pax_global_header00006660000000000000000000000064122441711770014520gustar00rootroot0000000000000052 comment=8e07d3432aea50fe3930589035721f196cbba8de statsmodels-0.5.0+git13-g8e07d34/000077500000000000000000000000001224417117700161525ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/.bzrignore000066400000000000000000000005071224417117700201560ustar00rootroot00000000000000*.py[oc] # setup.py working directory build # setup.py dist directory ./dist # Editor temporary/working/backup files *$ .*.sw[nop] .sw[nop] *~ [#]*# .#* *.bak *.tmp *.tgz *.rej *.org .project *.diff .settings/ *.svn/ *.log.py # Egg metadata ./*.egg-info # The shelf plugin uses this dir ./.shelf # Mac droppings .DS_Store help statsmodels-0.5.0+git13-g8e07d34/.coveragerc000066400000000000000000000010701224417117700202710ustar00rootroot00000000000000# .coveragerc to control coverage.py [run] branch = False [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about missing debug-only code: def __repr__ if self\.debug # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: ignore_errors = False [html] directory = coverage_html_reportstatsmodels-0.5.0+git13-g8e07d34/.gitattributes000066400000000000000000000000151224417117700210410ustar00rootroot00000000000000* text=auto statsmodels-0.5.0+git13-g8e07d34/.gitignore000066400000000000000000000012241224417117700201410ustar00rootroot00000000000000*.py[oc] # setup.py working directory build # setup.py dist directory ./dist dist #docs build and others #generated #not yet? generated for dataset not rebuild docs/source/generated docs/source/dev/generated docs/source/examples/generated # generated c source and built extensions *.c *.so *.pyd # repository directories for bzr-git .bzr .git marks.git marks.bzr # Editor temporary/working/backup files *$ .*.sw[nop] .sw[nop] *~ [#]*# .#* *.bak *.tmp *.tgz *.rej *.org .project *.diff .settings/ *.svn/ *.log.py # Egg metadata ./*.egg-info # The shelf plugin uses this dir ./.shelf # Mac droppings .DS_Store help # Project specific statsmodels/version.py statsmodels-0.5.0+git13-g8e07d34/.mailmap000066400000000000000000000147421224417117700176030ustar00rootroot00000000000000Alexander W Blocker Alexander W Blocker Alexis Roche Alexis Roche Ana Martinez Pardo Ana Martinez Pardo anov anov avishaylivne avishaylivne Bart Baker Bart Baker Bart Baker bartbkr Bart Baker bartbkr@gmail.com Benjamin Thyreau benjamin.thyreau <> brian.hawthorne <> brian.hawthorne <> Bruno Rodrigues Bruno Rodrigues Carl Vogel Carl Vogel Chad Fulton Chad Fulton Chris Jordan-Squire Chris Jordan-Squire Christian Prinoth Christian Prinoth Christopher Burns cburns <> Christopher Burns Chris Christopher Burns Christopher Burns Cindee Madison Cindee Madison Daniel B. Smith Daniel B. Smith davclark <> davclark <> dengemann dengemann Dieter Vandenbussche Dieter Vandenbussche Dougal Sutherland Dougal Sutherland Enrico Giampieri Enrico Giampieri evelynmitchell evelynmitchell Fernando Perez fdo.perez <> Fernando Perez Fernando Perez Gael Varoquaux Gael Varoquaux George Panterov George Panterov Grayson Grayson Jan Schulz Jan Schulz Jarrod Millman jarrod.millman <> Jarrod Millman Jarrod Millman Jeff Reback jreback Jonathan Taylor jonathan.taylor <> Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor Jonathan Taylor jtaylo Josef Perktold Josef Perktold Justin Grana Justin Grana langmore langmore Matthew Brett matthew.brett <> Matthew Brett Matthew Brett <> Matthew Brett Matthew Brett Matthew Brett Matthew Brett Matthieu Brucher Matthieu Brucher michael.castelle <> michael.castelle <> Mike Crowe Mike Crowe Mike Crowe Mike Crowe Mike Crowe Mike Nathaniel J. Smith Nathaniel J. Smith otterb otterb padarn padarn Paris Sprint Account Paris Sprint Account Paul Hobson Paul Hobson Peter Prettenhofer Peter Prettenhofer Pietro Battiston Pietro Battiston Ralf Gommers Ralf Gommers Richard T. Guy Richard T. Guy Robert Cimrman Robert Cimrman Roger Lew Roger Lew scottpiraino scottpiraino sebastien.meriaux <> sebastien.meriaux <> Skipper Seabold jsseabold <> Skipper Seabold jsseabold Skipper Seabold Skipper Seabold skipper seabold skipper seabold Skipper Seabold skipper Skipper Seabold skipper Steve Genoud Steve Genoud Thomas Haslwanter Thomas Haslwanter Thomas Kluyver Thomas Kluyver tim.leslie <> tim.leslie <> timmie timmie Tom Augspurger TomAugspurger Tom Waite Tom Waite Tom Waite twaite Trent Hauck Trent Hauck Trent Hauck tshauck tylerhartley tylerhartley Vincent Arel-Bundock Vincent Arel-Bundock Vincent Davis Vincent Davis VirgileFritsch VirgileFritsch Wes McKinney Wes McKinney Wes McKinney Wes McKinney Yaroslav Halchenko Yaroslav Halchenko zed zed statsmodels-0.5.0+git13-g8e07d34/.travis.yml000066400000000000000000000042071224417117700202660ustar00rootroot00000000000000# Adapted from M. Brett's .yaml file for nipy: # https://github.com/nipy/nipy/blob/master/.travis.yml # # We pretend to be erlang because we can't use the python support in # travis-ci; it uses virtualenvs, they do not have numpy, scipy, matplotlib, # and it is impractical to build them language: erlang notifications: email: - statsmodels-commits@googlegroups.com env: # Enable python 2 and python 3 builds. Python3.2 available in Ubuntu 12.04. - PYTHON=python PYSUF='' - PYTHON=python3 PYSUF=3 install: - sudo apt-get update - sudo apt-get install $PYTHON-dev - sudo apt-get install $PYTHON-numpy - sudo apt-get install $PYTHON-scipy - sudo apt-get install $PYTHON-setuptools - sudo apt-get install $PYTHON-nose # Cython needs manual install under Python 3 - if [ "${PYSUF}" == "3" ]; then wget http://cython.org/release/Cython-0.17.1.tar.gz ; tar xfvz Cython-0.17.1.tar.gz ; cd Cython-0.17.1 ; sudo python3 setup.py install ; cd .. ; else sudo apt-get install cython ; fi - echo ${DISTRIB_CODENAME} - wget -O- http://neuro.debian.net/lists/precise.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list - sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9 - sudo apt-get update -qq - sudo apt-get install $PYTHON-dateutil - sudo apt-get --no-install-recommends install $PYTHON-pandas - sudo apt-get --no-install-recommends install $PYTHON-pandas-lib - sudo easy_install$PYSUF -U patsy script: - sudo $PYTHON setup.py install # Ubuntu 12.04 installs statsmodels under the wrong path for Python 3 - if [ "${PYSUF}" == "3" ]; then sudo mv /usr/local/lib/python3.2/dist-packages/statsmodels-*/statsmodels /usr/local/lib/python3.2/dist-packages/statsmodels ; sudo rm -rf /usr/lib/python3.2/dist-packages/statsmodels-* ; fi # For some reason, Python 3 will try to work with the build directory. Get out of folder to avoid breakage - cd ../ - sudo $PYTHON -c "import statsmodels as sm; a=sm.test(); import sys; sys.exit((len(a.failures)+len(a.errors))>0)" statsmodels-0.5.0+git13-g8e07d34/CHANGES.md000066400000000000000000000003751224417117700175510ustar00rootroot00000000000000Release Notes ============= The list of changes for each statsmodels release can be found [here](http://statsmodels.sourceforge.net/devel/release/index.html). Full details are available in the [commit logs](https://github.com/statsmodels/statsmodels). statsmodels-0.5.0+git13-g8e07d34/CONTRIBUTING.rst000066400000000000000000000027121224417117700206150ustar00rootroot00000000000000Contributing guidelines ======================= This page explains how you can contribute to the development of `statsmodels` by submitting patches, statistical tests, new models, or examples. `statsmodels` is developed on `Github `_ using the `Git `_ version control system. License ~~~~~~~ Statsmodels is released under the `Modified (3-clause) BSD license `_. Submitting a Patch ~~~~~~~~~~~~~~~~~~ So you want to submit a patch to `statsmodels`?. Great news! Here are the steps you need to take. 1. `Fork `_ the `statsmodels repository `_ on Github. 2. `Create a new feature branch `_ + Each branch must be self-contained, with a single new feature or bugfix. + Patches must always include tests. See our `notes on testing `_. 3. `Submit a pull request `_ Mailing List ~~~~~~~~~~~~ Conversations about development take place on the `statsmodels mailing list `__. Learn More ~~~~~~~~~~ The ``statsmodels`` documentation's `developer page `_ offers much more detailed information about the process. statsmodels-0.5.0+git13-g8e07d34/COPYRIGHTS.txt000066400000000000000000000267231224417117700204200ustar00rootroot00000000000000 The license of statsmodels can be found in LICENSE.txt statsmodels contains code or derivative code from several other packages. Some modules also note the author of individual contributions, or author of code that formed the basis for the derived or translated code. The copyright statements for the datasets are attached to the individual datasets, most datasets are in public domain, and we don't claim any copyright on any of them. In the following, we collect copyright statements of code from other packages, all of which are either a version of BSD or MIT licensed: numpy scipy pandas matplotlib scikit-learn qsturng-py http://code.google.com/p/qsturng-py/ numpy (statsmodels.compatnp contains copy of entire model) ---------------------------------------------------------- Copyright (c) 2005-2009, NumPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the NumPy Developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. --------------------------------------------------------------------- scipy ----- Copyright (c) 2001, 2002 Enthought, Inc. All rights reserved. Copyright (c) 2003-2009 SciPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of the Enthought nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. --------------------------------------------------------------------------- pandas ------ Copyright (c) 2008-2009 AQR Capital Management, LLC All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ---------------------------------------------------------------------- matplotlib (copied from license.py) ----------------------------------- LICENSE AGREEMENT FOR MATPLOTLIB %(version)s -------------------------------------- 1. This LICENSE AGREEMENT is between John D. Hunter ("JDH"), and the Individual or Organization ("Licensee") accessing and otherwise using matplotlib software in source or binary form and its associated documentation. 2. Subject to the terms and conditions of this License Agreement, JDH hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use matplotlib %(version)s alone or in any derivative version, provided, however, that JDH's License Agreement and JDH's notice of copyright, i.e., "Copyright (c) 2002-%(year)d John D. Hunter; All Rights Reserved" are retained in matplotlib %(version)s alone or in any derivative version prepared by Licensee. 3. In the event Licensee prepares a derivative work that is based on or incorporates matplotlib %(version)s or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to matplotlib %(version)s. 4. JDH is making matplotlib %(version)s available to Licensee on an "AS IS" basis. JDH MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, JDH MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB %(version)s WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. 5. JDH SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB %(version)s FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING MATPLOTLIB %(version)s, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. 6. This License Agreement will automatically terminate upon a material breach of its terms and conditions. 7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between JDH and Licensee. This License Agreement does not grant permission to use JDH trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party. 8. By copying, installing or otherwise using matplotlib %(version)s, Licensee agrees to be bound by the terms and conditions of this License Agreement. -------------------------------------------------------------------------- scikits-learn ------------- New BSD License Copyright (c) 2007 - 2010 Scikit-Learn Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of the Scikit-learn Developers nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. --------------------------------------------------------------------------- qsturng-py (code included in statsmodels.stats.libqsturng) -------------------------------------------------------------- Copyright (c) 2011, Roger Lew [see LICENSE.txt] All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the organizations affiliated with the contributors or the names of its contributors themselves may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ---------------------------------------------------------- statsmodels-0.5.0+git13-g8e07d34/INSTALL.txt000066400000000000000000000042301224417117700200200ustar00rootroot00000000000000Dependencies ------------ python >= 2.6 www.python.org numpy >= 1.5.1 www.numpy.org scipy >= 0.7 www.scipy.org pandas >= 0.7.1 pandas.pydata.org patsy >= 0.1.0 patsy.readthedocs.org cython >= 0.15.1 http://cython.org/ Cython is required if you are building the source from github. However, if you have are building from source distribution archive then the generated C files are included and Cython is not necessary. Optional Dependencies --------------------- matplotlib >= 1.0.0 http://matplotlib.sf.net/ Matplotlib is needed for plotting functionality and running many of the examples. sphinx >= 1.0.0 http://sphinx.pocoo.org/ Sphinx is used to build the documentation. nose >= 1.0.0 http://readthedocs.org/docs/nose/en/latest/ Nose is needed to run the tests. Easy Install ------------ To get the latest release using easy_install you need setuptools (easy_install): http://peak.telecommunity.com/DevCenter/EasyInstall Then you can do (with proper permissions): easy_install -U statsmodels Ubuntu/Debian ------------- On Ubuntu you can get dependencies through: sudo apt-get install python python-dev python-setuptools python-numpy python-scipy easy_install -U pandas easy_install -U cython Alternatively, you can install from the NeuroDebian repository: http://neuro.debian.net Installing from Source ---------------------- Download and extract the source distribution from PyPI or github http://pypi.python.org/pypi/statsmodels https://github.com/statsmodels/statsmodels/tags Or clone the bleeding edge code from our repository on github at git clone git://github.com/statsmodels/statsmodels.git In the statsmodels directory do (with proper permissions) python setup.py build python setup.py install You will need a C compiler installed. Installing from Source on Windows --------------------------------- See http://statsmodels.sf.net/devel/install.html#windows. Documentation ------------- You may find more information about the project and installation in our documentation http://statsmodels.sf.net/devel/install.html statsmodels-0.5.0+git13-g8e07d34/LICENSE.txt000066400000000000000000000031441224417117700177770ustar00rootroot00000000000000Copyright (C) 2006, Jonathan E. Taylor All rights reserved. Copyright (c) 2006-2008 Scipy Developers. All rights reserved. Copyright (c) 2009-2012 Statsmodels Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of Statsmodels nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL STATSMODELS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. statsmodels-0.5.0+git13-g8e07d34/MANIFEST.in000066400000000000000000000027511224417117700177150ustar00rootroot00000000000000global-include *.csv *.py *.txt #scikits*.* include MANIFEST.in #exclude docs/build/htmlhelp* recursive-exclude build * recursive-exclude dist * recursive-exclude tools * include tools/examples_rst.py include tools/hash_funcs.py graft statsmodels/datasets graft statsmodels/tests graft statsmodels/sandbox/regression/data graft statsmodels/sandbox/tests graft statsmodels/sandbox/tsa/examples graft statsmodels/tsa/vector_ar/data recursive-include docs/source * exclude docs/source/generated/* recursive-include docs/sphinxext * recursive-include docs/themes * recursive-exclude docs/build * #recursive-include docs/build/html * recursive-exclude docs/build/htmlhelp * include statsmodels/statsmodelsdoc.chm include docs/make.bat include docs/Makefile #include docs mak* #include docs GLM* recursive-include examples * #missed files: .npz, .npy include statsmodels/tsa/vector_ar/tests/results/vars_results.npz include statsmodels/iolib/tests/results/* include statsmodels/stats/tests/results/influence_lsdiag_R.json include statsmodels/sandbox/panel/test_data.txt include statsmodels/stats/tests/results/influence_measures_R.csv include statsmodels/stats/libqsturng/tests/results/* include statsmodels/stats/libqsturng/tests/bootleg.dat include statsmodels/stats/libqsturng/tests/bootleg.csv include statsmodels/stats/libqsturng/CH.r include statsmodels/stats/libqsturng/LICENSE.txt include statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt global-exclude *~ *.swp *.pyc *.bak *.pyx statsmodels-0.5.0+git13-g8e07d34/README.txt000066400000000000000000000061041224417117700176510ustar00rootroot00000000000000What Statsmodels is =================== What it is ========== Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features ============= * linear regression models: Generalized least squares (including weighted least squares and least squares with autoregressive errors), ordinary least squares. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate time series analysis: AR, ARIMA - vector autoregressive models, VAR and structural VAR - descriptive statistics and process models for time series analysis * nonparametric : (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests - diagnostics and specification tests - goodness-of-fit and normality tests - functions for multiple testing - various additional statistical tests * iolib - Tools for reading Stata .dta files into numpy arrays. - printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of developement and testing which is not considered "production ready". This covers among others Mixed (repeated measures) Models, GARCH models, general method of moments (GMM) estimators, kernel regression, various extensions to scipy.stats.distributions, panel data models, generalized additive models and information theoretic measures. Where to get it =============== The master branch on GitHub is the most up to date code https://www.github.com/statsmodels/statsmodels Source download of release tags are available on GitHub https://github.com/statsmodels/statsmodels/tags Binaries and source distributions are available from PyPi http://pypi.python.org/pypi/statsmodels/ Installation from sources ========================= See INSTALL.txt for requirements or see the documentation http://statsmodels.sf.net/devel/install.html License ======= Modified BSD (3-clause) Documentation ============= The official documentation is hosted on SourceForge http://statsmodels.sf.net/ Windows Help ============ The source distribution for Windows includes a htmlhelp file (statsmodels.chm). This can be opened from the python interpreter :: >>> import statsmodels.api as sm >>> sm.open_help() Discussion and Development ========================== Discussions take place on our mailing list. http://groups.google.com/group/pystatsmodels We are very interested in feedback about usability and suggestions for improvements. Bug Reports =========== Bug reports can be submitted to the issue tracker at https://github.com/statsmodels/statsmodels/issues statsmodels-0.5.0+git13-g8e07d34/README_l1.txt000066400000000000000000000023771224417117700202550ustar00rootroot00000000000000What the l1 addition is ======================= A slight modification that allows l1 regularized LikelihoodModel. Regularization is handled by a fit_regularized method. Main Files ========== l1_demo/demo.py $ python demo.py --get_l1_slsqp_results logit does a quick demo of the regularization using logistic regression. l1_demo/sklearn_compare.py $ python sklearn_compare.py Plots a comparison of regularization paths. Modify the source to use different datasets. statsmodels/base/l1_cvxopt.py fit_l1_cvxopt_cp() Fit likelihood model using l1 regularization. Use the CVXOPT package. Lots of small functions supporting fit_l1_cvxopt_cp statsmodels/base/l1_slsqp.py fit_l1_slsqp() Fit likelihood model using l1 regularization. Use scipy.optimize Lots of small functions supporting fit_l1_slsqp statsmodels/base/l1_solvers_common.py Common methods used by l1 solvers statsmodels/base/model.py Likelihoodmodel.fit() 3 lines modified to allow for importing and calling of l1 fitting functions statsmodels/discrete/discrete_model.py L1MultinomialResults class Child of MultinomialResults MultinomialModel.fit() 3 lines re-directing l1 fit results to the L1MultinomialResults class statsmodels-0.5.0+git13-g8e07d34/build_bdists.bat000066400000000000000000000004231224417117700213100ustar00rootroot00000000000000call tools\build_win_bdist64-py26.bat call tools\build_win_bdist32-py26.bat call tools\build_win_bdist64-py27.bat call tools\build_win_bdist32-py27.bat call tools\build_win_bdist32-py32.bat call tools\build_win_bdist64-py32.bat call python setup.py sdist --formats=zip,gztar statsmodels-0.5.0+git13-g8e07d34/docs/000077500000000000000000000000001224417117700171025ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/GLMNotes.lyx000066400000000000000000000532311224417117700212740ustar00rootroot00000000000000#LyX 1.6.2 created this file. For more info see http://www.lyx.org/ \lyxformat 345 \begin_document \begin_header \textclass article \use_default_options true \language english \inputencoding auto \font_roman default \font_sans default \font_typewriter default \font_default_family default \font_sc false \font_osf false \font_sf_scale 100 \font_tt_scale 100 \graphics default \paperfontsize default \spacing single \use_hyperref false \papersize default \use_geometry true \use_amsmath 1 \use_esint 1 \cite_engine basic \use_bibtopic false \paperorientation portrait \leftmargin 1in \topmargin 1in \rightmargin 1in \bottommargin 1in \secnumdepth 3 \tocdepth 3 \paragraph_separation indent \defskip medskip \quotes_language english \papercolumns 1 \papersides 1 \paperpagestyle default \tracking_changes false \output_changes false \author "" \author "" \end_header \begin_body \begin_layout Standard Variance Functions: \end_layout \begin_layout Standard Constant: \begin_inset Formula $\boldsymbol{1}$ \end_inset \end_layout \begin_layout Standard Power: \begin_inset Formula $\boldsymbol{X}^{2}$ \end_inset \end_layout \begin_layout Standard Binomial: \begin_inset Formula $np(1-p)\text{ where }p=\frac{\mu}{n};\,\, V(\mu)=np(1-p)$ \end_inset \end_layout \begin_layout Standard \begin_inset Formula $\frac{\partial\mu}{\partial\eta}$ \end_inset \end_layout \begin_layout Standard Links: initialization of base class returns the actual mean vector \begin_inset Formula $\boldsymbol{\mu}$ \end_inset ; \begin_inset Formula $p$ \end_inset in the logit and subclasses; \begin_inset Formula $x$ \end_inset elsewhere. \end_layout \begin_layout Standard \begin_inset Float table placement H wide false sideways false status open \begin_layout Plain Layout \begin_inset Tabular \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Link \begin_inset Formula $g(p)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Inverse \begin_inset Formula $g^{-1}(p)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Analytic Derivative \begin_inset Formula $g^{\prime}(p)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Logit \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $z=\log\frac{p}{1-p}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $p=\frac{e^{z}}{1+e^{z}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{\prime}(p)=\frac{1}{p(1-p)}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Power \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $z=x^{\text{pow}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $x=z^{\frac{1}{\text{pow}}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{\prime}(x)=\text{pow}\cdot x^{\text{power}-1}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Inverse \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout same as above with \begin_inset Formula $\text{pow}=-1$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Square Root \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\text{pow}=0.5$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Identity \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\text{pow}=1$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Log \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $z=\log x$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{-1}(z)=e^{z}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{\prime}(x)=\frac{1}{x}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout CDFLink/Probit \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $z=\Phi^{-1}(p)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $p=\Phi(z)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{\prime}(x)=\frac{1}{\int_{-\infty}^{p}f(t)dt}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Cauchy \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout same as the above with the Cauchy distribution \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout CLogLog \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $z=\log(-\log p)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $p=e^{-e^{z}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $g^{\prime}(p)=-\frac{1}{p\log p}$ \end_inset \end_layout \end_inset \end_inset \end_layout \begin_layout Plain Layout \begin_inset Caption \begin_layout Plain Layout Link Functions \end_layout \end_inset \end_layout \end_inset \end_layout \begin_layout Standard Initializing the family sets a link property and a variance based on the link(?) \end_layout \begin_layout Standard \begin_inset Float table placement H wide false sideways false status open \begin_layout Plain Layout \begin_inset Tabular \begin_inset Text \begin_layout Plain Layout Family \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Weights \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Deviance \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout DevResid \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Fitted \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Predict \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Base Class \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\frac{1}{(g^{\prime}(\mu))^{2}\cdot V(\mu)}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\frac{\sum_{i}\text{DevResid}^{2}}{\text{scale}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\left(Y-\mu\right)\cdot\sqrt{\text{weights}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\mu=g^{-1}(\eta)$ \end_inset * \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\eta=g(\mu)$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Poisson \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\text{sign}\left(Y-\mu\right)\sqrt{2Y\log\frac{Y}{\mu}-2(Y-\mu)}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Gaussian \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\frac{\left(Y-\mu\right)}{\text{\sqrt{\text{scale}\cdot V\left(\mu\right)}}}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Gamma \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Bug? \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Binomial \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \begin_inset Formula $\text{sign}\left(Y-\mu\right)\sqrt{-2Y\log\frac{\mu}{n}+\left(n-Y\right)\log\left(1-\frac{\mu}{n}\right)}$ \end_inset \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout Inverse Gaussian \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout ? \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \begin_inset Text \begin_layout Plain Layout \end_layout \end_inset \end_inset \end_layout \begin_layout Plain Layout \begin_inset Caption \begin_layout Plain Layout Families \end_layout \end_inset \end_layout \begin_layout Plain Layout * \begin_inset Formula $\eta$ \end_inset is the linear predictor ie., \begin_inset Formula $X\beta$ \end_inset in the generalized linear model \end_layout \end_inset \end_layout \end_body \end_document 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SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = build TOOLSPATH = ../tools/ EXAMPLEBUILD = examples_rst.py NOTEBOOKBUILD = nbgenerate.py FOLDTOC = fold_toc.py # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(BUILDDIR)/* -rm -rf source/generated/ -rm -rf source/dev/generated/ cleanall: clean cleancache cleancache: -rm source/examples/generated/* -rm -rf source/examples/notebooks/generated/* -rm -rf ../tools/hash_dict.pickle html: # generate the examples rst files @echo "Generating reST from examples folder" $(TOOLSPATH)$(EXAMPLEBUILD) @echo "Generating notebooks from examples/notebooks folder" $(TOOLSPATH)$(NOTEBOOKBUILD) $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html $(TOOLSPATH)$(EXAMPLEBUILD) $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/examples/index.html ../_static $(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/esip.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/esip.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/esip" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/esip" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex ./fix_longtable.py $(BUILDDIR) @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex ./fix_longtable.py $(BUILDDIR) @echo "Running LaTeX files through pdflatex..." make -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." statsmodels-0.5.0+git13-g8e07d34/docs/fix_longtable.py000066400000000000000000000010351224417117700222700ustar00rootroot00000000000000#!/usr/bin/env python import sys import os BUILDDIR = sys.argv[-1] read_file_path = os.path.join(BUILDDIR,'latex','statsmodels.tex') write_file_path = os.path.join(BUILDDIR, 'latex','statsmodels_tmp.tex') read_file = open(read_file_path,'r') write_file = open(write_file_path, 'w') for line in read_file: if 'longtable}{LL' in line: line = line.replace('longtable}{LL', 'longtable}{|l|l|') write_file.write(line) read_file.close() write_file.close() os.remove(read_file_path) os.rename(write_file_path, read_file_path) statsmodels-0.5.0+git13-g8e07d34/docs/make.bat000066400000000000000000000114211224417117700205060ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=build set TOOLSPATH=../tools set EXAMPLEBUILD=examples_rst.py set FOLDTOC=fold_toc.py set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( python %TOOLSPATH%/%EXAMPLEBUILD% %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/index.html python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/examples/index.html ../_static python %TOOLSPATH%/%FOLDTOC% %BUILDDIR%/html/dev/index.html ../_static echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\esip.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\esip.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 start python fix_longtable.py %BUILDDIR% echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end 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nÝa˜,IEND®B`‚statsmodels-0.5.0+git13-g8e07d34/docs/source/_static/facebox.css000066400000000000000000000022071224417117700241520ustar00rootroot00000000000000#facebox { position: absolute; top: 0; left: 0; z-index: 100; text-align: left; } #facebox .popup{ position:relative; border:3px solid rgba(0,0,0,0); -webkit-border-radius:5px; -moz-border-radius:5px; border-radius:5px; -webkit-box-shadow:0 0 18px rgba(0,0,0,0.4); -moz-box-shadow:0 0 18px rgba(0,0,0,0.4); box-shadow:0 0 18px rgba(0,0,0,0.4); } #facebox .content { display:table; width: 370px; padding: 10px; background: #fff; -webkit-border-radius:4px; -moz-border-radius:4px; border-radius:4px; } #facebox .content > p:first-child{ margin-top:0; } #facebox .content > p:last-child{ margin-bottom:0; } #facebox .close{ position:absolute; top:5px; right:5px; padding:2px; background:#fff; } #facebox .close img{ opacity:0.3; } #facebox .close:hover img{ opacity:1.0; } #facebox .loading { text-align: center; } #facebox .image { text-align: center; } #facebox img { border: 0; margin: 0; } #facebox_overlay { position: fixed; top: 0px; left: 0px; height:100%; width:100%; } .facebox_hide { z-index:-100; } .facebox_overlayBG { background-color: #000; z-index: 99; }statsmodels-0.5.0+git13-g8e07d34/docs/source/_static/facebox.js000066400000000000000000000221751224417117700240040ustar00rootroot00000000000000/* * Facebox (for jQuery) * version: 1.2 (05/05/2008) * @requires jQuery v1.2 or later * * Examples at http://famspam.com/facebox/ * * Licensed under the MIT: * http://www.opensource.org/licenses/mit-license.php * * Copyright 2007, 2008 Chris Wanstrath [ chris@ozmm.org ] * * Usage: * * jQuery(document).ready(function() { * jQuery('a[rel*=facebox]').facebox() * }) * * Terms * Loads the #terms div in the box * * Terms * Loads the terms.html page in the box * * Terms * Loads the terms.png image in the box * * * You can also use it programmatically: * * jQuery.facebox('some html') * jQuery.facebox('some html', 'my-groovy-style') * * The above will open a facebox with "some html" as the content. * * jQuery.facebox(function($) { * $.get('blah.html', function(data) { $.facebox(data) }) * }) * * The above will show a loading screen before the passed function is called, * allowing for a better ajaxy experience. * * The facebox function can also display an ajax page, an image, or the contents of a div: * * jQuery.facebox({ ajax: 'remote.html' }) * jQuery.facebox({ ajax: 'remote.html' }, 'my-groovy-style') * jQuery.facebox({ image: 'stairs.jpg' }) * jQuery.facebox({ image: 'stairs.jpg' }, 'my-groovy-style') * jQuery.facebox({ div: '#box' }) * jQuery.facebox({ div: '#box' }, 'my-groovy-style') * * Want to close the facebox? Trigger the 'close.facebox' document event: * * jQuery(document).trigger('close.facebox') * * Facebox also has a bunch of other hooks: * * loading.facebox * beforeReveal.facebox * reveal.facebox (aliased as 'afterReveal.facebox') * init.facebox * afterClose.facebox * * Simply bind a function to any of these hooks: * * $(document).bind('reveal.facebox', function() { ...stuff to do after the facebox and contents are revealed... }) * */ (function($) { $.facebox = function(data, klass) { $.facebox.loading() if (data.ajax) fillFaceboxFromAjax(data.ajax, klass) else if (data.image) fillFaceboxFromImage(data.image, klass) else if (data.div) fillFaceboxFromHref(data.div, klass) else if ($.isFunction(data)) data.call($) else $.facebox.reveal(data, klass) } /* * Public, $.facebox methods */ $.extend($.facebox, { settings: { opacity : 0.2, overlay : true, /* I don't know why absolute paths don't work. If you try to use facebox * outside of the examples folder these images won't show up. */ loadingImage : '../../_static/loading.gif', closeImage : '../../_static/closelabel.png', imageTypes : [ 'png', 'jpg', 'jpeg', 'gif' ], faceboxHtml : '\ ' }, loading: function() { init() if ($('#facebox .loading').length == 1) return true showOverlay() $('#facebox .content').empty() $('#facebox .body').children().hide().end(). append('
') $('#facebox').css({ top: getPageScroll()[1] + (getPageHeight() / 10), left: $(window).width() / 2 - 205 }).show() $(document).bind('keydown.facebox', function(e) { if (e.keyCode == 27) $.facebox.close() return true }) $(document).trigger('loading.facebox') }, reveal: function(data, klass) { $(document).trigger('beforeReveal.facebox') if (klass) $('#facebox .content').addClass(klass) $('#facebox .content').append(data) $('#facebox .loading').remove() $('#facebox .body').children().fadeIn('normal') $('#facebox').css('left', $(window).width() / 2 - ($('#facebox .popup').width() / 2)) $(document).trigger('reveal.facebox').trigger('afterReveal.facebox') }, close: function() { $(document).trigger('close.facebox') return false } }) /* * Public, $.fn methods */ $.fn.facebox = function(settings) { if ($(this).length == 0) return init(settings) function clickHandler() { $.facebox.loading(true) // support for rel="facebox.inline_popup" syntax, to add a class // also supports deprecated "facebox[.inline_popup]" syntax var klass = this.rel.match(/facebox\[?\.(\w+)\]?/) if (klass) klass = klass[1] fillFaceboxFromHref(this.href, klass) return false } return this.bind('click.facebox', clickHandler) } /* * Private methods */ // called one time to setup facebox on this page function init(settings) { if ($.facebox.settings.inited) return true else $.facebox.settings.inited = true $(document).trigger('init.facebox') makeCompatible() var imageTypes = $.facebox.settings.imageTypes.join('|') $.facebox.settings.imageTypesRegexp = new RegExp('\.(' + imageTypes + ')$', 'i') if (settings) $.extend($.facebox.settings, settings) $('body').append($.facebox.settings.faceboxHtml) var preload = [ new Image(), new Image() ] preload[0].src = $.facebox.settings.closeImage preload[1].src = $.facebox.settings.loadingImage $('#facebox').find('.b:first, .bl').each(function() { preload.push(new Image()) preload.slice(-1).src = $(this).css('background-image').replace(/url\((.+)\)/, '$1') }) $('#facebox .close').click($.facebox.close) $('#facebox .close_image').attr('src', $.facebox.settings.closeImage) } // getPageScroll() by quirksmode.com function getPageScroll() { var xScroll, yScroll; if (self.pageYOffset) { yScroll = self.pageYOffset; xScroll = self.pageXOffset; } else if (document.documentElement && document.documentElement.scrollTop) { // Explorer 6 Strict yScroll = document.documentElement.scrollTop; xScroll = document.documentElement.scrollLeft; } else if (document.body) {// all other Explorers yScroll = document.body.scrollTop; xScroll = document.body.scrollLeft; } return new Array(xScroll,yScroll) } // Adapted from getPageSize() by quirksmode.com function getPageHeight() { var windowHeight if (self.innerHeight) { // all except Explorer windowHeight = self.innerHeight; } else if (document.documentElement && document.documentElement.clientHeight) { // Explorer 6 Strict Mode windowHeight = document.documentElement.clientHeight; } else if (document.body) { // other Explorers windowHeight = document.body.clientHeight; } return windowHeight } // Backwards compatibility function makeCompatible() { var $s = $.facebox.settings $s.loadingImage = $s.loading_image || $s.loadingImage $s.closeImage = $s.close_image || $s.closeImage $s.imageTypes = $s.image_types || $s.imageTypes $s.faceboxHtml = $s.facebox_html || $s.faceboxHtml } // Figures out what you want to display and displays it // formats are: // div: #id // image: blah.extension // ajax: anything else function fillFaceboxFromHref(href, klass) { // div if (href.match(/#/)) { var url = window.location.href.split('#')[0] var target = href.replace(url,'') if (target == '#') return $.facebox.reveal($(target).html(), klass) // image } else if (href.match($.facebox.settings.imageTypesRegexp)) { fillFaceboxFromImage(href, klass) // ajax } else { fillFaceboxFromAjax(href, klass) } } function fillFaceboxFromImage(href, klass) { var image = new Image() image.onload = function() { $.facebox.reveal('
', klass) } image.src = href } function fillFaceboxFromAjax(href, klass) { $.get(href, function(data) { $.facebox.reveal(data, klass) }) } function skipOverlay() { return $.facebox.settings.overlay == false || $.facebox.settings.opacity === null } function showOverlay() { if (skipOverlay()) return if ($('#facebox_overlay').length == 0) $("body").append('
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statsmodels-0.5.0+git13-g8e07d34/docs/source/_templates/000077500000000000000000000000001224417117700225375ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/_templates/autosummary/000077500000000000000000000000001224417117700251255ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/_templates/autosummary/class.rst000066400000000000000000000010631224417117700267640ustar00rootroot00000000000000{{ fullname }} {{ underline }} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} {% block methods %} {% if methods %} .. rubric:: Methods .. autosummary:: :toctree: {% for item in methods %} {% if item != '__init__' %} ~{{ name }}.{{ item }} {% endif %} {%- endfor %} {% endif %} {% endblock %} {% block attributes %} {% if attributes %} .. rubric:: Attributes .. autosummary:: {% for item in attributes %} ~{{ name }}.{{ item }} {%- endfor %} {% endif %} {% endblock %} statsmodels-0.5.0+git13-g8e07d34/docs/source/_templates/autosummary/glmfamilies.rst000066400000000000000000000005431224417117700301520ustar00rootroot00000000000000{{ fullname }} {{ underline }} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} {% block methods %} {% if methods %} .. rubric:: Methods .. autosummary:: :toctree: {% for item in methods %} {% if item != '__init__' %} ~{{ name }}.{{ item }} {% endif %} {%- endfor %} {% endif %} {% endblock %} statsmodels-0.5.0+git13-g8e07d34/docs/source/anova.rst000066400000000000000000000015721224417117700222450ustar00rootroot00000000000000.. currentmodule:: statsmodels.stats.anova .. _anova: ANOVA ===== Analysis of Variance models Examples -------- .. ipython:: python import statsmodels.api as sm from statsmodels.formula.api import ols moore = sm.datasets.get_rdataset("Moore", "car", cache=True) # load data data = moore.data data = data.rename(columns={"partner.status" : "partner_status"}) # make name pythonic moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)', data=data).fit() table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame print table A more detailed example can be found here: .. toctree:: :maxdepth: 1 examples/generated/example_interactions Module Reference ---------------- .. autosummary:: :toctree: generated/ anova_lm statsmodels-0.5.0+git13-g8e07d34/docs/source/conf.py000066400000000000000000000261421224417117700217060ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # statsmodels documentation build configuration file, created by # sphinx-quickstart on Sat Jan 22 11:17:58 2011. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../sphinxext')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.pngmath', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.inheritance_diagram', 'matplotlib.sphinxext.plot_directive', 'matplotlib.sphinxext.only_directives', 'ipython_console_highlighting', 'ipython_directive', 'numpy_ext.numpydoc', 'github' # for GitHub links ] import sphinx if sphinx.__version__ == '1.1.3': print ("WARNING: Not building inheritance diagrams on sphinx 1.1.3. " "See https://github.com/statsmodels/statsmodels/issues/1002") extensions.remove('sphinx.ext.inheritance_diagram') # plot_directive is broken on old matplotlib from matplotlib import __version__ as mpl_version from distutils.version import LooseVersion if LooseVersion(mpl_version) < LooseVersion('1.0.1'): extensions.remove('matplotlib.sphinxext.plot_directive') extensions.append('numpy_ext.plot_directive') # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'statsmodels' copyright = u'2009-2013, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers' autosummary_generate = True autoclass_content = 'class' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # from statsmodels.version import version, full_version release = version # The full version, including dev tag. version = full_version # set inheritance_graph_attrs # you need graphviz installed to use this # see: http://sphinx.pocoo.org/ext/inheritance.html # and graphviz dot documentation http://www.graphviz.org/content/attrs #NOTE: giving the empty string to size allows graphviz to figure out # the size inheritance_graph_attrs = dict(size='""', ratio="compress", fontsize=14, rankdir="LR") #inheritance_node_attrs = dict(shape='ellipse', fontsize=14, height=0.75, # color='dodgerblue1', style='filled') # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['*/autosummary/class.rst', '*/autosummary/glmfamilies.rst'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. add_function_parentheses = False # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. #html_theme = 'default' if 'htmlhelp' in sys.argv: #html_theme = 'statsmodels_htmlhelp' #doesn't look nice yet html_theme = 'default' print '################# using statsmodels_htmlhelp ############' else: html_theme = 'statsmodels' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ['../themes'] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = 'images/statsmodels_hybi_banner.png' # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = 'images/statsmodels_hybi_favico.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = {'index' : ['indexsidebar.html','searchbox.html','sidelinks.html']} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. html_domain_indices = False # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'statsmodelsdoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'statsmodels.tex', u'statsmodels Documentation', u'Josef Perktold, Skipper Seabold', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # pngmath options # http://sphinx-doc.org/ext/math.html#module-sphinx.ext.pngmath pngmath_latex_preamble=r'\usepackage[active]{preview}' # + other custom stuff for inline math, such as non-default math fonts etc. pngmath_use_preview=True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'statsmodels', u'statsmodels Documentation', [u'Josef Perktold, Skipper Seabold, Jonathan Taylor'], 1) ] # -- Options for Epub output --------------------------------------------------- # Bibliographic Dublin Core info. epub_title = u'statsmodels' epub_author = u'Josef Perktold, Skipper Seabold' epub_publisher = u'Josef Perktold, Skipper Seabold' epub_copyright = u'2009-2013, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers' # The language of the text. It defaults to the language option # or en if the language is not set. #epub_language = '' # The scheme of the identifier. Typical schemes are ISBN or URL. #epub_scheme = '' # The unique identifier of the text. This can be a ISBN number # or the project homepage. #epub_identifier = '' # A unique identification for the text. #epub_uid = '' # HTML files that should be inserted before the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_pre_files = [] # HTML files shat should be inserted after the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_post_files = [] # A list of files that should not be packed into the epub file. #epub_exclude_files = [] # The depth of the table of contents in toc.ncx. #epub_tocdepth = 3 # Allow duplicate toc entries. #epub_tocdup = True # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'numpy' : ('http://docs.scipy.org/doc/numpy/', None), 'python' : ('http://docs.python.org/3.2', None), 'pydagogue' : ('http://matthew-brett.github.com/pydagogue/', None), 'patsy' : ('http://patsy.readthedocs.org/en/latest/', None), 'pandas' : ('http://pandas.pydata.org/pandas-docs/dev/', None), } from os.path import dirname, abspath, join plot_basedir = join(dirname(dirname(os.path.abspath(__file__))), 'source') # ghissue config github_project_url = "https://github.com/statsmodels/statsmodels" statsmodels-0.5.0+git13-g8e07d34/docs/source/contrasts.rst000066400000000000000000000243731224417117700231650ustar00rootroot00000000000000:orphan: Patsy: Contrast Coding Systems for categorical variables =========================================================== .. note:: This document is based heavily on `this excellent resource from UCLA `__. A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses. In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model's intercept. On the other hand, a set of *contrasts* for a categorical variable with `k` levels is a set of `k-1` functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding isn't wrong *per se*. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context. To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let's load the data. .. ipython:: python :suppress: import numpy as np np.set_printoptions(precision=4, suppress=True) from patsy.contrasts import ContrastMatrix def _name_levels(prefix, levels): return ["[%s%s]" % (prefix, level) for level in levels] class Simple(object): def _simple_contrast(self, levels): nlevels = len(levels) contr = -1./nlevels * np.ones((nlevels, nlevels-1)) contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels return contr def code_with_intercept(self, levels): contrast = np.column_stack((np.ones(len(levels)), self._simple_contrast(levels))) return ContrastMatrix(contrast, _name_levels("Simp.", levels)) def code_without_intercept(self, levels): contrast = self._simple_contrast(levels) return ContrastMatrix(contrast, _name_levels("Simp.", levels[:-1])) Example Data ------------ .. ipython:: python import pandas url = 'http://www.ats.ucla.edu/stat/data/hsb2.csv' hsb2 = pandas.read_table(url, delimiter=",") It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian)). .. ipython:: hsb2.groupby('race')['write'].mean() Treatment (Dummy) Coding ------------------------ Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be .. ipython:: python from patsy.contrasts import Treatment levels = [1,2,3,4] contrast = Treatment(reference=0).code_without_intercept(levels) print contrast.matrix Here we used `reference=0`, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let's look at how this would encode the `race` variable. .. ipython:: python contrast.matrix[hsb2.race-1, :][:20] This is a bit of a trick, as the `race` category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won't work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above .. ipython:: python from statsmodels.formula.api import ols mod = ols("write ~ C(race, Treatment)", data=hsb2) res = mod.fit() print res.summary() We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this. Simple Coding ------------- Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. See :ref:`user-defined` for how to implement the Simple contrast. .. ipython:: python contrast = Simple().code_without_intercept(levels) print contrast.matrix mod = ols("write ~ C(race, Simple)", data=hsb2) res = mod.fit() print res.summary() Sum (Deviation) Coding ---------------------- Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others. .. ipython:: python from patsy.contrasts import Sum contrast = Sum().code_without_intercept(levels) print contrast.matrix mod = ols("write ~ C(race, Sum)", data=hsb2) res = mod.fit() print res.summary() This correspons to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level. .. ipython:: python hsb2.groupby('race')['write'].mean().mean() Backward Difference Coding -------------------------- In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable. .. ipython:: python from patsy.contrasts import Diff contrast = Diff().code_without_intercept(levels) print contrast.matrix mod = ols("write ~ C(race, Diff)", data=hsb2) res = mod.fit() print res.summary() For example, here the coefficient on level 1 is the mean of `write` at level 2 compared with the mean at level 1. Ie., .. ipython:: python res.params["C(race, Diff)[D.1]"] hsb2.groupby('race').mean()["write"][2] - \ hsb2.groupby('race').mean()["write"][1] Helmert Coding -------------- Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name 'reverse' being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so: .. ipython:: python from patsy.contrasts import Helmert contrast = Helmert().code_without_intercept(levels) print contrast.matrix mod = ols("write ~ C(race, Helmert)", data=hsb2) res = mod.fit() print res.summary() To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4 .. ipython:: python grouped = hsb2.groupby('race') grouped.mean()["write"][4] - grouped.mean()["write"][:3].mean() As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same. .. ipython:: python k = 4 1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean()) k = 3 1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean()) Orthogonal Polynomial Coding ---------------------------- The coefficients taken on by polynomial coding for `k=4` levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order `k-1`. Since `race` is not an ordered factor variable let's use `read` as an example. First we need to create an ordered categorical from `read`. .. ipython:: python _, bins = np.histogram(hsb2.read, 3) try: # requires numpy master readcat = np.digitize(hsb2.read, bins, True) except: readcat = np.digitize(hsb2.read, bins) hsb2['readcat'] = readcat hsb2.groupby('readcat').mean()['write'] .. ipython:: python from patsy.contrasts import Poly levels = hsb2.readcat.unique().tolist() contrast = Poly().code_without_intercept(levels) print contrast.matrix mod = ols("write ~ C(readcat, Poly)", data=hsb2) res = mod.fit() print res.summary() As you can see, readcat has a significant linear effect on the dependent variable `write` but not a significant quadratic or cubic effect. .. _user-defined: User-Defined Coding ------------------- If you want to use your own coding, you must do so by writing a coding class that contains a code_with_intercept and a code_without_intercept method that return a `patsy.contrast.ContrastMatrix` instance. .. ipython:: python from patsy.contrasts import ContrastMatrix def _name_levels(prefix, levels): return ["[%s%s]" % (prefix, level) for level in levels] class Simple(object): def _simple_contrast(self, levels): nlevels = len(levels) contr = -1./nlevels * np.ones((nlevels, nlevels-1)) contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels return contr def code_with_intercept(self, levels): contrast = np.column_stack((np.ones(len(levels)), self._simple_contrast(levels))) return ContrastMatrix(contrast, _name_levels("Simp.", levels)) def code_without_intercept(self, levels): contrast = self._simple_contrast(levels) return ContrastMatrix(contrast, _name_levels("Simp.", levels[:-1])) mod = ols("write ~ C(race, Simple)", data=hsb2) res = mod.fit() statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/000077500000000000000000000000001224417117700222125ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/dataset_proposal.rst000066400000000000000000000133331224417117700263130ustar00rootroot00000000000000:orphan: .. _dataset_proposal: Dataset for statmodels: design proposal =============================================== One of the thing numpy/scipy is missing now is a set of datasets, available for demo, courses, etc. For example, R has a set of dataset available at the core. The expected usage of the datasets are the following: - examples, tutorials for model usage - testing of model usage vs. other statistical packages That is, a dataset is not only data, but also some meta-data. The goal of this proposal is to propose common practices for organizing the data, in a way which is both straightforward, and does not prevent specific usage of the data. Background ---------- This proposal was adapted from David Cournapeau's original proposal for a datasets package for scipy and the learn scikit. It has been adapted for use in the statsmodels scikit. The structure of the datasets itself, while specific to statsmodels, should be general enough such that it might be used for other types of data (e.g., in the learn scikit or scipy itself). Organization ------------ Each dataset is a directory in the `datasets` directory and defines a python package (e.g. has the __init__.py file). Each package is expected to define the function load, returning the corresponding data. For example, to access datasets data1, you should be able to do:: >>> from statsmodels.datasets.data1 import load >>> d = load() # -> d is a Dataset object, see below The `load` function is expected to return the `Dataset` object, which has certain common attributes that make it readily usable in tests and examples. Load can do whatever it wants: fetching data from a file (python script, csv file, etc...), from the internet, etc. However, it is strongly recommended that each dataset directory contain a csv file with the dataset and its variables in the same form as returned by load so that the dataset can easily be loaded into other statistical packages. In addition, an optional (though recommended) sub-directory src should contain the dataset in its original form if it was "cleaned" (ie., variable transformations) in order to put it into the format needed for statsmodels. Some special variables must be defined for each package, containing a Python string: - COPYRIGHT: copyright informations - SOURCE: where the data are coming from - DESCHOSRT: short description - DESCLONG: long description - NOTE: some notes on the datasets. See `datasets/data_template.py` for more information. Format of the data ------------------ This is strongly suggested a practice for the `Dataset` object returned by the load function. Instead of using classes to provide meta-data, the Bunch pattern is used. :: class Bunch(dict): def __init__(self,**kw): dict.__init__(self,kw) self.__dict__ = self See this `Reference `_ In practice, you can use :: >>> from statsmodels.datasets import Dataset as the default collector as in `datasets/data_template.py`. The advantage of the Bunch pattern is that it preserves look-up by attribute. The key goals are: - For people who just want the data, there is no extra burden - For people who need more, they can easily extract what they need from the returned values. Higher level abstractions can be built easily from this model. - All possible datasets should fit into this model. For the datasets to be useful in statsmodels the Dataset object returned by load has the following conventions and attributes: - Calling the object itself returns the plain ndarray of the full dataset. - `data`: A recarray containing the actual data. It is assumed that all of the data can safely be cast to a float at this point. - `raw_data`: This is the plain ndarray version of 'data'. - `names`: this returns data.dtype.names so that name[i] is the i-th column in 'raw_data'. - `endog`: this value is provided for convenience in tests and examples - `exog`: this value is provided for convenience in tests and examples - `endog_name`: the name of the endog attribute - `exog_name`: the names of the exog attribute This contains enough information to get all useful information through introspection and simple functions. Further, attributes are easily added that may be useful for other packages. Adding a dataset ---------------- See the :ref:`notes on adding a dataset `. Example Usage ------------- :: >>> from statsmodels import datasets >>> data = datasets.longley.load() Remaining problems: ------------------- - If the dataset is big and cannot fit into memory, what kind of API do we want to avoid loading all the data in memory ? Can we use memory mapped arrays ? - Missing data: I thought about subclassing both record arrays and masked arrays classes, but I don't know if this is feasable, or even makes sense. I have the feeling that some Data mining software use Nan (for example, weka seems to use float internally), but this prevents them from representing integer data. - What to do with non-float data, i.e., strings or categorical variables? Current implementation ---------------------- An implementation following the above design is available in `statsmodels`. Note ---- Although the datasets package emerged from the learn package, we try to keep it independant from everything else, that is once we agree on the remaining problems and where the package should go, it can easily be put elsewhere without too much trouble. If there is interest in re-using the datasets package, please contact the developers on the `mailing list `_. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/000077500000000000000000000000001224417117700241505ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/anes96.rst000066400000000000000000000051401224417117700260070ustar00rootroot00000000000000American National Election Survey 1996 ====================================== Description ----------- This data is a subset of the American National Election Studies of 1996. Notes ----- Number of observations - 944 Numner of variables - 10 Variables name definitions:: popul - Census place population in 1000s TVnews - Number of times per week that respondent watches TV news. PID - Party identification of respondent. 0 - Strong Democrat 1 - Weak Democrat 2 - Independent-Democrat 3 - Independent-Indpendent 4 - Independent-Republican 5 - Weak Republican 6 - Strong Republican age : Age of respondent. educ - Education level of respondent 1 - 1-8 grades 2 - Some high school 3 - High school graduate 4 - Some college 5 - College degree 6 - Master's degree 7 - PhD income - Income of household 1 - None or less than $2,999 2 - $3,000-$4,999 3 - $5,000-$6,999 4 - $7,000-$8,999 5 - $9,000-$9,999 6 - $10,000-$10,999 7 - $11,000-$11,999 8 - $12,000-$12,999 9 - $13,000-$13,999 10 - $14,000-$14.999 11 - $15,000-$16,999 12 - $17,000-$19,999 13 - $20,000-$21,999 14 - $22,000-$24,999 15 - $25,000-$29,999 16 - $30,000-$34,999 17 - $35,000-$39,999 18 - $40,000-$44,999 19 - $45,000-$49,999 20 - $50,000-$59,999 21 - $60,000-$74,999 22 - $75,000-89,999 23 - $90,000-$104,999 24 - $105,000 and over vote - Expected vote 0 - Clinton 1 - Dole The following 3 variables all take the values: 1 - Extremely liberal 2 - Liberal 3 - Slightly liberal 4 - Moderate 5 - Slightly conservative 6 - Conservative 7 - Extremely Conservative selfLR - Respondent's self-reported political leanings from "Left" to "Right". ClinLR - Respondents impression of Bill Clinton's political leanings from "Left" to "Right". DoleLR - Respondents impression of Bob Dole's political leanings from "Left" to "Right". logpopul - log(popul + .1) Source ------ http://www.electionstudies.org/ The American National Election Studies. Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/cancer.rst000066400000000000000000000010761224417117700261410ustar00rootroot00000000000000Breast Cancer Data ================== Description ----------- The number of breast cancer observances in various counties Notes ----- Number of observations: 301 Number of variables: 2 Variable name definitions: cancer - The number of breast cancer observances population - The population of the county Source ------ This is the breast cancer data used in Owen's empirical likelihood. It is taken from Rice, J.A. Mathematical Statistics and Data Analysis. http://www.thomsonedu.com/statistics/discipline_content/dataLibrary.html Copyright --------- ??? statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/ccard.rst000066400000000000000000000011671224417117700257630ustar00rootroot00000000000000Bill Greene's credit scoring data. ================================== Description ----------- More information on this data can be found on the homepage for Greene's `Econometric Analysis`. See source. Notes ----- Number of observations - 72 Number of variables - 5 Variable name definitions - See Source for more information on the variables. Source ------ William Greene's `Econometric Analysis` More information can be found at the web site of the text: http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm Copyright --------- Used with express permission of the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/committee.rst000066400000000000000000000032661224417117700266770ustar00rootroot00000000000000First 100 days of the US House of Representatives 1995 ====================================================== Description ----------- The example in Gill, seeks to explain the number of bill assignments in the first 100 days of the US' 104th House of Representatives. The response variable is the number of bill assignments in the first 100 days over 20 Committees. The explanatory variables in the example are the number of assignments in the first 100 days of the 103rd House, the number of members on the committee, the number of subcommittees, the log of the number of staff assigned to the committee, a dummy variable indicating whether the committee is a high prestige committee, and an interaction term between the number of subcommittees and the log of the staff size. The data returned by load are not cleaned to represent the above example. Notes ----- Number of Observations - 20 Number of Variables - 6 Variable name definitions:: BILLS104 - Number of bill assignments in the first 100 days of the 104th House of Representatives. SIZE - Number of members on the committee. SUBS - Number of subcommittees. STAFF - Number of staff members assigned to the committee. PRESTIGE - PRESTIGE == 1 is a high prestige committee. BILLS103 - Number of bill assignments in the first 100 days of the 103rd House of Representatives. Committee names are included as a variable in the data file though not returned by load. Source ------ Jeff Gill's `Generalized Linear Models: A Unifited Approach` http://jgill.wustl.edu/research/books.html Copyright --------- Used with express permission from the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/copper.rst000066400000000000000000000024161224417117700261750ustar00rootroot00000000000000World Copper Market 1951-1975 Dataset ===================================== Description ----------- This data describes the world copper market from 1951 through 1975. In an example, in Gill, the outcome variable (of a 2 stage estimation) is the world consumption of copper for the 25 years. The explanatory variables are the world consumption of copper in 1000 metric tons, the constant dollar adjusted price of copper, the price of a substitute, aluminum, an index of real per capita income base 1970, an annual measure of manufacturer inventory change, and a time trend. Notes ----- Number of Observations - 25 Number of Variables - 6 Variable name definitions:: WORLDCONSUMPTION - World consumption of copper (in 1000 metric tons) COPPERPRICE - Constant dollar adjusted price of copper INCOMEINDEX - An index of real per capita income (base 1970) ALUMPRICE - The price of aluminum INVENTORYINDEX - A measure of annual manufacturer inventory trend TIME - A time trend Years are included in the data file though not returned by load. Source ------ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html Copyright --------- Used with express permission from the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/cpunish.rst000066400000000000000000000030151224417117700263520ustar00rootroot00000000000000US Capital Punishment dataset. ============================== Description ----------- This data describes the number of times capital punishment is implemented at the state level for the year 1997. The outcome variable is the number of executions. There were executions in 17 states. Included in the data are explanatory variables for median per capita income in dollars, the percent of the population classified as living in poverty, the percent of Black citizens in the population, the rate of violent crimes per 100,000 residents for 1996, a dummy variable indicating whether the state is in the South, and (an estimate of) the proportion of the population with a college degree of some kind. Notes ----- Number of Observations - 17 Number of Variables - 7 Variable name definitions:: EXECUTIONS - Executions in 1996 INCOME - Median per capita income in 1996 dollars PERPOVERTY - Percent of the population classified as living in poverty PERBLACK - Percent of black citizens in the population VC100k96 - Rate of violent crimes per 100,00 residents for 1996 SOUTH - SOUTH == 1 indicates a state in the South DEGREE - An esimate of the proportion of the state population with a college degree of some kind State names are included in the data file, though not returned by load. Source ------ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html Copyright --------- Used with express permission from the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/elnino.rst000066400000000000000000000013571224417117700261740ustar00rootroot00000000000000El Nino - Sea Surface Temperatures ================================== Description ----------- This data contains the averaged monthly sea surface temperature in degrees Celcius of the Pacific Ocean, between 0-10 degrees South and 90-80 degrees West, from 1950 to 2010. This dataset was obtained from NOAA. Notes ----- Number of Observations - 61 x 12 Number of Variables - 1 Variable name definitions:: TEMPERATURE - average sea surface temperature in degrees Celcius (12 columns, one per month). Source ------ National Oceanic and Atmospheric Administration's National Weather Service ERSST.V3B dataset, Nino 1+2 http://www.cpc.ncep.noaa.gov/data/indices/ Copyright --------- This data is in the public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/fair.rst000066400000000000000000000034511224417117700256260ustar00rootroot00000000000000Affairs dataset =============== Description ----------- Extramarital affair data used to explain the allocation of an individual's time among work, time spent with a spouse, and time spent with a paramour. The data is used as an example of regression with censored data. Notes ----- Number of observations: 6366 Number of variables: 9 Variable name definitions:: rate_marriage : How rate marriage, 1 = very poor, 2 = poor, 3 = fair, 4 = good, 5 = very good age : Age yrs_married : No. years married. Interval approximations. See original paper for detailed explanation. children : No. children religious : How relgious, 1 = not, 2 = mildly, 3 = fairly, 4 = strongly educ : Level of education, 9 = grade school, 12 = high school, 14 = some college, 16 = college graduate, 17 = some graduate school, 20 = advanced degree occupation : 1 = student, 2 = farming, agriculture; semi-skilled, or unskilled worker; 3 = white-colloar; 4 = teacher counselor social worker, nurse; artist, writers; technician, skilled worker, 5 = managerial, administrative, business, 6 = professional with advanced degree occupation_husb : Husband's occupation. Same as occupation. affairs : measure of time spent in extramarital affairs See the original paper for more details. Source ------ Fair, Ray. 1978. "A Theory of Extramarital Affairs," `Journal of Political Economy`, February, 45-61. The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm Copyright --------- Included with permission of the author. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/grunfeld.rst000066400000000000000000000022561224417117700265150ustar00rootroot00000000000000Grunfeld (1950) Investment Data =============================== Description ----------- Grunfeld (1950) Investment Data for 11 U.S. Firms. Notes ----- Number of observations - 220 (20 years for 11 firms) Number of variables - 5 Variables name definitions:: invest - Gross investment in 1947 dollars value - Market value as of Dec. 31 in 1947 dollars capital - Stock of plant and equipment in 1947 dollars firm - General Motors, US Steel, General Electric, Chrysler, Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear, Diamond Match, American Steel year - 1935 - 1954 Note that raw_data has firm expanded to dummy variables, since it is a string categorical variable. Source ------ This is the Grunfeld (1950) Investment Data. The source for the data was the original 11-firm data set from Grunfeld's Ph.D. thesis recreated by Kleiber and Zeileis (2008) "The Grunfeld Data at 50". The data can be found here. http://statmath.wu-wien.ac.at/~zeileis/grunfeld/ For a note on the many versions of the Grunfeld data circulating see: http://www.stanford.edu/~clint/bench/grunfeld.htm Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/heart.rst000066400000000000000000000011471224417117700260100ustar00rootroot00000000000000Transplant Survival Data ======================== Description ----------- This data contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored. Notes ----- Number of Observations - 69 Number of Variables - 3 Variable name definitions:: death - Days after surgery until death age - age at the time of surgery censored - indicates if an observation is censored. 1 is uncensored Source ------ Miller, R. (1976). Least squares regression with censored dara. Biometrica, 63 (3). 449-464. Copyright --------- ??? statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/longley.rst000066400000000000000000000016231224417117700263550ustar00rootroot00000000000000Longley dataset =============== Description ----------- The Longley dataset contains various US macroeconomic variables that are known to be highly collinear. It has been used to appraise the accuracy of least squares routines. Notes ----- Number of Observations - 16 Number of Variables - 6 Variable name definitions:: TOTEMP - Total Employment GNPDEFL - GNP deflator GNP - GNP UNEMP - Number of unemployed ARMED - Size of armed forces POP - Population YEAR - Year (1947 - 1962) Source ------ The classic 1967 Longley Data http://www.itl.nist.gov/div898/strd/lls/data/Longley.shtml :: Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the Electronic Comptuer from the Point of View of the User." Journal of the American Statistical Association. 62.319, 819-41. Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/macrodata.rst000066400000000000000000000041361224417117700266410ustar00rootroot00000000000000United States Macroeconomic data ================================ Description ----------- US Macroeconomic Data for 1959Q1 - 2009Q3 Notes ----- Number of Observations - 203 Number of Variables - 14 Variable name definitions:: year - 1959q1 - 2009q3 quarter - 1-4 realgdp - Real gross domestic product (Bil. of chained 2005 US$, seasonally adjusted annual rate) realcons - Real personal consumption expenditures (Bil. of chained 2005 US$, seasonally adjusted annual rate) realinv - Real gross private domestic investment (Bil. of chained 2005 US$, seasonally adjusted annual rate) realgovt - Real federal consumption expenditures & gross investment (Bil. of chained 2005 US$, seasonally adjusted annual rate) realdpi - Real private disposable income (Bil. of chained 2005 US$, seasonally adjusted annual rate) cpi - End of the quarter consumer price index for all urban consumers: all items (1982-84 = 100, seasonally adjusted). m1 - End of the quarter M1 nominal money stock (Seasonally adjusted) tbilrate - Quarterly monthly average of the monthly 3-month treasury bill: secondary market rate unemp - Seasonally adjusted unemployment rate (%) pop - End of the quarter total population: all ages incl. armed forces over seas infl - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400) realint - Real interest rate (tbilrate - infl) Source ------ Compiled by Skipper Seabold. All data are from the Federal Reserve Bank of St. Louis [1] except the unemployment rate which was taken from the National Bureau of Labor Statistics [2]. :: [1] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve Bank of St. Louis; http://research.stlouisfed.org/fred2/; accessed December 15, 2009. [2] Data Source: Bureau of Labor Statistics, U.S. Department of Labor; http://www.bls.gov/data/; accessed December 15, 2009. Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/randhie.rst000066400000000000000000000026271224417117700263230ustar00rootroot00000000000000RAND Health Insurance Experiment Data ===================================== Description ----------- Notes ----- Number of observations - 20,190 Number of variables - 10 Variable name definitions:: mdvis - Number of outpatient visits to an MD lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100 idp - 1 if individual deductible plan, 0 otherwise lpi - ln(max(1, annual participation incentive payment)) fmde - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise physlm - 1 if the person has a physical limitation disea - number of chronic diseases hlthg - 1 if self-rated health is good hlthf - 1 if self-rated health is fair hlthp - 1 if self-rated health is poor (Omitted category is excellent self-rated health) Source ------ The data was collected by the RAND corporation as part of the Health Insurance Experiment (HIE). http://www.rand.org/health/projects/hie/ This data was used in:: Cameron, A.C. amd Trivedi, P.K. 2005. `Microeconometrics: Methods and Applications,` Cambridge: New York. And was obtained from: See randhie/src for the original data and description. The data included here contains only a subset of the original data. The data varies slightly compared to that reported in Cameron and Trivedi. Copyright --------- This is in the public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/scotland.rst000066400000000000000000000035041224417117700265130ustar00rootroot00000000000000Taxation Powers Vote for the Scottish Parliamant 1997 ===================================================== Description ----------- This data is based on the example in Gill and describes the proportion of voters who voted Yes to grant the Scottish Parliament taxation powers. The data are divided into 32 council districts. This example's explanatory variables include the amount of council tax collected in pounds sterling as of April 1997 per two adults before adjustments, the female percentage of total claims for unemployment benefits as of January, 1998, the standardized mortality rate (UK is 100), the percentage of labor force participation, regional GDP, the percentage of children aged 5 to 15, and an interaction term between female unemployment and the council tax. The original source files and variable information are included in /scotland/src/ Notes ----- Number of Observations - 32 (1 for each Scottish district) Number of Variables - 8 Variable name definitions:: YES - Proportion voting yes to granting taxation powers to the Scottish parliament. COUTAX - Amount of council tax collected in pounds steling as of April '97 UNEMPF - Female percentage of total unemployment benefits claims as of January 1998 MOR - The standardized mortality rate (UK is 100) ACT - Labor force participation (Short for active) GDP - GDP per county AGE - Percentage of children aged 5 to 15 in the county COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF Council district names are included in the data file, though are not returned by load. Source ------ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html Copyright --------- Used with express permission from the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/spector.rst000066400000000000000000000016751224417117700263720ustar00rootroot00000000000000Spector and Mazzeo (1980) - Program Effectiveness Data ====================================================== Description ----------- Experimental data on the effectiveness of the personalized system of instruction (PSI) program Notes ----- Number of Observations - 32 Number of Variables - 4 Variable name definitions:: Grade - binary variable indicating whether or not a student's grade improved. 1 indicates an improvement. TUCE - Test score on economics test PSI - participation in program GPA - Student's grade point average Source ------ http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm The raw data was downloaded from Bill Greene's Econometric Analysis web site, though permission was obtained from the original researcher, Dr. Lee Spector, Professor of Economics, Ball State University. Copyright --------- Used with express permission of the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/stackloss.rst000066400000000000000000000015201224417117700267060ustar00rootroot00000000000000Stack loss data =============== Description ----------- The stack loss plant data of Brownlee (1965) contains 21 days of measurements from a plant's oxidation of ammonia to nitric acid. The nitric oxide pollutants are captured in an absorption tower. Notes ----- Number of Observations - 21 Number of Variables - 4 Variable name definitions:: STACKLOSS - 10 times the percentage of ammonia going into the plant that escapes from the absoroption column AIRFLOW - Rate of operation of the plant WATERTEMP - Cooling water temperature in the absorption tower ACIDCONC - Acid concentration of circulating acid minus 50 times 10. Source ------ Brownlee, K. A. (1965), "Statistical Theory and Methodology in Science and Engineering", 2nd edition, New York:Wiley. Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/star98.rst000066400000000000000000000042271224417117700260410ustar00rootroot00000000000000Star98 Educational Dataset ========================== Description ----------- This data is on the California education policy and outcomes (STAR program results for 1998. The data measured standardized testing by the California Department of Education that required evaluation of 2nd - 11th grade students by the the Stanford 9 test on a variety of subjects. This dataset is at the level of the unified school district and consists of 303 cases. The binary response variable represents the number of 9th graders scoring over the national median value on the mathematics exam. The data used in this example is only a subset of the original source. Notes ----- Number of Observations - 303 (counties in California). Number of Variables - 13 and 8 interaction terms. Definition of variables names:: NABOVE - Total number of students above the national median for the math section. NBELOW - Total number of students below the national median for the math section. LOWINC - Percentage of low income students PERASIAN - Percentage of Asian student PERBLACK - Percentage of black students PERHISP - Percentage of Hispanic students PERMINTE - Percentage of minority teachers AVYRSEXP - Sum of teachers' years in educational service divided by the number of teachers. AVSALK - Total salary budget including benefits divided by the number of full-time teachers (in thousands) PERSPENK - Per-pupil spending (in thousands) PTRATIO - Pupil-teacher ratio. PCTAF - Percentage of students taking UC/CSU prep courses PCTCHRT - Percentage of charter schools PCTYRRND - Percentage of year-round schools The below variables are interaction terms of the variables defined above. PERMINTE_AVYRSEXP PEMINTE_AVSAL AVYRSEXP_AVSAL PERSPEN_PTRATIO PERSPEN_PCTAF PTRATIO_PCTAF PERMINTE_AVTRSEXP_AVSAL PERSPEN_PTRATIO_PCTAF Source ------ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html Copyright --------- Used with express permission from the original author, who retains all rights. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/statecrime.rst000066400000000000000000000033761224417117700270530ustar00rootroot00000000000000Statewide Crime Data 2009 ========================= Description ----------- State crime data 2009 Notes ----- Number of observations: 51 Number of variables: 8 Variable name definitions: state All 50 states plus DC. violent Rate of violent crimes / 100,000 population. Includes murder, forcible rape, robbery, and aggravated assault. Numbers for Illinois and Minnesota do not include forcible rapes. Footnote included with the American Statistical Abstract table reads: "The data collection methodology for the offense of forcible rape used by the Illinois and the Minnesota state Uniform Crime Reporting (UCR) Programs (with the exception of Rockford, Illinois, and Minneapolis and St. Paul, Minnesota) does not comply with national UCR guidelines. Consequently, their state figures for forcible rape and violent crime (of which forcible rape is a part) are not published in this table." murder Rate of murders / 100,000 population. hs_grad Precent of population having graduated from high school or higher. poverty % of individuals below the poverty line white Percent of population that is one race - white only. From 2009 American Community Survey single Calculated from 2009 1-year American Community Survey obtained obtained from Census. Variable is Male householder, no wife present, family household combined with Female household, no husband prsent, family household, divided by the total number of Family households. urban % of population in Urbanized Areas as of 2010 Census. Urbanized Areas are area of 50,000 or more people. Source ------ All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below. Copyright --------- Public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/strikes.rst000066400000000000000000000014721224417117700263720ustar00rootroot00000000000000U.S. Strike Duration Data ========================= Description ----------- Contains data on the length of strikes in US manufacturing and unanticipated industrial production. The data is a subset of the data originally used by Kennan. The data here is data for the months of June only to avoid seasonal issues. Notes ----- Number of observations - 62 Number of variables - 2 Variable name definitions:: duration - duration of the strike in days iprod - unanticipated industrial production Source ------ This is a subset of the data used in Kennan (1985). It was originally published by the Bureau of Labor Statistics. :: Kennan, J. 1985. "The duration of contract strikes in US manufacturing. `Journal of Econometrics` 28.1, 5-28. Copyright --------- This is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/generated/sunspots.rst000066400000000000000000000012401224417117700265750ustar00rootroot00000000000000Yearly sunspots data 1700-2008 ============================== Description ----------- Yearly (1700-2008) data on sunspots from the National Geophysical Data Center. Notes ----- Number of Observations - 309 (Annual 1700 - 2008) Number of Variables - 1 Variable name definitions:: SUNACTIVITY - Number of sunspots for each year The data file contains a 'YEAR' variable that is not returned by load. Source ------ http://www.ngdc.noaa.gov/stp/SOLAR/ftpsunspotnumber.html The original dataset contains monthly data on sunspot activity in the file ./src/sunspots_yearly.dat. There is also sunspots_monthly.dat. Copyright --------- This data is public domain. statsmodels-0.5.0+git13-g8e07d34/docs/source/datasets/index.rst000066400000000000000000000064111224417117700240550ustar00rootroot00000000000000.. _datasets: .. currentmodule:: statsmodels.datasets .. ipython:: python :suppress: import numpy as np np.set_printoptions(suppress=True) The Datasets Package ==================== ``statsmodels`` provides data sets (i.e. data *and* meta-data) for use in examples, tutorials, model testing, etc. Using Datasets from R --------------------- The `Rdatasets project `__ gives access to the datasets available in R's core datasets package and many other common R packages. All of these datasets are available to statsmodels by using the :func:`get_rdataset` function. For example: .. ipython:: python import statsmodels.api as sm duncan_prestige = sm.datasets.get_rdataset("Duncan", "car") print duncan_prestige.__doc__ duncan_prestige.data.head(5) R Datasets Function Reference ----------------------------- .. autosummary:: :toctree: ./ get_rdataset get_data_home clear_data_home Available Datasets ------------------ .. toctree:: :maxdepth: 1 :glob: generated/* Usage ----- Load a dataset: .. ipython:: python import statsmodels.api as sm data = sm.datasets.longley.load() The `Dataset` object follows the bunch pattern explained in :ref:`proposal `. Most datasets hold convenient representations of the data in the attributes `endog` and `exog`: .. ipython:: python data.endog[:5] data.exog[:5,:] Univariate datasets, however, do not have an `exog` attribute. Variable names can be obtained by typing: .. ipython:: python data.endog_name data.exog_name If the dataset does not have a clear interpretation of what should be an `endog` and `exog`, then you can always access the `data` or `raw_data` attributes. This is the case for the `macrodata` dataset, which is a collection of US macroeconomic data rather than a dataset with a specific example in mind. The `data` attribute contains a record array of the full dataset and the `raw_data` attribute contains an ndarray with the names of the columns given by the `names` attribute. .. ipython:: python type(data.data) type(data.raw_data) data.names Loading data as pandas objects ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For many users it may be preferable to get the datasets as a pandas DataFrame or Series object. Each of the dataset modules is equipped with a ``load_pandas`` method which returns a ``Dataset`` instance with the data as pandas objects: .. ipython:: python data = sm.datasets.longley.load_pandas() data.exog data.endog With pandas integration in the estimation classes, the metadata will be attached to model results: .. ipython:: python y, x = data.endog, data.exog res = sm.OLS(y, x).fit() res.params res.summary() Extra Information ^^^^^^^^^^^^^^^^^ If you want to know more about the dataset itself, you can access the following, again using the Longley dataset as an example :: >>> dir(sm.datasets.longley)[:6] ['COPYRIGHT', 'DESCRLONG', 'DESCRSHORT', 'NOTE', 'SOURCE', 'TITLE'] Additional information ---------------------- * The idea for a datasets package was originally proposed by David Cournapeau and can be found :ref:`here ` with updates by Skipper Seabold. * To add datasets, see the :ref:`notes on adding a dataset `. statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/000077500000000000000000000000001224417117700211605ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/dataset_notes.rst000066400000000000000000000051061224417117700245510ustar00rootroot00000000000000.. _add_data: Datasets ======== For a list of currently available datasets and usage instructions, see the :ref:`datasets page `. License ------- To be considered for inclusion in `statsmodels`, a dataset must be in the public domain, distributed under a BSD-compatible license, or we must obtain permission from the original author. Adding a dataset: An example ---------------------------- The Nile River data measures the volume of the discharge of the Nile River at Aswan for the years 1871 to 1970. The data are copied from the paper of Cobb (1978). **Step 1**: Create a directory `datasets/nile/` **Step 2**: Add `datasets/nile/nile.csv` and a new file `datasets/__init__.py` which contains :: from data import * **Step 3**: If `nile.csv` is a transformed/cleaned version of the original data, create a `nile/src` directory and include the original raw data there. In the `nile` case, this step is not necessary. **Step 4**: Copy `datasets/template_data.py` to `nile/data.py`. Edit `nile/data.py` by filling-in strings for COPYRIGHT, TITLE, SOURCE, DESCRSHORT, DESCLONG, and NOTE. :: COPYRIGHT = """This is public domain.""" TITLE = """Nile River Data""" SOURCE = """ Cobb, G.W. 1978. The Problem of the Nile: Conditional Solution to a Changepoint Problem. Biometrika. 65.2, 243-251, """ DESCRSHORT = """Annual Nile River Volume at Aswan, 1871-1970"" DESCRLONG = """AAnnual Nile River Volume at Aswan, 1871-1970. The units of measurement are 1e9 m^{3}, and there is an apparent changepoint near 1898.""" NOTE = """ Number of observations: 100 Number of variables: 2 Variable name definitions: year - Year of observation volume - Nile River volume at Aswan The data were originally used in Cobb (1987, See SOURCE). The author acknowledges that the data were originally compiled from various sources by Dr. Barbara Bell, Center for Astrophysics, Cambridge, Massachusetts. The data set is also used as an example in many textbooks and software packages. """ **Step 5:** Edit the docstring of the `load` function in `data.py` to specify which dataset will be loaded. Also edit the path and the indices for the `endog` and `exog` attributes. In the `nile` case, there is no `exog`, so everything referencing `exog` is not used. The `year` variable is also not used. **Step 6:** Edit the `datasets/__init__.py` to import the directory. That's it! The result can be found `here `_ for reference. statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/examples.rst000066400000000000000000000055531224417117700235400ustar00rootroot00000000000000.. _examples: Examples ======== Examples are invaluable for new users who hope to get up and running quickly with `statsmodels`, and they are extremely useful to those who wish to explore new features of `statsmodels`. We hope to provide documentation and tutorials for as many models and use-cases as possible! Most user-contributed examples/tutorials/recipes should be placed on the `statsmodels examples wiki page `_ That wiki page is freely editable. Please post your cool tricks, examples, and recipes on there! If you would rather have your example file officially accepted to the `statsmodels` distribution and posted on this website, you will need to go through the normal `patch submission process `_. File Format ~~~~~~~~~~~ Examples are simple runnable python scripts that go in the top-level examples directory. We use the `ipython_directive for Sphinx `_ to convert them automatically to `reStructuredText `_ and html at build time. Each line of the script is executed; both the python code and the printed results are shown in the output file. Lines that are commented out using the hash symbol ``#`` are rendered as reST markup. **Comments**: "True" comments that should not appear in the output file should be written on lines that start with ``#..``. **Error handling**: Syntax errors in pure Python will raise an error during the build process. If you need to show a SyntaxError, an alternative would be to provide a verbatim copy of an IPython session encased in a ReST code block instead of pure Python code. **Suppressing lines**: To suppress a line in the built documentation, follow it with a semicolon. **Figures**: To save a figure, prepend the line directly before the plotting command with ``#@savefig file_name.png width=4in``, for example. You do not need to call show or close. **IPython magics**: You can use IPython magics by writing a line like this: ``#%timeit X = np.empty((1000,1000))``. Make Life Easier ~~~~~~~~~~~~~~~~ To save you some time and to make the new examples nicely fit into the existing ones consider the following points. **Look at examples source code** to get a feel for how statsmodels examples should look like. **PEP8 syntax checker** install a [PEP8] http://pypi.python.org/pypi/pep8 syntax checker for you editor. It will not only make your code look nicer but also serves as `pre-debugger`. Note that some of doc directives explained above imply pep8 violations. Also, for the sake of readability it's a local convention not to add white spaces around power operators, e.g. `x * 2 + y**2 + z`. **build docs** run `make html` from the docs directory to see how your example looks in the fully rendered html pages. statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/git_notes.rst000066400000000000000000000226451224417117700237160ustar00rootroot00000000000000Working with the Statsmodels Code ================================= Github ------ The `statsmodels` code base is hosted on `Github `_. To contribute you will need to `sign up for a free Github account `_. Version Control and Git ----------------------- We use the `Git `_ version control system for development. Git allows many people to work together on the same project. In a nutshell, it allows you to make changes to the code independent of others who may also be working on the code and allows you to easily contribute your changes to the codebase. It also keeps a complete history of all changes to the code, so you can easily undo changes or see when a change was made, by whom, and why. To install and configure Git, and to setup SSH keys, see: + `Linux users `_ + `Windows users `_ + `Mac users `_ To learn more about Git, you may want to visit: + `Git documentation (book and videos) `_ + `Github help pages `_ + `NumPy documentation `_ + `Matthew Brett's Pydagogue `_ Below, we describe the bare minimum git commands you need to contribute to `statsmodels`. Statsmodels Git/Github Workflow ------------------------------- Forking and cloning ~~~~~~~~~~~~~~~~~~~ After setting up git, you need to fork the main `statsmodels` repository. To do this, visit the `statsmodels project page `_ and hit the fork button (see `this page `_ for details). This should take you to your fork's page. Then, you want to clone the fork to your machine:: git clone git@github.com:your-user-name/statsmodels.git statsmodels-yourname cd statsmodels-yourname git remote add upstream git://github.com/statsmodels/statsmodels.git The first line creates a directory named `statsmodels-yourname`. The third line sets-up a read-only connection to the upstream statsmodels repository. This will allow you to periodically update your local code with changes in the upstream. Create a Branch ~~~~~~~~~~~~~~~ All changes to the code should be made in a feature branch. To create a branch, type:: git branch shiny-new-feature git checkout shiny-new-feature Doing:: git branch will give something like:: * shiny-new-feature master to indicate that you are now on the `shiny-new-feature` branch. Making changes ~~~~~~~~~~~~~~ Hack away! Make any changes that you want, but please keep the work in your branch completely confined to one specific topic, bugfix, or feature implementation. You can work across multiple files and have many commits, but the changes should all be related to the feature of the feature branch, whatever that may be. Now imagine that you changed the file `foo.py`. You can see your changes by typing:: git status This will print something like:: # On branch shiny-new-feature # Changes not staged for commit: # (use "git add ..." to update what will be committed) # (use "git checkout -- ..." to discard changes in working directory) # # modified: relative/path/to/foo.py # no changes added to commit (use "git add" and/or "git commit -a") Before you can commit these changes, you have to `add`, or `stage`, the changes. You can do this by typing:: git add path/to/foo.py Then check the status to make sure your commit looks okay:: git status should give something like:: # On branch shiny-new-feature # Changes to be committed: # (use "git reset HEAD ..." to unstage) # # modified: /relative/path/to/foo.py # Pushing your changes ~~~~~~~~~~~~~~~~~~~~ At any time you can push your feature branch (and any changes) to your github (fork) repository by:: git push origin shiny-new-feature Here `origin` is the default name given to your remote repository. You can see the remote repositories by:: git remote -v If you added the upstream repository as described above you will see something like:: origin git@github.com:yourname/statsmodels.git (fetch) origin git@github.com:yourname/statsmodels.git (push) upstream git://github.com/statsmodels/statsmodels.git (fetch) upstream git://github.com/statsmodels/statsmodels.git (push) Before you push any commits, however, it is *highly* recommended that you make sure what you are pushing makes sense and looks clean. You can review your change history by:: git log --oneline --graph It pays to take care of things locally before you push them to github. So when in doubt, don't push. Also see the advice on keeping your history clean in :ref:`merge-vs-rebase`. .. _pull-requests: Pull Requests ~~~~~~~~~~~~~ When you are ready to ask for a code review, we recommend that you file a pull request. Before you do so you should check your changeset yourself. You can do this by using `compare view `__ on github. #. Navigate to your repository on github. #. Click on `Branch List` #. Click on the `Compare` button for your feature branch, `shiny-new-feature`. #. Select the `base` and `compare` branches, if necessary. This will be `master` and `shiny-new-feature`, respectively. #. From here you will see a nice overview of your changes. If anything is amiss, you can fix it. If everything looks good you are read to make a `pull request `__. #. Navigate to your repository on github. #. Click on the `Pull Request` button. #. You can then click on `Commits` and `Files Changed` to make sure everything looks okay one last time. #. Write a description of your changes in the `Preview Discussion` tab. #. Click `Send Pull Request`. Your request will then be reviewed. If you need to go back and make more changes, you can make them in your branch and push them to github and the pull request will be automatically updated. One last thing to note. If there has been a lot of work in upstream/master since you started your patch, you might want to rebase. However, you can probably get away with not rebasing if these changes are unrelated to the work you have done in the `shiny-new-feature` branch. If you can avoid it, then don't rebase. If you have to, try to do it once and when you are at the end of your changes. Read on for some notes on :ref:`merge-vs-rebase`. Advanced Topics --------------- .. _merge-vs-rebase: Merging vs. Rebasing ~~~~~~~~~~~~~~~~~~~~ This is a topic that has been discussed at great length and with considerable more expertise than we can offer here. This section will provide some resources for further reading and some advice. The focus, though, will be for those who wish to submit pull requests for a feature branch. For these cases rebase should be preferred. A rebase replays commits from one branch on top of another branch to preserve a linear history. Recall that your commits were tested against a (possibly) older version of master from which you started your branch, so if you rebase, you could introduce bugs. However, if you have only a few commits, this might not be such a concern. One great place to start learning about rebase is :ref:`rebasing without tears `. In particular, `heed the warnings `__. Namely, **always make a new branch before doing a rebase**. This is good general advice for working with git. I would also add **never use rebase on work that has already been published**. If another developer is using your work, don't rebase!! As for merging, **never merge from trunk into your feature branch**. You will, however, want to check that your work will merge cleanly into trunk. This will help out the reviewers. You can do this in your local repository by merging your work into your master (or any branch that tracks remote master) and :ref:`run-tests`. Deleting Branches ~~~~~~~~~~~~~~~~~ Once your feature branch is accepted into upstream, you might want to get rid of it. First you'll want to merge upstream master into your branch. That way git will know that it can safely delete your branch:: git fetch upstream git checkout master git merge upstream/master Then you can just do:: git -d shiny-new-feature Make sure you use a lower-case -d. That way, git will complain if your feature branch has not actually been merged. The branch will still exist on github however. To delete the branch on github, do:: git push origin :shiny-new-feature branch .. Squashing with Rebase .. ^^^^^^^^^^^^^^^^^^^^^ .. You've made a bunch of incremental commits, but you think they might be better off together as one .. commit. You can do this with an interactive rebase. As usual, **only do this when you have local .. commits. Do not edit the history of changes that have been pushed.** .. see this reference http://gitready.com/advanced/2009/02/10/squashing-commits-with-rebase.html Git for Bzr Users ~~~~~~~~~~~~~~~~~ :: git pull != bzr pull :: git pull = git fetch + git merge Of course, you could:: git pull --rebase = git fetch + git rebase :: git merge != bzr merge git merge == bzr merge + bzr commit git merge --no-commit == bzr merge statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/images/000077500000000000000000000000001224417117700224255ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/images/git_merge.png000066400000000000000000001055401224417117700251020ustar00rootroot00000000000000‰PNG  IHDRÏž}È.tiCCPicmxÚíÚUP–Ñ×(ðõ~1Q†¶¶VðŸ˜Pð‰Š [Ðøp?5¹übɽó¶÷[¨Ùð_{´‹«b Œ ¿¹?0|ÿæ ÀØ €äÃ/ØÇ©ùh;cä‘ä`g @š R}ìŒØ_©>>ÑA¼_€éãÀÇö7÷ àSf¼_P,Ÿ-žáÀôÀëûÄøHžÿ¿p)"؆‡GúH¹€´_Tt,€Ô!wqucþ}–O€æ‚õj᧚×xŽý§&U À{ Ýî?µe;@áz¨¦ Õ÷rssY€P°^°¹¹znss½ó 7Ì/.:þï},ÈÀ à!iPUÐ]0S°pWð?†pˆ†}©¹'¡ÎÂy¸MÐ× ú`F` Á4ÌÂø_á¬"B@¨áA„ DQA¶ úˆ)b‰Ø!®ˆ7ˆ„#qH’†ä …Ȥ ¹€´ H/2„Œ!SÈ,²€,!+(ŠRP*€J Š¨j€Z ö¨ˆF¡ûÑ4ô(z­BÐ+h:ŒN¢3èô;º‰!c81LŒF c„±Æ¸b0Q˜$Læ¦Óˆ¹Ž¹ǼÄ|ÄüÆb±ìX!¬œ4N g†sÂàbqGpE¸j\;n7›Ç-ã1x^¯7Å;áƒð øl| ¾?ŠŸÅ#`œ)‚6ÁŠàIˆ"¤N.º£„9Âw"‘ÈOT"ˆ¡Ädb!±–ØI%Α¨$’&É’äMŠ#å*IWH#¤YÒO2•,JÖ&ÛÈIäBòEr/ù y‘‚£RÔ)–?J"¥ˆÒ@ <£,S©TqªÕ‰AͤVQ;©ÔÏ4É ËÚɦËvÈ~”’³“Ëë”[”•w’Ï‘ï‘ÿ® £à©P¤0¤°®¨¦¢X¡8©DQ2TÚ§Ô¤ôZ™_ÙN9[¹Où·Š²JÊY•'ª¬ªæª‡T;T—ÔdÕüÔÊÔ©³ª›«VïVÿ©¡¬ªQ£1£É§é¨™¯9¢…×2ÒJÖº®õc‹Ê–ð-õ[Þj‹i{k—kOëpë8èê<Ð¥éZêfëÞÙŠßj²5mk¿¢g —¢×£·¡¯¯Ÿ¬ScÛ¶m)Ûz ÀÀÐà°Á-Cœ¡™a–á°Õh§QÑ„1—±‹q©ñK““ &ŸL•McM;L׶lOß>lÆbfovÆ쥹˜yˆy³ùº;Rwܱ`±p´(µxe)k¹×²Ã ¬Ì­ò­¦¬™ÖÁÖ­Ö+;væí|d#ddÓj³jkj{Üöé.ñ]»®ÛaílìJìÞØ«Ø'Ù90¼.9üqÜîXè8ã$ïtÀéŽ3ÃÙ×¹ÅyÃÅÊ¥ÄeÁu‹k†ë#7q·X·ww?÷6Œ‡½GµÇ÷Ý&»Oî~ë©å™í9í%ï•ì5æ-êë}LJ×'̧Ǘîà{ÝêçíwÙŸèïá߀ p hĺ6aƒÜ‚Z‚ñÁÁm!¤¯«¡´PÿÐî={öìã‹   O ¡‘1©Yù9Ê,ª*ju¯ÓÞ–hJt`t_Œ@LBÌD¬Rlnì»8ã¸Ê¸µx×ø«û8öEíMKÈNx·ßtõô€÷žD¡Äƒ‰Ï“¶&•&­t?Ø•ÌŸœ”üÆ}Üü”ó™ësÕ¢übÇ“/c_=¾.,Å}C¿_X®ÿ®õýöûs?#nüÊûÍÿ»þöŸ»+®+ïWÖˆkgÖeÖ;7,7žoFü³À? ü³À? ü³À? ü³À? ü³À? ü³À? ü³À? ü³Àÿ¿ˆò‰ö  ßêh®cÌ÷üïå@œ  ^P ï $ù„z Ó?Ììyœ'^ ÿ›0E" ‘Ç(Ï©?Yh¬Zl»ÙÓéãŒ\tn m^>S~#eAn!Tèƒð$ó†H½h±XŽx²D´dˆ”—´³Œ³¬³œ³¼»B€b¸Ò~ål•ÕFµõU-î-[µÝu’tó·–ë5èwlë7x`8c´h¼fJÛ.d¦bn¸c—Ee¢Õqëš6#¶ó»VìÙ¤·9¹8ǹ»öº}ñÜmáïUí=æ ~ªþ¾ÅÁ”¡y{&Ãù"B"»öÒ£Ãc†ãdãí›HX<‰lIâÕ“MSly¦M‹>•šéše‘­‘#œ‹Í}—w÷hͱCÇwçÈÒ WŠæOŒœ¼|ªôtZqøçãR¥2þrRùŸŠgg*'«î»]}ã|gMMmF]Øû‹[ê™ Ä†/S—šššK[2Z£Û¼Ú­/ë^‘½Ê{ wí[ÇÌõ‘κ®”n§J7É7ßôô÷Vô%÷{Ü’¸M»ýcpî΃¡›w[†kFÊîÝÏÝ÷ `l×C½qÙ ¾IÂäGo?~rwªûiëtݳ³ÏO¿(zyt&w6k.ãUÞ|Áëü7Ùo¾‹y²àóÁçcЧ„Ïe‹ã_E—N-Ë~ýóñïÕÕÈ ÉÍM €ØÂ)ø‰è YÈkÔíÀhcîaqÜm|6Á¨Mb’¹(üTš‹«'Û~öbúUŽIÆ.àfááåáâç@Þ >º.\Å̉õ³7•P“”–”f•¡ÉåyD§HR¢)sªð«Jªiªo×pÓ Õ:¼å´v½N›nËÖ&½Kú·Õ\4¬7ª7n4i2mÝÞnvÕ¼sG¯ÅmË»V£ÖwNÙÌؾÞõÁnÉþ#ΉËYÑÅÒ5Øíˆ{¥ÇÝÏ|Ü>_¿@¶»[ôíÄ›“S§îŸ(î:Ó^ÒRÚTÖTÞZqålwe_Õй®êòó™51µ^uÖ¶^”¯j 77.-7-4϶LµÞk»ÝÞu¹ñJÅÕüki ×÷tîî²í6¾¡yS¦G —­Ó÷³ÿãÀÜ­ÉÛ÷îtµß½4\?R{ïâýžÑéßÆ&T&Ù?x?•õ´tºåÙàó¹+3|³ºsÞ¯2ç›_?}K~§ó>l¡öÃëObŸƒÛ¾¢KNßê¿cøÿ¼ý[êϱ•kîë77¹77, úÅðáFœrdµD[1"˜b,¶§‹{†O!Èž HÖdVò0%—º“ÆF»Ïr„U‡õ [9»9ûgz‡8Ç †3c‰3—KŠë·–§š×”wï¿ÿ AUÁy¡"aá?ÌF?QaÑgb§Å$x$¦$OJYJ}—®”±’ù%{^Î^#E!HQ@ñ¡R¦ò6å•+ª{Õ”Õ>©×k„hÊk~ÔjØ¡­¢ýCçšnâVc=²Þ¸~É6_9ƒŸ†}FG=LMÖMl?o–hn³CÆc1cÙmUb°ÓÕFÉfÃöÞ®2»{C^‡¯Ž#Nœ3\B]­Ý4Ü™4µÝŸ<_zMxùôú^ókò?Px8(2Ø;Ä>tûÝ0¥pÉf$_÷^ÎhŽz,GW<ß>¡‰ýòTÕ’x“–%ŸM‰>d–Ê›ºp¸+íØŸtõ BƳ̶¬Ülßœm¹¹+yOv+;¾7ߤ€»`±ðnQ݉¬“{N96)Ö8£T"_ªP¦V®_auÖ³2¶êø¹†êçjnÕÖ ]½ø¤~¶áSãZ¥Y¨E­Õ¼Í«=þòÑ+uWo^›êøÒIì2쮾±ÒcÑ{¶oc øÖì`øýžûãcÒãý.>eyÁòJô}ï²Àæ&Àßy:^à à4àˆ(hŠà&ØÒ´%ë²2ˆdàÿþ?8@LÁ  à6ÌÀ„(#VH(’‰Ô"ƒÈ;”Œ*¢hZ‡N`0 L(¦ó+Ž Â6b—qº¸tܼ>ßI vZˆ8¢;±…D$ù:É ò^ò}Š,%“ò–jF­¥‘ha´G,&,WX%YËØl¹ìXööuúú G"Ç&#“ÌYÀÅÏUíÊÝÏcÏóŠ7žÂwŽ_‡B L+X&¤"tGØ[x…yZD]dR4VŒ!vUÜM|]¢\ÒHòT®´¢ô™8Y^Ù.9Oy?§`¢ðV1WI^iTù€Š‰ª êµ'êíÅš‰ZA[viëèéªmÕÔÛ¦o±ÍÛ ÑðœÑ=ÔT{{²ÙкÅ.Ë|«É¼6¾¶—vmØÛ8T9þr¶p©v]wwô¸ä‰÷òò¾æËêæ?¨Ôb:f>õ;:=–3®fßÖ„‡B“0+StÍÎ>¢š>›Ùš}<7æ¨ÝqµFáê‰éS7Š+JÊœ+”+‰U3Õ—k2êœ.Ê4¿5Í·Lµ_¾uµ¿£§ózwÇÍ+½mýÍ·. 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«íÖ­[E¿.]ºô+µ–––¼q㆟ŸŸh3±¯ÔVä{zzjkk³ãOŸ>©¨¨0u&µ%B¾€ö)((ˆvZ„ÚŠºYAóæÍ7n\XCÿúë/¶X\\Ü¿ù¶ßUÛÈÈHü‰æ8Ú„<’““£¥¥/U¤¶B¡Š‰cx¯jjj‡*¬¡ð…•””bcc?|ø0räHœôòò*¦Ú^ºtIò[\“ÚQVèÝ»÷Š+Dj;vìX[[[H¤ªªêüùóÙy‘†Â«íÑ£þjjjº`Á\¾|¹˜j;±ÊpR[‚SÂÃÃÍÌÌXg‚¨ß6>>Þë¿ýË«W¯>~üX¢oINN611ùÉù¤¶Að›™3g9räó7oÉJ‘-[¶¬Y³†²šÔ– ˆ|,--EóÇR[‚ R[‚ ‚Ô– ‚Ô– ‚Ô– ‚ µ%‚àÿnSÀí&ŸIEND®B`‚statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/index.rst000066400000000000000000000026441224417117700230270ustar00rootroot00000000000000Developer Page -------------- This page explains how you can contribute to the development of `statsmodels` by submitting patches, statistical tests, new models, or examples. `statsmodels` is developed on `Github `_ using the `Git `_ version control system. License ~~~~~~~ Statsmodels is released under the `Modified (3-clause) BSD license `_. Submitting a Patch ~~~~~~~~~~~~~~~~~~~ So you want to submit a patch to `statsmodels`?. Great news! Here are the steps you need to take. 1. `Fork `_ the `statsmodels repository `_ on Github. 2. `Create a new feature branch `_ + Each branch must be self-contained, with a single new feature or bugfix. + Patches must always include tests. See our `notes on testing `_. 3. `Submit a pull request `_ Mailing List ~~~~~~~~~~~~ Conversations about development take place on the `statsmodels mailing list `__. Contents ~~~~~~~~ .. toctree:: :maxdepth: 3 git_notes maintainer_notes test_notes naming_conventions dataset_notes examples roadmap_todo internal statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/internal.rst000066400000000000000000000033321224417117700235270ustar00rootroot00000000000000.. _model: Internal Classes ================ The following summarizes classes and functions that are not intended to be directly used, but of interest only for internal use or for a developer who wants to extend on existing model classes. Module Reference ---------------- Model and Results Classes ^^^^^^^^^^^^^^^^^^^^^^^^^ These are the base classes for both the estimation models and the results. They are not directly useful, but layout the structure of the subclasses and define some common methods. .. currentmodule:: statsmodels.base.model .. autosummary:: :toctree: generated/ Model LikelihoodModel GenericLikelihoodModel Results LikelihoodModelResults ResultMixin GenericLikelihoodModelResults .. inheritance-diagram:: statsmodels.base.model statsmodels.discrete.discrete_model statsmodels.regression.linear_model statsmodels.miscmodels.count :parts: 3 .. inheritance-diagram:: statsmodels.regression.linear_model.GLS statsmodels.regression.linear_model.WLS statsmodels.regression.linear_model.OLS statsmodels.regression.linear_model.GLSAR :parts: 1 Linear Model ^^^^^^^^^^^^ .. inheritance-diagram:: statsmodels.regression.linear_model :parts: 1 Generalized Linear Model ^^^^^^^^^^^^^^^^^^^^^^^^ .. inheritance-diagram:: statsmodels.genmod.generalized_linear_model statsmodels.genmod.families.family statsmodels.genmod.families.links :parts: 1 Discrete Model ^^^^^^^^^^^^^^ .. inheritance-diagram:: statsmodels.discrete.discrete_model :parts: 1 Robust Model ^^^^^^^^^^^^ .. inheritance-diagram:: statsmodels.robust.robust_linear_model :parts: 1 Vector Autoregressive Model ^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. inheritance-diagram:: statsmodels.tsa.vector_ar.var_model :parts: 3 statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/maintainer_notes.rst000066400000000000000000000145421224417117700252570ustar00rootroot00000000000000Maintainer Notes ================ This is for those with read-write access to upstream. It is recommended to name the upstream remote something to remind you that it is read-write:: git remote add upstream-rw git@github.com:statsmodels/statsmodels.git git fetch upstream-rw Git Workflow ------------ Grabbing Changes from Others ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you need to push changes from others, you can link to their repository by doing:: git remote add contrib-name git://github.com/contrib-name/statsmodels.git get fetch contrib-name git branch shiny-new-feature --track contrib-name/shiny-new-feature git checkout shiny-new-feature The rest of the below assumes you are on your or someone else's branch with the changes you want to push upstream. .. _rebasing: Rebasing ~~~~~~~~ If there are only a few commits, you can rebase to keep a linear history:: git fetch upstream-rw git rebase upstream-rw/master Rebasing will not automatically close the pull request however, if there is one, so don't forget to do this. .. _merging: Merging ~~~~~~~ If there is a long series of related commits, then you'll want to merge. You may ask yourself, :ref:`ff-no-ff`? See below for more on this choice. Once decided you can do:: git fetch upstream-rw git merge --no-ff upstream-rw/master Merging will automaticall close the pull request on github. Check the History ~~~~~~~~~~~~~~~~~ This is very important. Again, any and all fixes should be made locally before pushing to the repository:: git log --oneline --graph This shows the history in a compact way of the current branch. This:: git log -p upstream-rw/master.. shows the log of commits excluding those that can be reached from upstream-rw/master, and including those that can be reached from current HEAD. That is, those changes unique to this branch versus upstream-rw/master. See :ref:`Pydagogue ` for more on using dots with log and also for using :ref:`dots with diff `. Push Your Feature Branch ~~~~~~~~~~~~~~~~~~~~~~~~ All the changes look good? You can push your feature branch after :ref:`merging` or :ref:`rebasing` by:: git push upstream-rw shiny-new-feature:master Cherry-Picking ~~~~~~~~~~~~~~ Say you are interested in some commit in another branch, but want to leave the other ones for now. You can do this with a cherry-pick. Use `git log --oneline` to find the commit that you want to cherry-pick. Say you want commit `dd9ff35` from the `shiny-new-feature` branch. You want to apply this commit to master. You simply do:: git checkout master git cherry-pick dd9ff35 And that's all. This commit is now applied as a new commit in master. .. _ff-no-ff: Merging: To Fast-Forward or Not To Fast-Forward ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ By default, `git merge` is a fast-forward merge. What does this mean, and when do you want to avoid this? .. figure:: images/git_merge.png :alt: git merge diagram :scale: 100% :align: center (source `nvie.com `__, post `"A successful Git branching model" `__) The fast-forward merge does not create a merge commit. This means that the existence of the feature branch is lost in the history. The fast-forward is the default for Git basically because branches are cheap and, therefore, *usually* short-lived. If on the other hand, you have a long-lived feature branch or are following an iterative workflow on the feature branch (i.e. merge into master, then go back to feature branch and add more commits), then it makes sense to include only the merge in the main branch, rather than all the intermediate commits of the feature branch, so you should use:: git merge --no-ff Handling Pull Requests ~~~~~~~~~~~~~~~~~~~~~~ You can apply a pull request through `fetch `__ and `merge `__. In your local copy of the main repo:: git checkout master git remote add contrib-name git://github.com/contrib-name/statsmodels.git git fetch contrib-name git merge contrib-name/shiny-new-feature Check that the merge applies cleanly and the history looks good. Edit the merge message. Add a short explanation of what the branch did along with a 'Closes gh-XXX.' string. This will auto-close the pull request and link the ticket and closing commit. To automatically close the issue, you can use any of:: gh-XXX GH-XXX #XXX in the commit message. Any and all problems need to be taken care of locally before doing:: git push origin master Releasing --------- #. Fix the version number. Open setup.py and set:: ISRELEASED = True #. Clean the working tree with:: git clean -xdf But you might want to do a dry-run first:: git clean -xdfn #. Tag the release. For a release candidate, for example:: git tag -a v0.3.0rc1 -m "Version 0.3.0 Release Candidate 1" 7b2fb29 #. If on a new minor release (major.minor.micro format) start a new maintenance branch, for example:: git checkout -b maintenance/0.3.x Any bug fixes and maintenance commits for this minor release go into this branch and are merged back into master. If we have another micro release, then we make the tag and do this release in this branch and then merge the changes back into master so the tag is reachable. Check that the version number is still ok after the merge, fixing any conflicts here. #. Upload the source distribution to PyPI:: python setup.py sdist --formats=gztar,zip register upload #. Go back to setup.py and set `isreleased = False` and bump the major version in master. #. Update the version numbers in the statsmodels/statsmodels-website repo. These are in conf.py. Also upload the released version docs to stable/. #. Make an announcment #. Profit Commit Comments --------------- Prefix commit messages in the master branch of the main shared repository with the following:: ENH: Feature implementation BUG: Bug fix STY: Coding style changes (indenting, braces, code cleanup) DOC: Sphinx documentation, docstring, or comment changes CMP: Compiled code issues, regenerating C code with Cython, etc. REL: Release related commit TST: Change to a test, adding a test. Only used if not directly related to a bug. REF: Refactoring changes statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/naming_conventions.rst000066400000000000000000000075251224417117700256210ustar00rootroot00000000000000Naming Conventions ------------------ File and Directory Names ~~~~~~~~~~~~~~~~~~~~~~~~ Our directory tree stripped down looks something like:: statsmodels/ __init__.py api.py discrete/ __init__.py discrete_model.py tests/ results/ tsa/ __init__.py api.py tsatools.py stattools.py arima_model.py arima_process.py vector_ar/ __init__.py var_model.py tests/ results/ tests/ results/ stats/ __init__.py api.py stattools.py tests/ tools/ __init__.py tools.py decorators.py tests/ The submodules are arranged by topic, `discrete` for discrete choice models, or `tsa` for time series analysis. The submodules that can be import heavy contain an empty __init__.py, except for some testing code for running tests for the submodules. The namespace to be imported in in `api.py`. That way, we can import selectively and not have to import a lot of code that we don't need. Helper functions are usually put in files named `tools.py` and statistical functions, such as statistical tests are placed in `stattools.py`. Everything has directories for :ref:`tests `. `endog` & `exog` ~~~~~~~~~~~~~~~~ Our working definition of a statistical model is an object that has both endogenous and exogenous data defined as well as a statistical relationship. In place of endogenous and exogenous one can often substitute the terms left hand side (LHS) and right hand side (RHS), dependent and independent variables, regressand and regressors, outcome and design, response variable and explanatory variable, respectively. The usage is quite often domain specific; however, we have chosen to use `endog` and `exog` almost exclusively, since the principal developers of statsmodels have a background in econometrics, and this feels most natural. This means that all of the models are objects with `endog` and `exog` defined, though in some cases `exog` is None for convenience (for instance, with an autoregressive process). Each object also defines a `fit` (or similar) method that returns a model-specific results object. In addition there are some functions, e.g. for statistical tests or convenience functions. See also the related explanation in :ref:`endog_exog`. Variable Names ~~~~~~~~~~~~~~ All of our models assume that data is arranged with variables in columns. Thus, internally the data is all 2d arrays. By convention, we will prepend a `k_` to variable names that indicate moving over axis 1 (columns), and `n_` to variables that indicate moving over axis 0 (rows). The main exception to the underscore is that `nobs` should indicate the number of observations. For example, in the time-series ARMA model we have:: `k_ar` - The number of AR lags included in the RHS variables `k_ma` - The number of MA lags included in the RHS variables `k_trend` - The number of trend variables included in the RHS variables `k_exog` - The number of exogenous variables included in the RHS variables excluding the trend terms `n_totobs` - The total number of observations for the LHS variables including the pre-sample values Options ~~~~~~~ We are using similar options in many classes, methods and functions. They should follow a standardized pattern if they recurr frequently. :: `missing` ['none', 'drop', 'raise'] define whether inputs are checked for nans, and how they are treated `alpha` (float in (0, 1)) significance level for hypothesis tests and confidence intervals, e.g. `alpha=0.05` patterns :: `return_xxx` : boolean to indicate optional or different returns (not `ret_xxx`) statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/roadmap_todo.rst000066400000000000000000000031521224417117700243630ustar00rootroot00000000000000Get Involved ============ Where to Start? --------------- Use grep or download a tool like `grin `__ to search the code for TODO notes:: grin -i -I "*.py*" todo This shows almost 700 TODOs in the code base right now. Feel free to inquire on the mailing list about any of these. Sandbox ------- We currently have a large amount code in the :ref:`sandbox`. The medium term goal is to move much of this to feature branches as it gets worked on and remove the sandbox folder. Many of these models and functions are close to done, however, and we welcome any and all contributions to complete them, including refactoring, documentation, and tests. These models include generalized additive models (GAM), information theoretic models such as maximum entropy, survival models, systems of equation models, restricted least squares, panel data models, and time series models such as (G)ARCH. .. .. toctree:: .. :maxdepth: 4 .. .. ../sandbox Contribute an Example --------------------- Link to examples documentation. Examples and technical documentation. Contribute to the Gallery ------------------------- Link to the Gallery. Roadmap to 0.6 ============== Work on any of the big picture ideas is very welcome. Implementing these ideas requires some thought and changes will likely affect all the codebase. Core Development ---------------- * Refactoring models structure to have consistent variable naming, methods, and signatures. Make sure `DRY `__ is respected. Statistics ---------- * Bootstrapping, jackknifing, or re-sampling framework. statsmodels-0.5.0+git13-g8e07d34/docs/source/dev/test_notes.rst000066400000000000000000000113521224417117700241030ustar00rootroot00000000000000.. _testing: Testing ======= Test Driven Development ~~~~~~~~~~~~~~~~~~~~~~~ We strive to follow a `Test Driven Development (TDD) `_ pattern. All models or statistical functions that are added to the main code base are to have tests versus an existing statistical package, if possible. Introduction to Nose ~~~~~~~~~~~~~~~~~~~~ Like many packages, statsmodels uses the `Nose testing system `__ and the convenient extensions in `numpy.testing `__. Nose itself is an extension of :mod:`Python's unittest `. Nose will find any file, directory, function, or class name that matches the regular expression ``(?:^|[b_./-])[Tt]est``. This is mainly functions that begin with test* and classes that begin with Test*. .. _run-tests: Running the Test Suite ~~~~~~~~~~~~~~~~~~~~~~ You can run all the tests by:: >>> import statsmodels.api as sm >>> sm.test() You can test submodules by:: >>> sm.discrete.test() How To Write A Test ~~~~~~~~~~~~~~~~~~~ NumPy provides a good introduction to unit testing with Nose and NumPy extensions `here `__. It is worth a read for some more details. Here, we will document a few conventions we follow that are worth mentioning. Often we want to test a whole model at once rather than just one function, for example. The following is a pared down version test_discrete.py. In this case, several different models with different options need to be tested. The tests look something like .. code-block:: python from numpy.testing import assert_almost_equal import statsmodels.api as sm from results.results_discrete import Spector class CheckDiscreteResults(object): """ res2 are the results. res1 are the values from statsmodels """ def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, 4) decimal_tvalues = 4 def test_tvalues(self): assert_almost_equal(self.res1.params, self.res2.params, self.decimal_tvalues) # ... as many more tests as there are common results class TestProbitNewton(CheckDiscreteResults): """ Tests the Probit model using Newton's method for fitting. """ @classmethod def setupClass(cls): # set up model data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog) cls.res1 = sm.Probit(data.endog, data.exog).fit(method='newton', disp=0) # set up results res2 = Spector() res2.probit() cls.res2 = res2 # set up precision cls.decimal_tvalues = 3 def test_model_specifc(self): assert_almost_equal(self.res1.foo, self.res2.foo, 4) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False) The main workhorse is the `CheckDiscreteResults` class. Notice that we can set the level of precision for `tvalues` to be different than the default in the subclass `TestProbitNewton`. All of the test classes have a `setupClass` :func:`python:classmethod`. Otherwise, Nose would reinstantiate the class before every single test method. If the fitting of the model is time consuming, then this is clearly undesirable. Finally, we have a script at the bottom so that we can run the tests should be running the Python file. Test Results ~~~~~~~~~~~~ The test results are the final piece of the above example. For many tests, especially those for the models, there are many results against which you would like to test. It makes sense then to separate the hard-coded results from the actual tests to make the tests more readable. If there are only a few results it's not necessary to separate the results. We often take results from some other statistical package. It is important to document where you got the results from and why they might differ from the results that we get. Each tests folder has a results subdirectory. Consider the folder structure for the discrete models:: tests/ __init__.py test_discrete.py results/ __init__.py results_discrete.py nbinom_resids.csv It is up to you how best to structure the results. In the discrete model example, you will notice that there are result classes based around particular datasets with a method for loading different model results for that dataset. You can also include text files that hold results to be loaded by results classes if it is easier than putting them in the class itself. statsmodels-0.5.0+git13-g8e07d34/docs/source/diagnostic.rst000066400000000000000000000175751224417117700232770ustar00rootroot00000000000000:orphan: .. _diagnostics: Regression Diagnostics and Specification Tests ============================================== Introduction ------------ In many cases of statistical analysis, we are not sure whether our statistical model is correctly specified. For example when using ols, then linearity and homoscedasticity are assumed, some test statistics additionally assume that the errors are normally distributed or that we have a large sample. Since our results depend on these statistical assumptions, the results are only correct of our assumptions hold (at least approximately). One solution to the problem of uncertainty about the correct specification is to use robust methods, for example robust regression or robust covariance (sandwich) estimators. The second approach is to test whether our sample is consistent with these assumptions. The following briefly summarizes specification and diagnostics tests for linear regression. Heteroscedasticity Tests ------------------------ For these test the null hypothesis is that all observations have the same error variance, i.e. errors are homoscedastic. The tests differ in which kind of heteroscedasticity is considered as alternative hypothesis. They also vary in the power of the test for different types of heteroscedasticity. :py:func:`het_breushpagan ` Lagrange Multiplier Heteroscedasticity Test by Breush-Pagan :py:func:`het_white ` Lagrange Multiplier Heteroscedasticity Test by White :py:func:`het_goldfeldquandt ` test whether variance is the same in 2 subsamples Autocorrelation Tests --------------------- This group of test whether the regression residuals are not autocorrelated. They assume that observations are ordered by time. :py:func:`durbin_watson ` - Durbin-Watson test for no autocorrelation of residuals - printed with summary() :py:func:`acorr_ljungbox ` - Ljung-Box test for no autocorrelation of residuals - also returns Box-Pierce statistic :py:func:`acorr_breush_godfrey ` - Breush-Pagan test for no autocorrelation of residuals missing - ? Non-Linearity Tests ------------------- :py:func:`linear_harvey_collier ` - Multiplier test for Null hypothesis that linear specification is correct :py:func:`acorr_linear_rainbow ` - Multiplier test for Null hypothesis that linear specification is correct. :py:func:`acorr_linear_lm ` - Lagrange Multiplier test for Null hypothesis that linear specification is correct. This tests against specific functional alternatives. Tests for Structural Change, Parameter Stability ------------------------------------------------ Test whether all or some regression coefficient are constant over the entire data sample. Known Change Point ^^^^^^^^^^^^^^^^^^ OneWayLS : - flexible ols wrapper for testing identical regression coefficients across predefined subsamples (eg. groups) missing - predictive test: Greene, number of observations in subsample is smaller than number of regressors Unknown Change Point ^^^^^^^^^^^^^^^^^^^^ :py:func:`breaks_cusumolsresid ` - cusum test for parameter stability based on ols residuals :py:func:`breaks_hansen ` - test for model stability, breaks in parameters for ols, Hansen 1992 :py:func:`recursive_olsresiduals ` Calculate recursive ols with residuals and cusum test statistic. This is currently mainly helper function for recursive residual based tests. However, since it uses recursive updating and doesn't estimate separate problems it should be also quite efficient as expanding OLS function. missing - supLM, expLM, aveLM (Andrews, Andrews/Ploberger) - R-structchange also has musum (moving cumulative sum tests) - test on recursive parameter estimates, which are there? Mutlicollinearity Tests -------------------------------- conditionnum (statsmodels.stattools) - -- needs test vs Stata -- - cf Grene (3rd ed.) pp 57-8 numpy.linalg.cond - (for more general condition numbers, but no behind the scenes help for design preparation) Variance Inflation Factors This is currently together with influence and outlier measures (with some links to other tests here: http://www.stata.com/help.cgi?vif) Normality and Distribution Tests -------------------------------- :py:func:`jarque_bera ` - printed with summary() - test for normal distribution of residuals Normality tests in scipy stats need to find list again :py:func:`omni_normtest ` - test for normal distribution of residuals - printed with summary() :py:func:`normal_ad ` - Anderson Darling test for normality with estimated mean and variance :py:func:`kstest_normal ` :py:func:`lillifors ` Lillifors test for normality, this is a Kolmogorov-Smirnov tes with for normality with estimated mean and variance. lillifors is an alias for kstest_normal qqplot, scipy.stats.probplot other goodness-of-fit tests for distributions in scipy.stats and enhancements - kolmogorov-smirnov - anderson : Anderson-Darling - likelihood-ratio, ... - chisquare tests, powerdiscrepancy : needs wrapping (for binning) Outlier and Influence Diagnostic Measures ----------------------------------------- These measures try to identify observations that are outliers, with large residual, or observations that have a large influence on the regression estimates. Robust Regression, RLM, can be used to both estimate in an outlier robust way as well as identify outlier. The advantage of RLM that the estimation results are not strongly influenced even if there are many outliers, while most of the other measures are better in identifying individual outliers and might not be able to identify groups of outliers. :py:class:`RLM ` example from example_rlm.py :: import statsmodels.api as sm ### Example for using Huber's T norm with the default ### median absolute deviation scaling data = sm.datasets.stackloss.Load() data.exog = sm.add_constant(data.exog) huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) hub_results = huber_t.fit() print hub_results.weights And the weights give an idea of how much a particular observation is down-weighted according to the scaling asked for. :py:class:`Influence ` Class in stats.outliers_influence, most standard measures for outliers and influence are available as methods or attributes given a fitted OLS model. This is mainly written for OLS, some but not all measures are also valid for other models. Some of these statistics can be calculated from an OLS results instance, others require that an OLS is estimated for each left out variable. - resid_press - resid_studentized_external - resid_studentized_internal - ess_press - hat_matrix_diag - cooks_distance - Cook's Distance `Wikipedia `_ (with some other links) - cov_ratio - dfbetas - dffits - dffits_internal - det_cov_params_not_obsi - params_not_obsi - sigma2_not_obsi Unit Root Tests --------------- :py:func:`unitroot_adf ` - same as adfuller but with different signature statsmodels-0.5.0+git13-g8e07d34/docs/source/discretemod.rst000066400000000000000000000050301224417117700234340ustar00rootroot00000000000000.. currentmodule:: statsmodels.discrete.discrete_model .. _discretemod: Regression with Discrete Dependent Variable =========================================== Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson) data. See `Module Reference`_ for commands and arguments. Examples -------- :: # Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog) # Logit Model logit_mod = sm.Logit(spector_data.endog, spector_data.exog) logit_res = logit_mod.fit() print logit_res.summary() Detailed examples can be found here: .. toctree:: :maxdepth: 2 examples/generated/example_discrete Technical Documentation ----------------------- Currently all models are estimated by Maximum Likelihood and assume independently and identically distributed errors. All discrete regression models define the same methods and follow the same structure, which is similar to the regression results but with some methods specific to discrete models. Additionally some of them contain additional model specific methods and attributes. References ^^^^^^^^^^ General references for this class of models are:: A.C. Cameron and P.K. Trivedi. `Regression Analysis of Count Data`. Cambridge, 1998 G.S. Madalla. `Limited-Dependent and Qualitative Variables in Econometrics`. Cambridge, 1983. W. Greene. `Econometric Analysis`. Prentice Hall, 5th. edition. 2003. Module Reference ---------------- The specific model classes are: .. autosummary:: :toctree: generated/ Logit Probit MNLogit Poisson NegativeBinomial The specific result classes are: .. autosummary:: :toctree: generated/ LogitResults ProbitResults CountResults MultinomialResults NegativeBinomialAncillaryResults :class:`DiscreteModel` is a superclass of all discrete regression models. The estimation results are returned as an instance of one of the subclasses of :class:`DiscreteResults`. Each category of models, binary, count and multinomial, have their own intermediate level of model and results classes. This intermediate classes are mostly to facilitate the implementation of the methods and attributes defined by :class:`DiscreteModel` and :class:`DiscreteResults`. .. autosummary:: :toctree: generated/ DiscreteModel DiscreteResults BinaryModel BinaryResults CountModel MultinomialModel statsmodels-0.5.0+git13-g8e07d34/docs/source/distributions.rst000066400000000000000000000031531224417117700240400ustar00rootroot00000000000000.. currentmodule:: statsmodels.sandbox.distributions .. _distributions: Distributions ============= This section collects various additional functions and methods for statistical distributions. Empirical Distributions ----------------------- .. currentmodule:: statsmodels.distributions.empirical_distribution .. autosummary:: :toctree: generated/ ECDF StepFunction Distribution Extras ------------------- .. currentmodule:: statsmodels.sandbox.distributions.extras *Skew Distributions* .. autosummary:: :toctree: generated/ SkewNorm_gen SkewNorm2_gen ACSkewT_gen skewnorm2 *Distributions based on Gram-Charlier expansion* .. autosummary:: :toctree: generated/ pdf_moments_st pdf_mvsk pdf_moments NormExpan_gen *cdf of multivariate normal* wrapper for scipy.stats .. autosummary:: :toctree: generated/ mvstdnormcdf mvnormcdf Univariate Distributions by non-linear Transformations ------------------------------------------------------ Univariate distributions can be generated from a non-linear transformation of an existing univariate distribution. `Transf_gen` is a class that can generate a new distribution from a monotonic transformation, `TransfTwo_gen` can use hump-shaped or u-shaped transformation, such as abs or square. The remaining objects are special cases. .. currentmodule:: statsmodels.sandbox.distributions.transformed .. autosummary:: :toctree: generated/ TransfTwo_gen Transf_gen ExpTransf_gen LogTransf_gen SquareFunc absnormalg invdnormalg loggammaexpg lognormalg negsquarenormalg squarenormalg squaretg statsmodels-0.5.0+git13-g8e07d34/docs/source/emplike.rst000066400000000000000000000034431224417117700225660ustar00rootroot00000000000000.. currentmodule:: statsmodels.emplike .. _emplike: Empirical Likelihood :mod:`emplike` ==================================== Introduction ------------ Empirical likelihood is a method of nonparametric inference and estimation that lifts the obligation of having to specify a family of underlying distributions. Moreover, empirical likelihood methods do not require re-sampling but still uniquely determine confidence regions whose shape mirrors the shape of the data. In essence, empirical likelihood attempts to combine the benefits of parametric and nonparametric methods while limiting their shortcomings. The main difficulties of empirical likelihood is the computationally intensive methods required to conduct inference. :mod:`statsmodels.emplike` attempts to provide a user-friendly interface that allows the end user to effectively conduct empirical likelihood analysis without having to concern themselves with the computational burdens. Currently, :mod:`emplike` provides methods to conduct hypothesis tests and form confidence intervals for descriptive statistics. Empirical likelihood estimation and inference in a regression, accelerated failure time and instrumental variable model are currently under development. References ^^^^^^^^^^ The main reference for empirical likelihood is:: Owen, A.B. "Empirical Likelihood." Chapman and Hall, 2001. Examples -------- .. code-block:: python import numpy as np import statsmodels.api as sm # Generate Data x = np.random.standard_normal(50) # initiate EL el = sm.emplike.DescStat(x) # confidence interval for the mean el.ci_mean() # test variance is 1 el.test_var(1) Module Reference ---------------- .. autosummary:: :toctree: generated/ descriptive.DescStat descriptive.DescStatUV descriptive.DescStatMV statsmodels-0.5.0+git13-g8e07d34/docs/source/endog_exog.rst000066400000000000000000000072571224417117700232650ustar00rootroot00000000000000.. _endog_exog: ``endog``, ``exog``, what's that? ================================= Statsmodels is using ``endog`` and ``exog`` as names for the data, the observed variables that are used in an estimation problem. Other names that are often used in different statistical packages or text books are, for example, ===================== ====================== endog exog ===================== ====================== y x y variable x variable left hand side (LHS) right hand side (RHS) dependent variable independent variable regressand regressors outcome design response variable explanatory variable ===================== ====================== The usage is quite often domain and model specific; however, we have chosen to use `endog` and `exog` almost exclusively. A mnenomic hint to keep the two terms apart is that exogenous has an "x", as in x-variable, in it's name. `x` and `y` are one letter names that are sometimes used for temporary variables and are not informative in itself. To avoid one letter names we decided to use descriptive names and settled on ``endog`` and ``exog``. Since this has been criticized, this might change in future. Background ---------- Some informal definitions of the terms are `endogenous`: caused by factors within the system `exogenous`: caused by factors outside the system *Endogenous variables designates variables in an economic/econometric model that are explained, or predicted, by that model.* http://stats.oecd.org/glossary/detail.asp?ID=794 *Exogenous variables designates variables that appear in an economic/econometric model, but are not explained by that model (i.e. they are taken as given by the model).* http://stats.oecd.org/glossary/detail.asp?ID=890 In econometrics and statistics the terms are defined more formally, and different definitions of exogeneity (weak, strong, strict) are used depending on the model. The usage in statsmodels as variable names cannot always be interpreted in a formal sense, but tries to follow the same principle. In the simplest form, a model relates an observed variable, y, to another set of variables, x, in some linear or nonlinear form :: y = f(x, beta) + noise y = x * beta + noise However, to have a statistical model we need additional assumptions on the properties of the explanatory variables, x, and the noise. One standard assumption for many basic models is that x is not correlated with the noise. In a more general definition, x being exogenous means that we do not have to consider how the explanatory variables in x were generated, whether by design or by random draws from some underlying distribution, when we want to estimate the effect or impact that x has on y, or test a hypothesis about this effect. In other words, y is *endogenous* to our model, x is *exogenous* to our model for the estimation. As an example, suppose you run an experiment and for the second session some subjects are not available anymore. Is the drop-out relevant for the conclusions you draw for the experiment? In other words, can we treat the drop-out decision as exogenous for our problem. It is up to the user to know (or to consult a text book to find out) what the underlying statistical assumptions for the models are. As an example, ``exog`` in ``OLS`` can have lagged dependent variables if the error or noise term is independently distributed over time (or uncorrelated over time). However, if the error terms are autocorrelated, then OLS does not have good statistical properties (is inconsistent) and the correct model will be ARMAX. ``statsmodels`` has functions for regression diagnostics to test whether some of the assumptions are justified or not. statsmodels-0.5.0+git13-g8e07d34/docs/source/example_formulas.rst000066400000000000000000000260711224417117700245050ustar00rootroot00000000000000.. _formula_examples: Fitting models using R-style formulas ===================================== Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the `patsy `_ package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: - `Patsy formula language description `_ Loading modules and functions ----------------------------- .. code:: python import statsmodels.formula.api as sm import numpy as np import pandas Notice that we called ``statsmodels.formula.api`` instead of the usual ``statsmodels.api``. The ``formula.api`` hosts many of the same functions found in ``api`` (e.g. OLS, GLM), but it also holds lower case counterparts for most of these models. In general, lower case models accept ``formula`` and ``df`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``df`` takes a `pandas `_ data frame. ``dir(sm)`` will print a list of available models. Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)`` OLS regression using formulas ----------------------------- To begin, we fit the linear model described on the `Getting Started `_ page. Download the data, subset columns, and list-wise delete to remove missing observations: .. code:: python url = "http://vincentarelbundock.github.com/Rdatasets/csv/HistData/Guerry.csv" df = pandas.read_csv(url) df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna() df.head() .. parsed-literal:: Lottery Literacy Wealth Region 0 41 37 73 E 1 38 51 22 N 2 66 13 61 C 3 80 46 76 E 4 79 69 83 E Fit the model: .. code:: python mod = sm.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df) res = mod.fit() print res.summary() .. parsed-literal:: OLS Regression Results ============================================================================== Dep. Variable: Lottery R-squared: 0.338 Model: OLS Adj. R-squared: 0.287 Method: Least Squares F-statistic: 6.636 Date: Sun, 13 Jan 2013 Prob (F-statistic): 1.07e-05 Time: 10:38:36 Log-Likelihood: -375.30 No. Observations: 85 AIC: 764.6 Df Residuals: 78 BIC: 781.7 Df Model: 6 =============================================================================== coef std err t P>|t| [95.0% Conf. Int.] ------------------------------------------------------------------------------- Intercept 38.6517 9.456 4.087 0.000 19.826 57.478 Region[T.E] -15.4278 9.727 -1.586 0.117 -34.793 3.938 Region[T.N] -10.0170 9.260 -1.082 0.283 -28.453 8.419 Region[T.S] -4.5483 7.279 -0.625 0.534 -19.039 9.943 Region[T.W] -10.0913 7.196 -1.402 0.165 -24.418 4.235 Literacy -0.1858 0.210 -0.886 0.378 -0.603 0.232 Wealth 0.4515 0.103 4.390 0.000 0.247 0.656 ============================================================================== Omnibus: 3.049 Durbin-Watson: 1.785 Prob(Omnibus): 0.218 Jarque-Bera (JB): 2.694 Skew: -0.340 Prob(JB): 0.260 Kurtosis: 2.454 Cond. No. 371. ============================================================================== Categorical variables --------------------- Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. ``patsy``'s default is also to include an intercept, so we automatically dropped one of the *Region* categories. If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator: .. code:: python res = sm.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit() print res.params .. parsed-literal:: Intercept 38.651655 C(Region)[T.E] -15.427785 C(Region)[T.N] -10.016961 C(Region)[T.S] -4.548257 C(Region)[T.W] -10.091276 Literacy -0.185819 Wealth 0.451475 Examples more advanced features ``patsy``'s categorical variables function can be found here: `Patsy: Contrast Coding Systems for categorical variables `_ Operators --------- We have already seen that "~" separates the left-hand side of the model from the right-hand side, and that "+" adds new columns to the design matrix. Removing variables ~~~~~~~~~~~~~~~~~~ The "-" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: .. code:: python res = sm.ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit() print res.params .. parsed-literal:: C(Region)[C] 38.651655 C(Region)[E] 23.223870 C(Region)[N] 28.634694 C(Region)[S] 34.103399 C(Region)[W] 28.560379 Literacy -0.185819 Wealth 0.451475 Multiplicative interactions ~~~~~~~~~~~~~~~~~~~~~~~~~~~ ":" adds a new column to the design matrix with the product of the other two columns. "\*" will also include the individual columns that were multiplied together: .. code:: python res1 = sm.ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit() res2 = sm.ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit() print res1.params, '\n' print res2.params .. parsed-literal:: Literacy:Wealth 0.018176 Literacy 0.427386 Wealth 1.080987 Literacy:Wealth -0.013609 Many other things are possible with operators. Please consult the `patsy docs `_ to learn more. Functions --------- You can apply vectorized functions to the variables in your model: .. code:: python res = sm.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit() print res.params .. parsed-literal:: Intercept 115.609119 np.log(Literacy) -20.393959 Define a custom function: .. code:: python def log_plus_1(x): return np.log(x) + 1. res = sm.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit() print res.params .. parsed-literal:: Intercept 136.003079 log_plus_1(Literacy) -20.393959 Using formulas with models that do not (yet) support them --------------------------------------------------------- Even if a given ``statsmodels`` function does not support formulas, you can still use ``patsy``'s formula language to produce design matrices. Those matrices can then be fed to the fitting function as ``endog`` and ``exog`` arguments. To generate ``numpy`` arrays: .. code:: python import patsy f = 'Lottery ~ Literacy * Wealth' y,X = patsy.dmatrices(f, df, return_type='dataframe') print y[:5] print X[:5] .. parsed-literal:: Lottery 0 41 1 38 2 66 3 80 4 79 Intercept Literacy Wealth Literacy:Wealth 0 1 37 73 2701 1 1 51 22 1122 2 1 13 61 793 3 1 46 76 3496 4 1 69 83 5727 To generate pandas data frames: .. code:: python f = 'Lottery ~ Literacy * Wealth' y,X = patsy.dmatrices(f, df, return_type='dataframe') print y[:5] print X[:5] .. parsed-literal:: Lottery 0 41 1 38 2 66 3 80 4 79 Intercept Literacy Wealth Literacy:Wealth 0 1 37 73 2701 1 1 51 22 1122 2 1 13 61 793 3 1 46 76 3496 4 1 69 83 5727 .. code:: python print sm.OLS(y, X).fit().summary() .. parsed-literal:: OLS Regression Results ============================================================================== Dep. Variable: Lottery R-squared: 0.309 Model: OLS Adj. R-squared: 0.283 Method: Least Squares F-statistic: 12.06 Date: Sun, 13 Jan 2013 Prob (F-statistic): 1.32e-06 Time: 10:38:36 Log-Likelihood: -377.13 No. Observations: 85 AIC: 762.3 Df Residuals: 81 BIC: 772.0 Df Model: 3 =================================================================================== coef std err t P>|t| [95.0% Conf. Int.] ----------------------------------------------------------------------------------- Intercept 38.6348 15.825 2.441 0.017 7.149 70.121 Literacy -0.3522 0.334 -1.056 0.294 -1.016 0.312 Wealth 0.4364 0.283 1.544 0.126 -0.126 0.999 Literacy:Wealth -0.0005 0.006 -0.085 0.933 -0.013 0.012 ============================================================================== Omnibus: 4.447 Durbin-Watson: 1.953 Prob(Omnibus): 0.108 Jarque-Bera (JB): 3.228 Skew: -0.332 Prob(JB): 0.199 Kurtosis: 2.314 Cond. No. 1.40e+04 ============================================================================== The condition number is large, 1.4e+04. This might indicate that there are strong multicollinearity or other numerical problems. statsmodels-0.5.0+git13-g8e07d34/docs/source/examples/000077500000000000000000000000001224417117700222205ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/examples/index.rst000066400000000000000000000017001224417117700240570ustar00rootroot00000000000000:orphan: Statsmodels Examples ==================== This page provides a series of examples, tutorials and recipes to help you get started with ``statsmodels``. Each of these examples is also made available in IPython Notebook format on the `examples wiki page `_. Of course, you can edit the examples wiki page freely; please add any example, tutorial or cool `statsmodels` trick you feel is worth sharing. Thanks for helping us make this resource more useful! The script files for these examples and the notebooks are available in the top level examples folder in the source distribution. `Examples wiki page `_ The Examples ------------ .. toctree:: :maxdepth: 3 :glob: generated/* Notebook Examples ----------------- .. toctree:: :maxdepth: 3 :glob: notebooks/generated/* statsmodels-0.5.0+git13-g8e07d34/docs/source/extending.rst.TXT000066400000000000000000000000001224417117700235650ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/genericmle.rst.TXT000066400000000000000000000000001224417117700237120ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/gettingstarted.rst000066400000000000000000000144301224417117700241660ustar00rootroot00000000000000Getting started =============== This very simple case-study is designed to get you up-and-running quickly with ``statsmodels``. Starting from raw data, we will show the steps needed to estimate a statistical model and to draw a diagnostic plot. We will only use functions provided by ``statsmodels`` or its ``pandas`` and ``patsy`` dependencies. Loading modules and functions ----------------------------- After `installing statsmodels and its dependencies `_, we load a few modules and functions: .. ipython:: python import statsmodels.api as sm import pandas from patsy import dmatrices `pandas `_ builds on ``numpy`` arrays to provide rich data structures and data analysis tools. The ``pandas.DataFrame`` function provides labelled arrays of (potentially heterogenous) data, similar to the ``R`` "data.frame". The ``pandas.read_csv`` function can be used to convert a comma-separated values file to a ``DataFrame`` object. `patsy `_ is a Python library for describing statistical models and building `Design Matrices `_ using ``R``-like formulas. Data ---- We download the `Guerry dataset `_, a collection of historical data used in support of Andre-Michel Guerry's 1833 *Essay on the Moral Statistics of France*. The data set is hosted online in comma-separated values format (CSV) by the `Rdatasets `_ repository. We could download the file locally and then load it using ``read_csv``, but ``pandas`` takes care of all of this automatically for us: .. ipython:: python url = "http://vincentarelbundock.github.com/Rdatasets/csv/HistData/Guerry.csv" #the next two lines are not necessary with a recent version of pandas from urllib2 import urlopen url = urlopen(url) df = pandas.read_csv(url) The `Input/Output doc page `_ shows how to import from various other formats. We select the variables of interest and look at the bottom 5 rows: .. ipython:: python vars = ['Department', 'Lottery', 'Literacy', 'Wealth', 'Region'] df = df[vars] df[-5:] Notice that there is one missing observation in the *Region* column. We eliminate it using a ``DataFrame`` method provided by ``pandas``: .. ipython:: python df = df.dropna() df[-5:] Substantive motivation and model -------------------------------- We want to know whether literacy rates in the 86 French departments are associated with per capita wagers on the Royal Lottery in the 1820s. We need to control for the level of wealth in each department, and we also want to include a series of dummy variables on the right-hand side of our regression equation to control for unobserved heterogeneity due to regional effects. The model is estimated using ordinary least squares regression (OLS). Design matrices (*endog* & *exog*) ---------------------------------- To fit most of the models covered by ``statsmodels``, you will need to create two design matrices. The first is a matrix of endogenous variable(s) (i.e. dependent, response, regressand, etc.). The second is a matrix of exogenous variable(s) (i.e. independent, predictor, regressor, etc.). The OLS coefficient estimates are calculated as usual: .. math:: \hat{\beta} = (X'X)^{-1} X'y where :math:`y` is an :math:`N \times 1` column of data on lottery wagers per capita (*Lottery*). :math:`X` is :math:`N \times 7` with an intercept, the *Literacy* and *Wealth* variables, and 4 region binary variables. The ``patsy`` module provides a convenient function to prepare design matrices using ``R``-like formulas. You can find more information here: http://patsy.readthedocs.org We use ``patsy``'s ``dmatrices`` function to create design matrices: .. ipython:: python y, X = dmatrices('Lottery ~ Literacy + Wealth + Region', data=df, return_type='dataframe') The resulting matrices/data frames look like this: .. ipython:: python y[:3] X[:3] Notice that ``dmatrices`` has * split the categorical *Region* variable into a set of indicator variables. * added a constant to the exogenous regressors matrix. * returned ``pandas`` DataFrames instead of simple numpy arrays. This is useful because DataFrames allow ``statsmodels`` to carry-over meta-data (e.g. variable names) when reporting results. The above behavior can of course be altered. See the `patsy doc pages `_. Model fit and summary --------------------- Fitting a model in ``statsmodels`` typically involves 3 easy steps: 1. Use the model class to describe the model 2. Fit the model using a class method 3. Inspect the results using a summary method For OLS, this is achieved by: .. ipython:: python mod = sm.OLS(y, X) # Describe model res = mod.fit() # Fit model print res.summary() # Summarize model The ``res`` object has many useful attributes. For example, we can extract parameter estimates and r-squared by typing: .. ipython:: python res.params res.rsquared Type ``dir(res)`` for a full list of attributes. For more information and examples, see the `Regression doc page `_ Diagnostics and specification tests ----------------------------------- ``statsmodels`` allows you to conduct a range of useful `regression diagnostics and specification tests `_. For instance, apply the Rainbow test for linearity (the null hypothesis is that the relationship is properly modelled as linear): .. ipython:: python sm.stats.linear_rainbow(res) Admittedly, the output produced above is not very verbose, but we know from reading the `docstring `_ (also, ``print sm.stats.linear_rainbow.__doc__``) that the first number is an F-statistic and that the second is the p-value. ``statsmodels`` also provides graphics functions. For example, we can draw a plot of partial regression for a set of regressors by: .. ipython:: python from statsmodels.graphics.regressionplots import plot_partregress @savefig gettingstarted_0.png plot_partregress(res) More ---- Congratulations! You're ready to move on to other topics in the `Table of Contents `_ statsmodels-0.5.0+git13-g8e07d34/docs/source/glm.rst000066400000000000000000000045651224417117700217250ustar00rootroot00000000000000.. currentmodule:: statsmodels.genmod.generalized_linear_model .. _glm: Generalized Linear Models ========================= Generalized linear models currently supports estimation using the one-parameter exponential families See `Module Reference`_ for commands and arguments. Examples -------- :: # Load modules and data import statsmodels.api as sm data = sm.datasets.scotland.load() data.exog = sm.add_constant(data.exog) # Instantiate a gamma family model with the default link function. gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma()) gamma_results = gamma_model.fit() Detailed examples can be found here: .. toctree:: :maxdepth: 1 examples/generated/example_glm Technical Documentation ----------------------- .. ..glm_techn1 .. ..glm_techn2 References ^^^^^^^^^^ * Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach. SAGE QASS Series. * Green, PJ. 1984. “Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives.†Journal of the Royal Statistical Society, Series B, 46, 149-192. * Hardin, J.W. and Hilbe, J.M. 2007. “Generalized Linear Models and Extensions.†2nd ed. Stata Press, College Station, TX. * McCullagh, P. and Nelder, J.A. 1989. “Generalized Linear Models.†2nd ed. Chapman & Hall, Boca Rotan. Module Reference ---------------- Model Class ^^^^^^^^^^^ .. autosummary:: :toctree: generated/ GLM Results Class ^^^^^^^^^^^^^ .. autosummary:: :toctree: generated/ GLMResults Families ^^^^^^^^ The distribution families currently implemented are .. currentmodule:: statsmodels.genmod.families.family .. autosummary:: :toctree: generated/ :template: autosummary/glmfamilies.rst Family Binomial Gamma Gaussian InverseGaussian NegativeBinomial Poisson Link Functions ^^^^^^^^^^^^^^ The link functions currently implemented are the following. Not all link functions are available for each distribution family. The list of available link functions can be obtained by :: >>> sm.families.family..links .. currentmodule:: statsmodels.genmod.families.links .. autosummary:: :toctree: generated/ Link CDFLink CLogLog Log Logit NegativeBinomial Power cauchy cloglog identity inverse_power inverse_squared log logit nbinom probit statsmodels-0.5.0+git13-g8e07d34/docs/source/glm_techn1.rst.TXT000066400000000000000000000002251224417117700236320ustar00rootroot00000000000000.. currentmodule:: statsmodels.glm .. _glm_techn1: Technical Documentation ======================= Introduction ------------ Just a placeholder statsmodels-0.5.0+git13-g8e07d34/docs/source/glm_techn2.rst.TXT000066400000000000000000000002671224417117700236410ustar00rootroot00000000000000.. currentmodule:: statsmodels.glm .. _glm_techn2: Technical Documentation - part 2 ================================ Implementation Notes -------------------- Just a placeholder statsmodels-0.5.0+git13-g8e07d34/docs/source/gmm.rst000066400000000000000000000025111224417117700217130ustar00rootroot00000000000000.. currentmodule:: statsmodels.sandbox.regression.gmm .. _gmm: Generalized Method of Moments :mod:`gmm` ======================================== :mod:`statsmodels.gmm` contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and numerically more accurate. Currently, GMM takes arbitrary non-linear moment conditions and calculates the estimates either for a given weighting matrix or iteratively by alternating between estimating the optimal weighting matrix and estimating the parameters. Implementing models with different moment conditions is done by subclassing GMM. In the minimal implementation only the moment conditions, `momcond` have to be defined. .. currentmodule:: statsmodels.sandbox.regression.gmm Module Reference """""""""""""""" .. autosummary:: :toctree: generated/ GMM GMMResults IV2SLS not sure what the status is on the following .. autosummary:: :toctree: generated/ IVGMM NonlinearIVGMM DistQuantilesGMM statsmodels-0.5.0+git13-g8e07d34/docs/source/gmm_techn1.rst.TXT000066400000000000000000000035321224417117700236370ustar00rootroot00000000000000.. currentmodule:: statsmodels.sandbox.regression.gmm .. _gmm_techn1: Technical Documentation ======================= Introduction ------------ Generalized Method of Moments is an extension of the Method of Moments if there are more moment conditions than parameters that are estimated. simple example General Structure and Implementation ------------------------------------ The main class for GMM estimation, makes little assumptions about the moment conditions. It is designed for the general case when moment conditions are given as function by the user. :: def momcond(params) which should return a two dimensional array with observation in rows and moment conditions in columns. Denote this function by `$g(\theta)$`. Then the GMM estimator is given as the solution to the maximization problem: ..math: max_{\theta) g(theta)' W g(theta) (1) The weighting matrix can be estimated in several different ways. The basic method `fitgmm` takes the weighting matrix as argument or if it is not given takes the identity matrix and maximizes (1) taking W as given. Since the optimizing functions solve minimization problems, we usually minimizes the negative of the objective function. `fit_iterative` calculates the optimal weighting matrix and maximizes the criterion function in alternating steps. The number of iterations can be given as an argument to this fit method. The optimal weighting matrix, which is the covariance matrix of the moment conditions, can be estimated in different ways. Kernel and shrinkage estimators are planned but not yet implemented. TODO The GMM class itself does not define any moment conditions. To get an estimator for given moment conditions, GMM needs to be subclassed. The basic structure of writing new models based on the generic MLE or GMM framework and subclassing is described in `extending.rst` (TODO: link) As an example statsmodels-0.5.0+git13-g8e07d34/docs/source/graphics.rst000066400000000000000000000023631224417117700227400ustar00rootroot00000000000000.. currentmodule:: statsmodels.graphics .. _graphics: Graphics ======== .. automodule:: statsmodels.graphics Goodness of Fit Plots --------------------- .. autosummary:: :toctree: generated/ gofplots.qqplot gofplots.qqline gofplots.qqplot_2samples gofplots.ProbPlot Boxplots -------- .. autosummary:: :toctree: generated/ boxplots.violinplot boxplots.beanplot Correlation Plots ------------------ .. autosummary:: :toctree: generated/ correlation.plot_corr correlation.plot_corr_grid plot_grids.scatter_ellipse Functional Plots ---------------- .. autosummary:: :toctree: generated/ functional.fboxplot functional.rainbowplot functional.banddepth Regression Plots ---------------- .. autosummary:: 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This makes most functions and classes conveniently available within one or two levels, without making the "sm" namespace too crowded. To see what functions and classes available, you can type the following (or use the namespace exploration features of IPython, Spyder, IDLE, etc.): .. code-block:: python >>> dir(sm) ['GLM', 'GLS', 'GLSAR', 'Logit', 'MNLogit', 'OLS', 'Poisson', 'Probit', 'RLM', 'WLS', '__builtins__', '__doc__', '__file__', '__name__', '__package__', 'add_constant', 'categorical', 'datasets', 'distributions', 'families', 'graphics', 'iolib', 'nonparametric', 'qqplot', 'regression', 'robust', 'stats', 'test', 'tools', 'tsa', 'version'] >>> dir(sm.graphics) ['__builtins__', '__doc__', '__file__', '__name__', '__package__', 'abline_plot', 'beanplot', 'fboxplot', 'interaction_plot', 'qqplot', 'rainbow', 'rainbowplot', 'violinplot'] >>> dir(sm.tsa) ['AR', 'ARMA', 'DynamicVAR', 'SVAR', 'VAR', '__builtins__', '__doc__', '__file__', '__name__', '__package__', 'acf', 'acovf', 'add_lag', 'add_trend', 'adfuller', 'ccf', 'ccovf', 'datetools', 'detrend', 'filters', 'grangercausalitytests', 'interp', 'lagmat', 'lagmat2ds', 'pacf', 'pacf_ols', 'pacf_yw', 'periodogram', 'q_stat', 'stattools', 'tsatools', 'var'] Notes ^^^^^ The `api` modules may not include all the public functionality of statsmodels. If you find something that should be added to the api, please file an issue on github or report it to the mailing list. The subpackages of statsmodels include `api.py` modules that are mainly intended to collect the imports needed for those subpackages. The `subpackage/api.py` files are imported into statsmodels api, for example :: from .nonparametric import api as nonparametric Users do not need to load the `subpackage/api.py` modules directly. Direct import for programs -------------------------- ``statsmodels`` submodules are arranged by topic (e.g. `discrete` for discrete choice models, or `tsa` for time series analysis). Our directory tree (stripped down) looks something like this:: statsmodels/ __init__.py api.py discrete/ __init__.py discrete_model.py tests/ results/ tsa/ __init__.py api.py tsatools.py stattools.py arima_model.py arima_process.py vector_ar/ __init__.py var_model.py tests/ results/ tests/ results/ stats/ __init__.py api.py stattools.py tests/ tools/ __init__.py tools.py decorators.py tests/ The submodules that can be import heavy contain an empty `__init__.py`, except for some testing code for running tests for the submodules. The intention is to change all directories to have an `api.py` and empty `__init__.py` in the next release. Import examples ^^^^^^^^^^^^^^^ Functions and classes:: from statsmodels.regression.linear_model import OLS, WLS from statsmodels.tools.tools import rank, add_constant Modules :: from statsmodels.datasets import macrodata import statsmodels.stats import diagnostic Modules with aliases :: import statsmodels.regression.linear_model as lm import statsmodels.stats.diagnostic as smsdia import statsmodels.stats.outliers_influence as oi We do not have currently a convention for aliases of submodules. statsmodels-0.5.0+git13-g8e07d34/docs/source/index.rst000066400000000000000000000051131224417117700222430ustar00rootroot00000000000000.. :tocdepth: 2 Welcome to Statsmodels's Documentation ====================================== :mod:`statsmodels` is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are avalable for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at `sourceforge `__. Minimal Examples ---------------- Since version ``0.5.0`` of ``statsmodels``, you can use R-style formulas together with ``pandas`` data frames to fit your models. Here is a simple example using ordinary least squares: .. code-block:: python import numpy as np import pandas as pd import statsmodels.formula.api as smf # Load data url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv' dat = pd.read_csv(url) # Fit regression model (using the natural log of one of the regressors) results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit() # Inspect the results print results.summary() You can also use ``numpy`` arrays instead of formulas: .. code-block:: python import numpy as np import statsmodels.api as sm # Generate artificial data (2 regressors + constant) nobs = 100 X = np.random.random((nobs, 2)) X = sm.add_constant(X) beta = [1, .1, .5] e = np.random.random(nobs) y = np.dot(X, beta) + e # Fit regression model results = sm.OLS(y, X).fit() # Inspect the results print results.summary() Have a look at `dir(results)` to see available results. Attributes are described in `results.__doc__` and results methods have their own docstrings. Basic Documentation ------------------- .. toctree:: :maxdepth: 3 introduction release/index gettingstarted example_formulas install related Information about the structure and development of statsmodels: .. toctree:: :maxdepth: 1 endog_exog importpaths pitfalls dev/index dev/internal Table of Contents ----------------- .. toctree:: :maxdepth: 3 regression glm rlm discretemod anova tsa stats nonparametric gmm emplike miscmodels distributions graphics iolib tools datasets/index sandbox Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search` statsmodels-0.5.0+git13-g8e07d34/docs/source/install.rst000066400000000000000000000107571224417117700226140ustar00rootroot00000000000000:orphan: .. _install: Installation ------------ Using setuptools ~~~~~~~~~~~~~~~~ To obtain the latest released version of statsmodels using `setuptools `__:: easy_install -U statsmodels Or follow `this link to our PyPI page `__. Obtaining the Source ~~~~~~~~~~~~~~~~~~~~ We do not release very often but the master branch of our source code is usually fine for everyday use. You can get the latest source from our `github repository `__. Or if you have git installed:: git clone git://github.com/statsmodels/statsmodels.git If you want to keep up to date with the source on github just periodically do:: git pull in the statsmodels directory. Windows Nightly Binaries ~~~~~~~~~~~~~~~~~~~~~~~~ If you are not able to follow the build instructions below, we upload nightly builds of the GitHub repository to `http://statsmodels.sourceforge.net/binaries/ `__. Installation from Source ~~~~~~~~~~~~~~~~~~~~~~~~ You will need a C compiler installed to build statsmodels. If you are building from the github source and not a source release, then you will also need Cython. You can follow the instructions below to get a C compiler setup for Windows. Linux ^^^^^ Once you have obtained the source, you can do (with appropriate permissions):: python setup.py install Or:: python setup.py build python setup.py install Windows ^^^^^^^ You can build 32-bit version of the code on windows using mingw32. First, get and install `mingw32 `__. Then, you'll need to edit distutils.cfg. This is usually found somewhere like C:\Python27\Lib\distutils\distutils.cfg. Add these lines:: [build] compiler=mingw32 Then in the statsmodels directory do:: python setup.py build python setup.py install OR You can build 32-bit or 64-bit versions of the code using the Microsoft SDK. Detailed instructions can be found on the Cython wiki `here `__. The gist of these instructions follow. You will need to download the free Windows SDK C/C++ compiler from Microsoft. You must use the **Microsoft Windows SDK for Windows 7 and .NET Framework 3.5 SP1** to be comptible with Python 2.6, 2.7, 3.1, and 3.2. The link for the 3.5 SP1 version is `http://www.microsoft.com/downloads/en/details.aspx?familyid=71DEB800-C591-4F97-A900-BEA146E4FAE1&displaylang=en `__ For Python 3.3, you need to use the **Microsoft Windows SDK for Windows 7 and .NET Framework 4**, available from `http://www.microsoft.com/en-us/download/details.aspx?id=8279 `__ For 7.0, get the ISO file GRMSDKX_EN_DVD.iso for AMD64. After you install this, open the SDK Command Shell (Start -> All Programs -> Microsoft Windows SDK v7.0 -> CMD Shell). CD to the statsmodels directory and type:: set DISTUTILS_USE_SDK=1 To build a 64-bit application type:: setenv /x64 /release To build a 32-bit application type:: setenv /x86 /release The prompt should change colors to green. Then proceed as usual to install:: python setup.py build python setup.py install For 7.1, the instructions are exactly the same, except you use the download link provided above and make sure you are using SDK 7.1. If you want to accomplish the same without opening up the SDK CMD SHELL, then you can use these commands at the CMD Prompt or in a batch file.:: setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x64 /release set DISTUTILS_USE_SDK=1 Replace `/x64` with `/x86` and `v7.0` with `v7.1` as needed. Dependencies ~~~~~~~~~~~~ * `Python `__ >= 2.6, including Python 3.x * `NumPy `__ >= 1.5.0 * `SciPy `__ >= 0.7 * `Pandas `__ >= 0.7.1 * `Patsy `__ >= 0.1.0 * `Cython `__ >= 15.1, Needed if you want to build the code from github and not a source distribution. Optional Dependencies ~~~~~~~~~~~~~~~~~~~~~ * `Matplotlib `__ is needed for plotting functions and running many of the examples. * `Nose `__ is required to run the test suite. statsmodels-0.5.0+git13-g8e07d34/docs/source/introduction.rst000066400000000000000000000202501224417117700236540ustar00rootroot00000000000000.. currentmodule:: statsmodels ************ Introduction ************ Background ---------- Scipy.stats.models was originally written by Jonathan Taylor. For some time it was part of scipy but then removed from it. During the Google Summer of Code 2009, stats.models was corrected, tested and enhanced and released as a new package. Since then we have continued to improve the existing models and added new statistical methods. Main Features and Current Status -------------------------------- statsmodels 0.4 is a pure python package, with one optional cython based extension that provides a considerable speed improvement for ARIMA estimation. Future releases will depend on cython generated extensions. statsmodels includes: * regression: Generalized least squares (including weighted least squares and least squares with autoregressive errors), ordinary least squares. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate time series analysis: AR, ARIMA - vector autoregressive models, VAR and structural VAR - descriptive statistics and process models for time series analysis * nonparametric : (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests - diagnostics and specification tests - goodness-of-fit and normality tests - functions for multiple testing - various additional statistical tests * iolib: Tools for reading Stata .dta files into numpy arrays. (not yet ported to Python 3) * iolob: printing table output to ascii, latex, and html * miscellaneous models statsmodels contains a sandbox folder, which includes some of the original stats.models code that has not yet been rewritten and tested. The sandbox also contains models and functions that we are currently developing. This code is in various stages of development from early stages to almost finished, but not sufficiently tested or with an API that is still in flux. Some of the code in the advanced state covers among others Mixed (repeated measures) Models, GARCH models, general method of moments (GMM) estimators, kernel regression and kernel density estimation, and various extensions to scipy.stats.distributions. The code is written for plain NumPy arrays so that statsmodels can be used as a library for any kind of data structure users might have. However, in order to make the data handling easier, some time series specific models rely on pandas, and we have plans to integrate pandas in future releases of statsmodels. We have also included several datasets from the public domain and by permission for tests and examples. The datasets are set up so that it is easy to add more datasets. Python 3 -------- statsmodels has been ported and tested for Python 3.2. Python 3 version of the code is automatically created during setup by running 2to3.py over the statsmodels source (excluding examples). The STATA file reader and writer in iolib.foreign has not been ported yet. A recent development version of matplotlib for Python 3 runs without problems with our examples and tests. Running the test suite with Python 3.2 shows only one errors related to unported STATA file reader. Testing ------- Most results have been verified with at least one other statistical package: R, Stata or SAS. The guiding principal for the initial rewrite and for continued development is that all numbers have to be verified. Some statistical methods are tested with Monte Carlo studies. While we strife to follow this test driven approach, there is no guarantee that the code is bug-free and always works. Some auxiliary function are still insufficiently tested, some edge cases might not be correctly taken into account, and the possibility of numerical problems is inherent to many of the statistical models. We especially appreciate any help and reports for these kind of problems so we can keep improving the existing models. Looking Forward --------------- We would like to invite everyone to give statsmodels a test drive, use it, and report comments, possibilities for improvement and bugs to the statsmodels mailing list http://groups.google.com/group/pystatsmodels or file tickets on our issue tracker at https://github.com/statsmodels/statsmodels/issues The source code is available from https://github.com/statsmodels/statsmodels. Our plans for the future include improving the coverage of statistical models, methods and tests that any basic statistics package should provide. But the main direction for the expansion of statsmodels depends on the requirements and interests of the developers and contributers. The current maintainers are mostly interested in econometrics and time series analysis, but we would like to invite any users or developers to contribute their own extensions to existing models, or new models. To speed up improvements that are waiting in the sandbox, any help with providing test cases, reviewing or improving the code would be very appreciated. Planned Extensions ~~~~~~~~~~~~~~~~~~ Big changes that are planned for the next release will improve the usability of statsmodels especially for interactive work. * Metainformation about data and models: Currently the models essentially use no information about the design matrix and just treat it as numpy array. Some information like variable names are included with the wrapper for use with Pandas or other data structures. * Formulas similar to R: This will provide a faster way to interactively define models and contrast matrices, and will provide additional information especially for categorical variables. (Nathaniel Smith) Various models that are work in progress where the time to inclusion in statsmodels proper will depend on the available developer time and interests: Bayesian dynamic linear models (Wes) more Kalman filter based time series analysis (Skipper) New models (roughly in order of completeness): general method of moments (GMM) estimators, kernel regression, kernel density estimation, various extensions to scipy.stats.distributions, GARCH models, copulas, system of equation models, panel data models, more discrete choice models, mixed effects models, survival models. Resampling approaches like bootstrap and permutation for tests and estimator statistics. Code Stability ~~~~~~~~~~~~~~ The existing models are mostly settled in their user interface and we do not expect many changes anymore. One area that will need adjustment is how formulas and meta information are included. New models that have just been included might require adjustments as we gain more experience and obtain feedback by users. As we expand the range of models, we keep improving the framework for different estimators and statistical tests, so further changes will be necessary. In 0.3 we reorganized the internal location of the code and import paths which will make future enhancements less interruptive. In 0.4 most models obtained a wrapper that stores and returns additional information from richer data structures like data structures in Pandas and structured arrays. In 0.4 also prediction has been improved in many cases and made more consistent across models. Although there is no guarantee yet on API stability, we try to keep changes that require adjustments by existing users to a minimal level. Financial Support ----------------- We are grateful for the financial support that we obtained for the developement of statsmodels: Google `www.google.com `_ : two Google Summer of Code, GSOC 2009, GSOC 2010 and GSOC 2011 AQR `www.aqr.com `_ : financial sponsor for the work on Vector Autoregressive Models (VAR) by Wes McKinney We would also like to thank our hosting providers, `github `_ for the public code repository, `sourceforge `_ for hosting our documentation and `python.org `_ for making our downloads available on pypi. Josef Perktold and Skipper Seabold (maintainers) statsmodels-0.5.0+git13-g8e07d34/docs/source/iolib.rst000066400000000000000000000015661224417117700222420ustar00rootroot00000000000000.. currentmodule:: statsmodels.iolib .. _iolib: Input-Output :mod:`iolib` ========================= ``statsmodels`` offers some functions for input and output. These include a reader for STATA files, a class for generating tables for printing in several formats and two helper functions for pickling. Users can also leverage the powerful input/output functions provided by :ref:`pandas.io `. Among other things, ``pandas`` (a ``statsmodels`` dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). Examples -------- `SimpleTable: Basic example `_ Module Reference ---------------- .. autosummary:: :toctree: generated/ foreign.StataReader foreign.StataWriter foreign.genfromdta foreign.savetxt table.SimpleTable table.csv2st smpickle.save_pickle smpickle.load_pickle statsmodels-0.5.0+git13-g8e07d34/docs/source/miscmodels.rst000066400000000000000000000033571224417117700233030ustar00rootroot00000000000000 .. currentmodule:: statsmodels.miscmodels .. _miscmodels: Other Models :mod:`miscmodels` ============================== :mod:`statsmodels.miscmodels` contains model classes and that do not yet fit into any other category, or are basic implementations that are not yet polished and will most likely still change. Some of these models were written as examples for the generic maximum likelihood framework, and there will be others that might be based on general method of moments. The models in this category have been checked for basic cases, but might be more exposed to numerical problems than the complete implementation. For example, count.Poisson has been added using only the generic maximum likelihood framework, the standard errors are based on the numerical evaluation of the Hessian, while discretemod.Poisson uses analytical Gradients and Hessian and will be more precise, especially in cases when there is strong multicollinearity. On the other hand, by subclassing GenericLikelihoodModel, it is easy to add new models, another example can be seen in the zero inflated Poisson model, miscmodels.count. Count Models :mod:`count` -------------------------- .. currentmodule:: statsmodels.miscmodels.count .. autosummary:: :toctree: generated/ PoissonGMLE PoissonOffsetGMLE PoissonZiGMLE Linear Model with t-distributed errors -------------------------------------- This is a class that shows that a new model can be defined by only specifying the method for the loglikelihood. All result statistics are inherited from the generic likelihood model and result classes. The results have been checked against R for a simple case. .. currentmodule:: statsmodels.miscmodels.tmodel .. autosummary:: :toctree: generated/ TLinearModel statsmodels-0.5.0+git13-g8e07d34/docs/source/missing.rst000066400000000000000000000037001224417117700226050ustar00rootroot00000000000000:orphan: .. _missing_data: Missing Data ------------ All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing .. code-block:: python >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> # add in some missing data >>> missing_idx = np.array([False] * len(data.endog)) >>> missing_idx[[4, 10, 15]] = True >>> data.endog[missing_idx] = np.nan >>> ols_model = sm.OLS(data.endog, data.exog) >>> ols_fit = ols_model.fit() >>> print ols_fit.params [ nan nan nan nan nan nan nan] This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use `missing = 'raise'`. This will raise a `MissingDataError` during model instantiation if missing data is present so that you know something was wrong in your input data. .. code-block:: python >>> ols_model = sm.OLS(data.endog, data.exog, missing='raise') If you want statsmodels to handle the missing data by dropping the observations, use `missing = 'drop'`. .. code-block:: python >>> ols_model = sm.OLS(data.endog, data.exog, missing='drop') We are considering adding a configuration framework so that you can set the option with a global setting. We would also like to allow users to pass in their own functions for missing data so that you can do custom imputation or data augmentation. This is also not implemented yet. Implementation Details ---------------------- Internally, this function uses `pandas.isnull `_. Anything that returns True from this function will be treated as missing data. statsmodels-0.5.0+git13-g8e07d34/docs/source/nonparametric.rst000066400000000000000000000067061224417117700240070ustar00rootroot00000000000000.. currentmodule:: statsmodels.nonparametric .. _nonparametric: Nonparametric Methods :mod:`nonparametric` ========================================== This section collects various methods in nonparametric statistics. This includes kernel density estimation for univariate and multivariate data, kernel regression and locally weighted scatterplot smoothing (lowess). sandbox.nonparametric contains additional functions that are work in progress or don't have unit tests yet. We are planning to include here nonparametric density estimators, especially based on kernel or orthogonal polynomials, smoothers, and tools for nonparametric models and methods in other parts of statsmodels. Kernel density estimation ------------------------- The kernel density estimation (KDE) functionality is split between univariate and multivariate estimation, which are implemented in quite different ways. Univariate estimation (as provided by `KDEUnivariate`) uses FFT transforms, which makes it quite fast. Therefore it should be preferred for *continuous, univariate* data if speed is important. It supports using different kernels; bandwidth estimation is done only by a rule of thumb (Scott or Silverman). Multivariate estimation (as provided by `KDEMultivariate`) uses product kernels. It supports least squares and maximum likelihood cross-validation for bandwidth estimation, as well as estimating mixed continuous, ordered and unordered data. The default kernels (Gaussian, Wang-Ryzin and Aitchison-Aitken) cannot be altered at the moment however. Direct estimation of the conditional density (:math:`P(X | Y) = P(X, Y) / P(Y)`) is supported by `KDEMultivariateConditional`. `KDEMultivariate` can do univariate estimation as well, but is up to two orders of magnitude slower than `KDEUnivariate`. Kernel regression ----------------- Kernel regression (as provided by `KernelReg`) is based on the same product kernel approach as `KDEMultivariate`, and therefore has the same set of features (mixed data, cross-validated bandwidth estimation, kernels) as described above for `KDEMultivariate`. Censored regression is provided by `KernelCensoredReg`. Note that code for semi-parametric partial linear models and single index models, based on `KernelReg`, can be found in the sandbox. References ---------- * B.W. Silverman, "Density Estimation for Statistics and Data Analysis" * J.S. Racine, "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics, Vol. 3, No. 1, pp. 1-88, 2008. * Q. Li and J.S. Racine, "Nonparametric econometrics: theory and practice", Princeton University Press, 2006. * Hastie, Tibshirani and Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Springer, 2009. Module Reference ---------------- The public functions and classes are .. autosummary:: :toctree: generated/ smoothers_lowess.lowess kde.KDEUnivariate kernel_density.KDEMultivariate kernel_density.KDEMultivariateConditional kernel_density.EstimatorSettings kernel_regression.KernelReg kernel_regression.KernelCensoredReg helper functions for kernel bandwidths .. autosummary:: :toctree: generated/ bandwidths.bw_scott bandwidths.bw_silverman bandwidths.select_bandwidth There are some examples for nonlinear functions in :mod:`statsmodels.nonparametric.dgp_examples` The sandbox.nonparametric contains additional insufficiently tested classes for testing functional form and for semi-linear and single index models. statsmodels-0.5.0+git13-g8e07d34/docs/source/pitfalls.rst000066400000000000000000000076271224417117700227660ustar00rootroot00000000000000Pitfalls ======== This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, "non-standard" use of models, or edge cases. statsmodels provides several warnings and helper functions for diagnostic checking (see this `blog article `_ for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time. While the underlying statistical problems are the same for all statistical packages, software implementations differ in the way extreme or corner cases are handled. Please report corner cases for which the models might not work, so we can treat them appropriately. Repeated calls to fit with different parameters ----------------------------------------------- Result instances often need to access attributes from the corresponding model instance. Fitting a model multiple times with different arguments can change model attributes. This means that the result instance may no longer point to the correct model attributes after the model has been re-fit. It is therefore best practice to create separate model instances when we want to fit a model using different fit function arguments. For example, this works without problem because we are not keeping the results instance for further use :: mod = AR(endog) aic = [] for lag in range(1,11): res = mod.fit(maxlag=lag) aic.append(res.aic) However, when we want to hold on to two different estimation results, then it is recommended to create two separate model instances. :: mod1 = RLM(endog, exog) res1 = mod1.fit(scale_est='mad') mod2 = RLM(endog, exog) res2 = mod2.fit(scale_est='stand_mad') Unidentified Parameters ----------------------- Rank deficient exog, perfect multicollinearity ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Models based on linear models, GLS, RLM, GLM and similar, use a generalized inverse. This means that: + Rank deficient matrices will not raise an error + Cases of almost perfect multicollinearity or ill-conditioned design matrices might produce numerically unstable results. Users need to manually check the rank or condition number of the matrix if this is not the desired behavior Note: Statsmodels currently fails on the NIST benchmark case for Filip if the data is not rescaled, see `this blog `_ Incomplete convergence in maximum likelihood estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In some cases, the maximum likelihood estimator might not exist, parameters might be infinite or not unique (e.g. (quasi-)separation in models with binary endogenous variable). Under the default settings, statsmodels will print a warning if the optimization algorithm stops without reaching convergence. However, it is important to know that the convergence criteria may sometimes falsely indicate convergence (e.g. if the value of the objective function converged but not the parameters). In general, a user needs to verify convergence. For binary Logit and Probit models, statsmodels raises an exception if perfect prediction is detected. There is, however, no check for quasi-perfect prediction. Other Problems -------------- Insufficient variation in the data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It is possible that there is insufficient variation in the data for small datasets or for data with small groups in categorical variables. In these cases, the results might not be identified or some hidden problems might occur. The only currently known case is a perfect fit in robust linear model estimation. For RLM, if residuals are equal to zero, then it does not cause an exception, but having this perfect fit can produce NaNs in some results (scale=0 and 0/0 division) (issue #55). statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/000077500000000000000000000000001224417117700215435ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_boxplot_beanplot.py000066400000000000000000000016171224417117700273550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri May 04 00:22:40 2012 Author: Ralf Gommers """ import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm data = sm.datasets.anes96.load_pandas() party_ID = np.arange(7) labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", "Independent-Indpendent", "Independent-Republican", "Weak Republican", "Strong Republican"] plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible age = [data.exog['age'][data.endog == id] for id in party_ID] fig = plt.figure() ax = fig.add_subplot(111) sm.graphics.beanplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") #plt.show() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_boxplot_violinplot.py000066400000000000000000000016261224417117700277500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri May 04 00:11:32 2012 Author: Ralf Gommers """ import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm data = sm.datasets.anes96.load_pandas() party_ID = np.arange(7) labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", "Independent-Indpendent", "Independent-Republican", "Weak Republican", "Strong Republican"] plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible age = [data.exog['age'][data.endog == id] for id in party_ID] fig = plt.figure() ax = fig.add_subplot(111) sm.graphics.violinplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") #plt.show() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_functional_fboxplot.py000066400000000000000000000017301224417117700300550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri May 04 11:10:51 2012 Author: Ralf Gommers """ #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea #surface temperature data. import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm data = sm.datasets.elnino.load() #Create a functional boxplot. We see that the years 1982-83 and 1997-98 are #outliers; these are the years where El Nino (a climate pattern #characterized by warming up of the sea surface and higher air pressures) #occurred with unusual intensity. fig = plt.figure() ax = fig.add_subplot(111) res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58, labels=data.raw_data[:, 0].astype(int), ax=ax) ax.set_xlabel("Month of the year") ax.set_ylabel("Sea surface temperature (C)") ax.set_xticks(np.arange(13, step=3) - 1) ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) ax.set_xlim([-0.2, 11.2]) #plt.show() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_functional_rainbowplot.py000066400000000000000000000012161224417117700305570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri May 04 11:08:56 2012 Author: Ralf Gommers """ #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea #surface temperature data. import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm data = sm.datasets.elnino.load() #Create a rainbow plot: fig = plt.figure() ax = fig.add_subplot(111) res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax) ax.set_xlabel("Month of the year") ax.set_ylabel("Sea surface temperature (C)") ax.set_xticks(np.arange(13, step=3) - 1) ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) ax.set_xlim([-0.2, 11.2]) #plt.show() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_gofplots_qqplot.py000066400000000000000000000035671224417117700272450ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun May 06 05:32:15 2012 Author: Josef Perktold editted by: Paul Hobson (2012-08-19) """ from scipy import stats from matplotlib import pyplot as plt import statsmodels.api as sm #example from docstring data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=True) mod_fit = sm.OLS(data.endog, data.exog).fit() res = mod_fit.resid left = -1.8 #x coordinate for text insert fig = plt.figure() ax = fig.add_subplot(2, 2, 1) sm.graphics.qqplot(res, ax=ax) top = ax.get_ylim()[1] * 0.75 txt = ax.text(left, top, 'no keywords', verticalalignment='top') txt.set_bbox(dict(facecolor='k', alpha=0.1)) ax = fig.add_subplot(2, 2, 2) sm.graphics.qqplot(res, line='s', ax=ax) top = ax.get_ylim()[1] * 0.75 txt = ax.text(left, top, "line='s'", verticalalignment='top') txt.set_bbox(dict(facecolor='k', alpha=0.1)) ax = fig.add_subplot(2, 2, 3) sm.graphics.qqplot(res, line='45', fit=True, ax=ax) ax.set_xlim(-2, 2) top = ax.get_ylim()[1] * 0.75 txt = ax.text(left, top, "line='45', \nfit=True", verticalalignment='top') txt.set_bbox(dict(facecolor='k', alpha=0.1)) ax = fig.add_subplot(2, 2, 4) sm.graphics.qqplot(res, dist=stats.t, line='45', fit=True, ax=ax) ax.set_xlim(-2, 2) top = ax.get_ylim()[1] * 0.75 txt = ax.text(left, top, "dist=stats.t, \nline='45', \nfit=True", verticalalignment='top') txt.set_bbox(dict(facecolor='k', alpha=0.1)) fig.tight_layout() plt.gcf() # example with the new ProbPlot class import numpy as np x = np.random.normal(loc=8.25, scale=3.5, size=37) y = np.random.normal(loc=8.00, scale=3.25, size=37) pp_x = sm.ProbPlot(x, fit=True) pp_y = sm.ProbPlot(y, fit=True) # probability of exceedance fig2 = pp_x.probplot(exceed=True) # compare x quantiles to y quantiles fig3 = pp_x.qqplot(other=pp_y, line='45') # same as above with probabilities/percentiles fig4 = pp_x.ppplot(other=pp_y, line='45') statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/graphics_plot_fit_ex.py000066400000000000000000000013221224417117700263070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Monday April 1st 2013 Author: Padarn Wilson """ # Load the Statewide Crime data set and perform linear regression with # 'poverty' and 'hs_grad' as variables and 'muder' as the response import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np data = sm.datasets.statecrime.load_pandas().data murder = data['murder'] X = data[['poverty', 'hs_grad']] X["constant"] = 1 y = murder model = sm.OLS(y, X) results = model.fit() # Create a plot just for the variable 'Poverty': fig, ax = plt.subplots() fig = sm.graphics.plot_fit(results, 0, ax=ax) ax.set_ylabel("Murder Rate") ax.set_xlabel("Poverty Level") ax.set_title("Linear Regression") plt.show() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_acorr.py000066400000000000000000000000561224417117700251320ustar00rootroot00000000000000from var_plots import plot_acorr plot_acorr() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_fevd.py000066400000000000000000000000541224417117700247460ustar00rootroot00000000000000from var_plots import plot_fevd plot_fevd() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_forecast.py000066400000000000000000000000641224417117700256310ustar00rootroot00000000000000from var_plots import plot_forecast plot_forecast() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_input.py000066400000000000000000000000561224417117700251630ustar00rootroot00000000000000from var_plots import plot_input plot_input() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_irf.py000066400000000000000000000000521224417117700246000ustar00rootroot00000000000000from var_plots import plot_irf plot_irf() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plot_irf_cum.py000066400000000000000000000000621224417117700254450ustar00rootroot00000000000000from var_plots import plot_irf_cum plot_irf_cum() statsmodels-0.5.0+git13-g8e07d34/docs/source/plots/var_plots.py000066400000000000000000000015041224417117700241260ustar00rootroot00000000000000import numpy as np from statsmodels.tsa.api import VAR from statsmodels.api import datasets as ds from statsmodels.tsa.base.datetools import dates_from_str import pandas mdata = ds.macrodata.load_pandas().data # prepare the dates index dates = mdata[['year', 'quarter']].astype(int).astype(str) quarterly = dates["year"] + "Q" + dates["quarter"] quarterly = dates_from_str(quarterly) mdata = mdata[['realgdp','realcons','realinv']] mdata.index = pandas.DatetimeIndex(quarterly) data = np.log(mdata).diff().dropna() model = VAR(data) est = model.fit(maxlags=2) def plot_input(): est.plot() def plot_acorr(): est.plot_acorr() def plot_irf(): est.irf().plot() def plot_irf_cum(): irf = est.irf() irf.plot_cum_effects() def plot_forecast(): est.plot_forecast(10) def plot_fevd(): est.fevd(20).plot() statsmodels-0.5.0+git13-g8e07d34/docs/source/regression.rst000066400000000000000000000121751224417117700233220ustar00rootroot00000000000000.. currentmodule:: statsmodels.regression.linear_model .. _regression: Linear Regression ================= Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See `Module Reference`_ for commands and arguments. Examples -------- :: # Load modules and data import numpy as np import statsmodels.api as sm spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog, prepend=False) # Fit and summarize OLS model mod = sm.OLS(spector_data.endog, spector_data.exog) res = mod.fit() print res.summary() Detailed examples can be found here: .. toctree:: :maxdepth: 1 examples/generated/example_ols examples/generated/example_wls examples/generated/example_gls Technical Documentation ----------------------- The statistical model is assumed to be :math:`Y = X\beta + \mu`, where :math:`\mu\sim N\left(0,\sigma^{2}\Sigma\right)` depending on the assumption on :math:`\Sigma`, we have currently four classes available * GLS : generalized least squares for arbitrary covariance :math:`\Sigma` * OLS : ordinary least squares for i.i.d. errors :math:`\Sigma=\textbf{I}` * WLS : weighted least squares for heteroskedastic errors :math:`\text{diag}\left (\Sigma\right)` * GLSAR : feasible generalized least squares with autocorrelated AR(p) errors :math:`\Sigma=\Sigma\left(\rho\right)` All regression models define the same methods and follow the same structure, and can be used in a similar fashion. Some of them contain additional model specific methods and attributes. GLS is the superclass of the other regression classes. .. Class hierachy: TODO .. yule_walker is not a full model class, but a function that estimate the .. parameters of a univariate autoregressive process, AR(p). It is used in GLSAR, .. but it can also be used independently of any models. yule_walker only .. calculates the estimates and the standard deviation of the lag parameters but .. not the additional regression statistics. We hope to include yule-walker in .. future in a separate univariate time series class. A similar result can be .. obtained with GLSAR if only the constant is included as regressors. In this .. case the parameter estimates of the lag estimates are not reported, however .. additional statistics, for example aic, become available. References ^^^^^^^^^^ General reference for regression models: * D.C. Montgomery and E.A. Peck. "Introduction to Linear Regression Analysis." 2nd. Ed., Wiley, 1992. Econometrics references for regression models: * R.Davidson and J.G. MacKinnon. "Econometric Theory and Methods," Oxford, 2004. * W.Green. "Econometric Analysis," 5th ed., Pearson, 2003. .. toctree:: .. :maxdepth: 1 .. .. regression_techn1 Attributes ^^^^^^^^^^ The following is more verbose description of the attributes which is mostly common to all regression classes pinv_wexog : array | `pinv_wexog` is the `p` x `n` Moore-Penrose pseudoinverse of the | whitened design matrix. Approximately equal to | :math:`\left(X^{T}\Sigma^{-1}X\right)^{-1}X^{T}\Psi` | where :math:`\Psi` is given by :math:`\Psi\Psi^{T}=\Sigma^{-1}` cholsimgainv : array | n x n upper triangular matrix such that | :math:`\Psi\Psi^{T}=\Sigma^{-1}` | :math:`cholsigmainv=\Psi^{T}` df_model : float The model degrees of freedom is equal to `p` - 1, where `p` is the number of regressors. Note that the intercept is not counted as using a degree of freedom here. df_resid : float The residual degrees of freedom is equal to the number of observations `n` less the number of parameters `p`. Note that the intercept is counted as using a degree of freedom here. llf : float The value of the likelihood function of the fitted model. nobs : float The number of observations `n` normalized_cov_params : array | A `p` x `p` array | :math:`(X^{T}\Sigma^{-1}X)^{-1}` sigma : array | `sigma` is the n x n strucutre of the covariance matrix of the error terms | :math:`\mu\sim N\left(0,\sigma^{2}\Sigma\right)` wexog : array | `wexog` is the whitened design matrix. | :math:`\Psi^{T}X` wendog : array | The whitened response variable. | :math:`\Psi^{T}Y` Module Reference ---------------- Model Classes ^^^^^^^^^^^^^ .. autosummary:: :toctree: generated/ OLS GLS WLS GLSAR yule_walker .. currentmodule:: statsmodels.regression.quantile_regression .. autosummary:: :toctree: generated/ QuantReg Results Classes ^^^^^^^^^^^^^^^ Fitting a linear regression model returns a results class. OLS has a specific results class with some additional methods compared to the results class of the other linear models. .. currentmodule:: statsmodels.regression.linear_model .. autosummary:: :toctree: generated/ RegressionResults OLSResults .. currentmodule:: statsmodels.regression.quantile_regression .. autosummary:: :toctree: generated/ QuantRegResults statsmodels-0.5.0+git13-g8e07d34/docs/source/regression_techn1.rst.TXT000066400000000000000000000002431224417117700252330ustar00rootroot00000000000000.. currentmodule:: statsmodels.regression .. _regression-techn1: Technical Documentation ======================= Introduction ------------ Just a placeholder statsmodels-0.5.0+git13-g8e07d34/docs/source/related.rst000066400000000000000000000165461224417117700225700ustar00rootroot00000000000000.. _related: .. currentmodule:: statsmodels Related Packages ================ These are some python packages that have a related purpose and can be useful in combination with statsmodels. The selection in this list is biased towards packages that might be directly useful for data handling and statistical analysis, and towards those that have a BSD compatible license, which implies that we are not restricted in looking at the source to learn of different ways of implementation or of different algorithms. The following descriptions are taken from the websites with small adjustments. Data Handling ------------- Pandas ^^^^^^ http://pypi.python.org/pypi/pandas "This project aims to provide the following * A set of fast NumPy-based data structures optimized for panel, time series, and cross-sectional data analysis. * A set of tools for loading such data from various sources and providing efficient ways to persist the data. * A robust statistics and econometrics library which closely integrates with the core data structures." License: New BSD Language: Python, Cython, binary distribution available for win32-py25, but easy to build with MinGW *Comments* Uses statsmodels as optional dependency for statistical analysis, but has additional statistical and econometrics algorithms that focus on panel data analysis, mostly in the time dimension. It has several data structures that allow dictionary access to the underlying 1, 2, or 3 dimensional arrays. It was initially focused on a two-dimensional representation of the data, but now also allows for different representation of three-dimensional arrays. It allows for arbitrary axis labels, but offers also a convenient time series class. Tabular ^^^^^^^ http://pypi.python.org/pypi/tabular "Tabular data container and associated convenience routines in Python Tabular is a package of Python modules for working with tabular data. Its main object is the tabarray class, a data structure for holding and manipulating tabular data. The tabarray object is based on the ndarray object from the Numerical Python package (NumPy), and the Tabular package is built to interface well with NumPy in general. " License: MIT Language: Python *Comments* Uses numpys structured arrays as basic building block. Focused on spreadsheet-style operations for working with two-dimensional tables and associated data handling and analysis. It is instructive to read the code of tabular for working with structured arrays. La ^^ http://pypi.python.org/pypi/la "Label the rows, columns, any dimension, of your NumPy arrays. The main class of the la package is a labeled array, larry. A larry consists of a data array and a label list. The data array is stored as a NumPy array and the label list as a list of lists. " License: BSD Language: Python *Comments* The data handling is in intention similar to pandas but closer to working with standard numpy ndarrays. The main addition to numpy arrays are arbitrary labels for each axis of the array. Larry delegates to numpy functions but does not subclass numpy's ndarrays. It also provides functions for basic descriptive statistics. Data Analysis ------------- Pymc ^^^^ http://pypi.python.org/pypi/pymc "Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. PyMC is a python module that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems."" License: MIT, Academic Free License (?) Language: Python, C, Fortran binary (bundle ?) installer *Comments* This is to some extent the modern Bayesian analog of statsmodels. It is by far the most mature project in this group including statsmodels. Scikits.talkbox ^^^^^^^^^^^^^^^ http://pypi.python.org/pypi/scikits.talkbox Talkbox is set of python modules for speech/signal processing. The goal of this toolbox is to be a sandbox for features which may end up in scipy at some point. License: BSD Language: Python, C optional *Comments* Although specialized on speech processing, talkbox has some accessible and useful functions for time series analysis, especially a fast implementation for estimating AR models (with ...) and spectral density based on estimated AR coefficients. Nitime ^^^^^^ http://github.com/fperez/nitime "Nitime is a library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code." License: BSD Language: Python *Comments* Althoug focused on neuroscience, the algorithms for time series analysis are independent of the data representation and can be used with numpy arrays. Current focus is on spectral analysis including coherence between several time series. KF - Kalman Filter ^^^^^^^^^^^^^^^^^^ http://pypi.python.org/pypi/KF "This project was started to test different avaiable tools to track mutual funds and hedge fund using Capital Asset Pricing Model (CAPM thereafter) introduced my Sharpe and Arbitrage Pricing Theory (APT thereafter) introduced by Ross. " * License : BSD -check * Language Python (requires cvxopt) *Comments* Very young project but with a similar, although narrower, focus as pandas and (parts of) statsmodels. Uses Kalman Filter for rolling linear regression and allows for equality and inequality constraints in the estimation. Includes its own time series class, and the estimation seems (?) to depend on it. Domain-specific Data Analysis ----------------------------- The following packages contain interesting statistical algorithms, however they are tightly focused on their application, and are or might be more difficult to use "from the outside". (Descriptions are taken from websites) Pymvpa ^^^^^^ PyMVPA is a Python module intended to ease pattern classification analyses of large datasets http://pymvpa.org/ License: MIT Nipy ^^^^ Nipy aims to provide a complete Python environment for the analysis of structural and functional neuroimaging data http://nipy.sourceforge.net/ License: BSD Biopython ^^^^^^^^^ Biopython is a set of tools for biological computation http://biopython.org/wiki/Main_Page License: http://www.biopython.org/DIST/LICENSE similar to MIT (?)) Pysal ^^^^^ A library for exploratory spatial analysis and geocomputation http://code.google.com/p/pysal/ License: BSD glu-genetics ^^^^^^^^^^^^ A broad array of tools to store, clean, and analyze data generated by whole-genome or candidate gene association scans. http://code.google.com/p/glu-genetics/ License: BSD Other packages -------------- There exists a large number of machine learning packages in python, many of them with a well established code base. Unfortunately, none of the packages with a wider coverage of algorithms has a scipy compatible license. A listing can be found at http://mloss.org/software/language/python/ scikits.learn includes several machine learning algorithms and is currently undergoing a cleanup and enhancement http://pypi.python.org/pypi/scikits.learn/0.1 . Other packages are available that provide additional functionality, especially openopt which offers additional optimization routines compared to the ones in scipy. statsmodels-0.5.0+git13-g8e07d34/docs/source/release/000077500000000000000000000000001224417117700220225ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/source/release/github-stats-0.5.rst000066400000000000000000000520231224417117700254740ustar00rootroot00000000000000.. _issues_list_05: Issues closed in the 0.5.0 development cycle ============================================ Issued closed in 0.5.0 ----------------------- GitHub stats for release 0.5.0 (07/02/2012/ - 08/14/2013/). We closed a total of 380 issues, 172 pull requests and 208 regular issues. This is the full list (generated with the script :file:`tools/github_stats.py`): This list is automatically generated, and may be incomplete: Pull Requests (172): * :ghpull:`1015`: DOC: Bump version. Remove done tasks. * :ghpull:`1010`: DOC/RLS: Update release notes workflow. Help Needed! * :ghpull:`1014`: DOC: nbgenerate does not like the comment at end of line. * :ghpull:`1012`: DOC: Add link to notebook and crosslink ref. Closes #924. * :ghpull:`997`: misc, tests, diagnostic * :ghpull:`1009`: MAINT: Add .mailmap file. * :ghpull:`817`: Add 3 new unit tests for arima_process * :ghpull:`1001`: BUG include_package_data for install closes #907 * :ghpull:`1005`: GITHUB: Contributing guidlines * :ghpull:`1007`: Cleanup docs for release * :ghpull:`1003`: BUG: Workaround for bug in sphinx 1.1.3. See #1002. * :ghpull:`1004`: DOC: Update maintainer notes with branching instructions. * :ghpull:`1000`: BUG: Support pandas 0.8.0. * :ghpull:`996`: BUG: Handle combo of pandas 0.8.0 and dateutils 1.5.0 * :ghpull:`995`: ENH: Print dateutil version. * :ghpull:`994`: ENH: Fail gracefully for version not found. * :ghpull:`993`: More conservative error catching in TimeSeriesModel * :ghpull:`992`: Misc fixes 12: adjustments to unit test * :ghpull:`985`: MAINT: Print versions script. * :ghpull:`986`: ENH: Prefer to_offset to get_offset. Closes #964. * :ghpull:`984`: COMPAT: Pandas 0.8.1 compatibility. Closes #983. * :ghpull:`982`: Misc fixes 11 * :ghpull:`978`: TST: generic mle pareto disable bsejac tests with estimated loc * :ghpull:`977`: BUG python 3.3 fix for numpy str TypeError, see #633 * :ghpull:`975`: Misc fixes 10 numdiff * :ghpull:`970`: BUG: array too long, raises exception with newer numpy closes #967 * :ghpull:`965`: Vincent summary2 rebased * :ghpull:`933`: Update and improve GenericlikelihoodModel and miscmodels * :ghpull:`950`: BUG/REF mcnemar fix exact pvalue, allow table as input * :ghpull:`951`: Pylint emplike formula genmod * :ghpull:`956`: Fix a docstring in KDEMultivariateConditional. * :ghpull:`949`: BUG fix lowess sort when nans closes #946 * :ghpull:`932`: ENH: support basinhopping solver in LikelihoodModel.fit() * :ghpull:`927`: DOC: clearer minimal example * :ghpull:`919`: Ols summary crash * :ghpull:`918`: Fixes10 emplike lowess * :ghpull:`909`: Bugs in GLM pvalues, more tests, pylint * :ghpull:`906`: ENH: No fmax with Windows SDK so define inline. * :ghpull:`905`: MAINT more fixes * :ghpull:`898`: Misc fixes 7 * :ghpull:`896`: Quantreg rebase2 * :ghpull:`895`: Fixes issue #832 * :ghpull:`893`: ENH: Remove unneeded restriction on low. Closes #867. * :ghpull:`894`: MAINT: Remove broken function. Keep deprecation. Closes #781. * :ghpull:`856`: Carljv improved lowess rebased2 * :ghpull:`884`: Pyflakes cleanup * :ghpull:`887`: BUG: Fix kde caching * :ghpull:`883`: Fixed pyflakes issue in discrete module * :ghpull:`882`: Update predstd.py * :ghpull:`871`: Update of sandbox doc * :ghpull:`631`: WIP: Correlation positive semi definite * :ghpull:`857`: BLD: apt get dependencies from Neurodebian, whitespace cleanup * :ghpull:`855`: AnaMP issue 783 mixture rvs tests rebased * :ghpull:`854`: Enrico multinear rebased * :ghpull:`849`: Tyler tukeyhsd rebased * :ghpull:`848`: BLD TravisCI use python-dateutil package * :ghpull:`784`: Misc07 cleanup multipletesting and proportions * :ghpull:`841`: ENH: Add load function to main API. Closes #840. * :ghpull:`820`: Ensure that tuples are not considered as data, not as data containers * :ghpull:`822`: DOC: Update for Cython changes. * :ghpull:`765`: Fix build issues * :ghpull:`800`: Automatically generate output from notebooks * :ghpull:`802`: BUG: Use two- not one-sided t-test in t_test. Closes #740. * :ghpull:`806`: ENH: Import formula.api in statsmodels.api namespace. * :ghpull:`803`: ENH: Fix arima error message for bad start_params * :ghpull:`801`: DOC: Fix ANOVA section titles * :ghpull:`795`: Negative Binomial Rebased * :ghpull:`787`: Origintests * :ghpull:`794`: ENH: Allow pandas-in/pandas-out in tsa.filters * :ghpull:`791`: Github stats for release notes * :ghpull:`779`: added np.asarray call to durbin_watson in stattools * :ghpull:`772`: Anova docs * :ghpull:`776`: BUG: Fix dates_from_range with length. Closes #775. * :ghpull:`774`: BUG: Attach prediction start date in AR. Closes #773. * :ghpull:`767`: MAINT: Remove use of deprecated from examples and docs. * :ghpull:`762`: ENH: Add new residuals to wrapper * :ghpull:`754`: Fix arima predict * :ghpull:`760`: ENH: Adjust for k_trend in information criteria. Closes #324. * :ghpull:`761`: ENH: Fixes and tests sign_test. Closes #642. * :ghpull:`759`: Fix 236 * :ghpull:`758`: DOC: Update VAR docs. Closes #537. * :ghpull:`752`: Discrete cleanup * :ghpull:`750`: VAR with 1d array * :ghpull:`748`: Remove reference to new_t_test and new_f_test. * :ghpull:`739`: DOC: Remove outdated note in docstring * :ghpull:`732`: BLD: Check for patsy dependency at build time + docs * :ghpull:`731`: Handle wrapped * :ghpull:`730`: Fix opt fulloutput * :ghpull:`729`: Get rid of warnings in docs build * :ghpull:`698`: update url for hsb2 dataset * :ghpull:`727`: DOC: Fix indent and add missing params to linear models. Closes #709. * :ghpull:`726`: CLN: Remove unused method. Closes #694 * :ghpull:`725`: BUG: Should call anova_single. Closes #702. * :ghpull:`723`: Rootfinding for Power * :ghpull:`722`: Handle pandas.Series with names in make_lags * :ghpull:`714`: Fix 712 * :ghpull:`668`: Allow for any pandas frequency to be used in TimeSeriesModel. * :ghpull:`711`: Misc06 - bug fixes * :ghpull:`708`: BUG: Fix one regressor case for conf_int. Closes #706. * :ghpull:`700`: Bugs rebased * :ghpull:`680`: BUG: Swap arguments in fftconvolve for scipy >= 0.12.0 * :ghpull:`640`: Misc fixes 05 * :ghpull:`663`: a typo in runs.py doc string for mcnemar test * :ghpull:`652`: WIP: fixing pyflakes / pep8, trying to improve readability * :ghpull:`619`: DOC: intro to formulas * :ghpull:`648`: BF: Make RLM stick to Huber's description * :ghpull:`649`: Bug Fix * :ghpull:`637`: Pyflakes cleanup * :ghpull:`634`: VAR DOC typo * :ghpull:`623`: Slowtests * :ghpull:`621`: MAINT: in setup.py, only catch ImportError for pandas. * :ghpull:`590`: Cleanup test output * :ghpull:`591`: Interrater agreement and reliability measures * :ghpull:`618`: Docs fix the main warnings and errors during sphinx build * :ghpull:`610`: nonparametric examples and some fixes * :ghpull:`578`: Fix 577 * :ghpull:`575`: MNT: Remove deprecated scikits namespace * :ghpull:`499`: WIP: Handle constant * :ghpull:`567`: Remove deprecated * :ghpull:`571`: Dataset docs * :ghpull:`561`: Grab rdatasets * :ghpull:`570`: DOC: Fixed links to Rdatasets * :ghpull:`524`: DOC: Clean up discrete model documentation. * :ghpull:`506`: ENH: Re-use effects if model fit with QR * :ghpull:`556`: WIP: L1 doc fix * :ghpull:`564`: TST: Use native integer to avoid issues in dtype asserts * :ghpull:`543`: Travis CI using M.Brett nipy hack * :ghpull:`558`: Plot cleanup * :ghpull:`541`: Replace pandas DataMatrix with DataFrame * :ghpull:`534`: Stata test fixes * :ghpull:`532`: Compat 323 * :ghpull:`531`: DOC: Add ECDF to distributions docs * :ghpull:`526`: ENH: Add class to write Stata binary dta files * :ghpull:`521`: DOC: Add abline plot to docs * :ghpull:`518`: Small fixes: interaction_plot * :ghpull:`508`: ENH: Avoid taking cholesky decomposition of diagonal matrix * :ghpull:`509`: DOC: Add ARIMA to docs * :ghpull:`510`: DOC: realdpi is disposable personal income. Closes #394. * :ghpull:`507`: ENH: Protect numdifftools import. Closes #45 * :ghpull:`504`: Fix weights * :ghpull:`498`: DOC: Add patys requirement to install docs * :ghpull:`491`: Make _data a public attribute. * :ghpull:`494`: DOC: Fix pandas links * :ghpull:`492`: added intersphinx for pandas * :ghpull:`422`: Handle missing data * :ghpull:`485`: ENH: Improve error message for pandas objects without dates in index * :ghpull:`428`: Remove other data * :ghpull:`483`: Arima predict bug * :ghpull:`482`: TST: Do array-array comparison when using numpy.testing * :ghpull:`471`: Formula rename df -> data * :ghpull:`473`: Vincent docs tweak rebased * :ghpull:`468`: Docs 050 * :ghpull:`462`: El aft rebased * :ghpull:`461`: TST: numpy 1.5.1 compatibility * :ghpull:`460`: Emplike desc reg rebase * :ghpull:`410`: Discrete model marginal effects * :ghpull:`417`: Numdiff cleanup * :ghpull:`398`: Improved plot_corr and plot_corr_grid functions. * :ghpull:`401`: BUG: Finish refactoring margeff for dummy. Closes #399. * :ghpull:`400`: MAINT: remove lowess.py, which was kept in 0.4.x for backwards compatibi... * :ghpull:`371`: BF+TEST: fixes, checks and tests for isestimable * :ghpull:`351`: ENH: Copy diagonal before write for upcoming numpy changes * :ghpull:`384`: REF: Move mixture_rvs out of sandbox. * :ghpull:`368`: ENH: Add polished version of acf/pacf plots with confidence intervals * :ghpull:`378`: Infer freq * :ghpull:`374`: ENH: Add Fair's extramarital affair dataset. From tobit-model branch. * :ghpull:`358`: ENH: Add method to OLSResults for outlier detection * :ghpull:`369`: ENH: allow predict to pass through patsy for transforms * :ghpull:`352`: Formula integration rebased * :ghpull:`360`: REF: Deprecate order in fit and move to ARMA init * :ghpull:`366`: Version fixes * :ghpull:`359`: DOC: Fix sphinx warnings Issues (208): * :ghissue:`1036`: Series no longer inherits from ndarray * :ghissue:`1038`: DataFrame with integer names not handled in ARIMA * :ghissue:`1028`: Test fail with windows and Anaconda - Low priority * :ghissue:`676`: acorr_breush_godfrey undefined nlags * :ghissue:`922`: lowess returns inconsistent with option * :ghissue:`425`: no bse in robust with norm=TrimmedMean * :ghissue:`1025`: add_constant incorrectly detects constant column * :ghissue:`533`: py3 compatibility ``pandas.read_csv(urlopen(...))`` * :ghissue:`662`: doc: install instruction: explicit about removing scikits.statsmodels * :ghissue:`910`: test failure Ubuntu TestARMLEConstant.test_dynamic_predict * :ghissue:`80`: t_model: f_test, t_test don't work * :ghissue:`432`: GenericLikelihoodModel change default for score and hessian * :ghissue:`454`: BUG/ENH: HuberScale instance is not used, allow user defined scale estimator * :ghissue:`98`: check connection or connect summary to variable names in wrappers * :ghissue:`418`: BUG: MNLogit loglikeobs, jac * :ghissue:`1017`: nosetests warnings * :ghissue:`924`: DOCS link in notebooks to notebook for download * :ghissue:`1011`: power ttest endless loop possible * :ghissue:`907`: BLD data_files for stats.libqsturng * :ghissue:`328`: consider moving example scripts into IPython notebooks * :ghissue:`1002`: Docs won't build with Sphinx 1.1.3 * :ghissue:`69`: Make methods like compare_ftest work with wrappers * :ghissue:`503`: summary_old in RegressionResults * :ghissue:`991`: TST precision of normal_power * :ghissue:`945`: Installing statsmodels from github? * :ghissue:`964`: Prefer to_offset not get_offset in tsa stuff * :ghissue:`983`: bug: pandas 0.8.1 incompatibility * :ghissue:`899`: build_ext inplace doesn't cythonize * :ghissue:`923`: location of initialization code * :ghissue:`980`: auto lag selection in S_hac_simple * :ghissue:`968`: genericMLE Ubuntu test failure * :ghissue:`633`: python 3.3 compatibility * :ghissue:`728`: test failure for solve_power with fsolve * :ghissue:`971`: numdiff test cases * :ghissue:`976`: VAR Model does not work in 1D * :ghissue:`972`: numdiff: epsilon has no minimum value * :ghissue:`967`: lowes test failure Ubuntu * :ghissue:`948`: nonparametric tests: mcnemar, cochranq unit test * :ghissue:`963`: BUG in runstest_2sample * :ghissue:`946`: Issue with lowess() smoother in statsmodels * :ghissue:`868`: k_vars > nobs * :ghissue:`917`: emplike emplikeAFT stray dimensions * :ghissue:`264`: version comparisons need to be made more robust (may be just use LooseVersion) * :ghissue:`674`: failure in test_foreign, pandas testing * :ghissue:`828`: GLMResults inconsistent distribution in pvalues * :ghissue:`908`: RLM missing test for tvalues, pvalues * :ghissue:`463`: formulas missing in docs * :ghissue:`256`: discrete Nbin has zero test coverage * :ghissue:`831`: test errors running bdist * :ghissue:`733`: Docs: interrater cohens_kappa is missing * :ghissue:`897`: lowess failure - sometimes * :ghissue:`902`: test failure tsa.filters precision too high * :ghissue:`901`: test failure stata_writer_pandas, newer versions of pandas * :ghissue:`900`: ARIMA.__new__ errors on python 3.3 * :ghissue:`832`: notebook errors * :ghissue:`867`: Baxter King has unneeded limit on value for low? * :ghissue:`781`: discreteResults margeff method not tests, obsolete * :ghissue:`870`: discrete unit tests duplicates * :ghissue:`630`: problems in regression plots * :ghissue:`885`: Caching behavior for KDEUnivariate icdf * :ghissue:`869`: sm.tsa.ARMA(..., order=(p,q)) gives "__init__() got an unexpected keyword argument 'order'" error * :ghissue:`783`: statsmodels\distributions\mixture_rvs.py no unit tests * :ghissue:`824`: Multicomparison w/Pandas Series * :ghissue:`789`: presentation of multiple comparison results * :ghissue:`764`: BUG: multipletests incorrect reject for Holm-Sidak * :ghissue:`766`: multipletests - status and tests of 2step FDR procedures * :ghissue:`763`: Bug: multipletests raises exception with empty array * :ghissue:`840`: sm.load should be in the main API namespace * :ghissue:`830`: invalid version number * :ghissue:`821`: Fail gracefully when extensions are not built * :ghissue:`204`: Cython extensions built twice? * :ghissue:`689`: tutorial notebooks * :ghissue:`740`: why does t_test return one-sided p-value * :ghissue:`804`: What goes in statsmodels.formula.api? * :ghissue:`675`: Improve error message for ARMA SVD convergence failure. * :ghissue:`15`: arma singular matrix * :ghissue:`559`: Add Rdatasets to optional dependencies list * :ghissue:`796`: Prediction Standard Errors * :ghissue:`793`: filters are not pandas aware * :ghissue:`785`: Negative R-squared * :ghissue:`777`: OLS residuals returned as Pandas series when endog and exog are Pandas series * :ghissue:`770`: Add ANOVA to docs * :ghissue:`775`: Bug in dates_from_range * :ghissue:`773`: AR model pvalues error with Pandas * :ghissue:`768`: multipletests: numerical problems at threshold * :ghissue:`355`: add draw if interactive to plotting functions * :ghissue:`625`: Exog is not correctly handled in ARIMA predict * :ghissue:`626`: ARIMA summary does not print exogenous variable coefficients * :ghissue:`657`: order (0,1) breaks ARMA forecast * :ghissue:`736`: ARIMA predict problem for ARMA model * :ghissue:`324`: ic in ARResults, aic, bic, hqic, fpe inconsistent definition? * :ghissue:`642`: sign_test check * :ghissue:`236`: AR start_params broken * :ghissue:`235`: tests hang on Windows * :ghissue:`156`: matplotlib deprecated legend ? var plots * :ghissue:`331`: Remove stale tests * :ghissue:`592`: test failures in datetools * :ghissue:`537`: Var Models * :ghissue:`755`: Unable to access AR fit parameters when model is estimated with pandas.DataFrame * :ghissue:`670`: discrete: numerically useless clipping * :ghissue:`515`: MNLogit residuals raise a TypeError * :ghissue:`225`: discrete models only define deviance residuals * :ghissue:`594`: remove skiptest in TestProbitCG * :ghissue:`681`: Dimension Error in discrete_model.py When Running test_dummy_* * :ghissue:`744`: DOC: new_f_test * :ghissue:`549`: Ship released patsy source in statsmodels * :ghissue:`588`: patsy is a hard dependency? * :ghissue:`716`: Tests missing for functions if pandas is used * :ghissue:`715`: statmodels regression plots not working with pandas datatypes * :ghissue:`450`: BUG: full_output in optimizers Likelihood model * :ghissue:`709`: DOCstrings linear models don't have missing params * :ghissue:`370`: BUG weightstats has wrong cov * :ghissue:`694`: DiscreteMargins duplicate method * :ghissue:`702`: bug, pylint stats.anova * :ghissue:`423`: Handling of constant across models * :ghissue:`456`: BUG: ARMA date handling incompatibility with recent pandas * :ghissue:`514`: NaNs in Multinomial * :ghissue:`405`: Check for existing old version of scikits.statsmodels? * :ghissue:`586`: Segmentation fault with OLS * :ghissue:`721`: Unable to run AR on named time series objects * :ghissue:`125`: caching pinv_wexog broke iterative fit - GLSAR * :ghissue:`712`: TSA bug with frequency inference * :ghissue:`319`: Timeseries Frequencies * :ghissue:`707`: .summary with alpha ignores parsed value * :ghissue:`673`: nonparametric: bug in _kernel_base * :ghissue:`710`: test_power failures * :ghissue:`706`: .conf_int() fails on linear regression without intercept * :ghissue:`679`: Test Baxter King band-pass filter fails with scipy 0.12 beta1 * :ghissue:`552`: influence outliers breaks when regressing on constant * :ghissue:`639`: test folders not on python path * :ghissue:`565`: omni_normtest doesn't propagate the axis argument * :ghissue:`563`: error in doc generation for AR.fit * :ghissue:`109`: TestProbitCG failure on Ubuntu * :ghissue:`661`: from scipy import comb fails on the latest scipy 0.11.0 * :ghissue:`413`: DOC: example_discrete.py missing from 0.5 documentation * :ghissue:`644`: FIX: factor plot + examples broken * :ghissue:`645`: STY: pep8 violations in many examples * :ghissue:`173`: doc sphinx warnings * :ghissue:`601`: bspline.py dependency on old scipy.stats.models * :ghissue:`103`: ecdf and step function conventions * :ghissue:`18`: Newey-West sandwich covariance is missing * :ghissue:`279`: cov_nw_panel not tests, example broken * :ghissue:`150`: precision in test_discrete.TestPoissonNewton.test_jac ? * :ghissue:`480`: rescale loglike for optimization * :ghissue:`627`: Travis-CI support for scipy * :ghissue:`622`: mark tests as slow in emplike * :ghissue:`589`: OLS F-statistic error * :ghissue:`572`: statsmodels/tools/data.py Stuck looking for la.py * :ghissue:`580`: test errors in graphics * :ghissue:`577`: PatsyData detection buglet * :ghissue:`470`: remove deprecated features * :ghissue:`573`: lazy imports are (possibly) very slow * :ghissue:`438`: New results instances are not in online documentation * :ghissue:`542`: Regression plots fail when Series objects passed to sm.OLS * :ghissue:`239`: release 0.4.x * :ghissue:`530`: l1 docs issues * :ghissue:`539`: test for statwriter (failure) * :ghissue:`490`: Travis CI on PRs * :ghissue:`252`: doc: distributions.rst refers to sandbox only * :ghissue:`85`: release 0.4 * :ghissue:`65`: MLE fit of AR model has no tests * :ghissue:`522`: ``test`` doesn't propagate arguments to nose * :ghissue:`517`: missing array conversion or shape in linear model * :ghissue:`523`: test failure with ubuntu decimals too large * :ghissue:`520`: web site documentation, source not updated * :ghissue:`488`: Avoid cholesky decomposition of diagonal matrices in linear regression models * :ghissue:`394`: Definition in macrodata NOTE * :ghissue:`45`: numdifftools dependency * :ghissue:`501`: WLS/GLS post estimation results * :ghissue:`500`: WLS fails if weights is a pandas.Series * :ghissue:`27`: add hasconstant indicator for R-squared and df calculations * :ghissue:`497`: DOC: add patsy? * :ghissue:`495`: ENH: add footer SimpleTable * :ghissue:`402`: model._data -> model.data? * :ghissue:`477`: VAR NaN Bug * :ghissue:`421`: Enhancment: Handle Missing Data * :ghissue:`489`: Expose model._data as model.data * :ghissue:`315`: tsa models assume pandas object indices are dates * :ghissue:`440`: arima predict is broken for steps > q and q != 1 * :ghissue:`458`: TST BUG? comparing pandas and array in tests, formula * :ghissue:`464`: from_formula signature * :ghissue:`245`: examples in docs: make nicer * :ghissue:`466`: broken example, pandas * :ghissue:`57`: Unhelpful error from bad exog matrix in model.py * :ghissue:`271`: ARMA.geterrors requires model to be fit * :ghissue:`350`: Writing to array returned np.diag * :ghissue:`354`: example_rst does not copy unchanged files over * :ghissue:`467`: Install issues with Pandas * :ghissue:`444`: ARMA example on stable release website not working * :ghissue:`377`: marginal effects count and discrete adjustments * :ghissue:`426`: "svd" method not supported for OLS.fit() * :ghissue:`409`: Move numdiff out of the sandbox * :ghissue:`416`: Switch to complex-step Hessian for AR(I)MA * :ghissue:`415`: bug in kalman_loglike_complex * :ghissue:`397`: plot_corr axis text labeling not working (with fix) * :ghissue:`399`: discrete errors due to incorrect in-place operation * :ghissue:`389`: VAR test_normality is broken with KeyError * :ghissue:`388`: Add tsaplots to graphics.api as graphics.tsa * :ghissue:`387`: predict date wasn't getting set with start = None * :ghissue:`386`: p-values not returned from acf * :ghissue:`385`: Allow AR.select_order to work without model being fit * :ghissue:`383`: Move mixture_rvs out of sandbox. * :ghissue:`248`: ARMA breaks with a 1d exog * :ghissue:`273`: When to give order for AR/AR(I)MA * :ghissue:`363`: examples folder -> tutorials folder * :ghissue:`346`: docs in sitepackages * :ghissue:`353`: PACF docs raise a sphinx warning * :ghissue:`348`: python 3.2.3 test failure zip_longest statsmodels-0.5.0+git13-g8e07d34/docs/source/release/index.rst000066400000000000000000000013461224417117700236670ustar00rootroot00000000000000.. During each release add in this folder information about important changes. .. Each versionx.x.rst file should have four main sections. .. (1) Major features (2) Important bug fixes (3) API breakage (4) Credits .. The github-stats-x.x.rst files are generated by tools/github_stats.py with .. some cleanup afterwards. I do python github_stats.py > github-stats-x.x.rst. .. As of the 0.5 release, this script asks for your github name and password .. to download the statistics. .. _whatsnew_index: ========================= What's new in Statsmodels ========================= .. toctree:: :maxdepth: 1 version0.5 github-stats-0.5 For an overview of changes that occured previous to the 0.5.0 release see :ref:`old_changes`. statsmodels-0.5.0+git13-g8e07d34/docs/source/release/old_changes.rst000066400000000000000000000153741224417117700250340ustar00rootroot00000000000000:orphan: .. _old_changes: Pre 0.5.0 Release History ========================= 0.5.0 ----- *Main Changes and Additions* * Add patsy dependency *Compatibility and Deprecation* * cleanup of import paths (lowess) * *Bug Fixes* * input shapes of tools.isestimable * *Enhancements and Additions* * formula integration based on patsy (new dependency) * Time series analysis - ARIMA modeling - enhanced forecasting based on pandas datetime handling * expanded margins for discrete models * OLS outlier test * empirical likelihood - Google Summer of Code 2012 project - inference for descriptive statistics - inference for regression models - accelerated failure time models * expanded probability plots * improved graphics - plotcorr - acf and pacf * new datasets * new and improved tools - numdiff numerical differentiation 0.4.3 ----- The only change compared to 0.4.2 is for compatibility with python 3.2.3 (changed behavior of 2to3) 0.4.2 ----- This is a bug-fix release, that affects mainly Big-Endian machines. *Bug Fixes* * discrete_model.MNLogit fix summary method * tsa.filters.hp_filter don't use umfpack on Big-Endian machine (scipy bug) * the remaining fixes are in the test suite, either precision problems on some machines or incorrect testing on Big-Endian machines. 0.4.1 ----- This is a backwards compatible (according to our test suite) release with bug fixes and code cleanup. *Bug Fixes* * build and distribution fixes * lowess correct distance calculation * genmod correction CDFlink derivative * adfuller _autolag correct calculation of optimal lag * het_arch, het_lm : fix autolag and store options * GLSAR: incorrect whitening for lag>1 *Other Changes* * add lowess and other functions to api and documentation * rename lowess module (old import path will be removed at next release) * new robust sandwich covariance estimators, moved out of sandbox * compatibility with pandas 0.8 * new plots in statsmodels.graphics - ABLine plot - interaction plot 0.4.0 ----- *Main Changes and Additions* * Added pandas dependency. * Cython source is built automatically if cython and compiler are present * Support use of dates in timeseries models * Improved plots - Violin plots - Bean Plots - QQ Plots * Added lowess function * Support for pandas Series and DataFrame objects. Results instances return pandas objects if the models are fit using pandas objects. * Full Python 3 compatibility * Fix bugs in genfromdta. Convert Stata .dta format to structured array preserving all types. Conversion is much faster now. * Improved documentation * Models and results are pickleable via save/load, optionally saving the model data. * Kernel Density Estimation now uses Cython and is considerably faster. * Diagnostics for outlier and influence statistics in OLS * Added El Nino Sea Surface Temperatures dataset * Numerous bug fixes * Internal code refactoring * Improved documentation including examples as part of HTML *Changes that break backwards compatibility* * Deprecated scikits namespace. The recommended import is now:: import statsmodels.api as sm * model.predict methods signature is now (params, exog, ...) where before it assumed that the model had been fit and omitted the params argument. * For consistency with other multi-equation models, the parameters of MNLogit are now transposed. * tools.tools.ECDF -> distributions.ECDF * tools.tools.monotone_fn_inverter -> distributions.monotone_fn_inverter * tools.tools.StepFunction -> distributions.StepFunction 0.3.1 ----- * Removed academic-only WFS dataset. * Fix easy_install issue on Windows. 0.3.0 ----- *Changes that break backwards compatibility* Added api.py for importing. So the new convention for importing is:: import statsmodels.api as sm Importing from modules directly now avoids unnecessary imports and increases the import speed if a library or user only needs specific functions. * sandbox/output.py -> iolib/table.py * lib/io.py -> iolib/foreign.py (Now contains Stata .dta format reader) * family -> families * families.links.inverse -> families.links.inverse_power * Datasets' Load class is now load function. * regression.py -> regression/linear_model.py * discretemod.py -> discrete/discrete_model.py * rlm.py -> robust/robust_linear_model.py * glm.py -> genmod/generalized_linear_model.py * model.py -> base/model.py * t() method -> tvalues attribute (t() still exists but raises a warning) *Main changes and additions* * Numerous bugfixes. * Time Series Analysis model (tsa) - Vector Autoregression Models VAR (tsa.VAR) - Autogressive Models AR (tsa.AR) - Autoregressive Moving Average Models ARMA (tsa.ARMA) optionally uses Cython for Kalman Filtering use setup.py install with option --with-cython - Baxter-King band-pass filter (tsa.filters.bkfilter) - Hodrick-Prescott filter (tsa.filters.hpfilter) - Christiano-Fitzgerald filter (tsa.filters.cffilter) * Improved maximum likelihood framework uses all available scipy.optimize solvers * Refactor of the datasets sub-package. * Added more datasets for examples. * Removed RPy dependency for running the test suite. * Refactored the test suite. * Refactored codebase/directory structure. * Support for offset and exposure in GLM. * Removed data_weights argument to GLM.fit for Binomial models. * New statistical tests, especially diagnostic and specification tests * Multiple test correction * General Method of Moment framework in sandbox * Improved documentation * and other additions 0.2.0 ----- *Main changes* * renames for more consistency RLM.fitted_values -> RLM.fittedvalues GLMResults.resid_dev -> GLMResults.resid_deviance * GLMResults, RegressionResults: lazy calculations, convert attributes to properties with _cache * fix tests to run without rpy * expanded examples in examples directory * add PyDTA to lib.io -- functions for reading Stata .dta binary files and converting them to numpy arrays * made tools.categorical much more robust * add_constant now takes a prepend argument * fix GLS to work with only a one column design *New* * add four new datasets - A dataset from the American National Election Studies (1996) - Grunfeld (1950) investment data - Spector and Mazzeo (1980) program effectiveness data - A US macroeconomic dataset * add four new Maximum Likelihood Estimators for models with a discrete dependent variables with examples - Logit - Probit - MNLogit (multinomial logit) - Poisson *Sandbox* * add qqplot in sandbox.graphics * add sandbox.tsa (time series analysis) and sandbox.regression (anova) * add principal component analysis in sandbox.tools * add Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares for systems of equations in sandbox.sysreg.Sem2SLS * add restricted least squares (RLS) 0.1.0b1 ------- * initial release statsmodels-0.5.0+git13-g8e07d34/docs/source/release/version0.5.rst000066400000000000000000000370661224417117700245000ustar00rootroot00000000000000=========== 0.5 Release =========== Release 0.5.0 ============= Statsmodels 0.5 is a large and very exciting release that brings together a year of work done by 38 authors, including over 2000 commits. It contains many new features and a large amount of bug fixes detailed below. See the :ref:`list of fixed issues ` for specific closed issues. The following major new features appear in this version. Support for Model Formulas via Patsy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Statsmodels now supports fitting models with a formula. This functionality is provided by `patsy `_. Patsy is now a dependency for statsmodels. Models can be individually imported from the ``statsmodels.formula.api`` namespace or you can import them all as:: import statsmodels.formula.api as smf Alternatively, each model in the usual ``statsmodels.api`` namespace has a ``from_formula`` classmethod that will create a model using a formula. Formulas are also available for specifying linear hypothesis tests using the ``t_test`` and ``f_test`` methods after model fitting. A typical workflow can now look something like this. .. code-block:: python import numpy as np import pandas as pd import statsmodels.formula.api as smf url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv' data = pd.read_csv(url) # Fit regression model (using the natural log of one of the regressors) results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=data).fit() See :ref:`here for some more documentation of using formulas in statsmodels ` Empirical Likelihood (Google Summer of Code 2012 project) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Empirical Likelihood-Based Inference for moments of univariate and multivariate variables is available as well as EL-based ANOVA tests. EL-based linear regression, including the regression through the origin model. In addition, the accelerated failure time model for inference on a linear regression model with a randomly right censored endogenous variable is available. Analysis of Variance (ANOVA) Modeling ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support for ANOVA is now available including type I, II, and III sums of squares. See :ref:`anova`. .. currentmodule:: statsmodels.nonparametric Multivariate Kernel Density Estimators (GSoC 2012 project) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Kernel density estimation has been extended to handle multivariate estimation as well via product kernels. It is available as :class:`sm.nonparametric.KDEMultivariate `. It supports least squares and maximum likelihood cross-validation for bandwidth estimation, as well as mixed continuous, ordered, and unordered categorical data. Conditional density estimation is also available via :class:`sm.nonparametric.KDEMUltivariateConditional `. Nonparameteric Regression (GSoC 2012 project) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Kernel regression models are now available via :class:`sm.nonparametric.KernelReg `. It is based on the product kernel mentioned above, so it also has the same set of features including support for cross-validation as well as support for estimation mixed continuous and categorical variables. Censored kernel regression is also provided by `kernel_regression.KernelCensoredReg`. Quantile Regression Model ~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.regression.quantile_regression Quantile regression is supported via the :class:`sm.QuantReg ` class. Kernel and bandwidth selection options are available for estimating the asymptotic covariance matrix using a kernel density estimator. Negative Binomial Regression Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.discrete.discrete_model It is now possible to fit negative binomial models for count data via maximum-likelihood using the :class:`sm.NegativeBinomial ` class. ``NB1``, ``NB2``, and ``geometric`` variance specifications are available. l1-penalized Discrete Choice Models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A new optimization method has been added to the discrete models, which includes Logit, Probit, MNLogit and Poisson, that makes it possible to estimate the models with an l1, linear, penalization. This shrinks parameters towards zero and can set parameters that are not very different from zero to zero. This is especially useful if there are a large number of explanatory variables and a large associated number of parameters. `CVXOPT `_ is now an optional dependency that can be used for fitting these models. New and Improved Graphics ~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.graphics * **ProbPlot**: A new `ProbPlot` object has been added to provide a simple interface to create P-P, Q-Q, and probability plots with options to fit a distribution and show various reference lines. In the case of Q-Q and P-P plots, two different samples can be compared with the `other` keyword argument. :func:`sm.graphics.ProbPlot ` .. code-block:: python import numpy as np import statsmodels.api as sm x = np.random.normal(loc=1.12, scale=0.25, size=37) y = np.random.normal(loc=0.75, scale=0.45, size=37) ppx = sm.ProbPlot(x) ppy = sm.ProbPlot(y) fig1 = ppx.qqplot() fig2 = ppx.qqplot(other=ppy) * **Mosaic Plot**: Create a mosaic plot from a contingency table. This allows you to visualize multivariate categorical data in a rigorous and informative way. Available with :func:`sm.graphics.mosaic `. * **Interaction Plot**: Interaction plots now handle categorical factors as well as other improviments. :func:`sm.graphics.interaction_plot `. * **Regression Plots**: The regression plots have been refactored and improved. They can now handle pandas objects and regression results instances appropriately. See :func:`sm.graphics.plot_fit `, :func:`sm.graphics.plot_regress_exog `, :func:`sm.graphics.plot_partregress `, :func:`sm.graphics.plot_ccpr `, :func:`sm.graphics.abline_plot `, :func:`sm.graphics.influence_plot `, and :func:`sm.graphics.plot_leverage_resid2 `. .. currentmodule:: statsmodels.stats.power Power and Sample Size Calculations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The power module (``statsmodel.stats.power``) currently implements power and sample size calculations for the t-tests (:class:`sm.stats.TTestPower `, :class:`sm.stats.TTestIndPower `), normal based test (:class:`sm.stats.NormIndPower `), F-tests (:class:`sm.stats.FTestPower `, `:class:sm.stats.FTestAnovaPower `) and Chisquare goodness of fit (:class:`sm.stats.GofChisquarePower `) test. The implementation is class based, but the module also provides three shortcut functions, :func:`sm.stats.tt_solve_power `, :func:`sm.stats.tt_ind_solve_power ` and :func:`sm.stats.zt_ind_solve_power ` to solve for any one of the parameters of the power equations. See this `blog post `_ for a more in-depth description of the additions. Other important new features ---------------------------- * **IPython notebook examples**: Many of our examples have been converted or added as IPython notebooks now. They are available `here `_. * **Improved marginal effects for discrete choice models**: Expanded options for obtaining marginal effects after the estimation of nonlinear discrete choice models are available. See :py:meth:`get_margeff `. * **OLS influence outlier measures**: After the estimation of a model with OLS, the common set of influence and outlier measures and a outlier test are now available attached as methods ``get_influnce`` and ``outlier_test`` to the Results instance. See :py:class:`OLSInfluence ` and :func:`outlier_test `. * **New datasets**: New :ref:`datasets ` are available for examples. * **Access to R datasets**: We now have access to many of the same datasets available to R users through the `Rdatasets project `_. You can access these using the :func:`sm.datasets.get_rdataset ` function. This function also includes caching of these datasets. * **Improved numerical differentiation tools**: Numerical differentiation routines have been greatly improved and expanded to cover all the routines discussed in:: Ridout, M.S. (2009) Statistical applications of the complex-step method of numerical differentiation. The American Statistician, 63, 66-74 See the :ref:`sm.tools.numdiff ` module. * **Consistent constant handling across models**: Result statistics no longer rely on the assumption that a constant is present in the model. * **Missing value handling across models**: Users can now control what models do in the presence of missing values via the ``missing`` keyword available in the instantiation of every model. The options are ``'none'``, ``'drop'``, and ``'raise'``. The default is ``'none'``, which does no missing value checks. To drop missing values use ``'drop'``. And ``'raise'`` will raise an error in the presence of any missing data. .. currentmodule:: statsmodels.iolib * **Ability to write Stata datasets**: Added the ability to write Stata ``.dta`` files. See :class:`sm.iolib.StataWriter `. .. currentmodule:: statsmodels.tsa.arima_model * **ARIMA modeling**: Statsmodels now has support for fitting Autoregressive Integrated Moving Average (ARIMA) models. See :class:`ARIMA` and :class:`ARIMAResults` for more information. * **Support for dynamic prediction in AR(I)MA models**: It is now possible to obtain dynamic in-sample forecast values in :class:`ARMA` and :class:`ARIMA` models. * **Improved Pandas integration**: Statsmodels now supports all frequencies available in pandas for time-series modeling. These are used for intelligent dates handling for prediction. These features are available, if you pass a pandas Series or DataFrame with a DatetimeIndex to a time-series model. .. currentmodule:: statsmodels * **New statistical hypothesis tests**: Added statistics for calculating interrater agreement including Cohen's kappa and Fleiss' kappa (See :ref:`interrater`), statistics and hypothesis tests for proportions (See :ref:`proportion stats `), Tukey HSD (with plot) was added as an enhancement to the multiple comparison tests (:class:`sm.stats.multicomp.MultiComparison `, :func:`sm.stats.multicomp.pairwise_tukeyhsd `). Weighted statistics and t tests were enhanced with new options. Tests of equivalence for one sample and two independent or paired samples were added based on t tests and z tests (See :ref:`tost`). Major Bugs fixed ---------------- * Post-estimation statistics for weighted least squares that depended on the centered total sum of squares were not correct. These are now correct and tested. See :ghissue:`501`. * Regression through the origin models now correctly use uncentered total sum of squares in post-estimation statistics. This affected the :math:`R^2` value in linear models without a constant. See :ghissue:`27`. Backwards incompatible changes and deprecations ----------------------------------------------- * Cython code is now non-optional. You will need a C compiler to build from source. If building from github and not a source release, you will also need Cython installed. See the :ref:`installation documentation `. * The ``q_matrix`` keyword to `t_test` and `f_test` for linear models is deprecated. You can now specify linear hypotheses using formulas. .. currentmodule:: statsmodels.tsa * The ``conf_int`` keyword to :func:`sm.tsa.acf ` is deprecated. * The ``names`` argument is deprecated in :class:`sm.tsa.VAR ` and `sm.tsa.SVAR `. This is now automatically detected and handled. .. currentmodule:: statsmodels.tsa * The ``order`` keyword to :py:meth:`sm.tsa.ARMA.fit ` is deprecated. It is now passed in during model instantiation. .. currentmodule:: statsmodels.distributions * The empirical distribution function (:class:`sm.distributions.ECDF `) and supporting functions have been moved to ``statsmodels.distributions``. Their old paths have been deprecated. * The ``margeff`` method of the discrete choice models has been deprecated. Use ``get_margeff`` instead. See above. Also, the vague ``resid`` attribute of the discrete choice models has been deprecated in favor of the more descriptive ``resid_dev`` to indicate that they are deviance residuals. .. currentmodule:: statsmodels.nonparametric.kde * The class ``KDE`` has been deprecated and renamed to :class:`KDEUnivariate` to distinguish it from the new ``KDEMultivariate``. See above. Development summary and credits ------------------------------- The previous version (statsmodels 0.4.3) was released on July 2, 2012. Since then we have closed a total of 380 issues, 172 pull requests and 208 regular issues. The :ref:`detailed list` can be viewed. This release is a result of the work of the following 38 authors who contributed total of 2032 commits. If for any reason, we've failed to list your name in the below, please contact us: * Ana Martinez Pardo * anov * avishaylivne * Bruno Rodrigues * Carl Vogel * Chad Fulton * Christian Prinoth * Daniel B. Smith * dengemann * Dieter Vandenbussche * Dougal Sutherland * Enrico Giampieri * evelynmitchell * George Panterov * Grayson * Jan Schulz * Josef Perktold * Jeff Reback * Justin Grana * langmore * Matthew Brett * Nathaniel J. Smith * otterb * padarn * Paul Hobson * Pietro Battiston * Ralf Gommers * Richard T. Guy * Robert Cimrman * Skipper Seabold * Thomas Haslwanter * timmie * Tom Augspurger * Trent Hauck * tylerhartley * Vincent Arel-Bundock * VirgileFritsch * Zhenya .. note:: Obtained by running ``git log v0.4.3..HEAD --format='* %aN <%aE>' | sed 's/@/\-at\-/' | sed 's/<>//' | sort -u``. statsmodels-0.5.0+git13-g8e07d34/docs/source/rlm.rst000066400000000000000000000033671224417117700217370ustar00rootroot00000000000000.. currentmodule:: statsmodels.robust .. _rlm: Robust Linear Models ==================== Robust linear models with support for the M-estimators listed under `Norms`_. See `Module Reference`_ for commands and arguments. Examples -------- :: # Load modules and data import statsmodels.api as sm data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) # Fit model and print summary rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) rlm_results = rlm_model.fit() print rlm_results.params Detailed examples can be found here: .. toctree:: :maxdepth: 1 examples/generated/example_rlm Technical Documentation ----------------------- .. toctree:: :maxdepth: 1 rlm_techn1 References ^^^^^^^^^^ * PJ Huber. ‘Robust Statistics’ John Wiley and Sons, Inc., New York. 1981. * PJ Huber. 1973, ‘The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.’ The Annals of Statistics, 1.5, 799-821. * R Venables, B Ripley. ‘Modern Applied Statistics in S’ Springer, New York, Module Reference ---------------- Model Classes ^^^^^^^^^^^^^ .. currentmodule:: statsmodels.robust.robust_linear_model .. autosummary:: :toctree: generated/ RLM Model Results ^^^^^^^^^^^^^ .. autosummary:: :toctree: generated/ RLMResults .. _norms: Norms ^^^^^ .. currentmodule:: statsmodels.robust.norms .. autosummary:: :toctree: generated/ AndrewWave Hampel HuberT LeastSquares RamsayE RobustNorm TrimmedMean TukeyBiweight estimate_location Scale ^^^^^ .. currentmodule:: statsmodels.robust.scale .. autosummary:: :toctree: generated/ Huber HuberScale mad huber hubers_scale stand_mad statsmodels-0.5.0+git13-g8e07d34/docs/source/rlm_techn1.rst000066400000000000000000000005621224417117700231730ustar00rootroot00000000000000.. currentmodule:: statsmodels.rlm .. _rlm_techn1: Weight Functions ---------------- Andrew's Wave .. image:: images/aw.png Hampel 17A .. image:: images/hl.png Huber's t .. image:: images/ht.png Least Squares .. image:: images/ls.png Ramsay's Ea .. image:: images/re.png Trimmed Mean .. image:: images/tm.png Tukey's Biweight .. image:: images/tk.png statsmodels-0.5.0+git13-g8e07d34/docs/source/sandbox.rst000066400000000000000000000117111224417117700225730ustar00rootroot00000000000000.. currentmodule:: statsmodels.sandbox .. _sandbox: Sandbox ======= This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All sandbox modules have to be explicitly imported to indicate that they are not yet part of the core of statsmodels. The quality and testing of the sandbox code varies widely. .. automodule:: statsmodels.sandbox Examples -------- There are some examples in the `sandbox.examples` folder. Additional examples are directly included in the modules and in subfolders of the sandbox. Module Reference ---------------- Time Series analysis :mod:`tsa` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In this part we develop models and functions that will be useful for time series analysis. Most of the models and function have been moved to :mod:`statsmodels.tsa`. Currently, GARCH models remain in development stage in `sandbox.tsa`. .. currentmodule:: statsmodels.sandbox Moving Window Statistics """""""""""""""""""""""" Most moving window statistics, like rolling mean, moments (up to 4th order), min, max, mean, and variance, are covered by the functions for `Moving (rolling) statistics/moments `_ in Pandas. .. autosummary:: :toctree: generated/ tsa.movstat Regression and ANOVA ^^^^^^^^^^^^^^^^^^^^ .. currentmodule:: statsmodels.sandbox.regression.anova_nistcertified The following two ANOVA functions are fully tested against the NIST test data for balanced one-way ANOVA. ``anova_oneway`` follows the same pattern as the oneway anova function in scipy.stats but with higher precision for badly scaled problems. ``anova_ols`` produces the same results as the one way anova however using the OLS model class. It also verifies against the NIST tests, with some problems in the worst scaled cases. It shows how to do simple ANOVA using statsmodels in three lines and is also best taken as a recipe. .. autosummary:: :toctree: generated/ anova_oneway anova_ols The following are helper functions for working with dummy variables and generating ANOVA results with OLS. They are best considered as recipes since they were written with a specific use in mind. These function will eventually be rewritten or reorganized. .. currentmodule:: statsmodels.sandbox.regression .. autosummary:: :toctree: generated/ try_ols_anova.data2dummy try_ols_anova.data2groupcont try_ols_anova.data2proddummy try_ols_anova.dropname try_ols_anova.form2design The following are helper functions for group statistics where groups are defined by a label array. The qualifying comments for the previous group apply also to this group of functions. .. autosummary:: :toctree: generated/ try_catdata.cat2dummy try_catdata.convertlabels try_catdata.groupsstats_1d try_catdata.groupsstats_dummy try_catdata.groupstatsbin try_catdata.labelmeanfilter try_catdata.labelmeanfilter_nd try_catdata.labelmeanfilter_str Additional to these functions, sandbox regression still contains several examples, that are illustrative of the use of the regression models of statsmodels. Systems of Regression Equations and Simultaneous Equations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The following are for fitting systems of equations models. Though the returned parameters have been verified as accurate, this code is still very experimental, and the usage of the models will very likely change significantly before they are added to the main codebase. .. currentmodule:: statsmodels.sandbox.sysreg .. autosummary:: :toctree: generated/ SUR Sem2SLS Miscellaneous ^^^^^^^^^^^^^ .. currentmodule:: statsmodels.sandbox.tools.tools_tsa Tools for Time Series Analysis """""""""""""""""""""""""""""" nothing left in here Tools: Principal Component Analysis """"""""""""""""""""""""""""""""""" .. currentmodule:: statsmodels.sandbox.tools.tools_pca .. autosummary:: :toctree: generated/ pca pcasvd Descriptive Statistics Printing """"""""""""""""""""""""""""""" .. currentmodule:: statsmodels.sandbox .. autosummary:: :toctree: generated/ descstats.sign_test descstats.descstats Original stats.models ^^^^^^^^^^^^^^^^^^^^^ None of these are fully working. The formula framework is used by cox and mixed. **Mixed Effects Model with Repeated Measures using an EM Algorithm** :mod:`statsmodels.sandbox.mixed` **Cox Proportional Hazards Model** :mod:`statsmodels.sandbox.cox` **Generalized Additive Models** :mod:`statsmodels.sandbox.gam` **Formula** :mod:`statsmodels.sandbox.formula` statsmodels-0.5.0+git13-g8e07d34/docs/source/stats.rst000066400000000000000000000203351224417117700222750ustar00rootroot00000000000000.. currentmodule:: statsmodels.stats .. _stats: Statistics :mod:`stats` ======================= This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around. We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. .. _stattools: Residual Diagnostics and Specification Tests -------------------------------------------- .. currentmodule:: statsmodels.stats.stattools .. autosummary:: :toctree: generated/ durbin_watson jarque_bera omni_normtest .. currentmodule:: statsmodels.stats.diagnostic .. autosummary:: :toctree: generated/ acorr_ljungbox acorr_breush_godfrey HetGoldfeldQuandt het_goldfeldquandt het_breushpagan het_white het_arch linear_harvey_collier linear_rainbow linear_lm breaks_cusumolsresid breaks_hansen recursive_olsresiduals CompareCox compare_cox CompareJ compare_j unitroot_adf normal_ad kstest_normal lillifors Outliers and influence measures ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.stats.outliers_influence .. autosummary:: :toctree: generated/ OLSInfluence variance_inflation_factor See also the notes on :ref:`notes on regression diagnostics ` Sandwich Robust Covariances --------------------------- The following functions calculate covariance matrices and standard errors for the parameter estimates that are robust to heteroscedasticity and autocorrelation in the errors. Similar to the methods that are available for the LinearModelResults, these methods are designed for use with OLS. .. currentmodule:: statsmodels.stats .. autosummary:: :toctree: generated/ sandwich_covariance.cov_hac sandwich_covariance.cov_nw_panel sandwich_covariance.cov_nw_groupsum sandwich_covariance.cov_cluster sandwich_covariance.cov_cluster_2groups sandwich_covariance.cov_white_simple The following are standalone versions of the heteroscedasticity robust standard errors attached to LinearModelResults .. autosummary:: :toctree: generated/ sandwich_covariance.cov_hc0 sandwich_covariance.cov_hc1 sandwich_covariance.cov_hc2 sandwich_covariance.cov_hc3 sandwich_covariance.se_cov Goodness of Fit Tests and Measures ---------------------------------- some tests for goodness of fit for univariate distributions .. currentmodule:: statsmodels.stats.gof .. autosummary:: :toctree: generated/ powerdiscrepancy gof_chisquare_discrete gof_binning_discrete chisquare_effectsize .. currentmodule:: statsmodels.stats.diagnostic .. autosummary:: :toctree: generated/ normal_ad kstest_normal lillifors Non-Parametric Tests -------------------- .. currentmodule:: statsmodels.sandbox.stats.runs .. autosummary:: :toctree: generated/ mcnemar symmetry_bowker median_test_ksample runstest_1samp runstest_2samp cochrans_q Runs .. currentmodule:: statsmodels.stats.descriptivestats .. autosummary:: :toctree: generated/ sign_test .. _interrater: Interrater Reliability and Agreement ------------------------------------ The main function that statsmodels has currently available for interrater agreement measures and tests is Cohen's Kappa. Fleiss' Kappa is currently only implemented as a measures but without associated results statistics. .. currentmodule:: statsmodels.stats.inter_rater .. autosummary:: :toctree: generated/ cohens_kappa fleiss_kappa to_table aggregate_raters Multiple Tests and Multiple Comparison Procedures ------------------------------------------------- `multipletests` is a function for p-value correction, which also includes p-value correction based on fdr in `fdrcorrection`. `tukeyhsd` performs simulatenous testing for the comparison of (independent) means. These three functions are verified. GroupsStats and MultiComparison are convenience classes to multiple comparisons similar to one way ANOVA, but still in developement .. currentmodule:: statsmodels.sandbox.stats.multicomp .. autosummary:: :toctree: generated/ multipletests fdrcorrection0 GroupsStats MultiComparison TukeyHSDResults .. currentmodule:: statsmodels.stats.multicomp .. autosummary:: :toctree: generated/ pairwise_tukeyhsd The following functions are not (yet) public .. currentmodule:: statsmodels.sandbox.stats.multicomp .. autosummary:: :toctree: generated/ varcorrection_pairs_unbalanced varcorrection_pairs_unequal varcorrection_unbalanced varcorrection_unequal StepDown catstack ccols compare_ordered distance_st_range ecdf get_tukeyQcrit homogeneous_subsets line maxzero maxzerodown mcfdr qcrit randmvn rankdata rejectionline set_partition set_remove_subs tiecorrect .. _tost: Basic Statistics and t-Tests with frequency weights --------------------------------------------------- Besides basic statistics, like mean, variance, covariance and correlation for data with case weights, the classes here provide one and two sample tests for means. The t-tests have more options than those in scipy.stats, but are more restrictive in the shape of the arrays. Confidence intervals for means are provided based on the same assumptions as the t-tests. Additionally, tests for equivalence of means are available for one sample and for two, either paired or independent, samples. These tests are based on TOST, two one-sided tests, which have as null hypothesis that the means are not "close" to each other. .. currentmodule:: statsmodels.stats.weightstats .. autosummary:: :toctree: generated/ DescrStatsW CompareMeans ttest_ind ttost_ind ttost_paired ztest ztost zconfint weightstats also contains tests and confidence intervals based on summary data .. currentmodule:: statsmodels.stats.weightstats .. autosummary:: :toctree: generated/ _tconfint_generic _tstat_generic _zconfint_generic _zstat_generic _zstat_generic2 Power and Sample Size Calculations ---------------------------------- The :mod:`power` module currently implements power and sample size calculations for the t-tests, normal based test, F-tests and Chisquare goodness of fit test. The implementation is class based, but the module also provides three shortcut functions, ``tt_solve_power``, ``tt_ind_solve_power`` and ``zt_ind_solve_power`` to solve for any one of the parameters of the power equations. .. currentmodule:: statsmodels.stats.power .. autosummary:: :toctree: generated/ TTestIndPower TTestPower GofChisquarePower NormalIndPower FTestAnovaPower FTestPower tt_solve_power tt_ind_solve_power zt_ind_solve_power .. _proportion_stats: Proportion ---------- Also available are hypothesis test, confidence intervals and effect size for proportions that can be used with NormalIndPower. .. currentmodule:: statsmodels.stats.proportion .. autosummary:: :toctree: generated proportion_confint proportion_effectsize binom_test binom_test_reject_interval binom_tost binom_tost_reject_interval proportions_ztest proportions_ztost proportions_chisquare proportions_chisquare_allpairs proportions_chisquare_pairscontrol proportion_effectsize power_binom_tost power_ztost_prop samplesize_confint_proportion Moment Helpers -------------- When there are missing values, then it is possible that a correlation or covariance matrix is not positive semi-definite. The following three functions can be used to find a correlation or covariance matrix that is positive definite and close to the original matrix. .. currentmodule:: statsmodels.stats.correlation_tools .. autosummary:: :toctree: generated/ corr_nearest corr_clipped cov_nearest These are utility functions to convert between central and non-central moments, skew, kurtosis and cummulants. .. currentmodule:: statsmodels.stats.moment_helpers .. autosummary:: :toctree: generated/ cum2mc mc2mnc mc2mvsk mnc2cum mnc2mc mnc2mvsk mvsk2mc mvsk2mnc cov2corr corr2cov se_cov statsmodels-0.5.0+git13-g8e07d34/docs/source/tools.rst000066400000000000000000000046551224417117700223060ustar00rootroot00000000000000.. currentmodule:: statsmodels.tools .. _tools: Tools ===== Our tool collection contains some convenience functions for users and functions that were written mainly for internal use. Additional to this tools directory, several other subpackages have their own tools modules, for example :mod:`statsmodels.tsa.tsatools` Module Reference ---------------- Basic tools :mod:`tools` ^^^^^^^^^^^^^^^^^^^^^^^^ These are basic and miscellaneous tools. The full import path is `statsmodels.tools.tools`. .. autosummary:: :toctree: generated/ tools.add_constant The next group are mostly helper functions that are not separately tested or insufficiently tested. .. autosummary:: :toctree: generated/ tools.categorical tools.ECDF tools.clean0 tools.fullrank tools.isestimable tools.monotone_fn_inverter tools.rank tools.recipr tools.recipr0 tools.unsqueeze .. _numdiff: Numerical Differentiation ^^^^^^^^^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: generated/ numdiff.approx_fprime numdiff.approx_fprime_cs numdiff.approx_hess1 numdiff.approx_hess2 numdiff.approx_hess3 numdiff.approx_hess_cs Measure for fit performance :mod:`eval_measures` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The first group of function in this module are standalone versions of information criteria, aic bic and hqic. The function with `_sigma` suffix take the error sum of squares as argument, those without, take the value of the log-likelihood, `llf`, as argument. The second group of function are measures of fit or prediction performance, which are mostly one liners to be used as helper functions. All of those calculate a performance or distance statistic for the difference between two arrays. For example in the case of Monte Carlo or cross-validation, the first array would be the estimation results for the different replications or draws, while the second array would be the true or observed values. .. currentmodule:: statsmodels.tools .. autosummary:: :toctree: generated/ eval_measures.aic eval_measures.aic_sigma eval_measures.aicc eval_measures.aicc_sigma eval_measures.bic eval_measures.bic_sigma eval_measures.hqic eval_measures.hqic_sigma eval_measures.bias eval_measures.iqr eval_measures.maxabs eval_measures.meanabs eval_measures.medianabs eval_measures.medianbias eval_measures.mse eval_measures.rmse eval_measures.stde eval_measures.vare statsmodels-0.5.0+git13-g8e07d34/docs/source/tsa.rst000066400000000000000000000135341224417117700217310ustar00rootroot00000000000000.. currentmodule:: statsmodels.tsa .. _tsa: Time Series analysis :mod:`tsa` =============================== :mod:`statsmodels.tsa` contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Additionally, related statistical tests and some useful helper functions are available. Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters. Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. The module structure is within statsmodels.tsa is - stattools : empirical properties and tests, acf, pacf, granger-causality, adf unit root test, ljung-box test and others. - ar_model : univariate autoregressive process, estimation with conditional and exact maximum likelihood and conditional least-squares - arima_model : univariate ARMA process, estimation with conditional and exact maximum likelihood and conditional least-squares - vector_ar, var : vector autoregressive process (VAR) estimation models, impulse response analysis, forecast error variance decompositions, and data visualization tools - kalmanf : estimation classes for ARMA and other models with exact MLE using Kalman Filter - arma_process : properties of arma processes with given parameters, this includes tools to convert between ARMA, MA and AR representation as well as acf, pacf, spectral density, impulse response function and similar - sandbox.tsa.fftarma : similar to arma_process but working in frequency domain - tsatools : additional helper functions, to create arrays of lagged variables, construct regressors for trend, detrend and similar. - filters : helper function for filtering time series Some additional functions that are also useful for time series analysis are in other parts of statsmodels, for example additional statistical tests. Some related functions are also available in matplotlib, nitime, and scikits.talkbox. Those functions are designed more for the use in signal processing where longer time series are available and work more often in the frequency domain. .. currentmodule:: statsmodels.tsa Descriptive Statistics and Tests """""""""""""""""""""""""""""""" .. autosummary:: :toctree: generated/ stattools.acovf stattools.acf stattools.pacf stattools.pacf_yw stattools.pacf_ols stattools.ccovf stattools.ccf stattools.periodogram stattools.adfuller stattools.q_stat stattools.grangercausalitytests stattools.levinson_durbin Estimation """""""""" The following are the main estimation classes, which can be accessed through statsmodels.tsa.api and their result classes Univariate Autogressive Processes (AR) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.tsa .. autosummary:: :toctree: generated/ ar_model.AR ar_model.ARResults Autogressive Moving-Average Processes (ARMA) and Kalman Filter ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: statsmodels.tsa .. autosummary:: :toctree: generated/ arima_model.ARMA arima_model.ARMAResults arima_model.ARIMA arima_model.ARIMAResults kalmanf.kalmanfilter.KalmanFilter Vector Autogressive Processes (VAR) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: generated/ vector_ar.var_model.VAR vector_ar.var_model.VARResults vector_ar.dynamic.DynamicVAR .. seealso:: :ref:`VAR documentation ` .. currentmodule:: statsmodels.tsa Vector Autogressive Processes (VAR) """"""""""""""""""""""""""""""""""" Besides estimation, several process properties and additional results after estimation are available for vector autoregressive processes. .. autosummary:: :toctree: generated/ vector_ar.var_model.VAR vector_ar.var_model.VARProcess vector_ar.var_model.VARResults vector_ar.irf.IRAnalysis vector_ar.var_model.FEVD vector_ar.dynamic.DynamicVAR .. seealso:: :ref:`VAR documentation ` ARMA Process """""""""""" The following are tools to work with the theoretical properties of an ARMA process for given lag-polynomials. .. autosummary:: :toctree: generated/ arima_process.ArmaProcess arima_process.ar2arma arima_process.arma2ar arima_process.arma2ma arima_process.arma_acf arima_process.arma_acovf arima_process.arma_generate_sample arima_process.arma_impulse_response arima_process.arma_pacf arima_process.arma_periodogram arima_process.deconvolve arima_process.index2lpol arima_process.lpol2index arima_process.lpol_fiar arima_process.lpol_fima arima_process.lpol_sdiff .. currentmodule:: statsmodels .. autosummary:: :toctree: generated/ sandbox.tsa.fftarma.ArmaFft .. currentmodule:: statsmodels.tsa Other Time Series Filters """"""""""""""""""""""""" .. autosummary:: :toctree: generated/ filters.bkfilter filters.hpfilter filters.arfilter filters.cffilter filters.miso_lfilter filters.filtertools.fftconvolve3 filters.filtertools.fftconvolveinv TSA Tools """"""""" .. autosummary:: :toctree: generated/ tsatools.add_constant tsatools.add_trend tsatools.detrend tsatools.lagmat tsatools.lagmat2ds VARMA Process """"""""""""" .. autosummary:: :toctree: generated/ varma_process.VarmaPoly Interpolation """"""""""""" .. autosummary:: :toctree: generated/ interp.denton.dentonm statsmodels-0.5.0+git13-g8e07d34/docs/source/tsastats.rst.TXT000066400000000000000000000010331224417117700234550ustar00rootroot00000000000000.. currentmodule:: statsmodels.tsa.tsatools Time Series Analysis ==================== These are some of the helper functions for doing time series analysis. First we can load some a some data from the US Macro Economy 1959:Q1 - 2009:Q3. :: >>> data = sm.datasets.macrodata.load() The macro dataset is a structured array. :: >>> data = data.data[['year','quarter','realgdp','tbilrate','cpi','unemp']] We can add a lag like so :: >>> data = sm.tsa.add_lag(data, 'realgdp', lags=2) TODO: -scikits.timeseries -link in to var docs statsmodels-0.5.0+git13-g8e07d34/docs/source/vector_ar.rst000066400000000000000000000413331224417117700231240ustar00rootroot00000000000000:orphan: .. currentmodule:: statsmodels.tsa.vector_ar.var_model .. _var: Vector Autoregressions :mod:`tsa.vector_ar` =========================================== VAR(p) processes ---------------- We are interested in modeling a :math:`T \times K` multivariate time series :math:`Y`, where :math:`T` denotes the number of observations and :math:`K` the number of variables. One way of estimating relationships between the time series and their lagged values is the *vector autoregression process*: .. math:: Y_t = A_1 Y_{t-1} + \ldots + A_p Y_{t-p} + u_t u_t \sim {\sf Normal}(0, \Sigma_u) where :math:`A_i` is a :math:`K \times K` coefficient matrix. We follow in large part the methods and notation of `Lutkepohl (2005) `__, which we will not develop here. Model fitting ~~~~~~~~~~~~~ .. note:: The classes referenced below are accessible via the :mod:`statsmodels.tsa.api` module. To estimate a VAR model, one must first create the model using an `ndarray` of homogeneous or structured dtype. When using a structured or record array, the class will use the passed variable names. Otherwise they can be passed explicitly: :: # some example data >>> import pandas >>> mdata = sm.datasets.macrodata.load_pandas().data # prepare the dates index >>> dates = mdata[['year', 'quarter']].astype(int).astype(str) >>> quarterly = dates["year"] + "Q" + dates["quarter"] >>> from statsmodels.tsa.base.datetools import dates_from_str >>> quarterly = dates_from_str(quarterly) >>> mdata = mdata[['realgdp','realcons','realinv']] >>> mdata.index = pandas.DatetimeIndex(quarterly) >>> data = np.log(mdata).diff().dropna() # make a VAR model >>> model = VAR(data) .. note:: The :class:`VAR` class assumes that the passed time series are stationary. Non-stationary or trending data can often be transformed to be stationary by first-differencing or some other method. For direct analysis of non-stationary time series, a standard stable VAR(p) model is not appropriate. To actually do the estimation, call the `fit` method with the desired lag order. Or you can have the model select a lag order based on a standard information criterion (see below): :: >>> results = model.fit(2) >>> results.summary() Summary of Regression Results ================================== Model: VAR Method: OLS Date: Fri, 08, Jul, 2011 Time: 11:30:22 -------------------------------------------------------------------- No. of Equations: 3.00000 BIC: -27.5830 Nobs: 200.000 HQIC: -27.7892 Log likelihood: 1962.57 FPE: 7.42129e-13 AIC: -27.9293 Det(Omega_mle): 6.69358e-13 -------------------------------------------------------------------- Results for equation realgdp ============================================================================== coefficient std. error t-stat prob ------------------------------------------------------------------------------ const 0.001527 0.001119 1.365 0.174 L1.realgdp -0.279435 0.169663 -1.647 0.101 L1.realcons 0.675016 0.131285 5.142 0.000 L1.realinv 0.033219 0.026194 1.268 0.206 L2.realgdp 0.008221 0.173522 0.047 0.962 L2.realcons 0.290458 0.145904 1.991 0.048 L2.realinv -0.007321 0.025786 -0.284 0.777 ============================================================================== Results for equation realcons ============================================================================== coefficient std. error t-stat prob ------------------------------------------------------------------------------ const 0.005460 0.000969 5.634 0.000 L1.realgdp -0.100468 0.146924 -0.684 0.495 L1.realcons 0.268640 0.113690 2.363 0.019 L1.realinv 0.025739 0.022683 1.135 0.258 L2.realgdp -0.123174 0.150267 -0.820 0.413 L2.realcons 0.232499 0.126350 1.840 0.067 L2.realinv 0.023504 0.022330 1.053 0.294 ============================================================================== Results for equation realinv ============================================================================== coefficient std. error t-stat prob ------------------------------------------------------------------------------ const -0.023903 0.005863 -4.077 0.000 L1.realgdp -1.970974 0.888892 -2.217 0.028 L1.realcons 4.414162 0.687825 6.418 0.000 L1.realinv 0.225479 0.137234 1.643 0.102 L2.realgdp 0.380786 0.909114 0.419 0.676 L2.realcons 0.800281 0.764416 1.047 0.296 L2.realinv -0.124079 0.135098 -0.918 0.360 ============================================================================== Correlation matrix of residuals realgdp realcons realinv realgdp 1.000000 0.603316 0.750722 realcons 0.603316 1.000000 0.131951 realinv 0.750722 0.131951 1.000000 Several ways to visualize the data using `matplotlib` are available. Plotting input time series: :: >>> results.plot() .. plot:: plots/var_plot_input.py Plotting time series autocorrelation function: :: >>> results.plot_acorr() .. plot:: plots/var_plot_acorr.py Lag order selection ~~~~~~~~~~~~~~~~~~~ Choice of lag order can be a difficult problem. Standard analysis employs likelihood test or information criteria-based order selection. We have implemented the latter, accessable through the :class:`VAR` class: :: >>> model.select_order(15) VAR Order Selection ====================================================== aic bic fpe hqic ------------------------------------------------------ 0 -27.70 -27.65 9.358e-13 -27.68 1 -28.02 -27.82* 6.745e-13 -27.94* 2 -28.03 -27.66 6.732e-13 -27.88 3 -28.04* -27.52 6.651e-13* -27.83 4 -28.03 -27.36 6.681e-13 -27.76 5 -28.02 -27.19 6.773e-13 -27.69 6 -27.97 -26.98 7.147e-13 -27.57 7 -27.93 -26.79 7.446e-13 -27.47 8 -27.94 -26.64 7.407e-13 -27.41 9 -27.96 -26.50 7.280e-13 -27.37 10 -27.91 -26.30 7.629e-13 -27.26 11 -27.86 -26.09 8.076e-13 -27.14 12 -27.83 -25.91 8.316e-13 -27.05 13 -27.80 -25.73 8.594e-13 -26.96 14 -27.80 -25.57 8.627e-13 -26.90 15 -27.81 -25.43 8.599e-13 -26.85 ====================================================== * Minimum {'aic': 3, 'bic': 1, 'fpe': 3, 'hqic': 1} When calling the `fit` function, one can pass a maximum number of lags and the order criterion to use for order selection: :: >>> results = model.fit(maxlags=15, ic='aic') Forecasting ~~~~~~~~~~~ The linear predictor is the optimal h-step ahead forecast in terms of mean-squared error: .. math:: y_t(h) = \nu + A_1 y_t(h − 1) + \cdots + A_p y_t(h − p) We can use the `forecast` function to produce this forecast. Note that we have to specify the "initial value" for the forecast: :: >>> lag_order = results.k_ar >>> results.forecast(data.values[-lagorder:], 5) array([[ 0.00616044, 0.00500006, 0.00916198], [ 0.00427559, 0.00344836, -0.00238478], [ 0.00416634, 0.0070728 , -0.01193629], [ 0.00557873, 0.00642784, 0.00147152], [ 0.00626431, 0.00666715, 0.00379567]]) The `forecast_interval` function will produce the above forecast along with asymptotic standard errors. These can be visualized using the `plot_forecast` function: .. plot:: plots/var_plot_forecast.py Impulse Response Analysis ------------------------- *Impulse responses* are of interest in econometric studies: they are the estimated responses to a unit impulse in one of the variables. They are computed in practice using the MA(:math:`\infty`) representation of the VAR(p) process: .. math:: Y_t = \mu + \sum_{i=0}^\infty \Phi_i u_{t-i} We can perform an impulse response analysis by calling the `irf` function on a `VARResults` object: :: >>> irf = results.irf(10) These can be visualized using the `plot` function, in either orthogonalized or non-orthogonalized form. Asymptotic standard errors are plotted by default at the 95% significance level, which can be modified by the user. .. note:: Orthogonalization is done using the Cholesky decomposition of the estimated error covariance matrix :math:`\hat \Sigma_u` and hence interpretations may change depending on variable ordering. :: >>> irf.plot(orth=False) .. plot:: plots/var_plot_irf.py Note the `plot` function is flexible and can plot only variables of interest if so desired: :: >>> irf.plot(impulse='realgdp') The cumulative effects :math:`\Psi_n = \sum_{i=0}^n \Phi_i` can be plotted with the long run effects as follows: :: >>> irf.plot_cum_effects(orth=False) .. plot:: plots/var_plot_irf_cum.py Forecast Error Variance Decomposition (FEVD) -------------------------------------------- Forecast errors of component j on k in an i-step ahead forecast can be decomposed using the orthogonalized impulse responses :math:`\Theta_i`: .. math:: \omega_{jk, i} = \sum_{i=0}^{h-1} (e_j^\prime \Theta_i e_k)^2 / \mathrm{MSE}_j(h) \mathrm{MSE}_j(h) = \sum_{i=0}^{h-1} e_j^\prime \Phi_i \Sigma_u \Phi_i^\prime e_j These are computed via the `fevd` function up through a total number of steps ahead: :: >>> fevd = results.fevd(5) >>> fevd.summary() FEVD for realgdp realgdp realcons realinv 0 1.000000 0.000000 0.000000 1 0.864889 0.129253 0.005858 2 0.816725 0.177898 0.005378 3 0.793647 0.197590 0.008763 4 0.777279 0.208127 0.014594 FEVD for realcons realgdp realcons realinv 0 0.359877 0.640123 0.000000 1 0.358767 0.635420 0.005813 2 0.348044 0.645138 0.006817 3 0.319913 0.653609 0.026478 4 0.317407 0.652180 0.030414 FEVD for realinv realgdp realcons realinv 0 0.577021 0.152783 0.270196 1 0.488158 0.293622 0.218220 2 0.478727 0.314398 0.206874 3 0.477182 0.315564 0.207254 4 0.466741 0.324135 0.209124 They can also be visualized through the returned :class:`FEVD` object: :: >>> results.fevd(20).plot() .. plot:: plots/var_plot_fevd.py Statistical tests ----------------- A number of different methods are provided to carry out hypothesis tests about the model results and also the validity of the model assumptions (normality, whiteness / "iid-ness" of errors, etc.). Granger causality ~~~~~~~~~~~~~~~~~ One is often interested in whether a variable or group of variables is "causal" for another variable, for some definition of "causal". In the context of VAR models, one can say that a set of variables are Granger-causal within one of the VAR equations. We will not detail the mathematics or definition of Granger causality, but leave it to the reader. The :class:`VARResults` object has the `test_causality` method for performing either a Wald (:math:`\chi^2`) test or an F-test. :: >>> results.test_causality('realgdp', ['realinv', 'realcons'], kind='f') Granger causality f-test ============================================================= Test statistic Critical Value p-value df ------------------------------------------------------------- 6.999888 2.114554 0.000 (6, 567) ============================================================= H_0: ['realinv', 'realcons'] do not Granger-cause realgdp Conclusion: reject H_0 at 5.00% significance level [88]: {'conclusion': 'reject', 'crit_value': 2.1145543864562706, 'df': (6, 567), 'pvalue': 3.3805963773886478e-07, 'signif': 0.05, 'statistic': 6.9998875522543473} Normality ~~~~~~~~~ Whiteness of residuals ~~~~~~~~~~~~~~~~~~~~~~ Dynamic Vector Autoregressions ------------------------------ .. note:: To use this functionality, `pandas `__ must be installed. See the `pandas documentation `__ for more information on the below data structures. One is often interested in estimating a moving-window regression on time series data for the purposes of making forecasts throughout the data sample. For example, we may wish to produce the series of 2-step-ahead forecasts produced by a VAR(p) model estimated at each point in time. :: >>> data Index: 500 entries , 2000-01-03 00:00:00 to 2001-11-30 00:00:00 A 500 non-null values B 500 non-null values C 500 non-null values D 500 non-null values >>> var = DynamicVAR(data, lag_order=2, window_type='expanding') The estimated coefficients for the dynamic model are returned as a :class:`pandas.WidePanel` object, which can allow you to easily examine, for example, all of the model coefficients by equation or by date: :: >>> var.coefs Dimensions: 9 (items) x 489 (major) x 4 (minor) Items: L1.A to intercept Major axis: 2000-01-18 00:00:00 to 2001-11-30 00:00:00 Minor axis: A to D # all estimated coefficients for equation A >>> var.coefs.minor_xs('A').info() Index: 489 entries , 2000-01-18 00:00:00 to 2001-11-30 00:00:00 Data columns: L1.A 489 non-null values L1.B 489 non-null values L1.C 489 non-null values L1.D 489 non-null values L2.A 489 non-null values L2.B 489 non-null values L2.C 489 non-null values L2.D 489 non-null values intercept 489 non-null values dtype: float64(9) # coefficients on 11/30/2001 >>> var.coefs.major_xs(datetime(2001, 11, 30)).T A B C D L1.A 0.9567 -0.07389 0.0588 -0.02848 L1.B -0.00839 0.9757 -0.004945 0.005938 L1.C -0.01824 0.1214 0.8875 0.01431 L1.D 0.09964 0.02951 0.05275 1.037 L2.A 0.02481 0.07542 -0.04409 0.06073 L2.B 0.006359 0.01413 0.02667 0.004795 L2.C 0.02207 -0.1087 0.08282 -0.01921 L2.D -0.08795 -0.04297 -0.06505 -0.06814 intercept 0.07778 -0.283 -0.1009 -0.6426 Dynamic forecasts for a given number of steps ahead can be produced using the `forecast` function and return a :class:`pandas.DataMatrix` object: :: >>> In [76]: var.forecast(2) A B C D 2001-11-23 00:00:00 -6.661 43.18 33.43 -23.71 2001-11-26 00:00:00 -5.942 43.58 34.04 -22.13 2001-11-27 00:00:00 -6.666 43.64 33.99 -22.85 2001-11-28 00:00:00 -6.521 44.2 35.34 -24.29 2001-11-29 00:00:00 -6.432 43.92 34.85 -26.68 2001-11-30 00:00:00 -5.445 41.98 34.87 -25.94 The forecasts can be visualized using `plot_forecast`: :: >>> var.plot_forecast(2) Class Reference --------------- .. currentmodule:: statsmodels.tsa.vector_ar .. autosummary:: :toctree: generated/ var_model.VAR var_model.VARProcess var_model.VARResults irf.IRAnalysis var_model.FEVD dynamic.DynamicVAR statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/000077500000000000000000000000001224417117700211345ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/LICENSE.txt000066400000000000000000000136231224417117700227640ustar00rootroot00000000000000------------------------------------------------------------------------------- The files - numpydoc.py - autosummary.py - autosummary_generate.py - docscrape.py - docscrape_sphinx.py - phantom_import.py have the following license: Copyright (C) 2008 Stefan van der Walt , Pauli Virtanen Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. 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By copying, installing or otherwise using matplotlib 0.98.3, Licensee agrees to be bound by the terms and conditions of this License Agreement. statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/MANIFEST.in000066400000000000000000000000531224417117700226700ustar00rootroot00000000000000recursive-include tests *.py include *.txt statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/README.txt000066400000000000000000000020711224417117700226320ustar00rootroot00000000000000===================================== numpydoc -- Numpy's Sphinx extensions ===================================== Numpy's documentation uses several custom extensions to Sphinx. These are shipped in this ``numpydoc`` package, in case you want to make use of them in third-party projects. The following extensions are available: - ``numpydoc``: support for the Numpy docstring format in Sphinx, and add the code description directives ``np-function``, ``np-cfunction``, etc. that support the Numpy docstring syntax. - ``numpydoc.traitsdoc``: For gathering documentation about Traits attributes. - ``numpydoc.plot_directives``: Adaptation of Matplotlib's ``plot::`` directive. Note that this implementation may still undergo severe changes or eventually be deprecated. - ``numpydoc.only_directives``: (DEPRECATED) - ``numpydoc.autosummary``: (DEPRECATED) An ``autosummary::`` directive. Available in Sphinx 0.6.2 and (to-be) 1.0 as ``sphinx.ext.autosummary``, and it the Sphinx 1.0 version is recommended over that included in Numpydoc. statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/github.py000066400000000000000000000124031224417117700227700ustar00rootroot00000000000000"""Define text roles for GitHub * ghissue - Issue * ghpull - Pull Request * ghuser - User Adapted from bitbucket example here: https://bitbucket.org/birkenfeld/sphinx-contrib/src/tip/bitbucket/sphinxcontrib/bitbucket.py Authors ------- * Doug Hellmann * Min RK """ # # Original Copyright (c) 2010 Doug Hellmann. All rights reserved. # from docutils import nodes, utils from docutils.parsers.rst.roles import set_classes def make_link_node(rawtext, app, type, slug, options): """Create a link to a github resource. :param rawtext: Text being replaced with link node. :param app: Sphinx application context :param type: Link type (issues, changeset, etc.) :param slug: ID of the thing to link to :param options: Options dictionary passed to role func. """ try: base = app.config.github_project_url if not base: raise AttributeError if not base.endswith('/'): base += '/' except AttributeError as err: raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) ref = base + type + '/' + slug + '/' set_classes(options) prefix = "#" if type == 'pull': prefix = "PR " + prefix node = nodes.reference(rawtext, prefix + utils.unescape(slug), refuri=ref, **options) return node def ghissue_role(name, rawtext, text, lineno, inliner, options={}, content=[]): """Link to a GitHub issue. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ try: issue_num = int(text) if issue_num <= 0: raise ValueError except ValueError: msg = inliner.reporter.error( 'GitHub issue number must be a number greater than or equal to 1; ' '"%s" is invalid.' % text, line=lineno) prb = inliner.problematic(rawtext, rawtext, msg) return [prb], [msg] app = inliner.document.settings.env.app #app.info('issue %r' % text) if 'pull' in name.lower(): category = 'pull' elif 'issue' in name.lower(): category = 'issues' else: msg = inliner.reporter.error( 'GitHub roles include "ghpull" and "ghissue", ' '"%s" is invalid.' % name, line=lineno) prb = inliner.problematic(rawtext, rawtext, msg) return [prb], [msg] node = make_link_node(rawtext, app, category, str(issue_num), options) return [node], [] def ghuser_role(name, rawtext, text, lineno, inliner, options={}, content=[]): """Link to a GitHub user. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ app = inliner.document.settings.env.app #app.info('user link %r' % text) ref = 'https://www.github.com/' + text node = nodes.reference(rawtext, text, refuri=ref, **options) return [node], [] def ghcommit_role(name, rawtext, text, lineno, inliner, options={}, content=[]): """Link to a GitHub commit. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ app = inliner.document.settings.env.app #app.info('user link %r' % text) try: base = app.config.github_project_url if not base: raise AttributeError if not base.endswith('/'): base += '/' except AttributeError as err: raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) ref = base + text node = nodes.reference(rawtext, text[:6], refuri=ref, **options) return [node], [] def setup(app): """Install the plugin. :param app: Sphinx application context. """ app.info('Initializing GitHub plugin') app.add_role('ghissue', ghissue_role) app.add_role('ghpull', ghissue_role) app.add_role('ghuser', ghuser_role) app.add_role('ghcommit', ghcommit_role) app.add_config_value('github_project_url', None, 'env') return statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/ipython_console_highlighting.py000066400000000000000000000101221224417117700274430ustar00rootroot00000000000000"""reST directive for syntax-highlighting ipython interactive sessions. XXX - See what improvements can be made based on the new (as of Sept 2009) 'pycon' lexer for the python console. At the very least it will give better highlighted tracebacks. """ #----------------------------------------------------------------------------- # Needed modules # Standard library import re # Third party from pygments.lexer import Lexer, do_insertions from pygments.lexers.agile import (PythonConsoleLexer, PythonLexer, PythonTracebackLexer) from pygments.token import Comment, Generic from sphinx import highlighting #----------------------------------------------------------------------------- # Global constants line_re = re.compile('.*?\n') #----------------------------------------------------------------------------- # Code begins - classes and functions class IPythonConsoleLexer(Lexer): """ For IPython console output or doctests, such as: .. sourcecode:: ipython In [1]: a = 'foo' In [2]: a Out[2]: 'foo' In [3]: print a foo In [4]: 1 / 0 Notes: - Tracebacks are not currently supported. - It assumes the default IPython prompts, not customized ones. """ name = 'IPython console session' aliases = ['ipython'] mimetypes = ['text/x-ipython-console'] input_prompt = re.compile("(In \[[0-9]+\]: )|( \.\.\.+:)") output_prompt = re.compile("(Out\[[0-9]+\]: )|( \.\.\.+:)") continue_prompt = re.compile(" \.\.\.+:") tb_start = re.compile("\-+") def get_tokens_unprocessed(self, text): pylexer = PythonLexer(**self.options) tblexer = PythonTracebackLexer(**self.options) curcode = '' insertions = [] for match in line_re.finditer(text): line = match.group() input_prompt = self.input_prompt.match(line) continue_prompt = self.continue_prompt.match(line.rstrip()) output_prompt = self.output_prompt.match(line) if line.startswith("#"): insertions.append((len(curcode), [(0, Comment, line)])) elif input_prompt is not None: insertions.append((len(curcode), [(0, Generic.Prompt, input_prompt.group())])) curcode += line[input_prompt.end():] elif continue_prompt is not None: insertions.append((len(curcode), [(0, Generic.Prompt, continue_prompt.group())])) curcode += line[continue_prompt.end():] elif output_prompt is not None: # Use the 'error' token for output. We should probably make # our own token, but error is typicaly in a bright color like # red, so it works fine for our output prompts. insertions.append((len(curcode), [(0, Generic.Error, output_prompt.group())])) curcode += line[output_prompt.end():] else: if curcode: for item in do_insertions(insertions, pylexer.get_tokens_unprocessed(curcode)): yield item curcode = '' insertions = [] yield match.start(), Generic.Output, line if curcode: for item in do_insertions(insertions, pylexer.get_tokens_unprocessed(curcode)): yield item def setup(app): """Setup as a sphinx extension.""" # This is only a lexer, so adding it below to pygments appears sufficient. # But if somebody knows that the right API usage should be to do that via # sphinx, by all means fix it here. At least having this setup.py # suppresses the sphinx warning we'd get without it. pass #----------------------------------------------------------------------------- # Register the extension as a valid pygments lexer highlighting.lexers['ipython'] = IPythonConsoleLexer() statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/ipython_directive.py000066400000000000000000000657471224417117700252610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Sphinx directive to support embedded IPython code. This directive allows pasting of entire interactive IPython sessions, prompts and all, and their code will actually get re-executed at doc build time, with all prompts renumbered sequentially. It also allows you to input code as a pure python input by giving the argument python to the directive. The output looks like an interactive ipython section. To enable this directive, simply list it in your Sphinx ``conf.py`` file (making sure the directory where you placed it is visible to sphinx, as is needed for all Sphinx directives). By default this directive assumes that your prompts are unchanged IPython ones, but this can be customized. The configurable options that can be placed in conf.py are ipython_savefig_dir: The directory in which to save the figures. This is relative to the Sphinx source directory. The default is `html_static_path`. ipython_rgxin: The compiled regular expression to denote the start of IPython input lines. The default is re.compile('In \[(\d+)\]:\s?(.*)\s*'). You shouldn't need to change this. ipython_rgxout: The compiled regular expression to denote the start of IPython output lines. The default is re.compile('Out\[(\d+)\]:\s?(.*)\s*'). You shouldn't need to change this. ipython_promptin: The string to represent the IPython input prompt in the generated ReST. The default is 'In [%d]:'. This expects that the line numbers are used in the prompt. ipython_promptout: The string to represent the IPython prompt in the generated ReST. The default is 'Out [%d]:'. This expects that the line numbers are used in the prompt. ToDo ---- - Turn the ad-hoc test() function into a real test suite. - Break up ipython-specific functionality from matplotlib stuff into better separated code. Authors ------- - John D Hunter: orignal author. - Fernando Perez: refactoring, documentation, cleanups, port to 0.11. - VáclavÅ milauer : Prompt generalizations. - Skipper Seabold, refactoring, cleanups, pure python addition """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Stdlib import cStringIO import os import re import sys import tempfile import ast import time # To keep compatibility with various python versions try: from hashlib import md5 except ImportError: from md5 import md5 # Third-party import matplotlib import sphinx from docutils.parsers.rst import directives from docutils import nodes from sphinx.util.compat import Directive matplotlib.use('Agg') # Our own from IPython import Config, InteractiveShell from IPython.core.profiledir import ProfileDir from IPython.utils import io #----------------------------------------------------------------------------- # Globals #----------------------------------------------------------------------------- # for tokenizing blocks COMMENT, INPUT, OUTPUT = range(3) #----------------------------------------------------------------------------- # Functions and class declarations #----------------------------------------------------------------------------- def block_parser(part, rgxin, rgxout, fmtin, fmtout): """ part is a string of ipython text, comprised of at most one input, one ouput, comments, and blank lines. The block parser parses the text into a list of:: blocks = [ (TOKEN0, data0), (TOKEN1, data1), ...] where TOKEN is one of [COMMENT | INPUT | OUTPUT ] and data is, depending on the type of token:: COMMENT : the comment string INPUT: the (DECORATOR, INPUT_LINE, REST) where DECORATOR: the input decorator (or None) INPUT_LINE: the input as string (possibly multi-line) REST : any stdout generated by the input line (not OUTPUT) OUTPUT: the output string, possibly multi-line """ block = [] lines = part.split('\n') N = len(lines) i = 0 decorator = None while 1: if i==N: # nothing left to parse -- the last line break line = lines[i] i += 1 line_stripped = line.strip() if line_stripped.startswith('#'): block.append((COMMENT, line)) continue if line_stripped.startswith('@'): # we're assuming at most one decorator -- may need to # rethink decorator = line_stripped continue # does this look like an input line? matchin = rgxin.match(line) if matchin: lineno, inputline = int(matchin.group(1)), matchin.group(2) # the ....: continuation string continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2)) Nc = len(continuation) # input lines can continue on for more than one line, if # we have a '\' line continuation char or a function call # echo line 'print'. The input line can only be # terminated by the end of the block or an output line, so # we parse out the rest of the input line if it is # multiline as well as any echo text rest = [] while i 1: if input_lines[-1] != "": input_lines.append('') # make sure there's a blank line # so splitter buffer gets reset continuation = ' %s:'%''.join(['.']*(len(str(lineno))+2)) Nc = len(continuation) if is_savefig: image_file, image_directive = self.process_image(decorator) ret = [] is_semicolon = False for i, line in enumerate(input_lines): if line.endswith(';'): is_semicolon = True if i==0: # process the first input line if is_verbatim: self.process_input_line('') self.IP.execution_count += 1 # increment it anyway else: # only submit the line in non-verbatim mode self.process_input_line(line, store_history=True) formatted_line = '%s %s'%(input_prompt, line) else: # process a continuation line if not is_verbatim: self.process_input_line(line, store_history=True) formatted_line = '%s %s'%(continuation, line) if not is_suppress: ret.append(formatted_line) if not is_suppress and len(rest.strip()) and is_verbatim: # the "rest" is the standard output of the # input, which needs to be added in # verbatim mode ret.append(rest) self.cout.seek(0) output = self.cout.read() if not is_suppress and not is_semicolon: ret.append(output) elif is_semicolon: # get spacing right ret.append('') self.cout.truncate(0) return (ret, input_lines, output, is_doctest, image_file, image_directive) #print 'OUTPUT', output # dbg def process_output(self, data, output_prompt, input_lines, output, is_doctest, image_file): """Process data block for OUTPUT token.""" if is_doctest: submitted = data.strip() found = output if found is not None: found = found.strip() # XXX - fperez: in 0.11, 'output' never comes with the prompt # in it, just the actual output text. So I think all this code # can be nuked... # the above comment does not appear to be accurate... (minrk) ind = found.find(output_prompt) if ind<0: e='output prompt="%s" does not match out line=%s' % \ (output_prompt, found) raise RuntimeError(e) found = found[len(output_prompt):].strip() if found!=submitted: e = ('doctest failure for input_lines="%s" with ' 'found_output="%s" and submitted output="%s"' % (input_lines, found, submitted) ) raise RuntimeError(e) #print 'doctest PASSED for input_lines="%s" with found_output="%s" and submitted output="%s"'%(input_lines, found, submitted) def process_comment(self, data): """Process data fPblock for COMMENT token.""" if not self.is_suppress: return [data] def save_image(self, image_file): """ Saves the image file to disk. """ self.ensure_pyplot() command = 'plt.gcf().savefig("%s")'%image_file #print 'SAVEFIG', command # dbg self.process_input_line('bookmark ipy_thisdir', store_history=False) self.process_input_line('cd -b ipy_savedir', store_history=False) self.process_input_line(command, store_history=False) self.process_input_line('cd -b ipy_thisdir', store_history=False) self.process_input_line('bookmark -d ipy_thisdir', store_history=False) self.clear_cout() def process_block(self, block): """ process block from the block_parser and return a list of processed lines """ ret = [] output = None input_lines = None lineno = self.IP.execution_count input_prompt = self.promptin%lineno output_prompt = self.promptout%lineno image_file = None image_directive = None for token, data in block: if token==COMMENT: out_data = self.process_comment(data) elif token==INPUT: (out_data, input_lines, output, is_doctest, image_file, image_directive) = \ self.process_input(data, input_prompt, lineno) elif token==OUTPUT: out_data = \ self.process_output(data, output_prompt, input_lines, output, is_doctest, image_file) if out_data: ret.extend(out_data) # save the image files if image_file is not None: self.save_image(image_file) return ret, image_directive def ensure_pyplot(self): if self._pyplot_imported: return self.process_input_line('import matplotlib.pyplot as plt', store_history=False) def process_pure_python(self, content): """ content is a list of strings. it is unedited directive conent This runs it line by line in the InteractiveShell, prepends prompts as needed capturing stderr and stdout, then returns the content as a list as if it were ipython code """ output = [] savefig = False # keep up with this to clear figure multiline = False # to handle line continuation multiline_start = None fmtin = self.promptin block = '\n'.join(content) # remove blank lines block = re.sub('\n+', '\n', block) content = block.split('\n') # if any figures, make sure you can handle them and no other figures exist if re.search('^\s*@savefig', block, flags=re.MULTILINE): self.ensure_pyplot() self.process_input_line('plt.clf()', store_history=False) self.clear_cout() # sub out the pseudo-decorators so we can parse block = re.sub('@(?=[savefig|suppress|verbatim|doctest])', '#@', block) # this is going to raise an error if there's problems # in the python. if you want errors, make an ipython block parsed_block = ast.parse(block) in_lines = [i.lineno for i in parsed_block.body] output = [] ct = 1 for lineno, line in enumerate(content): line_stripped = line.strip('\n') if lineno + 1 in in_lines: # this is an input line modified = u"%s %s" % (fmtin % ct, line_stripped) ct += 1 elif line.startswith('@'): # is it a decorator? modified = line else: # this is something else continuation = u' %s:'% ''.join(['.']*(len(str(ct))+2)) modified = u'%s %s' % (continuation, line) output.append(modified) output = re.sub('#@(?=[savefig|suppress|verbatim|doctest])', '@', '\n'.join(output)).split('\n') # put blank lines after input lines for i in in_lines[1:][::-1]: output.insert(i-1, u'') # fix the spacing for decorators # might be a cleaner regex for # \n@savefig name.png\n\n -> \n\n@savefig name.png\n decpat1 = '(?<=@[savefig|suppress|verbatim|doctest])(?P.+)\n\n' output = re.sub(decpat1, '\g\n','\n'.join(output)) decpat2 = '\n(?=@[savefig|suppress|verbatim|doctest])' output = re.sub(decpat2, '\n\n', output).split('\n') return output class IpythonDirective(Directive): has_content = True required_arguments = 0 optional_arguments = 4 # python, suppress, verbatim, doctest final_argumuent_whitespace = True option_spec = { 'python': directives.unchanged, 'suppress' : directives.flag, 'verbatim' : directives.flag, 'doctest' : directives.flag, } shell = EmbeddedSphinxShell() def get_config_options(self): # contains sphinx configuration variables config = self.state.document.settings.env.config # get config variables to set figure output directory confdir = self.state.document.settings.env.app.confdir savefig_dir = config.ipython_savefig_dir source_dir = os.path.dirname(self.state.document.current_source) if savefig_dir is None: savefig_dir = config.html_static_path if isinstance(savefig_dir, list): savefig_dir = savefig_dir[0] # safe to assume only one path? savefig_dir = os.path.join(confdir, savefig_dir) # get regex and prompt stuff rgxin = config.ipython_rgxin rgxout = config.ipython_rgxout promptin = config.ipython_promptin promptout = config.ipython_promptout return savefig_dir, source_dir, rgxin, rgxout, promptin, promptout def setup(self): # make a file in this directory, if there's already one # if it's older than 5 minutes, delete it # this needs a more robust solution cur_dir = os.path.normpath( os.path.join(self.state.document.settings.env.srcdir, '..')) tmp_file = os.path.join(cur_dir, 'seen_docs.temp') if os.path.exists(tmp_file): file_t = os.path.getmtime(tmp_file) now_t = time.time() if (now_t - file_t)/60. >= 5: docs = [] os.remove(tmp_file) else: docs = open(tmp_file, 'r').read().split('\n') if not self.state.document.current_source in docs: self.shell.IP.history_manager.reset() self.shell.IP.execution_count = 1 else: # haven't processed any docs yet docs = [] # get config values (savefig_dir, source_dir, rgxin, rgxout, promptin, promptout) = self.get_config_options() # and attach to shell so we don't have to pass them around self.shell.rgxin = rgxin self.shell.rgxout = rgxout self.shell.promptin = promptin self.shell.promptout = promptout self.shell.savefig_dir = savefig_dir self.shell.source_dir = source_dir # setup bookmark for saving figures directory self.shell.process_input_line('bookmark ipy_savedir %s'%savefig_dir, store_history=False) self.shell.clear_cout() # write the filename to a tempfile because it's been "seen" now if not self.state.document.current_source in docs: fout = open(tmp_file, 'a') fout.write(self.state.document.current_source+'\n') fout.close() return rgxin, rgxout, promptin, promptout def teardown(self): # delete last bookmark self.shell.process_input_line('bookmark -d ipy_savedir', store_history=False) self.shell.clear_cout() def run(self): debug = False #TODO, any reason block_parser can't be a method of embeddable shell # then we wouldn't have to carry these around rgxin, rgxout, promptin, promptout = self.setup() options = self.options self.shell.is_suppress = 'suppress' in options self.shell.is_doctest = 'doctest' in options self.shell.is_verbatim = 'verbatim' in options # handle pure python code if 'python' in self.arguments: content = self.content self.content = self.shell.process_pure_python(content) parts = '\n'.join(self.content).split('\n\n') lines = ['.. code-block:: ipython',''] figures = [] for part in parts: block = block_parser(part, rgxin, rgxout, promptin, promptout) if len(block): rows, figure = self.shell.process_block(block) for row in rows: lines.extend([' %s'%line for line in row.split('\n')]) if figure is not None: figures.append(figure) #text = '\n'.join(lines) #figs = '\n'.join(figures) for figure in figures: lines.append('') lines.extend(figure.split('\n')) lines.append('') #print lines if len(lines)>2: if debug: print '\n'.join(lines) else: #NOTE: this raises some errors, what's it for? #print 'INSERTING %d lines'%len(lines) self.state_machine.insert_input( lines, self.state_machine.input_lines.source(0)) text = '\n'.join(lines) txtnode = nodes.literal_block(text, text) txtnode['language'] = 'ipython' #imgnode = nodes.image(figs) # cleanup #self.teardown() # this gets called on _every_ exit from a block return []#, imgnode] # Enable as a proper Sphinx directive def setup(app): setup.app = app app.add_directive('ipython', IpythonDirective) app.add_config_value('ipython_savefig_dir', None, True) app.add_config_value('ipython_rgxin', re.compile('In \[(\d+)\]:\s?(.*)\s*'), True) app.add_config_value('ipython_rgxout', re.compile('Out\[(\d+)\]:\s?(.*)\s*'), True) app.add_config_value('ipython_promptin', 'In [%d]:', True) app.add_config_value('ipython_promptout', 'Out[%d]:', True) # Simple smoke test, needs to be converted to a proper automatic test. def test(): examples = [ r""" In [9]: pwd Out[9]: '/home/jdhunter/py4science/book' In [10]: cd bookdata/ /home/jdhunter/py4science/book/bookdata In [2]: from pylab import * In [2]: ion() In [3]: im = imread('stinkbug.png') @savefig mystinkbug.png width=4in In [4]: imshow(im) Out[4]: """, r""" In [1]: x = 'hello world' # string methods can be # used to alter the string @doctest In [2]: x.upper() Out[2]: 'HELLO WORLD' @verbatim In [3]: x.st x.startswith x.strip """, r""" In [130]: url = 'http://ichart.finance.yahoo.com/table.csv?s=CROX\ .....: &d=9&e=22&f=2009&g=d&a=1&br=8&c=2006&ignore=.csv' In [131]: print url.split('&') ['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22', 'f=2009', 'g=d', 'a=1', 'b=8', 'c=2006', 'ignore=.csv'] In [60]: import urllib """, r"""\ In [133]: import numpy.random @suppress In [134]: numpy.random.seed(2358) @doctest In [135]: numpy.random.rand(10,2) Out[135]: array([[ 0.64524308, 0.59943846], [ 0.47102322, 0.8715456 ], [ 0.29370834, 0.74776844], [ 0.99539577, 0.1313423 ], [ 0.16250302, 0.21103583], [ 0.81626524, 0.1312433 ], [ 0.67338089, 0.72302393], [ 0.7566368 , 0.07033696], [ 0.22591016, 0.77731835], [ 0.0072729 , 0.34273127]]) """, r""" In [106]: print x jdh In [109]: for i in range(10): .....: print i .....: .....: 0 1 2 3 4 5 6 7 8 9 """, r""" In [144]: from pylab import * In [145]: ion() # use a semicolon to suppress the output @savefig test_hist.png width=4in In [151]: hist(np.random.randn(10000), 100); @savefig test_plot.png width=4in In [151]: plot(np.random.randn(10000), 'o'); """, r""" # use a semicolon to suppress the output In [151]: plt.clf() @savefig plot_simple.png width=4in In [151]: plot([1,2,3]) @savefig hist_simple.png width=4in In [151]: hist(np.random.randn(10000), 100); """, r""" # update the current fig In [151]: ylabel('number') In [152]: title('normal distribution') @savefig hist_with_text.png In [153]: grid(True) """, ] # skip local-file depending first example: examples = examples[1:] #ipython_directive.DEBUG = True # dbg #options = dict(suppress=True) # dbg options = dict() for example in examples: content = example.split('\n') ipython_directive('debug', arguments=None, options=options, content=content, lineno=0, content_offset=None, block_text=None, state=None, state_machine=None, ) # Run test suite as a script if __name__=='__main__': if not os.path.isdir('_static'): os.mkdir('_static') test() print 'All OK? Check figures in _static/' statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/000077500000000000000000000000001224417117700231645ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/__init__.py000066400000000000000000000000001224417117700252630ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/docscrape.py000066400000000000000000000355121224417117700255070ustar00rootroot00000000000000"""Extract reference documentation from the NumPy source tree. """ import inspect import textwrap import re import pydoc from StringIO import StringIO from warnings import warn class Reader(object): """A line-based string reader. """ def __init__(self, data): """ Parameters ---------- data : str String with lines separated by '\n'. """ if isinstance(data,list): self._str = data else: self._str = data.split('\n') # store string as list of lines self.reset() def __getitem__(self, n): return self._str[n] def reset(self): self._l = 0 # current line nr def read(self): if not self.eof(): out = self[self._l] self._l += 1 return out else: return '' def seek_next_non_empty_line(self): for l in self[self._l:]: if l.strip(): break else: self._l += 1 def eof(self): return self._l >= len(self._str) def read_to_condition(self, condition_func): start = self._l for line in self[start:]: if condition_func(line): return self[start:self._l] self._l += 1 if self.eof(): return self[start:self._l+1] return [] def read_to_next_empty_line(self): self.seek_next_non_empty_line() def is_empty(line): return not line.strip() return self.read_to_condition(is_empty) def read_to_next_unindented_line(self): def is_unindented(line): return (line.strip() and (len(line.lstrip()) == len(line))) return self.read_to_condition(is_unindented) def peek(self,n=0): if self._l + n < len(self._str): return self[self._l + n] else: return '' def is_empty(self): return not ''.join(self._str).strip() class NumpyDocString(object): def __init__(self, docstring, config={}): docstring = textwrap.dedent(docstring).split('\n') self._doc = Reader(docstring) self._parsed_data = { 'Signature': '', 'Summary': [''], 'Extended Summary': [], 'Parameters': [], 'Returns': [], 'Raises': [], 'Warns': [], 'Other Parameters': [], 'Attributes': [], 'Methods': [], 'See Also': [], 'Notes': [], 'Warnings': [], 'References': '', 'Examples': '', 'index': {} } self._parse() def __getitem__(self,key): return self._parsed_data[key] def __setitem__(self,key,val): if not self._parsed_data.has_key(key): warn("Unknown section %s" % key) else: self._parsed_data[key] = val def _is_at_section(self): self._doc.seek_next_non_empty_line() if self._doc.eof(): return False l1 = self._doc.peek().strip() # e.g. Parameters if l1.startswith('.. index::'): return True l2 = self._doc.peek(1).strip() # ---------- or ========== return l2.startswith('-'*len(l1)) or l2.startswith('='*len(l1)) def _strip(self,doc): i = 0 j = 0 for i,line in enumerate(doc): if line.strip(): break for j,line in enumerate(doc[::-1]): if line.strip(): break return doc[i:len(doc)-j] def _read_to_next_section(self): section = self._doc.read_to_next_empty_line() while not self._is_at_section() and not self._doc.eof(): if not self._doc.peek(-1).strip(): # previous line was empty section += [''] section += self._doc.read_to_next_empty_line() return section def _read_sections(self): while not self._doc.eof(): data = self._read_to_next_section() name = data[0].strip() if name.startswith('..'): # index section yield name, data[1:] elif len(data) < 2: yield StopIteration else: yield name, self._strip(data[2:]) def _parse_param_list(self,content): r = Reader(content) params = [] while not r.eof(): header = r.read().strip() if ' : ' in header: arg_name, arg_type = header.split(' : ')[:2] else: arg_name, arg_type = header, '' desc = r.read_to_next_unindented_line() desc = dedent_lines(desc) params.append((arg_name,arg_type,desc)) return params _name_rgx = re.compile(r"^\s*(:(?P\w+):`(?P[a-zA-Z0-9_.-]+)`|" r" (?P[a-zA-Z0-9_.-]+))\s*", re.X) def _parse_see_also(self, content): """ func_name : Descriptive text continued text another_func_name : Descriptive text func_name1, func_name2, :meth:`func_name`, func_name3 """ items = [] def parse_item_name(text): """Match ':role:`name`' or 'name'""" m = self._name_rgx.match(text) if m: g = m.groups() if g[1] is None: return g[3], None else: return g[2], g[1] raise ValueError("%s is not a item name" % text) def push_item(name, rest): if not name: return name, role = parse_item_name(name) items.append((name, list(rest), role)) del rest[:] current_func = None rest = [] for line in content: if not line.strip(): continue m = self._name_rgx.match(line) if m and line[m.end():].strip().startswith(':'): push_item(current_func, rest) current_func, line = line[:m.end()], line[m.end():] rest = [line.split(':', 1)[1].strip()] if not rest[0]: rest = [] elif not line.startswith(' '): push_item(current_func, rest) current_func = None if ',' in line: for func in line.split(','): push_item(func, []) elif line.strip(): current_func = line elif current_func is not None: rest.append(line.strip()) push_item(current_func, rest) return items def _parse_index(self, section, content): """ .. index: default :refguide: something, else, and more """ def strip_each_in(lst): return [s.strip() for s in lst] out = {} section = section.split('::') if len(section) > 1: out['default'] = strip_each_in(section[1].split(','))[0] for line in content: line = line.split(':') if len(line) > 2: out[line[1]] = strip_each_in(line[2].split(',')) return out def _parse_summary(self): """Grab signature (if given) and summary""" if self._is_at_section(): return summary = self._doc.read_to_next_empty_line() summary_str = " ".join([s.strip() for s in summary]).strip() if re.compile('^([\w., ]+=)?\s*[\w\.]+\(.*\)$').match(summary_str): self['Signature'] = summary_str if not self._is_at_section(): self['Summary'] = self._doc.read_to_next_empty_line() else: self['Summary'] = summary if not self._is_at_section(): self['Extended Summary'] = self._read_to_next_section() def _parse(self): self._doc.reset() self._parse_summary() for (section,content) in self._read_sections(): if not section.startswith('..'): section = ' '.join([s.capitalize() for s in section.split(' ')]) if section in ('Parameters', 'Attributes', 'Methods', 'Returns', 'Raises', 'Warns'): self[section] = self._parse_param_list(content) elif section.startswith('.. index::'): self['index'] = self._parse_index(section, content) elif section == 'See Also': self['See Also'] = self._parse_see_also(content) else: self[section] = content # string conversion routines def _str_header(self, name, symbol='-'): return [name, len(name)*symbol] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): if self['Signature']: return [self['Signature'].replace('*','\*')] + [''] else: return [''] def _str_summary(self): if self['Summary']: return self['Summary'] + [''] else: return [] def _str_extended_summary(self): if self['Extended Summary']: return self['Extended Summary'] + [''] else: return [] def _str_param_list(self, name): out = [] if self[name]: out += self._str_header(name) for param,param_type,desc in self[name]: out += ['%s : %s' % (param, param_type)] out += self._str_indent(desc) out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += self[name] out += [''] return out def _str_see_also(self, func_role): if not self['See Also']: return [] out = [] out += self._str_header("See Also") last_had_desc = True for func, desc, role in self['See Also']: if role: link = ':%s:`%s`' % (role, func) elif func_role: link = ':%s:`%s`' % (func_role, func) else: link = "`%s`_" % func if desc or last_had_desc: out += [''] out += [link] else: out[-1] += ", %s" % link if desc: out += self._str_indent([' '.join(desc)]) last_had_desc = True else: last_had_desc = False out += [''] return out def _str_index(self): idx = self['index'] out = [] out += ['.. index:: %s' % idx.get('default','')] for section, references in idx.iteritems(): if section == 'default': continue out += [' :%s: %s' % (section, ', '.join(references))] return out def __str__(self, func_role=''): out = [] out += self._str_signature() out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters','Returns','Raises'): out += self._str_param_list(param_list) out += self._str_section('Warnings') out += self._str_see_also(func_role) for s in ('Notes','References','Examples'): out += self._str_section(s) for param_list in ('Attributes', 'Methods'): out += self._str_param_list(param_list) out += self._str_index() return '\n'.join(out) def indent(str,indent=4): indent_str = ' '*indent if str is None: return indent_str lines = str.split('\n') return '\n'.join(indent_str + l for l in lines) def dedent_lines(lines): """Deindent a list of lines maximally""" return textwrap.dedent("\n".join(lines)).split("\n") def header(text, style='-'): return text + '\n' + style*len(text) + '\n' class FunctionDoc(NumpyDocString): def __init__(self, func, role='func', doc=None, config={}): self._f = func self._role = role # e.g. "func" or "meth" if doc is None: if func is None: raise ValueError("No function or docstring given") doc = inspect.getdoc(func) or '' NumpyDocString.__init__(self, doc) if not self['Signature'] and func is not None: func, func_name = self.get_func() try: # try to read signature argspec = inspect.getargspec(func) argspec = inspect.formatargspec(*argspec) argspec = argspec.replace('*','\*') signature = '%s%s' % (func_name, argspec) except TypeError, e: signature = '%s()' % func_name self['Signature'] = signature def get_func(self): func_name = getattr(self._f, '__name__', self.__class__.__name__) if inspect.isclass(self._f): func = getattr(self._f, '__call__', self._f.__init__) else: func = self._f return func, func_name def __str__(self): out = '' func, func_name = self.get_func() signature = self['Signature'].replace('*', '\*') roles = {'func': 'function', 'meth': 'method'} if self._role: if not roles.has_key(self._role): print "Warning: invalid role %s" % self._role out += '.. %s:: %s\n \n\n' % (roles.get(self._role,''), func_name) out += super(FunctionDoc, self).__str__(func_role=self._role) return out class ClassDoc(NumpyDocString): def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc, config={}): if not inspect.isclass(cls) and cls is not None: raise ValueError("Expected a class or None, but got %r" % cls) self._cls = cls if modulename and not modulename.endswith('.'): modulename += '.' self._mod = modulename if doc is None: if cls is None: raise ValueError("No class or documentation string given") doc = pydoc.getdoc(cls) NumpyDocString.__init__(self, doc) if config.get('show_class_members', True): if not self['Methods']: self['Methods'] = [(name, '', '') for name in sorted(self.methods)] if not self['Attributes']: self['Attributes'] = [(name, '', '') for name in sorted(self.properties)] @property def methods(self): if self._cls is None: return [] return [name for name,func in inspect.getmembers(self._cls) if not name.startswith('_') and callable(func)] @property def properties(self): if self._cls is None: return [] return [name for name,func in inspect.getmembers(self._cls) if not name.startswith('_') and func is None] statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/docscrape_sphinx.py000066400000000000000000000170271224417117700271010ustar00rootroot00000000000000import re, inspect, textwrap, pydoc import sphinx from docscrape import NumpyDocString, FunctionDoc, ClassDoc class SphinxDocString(NumpyDocString): def __init__(self, docstring, config={}): self.use_plots = config.get('use_plots', False) NumpyDocString.__init__(self, docstring, config=config) # string conversion routines def _str_header(self, name, symbol='`'): return ['.. rubric:: ' + name, ''] def _str_field_list(self, name): return [':' + name + ':'] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' '*indent + line] return out def _str_signature(self): return [''] if self['Signature']: return ['``%s``' % self['Signature']] + [''] else: return [''] def _str_summary(self): return self['Summary'] + [''] def _str_extended_summary(self): return self['Extended Summary'] + [''] def _str_param_list(self, name): out = [] if self[name]: out += self._str_field_list(name) out += [''] for param,param_type,desc in self[name]: out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) out += [''] out += self._str_indent(desc,8) out += [''] return out @property def _obj(self): if hasattr(self, '_cls'): return self._cls elif hasattr(self, '_f'): return self._f return None def _str_member_list(self, name): """ Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] prefix = getattr(self, '_name', '') if prefix: prefix = '~%s.' % prefix autosum = [] others = [] for param, param_type, desc in self[name]: param = param.strip() if not self._obj or hasattr(self._obj, param): autosum += [" %s%s" % (prefix, param)] else: others.append((param, param_type, desc)) if autosum: out += ['.. autosummary::', ' :toctree:', ''] out += autosum if others: maxlen_0 = max([len(x[0]) for x in others]) maxlen_1 = max([len(x[1]) for x in others]) hdr = "="*maxlen_0 + " " + "="*maxlen_1 + " " + "="*10 fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1) n_indent = maxlen_0 + maxlen_1 + 4 out += [hdr] for param, param_type, desc in others: out += [fmt % (param.strip(), param_type)] out += self._str_indent(desc, n_indent) out += [hdr] out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += [''] content = textwrap.dedent("\n".join(self[name])).split("\n") out += content out += [''] return out def _str_see_also(self, func_role): out = [] if self['See Also']: see_also = super(SphinxDocString, self)._str_see_also(func_role) out = ['.. seealso::', ''] out += self._str_indent(see_also[2:]) return out def _str_warnings(self): out = [] if self['Warnings']: out = ['.. warning::', ''] out += self._str_indent(self['Warnings']) return out def _str_index(self): idx = self['index'] out = [] if len(idx) == 0: return out out += ['.. index:: %s' % idx.get('default','')] for section, references in idx.iteritems(): if section == 'default': continue elif section == 'refguide': out += [' single: %s' % (', '.join(references))] else: out += [' %s: %s' % (section, ','.join(references))] return out def _str_references(self): out = [] if self['References']: out += self._str_header('References') if isinstance(self['References'], str): self['References'] = [self['References']] out.extend(self['References']) out += [''] # Latex collects all references to a separate bibliography, # so we need to insert links to it if sphinx.__version__ >= "0.6": out += ['.. only:: latex',''] else: out += ['.. latexonly::',''] items = [] for line in self['References']: m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I) if m: items.append(m.group(1)) out += [' ' + ", ".join(["[%s]_" % item for item in items]), ''] return out def _str_examples(self): examples_str = "\n".join(self['Examples']) if (self.use_plots and 'import matplotlib' in examples_str and 'plot::' not in examples_str): out = [] out += self._str_header('Examples') out += ['.. plot::', ''] out += self._str_indent(self['Examples']) out += [''] return out else: return self._str_section('Examples') def __str__(self, indent=0, func_role="obj"): out = [] out += self._str_signature() out += self._str_index() + [''] out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters', 'Returns', 'Raises'): out += self._str_param_list(param_list) out += self._str_warnings() out += self._str_see_also(func_role) out += self._str_section('Notes') out += self._str_references() out += self._str_examples() for param_list in ('Attributes', 'Methods'): out += self._str_member_list(param_list) out = self._str_indent(out,indent) return '\n'.join(out) class SphinxFunctionDoc(SphinxDocString, FunctionDoc): def __init__(self, obj, doc=None, config={}): self.use_plots = config.get('use_plots', False) FunctionDoc.__init__(self, obj, doc=doc, config=config) class SphinxClassDoc(SphinxDocString, ClassDoc): def __init__(self, obj, doc=None, func_doc=None, config={}): self.use_plots = config.get('use_plots', False) ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config) class SphinxObjDoc(SphinxDocString): def __init__(self, obj, doc=None, config={}): self._f = obj SphinxDocString.__init__(self, doc, config=config) def get_doc_object(obj, what=None, doc=None, config={}): if what is None: if inspect.isclass(obj): what = 'class' elif inspect.ismodule(obj): what = 'module' elif callable(obj): what = 'function' else: what = 'object' if what == 'class': return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc, config=config) elif what in ('function', 'method'): return SphinxFunctionDoc(obj, doc=doc, config=config) else: if doc is None: doc = pydoc.getdoc(obj) return SphinxObjDoc(obj, doc, config=config) statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/numpydoc.py000066400000000000000000000130221224417117700253720ustar00rootroot00000000000000""" ======== numpydoc ======== Sphinx extension that handles docstrings in the Numpy standard format. [1] It will: - Convert Parameters etc. sections to field lists. - Convert See Also section to a See also entry. - Renumber references. - Extract the signature from the docstring, if it can't be determined otherwise. .. [1] http://projects.scipy.org/numpy/wiki/CodingStyleGuidelines#docstring-standard """ import os, re, pydoc from docscrape_sphinx import get_doc_object, SphinxDocString from sphinx.util.compat import Directive import inspect def mangle_docstrings(app, what, name, obj, options, lines, reference_offset=[0]): cfg = dict(use_plots=app.config.numpydoc_use_plots, show_class_members=app.config.numpydoc_show_class_members) if what == 'module': # Strip top title title_re = re.compile(ur'^\s*[#*=]{4,}\n[a-z0-9 -]+\n[#*=]{4,}\s*', re.I|re.S) lines[:] = title_re.sub(u'', u"\n".join(lines)).split(u"\n") else: doc = get_doc_object(obj, what, u"\n".join(lines), config=cfg) lines[:] = unicode(doc).split(u"\n") if app.config.numpydoc_edit_link and hasattr(obj, '__name__') and \ obj.__name__: if hasattr(obj, '__module__'): v = dict(full_name=u"%s.%s" % (obj.__module__, obj.__name__)) else: v = dict(full_name=obj.__name__) lines += [u'', u'.. htmlonly::', ''] lines += [u' %s' % x for x in (app.config.numpydoc_edit_link % v).split("\n")] # replace reference numbers so that there are no duplicates references = [] for line in lines: line = line.strip() m = re.match(ur'^.. \[([a-z0-9_.-])\]', line, re.I) if m: references.append(m.group(1)) # start renaming from the longest string, to avoid overwriting parts references.sort(key=lambda x: -len(x)) if references: for i, line in enumerate(lines): for r in references: if re.match(ur'^\d+$', r): new_r = u"R%d" % (reference_offset[0] + int(r)) else: new_r = u"%s%d" % (r, reference_offset[0]) lines[i] = lines[i].replace(u'[%s]_' % r, u'[%s]_' % new_r) lines[i] = lines[i].replace(u'.. [%s]' % r, u'.. [%s]' % new_r) reference_offset[0] += len(references) def mangle_signature(app, what, name, obj, options, sig, retann): # Do not try to inspect classes that don't define `__init__` if (inspect.isclass(obj) and (not hasattr(obj, '__init__') or 'initializes x; see ' in pydoc.getdoc(obj.__init__))): return '', '' if not (callable(obj) or hasattr(obj, '__argspec_is_invalid_')): return if not hasattr(obj, '__doc__'): return doc = SphinxDocString(pydoc.getdoc(obj)) if doc['Signature']: sig = re.sub(u"^[^(]*", u"", doc['Signature']) return sig, u'' def setup(app, get_doc_object_=get_doc_object): global get_doc_object get_doc_object = get_doc_object_ app.connect('autodoc-process-docstring', mangle_docstrings) app.connect('autodoc-process-signature', mangle_signature) app.add_config_value('numpydoc_edit_link', None, False) app.add_config_value('numpydoc_use_plots', None, False) #app.add_config_value('numpydoc_show_class_members', True, True) app.add_config_value('numpydoc_show_class_members', False, False) # Extra mangling domains app.add_domain(NumpyPythonDomain) app.add_domain(NumpyCDomain) #------------------------------------------------------------------------------ # Docstring-mangling domains #------------------------------------------------------------------------------ from docutils.statemachine import ViewList from sphinx.domains.c import CDomain from sphinx.domains.python import PythonDomain class ManglingDomainBase(object): directive_mangling_map = {} def __init__(self, *a, **kw): super(ManglingDomainBase, self).__init__(*a, **kw) self.wrap_mangling_directives() def wrap_mangling_directives(self): for name, objtype in self.directive_mangling_map.items(): self.directives[name] = wrap_mangling_directive( self.directives[name], objtype) class NumpyPythonDomain(ManglingDomainBase, PythonDomain): name = 'np' directive_mangling_map = { 'function': 'function', 'class': 'class', 'exception': 'class', 'method': 'function', 'classmethod': 'function', 'staticmethod': 'function', 'attribute': 'attribute', } class NumpyCDomain(ManglingDomainBase, CDomain): name = 'np-c' directive_mangling_map = { 'function': 'function', 'member': 'attribute', 'macro': 'function', 'type': 'class', 'var': 'object', } def wrap_mangling_directive(base_directive, objtype): class directive(base_directive): def run(self): env = self.state.document.settings.env name = None if self.arguments: m = re.match(r'^(.*\s+)?(.*?)(\(.*)?', self.arguments[0]) name = m.group(2).strip() if not name: name = self.arguments[0] lines = list(self.content) mangle_docstrings(env.app, objtype, name, None, None, lines) self.content = ViewList(lines, self.content.parent) return base_directive.run(self) return directive statsmodels-0.5.0+git13-g8e07d34/docs/sphinxext/numpy_ext/plot_directive.py000066400000000000000000000476571224417117700265750ustar00rootroot00000000000000""" A special directive for generating a matplotlib plot. .. warning:: This is a hacked version of plot_directive.py from Matplotlib. It's very much subject to change! Usage ----- Can be used like this:: .. plot:: examples/example.py .. plot:: import matplotlib.pyplot as plt plt.plot([1,2,3], [4,5,6]) .. plot:: A plotting example: >>> import matplotlib.pyplot as plt >>> plt.plot([1,2,3], [4,5,6]) The content is interpreted as doctest formatted if it has a line starting with ``>>>``. The ``plot`` directive supports the options format : {'python', 'doctest'} Specify the format of the input include-source : bool Whether to display the source code. Default can be changed in conf.py and the ``image`` directive options ``alt``, ``height``, ``width``, ``scale``, ``align``, ``class``. Configuration options --------------------- The plot directive has the following configuration options: plot_include_source Default value for the include-source option plot_pre_code Code that should be executed before each plot. plot_basedir Base directory, to which plot:: file names are relative to. (If None or empty, file names are relative to the directoly where the file containing the directive is.) plot_formats File formats to generate. List of tuples or strings:: [(suffix, dpi), suffix, ...] that determine the file format and the DPI. For entries whose DPI was omitted, sensible defaults are chosen. plot_html_show_formats Whether to show links to the files in HTML. TODO ---- * Refactor Latex output; now it's plain images, but it would be nice to make them appear side-by-side, or in floats. """ import sys, os, glob, shutil, imp, warnings, cStringIO, re, textwrap, traceback import sphinx import warnings warnings.warn("A plot_directive module is also available under " "matplotlib.sphinxext; expect this numpydoc.plot_directive " "module to be deprecated after relevant features have been " "integrated there.", FutureWarning, stacklevel=2) #------------------------------------------------------------------------------ # Registration hook #------------------------------------------------------------------------------ def setup(app): setup.app = app setup.config = app.config setup.confdir = app.confdir app.add_config_value('plot_pre_code', '', True) app.add_config_value('plot_include_source', False, True) app.add_config_value('plot_formats', ['png', 'hires.png', 'pdf'], True) app.add_config_value('plot_basedir', None, True) app.add_config_value('plot_html_show_formats', True, True) app.add_directive('plot', plot_directive, True, (0, 1, False), **plot_directive_options) #------------------------------------------------------------------------------ # plot:: directive #------------------------------------------------------------------------------ from docutils.parsers.rst import directives from docutils import nodes def plot_directive(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine): return run(arguments, content, options, state_machine, state, lineno) plot_directive.__doc__ = __doc__ def _option_boolean(arg): if not arg or not arg.strip(): # no argument given, assume used as a flag return True elif arg.strip().lower() in ('no', '0', 'false'): return False elif arg.strip().lower() in ('yes', '1', 'true'): return True else: raise ValueError('"%s" unknown boolean' % arg) def _option_format(arg): return directives.choice(arg, ('python', 'lisp')) def _option_align(arg): return directives.choice(arg, ("top", "middle", "bottom", "left", "center", "right")) plot_directive_options = {'alt': directives.unchanged, 'height': directives.length_or_unitless, 'width': directives.length_or_percentage_or_unitless, 'scale': directives.nonnegative_int, 'align': _option_align, 'class': directives.class_option, 'include-source': _option_boolean, 'format': _option_format, } #------------------------------------------------------------------------------ # Generating output #------------------------------------------------------------------------------ from docutils import nodes, utils try: # Sphinx depends on either Jinja or Jinja2 import jinja2 def format_template(template, **kw): return jinja2.Template(template).render(**kw) except ImportError: import jinja def format_template(template, **kw): return jinja.from_string(template, **kw) TEMPLATE = """ {{ source_code }} {{ only_html }} {% if source_link or (html_show_formats and not multi_image) %} ( {%- if source_link -%} `Source code <{{ source_link }}>`__ {%- endif -%} {%- if html_show_formats and not multi_image -%} {%- for img in images -%} {%- for fmt in img.formats -%} {%- if source_link or not loop.first -%}, {% endif -%} `{{ fmt }} <{{ dest_dir }}/{{ img.basename }}.{{ fmt }}>`__ {%- endfor -%} {%- endfor -%} {%- endif -%} ) {% endif %} {% for img in images %} .. figure:: {{ build_dir }}/{{ img.basename }}.png {%- for option in options %} {{ option }} {% endfor %} {% if html_show_formats and multi_image -%} ( {%- for fmt in img.formats -%} {%- if not loop.first -%}, {% endif -%} `{{ fmt }} <{{ dest_dir }}/{{ img.basename }}.{{ fmt }}>`__ {%- endfor -%} ) {%- endif -%} {% endfor %} {{ only_latex }} {% for img in images %} .. image:: {{ build_dir }}/{{ img.basename }}.pdf {% endfor %} """ class ImageFile(object): def __init__(self, basename, dirname): self.basename = basename self.dirname = dirname self.formats = [] def filename(self, format): return os.path.join(self.dirname, "%s.%s" % (self.basename, format)) def filenames(self): return [self.filename(fmt) for fmt in self.formats] def run(arguments, content, options, state_machine, state, lineno): if arguments and content: raise RuntimeError("plot:: directive can't have both args and content") document = state_machine.document config = document.settings.env.config options.setdefault('include-source', config.plot_include_source) # determine input rst_file = document.attributes['source'] rst_dir = os.path.dirname(rst_file) if arguments: if not config.plot_basedir: source_file_name = os.path.join(rst_dir, directives.uri(arguments[0])) else: source_file_name = os.path.join(setup.confdir, config.plot_basedir, directives.uri(arguments[0])) code = open(source_file_name, 'r').read() output_base = os.path.basename(source_file_name) else: source_file_name = rst_file code = textwrap.dedent("\n".join(map(str, content))) counter = document.attributes.get('_plot_counter', 0) + 1 document.attributes['_plot_counter'] = counter base, ext = os.path.splitext(os.path.basename(source_file_name)) output_base = '%s-%d.py' % (base, counter) base, source_ext = os.path.splitext(output_base) if source_ext in ('.py', '.rst', '.txt'): output_base = base else: source_ext = '' # ensure that LaTeX includegraphics doesn't choke in foo.bar.pdf filenames output_base = output_base.replace('.', '-') # is it in doctest format? is_doctest = contains_doctest(code) if options.has_key('format'): if options['format'] == 'python': is_doctest = False else: is_doctest = True # determine output directory name fragment source_rel_name = relpath(source_file_name, setup.confdir) source_rel_dir = os.path.dirname(source_rel_name) while source_rel_dir.startswith(os.path.sep): source_rel_dir = source_rel_dir[1:] # build_dir: where to place output files (temporarily) build_dir = os.path.join(os.path.dirname(setup.app.doctreedir), 'plot_directive', source_rel_dir) if not os.path.exists(build_dir): os.makedirs(build_dir) # output_dir: final location in the builder's directory dest_dir = os.path.abspath(os.path.join(setup.app.builder.outdir, source_rel_dir)) # how to link to files from the RST file dest_dir_link = os.path.join(relpath(setup.confdir, rst_dir), source_rel_dir).replace(os.path.sep, '/') build_dir_link = relpath(build_dir, rst_dir).replace(os.path.sep, '/') source_link = dest_dir_link + '/' + output_base + source_ext # make figures try: results = makefig(code, source_file_name, build_dir, output_base, config) errors = [] except PlotError, err: reporter = state.memo.reporter sm = reporter.system_message( 2, "Exception occurred in plotting %s: %s" % (output_base, err), line=lineno) results = [(code, [])] errors = [sm] # generate output restructuredtext total_lines = [] for j, (code_piece, images) in enumerate(results): if options['include-source']: if is_doctest: lines = [''] lines += [row.rstrip() for row in code_piece.split('\n')] else: lines = ['.. code-block:: python', ''] lines += [' %s' % row.rstrip() for row in code_piece.split('\n')] source_code = "\n".join(lines) else: source_code = "" opts = [':%s: %s' % (key, val) for key, val in options.items() if key in ('alt', 'height', 'width', 'scale', 'align', 'class')] only_html = ".. only:: html" only_latex = ".. only:: latex" if j == 0: src_link = source_link else: src_link = None result = format_template( TEMPLATE, dest_dir=dest_dir_link, build_dir=build_dir_link, source_link=src_link, multi_image=len(images) > 1, only_html=only_html, only_latex=only_latex, options=opts, images=images, source_code=source_code, html_show_formats=config.plot_html_show_formats) total_lines.extend(result.split("\n")) total_lines.extend("\n") if total_lines: state_machine.insert_input(total_lines, source=source_file_name) # copy image files to builder's output directory if not os.path.exists(dest_dir): os.makedirs(dest_dir) for code_piece, images in results: for img in images: for fn in img.filenames(): shutil.copyfile(fn, os.path.join(dest_dir, os.path.basename(fn))) # copy script (if necessary) if source_file_name == rst_file: target_name = os.path.join(dest_dir, output_base + source_ext) f = open(target_name, 'w') f.write(unescape_doctest(code)) f.close() return errors #------------------------------------------------------------------------------ # Run code and capture figures #------------------------------------------------------------------------------ import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.image as image from matplotlib import _pylab_helpers import exceptions def contains_doctest(text): try: # check if it's valid Python as-is compile(text, '', 'exec') return False except SyntaxError: pass r = re.compile(r'^\s*>>>', re.M) m = r.search(text) return bool(m) def unescape_doctest(text): """ Extract code from a piece of text, which contains either Python code or doctests. """ if not contains_doctest(text): return text code = "" for line in text.split("\n"): m = re.match(r'^\s*(>>>|\.\.\.) (.*)$', line) if m: code += m.group(2) + "\n" elif line.strip(): code += "# " + line.strip() + "\n" else: code += "\n" return code def split_code_at_show(text): """ Split code at plt.show() """ parts = [] is_doctest = contains_doctest(text) part = [] for line in text.split("\n"): if (not is_doctest and line.strip() == 'plt.show()') or \ (is_doctest and line.strip() == '>>> plt.show()'): part.append(line) parts.append("\n".join(part)) part = [] else: part.append(line) if "\n".join(part).strip(): parts.append("\n".join(part)) return parts class PlotError(RuntimeError): pass def run_code(code, code_path, ns=None): # Change the working directory to the directory of the example, so # it can get at its data files, if any. pwd = os.getcwd() old_sys_path = list(sys.path) if code_path is not None: dirname = os.path.abspath(os.path.dirname(code_path)) os.chdir(dirname) sys.path.insert(0, dirname) # Redirect stdout stdout = sys.stdout sys.stdout = cStringIO.StringIO() # Reset sys.argv old_sys_argv = sys.argv sys.argv = [code_path] try: try: code = unescape_doctest(code) if ns is None: ns = {} if not ns: exec setup.config.plot_pre_code in ns exec code in ns except (Exception, SystemExit), err: raise PlotError(traceback.format_exc()) finally: os.chdir(pwd) sys.argv = old_sys_argv sys.path[:] = old_sys_path sys.stdout = stdout return ns #------------------------------------------------------------------------------ # Generating figures #------------------------------------------------------------------------------ def out_of_date(original, derived): """ Returns True if derivative is out-of-date wrt original, both of which are full file paths. """ return (not os.path.exists(derived) or os.stat(derived).st_mtime < os.stat(original).st_mtime) def makefig(code, code_path, output_dir, output_base, config): """ Run a pyplot script *code* and save the images under *output_dir* with file names derived from *output_base* """ # -- Parse format list default_dpi = {'png': 80, 'hires.png': 200, 'pdf': 50} formats = [] for fmt in config.plot_formats: if isinstance(fmt, str): formats.append((fmt, default_dpi.get(fmt, 80))) elif type(fmt) in (tuple, list) and len(fmt)==2: formats.append((str(fmt[0]), int(fmt[1]))) else: raise PlotError('invalid image format "%r" in plot_formats' % fmt) # -- Try to determine if all images already exist code_pieces = split_code_at_show(code) # Look for single-figure output files first all_exists = True img = ImageFile(output_base, output_dir) for format, dpi in formats: if out_of_date(code_path, img.filename(format)): all_exists = False break img.formats.append(format) if all_exists: return [(code, [img])] # Then look for multi-figure output files results = [] all_exists = True for i, code_piece in enumerate(code_pieces): images = [] for j in xrange(1000): img = ImageFile('%s_%02d_%02d' % (output_base, i, j), output_dir) for format, dpi in formats: if out_of_date(code_path, img.filename(format)): all_exists = False break img.formats.append(format) # assume that if we have one, we have them all if not all_exists: all_exists = (j > 0) break images.append(img) if not all_exists: break results.append((code_piece, images)) if all_exists: return results # -- We didn't find the files, so build them results = [] ns = {} for i, code_piece in enumerate(code_pieces): # Clear between runs plt.close('all') # Run code run_code(code_piece, code_path, ns) # Collect images images = [] fig_managers = _pylab_helpers.Gcf.get_all_fig_managers() for j, figman in enumerate(fig_managers): if len(fig_managers) == 1 and len(code_pieces) == 1: img = ImageFile(output_base, output_dir) else: img = ImageFile("%s_%02d_%02d" % (output_base, i, j), output_dir) images.append(img) for format, dpi in formats: try: figman.canvas.figure.savefig(img.filename(format), dpi=dpi) except exceptions.BaseException, err: raise PlotError(traceback.format_exc()) img.formats.append(format) # Results results.append((code_piece, images)) return results #------------------------------------------------------------------------------ # Relative pathnames #------------------------------------------------------------------------------ try: from os.path import relpath except ImportError: # Copied from Python 2.7 if 'posix' in sys.builtin_module_names: def relpath(path, start=os.path.curdir): """Return a relative version of a path""" from os.path import sep, curdir, join, abspath, commonprefix, \ pardir if not path: raise ValueError("no path specified") start_list = abspath(start).split(sep) path_list = abspath(path).split(sep) # Work out how much of the filepath is shared by start and path. i = len(commonprefix([start_list, path_list])) rel_list = [pardir] * (len(start_list)-i) + path_list[i:] if not rel_list: return curdir return join(*rel_list) elif 'nt' in sys.builtin_module_names: def relpath(path, start=os.path.curdir): """Return a relative version of a path""" from os.path import sep, curdir, join, abspath, commonprefix, \ pardir, splitunc if not path: raise ValueError("no path specified") start_list = abspath(start).split(sep) path_list = abspath(path).split(sep) if start_list[0].lower() != path_list[0].lower(): unc_path, rest = splitunc(path) unc_start, rest = splitunc(start) if bool(unc_path) ^ bool(unc_start): raise ValueError("Cannot mix UNC and non-UNC paths (%s and %s)" % (path, start)) else: raise ValueError("path is on drive %s, start on drive %s" % (path_list[0], start_list[0])) # Work out how much of the filepath is shared by start and path. for i in range(min(len(start_list), len(path_list))): if start_list[i].lower() != path_list[i].lower(): break else: i += 1 rel_list = [pardir] * (len(start_list)-i) + path_list[i:] if not rel_list: return curdir return join(*rel_list) else: raise RuntimeError("Unsupported platform (no relpath available!)") statsmodels-0.5.0+git13-g8e07d34/docs/themes/000077500000000000000000000000001224417117700203675ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/000077500000000000000000000000001224417117700227315ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/indexsidebar.html000066400000000000000000000027631224417117700262700ustar00rootroot00000000000000

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{% if 'dev' in version %}

This documentation is for version {{ version }}, which is not released yet. Grab the source code from Github to install this version. You can go to the documentation for the last release here.

{% else %}

This documentation is for the {{ release }} release. You can install it with:

easy_install -U statsmodels

Or get it from the Python Package Index. Documentation for the current development version is here.

{% endif %}

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statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/layout.html000066400000000000000000000035431224417117700251410ustar00rootroot00000000000000{# statsmodels/layout.hml :copyright: Skipper Seabold :license: BSD #} {% extends "basic/layout.html" %} {% block extrahead %} {% endblock %} {% set reldelim1 = ' |' %} {% block sidebarlogo %}{% endblock %} {# override to not display, we keep the "logo" at the very top #} {% if pagename == 'index' %} {% set title = 'StatsModels: Statistics in Python' %} {% endif %} {% block rootrellink %}
  • Install
    •  | 
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    •  | 
  • Examples
    •  |  {% endblock %} {# Render the Header with Banner #} {% block header %}
    {% if logo %} Logo {% endif %}
    {% endblock %} {#{% block document %}
    • Download
    • Support
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    • Develop
    • Examples
    {{ super() }} {% endblock %}#} {#{% block document %} {% block relbaritems %} {% if pagename != 'index' %} {{ title }} {% endif %} {% endblock %} {{ super() }} {% endblock %}#} {#{% block relbar1 %}{% endblock %} #} {% block relbar2 %}{% endblock %} statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/relations.html000066400000000000000000000020311224417117700256130ustar00rootroot00000000000000{# basic/relations.html ~~~~~~~~~~~~~~~~~~~~ Sphinx sidebar template: relation links. :copyright: Copyright 2007-2010 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. #} {%- if prev %}

    {{ _('Previous topic') }}

    {%- if prev.title[:19] == "statsmodels" %}

    {{ "sm." ~ prev.title[20:] }}

    {%- else %}

    {{ prev.title }}

    {%- endif %} {%- endif %} {%- if next %}

    {{ _('Next topic') }}

    {%- if next.title[:19] == "statsmodels" %}

    {{ "sm." ~ next.title[20:] }}

    {%- else %}

    {{ next.title }}

    {%- endif %} {%- endif %} statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/sidelinks.html000066400000000000000000000004151224417117700256040ustar00rootroot00000000000000

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    statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/static/000077500000000000000000000000001224417117700242205ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/static/nature.css_t000066400000000000000000000113401224417117700265520ustar00rootroot00000000000000/* * nature.css_t * ~~~~~~~~~~~~ * * Sphinx stylesheet -- nature theme. * * :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ /* background-color: #ffffff; (For text) background-color: #dbe0ea; (For banner and sidebar headers) */ @import url("basic.css"); /* ---------------- Header ---------------- */ div.header { background-color: #dbe0ea; color: #272A2D; } div.headerwrap { background-color: #dbe0ea; min-width: 1030px; min-height: 85px; margin-left: auto; margin-right: auto; } div.navbar ul { list-style-type: none; margin-left: 230px; text-align: right; position: absolute; right: 105px; top: 103px; } div.navbar li { display: inline; /*color: #272A2D;*/ border-bottom-right-radius: 30px; border-bottom-left-radius: 30px; padding: 0px 15px } div.related { background-color: #dbe0ea; line-height: 32px; color: #272A2D; font-size: 100%; border-top: 1px solid white; border-bottom: 1px solid white; min-width: 1030px; } div.related a { color: #272A2D; font-weight: normal; text-decoration: none } /* ----------- Page layout --------- */ body { font-family: Arial, sans-serif; font-size: 100%; /*background-color: #ffffff;*/ color: #272A2D; margin: 0 80px; padding: 0; min-width: 640px; } div.documentwrapper { background-color: #ffffff; float: left; width: 100%; } div.bodywrapper { margin: 0 0 0 230px; } hr { border: 1px solid #B1B4B6; } div.document { min-width: 1030px; } div.body { min-width: 740px; padding: 0 30px 30px 30px; /*font-size: 0.9em;*/ } /* -------------- Footer -------------- */ div.footer { background-color: ffffff; color: #272A2D; width: 100%; padding: 13px 0; text-align: center; font-size: 75%; } /* -------------- Sidebar -------------- */ div.sphinxsidebar { /*background-color: #dbe0ea;*/ /*background-color: #ffffff;*/ font-size: 0.85em; 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â|†Ò÷ùÿ”Þ––ã>’lÉ÷|’)ÿ¨è>$¯97æ€ÆÖØ[ÿþõg²$@¶LIEND®B`‚statsmodels-0.5.0+git13-g8e07d34/docs/themes/statsmodels/theme.conf000066400000000000000000000001071224417117700247000ustar00rootroot00000000000000[theme] inherit = basic stylesheet = nature.css pygments_style = tango statsmodels-0.5.0+git13-g8e07d34/examples/000077500000000000000000000000001224417117700177705ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/examples/example_discrete.py000066400000000000000000000075031224417117700236640ustar00rootroot00000000000000"""Discrete Data Models """ import numpy as np import statsmodels.api as sm # Load data from Spector and Mazzeo (1980). Examples follow Greene's # Econometric Analysis Ch. 21 (5th Edition). spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog, prepend=False) # Inspect the data: spector_data.exog[:5, :] spector_data.endog[:5] # Linear Probability Model (OLS) #------------------------------- lpm_mod = sm.OLS(spector_data.endog, spector_data.exog) lpm_res = lpm_mod.fit() print lpm_res.params[:-1] #Logit Model #----------- logit_mod = sm.Logit(spector_data.endog, spector_data.exog) logit_res = logit_mod.fit() print logit_res.params logit_margeff = logit_res.get_margeff(method='dydx', at='overall') print logit_margeff.summary() #l1 regularized logit #-------------------- # The regularization parameter alpha should be a scalar or have the same shape # as results.params alpha = 0.1 * len(spector_data.endog) * np.ones(spector_data.exog.shape[1]) # Choose not to regularize the constant alpha[-1] = 0 logit_l1_res = logit_mod.fit_regularized(method='l1', alpha=alpha) print logit_l1_res.summary() # As in all the discrete data models presented below, we can print a nice # summary of results: print logit_res.summary() #Probit Model #------------ probit_mod = sm.Probit(spector_data.endog, spector_data.exog) probit_res = probit_mod.fit() print probit_res.params probit_margeff = probit_res.get_margeff() print probit_margeff.summary() #Multinomial Logit #----------------- # Load data from the American National Election Studies: anes_data = sm.datasets.anes96.load() anes_exog = anes_data.exog anes_exog = sm.add_constant(anes_exog, prepend=False) # Inspect the data: print anes_data.exog[:5, :] print anes_data.endog[:5] # Fit MNL model mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog) mlogit_res = mlogit_mod.fit() print mlogit_res.params mlogit_margeff = mlogit_res.get_margeff() print mlogit_margeff.summary() #l1 regularized Multinomial Logit #-------------------------------- # The regularization parameter alpha should be a scalar or have the same shape as # as results.params alpha = 10 * np.ones((mlogit_mod.K, mlogit_mod.J - 1)) # Choose not to regularize the constant alpha[-1, :] = 0 mlogit_mod2 = sm.MNLogit(anes_data.endog, anes_exog) mlogit_l1_res = mlogit_mod2.fit_regularized(method='l1', alpha=alpha) print mlogit_l1_res.summary() #Poisson model #------------- # Load the Rand data. Note that this example is similar to Cameron and # Trivedi's `Microeconometrics` Table 20.5, but it is slightly different # because of minor changes in the data. rand_data = sm.datasets.randhie.load() rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1) rand_exog = sm.add_constant(rand_exog, prepend=False) # Fit Poisson model: poisson_mod = sm.Poisson(rand_data.endog, rand_exog) poisson_res = poisson_mod.fit(method="newton") print poisson_res.summary() poisson_margeff = poisson_res.get_margeff() print poisson_margeff.summary() # l1 regularized Poisson model poisson_mod2 = sm.Poisson(rand_data.endog, rand_exog) alpha = 0.1 * len(rand_data.endog) * np.ones(rand_exog.shape[1]) alpha[-1] = 0 poisson_l1_res = poisson_mod2.fit_regularized(method='l1', alpha=alpha) # Negative binomial model #------------------------ # The negative binomial model gives slightly different results: mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog) res_nbin = mod_nbin.fit(disp=False) print res_nbin.summary() #Alternative solvers #------------------- # The default method for fitting discrete data MLE models is Newton-Raphson. # You can use other solvers by using the ``method`` argument: mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=500) #.. The below needs a lot of iterations to get it right? #.. TODO: Add a technical note on algorithms #.. mlogit_res = mlogit_mod.fit(method='ncg') # this takes forever statsmodels-0.5.0+git13-g8e07d34/examples/example_formula_glm.py000066400000000000000000000020571224417117700243650ustar00rootroot00000000000000"""GLM Formula Example """ import statsmodels.api as sm star98 = sm.datasets.star98.load_pandas().data formula = ('SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT ' '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF') dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF']] endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW')) del dta['NABOVE'] dta['SUCCESS'] = endog mod = sm.GLM.from_formula(formula=formula, data=dta, family=sm.families.Binomial()).fit() # try passing a formula object, using arbitrary user-injected code def double_it(x): return 2 * x formula = ('SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + ' 'PCTCHRT + PCTYRRND + PERMINTE*AVYRSEXP*AVSALK' '+ PERSPENK*PTRATIO*PCTAF') mod2 = sm.GLM.from_formula(formula=formula, data=dta, family=sm.families.Binomial()).fit() statsmodels-0.5.0+git13-g8e07d34/examples/example_glm.py000066400000000000000000000101611224417117700226330ustar00rootroot00000000000000"""Generalized Linear Models """ import numpy as np import statsmodels.api as sm from scipy import stats from matplotlib import pyplot as plt #GLM: Binomial response data #--------------------------- #Load data #^^^^^^^^^ # In this example, we use the Star98 dataset which was taken with permission # from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook # information can be obtained by typing: print sm.datasets.star98.NOTE # Load the data and add a constant to the exogenous (independent) variables: data = sm.datasets.star98.load() data.exog = sm.add_constant(data.exog, prepend=False) # The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): print data.endog[:5, :] # The independent variables include all the other variables described above, as # well as the interaction terms: print data.exog[:2, :] #Fit and summary #^^^^^^^^^^^^^^^ glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial()) res = glm_binom.fit() print res.summary() #Quantities of interest #^^^^^^^^^^^^^^^^^^^^^^ # Total number of trials: print data.endog[0].sum() # Parameter estimates: print res.params # The corresponding t-values: print res.tvalues # First differences: We hold all explanatory variables constant at their means # and manipulate the percentage of low income households to assess its impact # on the response variables: means = data.exog.mean(axis=0) means25 = means.copy() means25[0] = stats.scoreatpercentile(data.exog[:, 0], 25) means75 = means.copy() means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:, 0], 75) resp_25 = res.predict(means25) resp_75 = res.predict(means75) diff = resp_75 - resp_25 # The interquartile first difference for the percentage of low income # households in a school district is: print '%2.4f%%' % (diff * 100) #Plots #^^^^^ # We extract information that will be used to draw some interesting plots: nobs = res.nobs y = data.endog[:, 0] / data.endog.sum(1) yhat = res.mu # Plot yhat vs y: plt.figure(); plt.scatter(yhat, y); line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=False)).fit().params fit = lambda x: line_fit[1] + line_fit[0] * x # better way in scipy? plt.plot(np.linspace(0, 1, nobs), fit(np.linspace(0, 1, nobs))); plt.title('Model Fit Plot'); plt.ylabel('Observed values'); #@savefig glm_fitted.png plt.xlabel('Fitted values'); # Plot yhat vs. Pearson residuals: plt.figure(); plt.scatter(yhat, res.resid_pearson); plt.plot([0.0, 1.0], [0.0, 0.0], 'k-'); plt.title('Residual Dependence Plot'); plt.ylabel('Pearson Residuals'); #@savefig glm_resids.png plt.xlabel('Fitted values'); #Histogram of standardized deviance residuals plt.figure(); resid = res.resid_deviance.copy() resid_std = (resid - resid.mean()) / resid.std() plt.hist(resid_std, bins=25); #@savefig glm_hist_res.png plt.title('Histogram of standardized deviance residuals'); #QQ Plot of Deviance Residuals from statsmodels import graphics #@savefig glm_qqplot.png graphics.gofplots.qqplot(resid, line='r'); #GLM: Gamma for proportional count response #------------------------------------------ #Load data #^^^^^^^^^ # In the example above, we printed the ``NOTE`` attribute to learn about the # Star98 dataset. Statsmodels datasets ships with other useful information. For # example: print sm.datasets.scotland.DESCRLONG # Load the data and add a constant to the exogenous variables: data2 = sm.datasets.scotland.load() data2.exog = sm.add_constant(data2.exog, prepend=False) print data2.exog[:5, :] print data2.endog[:5] #Fit and summary #^^^^^^^^^^^^^^^ glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma()) glm_results = glm_gamma.fit() print glm_results.summary() #GLM: Gaussian distribution with a noncanonical link #--------------------------------------------------- #Artificial data #^^^^^^^^^^^^^^^ nobs2 = 100 x = np.arange(nobs2) np.random.seed(54321) X = np.column_stack((x, x**2)) X = sm.add_constant(X, prepend=False) lny = np.exp(-(.03 * x + .0001 * x**2 - 1.0)) + .001 * np.random.rand(nobs2) #Fit and summary #^^^^^^^^^^^^^^^ gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log)) gauss_log_results = gauss_log.fit() print gauss_log_results.summary() statsmodels-0.5.0+git13-g8e07d34/examples/example_gls.py000066400000000000000000000044731224417117700226520ustar00rootroot00000000000000"""Generalized Least Squares """ import statsmodels.api as sm data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog) # The Longley dataset is a time series dataset # Let's assume that the data is heteroskedastic and that we know # the nature of the heteroskedasticity. We can then define # `sigma` and use it to give us a GLS model # First we will obtain the residuals from an OLS fit ols_resid = sm.OLS(data.endog, data.exog).fit().resid # Assume that the error terms follow an AR(1) process with a trend # resid[i] = beta_0 + rho*resid[i-1] + e[i] # where e ~ N(0,some_sigma**2) # and that rho is simply the correlation of the residuals # a consistent estimator for rho is to regress the residuals # on the lagged residuals resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit() print resid_fit.tvalues[1] print resid_fit.pvalues[1] # While we don't have strong evidence that the errors follow an AR(1) # process we continue rho = resid_fit.params[1] # As we know, an AR(1) process means that near-neighbors have a stronger # relation so we can give this structure by using a toeplitz matrix from scipy.linalg import toeplitz toeplitz(range(5)) #.. array([[0, 1, 2, 3, 4], #.. [1, 0, 1, 2, 3], #.. [2, 1, 0, 1, 2], #.. [3, 2, 1, 0, 1], #.. [4, 3, 2, 1, 0]]) order = toeplitz(range(len(ols_resid))) # so that our error covariance structure is actually rho**order # which defines an autocorrelation structure sigma = rho**order gls_model = sm.GLS(data.endog, data.exog, sigma=sigma) gls_results = gls_model.fit() # of course, the exact rho in this instance is not known so it # it might make more sense to use feasible gls, which currently only # has experimental support # We can use the GLSAR model with one lag, to get to a similar result glsar_model = sm.GLSAR(data.endog, data.exog, 1) glsar_results = glsar_model.iterative_fit(1) # comparing gls and glsar results, we see that there are some small # differences in the parameter estimates and the resulting standard # errors of the parameter estimate. This might be do to the numerical # differences in the algorithm, e.g. the treatment of initial conditions, # because of the small number of observations in the longley dataset. print gls_results.params print glsar_results.params print gls_results.bse print glsar_results.bse statsmodels-0.5.0+git13-g8e07d34/examples/example_glsar.py000066400000000000000000000111161224417117700231650ustar00rootroot00000000000000""" Generalized Least Squares with AR Errors 6 examples for GLSAR with artificial data """ #.. note: These examples were written mostly to cross-check results. It is still being # written, and GLSAR is still being worked on. import numpy as np import numpy.testing as npt from scipy import signal import statsmodels.api as sm from statsmodels.regression.linear_model import GLSAR, yule_walker examples_all = range(10) + ['test_copy'] examples = examples_all # [5] if 0 in examples: print '\n Example 0' X = np.arange(1, 8) X = sm.add_constant(X) Y = np.array((1, 3, 4, 5, 8, 10, 9)) rho = 2 model = GLSAR(Y, X, 2) for i in range(6): results = model.fit() print 'AR coefficients:', model.rho rho, sigma = yule_walker(results.resid, order=model.order) model = GLSAR(Y, X, rho) par0 = results.params print 'params fit', par0 model0if = GLSAR(Y, X, 2) res = model0if.iterative_fit(6) print 'iterativefit beta', res.params results.tvalues # XXX is this correct? it does equal params/bse # but isn't the same as the AR example (which was wrong in the first place..) print results.t_test([0, 1]) # are sd and t correct? vs print results.f_test(np.eye(2)) rhotrue = np.array([0.5, 0.2]) nlags = np.size(rhotrue) beta = np.array([0.1, 2]) noiseratio = 0.5 nsample = 2000 x = np.arange(nsample) X1 = sm.add_constant(x, prepend=False) wnoise = noiseratio * np.random.randn(nsample + nlags) #.. noise = noise[1:] + rhotrue*noise[:-1] # wrong this is not AR #.. find my drafts for univariate ARMA functions # generate AR(p) if np.size(rhotrue) == 1: # replace with scipy.signal.lfilter, keep for testing arnoise = np.zeros(nsample + 1) for i in range(1, nsample + 1): arnoise[i] = rhotrue * arnoise[i - 1] + wnoise[i] noise = arnoise[1:] an = signal.lfilter([1], np.hstack((1, -rhotrue)), wnoise[1:]) print 'simulate AR(1) difference', np.max(np.abs(noise - an)) else: noise = signal.lfilter([1], np.hstack((1, -rhotrue)), wnoise)[nlags:] # generate GLS model with AR noise y1 = np.dot(X1, beta) + noise if 1 in examples: print '\nExample 1: iterative_fit and repeated calls' mod1 = GLSAR(y1, X1, 1) res = mod1.iterative_fit() print res.params print mod1.rho mod1 = GLSAR(y1, X1, 2) for i in range(5): res1 = mod1.iterative_fit(2) # mod1.fit() print mod1.rho print res1.params if 2 in examples: print '\nExample 2: iterative fitting of first model' print 'with AR(0)', par0 parold = par0 mod0 = GLSAR(Y, X, 1) for i in range(5): #print mod0.wexog.sum() #print mod0.pinv_wexog.sum() res0 = mod0.iterative_fit(1) print 'rho', mod0.rho, parnew = res0.params print 'params', parnew, print 'params change in iteration', parnew - parold parold = parnew # generate pure AR(p) process Y = noise #example with no regressor, #results now have same estimated rho as yule-walker directly if 3 in examples: print '\nExample 3: pure AR(2), GLSAR versus Yule_Walker' model3 = GLSAR(Y, rho=2) for i in range(5): results = model3.fit() print "AR coefficients:", model3.rho, results.params rho, sigma = yule_walker(results.resid, order=model3.order) model3 = GLSAR(Y, rho=rho) if 'test_copy' in examples: xx = X.copy() rhoyw, sigmayw = yule_walker(xx[:, 0], order=2) print rhoyw, sigmayw print (xx == X).all() # test for unchanged array (fixed) yy = Y.copy() rhoyw, sigmayw = yule_walker(yy, order=2) print rhoyw, sigmayw print (yy == Y).all() # test for unchanged array (fixed) if 4 in examples: print '\nExample 4: demeaned pure AR(2), GLSAR versus Yule_Walker' Ydemeaned = Y - Y.mean() model4 = GLSAR(Ydemeaned, rho=2) for i in range(5): results = model4.fit() print "AR coefficients:", model3.rho, results.params rho, sigma = yule_walker(results.resid, order=model4.order) model4 = GLSAR(Ydemeaned, rho=rho) if 5 in examples: print '\nExample 5: pure AR(2), GLSAR iterative_fit versus Yule_Walker' model3a = GLSAR(Y, rho=1) res3a = model3a.iterative_fit(5) print res3a.params print model3a.rho rhoyw, sigmayw = yule_walker(Y, order=1) print rhoyw, sigmayw npt.assert_array_almost_equal(model3a.rho, rhoyw, 15) for i in range(6): model3b = GLSAR(Y, rho=0.1) print i, model3b.iterative_fit(i).params, model3b.rho model3b = GLSAR(Y, rho=0.1) for i in range(6): print i, model3b.iterative_fit(2).params, model3b.rho statsmodels-0.5.0+git13-g8e07d34/examples/example_gmle.py000066400000000000000000000133611224417117700230050ustar00rootroot00000000000000'''Generic Maximum Likelihood Models''' #This tutorial explains how to quickly implement new maximum likelihood models #in ``statsmodels``. The `GenericLikelihoodModel #<../../dev/generated/statsmodels.base.model.GenericLikelihoodModel.html#statsmodels.base.model.GenericLikelihoodModel>`_ #class eases the process by providing tools such as automatic numeric #differentiation and a unified interface to ``scipy`` optimization functions. #Using ``statsmodels``, users can fit new MLE models simply by "plugging-in" a #log-likelihood function. # #Negative Binomial Regression for Count Data #------------------------------------------- #Consider a negative binomial regression model for count data with #log-likelihood (type NB-2) function expressed as: #.. math:: # # \mathcal{L}(\beta_j; y, \alpha) = \sum_{i=1}^n y_i ln # \left ( \frac{\alpha exp(X_i'\beta)}{1+\alpha exp(X_i'\beta)} \right ) - # \frac{1}{\alpha} ln(1+\alpha exp(X_i'\beta)) \\ # + ln \Gamma (y_i + 1/\alpha) - ln \Gamma (y_i+1) - ln \Gamma (1/\alpha) #with a matrix of regressors :math:`X`, a vector of coefficients :math:`\beta`, #and the negative binomial heterogeneity parameter :math:`\alpha`. #Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood #simply as: import numpy as np from scipy.stats import nbinom def _ll_nb2(y, X, beta, alph): mu = np.exp(np.dot(X, beta)) size = 1 / alph prob = size / (size + mu) ll = nbinom.logpmf(y, size, prob) return ll #New Model Class #--------------- #We create a new model class which inherits from ``GenericLikelihoodModel``: from statsmodels.base.model import GenericLikelihoodModel class NBin(GenericLikelihoodModel): def __init__(self, endog, exog, **kwds): super(NBin, self).__init__(endog, exog, **kwds) def nloglikeobs(self, params): alph = params[-1] beta = params[:-1] ll = _ll_nb2(self.endog, self.exog, beta, alph) return -ll def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds): if start_params == None: # Reasonable starting values start_params = np.append(np.zeros(self.exog.shape[1]), .5) start_params[0] = np.log(self.endog.mean()) return super(NBin, self).fit(start_params=start_params, maxiter=maxiter, maxfun=maxfun, **kwds) #Two important things to notice: #+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). #+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization. #That's it! You're done! #Usage Example #------------- #The `Medpar `_ #dataset is hosted in CSV format at the `Rdatasets repository #`_. We use the ``read_csv`` #function from the `Pandas library `_ to load the data #in memory. We then print the first few columns: import pandas as pd url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv' medpar = pd.read_csv(url) medpar.head() #The model we are interested in has a vector of non-negative integers as #dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``, #``type3``, ``hmo``, ``white``. #For estimation, we need to create 2 numpy arrays (pandas DataFrame should also #work): a 1d array of length *N* to hold ``los`` values, and a *N* by 5 #array to hold our 5 regressors. These arrays can be constructed manually or #using any number of helper functions; the details matter little for our current #purposes. Here, we build the arrays we need using the `Patsy #`_ package: import patsy y, X = patsy.dmatrices('los~type2+type3+hmo+white', medpar) print y[:5] print X[:5] #Then, we fit the model and extract some information: mod = NBin(y, X) res = mod.fit() # Extract parameter estimates, standard errors, p-values, AIC, etc.: res.params res.bse res.pvalues res.aic #As usual, you can obtain a full list of available information by typing #``dir(res)``. # #To ensure that the above results are sound, we compare them to results # obtained using the MASS implementation for R:: # # url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv' # medpar = read.csv(url) # f = los~factor(type)+hmo+white # # library(MASS) # mod = glm.nb(f, medpar) # coef(summary(mod)) # Estimate Std. Error z value Pr(>|z|) # (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257 # factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05 # factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20 # hmo -0.06795522 0.05321375 -1.277024 2.015939e-01 # white -0.12906544 0.06836272 -1.887951 5.903257e-02 #Numerical precision #^^^^^^^^^^^^^^^^^^^ #The ``statsmodels`` and ``R`` parameter estimates agree up to the fourth #decimal. The standard errors, however, agree only up to the second decimal. #This discrepancy may be the result of imprecision in our Hessian numerical #estimates. In the current context, the difference between ``MASS`` and #``statsmodels`` standard error estimates is substantively irrelevant, but it #highlights the fact that users who need very precise estimates may not always #want to rely on default settings when using numerical derivatives. In such #cases, it may be better to use analytical derivatives with the `LikelihoodModel #<../../dev/generated/statsmodels.base.model.GenericLikelihoodModel.html#statsmodels.base.model.GenericLikelihoodModel>`_ #class. statsmodels-0.5.0+git13-g8e07d34/examples/example_interaction_categorial.py000066400000000000000000000016151224417117700265710ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Plot Interaction of Categorical Factors """ # In this example, we will vizualize the interaction between # categorical factors. First, we will create some categorical # data are initialized. Then plotted using the interaction_plot # function which internally recodes the x-factor categories to # ingegers. import numpy as np import matplotlib.pyplot as plt from statsmodels.graphics.factorplots import interaction_plot from pandas import Series np.random.seed(12345) weight = Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight') nutrition = Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition') days = np.log(np.random.randint(1, 30, size=60)) plt.figure(figsize=(6, 6)); #@savefig interaction_plot_categorial.png align=center interaction_plot(x=weight, trace=nutrition, response=days, colors=['red', 'blue'], markers=['D', '^'], ms=10); statsmodels-0.5.0+git13-g8e07d34/examples/example_interactions.py000066400000000000000000000262101224417117700245600ustar00rootroot00000000000000"""Interactions and ANOVA """ #.. note:: This script is based heavily on Jonathan Taylor's class notes # http://www.stanford.edu/class/stats191/interactions.html from urllib2 import urlopen import numpy as np #@suppress np.set_printoptions(precision=4, suppress=True) import pandas import matplotlib.pyplot as plt from statsmodels.formula.api import ols from statsmodels.graphics.api import interaction_plot, abline_plot from statsmodels.stats.anova import anova_lm # load data try: salary_table = pandas.read_csv('./salary.table') except: print "fetching from website" url = 'http://stats191.stanford.edu/data/salary.table' #the next line is not necessary with recent version of pandas url = urlopen(url) salary_table = pandas.read_table(url) salary_table.to_csv('salary.table', index=False) E = salary_table.E M = salary_table.M X = salary_table.X S = salary_table.S # Take a look at the data plt.figure(figsize=(6, 6)); symbols = ['D', '^'] colors = ['r', 'g', 'blue'] factor_groups = salary_table.groupby(['E', 'M']) for values, group in factor_groups: i, j = values plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i - 1], s=144) plt.xlabel('Experience'); #@savefig raw_data_interactions.png align=center plt.ylabel('Salary'); # Fit a linear model formula = 'S ~ C(E) + C(M) + X' lm = ols(formula, salary_table).fit() print lm.summary() # Have a look at the created design matrix lm.model.exog[:20] # Or since we initially passed in a DataFrame, we have a DataFrame available in lm.model.data.orig_exog # We keep a reference to the original untouched data in lm.model.data.frame # Get influence statistics infl = lm.get_influence() print infl.summary_table() # or get a dataframe df_infl = infl.summary_frame() #Now plot the reiduals within the groups separately resid = lm.resid plt.figure(figsize=(6, 6)); for values, group in factor_groups: i, j = values group_num = i * 2 + j - 1 # for plotting purposes x = [group_num] * len(group) plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i - 1], s=144, edgecolors='black') plt.xlabel('Group'); #@savefig residual_groups.png align=center plt.ylabel('Residuals'); # now we will test some interactions using anova or f_test interX_lm = ols("S ~ C(E) * X + C(M)", salary_table).fit() print interX_lm.summary() # Do an ANOVA check table1 = anova_lm(lm, interX_lm) print table1 interM_lm = ols("S ~ X + C(E)*C(M)", data=salary_table).fit() print interM_lm.summary() table2 = anova_lm(lm, interM_lm) print table2 # The design matrix as a DataFrame interM_lm.model.data.orig_exog # The design matrix as an ndarray interM_lm.model.exog interM_lm.model.exog_names infl = interM_lm.get_influence() resid = infl.resid_studentized_internal plt.figure(figsize=(6, 6)); for values, group in factor_groups: i, j = values idx = group.index plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i - 1], s=144, edgecolors='black') plt.xlabel('X'); #@savefig standardized_resids.png align=center plt.ylabel('standardized resids'); # Looks like one observation is an outlier. #TODO: do we have Bonferonni outlier test? drop_idx = abs(resid).argmax() print drop_idx # zero-based index idx = salary_table.index.drop([drop_idx]) lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit() print lm32.summary() interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit() print interX_lm32.summary() table3 = anova_lm(lm32, interX_lm32) print table3 interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit() table4 = anova_lm(lm32, interM_lm32) print table4 # Replot the residuals try: resid = interM_lm32.get_influence().summary_frame()['standard_resid'] except: resid = interM_lm32.get_influence().summary_frame()['standard_resid'] plt.figure(figsize=(6, 6)); for values, group in factor_groups: i, j = values idx = group.index plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i - 1], s=144, edgecolors='black') plt.xlabel('X[~[32]]'); #@savefig standardized_drop32.png align=center plt.ylabel('standardized resids'); # Plot the fitted values lm_final = ols('S ~ X + C(E)*C(M)', data=salary_table.drop([drop_idx])).fit() mf = lm_final.model.data.orig_exog lstyle = ['-', '--'] plt.figure(figsize=(6, 6)); for values, group in factor_groups: i, j = values idx = group.index plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i - 1], s=144, edgecolors='black') # drop NA because there is no idx 32 in the final model plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(), ls=lstyle[j], color=colors[i - 1]) plt.xlabel('Experience'); #@savefig fitted_drop32.png align=center plt.ylabel('Salary'); # From our first look at the data, the difference between Master's and PhD in # the management group is different than in the non-management group. # This is an interaction between the two qualitative variables management, M # and education,E. We can visualize this by first removing the effect of # experience, then plotting the means within each of the 6 groups # using interaction.plot. U = S - X * interX_lm32.params['X'] plt.figure(figsize=(6, 6)); #@savefig interaction_plot.png align=center interaction_plot(E, M, U, colors=['red', 'blue'], markers=['^', 'D'], markersize=10, ax=plt.gca()) # Minority Employment Data # ------------------------ try: minority_table = pandas.read_table('minority.table') except: # don't have data already url = 'http://stats191.stanford.edu/data/minority.table' #the next line is not necessary with recent version of pandas url = urlopen(url) minority_table = pandas.read_table(url) factor_group = minority_table.groupby(['ETHN']) plt.figure(figsize=(6, 6)); colors = ['purple', 'green'] markers = ['o', 'v'] for factor, group in factor_group: plt.scatter(group['TEST'], group['JPERF'], color=colors[factor], marker=markers[factor], s=12**2) plt.xlabel('TEST'); #@savefig group_test.png align=center plt.ylabel('JPERF'); min_lm = ols('JPERF ~ TEST', data=minority_table).fit() print min_lm.summary() plt.figure(figsize=(6, 6)); for factor, group in factor_group: plt.scatter(group['TEST'], group['JPERF'], color=colors[factor], marker=markers[factor], s=12**2) plt.xlabel('TEST') plt.ylabel('JPERF') #@savefig abline.png align=center abline_plot(model_results=min_lm, ax=plt.gca()) min_lm2 = ols('JPERF ~ TEST + TEST:ETHN', data=minority_table).fit() print min_lm2.summary() plt.figure(figsize=(6, 6)); for factor, group in factor_group: plt.scatter(group['TEST'], group['JPERF'], color=colors[factor], marker=markers[factor], s=12**2) abline_plot(intercept=min_lm2.params['Intercept'], slope=min_lm2.params['TEST'], ax=plt.gca(), color='purple') #@savefig abline2.png align=center abline_plot(intercept=min_lm2.params['Intercept'], slope=min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'], ax=plt.gca(), color='green') min_lm3 = ols('JPERF ~ TEST + ETHN', data=minority_table).fit() print min_lm3.summary() plt.figure(figsize=(6, 6)); for factor, group in factor_group: plt.scatter(group['TEST'], group['JPERF'], color=colors[factor], marker=markers[factor], s=12**2) abline_plot(intercept=min_lm3.params['Intercept'], slope=min_lm3.params['TEST'], ax=plt.gca(), color='purple') #@savefig abline3.png align=center abline_plot(intercept=min_lm3.params['Intercept'] + min_lm3.params['ETHN'], slope=min_lm3.params['TEST'], ax=plt.gca(), color='green') min_lm4 = ols('JPERF ~ TEST * ETHN', data=minority_table).fit() print min_lm4.summary() plt.figure(figsize=(6, 6)); for factor, group in factor_group: plt.scatter(group['TEST'], group['JPERF'], color=colors[factor], marker=markers[factor], s=12**2) abline_plot(intercept=min_lm4.params['Intercept'], slope=min_lm4.params['TEST'], ax=plt.gca(), color='purple') #@savefig abline4.png align=center abline_plot(intercept=min_lm4.params['Intercept'] + min_lm4.params['ETHN'], slope=min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'], ax=plt.gca(), color='green') # is there any effect of ETHN on slope or intercept table5 = anova_lm(min_lm, min_lm4) print table5 # is there any effect of ETHN on intercept table6 = anova_lm(min_lm, min_lm3) print table6 # is there any effect of ETHN on slope table7 = anova_lm(min_lm, min_lm2) print table7 # is it just the slope or both? table8 = anova_lm(min_lm2, min_lm4) print table8 # One-way ANOVA # ------------- try: rehab_table = pandas.read_csv('rehab.table') except: url = 'http://stats191.stanford.edu/data/rehab.csv' #the next line is not necessary with recent version of pandas url = urlopen(url) rehab_table = pandas.read_table(url, delimiter=",") rehab_table.to_csv('rehab.table') plt.figure(figsize=(6, 6)); #@savefig plot_boxplot.png align=center rehab_table.boxplot('Time', 'Fitness', ax=plt.gca()) rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit() table9 = anova_lm(rehab_lm) print table9 print rehab_lm.model.data.orig_exog print rehab_lm.summary() # Two-way ANOVA # ------------- try: kidney_table = pandas.read_table('./kidney.table') except: url = 'http://stats191.stanford.edu/data/kidney.table' #the next line is not necessary with recent version of pandas url = urlopen(url) kidney_table = pandas.read_table(url, delimiter=" *") # Explore the dataset print kidney_table.groupby(['Weight', 'Duration']).size() # balanced panel kt = kidney_table plt.figure(figsize=(6, 6)); #@savefig kidney_interactiong.png align=center interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days'] + 1), colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=plt.gca()) # You have things available in the calling namespace available # in the formula evaluation namespace kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit() table10 = anova_lm(kidney_lm) print anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)', data=kt).fit(), kidney_lm) print anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(), ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)', data=kt).fit()) print anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(), ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)', data=kt).fit()) # Sum of squares # -------------- # # Illustrates the use of different types of sums of squares (I,II,II) # and how the Sum contrast can be used to produce the same output between # the 3. # Types I and II are equivalent under a balanced design. # Don't use Type III with non-orthogonal contrast - ie., Treatment sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)', data=kt).fit() print anova_lm(sum_lm) print anova_lm(sum_lm, typ=2) print anova_lm(sum_lm, typ=3) nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)', data=kt).fit() print anova_lm(nosum_lm) print anova_lm(nosum_lm, typ=2) print anova_lm(nosum_lm, typ=3) statsmodels-0.5.0+git13-g8e07d34/examples/example_ols.py000066400000000000000000000122751224417117700226610ustar00rootroot00000000000000"""Ordinary Least Squares """ import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std np.random.seed(9876789) #OLS Estimation #-------------- #Artificial data #^^^^^^^^^^^^^^^^ nsample = 100 x = np.linspace(0, 10, 100) X = np.column_stack((x, x**2)) beta = np.array([1, 0.1, 10]) e = np.random.normal(size=nsample) # Our model needs an intercept so we add a column of 1s: X = sm.add_constant(X, prepend=False) y = np.dot(X, beta) + e # Inspect data print X[:5, :] print y[:5] #Fit and summary #^^^^^^^^^^^^^^^ model = sm.OLS(y, X) results = model.fit() print results.summary() # Quantities of interest can be extracted directly from the fitted model. Type # ``dir(results)`` for a full list. Here are some examples: print results.params print results.rsquared #OLS non-linear curve but linear in parameters #--------------------------------------------- #Artificial data #^^^^^^^^^^^^^^^ # Non-linear relationship between x and y nsample = 50 sig = 0.5 x = np.linspace(0, 20, nsample) X = np.c_[x, np.sin(x), (x - 5)**2, np.ones(nsample)] beta = [0.5, 0.5, -0.02, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) # Fit and summary #^^^^^^^^^^^^^^^^ res = sm.OLS(y, X).fit() print res.summary() # Extract other quantities of interest print res.params print res.bse print res.predict() # Draw a plot to compare the true relationship to OLS predictions. Confidence # intervals around the predictions are built using the ``wls_prediction_std`` # command. plt.figure(); plt.plot(x, y, 'o', x, y_true, 'b-'); prstd, iv_l, iv_u = wls_prediction_std(res); plt.plot(x, res.fittedvalues, 'r--.'); plt.plot(x, iv_u, 'r--'); plt.plot(x, iv_l, 'r--'); #@savefig ols_predict_0.png plt.title('blue: true, red: OLS'); #OLS with dummy variables #------------------------ #Artificial data #^^^^^^^^^^^^^^^^ # We create 3 groups which will be modelled using dummy variables. Group 0 is # the omitted/benchmark category. nsample = 50 groups = np.zeros(nsample, int) groups[20:40] = 1 groups[40:] = 2 dummy = (groups[:, None] == np.unique(groups)).astype(float) x = np.linspace(0, 20, nsample) X = np.c_[x, dummy[:, 1:], np.ones(nsample)] beta = [1., 3, -3, 10] y_true = np.dot(X, beta) e = np.random.normal(size=nsample) y = y_true + e # Inspect the data print X[:5, :] print y[:5] print groups print dummy[:5, :] #Fit and summary #^^^^^^^^^^^^^^^ res2 = sm.OLS(y, X).fit() print res.summary() print res2.params print res2.bse print res.predict() # Draw a plot to compare the true relationship to OLS predictions. prstd, iv_l, iv_u = wls_prediction_std(res2); plt.figure(); plt.plot(x, y, 'o', x, y_true, 'b-'); plt.plot(x, res2.fittedvalues, 'r--.'); plt.plot(x, iv_u, 'r--'); plt.plot(x, iv_l, 'r--'); #@savefig ols_predict_1.png plt.title('blue: true, red: OLS'); #Joint hypothesis tests #---------------------- #F test #^^^^^^ # We want to test the hypothesis that both coefficients on the dummy variables # are equal to zero, that is, :math:`R \times \beta = 0`. An F test leads us to # strongly reject the null hypothesis of identical constant in the 3 groups: R = [[0, 1, 0, 0], [0, 0, 1, 0]] print np.array(R) print res2.f_test(R) #T test #^^^^^^ # We want to test the null hypothesis that the effects of the 2nd and 3rd # groups add to zero. The T-test allows us to reject the Null (but note the # one-sided p-value): R = [0, 1, -1, 0] print res2.t_test(R) #Small group effects #^^^^^^^^^^^^^^^^^^^ # If we generate artificial data with smaller group effects, the T test can no # longer reject the Null hypothesis: beta = [1., 0.3, -0.0, 10] y_true = np.dot(X, beta) y = y_true + np.random.normal(size=nsample) res3 = sm.OLS(y, X).fit() print res3.f_test(R) #Multicollinearity #----------------- #Data #^^^^ # The Longley dataset is well known to have high multicollinearity, that is, # the exogenous predictors are highly correlated. This is problematic because # it can affect the stability of our coefficient estimates as we make minor # changes to model specification. from statsmodels.datasets.longley import load y = load().endog X = load().exog X = sm.tools.add_constant(X, prepend=False) #Fit and summary #^^^^^^^^^^^^^^^ ols_model = sm.OLS(y, X) ols_results = ols_model.fit() print ols_results.summary() #Condition number #^^^^^^^^^^^^^^^^ # One way to assess multicollinearity is to compute the condition number. # Values over 20 are worrisome (see Greene 4.9). The first step is to normalize # the independent variables to have unit length: norm_x = np.ones_like(X) for i in range(int(ols_model.df_model)): norm_x[:, i] = X[:, i] / np.linalg.norm(X[:, i]) norm_xtx = np.dot(norm_x.T, norm_x) # Then, we take the square root of the ratio of the biggest to the smallest # eigen values. eigs = np.linalg.eigvals(norm_xtx) condition_number = np.sqrt(eigs.max() / eigs.min()) print condition_number #Dropping an observation #^^^^^^^^^^^^^^^^^^^^^^^ # Greene also points out that dropping a single observation can have a dramatic # effect on the coefficient estimates: ols_results2 = sm.OLS(y[:-1], X[:-1, :]).fit() res_dropped = ols_results.params / ols_results2.params * 100 - 100 print 'Percentage change %4.2f%%\n' * 7 % tuple(i for i in res_dropped) statsmodels-0.5.0+git13-g8e07d34/examples/example_ols_table.py000066400000000000000000000037151224417117700240270ustar00rootroot00000000000000"""Example: statsmodels.OLS """ from statsmodels.datasets.longley import load import statsmodels.api as sm from statsmodels.iolib.table import SimpleTable, default_txt_fmt import numpy as np data = load() data_orig = (data.endog.copy(), data.exog.copy()) #.. Note: In this example using zscored/standardized variables has no effect on #.. regression estimates. Are there no numerical problems? rescale = 0 #0: no rescaling, 1:demean, 2:standardize, 3:standardize and transform back rescale_ratio = data.endog.std() / data.exog.std(0) if rescale > 0: # rescaling data.endog -= data.endog.mean() data.exog -= data.exog.mean(0) if rescale > 1: data.endog /= data.endog.std() data.exog /= data.exog.std(0) #skip because mean has been removed, but dimension is hardcoded in table data.exog = sm.tools.add_constant(data.exog, prepend=False) ols_model = sm.OLS(data.endog, data.exog) ols_results = ols_model.fit() # the Longley dataset is well known to have high multicollinearity # one way to find the condition number is as follows #Find OLS parameters for model with one explanatory variable dropped resparams = np.nan * np.ones((7, 7)) res = sm.OLS(data.endog, data.exog).fit() resparams[:, 0] = res.params indall = range(7) for i in range(6): ind = indall[:] del ind[i] res = sm.OLS(data.endog, data.exog[:, ind]).fit() resparams[ind, i + 1] = res.params if rescale == 1: pass if rescale == 3: resparams[:-1, :] *= rescale_ratio[:, None] txt_fmt1 = default_txt_fmt numformat = '%10.4f' txt_fmt1 = dict(data_fmts=[numformat]) rowstubs = data.names[1:] + ['const'] headers = ['all'] + ['drop %s' % name for name in data.names[1:]] tabl = SimpleTable(resparams, headers, rowstubs, txt_fmt=txt_fmt1) nanstring = numformat % np.nan nn = len(nanstring) nanrep = ' ' * (nn - 1) nanrep = nanrep[:nn // 2] + '-' + nanrep[nn // 2:] print 'Longley data - sensitivity to dropping an explanatory variable' print str(tabl).replace(nanstring, nanrep) statsmodels-0.5.0+git13-g8e07d34/examples/example_ols_tftest.py000066400000000000000000000135401224417117700242460ustar00rootroot00000000000000"""examples for usage of F-test on linear restrictions in OLS linear restriction is R \beta = 0 R is (nr,nk), beta is (nk,1) (in matrix notation) TODO: clean this up for readability and explain Notes ----- This example was written mostly for cross-checks and refactoring. """ import numpy as np import numpy.testing as npt import statsmodels.api as sm print '\n\n Example 1: Longley Data, high multicollinearity' data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=False) res = sm.OLS(data.endog, data.exog).fit() # test pairwise equality of some coefficients R2 = [[0, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 1, -1, 0]] Ftest = res.f_test(R2) print repr((Ftest.fvalue, Ftest.pvalue)) # use repr to get more digits # 9.740461873303655 0.0056052885317360301 ##Compare to R (after running R_lm.s in the longley folder) ## ##> library(car) ##> linear.hypothesis(m1, c("GNP = UNEMP","POP = YEAR")) ##Linear hypothesis test ## ##Hypothesis: ##GNP - UNEMP = 0 ##POP - YEAR = 0 ## ##Model 1: TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR ##Model 2: restricted model ## ## Res.Df RSS Df Sum of Sq F Pr(>F) ##1 9 836424 ##2 11 2646903 -2 -1810479 9.7405 0.005605 ** print 'Regression Results Summary' print res.summary() print '\n F-test whether all variables have zero effect' R = np.eye(7)[:-1, :] Ftest0 = res.f_test(R) print repr((Ftest0.fvalue, Ftest0.pvalue)) print '%r' % res.fvalue npt.assert_almost_equal(res.fvalue, Ftest0.fvalue, decimal=9) ttest0 = res.t_test(R[0, :]) print repr((ttest0.tvalue, ttest0.pvalue)) betatval = res.tvalues betatval[0] npt.assert_almost_equal(betatval[0], ttest0.tvalue, decimal=15) """ # several ttests at the same time # currently not checked for this, but it (kind of) works >>> ttest0 = res.t_test(R[:2,:]) >>> print repr((ttest0.t, ttest0.pvalue)) (array([[ 0.17737603, NaN], [ NaN, -1.06951632]]), array([[ 0.43157042, 1. ], [ 1. , 0.84365947]])) >>> ttest0 = res.t_test(R) >>> ttest0.t array([[ 1.77376028e-01, NaN, NaN, NaN, -1.43660623e-02, 2.15494063e+01], [ NaN, -1.06951632e+00, -1.62440215e+01, -1.78173553e+01, NaN, NaN], [ NaN, -2.88010561e-01, -4.13642736e+00, -4.06097408e+00, NaN, NaN], [ NaN, -6.17679489e-01, -7.94027056e+00, -4.82198531e+00, NaN, NaN], [ 4.23409809e+00, NaN, NaN, NaN, -2.26051145e-01, 2.89324928e+02], [ 1.77445341e-01, NaN, NaN, NaN, -8.08336103e-03, 4.01588981e+00]]) >>> betatval array([ 0.17737603, -1.06951632, -4.13642736, -4.82198531, -0.22605114, 4.01588981, -3.91080292]) >>> ttest0.t array([ 0.17737603, -1.06951632, -4.13642736, -4.82198531, -0.22605114, 4.01588981]) """ print '\nsimultaneous t-tests' ttest0 = res.t_test(R2) t2 = ttest0.tvalue print ttest0.tvalue print t2 t2a = np.r_[res.t_test(np.array(R2)[0, :]).tvalue, res.t_test(np.array(R2)[1, :]).tvalue] print t2 - t2a t2pval = ttest0.pvalue print '%r' % t2pval # reject # array([ 9.33832896e-04, 9.98483623e-01]) print 'reject' print '%r' % (t2pval < 0.05) # f_test needs 2-d currently Ftest2a = res.f_test(np.asarray(R2)[:1, :]) print repr((Ftest2a.fvalue, Ftest2a.pvalue)) Ftest2b = res.f_test(np.asarray(R2)[1:2, :]) print repr((Ftest2b.fvalue, Ftest2b.pvalue)) print '\nequality of t-test and F-test' print t2a**2 - np.array((Ftest2a.fvalue, Ftest2b.fvalue)) npt.assert_almost_equal(t2a**2, np.vstack((Ftest2a.fvalue, Ftest2b.fvalue))) #npt.assert_almost_equal(t2pval, np.array((Ftest2a.pvalue, Ftest2b.pvalue))) npt.assert_almost_equal(t2pval * 2, np.c_[Ftest2a.pvalue, Ftest2b.pvalue].squeeze()) print '\n\n Example 2: Artificial Data' nsample = 100 ncat = 4 sigma = 2 xcat = np.linspace(0, ncat - 1, nsample).round()[:, np.newaxis] dummyvar = (xcat == np.arange(ncat)).astype(float) beta = np.array([0., 2, -2, 1])[:, np.newaxis] ytrue = np.dot(dummyvar, beta) X = sm.tools.add_constant(dummyvar[:, :-1], prepend=False) y = ytrue + sigma * np.random.randn(nsample, 1) mod2 = sm.OLS(y[:, 0], X) res2 = mod2.fit() print res2.summary() R3 = np.eye(ncat)[:-1, :] Ftest = res2.f_test(R3) print repr((Ftest.fvalue, Ftest.pvalue)) R3 = np.atleast_2d([0, 1, -1, 2]) Ftest = res2.f_test(R3) print repr((Ftest.fvalue, Ftest.pvalue)) print 'simultaneous t-test for zero effects' R4 = np.eye(ncat)[:-1, :] ttest = res2.t_test(R4) print repr((ttest.tvalue, ttest.pvalue)) R5 = np.atleast_2d([0, 1, 1, 2]) np.dot(R5, res2.params) Ftest = res2.f_test(R5) print repr((Ftest.fvalue, Ftest.pvalue)) ttest = res2.t_test(R5) #print repr((ttest.t, ttest.pvalue)) print repr((ttest.tvalue, ttest.pvalue)) R6 = np.atleast_2d([1, -1, 0, 0]) np.dot(R6, res2.params) Ftest = res2.f_test(R6) print repr((Ftest.fvalue, Ftest.pvalue)) ttest = res2.t_test(R6) #print repr((ttest.t, ttest.pvalue)) print repr((ttest.tvalue, ttest.pvalue)) R7 = np.atleast_2d([1, 0, 0, 0]) np.dot(R7, res2.params) Ftest = res2.f_test(R7) print repr((Ftest.fvalue, Ftest.pvalue)) ttest = res2.t_test(R7) #print repr((ttest.t, ttest.pvalue)) print repr((ttest.tvalue, ttest.pvalue)) print '\nExample: 2 categories: replicate stats.glm and stats.ttest_ind' mod2 = sm.OLS(y[xcat.flat < 2][:, 0], X[xcat.flat < 2, :][:, (0, -1)]) res2 = mod2.fit() R8 = np.atleast_2d([1, 0]) np.dot(R8, res2.params) Ftest = res2.f_test(R8) print repr((Ftest.fvalue, Ftest.pvalue)) print repr((np.sqrt(Ftest.fvalue), Ftest.pvalue)) ttest = res2.t_test(R8) #print repr(ttest.t), ttest.pvalue)) print repr((ttest.tvalue, ttest.pvalue)) from scipy import stats print stats.glm(y[xcat < 2].ravel(), xcat[xcat < 2].ravel()) print stats.ttest_ind(y[xcat == 0], y[xcat == 1]) #TODO: compare with f_oneway statsmodels-0.5.0+git13-g8e07d34/examples/example_predict.py000066400000000000000000000017431224417117700235140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Out of sample prediction """ import numpy as np import statsmodels.api as sm #Create some data nsample = 50 sig = 0.25 x1 = np.linspace(0, 20, nsample) X = np.c_[x1, np.sin(x1), (x1 - 5)**2, np.ones(nsample)] beta = [0.5, 0.5, -0.02, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) #Setup and estimate the model olsmod = sm.OLS(y, X) olsres = olsmod.fit() print olsres.params print olsres.bse #In-sample prediction ypred = olsres.predict(X) #Create a new sample of explanatory variables Xnew, predict and plot x1n = np.linspace(20.5, 25, 10) Xnew = np.c_[x1n, np.sin(x1n), (x1n - 5)**2, np.ones(10)] ynewpred = olsres.predict(Xnew) # predict out of sample print ypred import matplotlib.pyplot as plt plt.figure(); plt.plot(x1, y, 'o', x1, y_true, 'b-'); plt.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r'); #@savefig ols_predict.png plt.title('OLS prediction, blue: true and data, fitted/predicted values:red'); statsmodels-0.5.0+git13-g8e07d34/examples/example_rlm.py000066400000000000000000000060101224417117700226440ustar00rootroot00000000000000""" Robust Linear Models Notes ----- The syntax for the arguments will be shortened to accept string arguments in the future. """ import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std #Estimating RLM #-------------- # Load data data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) # Huber's T norm with the (default) median absolute deviation scaling huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) hub_results = huber_t.fit() print hub_results.params print hub_results.bse varnames = ['var_%d' % i for i in range(len(hub_results.params))] print hub_results.summary(yname='y', xname=varnames) # Huber's T norm with 'H2' covariance matrix hub_results2 = huber_t.fit(cov="H2") print hub_results2.params print hub_results2.bse # Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave()) andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov='H3') print andrew_results.params # See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for # scale options #Comparing OLS and RLM #--------------------- #Artificial data #^^^^^^^^^^^^^^^ nsample = 50 x1 = np.linspace(0, 20, nsample) X = np.c_[x1, (x1 - 5)**2, np.ones(nsample)] sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger beta = [0.5, -0.0, 5.] y_true2 = np.dot(X, beta) y2 = y_true2 + sig * 1. * np.random.normal(size=nsample) y2[[39, 41, 43, 45, 48]] -= 5 # add some outliers (10% of nsample) #Example: quadratic function with linear truth #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Note that the quadratic term in OLS regression will capture outlier effects. res = sm.OLS(y2, X).fit() print res.params print res.bse print res.predict # Estimate RLM resrlm = sm.RLM(y2, X).fit() print resrlm.params print resrlm.bse # Draw a plot to compare OLS estimates to the robust estimates plt.figure(); plt.plot(x1, y2, 'o', x1, y_true2, 'b-'); prstd, iv_l, iv_u = wls_prediction_std(res); plt.plot(x1, res.fittedvalues, 'r-'); plt.plot(x1, iv_u, 'r--'); plt.plot(x1, iv_l, 'r--'); plt.plot(x1, resrlm.fittedvalues, 'g.-'); #@savefig rlm_ols_0.png plt.title('blue: true, red: OLS, green: RLM'); #Example: linear function with linear truth #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Fit a new OLS model using only the linear term and the constant X2 = X[:, [0, 2]] res2 = sm.OLS(y2, X2).fit() print res2.params print res2.bse # Estimate RLM resrlm2 = sm.RLM(y2, X2).fit() print resrlm2.params print resrlm2.bse # Draw a plot to compare OLS estimates to the robust estimates prstd, iv_l, iv_u = wls_prediction_std(res2) plt.figure(); plt.plot(x1, y2, 'o', x1, y_true2, 'b-'); plt.plot(x1, res2.fittedvalues, 'r-'); plt.plot(x1, iv_u, 'r--'); plt.plot(x1, iv_l, 'r--'); plt.plot(x1, resrlm2.fittedvalues, 'g.-'); #@savefig rlm_ols_1.png plt.title('blue: true, red: OLS, green: RLM'); statsmodels-0.5.0+git13-g8e07d34/examples/example_wls.py000066400000000000000000000053711224417117700226700ustar00rootroot00000000000000"""Weighted Least Squares """ import numpy as np from scipy import stats import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std from statsmodels.iolib.table import (SimpleTable, default_txt_fmt) np.random.seed(1024) # WLS Estimation # -------------- # Artificial data: Heteroscedasticity 2 groups # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # Model assumptions: # # * Misspecificaion: true model is quadratic, estimate only linear # * Independent noise/error term # * Two groups for error variance, low and high variance groups nsample = 50 x = np.linspace(0, 20, nsample) X = np.c_[x, (x - 5)**2, np.ones(nsample)] beta = [0.5, -0.01, 5.] sig = 0.5 w = np.ones(nsample) w[nsample * 6 / 10:] = 3 y_true = np.dot(X, beta) e = np.random.normal(size=nsample) y = y_true + sig * w * e X = X[:, [0, 2]] #WLS knowing the true variance ratio of heteroscedasticity #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ mod_wls = sm.WLS(y, X, weights=1. / w) res_wls = mod_wls.fit() print res_wls.summary() #OLS vs. WLS #----------- # Estimate an OLS model for comparison res_ols = sm.OLS(y, X).fit() # Compare the estimated parameters in WLS and OLS print res_ols.params print res_wls.params # Compare the WLS standard errors to heteroscedasticity corrected OLS standard # errors: se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se], [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]]) se = np.round(se, 4) colnames = 'x1', 'const' rownames = 'WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3' tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt) print tabl # Calculate OLS prediction interval covb = res_ols.cov_params() prediction_var = res_ols.mse_resid + (X * np.dot(covb, X.T).T).sum(1) prediction_std = np.sqrt(prediction_var) tppf = stats.t.ppf(0.975, res_ols.df_resid) # Draw a plot to compare predicted values in WLS and OLS: prstd, iv_l, iv_u = wls_prediction_std(res_wls) plt.figure(); plt.plot(x, y, 'o', x, y_true, 'b-'); plt.plot(x, res_ols.fittedvalues, 'r--'); plt.plot(x, res_ols.fittedvalues + tppf * prediction_std, 'r--'); plt.plot(x, res_ols.fittedvalues - tppf * prediction_std, 'r--'); plt.plot(x, res_wls.fittedvalues, 'g--.'); plt.plot(x, iv_u, 'g--'); plt.plot(x, iv_l, 'g--'); #@savefig wls_ols_0.png plt.title('blue: true, red: OLS, green: WLS'); # Feasible Weighted Least Squares (2-stage FWLS) # ---------------------------------------------- resid1 = res_ols.resid[w == 1.] var1 = resid1.var(ddof=int(res_ols.df_model) + 1) resid2 = res_ols.resid[w != 1.] var2 = resid2.var(ddof=int(res_ols.df_model) + 1) w_est = w.copy() w_est[w != 1.] = np.sqrt(var2) / np.sqrt(var1) res_fwls = sm.WLS(y, X, 1. / w_est).fit() print res_fwls.summary() statsmodels-0.5.0+git13-g8e07d34/examples/ipynb/000077500000000000000000000000001224417117700211115ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/examples/ipynb/example_quantreg.ipynb000066400000000000000000000142421224417117700255200ustar00rootroot00000000000000{ "metadata": { "name": "example_quantreg" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quantile regression\n", "\n", "This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n", "\n", "* Koenker, Roger and Kevin F. Hallock. \"Quantile Regressioin\". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143\u2013156\n", "\n", "We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). \n", "\n", "## Setup\n", "\n", "We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import patsy\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "import matplotlib.pyplot as plt\n", "from statsmodels.regression.quantile_regression import QuantReg\n", "\n", "data = sm.datasets.engel.load_pandas().data\n", "data.head()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Least Absolute Deviation\n", "\n", "The LAD model is a special case of quantile regression where q=0.5" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = smf.quantreg('foodexp ~ income', data)\n", "res = mod.fit(q=.5)\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the results\n", "\n", "We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare data for plotting\n", "\n", "For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary." ] }, { "cell_type": "code", "collapsed": false, "input": [ "quantiles = np.arange(.05, .96, .1)\n", "def fit_model(q):\n", " res = mod.fit(q=q)\n", " return [q, res.params['Intercept'], res.params['income']] + \\\n", " res.conf_int().ix['income'].tolist()\n", " \n", "models = [fit_model(x) for x in quantiles]\n", "models = pd.DataFrame(models, columns=['q', 'a', 'b','lb','ub'])\n", "\n", "ols = smf.ols('foodexp ~ income', data).fit()\n", "ols_ci = ols.conf_int().ix['income'].tolist()\n", "ols = dict(a = ols.params['Intercept'],\n", " b = ols.params['income'],\n", " lb = ols_ci[0],\n", " ub = ols_ci[1])\n", "\n", "print models\n", "print ols" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First plot\n", "\n", "This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n", "\n", "1. Food expenditure increases with income\n", "2. The *dispersion* of food expenditure increases with income\n", "3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.arange(data.income.min(), data.income.max(), 50)\n", "get_y = lambda a, b: a + b * x\n", "\n", "for i in range(models.shape[0]):\n", " y = get_y(models.a[i], models.b[i])\n", " plt.plot(x, y, linestyle='dotted', color='grey')\n", " \n", "y = get_y(ols['a'], ols['b'])\n", "plt.plot(x, y, color='red', label='OLS')\n", "\n", "plt.scatter(data.income, data.foodexp, alpha=.2)\n", "plt.xlim((240, 3000))\n", "plt.ylim((240, 2000))\n", "plt.legend()\n", "plt.xlabel('Income')\n", "plt.ylabel('Food expenditure')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second plot\n", "\n", "The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confindence interval.\n", "\n", "In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import rc\n", "rc('text', usetex=True)\n", "n = models.shape[0]\n", "p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n", "p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')\n", "p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')\n", "p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n", "p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')\n", "p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')\n", "plt.ylabel(r'\\beta_\\mbox{income}')\n", "plt.xlabel('Quantiles of the conditional food expenditure distribution')\n", "plt.legend()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/000077500000000000000000000000001224417117700217735ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/contrasts.ipynb000066400000000000000000000372121224417117700250630ustar00rootroot00000000000000{ "metadata": { "name": "contrasts" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Contrasts Overview" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This document is based heavily on this excellent resource from UCLA http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.\n", "\n", "In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model's intercept. On the other hand, a set of *contrasts* for a categorical variable with `k` levels is a set of `k-1` functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding isn't wrong *per se*. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context.\n", "\n", "To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let's load the data." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Example Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas\n", "url = 'http://www.ats.ucla.edu/stat/data/hsb2.csv'\n", "hsb2 = pandas.read_table(url, delimiter=\",\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian))." ] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2.groupby('race')['write'].mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Treatment (Dummy) Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import Treatment\n", "levels = [1,2,3,4]\n", "contrast = Treatment(reference=0).code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "Here we used `reference=0`, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let's look at how this would encode the `race` variable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2.race.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print contrast.matrix[hsb2.race-1, :][:20]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.categorical(hsb2.race.values)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "This is a bit of a trick, as the `race` category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won't work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.formula.api import ols\n", "mod = ols(\"write ~ C(race, Treatment)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Simple Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. Patsy doesn't have the Simple contrast included, but you can easily define your own contrasts. To do so, write a class that contains a code_with_intercept and a code_without_intercept method that returns a patsy.contrast.ContrastMatrix instance" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import ContrastMatrix\n", "\n", "def _name_levels(prefix, levels):\n", " return [\"[%s%s]\" % (prefix, level) for level in levels]\n", "\n", "class Simple(object):\n", " def _simple_contrast(self, levels):\n", " nlevels = len(levels)\n", " contr = -1./nlevels * np.ones((nlevels, nlevels-1))\n", " contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels\n", " return contr\n", "\n", " def code_with_intercept(self, levels):\n", " contrast = np.column_stack((np.ones(len(levels)),\n", " self._simple_contrast(levels)))\n", " return ContrastMatrix(contrast, _name_levels(\"Simp.\", levels))\n", "\n", " def code_without_intercept(self, levels):\n", " contrast = self._simple_contrast(levels)\n", " return ContrastMatrix(contrast, _name_levels(\"Simp.\", levels[:-1]))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2.groupby('race')['write'].mean().mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "contrast = Simple().code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(\"write ~ C(race, Simple)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Sum (Deviation) Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import Sum\n", "contrast = Sum().code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(\"write ~ C(race, Sum)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "This corresponds to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level." ] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2.groupby('race')['write'].mean().mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Backward Difference Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import Diff\n", "contrast = Diff().code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(\"write ~ C(race, Diff)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "For example, here the coefficient on level 1 is the mean of `write` at level 2 compared with the mean at level 1. Ie.," ] }, { "cell_type": "code", "collapsed": false, "input": [ "res.params[\"C(race, Diff)[D.1]\"]\n", "hsb2.groupby('race').mean()[\"write\"][2] - \\\n", " hsb2.groupby('race').mean()[\"write\"][1]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Helmert Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name 'reverse' being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import Helmert\n", "contrast = Helmert().code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(\"write ~ C(race, Helmert)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4" ] }, { "cell_type": "code", "collapsed": false, "input": [ "grouped = hsb2.groupby('race')\n", "grouped.mean()[\"write\"][4] - grouped.mean()[\"write\"][:3].mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same." ] }, { "cell_type": "code", "collapsed": false, "input": [ "k = 4\n", "1./k * (grouped.mean()[\"write\"][k] - grouped.mean()[\"write\"][:k-1].mean())\n", "k = 3\n", "1./k * (grouped.mean()[\"write\"][k] - grouped.mean()[\"write\"][:k-1].mean())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Orthogonal Polynomial Coding" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The coefficients taken on by polynomial coding for `k=4` levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order `k-1`. Since `race` is not an ordered factor variable let's use `read` as an example. First we need to create an ordered categorical from `read`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "hsb2['readcat'] = pandas.cut(hsb2.read, bins=3)\n", "hsb2.groupby('readcat').mean()['write']" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from patsy.contrasts import Poly\n", "levels = hsb2.readcat.unique().tolist()\n", "contrast = Poly().code_without_intercept(levels)\n", "print contrast.matrix" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(\"write ~ C(readcat, Poly)\", data=hsb2)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "As you can see, readcat has a significant linear effect on the dependent variable `write` but not a significant quadratic or cubic effect." ] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/discrete_choice.ipynb000066400000000000000000000360051224417117700261560ustar00rootroot00000000000000{ "metadata": { "name": "discrete_choice" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Discrete Choice Models" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Fair's Affair data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A survey of women only was conducted in 1974 by *Redbook* asking about extramarital affairs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import logit, probit, poisson, ols" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.fair.SOURCE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.fair.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.fair.load_pandas().data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta['affair'] = (dta['affairs'] > 0).astype(float)\n", "print dta.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print dta.describe()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "affair_mod = logit(\"affair ~ occupation + educ + occupation_husb\" \n", " \"+ rate_marriage + age + yrs_married + children\"\n", " \" + religious\", dta).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print affair_mod.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "How well are we predicting?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "affair_mod.pred_table()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mfx = affair_mod.get_margeff()\n", "print mfx.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "respondent1000 = dta.ix[1000]\n", "print respondent1000" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "resp = dict(zip(range(1,9), respondent1000[[\"occupation\", \"educ\", \n", " \"occupation_husb\", \"rate_marriage\", \n", " \"age\", \"yrs_married\", \"children\", \n", " \"religious\"]].tolist()))\n", "resp.update({0 : 1})\n", "print resp" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mfx = affair_mod.get_margeff(atexog=resp)\n", "print mfx.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "affair_mod.predict(respondent1000)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "affair_mod.fittedvalues[1000]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "affair_mod.model.cdf(affair_mod.fittedvalues[1000])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "The \"correct\" model here is likely the Tobit model. We have an work in progress branch \"tobit-model\" on github, if anyone is interested in censored regression models." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Exercise: Logit vs Probit" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "support = np.linspace(-6, 6, 1000)\n", "ax.plot(support, stats.logistic.cdf(support), 'r-', label='Logistic')\n", "ax.plot(support, stats.norm.cdf(support), label='Probit')\n", "ax.legend();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "support = np.linspace(-6, 6, 1000)\n", "ax.plot(support, stats.logistic.pdf(support), 'r-', label='Logistic')\n", "ax.plot(support, stats.norm.pdf(support), label='Probit')\n", "ax.legend();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Genarlized Linear Model Example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.star98.SOURCE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.star98.DESCRLONG" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.star98.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.star98.load_pandas().data\n", "print dta.columns" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "formula = 'NABOVE + NBELOW ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT '\n", "formula += '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Aside: Binomial distribution" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Toss a six-sided die 5 times, what's the probability of exactly 2 fours?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stats.binom(5, 1./6).pmf(2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.misc import comb\n", "comb(5,2) * (1/6.)**2 * (5/6.)**3" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.formula.api import glm\n", "glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print glm_mod.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "The number of trials " ] }, { "cell_type": "code", "collapsed": false, "input": [ "glm_mod.model.data.orig_endog.sum(1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact\n", "on the response variables:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "exog = glm_mod.model.data.orig_exog # get the dataframe" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "means25 = exog.mean()\n", "print means25" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "means25['LOWINC'] = exog['LOWINC'].quantile(.25)\n", "print means25" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "means75 = exog.mean()\n", "means75['LOWINC'] = exog['LOWINC'].quantile(.75)\n", "print means75" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "resp25 = glm_mod.predict(means25)\n", "resp75 = glm_mod.predict(means75)\n", "diff = resp75 - resp25" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "The interquartile first difference for the percentage of low income households in a school district is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"%2.4f%%\" % (diff[0]*100)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "nobs = glm_mod.nobs\n", "y = glm_mod.model.endog\n", "yhat = glm_mod.mu" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.graphics.api import abline_plot\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, ylabel='Observed Values', xlabel='Fitted Values')\n", "ax.scatter(yhat, y)\n", "y_vs_yhat = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n", "fig = abline_plot(model_results=y_vs_yhat, ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Plot fitted values vs Pearson residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pearson residuals are defined to be \n", "\n", "$$\\frac{(y - \\mu)}{\\sqrt{(var(\\mu))}}$$\n", "\n", "where var is typically determined by the family. E.g., binomial variance is $np(1 - p)$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, title='Residual Dependence Plot', xlabel='Fitted Values',\n", " ylabel='Pearson Residuals')\n", "ax.scatter(yhat, stats.zscore(glm_mod.resid_pearson))\n", "ax.axis('tight')\n", "ax.plot([0.0, 1.0],[0.0, 0.0], 'k-');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Histogram of standardized deviance residuals with Kernel Density Estimate overlayed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The definition of the deviance residuals depends on the family. For the Binomial distribution this is \n", "\n", "$$r_{dev} = sign\\(Y-\\mu\\)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n", "\n", "They can be used to detect ill-fitting covariates" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resid = glm_mod.resid_deviance\n", "resid_std = stats.zscore(resid) \n", "kde_resid = sm.nonparametric.KDEUnivariate(resid_std)\n", "kde_resid.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, title=\"Standardized Deviance Residuals\")\n", "ax.hist(resid_std, bins=25, normed=True);\n", "ax.plot(kde_resid.support, kde_resid.density, 'r');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "QQ-plot of deviance residuals" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "fig = sm.graphics.qqplot(resid, line='r', ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_arma.ipynb000066400000000000000000000042111224417117700254670ustar00rootroot00000000000000{ "metadata": { "name": "ex_arma2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Autoregressive Moving Average (ARMA) Model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm\n", "from statsmodels.tsa.arima_process import arma_generate_sample\n", "np.random.seed(12345)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate some data from an ARMA process:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "arparams = np.array([.75, -.25])\n", "maparams = np.array([.65, .35])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated." ] }, { "cell_type": "code", "collapsed": false, "input": [ "arparams = np.r_[1, -arparams]\n", "maparam = np.r_[1, maparams]\n", "nobs = 250\n", "y = arma_generate_sample(arparams, maparams, nobs)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas\n", "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", "y = pandas.TimeSeries(y, index=dates)\n", "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", "arma_res = arma_mod.fit(trend='nc', disp=-1)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_discrete.ipynb000066400000000000000000000155151224417117700263620ustar00rootroot00000000000000{ "metadata": { "name": "example_discrete" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Discrete Choice Models Overview" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data\n", "\n", "Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "spector_data = sm.datasets.spector.load()\n", "spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print spector_data.exog[:5,:]\n", "print spector_data.endog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Probability Model (OLS)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)\n", "lpm_res = lpm_mod.fit()\n", "print 'Parameters: ', lpm_res.params[:-1]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logit Model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "logit_mod = sm.Logit(spector_data.endog, spector_data.exog)\n", "logit_res = logit_mod.fit(disp=0)\n", "print 'Parameters: ', logit_res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Marginal Effects" ] }, { "cell_type": "code", "collapsed": false, "input": [ "margeff = logit_res.get_margeff()\n", "print margeff.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in all the discrete data models presented below, we can print a nice summary of results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print logit_res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probit Model " ] }, { "cell_type": "code", "collapsed": false, "input": [ "probit_mod = sm.Probit(spector_data.endog, spector_data.exog)\n", "probit_res = probit_mod.fit()\n", "probit_margeff = probit_res.get_margeff()\n", "print 'Parameters: ', probit_res.params\n", "print 'Marginal effects: '\n", "print probit_margeff.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multinomial Logit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load data from the American National Election Studies:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "anes_data = sm.datasets.anes96.load()\n", "anes_exog = anes_data.exog\n", "anes_exog = sm.add_constant(anes_exog, prepend=False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print anes_data.exog[:5,:]\n", "print anes_data.endog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit MNL model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)\n", "mlogit_res = mlogit_mod.fit()\n", "print mlogit_res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Poisson\n", "\n", "Load the Rand data. Note that this example is similar to Cameron and Trivedi's `Microeconometrics` Table 20.5, but it is slightly different because of minor changes in the data. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "rand_data = sm.datasets.randhie.load()\n", "rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)\n", "rand_exog = sm.add_constant(rand_exog, prepend=False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit Poisson model: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "poisson_mod = sm.Poisson(rand_data.endog, rand_exog)\n", "poisson_res = poisson_mod.fit(method=\"newton\")\n", "print poisson_res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Negative Binomial\n", "\n", "The negative binomial model gives slightly different results. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)\n", "res_nbin = mod_nbin.fit(disp=False)\n", "print res_nbin.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Alternative solvers\n", "\n", "The default method for fitting discrete data MLE models is Newton-Raphson. You can use other solvers by using the ``method`` argument: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)\n", "print mlogit_res.summary()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_formulas.ipynb000066400000000000000000000235361224417117700264120ustar00rootroot00000000000000{ "metadata": { "name": "example_formulas" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Fitting models using R-style formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: \n", "\n", "* [Patsy formula language description](http://patsy.readthedocs.org/)\n", "\n", "## Loading modules and functions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Import convention" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can import explicitly from statsmodels.formula.api" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.formula.api import ols" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can just use the `formula` namespace of the main `statsmodels.api`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.formula.ols" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or you can use the following conventioin" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.formula as smf" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These names are just a convenient way to get access to each model's `from_formula` classmethod. See, for instance" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.OLS.from_formula" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n", "\n", "``dir(sm.formula)`` will print a list of available models. \n", "\n", "Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## OLS regression using formulas\n", "\n", "To begin, we fit the linear model described on the [Getting Started](gettingstarted.html) page. Download the data, subset columns, and list-wise delete to remove missing observations:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\n", "df.head()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n", "res = mod.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical variables\n", "\n", "Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. `patsy`'s default is also to include an intercept, so we automatically dropped one of the *Region* categories.\n", "\n", "If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()\n", "print res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Patsy's mode advanced features for categorical variables are discussed in: [Patsy: Contrast Coding Systems for categorical variables](contrasts.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Operators\n", "\n", "We have already seen that \"~\" separates the left-hand side of the model from the right-hand side, and that \"+\" adds new columns to the design matrix. \n", "\n", "### Removing variables\n", "\n", "The \"-\" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()\n", "print res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiplicative interactions\n", "\n", "\":\" adds a new column to the design matrix with the interaction of the other two columns. \"*\" will also include the individual columns that were multiplied together:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()\n", "res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()\n", "print res1.params, '\\n'\n", "print res2.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many other things are possible with operators. Please consult the [patsy docs](https://patsy.readthedocs.org/en/latest/formulas.html) to learn more." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions\n", "\n", "You can apply vectorized functions to the variables in your model: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = sm.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n", "print res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a custom function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def log_plus_1(x):\n", " return np.log(x) + 1.\n", "res = sm.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n", "print res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any function that is in the calling namespace is available to the formula." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using formulas with models that do not (yet) support them\n", "\n", "Even if a given `statsmodels` function does not support formulas, you can still use `patsy`'s formula language to produce design matrices. Those matrices \n", "can then be fed to the fitting function as `endog` and `exog` arguments. \n", "\n", "To generate ``numpy`` arrays: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import patsy\n", "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n", "print y[:5]\n", "print X[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate pandas data frames: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n", "print y[:5]\n", "print X[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.OLS(y, X).fit().summary()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_glm.ipynb000066400000000000000000000231311224417117700253300ustar00rootroot00000000000000{ "metadata": { "name": "example_glm" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Generalized Linear Models" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm\n", "from scipy import stats\n", "from matplotlib import pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Binomial response data\n", "\n", "### Load data\n", "\n", " In this example, we use the Star98 dataset which was taken with permission\n", " from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook\n", " information can be obtained by typing: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.star98.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the data and add a constant to the exogenous (independent) variables:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = sm.datasets.star98.load()\n", "data.exog = sm.add_constant(data.exog, prepend=False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): " ] }, { "cell_type": "code", "collapsed": false, "input": [ "print data.endog[:5,:]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The independent variables include all the other variables described above, as\n", " well as the interaction terms:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print data.exog[:2,:]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n", "res = glm_binom.fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quantities of interest" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Total number of trials:', data.endog[0].sum()\n", "print 'Parameters: ', res.params\n", "print 'T-values: ', res.tvalues" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "means = data.exog.mean(axis=0)\n", "means25 = means.copy()\n", "means25[0] = stats.scoreatpercentile(data.exog[:,0], 25)\n", "means75 = means.copy()\n", "means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:,0], 75)\n", "resp_25 = res.predict(means25)\n", "resp_75 = res.predict(means75)\n", "diff = resp_75 - resp_25" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The interquartile first difference for the percentage of low income households in a school district is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"%2.4f%%\" % (diff*100)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plots\n", "\n", " We extract information that will be used to draw some interesting plots: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "nobs = res.nobs\n", "y = data.endog[:,0]/data.endog.sum(1)\n", "yhat = res.mu" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot yhat vs y:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.graphics.api import abline_plot" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "ax.scatter(yhat, y)\n", "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n", "abline_plot(model_results=line_fit, ax=ax)\n", "\n", "\n", "ax.set_title('Model Fit Plot')\n", "ax.set_ylabel('Observed values')\n", "ax.set_xlabel('Fitted values');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot yhat vs. Pearson residuals:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "\n", "ax.scatter(yhat, res.resid_pearson)\n", "ax.hlines(0, 0, 1)\n", "ax.set_xlim(0, 1)\n", "ax.set_title('Residual Dependence Plot')\n", "ax.set_ylabel('Pearson Residuals')\n", "ax.set_xlabel('Fitted values')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histogram of standardized deviance residuals:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy import stats\n", "\n", "fig, ax = plt.subplots()\n", "\n", "resid = res.resid_deviance.copy()\n", "resid_std = stats.zscore(resid)\n", "ax.hist(resid_std, bins=25)\n", "ax.set_title('Histogram of standardized deviance residuals');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "QQ Plot of Deviance Residuals:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels import graphics\n", "graphics.gofplots.qqplot(resid, line='r')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Gamma for proportional count response\n", "\n", "### Load data\n", "\n", " In the example above, we printed the ``NOTE`` attribute to learn about the\n", " Star98 dataset. Statsmodels datasets ships with other useful information. For\n", " example: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.scotland.DESCRLONG" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Load the data and add a constant to the exogenous variables:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data2 = sm.datasets.scotland.load()\n", "data2.exog = sm.add_constant(data2.exog, prepend=False)\n", "print data2.exog[:5,:]\n", "print data2.endog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())\n", "glm_results = glm_gamma.fit()\n", "print glm_results.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Gaussian distribution with a noncanonical link\n", "\n", "### Artificial data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nobs2 = 100\n", "x = np.arange(nobs2)\n", "np.random.seed(54321)\n", "X = np.column_stack((x,x**2))\n", "X = sm.add_constant(X, prepend=False)\n", "lny = np.exp(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))\n", "gauss_log_results = gauss_log.fit()\n", "print gauss_log_results.summary()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_gls.ipynb000066400000000000000000000124041224417117700253370ustar00rootroot00000000000000{ "metadata": { "name": "example_gls" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Generalized Least Squares" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm\n", "import numpy as np\n", "from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Longley dataset is a time series dataset: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = sm.datasets.longley.load()\n", "data.exog = sm.add_constant(data.exog)\n", "print data.exog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " Let's assume that the data is heteroskedastic and that we know\n", " the nature of the heteroskedasticity. We can then define\n", " `sigma` and use it to give us a GLS model\n", "\n", " First we will obtain the residuals from an OLS fit" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ols_resid = sm.OLS(data.endog, data.exog).fit().resid" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume that the error terms follow an AR(1) process with a trend:\n", "\n", "$\\epsilon_i = \\beta_0 + \\rho\\epsilon_{i-1} + \\eta_i$\n", "\n", "where $\\eta \\sim N(0,\\Sigma^2)$\n", " \n", "and that $\\rho$ is simply the correlation of the residual a consistent estimator for rho is to regress the residuals on the lagged residuals" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()\n", "print resid_fit.tvalues[1]\n", "print resid_fit.pvalues[1]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " While we don't have strong evidence that the errors follow an AR(1)\n", " process we continue" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rho = resid_fit.params[1]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we know, an AR(1) process means that near-neighbors have a stronger\n", " relation so we can give this structure by using a toeplitz matrix" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.linalg import toeplitz\n", "\n", "toeplitz(range(5))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "order = toeplitz(range(len(ols_resid)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "so that our error covariance structure is actually rho**order\n", " which defines an autocorrelation structure" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sigma = rho**order\n", "gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)\n", "gls_results = gls_model.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, the exact rho in this instance is not known so it it might make more sense to use feasible gls, which currently only has experimental support. \n", "\n", "We can use the GLSAR model with one lag, to get to a similar result:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "glsar_model = sm.GLSAR(data.endog, data.exog, 1)\n", "glsar_results = glsar_model.iterative_fit(1)\n", "print glsar_results.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing gls and glsar results, we see that there are some small\n", " differences in the parameter estimates and the resulting standard\n", " errors of the parameter estimate. This might be do to the numerical\n", " differences in the algorithm, e.g. the treatment of initial conditions,\n", " because of the small number of observations in the longley dataset." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print gls_results.params\n", "print glsar_results.params\n", "print gls_results.bse\n", "print glsar_results.bse" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_gmle.ipynb000066400000000000000000000235521224417117700255040ustar00rootroot00000000000000{ "metadata": { "name": "example_gmle" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Generic Maximum Likelihood Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. \n", "\n", "## Example: Negative Binomial Regression for Count Data\n", "\n", "Consider a negative binomial regression model for count data with\n", "log-likelihood (type NB-2) function expressed as:\n", "\n", "$$\n", " \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n", " \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n", " \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n", "$$\n", "\n", "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n", "and the negative binomial heterogeneity parameter $\\alpha$. \n", "\n", "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n", "simply as:\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy.stats import nbinom" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def _ll_nb2(y, X, beta, alph):\n", " mu = np.exp(np.dot(X, beta))\n", " size = 1/alph\n", " prob = size/(size+mu)\n", " ll = nbinom.logpmf(y, size, prob)\n", " return ll" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Model Class\n", "\n", "We create a new model class which inherits from ``GenericLikelihoodModel``:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.base.model import GenericLikelihoodModel" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class NBin(GenericLikelihoodModel):\n", " def __init__(self, endog, exog, **kwds):\n", " super(NBin, self).__init__(endog, exog, **kwds)\n", " \n", " def nloglikeobs(self, params):\n", " alph = params[-1]\n", " beta = params[:-1]\n", " ll = _ll_nb2(self.endog, self.exog, beta, alph)\n", " return -ll \n", " \n", " def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n", " # we have one additional parameter and we need to add it for summary\n", " self.exog_names.append('alpha')\n", " if start_params == None:\n", " # Reasonable starting values\n", " start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n", " # intercept\n", " start_params[-2] = np.log(self.endog.mean())\n", " return super(NBin, self).fit(start_params=start_params, \n", " maxiter=maxiter, maxfun=maxfun, \n", " **kwds) " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two important things to notice: \n", "\n", "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n", "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n", " \n", "That's it! You're done!\n", "\n", "Usage Example\n", "-------------\n", "\n", "The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)\n", "dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``\n", "function from the [Pandas library](http://pandas.pydata.org) to load the data\n", "in memory. We then print the first few columns: \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n", "\n", "medpar.head()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model we are interested in has a vector of non-negative integers as\n", "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n", "``type3``, ``hmo``, ``white``.\n", "\n", "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects." ] }, { "cell_type": "code", "collapsed": false, "input": [ "y = medpar.los\n", "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]]\n", "X[\"constant\"] = 1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we fit the model and extract some information: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "mod = NBin(y, X)\n", "res = mod.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Extract parameter estimates, standard errors, p-values, AIC, etc.:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Parameters: ', res.params\n", "print 'Standard errors: ', res.bse\n", "print 'P-values: ', res.pvalues\n", "print 'AIC: ', res.aic" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, you can obtain a full list of available information by typing\n", "``dir(res)``.\n", "We can also look at the summary of the estimation results." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian." ] }, { "cell_type": "code", "collapsed": false, "input": [ "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n", "print res_nbin.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print res_nbin.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print res_nbin.bse" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we could compare them to results obtained using the MASS implementation for R:\n", "\n", " url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'\n", " medpar = read.csv(url)\n", " f = los~factor(type)+hmo+white\n", " \n", " library(MASS)\n", " mod = glm.nb(f, medpar)\n", " coef(summary(mod))\n", " Estimate Std. Error z value Pr(>|z|)\n", " (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257\n", " factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05\n", " factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20\n", " hmo -0.06795522 0.05321375 -1.277024 2.015939e-01\n", " white -0.12906544 0.06836272 -1.887951 5.903257e-02\n", "\n", "## Numerical precision \n", "\n", "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class.\n" ] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_interactions.ipynb000066400000000000000000000471331224417117700272630ustar00rootroot00000000000000{ "metadata": { "name": "example_interactions" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Interactions and ANOVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html\n", "\n", "Download and format data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "np.set_printoptions(precision=4, suppress=True)\n", "import statsmodels.api as sm\n", "import pandas\n", "import matplotlib.pyplot as plt\n", "from statsmodels.formula.api import ols\n", "from statsmodels.graphics.api import interaction_plot, abline_plot\n", "from statsmodels.stats.anova import anova_lm\n", "\n", "try:\n", " salary_table = pandas.read_csv('salary.table')\n", "except: # recent pandas can read URL without urlopen\n", " from urllib2 import urlopen\n", " url = 'http://stats191.stanford.edu/data/salary.table'\n", " fh = urlopen(url)\n", " salary_table = pandas.read_table(fh)\n", " salary_table.to_csv('salary.table')\n", "\n", "E = salary_table.E\n", "M = salary_table.M\n", "X = salary_table.X\n", "S = salary_table.S" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(6,6))\n", "symbols = ['D', '^']\n", "colors = ['r', 'g', 'blue']\n", "factor_groups = salary_table.groupby(['E','M'])\n", "for values, group in factor_groups:\n", " i,j = values\n", " plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n", " s=144)\n", "plt.xlabel('Experience');\n", "plt.ylabel('Salary');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit a linear model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "formula = 'S ~ C(E) + C(M) + X'\n", "lm = ols(formula, salary_table).fit()\n", "print lm.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Have a look at the created design matrix: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "lm.model.exog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or since we initially passed in a DataFrame, we have a DataFrame available in" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lm.model.data.orig_exog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We keep a reference to the original untouched data in" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lm.model.data.frame[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Influence statistics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "infl = lm.get_influence()\n", "print infl.summary_table()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or get a dataframe" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df_infl = infl.summary_frame()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "df_infl[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now plot the reiduals within the groups separately:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resid = lm.resid\n", "plt.figure(figsize=(6,6));\n", "for values, group in factor_groups:\n", " i,j = values\n", " group_num = i*2 + j - 1 # for plotting purposes\n", " x = [group_num] * len(group)\n", " plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('Group');\n", "plt.ylabel('Residuals');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will test some interactions using anova or f_test" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n", "print interX_lm.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do an ANOVA check" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.stats.api import anova_lm\n", "\n", "table1 = anova_lm(lm, interX_lm)\n", "print table1\n", "\n", "interM_lm = ols(\"S ~ X + C(E)*C(M)\", df=salary_table).fit()\n", "print interM_lm.summary()\n", "\n", "table2 = anova_lm(lm, interM_lm)\n", "print table2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The design matrix as a DataFrame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interM_lm.model.data.orig_exog[:5]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The design matrix as an ndarray" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interM_lm.model.exog\n", "interM_lm.model.exog_names" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "infl = interM_lm.get_influence()\n", "resid = infl.resid_studentized_internal\n", "plt.figure(figsize=(6,6))\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('X');\n", "plt.ylabel('standardized resids');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like one observation is an outlier." ] }, { "cell_type": "code", "collapsed": false, "input": [ "drop_idx = abs(resid).argmax()\n", "print drop_idx # zero-based index\n", "idx = salary_table.index.drop(drop_idx)\n", "\n", "lm32 = ols('S ~ C(E) + X + C(M)', df=salary_table, subset=idx).fit()\n", "\n", "print lm32.summary()\n", "\n", "interX_lm32 = ols('S ~ C(E) * X + C(M)', df=salary_table, subset=idx).fit()\n", "\n", "print interX_lm32.summary()\n", "\n", "table3 = anova_lm(lm32, interX_lm32)\n", "print table3\n", "\n", "interM_lm32 = ols('S ~ X + C(E) * C(M)', df=salary_table, subset=idx).fit()\n", "\n", "table4 = anova_lm(lm32, interM_lm32)\n", "print table4" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Replot the residuals" ] }, { "cell_type": "code", "collapsed": false, "input": [ "try:\n", " resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n", "except:\n", " resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n", "\n", "plt.figure(figsize=(6,6))\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('X[~[32]]');\n", "plt.ylabel('standardized resids');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Plot the fitted values" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lm_final = ols('S ~ X + C(E)*C(M)', df = salary_table.drop([drop_idx])).fit()\n", "mf = lm_final.model.data.orig_exog\n", "lstyle = ['-','--']\n", "\n", "plt.figure(figsize=(6,6))\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", " # drop NA because there is no idx 32 in the final model\n", " plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),\n", " ls=lstyle[j], color=colors[i-1])\n", "plt.xlabel('Experience');\n", "plt.ylabel('Salary');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot." ] }, { "cell_type": "code", "collapsed": false, "input": [ "U = S - X * interX_lm32.params['X']\n", "\n", "plt.figure(figsize=(6,6))\n", "interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n", " markersize=10, ax=plt.gca())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Minority Employment Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "try:\n", " minority_table = pandas.read_table('minority.table')\n", "except: # don't have data already\n", " url = 'http://stats191.stanford.edu/data/minority.table'\n", " minority_table = pandas.read_table(url)\n", "\n", "factor_group = minority_table.groupby(['ETHN'])\n", "\n", "plt.figure(figsize=(6,6))\n", "colors = ['purple', 'green']\n", "markers = ['o', 'v']\n", "for factor, group in factor_group:\n", " plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "plt.xlabel('TEST');\n", "plt.ylabel('JPERF');\n", "\n", "min_lm = ols('JPERF ~ TEST', df=minority_table).fit()\n", "print min_lm.summary()\n", "\n", "plt.figure(figsize=(6,6));\n", "for factor, group in factor_group:\n", " plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "plt.xlabel('TEST')\n", "plt.ylabel('JPERF')\n", "abline_plot(model_results = min_lm, ax=plt.gca());\n", "\n", "min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',\n", " df=minority_table).fit()\n", "\n", "print min_lm2.summary()\n", "\n", "plt.figure(figsize=(6,6));\n", "for factor, group in factor_group:\n", " plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "abline_plot(intercept = min_lm2.params['Intercept'],\n", " slope = min_lm2.params['TEST'], ax=plt.gca(), color='purple');\n", "abline_plot(intercept = min_lm2.params['Intercept'],\n", " slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n", " ax=plt.gca(), color='green');\n", "\n", "\n", "min_lm3 = ols('JPERF ~ TEST + ETHN', df = minority_table).fit()\n", "print min_lm3.summary()\n", "\n", "plt.figure(figsize=(6,6));\n", "for factor, group in factor_group:\n", " plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "abline_plot(intercept = min_lm3.params['Intercept'],\n", " slope = min_lm3.params['TEST'], ax=plt.gca(), color='purple');\n", "abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n", " slope = min_lm3.params['TEST'], ax=plt.gca(), color='green');\n", "\n", "\n", "min_lm4 = ols('JPERF ~ TEST * ETHN', df = minority_table).fit()\n", "print min_lm4.summary()\n", "\n", "plt.figure(figsize=(6,6));\n", "for factor, group in factor_group:\n", " plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "abline_plot(intercept = min_lm4.params['Intercept'],\n", " slope = min_lm4.params['TEST'], ax=plt.gca(), color='purple');\n", "abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n", " slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n", " ax=plt.gca(), color='green');\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# is there any effect of ETHN on slope or intercept?\n", "table5 = anova_lm(min_lm, min_lm4)\n", "print table5\n", "\n", "# is there any effect of ETHN on intercept\n", "table6 = anova_lm(min_lm, min_lm3)\n", "print table6\n", "\n", "# is there any effect of ETHN on slope\n", "table7 = anova_lm(min_lm, min_lm2)\n", "print table7\n", "\n", "# is it just the slope or both?\n", "table8 = anova_lm(min_lm2, min_lm4)\n", "print table8" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## One-way ANOVA" ] }, { "cell_type": "code", "collapsed": false, "input": [ "try:\n", " rehab_table = pandas.read_csv('rehab.table')\n", "except:\n", " url = 'http://stats191.stanford.edu/data/rehab.csv'\n", " rehab_table = pandas.read_table(url, delimiter=\",\")\n", " rehab_table.to_csv('rehab.table')\n", "\n", "plt.figure(figsize=(6,6))\n", "rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())\n", "\n", "rehab_lm = ols('Time ~ C(Fitness)', df=rehab_table).fit()\n", "table9 = anova_lm(rehab_lm)\n", "print table9\n", "\n", "print rehab_lm.model.data.orig_exog\n", "\n", "print rehab_lm.summary()\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two-way ANOVA" ] }, { "cell_type": "code", "collapsed": false, "input": [ "try:\n", " kidney_table = pandas.read_table('./kidney.table')\n", "except:\n", " url = 'http://stats191.stanford.edu/data/kidney.table'\n", " kidney_table = pandas.read_table(url, delimiter=\" *\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Explore the dataset" ] }, { "cell_type": "code", "collapsed": false, "input": [ "kidney_table.groupby(['Weight', 'Duration']).size()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Balanced panel" ] }, { "cell_type": "code", "collapsed": false, "input": [ "kt = kidney_table\n", "plt.figure(figsize=(6,6))\n", "interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n", " colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have things available in the calling namespace available in the formula evaluation namespace" ] }, { "cell_type": "code", "collapsed": false, "input": [ "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', df=kt).fit()\n", "\n", "table10 = anova_lm(kidney_lm)\n", "\n", "print anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n", " df=kt).fit(), kidney_lm)\n", "print anova_lm(ols('np.log(Days+1) ~ C(Duration)', df=kt).fit(),\n", " ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n", " df=kt).fit())\n", "print anova_lm(ols('np.log(Days+1) ~ C(Weight)', df=kt).fit(),\n", " ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n", " df=kt).fit())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sum of squares\n", "\n", " Illustrates the use of different types of sums of squares (I,II,II)\n", " and how the Sum contrast can be used to produce the same output between\n", " the 3.\n", "\n", " Types I and II are equivalent under a balanced design.\n", "\n", " Don't use Type III with non-orthogonal contrast - ie., Treatment" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n", " df=kt).fit()\n", "\n", "print anova_lm(sum_lm)\n", "print anova_lm(sum_lm, typ=2)\n", "print anova_lm(sum_lm, typ=3)\n", "\n", "nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n", " df=kt).fit()\n", "print anova_lm(nosum_lm)\n", "print anova_lm(nosum_lm, typ=2)\n", "print anova_lm(nosum_lm, typ=3)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_ols.ipynb000066400000000000000000000320401224417117700253450ustar00rootroot00000000000000{ "metadata": { "name": "example_ols" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Ordinary Least Squares" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "np.random.seed(9876789)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS estimation\n", "\n", "Artificial data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 100\n", "x = np.linspace(0, 10, 100)\n", "X = np.column_stack((x, x**2))\n", "beta = np.array([1, 0.1, 10])\n", "e = np.random.normal(size=nsample)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model needs an intercept so we add a column of 1s:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "print results.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quantities of interest can be extracted directly from the fitted model. Type ``dir(results)`` for a full list. Here are some examples: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Parameters: ', results.params\n", "print 'R2: ', results.rsquared" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS non-linear curve but linear in parameters\n", "\n", "We simulate artificial data with a non-linear relationship between x and y:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 50\n", "sig = 0.5\n", "x = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n", "beta = [0.5, 0.5, -0.02, 5.]\n", "\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = sm.OLS(y, X).fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract other quantities of interest:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Parameters: ', res.params\n", "print 'Standard errors: ', res.bse\n", "print 'Predicted values: ', res.predict()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the ``wls_prediction_std`` command." ] }, { "cell_type": "code", "collapsed": false, "input": [ "prstd, iv_l, iv_u = wls_prediction_std(res)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax.plot(x, y, 'o', label=\"data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res.fittedvalues, 'r--.', label=\"OLS\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "ax.legend(loc='best');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS with dummy variables\n", "\n", "We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 50\n", "groups = np.zeros(nsample, int)\n", "groups[20:40] = 1\n", "groups[40:] = 2\n", "#dummy = (groups[:,None] == np.unique(groups)).astype(float)\n", "\n", "dummy = sm.categorical(groups, drop=True)\n", "x = np.linspace(0, 20, nsample)\n", "# drop reference category\n", "X = np.column_stack((x, dummy[:,1:]))\n", "X = sm.add_constant(X, prepend=False)\n", "\n", "beta = [1., 3, -3, 10]\n", "y_true = np.dot(X, beta)\n", "e = np.random.normal(size=nsample)\n", "y = y_true + e" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print X[:5,:]\n", "print y[:5]\n", "print groups\n", "print dummy[:5,:]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res2 = sm.OLS(y, X).fit()\n", "print res.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "prstd, iv_l, iv_u = wls_prediction_std(res2)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax.plot(x, y, 'o', label=\"Data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res2.fittedvalues, 'r--.', label=\"Predicted\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "ax.legend(loc=\"best\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Joint hypothesis test\n", "\n", "### F test\n", "\n", "We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \\times \\beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n", "print np.array(R)\n", "print res2.f_test(R)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use formula-like syntax to test hypotheses" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print res2.f_test(\"x2 = x3 = 0\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Small group effects\n", "\n", "If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "beta = [1., 0.3, -0.0, 10]\n", "y_true = np.dot(X, beta)\n", "y = y_true + np.random.normal(size=nsample)\n", "\n", "res3 = sm.OLS(y, X).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print res3.f_test(R)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print res3.f_test(\"x2 = x3 = 0\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multicollinearity\n", "\n", "The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.datasets.longley import load_pandas\n", "y = load_pandas().endog\n", "X = load_pandas().exog\n", "X = sm.add_constant(X)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ols_model = sm.OLS(y, X)\n", "ols_results = ols_model.fit()\n", "print ols_results.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Condition number\n", "\n", "One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i, name in enumerate(X):\n", " if name == \"const\":\n", " continue\n", " norm_x[:,i] = X[name]/np.linalg.norm(X[name])\n", "norm_xtx = np.dot(norm_x.T,norm_x)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we take the square root of the ratio of the biggest to the smallest eigen values. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "eigs = np.linalg.eigvals(norm_xtx)\n", "condition_number = np.sqrt(eigs.max() / eigs.min())\n", "print condition_number" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dropping an observation\n", "\n", "Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()\n", "print \"Percentage change %4.2f%%\\n\"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out." ] }, { "cell_type": "code", "collapsed": false, "input": [ "infl = ols_results.get_influence()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general we may consider DBETAS in absolute value greater than $2/\\sqrt{N}$ to be influential observations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "2./len(X)**.5" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print infl.summary_frame().filter(regex=\"dfb\")" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_predict.ipynb000066400000000000000000000104431224417117700262050ustar00rootroot00000000000000{ "metadata": { "name": "example_predict" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Out of sample prediction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Artificial data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 50\n", "sig = 0.25\n", "x1 = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x1, np.sin(x1), (x1-5)**2))\n", "X = sm.add_constant(X)\n", "beta = [5., 0.5, 0.5, -0.02]\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation " ] }, { "cell_type": "code", "collapsed": false, "input": [ "olsmod = sm.OLS(y, X)\n", "olsres = olsmod.fit()\n", "print olsres.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In-sample prediction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ypred = olsres.predict(X)\n", "print ypred" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a new sample of explanatory variables Xnew, predict and plot" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x1n = np.linspace(20.5,25, 10)\n", "Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2))\n", "Xnew = sm.add_constant(Xnew)\n", "ynewpred = olsres.predict(Xnew) # predict out of sample\n", "print ynewpred" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot comparison" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x1, y, 'o', label=\"Data\")\n", "ax.plot(x1, y_true, 'b-', label=\"True\")\n", "ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label=\"OLS prediction\")\n", "ax.legend(loc=\"best\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Predicting with Formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using formulas can make both estimation and prediction a lot easier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.formula.api import ols\n", "\n", "data = {\"x1\" : x1, \"y\" : y}\n", "\n", "res = ols(\"y ~ x1 + np.sin(x1) + I((x1-5)**2)\", data=data).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `I` to indicate use of the Identity transform. Ie., we don't want any expansion magic from using `**2`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we only have to pass the single variable and we get the transformed right-hand side variables automatically" ] }, { "cell_type": "code", "collapsed": false, "input": [ "res.predict(exog=dict(x1=x1n))" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_regression_plots.ipynb000066400000000000000000000424131224417117700301560ustar00rootroot00000000000000{ "metadata": { "name": "example_regression_plots" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Regression Plots" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import ols" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Duncan's Prestige Dataset" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Load the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a utility function to load any R dataset available from the great Rdatasets package." ] }, { "cell_type": "code", "collapsed": false, "input": [ "prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "prestige.head()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "prestige_model = ols(\"prestige ~ income + education\", data=prestige).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print prestige_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Influence plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Influence plots show the (externally) studentized residuals vs. the leverage of each observation as measured by the hat matrix.\n", "\n", "Externally studentized residuals are residuals that are scaled by their standard deviation where \n", "\n", "$$var(\\\\hat{\\epsilon}_i)=\\hat{\\sigma}^2_i(1-h_{ii})$$\n", "\n", "with\n", "\n", "$$\\hat{\\sigma}^2_i=\\frac{1}{n - p - 1 \\;\\;}\\sum_{j}^{n}\\;\\;\\;\\forall \\;\\;\\; j \\neq i$$\n", "\n", "$n$ is the number of observations and $p$ is the number of regressors. $h_{ii}$ is the $i$-th diagonal element of the hat matrix\n", "\n", "$$H=X(X^{\\;\\prime}X)^{-1}X^{\\;\\prime}$$\n", "\n", "The influence of each point can be visualized by the criterion keyword argument. Options are Cook's distance and DFFITS, two measures of influence." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.influence_plot(prestige_model, ax=ax, criterion=\"cooks\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see there are a few worrisome observations. Both contractor and reporter have low leverage but a large residual.
    \n", "RR.engineer has small residual and large leverage. Conductor and minister have both high leverage and large residuals, and,
    \n", "therefore, large influence." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Partial Regression Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are doing multivariate regressions, we cannot just look at individual bivariate plots to discern relationships.
    \n", "Instead, we want to look at the relationship of the dependent variable and independent variables conditional on the other
    \n", "independent variables. We can do this through using partial regression plots, otherwise known as added variable plots.
    \n", "\n", "In a partial regression plot, to discern the relationship between the response variable and the $k$-th variabe, we compute
    \n", "the residuals by regressing the response variable versus the independent variables excluding $X_k$. We can denote this by
    \n", "$X_{\\sim k}$. We then compute the residuals by regressing $X_k$ on $X_{\\sim k}$. The partial regression plot is the plot
    \n", "of the former versus the latter residuals.
    \n", "\n", "The notable points of this plot are that the fitted line has slope $\\beta_k$ and intercept zero. The residuals of this plot
    \n", "are the same as those of the least squares fit of the original model with full $X$. You can discern the effects of the
    \n", "individual data values on the estimation of a coefficient easily. If obs_labels is True, then these points are annotated
    \n", "with their observation label. You can also see the violation of underlying assumptions such as homooskedasticity and
    \n", "linearity." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"income\", \"education\"], data=prestige, ax=ax)\n", "ax = fig.axes[0]\n", "\n", "ax.set_xlim(-2e-15, 1e-14)\n", "ax.set_ylim(-25, 30);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fix, ax = plt.subplots(figsize=(12,14))\n", "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"education\"], data=prestige, ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the partial regression plot confirms the influence of conductor, minister, and RR.engineer on the partial relationship between income and prestige. The cases greatly decrease the effect of income on prestige. Dropping these cases confirms this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "subset = ~prestige.index.isin([\"conductor\", \"RR.engineer\", \"minister\"])\n", "prestige_model2 = ols(\"prestige ~ income + education\", data=prestige, subset=subset).fit()\n", "print prestige_model2.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a quick check of all the regressors, you can use plot_partregress_grid. These plots will not label the
    \n", "points, but you can use them to identify problems and then use plot_partregress to get more information." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress_grid(prestige_model, fig=fig)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Component-Component plus Residual (CCPR) Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CCPR plot provides a way to judge the effect of one regressor on the
    \n", "response variable by taking into account the effects of the other
    \n", "independent variables. The partial residuals plot is defined as
    \n", "$\\text{Residuals} + B_iX_i \\text{ }\\text{ }$ versus $X_i$. The component adds $B_iX_i$ versus
    \n", "$X_i$ to show where the fitted line would lie. Care should be taken if $X_i$
    \n", "is highly correlated with any of the other independent variables. If this
    \n", "is the case, the variance evident in the plot will be an underestimate of
    \n", "the true variance." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "fig = sm.graphics.plot_ccpr(prestige_model, \"education\", ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the relationship between the variation in prestige explained by education conditional on income seems to be linear, though you can see there are some observations that are exerting considerable influence on the relationship. We can quickly look at more than one variable by using plot_ccpr_grid." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12, 8))\n", "fig = sm.graphics.plot_ccpr_grid(prestige_model, fig=fig)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Regression Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot_regress_exog function is a convenience function that gives a 2x2 plot containing the dependent variable and fitted values with confidence intervals vs. the independent variable chosen, the residuals of the model vs. the chosen independent variable, a partial regression plot, and a CCPR plot. This function can be used for quickly checking modeling assumptions with respect to a single regressor." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_regress_exog(prestige_model, \"education\", fig=fig)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Fit Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot_fit function plots the fitted values versus a chosen independent variable. It includes prediction confidence intervals and optionally plots the true dependent variable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "fig = sm.graphics.plot_fit(prestige_model, \"education\", ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Statewide Crime 2009 Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the following to http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg_self_assessment_answers4.htm\n", "\n", "Though the data here is not the same as in that example. You could run that example by uncommenting the necessary cells below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#dta = pandas.read_csv(\"http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv\")\n", "#dta = dta.set_index(\"State\", inplace=True).dropna()\n", "#dta.rename(columns={\"VR\" : \"crime\",\n", "# \"MR\" : \"murder\",\n", "# \"M\" : \"pctmetro\",\n", "# \"W\" : \"pctwhite\",\n", "# \"H\" : \"pcths\",\n", "# \"P\" : \"poverty\",\n", "# \"S\" : \"single\"\n", "# }, inplace=True)\n", "#\n", "#crime_model = ols(\"murder ~ pctmetro + poverty + pcths + single\", data=dta).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.statecrime.load_pandas().data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "crime_model = ols(\"murder ~ urban + poverty + hs_grad + single\", data=dta).fit()\n", "print crime_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Partial Regression Plots" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress_grid(crime_model, fig=fig)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress(\"murder\", \"hs_grad\", [\"urban\", \"poverty\", \"single\"], ax=ax, data=dta)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Leverage-Resid2 Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Closely related to the influence_plot is the leverage-resid2 plot." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = sm.graphics.plot_leverage_resid2(crime_model, ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Influence Plot" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = sm.graphics.influence_plot(crime_model, ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Using robust regression to correct for outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Part of the problem here in recreating the Stata results is that M-estimators are not robust to leverage points. MM-estimators should do better with this examples." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.formula.api import rlm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "rob_crime_model = rlm(\"murder ~ urban + poverty + hs_grad + single\", data=dta, \n", " M=sm.robust.norms.TukeyBiweight(3)).fit(conv=\"weights\")\n", "print rob_crime_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#rob_crime_model = rlm(\"murder ~ pctmetro + poverty + pcths + single\", data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv=\"weights\")\n", "#print rob_crime_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There aren't yet an influence diagnostics as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))" ] }, { "cell_type": "code", "collapsed": false, "input": [ "weights = rob_crime_model.weights\n", "idx = weights > 0\n", "X = rob_crime_model.model.exog[idx]\n", "ww = weights[idx] / weights[idx].mean()\n", "hat_matrix_diag = ww*(X*np.linalg.pinv(X).T).sum(1)\n", "resid = rob_crime_model.resid\n", "resid2 = resid**2\n", "resid2 /= resid2.sum()\n", "nobs = int(idx.sum())\n", "hm = hat_matrix_diag.mean()\n", "rm = resid2.mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.graphics import utils\n", "fig, ax = plt.subplots(figsize=(12,8))\n", "ax.plot(resid2[idx], hat_matrix_diag, 'o')\n", "ax = utils.annotate_axes(range(nobs), labels=rob_crime_model.model.data.row_labels[idx], \n", " points=zip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,\n", " size=\"large\", ax=ax)\n", "ax.set_xlabel(\"resid2\")\n", "ax.set_ylabel(\"leverage\")\n", "ylim = ax.get_ylim()\n", "ax.vlines(rm, *ylim)\n", "xlim = ax.get_xlim()\n", "ax.hlines(hm, *xlim)\n", "ax.margins(0,0)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_rlm.ipynb000066400000000000000000000152241224417117700253470ustar00rootroot00000000000000{ "metadata": { "name": "example_rlm" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Robust Linear Models" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation\n", "\n", "Load data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = sm.datasets.stackloss.load()\n", "data.exog = sm.add_constant(data.exog)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Huber's T norm with the (default) median absolute deviation scaling" ] }, { "cell_type": "code", "collapsed": false, "input": [ "huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())\n", "hub_results = huber_t.fit()\n", "print hub_results.params\n", "print hub_results.bse\n", "print hub_results.summary(yname='y',\n", " xname=['var_%d' % i for i in range(len(hub_results.params))])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Huber's T norm with 'H2' covariance matrix" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hub_results2 = huber_t.fit(cov=\"H2\")\n", "print hub_results2.params\n", "print hub_results2.bse" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix" ] }, { "cell_type": "code", "collapsed": false, "input": [ "andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())\n", "andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov=\"H3\")\n", "print 'Parameters: ', andrew_results.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for scale options\n", "\n", "## Comparing OLS and RLM\n", "\n", "Artificial data with outliers:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 50\n", "x1 = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x1, (x1-5)**2))\n", "X = sm.add_constant(X)\n", "sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger\n", "beta = [5, 0.5, -0.0]\n", "y_true2 = np.dot(X, beta)\n", "y2 = y_true2 + sig*1. * np.random.normal(size=nsample)\n", "y2[[39,41,43,45,48]] -= 5 # add some outliers (10% of nsample)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 1: quadratic function with linear truth\n", "\n", "Note that the quadratic term in OLS regression will capture outlier effects. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "res = sm.OLS(y2, X).fit()\n", "print res.params\n", "print res.bse\n", "print res.predict()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate RLM:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resrlm = sm.RLM(y2, X).fit()\n", "print resrlm.params\n", "print resrlm.bse" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare OLS estimates to the robust estimates:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(x1, y2, 'o',label=\"data\")\n", "ax.plot(x1, y_true2, 'b-', label=\"True\")\n", "prstd, iv_l, iv_u = wls_prediction_std(res)\n", "ax.plot(x1, res.fittedvalues, 'r-', label=\"OLS\")\n", "ax.plot(x1, iv_u, 'r--')\n", "ax.plot(x1, iv_l, 'r--')\n", "ax.plot(x1, resrlm.fittedvalues, 'g.-', label=\"RLM\")\n", "ax.legend(loc=\"best\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2: linear function with linear truth\n", "\n", "Fit a new OLS model using only the linear term and the constant:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X2 = X[:,[0,1]] \n", "res2 = sm.OLS(y2, X2).fit()\n", "print res2.params\n", "print res2.bse" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate RLM:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resrlm2 = sm.RLM(y2, X2).fit()\n", "print resrlm2.params\n", "print resrlm2.bse" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare OLS estimates to the robust estimates:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "prstd, iv_l, iv_u = wls_prediction_std(res2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x1, y2, 'o', label=\"data\")\n", "ax.plot(x1, y_true2, 'b-', label=\"True\")\n", "ax.plot(x1, res2.fittedvalues, 'r-', label=\"OLS\")\n", "ax.plot(x1, iv_u, 'r--')\n", "ax.plot(x1, iv_l, 'r--')\n", "ax.plot(x1, resrlm2.fittedvalues, 'g.-', label=\"RLM\")\n", "ax.legend(loc=\"best\")" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_tsa_dates.ipynb000066400000000000000000000067701224417117700265320ustar00rootroot00000000000000{ "metadata": { "name": "ex_dates" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using dates with timeseries models" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import statsmodels.api as sm\n", "import numpy as np\n", "import pandas" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting started" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = sm.datasets.sunspots.load()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right now an annual date series must be datetimes at the end of the year." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from datetime import datetime\n", "dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Pandas\n", "\n", "Make a pandas TimeSeries or DataFrame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "endog = pandas.TimeSeries(data.endog, index=dates)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate the model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ar_model = sm.tsa.AR(endog, freq='A')\n", "pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Out-of-sample prediction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pred = pandas_ar_res.predict(start='2005', end='2015')\n", "print pred" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using explicit dates" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')\n", "ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)\n", "pred = ar_res.predict(start='2005', end='2015')\n", "print pred" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print ar_res.data.predict_dates" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict." ] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/example_wls.ipynb000066400000000000000000000130071224417117700253570ustar00rootroot00000000000000{ "metadata": { "name": "example_wls" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Weighted Least Squares" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)\n", "np.random.seed(1024)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WLS Estimation\n", "\n", "### Artificial data: Heteroscedasticity 2 groups \n", "\n", "Model assumptions:\n", "\n", " * Misspecificaion: true model is quadratic, estimate only linear\n", " * Independent noise/error term\n", " * Two groups for error variance, low and high variance groups" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nsample = 50\n", "x = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x, (x - 5)**2))\n", "X = sm.add_constant(X)\n", "beta = [5., 0.5, -0.01]\n", "sig = 0.5\n", "w = np.ones(nsample)\n", "w[nsample * 6/10:] = 3\n", "y_true = np.dot(X, beta)\n", "e = np.random.normal(size=nsample)\n", "y = y_true + sig * w * e \n", "X = X[:,[0,1]]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WLS knowing the true variance ratio of heteroscedasticity" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mod_wls = sm.WLS(y, X, weights=1./w)\n", "res_wls = mod_wls.fit()\n", "print res_wls.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS vs. WLS\n", "\n", "Estimate an OLS model for comparison: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "res_ols = sm.OLS(y, X).fit()\n", "print res_ols.params\n", "print res_wls.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the WLS standard errors to heteroscedasticity corrected OLS standard errors:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se], \n", " [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]])\n", "se = np.round(se,4)\n", "colnames = ['x1', 'const']\n", "rownames = ['WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3']\n", "tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)\n", "print tabl" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate OLS prediction interval:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "covb = res_ols.cov_params()\n", "prediction_var = res_ols.mse_resid + (X * np.dot(covb,X.T).T).sum(1)\n", "prediction_std = np.sqrt(prediction_var)\n", "tppf = stats.t.ppf(0.975, res_ols.df_resid)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "prstd_ols, iv_l_ols, iv_u_ols = wls_prediction_std(res_ols)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare predicted values in WLS and OLS:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "prstd, iv_l, iv_u = wls_prediction_std(res_wls)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x, y, 'o', label=\"Data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "# OLS\n", "ax.plot(x, res_ols.fittedvalues, 'r--')\n", "ax.plot(x, iv_u_ols, 'r--', label=\"OLS\")\n", "ax.plot(x, iv_l_ols, 'r--')\n", "# WLS\n", "ax.plot(x, res_wls.fittedvalues, 'g--.')\n", "ax.plot(x, iv_u, 'g--', label=\"WLS\")\n", "ax.plot(x, iv_l, 'g--')\n", "ax.legend(loc=\"best\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feasible Weighted Least Squares (2-stage FWLS)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resid1 = res_ols.resid[w==1.]\n", "var1 = resid1.var(ddof=int(res_ols.df_model)+1)\n", "resid2 = res_ols.resid[w!=1.]\n", "var2 = resid2.var(ddof=int(res_ols.df_model)+1)\n", "w_est = w.copy()\n", "w_est[w!=1.] = np.sqrt(var2) / np.sqrt(var1)\n", "res_fwls = sm.WLS(y, X, 1./w_est).fit()\n", "print res_fwls.summary()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/generic_mle.ipynb000066400000000000000000000060251224417117700253120ustar00rootroot00000000000000{ "metadata": { "name": "generic_mle" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Generic Maximum Likelihood Estimator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "from statsmodels.base.model import GenericLikelihoodModel" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.spector.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data = sm.datasets.spector.load_pandas()\n", "exog = sm.add_constant(data.exog, prepend=True)\n", "endog = data.endog" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm_probit = sm.Probit(endog, exog).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* To create your own Likelihood Model, you just need to overwrite the loglike method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class MyProbit(GenericLikelihoodModel):\n", " def loglike(self, params):\n", " exog = self.exog\n", " endog = self.endog\n", " q = 2 * endog - 1\n", " return stats.norm.logcdf(q*np.dot(exog, params)).sum()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "my_probit = MyProbit(endog, exog).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm_probit.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm_probit.cov_params()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print my_probit.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "You can get the variance-covariance of the parameters. Notice that we didn't have to provide Hessian or Score functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print my_probit.cov_params()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/kernel_density.ipynb000066400000000000000000000121551224417117700260610ustar00rootroot00000000000000{ "metadata": { "name": "kernel_density" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Kernel Density Estimation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.distributions.mixture_rvs import mixture_rvs" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "A univariate example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(12345)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm],\n", " kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "kde = sm.nonparametric.KDEUnivariate(obs_dist1)\n", "kde.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.hist(obs_dist1, bins=50, normed=True, color='red')\n", "ax.plot(kde.support, kde.density, lw=2, color='black');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "obs_dist2 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta],\n", " kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5))))\n", "\n", "kde2 = sm.nonparametric.KDEUnivariate(obs_dist2)\n", "kde2.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.hist(obs_dist2, bins=50, normed=True, color='red')\n", "ax.plot(kde2.support, kde2.density, lw=2, color='black');" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "The fitted KDE object is a full non-parametric distribution." ] }, { "cell_type": "code", "collapsed": false, "input": [ "obs_dist3 = mixture_rvs([.25,.75], size=1000, dist=[stats.norm, stats.norm],\n", " kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))\n", "kde3 = sm.nonparametric.KDEUnivariate(obs_dist3)\n", "kde3.fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "kde3.entropy" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "kde3.evaluate(-1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "CDF" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde3.support, kde3.cdf);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Cumulative Hazard Function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde3.support, kde3.cumhazard);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Inverse CDF" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde3.support, kde3.icdf);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Survival Function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde3.support, kde3.sf);" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/robust_models.ipynb000066400000000000000000000557741224417117700257410ustar00rootroot00000000000000{ "metadata": { "name": "robust_models" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "M-Estimators for Robust Linear Modeling" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* An M-estimator minimizes the function \n", "\n", "$$Q(e_i, \\rho) = \\sum_i~\\rho(\\frac{e_i}{s})$$\n", "\n", "where $\\rho$ is a symmetric function of the residuals \n", "\n", "* The effect of $\\rho$ is to reduce the influence of outliers\n", "* $s$ is an estimate of scale. \n", "* The robust estimates $\\hat{\\beta}$ are computed by the iteratively re-weighted least squares algorithm" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "* We have several choices available for the weighting functions to be used" ] }, { "cell_type": "code", "collapsed": false, "input": [ "norms = sm.robust.norms" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_weights(support, weights_func, xlabels, xticks):\n", " fig = plt.figure(figsize=(12,8))\n", " ax = fig.add_subplot(111)\n", " ax.plot(support, weights_func(support))\n", " ax.set_xticks(xticks)\n", " ax.set_xticklabels(xlabels, fontsize=16)\n", " ax.set_ylim(-.1, 1.1)\n", " return ax" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Andrew's Wave" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.AndrewWave.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "a = 1.339\n", "support = np.linspace(-np.pi*a, np.pi*a, 100)\n", "andrew = norms.AndrewWave(a=a)\n", "plot_weights(support, andrew.weights, ['$-\\pi*a$', '0', '$\\pi*a$'], [-np.pi*a, 0, np.pi*a]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Hampel's 17A" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.Hampel.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c = 8\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "hampel = norms.Hampel(a=2., b=4., c=c)\n", "plot_weights(support, hampel.weights, ['3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Huber's t" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.HuberT.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "t = 1.345\n", "support = np.linspace(-3*t, 3*t, 1000)\n", "huber = norms.HuberT(t=t)\n", "plot_weights(support, huber.weights, ['-3*t', '0', '3*t'], [-3*t, 0, 3*t]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Least Squares" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.LeastSquares.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "support = np.linspace(-3, 3, 1000)\n", "lst_sq = norms.LeastSquares()\n", "plot_weights(support, lst_sq.weights, ['-3', '0', '3'], [-3, 0, 3]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Ramsay's Ea" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.RamsayE.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "a = .3\n", "support = np.linspace(-3*a, 3*a, 1000)\n", "ramsay = norms.RamsayE(a=a)\n", "plot_weights(support, ramsay.weights, ['-3*a', '0', '3*a'], [-3*a, 0, 3*a]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Trimmed Mean" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.TrimmedMean.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c = 2\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "trimmed = norms.TrimmedMean(c=c)\n", "plot_weights(support, trimmed.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Tukey's Biweight" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(norms.TukeyBiweight.weights)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c = 4.685\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "tukey = norms.TukeyBiweight(c=c)\n", "plot_weights(support, tukey.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Scale Estimators" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "* Robust estimates of the location" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.array([1, 2, 3, 4, 500])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The mean is not a robust estimator of location" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x.mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The median, on the other hand, is a robust estimator with a breakdown point of 50%" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.median(x)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* Analagously for the scale\n", "* The standard deviation is not robust" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x.std()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Median Absolute Deviation\n", "\n", "$$ median_i |X_i - median_j(X_j)|) $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standardized Median Absolute Deviation is a consistent estimator for $\\hat{\\sigma}$\n", "\n", "$$\\hat{\\sigma}=K \\cdot MAD$$\n", "\n", "where $K$ depends on the distribution. For the normal distribution for example,\n", "\n", "$$K = \\Phi^{-1}(.75)$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "stats.norm.ppf(.75)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print x" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.robust.scale.stand_mad(x)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "np.array([1,2,3,4,5.]).std()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* The default for Robust Linear Models is MAD\n", "* another popular choice is Huber's proposal 2" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(12345)\n", "fat_tails = stats.t(6).rvs(40)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "kde = sm.nonparametric.KDE(fat_tails)\n", "kde.fit()\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde.support, kde.density);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print fat_tails.mean(), fat_tails.std()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print stats.norm.fit(fat_tails)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print stats.t.fit(fat_tails, f0=6)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "huber = sm.robust.scale.Huber()\n", "loc, scale = huber(fat_tails)\n", "print loc, scale" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.robust.stand_mad(fat_tails)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.robust.stand_mad(fat_tails, c=stats.t(6).ppf(.75))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.robust.scale.mad(fat_tails)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Duncan's Occupational Prestige data - M-estimation for outliers" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.graphics.api import abline_plot\n", "from statsmodels.formula.api import ols, rlm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print prestige.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,12))\n", "ax1 = fig.add_subplot(211, xlabel='Income', ylabel='Prestige')\n", "ax1.scatter(prestige.income, prestige.prestige)\n", "xy_outlier = prestige.ix['minister'][['income','prestige']]\n", "ax1.annotate('Minister', xy_outlier, xy_outlier+1, fontsize=16)\n", "ax2 = fig.add_subplot(212, xlabel='Education',\n", " ylabel='Prestige')\n", "ax2.scatter(prestige.education, prestige.prestige);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "ols_model = ols('prestige ~ income + education', prestige).fit()\n", "print ols_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "infl = ols_model.get_influence()\n", "student = infl.summary_frame()['student_resid']\n", "print student" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print student.ix[np.abs(student) > 2]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print infl.summary_frame().ix['minister']" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sidak = ols_model.outlier_test('sidak')\n", "sidak.sort('unadj_p', inplace=True)\n", "print sidak" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fdr = ols_model.outlier_test('fdr_bh')\n", "fdr.sort('unadj_p', inplace=True)\n", "print fdr" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "rlm_model = rlm('prestige ~ income + education', prestige).fit()\n", "print rlm_model.summary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print rlm_model.weights" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Data is on the luminosity and temperature of 47 stars in the direction of Cygnus." ] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.get_rdataset(\"starsCYG\", \"robustbase\", cache=True).data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib.patches import Ellipse\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, xlabel='log(Temp)', ylabel='log(Light)', title='Hertzsprung-Russell Diagram of Star Cluster CYG OB1')\n", "ax.scatter(*dta.values.T)\n", "# highlight outliers\n", "e = Ellipse((3.5, 6), .2, 1, alpha=.25, color='r')\n", "ax.add_patch(e);\n", "ax.annotate('Red giants', xy=(3.6, 6), xytext=(3.8, 6),\n", " arrowprops=dict(facecolor='black', shrink=0.05, width=2),\n", " horizontalalignment='left', verticalalignment='bottom',\n", " clip_on=True, # clip to the axes bounding box\n", " fontsize=16,\n", " )\n", "# annotate these with their index\n", "for i,row in dta.ix[dta['log.Te'] < 3.8].iterrows():\n", " ax.annotate(i, row, row + .01, fontsize=14)\n", "xlim, ylim = ax.get_xlim(), ax.get_ylim()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "Image(filename='star_diagram.png')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "y = dta['log.light']\n", "X = sm.add_constant(dta['log.Te'], prepend=True)\n", "ols_model = sm.OLS(y, X).fit()\n", "abline_plot(model_results=ols_model, ax=ax)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()\n", "abline_plot(model_results=rlm_mod, ax=ax, color='red')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Why? Because M-estimators are not robust to leverage points." ] }, { "cell_type": "code", "collapsed": false, "input": [ "infl = ols_model.get_influence()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "h_bar = 2*(ols_model.df_model + 1 )/ols_model.nobs\n", "hat_diag = infl.summary_frame()['hat_diag']\n", "hat_diag.ix[hat_diag > h_bar]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sidak2 = ols_model.outlier_test('sidak')\n", "sidak2.sort('unadj_p', inplace=True)\n", "print sidak2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fdr2 = ols_model.outlier_test('fdr_bh')\n", "fdr2.sort('unadj_p', inplace=True)\n", "print fdr2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Let's delete that line" ] }, { "cell_type": "code", "collapsed": false, "input": [ "del ax.lines[-1]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "weights = np.ones(len(X))\n", "weights[X[X['log.Te'] < 3.8].index.values - 1] = 0\n", "wls_model = sm.WLS(y, X, weights=weights).fit()\n", "abline_plot(model_results=wls_model, ax=ax, color='green')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* MM estimators are good for this type of problem, unfortunately, we don't yet have these yet. \n", "* It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook." ] }, { "cell_type": "code", "collapsed": false, "input": [ "yy = y.values[:,None]\n", "xx = X['log.Te'].values[:,None]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rmagic\n", "\n", "%R library(robustbase)\n", "%Rpush yy xx\n", "%R mod <- lmrob(yy ~ xx);\n", "%R params <- mod$coefficients;\n", "%Rpull params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%R print(mod)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "abline_plot(intercept=params[0], slope=params[1], ax=ax, color='green')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Exercise: Breakdown points of M-estimator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(12345)\n", "nobs = 200\n", "beta_true = np.array([3, 1, 2.5, 3, -4])\n", "X = np.random.uniform(-20,20, size=(nobs, len(beta_true)-1))\n", "# stack a constant in front\n", "X = sm.add_constant(X, prepend=True) # np.c_[np.ones(nobs), X]\n", "mc_iter = 500\n", "contaminate = .25 # percentage of response variables to contaminate" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "all_betas = []\n", "for i in range(mc_iter):\n", " y = np.dot(X, beta_true) + np.random.normal(size=200)\n", " random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))\n", " y[random_idx] = np.random.uniform(-750, 750)\n", " beta_hat = sm.RLM(y, X).fit().params\n", " all_betas.append(beta_hat)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "all_betas = np.asarray(all_betas)\n", "se_loss = lambda x : np.linalg.norm(x, ord=2)**2\n", "se_beta = map(se_loss, all_betas - beta_true)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Squared error loss" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.array(se_beta).mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "all_betas.mean(0)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "beta_true" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "se_loss(all_betas.mean(0) - beta_true)" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] } statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/star_diagram.png000066400000000000000000005267061224417117700251560ustar00rootroot00000000000000‰PNG  IHDRHE©âŸ&sRGB®ÎébKGDÿÿÿ ½§“ pHYs a Äî–@µtIMEÜ6xá IDATxÚì½]¬¬ÛU%6Æ\_Õ9þiÿ`·Œ!ü…tÓ`Àjµ€ü!"H“ˆ— ¡ÄHoH<€DìÒÀ‹_HH-E$J@<@D;Q'nºóg‡Vè@‹"è¸}}N}kΑ‡9×úVí½Ïõ½— >÷ÞZ¶÷OíÚµ«¾ZcÍ1Ç’¾ð ¿·u[·u[·u[¯‹e·§à¶në¶në¶nÀv[·u[·u[·u¶Ûº­Ûº­Ûº­°ÝÖmÝÖmÝÖmÝ€í¶në¶në¶nÀv[·u[·u[·u¶Ûº­Ûº­Ûº­°ÝÖmÝÖmÝÖmý®í òw¾ç=ïùÒ/ýRI·—ü¶në¶nëu°"âãÿø¾ïo\`ûöoÿöÿñÿƒ?øƒÛÕp[·u[·õ:@µ/ø‚/øò/ÿò?ú£?z㛤}èC?ñ?q» 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‚ÀÀ@ƒÙ»wïîÝ»{{{¡rƒÄ'Lh4úþ""" äëõë×}}}¯_¿oáäääææF/ÐÞÞîääôH¿Qmm-ò¡(ÊÃÃC¯×Ca\__o4nܸW^y¥°°pæÌ™¨bllìÃêuww·B¡€µ®]»­ <¨««Cüý÷ßëõúãÇ“$ù믿¦¦¦J¥R¨J:;;#)ŽÁ‚ ƒŠ|õÕWÅÅÅqqq£Gk×®½uëܼ¡(*00Çã-\¸ 6‚ þÎ…ßÍ›7x<ž··÷Ê•+oß¾íéé)“ɸ\îìÙ³]\\bbblllêêêJKK}}}'NœÈãñf̘add$“ÉþÜòïíÛ·¯¸¸ØÇLJÇã%'' ©TJQÒ¥(Š¢;hÀ&õôô¬\¹R(òx¼øøø×_½¦¦æÖ­['55•Çã………mÚ´éâÅ‹ô{µ´´LŸ>ÝÃÃcäÈ‘ëÖ­»}ûv@@@.—‡„„X[[_¼x1..ÎÏÏÏÕÕõË/¿¼|ù2Aôn)((puu‰‰áñxãÆ[»v-t¥A”——C3 j*I’¨ý÷0à“O>IKK5j”››ÛÒ¥KÛÛÛaE©T Ÿ%$$äý÷ß?räˆR©‰DÙÙÙ'NLNNf³Ù°$½»¶lÙRVV6zôh77·={öÔÕÕA-##ƒÏç{xxdeeåææB5tÆŒ</ ÀÜܼ»»ÛÜÜ\§ÓuwwãçÅtqq¹{÷.î Ì3„••Õ´iÓüýýÕjuYYYuuµN§ØÚÚ&$$0Œœœœ‘#G–——s8œàààòòr¤ÉÍš5 N‚Ó§O—Éd*•Ê××·ªªŠÁ`„‡‡Oš4©¾¾þĉÐœÈb±fÎœéååUWWWXX800ðp“Œýüüjjjà¡­­íœ9sœœœªªª***´Z­««kgg烠^¥Õj¡ “ÉŒŠŠú駟víÚµjÕª°°°±X\UU¥R©àÑØØxâĉžž&“Švì¸\î¢E‹T*U^^žZ­7n\II‰™™Ùœ9s***nß¾íàà””ÔÛÛ ]"˜L¦ÏùóçáX,VLLŒ@ hjjÊÏÏïêê2xº«W¯†„„ N›6-??ßÝÝ]§Óýþûï°î„ Îœ9cPeØ°aóçϧ(*77—Ïçß½{÷Î;$I …Â)S¦Ü¹sçرc …¶³³»ÿþرc-Z4wî\è҂̹öööóæÍ333+,,„Î#€Ñ£GÇÇÇ@&“+~ÿý÷ãÇ+•Ê¤¤¤aÆ}ñÅø­yÀ^‘Ìa×®]kä|Z,\¸ð/þ¯ö˜H$KKK˜.,,ŒŽŽþ/ž——‡‡Ö‹6Eb0C…ð Xÿ8'OžüàƒþŸ.þÞ{ïݸqC,ß»wïâÅ‹t×ÐÇdøðá§OŸÆC kl 󰶶~êØÄ<'kDÜ f(w1˜Ç¯0 ƒƒÁ`0X°a0 óø@%OʵÉò¹IEND®B`‚statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/tsa_arma.ipynb000066400000000000000000000301131224417117700246230ustar00rootroot00000000000000{ "metadata": { "name": "tsa_arma" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "ARMA Example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import pandas\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.graphics.api import qqplot" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sunpots Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.sunspots.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.sunspots.load_pandas().data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n", "del dta[\"YEAR\"]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta.plot(figsize=(12,8));" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()\n", "print arma_mod20.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print arma_mod30.params" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Does our model obey the theory?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sm.stats.durbin_watson(arma_mod30.resid.values)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax = arma_mod30.resid.plot(ax=ax);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "resid = arma_mod30.resid" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "stats.normaltest(resid)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "fig = qqplot(resid, line='q', ax=ax, fit=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print table.set_index('lag')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This indicates a lack of fit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* In-sample dynamic prediction. How good does our model do?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)\n", "print predict_sunspots" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = dta.ix['1950':].plot(figsize=(12,8))\n", "ax = predict_sunspots.plot(ax=ax, style='r--', label='Dynamic Prediction');\n", "ax.legend();\n", "ax.axis((-20.0, 38.0, -4.0, 200.0));" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def mean_forecast_err(y, yhat):\n", " return y.sub(yhat).mean()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Simulated ARMA(4,1): Model Identification is Difficult" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(1234)\n", "# include zero-th lag\n", "arparams = np.array([1, .75, -.65, -.55, .9])\n", "maparams = np.array([1, .65])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Let's make sure this models is estimable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_t = ArmaProcess(arparams, maparams)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_t.isinvertible()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_t.isstationary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* What does this mean?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(arma_t.generate_sample(size=50));" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arparams = np.array([1, .35, -.15, .55, .1])\n", "maparams = np.array([1, .65])\n", "arma_t = ArmaProcess(arparams, maparams)\n", "arma_t.isstationary()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma_rvs = arma_t.generate_sample(size=500, burnin=250, scale=2.5)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags. \n", "* The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags." ] }, { "cell_type": "code", "collapsed": false, "input": [ "arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()\n", "resid = arma11.resid\n", "r,q,p = sm.tsa.acf(resid, qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print table.set_index('lag')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()\n", "resid = arma41.resid\n", "r,q,p = sm.tsa.acf(resid, qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print table.set_index('lag')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Exercise: How good of in-sample prediction can you do for another series, say, CPI" ] }, { "cell_type": "code", "collapsed": false, "input": [ "macrodta = sm.datasets.macrodata.load_pandas().data\n", "macrodta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n", "cpi = macrodta[\"cpi\"]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Hint: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax = cpi.plot(ax=ax);\n", "ax.legend();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "P-value of the unit-root test, resoundly rejects the null of no unit-root." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.tsa.adfuller(cpi)[1]" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/notebooks/tsa_filters.ipynb000066400000000000000000000214621224417117700253620ustar00rootroot00000000000000{ "metadata": { "name": "tsa_filters" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Time Series Filters" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta = sm.datasets.macrodata.load_pandas().data" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n", "print index" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dta.index = index\n", "del dta['year']\n", "del dta['quarter']" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.datasets.macrodata.NOTE" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print dta.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "dta.realgdp.plot(ax=ax);\n", "legend = ax.legend(loc = 'upper left');\n", "legend.prop.set_size(20);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Hodrick-Prescott Filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Hodrick-Prescott filter separates a time-series $y_t$ into a trend $\\tau_t$ and a cyclical component $\\zeta_t$ \n", "\n", "$$y_t = \\tau_t + \\zeta_t$$\n", "\n", "The components are determined by minimizing the following quadratic loss function\n", "\n", "$$\\min_{\\\\{ \\tau_{t}\\\\} }\\sum_{t}^{T}\\zeta_{t}^{2}+\\lambda\\sum_{t=1}^{T}\\left[\\left(\\tau_{t}-\\tau_{t-1}\\right)-\\left(\\tau_{t-1}-\\tau_{t-2}\\right)\\right]^{2}$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gdp_cycle, gdp_trend = sm.tsa.filters.hpfilter(dta.realgdp)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "gdp_decomp = dta[['realgdp']]\n", "gdp_decomp[\"cycle\"] = gdp_cycle\n", "gdp_decomp[\"trend\"] = gdp_trend" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "gdp_decomp[[\"realgdp\", \"trend\"]][\"2000-03-31\":].plot(ax=ax, fontsize=16);\n", "legend = ax.get_legend()\n", "legend.prop.set_size(20);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Baxter-King approximate band-pass filter: Inflation and Unemployment" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Explore the hypothesis that inflation and unemployment are counter-cyclical." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Baxter-King filter is intended to explictly deal with the periodicty of the business cycle. By applying their band-pass filter to a series, they produce a new series that does not contain fluctuations at higher or lower than those of the business cycle. Specifically, the BK filter takes the form of a symmetric moving average \n", "\n", "$$y_{t}^{*}=\\sum_{k=-K}^{k=K}a_ky_{t-k}$$\n", "\n", "where $a_{-k}=a_k$ and $\\sum_{k=-k}^{K}a_k=0$ to eliminate any trend in the series and render it stationary if the series is I(1) or I(2).\n", "\n", "For completeness, the filter weights are determined as follows\n", "\n", "$$a_{j} = B_{j}+\\theta\\text{ for }j=0,\\pm1,\\pm2,\\dots,\\pm K$$\n", "\n", "$$B_{0} = \\frac{\\left(\\omega_{2}-\\omega_{1}\\right)}{\\pi}$$\n", "$$B_{j} = \\frac{1}{\\pi j}\\left(\\sin\\left(\\omega_{2}j\\right)-\\sin\\left(\\omega_{1}j\\right)\\right)\\text{ for }j=0,\\pm1,\\pm2,\\dots,\\pm K$$\n", "\n", "where $\\theta$ is a normalizing constant such that the weights sum to zero.\n", "\n", "$$\\theta=\\frac{-\\sum_{j=-K^{K}b_{j}}}{2K+1}$$\n", "\n", "$$\\omega_{1}=\\frac{2\\pi}{P_{H}}$$\n", "\n", "$$\\omega_{2}=\\frac{2\\pi}{P_{L}}$$\n", "\n", "$P_L$ and $P_H$ are the periodicity of the low and high cut-off frequencies. Following Burns and Mitchell's work on US business cycles which suggests cycles last from 1.5 to 8 years, we use $P_L=6$ and $P_H=32$ by default." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bk_cycles = sm.tsa.filters.bkfilter(dta[[\"infl\",\"unemp\"]])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "* We lose K observations on both ends. It is suggested to use K=12 for quarterly data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(14,10))\n", "ax = fig.add_subplot(111)\n", "bk_cycles.plot(ax=ax, style=['r--', 'b-']);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Christiano-Fitzgerald approximate band-pass filter: Inflation and Unemployment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Christiano-Fitzgerald filter is a generalization of BK and can thus also be seen as weighted moving average. However, the CF filter is asymmetric about $t$ as well as using the entire series. The implementation of their filter involves the\n", "calculations of the weights in\n", "\n", "$$y_{t}^{*}=B_{0}y_{t}+B_{1}y_{t+1}+\\dots+B_{T-1-t}y_{T-1}+\\tilde B_{T-t}y_{T}+B_{1}y_{t-1}+\\dots+B_{t-2}y_{2}+\\tilde B_{t-1}y_{1}$$\n", "\n", "for $t=3,4,...,T-2$, where\n", "\n", "$$B_{j} = \\frac{\\sin(jb)-\\sin(ja)}{\\pi j},j\\geq1$$\n", "\n", "$$B_{0} = \\frac{b-a}{\\pi},a=\\frac{2\\pi}{P_{u}},b=\\frac{2\\pi}{P_{L}}$$\n", "\n", "$\\tilde B_{T-t}$ and $\\tilde B_{t-1}$ are linear functions of the $B_{j}$'s, and the values for $t=1,2,T-1,$ and $T$ are also calculated in much the same way. $P_{U}$ and $P_{L}$ are as described above with the same interpretation." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The CF filter is appropriate for series that may follow a random walk." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.tsa.stattools.adfuller(dta['unemp'])[:3]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print sm.tsa.stattools.adfuller(dta['infl'])[:3]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[[\"infl\",\"unemp\"]])\n", "print cf_cycles.head(10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(14,10))\n", "ax = fig.add_subplot(111)\n", "cf_cycles.plot(ax=ax, style=['r--','b-']);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filtering assumes *a priori* that business cycles exist. Due to this assumption, many macroeconomic models seek to create models that match the shape of impulse response functions rather than replicating properties of filtered series. See VAR notebook." ] } ], "metadata": {} } ] }statsmodels-0.5.0+git13-g8e07d34/examples/run_all.py000066400000000000000000000031701224417117700217770ustar00rootroot00000000000000"""run all examples to make sure we don't get an exception Note: If an example contaings plt.show(), then all plot windows have to be closed manually, at least in my setup. uncomment plt.show() to show all plot windows """ stop_on_error = True filelist = ['example_glsar.py', 'example_wls.py', 'example_gls.py', 'example_glm.py', 'example_ols_tftest.py', # 'example_rpy.py', 'example_ols.py', 'example_rlm.py', 'example_discrete.py', 'example_predict.py', 'example_ols_table.py', # time series 'tsa/ex_arma2.py', 'tsa/ex_dates.py'] if __name__ == '__main__': #temporarily disable show import matplotlib.pyplot as plt plt_show = plt.show def noop(*args): pass plt.show = noop msg = """Are you sure you want to run all of the examples? This is done mainly to check that they are up to date. (y/n) >>> """ cont = raw_input(msg) if 'y' in cont.lower(): for run_all_f in filelist: try: print '\n\nExecuting example file', run_all_f print '-----------------------' + '-' * len(run_all_f) execfile(run_all_f) except: # f might be overwritten in the executed file print '**********************' + '*' * len(run_all_f) print 'ERROR in example file', run_all_f print '**********************' + '*' * len(run_all_f) if stop_on_error: raise # reenable show after closing windows plt.close('all') plt.show = plt_show plt.show() statsmodels-0.5.0+git13-g8e07d34/examples/try_wls.py000066400000000000000000000372601224417117700220550ustar00rootroot00000000000000""" Weighted Least Squares example is extended to look at the meaning of rsquared in WLS, at outliers, compares with RLM and a short bootstrap """ import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt data = sm.datasets.ccard.load() data.exog = sm.add_constant(data.exog, prepend=False) ols_fit = sm.OLS(data.endog, data.exog).fit() # perhaps the residuals from this fit depend on the square of income incomesq = data.exog[:,2] plt.scatter(incomesq, ols_fit.resid) #@savefig wls_resid_check.png plt.grid() # If we think that the variance is proportional to income**2 # we would want to weight the regression by income # the weights argument in WLS weights the regression by its square root # and since income enters the equation, if we have income/income # it becomes the constant, so we would want to perform # this type of regression without an explicit constant in the design #..data.exog = data.exog[:,:-1] wls_fit = sm.WLS(data.endog, data.exog[:,:-1], weights=1/incomesq).fit() # This however, leads to difficulties in interpreting the post-estimation # statistics. Statsmodels does not yet handle this elegantly, but # the following may be more appropriate # explained sum of squares ess = wls_fit.uncentered_tss - wls_fit.ssr # rsquared rsquared = ess/wls_fit.uncentered_tss # mean squared error of the model mse_model = ess/(wls_fit.df_model + 1) # add back the dof of the constant # f statistic fvalue = mse_model/wls_fit.mse_resid # adjusted r-squared rsquared_adj = 1 -(wls_fit.nobs)/(wls_fit.df_resid)*(1-rsquared) #Trying to figure out what's going on in this example #---------------------------------------------------- #JP: I need to look at this again. Even if I exclude the weight variable # from the regressors and keep the constant in then the reported rsquared # stays small. Below also compared using squared or sqrt of weight variable. # TODO: need to add 45 degree line to graphs wls_fit3 = sm.WLS(data.endog, data.exog[:,(0,1,3,4)], weights=1/incomesq).fit() print wls_fit3.summary() print 'corrected rsquared', print (wls_fit3.uncentered_tss - wls_fit3.ssr)/wls_fit3.uncentered_tss plt.figure(); plt.title('WLS dropping heteroscedasticity variable from regressors'); plt.plot(data.endog, wls_fit3.fittedvalues, 'o'); plt.xlim([0,2000]); #@savefig wls_drop_het.png plt.ylim([0,2000]); print 'raw correlation of endog and fittedvalues' print np.corrcoef(data.endog, wls_fit.fittedvalues) print 'raw correlation coefficient of endog and fittedvalues squared' print np.corrcoef(data.endog, wls_fit.fittedvalues)[0,1]**2 # compare with robust regression, # heteroscedasticity correction downweights the outliers rlm_fit = sm.RLM(data.endog, data.exog).fit() plt.figure(); plt.title('using robust for comparison'); plt.plot(data.endog, rlm_fit.fittedvalues, 'o'); plt.xlim([0,2000]); #@savefig wls_robust_compare.png plt.ylim([0,2000]); #What is going on? A more systematic look at the data #---------------------------------------------------- # two helper functions def getrsq(fitresult): '''calculates rsquared residual, total and explained sums of squares Parameters ---------- fitresult : instance of Regression Result class, or tuple of (resid, endog) arrays regression residuals and endogenous variable Returns ------- rsquared residual sum of squares (centered) total sum of squares explained sum of squares (for centered) ''' if hasattr(fitresult, 'resid') and hasattr(fitresult, 'model'): resid = fitresult.resid endog = fitresult.model.endog nobs = fitresult.nobs else: resid = fitresult[0] endog = fitresult[1] nobs = resid.shape[0] rss = np.dot(resid, resid) tss = np.var(endog)*nobs return 1-rss/tss, rss, tss, tss-rss def index_trim_outlier(resid, k): '''returns indices to residual array with k outliers removed Parameters ---------- resid : array_like, 1d data vector, usually residuals of a regression k : int number of outliers to remove Returns ------- trimmed_index : array, 1d index array with k outliers removed outlier_index : array, 1d index array of k outliers Notes ----- Outliers are defined as the k observations with the largest absolute values. ''' sort_index = np.argsort(np.abs(resid)) # index of non-outlier trimmed_index = np.sort(sort_index[:-k]) outlier_index = np.sort(sort_index[-k:]) return trimmed_index, outlier_index #Comparing estimation results for ols, rlm and wls with and without outliers #--------------------------------------------------------------------------- #ols_test_fit = sm.OLS(data.endog, data.exog).fit() olskeep, olsoutl = index_trim_outlier(ols_fit.resid, 2) print 'ols outliers', olsoutl, ols_fit.resid[olsoutl] ols_fit_rm2 = sm.OLS(data.endog[olskeep], data.exog[olskeep,:]).fit() rlm_fit_rm2 = sm.RLM(data.endog[olskeep], data.exog[olskeep,:]).fit() #weights = 1/incomesq results = [ols_fit, ols_fit_rm2, rlm_fit, rlm_fit_rm2] #Note: I think incomesq is already square for weights in [1/incomesq, 1/incomesq**2, np.sqrt(incomesq)]: print '\nComparison OLS and WLS with and without outliers' wls_fit0 = sm.WLS(data.endog, data.exog, weights=weights).fit() wls_fit_rm2 = sm.WLS(data.endog[olskeep], data.exog[olskeep,:], weights=weights[olskeep]).fit() wlskeep, wlsoutl = index_trim_outlier(ols_fit.resid, 2) print '2 outliers candidates and residuals' print wlsoutl, wls_fit.resid[olsoutl] # redundant because ols and wls outliers are the same: ##wls_fit_rm2_ = sm.WLS(data.endog[wlskeep], data.exog[wlskeep,:], ## weights=1/incomesq[wlskeep]).fit() print 'outliers ols, wls:', olsoutl, wlsoutl print 'rsquared' print 'ols vs ols rm2', ols_fit.rsquared, ols_fit_rm2.rsquared print 'wls vs wls rm2', wls_fit0.rsquared, wls_fit_rm2.rsquared #, wls_fit_rm2_.rsquared print 'compare R2_resid versus R2_wresid' print 'ols minus 2', getrsq(ols_fit_rm2)[0], print getrsq((ols_fit_rm2.wresid, ols_fit_rm2.model.wendog))[0] print 'wls ', getrsq(wls_fit)[0], print getrsq((wls_fit.wresid, wls_fit.model.wendog))[0] print 'wls minus 2', getrsq(wls_fit_rm2)[0], # next is same as wls_fit_rm2.rsquared for cross checking print getrsq((wls_fit_rm2.wresid, wls_fit_rm2.model.wendog))[0] #print getrsq(wls_fit_rm2_)[0], #print getrsq((wls_fit_rm2_.wresid, wls_fit_rm2_.model.wendog))[0] results.extend([wls_fit0, wls_fit_rm2]) print ' ols ols_rm2 rlm rlm_rm2 wls (lin) wls_rm2 (lin) wls (squ) wls_rm2 (squ) wls (sqrt) wls_rm2 (sqrt)' print 'Parameter estimates' print np.column_stack([r.params for r in results]) print 'R2 original data, next line R2 weighted data' print np.column_stack([getattr(r, 'rsquared', None) for r in results]) print 'Standard errors' print np.column_stack([getattr(r, 'bse', None) for r in results]) print 'Heteroscedasticity robust standard errors (with ols)' print 'with outliers' print np.column_stack([getattr(ols_fit, se, None) for se in ['HC0_se', 'HC1_se', 'HC2_se', 'HC3_se']]) #..''' #.. #.. ols ols_rm2 rlm rlm_rm2 wls (lin) wls_rm2 (lin) wls (squ) wls_rm2 (squ) wls (sqrt) wls_rm2 (sqrt) #..Parameter estimates #..[[ -3.08181404 -5.06103843 -4.98510966 -5.34410309 -2.69418516 -3.1305703 -1.43815462 -1.58893054 -3.57074829 -6.80053364] #.. [ 234.34702702 115.08753715 129.85391456 109.01433492 158.42697752 128.38182357 60.95113284 100.25000841 254.82166855 103.75834726] #.. [ -14.99684418 -5.77558429 -6.46204829 -4.77409191 -7.24928987 -7.41228893 6.84943071 -3.34972494 -16.40524256 -4.5924465 ] #.. [ 27.94090839 85.46566835 89.91389709 95.85086459 60.44877369 79.7759146 55.9884469 60.97199734 -3.8085159 84.69170048] #.. [-237.1465136 39.51639838 -15.50014814 31.39771833 -114.10886935 -40.04207242 -6.41976501 -38.83583228 -260.72084271 117.20540179]] #.. #..R2 original data, next line R2 weighted data #..[[ 0.24357792 0.31745994 0.19220308 0.30527648 0.22861236 0.3112333 0.06573949 0.29366904 0.24114325 0.31218669]] #..[[ 0.24357791 0.31745994 None None 0.05936888 0.0679071 0.06661848 0.12769654 0.35326686 0.54681225]] #.. #..-> R2 with weighted data is jumping all over #.. #..standard errors #..[[ 5.51471653 3.31028758 2.61580069 2.39537089 3.80730631 2.90027255 2.71141739 2.46959477 6.37593755 3.39477842] #.. [ 80.36595035 49.35949263 38.12005692 35.71722666 76.39115431 58.35231328 87.18452039 80.30086861 86.99568216 47.58202096] #.. [ 7.46933695 4.55366113 3.54293763 3.29509357 9.72433732 7.41259156 15.15205888 14.10674821 7.18302629 3.91640711] #.. [ 82.92232357 50.54681754 39.33262384 36.57639175 58.55088753 44.82218676 43.11017757 39.31097542 96.4077482 52.57314209] #.. [ 199.35166485 122.1287718 94.55866295 88.3741058 139.68749646 106.89445525 115.79258539 105.99258363 239.38105863 130.32619908]] #.. #..robust standard errors (with ols) #..with outliers #.. HC0_se HC1_se HC2_se HC3_se' #..[[ 3.30166123 3.42264107 3.4477148 3.60462409] #.. [ 88.86635165 92.12260235 92.08368378 95.48159869] #.. [ 6.94456348 7.19902694 7.19953754 7.47634779] #.. [ 92.18777672 95.56573144 95.67211143 99.31427277] #.. [ 212.9905298 220.79495237 221.08892661 229.57434782]] #.. #..removing 2 outliers #..[[ 2.57840843 2.67574088 2.68958007 2.80968452] #.. [ 36.21720995 37.58437497 37.69555106 39.51362437] #.. [ 3.1156149 3.23322638 3.27353882 3.49104794] #.. [ 50.09789409 51.98904166 51.89530067 53.79478834] #.. [ 94.27094886 97.82958699 98.25588281 102.60375381]] #.. #.. #..''' # a quick bootstrap analysis # -------------------------- # #(I didn't check whether this is fully correct statistically) #**With OLS on full sample** nobs, nvar = data.exog.shape niter = 2000 bootres = np.zeros((niter, nvar*2)) for it in range(niter): rind = np.random.randint(nobs, size=nobs) endog = data.endog[rind] exog = data.exog[rind,:] res = sm.OLS(endog, exog).fit() bootres[it, :nvar] = res.params bootres[it, nvar:] = res.bse np.set_printoptions(linewidth=200) print 'Bootstrap Results of parameters and parameter standard deviation OLS' print 'Parameter estimates' print 'median', np.median(bootres[:,:5], 0) print 'mean ', np.mean(bootres[:,:5], 0) print 'std ', np.std(bootres[:,:5], 0) print 'Standard deviation of parameter estimates' print 'median', np.median(bootres[:,5:], 0) print 'mean ', np.mean(bootres[:,5:], 0) print 'std ', np.std(bootres[:,5:], 0) plt.figure() for i in range(4): plt.subplot(2,2,i+1) plt.hist(bootres[:,i],50) plt.title('var%d'%i) #@savefig wls_bootstrap.png plt.figtext(0.5, 0.935, 'OLS Bootstrap', ha='center', color='black', weight='bold', size='large') #**With WLS on sample with outliers removed** data_endog = data.endog[olskeep] data_exog = data.exog[olskeep,:] incomesq_rm2 = incomesq[olskeep] nobs, nvar = data_exog.shape niter = 500 # a bit slow bootreswls = np.zeros((niter, nvar*2)) for it in range(niter): rind = np.random.randint(nobs, size=nobs) endog = data_endog[rind] exog = data_exog[rind,:] res = sm.WLS(endog, exog, weights=1/incomesq[rind,:]).fit() bootreswls[it, :nvar] = res.params bootreswls[it, nvar:] = res.bse print 'Bootstrap Results of parameters and parameter standard deviation', print 'WLS removed 2 outliers from sample' print 'Parameter estimates' print 'median', np.median(bootreswls[:,:5], 0) print 'mean ', np.mean(bootreswls[:,:5], 0) print 'std ', np.std(bootreswls[:,:5], 0) print 'Standard deviation of parameter estimates' print 'median', np.median(bootreswls[:,5:], 0) print 'mean ', np.mean(bootreswls[:,5:], 0) print 'std ', np.std(bootreswls[:,5:], 0) plt.figure() for i in range(4): plt.subplot(2,2,i+1) plt.hist(bootreswls[:,i],50) plt.title('var%d'%i) #@savefig wls_bootstrap_rm2.png plt.figtext(0.5, 0.935, 'WLS rm2 Bootstrap', ha='center', color='black', weight='bold', size='large') #..plt.show() #..plt.close('all') #:: # # The following a random variables not fixed by a seed # # Bootstrap Results of parameters and parameter standard deviation # OLS # # Parameter estimates # median [ -3.26216383 228.52546429 -14.57239967 34.27155426 -227.02816597] # mean [ -2.89855173 234.37139359 -14.98726881 27.96375666 -243.18361746] # std [ 3.78704907 97.35797802 9.16316538 94.65031973 221.79444244] # # Standard deviation of parameter estimates # median [ 5.44701033 81.96921398 7.58642431 80.64906783 200.19167735] # mean [ 5.44840542 86.02554883 8.56750041 80.41864084 201.81196849] # std [ 1.43425083 29.74806562 4.22063268 19.14973277 55.34848348] # # Bootstrap Results of parameters and parameter standard deviation # WLS removed 2 outliers from sample # # Parameter estimates # median [ -3.95876112 137.10419042 -9.29131131 88.40265447 -44.21091869] # mean [ -3.67485724 135.42681207 -8.7499235 89.74703443 -46.38622848] # std [ 2.96908679 56.36648967 7.03870751 48.51201918 106.92466097] # # Standard deviation of parameter estimates # median [ 2.89349748 59.19454402 6.70583332 45.40987953 119.05241283] # mean [ 2.97600894 60.14540249 6.92102065 45.66077486 121.35519673] # std [ 0.55378808 11.77831934 1.69289179 7.4911526 23.72821085] # # # #Conclusion: problem with outliers and possibly heteroscedasticity #----------------------------------------------------------------- # #in bootstrap results # #* bse in OLS underestimates the standard deviation of the parameters # compared to standard deviation in bootstrap #* OLS heteroscedasticity corrected standard errors for the original # data (above) are close to bootstrap std #* using WLS with 2 outliers removed has a relatively good match between # the mean or median bse and the std of the parameter estimates in the # bootstrap # #We could also include rsquared in bootstrap, and do it also for RLM. #The problems could also mean that the linearity assumption is violated, #e.g. try non-linear transformation of exog variables, but linear #in parameters. # # #for statsmodels # # * In this case rsquared for original data looks less random/arbitrary. # * Don't change definition of rsquared from centered tss to uncentered # tss when calculating rsquared in WLS if the original exog contains # a constant. The increase in rsquared because of a change in definition # will be very misleading. # * Whether there is a constant in the transformed exog, wexog, or not, # might affect also the degrees of freedom calculation, but I haven't # checked this. I would guess that the df_model should stay the same, # but needs to be verified with a textbook. # * df_model has to be adjusted if the original data does not have a # constant, e.g. when regressing an endog on a single exog variable # without constant. This case might require also a redefinition of # the rsquare and f statistic for the regression anova to use the # uncentered tss. # This can be done through keyword parameter to model.__init__ or # through autodedection with hasconst = (exog.var(0)<1e-10).any() # I'm not sure about fixed effects with a full dummy set but # without a constant. In this case autodedection wouldn't work this # way. Also, I'm not sure whether a ddof keyword parameter can also # handle the hasconst case. statsmodels-0.5.0+git13-g8e07d34/examples/tsa/000077500000000000000000000000001224417117700205575ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/examples/tsa/ex_arma2.py000066400000000000000000000015421224417117700226310ustar00rootroot00000000000000""" Autoregressive Moving Average (ARMA) Model """ import numpy as np import statsmodels.api as sm # Generate some data from an ARMA process from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) # The conventions of the arma_generate function require that we specify a # 1 for the zero-lag of the AR and MA parameters and that the AR parameters # be negated. ar = np.r_[1, -arparams] ma = np.r_[1, maparams] nobs = 250 y = arma_generate_sample(ar, ma, nobs) # Now, optionally, we can add some dates information. For this example, # we'll use a pandas time series. import pandas dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) y = pandas.TimeSeries(y, index=dates) arma_mod = sm.tsa.ARMA(y, order=(2, 2)) arma_res = arma_mod.fit(trend='nc', disp=-1) statsmodels-0.5.0+git13-g8e07d34/examples/tsa/ex_dates.py000066400000000000000000000023361224417117700227310ustar00rootroot00000000000000""" Using dates with timeseries models """ import statsmodels.api as sm import pandas # Getting started # --------------- data = sm.datasets.sunspots.load() # Right now an annual date series must be datetimes at the end of the year. dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) # Using Pandas # ------------ # Make a pandas TimeSeries or DataFrame endog = pandas.TimeSeries(data.endog, index=dates) # and instantiate the model ar_model = sm.tsa.AR(endog, freq='A') pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) # Let's do some out-of-sample prediction pred = pandas_ar_res.predict(start='2005', end='2015') print pred # Using explicit dates # -------------------- ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A') ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1) pred = ar_res.predict(start='2005', end='2015') print pred # This just returns a regular array, but since the model has date information # attached, you can get the prediction dates in a roundabout way. print ar_res.data.predict_dates # This attribute only exists if predict has been called. It holds the dates # associated with the last call to predict. #..TODO: should this be attached to the results instance? statsmodels-0.5.0+git13-g8e07d34/examples/tsa/example_arima.py000066400000000000000000000030361224417117700237370ustar00rootroot00000000000000from statsmodels.datasets.macrodata import load_pandas from statsmodels.tsa.base.datetools import dates_from_range from statsmodels.tsa.arima_model import ARIMA import matplotlib.pyplot as plt import numpy as np import statsmodels.api as sm plt.interactive(False) # let's examine an ARIMA model of CPI cpi = load_pandas().data['cpi'] dates = dates_from_range('1959q1', '2009q3') cpi.index = dates res = ARIMA(cpi, (1, 1, 1), freq='Q').fit() print res.summary() # we can look at the series cpi.diff().plot() # maybe logs are better log_cpi = np.log(cpi) # check the ACF and PCF plots acf, confint_acf = sm.tsa.acf(log_cpi.diff().values[1:], confint=95) # center the confidence intervals about zero #confint_acf -= confint_acf.mean(1)[:, None] pacf = sm.tsa.pacf(log_cpi.diff().values[1:], method='ols') # confidence interval is now an option to pacf from scipy import stats confint_pacf = stats.norm.ppf(1 - .025) * np.sqrt(1 / 202.) fig = plt.figure() ax = fig.add_subplot(121) ax.set_title('Autocorrelation') ax.plot(range(41), acf, 'bo', markersize=5) ax.vlines(range(41), 0, acf) ax.fill_between(range(41), confint_acf[:, 0], confint_acf[:, 1], alpha=.25) fig.tight_layout() ax = fig.add_subplot(122, sharey=ax) ax.vlines(range(41), 0, pacf) ax.plot(range(41), pacf, 'bo', markersize=5) ax.fill_between(range(41), -confint_pacf, confint_pacf, alpha=.25) #NOTE: you'll be able to just to this when tsa-plots is in master #sm.graphics.acf_plot(x, nlags=40) #sm.graphics.pacf_plot(x, nlags=40) # still some seasonality # try an arma(1, 1) with ma(4) term statsmodels-0.5.0+git13-g8e07d34/ez_setup.py000066400000000000000000000243001224417117700203610ustar00rootroot00000000000000#!python """Bootstrap setuptools installation If you want to use setuptools in your package's setup.py, just include this file in the same directory with it, and add this to the top of your setup.py:: from ez_setup import use_setuptools use_setuptools() If you want to require a specific version of setuptools, set a download mirror, or use an alternate download directory, you can do so by supplying the appropriate options to ``use_setuptools()``. This file can also be run as a script to install or upgrade setuptools. """ import sys DEFAULT_VERSION = "0.6c11" DEFAULT_URL = "http://pypi.python.org/packages/%s/s/setuptools/" % sys.version[ :3] md5_data = { 'setuptools-0.6b1-py2.3.egg': '8822caf901250d848b996b7f25c6e6ca', 'setuptools-0.6b1-py2.4.egg': 'b79a8a403e4502fbb85ee3f1941735cb', 'setuptools-0.6b2-py2.3.egg': '5657759d8a6d8fc44070a9d07272d99b', 'setuptools-0.6b2-py2.4.egg': '4996a8d169d2be661fa32a6e52e4f82a', 'setuptools-0.6b3-py2.3.egg': 'bb31c0fc7399a63579975cad9f5a0618', 'setuptools-0.6b3-py2.4.egg': '38a8c6b3d6ecd22247f179f7da669fac', 'setuptools-0.6b4-py2.3.egg': '62045a24ed4e1ebc77fe039aa4e6f7e5', 'setuptools-0.6b4-py2.4.egg': '4cb2a185d228dacffb2d17f103b3b1c4', 'setuptools-0.6c1-py2.3.egg': 'b3f2b5539d65cb7f74ad79127f1a908c', 'setuptools-0.6c1-py2.4.egg': 'b45adeda0667d2d2ffe14009364f2a4b', 'setuptools-0.6c10-py2.3.egg': 'ce1e2ab5d3a0256456d9fc13800a7090', 'setuptools-0.6c10-py2.4.egg': '57d6d9d6e9b80772c59a53a8433a5dd4', 'setuptools-0.6c10-py2.5.egg': 'de46ac8b1c97c895572e5e8596aeb8c7', 'setuptools-0.6c10-py2.6.egg': '58ea40aef06da02ce641495523a0b7f5', 'setuptools-0.6c11-py2.3.egg': '2baeac6e13d414a9d28e7ba5b5a596de', 'setuptools-0.6c11-py2.4.egg': 'bd639f9b0eac4c42497034dec2ec0c2b', 'setuptools-0.6c11-py2.5.egg': '64c94f3bf7a72a13ec83e0b24f2749b2', 'setuptools-0.6c11-py2.6.egg': 'bfa92100bd772d5a213eedd356d64086', 'setuptools-0.6c2-py2.3.egg': 'f0064bf6aa2b7d0f3ba0b43f20817c27', 'setuptools-0.6c2-py2.4.egg': '616192eec35f47e8ea16cd6a122b7277', 'setuptools-0.6c3-py2.3.egg': 'f181fa125dfe85a259c9cd6f1d7b78fa', 'setuptools-0.6c3-py2.4.egg': 'e0ed74682c998bfb73bf803a50e7b71e', 'setuptools-0.6c3-py2.5.egg': 'abef16fdd61955514841c7c6bd98965e', 'setuptools-0.6c4-py2.3.egg': 'b0b9131acab32022bfac7f44c5d7971f', 'setuptools-0.6c4-py2.4.egg': '2a1f9656d4fbf3c97bf946c0a124e6e2', 'setuptools-0.6c4-py2.5.egg': '8f5a052e32cdb9c72bcf4b5526f28afc', 'setuptools-0.6c5-py2.3.egg': 'ee9fd80965da04f2f3e6b3576e9d8167', 'setuptools-0.6c5-py2.4.egg': 'afe2adf1c01701ee841761f5bcd8aa64', 'setuptools-0.6c5-py2.5.egg': 'a8d3f61494ccaa8714dfed37bccd3d5d', 'setuptools-0.6c6-py2.3.egg': '35686b78116a668847237b69d549ec20', 'setuptools-0.6c6-py2.4.egg': '3c56af57be3225019260a644430065ab', 'setuptools-0.6c6-py2.5.egg': 'b2f8a7520709a5b34f80946de5f02f53', 'setuptools-0.6c7-py2.3.egg': '209fdf9adc3a615e5115b725658e13e2', 'setuptools-0.6c7-py2.4.egg': '5a8f954807d46a0fb67cf1f26c55a82e', 'setuptools-0.6c7-py2.5.egg': '45d2ad28f9750e7434111fde831e8372', 'setuptools-0.6c8-py2.3.egg': '50759d29b349db8cfd807ba8303f1902', 'setuptools-0.6c8-py2.4.egg': 'cba38d74f7d483c06e9daa6070cce6de', 'setuptools-0.6c8-py2.5.egg': '1721747ee329dc150590a58b3e1ac95b', 'setuptools-0.6c9-py2.3.egg': 'a83c4020414807b496e4cfbe08507c03', 'setuptools-0.6c9-py2.4.egg': '260a2be2e5388d66bdaee06abec6342a', 'setuptools-0.6c9-py2.5.egg': 'fe67c3e5a17b12c0e7c541b7ea43a8e6', 'setuptools-0.6c9-py2.6.egg': 'ca37b1ff16fa2ede6e19383e7b59245a', } import sys import os try: from hashlib import md5 except ImportError: from md5 import md5 def _validate_md5(egg_name, data): if egg_name in md5_data: digest = md5(data).hexdigest() if digest != md5_data[egg_name]: print >>sys.stderr, ( "md5 validation of %s failed! (Possible download problem?)" % egg_name ) sys.exit(2) return data def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, download_delay=15 ): """Automatically find/download setuptools and make it available on sys.path `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where setuptools will be downloaded, if it is not already available. If `download_delay` is specified, it should be the number of seconds that will be paused before initiating a download, should one be required. If an older version of setuptools is installed, this routine will print a message to ``sys.stderr`` and raise SystemExit in an attempt to abort the calling script. """ was_imported = 'pkg_resources' in sys.modules or 'setuptools' in sys.modules def do_download(): egg = download_setuptools( version, download_base, to_dir, download_delay) sys.path.insert(0, egg) import setuptools setuptools.bootstrap_install_from = egg try: import pkg_resources except ImportError: return do_download() try: pkg_resources.require("setuptools>=" + version) return except pkg_resources.VersionConflict, e: if was_imported: print >>sys.stderr, ( "The required version of setuptools (>=%s) is not available, and\n" "can't be installed while this script is running. Please install\n" " a more recent version first, using 'easy_install -U setuptools'." "\n\n(Currently using %r)" ) % (version, e.args[0]) sys.exit(2) else: del pkg_resources, sys.modules['pkg_resources'] # reload ok return do_download() except pkg_resources.DistributionNotFound: return do_download() def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, delay=15 ): """Download setuptools from a specified location and return its filename `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. """ import urllib2 import shutil egg_name = "setuptools-%s-py%s.egg" % (version, sys.version[:3]) url = download_base + egg_name saveto = os.path.join(to_dir, egg_name) src = dst = None if not os.path.exists(saveto): # Avoid repeated downloads try: from distutils import log if delay: log.warn(""" --------------------------------------------------------------------------- This script requires setuptools version %s to run (even to display help). I will attempt to download it for you (from %s), but you may need to enable firewall access for this script first. I will start the download in %d seconds. (Note: if this machine does not have network access, please obtain the file %s and place it in this directory before rerunning this script.) ---------------------------------------------------------------------------""", version, download_base, delay, url ) from time import sleep sleep(delay) log.warn("Downloading %s", url) src = urllib2.urlopen(url) # Read/write all in one block, so we don't create a corrupt file # if the download is interrupted. data = _validate_md5(egg_name, src.read()) dst = open(saveto, "wb") dst.write(data) finally: if src: src.close() if dst: dst.close() return os.path.realpath(saveto) def main(argv, version=DEFAULT_VERSION): """Install or upgrade setuptools and EasyInstall""" try: import setuptools except ImportError: egg = None try: egg = download_setuptools(version, delay=0) sys.path.insert(0, egg) from setuptools.command.easy_install import main return main(list(argv) + [egg]) # we're done here finally: if egg and os.path.exists(egg): os.unlink(egg) else: if setuptools.__version__ == '0.0.1': print >>sys.stderr, ( "You have an obsolete version of setuptools installed. Please\n" "remove it from your system entirely before rerunning this script." ) sys.exit(2) req = "setuptools>=" + version import pkg_resources try: pkg_resources.require(req) except pkg_resources.VersionConflict: try: from setuptools.command.easy_install import main except ImportError: from easy_install import main main(list(argv) + [download_setuptools(delay=0)]) sys.exit(0) # try to force an exit else: if argv: from setuptools.command.easy_install import main main(argv) else: print "Setuptools version", version, "or greater has been installed." print '(Run "ez_setup.py -U setuptools" to reinstall or upgrade.)' def update_md5(filenames): """Update our built-in md5 registry""" import re for name in filenames: base = os.path.basename(name) f = open(name, 'rb') md5_data[base] = md5(f.read()).hexdigest() f.close() data = [" %r: %r,\n" % it for it in md5_data.items()] data.sort() repl = "".join(data) import inspect srcfile = inspect.getsourcefile(sys.modules[__name__]) f = open(srcfile, 'rb') src = f.read() f.close() match = re.search("\nmd5_data = {\n([^}]+)}", src) if not match: print >>sys.stderr, "Internal error!" sys.exit(2) src = src[:match.start(1)] + repl + src[match.end(1):] f = open(srcfile, 'w') f.write(src) f.close() if __name__ == '__main__': if len(sys.argv) > 2 and sys.argv[1] == '--md5update': update_md5(sys.argv[2:]) else: main(sys.argv[1:]) statsmodels-0.5.0+git13-g8e07d34/setup.cfg000066400000000000000000000000001224417117700177610ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/setup.py000066400000000000000000000412421224417117700176670ustar00rootroot00000000000000""" Much of the build system code was adapted from work done by the pandas developers [1], which was in turn based on work done in pyzmq [2] and lxml [3]. [1] http://pandas.pydata.org [2] http://zeromq.github.io/pyzmq/ [3] http://lxml.de/ """ import os from os.path import splitext, basename, join as pjoin import sys import subprocess import re # temporarily redirect config directory to prevent matplotlib importing # testing that for writeable directory which results in sandbox error in # certain easy_install versions os.environ["MPLCONFIGDIR"] = "." # may need to work around setuptools bug by providing a fake Pyrex try: import Cython sys.path.insert(0, pjoin(os.path.dirname(__file__), "fake_pyrex")) except ImportError: pass # try bootstrapping setuptools if it doesn't exist try: import pkg_resources try: pkg_resources.require("setuptools>=0.6c5") except pkg_resources.VersionConflict: from ez_setup import use_setuptools use_setuptools(version="0.6c5") from setuptools import setup, Command, find_packages _have_setuptools = True except ImportError: # no setuptools installed from distutils.core import setup, Command _have_setuptools = False setuptools_kwargs = {} if sys.version_info[0] >= 3: setuptools_kwargs = {'use_2to3': True, 'zip_safe': False, #'use_2to3_exclude_fixers': [], } if not _have_setuptools: sys.exit("need setuptools/distribute for Py3k" "\n$ pip install distribute") else: setuptools_kwargs = { 'install_requires': [], 'zip_safe': False, } if not _have_setuptools: setuptools_kwargs = {} curdir = os.path.abspath(os.path.dirname(__file__)) README = open(pjoin(curdir, "README.txt")).read() DISTNAME = 'statsmodels' DESCRIPTION = 'Statistical computations and models for use with SciPy' LONG_DESCRIPTION = README MAINTAINER = 'Skipper Seabold, Josef Perktold' MAINTAINER_EMAIL ='pystatsmodels@googlegroups.com' URL = 'http://statsmodels.sourceforge.net/' LICENSE = 'BSD License' DOWNLOAD_URL = '' from distutils.extension import Extension from distutils.command.build import build from distutils.command.sdist import sdist from distutils.command.build_ext import build_ext as _build_ext try: from Cython.Distutils import build_ext as _build_ext # from Cython.Distutils import Extension # to get pyrex debugging symbols cython = True except ImportError: cython = False class build_ext(_build_ext): def build_extensions(self): numpy_incl = pkg_resources.resource_filename('numpy', 'core/include') for ext in self.extensions: if (hasattr(ext, 'include_dirs') and not numpy_incl in ext.include_dirs): ext.include_dirs.append(numpy_incl) _build_ext.build_extensions(self) def strip_rc(version): return re.sub(r"rc\d+$", "", version) def check_dependency_versions(min_versions): """ Don't let setuptools do this. It's rude. Just makes sure it can import the packages and if not, stops the build process. """ from distutils.version import StrictVersion try: from numpy.version import short_version as npversion except ImportError: raise ImportError("statsmodels requires numpy") try: from scipy.version import short_version as spversion except ImportError: try: # scipy 0.7.0 from scipy.version import version as spversion except ImportError: raise ImportError("statsmodels requires scipy") try: from pandas.version import version as pversion except ImportError: raise ImportError("statsmodels requires pandas") try: from patsy import __version__ as patsy_version except ImportError: raise ImportError("statsmodels requires patsy. http://patsy.readthedocs.org") try: assert StrictVersion(strip_rc(npversion)) >= min_versions['numpy'] except AssertionError: raise ImportError("Numpy version is %s. Requires >= %s" % (npversion, min_versions['numpy'])) try: assert StrictVersion(strip_rc(spversion)) >= min_versions['scipy'] except AssertionError: raise ImportError("Scipy version is %s. Requires >= %s" % (spversion, min_versions['scipy'])) try: #NOTE: not sure how robust this regex is but it at least allows # double digit version numbering pversion = re.match("\d*\.\d*\.\d*", pversion).group() assert StrictVersion(pversion) >= min_versions['pandas'] except AssertionError: raise ImportError("Pandas version is %s. Requires >= %s" % (pversion, min_versions['pandas'])) try: # patsy dev looks like 0.1.0+dev pversion = re.match("\d*\.\d*\.\d*", patsy_version).group() assert StrictVersion(pversion) >= min_versions['patsy'] except AssertionError: raise ImportError("Patsy version is %s. Requires >= %s" % (pversion, min_versions["patsy"])) MAJ = 0 MIN = 5 REV = 0 ISRELEASED = True VERSION = '%d.%d.%d' % (MAJ,MIN,REV) classifiers = [ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.2', 'Operating System :: OS Independent', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Topic :: Scientific/Engineering'] # Return the git revision as a string def git_version(): def _minimal_ext_cmd(cmd): # construct minimal environment env = {} for k in ['SYSTEMROOT', 'PATH']: v = os.environ.get(k) if v is not None: env[k] = v # LANGUAGE is used on win32 env['LANGUAGE'] = 'C' env['LANG'] = 'C' env['LC_ALL'] = 'C' out = subprocess.Popen(" ".join(cmd), stdout = subprocess.PIPE, env=env, shell=True).communicate()[0] return out try: out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) GIT_REVISION = out.strip().decode('ascii') except OSError: GIT_REVISION = "Unknown" return GIT_REVISION def write_version_py(filename=pjoin(curdir, 'statsmodels/version.py')): cnt = "\n".join(["", "# THIS FILE IS GENERATED FROM SETUP.PY", "short_version = '%(version)s'", "version = '%(version)s'", "full_version = '%(full_version)s'", "git_revision = '%(git_revision)s'", "release = %(isrelease)s", "", "if not release:", " version = full_version"]) # Adding the git rev number needs to be done inside write_version_py(), # otherwise the import of numpy.version messes up the build under Python 3. FULLVERSION = VERSION dowrite = True if os.path.exists('.git'): GIT_REVISION = git_version() elif os.path.exists(filename): # must be a source distribution, use existing version file try: from statsmodels.version import git_revision as GIT_REVISION except ImportError: dowrite = False else: GIT_REVISION = "Unknown" if not ISRELEASED: FULLVERSION += '.dev-' + GIT_REVISION[:7] if dowrite: try: a = open(filename, 'w') a.write(cnt % {'version': VERSION, 'full_version' : FULLVERSION, 'git_revision' : GIT_REVISION, 'isrelease': str(ISRELEASED)}) finally: a.close() try: from distutils.command.build_py import build_py_2to3 as build_py except ImportError: # 2.x from distutils.command.build_py import build_py class CleanCommand(Command): """Custom distutils command to clean the .so and .pyc files.""" user_options = [("all", "a", "")] def initialize_options(self): self.all = True self._clean_me = [] self._clean_trees = [] self._clean_exclude = ["bspline_ext.c", "bspline_impl.c"] for root, dirs, files in list(os.walk('statsmodels')): for f in files: if f in self._clean_exclude: continue if os.path.splitext(f)[-1] in ('.pyc', '.so', '.o', '.pyo', '.pyd', '.c', '.orig'): self._clean_me.append(pjoin(root, f)) for d in dirs: if d == '__pycache__': self._clean_trees.append(pjoin(root, d)) for d in ('build',): if os.path.exists(d): self._clean_trees.append(d) def finalize_options(self): pass def run(self): for clean_me in self._clean_me: try: os.unlink(clean_me) except Exception: pass for clean_tree in self._clean_trees: try: import shutil shutil.rmtree(clean_tree) except Exception: pass class CheckSDist(sdist): """Custom sdist that ensures Cython has compiled all pyx files to c.""" _pyxfiles = ['statsmodels/nonparametric/linbin.pyx', 'statsmodels/nonparametric/_smoothers_lowess.pyx', 'statsmodels/tsa/kalmanf/kalman_loglike.pyx'] def initialize_options(self): sdist.initialize_options(self) ''' self._pyxfiles = [] for root, dirs, files in os.walk('statsmodels'): for f in files: if f.endswith('.pyx'): self._pyxfiles.append(pjoin(root, f)) ''' def run(self): if 'cython' in cmdclass: self.run_command('cython') else: for pyxfile in self._pyxfiles: cfile = pyxfile[:-3] + 'c' msg = "C-source file '%s' not found." % (cfile) +\ " Run 'setup.py cython' before sdist." assert os.path.isfile(cfile), msg sdist.run(self) class CheckingBuildExt(build_ext): """Subclass build_ext to get clearer report if Cython is necessary.""" def check_cython_extensions(self, extensions): for ext in extensions: for src in ext.sources: if not os.path.exists(src): raise Exception("""Cython-generated file '%s' not found. Cython is required to compile statsmodels from a development branch. Please install Cython or download a source release of statsmodels. """ % src) def build_extensions(self): self.check_cython_extensions(self.extensions) build_ext.build_extensions(self) class CythonCommand(build_ext): """Custom distutils command subclassed from Cython.Distutils.build_ext to compile pyx->c, and stop there. All this does is override the C-compile method build_extension() with a no-op.""" def build_extension(self, ext): pass class DummyBuildSrc(Command): """ numpy's build_src command interferes with Cython's build_ext. """ user_options = [] def initialize_options(self): self.py_modules_dict = {} def finalize_options(self): pass def run(self): pass cmdclass = {'clean': CleanCommand, 'build': build, 'sdist': CheckSDist} if cython: suffix = ".pyx" cmdclass["build_ext"] = CheckingBuildExt cmdclass["cython"] = CythonCommand else: suffix = ".c" cmdclass["build_src"] = DummyBuildSrc cmdclass["build_ext"] = CheckingBuildExt lib_depends = [] def srcpath(name=None, suffix='.pyx', subdir='src'): return pjoin('statsmodels', subdir, name + suffix) if suffix == ".pyx": lib_depends = [srcpath(f, suffix=".pyx") for f in lib_depends] else: lib_depends = [] common_include = [] # some linux distros require it libraries = ['m'] if 'win32' not in sys.platform else [] ext_data = dict( kalman_loglike = {"pyxfile" : "tsa/kalmanf/kalman_loglike", "depends" : [], "sources" : []}, linbin = {"pyxfile" : "nonparametric/linbin", "depends" : [], "sources" : []}, _smoothers_lowess = {"pyxfile" : "nonparametric/_smoothers_lowess", "depends" : [], "sources" : []} ) def pxd(name): return os.path.abspath(pjoin('pandas', name + '.pxd')) extensions = [] for name, data in ext_data.items(): sources = [srcpath(data['pyxfile'], suffix=suffix, subdir='')] pxds = [pxd(x) for x in data.get('pxdfiles', [])] destdir = ".".join(os.path.dirname(data["pyxfile"]).split("/")) if suffix == '.pyx' and pxds: sources.extend(pxds) sources.extend(data.get('sources', [])) include = data.get('include', common_include) obj = Extension('statsmodels.%s.%s' % (destdir, name), sources=sources, depends=data.get('depends', []), include_dirs=include) extensions.append(obj) if suffix == '.pyx' and 'setuptools' in sys.modules: # undo dumb setuptools bug clobbering .pyx sources back to .c for ext in extensions: if ext.sources[0].endswith('.c'): root, _ = os.path.splitext(ext.sources[0]) ext.sources[0] = root + suffix if _have_setuptools: setuptools_kwargs["test_suite"] = "nose.collector" from os.path import relpath def get_data_files(): sep = os.path.sep # install the datasets data_files = {} root = pjoin(curdir, "statsmodels", "datasets") for i in os.listdir(root): if i is "tests": continue path = pjoin(root, i) if os.path.isdir(path): data_files.update({relpath(path, start=curdir).replace(sep, ".") : ["*.csv", "*.dta"]}) # add all the tests and results files for r, ds, fs in os.walk(pjoin(curdir, "statsmodels")): if r.endswith('results') and 'sandbox' not in r: data_files.update({relpath(r, start=curdir).replace(sep, ".") : ["*.csv", "*.txt"]}) return data_files if __name__ == "__main__": if os.path.exists('MANIFEST'): os.unlink('MANIFEST') min_versions = { 'numpy' : '1.4.0', 'scipy' : '0.7.0', 'pandas' : '0.7.1', 'patsy' : '0.1.0', } if sys.version_info[0] == 3 and sys.version_info[1] >= 3: # 3.3 needs numpy 1.7+ min_versions.update({"numpy" : "1.7.0b2"}) check_dependency_versions(min_versions) write_version_py() # this adds *.csv and *.dta files in datasets folders # and *.csv and *.txt files in test/results folders package_data = get_data_files() packages = find_packages() packages.append("statsmodels.tsa.vector_ar.data") package_data["statsmodels.datasets.tests"].append("*.zip") package_data["statsmodels.iolib.tests.results"].append("*.dta") package_data["statsmodels.stats.tests.results"].append("*.json") package_data["statsmodels.tsa.vector_ar.tests.results"].append("*.npz") # data files that don't follow the tests/results pattern. should fix. package_data.update({"statsmodels.stats.tests" : ["*.txt"]}) # the next two are in the sdist, but I don't manage to get them installed package_data.update({"statsmodels.stats.libqstrung" : ["*.r", "*.txt", "*.dat"]}) package_data.update({"statsmodels.stats.libqstrung.tests" : ["*.csv", "*.dat"]}) package_data.update({"statsmodels.tsa.vector_ar.data" : ["*.dat"]}) package_data.update({"statsmodels.tsa.vector_ar.data" : ["*.dat"]}) # Why are we installing this stuff? #TODO: deal with this. Not sure if it ever worked for bdists #('docs/build/htmlhelp/statsmodelsdoc.chm', # 'statsmodels/statsmodelsdoc.chm') setup(name = DISTNAME, version = VERSION, maintainer = MAINTAINER, ext_modules = extensions, maintainer_email = MAINTAINER_EMAIL, description = DESCRIPTION, license = LICENSE, url = URL, download_url = DOWNLOAD_URL, long_description = LONG_DESCRIPTION, classifiers = classifiers, platforms = 'any', cmdclass = cmdclass, packages = packages, package_data = package_data, include_package_data=True, **setuptools_kwargs) statsmodels-0.5.0+git13-g8e07d34/statsmodels/000077500000000000000000000000001224417117700205145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/LICENSE.txt000066400000000000000000000030011224417117700223310ustar00rootroot00000000000000Copyright (C) 2006, Jonathan E. Taylor All rights reserved. Copyright (c) 2006-2008 Scipy Developers. All rights reserved. Copyright (c) 2009 Statsmodels Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. The name of the author may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. statsmodels-0.5.0+git13-g8e07d34/statsmodels/TODO.txt000066400000000000000000000010411224417117700220160ustar00rootroot00000000000000Tests TODO ---------- Test I/O of models wrt array types, dimensions - add checks in all top class for data Known Issues ---------- Need to clip mu's in GLM to avoid np.log(0), etc. (done for gamma) Regression will not work with a 1d array for exog (pinv needs two), then other calculations need checking and changing TODO ----- Make a recarray dataset and masked dataset for testing and development Rename bse Add tvalues attribute to results instead of calling t method? note the tests requirements somewhere (rpy, R, car library) statsmodels-0.5.0+git13-g8e07d34/statsmodels/__init__.py000066400000000000000000000050671224417117700226350ustar00rootroot00000000000000# # models - Statistical Models # from __future__ import with_statement __docformat__ = 'restructuredtext' #from version import __version__ #from info import __doc__ #from regression import * #from genmod.glm import * #from robust.rlm import * #from discrete.discretemod import * #import tsa #from tools.tools import add_constant, chain_dot #import base.model #import tools.tools #import datasets #import glm.families #import stats.stattools #import iolib from numpy import errstate #__all__ = filter(lambda s:not s.startswith('_'),dir()) from numpy.testing import Tester class NoseWrapper(Tester): ''' This is simply a monkey patch for numpy.testing.Tester. It allows extra_argv to be changed from its default None to ['--exe'] so that the tests can be run the same across platforms. It also takes kwargs that are passed to numpy.errstate to suppress floating point warnings. ''' def test(self, label='fast', verbose=1, extra_argv=['--exe'], doctests=False, coverage=False, **kwargs): ''' Run tests for module using nose %(test_header)s doctests : boolean If True, run doctests in module, default False coverage : boolean If True, report coverage of NumPy code, default False (Requires the coverage module: http://nedbatchelder.com/code/modules/coverage.html) kwargs Passed to numpy.errstate. See its documentation for details. ''' # cap verbosity at 3 because nose becomes *very* verbose beyond that verbose = min(verbose, 3) from numpy.testing import utils utils.verbose = verbose if doctests: print("Running unit tests and doctests for %s" % self.package_name) else: print("Running unit tests for %s" % self.package_name) self._show_system_info() # reset doctest state on every run import doctest doctest.master = None argv, plugins = self.prepare_test_args(label, verbose, extra_argv, doctests, coverage) from numpy.testing.noseclasses import NumpyTestProgram from warnings import simplefilter #, catch_warnings with errstate(**kwargs): ## with catch_warnings(): simplefilter('ignore', category=DeprecationWarning) t = NumpyTestProgram(argv=argv, exit=False, plugins=plugins) return t.result test = NoseWrapper().test try: from .version import version as __version__ except ImportError: __version__ = 'not-yet-built' statsmodels-0.5.0+git13-g8e07d34/statsmodels/api.py000066400000000000000000000022561224417117700216440ustar00rootroot00000000000000import iolib, datasets, tools from tools.tools import add_constant, categorical import regression from .regression.linear_model import OLS, GLS, WLS, GLSAR from .regression.quantile_regression import QuantReg from .genmod.generalized_linear_model import GLM from .genmod import families import robust from .robust.robust_linear_model import RLM from .discrete.discrete_model import (Poisson, Logit, Probit, MNLogit, NegativeBinomial) from .tsa import api as tsa from .nonparametric import api as nonparametric import distributions from __init__ import test from . import version from info import __doc__ from graphics.gofplots import qqplot, qqplot_2samples, qqline, ProbPlot from .graphics import api as graphics from .stats import api as stats from .emplike import api as emplike from .formula import api as formula from .iolib.smpickle import load_pickle as load from .tools.print_version import show_versions import os chmpath = os.path.join(os.path.dirname(__file__), 'statsmodelsdoc.chm') if os.path.exists(chmpath): def open_help(chmpath=chmpath): from subprocess import Popen p = Popen(chmpath, shell=True) del os del chmpath statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/000077500000000000000000000000001224417117700214265ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/__init__.py000066400000000000000000000001031224417117700235310ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/data.py000066400000000000000000000330101224417117700227060ustar00rootroot00000000000000""" Base tools for handling various kinds of data structures, attaching metadata to results, and doing data cleaning """ import numpy as np from pandas import DataFrame, Series, TimeSeries, isnull from statsmodels.tools.decorators import (resettable_cache, cache_readonly, cache_writable) import statsmodels.tools.data as data_util try: reduce pass except NameError: #python 3.2 from functools import reduce class MissingDataError(Exception): pass def _asarray_2dcolumns(x): if np.asarray(x).ndim > 1 and np.asarray(x).squeeze().ndim == 1: return def _asarray_2d_null_rows(x): """ Makes sure input is an array and is 2d. Makes sure output is 2d. True indicates a null in the rows of 2d x. """ #Have to have the asarrays because isnull doesn't account for array-like #input x = np.asarray(x) if x.ndim == 1: x = x[:,None] return np.any(isnull(x), axis=1)[:,None] def _nan_rows(*arrs): """ Returns a boolean array which is True where any of the rows in any of the _2d_ arrays in arrs are NaNs. Inputs can be any mixture of Series, DataFrames or array-like. """ if len(arrs) == 1: arrs += ([[False]],) def _nan_row_maybe_two_inputs(x, y): # check for dtype bc dataframe has dtypes x_is_boolean_array = hasattr(x, 'dtype') and x.dtype == bool and x return np.logical_or(_asarray_2d_null_rows(x), (x_is_boolean_array | _asarray_2d_null_rows(y))) return reduce(_nan_row_maybe_two_inputs, arrs).squeeze() class ModelData(object): """ Class responsible for handling input data and extracting metadata into the appropriate form """ def __init__(self, endog, exog=None, missing='none', hasconst=None, **kwargs): if missing != 'none': arrays, nan_idx = self._handle_missing(endog, exog, missing, **kwargs) self.missing_row_idx = nan_idx self.__dict__.update(arrays) # attach all the data arrays self.orig_endog = self.endog self.orig_exog = self.exog self.endog, self.exog = self._convert_endog_exog(self.endog, self.exog) else: self.__dict__.update(kwargs) # attach the extra arrays anyway self.orig_endog = endog self.orig_exog = exog self.endog, self.exog = self._convert_endog_exog(endog, exog) # this has side-effects, attaches k_constant and const_idx self._handle_constant(hasconst) self._check_integrity() self._cache = resettable_cache() def _handle_constant(self, hasconst): if hasconst is not None: if hasconst: self.k_constant = 1 self.const_idx = None else: self.k_constant = 0 self.const_idx = None else: try: # to detect where the constant is const_idx = np.where(self.exog.var(axis = 0) == 0)[0].squeeze() self.k_constant = const_idx.size if self.k_constant > 1: raise ValueError("More than one constant detected.") else: self.const_idx = const_idx except: # should be an index error but who knows, means no const self.const_idx = None self.k_constant = 0 def _drop_nans(self, x, nan_mask): return x[nan_mask] def _drop_nans_2d(self, x, nan_mask): return x[nan_mask][:, nan_mask] def _handle_missing(self, endog, exog, missing, **kwargs): """ This returns a dictionary with keys endog, exog and the keys of kwargs. It preserves Nones. """ none_array_names = [] if exog is not None: combined = (endog, exog) combined_names = ['endog', 'exog'] else: combined = (endog,) combined_names = ['endog'] none_array_names += ['exog'] # deal with other arrays combined_2d = () combined_2d_names = [] if len(kwargs): for key, value_array in kwargs.iteritems(): if value_array is None or value_array.ndim == 0: none_array_names += [key] continue # grab 1d arrays if value_array.ndim == 1: combined += (value_array,) combined_names += [key] elif value_array.squeeze().ndim == 1: combined += (value_array,) combined_names += [key] # grab 2d arrays that are _assumed_ to be symmetric elif value_array.ndim == 2: combined_2d += (value_array,) combined_2d_names += [key] else: raise ValueError("Arrays with more than 2 dimensions " "aren't yet handled") nan_mask = _nan_rows(*combined) if combined_2d: nan_mask = _nan_rows(*(nan_mask[:,None],) + combined_2d) if missing == 'raise' and np.any(nan_mask): raise MissingDataError("NaNs were encountered in the data") elif missing == 'drop': nan_mask = ~nan_mask drop_nans = lambda x : self._drop_nans(x, nan_mask) drop_nans_2d = lambda x : self._drop_nans_2d(x, nan_mask) combined = dict(zip(combined_names, map(drop_nans, combined))) if combined_2d: combined.update(dict(zip(combined_2d_names, map(drop_nans_2d, combined_2d)))) if none_array_names: combined.update(dict(zip(none_array_names, [None]*len(none_array_names) ))) return combined, np.where(~nan_mask)[0].tolist() else: raise ValueError("missing option %s not understood" % missing) def _convert_endog_exog(self, endog, exog): # for consistent outputs if endog is (n,1) yarr = self._get_yarr(endog) xarr = None if exog is not None: xarr = self._get_xarr(exog) if xarr.ndim == 1: xarr = xarr[:, None] if xarr.ndim != 2: raise ValueError("exog is not 1d or 2d") return yarr, xarr @cache_writable() def ynames(self): endog = self.orig_endog ynames = self._get_names(endog) if not ynames: ynames = _make_endog_names(self.endog) if len(ynames) == 1: return ynames[0] else: return list(ynames) @cache_writable() def xnames(self): exog = self.orig_exog if exog is not None: xnames = self._get_names(exog) if not xnames: xnames = _make_exog_names(self.exog) return list(xnames) return None @cache_readonly def row_labels(self): exog = self.orig_exog if exog is not None: row_labels = self._get_row_labels(exog) else: endog = self.orig_endog row_labels = self._get_row_labels(endog) return row_labels def _get_row_labels(self, arr): return None def _get_names(self, arr): if isinstance(arr, DataFrame): return list(arr.columns) elif isinstance(arr, Series): if arr.name: return [arr.name] else: return else: try: return arr.dtype.names except AttributeError: pass return None def _get_yarr(self, endog): if data_util._is_structured_ndarray(endog): endog = data_util.struct_to_ndarray(endog) endog = np.asarray(endog) if len(endog) == 1: # never squeeze to a scalar if endog.ndim == 1: return endog elif endog.ndim > 1: return np.asarray([endog.squeeze()]) return endog.squeeze() def _get_xarr(self, exog): if data_util._is_structured_ndarray(exog): exog = data_util.struct_to_ndarray(exog) return np.asarray(exog) def _check_integrity(self): if self.exog is not None: if len(self.exog) != len(self.endog): raise ValueError("endog and exog matrices are different sizes") def wrap_output(self, obj, how='columns'): if how == 'columns': return self.attach_columns(obj) elif how == 'rows': return self.attach_rows(obj) elif how == 'cov': return self.attach_cov(obj) elif how == 'dates': return self.attach_dates(obj) elif how == 'columns_eq': return self.attach_columns_eq(obj) elif how == 'cov_eq': return self.attach_cov_eq(obj) else: return obj def attach_columns(self, result): return result def attach_columns_eq(self, result): return result def attach_cov(self, result): return result def attach_cov_eq(self, result): return result def attach_rows(self, result): return result def attach_dates(self, result): return result class PatsyData(ModelData): def _get_names(self, arr): return arr.design_info.column_names class PandasData(ModelData): """ Data handling class which knows how to reattach pandas metadata to model results """ def _drop_nans(self, x, nan_mask): if hasattr(x, 'ix'): return x.ix[nan_mask] else: # extra arguments could be plain ndarrays return super(PandasData, self)._drop_nans(x, nan_mask) def _drop_nans_2d(self, x, nan_mask): if hasattr(x, 'ix'): return x.ix[nan_mask].ix[:, nan_mask] else: # extra arguments could be plain ndarrays return super(PandasData, self)._drop_nans_2d(x, nan_mask) def _check_integrity(self): try: endog, exog = self.orig_endog, self.orig_exog # exog can be None and we could be upcasting one or the other if exog is not None and (hasattr(endog, 'index') and hasattr(exog, 'index')): assert self.orig_endog.index.equals(self.orig_exog.index) except AssertionError: raise ValueError("The indices for endog and exog are not aligned") super(PandasData, self)._check_integrity() def _get_row_labels(self, arr): try: return arr.index except AttributeError: # if we've gotten here it's because endog is pandas and # exog is not, so just return the row labels from endog return self.orig_endog.index def attach_columns(self, result): # this can either be a 1d array or a scalar # don't squeeze because it might be a 2d row array # if it needs a squeeze, the bug is elsewhere if result.ndim <= 1: return Series(result, index=self.xnames) else: # for e.g., confidence intervals return DataFrame(result, index=self.xnames) def attach_columns_eq(self, result): return DataFrame(result, index=self.xnames, columns=self.ynames) def attach_cov(self, result): return DataFrame(result, index=self.xnames, columns=self.xnames) def attach_cov_eq(self, result): return DataFrame(result, index=self.ynames, columns=self.ynames) def attach_rows(self, result): # assumes if len(row_labels) > len(result) it's bc it was truncated # at the front, for AR lags, for example if result.squeeze().ndim == 1: return Series(result, index=self.row_labels[-len(result):]) else: # this is for VAR results, may not be general enough return DataFrame(result, index=self.row_labels[-len(result):], columns=self.ynames) def attach_dates(self, result): return TimeSeries(result, index=self.predict_dates) def _make_endog_names(endog): if endog.ndim == 1 or endog.shape[1] == 1: ynames = ['y'] else: # for VAR ynames = ['y%d' % (i+1) for i in range(endog.shape[1])] return ynames def _make_exog_names(exog): exog_var = exog.var(0) if (exog_var == 0).any(): # assumes one constant in first or last position # avoid exception if more than one constant const_idx = exog_var.argmin() exog_names = ['x%d' % i for i in range(1,exog.shape[1])] exog_names.insert(const_idx, 'const') else: exog_names = ['x%d' % i for i in range(1,exog.shape[1]+1)] return exog_names def handle_data(endog, exog, missing='none', hasconst=None, **kwargs): """ Given inputs """ # deal with lists and tuples up-front if isinstance(endog, (list, tuple)): endog = np.asarray(endog) if isinstance(exog, (list, tuple)): exog = np.asarray(exog) if data_util._is_using_ndarray_type(endog, exog): klass = ModelData elif data_util._is_using_pandas(endog, exog): klass = PandasData elif data_util._is_using_patsy(endog, exog): klass = PatsyData # keep this check last elif data_util._is_using_ndarray(endog, exog): klass = ModelData else: raise ValueError('unrecognized data structures: %s / %s' % (type(endog), type(exog))) return klass(endog, exog=exog, missing=missing, hasconst=hasconst, **kwargs) statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/l1_cvxopt.py000066400000000000000000000145731224417117700237310ustar00rootroot00000000000000""" Holds files for l1 regularization of LikelihoodModel, using cvxopt. """ import numpy as np import statsmodels.base.l1_solvers_common as l1_solvers_common from cvxopt import solvers, matrix def fit_l1_cvxopt_cp( f, score, start_params, args, kwargs, disp=False, maxiter=100, callback=None, retall=False, full_output=False, hess=None): """ Solve the l1 regularized problem using cvxopt.solvers.cp Specifically: We convert the convex but non-smooth problem .. math:: \\min_\\beta f(\\beta) + \\sum_k\\alpha_k |\\beta_k| via the transformation to the smooth, convex, constrained problem in twice as many variables (adding the "added variables" :math:`u_k`) .. math:: \\min_{\\beta,u} f(\\beta) + \\sum_k\\alpha_k u_k, subject to .. math:: -u_k \\leq \\beta_k \\leq u_k. Parameters ---------- All the usual parameters from LikelhoodModel.fit alpha : non-negative scalar or numpy array (same size as parameters) The weight multiplying the l1 penalty term trim_mode : 'auto, 'size', or 'off' If not 'off', trim (set to zero) parameters that would have been zero if the solver reached the theoretical minimum. If 'auto', trim params using the Theory above. If 'size', trim params if they have very small absolute value size_trim_tol : float or 'auto' (default = 'auto') For use when trim_mode === 'size' auto_trim_tol : float For sue when trim_mode == 'auto'. Use qc_tol : float Print warning and don't allow auto trim when (ii) in "Theory" (above) is violated by this much. qc_verbose : Boolean If true, print out a full QC report upon failure abstol : float absolute accuracy (default: 1e-7). reltol : float relative accuracy (default: 1e-6). feastol : float tolerance for feasibility conditions (default: 1e-7). refinement : int number of iterative refinement steps when solving KKT equations (default: 1). """ start_params = np.array(start_params).ravel('F') ## Extract arguments # k_params is total number of covariates, possibly including a leading constant. k_params = len(start_params) # The start point x0 = np.append(start_params, np.fabs(start_params)) x0 = matrix(x0, (2 * k_params, 1)) # The regularization parameter alpha = np.array(kwargs['alpha_rescaled']).ravel('F') # Make sure it's a vector alpha = alpha * np.ones(k_params) assert alpha.min() >= 0 ## Wrap up functions for cvxopt f_0 = lambda x: _objective_func(f, x, k_params, alpha, *args) Df = lambda x: _fprime(score, x, k_params, alpha) G = _get_G(k_params) # Inequality constraint matrix, Gx \leq h h = matrix(0.0, (2 * k_params, 1)) # RHS in inequality constraint H = lambda x, z: _hessian_wrapper(hess, x, z, k_params) ## Define the optimization function def F(x=None, z=None): if x is None: return 0, x0 elif z is None: return f_0(x), Df(x) else: return f_0(x), Df(x), H(x, z) ## Convert optimization settings to cvxopt form solvers.options['show_progress'] = disp solvers.options['maxiters'] = maxiter if 'abstol' in kwargs: solvers.options['abstol'] = kwargs['abstol'] if 'reltol' in kwargs: solvers.options['reltol'] = kwargs['reltol'] if 'feastol' in kwargs: solvers.options['feastol'] = kwargs['feastol'] if 'refinement' in kwargs: solvers.options['refinement'] = kwargs['refinement'] ### Call the optimizer results = solvers.cp(F, G, h) x = np.asarray(results['x']).ravel() params = x[:k_params] ### Post-process # QC qc_tol = kwargs['qc_tol'] qc_verbose = kwargs['qc_verbose'] passed = l1_solvers_common.qc_results( params, alpha, score, qc_tol, qc_verbose) # Possibly trim trim_mode = kwargs['trim_mode'] size_trim_tol = kwargs['size_trim_tol'] auto_trim_tol = kwargs['auto_trim_tol'] params, trimmed = l1_solvers_common.do_trim_params( params, k_params, alpha, score, passed, trim_mode, size_trim_tol, auto_trim_tol) ### Pack up return values for statsmodels # TODO These retvals are returned as mle_retvals...but the fit wasn't ML if full_output: fopt = f_0(x) gopt = float('nan') # Objective is non-differentiable hopt = float('nan') iterations = float('nan') converged = 'True' if results['status'] == 'optimal'\ else results['status'] retvals = { 'fopt': fopt, 'converged': converged, 'iterations': iterations, 'gopt': gopt, 'hopt': hopt, 'trimmed': trimmed} else: x = np.array(results['x']).ravel() params = x[:k_params] ### Return results if full_output: return params, retvals else: return params def _objective_func(f, x, k_params, alpha, *args): """ The regularized objective function. """ x_arr = np.asarray(x) params = x_arr[:k_params].ravel() u = x_arr[k_params:] # Call the numpy version objective_func_arr = f(params, *args) + (alpha * u).sum() # Return return matrix(objective_func_arr) def _fprime(score, x, k_params, alpha): """ The regularized derivative. """ x_arr = np.asarray(x) params = x_arr[:k_params].ravel() # Call the numpy version # The derivative just appends a vector of constants fprime_arr = np.append(score(params), alpha) # Return return matrix(fprime_arr, (1, 2 * k_params)) def _get_G(k_params): """ The linear inequality constraint matrix. """ I = np.eye(k_params) A = np.concatenate((-I, -I), axis=1) B = np.concatenate((I, -I), axis=1) C = np.concatenate((A, B), axis=0) # Return return matrix(C) def _hessian_wrapper(hess, x, z, k_params): """ Wraps the hessian up in the form for cvxopt. cvxopt wants the hessian of the objective function and the constraints. Since our constraints are linear, this part is all zeros. """ x_arr = np.asarray(x) params = x_arr[:k_params].ravel() zh_x = np.asarray(z[0]) * hess(params) zero_mat = np.zeros(zh_x.shape) A = np.concatenate((zh_x, zero_mat), axis=1) B = np.concatenate((zero_mat, zero_mat), axis=1) zh_x_ext = np.concatenate((A, B), axis=0) return matrix(zh_x_ext, (2 * k_params, 2 * k_params)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/l1_slsqp.py000066400000000000000000000126351224417117700235450ustar00rootroot00000000000000""" Holds files for l1 regularization of LikelihoodModel, using scipy.optimize.slsqp """ import numpy as np from scipy.optimize import fmin_slsqp import statsmodels.base.l1_solvers_common as l1_solvers_common def fit_l1_slsqp( f, score, start_params, args, kwargs, disp=False, maxiter=1000, callback=None, retall=False, full_output=False, hess=None): """ Solve the l1 regularized problem using scipy.optimize.fmin_slsqp(). Specifically: We convert the convex but non-smooth problem .. math:: \\min_\\beta f(\\beta) + \\sum_k\\alpha_k |\\beta_k| via the transformation to the smooth, convex, constrained problem in twice as many variables (adding the "added variables" :math:`u_k`) .. math:: \\min_{\\beta,u} f(\\beta) + \\sum_k\\alpha_k u_k, subject to .. math:: -u_k \\leq \\beta_k \\leq u_k. Parameters ---------- All the usual parameters from LikelhoodModel.fit alpha : non-negative scalar or numpy array (same size as parameters) The weight multiplying the l1 penalty term trim_mode : 'auto, 'size', or 'off' If not 'off', trim (set to zero) parameters that would have been zero if the solver reached the theoretical minimum. If 'auto', trim params using the Theory above. If 'size', trim params if they have very small absolute value size_trim_tol : float or 'auto' (default = 'auto') For use when trim_mode === 'size' auto_trim_tol : float For sue when trim_mode == 'auto'. Use qc_tol : float Print warning and don't allow auto trim when (ii) in "Theory" (above) is violated by this much. qc_verbose : Boolean If true, print out a full QC report upon failure acc : float (default 1e-6) Requested accuracy as used by slsqp """ start_params = np.array(start_params).ravel('F') ### Extract values # k_params is total number of covariates, # possibly including a leading constant. k_params = len(start_params) # The start point x0 = np.append(start_params, np.fabs(start_params)) # alpha is the regularization parameter alpha = np.array(kwargs['alpha_rescaled']).ravel('F') # Make sure it's a vector alpha = alpha * np.ones(k_params) assert alpha.min() >= 0 # Convert display parameters to scipy.optimize form disp_slsqp = _get_disp_slsqp(disp, retall) # Set/retrieve the desired accuracy acc = kwargs.setdefault('acc', 1e-10) ### Wrap up for use in fmin_slsqp func = lambda x_full: _objective_func(f, x_full, k_params, alpha, *args) f_ieqcons_wrap = lambda x_full: _f_ieqcons(x_full, k_params) fprime_wrap = lambda x_full: _fprime(score, x_full, k_params, alpha) fprime_ieqcons_wrap = lambda x_full: _fprime_ieqcons(x_full, k_params) ### Call the solver results = fmin_slsqp( func, x0, f_ieqcons=f_ieqcons_wrap, fprime=fprime_wrap, acc=acc, iter=maxiter, disp=disp_slsqp, full_output=full_output, fprime_ieqcons=fprime_ieqcons_wrap) params = np.asarray(results[0][:k_params]) ### Post-process # QC qc_tol = kwargs['qc_tol'] qc_verbose = kwargs['qc_verbose'] passed = l1_solvers_common.qc_results( params, alpha, score, qc_tol, qc_verbose) # Possibly trim trim_mode = kwargs['trim_mode'] size_trim_tol = kwargs['size_trim_tol'] auto_trim_tol = kwargs['auto_trim_tol'] params, trimmed = l1_solvers_common.do_trim_params( params, k_params, alpha, score, passed, trim_mode, size_trim_tol, auto_trim_tol) ### Pack up return values for statsmodels optimizers # TODO These retvals are returned as mle_retvals...but the fit wasn't ML. # This could be confusing someday. if full_output: x_full, fx, its, imode, smode = results fopt = func(np.asarray(x_full)) converged = 'True' if imode == 0 else smode iterations = its gopt = float('nan') # Objective is non-differentiable hopt = float('nan') retvals = { 'fopt': fopt, 'converged': converged, 'iterations': iterations, 'gopt': gopt, 'hopt': hopt, 'trimmed': trimmed} ### Return if full_output: return params, retvals else: return params def _get_disp_slsqp(disp, retall): if disp or retall: if disp: disp_slsqp = 1 if retall: disp_slsqp = 2 else: disp_slsqp = 0 return disp_slsqp def _objective_func(f, x_full, k_params, alpha, *args): """ The regularized objective function """ x_params = x_full[:k_params] x_added = x_full[k_params:] ## Return return f(x_params, *args) + (alpha * x_added).sum() def _fprime(score, x_full, k_params, alpha): """ The regularized derivative """ x_params = x_full[:k_params] # The derivative just appends a vector of constants return np.append(score(x_params), alpha) def _f_ieqcons(x_full, k_params): """ The inequality constraints. """ x_params = x_full[:k_params] x_added = x_full[k_params:] # All entries in this vector must be \geq 0 in a feasible solution return np.append(x_params + x_added, x_added - x_params) def _fprime_ieqcons(x_full, k_params): """ Derivative of the inequality constraints """ I = np.eye(k_params) A = np.concatenate((I, I), axis=1) B = np.concatenate((-I, I), axis=1) C = np.concatenate((A, B), axis=0) ## Return return C statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/l1_solvers_common.py000066400000000000000000000124621224417117700254460ustar00rootroot00000000000000""" Holds common functions for l1 solvers. """ import numpy as np def qc_results(params, alpha, score, qc_tol, qc_verbose=False): """ Theory dictates that one of two conditions holds: i) abs(score[i]) == alpha[i] and params[i] != 0 ii) abs(score[i]) <= alpha[i] and params[i] == 0 qc_results checks to see that (ii) holds, within qc_tol qc_results also checks for nan or results of the wrong shape. Parameters ---------- params : np.ndarray model parameters. Not including the added variables x_added. alpha : np.ndarray regularization coefficients score : function Gradient of unregularized objective function qc_tol : float Tolerance to hold conditions (i) and (ii) to for QC check. qc_verbose : Boolean If true, print out a full QC report upon failure Returns ------- passed : Boolean True if QC check passed qc_dict : Dictionary Keys are fprime, alpha, params, passed_array Prints ------ Warning message if QC check fails. """ ## Check for fatal errors assert not np.isnan(params).max() assert (params == params.ravel('F')).min(), \ "params should have already been 1-d" ## Start the theory compliance check fprime = score(params) k_params = len(params) passed_array = np.array([True] * k_params) for i in xrange(k_params): if alpha[i] > 0: # If |fprime| is too big, then something went wrong if (abs(fprime[i]) - alpha[i]) / alpha[i] > qc_tol: passed_array[i] = False qc_dict = dict( fprime=fprime, alpha=alpha, params=params, passed_array=passed_array) passed = passed_array.min() if not passed: num_failed = (passed_array == False).sum() message = 'QC check did not pass for %d out of %d parameters' % ( num_failed, k_params) message += '\nTry increasing solver accuracy or number of iterations'\ ', decreasing alpha, or switch solvers' if qc_verbose: message += _get_verbose_addon(qc_dict) print message return passed def _get_verbose_addon(qc_dict): alpha = qc_dict['alpha'] params = qc_dict['params'] fprime = qc_dict['fprime'] passed_array = qc_dict['passed_array'] addon = '\n------ verbose QC printout -----------------' addon = '\n------ Recall the problem was rescaled by 1 / nobs ---' addon += '\n|%-10s|%-10s|%-10s|%-10s|' % ( 'passed', 'alpha', 'fprime', 'param') addon += '\n--------------------------------------------' for i in xrange(len(alpha)): addon += '\n|%-10s|%-10.3e|%-10.3e|%-10.3e|' % ( passed_array[i], alpha[i], fprime[i], params[i]) return addon def do_trim_params(params, k_params, alpha, score, passed, trim_mode, size_trim_tol, auto_trim_tol): """ Trims (set to zero) params that are zero at the theoretical minimum. Uses heuristics to account for the solver not actually finding the minimum. In all cases, if alpha[i] == 0, then don't trim the ith param. In all cases, do nothing with the added variables. Parameters ---------- params : np.ndarray model parameters. Not including added variables. k_params : Int Number of parameters alpha : np.ndarray regularization coefficients score : Function. score(params) should return a 1-d vector of derivatives of the unpenalized objective function. passed : Boolean True if the QC check passed trim_mode : 'auto, 'size', or 'off' If not 'off', trim (set to zero) parameters that would have been zero if the solver reached the theoretical minimum. If 'auto', trim params using the Theory above. If 'size', trim params if they have very small absolute value size_trim_tol : float or 'auto' (default = 'auto') For use when trim_mode === 'size' auto_trim_tol : float For sue when trim_mode == 'auto'. Use qc_tol : float Print warning and don't allow auto trim when (ii) in "Theory" (above) is violated by this much. Returns ------- params : np.ndarray Trimmed model parameters trimmed : np.ndarray of Booleans trimmed[i] == True if the ith parameter was trimmed. """ ## Trim the small params trimmed = [False] * k_params if trim_mode == 'off': trimmed = np.array([False] * k_params) elif trim_mode == 'auto' and not passed: print "Could not trim params automatically due to failed QC "\ "check. Trimming using trim_mode == 'size' will still work." trimmed = np.array([False] * k_params) elif trim_mode == 'auto' and passed: fprime = score(params) for i in xrange(k_params): if alpha[i] != 0: if (alpha[i] - abs(fprime[i])) / alpha[i] > auto_trim_tol: params[i] = 0.0 trimmed[i] = True elif trim_mode == 'size': for i in xrange(k_params): if alpha[i] != 0: if abs(params[i]) < size_trim_tol: params[i] = 0.0 trimmed[i] = True else: raise Exception( "trim_mode == %s, which is not recognized" % (trim_mode)) return params, np.asarray(trimmed) statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/model.py000066400000000000000000002056251224417117700231120ustar00rootroot00000000000000import numpy as np from scipy import optimize, stats from statsmodels.base.data import handle_data from statsmodels.tools.tools import recipr, nan_dot from statsmodels.stats.contrast import ContrastResults from statsmodels.tools.decorators import (resettable_cache, cache_readonly) import statsmodels.base.wrapper as wrap from statsmodels.tools.numdiff import approx_fprime from statsmodels.formula import handle_formula_data _model_params_doc = """\ Parameters ---------- endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where `nobs` is the number of observations and `k` is the number of regressors. An interecept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`.""" _missing_param_doc = """missing : str Available options are 'none', 'drop', and 'raise'. If 'none', no nan checking is done. If 'drop', any observations with nans are dropped. If 'raise', an error is raised. Default is 'none.' """ _extra_param_doc = """hasconst : None or bool Indicates whether the RHS includes a user-supplied constant. If True, a constant is not checked for and k_constant is set to 1 and all result statistics are calculated as if a constant is present. If False, a constant is not checked for and k_constant is set to 0. """ class Model(object): __doc__ = """ A (predictive) statistical model. Intended to be subclassed not used. %(params_doc)s %(extra_params_doc)s Notes ----- `endog` and `exog` are references to any data provided. So if the data is already stored in numpy arrays and it is changed then `endog` and `exog` will change as well. """ % {'params_doc' : _model_params_doc, 'extra_params_doc' : _missing_param_doc + _extra_param_doc} def __init__(self, endog, exog=None, **kwargs): missing = kwargs.pop('missing', 'none') hasconst = kwargs.pop('hasconst', None) self.data = handle_data(endog, exog, missing, hasconst, **kwargs) self.k_constant = self.data.k_constant self.exog = self.data.exog self.endog = self.data.endog # kwargs arrays could have changed, easier to just attach here for key in kwargs: # pop so we don't start keeping all these twice or references setattr(self, key, self.data.__dict__.pop(key)) self._data_attr = [] self._data_attr.extend(['exog', 'endog', 'data.exog', 'data.endog', 'data.orig_endog', 'data.orig_exog']) @classmethod def from_formula(cls, formula, data, subset=None, *args, **kwargs): """ Create a Model from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a `pandas.DataFrame` args : extra arguments These are passed to the model kwargs : extra keyword arguments These are passed to the model. Returns ------- model : Model instance Notes ------ data must define __getitem__ with the keys in the formula terms args and kwargs are passed on to the model instantiation. E.g., a numpy structured or rec array, a dictionary, or a pandas DataFrame. """ #TODO: provide a docs template for args/kwargs from child models #TODO: subset could use syntax. issue #469. if subset is not None: data= data.ix[subset] endog, exog = handle_formula_data(data, None, formula) mod = cls(endog, exog, *args, **kwargs) mod.formula = formula # since we got a dataframe, attach the original mod.data.frame = data return mod @property def endog_names(self): return self.data.ynames @property def exog_names(self): return self.data.xnames def fit(self): """ Fit a model to data. """ raise NotImplementedError def predict(self, params, exog=None, *args, **kwargs): """ After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models. """ raise NotImplementedError class LikelihoodModel(Model): """ Likelihood model is a subclass of Model. """ def __init__(self, endog, exog=None, **kwargs): super(LikelihoodModel, self).__init__(endog, exog, **kwargs) self.initialize() def initialize(self): """ Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed. """ pass # TODO: if the intent is to re-initialize the model with new data then this # method needs to take inputs... def loglike(self, params): """ Log-likelihood of model. """ raise NotImplementedError def score(self, params): """ Score vector of model. The gradient of logL with respect to each parameter. """ raise NotImplementedError def information(self, params): """ Fisher information matrix of model Returns -Hessian of loglike evaluated at params. """ raise NotImplementedError def hessian(self, params): """ The Hessian matrix of the model """ raise NotImplementedError def fit(self, start_params=None, method='newton', maxiter=100, full_output=True, disp=True, fargs=(), callback=None, retall=False, **kwargs): """ Fit method for likelihood based models Parameters ---------- start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str {'newton','nm','bfgs','powell','cg','ncg','basinhopping'} Method can be 'newton' for Newton-Raphson, 'nm' for Nelder-Mead, 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno, 'powell' for modified Powell's method, 'cg' for conjugate gradient, 'ncg' for Newton- conjugate gradient or 'basinhopping' for global basin-hopping solver, if available. `method` determines which solver from scipy.optimize is used. The explicit arguments in `fit` are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.. maxiter : int The maximum number of iterations to perform. full_output : bool Set to True to have all available output in the Results object's mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. disp : bool Set to True to print convergence messages. fargs : tuple Extra arguments passed to the likelihood function, i.e., loglike(x,*args) callback : callable callback(xk) Called after each iteration, as callback(xk), where xk is the current parameter vector. retall : bool Set to True to return list of solutions at each iteration. Available in Results object's mle_retvals attribute. Notes ----- The 'basinhopping' solver ignores `maxiter`, `retall`, `full_output` explicit arguments. Optional arguments for the solvers (available in Results.mle_settings):: 'newton' tol : float Relative error in params acceptable for convergence. 'nm' -- Nelder Mead xtol : float Relative error in params acceptable for convergence ftol : float Relative error in loglike(params) acceptable for convergence maxfun : int Maximum number of function evaluations to make. 'bfgs' gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon If fprime is approximated, use this value for the step size. Only relevant if LikelihoodModel.score is None. 'cg' gtol : float Stop when norm of gradient is less than gtol. norm : float Order of norm (np.Inf is max, -np.Inf is min) epsilon : float If fprime is approximated, use this value for the step size. Can be scalar or vector. Only relevant if Likelihoodmodel.score is None. 'ncg' fhess_p : callable f'(x,*args) Function which computes the Hessian of f times an arbitrary vector, p. Should only be supplied if LikelihoodModel.hessian is None. avextol : float Stop when the average relative error in the minimizer falls below this amount. epsilon : float or ndarray If fhess is approximated, use this value for the step size. Only relevant if Likelihoodmodel.hessian is None. 'powell' xtol : float Line-search error tolerance ftol : float Relative error in loglike(params) for acceptable for convergence. maxfun : int Maximum number of function evaluations to make. start_direc : ndarray Initial direction set. 'basinhopping' niter : integer The number of basin hopping iterations. niter_success : integer Stop the run if the global minimum candidate remains the same for this number of iterations. T : float The "temperature" parameter for the accept or reject criterion. Higher "temperatures" mean that larger jumps in function value will be accepted. For best results `T` should be comparable to the separation (in function value) between local minima. stepsize : float Initial step size for use in the random displacement. interval : integer The interval for how often to update the `stepsize`. minimizer : dict Extra keyword arguments to be passed to the minimizer `scipy.optimize.minimize()`, for example 'method' - the minimization method (e.g. 'L-BFGS-B'), or 'tol' - the tolerance for termination. Other arguments are mapped from explicit argument of `fit`: - `args` <- `fargs` - `jac` <- `score` - `hess` <- `hess` """ # Extract kwargs specific to fit_regularized calling fit extra_fit_funcs = kwargs.setdefault('extra_fit_funcs', dict()) cov_params_func = kwargs.setdefault('cov_params_func', None) Hinv = None # JP error if full_output=0, Hinv not defined methods = ['newton', 'nm', 'bfgs', 'powell', 'cg', 'ncg', 'basinhopping'] methods += extra_fit_funcs.keys() if start_params is None: if hasattr(self, 'start_params'): start_params = self.start_params elif self.exog is not None: # fails for shape (K,)? start_params = [0] * self.exog.shape[1] else: raise ValueError("If exog is None, then start_params should " "be specified") if method.lower() not in methods: message = "Unknown fit method %s" % method raise ValueError(message) method = method.lower() # TODO: separate args from nonarg taking score and hessian, ie., # user-supplied and numerically evaluated estimate frprime doesn't take # args in most (any?) of the optimize function nobs = self.endog.shape[0] f = lambda params, *args: -self.loglike(params, *args) / nobs score = lambda params: -self.score(params) / nobs try: hess = lambda params: -self.hessian(params) / nobs except: hess = None fit_funcs = { 'newton': _fit_mle_newton, 'nm': _fit_mle_nm, # Nelder-Mead 'bfgs': _fit_mle_bfgs, 'cg': _fit_mle_cg, 'ncg': _fit_mle_ncg, 'powell': _fit_mle_powell, 'basinhopping': _fit_mle_basinhopping, } if extra_fit_funcs: fit_funcs.update(extra_fit_funcs) if method == 'newton': score = lambda params: self.score(params) / nobs hess = lambda params: self.hessian(params) / nobs #TODO: why are score and hess positive? func = fit_funcs[method] xopt, retvals = func(f, score, start_params, fargs, kwargs, disp=disp, maxiter=maxiter, callback=callback, retall=retall, full_output=full_output, hess=hess) if not full_output: # xopt should be None and retvals is argmin xopt = retvals elif cov_params_func: Hinv = cov_params_func(self, xopt, retvals) elif method == 'newton' and full_output: Hinv = np.linalg.inv(-retvals['Hessian']) / nobs else: try: Hinv = np.linalg.inv(-1 * self.hessian(xopt)) except: #might want custom warning ResultsWarning? NumericalWarning? from warnings import warn warndoc = ('Inverting hessian failed, no bse or ' 'cov_params available') warn(warndoc, Warning) Hinv = None #TODO: add Hessian approximation and change the above if needed mlefit = LikelihoodModelResults(self, xopt, Hinv, scale=1.) #TODO: hardcode scale? if isinstance(retvals, dict): mlefit.mle_retvals = retvals optim_settings = {'optimizer': method, 'start_params': start_params, 'maxiter': maxiter, 'full_output': full_output, 'disp': disp, 'fargs': fargs, 'callback': callback, 'retall': retall} optim_settings.update(kwargs) mlefit.mle_settings = optim_settings return mlefit def _fit_mle_newton(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): tol = kwargs.setdefault('tol', 1e-8) iterations = 0 oldparams = np.inf newparams = np.asarray(start_params) if retall: history = [oldparams, newparams] while (iterations < maxiter and np.any(np.abs(newparams - oldparams) > tol)): H = hess(newparams) oldparams = newparams newparams = oldparams - np.dot(np.linalg.inv(H), score(oldparams)) if retall: history.append(newparams) if callback is not None: callback(newparams) iterations += 1 fval = f(newparams, *fargs) # this is the negative likelihood if iterations == maxiter: warnflag = 1 if disp: print ("Warning: Maximum number of iterations has been " "exceeded.") print " Current function value: %f" % fval print " Iterations: %d" % iterations else: warnflag = 0 if disp: print "Optimization terminated successfully." print " Current function value: %f" % fval print " Iterations %d" % iterations if full_output: (xopt, fopt, niter, gopt, hopt) = (newparams, f(newparams, *fargs), iterations, score(newparams), hess(newparams)) converged = not warnflag retvals = {'fopt': fopt, 'iterations': niter, 'score': gopt, 'Hessian': hopt, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': history}) else: retvals = newparams xopt = None return xopt, retvals def _fit_mle_bfgs(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05) norm = kwargs.setdefault('norm', np.Inf) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_bfgs(f, start_params, score, args=fargs, gtol=gtol, norm=norm, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag = retvals else: (xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag, allvecs) = retvals converged = not warnflag retvals = {'fopt': fopt, 'gopt': gopt, 'Hinv': Hinv, 'fcalls': fcalls, 'gcalls': gcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = None return xopt, retvals def _fit_mle_nm(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): xtol = kwargs.setdefault('xtol', 0.0001) ftol = kwargs.setdefault('ftol', 0.0001) maxfun = kwargs.setdefault('maxfun', None) retvals = optimize.fmin(f, start_params, args=fargs, xtol=xtol, ftol=ftol, maxiter=maxiter, maxfun=maxfun, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, niter, fcalls, warnflag = retvals else: xopt, fopt, niter, fcalls, warnflag, allvecs = retvals converged = not warnflag retvals = {'fopt': fopt, 'iterations': niter, 'fcalls': fcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = None return xopt, retvals def _fit_mle_cg(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05) norm = kwargs.setdefault('norm', np.Inf) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_cg(f, start_params, score, gtol=gtol, norm=norm, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, fcalls, gcalls, warnflag = retvals else: xopt, fopt, fcalls, gcalls, warnflag, allvecs = retvals converged = not warnflag retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = None return xopt, retvals def _fit_mle_ncg(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): fhess_p = kwargs.setdefault('fhess_p', None) avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05) epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08) retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p, fhess=hess, args=fargs, avextol=avextol, epsilon=epsilon, maxiter=maxiter, full_output=full_output, disp=disp, retall=retall, callback=callback) if full_output: if not retall: xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals else: xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\ retvals converged = not warnflag retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls, 'hcalls': hcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = None return xopt, retvals def _fit_mle_powell(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): xtol = kwargs.setdefault('xtol', 0.0001) ftol = kwargs.setdefault('ftol', 0.0001) maxfun = kwargs.setdefault('maxfun', None) start_direc = kwargs.setdefault('start_direc', None) retvals = optimize.fmin_powell(f, start_params, args=fargs, xtol=xtol, ftol=ftol, maxiter=maxiter, maxfun=maxfun, full_output=full_output, disp=disp, retall=retall, callback=callback, direc=start_direc) if full_output: if not retall: xopt, fopt, direc, niter, fcalls, warnflag = retvals else: xopt, fopt, direc, niter, fcalls, warnflag, allvecs =\ retvals converged = not warnflag retvals = {'fopt': fopt, 'direc': direc, 'iterations': niter, 'fcalls': fcalls, 'warnflag': warnflag, 'converged': converged} if retall: retvals.update({'allvecs': allvecs}) else: xopt = None return xopt, retvals def _fit_mle_basinhopping(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None): if not 'basinhopping' in vars(optimize): msg = 'basinhopping solver is not available, use e.g. bfgs instead!' raise ValueError(msg) from copy import copy kwargs = copy(kwargs) niter = kwargs.setdefault('niter', 100) niter_success = kwargs.setdefault('niter_success', None) T = kwargs.setdefault('T', 1.0) stepsize = kwargs.setdefault('stepsize', 0.5) interval = kwargs.setdefault('interval', 50) minimizer_kwargs = kwargs.get('minimizer', {}) minimizer_kwargs['args'] = fargs minimizer_kwargs['jac'] = score minimizer_kwargs['hess'] = hess res = optimize.basinhopping(f, start_params, minimizer_kwargs=minimizer_kwargs, niter=niter, niter_success=niter_success, T=T, stepsize=stepsize, disp=disp, callback=callback, interval=interval) if full_output: xopt, fopt, niter, fcalls = res.x, res.fun, res.nit, res.nfev converged = 'completed successfully' in res.message[0] retvals = {'fopt': fopt, 'iterations': niter, 'fcalls': fcalls, 'converged': converged} else: xopt = None return xopt, retvals #TODO: the below is unfinished class GenericLikelihoodModel(LikelihoodModel): """ Allows the fitting of any likelihood function via maximum likelihood. A subclass needs to specify at least the log-likelihood If the log-likelihood is specified for each observation, then results that require the Jacobian will be available. (The other case is not tested yet.) Notes ----- Optimization methods that require only a likelihood function are 'nm' and 'powell' Optimization methods that require a likelihood function and a score/gradient are 'bfgs', 'cg', and 'ncg'. A function to compute the Hessian is optional for 'ncg'. Optimization method that require a likelihood function, a score/gradient, and a Hessian is 'newton' If they are not overwritten by a subclass, then numerical gradient, Jacobian and Hessian of the log-likelihood are caclulated by numerical forward differentiation. This might results in some cases in precision problems, and the Hessian might not be positive definite. Even if the Hessian is not positive definite the covariance matrix of the parameter estimates based on the outer product of the Jacobian might still be valid. Examples -------- see also subclasses in directory miscmodels import statsmodels.api as sm data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog) # in this dir from model import GenericLikelihoodModel probit_mod = sm.Probit(data.endog, data.exog) probit_res = probit_mod.fit() loglike = probit_mod.loglike score = probit_mod.score mod = GenericLikelihoodModel(data.endog, data.exog, loglike, score) res = mod.fit(method="nm", maxiter = 500) import numpy as np np.allclose(res.params, probit_res.params) """ def __init__(self, endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds): # let them be none in case user wants to use inheritance if not loglike is None: self.loglike = loglike if not score is None: self.score = score if not hessian is None: self.hessian = hessian self.confint_dist = stats.norm self.__dict__.update(kwds) # TODO: data structures? #TODO temporary solution, force approx normal #self.df_model = 9999 #somewhere: CacheWriteWarning: The attribute 'df_model' cannot be overwritten super(GenericLikelihoodModel, self).__init__(endog, exog, missing=missing) # this won't work for ru2nmnl, maybe np.ndim of a dict? if exog is not None: #try: self.nparams = (exog.shape[1] if np.ndim(exog) == 2 else 1) if extra_params_names is not None: self._set_extra_params_names(extra_params_names) def _set_extra_params_names(self, extra_params_names): # check param_names if extra_params_names is not None: if self.exog is not None: self.exog_names.extend(extra_params_names) else: self.data.xnames = extra_params_names self.nparams = len(self.exog_names) #this is redundant and not used when subclassing def initialize(self): if not self.score: # right now score is not optional self.score = approx_fprime if not self.hessian: pass else: # can use approx_hess_p if we have a gradient if not self.hessian: pass #Initialize is called by #statsmodels.model.LikelihoodModel.__init__ #and should contain any preprocessing that needs to be done for a model. from statsmodels.tools import tools if self.exog is not None: self.df_model = float(tools.rank(self.exog) - 1) # assumes constant self.df_resid = float(self.exog.shape[0] - tools.rank(self.exog)) else: self.df_model = np.nan self.df_resid = np.nan super(GenericLikelihoodModel, self).initialize() def expandparams(self, params): ''' expand to full parameter array when some parameters are fixed Parameters ---------- params : array reduced parameter array Returns ------- paramsfull : array expanded parameter array where fixed parameters are included Notes ----- Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. *developer notes:* This can be used in the log-likelihood to ... this could also be replaced by a more general parameter transformation. ''' paramsfull = self.fixed_params.copy() paramsfull[self.fixed_paramsmask] = params return paramsfull def reduceparams(self, params): return params[self.fixed_paramsmask] def loglike(self, params): return self.loglikeobs(params).sum(0) def nloglike(self, params): return -self.loglikeobs(params).sum(0) def loglikeobs(self, params): return -self.nloglikeobs(params) def score(self, params): ''' Gradient of log-likelihood evaluated at params ''' kwds = {} kwds.setdefault('centered', True) return approx_fprime(params, self.loglike, **kwds).ravel() def jac(self, params, **kwds): ''' Jacobian/Gradient of log-likelihood evaluated at params for each observation. ''' #kwds.setdefault('epsilon', 1e-4) kwds.setdefault('centered', True) return approx_fprime(params, self.loglikeobs, **kwds) def hessian(self, params): ''' Hessian of log-likelihood evaluated at params ''' from statsmodels.tools.numdiff import approx_hess # need options for hess (epsilon) return approx_hess(params, self.loglike) def fit(self, start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs): """ Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit """ if start_params is None: if hasattr(self, 'start_params'): start_params = self.start_params else: start_params = 0.1 * np.ones(self.nparams) fit_method = super(GenericLikelihoodModel, self).fit mlefit = fit_method(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) genericmlefit = GenericLikelihoodModelResults(self, mlefit) #amend param names exog_names = [] if (self.exog_names is None) else self.exog_names k_miss = len(exog_names) - len(mlefit.params) if not k_miss == 0: if k_miss < 0: self._set_extra_params_names( ['par%d' % i for i in range(-k_miss)]) else: # I don't want to raise after we have already fit() import warnings warnings.warn('more exog_names than parameters', UserWarning) return genericmlefit #fit.__doc__ += LikelihoodModel.fit.__doc__ class Results(object): """ Class to contain model results Parameters ---------- model : class instance the previously specified model instance params : array parameter estimates from the fit model """ def __init__(self, model, params, **kwd): self.__dict__.update(kwd) self.initialize(model, params, **kwd) self._data_attr = [] def initialize(self, model, params, **kwd): self.params = params self.model = model if hasattr(model, 'k_constant'): self.k_constant = model.k_constant def predict(self, exog=None, transform=True, *args, **kwargs): """ Call self.model.predict with self.params as the first argument. Parameters ---------- exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. Returns ------- See self.model.predict """ if transform and hasattr(self.model, 'formula') and exog is not None: from patsy import dmatrix exog = dmatrix(self.model.data.orig_exog.design_info.builder, exog) return self.model.predict(self.params, exog, *args, **kwargs) #TODO: public method? class LikelihoodModelResults(Results): """ Class to contain results from likelihood models Parameters ----------- model : LikelihoodModel instance or subclass instance LikelihoodModelResults holds a reference to the model that is fit. params : 1d array_like parameter estimates from estimated model normalized_cov_params : 2d array Normalized (before scaling) covariance of params. (dot(X.T,X))**-1 scale : float For (some subset of models) scale will typically be the mean square error from the estimated model (sigma^2) Returns ------- **Attributes** mle_retvals : dict Contains the values returned from the chosen optimization method if full_output is True during the fit. Available only if the model is fit by maximum likelihood. See notes below for the output from the different methods. mle_settings : dict Contains the arguments passed to the chosen optimization method. Available if the model is fit by maximum likelihood. See LikelihoodModel.fit for more information. model : model instance LikelihoodResults contains a reference to the model that is fit. params : ndarray The parameters estimated for the model. scale : float The scaling factor of the model given during instantiation. tvalues : array The t-values of the standard errors. Notes -------- The covariance of params is given by scale times normalized_cov_params. Return values by solver if full_ouput is True during fit: 'newton' fopt : float The value of the (negative) loglikelihood at its minimum. iterations : int Number of iterations performed. score : ndarray The score vector at the optimum. Hessian : ndarray The Hessian at the optimum. warnflag : int 1 if maxiter is exceeded. 0 if successful convergence. converged : bool True: converged. False: did not converge. allvecs : list List of solutions at each iteration. 'nm' fopt : float The value of the (negative) loglikelihood at its minimum. iterations : int Number of iterations performed. warnflag : int 1: Maximum number of function evaluations made. 2: Maximum number of iterations reached. converged : bool True: converged. False: did not converge. allvecs : list List of solutions at each iteration. 'bfgs' fopt : float Value of the (negative) loglikelihood at its minimum. gopt : float Value of gradient at minimum, which should be near 0. Hinv : ndarray value of the inverse Hessian matrix at minimum. Note that this is just an approximation and will often be different from the value of the analytic Hessian. fcalls : int Number of calls to loglike. gcalls : int Number of calls to gradient/score. warnflag : int 1: Maximum number of iterations exceeded. 2: Gradient and/or function calls are not changing. converged : bool True: converged. False: did not converge. allvecs : list Results at each iteration. 'powell' fopt : float Value of the (negative) loglikelihood at its minimum. direc : ndarray Current direction set. iterations : int Number of iterations performed. fcalls : int Number of calls to loglike. warnflag : int 1: Maximum number of function evaluations. 2: Maximum number of iterations. converged : bool True : converged. False: did not converge. allvecs : list Results at each iteration. 'cg' fopt : float Value of the (negative) loglikelihood at its minimum. fcalls : int Number of calls to loglike. gcalls : int Number of calls to gradient/score. warnflag : int 1: Maximum number of iterations exceeded. 2: Gradient and/ or function calls not changing. converged : bool True: converged. False: did not converge. allvecs : list Results at each iteration. 'ncg' fopt : float Value of the (negative) loglikelihood at its minimum. fcalls : int Number of calls to loglike. gcalls : int Number of calls to gradient/score. hcalls : int Number of calls to hessian. warnflag : int 1: Maximum number of iterations exceeded. converged : bool True: converged. False: did not converge. allvecs : list Results at each iteration. """ def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(LikelihoodModelResults, self).__init__(model, params) self.normalized_cov_params = normalized_cov_params self.scale = scale def normalized_cov_params(self): raise NotImplementedError @cache_readonly def llf(self): return self.model.loglike(self.params) @cache_readonly def bse(self): return np.sqrt(np.diag(self.cov_params())) @cache_readonly def tvalues(self): """ Return the t-statistic for a given parameter estimate. """ return self.params / self.bse @cache_readonly def pvalues(self): return stats.norm.sf(np.abs(self.tvalues)) * 2 def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None, other=None): """ Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters ---------- r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, optional Must be used on its own. Can be 0d or 1d see below. scale : float, optional Can be specified or not. Default is None, which means that the scale argument is taken from the model. other : array-like, optional Can be used when r_matrix is specified. Returns ------- (The below are assumed to be in matrix notation.) cov : ndarray If no argument is specified returns the covariance matrix of a model (scale)*(X.T X)^(-1) If contrast is specified it pre and post-multiplies as follows (scale) * r_matrix (X.T X)^(-1) r_matrix.T If contrast and other are specified returns (scale) * r_matrix (X.T X)^(-1) other.T If column is specified returns (scale) * (X.T X)^(-1)[column,column] if column is 0d OR (scale) * (X.T X)^(-1)[column][:,column] if column is 1d """ if (hasattr(self, 'mle_settings') and self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): dot_fun = nan_dot else: dot_fun = np.dot if cov_p is None and self.normalized_cov_params is None: raise ValueError('need covariance of parameters for computing ' '(unnormalized) covariances') if column is not None and (r_matrix is not None or other is not None): raise ValueError('Column should be specified without other ' 'arguments.') if other is not None and r_matrix is None: raise ValueError('other can only be specified with r_matrix') if cov_p is None: if scale is None: scale = self.scale cov_p = self.normalized_cov_params * scale if column is not None: column = np.asarray(column) if column.shape == (): return cov_p[column, column] else: #return cov_p[column][:, column] return cov_p[column[:, None], column] elif r_matrix is not None: r_matrix = np.asarray(r_matrix) if r_matrix.shape == (): raise ValueError("r_matrix should be 1d or 2d") if other is None: other = r_matrix else: other = np.asarray(other) tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other))) return tmp else: #if r_matrix is None and column is None: return cov_p #TODO: make sure this works as needed for GLMs def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None): """ Compute a t-test for a joint linear hypothesis of the form Rb = q Parameters ---------- r_matrix : array-like, str, tuple - array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. - str : The full hypotheses to test can be given as a string. See the examples. - tuple : A tuple of arrays in the form (R, q), since q_matrix is deprecated. q_matrix : array-like or scalar, optional This is deprecated. See `r_matrix` and the examples for more information on new usage. Can be either a scalar or a length p row vector. If omitted and r_matrix is an array, `q_matrix` is assumed to be a conformable array of zeros. cov_p : array-like, optional An alternative estimate for the parameter covariance matrix. If None is given, self.normalized_cov_params is used. scale : float, optional An optional `scale` to use. Default is the scale specified by the model fit. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> results = sm.OLS(data.endog, data.exog).fit() >>> r = np.zeros_like(results.params) >>> r[5:] = [1,-1] >>> print r [ 0. 0. 0. 0. 0. 1. -1.] r tests that the coefficients on the 5th and 6th independent variable are the same. >>>T_Test = results.t_test(r) >>>print T_test >>> T_test.effect -1829.2025687192481 >>> T_test.sd 455.39079425193762 >>> T_test.tvalue -4.0167754636411717 >>> T_test.pvalue 0.0015163772380899498 Alternatively, you can specify the hypothesis tests using a string >>> dta = sm.datasets.longley.load_pandas().data >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' >>> results = ols(formula, dta).fit() >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1' >>> t_test = results.t_test(hypotheses) >>> print t_test See also --------- tvalues : individual t statistics f_test : for F tests patsy.DesignInfo.linear_constraint """ from patsy import DesignInfo if q_matrix is not None: from warnings import warn warn("The `q_matrix` keyword is deprecated and will be removed " "in 0.6.0. See the documentation for the new API", FutureWarning) r_matrix = (r_matrix, q_matrix) LC = DesignInfo(self.model.exog_names).linear_constraint(r_matrix) r_matrix, q_matrix = LC.coefs, LC.constants num_ttests = r_matrix.shape[0] num_params = r_matrix.shape[1] if cov_p is None and self.normalized_cov_params is None: raise ValueError('Need covariance of parameters for computing ' 'T statistics') if num_params != self.params.shape[0]: raise ValueError('r_matrix and params are not aligned') if q_matrix is None: q_matrix = np.zeros(num_ttests) else: q_matrix = np.asarray(q_matrix) q_matrix = q_matrix.squeeze() if q_matrix.size > 1: if q_matrix.shape[0] != num_ttests: raise ValueError("r_matrix and q_matrix must have the same " "number of rows") _t = _sd = None _effect = np.dot(r_matrix, self.params) # nan_dot multiplies with the convention nan * 0 = 0 # Perform the test if num_ttests > 1: _sd = np.sqrt(np.diag(self.cov_params( r_matrix=r_matrix, cov_p=cov_p))) else: _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p)) _t = (_effect - q_matrix) * recipr(_sd) return ContrastResults(effect=_effect, t=_t, sd=_sd, df_denom=self.model.df_resid) #TODO: untested for GLMs? def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0, invcov=None): """ Compute an F-test for a joint linear hypothesis. Parameters ---------- r_matrix : array-like, str, or tuple - array : An r x k array where r is the number of restrictions to test and k is the number of regressors. - str : The full hypotheses to test can be given as a string. See the examples. - tuple : A tuple of arrays in the form (R, q), since q_matrix is deprecated. q_matrix : array-like This is deprecated. See `r_matrix` and the examples for more information on new usage. Can be either a scalar or a length p row vector. If omitted and r_matrix is an array, `q_matrix` is assumed to be a conformable array of zeros. cov_p : array-like, optional An alternative estimate for the parameter covariance matrix. If None is given, self.normalized_cov_params is used. scale : float, optional Default is 1.0 for no scaling. invcov : array-like, optional A q x q array to specify an inverse covariance matrix based on a restrictions matrix. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> results = sm.OLS(data.endog, data.exog).fit() >>> A = np.identity(len(results.params)) >>> A = A[1:,:] This tests that each coefficient is jointly statistically significantly different from zero. >>> print results.f_test(A) Compare this to >>> results.F 330.2853392346658 >>> results.F_p 4.98403096572e-10 >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1])) This tests that the coefficient on the 2nd and 3rd regressors are equal and jointly that the coefficient on the 5th and 6th regressors are equal. >>> print results.f_test(B) Alternatively, you can specify the hypothesis tests using a string >>> from statsmodels.datasets import longley >>> from statsmodels.formula.api import ols >>> dta = longley.load_pandas().data >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' >>> results = ols(formula, dta).fit() >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)' >>> f_test = results.f_test(hypotheses) >>> print f_test See also -------- statsmodels.contrasts statsmodels.model.t_test patsy.DesignInfo.linear_constraint Notes ----- The matrix `r_matrix` is assumed to be non-singular. More precisely, r_matrix (pX pX.T) r_matrix.T is assumed invertible. Here, pX is the generalized inverse of the design matrix of the model. There can be problems in non-OLS models where the rank of the covariance of the noise is not full. """ from patsy import DesignInfo if q_matrix is not None: from warnings import warn warn("The `q_matrix` keyword is deprecated and will be removed " "in 0.6.0. See the documentation for the new API", FutureWarning) r_matrix = (r_matrix, q_matrix) LC = DesignInfo(self.model.exog_names).linear_constraint(r_matrix) r_matrix, q_matrix = LC.coefs, LC.constants if (self.normalized_cov_params is None and cov_p is None and invcov is None): raise ValueError('need covariance of parameters for computing ' 'F statistics') cparams = np.dot(r_matrix, self.params[:, None]) J = float(r_matrix.shape[0]) # number of restrictions if q_matrix is None: q_matrix = np.zeros(J) else: q_matrix = np.asarray(q_matrix) if q_matrix.ndim == 1: q_matrix = q_matrix[:, None] if q_matrix.shape[0] != J: raise ValueError("r_matrix and q_matrix must have the same " "number of rows") Rbq = cparams - q_matrix if invcov is None: cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p) if np.isnan(cov_p).max(): raise ValueError("r_matrix performs f_test for using " "dimensions that are asymptotically non-normal") invcov = np.linalg.inv(cov_p) if (hasattr(self, 'mle_settings') and self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): F = nan_dot(nan_dot(Rbq.T, invcov), Rbq) / J else: F = np.dot(np.dot(Rbq.T, invcov), Rbq) / J return ContrastResults(F=F, df_denom=self.model.df_resid, df_num=invcov.shape[0]) def conf_int(self, alpha=.05, cols=None, method='default'): """ Returns the confidence interval of the fitted parameters. Parameters ---------- alpha : float, optional The `alpha` level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. cols : array-like, optional `cols` specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. "Default" : uses self.bse which is based on inverse Hessian for MLE "jhj" : "jac" : "boot-bse" "boot_quant" "profile" Returns -------- conf_int : array Each row contains [lower, upper] confidence interval Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> results = sm.OLS(data.endog, data.exog).fit() >>> results.conf_int() array([[-5496529.48322745, -1467987.78596704], [ -177.02903529, 207.15277984], [ -0.1115811 , 0.03994274], [ -3.12506664, -0.91539297], [ -1.5179487 , -0.54850503], [ -0.56251721, 0.460309 ], [ 798.7875153 , 2859.51541392]]) >>> results.conf_int(cols=(2,3)) array([[-0.1115811 , 0.03994274], [-3.12506664, -0.91539297]]) Notes ----- The confidence interval is based on the standard normal distribution. Models wish to use a different distribution should overwrite this method. """ bse = self.bse dist = stats.norm q = dist.ppf(1 - alpha / 2) if cols is None: lower = self.params - q * bse upper = self.params + q * bse else: cols = np.asarray(cols) lower = self.params[cols] - q * bse[cols] upper = self.params[cols] + q * bse[cols] return np.asarray(zip(lower, upper)) def save(self, fname, remove_data=False): ''' save a pickle of this instance Parameters ---------- fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes ----- If remove_data is true and the model result does not implement a remove_data method then this will raise an exception. ''' from statsmodels.iolib.smpickle import save_pickle if remove_data: self.remove_data() save_pickle(self, fname) @classmethod def load(cls, fname): ''' load a pickle, (class method) Parameters ---------- fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns ------- unpickled instance ''' from statsmodels.iolib.smpickle import load_pickle return load_pickle(fname) def remove_data(self): '''remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. .. warning:: Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is accessed that has been set to None. Not fully tested for time series models, tsa, and might delete too much for prediction or not all that would be possible. The list of arrays to delete is maintained as an attribute of the result and model instance, except for cached values. These lists could be changed before calling remove_data. ''' def wipe(obj, att): #get to last element in attribute path p = att.split('.') att_ = p.pop(-1) try: obj_ = reduce(getattr, [obj] + p) #print repr(obj), repr(att) #print hasattr(obj_, att_) if hasattr(obj_, att_): #print 'removing3', att_ setattr(obj_, att_, None) except AttributeError: pass model_attr = ['model.'+ i for i in self.model._data_attr] for att in self._data_attr + model_attr: #print 'removing', att wipe(self, att) data_in_cache = getattr(self, 'data_in_cache', []) data_in_cache += ['fittedvalues', 'resid', 'wresid'] for key in data_in_cache: try: self._cache[key] = None except (AttributeError, KeyError): pass class LikelihoodResultsWrapper(wrap.ResultsWrapper): _attrs = { 'params': 'columns', 'bse': 'columns', 'pvalues': 'columns', 'tvalues': 'columns', 'resid': 'rows', 'fittedvalues': 'rows', 'normalized_cov_params': 'cov', } _wrap_attrs = _attrs _wrap_methods = { 'cov_params': 'cov', 'conf_int': 'columns' } wrap.populate_wrapper(LikelihoodResultsWrapper, LikelihoodModelResults) class ResultMixin(object): @cache_readonly def df_modelwc(self): # collect different ways of defining the number of parameters, used for # aic, bic if hasattr(self, 'df_model'): if hasattr(self, 'hasconst'): hasconst = self.hasconst else: # default assumption hasconst = 1 return self.df_model + hasconst else: return self.params.size @cache_readonly def aic(self): return -2 * self.llf + 2 * (self.df_modelwc) @cache_readonly def bic(self): return -2 * self.llf + np.log(self.nobs) * (self.df_modelwc) @cache_readonly def jacv(self): '''cached Jacobian of log-likelihood ''' return self.model.jac(self.params) @cache_readonly def hessv(self): '''cached Hessian of log-likelihood ''' return self.model.hessian(self.params) @cache_readonly def covjac(self): ''' covariance of parameters based on outer product of jacobian of log-likelihood ''' ## if not hasattr(self, '_results'): ## raise ValueError('need to call fit first') ## #self.fit() ## self.jacv = jacv = self.jac(self._results.params) jacv = self.jacv return np.linalg.inv(np.dot(jacv.T, jacv)) @cache_readonly def covjhj(self): '''covariance of parameters based on HJJH dot product of Hessian, Jacobian, Jacobian, Hessian of likelihood name should be covhjh ''' jacv = self.jacv ## hessv = self.hessv ## hessinv = np.linalg.inv(hessv) ## self.hessinv = hessinv hessinv = self.cov_params() return np.dot(hessinv, np.dot(np.dot(jacv.T, jacv), hessinv)) @cache_readonly def bsejhj(self): '''standard deviation of parameter estimates based on covHJH ''' return np.sqrt(np.diag(self.covjhj)) @cache_readonly def bsejac(self): '''standard deviation of parameter estimates based on covjac ''' return np.sqrt(np.diag(self.covjac)) def bootstrap(self, nrep=100, method='nm', disp=0, store=1): '''simple bootstrap to get mean and variance of estimator see notes Parameters ---------- nrep : int number of bootstrap replications method : str optimization method to use disp : bool If true, then optimization prints results store : bool If true, then parameter estimates for all bootstrap iterations are attached in self.bootstrap_results Returns ------- mean : array mean of parameter estimates over bootstrap replications std : array standard deviation of parameter estimates over bootstrap replications Notes ----- This was mainly written to compare estimators of the standard errors of the parameter estimates. It uses independent random sampling from the original endog and exog, and therefore is only correct if observations are independently distributed. This will be moved to apply only to models with independently distributed observations. ''' results = [] print self.model.__class__ hascloneattr = True if hasattr(self, 'cloneattr') else False for i in xrange(nrep): rvsind = np.random.randint(self.nobs - 1, size=self.nobs) #this needs to set startparam and get other defining attributes #need a clone method on model fitmod = self.model.__class__(self.endog[rvsind], self.exog[rvsind, :]) if hascloneattr: for attr in self.model.cloneattr: setattr(fitmod, attr, getattr(self.model, attr)) fitres = fitmod.fit(method=method, disp=disp) results.append(fitres.params) results = np.array(results) if store: self.bootstrap_results = results return results.mean(0), results.std(0), results def get_nlfun(self, fun): #I think this is supposed to get the delta method that is currently #in miscmodels count (as part of Poisson example) pass class GenericLikelihoodModelResults(LikelihoodModelResults, ResultMixin): """ A results class for the discrete dependent variable models. ..Warning : The following description has not been updated to this version/class. Where are AIC, BIC, ....? docstring looks like copy from discretemod Parameters ---------- model : A DiscreteModel instance mlefit : instance of LikelihoodResults This contains the numerical optimization results as returned by LikelihoodModel.fit(), in a superclass of GnericLikelihoodModels Returns ------- *Attributes* Warning most of these are not available yet aic : float Akaike information criterion. -2*(`llf` - p) where p is the number of regressors including the intercept. bic : float Bayesian information criterion. -2*`llf` + ln(`nobs`)*p where p is the number of regressors including the intercept. bse : array The standard errors of the coefficients. df_resid : float See model definition. df_model : float See model definition. fitted_values : array Linear predictor XB. llf : float Value of the loglikelihood llnull : float Value of the constant-only loglikelihood llr : float Likelihood ratio chi-squared statistic; -2*(`llnull` - `llf`) llr_pvalue : float The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom `df_model`. prsquared : float McFadden's pseudo-R-squared. 1 - (`llf`/`llnull`) """ def __init__(self, model, mlefit): # super(DiscreteResults, self).__init__(model, params, # np.linalg.inv(-hessian), scale=1.) self.model = model self.endog = model.endog self.exog = model.exog self.nobs = model.endog.shape[0] # TODO: possibly move to model.fit() # and outsource together with patching names if hasattr(model, 'df_model'): self.df_model = model.df_model else: self.df_model = len(mlefit.params) # retrofitting the model, used in t_test TODO: check design self.model.df_model = self.df_model if hasattr(model, 'df_resid'): self.df_resid = model.df_resid else: self.df_resid = self.endog.shape[0] - self.df_model # retrofitting the model, used in t_test TODO: check design self.model.df_resid = self.df_resid self._cache = resettable_cache() self.__dict__.update(mlefit.__dict__) def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ----------- yname : string, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Maximum Likelihood']), ('Date:', None), ('Time:', None), ('No. Observations:', None), ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling ('Df Model:', None), #[self.df_model]) ] top_right = [#('R-squared:', ["%#8.3f" % self.rsquared]), #('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]), #('F-statistic:', ["%#8.4g" % self.fvalue] ), #('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]), ('Log-Likelihood:', None), #["%#6.4g" % self.llf]), ('AIC:', ["%#8.4g" % self.aic]), ('BIC:', ["%#8.4g" % self.bic]) ] if title is None: title = self.model.__class__.__name__ + ' ' + "Results" #create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False) return smry statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/tests/000077500000000000000000000000001224417117700225705ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/tests/__init__.py000066400000000000000000000000001224417117700246670ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/tests/test_data.py000066400000000000000000000626501224417117700251230ustar00rootroot00000000000000import numpy as np import pandas import pandas.util.testing as ptesting from statsmodels.base import data as sm_data #class TestDates(object): # @classmethod # def setupClass(cls): # nrows = 10 # cls.dates_result = cls.dates_results = np.random.random(nrows) # # def test_dates(self): # np.testing.assert_equal(data.wrap_output(self.dates_input, 'dates'), # self.dates_result) class TestArrays(object): @classmethod def setupClass(cls): cls.endog = np.random.random(10) cls.exog = np.c_[np.ones(10), np.random.random((10,2))] cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_result = cls.col_input = np.random.random(nvars) cls.row_result = cls.row_input = np.random.random(nrows) cls.cov_result = cls.cov_input = np.random.random((nvars, nvars)) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = 'y' cls.row_labels = None def test_orig(self): np.testing.assert_equal(self.data.orig_endog, self.endog) np.testing.assert_equal(self.data.orig_exog, self.exog) def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog) np.testing.assert_equal(self.data.exog, self.exog) def test_attach(self): data = self.data # this makes sure what the wrappers need work but not the wrapped # results themselves np.testing.assert_equal(data.wrap_output(self.col_input, 'columns'), self.col_result) np.testing.assert_equal(data.wrap_output(self.row_input, 'rows'), self.row_result) np.testing.assert_equal(data.wrap_output(self.cov_input, 'cov'), self.cov_result) def test_names(self): data = self.data np.testing.assert_equal(data.xnames, self.xnames) np.testing.assert_equal(data.ynames, self.ynames) def test_labels(self): #HACK: because numpy master after NA stuff assert_equal fails on # pandas indices np.testing.assert_(np.all(self.data.row_labels == self.row_labels)) class TestArrays2dEndog(TestArrays): @classmethod def setupClass(cls): super(TestArrays2dEndog, cls).setupClass() cls.endog = np.random.random((10,1)) cls.exog = np.c_[np.ones(10), np.random.random((10,2))] cls.data = sm_data.handle_data(cls.endog, cls.exog) #cls.endog = endog.squeeze() def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.squeeze()) np.testing.assert_equal(self.data.exog, self.exog) class TestArrays1dExog(TestArrays): @classmethod def setupClass(cls): super(TestArrays1dExog, cls).setupClass() cls.endog = np.random.random(10) exog = np.random.random(10) cls.data = sm_data.handle_data(cls.endog, exog) cls.exog = exog[:,None] cls.xnames = ['x1'] cls.ynames = 'y' def test_orig(self): np.testing.assert_equal(self.data.orig_endog, self.endog) np.testing.assert_equal(self.data.orig_exog, self.exog.squeeze()) class TestDataFrames(TestArrays): @classmethod def setupClass(cls): cls.endog = pandas.DataFrame(np.random.random(10), columns=['y_1']) exog = pandas.DataFrame(np.random.random((10,2)), columns=['x_1','x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y_1' cls.row_labels = cls.exog.index def test_orig(self): ptesting.assert_frame_equal(self.data.orig_endog, self.endog) ptesting.assert_frame_equal(self.data.orig_exog, self.exog) def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.values.squeeze()) np.testing.assert_equal(self.data.exog, self.exog.values) def test_attach(self): data = self.data # this makes sure what the wrappers need work but not the wrapped # results themselves ptesting.assert_series_equal(data.wrap_output(self.col_input, 'columns'), self.col_result) ptesting.assert_series_equal(data.wrap_output(self.row_input, 'rows'), self.row_result) ptesting.assert_frame_equal(data.wrap_output(self.cov_input, 'cov'), self.cov_result) class TestLists(TestArrays): @classmethod def setupClass(cls): super(TestLists, cls).setupClass() cls.endog = np.random.random(10).tolist() cls.exog = np.c_[np.ones(10), np.random.random((10,2))].tolist() cls.data = sm_data.handle_data(cls.endog, cls.exog) class TestRecarrays(TestArrays): @classmethod def setupClass(cls): super(TestRecarrays, cls).setupClass() cls.endog = np.random.random(9).view([('y_1', 'f8')]).view(np.recarray) exog = np.random.random(9*3).view([('const', 'f8'),('x_1', 'f8'), ('x_2', 'f8')]).view(np.recarray) exog['const'] = 1 cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y_1' def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.view(float)) np.testing.assert_equal(self.data.exog, self.exog.view((float,3))) class TestStructarrays(TestArrays): @classmethod def setupClass(cls): super(TestStructarrays, cls).setupClass() cls.endog = np.random.random(9).view([('y_1', 'f8')]).view(np.recarray) exog = np.random.random(9*3).view([('const', 'f8'),('x_1', 'f8'), ('x_2', 'f8')]).view(np.recarray) exog['const'] = 1 cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y_1' def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.view(float)) np.testing.assert_equal(self.data.exog, self.exog.view((float,3))) class TestListDataFrame(TestDataFrames): @classmethod def setupClass(cls): cls.endog = np.random.random(10).tolist() exog = pandas.DataFrame(np.random.random((10,2)), columns=['x_1','x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y' cls.row_labels = cls.exog.index def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog) np.testing.assert_equal(self.data.exog, self.exog.values) def test_orig(self): np.testing.assert_equal(self.data.orig_endog, self.endog) ptesting.assert_frame_equal(self.data.orig_exog, self.exog) class TestDataFrameList(TestDataFrames): @classmethod def setupClass(cls): cls.endog = pandas.DataFrame(np.random.random(10), columns=['y_1']) exog = pandas.DataFrame(np.random.random((10,2)), columns=['x1','x2']) exog.insert(0, 'const', 1) cls.exog = exog.values.tolist() cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = 'y_1' cls.row_labels = cls.endog.index def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.values.squeeze()) np.testing.assert_equal(self.data.exog, self.exog) def test_orig(self): ptesting.assert_frame_equal(self.data.orig_endog, self.endog) np.testing.assert_equal(self.data.orig_exog, self.exog) class TestArrayDataFrame(TestDataFrames): @classmethod def setupClass(cls): cls.endog = np.random.random(10) exog = pandas.DataFrame(np.random.random((10,2)), columns=['x_1','x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y' cls.row_labels = cls.exog.index def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog) np.testing.assert_equal(self.data.exog, self.exog.values) def test_orig(self): np.testing.assert_equal(self.data.orig_endog, self.endog) ptesting.assert_frame_equal(self.data.orig_exog, self.exog) class TestDataFrameArray(TestDataFrames): @classmethod def setupClass(cls): cls.endog = pandas.DataFrame(np.random.random(10), columns=['y_1']) exog = pandas.DataFrame(np.random.random((10,2)), columns=['x1','x2']) # names mimic defaults exog.insert(0, 'const', 1) cls.exog = exog.values cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = 'y_1' cls.row_labels = cls.endog.index def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.values.squeeze()) np.testing.assert_equal(self.data.exog, self.exog) def test_orig(self): ptesting.assert_frame_equal(self.data.orig_endog, self.endog) np.testing.assert_equal(self.data.orig_exog, self.exog) class TestSeriesDataFrame(TestDataFrames): @classmethod def setupClass(cls): cls.endog = pandas.Series(np.random.random(10), name='y_1') exog = pandas.DataFrame(np.random.random((10,2)), columns=['x_1','x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = 'y_1' cls.row_labels = cls.exog.index def test_orig(self): ptesting.assert_series_equal(self.data.orig_endog, self.endog) ptesting.assert_frame_equal(self.data.orig_exog, self.exog) class TestSeriesSeries(TestDataFrames): @classmethod def setupClass(cls): cls.endog = pandas.Series(np.random.random(10), name='y_1') exog = pandas.Series(np.random.random(10), name='x_1') cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 1 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index = [exog.name]) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index = exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = [exog.name], columns = [exog.name]) cls.xnames = ['x_1'] cls.ynames = 'y_1' cls.row_labels = cls.exog.index def test_orig(self): ptesting.assert_series_equal(self.data.orig_endog, self.endog) ptesting.assert_series_equal(self.data.orig_exog, self.exog) def test_endogexog(self): np.testing.assert_equal(self.data.endog, self.endog.values.squeeze()) np.testing.assert_equal(self.data.exog, self.exog.values[:,None]) def test_alignment(): """ Fix Issue #206 """ from statsmodels.regression.linear_model import OLS from statsmodels.datasets.macrodata import load_pandas d = load_pandas().data #growth rates gs_l_realinv = 400 * np.log(d['realinv']).diff().dropna() gs_l_realgdp = 400 * np.log(d['realgdp']).diff().dropna() lint = d['realint'][:-1] # incorrect indexing for test purposes endog = gs_l_realinv # re-index because they won't conform to lint realgdp = gs_l_realgdp.reindex(lint.index, method='bfill') data = dict(const=np.ones_like(lint), lrealgdp=realgdp, lint=lint) exog = pandas.DataFrame(data) # which index do we get?? np.testing.assert_raises(ValueError, OLS, *(endog, exog)) class TestMultipleEqsArrays(TestArrays): @classmethod def setupClass(cls): cls.endog = np.random.random((10,4)) cls.exog = np.c_[np.ones(10), np.random.random((10,2))] cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 neqs = 4 cls.col_result = cls.col_input = np.random.random(nvars) cls.row_result = cls.row_input = np.random.random(nrows) cls.cov_result = cls.cov_input = np.random.random((nvars, nvars)) cls.cov_eq_result = cls.cov_eq_input = np.random.random((neqs,neqs)) cls.col_eq_result = cls.col_eq_input = np.array((neqs, nvars)) cls.xnames = ['const', 'x1', 'x2'] cls.ynames = ['y1', 'y2', 'y3', 'y4'] cls.row_labels = None def test_attach(self): data = self.data # this makes sure what the wrappers need work but not the wrapped # results themselves np.testing.assert_equal(data.wrap_output(self.col_input, 'columns'), self.col_result) np.testing.assert_equal(data.wrap_output(self.row_input, 'rows'), self.row_result) np.testing.assert_equal(data.wrap_output(self.cov_input, 'cov'), self.cov_result) np.testing.assert_equal(data.wrap_output(self.cov_eq_input, 'cov_eq'), self.cov_eq_result) np.testing.assert_equal(data.wrap_output(self.col_eq_input, 'columns_eq'), self.col_eq_result) class TestMultipleEqsDataFrames(TestDataFrames): @classmethod def setupClass(cls): cls.endog = endog = pandas.DataFrame(np.random.random((10,4)), columns=['y_1', 'y_2', 'y_3', 'y_4']) exog = pandas.DataFrame(np.random.random((10,2)), columns=['x_1','x_2']) exog.insert(0, 'const', 1) cls.exog = exog cls.data = sm_data.handle_data(cls.endog, cls.exog) nrows = 10 nvars = 3 neqs = 4 cls.col_input = np.random.random(nvars) cls.col_result = pandas.Series(cls.col_input, index=exog.columns) cls.row_input = np.random.random(nrows) cls.row_result = pandas.Series(cls.row_input, index=exog.index) cls.cov_input = np.random.random((nvars, nvars)) cls.cov_result = pandas.DataFrame(cls.cov_input, index = exog.columns, columns = exog.columns) cls.cov_eq_input = np.random.random((neqs, neqs)) cls.cov_eq_result = pandas.DataFrame(cls.cov_eq_input, index=endog.columns, columns=endog.columns) cls.col_eq_input = np.random.random((nvars, neqs)) cls.col_eq_result = pandas.DataFrame(cls.col_eq_input, index=exog.columns, columns=endog.columns) cls.xnames = ['const', 'x_1', 'x_2'] cls.ynames = ['y_1', 'y_2', 'y_3', 'y_4'] cls.row_labels = cls.exog.index def test_attach(self): data = self.data ptesting.assert_series_equal(data.wrap_output(self.col_input, 'columns'), self.col_result) ptesting.assert_series_equal(data.wrap_output(self.row_input, 'rows'), self.row_result) ptesting.assert_frame_equal(data.wrap_output(self.cov_input, 'cov'), self.cov_result) ptesting.assert_frame_equal(data.wrap_output(self.cov_eq_input, 'cov_eq'), self.cov_eq_result) ptesting.assert_frame_equal(data.wrap_output(self.col_eq_input, 'columns_eq'), self.col_eq_result) class TestMissingArray(object): @classmethod def setupClass(cls): X = np.random.random((25,4)) y = np.random.random(25) y[10] = np.nan X[2,3] = np.nan X[14,2] = np.nan cls.y, cls.X = y, X def test_raise(self): np.testing.assert_raises(Exception, sm_data.handle_data, (self.y, self.X, 'raise')) def test_drop(self): y = self.y X = self.X combined = np.c_[y, X] idx = ~np.isnan(combined).any(axis=1) y = y[idx] X = X[idx] data = sm_data.handle_data(self.y, self.X, 'drop') np.testing.assert_array_equal(data.endog, y) np.testing.assert_array_equal(data.exog, X) def test_none(self): data = sm_data.handle_data(self.y, self.X, 'none') np.testing.assert_array_equal(data.endog, self.y) np.testing.assert_array_equal(data.exog, self.X) def test_endog_only_raise(self): np.testing.assert_raises(Exception, sm_data.handle_data, (self.y, None, 'raise')) def test_endog_only_drop(self): y = self.y y = y[~np.isnan(y)] data = sm_data.handle_data(self.y, None, 'drop') np.testing.assert_array_equal(data.endog, y) def test_mv_endog(self): y = self.X y = y[~np.isnan(y).any(axis=1)] data = sm_data.handle_data(self.X, None, 'drop') np.testing.assert_array_equal(data.endog, y) def test_extra_kwargs_2d(self): sigma = np.random.random((25, 25)) sigma = sigma + sigma.T - np.diag(np.diag(sigma)) data = sm_data.handle_data(self.y, self.X, 'drop', sigma=sigma) idx = ~np.isnan(np.c_[self.y, self.X]).any(axis=1) sigma = sigma[idx][:,idx] np.testing.assert_array_equal(data.sigma, sigma) def test_extra_kwargs_1d(self): weights = np.random.random(25) data = sm_data.handle_data(self.y, self.X, 'drop', weights=weights) idx = ~np.isnan(np.c_[self.y, self.X]).any(axis=1) weights = weights[idx] np.testing.assert_array_equal(data.weights, weights) class TestMissingPandas(object): @classmethod def setupClass(cls): X = np.random.random((25,4)) y = np.random.random(25) y[10] = np.nan X[2,3] = np.nan X[14,2] = np.nan cls.y, cls.X = pandas.Series(y), pandas.DataFrame(X) def test_raise(self): np.testing.assert_raises(Exception, sm_data.handle_data, (self.y, self.X, 'raise')) def test_drop(self): y = self.y X = self.X combined = np.c_[y, X] idx = ~np.isnan(combined).any(axis=1) y = y.ix[idx] X = X.ix[idx] data = sm_data.handle_data(self.y, self.X, 'drop') np.testing.assert_array_equal(data.endog, y.values) ptesting.assert_series_equal(data.orig_endog, self.y.ix[idx]) np.testing.assert_array_equal(data.exog, X.values) ptesting.assert_frame_equal(data.orig_exog, self.X.ix[idx]) def test_none(self): data = sm_data.handle_data(self.y, self.X, 'none') np.testing.assert_array_equal(data.endog, self.y.values) np.testing.assert_array_equal(data.exog, self.X.values) def test_endog_only_raise(self): np.testing.assert_raises(Exception, sm_data.handle_data, (self.y, None, 'raise')) def test_endog_only_drop(self): y = self.y y = y.dropna() data = sm_data.handle_data(self.y, None, 'drop') np.testing.assert_array_equal(data.endog, y.values) def test_mv_endog(self): y = self.X y = y.ix[~np.isnan(y.values).any(axis=1)] data = sm_data.handle_data(self.X, None, 'drop') np.testing.assert_array_equal(data.endog, y.values) def test_labels(self): 2, 10, 14 labels = pandas.Index([0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) data = sm_data.handle_data(self.y, self.X, 'drop') np.testing.assert_(data.row_labels.equals(labels)) class TestConstant(object): @classmethod def setupClass(cls): from statsmodels.datasets.longley import load_pandas cls.data = load_pandas() def test_array_constant(self): exog = self.data.exog.copy() exog['const'] = 1 data = sm_data.handle_data(self.data.endog.values, exog.values) np.testing.assert_equal(data.k_constant, 1) np.testing.assert_equal(data.const_idx, 6) def test_pandas_constant(self): exog = self.data.exog.copy() exog['const'] = 1 data = sm_data.handle_data(self.data.endog, exog) np.testing.assert_equal(data.k_constant, 1) np.testing.assert_equal(data.const_idx, 6) def test_pandas_noconstant(self): exog = self.data.exog.copy() data = sm_data.handle_data(self.data.endog, exog) np.testing.assert_equal(data.k_constant, 0) np.testing.assert_equal(data.const_idx, None) def test_array_noconstant(self): exog = self.data.exog.copy() data = sm_data.handle_data(self.data.endog.values, exog.values) np.testing.assert_equal(data.k_constant, 0) np.testing.assert_equal(data.const_idx, None) if __name__ == "__main__": import nose #nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], # exit=False) nose.runmodule(argv=[__file__, '-vvs', '-x'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/tests/test_optimize.py000066400000000000000000000025551224417117700260500ustar00rootroot00000000000000from numpy.testing import assert_ from statsmodels.base.model import (_fit_mle_newton, _fit_mle_nm, _fit_mle_bfgs, _fit_mle_cg, _fit_mle_ncg, _fit_mle_powell) fit_funcs = { 'newton': _fit_mle_newton, 'nm': _fit_mle_nm, # Nelder-Mead 'bfgs': _fit_mle_bfgs, 'cg': _fit_mle_cg, 'ncg': _fit_mle_ncg, 'powell': _fit_mle_powell } def dummy_func(x): return x**2 def dummy_score(x): return 2*x def dummy_hess(x): return [[2]] def test_full_output_false(): # just a smoke test # newton needs f, score, start, fargs, kwargs # bfgs needs f, score start, fargs, kwargs # nm needs "" # cg "" # ncg "" # powell "" for method in fit_funcs: func = fit_funcs[method] if method == "newton": xopts, retvals = func(dummy_func, dummy_score, [1], (), {}, hess=dummy_hess, full_output=False, disp=0) else: xopts, retvals = func(dummy_func, dummy_score, [1], (), {}, full_output=False, disp=0) assert_(xopts == None) if method == "powell": #NOTE: I think I reported this? Might be version/optimize API # dependent assert_(retvals.shape == () and retvals.size == 1) else: assert_(len(retvals)==1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/tests/test_shrink_pickle.py000066400000000000000000000156351224417117700270400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 09 16:00:27 2012 Author: Josef Perktold """ import pickle import numpy as np import statsmodels.api as sm from numpy.testing import assert_ from nose import SkipTest import platform iswin = platform.system() == 'Windows' npversionless15 = np.__version__ < '1.5' winoldnp = iswin & npversionless15 def check_pickle(obj): from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() pickle.dump(obj, fh) plen = fh.tell() fh.seek(0, 0) res = pickle.load(fh) fh.close() return res, plen class RemoveDataPickle(object): def __init__(self): self.predict_kwds = {} @classmethod def setup_class(self): nobs = 10000 np.random.seed(987689) x = np.random.randn(nobs, 3) x = sm.add_constant(x) self.exog = x self.xf = 0.25 * np.ones((2, 4)) def test_remove_data_pickle(self): if winoldnp: raise SkipTest results = self.results xf = self.xf pred_kwds = self.predict_kwds pred1 = results.predict(xf, **pred_kwds) #create some cached attributes results.summary() res = results.summary2() # SMOKE test also summary2 # uncomment the following to check whether tests run (7 failures now) #np.testing.assert_equal(res, 1) #check pickle unpickle works on full results #TODO: drop of load save is tested res, l = check_pickle(results._results) #remove data arrays, check predict still works results.remove_data() pred2 = results.predict(xf, **pred_kwds) np.testing.assert_equal(pred2, pred1) #pickle, unpickle reduced array res, l = check_pickle(results._results) #for testing attach res self.res = res #Note: 10000 is just a guess for the limit on the length of the pickle assert_(l < 10000, msg='pickle length not %d < %d' % (l, 10000)) pred3 = results.predict(xf, **pred_kwds) np.testing.assert_equal(pred3, pred1) def test_remove_data_docstring(self): assert_(self.results.remove_data.__doc__ is not None) def test_pickle_wrapper(self): from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() # use cPickle with binary content # test unwrapped results load save pickle self.results._results.save(fh) fh.seek(0, 0) res_unpickled = self.results._results.__class__.load(fh) assert_(type(res_unpickled) is type(self.results._results)) # test wrapped results load save fh.seek(0, 0) self.results.save(fh) fh.seek(0, 0) res_unpickled = self.results.__class__.load(fh) fh.close() # print type(res_unpickled) assert_(type(res_unpickled) is type(self.results)) before = sorted(self.results.__dict__.keys()) after = sorted(res_unpickled.__dict__.keys()) assert_(before == after, msg='not equal %r and %r' % (before, after)) before = sorted(self.results._results.__dict__.keys()) after = sorted(res_unpickled._results.__dict__.keys()) assert_(before == after, msg='not equal %r and %r' % (before, after)) before = sorted(self.results.model.__dict__.keys()) after = sorted(res_unpickled.model.__dict__.keys()) assert_(before == after, msg='not equal %r and %r' % (before, after)) before = sorted(self.results._cache.keys()) after = sorted(res_unpickled._cache.keys()) assert_(before == after, msg='not equal %r and %r' % (before, after)) class TestRemoveDataPickleOLS(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog np.random.seed(987689) y = x.sum(1) + np.random.randn(x.shape[0]) self.results = sm.OLS(y, self.exog).fit() class TestRemoveDataPickleWLS(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog np.random.seed(987689) y = x.sum(1) + np.random.randn(x.shape[0]) self.results = sm.WLS(y, self.exog, weights=np.ones(len(y))).fit() class TestRemoveDataPicklePoisson(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog np.random.seed(987689) y_count = np.random.poisson(np.exp(x.sum(1) - x.mean())) model = sm.Poisson(y_count, x) #, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default # use start_params to converge faster start_params = np.array([0.75334818, 0.99425553, 1.00494724, 1.00247112]) self.results = model.fit(start_params=start_params, method='bfgs', disp=0) #TODO: temporary, fixed in master self.predict_kwds = dict(exposure=1, offset=0) class TestRemoveDataPickleNegativeBinomial(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test np.random.seed(987689) data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) mod = sm.NegativeBinomial(data.endog, data.exog) self.results = mod.fit(disp=0) class TestRemoveDataPickleLogit(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog nobs = x.shape[0] np.random.seed(987689) y_bin = (np.random.rand(nobs) < 1.0 / (1 + np.exp(x.sum(1) - x.mean()))).astype(int) model = sm.Logit(y_bin, x) #, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default # use start_params to converge faster start_params = np.array([-0.73403806, -1.00901514, -0.97754543, -0.95648212]) self.results = model.fit(start_params=start_params, method='bfgs', disp=0) class TestRemoveDataPickleRLM(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog np.random.seed(987689) y = x.sum(1) + np.random.randn(x.shape[0]) self.results = sm.RLM(y, self.exog).fit() class TestRemoveDataPickleGLM(RemoveDataPickle): def setup(self): #fit for each test, because results will be changed by test x = self.exog np.random.seed(987689) y = x.sum(1) + np.random.randn(x.shape[0]) self.results = sm.GLM(y, self.exog).fit() if __name__ == '__main__': for cls in [TestRemoveDataPickleOLS, TestRemoveDataPickleWLS, TestRemoveDataPicklePoisson, TestRemoveDataPickleNegativeBinomial, TestRemoveDataPickleLogit, TestRemoveDataPickleRLM, TestRemoveDataPickleGLM]: print cls cls.setup_class() tt = cls() tt.setup() tt.test_remove_data_pickle() tt.test_remove_data_docstring() tt.test_pickle_wrapper() statsmodels-0.5.0+git13-g8e07d34/statsmodels/base/wrapper.py000066400000000000000000000100721224417117700234600ustar00rootroot00000000000000import inspect import functools import numpy as np class ResultsWrapper(object): """ Class which wraps a statsmodels estimation Results class and steps in to reattach metadata to results (if available) """ _wrap_attrs = {} _wrap_methods = {} def __init__(self, results): self._results = results self.__doc__ = results.__doc__ def __dir__(self): return [x for x in dir(self._results)] def __getattribute__(self, attr): get = lambda name: object.__getattribute__(self, name) try: results = get('_results') except AttributeError: pass try: return get(attr) except AttributeError: pass obj = getattr(results, attr) data = results.model.data how = self._wrap_attrs.get(attr) if how: obj = data.wrap_output(obj, how=how) return obj def __getstate__(self): #print 'pickling wrapper', self.__dict__ return self.__dict__ def __setstate__(self, dict_): #print 'unpickling wrapper', dict_ self.__dict__.update(dict_) def save(self, fname, remove_data=False): '''save a pickle of this instance Parameters ---------- fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. ''' from statsmodels.iolib.smpickle import save_pickle if remove_data: self.remove_data() save_pickle(self, fname) @classmethod def load(cls, fname): from statsmodels.iolib.smpickle import load_pickle return load_pickle(fname) def union_dicts(*dicts): result = {} for d in dicts: result.update(d) return result def make_wrapper(func, how): @functools.wraps(func) def wrapper(self, *args, **kwargs): results = object.__getattribute__(self, '_results') data = results.model.data return data.wrap_output(func(results, *args, **kwargs), how) argspec = inspect.getargspec(func) formatted = inspect.formatargspec(argspec[0], varargs=argspec[1], defaults=argspec[3]) try: func_name = func.im_func.func_name except AttributeError: #Python 3 func_name = func.__name__ wrapper.__doc__ = "%s%s\n%s" % (func_name, formatted, wrapper.__doc__) return wrapper def populate_wrapper(klass, wrapping): for meth, how in klass._wrap_methods.iteritems(): if not hasattr(wrapping, meth): continue func = getattr(wrapping, meth) wrapper = make_wrapper(func, how) setattr(klass, meth, wrapper) if __name__ == '__main__': import statsmodels.api as sm from pandas import DataFrame data = sm.datasets.longley.load() df = DataFrame(data.exog, columns=data.exog_name) y = data.endog # data.exog = sm.add_constant(data.exog) df['intercept'] = 1. olsresult = sm.OLS(y, df).fit() rlmresult = sm.RLM(y, df).fit() # olswrap = RegressionResultsWrapper(olsresult) # rlmwrap = RLMResultsWrapper(rlmresult) data = sm.datasets.wfs.load() # get offset offset = np.log(data.exog[:, -1]) exog = data.exog[:, :-1] # convert dur to dummy exog = sm.tools.categorical(exog, col=0, drop=True) # drop reference category # convert res to dummy exog = sm.tools.categorical(exog, col=0, drop=True) # convert edu to dummy exog = sm.tools.categorical(exog, col=0, drop=True) # drop reference categories and add intercept exog = sm.add_constant(exog[:, [1, 2, 3, 4, 5, 7, 8, 10, 11, 12]], prepend=False) endog = np.round(data.endog) mod = sm.GLM(endog, exog, family=sm.families.Poisson()).fit() # glmwrap = GLMResultsWrapper(mod) statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/000077500000000000000000000000001224417117700223355ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/__init__.py000066400000000000000000000000001224417117700244340ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/collections.py000066400000000000000000000007221224417117700252260ustar00rootroot00000000000000'''backported compatibility functions for Python's collections ''' try: #python >= 2.7 from collections import OrderedDict except ImportError: #http://code.activestate.com/recipes/576693/ #author: Raymond Hettinger from ordereddict import OrderedDict try: #python >= 2.7 from collections import Counter except ImportError: #http://code.activestate.com/recipes/576611/ #author: Raymond Hettinger from counter import Counter statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/counter.py000066400000000000000000000145001224417117700243660ustar00rootroot00000000000000'''Compatibility module for collections.Counter for python < 2.7 Author: Raymond Hettinger License: MIT License http://code.activestate.com/recipes/576611/ , downloaded 2013-03-08 ''' from operator import itemgetter from heapq import nlargest from itertools import repeat, ifilter class Counter(dict): '''Dict subclass for counting hashable objects. Sometimes called a bag or multiset. Elements are stored as dictionary keys and their counts are stored as dictionary values. >>> Counter('zyzygy') Counter({'y': 3, 'z': 2, 'g': 1}) ''' def __init__(self, iterable=None, **kwds): '''Create a new, empty Counter object. And if given, count elements from an input iterable. Or, initialize the count from another mapping of elements to their counts. >>> c = Counter() # a new, empty counter >>> c = Counter('gallahad') # a new counter from an iterable >>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping >>> c = Counter(a=4, b=2) # a new counter from keyword args ''' self.update(iterable, **kwds) def __missing__(self, key): return 0 def most_common(self, n=None): '''List the n most common elements and their counts from the most common to the least. If n is None, then list all element counts. >>> Counter('abracadabra').most_common(3) [('a', 5), ('r', 2), ('b', 2)] ''' if n is None: return sorted(self.iteritems(), key=itemgetter(1), reverse=True) return nlargest(n, self.iteritems(), key=itemgetter(1)) def elements(self): '''Iterator over elements repeating each as many times as its count. >>> c = Counter('ABCABC') >>> sorted(c.elements()) ['A', 'A', 'B', 'B', 'C', 'C'] If an element's count has been set to zero or is a negative number, elements() will ignore it. ''' for elem, count in self.iteritems(): for _ in repeat(None, count): yield elem # Override dict methods where the meaning changes for Counter objects. @classmethod def fromkeys(cls, iterable, v=None): raise NotImplementedError( 'Counter.fromkeys() is undefined. Use Counter(iterable) instead.') def update(self, iterable=None, **kwds): '''Like dict.update() but add counts instead of replacing them. Source can be an iterable, a dictionary, or another Counter instance. >>> c = Counter('which') >>> c.update('witch') # add elements from another iterable >>> d = Counter('watch') >>> c.update(d) # add elements from another counter >>> c['h'] # four 'h' in which, witch, and watch 4 ''' if iterable is not None: if hasattr(iterable, 'iteritems'): if self: self_get = self.get for elem, count in iterable.iteritems(): self[elem] = self_get(elem, 0) + count else: dict.update(self, iterable) # fast path when counter is empty else: self_get = self.get for elem in iterable: self[elem] = self_get(elem, 0) + 1 if kwds: self.update(kwds) def copy(self): 'Like dict.copy() but returns a Counter instance instead of a dict.' return Counter(self) def __delitem__(self, elem): 'Like dict.__delitem__() but does not raise KeyError for missing values.' if elem in self: dict.__delitem__(self, elem) def __repr__(self): if not self: return '%s()' % self.__class__.__name__ items = ', '.join(map('%r: %r'.__mod__, self.most_common())) return '%s({%s})' % (self.__class__.__name__, items) # Multiset-style mathematical operations discussed in: # Knuth TAOCP Volume II section 4.6.3 exercise 19 # and at http://en.wikipedia.org/wiki/Multiset # # Outputs guaranteed to only include positive counts. # # To strip negative and zero counts, add-in an empty counter: # c += Counter() def __add__(self, other): '''Add counts from two counters. >>> Counter('abbb') + Counter('bcc') Counter({'b': 4, 'c': 2, 'a': 1}) ''' if not isinstance(other, Counter): return NotImplemented result = Counter() for elem in set(self) | set(other): newcount = self[elem] + other[elem] if newcount > 0: result[elem] = newcount return result def __sub__(self, other): ''' Subtract count, but keep only results with positive counts. >>> Counter('abbbc') - Counter('bccd') Counter({'b': 2, 'a': 1}) ''' if not isinstance(other, Counter): return NotImplemented result = Counter() for elem in set(self) | set(other): newcount = self[elem] - other[elem] if newcount > 0: result[elem] = newcount return result def __or__(self, other): '''Union is the maximum of value in either of the input counters. >>> Counter('abbb') | Counter('bcc') Counter({'b': 3, 'c': 2, 'a': 1}) ''' if not isinstance(other, Counter): return NotImplemented _max = max result = Counter() for elem in set(self) | set(other): newcount = _max(self[elem], other[elem]) if newcount > 0: result[elem] = newcount return result def __and__(self, other): ''' Intersection is the minimum of corresponding counts. >>> Counter('abbb') & Counter('bcc') Counter({'b': 1}) ''' if not isinstance(other, Counter): return NotImplemented _min = min result = Counter() if len(self) < len(other): self, other = other, self for elem in ifilter(self.__contains__, other): newcount = _min(self[elem], other[elem]) if newcount > 0: result[elem] = newcount return result if __name__ == '__main__': import doctest print doctest.testmod() statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/iter_compat.py000066400000000000000000000020011224417117700252060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Feb 29 10:12:38 2012 Author: Josef Perktold License: BSD-3 """ import itertools try: #python 2.6, 2.7 zip_longest = itertools.izip_longest pass except AttributeError: #python 3.2 zip_longest = itertools.zip_longest try: from itertools import combinations except ImportError: #from python 2.6 documentation def combinations(iterable, r): # combinations('ABCD', 2) --> AB AC AD BC BD CD # combinations(range(4), 3) --> 012 013 023 123 pool = tuple(iterable) n = len(pool) if r > n: return indices = range(r) yield tuple(pool[i] for i in indices) while True: for i in reversed(range(r)): if indices[i] != i + n - r: break else: return indices[i] += 1 for j in range(i+1, r): indices[j] = indices[j-1] + 1 yield tuple(pool[i] for i in indices) statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/np_compat.py000066400000000000000000000127061224417117700246750ustar00rootroot00000000000000'''Compatibility functions for numpy versions in lib np.unique --------- Behavior changed in 1.6.2 and doesn't work for structured arrays if return_index=True. Only needed for this case, use np.unique otherwise License: np_unique below is copied form the numpy source before the change and is distributed under the BSD-3 license Copyright (c) 2005-2009, NumPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the NumPy Developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' import numpy as np if np.__version__ < '1.6.2': npc_unique = np.unique else: def npc_unique(ar, return_index=False, return_inverse=False): """ Find the unique elements of an array. Returns the sorted unique elements of an array. There are two optional outputs in addition to the unique elements: the indices of the input array that give the unique values, and the indices of the unique array that reconstruct the input array. Parameters ---------- ar : array_like Input array. This will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of `ar` that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array that can be used to reconstruct `ar`. Returns ------- unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the unique values in the (flattened) original array. Only provided if `return_index` is True. unique_inverse : ndarray, optional The indices to reconstruct the (flattened) original array from the unique array. Only provided if `return_inverse` is True. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> np.unique([1, 1, 2, 2, 3, 3]) array([1, 2, 3]) >>> a = np.array([[1, 1], [2, 3]]) >>> np.unique(a) array([1, 2, 3]) Return the indices of the original array that give the unique values: >>> a = np.array(['a', 'b', 'b', 'c', 'a']) >>> u, indices = np.unique(a, return_index=True) >>> u array(['a', 'b', 'c'], dtype='|S1') >>> indices array([0, 1, 3]) >>> a[indices] array(['a', 'b', 'c'], dtype='|S1') Reconstruct the input array from the unique values: >>> a = np.array([1, 2, 6, 4, 2, 3, 2]) >>> u, indices = np.unique(a, return_inverse=True) >>> u array([1, 2, 3, 4, 6]) >>> indices array([0, 1, 4, 3, 1, 2, 1]) >>> u[indices] array([1, 2, 6, 4, 2, 3, 2]) """ try: ar = ar.flatten() except AttributeError: if not return_inverse and not return_index: items = sorted(set(ar)) return np.asarray(items) else: ar = np.asanyarray(ar).flatten() if ar.size == 0: if return_inverse and return_index: return ar, np.empty(0, np.bool), np.empty(0, np.bool) elif return_inverse or return_index: return ar, np.empty(0, np.bool) else: return ar if return_inverse or return_index: perm = ar.argsort() aux = ar[perm] flag = np.concatenate(([True], aux[1:] != aux[:-1])) if return_inverse: iflag = np.cumsum(flag) - 1 iperm = perm.argsort() if return_index: return aux[flag], perm[flag], iflag[iperm] else: return aux[flag], iflag[iperm] else: return aux[flag], perm[flag] else: ar.sort() flag = np.concatenate(([True], ar[1:] != ar[:-1])) return ar[flag] statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/ordereddict.py000066400000000000000000000213671224417117700252100ustar00rootroot00000000000000# Backport of OrderedDict() class that runs on Python 2.4, 2.5, 2.6, 2.7 and pypy. # Passes Python2.7's test suite and incorporates all the latest updates. #Author: Raymond Hettinger #License: MIT License #http://code.activestate.com/recipes/576693/ revision 9, downloaded 2012-03-28 try: from thread import get_ident as _get_ident except ImportError: from dummy_thread import get_ident as _get_ident try: from _abcoll import KeysView, ValuesView, ItemsView except ImportError: pass class OrderedDict(dict): 'Dictionary that remembers insertion order' # An inherited dict maps keys to values. # The inherited dict provides __getitem__, __len__, __contains__, and get. # The remaining methods are order-aware. # Big-O running times for all methods are the same as for regular dictionaries. # The internal self.__map dictionary maps keys to links in a doubly linked list. # The circular doubly linked list starts and ends with a sentinel element. # The sentinel element never gets deleted (this simplifies the algorithm). # Each link is stored as a list of length three: [PREV, NEXT, KEY]. def __init__(self, *args, **kwds): '''Initialize an ordered dictionary. Signature is the same as for regular dictionaries, but keyword arguments are not recommended because their insertion order is arbitrary. ''' if len(args) > 1: raise TypeError('expected at most 1 arguments, got %d' % len(args)) try: self.__root except AttributeError: self.__root = root = [] # sentinel node root[:] = [root, root, None] self.__map = {} self.__update(*args, **kwds) def __setitem__(self, key, value, dict_setitem=dict.__setitem__): 'od.__setitem__(i, y) <==> od[i]=y' # Setting a new item creates a new link which goes at the end of the linked # list, and the inherited dictionary is updated with the new key/value pair. if key not in self: root = self.__root last = root[0] last[1] = root[0] = self.__map[key] = [last, root, key] dict_setitem(self, key, value) def __delitem__(self, key, dict_delitem=dict.__delitem__): 'od.__delitem__(y) <==> del od[y]' # Deleting an existing item uses self.__map to find the link which is # then removed by updating the links in the predecessor and successor nodes. dict_delitem(self, key) link_prev, link_next, key = self.__map.pop(key) link_prev[1] = link_next link_next[0] = link_prev def __iter__(self): 'od.__iter__() <==> iter(od)' root = self.__root curr = root[1] while curr is not root: yield curr[2] curr = curr[1] def __reversed__(self): 'od.__reversed__() <==> reversed(od)' root = self.__root curr = root[0] while curr is not root: yield curr[2] curr = curr[0] def clear(self): 'od.clear() -> None. Remove all items from od.' try: for node in self.__map.itervalues(): del node[:] root = self.__root root[:] = [root, root, None] self.__map.clear() except AttributeError: pass dict.clear(self) def popitem(self, last=True): '''od.popitem() -> (k, v), return and remove a (key, value) pair. Pairs are returned in LIFO order if last is true or FIFO order if false. ''' if not self: raise KeyError('dictionary is empty') root = self.__root if last: link = root[0] link_prev = link[0] link_prev[1] = root root[0] = link_prev else: link = root[1] link_next = link[1] root[1] = link_next link_next[0] = root key = link[2] del self.__map[key] value = dict.pop(self, key) return key, value # -- the following methods do not depend on the internal structure -- def keys(self): 'od.keys() -> list of keys in od' return list(self) def values(self): 'od.values() -> list of values in od' return [self[key] for key in self] def items(self): 'od.items() -> list of (key, value) pairs in od' return [(key, self[key]) for key in self] def iterkeys(self): 'od.iterkeys() -> an iterator over the keys in od' return iter(self) def itervalues(self): 'od.itervalues -> an iterator over the values in od' for k in self: yield self[k] def iteritems(self): 'od.iteritems -> an iterator over the (key, value) items in od' for k in self: yield (k, self[k]) def update(*args, **kwds): '''od.update(E, **F) -> None. Update od from dict/iterable E and F. If E is a dict instance, does: for k in E: od[k] = E[k] If E has a .keys() method, does: for k in E.keys(): od[k] = E[k] Or if E is an iterable of items, does: for k, v in E: od[k] = v In either case, this is followed by: for k, v in F.items(): od[k] = v ''' if len(args) > 2: raise TypeError('update() takes at most 2 positional ' 'arguments (%d given)' % (len(args),)) elif not args: raise TypeError('update() takes at least 1 argument (0 given)') self = args[0] # Make progressively weaker assumptions about "other" other = () if len(args) == 2: other = args[1] if isinstance(other, dict): for key in other: self[key] = other[key] elif hasattr(other, 'keys'): for key in other.keys(): self[key] = other[key] else: for key, value in other: self[key] = value for key, value in kwds.items(): self[key] = value __update = update # let subclasses override update without breaking __init__ __marker = object() def pop(self, key, default=__marker): '''od.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised. ''' if key in self: result = self[key] del self[key] return result if default is self.__marker: raise KeyError(key) return default def setdefault(self, key, default=None): 'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od' if key in self: return self[key] self[key] = default return default def __repr__(self, _repr_running={}): 'od.__repr__() <==> repr(od)' call_key = id(self), _get_ident() if call_key in _repr_running: return '...' _repr_running[call_key] = 1 try: if not self: return '%s()' % (self.__class__.__name__,) return '%s(%r)' % (self.__class__.__name__, self.items()) finally: del _repr_running[call_key] def __reduce__(self): 'Return state information for pickling' items = [[k, self[k]] for k in self] inst_dict = vars(self).copy() for k in vars(OrderedDict()): inst_dict.pop(k, None) if inst_dict: return (self.__class__, (items,), inst_dict) return self.__class__, (items,) def copy(self): 'od.copy() -> a shallow copy of od' return self.__class__(self) @classmethod def fromkeys(cls, iterable, value=None): '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S and values equal to v (which defaults to None). ''' d = cls() for key in iterable: d[key] = value return d def __eq__(self, other): '''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive while comparison to a regular mapping is order-insensitive. ''' if isinstance(other, OrderedDict): return len(self)==len(other) and self.items() == other.items() return dict.__eq__(self, other) def __ne__(self, other): return not self == other # -- the following methods are only used in Python 2.7 -- def viewkeys(self): "od.viewkeys() -> a set-like object providing a view on od's keys" return KeysView(self) def viewvalues(self): "od.viewvalues() -> an object providing a view on od's values" return ValuesView(self) def viewitems(self): "od.viewitems() -> a set-like object providing a view on od's items" return ItemsView(self) statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/py3k.py000066400000000000000000000034421224417117700236000ustar00rootroot00000000000000""" Python 3 compatibility tools. """ __all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar', 'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested', 'asstr', 'open_latin1'] import sys if sys.version_info[0] >= 3: import io bytes = bytes unicode = str asunicode = str def asbytes(s): if isinstance(s, bytes): return s return s.encode('latin1') def asstr(s): if isinstance(s, str): return s return s.decode('latin1') def asstr2(s): #added JP, not in numpy version if isinstance(s, str): return s elif isinstance(s, bytes): return s.decode('latin1') else: return str(s) def isfileobj(f): return isinstance(f, io.FileIO) def open_latin1(filename, mode='r'): return open(filename, mode=mode, encoding='iso-8859-1') strchar = 'U' from io import BytesIO, StringIO #statsmodels else: bytes = str unicode = unicode asbytes = str asstr = str asstr2 = str strchar = 'S' def isfileobj(f): return isinstance(f, file) def asunicode(s): if isinstance(s, unicode): return s return s.decode('ascii') def open_latin1(filename, mode='r'): return open(filename, mode=mode) from StringIO import StringIO BytesIO = StringIO def getexception(): return sys.exc_info()[1] def asbytes_nested(x): if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)): return [asbytes_nested(y) for y in x] else: return asbytes(x) def asunicode_nested(x): if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)): return [asunicode_nested(y) for y in x] else: return asunicode(x) statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/tests/000077500000000000000000000000001224417117700234775ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/tests/__init__.py000066400000000000000000000000001224417117700255760ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/tests/test_collections.py000066400000000000000000000005131224417117700274250ustar00rootroot00000000000000 from numpy.testing import assert_ from statsmodels.compatnp.collections import Counter def test_counter(): #just check a basic example c = Counter('gallahad') res = [('a', 3), ('d', 1), ('g', 1), ('h', 1), ('l', 2)] msg = 'gallahad fails\n'+repr(sorted(c.items())) assert_(sorted(c.items()) == res, msg=msg) statsmodels-0.5.0+git13-g8e07d34/statsmodels/compatnp/tests/test_itercompat.py000066400000000000000000000023141224417117700272570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Feb 29 10:34:00 2012 Author: Josef Perktold """ from numpy.testing import assert_ from statsmodels.compatnp.iter_compat import zip_longest, combinations def test_zip_longest(): lili = [['a0', 'b0', 'c0', 'd0'], ['a1', 'b1', 'c1'], ['a2', 'b2', 'c2', 'd2'], ['a3', 'b3', 'c3', 'd3'], ['a4', 'b4']] transposed = [('a0', 'a1', 'a2', 'a3', 'a4'), ('b0', 'b1', 'b2', 'b3', 'b4'), ('c0', 'c1', 'c2', 'c3', None), ('d0', None, 'd2', 'd3', None)] assert_(list(zip_longest(*lili)) == transposed, '%r not equal %r' % ( zip_longest(*lili), transposed)) def test_combinations(): actual = list(combinations('ABCD', 2)) desired = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')] assert_(actual == desired, '%r not equal %r' % (actual, desired)) actual = list(combinations(range(4), 3)) desired = [(0, 1, 2), (0, 1, 3), (0, 2, 3), (1, 2, 3)] assert_(actual == desired, '%r not equal %r' % (actual, desired)) if __name__ == '__main__': test_zip_longest() test_combinations() statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/000077500000000000000000000000001224417117700223245ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/COPYING000066400000000000000000000034611224417117700233630ustar00rootroot00000000000000Last Change: Tue Jul 17 05:00 PM 2007 J The code and descriptive text is copyrighted and offered under the terms of the BSD License from the authors; see below. However, the actual dataset may have a different origin and intellectual property status. See the SOURCE and COPYRIGHT variables for this information. Copyright (c) 2007 David Cournapeau All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the author nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/README.txt000066400000000000000000000031701224417117700240230ustar00rootroot00000000000000This README was copied from http://projects.scipy.org/scikits/browser/trunk/learn/scikits/learn/datasets/ ----------------------------------------------------------------------------- Last Change: Tue Jul 17 04:00 PM 2007 J This packages datasets defines a set of packages which contain datasets useful for demo, examples, etc... This can be seen as an equivalent of the R dataset package, but for python. Each subdir is a python package, and should define the function load, returning the corresponding data. For example, to access datasets data1, you should be able to do: >> from datasets.data1 import load >> d = load() # -> d contains the data of the datasets data1 load can do whatever it wants: fetching data from a file (python script, csv file, etc...), from the internet, etc... Some special variables must be defined for each package, containing a python string: - COPYRIGHT: copyright informations - SOURCE: where the data are coming from - DESCHOSRT: short description - DESCLONG: long description - NOTE: some notes on the datasets. For the datasets to be useful in the learn scikits, which is the project which initiated this datasets package, the data returned by load has to be a dict with the following conventions: - 'data': this value should be a record array containing the actual data. - 'label': this value should be a rank 1 array of integers, contains the label index for each sample, that is label[i] should be the label index of data[i]. - 'class': a record array such as class[i] is the class name. In other words, this makes the correspondance label index <> label name. statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/__init__.py000066400000000000000000000006141224417117700244360ustar00rootroot00000000000000""" Datasets module """ #__all__ = filter(lambda s:not s.startswith('_'),dir()) from . import (anes96, cancer, committee, ccard, copper, cpunish, elnino, engel, grunfeld, longley, macrodata, nile, randhie, scotland, spector, stackloss, star98, strikes, sunspots, fair, heart, statecrime) from utils import get_rdataset, get_data_home, clear_data_home statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/000077500000000000000000000000001224417117700234315ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/__init__.py000066400000000000000000000000231224417117700255350ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/anes96.csv000066400000000000000000000521261224417117700252610ustar00rootroot00000000000000'popul' 'TVnews' 'selfLR' 'ClinLR' 'DoleLR' 'PID' 'age' 'educ' 'income' 'vote' 0 7 7 1 6 6 36 3 1 1 190 1 3 3 5 1 20 4 1 0 31 7 2 2 6 1 24 6 1 0 83 4 3 4 5 1 28 6 1 0 640 7 5 6 4 0 68 6 1 0 110 3 3 4 6 1 21 4 1 0 100 7 5 6 4 1 77 4 1 0 31 1 5 4 5 4 21 4 1 0 180 7 4 6 3 3 31 4 1 0 2800 0 3 3 7 0 39 3 1 0 1600 0 3 2 4 4 26 2 1 0 330 5 4 3 6 1 31 4 1 0 190 2 5 4 6 5 22 4 1 1 100 7 4 4 6 0 42 5 1 0 1000 7 5 7 4 0 74 1 1 0 0 7 6 7 5 0 62 3 1 0 130 7 4 4 5 1 58 3 1 0 5 5 3 3 6 1 24 6 1 0 33 7 6 2 6 5 51 4 1 1 19 2 2 1 4 0 36 3 2 0 74 7 4 4 7 2 88 2 2 0 190 0 2 4 6 2 20 4 2 0 12 3 4 6 3 2 27 3 2 0 0 7 6 1 6 6 44 4 2 1 19 0 4 2 2 1 45 3 2 0 0 2 4 3 6 1 21 4 2 0 390 5 3 4 7 1 40 5 2 0 40 7 4 3 4 0 40 6 2 0 3 3 5 5 4 1 48 3 2 0 450 3 4 7 1 0 34 3 2 0 350 0 3 4 7 2 26 2 2 0 64 3 4 4 2 1 60 2 3 0 3 0 4 4 3 0 32 3 3 0 0 1 4 3 7 1 31 3 3 0 640 7 7 5 7 4 33 3 3 1 0 7 3 4 6 0 57 3 3 0 12 7 4 3 6 1 84 3 3 0 62 6 7 2 7 5 75 3 3 1 31 2 7 2 6 6 19 4 3 1 0 1 3 2 6 1 47 6 3 0 180 6 5 5 5 0 51 2 3 0 640 3 6 4 4 5 40 3 3 0 110 0 2 3 6 1 22 6 3 0 100 1 7 7 5 6 35 2 3 0 100 7 4 4 7 2 43 5 3 0 11 3 6 6 3 2 76 6 3 0 0 7 4 3 1 6 45 3 3 1 4 7 4 6 6 0 88 2 3 0 35 6 4 4 2 1 46 3 4 0 0 1 3 4 5 2 22 6 4 0 0 7 5 1 6 5 68 3 4 1 0 2 5 2 6 5 38 3 4 1 33 7 4 3 6 3 69 2 4 0 270 2 5 4 3 0 67 3 4 0 45 7 2 4 6 0 88 4 4 0 40 3 6 2 5 5 68 3 4 1 6 1 5 2 4 2 76 3 4 1 2 7 4 4 6 0 72 2 4 0 0 0 6 2 6 6 37 6 4 1 35 3 4 2 6 0 69 3 4 0 83 0 2 4 6 0 33 6 4 0 3500 7 2 2 6 0 34 4 4 0 100 2 4 4 7 2 30 3 4 0 350 2 3 3 6 1 19 3 4 0 100 3 4 6 2 0 44 3 4 0 67 1 4 4 7 1 64 3 4 0 30 5 7 7 2 0 37 4 4 0 0 7 6 3 5 4 31 5 5 1 0 0 6 1 5 4 88 4 5 1 6 7 6 2 6 6 77 4 5 1 350 1 4 5 6 5 30 6 5 0 400 1 2 3 7 1 32 4 5 0 15 7 6 2 6 6 59 1 5 1 0 0 4 4 4 3 47 4 5 0 3 2 4 6 5 1 22 3 5 0 22 5 4 2 6 2 55 3 5 0 64 2 2 1 3 0 24 2 5 0 32 5 3 7 4 1 65 1 5 0 390 7 3 6 2 2 24 3 5 0 0 7 3 4 5 3 30 3 5 0 0 7 4 5 2 3 73 3 5 0 59 5 3 3 5 1 73 5 5 0 0 6 4 3 6 2 91 1 5 0 35 7 3 2 5 0 71 2 5 0 0 2 6 4 5 4 34 4 5 1 170 7 4 3 2 0 48 2 6 0 12 1 6 2 6 5 42 4 6 1 40 4 6 5 4 0 72 2 6 0 31 2 3 4 6 6 20 4 6 1 31 7 2 2 7 0 22 4 6 0 1600 1 3 3 6 1 24 6 6 0 1 1 4 2 7 2 39 6 6 0 4 7 6 1 6 6 83 5 6 1 190 0 6 2 6 6 39 3 6 1 53 3 5 3 6 1 33 5 6 0 31 7 4 3 6 1 53 3 6 1 16 7 5 3 6 5 82 3 6 1 33 5 4 3 5 6 82 3 6 1 0 3 5 3 6 5 47 6 7 1 0 3 4 2 7 4 68 3 7 0 0 7 4 3 5 0 84 6 7 0 27 2 6 1 6 5 35 5 7 1 84 7 4 5 6 1 67 2 7 0 22 3 5 3 5 4 33 2 7 1 0 3 3 3 5 0 49 7 7 0 3500 0 4 3 7 0 91 1 7 0 390 7 4 5 3 1 43 3 7 0 0 7 4 3 2 6 65 4 7 0 16 7 5 6 3 0 69 3 7 0 200 0 5 5 4 1 56 4 8 0 640 0 2 3 5 0 24 6 8 0 0 7 4 4 5 0 77 3 8 0 45 7 6 3 7 0 74 3 8 0 12 0 7 3 6 6 25 6 8 1 20 7 6 2 5 4 85 1 8 1 7300 5 7 7 6 3 21 2 8 0 64 7 6 3 1 0 24 4 8 0 13 7 5 4 7 4 73 4 8 0 190 0 4 5 3 2 37 3 8 0 9 4 4 5 1 2 35 4 8 0 0 7 4 4 7 0 47 3 8 0 170 2 4 2 6 6 21 3 8 1 640 7 3 6 4 0 55 5 8 0 9 4 6 3 6 6 30 6 8 1 0 4 5 3 6 4 76 7 8 1 7300 5 3 4 3 3 36 4 8 0 2800 0 1 1 7 0 38 3 9 0 0 7 2 3 5 0 67 3 9 0 30 7 7 3 7 6 70 2 9 1 44 7 5 3 7 2 78 4 9 0 7300 1 2 2 7 3 27 6 9 0 330 4 3 5 6 1 51 4 9 0 3 0 6 7 3 5 33 4 9 0 51 2 6 1 5 6 80 6 9 1 29 5 4 1 6 1 79 1 9 0 630 2 6 4 5 4 66 1 9 1 170 0 4 1 6 0 32 3 10 0 33 7 4 5 7 0 70 2 10 0 0 3 2 3 6 3 42 3 10 0 9 5 5 4 5 5 73 4 10 1 22 4 4 4 6 0 87 2 10 0 100 0 7 5 1 1 30 5 10 0 2 2 4 4 5 3 52 3 10 0 0 6 5 3 6 1 62 4 10 0 50 7 6 3 4 0 67 3 10 0 15 4 6 3 4 4 37 6 10 0 3 4 3 5 7 0 37 4 10 0 720 5 1 5 6 1 64 6 10 0 640 7 1 1 5 0 34 3 10 0 5 7 4 4 7 0 70 3 10 0 24 2 6 2 6 6 31 5 10 1 22 7 2 2 6 0 29 6 11 0 55 7 4 5 4 1 71 2 11 0 0 2 4 4 4 0 67 1 11 0 1600 5 4 4 6 0 41 7 11 0 170 6 1 2 6 0 49 6 11 0 1000 7 4 4 5 0 42 5 11 0 63 0 6 3 2 0 78 2 11 0 110 0 4 1 6 1 24 3 11 0 16 7 4 6 6 1 29 3 11 0 100 3 4 2 6 5 39 5 11 1 7300 3 5 3 6 1 19 4 11 0 22 2 4 2 7 1 32 5 11 0 71 3 4 2 6 5 69 3 11 1 900 4 5 2 5 5 83 3 11 1 35 7 4 1 5 4 76 2 11 1 2 7 7 1 2 0 62 2 11 0 83 2 3 3 6 0 47 7 11 0 370 5 6 7 4 0 35 3 11 0 12 0 4 5 3 4 23 3 11 0 370 7 4 4 1 1 79 4 11 0 100 7 6 2 6 5 64 5 11 1 470 7 6 2 4 5 70 4 11 1 22 7 6 1 6 6 87 5 11 1 2800 0 3 6 1 0 28 2 12 0 47 5 3 5 7 1 58 3 12 0 900 5 4 4 6 1 85 2 12 0 330 7 3 6 4 0 62 3 12 0 84 0 3 2 7 1 26 6 12 0 0 0 6 2 5 5 28 3 12 1 33 3 6 1 7 6 88 2 12 1 53 7 2 3 6 0 57 6 12 0 8 7 2 2 6 0 78 3 12 0 2 7 4 4 2 0 56 3 12 0 0 0 4 6 3 3 46 5 12 0 0 2 4 4 3 5 20 3 12 1 0 0 5 6 4 1 24 4 12 0 0 7 5 2 6 2 72 4 12 0 15 7 2 4 7 1 51 4 12 0 900 0 6 2 5 6 34 6 12 1 30 2 4 2 6 1 21 4 12 0 0 7 4 4 6 2 74 7 12 0 170 3 4 4 6 1 48 1 12 0 900 2 3 3 7 5 28 3 12 0 0 6 7 1 7 5 38 2 12 1 1600 7 4 6 1 0 70 3 12 0 0 7 4 5 4 0 72 2 12 0 2800 0 4 5 6 0 41 3 12 0 110 5 3 4 5 1 50 7 12 0 1 7 6 2 5 5 73 3 12 1 3 5 5 2 4 0 79 6 12 0 0 4 5 1 4 5 76 2 12 1 22 0 5 3 5 5 62 5 12 1 63 3 6 2 6 6 30 6 12 1 290 0 6 3 6 5 35 4 12 1 2 7 1 2 7 1 66 4 12 0 40 0 2 4 6 0 35 4 12 0 67 0 6 1 5 6 57 6 12 1 0 5 4 5 4 5 37 5 12 1 470 7 5 5 2 1 61 3 13 0 0 7 6 2 6 6 56 3 13 1 4 6 3 4 5 1 53 3 13 0 20 0 4 5 3 2 24 6 13 1 2800 7 4 1 6 5 74 3 13 1 0 0 4 4 3 1 36 3 13 0 1 0 6 2 4 5 30 5 13 1 640 0 4 7 4 1 55 2 13 0 170 3 3 2 7 2 35 6 13 0 270 2 3 4 6 0 26 4 13 0 390 0 3 4 6 2 25 4 13 0 16 2 6 7 4 3 27 3 13 0 11 7 4 1 6 5 66 3 13 1 0 1 5 2 6 2 39 2 13 0 270 7 1 1 2 2 58 5 13 0 170 2 4 4 4 0 53 3 13 1 900 7 6 7 4 0 76 3 13 0 270 7 5 2 7 1 51 3 13 0 0 7 4 2 7 0 70 2 13 0 350 3 6 3 6 6 68 4 13 1 0 0 5 4 5 2 32 3 13 1 6 0 5 4 5 5 55 3 13 0 290 7 2 2 6 0 52 4 13 0 630 7 6 4 6 4 73 2 13 1 900 0 5 4 7 0 42 2 13 0 31 2 4 4 3 4 23 5 13 1 1600 5 2 3 6 0 30 7 14 0 71 7 2 2 7 0 68 4 14 0 200 7 5 2 3 2 68 3 14 0 0 0 6 4 7 3 68 6 14 0 30 5 2 3 6 0 38 5 14 0 10 1 4 3 6 2 74 3 14 0 0 7 5 6 3 0 59 2 14 0 900 2 5 2 5 2 73 2 14 0 71 7 2 3 6 0 79 3 14 0 22 3 7 1 6 5 28 4 14 1 0 7 6 2 6 6 50 3 14 1 0 4 6 3 1 1 36 4 14 0 0 3 6 2 6 6 50 3 14 1 0 1 6 2 6 6 61 3 14 1 7300 3 2 2 6 0 37 4 14 0 83 0 3 4 7 0 29 6 14 0 93 7 2 3 7 0 39 4 14 0 0 7 4 5 4 2 83 6 14 1 51 7 6 1 5 4 68 6 14 1 31 2 6 1 5 6 25 4 14 1 93 2 1 3 6 1 41 6 14 0 0 7 3 2 6 1 67 3 14 0 0 3 4 2 6 4 36 6 14 1 31 4 6 2 6 4 66 4 14 1 900 1 3 2 7 1 55 4 14 0 0 4 3 2 6 2 42 5 14 0 2 7 6 3 5 5 42 3 14 1 110 3 4 5 7 1 36 3 14 0 63 1 6 4 6 4 53 5 14 1 900 0 3 2 5 1 36 5 14 0 31 3 4 3 6 2 29 7 14 0 510 1 4 4 6 0 31 3 14 0 270 2 3 4 6 1 43 6 14 0 9 3 3 4 7 1 33 6 14 0 3 1 6 6 2 0 63 3 14 0 29 1 5 2 4 2 25 5 14 0 45 2 3 2 6 0 72 4 14 0 83 5 3 3 6 1 40 4 14 0 22 7 4 2 6 2 27 6 14 0 15 3 5 4 7 2 26 4 15 0 110 7 5 2 5 6 67 3 15 1 8 0 4 5 5 1 21 3 15 0 11 5 6 2 6 6 27 7 15 1 56 4 6 5 3 0 78 6 15 1 8 3 4 4 3 1 32 3 15 0 100 5 4 2 6 1 68 4 15 0 900 7 4 2 6 5 76 4 15 0 67 7 3 3 6 0 33 5 15 0 35 4 6 2 5 4 38 2 15 1 35 3 4 3 5 1 49 7 15 0 22 0 5 4 6 5 61 4 15 1 110 7 7 1 5 4 57 3 15 1 12 0 4 2 5 2 20 3 15 0 7300 7 4 2 4 4 63 6 15 1 0 0 4 1 5 4 53 3 15 1 19 5 4 2 6 5 35 4 15 1 470 3 4 4 7 0 39 3 15 0 4 3 3 5 3 4 48 3 15 1 640 4 6 2 5 4 62 3 15 1 640 2 4 2 3 1 30 5 15 0 200 7 7 1 4 6 26 6 15 1 0 7 3 2 4 5 74 6 15 1 29 4 3 4 6 2 37 5 15 0 330 2 4 5 5 1 43 5 15 0 19 5 5 1 4 6 68 3 15 1 1 7 5 3 4 5 73 5 15 1 110 5 6 1 6 6 60 7 15 1 0 7 5 4 6 1 35 3 15 0 350 4 3 4 6 0 29 6 15 0 2 5 3 3 5 1 25 6 15 0 0 7 2 2 6 0 25 7 15 0 7 4 2 4 5 0 70 6 15 0 71 1 6 2 5 6 41 3 15 1 53 0 4 2 6 1 37 6 15 0 0 0 6 1 6 6 39 5 15 1 2 6 3 4 6 0 35 4 15 0 190 4 2 3 6 0 62 7 15 0 31 0 3 2 6 1 30 7 15 0 16 7 4 2 6 0 74 4 15 0 22 7 3 3 4 5 47 3 15 0 3 4 4 5 3 1 43 6 15 0 0 6 5 3 6 5 64 2 15 1 0 7 4 3 5 2 75 4 15 1 67 3 4 4 7 1 27 6 15 0 40 7 4 4 6 0 21 3 15 0 74 4 2 2 6 1 70 2 15 0 3 6 5 2 6 5 67 3 15 1 140 7 6 4 5 6 82 5 15 0 14 0 2 2 6 0 40 6 15 0 110 0 5 3 6 5 26 4 15 1 35 3 4 3 5 1 29 6 15 0 0 1 4 5 6 2 28 6 15 0 310 7 6 4 3 5 65 3 15 0 900 2 6 5 3 1 25 3 15 0 0 7 3 2 7 1 65 2 15 0 11 4 6 2 6 5 38 5 15 1 0 2 4 3 5 5 72 7 15 1 270 7 3 2 7 1 67 3 15 0 51 7 3 1 7 0 74 3 15 0 11 5 2 4 4 2 71 6 15 0 2 0 4 6 4 0 47 3 15 0 20 7 5 4 6 1 69 1 15 0 31 3 3 3 6 1 29 6 15 0 2 0 6 2 5 6 34 3 15 1 5 3 4 3 6 0 43 4 15 0 22 7 2 1 7 0 30 3 15 0 0 7 5 5 4 0 76 2 15 0 27 0 2 4 6 1 26 5 16 0 7 7 4 3 6 2 76 5 16 0 0 0 1 4 6 1 42 7 16 0 0 4 3 1 4 5 33 3 16 1 0 1 6 2 5 5 25 3 16 1 2800 0 2 2 7 0 51 4 16 0 0 4 4 2 5 4 57 3 16 1 22 1 6 1 5 6 21 4 16 1 9 7 5 1 4 5 79 7 16 1 0 1 6 2 5 5 35 5 16 1 9 5 4 2 5 4 57 6 16 1 0 1 4 4 6 5 32 6 16 0 37 5 4 5 5 2 51 6 16 0 23 0 5 2 4 6 62 7 16 1 0 4 5 1 6 5 48 4 16 1 0 7 7 1 6 6 39 3 16 1 0 5 3 2 6 5 26 6 16 1 40 0 4 2 4 1 38 3 16 1 0 5 3 3 4 0 50 4 16 0 9 2 5 5 6 4 33 3 16 1 15 5 4 3 6 2 36 3 16 0 640 5 4 4 6 0 24 6 16 0 0 4 3 2 6 0 25 5 16 0 0 7 6 5 3 0 62 3 16 0 0 2 4 3 3 6 33 3 16 1 0 7 6 2 5 6 53 6 16 1 22 7 6 2 5 6 68 6 16 1 22 7 5 4 6 1 68 3 16 0 10 1 6 1 5 5 38 3 16 1 29 1 4 5 3 0 58 1 16 0 170 7 4 2 6 5 34 6 16 1 4 2 4 3 4 1 58 2 16 0 11 0 3 4 7 1 35 5 16 0 31 3 7 2 6 6 42 6 16 1 0 7 4 4 6 0 54 3 16 0 0 7 6 2 5 6 69 3 16 1 360 2 4 6 5 0 35 4 16 0 0 7 6 1 5 6 66 4 16 1 900 2 3 2 7 0 58 3 16 0 51 5 2 4 7 1 41 3 16 0 0 2 5 2 6 4 35 6 16 0 110 0 3 3 6 1 40 7 16 0 1 7 4 7 2 0 53 1 16 0 8 5 6 2 6 5 67 6 16 1 5 5 4 4 6 0 32 5 16 0 87 4 3 4 6 0 41 4 16 0 3 1 2 1 6 1 43 7 16 0 51 1 5 3 2 0 65 2 16 0 350 7 3 3 7 0 60 5 16 0 3 7 5 3 6 0 77 6 16 0 630 0 6 5 4 1 35 4 16 0 180 4 6 7 5 1 48 4 16 0 0 0 6 2 6 6 52 3 16 1 35 7 5 3 6 2 43 7 16 0 0 7 6 2 6 6 43 5 16 1 0 7 6 2 6 6 67 4 16 1 6 1 4 4 7 0 56 3 16 0 7300 2 3 3 4 0 62 4 16 0 2 1 7 7 5 0 62 3 16 0 35 3 3 2 6 1 22 6 16 0 0 0 2 2 6 1 21 5 16 0 45 3 6 1 6 6 34 3 16 1 0 7 4 3 5 0 70 3 16 0 5 1 3 2 6 2 50 3 16 0 35 5 6 1 6 6 42 4 16 1 900 5 6 2 6 6 73 3 16 1 35 2 3 1 6 0 57 7 16 0 0 7 6 1 5 6 40 6 16 1 0 2 6 1 6 6 58 6 16 1 11 7 4 6 2 1 62 3 16 0 40 1 3 5 3 1 44 3 17 0 0 3 5 3 7 6 30 6 17 0 23 7 3 3 7 0 76 5 17 0 270 3 4 3 3 1 50 4 17 0 9 1 6 2 5 5 41 4 17 1 0 6 6 3 6 6 77 3 17 1 0 3 6 3 5 6 35 6 17 1 0 7 4 3 4 3 39 4 17 0 2 0 4 2 4 4 72 3 17 1 0 2 6 2 5 6 42 7 17 1 16 7 5 2 6 5 85 2 17 1 7300 0 4 2 6 3 79 4 17 0 0 5 2 3 6 0 39 4 17 0 23 4 6 1 5 6 58 6 17 1 42 0 4 2 6 5 27 6 17 1 2 4 4 3 5 1 43 5 17 0 0 0 3 3 6 0 58 4 17 0 42 7 4 3 6 4 28 7 17 1 470 2 5 3 6 5 27 6 17 0 42 0 3 6 5 2 40 3 17 0 0 5 1 3 6 0 43 6 17 0 40 7 5 4 6 5 64 3 17 1 180 7 2 2 6 0 39 6 17 0 110 7 4 2 6 4 76 4 17 1 140 5 3 3 6 0 64 5 17 0 0 4 4 6 3 2 28 4 17 0 0 2 4 2 6 1 45 3 17 0 190 1 6 2 6 6 22 3 17 1 35 1 3 3 6 1 27 6 17 0 45 0 1 2 7 0 31 4 17 0 170 1 2 2 7 0 34 6 17 0 0 4 2 3 7 0 30 4 17 0 0 2 6 1 6 6 64 3 17 1 35 0 6 1 6 4 36 6 17 1 5 7 4 3 5 4 31 2 17 1 350 1 4 3 4 5 37 3 17 1 4 0 6 4 2 1 48 3 17 0 70 0 5 7 7 3 41 3 17 0 8 2 5 2 6 5 25 4 17 1 12 5 4 2 6 6 82 3 17 1 5 1 6 2 5 4 36 7 17 1 0 7 6 2 5 1 47 3 17 1 16 6 2 3 6 0 67 4 17 0 0 0 4 6 2 5 24 4 17 1 9 0 4 5 3 5 33 6 17 1 14 7 4 1 5 1 59 4 17 1 22 7 7 1 6 6 71 4 17 1 0 7 6 2 5 6 36 3 17 1 1 3 3 3 5 0 41 3 17 0 1600 5 6 2 6 6 38 6 17 1 7300 1 6 3 6 6 32 3 17 1 19 0 5 6 7 1 32 4 17 0 9 6 5 4 6 0 75 6 17 0 0 7 5 2 4 4 52 3 17 1 1600 5 2 3 6 0 29 7 17 0 12 7 3 4 6 0 71 3 17 0 1 0 6 2 5 2 33 6 17 0 0 3 1 2 7 2 67 7 17 0 0 2 6 1 6 6 49 4 17 1 0 0 2 3 5 1 31 7 17 0 9 7 3 4 6 1 53 2 17 0 0 3 2 3 7 0 35 7 17 0 170 7 6 2 6 6 49 7 18 1 3 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7 6 1 6 6 81 5 22 1 20 1 5 2 6 4 29 4 22 1 70 3 5 3 4 0 45 6 22 0 31 3 5 2 6 5 21 4 22 1 3 7 2 4 3 6 33 6 22 1 9 7 4 4 2 2 44 3 23 0 59 1 2 2 6 4 52 7 23 0 27 2 3 5 7 1 38 4 23 0 51 4 2 3 6 2 44 5 23 0 9 7 6 2 6 6 87 7 23 1 0 2 7 2 6 5 22 3 23 1 88 0 3 3 5 2 32 6 23 1 67 0 4 5 6 4 69 3 23 0 29 2 6 2 5 6 49 6 23 1 5 0 6 2 6 6 53 3 23 1 0 0 6 1 6 5 44 6 23 1 900 1 6 2 7 5 34 6 23 1 18 1 6 1 5 5 55 7 23 1 190 2 3 3 6 0 35 6 23 0 2 7 4 5 5 3 55 3 23 0 5 3 6 3 5 5 27 6 23 1 56 3 4 4 5 1 26 6 23 0 75 7 4 2 6 5 54 6 23 0 56 0 5 2 6 5 42 4 23 1 0 5 6 1 6 6 57 7 23 1 0 0 7 1 4 6 54 6 23 1 75 6 6 3 6 6 55 6 23 1 1600 7 5 3 6 6 50 7 23 1 15 0 5 5 6 1 57 7 23 0 19 3 5 3 7 3 46 4 23 0 16 7 6 2 6 6 53 6 23 1 42 2 3 3 5 0 32 7 23 0 18 5 5 2 5 3 53 5 23 1 0 3 3 3 6 1 39 6 23 0 310 1 5 2 5 4 47 6 23 1 1600 7 5 2 4 2 57 4 23 0 23 5 6 3 6 6 49 6 23 1 20 1 5 4 6 5 31 5 23 0 51 5 5 3 5 5 43 6 23 1 0 2 5 2 5 4 44 6 23 1 0 4 4 3 6 0 39 6 23 0 0 2 6 1 6 5 49 4 23 1 18 7 5 4 6 4 72 6 23 1 7300 7 5 2 6 5 50 6 23 1 110 1 5 2 6 6 28 4 23 1 0 0 5 2 7 3 48 7 23 1 3500 1 3 4 7 1 32 6 23 0 720 7 5 5 5 1 63 4 23 0 9 4 4 5 6 5 36 4 23 1 47 7 6 3 6 6 36 6 23 1 350 7 3 2 7 2 53 3 23 0 0 5 2 2 6 2 44 7 23 0 0 0 4 2 6 6 41 7 24 1 83 0 2 3 6 1 56 7 24 0 1 4 4 4 6 2 63 7 24 0 190 7 2 4 6 0 52 6 24 0 0 7 3 3 7 2 43 7 24 0 12 7 4 3 6 2 40 3 24 0 9 5 5 1 7 4 69 4 24 1 23 7 2 2 6 0 49 7 24 0 9 1 3 3 6 2 65 7 24 0 18 5 6 1 6 5 53 7 24 1 0 5 5 3 5 6 50 4 24 1 12 3 2 4 6 0 27 5 24 0 0 6 5 3 5 5 44 6 24 1 170 2 2 3 5 0 54 7 24 0 0 7 4 2 6 4 33 5 24 1 9 0 4 4 6 3 48 7 24 0 23 3 5 2 6 5 54 5 24 1 0 0 6 2 5 6 56 3 24 1 9 1 2 4 7 2 34 7 24 0 290 7 6 4 7 6 41 6 24 0 1 0 5 1 5 6 40 6 24 1 350 1 7 2 6 6 55 6 24 1 20 0 4 3 5 5 38 6 24 1 0 3 6 2 6 6 40 6 24 1 23 1 6 1 6 6 46 4 24 1 150 4 3 3 4 0 26 6 24 0 31 0 5 2 7 3 49 6 24 0 0 7 4 1 5 6 51 5 24 1 9 2 5 2 6 4 46 6 24 0 47 0 3 4 6 2 40 7 24 0 900 0 3 4 7 2 30 5 24 0 83 3 2 3 6 2 45 5 24 0 18 7 5 4 6 4 52 7 24 1 0 0 6 1 5 6 36 6 24 1 20 0 4 3 5 3 49 6 24 0 24 7 3 4 5 1 38 7 24 0 18 0 2 4 6 1 51 7 24 0 9 3 3 2 5 1 47 6 24 0 0 1 6 1 5 6 52 7 24 1 9 0 6 2 6 6 33 6 24 1 0 4 4 2 6 6 50 4 24 1 18 7 6 2 5 4 48 7 24 1 19 3 2 2 6 0 36 6 24 0 31 3 2 3 6 1 35 7 24 0 3500 7 7 3 5 4 34 7 24 0 0 7 2 4 5 2 53 6 24 0 33 0 4 3 6 2 33 7 24 0 0 1 6 3 6 6 52 6 24 1 18 3 4 3 6 4 44 7 24 0 0 0 3 4 4 0 48 6 24 0 31 3 5 2 6 5 20 4 24 1 0 5 3 2 4 6 45 6 24 1 59 7 4 2 6 2 70 3 24 0 0 0 3 3 4 2 39 3 24 0 7300 7 3 3 5 1 40 7 24 1 75 4 5 2 7 5 62 6 24 1 0 7 5 2 6 4 46 6 24 1 27 7 4 4 7 2 46 3 24 0 1600 7 4 2 5 6 56 7 24 1 0 7 6 3 6 6 55 7 24 1 0 7 6 2 6 6 41 4 24 1 7300 1 2 3 6 0 43 7 24 0 16 7 7 1 7 6 34 3 24 1 0 7 7 1 6 4 73 6 24 1 0 7 5 2 6 6 50 6 24 1 0 3 6 2 7 5 43 6 24 1 0 6 6 2 5 6 46 7 24 1 18 7 4 2 6 3 61 7 24 1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/data.py000066400000000000000000000074531224417117700247250ustar00rootroot00000000000000"""American National Election Survey 1996""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = __doc__ SOURCE = """ http://www.electionstudies.org/ The American National Election Studies. """ DESCRSHORT = """This data is a subset of the American National Election Studies of 1996.""" DESCRLONG = DESCRSHORT NOTE = """ Number of observations - 944 Numner of variables - 10 Variables name definitions:: popul - Census place population in 1000s TVnews - Number of times per week that respondent watches TV news. PID - Party identification of respondent. 0 - Strong Democrat 1 - Weak Democrat 2 - Independent-Democrat 3 - Independent-Indpendent 4 - Independent-Republican 5 - Weak Republican 6 - Strong Republican age : Age of respondent. educ - Education level of respondent 1 - 1-8 grades 2 - Some high school 3 - High school graduate 4 - Some college 5 - College degree 6 - Master's degree 7 - PhD income - Income of household 1 - None or less than $2,999 2 - $3,000-$4,999 3 - $5,000-$6,999 4 - $7,000-$8,999 5 - $9,000-$9,999 6 - $10,000-$10,999 7 - $11,000-$11,999 8 - $12,000-$12,999 9 - $13,000-$13,999 10 - $14,000-$14.999 11 - $15,000-$16,999 12 - $17,000-$19,999 13 - $20,000-$21,999 14 - $22,000-$24,999 15 - $25,000-$29,999 16 - $30,000-$34,999 17 - $35,000-$39,999 18 - $40,000-$44,999 19 - $45,000-$49,999 20 - $50,000-$59,999 21 - $60,000-$74,999 22 - $75,000-89,999 23 - $90,000-$104,999 24 - $105,000 and over vote - Expected vote 0 - Clinton 1 - Dole The following 3 variables all take the values: 1 - Extremely liberal 2 - Liberal 3 - Slightly liberal 4 - Moderate 5 - Slightly conservative 6 - Conservative 7 - Extremely Conservative selfLR - Respondent's self-reported political leanings from "Left" to "Right". ClinLR - Respondents impression of Bill Clinton's political leanings from "Left" to "Right". DoleLR - Respondents impression of Bob Dole's political leanings from "Left" to "Right". logpopul - log(popul + .1) """ from numpy import recfromtxt, column_stack, array, log import numpy.lib.recfunctions as nprf from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """Load the anes96 data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=5, exog_idx=[10,2,6,7,8], dtype=float) def load_pandas(): """Load the anes96 data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=5, exog_idx=[10,2,6,7,8], dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/anes96.csv',"rb"), delimiter="\t", names = True, dtype=float) logpopul = log(data['popul'] + .1) data = nprf.append_fields(data, 'logpopul', logpopul, usemask=False, asrecarray=True) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/src/000077500000000000000000000000001224417117700242205ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/anes96/src/anes96.csv000066400000000000000000000565131224417117700260540ustar00rootroot00000000000000popul TVnews selfLR ClinLR DoleLR PID age educ income vote reldist 0 7 7 1 6 6 36 3 1 1 -5 190 1 3 3 5 1 20 4 1 0 2 31 7 2 2 6 1 24 6 1 0 4 83 4 3 4 5 1 28 6 1 0 1 640 7 5 6 4 0 68 6 1 0 0 110 3 3 4 6 1 21 4 1 0 2 100 7 5 6 4 1 77 4 1 0 0 31 1 5 4 5 4 21 4 1 0 -1 180 7 4 6 3 3 31 4 1 0 -1 2800 0 3 3 7 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3 3 6 0 62 6 21 0 3 0 7 4 3 6 3 40 4 21 0 1 180 1 6 1 6 6 30 4 21 1 -5 3 6 3 2 6 2 47 3 21 0 2 51 2 5 2 6 1 41 6 21 0 -2 9 2 6 1 6 6 35 7 21 1 -5 0 7 5 2 6 6 45 4 21 1 -2 0 2 4 2 6 4 34 6 21 1 0 71 2 5 7 2 2 55 3 21 0 1 290 1 5 3 6 2 37 4 21 0 -1 45 3 5 2 4 4 61 7 21 1 -2 0 3 4 5 4 1 62 2 21 0 -1 26 1 5 2 6 6 54 6 21 1 -2 87 3 2 3 6 1 33 6 21 0 3 0 0 2 3 6 1 50 6 21 0 3 630 0 3 4 7 2 37 3 21 0 3 50 5 5 3 6 3 44 3 21 1 -1 35 7 4 3 4 3 78 3 21 0 -1 180 7 6 2 4 6 56 3 21 0 -2 32 0 2 3 4 2 29 6 21 0 1 0 7 3 2 5 4 52 6 21 0 1 51 1 6 4 5 1 31 3 21 0 -1 40 0 6 3 6 6 34 7 21 1 -3 0 2 5 3 6 5 31 7 21 1 -1 0 7 4 3 5 2 43 6 21 0 0 0 1 4 4 3 5 31 3 21 1 1 1 6 6 1 6 6 63 7 21 1 -5 7 2 2 3 7 4 38 4 21 0 4 0 2 6 2 6 6 31 5 21 1 -4 71 2 2 1 7 0 64 3 21 0 4 75 2 3 2 5 5 55 7 21 0 1 55 1 2 2 6 0 41 3 21 0 4 290 4 3 4 5 5 38 4 21 1 1 88 4 6 3 6 5 28 6 21 1 -3 0 7 4 3 5 5 42 5 21 1 0 16 7 2 4 3 1 43 4 21 0 -1 75 1 5 1 6 5 37 4 21 1 -3 220 1 4 2 6 3 47 5 21 0 0 3 5 5 1 6 5 52 7 21 1 -3 130 0 5 2 6 5 32 4 21 1 -2 0 5 4 4 3 2 29 3 21 0 1 110 2 6 2 5 4 56 3 21 1 -3 12 7 4 3 5 5 63 7 21 1 0 180 3 3 3 6 1 35 5 21 0 3 93 7 4 3 5 3 36 4 21 1 0 170 7 4 2 7 0 75 5 21 0 1 31 5 3 3 6 1 48 6 21 0 3 62 4 7 2 6 6 36 5 21 1 -4 30 4 4 3 6 2 34 6 21 0 1 66 7 4 3 7 1 35 5 21 0 2 3 3 5 2 5 6 50 4 21 1 -3 18 3 5 2 5 6 39 7 21 1 -3 350 5 5 4 6 5 70 7 21 1 0 71 7 4 2 5 6 76 3 21 1 -1 3500 5 5 2 5 5 35 6 21 1 -3 0 0 3 4 3 3 53 7 21 1 -1 360 6 5 2 6 4 46 6 21 1 -2 81 2 5 2 5 4 34 4 21 1 -3 350 5 4 4 6 5 69 4 21 0 2 190 1 5 2 5 1 32 3 21 0 -3 0 7 5 1 6 6 50 2 21 1 -3 290 1 5 3 6 6 35 6 21 1 -1 18 0 6 1 6 6 67 5 21 1 -5 11 3 5 5 6 5 47 3 21 0 1 2 2 6 3 5 4 50 6 21 1 -2 570 0 6 2 6 4 32 3 21 1 -4 310 3 5 4 6 2 58 5 21 0 0 1 7 3 2 7 0 49 7 21 0 3 0 2 6 3 6 1 43 4 21 1 -3 35 1 5 2 6 5 24 6 21 1 -2 22 7 5 3 4 2 58 7 21 0 -1 2 1 2 2 5 0 43 4 21 0 3 0 7 4 3 6 5 59 3 21 1 1 0 3 6 1 6 6 40 5 21 1 -5 310 0 5 2 7 0 35 4 21 0 -1 470 5 2 3 5 1 48 4 21 0 2 0 4 6 1 6 6 40 3 21 1 -5 270 3 3 2 7 0 48 3 21 0 3 110 0 2 4 6 0 47 7 21 0 2 50 3 6 4 1 1 23 3 21 0 3 0 0 6 1 6 6 38 3 21 1 -5 3 7 6 2 6 6 81 7 21 1 -4 31 5 3 3 6 1 48 6 21 0 3 22 7 5 6 2 4 52 3 21 0 2 83 2 4 1 6 3 24 6 21 1 -1 9 0 4 2 6 1 21 5 21 0 0 5 7 5 2 6 5 70 6 21 1 -2 0 7 7 1 7 6 24 6 21 1 -6 0 7 4 3 5 1 57 7 21 0 0 0 6 3 5 6 4 37 6 21 0 1 27 1 3 5 3 5 25 5 21 0 -2 110 1 6 5 1 1 33 3 21 0 4 0 7 4 6 3 0 45 3 22 0 -1 0 3 5 2 7 5 42 6 22 0 -1 350 3 3 3 6 2 47 6 22 0 3 0 7 6 1 6 6 51 7 22 1 -5 5 7 5 4 6 5 85 2 22 0 0 15 4 3 3 6 2 32 7 22 0 3 35 7 3 3 6 2 31 7 22 0 3 0 2 4 3 6 2 23 6 22 0 1 75 3 3 3 6 0 42 6 22 0 3 0 5 6 2 5 5 55 6 22 1 -3 16 7 6 2 6 6 45 6 22 1 -4 0 1 6 2 5 6 35 7 22 1 -3 0 0 2 4 6 0 45 6 22 0 2 0 0 3 3 5 2 42 3 22 0 2 4 1 6 2 6 5 37 4 22 1 -4 62 0 4 4 4 5 38 3 22 1 0 0 3 2 2 6 1 47 7 22 0 4 4 7 4 2 6 2 32 6 22 0 0 56 2 5 2 6 5 35 6 22 1 -2 2 6 4 2 6 5 38 4 22 1 0 0 0 4 4 4 3 40 3 22 0 0 75 7 6 2 5 6 62 6 22 1 -3 10 2 2 2 6 1 28 7 22 0 4 0 6 6 2 5 6 59 5 22 1 -3 0 2 1 2 6 0 25 6 22 0 4 220 2 4 2 6 6 31 6 22 1 0 0 1 7 2 6 6 45 4 22 1 -4 75 0 5 2 7 5 42 6 22 0 -1 0 3 2 2 5 1 56 7 22 0 3 140 5 3 3 6 1 47 7 22 0 3 290 1 6 2 5 5 38 6 22 1 -3 350 7 4 3 7 2 47 6 22 0 2 55 1 5 2 6 6 49 4 22 1 -2 31 3 3 3 6 2 29 7 22 0 3 17 7 2 2 6 0 57 6 22 0 4 51 4 6 3 5 6 68 3 22 1 -2 140 7 2 2 4 0 76 6 22 0 2 9 4 6 2 6 5 66 6 22 1 -4 0 7 5 1 6 4 59 4 22 1 -3 640 7 5 3 6 4 37 7 22 0 -1 32 2 5 2 6 6 38 6 22 1 -2 5 7 6 2 6 5 47 7 22 1 -4 8 1 5 2 4 5 36 7 22 1 -2 18 0 5 4 6 0 45 7 22 0 0 0 6 5 2 6 5 39 7 22 1 -2 0 1 3 2 6 1 34 6 22 0 2 0 3 6 6 4 0 49 4 22 0 2 31 2 6 2 6 5 36 6 22 1 -4 350 7 6 1 6 6 81 5 22 1 -5 20 1 5 2 6 4 29 4 22 1 -2 70 3 5 3 4 0 45 6 22 0 -1 31 3 5 2 6 5 21 4 22 1 -2 3 7 2 4 3 6 33 6 22 1 -1 9 7 4 4 2 2 44 3 23 0 2 59 1 2 2 6 4 52 7 23 0 4 27 2 3 5 7 1 38 4 23 0 2 51 4 2 3 6 2 44 5 23 0 3 9 7 6 2 6 6 87 7 23 1 -4 0 2 7 2 6 5 22 3 23 1 -4 88 0 3 3 5 2 32 6 23 1 2 67 0 4 5 6 4 69 3 23 0 1 29 2 6 2 5 6 49 6 23 1 -3 5 0 6 2 6 6 53 3 23 1 -4 0 0 6 1 6 5 44 6 23 1 -5 900 1 6 2 7 5 34 6 23 1 -3 18 1 6 1 5 5 55 7 23 1 -4 190 2 3 3 6 0 35 6 23 0 3 2 7 4 5 5 3 55 3 23 0 0 5 3 6 3 5 5 27 6 23 1 -2 56 3 4 4 5 1 26 6 23 0 1 75 7 4 2 6 5 54 6 23 0 0 56 0 5 2 6 5 42 4 23 1 -2 0 5 6 1 6 6 57 7 23 1 -5 0 0 7 1 4 6 54 6 23 1 -3 75 6 6 3 6 6 55 6 23 1 -3 1600 7 5 3 6 6 50 7 23 1 -1 15 0 5 5 6 1 57 7 23 0 1 19 3 5 3 7 3 46 4 23 0 0 16 7 6 2 6 6 53 6 23 1 -4 42 2 3 3 5 0 32 7 23 0 2 18 5 5 2 5 3 53 5 23 1 -3 0 3 3 3 6 1 39 6 23 0 3 310 1 5 2 5 4 47 6 23 1 -3 1600 7 5 2 4 2 57 4 23 0 -2 23 5 6 3 6 6 49 6 23 1 -3 20 1 5 4 6 5 31 5 23 0 0 51 5 5 3 5 5 43 6 23 1 -2 0 2 5 2 5 4 44 6 23 1 -3 0 4 4 3 6 0 39 6 23 0 1 0 2 6 1 6 5 49 4 23 1 -5 18 7 5 4 6 4 72 6 23 1 0 7300 7 5 2 6 5 50 6 23 1 -2 110 1 5 2 6 6 28 4 23 1 -2 0 0 5 2 7 3 48 7 23 1 -1 3500 1 3 4 7 1 32 6 23 0 3 720 7 5 5 5 1 63 4 23 0 0 9 4 4 5 6 5 36 4 23 1 1 47 7 6 3 6 6 36 6 23 1 -3 350 7 3 2 7 2 53 3 23 0 3 0 5 2 2 6 2 44 7 23 0 4 0 0 4 2 6 6 41 7 24 1 0 83 0 2 3 6 1 56 7 24 0 3 1 4 4 4 6 2 63 7 24 0 2 190 7 2 4 6 0 52 6 24 0 2 0 7 3 3 7 2 43 7 24 0 4 12 7 4 3 6 2 40 3 24 0 1 9 5 5 1 7 4 69 4 24 1 -2 23 7 2 2 6 0 49 7 24 0 4 9 1 3 3 6 2 65 7 24 0 3 18 5 6 1 6 5 53 7 24 1 -5 0 5 5 3 5 6 50 4 24 1 -2 12 3 2 4 6 0 27 5 24 0 2 0 6 5 3 5 5 44 6 24 1 -2 170 2 2 3 5 0 54 7 24 0 2 0 7 4 2 6 4 33 5 24 1 0 9 0 4 4 6 3 48 7 24 0 2 23 3 5 2 6 5 54 5 24 1 -2 0 0 6 2 5 6 56 3 24 1 -3 9 1 2 4 7 2 34 7 24 0 3 290 7 6 4 7 6 41 6 24 0 -1 1 0 5 1 5 6 40 6 24 1 -4 350 1 7 2 6 6 55 6 24 1 -4 20 0 4 3 5 5 38 6 24 1 0 0 3 6 2 6 6 40 6 24 1 -4 23 1 6 1 6 6 46 4 24 1 -5 150 4 3 3 4 0 26 6 24 0 1 31 0 5 2 7 3 49 6 24 0 -1 0 7 4 1 5 6 51 5 24 1 -2 9 2 5 2 6 4 46 6 24 0 -2 47 0 3 4 6 2 40 7 24 0 2 900 0 3 4 7 2 30 5 24 0 3 83 3 2 3 6 2 45 5 24 0 3 18 7 5 4 6 4 52 7 24 1 0 0 0 6 1 5 6 36 6 24 1 -4 20 0 4 3 5 3 49 6 24 0 0 24 7 3 4 5 1 38 7 24 0 1 18 0 2 4 6 1 51 7 24 0 2 9 3 3 2 5 1 47 6 24 0 1 0 1 6 1 5 6 52 7 24 1 -4 9 0 6 2 6 6 33 6 24 1 -4 0 4 4 2 6 6 50 4 24 1 0 18 7 6 2 5 4 48 7 24 1 -3 19 3 2 2 6 0 36 6 24 0 4 31 3 2 3 6 1 35 7 24 0 3 3500 7 7 3 5 4 34 7 24 0 -2 0 7 2 4 5 2 53 6 24 0 1 33 0 4 3 6 2 33 7 24 0 1 0 1 6 3 6 6 52 6 24 1 -3 18 3 4 3 6 4 44 7 24 0 1 0 0 3 4 4 0 48 6 24 0 0 31 3 5 2 6 5 20 4 24 1 -2 0 5 3 2 4 6 45 6 24 1 0 59 7 4 2 6 2 70 3 24 0 0 0 0 3 3 4 2 39 3 24 0 1 7300 7 3 3 5 1 40 7 24 1 2 75 4 5 2 7 5 62 6 24 1 -1 0 7 5 2 6 4 46 6 24 1 -2 27 7 4 4 7 2 46 3 24 0 3 1600 7 4 2 5 6 56 7 24 1 -1 0 7 6 3 6 6 55 7 24 1 -3 0 7 6 2 6 6 41 4 24 1 -4 7300 1 2 3 6 0 43 7 24 0 3 16 7 7 1 7 6 34 3 24 1 -6 0 7 7 1 6 4 73 6 24 1 -5 0 7 5 2 6 6 50 6 24 1 -2 0 3 6 2 7 5 43 6 24 1 -3 0 6 6 2 5 6 46 7 24 1 -3 18 7 4 2 6 3 61 7 24 1 0 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cancer/000077500000000000000000000000001224417117700235575ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cancer/__init__.py000066400000000000000000000000231224417117700256630ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cancer/cancer.csv000077500000000000000000000046621224417117700255420ustar00rootroot00000000000000cancer,population 1,445 0,559 3,677 4,681 3,746 4,869 1,950 5,976 5,1096 5,1098 5,1114 7,1125 5,1236 6,1285 3,1291 3,1318 2,1323 8,1327 9,1438 7,1479 4,1536 6,1598 6,1635 11,1667 4,1696 7,1792 7,1795 4,1808 6,1838 16,1838 3,1847 8,1933 8,1959 4,1990 9,2003 10,2070 7,2091 8,2099 5,2104 11,2147 4,2154 12,2163 11,2172 9,2174 13,2183 17,2193 11,2210 10,2212 4,2236 4,2245 8,2261 6,2317 8,2333 16,2393 10,2404 4,2419 11,2462 10,2476 11,2477 9,2483 11,2511 14,2591 6,2624 8,2690 12,2731 15,2735 9,2736 13,2747 18,2782 15,2783 12,2793 11,2891 12,2894 12,2906 14,2929 12,2935 3,2962 5,3054 7,3112 9,3118 11,3185 14,3217 18,3236 11,3290 11,3314 4,3316 13,3401 10,3409 10,3426 9,3470 11,3488 12,3511 4,3549 16,3571 20,3578 5,3620 15,3654 15,3680 12,3683 7,3688 7,3706 12,3733 21,3800 16,3802 13,3832 16,3863 8,3891 12,4008 20,4093 21,4149 15,4162 13,4223 13,4232 10,4312 22,4329 14,4331 16,4399 13,4470 24,4618 27,4669 16,4681 28,4737 11,4784 12,4829 14,4857 26,4918 27,4967 17,5041 20,5051 12,5077 20,5107 12,5108 24,5124 27,5156 25,5167 19,5211 21,5246 8,5743 15,5773 22,5932 21,5983 37,5989 23,5998 18,6021 25,6035 26,6074 17,6134 27,6175 13,6220 13,6296 15,6445 33,6624 24,6841 23,6868 18,6903 24,6904 21,6916 32,6934 23,6978 32,7014 16,7025 29,7031 33,7115 20,7256 19,7288 27,7304 10,7367 34,7376 36,7407 26,7408 33,7503 24,7599 37,7743 34,7760 37,7910 20,7917 28,7957 30,7984 27,8004 45,8208 39,8249 29,8289 22,8313 27,8377 19,8396 30,8468 34,8493 35,8531 21,8773 18,8866 41,9091 34,9215 51,9225 30,9243 32,9435 38,9445 18,9468 42,9563 60,9605 19,9841 29,9994 17,10033 29,10049 41,10144 31,10303 35,10416 27,10461 37,10670 18,10844 41,10875 39,10890 41,11105 61,11622 46,12038 47,12173 36,12181 43,12608 45,12775 46,12915 45,13021 49,13142 55,13206 64,13407 64,13647 66,13870 57,13989 53,14089 51,14197 36,14620 28,14816 59,14952 39,15039 73,15049 41,15179 48,15204 37,16161 72,16239 72,16427 48,16462 62,16793 51,16925 71,17027 60,17201 70,17526 59,17666 91,17692 52,17742 65,18482 77,18731 84,18835 51,19274 66,19818 53,19906 58,20065 75,20140 88,20268 83,20539 48,20639 69,20969 41,21353 73,21757 79,22811 63,23245 90,23258 92,24296 60,24351 63,24692 63,24896 75,25275 70,25405 90,25715 111,26245 103,26408 117,26691 118,28024 40,28270 83,28477 90,29254 97,29422 92,30125 104,30538 96,34109 142,35112 105,35876 145,36307 160,39023 127,40756 169,42997 104,47672 179,49126 152,53464 163,56529 167,59634 302,60161 246,62398 236,62652 250,62931 267,63476 244,66676 248,74005 360,88456 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cancer/data.py000077500000000000000000000032601224417117700250460ustar00rootroot00000000000000"""Breast Cancer Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """???""" TITLE = """Breast Cancer Data""" SOURCE = """ This is the breast cancer data used in Owen's empirical likelihood. It is taken from Rice, J.A. Mathematical Statistics and Data Analysis. http://www.thomsonedu.com/statistics/discipline_content/dataLibrary.html """ DESCRSHORT = """Breast Cancer and county population""" DESCRLONG = """The number of breast cancer observances in various counties""" #suggested notes NOTE = """ Number of observations: 301 Number of variables: 2 Variable name definitions: cancer - The number of breast cancer observances population - The population of the county """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray(data, endog_idx=0, exog_idx=None, dtype=float) def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/cancer.csv', 'rb'), delimiter=",", names = True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/000077500000000000000000000000001224417117700234005ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/R_wls.s000066400000000000000000000005631224417117700246560ustar00rootroot00000000000000d <- read.csv('./ccard.csv') attach(d) m1 <- lm(AVGEXP ~ AGE + INCOME + INCOMESQ + OWNRENT, weights=1/INCOMESQ) results <- summary(m1) m2 <- lm(AVGEXP ~ AGE + INCOME + INCOMESQ + OWNRENT - 1, weights=1/INCOMESQ) results2 <- summary(m2) print('m1 has a constant, which theoretically should be INCOME') print('m2 include -1 for no constant') print('See ccard/R_wls.s') statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/__init__.py000066400000000000000000000000231224417117700255040ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/ccard.csv000066400000000000000000000030431224417117700251710ustar00rootroot00000000000000"AVGEXP","AGE","INCOME","INCOMESQ","OWNRENT" 124.98,38,4.52,20.4304,1 9.85,33,2.42,5.8564,0 15,34,4.5,20.25,1 137.87,31,2.54,6.4516,0 546.5,32,9.79,95.8441,1 92,23,2.5,6.25,0 40.83,28,3.96,15.6816,0 150.79,29,2.37,5.6169,1 777.82,37,3.8,14.44,1 52.58,28,3.2,10.24,0 256.66,31,3.95,15.6025,1 78.87,29,2.45,6.0025,1 42.62,35,1.91,3.6481,1 335.43,41,3.2,10.24,1 248.72,40,4,16,1 548.03,40,10,100,1 43.34,35,2.35,5.5225,1 218.52,34,2,4,1 170.64,36,4,16,0 37.58,43,5.14,26.4196,1 502.2,30,4.51,20.3401,0 73.18,22,1.5,2.25,0 1532.77,40,5.5,30.25,1 42.69,22,2.03,4.1209,0 417.83,29,3.2,10.24,0 552.72,21,2.47,6.1009,1 222.54,24,3,9,0 541.3,43,3.54,12.5316,1 568.77,37,5.7,32.49,1 344.47,27,3.5,12.25,0 405.35,28,4.6,21.16,1 310.94,26,3,9,1 53.65,23,2.59,6.7081,0 63.92,30,1.51,2.2801,0 165.85,30,1.85,3.4225,0 9.58,38,2.6,6.76,0 319.49,36,2,4,0 83.08,26,2.35,5.5225,0 644.83,28,7,49,1 93.2,24,2,4,0 105.04,21,1.7,2.89,0 34.13,24,2.8,7.84,0 41.19,26,2.4,5.76,0 169.89,33,3,9,0 1898.03,34,4.8,23.04,0 810.39,33,3.18,10.1124,0 32.78,21,1.5,2.25,0 95.8,25,3,9,0 27.78,27,2.28,5.1984,0 215.07,26,2.8,7.84,0 79.51,22,2.7,7.29,0 306.03,41,6,36,0 104.54,42,3.9,15.21,0 642.47,25,3.07,9.4249,0 308.05,31,2.46,6.0516,1 186.35,27,2,4,0 56.15,33,3.25,10.5625,0 129.37,37,2.72,7.3984,0 93.11,27,2.2,4.84,0 292.66,24,3.75,14.0625,0 98.46,25,2.88,8.2944,0 258.55,36,3.05,9.3025,0 101.68,33,2.55,6.5025,0 65.25,55,2.64,6.9696,1 108.61,20,1.65,2.7225,0 49.56,29,2.4,5.76,0 235.57,41,7.24,52.4176,1 68.38,43,2.4,5.76,0 474.15,33,6,36,1 234.05,25,3.6,12.96,0 451.2,26,5,25,1 251.52,46,5.5,30.25,1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/data.py000066400000000000000000000030641224417117700246660ustar00rootroot00000000000000"""Bill Greene's credit scoring data.""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission of the original author, who retains all rights.""" TITLE = __doc__ SOURCE = """ William Greene's `Econometric Analysis` More information can be found at the web site of the text: http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm """ DESCRSHORT = """William Greene's credit scoring data""" DESCRLONG = """More information on this data can be found on the homepage for Greene's `Econometric Analysis`. See source. """ NOTE = """ Number of observations - 72 Number of variables - 5 Variable name definitions - See Source for more information on the variables. """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """Load the credit card data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """Load the credit card data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/ccard.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/src/000077500000000000000000000000001224417117700241675ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/src/ccard.csv000066400000000000000000000040511224417117700257600ustar00rootroot00000000000000"MDR","Acc","Age","Income","Avgexp","Ownrent","Selfempl" 0,1,38,4.52,124.98,1,0 0,1,33,2.42,9.85,0,0 0,1,34,4.5,15,1,0 0,1,31,2.54,137.87,0,0 0,1,32,9.79,546.5,1,0 0,1,23,2.5,92,0,0 0,1,28,3.96,40.83,0,0 0,1,29,2.37,150.79,1,0 0,1,37,3.8,777.82,1,0 0,1,28,3.2,52.58,0,0 0,1,31,3.95,256.66,1,0 0,0,42,1.98,0,1,0 0,0,30,1.73,0,1,0 0,1,29,2.45,78.87,1,0 0,1,35,1.91,42.62,1,0 0,1,41,3.2,335.43,1,0 0,1,40,4,248.72,1,0 7,0,30,3,0,1,0 0,1,40,10,548.03,1,1 3,0,46,3.4,0,0,0 0,1,35,2.35,43.34,1,0 1,0,25,1.88,0,0,0 0,1,34,2,218.52,1,0 1,1,36,4,170.64,0,0 0,1,43,5.14,37.58,1,0 0,1,30,4.51,502.2,0,0 0,0,22,3.84,0,0,1 0,1,22,1.5,73.18,0,0 0,0,34,2.5,0,1,0 0,1,40,5.5,1532.77,1,0 0,1,22,2.03,42.69,0,0 1,1,29,3.2,417.83,0,0 1,0,25,3.15,0,1,0 0,1,21,2.47,552.72,1,0 0,1,24,3,222.54,0,0 0,1,43,3.54,541.3,1,0 0,0,43,2.28,0,0,0 0,1,37,5.7,568.77,1,0 0,1,27,3.5,344.47,0,0 0,1,28,4.6,405.35,1,0 0,1,26,3,310.94,1,0 0,1,23,2.59,53.65,0,0 0,1,30,1.51,63.92,0,0 0,1,30,1.85,165.85,0,0 0,1,38,2.6,9.58,0,0 0,0,28,1.8,0,0,1 0,1,36,2,319.49,0,0 0,0,38,3.26,0,0,0 0,1,26,2.35,83.08,0,0 0,1,28,7,644.83,1,0 0,0,50,3.6,0,0,0 0,1,24,2,93.2,0,0 0,1,21,1.7,105.04,0,0 0,1,24,2.8,34.13,0,0 0,1,26,2.4,41.19,0,0 1,1,33,3,169.89,0,0 0,1,34,4.8,1898.03,0,0 0,1,33,3.18,810.39,0,0 0,0,45,1.8,0,0,0 0,1,21,1.5,32.78,0,0 2,1,25,3,95.8,0,0 0,1,27,2.28,27.78,0,0 0,1,26,2.8,215.07,0,0 0,1,22,2.7,79.51,0,0 3,0,27,4.9,0,1,0 0,0,26,2.5,0,0,1 0,1,41,6,306.03,0,1 0,1,42,3.9,104.54,0,0 0,0,22,5.1,0,0,0 0,1,25,3.07,642.47,0,0 0,1,31,2.46,308.05,1,0 0,1,27,2,186.35,0,0 0,1,33,3.25,56.15,0,0 0,1,37,2.72,129.37,0,0 0,1,27,2.2,93.11,0,0 1,0,24,4.1,0,0,0 0,1,24,3.75,292.66,0,0 0,1,25,2.88,98.46,0,0 0,1,36,3.05,258.55,0,0 0,1,33,2.55,101.68,0,0 0,0,33,4,0,0,0 1,1,55,2.64,65.25,1,0 0,1,20,1.65,108.61,0,0 0,1,29,2.4,49.56,0,0 3,0,40,3.71,0,0,0 0,1,41,7.24,235.57,1,0 0,0,41,4.39,0,1,0 0,0,35,3.3,0,1,0 0,0,24,2.3,0,0,0 1,0,54,4.18,0,0,0 2,0,34,2.49,0,0,0 0,0,45,2.81,0,1,0 0,1,43,2.4,68.38,0,0 4,0,35,1.5,0,0,0 2,0,36,8.4,0,0,0 0,1,22,1.56,0,0,0 1,1,33,6,474.15,1,0 1,1,25,3.6,234.05,0,0 0,1,26,5,451.2,1,0 0,1,46,5.5,251.52,1,0 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/ccard/src/names.txt000066400000000000000000000004231224417117700260320ustar00rootroot00000000000000MDR = Number of derogator reports Acc = Credit card application accpeted (1=yes) Age = Age in years + 12ths of a year Income = Income divided by 10,000 Avgexp = Avg. monthly credit card expenditure Ownrent = Indiviual owns(1) or rents(0) home Selfempl = (1=yes, 0=no) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/000077500000000000000000000000001224417117700243125ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/R_committee.s000066400000000000000000000006151224417117700267470ustar00rootroot00000000000000### SETUP ### d <- read.table("./committee.csv",sep=",", header=T) attach(d) LNSTAFF <- log(STAFF) SUBS.LNSTAFF <- SUBS*LNSTAFF library(MASS) #m1 <- glm.nb(BILLS104 ~ SIZE + SUBS + LNSTAFF + PRESTIGE + BILLS103 + SUBS.LNSTAFF) m1 <- glm(BILLS104 ~ SIZE + SUBS + LNSTAFF + PRESTIGE + BILLS103 + SUBS.LNSTAFF, family=negative.binomial(1)) # Disp should be 1 by default results <- summary.glm(m1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/__init__.py000066400000000000000000000000231224417117700264160ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/committee.csv000066400000000000000000000013101224417117700270100ustar00rootroot00000000000000"COMMITTEE","BILLS104","SIZE","SUBS","STAFF","PRESTIGE","BILLS103" "Appropriations",6,58,13,109,1,9 "Budget",23,42,0,39,1,101 "Rules",44,13,2,25,1,54 "Ways_and_Means",355,39,5,23,1,542 "Banking",125,51,5,61,0,101 "Economic_Educ_Oppor",131,43,5,69,0,158 "Commerce",271,49,4,79,0,196 "International_Relations",63,44,3,68,0,40 "Government_Reform",149,51,7,99,0,72 "Judiciary",253,35,5,56,0,168 "Agriculture",81,49,5,46,0,60 "National_Security",89,55,7,48,0,75 "Resources",142,44,5,58,0,98 "TransInfrastructure",155,61,6,74,0,69 "Science",27,50,4,58,0,25 "Small_Business",8,43,4,29,0,9 "Veterans_Affairs",28,33,3,36,0,41 "House_Oversight",68,12,0,24,0,233 "Stds_of_Conduct",1,10,0,9,0,0 "Intelligence",4,16,2,24,0,2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/data.py000066400000000000000000000047411224417117700256030ustar00rootroot00000000000000"""First 100 days of the US House of Representatives 1995""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission from the original author, who retains all rights.""" TITLE = __doc__ SOURCE = """ Jeff Gill's `Generalized Linear Models: A Unifited Approach` http://jgill.wustl.edu/research/books.html """ DESCRSHORT = """Number of bill assignments in the 104th House in 1995""" DESCRLONG = """The example in Gill, seeks to explain the number of bill assignments in the first 100 days of the US' 104th House of Representatives. The response variable is the number of bill assignments in the first 100 days over 20 Committees. The explanatory variables in the example are the number of assignments in the first 100 days of the 103rd House, the number of members on the committee, the number of subcommittees, the log of the number of staff assigned to the committee, a dummy variable indicating whether the committee is a high prestige committee, and an interaction term between the number of subcommittees and the log of the staff size. The data returned by load are not cleaned to represent the above example. """ NOTE = """Number of Observations - 20 Number of Variables - 6 Variable name definitions:: BILLS104 - Number of bill assignments in the first 100 days of the 104th House of Representatives. SIZE - Number of members on the committee. SUBS - Number of subcommittees. STAFF - Number of staff members assigned to the committee. PRESTIGE - PRESTIGE == 1 is a high prestige committee. BILLS103 - Number of bill assignments in the first 100 days of the 103rd House of Representatives. Committee names are included as a variable in the data file though not returned by load. """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """Load the committee data and returns a data class. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/committee.csv', 'rb'), delimiter=",", names=True, dtype=float, usecols=(1,2,3,4,5,6)) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/src/000077500000000000000000000000001224417117700251015ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/committee/src/committee.dat000066400000000000000000000026561224417117700275720ustar00rootroot00000000000000 SIZE SUBS STAFF PRESTIGE POLICY CONSTIT SERVICE BILLS103 BILLS104 Appropriations 58 13 109 1 0 0 0 9 6 Budget 42 0 39 1 0 0 0 101 23 Rules 13 2 25 1 0 0 0 54 44 Ways_and_Means 39 5 23 1 0 0 0 542 355 Banking 51 5 61 0 1 0 0 101 125 Economic_Educ_Oppor 43 5 69 0 1 0 0 158 131 Commerce 49 4 79 0 1 0 0 196 271 International_Relations 44 3 68 0 1 0 0 40 63 Government_Reform 51 7 99 0 1 0 0 72 149 Judiciary 35 5 56 0 1 0 0 168 253 Agriculture 49 5 46 0 0 1 0 60 81 National_Security 55 7 48 0 0 1 0 75 89 Resources 44 5 58 0 0 1 0 98 142 TransInfrastructure 61 6 74 0 0 1 0 69 155 Science 50 4 58 0 0 1 0 25 27 Small_Business 43 4 29 0 0 1 0 9 8 Veterans_Affairs 33 3 36 0 0 1 0 41 28 House_Oversight 12 0 24 0 0 0 1 233 68 Stds_of_Conduct 10 0 9 0 0 0 1 0 1 Intelligence 16 2 24 0 0 0 1 2 4 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/000077500000000000000000000000001224417117700236145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/__init__.py000066400000000000000000000000231224417117700257200ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/copper.csv000066400000000000000000000017261224417117700256270ustar00rootroot00000000000000"YEAR","WORLDCONSUMPTION","COPPERPRICE","INCOMEINDEX","ALUMPRICE","INVENTORYINDEX","TIME" 1951,3173,26.56,0.7,19.76,0.98,1 1952,3281.1,27.31,0.71,20.78,1.04,2 1953,3135.7,32.95,0.72,22.55,1.05,3 1954,3359.1,33.9,0.7,23.06,0.97,4 1955,3755.1,42.7,0.74,24.93,1.02,5 1956,3875.9,46.11,0.74,26.5,1.04,6 1957,3905.7,31.7,0.74,27.24,0.98,7 1958,3957.6,27.23,0.72,26.21,0.98,8 1959,4279.1,32.89,0.75,26.09,1.03,9 1960,4627.9,33.78,0.77,27.4,1.03,10 1961,4910.2,31.66,0.76,26.94,0.98,11 1962,4908.4,32.28,0.79,25.18,1,12 1963,5327.9,32.38,0.83,23.94,0.97,13 1964,5878.4,33.75,0.85,25.07,1.03,14 1965,6075.2,36.25,0.89,25.37,1.08,15 1966,6312.7,36.24,0.93,24.55,1.05,16 1967,6056.8,38.23,0.95,24.98,1.03,17 1968,6375.9,40.83,0.99,24.96,1.03,18 1969,6974.3,44.62,1,25.52,0.99,19 1970,7101.6,52.27,1,26.01,1,20 1971,7071.7,45.16,1.02,25.46,0.96,21 1972,7754.8,42.5,1.07,22.17,0.97,22 1973,8480.3,43.7,1.12,18.56,0.98,23 1974,8105.2,47.88,1.1,21.32,1.01,24 1975,7157.2,36.33,1.07,22.75,0.94,25 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/data.py000066400000000000000000000044141224417117700251020ustar00rootroot00000000000000"""World Copper Prices 1951-1975 dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission from the original author, who retains all rights.""" TITLE = "World Copper Market 1951-1975 Dataset" SOURCE = """ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html """ DESCRSHORT = """World Copper Market 1951-1975""" DESCRLONG = """This data describes the world copper market from 1951 through 1975. In an example, in Gill, the outcome variable (of a 2 stage estimation) is the world consumption of copper for the 25 years. The explanatory variables are the world consumption of copper in 1000 metric tons, the constant dollar adjusted price of copper, the price of a substitute, aluminum, an index of real per capita income base 1970, an annual measure of manufacturer inventory change, and a time trend. """ NOTE = """ Number of Observations - 25 Number of Variables - 6 Variable name definitions:: WORLDCONSUMPTION - World consumption of copper (in 1000 metric tons) COPPERPRICE - Constant dollar adjusted price of copper INCOMEINDEX - An index of real per capita income (base 1970) ALUMPRICE - The price of aluminum INVENTORYINDEX - A measure of annual manufacturer inventory trend TIME - A time trend Years are included in the data file though not returned by load. """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the copper data and returns a Dataset class. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/copper.csv', 'rb'), delimiter=",", names=True, dtype=float, usecols=(1,2,3,4,5,6)) return data def load_pandas(): """ Load the copper data and returns a Dataset class. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/src/000077500000000000000000000000001224417117700244035ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/copper/src/copper.dat000066400000000000000000000020531224417117700263650ustar00rootroot00000000000000YEAR WORLDCONSUMPTION COPPERPRICE INCOMEINDEX ALUMPRICE INVENTORYINDEX TIME 1951 3173.0 26.56 0.70 19.76 0.97679 1 1952 3281.1 27.31 0.71 20.78 1.03937 2 1953 3135.7 32.95 0.72 22.55 1.05153 3 1954 3359.1 33.90 0.70 23.06 0.97312 4 1955 3755.1 42.70 0.74 24.93 1.02349 5 1956 3875.9 46.11 0.74 26.50 1.04135 6 1957 3905.7 31.70 0.74 27.24 0.97686 7 1958 3957.6 27.23 0.72 26.21 0.98069 8 1959 4279.1 32.89 0.75 26.09 1.02888 9 1960 4627.9 33.78 0.77 27.40 1.03392 10 1961 4910.2 31.66 0.76 26.94 0.97922 11 1962 4908.4 32.28 0.79 25.18 0.99679 12 1963 5327.9 32.38 0.83 23.94 0.96630 13 1964 5878.4 33.75 0.85 25.07 1.02915 14 1965 6075.2 36.25 0.89 25.37 1.07950 15 1966 6312.7 36.24 0.93 24.55 1.05073 16 1967 6056.8 38.23 0.95 24.98 1.02788 17 1968 6375.9 40.83 0.99 24.96 1.02799 18 1969 6974.3 44.62 1.00 25.52 0.99151 19 1970 7101.6 52.27 1.00 26.01 1.00191 20 1971 7071.7 45.16 1.02 25.46 0.95644 21 1972 7754.8 42.50 1.07 22.17 0.96947 22 1973 8480.3 43.70 1.12 18.56 0.98220 23 1974 8105.2 47.88 1.10 21.32 1.00793 24 1975 7157.2 36.33 1.07 22.75 0.93810 25 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/000077500000000000000000000000001224417117700237755ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/R_cpunish.s000066400000000000000000000004331224417117700261130ustar00rootroot00000000000000### SETUP ### d <- read.table("./cpunish.csv",sep=",", header=T) attach(d) LN_VC100k96 = log(VC100k96) ### MODEL ### m1 <- glm(EXECUTIONS ~ INCOME + PERPOVERTY + PERBLACK + LN_VC100k96 + SOUTH + DEGREE, family=poisson) results <- summary.glm(m1) results results['coefficients'] statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/__init__.py000066400000000000000000000000231224417117700261010ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/cpunish.csv000066400000000000000000000013521224417117700261640ustar00rootroot00000000000000"STATE","EXECUTIONS","INCOME","PERPOVERTY","PERBLACK","VC100k96","SOUTH","DEGREE" "Texas",37,34453,16.7,12.2,644,1,0.16 "Virginia",9,41534,12.5,20,351,1,0.27 "Missouri",6,35802,10.6,11.2,591,0,0.21 "Arkansas",4,26954,18.4,16.1,524,1,0.16 "Alabama",3,31468,14.8,25.9,565,1,0.19 "Arizona",2,32552,18.8,3.5,632,0,0.25 "Illinois",2,40873,11.6,15.3,886,0,0.25 "South_Carolina",2,34861,13.1,30.1,997,1,0.21 "Colorado",1,42562,9.4,4.3,405,0,0.31 "Florida",1,31900,14.3,15.4,1051,1,0.24 "Indiana",1,37421,8.2,8.2,537,0,0.19 "Kentucky",1,33305,16.4,7.2,321,0,0.16 "Louisiana",1,32108,18.4,32.1,929,1,0.18 "Maryland",1,45844,9.3,27.4,931,0,0.29 "Nebraska",1,34743,10,4,435,0,0.24 "Oklahoma",1,29709,15.2,7.7,597,0,0.21 "Oregon",1,36777,11.7,1.8,463,0,0.25 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/data.py000066400000000000000000000047641224417117700252730ustar00rootroot00000000000000"""US Capital Punishment dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission from the original author, who retains all rights.""" TITLE = __doc__ SOURCE = """ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html """ DESCRSHORT = """Number of state executions in 1997""" DESCRLONG = """This data describes the number of times capital punishment is implemented at the state level for the year 1997. The outcome variable is the number of executions. There were executions in 17 states. Included in the data are explanatory variables for median per capita income in dollars, the percent of the population classified as living in poverty, the percent of Black citizens in the population, the rate of violent crimes per 100,000 residents for 1996, a dummy variable indicating whether the state is in the South, and (an estimate of) the proportion of the population with a college degree of some kind. """ NOTE = """ Number of Observations - 17 Number of Variables - 7 Variable name definitions:: EXECUTIONS - Executions in 1996 INCOME - Median per capita income in 1996 dollars PERPOVERTY - Percent of the population classified as living in poverty PERBLACK - Percent of black citizens in the population VC100k96 - Rate of violent crimes per 100,00 residents for 1996 SOUTH - SOUTH == 1 indicates a state in the South DEGREE - An esimate of the proportion of the state population with a college degree of some kind State names are included in the data file, though not returned by load. """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the cpunish data and return a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Load the cpunish data and return a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/cpunish.csv', 'rb'), delimiter=",", names=True, dtype=float, usecols=(1,2,3,4,5,6,7)) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/src/000077500000000000000000000000001224417117700245645ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/cpunish/src/cpunish.dat000066400000000000000000000037151224417117700267350ustar00rootroot00000000000000STATE EXECUTIONS INCOME PERPOVERTY PERBLACK VC100k96 SOUTH <9thGRADE 9thTO12th HSOREQUIV SOMECOLL AADEGREE BACHELORS GRAD/PROF Texas 37 34453 16.7 12.2 644 1 1492112 1924831 3153187 2777973 598956 530849 673250 Virginia 9 41534 12.5 20.0 351 1 461475 669851 1297714 969191 244488 676710 363602 Missouri 6 35802 10.6 11.2 591 0 391097 578440 1251550 785555 170146 420521 204294 Arkansas 4 26954 18.4 16.1 524 1 234071 328690 571252 323016 62246 143038 67144 Alabama 3 31468 14.8 25.9 565 1 362434 597455 875703 575123 146228 281466 142177 Arizona 2 32552 18.8 3.5 632 0 224662 368279 708340 724228 173801 325575 161560 Illinois 2 40873 11.6 15.3 886 0 786815 1203134 2531465 1817238 490791 1101193 552145 South_Carolina 2 34861 13.1 30.1 997 1 303694 479916 776053 466145 152671 267365 118811 Colorado 1 42562 9.4 4.3 405 0 124477 270560 654510 630445 161331 402917 190168 Florida 1 31900 14.3 15.4 1051 1 883820 1706839 3045682 2054574 682005 1133053 567453 Indiana 1 37421 8.2 8.2 537 0 310403 673362 1530741 775605 212379 360087 224057 Kentucky 1 33305 16.4 7.2 321 0 456107 467956 881795 476362 108409 209055 129994 Louisiana 1 32108 18.4 32.1 929 1 391630 534570 951832 586477 94409 288154 143624 Maryland 1 45844 9.3 27.4 931 0 257518 514788 1044976 744604 182465 532883 342012 Nebraska 1 34743 10.0 4.0 435 0 81690 124792 388540 272981 80956 141231 59008 Oklahoma 1 29709 15.2 7.7 597 0 201228 375155 706003 539511 113434 253635 119774 Oregon 1 36777 11.7 1.8 463 0 122513 283409 613983 561176 139269 267161 130403 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/000077500000000000000000000000001224417117700236105ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/__init__.py000066400000000000000000000000231224417117700257140ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/data.py000066400000000000000000000033351224417117700250770ustar00rootroot00000000000000"""El Nino dataset, 1950 - 2010""" __docformat__ = 'restructuredtext' COPYRIGHT = """This data is in the public domain.""" TITLE = """El Nino - Sea Surface Temperatures""" SOURCE = """ National Oceanic and Atmospheric Administration's National Weather Service ERSST.V3B dataset, Nino 1+2 http://www.cpc.ncep.noaa.gov/data/indices/ """ DESCRSHORT = """Averaged monthly sea surface temperature - Pacific Ocean.""" DESCRLONG = """This data contains the averaged monthly sea surface temperature in degrees Celcius of the Pacific Ocean, between 0-10 degrees South and 90-80 degrees West, from 1950 to 2010. This dataset was obtained from NOAA. """ NOTE = """ Number of Observations - 61 x 12 Number of Variables - 1 Variable name definitions:: TEMPERATURE - average sea surface temperature in degrees Celcius (12 columns, one per month). """ from numpy import recfromtxt, column_stack, array from pandas import DataFrame from statsmodels.datasets.utils import Dataset from os.path import dirname, abspath def load(): """ Load the El Nino data and return a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. Notes ----- The elnino Dataset instance does not contain endog and exog attributes. """ data = _get_data() names = data.dtype.names dataset = Dataset(data=data, names=names) return dataset def load_pandas(): dataset = load() dataset.data = DataFrame(dataset.data) return dataset def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/elnino.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/elnino.csv000066400000000000000000000126041224417117700256140ustar00rootroot00000000000000"YEAR","JAN","FEB","MAR","APR","MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC" 1950,23.110,24.200,25.370,23.860,23.030,21.570,20.630,20.150,19.670,20.030,20.020,21.800 1951,24.190,25.280,25.600,25.370,24.790,24.690,23.860,22.320,21.440,21.770,22.330,22.890 1952,24.520,26.210,26.370,24.730,23.710,22.340,20.890,20.020,19.630,20.400,20.770,22.390 1953,24.150,26.340,27.360,27.030,25.470,23.490,22.200,21.450,21.250,20.950,21.600,22.440 1954,23.020,25.000,25.330,22.970,21.730,20.770,19.520,19.330,18.950,19.110,20.270,21.300 1955,23.750,24.820,25.140,24.220,22.160,21.200,20.460,19.630,19.240,19.160,19.840,21.190 1956,23.240,24.710,25.900,24.660,23.140,22.040,21.470,20.550,19.890,19.690,20.570,21.580 1957,23.130,26.300,27.630,27.150,26.720,25.040,23.830,22.340,21.800,21.800,22.390,23.690 1958,24.890,26.550,27.090,26.370,24.710,23.230,22.310,20.720,20.620,21.050,21.520,22.500 1959,23.970,25.900,26.940,25.840,24.230,22.570,21.500,20.150,20.230,20.860,21.880,22.550 1960,24.400,25.590,26.010,24.660,23.530,21.830,20.730,20.100,20.560,20.270,20.930,22.740 1961,24.580,26.660,25.950,25.170,23.600,22.360,20.520,19.970,19.700,20.070,21.090,22.110 1962,24.020,25.350,24.470,23.430,23.030,21.810,20.600,20.170,20.020,20.140,20.990,21.820 1963,23.810,25.360,26.020,24.670,23.970,22.410,21.800,21.310,21.000,21.130,21.640,22.550 1964,24.150,25.080,25.300,24.610,21.930,21.440,20.250,19.480,19.670,19.790,20.880,21.830 1965,24.220,26.170,26.710,27.010,26.090,24.600,23.260,22.540,21.260,21.570,22.290,23.350 1966,25.150,25.880,25.350,24.260,22.920,21.800,20.850,20.170,20.040,20.510,21.030,22.250 1967,23.660,25.540,25.550,24.980,23.770,22.040,21.100,19.840,19.080,19.470,20.200,21.280 1968,23.190,24.880,25.110,23.970,22.440,21.700,21.250,20.970,21.230,21.120,21.680,23.200 1969,24.670,25.560,27.090,26.660,26.070,24.390,22.470,21.060,20.880,21.740,22.470,23.600 1970,25.020,25.760,25.530,24.760,22.940,21.270,19.690,19.270,19.500,20.160,20.610,21.770 1971,23.330,24.580,25.240,24.950,23.290,21.600,21.010,19.970,19.740,19.880,20.940,21.990 1972,24.510,26.660,27.090,26.250,25.470,25.010,24.110,23.420,22.120,22.580,23.320,24.890 1973,26.030,26.480,26.270,24.870,23.440,21.760,20.840,19.470,19.490,19.800,20.710,21.740 1974,23.290,24.870,25.690,25.280,24.350,22.480,21.580,20.730,20.150,19.880,20.680,21.410 1975,23.550,24.950,26.060,25.530,23.710,21.840,21.050,19.970,19.140,19.170,19.440,21.050 1976,23.510,25.360,25.880,25.720,25.110,24.460,23.300,21.910,21.560,21.690,22.140,23.290 1977,24.930,25.790,26.130,25.290,23.880,22.700,21.630,20.250,19.820,20.610,21.290,22.470 1978,24.320,25.770,25.390,24.730,23.150,21.890,21.010,19.660,19.980,20.220,21.620,22.940 1979,24.710,25.600,25.930,25.580,24.520,23.280,21.790,21.050,21.150,21.430,21.950,22.970 1980,24.350,25.730,26.460,25.710,24.460,22.880,21.260,20.570,20.450,20.430,21.230,22.340 1981,22.980,24.900,25.940,24.890,23.900,22.570,21.100,20.030,20.090,20.580,21.260,22.600 1982,24.360,25.420,25.400,24.960,24.210,23.350,22.500,21.890,22.040,22.880,24.570,25.890 1983,27.250,28.230,28.850,28.820,28.370,27.430,25.730,23.880,22.260,22.220,22.210,23.190 1984,24.320,25.120,25.750,25.400,23.580,22.300,21.530,20.640,20.730,20.620,21.700,22.470 1985,23.840,24.830,25.600,24.280,22.670,21.840,20.750,19.900,19.860,20.260,20.970,22.490 1986,24.310,25.900,25.780,24.860,23.350,22.030,21.640,21.070,21.130,21.460,22.170,23.530 1987,25.600,27.020,27.890,26.950,25.960,24.040,23.000,21.920,22.000,22.540,22.840,23.510 1988,24.760,25.740,25.710,24.680,23.180,21.660,20.590,19.630,19.440,19.830,20.960,22.250 1989,24.360,26.020,26.210,25.540,23.360,22.140,21.270,20.860,20.170,20.520,21.440,22.610 1990,24.220,26.170,26.150,25.150,24.140,22.760,21.360,20.700,20.280,20.400,21.190,22.290 1991,23.990,25.590,26.310,25.150,24.440,23.280,22.390,21.390,21.220,21.730,22.400,23.750 1992,25.020,26.620,27.720,27.580,26.440,23.860,21.840,20.870,20.850,21.150,21.840,22.790 1993,24.680,26.460,27.070,26.840,25.600,24.110,22.610,21.650,21.110,21.710,22.070,22.860 1994,24.560,25.890,25.750,24.490,23.520,22.310,21.170,20.220,20.650,22.040,22.270,23.750 1995,25.480,26.250,26.090,24.320,23.370,22.430,21.420,20.460,20.500,20.620,21.490,22.030 1996,23.810,25.520,26.280,24.000,23.110,21.660,20.720,20.230,20.430,20.520,20.770,21.680 1997,23.700,26.080,27.170,26.740,26.770,26.150,25.590,24.950,24.690,24.640,25.850,27.080 1998,28.120,28.820,29.240,28.450,27.360,25.190,23.610,22.270,21.310,21.370,21.600,22.810 1999,24.230,25.730,26.470,24.530,23.640,22.090,21.360,20.670,20.080,20.460,20.620,22.420 2000,24.010,25.380,25.670,25.530,24.270,22.930,21.470,20.070,20.640,20.900,20.670,22.080 2001,24.240,26.110,26.890,25.990,23.980,22.710,21.480,20.240,19.730,20.140,20.680,21.730 2002,24.090,26.230,27.390,26.440,25.290,23.280,21.640,21.320,21.420,21.850,22.850,24.050 2003,25.010,26.270,26.910,25.410,23.240,22.150,21.500,21.250,20.750,21.700,22.330,23.600 2004,25.090,26.470,26.120,25.270,23.440,22.540,21.260,20.790,20.830,21.560,22.880,23.390 2005,24.610,25.090,25.230,25.210,24.310,22.600,21.610,20.470,20.000,19.890,20.610,22.200 2006,24.760,26.520,26.220,24.290,23.840,22.820,22.200,21.890,21.930,22.460,22.610,24.150 2007,25.820,26.810,26.410,24.960,23.050,21.610,21.050,19.950,19.850,19.310,19.820,21.150 2008,24.240,26.390,26.910,25.680,24.430,23.190,23.020,22.140,21.600,21.390,21.540,22.730 2009,24.390,25.530,25.480,25.840,24.950,24.090,23.090,22.030,21.480,21.640,21.990,23.210 2010,24.700,26.160,26.540,26.040,24.750,23.260,21.110,19.490,19.280,19.730,20.440,22.070 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/src/000077500000000000000000000000001224417117700243775ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/elnino/src/elnino.dat000066400000000000000000001504051224417117700263620ustar00rootroot00000000000000 YR MON NINO1+2 ANOM NINO3 ANOM NINO4 ANOM NINO3.4 ANOM 1950 1 23.11 -1.42 23.74 -1.91 27.03 -1.12 24.83 -1.72 1950 2 24.20 -1.71 24.92 -1.45 27.15 -0.92 25.20 -1.53 1950 3 25.37 -1.00 26.33 -0.80 27.06 -1.11 26.03 -1.20 1950 4 23.86 -1.71 26.46 -0.97 27.29 -1.10 26.36 -1.30 1950 5 23.03 -1.39 25.72 -1.38 27.59 -1.10 26.19 -1.58 1950 6 21.57 -1.50 25.55 -0.92 27.97 -0.70 26.52 -1.01 1950 7 20.63 -1.34 24.97 -0.68 27.83 -0.78 26.42 -0.69 1950 8 20.15 -0.86 24.61 -0.45 27.72 -0.78 25.98 -0.77 1950 9 19.67 -1.10 24.22 -0.72 27.66 -0.85 25.78 -0.92 1950 10 20.03 -1.04 24.39 -0.56 27.44 -1.07 25.96 -0.68 1950 11 20.02 -1.72 24.00 -1.05 27.35 -1.11 25.64 -0.96 1950 12 21.80 -1.11 24.30 -0.91 27.30 -1.05 25.50 -1.07 1951 1 24.19 -0.35 25.00 -0.65 27.36 -0.79 25.46 -1.09 1951 2 25.28 -0.64 25.80 -0.57 27.27 -0.80 25.78 -0.96 1951 3 25.60 -0.78 26.91 -0.22 27.96 -0.21 26.72 -0.51 1951 4 25.37 -0.20 27.31 -0.11 28.49 0.09 27.24 -0.43 1951 5 24.79 0.37 27.08 -0.02 29.04 0.35 27.68 -0.09 1951 6 24.69 1.62 26.57 0.10 28.74 0.07 27.46 -0.07 1951 7 23.86 1.88 26.54 0.89 28.82 0.21 27.72 0.61 1951 8 22.32 1.31 25.67 0.62 28.53 0.04 27.36 0.61 1951 9 21.44 0.67 25.56 0.62 28.62 0.11 27.51 0.81 1951 10 21.77 0.70 25.85 0.90 28.71 0.20 27.43 0.78 1951 11 22.33 0.59 26.18 1.13 28.74 0.29 27.48 0.87 1951 12 22.89 -0.01 25.98 0.77 28.53 0.19 27.12 0.55 1952 1 24.52 -0.02 25.85 0.20 28.35 0.19 26.85 0.30 1952 2 26.21 0.29 26.46 0.08 28.08 0.00 26.79 0.06 1952 3 26.37 0.00 27.18 0.05 28.10 -0.07 27.32 0.09 1952 4 24.73 -0.84 27.41 -0.02 28.52 0.12 27.88 0.21 1952 5 23.71 -0.71 26.92 -0.18 28.69 -0.01 27.99 0.21 1952 6 22.34 -0.73 25.84 -0.63 28.47 -0.20 27.33 -0.20 1952 7 20.89 -1.09 24.92 -0.73 28.24 -0.37 26.72 -0.38 1952 8 20.02 -0.99 24.50 -0.56 28.09 -0.40 26.46 -0.29 1952 9 19.63 -1.14 24.43 -0.51 28.55 0.04 26.54 -0.17 1952 10 20.40 -0.67 24.52 -0.42 28.25 -0.26 26.54 -0.10 1952 11 20.77 -0.97 24.36 -0.69 28.28 -0.17 26.36 -0.24 1952 12 22.39 -0.52 24.88 -0.33 28.30 -0.04 26.53 -0.04 1953 1 24.15 -0.39 25.77 0.11 28.31 0.15 26.85 0.29 1953 2 26.34 0.42 26.74 0.36 28.26 0.18 27.19 0.45 1953 3 27.36 0.98 27.57 0.44 28.10 -0.07 27.68 0.45 1953 4 27.03 1.46 27.91 0.49 28.53 0.14 28.19 0.53 1953 5 25.47 1.05 27.39 0.28 28.92 0.23 28.29 0.52 1953 6 23.49 0.42 26.77 0.30 29.18 0.51 28.02 0.49 1953 7 22.20 0.23 25.81 0.16 29.02 0.41 27.52 0.42 1953 8 21.45 0.44 25.20 0.14 28.83 0.34 27.16 0.41 1953 9 21.25 0.49 25.17 0.24 28.61 0.09 27.13 0.43 1953 10 20.95 -0.12 25.07 0.13 28.91 0.41 27.02 0.38 1953 11 21.60 -0.14 25.25 0.19 28.91 0.46 26.96 0.36 1953 12 22.44 -0.47 25.55 0.34 28.74 0.40 26.99 0.42 1954 1 23.02 -1.52 25.73 0.07 28.30 0.14 27.03 0.47 1954 2 25.00 -0.92 26.43 0.05 28.16 0.08 27.22 0.48 1954 3 25.33 -1.05 26.85 -0.28 27.89 -0.28 27.21 -0.02 1954 4 22.97 -2.60 25.85 -1.57 28.20 -0.19 26.87 -0.80 1954 5 21.73 -2.69 25.67 -1.44 28.40 -0.30 27.06 -0.71 1954 6 20.77 -2.30 25.20 -1.27 28.32 -0.35 26.93 -0.60 1954 7 19.52 -2.46 24.47 -1.17 28.00 -0.61 26.37 -0.74 1954 8 19.33 -1.68 23.83 -1.23 27.76 -0.73 25.73 -1.02 1954 9 18.95 -1.82 23.51 -1.43 27.61 -0.90 25.38 -1.33 1954 10 19.11 -1.96 23.63 -1.31 27.54 -0.97 25.51 -1.13 1954 11 20.27 -1.47 23.86 -1.19 27.53 -0.92 25.67 -0.93 1954 12 21.30 -1.61 23.79 -1.42 27.61 -0.74 25.37 -1.20 1955 1 23.75 -0.79 24.79 -0.86 27.60 -0.56 25.54 -1.02 1955 2 24.82 -1.10 25.76 -0.62 27.30 -0.77 25.90 -0.84 1955 3 25.14 -1.23 26.35 -0.78 27.37 -0.81 26.37 -0.86 1955 4 24.22 -1.35 26.32 -1.11 27.82 -0.57 26.66 -1.01 1955 5 22.16 -2.26 25.52 -1.58 28.03 -0.66 26.66 -1.11 1955 6 21.20 -1.87 25.08 -1.39 28.08 -0.59 26.56 -0.97 1955 7 20.46 -1.52 24.55 -1.10 28.14 -0.47 26.31 -0.80 1955 8 19.63 -1.38 23.69 -1.37 27.94 -0.55 25.60 -1.15 1955 9 19.24 -1.53 23.36 -1.57 27.71 -0.81 25.63 -1.07 1955 10 19.16 -1.91 22.71 -2.24 27.22 -1.29 24.66 -1.98 1955 11 19.84 -1.90 22.66 -2.39 26.79 -1.66 24.29 -2.32 1955 12 21.19 -1.71 23.08 -2.13 27.20 -1.15 24.58 -1.99 1956 1 23.24 -1.30 24.47 -1.18 27.18 -0.97 25.28 -1.27 1956 2 24.71 -1.20 25.80 -0.57 27.27 -0.81 26.00 -0.73 1956 3 25.90 -0.47 26.84 -0.29 27.49 -0.68 26.62 -0.61 1956 4 24.66 -0.91 27.12 -0.30 27.72 -0.68 26.96 -0.70 1956 5 23.14 -1.28 26.63 -0.47 28.11 -0.58 27.23 -0.54 1956 6 22.04 -1.03 25.87 -0.60 28.22 -0.46 26.99 -0.54 1956 7 21.47 -0.51 24.82 -0.82 28.07 -0.54 26.37 -0.73 1956 8 20.55 -0.46 24.16 -0.90 27.67 -0.82 25.91 -0.84 1956 9 19.89 -0.88 23.97 -0.97 27.71 -0.80 25.91 -0.79 1956 10 19.69 -1.39 24.01 -0.94 27.92 -0.59 25.83 -0.81 1956 11 20.57 -1.17 23.91 -1.14 28.02 -0.43 25.64 -0.97 1956 12 21.58 -1.32 24.06 -1.15 28.09 -0.26 25.71 -0.86 1957 1 23.13 -1.40 25.05 -0.60 28.03 -0.12 26.09 -0.47 1957 2 26.30 0.38 26.23 -0.15 28.10 0.02 26.58 -0.16 1957 3 27.63 1.26 27.55 0.42 28.33 0.16 27.65 0.41 1957 4 27.15 1.59 27.94 0.51 28.86 0.47 28.34 0.68 1957 5 26.72 2.30 27.81 0.71 29.15 0.46 28.59 0.82 1957 6 25.04 1.97 27.30 0.84 28.83 0.16 28.28 0.75 1957 7 23.83 1.85 26.95 1.31 28.73 0.12 28.11 1.00 1957 8 22.34 1.33 26.14 1.08 28.91 0.41 27.75 1.00 1957 9 21.80 1.03 25.71 0.77 28.96 0.45 27.54 0.84 1957 10 21.80 0.73 25.68 0.74 29.12 0.61 27.49 0.85 1957 11 22.39 0.65 26.23 1.18 29.12 0.67 27.79 1.19 1957 12 23.69 0.78 26.64 1.43 29.08 0.74 28.08 1.51 1958 1 24.89 0.35 27.04 1.38 29.26 1.10 28.37 1.81 1958 2 26.55 0.63 27.57 1.20 29.28 1.21 28.41 1.68 1958 3 27.09 0.72 27.85 0.72 29.04 0.86 28.34 1.11 1958 4 26.37 0.80 27.86 0.44 29.04 0.65 28.35 0.69 1958 5 24.71 0.29 27.39 0.28 29.21 0.52 28.35 0.57 1958 6 23.23 0.16 26.70 0.23 29.13 0.46 28.05 0.52 1958 7 22.31 0.33 25.73 0.09 28.89 0.28 27.48 0.37 1958 8 20.72 -0.29 24.97 -0.08 28.53 0.04 26.90 0.15 1958 9 20.62 -0.14 24.45 -0.48 28.50 -0.02 26.50 -0.20 1958 10 21.05 -0.02 24.65 -0.30 28.68 0.17 26.61 -0.03 1958 11 21.52 -0.22 25.06 0.01 28.79 0.34 26.89 0.29 1958 12 22.50 -0.40 25.02 -0.19 28.76 0.41 26.77 0.20 1959 1 23.97 -0.57 25.75 0.10 28.63 0.48 27.14 0.58 1959 2 25.90 -0.01 26.50 0.13 28.38 0.31 27.25 0.51 1959 3 26.94 0.56 27.17 0.04 28.48 0.31 27.55 0.32 1959 4 25.84 0.27 27.56 0.14 28.75 0.36 27.93 0.27 1959 5 24.23 -0.19 26.94 -0.16 28.85 0.16 27.80 0.03 1959 6 22.57 -0.50 26.07 -0.39 28.39 -0.28 27.34 -0.19 1959 7 21.50 -0.47 24.94 -0.71 28.30 -0.31 26.56 -0.55 1959 8 20.15 -0.86 24.40 -0.66 28.27 -0.22 26.32 -0.43 1959 9 20.23 -0.54 24.37 -0.57 28.19 -0.32 26.17 -0.53 1959 10 20.86 -0.21 24.67 -0.28 28.21 -0.30 26.42 -0.22 1959 11 21.88 0.14 24.74 -0.31 28.12 -0.34 26.32 -0.28 1959 12 22.55 -0.36 24.94 -0.27 28.07 -0.27 26.44 -0.13 1960 1 24.40 -0.14 25.34 -0.32 27.77 -0.38 26.25 -0.31 1960 2 25.59 -0.32 25.87 -0.50 27.49 -0.58 26.27 -0.47 1960 3 26.01 -0.36 26.75 -0.38 27.77 -0.40 26.97 -0.26 1960 4 24.66 -0.91 27.03 -0.39 28.08 -0.31 27.51 -0.16 1960 5 23.53 -0.89 26.66 -0.45 28.52 -0.18 27.62 -0.16 1960 6 21.83 -1.24 25.95 -0.52 28.35 -0.32 27.24 -0.29 1960 7 20.73 -1.25 25.38 -0.26 28.31 -0.30 27.07 -0.04 1960 8 20.10 -0.91 24.95 -0.11 28.19 -0.30 26.85 0.10 1960 9 20.56 -0.20 24.70 -0.24 28.32 -0.20 26.60 -0.11 1960 10 20.27 -0.80 24.24 -0.71 28.33 -0.17 26.35 -0.29 1960 11 20.93 -0.81 24.31 -0.75 28.37 -0.09 26.34 -0.26 1960 12 22.74 -0.17 24.73 -0.48 28.36 0.02 26.41 -0.16 1961 1 24.58 0.04 25.19 -0.46 27.94 -0.21 26.38 -0.17 1961 2 26.66 0.75 26.25 -0.13 27.85 -0.23 26.61 -0.13 1961 3 25.95 -0.42 26.83 -0.30 27.92 -0.26 27.00 -0.24 1961 4 25.17 -0.40 27.40 -0.02 27.96 -0.43 27.50 -0.16 1961 5 23.60 -0.82 27.08 -0.02 28.38 -0.31 27.85 0.08 1961 6 22.36 -0.71 26.37 -0.10 28.49 -0.18 27.86 0.33 1961 7 20.52 -1.46 24.95 -0.70 28.33 -0.28 27.16 0.05 1961 8 19.97 -1.04 24.20 -0.86 28.12 -0.37 26.43 -0.32 1961 9 19.70 -1.07 23.65 -1.29 28.19 -0.33 25.97 -0.73 1961 10 20.07 -1.01 23.89 -1.06 28.09 -0.42 25.94 -0.71 1961 11 21.09 -0.65 24.38 -0.67 28.26 -0.19 26.20 -0.40 1961 12 22.11 -0.80 24.75 -0.46 28.06 -0.28 26.20 -0.37 1962 1 24.02 -0.52 25.23 -0.42 27.66 -0.49 26.12 -0.43 1962 2 25.35 -0.56 26.07 -0.31 27.57 -0.51 26.28 -0.46 1962 3 24.47 -1.90 26.49 -0.64 27.83 -0.34 26.80 -0.44 1962 4 23.43 -2.14 26.59 -0.83 28.19 -0.20 27.22 -0.44 1962 5 23.03 -1.39 26.28 -0.82 28.23 -0.47 27.23 -0.54 1962 6 21.81 -1.26 25.83 -0.64 28.30 -0.37 27.18 -0.35 1962 7 20.60 -1.37 25.21 -0.44 28.24 -0.37 26.93 -0.17 1962 8 20.17 -0.84 24.72 -0.33 28.15 -0.34 26.52 -0.23 1962 9 20.02 -0.75 24.20 -0.74 28.11 -0.40 26.07 -0.63 1962 10 20.14 -0.93 24.23 -0.72 28.10 -0.41 26.06 -0.58 1962 11 20.99 -0.75 24.10 -0.95 28.12 -0.33 25.88 -0.72 1962 12 21.82 -1.09 24.12 -1.09 27.96 -0.38 25.75 -0.82 1963 1 23.81 -0.73 24.96 -0.69 27.74 -0.41 25.86 -0.69 1963 2 25.36 -0.55 25.91 -0.47 27.70 -0.38 26.39 -0.35 1963 3 26.02 -0.36 27.06 -0.07 28.01 -0.16 27.31 0.08 1963 4 24.67 -0.90 27.66 0.23 28.28 -0.11 27.87 0.20 1963 5 23.97 -0.45 27.10 0.00 28.40 -0.30 27.71 -0.06 1963 6 22.41 -0.66 26.74 0.27 28.38 -0.29 27.69 0.16 1963 7 21.80 -0.18 26.36 0.71 28.72 0.11 27.91 0.80 1963 8 21.31 0.30 25.80 0.74 29.01 0.51 27.63 0.88 1963 9 21.00 0.23 25.41 0.48 29.03 0.52 27.47 0.76 1963 10 21.13 0.06 25.58 0.63 29.07 0.56 27.56 0.92 1963 11 21.64 -0.10 25.83 0.78 29.11 0.66 27.61 1.00 1963 12 22.55 -0.35 26.08 0.87 28.87 0.52 27.71 1.14 1964 1 24.15 -0.39 26.07 0.42 28.70 0.55 27.43 0.87 1964 2 25.08 -0.83 26.41 0.04 28.39 0.31 27.27 0.53 1964 3 25.30 -1.08 26.76 -0.37 28.08 -0.09 27.10 -0.13 1964 4 24.61 -0.96 26.34 -1.09 28.24 -0.15 27.06 -0.60 1964 5 21.93 -2.49 25.56 -1.54 28.39 -0.30 26.94 -0.84 1964 6 21.44 -1.63 25.10 -1.37 28.06 -0.61 26.66 -0.87 1964 7 20.25 -1.72 24.82 -0.82 27.87 -0.74 26.42 -0.69 1964 8 19.48 -1.53 23.73 -1.33 27.64 -0.85 25.74 -1.01 1964 9 19.67 -1.10 23.89 -1.04 27.38 -1.13 25.49 -1.22 1964 10 19.79 -1.28 23.95 -0.99 27.38 -1.13 25.53 -1.11 1964 11 20.88 -0.86 24.04 -1.02 27.34 -1.11 25.40 -1.20 1964 12 21.83 -1.08 23.86 -1.35 27.25 -1.10 25.42 -1.15 1965 1 24.22 -0.32 24.84 -0.81 27.57 -0.59 25.78 -0.77 1965 2 26.17 0.26 25.96 -0.41 27.77 -0.31 26.37 -0.37 1965 3 26.71 0.33 27.07 -0.06 27.93 -0.24 27.12 -0.11 1965 4 27.01 1.45 27.66 0.23 28.06 -0.33 27.62 -0.05 1965 5 26.09 1.67 27.68 0.57 28.70 0.01 28.07 0.29 1965 6 24.60 1.54 27.24 0.78 29.02 0.35 28.19 0.66 1965 7 23.26 1.29 26.64 0.99 29.08 0.47 28.02 0.91 1965 8 22.54 1.53 26.25 1.20 29.19 0.70 28.07 1.32 1965 9 21.26 0.49 26.07 1.13 29.11 0.60 28.03 1.32 1965 10 21.57 0.50 26.15 1.21 29.30 0.80 28.26 1.62 1965 11 22.29 0.55 26.50 1.45 29.17 0.72 28.29 1.69 1965 12 23.35 0.44 26.59 1.38 29.03 0.68 28.07 1.50 1966 1 25.15 0.62 26.70 1.05 28.87 0.71 27.75 1.19 1966 2 25.88 -0.03 26.86 0.49 28.85 0.78 27.66 0.92 1966 3 25.35 -1.02 27.26 0.13 29.18 1.01 28.22 0.99 1966 4 24.26 -1.31 27.40 -0.03 28.99 0.60 28.24 0.58 1966 5 22.92 -1.50 26.28 -0.82 29.08 0.39 27.67 -0.10 1966 6 21.80 -1.27 26.08 -0.39 29.13 0.46 27.77 0.24 1966 7 20.85 -1.13 25.42 -0.22 29.03 0.42 27.42 0.31 1966 8 20.17 -0.84 24.51 -0.55 28.76 0.27 26.65 -0.10 1966 9 20.04 -0.73 24.17 -0.77 28.71 0.20 26.49 -0.22 1966 10 20.51 -0.56 24.42 -0.53 28.57 0.06 26.40 -0.25 1966 11 21.03 -0.71 24.26 -0.79 28.65 0.20 26.42 -0.19 1966 12 22.25 -0.66 24.30 -0.91 28.27 -0.07 26.21 -0.36 1967 1 23.66 -0.88 25.09 -0.56 27.69 -0.46 26.18 -0.38 1967 2 25.54 -0.37 26.09 -0.28 27.48 -0.60 26.35 -0.39 1967 3 25.55 -0.83 26.46 -0.67 27.78 -0.39 26.67 -0.56 1967 4 24.98 -0.59 26.46 -0.96 28.40 0.01 26.91 -0.76 1967 5 23.77 -0.65 26.69 -0.41 28.80 0.11 27.49 -0.28 1967 6 22.04 -1.03 26.40 -0.07 28.69 0.02 27.62 0.09 1967 7 21.10 -0.88 25.32 -0.32 28.62 0.01 27.17 0.06 1967 8 19.84 -1.17 24.30 -0.76 28.42 -0.07 26.62 -0.13 1967 9 19.08 -1.68 23.69 -1.25 28.19 -0.32 26.10 -0.60 1967 10 19.47 -1.60 23.80 -1.14 28.32 -0.19 26.19 -0.45 1967 11 20.20 -1.54 24.01 -1.04 28.43 -0.02 26.26 -0.34 1967 12 21.28 -1.62 24.14 -1.07 28.27 -0.08 26.10 -0.47 1968 1 23.19 -1.35 24.40 -1.25 27.92 -0.24 25.78 -0.78 1968 2 24.88 -1.04 25.00 -1.38 27.80 -0.28 25.79 -0.95 1968 3 25.11 -1.27 25.86 -1.27 27.87 -0.30 26.38 -0.85 1968 4 23.97 -1.60 26.57 -0.85 28.16 -0.24 27.09 -0.58 1968 5 22.44 -1.98 26.19 -0.92 28.30 -0.40 27.15 -0.62 1968 6 21.70 -1.36 26.22 -0.25 28.90 0.23 27.69 0.16 1968 7 21.25 -0.72 26.00 0.35 28.70 0.09 27.61 0.51 1968 8 20.97 -0.04 25.28 0.22 28.68 0.19 27.12 0.37 1968 9 21.23 0.46 24.96 0.02 28.66 0.15 26.90 0.19 1968 10 21.12 0.05 25.17 0.22 28.77 0.26 26.99 0.34 1968 11 21.68 -0.06 25.35 0.29 29.17 0.72 27.38 0.78 1968 12 23.20 0.29 25.79 0.58 29.17 0.83 27.42 0.85 1969 1 24.67 0.13 26.27 0.62 29.11 0.95 27.55 0.99 1969 2 25.56 -0.36 26.92 0.54 29.28 1.20 27.95 1.21 1969 3 27.09 0.72 27.63 0.50 28.99 0.82 28.02 0.79 1969 4 26.66 1.10 27.94 0.52 29.03 0.64 28.39 0.73 1969 5 26.07 1.65 27.92 0.81 29.21 0.52 28.50 0.73 1969 6 24.39 1.32 27.05 0.58 29.09 0.42 27.99 0.46 1969 7 22.47 0.50 25.84 0.19 28.85 0.24 27.30 0.20 1969 8 21.06 0.05 25.41 0.35 28.93 0.44 27.16 0.41 1969 9 20.88 0.12 25.46 0.52 29.14 0.63 27.34 0.63 1969 10 21.74 0.67 25.69 0.75 29.20 0.69 27.50 0.86 1969 11 22.47 0.73 25.75 0.70 29.20 0.75 27.35 0.74 1969 12 23.60 0.69 26.16 0.95 28.87 0.52 27.30 0.73 1970 1 25.02 0.48 26.38 0.73 28.57 0.42 27.05 0.50 1970 2 25.76 -0.16 26.52 0.14 28.57 0.50 27.02 0.29 1970 3 25.53 -0.85 26.97 -0.16 28.52 0.35 27.35 0.12 1970 4 24.76 -0.81 27.20 -0.23 28.73 0.34 27.96 0.29 1970 5 22.94 -1.48 26.44 -0.66 28.97 0.28 27.77 0.00 1970 6 21.27 -1.80 25.30 -1.17 28.75 0.08 27.14 -0.39 1970 7 19.69 -2.28 24.18 -1.47 28.66 0.05 26.48 -0.63 1970 8 19.27 -1.74 23.69 -1.37 28.16 -0.34 25.86 -0.89 1970 9 19.50 -1.26 23.89 -1.05 27.88 -0.64 25.86 -0.84 1970 10 20.16 -0.91 23.80 -1.14 27.86 -0.65 25.79 -0.85 1970 11 20.61 -1.13 23.78 -1.28 27.91 -0.54 25.83 -0.77 1970 12 21.77 -1.13 24.01 -1.20 27.44 -0.91 25.47 -1.10 1971 1 23.33 -1.21 24.19 -1.47 26.94 -1.21 25.09 -1.46 1971 2 24.58 -1.34 24.91 -1.47 27.07 -1.00 25.40 -1.34 1971 3 25.24 -1.13 26.01 -1.11 27.25 -0.92 26.10 -1.13 1971 4 24.95 -0.62 26.61 -0.81 27.63 -0.76 26.78 -0.89 1971 5 23.29 -1.13 26.26 -0.85 28.02 -0.67 27.06 -0.71 1971 6 21.60 -1.47 25.44 -1.03 28.03 -0.64 26.70 -0.83 1971 7 21.01 -0.96 24.78 -0.86 27.93 -0.68 26.29 -0.82 1971 8 19.97 -1.04 23.90 -1.16 27.84 -0.66 25.99 -0.76 1971 9 19.74 -1.03 23.92 -1.02 27.75 -0.77 25.94 -0.76 1971 10 19.88 -1.19 23.73 -1.21 27.80 -0.71 25.65 -0.99 1971 11 20.94 -0.80 23.93 -1.12 27.87 -0.58 25.69 -0.91 1971 12 21.99 -0.92 23.92 -1.29 27.98 -0.36 25.55 -1.02 1972 1 24.51 -0.03 25.02 -0.63 27.97 -0.19 25.84 -0.72 1972 2 26.66 0.75 26.04 -0.34 28.15 0.07 26.42 -0.32 1972 3 27.09 0.72 27.12 -0.01 28.36 0.19 27.21 -0.02 1972 4 26.25 0.68 27.78 0.35 28.82 0.43 27.93 0.26 1972 5 25.47 1.05 27.62 0.52 29.21 0.52 28.27 0.49 1972 6 25.01 1.95 27.37 0.90 29.19 0.52 28.23 0.70 1972 7 24.11 2.13 27.14 1.49 29.09 0.48 28.18 1.07 1972 8 23.42 2.41 26.86 1.81 28.92 0.43 28.10 1.35 1972 9 22.12 1.35 26.48 1.54 29.10 0.58 28.10 1.40 1972 10 22.58 1.51 26.83 1.89 29.44 0.93 28.46 1.82 1972 11 23.32 1.58 27.17 2.11 29.48 1.03 28.69 2.09 1972 12 24.89 1.99 27.60 2.39 29.35 1.00 28.81 2.24 1973 1 26.03 1.49 27.52 1.86 29.03 0.88 28.44 1.88 1973 2 26.48 0.56 27.24 0.87 28.77 0.70 28.00 1.26 1973 3 26.27 -0.10 27.37 0.24 28.52 0.35 27.71 0.48 1973 4 24.87 -0.70 26.87 -0.55 28.39 0.00 27.39 -0.28 1973 5 23.44 -0.98 26.06 -1.04 28.51 -0.18 27.13 -0.65 1973 6 21.76 -1.31 25.34 -1.12 28.23 -0.44 26.74 -0.79 1973 7 20.84 -1.14 24.31 -1.34 28.01 -0.60 25.95 -1.16 1973 8 19.47 -1.54 23.69 -1.36 27.65 -0.84 25.46 -1.29 1973 9 19.49 -1.28 23.56 -1.38 27.53 -0.98 25.32 -1.39 1973 10 19.80 -1.27 23.49 -1.46 27.32 -1.19 25.03 -1.61 1973 11 20.71 -1.03 23.35 -1.71 26.97 -1.48 24.50 -2.11 1973 12 21.74 -1.17 23.40 -1.81 26.69 -1.65 24.43 -2.14 1974 1 23.29 -1.25 23.93 -1.72 26.50 -1.65 24.52 -2.04 1974 2 24.87 -1.04 25.11 -1.27 26.87 -1.21 25.12 -1.61 1974 3 25.69 -0.68 26.28 -0.85 27.21 -0.96 25.91 -1.32 1974 4 25.28 -0.29 26.84 -0.58 27.46 -0.93 26.63 -1.03 1974 5 24.35 -0.07 26.56 -0.54 27.94 -0.75 26.84 -0.93 1974 6 22.48 -0.58 25.91 -0.55 27.97 -0.70 26.71 -0.82 1974 7 21.58 -0.40 25.30 -0.35 27.83 -0.78 26.48 -0.63 1974 8 20.73 -0.28 24.82 -0.24 27.97 -0.52 26.37 -0.38 1974 9 20.15 -0.62 24.38 -0.56 27.91 -0.61 26.23 -0.47 1974 10 19.88 -1.19 24.14 -0.81 27.74 -0.77 25.92 -0.73 1974 11 20.68 -1.06 24.07 -0.98 27.72 -0.73 25.63 -0.97 1974 12 21.41 -1.50 24.31 -0.90 27.61 -0.74 25.70 -0.87 1975 1 23.55 -0.99 25.34 -0.31 27.86 -0.30 26.18 -0.37 1975 2 24.95 -0.97 25.74 -0.64 27.96 -0.12 26.22 -0.52 1975 3 26.06 -0.32 26.49 -0.64 27.76 -0.41 26.44 -0.79 1975 4 25.53 -0.04 27.08 -0.34 27.83 -0.56 26.99 -0.67 1975 5 23.71 -0.72 26.27 -0.83 27.95 -0.74 26.97 -0.80 1975 6 21.84 -1.22 25.29 -1.18 27.71 -0.96 26.29 -1.24 1975 7 21.05 -0.92 24.72 -0.93 27.57 -1.04 25.98 -1.12 1975 8 19.97 -1.04 24.15 -0.91 27.29 -1.21 25.47 -1.28 1975 9 19.14 -1.63 23.67 -1.27 26.91 -1.60 25.20 -1.50 1975 10 19.17 -1.90 23.40 -1.54 26.76 -1.75 24.87 -1.77 1975 11 19.44 -2.30 23.73 -1.33 26.80 -1.66 25.10 -1.50 1975 12 21.05 -1.85 23.39 -1.82 26.84 -1.50 24.76 -1.81 1976 1 23.51 -1.02 23.91 -1.74 26.92 -1.23 24.72 -1.84 1976 2 25.36 -0.56 25.31 -1.07 27.19 -0.89 25.58 -1.16 1976 3 25.88 -0.49 26.46 -0.66 27.56 -0.61 26.61 -0.62 1976 4 25.72 0.15 26.94 -0.49 27.98 -0.42 26.99 -0.67 1976 5 25.11 0.69 26.98 -0.12 28.32 -0.37 27.27 -0.50 1976 6 24.46 1.39 26.95 0.48 28.32 -0.35 27.34 -0.19 1976 7 23.30 1.33 26.29 0.65 28.42 -0.19 27.26 0.16 1976 8 21.91 0.90 25.87 0.81 28.34 -0.15 27.09 0.34 1976 9 21.56 0.79 25.76 0.83 28.50 -0.01 27.12 0.42 1976 10 21.69 0.62 25.97 1.03 28.82 0.31 27.48 0.84 1976 11 22.14 0.40 25.93 0.87 28.92 0.47 27.42 0.82 1976 12 23.29 0.39 25.91 0.70 28.45 0.10 27.17 0.60 1977 1 24.93 0.39 26.57 0.92 28.38 0.23 27.31 0.76 1977 2 25.79 -0.13 27.03 0.65 28.11 0.04 27.15 0.41 1977 3 26.13 -0.24 27.51 0.38 28.28 0.10 27.57 0.34 1977 4 25.29 -0.28 27.02 -0.41 28.50 0.10 27.61 -0.05 1977 5 23.88 -0.54 27.04 -0.06 28.78 0.09 27.97 0.19 1977 6 22.70 -0.37 26.56 0.09 28.97 0.30 27.96 0.43 1977 7 21.63 -0.34 25.70 0.05 29.13 0.52 27.56 0.45 1977 8 20.25 -0.76 24.81 -0.25 28.87 0.38 26.93 0.18 1977 9 19.82 -0.95 24.97 0.04 28.98 0.47 27.21 0.50 1977 10 20.61 -0.46 25.34 0.39 28.96 0.45 27.37 0.73 1977 11 21.29 -0.45 25.41 0.35 29.10 0.65 27.28 0.67 1977 12 22.47 -0.44 25.54 0.33 29.11 0.76 27.37 0.80 1978 1 24.32 -0.22 25.92 0.27 28.80 0.65 27.32 0.77 1978 2 25.77 -0.15 26.46 0.09 28.48 0.41 27.17 0.43 1978 3 25.39 -0.98 26.83 -0.30 28.26 0.09 27.24 0.01 1978 4 24.73 -0.84 26.66 -0.77 28.28 -0.11 27.26 -0.41 1978 5 23.15 -1.27 26.13 -0.97 28.72 0.03 27.36 -0.42 1978 6 21.89 -1.18 25.68 -0.79 28.56 -0.11 27.16 -0.37 1978 7 21.01 -0.96 25.08 -0.57 28.26 -0.35 26.81 -0.29 1978 8 19.66 -1.35 24.35 -0.71 28.25 -0.24 26.23 -0.52 1978 9 19.98 -0.79 24.33 -0.61 28.43 -0.08 26.20 -0.50 1978 10 20.22 -0.86 24.72 -0.22 28.35 -0.16 26.40 -0.24 1978 11 21.62 -0.12 24.94 -0.11 28.37 -0.09 26.43 -0.17 1978 12 22.94 0.03 25.34 0.13 28.27 -0.08 26.48 -0.09 1979 1 24.71 0.17 25.43 -0.23 28.40 0.25 26.45 -0.11 1979 2 25.60 -0.32 26.19 -0.19 28.22 0.14 26.60 -0.13 1979 3 25.93 -0.44 27.25 0.12 28.26 0.09 27.48 0.25 1979 4 25.58 0.01 27.66 0.23 28.35 -0.05 27.87 0.20 1979 5 24.52 0.10 27.26 0.16 28.70 0.01 27.74 -0.03 1979 6 23.28 0.22 26.54 0.08 28.74 0.07 27.55 0.02 1979 7 21.79 -0.19 25.56 -0.09 28.66 0.05 26.95 -0.15 1979 8 21.05 0.04 25.29 0.23 28.36 -0.13 26.77 0.02 1979 9 21.15 0.39 25.58 0.65 28.67 0.16 27.15 0.45 1979 10 21.43 0.36 25.54 0.60 28.81 0.31 27.01 0.36 1979 11 21.95 0.21 25.58 0.53 28.87 0.42 27.08 0.48 1979 12 22.97 0.07 25.68 0.47 28.79 0.45 27.09 0.52 1980 1 24.35 -0.18 26.12 0.47 28.62 0.47 27.10 0.54 1980 2 25.73 -0.18 26.52 0.15 28.47 0.39 27.03 0.29 1980 3 26.46 0.08 27.05 -0.07 28.51 0.34 27.38 0.15 1980 4 25.71 0.14 27.33 -0.10 28.70 0.30 27.78 0.12 1980 5 24.46 0.04 27.15 0.05 29.08 0.39 27.99 0.21 1980 6 22.88 -0.19 26.76 0.29 29.15 0.48 27.99 0.46 1980 7 21.26 -0.71 25.65 0.00 28.95 0.34 27.35 0.24 1980 8 20.57 -0.44 24.87 -0.19 28.47 -0.02 26.62 -0.13 1980 9 20.45 -0.32 24.86 -0.08 28.50 -0.01 26.57 -0.13 1980 10 20.43 -0.64 24.62 -0.32 28.66 0.15 26.51 -0.13 1980 11 21.23 -0.51 25.07 0.02 28.58 0.13 26.63 0.03 1980 12 22.34 -0.56 25.28 0.07 28.44 0.10 26.58 0.01 1981 1 22.98 -1.56 25.05 -0.61 28.08 -0.08 26.17 -0.39 1981 2 24.90 -1.01 25.63 -0.75 27.94 -0.14 26.13 -0.60 1981 3 25.94 -0.43 26.74 -0.39 28.08 -0.09 26.75 -0.48 1981 4 24.89 -0.68 27.10 -0.32 28.17 -0.22 27.32 -0.34 1981 5 23.90 -0.52 26.74 -0.37 28.34 -0.35 27.41 -0.36 1981 6 22.57 -0.50 26.26 -0.20 28.36 -0.31 27.35 -0.18 1981 7 21.10 -0.87 25.26 -0.39 28.29 -0.32 26.66 -0.44 1981 8 20.03 -0.98 24.43 -0.63 28.22 -0.28 26.25 -0.50 1981 9 20.09 -0.68 24.74 -0.20 28.30 -0.22 26.54 -0.17 1981 10 20.58 -0.49 24.92 -0.03 28.45 -0.06 26.52 -0.12 1981 11 21.26 -0.48 24.83 -0.22 28.47 0.02 26.41 -0.19 1981 12 22.60 -0.30 25.23 0.03 28.43 0.09 26.47 -0.10 1982 1 24.36 -0.18 25.96 0.30 28.19 0.03 26.69 0.14 1982 2 25.42 -0.49 26.55 0.17 28.04 -0.04 26.71 -0.02 1982 3 25.40 -0.98 27.06 -0.07 28.37 0.19 27.36 0.13 1982 4 24.96 -0.61 27.52 0.10 28.78 0.39 27.90 0.24 1982 5 24.21 -0.21 27.66 0.56 29.21 0.52 28.42 0.65 1982 6 23.35 0.29 27.31 0.84 29.20 0.53 28.32 0.79 1982 7 22.50 0.52 26.33 0.68 28.83 0.22 27.65 0.54 1982 8 21.89 0.88 26.21 1.15 28.81 0.32 27.64 0.89 1982 9 22.04 1.27 26.82 1.88 28.98 0.47 28.21 1.50 1982 10 22.88 1.81 27.31 2.37 29.37 0.86 28.68 2.04 1982 11 24.57 2.83 27.68 2.63 29.18 0.73 28.72 2.11 1982 12 25.89 2.98 28.23 3.02 29.13 0.79 28.94 2.37 1983 1 27.25 2.71 28.60 2.95 28.96 0.81 28.93 2.37 1983 2 28.23 2.32 28.63 2.26 28.80 0.73 28.75 2.01 1983 3 28.85 2.48 28.91 1.78 28.81 0.64 28.76 1.53 1983 4 28.82 3.25 29.06 1.63 28.81 0.42 28.74 1.07 1983 5 28.37 3.95 28.98 1.87 29.21 0.51 28.82 1.05 1983 6 27.43 4.36 28.19 1.72 28.98 0.31 28.27 0.74 1983 7 25.73 3.75 26.66 1.01 28.64 0.03 27.18 0.07 1983 8 23.88 2.87 25.78 0.72 28.35 -0.14 26.51 -0.24 1983 9 22.26 1.49 25.02 0.09 28.05 -0.46 26.18 -0.52 1983 10 22.22 1.15 24.48 -0.46 27.92 -0.59 25.75 -0.89 1983 11 22.21 0.47 24.19 -0.86 27.73 -0.73 25.54 -1.06 1983 12 23.19 0.29 24.58 -0.63 27.75 -0.59 25.82 -0.75 1984 1 24.32 -0.21 25.30 -0.35 27.64 -0.51 26.14 -0.42 1984 2 25.12 -0.79 26.43 0.05 27.60 -0.48 26.72 -0.02 1984 3 25.75 -0.63 27.14 0.01 27.59 -0.58 27.05 -0.19 1984 4 25.40 -0.17 27.24 -0.18 27.78 -0.61 27.30 -0.37 1984 5 23.58 -0.84 26.38 -0.72 28.19 -0.51 27.31 -0.47 1984 6 22.30 -0.77 25.50 -0.97 28.38 -0.29 26.95 -0.58 1984 7 21.53 -0.44 25.04 -0.60 28.47 -0.14 26.84 -0.26 1984 8 20.64 -0.37 24.81 -0.25 28.30 -0.19 26.67 -0.08 1984 9 20.73 -0.04 24.63 -0.30 28.26 -0.25 26.43 -0.27 1984 10 20.62 -0.45 24.34 -0.61 28.19 -0.32 26.15 -0.49 1984 11 21.70 -0.04 24.23 -0.83 27.92 -0.53 25.60 -1.00 1984 12 22.47 -0.44 24.10 -1.11 27.60 -0.74 25.38 -1.19 1985 1 23.84 -0.70 24.68 -0.97 27.55 -0.60 25.58 -0.98 1985 2 24.83 -1.08 25.65 -0.72 27.68 -0.39 26.11 -0.63 1985 3 25.60 -0.77 26.45 -0.68 27.59 -0.58 26.53 -0.71 1985 4 24.28 -1.29 26.63 -0.79 27.78 -0.61 26.93 -0.74 1985 5 22.67 -1.75 26.26 -0.84 27.99 -0.70 27.09 -0.68 1985 6 21.84 -1.23 25.66 -0.81 28.09 -0.58 26.92 -0.61 1985 7 20.75 -1.23 24.72 -0.93 28.32 -0.30 26.64 -0.46 1985 8 19.90 -1.11 24.12 -0.94 28.34 -0.16 26.28 -0.47 1985 9 19.86 -0.91 24.05 -0.88 28.32 -0.20 26.08 -0.62 1985 10 20.26 -0.81 24.23 -0.71 28.40 -0.11 26.22 -0.42 1985 11 20.97 -0.77 24.40 -0.65 28.48 0.03 26.39 -0.22 1985 12 22.49 -0.41 24.56 -0.65 28.39 0.05 26.19 -0.38 1986 1 24.31 -0.23 24.99 -0.67 27.96 -0.19 26.04 -0.52 1986 2 25.90 -0.02 26.04 -0.33 27.95 -0.13 26.26 -0.48 1986 3 25.78 -0.59 26.92 -0.21 28.11 -0.06 26.99 -0.24 1986 4 24.86 -0.71 27.30 -0.13 28.23 -0.17 27.65 -0.02 1986 5 23.35 -1.07 26.78 -0.32 28.53 -0.17 27.58 -0.20 1986 6 22.03 -1.04 26.27 -0.20 28.76 0.09 27.56 0.03 1986 7 21.64 -0.33 25.82 0.18 28.75 0.14 27.39 0.28 1986 8 21.07 0.06 25.38 0.33 28.95 0.46 27.21 0.46 1986 9 21.13 0.36 25.38 0.44 29.20 0.69 27.35 0.65 1986 10 21.46 0.39 25.65 0.70 29.27 0.76 27.60 0.95 1986 11 22.17 0.43 25.94 0.88 29.33 0.88 27.67 1.07 1986 12 23.53 0.63 26.26 1.05 28.97 0.63 27.75 1.18 1987 1 25.60 1.07 26.74 1.09 28.81 0.65 27.82 1.27 1987 2 27.02 1.10 27.60 1.23 28.82 0.74 28.02 1.29 1987 3 27.89 1.51 28.38 1.26 28.95 0.77 28.49 1.26 1987 4 26.95 1.38 28.63 1.20 28.97 0.58 28.68 1.02 1987 5 25.96 1.53 28.30 1.19 29.24 0.55 28.70 0.93 1987 6 24.04 0.97 27.51 1.04 29.44 0.77 28.67 1.14 1987 7 23.00 1.03 26.94 1.30 29.37 0.76 28.52 1.42 1987 8 21.92 0.91 26.59 1.53 29.40 0.91 28.48 1.73 1987 9 22.00 1.23 26.62 1.68 29.47 0.96 28.42 1.71 1987 10 22.54 1.47 26.19 1.25 29.67 1.17 28.08 1.43 1987 11 22.84 1.10 26.21 1.16 29.53 1.08 27.94 1.34 1987 12 23.51 0.60 26.33 1.12 29.25 0.90 27.60 1.03 1988 1 24.76 0.22 26.47 0.82 28.97 0.81 27.49 0.94 1988 2 25.74 -0.18 26.43 0.05 28.54 0.46 27.01 0.28 1988 3 25.71 -0.66 27.10 -0.03 28.46 0.29 27.45 0.22 1988 4 24.68 -0.89 27.01 -0.41 28.41 0.02 27.56 -0.10 1988 5 23.18 -1.24 25.88 -1.22 28.26 -0.43 26.97 -0.80 1988 6 21.66 -1.41 24.61 -1.86 28.00 -0.67 26.21 -1.32 1988 7 20.59 -1.38 23.79 -1.85 27.95 -0.67 25.66 -1.45 1988 8 19.63 -1.38 23.71 -1.35 28.07 -0.43 25.71 -1.04 1988 9 19.44 -1.32 23.88 -1.06 27.90 -0.61 25.72 -0.99 1988 10 19.83 -1.24 23.41 -1.53 27.12 -1.39 24.82 -1.82 1988 11 20.96 -0.78 23.45 -1.61 27.06 -1.39 24.73 -1.87 1988 12 22.25 -0.66 23.50 -1.71 26.85 -1.49 24.68 -1.89 1989 1 24.36 -0.18 24.27 -1.38 26.63 -1.52 24.70 -1.86 1989 2 26.02 0.10 25.49 -0.89 26.75 -1.33 25.34 -1.40 1989 3 26.21 -0.17 26.23 -0.90 27.25 -0.93 26.12 -1.12 1989 4 25.54 -0.03 26.85 -0.57 27.71 -0.68 26.83 -0.83 1989 5 23.36 -1.06 26.58 -0.53 28.24 -0.46 27.18 -0.60 1989 6 22.14 -0.93 26.42 -0.05 28.12 -0.55 27.23 -0.30 1989 7 21.27 -0.70 25.32 -0.32 28.23 -0.38 26.80 -0.31 1989 8 20.86 -0.15 24.73 -0.33 28.10 -0.39 26.43 -0.32 1989 9 20.17 -0.60 24.67 -0.27 28.37 -0.14 26.46 -0.24 1989 10 20.52 -0.55 24.61 -0.34 28.27 -0.24 26.31 -0.34 1989 11 21.44 -0.30 24.56 -0.49 28.10 -0.36 26.29 -0.31 1989 12 22.61 -0.30 24.92 -0.29 28.51 0.17 26.58 0.01 1990 1 24.22 -0.32 25.45 -0.20 28.49 0.33 26.63 0.07 1990 2 26.17 0.26 26.54 0.16 28.58 0.50 27.03 0.29 1990 3 26.15 -0.22 27.01 -0.12 28.69 0.51 27.39 0.16 1990 4 25.15 -0.42 27.56 0.14 28.83 0.44 27.94 0.27 1990 5 24.14 -0.29 27.42 0.32 28.93 0.23 28.08 0.30 1990 6 22.76 -0.31 26.55 0.08 28.97 0.30 27.63 0.10 1990 7 21.36 -0.61 25.82 0.18 29.01 0.40 27.42 0.31 1990 8 20.70 -0.31 25.32 0.26 28.99 0.49 27.16 0.41 1990 9 20.28 -0.49 25.15 0.21 28.92 0.41 26.96 0.25 1990 10 20.40 -0.67 24.96 0.02 29.10 0.59 27.01 0.37 1990 11 21.19 -0.55 25.00 -0.06 29.13 0.68 26.88 0.28 1990 12 22.29 -0.61 25.23 0.02 29.16 0.81 26.96 0.39 1991 1 23.99 -0.55 25.84 0.19 28.96 0.81 27.07 0.51 1991 2 25.59 -0.33 26.50 0.12 28.71 0.63 27.12 0.38 1991 3 26.31 -0.06 27.12 -0.01 28.58 0.40 27.38 0.15 1991 4 25.15 -0.42 27.65 0.22 28.94 0.55 28.10 0.44 1991 5 24.44 0.02 27.64 0.54 29.37 0.68 28.36 0.58 1991 6 23.28 0.21 27.43 0.96 29.28 0.61 28.41 0.88 1991 7 22.39 0.42 26.75 1.10 29.27 0.66 28.17 1.07 1991 8 21.39 0.38 25.71 0.65 29.23 0.74 27.73 0.98 1991 9 21.22 0.45 25.32 0.39 29.24 0.73 27.42 0.71 1991 10 21.73 0.66 25.63 0.69 29.44 0.93 27.62 0.98 1991 11 22.40 0.66 26.10 1.04 29.40 0.95 27.93 1.33 1991 12 23.75 0.85 26.42 1.21 29.46 1.12 28.32 1.75 1992 1 25.02 0.48 27.03 1.38 29.03 0.87 28.36 1.81 1992 2 26.62 0.71 27.61 1.23 29.00 0.92 28.43 1.69 1992 3 27.72 1.34 28.22 1.09 29.03 0.86 28.66 1.42 1992 4 27.58 2.01 28.79 1.37 29.16 0.77 29.05 1.39 1992 5 26.44 2.02 28.49 1.38 29.39 0.70 29.01 1.24 1992 6 23.86 0.79 27.28 0.82 29.28 0.60 28.35 0.82 1992 7 21.84 -0.14 25.84 0.19 29.15 0.54 27.56 0.45 1992 8 20.87 -0.14 25.11 0.06 28.92 0.43 26.96 0.21 1992 9 20.85 0.08 24.90 -0.03 28.83 0.32 26.66 -0.04 1992 10 21.15 0.08 24.65 -0.29 28.69 0.18 26.39 -0.25 1992 11 21.84 0.10 24.77 -0.29 28.67 0.21 26.60 0.00 1992 12 22.79 -0.12 24.97 -0.24 28.82 0.47 26.72 0.15 1993 1 24.68 0.15 25.79 0.14 28.71 0.56 26.87 0.31 1993 2 26.46 0.55 26.94 0.57 28.37 0.29 27.20 0.46 1993 3 27.07 0.69 27.64 0.51 28.50 0.32 27.71 0.48 1993 4 26.84 1.28 28.43 1.00 28.78 0.38 28.50 0.83 1993 5 25.60 1.18 28.33 1.23 29.03 0.34 28.70 0.93 1993 6 24.11 1.04 27.03 0.56 29.03 0.36 28.08 0.55 1993 7 22.61 0.64 26.19 0.54 29.01 0.40 27.59 0.49 1993 8 21.65 0.64 25.52 0.46 28.86 0.37 27.01 0.26 1993 9 21.11 0.34 25.25 0.31 29.02 0.51 27.14 0.43 1993 10 21.71 0.64 25.28 0.33 28.84 0.33 27.01 0.36 1993 11 22.07 0.33 25.20 0.14 28.88 0.43 26.92 0.32 1993 12 22.86 -0.05 25.30 0.09 28.85 0.50 26.79 0.22 1994 1 24.56 0.02 25.84 0.18 28.54 0.39 26.74 0.19 1994 2 25.89 -0.03 26.40 0.02 28.27 0.20 26.97 0.23 1994 3 25.75 -0.62 26.88 -0.24 28.46 0.28 27.52 0.29 1994 4 24.49 -1.08 27.15 -0.28 28.82 0.43 28.13 0.46 1994 5 23.52 -0.90 27.17 0.06 29.20 0.51 28.27 0.50 1994 6 22.31 -0.76 26.63 0.17 29.27 0.60 28.09 0.56 1994 7 21.17 -0.80 25.61 -0.04 29.49 0.88 27.68 0.57 1994 8 20.22 -0.79 24.93 -0.13 29.51 1.02 27.40 0.65 1994 9 20.65 -0.12 24.97 0.03 29.38 0.87 27.25 0.54 1994 10 22.04 0.97 25.65 0.70 29.51 1.00 27.53 0.89 1994 11 22.27 0.53 26.17 1.12 29.60 1.15 27.95 1.35 1994 12 23.75 0.85 26.23 1.02 29.56 1.22 28.02 1.45 1995 1 25.48 0.94 26.51 0.86 29.24 1.09 27.74 1.18 1995 2 26.25 0.33 26.90 0.52 29.16 1.09 27.67 0.94 1995 3 26.09 -0.28 27.26 0.13 29.23 1.05 27.91 0.68 1995 4 24.32 -1.25 27.20 -0.23 29.26 0.87 28.17 0.50 1995 5 23.37 -1.05 26.55 -0.55 29.36 0.67 27.87 0.10 1995 6 22.43 -0.64 26.30 -0.17 29.21 0.54 27.80 0.27 1995 7 21.42 -0.55 25.65 0.00 29.03 0.42 27.24 0.14 1995 8 20.46 -0.55 24.36 -0.70 28.81 0.32 26.42 -0.33 1995 9 20.50 -0.27 24.05 -0.89 28.59 0.08 26.20 -0.50 1995 10 20.62 -0.45 24.16 -0.78 28.52 0.01 26.08 -0.57 1995 11 21.49 -0.25 24.22 -0.84 28.32 -0.13 25.82 -0.78 1995 12 22.03 -0.87 24.45 -0.76 28.13 -0.22 25.86 -0.71 1996 1 23.81 -0.73 25.06 -0.59 27.89 -0.27 25.83 -0.73 1996 2 25.52 -0.40 25.84 -0.54 27.61 -0.46 25.98 -0.76 1996 3 26.28 -0.09 26.75 -0.38 27.91 -0.27 26.73 -0.50 1996 4 24.00 -1.57 26.83 -0.59 28.25 -0.15 27.42 -0.25 1996 5 23.11 -1.31 26.63 -0.47 28.62 -0.07 27.65 -0.12 1996 6 21.66 -1.41 26.10 -0.37 28.71 0.04 27.49 -0.04 1996 7 20.72 -1.26 25.29 -0.36 28.65 0.04 27.02 -0.09 1996 8 20.23 -0.78 24.78 -0.28 28.62 0.12 26.75 0.00 1996 9 20.43 -0.34 24.53 -0.41 28.57 0.05 26.44 -0.27 1996 10 20.52 -0.55 24.52 -0.42 28.62 0.11 26.49 -0.15 1996 11 20.77 -0.97 24.56 -0.49 28.62 0.17 26.43 -0.17 1996 12 21.68 -1.22 24.31 -0.90 28.52 0.18 26.13 -0.44 1997 1 23.70 -0.83 24.80 -0.85 28.42 0.26 26.09 -0.47 1997 2 26.08 0.16 25.78 -0.60 28.53 0.45 26.46 -0.28 1997 3 27.17 0.80 26.97 -0.16 28.73 0.56 27.13 -0.10 1997 4 26.74 1.17 27.50 0.07 29.33 0.94 27.97 0.31 1997 5 26.77 2.35 28.03 0.92 29.51 0.82 28.63 0.85 1997 6 26.15 3.08 28.23 1.76 29.37 0.70 28.86 1.33 1997 7 25.59 3.61 27.99 2.34 29.41 0.80 28.86 1.76 1997 8 24.95 3.94 27.75 2.69 29.31 0.82 28.69 1.94 1997 9 24.69 3.92 27.82 2.88 29.54 1.03 28.89 2.19 1997 10 24.64 3.57 28.09 3.15 29.51 1.00 29.14 2.50 1997 11 25.85 4.11 28.40 3.35 29.45 1.00 29.13 2.52 1997 12 27.08 4.18 28.53 3.32 29.27 0.93 29.04 2.48 1998 1 28.12 3.58 28.71 3.06 29.06 0.91 28.98 2.43 1998 2 28.82 2.90 28.86 2.48 28.90 0.82 28.71 1.97 1998 3 29.24 2.87 29.10 1.97 28.83 0.66 28.66 1.43 1998 4 28.45 2.88 29.16 1.74 28.77 0.38 28.64 0.98 1998 5 27.36 2.94 28.53 1.42 28.87 0.18 28.47 0.70 1998 6 25.19 2.12 27.04 0.57 28.46 -0.21 27.41 -0.12 1998 7 23.61 1.64 25.60 -0.05 28.30 -0.31 26.52 -0.59 1998 8 22.27 1.26 24.78 -0.28 27.99 -0.50 25.91 -0.84 1998 9 21.31 0.54 24.19 -0.75 27.83 -0.68 25.66 -1.05 1998 10 21.37 0.30 24.08 -0.86 27.51 -1.00 25.46 -1.19 1998 11 21.60 -0.14 24.40 -0.66 27.41 -1.04 25.48 -1.12 1998 12 22.81 -0.10 24.25 -0.96 27.19 -1.16 25.12 -1.45 1999 1 24.23 -0.31 24.41 -1.24 26.89 -1.26 24.96 -1.59 1999 2 25.73 -0.19 25.56 -0.82 26.90 -1.18 25.58 -1.16 1999 3 26.47 0.10 26.81 -0.32 27.14 -1.04 26.43 -0.80 1999 4 24.53 -1.04 26.90 -0.52 27.66 -0.73 26.92 -0.75 1999 5 23.64 -0.78 26.55 -0.56 28.14 -0.55 26.99 -0.78 1999 6 22.09 -0.98 25.89 -0.58 28.05 -0.62 26.66 -0.87 1999 7 21.36 -0.61 25.03 -0.62 27.89 -0.72 26.28 -0.83 1999 8 20.67 -0.34 24.36 -0.70 27.77 -0.73 25.82 -0.93 1999 9 20.08 -0.69 23.91 -1.03 27.96 -0.56 25.77 -0.94 1999 10 20.46 -0.61 23.77 -1.18 27.93 -0.58 25.62 -1.03 1999 11 20.62 -1.12 23.72 -1.34 27.54 -0.91 25.20 -1.40 1999 12 22.42 -0.48 23.76 -1.45 27.30 -1.05 24.97 -1.60 2000 1 24.01 -0.53 24.12 -1.53 27.17 -0.99 24.88 -1.68 2000 2 25.38 -0.54 25.35 -1.03 26.88 -1.20 25.23 -1.50 2000 3 25.67 -0.70 26.78 -0.35 26.95 -1.22 26.27 -0.96 2000 4 25.53 -0.04 27.46 0.04 27.40 -0.99 26.99 -0.68 2000 5 24.27 -0.15 26.85 -0.26 27.90 -0.79 27.10 -0.67 2000 6 22.93 -0.13 26.01 -0.46 28.25 -0.42 26.97 -0.56 2000 7 21.47 -0.50 25.24 -0.41 28.41 -0.20 26.69 -0.41 2000 8 20.07 -0.94 24.74 -0.31 28.28 -0.21 26.46 -0.29 2000 9 20.64 -0.13 24.73 -0.21 28.37 -0.14 26.32 -0.39 2000 10 20.90 -0.17 24.60 -0.34 28.27 -0.24 26.11 -0.53 2000 11 20.67 -1.07 24.43 -0.62 28.06 -0.39 26.01 -0.59 2000 12 22.08 -0.83 24.73 -0.48 27.66 -0.68 25.83 -0.74 2001 1 24.24 -0.29 25.37 -0.29 27.49 -0.66 25.93 -0.63 2001 2 26.11 0.20 26.38 0.01 27.38 -0.70 26.24 -0.49 2001 3 26.89 0.52 27.31 0.18 27.68 -0.50 26.89 -0.35 2001 4 25.99 0.42 27.52 0.09 28.16 -0.23 27.38 -0.28 2001 5 23.98 -0.44 27.21 0.11 28.65 -0.04 27.70 -0.08 2001 6 22.71 -0.36 26.44 -0.03 28.91 0.24 27.60 0.07 2001 7 21.48 -0.50 25.63 -0.02 29.12 0.51 27.37 0.26 2001 8 20.24 -0.77 25.03 -0.03 28.98 0.48 26.95 0.20 2001 9 19.73 -1.04 24.49 -0.45 29.11 0.60 26.74 0.04 2001 10 20.14 -0.93 24.59 -0.36 29.08 0.58 26.66 0.02 2001 11 20.68 -1.06 24.53 -0.53 28.93 0.48 26.47 -0.13 2001 12 21.73 -1.17 24.79 -0.42 28.77 0.42 26.32 -0.25 2002 1 24.09 -0.45 25.23 -0.42 28.84 0.69 26.49 -0.07 2002 2 26.23 0.31 26.22 -0.15 28.70 0.63 26.83 0.09 2002 3 27.39 1.01 27.51 0.38 28.62 0.44 27.48 0.25 2002 4 26.44 0.87 27.71 0.28 29.14 0.75 28.00 0.34 2002 5 25.29 0.87 27.68 0.58 29.61 0.92 28.44 0.66 2002 6 23.28 0.21 27.30 0.83 29.58 0.91 28.48 0.95 2002 7 21.64 -0.34 26.31 0.67 29.53 0.92 28.03 0.93 2002 8 21.32 0.31 25.62 0.56 29.41 0.92 27.67 0.92 2002 9 21.42 0.65 25.69 0.75 29.41 0.90 27.75 1.05 2002 10 21.85 0.78 25.94 1.00 29.52 1.01 27.95 1.31 2002 11 22.85 1.11 26.43 1.37 29.66 1.21 28.14 1.54 2002 12 24.05 1.15 26.62 1.41 29.57 1.23 28.08 1.52 2003 1 25.01 0.47 26.68 1.02 29.09 0.94 27.68 1.12 2003 2 26.27 0.35 27.01 0.63 28.94 0.86 27.63 0.89 2003 3 26.91 0.53 27.48 0.35 28.95 0.78 27.84 0.61 2003 4 25.41 -0.15 27.22 -0.20 28.94 0.55 27.75 0.08 2003 5 23.24 -1.18 26.44 -0.66 29.01 0.32 27.42 -0.36 2003 6 22.15 -0.91 26.20 -0.27 29.17 0.50 27.56 0.03 2003 7 21.50 -0.48 26.16 0.52 29.19 0.58 27.66 0.55 2003 8 21.25 0.24 25.65 0.59 29.09 0.59 27.32 0.57 2003 9 20.75 -0.02 25.20 0.26 29.05 0.54 27.10 0.40 2003 10 21.70 0.63 25.58 0.63 29.22 0.71 27.33 0.69 2003 11 22.33 0.59 25.85 0.80 29.08 0.62 27.13 0.53 2003 12 23.60 0.70 26.07 0.86 28.97 0.63 27.04 0.47 2004 1 25.09 0.55 26.23 0.58 28.71 0.56 26.90 0.35 2004 2 26.47 0.55 26.81 0.44 28.61 0.53 27.04 0.31 2004 3 26.12 -0.26 27.47 0.34 28.53 0.36 27.38 0.15 2004 4 25.27 -0.30 27.62 0.19 28.82 0.43 27.91 0.24 2004 5 23.44 -0.98 26.96 -0.14 29.18 0.49 28.02 0.24 2004 6 22.54 -0.53 26.55 0.08 29.24 0.57 27.93 0.40 2004 7 21.26 -0.72 25.94 0.30 29.36 0.75 27.86 0.75 2004 8 20.79 -0.22 25.47 0.41 29.47 0.98 27.67 0.92 2004 9 20.83 0.06 25.42 0.48 29.54 1.02 27.58 0.88 2004 10 21.56 0.49 25.64 0.70 29.53 1.03 27.53 0.89 2004 11 22.88 1.14 25.84 0.79 29.49 1.04 27.37 0.77 2004 12 23.39 0.49 25.95 0.74 29.41 1.07 27.38 0.81 2005 1 24.61 0.07 26.10 0.45 29.23 1.08 27.32 0.76 2005 2 25.09 -0.83 26.38 0.00 28.91 0.83 27.17 0.43 2005 3 25.23 -1.14 27.12 -0.01 28.89 0.71 27.63 0.40 2005 4 25.21 -0.36 27.62 0.20 29.03 0.64 28.02 0.36 2005 5 24.31 -0.11 27.56 0.46 29.24 0.55 28.30 0.52 2005 6 22.60 -0.47 26.86 0.39 29.20 0.53 27.99 0.46 2005 7 21.61 -0.37 26.07 0.42 28.93 0.32 27.45 0.34 2005 8 20.47 -0.54 25.44 0.38 28.78 0.29 27.11 0.36 2005 9 20.00 -0.77 24.88 -0.05 28.85 0.34 26.79 0.09 2005 10 19.89 -1.18 24.83 -0.12 28.85 0.34 26.70 0.06 2005 11 20.61 -1.13 23.92 -1.14 28.65 0.19 26.13 -0.48 2005 12 22.20 -0.70 24.01 -1.20 28.34 0.00 25.80 -0.77 2006 1 24.76 0.22 24.66 -0.99 27.91 -0.25 25.68 -0.87 2006 2 26.52 0.60 26.02 -0.35 27.58 -0.50 26.15 -0.59 2006 3 26.22 -0.15 26.83 -0.30 27.82 -0.35 26.80 -0.43 2006 4 24.29 -1.28 27.18 -0.24 28.36 -0.04 27.54 -0.13 2006 5 23.84 -0.58 27.30 0.19 28.91 0.21 27.91 0.13 2006 6 22.82 -0.24 26.73 0.27 29.15 0.48 27.82 0.29 2006 7 22.20 0.22 25.93 0.28 29.14 0.53 27.36 0.25 2006 8 21.89 0.88 25.68 0.62 29.20 0.71 27.25 0.50 2006 9 21.93 1.16 25.77 0.83 29.30 0.79 27.32 0.61 2006 10 22.46 1.39 26.00 1.06 29.43 0.92 27.47 0.83 2006 11 22.61 0.87 26.39 1.33 29.55 1.09 27.81 1.21 2006 12 24.15 1.24 26.63 1.42 29.45 1.11 27.86 1.29 2007 1 25.82 1.28 26.68 1.03 28.98 0.82 27.36 0.81 2007 2 26.81 0.90 26.56 0.18 28.69 0.61 26.98 0.24 2007 3 26.41 0.04 26.77 -0.36 28.81 0.64 27.25 0.02 2007 4 24.96 -0.61 26.93 -0.50 28.88 0.49 27.56 -0.10 2007 5 23.05 -1.37 26.40 -0.70 28.90 0.21 27.54 -0.23 2007 6 21.61 -1.46 26.11 -0.35 28.92 0.25 27.54 0.01 2007 7 21.05 -0.93 25.14 -0.51 28.80 0.19 26.99 -0.12 2007 8 19.95 -1.06 24.24 -0.82 28.57 0.08 26.41 -0.34 2007 9 19.85 -0.91 23.84 -1.10 28.21 -0.31 25.86 -0.84 2007 10 19.31 -1.76 23.85 -1.09 28.09 -0.42 25.73 -0.92 2007 11 19.82 -1.92 23.76 -1.30 27.90 -0.56 25.47 -1.14 2007 12 21.15 -1.76 23.91 -1.30 27.70 -0.64 25.40 -1.17 2008 1 24.24 -0.30 24.42 -1.23 27.18 -0.98 25.06 -1.50 2008 2 26.39 0.47 25.34 -1.04 26.79 -1.29 25.19 -1.55 2008 3 26.91 0.53 26.54 -0.59 27.22 -0.95 26.16 -1.07 2008 4 25.68 0.12 27.36 -0.07 27.53 -0.86 26.97 -0.70 2008 5 24.43 0.00 27.15 0.05 27.93 -0.76 27.19 -0.58 2008 6 23.19 0.12 26.49 0.02 28.10 -0.57 27.15 -0.38 2008 7 23.02 1.05 26.01 0.36 28.18 -0.43 27.00 -0.11 2008 8 22.14 1.13 25.56 0.51 28.27 -0.22 26.81 0.06 2008 9 21.60 0.84 25.36 0.42 28.25 -0.27 26.70 0.00 2008 10 21.39 0.32 25.48 0.53 28.23 -0.28 26.66 0.02 2008 11 21.54 -0.20 25.25 0.20 28.16 -0.29 26.52 -0.08 2008 12 22.73 -0.18 24.59 -0.62 27.83 -0.52 25.78 -0.79 2009 1 24.39 -0.15 25.26 -0.39 27.47 -0.69 25.69 -0.87 2009 2 25.53 -0.38 26.16 -0.22 27.34 -0.74 26.02 -0.71 2009 3 25.48 -0.89 26.62 -0.51 27.85 -0.32 26.66 -0.57 2009 4 25.84 0.27 27.38 -0.04 28.36 -0.03 27.53 -0.13 2009 5 24.95 0.52 27.57 0.47 29.09 0.40 28.10 0.33 2009 6 24.09 1.02 27.15 0.68 29.15 0.48 28.06 0.53 2009 7 23.09 1.12 26.56 0.91 29.13 0.52 27.88 0.78 2009 8 22.03 1.02 25.90 0.84 29.20 0.71 27.51 0.76 2009 9 21.48 0.71 25.74 0.80 29.24 0.73 27.48 0.78 2009 10 21.64 0.57 25.82 0.87 29.68 1.17 27.73 1.09 2009 11 21.99 0.25 26.13 1.07 29.90 1.44 28.17 1.57 2009 12 23.21 0.31 26.69 1.48 29.80 1.45 28.51 1.94 2010 1 24.70 0.17 26.86 1.21 29.58 1.42 28.32 1.76 2010 2 26.16 0.24 27.37 1.00 29.25 1.18 28.19 1.45 2010 3 26.54 0.16 28.04 0.91 29.18 1.01 28.46 1.23 2010 4 26.04 0.47 28.08 0.66 29.28 0.89 28.49 0.82 2010 5 24.75 0.33 27.34 0.24 29.27 0.57 28.13 0.36 2010 6 23.26 0.19 26.32 -0.15 28.76 0.09 27.35 -0.18 2010 7 21.11 -0.87 24.95 -0.70 28.33 -0.28 26.44 -0.67 2010 8 19.49 -1.52 24.21 -0.85 27.82 -0.68 25.70 -1.05 2010 9 19.28 -1.49 23.83 -1.11 27.47 -1.05 25.36 -1.34 2010 10 19.73 -1.34 23.67 -1.28 27.35 -1.16 25.25 -1.40 2010 11 20.44 -1.30 23.68 -1.37 27.47 -0.99 25.20 -1.41 2010 12 22.07 -0.83 24.00 -1.21 27.54 -0.81 25.29 -1.28 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/engel/000077500000000000000000000000001224417117700234165ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/engel/__init__.py000066400000000000000000000000231224417117700255220ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/engel/data.py000066400000000000000000000036341224417117700247070ustar00rootroot00000000000000#! /usr/bin/env python """Name of dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = """Engel (1857) food expenditure data""" SOURCE = """ This dataset was used in Koenker and Bassett (1982) and distributed alongside the ``quantreg`` package for R. Koenker, R. and Bassett, G (1982) Robust Tests of Heteroscedasticity based on Regression Quantiles; Econometrica 50, 43-61. Roger Koenker (2012). quantreg: Quantile Regression. R package version 4.94. http://CRAN.R-project.org/package=quantreg """ DESCRSHORT = """Engel food expenditure data.""" DESCRLONG = """Data on income and food expenditure for 235 working class households in 1857 Belgium.""" #suggested notes NOTE = """ Number of observations: 235 Number of variables: 2 Variable name definitions: income - annual household income (Belgian francs) foodexp - annual household food expenditure (Belgian francs) """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray(data, endog_idx=0, exog_idx=None, dtype=float) def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/engel.csv', 'rb'), delimiter=",", names = True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/engel/engel.csv000066400000000000000000000174041224417117700252330ustar00rootroot00000000000000income","foodexp" 420.157650843928,255.839424594576 541.411706721205,310.958667059145 901.157456651663,485.680014171085 639.08022868883,402.997355544163 750.875605828476,495.560774933776 945.7989310482,633.797815132393 829.397886847186,630.756567705217 979.164835837914,700.440904266032 1309.87894037831,830.95862150535 1492.39874437426,815.360217290461 502.838980218067,338.001387329587 616.71684724229,412.361338428638 790.922511291545,520.000617981121 555.878641643074,452.401472907332 713.44118368415,512.720057836885 838.756132722629,658.839530303595 535.076645900387,392.599496749012 596.440805359932,443.558633799595 924.561896582212,640.11637771341 487.758301817955,333.839386975747 692.639734238467,466.958318528848 997.876977824175,543.396904263231 506.999491179525,317.719843756484 654.158711401758,424.320896352573 933.919270183382,518.961656053407 433.681329252018,338.001387329587 587.596213341684,419.641174631375 896.474635643808,476.320048933563 454.478223782524,386.36016331012 584.998919370362,423.278349805554 800.799016617394,503.35717119023 502.436869899465,354.638868545299 713.519666530832,497.31816882899 906.000628725279,588.519463958722 880.596923786325,654.597144597117 796.828919368547,550.727427597965 854.87905261442,528.376976714303 1167.37159427026,640.481348055373 523.800035579844,401.320355546637 670.779185433114,435.999021900918 377.058368850099,276.560609645838 851.543001748297,588.348818059025 1121.09369365137,664.19780220393 625.517933400268,444.860166410776 805.537696174017,462.899514863823 558.581201465033,377.779238546012 884.400487319312,553.15043561635 1257.49886576834,810.896154761447 2051.17894099901,1067.95405614074 1466.33299214604,1049.87879168732 730.098896774438,522.701209676143 800.799016617394,572.080662617684 1245.69638760971,907.396944580766 1201.00021368196,811.57759369573 634.400209148622,427.797520201042 956.231463138833,649.998464123521 1148.60104702955,860.600154559902 1768.82364982203,1143.42108582835 2822.53303466609,2032.67919020832 922.354832141967,590.618327009967 2293.19197144236,1570.39113831708 627.472597042135,483.480024661708 889.980898378682,600.480399087121 1162.19995392922,696.202105745481 1197.07935707754,774.796179057776 530.797185462728,390.598430399482 1142.15259256708,612.56190089616 1088.00388697846,708.762151593178 484.661156316503,296.919185772441 1536.02010067662,1071.46269646941 678.897392141278,496.597580037315 671.880165854403,503.39744137471 690.468256477202,357.641110879089 860.694825783725,430.337592716917 873.309484596291,624.699041398883 894.459835214087,582.54125094185 1148.64699199033,580.221542246938 926.876192988813,543.88074278423 839.041358293354,588.637179622688 829.49742056551,627.99989545798 1264.00432957802,712.101174262357 1937.97714636527,968.394940061335 698.831741432851,482.581587621304 920.419919363967,593.169385581544 1897.57113999412,1033.56575426925 891.682386298282,693.679473449282 889.6783563083,693.679473449282 1221.48181114807,761.279065822776 544.599139402161,361.398056338879 1031.44911476975,628.452218199356 1462.94967217323,771.44856441849 830.435282442915,757.118666447887 975.04146665886,821.59699408795 1337.99833996025,1022.3201704203 867.64270523161,679.440727360101 725.745933703277,538.749115686801 989.005606041026,679.998097246959 1525.00047421177,977.003270959677 672.196023753147,561.201488176248 923.397682201603,728.399746623589 472.321548280698,372.318622584609 590.760092419618,361.52095256921 940.921768902254,517.919590751206 643.357141462033,459.817650989876 2551.66151377579,863.919851470995 1795.32258882792,831.440717300088 1165.77339020587,534.761043791083 815.62117431089,392.050242021584 1264.20658645087,934.975195444102 1095.40561684263,813.308096301318 447.447852411217,263.709996170628 1178.97417954918,769.083849021127 975.802295687602,630.586286420944 1017.85220007186,645.987400679307 423.879832013577,319.558386349475 558.776739102636,348.451830104442 943.248718270872,614.506803163417 1348.30024452767,662.009562038758 2340.61735387973,1504.37077491258 587.179168098792,406.218015716283 1540.97405699008,692.1688993212 1115.84807481956,588.137050604953 1044.68428590432,511.260885979623 1389.79286912095,700.559989341702 2497.78595383858,1301.14509754583 1585.38094296753,879.066017413729 1862.04383602348,912.885078250154 2008.8546243469,1509.7811718462 697.309947594492,484.060548936204 571.251746766114,399.670317095029 598.346489479165,444.100105563446 461.097722534667,248.810111222236 977.110747230569,527.801359578015 883.984916757004,500.631349055309 718.359399343153,436.810734740578 543.897059451262,374.799044124691 1587.34803468162,726.392135101241 4957.81302447901,1827.1999644396 969.68376220836,523.49108470832 419.998021268147,334.99982183865 561.998960360707,473.200883889103 689.598814337616,581.202945705675 1398.5202607169,929.75396743285 820.816847571835,591.197416678028 875.171616264683,637.548275219478 1392.44991016741,674.950935444337 1256.31737222348,776.758895179758 1362.85902988814,959.516968477919 1999.25521962472,1250.96433391432 1209.47299210597,737.820079380279 1125.0356465964,810.677242354166 1827.40096749065,983.000864895715 1014.15395295671,708.896829195949 880.394409783857,633.120014158391 2432.39099224122,1424.80465628899 1177.85468592228,830.95862150535 1222.5938655042,925.579474233555 1519.58112468928,1162.00239672989 687.663761691575,383.457962724518 953.11922427465,621.117329201764 953.11922427465,621.117329201764 953.11922427465,621.117329201764 939.04180595405,548.600231233027 1283.40245730726,745.235294456105 1511.57888086542,837.80049554801 1342.58212809281,795.34024163682 511.797991841133,418.597567936084 689.798826994207,508.797449887606 1532.30742618103,883.278011623353 1056.08082482784,742.527569034242 387.319525632704,242.32020192074 387.319525632704,242.32020192074 410.998678844171,266.00098186588 832.755431984518,614.75880260907 614.998605300573,385.318397885378 887.465812960725,515.619969025557 1024.81767735549,708.478703425954 1006.43533940422,734.235630810297 725.999989237198,433.000983776039 494.417350707113,327.418772037758 748.641327251627,429.039933638385 987.64171960278,619.640827692018 788.09607104789,400.798978053224 831.79831368624,620.800640126449 1139.49447493599,819.996440995918 507.516894491359,360.878017655466 576.197221195524,395.760804612775 696.59905406528,442.000052121661 650.817978373872,404.03843113149 949.580207412905,670.799309110531 497.119281341608,297.5701513364 570.167398903935,353.488163131684 724.730600295316,383.93758487932 408.339934376479,284.800803268761 638.671348198183,431.099964756245 1225.78900333424,801.351758612919 715.370077715236,448.451258996436 800.470756318541,577.911070749254 975.597398720442,570.521012300081 1613.75654835121,865.320535878418 608.501878790717,444.557764325978 958.663378279956,680.419826789901 835.942641428986,576.277894453487 873.737511101673,631.798175131743 951.44320976533,608.641850318947 473.002181248345,300.999920310599 601.003044352051,377.998414059123 713.99788489267,397.001476569931 829.29836507219,588.519463958722 959.795268750367,681.761575036767 1212.96129506839,807.360270100159 958.874307424532,696.801097085781 1129.44314702273,811.196241851397 1943.04187331083,1305.72014134299 539.638785503439,442.000052121661 463.599021125583,353.601297444411 562.640004501253,468.000797511221 736.758383070776,526.75734679978 1415.44606403432,890.239030266526 2208.78970565549,1318.80328223672 636.000913716338,331.000537750629 759.401026863119,416.401525383919 1078.83819961899,596.840554600565 499.751012804117,408.499218009175 1020.02253584507,775.020902533032 1595.1610788589,1138.16204602323 776.595792938105,485.519766208776 1230.92352504251,772.761142353133 1807.95201459536,993.96301727137 415.440747985411,305.438973730216 440.51742415637,306.519078570937 541.200597324592,299.199327967538 581.35989168777,468.000797511221 743.077242789534,522.601905880464 1057.67671146451,750.320163419201 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/000077500000000000000000000000001224417117700232455ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/__init__.py000066400000000000000000000000231224417117700253510ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/data.py000066400000000000000000000056671224417117700245460ustar00rootroot00000000000000#! /usr/bin/env python """Fair's Extramarital Affairs Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """Included with permission of the author.""" TITLE = """Affairs dataset""" SOURCE = """ Fair, Ray. 1978. "A Theory of Extramarital Affairs," `Journal of Political Economy`, February, 45-61. The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm """ DESCRSHORT = """Extramarital affair data.""" DESCRLONG = """Extramarital affair data used to explain the allocation of an individual's time among work, time spent with a spouse, and time spent with a paramour. The data is used as an example of regression with censored data.""" #suggested notes NOTE = """ Number of observations: 6366 Number of variables: 9 Variable name definitions: rate_marriage : How rate marriage, 1 = very poor, 2 = poor, 3 = fair, 4 = good, 5 = very good age : Age yrs_married : No. years married. Interval approximations. See original paper for detailed explanation. children : No. children religious : How relgious, 1 = not, 2 = mildly, 3 = fairly, 4 = strongly educ : Level of education, 9 = grade school, 12 = high school, 14 = some college, 16 = college graduate, 17 = some graduate school, 20 = advanced degree occupation : 1 = student, 2 = farming, agriculture; semi-skilled, or unskilled worker; 3 = white-colloar; 4 = teacher counselor social worker, nurse; artist, writers; technician, skilled worker, 5 = managerial, administrative, business, 6 = professional with advanced degree occupation_husb : Husband's occupation. Same as occupation. affairs : measure of time spent in extramarital affairs See the original paper for more details. """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray(data, endog_idx=8, exog_idx=None, dtype=float) def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=8, exog_idx=None, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/fair.csv', 'rb'), delimiter=",", names = True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/fair.csv000066400000000000000000004504101224417117700247070ustar00rootroot00000000000000"rate_marriage","age","yrs_married","children","religious","educ","occupation","occupation_husb","affairs" 3,32,9,3,3,17,2,5,0.1111111 3,27,13,3,1,14,3,4,3.2307692 4,22,2.5,0,1,16,3,5,1.3999996 4,37,16.5,4,3,16,5,5,0.7272727 5,27,9,1,1,14,3,4,4.666666 4,27,9,0,2,14,3,4,4.666666 5,37,23,5.5,2,12,5,4,0.8521735 5,37,23,5.5,2,12,2,3,1.826086 3,22,2.5,0,2,12,3,3,4.7999992 3,27,6,0,1,16,3,5,1.333333 2,27,6,2,1,16,3,5,3.2666645 5,27,6,2,3,14,3,5,2.041666 3,37,16.5,5.5,1,12,2,3,0.4848484 5,27,6,0,2,14,3,2,2 4,22,6,1,1,14,4,4,3.2666645 4,37,9,2,2,14,3,6,1.3611107 4,27,6,1,1,12,3,5,2 1,37,23,5.5,4,14,5,2,1.826086 2,42,23,2,2,20,4,4,1.826086 4,37,6,0,2,16,5,4,2.041666 5,22,2.5,0,2,14,3,4,7.8399963 3,37,16.5,5.5,2,9,3,2,2.545454 3,42,23,5.5,3,12,5,4,0.5326087 2,27,9,2,4,20,3,4,0.6222222 4,27,6,1,2,12,5,4,0.5833333 5,27,2.5,0,3,16,4,1,4.7999992 2,27,6,2,2,12,2,5,0.1666666 5,37,13,1,3,12,3,4,0.6153846 2,32,16.5,2,2,12,4,2,1.1878777 3,27,6,1,1,14,3,6,11.1999989 3,32,16.5,4,3,14,5,5,1.1878777 3,27,9,2,1,14,3,4,2.1777763 3,37,16.5,3,3,14,3,2,0.4848484 4,32,16.5,5.5,4,12,2,4,0.7272727 5,42,16.5,4,3,16,4,6,0.7272727 3,27,9,2,2,12,5,2,1.333333 3,17.5,0.5,0,1,12,3,2,7 4,42,23,5.5,2,20,3,2,0.5217391 5,37,16.5,3,3,12,3,5,0.2121212 4,22,2.5,1,2,14,3,5,0.4 4,27,2.5,0,2,16,4,1,1.3999996 4,22,2.5,0,2,14,3,6,3.1999998 4,37,13,3,2,16,4,5,1.5076914 4,22,2.5,0,2,16,3,4,7.8399963 4,22,2.5,0,1,14,1,2,7.8399963 5,22,2.5,0,3,12,3,2,0.4 5,22,2.5,0,3,16,4,5,4.8999996 3,42,23,4,3,16,5,5,0.0434783 5,32,13,3,3,14,4,4,0.0769231 5,22,6,2,2,14,3,4,0.5833333 3,27,2.5,1,4,17,4,6,4.7999992 2,42,23,3,3,14,3,5,0.8521735 4,22,2.5,0,1,17,6,6,4.7999992 2,42,23,3,3,14,3,5,2.9217386 4,42,23,2,2,14,4,4,0.5217391 4,42,23,3,3,12,5,4,0.5326087 4,37,16.5,2,3,14,3,4,0.4848484 4,27,2.5,0,2,20,4,5,7.8399963 2,32,9,2,2,12,2,2,1.333333 4,42,13,0,1,14,3,4,1.5076914 4,22,6,2,1,12,3,4,1.333333 5,32,16.5,3,3,14,2,4,1.1878777 4,42,13,0,2,12,3,2,1.5076914 5,27,9,1,3,14,5,5,1.333333 5,22,6,2,2,12,2,5,7 2,27,16.5,2,3,16,4,5,0.7272727 5,37,13,2,1,12,3,4,1.5076914 5,27,6,0,2,14,3,4,2.041666 2,27,2.5,1,1,12,2,3,26.8799896 5,42,23,5.5,4,20,5,4,0.5326087 5,27,6,0,2,16,5,2,2 5,37,16.5,3,2,14,3,5,0.2121212 2,32,9,2,2,16,3,5,1.333333 3,37,16.5,5.5,3,12,5,5,2.545454 5,27,6,2,2,12,3,4,0.1666666 5,32,16.5,3,1,12,4,4,0.4848484 5,27,9,2,1,14,5,1,1.333333 4,22,2.5,0,2,16,5,5,0.4 5,32,16.5,2,4,14,5,2,0.4848484 2,22,6,2,2,14,2,5,0.1666666 3,32,13,3,1,12,4,5,1.5076914 5,32,16.5,3,2,12,3,4,0.0606061 3,27,6,2,1,14,3,6,2.041666 5,22,2.5,0,1,20,4,4,0.4 3,32,9,2,3,14,3,5,0.8888888 3,22,2.5,1,2,14,3,2,4.7999992 3,22,2.5,0,2,16,3,4,4.7999992 5,27,9,2,1,16,4,6,1.333333 3,42,23,2,2,12,2,2,0.1521739 3,37,16.5,3,2,17,3,2,1.1878777 5,32,13,2,2,12,2,6,0.9423077 1,27,13,2,2,12,4,4,1.5076914 5,27,2.5,0,2,14,3,2,0.4 5,27,9,0,1,14,4,4,0.1111111 5,32,13,2,2,16,3,3,0.9423077 4,27,9,2,2,14,3,5,0.3888888 3,22,2.5,1,3,14,5,4,4.8999996 2,37,23,2,2,12,5,5,0.5326087 2,27,6,2,1,14,3,5,2.041666 4,27,2.5,1,2,12,5,2,4.8999996 3,37,13,5.5,2,12,5,5,5.1692305 5,37,16.5,4,1,12,3,6,1.696969 5,22,2.5,0,1,14,3,5,1.3999996 4,42,13,0,1,14,5,5,0.0769231 5,27,9,1,1,14,5,5,2.1777763 4,22,2.5,0,1,14,3,4,1.3999996 4,22,2.5,1,2,14,5,4,7.8399963 4,27,6,2,3,16,4,6,0.5833333 2,32,16.5,1,2,14,3,5,2.7151508 3,27,13,2,1,14,3,4,0.9423077 4,22,0.5,0,2,12,3,2,7 5,27,6,1,1,16,4,3,3.2666645 2,27,9,2,2,14,3,5,4.666666 3,27,9,2,2,12,3,4,2.1777763 4,32,16.5,3,3,12,2,3,0.4848484 5,22,2.5,0,1,12,3,3,1.3999996 4,22,2.5,1,2,14,4,4,3.1999998 1,22,2.5,0,2,9,2,4,11.1999998 3,32,13,2,1,20,6,6,1.5076914 5,32,9,1,2,14,3,5,1.333333 5,27,2.5,0,1,20,6,4,1.3999996 4,27,9,2,2,14,2,5,0.3888888 4,27,6,0,3,12,5,2,2 2,22,2.5,0,3,14,3,2,4.8999996 3,27,9,2,3,17,4,6,1.3611107 4,27,6,2,1,14,3,4,2.041666 5,22,2.5,1,1,12,2,2,1.3999996 4,27,16.5,4,3,12,2,6,0.2121212 2,27,6,1,2,14,4,3,3.2666645 2,32,6,1,1,17,4,4,7.4666672 5,27,6,2,2,16,4,3,0.5833333 4,42,23,3,3,17,4,4,0.5217391 4,32,9,2,1,16,4,6,0.1111111 3,42,23,4,3,14,4,3,0.5217391 5,27,6,2,1,12,5,2,2 2,42,23,4,4,20,4,5,0.1521739 3,32,6,0,2,20,4,5,4.666666 5,27,2.5,0,2,14,3,2,1.3999996 2,42,16.5,3,3,12,3,5,1.1878777 4,37,23,3,2,12,5,4,1.826086 3,27,2.5,0,2,17,4,4,1.3999996 3,32,13,3,3,20,4,6,3.2307692 3,27,2.5,0,2,14,3,4,4.7999992 3,42,16.5,2,1,16,3,5,1.1878777 4,22,2.5,0,2,14,5,5,0.4 5,27,6,0,3,14,3,5,0.5833333 3,22,2.5,0,1,12,2,4,7.8399963 4,27,6,0,3,14,2,4,11.1999989 3,27,9,2,2,16,3,4,1.3611107 2,27,9,1,2,14,2,2,4.9777775 3,22,2.5,0,3,12,3,3,4.7999992 4,32,16.5,4,4,14,3,5,2.7151508 5,32,2.5,2,2,14,3,4,0.4 4,27,2.5,0,2,14,3,5,0.4 5,32,9,3,3,16,4,4,0.1111111 3,32,13,2,2,12,3,5,0.2692307 3,32,16.5,3,3,12,3,2,0.7272727 5,42,23,2,2,14,5,5,0.5217391 3,27,16.5,4,3,14,4,2,0.7424242 3,32,16.5,4,2,20,4,4,0.7272727 4,22,2.5,2,2,14,5,2,4.7999992 5,27,6,0,1,12,3,4,7.4666672 4,37,16.5,3,4,16,5,5,0.2121212 3,22,2.5,0,2,12,3,5,1.3999996 4,32,13,2,2,14,3,5,1.5076914 5,42,23,5.5,3,17,4,4,0.5217391 2,27,6,2,3,12,2,2,1.333333 5,22,2.5,1,2,16,3,6,1.3999996 3,42,23,2,3,12,2,2,0.5326087 5,42,23,3,1,14,3,5,1.826086 4,22,2.5,0,3,12,3,4,4.8999996 3,27,9,1,4,14,5,5,0.1111111 2,37,23,3,3,12,2,4,4.173913 3,32,9,3,3,14,4,5,0.3888888 5,37,16.5,4,3,12,3,4,1.1878777 3,27,9,2,2,12,5,2,1.333333 3,27,6,1,2,17,2,2,1.333333 5,22,2.5,0,3,14,3,3,1.3999996 5,27,6,0,2,17,4,5,1.333333 4,22,2.5,0,2,12,4,2,4.8999996 4,27,6,0,2,12,3,4,4.666666 3,32,9,1,2,12,3,3,0.1111111 1,42,23,4,3,12,3,5,0.8521735 2,22,6,2,2,12,3,4,1.333333 4,32,16.5,2,2,12,2,2,0.4848484 4,32,13,3,3,14,2,2,0.9423077 5,27,9,2,1,12,3,3,0.3888888 3,42,23,3,2,12,3,2,0.5217391 3,37,16.5,1,3,20,6,6,2.7151508 5,32,13,2,1,12,5,2,0.9423077 4,27,13,3,1,12,4,2,3.4461536 2,37,13,0,2,14,3,5,0.9230769 3,27,9,1,1,12,2,2,3.1111107 5,27,6,0,1,14,5,5,3.2666645 2,42,23,2,3,12,5,2,0.8521735 5,27,6,0,2,16,3,4,0.5833333 4,22,2.5,0,4,16,5,4,7.8399963 5,22,2.5,0,2,16,4,4,1.3999996 5,42,13,0,3,12,3,2,1.5076914 4,22,2.5,1,1,16,3,4,1.3999996 4,32,9,1,3,16,3,5,1.3611107 4,27,9,2,2,12,4,2,0.3888888 5,27,6,0,1,16,5,2,2.041666 4,32,16.5,3,2,14,5,4,2.545454 2,42,2.5,0,4,20,6,5,4.7999992 2,42,23,5.5,4,14,3,6,0.3478261 4,42,23,4,2,12,3,2,0.3478261 3,22,2.5,0,3,16,5,5,4.7999992 2,22,2.5,0,3,14,3,5,1.3999996 4,22,2.5,0,3,14,3,3,4.7999992 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5,27,2.5,0,3,20,4,5,4.7999992 2,42,23,3,3,14,4,2,0.5217391 3,27,6,0,1,14,3,2,0.5833333 4,27,6,1,2,12,3,4,2.041666 2,22,2.5,0,3,12,3,3,3.1999998 3,27,2.5,0,3,12,2,2,7.8399963 4,42,23,2,2,17,5,2,0.8521735 5,32,13,2,1,14,2,5,0.9230769 5,32,13,0,1,12,5,4,2.1538458 4,27,9,2,2,16,4,5,0.3888888 5,42,23,4,3,17,4,4,0.1521739 3,27,9,3,1,16,4,5,0.1111111 5,42,13,0,2,14,5,3,0.9230769 5,32,16.5,2,2,16,5,5,0.2121212 1,42,23,2,1,12,3,3,1.826086 4,27,6,0,1,12,3,4,0.5833333 5,42,23,3,2,12,5,4,1.9478254 4,27,9,2,3,16,4,5,1.333333 2,32,13,3,3,20,4,5,0.0769231 4,22,2.5,0,2,12,3,2,11.1999998 3,32,16.5,3,2,12,3,2,0.7272727 5,27,6,1,3,12,5,2,2 3,27,2.5,0,1,12,3,4,3.1999998 5,22,2.5,0,2,16,4,6,1.3999996 3,32,13,0,2,16,4,5,0.9230769 4,37,16.5,2,2,20,4,2,0.7424242 4,27,6,1,2,17,4,5,2.041666 4,37,23,4,2,12,3,2,0.1521739 4,22,2.5,2,2,14,3,5,4.8999996 3,27,6,1,3,17,4,4,1.333333 4,32,16.5,3,4,12,3,5,1.1878777 5,22,6,2,3,14,3,5,7.4666672 4,32,13,2,3,14,3,2,0.0769231 4,27,9,1,1,14,2,5,1.3611107 3,32,16.5,2,2,12,3,4,0.7272727 4,27,13,2,2,12,3,2,0.0769231 5,27,9,1,3,12,2,3,0.3888888 3,27,9,0,1,12,3,4,16 4,27,9,0,3,14,5,6,4.9777775 3,22,2.5,0,1,12,3,2,7.8399963 3,32,13,2,3,20,4,5,0.0769231 4,42,23,5.5,2,14,5,3,0.5326087 5,22,2.5,0,2,16,3,4,4.8999996 5,27,9,1,2,14,4,5,0.3888888 5,37,13,2,3,14,4,3,0.2692307 5,27,2.5,1,3,16,3,3,1.3999996 4,27,6,0,2,14,3,2,0.5833333 5,22,2.5,0,2,12,4,4,7.8399963 5,27,6,1,3,12,2,3,1.333333 4,32,9,2,2,16,5,5,1.333333 4,32,6,2,3,14,4,2,2.041666 2,22,2.5,1,2,17,2,2,11.1999998 5,27,2.5,0,2,17,5,5,4.7999992 5,37,16.5,3,2,14,4,4,1.1878777 3,27,2.5,1,3,14,5,3,4.7999992 4,27,6,1,1,14,3,4,0.1666666 5,27,6,2,3,12,2,4,3.2666645 4,42,23,3,2,12,3,2,1.217391 3,27,6,2,3,14,3,5,0.5833333 5,22,6,1,3,14,1,4,2 5,37,23,3,2,12,3,4,0.5326087 3,37,16.5,3,3,12,3,4,1.1878777 4,42,23,4,2,9,4,4,0.5217391 4,42,23,3,3,12,3,5,0.5217391 4,37,23,2,2,16,4,6,0.1521739 5,32,16.5,4,1,12,2,1,0.4848484 4,27,6,2,3,14,2,6,1.333333 3,32,16.5,2,2,12,2,2,1.1878777 2,27,6,1,2,12,3,2,0.5833333 4,22,2.5,0,1,14,3,4,4.7999992 3,22,6,1,2,14,3,2,0.5833333 2,27,6,0,3,12,3,3,0.1666666 4,27,6,1,2,12,2,5,3.2666645 3,22,2.5,1,1,12,5,5,4.8999996 2,37,16.5,3,2,14,3,5,1.1878777 5,32,13,2,2,16,4,2,3.2307692 5,22,2.5,0,1,12,3,2,1.3999996 4,22,6,1,3,14,4,1,0.5833333 4,22,0.5,0,2,12,5,2,2 3,27,2.5,0,2,20,4,4,1.3999996 5,27,2.5,0,3,12,2,2,1.3999996 4,22,2.5,0,3,14,3,2,1.3999996 4,42,16.5,2,1,16,4,5,2.7151508 5,32,13,1,2,12,5,2,1.5076914 5,32,13,3,3,14,3,5,0.9230769 5,32,2.5,0,3,14,4,2,4.7999992 3,32,13,1,1,16,4,5,0.9423077 5,42,23,2,3,14,5,6,1.217391 4,27,6,1,1,14,3,2,2.041666 3,37,16.5,4,3,12,2,5,1.1878777 5,37,16.5,2,2,12,2,4,0.2121212 2,32,9,1,2,12,3,3,1.333333 2,32,13,0,2,14,5,4,0.2692307 4,22,0.5,0,1,12,3,4,2 4,22,6,0,1,12,3,4,0.1666666 3,37,16.5,3,3,12,2,2,0.2121212 3,27,2.5,0,2,17,4,5,3.1999998 3,22,2.5,0,2,17,5,4,0.4 4,32,16.5,2,2,14,3,4,0.7272727 4,42,16.5,2,3,17,5,6,0.4848484 4,32,16.5,5.5,3,12,2,2,1.1878777 3,32,13,2,3,14,2,2,0.6153846 4,42,23,2,2,20,4,5,0.8521735 3,32,9,1,2,16,4,3,4.666666 4,22,2.5,0,2,14,3,2,1.3999996 3,37,16.5,3,3,12,3,3,5.818181 3,42,23,3,1,14,3,4,0.5217391 1,32,16.5,3,3,12,4,2,0.7424242 2,37,16.5,5.5,2,12,2,5,2.545454 5,32,9,0,3,12,3,5,2.1777763 2,32,16.5,4,2,14,3,5,2.7151508 3,22,2.5,0,2,17,4,1,16.7999878 5,32,2.5,0,2,16,5,5,4.8999996 3,27,6,1,1,17,4,5,0.1666666 4,27,6,0,1,17,4,1,3.2666645 3,37,16.5,2,2,14,4,2,0.2121212 4,37,16.5,2,1,14,3,3,0.2121212 5,42,23,2,3,14,3,4,0.5217391 4,32,13,2,1,17,4,2,0.9230769 3,27,9,0,1,14,3,5,4.9777775 3,32,13,2,1,12,3,6,0.9423077 3,27,6,2,3,17,4,6,2.041666 2,37,16.5,3,2,12,2,4,0.7272727 2,22,6,2,1,14,3,4,0.1666666 3,17.5,2.5,1,3,12,4,2,4.8999996 3,27,6,3,2,14,2,2,0.1666666 3,22,2.5,0,1,12,2,5,1.3999996 4,42,23,3,2,14,3,5,1.826086 2,27,6,2,3,14,3,3,2 4,32,16.5,5.5,2,14,3,2,2.545454 4,27,2.5,0,3,12,3,5,0.4 4,27,6,2,3,12,3,2,2 4,27,2.5,0,3,16,4,5,4.7999992 5,27,6,0,1,16,4,5,4.666666 2,22,6,2,2,12,2,3,0.5833333 3,27,2.5,0,1,20,4,4,4.7999992 4,32,16.5,3,3,14,3,5,0.7272727 5,32,13,1,3,17,4,4,0.9423077 3,22,2.5,1,2,12,2,4,7.8399963 4,22,2.5,0,1,14,3,3,1.3999996 5,32,9,2,3,16,4,4,1.333333 5,32,2.5,0,3,14,5,4,0.4 5,27,9,2,1,14,2,4,1.333333 4,32,9,1,2,14,5,5,0.8888888 4,22,2.5,0,2,14,3,5,1.3999996 4,42,23,1,3,12,3,3,2.9217386 4,27,9,2,2,16,4,4,1.333333 4,32,16.5,3,2,17,4,2,0.4848484 4,27,6,2,2,16,4,5,7.4666672 4,37,16.5,2,2,14,4,5,0.7272727 5,27,6,1,3,16,4,4,4.666666 2,27,6,1,2,12,2,2,4.666666 4,22,2.5,1,1,12,2,2,3.1999998 3,37,13,1,2,12,3,2,0.9423077 4,42,23,2,2,14,3,5,0.1521739 5,22,2.5,0,1,14,3,2,0.4 3,42,23,2,1,17,4,6,4.173913 4,32,16.5,2,3,12,3,4,0.7272727 1,32,16.5,3,3,14,3,4,0.0606061 5,27,13,2,3,12,3,2,0.9230769 3,27,6,0,1,17,5,4,3.2666645 5,42,23,5.5,2,14,4,4,0.5326087 4,32,16.5,1,3,16,4,5,0.0606061 2,22,2.5,0,1,12,3,2,4.7999992 2,42,23,1,3,14,4,2,0.5326087 2,22,6,0,2,12,3,4,0.5833333 4,27,6,0,1,16,5,1,0.5833333 4,42,23,1,4,17,3,2,1.826086 5,42,23,2,1,12,3,2,0.3478261 4,27,6,2,2,16,4,2,2 3,22,2.5,0,1,12,3,2,1.3999996 3,27,13,3,2,14,5,5,3.4461536 4,32,13,2,2,14,4,5,2.1538458 4,27,9,3,3,14,4,3,0.3888888 3,32,9,1,2,14,3,4,0.3888888 3,27,9,2,1,14,4,2,1.333333 5,37,16.5,5.5,3,12,2,2,1.1878777 3,27,6,1,4,14,3,5,4.666666 2,27,9,1,1,14,5,3,4.666666 4,27,2.5,0,2,17,4,6,7.8399963 4,32,13,1,1,20,4,4,1.5076914 1,42,23,4,3,12,3,4,1.9478254 4,32,16.5,1,1,14,5,5,0.7424242 1,42,23,5.5,2,12,2,2,0.5217391 4,37,13,1,2,12,3,5,1.5076914 4,32,16.5,2,2,14,5,5,1.1878777 4,42,23,3,2,14,4,4,0.0434783 5,27,6,1,3,14,3,6,2.041666 3,37,16.5,4,3,12,3,2,0.7272727 3,37,23,3,3,9,4,2,0.1521739 4,37,16.5,2,2,12,3,4,2.545454 3,27,6,2,1,14,4,5,1.333333 4,37,13,0,3,12,3,2,1.5076914 2,27,2.5,1,3,16,4,4,4.8999996 4,42,23,5.5,3,12,5,5,0.5326087 3,32,16.5,3,2,12,2,2,0.7424242 4,37,16.5,3,2,12,5,2,0.2121212 3,42,13,0,3,12,4,4,0.9230769 4,37,13,4,3,14,4,2,0.6153846 2,32,16.5,4,2,14,5,4,1.1878777 3,27,13,2,1,14,4,4,2.1538458 5,22,2.5,0,2,14,4,5,0.4 4,22,2.5,1,2,14,4,5,0.4 3,32,2.5,0,1,16,4,5,17.9199982 5,27,6,2,3,14,3,5,3.2666645 5,32,16.5,3,4,12,2,3,0.0606061 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5,22,2.5,0,3,14,3,4,7.8399963 5,32,9,1,2,14,2,4,1.333333 4,37,16.5,2,3,12,3,6,0.4848484 3,22,2.5,0,3,12,3,4,1.3999996 4,37,16.5,4,2,12,3,5,0.7272727 4,27,2.5,0,3,17,4,1,1.3999996 3,27,6,0,1,14,3,5,2.041666 5,32,16.5,2,2,20,4,4,0.7424242 2,22,2.5,1,2,12,5,5,0.4 4,42,16.5,2,1,14,5,4,0.7424242 5,42,13,3,4,14,2,2,1.5076914 5,32,13,1,3,16,6,6,0.2692307 5,37,13,0,1,14,4,5,0.0769231 2,22,6,0,1,20,6,4,3.2666645 4,22,2.5,0,2,12,3,4,4.8999996 4,32,16.5,1,2,12,3,4,0.7272727 4,27,6,2,2,12,2,2,1.333333 2,37,16.5,2,2,12,3,3,0.7272727 3,22,6,2,3,12,3,2,3.2666645 4,27,13,2,2,16,4,5,3.2307692 4,22,6,1,2,12,2,5,2 4,17.5,0.5,0,3,14,3,4,7 5,27,2.5,0,2,17,5,1,7.8399963 5,27,9,1,3,14,3,4,1.333333 2,22,2.5,1,3,12,2,2,4.7999992 4,42,23,5.5,3,9,2,4,0.8521735 3,32,16.5,3,3,14,4,5,1.696969 4,37,13,1,1,12,5,5,0.9423077 2,32,16.5,3,2,12,3,5,0.7272727 4,32,13,2,2,14,2,5,0.2692307 4,22,2.5,0,2,14,1,1,0.4 5,22,2.5,0,2,12,3,2,4.8999996 4,32,16.5,3,2,12,4,2,1.1878777 4,37,16.5,3,2,12,3,3,0.2121212 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5,42,23,4,4,14,1,6,0.8521735 4,32,9,1,1,16,4,5,2.1777763 4,22,2.5,2,3,12,2,2,11.1999998 3,42,23,5.5,3,14,3,5,0.8521735 3,22,2.5,1,1,12,5,4,4.7999992 5,27,9,2,2,17,2,2,2.1777763 3,22,2.5,0,1,12,2,4,3.1999998 4,32,13,1,2,12,3,3,0.2692307 4,37,16.5,1,2,12,3,4,4.0727262 5,32,9,3,2,17,4,6,0.1111111 4,27,9,1,2,12,2,3,1.333333 3,27,2.5,1,3,14,3,2,4.8999996 2,22,2.5,1,2,16,4,3,1.3999996 1,42,16.5,5.5,3,14,5,5,0.7424242 3,27,13,2,2,9,2,4,0.2692307 4,42,23,3,4,14,3,2,0.5217391 3,27,9,2,2,12,3,4,7.4666662 3,27,6,2,2,14,3,1,2.041666 5,27,2.5,0,3,14,3,2,0.4 3,27,2.5,0,3,16,3,5,3.1999998 3,22,2.5,0,3,16,4,2,0.4 3,42,23,3,1,12,3,3,2.9217386 5,42,23,2,2,12,2,5,0.1521739 4,27,2.5,0,1,17,4,4,4.8999996 5,27,6,0,2,14,3,5,0.1666666 4,32,16.5,4,2,14,4,4,0.7272727 4,27,6,0,3,12,3,2,3.2666645 4,32,9,2,2,14,3,4,2.1777763 3,27,9,2,1,14,3,4,0.8888888 3,27,2.5,1,2,16,2,2,1.3999996 5,27,13,2,2,9,2,4,0.9230769 2,27,13,2,2,12,3,4,1.5076914 3,42,16.5,4,2,14,3,5,0.2121212 3,32,9,2,1,14,3,2,1.3611107 5,32,13,0,2,12,3,5,0.2692307 1,27,9,2,2,12,2,4,2.1777763 3,32,13,3,2,12,3,2,0.9423077 5,27,2.5,0,3,14,5,5,1.3999996 4,32,6,1,1,16,4,4,0.1666666 5,27,13,3,1,14,3,4,3.4461536 5,27,2.5,0,3,20,6,6,1.3999996 5,32,16.5,4,2,12,2,4,1.1878777 4,37,13,0,2,12,3,3,1.5076914 3,22,2.5,0,3,16,4,4,0.4 5,37,13,2,4,17,4,6,0.9230769 4,22,2.5,2,1,12,3,4,0.4 1,42,16.5,2,4,16,5,2,0.2121212 3,37,16.5,3,3,17,4,4,2.7151508 4,22,2.5,0,3,17,3,4,0.4 5,32,13,2,2,20,4,2,1.5076914 3,37,16.5,2,3,17,4,5,2.545454 4,32,16.5,4,3,12,2,4,0.2121212 4,32,16.5,2,2,14,3,5,0.7272727 5,37,16.5,2,4,17,3,3,0.7272727 3,27,9,2,1,12,3,5,0.3888888 3,42,23,3,3,9,3,5,1.9478254 4,32,9,2,1,20,4,5,1.333333 4,22,6,2,2,12,3,3,0.5833333 1,32,13,2,2,16,6,5,1.5076914 4,22,2.5,0,1,12,2,2,4.7999992 4,27,9,2,1,12,3,4,0.3888888 3,27,6,0,2,14,4,5,2 4,32,13,4,2,12,3,5,0.0769231 5,42,23,3,4,12,2,4,1.217391 2,37,16.5,3,1,14,3,4,1.1878777 3,32,9,0,2,14,3,5,1.333333 4,37,16.5,2,3,14,3,6,0.7272727 1,32,16.5,4,3,12,3,5,1.1878777 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4,27,9,1,1,14,3,5,0.8888888 5,27,6,0,3,12,3,4,0.5833333 4,37,13,2,3,17,4,6,0.2692307 3,42,23,2,2,14,3,4,0.8521735 5,22,2.5,0,1,17,5,1,0.4 2,37,23,5.5,2,14,3,5,0.1521739 4,32,2.5,0,2,12,3,2,4.7999992 4,27,2.5,0,3,12,3,3,1.3999996 2,27,2.5,0,2,14,3,5,1.3999996 3,22,2.5,0,3,14,3,6,3.1999998 3,42,23,5.5,2,12,4,2,1.9478254 3,37,16.5,3,3,14,3,3,8.727272 2,27,9,0,2,14,3,4,4.666666 2,37,16.5,3,1,12,3,2,2.545454 3,22,2.5,2,3,17,4,2,3.1999998 4,22,6,0,3,12,3,4,2.041666 4,37,16.5,4,1,14,5,4,0.7272727 4,27,9,3,3,14,3,2,0.1111111 4,22,2.5,0,1,14,2,5,0.4 2,42,23,4,2,14,3,5,0.8521735 5,27,6,0,2,14,5,2,0.5833333 2,27,6,1,1,12,3,5,0.5833333 3,32,16.5,2,2,12,3,2,0.7272727 4,37,23,5.5,2,12,4,2,0.5326087 4,17.5,2.5,0,1,12,4,2,17.9199982 5,22,2.5,1,1,14,3,5,57.5999908 4,22,2.5,0,2,14,5,4,1.3999996 3,27,9,2,2,14,3,5,4.666666 2,32,9,2,2,16,4,5,2.1777763 5,22,2.5,0,1,14,2,2,0.4 3,22,2.5,1,3,14,5,4,0.4 3,27,9,2,3,12,5,5,7.4666662 4,27,16.5,5.5,2,14,4,4,0.0606061 4,32,13,2,3,14,3,5,0.2692307 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3,37,23,4,2,12,3,2,1.9478254 4,37,16.5,3,3,12,5,2,0.7424242 5,22,2.5,1,2,14,4,1,7.8399963 2,27,2.5,0,1,14,3,3,11.1999998 5,22,2.5,1,2,16,4,4,1.3999996 4,22,2.5,1,3,12,3,3,4.7999992 4,27,6,2,2,14,3,5,0.1666666 5,22,2.5,0,3,16,4,4,0.4 4,27,6,1,3,16,3,3,1.333333 5,42,23,3,2,17,5,5,0.0434783 5,37,16.5,4,2,12,3,2,1.696969 2,27,6,0,1,14,3,4,7 4,32,16.5,3,4,14,3,5,1.1878777 5,37,16.5,5.5,3,20,4,6,0.7424242 5,27,2.5,2,2,16,3,2,0.4 3,27,9,2,2,14,3,6,4.666666 4,32,13,4,3,12,3,4,0.2692307 5,27,9,1,3,12,3,4,2.1777763 5,32,6,3,1,14,3,2,4.666666 4,32,16.5,3,1,12,3,4,1.696969 3,27,9,0,1,12,3,5,7.4666662 4,32,13,1,3,14,3,5,1.5076914 3,42,23,2,3,9,2,2,0.5217391 3,22,6,1,2,12,3,2,1.333333 4,37,23,3,3,12,5,2,0.3478261 5,42,23,3,3,16,4,2,0.3478261 2,22,6,0,2,12,3,3,3.2666645 3,37,13,2,2,12,3,5,0.2692307 3,32,13,2,2,16,3,5,0.9230769 3,22,2.5,0,2,12,3,3,0.4 3,42,23,5.5,3,14,3,4,0.5326087 2,32,16.5,2,2,16,4,6,0.7424242 4,27,6,2,3,14,5,4,2 4,37,16.5,3,4,16,4,5,0.7424242 5,37,23,3,1,12,3,5,0.3478261 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3,27,6,3,3,14,3,5,2.041666 4,22,2.5,5.5,3,12,3,2,1.3999996 5,22,6,2,3,12,3,5,0.5833333 3,27,6,1,2,16,4,4,0.5833333 3,22,2.5,0,3,14,3,5,3.1999998 4,32,16.5,5.5,2,12,3,5,0.2121212 4,27,9,2,3,12,3,2,0.8888888 2,37,23,3,3,12,3,4,0.0434783 3,37,23,4,3,12,5,2,0.0434783 3,32,16.5,5.5,2,12,3,3,2.545454 3,22,2.5,0,2,16,5,4,1.3999996 5,37,23,4,3,12,2,2,1.217391 5,22,2.5,0,3,12,3,2,1.3999996 5,27,9,1,3,12,2,2,0.3888888 5,32,13,3,1,12,2,2,0.6153846 3,32,16.5,2,3,12,2,2,1.1878777 5,32,16.5,2,1,16,4,2,4.0727262 4,27,6,0,2,16,4,2,2 4,22,2.5,0,3,16,3,5,1.3999996 2,22,2.5,0,3,14,3,2,4.7999992 4,37,16.5,2,3,14,3,5,8.727272 5,27,6,1,4,16,4,4,2.041666 3,22,6,1,3,12,4,4,4.666666 2,32,9,2,3,14,2,4,1.3611107 4,27,6,2,1,14,3,1,3.2666645 3,27,2.5,0,1,17,5,4,16.7999878 2,22,6,1,3,12,3,5,2.041666 3,27,9,2,3,12,2,2,2.1777763 3,37,23,4,1,17,4,4,0.5217391 3,42,23,5.5,4,20,4,4,0.5326087 5,22,2.5,1,3,12,6,2,26.8799896 4,37,16.5,3,2,12,2,2,0.7272727 5,27,6,0,2,12,3,2,11.1999989 3,22,2.5,0,2,14,5,2,11.1999998 3,32,16.5,3,2,14,5,5,2.7151508 5,27,6,0,3,16,5,5,3.2666645 2,42,23,3,2,12,3,4,0.0434783 4,27,2.5,0,3,20,4,5,7.8399963 5,22,2.5,0,3,14,3,5,0.4 3,27,6,1,3,12,3,4,0.5833333 5,32,13,1,2,14,4,3,0.9423077 2,32,2.5,0,3,20,4,4,1.3999996 2,27,9,2,2,16,4,5,0.1111111 5,22,2.5,0,2,14,3,3,1.3999996 4,27,2.5,0,2,17,4,5,0.4 4,27,0.5,0,3,12,3,4,2 4,32,13,2,4,14,4,5,0.2692307 3,27,9,1,3,12,2,2,2.1777763 3,32,13,2,2,12,3,5,3.4461536 5,27,9,2,2,12,3,5,0.3888888 5,22,6,1,2,12,3,2,1.333333 3,32,13,0,1,12,3,4,3.4461536 3,37,23,2,3,14,4,5,0.3478261 5,32,16.5,1,3,12,3,2,1.1878777 3,32,16.5,3,1,14,3,6,1.1878777 5,22,6,0,1,14,4,4,7 4,37,13,0,2,20,2,6,5.1692305 3,42,23,3,2,14,5,4,0.5217391 4,22,2.5,0,3,14,3,4,7.8399963 4,27,13,3,3,14,6,5,5.1692305 2,42,23,1,1,20,6,6,1.9478254 2,27,9,2,3,14,2,2,1.3611107 5,27,6,1,2,14,4,2,2.041666 5,37,13,4,2,12,4,5,1.5076914 2,22,6,1,2,14,3,2,3.2666645 3,17.5,2.5,0,4,14,3,4,3.1999998 5,22,2.5,1,3,14,2,4,16.7999878 3,27,6,0,3,14,3,4,2 5,27,9,0,1,14,3,5,2.1777763 3,32,13,3,2,14,3,6,1.5076914 4,37,23,3,3,12,3,4,0.5217391 4,42,23,1,2,12,5,5,0.3478261 3,32,9,2,3,16,3,5,2.1777763 5,42,23,3,4,14,5,6,0.5217391 4,27,9,2,2,12,3,4,1.3611107 4,32,16.5,3,3,14,5,5,1.1878777 2,22,2.5,0,1,14,3,4,11.1999998 3,37,16.5,3,2,12,3,5,5.818181 3,22,2.5,0,2,14,3,4,0.4 4,27,6,0,1,17,4,6,3.2666645 5,32,13,3,3,14,6,6,0.2692307 5,42,23,3,2,12,4,2,2.9217386 5,22,2.5,1,3,14,3,2,1.3999996 4,42,23,2,3,12,3,5,1.826086 4,32,16.5,2,4,14,3,5,0.7272727 3,27,2.5,2,1,14,4,2,3.1999998 5,42,16.5,2,1,20,4,4,0.2121212 5,22,2.5,0,2,14,5,4,0.4 3,22,2.5,0,1,14,5,4,4.7999992 4,27,2.5,0,2,16,3,5,3.1999998 3,27,6,1,3,14,3,4,0.5833333 4,42,23,2,3,14,3,5,0.5326087 2,37,2.5,1,3,14,3,2,1.3999996 2,42,23,2,3,14,3,5,0.5326087 1,42,23,2,1,14,5,5,1.826086 5,27,13,1,1,12,2,4,0.9423077 5,32,9,1,3,20,3,3,4.9777775 3,27,9,1,1,16,5,4,0.3888888 4,27,9,2,2,12,4,4,2.1777763 4,27,6,2,3,14,6,5,2.041666 4,27,9,1,2,20,3,5,2.1777763 2,37,16.5,3,2,20,4,4,0.2121212 3,27,9,2,3,14,3,5,0.3888888 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1,32,13,3,3,12,2,4,0.0769231 5,22,2.5,1,3,12,3,4,4.8999996 4,22,2.5,0,2,14,3,5,4.7999992 3,22,2.5,0,2,16,4,4,4.8999996 5,37,16.5,2,3,12,5,5,4.0727262 4,17.5,2.5,0,2,12,2,5,4.7999992 5,42,23,2,3,17,5,2,0.5326087 3,32,13,2,3,14,5,2,3.2307692 3,42,23,3,2,12,4,4,1.217391 4,27,2.5,0,3,14,3,4,1.3999996 5,42,13,0,2,12,5,5,0.9230769 2,32,9,2,4,16,4,4,2.1777763 3,27,6,1,3,14,3,4,3.2666645 2,27,9,3,3,12,5,4,2.1777763 3,27,6,2,3,14,4,6,0.5833333 5,27,9,2,1,14,3,4,4.9777775 2,27,13,1,3,12,3,2,0.0769231 4,27,13,2,2,12,3,4,0.0769231 5,22,2.5,0,3,12,3,4,4.7999992 4,27,9,0,3,12,2,4,4.666666 3,27,9,2,3,12,4,5,0.3888888 3,22,2.5,0,2,17,2,6,3.1999998 3,27,6,1,3,14,3,3,2.041666 4,27,6,0,2,14,5,3,2 4,32,16.5,3,3,14,5,5,0.0606061 4,32,16.5,2,3,12,5,5,0.7424242 3,42,16.5,2,4,16,5,5,0.2121212 3,32,6,2,3,14,3,4,2 3,37,23,4,4,9,5,5,0.5326087 1,42,16.5,1,2,12,3,5,1.1878777 2,42,23,5.5,3,14,2,2,0.5217391 5,37,16.5,4,4,14,3,3,0.0606061 5,42,23,1,3,14,3,5,0.8521735 3,22,2.5,1,2,12,2,2,7.8399963 5,22,2.5,0,2,16,5,4,4.8999996 2,27,6,1,2,14,3,4,0.1666666 1,32,13,2,2,16,4,6,0.0769231 3,32,16.5,2,2,16,4,2,1.1878777 5,27,9,1,3,14,3,5,2.1777763 3,22,6,1,1,12,3,2,3.2666645 4,27,6,2,3,12,3,2,1.333333 4,37,16.5,2,4,17,6,6,0.4848484 4,22,2.5,0,3,12,3,3,0.4 1,42,23,3,3,14,3,5,0.8521735 2,22,2.5,0,2,12,3,5,4.7999992 4,37,13,1,3,20,4,2,1.5076914 2,27,6,1,2,12,2,2,0.5833333 5,27,9,2,3,20,4,4,0.3888888 3,42,23,3,3,12,2,2,1.826086 3,42,16.5,2,3,14,3,2,0.7272727 2,32,16.5,1,1,17,4,6,0.2121212 4,22,2.5,1,2,14,3,5,0.4 5,42,23,3,4,16,3,4,0.8521735 4,27,13,2,3,14,3,5,3.2307692 1,32,13,1,2,12,3,3,0.0769231 3,42,23,5.5,2,14,5,6,0.5217391 5,37,23,2,3,12,3,5,1.826086 5,22,2.5,0,1,14,4,1,4.8999996 3,27,6,0,2,14,3,6,2 4,22,2.5,1,1,14,2,2,1.3999996 5,37,13,2,1,12,3,4,0.9423077 4,42,23,3,2,14,3,3,0.8521735 3,27,6,2,3,20,4,2,0.5833333 4,27,6,2,2,12,2,5,2.041666 2,27,2.5,1,3,14,2,4,7.8399963 5,42,23,4,1,12,5,6,0.1521739 3,42,13,0,3,17,3,2,0.9230769 5,27,6,0,3,14,3,5,0.5833333 4,22,2.5,0,3,14,3,2,1.3999996 3,27,13,3,3,12,4,4,5.1692305 3,32,9,0,2,14,3,4,0.8888888 1,37,16.5,1,3,14,2,5,0.2121212 3,37,23,2,3,12,5,5,0.8521735 2,32,16.5,2,2,12,3,4,0.7272727 3,27,6,0,1,16,4,1,7.4666672 4,42,16.5,3,2,14,5,4,0.7424242 1,42,16.5,1,3,12,5,5,1.1878777 3,32,16.5,3,4,12,4,2,0.7272727 5,27,6,1,4,14,3,3,2.041666 2,32,16.5,2,2,14,2,3,4.0727262 2,17.5,2.5,0,2,12,3,2,16.7999878 4,32,16.5,2,3,14,3,4,0.7424242 3,22,2.5,0,3,14,3,4,7.8399963 5,27,6,0,3,16,3,6,2 5,37,23,3,3,12,3,5,0.1521739 3,32,13,0,2,12,5,2,0.2692307 4,42,23,1,2,14,5,5,0.0434783 1,42,23,2,3,14,3,4,2.9217386 2,37,23,3,2,12,3,3,1.9478254 4,22,0.5,0,3,16,4,6,7 5,27,9,2,2,14,3,4,0.1111111 4,22,0.5,0,1,14,5,4,16 3,27,9,2,2,17,4,4,1.3611107 4,42,23,2,3,14,3,5,1.217391 4,22,2.5,0,2,14,5,5,1.3999996 4,22,2.5,0,3,14,3,4,17.9199982 4,27,13,2,3,14,5,5,0.9423077 4,27,6,1,3,16,4,6,2.041666 3,42,23,3,2,14,2,2,0.5217391 3,27,6,2,1,12,4,3,2.041666 5,42,23,2,2,16,3,5,0.8521735 3,42,23,2,4,14,3,6,0.0434783 5,32,16.5,1,3,14,3,5,0.2121212 4,27,6,2,3,14,5,5,0.1666666 2,37,23,3,1,14,5,5,0.1521739 4,22,6,1,3,14,3,1,3.2666645 2,27,9,2,3,12,3,4,1.333333 5,27,9,2,1,14,4,4,2.1777763 4,27,2.5,0,3,14,3,4,16.7999878 5,37,16.5,2,2,14,3,5,0.7272727 3,22,2.5,1,2,14,2,2,0.4 4,27,2.5,1,2,16,3,6,0.4 1,27,6,1,1,12,3,5,0.1666666 4,37,16.5,5.5,3,17,4,2,0.2121212 5,22,2.5,0,3,12,4,3,4.8999996 2,22,2.5,1,1,14,2,6,1.3999996 5,42,16.5,2,3,12,3,3,0.7424242 3,22,6,1,2,12,4,4,1.333333 3,37,23,3,3,12,3,4,1.9478254 4,27,6,1,3,14,3,2,2.041666 2,32,16.5,4,2,12,3,4,1.1878777 3,27,9,1,2,14,3,5,0.3888888 5,32,16.5,3,3,16,4,6,0.2121212 5,22,6,1,2,12,3,2,0.5833333 3,27,13,3,2,12,2,4,0.2692307 5,22,2.5,0,3,12,3,3,1.3999996 4,32,2.5,0,2,14,5,6,1.3999996 3,27,6,1,2,14,3,5,2.041666 3,27,6,2,3,12,3,6,0.1666666 2,32,13,2,3,12,3,3,0.2692307 4,32,16.5,2,3,14,2,5,0.2121212 3,27,6,2,2,12,5,2,2 4,27,9,2,1,17,4,5,2.1777763 4,37,16.5,2,2,12,4,4,1.1878777 3,22,2.5,0,3,16,3,4,4.7999992 4,27,9,2,2,14,5,5,1.3611107 3,32,16.5,4,2,12,3,5,1.696969 3,27,9,2,4,14,3,2,2.1777763 4,32,13,2,2,16,2,5,0.2692307 2,37,23,2,3,14,3,5,0.5217391 4,22,2.5,0,1,17,3,4,17.9199982 4,27,6,0,1,17,4,4,0.1666666 4,27,6,1,2,12,3,6,7 3,22,6,0,3,12,3,2,0.5833333 3,42,23,3,2,12,3,2,1.826086 4,27,6,3,3,16,3,2,2.041666 4,32,16.5,3,3,12,3,3,1.1878777 3,27,13,2,3,12,5,5,3.4461536 3,27,16.5,2,4,14,3,4,0.2121212 3,27,6,3,2,14,2,2,0.1666666 5,22,2.5,0,3,12,5,2,0.4 4,42,23,2,2,16,5,5,1.217391 1,22,2.5,1,3,14,3,5,4.8999996 4,42,23,3,3,16,4,3,0.1521739 3,32,16.5,2,4,16,4,4,0.7424242 3,32,16.5,2,4,12,3,3,0.7272727 4,22,2.5,1,2,14,3,3,4.7999992 3,22,6,1,3,14,3,5,0.5833333 4,27,6,0,2,12,3,4,0.1666666 5,32,16.5,4,4,9,2,4,0.0606061 1,27,6,1,3,20,4,4,2 2,22,2.5,0,1,14,3,2,4.8999996 5,27,9,3,4,14,2,5,0.3888888 3,27,6,1,2,16,3,1,7 4,32,16.5,2,1,16,5,5,2.545454 5,27,6,0,3,12,3,3,2.041666 4,42,23,2,3,12,5,2,0.5326087 3,27,9,2,2,12,3,4,2.1777763 4,27,9,0,3,14,3,3,2.1777763 4,27,13,2,3,17,4,2,3.4461536 5,32,16.5,2,3,17,4,4,1.1878777 4,27,6,1,2,12,5,4,3.2666645 4,42,23,4,4,12,5,5,0.5326087 1,42,23,2,4,14,3,2,0.3478261 2,22,2.5,1,3,14,3,4,1.3999996 1,32,9,3,2,14,4,4,1.3611107 3,27,6,1,3,12,4,3,0.5833333 4,27,2.5,0,1,16,4,1,4.7999992 5,42,23,2,1,17,4,5,0.5217391 5,32,16.5,2,3,14,3,5,0.4848484 4,32,16.5,2,4,12,3,4,2.545454 5,42,23,3,3,14,4,2,0.5326087 4,37,16.5,2,1,16,3,5,0.7272727 2,32,9,0,2,12,3,4,0.1111111 4,37,16.5,2,4,12,3,4,0.4848484 3,32,6,3,2,14,3,4,0.5833333 5,37,23,2,2,14,3,5,0.3478261 2,32,13,0,2,12,3,4,1.5076914 4,32,13,2,3,14,3,4,3.2307692 4,37,16.5,3,3,14,3,3,0.2121212 4,22,2.5,0,2,14,3,5,4.7999992 5,42,23,4,2,14,3,4,0.8521735 3,32,16.5,2,4,12,3,5,1.1878777 4,42,23,4,3,9,3,5,1.9478254 5,22,2.5,1,2,12,3,4,4.8999996 2,32,9,0,2,14,3,4,0.8888888 5,32,13,1,2,14,3,5,0.2692307 2,32,16.5,3,2,12,5,2,0.7424242 5,27,6,1,1,14,5,5,2 4,42,23,4,2,14,3,4,0.8521735 3,27,6,1,2,14,5,2,3.2666645 3,32,13,1,1,16,4,2,0.6153846 2,27,6,2,3,12,3,5,0.1666666 4,27,9,2,3,12,4,4,1.3611107 5,22,2.5,1,1,14,3,2,11.1999998 2,22,2.5,1,3,14,5,6,7.8399963 5,22,2.5,0,1,16,5,4,0.4 4,37,23,4,3,14,3,5,1.9478254 2,32,16.5,4,3,12,3,4,0.7424242 3,22,6,1,3,12,3,4,2.041666 3,32,9,2,3,12,3,4,16 1,27,2.5,0,2,12,4,3,4.8999996 5,42,23,2,3,14,3,4,0.5217391 4,27,13,1,2,12,3,4,0.0769231 4,32,9,2,3,17,3,4,1.3611107 5,42,23,4,4,14,5,4,0.5326087 4,27,6,2,3,12,2,2,2.041666 5,32,16.5,2,4,16,5,6,1.696969 5,27,9,2,1,16,2,5,0.3888888 4,22,6,1,2,12,2,1,2.041666 5,32,13,2,1,12,3,4,0.0769231 3,42,23,2,3,12,3,5,0.1521739 4,27,16.5,4,3,17,4,6,2.545454 3,37,16.5,2,3,12,3,4,0.7272727 5,27,6,2,3,14,4,2,3.2666645 2,17.5,2.5,1,3,12,3,2,1.3999996 4,22,2.5,1,2,14,3,5,1.3999996 5,27,6,1,3,14,3,3,2.041666 5,27,6,2,2,12,4,4,0.1666666 3,37,23,3,4,12,5,2,0.8521735 3,27,6,3,2,16,4,4,0.5833333 3,22,2.5,1,2,12,3,2,1.3999996 5,27,13,3,1,12,3,5,1.5076914 4,27,9,1,4,14,3,3,1.3611107 1,37,23,5.5,2,12,3,4,0.5217391 5,42,23,3,1,16,4,6,0.8521735 4,27,2.5,0,1,16,5,6,7.8399963 4,32,13,2,2,12,5,4,0.9423077 3,27,9,2,2,14,5,5,1.3611107 4,22,2.5,0,1,14,3,4,4.8999996 4,22,2.5,0,1,12,3,2,1.3999996 4,27,6,1,3,17,4,5,0.1666666 4,37,23,5.5,2,12,3,4,0.5217391 5,32,16.5,2,1,12,2,4,0.2121212 2,22,6,2,4,17,3,5,2 2,22,2.5,0,1,17,4,4,26.8799896 4,27,6,0,3,14,2,2,7.4666672 5,22,2.5,0,2,14,3,5,1.3999996 5,42,23,3,3,12,3,5,2.782608 5,32,13,2,2,14,5,4,0.9423077 5,32,13,2,2,14,5,4,1.5076914 3,42,23,5.5,3,20,6,4,2.9217386 5,22,2.5,1,1,12,3,5,1.3999996 2,22,2.5,0,1,16,3,5,17.9199982 3,42,23,2,3,16,4,5,0.1521739 3,27,6,0,2,12,3,4,4.666666 3,27,13,0,3,12,3,5,0.9423077 4,22,2.5,0,2,12,2,4,4.7999992 3,32,13,2,3,14,3,3,1.5076914 5,37,16.5,3,2,12,3,5,0.7272727 3,27,9,1,3,14,3,3,2.1777763 5,42,23,2,2,12,5,4,0.8521735 5,27,2.5,0,1,17,4,5,4.8999996 3,32,0.5,0,2,16,4,5,24.5 4,32,13,2,3,12,3,1,0.0769231 3,42,23,5.5,3,12,3,2,0.1521739 1,42,23,2,3,14,3,5,0.8521735 3,37,16.5,2,1,17,4,2,0.7272727 4,27,9,1,1,14,3,4,1.3611107 4,22,2.5,0,3,12,3,2,1.3999996 4,37,23,5.5,2,14,3,6,1.217391 5,32,9,3,2,14,2,6,0.3888888 4,37,9,3,3,16,4,2,2.1777763 5,37,16.5,3,3,16,3,4,1.696969 4,32,13,2,2,12,5,5,0.2692307 3,37,23,3,4,12,3,5,0.5217391 4,42,23,3,4,16,4,2,1.217391 3,27,6,1,2,12,3,4,0.5833333 5,42,23,2,3,16,3,5,0.5326087 5,27,6,1,3,20,5,6,1.333333 3,37,16.5,3,2,12,3,2,2.7151508 3,27,13,2,3,12,3,5,0.9230769 5,37,23,3,1,12,3,4,0.0434783 3,42,23,5.5,3,12,3,4,0.0434783 2,27,9,0,2,17,2,3,1.3611107 2,22,2.5,0,3,12,2,3,0.4 4,27,13,2,2,12,3,2,0.0769231 4,32,9,1,1,16,4,5,4.9777775 3,32,9,3,3,14,3,6,2.1777763 4,32,13,1,3,14,5,5,0.2692307 5,22,2.5,0,3,16,4,1,1.3999996 3,27,6,1,2,17,4,4,4.666666 4,27,2.5,0,2,16,4,2,7.8399963 3,27,6,0,1,16,4,4,1.333333 3,42,16.5,4,3,16,2,4,0.2121212 1,27,2.5,1,3,16,4,4,1.3999996 5,27,9,1,2,14,4,4,3.1111107 4,42,23,2,3,12,3,5,1.826086 4,27,13,2,2,12,3,4,0.9230769 5,32,13,1,1,14,4,4,0.9423077 4,22,2.5,1,2,14,3,4,7.8399963 2,27,9,2,2,14,5,5,4.666666 5,22,2.5,0,4,14,3,4,4.8999996 3,37,16.5,3,3,14,3,3,0.2121212 3,27,6,1,3,14,3,4,7 4,32,13,2,1,12,2,4,3.4461536 3,42,23,2,2,16,4,5,0.5217391 4,37,16.5,3,3,12,3,5,0.2121212 4,32,13,2,1,12,2,4,1.5076914 3,42,23,5.5,2,12,3,4,2.9217386 3,27,9,1,1,14,3,4,1.3611107 5,32,16.5,2,3,12,3,2,0.7424242 5,32,16.5,1,2,14,5,5,0.2121212 5,27,6,2,2,20,6,6,4.666666 4,27,6,2,3,12,3,2,0.1666666 3,22,6,1,3,14,5,4,0.5833333 5,27,16.5,3,3,14,5,5,0.2121212 5,22,2.5,0,2,14,5,6,4.8999996 3,42,23,5.5,3,14,3,5,0.5217391 4,37,23,3,2,12,3,3,0.5217391 1,42,16.5,3,4,17,4,4,1.1878777 3,27,2.5,0,2,16,4,2,4.7999992 2,27,6,2,2,12,5,1,0.1666666 5,27,9,3,2,16,4,4,0.1111111 5,27,6,2,2,12,5,3,0.5833333 3,32,16.5,3,1,14,2,2,0.4848484 4,27,6,2,2,14,3,4,0.5833333 4,27,0.5,0,3,17,4,6,7 4,22,6,1,2,12,3,3,3.2666645 5,42,23,2,1,14,6,6,2.9217386 4,27,6,2,4,14,5,4,0.1666666 5,27,6,1,2,16,4,5,2.041666 3,42,13,0,3,17,4,5,0.2692307 3,42,23,3,3,12,2,2,1.217391 5,27,6,1,1,16,4,4,2.041666 5,22,6,0,3,14,3,4,2.041666 3,42,23,3,2,14,3,4,0.8521735 4,22,6,0,2,16,4,5,0.5833333 5,32,13,2,1,14,3,5,3.2307692 3,22,6,2,3,14,2,2,11.1999989 5,27,2.5,0,2,14,3,2,1.3999996 2,42,23,5.5,2,14,3,2,0.0434783 4,37,23,3,1,14,3,4,0.8521735 5,22,2.5,0,1,14,3,4,7.8399963 5,27,2.5,0,1,17,4,5,0.4 4,27,9,1,2,16,3,5,0.3888888 4,27,9,3,3,14,3,4,1.3611107 4,42,23,4,2,16,4,5,0.8521735 4,37,16.5,4,3,14,3,2,0.7424242 4,32,9,0,2,12,3,3,1.333333 4,32,13,0,2,14,3,6,0.9423077 2,22,6,2,2,12,3,4,0.5833333 3,42,23,3,2,14,5,5,0.5326087 4,32,13,0,3,14,3,5,0.9230769 4,37,16.5,4,3,14,3,4,0.7424242 3,37,23,3,2,12,3,5,0.8521735 4,32,16.5,2,1,14,3,4,2.545454 4,27,6,1,2,16,3,5,2 4,22,2.5,0,2,16,4,5,3.1999998 2,22,2.5,0,3,12,3,2,1.3999996 4,27,6,1,2,14,3,3,1.333333 3,32,16.5,2,2,12,3,2,2.7151508 5,17.5,2.5,2,2,12,2,5,4.7999992 3,22,2.5,0,3,20,3,1,7.8399963 2,37,23,3,3,12,3,2,0.8521735 4,32,16.5,5.5,3,14,5,5,0.7424242 3,42,23,3,4,14,3,5,0.1521739 5,22,2.5,0,2,14,2,3,4.8999996 4,27,9,0,2,14,3,4,3.1111107 3,22,2.5,2,2,12,3,4,0.4 3,42,23,2,4,14,4,6,1.217391 5,42,23,5.5,2,14,3,2,0.5326087 4,27,6,0,3,14,6,6,2.041666 4,42,23,4,3,14,3,4,0.5217391 3,32,16.5,2,2,12,3,5,0.3393939 3,27,2.5,0,1,16,3,4,4.8999996 3,32,13,2,4,14,4,2,5.1692305 2,37,16.5,2,2,14,3,5,1.1878777 4,22,6,2,2,12,2,4,2 4,32,9,3,1,16,2,4,0.1111111 3,42,23,4,2,12,3,5,0.3478261 3,32,13,2,3,12,3,3,2.1538458 4,22,2.5,0,2,14,2,1,0.4 3,37,16.5,2,2,12,2,2,0.7272727 3,27,6,0,2,14,3,5,2 4,42,23,3,3,12,5,4,2.9217386 4,42,23,3,2,14,5,4,1.217391 2,42,23,5.5,4,14,2,5,0.1521739 5,27,6,0,2,12,5,4,0.5833333 4,32,16.5,1,2,14,3,4,0.7424242 4,37,16.5,3,3,12,4,2,1.696969 5,42,23,5.5,3,14,3,6,0.1521739 3,37,16.5,3,2,14,5,4,0.7272727 2,37,13,1,1,16,4,4,0.9423077 4,27,6,0,3,16,5,6,0.5833333 4,42,23,4,3,16,4,2,0.8521735 5,27,2.5,0,2,14,3,4,3.1999998 2,42,23,3,1,12,3,5,2.9217386 5,37,13,2,1,14,3,6,0.2692307 4,32,16.5,2,1,12,5,2,1.1878777 4,37,16.5,3,1,14,3,2,0.4848484 3,27,2.5,0,1,14,5,4,1.3999996 4,22,2.5,0,1,14,3,3,4.8999996 4,37,16.5,2,2,14,3,5,1.1878777 3,27,6,2,2,12,3,4,0.1666666 4,22,6,1,2,12,3,4,0.1666666 5,32,13,2,2,14,4,5,0.2692307 2,37,23,3,3,12,5,2,0.5326087 2,37,16.5,2,3,14,3,5,2.545454 4,32,16.5,2,2,14,5,4,0.7272727 5,37,16.5,5.5,4,20,4,6,0.7424242 3,32,16.5,3,2,12,3,2,1.1878777 3,27,9,4,3,14,3,5,2.1777763 3,37,13,0,1,14,5,4,3.2307692 2,27,2.5,2,2,14,4,5,4.8999996 4,27,9,2,1,14,3,4,0.8888888 4,42,23,2,3,14,3,4,0.3478261 5,42,23,2,1,14,5,5,0.8521735 4,42,23,4,2,12,3,3,0.5217391 4,37,16.5,4,3,14,3,5,0.2121212 4,32,16.5,3,2,12,2,2,1.1878777 2,37,23,2,2,12,4,4,0.0434783 4,42,23,1,3,20,4,4,0.5217391 4,22,2.5,1,2,14,4,4,4.8999996 1,32,13,2,2,14,4,4,2.1538458 3,42,23,3,2,14,3,5,1.826086 4,22,2.5,0,2,16,3,2,1.3999996 5,37,23,3,3,12,3,5,0.1521739 3,22,2.5,0,2,14,2,2,4.8999996 1,42,23,3,3,12,5,5,1.9478254 3,22,2.5,0,2,14,3,4,4.8999996 3,37,23,2,3,12,3,5,1.9478254 4,22,2.5,0,2,12,5,4,0.4 5,32,9,2,3,14,5,4,0.1111111 1,37,16.5,4,2,14,3,6,1.1878777 3,42,23,1,3,12,3,4,0.8521735 5,27,9,2,1,12,3,2,1.3611107 4,22,2.5,1,2,14,2,4,17.9199982 5,42,23,3,2,14,3,5,0.8521735 4,27,6,1,3,14,5,5,2 5,22,2.5,0,1,14,3,1,1.3999996 3,37,16.5,2,3,16,3,4,0.7272727 3,32,16.5,2,3,12,5,5,2.545454 5,37,16.5,3,3,14,2,5,0.7272727 3,27,6,2,1,16,4,3,0.5833333 4,27,9,1,2,20,4,4,0.1111111 4,27,6,0,2,14,3,4,2.041666 5,32,13,2,2,14,3,4,0.9423077 4,42,23,2,2,12,4,4,1.217391 2,37,16.5,2,2,12,5,4,0.4848484 4,27,2.5,0,2,17,5,5,0.4 4,37,16.5,2,2,17,4,5,0.2121212 4,32,13,2,2,12,3,3,0.9230769 4,32,9,2,1,14,3,4,1.3611107 4,32,9,2,2,14,3,5,0.1111111 4,32,13,2,3,12,5,5,0.9423077 4,32,13,3,2,14,3,2,2.1538458 4,22,2.5,1,1,14,2,2,1.3999996 5,27,16.5,2,3,12,5,4,0.2121212 4,32,2.5,0,3,14,2,6,4.8999996 3,32,16.5,1,2,14,2,4,0.2121212 3,37,16.5,3,2,16,4,3,1.1878777 5,42,23,4,2,12,5,5,0.8521735 5,27,6,2,3,14,3,4,3.2666645 4,22,2.5,1,3,12,3,2,1.3999996 4,42,23,3,1,20,6,6,1.217391 4,32,9,2,1,17,4,5,2.1777763 5,32,2.5,0,2,20,6,6,1.3999996 3,27,6,1,2,14,3,6,0.5833333 1,22,2.5,0,1,14,4,4,4.8999996 3,32,16.5,2,2,14,3,2,0.7424242 4,42,23,2,2,12,3,3,0.1521739 4,22,6,1,3,14,3,5,0.5833333 5,42,23,3,2,14,3,5,0.5326087 3,27,13,2,2,12,4,3,3.4461536 4,32,13,2,2,14,5,4,0.2692307 3,27,2.5,0,3,14,3,4,3.1999998 2,22,2.5,0,3,12,3,2,0.4 5,42,23,2,2,20,4,4,1.826086 3,27,6,1,2,14,2,2,0.5833333 5,42,23,2,2,20,3,6,0.5326087 3,37,23,4,1,12,3,2,0.3478261 5,42,23,4,4,20,6,6,6.260869 5,27,6,0,1,17,4,5,1.333333 3,32,13,2,1,17,4,6,0.2692307 2,27,2.5,0,1,14,5,5,1.3999996 4,27,9,3,2,12,3,6,1.333333 5,27,6,1,1,14,3,5,2 4,22,0.5,0,2,14,4,5,24.5 4,17.5,2.5,1,2,12,3,2,1.3999996 3,27,2.5,1,2,14,3,4,1.3999996 5,32,16.5,3,2,14,2,4,0.0606061 4,42,23,5.5,1,14,3,5,0.5326087 5,22,2.5,0,1,14,3,4,4.8999996 5,42,23,2,2,20,1,5,0.1521739 5,42,23,3,3,14,3,5,0.0434783 2,42,23,3,3,16,4,4,0.1521739 4,32,16.5,4,2,12,3,5,0.4848484 4,37,23,4,2,12,3,4,0.8521735 4,42,23,2,1,14,5,3,0.5326087 5,22,2.5,0,4,14,3,4,1.3999996 4,22,2.5,0,1,16,3,5,1.3999996 4,42,23,3,3,14,5,5,1.9478254 4,42,23,3,2,14,3,5,1.9478254 2,22,2.5,0,2,14,3,5,0.4 3,42,23,3,1,20,4,4,0.0434783 3,37,16.5,3,4,12,3,2,2.545454 4,27,16.5,2,1,12,3,2,0.2121212 3,27,6,1,1,16,4,1,3.2666645 5,32,13,2,2,14,4,4,0.9230769 4,32,13,3,2,12,2,4,0.6153846 5,42,13,0,1,16,3,6,1.5076914 4,32,9,2,3,14,3,4,2.1777763 3,42,16.5,1,3,17,3,3,0.7424242 4,42,23,3,1,14,3,5,0.8521735 1,32,13,3,3,14,3,4,1.5076914 4,27,6,2,2,14,3,5,1.333333 4,27,6,2,2,14,5,4,3.2666645 5,27,2.5,0,3,16,3,5,1.3999996 4,22,2.5,0,1,16,4,2,1.3999996 4,27,6,3,3,14,4,2,2.041666 4,42,23,2,1,16,4,5,0.1521739 3,27,6,1,4,14,5,4,0.5833333 3,32,13,0,2,16,3,6,0.9230769 1,27,6,2,3,14,3,2,3.2666645 3,32,13,2,2,14,3,2,2.1538458 3,37,16.5,4,2,14,4,4,0.7424242 4,37,16.5,3,4,14,4,4,0.2121212 4,32,16.5,4,3,14,3,5,0.2121212 5,27,2.5,0,1,14,3,5,4.7999992 3,22,2.5,0,2,14,3,1,0.4 5,32,23,2,3,14,5,6,0.5217391 3,27,9,1,3,14,3,2,1.333333 5,37,23,3,2,14,5,4,0.3478261 3,27,6,1,3,20,4,4,3.2666645 1,27,9,1,3,14,2,4,0.3888888 4,32,13,4,2,16,4,2,0.2692307 3,32,16.5,3,3,14,5,4,4.0727262 4,42,23,3,3,14,4,5,0.1521739 4,32,16.5,4,2,14,2,5,1.696969 3,27,9,4,4,14,4,4,0.3888888 4,27,6,1,2,12,2,4,0.1666666 2,22,6,1,3,14,3,5,2 4,22,2.5,1,2,14,3,5,1.3999996 3,22,2.5,1,3,14,3,4,3.1999998 5,22,2.5,0,1,14,3,2,1.3999996 5,37,23,3,1,12,5,2,1.826086 3,27,9,1,1,14,3,6,4.666666 4,27,2.5,0,1,14,5,5,7.8399963 5,27,2.5,0,1,16,4,2,1.3999996 3,22,6,2,3,12,3,2,0.5833333 4,37,23,2,2,12,3,2,0.1521739 3,22,2.5,0,2,14,3,4,4.7999992 5,27,2.5,0,2,17,4,5,1.3999996 4,22,2.5,0,1,14,5,2,4.8999996 4,27,6,1,2,14,3,3,7 4,27,6,2,2,14,4,4,0.5833333 4,32,13,0,2,12,3,3,3.2307692 4,27,9,2,2,12,2,4,1.3611107 3,22,2.5,0,1,14,3,2,7.8399963 5,27,2.5,0,1,14,2,4,7.8399963 3,37,23,4,2,14,3,4,1.9478254 5,27,9,3,2,16,4,4,1.333333 4,22,2.5,0,2,12,2,2,1.3999996 2,32,16.5,3,1,14,4,4,1.1878777 3,27,9,2,2,12,3,2,1.3611107 4,32,16.5,3,1,16,4,5,0.7272727 3,27,9,1,2,12,5,4,0.1111111 5,22,2.5,0,1,14,2,2,7.8399963 4,42,23,2,3,14,5,5,0.5326087 5,27,6,1,4,16,4,1,0.1666666 4,22,2.5,0,1,12,2,2,4.8999996 3,22,0.5,0,1,14,5,5,7 5,42,23,5.5,3,14,5,4,1.9478254 4,37,16.5,2,2,12,3,4,0.2121212 4,22,2.5,0,2,12,3,3,3.1999998 3,22,6,1,2,12,3,2,2.041666 4,42,23,4,3,17,6,3,2.782608 4,22,6,0,4,14,5,4,2.041666 2,27,9,3,3,14,4,4,0.1111111 3,27,6,1,1,14,5,4,7 5,42,23,5.5,3,16,4,6,0.0434783 4,37,13,3,1,12,4,4,0.6153846 4,37,16.5,2,3,12,5,5,0.7272727 4,37,13,3,3,16,3,4,0.2692307 5,22,6,2,2,12,2,4,4.666666 4,27,2.5,0,2,17,4,6,16.7999878 2,22,6,2,3,14,3,5,3.2666645 3,22,6,1,2,12,4,4,1.333333 5,32,9,2,2,14,4,4,0.1111111 3,27,9,3,2,12,3,2,0.3888888 3,32,13,2,3,14,3,5,0.2692307 2,27,9,1,2,16,4,5,4.9777775 4,27,6,1,1,17,3,5,0.5833333 4,32,2.5,0,2,20,6,6,16.7999878 5,32,13,2,1,12,3,5,0.9423077 4,32,16.5,1,1,14,5,5,1.696969 5,27,2.5,0,3,12,4,5,0.4 3,42,23,4,3,16,4,2,0.3478261 4,22,6,1,3,14,3,4,2.041666 4,27,6,0,3,14,3,4,0.1666666 4,17.5,2.5,0,2,12,3,4,7.8399963 4,22,2.5,0,1,12,3,4,0.4 1,37,13,3,1,17,5,6,0.0769231 2,32,16.5,2,1,12,5,2,2.7151508 3,37,23,3,3,12,3,5,0.1521739 5,27,9,1,3,12,3,5,1.3611107 4,27,6,1,1,12,3,3,3.2666645 3,37,23,4,2,12,3,2,1.826086 4,37,23,5.5,3,12,5,5,0.1521739 1,22,2.5,1,1,12,3,4,1.3999996 3,22,2.5,0,1,12,3,5,4.7999992 4,27,2.5,0,1,16,4,2,7.8399963 4,32,13,1,3,16,3,5,0.6153846 4,27,2.5,0,2,12,3,4,7.8399963 2,32,9,0,2,16,3,2,1.3611107 5,42,23,4,1,20,4,5,0.5217391 4,22,6,1,1,12,3,5,2 3,42,23,4,2,14,3,5,0.3478261 5,27,6,0,3,12,3,4,2.041666 4,42,16.5,2,2,12,3,4,0.2121212 5,22,2.5,0,2,16,4,1,3.1999998 4,42,23,5.5,4,20,4,2,0.1521739 5,27,9,2,3,14,4,5,0.3888888 4,27,2.5,1,3,16,6,5,4.7999992 3,27,9,0,3,16,5,5,1.333333 5,37,23,3,2,14,3,5,0.1521739 4,37,16.5,2,3,17,4,2,1.696969 3,27,6,1,1,17,4,5,2.041666 5,32,16.5,3,4,14,3,6,0.7272727 5,37,16.5,2,3,12,3,4,0.7424242 3,27,6,1,4,17,4,5,7 3,37,16.5,3,2,14,3,4,2.545454 5,27,9,1,1,14,3,4,0.3888888 3,32,9,3,2,14,5,5,1.3611107 2,42,23,3,2,12,3,5,0.8521735 4,32,13,2,1,14,3,5,1.5076914 4,22,2.5,1,2,14,3,2,4.7999992 5,27,9,2,2,12,3,6,0.3888888 5,32,16.5,2,1,12,3,6,0.0606061 4,32,16.5,2,2,12,2,4,1.696969 4,42,23,3,2,12,3,5,1.9478254 4,32,9,2,3,9,3,5,1.333333 2,27,6,2,3,12,3,4,2.041666 5,42,23,2,3,16,4,5,0.2434782 4,37,23,3,4,12,5,4,0.5326087 4,32,16.5,3,2,12,5,5,3.878787 3,27,9,1,2,12,3,4,7.1111107 2,37,13,2,3,14,3,4,0.9230769 4,32,16.5,3,3,14,4,3,0.2121212 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4,32,13,2,2,14,5,5,0.2692307 5,27,9,2,3,14,4,4,1.333333 5,42,16.5,4,3,16,5,4,0.0606061 1,37,13,1,4,16,3,5,1.5076914 5,32,9,3,2,16,4,5,0.3888888 3,22,2.5,0,2,12,3,3,25.5999908 3,27,2.5,0,4,14,5,6,0.4 3,22,2.5,0,2,12,3,5,1.3999996 2,32,16.5,2,1,16,6,6,0.7272727 4,32,9,1,3,14,3,5,0.8888888 2,42,16.5,2,3,12,3,3,1.1878777 4,37,16.5,3,2,12,5,2,4.0727262 2,37,16.5,3,2,12,3,5,0.2121212 4,22,2.5,1,3,12,3,4,1.3999996 3,32,16.5,3,1,12,5,4,0.7272727 3,32,9,1,1,14,2,4,2.1777763 1,42,23,3,2,14,3,5,0.8521735 5,22,2.5,0,1,14,3,2,7.8399963 3,42,23,4,1,14,3,5,0.1521739 4,27,9,2,2,12,1,5,3.1111107 4,27,9,1,3,14,3,5,3.1111107 4,22,2.5,0,1,16,4,4,1.3999996 4,37,16.5,5.5,3,12,2,4,0.2121212 3,22,6,2,1,12,3,4,1.333333 1,42,16.5,5.5,2,12,2,5,0.2121212 3,32,9,2,4,17,4,2,2.1777763 4,32,13,2,2,12,3,5,0.9423077 3,37,16.5,2,2,16,4,5,1.1878777 3,37,16.5,3,3,17,4,6,0.7272727 4,32,13,2,2,14,5,4,1.5076914 3,42,23,3,1,14,4,4,1.217391 1,32,16.5,2,3,14,4,2,2.545454 1,42,23,2,3,12,3,4,0.8521735 5,37,6,2,3,17,4,4,0.5833333 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5,42,23,2,3,17,4,2,0.1521739 1,22,2.5,1,2,14,3,2,4.7999992 3,27,6,1,2,12,3,4,3.2666645 3,22,6,0,3,14,5,4,2 4,27,9,2,2,16,4,4,2.1777763 3,22,6,1,2,9,2,2,0.1666666 4,32,16.5,3,2,16,5,5,0.2121212 5,37,16.5,1,3,14,3,2,0.4848484 2,27,9,2,3,14,2,2,0.8888888 4,32,13,0,2,14,3,5,0.2692307 4,37,23,5.5,3,14,4,5,0.8521735 4,27,13,2,3,12,3,5,0.2692307 3,27,9,1,1,16,3,6,1.3611107 3,32,13,3,2,14,3,4,0.2692307 2,27,9,1,3,14,3,5,1.3611107 3,22,6,2,2,12,3,2,1.333333 3,32,16.5,2,2,12,3,5,0.0606061 3,42,23,2,3,12,2,3,2.9217386 4,27,6,1,3,14,3,3,2.041666 2,27,13,2,1,12,4,5,1.5076914 3,42,23,3,2,12,3,4,0.1521739 2,42,23,4,2,12,5,5,0.5217391 5,42,23,4,4,14,3,2,0.1521739 4,27,6,1,1,12,5,6,7 3,42,23,1,2,14,5,6,0.5326087 1,32,13,0,3,14,3,5,0.9423077 5,32,13,2,3,14,4,2,0.9230769 3,27,6,0,1,16,3,2,2.041666 3,27,9,2,3,14,5,5,1.333333 3,27,6,1,3,16,4,5,0.5833333 4,22,2.5,0,1,12,3,2,1.3999996 2,42,23,2,3,12,5,5,0.8521735 4,22,2.5,0,3,14,5,4,3.1999998 4,27,9,2,2,20,4,6,0.3888888 2,27,2.5,1,2,14,2,4,1.3999996 4,37,23,4,3,14,3,3,4.173913 3,27,9,1,2,14,3,4,1.333333 5,22,2.5,0,2,12,3,2,1.3999996 4,27,6,1,2,12,3,4,0.1666666 5,32,13,1,3,14,5,5,0.9230769 5,32,13,1,2,12,3,4,0.2692307 4,27,2.5,0,3,14,5,4,4.7999992 4,27,9,1,3,14,3,5,0.1111111 5,32,13,2,2,12,5,3,0.9423077 4,27,2.5,0,3,12,3,3,0.4 3,32,13,2,2,14,3,3,0.2692307 4,27,6,1,3,12,3,4,0.5833333 5,37,23,4,3,16,5,5,0.5217391 4,27,9,2,2,12,5,5,2.1777763 2,42,16.5,3,3,16,3,5,2.545454 4,27,6,0,2,14,3,5,2.041666 3,22,2.5,1,3,12,5,5,0.4 4,32,13,2,2,14,3,5,1.5076914 3,27,6,1,2,14,3,4,3.2666645 3,27,13,1,1,12,3,3,0.6153846 3,27,6,0,1,16,6,6,2.041666 3,32,16.5,3,2,14,3,4,1.1878777 5,42,23,3,3,14,3,4,0.8521735 4,22,6,0,3,12,3,5,0.5833333 4,32,16.5,2,3,12,3,4,0.2121212 1,37,16.5,3,2,14,4,4,0.2121212 3,22,2.5,0,3,16,3,4,4.8999996 3,27,6,0,3,17,5,5,3.2666645 3,37,16.5,2,3,16,5,3,0.0606061 2,32,13,3,2,12,3,2,0.9423077 4,37,23,2,3,14,5,2,0.1521739 4,27,9,2,4,17,4,4,1.3611107 4,27,9,1,2,14,3,5,0.1111111 2,42,16.5,5.5,2,16,3,5,2.545454 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5,22,2.5,0,2,14,5,5,0 4,22,2.5,0,1,12,5,4,0 4,22,2.5,0,3,14,3,2,0 4,22,6,3,2,14,4,2,0 5,27,2.5,0,3,17,4,5,0 4,22,0.5,0,2,14,3,5,0 5,22,0.5,0,3,17,4,4,0 4,27,9,0,2,12,3,2,0 4,27,0.5,0,3,16,4,6,0 4,27,6,2,3,12,2,6,0 4,22,2.5,0,2,14,4,4,0 5,22,6,0,3,12,4,6,0 4,22,0.5,0,2,12,3,5,0 5,17.5,0.5,0,2,12,4,2,0 5,27,2.5,1,1,17,4,5,0 3,27,9,2,1,14,3,5,0 4,27,0.5,2,2,14,3,5,0 4,22,2.5,0,2,14,2,5,0 4,27,2.5,0,3,12,3,2,0 2,27,6,2,2,14,3,2,0 5,27,2.5,0,3,14,2,4,0 5,42,9,0,4,20,4,4,0 5,22,2.5,0,2,14,4,3,0 5,37,13,5.5,4,17,4,5,0 4,42,23,2,3,14,4,3,0 5,32,16.5,3,4,14,4,3,0 5,37,16.5,1,3,12,3,5,0 5,22,2.5,0,3,14,3,5,0 5,27,2.5,0,3,20,6,1,0 4,42,23,3,3,14,4,5,0 4,42,23,4,1,16,4,5,0 3,27,2.5,0,2,16,3,5,0 5,37,16.5,3,2,16,4,3,0 5,42,23,5.5,3,14,4,4,0 4,22,2.5,1,1,14,3,4,0 5,27,9,1,3,12,2,5,0 5,22,2.5,0,2,14,3,6,0 4,22,2.5,1,2,14,2,2,0 3,42,23,3,4,12,3,4,0 5,42,23,3,3,12,3,4,0 4,32,13,2,1,14,3,4,0 4,37,16.5,5.5,3,12,4,5,0 4,27,6,1,4,12,3,4,0 5,42,23,5.5,3,16,4,5,0 5,32,6,2,2,16,2,5,0 2,32,9,2,4,14,4,5,0 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5,32,13,3,3,14,3,5,0 5,22,0.5,1,3,14,4,4,0 5,22,2.5,0,2,12,3,4,0 5,22,2.5,0,2,12,2,4,0 4,22,2.5,0,1,12,3,3,0 4,27,6,1,1,14,3,4,0 5,37,23,4,3,16,5,5,0 5,22,2.5,0,2,16,4,4,0 3,37,16.5,1,2,20,4,4,0 4,27,2.5,1,3,14,4,2,0 5,27,2.5,0,2,17,4,4,0 5,42,23,3,3,17,4,2,0 5,32,9,2,3,14,4,3,0 5,22,0.5,0,4,14,3,5,0 3,27,6,1,2,12,5,4,0 3,27,9,1,2,12,3,4,0 5,22,2.5,0,3,16,5,4,0 4,32,9,3,4,16,4,4,0 2,27,6,1,4,20,5,6,0 5,22,2.5,0,3,14,4,2,0 5,27,2.5,0,3,16,4,5,0 4,32,13,3,2,12,2,5,0 4,22,0.5,0,1,14,3,5,0 5,27,9,1,2,12,3,5,0 5,37,16.5,3,2,14,3,4,0 5,22,2.5,0,3,16,4,5,0 4,22,2.5,0,2,12,3,2,0 4,42,23,1,2,12,3,3,0 1,27,6,1,2,16,3,5,0 5,27,6,1,3,20,4,5,0 5,27,2.5,0,4,14,3,4,0 5,27,2.5,0,3,14,3,4,0 5,32,9,0,2,12,4,5,0 5,22,2.5,0,3,17,5,5,0 2,37,16.5,3,3,12,3,4,0 5,27,6,0,3,17,4,6,0 4,27,6,0,1,12,3,3,0 4,27,9,1,3,16,3,2,0 5,22,2.5,1,3,12,5,5,0 4,17.5,2.5,0,3,14,3,1,0 5,27,2.5,0,2,16,4,5,0 4,22,2.5,0,2,12,3,2,0 5,27,9,2,3,12,2,2,0 4,32,9,1,3,17,3,6,0 4,37,16.5,4,2,14,4,3,0 4,27,9,0,3,14,2,5,0 5,37,13,3,3,20,4,6,0 4,27,2.5,1,3,16,4,2,0 5,27,2.5,0,2,14,4,6,0 4,22,2.5,0,2,12,3,2,0 4,27,6,0,2,14,3,5,0 4,27,13,3,2,12,3,4,0 4,27,6,1,3,14,5,5,0 5,27,6,1,4,16,4,4,0 3,27,9,0,2,14,3,5,0 4,42,23,3,4,14,3,3,0 4,32,13,2,2,12,5,5,0 5,27,6,0,4,20,4,1,0 5,37,23,4,2,14,3,4,0 5,22,2.5,0,2,12,3,4,0 5,22,2.5,1,4,14,3,4,0 4,22,2.5,0,3,12,3,4,0 4,17.5,2.5,0,2,12,3,4,0 3,22,2.5,1,3,12,3,4,0 5,17.5,0.5,0,4,12,3,2,0 4,37,23,3,4,9,2,2,0 5,42,23,4,2,14,5,4,0 4,32,16.5,2,3,14,3,4,0 4,22,2.5,2,3,14,5,4,0 5,17.5,0.5,0,2,12,3,4,0 5,37,16.5,3,4,14,3,4,0 4,27,9,1,3,12,3,3,0 5,37,16.5,3,2,14,3,3,0 3,42,13,2,3,17,4,4,0 5,37,23,4,4,16,4,6,0 5,37,23,1,3,14,3,5,0 4,42,23,3,3,17,4,2,0 4,27,6,1,3,20,4,4,0 5,32,9,0,4,20,4,6,0 4,27,2.5,0,4,17,4,4,0 3,17.5,2.5,0,3,12,4,4,0 5,22,2.5,0,3,12,2,2,0 5,37,13,3,1,20,4,5,0 5,27,16.5,2,3,12,4,4,0 4,22,2.5,1,3,14,3,5,0 4,42,16.5,4,3,9,2,2,0 4,22,2.5,0,2,14,3,5,0 4,42,23,2,3,14,4,2,0 5,32,13,2,2,14,2,6,0 4,32,13,2,3,17,4,5,0 5,22,2.5,1,1,12,5,1,0 5,22,2.5,0,4,16,4,2,0 5,37,9,0,3,20,4,6,0 5,42,23,2,3,14,3,5,0 5,32,9,2,3,16,4,4,0 5,22,6,0,1,12,3,2,0 4,27,6,2,2,12,2,2,0 4,22,2.5,0,2,12,3,4,0 5,27,2.5,0,4,16,4,4,0 5,32,16.5,3,4,14,2,4,0 4,37,16.5,2,1,12,3,4,0 5,22,2.5,0,3,12,3,3,0 4,37,16.5,4,2,12,2,2,0 3,27,9,1,3,17,4,1,0 5,22,2.5,0,1,17,2,2,0 5,22,2.5,0,1,14,3,4,0 5,22,2.5,0,2,16,4,5,0 5,27,6,0,2,16,3,5,0 4,22,2.5,0,3,14,5,4,0 5,27,2.5,0,1,17,4,6,0 5,42,23,1,4,20,4,3,0 4,17.5,0.5,0,2,14,3,1,0 5,22,6,1,3,12,2,3,0 4,22,6,2,2,9,2,3,0 5,27,2.5,0,4,16,5,4,0 4,22,2.5,1,2,12,3,2,0 3,32,2.5,0,4,17,6,6,0 5,37,16.5,3,4,14,4,6,0 5,32,13,4,2,12,5,1,0 3,42,23,3,4,16,3,5,0 5,27,2.5,0,3,12,3,4,0 5,32,2.5,1,3,20,4,4,0 5,27,9,2,3,12,2,4,0 3,27,2.5,0,2,14,3,3,0 5,17.5,2.5,1,2,12,5,2,0 4,37,16.5,3,2,17,4,5,0 4,27,2.5,1,3,16,4,6,0 5,22,2.5,1,3,14,3,5,0 4,27,2.5,0,1,20,5,5,0 5,22,2.5,0,3,14,5,5,0 5,42,23,5.5,3,14,4,4,0 5,42,23,4,3,14,3,2,0 5,37,13,2,2,20,6,6,0 5,27,13,2,3,14,4,5,0 4,27,13,2,2,12,2,2,0 5,37,23,3,2,12,4,4,0 5,22,2.5,0,2,17,5,3,0 5,27,6,1,2,12,2,4,0 5,22,2.5,0,2,14,3,4,0 5,37,13,2,3,16,4,4,0 5,27,6,0,2,17,5,6,0 5,27,0.5,0,3,12,3,4,0 2,37,16.5,3,1,14,3,2,0 5,37,23,2,3,20,4,4,0 2,32,9,2,4,14,3,5,0 5,32,13,2,4,20,4,5,0 5,22,2.5,1,4,16,4,5,0 5,22,2.5,0,3,20,4,4,0 3,37,13,3,2,16,4,3,0 3,17.5,2.5,1,1,9,3,3,0 4,27,2.5,0,2,14,3,1,0 1,27,6,1,3,14,3,4,0 4,27,6,2,2,14,4,5,0 2,42,23,2,2,14,3,4,0 5,32,13,2,3,14,4,6,0 4,22,2.5,1,3,14,3,4,0 4,27,2.5,0,1,14,2,5,0 4,22,2.5,0,3,20,6,6,0 3,37,2.5,0,3,12,3,3,0 5,27,2.5,0,3,17,4,4,0 5,27,2.5,0,3,16,3,5,0 4,27,6,1,2,20,4,2,0 4,27,9,2,2,14,4,4,0 4,27,2.5,0,3,17,4,4,0 5,27,6,0,4,16,4,5,0 5,42,23,5.5,3,12,3,2,0 5,22,2.5,0,3,16,4,1,0 5,22,0.5,0,3,14,2,2,0 4,22,2.5,1,3,14,2,4,0 5,22,2.5,1,4,12,3,4,0 5,42,23,4,3,14,5,4,0 5,27,6,0,4,14,4,4,0 5,42,23,2,3,12,2,2,0 4,32,13,3,3,16,4,2,0 5,27,13,3,3,16,4,2,0 5,27,9,1,2,14,4,5,0 4,22,2.5,0,2,16,4,1,0 5,17.5,2.5,0,4,12,3,5,0 4,32,16.5,2,2,12,3,4,0 5,27,9,1,3,12,3,5,0 4,22,2.5,0,4,14,4,2,0 5,22,2.5,1,2,12,3,2,0 5,27,0.5,0,4,20,4,4,0 5,37,16.5,3,3,14,5,5,0 5,32,13,2,4,14,3,6,0 4,22,0.5,0,2,16,3,1,0 5,42,23,2,4,12,3,2,0 5,22,2.5,2,2,14,3,5,0 5,42,23,4,4,12,3,5,0 4,27,6,0,3,12,3,4,0 5,32,13,3,3,12,3,5,0 5,32,13,4,2,14,4,4,0 3,27,6,2,4,14,3,1,0 4,22,2.5,0,3,16,5,5,0 5,22,2.5,0,2,14,3,3,0 5,32,13,2,3,17,4,3,0 4,32,13,1,1,16,5,5,0 5,22,2.5,0,2,14,3,1,0 5,32,6,1,3,14,3,4,0 4,22,2.5,0,2,16,2,4,0 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/src/000077500000000000000000000000001224417117700240345ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/fair/src/1978ADAT.asc000066400000000000000000015761621224417117700256100ustar00rootroot00000000000000PT data: 15 variables per line; they are: identifier, not used, constant, z1, z2, z3, z4, z5, z6, not used, z7, z8, yPT, not used, not used RB data: 14 variables per line; they are: identifier, constant, v1, v2, v3, v4, v5, v6, not used, v7, v8, yRB, not used, not used See Table 1 in the article for the notation. BEGIN PT data (601 observations): 4 0 1. 1 37.0 10.000 0 3 18. 40.0 7 4 0. 0. 1. 5 0 1. 0 27.0 4.000 0 4 14. 20.0 6 4 0. 0. 1. 11 0 1. 0 32.0 15.000 1 1 12. 12.5 1 4 0. 0. 1. 16 0 1. 1 57.0 15.000 1 5 18. 12.5 6 5 0. 0. 1. 23 0 1. 1 22.0 0.750 0 2 17. 7.5 6 3 0. 0. 1. 29 0 1. 0 32.0 1.500 0 2 17. 7.5 5 5 0. 0. 1. 44 0 1. 0 22.0 0.750 0 2 12. 12.5 1 3 0. 0. 1. 45 0 1. 1 57.0 15.000 1 2 14. 20.0 4 4 0. 0. 1. 47 0 1. 0 32.0 15.000 1 4 16. 20.0 1 2 0. 0. 1. 49 0 1. 1 22.0 1.500 0 4 14. 12.5 4 5 0. 0. 1. 50 0 1. 1 37.0 15.000 1 2 20. 20.0 7 2 0. 0. 1. 55 0 1. 1 27.0 4.000 1 4 18. 12.5 6 4 0. 0. 1. 64 0 1. 1 47.0 15.000 1 5 17. 12.5 6 4 0. 0. 1. 80 0 1. 0 22.0 1.500 0 2 17. 12.5 5 4 0. 0. 1. 86 0 1. 0 27.0 4.000 0 4 14. 7.5 5 4 0. 0. 1. 93 0 1. 0 37.0 15.000 1 1 17. 20.0 5 5 0. 0. 1. 108 0 1. 0 37.0 15.000 1 2 18. 20.0 4 3 0. 0. 1. 114 0 1. 0 22.0 0.750 0 3 16. 7.5 5 4 0. 0. 1. 115 0 1. 0 22.0 1.500 0 2 16. 7.5 5 5 0. 0. 1. 116 0 1. 0 27.0 10.000 1 2 14. 7.5 1 5 0. 0. 1. 123 0 1. 0 22.0 1.500 0 2 16. 12.5 5 5 0. 0. 1. 127 0 1. 0 22.0 1.500 0 2 16. 7.5 5 5 0. 0. 1. 129 0 1. 0 27.0 10.000 1 4 16. 20.0 5 4 0. 0. 1. 134 0 1. 0 32.0 10.000 1 3 14. 7.5 1 5 0. 0. 1. 137 0 1. 1 37.0 4.000 1 2 20. 20.0 6 4 0. 0. 1. 139 0 1. 0 22.0 1.500 0 2 18. 12.5 5 5 0. 0. 1. 147 0 1. 0 27.0 7.000 0 4 16. 12.5 1 5 0. 0. 1. 151 0 1. 1 42.0 15.000 1 5 20. 12.5 6 4 0. 0. 1. 153 0 1. 1 27.0 4.000 1 3 16. 12.5 5 5 0. 0. 1. 155 0 1. 0 27.0 4.000 1 3 17. 12.5 5 4 0. 0. 1. 162 0 1. 1 42.0 15.000 1 4 20. 20.0 6 3 0. 0. 1. 163 0 1. 0 22.0 1.500 0 3 16. 12.5 5 5 0. 0. 1. 165 0 1. 1 27.0 0.417 0 4 17. 7.5 6 4 0. 0. 1. 168 0 1. 0 42.0 15.000 1 5 14. 20.0 5 4 0. 0. 1. 170 0 1. 1 32.0 4.000 1 1 18. 20.0 6 4 0. 0. 1. 172 0 1. 0 22.0 1.500 0 4 16. 7.5 5 3 0. 0. 1. 184 0 1. 0 42.0 15.000 1 3 12. 20.0 1 4 0. 0. 1. 187 0 1. 0 22.0 4.000 0 4 17. 20.0 5 5 0. 0. 1. 192 0 1. 1 22.0 1.500 1 1 14. 7.5 3 5 0. 0. 1. 194 0 1. 0 22.0 0.750 0 3 16. 7.5 1 5 0. 0. 1. 210 0 1. 1 32.0 10.000 1 5 20. 12.5 6 5 0. 0. 1. 217 0 1. 1 52.0 15.000 1 5 18. 7.5 6 3 0. 0. 1. 220 0 1. 0 22.0 0.417 0 5 14. 12.5 1 4 0. 0. 1. 224 0 1. 0 27.0 4.000 1 2 18. 4.0 6 1 0. 0. 1. 227 0 1. 0 32.0 7.000 1 5 17. 12.5 5 3 0. 0. 1. 228 0 1. 1 22.0 4.000 0 3 16. 7.5 5 5 0. 0. 1. 239 0 1. 0 27.0 7.000 1 4 18. 40.0 6 5 0. 0. 1. 241 0 1. 0 42.0 15.000 1 2 18. 20.0 5 4 0. 0. 1. 245 0 1. 1 27.0 1.500 1 4 16. 7.5 3 5 0. 0. 1. 249 0 1. 1 42.0 15.000 1 2 20. 20.0 6 4 0. 0. 1. 262 0 1. 0 22.0 0.750 0 5 14. 20.0 3 5 0. 0. 1. 265 0 1. 1 32.0 7.000 1 2 20. 20.0 6 4 0. 0. 1. 267 0 1. 1 27.0 4.000 1 5 20. 7.5 6 5 0. 0. 1. 269 0 1. 1 27.0 10.000 1 4 20. 7.5 6 4 0. 0. 1. 271 0 1. 1 22.0 4.000 0 1 18. 20.0 5 5 0. 0. 1. 277 0 1. 0 37.0 15.000 1 4 14. 12.5 3 1 0. 0. 1. 290 0 1. 1 22.0 1.500 1 5 16. 20.0 4 4 0. 0. 1. 292 0 1. 0 37.0 15.000 1 4 17. 20.0 1 5 0. 0. 1. 293 0 1. 0 27.0 0.750 0 4 17. 12.5 5 4 0. 0. 1. 295 0 1. 1 32.0 10.000 1 4 20. 12.5 6 4 0. 0. 1. 299 0 1. 0 47.0 15.000 1 5 14. 40.0 7 2 0. 0. 1. 320 0 1. 1 37.0 10.000 1 3 20. 40.0 6 4 0. 0. 1. 321 0 1. 0 22.0 0.750 0 2 16. 7.5 5 5 0. 0. 1. 324 0 1. 1 27.0 4.000 0 2 18. 12.5 4 5 0. 0. 1. 334 0 1. 1 32.0 7.000 0 4 20. 7.5 6 4 0. 0. 1. 351 0 1. 1 42.0 15.000 1 2 17. 40.0 3 5 0. 0. 1. 355 0 1. 1 37.0 10.000 1 4 20. 7.5 6 4 0. 0. 1. 361 0 1. 0 47.0 15.000 1 3 17. 20.0 6 5 0. 0. 1. 362 0 1. 0 22.0 1.500 0 5 16. 7.5 5 5 0. 0. 1. 366 0 1. 0 27.0 1.500 0 2 16. 20.0 6 4 0. 0. 1. 370 0 1. 0 27.0 4.000 0 3 17. 7.5 5 5 0. 0. 1. 374 0 1. 0 32.0 10.000 1 5 14. 12.5 4 5 0. 0. 1. 378 0 1. 0 22.0 0.125 0 2 12. 7.5 5 5 0. 0. 1. 381 0 1. 1 47.0 15.000 1 4 14. 20.0 4 3 0. 0. 1. 382 0 1. 1 32.0 15.000 1 1 14. 40.0 5 5 0. 0. 1. 383 0 1. 1 27.0 7.000 1 4 16. 12.5 5 5 0. 0. 1. 384 0 1. 0 22.0 1.500 1 3 16. 7.5 5 5 0. 0. 1. 400 0 1. 1 27.0 4.000 1 3 17. 7.5 6 5 0. 0. 1. 403 0 1. 0 22.0 1.500 0 3 16. 7.5 5 5 0. 0. 1. 409 0 1. 1 57.0 15.000 1 2 14. 20.0 7 2 0. 0. 1. 412 0 1. 1 17.5 1.500 1 3 18. 20.0 6 5 0. 0. 1. 413 0 1. 1 57.0 15.000 1 4 20. 40.0 6 5 0. 0. 1. 416 0 1. 0 22.0 0.750 0 2 16. 20.0 3 4 0. 0. 1. 418 0 1. 1 42.0 4.000 0 4 17. 12.5 3 3 0. 0. 1. 422 0 1. 0 22.0 1.500 1 4 12. 12.5 1 5 0. 0. 1. 435 0 1. 0 22.0 0.417 0 1 17. 4.0 6 4 0. 0. 1. 439 0 1. 0 32.0 15.000 1 4 17. 12.5 5 5 0. 0. 1. 445 0 1. 0 27.0 1.500 0 3 18. 12.5 5 2 0. 0. 1. 447 0 1. 0 22.0 1.500 1 3 14. 7.5 1 5 0. 0. 1. 448 0 1. 0 37.0 15.000 1 3 14. 40.0 1 4 0. 0. 1. 449 0 1. 0 32.0 15.000 1 4 14. 20.0 3 4 0. 0. 1. 478 0 1. 1 37.0 10.000 1 2 14. 12.5 5 3 0. 0. 1. 482 0 1. 1 37.0 10.000 1 4 16. 12.5 5 4 0. 0. 1. 486 0 1. 1 57.0 15.000 1 5 20. 12.5 5 3 0. 0. 1. 489 0 1. 1 27.0 0.417 0 1 16. 7.5 3 4 0. 0. 1. 490 0 1. 0 42.0 15.000 1 5 14. 12.5 1 5 0. 0. 1. 491 0 1. 1 57.0 15.000 1 3 16. 20.0 6 1 0. 0. 1. 492 0 1. 1 37.0 10.000 1 1 16. 7.5 6 4 0. 0. 1. 503 0 1. 1 37.0 15.000 1 3 17. 40.0 5 5 0. 0. 1. 508 0 1. 1 37.0 15.000 1 4 20. 20.0 6 5 0. 0. 1. 509 0 1. 0 27.0 10.000 1 5 14. 12.5 1 5 0. 0. 1. 512 0 1. 1 37.0 10.000 1 2 18. 20.0 6 4 0. 0. 1. 515 0 1. 0 22.0 0.125 0 4 12. 12.5 4 5 0. 0. 1. 517 0 1. 1 57.0 15.000 1 5 20. 40.0 6 5 0. 0. 1. 532 0 1. 0 37.0 15.000 1 4 18. 40.0 6 4 0. 0. 1. 533 0 1. 1 22.0 4.000 1 4 14. 7.5 6 4 0. 0. 1. 535 0 1. 1 27.0 7.000 1 4 18. 7.5 5 4 0. 0. 1. 537 0 1. 1 57.0 15.000 1 4 20. 40.0 5 4 0. 0. 1. 538 0 1. 1 32.0 15.000 1 3 14. 12.5 6 3 0. 0. 1. 543 0 1. 0 22.0 1.500 0 2 14. 12.5 5 4 0. 0. 1. 547 0 1. 0 32.0 7.000 1 4 17. 40.0 1 5 0. 0. 1. 550 0 1. 0 37.0 15.000 1 4 17. 40.0 6 5 0. 0. 1. 558 0 1. 0 32.0 1.500 0 5 18. 40.0 5 5 0. 0. 1. 571 0 1. 1 42.0 10.000 1 5 20. 40.0 7 4 0. 0. 1. 578 0 1. 0 27.0 7.000 0 3 16. 12.5 5 4 0. 0. 1. 583 0 1. 1 37.0 15.000 0 4 20. 40.0 6 5 0. 0. 1. 586 0 1. 1 37.0 15.000 1 4 14. 12.5 3 2 0. 0. 1. 594 0 1. 1 32.0 10.000 0 5 18. 20.0 6 4 0. 0. 1. 597 0 1. 0 22.0 0.750 0 4 16. 7.5 1 5 0. 0. 1. 602 0 1. 0 27.0 7.000 1 4 12. 7.5 2 4 0. 0. 1. 603 0 1. 0 27.0 7.000 1 2 16. 12.5 2 5 0. 0. 1. 604 0 1. 0 42.0 15.000 1 5 18. 40.0 5 4 0. 0. 1. 612 0 1. 1 42.0 15.000 1 4 17. 20.0 5 3 0. 0. 1. 613 0 1. 0 27.0 7.000 1 2 16. 20.0 1 2 0. 0. 1. 621 0 1. 0 22.0 1.500 0 3 16. 12.5 5 5 0. 0. 1. 627 0 1. 1 37.0 15.000 1 5 20. 40.0 6 5 0. 0. 1. 630 0 1. 0 22.0 0.125 0 2 14. 12.5 4 5 0. 0. 1. 631 0 1. 1 27.0 1.500 0 4 16. 7.5 5 5 0. 0. 1. 632 0 1. 1 32.0 1.500 0 2 18. 20.0 6 5 0. 0. 1. 639 0 1. 1 27.0 1.500 0 2 17. 7.5 6 5 0. 0. 1. 645 0 1. 0 27.0 10.000 1 4 16. 12.5 1 3 0. 0. 1. 647 0 1. 1 42.0 15.000 1 4 18. 12.5 6 5 0. 0. 1. 648 0 1. 0 27.0 1.500 0 2 16. 7.5 6 5 0. 0. 1. 651 0 1. 1 27.0 4.000 0 2 18. 12.5 6 3 0. 0. 1. 655 0 1. 0 32.0 10.000 1 3 14. 7.5 5 3 0. 0. 1. 667 0 1. 0 32.0 15.000 1 3 18. 20.0 5 4 0. 0. 1. 670 0 1. 0 22.0 0.750 0 2 18. 4.0 6 5 0. 0. 1. 671 0 1. 0 37.0 15.000 1 2 16. 7.5 1 4 0. 0. 1. 673 0 1. 1 27.0 4.000 1 4 20. 12.5 5 5 0. 0. 1. 701 0 1. 1 27.0 4.000 0 1 20. 20.0 5 4 0. 0. 1. 705 0 1. 0 27.0 10.000 1 2 12. 7.5 1 4 0. 0. 1. 706 0 1. 0 32.0 15.000 1 5 18. 20.0 6 4 0. 0. 1. 709 0 1. 1 27.0 7.000 1 5 12. 7.5 5 3 0. 0. 1. 717 0 1. 1 52.0 15.000 1 2 18. 12.5 5 4 0. 0. 1. 719 0 1. 1 27.0 4.000 0 3 20. 12.5 6 3 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1. 1481 0 1. 0 27.0 1.500 0 2 16. 7.5 5 5 0. 0. 1. 1482 0 1. 0 27.0 1.500 0 3 17. 12.5 5 5 0. 0. 1. 1496 0 1. 1 22.0 0.125 0 5 16. 7.5 4 4 0. 0. 1. 1497 0 1. 0 27.0 4.000 1 4 16. 7.5 1 5 0. 0. 1. 1504 0 1. 0 27.0 4.000 1 4 12. 7.5 1 5 0. 0. 1. 1513 0 1. 0 47.0 15.000 1 2 14. 40.0 5 5 0. 0. 1. 1515 0 1. 0 32.0 15.000 1 3 14. 12.5 5 3 0. 0. 1. 1534 0 1. 1 42.0 7.000 1 2 16. 12.5 5 5 0. 0. 1. 1535 0 1. 1 22.0 0.750 0 4 16. 7.5 6 4 0. 0. 1. 1536 0 1. 1 27.0 0.125 0 3 20. 7.5 6 5 0. 0. 1. 1540 0 1. 1 32.0 10.000 1 3 20. 20.0 6 5 0. 0. 1. 1551 0 1. 0 22.0 0.417 0 5 14. 12.5 4 5 0. 0. 1. 1555 0 1. 0 47.0 15.000 1 5 14. 12.5 1 4 0. 0. 1. 1557 0 1. 0 32.0 10.000 1 3 14. 40.0 1 5 0. 0. 1. 1566 0 1. 1 57.0 15.000 1 4 17. 12.5 5 5 0. 0. 1. 1567 0 1. 1 27.0 4.000 1 3 20. 12.5 6 5 0. 0. 1. 1576 0 1. 0 32.0 7.000 1 4 17. 12.5 1 5 0. 0. 1. 1584 0 1. 0 37.0 10.000 1 4 16. 40.0 1 5 0. 0. 1. 1585 0 1. 0 32.0 10.000 1 1 18. 20.0 1 4 0. 0. 1. 1590 0 1. 0 22.0 4.000 0 3 14. 7.5 1 4 0. 0. 1. 1594 0 1. 0 27.0 7.000 1 4 14. 7.5 3 2 0. 0. 1. 1595 0 1. 1 57.0 15.000 1 5 18. 20.0 5 2 0. 0. 1. 1603 0 1. 1 32.0 7.000 1 2 18. 7.5 5 5 0. 0. 1. 1608 0 1. 0 27.0 1.500 0 4 17. 7.5 1 3 0. 0. 1. 1609 0 1. 1 22.0 1.500 0 4 14. 7.5 5 5 0. 0. 1. 1615 0 1. 0 22.0 1.500 1 4 14. 4.0 5 4 0. 0. 1. 1616 0 1. 0 32.0 7.000 1 3 16. 12.5 1 5 0. 0. 1. 1617 0 1. 0 47.0 15.000 1 3 16. 20.0 5 4 0. 0. 1. 1620 0 1. 0 22.0 0.750 0 3 16. 40.0 1 5 0. 0. 1. 1621 0 1. 0 22.0 1.500 1 2 14. 7.5 5 5 0. 0. 1. 1637 0 1. 0 27.0 4.000 1 1 16. 7.5 5 5 0. 0. 1. 1638 0 1. 1 52.0 15.000 1 4 16. 20.0 5 5 0. 0. 1. 1650 0 1. 1 32.0 10.000 1 4 20. 40.0 6 5 0. 0. 1. 1654 0 1. 1 47.0 15.000 1 4 16. 20.0 6 4 0. 0. 1. 1665 0 1. 0 27.0 7.000 1 2 14. 7.5 1 2 0. 0. 1. 1670 0 1. 0 22.0 1.500 0 4 14. 20.0 4 5 0. 0. 1. 1671 0 1. 0 32.0 10.000 1 2 16. 20.0 5 4 0. 0. 1. 1675 0 1. 0 22.0 0.750 0 2 16. 12.5 5 4 0. 0. 1. 1688 0 1. 0 22.0 1.500 0 2 16. 12.5 5 5 0. 0. 1. 1691 0 1. 0 42.0 15.000 1 3 18. 20.0 6 4 0. 0. 1. 1695 0 1. 0 27.0 7.000 1 5 14. 20.0 4 5 0. 0. 1. 1698 0 1. 1 42.0 15.000 1 4 16. 20.0 4 4 0. 0. 1. 1704 0 1. 0 57.0 15.000 1 3 18. 20.0 5 2 0. 0. 1. 1705 0 1. 1 42.0 15.000 1 3 18. 12.5 6 2 0. 0. 1. 1711 0 1. 0 32.0 7.000 1 2 14. 7.5 1 2 0. 0. 1. 1719 0 1. 1 22.0 4.000 0 5 12. 7.5 4 5 0. 0. 1. 1723 0 1. 0 22.0 1.500 0 1 16. 7.5 6 5 0. 0. 1. 1726 0 1. 0 22.0 0.750 0 1 14. 7.5 4 5 0. 0. 1. 1749 0 1. 0 32.0 15.000 1 4 12. 20.0 1 5 0. 0. 1. 1752 0 1. 1 22.0 1.500 0 2 18. 12.5 5 3 0. 0. 1. 1754 0 1. 1 27.0 4.000 1 5 17. 7.5 2 5 0. 0. 1. 1758 0 1. 0 27.0 4.000 1 4 12. 7.5 1 5 0. 0. 1. 1761 0 1. 1 42.0 15.000 1 5 18. 7.5 5 4 0. 0. 1. 1773 0 1. 1 32.0 1.500 0 2 20. 20.0 7 3 0. 0. 1. 1775 0 1. 1 57.0 15.000 0 4 9. 7.5 3 1 0. 0. 1. 1786 0 1. 1 37.0 7.000 0 4 18. 12.5 5 5 0. 0. 1. 1793 0 1. 1 52.0 15.000 1 2 17. 20.0 5 4 0. 0. 1. 1799 0 1. 1 47.0 15.000 1 4 17. 20.0 6 5 0. 0. 1. 1803 0 1. 0 27.0 7.000 0 2 17. 20.0 5 4 0. 0. 1. 1806 0 1. 0 27.0 7.000 1 4 14. 40.0 5 5 0. 0. 1. 1807 0 1. 0 22.0 4.000 0 2 14. 12.5 3 3 0. 0. 1. 1808 0 1. 1 37.0 7.000 1 2 20. 40.0 6 5 0. 0. 1. 1814 0 1. 1 27.0 7.000 0 4 12. 7.5 4 3 0. 0. 1. 1815 0 1. 1 42.0 10.000 1 4 18. 40.0 6 4 0. 0. 1. 1818 0 1. 0 22.0 1.500 0 3 14. 7.5 1 5 0. 0. 1. 1827 0 1. 0 22.0 4.000 1 2 14. 12.5 1 3 0. 0. 1. 1834 0 1. 0 57.0 15.000 0 4 20. 20.0 6 5 0. 0. 1. 1835 0 1. 1 37.0 15.000 1 4 14. 12.5 4 3 0. 0. 1. 1843 0 1. 0 27.0 7.000 1 3 18. 12.5 5 5 0. 0. 1. 1846 0 1. 0 17.5 10.000 0 4 14. 20.0 4 5 0. 0. 1. 1850 0 1. 1 22.0 4.000 1 4 16. 12.5 5 5 0. 0. 1. 1851 0 1. 0 27.0 4.000 1 2 16. 12.5 1 4 0. 0. 1. 1854 0 1. 0 37.0 15.000 1 2 14. 12.5 5 1 0. 0. 1. 1859 0 1. 0 22.0 1.500 0 5 14. 4.0 1 4 0. 0. 1. 1861 0 1. 1 27.0 7.000 1 2 20. 7.5 5 4 0. 0. 1. 1866 0 1. 1 27.0 4.000 1 4 14. 7.5 5 5 0. 0. 1. 1873 0 1. 1 22.0 0.125 0 1 16. 7.5 3 5 0. 0. 1. 1875 0 1. 0 27.0 7.000 1 4 14. 20.0 1 4 0. 0. 1. 1885 0 1. 0 32.0 15.000 1 5 16. 12.5 5 3 0. 0. 1. 1892 0 1. 1 32.0 10.000 1 4 18. 12.5 5 4 0. 0. 1. 1895 0 1. 0 32.0 15.000 1 2 14. 7.5 3 4 0. 0. 1. 1896 0 1. 0 22.0 1.500 0 3 17. 7.5 5 5 0. 0. 1. 1897 0 1. 1 27.0 4.000 1 4 17. 7.5 4 4 0. 0. 1. 1899 0 1. 0 52.0 15.000 1 5 14. 12.5 1 5 0. 0. 1. 1904 0 1. 0 27.0 7.000 1 2 12. 20.0 1 2 0. 0. 1. 1905 0 1. 0 27.0 7.000 1 3 12. 12.5 1 4 0. 0. 1. 1908 0 1. 0 42.0 15.000 1 2 14. 20.0 1 4 0. 0. 1. 1916 0 1. 0 42.0 15.000 1 4 14. 20.0 5 4 0. 0. 1. 1918 0 1. 1 27.0 7.000 1 4 14. 7.5 3 3 0. 0. 1. 1920 0 1. 1 27.0 7.000 1 2 20. 20.0 6 2 0. 0. 1. 1930 0 1. 0 42.0 15.000 1 3 12. 20.0 3 3 0. 0. 1. 1940 0 1. 1 27.0 4.000 1 3 16. 7.5 3 5 0. 0. 1. 1947 0 1. 0 27.0 7.000 1 3 14. 40.0 1 4 0. 0. 1. 1949 0 1. 0 22.0 1.500 0 2 14. 12.5 4 5 0. 0. 1. 1951 0 1. 0 27.0 4.000 1 4 14. 12.5 1 4 0. 0. 1. 1952 0 1. 0 22.0 4.000 0 4 14. 4.0 5 5 0. 0. 1. 1960 0 1. 0 22.0 1.500 0 2 16. 20.0 4 5 0. 0. 1. 9001 0 1. 1 47.0 15.000 0 4 14. 12.5 5 4 0. 0. 1. 9012 0 1. 1 37.0 10.000 1 2 18. 12.5 6 2 0. 0. 1. 9023 0 1. 1 37.0 15.000 1 3 17. 40.0 5 4 0. 0. 1. 9029 0 1. 0 27.0 4.000 1 2 16. 7.5 1 4 0. 0. 1. 6 3 1. 1 27.0 1.500 0 3 18. 20.0 4 4 3. 0. 1. 12 3 1. 0 27.0 4.000 1 3 17. 12.5 1 5 3. 0. 1. 43 4 1. 1 37.0 15.000 1 5 18. 12.5 6 2 7. 0. 1. 53 6 1. 0 32.0 10.000 1 3 17. 40.0 5 2 12. 0. 1. 67 1 1. 1 22.0 0.125 0 4 16. 20.0 5 5 1. 0. 1. 79 1 1. 0 22.0 1.500 1 2 14. 4.0 1 5 1. 0. 1. 122 6 1. 1 37.0 15.000 1 4 14. 7.5 5 2 12. 0. 1. 126 4 1. 0 22.0 1.500 0 2 14. 4.0 3 4 7. 0. 1. 133 2 1. 1 37.0 15.000 1 2 18. 20.0 6 4 2. 0. 1. 138 3 1. 0 32.0 15.000 1 4 12. 12.5 3 2 3. 0. 1. 154 1 1. 0 37.0 15.000 1 4 14. 12.5 4 2 1. 0. 1. 159 4 1. 0 42.0 15.000 1 3 17. 40.0 1 4 7. 0. 1. 174 6 1. 0 42.0 15.000 1 5 9. 12.5 4 1 12. 0. 1. 176 5 1. 1 37.0 10.000 1 2 20. 40.0 6 2 12. 0. 1. 181 6 1. 0 32.0 15.000 1 3 14. 12.5 1 2 12. 0. 1. 182 3 1. 1 27.0 4.000 0 1 18. 7.5 6 5 3. 0. 1. 186 4 1. 1 37.0 10.000 1 2 18. 40.0 7 3 7. 0. 1. 189 4 1. 0 27.0 4.000 0 3 17. 7.5 5 5 7. 0. 1. 204 1 1. 1 42.0 15.000 1 4 16. 20.0 5 5 1. 0. 1. 215 1 1. 0 47.0 15.000 1 5 14. 12.5 4 5 1. 0. 1. 232 4 1. 0 27.0 4.000 1 3 18. 12.5 5 4 7. 0. 1. 233 1 1. 0 27.0 7.000 1 5 14. 4.0 1 4 1. 0. 1. 252 6 1. 1 27.0 1.500 1 3 17. 20.0 5 4 12. 0. 1. 253 5 1. 0 27.0 7.000 1 4 14. 12.5 6 2 12. 0. 1. 274 3 1. 0 42.0 15.000 1 4 16. 20.0 5 4 3. 0. 1. 275 4 1. 0 27.0 10.000 1 4 12. 12.5 7 3 7. 0. 1. 287 1 1. 1 27.0 1.500 0 2 18. 12.5 5 2 1. 0. 1. 288 1 1. 1 32.0 4.000 0 4 20. 20.0 6 4 1. 0. 1. 325 1 1. 0 27.0 7.000 1 3 14. 7.5 1 3 1. 0. 1. 328 3 1. 0 32.0 10.000 1 4 14. 7.5 1 4 3. 0. 1. 344 3 1. 1 27.0 4.000 1 2 18. 12.5 7 2 3. 0. 1. 353 1 1. 0 17.5 0.750 0 5 14. 7.5 4 5 1. 0. 1. 354 1 1. 0 32.0 10.000 1 4 18. 20.0 1 5 1. 0. 1. 367 4 1. 0 32.0 7.000 1 2 17. 20.0 6 4 7. 0. 1. 369 4 1. 1 37.0 15.000 1 2 20. 20.0 6 4 7. 0. 1. 390 4 1. 0 37.0 10.000 0 1 20. 20.0 5 3 7. 0. 1. 392 5 1. 0 32.0 10.000 1 2 16. 12.5 5 5 12. 0. 1. 423 4 1. 1 52.0 15.000 1 2 20. 40.0 6 4 7. 0. 1. 432 4 1. 0 42.0 15.000 1 1 12. 12.5 1 3 7. 0. 1. 436 1 1. 1 52.0 15.000 1 2 20. 40.0 6 3 1. 0. 1. 483 2 1. 1 37.0 15.000 1 3 18. 12.5 6 5 2. 0. 1. 513 5 1. 0 22.0 4.000 0 3 12. 12.5 3 4 12. 0. 1. 516 6 1. 1 27.0 7.000 1 1 18. 12.5 6 2 12. 0. 1. 518 1 1. 1 27.0 4.000 1 3 18. 12.5 5 5 1. 0. 1. 520 7 1. 1 47.0 15.000 1 4 17. 12.5 6 5 12. 0. 1. 526 6 1. 0 42.0 15.000 1 4 12. 20.0 1 1 12. 0. 1. 528 4 1. 1 27.0 4.000 0 3 14. 7.5 3 4 7. 0. 1. 553 4 1. 0 32.0 7.000 1 4 18. 12.5 4 5 7. 0. 1. 576 1 1. 1 32.0 0.417 1 3 12. 7.5 3 4 1. 0. 1. 611 3 1. 1 47.0 15.000 1 5 16. 12.5 5 4 3. 0. 1. 625 5 1. 1 37.0 15.000 1 2 20. 40.0 5 4 12. 0. 1. 635 4 1. 1 22.0 4.000 1 2 17. 7.5 6 4 7. 0. 1. 646 1 1. 1 27.0 4.000 0 2 14. 12.5 4 5 1. 0. 1. 657 4 1. 0 52.0 15.000 1 5 16. 12.5 1 3 7. 0. 1. 659 1 1. 1 27.0 4.000 0 3 14. 12.5 3 3 1. 0. 1. 666 1 1. 0 27.0 10.000 1 4 16. 12.5 1 4 1. 0. 1. 679 1 1. 1 32.0 7.000 1 3 14. 20.0 7 4 1. 0. 1. 729 4 1. 1 32.0 7.000 1 2 18. 20.0 4 1 7. 0. 1. 755 3 1. 1 22.0 1.500 0 1 14. 20.0 3 2 3. 0. 1. 758 4 1. 1 22.0 4.000 1 3 18. 12.5 6 4 7. 0. 1. 770 4 1. 1 42.0 15.000 1 4 20. 40.0 6 4 7. 0. 1. 786 2 1. 0 57.0 15.000 1 1 18. 40.0 5 4 2. 0. 1. 797 4 1. 0 32.0 4.000 1 3 18. 12.5 5 2 7. 0. 1. 811 1 1. 1 27.0 4.000 1 1 16. 20.0 4 4 1. 0. 1. 834 4 1. 1 32.0 7.000 1 4 16. 12.5 1 4 7. 0. 1. 858 2 1. 1 57.0 15.000 1 1 17. 20.0 4 4 2. 0. 1. 885 4 1. 0 42.0 15.000 1 4 14. 20.0 5 2 7. 0. 1. 893 4 1. 1 37.0 10.000 1 1 18. 12.5 5 3 7. 0. 1. 927 3 1. 1 42.0 15.000 1 3 17. 12.5 6 1 3. 0. 1. 928 1 1. 0 52.0 15.000 1 3 14. 12.5 4 4 1. 0. 1. 933 2 1. 0 27.0 7.000 1 3 17. 12.5 5 3 2. 0. 1. 951 6 1. 1 32.0 7.000 1 2 12. 20.0 4 2 12. 0. 1. 968 1 1. 1 22.0 4.000 0 4 14. 7.5 2 5 1. 0. 1. 972 3 1. 1 27.0 7.000 1 3 18. 12.5 6 4 3. 0. 1. 975 6 1. 0 37.0 15.000 1 1 18. 12.5 5 5 12. 0. 1. 977 4 1. 0 32.0 15.000 1 3 17. 20.0 1 3 7. 0. 1. 981 4 1. 0 27.0 7.000 0 2 17. 20.0 5 5 7. 0. 1. 986 1 1. 0 32.0 7.000 1 3 17. 12.5 5 3 1. 0. 1. 1002 1 1. 1 32.0 1.500 1 2 14. 7.5 2 4 1. 0. 1. 1007 6 1. 0 42.0 15.000 1 4 14. 12.5 1 2 12. 0. 1. 1011 4 1. 1 32.0 10.000 1 3 14. 20.0 5 4 7. 0. 1. 1035 4 1. 1 37.0 4.000 1 1 20. 20.0 6 3 7. 0. 1. 1050 1 1. 0 27.0 4.000 1 2 16. 12.5 5 3 1. 0. 1. 1056 5 1. 0 42.0 15.000 1 3 14. 20.0 4 3 12. 0. 1. 1057 1 1. 1 27.0 10.000 1 5 20. 20.0 6 5 1. 0. 1. 1075 6 1. 1 37.0 10.000 1 2 20. 40.0 6 2 12. 0. 1. 1080 6 1. 0 27.0 7.000 1 1 14. 12.5 3 3 12. 0. 1. 1125 3 1. 0 27.0 7.000 1 4 12. 7.5 1 2 3. 0. 1. 1131 3 1. 1 32.0 10.000 1 2 14. 12.5 4 4 3. 0. 1. 1138 7 1. 0 17.5 0.750 1 2 12. 7.5 1 3 12. 0. 1. 1150 5 1. 0 32.0 15.000 1 3 18. 40.0 5 4 12. 0. 1. 1163 2 1. 0 22.0 7.000 0 4 14. 20.0 4 3 2. 0. 1. 1169 1 1. 1 32.0 7.000 1 4 20. 20.0 6 5 1. 0. 1. 1198 4 1. 1 27.0 4.000 1 2 18. 12.5 6 2 7. 0. 1. 1204 1 1. 0 22.0 1.500 1 5 14. 7.5 5 3 1. 0. 1. 1218 7 1. 0 32.0 15.000 0 3 17. 7.5 5 1 12. 0. 1. 1230 5 1. 0 42.0 15.000 1 2 12. 20.0 1 2 12. 0. 1. 1236 4 1. 1 42.0 15.000 1 3 20. 40.0 5 4 7. 0. 1. 1247 6 1. 1 32.0 10.000 0 2 18. 20.0 4 2 12. 0. 1. 1259 5 1. 0 32.0 15.000 1 3 9. 12.5 1 1 12. 0. 1. 1294 4 1. 1 57.0 15.000 1 5 20. 12.5 4 5 7. 0. 1. 1353 5 1. 1 47.0 15.000 1 4 20. 40.0 6 4 12. 0. 1. 1370 2 1. 0 42.0 15.000 1 2 17. 20.0 6 3 2. 0. 1. 1427 6 1. 1 37.0 15.000 1 3 17. 40.0 6 3 12. 0. 1. 1445 5 1. 1 37.0 15.000 1 5 17. 12.5 5 2 12. 0. 1. 1460 4 1. 1 27.0 10.000 1 2 20. 12.5 6 4 7. 0. 1. 1480 2 1. 1 37.0 15.000 1 2 16. 20.0 5 4 2. 0. 1. 1505 6 1. 0 32.0 15.000 1 1 14. 20.0 5 2 12. 0. 1. 1543 4 1. 1 32.0 10.000 1 3 17. 7.5 6 3 7. 0. 1. 1548 2 1. 1 37.0 15.000 1 4 18. 12.5 5 1 2. 0. 1. 1550 4 1. 0 27.0 1.500 0 2 17. 12.5 5 5 7. 0. 1. 1561 3 1. 0 47.0 15.000 1 2 17. 20.0 5 2 3. 0. 1. 1564 6 1. 1 37.0 15.000 1 2 17. 20.0 5 4 12. 0. 1. 1573 5 1. 0 27.0 4.000 0 2 14. 20.0 5 5 12. 0. 1. 1575 2 1. 0 27.0 10.000 1 4 14. 12.5 1 5 2. 0. 1. 1599 1 1. 0 22.0 4.000 1 3 16. 12.5 1 3 1. 0. 1. 1622 6 1. 1 52.0 7.000 0 4 16. 7.5 5 5 12. 0. 1. 1629 2 1. 0 27.0 4.000 1 1 16. 7.5 3 5 2. 0. 1. 1664 4 1. 0 37.0 15.000 1 2 17. 40.0 6 4 7. 0. 1. 1669 2 1. 0 27.0 4.000 0 1 17. 4.0 3 1 2. 0. 1. 1674 7 1. 0 17.5 0.750 1 2 12. 7.5 3 5 12. 0. 1. 1682 4 1. 0 32.0 15.000 1 5 18. 12.5 5 4 7. 0. 1. 1685 4 1. 0 22.0 4.000 0 1 16. 7.5 3 5 7. 0. 1. 1697 2 1. 1 32.0 4.000 1 4 18. 20.0 6 4 2. 0. 1. 1716 1 1. 0 22.0 1.500 1 3 18. 20.0 5 2 1. 0. 1. 1730 3 1. 0 42.0 15.000 1 2 17. 40.0 5 4 3. 0. 1. 1731 1 1. 1 32.0 7.000 1 4 16. 12.5 4 4 1. 0. 1. 1732 5 1. 1 37.0 15.000 0 3 14. 20.0 6 2 12. 0. 1. 1743 1 1. 1 42.0 15.000 1 3 16. 40.0 6 3 1. 0. 1. 1751 1 1. 1 27.0 4.000 1 1 18. 7.5 5 4 1. 0. 1. 1757 2 1. 1 37.0 15.000 1 4 20. 40.0 7 3 2. 0. 1. 1763 4 1. 1 37.0 15.000 1 3 20. 40.0 6 4 7. 0. 1. 1766 3 1. 1 22.0 1.500 0 2 12. 12.5 3 3 3. 0. 1. 1772 3 1. 1 32.0 4.000 1 3 20. 20.0 6 2 3. 0. 1. 1776 2 1. 1 32.0 15.000 1 5 20. 20.0 6 5 2. 0. 1. 1782 5 1. 0 52.0 15.000 1 1 18. 40.0 5 5 12. 0. 1. 1784 5 1. 1 47.0 15.000 0 1 18. 40.0 6 5 12. 0. 1. 1791 3 1. 0 32.0 15.000 1 4 16. 12.5 4 4 3. 0. 1. 1831 4 1. 0 32.0 15.000 1 3 14. 12.5 3 2 7. 0. 1. 1840 4 1. 0 27.0 7.000 1 4 16. 20.0 1 2 7. 0. 1. 1844 5 1. 1 42.0 15.000 1 3 18. 12.5 6 2 12. 0. 1. 1856 4 1. 0 42.0 15.000 1 2 14. 12.5 3 2 7. 0. 1. 1876 5 1. 1 27.0 7.000 1 2 17. 12.5 5 4 12. 0. 1. 1929 3 1. 1 32.0 10.000 1 4 14. 7.5 4 3 3. 0. 1. 1935 4 1. 1 47.0 15.000 1 3 16. 20.0 4 2 7. 0. 1. 1938 1 1. 1 22.0 1.500 1 1 12. 7.5 2 5 1. 0. 1. 1941 4 1. 0 32.0 10.000 1 2 18. 7.5 5 4 7. 0. 1. 1954 2 1. 1 32.0 10.000 1 2 17. 20.0 6 5 2. 0. 1. 1959 2 1. 1 22.0 7.000 1 3 18. 20.0 6 2 2. 0. 1. 9010 1 1. 0 32.0 15.000 1 3 14. 40.0 1 5 1. 0. 1. END OF PT DATA BEGIN RB DATA (6366 observations): 3. 1. 3. 32.0 9.0 3.0 3. 17.0 40.0 2. 5. 0.1111111 0. 1. 12. 1. 3. 27.0 13.0 3.0 1. 14.0 20.0 3. 4. 3.2307692 0. 1. 51. 1. 4. 22.0 2.5 0.0 1. 16.0 11.5 3. 5. 1.3999996 0. 1. 53. 1. 4. 37.0 16.5 4.0 3. 16.0 20.0 5. 5. 0.7272727 0. 1. 54. 1. 5. 27.0 9.0 1.0 1. 14.0 14.0 3. 4. 4.6666660 0. 1. 74. 1. 4. 27.0 9.0 0.0 2. 14.0 20.0 3. 4. 4.6666660 0. 1. 81. 1. 5. 37.0 23.0 5.5 2. 12.0 20.0 5. 4. 0.8521735 0. 1. 83. 1. 5. 37.0 23.0 5.5 2. 12.0 20.0 2. 3. 1.8260860 0. 1. 89. 1. 3. 22.0 2.5 0.0 2. 12.0 11.5 3. 3. 4.7999992 0. 1. 102. 1. 3. 27.0 6.0 0.0 1. 16.0 20.0 3. 5. 1.3333330 0. 1. 135. 1. 2. 27.0 6.0 2.0 1. 16.0 20.0 3. 5. 3.2666645 0. 1. 137. 1. 5. 27.0 6.0 2.0 3. 14.0 14.0 3. 5. 2.0416660 0. 1. 144. 1. 3. 37.0 16.5 5.5 1. 12.0 11.5 2. 3. 0.4848484 0. 1. 160. 1. 5. 27.0 6.0 0.0 2. 14.0 20.0 3. 2. 2.0000000 0. 1. 168. 1. 4. 22.0 6.0 1.0 1. 14.0 14.0 4. 4. 3.2666645 0. 1. 182. 1. 4. 37.0 9.0 2.0 2. 14.0 40.0 3. 6. 1.3611107 0. 1. 202. 1. 4. 27.0 6.0 1.0 1. 12.0 20.0 3. 5. 2.0000000 0. 1. 228. 1. 1. 37.0 23.0 5.5 4. 14.0 14.0 5. 2. 1.8260860 0. 1. 239. 1. 2. 42.0 23.0 2.0 2. 20.0 20.0 4. 4. 1.8260860 0. 1. 243. 1. 4. 37.0 6.0 0.0 2. 16.0 20.0 5. 4. 2.0416660 0. 1. 248. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 3. 4. 7.8399963 0. 1. 251. 1. 3. 37.0 16.5 5.5 2. 9.0 40.0 3. 2. 2.5454540 0. 1. 264. 1. 3. 42.0 23.0 5.5 3. 12.0 14.0 5. 4. 0.5326087 0. 1. 266. 1. 2. 27.0 9.0 2.0 4. 20.0 20.0 3. 4. 0.6222222 0. 1. 280. 1. 4. 27.0 6.0 1.0 2. 12.0 11.5 5. 4. 0.5833333 0. 1. 284. 1. 5. 27.0 2.5 0.0 3. 16.0 14.0 4. 1. 4.7999992 0. 1. 286. 1. 2. 27.0 6.0 2.0 2. 12.0 11.5 2. 5. 0.1666666 0. 1. 289. 1. 5. 37.0 13.0 1.0 3. 12.0 11.5 3. 4. 0.6153846 0. 1. 293. 1. 2. 32.0 16.5 2.0 2. 12.0 14.0 4. 2. 1.1878777 0. 1. 296. 1. 3. 27.0 6.0 1.0 1. 14.0 14.0 3. 6. 11.1999989 0. 1. 297. 1. 3. 32.0 16.5 4.0 3. 14.0 20.0 5. 5. 1.1878777 0. 1. 300. 1. 3. 27.0 9.0 2.0 1. 14.0 20.0 3. 4. 2.1777763 0. 1. 308. 1. 3. 37.0 16.5 3.0 3. 14.0 14.0 3. 2. 0.4848484 0. 1. 312. 1. 4. 32.0 16.5 5.5 4. 12.0 14.0 2. 4. 0.7272727 0. 1. 320. 1. 5. 42.0 16.5 4.0 3. 16.0 40.0 4. 6. 0.7272727 0. 1. 322. 1. 3. 27.0 9.0 2.0 2. 12.0 14.0 5. 2. 1.3333330 0. 1. 332. 1. 3. 17.5 0.5 0.0 1. 12.0 11.5 3. 2. 7.0000000 0. 1. 339. 1. 4. 42.0 23.0 5.5 2. 20.0 4.0 3. 2. 0.5217391 0. 1. 354. 1. 5. 37.0 16.5 3.0 3. 12.0 20.0 3. 5. 0.2121212 0. 1. 359. 1. 4. 22.0 2.5 1.0 2. 14.0 20.0 3. 5. 0.4000000 0. 1. 361. 1. 4. 27.0 2.5 0.0 2. 16.0 20.0 4. 1. 1.3999996 0. 1. 368. 1. 4. 22.0 2.5 0.0 2. 14.0 14.0 3. 6. 3.1999998 0. 1. 370. 1. 4. 37.0 13.0 3.0 2. 16.0 40.0 4. 5. 1.5076914 0. 1. 373. 1. 4. 22.0 2.5 0.0 2. 16.0 14.0 3. 4. 7.8399963 0. 1. 386. 1. 4. 22.0 2.5 0.0 1. 14.0 11.5 1. 2. 7.8399963 0. 1. 387. 1. 5. 22.0 2.5 0.0 3. 12.0 14.0 3. 2. 0.4000000 0. 1. 410. 1. 5. 22.0 2.5 0.0 3. 16.0 20.0 4. 5. 4.8999996 0. 1. 411. 1. 3. 42.0 23.0 4.0 3. 16.0 40.0 5. 5. 0.0434783 0. 1. 425. 1. 5. 32.0 13.0 3.0 3. 14.0 14.0 4. 4. 0.0769231 0. 1. 431. 1. 5. 22.0 6.0 2.0 2. 14.0 14.0 3. 4. 0.5833333 0. 1. 471. 1. 3. 27.0 2.5 1.0 4. 17.0 6.5 4. 6. 4.7999992 0. 1. 473. 1. 2. 42.0 23.0 3.0 3. 14.0 20.0 3. 5. 0.8521735 0. 1. 478. 1. 4. 22.0 2.5 0.0 1. 17.0 40.0 6. 6. 4.7999992 0. 1. 496. 1. 2. 42.0 23.0 3.0 3. 14.0 20.0 3. 5. 2.9217386 0. 1. 510. 1. 4. 42.0 23.0 2.0 2. 14.0 14.0 4. 4. 0.5217391 0. 1. 512. 1. 4. 42.0 23.0 3.0 3. 12.0 20.0 5. 4. 0.5326087 0. 1. 516. 1. 4. 37.0 16.5 2.0 3. 14.0 20.0 3. 4. 0.4848484 0. 1. 521. 1. 4. 27.0 2.5 0.0 2. 20.0 20.0 4. 5. 7.8399963 0. 1. 529. 1. 2. 32.0 9.0 2.0 2. 12.0 9.0 2. 2. 1.3333330 0. 1. 533. 1. 4. 42.0 13.0 0.0 1. 14.0 20.0 3. 4. 1.5076914 0. 1. 536. 1. 4. 22.0 6.0 2.0 1. 12.0 11.5 3. 4. 1.3333330 0. 1. 544. 1. 5. 32.0 16.5 3.0 3. 14.0 20.0 2. 4. 1.1878777 0. 1. 546. 1. 4. 42.0 13.0 0.0 2. 12.0 11.5 3. 2. 1.5076914 0. 1. 547. 1. 5. 27.0 9.0 1.0 3. 14.0 20.0 5. 5. 1.3333330 0. 1. 565. 1. 5. 22.0 6.0 2.0 2. 12.0 14.0 2. 5. 7.0000000 0. 1. 575. 1. 2. 27.0 16.5 2.0 3. 16.0 20.0 4. 5. 0.7272727 0. 1. 577. 1. 5. 37.0 13.0 2.0 1. 12.0 14.0 3. 4. 1.5076914 0. 1. 584. 1. 5. 27.0 6.0 0.0 2. 14.0 20.0 3. 4. 2.0416660 0. 1. 587. 1. 2. 27.0 2.5 1.0 1. 12.0 11.5 2. 3. 26.8799896 0. 1. 588. 1. 5. 42.0 23.0 5.5 4. 20.0 40.0 5. 4. 0.5326087 0. 1. 612. 1. 5. 27.0 6.0 0.0 2. 16.0 20.0 5. 2. 2.0000000 0. 1. 616. 1. 5. 37.0 16.5 3.0 2. 14.0 20.0 3. 5. 0.2121212 0. 1. 618. 1. 2. 32.0 9.0 2.0 2. 16.0 20.0 3. 5. 1.3333330 0. 1. 630. 1. 3. 37.0 16.5 5.5 3. 12.0 20.0 5. 5. 2.5454540 0. 1. 633. 1. 5. 27.0 6.0 2.0 2. 12.0 20.0 3. 4. 0.1666666 0. 1. 648. 1. 5. 32.0 16.5 3.0 1. 12.0 20.0 4. 4. 0.4848484 0. 1. 652. 1. 5. 27.0 9.0 2.0 1. 14.0 11.5 5. 1. 1.3333330 0. 1. 672. 1. 4. 22.0 2.5 0.0 2. 16.0 20.0 5. 5. 0.4000000 0. 1. 674. 1. 5. 32.0 16.5 2.0 4. 14.0 14.0 5. 2. 0.4848484 0. 1. 706. 1. 2. 22.0 6.0 2.0 2. 14.0 9.0 2. 5. 0.1666666 0. 1. 715. 1. 3. 32.0 13.0 3.0 1. 12.0 11.5 4. 5. 1.5076914 0. 1. 733. 1. 5. 32.0 16.5 3.0 2. 12.0 20.0 3. 4. 0.0606061 0. 1. 734. 1. 3. 27.0 6.0 2.0 1. 14.0 11.5 3. 6. 2.0416660 0. 1. 736. 1. 5. 22.0 2.5 0.0 1. 20.0 14.0 4. 4. 0.4000000 0. 1. 740. 1. 3. 32.0 9.0 2.0 3. 14.0 20.0 3. 5. 0.8888888 0. 1. 771. 1. 3. 22.0 2.5 1.0 2. 14.0 11.5 3. 2. 4.7999992 0. 1. 775. 1. 3. 22.0 2.5 0.0 2. 16.0 9.0 3. 4. 4.7999992 0. 1. 779. 1. 5. 27.0 9.0 2.0 1. 16.0 40.0 4. 6. 1.3333330 0. 1. 780. 1. 3. 42.0 23.0 2.0 2. 12.0 14.0 2. 2. 0.1521739 0. 1. 784. 1. 3. 37.0 16.5 3.0 2. 17.0 20.0 3. 2. 1.1878777 0. 1. 808. 1. 5. 32.0 13.0 2.0 2. 12.0 20.0 2. 6. 0.9423077 0. 1. 822. 1. 1. 27.0 13.0 2.0 2. 12.0 11.5 4. 4. 1.5076914 0. 1. 825. 1. 5. 27.0 2.5 0.0 2. 14.0 20.0 3. 2. 0.4000000 0. 1. 827. 1. 5. 27.0 9.0 0.0 1. 14.0 20.0 4. 4. 0.1111111 0. 1. 839. 1. 5. 32.0 13.0 2.0 2. 16.0 20.0 3. 3. 0.9423077 0. 1. 860. 1. 4. 27.0 9.0 2.0 2. 14.0 20.0 3. 5. 0.3888888 0. 1. 865. 1. 3. 22.0 2.5 1.0 3. 14.0 11.5 5. 4. 4.8999996 0. 1. 866. 1. 2. 37.0 23.0 2.0 2. 12.0 40.0 5. 5. 0.5326087 0. 1. 882. 1. 2. 27.0 6.0 2.0 1. 14.0 20.0 3. 5. 2.0416660 0. 1. 922. 1. 4. 27.0 2.5 1.0 2. 12.0 11.5 5. 2. 4.8999996 0. 1. 946. 1. 3. 37.0 13.0 5.5 2. 12.0 40.0 5. 5. 5.1692305 0. 1. 948. 1. 5. 37.0 16.5 4.0 1. 12.0 40.0 3. 6. 1.6969690 0. 1. 954. 1. 5. 22.0 2.5 0.0 1. 14.0 20.0 3. 5. 1.3999996 0. 1. 955. 1. 4. 42.0 13.0 0.0 1. 14.0 11.5 5. 5. 0.0769231 0. 1. 961. 1. 5. 27.0 9.0 1.0 1. 14.0 20.0 5. 5. 2.1777763 0. 1. 980. 1. 4. 22.0 2.5 0.0 1. 14.0 11.5 3. 4. 1.3999996 0. 1. 1000. 1. 4. 22.0 2.5 1.0 2. 14.0 20.0 5. 4. 7.8399963 0. 1. 1011. 1. 4. 27.0 6.0 2.0 3. 16.0 20.0 4. 6. 0.5833333 0. 1. 1013. 1. 2. 32.0 16.5 1.0 2. 14.0 40.0 3. 5. 2.7151508 0. 1. 1024. 1. 3. 27.0 13.0 2.0 1. 14.0 20.0 3. 4. 0.9423077 0. 1. 1029. 1. 4. 22.0 0.5 0.0 2. 12.0 20.0 3. 2. 7.0000000 0. 1. 1035. 1. 5. 27.0 6.0 1.0 1. 16.0 20.0 4. 3. 3.2666645 0. 1. 1037. 1. 2. 27.0 9.0 2.0 2. 14.0 11.5 3. 5. 4.6666660 0. 1. 1041. 1. 3. 27.0 9.0 2.0 2. 12.0 14.0 3. 4. 2.1777763 0. 1. 1047. 1. 4. 32.0 16.5 3.0 3. 12.0 20.0 2. 3. 0.4848484 0. 1. 1049. 1. 5. 22.0 2.5 0.0 1. 12.0 20.0 3. 3. 1.3999996 0. 1. 1071. 1. 4. 22.0 2.5 1.0 2. 14.0 11.5 4. 4. 3.1999998 0. 1. 1072. 1. 1. 22.0 2.5 0.0 2. 9.0 6.5 2. 4. 11.1999998 0. 1. 1074. 1. 3. 32.0 13.0 2.0 1. 20.0 40.0 6. 6. 1.5076914 0. 1. 1101. 1. 5. 32.0 9.0 1.0 2. 14.0 40.0 3. 5. 1.3333330 0. 1. 1118. 1. 5. 27.0 2.5 0.0 1. 20.0 20.0 6. 4. 1.3999996 0. 1. 1125. 1. 4. 27.0 9.0 2.0 2. 14.0 9.0 2. 5. 0.3888888 0. 1. 1128. 1. 4. 27.0 6.0 0.0 3. 12.0 11.5 5. 2. 2.0000000 0. 1. 1129. 1. 2. 22.0 2.5 0.0 3. 14.0 20.0 3. 2. 4.8999996 0. 1. 1130. 1. 3. 27.0 9.0 2.0 3. 17.0 9.0 4. 6. 1.3611107 0. 1. 1151. 1. 4. 27.0 6.0 2.0 1. 14.0 40.0 3. 4. 2.0416660 0. 1. 1152. 1. 5. 22.0 2.5 1.0 1. 12.0 20.0 2. 2. 1.3999996 0. 1. 1158. 1. 4. 27.0 16.5 4.0 3. 12.0 40.0 2. 6. 0.2121212 0. 1. 1165. 1. 2. 27.0 6.0 1.0 2. 14.0 11.5 4. 3. 3.2666645 0. 1. 1187. 1. 2. 32.0 6.0 1.0 1. 17.0 11.5 4. 4. 7.4666672 0. 1. 1194. 1. 5. 27.0 6.0 2.0 2. 16.0 20.0 4. 3. 0.5833333 0. 1. 1202. 1. 4. 42.0 23.0 3.0 3. 17.0 20.0 4. 4. 0.5217391 0. 1. 1207. 1. 4. 32.0 9.0 2.0 1. 16.0 20.0 4. 6. 0.1111111 0. 1. 1223. 1. 3. 42.0 23.0 4.0 3. 14.0 20.0 4. 3. 0.5217391 0. 1. 1232. 1. 5. 27.0 6.0 2.0 1. 12.0 9.0 5. 2. 2.0000000 0. 1. 1233. 1. 2. 42.0 23.0 4.0 4. 20.0 40.0 4. 5. 0.1521739 0. 1. 1251. 1. 3. 32.0 6.0 0.0 2. 20.0 40.0 4. 5. 4.6666660 0. 1. 1259. 1. 5. 27.0 2.5 0.0 2. 14.0 20.0 3. 2. 1.3999996 0. 1. 1261. 1. 2. 42.0 16.5 3.0 3. 12.0 20.0 3. 5. 1.1878777 0. 1. 1269. 1. 4. 37.0 23.0 3.0 2. 12.0 20.0 5. 4. 1.8260860 0. 1. 1271. 1. 3. 27.0 2.5 0.0 2. 17.0 14.0 4. 4. 1.3999996 0. 1. 1278. 1. 3. 32.0 13.0 3.0 3. 20.0 40.0 4. 6. 3.2307692 0. 1. 1288. 1. 3. 27.0 2.5 0.0 2. 14.0 40.0 3. 4. 4.7999992 0. 1. 1296. 1. 3. 42.0 16.5 2.0 1. 16.0 40.0 3. 5. 1.1878777 0. 1. 1301. 1. 4. 22.0 2.5 0.0 2. 14.0 20.0 5. 5. 0.4000000 0. 1. 1314. 1. 5. 27.0 6.0 0.0 3. 14.0 20.0 3. 5. 0.5833333 0. 1. 1320. 1. 3. 22.0 2.5 0.0 1. 12.0 11.5 2. 4. 7.8399963 0. 1. 1325. 1. 4. 27.0 6.0 0.0 3. 14.0 40.0 2. 4. 11.1999989 0. 1. 1333. 1. 3. 27.0 9.0 2.0 2. 16.0 14.0 3. 4. 1.3611107 0. 1. 1334. 1. 2. 27.0 9.0 1.0 2. 14.0 20.0 2. 2. 4.9777775 0. 1. 1337. 1. 3. 22.0 2.5 0.0 3. 12.0 20.0 3. 3. 4.7999992 0. 1. 1339. 1. 4. 32.0 16.5 4.0 4. 14.0 20.0 3. 5. 2.7151508 0. 1. 1349. 1. 5. 32.0 2.5 2.0 2. 14.0 20.0 3. 4. 0.4000000 0. 1. 1351. 1. 4. 27.0 2.5 0.0 2. 14.0 40.0 3. 5. 0.4000000 0. 1. 1356. 1. 5. 32.0 9.0 3.0 3. 16.0 11.5 4. 4. 0.1111111 0. 1. 1364. 1. 3. 32.0 13.0 2.0 2. 12.0 20.0 3. 5. 0.2692307 0. 1. 1368. 1. 3. 32.0 16.5 3.0 3. 12.0 20.0 3. 2. 0.7272727 0. 1. 1369. 1. 5. 42.0 23.0 2.0 2. 14.0 20.0 5. 5. 0.5217391 0. 1. 1370. 1. 3. 27.0 16.5 4.0 3. 14.0 20.0 4. 2. 0.7424242 0. 1. 1374. 1. 3. 32.0 16.5 4.0 2. 20.0 20.0 4. 4. 0.7272727 0. 1. 1378. 1. 4. 22.0 2.5 2.0 2. 14.0 14.0 5. 2. 4.7999992 0. 1. 1392. 1. 5. 27.0 6.0 0.0 1. 12.0 20.0 3. 4. 7.4666672 0. 1. 1396. 1. 4. 37.0 16.5 3.0 4. 16.0 40.0 5. 5. 0.2121212 0. 1. 1410. 1. 3. 22.0 2.5 0.0 2. 12.0 6.5 3. 5. 1.3999996 0. 1. 1411. 1. 4. 32.0 13.0 2.0 2. 14.0 20.0 3. 5. 1.5076914 0. 1. 1422. 1. 5. 42.0 23.0 5.5 3. 17.0 20.0 4. 4. 0.5217391 0. 1. 1455. 1. 2. 27.0 6.0 2.0 3. 12.0 11.5 2. 2. 1.3333330 0. 1. 1465. 1. 5. 22.0 2.5 1.0 2. 16.0 20.0 3. 6. 1.3999996 0. 1. 1486. 1. 3. 42.0 23.0 2.0 3. 12.0 11.5 2. 2. 0.5326087 0. 1. 1495. 1. 5. 42.0 23.0 3.0 1. 14.0 40.0 3. 5. 1.8260860 0. 1. 1499. 1. 4. 22.0 2.5 0.0 3. 12.0 20.0 3. 4. 4.8999996 0. 1. 1516. 1. 3. 27.0 9.0 1.0 4. 14.0 20.0 5. 5. 0.1111111 0. 1. 1519. 1. 2. 37.0 23.0 3.0 3. 12.0 20.0 2. 4. 4.1739130 0. 1. 1553. 1. 3. 32.0 9.0 3.0 3. 14.0 20.0 4. 5. 0.3888888 0. 1. 1554. 1. 5. 37.0 16.5 4.0 3. 12.0 14.0 3. 4. 1.1878777 0. 1. 1559. 1. 3. 27.0 9.0 2.0 2. 12.0 14.0 5. 2. 1.3333330 0. 1. 1560. 1. 3. 27.0 6.0 1.0 2. 17.0 6.5 2. 2. 1.3333330 0. 1. 1563. 1. 5. 22.0 2.5 0.0 3. 14.0 14.0 3. 3. 1.3999996 0. 1. 1568. 1. 5. 27.0 6.0 0.0 2. 17.0 20.0 4. 5. 1.3333330 0. 1. 1591. 1. 4. 22.0 2.5 0.0 2. 12.0 14.0 4. 2. 4.8999996 0. 1. 1595. 1. 4. 27.0 6.0 0.0 2. 12.0 20.0 3. 4. 4.6666660 0. 1. 1610. 1. 3. 32.0 9.0 1.0 2. 12.0 20.0 3. 3. 0.1111111 0. 1. 1611. 1. 1. 42.0 23.0 4.0 3. 12.0 20.0 3. 5. 0.8521735 0. 1. 1612. 1. 2. 22.0 6.0 2.0 2. 12.0 20.0 3. 4. 1.3333330 0. 1. 1617. 1. 4. 32.0 16.5 2.0 2. 12.0 14.0 2. 2. 0.4848484 0. 1. 1635. 1. 4. 32.0 13.0 3.0 3. 14.0 11.5 2. 2. 0.9423077 0. 1. 1639. 1. 5. 27.0 9.0 2.0 1. 12.0 20.0 3. 3. 0.3888888 0. 1. 1649. 1. 3. 42.0 23.0 3.0 2. 12.0 20.0 3. 2. 0.5217391 0. 1. 1673. 1. 3. 37.0 16.5 1.0 3. 20.0 20.0 6. 6. 2.7151508 0. 1. 1674. 1. 5. 32.0 13.0 2.0 1. 12.0 20.0 5. 2. 0.9423077 0. 1. 1677. 1. 4. 27.0 13.0 3.0 1. 12.0 9.0 4. 2. 3.4461536 0. 1. 1688. 1. 2. 37.0 13.0 0.0 2. 14.0 20.0 3. 5. 0.9230769 0. 1. 1690. 1. 3. 27.0 9.0 1.0 1. 12.0 11.5 2. 2. 3.1111107 0. 1. 1708. 1. 5. 27.0 6.0 0.0 1. 14.0 40.0 5. 5. 3.2666645 0. 1. 1711. 1. 2. 42.0 23.0 2.0 3. 12.0 14.0 5. 2. 0.8521735 0. 1. 1712. 1. 5. 27.0 6.0 0.0 2. 16.0 11.5 3. 4. 0.5833333 0. 1. 1713. 1. 4. 22.0 2.5 0.0 4. 16.0 14.0 5. 4. 7.8399963 0. 1. 1720. 1. 5. 22.0 2.5 0.0 2. 16.0 6.5 4. 4. 1.3999996 0. 1. 1724. 1. 5. 42.0 13.0 0.0 3. 12.0 11.5 3. 2. 1.5076914 0. 1. 1742. 1. 4. 22.0 2.5 1.0 1. 16.0 20.0 3. 4. 1.3999996 0. 1. 1757. 1. 4. 32.0 9.0 1.0 3. 16.0 14.0 3. 5. 1.3611107 0. 1. 1773. 1. 4. 27.0 9.0 2.0 2. 12.0 11.5 4. 2. 0.3888888 0. 1. 1793. 1. 5. 27.0 6.0 0.0 1. 16.0 14.0 5. 2. 2.0416660 0. 1. 1796. 1. 4. 32.0 16.5 3.0 2. 14.0 20.0 5. 4. 2.5454540 0. 1. 1802. 1. 2. 42.0 2.5 0.0 4. 20.0 40.0 6. 5. 4.7999992 0. 1. 1805. 1. 2. 42.0 23.0 5.5 4. 14.0 14.0 3. 6. 0.3478261 0. 1. 1822. 1. 4. 42.0 23.0 4.0 2. 12.0 20.0 3. 2. 0.3478261 0. 1. 1827. 1. 3. 22.0 2.5 0.0 3. 16.0 20.0 5. 5. 4.7999992 0. 1. 1837. 1. 2. 22.0 2.5 0.0 3. 14.0 20.0 3. 5. 1.3999996 0. 1. 1844. 1. 4. 22.0 2.5 0.0 3. 14.0 14.0 3. 3. 4.7999992 0. 1. 1852. 1. 4. 32.0 16.5 4.0 3. 14.0 11.5 4. 4. 0.2121212 0. 1. 1855. 1. 5. 32.0 13.0 2.0 4. 17.0 14.0 4. 5. 0.9423077 0. 1. 1863. 1. 4. 32.0 13.0 3.0 4. 12.0 20.0 3. 5. 1.5076914 0. 1. 1872. 1. 5. 27.0 9.0 2.0 1. 14.0 11.5 4. 4. 1.3611107 0. 1. 1889. 1. 4. 27.0 6.0 1.0 4. 16.0 11.5 3. 4. 0.5833333 0. 1. 1891. 1. 4. 27.0 9.0 2.0 3. 12.0 11.5 2. 4. 1.3333330 0. 1. 1904. 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1929. 1. 5. 27.0 6.0 1.0 3. 12.0 14.0 3. 4. 0.0 0. 1. 1932. 1. 3. 42.0 23.0 5.5 2. 14.0 40.0 4. 5. 0.0 0. 1. 1943. 1. 5. 27.0 6.0 0.0 3. 20.0 40.0 4. 4. 0.0 0. 1. 1944. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 3. 2. 0.0 0. 1. 1945. 1. 4. 27.0 2.5 0.0 3. 12.0 20.0 3. 3. 0.0 0. 1. 1948. 1. 5. 22.0 2.5 0.0 3. 16.0 20.0 3. 5. 0.0 0. 1. 1949. 1. 2. 32.0 2.5 1.0 2. 12.0 14.0 5. 2. 0.0 0. 1. 1955. 1. 4. 27.0 2.5 1.0 2. 14.0 14.0 4. 4. 0.0 0. 1. 1956. 1. 4. 27.0 0.5 0.0 3. 14.0 14.0 3. 4. 0.0 0. 1. 1959. 1. 4. 32.0 9.0 1.0 3. 17.0 14.0 4. 4. 0.0 0. 1. 1962. 1. 4. 27.0 9.0 2.0 2. 17.0 20.0 4. 5. 0.0 0. 1. 1963. 1. 5. 22.0 2.5 0.0 4. 14.0 6.5 4. 4. 0.0 0. 1. 1985. 1. 4. 22.0 2.5 0.0 2. 12.0 14.0 2. 2. 0.0 0. 1. 1996. 1. 5. 27.0 6.0 2.0 2. 16.0 20.0 4. 1. 0.0 0. 1. 1997. 1. 4. 27.0 6.0 0.0 2. 12.0 14.0 3. 4. 0.0 0. 1. 1999. 1. 4. 32.0 13.0 2.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2009. 1. 4. 37.0 16.5 2.0 1. 20.0 40.0 4. 6. 0.0 0. 1. 2013. 1. 4. 42.0 23.0 3.0 2. 12.0 11.5 3. 5. 0.0 0. 1. 2017. 1. 1. 42.0 23.0 5.5 3. 14.0 20.0 2. 5. 0.0 0. 1. 2018. 1. 4. 27.0 2.5 0.0 1. 12.0 20.0 3. 2. 0.0 0. 1. 2029. 1. 4. 27.0 6.0 1.0 3. 14.0 11.5 2. 5. 0.0 0. 1. 2030. 1. 3. 42.0 16.5 5.5 2. 14.0 40.0 3. 6. 0.0 0. 1. 2033. 1. 5. 22.0 2.5 0.0 2. 16.0 6.5 3. 1. 0.0 0. 1. 2040. 1. 4. 27.0 2.5 1.0 2. 12.0 14.0 3. 2. 0.0 0. 1. 2043. 1. 5. 37.0 16.5 4.0 3. 16.0 40.0 4. 5. 0.0 0. 1. 2045. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 3. 3. 0.0 0. 1. 2049. 1. 4. 27.0 2.5 0.0 3. 14.0 11.5 3. 1. 0.0 0. 1. 2057. 1. 4. 22.0 2.5 0.0 1. 14.0 11.5 2. 2. 0.0 0. 1. 2059. 1. 5. 22.0 2.5 0.0 2. 12.0 14.0 3. 4. 0.0 0. 1. 2068. 1. 5. 22.0 0.5 0.0 3. 12.0 14.0 3. 3. 0.0 0. 1. 2069. 1. 3. 42.0 23.0 2.0 1. 20.0 40.0 4. 4. 0.0 0. 1. 2071. 1. 5. 32.0 16.5 1.0 3. 17.0 40.0 5. 4. 0.0 0. 1. 2074. 1. 5. 27.0 9.0 1.0 2. 14.0 20.0 3. 4. 0.0 0. 1. 2081. 1. 5. 27.0 2.5 1.0 3. 14.0 11.5 3. 5. 0.0 0. 1. 2083. 1. 4. 27.0 6.0 0.0 2. 16.0 14.0 3. 3. 0.0 0. 1. 2084. 1. 5. 22.0 0.5 0.0 3. 14.0 9.0 3. 2. 0.0 0. 1. 2085. 1. 4. 32.0 9.0 0.0 1. 17.0 20.0 4. 4. 0.0 0. 1. 2089. 1. 3. 37.0 16.5 3.0 3. 12.0 11.5 3. 3. 0.0 0. 1. 2091. 1. 5. 27.0 2.5 0.0 3. 12.0 14.0 3. 4. 0.0 0. 1. 2092. 1. 5. 22.0 2.5 0.0 4. 14.0 40.0 3. 2. 0.0 0. 1. 2094. 1. 4. 32.0 13.0 3.0 3. 17.0 20.0 4. 4. 0.0 0. 1. 2097. 1. 5. 42.0 23.0 5.5 4. 17.0 40.0 5. 5. 0.0 0. 1. 2103. 1. 4. 27.0 2.5 0.0 2. 16.0 20.0 4. 4. 0.0 0. 1. 2117. 1. 5. 42.0 16.5 4.0 3. 14.0 20.0 3. 4. 0.0 0. 1. 2118. 1. 3. 27.0 2.5 0.0 3. 12.0 20.0 3. 3. 0.0 0. 1. 2119. 1. 5. 27.0 6.0 2.0 4. 16.0 40.0 4. 6. 0.0 0. 1. 2121. 1. 3. 22.0 0.5 0.0 3. 12.0 9.0 5. 4. 0.0 0. 1. 2127. 1. 4. 42.0 23.0 3.0 2. 14.0 40.0 3. 4. 0.0 0. 1. 2131. 1. 5. 27.0 2.5 0.0 2. 16.0 20.0 4. 5. 0.0 0. 1. 2148. 1. 5. 22.0 0.5 0.0 3. 12.0 20.0 3. 2. 0.0 0. 1. 2151. 1. 5. 22.0 2.5 0.0 2. 12.0 20.0 3. 4. 0.0 0. 1. 2154. 1. 5. 37.0 16.5 2.0 1. 12.0 40.0 3. 5. 0.0 0. 1. 2162. 1. 3. 22.0 6.0 2.0 2. 12.0 11.5 3. 2. 0.0 0. 1. 2171. 1. 2. 42.0 23.0 3.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2172. 1. 4. 32.0 2.5 0.0 1. 17.0 40.0 5. 5. 0.0 0. 1. 2175. 1. 5. 22.0 2.5 0.0 3. 16.0 11.5 3. 4. 0.0 0. 1. 2181. 1. 4. 22.0 0.5 0.0 3. 14.0 9.0 3. 4. 0.0 0. 1. 2184. 1. 5. 27.0 6.0 1.0 1. 14.0 14.0 4. 4. 0.0 0. 1. 2186. 1. 4. 27.0 2.5 0.0 3. 16.0 40.0 4. 4. 0.0 0. 1. 2188. 1. 5. 32.0 6.0 3.0 1. 16.0 20.0 3. 5. 0.0 0. 1. 2190. 1. 5. 32.0 16.5 2.0 1. 12.0 20.0 2. 3. 0.0 0. 1. 2193. 1. 4. 42.0 16.5 3.0 2. 14.0 40.0 3. 2. 0.0 0. 1. 2194. 1. 5. 22.0 0.5 0.0 2. 12.0 11.5 2. 2. 0.0 0. 1. 2197. 1. 5. 17.5 0.5 0.0 2. 12.0 4.0 2. 4. 0.0 0. 1. 2198. 1. 5. 42.0 23.0 3.0 3. 14.0 20.0 4. 4. 0.0 0. 1. 2200. 1. 2. 22.0 0.5 0.0 1. 14.0 20.0 3. 2. 0.0 0. 1. 2201. 1. 5. 27.0 2.5 0.0 1. 16.0 40.0 4. 5. 0.0 0. 1. 2204. 1. 5. 27.0 16.5 3.0 1. 12.0 11.5 3. 5. 0.0 0. 1. 2207. 1. 5. 22.0 0.5 0.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2211. 1. 4. 32.0 9.0 2.0 3. 20.0 40.0 6. 5. 0.0 0. 1. 2213. 1. 4. 22.0 0.5 0.0 2. 14.0 6.5 2. 1. 0.0 0. 1. 2214. 1. 4. 27.0 6.0 2.0 4. 14.0 9.0 3. 5. 0.0 0. 1. 2215. 1. 1. 27.0 9.0 4.0 1. 12.0 9.0 4. 2. 0.0 0. 1. 2224. 1. 4. 22.0 2.5 0.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2227. 1. 5. 22.0 2.5 0.0 2. 16.0 20.0 4. 5. 0.0 0. 1. 2233. 1. 5. 22.0 2.5 1.0 2. 12.0 11.5 2. 2. 0.0 0. 1. 2237. 1. 5. 17.5 0.5 0.0 3. 12.0 11.5 5. 2. 0.0 0. 1. 2239. 1. 4. 32.0 9.0 2.0 3. 14.0 14.0 2. 4. 0.0 0. 1. 2245. 1. 5. 27.0 6.0 0.0 3. 17.0 20.0 5. 5. 0.0 0. 1. 2246. 1. 5. 22.0 0.5 0.0 2. 12.0 9.0 3. 2. 0.0 0. 1. 2250. 1. 5. 32.0 9.0 0.0 3. 12.0 40.0 3. 4. 0.0 0. 1. 2252. 1. 3. 27.0 9.0 3.0 1. 12.0 20.0 3. 4. 0.0 0. 1. 2254. 1. 5. 22.0 2.5 0.0 1. 12.0 4.0 3. 1. 0.0 0. 1. 2261. 1. 5. 42.0 16.5 4.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2262. 1. 5. 27.0 2.5 1.0 2. 16.0 40.0 4. 5. 0.0 0. 1. 2263. 1. 3. 22.0 2.5 0.0 3. 12.0 11.5 3. 4. 0.0 0. 1. 2264. 1. 5. 22.0 2.5 1.0 1. 14.0 11.5 3. 5. 0.0 0. 1. 2284. 1. 4. 27.0 16.5 3.0 1. 16.0 20.0 5. 5. 0.0 0. 1. 2285. 1. 5. 27.0 2.5 1.0 3. 16.0 20.0 4. 5. 0.0 0. 1. 2295. 1. 3. 27.0 2.5 1.0 1. 17.0 11.5 2. 5. 0.0 0. 1. 2297. 1. 5. 22.0 2.5 0.0 3. 14.0 6.5 3. 4. 0.0 0. 1. 2301. 1. 5. 37.0 16.5 2.0 3. 20.0 40.0 6. 6. 0.0 0. 1. 2321. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 5. 4. 0.0 0. 1. 2322. 1. 5. 27.0 2.5 0.0 3. 14.0 20.0 3. 4. 0.0 0. 1. 2326. 1. 4. 22.0 2.5 0.0 3. 16.0 6.5 3. 1. 0.0 0. 1. 2327. 1. 4. 27.0 6.0 0.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2332. 1. 1. 27.0 6.0 1.0 2. 20.0 40.0 4. 5. 0.0 0. 1. 2335. 1. 5. 22.0 0.5 0.0 3. 14.0 14.0 2. 2. 0.0 0. 1. 2345. 1. 5. 32.0 13.0 4.0 3. 12.0 11.5 3. 4. 0.0 0. 1. 2347. 1. 5. 22.0 6.0 0.0 2. 12.0 40.0 3. 5. 0.0 0. 1. 2351. 1. 5. 37.0 16.5 2.0 3. 16.0 20.0 4. 5. 0.0 0. 1. 2353. 1. 3. 22.0 6.0 2.0 3. 12.0 11.5 2. 4. 0.0 0. 1. 2356. 1. 4. 22.0 0.5 0.0 3. 16.0 20.0 3. 5. 0.0 0. 1. 2361. 1. 5. 27.0 2.5 0.0 3. 14.0 20.0 3. 2. 0.0 0. 1. 2366. 1. 4. 22.0 2.5 0.0 4. 14.0 14.0 3. 4. 0.0 0. 1. 2367. 1. 5. 22.0 2.5 0.0 1. 12.0 11.5 3. 2. 0.0 0. 1. 2368. 1. 5. 27.0 6.0 0.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2369. 1. 3. 27.0 0.5 0.0 3. 20.0 40.0 6. 6. 0.0 0. 1. 2371. 1. 4. 32.0 13.0 2.0 3. 12.0 20.0 5. 5. 0.0 0. 1. 2373. 1. 3. 22.0 2.5 0.0 2. 16.0 14.0 3. 6. 0.0 0. 1. 2377. 1. 5. 32.0 2.5 0.0 2. 20.0 20.0 4. 4. 0.0 0. 1. 2382. 1. 4. 22.0 2.5 0.0 3. 17.0 40.0 4. 5. 0.0 0. 1. 2385. 1. 5. 22.0 2.5 0.0 3. 17.0 20.0 4. 5. 0.0 0. 1. 2387. 1. 3. 22.0 0.5 0.0 3. 16.0 20.0 3. 4. 0.0 0. 1. 2389. 1. 5. 27.0 6.0 1.0 1. 14.0 14.0 3. 4. 0.0 0. 1. 2394. 1. 5. 32.0 16.5 1.0 3. 12.0 9.0 2. 2. 0.0 0. 1. 2397. 1. 3. 37.0 23.0 3.0 3. 12.0 20.0 3. 4. 0.0 0. 1. 2399. 1. 5. 22.0 2.5 0.0 1. 20.0 20.0 4. 5. 0.0 0. 1. 2401. 1. 5. 27.0 6.0 0.0 1. 12.0 14.0 3. 4. 0.0 0. 1. 2405. 1. 5. 27.0 6.0 0.0 2. 14.0 20.0 3. 5. 0.0 0. 1. 2408. 1. 3. 37.0 23.0 5.5 1. 12.0 14.0 3. 4. 0.0 0. 1. 2410. 1. 3. 42.0 16.5 2.0 2. 12.0 40.0 2. 6. 0.0 0. 1. 2411. 1. 4. 27.0 6.0 2.0 2. 12.0 20.0 2. 2. 0.0 0. 1. 2417. 1. 5. 22.0 2.5 0.0 2. 16.0 14.0 4. 4. 0.0 0. 1. 2418. 1. 5. 22.0 0.5 0.0 3. 12.0 9.0 2. 5. 0.0 0. 1. 2423. 1. 4. 42.0 23.0 2.0 3. 12.0 20.0 2. 5. 0.0 0. 1. 2425. 1. 5. 37.0 16.5 2.0 1. 14.0 40.0 5. 6. 0.0 0. 1. 2428. 1. 5. 42.0 23.0 4.0 4. 12.0 40.0 3. 5. 0.0 0. 1. 2429. 1. 2. 22.0 2.5 0.0 3. 14.0 9.0 3. 4. 0.0 0. 1. 2433. 1. 4. 27.0 6.0 2.0 1. 14.0 20.0 3. 4. 0.0 0. 1. 2434. 1. 3. 22.0 0.5 0.0 2. 17.0 14.0 3. 5. 0.0 0. 1. 2435. 1. 3. 27.0 6.0 2.0 2. 16.0 20.0 4. 5. 0.0 0. 1. 2445. 1. 5. 32.0 13.0 3.0 2. 20.0 40.0 3. 6. 0.0 0. 1. 2454. 1. 5. 42.0 23.0 4.0 4. 16.0 20.0 2. 5. 0.0 0. 1. 2461. 1. 4. 22.0 2.5 0.0 3. 16.0 20.0 4. 4. 0.0 0. 1. 2470. 1. 5. 27.0 6.0 1.0 3. 20.0 40.0 4. 6. 0.0 0. 1. 2471. 1. 2. 42.0 23.0 5.5 3. 12.0 20.0 2. 5. 0.0 0. 1. 2472. 1. 5. 22.0 6.0 0.0 4. 14.0 6.5 3. 2. 0.0 0. 1. 2478. 1. 5. 22.0 6.0 1.0 1. 12.0 11.5 3. 5. 0.0 0. 1. 2480. 1. 5. 27.0 2.5 0.0 2. 12.0 20.0 3. 4. 0.0 0. 1. 2492. 1. 5. 32.0 13.0 3.0 4. 16.0 14.0 4. 6. 0.0 0. 1. 2493. 1. 5. 27.0 0.5 0.0 2. 12.0 14.0 3. 2. 0.0 0. 1. 2501. 1. 4. 37.0 16.5 2.0 2. 12.0 20.0 2. 2. 0.0 0. 1. 2505. 1. 4. 32.0 2.5 0.0 3. 20.0 20.0 4. 4. 0.0 0. 1. 2506. 1. 5. 32.0 2.5 1.0 3. 12.0 20.0 3. 4. 0.0 0. 1. 2513. 1. 4. 27.0 2.5 0.0 2. 12.0 14.0 3. 4. 0.0 0. 1. 2519. 1. 5. 27.0 6.0 1.0 3. 12.0 11.5 3. 4. 0.0 0. 1. 2520. 1. 4. 27.0 6.0 1.0 1. 20.0 20.0 4. 4. 0.0 0. 1. 2522. 1. 5. 22.0 2.5 0.0 1. 12.0 20.0 3. 5. 0.0 0. 1. 2524. 1. 5. 32.0 16.5 2.0 3. 17.0 11.5 3. 5. 0.0 0. 1. 2535. 1. 5. 22.0 2.5 0.0 1. 16.0 20.0 5. 5. 0.0 0. 1. 2537. 1. 5. 27.0 6.0 1.0 3. 20.0 20.0 6. 6. 0.0 0. 1. 2542. 1. 3. 27.0 6.0 2.0 3. 20.0 20.0 4. 5. 0.0 0. 1. 2544. 1. 5. 27.0 9.0 1.0 2. 16.0 11.5 4. 4. 0.0 0. 1. 2545. 1. 3. 42.0 23.0 4.0 3. 14.0 20.0 3. 4. 0.0 0. 1. 2549. 1. 5. 27.0 6.0 2.0 2. 20.0 14.0 4. 3. 0.0 0. 1. 2555. 1. 4. 27.0 6.0 2.0 3. 14.0 14.0 3. 4. 0.0 0. 1. 2558. 1. 5. 42.0 23.0 3.0 2. 12.0 20.0 5. 4. 0.0 0. 1. 2563. 1. 4. 22.0 2.5 0.0 3. 16.0 14.0 3. 5. 0.0 0. 1. 2569. 1. 5. 32.0 16.5 3.0 3. 12.0 14.0 3. 4. 0.0 0. 1. 2573. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 1. 5. 0.0 0. 1. 2574. 1. 5. 27.0 6.0 0.0 3. 14.0 20.0 4. 5. 0.0 0. 1. 2576. 1. 5. 22.0 2.5 0.0 1. 12.0 9.0 2. 1. 0.0 0. 1. 2580. 1. 4. 27.0 6.0 1.0 1. 14.0 20.0 4. 4. 0.0 0. 1. 2581. 1. 4. 22.0 2.5 0.0 3. 16.0 11.5 2. 4. 0.0 0. 1. 2582. 1. 4. 22.0 2.5 0.0 4. 12.0 20.0 3. 2. 0.0 0. 1. 2586. 1. 5. 17.5 2.5 0.0 1. 12.0 9.0 2. 4. 0.0 0. 1. 2592. 1. 4. 37.0 16.5 3.0 3. 12.0 11.5 4. 5. 0.0 0. 1. 2593. 1. 4. 27.0 6.0 1.0 2. 12.0 11.5 4. 4. 0.0 0. 1. 2595. 1. 3. 37.0 13.0 0.0 2. 14.0 20.0 5. 6. 0.0 0. 1. 2597. 1. 4. 37.0 16.5 4.0 3. 12.0 14.0 2. 4. 0.0 0. 1. 2598. 1. 4. 27.0 2.5 0.0 1. 14.0 40.0 5. 6. 0.0 0. 1. 2599. 1. 5. 42.0 23.0 2.0 2. 12.0 40.0 5. 5. 0.0 0. 1. 2600. 1. 5. 22.0 2.5 0.0 4. 14.0 11.5 2. 2. 0.0 0. 1. 2601. 1. 5. 22.0 2.5 0.0 3. 16.0 9.0 3. 1. 0.0 0. 1. 2610. 1. 5. 27.0 0.5 0.0 3. 20.0 40.0 4. 4. 0.0 0. 1. 2616. 1. 5. 27.0 6.0 1.0 2. 20.0 14.0 4. 6. 0.0 0. 1. 2619. 1. 4. 22.0 2.5 0.0 2. 16.0 9.0 5. 2. 0.0 0. 1. 2622. 1. 5. 22.0 2.5 0.0 3. 12.0 20.0 2. 3. 0.0 0. 1. 2623. 1. 5. 22.0 0.5 0.0 3. 12.0 20.0 3. 2. 0.0 0. 1. 2631. 1. 4. 37.0 9.0 5.5 4. 12.0 20.0 4. 2. 0.0 0. 1. 2646. 1. 3. 27.0 0.5 0.0 2. 12.0 9.0 3. 2. 0.0 0. 1. 2671. 1. 5. 27.0 2.5 0.0 2. 20.0 20.0 4. 5. 0.0 0. 1. 2674. 1. 3. 22.0 2.5 0.0 1. 14.0 14.0 3. 3. 0.0 0. 1. 2680. 1. 3. 42.0 16.5 4.0 4. 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4. 27.0 9.0 1.0 3. 12.0 14.0 3. 3. 0.0 0. 1. 17426. 1. 5. 37.0 16.5 3.0 2. 14.0 11.5 3. 3. 0.0 0. 1. 17431. 1. 3. 42.0 13.0 2.0 3. 17.0 20.0 4. 4. 0.0 0. 1. 17433. 1. 5. 37.0 23.0 4.0 4. 16.0 20.0 4. 6. 0.0 0. 1. 17434. 1. 5. 37.0 23.0 1.0 3. 14.0 20.0 3. 5. 0.0 0. 1. 17445. 1. 4. 42.0 23.0 3.0 3. 17.0 40.0 4. 2. 0.0 0. 1. 17449. 1. 4. 27.0 6.0 1.0 3. 20.0 20.0 4. 4. 0.0 0. 1. 17450. 1. 5. 32.0 9.0 0.0 4. 20.0 20.0 4. 6. 0.0 0. 1. 17452. 1. 4. 27.0 2.5 0.0 4. 17.0 20.0 4. 4. 0.0 0. 1. 17454. 1. 3. 17.5 2.5 0.0 3. 12.0 6.5 4. 4. 0.0 0. 1. 17471. 1. 5. 22.0 2.5 0.0 3. 12.0 40.0 2. 2. 0.0 0. 1. 17472. 1. 5. 37.0 13.0 3.0 1. 20.0 20.0 4. 5. 0.0 0. 1. 17473. 1. 5. 27.0 16.5 2.0 3. 12.0 9.0 4. 4. 0.0 0. 1. 17474. 1. 4. 22.0 2.5 1.0 3. 14.0 11.5 3. 5. 0.0 0. 1. 17506. 1. 4. 42.0 16.5 4.0 3. 9.0 11.5 2. 2. 0.0 0. 1. 17508. 1. 4. 22.0 2.5 0.0 2. 14.0 9.0 3. 5. 0.0 0. 1. 17509. 1. 4. 42.0 23.0 2.0 3. 14.0 20.0 4. 2. 0.0 0. 1. 17517. 1. 5. 32.0 13.0 2.0 2. 14.0 20.0 2. 6. 0.0 0. 1. 17520. 1. 4. 32.0 13.0 2.0 3. 17.0 20.0 4. 5. 0.0 0. 1. 17523. 1. 5. 22.0 2.5 1.0 1. 12.0 9.0 5. 1. 0.0 0. 1. 17533. 1. 5. 22.0 2.5 0.0 4. 16.0 20.0 4. 2. 0.0 0. 1. 17544. 1. 5. 37.0 9.0 0.0 3. 20.0 40.0 4. 6. 0.0 0. 1. 17547. 1. 5. 42.0 23.0 2.0 3. 14.0 9.0 3. 5. 0.0 0. 1. 17551. 1. 5. 32.0 9.0 2.0 3. 16.0 11.5 4. 4. 0.0 0. 1. 17553. 1. 5. 22.0 6.0 0.0 1. 12.0 14.0 3. 2. 0.0 0. 1. 17558. 1. 4. 27.0 6.0 2.0 2. 12.0 9.0 2. 2. 0.0 0. 1. 17564. 1. 4. 22.0 2.5 0.0 2. 12.0 11.5 3. 4. 0.0 0. 1. 17569. 1. 5. 27.0 2.5 0.0 4. 16.0 6.5 4. 4. 0.0 0. 1. 17573. 1. 5. 32.0 16.5 3.0 4. 14.0 20.0 2. 4. 0.0 0. 1. 17574. 1. 4. 37.0 16.5 2.0 1. 12.0 20.0 3. 4. 0.0 0. 1. 17575. 1. 5. 22.0 2.5 0.0 3. 12.0 9.0 3. 3. 0.0 0. 1. 17578. 1. 4. 37.0 16.5 4.0 2. 12.0 11.5 2. 2. 0.0 0. 1. 17580. 1. 3. 27.0 9.0 1.0 3. 17.0 14.0 4. 1. 0.0 0. 1. 17585. 1. 5. 22.0 2.5 0.0 1. 17.0 9.0 2. 2. 0.0 0. 1. 17596. 1. 5. 22.0 2.5 0.0 1. 14.0 9.0 3. 4. 0.0 0. 1. 17598. 1. 5. 22.0 2.5 0.0 2. 16.0 14.0 4. 5. 0.0 0. 1. 17602. 1. 5. 27.0 6.0 0.0 2. 16.0 20.0 3. 5. 0.0 0. 1. 17608. 1. 4. 22.0 2.5 0.0 3. 14.0 11.5 5. 4. 0.0 0. 1. 17616. 1. 5. 27.0 2.5 0.0 1. 17.0 11.5 4. 6. 0.0 0. 1. 17620. 1. 5. 42.0 23.0 1.0 4. 20.0 40.0 4. 3. 0.0 0. 1. 17625. 1. 4. 17.5 0.5 0.0 2. 14.0 4.0 3. 1. 0.0 0. 1. 17627. 1. 5. 22.0 6.0 1.0 3. 12.0 11.5 2. 3. 0.0 0. 1. 17636. 1. 4. 22.0 6.0 2.0 2. 9.0 6.5 2. 3. 0.0 0. 1. 17643. 1. 5. 27.0 2.5 0.0 4. 16.0 20.0 5. 4. 0.0 0. 1. 17646. 1. 4. 22.0 2.5 1.0 2. 12.0 6.5 3. 2. 0.0 0. 1. 17651. 1. 3. 32.0 2.5 0.0 4. 17.0 14.0 6. 6. 0.0 0. 1. 17661. 1. 5. 37.0 16.5 3.0 4. 14.0 20.0 4. 6. 0.0 0. 1. 17668. 1. 5. 32.0 13.0 4.0 2. 12.0 11.5 5. 1. 0.0 0. 1. 17669. 1. 3. 42.0 23.0 3.0 4. 16.0 14.0 3. 5. 0.0 0. 1. 17673. 1. 5. 27.0 2.5 0.0 3. 12.0 20.0 3. 4. 0.0 0. 1. 17674. 1. 5. 32.0 2.5 1.0 3. 20.0 20.0 4. 4. 0.0 0. 1. 17676. 1. 5. 27.0 9.0 2.0 3. 12.0 9.0 2. 4. 0.0 0. 1. 17690. 1. 3. 27.0 2.5 0.0 2. 14.0 14.0 3. 3. 0.0 0. 1. 17691. 1. 5. 17.5 2.5 1.0 2. 12.0 6.5 5. 2. 0.0 0. 1. 17696. 1. 4. 37.0 16.5 3.0 2. 17.0 40.0 4. 5. 0.0 0. 1. 17700. 1. 4. 27.0 2.5 1.0 3. 16.0 40.0 4. 6. 0.0 0. 1. 17705. 1. 5. 22.0 2.5 1.0 3. 14.0 11.5 3. 5. 0.0 0. 1. 17706. 1. 4. 27.0 2.5 0.0 1. 20.0 40.0 5. 5. 0.0 0. 1. 17707. 1. 5. 22.0 2.5 0.0 3. 14.0 14.0 5. 5. 0.0 0. 1. 17716. 1. 5. 42.0 23.0 5.5 3. 14.0 14.0 4. 4. 0.0 0. 1. 17718. 1. 5. 42.0 23.0 4.0 3. 14.0 14.0 3. 2. 0.0 0. 1. 17721. 1. 5. 37.0 13.0 2.0 2. 20.0 40.0 6. 6. 0.0 0. 1. 17723. 1. 5. 27.0 13.0 2.0 3. 14.0 20.0 4. 5. 0.0 0. 1. 17726. 1. 4. 27.0 13.0 2.0 2. 12.0 6.5 2. 2. 0.0 0. 1. 17742. 1. 5. 37.0 23.0 3.0 2. 12.0 14.0 4. 4. 0.0 0. 1. 17748. 1. 5. 22.0 2.5 0.0 2. 17.0 11.5 5. 3. 0.0 0. 1. 17750. 1. 5. 27.0 6.0 1.0 2. 12.0 14.0 2. 4. 0.0 0. 1. 17752. 1. 5. 22.0 2.5 0.0 2. 14.0 11.5 3. 4. 0.0 0. 1. 17758. 1. 5. 37.0 13.0 2.0 3. 16.0 20.0 4. 4. 0.0 0. 1. 17780. 1. 5. 27.0 6.0 0.0 2. 17.0 20.0 5. 6. 0.0 0. 1. 17786. 1. 5. 27.0 0.5 0.0 3. 12.0 40.0 3. 4. 0.0 0. 1. 17796. 1. 2. 37.0 16.5 3.0 1. 14.0 14.0 3. 2. 0.0 0. 1. 17797. 1. 5. 37.0 23.0 2.0 3. 20.0 40.0 4. 4. 0.0 0. 1. 17802. 1. 2. 32.0 9.0 2.0 4. 14.0 14.0 3. 5. 0.0 0. 1. 17803. 1. 5. 32.0 13.0 2.0 4. 20.0 20.0 4. 5. 0.0 0. 1. 17805. 1. 5. 22.0 2.5 1.0 4. 16.0 11.5 4. 5. 0.0 0. 1. 17812. 1. 5. 22.0 2.5 0.0 3. 20.0 11.5 4. 4. 0.0 0. 1. 17813. 1. 3. 37.0 13.0 3.0 2. 16.0 11.5 4. 3. 0.0 0. 1. 17818. 1. 3. 17.5 2.5 1.0 1. 9.0 6.5 3. 3. 0.0 0. 1. 17820. 1. 4. 27.0 2.5 0.0 2. 14.0 11.5 3. 1. 0.0 0. 1. 17821. 1. 1. 27.0 6.0 1.0 3. 14.0 14.0 3. 4. 0.0 0. 1. 17824. 1. 4. 27.0 6.0 2.0 2. 14.0 40.0 4. 5. 0.0 0. 1. 17829. 1. 2. 42.0 23.0 2.0 2. 14.0 20.0 3. 4. 0.0 0. 1. 17832. 1. 5. 32.0 13.0 2.0 3. 14.0 20.0 4. 6. 0.0 0. 1. 17841. 1. 4. 22.0 2.5 1.0 3. 14.0 11.5 3. 4. 0.0 0. 1. 17866. 1. 4. 27.0 2.5 0.0 1. 14.0 9.0 2. 5. 0.0 0. 1. 17872. 1. 4. 22.0 2.5 0.0 3. 20.0 40.0 6. 6. 0.0 0. 1. 17873. 1. 3. 37.0 2.5 0.0 3. 12.0 11.5 3. 3. 0.0 0. 1. 17876. 1. 5. 27.0 2.5 0.0 3. 17.0 20.0 4. 4. 0.0 0. 1. 17878. 1. 5. 27.0 2.5 0.0 3. 16.0 20.0 3. 5. 0.0 0. 1. 17879. 1. 4. 27.0 6.0 1.0 2. 20.0 20.0 4. 2. 0.0 0. 1. 17880. 1. 4. 27.0 9.0 2.0 2. 14.0 20.0 4. 4. 0.0 0. 1. 17892. 1. 4. 27.0 2.5 0.0 3. 17.0 11.5 4. 4. 0.0 0. 1. 17895. 1. 5. 27.0 6.0 0.0 4. 16.0 14.0 4. 5. 0.0 0. 1. 17901. 1. 5. 42.0 23.0 5.5 3. 12.0 14.0 3. 2. 0.0 0. 1. 17905. 1. 5. 22.0 2.5 0.0 3. 16.0 9.0 4. 1. 0.0 0. 1. 17914. 1. 5. 22.0 0.5 0.0 3. 14.0 9.0 2. 2. 0.0 0. 1. 17916. 1. 4. 22.0 2.5 1.0 3. 14.0 9.0 2. 4. 0.0 0. 1. 17925. 1. 5. 22.0 2.5 1.0 4. 12.0 6.5 3. 4. 0.0 0. 1. 17928. 1. 5. 42.0 23.0 4.0 3. 14.0 14.0 5. 4. 0.0 0. 1. 17929. 1. 5. 27.0 6.0 0.0 4. 14.0 6.5 4. 4. 0.0 0. 1. 17934. 1. 5. 42.0 23.0 2.0 3. 12.0 40.0 2. 2. 0.0 0. 1. 17939. 1. 4. 32.0 13.0 3.0 3. 16.0 20.0 4. 2. 0.0 0. 1. 17940. 1. 5. 27.0 13.0 3.0 3. 16.0 14.0 4. 2. 0.0 0. 1. 17943. 1. 5. 27.0 9.0 1.0 2. 14.0 20.0 4. 5. 0.0 0. 1. 17946. 1. 4. 22.0 2.5 0.0 2. 16.0 6.5 4. 1. 0.0 0. 1. 17947. 1. 5. 17.5 2.5 0.0 4. 12.0 14.0 3. 5. 0.0 0. 1. 17952. 1. 4. 32.0 16.5 2.0 2. 12.0 20.0 3. 4. 0.0 0. 1. 17953. 1. 5. 27.0 9.0 1.0 3. 12.0 40.0 3. 5. 0.0 0. 1. 17960. 1. 4. 22.0 2.5 0.0 4. 14.0 11.5 4. 2. 0.0 0. 1. 17961. 1. 5. 22.0 2.5 1.0 2. 12.0 11.5 3. 2. 0.0 0. 1. 17962. 1. 5. 27.0 0.5 0.0 4. 20.0 20.0 4. 4. 0.0 0. 1. 17963. 1. 5. 37.0 16.5 3.0 3. 14.0 20.0 5. 5. 0.0 0. 1. 17967. 1. 5. 32.0 13.0 2.0 4. 14.0 20.0 3. 6. 0.0 0. 1. 17971. 1. 4. 22.0 0.5 0.0 2. 16.0 9.0 3. 1. 0.0 0. 1. 17973. 1. 5. 42.0 23.0 2.0 4. 12.0 20.0 3. 2. 0.0 0. 1. 17975. 1. 5. 22.0 2.5 2.0 2. 14.0 6.5 3. 5. 0.0 0. 1. 17978. 1. 5. 42.0 23.0 4.0 4. 12.0 20.0 3. 5. 0.0 0. 1. 17981. 1. 4. 27.0 6.0 0.0 3. 12.0 20.0 3. 4. 0.0 0. 1. 17982. 1. 5. 32.0 13.0 3.0 3. 12.0 20.0 3. 5. 0.0 0. 1. 17990. 1. 5. 32.0 13.0 4.0 2. 14.0 20.0 4. 4. 0.0 0. 1. 17997. 1. 3. 27.0 6.0 2.0 4. 14.0 6.5 3. 1. 0.0 0. 1. 17998. 1. 4. 22.0 2.5 0.0 3. 16.0 11.5 5. 5. 0.0 0. 1. 17999. 1. 5. 22.0 2.5 0.0 2. 14.0 20.0 3. 3. 0.0 0. 1. 19006. 1. 5. 32.0 13.0 2.0 3. 17.0 40.0 4. 3. 0.0 0. 1. 19009. 1. 4. 32.0 13.0 1.0 1. 16.0 40.0 5. 5. 0.0 0. 1. 19013. 1. 5. 22.0 2.5 0.0 2. 14.0 6.5 3. 1. 0.0 0. 1. 19015. 1. 5. 32.0 6.0 1.0 3. 14.0 20.0 3. 4. 0.0 0. 1. 19020. 1. 4. 22.0 2.5 0.0 2. 16.0 4.0 2. 4. 0.0 0. 1. END OF RB DATA statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/000077500000000000000000000000001224417117700241325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/__init__.py000066400000000000000000000000231224417117700262360ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/data.py000066400000000000000000000052101224417117700254130ustar00rootroot00000000000000"""Grunfeld (1950) Investment Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = __doc__ SOURCE = """This is the Grunfeld (1950) Investment Data. The source for the data was the original 11-firm data set from Grunfeld's Ph.D. thesis recreated by Kleiber and Zeileis (2008) "The Grunfeld Data at 50". The data can be found here. http://statmath.wu-wien.ac.at/~zeileis/grunfeld/ For a note on the many versions of the Grunfeld data circulating see: http://www.stanford.edu/~clint/bench/grunfeld.htm """ DESCRSHORT = """Grunfeld (1950) Investment Data for 11 U.S. Firms.""" DESCRLONG = DESCRSHORT NOTE = """Number of observations - 220 (20 years for 11 firms) Number of variables - 5 Variables name definitions:: invest - Gross investment in 1947 dollars value - Market value as of Dec. 31 in 1947 dollars capital - Stock of plant and equipment in 1947 dollars firm - General Motors, US Steel, General Electric, Chrysler, Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear, Diamond Match, American Steel year - 1935 - 1954 Note that raw_data has firm expanded to dummy variables, since it is a string categorical variable. """ from numpy import recfromtxt, column_stack, array from statsmodels.tools import categorical from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Loads the Grunfeld data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. Notes ----- raw_data has the firm variable expanded to dummy variables for each firm (ie., there is no reference dummy) """ data = _get_data() raw_data = categorical(data, col='firm', drop=True) ds = du.process_recarray(data, endog_idx=0, stack=False) ds.raw_data = raw_data return ds def load_pandas(): """ Loads the Grunfeld data and returns a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. Notes ----- raw_data has the firm variable expanded to dummy variables for each firm (ie., there is no reference dummy) """ from pandas import DataFrame data = _get_data() raw_data = categorical(data, col='firm', drop=True) ds = du.process_recarray_pandas(data, endog_idx=0) ds.raw_data = DataFrame(raw_data) return ds def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/grunfeld.csv','rb'), delimiter=",", names=True, dtype="f8,f8,f8,a17,f8") return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/grunfeld.csv000066400000000000000000000167151224417117700264670ustar00rootroot00000000000000invest,value,capital,firm,year 317.6,3078.5,2.8,General Motors,1935 391.8,4661.7,52.6,General Motors,1936 410.6,5387.1,156.9,General Motors,1937 257.7,2792.2,209.2,General Motors,1938 330.8,4313.2,203.4,General Motors,1939 461.2,4643.9,207.2,General Motors,1940 512,4551.2,255.2,General Motors,1941 448,3244.1,303.7,General Motors,1942 499.6,4053.7,264.1,General Motors,1943 547.5,4379.3,201.6,General Motors,1944 561.2,4840.9,265,General Motors,1945 688.1,4900.9,402.2,General Motors,1946 568.9,3526.5,761.5,General Motors,1947 529.2,3254.7,922.4,General Motors,1948 555.1,3700.2,1020.1,General Motors,1949 642.9,3755.6,1099,General Motors,1950 755.9,4833,1207.7,General Motors,1951 891.2,4924.9,1430.5,General Motors,1952 1304.4,6241.7,1777.3,General Motors,1953 1486.7,5593.6,2226.3,General Motors,1954 209.9,1362.4,53.8,US Steel,1935 355.3,1807.1,50.5,US Steel,1936 469.9,2676.3,118.1,US Steel,1937 262.3,1801.9,260.2,US Steel,1938 230.4,1957.3,312.7,US Steel,1939 361.6,2202.9,254.2,US Steel,1940 472.8,2380.5,261.4,US Steel,1941 445.6,2168.6,298.7,US Steel,1942 361.6,1985.1,301.8,US Steel,1943 288.2,1813.9,279.1,US Steel,1944 258.7,1850.2,213.8,US Steel,1945 420.3,2067.7,132.6,US Steel,1946 420.5,1796.7,264.8,US Steel,1947 494.5,1625.8,306.9,US Steel,1948 405.1,1667,351.1,US Steel,1949 418.8,1677.4,357.8,US Steel,1950 588.2,2289.5,342.1,US Steel,1951 645.5,2159.4,444.2,US Steel,1952 641,2031.3,623.6,US Steel,1953 459.3,2115.5,669.7,US Steel,1954 33.1,1170.6,97.8,General Electric,1935 45,2015.8,104.4,General Electric,1936 77.2,2803.3,118,General Electric,1937 44.6,2039.7,156.2,General Electric,1938 48.1,2256.2,172.6,General Electric,1939 74.4,2132.2,186.6,General Electric,1940 113,1834.1,220.9,General Electric,1941 91.9,1588,287.8,General Electric,1942 61.3,1749.4,319.9,General Electric,1943 56.8,1687.2,321.3,General Electric,1944 93.6,2007.7,319.6,General Electric,1945 159.9,2208.3,346,General Electric,1946 147.2,1656.7,456.4,General Electric,1947 146.3,1604.4,543.4,General Electric,1948 98.3,1431.8,618.3,General Electric,1949 93.5,1610.5,647.4,General Electric,1950 135.2,1819.4,671.3,General Electric,1951 157.3,2079.7,726.1,General Electric,1952 179.5,2371.6,800.3,General Electric,1953 189.6,2759.9,888.9,General Electric,1954 40.29,417.5,10.5,Chrysler,1935 72.76,837.8,10.2,Chrysler,1936 66.26,883.9,34.7,Chrysler,1937 51.6,437.9,51.8,Chrysler,1938 52.41,679.7,64.3,Chrysler,1939 69.41,727.8,67.1,Chrysler,1940 68.35,643.6,75.2,Chrysler,1941 46.8,410.9,71.4,Chrysler,1942 47.4,588.4,67.1,Chrysler,1943 59.57,698.4,60.5,Chrysler,1944 88.78,846.4,54.6,Chrysler,1945 74.12,893.8,84.8,Chrysler,1946 62.68,579,96.8,Chrysler,1947 89.36,694.6,110.2,Chrysler,1948 78.98,590.3,147.4,Chrysler,1949 100.66,693.5,163.2,Chrysler,1950 160.62,809,203.5,Chrysler,1951 145,727,290.6,Chrysler,1952 174.93,1001.5,346.1,Chrysler,1953 172.49,703.2,414.9,Chrysler,1954 39.68,157.7,183.2,Atlantic Refining,1935 50.73,167.9,204,Atlantic Refining,1936 74.24,192.9,236,Atlantic Refining,1937 53.51,156.7,291.7,Atlantic Refining,1938 42.65,191.4,323.1,Atlantic Refining,1939 46.48,185.5,344,Atlantic Refining,1940 61.4,199.6,367.7,Atlantic Refining,1941 39.67,189.5,407.2,Atlantic Refining,1942 62.24,151.2,426.6,Atlantic Refining,1943 52.32,187.7,470,Atlantic Refining,1944 63.21,214.7,499.2,Atlantic Refining,1945 59.37,232.9,534.6,Atlantic Refining,1946 58.02,249,566.6,Atlantic Refining,1947 70.34,224.5,595.3,Atlantic Refining,1948 67.42,237.3,631.4,Atlantic Refining,1949 55.74,240.1,662.3,Atlantic Refining,1950 80.3,327.3,683.9,Atlantic Refining,1951 85.4,359.4,729.3,Atlantic Refining,1952 91.9,398.4,774.3,Atlantic Refining,1953 81.43,365.7,804.9,Atlantic Refining,1954 20.36,197,6.5,IBM,1935 25.98,210.3,15.8,IBM,1936 25.94,223.1,27.7,IBM,1937 27.53,216.7,39.2,IBM,1938 24.6,286.4,48.6,IBM,1939 28.54,298,52.5,IBM,1940 43.41,276.9,61.5,IBM,1941 42.81,272.6,80.5,IBM,1942 27.84,287.4,94.4,IBM,1943 32.6,330.3,92.6,IBM,1944 39.03,324.4,92.3,IBM,1945 50.17,401.9,94.2,IBM,1946 51.85,407.4,111.4,IBM,1947 64.03,409.2,127.4,IBM,1948 68.16,482.2,149.3,IBM,1949 77.34,673.8,164.4,IBM,1950 95.3,676.9,177.2,IBM,1951 99.49,702,200,IBM,1952 127.52,793.5,211.5,IBM,1953 135.72,927.3,238.7,IBM,1954 24.43,138,100.2,Union Oil,1935 23.21,200.1,125,Union Oil,1936 32.78,210.1,142.4,Union Oil,1937 32.54,161.2,165.1,Union Oil,1938 26.65,161.7,194.8,Union Oil,1939 33.71,145.1,222.9,Union Oil,1940 43.5,110.6,252.1,Union Oil,1941 34.46,98.1,276.3,Union Oil,1942 44.28,108.8,300.3,Union Oil,1943 70.8,118.2,318.2,Union Oil,1944 44.12,126.5,336.2,Union Oil,1945 48.98,156.7,351.2,Union Oil,1946 48.51,119.4,373.6,Union Oil,1947 50,129.1,389.4,Union Oil,1948 50.59,134.8,406.7,Union Oil,1949 42.53,140.8,429.5,Union Oil,1950 64.77,179,450.6,Union Oil,1951 72.68,178.1,466.9,Union Oil,1952 73.86,186.8,486.2,Union Oil,1953 89.51,192.7,511.3,Union Oil,1954 12.93,191.5,1.8,Westinghouse,1935 25.9,516,0.8,Westinghouse,1936 35.05,729,7.4,Westinghouse,1937 22.89,560.4,18.1,Westinghouse,1938 18.84,519.9,23.5,Westinghouse,1939 28.57,628.5,26.5,Westinghouse,1940 48.51,537.1,36.2,Westinghouse,1941 43.34,561.2,60.8,Westinghouse,1942 37.02,617.2,84.4,Westinghouse,1943 37.81,626.7,91.2,Westinghouse,1944 39.27,737.2,92.4,Westinghouse,1945 53.46,760.5,86,Westinghouse,1946 55.56,581.4,111.1,Westinghouse,1947 49.56,662.3,130.6,Westinghouse,1948 32.04,583.8,141.8,Westinghouse,1949 32.24,635.2,136.7,Westinghouse,1950 54.38,723.8,129.7,Westinghouse,1951 71.78,864.1,145.5,Westinghouse,1952 90.08,1193.5,174.8,Westinghouse,1953 68.6,1188.9,213.5,Westinghouse,1954 26.63,290.6,162,Goodyear,1935 23.39,291.1,174,Goodyear,1936 30.65,335,183,Goodyear,1937 20.89,246,198,Goodyear,1938 28.78,356.2,208,Goodyear,1939 26.93,289.8,223,Goodyear,1940 32.08,268.2,234,Goodyear,1941 32.21,213.3,248,Goodyear,1942 35.69,348.2,274,Goodyear,1943 62.47,374.2,282,Goodyear,1944 52.32,387.2,316,Goodyear,1945 56.95,347.4,302,Goodyear,1946 54.32,291.9,333,Goodyear,1947 40.53,297.2,359,Goodyear,1948 32.54,276.9,370,Goodyear,1949 43.48,274.6,376,Goodyear,1950 56.49,339.9,391,Goodyear,1951 65.98,474.8,414,Goodyear,1952 66.11,496,443,Goodyear,1953 49.34,474.5,468,Goodyear,1954 2.54,70.91,4.5,Diamond Match,1935 2,87.94,4.71,Diamond Match,1936 2.19,82.2,4.57,Diamond Match,1937 1.99,58.72,4.56,Diamond Match,1938 2.03,80.54,4.38,Diamond Match,1939 1.81,86.47,4.21,Diamond Match,1940 2.14,77.68,4.12,Diamond Match,1941 1.86,62.16,3.83,Diamond Match,1942 0.93,62.24,3.58,Diamond Match,1943 1.18,61.82,3.41,Diamond Match,1944 1.36,65.85,3.31,Diamond Match,1945 2.24,69.54,3.23,Diamond Match,1946 3.81,64.97,3.9,Diamond Match,1947 5.66,68,5.38,Diamond Match,1948 4.21,71.24,7.39,Diamond Match,1949 3.42,69.05,8.74,Diamond Match,1950 4.67,83.04,9.07,Diamond Match,1951 6,74.42,9.93,Diamond Match,1952 6.53,63.51,11.68,Diamond Match,1953 5.12,58.12,14.33,Diamond Match,1954 2.938,30.284,52.011,American Steel,1935 5.643,43.909,52.903,American Steel,1936 10.233,107.02,54.499,American Steel,1937 4.046,68.306,59.722,American Steel,1938 3.326,84.164,61.659,American Steel,1939 4.68,69.157,62.243,American Steel,1940 5.732,60.148,63.361,American Steel,1941 12.117,49.332,64.861,American Steel,1942 15.276,75.18,67.953,American Steel,1943 9.275,62.05,69.59,American Steel,1944 9.577,59.152,69.144,American Steel,1945 3.956,68.424,70.269,American Steel,1946 3.834,48.505,71.051,American Steel,1947 5.97,40.507,71.508,American Steel,1948 6.433,39.961,73.827,American Steel,1949 4.77,36.494,75.847,American Steel,1950 6.532,46.082,77.367,American Steel,1951 7.329,57.616,78.631,American Steel,1952 9.02,57.441,80.215,American Steel,1953 6.281,47.165,83.788,American Steel,1954 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/src/000077500000000000000000000000001224417117700247215ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/grunfeld/src/grunfeld.csv000066400000000000000000000167151224417117700272560ustar00rootroot00000000000000invest,value,capital,firm,year 317.6,3078.5,2.8,General Motors,1935 391.8,4661.7,52.6,General Motors,1936 410.6,5387.1,156.9,General Motors,1937 257.7,2792.2,209.2,General Motors,1938 330.8,4313.2,203.4,General Motors,1939 461.2,4643.9,207.2,General Motors,1940 512,4551.2,255.2,General Motors,1941 448,3244.1,303.7,General Motors,1942 499.6,4053.7,264.1,General Motors,1943 547.5,4379.3,201.6,General Motors,1944 561.2,4840.9,265,General Motors,1945 688.1,4900.9,402.2,General Motors,1946 568.9,3526.5,761.5,General Motors,1947 529.2,3254.7,922.4,General Motors,1948 555.1,3700.2,1020.1,General Motors,1949 642.9,3755.6,1099,General Motors,1950 755.9,4833,1207.7,General Motors,1951 891.2,4924.9,1430.5,General Motors,1952 1304.4,6241.7,1777.3,General Motors,1953 1486.7,5593.6,2226.3,General Motors,1954 209.9,1362.4,53.8,US Steel,1935 355.3,1807.1,50.5,US Steel,1936 469.9,2676.3,118.1,US Steel,1937 262.3,1801.9,260.2,US Steel,1938 230.4,1957.3,312.7,US Steel,1939 361.6,2202.9,254.2,US Steel,1940 472.8,2380.5,261.4,US Steel,1941 445.6,2168.6,298.7,US Steel,1942 361.6,1985.1,301.8,US Steel,1943 288.2,1813.9,279.1,US Steel,1944 258.7,1850.2,213.8,US Steel,1945 420.3,2067.7,132.6,US Steel,1946 420.5,1796.7,264.8,US Steel,1947 494.5,1625.8,306.9,US Steel,1948 405.1,1667,351.1,US Steel,1949 418.8,1677.4,357.8,US Steel,1950 588.2,2289.5,342.1,US Steel,1951 645.5,2159.4,444.2,US Steel,1952 641,2031.3,623.6,US Steel,1953 459.3,2115.5,669.7,US Steel,1954 33.1,1170.6,97.8,General Electric,1935 45,2015.8,104.4,General Electric,1936 77.2,2803.3,118,General Electric,1937 44.6,2039.7,156.2,General Electric,1938 48.1,2256.2,172.6,General Electric,1939 74.4,2132.2,186.6,General Electric,1940 113,1834.1,220.9,General Electric,1941 91.9,1588,287.8,General Electric,1942 61.3,1749.4,319.9,General Electric,1943 56.8,1687.2,321.3,General Electric,1944 93.6,2007.7,319.6,General Electric,1945 159.9,2208.3,346,General Electric,1946 147.2,1656.7,456.4,General Electric,1947 146.3,1604.4,543.4,General Electric,1948 98.3,1431.8,618.3,General Electric,1949 93.5,1610.5,647.4,General Electric,1950 135.2,1819.4,671.3,General Electric,1951 157.3,2079.7,726.1,General Electric,1952 179.5,2371.6,800.3,General Electric,1953 189.6,2759.9,888.9,General Electric,1954 40.29,417.5,10.5,Chrysler,1935 72.76,837.8,10.2,Chrysler,1936 66.26,883.9,34.7,Chrysler,1937 51.6,437.9,51.8,Chrysler,1938 52.41,679.7,64.3,Chrysler,1939 69.41,727.8,67.1,Chrysler,1940 68.35,643.6,75.2,Chrysler,1941 46.8,410.9,71.4,Chrysler,1942 47.4,588.4,67.1,Chrysler,1943 59.57,698.4,60.5,Chrysler,1944 88.78,846.4,54.6,Chrysler,1945 74.12,893.8,84.8,Chrysler,1946 62.68,579,96.8,Chrysler,1947 89.36,694.6,110.2,Chrysler,1948 78.98,590.3,147.4,Chrysler,1949 100.66,693.5,163.2,Chrysler,1950 160.62,809,203.5,Chrysler,1951 145,727,290.6,Chrysler,1952 174.93,1001.5,346.1,Chrysler,1953 172.49,703.2,414.9,Chrysler,1954 39.68,157.7,183.2,Atlantic Refining,1935 50.73,167.9,204,Atlantic Refining,1936 74.24,192.9,236,Atlantic Refining,1937 53.51,156.7,291.7,Atlantic Refining,1938 42.65,191.4,323.1,Atlantic Refining,1939 46.48,185.5,344,Atlantic Refining,1940 61.4,199.6,367.7,Atlantic Refining,1941 39.67,189.5,407.2,Atlantic Refining,1942 62.24,151.2,426.6,Atlantic Refining,1943 52.32,187.7,470,Atlantic Refining,1944 63.21,214.7,499.2,Atlantic Refining,1945 59.37,232.9,534.6,Atlantic Refining,1946 58.02,249,566.6,Atlantic Refining,1947 70.34,224.5,595.3,Atlantic Refining,1948 67.42,237.3,631.4,Atlantic Refining,1949 55.74,240.1,662.3,Atlantic Refining,1950 80.3,327.3,683.9,Atlantic Refining,1951 85.4,359.4,729.3,Atlantic Refining,1952 91.9,398.4,774.3,Atlantic Refining,1953 81.43,365.7,804.9,Atlantic Refining,1954 20.36,197,6.5,IBM,1935 25.98,210.3,15.8,IBM,1936 25.94,223.1,27.7,IBM,1937 27.53,216.7,39.2,IBM,1938 24.6,286.4,48.6,IBM,1939 28.54,298,52.5,IBM,1940 43.41,276.9,61.5,IBM,1941 42.81,272.6,80.5,IBM,1942 27.84,287.4,94.4,IBM,1943 32.6,330.3,92.6,IBM,1944 39.03,324.4,92.3,IBM,1945 50.17,401.9,94.2,IBM,1946 51.85,407.4,111.4,IBM,1947 64.03,409.2,127.4,IBM,1948 68.16,482.2,149.3,IBM,1949 77.34,673.8,164.4,IBM,1950 95.3,676.9,177.2,IBM,1951 99.49,702,200,IBM,1952 127.52,793.5,211.5,IBM,1953 135.72,927.3,238.7,IBM,1954 24.43,138,100.2,Union Oil,1935 23.21,200.1,125,Union Oil,1936 32.78,210.1,142.4,Union Oil,1937 32.54,161.2,165.1,Union Oil,1938 26.65,161.7,194.8,Union Oil,1939 33.71,145.1,222.9,Union Oil,1940 43.5,110.6,252.1,Union Oil,1941 34.46,98.1,276.3,Union Oil,1942 44.28,108.8,300.3,Union Oil,1943 70.8,118.2,318.2,Union Oil,1944 44.12,126.5,336.2,Union Oil,1945 48.98,156.7,351.2,Union Oil,1946 48.51,119.4,373.6,Union Oil,1947 50,129.1,389.4,Union Oil,1948 50.59,134.8,406.7,Union Oil,1949 42.53,140.8,429.5,Union Oil,1950 64.77,179,450.6,Union Oil,1951 72.68,178.1,466.9,Union Oil,1952 73.86,186.8,486.2,Union Oil,1953 89.51,192.7,511.3,Union Oil,1954 12.93,191.5,1.8,Westinghouse,1935 25.9,516,0.8,Westinghouse,1936 35.05,729,7.4,Westinghouse,1937 22.89,560.4,18.1,Westinghouse,1938 18.84,519.9,23.5,Westinghouse,1939 28.57,628.5,26.5,Westinghouse,1940 48.51,537.1,36.2,Westinghouse,1941 43.34,561.2,60.8,Westinghouse,1942 37.02,617.2,84.4,Westinghouse,1943 37.81,626.7,91.2,Westinghouse,1944 39.27,737.2,92.4,Westinghouse,1945 53.46,760.5,86,Westinghouse,1946 55.56,581.4,111.1,Westinghouse,1947 49.56,662.3,130.6,Westinghouse,1948 32.04,583.8,141.8,Westinghouse,1949 32.24,635.2,136.7,Westinghouse,1950 54.38,723.8,129.7,Westinghouse,1951 71.78,864.1,145.5,Westinghouse,1952 90.08,1193.5,174.8,Westinghouse,1953 68.6,1188.9,213.5,Westinghouse,1954 26.63,290.6,162,Goodyear,1935 23.39,291.1,174,Goodyear,1936 30.65,335,183,Goodyear,1937 20.89,246,198,Goodyear,1938 28.78,356.2,208,Goodyear,1939 26.93,289.8,223,Goodyear,1940 32.08,268.2,234,Goodyear,1941 32.21,213.3,248,Goodyear,1942 35.69,348.2,274,Goodyear,1943 62.47,374.2,282,Goodyear,1944 52.32,387.2,316,Goodyear,1945 56.95,347.4,302,Goodyear,1946 54.32,291.9,333,Goodyear,1947 40.53,297.2,359,Goodyear,1948 32.54,276.9,370,Goodyear,1949 43.48,274.6,376,Goodyear,1950 56.49,339.9,391,Goodyear,1951 65.98,474.8,414,Goodyear,1952 66.11,496,443,Goodyear,1953 49.34,474.5,468,Goodyear,1954 2.54,70.91,4.5,Diamond Match,1935 2,87.94,4.71,Diamond Match,1936 2.19,82.2,4.57,Diamond Match,1937 1.99,58.72,4.56,Diamond Match,1938 2.03,80.54,4.38,Diamond Match,1939 1.81,86.47,4.21,Diamond Match,1940 2.14,77.68,4.12,Diamond Match,1941 1.86,62.16,3.83,Diamond Match,1942 0.93,62.24,3.58,Diamond Match,1943 1.18,61.82,3.41,Diamond Match,1944 1.36,65.85,3.31,Diamond Match,1945 2.24,69.54,3.23,Diamond Match,1946 3.81,64.97,3.9,Diamond Match,1947 5.66,68,5.38,Diamond Match,1948 4.21,71.24,7.39,Diamond Match,1949 3.42,69.05,8.74,Diamond Match,1950 4.67,83.04,9.07,Diamond Match,1951 6,74.42,9.93,Diamond Match,1952 6.53,63.51,11.68,Diamond Match,1953 5.12,58.12,14.33,Diamond Match,1954 2.938,30.284,52.011,American Steel,1935 5.643,43.909,52.903,American Steel,1936 10.233,107.02,54.499,American Steel,1937 4.046,68.306,59.722,American Steel,1938 3.326,84.164,61.659,American Steel,1939 4.68,69.157,62.243,American Steel,1940 5.732,60.148,63.361,American Steel,1941 12.117,49.332,64.861,American Steel,1942 15.276,75.18,67.953,American Steel,1943 9.275,62.05,69.59,American Steel,1944 9.577,59.152,69.144,American Steel,1945 3.956,68.424,70.269,American Steel,1946 3.834,48.505,71.051,American Steel,1947 5.97,40.507,71.508,American Steel,1948 6.433,39.961,73.827,American Steel,1949 4.77,36.494,75.847,American Steel,1950 6.532,46.082,77.367,American Steel,1951 7.329,57.616,78.631,American Steel,1952 9.02,57.441,80.215,American Steel,1953 6.281,47.165,83.788,American Steel,1954 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/heart/000077500000000000000000000000001224417117700234275ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/heart/__init__.py000066400000000000000000000000231224417117700255330ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/heart/data.py000066400000000000000000000034531224417117700247170ustar00rootroot00000000000000"""Heart Transplant Data, Miller 1976""" __docformat__ = 'restructuredtext' COPYRIGHT = """???""" TITLE = """Transplant Survival Data""" SOURCE = """ Miller, R. (1976). Least squares regression with censored dara. Biometrica, 63 (3). 449-464. """ DESCRSHORT = """Survival times after receiving a heart transplant""" DESCRLONG = """This data contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored. """ NOTE = """ Number of Observations - 69 Number of Variables - 3 Variable name definitions:: death - Days after surgery until death age - age at the time of surgery censored - indicates if an observation is censored. 1 is uncensored """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx dset = du.process_recarray(data, endog_idx=0, exog_idx=None, dtype=float) dset.censors = dset.exog[:,0] dset.exog = dset.exog[:,1] return dset def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/heart.csv', 'rb'), delimiter=",", names = True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/heart/heart.csv000066400000000000000000000040201224417117700252430ustar00rootroot00000000000000survival,censors,age 15.000000,1.000000,54.300000 3.000000,1.000000,40.400000 624.000000,1.000000,51.000000 46.000000,1.000000,42.500000 127.000000,1.000000,48.000000 64.000000,1.000000,54.600000 1350.000000,1.000000,54.100000 280.000000,1.000000,49.500000 23.000000,1.000000,56.900000 10.000000,1.000000,55.300000 1024.000000,1.000000,43.400000 39.000000,1.000000,42.800000 730.000000,1.000000,58.400000 136.000000,1.000000,52.000000 1775.000000,0.000000,33.300000 1.000000,1.000000,54.200000 836.000000,1.000000,45.000000 60.000000,1.000000,64.500000 1536.000000,0.000000,49.000000 1549.000000,0.000000,40.600000 54.000000,1.000000,49.000000 47.000000,1.000000,61.500000 1.000000,1.000000,41.500000 51.000000,1.000000,50.500000 1367.000000,0.000000,48.600000 1264.000000,0.000000,45.500000 44.000000,1.000000,36.200000 994.000000,1.000000,48.600000 51.000000,1.000000,47.200000 1106.000000,0.000000,36.800000 897.000000,1.000000,46.100000 253.000000,1.000000,48.800000 147.000000,1.000000,47.500000 51.000000,1.000000,52.500000 875.000000,0.000000,38.900000 322.000000,1.000000,48.100000 838.000000,0.000000,41.600000 65.000000,1.000000,49.100000 815.000000,0.000000,32.700000 551.000000,1.000000,48.900000 66.000000,1.000000,51.300000 228.000000,1.000000,19.700000 65.000000,1.000000,45.200000 660.000000,0.000000,48.000000 25.000000,1.000000,53.000000 589.000000,0.000000,47.500000 592.000000,0.000000,26.700000 63.000000,1.000000,56.400000 12.000000,1.000000,29.200000 499.000000,0.000000,52.200000 305.000000,0.000000,49.300000 29.000000,1.000000,54.000000 456.000000,0.000000,46.500000 439.000000,0.000000,52.900000 48.000000,1.000000,53.400000 297.000000,1.000000,42.800000 389.000000,0.000000,48.900000 50.000000,1.000000,46.400000 339.000000,0.000000,54.400000 68.000000,1.000000,51.400000 26.000000,1.000000,52.500000 30.000000,0.000000,45.800000 237.000000,0.000000,47.800000 161.000000,1.000000,43.800000 14.000000,1.000000,40.300000 167.000000,0.000000,26.700000 110.000000,0.000000,23.700000 13.000000,0.000000,28.900000 1.000000,0.000000,35.200000 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/000077500000000000000000000000001224417117700237755ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/R_gls.s000066400000000000000000000021541224417117700252310ustar00rootroot00000000000000### GLS Example with Longley Data ### Done the long way... d <- read.table('./longley.csv', sep=',', header=T) attach(d) m1 <- lm(TOTEMP ~ GNP + POP) rho <- cor(m1$res[-1],m1$res[-16]) sigma <- diag(16) # diagonal matrix of ones sigma <- rho^abs(row(sigma)-col(sigma)) # row sigma is a matrix of the row index # col sigma is a matrix of the column index # this gives a upper-lower triangle with the # covariance structure of an AR1 process... sigma_inv <- solve(sigma) # inverse of sigma x <- model.matrix(m1) xPrimexInv <- solve(t(x) %*% sigma_inv %*% x) beta <- xPrimexInv %*% t(x) %*% sigma_inv %*% TOTEMP beta # residuals res <- TOTEMP - x %*% beta # whitened residuals, not sure if this is right # xPrimexInv is different than cholsigmainv obviously... wres = sigma_inv %*% TOTEMP - sigma_inv %*% x %*% beta sig <- sqrt(sum(res^2)/m1$df) wsig <- sqrt(sum(wres^2)/m1$df) wvc <- sqrt(diag(xPrimexInv))*wsig vc <- sqrt(diag(xPrimexInv))*sig vc ### Attempt to use a varFunc for GLS library(nlme) m1 <- gls(TOTEMP ~ GNP + POP, correlation=corAR1(value=rho, fixed=TRUE)) results <- summary(m1) bse <- sqrt(diag(vcov(m1))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/R_lm.s000066400000000000000000000002731224417117700250540ustar00rootroot00000000000000d <- read.table('./longley.csv', sep=',', header=T) attach(d) library(nlme) # to be able to get BIC m1 <- lm(TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR) results <-summary(m1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/__init__.py000066400000000000000000000000231224417117700261010ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/data.py000066400000000000000000000035371224417117700252700ustar00rootroot00000000000000"""Longley dataset""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = __doc__ SOURCE = """ The classic 1967 Longley Data http://www.itl.nist.gov/div898/strd/lls/data/Longley.shtml :: Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the Electronic Comptuer from the Point of View of the User." Journal of the American Statistical Association. 62.319, 819-41. """ DESCRSHORT = """""" DESCRLONG = """The Longley dataset contains various US macroeconomic variables that are known to be highly collinear. It has been used to appraise the accuracy of least squares routines.""" NOTE = """ Number of Observations - 16 Number of Variables - 6 Variable name definitions:: TOTEMP - Total Employment GNPDEFL - GNP deflator GNP - GNP UNEMP - Number of unemployed ARMED - Size of armed forces POP - Population YEAR - Year (1947 - 1962) """ from numpy import recfromtxt, array, column_stack from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the Longley data and return a Dataset class. Returns ------- Dataset instance See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Load the Longley data and return a Dataset class. Returns ------- Dataset instance See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath+'/longley.csv',"rb"), delimiter=",", names=True, dtype=float, usecols=(1,2,3,4,5,6,7)) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/longley/longley.csv000066400000000000000000000013461224417117700261670ustar00rootroot00000000000000"Obs","TOTEMP","GNPDEFL","GNP","UNEMP","ARMED","POP","YEAR" 1,60323,83,234289,2356,1590,107608,1947 2,61122,88.5,259426,2325,1456,108632,1948 3,60171,88.2,258054,3682,1616,109773,1949 4,61187,89.5,284599,3351,1650,110929,1950 5,63221,96.2,328975,2099,3099,112075,1951 6,63639,98.1,346999,1932,3594,113270,1952 7,64989,99,365385,1870,3547,115094,1953 8,63761,100,363112,3578,3350,116219,1954 9,66019,101.2,397469,2904,3048,117388,1955 10,67857,104.6,419180,2822,2857,118734,1956 11,68169,108.4,442769,2936,2798,120445,1957 12,66513,110.8,444546,4681,2637,121950,1958 13,68655,112.6,482704,3813,2552,123366,1959 14,69564,114.2,502601,3931,2514,125368,1960 15,69331,115.7,518173,4806,2572,127852,1961 16,70551,116.9,554894,4007,2827,130081,1962 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/macrodata/000077500000000000000000000000001224417117700242575ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/macrodata/__init__.py000066400000000000000000000000231224417117700263630ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/macrodata/data.py000066400000000000000000000060061224417117700255440ustar00rootroot00000000000000"""United States Macroeconomic data""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = __doc__ SOURCE = """ Compiled by Skipper Seabold. All data are from the Federal Reserve Bank of St. Louis [1] except the unemployment rate which was taken from the National Bureau of Labor Statistics [2]. :: [1] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve Bank of St. Louis; http://research.stlouisfed.org/fred2/; accessed December 15, 2009. [2] Data Source: Bureau of Labor Statistics, U.S. Department of Labor; http://www.bls.gov/data/; accessed December 15, 2009. """ DESCRSHORT = """US Macroeconomic Data for 1959Q1 - 2009Q3""" DESCRLONG = DESCRSHORT NOTE = """ Number of Observations - 203 Number of Variables - 14 Variable name definitions:: year - 1959q1 - 2009q3 quarter - 1-4 realgdp - Real gross domestic product (Bil. of chained 2005 US$, seasonally adjusted annual rate) realcons - Real personal consumption expenditures (Bil. of chained 2005 US$, seasonally adjusted annual rate) realinv - Real gross private domestic investment (Bil. of chained 2005 US$, seasonally adjusted annual rate) realgovt - Real federal consumption expenditures & gross investment (Bil. of chained 2005 US$, seasonally adjusted annual rate) realdpi - Real private disposable income (Bil. of chained 2005 US$, seasonally adjusted annual rate) cpi - End of the quarter consumer price index for all urban consumers: all items (1982-84 = 100, seasonally adjusted). m1 - End of the quarter M1 nominal money stock (Seasonally adjusted) tbilrate - Quarterly monthly average of the monthly 3-month treasury bill: secondary market rate unemp - Seasonally adjusted unemployment rate (%) pop - End of the quarter total population: all ages incl. armed forces over seas infl - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400) realint - Real interest rate (tbilrate - infl) """ from numpy import recfromtxt, column_stack, array from pandas import DataFrame from statsmodels.datasets.utils import Dataset from os.path import dirname, abspath def load(): """ Load the US macro data and return a Dataset class. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. Notes ----- The macrodata Dataset instance does not contain endog and exog attributes. """ data = _get_data() names = data.dtype.names dataset = Dataset(data=data, names=names) return dataset def load_pandas(): dataset = load() dataset.data = DataFrame(dataset.data) return dataset def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/macrodata.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/macrodata/macrodata.csv000066400000000000000000000426451224417117700267420ustar00rootroot00000000000000"year","quarter","realgdp","realcons","realinv","realgovt","realdpi","cpi","m1","tbilrate","unemp","pop","infl","realint" 1959,1,2710.349,1707.4,286.898,470.045,1886.9,28.980,139.7,2.82,5.8,177.146,0,0 1959,2,2778.801,1733.7,310.859,481.301,1919.7,29.150,141.7,3.08,5.1,177.830,2.34,0.74 1959,3,2775.488,1751.8,289.226,491.260,1916.4,29.350,140.5,3.82,5.3,178.657,2.74,1.09 1959,4,2785.204,1753.7,299.356,484.052,1931.3,29.370,140,4.33,5.6,179.386,0.27,4.06 1960,1,2847.699,1770.5,331.722,462.199,1955.5,29.540,139.6,3.50,5.2,180.007,2.31,1.19 1960,2,2834.390,1792.9,298.152,460.400,1966.1,29.550,140.2,2.68,5.2,180.671,0.14,2.55 1960,3,2839.022,1785.8,296.375,474.676,1967.8,29.750,140.9,2.36,5.6,181.528,2.7,-0.34 1960,4,2802.616,1788.2,259.764,476.434,1966.6,29.840,141.1,2.29,6.3,182.287,1.21,1.08 1961,1,2819.264,1787.7,266.405,475.854,1984.5,29.810,142.1,2.37,6.8,182.992,-0.4,2.77 1961,2,2872.005,1814.3,286.246,480.328,2014.4,29.920,142.9,2.29,7,183.691,1.47,0.81 1961,3,2918.419,1823.1,310.227,493.828,2041.9,29.980,144.1,2.32,6.8,184.524,0.8,1.52 1961,4,2977.830,1859.6,315.463,502.521,2082.0,30.040,145.2,2.60,6.2,185.242,0.8,1.8 1962,1,3031.241,1879.4,334.271,520.960,2101.7,30.210,146.4,2.73,5.6,185.874,2.26,0.47 1962,2,3064.709,1902.5,331.039,523.066,2125.2,30.220,146.5,2.78,5.5,186.538,0.13,2.65 1962,3,3093.047,1917.9,336.962,538.838,2137.0,30.380,146.7,2.78,5.6,187.323,2.11,0.67 1962,4,3100.563,1945.1,325.650,535.912,2154.6,30.440,148.3,2.87,5.5,188.013,0.79,2.08 1963,1,3141.087,1958.2,343.721,522.917,2172.5,30.480,149.7,2.90,5.8,188.580,0.53,2.38 1963,2,3180.447,1976.9,348.730,518.108,2193.1,30.690,151.3,3.03,5.7,189.242,2.75,0.29 1963,3,3240.332,2003.8,360.102,546.893,2217.9,30.750,152.6,3.38,5.5,190.028,0.78,2.6 1963,4,3264.967,2020.6,364.534,532.383,2254.6,30.940,153.7,3.52,5.6,190.668,2.46,1.06 1964,1,3338.246,2060.5,379.523,529.686,2299.6,30.950,154.8,3.51,5.5,191.245,0.13,3.38 1964,2,3376.587,2096.7,377.778,526.175,2362.1,31.020,156.8,3.47,5.2,191.889,0.9,2.57 1964,3,3422.469,2135.2,386.754,522.008,2392.7,31.120,159.2,3.53,5,192.631,1.29,2.25 1964,4,3431.957,2141.2,389.910,514.603,2420.4,31.280,160.7,3.76,5,193.223,2.05,1.71 1965,1,3516.251,2188.8,429.145,508.006,2447.4,31.380,162,3.93,4.9,193.709,1.28,2.65 1965,2,3563.960,2213.0,429.119,508.931,2474.5,31.580,163.1,3.84,4.7,194.303,2.54,1.3 1965,3,3636.285,2251.0,444.444,529.446,2542.6,31.650,166,3.93,4.4,194.997,0.89,3.04 1965,4,3724.014,2314.3,446.493,544.121,2594.1,31.880,169.1,4.35,4.1,195.539,2.9,1.46 1966,1,3815.423,2348.5,484.244,556.593,2618.4,32.280,171.8,4.62,3.9,195.999,4.99,-0.37 1966,2,3828.124,2354.5,475.408,571.371,2624.7,32.450,170.3,4.65,3.8,196.560,2.1,2.55 1966,3,3853.301,2381.5,470.697,594.514,2657.8,32.850,171.2,5.23,3.8,197.207,4.9,0.33 1966,4,3884.520,2391.4,472.957,599.528,2688.2,32.900,171.9,5.00,3.7,197.736,0.61,4.39 1967,1,3918.740,2405.3,460.007,640.682,2728.4,33.100,174.2,4.22,3.8,198.206,2.42,1.8 1967,2,3919.556,2438.1,440.393,631.430,2750.8,33.400,178.1,3.78,3.8,198.712,3.61,0.17 1967,3,3950.826,2450.6,453.033,641.504,2777.1,33.700,181.6,4.42,3.8,199.311,3.58,0.84 1967,4,3980.970,2465.7,462.834,640.234,2797.4,34.100,184.3,4.90,3.9,199.808,4.72,0.18 1968,1,4063.013,2524.6,472.907,651.378,2846.2,34.400,186.6,5.18,3.7,200.208,3.5,1.67 1968,2,4131.998,2563.3,492.026,646.145,2893.5,34.900,190.5,5.50,3.5,200.706,5.77,-0.28 1968,3,4160.267,2611.5,476.053,640.615,2899.3,35.300,194,5.21,3.5,201.290,4.56,0.65 1968,4,4178.293,2623.5,480.998,636.729,2918.4,35.700,198.7,5.85,3.4,201.760,4.51,1.34 1969,1,4244.100,2652.9,512.686,633.224,2923.4,36.300,200.7,6.08,3.4,202.161,6.67,-0.58 1969,2,4256.460,2669.8,508.601,623.160,2952.9,36.800,201.7,6.49,3.4,202.677,5.47,1.02 1969,3,4283.378,2682.7,520.360,623.613,3012.9,37.300,202.9,7.02,3.6,203.302,5.4,1.63 1969,4,4263.261,2704.1,492.334,606.900,3034.9,37.900,206.2,7.64,3.6,203.849,6.38,1.26 1970,1,4256.573,2720.7,476.925,594.888,3050.1,38.500,206.7,6.76,4.2,204.401,6.28,0.47 1970,2,4264.289,2733.2,478.419,576.257,3103.5,38.900,208,6.66,4.8,205.052,4.13,2.52 1970,3,4302.259,2757.1,486.594,567.743,3145.4,39.400,212.9,6.15,5.2,205.788,5.11,1.04 1970,4,4256.637,2749.6,458.406,564.666,3135.1,39.900,215.5,4.86,5.8,206.466,5.04,-0.18 1971,1,4374.016,2802.2,517.935,542.709,3197.3,40.100,220,3.65,5.9,207.065,2,1.65 1971,2,4398.829,2827.9,533.986,534.905,3245.3,40.600,224.9,4.76,5.9,207.661,4.96,-0.19 1971,3,4433.943,2850.4,541.010,532.646,3259.7,40.900,227.2,4.70,6,208.345,2.94,1.75 1971,4,4446.264,2897.8,524.085,516.140,3294.2,41.200,230.1,3.87,6,208.917,2.92,0.95 1972,1,4525.769,2936.5,561.147,518.192,3314.9,41.500,235.6,3.55,5.8,209.386,2.9,0.64 1972,2,4633.101,2992.6,595.495,526.473,3346.1,41.800,238.8,3.86,5.7,209.896,2.88,0.98 1972,3,4677.503,3038.8,603.970,498.116,3414.6,42.200,245,4.47,5.6,210.479,3.81,0.66 1972,4,4754.546,3110.1,607.104,496.540,3550.5,42.700,251.5,5.09,5.3,210.985,4.71,0.38 1973,1,4876.166,3167.0,645.654,504.838,3590.7,43.700,252.7,5.98,5,211.420,9.26,-3.28 1973,2,4932.571,3165.4,675.837,497.033,3626.2,44.200,257.5,7.19,4.9,211.909,4.55,2.64 1973,3,4906.252,3176.7,649.412,475.897,3644.4,45.600,259,8.06,4.8,212.475,12.47,-4.41 1973,4,4953.050,3167.4,674.253,476.174,3688.9,46.800,263.8,7.68,4.8,212.932,10.39,-2.71 1974,1,4909.617,3139.7,631.230,491.043,3632.3,48.100,267.2,7.80,5.1,213.361,10.96,-3.16 1974,2,4922.188,3150.6,628.102,490.177,3601.1,49.300,269.3,7.89,5.2,213.854,9.86,-1.96 1974,3,4873.520,3163.6,592.672,492.586,3612.4,51.000,272.3,8.16,5.6,214.451,13.56,-5.4 1974,4,4854.340,3117.3,598.306,496.176,3596.0,52.300,273.9,6.96,6.6,214.931,10.07,-3.11 1975,1,4795.295,3143.4,493.212,490.603,3581.9,53.000,276.2,5.53,8.2,215.353,5.32,0.22 1975,2,4831.942,3195.8,476.085,486.679,3749.3,54.000,283.7,5.57,8.9,215.973,7.48,-1.91 1975,3,4913.328,3241.4,516.402,498.836,3698.6,54.900,285.4,6.27,8.5,216.587,6.61,-0.34 1975,4,4977.511,3275.7,530.596,500.141,3736.0,55.800,288.4,5.26,8.3,217.095,6.5,-1.24 1976,1,5090.663,3341.2,585.541,495.568,3791.0,56.100,294.7,4.91,7.7,217.528,2.14,2.77 1976,2,5128.947,3371.8,610.513,494.532,3822.2,57.000,297.2,5.28,7.6,218.035,6.37,-1.09 1976,3,5154.072,3407.5,611.646,493.141,3856.7,57.900,302,5.05,7.7,218.644,6.27,-1.22 1976,4,5191.499,3451.8,615.898,494.415,3884.4,58.700,308.3,4.57,7.8,219.179,5.49,-0.92 1977,1,5251.762,3491.3,646.198,498.509,3887.5,60.000,316,4.60,7.5,219.684,8.76,-4.16 1977,2,5356.131,3510.6,696.141,506.695,3931.8,60.800,320.2,5.06,7.1,220.239,5.3,-0.24 1977,3,5451.921,3544.1,734.078,509.605,3990.8,61.600,326.4,5.82,6.9,220.904,5.23,0.59 1977,4,5450.793,3597.5,713.356,504.584,4071.2,62.700,334.4,6.20,6.6,221.477,7.08,-0.88 1978,1,5469.405,3618.5,727.504,506.314,4096.4,63.900,339.9,6.34,6.3,221.991,7.58,-1.24 1978,2,5684.569,3695.9,777.454,518.366,4143.4,65.500,347.6,6.72,6,222.585,9.89,-3.18 1978,3,5740.300,3711.4,801.452,520.199,4177.1,67.100,353.3,7.64,6,223.271,9.65,-2.01 1978,4,5816.222,3741.3,819.689,524.782,4209.8,68.500,358.6,9.02,5.9,223.865,8.26,0.76 1979,1,5825.949,3760.2,819.556,525.524,4255.9,70.600,368,9.42,5.9,224.438,12.08,-2.66 1979,2,5831.418,3758.0,817.660,532.040,4226.1,73.000,377.2,9.30,5.7,225.055,13.37,-4.07 1979,3,5873.335,3794.9,801.742,531.232,4250.3,75.200,380.8,10.49,5.9,225.801,11.88,-1.38 1979,4,5889.495,3805.0,786.817,531.126,4284.3,78.000,385.8,11.94,5.9,226.451,14.62,-2.68 1980,1,5908.467,3798.4,781.114,548.115,4296.2,80.900,383.8,13.75,6.3,227.061,14.6,-0.85 1980,2,5787.373,3712.2,710.640,561.895,4236.1,82.600,394,7.90,7.3,227.726,8.32,-0.42 1980,3,5776.617,3752.0,656.477,554.292,4279.7,84.700,409,10.34,7.7,228.417,10.04,0.3 1980,4,5883.460,3802.0,723.220,556.130,4368.1,87.200,411.3,14.75,7.4,228.937,11.64,3.11 1981,1,6005.717,3822.8,795.091,567.618,4358.1,89.100,427.4,13.95,7.4,229.403,8.62,5.32 1981,2,5957.795,3822.8,757.240,584.540,4358.6,91.500,426.9,15.33,7.4,229.966,10.63,4.69 1981,3,6030.184,3838.3,804.242,583.890,4455.4,93.400,428.4,14.58,7.4,230.641,8.22,6.36 1981,4,5955.062,3809.3,773.053,590.125,4464.4,94.400,442.7,11.33,8.2,231.157,4.26,7.07 1982,1,5857.333,3833.9,692.514,591.043,4469.6,95.000,447.1,12.95,8.8,231.645,2.53,10.42 1982,2,5889.074,3847.7,691.900,596.403,4500.8,97.500,448,11.97,9.4,232.188,10.39,1.58 1982,3,5866.370,3877.2,683.825,605.370,4520.6,98.100,464.5,8.10,9.9,232.816,2.45,5.65 1982,4,5871.001,3947.9,622.930,623.307,4536.4,97.900,477.2,7.96,10.7,233.322,-0.82,8.77 1983,1,5944.020,3986.6,645.110,630.873,4572.2,98.800,493.2,8.22,10.4,233.781,3.66,4.56 1983,2,6077.619,4065.7,707.372,644.322,4605.5,99.800,507.8,8.69,10.1,234.307,4.03,4.66 1983,3,6197.468,4137.6,754.937,662.412,4674.7,100.800,517.2,8.99,9.4,234.907,3.99,5.01 1983,4,6325.574,4203.2,834.427,639.197,4771.1,102.100,525.1,8.89,8.5,235.385,5.13,3.76 1984,1,6448.264,4239.2,921.763,644.635,4875.4,103.300,535,9.43,7.9,235.839,4.67,4.76 1984,2,6559.594,4299.9,952.841,664.839,4959.4,104.100,540.9,9.94,7.5,236.348,3.09,6.85 1984,3,6623.343,4333.0,974.989,662.294,5036.6,105.100,543.7,10.19,7.4,236.976,3.82,6.37 1984,4,6677.264,4390.1,958.993,684.282,5084.5,105.700,557,8.14,7.3,237.468,2.28,5.87 1985,1,6740.275,4464.6,927.375,691.613,5072.0,107.000,570.4,8.25,7.3,237.900,4.89,3.36 1985,2,6797.344,4505.2,943.383,708.524,5172.7,107.700,589.1,7.17,7.3,238.466,2.61,4.56 1985,3,6903.523,4590.8,932.959,732.305,5140.7,108.500,607.8,7.13,7.2,239.113,2.96,4.17 1985,4,6955.918,4600.9,969.434,732.026,5193.9,109.900,621.4,7.14,7,239.638,5.13,2.01 1986,1,7022.757,4639.3,967.442,728.125,5255.8,108.700,641,6.56,7,240.094,-4.39,10.95 1986,2,7050.969,4688.7,945.972,751.334,5315.5,109.500,670.3,6.06,7.2,240.651,2.93,3.13 1986,3,7118.950,4770.7,916.315,779.770,5343.3,110.200,694.9,5.31,7,241.274,2.55,2.76 1986,4,7153.359,4799.4,917.736,767.671,5346.5,111.400,730.2,5.44,6.8,241.784,4.33,1.1 1987,1,7193.019,4792.1,945.776,772.247,5379.4,112.700,743.9,5.61,6.6,242.252,4.64,0.97 1987,2,7269.510,4856.3,947.100,782.962,5321.0,113.800,743,5.67,6.3,242.804,3.89,1.79 1987,3,7332.558,4910.4,948.055,783.804,5416.2,115.000,756.2,6.19,6,243.446,4.2,1.99 1987,4,7458.022,4922.2,1021.980,795.467,5493.1,116.000,756.2,5.76,5.9,243.981,3.46,2.29 1988,1,7496.600,5004.4,964.398,773.851,5562.1,117.200,768.1,5.76,5.7,244.445,4.12,1.64 1988,2,7592.881,5040.8,987.858,765.980,5614.3,118.500,781.4,6.48,5.5,245.021,4.41,2.07 1988,3,7632.082,5080.6,994.204,760.245,5657.5,119.900,783.3,7.22,5.5,245.693,4.7,2.52 1988,4,7733.991,5140.4,1007.371,783.065,5708.5,121.200,785.7,8.03,5.3,246.224,4.31,3.72 1989,1,7806.603,5159.3,1045.975,767.024,5773.4,123.100,779.2,8.67,5.2,246.721,6.22,2.44 1989,2,7865.016,5182.4,1033.753,784.275,5749.8,124.500,777.8,8.15,5.2,247.342,4.52,3.63 1989,3,7927.393,5236.1,1021.604,791.819,5787.0,125.400,786.6,7.76,5.3,248.067,2.88,4.88 1989,4,7944.697,5261.7,1011.119,787.844,5831.3,127.500,795.4,7.65,5.4,248.659,6.64,1.01 1990,1,8027.693,5303.3,1021.070,799.681,5875.1,128.900,806.2,7.80,5.3,249.306,4.37,3.44 1990,2,8059.598,5320.8,1021.360,800.639,5913.9,130.500,810.1,7.70,5.3,250.132,4.93,2.76 1990,3,8059.476,5341.0,997.319,793.513,5918.1,133.400,819.8,7.33,5.7,251.057,8.79,-1.46 1990,4,7988.864,5299.5,934.248,800.525,5878.2,134.700,827.2,6.67,6.1,251.889,3.88,2.79 1991,1,7950.164,5284.4,896.210,806.775,5896.3,135.100,843.2,5.83,6.6,252.643,1.19,4.65 1991,2,8003.822,5324.7,891.704,809.081,5941.1,136.200,861.5,5.54,6.8,253.493,3.24,2.29 1991,3,8037.538,5345.0,913.904,793.987,5953.6,137.200,878,5.18,6.9,254.435,2.93,2.25 1991,4,8069.046,5342.6,948.891,778.378,5992.4,138.300,910.4,4.14,7.1,255.214,3.19,0.95 1992,1,8157.616,5434.5,927.796,778.568,6082.9,139.400,943.8,3.88,7.4,255.992,3.17,0.71 1992,2,8244.294,5466.7,988.912,777.762,6129.5,140.500,963.2,3.50,7.6,256.894,3.14,0.36 1992,3,8329.361,5527.1,999.135,786.639,6160.6,141.700,1003.8,2.97,7.6,257.861,3.4,-0.44 1992,4,8417.016,5594.6,1030.758,787.064,6248.2,142.800,1030.4,3.12,7.4,258.679,3.09,0.02 1993,1,8432.485,5617.2,1054.979,762.901,6156.5,143.800,1047.6,2.92,7.2,259.414,2.79,0.13 1993,2,8486.435,5671.1,1063.263,752.158,6252.3,144.500,1084.5,3.02,7.1,260.255,1.94,1.08 1993,3,8531.108,5732.7,1062.514,744.227,6265.7,145.600,1113,3.00,6.8,261.163,3.03,-0.04 1993,4,8643.769,5783.7,1118.583,748.102,6358.1,146.300,1131.6,3.05,6.6,261.919,1.92,1.13 1994,1,8727.919,5848.1,1166.845,721.288,6332.6,147.200,1141.1,3.48,6.6,262.631,2.45,1.02 1994,2,8847.303,5891.5,1234.855,717.197,6440.6,148.400,1150.5,4.20,6.2,263.436,3.25,0.96 1994,3,8904.289,5938.7,1212.655,736.890,6487.9,149.400,1150.1,4.68,6,264.301,2.69,2 1994,4,9003.180,5997.3,1269.190,716.702,6574.0,150.500,1151.4,5.53,5.6,265.044,2.93,2.6 1995,1,9025.267,6004.3,1282.090,715.326,6616.6,151.800,1149.3,5.72,5.5,265.755,3.44,2.28 1995,2,9044.668,6053.5,1247.610,712.492,6617.2,152.600,1145.4,5.52,5.7,266.557,2.1,3.42 1995,3,9120.684,6107.6,1235.601,707.649,6666.8,153.500,1137.3,5.32,5.7,267.456,2.35,2.97 1995,4,9184.275,6150.6,1270.392,681.081,6706.2,154.700,1123.5,5.17,5.6,268.151,3.11,2.05 1996,1,9247.188,6206.9,1287.128,695.265,6777.7,156.100,1124.8,4.91,5.5,268.853,3.6,1.31 1996,2,9407.052,6277.1,1353.795,705.172,6850.6,157.000,1112.4,5.09,5.5,269.667,2.3,2.79 1996,3,9488.879,6314.6,1422.059,692.741,6908.9,158.200,1086.1,5.04,5.3,270.581,3.05,2 1996,4,9592.458,6366.1,1418.193,690.744,6946.8,159.400,1081.5,4.99,5.3,271.360,3.02,1.97 1997,1,9666.235,6430.2,1451.304,681.445,7008.9,159.900,1063.8,5.10,5.2,272.083,1.25,3.85 1997,2,9809.551,6456.2,1543.976,693.525,7061.5,160.400,1066.2,5.01,5,272.912,1.25,3.76 1997,3,9932.672,6566.0,1571.426,691.261,7142.4,161.500,1065.5,5.02,4.9,273.852,2.73,2.29 1997,4,10008.874,6641.1,1596.523,690.311,7241.5,162.000,1074.4,5.11,4.7,274.626,1.24,3.88 1998,1,10103.425,6707.2,1672.732,668.783,7406.2,162.200,1076.1,5.02,4.6,275.304,0.49,4.53 1998,2,10194.277,6822.6,1652.716,687.184,7512.0,163.200,1075,4.98,4.4,276.115,2.46,2.52 1998,3,10328.787,6913.1,1700.071,681.472,7591.0,163.900,1086,4.49,4.5,277.003,1.71,2.78 1998,4,10507.575,7019.1,1754.743,688.147,7646.5,164.700,1097.8,4.38,4.4,277.790,1.95,2.43 1999,1,10601.179,7088.3,1809.993,683.601,7698.4,165.900,1101.9,4.39,4.3,278.451,2.9,1.49 1999,2,10684.049,7199.9,1803.674,683.594,7716.0,166.700,1098.7,4.54,4.3,279.295,1.92,2.62 1999,3,10819.914,7286.4,1848.949,697.936,7765.9,168.100,1102.3,4.75,4.2,280.203,3.35,1.41 1999,4,11014.254,7389.2,1914.567,713.445,7887.7,169.300,1121.9,5.20,4.1,280.976,2.85,2.35 2000,1,11043.044,7501.3,1887.836,685.216,8053.4,170.900,1113.5,5.63,4,281.653,3.76,1.87 2000,2,11258.454,7571.8,2018.529,712.641,8135.9,172.700,1103,5.81,3.9,282.385,4.19,1.62 2000,3,11267.867,7645.9,1986.956,698.827,8222.3,173.900,1098.7,6.07,4,283.190,2.77,3.3 2000,4,11334.544,7713.5,1987.845,695.597,8234.6,175.600,1097.7,5.70,3.9,283.900,3.89,1.81 2001,1,11297.171,7744.3,1882.691,710.403,8296.5,176.400,1114.9,4.39,4.2,284.550,1.82,2.57 2001,2,11371.251,7773.5,1876.650,725.623,8273.7,177.400,1139.7,3.54,4.4,285.267,2.26,1.28 2001,3,11340.075,7807.7,1837.074,730.493,8484.5,177.600,1166,2.72,4.8,286.047,0.45,2.27 2001,4,11380.128,7930.0,1731.189,739.318,8385.5,177.700,1190.9,1.74,5.5,286.728,0.23,1.51 2002,1,11477.868,7957.3,1789.327,756.915,8611.6,179.300,1185.9,1.75,5.7,287.328,3.59,-1.84 2002,2,11538.770,7997.8,1810.779,774.408,8658.9,180.000,1199.5,1.70,5.8,288.028,1.56,0.14 2002,3,11596.430,8052.0,1814.531,786.673,8629.2,181.200,1204,1.61,5.7,288.783,2.66,-1.05 2002,4,11598.824,8080.6,1813.219,799.967,8649.6,182.600,1226.8,1.20,5.8,289.421,3.08,-1.88 2003,1,11645.819,8122.3,1813.141,800.196,8681.3,183.200,1248.4,1.14,5.9,290.019,1.31,-0.17 2003,2,11738.706,8197.8,1823.698,838.775,8812.5,183.700,1287.9,0.96,6.2,290.704,1.09,-0.13 2003,3,11935.461,8312.1,1889.883,839.598,8935.4,184.900,1297.3,0.94,6.1,291.449,2.6,-1.67 2003,4,12042.817,8358.0,1959.783,845.722,8986.4,186.300,1306.1,0.90,5.8,292.057,3.02,-2.11 2004,1,12127.623,8437.6,1970.015,856.570,9025.9,187.400,1332.1,0.94,5.7,292.635,2.35,-1.42 2004,2,12213.818,8483.2,2055.580,861.440,9115.0,189.100,1340.5,1.21,5.6,293.310,3.61,-2.41 2004,3,12303.533,8555.8,2082.231,876.385,9175.9,190.800,1361,1.63,5.4,294.066,3.58,-1.95 2004,4,12410.282,8654.2,2125.152,865.596,9303.4,191.800,1366.6,2.20,5.4,294.741,2.09,0.11 2005,1,12534.113,8719.0,2170.299,869.204,9189.6,193.800,1357.8,2.69,5.3,295.308,4.15,-1.46 2005,2,12587.535,8802.9,2131.468,870.044,9253.0,194.700,1366.6,3.01,5.1,295.994,1.85,1.16 2005,3,12683.153,8865.6,2154.949,890.394,9308.0,199.200,1375,3.52,5,296.770,9.14,-5.62 2005,4,12748.699,8888.5,2232.193,875.557,9358.7,199.400,1380.6,4.00,4.9,297.435,0.4,3.6 2006,1,12915.938,8986.6,2264.721,900.511,9533.8,200.700,1380.5,4.51,4.7,298.061,2.6,1.91 2006,2,12962.462,9035.0,2261.247,892.839,9617.3,202.700,1369.2,4.82,4.7,298.766,3.97,0.85 2006,3,12965.916,9090.7,2229.636,892.002,9662.5,201.900,1369.4,4.90,4.7,299.593,-1.58,6.48 2006,4,13060.679,9181.6,2165.966,894.404,9788.8,203.574,1373.6,4.92,4.4,300.320,3.3,1.62 2007,1,13099.901,9265.1,2132.609,882.766,9830.2,205.920,1379.7,4.95,4.5,300.977,4.58,0.36 2007,2,13203.977,9291.5,2162.214,898.713,9842.7,207.338,1370,4.72,4.5,301.714,2.75,1.97 2007,3,13321.109,9335.6,2166.491,918.983,9883.9,209.133,1379.2,4.00,4.7,302.509,3.45,0.55 2007,4,13391.249,9363.6,2123.426,925.110,9886.2,212.495,1377.4,3.01,4.8,303.204,6.38,-3.37 2008,1,13366.865,9349.6,2082.886,943.372,9826.8,213.997,1384,1.56,4.9,303.803,2.82,-1.26 2008,2,13415.266,9351.0,2026.518,961.280,10059.0,218.610,1409.3,1.74,5.4,304.483,8.53,-6.79 2008,3,13324.600,9267.7,1990.693,991.551,9838.3,216.889,1474.7,1.17,6,305.270,-3.16,4.33 2008,4,13141.920,9195.3,1857.661,1007.273,9920.4,212.174,1576.5,0.12,6.9,305.952,-8.79,8.91 2009,1,12925.410,9209.2,1558.494,996.287,9926.4,212.671,1592.8,0.22,8.1,306.547,0.94,-0.71 2009,2,12901.504,9189.0,1456.678,1023.528,10077.5,214.469,1653.6,0.18,9.2,307.226,3.37,-3.19 2009,3,12990.341,9256.0,1486.398,1044.088,10040.6,216.385,1673.9,0.12,9.6,308.013,3.56,-3.44 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/macrodata/macrodata.dta000066400000000000000000000317071224417117700267140ustar00rootroot00000000000000rË^kÐP­A€á­A°DxJÿCù4@`\A@×[A27 May 2010 23:49üûþþþþþþþþþþþþyearquarterrealgdprealconsrealinvrealgovtrealdpicpim1tbilrateunemppopinflrealint%8.0g%8.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g%9.0g§–e)EÍlÕDòrCÃëCÍÜëD ×çA3³ Cáz4@š™¹@`%1C§Ѭ-Ef¶ØDôm›C‡¦ðCföïD33éA3³ C¸E@33£@{Ô1CÂ@¤p=?§Ïw-EšùÚDîœCH¡õCÍŒïDÍÌêA€ Cázt@š™©@1¨2C)\/@…‹?§D.Ef6ÛD‘­•C¨òCšiñDÃõêA C\Š@33³@Ñb3Cq=Š>…ë@¨/û1EPÝDjÜ¥CyçCpôDìQìAš™ C`@ff¦@Ë4C ×@ìQ˜?¨=&1EÍàDu•C33æC3ÃõDffìA33 C…+@ff¦@Ç«4C)\>33#@¨Zp1Eš9ßD0”C‡VíCšùõDîAfæ C= @33³@+‡5CÍÌ,@{®¾¨Û)/Ef†ßDËáC7îC3ÓõDR¸îAš C\@š™É@yI6CHáš?q=Š?©940EfvßD×3…CPííCøDázîAšC®@š™Ù@ôý6CÍÌ̾®G1@©€3EšÉâD}Cü)ðCÍÌûD)\ïAfæC\@à@å°7Cö(¼?)\O?©´f6E3ããD›CüéöCÍ<ÿD ×ïAšCáz@š™Ù@%†8CÍÌL?\Â?©H:E3sèDD»C°BûC 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ÑþÿÿÿÒÓÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ l ÉBäREADME Monthly Quarterly=¼%r8X"1ÈÿArial1ÈÿArial1ÈÿArial1ÈÿArial1ÈÿArial1È Arial ¤ YYYY-MM-DD¥0.000¦0.0§0.00àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À àõÿ À à À à À ठÀ à#À ॠÀ ঠÀ ॠÀ ॠÀ ॠÀ ঠÀ ঠÀ à À ৠÀ à¤!À à¤"À à"À à!À à#À “€ÿ’â8ÿÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€€€ÀÀÀ€€€™™ÿ™3fÿÿÌÌÿÿffÿ€€fÌÌÌÿ€ÿÿÿÿÿÿ€€€€€ÿÌÿÌÿÿÌÿÌÿÿ™™Ìÿÿ™ÌÌ™ÿÿÌ™3fÿ3ÌÌ™ÌÿÌÿ™ÿfff™–––3f3™f333™3™3f33™333… ÛREADME…·#Monthly…F6 Quarterly l ÉU} ¸ } ¸ } ¸< Monthly Quarterly*+€‚Áƒ„&è?'è?(ð?)ð?¡"dXXà?à? Data List: macrodata Data Updated: 2009-12-11,$FRED (Federal Reserve Economic Data)2*Link: http://research.stlouisfed.org/fred2;3Help: http://research.stlouisfed.org/fred2/help-faq"Economic Research Division)!Federal Reserve Bank of St. Louis  Series ID: CPIAUCSL Title:? 7Consumer Price Index For All Urban Consumers: All ItemsSource:<4U.S. Department of Labor: Bureau of Labor StatisticsRelease:Consumer Price IndexUnits:Index 1982-84=100 Frequency:MonthlySeasonal Adjustment:Seasonally AdjustedNotes:Handbook of Methods -L D(http://stats.bls.gov:80/opub/hom/homch17_itc.htm) Understanding the)!!CPI: Frequently Asked Questions -0"((http://stats.bls.gov:80/cpi/cpifaq.htm)& Series ID:'DPIC96)Title:'*Real Disposable Personal Income,Source:@-8U.S. Department of Commerce: Bureau of Economic Analysis/Release:0Gross Domestic Product2Units:(3 Billions of Chained 2005 Dollars5 Frequency:6 Quarterly8Seasonal Adjustment:'9Seasonally Adjusted Annual Rate;Notes:I<AA Guide to the National Income and Product Accounts of the UnitedF=>States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)A Series ID:BFGCEC96DTitle:KECReal Federal Consumption Expenditures & Gross Investment, 3 DecimalGSource:@H8U.S. Department of Commerce: Bureau of Economic AnalysisJRelease:KGross Domestic ProductMUnits:(N Billions of Chained 2005 DollarsP Frequency:Q QuarterlySSeasonal Adjustment:'TSeasonally Adjusted Annual RateVNotes:GW?Data prior to 1990 for chained dollars were calculated by Haver1X)Analytics using chained quantity indexes.YIZAA Guide to the National Income and Product Accounts of the UnitedF[>States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)_ Series ID:`GDPC96bTitle:.c&Real Gross Domestic Product, 3 DecimaleSource:@f8U.S. Department of Commerce: Bureau of Economic AnalysishRelease:iGross Domestic ProductkUnits:(l Billions of Chained 2005 Dollarsn Frequency:o QuarterlyqSeasonal Adjustment:'rSeasonally Adjusted Annual RatetNotes:IuAA Guide to the National Income and Product Accounts of the UnitedFv>States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)z Series ID:{GPDIC96}Title:9~1Real Gross Private Domestic Investment, 3 Decimal€Source:@8U.S. Department of Commerce: Bureau of Economic AnalysisƒRelease:„Gross Domestic Product†Units:(‡ Billions of Chained 2005 Dollars‰ Frequency:Š QuarterlyŒSeasonal Adjustment:'Seasonally Adjusted Annual RateNotes:IAA Guide to the National Income and Product Accounts of the UnitedF‘>States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)• Series ID: –M1SL˜Title:™M1 Money Stock›Source:8œ0Board of Governors of the Federal Reserve SystemžRelease: ŸH.6 Money Stock Measures¡Units:¢Billions of Dollars¤ Frequency:¥Monthly§Seasonal Adjustment:¨Seasonally AdjustedªNotes:F«>Ml includes funds that are readily accessible for spending. M1L¬Dconsists of: (1) currency outside the U.S. Treasury, Federal ReserveH­@Banks, and the vaults of depository institutions; (2) traveler'sE®=checks of nonbank issuers; (3) demand deposits; and (4) otherN¯Fcheckable deposits (OCDs), which consist primarily of negotiable orderJ°Bof withdrawal (NOW) accounts at depository institutions and creditK±Cunion share draft accounts. Seasonally adjusted M1 is calculated byL²Dsumming currency, traveler's checks, demand deposits, and OCDs, each'³seasonally adjusted separately.· Series ID:¸PCECC96ºTitle:.»&Real Personal Consumption Expenditures½Source:@¾8U.S. Department of Commerce: Bureau of Economic AnalysisÀRelease:ÁGross Domestic ProductÃUnits:(Ä Billions of Chained 2005 DollarsÆ Frequency:Ç QuarterlyÉSeasonal Adjustment:'ÊSeasonally Adjusted Annual RateÌNotes:IÍAA Guide to the National Income and Product Accounts of the UnitedFÎ>States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)Ò Series ID: ÓPOPÕTitle:BÖ:Total Population: All Ages including Armed Forces OverseasØSource:2Ù*U.S. Department of Commerce: Census BureauÛRelease:-Ü%Monthly National Population EstimatesÞUnits:ß Thousandsá Frequency:âMonthlyäSeasonal Adjustment:åNot ApplicableçNotes:Eè=The intercensal estimates for 1990-2000 for the United StatesFé>population are produced by converting the 1990-2000 postcensalNêFestimates prepared previously for the U. S. to account for differencesLëDbetween the postcensal estimates in 2000 and census counts (error ofLìDclosure). The postcensal estimates for 1990 to 2000 were produced byIíAupdating the resident population enumerated in the 1990 census byNîFestimates of the components of population change between April 1, 1990IïAand April 1, 2000-- births to U.S. resident women, deaths to U.S.MðEresidents, net international migration (incl legal & residual foreignNñFborn), and net movement of the U.S. armed forces and civilian citizensJòBto the United States. Intercensal population estimates for 1990 toJóB2000 are derived from the postcensal estimates by distributing theJôBerror of closure over the decade by month. The method used for theMõE1990s for distributing the error of closure is the same that was usedHö@for the 1980s. This method produces an intercensal estimate as aI÷Afunction of time and the postcensal estimates,using the followingMøEformula: the population at time t is equal to the postcensal estimateMùEat time t multiplied by a function. The function is the April 1, 2000LúDcensus count divided by the April 1, 2000 postcensal estimate raised*û"to the power of t divided by 3653.ÿ Series ID: TB3MSTitle:4,3-Month Treasury Bill: Secondary Market RateSource:80Board of Governors of the Federal Reserve SystemRelease:$ H.15 Selected Interest Rates Units: Percent Frequency:MonthlySeasonal Adjustment:Not ApplicableNotes:1)Averages of Business Days, Discount Basis> ¶ l ÉU} ¸} ¸ } ¸ } ¸ } ¸ README Quarterly*+€‚Áƒ„&è?'è?(ð?)ð?¡"dXXà?à?  DATECPIAUCSL M1SL POP TB3MSBÈ@ ×£p= ç?€QÈ@×£p= ×ã?€_È@¸…ëQ¸Î?oÈ@333333Ã?~È@{®GázÄ?€È@333333Ã?€œÈ@333333Ã?¬È@R¸…ëQÈ? €»È@ áz®GáÊ? €ÊÈ@ Ház®GÑ? 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LNS14000000Q,1990,Q01,5.3 LNS14000000Q,1990,Q02,5.3 LNS14000000Q,1990,Q03,5.7 LNS14000000Q,1990,Q04,6.1 LNS14000000Q,1991,Q01,6.6 LNS14000000Q,1991,Q02,6.8 LNS14000000Q,1991,Q03,6.9 LNS14000000Q,1991,Q04,7.1 LNS14000000Q,1992,Q01,7.4 LNS14000000Q,1992,Q02,7.6 LNS14000000Q,1992,Q03,7.6 LNS14000000Q,1992,Q04,7.4 LNS14000000Q,1993,Q01,7.2 LNS14000000Q,1993,Q02,7.1 LNS14000000Q,1993,Q03,6.8 LNS14000000Q,1993,Q04,6.6 LNS14000000Q,1994,Q01,6.6 LNS14000000Q,1994,Q02,6.2 LNS14000000Q,1994,Q03,6.0 LNS14000000Q,1994,Q04,5.6 LNS14000000Q,1995,Q01,5.5 LNS14000000Q,1995,Q02,5.7 LNS14000000Q,1995,Q03,5.7 LNS14000000Q,1995,Q04,5.6 LNS14000000Q,1996,Q01,5.5 LNS14000000Q,1996,Q02,5.5 LNS14000000Q,1996,Q03,5.3 LNS14000000Q,1996,Q04,5.3 LNS14000000Q,1997,Q01,5.2 LNS14000000Q,1997,Q02,5.0 LNS14000000Q,1997,Q03,4.9 LNS14000000Q,1997,Q04,4.7 LNS14000000Q,1998,Q01,4.6 LNS14000000Q,1998,Q02,4.4 LNS14000000Q,1998,Q03,4.5 LNS14000000Q,1998,Q04,4.4 LNS14000000Q,1999,Q01,4.3 LNS14000000Q,1999,Q02,4.3 LNS14000000Q,1999,Q03,4.2 LNS14000000Q,1999,Q04,4.1 LNS14000000Q,2000,Q01,4.0 LNS14000000Q,2000,Q02,3.9 LNS14000000Q,2000,Q03,4.0 LNS14000000Q,2000,Q04,3.9 LNS14000000Q,2001,Q01,4.2 LNS14000000Q,2001,Q02,4.4 LNS14000000Q,2001,Q03,4.8 LNS14000000Q,2001,Q04,5.5 LNS14000000Q,2002,Q01,5.7 LNS14000000Q,2002,Q02,5.8 LNS14000000Q,2002,Q03,5.7 LNS14000000Q,2002,Q04,5.8 LNS14000000Q,2003,Q01,5.9 LNS14000000Q,2003,Q02,6.2 LNS14000000Q,2003,Q03,6.1 LNS14000000Q,2003,Q04,5.8 LNS14000000Q,2004,Q01,5.7 LNS14000000Q,2004,Q02,5.6 LNS14000000Q,2004,Q03,5.4 LNS14000000Q,2004,Q04,5.4 LNS14000000Q,2005,Q01,5.3 LNS14000000Q,2005,Q02,5.1 LNS14000000Q,2005,Q03,5.0 LNS14000000Q,2005,Q04,4.9 LNS14000000Q,2006,Q01,4.7 LNS14000000Q,2006,Q02,4.7 LNS14000000Q,2006,Q03,4.7 LNS14000000Q,2006,Q04,4.4 LNS14000000Q,2007,Q01,4.5 LNS14000000Q,2007,Q02,4.5 LNS14000000Q,2007,Q03,4.7 LNS14000000Q,2007,Q04,4.8 LNS14000000Q,2008,Q01,4.9 LNS14000000Q,2008,Q02,5.4 LNS14000000Q,2008,Q03,6.0 LNS14000000Q,2008,Q04,6.9 LNS14000000Q,2009,Q01,8.1 LNS14000000Q,2009,Q02,9.2 LNS14000000Q,2009,Q03,9.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/nile/000077500000000000000000000000001224417117700232535ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/nile/__init__.py000066400000000000000000000000231224417117700253570ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/nile/data.py000066400000000000000000000032661224417117700245450ustar00rootroot00000000000000"""Name of dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """E.g., This is public domain.""" TITLE = """Title of the dataset""" SOURCE = """ This section should provide a link to the original dataset if possible and attribution and correspondance information for the dataset's original author if so desired. """ DESCRSHORT = """A short description.""" DESCRLONG = """A longer description of the dataset.""" #suggested notes NOTE = """ Number of observations: Number of variables: Variable name definitions: Any other useful information that does not fit into the above categories. """ from numpy import recfromtxt, column_stack, array from pandas import Series, DataFrame from statsmodels.datasets.utils import Dataset from os.path import dirname, abspath def load(): """ Load the Nile data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() names = list(data.dtype.names) endog_name = 'volume' endog = array(data[endog_name], dtype=float) dataset = Dataset(data=data, names=[endog_name], endog=endog, endog_name=endog_name) return dataset def load_pandas(): data = DataFrame(_get_data()) # TODO: time series endog = Series(data['volume'], index=data['year'].astype(int)) dataset = Dataset(data=data, names=list(data.columns), endog=endog, endog_name='volume') return dataset def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/nile.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/nile/nile.csv000066400000000000000000000016561224417117700247270ustar00rootroot00000000000000year,volume 1871,1120 1872,1160 1873,963 1874,1210 1875,1160 1876,1160 1877,813 1878,1230 1879,1370 1880,1140 1881,995 1882,935 1883,1110 1884,994 1885,1020 1886,960 1887,1180 1888,799 1889,958 1890,1140 1891,1100 1892,1210 1893,1150 1894,1250 1895,1260 1896,1220 1897,1030 1898,1100 1899,774 1900,840 1901,874 1902,694 1903,940 1904,833 1905,701 1906,916 1907,692 1908,1020 1909,1050 1910,969 1911,831 1912,726 1913,456 1914,824 1915,702 1916,1120 1917,1100 1918,832 1919,764 1920,821 1921,768 1922,845 1923,864 1924,862 1925,698 1926,845 1927,744 1928,796 1929,1040 1930,759 1931,781 1932,865 1933,845 1934,944 1935,984 1936,897 1937,822 1938,1010 1939,771 1940,676 1941,649 1942,846 1943,812 1944,742 1945,801 1946,1040 1947,860 1948,874 1949,848 1950,890 1951,744 1952,749 1953,838 1954,1050 1955,918 1956,986 1957,797 1958,923 1959,975 1960,815 1961,1020 1962,906 1963,901 1964,1170 1965,912 1966,746 1967,919 1968,718 1969,714 1970,740 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/randhie/000077500000000000000000000000001224417117700237365ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/randhie/__init__.py000066400000000000000000000000231224417117700260420ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/randhie/data.py000066400000000000000000000050541224417117700252250ustar00rootroot00000000000000"""RAND Health Insurance Experiment Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is in the public domain.""" TITLE = __doc__ SOURCE = """ The data was collected by the RAND corporation as part of the Health Insurance Experiment (HIE). http://www.rand.org/health/projects/hie/ This data was used in:: Cameron, A.C. amd Trivedi, P.K. 2005. `Microeconometrics: Methods and Applications,` Cambridge: New York. And was obtained from: See randhie/src for the original data and description. The data included here contains only a subset of the original data. The data varies slightly compared to that reported in Cameron and Trivedi. """ DESCRSHORT = """The RAND Co. Health Insurance Experiment Data""" DESCRLONG = """""" NOTE = """ Number of observations - 20,190 Number of variables - 10 Variable name definitions:: mdvis - Number of outpatient visits to an MD lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100 idp - 1 if individual deductible plan, 0 otherwise lpi - ln(max(1, annual participation incentive payment)) fmde - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise physlm - 1 if the person has a physical limitation disea - number of chronic diseases hlthg - 1 if self-rated health is good hlthf - 1 if self-rated health is fair hlthp - 1 if self-rated health is poor (Omitted category is excellent self-rated health) """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath PATH = '%s/%s' % (dirname(abspath(__file__)), 'randhie.csv') def load(): """ Loads the RAND HIE data and returns a Dataset class. ---------- endog - response variable, mdvis exog - design Returns Load instance: a class of the data with array attrbutes 'endog' and 'exog' """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Loads the RAND HIE data and returns a Dataset class. ---------- endog - response variable, mdvis exog - design Returns Load instance: a class of the data with array attrbutes 'endog' and 'exog' """ from pandas import read_csv data = read_csv(PATH) return du.process_recarray_pandas(data, endog_idx=0) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(PATH, "rb"), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/randhie/randhie.csv000066400000000000000000026652251224417117700261070ustar00rootroot00000000000000mdvis,lncoins,idp,lpi,fmde,physlm,disea,hlthg,hlthf,hlthp 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 2,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 1,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 1,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 0,4.61512,1,6.907755,0,0,13.73189,0,0,0 6,4.61512,1,6.907755,0,0,13.73189,1,0,0 2,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 0,4.61512,1,6.907755,0,0,13.73189,1,0,0 1,0,1,6.109248,0,0,13.73189,1,0,0 0,0,1,6.109248,0,0,13.73189,1,0,0 0,0,1,6.109248,0,0,13.73189,1,0,0 0,0,1,6.109248,0,0,13.73189,1,0,0 0,0,1,6.109248,0,0,13.73189,1,0,0 1,0,1,6.109248,0,1,13,1,0,0 0,0,1,6.109248,0,1,13,1,0,0 1,0,1,6.109248,0,1,13,1,0,0 2,0,1,6.109248,0,1,13,1,0,0 4,0,1,6.109248,0,1,13,1,0,0 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leading digit is sit black float %9.0g black income float %9.0g income based on annual income r xage float %9.0g age that year female float %9.0g female educdec float %9.0g education of decision maker time float %9.0g time eligible during the year outpdol float %9.0g outpatient exp. excl. ment and drugdol float %9.0g drugs purchased, outpatient suppdol float %9.0g supplies purchased, outpatient mentdol float %9.0g psychotherapy exp., outpatient inpdol float %9.0g inpatient exp., facilities & md meddol float %9.0g medical exp excl outpatient men totadm float %9.0g number of hosp. admissions inpmis float %9.0g missing any inpatient charges mentvis float %9.0g number psychotherapy visits mdvis float %9.0g number face-to-fact md visits notmdvis float %9.0g number face-to-face, not-md vis num float %9.0g family size mhi float %9.0g mental health index -- baselin disea float %9.0g count of chronic diseases -- ba physlm float %9.0g physical limitations -- baselin ghindx float %9.0g general health index -- baselin mdeoff float %9.0g maximum expenditure offer pioff float %9.0g participation incentive child float %9.0g child fchild float %9.0g female child lfam float %9.0g log of family size lpi float %9.0g log participation incentive idp float %9.0g individual deductible plan logc float %9.0g log(coinsurance+1) fmde float %9.0g function of mdeoff hlthg float %9.0g good health hlthf float %9.0g fair health hlthp float %9.0g poor health xghindx float %9.0g ghi with imputation linc float %9.0g lnum float %9.0g lnmeddol float %9.0g binexp float %9.0g statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/randhie/src/randhie.csv000066400000000000000000160742761224417117700267030ustar00rootroot00000000000000plan,site,coins,tookphys,year,zper,black,income,xage,female,educdec,time,outpdol,drugdol,suppdol,mentdol,inpdol,meddol,totadm,inpmis,mentvis,mdvis,notmdvis,num,mhi,disea,physlm,ghindx,mdeoff,pioff,child,fchild,lfam,lpi,idp,logc,fmde,hlthg,hlthf,hlthp,xghindx,linc,lnum,lnmeddol,binexp 3,1,100,0,1,125024,1,13748.76,42.87748,0,12,1,0,8.451119,0,0,0,8.451119,0,0,0,0,0,4,95,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.2078,9.528776,1.386294,2.134299,1 3,1,100,0,2,125024,1,13748.76,43.87748,0,12,1,48.78706,13.28841,0,0,0,62.07547,0,0,0,2,0,4,95,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.2078,9.528776,1.386294,4.128351,1 3,1,100,0,3,125024,1,13748.76,44.87748,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,95,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.2078,9.528776,1.386294,,0 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3,1,100,0,1,125027,1,13748.76,43.14305,1,12,1,60.6596,42.93286,0,0,0,103.5925,0,0,0,6,0,4,96.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84741,9.528776,1.386294,4.640465,1 3,1,100,0,2,125027,1,13748.76,44.14305,1,12,1,242.1186,11.07817,37.38544,0,0,290.5822,0,0,0,2,1,4,96.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84741,9.528776,1.386294,5.671886,1 3,1,100,0,3,125027,1,13748.76,45.14305,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,96.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84741,9.528776,1.386294,,0 3,1,100,0,4,125027,1,13748.76,46.14305,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,96.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84741,9.528776,1.386294,,0 3,1,100,0,5,125027,1,13748.76,47.14305,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,96.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84741,9.528776,1.386294,,0 1,1,0,1,1,125057,1,5107.692,13.54962,1,12,1,12.79001,0,0,0,382.9268,395.7168,1,0,0,1,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,80.28564,8.538699,1.098612,5.980699,1 1,1,0,1,2,125057,1,5107.692,14.54962,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,80.28564,8.538699,1.098612,,0 1,1,0,1,3,125057,1,5107.692,15.54962,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,80.28564,8.538699,1.098612,,0 1,1,0,1,4,125057,1,5107.692,16.54962,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,80.28564,8.538699,1.098612,,0 1,1,0,1,5,125057,1,5107.692,17.54962,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,80.28564,8.538699,1.098612,,0 1,1,0,1,1,125058,1,5107.692,15.28268,1,12,1,18.4414,0,0,0,0,18.4414,0,0,0,1,0,3,61.1,13,1,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,63.74599,8.538699,1.098612,2.914598,1 1,1,0,1,2,125058,1,5107.692,16.28268,1,12,1,0,2.39521,0,0,0,2.39521,0,0,0,0,0,3,61.1,13,1,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,63.74599,8.538699,1.098612,.8734707,1 1,1,0,1,3,125058,1,5107.692,17.28268,1,12,1,4.955401,17.09613,0,0,0,22.05154,0,0,0,1,0,3,61.1,13,1,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,63.74599,8.538699,1.098612,3.093382,1 1,1,0,1,4,125058,1,5107.692,18.28268,1,12,1,29.8302,25.7687,0,0,0,55.5989,0,0,0,2,0,3,61.1,13,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.74599,8.538699,1.098612,4.018163,1 1,1,0,1,5,125058,1,5107.692,19.28268,1,12,1,122.0025,11.75852,0,0,0,133.761,0,0,0,4,0,3,61.1,13,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.74599,8.538699,1.098612,4.896055,1 1,1,0,1,1,125059,1,5107.692,38.83094,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,81.1,17.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,68.12782,8.538699,1.098612,,0 1,1,0,1,2,125059,1,5107.692,39.83094,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,81.1,17.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,68.12782,8.538699,1.098612,,0 1,1,0,1,3,125059,1,5107.692,40.83094,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,81.1,17.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,68.12782,8.538699,1.098612,,0 1,1,0,1,4,125059,1,5107.692,41.83094,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,81.1,17.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,68.12782,8.538699,1.098612,,0 1,1,0,1,5,125059,1,5107.692,42.83094,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,81.1,17.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,68.12782,8.538699,1.098612,,0 1,1,0,0,1,125075,1,1,24.60233,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,80,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,71.88312,.6931472,0,,0 1,1,0,0,2,125075,1,1,25.60233,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,80,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,71.88312,.6931472,0,,0 1,1,0,0,3,125075,1,1,26.60233,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,80,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,71.88312,.6931472,0,,0 11,1,0,0,1,125126,1,5899.918,15.74538,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,0,2,125126,1,5899.918,16.74538,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,0,3,125126,1,5899.918,17.74538,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,1,1,125127,1,5899.918,60.48734,1,9,1,5.889281,0,0,0,0,5.889281,0,0,0,1,0,4,71.6,4.3,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,63.0108,8.682863,1.386294,1.773134,1 11,1,0,1,2,125127,1,5899.918,61.48734,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,71.6,4.3,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,63.0108,8.682863,1.386294,,0 11,1,0,1,3,125127,1,5899.918,62.48734,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,71.6,4.3,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,63.0108,8.682863,1.386294,,0 11,1,0,0,1,125128,1,5899.918,14.59001,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,63.5,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,0,2,125128,1,5899.918,15.59001,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,63.5,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,0,3,125128,1,5899.918,16.59001,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,63.5,13,0,,0,406,1,1,1.386294,6.006353,0,0,0,1,0,0,59.67566,8.682863,1.386294,,0 11,1,0,1,1,125129,1,5899.918,59.27721,0,4,1,66.19553,67.13781,0,0,0,133.3333,0,0,0,1,0,4,71.6,13,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,61.37119,8.682863,1.386294,4.892852,1 11,1,0,1,2,125129,1,5899.918,60.27721,0,4,1,0,61.45552,0,0,0,61.45552,0,0,0,0,0,4,71.6,13,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,61.37119,8.682863,1.386294,4.118314,1 11,1,0,1,3,125129,1,5899.918,61.27721,0,4,1,695.5725,62.11302,5.405406,0,0,763.0909,0,0,0,7,32,4,71.6,13,0,,0,406,0,0,1.386294,6.006353,0,0,0,1,0,0,61.37119,8.682863,1.386294,6.637377,1 11,1,0,1,1,125133,1,3596.154,14.22313,0,12,1,11.99041,7.284173,0,0,0,19.27458,0,0,0,1,0,3,92.6,4.3,0,,0,0,1,0,1.098612,0,0,0,0,0,0,0,75.73199,8.187899,1.098612,2.958787,1 11,1,0,1,2,125133,1,3596.154,15.22313,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,92.6,4.3,0,,0,0,1,0,1.098612,0,0,0,0,0,0,0,75.73199,8.187899,1.098612,,0 11,1,0,1,3,125133,1,3596.154,16.22313,0,12,1,28.42893,0,0,0,0,28.42893,0,0,0,2,0,3,92.6,4.3,0,,0,0,1,0,1.098612,0,0,0,0,0,0,0,75.73199,8.187899,1.098612,3.347407,1 11,1,0,1,1,125134,1,3596.154,45.42916,0,7,1,455.9352,2.817746,0,0,0,458.753,0,0,0,8,0,3,83.2,0,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,67.10523,8.187899,1.098612,6.128512,1 11,1,0,1,2,125134,1,3596.154,46.42916,0,7,1,94.38663,0,40.40526,0,0,134.7919,0,0,0,1,1,3,83.2,0,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,67.10523,8.187899,1.098612,4.903732,1 11,1,0,1,3,125134,1,3596.154,47.42916,0,7,1,37.90524,0,0,0,1239.302,1277.207,1,0,0,2,0,3,83.2,0,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,67.10523,8.187899,1.098612,7.152431,1 11,1,0,1,1,125135,1,3596.154,37.11157,1,12,1,5.995204,4.586331,0,0,0,10.58153,0,0,0,1,0,3,68.4,8.7,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,74.57648,8.187899,1.098612,2.35911,1 11,1,0,1,2,125135,1,3596.154,38.11157,1,12,1,15.33406,14.0471,47.82585,0,0,77.20701,0,0,0,1,1,3,68.4,8.7,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,74.57648,8.187899,1.098612,4.34649,1 11,1,0,1,3,125135,1,3596.154,39.11157,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,68.4,8.7,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,74.57648,8.187899,1.098612,,0 2,1,100,0,1,125152,1,11468.98,55.32923,1,11,1,38.28033,11.23675,32.61484,0,0,82.13192,0,0,0,4,0,2,82.1,17.4,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,60.89297,9.347488,.6931472,4.408327,1 2,1,100,0,2,125152,1,11468.98,56.32923,1,11,1,120.4852,17.69811,0,0,0,138.1833,0,0,0,5,0,2,82.1,17.4,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,60.89297,9.347488,.6931472,4.928581,1 2,1,100,0,3,125152,1,11468.98,57.32923,1,11,1,126.5356,42.39312,52.37838,0,944.2113,1165.518,1,0,0,14,0,2,82.1,17.4,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,60.89297,9.347488,.6931472,7.060921,1 2,1,100,0,1,125153,1,11468.98,59.01711,0,7,1,20.61249,5.624264,0,0,0,26.23675,0,0,0,3,0,2,87.4,13,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,59.25336,9.347488,.6931472,3.267161,1 2,1,100,0,2,125153,1,11468.98,60.01711,0,7,1,18.86792,0,0,0,0,18.86792,0,0,0,3,0,2,87.4,13,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,59.25336,9.347488,.6931472,2.937463,1 2,1,100,0,3,125153,1,11468.98,61.01711,0,7,1,80.09828,3.759214,45.18919,0,0,129.0467,0,0,0,5,0,2,87.4,13,0,,900,900,0,0,.6931472,6.802395,1,0,0,1,0,0,59.25336,9.347488,.6931472,4.860174,1 7,1,25,0,1,125159,0,7586.229,10.00411,1,12,1,4.711425,2.915194,0,0,0,7.626619,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.48192,8.934221,1.609438,2.031645,1 7,1,25,0,2,125159,0,7586.229,11.00411,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.48192,8.934221,1.609438,,0 7,1,25,0,3,125159,0,7586.229,12.00411,1,12,1,32.92383,28.55037,0,0,0,61.4742,0,0,0,5,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.48192,8.934221,1.609438,4.118618,1 7,1,25,0,4,125159,0,7586.229,13.00411,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.48192,8.934221,1.609438,,0 7,1,25,0,5,125159,0,7586.229,14.00411,1,12,1,10.42101,0,0,0,0,10.42101,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.48192,8.934221,1.609438,2.343824,1 7,1,25,0,1,125160,0,7586.229,32.61328,0,13,1,17.66784,0,0,191.4016,0,17.66784,0,0,16,0,0,5,60,0,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,77.5937,8.934221,1.609438,2.871746,1 7,1,25,0,2,125160,0,7586.229,33.61328,0,13,1,0,0,0,0,0,0,0,0,0,0,0,5,60,0,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,77.5937,8.934221,1.609438,,0 7,1,25,0,3,125160,0,7586.229,34.61328,0,13,1,9.336609,7.174447,0,0,0,16.51106,0,0,0,2,0,5,60,0,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,77.5937,8.934221,1.609438,2.80403,1 7,1,25,0,4,125160,0,7586.229,35.61328,0,13,1,5.469462,4.010939,0,0,0,9.480401,0,0,0,1,0,5,60,0,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,77.5937,8.934221,1.609438,2.249227,1 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7,1,25,0,5,125162,0,7586.229,7.720739,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,80.76491,8.934221,1.609438,,0 7,1,25,0,1,125163,0,7586.229,8.309377,0,12,1,22.37927,3.533569,0,0,0,25.91284,0,0,0,4,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.9959,8.934221,1.609438,3.254739,1 7,1,25,0,2,125163,0,7586.229,9.309377,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.9959,8.934221,1.609438,,0 7,1,25,0,3,125163,0,7586.229,10.30938,0,12,1,22.60442,3.847666,0,0,0,26.45209,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.9959,8.934221,1.609438,3.275335,1 7,1,25,0,4,125163,0,7586.229,11.30938,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.9959,8.934221,1.609438,,0 7,1,25,0,5,125163,0,7586.229,12.30938,0,12,1,32.51355,0,0,0,0,32.51355,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.9959,8.934221,1.609438,3.481657,1 6,1,25,0,1,125164,0,7854.839,61.42642,0,13,1,15.90106,14.25206,62.77385,0,0,92.92697,0,0,0,1,1,3,85.3,30.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,77.34652,8.969012,1.098612,4.531814,1 6,1,25,0,2,125164,0,7854.839,62.42642,0,13,1,39.3531,1.859838,0,0,0,41.21294,0,0,0,1,1,3,85.3,30.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,77.34652,8.969012,1.098612,3.718752,1 6,1,25,0,3,125164,0,7854.839,63.42642,0,13,1,9.82801,0,55.45946,0,0,65.28747,0,0,0,0,1,3,85.3,30.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,77.34652,8.969012,1.098612,4.1788,1 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3,1,100,0,2,125185,0,12770.47,44.87132,0,12,1,22.64151,18.92183,42.81401,0,0,84.37736,0,0,0,2,1,6,58.9,17.4,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,0,1,0,56.85956,9.454969,1.791759,4.435299,1 3,1,100,0,3,125185,0,12770.47,45.87132,0,12,1,91.40049,6.388206,46.90909,0,0,144.6978,0,0,0,2,4,6,58.9,17.4,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,0,1,0,56.85956,9.454969,1.791759,4.974648,1 3,1,100,0,1,125186,0,12770.47,11.95072,1,12,1,2.944641,0,0,0,0,2.944641,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,78.63347,9.454969,1.791759,1.079987,1 3,1,100,0,2,125186,0,12770.47,12.95072,1,12,1,40.43127,0,11.10512,0,0,51.53639,0,0,0,3,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,78.63347,9.454969,1.791759,3.942288,1 3,1,100,0,3,125186,0,12770.47,13.95072,1,12,1,12.77641,4.078624,0,0,0,16.85504,0,0,0,2,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,78.63347,9.454969,1.791759,2.82465,1 3,1,100,0,1,125187,0,12770.47,14.44216,0,12,1,12.95642,8.36278,0,0,0,21.3192,0,0,0,2,0,6,77.9,8.7,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,3.059608,1 3,1,100,0,2,125187,0,12770.47,15.44216,0,12,1,11.32076,4.851752,0,0,0,16.17251,0,0,0,2,0,6,77.9,8.7,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,2.783313,1 3,1,100,0,3,125187,0,12770.47,16.44216,0,12,1,6.879607,6.388206,0,0,0,13.26781,0,0,0,1,0,6,77.9,8.7,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,2.585341,1 3,1,100,0,1,125188,0,12770.47,43.46064,1,12,1,27.38516,10.01178,0,0,0,37.39694,0,0,0,3,0,6,71.6,13,1,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,71.8888,9.454969,1.791759,3.621589,1 3,1,100,0,2,125188,0,12770.47,44.46064,1,12,1,24.25876,17.00809,58.59299,0,0,99.85984,0,0,0,1,1,6,71.6,13,1,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,71.8888,9.454969,1.791759,4.603767,1 3,1,100,0,3,125188,0,12770.47,45.46064,1,12,1,113.7592,0,0,0,785.9607,899.7199,1,0,0,4,0,6,71.6,13,1,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,71.8888,9.454969,1.791759,6.802083,1 3,1,100,0,1,125189,0,12770.47,17.13895,0,12,1,159.5995,8.863369,0,0,0,168.4629,0,0,0,20,0,6,71.6,21.7,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,5.126716,1 3,1,100,0,2,125189,0,12770.47,18.13895,0,12,1,81.9407,142.3989,44.86253,0,0,269.2021,0,0,0,15,1,6,71.6,21.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,5.595463,1 3,1,100,0,3,125189,0,12770.47,19.13895,0,12,1,124.8157,139.9853,37.27273,0,0,302.0737,0,0,0,11,1,6,71.6,21.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,73.87741,9.454969,1.791759,5.710671,1 11,1,0,0,1,125214,0,6866.625,54.21492,1,10,1,588.0447,447.4853,0,0,1195.518,2231.048,1,0,0,69,0,2,76.3,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,1,0,50.56934,8.834574,.6931472,7.710227,1 11,1,0,0,2,125214,0,6866.625,55.21492,1,10,1,496.6146,343.035,25.33693,0,576.097,1441.084,1,0,0,58,1,2,76.3,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,1,0,50.56934,8.834574,.6931472,7.27315,1 11,1,0,0,3,125214,0,6866.625,56.21492,1,10,1,799.8133,335.2776,28.2457,0,41.27764,1204.614,0,0,0,63,1,2,76.3,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,1,0,50.56934,8.834574,.6931472,7.093915,1 11,1,0,0,1,125215,0,6866.625,49.56879,0,9,1,17.66784,0,0,0,0,17.66784,0,0,0,2,0,2,87.5,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,0,0,70.33569,8.834574,.6931472,2.871746,1 11,1,0,0,2,125215,0,6866.625,50.56879,0,9,1,74.93262,9.347709,28.57143,0,0,112.8518,0,0,0,3,1,2,87.5,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,0,0,70.33569,8.834574,.6931472,4.726075,1 11,1,0,0,3,125215,0,6866.625,51.56879,0,9,1,18.18182,8.255528,23.11057,0,0,49.54791,0,0,0,2,1,2,87.5,13.73189,1,,0,598,0,0,.6931472,6.393591,0,0,0,0,0,0,70.33569,8.834574,.6931472,3.90294,1 11,1,0,1,1,125216,0,9106.079,6.171116,0,12,1,80.01189,26.79952,0,0,286.8055,393.6169,1,0,0,11,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,84.24345,9.116807,1.386294,5.975378,1 11,1,0,1,2,125216,0,9106.079,7.171116,0,12,1,198.6935,16.08601,0,0,0,214.7795,0,0,0,9,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,84.24345,9.116807,1.386294,5.369612,1 11,1,0,1,3,125216,0,9106.079,8.171116,0,12,1,34.68781,16.16948,0,0,0,50.85728,0,0,0,5,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,84.24345,9.116807,1.386294,3.929023,1 11,1,0,1,4,125216,0,9106.079,9.171116,0,12,1,23.86416,7.755851,0,0,0,31.62001,0,0,0,3,0,3,74.36826,13.73189,0,,0,0,1,0,1.098612,0,0,0,0,0,0,0,84.24345,9.116807,1.098612,3.45379,1 11,1,0,1,5,125216,0,9106.079,10.17112,0,12,1,153.8704,35.07783,0,0,0,188.9483,0,0,0,8,0,3,74.36826,13.73189,0,,0,0,1,0,1.098612,0,0,0,0,0,0,0,84.24345,9.116807,1.098612,5.241473,1 11,1,0,1,1,125217,0,9106.079,10.39288,0,12,1,36.28793,4.46163,0,0,0,40.74955,0,0,0,6,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.06646,9.116807,1.386294,3.707445,1 11,1,0,1,2,125217,0,9106.079,11.39288,0,12,1,18.50844,5.960806,0,0,0,24.46924,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.06646,9.116807,1.386294,3.197417,1 11,1,0,1,3,125217,0,9106.079,12.39288,0,12,1,23.78593,6.665015,0,0,0,30.45094,0,0,0,4,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.06646,9.116807,1.386294,3.416117,1 11,1,0,1,4,125217,0,9106.079,13.39288,0,12,1,15.14456,8.926113,0,0,0,24.07067,0,0,0,3,0,3,74.36826,13.73189,0,,0,0,1,0,1.098612,0,0,0,0,1,0,0,74.06646,9.116807,1.098612,3.180994,1 11,1,0,1,5,125217,0,9106.079,14.39288,0,12,1,23.55911,1.262095,0,0,0,24.8212,0,0,0,2,0,3,74.36826,13.73189,0,,0,0,1,0,1.098612,0,0,0,0,1,0,0,74.06646,9.116807,1.098612,3.211698,1 11,1,0,1,1,125218,0,9106.079,44.80219,0,10,1,197.204,6.097561,0,0,0,203.3016,0,0,0,6,0,4,80,17.4,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,69.65557,9.116807,1.386294,5.314691,1 11,1,0,1,2,125218,0,9106.079,45.80219,0,10,1,166.0316,21.09418,0,0,7141.557,7328.683,2,0,0,10,0,4,80,17.4,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,69.65557,9.116807,1.386294,8.899551,1 11,1,0,1,3,125218,0,9106.079,46.80219,0,10,1,444.9951,90.65907,29.58375,0,0,565.2379,0,0,0,12,31,4,80,17.4,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,69.65557,9.116807,1.386294,6.337246,1 11,1,0,1,1,125219,0,9106.079,33.42916,1,12,1,249.8513,130.3093,3.777514,0,0,383.9381,0,0,0,35,0,4,55.8,13,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,78.52583,9.116807,1.386294,5.950481,1 11,1,0,1,2,125219,0,9106.079,34.42916,1,12,1,381.0561,177.006,4.899292,0,1644.339,2207.3,1,0,0,9,15,4,55.8,13,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,78.52583,9.116807,1.386294,7.699525,1 11,1,0,1,3,125219,0,9106.079,35.42916,1,12,1,98.11695,149.3062,170.9613,59.46482,0,418.3846,0,0,4,6,0,4,55.8,13,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,78.52583,9.116807,1.386294,6.036401,1 11,1,0,1,4,125219,0,9106.079,36.42916,1,12,1,54.61221,145.5484,0,80.31207,1999.358,2199.518,1,0,5,6,0,3,55.8,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.52583,9.116807,1.098612,7.695993,1 11,1,0,1,5,125219,0,9106.079,37.42916,1,12,1,110.223,271.9941,257.0467,117.7955,0,639.2638,0,0,8,14,0,3,55.8,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.52583,9.116807,1.098612,6.460317,1 2,1,100,0,1,125220,0,12213.6,35.87132,0,17,1,33.57314,10.8813,0,0,0,44.45444,0,0,0,5,0,5,77.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.73786,9.410387,1.609438,3.794465,1 2,1,100,0,2,125220,0,12213.6,36.87132,0,17,1,23.54874,7.338445,0,0,0,30.88719,0,0,0,4,0,5,77.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.73786,9.410387,1.609438,3.430341,1 2,1,100,0,3,125220,0,12213.6,37.87132,0,17,1,43.81047,4.189526,37.0773,0,0,85.07731,0,0,0,2,1,5,77.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.73786,9.410387,1.609438,4.44356,1 2,1,100,0,4,125220,0,12213.6,38.87132,0,17,1,0,0,0,0,0,0,0,0,0,0,0,5,77.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.73786,9.410387,1.609438,,0 2,1,100,0,5,125220,0,12213.6,39.87132,0,17,1,0,0,0,0,0,0,0,0,0,0,0,5,77.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.73786,9.410387,1.609438,,0 2,1,100,0,1,125221,0,12213.6,10.94045,0,16,1,42.86571,.5395684,0,0,0,43.40528,0,0,0,1,2,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,3.770581,1 2,1,100,0,2,125221,0,12213.6,11.94045,0,16,1,29.84666,8.598028,26.85104,0,0,65.29573,0,0,0,2,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,4.178926,1 2,1,100,0,3,125221,0,12213.6,12.94045,0,16,1,7.381546,8.254364,0,0,0,15.63591,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,2.74957,1 2,1,100,0,4,125221,0,12213.6,13.94045,0,16,1,195.6893,3.665283,0,0,0,199.3545,0,0,0,5,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,5.295085,1 2,1,100,0,5,125221,0,12213.6,14.94045,0,16,1,90.71096,0,0,0,0,90.71096,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,4.507678,1 2,1,100,0,1,125222,0,12213.6,33.19096,1,16,1,45.20384,15.19784,0,0,0,60.40168,0,0,0,3,0,5,71.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,80.14037,9.410387,1.609438,4.101017,1 2,1,100,0,2,125222,0,12213.6,34.19096,1,16,1,21.35816,7.174151,0,0,0,28.53231,0,0,0,3,0,5,71.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,80.14037,9.410387,1.609438,3.351037,1 2,1,100,0,3,125222,0,12213.6,35.19096,1,16,1,23.94015,4.239401,0,0,0,28.17955,0,0,0,3,0,5,71.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,80.14037,9.410387,1.609438,3.338597,1 2,1,100,0,4,125222,0,12213.6,36.19096,1,16,1,6.915629,5.693868,0,0,0,12.6095,0,0,0,1,0,5,71.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,80.14037,9.410387,1.609438,2.53445,1 2,1,100,0,5,125222,0,12213.6,37.19096,1,16,1,49.93652,2.653407,0,0,0,52.58993,0,0,0,2,0,5,71.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,80.14037,9.410387,1.609438,3.962525,1 2,1,100,0,1,125223,0,12213.6,7.750855,0,16,1,34.77218,5.635491,0,0,0,40.40767,0,0,0,3,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,3.69902,1 2,1,100,0,2,125223,0,12213.6,8.750855,0,16,1,13.96495,10.35049,0,0,0,24.31544,0,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,3.191112,1 2,1,100,0,3,125223,0,12213.6,9.750855,0,16,1,29.55112,17.95511,0,0,0,47.50623,0,0,0,1,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,3.860861,1 2,1,100,0,4,125223,0,12213.6,10.75086,0,16,1,15.3988,9.22084,0,0,0,24.61964,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,3.203545,1 2,1,100,0,5,125223,0,12213.6,11.75086,0,16,1,11.84934,0,0,0,0,11.84934,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,87.05311,9.410387,1.609438,2.472273,1 2,1,100,0,1,125224,0,12213.6,6.606434,1,16,1,31.41487,10.52158,0,0,0,41.93645,0,0,0,2,1,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,86.53913,9.410387,1.609438,3.736156,1 2,1,100,0,2,125224,0,12213.6,7.606434,1,16,1,24.26068,11.22672,0,0,0,35.4874,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,86.53913,9.410387,1.609438,3.569178,1 2,1,100,0,3,125224,0,12213.6,8.606434,1,16,1,1.995013,0,0,0,0,1.995013,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,86.53913,9.410387,1.609438,.6906503,1 2,1,100,0,4,125224,0,12213.6,9.606434,1,16,1,14.01568,0,0,0,0,14.01568,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,86.53913,9.410387,1.609438,2.640176,1 2,1,100,0,5,125224,0,12213.6,10.60643,1,16,1,13.22471,0,0,0,0,13.22471,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,86.53913,9.410387,1.609438,2.582087,1 11,1,0,1,1,125229,0,22318.24,41.1143,1,12,1,151.6786,88.81295,26.6307,0,0,267.1223,0,0,0,16,0,8,74.7,21.7,1,,0,0,0,0,2.079442,0,0,0,0,1,0,0,68.03587,10.0132,2.079442,5.587707,1 11,1,0,1,2,125229,0,22318.24,42.1143,1,12,1,225.0822,26.31435,0,0,0,251.3965,0,0,0,16,0,8,74.7,21.7,1,,0,0,0,0,2.079442,0,0,0,0,1,0,0,68.03587,10.0132,2.079442,5.527031,1 11,1,0,1,3,125229,0,22318.24,43.1143,1,12,1,185.2868,61.14713,35.91022,0,0,282.3441,0,0,0,17,0,9,74.7,21.7,1,,0,0,0,0,2.197225,0,0,0,0,1,0,0,68.03587,10.0132,2.197225,5.643126,1 11,1,0,1,1,125230,0,22318.24,17.30869,0,12,1,5.395683,6.564748,0,0,0,11.96043,0,0,0,0,0,8,80,13,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,80.52389,10.0132,2.079442,2.481604,1 11,1,0,1,2,125230,0,22318.24,18.30869,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,80,13,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,80.52389,10.0132,2.079442,,0 11,1,0,1,3,125230,0,22318.24,19.30869,0,12,1,17.45636,9.077307,0,0,0,26.53367,0,0,0,3,0,9,80,13,0,,0,0,0,0,2.197225,0,0,0,0,0,0,0,80.52389,10.0132,2.197225,3.278414,1 11,1,0,1,1,125231,0,22318.24,18.41205,0,12,1,13.48921,5.749401,0,0,0,19.23861,0,0,0,2,0,8,52.6,0,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,78.67783,10.0132,2.079442,2.956919,1 11,1,0,1,2,125231,0,22318.24,19.41205,0,12,1,49.8357,15.33406,32.04819,0,0,97.21796,0,0,0,4,0,8,52.6,0,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,78.67783,10.0132,2.079442,4.576955,1 11,1,0,1,3,125231,0,22318.24,20.41205,0,12,1,32.91771,8.304239,0,0,700.02,741.2419,1,0,0,3,0,9,52.6,0,0,,0,0,0,0,2.197225,0,0,0,0,0,0,0,78.67783,10.0132,2.197225,6.608327,1 11,1,0,1,1,125232,0,22318.24,14.19576,0,12,1,59.95204,14.92806,0,0,0,74.8801,0,0,0,6,0,8,80,4.3,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,74.01256,10.0132,2.079442,4.315888,1 11,1,0,1,2,125232,0,22318.24,15.19576,0,12,1,21.90581,35.20263,0,0,0,57.10843,0,0,0,3,0,8,80,4.3,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,74.01256,10.0132,2.079442,4.044952,1 11,1,0,1,3,125232,0,22318.24,16.19576,0,12,1,19.95012,16.7581,0,0,0,36.70823,0,0,0,3,0,9,80,4.3,0,,0,0,1,0,2.197225,0,0,0,0,1,0,0,74.01256,10.0132,2.197225,3.603001,1 11,1,0,1,1,125233,0,22318.24,16.1807,1,12,1,90.22782,44.44844,27.56595,0,0,162.2422,0,0,0,7,0,8,75.8,0,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,74.24654,10.0132,2.079442,5.08909,1 11,1,0,1,2,125233,0,22318.24,17.1807,1,12,1,19.16758,27.40964,0,0,491.6375,538.2147,1,0,0,3,0,8,75.8,0,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,74.24654,10.0132,2.079442,6.288258,1 11,1,0,1,3,125233,0,22318.24,18.1807,1,12,1,334.2444,11.47132,0,0,11.97007,357.6858,0,0,0,7,0,9,75.8,0,0,,0,0,0,0,2.197225,0,0,0,0,0,0,0,74.24654,10.0132,2.197225,5.879655,1 11,1,0,1,1,125234,0,22318.24,43.61123,0,16,1,67.14629,178.0456,55.44365,0,0,300.6355,0,0,0,8,0,8,81.1,26.1,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,77.31422,10.0132,2.079442,5.705899,1 11,1,0,1,2,125234,0,22318.24,44.61123,0,16,1,65.71741,122.6451,0,0,0,188.3625,0,0,0,8,0,8,81.1,26.1,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,77.31422,10.0132,2.079442,5.238369,1 11,1,0,1,3,125234,0,22318.24,45.61123,0,16,1,70.82294,125.1122,46.90773,0,0,242.8429,0,0,0,4,0,9,81.1,26.1,0,,0,0,0,0,2.197225,0,0,0,0,0,0,0,77.31422,10.0132,2.197225,5.492415,1 11,1,0,1,1,125235,0,22318.24,10.42026,0,12,1,26.3789,21.07314,0,0,0,47.45204,0,0,0,4,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,84.64054,10.0132,2.079442,3.85972,1 11,1,0,1,2,125235,0,22318.24,11.42026,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,84.64054,10.0132,2.079442,,0 11,1,0,1,3,125235,0,22318.24,12.42026,0,12,1,41.39651,10.69825,0,0,0,52.09476,0,0,0,4,0,9,74.36826,13.73189,0,,0,0,1,0,2.197225,0,0,0,0,0,0,0,84.64054,10.0132,2.197225,3.953064,1 11,1,0,1,1,125236,0,22318.24,11.50445,0,12,1,59.95204,63.31535,64.04076,0,0,187.3082,0,0,0,8,0,8,74.36826,13.73189,1,,0,0,1,0,2.079442,0,0,0,0,1,0,0,74.46355,10.0132,2.079442,5.232755,1 11,1,0,1,2,125236,0,22318.24,12.50445,0,12,1,23.00109,41.15005,54.45783,0,0,118.609,0,0,0,4,0,8,74.36826,13.73189,1,,0,0,1,0,2.079442,0,0,0,0,1,0,0,74.46355,10.0132,2.079442,4.775832,1 11,1,0,1,3,125236,0,22318.24,13.50445,0,12,1,68.82793,62.04489,55.70075,0,0,186.5736,0,0,0,7,0,9,74.36826,13.73189,1,,0,0,1,0,2.197225,0,0,0,0,1,0,0,74.46355,10.0132,2.197225,5.228826,1 11,1,0,1,1,125258,0,7810.794,41.70842,0,12,1,78.61987,51.86199,7.703748,0,0,138.1856,0,0,0,7,0,5,77.9,13,1,,0,232,0,0,1.609438,5.446737,0,0,0,1,0,0,63.76923,8.96339,1.609438,4.928598,1 11,1,0,1,2,125258,0,7810.794,42.70842,0,12,1,192.1611,112.0577,87.09853,0,2911.241,3302.559,3,0,0,19,1,5,77.9,13,1,,0,232,0,0,1.609438,5.446737,0,0,0,1,0,0,63.76923,8.96339,1.609438,8.102453,1 11,1,0,1,3,125258,0,7810.794,43.70842,0,12,1,663.4787,111.7344,26.26363,0,1011.516,1812.993,1,0,0,0,2,5,77.9,13,1,,0,232,0,0,1.609438,5.446737,0,0,0,1,0,0,63.76923,8.96339,1.609438,7.502734,1 11,1,0,1,1,125259,0,7810.794,31.87406,1,10,1,101.7252,56.00238,0,0,0,157.7275,0,0,0,12,0,5,80,17.4,0,,0,232,0,0,1.609438,5.446737,0,0,0,0,0,0,76.00816,8.96339,1.609438,5.060869,1 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11,1,0,1,5,125426,0,9542.002,23.99726,0,12,1,23.2755,6.982649,0,0,0,30.25815,0,0,0,1,0,1,69.5,4.3,0,,0,0,0,0,0,0,0,0,0,0,0,0,78.01653,9.163564,0,3.409765,1 4,1,100,1,1,125432,0,2454.715,46.06434,0,17,1,108.813,112.3921,0,35.97122,0,221.205,0,0,5,11,1,4,38.9,8.7,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84441,7.806173,1.386294,5.39909,1 4,1,100,1,2,125432,0,2454.715,47.06434,0,17,1,370.4819,148.0449,23.46659,690.1698,2736.95,3278.943,3,0,48,44,1,4,38.9,8.7,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84441,7.806173,1.386294,8.095277,1 4,1,100,1,3,125432,0,2454.715,48.06434,0,17,1,168.0798,57.54115,0,139.6509,0,225.6209,0,0,7,7,0,4,38.9,8.7,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.84441,7.806173,1.386294,5.418856,1 4,1,100,1,1,125433,0,2454.715,17.00479,1,12,1,38.3693,.8693045,0,0,0,39.23861,0,0,0,1,0,4,76.8,13,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,64.73034,7.806173,1.386294,3.669661,1 4,1,100,1,2,125433,0,2454.715,18.00479,1,12,1,18.15444,2.102957,33.7678,0,0,54.02519,0,0,0,1,1,4,76.8,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,64.73034,7.806173,1.386294,3.98945,1 4,1,100,1,3,125433,0,2454.715,19.00479,1,12,1,22.44389,0,0,0,0,22.44389,0,0,0,3,0,4,76.8,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,64.73034,7.806173,1.386294,3.111018,1 4,1,100,1,1,125434,0,2454.715,9.601643,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,1,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,76.38216,7.806173,1.386294,,0 4,1,100,1,2,125434,0,2454.715,10.60164,1,12,1,52.35487,0,0,0,0,52.35487,0,0,0,3,1,4,74.36826,13.73189,1,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,76.38216,7.806173,1.386294,3.958045,1 4,1,100,1,3,125434,0,2454.715,11.60164,1,12,1,18.95262,0,0,0,0,18.95262,0,0,0,1,0,4,74.36826,13.73189,1,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,76.38216,7.806173,1.386294,2.941942,1 4,1,100,1,1,125435,0,2454.715,47.8987,1,12,1,53.95683,68.91487,0,0,0,122.8717,0,0,0,8,0,4,42.1,39.1,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,54.82101,7.806173,1.386294,4.811141,1 4,1,100,1,2,125435,0,2454.715,48.8987,1,12,1,60.78861,79.4414,30.33406,0,325.3012,495.8653,1,0,0,6,1,4,42.1,39.1,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,54.82101,7.806173,1.386294,6.206304,1 4,1,100,1,3,125435,0,2454.715,49.8987,1,12,1,22.44389,2.009975,0,0,0,24.45387,0,0,0,3,0,4,42.1,39.1,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,54.82101,7.806173,1.386294,3.196788,1 6,1,25,1,1,125454,0,1093.672,60.81862,1,6,1,32.71862,20.09518,0,0,0,52.8138,0,0,0,3,0,1,75.8,21.7,1,,226,226,0,0,0,5.420535,0,3.258096,6.806829,1,0,0,62.52559,6.99821,0,3.966773,1 6,1,25,1,2,125454,0,1093.672,61.81862,1,6,1,26.12956,15.77572,0,0,0,41.90528,0,0,0,3,1,1,75.8,21.7,1,,226,226,0,0,0,5.420535,0,3.258096,6.806829,1,0,0,62.52559,6.99821,0,3.735412,1 6,1,25,1,3,125454,0,1093.672,62.81862,1,6,1,16.84836,6.868186,0,0,0,23.71655,0,0,0,3,0,1,75.8,21.7,1,,226,226,0,0,0,5.420535,0,3.258096,6.806829,1,0,0,62.52559,6.99821,0,3.166173,1 6,1,25,1,1,125455,0,3467.742,21.80698,1,12,1,27.95955,0,41.047,0,0,69.00655,0,0,0,2,0,1,68.4,8.7,0,,530,530,0,0,0,6.272877,0,3.258096,7.659172,0,0,0,79.25372,8.151547,0,4.234201,1 6,1,25,1,2,125455,0,3467.742,22.80698,1,12,1,26.12956,9.450191,13.60915,0,0,49.1889,0,0,0,1,1,1,68.4,8.7,0,,530,530,0,0,0,6.272877,0,3.258096,7.659172,0,0,0,79.25372,8.151547,0,3.895668,1 6,1,25,1,3,125455,0,3467.742,23.80698,1,12,1,20.19326,0,0,0,0,20.19326,0,0,0,1,0,1,68.4,8.7,0,,530,530,0,0,0,6.272877,0,3.258096,7.659172,0,0,0,79.25372,8.151547,0,3.005349,1 3,1,100,0,1,125484,0,4986.759,33.18549,0,11,1,18.8457,2.915194,0,0,0,21.76089,0,0,0,3,0,3,77.9,0,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,67.92956,8.514742,1.098612,3.080115,1 3,1,100,0,2,125484,0,4986.759,34.18549,0,11,1,21.56334,0,0,0,0,21.56334,0,0,0,1,0,3,77.9,0,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,67.92956,8.514742,1.098612,3.070995,1 3,1,100,0,3,125484,0,4986.759,35.18549,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,77.9,0,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,67.92956,8.514742,1.098612,,0 3,1,100,0,1,125485,0,4986.759,6.672143,0,12,1,12.36749,28.97527,9.234393,0,360.424,411.0012,1,0,0,3,0,3,74.36826,13.73189,0,,1000,1000,1,0,1.098612,6.907755,1,0,0,1,0,0,73.90472,8.514742,1.098612,6.018596,1 3,1,100,0,2,125485,0,4986.759,7.672143,0,12,1,138.7062,37.19677,0,0,0,175.903,0,0,0,17,0,3,74.36826,13.73189,0,,1000,1000,1,0,1.098612,6.907755,1,0,0,1,0,0,73.90472,8.514742,1.098612,5.169932,1 3,1,100,0,3,125485,0,4986.759,8.672142,0,12,1,80.09828,7.149877,0,0,0,87.24815,0,0,0,21,1,3,74.36826,13.73189,0,,1000,1000,1,0,1.098612,6.907755,1,0,0,1,0,0,73.90472,8.514742,1.098612,4.468756,1 3,1,100,0,1,125486,0,4986.759,29.27858,1,12,1,19.43463,5.859835,0,0,0,25.29446,0,0,0,5,0,3,44.2,39.1,1,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,68.93575,8.514742,1.098612,3.230586,1 3,1,100,0,2,125486,0,4986.759,30.27858,1,12,1,62.53369,97.96765,53.36927,0,2370.226,2584.097,2,0,0,5,0,3,44.2,39.1,1,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,68.93575,8.514742,1.098612,7.857131,1 3,1,100,0,3,125486,0,4986.759,31.27858,1,12,1,33.90664,87.46928,0,0,0,121.3759,0,0,0,3,0,3,44.2,39.1,1,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,68.93575,8.514742,1.098612,4.798892,1 11,1,0,0,1,125501,1,9055.211,39.57837,1,12,1,348.0216,43.64508,0,0,0,391.6667,0,0,0,10,0,8,45,13.73189,1,,0,0,0,0,2.079442,0,0,0,0,0,0,1,66.24201,9.111206,2.079442,5.970411,1 11,1,0,0,2,125501,1,9055.211,40.57837,1,12,1,59.96714,7.721797,0,0,0,67.68893,0,0,0,1,0,8,45,13.73189,1,,0,0,0,0,2.079442,0,0,0,0,0,0,1,66.24201,9.111206,2.079442,4.214923,1 11,1,0,0,3,125501,1,9055.211,41.57837,1,12,1,3.990025,12.06983,0,0,0,16.05985,0,0,0,1,0,8,45,13.73189,1,,0,0,0,0,2.079442,0,0,0,0,0,0,1,66.24201,9.111206,2.079442,2.776322,1 11,1,0,0,4,125501,1,9055.211,42.57837,1,12,1,13.83126,21.96865,0,0,0,35.79991,0,0,0,2,0,8,45,13.73189,1,,0,0,0,0,2.079442,0,0,0,0,0,0,1,66.24201,9.111206,2.079442,3.577945,1 11,1,0,0,5,125501,1,9055.211,43.57837,1,12,1,26.02624,20.94795,3.537876,0,0,50.51206,0,0,0,1,0,8,45,13.73189,1,,0,0,0,0,2.079442,0,0,0,0,0,0,1,66.24201,9.111206,2.079442,3.922212,1 11,1,0,0,1,125502,1,9055.211,9.535934,1,12,1,64.7482,0,0,0,0,64.7482,0,0,0,2,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,78.46435,9.111206,2.079442,4.170506,1 11,1,0,0,2,125502,1,9055.211,10.53593,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,78.46435,9.111206,2.079442,,0 11,1,0,0,3,125502,1,9055.211,11.53593,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,78.46435,9.111206,2.079442,,0 11,1,0,0,4,125502,1,9055.211,12.53593,1,12,1,56.93868,0,0,0,0,56.93868,0,0,0,3,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,78.46435,9.111206,2.079442,4.041975,1 11,1,0,0,5,125502,1,9055.211,13.53593,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,78.46435,9.111206,2.079442,,0 11,1,0,0,1,125503,1,9055.211,4.971937,0,12,1,35.3717,1.858513,0,0,0,37.23022,0,0,0,1,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.5467,9.111206,2.079442,3.617121,1 11,1,0,0,2,125503,1,9055.211,5.971937,0,12,1,0,2.135816,0,0,0,2.135816,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.5467,9.111206,2.079442,.7588488,1 11,1,0,0,3,125503,1,9055.211,6.971937,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.5467,9.111206,2.079442,,0 11,1,0,0,4,125503,1,9055.211,7.971937,0,12,1,45.50484,0,0,0,0,45.50484,0,0,0,2,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.5467,9.111206,2.079442,3.817819,1 11,1,0,0,5,125503,1,9055.211,8.971937,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.5467,9.111206,2.079442,,0 11,1,0,0,1,125504,1,9055.211,16.19986,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,63.8,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.33212,9.111206,2.079442,,0 11,1,0,0,2,125504,1,9055.211,17.19986,0,12,1,146.2486,0,0,0,0,146.2486,0,0,0,7,0,8,63.8,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,0,0,0,75.33212,9.111206,2.079442,4.985308,1 11,1,0,0,3,125504,1,9055.211,18.19986,0,12,1,52.36908,0,0,0,0,52.36908,0,0,0,5,0,8,63.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,75.33212,9.111206,2.079442,3.958316,1 11,1,0,0,4,125504,1,9055.211,19.19986,0,12,1,0,5.928999,0,0,0,5.928999,0,0,0,0,0,8,63.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,75.33212,9.111206,2.079442,1.779855,1 11,1,0,0,5,125504,1,9055.211,20.19986,0,12,1,45.2391,0,0,0,0,45.2391,0,0,0,1,0,8,63.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,0,0,0,75.33212,9.111206,2.079442,3.811962,1 11,1,0,0,1,125505,1,9055.211,7.827516,0,12,1,25.77938,1.396883,0,0,0,27.17626,0,0,0,2,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,79.24362,9.111206,2.079442,3.302344,1 11,1,0,0,2,125505,1,9055.211,8.827516,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,79.24362,9.111206,2.079442,,0 11,1,0,0,3,125505,1,9055.211,9.827516,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,79.24362,9.111206,2.079442,,0 11,1,0,0,4,125505,1,9055.211,10.82752,0,12,1,5.532504,0,0,0,0,5.532504,0,0,0,1,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,79.24362,9.111206,2.079442,1.71064,1 11,1,0,0,5,125505,1,9055.211,11.82752,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,0,0,0,1,0,0,79.24362,9.111206,2.079442,,0 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11,1,0,0,2,125507,1,9055.211,38.17728,0,3,1,8.214677,4.791895,0,0,699.9124,712.9189,1,0,0,1,0,8,73.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,1,0,0,62.82508,9.111206,2.079442,6.569368,1 11,1,0,0,3,125507,1,9055.211,39.17728,0,3,1,0,0,0,0,0,0,0,0,0,0,0,8,73.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,1,0,0,62.82508,9.111206,2.079442,,0 11,1,0,0,4,125507,1,9055.211,40.17728,0,3,1,28.5846,5.048409,0,0,0,33.63301,0,0,0,6,0,8,73.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,1,0,0,62.82508,9.111206,2.079442,3.515508,1 11,1,0,0,5,125507,1,9055.211,41.17728,0,3,1,0,0,0,0,0,0,0,0,0,0,0,8,73.8,13.73189,0,,0,0,0,0,2.079442,0,0,0,0,1,0,0,62.82508,9.111206,2.079442,,0 11,1,0,0,1,125520,1,9542.002,13.06776,1,8,1,23.79536,8.090423,0,0,0,31.88578,0,0,0,3,0,3,74.36826,13.73189,0,,0,0,1,1,1.098612,0,0,0,0,1,0,0,78.41846,9.163564,1.098612,3.46216,1 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5,1,25,0,4,125535,1,4560.794,57.19576,1,9,1,1629.986,87.47349,369.29,0,10612.44,12699.19,6,0,0,48,99,1,81.3,13.73189,0,,305,305,0,0,0,5.720312,0,3.258096,7.106606,0,1,0,62.11581,8.425471,0,9.449294,1 5,1,25,0,5,125535,1,4560.794,58.19576,1,9,.2684931,1913.957,74.28693,70.22429,0,445.6665,2504.135,1,0,0,8,78,1,81.3,13.73189,0,,305,305,0,0,0,5.720312,0,3.258096,7.106606,0,1,0,62.11581,8.425471,0,7.825698,1 3,1,100,1,1,125558,0,10174.32,14.47775,1,12,1,34.50327,47.81678,0,0,0,82.32005,0,0,0,5,0,5,40,4.3,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,73.93777,9.22772,1.609438,4.410614,1 3,1,100,1,2,125558,0,10174.32,15.47775,1,12,1,53.89222,16.84812,0,0,0,70.74034,0,0,0,8,0,5,40,4.3,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,73.93777,9.22772,1.609438,4.259016,1 3,1,100,1,3,125558,0,10174.32,16.47775,1,12,1,96.25867,20.9663,0,0,0,117.225,0,0,0,14,0,5,40,4.3,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,73.93777,9.22772,1.609438,4.764095,1 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9,1,50,1,3,125570,0,10021.71,44.07529,0,10,1,90.68385,1.73439,0,14.8662,0,92.41824,0,0,3,7,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,69.59772,9.212609,1.386294,4.526324,1 2,1,100,1,1,125574,0,5724.566,27.76728,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,77.9,0,0,,359,359,0,0,1.098612,5.883322,1,0,0,1,0,0,72.43401,8.652697,1.098612,,0 2,1,100,1,2,125574,0,5724.566,28.76728,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,77.9,0,0,,359,359,0,0,1.098612,5.883322,1,0,0,1,0,0,72.43401,8.652697,1.098612,,0 2,1,100,1,3,125574,0,5724.566,29.76728,1,13,1,10.90188,0,0,0,0,10.90188,0,0,0,1,0,3,77.9,0,0,,359,359,0,0,1.098612,5.883322,1,0,0,1,0,0,72.43401,8.652697,1.098612,2.388936,1 2,1,100,1,1,125575,0,5724.566,8.008214,0,13,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,359,359,1,0,1.098612,5.883322,1,0,0,0,0,0,82.44528,8.652697,1.098612,,0 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2,1,100,1,4,125590,0,18062.99,20.12252,1,12,1,36.71409,2.225792,0,0,0,38.93988,0,0,0,3,0,5,76.8,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,68.8233,9.801676,1.609438,3.662019,1 2,1,100,1,5,125590,0,18062.99,21.12252,1,12,1,30.71098,6.327303,29.69289,0,0,66.73117,0,0,0,2,1,5,76.8,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,68.8233,9.801676,1.609438,4.200672,1 2,1,100,1,1,125591,0,18062.99,13.03765,1,12,1,83.28376,3.450327,398.5842,0,1679.494,2164.813,1,0,0,1,2,6,74.36826,13.73189,1,,1000,1000,1,1,1.791759,6.907755,1,0,0,0,1,0,66.11668,9.801676,1.791759,7.680089,1 2,1,100,1,2,125591,0,18062.99,14.03765,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,1,,1000,1000,1,1,1.791759,6.907755,1,0,0,0,1,0,66.11668,9.801676,1.791759,,0 2,1,100,1,3,125591,0,18062.99,15.03765,1,12,1,3.468781,0,0,0,0,3.468781,0,0,0,1,0,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,1,0,66.11668,9.801676,1.609438,1.243803,1 2,1,100,1,4,125591,0,18062.99,16.03765,1,12,1,39.23818,5.598898,0,0,0,44.83708,0,0,0,3,0,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,1,0,66.11668,9.801676,1.609438,3.803035,1 2,1,100,1,5,125591,0,18062.99,17.03765,1,12,1,31.55238,0,30.33656,0,0,61.88894,0,0,0,1,1,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,1,0,66.11668,9.801676,1.609438,4.125341,1 11,1,0,1,1,125613,0,1388.958,20.10404,0,9,1,20.5235,36.35931,0,0,0,56.88281,0,0,0,2,0,1,46.3,17.4,1,,0,0,0,0,0,0,0,0,0,1,0,0,68.92193,7.237029,0,4.040993,1 11,1,0,1,2,125613,0,1388.958,21.10404,0,9,1,34.38214,0,0,0,0,34.38214,0,0,0,1,0,1,46.3,17.4,1,,0,0,0,0,0,0,0,0,0,1,0,0,68.92193,7.237029,0,3.537537,1 11,1,0,1,3,125613,0,1388.958,22.10404,0,9,1,37.41328,5.649158,0,0,0,43.06244,0,0,0,3,0,1,46.3,17.4,1,,0,0,0,0,0,0,0,0,0,1,0,0,68.92193,7.237029,0,3.762651,1 11,1,0,1,4,125613,0,1388.958,23.10404,0,9,1,0,0,0,0,0,0,0,0,0,0,0,1,46.3,17.4,1,,0,0,0,0,0,0,0,0,0,1,0,0,68.92193,7.237029,0,,0 11,1,0,1,5,125613,0,1388.958,24.10404,0,9,1,21.03492,0,0,0,0,21.03492,0,0,0,1,0,1,46.3,17.4,1,,0,0,0,0,0,0,0,0,0,1,0,0,68.92193,7.237029,0,3.046184,1 11,1,0,1,1,125614,0,3273.573,57.48391,1,8,1,0,45.87745,0,26.76978,0,45.87745,0,0,3,0,0,1,83.2,13,0,,0,0,0,0,0,0,0,0,0,0,1,0,53.37024,8.093943,0,3.825974,1 11,1,0,1,2,125614,0,3273.573,58.48391,1,8,1,0,32.69461,0,32.66195,0,32.69461,0,0,4,0,0,1,83.2,13,0,,0,0,0,0,0,0,0,0,0,0,1,0,53.37024,8.093943,0,3.48721,1 11,1,0,1,3,125614,0,3273.573,59.48391,1,8,1,0,33.05748,0,22.29931,0,33.05748,0,0,3,0,0,1,83.2,13,0,,0,0,0,0,0,0,0,0,0,0,1,0,53.37024,8.093943,0,3.498248,1 11,1,0,1,4,125614,0,3273.573,60.48391,1,8,1,0,35.09867,0,22.94631,0,35.09867,0,0,3,0,0,1,83.2,13,0,,0,0,0,0,0,0,0,0,0,0,1,0,53.37024,8.093943,0,3.558163,1 11,1,0,1,5,125614,0,3273.573,61.48391,1,8,1,0,35.12411,0,33.65587,0,35.12411,0,0,4,0,0,1,83.2,13,0,,0,0,0,0,0,0,0,0,0,0,1,0,53.37024,8.093943,0,3.558888,1 6,1,25,1,1,125617,0,9542.002,6.817248,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,72.63244,9.163564,1.791759,,0 6,1,25,1,2,125617,0,9542.002,7.817248,0,12,1,2.695418,0,0,0,0,2.695418,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,72.63244,9.163564,1.791759,.9915532,1 6,1,25,1,3,125617,0,9542.002,8.817248,0,12,1,67.66585,0,4.570024,0,0,72.23587,0,0,0,3,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,72.63244,9.163564,1.791759,4.279937,1 6,1,25,1,4,125617,0,9542.002,9.817248,0,12,1,24.38469,0,0,0,782.0739,806.4585,1,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,72.63244,9.163564,1.791759,6.692652,1 6,1,25,1,5,125617,0,9542.002,10.81725,0,12,1,236.9737,0,0,0,0,236.9737,0,0,0,4,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,72.63244,9.163564,1.791759,5.467949,1 6,1,25,1,1,125618,0,9542.002,3.693361,1,12,1,45.78917,0,27.07892,0,0,72.86808,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.258096,8.294049,1,0,0,68.68684,9.163564,1.791759,4.288651,1 6,1,25,1,2,125618,0,9542.002,4.693361,1,12,1,2.695418,0,0,0,0,2.695418,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.258096,8.294049,1,0,0,68.68684,9.163564,1.791759,.9915532,1 6,1,25,1,3,125618,0,9542.002,5.693361,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.258096,8.294049,1,0,0,68.68684,9.163564,1.791759,,0 6,1,25,1,4,125618,0,9542.002,6.693361,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.258096,8.294049,1,0,0,68.68684,9.163564,1.791759,,0 6,1,25,1,5,125618,0,9542.002,7.693361,1,12,1,10.42101,0,0,0,0,10.42101,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.258096,8.294049,1,0,0,68.68684,9.163564,1.791759,2.343824,1 6,1,25,1,1,125619,0,9542.002,4.807666,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,69.20081,9.163564,1.791759,,0 6,1,25,1,2,125619,0,9542.002,5.807666,0,12,1,2.695418,0,0,0,0,2.695418,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,69.20081,9.163564,1.791759,.9915532,1 6,1,25,1,3,125619,0,9542.002,6.807666,0,12,1,14.74201,0,23.48894,0,0,38.23096,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,69.20081,9.163564,1.791759,3.643646,1 6,1,25,1,4,125619,0,9542.002,7.807666,0,12,1,60.28715,0,14.28897,0,0,74.57612,0,0,0,3,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,69.20081,9.163564,1.791759,4.311821,1 6,1,25,1,5,125619,0,9542.002,8.807666,0,12,1,10.42101,0,20.90871,0,0,31.32972,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,69.20081,9.163564,1.791759,3.444567,1 6,1,25,1,1,125620,0,9542.002,35.6961,0,10,1,0,0,33.23322,0,0,33.23322,0,0,0,0,0,6,58.9,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,67.52351,9.163564,1.791759,3.50355,1 6,1,25,1,2,125620,0,9542.002,36.6961,0,10,1,140.9704,7.385445,0,0,0,148.3558,0,0,0,3,0,6,58.9,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,67.52351,9.163564,1.791759,4.999613,1 6,1,25,1,3,125620,0,9542.002,37.6961,0,10,1,35.74939,0,0,0,0,35.74939,0,0,0,1,0,6,58.9,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,67.52351,9.163564,1.791759,3.576533,1 6,1,25,1,4,125620,0,9542.002,38.6961,0,10,1,31.9052,0,0,0,675.6426,707.5479,1,0,0,0,3,6,58.9,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,67.52351,9.163564,1.791759,6.561805,1 6,1,25,1,5,125620,0,9542.002,39.6961,0,10,1,0,0,0,0,0,0,0,0,0,0,0,6,58.9,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,67.52351,9.163564,1.791759,,0 6,1,25,1,1,125621,0,9542.002,26.33539,1,12,1,0,3.239105,0,0,0,3.239105,0,0,0,0,0,6,49.5,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,71.57712,9.163564,1.791759,1.175297,1 6,1,25,1,2,125621,0,9542.002,27.33539,1,12,1,77.35849,5.175202,0,0,0,82.53369,0,0,0,4,9,6,49.5,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,71.57712,9.163564,1.791759,4.413207,1 6,1,25,1,3,125621,0,9542.002,28.33539,1,12,1,78.50123,1.719902,0,0,1031.071,1111.292,1,0,0,2,0,6,49.5,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,71.57712,9.163564,1.791759,7.013279,1 6,1,25,1,4,125621,0,9542.002,29.33539,1,12,1,48.76937,0,0,0,0,48.76937,0,0,0,2,2,6,49.5,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,71.57712,9.163564,1.791759,3.887102,1 6,1,25,1,5,125621,0,9542.002,30.33539,1,12,1,10.42101,0,0,0,0,10.42101,0,0,0,0,0,6,49.5,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.258096,8.294049,1,0,0,71.57712,9.163564,1.791759,2.343824,1 2,1,100,0,1,125635,0,6916.232,46.73785,1,9,1,62.42638,12.60306,29.44641,0,0,104.4759,0,0,0,2,2,3,73.8,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,1,0,0,67.80335,8.841771,1.098612,4.648956,1 2,1,100,0,2,125635,0,6916.232,47.73785,1,9,1,8.625337,2.96496,0,0,0,11.5903,0,0,0,1,0,3,73.8,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,1,0,0,67.80335,8.841771,1.098612,2.450168,1 2,1,100,0,3,125635,0,6916.232,48.73785,1,9,1,8.845209,6.486486,0,0,0,15.3317,0,0,0,1,0,3,73.8,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,1,0,0,67.80335,8.841771,1.098612,2.729922,1 2,1,100,0,1,125636,0,6916.232,10.32717,0,9,1,1.766784,15.22379,0,0,0,16.99058,0,0,0,0,0,3,74.36826,13.73189,0,,300,300,1,0,1.098612,5.703783,1,0,0,1,0,0,75.82542,8.841771,1.098612,2.832659,1 2,1,100,0,2,125636,0,6916.232,11.32717,0,9,1,54.44744,14.76011,0,0,0,69.20755,0,0,0,5,0,3,74.36826,13.73189,0,,300,300,1,0,1.098612,5.703783,1,0,0,1,0,0,75.82542,8.841771,1.098612,4.23711,1 2,1,100,0,3,125636,0,6916.232,12.32717,0,9,1,4.422605,4.29484,0,0,0,8.717444,0,0,0,1,0,3,74.36826,13.73189,0,,300,300,1,0,1.098612,5.703783,1,0,0,1,0,0,75.82542,8.841771,1.098612,2.165326,1 2,1,100,0,1,125637,0,6916.232,53.96851,0,11,1,141.9317,27.04947,0,0,0,168.9812,0,0,0,6,3,3,85,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,0,0,0,72.67507,8.841771,1.098612,5.129787,1 2,1,100,0,2,125637,0,6916.232,54.96851,0,11,1,7.54717,0,0,0,0,7.54717,0,0,0,1,0,3,85,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,0,0,0,72.67507,8.841771,1.098612,2.021173,1 2,1,100,0,3,125637,0,6916.232,55.96851,0,11,1,7.371007,11.08108,0,0,0,18.45209,0,0,0,3,0,3,85,13.73189,0,,300,300,0,0,1.098612,5.703783,1,0,0,0,0,0,72.67507,8.841771,1.098612,2.915178,1 11,1,0,1,1,125658,0,9201.975,14.49144,1,12,1,32.97998,28.15077,0,0,0,61.13074,0,0,0,4,0,6,80,4.3,0,,0,0,1,1,1.791759,0,0,0,0,0,0,0,75.07128,9.127282,1.791759,4.113015,1 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11,1,0,0,4,125676,0,14410.96,58.15948,0,15,1,104.1955,0,0,0,0,104.1955,0,0,0,2,0,3,91.3,13.73189,0,,0,492,0,0,1.098612,6.198479,0,0,0,1,0,0,70.76144,9.575813,1.098612,4.646269,1 11,1,0,0,5,125676,0,14410.96,59.15948,0,15,1,22.85231,0,61.91282,0,912.8227,997.5878,1,0,0,2,0,3,91.3,13.73189,0,,0,492,0,0,1.098612,6.198479,0,0,0,1,0,0,70.76144,9.575813,1.098612,6.90534,1 11,1,0,0,1,125690,0,11982.63,13.87269,1,16,1,40.76739,10.47962,0,0,0,51.247,0,0,0,6,0,3,74.36826,13.73189,0,,0,288,1,1,1.098612,5.662961,0,0,0,0,0,0,86.44971,9.391296,1.098612,3.936657,1 11,1,0,0,2,125690,0,11982.63,14.87269,1,16,1,29.02519,13.5816,0,0,0,42.60679,0,0,0,4,0,3,74.36826,13.73189,0,,0,288,1,1,1.098612,5.662961,0,0,0,0,0,0,86.44971,9.391296,1.098612,3.752014,1 11,1,0,0,3,125690,0,11982.63,15.87269,1,16,1,39.4015,22.19451,0,0,0,61.59601,0,0,0,5,1,3,74.36826,13.73189,0,,0,288,1,1,1.098612,5.662961,0,0,0,0,0,0,86.44971,9.391296,1.098612,4.120597,1 11,1,0,0,4,125690,0,11982.63,16.87269,1,16,1,5.532504,28.13278,0,0,0,33.66528,0,0,0,1,0,3,74.36826,13.73189,0,,0,288,1,1,1.098612,5.662961,0,0,0,0,0,0,86.44971,9.391296,1.098612,3.516467,1 11,1,0,0,5,125690,0,11982.63,17.87269,1,16,1,28.98857,24.13457,24.96826,0,0,78.09141,0,0,0,2,1,3,74.36826,13.73189,0,,0,288,1,1,1.098612,5.662961,0,0,0,0,0,0,86.44971,9.391296,1.098612,4.35788,1 11,1,0,0,1,125691,0,11982.63,49.93292,1,16,1,121.7026,14.65827,32.3741,0,0,168.735,0,0,0,4,1,3,92.5,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,79.07426,9.391296,1.098612,5.12833,1 11,1,0,0,2,125691,0,11982.63,50.93292,1,16,1,31.76342,20.81051,17.25082,0,0,69.82475,0,0,0,4,1,3,92.5,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,79.07426,9.391296,1.098612,4.245988,1 11,1,0,0,3,125691,0,11982.63,51.93292,1,16,1,44.13965,12.74314,32.53865,0,0,89.42145,0,0,0,1,1,3,92.5,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,79.07426,9.391296,1.098612,4.493361,1 11,1,0,0,4,125691,0,11982.63,52.93292,1,16,1,8.759797,6.325496,0,0,0,15.08529,0,0,0,1,0,3,92.5,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,79.07426,9.391296,1.098612,2.71372,1 11,1,0,0,5,125691,0,11982.63,53.93292,1,16,1,38.51037,27.10538,32.16251,0,0,97.77825,0,0,0,2,1,3,92.5,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,79.07426,9.391296,1.098612,4.582702,1 11,1,0,0,1,125692,0,11982.63,54.93224,0,16,1,176.8585,98.89688,64.34652,0,0,340.1019,0,0,0,10,1,3,90,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,71.24387,9.391296,1.098612,5.829246,1 11,1,0,0,2,125692,0,11982.63,55.93224,0,16,1,73.38445,85.47098,19.49617,0,0,178.3516,0,0,0,7,2,3,90,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,71.24387,9.391296,1.098612,5.183757,1 11,1,0,0,3,125692,0,11982.63,56.93224,0,16,1,56.85786,81.89027,33.78554,0,0,172.5337,0,0,0,5,2,3,90,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,71.24387,9.391296,1.098612,5.150592,1 11,1,0,0,4,125692,0,11982.63,57.93224,0,16,1,39.18856,88.06824,23.6284,0,0,150.8852,0,0,0,4,2,3,90,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,71.24387,9.391296,1.098612,5.01652,1 11,1,0,0,5,125692,0,11982.63,58.93224,0,16,1,75.43377,56.97419,22.99619,0,0,155.4041,0,0,0,4,2,3,90,13.73189,0,,0,288,0,0,1.098612,5.662961,0,0,0,0,0,0,71.24387,9.391296,1.098612,5.046029,1 11,1,0,0,1,125782,0,7207.816,7.958932,1,12,1,6.6345,4.191797,0,0,0,10.8263,0,0,0,1,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,1,0,0,76.96274,8.88306,1.609438,2.381978,1 11,1,0,0,2,125782,0,7207.816,8.958932,1,12,1,5.506608,0,0,0,0,5.506608,0,0,0,1,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,1,0,0,76.96274,8.88306,1.609438,1.705949,1 11,1,0,0,3,125782,0,7207.816,9.958932,1,12,1,5.524862,0,0,0,0,5.524862,0,0,0,1,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,1,0,0,76.96274,8.88306,1.609438,1.709258,1 11,1,0,0,1,125783,0,7207.816,27.72895,1,12,1,30.75995,43.27503,0,0,0,74.03498,0,0,0,4,0,5,70,13.73189,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,71.7831,8.88306,1.609438,4.304538,1 11,1,0,0,2,125783,0,7207.816,28.72895,1,12,1,26.98238,34.0859,0,0,0,61.06828,0,0,0,4,1,5,70,13.73189,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,71.7831,8.88306,1.609438,4.111993,1 11,1,0,0,3,125783,0,7207.816,29.72895,1,12,1,43.69664,22.25013,17.96585,0,0,83.91261,0,0,0,4,1,5,70,13.73189,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,71.7831,8.88306,1.609438,4.429776,1 11,1,0,0,1,125784,0,7207.816,2.38193,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,1,0,0,73.53111,8.88306,1.609438,,0 11,1,0,0,2,125784,0,7207.816,3.38193,1,12,1,25.3304,9.388766,0,0,0,34.71916,0,0,0,4,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,1,0,0,73.53111,8.88306,1.609438,3.547292,1 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11,1,0,0,2,125786,0,7207.816,34.67009,0,12,1,16.51982,4.157489,20.14317,0,0,40.82048,0,0,0,2,1,5,92.5,13.73189,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,71.81962,8.88306,1.609438,3.709184,1 11,1,0,0,3,125786,0,7207.816,35.67009,0,12,1,27.12205,7.332998,0,0,0,34.45505,0,0,0,4,1,5,92.5,13.73189,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,71.81962,8.88306,1.609438,3.539655,1 8,1,50,0,1,125798,0,8419.976,3.496235,0,12,1,18.58513,5.785372,0,0,0,24.3705,0,0,0,3,0,4,74.36826,13.73189,0,,599,599,1,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,75.69691,9.038481,1.386294,3.193374,1 8,1,50,0,2,125798,0,8419.976,4.496235,0,12,1,32.85871,8.652793,0,0,0,41.5115,0,0,0,6,0,4,74.36826,13.73189,0,,599,599,1,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,75.69691,9.038481,1.386294,3.725971,1 8,1,50,0,3,125798,0,8419.976,5.496235,0,12,1,56.53366,2.493766,0,0,0,59.02743,0,0,0,4,0,4,74.36826,13.73189,0,,599,599,1,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,75.69691,9.038481,1.386294,4.078002,1 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8,1,50,0,3,125799,0,8419.976,29.74812,1,12,1,8.977556,5.21197,35.41147,0,1465.092,1514.693,1,0,0,0,1,4,82.5,13.73189,0,,599,599,0,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,78.26785,9.038481,1.386294,7.322968,1 8,1,50,0,4,125799,0,8419.976,30.74812,1,12,1,24.2047,23.02905,0,0,0,47.23375,0,0,0,2,0,5,82.5,13.73189,0,,599,599,0,0,1.609438,6.395262,0,3.931826,7.088409,0,0,0,78.26785,9.038481,1.609438,3.855109,1 8,1,50,0,5,125799,0,8419.976,31.74812,1,12,1,0,0,13.26703,0,0,13.26703,0,0,0,0,0,5,82.5,13.73189,0,,599,599,0,0,1.609438,6.395262,0,3.931826,7.088409,0,0,0,78.26785,9.038481,1.609438,2.585282,1 8,1,50,0,1,125800,0,8419.976,32.05202,0,14,1,0,0,0,0,0,0,0,0,0,0,0,4,87.5,13.73189,0,,599,599,0,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,77.63718,9.038481,1.386294,,0 8,1,50,0,2,125800,0,8419.976,33.05202,0,14,1,55.31216,23.84447,0,0,0,79.15662,0,0,0,5,0,4,87.5,13.73189,0,,599,599,0,0,1.386294,6.395262,0,3.931826,7.088409,0,0,0,77.63718,9.038481,1.386294,4.371428,1 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8,1,50,0,2,125801,0,8419.976,1.580424,1,12,1,37.23987,19.82475,0,0,0,57.06462,0,0,0,7,0,4,74.36826,13.73189,0,,599,599,1,1,1.386294,6.395262,0,3.931826,7.088409,0,0,0,79.42434,9.038481,1.386294,4.044184,1 8,1,50,0,3,125801,0,8419.976,2.580424,1,12,1,7.98005,0,0,0,0,7.98005,0,0,0,1,0,4,74.36826,13.73189,0,,599,599,1,1,1.386294,6.395262,0,3.931826,7.088409,0,0,0,79.42434,9.038481,1.386294,2.076945,1 8,1,50,0,4,125801,0,8419.976,3.580424,1,12,1,27.66252,0,0,0,0,27.66252,0,0,0,3,0,5,74.36826,13.73189,0,,599,599,1,1,1.609438,6.395262,0,3.931826,7.088409,0,0,0,79.42434,9.038481,1.609438,3.320078,1 8,1,50,0,5,125801,0,8419.976,4.580424,1,12,1,49.09014,0,0,0,0,49.09014,0,0,0,2,1,5,74.36826,13.73189,0,,599,599,1,1,1.609438,6.395262,0,3.931826,7.088409,0,0,0,79.42434,9.038481,1.609438,3.893658,1 8,1,50,0,1,125806,0,14051.49,28.63244,1,12,1,8.833922,18.49823,0,0,0,27.33216,0,0,0,1,0,3,83.8,13.73189,0,,718,718,0,0,1.098612,6.576469,0,3.931826,7.269617,0,0,0,79.52938,9.550555,1.098612,3.308064,1 8,1,50,0,2,125806,0,14051.49,29.63244,1,12,1,38.27493,14.47978,0,0,0,52.75472,0,0,0,4,2,3,83.8,13.73189,0,,718,718,0,0,1.098612,6.576469,0,3.931826,7.269617,0,0,0,79.52938,9.550555,1.098612,3.965653,1 8,1,50,0,3,125806,0,14051.49,30.63244,1,12,1,8.845209,22.13268,0,0,0,30.97789,0,0,0,1,0,3,83.8,13.73189,0,,718,718,0,0,1.098612,6.576469,0,3.931826,7.269617,0,0,0,79.52938,9.550555,1.098612,3.433274,1 8,1,50,0,1,125807,0,14051.49,8.531143,1,12,1,47.93875,7.832745,14.77032,0,0,70.54182,0,0,0,5,1,3,74.36826,13.73189,0,,718,718,1,1,1.098612,6.576469,0,3.931826,7.269617,1,0,0,79.35551,9.550555,1.098612,4.256206,1 8,1,50,0,2,125807,0,14051.49,9.531143,1,12,1,16.17251,2.560647,21.04582,0,0,39.77898,0,0,0,1,1,3,74.36826,13.73189,0,,718,718,1,1,1.098612,6.576469,0,3.931826,7.269617,1,0,0,79.35551,9.550555,1.098612,3.683339,1 8,1,50,0,3,125807,0,14051.49,10.53114,1,12,1,23.09582,0,11.05651,0,0,34.15233,0,0,0,2,1,3,74.36826,13.73189,0,,718,718,1,1,1.098612,6.576469,0,3.931826,7.269617,1,0,0,79.35551,9.550555,1.098612,3.530831,1 8,1,50,0,1,125808,0,14051.49,8.531143,0,12,1,63.60424,18.22144,0,0,0,81.82568,0,0,0,13,0,3,74.36826,13.73189,0,,718,718,1,0,1.098612,6.576469,0,3.931826,7.269617,0,1,0,60.19517,9.550555,1.098612,4.404591,1 8,1,50,0,2,125808,0,14051.49,9.531143,0,12,1,77.08895,21.96766,18.88949,0,0,117.9461,0,0,0,12,1,3,74.36826,13.73189,0,,718,718,1,0,1.098612,6.576469,0,3.931826,7.269617,0,1,0,60.19517,9.550555,1.098612,4.770227,1 8,1,50,0,3,125808,0,14051.49,10.53114,0,12,1,72.72727,13.09582,8.039312,0,0,93.86241,0,0,0,13,1,3,74.36826,13.73189,0,,718,718,1,0,1.098612,6.576469,0,3.931826,7.269617,0,1,0,60.19517,9.550555,1.098612,4.54183,1 6,1,25,0,1,125810,0,10455.96,8.303902,1,12,1,10.25332,0,0,0,0,10.25332,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,84.24701,9.255023,1.386294,2.327601,1 6,1,25,0,2,125810,0,10455.96,9.303902,1,12,1,5.506608,0,0,0,0,5.506608,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,84.24701,9.255023,1.386294,1.705949,1 6,1,25,0,3,125810,0,10455.96,10.3039,1,12,1,11.04972,0,0,0,166.6449,177.6946,1,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,84.24701,9.255023,1.386294,5.180067,1 6,1,25,0,1,125811,0,10455.96,38.2642,1,12,1,138.1182,6.574186,0,0,0,144.6924,0,0,0,5,0,4,83.8,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.34193,9.255023,1.386294,4.97461,1 6,1,25,0,2,125811,0,10455.96,39.2642,1,12,1,29.18502,13.51872,0,0,0,42.70374,0,0,0,3,0,4,83.8,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.34193,9.255023,1.386294,3.754287,1 6,1,25,0,3,125811,0,10455.96,40.2642,1,12,1,8.538423,7.157207,0,0,0,15.69563,0,0,0,1,0,4,83.8,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.34193,9.255023,1.386294,2.753382,1 6,1,25,0,1,125812,0,10455.96,15.65777,0,12,1,53.49819,51.538,0,0,0,105.0362,0,0,0,2,4,4,77.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,80.2968,9.255023,1.386294,4.654305,1 6,1,25,0,2,125812,0,10455.96,16.65777,0,12,1,6.60793,36.17841,0,0,0,42.78634,0,0,0,1,0,4,77.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,80.2968,9.255023,1.386294,3.756219,1 6,1,25,0,3,125812,0,10455.96,17.65777,0,12,1,24.61075,33.24962,0,0,0,57.86037,0,0,0,1,0,4,77.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,80.2968,9.255023,1.386294,4.058033,1 6,1,25,0,1,125813,0,10455.96,42.66393,0,12,1,15.07841,0,38.76357,0,0,53.84198,0,0,0,1,0,4,82.5,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.66858,9.255023,1.386294,3.986053,1 6,1,25,0,2,125813,0,10455.96,43.66393,0,12,1,89.75771,3.276432,0,0,0,93.03414,0,0,0,5,0,4,82.5,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.66858,9.255023,1.386294,4.532967,1 6,1,25,0,3,125813,0,10455.96,44.66393,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,82.5,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.66858,9.255023,1.386294,,0 6,1,25,0,1,125817,0,7893.92,27.98905,0,14,1,17.38609,11.95444,0,0,0,29.34053,0,0,0,3,0,2,80,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,0,0,77.89652,8.973975,.6931472,3.37897,1 6,1,25,0,2,125817,0,7893.92,28.98905,0,14,1,68.45564,33.51041,0,0,0,101.966,0,0,0,6,0,3,80,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,77.89652,8.973975,1.098612,4.62464,1 6,1,25,0,3,125817,0,7893.92,29.98905,0,14,1,28.91771,24.4389,0,0,0,53.35661,0,0,0,3,0,3,80,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,77.89652,8.973975,1.098612,3.976998,1 6,1,25,0,1,125818,0,7893.92,24.09309,1,12,1,80.03597,20.73741,0,0,1351.439,1452.212,2,0,0,5,2,2,86.3,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,0,0,78.52718,8.973975,.6931472,7.280843,1 6,1,25,0,2,125818,0,7893.92,25.09309,1,12,1,174.5619,19.24973,0,0,947.6287,1141.44,1,0,0,6,0,3,86.3,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.52718,8.973975,1.098612,7.040046,1 6,1,25,0,3,125818,0,7893.92,26.09309,1,12,1,48.00499,42.1197,0,0,0,90.12469,0,0,0,0,0,3,86.3,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.52718,8.973975,1.098612,4.501194,1 1,1,0,1,1,125819,0,6789.702,59.02533,1,14,1,19.03629,33.78941,0,0,0,52.8257,0,0,0,0,0,1,82.1,13,0,,150,93,0,0,0,4.532599,1,0,0,0,0,0,78.15913,8.82331,0,3.966998,1 1,1,0,1,2,125819,0,6789.702,60.02533,1,14,1,145.3457,14.31682,52.62384,0,0,212.2863,0,0,0,4,0,1,82.1,13,0,,150,93,0,0,0,4.532599,1,0,0,0,0,0,78.15913,8.82331,0,5.357936,1 1,1,0,1,3,125819,0,6789.702,61.02533,1,14,1,0,14.79187,0,0,0,14.79187,0,0,0,0,0,1,82.1,13,0,,150,93,0,0,0,4.532599,1,0,0,0,0,0,78.15913,8.82331,0,2.694078,1 4,1,100,0,1,125825,0,5982.63,21.49213,0,12,1,0,0,0,0,0,0,0,0,0,0,0,2,74.7,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,68.84245,8.696783,.6931472,,0 4,1,100,0,2,125825,0,5982.63,22.49213,0,12,1,4.043127,0,0,0,0,4.043127,0,0,0,1,0,2,74.7,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,68.84245,8.696783,.6931472,1.397018,1 4,1,100,0,3,125825,0,5982.63,23.49213,0,12,1,7.371007,6.422605,0,0,0,13.79361,0,0,0,1,0,3,74.7,0,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,1,0,0,68.84245,8.696783,1.098612,2.624206,1 4,1,100,0,4,125825,0,5982.63,24.49213,0,12,1,9.913401,2.402005,0,0,0,12.31541,0,0,0,2,0,4,74.7,0,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,68.84245,8.696783,1.386294,2.510851,1 4,1,100,0,5,125825,0,5982.63,25.49213,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.7,0,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,68.84245,8.696783,1.386294,,0 4,1,100,0,1,125826,0,5982.63,21.79055,1,13,1,0,8.551237,0,0,0,8.551237,0,0,0,0,0,2,92.6,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,70.0071,8.696783,.6931472,2.146076,1 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6,1,25,1,2,125892,0,19523.22,39.18207,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,95.8,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.21307,9.879411,1.386294,,0 6,1,25,1,3,125892,0,19523.22,40.18207,0,15,1,17.19902,0,0,0,0,17.19902,0,0,0,1,0,4,95.8,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.21307,9.879411,1.386294,2.844852,1 6,1,25,1,1,125893,0,19523.22,16.64066,0,14,1,41.8139,16.0424,0,0,0,57.8563,0,0,0,6,0,4,86.3,0,1,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,4.057962,1 6,1,25,1,2,125893,0,19523.22,17.64066,0,14,1,34.50135,1.617251,8.086253,0,0,44.20485,0,0,0,4,0,4,86.3,0,1,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,3.788835,1 6,1,25,1,3,125893,0,19523.22,18.64066,0,14,1,0,2.432432,0,0,0,2.432432,0,0,0,0,0,4,86.3,0,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,.8888918,1 6,1,25,1,1,125894,0,19523.22,17.66735,0,14,1,11.18964,0,0,0,0,11.18964,0,0,0,2,0,4,93.7,4.3,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,2.414988,1 6,1,25,1,2,125894,0,19523.22,18.66735,0,14,1,58.76011,2.458221,0,0,0,61.21833,0,0,0,3,0,4,93.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,4.114447,1 6,1,25,1,3,125894,0,19523.22,19.66735,0,14,1,145.4545,38.32924,0,0,0,183.7838,0,0,0,14,3,4,93.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.05912,9.879411,1.386294,5.21376,1 1,1,0,1,1,125907,0,18647.29,38.52703,0,8,1,0,0,0,0,0,0,0,0,0,0,0,3,93.7,0,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,75.67756,9.83351,1.098612,,0 1,1,0,1,2,125907,0,18647.29,39.52703,0,8,1,0,0,0,0,0,0,0,0,0,0,0,3,93.7,0,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,75.67756,9.83351,1.098612,,0 1,1,0,1,3,125907,0,18647.29,40.52703,0,8,1,43.58722,0,0,0,0,43.58722,0,0,0,1,0,3,93.7,0,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,75.67756,9.83351,1.098612,3.774764,1 1,1,0,1,4,125907,0,18647.29,41.52703,0,8,1,10.93892,0,0,0,0,10.93892,0,0,0,2,0,3,93.7,0,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,75.67756,9.83351,1.098612,2.392328,1 1,1,0,1,5,125907,0,18647.29,42.52703,0,8,1,19.17466,5.122968,29.18716,0,0,53.48479,0,0,0,2,0,3,93.7,0,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,75.67756,9.83351,1.098612,3.979397,1 1,1,0,1,1,125908,0,18647.29,38.16564,1,11,1,21.20141,10.3298,0,0,0,31.53121,0,0,0,2,0,3,82.1,13,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,76.56874,9.83351,1.098612,3.450978,1 1,1,0,1,2,125908,0,18647.29,39.16564,1,11,1,33.531,9.229111,0,0,0,42.76011,0,0,0,0,1,3,82.1,13,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,76.56874,9.83351,1.098612,3.755606,1 1,1,0,1,3,125908,0,18647.29,40.16564,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,82.1,13,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,76.56874,9.83351,1.098612,,0 1,1,0,1,4,125908,0,18647.29,41.16564,1,11,1,430.2963,13.79672,25.48314,0,0,469.5761,0,0,0,7,0,3,82.1,13,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,76.56874,9.83351,1.098612,6.15183,1 1,1,0,1,5,125908,0,18647.29,42.16564,1,11,1,22.50938,5.769071,0,0,0,28.27845,0,0,0,3,0,3,82.1,13,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,76.56874,9.83351,1.098612,3.3421,1 1,1,0,1,1,125909,0,18647.29,16.5421,1,11,1,15.90106,18.43345,0,0,0,34.33451,0,0,0,3,0,3,82.1,8.7,0,,113,113,1,1,1.098612,4.727388,1,0,0,0,0,0,72.18691,9.83351,1.098612,3.536151,1 1,1,0,1,2,125909,0,18647.29,17.5421,1,11,1,50.40431,10.27493,19.71968,0,0,80.39892,0,0,0,3,0,3,82.1,8.7,0,,113,113,1,1,1.098612,4.727388,1,0,0,0,0,0,72.18691,9.83351,1.098612,4.387001,1 1,1,0,1,3,125909,0,18647.29,18.5421,1,11,1,17.69042,9.77887,0,0,0,27.46929,0,0,0,3,0,3,82.1,8.7,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,72.18691,9.83351,1.098612,3.313069,1 1,1,0,1,4,125909,0,18647.29,19.5421,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,82.1,8.7,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,72.18691,9.83351,1.098612,,0 1,1,0,1,5,125909,0,18647.29,20.5421,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,82.1,8.7,0,,113,113,0,0,1.098612,4.727388,1,0,0,0,0,0,72.18691,9.83351,1.098612,,0 1,1,0,0,1,125962,0,3588.089,15.34292,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,80,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,0,0,0,70.76381,8.185654,1.098612,,0 1,1,0,0,2,125962,0,3588.089,16.34292,1,10,1,55.52561,0,1.886792,0,0,57.4124,0,0,0,2,2,3,80,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,0,0,0,70.76381,8.185654,1.098612,4.05026,1 1,1,0,0,3,125962,0,3588.089,17.34292,1,10,1,126.7813,0,0,0,0,126.7813,0,0,0,4,0,3,80,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,0,0,0,70.76381,8.185654,1.098612,4.842464,1 1,1,0,0,1,125963,0,3588.089,55.24162,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,83.8,13.73189,0,,450,0,0,0,1.098612,0,1,0,0,1,0,0,64.55314,8.185654,1.098612,,0 1,1,0,0,2,125963,0,3588.089,56.24162,1,10,1,15.09434,3.234501,0,0,0,18.32884,0,0,0,1,0,3,83.8,13.73189,0,,450,0,0,0,1.098612,0,1,0,0,1,0,0,64.55314,8.185654,1.098612,2.908476,1 1,1,0,0,3,125963,0,3588.089,57.24162,1,10,1,6.879607,0,0,0,0,6.879607,0,0,0,1,0,3,83.8,13.73189,0,,450,0,0,0,1.098612,0,1,0,0,1,0,0,64.55314,8.185654,1.098612,1.928561,1 1,1,0,0,1,125964,0,3588.089,58.2204,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,70,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,47.8484,8.185654,1.098612,,0 1,1,0,0,2,125964,0,3588.089,59.2204,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,70,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,47.8484,8.185654,1.098612,,0 1,1,0,0,3,125964,0,3588.089,60.2204,0,9,1,0,0,0,0,360.2555,360.2555,1,0,0,0,0,3,70,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,47.8484,8.185654,1.098612,5.886814,1 1,1,0,0,1,125965,0,1,21.65366,0,9,1,10.60071,0,0,0,0,10.60071,0,0,0,2,0,1,45,13.73189,0,,150,0,0,0,0,0,1,0,0,1,0,0,68.1502,.6931472,0,2.360921,1 1,1,0,0,2,125965,0,1,22.65366,0,9,1,9.703504,0,0,0,0,9.703504,0,0,0,1,0,1,45,13.73189,0,,150,0,0,0,0,0,1,0,0,1,0,0,68.1502,.6931472,0,2.272487,1 1,1,0,0,3,125965,0,1,23.65366,0,9,1,57.64128,0,0,0,483.8575,541.4988,2,1,0,2,0,2,45,13.73189,0,,150,0,0,0,.6931472,0,1,0,0,1,0,0,68.1502,.6931472,.6931472,6.294341,1 7,1,25,1,1,125967,1,1176.179,8.539356,1,5,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,340,340,1,1,1.791759,5.828946,0,3.258096,7.21524,1,0,0,76.93629,7.070876,1.791759,,0 7,1,25,1,2,125967,1,1176.179,9.539356,1,5,1,8.165487,0,0,0,0,8.165487,0,0,0,1,0,6,74.36826,13.73189,0,,340,340,1,1,1.791759,5.828946,0,3.258096,7.21524,1,0,0,76.93629,7.070876,1.791759,2.099916,1 7,1,25,1,1,125968,1,1176.179,16.41615,1,5,1,0,0,0,0,0,0,0,0,0,0,0,6,75.8,4.3,0,,340,340,1,1,1.791759,5.828946,0,3.258096,7.21524,1,0,0,58.89369,7.070876,1.791759,,0 7,1,25,1,2,125968,1,1176.179,17.41615,1,5,1,0,0,0,0,0,0,0,0,0,0,0,6,75.8,4.3,0,,340,340,1,1,1.791759,5.828946,0,3.258096,7.21524,1,0,0,58.89369,7.070876,1.791759,,0 7,1,25,1,1,125969,1,1176.179,17.52225,1,5,1,136.8233,5.264723,0,0,0,142.088,0,0,0,3,0,6,85.3,4.3,0,,340,340,1,1,1.791759,5.828946,0,3.258096,7.21524,1,0,0,58.69306,7.070876,1.791759,4.956447,1 7,1,25,1,2,125969,1,1176.179,18.52225,1,5,1,41.3718,3.739793,0,0,253.4295,298.5411,1,0,0,2,0,6,85.3,4.3,0,,340,340,0,0,1.791759,5.828946,0,3.258096,7.21524,1,0,0,58.69306,7.070876,1.791759,5.698908,1 7,1,25,1,3,125969,1,1176.179,19.52225,1,5,1,9.415262,0,0,0,0,9.415262,0,0,0,0,0,1,85.3,4.3,0,,340,340,0,0,0,5.828946,0,3.258096,7.21524,1,0,0,58.69306,7.070876,0,2.242332,1 7,1,25,1,4,125969,1,1176.179,20.52225,1,5,1,0,0,0,0,0,0,0,0,0,0,0,1,85.3,4.3,0,,340,340,0,0,0,5.828946,0,3.258096,7.21524,1,0,0,58.69306,7.070876,0,,0 7,1,25,1,1,125970,1,1176.179,1.850787,0,5,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,340,340,1,0,1.791759,5.828946,0,3.258096,7.21524,1,0,0,73.69083,7.070876,1.791759,,0 7,1,25,1,2,125970,1,1176.179,2.850787,0,5,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,340,340,1,0,1.791759,5.828946,0,3.258096,7.21524,1,0,0,73.69083,7.070876,1.791759,,0 7,1,25,1,1,125971,1,1176.179,45.83984,1,5,1,0,0,0,0,0,0,0,0,0,0,0,6,63.3,8.7,0,,340,340,0,0,1.791759,5.828946,0,3.258096,7.21524,0,1,0,51.09576,7.070876,1.791759,,0 7,1,25,1,2,125971,1,1176.179,46.83984,1,5,1,54.98095,10.90365,0,0,0,65.8846,0,0,0,5,0,6,63.3,8.7,0,,340,340,0,0,1.791759,5.828946,0,3.258096,7.21524,0,1,0,51.09576,7.070876,1.791759,4.187905,1 1,1,0,0,1,125983,0,16303.07,27.41684,1,12,1,15.07841,8.455971,0,0,0,23.53438,0,0,0,2,0,2,35,13.73189,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,72.96643,9.69917,.6931472,3.158462,1 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7,1,25,1,4,126139,0,9542.002,12.20465,1,14,1,22.59105,0,0,0,0,22.59105,0,0,0,1,1,6,74.36826,13.73189,0,,0,0,1,1,1.791759,0,0,3.258096,0,0,0,0,82.8158,9.163564,1.791759,3.117554,1 7,1,25,1,5,126139,0,9542.002,13.20465,1,14,1,73.63521,2.750741,368.1761,0,0,444.562,0,0,0,5,0,6,74.36826,13.73189,0,,0,0,1,1,1.791759,0,0,3.258096,0,0,0,0,82.8158,9.163564,1.791759,6.09709,1 4,1,100,1,1,126151,0,8923.697,43.38124,0,12,1,0,0,0,0,0,0,0,0,0,0,0,2,86.3,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,70.32339,9.096578,.6931472,,0 4,1,100,1,2,126151,0,8923.697,44.38124,0,12,1,0,0,0,0,0,0,0,0,0,0,0,2,86.3,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,70.32339,9.096578,.6931472,,0 4,1,100,1,3,126151,0,8923.697,45.38124,0,12,1,0,0,0,0,0,0,0,0,0,0,0,2,86.3,0,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,1,0,0,70.32339,9.096578,.6931472,,0 4,1,100,1,1,126152,0,8923.697,42.28337,1,12,1,10.79137,0,0,0,0,10.79137,0,0,0,1,0,2,90.5,4.3,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.46137,9.096578,.6931472,2.378747,1 4,1,100,1,2,126152,0,8923.697,43.28337,1,12,1,0,0,0,0,0,0,0,0,0,0,0,2,90.5,4.3,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.46137,9.096578,.6931472,,0 4,1,100,1,3,126152,0,8923.697,44.28337,1,12,1,5.985037,0,23.94015,0,0,29.92519,0,0,0,0,1,2,90.5,4.3,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.46137,9.096578,.6931472,3.3987,1 5,1,25,0,1,126156,0,4841.812,46.5243,1,9,1,27.67962,3.898704,0,0,0,31.57833,0,0,0,4,0,1,85.6,13,0,,375,375,0,0,0,5.926926,0,3.258096,7.313221,1,0,0,68.95127,8.48525,0,3.452471,1 5,1,25,0,2,126156,0,4841.812,47.5243,1,9,1,0,0,0,0,0,0,0,0,0,0,0,1,85.6,13,0,,375,375,0,0,0,5.926926,0,3.258096,7.313221,1,0,0,68.95127,8.48525,0,,0 5,1,25,0,3,126156,0,4841.812,48.5243,1,9,1,0,0,0,0,0,0,0,0,0,0,0,1,85.6,13,0,,375,375,0,0,0,5.926926,0,3.258096,7.313221,1,0,0,68.95127,8.48525,0,,0 5,1,25,0,4,126156,0,4841.812,49.5243,1,9,1,10.93892,0,30.44212,0,0,41.38104,0,0,0,0,1,1,85.6,13,0,,375,375,0,0,0,5.926926,0,3.258096,7.313221,1,0,0,68.95127,8.48525,0,3.722823,1 5,1,25,0,5,126156,0,4841.812,50.5243,1,9,1,0,0,0,0,0,0,0,0,0,0,0,1,85.6,13,0,,375,375,0,0,0,5.926926,0,3.258096,7.313221,1,0,0,68.95127,8.48525,0,,0 4,1,100,0,1,126157,0,9122.829,38.24504,0,12,1,13.68233,10.35098,0,0,0,24.03331,0,0,0,2,0,4,58.8,13.73189,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.64435,9.118645,1.386294,3.179441,1 4,1,100,0,2,126157,0,9122.829,39.24504,0,12,1,381.6004,0,37.54491,0,0,419.1454,0,0,0,1,45,4,58.8,13.73189,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.64435,9.118645,1.386294,6.038218,1 4,1,100,0,3,126157,0,9122.829,40.24504,0,12,1,5.946482,0,0,0,0,5.946482,0,0,0,0,1,4,58.8,13.73189,1,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,65.64435,9.118645,1.386294,1.7828,1 4,1,100,0,1,126158,0,9122.829,35.38124,1,10,1,20.82094,21.14813,0,0,0,41.96907,0,0,0,1,1,4,56.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,68.66209,9.118645,1.386294,3.736933,1 4,1,100,0,2,126158,0,9122.829,36.38124,1,10,1,221.0125,22.37343,6.53239,0,0,249.9184,0,0,0,1,33,4,56.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,68.66209,9.118645,1.386294,5.521134,1 4,1,100,0,3,126158,0,9122.829,37.38124,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,56.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,68.66209,9.118645,1.386294,,0 4,1,100,0,1,126159,0,9122.829,14.94045,0,10,1,25.58001,0,0,0,0,25.58001,0,0,0,1,1,4,82.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,69.60401,9.118645,1.386294,3.241811,1 4,1,100,0,2,126159,0,9122.829,15.94045,0,10,1,8.165487,0,0,0,0,8.165487,0,0,0,0,1,4,82.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,69.60401,9.118645,1.386294,2.099916,1 4,1,100,0,3,126159,0,9122.829,16.94045,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,82.5,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,69.60401,9.118645,1.386294,,0 4,1,100,0,1,126160,0,9122.829,18.52156,1,12,1,21.49911,5.562165,0,0,0,27.06127,0,0,0,3,1,4,91.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.81157,9.118645,1.386294,3.298104,1 4,1,100,0,2,126160,0,9122.829,19.52156,1,12,1,305.4763,14.70332,0,0,0,320.1796,0,0,0,8,36,4,91.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.81157,9.118645,1.386294,5.768882,1 4,1,100,0,3,126160,0,9122.829,20.52156,1,12,1,9.142715,.6937562,0,0,0,9.836472,0,0,0,0,1,4,91.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.81157,9.118645,1.386294,2.286097,1 3,1,100,0,1,126177,0,10341.81,52.85695,0,12,1,75.97173,6.089517,0,0,0,82.06125,0,0,0,3,0,5,86.3,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,66.41367,9.244047,1.609438,4.407466,1 3,1,100,0,2,126177,0,10341.81,53.85695,0,12,1,361.5903,18.72776,50.42049,0,6563.558,6994.296,2,0,0,12,0,5,86.3,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,66.41367,9.244047,1.609438,8.85285,1 3,1,100,0,3,126177,0,10341.81,54.85695,0,12,1,565.1351,59.71008,9.085995,0,834.1572,1468.089,1,0,0,18,1,5,86.3,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,66.41367,9.244047,1.609438,7.291717,1 3,1,100,0,1,126178,0,10341.81,53.33333,1,12,1,110.7185,38.45701,0,0,0,149.1755,0,0,0,8,0,5,80,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,74.55164,9.244047,1.609438,5.005124,1 3,1,100,0,2,126178,0,10341.81,54.33333,1,12,1,69.00269,47.7035,40.56065,0,1090.016,1247.283,1,0,0,4,1,5,80,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,74.55164,9.244047,1.609438,7.128723,1 3,1,100,0,3,126178,0,10341.81,55.33333,1,12,1,105.774,65.58723,51.59705,0,0,222.9582,0,0,0,13,1,5,80,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,74.55164,9.244047,1.609438,5.406984,1 3,1,100,0,1,126179,0,10341.81,12.38877,0,12,1,18.8457,3.374558,0,0,0,22.22026,0,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,85.67059,9.244047,1.609438,3.101004,1 3,1,100,0,2,126179,0,10341.81,13.38877,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,85.67059,9.244047,1.609438,,0 3,1,100,0,3,126179,0,10341.81,14.38877,0,12,1,19.65602,1.503685,0,0,0,21.1597,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,85.67059,9.244047,1.609438,3.052099,1 3,1,100,0,1,126180,0,10341.81,16.52841,1,12,1,15.31213,2.479388,0,0,0,17.79152,0,0,0,2,0,5,72.6,13,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,75.20459,9.244047,1.609438,2.878722,1 3,1,100,0,2,126180,0,10341.81,17.52841,1,12,1,21.56334,11.21833,0,0,0,32.78167,0,0,0,2,1,5,72.6,13,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,75.20459,9.244047,1.609438,3.48987,1 3,1,100,0,3,126180,0,10341.81,18.52841,1,12,1,61.42506,14.27518,3.415233,0,0,79.11548,0,0,0,4,0,5,72.6,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,75.20459,9.244047,1.609438,4.370909,1 3,1,100,0,1,126181,0,10341.81,18.2423,0,12.32507,1,91.8728,8.60424,0,0,0,100.477,0,0,0,6,0,5,42.1,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,78.95576,9.244047,1.609438,4.609929,1 3,1,100,0,2,126181,0,10341.81,19.2423,0,12.32507,1,3.234501,1.078167,0,0,0,4.312668,0,0,0,0,0,5,42.1,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,78.95576,9.244047,1.609438,1.461557,1 3,1,100,0,3,126181,0,10341.81,20.2423,0,12.32507,1,193.4742,22.92383,0,0,0,216.398,0,0,0,8,0,5,42.1,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,78.95576,9.244047,1.609438,5.37712,1 3,1,100,0,1,126182,0,9542.002,21.45927,1,12,1,0,0,0,0,0,0,0,0,0,0,0,1,72.6,8.7,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.1736,9.163564,0,,0 3,1,100,0,2,126182,0,9542.002,22.45927,1,12,1,0,.6630728,0,0,0,.6630728,0,0,0,0,0,1,72.6,8.7,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.1736,9.163564,0,-.4108706,1 3,1,100,0,3,126182,0,9542.002,23.45927,1,12,1,115.7248,41.2973,30.02948,0,0,187.0516,0,0,0,7,4,1,72.6,8.7,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.1736,9.163564,0,5.231384,1 5,1,25,1,1,126192,0,7179.28,53.90007,0,10,1,20.98321,4.664268,0,0,0,25.64748,0,0,0,1,0,3,45.3,30.4,0,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,63.23743,8.879094,1.098612,3.244445,1 5,1,25,1,2,126192,0,7179.28,54.90007,0,10,1,6.024096,3.450164,0,0,0,9.47426,0,0,0,1,0,3,45.3,30.4,0,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,63.23743,8.879094,1.098612,2.248579,1 5,1,25,1,3,126192,0,7179.28,55.90007,0,10,1,61.7207,2.488778,0,0,0,64.20947,0,0,0,2,0,3,45.3,30.4,0,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,63.23743,8.879094,1.098612,4.162151,1 5,1,25,1,4,126192,0,7179.28,56.90007,0,10,1,158.5293,29.65422,0,0,1485.177,1673.361,1,0,0,12,0,3,45.3,30.4,0,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,63.23743,8.879094,1.098612,7.422589,1 5,1,25,1,5,126192,0,7179.28,57.90007,0,10,1,35.12484,21.30766,1.481168,125.8951,0,57.91367,0,0,15,3,1,3,45.3,30.4,0,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,63.23743,8.879094,1.098612,4.058953,1 5,1,25,1,1,126193,0,7179.28,13.78782,0,12,1,61.15108,8.141487,0,0,0,69.29256,0,0,0,4,0,3,74.36826,13.73189,0,,799,799,1,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,78.66734,8.879094,1.098612,4.238338,1 5,1,25,1,2,126193,0,7179.28,14.78782,0,12,1,22.45345,2.924425,36.8839,0,0,62.26178,0,0,0,2,0,3,74.36826,13.73189,0,,799,799,1,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,78.66734,8.879094,1.098612,4.131348,1 5,1,25,1,3,126193,0,7179.28,15.78782,0,12,1,33.91521,11.08728,0,0,0,45.00249,0,0,0,3,0,3,74.36826,13.73189,0,,799,799,1,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,78.66734,8.879094,1.098612,3.806718,1 5,1,25,1,4,126193,0,7179.28,16.78782,0,12,1,19.59428,2.996773,27.18764,0,0,49.7787,0,0,0,2,0,3,74.36826,13.73189,0,,799,799,1,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,78.66734,8.879094,1.098612,3.907587,1 5,1,25,1,5,126193,0,7179.28,17.78782,0,12,1,35.89928,2.276767,0,0,0,38.17605,0,0,0,2,0,3,74.36826,13.73189,0,,799,799,1,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,78.66734,8.879094,1.098612,3.642208,1 5,1,25,1,1,126194,0,7179.28,34.96235,1,12,1,29.3765,17.90168,33.0036,0,0,80.28178,0,0,0,2,0,3,71.6,30.4,1,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,69.07321,8.879094,1.098612,4.385543,1 5,1,25,1,2,126194,0,7179.28,35.96235,1,12,1,9.857613,11.45674,0,0,0,21.31435,0,0,0,1,0,3,71.6,30.4,1,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,69.07321,8.879094,1.098612,3.059381,1 5,1,25,1,3,126194,0,7179.28,36.96235,1,12,1,25.93516,1.94015,0,0,0,27.87531,0,0,0,2,0,3,71.6,30.4,1,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,69.07321,8.879094,1.098612,3.327741,1 5,1,25,1,4,126194,0,7179.28,37.96235,1,12,1,30.42877,15.09451,0,0,0,45.52328,0,0,0,3,0,3,71.6,30.4,1,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,69.07321,8.879094,1.098612,3.818224,1 5,1,25,1,5,126194,0,7179.28,38.96235,1,12,1,121.4558,2.175201,0,276.6653,0,123.631,0,0,23,3,0,3,71.6,30.4,1,,799,799,0,0,1.098612,6.683361,0,3.258096,8.069655,1,0,0,69.07321,8.879094,1.098612,4.817301,1 5,1,25,1,1,126195,0,3355.459,19.83573,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,66.3,4.3,0,,259,259,0,0,0,5.556828,0,3.258096,6.943122,1,0,0,67.43752,8.118642,0,,0 5,1,25,1,2,126195,0,3355.459,20.83573,0,12,1,9.309967,0,36.05695,0,0,45.36692,0,0,0,0,1,1,66.3,4.3,0,,259,259,0,0,0,5.556828,0,3.258096,6.943122,1,0,0,67.43752,8.118642,0,3.814783,1 5,1,25,1,3,126195,0,3355.459,21.83573,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,66.3,4.3,0,,259,259,0,0,0,5.556828,0,3.258096,6.943122,1,0,0,67.43752,8.118642,0,,0 5,1,25,1,4,126195,0,3355.459,22.83573,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,66.3,4.3,0,,259,259,0,0,0,5.556828,0,3.258096,6.943122,1,0,0,67.43752,8.118642,0,,0 5,1,25,1,5,126195,0,3355.459,23.83573,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,66.3,4.3,0,,259,259,0,0,0,5.556828,0,3.258096,6.943122,1,0,0,67.43752,8.118642,0,,0 11,1,0,0,1,126198,0,11809.23,20.53388,0,13,1,61.24853,18.55124,34.06949,0,0,113.8693,0,0,0,7,0,3,85,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,0,0,0,79.07556,9.376721,1.098612,4.735051,1 11,1,0,0,2,126198,0,11809.23,21.53388,0,13,1,84.20485,23.42318,.4851752,0,0,108.1132,0,0,0,6,2,3,85,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,0,0,0,79.07556,9.376721,1.098612,4.683179,1 11,1,0,0,3,126198,0,11809.23,22.53388,0,13,1,217.1499,11.10565,32.33415,0,0,260.5897,0,0,0,12,1,3,85,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,0,0,0,79.07556,9.376721,1.098612,5.562947,1 11,1,0,0,1,126199,0,11809.23,48.39151,1,12,1,72.61484,5.270907,44.00471,0,0,121.8905,0,0,0,5,2,3,67.5,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,1,0,0,72.16116,9.376721,1.098612,4.803123,1 11,1,0,0,2,126199,0,11809.23,49.39151,1,12,1,212.3181,48.61995,.2695418,0,0,261.2076,0,0,0,10,0,3,67.5,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,1,0,0,72.16116,9.376721,1.098612,5.565315,1 11,1,0,0,3,126199,0,11809.23,50.39151,1,12,1,116.9533,11.7199,56.48649,0,0,185.1597,0,0,0,5,0,3,67.5,13.73189,0,,0,464,0,0,1.098612,6.139884,0,0,0,1,0,0,72.16116,9.376721,1.098612,5.221219,1 11,1,0,0,1,126200,0,11809.23,13.55236,0,12,1,20.61249,2.502945,0,0,0,23.11543,0,0,0,3,0,3,74.36826,13.73189,0,,0,464,1,0,1.098612,6.139884,0,0,0,0,0,0,87.52181,9.376721,1.098612,3.1405,1 11,1,0,0,2,126200,0,11809.23,14.55236,0,12,1,46.36119,10.08625,0,0,0,56.44744,0,0,0,7,0,3,74.36826,13.73189,0,,0,464,1,0,1.098612,6.139884,0,0,0,0,0,0,87.52181,9.376721,1.098612,4.03331,1 11,1,0,0,3,126200,0,11809.23,15.55236,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,0,464,1,0,1.098612,6.139884,0,0,0,0,0,0,87.52181,9.376721,1.098612,,0 7,1,25,1,1,126205,0,8393.3,6.31896,1,13,1,9.518144,2.647234,0,0,0,12.16538,0,0,0,1,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.258096,8.294049,0,0,0,83.31136,9.035308,1.098612,2.498594,1 7,1,25,1,2,126205,0,8393.3,7.31896,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.258096,8.294049,0,0,0,83.31136,9.035308,1.098612,,0 7,1,25,1,3,126205,0,8393.3,8.318959,1,13,1,14.8662,7.433102,0,0,0,22.29931,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,83.31136,9.035308,1.386294,3.104556,1 7,1,25,1,1,126206,0,8393.3,30.34634,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,80,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,75.11373,9.035308,1.098612,,0 7,1,25,1,2,126206,0,8393.3,31.34634,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,80,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,75.11373,9.035308,1.098612,,0 7,1,25,1,3,126206,0,8393.3,32.34634,0,12,1,26.16452,0,39.99009,0,0,66.15461,0,0,0,1,1,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.11373,9.035308,1.386294,4.191995,1 7,1,25,1,1,126207,0,8393.3,28.46817,1,13,1,30.63653,18.38192,0,0,0,49.01844,0,0,0,1,0,3,41.1,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,76.69158,9.035308,1.098612,3.892197,1 7,1,25,1,2,126207,0,8393.3,29.46817,1,13,1,5.443658,4.327708,3.266195,0,933.2335,946.2711,2,0,0,1,0,3,41.1,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,76.69158,9.035308,1.098612,6.852529,1 7,1,25,1,3,126207,0,8393.3,30.46817,1,13,1,37.6115,14.14272,0,0,0,51.75421,0,0,0,3,0,4,41.1,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.69158,9.035308,1.386294,3.946506,1 9,1,50,1,1,126208,0,12392.06,54.0616,0,12,1,131.1719,20.19631,0,0,0,151.3682,0,0,0,8,0,3,95.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.03518,9.424891,1.098612,5.019715,1 9,1,50,1,2,126208,0,12392.06,55.0616,0,12,1,101.0343,13.28253,0,0,2096.51,2210.827,2,0,0,8,1,3,95.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.03518,9.424891,1.098612,7.701122,1 9,1,50,1,3,126208,0,12392.06,56.0616,0,12,1,72.74529,16.82359,0,0,0,89.56888,0,0,0,8,0,3,95.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.03518,9.424891,1.098612,4.495008,1 9,1,50,1,1,126209,0,12392.06,15.93155,0,16,1,39.26234,11.60024,28.59607,0,0,79.45866,0,0,0,5,1,3,77.9,4.3,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,81.08195,9.424891,1.098612,4.375237,1 9,1,50,1,2,126209,0,12392.06,16.93155,0,16,1,10.77844,19.13446,0,0,0,29.9129,0,0,0,2,0,3,77.9,4.3,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,81.08195,9.424891,1.098612,3.39829,1 9,1,50,1,3,126209,0,12392.06,17.93155,0,16,1,54.60852,4.286422,24.28147,0,0,83.17641,0,0,0,3,2,3,77.9,4.3,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,81.08195,9.424891,1.098612,4.420964,1 9,1,50,1,1,126210,0,12392.06,50.22313,1,16,1,155.7406,39.29209,31.52885,0,0,226.5616,0,0,0,6,2,3,86.3,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,78.13973,9.424891,1.098612,5.423017,1 9,1,50,1,2,126210,0,12392.06,51.22313,1,16,1,50.08165,37.20741,31.85629,0,0,119.1453,0,0,0,4,1,3,86.3,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,78.13973,9.424891,1.098612,4.780344,1 9,1,50,1,3,126210,0,12392.06,52.22313,1,16,1,42.76511,39.51933,0,0,0,82.28444,0,0,0,5,0,3,86.3,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,78.13973,9.424891,1.098612,4.410182,1 10,1,50,0,1,126222,0,25825.28,54.11088,0,19,1,50.05889,0,0,38.86926,0,50.05889,0,0,3,0,0,2,81.3,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,77.43079,10.15915,.6931472,3.9132,1 10,1,50,0,2,126222,0,25825.28,55.11088,0,19,1,13.47709,0,0,266.8464,0,13.47709,0,0,15,0,0,2,81.3,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,77.43079,10.15915,.6931472,2.600991,1 10,1,50,0,3,126222,0,25825.28,56.11088,0,19,1,0,0,0,16.21622,0,0,0,0,1,0,0,2,81.3,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,77.43079,10.15915,.6931472,,0 10,1,50,0,1,126223,0,25825.28,52.73375,1,13,1,0,0,0,0,0,0,0,0,0,0,0,2,75,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,78.89941,10.15915,.6931472,,0 10,1,50,0,2,126223,0,25825.28,53.73375,1,13,1,0,0,0,0,0,0,0,0,0,0,0,2,75,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,78.89941,10.15915,.6931472,,0 10,1,50,0,3,126223,0,25825.28,54.73375,1,13,1,0,0,0,0,0,0,0,0,0,0,0,2,75,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,0,0,0,78.89941,10.15915,.6931472,,0 1,1,0,1,1,126231,0,3460.918,51.72895,1,12,1,128.9753,61.96113,17.85041,0,0,208.7868,0,0,0,7,0,1,85.3,13,0,,150,203,0,0,0,5.313206,1,0,0,0,0,0,79.21188,8.149578,0,5.341314,1 1,1,0,1,2,126231,0,3460.918,52.72895,1,12,1,170.8895,74.88949,44.60917,0,0,290.3882,0,0,0,9,2,1,85.3,13,0,,150,203,0,0,0,5.313206,1,0,0,0,0,0,79.21188,8.149578,0,5.671218,1 1,1,0,1,3,126231,0,3460.918,53.72895,1,12,1,47.66585,37.99509,62.63882,0,0,148.2998,0,0,0,5,0,1,85.3,13,0,,150,203,0,0,0,5.313206,1,0,0,0,0,0,79.21188,8.149578,0,4.999236,1 4,1,100,1,1,126240,0,9577.765,28.52019,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,52.6,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,59.58581,9.167304,1.386294,,0 4,1,100,1,2,126240,0,9577.765,29.52019,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,52.6,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,59.58581,9.167304,1.386294,,0 4,1,100,1,3,126240,0,9577.765,30.52019,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,52.6,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,1,0,59.58581,9.167304,1.386294,,0 4,1,100,1,1,126241,0,9577.765,32.32854,0,11,1,5.889281,0,0,0,0,5.889281,0,0,0,1,0,4,33.7,30.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.88706,9.167304,1.386294,1.773134,1 4,1,100,1,2,126241,0,9577.765,33.32854,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,33.7,30.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.88706,9.167304,1.386294,,0 4,1,100,1,3,126241,0,9577.765,34.32854,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,33.7,30.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.88706,9.167304,1.386294,,0 4,1,100,1,1,126242,0,9577.765,11.16222,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,,0 4,1,100,1,2,126242,0,9577.765,12.16222,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,,0 4,1,100,1,3,126242,0,9577.765,13.16222,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,,0 4,1,100,1,1,126243,0,9577.765,12.92539,0,9,1,21.3192,0,0,0,0,21.3192,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,3.059608,1 4,1,100,1,2,126243,0,9577.765,13.92539,0,9,1,8.086253,9.153639,0,0,0,17.23989,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,2.847226,1 4,1,100,1,3,126243,0,9577.765,14.92539,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.2337,9.167304,1.386294,,0 10,1,50,1,1,126250,0,11668.83,45.38809,1,11,1,36.27098,36.3729,33.71103,0,0,106.3549,0,0,0,4,0,3,80,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.51671,9.364761,1.098612,4.666782,1 10,1,50,1,2,126250,0,11668.83,46.38809,1,11,1,71.08434,16.78532,8.105148,0,0,95.97481,0,0,0,6,0,3,80,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.51671,9.364761,1.098612,4.564086,1 10,1,50,1,3,126250,0,11668.83,47.38809,1,11,1,46.13466,14.3591,37.29177,0,0,97.78554,0,0,0,4,1,3,80,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.51671,9.364761,1.098612,4.582777,1 10,1,50,1,4,126250,0,11668.83,48.38809,1,11,1,35.96127,9.400645,0,0,0,45.36192,0,0,0,3,0,3,80,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.51671,9.364761,1.098612,3.814673,1 10,1,50,1,5,126250,0,11668.83,49.38809,1,11,1,125.6877,34.24884,0,0,0,159.9365,0,0,0,8,0,3,80,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.51671,9.364761,1.098612,5.074777,1 10,1,50,1,1,126251,0,11668.83,46.72416,0,11,1,138.1895,26.90648,31.03717,0,990.5935,1186.727,2,0,0,6,0,3,71.6,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,73.89005,9.364761,1.098612,7.078954,1 10,1,50,1,2,126251,0,11668.83,47.72416,0,11,1,13.14348,11.65936,0,0,0,24.80285,0,0,0,2,0,3,71.6,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,73.89005,9.364761,1.098612,3.210958,1 10,1,50,1,3,126251,0,11668.83,48.72416,0,11,1,43.01746,14.46883,33.99002,0,0,91.47631,0,0,0,3,0,3,71.6,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,73.89005,9.364761,1.098612,4.51608,1 10,1,50,1,4,126251,0,11668.83,49.72416,0,11,1,48.4094,4.319963,0,0,0,52.72937,0,0,0,6,0,3,71.6,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,73.89005,9.364761,1.098612,3.965173,1 10,1,50,1,5,126251,0,11668.83,50.72416,0,11,1,11.42615,0,29.67414,0,0,41.1003,0,0,0,0,1,3,71.6,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,73.89005,9.364761,1.098612,3.716015,1 10,1,50,1,1,126252,0,11668.83,18.41752,0,11,1,32.07434,11.66067,0,0,632.9616,676.6967,1,0,0,3,0,3,82.1,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.67222,9.364761,1.098612,6.517223,1 10,1,50,1,2,126252,0,11668.83,19.41752,0,11,1,5.476451,2.584885,0,0,0,8.061337,0,0,0,1,0,3,82.1,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.67222,9.364761,1.098612,2.087079,1 10,1,50,1,3,126252,0,11668.83,20.41752,0,11,1,16.70823,2.992519,0,0,0,19.70075,0,0,0,2,0,3,82.1,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.67222,9.364761,1.098612,2.980657,1 10,1,50,1,4,126252,0,11668.83,21.41752,0,11,1,82.98755,15.20055,0,0,2542.153,2640.341,2,0,0,6,0,3,82.1,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.67222,9.364761,1.098612,7.878664,1 10,1,50,1,5,126252,0,11668.83,22.41752,0,11,1,95.64114,21.17647,0,0,0,116.8176,0,0,0,11,0,3,82.1,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,75.67222,9.364761,1.098612,4.760614,1 5,1,25,1,1,126256,0,17812.88,41.86721,0,16,1,24.98513,8.893516,27.35277,0,0,61.23141,0,0,0,1,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,73.33533,9.787733,1.386294,4.11466,1 5,1,25,1,2,126256,0,17812.88,42.86721,0,16,1,26.21121,0,0,0,0,26.21121,0,0,0,1,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,73.33533,9.787733,1.386294,3.266187,1 5,1,25,1,3,126256,0,17812.88,43.86721,0,16,1,98.11695,6.243806,9.32111,0,0,113.6819,0,0,0,1,2,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,73.33533,9.787733,1.386294,4.733404,1 5,1,25,1,4,126256,0,17812.88,44.86721,0,16,1,15.14456,0,44.60303,0,0,59.74759,0,0,0,1,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,73.33533,9.787733,1.386294,4.090129,1 5,1,25,1,5,126256,0,17812.88,45.86721,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,73.33533,9.787733,1.386294,,0 5,1,25,1,1,126257,0,17812.88,17.46201,0,15,1,28.55443,6.216538,0,0,0,34.77097,0,0,0,4,0,4,82.1,8.7,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.80552,9.787733,1.386294,3.548783,1 5,1,25,1,2,126257,0,17812.88,18.46201,0,15,1,50.62602,1.986935,0,0,0,52.61296,0,0,0,3,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.80552,9.787733,1.386294,3.962962,1 5,1,25,1,3,126257,0,17812.88,19.46201,0,15,1,25.76809,4.212091,0,0,0,29.98018,0,0,0,2,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.80552,9.787733,1.386294,3.400537,1 5,1,25,1,4,126257,0,17812.88,20.46201,0,15,1,23.40523,0,0,0,0,23.40523,0,0,0,1,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.80552,9.787733,1.386294,3.15296,1 5,1,25,1,5,126257,0,17812.88,21.46201,0,15,1,4.417333,0,0,0,0,4.417333,0,0,0,0,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,76.80552,9.787733,1.386294,1.485536,1 5,1,25,1,1,126258,0,17812.88,9.459274,1,15,1,41.047,27.79298,0,0,0,68.83997,0,0,0,8,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77095,9.787733,1.386294,4.231785,1 5,1,25,1,2,126258,0,17812.88,10.45927,1,15,1,22.319,3.810561,0,0,0,26.12956,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77095,9.787733,1.386294,3.263067,1 5,1,25,1,3,126258,0,17812.88,11.45927,1,15,1,22.29931,4.831516,0,0,0,27.13082,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77095,9.787733,1.386294,3.30067,1 5,1,25,1,4,126258,0,17812.88,12.45927,1,15,1,43.13905,12.09729,0,0,0,55.23635,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77095,9.787733,1.386294,4.011621,1 5,1,25,1,5,126258,0,17812.88,13.45927,1,15,1,47.11822,11.42196,0,0,0,58.54018,0,0,0,6,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77095,9.787733,1.386294,4.069713,1 5,1,25,1,1,126259,0,17812.88,39.07187,1,15,1,101.7252,0,0,0,0,101.7252,0,0,0,3,0,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.85065,9.787733,1.386294,4.622275,1 5,1,25,1,2,126259,0,17812.88,40.07187,1,15,1,126.0207,0,0,0,0,126.0207,0,0,0,3,0,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.85065,9.787733,1.386294,4.836446,1 5,1,25,1,3,126259,0,17812.88,41.07187,1,15,1,19.82161,2.750248,0,0,0,22.57185,0,0,0,2,0,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.85065,9.787733,1.386294,3.116704,1 5,1,25,1,4,126259,0,17812.88,42.07187,1,15,1,18.81597,0,0,0,0,18.81597,0,0,0,1,0,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.85065,9.787733,1.386294,2.934706,1 5,1,25,1,5,126259,0,17812.88,43.07187,1,15,1,109.8023,23.09634,3.407657,0,0,136.3063,0,0,0,4,0,4,80,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.85065,9.787733,1.386294,4.914905,1 7,1,25,0,1,126288,0,11939.83,4.073922,0,12,1,35.92462,0,0,0,0,35.92462,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.3802,9.387718,1.386294,3.581423,1 7,1,25,0,2,126288,0,11939.83,5.073922,0,12,1,28.84097,0,0,0,0,28.84097,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.3802,9.387718,1.386294,3.361797,1 7,1,25,0,3,126288,0,11939.83,6.073922,0,12,1,52.82555,0,4.511056,4.299754,0,57.33661,0,0,1,2,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.3802,9.387718,1.386294,4.048939,1 7,1,25,0,4,126288,0,11939.83,7.073922,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.3802,9.387718,1.386294,,0 7,1,25,0,5,126288,0,11939.83,8.073922,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.3802,9.387718,1.386294,,0 7,1,25,0,1,126289,0,11939.83,29.62628,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,91.6,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.3162,9.387718,1.386294,,0 7,1,25,0,2,126289,0,11939.83,30.62628,0,11,1,24.25876,0,0,0,0,24.25876,0,0,0,2,0,4,91.6,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.3162,9.387718,1.386294,3.188778,1 7,1,25,0,3,126289,0,11939.83,31.62628,0,11,1,0,0,0,19.04177,0,0,0,0,3,0,0,4,91.6,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.3162,9.387718,1.386294,,0 7,1,25,0,4,126289,0,11939.83,32.62628,0,11,1,87.39745,0,0,0,0,87.39745,0,0,0,3,0,4,91.6,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.3162,9.387718,1.386294,4.470466,1 7,1,25,0,5,126289,0,11939.83,33.62628,0,11,1,33.97249,0,0,0,0,33.97249,0,0,0,1,0,4,91.6,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.3162,9.387718,1.386294,3.525551,1 7,1,25,0,1,126290,0,11939.83,29.02943,1,12,1,438.1036,0,17.85041,0,0,455.9541,0,0,0,3,1,4,61.1,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.35686,9.387718,1.386294,6.122392,1 7,1,25,0,2,126290,0,11939.83,30.02943,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.35686,9.387718,1.386294,,0 7,1,25,0,3,126290,0,11939.83,31.02943,1,12,1,12.28501,0,32.40786,50.9828,0,44.69287,0,0,5,0,1,4,61.1,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.35686,9.387718,1.386294,3.799814,1 7,1,25,0,4,126290,0,11939.83,32.02943,1,12,1,458.7284,0,7.146764,0,1132.84,1598.715,1,0,0,3,0,4,61.1,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.35686,9.387718,1.386294,7.376956,1 7,1,25,0,5,126290,0,11939.83,33.02943,1,12,1,12.50521,0,0,0,0,12.50521,0,0,0,1,0,4,61.1,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.35686,9.387718,1.386294,2.526145,1 7,1,25,0,1,126291,0,11939.83,8.657084,1,12,1,75.97173,0,0,0,0,75.97173,0,0,0,2,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.36227,9.387718,1.386294,4.330361,1 7,1,25,0,2,126291,0,11939.83,9.657084,1,12,1,24.20485,0,0,0,0,24.20485,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.36227,9.387718,1.386294,3.186553,1 7,1,25,0,3,126291,0,11939.83,10.65708,1,12,1,20.63882,0,0,4.299754,0,20.63882,0,0,1,1,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.36227,9.387718,1.386294,3.027174,1 7,1,25,0,4,126291,0,11939.83,11.65708,1,12,1,116.4768,2.734731,0,0,0,119.2115,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.36227,9.387718,1.386294,4.780899,1 7,1,25,0,5,126291,0,11939.83,12.65708,1,12,1,91.9133,0,0,0,0,91.9133,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.36227,9.387718,1.386294,4.520846,1 5,1,25,1,1,126311,0,7136.477,35.90691,0,12,1,58.15348,2.93765,0,0,0,61.09113,0,0,0,2,0,3,85.3,8.7,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,78.56193,8.873115,1.098612,4.112367,1 5,1,25,1,2,126311,0,7136.477,36.90691,0,12,1,44.35926,0,0,0,0,44.35926,0,0,0,3,0,3,85.3,8.7,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,78.56193,8.873115,1.098612,3.792321,1 5,1,25,1,3,126311,0,7136.477,37.90691,0,12,1,444.1596,27.6808,0,0,0,471.8404,0,0,0,2,1,3,85.3,8.7,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,78.56193,8.873115,1.098612,6.156641,1 5,1,25,1,4,126311,0,7136.477,38.90691,0,12,1,76.07192,6.339327,52.0332,0,0,134.4444,0,0,0,5,1,3,85.3,8.7,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,78.56193,8.873115,1.098612,4.901151,1 5,1,25,1,5,126311,0,7136.477,39.90691,0,12,1,20.31316,8.78121,0,0,0,29.09437,0,0,0,5,0,3,85.3,8.7,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,78.56193,8.873115,1.098612,3.370545,1 5,1,25,1,1,126312,0,7136.477,.6680356,0,7,1,35.3717,2.068345,0,0,0,37.44005,0,0,0,6,0,3,74.36826,13.73189,0,,678,678,1,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,80.9538,8.873115,1.098612,3.622741,1 5,1,25,1,2,126312,0,7136.477,1.668036,0,7,1,8.214677,2.382256,0,0,0,10.59693,0,0,0,1,0,3,74.36826,13.73189,0,,678,678,1,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,80.9538,8.873115,1.098612,2.360565,1 5,1,25,1,3,126312,0,7136.477,2.668036,0,7,1,4.987531,0,0,0,0,4.987531,0,0,0,1,0,3,74.36826,13.73189,0,,678,678,1,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,80.9538,8.873115,1.098612,1.606941,1 5,1,25,1,4,126312,0,7136.477,3.668036,0,7,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,678,678,1,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,80.9538,8.873115,1.098612,,0 5,1,25,1,5,126312,0,7136.477,4.668036,0,7,1,22.85231,0,0,0,0,22.85231,0,0,0,2,1,3,74.36826,13.73189,0,,678,678,1,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,80.9538,8.873115,1.098612,3.129052,1 5,1,25,1,1,126313,0,7136.477,36.23819,1,7,1,11.39089,21.94245,0,0,0,33.33333,0,0,0,0,0,3,64.2,17.4,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,69.21714,8.873115,1.098612,3.506558,1 5,1,25,1,2,126313,0,7136.477,37.23819,1,7,1,43.81161,27.65608,0,0,0,71.46769,0,0,0,1,0,3,64.2,17.4,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,69.21714,8.873115,1.098612,4.269246,1 5,1,25,1,3,126313,0,7136.477,38.23819,1,7,1,23.94015,34.0399,26.58354,0,0,84.56359,0,0,0,1,1,3,64.2,17.4,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,69.21714,8.873115,1.098612,4.437504,1 5,1,25,1,4,126313,0,7136.477,39.23819,1,7,1,47.02628,47.30291,0,0,0,94.32919,0,0,0,3,0,3,64.2,17.4,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,69.21714,8.873115,1.098612,4.546791,1 5,1,25,1,5,126313,0,7136.477,40.23819,1,7,1,121.5404,33.64367,56.60601,0,0,211.7901,0,0,0,5,1,3,64.2,17.4,0,,678,678,0,0,1.098612,6.519147,0,3.258096,7.905442,0,0,0,69.21714,8.873115,1.098612,5.355596,1 5,1,25,1,1,126314,0,2291.563,19.36208,0,12,1,8.992805,0,38.21943,0,0,47.21223,0,0,0,0,1,1,73.7,0,0,,179,0,0,0,0,0,0,3.258096,6.57368,0,0,0,77.80665,7.737426,0,3.854653,1 5,1,25,1,2,126314,0,2291.563,20.36208,0,12,1,0,0,39.49069,0,0,39.49069,0,0,0,0,0,1,73.7,0,0,,179,0,0,0,0,0,0,3.258096,6.57368,0,0,0,77.80665,7.737426,0,3.676065,1 5,1,25,1,3,126314,0,2291.563,21.36208,0,12,1,8.977556,1.9202,31.79551,0,0,42.69327,0,0,0,0,1,1,73.7,0,0,,179,0,0,0,0,0,0,3.258096,6.57368,0,0,0,77.80665,7.737426,0,3.754041,1 5,1,25,1,4,126314,0,2291.563,22.36208,0,12,1,49.80175,0,20.71922,0,0,70.52098,0,0,0,1,0,1,73.7,0,0,,179,0,0,0,0,0,0,3.258096,6.57368,0,0,0,77.80665,7.737426,0,4.25591,1 5,1,25,1,5,126314,0,2291.563,23.36208,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,73.7,0,0,,179,0,0,0,0,0,0,3.258096,6.57368,0,0,0,77.80665,7.737426,0,,0 8,1,50,0,1,126319,0,3717.07,6.833675,0,13,1,12.58993,10.61151,0,0,0,23.20144,0,0,0,2,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,3.931826,0,0,0,0,84.2508,8.22096,1.386294,3.144214,1 8,1,50,0,2,126319,0,3717.07,7.833675,0,13,1,34.50164,15.03286,0,0,0,49.5345,0,0,0,5,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,3.931826,0,0,0,0,84.2508,8.22096,1.386294,3.902669,1 8,1,50,0,3,126319,0,3717.07,8.833675,0,13,1,42.39402,16.28429,0,0,0,58.6783,0,0,0,7,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,3.931826,0,0,0,0,84.2508,8.22096,1.386294,4.07207,1 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1,1,0,0,1,126333,0,8401.985,29.08419,1,16,1,60.6596,27.34393,42.40283,0,0,130.4064,0,0,0,4,1,3,87.4,13,0,,450,450,0,0,1.098612,6.109248,1,0,0,0,0,0,79.51961,9.036343,1.098612,4.870656,1 1,1,0,0,2,126333,0,8401.985,30.08419,1,16,1,2.695418,27.78976,0,0,736.7925,767.2776,1,0,0,0,0,3,87.4,13,0,,450,450,0,0,1.098612,6.109248,1,0,0,0,0,0,79.51961,9.036343,1.098612,6.642848,1 1,1,0,0,3,126333,0,8401.985,31.08419,1,16,1,26.04423,24.44226,0,0,0,50.48649,0,0,0,3,0,4,87.4,13,0,,450,450,0,0,1.386294,6.109248,1,0,0,0,0,0,79.51961,9.036343,1.386294,3.921706,1 11,1,0,1,1,126340,0,6321.658,17.30048,1,14,1,132.0643,70.28555,42.75431,0,0,245.1041,0,0,0,19,0,3,92.6,13,0,,0,0,1,1,1.098612,0,0,0,0,0,0,0,74.41844,8.751895,1.098612,5.501683,1 11,1,0,1,2,126340,0,6321.658,18.30048,1,14,1,22.86336,70.44093,0,0,0,93.3043,0,0,0,3,0,3,92.6,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,74.41844,8.751895,1.098612,4.535866,1 11,1,0,1,3,126340,0,6321.658,19.30048,1,14,1,101.0902,64.54411,45.16848,0,0,210.8028,0,0,0,10,0,3,92.6,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,74.41844,8.751895,1.098612,5.350923,1 11,1,0,1,1,126341,0,6321.658,53.70021,1,14,1,60.08328,23.3492,46.62106,0,0,130.0535,0,0,0,7,0,3,38.9,17.4,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.69423,8.751895,1.098612,4.867946,1 11,1,0,1,2,126341,0,6321.658,54.70021,1,14,1,67.55579,39.62983,4.545455,0,832.798,944.5291,1,0,0,9,0,3,38.9,17.4,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.69423,8.751895,1.098612,6.850687,1 11,1,0,1,3,126341,0,6321.658,55.70021,1,14,1,181.8632,15.36174,44.33102,0,0,241.556,0,0,0,6,1,3,38.9,17.4,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.69423,8.751895,1.098612,5.487102,1 11,1,0,1,1,126342,0,6321.658,53.65366,0,14,1,66.62701,12.0464,38.07257,0,0,116.746,0,0,0,5,6,3,52.6,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,76.12692,8.751895,1.098612,4.760001,1 11,1,0,1,2,126342,0,6321.658,54.65366,0,14,1,97.44148,37.34349,0,0,0,134.785,0,0,0,10,5,3,52.6,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,76.12692,8.751895,1.098612,4.903681,1 11,1,0,1,3,126342,0,6321.658,55.65366,0,14,1,46.08523,23.14172,37.16551,0,0,106.3925,0,0,0,1,5,3,52.6,13,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,76.12692,8.751895,1.098612,4.667135,1 11,1,0,1,1,126343,0,5065.757,23.02259,0,12,1,46.84117,6.632957,34.19393,0,0,87.66805,0,0,0,6,0,1,62.1,4.3,0,,0,52,0,0,0,3.951244,0,0,0,0,0,0,77.42352,8.530457,0,4.473557,1 11,1,0,1,2,126343,0,5065.757,24.02259,0,12,1,20.6859,8.328797,0,0,0,29.0147,0,0,0,4,0,1,62.1,4.3,0,,0,52,0,0,0,3.951244,0,0,0,0,0,0,77.42352,8.530457,0,3.367803,1 11,1,0,1,3,126343,0,5065.757,25.02259,0,12,1,48.56293,12.31417,29.51933,0,0,90.39643,0,0,0,7,0,1,62.1,4.3,0,,0,52,0,0,0,3.951244,0,0,0,0,0,0,77.42352,8.530457,0,4.504205,1 5,1,25,0,1,126350,0,9707.816,61.90281,1,11,1,107.3322,87.77974,39.3934,0,0,234.5053,0,0,0,15,0,1,95,13.73189,0,,716,716,0,0,0,6.57368,0,3.258096,7.959975,0,0,0,76.49065,9.18079,0,5.457478,1 5,1,25,0,2,126350,0,9707.816,62.90281,1,11,1,165.2291,86.33962,46.47979,0,1636.14,1934.189,1,0,0,16,0,1,95,13.73189,0,,716,716,0,0,0,6.57368,0,3.258096,7.959975,0,0,0,76.49065,9.18079,0,7.567443,1 5,1,25,0,3,126350,0,9707.816,63.90281,1,11,1,29.48403,50.71253,0,0,0,80.19656,0,0,0,3,0,1,95,13.73189,0,,716,716,0,0,0,6.57368,0,3.258096,7.959975,0,0,0,76.49065,9.18079,0,4.38448,1 5,1,25,0,1,126359,0,6277.295,32.53114,1,9,1,6.478209,0,0,0,0,6.478209,0,0,0,1,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,69.47372,8.744854,1.386294,1.868444,1 5,1,25,0,2,126359,0,6277.295,33.53114,1,9,1,25.87601,2.156334,0,0,0,28.03234,0,0,0,2,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,69.47372,8.744854,1.386294,3.333359,1 5,1,25,0,3,126359,0,6277.295,34.53114,1,9,1,46.06879,6.314497,0,0,0,52.38329,0,0,0,3,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,69.47372,8.744854,1.386294,3.958588,1 5,1,25,0,4,126359,0,6277.295,35.53114,1,9,1,26.89152,0,0,0,0,26.89152,0,0,0,2,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,69.47372,8.744854,1.386294,3.291811,1 5,1,25,0,5,126359,0,6277.295,36.53114,1,9,1,350.3126,6.669446,196.0192,0,0,553.0012,0,0,0,9,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,69.47372,8.744854,1.386294,6.31536,1 5,1,25,0,1,126360,0,6277.295,9.029432,1,9,1,19.43463,0,20.84806,0,0,40.28268,0,0,0,2,1,4,74.36826,13.73189,0,,500,500,1,1,1.386294,6.214608,0,3.258096,7.600903,1,0,0,76.85021,8.744854,1.386294,3.695922,1 5,1,25,0,2,126360,0,6277.295,10.02943,1,9,1,5.390836,2.495957,8.964959,0,0,16.85175,0,0,0,1,0,4,74.36826,13.73189,0,,500,500,1,1,1.386294,6.214608,0,3.258096,7.600903,1,0,0,76.85021,8.744854,1.386294,2.824455,1 5,1,25,0,3,126360,0,6277.295,11.02943,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,500,500,1,1,1.386294,6.214608,0,3.258096,7.600903,1,0,0,76.85021,8.744854,1.386294,,0 5,1,25,0,4,126360,0,6277.295,12.02943,1,9,1,46.03464,2.734731,0,0,0,48.76937,0,0,0,2,1,4,74.36826,13.73189,0,,500,500,1,1,1.386294,6.214608,0,3.258096,7.600903,1,0,0,76.85021,8.744854,1.386294,3.887102,1 5,1,25,0,5,126360,0,6277.295,13.02943,1,9,1,40.45436,5.360567,23.88495,0,492.0342,561.7341,1,0,0,2,0,4,74.36826,13.73189,0,,500,500,1,1,1.386294,6.214608,0,3.258096,7.600903,1,0,0,76.85021,8.744854,1.386294,6.331028,1 5,1,25,0,1,126361,0,6277.295,32.85695,0,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.12982,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,71.97958,8.744854,1.386294,,0 5,1,25,0,2,126361,0,6277.295,33.85695,0,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.12982,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,71.97958,8.744854,1.386294,,0 5,1,25,0,3,126361,0,6277.295,34.85695,0,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.12982,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,71.97958,8.744854,1.386294,,0 5,1,25,0,4,126361,0,6277.295,35.85695,0,12.32507,1,30.76572,0,0,0,0,30.76572,0,0,0,1,0,4,74.36826,13.73189,.12982,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,71.97958,8.744854,1.386294,3.426401,1 5,1,25,0,5,126361,0,6277.295,36.85695,0,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.12982,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,71.97958,8.744854,1.386294,,0 5,1,25,0,1,126362,0,6277.295,14.23956,0,9,1,104.2108,.8598351,0,0,0,105.0707,0,0,0,3,0,4,95,13.73189,0,,500,500,1,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,70.41564,8.744854,1.386294,4.654633,1 5,1,25,0,2,126362,0,6277.295,15.23956,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,95,13.73189,0,,500,500,1,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,70.41564,8.744854,1.386294,,0 5,1,25,0,3,126362,0,6277.295,16.23956,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,95,13.73189,0,,500,500,1,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,70.41564,8.744854,1.386294,,0 5,1,25,0,4,126362,0,6277.295,17.23956,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,95,13.73189,0,,500,500,1,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,70.41564,8.744854,1.386294,,0 5,1,25,0,5,126362,0,6277.295,18.23956,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,95,13.73189,0,,500,500,0,0,1.386294,6.214608,0,3.258096,7.600903,1,0,0,70.41564,8.744854,1.386294,,0 4,1,100,1,1,126384,0,4298.431,62.02327,1,11,1,26.50177,11.16019,0,0,0,37.66196,0,0,0,3,0,1,81.1,17.4,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,68.18743,8.366238,0,3.62865,1 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5,1,25,1,4,126388,0,7500,34.48528,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,65.3,4.3,0,,455,455,0,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,78.04445,8.922791,1.386294,,0 5,1,25,1,5,126388,0,7500,35.48528,0,12,1,13.46235,0,0,0,0,13.46235,0,0,0,1,0,4,65.3,4.3,0,,455,455,0,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,78.04445,8.922791,1.386294,2.599897,1 5,1,25,1,1,126389,0,7500,5.738535,0,12,1,11.89768,7.406306,0,0,0,19.30399,0,0,0,1,0,4,74.36826,13.73189,0,,455,455,1,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,82.59333,8.922791,1.386294,2.960312,1 5,1,25,1,2,126389,0,7500,6.738535,0,12,1,24.76864,5.389222,0,0,0,30.15787,0,0,0,3,0,4,74.36826,13.73189,0,,455,455,1,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,82.59333,8.922791,1.386294,3.406446,1 5,1,25,1,3,126389,0,7500,7.738535,0,12,1,28.74133,4.88107,0,0,0,33.6224,0,0,0,3,0,4,74.36826,13.73189,0,,455,455,1,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,82.59333,8.922791,1.386294,3.515193,1 5,1,25,1,4,126389,0,7500,8.738535,0,12,1,13.99725,2.294631,0,0,0,16.29188,0,0,0,2,0,4,74.36826,13.73189,0,,455,455,1,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,82.59333,8.922791,1.386294,2.790667,1 5,1,25,1,5,126389,0,7500,9.738535,0,12,1,34.939,0,0,0,0,34.939,0,0,0,2,0,4,74.36826,13.73189,0,,455,455,1,0,1.386294,6.120297,0,3.258096,7.506592,0,0,0,82.59333,8.922791,1.386294,3.553604,1 1,1,0,1,1,126390,0,4179.28,43.07734,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,80,17.4,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,70.18314,8.338134,1.098612,,0 1,1,0,1,2,126390,0,4179.28,44.07734,1,12,1,46.36119,2.695418,14.01617,0,0,63.07278,0,0,0,4,1,3,80,17.4,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,70.18314,8.338134,1.098612,4.144289,1 1,1,0,1,3,126390,0,4179.28,45.07734,1,12,1,8.353808,0,0,0,0,8.353808,0,0,0,1,0,3,80,17.4,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,70.18314,8.338134,1.098612,2.122718,1 1,1,0,1,4,126390,0,4179.28,46.07734,1,12,1,9.11577,0,0,0,0,9.11577,0,0,0,0,0,2,80,17.4,0,,450,460,0,0,.6931472,6.131227,1,0,0,1,0,0,70.18314,8.338134,.6931472,2.210006,1 1,1,0,1,5,126390,0,4179.28,47.07734,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,80,17.4,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,70.18314,8.338134,1.098612,,0 1,1,0,1,1,126391,0,4179.28,13.04586,0,12,1,5.300354,1.766784,0,0,0,7.067138,0,0,0,1,0,3,74.36826,13.73189,0,,450,460,1,0,1.098612,6.131227,1,0,0,1,0,0,75.23473,8.338134,1.098612,1.955456,1 1,1,0,1,2,126391,0,4179.28,14.04586,0,12,1,28.03234,0,19.11051,0,0,47.14286,0,0,0,1,1,3,74.36826,13.73189,0,,450,460,1,0,1.098612,6.131227,1,0,0,1,0,0,75.23473,8.338134,1.098612,3.853183,1 1,1,0,1,3,126391,0,4179.28,15.04586,0,12,1,62.89926,0,0,0,0,62.89926,0,0,0,3,0,3,74.36826,13.73189,0,,450,460,1,0,1.098612,6.131227,1,0,0,1,0,0,75.23473,8.338134,1.098612,4.141534,1 1,1,0,1,4,126391,0,4179.28,16.04586,0,12,1,75.90246,0,34.77211,0,0,110.6746,0,0,0,5,1,2,74.36826,13.73189,0,,450,460,1,0,.6931472,6.131227,1,0,0,1,0,0,75.23473,8.338134,.6931472,4.706594,1 1,1,0,1,5,126391,0,4179.28,17.04586,0,12,1,15.00625,.8336807,0,0,0,15.83993,0,0,0,1,0,3,74.36826,13.73189,0,,450,460,1,0,1.098612,6.131227,1,0,0,1,0,0,75.23473,8.338134,1.098612,2.762534,1 1,1,0,1,1,126392,0,4179.28,39.97262,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,76.8,4.3,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,67.17622,8.338134,1.098612,,0 1,1,0,1,2,126392,0,4179.28,40.97262,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,76.8,4.3,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,67.17622,8.338134,1.098612,,0 1,1,0,1,3,126392,0,4179.28,41.97262,0,11,1,66.58477,0,0,0,0,66.58477,0,0,0,1,0,3,76.8,4.3,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,67.17622,8.338134,1.098612,4.198476,1 1,1,0,1,5,126392,0,4179.28,43.97262,0,11,1,45.22718,0,4.126719,0,0,49.3539,0,0,0,2,0,3,76.8,4.3,0,,450,460,0,0,1.098612,6.131227,1,0,0,1,0,0,67.17622,8.338134,1.098612,3.899017,1 9,1,50,0,1,126410,0,10069.48,26.74607,1,16,1,200.8393,22.97362,0,0,0,223.8129,0,0,0,12,0,2,93.8,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,69.61801,9.217363,.6931472,5.41081,1 9,1,50,0,2,126410,0,10069.48,27.74607,1,16,1,465.3341,0,0,0,0,465.3341,0,0,0,5,0,2,93.8,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,69.61801,9.217363,.6931472,6.142756,1 9,1,50,0,3,126410,0,10069.48,28.74607,1,16,1,74.06483,0,0,0,0,74.06483,0,0,0,1,0,2,93.8,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,69.61801,9.217363,.6931472,4.304941,1 9,1,50,0,1,126411,0,10069.48,26.6475,0,16,1,0,0,0,0,0,0,0,0,0,0,0,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,72.70196,9.217363,.6931472,,0 9,1,50,0,2,126411,0,10069.48,27.6475,0,16,1,6.571742,0,36.8839,0,0,43.45564,0,0,0,0,1,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,72.70196,9.217363,.6931472,3.771741,1 9,1,50,0,3,126411,0,10069.48,28.6475,0,16,1,0,0,0,0,0,0,0,0,0,0,0,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,0,3.931826,7.600903,1,0,0,72.70196,9.217363,.6931472,,0 11,1,0,0,1,126417,0,7262.407,25.62354,1,18,1,24.14605,14.68787,0,0,0,38.83392,0,0,0,2,0,3,75,13.73189,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.44444,8.890604,1.098612,3.659294,1 11,1,0,0,2,126417,0,7262.407,26.62354,1,18,1,173.5849,26.81402,17.57412,0,0,217.9731,0,0,0,10,1,3,75,13.73189,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.44444,8.890604,1.098612,5.384371,1 11,1,0,0,3,126417,0,7262.407,27.62354,1,18,1,48.2801,10.79115,0,0,0,59.07125,0,0,0,5,0,3,75,13.73189,0,,0,0,0,0,1.098612,0,0,0,0,0,0,0,78.44444,8.890604,1.098612,4.078744,1 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7,1,25,0,2,126461,0,6078.164,50.24572,1,14,1,0,0,0,0,0,0,0,0,0,0,0,1,82.5,13.73189,0,,843,843,0,0,0,6.736967,0,3.258096,8.123261,0,0,0,79.31465,8.712623,0,,0 7,1,25,0,3,126461,0,6078.164,51.24572,1,14,1,0,0,0,0,0,0,0,0,0,0,0,1,82.5,13.73189,0,,843,843,0,0,0,6.736967,0,3.258096,8.123261,0,0,0,79.31465,8.712623,0,,0 2,1,100,0,1,126465,0,9542.002,59.37577,1,8,1,77.73852,111.331,0,0,0,189.0695,0,0,0,14,0,1,93.7,26.1,1,,107,0,0,0,0,0,1,0,0,0,0,0,74.02026,9.163564,0,5.242115,1 2,1,100,0,2,126465,0,9542.002,60.37577,1,8,.4098361,33.42318,51.71429,0,0,0,85.13747,0,0,0,3,1,1,93.7,26.1,1,,107,0,0,0,0,0,1,0,0,0,0,0,74.02026,9.163564,0,4.444267,1 1,1,0,0,1,126467,0,9542.002,51.60301,0,13,1,59.48174,17.95642,0,0,0,77.43816,0,0,0,7,0,3,52.6,21.7,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,69.33797,9.163564,1.098612,4.34948,1 1,1,0,0,2,126467,0,9542.002,52.60301,0,13,1,17.78976,0,0,0,0,17.78976,0,0,0,3,0,3,52.6,21.7,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,69.33797,9.163564,1.098612,2.878623,1 1,1,0,0,3,126467,0,9542.002,53.60301,0,13,1,28.13268,0,0,0,0,28.13268,0,0,0,2,0,3,52.6,21.7,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,69.33797,9.163564,1.098612,3.336932,1 1,1,0,0,1,126468,0,9542.002,18.52704,1,12,1,30.62426,9.045937,0,0,0,39.6702,0,0,0,1,0,3,42.1,13,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,72.70441,9.163564,1.098612,3.6806,1 1,1,0,0,2,126468,0,9542.002,19.52704,1,12,1,40.97035,5.369272,26.41509,0,0,72.75471,0,0,0,3,0,3,42.1,13,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,72.70441,9.163564,1.098612,4.287094,1 1,1,0,0,3,126468,0,9542.002,20.52704,1,12,1,42.01474,11.95086,0,0,767.0762,821.0417,1,0,0,2,0,3,42.1,13,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,72.70441,9.163564,1.098612,6.710574,1 1,1,0,0,1,126469,0,9542.002,47.10199,1,12,1,89.28151,26.27797,0,0,992.2438,1107.803,1,0,0,8,0,3,51.6,8.7,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,71.65771,9.163564,1.098612,7.010134,1 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8,1,50,1,4,126488,0,1728.908,50.95893,1,11,1,19.73382,9.343736,0,0,0,29.07756,0,0,0,2,0,4,64.2,26.1,0,,163,450,0,0,1.386294,6.109248,0,3.931826,5.786897,1,0,0,67.26075,7.455823,1.386294,3.369967,1 8,1,50,1,5,126488,0,1728.908,51.95893,1,11,1,276.7144,45.35128,47.48002,0,470.8162,840.3618,1,0,0,7,1,4,64.2,26.1,0,,163,450,0,0,1.386294,6.109248,0,3.931826,5.786897,1,0,0,67.26075,7.455823,1.386294,6.733832,1 8,1,50,1,1,126489,0,1728.908,15.86858,1,11,1,95.18144,36.38311,0,0,25263.65,25395.21,1,0,0,9,0,3,75.8,13,0,,163,450,1,1,1.098612,6.109248,0,3.931826,5.786897,1,0,0,63.92562,7.455823,1.098612,10.14232,1 8,1,50,1,2,126489,0,1728.908,16.86858,1,11,1,27.21829,27.10942,0,0,0,54.32771,0,0,0,3,0,4,75.8,13,0,,163,450,1,1,1.386294,6.109248,0,3.931826,5.786897,1,0,0,63.92562,7.455823,1.386294,3.995034,1 8,1,50,1,3,126489,0,1728.908,17.86858,1,11,1,80.94153,38.60258,41.94747,0,0,161.4916,0,0,0,7,2,4,75.8,13,0,,163,450,1,1,1.386294,6.109248,0,3.931826,5.786897,1,0,0,63.92562,7.455823,1.386294,5.084453,1 8,1,50,1,4,126489,0,1728.908,18.86858,1,11,1,76.8793,25.69527,0,0,0,102.5746,0,0,0,4,0,4,75.8,13,0,,163,450,0,0,1.386294,6.109248,0,3.931826,5.786897,1,0,0,63.92562,7.455823,1.386294,4.63059,1 8,1,50,1,5,126489,0,1728.908,19.86858,1,11,1,134.8296,25.7257,0,0,223.8199,384.3753,1,0,0,5,1,4,75.8,13,0,,163,450,0,0,1.386294,6.109248,0,3.931826,5.786897,1,0,0,63.92562,7.455823,1.386294,5.951619,1 7,1,25,1,1,126497,0,5035.244,58.32717,0,8,1,143.735,43.96283,25.94125,0,558.5552,772.1943,1,0,0,10,0,2,65.3,21.7,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,1,0,58.96577,8.524416,.6931472,6.649236,1 7,1,25,1,2,126497,0,5035.244,59.32717,0,8,1,15.88171,57.19058,30.77218,0,0,103.8445,0,0,0,2,1,2,65.3,21.7,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,1,0,58.96577,8.524416,.6931472,4.642894,1 7,1,25,1,1,126498,0,5035.244,49.82888,1,8,1,285.8513,28.84892,0,0,0,314.7002,0,0,0,6,0,2,80,8.7,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,1,0,0,63.43494,8.524416,.6931472,5.75162,1 7,1,25,1,2,126498,0,5035.244,50.82888,1,8,1,17.52464,11.33625,37.48083,0,448.7952,515.1369,1,0,0,2,1,2,80,8.7,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,1,0,0,63.43494,8.524416,.6931472,6.244433,1 3,1,100,0,1,126516,0,17296.53,33.73032,1,16,1,16.65675,11.08864,0,0,0,27.74539,0,0,0,2,0,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.55659,9.758319,1.609438,3.32307,1 3,1,100,0,2,126516,0,17296.53,34.73032,1,16,1,371.8018,30.44638,0,0,0,402.2482,0,0,0,10,0,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.55659,9.758319,1.609438,5.997069,1 3,1,100,0,3,126516,0,17296.53,35.73032,1,16,1,10.15857,43.91972,0,0,0,54.0783,0,0,0,1,0,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.55659,9.758319,1.609438,3.990433,1 3,1,100,0,1,126517,0,17296.53,9.330595,0,16,1,20.22606,17.93575,0,0,0,38.16181,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,77.79649,9.758319,1.609438,3.641835,1 3,1,100,0,2,126517,0,17296.53,10.3306,0,16,1,29.9129,4.599891,0,0,0,34.51279,0,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,77.79649,9.758319,1.609438,3.54133,1 3,1,100,0,3,126517,0,17296.53,11.3306,0,16,1,22.79485,3.657086,0,0,0,26.45193,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,77.79649,9.758319,1.609438,3.275329,1 3,1,100,0,1,126518,0,17296.53,10.42026,1,16,1,24.39024,1.754908,0,0,0,26.14515,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,83.2181,9.758319,1.609438,3.263664,1 3,1,100,0,2,126518,0,17296.53,11.42026,1,16,1,77.81709,13.05389,0,0,182.5259,273.3969,1,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,83.2181,9.758319,1.609438,5.610924,1 3,1,100,0,3,126518,0,17296.53,12.42026,1,16,1,14.37066,0,0,0,0,14.37066,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,83.2181,9.758319,1.609438,2.665189,1 3,1,100,0,1,126519,0,17296.53,34.54894,0,18,1,35.09816,49.55384,0,0,0,84.65199,0,0,0,5,0,5,80,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.61339,9.758319,1.609438,4.438549,1 3,1,100,0,2,126519,0,17296.53,35.54894,0,18,1,159.4992,37.40882,0,0,0,196.908,0,0,0,7,0,5,80,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.61339,9.758319,1.609438,5.282737,1 3,1,100,0,3,126519,0,17296.53,36.54894,0,18,1,101.5857,55.32706,0,0,0,156.9128,0,0,0,10,0,5,80,13.73189,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.61339,9.758319,1.609438,5.05569,1 3,1,100,0,1,126520,0,17296.53,4.112252,1,16,1,39.26234,15.84176,0,0,0,55.1041,0,0,0,5,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,71.73346,9.758319,1.609438,4.009224,1 3,1,100,0,2,126520,0,17296.53,5.112252,1,16,1,89.82036,26.32553,0,0,0,116.1459,0,0,0,6,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,71.73346,9.758319,1.609438,4.754847,1 3,1,100,0,3,126520,0,17296.53,6.112252,1,16,1,8.919723,16.0555,0,0,0,24.97522,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,71.73346,9.758319,1.609438,3.217884,1 8,1,50,0,1,126544,0,13919.98,11.49076,1,12,1,28.85748,2.37338,0,0,0,31.23086,0,0,0,2,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,84.23631,9.541152,1.098612,3.441407,1 8,1,50,0,2,126544,0,13919.98,12.49076,1,12,1,9.703504,0,0,0,0,9.703504,0,0,0,0,1,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,84.23631,9.541152,1.098612,2.272487,1 8,1,50,0,3,126544,0,13919.98,13.49076,1,12,1,7.371007,1.941032,0,0,0,9.312039,0,0,0,1,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,84.23631,9.541152,1.098612,2.231308,1 8,1,50,0,1,126545,0,13919.98,42.7269,1,12,1,283.5689,45.36514,0,0,0,328.9341,0,0,0,12,0,3,34.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,66.0592,9.541152,1.098612,5.795857,1 8,1,50,0,2,126545,0,13919.98,43.7269,1,12,1,264.469,92.54447,33.66038,0,0,390.6739,0,0,0,17,6,3,34.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,66.0592,9.541152,1.098612,5.967873,1 8,1,50,0,3,126545,0,13919.98,44.7269,1,12,1,0,47.2629,0,0,0,47.2629,0,0,0,0,0,3,34.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,66.0592,9.541152,1.098612,3.855726,1 8,1,50,0,1,126546,0,13919.98,43.47981,0,11,1,191.9906,6.861013,0,0,0,198.8516,0,0,0,2,0,3,74.7,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,61.27012,9.541152,1.098612,5.292559,1 8,1,50,0,2,126546,0,13919.98,44.47981,0,11,1,0,8.916442,0,0,0,8.916442,0,0,0,0,0,3,74.7,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,61.27012,9.541152,1.098612,2.187897,1 8,1,50,0,3,126546,0,13919.98,45.47981,0,11,1,32.43243,2.584767,53.40541,0,0,88.42261,0,0,0,1,0,3,74.7,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,61.27012,9.541152,1.098612,4.482128,1 3,1,100,1,1,126553,0,11575.06,3.471595,1,14,1,7.237636,0,0,0,0,7.237636,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,74.41945,9.356694,1.386294,1.979295,1 3,1,100,1,2,126553,0,11575.06,4.471595,1,14,1,18.72247,0,0,0,0,18.72247,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,74.41945,9.356694,1.386294,2.929724,1 3,1,100,1,3,126553,0,11575.06,5.471595,1,14,1,39.02562,2.556504,0,0,0,41.58212,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,74.41945,9.356694,1.386294,3.72767,1 3,1,100,1,4,126553,0,11575.06,6.471595,1,14,1,12.05378,0,0,0,0,12.05378,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,74.41945,9.356694,1.386294,2.489378,1 3,1,100,1,5,126553,0,11575.06,7.471595,1,14,1,16.58869,0,0,0,0,16.58869,0,0,0,1,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,74.41945,9.356694,1.386294,2.808721,1 3,1,100,1,1,126554,0,11575.06,32.55578,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,77.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.67448,9.356694,1.386294,,0 3,1,100,1,2,126554,0,11575.06,33.55578,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,77.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.67448,9.356694,1.386294,,0 3,1,100,1,3,126554,0,11575.06,34.55578,0,16,1,12.05424,0,14.1336,0,0,26.18785,0,0,0,0,1,4,77.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.67448,9.356694,1.386294,3.265295,1 3,1,100,1,4,126554,0,11575.06,35.55578,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,77.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.67448,9.356694,1.386294,,0 3,1,100,1,5,126554,0,11575.06,36.55578,0,16,1,11.90983,0,0,0,0,11.90983,0,0,0,0,1,4,77.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,71.67448,9.356694,1.386294,2.477364,1 3,1,100,1,1,126555,0,11575.06,6.401095,1,14,1,9.650181,8.27503,0,0,0,17.92521,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,77.85107,9.356694,1.386294,2.886208,1 3,1,100,1,2,126555,0,11575.06,7.401095,1,14,1,0,7.621145,0,0,0,7.621145,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,77.85107,9.356694,1.386294,2.030927,1 3,1,100,1,3,126555,0,11575.06,8.401095,1,14,1,15.57007,4.650929,0,0,0,20.22099,0,0,0,1,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,77.85107,9.356694,1.386294,3.006721,1 3,1,100,1,4,126555,0,11575.06,9.401095,1,14,1,12.05378,2.545202,20.34771,0,0,34.94669,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,77.85107,9.356694,1.386294,3.553824,1 3,1,100,1,5,126555,0,11575.06,10.4011,1,14,1,16.58869,0,12.44577,0,0,29.03445,0,0,0,1,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,1,0,0,77.85107,9.356694,1.386294,3.368483,1 3,1,100,1,1,126556,0,11575.06,31.86311,1,14,1,28.34741,0,25.84439,0,0,54.1918,0,0,0,2,1,4,86.3,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,72.42058,9.356694,1.386294,3.99253,1 3,1,100,1,2,126556,0,11575.06,32.86311,1,14,1,0,0,0,0,0,0,0,0,0,0,0,4,86.3,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,72.42058,9.356694,1.386294,,0 3,1,100,1,3,126556,0,11575.06,33.86311,1,14,1,10.54746,0,0,0,0,10.54746,0,0,0,1,0,4,86.3,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,72.42058,9.356694,1.386294,2.355886,1 3,1,100,1,4,126556,0,11575.06,34.86311,1,14,1,23.64395,1.687529,0,0,0,25.33148,0,0,0,1,1,4,86.3,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,72.42058,9.356694,1.386294,3.232048,1 3,1,100,1,5,126556,0,11575.06,35.86311,1,14,1,11.48448,0,0,0,0,11.48448,0,0,0,1,0,4,86.3,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,72.42058,9.356694,1.386294,2.440996,1 4,1,100,1,1,126566,0,8857.32,37.21561,0,13,1,80.33573,134.2626,0,0,0,214.5983,0,0,0,4,0,5,93.7,8.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.11638,9.089112,1.609438,5.368768,1 4,1,100,1,2,126566,0,8857.32,38.21561,0,13,1,108.4337,120.2026,22.32202,0,0,250.9584,0,0,0,3,0,5,93.7,8.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,72.11638,9.089112,1.609438,5.525287,1 4,1,100,1,3,126566,0,8857.32,39.21561,0,13,1,9.476309,0,0,0,0,9.476309,0,0,0,1,0,6,93.7,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,72.11638,9.089112,1.791759,2.248795,1 4,1,100,1,4,126566,0,8857.32,40.21561,0,13,1,9.22084,4.601199,0,0,0,13.82204,0,0,0,1,0,6,93.7,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,72.11638,9.089112,1.791759,2.626264,1 4,1,100,1,5,126566,0,8857.32,41.21561,0,13,1,.5289886,2.391028,0,0,0,2.920017,0,0,0,0,0,6,93.7,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,72.11638,9.089112,1.791759,1.071589,1 4,1,100,1,1,126567,0,8857.32,14.45311,0,12,1,29.3765,0,0,0,0,29.3765,0,0,0,0,0,5,95.8,0,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,79.27259,9.089112,1.609438,3.380195,1 4,1,100,1,2,126567,0,8857.32,15.45311,0,12,1,0,4.359255,0,0,0,4.359255,0,0,0,0,0,5,95.8,0,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,79.27259,9.089112,1.609438,1.472301,1 4,1,100,1,3,126567,0,8857.32,16.45311,0,12,1,26.18454,6.528678,0,0,0,32.71322,0,0,0,3,0,6,95.8,0,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,0,0,0,79.27259,9.089112,1.791759,3.487779,1 4,1,100,1,4,126567,0,8857.32,17.45311,0,12,1,23.0521,0,0,0,0,23.0521,0,0,0,2,0,6,95.8,0,0,,1000,1000,1,0,1.791759,6.907755,1,0,0,0,0,0,79.27259,9.089112,1.791759,3.137757,1 4,1,100,1,5,126567,0,8857.32,18.45311,0,12,1,8.463818,0,0,0,0,8.463818,0,0,0,1,0,6,95.8,0,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,0,0,0,79.27259,9.089112,1.791759,2.1358,1 4,1,100,1,1,126568,0,8857.32,13.40178,1,12,1,14.98801,3.52518,0,0,0,18.51319,0,0,0,1,0,5,74.36826,13.73189,.0277778,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,77.07726,9.089112,1.609438,2.918483,1 4,1,100,1,2,126568,0,8857.32,14.40178,1,12,1,6.024096,4.813801,16.59912,0,0,27.43702,0,0,0,1,0,5,74.36826,13.73189,.0277778,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,77.07726,9.089112,1.609438,3.311893,1 4,1,100,1,3,126568,0,8857.32,15.40178,1,12,1,17.45636,2.024938,0,0,0,19.4813,0,0,0,2,0,6,74.36826,13.73189,.0277778,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,77.07726,9.089112,1.791759,2.969455,1 4,1,100,1,4,126568,0,8857.32,16.40178,1,12,1,0,3.720608,0,0,0,3.720608,0,0,0,0,0,6,74.36826,13.73189,.0277778,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,77.07726,9.089112,1.791759,1.313887,1 4,1,100,1,5,126568,0,8857.32,17.40178,1,12,1,74.1642,3.643673,28.67964,0,0,106.4875,0,0,0,2,1,6,74.36826,13.73189,.0277778,,1000,1000,1,1,1.791759,6.907755,1,0,0,1,0,0,77.07726,9.089112,1.791759,4.668028,1 4,1,100,1,1,126569,0,8857.32,6.075291,1,12,1,4.196643,12.58993,0,0,0,16.78657,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,83.01283,9.089112,1.609438,2.820579,1 4,1,100,1,2,126569,0,8857.32,7.075291,1,12,1,16.977,11.17196,0,0,0,28.14896,0,0,0,1,1,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,0,0,0,83.01283,9.089112,1.609438,3.33751,1 4,1,100,1,3,126569,0,8857.32,8.075291,1,12,1,0,3.690773,0,0,0,3.690773,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,0,0,0,83.01283,9.089112,1.791759,1.305836,1 4,1,100,1,4,126569,0,8857.32,9.075291,1,12,1,29.50669,6.325496,0,0,0,35.83218,0,0,0,1,3,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,0,0,0,83.01283,9.089112,1.791759,3.578846,1 4,1,100,1,5,126569,0,8857.32,10.07529,1,12,1,17.45662,1.447313,0,0,0,18.90394,0,0,0,0,3,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,1,0,0,0,0,0,83.01283,9.089112,1.791759,2.93937,1 4,1,100,1,1,126570,0,8857.32,32.82683,1,12,1,40.76739,2.392086,0,0,0,43.15947,0,0,0,2,0,5,88.4,8.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,66.55627,9.089112,1.609438,3.764902,1 4,1,100,1,2,126570,0,8857.32,33.82683,1,12,1,60.24096,11.12815,0,0,1100.181,1171.55,1,0,0,6,0,5,88.4,8.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,66.55627,9.089112,1.609438,7.066083,1 4,1,100,1,3,126570,0,8857.32,34.82683,1,12,1,2.992519,2.109726,0,0,0,5.102244,0,0,0,0,0,6,88.4,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,66.55627,9.089112,1.791759,1.629681,1 4,1,100,1,4,126570,0,8857.32,35.82683,1,12,1,62.24067,0,0,0,0,62.24067,0,0,0,1,1,6,88.4,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,66.55627,9.089112,1.791759,4.131009,1 4,1,100,1,5,126570,0,8857.32,36.82683,1,12,1,18.30301,2.835379,0,0,0,21.13838,0,0,0,1,0,6,88.4,8.7,0,,1000,1000,0,0,1.791759,6.907755,1,0,0,1,0,0,66.55627,9.089112,1.791759,3.05109,1 9,1,50,1,1,126596,0,14330.02,35.03354,1,20,1,106.0071,42.30271,0,0,696.6019,844.9117,1,0,0,12,0,4,67.4,21.7,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,64.92197,9.570182,1.386294,6.739232,1 9,1,50,1,2,126596,0,14330.02,36.03354,1,20,1,100.2695,26.14016,8.625337,0,1220.782,1355.817,1,0,0,9,0,4,67.4,21.7,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,64.92197,9.570182,1.386294,7.212159,1 9,1,50,1,3,126596,0,14330.02,37.03354,1,20,1,83.53809,47.91155,0,0,0,131.4496,0,0,0,7,0,4,67.4,21.7,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,64.92197,9.570182,1.386294,4.878624,1 9,1,50,1,1,126597,0,14330.02,42.59822,0,24,1,21.20141,5.683156,49.85277,0,0,76.73734,0,0,0,3,0,4,68.4,13,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,68.74699,9.570182,1.386294,4.340388,1 9,1,50,1,2,126597,0,14330.02,43.59822,0,24,1,1.617251,2.754717,0,0,0,4.371968,0,0,0,2,0,4,68.4,13,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,68.74699,9.570182,1.386294,1.475213,1 9,1,50,1,3,126597,0,14330.02,44.59822,0,24,1,26.53563,4.127764,48.27027,0,0,78.93366,0,0,0,3,0,4,68.4,13,1,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,68.74699,9.570182,1.386294,4.368608,1 9,1,50,1,1,126598,0,14330.02,2.023272,0,20,1,55.35925,13.26266,4.711425,0,0,73.33334,0,0,0,10,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,4.295015,1 9,1,50,1,2,126598,0,14330.02,3.023272,0,20,1,51.21294,1.644205,0,0,0,52.85714,0,0,1,7,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,3.967593,1 9,1,50,1,3,126598,0,14330.02,4.023272,0,20,1,24.57002,11.46929,0,0,0,36.03931,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,3.58461,1 9,1,50,1,1,126599,0,14330.02,4.607803,0,20,1,324.7644,8.386337,0,0,0,333.1508,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,5.808595,1 9,1,50,1,2,126599,0,14330.02,5.607803,0,20,1,24.25876,4.684636,0,0,0,28.9434,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,3.365342,1 9,1,50,1,3,126599,0,14330.02,6.607803,0,20,1,15.23342,10.93366,0,0,0,26.16708,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,66.69353,9.570182,1.386294,3.264502,1 2,1,100,0,1,126609,0,9542.002,41.79603,1,12,1,45.34747,11.702,0,0,0,57.04947,0,0,0,4,0,4,90.5,8.7,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,72.1002,9.163564,1.386294,4.043919,1 2,1,100,0,2,126609,0,9542.002,42.79603,1,12,1,89.75742,4.911051,0,0,0,94.66846,0,0,0,3,0,4,90.5,8.7,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,72.1002,9.163564,1.386294,4.550381,1 2,1,100,0,3,126609,0,9542.002,43.79603,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,90.5,8.7,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,72.1002,9.163564,1.386294,,0 2,1,100,0,4,126609,0,9542.002,44.79603,1,12,1,13.67366,2.962625,0,0,0,16.63628,0,0,0,1,0,4,90.5,8.7,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,72.1002,9.163564,1.386294,2.811586,1 2,1,100,0,5,126609,0,9542.002,45.79603,1,12,1,38.76615,0,0,0,0,38.76615,0,0,0,1,0,4,90.5,8.7,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,72.1002,9.163564,1.386294,3.657547,1 2,1,100,0,1,126610,0,9542.002,14.57632,0,12,1,5.889281,0,0,0,0,5.889281,0,0,0,1,0,4,73.7,4.3,0,,0,0,1,0,1.386294,0,1,0,0,1,0,0,74.08881,9.163564,1.386294,1.773134,1 2,1,100,0,2,126610,0,9542.002,15.57632,0,12,1,13.47709,0,0,0,0,13.47709,0,0,0,1,0,4,73.7,4.3,0,,0,0,1,0,1.386294,0,1,0,0,1,0,0,74.08881,9.163564,1.386294,2.600991,1 2,1,100,0,3,126610,0,9542.002,16.57632,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,4.3,0,,0,0,1,0,1.386294,0,1,0,0,1,0,0,74.08881,9.163564,1.386294,,0 2,1,100,0,4,126610,0,9542.002,17.57632,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,4.3,0,,0,0,1,0,1.386294,0,1,0,0,1,0,0,74.08881,9.163564,1.386294,,0 2,1,100,0,5,126610,0,9542.002,18.57632,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,4.3,0,,0,0,0,0,1.386294,0,1,0,0,1,0,0,74.08881,9.163564,1.386294,,0 2,1,100,0,1,126611,0,9542.002,8.405202,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,1,0,0,1,0,0,79.46868,9.163564,1.386294,,0 2,1,100,0,2,126611,0,9542.002,9.405202,1,12,1,4.851752,3.28841,0,0,0,8.140162,0,0,0,1,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,1,0,0,1,0,0,79.46868,9.163564,1.386294,2.09681,1 2,1,100,0,3,126611,0,9542.002,10.4052,1,12,1,118.6732,23.14496,0,0,0,141.8182,0,0,0,8,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,1,0,0,1,0,0,79.46868,9.163564,1.386294,4.954546,1 2,1,100,0,4,126611,0,9542.002,11.4052,1,12,1,34.43482,15.10939,0,0,0,49.54421,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,1,0,0,1,0,0,79.46868,9.163564,1.386294,3.902865,1 2,1,100,0,5,126611,0,9542.002,12.4052,1,12,1,22.50938,21.21717,0,0,0,43.72655,0,0,0,2,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,1,0,0,1,0,0,79.46868,9.163564,1.386294,3.777956,1 2,1,100,0,1,126612,0,9542.002,19.47433,1,13,1,48.29211,40.22968,21.49588,0,0,110.0177,0,0,0,7,0,1,78.9,17.4,0,,0,0,0,0,0,0,1,0,0,1,0,0,74.33247,9.163564,0,4.700641,1 2,1,100,0,2,126612,0,9542.002,20.47433,1,13,1,44.20485,29.82749,0,0,0,74.03235,0,0,0,5,0,1,78.9,17.4,0,,0,0,0,0,0,0,1,0,0,1,0,0,74.33247,9.163564,0,4.304502,1 2,1,100,0,3,126612,0,9542.002,21.47433,1,13,1,45.70024,17.79852,0,0,0,63.49877,0,0,0,4,0,1,78.9,17.4,0,,0,0,0,0,0,0,1,0,0,1,0,0,74.33247,9.163564,0,4.151021,1 2,1,100,0,4,126612,0,9542.002,22.47433,1,13,1,34.27985,5.556062,0,0,0,39.83591,0,0,0,2,0,1,78.9,17.4,0,,0,0,0,0,0,0,1,0,0,1,0,0,74.33247,9.163564,0,3.684769,1 2,1,100,0,5,126612,0,9542.002,23.47433,1,13,1,33.34723,8.724468,0,0,0,42.0717,0,0,0,4,0,1,78.9,17.4,0,,0,0,0,0,0,0,1,0,0,1,0,0,74.33247,9.163564,0,3.739375,1 2,1,100,0,1,126613,0,9542.002,42.00411,0,12,1,24.44052,8.763251,37.27915,0,0,70.48292,0,0,0,1,0,4,77.9,13,0,,0,0,0,0,1.386294,0,1,0,0,0,1,0,66.69038,9.163564,1.386294,4.255371,1 2,1,100,0,2,126613,0,9542.002,43.00411,0,12,1,379.2668,48.46361,0,0,0,427.7305,0,0,0,6,0,4,77.9,13,0,,0,0,0,0,1.386294,0,1,0,0,0,1,0,66.69038,9.163564,1.386294,6.058493,1 2,1,100,0,3,126613,0,9542.002,44.00411,0,12,1,15.47912,16.09337,0,0,0,31.57248,0,0,0,2,0,4,77.9,13,0,,0,0,0,0,1.386294,0,1,0,0,0,1,0,66.69038,9.163564,1.386294,3.452286,1 2,1,100,0,4,126613,0,9542.002,45.00411,0,12,1,29.62625,24.09754,37.09207,0,0,90.81586,0,0,0,4,0,4,77.9,13,0,,0,0,0,0,1.386294,0,1,0,0,0,1,0,66.69038,9.163564,1.386294,4.508834,1 2,1,100,0,5,126613,0,9542.002,46.00411,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,77.9,13,0,,0,0,0,0,1.386294,0,1,0,0,0,1,0,66.69038,9.163564,1.386294,,0 1,1,0,0,1,126635,0,9092.432,1.092402,0,19,1,47.59072,9.970256,0,0,0,57.56097,0,0,0,8,0,4,74.36826,13.73189,0,,450,450,1,0,1.386294,6.109248,1,0,0,0,0,0,79.71203,9.115308,1.386294,4.052845,1 1,1,0,0,2,126635,0,9092.432,2.092402,0,19,1,31.8454,3.712575,0,0,0,35.55798,0,0,0,4,0,4,74.36826,13.73189,0,,450,450,1,0,1.386294,6.109248,1,0,0,0,0,0,79.71203,9.115308,1.386294,3.571164,1 1,1,0,0,3,126635,0,9092.432,3.092402,0,19,1,66.15461,10.53023,18.58276,0,0,95.26759,0,0,0,6,0,4,74.36826,13.73189,0,,450,450,1,0,1.386294,6.109248,1,0,0,0,0,0,79.71203,9.115308,1.386294,4.55669,1 1,1,0,0,4,126635,0,9092.432,4.092402,0,19,1,33.73107,5.043598,0,0,0,38.77467,0,0,0,4,0,4,74.36826,13.73189,0,,450,450,1,0,1.386294,6.109248,1,0,0,0,0,0,79.71203,9.115308,1.386294,3.657767,1 1,1,0,0,5,126635,0,9092.432,5.092402,0,19,1,40.80774,7.942785,28.18679,0,0,76.93732,0,0,0,3,0,4,74.36826,13.73189,0,,450,450,1,0,1.386294,6.109248,1,0,0,0,0,0,79.71203,9.115308,1.386294,4.342991,1 1,1,0,0,1,126636,0,9092.432,3.92334,1,19,1,27.36466,5.591909,0,0,0,32.95657,0,0,0,4,0,4,74.36826,13.73189,0,,450,450,1,1,1.386294,6.109248,1,0,0,0,0,0,79.19805,9.115308,1.386294,3.495191,1 1,1,0,0,2,126636,0,9092.432,4.92334,1,19,1,31.02885,5.035384,0,0,0,36.06424,0,0,0,3,0,4,74.36826,13.73189,0,,450,450,1,1,1.386294,6.109248,1,0,0,0,0,0,79.19805,9.115308,1.386294,3.585302,1 1,1,0,0,3,126636,0,9092.432,5.92334,1,19,1,84.14272,0,0,0,0,84.14272,0,0,0,2,1,4,74.36826,13.73189,0,,450,450,1,1,1.386294,6.109248,1,0,0,0,0,0,79.19805,9.115308,1.386294,4.432514,1 1,1,0,0,4,126636,0,9092.432,6.92334,1,19,1,19.04543,10.11014,0,0,0,29.15558,0,0,0,3,0,4,74.36826,13.73189,0,,450,450,1,1,1.386294,6.109248,1,0,0,0,0,0,79.19805,9.115308,1.386294,3.372646,1 1,1,0,0,5,126636,0,9092.432,7.92334,1,19,1,66.91628,14.11022,0,0,0,81.0265,0,0,0,3,0,4,74.36826,13.73189,0,,450,450,1,1,1.386294,6.109248,1,0,0,0,0,0,79.19805,9.115308,1.386294,4.394776,1 1,1,0,0,1,126637,0,9092.432,32.19439,0,19,1,14.8721,0,35.12195,0,0,49.99405,0,0,0,1,0,4,57.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,68.50468,9.115308,1.386294,3.911904,1 1,1,0,0,2,126637,0,9092.432,33.19439,0,19,1,0,0,0,0,0,0,0,0,0,0,0,4,57.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,68.50468,9.115308,1.386294,,0 1,1,0,0,3,126637,0,9092.432,34.19439,0,19,1,51.53617,2.685827,25.76809,0,0,79.99009,0,0,0,4,0,4,57.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,68.50468,9.115308,1.386294,4.381903,1 1,1,0,0,4,126637,0,9092.432,35.19439,0,19,1,116.5672,6.094539,0,0,0,122.6618,0,0,0,3,0,4,57.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,68.50468,9.115308,1.386294,4.809431,1 1,1,0,0,5,126637,0,9092.432,36.19439,0,19,1,3.365587,0,0,0,0,3.365587,0,0,0,0,0,4,57.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,68.50468,9.115308,1.386294,1.213602,1 1,1,0,0,1,126638,0,9092.432,30.88022,1,19,1,33.3135,27.72159,27.35277,0,0,88.38786,0,0,0,3,0,4,62.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,67.19316,9.115308,1.386294,4.481735,1 1,1,0,0,2,126638,0,9092.432,31.88022,1,19,1,26.12956,11.24115,0,0,0,37.37071,0,0,0,2,0,4,62.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,67.19316,9.115308,1.386294,3.620887,1 1,1,0,0,3,126638,0,9092.432,32.88022,1,19,1,14.8662,9.781962,0,0,0,24.64817,0,0,0,1,0,4,62.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,67.19316,9.115308,1.386294,3.204703,1 1,1,0,0,4,126638,0,9092.432,33.88022,1,19,1,48.64617,2.505737,0,0,0,51.15191,0,0,0,2,0,4,62.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,67.19316,9.115308,1.386294,3.9348,1 1,1,0,0,5,126638,0,9092.432,34.88022,1,19,1,24.8212,0,0,0,0,24.8212,0,0,0,2,0,4,62.5,13.73189,0,,450,450,0,0,1.386294,6.109248,1,0,0,1,0,0,67.19316,9.115308,1.386294,3.211698,1 6,1,25,0,1,126650,0,9743.176,32.97467,0,14,1,0,2.338045,0,0,0,2.338045,0,0,0,0,0,4,91.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,78.79201,9.184425,1.386294,.8493149,1 6,1,25,0,2,126650,0,9743.176,33.97467,0,14,1,43.66577,6.080863,0,0,0,49.74663,0,0,0,2,0,4,91.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,78.79201,9.184425,1.386294,3.906943,1 6,1,25,0,3,126650,0,9743.176,34.97467,0,14,1,7.371007,7.208845,0,0,0,14.57985,0,0,0,1,0,5,91.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,78.79201,9.184425,1.609438,2.679641,1 6,1,25,0,1,126651,0,9743.176,6.130048,0,12,1,50.79505,3.274441,0,0,0,54.06949,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,85.04264,9.184425,1.386294,3.99027,1 6,1,25,0,2,126651,0,9743.176,7.130048,0,12,1,18.86792,8.722372,0,0,0,27.5903,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,85.04264,9.184425,1.386294,3.317464,1 6,1,25,0,3,126651,0,9743.176,8.130048,0,12,1,64.37347,9.7543,0,0,0,74.12776,0,0,0,3,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,85.04264,9.184425,1.609438,4.30579,1 6,1,25,0,1,126652,0,9743.176,31.21424,1,12,1,80.09423,51.60188,0,0,0,131.6961,0,0,0,9,0,4,86.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.3639,9.184425,1.386294,4.880497,1 6,1,25,0,2,126652,0,9743.176,32.21424,1,12,1,124.5283,109.504,35.76819,0,4801.725,5071.525,1,0,0,11,0,4,86.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.3639,9.184425,1.386294,8.531397,1 6,1,25,0,3,126652,0,9743.176,33.21424,1,12,1,6.633907,58.45209,0,0,0,65.086,0,0,0,1,0,5,86.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.3639,9.184425,1.609438,4.175709,1 6,1,25,0,1,126653,0,9743.176,8.919918,0,12,1,130.7421,58.75147,0,0,0,189.4935,0,0,0,7,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.90643,9.184425,1.386294,5.244355,1 6,1,25,0,2,126653,0,9743.176,9.919918,0,12,1,90.29649,40.14016,26.95418,0,0,157.3908,0,0,0,31,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.90643,9.184425,1.386294,5.058732,1 6,1,25,0,3,126653,0,9743.176,10.91992,0,12,1,7.371007,18.92875,0,0,0,26.29976,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,73.90643,9.184425,1.609438,3.26956,1 11,1,0,1,1,126665,0,5583.747,3.039014,0,11,1,51.56815,9.408926,0,0,255.5609,316.538,1,0,0,5,1,5,74.36826,13.73189,0,,0,458,1,0,1.609438,6.126869,0,0,0,0,0,0,79.08629,8.627794,1.609438,5.757443,1 11,1,0,1,2,126665,0,5583.747,4.039014,0,11,1,62.67621,0,0,0,0,62.67621,0,0,0,3,0,6,74.36826,13.73189,0,,0,458,1,0,1.791759,6.126869,0,0,0,0,0,0,79.08629,8.627794,1.791759,4.137982,1 11,1,0,1,3,126665,0,5583.747,5.039014,0,11,1,47.21246,14.21396,0,0,0,61.42642,0,0,0,6,0,6,74.36826,13.73189,0,,0,458,1,0,1.791759,6.126869,0,0,0,0,0,0,79.08629,8.627794,1.791759,4.11784,1 11,1,0,1,4,126665,0,5583.747,6.039014,0,11,1,25.96198,5.656003,0,0,0,31.61799,0,0,0,4,0,6,74.36826,13.73189,0,,0,458,1,0,1.791759,6.126869,0,0,0,0,0,0,79.08629,8.627794,1.791759,3.453726,1 11,1,0,1,5,126665,0,5583.747,7.039014,0,11,1,13.61123,2.105487,20.44662,0,0,36.16333,0,0,0,1,1,6,74.36826,13.73189,0,,0,458,1,0,1.791759,6.126869,0,0,0,0,0,0,79.08629,8.627794,1.791759,3.588046,1 11,1,0,1,1,126666,0,5583.747,22.5243,1,11,1,15.68154,12.14113,0,0,1307.382,1335.205,1,0,0,3,0,5,96.8,8.7,0,,0,458,0,0,1.609438,6.126869,0,0,0,0,0,0,75.68143,8.627794,1.609438,7.19684,1 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1,1,0,1,3,126674,0,3711.042,26.17796,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,34.7,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,78.79221,8.219337,0,,0 1,1,0,1,4,126674,0,3711.042,27.17796,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,34.7,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,78.79221,8.219337,0,,0 1,1,0,1,5,126674,0,3711.042,28.17796,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,34.7,4.3,0,,150,0,0,0,0,0,1,0,0,0,0,0,78.79221,8.219337,0,,0 4,1,100,0,1,126678,0,4242.556,11.52088,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,75.05559,8.353157,1.94591,,0 4,1,100,0,2,126678,0,4242.556,12.52088,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,75.05559,8.353157,1.94591,,0 4,1,100,0,3,126678,0,4242.556,13.52088,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,75.05559,8.353157,1.94591,,0 4,1,100,0,1,126679,0,4242.556,10.59274,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,0,0,0,80.99117,8.353157,1.94591,,0 4,1,100,0,2,126679,0,4242.556,11.59274,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,0,0,0,80.99117,8.353157,1.94591,,0 4,1,100,0,3,126679,0,4242.556,12.59274,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,0,0,0,80.99117,8.353157,1.94591,,0 4,1,100,0,1,126680,0,4242.556,2.778919,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,68.34178,8.353157,1.94591,,0 4,1,100,0,2,126680,0,4242.556,3.778919,1,10,1,0,1.518781,0,0,0,1.518781,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,68.34178,8.353157,1.94591,.4179078,1 4,1,100,0,3,126680,0,4242.556,4.778919,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,1,0,0,1,0,0,68.34178,8.353157,1.94591,,0 4,1,100,0,1,126681,0,4242.556,30.5681,0,8,1,38.66746,16.00238,0,0,0,54.66984,0,0,0,4,1,7,60,13.73189,1,,1000,1000,0,0,1.94591,6.907755,1,0,0,0,1,0,59.22386,8.353157,1.94591,4.001312,1 4,1,100,0,2,126681,0,4242.556,31.5681,0,8,1,0,0,0,0,0,0,0,0,0,0,0,7,60,13.73189,1,,1000,1000,0,0,1.94591,6.907755,1,0,0,0,1,0,59.22386,8.353157,1.94591,,0 4,1,100,0,3,126681,0,4242.556,32.5681,0,8,1,23.29039,0,0,0,0,23.29039,0,0,0,3,0,7,60,13.73189,1,,1000,1000,0,0,1.94591,6.907755,1,0,0,0,1,0,59.22386,8.353157,1.94591,3.148041,1 4,1,100,0,1,126682,0,4242.556,28.33402,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,72.5,13.73189,0,,1000,1000,0,0,1.94591,6.907755,1,0,0,1,0,0,69.16129,8.353157,1.94591,,0 4,1,100,0,2,126682,0,4242.556,29.33402,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,72.5,13.73189,0,,1000,1000,0,0,1.94591,6.907755,1,0,0,1,0,0,69.16129,8.353157,1.94591,,0 4,1,100,0,3,126682,0,4242.556,30.33402,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,72.5,13.73189,0,,1000,1000,0,0,1.94591,6.907755,1,0,0,1,0,0,69.16129,8.353157,1.94591,,0 4,1,100,0,1,126683,0,4242.556,7.980835,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,1,0,0,71.32816,8.353157,1.94591,,0 4,1,100,0,2,126683,0,4242.556,8.980835,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,1,0,0,71.32816,8.353157,1.94591,,0 4,1,100,0,3,126683,0,4242.556,9.980835,0,10,1,0,1.778989,0,0,0,1.778989,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,1,0,0,71.32816,8.353157,1.94591,.5760453,1 4,1,100,0,1,126684,0,4242.556,9.38809,0,10,1,5.94884,1.302796,0,0,0,7.251636,0,0,0,1,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,0,0,0,82.46437,8.353157,1.94591,1.981227,1 4,1,100,0,2,126684,0,4242.556,10.38809,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,0,0,0,82.46437,8.353157,1.94591,,0 4,1,100,0,3,126684,0,4242.556,11.38809,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,1,0,0,0,0,0,82.46437,8.353157,1.94591,,0 11,1,0,1,1,126755,0,4586.849,50.34086,1,12,1,39.26234,2.492564,0,0,0,41.75491,0,0,0,4,0,3,77.8,17.4,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,71.87788,8.431167,1.098612,3.731817,1 11,1,0,1,2,126755,0,4586.849,51.34086,1,12,1,50.08165,16.44529,0,0,0,66.52695,0,0,0,5,0,3,77.8,17.4,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,71.87788,8.431167,1.098612,4.197607,1 11,1,0,1,3,126755,0,4586.849,52.34086,1,12,1,0,7.41328,0,0,0,7.41328,0,0,0,0,0,3,77.8,17.4,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,71.87788,8.431167,1.098612,2.003273,1 11,1,0,1,4,126755,0,4586.849,53.34086,1,12,1,0,12.29004,0,0,0,12.29004,0,0,0,0,0,3,77.8,17.4,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,71.87788,8.431167,1.098612,2.508789,1 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10,1,50,1,1,126765,0,13699.75,43.59753,1,12,1,53.59246,39.69376,23.26266,0,0,116.5489,0,0,0,5,1,3,74.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,1,0,62.05545,9.525206,1.098612,4.758311,1 10,1,50,1,2,126765,0,13699.75,44.59753,1,12,1,18.32884,31.47709,10.7062,0,0,60.51213,0,0,0,2,0,3,74.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,1,0,62.05545,9.525206,1.098612,4.102844,1 10,1,50,1,3,126765,0,13699.75,45.59753,1,12,1,63.39066,35.8231,30.60934,0,0,129.8231,0,0,0,5,1,3,74.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,1,0,62.05545,9.525206,1.098612,4.866173,1 10,1,50,1,4,126765,0,13699.75,46.59753,1,12,1,20.96627,27.52963,0,0,1209.435,1257.931,1,0,0,3,0,3,74.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,1,0,62.05545,9.525206,1.098612,7.137223,1 10,1,50,1,5,126765,0,13699.75,47.59753,1,12,1,45.01876,12.85536,0,0,0,57.87411,0,0,0,4,0,3,74.7,26.1,1,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,1,0,62.05545,9.525206,1.098612,4.05827,1 10,1,50,1,1,126766,0,13699.75,43.93977,0,8,1,10.60071,0,36.21908,0,0,46.81979,0,0,0,0,1,3,75.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,67.41241,9.525206,1.098612,3.846306,1 10,1,50,1,2,126766,0,13699.75,44.93977,0,8,1,0,0,0,0,0,0,0,0,0,0,0,3,75.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,67.41241,9.525206,1.098612,,0 10,1,50,1,3,126766,0,13699.75,45.93977,0,8,1,0,0,0,0,0,0,0,0,0,0,0,3,75.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,67.41241,9.525206,1.098612,,0 10,1,50,1,4,126766,0,13699.75,46.93977,0,8,1,15.49681,0,51.44029,0,0,66.9371,0,0,0,1,1,3,75.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,67.41241,9.525206,1.098612,4.203753,1 10,1,50,1,5,126766,0,13699.75,47.93977,0,8,1,0,0,0,0,0,0,0,0,0,0,0,3,75.8,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,67.41241,9.525206,1.098612,,0 10,1,50,1,1,126767,0,13699.75,15.26352,0,12,1,167.9623,6.036513,0,0,174.3522,348.351,1,0,0,4,13,3,83.2,13,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70143,9.525206,1.098612,5.85321,1 10,1,50,1,2,126767,0,13699.75,16.26352,0,12,1,24.25876,5.401617,13.33154,0,0,42.99191,0,0,0,2,0,3,83.2,13,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70143,9.525206,1.098612,3.761012,1 10,1,50,1,3,126767,0,13699.75,17.26352,0,12,1,7.371007,0,26.7027,0,0,34.07371,0,0,0,1,0,3,83.2,13,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70143,9.525206,1.098612,3.528526,1 10,1,50,1,4,126767,0,13699.75,18.26352,0,12,1,15.9526,0,0,0,0,15.9526,0,0,0,2,0,3,83.2,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70143,9.525206,1.098612,2.769622,1 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11,1,0,1,2,126794,1,9542.002,19.95688,1,12,1,23.40773,0,26.16767,0,677.6592,727.2346,1,0,0,1,1,3,93.7,4.3,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,67.68577,9.163564,1.098612,6.589249,1 11,1,0,1,3,126794,1,9542.002,20.95688,1,12,1,19.32607,4.44004,6.729435,0,0,30.49554,0,0,0,3,0,4,93.7,4.3,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.68577,9.163564,1.386294,3.41758,1 11,1,0,1,4,126794,1,9542.002,21.95688,1,12,1,9.178522,0,31.17485,0,559.8486,600.2019,1,0,0,0,1,4,93.7,4.3,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.68577,9.163564,1.386294,6.397266,1 11,1,0,1,5,126794,1,9542.002,22.95688,1,12,1,12.20025,4.636096,0,0,0,16.83635,0,0,0,2,0,5,93.7,4.3,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,67.68577,9.163564,1.609438,2.82354,1 11,1,0,1,1,126795,1,9542.002,51.71252,1,8,1,17.25164,0,43.42653,0,0,60.67817,0,0,0,2,1,3,64.2,8.7,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,61.64053,9.163564,1.098612,4.105584,1 11,1,0,1,2,126795,1,9542.002,52.71252,1,8,1,0,0,0,0,0,0,0,0,0,0,0,3,64.2,8.7,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,61.64053,9.163564,1.098612,,0 11,1,0,1,3,126795,1,9542.002,53.71252,1,8,1,0,0,0,0,0,0,0,0,0,0,0,4,64.2,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,61.64053,9.163564,1.386294,,0 11,1,0,1,4,126795,1,9542.002,54.71252,1,8,1,8.26067,0,34.87838,0,0,43.13905,0,0,0,0,1,4,64.2,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,61.64053,9.163564,1.386294,3.764429,1 11,1,0,1,5,126795,1,9542.002,55.71252,1,8,1,5.04838,0,0,0,0,5.04838,0,0,0,1,0,5,64.2,8.7,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,61.64053,9.163564,1.609438,1.619067,1 11,1,0,1,1,126796,1,9542.002,21.71937,0,12,1,21.41582,0,0,0,0,21.41582,0,0,0,0,0,3,80,8.7,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,66.78163,9.163564,1.098612,3.06413,1 11,1,0,1,2,126796,1,9542.002,22.71937,0,12,1,9.254219,0,0,0,0,9.254219,0,0,0,1,0,3,80,8.7,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,66.78163,9.163564,1.098612,2.22508,1 11,1,0,1,3,126796,1,9542.002,23.71937,0,12,1,116.997,4.578791,0,0,0,121.5758,0,0,0,8,0,4,80,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,66.78163,9.163564,1.386294,4.800538,1 11,1,0,1,4,126796,1,9542.002,24.71937,0,12,1,5.96604,0,0,0,0,5.96604,0,0,0,1,0,4,80,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,66.78163,9.163564,1.386294,1.786083,1 11,1,0,1,5,126796,1,9542.002,25.71937,0,12,1,42.49054,.8413967,.3575936,0,0,43.68953,0,0,0,3,0,5,80,8.7,0,,0,0,0,0,1.609438,0,0,0,0,1,0,0,66.78163,9.163564,1.609438,3.777108,1 11,1,0,0,1,126797,1,9542.002,23.11294,1,12,1,24.98513,9.571684,21.94527,0,0,56.50208,0,0,0,4,0,1,78.9,0,0,,0,72,0,0,0,4.276666,0,0,0,1,0,0,63.49755,9.163564,0,4.034277,1 11,1,0,0,2,126797,1,9542.002,24.11294,1,12,1,617.534,13.03212,18.96026,0,0,649.5264,0,0,0,10,1,1,78.9,0,0,,0,72,0,0,0,4.276666,0,0,0,1,0,0,63.49755,9.163564,0,6.476243,1 11,1,0,0,3,126797,1,9542.002,25.11294,1,12,1,406.5857,28.15164,0,0,29.73241,464.4698,0,0,0,8,0,1,78.9,0,0,,0,72,0,0,0,4.276666,0,0,0,1,0,0,63.49755,9.163564,0,6.140896,1 11,1,0,0,4,126797,1,9542.002,26.11294,1,12,1,519.9404,51.39054,38.84809,0,2560.996,3171.175,1,0,0,3,1,1,78.9,0,0,,0,72,0,0,0,4.276666,0,0,0,1,0,0,63.49755,9.163564,0,8.061857,1 11,1,0,0,5,126797,1,9542.002,27.11294,1,12,1,648.3172,59.20909,16.48717,0,0,724.0135,0,0,0,2,3,1,78.9,0,0,,0,72,0,0,0,4.276666,0,0,0,1,0,0,63.49755,9.163564,0,6.58481,1 4,1,100,0,1,126800,1,2297.767,52.39699,1,8,1,189.4582,0,0,0,0,189.4582,0,0,0,8,0,1,35.8,21.7,0,,0,0,0,0,0,0,1,0,0,0,0,1,62.06791,7.740128,0,5.244168,1 4,1,100,0,2,126800,1,2297.767,53.39699,1,8,1,0,0,0,0,0,0,0,0,0,0,0,1,35.8,21.7,0,,0,0,0,0,0,0,1,0,0,0,0,1,62.06791,7.740128,0,,0 4,1,100,0,3,126800,1,2297.767,54.39699,1,8,1,0,0,0,0,0,0,0,0,0,0,0,1,35.8,21.7,0,,0,0,0,0,0,0,1,0,0,0,0,1,62.06791,7.740128,0,,0 5,1,25,0,1,126805,1,898.263,13.59617,0,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.72706,6.801576,1.609438,,0 5,1,25,0,2,126805,1,898.263,14.59617,0,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.72706,6.801576,1.609438,,0 5,1,25,0,1,126806,1,898.263,11.47159,0,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.72706,6.801576,1.609438,,0 5,1,25,0,2,126806,1,898.263,12.47159,0,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.72706,6.801576,1.609438,,0 5,1,25,0,1,126807,1,898.263,9.409993,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,1,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.21308,6.801576,1.609438,,0 5,1,25,0,2,126807,1,898.263,10.40999,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,101,101,1,1,1.609438,4.61512,0,3.258096,6.001415,0,0,0,75.21308,6.801576,1.609438,,0 5,1,25,0,1,126808,1,898.263,14.93224,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,75.8,8.7,0,,101,101,1,1,1.609438,4.61512,0,3.258096,6.001415,0,0,0,65.00531,6.801576,1.609438,,0 5,1,25,0,2,126808,1,898.263,15.93224,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,75.8,8.7,0,,101,101,1,1,1.609438,4.61512,0,3.258096,6.001415,0,0,0,65.00531,6.801576,1.609438,,0 5,1,25,0,1,126809,1,898.263,32.79945,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,62.1,21.7,0,,101,101,0,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,69.38714,6.801576,1.609438,,0 5,1,25,0,2,126809,1,898.263,33.79945,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,62.1,21.7,0,,101,101,0,0,1.609438,4.61512,0,3.258096,6.001415,0,0,0,69.38714,6.801576,1.609438,,0 11,1,0,0,1,126815,1,3944.789,51.42231,1,6,1,307.2278,0,0,0,0,307.2278,0,0,0,2,0,1,94.7,13,0,,0,0,0,0,0,0,0,0,0,0,0,0,67.9421,8.280404,0,5.72759,1 11,1,0,0,2,126815,1,3944.789,52.42231,1,6,1,133.914,5.307567,0,0,0,139.2216,0,0,0,5,0,1,94.7,13,0,,0,0,0,0,0,0,0,0,0,0,0,0,67.9421,8.280404,0,4.936067,1 11,1,0,0,3,126815,1,3944.789,53.42231,1,6,1,129.2121,0,34.14272,0,0,163.3548,0,0,0,5,1,1,94.7,13,0,,0,0,0,0,0,0,0,0,0,0,0,0,67.9421,8.280404,0,5.095924,1 11,1,0,0,4,126815,1,3944.789,54.42231,1,6,1,128.4993,0,0,0,0,128.4993,0,0,0,5,0,1,94.7,13,0,,0,0,0,0,0,0,0,0,0,0,0,0,67.9421,8.280404,0,4.855924,1 11,1,0,0,5,126815,1,3944.789,55.42231,1,6,1,138.1994,0,46.38199,0,749.2806,933.862,1,0,0,5,0,1,94.7,13,0,,0,0,0,0,0,0,0,0,0,0,0,0,67.9421,8.280404,0,6.839329,1 9,1,50,1,1,126822,0,14151.37,7.118412,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,81.68566,9.557637,1.791759,,0 9,1,50,1,2,126822,0,14151.37,8.118412,1,12,1,8.259912,0,0,0,0,8.259912,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,81.68566,9.557637,1.791759,2.111414,1 9,1,50,1,3,126822,0,14151.37,9.118412,1,12,1,2.009041,0,0,0,0,2.009041,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,81.68566,9.557637,1.791759,.6976573,1 9,1,50,1,4,126822,0,14151.37,10.11841,1,12,1,2.318034,0,0,0,0,2.318034,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,81.68566,9.557637,1.791759,.8407196,1 9,1,50,1,5,126822,0,14151.37,11.11841,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,81.68566,9.557637,1.791759,,0 9,1,50,1,1,126823,0,14151.37,8.678987,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,,0 9,1,50,1,2,126823,0,14151.37,9.678987,1,12,1,46.25551,2.114537,28.19934,0,0,76.56938,0,0,0,6,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,4.338197,1 9,1,50,1,3,126823,0,14151.37,10.67899,1,12,1,24.10849,0,0,0,0,24.10849,0,0,0,5,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,3.182564,1 9,1,50,1,4,126823,0,14151.37,11.67899,1,12,1,49.95364,0,29.13305,0,0,79.08669,0,0,0,3,1,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,4.370544,1 9,1,50,1,5,126823,0,14151.37,12.67899,1,12,1,14.88728,0,11.90983,0,0,26.79711,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,3.288294,1 9,1,50,1,1,126824,0,14151.37,35.06092,1,12,1,3.015682,0,0,0,0,3.015682,0,0,0,0,0,6,72.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.27332,9.557637,1.791759,1.103826,1 9,1,50,1,2,126824,0,14151.37,36.06092,1,12,1,22.57709,0,0,0,0,22.57709,0,0,0,1,0,6,72.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.27332,9.557637,1.791759,3.116936,1 9,1,50,1,3,126824,0,14151.37,37.06092,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,72.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.27332,9.557637,1.791759,,0 9,1,50,1,4,126824,0,14151.37,38.06092,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,72.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.27332,9.557637,1.791759,,0 9,1,50,1,5,126824,0,14151.37,39.06092,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,72.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.27332,9.557637,1.791759,,0 9,1,50,1,1,126825,0,14151.37,13.82615,1,12,1,4.221954,1.206273,33.40772,0,0,38.83595,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,3.659346,1 9,1,50,1,2,126825,0,14151.37,14.82615,1,12,1,13.76652,0,0,0,0,13.76652,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,2.62224,1 9,1,50,1,3,126825,0,14151.37,15.82615,1,12,1,4.018081,0,95.42944,0,0,99.44752,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,4.59963,1 9,1,50,1,4,126825,0,14151.37,16.82615,1,12,1,17.61706,0,24.22346,0,0,41.84052,0,0,0,2,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,3.733865,1 9,1,50,1,5,126825,0,14151.37,17.82615,1,12,1,2.552105,0,6.682263,0,0,9.234368,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,85.92707,9.557637,1.791759,2.222932,1 9,1,50,1,1,126826,0,14151.37,41.97399,0,16,1,18.09409,0,0,0,0,18.09409,0,0,0,1,0,6,84.2,21.7,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,65.73057,9.557637,1.791759,2.895585,1 9,1,50,1,2,126826,0,14151.37,42.97399,0,16,1,0,0,17.84141,0,0,17.84141,0,0,0,0,0,6,84.2,21.7,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,65.73057,9.557637,1.791759,2.881522,1 9,1,50,1,3,126826,0,14151.37,43.97399,0,16,1,16.57458,0,0,0,0,16.57458,0,0,0,1,0,6,84.2,21.7,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,65.73057,9.557637,1.791759,2.80787,1 9,1,50,1,4,126826,0,14151.37,44.97399,0,16,1,13.90821,0,47.2879,0,0,61.19611,0,0,0,1,0,6,84.2,21.7,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,65.73057,9.557637,1.791759,4.114084,1 9,1,50,1,5,126826,0,14151.37,45.97399,0,16,1,11.48448,0,0,0,0,11.48448,0,0,0,2,0,6,84.2,21.7,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,65.73057,9.557637,1.791759,2.440996,1 9,1,50,1,1,126827,0,14151.37,12.54483,0,12,1,3.015682,0,0,0,0,3.015682,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,86.44105,9.557637,1.791759,1.103826,1 9,1,50,1,2,126827,0,14151.37,13.54483,0,12,1,98.01762,0,0,0,0,98.01762,0,0,0,4,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,86.44105,9.557637,1.791759,4.585147,1 9,1,50,1,3,126827,0,14151.37,14.54483,0,12,1,2.009041,0,0,0,0,2.009041,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,86.44105,9.557637,1.791759,.6976573,1 9,1,50,1,4,126827,0,14151.37,15.54483,0,12,1,115.9017,0,0,0,0,115.9017,0,0,0,3,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,86.44105,9.557637,1.791759,4.752743,1 9,1,50,1,5,126827,0,14151.37,16.54483,0,12,1,298.4688,33.93024,0,0,0,332.399,0,0,0,6,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,86.44105,9.557637,1.791759,5.806336,1 6,1,25,0,1,126835,0,12640.82,26.57084,1,8,1,14.13428,12.45583,0,0,0,26.59011,0,0,0,2,0,4,75.8,13,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,69.97394,9.444766,1.386294,3.280539,1 6,1,25,0,2,126835,0,12640.82,27.57084,1,8,1,16.17251,7.02965,0,0,0,23.20216,0,0,0,2,0,4,75.8,13,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,69.97394,9.444766,1.386294,3.144245,1 6,1,25,0,3,126835,0,12640.82,28.57084,1,8,1,8.845209,0,0,0,0,8.845209,0,0,0,1,0,4,75.8,13,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,69.97394,9.444766,1.386294,2.179876,1 6,1,25,0,4,126835,0,12640.82,29.57084,1,8,1,0,0,0,0,0,0,0,0,0,0,0,4,75.8,13,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,69.97394,9.444766,1.386294,,0 6,1,25,0,5,126835,0,12640.82,30.57084,1,8,1,42.93456,0,0,0,0,42.93456,0,0,0,4,0,4,75.8,13,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,69.97394,9.444766,1.386294,3.759677,1 6,1,25,0,1,126836,0,12640.82,30.97604,0,12,1,14.13428,11.42521,0,0,0,25.55948,0,0,0,2,0,4,72.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,72.21928,9.444766,1.386294,3.241008,1 6,1,25,0,2,126836,0,12640.82,31.97604,0,12,1,57.14286,8.533692,0,0,0,65.67655,0,0,0,9,0,4,72.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,72.21928,9.444766,1.386294,4.184742,1 6,1,25,0,3,126836,0,12640.82,32.97604,0,12,1,60.93366,10.4226,.7371007,0,0,72.09337,0,0,0,4,1,4,72.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,72.21928,9.444766,1.386294,4.277962,1 6,1,25,0,4,126836,0,12640.82,33.97604,0,12,1,5.925251,0,0,0,0,5.925251,0,0,0,1,0,4,72.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,72.21928,9.444766,1.386294,1.779223,1 6,1,25,0,5,126836,0,12640.82,34.97604,0,12,1,50.85452,0,0,0,0,50.85452,0,0,0,2,0,4,72.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,72.21928,9.444766,1.386294,3.928969,1 6,1,25,0,1,126837,0,12640.82,8.791239,0,8,1,154.1519,26.88457,0,0,0,181.0365,0,0,0,8,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,58.4809,9.444766,1.386294,5.198699,1 6,1,25,0,2,126837,0,12640.82,9.791239,0,8,1,72.29111,0,0,0,0,72.29111,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,58.4809,9.444766,1.386294,4.280701,1 6,1,25,0,3,126837,0,12640.82,10.79124,0,8,1,5.405406,3.552825,0,0,0,8.958231,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,58.4809,9.444766,1.386294,2.192573,1 6,1,25,0,4,126837,0,12640.82,11.79124,0,8,1,41.02097,0,0,0,0,41.02097,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,58.4809,9.444766,1.386294,3.714083,1 6,1,25,0,5,126837,0,12640.82,12.79124,0,8,1,29.28304,0,0,0,0,29.28304,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,58.4809,9.444766,1.386294,3.377008,1 6,1,25,0,1,126838,0,12640.82,11.44695,1,8,1,16.48999,7.232038,0,0,0,23.72203,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.60046,9.444766,1.386294,3.166404,1 6,1,25,0,2,126838,0,12640.82,12.44695,1,8,1,12.93801,4.819407,0,0,0,17.75741,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.60046,9.444766,1.386294,2.876803,1 6,1,25,0,3,126838,0,12640.82,13.44695,1,8,1,5.896806,6.943489,0,0,0,12.84029,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.60046,9.444766,1.386294,2.552588,1 6,1,25,0,4,126838,0,12640.82,14.44695,1,8,1,36.78213,0,.911577,0,0,37.69371,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.60046,9.444766,1.386294,3.629493,1 6,1,25,0,5,126838,0,12640.82,15.44695,1,8,1,31.88829,0,0,0,0,31.88829,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,78.60046,9.444766,1.386294,3.462239,1 5,1,25,0,1,126839,0,13224.14,26.36276,1,13,1,81.86102,31.21908,0,0,530.1649,643.245,1,0,0,7,0,4,71.6,13,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,70.1928,9.489875,1.386294,6.466526,1 5,1,25,0,2,126839,0,13224.14,27.36276,1,13,1,119.6766,33.57412,0,0,0,153.2507,0,0,0,13,0,4,71.6,13,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,70.1928,9.489875,1.386294,5.032075,1 5,1,25,0,3,126839,0,13224.14,28.36276,1,13,1,79.60688,13.03685,34.91892,0,0,127.5627,0,0,0,4,1,4,71.6,13,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,70.1928,9.489875,1.386294,4.848608,1 5,1,25,0,1,126840,0,13224.14,28.4627,0,12,1,5.300354,5.683156,0,0,0,10.98351,0,0,0,1,0,4,83.2,8.7,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,69.02815,9.489875,1.386294,2.396395,1 5,1,25,0,2,126840,0,13224.14,29.4627,0,12,1,4.851752,4.177897,0,0,0,9.02965,0,0,0,1,0,4,83.2,8.7,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,69.02815,9.489875,1.386294,2.200514,1 5,1,25,0,3,126840,0,13224.14,30.4627,0,12,1,102.8256,24.99754,0,0,0,127.8231,0,0,0,7,2,4,83.2,8.7,0,,679,679,0,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,69.02815,9.489875,1.386294,4.850647,1 5,1,25,0,1,126841,0,13224.14,5.155373,0,13,1,26.79623,9.952886,0,0,320.3769,357.126,1,0,0,2,1,4,74.36826,13.73189,0,,679,679,1,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,73.48592,9.489875,1.386294,5.878089,1 5,1,25,0,2,126841,0,13224.14,6.155373,0,13,1,12.93801,4.716981,0,0,0,17.65499,0,0,0,1,0,4,74.36826,13.73189,0,,679,679,1,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,73.48592,9.489875,1.386294,2.871018,1 5,1,25,0,3,126841,0,13224.14,7.155373,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,679,679,1,0,1.386294,6.520621,0,3.258096,7.906916,1,0,0,73.48592,9.489875,1.386294,,0 5,1,25,0,1,126842,0,13224.14,6.63655,1,13,1,1.766784,5.918728,0,0,0,7.685513,0,0,0,0,0,4,74.36826,13.73189,0,,679,679,1,1,1.386294,6.520621,0,3.258096,7.906916,1,0,0,76.40357,9.489875,1.386294,2.039337,1 5,1,25,0,2,126842,0,13224.14,7.63655,1,13,1,5.390836,0,0,0,0,5.390836,0,0,0,1,0,4,74.36826,13.73189,0,,679,679,1,1,1.386294,6.520621,0,3.258096,7.906916,1,0,0,76.40357,9.489875,1.386294,1.6847,1 5,1,25,0,3,126842,0,13224.14,8.63655,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,679,679,1,1,1.386294,6.520621,0,3.258096,7.906916,1,0,0,76.40357,9.489875,1.386294,,0 1,1,0,0,1,126859,0,10039.08,30.91581,0,14,1,191.8433,11.21319,33.5689,0,0,236.6254,0,0,0,5,2,3,56.8,30.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,66.07695,9.21434,1.098612,5.466478,1 1,1,0,0,2,126859,0,10039.08,31.91581,0,14,1,70.08086,1.617251,0,0,0,71.69811,0,0,0,2,0,3,56.8,30.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,66.07695,9.21434,1.098612,4.272464,1 1,1,0,0,3,126859,0,10039.08,32.91581,0,14,1,0,0,0,0,0,0,0,0,0,0,0,3,56.8,30.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,66.07695,9.21434,1.098612,,0 1,1,0,0,4,126859,0,10039.08,33.91581,0,14,1,34.18414,9.553328,12.13765,0,0,55.87511,0,0,0,3,1,3,56.8,30.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,66.07695,9.21434,1.098612,4.023119,1 1,1,0,0,5,126859,0,10039.08,34.91581,0,14,1,36.68195,5.944143,0,0,0,42.62609,0,0,0,4,0,3,56.8,30.4,0,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,66.07695,9.21434,1.098612,3.752467,1 1,1,0,0,1,126860,0,10039.08,26.48871,1,9,1,58.33333,32.86808,24.73498,0,1244.411,1360.347,2,0,0,2,1,3,64.2,17.4,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.57109,9.21434,1.098612,7.215496,1 1,1,0,0,2,126860,0,10039.08,27.48871,1,9,1,21.56334,0,0,0,514.6092,536.1725,1,0,0,1,0,3,64.2,17.4,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.57109,9.21434,1.098612,6.284456,1 1,1,0,0,3,126860,0,10039.08,28.48871,1,9,1,15.23342,4.117936,0,0,0,19.35135,0,0,0,1,0,3,64.2,17.4,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.57109,9.21434,1.098612,2.962762,1 1,1,0,0,4,126860,0,10039.08,29.48871,1,9,1,24.15679,5.720146,29.17047,0,0,59.0474,0,0,0,1,1,3,64.2,17.4,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.57109,9.21434,1.098612,4.078341,1 1,1,0,0,5,126860,0,10039.08,30.48871,1,9,1,100.0417,6.335973,0,0,0,106.3777,0,0,0,1,1,3,64.2,17.4,1,,450,450,0,0,1.098612,6.109248,1,0,0,1,0,0,63.57109,9.21434,1.098612,4.666996,1 1,1,0,0,1,126861,0,10039.08,7.359343,1,9,1,58.89281,7.108363,0,0,0,66.00117,0,0,0,9,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,72.6246,9.21434,1.098612,4.189672,1 1,1,0,0,2,126861,0,10039.08,8.359343,1,9,1,105.6604,21.6496,24.25876,0,0,151.5687,0,0,0,10,1,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,72.6246,9.21434,1.098612,5.021039,1 1,1,0,0,3,126861,0,10039.08,9.359343,1,9,1,73.46437,13.72482,0,0,0,87.18919,0,0,0,8,0,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,72.6246,9.21434,1.098612,4.468081,1 1,1,0,0,4,126861,0,10039.08,10.35934,1,9,1,56.06199,27.39289,20.4649,0,0,103.9198,0,0,0,6,1,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,72.6246,9.21434,1.098612,4.64362,1 1,1,0,0,5,126861,0,10039.08,11.35934,1,9,1,130.471,21.85911,10.83785,0,0,163.168,0,0,0,8,1,3,74.36826,13.73189,0,,450,450,1,1,1.098612,6.109248,1,0,0,1,0,0,72.6246,9.21434,1.098612,5.09478,1 9,1,50,1,1,126862,0,10156.33,29.44832,0,13,1,0,0,0,0,0,0,0,0,0,0,0,3,84.2,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,72.47523,9.22595,1.098612,,0 9,1,50,1,2,126862,0,10156.33,30.44832,0,13,1,30.12048,16.57722,0,0,0,46.6977,0,0,0,3,0,3,84.2,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,72.47523,9.22595,1.098612,3.843695,1 9,1,50,1,3,126862,0,10156.33,31.44832,0,13,1,5.985037,0,37.08229,0,0,43.06733,0,0,0,0,1,3,84.2,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,72.47523,9.22595,1.098612,3.762765,1 9,1,50,1,4,126862,0,10156.33,32.44832,0,13,1,23.51314,17.75011,0,0,0,41.26326,0,0,0,2,1,3,84.2,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,72.47523,9.22595,1.098612,3.719972,1 9,1,50,1,5,126862,0,10156.33,33.44832,0,13,1,40.71096,1.531951,.6347863,0,0,42.8777,0,0,0,2,0,3,84.2,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,72.47523,9.22595,1.098612,3.758352,1 9,1,50,1,1,126863,0,10156.33,2.915811,1,12,1,4.796163,18.59712,0,0,0,23.39329,0,0,0,1,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70722,9.22595,1.098612,3.152449,1 9,1,50,1,2,126863,0,10156.33,3.915811,1,12,1,0,8.849945,0,0,0,8.849945,0,0,0,0,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70722,9.22595,1.098612,2.180411,1 9,1,50,1,3,126863,0,10156.33,4.915811,1,12,1,17.95511,1.915212,0,0,0,19.87033,0,0,0,2,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70722,9.22595,1.098612,2.989228,1 9,1,50,1,4,126863,0,10156.33,5.915811,1,12,1,5.532504,15.67543,0,0,0,21.20793,0,0,0,1,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70722,9.22595,1.098612,3.054375,1 9,1,50,1,5,126863,0,10156.33,6.915811,1,12,1,5.924672,12.91578,0,0,0,18.84046,0,0,0,1,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.931826,7.600903,0,0,0,80.70722,9.22595,1.098612,2.936007,1 9,1,50,1,1,126864,0,10156.33,24.84599,1,12,1,76.73861,33.05156,0,0,0,109.7902,0,0,0,4,0,3,84.2,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,79.30963,9.22595,1.098612,4.698571,1 9,1,50,1,2,126864,0,10156.33,25.84599,1,12,1,10.9529,4.457831,0,0,0,15.41073,0,0,0,1,0,3,84.2,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,79.30963,9.22595,1.098612,2.735064,1 9,1,50,1,3,126864,0,10156.33,26.84599,1,12,1,40.89775,20.54364,0,0,0,61.44139,0,0,0,5,0,3,84.2,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,79.30963,9.22595,1.098612,4.118084,1 9,1,50,1,4,126864,0,10156.33,27.84599,1,12,1,43.79898,43.30567,0,0,0,87.10466,0,0,0,4,1,3,84.2,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,79.30963,9.22595,1.098612,4.46711,1 9,1,50,1,5,126864,0,10156.33,28.84599,1,12,1,26.66102,25.26449,0,0,0,51.92552,0,0,0,3,0,3,84.2,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,79.30963,9.22595,1.098612,3.94981,1 9,1,50,1,1,126865,0,17723.95,18.58179,0,11,1,4.759072,0,0,0,0,4.759072,0,0,0,1,0,6,65.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,68.26189,9.782728,1.791759,1.560053,1 9,1,50,1,2,126865,0,17723.95,19.58179,0,11,1,0,0,0,0,0,0,0,0,0,0,0,6,65.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,68.26189,9.782728,1.791759,,0 9,1,50,1,3,126865,0,17723.95,20.58179,0,11,1,0,0,0,0,0,0,0,0,0,0,0,7,65.3,8.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.931826,7.600903,1,0,0,68.26189,9.782728,1.94591,,0 9,1,50,1,1,126866,0,17723.95,15.44422,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,64.2,4.3,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,73.25742,9.782728,1.791759,,0 9,1,50,1,2,126866,0,17723.95,16.44422,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,64.2,4.3,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,73.25742,9.782728,1.791759,,0 9,1,50,1,3,126866,0,17723.95,17.44422,0,12,1,3.964321,0,0,0,0,3.964321,0,0,0,1,0,7,64.2,4.3,0,,1000,1000,1,0,1.94591,6.907755,0,3.931826,7.600903,1,0,0,73.25742,9.782728,1.94591,1.377335,1 9,1,50,1,1,126867,0,17723.95,17.24025,1,12,1,19.63117,5.205235,0,0,0,24.83641,0,0,0,2,0,6,46.3,0,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,1,0,0,67.93368,9.782728,1.791759,3.212311,1 9,1,50,1,2,126867,0,17723.95,18.24025,1,12,1,51.71475,4.148067,0,0,0,55.86282,0,0,0,2,1,6,46.3,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,67.93368,9.782728,1.791759,4.022899,1 9,1,50,1,3,126867,0,17723.95,19.24025,1,12,1,29.23687,6.778989,0,0,0,36.01586,0,0,0,6,0,7,46.3,0,0,,1000,1000,0,0,1.94591,6.907755,0,3.931826,7.600903,1,0,0,67.93368,9.782728,1.94591,3.583959,1 9,1,50,1,1,126868,0,17723.95,41.54415,0,12,1,7.733492,0,0,0,0,7.733492,0,0,0,1,0,6,73.7,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.6292,9.782728,1.791759,2.045561,1 9,1,50,1,2,126868,0,17723.95,42.54415,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,73.7,21.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,69.6292,9.782728,1.791759,,0 9,1,50,1,3,126868,0,17723.95,43.54415,0,12,1,12.3885,1.466799,0,0,0,13.8553,0,0,0,2,0,7,73.7,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.931826,7.600903,1,0,0,69.6292,9.782728,1.94591,2.628668,1 9,1,50,1,1,126869,0,17723.95,40.38877,1,12,1,21.41582,0,0,0,0,21.41582,0,0,0,0,0,6,60,30.4,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,71.26881,9.782728,1.791759,3.06413,1 9,1,50,1,2,126869,0,17723.95,41.38877,1,12,1,22.86336,2.928688,0,0,0,25.79205,0,0,0,2,0,6,60,30.4,1,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,71.26881,9.782728,1.791759,3.250066,1 9,1,50,1,3,126869,0,17723.95,42.38877,1,12,1,69.87116,11.04063,54.50941,0,1599.336,1734.757,2,0,0,6,1,7,60,30.4,1,,1000,1000,0,0,1.94591,6.907755,0,3.931826,7.600903,1,0,0,71.26881,9.782728,1.94591,7.458623,1 9,1,50,1,1,126870,0,17723.95,10.39836,1,12,1,9.518144,1.778703,0,0,0,11.29685,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,1,0,0,79.00697,9.782728,1.791759,2.424524,1 9,1,50,1,2,126870,0,17723.95,11.39836,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,1,0,0,79.00697,9.782728,1.791759,,0 9,1,50,1,3,126870,0,17723.95,12.39836,1,12,1,50.54509,10.41625,0,0,0,60.96135,0,0,0,7,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.931826,7.600903,1,0,0,79.00697,9.782728,1.94591,4.11024,1 9,1,50,1,1,126871,0,4004.963,20.87064,1,12,1,15.46698,17.07317,0,0,0,32.54015,0,0,0,2,0,1,83.2,21.7,0,,580.31,580.31,0,0,0,6.363563,0,3.931826,7.05671,1,0,0,71.42945,8.295539,0,3.482475,1 9,1,50,1,2,126871,0,4004.963,21.87064,1,12,1,23.9521,16.66848,0,0,0,40.62058,0,0,0,2,0,1,83.2,21.7,0,,580.31,580.31,0,0,0,6.363563,0,3.931826,7.05671,1,0,0,71.42945,8.295539,0,3.704275,1 9,1,50,1,3,126871,0,4004.963,22.87064,1,12,1,12.3885,5.133796,36.76908,0,0,54.29138,0,0,0,1,1,1,83.2,21.7,0,,580.31,580.31,0,0,0,6.363563,0,3.931826,7.05671,1,0,0,71.42945,8.295539,0,3.994365,1 8,1,50,1,1,126894,0,8056.452,28.20808,1,13,1,41.64188,7.013682,0,0,0,48.65556,0,0,0,2,0,4,68.4,8.7,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,79.3287,8.994352,1.386294,3.884766,1 8,1,50,1,2,126894,0,8056.452,29.20808,1,13,1,22.72727,0,0,0,0,22.72727,0,0,0,2,0,4,68.4,8.7,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,79.3287,8.994352,1.386294,3.123566,1 8,1,50,1,3,126894,0,8056.452,30.20808,1,13,1,0,7.190288,0,0,0,7.190288,0,0,0,0,0,4,68.4,8.7,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,79.3287,8.994352,1.386294,1.972731,1 8,1,50,1,4,126894,0,8056.452,31.20808,1,13,1,18.35704,9.307021,32.13401,0,0,59.79807,0,0,0,1,1,4,68.4,8.7,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,79.3287,8.994352,1.386294,4.090973,1 8,1,50,1,5,126894,0,8056.452,32.20808,1,13,1,7.57257,2.086664,0,0,0,9.659234,0,0,0,1,0,4,68.4,8.7,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,79.3287,8.994352,1.386294,2.267914,1 8,1,50,1,1,126895,0,8056.452,8.271048,1,13,1,13.08745,7.067222,0,0,0,20.15467,0,0,0,2,0,4,74.36826,13.73189,0,,525,525,1,1,1.386294,6.263398,0,3.931826,6.956545,0,0,0,77.73626,8.994352,1.386294,3.003436,1 8,1,50,1,2,126895,0,8056.452,9.271048,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,525,525,1,1,1.386294,6.263398,0,3.931826,6.956545,0,0,0,77.73626,8.994352,1.386294,,0 8,1,50,1,3,126895,0,8056.452,10.27105,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,525,525,1,1,1.386294,6.263398,0,3.931826,6.956545,0,0,0,77.73626,8.994352,1.386294,,0 8,1,50,1,4,126895,0,8056.452,11.27105,1,13,1,30.51859,0,22.54245,0,0,53.06104,0,0,0,2,1,4,74.36826,13.73189,0,,525,525,1,1,1.386294,6.263398,0,3.931826,6.956545,0,0,0,77.73626,8.994352,1.386294,3.971443,1 8,1,50,1,5,126895,0,8056.452,12.27105,1,13,1,33.65587,0,15.38494,0,0,49.04081,0,0,0,1,1,4,74.36826,13.73189,0,,525,525,1,1,1.386294,6.263398,0,3.931826,6.956545,0,0,0,77.73626,8.994352,1.386294,3.892653,1 8,1,50,1,1,126896,0,8056.452,5.842574,0,13,1,121.3563,8.596074,0,0,517.1624,647.1148,1,0,0,16,6,4,74.36826,13.73189,0,,525,525,1,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,83.45087,8.994352,1.386294,6.472524,1 8,1,50,1,2,126896,0,8056.452,6.842574,0,13,1,146.9788,2.280893,31.29015,0,0,180.5498,0,0,0,16,4,4,74.36826,13.73189,0,,525,525,1,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,83.45087,8.994352,1.386294,5.196007,1 8,1,50,1,3,126896,0,8056.452,7.842574,0,13,1,48.06739,0,37.80476,0,0,85.87215,0,0,0,7,0,4,74.36826,13.73189,0,,525,525,1,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,83.45087,8.994352,1.386294,4.452859,1 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8,1,50,1,4,126897,0,8056.452,32.61533,0,14,1,3.212483,3.969711,0,0,0,7.182194,0,0,0,1,0,4,75.8,17.4,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,78.43752,8.994352,1.386294,1.971605,1 8,1,50,1,5,126897,0,8056.452,33.61533,0,14,1,9.255363,8.140513,0,0,0,17.39588,0,0,0,2,0,4,75.8,17.4,0,,525,525,0,0,1.386294,6.263398,0,3.931826,6.956545,0,0,0,78.43752,8.994352,1.386294,2.856233,1 5,1,25,0,1,126899,1,1,6.8282,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,1,,72,72,1,1,.6931472,4.276666,0,3.258096,5.662961,0,1,0,63.00796,.6931472,.6931472,,0 5,1,25,0,2,126899,1,1,7.8282,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,1,,72,72,1,1,.6931472,4.276666,0,3.258096,5.662961,0,1,0,63.00796,.6931472,.6931472,,0 5,1,25,0,3,126899,1,1,8.828199,1,10,1,274.4472,29.65111,0,0,678.231,982.3292,1,0,0,29,4,2,74.36826,13.73189,1,,72,72,1,1,.6931472,4.276666,0,3.258096,5.662961,0,1,0,63.00796,.6931472,.6931472,6.889926,1 5,1,25,0,1,126900,1,1,48.79671,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,55.8,26.1,1,,72,72,0,0,.6931472,4.276666,0,3.258096,5.662961,0,1,0,51.71064,.6931472,.6931472,,0 5,1,25,0,2,126900,1,1,49.79671,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,55.8,26.1,1,,72,72,0,0,.6931472,4.276666,0,3.258096,5.662961,0,1,0,51.71064,.6931472,.6931472,,0 5,1,25,0,3,126900,1,1,50.79671,1,10,1,158.4767,53.55774,0,0,0,212.0344,0,0,0,12,0,2,55.8,26.1,1,,72,72,0,0,.6931472,4.276666,0,3.258096,5.662961,0,1,0,51.71064,.6931472,.6931472,5.356749,1 4,1,100,1,1,126916,0,13328.78,14.84736,1,18,1,52.94468,0,0,0,0,52.94468,0,0,0,3,0,4,68.4,8.7,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,75.75117,9.497756,1.386294,3.969248,1 4,1,100,1,2,126916,0,13328.78,15.84736,1,18,1,1.633097,1.252041,0,0,0,2.885139,0,0,0,0,0,4,68.4,8.7,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,75.75117,9.497756,1.386294,1.059573,1 4,1,100,1,3,126916,0,13328.78,16.84736,1,18,1,45.09415,12.63132,0,0,0,57.72547,0,0,0,7,0,4,68.4,8.7,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,75.75117,9.497756,1.386294,4.055698,1 4,1,100,1,4,126916,0,13328.78,17.84736,1,18,1,14.22671,13.24002,0,0,0,27.46673,0,0,0,2,0,4,68.4,8.7,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,75.75117,9.497756,1.386294,3.312975,1 4,1,100,1,5,126916,0,13328.78,18.84736,1,18,1,10.09676,5.889777,0,0,0,15.98654,0,0,0,1,0,4,68.4,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,75.75117,9.497756,1.386294,2.771747,1 4,1,100,1,1,126917,0,13328.78,45.85352,0,15,1,67.22189,2.135633,52.8376,0,0,122.1951,0,0,0,4,0,4,54.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.31355,9.497756,1.386294,4.805619,1 4,1,100,1,2,126917,0,13328.78,46.85352,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,54.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.31355,9.497756,1.386294,,0 4,1,100,1,3,126917,0,13328.78,47.85352,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,54.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.31355,9.497756,1.386294,,0 4,1,100,1,4,126917,0,13328.78,48.85352,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,54.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.31355,9.497756,1.386294,,0 4,1,100,1,5,126917,0,13328.78,49.85352,0,15,1,12.62095,0,40.44173,0,0,53.06268,0,0,0,1,0,4,54.7,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.31355,9.497756,1.386294,3.971474,1 4,1,100,1,1,126918,0,13328.78,16.09309,0,18,1,20.82094,0,0,0,0,20.82094,0,0,0,1,0,4,77.9,4.3,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.08788,9.497756,1.386294,3.035959,1 4,1,100,1,2,126918,0,13328.78,17.09309,0,18,1,35.38378,2.885139,28.16004,0,0,66.42896,0,0,0,3,0,4,77.9,4.3,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,81.08788,9.497756,1.386294,4.196133,1 4,1,100,1,3,126918,0,13328.78,18.09309,0,18,1,47.40833,0,0,0,0,47.40833,0,0,0,1,0,4,77.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,81.08788,9.497756,1.386294,3.858798,1 4,1,100,1,4,126918,0,13328.78,19.09309,0,18,1,16.06241,0,16.06241,0,0,32.12483,0,0,0,0,2,4,77.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,81.08788,9.497756,1.386294,3.469629,1 4,1,100,1,5,126918,0,13328.78,20.09309,0,18,1,80.77409,1.598654,0,0,0,82.37274,0,0,0,2,1,4,77.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,81.08788,9.497756,1.386294,4.411254,1 4,1,100,1,1,126919,0,13328.78,51.32923,1,18,1,66.27008,0,35.69304,0,0,101.9631,0,0,0,2,1,4,58.9,26.1,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.3842,9.497756,1.386294,4.624611,1 4,1,100,1,2,126919,0,13328.78,52.32923,1,18,1,48.44856,10.01633,0,0,0,58.46489,0,0,0,4,0,4,58.9,26.1,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.3842,9.497756,1.386294,4.068427,1 4,1,100,1,3,126919,0,13328.78,53.32923,1,18,1,16.35283,13.95441,0,0,0,30.30724,0,0,0,1,0,4,58.9,26.1,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.3842,9.497756,1.386294,3.411386,1 4,1,100,1,4,126919,0,13328.78,54.32923,1,18,1,80.31207,0,0,0,0,80.31207,0,0,0,4,0,4,58.9,26.1,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.3842,9.497756,1.386294,4.38592,1 4,1,100,1,5,126919,0,13328.78,55.32923,1,18,1,44.17333,0,0,250.3155,0,44.17333,0,0,17,6,0,4,58.9,26.1,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.3842,9.497756,1.386294,3.788121,1 4,1,100,1,1,126920,0,4392.68,1.919233,1,11,1,129.6231,0,0,0,0,129.6231,0,0,0,2,3,2,74.36826,13.73189,0,,75,75,1,1,.6931472,4.317488,1,0,0,0,0,0,75.32616,8.387922,.6931472,4.864631,1 4,1,100,1,2,126920,0,4392.68,2.919233,1,11,1,69.54178,11.37466,0,0,0,80.91644,0,0,0,8,0,2,74.36826,13.73189,0,,75,75,1,1,.6931472,4.317488,1,0,0,0,0,0,75.32616,8.387922,.6931472,4.393417,1 4,1,100,1,3,126920,0,4392.68,3.919233,1,11,1,45.70024,2.712531,0,0,0,48.41278,0,0,0,2,0,3,74.36826,13.73189,0,,75,75,1,1,1.098612,4.317488,1,0,0,0,0,0,75.32616,8.387922,1.098612,3.879764,1 4,1,100,1,4,126920,0,4392.68,4.919233,1,11,1,25.75205,20.1732,0,0,0,45.92525,0,0,0,4,0,3,74.36826,13.73189,0,,75,75,1,1,1.098612,4.317488,1,0,0,0,0,0,75.32616,8.387922,1.098612,3.827015,1 4,1,100,1,5,126920,0,4392.68,5.919233,1,11,1,63.77657,16.09421,0,0,0,79.87078,0,0,0,4,0,3,74.36826,13.73189,0,,75,75,1,1,1.098612,4.317488,1,0,0,0,0,0,75.32616,8.387922,1.098612,4.38041,1 4,1,100,1,1,126921,0,4392.68,18.73511,1,11,1,235.4535,17.23204,0,0,0,252.6855,0,0,0,8,0,2,52.6,21.7,0,,75,75,0,0,.6931472,4.317488,1,0,0,0,0,0,73.02412,8.387922,.6931472,5.532146,1 4,1,100,1,2,126921,0,4392.68,19.73511,1,11,1,170.0809,8.975741,0,140.1617,1719.865,1898.922,2,0,8,3,1,2,52.6,21.7,0,,75,75,0,0,.6931472,4.317488,1,0,0,0,0,0,73.02412,8.387922,.6931472,7.549042,1 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10,1,50,1,1,127055,0,14800.25,40.87885,1,12,1,34.77218,4.868105,0,0,0,39.64029,0,0,0,2,0,3,70.5,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.55615,9.602467,1.098612,3.679846,1 10,1,50,1,2,127055,0,14800.25,41.87885,1,12,1,203.4502,11.33078,40.71741,0,0,255.4984,0,0,0,5,2,3,70.5,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.55615,9.602467,1.098612,5.543216,1 10,1,50,1,3,127055,0,14800.25,42.87885,1,12,1,31.42145,16.4788,0,0,0,47.90025,0,0,0,4,0,3,70.5,21.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,77.55615,9.602467,1.098612,3.869121,1 10,1,50,1,1,127059,0,9848.635,34.73785,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,95.8,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,78.73395,9.195189,1.098612,,0 10,1,50,1,2,127059,0,9848.635,35.73785,0,12,1,6.53239,0,0,0,0,6.53239,0,0,0,1,0,3,95.8,0,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,0,0,0,78.73395,9.195189,1.098612,1.876773,1 10,1,50,1,3,127059,0,9848.635,36.73785,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,78.73395,9.195189,0,,0 10,1,50,1,1,127060,0,9848.635,10.09993,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,79.89645,9.195189,1.098612,,0 10,1,50,1,2,127060,0,9848.635,11.09993,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,1000,1000,1,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,79.89645,9.195189,1.098612,,0 10,1,50,1,1,127061,0,9848.635,33.32786,1,12,1,28.55443,56.45449,0,0,0,85.00893,0,0,0,4,0,3,87.4,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,66.92303,9.195189,1.098612,4.442756,1 10,1,50,1,2,127061,0,9848.635,34.32786,1,12,1,21.77463,1.986935,0,0,0,23.76157,0,0,0,2,0,3,87.4,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.931826,7.600903,1,0,0,66.92303,9.195189,1.098612,3.16807,1 10,1,50,1,1,127074,0,11740.07,22.89391,0,13,1,0,1.6247,0,0,0,1.6247,0,0,0,0,0,1,75.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,1,0,0,66.68941,9.370849,0,.4853233,1 10,1,50,1,2,127074,0,11740.07,23.89391,0,13,1,12.04819,0,0,0,0,12.04819,0,0,0,1,0,1,75.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,1,0,0,66.68941,9.370849,0,2.488915,1 10,1,50,1,3,127074,0,11740.07,24.89391,0,13,1,4.987531,0,0,0,0,4.987531,0,0,0,1,0,1,75.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,1,0,0,66.68941,9.370849,0,1.606941,1 10,1,50,1,4,127074,0,11740.07,25.89391,0,13,1,5.532504,0,0,0,0,5.532504,0,0,0,1,0,1,75.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,1,0,0,66.68941,9.370849,0,1.71064,1 10,1,50,1,5,127074,0,11740.07,26.89391,0,13,1,0,0,0,0,0,0,0,0,0,0,0,1,75.8,0,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,1,0,0,66.68941,9.370849,0,,0 4,1,100,1,1,127075,0,7539.082,.8158795,0,12,1,20.38369,1.306954,0,0,0,21.69065,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,80.7198,8.927988,1.386294,3.076881,1 4,1,100,1,2,127075,0,7539.082,1.81588,0,12,1,20.26287,1.484118,0,0,0,21.74699,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,80.7198,8.927988,1.386294,3.079475,1 4,1,100,1,3,127075,0,7539.082,2.81588,0,12,1,7.98005,0,0,0,0,7.98005,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,80.7198,8.927988,1.386294,2.076945,1 4,1,100,1,4,127075,0,7539.082,3.81588,0,12,1,14.75334,0,0,0,0,14.75334,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,80.7198,8.927988,1.386294,2.69147,1 4,1,100,1,5,127075,0,7539.082,4.815879,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,80.7198,8.927988,1.386294,,0 4,1,100,1,1,127076,0,7539.082,25.78508,0,12,1,17.38609,0,0,0,0,17.38609,0,0,0,1,0,4,97.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.25877,8.927988,1.386294,2.85567,1 4,1,100,1,2,127076,0,7539.082,26.78508,0,12,1,2.190581,0,47.27273,0,0,49.46331,0,0,0,0,0,4,97.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.25877,8.927988,1.386294,3.901231,1 4,1,100,1,3,127076,0,7539.082,27.78508,0,12,1,9.476309,0,0,0,0,9.476309,0,0,0,1,0,4,97.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.25877,8.927988,1.386294,2.248795,1 4,1,100,1,4,127076,0,7539.082,28.78508,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,97.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.25877,8.927988,1.386294,,0 4,1,100,1,5,127076,0,7539.082,29.78508,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,97.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.25877,8.927988,1.386294,,0 4,1,100,1,1,127077,0,7539.082,3.531827,0,12,1,16.18705,1.306954,0,0,0,17.49401,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,70.5428,8.927988,1.386294,2.861858,1 4,1,100,1,2,127077,0,7539.082,4.531827,0,12,1,50.38335,15.73932,0,0,307.6396,373.7623,1,0,0,6,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,70.5428,8.927988,1.386294,5.92362,1 4,1,100,1,3,127077,0,7539.082,5.531827,0,12,1,19.95012,1.391521,0,0,0,21.34165,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,70.5428,8.927988,1.386294,3.06066,1 4,1,100,1,4,127077,0,7539.082,6.531827,0,12,1,18.44168,3.379437,0,0,191.9087,213.7298,1,1,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,70.5428,8.927988,1.386294,5.364713,1 4,1,100,1,5,127077,0,7539.082,7.531827,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,70.5428,8.927988,1.386294,,0 4,1,100,1,1,127078,0,7539.082,23.96715,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,83.2,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,79.14996,8.927988,1.386294,,0 4,1,100,1,2,127078,0,7539.082,24.96715,1,12,1,42.71632,4.83023,34.33735,0,0,81.8839,0,0,0,4,1,4,83.2,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,79.14996,8.927988,1.386294,4.405303,1 4,1,100,1,3,127078,0,7539.082,25.96715,1,12,1,34.91272,3.241895,0,0,0,38.15461,0,0,0,3,0,4,83.2,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,79.14996,8.927988,1.386294,3.641647,1 4,1,100,1,4,127078,0,7539.082,26.96715,1,12,1,7.376671,2.434302,0,0,0,9.810973,0,0,0,1,0,4,83.2,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,79.14996,8.927988,1.386294,2.283501,1 4,1,100,1,5,127078,0,7539.082,27.96715,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,83.2,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,79.14996,8.927988,1.386294,,0 5,1,25,1,1,127097,0,8867.866,.9089665,1,16,1,110.1296,20.01178,0,0,0,130.1413,0,0,0,15,0,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,73.34382,9.090302,1.386294,4.868621,1 5,1,25,1,2,127097,0,8867.866,1.908966,1,16,1,21.02426,7.929919,0,0,0,28.95418,0,0,0,4,0,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,73.34382,9.090302,1.386294,3.365715,1 5,1,25,1,3,127097,0,8867.866,2.908967,1,16,1,52.92383,6.240786,0,0,0,59.16462,0,0,0,6,1,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,73.34382,9.090302,1.386294,4.080324,1 5,1,25,1,1,127098,0,8867.866,34.62286,0,16,1,125.4417,25.9364,0,0,0,151.3781,0,0,0,11,0,4,57.9,30.4,1,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,1,0,63.20214,9.090302,1.386294,5.019781,1 5,1,25,1,2,127098,0,8867.866,35.62286,0,16,1,13.47709,1.061995,0,0,0,14.53908,0,0,0,1,0,4,57.9,30.4,1,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,1,0,63.20214,9.090302,1.386294,2.676841,1 5,1,25,1,3,127098,0,8867.866,36.62286,0,16,1,133.1695,1.965602,50.84029,0,242.7568,428.7322,1,0,0,6,1,4,57.9,30.4,1,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,1,0,63.20214,9.090302,1.386294,6.060833,1 5,1,25,1,1,127099,0,8867.866,3.931554,1,16,1,44.75854,15.00589,0,0,0,59.76443,0,0,0,7,0,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,70.0731,9.090302,1.386294,4.090411,1 5,1,25,1,2,127099,0,8867.866,4.931554,1,16,1,32.8841,13.29919,0,0,0,46.18329,0,0,0,6,0,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,70.0731,9.090302,1.386294,3.832618,1 5,1,25,1,3,127099,0,8867.866,5.931554,1,16,1,26.53563,4.825553,0,0,0,31.36118,0,0,0,5,0,4,74.36826,13.73189,0,,640,640,1,1,1.386294,6.461468,0,3.258096,7.847763,1,0,0,70.0731,9.090302,1.386294,3.445571,1 5,1,25,1,1,127100,0,8867.866,33.295,1,16,1,50.05889,18.32155,45.02356,0,0,113.404,0,0,0,2,1,4,83.2,26.1,0,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,0,0,75.64986,9.090302,1.386294,4.730957,1 5,1,25,1,2,127100,0,8867.866,34.295,1,16,1,465.6334,18.71698,0,0,0,484.3504,0,0,0,9,0,4,83.2,26.1,0,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,0,0,75.64986,9.090302,1.386294,6.182808,1 5,1,25,1,3,127100,0,8867.866,35.295,1,16,1,84.02949,13.94595,0,0,0,97.97543,0,0,0,4,0,4,83.2,26.1,0,,640,640,0,0,1.386294,6.461468,0,3.258096,7.847763,0,0,0,75.64986,9.090302,1.386294,4.584717,1 11,1,0,0,1,127104,0,14157.57,52,1,13,1,42.56595,58.15348,34.43045,0,0,135.1499,0,0,0,4,0,2,82.5,13.73189,0,,0,166,0,0,.6931472,5.111988,0,0,0,1,0,0,67.18981,9.558075,.6931472,4.906384,1 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1,1,0,1,1,127140,0,10474.57,12.73922,0,11,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,77.94382,9.256801,2.079442,,0 1,1,0,1,2,127140,0,10474.57,13.73922,0,11,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,77.94382,9.256801,2.079442,,0 1,1,0,1,3,127140,0,10474.57,14.73922,0,11,1,26.26363,12.00198,0,0,0,38.26561,0,0,0,2,0,8,74.36826,13.73189,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,77.94382,9.256801,2.079442,3.644552,1 1,1,0,1,4,127140,0,10474.57,15.73922,0,11,1,96.94814,7.88894,0,0,0,104.8371,0,0,0,6,0,8,74.36826,13.73189,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,77.94382,9.256801,2.079442,4.652408,1 1,1,0,1,5,127140,0,10474.57,16.73922,0,11,1,9.255363,0,0,0,0,9.255363,0,0,0,1,0,8,74.36826,13.73189,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,77.94382,9.256801,2.079442,2.225203,1 1,1,0,1,1,127141,0,10474.57,17.51951,0,11,1,8.328376,0,0,0,0,8.328376,0,0,0,1,0,8,63.2,8.7,0,,450,0,1,0,2.079442,0,1,0,0,0,0,0,72.88496,9.256801,2.079442,2.119668,1 1,1,0,1,2,127141,0,10474.57,18.51951,0,11,1,0,0,0,0,0,0,0,0,0,0,0,8,63.2,8.7,0,,450,0,0,0,2.079442,0,1,0,0,0,0,0,72.88496,9.256801,2.079442,,0 1,1,0,1,3,127141,0,10474.57,19.51951,0,11,1,0,0,0,0,0,0,0,0,0,0,0,8,63.2,8.7,0,,450,0,0,0,2.079442,0,1,0,0,0,0,0,72.88496,9.256801,2.079442,,0 1,1,0,1,4,127141,0,10474.57,20.51951,0,11,1,39.59615,2.202845,0,0,0,41.79899,0,0,0,1,0,8,63.2,8.7,0,,450,0,0,0,2.079442,0,1,0,0,0,0,0,72.88496,9.256801,2.079442,3.732872,1 1,1,0,1,5,127141,0,10474.57,21.51951,0,11,1,33.44552,0,0,0,0,33.44552,0,0,0,1,0,8,63.2,8.7,0,,450,0,0,0,2.079442,0,1,0,0,0,0,0,72.88496,9.256801,2.079442,3.509918,1 11,1,0,1,1,127158,0,8062.035,60.58316,1,13,1,170.7317,82.06425,0,0,0,252.796,0,0,0,2,1,1,80,26.1,0,,0,0,0,0,0,0,0,0,0,1,0,0,73.09361,8.995046,0,5.532583,1 11,1,0,1,2,127158,0,8062.035,61.58316,1,13,1,219.9238,49.15623,44.37126,0,0,313.4513,0,0,0,38,0,1,80,26.1,0,,0,0,0,0,0,0,0,0,0,1,0,0,73.09361,8.995046,0,5.747644,1 11,1,0,1,3,127158,0,8062.035,62.58316,1,13,1,95.63924,79.23687,0,0,0,174.8761,0,0,0,22,0,1,80,26.1,0,,0,0,0,0,0,0,0,0,0,1,0,0,73.09361,8.995046,0,5.164078,1 11,1,0,0,1,127174,0,10403.23,13.7577,1,12,1,51.82568,15.08834,0,0,0,66.91402,0,0,0,6,0,8,74.36826,13.73189,0,,0,60,1,1,2.079442,4.094345,0,0,0,0,0,0,83.22755,9.249968,2.079442,4.203408,1 11,1,0,0,2,127174,0,10403.23,14.7577,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,,0 11,1,0,0,3,127174,0,10403.23,15.7577,1,12,1,14.25061,0,2.457002,0,0,16.70762,0,0,0,1,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,2.815865,1 11,1,0,0,4,127174,0,10403.23,16.7577,1,12,1,5.925251,0,0,0,0,5.925251,0,0,0,1,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,1.779223,1 11,1,0,0,5,127174,0,10403.23,17.7577,1,12,1,16.88203,2.755315,0,0,0,19.63735,0,0,0,1,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,2.977433,1 11,1,0,0,1,127175,0,10403.23,16.30938,0,12,1,34.74676,3.533569,36.9258,0,0,75.20612,0,0,0,4,1,8,80,0,0,,0,60,1,0,2.079442,4.094345,0,0,0,0,0,0,79.33826,9.249968,2.079442,4.320233,1 11,1,0,0,2,127175,0,10403.23,17.30938,0,12,1,28.57143,9.105122,0,0,0,37.67655,0,0,0,3,0,7,80,0,0,,0,60,1,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,3.629038,1 11,1,0,0,3,127175,0,10403.23,18.30938,0,12,1,12.53071,3.710074,0,0,0,16.24079,0,0,0,1,0,7,80,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,2.787526,1 11,1,0,0,4,127175,0,10403.23,19.30938,0,12,1,49.64449,3.755697,0,0,0,53.40018,0,0,0,3,0,7,80,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,3.977814,1 11,1,0,0,5,127175,0,10403.23,20.30938,0,12,1,138.1826,3.989162,0,0,0,142.1717,0,0,0,5,0,7,80,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,4.957036,1 11,1,0,0,1,127176,0,10403.23,17.25667,0,12,1,83.53946,4.941107,0,0,0,88.48057,0,0,0,5,1,8,92.2,0,0,,0,60,1,0,2.079442,4.094345,0,0,0,0,0,0,79.33826,9.249968,2.079442,4.482783,1 11,1,0,0,2,127176,0,10403.23,18.25667,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,92.2,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,,0 11,1,0,0,3,127176,0,10403.23,19.25667,0,12,1,8.353808,3.012285,0,0,0,11.36609,0,0,0,1,0,7,92.2,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,2.430635,1 11,1,0,0,4,127176,0,10403.23,20.25667,0,12,1,5.925251,7.452142,0,0,0,13.37739,0,0,0,1,0,7,92.2,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,2.593566,1 11,1,0,0,5,127176,0,10403.23,21.25667,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,92.2,0,0,,0,60,0,0,1.94591,4.094345,0,0,0,0,0,0,79.33826,9.249968,1.94591,,0 11,1,0,0,1,127177,0,10403.23,41.25941,0,17,.8876712,176.119,1.766784,0,0,0,177.8857,0,0,0,6,0,8,81.1,13,0,,0,60,0,0,2.079442,4.094345,0,0,0,0,0,0,76.12859,9.249968,2.079442,5.181141,1 11,1,0,0,1,127178,0,10403.23,6.485969,1,12,1,8.833922,0,0,0,0,8.833922,0,0,0,0,1,8,74.36826,13.73189,0,,0,60,1,1,2.079442,4.094345,0,0,0,0,0,0,83.22755,9.249968,2.079442,2.178599,1 11,1,0,0,2,127178,0,10403.23,7.485969,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,,0 11,1,0,0,3,127178,0,10403.23,8.485969,1,12,1,8.353808,0,0,0,0,8.353808,0,0,0,0,1,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,2.122718,1 11,1,0,0,4,127178,0,10403.23,9.485969,1,12,1,20.96627,4.175023,0,0,0,25.14129,0,0,0,3,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,3.224512,1 11,1,0,0,5,127178,0,10403.23,10.48597,1,12,1,74.19759,8.69529,0,0,0,82.89288,0,0,0,6,1,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,4.417549,1 11,1,0,0,1,127179,0,10403.23,10.9514,1,12,1,14.7232,0,0,0,0,14.7232,0,0,0,1,1,8,74.36826,13.73189,0,,0,60,1,1,2.079442,4.094345,0,0,0,0,0,0,83.22755,9.249968,2.079442,2.689425,1 11,1,0,0,2,127179,0,10403.23,11.9514,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,,0 11,1,0,0,3,127179,0,10403.23,12.9514,1,12,1,12.53071,0,0,0,0,12.53071,0,0,0,1,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,2.528183,1 11,1,0,0,4,127179,0,10403.23,13.9514,1,12,1,36.69098,4.453054,0,0,0,41.14403,0,0,0,2,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,3.717079,1 11,1,0,0,5,127179,0,10403.23,14.9514,1,12,1,41.89246,3.397249,0,0,0,45.2897,0,0,0,3,1,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,83.22755,9.249968,1.94591,3.81308,1 11,1,0,0,1,127180,0,10403.23,5.045859,1,12,1,27.67962,7.579505,0,0,0,35.25913,0,0,0,2,1,8,74.36826,13.73189,0,,0,60,1,1,2.079442,4.094345,0,0,0,0,0,0,79.79594,9.249968,2.079442,3.562724,1 11,1,0,0,2,127180,0,10403.23,6.045859,1,12,1,9.703504,6.938005,0,0,0,16.64151,0,0,0,2,0,7,74.36826,13.73189,0,,0,60,1,1,1.94591,4.094345,0,0,0,0,0,0,79.79594,9.249968,1.94591,2.8119,1 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11,1,0,1,5,127251,0,15527.12,7.915127,1,12,1,51.47978,17.15298,0,0,0,68.63277,0,0,0,6,1,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,81.03252,9.650408,2.079442,4.22877,1 11,1,0,1,1,127252,0,15527.12,19.70157,1,13,1,20.31802,2.120141,11.69611,0,0,34.13428,0,0,0,1,2,10,84.2,13,0,,0,0,0,0,2.302585,0,0,0,0,0,0,0,78.72446,9.650408,2.302585,3.530302,1 11,1,0,1,1,127253,0,15527.12,.881588,1,12,1,23.85159,25.17668,0,0,0,49.02827,0,0,0,3,0,10,74.36826,13.73189,0,,0,0,1,1,2.302585,0,0,0,0,0,0,0,81.03252,9.650408,2.302585,3.892397,1 11,1,0,1,2,127253,0,15527.12,1.881588,1,12,1,27.7628,22.74933,0,0,0,50.51213,0,0,0,5,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,81.03252,9.650408,2.079442,3.922214,1 11,1,0,1,3,127253,0,15527.12,2.881588,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,81.03252,9.650408,2.079442,,0 11,1,0,1,4,127253,0,15527.12,3.881588,1,12,1,51.95989,52.66636,0,0,0,104.6263,0,0,0,10,0,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,81.03252,9.650408,2.079442,4.650394,1 11,1,0,1,5,127253,0,15527.12,4.881588,1,12,1,254.2726,122.9054,0,0,0,377.178,0,0,0,51,1,8,74.36826,13.73189,0,,0,0,1,1,2.079442,0,0,0,0,0,0,0,81.03252,9.650408,2.079442,5.932717,1 3,1,100,1,1,127297,0,2896.791,6.773443,0,14,1,20.02356,0,0,0,0,20.02356,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,2.996909,1 3,1,100,1,2,127297,0,2896.791,7.773443,0,14,1,44.12399,2.668464,30.98652,0,0,77.77898,0,0,0,4,2,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,4.353871,1 3,1,100,1,3,127297,0,2896.791,8.773443,0,14,1,9.82801,2.447175,15.40541,0,0,27.68059,0,0,0,0,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,3.320731,1 3,1,100,1,4,127297,0,2896.791,9.773443,0,14,1,95.71559,15.01823,0,0,0,110.7338,0,0,0,6,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.386294,4.707129,1 3,1,100,1,5,127297,0,2896.791,10.77344,0,14,1,81.70071,46.26928,28.49104,0,0,156.461,0,0,0,23,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.386294,5.052807,1 3,1,100,1,1,127298,0,2896.791,41.39631,0,14,1,54.77032,90.02945,0,0,0,144.7998,0,0,0,23,0,5,62.1,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,73.19926,7.971704,1.609438,4.975352,1 3,1,100,1,2,127298,0,2896.791,42.39631,0,14,1,68.46362,0,0,0,0,68.46362,0,0,0,12,2,5,62.1,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,73.19926,7.971704,1.609438,4.226303,1 3,1,100,1,3,127298,0,2896.791,43.39631,0,14,1,61.42506,66.82064,0,0,0,128.2457,0,0,0,17,0,5,62.1,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,0,0,0,73.19926,7.971704,1.609438,4.853948,1 3,1,100,1,4,127298,0,2896.791,44.39631,0,14,1,72.47038,40,38.10392,0,0,150.5743,0,0,0,11,1,4,62.1,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.19926,7.971704,1.386294,5.014457,1 3,1,100,1,5,127298,0,2896.791,45.39631,0,14,1,98.79116,68.77866,12.08837,0,0,179.6582,0,0,0,13,0,4,62.1,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,73.19926,7.971704,1.386294,5.191056,1 3,1,100,1,1,127299,0,2896.791,36.58316,1,14,1,34.15783,4.652533,25.47703,0,0,64.2874,0,0,0,2,1,5,92.6,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.37424,7.971704,1.609438,4.163363,1 3,1,100,1,2,127299,0,2896.791,37.58316,1,14,1,12.39892,0,0,0,0,12.39892,0,0,0,2,0,5,92.6,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.37424,7.971704,1.609438,2.51761,1 3,1,100,1,3,127299,0,2896.791,38.58316,1,14,1,6.388206,0,0,0,0,6.388206,0,0,0,1,0,5,92.6,13,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,69.37424,7.971704,1.609438,1.854454,1 3,1,100,1,4,127299,0,2896.791,39.58316,1,14,1,345.0319,0,27.62534,0,0,372.6573,0,0,0,5,1,4,92.6,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,69.37424,7.971704,1.386294,5.920659,1 3,1,100,1,5,127299,0,2896.791,40.58316,1,14,1,16.67361,4.614423,0,0,0,21.28804,0,0,0,2,0,4,92.6,13,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,69.37424,7.971704,1.386294,3.058145,1 3,1,100,1,1,127300,0,2896.791,11.66324,0,14,1,9.422851,0,0,0,0,9.422851,0,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,2.243138,1 3,1,100,1,2,127300,0,2896.791,12.66324,0,14,1,13.47709,0,15.21294,0,0,28.69003,0,0,0,2,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,3.35655,1 3,1,100,1,3,127300,0,2896.791,13.66324,0,14,1,64.71745,2.432432,32.86486,0,0,100.0147,0,0,0,2,2,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.609438,4.605318,1 3,1,100,1,4,127300,0,2896.791,14.66324,0,14,1,27.34731,4.512306,0,0,0,31.85962,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.386294,3.461339,1 3,1,100,1,5,127300,0,2896.791,15.66324,0,14,1,47.10296,9.349729,29.62068,0,0,86.07336,0,0,0,5,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,82.37522,7.971704,1.386294,4.4552,1 3,1,100,1,1,127301,0,2896.791,14.59822,0,14,1,45.9364,13.69258,36.9258,0,0,96.55477,0,0,0,4,1,5,78.9,4.3,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,76.82748,7.971704,1.609438,4.57011,1 3,1,100,1,2,127301,0,2896.791,15.59822,0,14,1,18.32884,28.93262,0,0,0,47.26146,0,0,0,3,0,5,78.9,4.3,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,76.82748,7.971704,1.609438,3.855695,1 3,1,100,1,3,127301,0,2896.791,16.59822,0,14,1,63.88206,26.3145,31.32678,0,0,121.5233,0,0,0,5,1,5,78.9,4.3,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,0,0,0,76.82748,7.971704,1.609438,4.800107,1 5,1,25,1,1,127311,0,14820.32,42.91034,0,14,1,0,71.58273,0,0,0,71.58273,0,0,0,0,0,4,73.7,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,64.57561,9.603822,1.386294,4.270854,1 5,1,25,1,2,127311,0,14820.32,43.91034,0,14,1,13.69113,100.4107,39.32092,0,0,153.4228,0,0,0,0,1,4,73.7,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,64.57561,9.603822,1.386294,5.033197,1 5,1,25,1,3,127311,0,14820.32,44.91034,0,14,1,94.56359,112.9077,0,0,0,207.4713,0,0,0,5,0,4,73.7,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,64.57561,9.603822,1.386294,5.334993,1 5,1,25,1,4,127311,0,14820.32,45.91034,0,14,1,10.14292,77.39511,0,0,0,87.53803,0,0,0,1,0,4,73.7,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,64.57561,9.603822,1.386294,4.472074,1 5,1,25,1,5,127311,0,14820.32,46.91034,0,14,1,35.97122,47.20694,0,0,0,83.17816,0,0,0,5,0,4,73.7,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,64.57561,9.603822,1.386294,4.420985,1 5,1,25,1,1,127312,0,14820.32,17.10062,0,15,1,26.3789,1.798561,25.28177,0,0,53.45923,0,0,0,1,1,4,78.9,17.4,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.97823,9.603822,1.386294,3.978919,1 5,1,25,1,2,127312,0,14820.32,18.10062,0,15,1,41.07338,2.190581,0,0,0,43.26397,0,0,0,5,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.97823,9.603822,1.386294,3.76732,1 5,1,25,1,3,127312,0,14820.32,19.10062,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.97823,9.603822,1.386294,,0 5,1,25,1,4,127312,0,14820.32,20.10062,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.97823,9.603822,1.386294,,0 5,1,25,1,5,127312,0,14820.32,21.10062,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.97823,9.603822,1.386294,,0 5,1,25,1,1,127313,0,14820.32,7.244353,1,15,1,11.99041,0,0,0,0,11.99041,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,82.31068,9.603822,1.386294,2.484107,1 5,1,25,1,2,127313,0,14820.32,8.244353,1,15,1,24.09639,1.09529,31.68675,0,0,56.87842,0,0,0,2,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,82.31068,9.603822,1.386294,4.040916,1 5,1,25,1,3,127313,0,14820.32,9.244353,1,15,1,7.481297,0,0,0,0,7.481297,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,82.31068,9.603822,1.386294,2.012406,1 5,1,25,1,4,127313,0,14820.32,10.24435,1,15,1,25.81835,5.855233,31.51222,0,0,63.1858,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,82.31068,9.603822,1.386294,4.14608,1 5,1,25,1,5,127313,0,14820.32,11.24435,1,15,1,26.23783,0,3.377063,0,0,29.6149,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,82.31068,9.603822,1.386294,3.388278,1 5,1,25,1,1,127314,0,14820.32,42.61191,1,15,1,13.18945,0,0,0,0,13.18945,0,0,0,1,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,77.97667,9.603822,1.386294,2.579417,1 5,1,25,1,2,127314,0,14820.32,43.61191,1,15,1,72.8368,0,36.98248,0,0,109.8193,0,0,0,4,1,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,77.97667,9.603822,1.386294,4.698836,1 5,1,25,1,3,127314,0,14820.32,44.61191,1,15,1,128.5287,0,0,0,116.0898,244.6185,1,0,0,1,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,77.97667,9.603822,1.386294,5.4997,1 5,1,25,1,4,127314,0,14820.32,45.61191,1,15,1,87.59797,5.60166,0,0,0,93.19963,0,0,0,5,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,77.97667,9.603822,1.386294,4.534744,1 5,1,25,1,5,127314,0,14820.32,46.61191,1,15,1,135.8697,0,0,0,979.3737,1115.243,1,0,0,2,0,4,82.1,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,77.97667,9.603822,1.386294,7.016828,1 7,1,25,0,1,127315,0,9408.188,44.71458,0,16,1,8.833922,0,36.9258,0,0,45.75972,0,0,0,0,1,5,78.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,66.6989,9.149442,1.609438,3.823404,1 7,1,25,0,2,127315,0,9408.188,45.71458,0,16,1,6.469003,0,0,0,0,6.469003,0,0,0,1,0,5,78.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,66.6989,9.149442,1.609438,1.867022,1 7,1,25,0,3,127315,0,9408.188,46.71458,0,16,1,0,5.356266,0,0,0,5.356266,0,0,0,0,0,5,78.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,66.6989,9.149442,1.609438,1.678267,1 7,1,25,0,4,127315,0,9408.188,47.71458,0,16,1,9.11577,0,51.85506,0,0,60.97083,0,0,0,0,1,5,78.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,66.6989,9.149442,1.609438,4.110395,1 7,1,25,0,5,127315,0,9408.188,48.71458,0,16,1,5.418924,2.163401,0,0,0,7.582326,0,0,0,1,0,5,78.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,66.6989,9.149442,1.609438,2.02582,1 7,1,25,0,1,127316,0,9408.188,17.31143,0,12,1,24.14605,0,1.177856,0,0,25.32391,0,0,0,5,0,5,87.5,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,3.231749,1 7,1,25,0,2,127316,0,9408.188,18.31143,0,12,1,54.44744,6.134771,0,0,0,60.58221,0,0,0,3,3,5,87.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,4.104002,1 7,1,25,0,3,127316,0,9408.188,19.31143,0,12,1,168.7617,3.70516,0,0,0,172.4668,0,0,0,5,0,5,87.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,5.150205,1 7,1,25,0,4,127316,0,9408.188,20.31143,0,12,1,50.59253,0,0,0,0,50.59253,0,0,0,3,0,5,87.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,3.923804,1 7,1,25,0,5,127316,0,9408.188,21.31143,0,12,1,18.75782,.8336807,0,0,0,19.5915,0,0,0,2,0,5,87.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,2.975096,1 7,1,25,0,1,127317,0,9408.188,15.33744,0,12,1,32.39105,0,0,0,0,32.39105,0,0,0,3,0,5,76.3,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,3.477882,1 7,1,25,0,2,127317,0,9408.188,16.33744,0,12,1,29.11051,0,0,0,0,29.11051,0,0,0,1,2,5,76.3,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,3.371099,1 7,1,25,0,3,127317,0,9408.188,17.33744,0,12,1,30.95823,8.643735,0,0,0,39.60197,0,0,0,5,0,5,76.3,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,3.678879,1 7,1,25,0,4,127317,0,9408.188,18.33744,0,12,1,123.5187,2.39289,0,0,0,125.9116,0,0,0,2,13,5,76.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,4.83558,1 7,1,25,0,5,127317,0,9408.188,19.33744,0,12,1,265.5273,6.335973,32.23426,0,0,304.0975,0,0,0,33,3,5,76.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,72.956,9.149442,1.609438,5.717349,1 7,1,25,0,1,127318,0,9408.188,44.18617,1,12,1,24.14605,0,0,0,0,24.14605,0,0,0,3,0,5,82.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.91996,9.149442,1.609438,3.184121,1 7,1,25,0,2,127318,0,9408.188,45.18617,1,12,1,339.0836,0,0,0,0,339.0836,0,0,0,5,0,5,82.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.91996,9.149442,1.609438,5.826247,1 7,1,25,0,3,127318,0,9408.188,46.18617,1,12,1,16.21622,3.734644,0,0,0,19.95086,0,0,0,2,0,5,82.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.91996,9.149442,1.609438,2.993272,1 7,1,25,0,4,127318,0,9408.188,47.18617,1,12,1,76.11668,9.52598,33.16773,0,0,118.8104,0,0,0,8,0,5,82.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.91996,9.149442,1.609438,4.777529,1 7,1,25,0,5,127318,0,9408.188,48.18617,1,12,1,179.5248,30.37932,42.51772,0,0,252.4218,0,0,0,10,2,5,82.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.91996,9.149442,1.609438,5.531102,1 7,1,25,0,1,127319,0,9408.188,14.46133,1,12,1,30.3298,10.13545,0,0,0,40.46525,0,0,0,4,0,5,62.5,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.63226,9.149442,1.609438,3.700444,1 7,1,25,0,2,127319,0,9408.188,15.46133,1,12,1,361.3154,1.185984,0,0,0,362.5013,0,0,0,5,0,5,62.5,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.63226,9.149442,1.609438,5.893028,1 7,1,25,0,3,127319,0,9408.188,16.46133,1,12,1,76.90417,5.710074,0,0,0,82.61425,0,0,0,5,0,5,62.5,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.63226,9.149442,1.609438,4.414182,1 7,1,25,0,4,127319,0,9408.188,17.46133,1,12,1,36.46308,6.020966,0,0,667.0146,709.4987,1,0,0,5,0,5,62.5,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.63226,9.149442,1.609438,6.564559,1 7,1,25,0,5,127319,0,9408.188,18.46133,1,12,1,46.18591,23.84744,0,0,0,70.03335,0,0,0,4,0,5,62.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,67.63226,9.149442,1.609438,4.248971,1 5,1,25,0,1,127345,0,9044.045,13.45927,0,12,1,29.59364,0,0,0,0,29.59364,0,0,0,0,1,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,3.387559,1 5,1,25,0,2,127345,0,9044.045,14.45927,0,12,1,26.95418,0,0,0,0,26.95418,0,0,0,2,0,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,3.294138,1 5,1,25,0,3,127345,0,9044.045,15.45927,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,,0 5,1,25,0,1,127346,0,9044.045,11.54552,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,,0 5,1,25,0,2,127346,0,9044.045,12.54552,0,12,1,72.2372,0,0,0,0,72.2372,0,0,0,4,0,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,4.279955,1 5,1,25,0,3,127346,0,9044.045,13.54552,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.87675,9.109972,1.94591,,0 5,1,25,0,1,127347,0,9044.045,14.51608,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,78.8,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,73.47551,9.109972,1.94591,,0 5,1,25,0,2,127347,0,9044.045,15.51608,0,12,1,13.47709,0,0,0,0,13.47709,0,0,0,1,0,7,78.8,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,73.47551,9.109972,1.94591,2.600991,1 5,1,25,0,3,127347,0,9044.045,16.51608,0,12,1,17.51843,5.061425,0,0,0,22.57985,0,0,0,1,0,7,78.8,13.73189,0,,515,515,1,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,73.47551,9.109972,1.94591,3.117058,1 5,1,25,0,1,127348,0,9044.045,6.55989,1,12,1,8.244994,0,0,0,0,8.244994,0,0,0,1,0,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,2.109606,1 5,1,25,0,2,127348,0,9044.045,7.55989,1,12,1,3.773585,0,15.77359,0,0,19.54717,0,0,0,0,1,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,2.972831,1 5,1,25,0,3,127348,0,9044.045,8.559891,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,,0 5,1,25,0,1,127349,0,9044.045,10.0835,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,,0 5,1,25,0,2,127349,0,9044.045,11.0835,1,12,1,5.390836,1.078167,0,0,0,6.469003,0,0,0,1,0,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,1.867022,1 5,1,25,0,3,127349,0,9044.045,12.0835,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,515,515,1,1,1.94591,6.244167,0,3.258096,7.630461,1,0,0,77.36277,9.109972,1.94591,,0 5,1,25,0,1,127350,0,9044.045,32.64887,1,12,1,25.32391,12.04947,0,0,0,37.37338,0,0,0,3,0,7,73.8,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,71.59294,9.109972,1.94591,3.620959,1 5,1,25,0,2,127350,0,9044.045,33.64887,1,12,1,17.25067,12.02695,16.33962,16.17251,0,45.61725,0,0,2,0,1,7,73.8,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,71.59294,9.109972,1.94591,3.820286,1 5,1,25,0,3,127350,0,9044.045,34.64887,1,12,1,4.914005,4.732187,0,0,0,9.646192,0,0,0,1,0,7,73.8,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,71.59294,9.109972,1.94591,2.266563,1 5,1,25,0,1,127351,0,9044.045,41.00479,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,72.5,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,68.90664,9.109972,1.94591,,0 5,1,25,0,2,127351,0,9044.045,42.00479,0,12,1,8.086253,3.962264,0,0,0,12.04852,0,0,0,1,0,7,72.5,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,68.90664,9.109972,1.94591,2.488942,1 5,1,25,0,3,127351,0,9044.045,43.00479,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,72.5,13.73189,0,,515,515,0,0,1.94591,6.244167,0,3.258096,7.630461,1,0,0,68.90664,9.109972,1.94591,,0 3,1,100,0,1,127361,0,9542.002,8.287475,0,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,0,1.791759,5.645447,1,0,0,1,0,0,73.43399,9.163564,1.791759,,0 3,1,100,0,2,127361,0,9542.002,9.287475,0,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,0,1.791759,5.645447,1,0,0,1,0,0,73.43399,9.163564,1.791759,,0 3,1,100,0,3,127361,0,9542.002,10.28747,0,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,0,1.791759,5.645447,1,0,0,1,0,0,73.43399,9.163564,1.791759,,0 3,1,100,0,1,127362,0,9542.002,7.074606,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,72.92001,9.163564,1.791759,,0 3,1,100,0,2,127362,0,9542.002,8.074607,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,72.92001,9.163564,1.791759,,0 3,1,100,0,3,127362,0,9542.002,9.074607,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,72.92001,9.163564,1.791759,,0 3,1,100,0,1,127363,0,9542.002,9.166325,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.64929,9.163564,1.791759,,0 3,1,100,0,2,127363,0,9542.002,10.16632,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.64929,9.163564,1.791759,,0 3,1,100,0,3,127363,0,9542.002,11.16632,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.64929,9.163564,1.791759,,0 3,1,100,0,1,127364,0,9542.002,27.91239,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,82.1,0,0,,283,283,0,0,1.791759,5.645447,1,0,0,1,0,0,63.48573,9.163564,1.791759,,0 3,1,100,0,2,127364,0,9542.002,28.91239,1,10,1,8.086253,6.134771,0,0,0,14.22102,0,0,0,0,0,6,82.1,0,0,,283,283,0,0,1.791759,5.645447,1,0,0,1,0,0,63.48573,9.163564,1.791759,2.654721,1 3,1,100,0,3,127364,0,9542.002,29.91239,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,82.1,0,0,,283,283,0,0,1.791759,5.645447,1,0,0,1,0,0,63.48573,9.163564,1.791759,,0 3,1,100,0,1,127365,0,9542.002,4.43258,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.4884,9.163564,1.791759,,0 3,1,100,0,2,127365,0,9542.002,5.43258,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.4884,9.163564,1.791759,,0 3,1,100,0,3,127365,0,9542.002,6.43258,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,283,283,1,1,1.791759,5.645447,1,0,0,1,0,0,69.4884,9.163564,1.791759,,0 9,1,50,0,1,127368,0,9542.002,25.28131,0,15,1,46.94935,1.319199,0,0,0,48.26855,0,0,0,2,1,1,54.7,17.4,1,,550,550,0,0,0,6.309918,0,3.931826,7.003066,0,0,1,52.34806,9.163564,0,3.87678,1 9,1,50,0,2,127368,0,9542.002,26.28131,0,15,1,72.50674,0,459.6981,0,38649.81,39182.02,2,0,0,0,0,1,54.7,17.4,1,,550,550,0,0,0,6.309918,0,3.931826,7.003066,0,0,1,52.34806,9.163564,0,10.57597,1 9,1,50,0,1,127369,0,1524.194,22.71321,0,14,1,0,0,0,0,0,0,0,0,0,0,0,1,81.1,4.3,0,,760,760,0,0,0,6.633318,0,3.931826,7.326466,1,0,0,69.06231,7.329876,0,,0 9,1,50,0,2,127369,0,1524.194,23.71321,0,14,1,17.25067,9.299191,0,0,0,26.54987,0,0,0,3,0,1,81.1,4.3,0,,760,760,0,0,0,6.633318,0,3.931826,7.326466,1,0,0,69.06231,7.329876,0,3.279025,1 9,1,50,0,3,127369,0,1524.194,24.71321,0,14,1,0,3.710074,0,0,0,3.710074,0,0,0,0,0,1,81.1,4.3,0,,760,760,0,0,0,6.633318,0,3.931826,7.326466,1,0,0,69.06231,7.329876,0,1.311052,1 9,1,50,0,4,127369,0,1524.194,25.71321,0,14,1,0,0,0,0,0,0,0,0,0,0,0,1,81.1,4.3,0,,760,760,0,0,0,6.633318,0,3.931826,7.326466,1,0,0,69.06231,7.329876,0,,0 9,1,50,0,5,127369,0,1524.194,26.71321,0,14,1,72.94706,6.969571,0,0,0,79.91663,0,0,0,5,0,1,81.1,4.3,0,,760,760,0,0,0,6.633318,0,3.931826,7.326466,1,0,0,69.06231,7.329876,0,4.380984,1 11,1,0,0,1,127370,0,7700.372,25.62354,1,8,1,59.71049,30.5187,31.51387,0,505.4765,627.2195,1,0,0,4,1,4,22.7,13.73189,1,,0,0,0,0,1.386294,0,0,0,0,0,0,0,75.49265,8.949154,1.386294,6.441297,1 11,1,0,0,2,127370,0,7700.372,26.62354,1,8,1,25.88106,10.40749,0,11.01322,0,36.28855,0,0,1,3,0,4,22.7,13.73189,1,,0,0,0,0,1.386294,0,0,0,0,0,0,0,75.49265,8.949154,1.386294,3.591502,1 11,1,0,0,3,127370,0,7700.372,27.62354,1,8,1,14.06328,1.25565,0,0,0,15.31894,0,0,0,1,0,4,22.7,13.73189,1,,0,0,0,0,1.386294,0,0,0,0,0,0,0,75.49265,8.949154,1.386294,2.72909,1 11,1,0,0,1,127371,0,7700.372,5.587954,1,8,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,79.6293,8.949154,1.386294,,0 11,1,0,0,2,127371,0,7700.372,6.587954,1,8,1,20.09912,2.257709,0,0,696.5253,718.8821,1,0,0,0,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,79.6293,8.949154,1.386294,6.577697,1 11,1,0,0,3,127371,0,7700.372,7.587954,1,8,1,41.43646,14.23908,0,0,0,55.67554,0,0,0,5,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,79.6293,8.949154,1.386294,4.019541,1 11,1,0,0,1,127372,0,7700.372,4.388775,1,8,1,13.87214,0,0,0,0,13.87214,0,0,0,1,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,76.19768,8.949154,1.386294,2.629882,1 11,1,0,0,2,127372,0,7700.372,5.388775,1,8,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,76.19768,8.949154,1.386294,,0 11,1,0,0,3,127372,0,7700.372,6.388775,1,8,1,28.65394,2.009041,0,0,0,30.66298,0,0,0,2,0,4,74.36826,13.73189,0,,0,0,1,1,1.386294,0,0,0,0,0,0,0,76.19768,8.949154,1.386294,3.423056,1 11,1,0,0,1,127373,0,7700.372,29.94935,0,11,1,26.13993,2.050663,0,0,0,28.19059,0,0,0,1,0,4,61.3,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,74.60146,8.949154,1.386294,3.338988,1 11,1,0,0,2,127373,0,7700.372,30.94935,0,11,1,83.70044,0,0,0,0,83.70044,0,0,0,0,10,4,61.3,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,74.60146,8.949154,1.386294,4.427244,1 11,1,0,0,3,127373,0,7700.372,31.94935,0,11,1,96.93621,.5022601,0,0,0,97.43848,0,0,0,1,18,4,61.3,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,74.60146,8.949154,1.386294,4.579221,1 6,1,25,1,1,127378,0,3809.553,36.846,0,3,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,0,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,74.64081,8.24553,1.386294,,0 6,1,25,1,2,127378,0,3809.553,37.846,0,3,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,0,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,74.64081,8.24553,1.386294,,0 6,1,25,1,3,127378,0,3809.553,38.846,0,3,1,37.666,0,0,0,0,37.666,0,0,0,1,0,4,96.8,0,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,74.64081,8.24553,1.386294,3.628758,1 6,1,25,1,1,127379,0,3809.553,8.64887,0,7,1,13.68233,0,0,0,0,13.68233,0,0,0,0,1,4,74.36826,13.73189,0,,700,700,1,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,82.16232,8.24553,1.386294,2.616105,1 6,1,25,1,2,127379,0,3809.553,9.64887,0,7,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,700,700,1,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,82.16232,8.24553,1.386294,,0 6,1,25,1,3,127379,0,3809.553,10.64887,0,7,1,36.91774,0,0,0,0,36.91774,0,0,0,1,0,4,74.36826,13.73189,0,,700,700,1,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,82.16232,8.24553,1.386294,3.608692,1 6,1,25,1,1,127380,0,3809.553,24.06297,1,7,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,4.3,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,75.53199,8.24553,1.386294,,0 6,1,25,1,2,127380,0,3809.553,25.06297,1,7,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,4.3,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,75.53199,8.24553,1.386294,,0 6,1,25,1,3,127380,0,3809.553,26.06297,1,7,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,4.3,0,,700,700,0,0,1.386294,6.55108,0,3.258096,7.937375,0,0,0,75.53199,8.24553,1.386294,,0 6,1,25,1,1,127381,0,3809.553,9.500342,1,7,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,700,700,1,1,1.386294,6.55108,0,3.258096,7.937375,0,0,0,81.64835,8.24553,1.386294,,0 6,1,25,1,2,127381,0,3809.553,10.50034,1,7,1,16.98421,6.303756,0,0,0,23.28797,0,0,0,1,0,4,74.36826,13.73189,0,,700,700,1,1,1.386294,6.55108,0,3.258096,7.937375,0,0,0,81.64835,8.24553,1.386294,3.147937,1 6,1,25,1,3,127381,0,3809.553,11.50034,1,7,1,19.32607,0,0,0,0,19.32607,0,0,0,1,0,4,74.36826,13.73189,0,,700,700,1,1,1.386294,6.55108,0,3.258096,7.937375,0,0,0,81.64835,8.24553,1.386294,2.961455,1 11,1,0,1,1,127385,0,9658.188,13.39357,1,12,1,16.35931,0,0,0,0,16.35931,0,0,0,1,0,5,74.36826,13.73189,0,,0,0,1,1,1.609438,0,0,0,0,0,0,0,83.986,9.175665,1.609438,2.794797,1 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6,1,25,1,2,127472,0,9668.734,13.29295,0,13,1,19.71523,6.818182,17.16867,0,0,43.70208,0,0,0,2,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,78.55493,9.176756,1.609438,3.777396,1 6,1,25,1,3,127472,0,9668.734,14.29295,0,13,1,34.41396,2.119701,28.84289,14.96259,0,65.37656,0,0,1,3,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,78.55493,9.176756,1.609438,4.180164,1 6,1,25,1,4,127472,0,9668.734,15.29295,0,13,1,25.35731,2.581835,0,0,0,27.93914,0,0,0,2,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,78.55493,9.176756,1.609438,3.330029,1 6,1,25,1,5,127472,0,9668.734,16.29295,0,13,1,14.81168,0,23.7537,0,0,38.56538,0,0,0,1,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,78.55493,9.176756,1.609438,3.652355,1 6,1,25,1,1,127473,0,9668.734,38.16564,1,13,1,31.77458,20.44365,34.27458,0,0,86.49281,0,0,0,2,1,5,58.9,26.1,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.59937,9.176756,1.609438,4.460061,1 6,1,25,1,2,127473,0,9668.734,39.16564,1,13,1,101.862,40.36145,0,0,0,142.2234,0,0,0,3,0,5,58.9,26.1,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.59937,9.176756,1.609438,4.957399,1 6,1,25,1,3,127473,0,9668.734,40.16564,1,13,1,49.87531,32.49377,43.78055,0,0,126.1496,0,0,0,2,1,5,58.9,26.1,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.59937,9.176756,1.609438,4.837469,1 6,1,25,1,4,127473,0,9668.734,41.16564,1,13,1,47.02628,4.702628,0,0,0,51.72891,0,0,0,3,0,5,58.9,26.1,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.59937,9.176756,1.609438,3.946017,1 6,1,25,1,5,127473,0,9668.734,42.16564,1,13,1,42.31908,10.77021,25.6496,0,0,78.73889,0,0,0,4,1,5,58.9,26.1,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,0,1,0,56.59937,9.176756,1.609438,4.366137,1 6,1,25,1,1,127474,0,9668.734,14.28884,1,13,1,107.9137,82.73381,0,0,0,190.6475,0,0,0,34,0,5,74.7,4.3,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.77795,9.176756,1.609438,5.250426,1 6,1,25,1,2,127474,0,9668.734,15.28884,1,13,1,75.02738,95.0712,0,0,0,170.0986,0,0,0,24,1,5,74.7,4.3,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.77795,9.176756,1.609438,5.136378,1 6,1,25,1,3,127474,0,9668.734,16.28884,1,13,1,182.2943,45.40648,0,0,0,227.7007,0,0,0,28,1,5,74.7,4.3,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.77795,9.176756,1.609438,5.428032,1 6,1,25,1,4,127474,0,9668.734,17.28884,1,13,1,108.1143,4.495159,24.09405,0,0,136.7036,0,0,0,22,1,5,74.7,4.3,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.77795,9.176756,1.609438,4.917815,1 6,1,25,1,5,127474,0,9668.734,18.28884,1,13,1,94.79475,6.749894,0,0,535.1968,636.7415,1,0,0,24,0,5,74.7,4.3,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.77795,9.176756,1.609438,6.456364,1 1,1,0,0,1,127486,0,10792.37,5.193703,1,12,1,7.733492,0,0,0,0,7.733492,0,0,0,1,0,5,74.36826,13.73189,0,,450,450,1,1,1.609438,6.109248,1,0,0,0,0,0,81.95454,9.286687,1.609438,2.045561,1 1,1,0,0,2,127486,0,10792.37,6.193703,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,450,450,1,1,1.609438,6.109248,1,0,0,0,0,0,81.95454,9.286687,1.609438,,0 1,1,0,0,3,127486,0,10792.37,7.193703,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,450,450,1,1,1.609438,6.109248,1,0,0,0,0,0,81.95454,9.286687,1.609438,,0 1,1,0,0,1,127487,0,10792.37,18.14374,0,12.32507,1,45.21119,0,0,0,0,45.21119,0,0,0,2,0,5,81.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.01041,9.286687,1.609438,3.811345,1 1,1,0,0,2,127487,0,10792.37,19.14374,0,12.32507,1,37.56124,0,0,0,0,37.56124,0,0,0,1,0,5,81.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.01041,9.286687,1.609438,3.625973,1 1,1,0,0,3,127487,0,10792.37,20.14374,0,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,5,81.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.01041,9.286687,1.609438,,0 1,1,0,0,1,127488,0,10792.37,50.19849,1,12,1,33.3135,88.65556,2.379536,0,0,124.3486,0,0,0,2,0,5,55,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,77.65372,9.286687,1.609438,4.823089,1 1,1,0,0,2,127488,0,10792.37,51.19849,1,12,1,42.46053,63.02667,20.14153,0,0,125.6287,0,0,0,2,1,5,55,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,77.65372,9.286687,1.609438,4.833331,1 1,1,0,0,3,127488,0,10792.37,52.19849,1,12,1,20.81269,38.45887,0,0,0,59.27156,0,0,0,2,0,5,55,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,77.65372,9.286687,1.609438,4.082129,1 1,1,0,0,1,127489,0,10792.37,24.3039,1,12,1,48.1856,20.65437,0,0,0,68.83997,0,0,0,3,0,5,66.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.64108,9.286687,1.609438,4.231785,1 1,1,0,0,2,127489,0,10792.37,25.3039,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,66.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.64108,9.286687,1.609438,,0 1,1,0,0,3,127489,0,10792.37,26.3039,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,66.3,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,79.64108,9.286687,1.609438,,0 1,1,0,0,1,127490,0,10792.37,49.90281,0,10,1,0,0,0,0,0,0,0,0,0,0,0,5,68.8,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,73.81825,9.286687,1.609438,,0 1,1,0,0,2,127490,0,10792.37,50.90281,0,10,1,8.709853,0,20.14153,0,0,28.85139,0,0,0,0,1,5,68.8,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,73.81825,9.286687,1.609438,3.362158,1 1,1,0,0,3,127490,0,10792.37,51.90281,0,10,1,120.664,34.2666,0,0,0,154.9306,0,0,0,8,6,5,68.8,13.73189,0,,450,450,0,0,1.609438,6.109248,1,0,0,0,0,0,73.81825,9.286687,1.609438,5.042977,1 2,1,100,0,1,127496,0,4168.734,57.92471,0,12,1,0,2.255595,0,0,0,2.255595,0,0,0,0,0,1,74.7,17.4,0,,170,170,0,0,0,5.135798,1,0,0,0,1,0,65.84276,8.335608,0,.8134137,1 2,1,100,0,2,127496,0,4168.734,58.92471,0,12,1,57.68194,7.563342,0,0,0,65.24529,0,0,0,3,0,1,74.7,17.4,0,,170,170,0,0,0,5.135798,1,0,0,0,1,0,65.84276,8.335608,0,4.178154,1 2,1,100,0,3,127496,0,4168.734,59.92471,0,12,1,22.85012,32.3489,0,0,0,55.19902,0,0,0,2,0,1,74.7,17.4,0,,170,170,0,0,0,5.135798,1,0,0,0,1,0,65.84276,8.335608,0,4.010945,1 2,1,100,0,4,127496,0,4168.734,60.92471,0,12,1,0,22.1103,0,0,0,22.1103,0,0,0,0,0,1,74.7,17.4,0,,170,170,0,0,0,5.135798,1,0,0,0,1,0,65.84276,8.335608,0,3.096044,1 2,1,100,0,5,127496,0,4168.734,61.92471,0,12,1,93.58066,67.77824,27.44477,0,1607.753,1796.557,2,0,0,4,6,1,74.7,17.4,0,,170,170,0,0,0,5.135798,1,0,0,0,1,0,65.84276,8.335608,0,7.493627,1 5,1,25,1,1,127511,0,1191.067,53.73032,1,3,1,9.592326,0,34.77218,0,0,44.36451,0,0,0,0,1,1,71.6,30.4,1,,0,0,0,0,0,0,0,3.258096,0,0,1,0,52.28558,7.083444,0,3.79244,1 5,1,25,1,2,127511,0,1191.067,54.73032,1,3,1,0,0,0,0,0,0,0,0,0,0,0,1,71.6,30.4,1,,0,0,0,0,0,0,0,3.258096,0,0,1,0,52.28558,7.083444,0,,0 5,1,25,1,3,127511,0,1191.067,55.73032,1,3,1,0,0,0,0,0,0,0,0,0,0,0,1,71.6,30.4,1,,0,0,0,0,0,0,0,3.258096,0,0,1,0,52.28558,7.083444,0,,0 1,1,0,0,1,127513,0,7029.777,3.879534,1,9,1,0,14.46643,0,0,0,14.46643,0,0,0,0,0,4,74.36826,13.73189,0,,450,0,1,1,1.386294,0,1,0,0,0,1,0,57.77286,8.858052,1.386294,2.671831,1 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11,1,0,1,2,127595,0,10902.61,3.45859,0,9,1,32.31106,15.36145,0,0,0,47.67251,0,0,0,5,0,5,74.36826,13.73189,0,,0,291,1,0,1.609438,5.673323,0,0,0,1,0,0,72.88785,9.296849,1.609438,3.864355,1 11,1,0,1,3,127595,0,10902.61,4.45859,0,9,1,93.46633,59.89526,0,0,0,153.3616,0,0,0,14,0,5,74.36826,13.73189,0,,0,291,1,0,1.609438,5.673323,0,0,0,1,0,0,72.88785,9.296849,1.609438,5.032799,1 11,1,0,1,1,127596,0,10902.61,11.19507,0,9,1,24.58034,2.002398,23.80696,0,0,50.38969,0,0,0,2,1,5,74.36826,13.73189,0,,0,291,1,0,1.609438,5.673323,0,0,0,1,0,0,74.00797,9.296849,1.609438,3.919786,1 11,1,0,1,2,127596,0,10902.61,12.19507,0,9,1,65.71741,5.750274,14.30449,0,0,85.77218,0,0,0,5,1,5,74.36826,13.73189,0,,0,291,1,0,1.609438,5.673323,0,0,0,1,0,0,74.00797,9.296849,1.609438,4.451694,1 11,1,0,1,3,127596,0,10902.61,13.19507,0,9,1,262.2494,5.955112,24.49875,0,0,292.7032,0,0,0,5,1,5,74.36826,13.73189,0,,0,291,1,0,1.609438,5.673323,0,0,0,1,0,0,74.00797,9.296849,1.609438,5.679159,1 11,1,0,1,1,127597,0,10902.61,32.24915,1,9,1,80.33573,17.48801,31.32494,0,0,129.1487,0,0,0,5,2,5,78.9,34.8,0,,0,291,0,0,1.609438,5.673323,0,0,0,1,0,0,65.76151,9.296849,1.609438,4.860964,1 11,1,0,1,2,127597,0,10902.61,33.24915,1,9,1,349.0854,28.63089,0,0,0,377.7163,0,0,0,11,1,5,78.9,34.8,0,,0,291,0,0,1.609438,5.673323,0,0,0,1,0,0,65.76151,9.296849,1.609438,5.934144,1 11,1,0,1,3,127597,0,10902.61,34.24915,1,9,1,228.7581,46.49377,36.48379,0,394.8678,706.6035,1,0,0,11,1,5,78.9,34.8,0,,0,291,0,0,1.609438,5.673323,0,0,0,1,0,0,65.76151,9.296849,1.609438,6.56047,1 11,1,0,1,1,127598,0,10902.61,31.96441,0,12,1,74.34053,38.81894,31.32494,0,720.2338,864.7182,2,0,0,6,1,5,82.1,17.4,0,,0,291,0,0,1.609438,5.673323,0,0,0,0,0,1,53.44062,9.296849,1.609438,6.762403,1 11,1,0,1,2,127598,0,10902.61,32.96441,0,12,1,215.2245,33.61446,38.33516,0,0,287.1742,0,0,0,11,7,5,82.1,17.4,0,,0,291,0,0,1.609438,5.673323,0,0,0,0,0,1,53.44062,9.296849,1.609438,5.660089,1 11,1,0,1,3,127598,0,10902.61,33.96441,0,12,1,175.3865,88.24937,34.399,0,0,298.0349,0,0,0,11,1,5,82.1,17.4,0,,0,291,0,0,1.609438,5.673323,0,0,0,0,0,1,53.44062,9.296849,1.609438,5.697211,1 11,1,0,1,1,127599,0,10902.61,10.11088,1,9,1,34.17266,0,0,0,0,34.17266,0,0,0,3,1,5,74.36826,13.73189,0,,0,291,1,1,1.609438,5.673323,0,0,0,1,0,0,75.8055,9.296849,1.609438,3.531426,1 11,1,0,1,2,127599,0,10902.61,11.11088,1,9,1,49.28806,6.440307,30.33406,0,0,86.06243,0,0,0,6,1,5,74.36826,13.73189,0,,0,291,1,1,1.609438,5.673323,0,0,0,1,0,0,75.8055,9.296849,1.609438,4.455073,1 11,1,0,1,3,127599,0,10902.61,12.11088,1,9,1,17.45636,4.82793,31.79551,0,0,54.0798,0,0,0,2,1,5,74.36826,13.73189,0,,0,291,1,1,1.609438,5.673323,0,0,0,1,0,0,75.8055,9.296849,1.609438,3.990461,1 6,1,25,1,1,127600,0,7676.799,21.86995,1,15,1,23.98082,6.294964,31.95444,0,0,62.23022,0,0,0,2,0,4,84.2,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.52619,8.946088,1.386294,4.130841,1 6,1,25,1,2,127600,0,7676.799,22.86995,1,15,1,38.8828,13.36254,0,0,0,52.24535,0,0,0,4,0,4,84.2,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.52619,8.946088,1.386294,3.955951,1 6,1,25,1,3,127600,0,7676.799,23.86995,1,15,1,23.4414,2.319202,38.04988,0,0,63.81047,0,0,0,2,0,4,84.2,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.52619,8.946088,1.386294,4.155917,1 6,1,25,1,1,127601,0,7676.799,17.16906,1,12,1,13.18945,11.1211,0,0,0,24.31055,0,0,0,2,0,4,78.9,0,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,74.88385,8.946088,1.386294,3.19091,1 6,1,25,1,2,127601,0,7676.799,18.16906,1,12,1,33.954,0,31.47864,0,0,65.43264,0,0,0,3,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,74.88385,8.946088,1.386294,4.181021,1 6,1,25,1,3,127601,0,7676.799,19.16906,1,12,1,50.87282,7.082294,0,0,0,57.95511,0,0,0,4,0,4,78.9,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,74.88385,8.946088,1.386294,4.059669,1 6,1,25,1,1,127602,0,7676.799,53.17728,0,12,1,19.78417,49.43045,0,0,0,69.21463,0,0,0,2,0,4,85.3,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,63.87726,8.946088,1.386294,4.237212,1 6,1,25,1,2,127602,0,7676.799,54.17728,0,12,1,57.41512,31.06791,33.19277,0,0,121.6758,0,0,0,5,0,4,85.3,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,63.87726,8.946088,1.386294,4.80136,1 6,1,25,1,3,127602,0,7676.799,55.17728,0,12,1,75.5611,48.17955,0,0,0,123.7406,0,0,0,5,0,4,85.3,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,63.87726,8.946088,1.386294,4.818188,1 6,1,25,1,1,127603,0,7676.799,53.577,1,12,1,68.95084,51.25899,230.8154,0,1179.568,1530.594,1,0,0,2,0,4,71.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,65.51687,8.946088,1.386294,7.333411,1 6,1,25,1,2,127603,0,7676.799,54.577,1,12,1,62.43155,65.4436,42.91895,0,0,170.7941,0,0,0,5,0,4,71.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,65.51687,8.946088,1.386294,5.140459,1 6,1,25,1,3,127603,0,7676.799,55.577,1,12,1,22.44389,51.94514,0,0,0,74.38903,0,0,0,2,0,4,71.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,1,0,0,65.51687,8.946088,1.386294,4.309309,1 4,1,100,1,1,127611,0,11610.42,11.06639,0,13,1,113.6631,22.9682,0,0,0,136.6313,0,0,0,10,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,79.12157,9.359744,1.609438,4.917286,1 4,1,100,1,2,127611,0,11610.42,12.06639,0,13,1,8.086253,0,0,0,0,8.086253,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,79.12157,9.359744,1.609438,2.090165,1 4,1,100,1,3,127611,0,11610.42,13.06639,0,13,1,123.8329,13.2973,0,71.04177,1380.973,1518.103,1,0,13,13,8,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,79.12157,9.359744,1.609438,7.325217,1 4,1,100,1,1,127612,0,11610.42,8.689939,0,13,1,50.05889,63.03298,0,0,887.8975,1000.989,1,0,0,5,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,74.88016,9.359744,1.609438,6.908744,1 4,1,100,1,2,127612,0,11610.42,9.689939,0,13,1,105.4771,32.70081,0,182.2102,0,138.1779,0,0,21,7,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,74.88016,9.359744,1.609438,4.928542,1 4,1,100,1,3,127612,0,11610.42,10.68994,0,13,1,55.03685,17.09091,0,194.5209,0,72.12776,0,0,21,4,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,1,0,0,1,0,0,74.88016,9.359744,1.609438,4.278439,1 4,1,100,1,1,127613,0,11610.42,34.77618,0,12,1,106.0071,48.20966,0,0,204.9764,359.1932,1,0,0,8,1,5,53.7,17.4,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.79161,9.359744,1.609438,5.88386,1 4,1,100,1,2,127613,0,11610.42,35.77618,0,12,1,30.18868,22.73854,0,75.47169,0,52.92722,0,0,4,3,0,5,53.7,17.4,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.79161,9.359744,1.609438,3.968918,1 4,1,100,1,3,127613,0,11610.42,36.77618,0,12,1,74.69287,10.05405,0,171.9902,0,84.74693,0,0,7,5,1,5,53.7,17.4,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,64.79161,9.359744,1.609438,4.43967,1 4,1,100,1,1,127614,0,11610.42,35.45517,1,13,1,148.4099,50.30035,0,0,0,198.7103,0,0,0,15,0,5,38.9,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,67.10754,9.359744,1.609438,5.291848,1 4,1,100,1,2,127614,0,11610.42,36.45517,1,13,1,67.16982,43.47709,0,16.17251,0,110.6469,0,0,1,4,0,5,38.9,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,67.10754,9.359744,1.609438,4.706344,1 4,1,100,1,3,127614,0,11610.42,37.45517,1,13,1,187.715,87.88698,0,243.0319,0,275.602,0,0,24,9,4,5,38.9,21.7,0,,1000,1000,0,0,1.609438,6.907755,1,0,0,1,0,0,67.10754,9.359744,1.609438,5.618958,1 4,1,100,1,1,127615,0,11610.42,12.72005,1,13,1,130.7421,41.56655,30.62426,0,0,202.9329,0,0,0,22,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,78.60759,9.359744,1.609438,5.312875,1 4,1,100,1,2,127615,0,11610.42,13.72005,1,13,1,140.1617,31.34232,0,0,0,171.504,0,0,0,23,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,78.60759,9.359744,1.609438,5.144607,1 4,1,100,1,3,127615,0,11610.42,14.72005,1,13,1,110.5651,15.47912,26.04423,74.72236,0,152.0885,0,0,13,13,7,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,1,0,0,1,0,0,78.60759,9.359744,1.609438,5.024462,1 2,1,100,0,1,127628,0,8910.67,15.91786,0,12,1,0,4.640095,14.30101,0,0,18.94111,0,0,0,0,0,6,76.3,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,2.941334,1 2,1,100,0,2,127628,0,8910.67,16.91786,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,76.3,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,3,127628,0,8910.67,17.91786,0,12,1,12.3885,0,0,0,0,12.3885,0,0,0,1,0,6,76.3,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,2.516769,1 2,1,100,0,4,127628,0,8910.67,18.91786,0,12,1,3.212483,2.349702,0,0,0,5.562184,0,0,0,1,0,6,76.3,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,1.715991,1 2,1,100,0,5,127628,0,8910.67,19.91786,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,76.3,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,1,127629,0,8910.67,17.71389,0,12,1,19.54194,0,0,0,0,19.54194,0,0,0,0,0,6,92.5,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,2.972563,1 2,1,100,0,2,127629,0,8910.67,18.71389,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,3,127629,0,8910.67,19.71389,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,4,127629,0,8910.67,20.71389,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,5,127629,0,8910.67,21.71389,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,80.20427,9.095117,1.791759,,0 2,1,100,0,1,127630,0,8910.67,47.63039,0,12,1,10.70791,0,43.27781,0,0,53.98572,0,0,0,0,1,6,90,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,66.07664,9.095117,1.791759,3.98872,1 2,1,100,0,2,127630,0,8910.67,48.63039,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,90,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,66.07664,9.095117,1.791759,,0 2,1,100,0,3,127630,0,8910.67,49.63039,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,90,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,66.07664,9.095117,1.791759,,0 2,1,100,0,4,127630,0,8910.67,50.63039,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,90,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,66.07664,9.095117,1.791759,,0 2,1,100,0,5,127630,0,8910.67,51.63039,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,90,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,66.07664,9.095117,1.791759,,0 2,1,100,0,1,127631,0,8910.67,9.270363,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,84.45741,9.095117,1.791759,,0 2,1,100,0,2,127631,0,8910.67,10.27036,0,12,1,26.12956,4.082744,0,0,0,30.2123,0,0,0,1,1,6,74.36826,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,84.45741,9.095117,1.791759,3.408249,1 2,1,100,0,3,127631,0,8910.67,11.27036,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,84.45741,9.095117,1.791759,,0 2,1,100,0,4,127631,0,8910.67,12.27036,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,84.45741,9.095117,1.791759,,0 2,1,100,0,5,127631,0,8910.67,13.27036,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,624,624,1,0,1.791759,6.436151,1,0,0,0,0,0,84.45741,9.095117,1.791759,,0 2,1,100,0,1,127632,0,8910.67,47.85216,1,12,1,19.63117,0,39.78584,0,0,59.41702,0,0,0,1,1,6,82.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,70.76368,9.095117,1.791759,4.08458,1 2,1,100,0,2,127632,0,8910.67,48.85216,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,82.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,70.76368,9.095117,1.791759,,0 2,1,100,0,3,127632,0,8910.67,49.85216,1,12,1,29.23687,11.11497,33.45391,0,0,73.80575,0,0,0,2,1,6,82.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,70.76368,9.095117,1.791759,4.301436,1 2,1,100,0,4,127632,0,8910.67,50.85216,1,12,1,0,11.7531,0,0,0,11.7531,0,0,0,0,0,6,82.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,70.76368,9.095117,1.791759,2.464117,1 2,1,100,0,5,127632,0,8910.67,51.85216,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,82.5,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,1,0,0,70.76368,9.095117,1.791759,,0 2,1,100,0,1,127633,0,8910.67,14.77071,1,12,1,41.19572,2.40928,0,0,0,43.605,0,0,0,1,1,6,85,13.73189,0,,624,624,1,1,1.791759,6.436151,1,0,0,0,0,0,74.86757,9.095117,1.791759,3.775172,1 2,1,100,0,2,127633,0,8910.67,15.77071,1,12,1,13.06478,4.082744,26.12956,0,0,43.27708,0,0,0,0,1,6,85,13.73189,0,,624,624,1,1,1.791759,6.436151,1,0,0,0,0,0,74.86757,9.095117,1.791759,3.767623,1 2,1,100,0,3,127633,0,8910.67,16.7707,1,12,1,3.468781,0,0,0,0,3.468781,0,0,0,1,0,6,85,13.73189,0,,624,624,1,1,1.791759,6.436151,1,0,0,0,0,0,74.86757,9.095117,1.791759,1.243803,1 2,1,100,0,4,127633,0,8910.67,17.7707,1,12,1,22.48738,0,0,0,0,22.48738,0,0,0,3,0,6,85,13.73189,0,,624,624,1,1,1.791759,6.436151,1,0,0,0,0,0,74.86757,9.095117,1.791759,3.112954,1 2,1,100,0,5,127633,0,8910.67,18.7707,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,85,13.73189,0,,624,624,0,0,1.791759,6.436151,1,0,0,0,0,0,74.86757,9.095117,1.791759,,0 4,1,100,1,1,127637,0,2199.752,48.90623,1,13,1,21.10977,0,43.80579,0,0,64.91556,0,0,0,2,0,1,61.1,21.7,0,,403,403,0,0,0,5.998937,1,0,0,1,0,0,69.50401,7.696554,0,4.173087,1 4,1,100,1,2,127637,0,2199.752,49.90623,1,13,1,0,0,0,0,0,0,0,0,0,0,0,1,61.1,21.7,0,,403,403,0,0,0,5.998937,1,0,0,1,0,0,69.50401,7.696554,0,,0 4,1,100,1,3,127637,0,2199.752,50.90623,1,13,1,0,0,0,0,0,0,0,0,0,0,0,1,61.1,21.7,0,,403,403,0,0,0,5.998937,1,0,0,1,0,0,69.50401,7.696554,0,,0 4,1,100,1,4,127637,0,2199.752,51.90623,1,13,1,23.18034,0,25.656,0,0,48.83635,0,0,0,3,0,1,61.1,21.7,0,,403,403,0,0,0,5.998937,1,0,0,1,0,0,69.50401,7.696554,0,3.888475,1 4,1,100,1,5,127637,0,2199.752,52.90623,1,13,1,380.5827,0,55.08294,0,1714.241,2149.906,1,0,0,2,0,1,61.1,21.7,0,,403,403,0,0,0,5.998937,1,0,0,1,0,0,69.50401,7.696554,0,7.67318,1 11,1,0,1,1,127641,0,9150.124,.6872005,0,12,1,101.3189,18.94484,0,0,0,120.2638,0,0,0,6,1,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.789688,1 11,1,0,1,2,127641,0,9150.124,1.687201,0,12,1,79.68237,23.05586,0,0,0,102.7382,0,0,0,6,1,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.632185,1 11,1,0,1,3,127641,0,9150.124,2.687201,0,12,1,57.4813,22.81796,0,0,236.813,317.1122,1,0,0,6,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,5.759256,1 11,1,0,1,4,127641,0,9150.124,3.687201,0,12,1,89.5574,10.23513,0,0,0,99.79253,0,0,0,8,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.603093,1 11,1,0,1,5,127641,0,9150.124,4.687201,0,12,1,45.70461,15.88235,0,0,0,61.58697,0,0,0,6,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.12045,1 11,1,0,1,1,127642,0,9150.124,25.93566,1,12,1,98.32134,53.5012,0,0,0,151.8225,0,0,0,7,0,4,42.1,21.7,1,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.19587,9.121632,1.386294,5.022712,1 11,1,0,1,2,127642,0,9150.124,26.93566,1,12,1,63.52684,23.7678,0,0,0,87.29463,0,0,0,7,0,4,42.1,21.7,1,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.19587,9.121632,1.386294,4.469289,1 11,1,0,1,3,127642,0,9150.124,27.93566,1,12,1,44.88778,30.44888,0,0,0,75.33665,0,0,0,6,0,4,42.1,21.7,1,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.19587,9.121632,1.386294,4.321967,1 11,1,0,1,4,127642,0,9150.124,28.93566,1,12,1,117.1047,57.42278,0,0,0,174.5274,0,0,0,9,0,4,42.1,21.7,1,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.19587,9.121632,1.386294,5.162082,1 11,1,0,1,5,127642,0,9150.124,29.93566,1,12,1,118.2818,46.21244,0,0,724.4604,888.9547,1,0,0,6,0,4,42.1,21.7,1,,0,0,0,0,1.386294,0,0,0,0,1,0,0,67.19587,9.121632,1.386294,6.790046,1 11,1,0,1,1,127643,0,9150.124,4.982888,0,12,1,34.77218,1.199041,0,0,0,35.97122,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,3.582719,1 11,1,0,1,2,127643,0,9150.124,5.982888,0,12,1,83.7897,1.09529,0,0,0,84.88499,0,0,0,5,1,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.441298,1 11,1,0,1,3,127643,0,9150.124,6.982888,0,12,1,67.98504,0,0,0,0,67.98504,0,0,0,4,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,4.219288,1 11,1,0,1,4,127643,0,9150.124,7.982888,0,12,1,26.74043,7.076994,0,0,0,33.81743,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,3.520976,1 11,1,0,1,5,127643,0,9150.124,8.982888,0,12,1,10.57977,0,0,0,0,10.57977,0,0,0,1,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,74.24259,9.121632,1.386294,2.358944,1 11,1,0,1,1,127644,0,9150.124,26.34908,0,14,1,57.40408,6.792566,0,0,299.7602,363.9568,1,1,0,1,0,4,70.5,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,71.81533,9.121632,1.386294,5.897035,1 11,1,0,1,2,127644,0,9150.124,27.34908,0,14,1,107.667,2.88609,20.60241,0,0,131.1555,0,0,0,6,2,4,70.5,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,71.81533,9.121632,1.386294,4.876384,1 11,1,0,1,3,127644,0,9150.124,28.34908,0,14,1,0,4.927681,0,0,0,4.927681,0,0,0,0,0,4,70.5,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,71.81533,9.121632,1.386294,1.594869,1 11,1,0,1,4,127644,0,9150.124,29.34908,0,14,1,0,0,0,0,822.2914,822.2914,1,0,0,0,0,4,70.5,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,71.81533,9.121632,1.386294,6.712095,1 11,1,0,1,5,127644,0,9150.124,30.34908,0,14,1,11.00296,0,0,0,0,11.00296,0,0,0,1,0,4,70.5,8.7,0,,0,0,0,0,1.386294,0,0,0,0,1,0,0,71.81533,9.121632,1.386294,2.398165,1 6,1,25,0,1,127645,0,13521.71,39.54826,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,78.64024,9.512126,1.386294,,0 6,1,25,0,2,127645,0,13521.71,40.54826,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,78.64024,9.512126,1.386294,,0 6,1,25,0,3,127645,0,13521.71,41.54826,0,12,1,16.70762,0,0,0,0,16.70762,0,0,0,2,0,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,78.64024,9.512126,1.386294,2.815865,1 6,1,25,0,4,127645,0,13521.71,42.54826,0,12,1,30.08204,0,0,0,0,30.08204,0,0,0,1,0,3,76.3,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.64024,9.512126,1.098612,3.403929,1 6,1,25,0,5,127645,0,13521.71,43.54826,0,12,1,118.1117,3.980825,0,0,0,122.0925,0,0,0,3,0,3,76.3,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.64024,9.512126,1.098612,4.804779,1 6,1,25,0,1,127646,0,13521.71,36.74469,1,12,1,35.92462,15.04711,0,0,0,50.97173,0,0,0,4,0,4,78.8,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.53143,9.512126,1.386294,3.931271,1 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1,1,0,0,3,127956,0,2800.868,23.32238,1,13,1,0,0,0,0,0,0,0,0,0,0,0,1,67.5,13.73189,0,,300,300,0,0,0,5.703783,1,0,0,1,0,0,65.36804,7.938042,0,,0 11,1,0,1,1,127957,0,9542.002,4.867898,0,10,1,67.83274,5.647821,0,0,0,73.48057,0,0,0,4,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,70.7345,9.163564,1.386294,4.297021,1 11,1,0,1,2,127957,0,9542.002,5.867898,0,10,1,114.7439,24.42048,0,0,898.6685,1037.833,2,0,0,8,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,70.7345,9.163564,1.386294,6.94489,1 11,1,0,1,3,127957,0,9542.002,6.867898,0,10,1,35.87223,14.05405,0,0,0,49.92629,0,0,0,4,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,70.7345,9.163564,1.386294,3.910548,1 11,1,0,1,4,127957,0,9542.002,7.867898,0,10,1,37.16955,0,0,0,0,37.16955,0,0,0,1,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,1,0,0,70.7345,9.163564,1.386294,3.61549,1 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3,1,100,0,1,128072,0,13029.78,29.17454,1,16,1,7.793765,7.152278,30.98321,0,797.1523,843.0815,1,0,0,0,1,3,86.3,4.3,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,79.08138,9.475069,1.098612,6.737064,1 3,1,100,0,2,128072,0,13029.78,30.17454,1,16,1,32.85871,0,0,0,0,32.85871,0,0,0,1,0,3,86.3,4.3,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,79.08138,9.475069,1.098612,3.492217,1 3,1,100,0,3,128072,0,13029.78,31.17454,1,16,1,7.481297,2.438903,0,0,0,9.920199,0,0,0,1,0,3,86.3,4.3,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,79.08138,9.475069,1.098612,2.294573,1 3,1,100,0,4,128072,0,13029.78,32.17454,1,16,1,6.915629,0,0,0,0,6.915629,0,0,0,1,0,3,86.3,4.3,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,79.08138,9.475069,1.098612,1.933784,1 3,1,100,0,5,128072,0,13029.78,33.17454,1,16,1,8.463818,0,0,0,0,8.463818,0,0,0,2,0,3,86.3,4.3,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,79.08138,9.475069,1.098612,2.1358,1 5,1,25,0,1,128076,0,12376.42,15.24709,1,17,1,11.89768,0,30.18441,0,0,42.08209,0,0,0,0,1,4,63.8,13.73189,0,,762,762,1,1,1.386294,6.635947,0,3.258096,8.022241,0,0,0,72.40894,9.423629,1.386294,3.739622,1 5,1,25,0,2,128076,0,12376.42,16.24709,1,17,1,21.23027,6.069679,0,0,0,27.29995,0,0,0,1,0,4,63.8,13.73189,0,,762,762,1,1,1.386294,6.635947,0,3.258096,8.022241,0,0,0,72.40894,9.423629,1.386294,3.306885,1 5,1,25,0,3,128076,0,12376.42,17.24709,1,17,1,101.338,3.414272,0,0,9.910803,114.663,0,0,0,6,0,4,63.8,13.73189,0,,762,762,1,1,1.386294,6.635947,0,3.258096,8.022241,0,0,0,72.40894,9.423629,1.386294,4.741998,1 5,1,25,0,1,128077,0,12376.42,42.25051,1,17,1,86.85307,101.6776,0,0,0,188.5306,0,0,0,8,0,4,78.8,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.74407,9.423629,1.386294,5.239261,1 5,1,25,0,2,128077,0,12376.42,43.25051,1,17,1,178.6064,75.40011,0,0,0,254.0065,0,0,0,6,0,4,78.8,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.74407,9.423629,1.386294,5.53736,1 5,1,25,0,3,128077,0,12376.42,44.25051,1,17,1,69.87116,54.74232,0,0,0,124.6135,0,0,0,8,0,4,78.8,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.74407,9.423629,1.386294,4.825217,1 5,1,25,0,1,128078,0,12376.42,17.92197,0,17,1,88.10232,3.866746,32.69482,0,0,124.6639,0,0,0,3,1,4,82.5,13.73189,0,,762,762,1,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,77.74564,9.423629,1.386294,4.825621,1 5,1,25,0,2,128078,0,12376.42,18.92197,0,17,1,14.15351,3.756124,0,0,0,17.90964,0,0,0,2,0,4,82.5,13.73189,0,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,77.74564,9.423629,1.386294,2.885339,1 5,1,25,0,3,128078,0,12376.42,19.92197,0,17,1,0,0,0,0,0,0,0,0,0,0,0,4,82.5,13.73189,0,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,77.74564,9.423629,1.386294,,0 5,1,25,0,1,128079,0,12376.42,19.00616,0,12,1,41.047,0,33.28971,0,0,74.33671,0,0,0,3,1,4,35,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.48103,9.423629,1.386294,4.308605,1 5,1,25,0,2,128079,0,12376.42,20.00616,0,12,1,70.76756,8.633642,14.62167,0,0,94.02287,0,0,0,2,1,4,35,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.48103,9.423629,1.386294,4.543538,1 5,1,25,0,3,128079,0,12376.42,21.00616,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,35,13.73189,1,,762,762,0,0,1.386294,6.635947,0,3.258096,8.022241,0,0,0,75.48103,9.423629,1.386294,,0 11,1,0,0,1,128084,0,8041.563,4.835044,0,12,1,31.80212,5.57126,0,0,0,37.37338,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,3.620959,1 11,1,0,0,2,128084,0,8041.563,5.835044,0,12,1,46.36119,11.26146,0,0,0,57.62264,0,0,0,5,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,4.053916,1 11,1,0,0,3,128084,0,8041.563,6.835044,0,12,1,227.2727,82.0344,0,0,0,309.3071,0,0,0,23,2,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,5.734335,1 11,1,0,0,1,128085,0,8041.563,1.716632,0,12,1,22.9682,7.043581,0,0,0,30.01178,0,0,0,4,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,3.40159,1 11,1,0,0,2,128085,0,8041.563,2.716632,0,12,1,19.40701,0,0,0,0,19.40701,0,0,0,3,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,2.965634,1 11,1,0,0,3,128085,0,8041.563,3.716632,0,12,1,19.16462,2.815725,0,0,0,21.98034,0,0,0,2,0,4,74.36826,13.73189,0,,0,0,1,0,1.386294,0,0,0,0,0,0,0,78.92502,8.992503,1.386294,3.090149,1 11,1,0,0,1,128086,0,8041.563,36.61602,0,9,1,88.33923,0,0,0,0,88.33923,0,0,0,1,0,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,73.86172,8.992503,1.386294,4.481184,1 11,1,0,0,2,128086,0,8041.563,37.61602,0,9,1,53.36927,0,0,0,0,53.36927,0,0,0,1,0,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,73.86172,8.992503,1.386294,3.977235,1 11,1,0,0,3,128086,0,8041.563,38.61602,0,9,1,20.63882,0,0,0,0,20.63882,0,0,0,2,1,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,73.86172,8.992503,1.386294,3.027174,1 11,1,0,0,1,128087,0,8041.563,28.39699,1,12,1,146.7903,45.18846,0,0,0,191.9788,0,0,0,5,0,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,77.90237,8.992503,1.386294,5.257385,1 11,1,0,0,2,128087,0,8041.563,29.39699,1,12,1,56.60378,7.202157,0,0,0,63.80593,0,0,0,1,0,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,77.90237,8.992503,1.386294,4.155846,1 11,1,0,0,3,128087,0,8041.563,30.39699,1,12,1,62.89926,5.110565,0,0,0,68.00983,0,0,0,4,0,4,78.8,13.73189,0,,0,0,0,0,1.386294,0,0,0,0,0,0,0,77.90237,8.992503,1.386294,4.219652,1 11,1,0,0,1,128088,0,9542.002,27.37851,1,15,1,151.6786,141.9364,0,0,0,293.6151,0,0,0,11,0,3,28.8,13.73189,0,,0,258,0,0,1.098612,5.552959,0,0,0,1,0,0,73.3112,9.163564,1.098612,5.68227,1 11,1,0,0,2,128088,0,9542.002,28.37851,1,15,1,116.1008,116.5663,0,0,460.1259,692.793,1,0,0,3,0,3,28.8,13.73189,0,,0,258,0,0,1.098612,5.552959,0,0,0,1,0,0,73.3112,9.163564,1.098612,6.540731,1 11,1,0,0,3,128088,0,9542.002,29.37851,1,15,1,65.83541,142.0199,0,0,0,207.8554,0,0,0,6,0,3,28.8,13.73189,0,,0,258,0,0,1.098612,5.552959,0,0,0,1,0,0,73.3112,9.163564,1.098612,5.336843,1 11,1,0,0,4,128088,0,9542.002,30.37851,1,15,1,75.8414,85.0023,0,0,745.0945,905.9382,1,0,0,6,0,3,28.8,13.73189,0,,0,258,0,0,1.098612,5.552959,0,0,0,1,0,0,73.3112,9.163564,1.098612,6.808971,1 11,1,0,0,5,128088,0,9542.002,31.37851,1,15,1,410.8422,155.5438,24.41388,0,0,590.7998,0,0,0,2,1,4,28.8,13.73189,0,,0,258,0,0,1.386294,5.552959,0,0,0,1,0,0,73.3112,9.163564,1.386294,6.381477,1 11,1,0,0,1,128089,0,9542.002,3.017112,0,15,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,69.03037,9.163564,1.098612,,0 11,1,0,0,2,128089,0,9542.002,4.017112,0,15,1,9.857613,0,0,0,0,9.857613,0,0,0,1,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,69.03037,9.163564,1.098612,2.288244,1 11,1,0,0,3,128089,0,9542.002,5.017112,0,15,1,136.2095,15.18703,.798005,0,0,152.1945,0,0,0,5,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,69.03037,9.163564,1.098612,5.025159,1 11,1,0,0,4,128089,0,9542.002,6.017112,0,15,1,43.79898,18.55694,0,0,287.953,350.3089,1,0,0,6,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,69.03037,9.163564,1.098612,5.858815,1 11,1,0,0,5,128089,0,9542.002,7.017112,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,0,258,1,0,1.386294,5.552959,0,0,0,1,0,0,69.03037,9.163564,1.386294,,0 11,1,0,0,1,128090,0,9542.002,5.481177,0,15,1,35.97122,11.90048,0,0,0,47.8717,0,0,0,1,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,72.462,9.163564,1.098612,3.868525,1 11,1,0,0,2,128090,0,9542.002,6.481177,0,15,1,21.35816,14.23877,0,0,0,35.59693,0,0,0,1,1,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,72.462,9.163564,1.098612,3.572259,1 11,1,0,0,3,128090,0,9542.002,7.481177,0,15,1,9.476309,40.52369,0,0,0,50,0,0,0,2,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,72.462,9.163564,1.098612,3.912023,1 11,1,0,0,4,128090,0,9542.002,8.481177,0,15,1,261.2494,57.51498,7.607192,0,0,326.3716,0,0,0,11,0,3,74.36826,13.73189,0,,0,258,1,0,1.098612,5.552959,0,0,0,1,0,0,72.462,9.163564,1.098612,5.788037,1 11,1,0,0,5,128090,0,9542.002,9.481177,0,15,1,90.56284,34.84977,32.69996,410.4951,0,158.1126,0,0,26,5,1,4,74.36826,13.73189,0,,0,258,1,0,1.386294,5.552959,0,0,0,1,0,0,72.462,9.163564,1.386294,5.063307,1 4,1,100,0,1,128091,0,10933.62,27.28268,1,16,1,29.7442,10.61868,0,0,0,40.36288,0,0,0,4,0,1,70,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,72.76367,9.299689,0,3.697911,1 4,1,100,0,2,128091,0,10933.62,28.28268,1,16,1,50.62602,11.66032,0,0,0,62.28633,0,0,0,2,1,1,70,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,72.76367,9.299689,0,4.131742,1 4,1,100,0,3,128091,0,10933.62,29.28268,1,16,1,37.66105,11.61546,14.8662,0,0,64.14272,0,0,0,3,1,1,70,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,72.76367,9.299689,0,4.16111,1 4,1,100,0,4,128091,0,10933.62,30.28268,1,16,1,20.19275,22.69849,0,0,0,42.89124,0,0,0,3,0,1,70,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,72.76367,9.299689,0,3.758667,1 4,1,100,0,5,128091,0,10933.62,31.28268,1,16,1,10.93816,11.76693,0,0,0,22.70509,0,0,0,2,0,1,70,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,1,0,0,72.76367,9.299689,0,3.122589,1 4,1,100,0,1,128092,0,5406.948,27.50445,1,16,1,24.98513,76.14515,0,0,0,101.1303,0,0,0,3,0,1,76.3,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.18845,8.595625,0,4.61641,1 4,1,100,0,2,128092,0,5406.948,28.50445,1,16,1,29.94012,37.01688,0,0,0,66.95699,0,0,0,5,0,1,76.3,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.18845,8.595625,0,4.204051,1 4,1,100,0,3,128092,0,5406.948,29.50445,1,16,1,23.98414,40.51041,0,0,0,64.49455,0,0,0,2,2,1,76.3,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.18845,8.595625,0,4.166581,1 4,1,100,0,4,128092,0,5406.948,30.50445,1,16,1,11.47315,29.42175,0,0,0,40.89491,0,0,0,1,0,1,76.3,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.18845,8.595625,0,3.711005,1 4,1,100,0,5,128092,0,5406.948,31.50445,1,16,1,7.57257,0,0,0,0,7.57257,0,0,0,1,0,1,76.3,13.73189,0,,1000,1000,0,0,0,6.907755,1,0,0,0,0,0,76.18845,8.595625,0,2.024533,1 3,1,100,0,1,128093,0,6576.303,24.33676,1,13,1,17.66784,15.23557,0,0,0,32.90342,0,0,0,2,0,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,79.71188,8.79138,.6931472,3.493577,1 3,1,100,0,2,128093,0,6576.303,25.33676,1,13,1,18.86792,16.08086,0,0,0,34.94879,0,0,0,2,0,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,79.71188,8.79138,.6931472,3.553884,1 3,1,100,0,3,128093,0,6576.303,26.33676,1,13,1,19.16462,21.88698,0,0,0,41.0516,0,0,0,2,0,2,92.5,13.73189,0,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,79.71188,8.79138,.6931472,3.71483,1 3,1,100,0,1,128094,0,6576.303,37.62902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,.12982,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.8207,8.79138,.6931472,,0 3,1,100,0,2,128094,0,6576.303,38.62902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,.12982,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.8207,8.79138,.6931472,,0 3,1,100,0,3,128094,0,6576.303,39.62902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,.12982,,1000,1000,0,0,.6931472,6.907755,1,0,0,0,0,0,78.8207,8.79138,.6931472,,0 6,1,25,0,1,128097,0,4321.96,26.02601,1,13,1,25.61837,18.10954,0,0,0,43.72792,0,0,0,2,0,1,65,13.73189,0,,700,700,0,0,0,6.55108,0,3.258096,7.937375,0,0,0,78.92638,8.371696,0,3.777987,1 6,1,25,0,2,128097,0,4321.96,27.02601,1,13,1,25.33693,19.94609,0,0,0,45.28302,0,0,0,3,0,1,65,13.73189,0,,700,700,0,0,0,6.55108,0,3.258096,7.937375,0,0,0,78.92638,8.371696,0,3.812932,1 6,1,25,0,3,128097,0,4321.96,28.02601,1,13,1,72.60442,19.31204,0,0,0,91.91646,0,0,0,3,1,1,65,13.73189,0,,700,700,0,0,0,6.55108,0,3.258096,7.937375,0,0,0,78.92638,8.371696,0,4.52088,1 6,1,25,0,4,128097,0,4321.96,29.02601,1,13,1,16.86418,14.75387,0,0,0,31.61805,0,0,0,2,0,1,65,13.73189,0,,700,700,0,0,0,6.55108,0,3.258096,7.937375,0,0,0,78.92638,8.371696,0,3.453728,1 6,1,25,0,5,128097,0,4321.96,30.02601,1,13,1,32.93039,0,0,0,0,32.93039,0,0,0,4,0,1,65,13.73189,0,,700,700,0,0,0,6.55108,0,3.258096,7.937375,0,0,0,78.92638,8.371696,0,3.494396,1 2,1,100,1,1,128103,0,7545.561,10.15195,1,13,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,82.314,8.928847,1.609438,,0 2,1,100,1,2,128103,0,7545.561,11.15195,1,13,1,138.0066,14.50712,29.02519,0,0,181.5389,0,0,0,6,0,5,74.36826,13.73189,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,82.314,8.928847,1.609438,5.20147,1 2,1,100,1,3,128103,0,7545.561,12.15195,1,13,1,127.4314,3.266833,11.63092,0,0,142.3292,0,0,0,6,0,5,74.36826,13.73189,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,82.314,8.928847,1.609438,4.958143,1 2,1,100,1,1,128104,0,7545.561,37.71115,0,18,1,68.94485,33.4952,0,0,0,102.44,0,0,0,3,0,5,62.1,21.7,0,,399,399,0,0,1.609438,5.988961,1,0,0,0,0,0,78.36037,8.928847,1.609438,4.629278,1 2,1,100,1,2,128104,0,7545.561,38.71115,0,18,1,26.28697,33.16539,9.56736,0,0,69.01971,0,0,0,1,0,5,62.1,21.7,0,,399,399,0,0,1.609438,5.988961,1,0,0,0,0,0,78.36037,8.928847,1.609438,4.234392,1 2,1,100,1,3,128104,0,7545.561,39.71115,0,18,1,89.2818,47.7606,33.46634,0,4.488778,174.9975,0,0,0,2,0,5,62.1,21.7,0,,399,399,0,0,1.609438,5.988961,1,0,0,0,0,0,78.36037,8.928847,1.609438,5.164772,1 2,1,100,1,1,128105,0,7545.561,36.46544,1,13,1,119.3046,8.051558,0,0,0,127.3561,0,0,0,2,0,5,63.2,17.4,0,,399,399,0,0,1.609438,5.988961,1,0,0,1,0,0,72.59515,8.928847,1.609438,4.846987,1 2,1,100,1,2,128105,0,7545.561,37.46544,1,13,1,68.45564,14.81928,0,0,229.6112,312.8861,1,0,0,2,0,5,63.2,17.4,0,,399,399,0,0,1.609438,5.988961,1,0,0,1,0,0,72.59515,8.928847,1.609438,5.745839,1 2,1,100,1,3,128105,0,7545.561,38.46544,1,13,1,31.9202,29.04239,26.59352,0,0,87.55611,0,0,0,3,0,5,63.2,17.4,0,,399,399,0,0,1.609438,5.988961,1,0,0,1,0,0,72.59515,8.928847,1.609438,4.47228,1 2,1,100,1,1,128106,0,7545.561,13.4757,0,13,1,86.03117,27.79976,93.04556,0,0,206.8765,0,0,0,8,1,5,74.36826,13.73189,0,,399,399,1,0,1.609438,5.988961,1,0,0,1,0,0,72.65098,8.928847,1.609438,5.332122,1 2,1,100,1,2,128106,0,7545.561,14.4757,0,13,1,37.23987,66.91676,62.15772,0,2215.767,2382.081,2,0,0,3,0,5,74.36826,13.73189,0,,399,399,1,0,1.609438,5.988961,1,0,0,1,0,0,72.65098,8.928847,1.609438,7.77573,1 2,1,100,1,3,128106,0,7545.561,15.4757,0,13,1,320.6384,147.2618,127.2668,0,1133.676,1728.843,3,0,0,20,0,5,74.36826,13.73189,0,,399,399,1,0,1.609438,5.988961,1,0,0,1,0,0,72.65098,8.928847,1.609438,7.455208,1 2,1,100,1,1,128107,0,7545.561,15.72074,1,13,1,139.6882,0,19.70024,0,0,159.3885,0,0,0,7,0,5,71.6,8.7,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,74.71169,8.928847,1.609438,5.071344,1 2,1,100,1,2,128107,0,7545.561,16.72074,1,13,1,4.928806,2.749179,36.3253,0,0,44.00328,0,0,0,1,0,5,71.6,8.7,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,74.71169,8.928847,1.609438,3.784264,1 2,1,100,1,3,128107,0,7545.561,17.72074,1,13,1,75.31172,12.41895,13.74564,0,210.8778,312.3541,1,0,0,5,0,5,71.6,8.7,0,,399,399,1,1,1.609438,5.988961,1,0,0,0,0,0,74.71169,8.928847,1.609438,5.744138,1 10,1,50,1,1,128124,0,8723.945,23.91786,1,16,1,153.777,66.43285,0,0,0,220.2098,0,0,0,15,1,1,75.8,8.7,0,,200,200,0,0,0,5.298317,0,3.931826,5.991465,1,0,0,67.59779,9.073941,0,5.394581,1 10,1,50,1,2,128124,0,8723.945,24.91786,1,16,1,56.65389,20.18072,11.24863,0,788.3351,876.4184,1,0,0,2,1,1,75.8,8.7,0,,200,200,0,0,0,5.298317,0,3.931826,5.991465,1,0,0,67.59779,9.073941,0,6.775844,1 10,1,50,1,3,128124,0,8723.945,25.91786,1,16,1,29.42643,13.62095,0,0,0,43.04738,0,0,0,1,2,2,75.8,8.7,0,,200,200,0,0,.6931472,5.298317,0,3.931826,5.991465,1,0,0,67.59779,9.073941,.6931472,3.762301,1 10,1,50,1,4,128124,0,8723.945,26.91786,1,16,1,418.7736,18.03596,0,0,0,436.8096,0,0,0,4,1,2,75.8,8.7,0,,200,200,0,0,.6931472,5.298317,0,3.931826,5.991465,1,0,0,67.59779,9.073941,.6931472,6.079497,1 10,1,50,1,5,128124,0,8723.945,27.91786,1,16,1,71.94244,49.97461,0,0,0,121.9171,0,0,0,2,1,2,75.8,8.7,0,,200,200,0,0,.6931472,5.298317,0,3.931826,5.991465,1,0,0,67.59779,9.073941,.6931472,4.803341,1 8,1,50,1,1,128125,0,4032.878,24.02738,1,16,1,9.422851,1.766784,0,0,0,11.18964,0,0,0,2,0,1,89.5,8.7,0,,115,115,0,0,0,4.744932,0,3.931826,5.438079,1,0,0,71.18497,8.302484,0,2.414988,1 8,1,50,1,2,128125,0,4032.878,25.02738,1,16,1,0,0,0,0,0,0,0,0,0,0,0,1,89.5,8.7,0,,115,115,0,0,0,4.744932,0,3.931826,5.438079,1,0,0,71.18497,8.302484,0,,0 8,1,50,1,3,128125,0,4032.878,26.02738,1,16,1,0,0,0,0,0,0,0,0,0,0,0,1,89.5,8.7,0,,115,115,0,0,0,4.744932,0,3.931826,5.438079,1,0,0,71.18497,8.302484,0,,0 8,1,50,1,4,128125,0,4032.878,27.02738,1,16,1,9.571559,1.458523,0,0,0,11.03008,0,0,0,2,0,1,89.5,8.7,0,,115,115,0,0,0,4.744932,0,3.931826,5.438079,1,0,0,71.18497,8.302484,0,2.400626,1 8,1,50,1,5,128125,0,4032.878,28.02738,1,16,1,130.9712,6.490204,0,0,0,137.4614,0,0,0,5,0,1,89.5,8.7,0,,115,115,0,0,0,4.744932,0,3.931826,5.438079,1,0,0,71.18497,8.302484,0,4.923344,1 9,1,50,0,1,128127,0,9204.095,31.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95,13.73189,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,79.2669,9.127512,0,,0 9,1,50,0,2,128127,0,9204.095,32.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95,13.73189,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,79.2669,9.127512,0,,0 9,1,50,0,3,128127,0,9204.095,33.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95,13.73189,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,79.2669,9.127512,0,,0 9,1,50,0,4,128127,0,9204.095,34.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95,13.73189,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,79.2669,9.127512,0,,0 9,1,50,0,5,128127,0,9204.095,35.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,95,13.73189,0,,1000,1000,0,0,0,6.907755,0,3.931826,7.600903,0,0,0,79.2669,9.127512,0,,0 11,1,0,1,1,128129,0,11576.82,4.668036,0,12,1,26.1749,4.544914,0,0,0,30.71981,0,0,0,2,0,4,74.36826,13.73189,0,,0,291,1,0,1.386294,5.673323,0,0,0,0,0,0,82.35892,9.356847,1.386294,3.424908,1 11,1,0,1,2,128129,0,11576.82,5.668036,0,12,1,7.621121,3.162765,0,0,0,10.78389,0,0,0,1,0,4,74.36826,13.73189,0,,0,291,1,0,1.386294,5.673323,0,0,0,0,0,0,82.35892,9.356847,1.386294,2.378053,1 11,1,0,1,3,128129,0,11576.82,6.668036,0,12,1,90.68385,4.44004,0,0,0,95.12389,0,0,0,1,1,4,74.36826,13.73189,0,,0,291,1,0,1.386294,5.673323,0,0,0,0,0,0,82.35892,9.356847,1.386294,4.55518,1 11,1,0,1,4,128129,0,11576.82,7.668036,0,12,1,35.33731,6.883892,0,0,0,42.2212,0,0,0,3,0,4,74.36826,13.73189,0,,0,291,1,0,1.386294,5.673323,0,0,0,0,0,0,82.35892,9.356847,1.386294,3.742923,1 11,1,0,1,5,128129,0,11576.82,8.668036,0,12,1,12.62095,0,25.94026,0,0,38.56121,0,0,0,1,0,4,74.36826,13.73189,0,,0,291,1,0,1.386294,5.673323,0,0,0,0,0,0,82.35892,9.356847,1.386294,3.652247,1 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2,1,100,1,1,128157,0,8172.457,46.67214,0,9,1,9.518144,20.93992,0,0,0,30.45806,0,0,0,2,0,7,38.9,21.7,0,,0,0,0,0,1.94591,0,1,0,0,1,0,0,60.7702,9.008647,1.94591,3.416351,1 2,1,100,1,2,128157,0,8172.457,47.67214,0,9,1,0,0,0,0,0,0,0,0,0,0,0,8,38.9,21.7,0,,0,0,0,0,2.079442,0,1,0,0,1,0,0,60.7702,9.008647,2.079442,,0 2,1,100,1,3,128157,0,8172.457,48.67214,0,9,1,0,0,0,0,0,0,0,0,0,0,0,8,38.9,21.7,0,,0,0,0,0,2.079442,0,1,0,0,1,0,0,60.7702,9.008647,2.079442,,0 2,1,100,1,4,128157,0,8172.457,49.67214,0,9,1,0,0,0,0,0,0,0,0,0,0,0,8,38.9,21.7,0,,0,0,0,0,2.079442,0,1,0,0,1,0,0,60.7702,9.008647,2.079442,,0 2,1,100,1,5,128157,0,8172.457,50.67214,0,9,1,16.40724,0,0,0,350.0841,366.4914,1,0,0,1,0,9,38.9,21.7,0,,0,0,0,0,2.197225,0,1,0,0,1,0,0,60.7702,9.008647,2.197225,5.903975,1 2,1,100,1,1,128158,0,8172.457,14.77071,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,81.1,0,0,,0,0,1,0,1.94591,0,1,0,0,1,0,0,73.75163,9.008647,1.94591,,0 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2,1,100,1,3,128159,0,8172.457,10.91992,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,1,0,0,1,0,0,78.7121,9.008647,2.079442,,0 2,1,100,1,4,128159,0,8172.457,11.91992,0,12,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,0,1,0,2.079442,0,1,0,0,1,0,0,78.7121,9.008647,2.079442,,0 2,1,100,1,5,128159,0,8172.457,12.91992,0,12,1,0,0,0,0,0,0,0,0,0,0,0,9,74.36826,13.73189,0,,0,0,1,0,2.197225,0,1,0,0,1,0,0,78.7121,9.008647,2.197225,,0 2,1,100,1,1,128160,0,8172.457,40.27652,1,12,1,198.0964,6.30577,0,0,351.398,555.8001,1,1,0,5,0,7,44.2,21.7,0,,0,0,0,0,1.94591,0,1,0,0,1,0,0,65.55928,9.008647,1.94591,6.320409,1 2,1,100,1,2,128160,0,8172.457,41.27652,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,44.2,21.7,0,,0,0,0,0,2.079442,0,1,0,0,1,0,0,65.55928,9.008647,2.079442,,0 2,1,100,1,3,128160,0,8172.457,42.27652,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,44.2,21.7,0,,0,0,0,0,2.079442,0,1,0,0,1,0,0,65.55928,9.008647,2.079442,,0 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10,1,50,0,3,128228,1,9542.002,20.73511,0,11,1,0,0,0,0,0,0,0,0,0,0,0,2,85.3,8.7,0,,179,179,0,0,.6931472,5.187386,0,3.931826,5.880533,1,0,0,60.33361,9.163564,.6931472,,0 11,1,0,0,1,128239,1,1191.067,4.043806,0,10,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,0,,0,0,1,0,.6931472,0,0,0,0,0,1,0,70.64633,7.083444,.6931472,,0 11,1,0,0,2,128239,1,1191.067,5.043806,0,10,1,0,0,0,0,0,0,0,0,0,0,0,2,74.36826,13.73189,0,,0,0,1,0,.6931472,0,0,0,0,0,1,0,70.64633,7.083444,.6931472,,0 11,1,0,0,1,128240,1,1191.067,32.15606,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,90,13.73189,0,,0,0,0,0,.6931472,0,0,0,0,0,0,0,69.84738,7.083444,.6931472,,0 11,1,0,0,2,128240,1,1191.067,33.15606,1,10,1,0,0,0,0,0,0,0,0,0,0,0,2,90,13.73189,0,,0,0,0,0,.6931472,0,0,0,0,0,0,0,69.84738,7.083444,.6931472,,0 6,1,25,0,1,128248,0,14105.46,11.19507,0,12,1,77.59129,2.679623,33.54535,0,0,113.8163,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.27783,9.554388,1.386294,4.734585,1 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2,1,100,1,5,128303,1,9276.055,38.69131,1,12,1,16.82793,5.982331,0,0,0,22.81026,0,0,0,2,0,5,87.4,8.7,0,,650,650,0,0,1.609438,6.476973,1,0,0,0,0,0,73.93718,9.1353,1.609438,3.127211,1 2,1,100,1,1,128304,1,9276.055,4.062971,0,12,1,53.21237,6.20464,0,0,0,59.41702,0,0,0,3,1,5,74.36826,13.73189,0,,650,650,1,0,1.609438,6.476973,1,0,0,0,0,0,76.88281,9.1353,1.609438,4.08458,1 2,1,100,1,2,128304,1,9276.055,5.062971,0,12,1,4.899292,0,0,0,222.4551,227.3544,1,0,0,1,0,5,74.36826,13.73189,0,,650,650,1,0,1.609438,6.476973,1,0,0,0,0,0,76.88281,9.1353,1.609438,5.42651,1 2,1,100,1,3,128304,1,9276.055,6.062971,0,12,1,6.442022,0,0,0,0,6.442022,0,0,0,1,0,5,74.36826,13.73189,0,,650,650,1,0,1.609438,6.476973,1,0,0,0,0,0,76.88281,9.1353,1.609438,1.862842,1 2,1,100,1,4,128304,1,9276.055,7.062971,0,12,1,0,3.822855,0,0,0,3.822855,0,0,0,0,0,5,74.36826,13.73189,0,,650,650,1,0,1.609438,6.476973,1,0,0,0,0,0,76.88281,9.1353,1.609438,1.340997,1 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2,1,100,1,5,128305,1,9276.055,15.26899,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,,0 2,1,100,1,1,128306,1,9276.055,8.125941,1,12,1,23.20048,0,13.68233,0,0,36.88281,0,0,0,3,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,3.607746,1 2,1,100,1,2,128306,1,9276.055,9.125941,1,12,1,14.69788,0,32.99401,0,0,47.69189,0,0,0,2,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,3.864761,1 2,1,100,1,3,128306,1,9276.055,10.12594,1,12,1,7.928642,0,0,0,0,7.928642,0,0,0,1,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,2.070482,1 2,1,100,1,4,128306,1,9276.055,11.12594,1,12,1,37.76962,2.134007,28.75172,0,0,68.65535,0,0,0,4,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,4.229099,1 2,1,100,1,5,128306,1,9276.055,12.12594,1,12,1,5.04838,.8960875,0,0,0,5.944468,0,0,0,1,0,5,74.36826,13.73189,0,,650,650,1,1,1.609438,6.476973,1,0,0,0,0,0,78.84123,9.1353,1.609438,1.782461,1 1,1,0,1,1,128326,0,11965.26,35.28542,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,83.2,21.7,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,78.30748,9.389847,1.386294,,0 1,1,0,1,2,128326,0,11965.26,36.28542,0,16,1,13.60915,0,0,0,0,13.60915,0,0,0,1,0,4,83.2,21.7,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,78.30748,9.389847,1.386294,2.610742,1 1,1,0,1,3,128326,0,11965.26,37.28542,0,16,1,29.23687,0,0,0,0,29.23687,0,0,0,1,0,4,83.2,21.7,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,78.30748,9.389847,1.386294,3.375431,1 1,1,0,1,4,128326,0,11965.26,38.28542,0,16,1,0,0,22.00551,0,0,22.00551,0,0,0,0,0,4,83.2,21.7,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,78.30748,9.389847,1.386294,3.091293,1 1,1,0,1,5,128326,0,11965.26,39.28542,0,16,1,69.83593,5.107278,0,0,0,74.94321,0,0,0,2,0,5,83.2,21.7,0,,450,120,0,0,1.609438,4.787492,1,0,0,0,0,0,78.30748,9.389847,1.609438,4.31673,1 1,1,0,1,1,128327,0,11965.26,33.0924,1,15,1,0,0,0,0,0,0,0,0,0,0,0,4,72.6,0,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,79.98129,9.389847,1.386294,,0 1,1,0,1,2,128327,0,11965.26,34.0924,1,15,1,0,0,0,0,0,0,0,0,0,0,0,4,72.6,0,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,79.98129,9.389847,1.386294,,0 1,1,0,1,3,128327,0,11965.26,35.0924,1,15,1,0,0,0,0,0,0,0,0,0,0,0,4,72.6,0,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,79.98129,9.389847,1.386294,,0 1,1,0,1,4,128327,0,11965.26,36.0924,1,15,1,17.66866,20.21569,0,0,1247.728,1285.613,1,0,0,0,0,4,72.6,0,0,,450,120,0,0,1.386294,4.787492,1,0,0,0,0,0,79.98129,9.389847,1.386294,7.158991,1 1,1,0,1,5,128327,0,11965.26,37.0924,1,15,1,9.570888,3.819941,0,0,0,13.39083,0,0,0,1,0,5,72.6,0,0,,450,120,0,0,1.609438,4.787492,1,0,0,0,0,0,79.98129,9.389847,1.609438,2.59457,1 1,1,0,1,1,128328,0,11965.26,3.115674,1,15,1,9.518144,0,0,0,0,9.518144,0,0,0,1,0,4,74.36826,13.73189,0,,450,120,1,1,1.386294,4.787492,1,0,0,0,0,0,81.84772,9.389847,1.386294,2.2532,1 1,1,0,1,2,128328,0,11965.26,4.115674,1,15,1,9.798585,0,0,0,0,9.798585,0,0,0,1,0,4,74.36826,13.73189,0,,450,120,1,1,1.386294,4.787492,1,0,0,0,0,0,81.84772,9.389847,1.386294,2.282238,1 1,1,0,1,3,128328,0,11965.26,5.115674,1,15,1,13.37958,0,0,0,0,13.37958,0,0,0,1,0,4,74.36826,13.73189,0,,450,120,1,1,1.386294,4.787492,1,0,0,0,0,0,81.84772,9.389847,1.386294,2.59373,1 1,1,0,1,4,128328,0,11965.26,6.115674,1,15,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,450,120,1,1,1.386294,4.787492,1,0,0,0,0,0,81.84772,9.389847,1.386294,,0 1,1,0,1,5,128328,0,11965.26,7.115674,1,15,1,55.88978,0,0,0,0,55.88978,0,0,0,3,0,5,74.36826,13.73189,0,,450,120,1,1,1.609438,4.787492,1,0,0,0,0,0,81.84772,9.389847,1.609438,4.023382,1 1,1,0,1,1,128329,0,11965.26,9.319644,0,15,1,50.2677,0,0,0,0,50.2677,0,0,0,2,1,4,74.36826,13.73189,0,,450,120,1,0,1.386294,4.787492,1,0,0,0,0,0,85.79333,9.389847,1.386294,3.917363,1 1,1,0,1,2,128329,0,11965.26,10.31964,0,15,1,14.15351,0,0,0,0,14.15351,0,0,0,1,0,4,74.36826,13.73189,0,,450,120,1,0,1.386294,4.787492,1,0,0,0,0,0,85.79333,9.389847,1.386294,2.649963,1 1,1,0,1,3,128329,0,11965.26,11.31964,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,450,120,1,0,1.386294,4.787492,1,0,0,0,0,0,85.79333,9.389847,1.386294,,0 1,1,0,1,4,128329,0,11965.26,12.31964,0,15,1,22.3497,0,1.032584,0,0,23.38229,0,0,0,1,0,4,74.36826,13.73189,0,,450,120,1,0,1.386294,4.787492,1,0,0,0,0,0,85.79333,9.389847,1.386294,3.151979,1 1,1,0,1,5,128329,0,11965.26,13.31964,0,15,1,99.01556,0,3.828355,0,0,102.8439,0,0,0,4,0,5,74.36826,13.73189,0,,450,120,1,0,1.609438,4.787492,1,0,0,0,0,0,85.79333,9.389847,1.609438,4.633213,1 5,1,25,1,1,128360,0,13322.58,33.62628,0,16,1,11.89768,0,15.27662,0,0,27.1743,0,0,0,0,1,7,89.5,4.3,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,79.16077,9.497291,1.94591,3.302272,1 5,1,25,1,2,128360,0,13322.58,34.62628,0,16,1,0,0,0,0,0,0,0,0,0,0,0,7,89.5,4.3,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,79.16077,9.497291,1.94591,,0 5,1,25,1,3,128360,0,13322.58,35.62628,0,16,1,47.57185,30.00496,0,0,0,77.57681,0,0,0,5,1,7,89.5,4.3,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,79.16077,9.497291,1.94591,4.351268,1 5,1,25,1,1,128361,0,13322.58,7.008898,0,16,1,22.60559,8.774539,0,0,0,31.38013,0,0,0,4,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,3.446175,1 5,1,25,1,2,128361,0,13322.58,8.008898,0,16,1,9.254219,0,0,0,0,9.254219,0,0,0,2,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,2.22508,1 5,1,25,1,3,128361,0,13322.58,9.008898,0,16,1,11.39742,0,0,0,0,11.39742,0,0,0,1,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,2.433387,1 5,1,25,1,1,128362,0,13322.58,5.713894,0,16,1,14.8721,3.997621,0,0,0,18.86972,0,0,0,2,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,2.937559,1 5,1,25,1,2,128362,0,13322.58,6.713894,0,16,1,43.0049,0,0,0,0,43.0049,0,0,0,1,4,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,3.761314,1 5,1,25,1,3,128362,0,13322.58,7.713894,0,16,1,30.72349,3.528246,0,0,0,34.25174,0,0,0,2,3,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,86.65917,9.497291,1.94591,3.533737,1 5,1,25,1,1,128363,0,13322.58,4.654346,0,16,1,35.99048,5.48483,0,0,293.8132,335.2885,1,0,0,1,1,7,74.36826,13.73189,1,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,5.814991,1 5,1,25,1,2,128363,0,13322.58,5.654346,0,16,1,13.06478,1.475231,0,0,0,14.54001,0,0,0,2,0,7,74.36826,13.73189,1,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,2.676904,1 5,1,25,1,3,128363,0,13322.58,6.654346,0,16,1,6.442022,0,0,0,0,6.442022,0,0,0,1,0,7,74.36826,13.73189,1,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,1.862842,1 5,1,25,1,1,128364,0,13322.58,30.62286,1,16,1,39.55978,5.841761,0,0,0,45.40155,0,0,0,2,0,7,87.4,13,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,76.06387,9.497291,1.94591,3.815546,1 5,1,25,1,2,128364,0,13322.58,31.62286,1,16,1,13.60915,0,0,0,0,13.60915,0,0,0,1,0,7,87.4,13,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,76.06387,9.497291,1.94591,2.610742,1 5,1,25,1,3,128364,0,13322.58,32.62286,1,16,1,12.3885,0,0,0,0,12.3885,0,0,0,1,0,7,87.4,13,0,,964,964,0,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,76.06387,9.497291,1.94591,2.516769,1 5,1,25,1,1,128365,0,13322.58,.9117043,1,16,1,39.85723,0,0,0,0,39.85723,0,0,0,6,0,7,74.36826,13.73189,0,,964,964,1,1,1.94591,6.871091,0,3.258096,8.257385,0,0,0,82.71357,9.497291,1.94591,3.685304,1 5,1,25,1,2,128365,0,13322.58,1.911704,1,16,1,9.254219,0,0,0,0,9.254219,0,0,0,1,0,7,74.36826,13.73189,0,,964,964,1,1,1.94591,6.871091,0,3.258096,8.257385,0,0,0,82.71357,9.497291,1.94591,2.22508,1 5,1,25,1,3,128365,0,13322.58,2.911704,1,16,1,7.928642,0,0,0,0,7.928642,0,0,0,1,0,7,74.36826,13.73189,0,,964,964,1,1,1.94591,6.871091,0,3.258096,8.257385,0,0,0,82.71357,9.497291,1.94591,2.070482,1 5,1,25,1,1,128366,0,13322.58,3.389459,0,16,1,116.2998,2.082094,0,0,143.0696,261.4515,1,0,0,4,1,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,5.566249,1 5,1,25,1,2,128366,0,13322.58,4.389459,0,16,1,7.076756,0,0,0,0,7.076756,0,0,0,1,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,1.956816,1 5,1,25,1,3,128366,0,13322.58,5.389459,0,16,1,8.424182,0,0,0,0,8.424182,0,0,0,1,0,7,74.36826,13.73189,0,,964,964,1,0,1.94591,6.871091,0,3.258096,8.257385,0,0,0,83.22755,9.497291,1.94591,2.131106,1 5,1,25,0,1,128383,0,16140.09,46.6256,1,13,1,7.067138,17.55006,0,0,0,24.6172,0,0,0,1,0,3,85.3,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,67.22846,9.689123,1.098612,3.203445,1 5,1,25,0,2,128383,0,16140.09,47.6256,1,13,1,6.469003,9.757412,0,0,0,16.22642,0,0,0,1,0,3,85.3,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,67.22846,9.689123,1.098612,2.78664,1 5,1,25,0,3,128383,0,16140.09,48.6256,1,13,1,21.62162,15.74939,33.89189,0,0,71.2629,0,0,0,2,0,3,85.3,13,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,67.22846,9.689123,1.098612,4.266376,1 5,1,25,0,1,128384,0,16140.09,18.68309,0,12,1,0,5.182568,30.59482,0,0,35.77739,0,0,0,0,0,3,73.7,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.88491,9.689123,1.098612,3.577316,1 5,1,25,0,2,128384,0,16140.09,19.68309,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,73.7,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.88491,9.689123,1.098612,,0 5,1,25,0,3,128384,0,16140.09,20.68309,0,12,1,22.11302,0,25.16462,0,0,47.27764,0,0,0,1,0,3,73.7,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,78.88491,9.689123,1.098612,3.856037,1 5,1,25,0,1,128385,0,16140.09,52.03012,0,12,1,51.23675,73.16843,0,0,0,124.4052,0,0,0,4,0,3,87.4,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,63.23641,9.689123,1.098612,4.823544,1 5,1,25,0,2,128385,0,16140.09,53.03012,0,12,1,45.8221,73.8814,39.40701,0,0,159.1105,0,0,0,3,0,3,87.4,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,63.23641,9.689123,1.098612,5.069599,1 5,1,25,0,3,128385,0,16140.09,54.03012,0,12,1,43.24324,103.0713,22.08354,0,0,168.398,0,0,0,2,0,3,87.4,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,63.23641,9.689123,1.098612,5.12633,1 11,1,0,1,1,128411,0,9645.161,10.0178,0,12,1,76.14515,9.613325,0,0,0,85.75848,0,0,0,3,1,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,85.65124,9.174315,1.386294,4.451535,1 11,1,0,1,2,128411,0,9645.161,11.0178,0,12,1,44.638,3.135547,0,0,0,47.77354,0,0,0,6,1,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,85.65124,9.174315,1.386294,3.866472,1 11,1,0,1,3,128411,0,9645.161,12.0178,0,12,1,27.75025,5.673934,0,0,0,33.42418,0,0,0,3,0,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,85.65124,9.174315,1.386294,3.50928,1 11,1,0,1,4,128411,0,9645.161,13.0178,0,12,1,56.67738,0,0,0,0,56.67738,0,0,0,4,1,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,85.65124,9.174315,1.386294,4.037375,1 11,1,0,1,5,128411,0,9645.161,14.0178,0,12,1,75.72571,12.25494,0,0,0,87.98064,0,0,0,6,0,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,85.65124,9.174315,1.386294,4.477117,1 11,1,0,1,1,128412,0,9645.161,45.86448,1,12,1,75.49078,74.17014,11.8144,0,0,161.4753,0,0,0,4,0,4,76.8,13,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.59435,9.174315,1.386294,5.084352,1 11,1,0,1,2,128412,0,9645.161,46.86448,1,12,1,109.4175,106.0425,31.29015,0,0,246.7501,0,0,0,10,1,4,76.8,13,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.59435,9.174315,1.386294,5.508376,1 11,1,0,1,3,128412,0,9645.161,47.86448,1,12,1,103.0723,32.41328,0,0,0,135.4856,0,0,0,3,0,4,76.8,13,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.59435,9.174315,1.386294,4.908865,1 11,1,0,1,4,128412,0,9645.161,48.86448,1,12,1,83.98348,39.02249,47.47591,0,0,170.4819,0,0,0,7,1,4,76.8,13,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.59435,9.174315,1.386294,5.138629,1 11,1,0,1,5,128412,0,9645.161,49.86448,1,12,1,53.84939,105.7089,0,0,414.6992,574.2574,1,0,0,5,0,4,76.8,13,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.59435,9.174315,1.386294,6.353078,1 11,1,0,1,1,128413,0,9645.161,8.123203,0,12,1,49.97026,49.26829,0,0,0,99.23855,0,0,0,5,0,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,84.69201,9.174315,1.386294,4.597527,1 11,1,0,1,2,128413,0,9645.161,9.123203,0,12,1,53.34785,60.31573,28.44311,0,0,142.1067,0,0,0,3,4,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,84.69201,9.174315,1.386294,4.956578,1 11,1,0,1,3,128413,0,9645.161,10.1232,0,12,1,58.47374,63.35976,0,0,0,121.8335,0,0,0,5,3,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,84.69201,9.174315,1.386294,4.802655,1 11,1,0,1,4,128413,0,9645.161,11.1232,0,12,1,21.1106,29.25195,0,0,0,50.36255,0,0,0,2,1,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,84.69201,9.174315,1.386294,3.919248,1 11,1,0,1,5,128413,0,9645.161,12.1232,0,12,1,34.91796,30.53008,0,0,0,65.44804,0,0,0,4,1,4,74.36826,13.73189,0,,0,233,1,0,1.386294,5.451038,0,0,0,0,0,0,84.69201,9.174315,1.386294,4.181257,1 11,1,0,1,1,128414,0,9645.161,43.7755,0,16,1,137.4182,34.33076,0,0,0,171.749,0,0,0,4,0,4,94.7,4.3,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.32689,9.174315,1.386294,5.146034,1 11,1,0,1,2,128414,0,9645.161,44.7755,0,16,1,188.8949,33.78879,0,0,0,222.6837,0,0,0,7,0,4,94.7,4.3,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.32689,9.174315,1.386294,5.405753,1 11,1,0,1,3,128414,0,9645.161,45.7755,0,16,1,282.4579,30.66402,21.23389,0,0,334.3558,0,0,0,6,1,4,94.7,4.3,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.32689,9.174315,1.386294,5.812206,1 11,1,0,1,4,128414,0,9645.161,46.7755,0,16,1,293.2538,49.88527,0,0,0,343.1391,0,0,0,12,0,4,94.7,4.3,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.32689,9.174315,1.386294,5.838136,1 11,1,0,1,5,128414,0,9645.161,47.7755,0,16,1,21.87631,31.66597,0,0,0,53.54228,0,0,0,1,0,4,94.7,4.3,0,,0,233,0,0,1.386294,5.451038,0,0,0,0,0,0,77.32689,9.174315,1.386294,3.980472,1 2,1,100,1,1,128431,0,5694.789,27.08008,1,7,1,92.92566,50.67746,32.57794,0,306.1631,482.3441,1,0,0,13,1,5,66.3,34.8,1,,594,594,0,0,1.609438,6.386879,1,0,0,0,1,0,52.32401,8.647483,1.609438,6.178658,1 2,1,100,1,2,128431,0,5694.789,28.08008,1,7,1,117.1961,28.75137,0,0,0,145.9474,0,0,0,5,0,5,66.3,34.8,1,,594,594,0,0,1.609438,6.386879,1,0,0,0,1,0,52.32401,8.647483,1.609438,4.983246,1 2,1,100,1,3,128431,0,5694.789,29.08008,1,7,1,17.45636,9.900249,26.58354,0,0,53.94015,0,0,0,1,1,5,66.3,34.8,1,,594,594,0,0,1.609438,6.386879,1,0,0,0,1,0,52.32401,8.647483,1.609438,3.987875,1 2,1,100,1,1,128432,0,5694.789,7.937029,0,7,1,10.79137,7.847722,0,0,0,18.63909,0,0,0,1,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,59.21023,8.647483,1.609438,2.925261,1 2,1,100,1,2,128432,0,5694.789,8.93703,0,7,1,60.24096,4.156627,0,0,2155.75,2220.148,2,0,0,6,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,59.21023,8.647483,1.609438,7.705329,1 2,1,100,1,3,128432,0,5694.789,9.93703,0,7,1,555.3616,7.845387,0,0,2160.798,2724.005,2,0,0,7,2,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,59.21023,8.647483,1.609438,7.909858,1 2,1,100,1,1,128433,0,5694.789,10.80356,0,7,1,20.38369,1.193046,26.97842,0,0,48.55516,0,0,0,1,2,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,63.0433,8.647483,1.609438,3.8827,1 2,1,100,1,2,128433,0,5694.789,11.80356,0,7,1,50.38335,3.214677,0,0,0,53.59803,0,0,0,3,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,63.0433,8.647483,1.609438,3.981512,1 2,1,100,1,3,128433,0,5694.789,12.80356,0,7,1,30.1995,8.249376,0,0,358.9526,397.4015,1,0,0,2,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,1,0,63.0433,8.647483,1.609438,5.984947,1 2,1,100,1,1,128434,0,5694.789,4.172484,0,7,1,49.46043,12.11031,0,0,0,61.57074,0,0,0,4,1,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,0,0,75.11046,8.647483,1.609438,4.120187,1 2,1,100,1,2,128434,0,5694.789,5.172484,0,7,1,98.35706,5.219058,0,0,0,103.5761,0,0,0,6,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,0,0,75.11046,8.647483,1.609438,4.640307,1 2,1,100,1,3,128434,0,5694.789,6.172484,0,7,1,20.94763,7.246883,0,0,0,28.19451,0,0,0,2,0,5,74.36826,13.73189,0,,594,594,1,0,1.609438,6.386879,1,0,0,0,0,0,75.11046,8.647483,1.609438,3.339127,1 2,1,100,1,1,128435,0,5694.789,32.46544,0,12,1,185.7014,3.219424,0,0,0,188.9209,0,0,0,2,0,5,65.3,17.4,0,,594,594,0,0,1.609438,6.386879,1,0,0,1,0,0,70.86124,8.647483,1.609438,5.241328,1 2,1,100,1,2,128435,0,5694.789,33.46544,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,65.3,17.4,0,,594,594,0,0,1.609438,6.386879,1,0,0,1,0,0,70.86124,8.647483,1.609438,,0 2,1,100,1,3,128435,0,5694.789,34.46544,0,12,1,18.95262,2.049875,0,0,0,21.00249,0,0,0,1,0,5,65.3,17.4,0,,594,594,0,0,1.609438,6.386879,1,0,0,1,0,0,70.86124,8.647483,1.609438,3.044641,1 11,1,0,1,1,128438,0,14655.34,47.23614,1,12,1,89.32854,307.9616,39.47842,0,0,436.7686,0,0,0,6,1,3,46.3,13,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,78.72062,9.592628,1.098612,6.079403,1 11,1,0,1,2,128438,0,14655.34,48.23614,1,12,1,0,304.2169,0,0,0,304.2169,0,0,0,0,0,3,46.3,13,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,78.72062,9.592628,1.098612,5.717741,1 11,1,0,1,3,128438,0,14655.34,49.23614,1,12,1,14.96259,64.68828,16.6783,0,0,96.32918,0,0,0,1,1,3,46.3,13,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,78.72062,9.592628,1.098612,4.567771,1 11,1,0,1,4,128438,0,14655.34,50.23614,1,12,1,51.6367,44.92854,0,0,0,96.56524,0,0,0,3,1,3,46.3,13,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,78.72062,9.592628,1.098612,4.570219,1 11,1,0,1,5,128438,0,14655.34,51.23614,1,12,1,101.1426,62.95387,25.20525,0,451.3161,640.6179,1,0,0,2,0,3,46.3,13,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,78.72062,9.592628,1.098612,6.462433,1 11,1,0,1,1,128439,0,14655.34,18.45311,1,12,1,11.99041,0,29.73022,0,0,41.72062,0,0,0,0,1,3,65.3,4.3,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,79.76731,9.592628,1.098612,3.730996,1 11,1,0,1,2,128439,0,14655.34,19.45311,1,12,1,31.21577,5.969332,0,0,0,37.1851,0,0,0,4,0,3,65.3,4.3,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,79.76731,9.592628,1.098612,3.615908,1 11,1,0,1,3,128439,0,14655.34,20.45311,1,12,1,40.399,15.12219,24.0798,0,0,79.601,0,0,0,3,1,3,65.3,4.3,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,79.76731,9.592628,1.098612,4.377027,1 11,1,0,1,4,128439,0,14655.34,21.45311,1,12,1,22.59105,57.13232,0,0,0,79.72337,0,0,0,3,0,3,65.3,4.3,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,79.76731,9.592628,1.098612,4.378563,1 11,1,0,1,5,128439,0,14655.34,22.45311,1,12,1,28.35379,37.93906,27.08845,0,0,93.38129,0,0,0,2,1,3,65.3,4.3,0,,0,329,0,0,1.098612,5.796058,0,0,0,0,0,0,79.76731,9.592628,1.098612,4.536691,1 11,1,0,1,1,128440,0,14655.34,52.82409,0,12,1,39.56834,67.91367,16.91846,0,0,124.4005,0,0,0,4,1,3,66.3,17.4,0,,0,329,0,0,1.098612,5.796058,0,0,0,1,0,0,64.3789,9.592628,1.098612,4.823506,1 11,1,0,1,2,128440,0,14655.34,53.82409,0,12,1,83.24206,58.61446,0,0,3381.911,3523.768,1,0,0,11,0,3,66.3,17.4,0,,0,329,0,0,1.098612,5.796058,0,0,0,1,0,0,64.3789,9.592628,1.098612,8.167286,1 11,1,0,1,3,128440,0,14655.34,54.82409,0,12,1,70.07481,24.01496,18.95262,0,938.4589,1051.501,1,0,0,7,1,3,66.3,17.4,0,,0,329,0,0,1.098612,5.796058,0,0,0,1,0,0,64.3789,9.592628,1.098612,6.957974,1 11,1,0,1,4,128440,0,14655.34,55.82409,0,12,1,38.72752,27.70862,0,0,0,66.43614,0,0,0,3,0,3,66.3,17.4,0,,0,329,0,0,1.098612,5.796058,0,0,0,1,0,0,64.3789,9.592628,1.098612,4.196241,1 11,1,0,1,5,128440,0,14655.34,56.82409,0,12,1,69.4033,34.40542,0,0,0,103.8087,0,0,0,5,1,3,66.3,17.4,0,,0,329,0,0,1.098612,5.796058,0,0,0,1,0,0,64.3789,9.592628,1.098612,4.64255,1 2,1,100,0,1,128443,0,9138.338,3.674196,1,13,1,7.656066,0,0,0,0,7.656066,0,0,0,1,0,3,74.36826,13.73189,0,,890,890,1,1,1.098612,6.791222,1,0,0,0,0,0,78.11302,9.120343,1.098612,2.035498,1 2,1,100,0,2,128443,0,9138.338,4.674196,1,13,1,15.63342,2.12938,0,0,0,17.7628,0,0,0,2,0,4,74.36826,13.73189,0,,890,890,1,1,1.386294,6.791222,1,0,0,0,0,0,78.11302,9.120343,1.386294,2.877107,1 2,1,100,0,3,128443,0,9138.338,5.674196,1,13,1,13.26781,.982801,0,0,0,14.25061,0,0,0,1,0,4,74.36826,13.73189,0,,890,890,1,1,1.386294,6.791222,1,0,0,0,0,0,78.11302,9.120343,1.386294,2.6568,1 2,1,100,0,4,128443,0,9138.338,6.674196,1,13,1,15.04102,0,0,0,0,15.04102,0,0,0,1,0,4,74.36826,13.73189,0,,890,890,1,1,1.386294,6.791222,1,0,0,0,0,0,78.11302,9.120343,1.386294,2.710781,1 2,1,100,0,5,128443,0,9138.338,7.674196,1,13,1,22.50938,0,0,0,0,22.50938,0,0,0,3,0,4,74.36826,13.73189,0,,890,890,1,1,1.386294,6.791222,1,0,0,0,0,0,78.11302,9.120343,1.386294,3.113932,1 2,1,100,0,1,128444,0,9138.338,28.71184,0,14,1,337.1614,5.276796,21.66667,0,0,364.1048,0,0,0,4,1,3,50.5,0,0,,890,890,0,0,1.098612,6.791222,1,0,0,0,0,0,77.94995,9.120343,1.098612,5.897442,1 2,1,100,0,2,128444,0,9138.338,29.71184,0,14,1,41.53639,4.204852,0,0,0,45.74124,0,0,0,3,0,4,50.5,0,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,77.94995,9.120343,1.386294,3.823,1 2,1,100,0,3,128444,0,9138.338,30.71184,0,14,1,9.82801,5.174447,0,0,0,15.00246,0,0,0,2,0,4,50.5,0,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,77.94995,9.120343,1.386294,2.708214,1 2,1,100,0,4,128444,0,9138.338,31.71184,0,14,1,5.469462,0,0,0,0,5.469462,0,0,0,1,0,4,50.5,0,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,77.94995,9.120343,1.386294,1.69918,1 2,1,100,0,5,128444,0,9138.338,32.71184,0,14,1,77.2822,2.917882,0,0,0,80.20008,0,0,0,4,0,4,50.5,0,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,77.94995,9.120343,1.386294,4.384524,1 2,1,100,0,1,128445,0,9138.338,25.40452,1,13,1,85.39458,11.40754,0,0,665.3121,762.1143,1,0,0,2,0,3,84.2,8.7,0,,890,890,0,0,1.098612,6.791222,1,0,0,0,0,0,73.57805,9.120343,1.098612,6.636096,1 2,1,100,0,2,128445,0,9138.338,26.40452,1,13,1,21.02426,6.091644,0,0,0,27.1159,0,0,0,3,0,4,84.2,8.7,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,73.57805,9.120343,1.386294,3.30012,1 2,1,100,0,3,128445,0,9138.338,27.40452,1,13,1,43.24324,3.439803,0,0,0,46.68305,0,0,0,6,0,4,84.2,8.7,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,73.57805,9.120343,1.386294,3.843381,1 2,1,100,0,4,128445,0,9138.338,28.40452,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,8.7,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,73.57805,9.120343,1.386294,,0 2,1,100,0,5,128445,0,9138.338,29.40452,1,13,1,19.17466,0,0,0,0,19.17466,0,0,0,2,0,4,84.2,8.7,0,,890,890,0,0,1.386294,6.791222,1,0,0,0,0,0,73.57805,9.120343,1.386294,2.953589,1 11,1,0,0,1,128468,0,10325.68,19.80561,0,12,1,32.97998,9.140165,41.54299,0,0,83.66313,0,0,0,2,1,4,78.9,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.46669,9.242486,1.386294,4.426798,1 11,1,0,0,2,128468,0,10325.68,20.80561,0,12,1,57.68194,0,0,0,0,57.68194,0,0,0,3,0,4,78.9,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.46669,9.242486,1.386294,4.054944,1 11,1,0,0,3,128468,0,10325.68,21.80561,0,12,1,253.0713,5.479115,0,0,0,258.5504,0,0,0,1,22,4,78.9,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.46669,9.242486,1.386294,5.55509,1 11,1,0,0,4,128468,0,10325.68,22.80561,0,12,.2684931,260.711,0,0,0,0,260.711,0,0,0,0,21,4,78.9,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.46669,9.242486,1.386294,5.563413,1 11,1,0,0,1,128469,0,10325.68,40.51472,1,12,1,11.77856,0,0,0,0,11.77856,0,0,0,0,0,4,84.2,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.31118,9.242486,1.386294,2.466281,1 11,1,0,0,2,128469,0,10325.68,41.51472,1,12,1,43.66577,2.156334,366.5768,0,0,412.3989,0,0,0,4,0,4,84.2,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.31118,9.242486,1.386294,6.021991,1 11,1,0,0,3,128469,0,10325.68,42.51472,1,12,1,14.74201,0,0,0,271.9754,286.7174,1,0,0,2,0,4,84.2,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.31118,9.242486,1.386294,5.658497,1 11,1,0,0,4,128469,0,10325.68,43.51472,1,12,1,15.9526,0,15.9526,0,0,31.9052,0,0,0,1,0,4,84.2,4.3,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,78.31118,9.242486,1.386294,3.462769,1 11,1,0,0,5,128469,0,10325.68,44.51472,1,12,1,45.85244,5.814923,0,0,0,51.66736,0,0,0,2,0,3,84.2,4.3,0,,0,195,0,0,1.098612,5.273,0,0,0,0,0,0,78.31118,9.242486,1.098612,3.944826,1 11,1,0,0,1,128470,0,10325.68,40.4627,0,11,1,98.0742,16.16608,0,0,0,114.2403,0,0,0,7,1,4,83.2,0,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,73.53505,9.242486,1.386294,4.738304,1 11,1,0,0,2,128470,0,10325.68,41.4627,0,11,1,5.390836,0,0,0,0,5.390836,0,0,0,1,0,4,83.2,0,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,73.53505,9.242486,1.386294,1.6847,1 11,1,0,0,3,128470,0,10325.68,42.4627,0,11,1,269.7789,1.891892,29.27273,0,0,300.9435,0,0,0,2,13,4,83.2,0,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,73.53505,9.242486,1.386294,5.706923,1 11,1,0,0,4,128470,0,10325.68,43.4627,0,11,1,209.6627,0,0,0,0,209.6627,0,0,0,1,18,4,83.2,0,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,73.53505,9.242486,1.386294,5.3455,1 11,1,0,0,5,128470,0,10325.68,44.4627,0,11,1,125.2188,.2084202,0,0,0,125.4273,0,0,0,4,0,3,83.2,0,0,,0,195,0,0,1.098612,5.273,0,0,0,0,0,0,73.53505,9.242486,1.098612,4.831726,1 11,1,0,0,1,128471,0,10325.68,15.37851,0,12,1,14.13428,4.240283,0,0,0,18.37456,0,0,0,2,0,4,83.2,0,0,,0,195,1,0,1.386294,5.273,0,0,0,0,0,0,80.31274,9.242486,1.386294,2.910967,1 11,1,0,0,2,128471,0,10325.68,16.37851,0,12,1,33.42318,2.156334,0,0,0,35.57951,0,0,0,3,0,4,83.2,0,0,,0,195,1,0,1.386294,5.273,0,0,0,0,0,0,80.31274,9.242486,1.386294,3.57177,1 11,1,0,0,3,128471,0,10325.68,17.37851,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,83.2,0,0,,0,195,1,0,1.386294,5.273,0,0,0,0,0,0,80.31274,9.242486,1.386294,,0 11,1,0,0,4,128471,0,10325.68,18.37851,0,12,1,25.06837,0,0,0,0,25.06837,0,0,0,4,0,4,83.2,0,0,,0,195,0,0,1.386294,5.273,0,0,0,0,0,0,80.31274,9.242486,1.386294,3.221607,1 11,1,0,0,5,128471,0,10325.68,19.37851,0,12,1,31.26303,0,0,0,0,31.26303,0,0,0,1,0,3,83.2,0,0,,0,195,0,0,1.098612,5.273,0,0,0,0,0,0,80.31274,9.242486,1.098612,3.442436,1 7,1,25,0,1,128493,0,11517.37,12.82957,1,12,1,17.07892,1.177856,0,0,0,18.25677,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.87948,9.351699,1.386294,2.904536,1 7,1,25,0,2,128493,0,11517.37,13.82957,1,12,1,33.96227,0,0,0,0,33.96227,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.87948,9.351699,1.386294,3.52525,1 7,1,25,0,3,128493,0,11517.37,14.82957,1,12,1,21.62162,.982801,0,0,0,22.60442,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.87948,9.351699,1.386294,3.118146,1 7,1,25,0,4,128493,0,11517.37,15.82957,1,12,1,37.60255,0,32.38833,0,0,69.99088,0,0,0,4,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.87948,9.351699,1.386294,4.248365,1 7,1,25,0,5,128493,0,11517.37,16.82957,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,73.87948,9.351699,1.386294,,0 7,1,25,0,1,128494,0,11517.37,38.95414,0,10,1,50.05889,1.177856,0,0,0,51.23675,0,0,0,2,0,4,82.5,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,71.52279,9.351699,1.386294,3.936457,1 7,1,25,0,2,128494,0,11517.37,39.95414,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,82.5,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,71.52279,9.351699,1.386294,,0 7,1,25,0,3,128494,0,11517.37,40.95414,0,10,1,12.28501,0,28.99263,0,0,41.27764,0,0,0,1,0,4,82.5,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,71.52279,9.351699,1.386294,3.720321,1 7,1,25,0,4,128494,0,11517.37,41.95414,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,82.5,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,71.52279,9.351699,1.386294,,0 7,1,25,0,5,128494,0,11517.37,42.95414,0,10,1,172.5719,0,37.09879,0,0,209.6707,0,0,0,4,4,4,82.5,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,71.52279,9.351699,1.386294,5.345538,1 7,1,25,0,1,128495,0,11517.37,10.46133,1,12,1,9.422851,6.919906,0,0,0,16.34276,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,76.19097,9.351699,1.386294,2.793785,1 7,1,25,0,2,128495,0,11517.37,11.46133,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,76.19097,9.351699,1.386294,,0 7,1,25,0,3,128495,0,11517.37,12.46133,1,12,1,8.845209,0,0,0,0,8.845209,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,76.19097,9.351699,1.386294,2.179876,1 7,1,25,0,4,128495,0,11517.37,13.46133,1,12,1,10.25524,0,0,0,0,10.25524,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,76.19097,9.351699,1.386294,2.327789,1 7,1,25,0,5,128495,0,11517.37,14.46133,1,12,1,8.336807,0,0,0,0,8.336807,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,1,0,0,76.19097,9.351699,1.386294,2.12068,1 7,1,25,0,1,128496,0,11517.37,34.08624,1,12,1,283.2744,67.55595,27.67962,0,0,378.51,0,0,0,19,0,4,80,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.49276,9.351699,1.386294,5.936243,1 7,1,25,0,2,128496,0,11517.37,35.08624,1,12,1,76.54987,32.19946,0,0,0,108.7493,0,0,0,11,0,4,80,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.49276,9.351699,1.386294,4.689045,1 7,1,25,0,3,128496,0,11517.37,36.08624,1,12,1,40.29484,20.93366,0,0,0,61.2285,0,0,0,8,0,4,80,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.49276,9.351699,1.386294,4.114613,1 7,1,25,0,4,128496,0,11517.37,37.08624,1,12,1,20.96627,0,0,0,0,20.96627,0,0,0,1,1,4,80,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.49276,9.351699,1.386294,3.042915,1 7,1,25,0,5,128496,0,11517.37,38.08624,1,12,1,32.51355,4.997916,26.5694,0,0,64.08086,0,0,0,3,0,4,80,13.73189,1,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,1,0,56.49276,9.351699,1.386294,4.160146,1 10,1,50,0,1,128497,0,10301.49,7.545517,0,9,1,8.833922,0,0,0,0,8.833922,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,84.4333,9.240141,1.791759,2.178599,1 10,1,50,0,2,128497,0,10301.49,8.545517,0,9,1,90.02695,0,0,0,173.5094,263.5364,1,0,0,2,2,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,84.4333,9.240141,1.791759,5.574192,1 10,1,50,0,1,128498,0,10301.49,27.53183,1,9,1,30.03534,5.300354,0,0,0,35.33569,0,0,0,4,0,6,65,13.73189,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,63.61448,9.240141,1.791759,3.564893,1 10,1,50,0,2,128498,0,10301.49,28.53183,1,9,1,14.01617,1.617251,29.6496,0,0,45.28302,0,0,0,1,0,6,65,13.73189,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,63.61448,9.240141,1.791759,3.812932,1 10,1,50,0,1,128499,0,10301.49,10.04517,1,9,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,83.91933,9.240141,1.791759,,0 10,1,50,0,2,128499,0,10301.49,11.04517,1,9,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,83.91933,9.240141,1.791759,,0 10,1,50,0,1,128500,0,10301.49,11.32101,0,9,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,84.4333,9.240141,1.791759,,0 10,1,50,0,2,128500,0,10301.49,12.32101,0,9,1,7.008086,0,0,0,0,7.008086,0,0,0,1,0,6,74.36826,13.73189,0,,1000,1000,1,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,84.4333,9.240141,1.791759,1.947065,1 10,1,50,0,1,128501,0,10301.49,4.911705,1,9,1,101.2956,0,0,0,0,101.2956,0,0,0,4,2,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.4877,9.240141,1.791759,4.618043,1 10,1,50,0,2,128501,0,10301.49,5.911705,1,9,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.4877,9.240141,1.791759,,0 10,1,50,0,1,128502,0,10301.49,31.90417,0,8,1,8.462897,16.66078,0,0,0,25.12367,0,0,0,0,0,6,90,13.73189,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,61.55907,9.240141,1.791759,3.223811,1 10,1,50,0,2,128502,0,10301.49,32.90417,0,8,1,229.3801,2.32345,0,0,0,231.7035,0,0,0,2,1,6,90,13.73189,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,1,0,0,61.55907,9.240141,1.791759,5.445458,1 11,1,0,1,1,128520,0,9861.166,15.50171,0,12,1,19.63117,0,36.68055,0,0,56.31172,0,0,0,1,1,6,98.9,13,0,,0,196,1,0,1.791759,5.278115,0,0,0,0,0,0,80.40273,9.196461,1.791759,4.030903,1 11,1,0,1,2,128520,0,9861.166,16.50171,0,12,1,9.798585,1.633097,0,0,0,11.43168,0,0,0,2,0,6,98.9,13,0,,0,196,1,0,1.791759,5.278115,0,0,0,0,0,0,80.40273,9.196461,1.791759,2.436388,1 11,1,0,1,3,128520,0,9861.166,17.50171,0,12,1,53.51833,14.49455,0,0,0,68.01289,0,0,0,5,0,6,98.9,13,0,,0,196,1,0,1.791759,5.278115,0,0,0,0,0,0,80.40273,9.196461,1.791759,4.219697,1 11,1,0,1,4,128520,0,9861.166,18.50171,0,12,1,219.0225,36.85177,19.66498,0,0,275.5392,0,0,0,3,1,6,98.9,13,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,80.40273,9.196461,1.791759,5.61873,1 11,1,0,1,5,128520,0,9861.166,19.50171,0,12,1,33.65587,27.34539,0,0,0,61.00126,0,0,0,0,0,6,98.9,13,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,80.40273,9.196461,1.791759,4.110895,1 11,1,0,1,1,128521,0,9861.166,8.960985,1,12,1,27.95955,7.346817,0,0,0,35.30637,0,0,0,4,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,3.564063,1 11,1,0,1,2,128521,0,9861.166,9.960985,1,12,1,16.33097,3.701688,0,0,0,20.03266,0,0,0,3,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,2.997364,1 11,1,0,1,3,128521,0,9861.166,10.96099,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,,0 11,1,0,1,4,128521,0,9861.166,11.96099,1,12,1,11.93208,0,0,0,0,11.93208,0,0,0,1,1,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,2.47923,1 11,1,0,1,5,128521,0,9861.166,12.96099,1,12,1,18.93143,0,0,0,0,18.93143,0,0,0,2,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,2.940823,1 11,1,0,1,1,128522,0,9861.166,11.76454,1,12,1,224.0631,213.028,51.08864,0,0,488.1797,0,0,0,20,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,1,0,0,74.44305,9.196461,1.791759,6.190683,1 11,1,0,1,2,128522,0,9861.166,12.76454,1,12,1,44.09363,98.44855,0,0,319.4883,462.0305,1,0,0,9,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,1,0,0,74.44305,9.196461,1.791759,6.135631,1 11,1,0,1,3,128522,0,9861.166,13.76454,1,12,1,127.8494,71.67988,0,0,0,199.5292,0,0,0,26,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,1,0,0,74.44305,9.196461,1.791759,5.295961,1 11,1,0,1,4,128522,0,9861.166,14.76454,1,12,1,114.5021,82.07893,.6883892,0,407.8476,605.117,1,0,0,28,1,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,1,0,0,74.44305,9.196461,1.791759,6.405422,1 11,1,0,1,5,128522,0,9861.166,15.76454,1,12,1,225.6458,82.06563,0,0,0,307.7114,0,0,0,33,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,1,0,0,74.44305,9.196461,1.791759,5.729162,1 11,1,0,1,1,128523,0,9861.166,41.42368,0,12,1,151.6954,39.64902,36.68055,0,1444.325,1672.35,2,0,0,13,1,6,100,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,76.77452,9.196461,1.791759,7.421985,1 11,1,0,1,2,128523,0,9861.166,42.42368,0,12,1,91.99783,33.043,0,0,0,125.0408,0,0,0,11,0,6,100,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,76.77452,9.196461,1.791759,4.82864,1 11,1,0,1,3,128523,0,9861.166,43.42368,0,12,1,95.14371,26.53617,0,0,0,121.6799,0,0,0,12,0,6,100,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,76.77452,9.196461,1.791759,4.801394,1 11,1,0,1,4,128523,0,9861.166,44.42368,0,12,1,55.98899,34.69481,0,0,0,90.6838,0,0,0,7,0,6,100,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,76.77452,9.196461,1.791759,4.507379,1 11,1,0,1,5,128523,0,9861.166,45.42368,0,12,1,147.1435,32.96172,36.04544,0,0,216.1506,0,0,0,16,0,6,100,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,76.77452,9.196461,1.791759,5.375976,1 11,1,0,1,1,128524,0,9861.166,39.96167,1,12,1,42.23676,22.57585,24.98513,0,0,89.79774,0,0,0,6,0,6,77.9,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,79.44787,9.196461,1.791759,4.49756,1 11,1,0,1,2,128524,0,9861.166,40.96167,1,12,1,193.5493,5.770278,21.62221,0,0,220.9418,0,0,0,3,1,6,77.9,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,79.44787,9.196461,1.791759,5.397899,1 11,1,0,1,3,128524,0,9861.166,41.96167,1,12,1,48.06739,8.671952,0,0,0,56.73935,0,0,0,6,0,6,77.9,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,79.44787,9.196461,1.791759,4.038468,1 11,1,0,1,4,128524,0,9861.166,42.96167,1,12,1,64.70858,17.37035,53.7173,0,0,135.7962,0,0,0,7,1,6,77.9,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,79.44787,9.196461,1.791759,4.911156,1 11,1,0,1,5,128524,0,9861.166,43.96167,1,12,1,251.6702,37.54733,31.15692,0,0,320.3744,0,0,0,6,0,6,77.9,8.7,0,,0,196,0,0,1.791759,5.278115,0,0,0,0,0,0,79.44787,9.196461,1.791759,5.76949,1 11,1,0,1,1,128525,0,9861.166,10.65571,1,12,1,19.63117,1.784652,0,0,0,21.41582,0,0,0,2,0,6,74.36826,13.73189,0,,0,196,1,1,1.791759,5.278115,0,0,0,0,0,0,84.62004,9.196461,1.791759,3.06413,1 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5,1,25,0,1,128552,1,5445.176,5.965777,1,14,1,22.93165,0,0,0,0,22.93165,0,0,0,1,0,3,74.36826,13.73189,0,,359,359,1,1,1.098612,5.883322,0,3.258096,7.269617,1,0,0,79.50889,8.602669,1.098612,3.132518,1 5,1,25,0,2,128552,1,5445.176,6.965777,1,14,1,5.476451,0,0,0,0,5.476451,0,0,0,1,0,3,74.36826,13.73189,0,,359,359,1,1,1.098612,5.883322,0,3.258096,7.269617,1,0,0,79.50889,8.602669,1.098612,1.700457,1 5,1,25,0,3,128552,1,5445.176,7.965777,1,14,1,4.987531,0,0,0,0,4.987531,0,0,0,1,0,3,74.36826,13.73189,0,,359,359,1,1,1.098612,5.883322,0,3.258096,7.269617,1,0,0,79.50889,8.602669,1.098612,1.606941,1 1,1,0,1,1,128569,1,15095.53,24.39973,1,16,1,54.72933,0,0,0,0,54.72933,0,0,0,2,0,2,73.7,8.7,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,65.77286,9.62222,.6931472,4.0024,1 1,1,0,1,2,128569,1,15095.53,25.39973,1,16,1,117.583,0,29.1399,0,0,146.7229,0,0,0,6,1,2,73.7,8.7,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,65.77286,9.62222,.6931472,4.988546,1 1,1,0,1,3,128569,1,15095.53,26.39973,1,16,1,24.77701,0,0,0,865.4361,890.2131,1,0,0,1,0,2,73.7,8.7,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,65.77286,9.62222,.6931472,6.791461,1 1,1,0,1,1,128570,1,15095.53,28.9473,0,18,1,25.25282,0,0,0,346.3712,371.624,1,0,0,0,0,2,68.4,4.3,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,67.4578,9.62222,.6931472,5.917882,1 1,1,0,1,2,128570,1,15095.53,29.9473,0,18,1,121.3936,0,0,0,0,121.3936,0,0,0,7,0,2,68.4,4.3,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,67.4578,9.62222,.6931472,4.799038,1 1,1,0,1,3,128570,1,15095.53,30.9473,0,18,1,601.4371,20.48067,0,0,45.09415,667.0119,0,0,0,6,1,2,68.4,4.3,0,,300,300,0,0,.6931472,5.703783,1,0,0,1,0,0,67.4578,9.62222,.6931472,6.502808,1 7,1,25,0,1,128581,1,14949.13,36.22724,0,18,1,65.84217,38.27444,24.73498,106.0071,1154.152,1283.004,2,0,14,1,1,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,65.67462,9.612475,1.609438,7.156959,1 7,1,25,0,2,128581,1,14949.13,37.22724,0,18,1,29.6496,0,0,75.47169,1867.17,1896.819,1,0,14,0,0,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,65.67462,9.612475,1.609438,7.547934,1 7,1,25,0,3,128581,1,14949.13,38.22724,0,18,1,0,0,0,0,0,0,0,0,0,0,0,5,73.8,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,65.67462,9.612475,1.609438,,0 7,1,25,0,1,128582,1,14949.13,35.23888,1,16,1,310.6596,165.689,0,0,0,476.3486,0,0,0,37,1,5,47.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.22718,9.612475,1.609438,6.16615,1 7,1,25,0,2,128582,1,14949.13,36.23888,1,16,1,53.36927,164.3666,0,0,0,217.7359,0,0,0,6,0,5,47.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.22718,9.612475,1.609438,5.383283,1 7,1,25,0,3,128582,1,14949.13,37.23888,1,16,1,228.6241,155.8673,0,0,0,384.4914,0,0,0,11,1,5,47.5,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.258096,8.294049,1,0,0,68.22718,9.612475,1.609438,5.951921,1 7,1,25,0,1,128583,1,14949.13,8.536619,1,16,1,145.1708,13.39812,0,0,0,158.5689,0,0,0,6,1,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,88.60927,9.612475,1.609438,5.066189,1 7,1,25,0,2,128583,1,14949.13,9.536619,1,16,1,41.50943,21.45553,0,0,0,62.96496,0,0,0,8,0,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,88.60927,9.612475,1.609438,4.142578,1 7,1,25,0,3,128583,1,14949.13,10.53662,1,16,1,122.344,17.62162,0,0,403.6265,543.5922,1,0,0,5,0,5,74.36826,13.73189,1,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,88.60927,9.612475,1.609438,6.298199,1 7,1,25,0,1,128584,1,14949.13,11.48528,1,16,1,80.38869,7.714959,20.17668,0,0,108.2803,0,0,0,4,1,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,83.93784,9.612475,1.609438,4.684723,1 7,1,25,0,2,128584,1,14949.13,12.48528,1,16,1,0,0,28,0,0,28,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,83.93784,9.612475,1.609438,3.332205,1 7,1,25,0,3,128584,1,14949.13,13.48528,1,16,1,14.25061,0,0,0,0,14.25061,0,0,0,1,0,5,74.36826,13.73189,0,,1000,1000,1,1,1.609438,6.907755,0,3.258096,8.294049,1,0,0,83.93784,9.612475,1.609438,2.6568,1 7,1,25,0,1,128585,1,14949.13,10.31348,0,16,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,82.66042,9.612475,1.609438,,0 7,1,25,0,2,128585,1,14949.13,11.31348,0,16,1,0,0,0,0,0,0,0,0,0,0,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,82.66042,9.612475,1.609438,,0 7,1,25,0,3,128585,1,14949.13,12.31348,0,16,1,149.8772,0,0,0,1039.926,1189.803,1,0,0,6,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.258096,8.294049,0,0,0,82.66042,9.612475,1.609438,7.081543,1 5,1,25,1,1,128596,1,11635.86,39.49897,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,98.9,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,74.38396,9.361933,1.098612,,0 5,1,25,1,2,128596,1,11635.86,40.49897,0,12,1,67.22918,4.001089,0,0,1182.967,1254.197,1,0,0,3,0,2,98.9,4.3,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,0,0,74.38396,9.361933,.6931472,7.134251,1 5,1,25,1,3,128596,1,11635.86,41.49897,0,12,1,29.73241,0,0,0,185.8275,215.56,1,1,0,2,0,2,98.9,4.3,0,,1000,1000,0,0,.6931472,6.907755,0,3.258096,8.294049,0,0,0,74.38396,9.361933,.6931472,5.373239,1 5,1,25,1,1,128597,1,11635.86,4.405202,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,.0327869,,1000,1000,1,1,1.098612,6.907755,0,3.258096,8.294049,0,0,0,76.82845,9.361933,1.098612,,0 5,1,25,1,1,128598,1,11635.86,39.54826,1,12,1,273.7359,71.98096,0,0,858.2927,1204.01,3,0,0,5,0,3,82.1,8.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,74.3345,9.361933,1.098612,7.093412,1 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8,1,50,0,3,128709,0,11037.84,14.20534,1,12,1,3.468781,2.452924,0,0,0,5.921705,0,0,0,1,0,4,74.36826,13.73189,0,,690,690,1,1,1.386294,6.536692,0,3.931826,7.229839,0,0,0,86.05113,9.309175,1.386294,1.778624,1 10,1,50,0,1,128723,0,7967.122,37.25667,1,12,1,63.95003,4.134444,0,0,0,68.08447,0,0,0,2,0,5,81.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,71.8062,8.983204,1.609438,4.220749,1 10,1,50,0,2,128723,0,7967.122,38.25667,1,12,1,12.52041,0,0,0,0,12.52041,0,0,0,1,0,5,81.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,71.8062,8.983204,1.609438,2.52736,1 10,1,50,0,3,128723,0,7967.122,39.25667,1,12,1,48.56293,0,27.25471,0,0,75.81764,0,0,0,4,0,5,81.3,13.73189,0,,1000,1000,0,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,71.8062,8.983204,1.609438,4.328331,1 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10,1,50,0,3,128726,0,7967.122,13.54552,0,12,1,62.68583,1.73439,29.23687,0,0,93.65709,0,0,0,3,1,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.931826,7.600903,1,0,0,78.24947,8.983204,1.609438,4.53964,1 10,1,50,0,1,128727,0,7967.122,6.004107,0,12,1,42.23676,10.79714,24.24747,0,0,77.28138,0,0,0,5,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.931826,7.600903,1,0,0,76.31956,8.983204,1.609438,4.347453,1 10,1,50,0,2,128727,0,7967.122,7.004107,0,12,1,81.65487,2.150245,22.59118,0,350.92,457.3163,1,0,0,8,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.931826,7.600903,1,0,0,76.31956,8.983204,1.609438,6.125375,1 10,1,50,0,3,128727,0,7967.122,8.004107,0,12,1,24.77701,6.119921,7.680872,0,0,38.5778,0,0,0,3,0,5,74.36826,13.73189,0,,1000,1000,1,0,1.609438,6.907755,0,3.931826,7.600903,1,0,0,76.31956,8.983204,1.609438,3.652677,1 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9,1,50,1,1,128758,0,11526.05,17.26763,0,12,1,25.77938,0,31.95444,0,0,57.73381,0,0,0,2,0,5,66.3,8.7,0,,1000,1000,1,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,80.43655,9.352452,1.609438,4.055843,1 9,1,50,1,2,128758,0,11526.05,18.26763,0,12,1,6.845564,0,0,0,0,6.845564,0,0,0,1,0,6,66.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.43655,9.352452,1.791759,1.923601,1 9,1,50,1,3,128758,0,11526.05,19.26763,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,66.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.43655,9.352452,1.791759,,0 9,1,50,1,4,128758,0,11526.05,20.26763,0,12,1,24.89627,.9220839,0,0,0,25.81835,0,0,0,1,0,6,66.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.43655,9.352452,1.791759,3.251086,1 9,1,50,1,5,128758,0,11526.05,21.26763,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,66.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,80.43655,9.352452,1.791759,,0 9,1,50,1,1,128759,0,11526.05,37.77139,1,12,1,11.99041,4.376499,27.56595,0,961.0971,1005.03,1,0,0,1,0,5,80,8.7,0,,1000,1000,0,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,78.54103,9.352452,1.609438,6.912773,1 9,1,50,1,2,128759,0,11526.05,38.77139,1,12,1,10.9529,0,0,0,0,10.9529,0,0,0,1,0,6,80,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,78.54103,9.352452,1.791759,2.393605,1 9,1,50,1,3,128759,0,11526.05,39.77139,1,12,1,18.70324,0,0,0,0,18.70324,0,0,0,2,0,6,80,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,78.54103,9.352452,1.791759,2.928697,1 9,1,50,1,4,128759,0,11526.05,40.77139,1,12,1,7.837713,0,0,0,0,7.837713,0,0,0,0,0,6,80,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,78.54103,9.352452,1.791759,2.058947,1 9,1,50,1,5,128759,0,11526.05,41.77139,1,12,1,194.2023,11.91282,35.01904,0,0,241.1342,0,0,0,4,9,6,80,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,78.54103,9.352452,1.791759,5.485353,1 9,1,50,1,1,128760,0,11526.05,14.78713,1,12,1,35.3717,7.284173,0,0,0,42.65588,0,0,0,1,0,5,73.3,8.7,0,,1000,1000,1,1,1.609438,6.907755,0,3.931826,7.600903,0,0,0,75.09985,9.352452,1.609438,3.753165,1 9,1,50,1,2,128760,0,11526.05,15.78713,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,73.3,8.7,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,75.09985,9.352452,1.791759,,0 9,1,50,1,3,128760,0,11526.05,16.78713,1,12,1,38.52868,1.745636,0,0,0,40.27431,0,0,0,2,0,6,73.3,8.7,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,75.09985,9.352452,1.791759,3.695714,1 9,1,50,1,4,128760,0,11526.05,17.78713,1,12,1,42.41586,33.3656,.9220839,0,431.3278,508.0313,1,0,0,4,0,6,73.3,8.7,0,,1000,1000,1,1,1.791759,6.907755,0,3.931826,7.600903,0,0,0,75.09985,9.352452,1.791759,6.230543,1 9,1,50,1,5,128760,0,11526.05,18.78713,1,12,1,0,1.967837,0,0,0,1.967837,0,0,0,0,0,6,73.3,8.7,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,75.09985,9.352452,1.791759,.6769352,1 9,1,50,1,1,128761,0,11526.05,36.88706,0,18,1,57.85372,8.033573,38.21943,0,0,104.1067,0,0,0,3,0,5,71.6,0,0,,1000,1000,0,0,1.609438,6.907755,0,3.931826,7.600903,0,0,0,79.00905,9.352452,1.609438,4.645416,1 9,1,50,1,2,128761,0,11526.05,37.88706,0,18,1,41.07338,7.420591,0,0,0,48.49398,0,0,0,0,3,6,71.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,79.00905,9.352452,1.791759,3.88144,1 9,1,50,1,3,128761,0,11526.05,38.88706,0,18,1,47.38155,0,0,0,257.6559,305.0374,1,0,0,2,0,6,71.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,79.00905,9.352452,1.791759,5.720435,1 9,1,50,1,4,128761,0,11526.05,39.88706,0,18,1,0,0,0,0,0,0,0,0,0,0,0,6,71.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,79.00905,9.352452,1.791759,,0 9,1,50,1,5,128761,0,11526.05,40.88706,0,18,1,13.11892,0,27.74016,0,0,40.85908,0,0,0,1,0,6,71.6,0,0,,1000,1000,0,0,1.791759,6.907755,0,3.931826,7.600903,0,0,0,79.00905,9.352452,1.791759,3.710129,1 2,1,100,1,1,128767,0,9549.008,32.92539,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,80,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,77.67306,9.164297,1.386294,,0 2,1,100,1,2,128767,0,9549.008,33.92539,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,80,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,77.67306,9.164297,1.386294,,0 2,1,100,1,3,128767,0,9549.008,34.92539,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,80,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,77.67306,9.164297,1.386294,,0 2,1,100,1,1,128768,0,9549.008,29.96852,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,79.50489,9.164297,1.386294,,0 2,1,100,1,2,128768,0,9549.008,30.96852,1,12,1,24.49646,0,0,0,0,24.49646,0,0,0,1,0,4,84.2,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,79.50489,9.164297,1.386294,3.198529,1 2,1,100,1,3,128768,0,9549.008,31.96852,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,84.2,13,0,,900,900,0,0,1.386294,6.802395,1,0,0,0,0,0,79.50489,9.164297,1.386294,,0 2,1,100,1,1,128769,0,9549.008,9.245722,0,12,1,35.69304,6.71624,0,0,0,42.40928,0,0,0,3,0,4,74.36826,13.73189,0,,900,900,1,0,1.386294,6.802395,1,0,0,0,0,0,85.34759,9.164297,1.386294,3.747367,1 2,1,100,1,2,128769,0,9549.008,10.24572,0,12,1,4.899292,1.061513,0,0,0,5.960806,0,0,0,1,0,4,74.36826,13.73189,0,,900,900,1,0,1.386294,6.802395,1,0,0,0,0,0,85.34759,9.164297,1.386294,1.785206,1 2,1,100,1,3,128769,0,9549.008,11.24572,0,12,1,7.433102,4.658077,0,0,0,12.09118,0,0,0,1,0,4,74.36826,13.73189,0,,900,900,1,0,1.386294,6.802395,1,0,0,0,0,0,85.34759,9.164297,1.386294,2.492476,1 2,1,100,1,1,128770,0,9549.008,12.4052,1,12,1,16.65675,.9518144,0,0,0,17.60857,0,0,0,1,0,4,74.36826,13.73189,0,,900,900,1,1,1.386294,6.802395,1,0,0,0,0,0,84.83361,9.164297,1.386294,2.868386,1 2,1,100,1,2,128770,0,9549.008,13.4052,1,12,1,6.53239,0,0,0,0,6.53239,0,0,0,1,0,4,74.36826,13.73189,0,,900,900,1,1,1.386294,6.802395,1,0,0,0,0,0,84.83361,9.164297,1.386294,1.876773,1 2,1,100,1,3,128770,0,9549.008,14.4052,1,12,1,22.29931,0,0,0,0,22.29931,0,0,0,1,0,4,74.36826,13.73189,0,,900,900,1,1,1.386294,6.802395,1,0,0,0,0,0,84.83361,9.164297,1.386294,3.104556,1 6,1,25,1,1,128771,0,17027.92,30.54346,0,17,1,35.09816,0,34.19393,0,0,69.29209,0,0,0,2,0,3,90.5,8.7,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,79.23241,9.742668,1.098612,4.238331,1 6,1,25,1,2,128771,0,17027.92,31.54346,0,17,1,6.53239,4.654328,0,0,0,11.18672,0,0,0,1,0,4,90.5,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.23241,9.742668,1.386294,2.414727,1 6,1,25,1,3,128771,0,17027.92,32.54346,0,17,1,19.32607,8.414271,28.99901,0,0,56.73935,0,0,0,2,0,4,90.5,8.7,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.23241,9.742668,1.386294,4.038468,1 6,1,25,1,1,128772,0,17027.92,3.126626,1,15,1,42.23676,34.17609,0,0,0,76.41285,0,0,0,5,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,0,3.258096,8.294049,0,0,0,81.77524,9.742668,1.098612,4.336151,1 6,1,25,1,2,128772,0,17027.92,4.126626,1,15,1,268.6445,10.96897,0,0,0,279.6135,0,0,0,22,41,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77524,9.742668,1.386294,5.633408,1 6,1,25,1,3,128772,0,17027.92,5.126626,1,15,1,280.9713,40.88206,0,0,0,321.8533,0,0,0,41,32,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.258096,8.294049,0,0,0,81.77524,9.742668,1.386294,5.774096,1 6,1,25,1,1,128773,0,17027.92,28.36413,1,15,1,48.1856,19.36347,0,0,731.7133,799.2623,1,0,0,2,0,3,96.8,4.3,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,79.96555,9.742668,1.098612,6.683689,1 6,1,25,1,2,128773,0,17027.92,29.36413,1,15,1,263.2009,17.17474,0,0,2118.759,2399.135,1,0,0,22,0,4,96.8,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.96555,9.742668,1.386294,7.782863,1 6,1,25,1,3,128773,0,17027.92,30.36413,1,15,1,622.3984,220.7978,0,0,0,843.1962,0,0,0,65,0,4,96.8,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,79.96555,9.742668,1.386294,6.7372,1 11,1,0,1,1,128808,0,9253.722,31.62218,0,17,1,0,29.71823,0,0,0,29.71823,0,0,0,0,0,3,55.8,8.7,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,65.66811,9.132889,1.098612,3.391761,1 11,1,0,1,2,128808,0,9253.722,32.62218,0,17,1,113.6364,6.544359,0,21.90581,1063.527,1183.708,1,0,1,2,0,3,55.8,8.7,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,65.66811,9.132889,1.098612,7.076407,1 11,1,0,1,3,128808,0,9253.722,33.62218,0,17,1,0,0,0,0,0,0,0,0,0,0,0,3,55.8,8.7,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,65.66811,9.132889,1.098612,,0 11,1,0,1,1,128809,0,9253.722,28.39425,1,12,1,40.76739,3.657074,0,0,0,44.42446,0,0,0,2,1,3,71.6,17.4,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,71.41678,9.132889,1.098612,3.79379,1 11,1,0,1,2,128809,0,9253.722,29.39425,1,12,1,127.5192,2.135816,0,0,950.0547,1079.71,1,0,0,3,1,3,71.6,17.4,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,71.41678,9.132889,1.098612,6.984447,1 11,1,0,1,3,128809,0,9253.722,30.39425,1,12,1,29.92519,0,0,0,0,29.92519,0,0,0,1,0,3,71.6,17.4,0,,0,192,0,0,1.098612,5.257495,0,0,0,1,0,0,71.41678,9.132889,1.098612,3.3987,1 11,1,0,1,1,128810,0,9253.722,4.202601,1,12,1,44.96403,3.597122,0,0,0,48.56115,0,0,0,4,0,3,74.36826,13.73189,0,,0,192,1,1,1.098612,5.257495,0,0,0,0,0,0,79.40221,9.132889,1.098612,3.882824,1 11,1,0,1,2,128810,0,9253.722,5.202601,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,0,192,1,1,1.098612,5.257495,0,0,0,0,0,0,79.40221,9.132889,1.098612,,0 11,1,0,1,3,128810,0,9253.722,6.202601,1,12,1,18.95262,3.990025,0,0,0,22.94264,0,0,0,3,0,3,74.36826,13.73189,0,,0,192,1,1,1.098612,5.257495,0,0,0,0,0,0,79.40221,9.132889,1.098612,3.132997,1 11,1,0,1,1,128812,0,13159.43,13.07598,1,12,1,20.98321,3.357314,24.43645,0,0,48.77698,0,0,0,1,1,4,74.36826,13.73189,0,,0,60,1,1,1.386294,4.094345,0,0,0,0,0,0,83.29486,9.48497,1.386294,3.887259,1 11,1,0,1,2,128812,0,13159.43,14.07598,1,12,1,33.40635,8.871851,0,0,0,42.2782,0,0,0,2,0,4,74.36826,13.73189,0,,0,60,1,1,1.386294,4.094345,0,0,0,0,0,0,83.29486,9.48497,1.386294,3.744272,1 11,1,0,1,3,128812,0,13159.43,15.07598,1,12,1,12.46883,12.06983,23.69077,0,0,48.22943,0,0,0,1,0,4,74.36826,13.73189,0,,0,60,1,1,1.386294,4.094345,0,0,0,0,0,0,83.29486,9.48497,1.386294,3.875969,1 11,1,0,1,4,128812,0,13159.43,16.07598,1,12,1,46.10419,7.100046,0,0,0,53.20424,0,0,0,4,0,4,74.36826,13.73189,0,,0,60,1,1,1.386294,4.094345,0,0,0,0,0,0,83.29486,9.48497,1.386294,3.974138,1 11,1,0,1,5,128812,0,13159.43,17.07598,1,12,1,21.37114,6.157427,26.23783,0,0,53.7664,0,0,0,2,0,4,74.36826,13.73189,0,,0,60,1,1,1.386294,4.094345,0,0,0,0,0,0,83.29486,9.48497,1.386294,3.984649,1 11,1,0,1,1,128813,0,13159.43,9.453798,0,12,1,178.0576,62.73981,23.4952,0,534.982,799.2746,1,0,0,34,0,4,74.36826,13.73189,0,,0,60,1,0,1.386294,4.094345,0,0,0,1,0,0,74.60253,9.48497,1.386294,6.683704,1 11,1,0,1,2,128813,0,13159.43,10.4538,0,12,1,94.74261,19.27711,14.23877,0,0,128.2585,0,0,0,22,0,4,74.36826,13.73189,0,,0,60,1,0,1.386294,4.094345,0,0,0,1,0,0,74.60253,9.48497,1.386294,4.854048,1 11,1,0,1,3,128813,0,13159.43,11.4538,0,12,1,157.8554,16.68329,14.46384,0,0,189.0025,0,0,0,31,0,4,74.36826,13.73189,0,,0,60,1,0,1.386294,4.094345,0,0,0,1,0,0,74.60253,9.48497,1.386294,5.24176,1 11,1,0,1,4,128813,0,13159.43,12.4538,0,12,1,59.47441,5.532504,32.9645,0,0,97.97141,0,0,0,4,0,4,74.36826,13.73189,0,,0,60,1,0,1.386294,4.094345,0,0,0,1,0,0,74.60253,9.48497,1.386294,4.584676,1 11,1,0,1,5,128813,0,13159.43,13.4538,0,12,1,116.1659,9.331359,24.65087,0,0,150.1481,0,0,0,7,0,4,74.36826,13.73189,0,,0,60,1,0,1.386294,4.094345,0,0,0,1,0,0,74.60253,9.48497,1.386294,5.011622,1 11,1,0,1,1,128814,0,13159.43,37.65366,1,12,1,32.67386,216.0791,0,119.9041,0,248.753,0,0,5,2,0,4,36.8,17.4,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.39919,9.48497,1.386294,5.51646,1 11,1,0,1,2,128814,0,13159.43,38.65366,1,12,1,48.19277,181.4841,30.66813,150.6024,0,260.345,0,0,6,4,0,4,36.8,17.4,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.39919,9.48497,1.386294,5.562008,1 11,1,0,1,3,128814,0,13159.43,39.65366,1,12,1,48.37905,227.1571,38.65337,256.8578,0,314.1895,0,0,12,2,0,4,36.8,17.4,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.39919,9.48497,1.386294,5.749996,1 11,1,0,1,4,128814,0,13159.43,40.65366,1,12,1,107.8838,207.2845,0,228.2158,2219.751,2534.919,1,0,10,10,0,4,36.8,17.4,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.39919,9.48497,1.386294,7.837917,1 11,1,0,1,5,128814,0,13159.43,41.65366,1,12,1,178.375,130.1523,29.83496,152.3487,0,338.3622,0,0,6,12,0,4,36.8,17.4,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.39919,9.48497,1.386294,5.824117,1 11,1,0,1,1,128815,0,13159.43,40.26831,0,16,1,189.7482,37.33213,31.17506,0,0,258.2554,0,0,0,28,0,4,66.3,13,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.36005,9.48497,1.386294,5.553949,1 11,1,0,1,2,128815,0,13159.43,41.26831,0,16,1,47.64513,26.58817,18.07229,54.76451,0,92.30559,0,0,2,9,0,4,66.3,13,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.36005,9.48497,1.386294,4.525105,1 11,1,0,1,3,128815,0,13159.43,42.26831,0,16,1,14.96259,7.306733,34.16459,296.7581,0,56.43391,0,0,13,1,0,4,66.3,13,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.36005,9.48497,1.386294,4.03307,1 11,1,0,1,4,128815,0,13159.43,43.26831,0,16,1,65.00691,16.41309,23.0521,0,0,104.4721,0,0,0,9,0,4,66.3,13,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.36005,9.48497,1.386294,4.64892,1 11,1,0,1,5,128815,0,13159.43,44.26831,0,16,1,139.5472,27.06306,45.28142,0,0,211.8917,0,0,0,24,0,4,66.3,13,0,,0,60,0,0,1.386294,4.094345,0,0,0,0,0,0,73.36005,9.48497,1.386294,5.356075,1 11,1,0,1,1,128840,0,4372.829,58.75154,1,16,1,46.76259,107.7998,0,0,0,154.5623,0,0,0,6,0,1,68.4,17.4,0,,0,258,0,0,0,5.552959,0,0,0,1,0,0,66.1049,8.383394,0,5.040597,1 11,1,0,1,2,128840,0,4372.829,59.75154,1,16,1,36.14458,49.2333,52.65608,0,0,138.034,0,0,0,5,0,1,68.4,17.4,0,,0,258,0,0,0,5.552959,0,0,0,1,0,0,66.1049,8.383394,0,4.9275,1 11,1,0,1,3,128840,0,4372.829,60.75154,1,16,1,68.82793,63.85536,0,0,0,132.6833,0,0,0,6,0,1,68.4,17.4,0,,0,258,0,0,0,5.552959,0,0,0,1,0,0,66.1049,8.383394,0,4.887965,1 11,1,0,1,4,128840,0,4372.829,61.75154,1,16,1,95.43568,64.47672,0,0,0,159.9124,0,0,0,11,0,1,68.4,17.4,0,,0,258,0,0,0,5.552959,0,0,0,1,0,0,66.1049,8.383394,0,5.074626,1 11,1,0,1,5,128840,0,4372.829,62.75154,1,16,1,477.8671,66.08125,4.159966,0,0,548.1083,0,0,0,10,0,1,68.4,17.4,0,,0,258,0,0,0,5.552959,0,0,0,1,0,0,66.1049,8.383394,0,6.306473,1 11,1,0,0,1,128865,0,15260.55,36.5421,1,17,1,38.57479,40.97173,0,0,0,79.54652,0,0,0,4,0,4,57.9,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,79.29243,9.633092,1.386294,4.376342,1 11,1,0,0,2,128865,0,15260.55,37.5421,1,17,1,63.34232,40.48518,26.95418,0,0,130.7817,0,0,0,4,0,4,57.9,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,79.29243,9.633092,1.386294,4.873529,1 11,1,0,0,3,128865,0,15260.55,38.5421,1,17,1,244.5946,27.64128,12.77641,0,0,285.0123,0,0,0,5,0,4,57.9,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,79.29243,9.633092,1.386294,5.652532,1 11,1,0,0,4,128865,0,15260.55,39.5421,1,17,1,34.41203,18.79216,26.89152,0,0,80.09572,0,0,0,4,0,4,57.9,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,79.29243,9.633092,1.386294,4.383223,1 11,1,0,0,5,128865,0,15260.55,40.5421,1,17,1,142.9762,77.84493,0,0,0,220.8212,0,0,0,10,0,4,57.9,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,79.29243,9.633092,1.386294,5.397353,1 11,1,0,0,1,128866,0,15260.55,14.86379,0,17,1,135.8952,65.41814,30.62426,0,0,231.9376,0,0,0,16,0,4,49.5,13,0,,0,84,1,0,1.386294,4.430817,0,0,0,0,0,0,72.45129,9.633092,1.386294,5.446468,1 11,1,0,0,2,128866,0,15260.55,15.86379,0,17,1,38.81401,54.28571,0,0,0,93.09973,0,0,0,8,0,4,49.5,13,0,,0,84,1,0,1.386294,4.430817,0,0,0,0,0,0,72.45129,9.633092,1.386294,4.533671,1 11,1,0,0,3,128866,0,15260.55,16.86379,0,17,1,30.95823,73.34152,0,0,0,104.2998,0,0,0,7,0,4,49.5,13,0,,0,84,1,0,1.386294,4.430817,0,0,0,0,0,0,72.45129,9.633092,1.386294,4.647269,1 11,1,0,0,4,128866,0,15260.55,17.86379,0,17,1,26.89152,36.98724,0,0,0,63.87876,0,0,0,5,0,4,49.5,13,0,,0,84,1,0,1.386294,4.430817,0,0,0,0,0,0,72.45129,9.633092,1.386294,4.156987,1 11,1,0,0,5,128866,0,15260.55,18.86379,0,17,1,81.86327,33.38891,12.50521,0,0,127.7574,0,0,0,7,1,4,49.5,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,72.45129,9.633092,1.386294,4.850133,1 11,1,0,0,1,128867,0,15260.55,42.35455,0,17,1,35.48292,13.82803,0,0,0,49.31096,0,0,0,4,0,4,71.6,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,76.61908,9.633092,1.386294,3.898146,1 11,1,0,0,2,128867,0,15260.55,43.35455,0,17,1,13.47709,0,29.11051,0,0,42.5876,0,0,0,1,0,4,71.6,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,76.61908,9.633092,1.386294,3.751563,1 11,1,0,0,3,128867,0,15260.55,44.35455,0,17,1,30.58968,24.00491,0,0,0,54.59459,0,0,0,4,0,4,71.6,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,76.61908,9.633092,1.386294,3.999935,1 11,1,0,0,4,128867,0,15260.55,45.35455,0,17,1,55.6062,31.95078,36.00729,0,0,123.5643,0,0,0,4,0,4,71.6,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,76.61908,9.633092,1.386294,4.816761,1 11,1,0,0,5,128867,0,15260.55,46.35455,0,17,1,87.11964,7.23218,0,0,0,94.35181,0,0,0,2,0,4,71.6,13,0,,0,84,0,0,1.386294,4.430817,0,0,0,0,0,0,76.61908,9.633092,1.386294,4.54703,1 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11,1,0,1,5,129072,1,9542.002,13.23477,1,13,1,5.924672,0,0,0,0,5.924672,0,0,0,1,0,10,74.36826,13.73189,0,,0,325,1,1,2.302585,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.302585,1.779125,1 11,1,0,1,1,129073,1,9542.002,35.2909,1,13,1,140.1379,24.02878,0,0,329.3525,493.5192,1,0,0,5,0,8,75.8,8.7,0,,0,325,0,0,2.079442,5.783825,0,0,0,0,0,0,75.5062,9.163564,2.079442,6.201562,1 11,1,0,1,2,129073,1,9542.002,36.2909,1,13,1,559.874,9.884995,28.91566,0,939.9124,1538.587,1,0,0,8,0,8,75.8,8.7,0,,0,325,0,0,2.079442,5.783825,0,0,0,0,0,0,75.5062,9.163564,2.079442,7.33862,1 11,1,0,1,3,129073,1,9542.002,37.2909,1,13,1,52.36908,10.32419,0,0,0,62.69327,0,0,0,1,0,9,75.8,8.7,0,,0,325,0,0,2.197225,5.783825,0,0,0,0,0,0,75.5062,9.163564,2.197225,4.138254,1 11,1,0,1,4,129073,1,9542.002,38.2909,1,13,1,19.36376,0,0,0,0,19.36376,0,0,0,1,0,10,75.8,8.7,0,,0,325,0,0,2.302585,5.783825,0,0,0,0,0,0,75.5062,9.163564,2.302585,2.963403,1 11,1,0,1,5,129073,1,9542.002,39.2909,1,13,1,0,0,0,0,0,0,0,0,0,0,0,10,75.8,8.7,0,,0,325,0,0,2.302585,5.783825,0,0,0,0,0,0,75.5062,9.163564,2.302585,,0 11,1,0,1,1,129074,1,9542.002,10.78987,0,13,1,14.98801,0,30.70144,0,0,45.68945,0,0,0,1,0,8,74.36826,13.73189,0,,0,325,1,0,2.079442,5.783825,0,0,0,0,0,0,81.08335,9.163564,2.079442,3.821867,1 11,1,0,1,2,129074,1,9542.002,11.78987,0,13,1,3.833516,0,0,0,0,3.833516,0,0,0,1,0,8,74.36826,13.73189,0,,0,325,1,0,2.079442,5.783825,0,0,0,0,0,0,81.08335,9.163564,2.079442,1.343782,1 11,1,0,1,3,129074,1,9542.002,12.78987,0,13,1,24.93766,0,33.91521,0,0,58.85287,0,0,0,2,0,9,74.36826,13.73189,0,,0,325,1,0,2.197225,5.783825,0,0,0,0,0,0,81.08335,9.163564,2.197225,4.07504,1 11,1,0,1,4,129074,1,9542.002,13.78987,0,13,1,5.993546,0,0,0,0,5.993546,0,0,0,1,0,10,74.36826,13.73189,0,,0,325,1,0,2.302585,5.783825,0,0,0,0,0,0,81.08335,9.163564,2.302585,1.790683,1 11,1,0,1,5,129074,1,9542.002,14.78987,0,13,1,0,0,0,0,0,0,0,0,0,0,0,10,74.36826,13.73189,0,,0,325,1,0,2.302585,5.783825,0,0,0,0,0,0,81.08335,9.163564,2.302585,,0 11,1,0,1,1,129075,1,9542.002,20.62149,1,13,1,32.97362,28.34532,0,0,0,61.31894,0,0,0,4,0,8,67.4,0,0,,0,325,0,0,2.079442,5.783825,0,0,0,0,0,0,72.91712,9.163564,2.079442,4.116089,1 11,1,0,1,2,129075,1,9542.002,21.62149,1,13,1,37.73275,6.900329,0,0,0,44.63308,0,0,0,4,1,8,67.4,0,0,,0,325,0,0,2.079442,5.783825,0,0,0,0,0,0,72.91712,9.163564,2.079442,3.798475,1 11,1,0,1,3,129075,1,9542.002,22.62149,1,13,1,499.7756,17.60599,0,0,0,517.3815,0,0,0,9,0,9,67.4,0,0,,0,325,0,0,2.197225,5.783825,0,0,0,0,0,0,72.91712,9.163564,2.197225,6.248781,1 11,1,0,1,4,129075,1,9542.002,23.62149,1,13,1,151.2218,14.56893,0,0,0,165.7907,0,0,0,4,0,10,67.4,0,0,,0,325,0,0,2.302585,5.783825,0,0,0,0,0,0,72.91712,9.163564,2.302585,5.110726,1 11,1,0,1,5,129075,1,9542.002,24.62149,1,13,1,52.05247,0,0,0,0,52.05247,0,0,0,7,0,10,67.4,0,0,,0,325,0,0,2.302585,5.783825,0,0,0,0,0,0,72.91712,9.163564,2.302585,3.952252,1 11,1,0,1,1,129076,1,9542.002,46.31348,0,3,1,0,0,0,0,0,0,0,0,0,0,0,8,57.9,0,0,,0,325,0,0,2.079442,5.783825,0,0,0,1,0,0,60.2207,9.163564,2.079442,,0 11,1,0,1,2,129076,1,9542.002,47.31348,0,3,1,24.91785,.8214677,49.28806,0,0,75.02738,0,0,0,2,0,8,57.9,0,0,,0,325,0,0,2.079442,5.783825,0,0,0,1,0,0,60.2207,9.163564,2.079442,4.317853,1 11,1,0,1,3,129076,1,9542.002,48.31348,0,3,1,0,0,0,0,0,0,0,0,0,0,0,9,57.9,0,0,,0,325,0,0,2.197225,5.783825,0,0,0,1,0,0,60.2207,9.163564,2.197225,,0 11,1,0,1,4,129076,1,9542.002,49.31348,0,3,1,55.78608,7.330567,0,0,0,63.11664,0,0,0,4,0,10,57.9,0,0,,0,325,0,0,2.302585,5.783825,0,0,0,1,0,0,60.2207,9.163564,2.302585,4.144984,1 11,1,0,1,5,129076,1,9542.002,50.31348,0,3,1,0,0,0,0,0,0,0,0,0,0,0,10,57.9,0,0,,0,325,0,0,2.302585,5.783825,0,0,0,1,0,0,60.2207,9.163564,2.302585,,0 11,1,0,1,1,129077,1,9542.002,9.492128,1,13,1,0,0,0,0,0,0,0,0,0,0,0,8,74.36826,13.73189,0,,0,325,1,1,2.079442,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.079442,,0 11,1,0,1,2,129077,1,9542.002,10.49213,1,13,1,3.833516,0,0,0,0,3.833516,0,0,0,1,0,8,74.36826,13.73189,0,,0,325,1,1,2.079442,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.079442,1.343782,1 11,1,0,1,3,129077,1,9542.002,11.49213,1,13,1,9.975062,0,0,0,0,9.975062,0,0,0,1,0,9,74.36826,13.73189,0,,0,325,1,1,2.197225,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.197225,2.300088,1 11,1,0,1,4,129077,1,9542.002,12.49213,1,13,1,0,0,0,0,0,0,0,0,0,0,0,10,74.36826,13.73189,0,,0,325,1,1,2.302585,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.302585,,0 11,1,0,1,5,129077,1,9542.002,13.49213,1,13,1,0,0,0,0,0,0,0,0,0,0,0,10,74.36826,13.73189,0,,0,325,1,1,2.302585,5.783825,0,0,0,0,0,0,80.56938,9.163564,2.302585,,0 11,1,0,1,1,129082,1,5959.057,50.58453,1,9,1,27.95955,31.66568,0,0,0,59.62522,0,0,0,3,1,3,84.2,26.1,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,52.06449,8.692836,1.098612,4.088078,1 11,1,0,1,2,129082,1,5959.057,51.58453,1,9,1,228.0893,26.40174,0,0,0,254.491,0,0,0,7,10,3,84.2,26.1,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,52.06449,8.692836,1.098612,5.539266,1 11,1,0,1,3,129082,1,5959.057,52.58453,1,9,1,122.3984,40.05946,26.92765,0,0,189.3855,0,0,0,6,6,3,84.2,26.1,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,52.06449,8.692836,1.098612,5.243785,1 11,1,0,1,1,129083,1,5959.057,18.05613,1,12.32507,1,35.09816,.594884,0,0,0,35.69304,0,0,0,3,0,3,83.2,17.4,0,,0,313,0,0,1.098612,5.746203,0,0,0,0,1,0,62.13012,8.692836,1.098612,3.574956,1 11,1,0,1,2,129083,1,5959.057,19.05613,1,12.32507,1,69.67883,13.62003,0,0,0,83.29886,0,0,0,8,0,3,83.2,17.4,0,,0,313,0,0,1.098612,5.746203,0,0,0,0,1,0,62.13012,8.692836,1.098612,4.422435,1 11,1,0,1,3,129083,1,5959.057,20.05613,1,12.32507,1,0,0,0,0,0,0,0,0,0,0,0,3,83.2,17.4,0,,0,313,0,0,1.098612,5.746203,0,0,0,0,1,0,62.13012,8.692836,1.098612,,0 11,1,0,1,1,129084,1,5959.057,56,0,2,1,131.1719,67.55502,21.76086,0,1095.604,1316.092,1,0,0,9,1,3,75.8,34.8,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,60.90429,8.692836,1.098612,7.182422,1 11,1,0,1,2,129084,1,5959.057,57,0,2,1,136.6358,50.28851,0,0,0,186.9243,0,0,0,8,6,3,75.8,34.8,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,60.90429,8.692836,1.098612,5.230704,1 11,1,0,1,3,129084,1,5959.057,58,0,2,1,66.40238,43.68186,21.74926,0,0,131.8335,0,0,0,6,3,3,75.8,34.8,1,,0,313,0,0,1.098612,5.746203,0,0,0,0,0,1,60.90429,8.692836,1.098612,4.88154,1 11,1,0,0,1,129090,0,4912.531,49.28679,1,11,1,23.55713,55.73027,0,0,0,79.2874,0,0,0,2,0,1,87.5,13.73189,0,,0,0,0,0,0,0,0,0,0,0,0,0,75.54311,8.499748,0,4.373079,1 11,1,0,0,2,129090,0,4912.531,50.28679,1,11,1,80.86253,58.14016,0,0,0,139.0027,0,0,0,9,0,1,87.5,13.73189,0,,0,0,0,0,0,0,0,0,0,0,0,0,75.54311,8.499748,0,4.934494,1 11,1,0,0,3,129090,0,4912.531,51.28679,1,11,1,28.99263,28.7027,48.78624,0,0,106.4816,0,0,0,2,1,1,87.5,13.73189,0,,0,0,0,0,0,0,0,0,0,0,0,0,75.54311,8.499748,0,4.667972,1 11,1,0,0,4,129090,0,4912.531,52.28679,1,11,1,11.8505,29.98177,0,0,0,41.83227,0,0,0,1,0,1,87.5,13.73189,0,,0,0,0,0,0,0,0,0,0,0,0,0,75.54311,8.499748,0,3.733668,1 11,1,0,0,5,129090,0,4912.531,53.28679,1,11,1,27.09462,24.73948,45.30221,0,0,97.13631,0,0,0,4,1,1,87.5,13.73189,0,,0,0,0,0,0,0,0,0,0,0,0,0,75.54311,8.499748,0,4.576115,1 11,1,0,0,1,129091,0,7755.583,21.3963,0,11,1,0,0,0,0,0,0,0,0,0,0,0,1,83.8,13.73189,0,,0,204,0,0,0,5.31812,0,0,0,0,0,0,69.5586,8.956297,0,,0 11,1,0,0,2,129091,0,7755.583,22.3963,0,11,1,0,0,0,0,0,0,0,0,0,0,0,1,83.8,13.73189,0,,0,204,0,0,0,5.31812,0,0,0,0,0,0,69.5586,8.956297,0,,0 11,1,0,0,3,129091,0,7755.583,23.3963,0,11,1,0,0,0,0,0,0,0,0,0,0,0,2,83.8,13.73189,0,,0,204,0,0,.6931472,5.31812,0,0,0,0,0,0,69.5586,8.956297,.6931472,,0 11,1,0,0,4,129091,0,7755.583,24.3963,0,11,1,0,0,0,0,0,0,0,0,0,0,0,2,83.8,13.73189,0,,0,204,0,0,.6931472,5.31812,0,0,0,0,0,0,69.5586,8.956297,.6931472,,0 11,1,0,0,5,129091,0,7755.583,25.3963,0,11,1,14.58941,0,0,0,0,14.58941,0,0,0,1,0,2,83.8,13.73189,0,,0,204,0,0,.6931472,5.31812,0,0,0,0,0,0,69.5586,8.956297,.6931472,2.680296,1 7,1,25,1,1,129103,0,6910.05,5.472964,1,12,1,222.7218,12.71583,0,0,0,235.4377,0,0,0,12,1,4,74.36826,13.73189,0,,224,398,1,1,1.386294,5.986452,0,3.258096,6.79794,0,0,0,76.40307,8.840877,1.386294,5.461446,1 7,1,25,1,2,129103,0,6910.05,6.472964,1,12,1,127.0537,4.742607,0,0,0,131.7963,0,0,0,12,1,4,74.36826,13.73189,0,,224,398,1,1,1.386294,5.986452,0,3.258096,6.79794,0,0,0,76.40307,8.840877,1.386294,4.881258,1 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8,1,50,0,3,129412,0,8082.506,22.66256,0,12,1,12.46883,0,0,0,0,12.46883,0,0,0,0,1,1,91.3,13.73189,0,,399,399,0,0,0,5.988961,0,3.931826,6.682108,1,0,0,67.90727,8.997581,0,2.523232,1 2,1,100,0,1,129417,0,5361.663,19.10198,1,11,1,0,1.193046,0,0,0,1.193046,0,0,0,0,0,3,63.2,13,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,74.09004,8.587215,1.098612,.1765094,1 2,1,100,0,2,129417,0,5361.663,20.10198,1,11,1,13.69113,0,0,0,0,13.69113,0,0,0,0,0,3,63.2,13,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,74.09004,8.587215,1.098612,2.616748,1 2,1,100,0,3,129417,0,5361.663,21.10198,1,11,1,20.82294,8.468828,0,0,879.4514,908.7432,3,0,0,0,0,3,63.2,13,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,74.09004,8.587215,1.098612,6.812063,1 2,1,100,0,4,129417,0,5361.663,22.10198,1,11,1,49.51591,0,0,0,0,49.51591,0,0,0,1,0,4,63.2,13,0,,409,0,0,0,1.386294,0,1,0,0,0,0,0,74.09004,8.587215,1.386294,3.902294,1 2,1,100,0,5,129417,0,5361.663,23.10198,1,11,1,19.04359,2.391028,0,0,614.2192,635.6538,1,0,0,1,1,4,63.2,13,0,,409,0,0,0,1.386294,0,1,0,0,0,0,0,74.09004,8.587215,1.386294,6.454654,1 2,1,100,0,1,129418,0,5361.663,2.16564,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,409,0,1,1,1.098612,0,1,0,0,0,0,0,76.23925,8.587215,1.098612,,0 2,1,100,0,2,129418,0,5361.663,3.16564,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,409,0,1,1,1.098612,0,1,0,0,0,0,0,76.23925,8.587215,1.098612,,0 2,1,100,0,3,129418,0,5361.663,4.16564,1,11,1,6.48379,0,0,0,0,6.48379,0,0,0,1,0,3,74.36826,13.73189,0,,409,0,1,1,1.098612,0,1,0,0,0,0,0,76.23925,8.587215,1.098612,1.869305,1 2,1,100,0,4,129418,0,5361.663,5.16564,1,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,409,0,1,1,1.386294,0,1,0,0,0,0,0,76.23925,8.587215,1.386294,,0 2,1,100,0,5,129418,0,5361.663,6.16564,1,11,1,10.57977,0,0,0,0,10.57977,0,0,0,0,1,4,74.36826,13.73189,0,,409,0,1,1,1.386294,0,1,0,0,0,0,0,76.23925,8.587215,1.386294,2.358944,1 2,1,100,0,1,129419,0,5361.663,20.16153,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,69.5,8.7,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,73.19886,8.587215,1.098612,,0 2,1,100,0,2,129419,0,5361.663,21.16153,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,69.5,8.7,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,73.19886,8.587215,1.098612,,0 2,1,100,0,3,129419,0,5361.663,22.16153,0,11,1,134.8379,4.528678,24.97257,0,0,164.3392,0,0,0,7,1,3,69.5,8.7,0,,409,0,0,0,1.098612,0,1,0,0,0,0,0,73.19886,8.587215,1.098612,5.101933,1 2,1,100,0,4,129419,0,5361.663,23.16153,0,11,1,7.376671,0,0,0,0,7.376671,0,0,0,1,0,4,69.5,8.7,0,,409,0,0,0,1.386294,0,1,0,0,0,0,0,73.19886,8.587215,1.386294,1.998322,1 2,1,100,0,5,129419,0,5361.663,24.16153,0,11,1,116.8007,0,30.40626,0,0,147.2069,0,0,0,3,1,4,69.5,8.7,0,,409,0,0,0,1.386294,0,1,0,0,0,0,0,73.19886,8.587215,1.386294,4.991839,1 10,1,50,0,1,129425,0,13275.43,1.938398,0,17,1,32.97998,21.76678,0,0,0,54.74676,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.00792,9.493746,1.386294,4.002718,1 10,1,50,0,2,129425,0,13275.43,2.938398,0,17,1,61.2938,15.55256,0,0,0,76.84636,0,0,0,8,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.00792,9.493746,1.386294,4.341808,1 10,1,50,0,3,129425,0,13275.43,3.938398,0,17,1,78.86978,11.86732,0,0,0,90.7371,0,0,0,6,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.00792,9.493746,1.386294,4.507967,1 10,1,50,0,4,129425,0,13275.43,4.938398,0,17,1,100.7293,42.83956,0,0,393.938,537.5068,1,0,0,11,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.00792,9.493746,1.386294,6.286942,1 10,1,50,0,5,129425,0,13275.43,5.938398,0,17,1,33.76407,7.899125,0,0,0,41.66319,0,0,0,5,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.00792,9.493746,1.386294,3.729618,1 10,1,50,0,1,129426,0,13275.43,34.79261,0,20,1,7.067138,14.25206,0,0,0,21.3192,0,0,0,1,0,4,51.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,75.16824,9.493746,1.386294,3.059608,1 10,1,50,0,2,129426,0,13275.43,35.79261,0,20,1,11.32076,16.06469,0,0,0,27.38544,0,0,0,1,0,4,51.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,75.16824,9.493746,1.386294,3.310012,1 10,1,50,0,3,129426,0,13275.43,36.79261,0,20,1,7.862408,6.142506,0,0,0,14.00491,0,0,0,1,0,4,51.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,75.16824,9.493746,1.386294,2.639408,1 10,1,50,0,4,129426,0,13275.43,37.79261,0,20,1,95.03191,20.03191,0,0,0,115.0638,0,0,0,6,0,4,51.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,75.16824,9.493746,1.386294,4.745487,1 10,1,50,0,5,129426,0,13275.43,38.79261,0,20,1,48.77032,9.866611,0,0,0,58.63693,0,0,0,3,0,4,51.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,75.16824,9.493746,1.386294,4.071365,1 10,1,50,0,1,129427,0,13275.43,34.05339,1,17,1,230.2709,15.95995,0,0,0,246.2309,0,0,0,6,1,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,76.05943,9.493746,1.386294,5.506269,1 10,1,50,0,2,129427,0,13275.43,35.05339,1,17,1,243.6658,7.843666,0,0,0,251.5094,0,0,0,4,3,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,76.05943,9.493746,1.386294,5.527481,1 10,1,50,0,3,129427,0,13275.43,36.05339,1,17,1,31.94103,2.407862,16.21622,0,0,50.56511,0,0,0,2,1,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,76.05943,9.493746,1.386294,3.923262,1 10,1,50,0,4,129427,0,13275.43,37.05339,1,17,1,59.4804,21.28532,0,0,0,80.76572,0,0,0,3,1,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,76.05943,9.493746,1.386294,4.391552,1 10,1,50,0,5,129427,0,13275.43,38.05339,1,17,1,181.1171,16.87786,8.336807,0,0,206.3318,0,0,0,10,0,4,76.3,13.73189,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,76.05943,9.493746,1.386294,5.329485,1 10,1,50,0,1,129428,0,13275.43,6.009583,1,17,1,162.2497,27.67962,0,0,0,189.9293,0,0,0,30,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,85.16698,9.493746,1.386294,5.246652,1 10,1,50,0,2,129428,0,13275.43,7.009583,1,17,1,97.57413,20.61995,0,0,0,118.1941,0,0,0,18,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,85.16698,9.493746,1.386294,4.772328,1 10,1,50,0,3,129428,0,13275.43,8.009583,1,17,1,96.21622,12.72727,1.228501,0,0,110.172,0,0,0,18,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,85.16698,9.493746,1.386294,4.702043,1 10,1,50,0,4,129428,0,13275.43,9.009583,1,17,1,123.0629,22.21969,0,0,0,145.2826,0,0,0,17,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,85.16698,9.493746,1.386294,4.978681,1 10,1,50,0,5,129428,0,13275.43,10.00958,1,17,1,89.9333,14.8812,20.03752,0,0,124.852,0,0,0,14,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,85.16698,9.493746,1.386294,4.827129,1 7,1,25,0,1,129436,0,15786.6,36.2026,0,20,1,173.7338,27.31449,43.69847,0,0,244.7468,0,0,0,19,0,7,44.2,26.1,1,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,68.73036,9.66698,1.94591,5.500224,1 7,1,25,0,2,129436,0,15786.6,37.2026,0,20,1,167.1159,49.062,67.38544,261.4555,0,283.5634,0,0,20,23,0,7,44.2,26.1,1,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,68.73036,9.66698,1.94591,5.647436,1 7,1,25,0,3,129436,0,15786.6,38.2026,0,20,1,223.5627,62.92875,37.06142,0,0,323.5528,0,0,0,29,0,7,44.2,26.1,1,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,68.73036,9.66698,1.94591,5.779362,1 7,1,25,0,4,129436,0,15786.6,39.2026,0,20,1,208.5232,36.45396,1.189608,0,0,246.1668,0,0,0,27,0,7,44.2,26.1,1,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,68.73036,9.66698,1.94591,5.50601,1 7,1,25,0,5,129436,0,15786.6,40.2026,0,20,1,127.97,55.42309,34.74364,0,0,218.1367,0,0,0,25,0,7,44.2,26.1,1,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,68.73036,9.66698,1.94591,5.385122,1 7,1,25,0,1,129437,0,15786.6,12.64066,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,,0 7,1,25,0,2,129437,0,15786.6,13.64066,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,,0 7,1,25,0,3,129437,0,15786.6,14.64066,0,10,1,16.21622,.982801,0,0,0,17.19902,0,0,0,3,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,2.844852,1 7,1,25,0,4,129437,0,15786.6,15.64066,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,,0 7,1,25,0,5,129437,0,15786.6,16.64066,0,10,1,13.75573,2.551063,0,0,0,16.3068,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,2.791582,1 7,1,25,0,1,129438,0,15786.6,7.772758,0,10,1,5.300354,0,0,0,0,5.300354,0,0,0,1,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,1.667773,1 7,1,25,0,2,129438,0,15786.6,8.772758,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,,0 7,1,25,0,3,129438,0,15786.6,9.772758,0,10,1,20.14742,2.457002,0,0,0,22.60442,0,0,0,3,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,3.118146,1 7,1,25,0,4,129438,0,15786.6,10.77276,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,,0 7,1,25,0,5,129438,0,15786.6,11.77276,0,10,1,17.5073,8.520217,0,0,0,26.02751,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,3.259154,1 7,1,25,0,1,129439,0,15786.6,10.02327,0,10,1,9.422851,9.729093,0,0,0,19.15194,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,2.952404,1 7,1,25,0,2,129439,0,15786.6,11.02327,0,10,1,43.39622,8.25876,0,0,0,51.65499,0,0,0,6,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,3.944587,1 7,1,25,0,3,129439,0,15786.6,12.02327,0,10,1,44.22604,4.157248,0,0,0,48.38329,0,0,0,4,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,3.879155,1 7,1,25,0,4,129439,0,15786.6,13.02327,0,10,1,101.6408,3.646308,5.528715,0,0,110.8159,0,0,0,3,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,4.70787,1 7,1,25,0,5,129439,0,15786.6,14.02327,0,10,1,83.47228,4.172572,0,0,0,87.64485,0,0,0,7,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,4.473293,1 7,1,25,0,1,129440,0,15786.6,13.55784,1,10,1,20.02356,1.177856,41.23675,0,0,62.43816,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.06523,9.66698,1.94591,4.134177,1 7,1,25,0,2,129440,0,15786.6,14.55784,1,10,1,45.8221,8.334231,0,0,0,54.15633,0,0,0,4,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.06523,9.66698,1.94591,3.991875,1 7,1,25,0,3,129440,0,15786.6,15.55784,1,10,1,33.41523,6.914005,0,0,0,40.32924,0,0,0,5,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.06523,9.66698,1.94591,3.697077,1 7,1,25,0,4,129440,0,15786.6,16.55784,1,10,1,27.34731,1.823154,0,0,0,29.17047,0,0,0,4,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.06523,9.66698,1.94591,3.373157,1 7,1,25,0,5,129440,0,15786.6,17.55784,1,10,1,31.88829,5.060442,0,0,0,36.94873,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,1,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.06523,9.66698,1.94591,3.609531,1 7,1,25,0,1,129441,0,15786.6,33.65914,1,10,1,99.52885,47.54417,0,0,0,147.073,0,0,0,4,0,7,24.2,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,63.85081,9.66698,1.94591,4.990929,1 7,1,25,0,2,129441,0,15786.6,34.65914,1,10,1,115.903,50.22102,0,318.0593,0,166.124,0,0,23,6,0,7,24.2,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,63.85081,9.66698,1.94591,5.112734,1 7,1,25,0,3,129441,0,15786.6,35.65914,1,10,1,56.01966,17.88206,0,0,0,73.90172,0,0,0,2,0,7,24.2,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,63.85081,9.66698,1.94591,4.302736,1 7,1,25,0,4,129441,0,15786.6,36.65914,1,10,1,295.8067,4.922516,0,0,0,300.7292,0,0,0,1,0,7,24.2,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,63.85081,9.66698,1.94591,5.70621,1 7,1,25,0,5,129441,0,15786.6,37.65914,1,10,1,75.4481,5.939975,0,0,0,81.38808,0,0,0,6,0,7,24.2,21.7,0,,1000,1000,0,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,63.85081,9.66698,1.94591,4.399229,1 7,1,25,0,1,129442,0,15786.6,11.68515,0,10,1,41.22497,4.658422,0,0,0,45.88339,0,0,0,5,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,3.826103,1 7,1,25,0,2,129442,0,15786.6,12.68515,0,10,1,17.25067,0,0,0,0,17.25067,0,0,0,2,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,2.847851,1 7,1,25,0,3,129442,0,15786.6,13.68515,0,10,1,12.28501,.982801,0,0,0,13.26781,0,0,0,3,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,2.585341,1 7,1,25,0,4,129442,0,15786.6,14.68515,0,10,1,33.52325,2.461258,0,0,641.6135,677.598,1,0,0,4,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,6.518554,1 7,1,25,0,5,129442,0,15786.6,15.68515,0,10,1,6.252605,0,0,0,0,6.252605,0,0,0,1,0,7,74.36826,13.73189,0,,1000,1000,1,0,1.94591,6.907755,0,3.258096,8.294049,1,0,0,76.57921,9.66698,1.94591,1.832998,1 2,1,100,0,1,129446,0,2588.71,4.533881,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,400,405.56,1,0,1.386294,6.005269,1,0,0,0,0,0,79.54541,7.859301,1.386294,,0 2,1,100,0,2,129446,0,2588.71,5.533881,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,400,405.56,1,0,1.386294,6.005269,1,0,0,0,0,0,79.54541,7.859301,1.386294,,0 2,1,100,0,3,129446,0,2588.71,6.533881,0,12,1,12.46883,0,31.27182,0,0,43.74065,0,0,0,0,1,4,74.36826,13.73189,0,,400,405.56,1,0,1.386294,6.005269,1,0,0,0,0,0,79.54541,7.859301,1.386294,3.778278,1 2,1,100,0,1,129447,0,2588.71,2.469541,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,400,405.56,1,1,1.386294,6.005269,1,0,0,0,0,0,79.03143,7.859301,1.386294,,0 2,1,100,0,2,129447,0,2588.71,3.469541,1,12,1,2.738226,4.441402,0,0,0,7.179627,0,0,0,1,0,4,74.36826,13.73189,0,,400,405.56,1,1,1.386294,6.005269,1,0,0,0,0,0,79.03143,7.859301,1.386294,1.971247,1 2,1,100,0,3,129447,0,2588.71,4.469542,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,400,405.56,1,1,1.386294,6.005269,1,0,0,0,0,0,79.03143,7.859301,1.386294,,0 2,1,100,0,1,129448,0,2588.71,24.95004,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,61.3,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.70184,7.859301,1.386294,,0 2,1,100,0,2,129448,0,2588.71,25.95004,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,61.3,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.70184,7.859301,1.386294,,0 2,1,100,0,3,129448,0,2588.71,26.95004,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,61.3,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.70184,7.859301,1.386294,,0 2,1,100,0,1,129449,0,2588.71,27.07734,0,15,1,0,0,0,0,0,0,0,0,0,0,0,4,77.5,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.07117,7.859301,1.386294,,0 2,1,100,0,2,129449,0,2588.71,28.07734,0,15,1,5.476451,7.738225,0,0,0,13.21468,0,0,0,0,1,4,77.5,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.07117,7.859301,1.386294,2.581328,1 2,1,100,0,3,129449,0,2588.71,29.07734,0,15,1,7.481297,2.523691,22.06983,0,0,32.07481,0,0,0,1,0,4,77.5,13.73189,0,,400,405.56,0,0,1.386294,6.005269,1,0,0,0,0,0,78.07117,7.859301,1.386294,3.468071,1 4,1,100,1,1,129458,0,8129.032,27.72622,0,12,1,14.98801,0,0,0,0,14.98801,0,0,0,1,0,4,76.8,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,77.82298,9.00332,1.386294,2.707251,1 4,1,100,1,2,129458,0,8129.032,28.72622,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,76.8,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,77.82298,9.00332,1.386294,,0 4,1,100,1,3,129458,0,8129.032,29.72622,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,76.8,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,77.82298,9.00332,1.386294,,0 4,1,100,1,1,129459,0,8129.032,7.104723,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,84.02346,9.00332,1.386294,,0 4,1,100,1,2,129459,0,8129.032,8.104723,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,84.02346,9.00332,1.386294,,0 4,1,100,1,3,129459,0,8129.032,9.104723,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,1,0,0,0,0,0,84.02346,9.00332,1.386294,,0 4,1,100,1,1,129460,0,8129.032,26.54894,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.97468,9.00332,1.386294,,0 4,1,100,1,2,129460,0,8129.032,27.54894,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.97468,9.00332,1.386294,,0 4,1,100,1,3,129460,0,8129.032,28.54894,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,8.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,0,0,0,78.97468,9.00332,1.386294,,0 4,1,100,1,1,129461,0,8129.032,6.162902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,84.53744,9.00332,1.386294,,0 4,1,100,1,2,129461,0,8129.032,7.162902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,84.53744,9.00332,1.386294,,0 4,1,100,1,3,129461,0,8129.032,8.162902,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,0,0,0,84.53744,9.00332,1.386294,,0 10,1,50,0,1,129466,0,4528.536,20.27105,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,85.3,4.3,0,,750,750,0,0,0,6.620073,0,3.931826,7.313221,0,0,0,77.9943,8.418375,0,,0 10,1,50,0,2,129466,0,4528.536,21.27105,0,12,1,33.82749,0,0,0,0,33.82749,0,0,0,0,1,1,85.3,4.3,0,,750,750,0,0,0,6.620073,0,3.931826,7.313221,0,0,0,77.9943,8.418375,0,3.521274,1 10,1,50,0,3,129466,0,4528.536,22.27105,0,12,1,0,0,0,0,0,0,0,0,0,0,0,1,85.3,4.3,0,,750,750,0,0,0,6.620073,0,3.931826,7.313221,0,0,0,77.9943,8.418375,0,,0 4,1,100,1,1,129467,1,7848.96,32.72827,0,16,1,5.94884,0,0,0,0,5.94884,0,0,0,1,0,4,91.6,17.4,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,66.55058,8.968264,1.386294,1.783196,1 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4,1,100,1,2,129468,1,7848.96,31.68583,1,16,1,90.36472,4.899292,0,0,791.3609,886.6249,2,0,0,5,0,4,92.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.45471,8.968264,1.386294,6.787422,1 4,1,100,1,3,129468,1,7848.96,32.68583,1,16,1,4.955401,0,0,0,0,4.955401,0,0,0,1,0,4,92.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.45471,8.968264,1.386294,1.600478,1 4,1,100,1,4,129468,1,7848.96,33.68583,1,16,1,303.1804,.9178522,0,0,0,304.0982,0,0,0,4,0,4,92.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.45471,8.968264,1.386294,5.71735,1 4,1,100,1,5,129468,1,7848.96,34.68583,1,16,1,71.51872,10.53849,14.50989,0,1084.666,1181.233,1,0,0,1,1,4,92.6,21.7,0,,1000,1000,0,0,1.386294,6.907755,1,0,0,1,0,0,67.45471,8.968264,1.386294,7.074314,1 4,1,100,1,1,129469,1,7848.96,9.437371,0,16,1,21.41582,0,0,0,0,21.41582,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,3.06413,1 4,1,100,1,2,129469,1,7848.96,10.43737,0,16,1,13.60915,0,0,0,0,13.60915,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,2.610742,1 4,1,100,1,3,129469,1,7848.96,11.43737,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,,0 4,1,100,1,4,129469,1,7848.96,12.43737,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,,0 4,1,100,1,5,129469,1,7848.96,13.43737,0,16,1,49.81068,0,0,0,0,49.81068,0,0,0,3,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,3.90823,1 4,1,100,1,1,129470,1,7848.96,7.318275,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,,0 4,1,100,1,2,129470,1,7848.96,8.318275,0,16,1,13.60915,0,0,0,0,13.60915,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,2.610742,1 4,1,100,1,3,129470,1,7848.96,9.318275,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,,0 4,1,100,1,4,129470,1,7848.96,10.31828,0,16,1,17.89812,0,0,0,0,17.89812,0,0,0,1,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,2.884696,1 4,1,100,1,5,129470,1,7848.96,11.31828,0,16,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,1,0,0,1,0,0,82.78881,8.968264,1.386294,,0 6,1,25,0,1,129474,0,10085.61,1.708419,0,14,1,48.78049,6.472338,0,0,0,55.25283,0,0,0,10,0,4,74.36826,13.73189,0,,1000,1000,1,0,1.386294,6.907755,0,3.258096,8.294049,0,0,0,75.35837,9.218964,1.386294,4.011919,1 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5,1,25,0,4,129500,0,13740.69,33.81177,0,12,1,22.48738,0,0,0,0,22.48738,0,0,0,2,0,3,77.5,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,66.10543,9.52819,1.098612,3.112954,1 5,1,25,0,5,129500,0,13740.69,34.81177,0,12,1,15.14514,5.961296,0,0,0,21.10644,0,0,0,2,0,3,77.5,13.73189,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,66.10543,9.52819,1.098612,3.049578,1 11,1,0,0,1,129521,0,10726.43,30.55441,0,19,1,121.6538,141.743,29.2207,0,0,292.6175,0,0,0,8,0,3,83.8,13.73189,1,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,75.44648,9.280559,1.098612,5.678866,1 11,1,0,0,2,129521,0,10726.43,31.55441,0,19,1,76.4834,78.68808,0,0,0,155.1715,0,0,0,2,0,3,83.8,13.73189,1,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,75.44648,9.280559,1.098612,5.044531,1 11,1,0,0,3,129521,0,10726.43,32.55442,0,19,1,77.7998,150.7929,0,0,0,228.5927,0,0,0,6,0,3,83.8,13.73189,1,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,75.44648,9.280559,1.098612,5.431942,1 11,1,0,0,4,129521,0,10726.43,33.55442,0,19,1,39.46765,137.6549,96.37448,0,0,273.497,0,0,0,4,0,4,83.8,13.73189,1,,0,101,0,0,1.386294,4.61512,0,0,0,0,0,0,75.44648,9.280559,1.386294,5.61129,1 11,1,0,0,5,129521,0,10726.43,34.55442,0,19,1,62.68406,185.7804,32.5873,0,0,281.0518,0,0,0,3,0,4,83.8,13.73189,1,,0,101,0,0,1.386294,4.61512,0,0,0,0,0,0,75.44648,9.280559,1.386294,5.638539,1 11,1,0,0,1,129522,0,10726.43,27.24162,1,13,1,201.6657,25.49673,0,0,0,227.1624,0,0,0,7,0,3,56.3,13.73189,0,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,77.56129,9.280559,1.098612,5.425665,1 11,1,0,0,2,129522,0,10726.43,28.24162,1,13,1,320.577,8.747958,0,0,0,329.325,0,0,0,6,3,3,56.3,13.73189,0,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,77.56129,9.280559,1.098612,5.797045,1 11,1,0,0,3,129522,0,10726.43,29.24162,1,13,1,40.95639,17.46779,2.973241,0,955.8077,1017.205,1,0,0,0,0,3,56.3,13.73189,0,,0,101,0,0,1.098612,4.61512,0,0,0,0,0,0,77.56129,9.280559,1.098612,6.924814,1 11,1,0,0,4,129522,0,10726.43,30.24162,1,13,1,13.76778,6.815053,0,0,0,20.58284,0,0,0,0,0,4,56.3,13.73189,0,,0,101,0,0,1.386294,4.61512,0,0,0,0,0,0,77.56129,9.280559,1.386294,3.024457,1 11,1,0,0,5,129522,0,10726.43,31.24162,1,13,1,426.2726,29.44888,0,0,2364.295,2820.017,2,0,0,10,0,4,56.3,13.73189,0,,0,101,0,0,1.386294,4.61512,0,0,0,0,0,0,77.56129,9.280559,1.386294,7.944498,1 11,1,0,0,1,129523,0,10726.43,3.789186,1,13,1,58.89352,39.68471,0,0,0,98.57822,0,0,0,9,0,3,74.36826,13.73189,1,,0,101,1,1,1.098612,4.61512,0,0,0,0,0,0,80.52754,9.280559,1.098612,4.59085,1 11,1,0,0,2,129523,0,10726.43,4.789186,1,13,1,54.70876,21.38269,0,0,0,76.09145,0,0,0,6,0,3,74.36826,13.73189,1,,0,101,1,1,1.098612,4.61512,0,0,0,0,0,0,80.52754,9.280559,1.098612,4.331936,1 11,1,0,0,3,129523,0,10726.43,5.789186,1,13,1,110.5104,24.52924,0,0,0,135.0396,0,0,0,9,0,3,74.36826,13.73189,1,,0,101,1,1,1.098612,4.61512,0,0,0,0,0,0,80.52754,9.280559,1.098612,4.905569,1 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8,1,50,1,1,129525,0,4029.156,28.14237,1,10,1,46.52533,11.96113,0,0,0,58.48645,0,0,0,2,1,1,69.5,13,0,,200,219,0,0,0,5.389072,0,3.931826,5.991465,0,0,0,75.91062,8.30156,0,4.068795,1 8,1,50,1,2,129525,0,4029.156,29.14237,1,10,1,17.52022,1.590297,0,0,0,19.11051,0,0,0,1,0,1,69.5,13,0,,200,219,0,0,0,5.389072,0,3.931826,5.991465,0,0,0,75.91062,8.30156,0,2.950238,1 8,1,50,1,3,129525,0,4029.156,30.14237,1,10,1,12.53071,0,0,0,0,12.53071,0,0,0,1,0,1,69.5,13,0,,200,219,0,0,0,5.389072,0,3.931826,5.991465,0,0,0,75.91062,8.30156,0,2.528183,1 11,1,0,1,1,129533,0,5911.911,25.68378,0,13,1,56.09756,24.71743,0,0,0,80.81499,0,0,0,7,2,3,67.4,0,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,68.15562,8.684894,1.098612,4.392162,1 11,1,0,1,2,129533,0,5911.911,26.68378,0,13,1,13.3914,12.24823,0,0,0,25.63963,0,0,0,2,0,3,67.4,0,0,,0,0,0,0,1.098612,0,0,0,0,1,0,0,68.15562,8.684894,1.098612,3.244139,1 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11,1,0,0,3,129590,0,6960.298,4.247776,0,12,1,261.8673,91.94595,0,0,0,353.8133,0,0,0,41,1,3,74.36826,13.73189,0,,0,120,1,0,1.098612,4.787492,0,0,0,0,0,0,74.35735,8.848122,1.098612,5.868769,1 11,1,0,0,1,129591,0,6960.298,25.54689,0,12,1,20.02356,6.289752,38.15665,0,0,64.46996,0,0,0,2,1,3,67.5,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,0,0,0,76.87543,8.848122,1.098612,4.166199,1 11,1,0,0,2,129591,0,6960.298,26.54689,0,12,1,28.03234,11.51482,20.28032,0,0,59.82749,0,0,0,3,1,3,67.5,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,0,0,0,76.87543,8.848122,1.098612,4.091465,1 11,1,0,0,3,129591,0,6960.298,27.54689,0,12,1,27.02703,10.32924,34.11302,0,0,71.46928,0,0,0,3,1,3,67.5,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,0,0,0,76.87543,8.848122,1.098612,4.269268,1 11,1,0,0,1,129592,0,6960.298,24.89254,1,12,1,104.2403,10.76561,1.766784,0,0,116.7727,0,0,0,7,1,3,73.8,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,1,0,0,66.00516,8.848122,1.098612,4.760229,1 11,1,0,0,2,129592,0,6960.298,25.89254,1,12,1,86.79245,34.80323,0,0,0,121.5957,0,0,0,10,0,3,73.8,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,1,0,0,66.00516,8.848122,1.098612,4.800702,1 11,1,0,0,3,129592,0,6960.298,26.89254,1,12,1,77.64128,43.24324,0,0,0,120.8845,0,0,0,10,1,3,73.8,13.73189,0,,0,120,0,0,1.098612,4.787492,0,0,0,1,0,0,66.00516,8.848122,1.098612,4.794836,1 1,1,0,0,1,129681,0,9542.002,61.9384,1,4,1,63.01531,91.47232,41.84924,0,409.9293,606.2662,1,0,0,7,0,3,68.8,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,40.99886,9.163564,1.098612,6.407319,1 1,1,0,0,2,129681,0,9542.002,62.9384,1,4,1,51.21294,80.469,0,0,0,131.6819,0,0,0,6,0,3,68.8,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,40.99886,9.163564,1.098612,4.88039,1 1,1,0,0,3,129681,0,9542.002,63.9384,1,4,1,387.3956,172.4619,0,0,15062.12,15621.98,3,0,0,11,2,3,68.8,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,0,1,40.99886,9.163564,1.098612,9.656434,1 1,1,0,0,1,129682,0,9542.002,14.48597,1,4,1,71.26031,12.50294,0,0,0,83.76325,0,0,0,4,0,3,52.5,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,1,0,0,63.93338,9.163564,1.098612,4.427994,1 1,1,0,0,2,129682,0,9542.002,15.48597,1,4,1,53.47709,27.09434,29.85984,0,1364.836,1475.267,2,0,0,7,0,3,52.5,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,1,0,0,63.93338,9.163564,1.098612,7.296594,1 1,1,0,0,3,129682,0,9542.002,16.48597,1,4,1,49.45946,18.36364,0,0,0,67.8231,0,0,0,6,1,3,52.5,13.73189,0,,450,0,1,1,1.098612,0,1,0,0,1,0,0,63.93338,9.163564,1.098612,4.216903,1 1,1,0,0,1,129683,0,9542.002,61.5332,0,4,1,19.43463,11.1543,41.84924,0,0,72.43816,0,0,0,2,0,3,35,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,1,0,55.00333,9.163564,1.098612,4.282733,1 1,1,0,0,2,129683,0,9542.002,62.5332,0,4,1,8.625337,13.19137,0,0,0,21.81671,0,0,0,2,0,3,35,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,1,0,55.00333,9.163564,1.098612,3.082676,1 1,1,0,0,3,129683,0,9542.002,63.5332,0,4,1,134.8894,23.32187,0,0,0,158.2113,0,0,0,4,0,3,35,13.73189,1,,450,0,0,0,1.098612,0,1,0,0,0,1,0,55.00333,9.163564,1.098612,5.063931,1 10,1,50,1,1,129684,0,6771.712,4.120465,1,13,1,45.50863,3.491969,0,0,0,49.0006,0,0,0,2,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,3.891832,1 10,1,50,1,2,129684,0,6771.712,5.120465,1,13,1,39.7387,3.739793,0,0,0,43.4785,0,0,0,4,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,3.772266,1 10,1,50,1,3,129684,0,6771.712,6.120465,1,13,1,9.910803,0,23.20119,0,0,33.11199,0,0,0,0,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,3.499896,1 10,1,50,1,1,129685,0,6771.712,5.207392,1,13,1,22.60559,10.5235,0,0,0,33.12909,0,0,0,2,0,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,3.500412,1 10,1,50,1,2,129685,0,6771.712,6.207392,1,13,1,49.53729,1.62221,20.37017,0,0,71.52967,0,0,0,1,1,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,4.270113,1 10,1,50,1,3,129685,0,6771.712,7.207392,1,13,1,27.25471,2.477701,36.43211,0,0,66.16452,0,0,0,1,2,4,74.36826,13.73189,0,,1000,1000,1,1,1.386294,6.907755,0,3.931826,7.600903,0,0,0,79.02408,8.820657,1.386294,4.192144,1 10,1,50,1,1,129686,0,6771.712,24.282,1,13,1,39.26234,13.38489,0,0,0,52.64723,0,0,0,3,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,67.72769,8.820657,1.386294,3.963614,1 10,1,50,1,2,129686,0,6771.712,25.282,1,13,1,95.80838,29.86391,0,0,0,125.6723,0,0,0,6,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,67.72769,8.820657,1.386294,4.833678,1 10,1,50,1,3,129686,0,6771.712,26.282,1,13,1,164.668,36.86819,13.46383,0,0,215,0,0,0,13,0,4,78.9,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,67.72769,8.820657,1.386294,5.370638,1 10,1,50,1,1,129687,0,6771.712,26.52704,0,14,1,0,0,0,0,0,0,0,0,0,0,0,4,76.8,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.59797,8.820657,1.386294,,0 10,1,50,1,2,129687,0,6771.712,27.52704,0,14,1,0,0,0,0,0,0,0,0,0,0,0,4,76.8,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.59797,8.820657,1.386294,,0 10,1,50,1,3,129687,0,6771.712,28.52704,0,14,1,0,0,0,0,0,0,0,0,0,0,0,4,76.8,0,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.59797,8.820657,1.386294,,0 10,1,50,0,1,129693,0,4394.988,27.54552,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,1,0,61.37853,8.388448,1.386294,,0 10,1,50,0,2,129693,0,4394.988,28.54552,0,10,1,5.390836,0,0,0,453.6604,459.0512,1,0,0,1,0,4,78.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,1,0,61.37853,8.388448,1.386294,6.129162,1 10,1,50,0,3,129693,0,4394.988,29.54552,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,1,0,61.37853,8.388448,1.386294,,0 10,1,50,0,4,129693,0,4394.988,30.54552,0,10,1,53.6691,0,6.836828,0,0,60.50592,0,0,0,2,0,4,78.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,1,0,61.37853,8.388448,1.386294,4.102741,1 10,1,50,0,5,129693,0,4394.988,31.54552,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,78.9,4.3,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,0,1,0,61.37853,8.388448,1.386294,,0 10,1,50,0,1,129694,0,4394.988,25.69199,1,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,68.15965,8.388448,1.386294,,0 10,1,50,0,2,129694,0,4394.988,26.69199,1,11,1,0,0,7.719676,0,0,7.719676,0,0,0,0,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,68.15965,8.388448,1.386294,2.043772,1 10,1,50,0,3,129694,0,4394.988,27.69199,1,11,1,0,5.208845,0,0,0,5.208845,0,0,0,0,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,68.15965,8.388448,1.386294,1.650358,1 10,1,50,0,4,129694,0,4394.988,28.69199,1,11,1,68.82407,11.54512,0,0,0,80.36919,0,0,0,6,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,68.15965,8.388448,1.386294,4.386631,1 10,1,50,0,5,129694,0,4394.988,29.69199,1,11,1,65.23551,0,0,0,0,65.23551,0,0,0,2,0,4,74.7,17.4,0,,1000,1000,0,0,1.386294,6.907755,0,3.931826,7.600903,1,0,0,68.15965,8.388448,1.386294,4.178004,1 10,1,50,0,1,129695,0,4394.988,4.676249,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.0327869,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.88596,8.388448,1.386294,,0 10,1,50,0,2,129695,0,4394.988,5.676249,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.0327869,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.88596,8.388448,1.386294,,0 10,1,50,0,3,129695,0,4394.988,6.676249,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.0327869,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.88596,8.388448,1.386294,,0 10,1,50,0,4,129695,0,4394.988,7.676249,0,11,1,5.925251,1.937101,0,0,0,7.862352,0,0,0,1,0,4,74.36826,13.73189,.0327869,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.88596,8.388448,1.386294,2.062086,1 10,1,50,0,5,129695,0,4394.988,8.67625,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,74.36826,13.73189,.0327869,,1000,1000,1,0,1.386294,6.907755,0,3.931826,7.600903,0,0,0,78.88596,8.388448,1.386294,,0 2,1,100,0,1,129696,0,7392.06,21.99589,0,12,1,5.94884,0,0,0,0,5.94884,0,0,0,1,0,3,71.6,8.7,0,,599,599,0,0,1.098612,6.395262,1,0,0,1,0,0,68.6146,8.908297,1.098612,1.783196,1 2,1,100,0,2,129696,0,7392.06,22.99589,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,71.6,8.7,0,,599,599,0,0,1.098612,6.395262,1,0,0,1,0,0,68.6146,8.908297,1.098612,,0 2,1,100,0,3,129696,0,7392.06,23.99589,0,12,1,9.910803,0,0,0,0,9.910803,0,0,0,2,0,3,71.6,8.7,0,,599,599,0,0,1.098612,6.395262,1,0,0,1,0,0,68.6146,8.908297,1.098612,2.293625,1 2,1,100,0,1,129697,0,7392.06,20.45996,1,11,1,5.94884,0,0,0,0,5.94884,0,0,0,1,0,3,31.6,17.4,1,,599,599,0,0,1.098612,6.395262,1,0,0,0,1,0,54.00756,8.908297,1.098612,1.783196,1 2,1,100,0,2,129697,0,7392.06,21.45996,1,11,1,40.82743,11.5841,0,0,0,52.41154,0,0,0,0,0,3,31.6,17.4,1,,599,599,0,0,1.098612,6.395262,1,0,0,0,1,0,54.00756,8.908297,1.098612,3.959127,1 2,1,100,0,3,129697,0,7392.06,22.45996,1,11,1,15.36174,15.66898,0,0,762.1853,793.2161,1,0,0,2,0,3,31.6,17.4,1,,599,599,0,0,1.098612,6.395262,1,0,0,0,1,0,54.00756,8.908297,1.098612,6.676095,1 2,1,100,0,1,129698,0,7392.06,.991102,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,599,599,1,1,1.098612,6.395262,1,0,0,1,0,0,69.01308,8.908297,1.098612,,0 2,1,100,0,2,129698,0,7392.06,1.991102,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,599,599,1,1,1.098612,6.395262,1,0,0,1,0,0,69.01308,8.908297,1.098612,,0 2,1,100,0,3,129698,0,7392.06,2.991102,1,11,1,16.35283,0,0,0,0,16.35283,0,0,0,2,0,3,74.36826,13.73189,0,,599,599,1,1,1.098612,6.395262,1,0,0,1,0,0,69.01308,8.908297,1.098612,2.794401,1 3,1,100,1,1,129699,0,4174.593,18.51061,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,17.4,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,63.95783,8.337011,1.386294,,0 3,1,100,1,2,129699,0,4174.593,19.51061,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,17.4,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,63.95783,8.337011,1.386294,,0 3,1,100,1,3,129699,0,4174.593,20.51061,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,17.4,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,63.95783,8.337011,1.386294,,0 3,1,100,1,4,129699,0,4174.593,21.51061,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,73.7,17.4,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,63.95783,8.337011,1.386294,,0 3,1,100,1,1,129700,0,4174.593,16.45448,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,8.7,0,,513,0,1,1,1.386294,0,1,0,0,1,0,0,60.48014,8.337011,1.386294,,0 3,1,100,1,2,129700,0,4174.593,17.45448,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,8.7,0,,513,0,1,1,1.386294,0,1,0,0,1,0,0,60.48014,8.337011,1.386294,,0 3,1,100,1,3,129700,0,4174.593,18.45448,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,8.7,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,60.48014,8.337011,1.386294,,0 3,1,100,1,4,129700,0,4174.593,19.45448,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,61.1,8.7,0,,513,0,0,0,1.386294,0,1,0,0,1,0,0,60.48014,8.337011,1.386294,,0 3,1,100,1,5,129700,0,4174.593,20.45448,1,9,1,0,0,0,0,0,0,0,0,0,0,0,3,61.1,8.7,0,,513,0,0,0,1.098612,0,1,0,0,1,0,0,60.48014,8.337011,1.098612,,0 3,1,100,1,1,129701,0,4174.593,51.77823,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,42.1,34.8,0,,513,0,0,0,1.386294,0,1,0,0,0,1,0,48.03789,8.337011,1.386294,,0 3,1,100,1,2,129701,0,4174.593,52.77823,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,42.1,34.8,0,,513,0,0,0,1.386294,0,1,0,0,0,1,0,48.03789,8.337011,1.386294,,0 3,1,100,1,3,129701,0,4174.593,53.77823,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,42.1,34.8,0,,513,0,0,0,1.386294,0,1,0,0,0,1,0,48.03789,8.337011,1.386294,,0 3,1,100,1,4,129701,0,4174.593,54.77823,1,9,1,0,0,0,0,0,0,0,0,0,0,0,4,42.1,34.8,0,,513,0,0,0,1.386294,0,1,0,0,0,1,0,48.03789,8.337011,1.386294,,0 3,1,100,1,5,129701,0,4174.593,55.77823,1,9,1,0,0,0,0,0,0,0,0,0,0,0,3,42.1,34.8,0,,513,0,0,0,1.098612,0,1,0,0,0,1,0,48.03789,8.337011,1.098612,,0 3,1,100,1,1,129702,0,4174.593,55.30732,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,4.3,0,,513,0,0,0,1.386294,0,1,0,0,0,0,0,72.67507,8.337011,1.386294,,0 3,1,100,1,2,129702,0,4174.593,56.30732,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,4.3,0,,513,0,0,0,1.386294,0,1,0,0,0,0,0,72.67507,8.337011,1.386294,,0 3,1,100,1,3,129702,0,4174.593,57.30732,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,4.3,0,,513,0,0,0,1.386294,0,1,0,0,0,0,0,72.67507,8.337011,1.386294,,0 3,1,100,1,4,129702,0,4174.593,58.30732,0,9,1,0,0,0,0,0,0,0,0,0,0,0,4,96.8,4.3,0,,513,0,0,0,1.386294,0,1,0,0,0,0,0,72.67507,8.337011,1.386294,,0 3,1,100,1,5,129702,0,4174.593,59.30732,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,96.8,4.3,0,,513,0,0,0,1.098612,0,1,0,0,0,0,0,72.67507,8.337011,1.098612,,0 4,1,100,0,1,129703,0,9542.002,21.23751,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,87.4,13,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,72.72614,9.163564,1.098612,,0 4,1,100,0,1,129704,0,9542.002,23.34292,0,14,1,0,0,0,0,0,0,0,0,0,0,0,3,76.8,13,0,,1000,1000,0,0,1.098612,6.907755,1,0,0,0,0,0,72.09547,9.163564,1.098612,,0 4,1,100,0,1,129705,0,9542.002,.6652977,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,74.36826,13.73189,0,,1000,1000,1,1,1.098612,6.907755,1,0,0,1,0,0,69.41489,9.163564,1.098612,,0 5,1,25,1,1,129706,0,13354.84,54.72964,1,13,1,191.8917,231.4515,71.64188,0,0,494.9851,0,0,0,7,1,3,86.3,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,66.29183,9.499709,1.098612,6.204528,1 5,1,25,1,2,129706,0,13354.84,55.72964,1,13,1,313.865,226.3963,18.50844,0,0,558.7697,0,0,0,5,21,3,86.3,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,66.29183,9.499709,1.098612,6.325737,1 5,1,25,1,3,129706,0,13354.84,56.72964,1,13,1,183.3499,280.3023,50.54509,0,0,514.1972,0,0,0,4,25,3,86.3,17.4,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,66.29183,9.499709,1.098612,6.242607,1 5,1,25,1,1,129707,0,13354.84,53.22108,0,18,1,67.22189,3.099346,0,0,0,70.32124,0,0,0,2,7,3,71.6,26.1,0,,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,0,0,0,76.58466,9.499709,1.098612,4.253074,1 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19,2,25,0,1,225151,0,8428.739,39.07734,0,12,1,38.10561,4.082744,0,0,0,42.18835,0,0,0,3,0,3,93.1,3.4,0,81.8,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,81.8,9.039521,1.098612,3.742144,1 19,2,25,0,2,225151,0,8428.739,40.07734,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,93.1,3.4,0,81.8,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,81.8,9.039521,1.098612,,0 19,2,25,0,3,225151,0,8428.739,41.07734,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,93.1,3.4,0,81.8,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,81.8,9.039521,1.098612,,0 19,2,25,0,1,225152,0,8428.739,38.10267,1,12,1,19.86935,1.829069,0,0,0,21.69842,0,0,0,1,0,3,85.6,10.3,0,67,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,67,9.039521,1.098612,3.07724,1 19,2,25,0,2,225152,0,8428.739,39.10267,1,12,1,67.889,0,0,0,0,67.889,0,0,0,3,0,3,85.6,10.3,0,67,1000,1000,0,0,1.098612,6.907755,0,3.258096,8.294049,1,0,0,67,9.039521,1.098612,4.217874,1 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18,2,25,1,1,226798,0,10598.83,31.01164,0,11,1,8.56531,2.189507,0,0,0,10.75482,0,0,0,1,0,5,79.8,3.4,0,67,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,67,9.268593,1.609438,2.375354,1 18,2,25,1,2,226798,0,10598.83,32.01163,0,11,1,38.5798,0,0,0,0,38.5798,0,0,0,1,0,5,79.8,3.4,0,67,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,67,9.268593,1.609438,3.652729,1 18,2,25,1,3,226798,0,10598.83,33.01163,0,11,1,0,0,0,0,0,0,0,0,0,0,0,5,79.8,3.4,0,67,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,67,9.268593,1.609438,,0 18,2,25,1,1,226799,0,10598.83,10.32991,0,12,1,101.0439,6.466809,0,0,0,107.5107,0,0,0,4,0,5,71.7,11.84267,0,93.3,1000,1076.54,1,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,93.3,9.268593,1.609438,4.67759,1 18,2,25,1,2,226799,0,10598.83,11.32991,0,12,1,34.65105,0,35.27086,0,0,69.92191,0,0,0,2,0,5,71.7,11.84267,0,93.3,1000,1076.54,1,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,93.3,9.268593,1.609438,4.247379,1 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18,2,25,1,1,226801,0,10598.83,7.540041,0,12,1,10.70664,0,0,0,0,10.70664,0,0,0,1,0,5,88.3,11.84267,0,96.3,1000,1076.54,1,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,96.3,9.268593,1.609438,2.370864,1 18,2,25,1,2,226801,0,10598.83,8.540041,0,12,1,41.50805,0,0,0,0,41.50805,0,0,0,2,0,5,88.3,11.84267,0,96.3,1000,1076.54,1,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,96.3,9.268593,1.609438,3.725888,1 18,2,25,1,3,226801,0,10598.83,9.540041,0,12,1,261.2692,0,0,0,0,261.2692,0,0,0,3,0,5,88.3,11.84267,0,96.3,1000,1076.54,1,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,96.3,9.268593,1.609438,5.565551,1 18,2,25,1,1,226802,0,10598.83,29.10335,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,72.3,10.3,1,77.3,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,77.3,9.268593,1.609438,,0 18,2,25,1,2,226802,0,10598.83,30.10335,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,72.3,10.3,1,77.3,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,77.3,9.268593,1.609438,,0 18,2,25,1,3,226802,0,10598.83,31.10335,1,12,1,55.78139,0,31.44535,0,0,87.22674,0,0,0,2,0,5,72.3,10.3,1,77.3,1000,1076.54,0,0,1.609438,6.981507,0,3.258096,8.294049,1,0,0,77.3,9.268593,1.609438,4.468511,1 11,2,0,1,1,226825,0,4219.355,58.09446,0,8,1,244.0445,8.125993,1.810482,0,1778.481,2032.462,2,0,0,10,0,1,48.9,6.9,1,73.9,0,0,0,0,0,0,0,0,0,0,0,0,73.9,8.347674,0,7.617003,1 11,2,0,1,2,226825,0,4219.355,59.09446,0,8,1,42.51931,1.954633,591.4913,0,0,635.9653,0,0,0,4,1,1,48.9,6.9,1,73.9,0,0,0,0,0,0,0,0,0,0,0,0,73.9,8.347674,0,6.455144,1 11,2,0,1,3,226825,0,4219.355,60.09446,0,8,1,0,17.73921,14.0721,0,0,31.8113,0,0,0,0,0,1,48.9,6.9,1,73.9,0,0,0,0,0,0,0,0,0,0,0,0,73.9,8.347674,0,3.459822,1 6,2,25,0,1,226831,0,12080.35,25.4319,0,14,1,53.84213,0,0,0,0,53.84213,0,0,0,2,0,1,86.2,6.9,0,77.3,1000,1000,0,0,0,6.907755,0,3.258096,8.294049,0,0,0,77.3,9.399418,0,3.986056,1 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18,2,25,1,1,226835,1,222.8739,2.069815,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,81.35272,11.84267,0,66.7,416.4,416.4,1,0,1.609438,6.031646,0,3.258096,7.417941,0,1,0,66.7,5.411083,1.609438,,0 18,2,25,1,2,226835,1,222.8739,3.069815,0,12,1,35.81081,1.858108,0,0,0,37.66892,0,0,0,2,0,5,81.35272,11.84267,0,66.7,416.4,416.4,1,0,1.609438,6.031646,0,3.258096,7.417941,0,1,0,66.7,5.411083,1.609438,3.628835,1 18,2,25,1,3,226835,1,222.8739,4.069815,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,81.35272,11.84267,0,66.7,416.4,416.4,1,0,1.609438,6.031646,0,3.258096,7.417941,0,1,0,66.7,5.411083,1.609438,,0 18,2,25,1,1,226836,1,222.8739,14.22313,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,64.4,0,0,65.9,416.4,416.4,1,0,1.609438,6.031646,0,3.258096,7.417941,0,1,0,65.9,5.411083,1.609438,,0 18,2,25,1,2,226836,1,222.8739,15.22313,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,64.4,0,0,65.9,416.4,416.4,1,0,1.609438,6.031646,0,3.258096,7.417941,0,1,0,65.9,5.411083,1.609438,,0 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11,3,0,0,2,327742,0,14627.57,13.96099,1,14,1,0,0,0,0,0,0,0,0,0,0,0,5,70,9.967326,0,18.5,0,521.28,1,1,1.609438,6.256287,0,0,0,0,1,0,18.5,9.590732,1.609438,,0 11,3,0,0,3,327742,0,14627.57,14.96099,1,14,1,37.40327,24.07567,0,0,0,61.47894,0,0,0,4,0,5,70,9.967326,0,18.5,0,521.28,1,1,1.609438,6.256287,0,0,0,0,1,0,18.5,9.590732,1.609438,4.118695,1 11,3,0,0,1,327743,0,14627.57,11.87406,0,14,1,33.70787,0,0,0,0,33.70787,0,0,0,2,0,5,48.3,9.967326,0,63,0,521.28,1,0,1.609438,6.256287,0,0,0,1,0,0,63,9.590732,1.609438,3.517731,1 11,3,0,0,2,327743,0,14627.57,12.87406,0,14,1,10.78294,0,0,0,0,10.78294,0,0,0,2,0,5,48.3,9.967326,0,63,0,521.28,1,0,1.609438,6.256287,0,0,0,1,0,0,63,9.590732,1.609438,2.377965,1 11,3,0,0,3,327743,0,14627.57,13.87406,0,14,1,236.9046,29.77214,0,0,0,266.6767,0,0,0,5,0,5,48.3,9.967326,0,63,0,521.28,1,0,1.609438,6.256287,0,0,0,1,0,0,63,9.590732,1.609438,5.586037,1 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13,3,0,0,2,327892,0,9591.202,13.05202,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,88.3,9.967326,0,85.2,450,450,1,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,85.2,9.168706,1.098612,,0 13,3,0,0,3,327892,0,9591.202,14.05202,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,88.3,9.967326,0,85.2,450,450,1,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,85.2,9.168706,1.098612,,0 13,3,0,0,1,327893,0,9591.202,48.91171,0,11,1,0,0,0,0,0,0,0,0,0,0,0,4,81.4,0,0,86.4,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,0,0,0,86.4,9.168706,1.386294,,0 13,3,0,0,2,327893,0,9591.202,49.91171,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,81.4,0,0,86.4,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,86.4,9.168706,1.098612,,0 13,3,0,0,3,327893,0,9591.202,50.91171,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,81.4,0,0,86.4,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,86.4,9.168706,1.098612,,0 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5,5,25,0,1,525405,1,5774.706,3.578371,1,12,1,58.3157,0,0,0,808.7177,867.0334,1,0,0,0,0,5,77.40034,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,6.765078,1 5,5,25,0,2,525405,1,5774.706,4.578371,1,12,1,7.593014,0,0,0,0,7.593014,0,0,0,0,0,5,77.40034,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,2.027229,1 5,5,25,0,3,525405,1,5774.706,5.578371,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,77.40034,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,,0 5,5,25,0,1,525406,1,5774.706,28.00548,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,81.4,0,0,81.8,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,0,1,0,81.8,8.661416,1.609438,,0 5,5,25,0,2,525406,1,5774.706,29.00548,0,12,1,3.796507,0,0,0,0,3.796507,0,0,0,1,0,5,81.4,0,0,81.8,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,0,1,0,81.8,8.661416,1.609438,1.334081,1 5,5,25,0,3,525406,1,5774.706,30.00548,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,81.4,0,0,81.8,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,0,1,0,81.8,8.661416,1.609438,,0 5,5,25,0,1,525407,1,5774.706,25.17728,1,12,1,11.00296,3.554803,0,0,0,14.55777,0,0,0,0,0,5,77.7,3.4,0,69.3,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,1,0,0,69.3,8.661416,1.609438,2.678125,1 5,5,25,0,2,525407,1,5774.706,26.17728,1,12,1,0,0,0,0,326.4996,326.4996,1,0,0,0,0,5,77.7,3.4,0,69.3,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,1,0,0,69.3,8.661416,1.609438,5.788429,1 5,5,25,0,3,525407,1,5774.706,27.17728,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,77.7,3.4,0,69.3,80.25,646.36,0,0,1.609438,6.471356,0,3.258096,5.771441,1,0,0,69.3,8.661416,1.609438,,0 5,5,25,0,1,525408,1,5774.706,5.812457,1,12,1,15.42531,0,0,0,0,15.42531,0,0,0,0,0,5,86.7,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,2.73601,1 5,5,25,0,2,525408,1,5774.706,6.812457,1,12,1,0,0,0,0,0,0,0,0,0,0,0,5,86.7,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,,0 5,5,25,0,3,525408,1,5774.706,7.812457,1,12,1,13.84083,0,0,0,0,13.84083,0,0,0,0,0,5,86.7,10.57626,0,88.9,80.25,646.36,1,1,1.609438,6.471356,0,3.258096,5.771441,1,0,0,88.9,8.661416,1.609438,2.627623,1 13,5,0,0,1,525423,1,9648.745,16.99384,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,82.4,0,0,81.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,81.8,9.174686,1.791759,,0 13,5,0,0,2,525423,1,9648.745,17.99384,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,82.4,0,0,81.8,450,450,1,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,81.8,9.174686,1.609438,,0 13,5,0,0,3,525423,1,9648.745,18.99384,0,12,1,0,1.35506,0,0,0,1.35506,0,0,0,0,0,5,82.4,0,0,81.8,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,81.8,9.174686,1.609438,.3038457,1 13,5,0,0,1,525424,1,9648.745,20.0794,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,93.1,3.4,0,76.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,76.1,9.174686,1.791759,,0 13,5,0,0,2,525424,1,9648.745,21.0794,1,10,1,0,0,0,0,0,0,0,0,0,0,0,5,93.1,3.4,0,76.1,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,76.1,9.174686,1.609438,,0 13,5,0,0,3,525424,1,9648.745,22.0794,1,10,1,0,7.025729,0,0,0,7.025729,0,0,0,0,0,5,93.1,3.4,0,76.1,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,76.1,9.174686,1.609438,1.949579,1 13,5,0,0,1,525425,1,9648.745,13.80972,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,96.7,10.57626,.1442925,88.9,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,88.9,9.174686,1.791759,,0 13,5,0,0,1,525426,1,9648.745,36.27926,1,12,1,6.310475,0,0,0,0,6.310475,0,0,0,0,0,6,77.40034,10.57626,.1442925,,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,70.68995,9.174686,1.791759,1.842211,1 13,5,0,0,2,525426,1,9648.745,37.27926,1,12,1,3.777862,1.511145,0,0,812.9958,818.2849,1,0,0,1,0,5,77.40034,10.57626,.1442925,,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,70.68995,9.174686,1.609438,6.707211,1 13,5,0,0,3,525426,1,9648.745,38.27926,1,12,1,0,4.082333,0,0,0,4.082333,0,0,0,0,0,5,77.40034,10.57626,.1442925,,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,70.68995,9.174686,1.609438,1.406669,1 13,5,0,0,1,525427,1,9648.745,18.3436,0,10.96978,1,0,0,0,0,0,0,0,0,0,0,0,6,90.4,0,0,80.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.791759,,0 13,5,0,0,2,525427,1,9648.745,19.3436,0,10.96978,1,0,0,0,0,0,0,0,0,0,0,0,5,90.4,0,0,80.7,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.609438,,0 13,5,0,0,3,525427,1,9648.745,20.3436,0,10.96978,1,0,0,0,0,0,0,0,0,0,0,0,5,90.4,0,0,80.7,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.609438,,0 13,5,0,0,1,525428,1,9648.745,43.84121,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,94.1,6.9,0,80.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.791759,,0 13,5,0,0,2,525428,1,9648.745,44.84121,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,94.1,6.9,0,80.7,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.609438,,0 13,5,0,0,3,525428,1,9648.745,45.84121,0,12,1,0,0,0,0,0,0,0,0,0,0,0,5,94.1,6.9,0,80.7,450,450,0,0,1.609438,6.109248,1,4.564348,6.160541,0,0,0,80.7,9.174686,1.609438,,0 13,5,0,0,1,525438,0,7237.583,30.45038,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,97.9,3.4,0,71.6,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,1,0,71.6,8.88718,1.098612,,0 13,5,0,0,2,525438,0,7237.583,31.45038,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,97.9,3.4,0,71.6,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,1,0,71.6,8.88718,1.098612,,0 13,5,0,0,3,525438,0,7237.583,32.45037,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,97.9,3.4,0,71.6,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,1,0,71.6,8.88718,1.098612,,0 13,5,0,1,1,525439,0,7237.583,11.00616,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,100,10.57626,0,92.6,450,450,1,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,92.6,8.88718,1.098612,,0 13,5,0,1,2,525439,0,7237.583,12.00616,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,100,10.57626,0,92.6,450,450,1,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,92.6,8.88718,1.098612,,0 13,5,0,1,3,525439,0,7237.583,13.00616,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,100,10.57626,0,92.6,450,450,1,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,92.6,8.88718,1.098612,,0 13,5,0,1,1,525440,0,7237.583,29.96852,1,9,1,0,5.469079,0,0,0,5.469079,0,0,0,0,0,3,96.3,3.4,0,77.4,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,77.4,8.88718,1.098612,1.69911,1 13,5,0,1,2,525440,0,7237.583,30.96852,1,9,1,0,0,0,0,0,0,0,0,0,0,0,3,96.3,3.4,0,77.4,450,450,0,0,1.098612,6.109248,1,4.564348,6.160541,0,0,0,77.4,8.88718,1.098612,,0 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18,5,25,0,3,525529,0,6674.895,41.48802,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,90.4,10.3,0,96.6,750,750,0,0,1.098612,6.620073,0,3.258096,8.006368,1,0,0,96.6,8.806258,1.098612,,0 18,5,25,0,1,525531,0,6674.895,40.53114,0,10,1,41.39651,0,0,0,0,41.39651,0,0,0,0,2,3,85.1,17.2,0,60.2,750,750,0,0,1.098612,6.620073,0,3.258096,8.006368,1,0,0,60.2,8.806258,1.098612,3.723197,1 18,5,25,0,2,525531,0,6674.895,41.53114,0,10,1,146.1503,0,0,0,0,146.1503,0,0,0,4,0,3,85.1,17.2,0,60.2,750,750,0,0,1.098612,6.620073,0,3.258096,8.006368,1,0,0,60.2,8.806258,1.098612,4.984635,1 18,5,25,0,3,525531,0,6674.895,42.53114,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,85.1,17.2,0,60.2,750,750,0,0,1.098612,6.620073,0,3.258096,8.006368,1,0,0,60.2,8.806258,1.098612,,0 18,5,25,0,1,525532,0,6674.895,16.89528,0,11,1,45.88529,0,0,0,0,45.88529,0,0,0,2,3,3,55.9,3.4,0,61.4,750,750,1,0,1.098612,6.620073,0,3.258096,8.006368,0,0,0,61.4,8.806258,1.098612,3.826144,1 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16,5,95,1,1,525534,1,1451.101,9.607119,0,11,1,0,0,0,0,702.0736,702.0736,1,0,0,0,0,5,98.3,10.57626,0,81.5,510.6,510.6,1,0,1.609438,6.235587,0,4.564348,6.28688,0,0,0,81.5,7.280766,1.609438,6.554038,1 16,5,95,1,2,525534,1,1451.101,10.60712,0,11,1,0,0,0,0,258.1625,258.1625,1,1,0,0,0,5,98.3,10.57626,0,81.5,510.6,510.6,1,0,1.609438,6.235587,0,4.564348,6.28688,0,0,0,81.5,7.280766,1.609438,5.553589,1 16,5,95,1,3,525534,1,1451.101,11.60712,0,11,1,93.07958,41.19723,0,0,996.8858,1131.163,1,0,0,6,0,5,98.3,10.57626,0,81.5,510.6,510.6,1,0,1.609438,6.235587,0,4.564348,6.28688,0,0,0,81.5,7.280766,1.609438,7.031001,1 16,5,95,1,1,525535,1,1451.101,18.88022,1,10.96978,1,0,0,0,0,0,0,0,0,0,0,0,5,70.2,0,0,61.4,510.6,510.6,0,0,1.609438,6.235587,0,4.564348,6.28688,0,0,0,61.4,7.280766,1.609438,,0 16,5,95,1,2,525535,1,1451.101,19.88022,1,10.96978,1,8.352316,0,0,0,0,8.352316,0,0,0,1,0,5,70.2,0,0,61.4,510.6,510.6,0,0,1.609438,6.235587,0,4.564348,6.28688,0,0,0,61.4,7.280766,1.609438,2.122539,1 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14,5,95,1,2,525553,1,3421.403,28.36756,1,12,1,0,4.652672,0,0,0,4.652672,0,0,0,0,0,3,26.1,6.9,1,34.1,142.55,142.55,0,0,1.098612,4.959693,0,4.564348,5.010986,1,0,0,34.1,8.138098,1.098612,1.537442,1 14,5,95,1,3,525553,1,3421.403,29.36756,1,12,1,0,0,0,0,453.6585,453.6585,1,0,0,0,0,3,26.1,6.9,1,34.1,142.55,142.55,0,0,1.098612,4.959693,0,4.564348,5.010986,1,0,0,34.1,8.138098,1.098612,6.117345,1 14,5,95,1,1,525554,1,3421.403,26.67488,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,63.8,13.8,1,52.3,142.55,142.55,0,0,1.098612,4.959693,0,4.564348,5.010986,0,0,0,52.3,8.138098,1.098612,,0 14,5,95,1,2,525554,1,3421.403,27.67488,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,63.8,13.8,1,52.3,142.55,142.55,0,0,1.098612,4.959693,0,4.564348,5.010986,0,0,0,52.3,8.138098,1.098612,,0 14,5,95,1,3,525554,1,3421.403,28.67488,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,63.8,13.8,1,52.3,142.55,142.55,0,0,1.098612,4.959693,0,4.564348,5.010986,0,0,0,52.3,8.138098,1.098612,,0 11,5,0,1,1,525555,1,769.5853,33.47844,1,12,1,8.081667,0,0,0,0,8.081667,0,0,0,0,0,6,63.3,10.3,0,83,0,0,0,0,1.791759,0,0,0,0,0,0,0,83,6.647151,1.791759,2.089598,1 11,5,0,1,2,525555,1,769.5853,34.47844,1,12,1,15.64886,.9770992,0,0,0,16.62595,0,0,0,2,0,6,63.3,10.3,0,83,0,0,0,0,1.791759,0,0,0,0,0,0,0,83,6.647151,1.791759,2.810965,1 11,5,0,1,3,525555,1,769.5853,35.47844,1,12,1,54.3554,10.79094,0,0,0,65.14634,0,0,0,4,0,6,63.3,10.3,0,83,0,0,0,0,1.791759,0,0,0,0,0,0,0,83,6.647151,1.791759,4.176636,1 11,5,0,1,1,525556,1,769.5853,12.44353,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,75,10.57626,0,77.8,0,0,1,0,1.791759,0,0,0,0,0,0,0,77.8,6.647151,1.791759,,0 11,5,0,1,2,525556,1,769.5853,13.44353,0,12,1,3.816794,0,0,0,0,3.816794,0,0,0,1,0,6,75,10.57626,0,77.8,0,0,1,0,1.791759,0,0,0,0,0,0,0,77.8,6.647151,1.791759,1.339411,1 11,5,0,1,3,525556,1,769.5853,14.44353,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,75,10.57626,0,77.8,0,0,1,0,1.791759,0,0,0,0,0,0,0,77.8,6.647151,1.791759,,0 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11,5,0,0,1,525586,0,4301.075,8.147844,0,8,1,7.993269,0,0,0,0,7.993269,0,0,0,0,0,4,85,10.57626,.1442925,74.1,0,147.36,1,0,1.386294,4.992878,0,0,0,0,0,0,74.1,8.366853,1.386294,2.0786,1 11,5,0,0,2,525586,0,4301.075,9.147844,0,8,1,11.33358,0,0,0,0,11.33358,0,0,0,0,1,4,85,10.57626,.1442925,74.1,0,147.36,1,0,1.386294,4.992878,0,0,0,0,0,0,74.1,8.366853,1.386294,2.42777,1 11,5,0,0,3,525586,0,4301.075,10.14784,0,8,1,0,0,0,0,0,0,0,0,0,0,0,4,85,10.57626,.1442925,74.1,0,147.36,1,0,1.386294,4.992878,0,0,0,0,0,0,74.1,8.366853,1.386294,,0 11,5,0,0,1,525587,0,4301.075,40.20534,0,13,1,0,8.93984,0,0,0,8.93984,0,0,0,0,0,4,64.9,6.9,1,60.2,0,147.36,0,0,1.386294,4.992878,0,0,0,1,0,0,60.2,8.366853,1.386294,2.190518,1 11,5,0,0,2,525587,0,4301.075,41.20534,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,64.9,6.9,1,60.2,0,147.36,0,0,1.386294,4.992878,0,0,0,1,0,0,60.2,8.366853,1.386294,,0 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11,5,0,0,2,525589,0,4301.075,26.8809,1,8,1,11.33358,0,0,0,0,11.33358,0,0,0,0,1,4,78.7,13.8,0,63.1,0,147.36,0,0,1.386294,4.992878,0,0,0,1,0,0,63.1,8.366853,1.386294,2.42777,1 11,5,0,0,3,525589,0,4301.075,27.8809,1,8,1,42.88165,9.73928,0,0,0,52.62093,0,0,0,0,0,4,78.7,13.8,0,63.1,0,147.36,0,0,1.386294,4.992878,0,0,0,1,0,0,63.1,8.366853,1.386294,3.963114,1 16,5,95,0,1,525593,1,6674.895,36.65435,1,9,1,0,0,0,0,0,0,0,0,0,0,0,3,63.3,10.3,0,76.1,176.4,176.4,0,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,76.1,8.806258,1.098612,,0 16,5,95,0,2,525593,1,6674.895,37.65435,1,9,1,11.62791,0,0,0,0,11.62791,0,0,0,0,0,3,63.3,10.3,0,76.1,176.4,176.4,0,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,76.1,8.806258,1.098612,2.453408,1 16,5,95,0,3,525593,1,6674.895,38.65435,1,9,1,0,0,0,0,0,0,0,0,0,0,0,3,63.3,10.3,0,76.1,176.4,176.4,0,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,76.1,8.806258,1.098612,,0 16,5,95,0,1,525595,1,6674.895,14.87748,0,9,1,7.919967,0,0,0,0,7.919967,0,0,0,0,0,3,77.40034,10.57626,.1442925,,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,70.68995,8.806258,1.098612,2.069387,1 16,5,95,0,2,525595,1,6674.895,15.87748,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,77.40034,10.57626,.1442925,,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,70.68995,8.806258,1.098612,,0 16,5,95,0,3,525595,1,6674.895,16.87748,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,77.40034,10.57626,.1442925,,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,1,0,0,70.68995,8.806258,1.098612,,0 16,5,95,0,1,525596,1,6674.895,6.913073,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,88.3,10.57626,.1442925,85.2,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,0,0,0,85.2,8.806258,1.098612,,0 16,5,95,0,2,525596,1,6674.895,7.913073,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,88.3,10.57626,.1442925,85.2,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,0,0,0,85.2,8.806258,1.098612,,0 16,5,95,0,3,525596,1,6674.895,8.913074,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,88.3,10.57626,.1442925,85.2,176.4,176.4,1,0,1.098612,5.172754,0,4.564348,5.224048,0,0,0,85.2,8.806258,1.098612,,0 11,5,0,1,1,525617,1,1426.523,12.2026,0,7,1,10.57977,0,23.69869,0,0,34.27846,0,0,0,0,1,7,90,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,3.534517,1 11,5,0,1,2,525617,1,1426.523,13.2026,0,7,1,0,0,0,0,0,0,0,0,0,0,0,7,90,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,3,525617,1,1426.523,14.2026,0,7,1,0,0,0,0,0,0,0,0,0,0,0,7,90,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,1,525618,1,1426.523,11.28542,0,7,1,0,0,0,0,0,0,0,0,0,0,0,7,95,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,0,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,2,525618,1,1426.523,12.28542,0,7,1,13.09795,0,0,0,0,13.09795,0,0,0,0,0,7,95,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,0,0,0,96.3,7.263696,1.94591,2.572456,1 11,5,0,1,3,525618,1,1426.523,13.28542,0,7,1,62.28374,3.442907,23.87543,0,267.8201,357.4221,1,1,0,3,1,7,95,10.57626,0,96.3,0,0,1,0,1.94591,0,0,0,0,0,0,0,96.3,7.263696,1.94591,5.878918,1 11,5,0,1,1,525619,1,1426.523,20.0794,1,10,1,28.24799,1.815489,23.69869,0,0,53.76217,0,0,0,0,1,7,51.6,6.9,0,71.6,0,0,0,0,1.94591,0,0,0,0,0,0,0,71.6,7.263696,1.94591,3.98457,1 11,5,0,1,2,525619,1,1426.523,21.0794,1,10,1,7.213364,43.92559,0,0,2274.108,2325.247,1,0,0,0,0,7,51.6,6.9,0,71.6,0,0,0,0,1.94591,0,0,0,0,0,0,0,71.6,7.263696,1.94591,7.751582,1 11,5,0,1,3,525619,1,1426.523,22.0794,1,10,1,20.41522,10.34602,21.79931,0,0,52.56055,0,0,0,1,1,7,51.6,6.9,0,71.6,0,0,0,0,1.94591,0,0,0,0,0,0,0,71.6,7.263696,1.94591,3.961966,1 11,5,0,1,1,525620,1,1426.523,35.14305,1,7,1,10.57977,0,23.69869,0,0,34.27846,0,0,0,0,1,7,85.1,6.9,0,81.8,0,0,0,0,1.94591,0,0,0,0,1,0,0,81.8,7.263696,1.94591,3.534517,1 11,5,0,1,2,525620,1,1426.523,36.14305,1,7,1,0,0,0,0,0,0,0,0,0,0,0,7,85.1,6.9,0,81.8,0,0,0,0,1.94591,0,0,0,0,1,0,0,81.8,7.263696,1.94591,,0 11,5,0,1,3,525620,1,1426.523,37.14305,1,7,1,8.650519,0,22.14533,0,0,30.79585,0,0,0,0,1,7,85.1,6.9,0,81.8,0,0,0,0,1.94591,0,0,0,0,1,0,0,81.8,7.263696,1.94591,3.42738,1 11,5,0,1,1,525621,1,1426.523,14.09719,1,7,1,10.57977,0,23.69869,0,0,34.27846,0,0,0,0,1,7,100,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,3.534517,1 11,5,0,1,2,525621,1,1426.523,15.09719,1,7,1,0,0,0,0,0,0,0,0,0,0,0,7,100,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,3,525621,1,1426.523,16.09719,1,7,1,14.87889,8.079585,20.41522,0,0,43.3737,0,0,0,1,1,7,100,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,3.769853,1 11,5,0,1,1,525622,1,1426.523,15.154,0,7,1,0,0,0,0,0,0,0,0,0,0,0,7,97.3,0,0,96.6,0,0,1,0,1.94591,0,0,0,0,1,0,0,96.6,7.263696,1.94591,,0 11,5,0,1,2,525622,1,1426.523,16.15401,0,7,1,0,0,0,0,0,0,0,0,0,0,0,7,97.3,0,0,96.6,0,0,1,0,1.94591,0,0,0,0,1,0,0,96.6,7.263696,1.94591,,0 11,5,0,1,1,525624,1,1426.523,14.09719,1,7,1,0,0,0,0,0,0,0,0,0,0,0,7,85,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,2,525624,1,1426.523,15.09719,1,7,1,0,0,0,0,0,0,0,0,0,0,0,7,85,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,3,525624,1,1426.523,16.09719,1,7,1,0,0,0,0,0,0,0,0,0,0,0,7,85,10.57626,0,96.3,0,0,1,1,1.94591,0,0,0,0,1,0,0,96.3,7.263696,1.94591,,0 11,5,0,1,1,525628,0,8880.185,34.78166,0,12,1,150.6559,39.1113,1.481168,0,0,191.2484,0,0,0,12,0,5,89.4,3.4,0,75,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,75,9.09169,1.609438,5.253573,1 11,5,0,1,2,525628,0,8880.185,35.78166,0,12,1,157.5551,22.35004,0,0,0,179.9051,0,0,0,10,0,5,89.4,3.4,0,75,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,75,9.09169,1.609438,5.19243,1 11,5,0,1,3,525628,0,8880.185,36.78166,0,12,1,87.88927,30.34256,0,0,0,118.2318,0,0,0,10,0,5,89.4,3.4,0,75,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,75,9.09169,1.609438,4.772647,1 11,5,0,1,1,525629,0,8880.185,31.92882,1,12,1,79.55988,48.15912,0,0,872.6196,1000.339,1,0,0,8,0,5,75.5,10.3,1,73.7,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,73.7,9.09169,1.609438,6.908094,1 11,5,0,1,2,525629,0,8880.185,32.92882,1,12,1,17.46393,17.73348,0,0,1247.912,1283.109,2,1,0,2,0,5,75.5,10.3,1,73.7,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,73.7,9.09169,1.609438,7.157042,1 11,5,0,1,3,525629,0,8880.185,33.92882,1,12,1,134.6021,21.53287,0,0,0,156.1349,0,0,0,7,0,5,75.5,10.3,1,73.7,0,646.36,0,0,1.609438,6.471356,0,0,0,0,0,0,73.7,9.09169,1.609438,5.050721,1 11,5,0,1,1,525630,0,8880.185,10.05339,1,12,1,0,2.06094,0,0,0,2.06094,0,0,0,0,0,5,93.3,10.57626,0,92.6,0,646.36,1,1,1.609438,6.471356,0,0,0,0,0,0,92.6,9.09169,1.609438,.723162,1 11,5,0,1,2,525630,0,8880.185,11.05339,1,12,1,5.694761,6.545178,0,0,0,12.23994,0,0,0,1,0,5,93.3,10.57626,0,92.6,0,646.36,1,1,1.609438,6.471356,0,0,0,0,0,0,92.6,9.09169,1.609438,2.504704,1 11,5,0,1,3,525630,0,8880.185,12.05339,1,12,1,32.17993,1.346021,0,0,0,33.52595,0,0,0,1,0,5,93.3,10.57626,0,92.6,0,646.36,1,1,1.609438,6.471356,0,0,0,0,0,0,92.6,9.09169,1.609438,3.51232,1 11,5,0,1,1,525631,0,8880.185,5.377139,0,12,1,19.46678,16.35209,0,0,0,35.81887,0,0,0,4,0,5,83.3,10.57626,0,51.9,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,51.9,9.09169,1.609438,3.578475,1 11,5,0,1,2,525631,0,8880.185,6.377139,0,12,1,11.38952,18.95976,0,0,0,30.34928,0,0,0,2,0,5,83.3,10.57626,0,51.9,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,51.9,9.09169,1.609438,3.412773,1 11,5,0,1,3,525631,0,8880.185,7.377139,0,12,1,25.95156,21.89619,0,0,0,47.84775,0,0,0,5,0,5,83.3,10.57626,0,51.9,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,51.9,9.09169,1.609438,3.868024,1 11,5,0,1,1,525632,0,8880.185,2.882957,0,12,1,60.51629,10.44012,0,0,0,70.95641,0,0,0,8,0,5,77.40034,10.57626,0,59.3,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,59.3,9.09169,1.609438,4.262066,1 11,5,0,1,2,525632,0,8880.185,3.882957,0,12,1,57.70691,3.02202,0,0,0,60.72893,0,0,0,3,0,5,77.40034,10.57626,0,59.3,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,59.3,9.09169,1.609438,4.10642,1 11,5,0,1,3,525632,0,8880.185,4.882957,0,12,1,23.18339,2.885813,0,0,537.7162,563.7855,1,0,0,2,0,5,77.40034,10.57626,0,59.3,0,646.36,1,0,1.609438,6.471356,0,0,0,0,0,0,59.3,9.09169,1.609438,6.334674,1 5,5,25,0,1,525656,1,0,39.29911,0,5,1,0,0,0,0,0,0,0,0,0,0,0,8,62.2,0,0,76.1,333.75,333.75,0,0,2.079442,5.810392,0,3.258096,7.196687,0,0,0,76.1,0,2.079442,,0 5,5,25,0,2,525656,1,0,40.29911,0,5,1,0,0,0,0,0,0,0,0,0,0,0,9,62.2,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,0,0,76.1,0,2.197225,,0 5,5,25,0,3,525656,1,0,41.29911,0,5,1,0,0,0,0,0,0,0,0,0,0,0,9,62.2,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,0,0,76.1,0,2.197225,,0 5,5,25,0,4,525656,1,0,42.29911,0,5,1,0,0,0,0,0,0,0,0,0,0,0,9,62.2,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,0,0,76.1,0,2.197225,,0 5,5,25,0,5,525656,1,0,43.29911,0,5,1,10.14885,0,0,0,0,10.14885,0,0,0,0,0,9,62.2,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,0,0,76.1,0,2.197225,2.31736,1 5,5,25,1,1,525657,1,0,10.00958,1,6,1,0,0,0,0,0,0,0,0,0,0,0,8,83.3,10.57626,0,63,333.75,333.75,1,1,2.079442,5.810392,0,3.258096,7.196687,0,1,0,63,0,2.079442,,0 5,5,25,1,2,525657,1,0,11.00958,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,83.3,10.57626,0,63,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,0,1,0,63,0,2.197225,,0 5,5,25,1,3,525657,1,0,12.00958,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,83.3,10.57626,0,63,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,0,1,0,63,0,2.197225,,0 5,5,25,1,4,525657,1,0,13.00958,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,83.3,10.57626,0,63,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,0,1,0,63,0,2.197225,,0 5,5,25,1,5,525657,1,0,14.00958,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,83.3,10.57626,0,63,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,0,1,0,63,0,2.197225,,0 5,5,25,1,1,525658,1,0,44.6872,1,6,1,8.599508,0,0,0,0,8.599508,0,0,0,0,0,8,63.3,6.9,0,50,333.75,333.75,0,0,2.079442,5.810392,0,3.258096,7.196687,0,1,0,50,0,2.079442,2.151705,1 5,5,25,1,2,525658,1,0,45.6872,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,63.3,6.9,0,50,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,50,0,2.197225,,0 5,5,25,1,3,525658,1,0,46.6872,1,6,1,17.5073,0,0,0,0,17.5073,0,0,0,0,0,9,63.3,6.9,0,50,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,50,0,2.197225,2.862618,1 5,5,25,1,4,525658,1,0,47.6872,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,63.3,6.9,0,50,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,50,0,2.197225,,0 5,5,25,1,5,525658,1,0,48.6872,1,6,1,44.99323,3.697564,35.53112,0,0,84.22192,0,0,0,0,2,9,63.3,6.9,0,50,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,50,0,2.197225,4.433455,1 5,5,25,1,1,525659,1,0,13.4757,0,6,1,8.599508,0,0,0,0,8.599508,0,0,0,0,0,8,98.3,10.57626,0,59.3,333.75,333.75,1,0,2.079442,5.810392,0,3.258096,7.196687,1,0,0,59.3,0,2.079442,2.151705,1 5,5,25,1,2,525659,1,0,14.4757,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,98.3,10.57626,0,59.3,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.3,0,2.197225,,0 5,5,25,1,3,525659,1,0,15.4757,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,98.3,10.57626,0,59.3,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.3,0,2.197225,,0 5,5,25,1,4,525659,1,0,16.4757,0,6,1,316.2978,0,0,0,0,316.2978,0,0,0,2,0,9,98.3,10.57626,0,59.3,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.3,0,2.197225,5.756684,1 5,5,25,1,5,525659,1,0,17.4757,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,98.3,10.57626,0,59.3,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.3,0,2.197225,,0 5,5,25,1,1,525660,1,0,11.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,8,80,10.57626,0,77.8,333.75,333.75,1,0,2.079442,5.810392,0,3.258096,7.196687,1,0,0,77.8,0,2.079442,,0 5,5,25,1,2,525660,1,0,12.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,80,10.57626,0,77.8,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,77.8,0,2.197225,,0 5,5,25,1,3,525660,1,0,13.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,80,10.57626,0,77.8,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,77.8,0,2.197225,,0 5,5,25,1,4,525660,1,0,14.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,80,10.57626,0,77.8,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,77.8,0,2.197225,,0 5,5,25,1,5,525660,1,0,15.77002,0,6,1,10.14885,2.405277,0,0,0,12.55413,0,0,0,0,0,9,80,10.57626,0,77.8,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,77.8,0,2.197225,2.530049,1 5,5,25,1,1,525661,1,0,16.75565,1,6,1,0,0,0,0,0,0,1,1,0,0,0,8,54.8,3.4,0,54.5,333.75,333.75,1,1,2.079442,5.810392,0,3.258096,7.196687,0,1,0,54.5,0,2.079442,,0 5,5,25,1,2,525661,1,0,17.75565,1,6,1,0,1.244303,0,0,0,1.244303,0,0,0,0,0,9,54.8,3.4,0,54.5,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,0,1,0,54.5,0,2.197225,.2185752,1 5,5,25,1,3,525661,1,0,18.75565,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,54.8,3.4,0,54.5,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,54.5,0,2.197225,,0 5,5,25,1,4,525661,1,0,19.75565,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,54.8,3.4,0,54.5,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,54.5,0,2.197225,,0 5,5,25,1,5,525661,1,0,20.75565,1,6,1,27.74019,1.589986,0,0,0,29.33018,0,0,0,1,0,9,54.8,3.4,0,54.5,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,54.5,0,2.197225,3.378617,1 5,5,25,0,1,525662,1,0,15.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,8,58.5,0,0,76.1,333.75,333.75,1,0,2.079442,5.810392,0,3.258096,7.196687,0,1,0,76.1,0,2.079442,,0 5,5,25,0,2,525662,1,0,16.77002,0,6,1,6.836828,0,0,0,0,6.836828,0,0,0,1,0,9,58.5,0,0,76.1,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,76.1,0,2.197225,1.922324,1 5,5,25,0,3,525662,1,0,17.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,58.5,0,0,76.1,333.75,333.75,1,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,76.1,0,2.197225,,0 5,5,25,0,4,525662,1,0,18.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,58.5,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,76.1,0,2.197225,,0 5,5,25,0,5,525662,1,0,19.77002,0,6,1,0,0,0,0,0,0,0,0,0,0,0,9,58.5,0,0,76.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,0,1,0,76.1,0,2.197225,,0 5,5,25,0,1,525663,1,0,14.61465,1,6,1,0,0,0,0,0,0,0,0,0,0,0,8,60.1,3.4,0,59.1,333.75,333.75,1,1,2.079442,5.810392,0,3.258096,7.196687,1,0,0,59.1,0,2.079442,,0 5,5,25,0,2,525663,1,0,15.61465,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,60.1,3.4,0,59.1,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.1,0,2.197225,,0 5,5,25,0,3,525663,1,0,16.61465,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,60.1,3.4,0,59.1,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.1,0,2.197225,,0 5,5,25,0,4,525663,1,0,17.61465,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,60.1,3.4,0,59.1,333.75,333.75,1,1,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.1,0,2.197225,,0 5,5,25,0,5,525663,1,0,18.61465,1,6,1,0,0,0,0,0,0,0,0,0,0,0,9,60.1,3.4,0,59.1,333.75,333.75,0,0,2.197225,5.810392,0,3.258096,7.196687,1,0,0,59.1,0,2.197225,,0 11,5,0,1,1,525705,0,12350.23,36.18344,0,9,1,0,0,0,0,0,0,0,0,0,0,0,2,78.2,0,0,84.1,0,308.42,0,0,.6931472,5.731462,0,0,0,0,0,0,84.1,9.421511,.6931472,,0 11,5,0,1,2,525705,0,12350.23,37.18344,0,9,1,7.972665,8.363706,0,0,0,16.33637,0,0,0,1,0,2,78.2,0,0,84.1,0,308.42,0,0,.6931472,5.731462,0,0,0,0,0,0,84.1,9.421511,.6931472,2.793394,1 11,5,0,1,3,525705,0,12350.23,38.18344,0,9,1,366.436,37.78547,0,0,309.6886,713.91,1,0,0,4,0,2,78.2,0,0,84.1,0,308.42,0,0,.6931472,5.731462,0,0,0,0,0,0,84.1,9.421511,.6931472,6.570757,1 11,5,0,1,1,525706,0,12350.23,36.99384,1,12,1,19.46678,3.440542,0,0,0,22.90732,0,0,0,1,0,2,84,13.8,0,83,0,308.42,0,0,.6931472,5.731462,0,0,0,0,1,0,83,9.421511,.6931472,3.131457,1 11,5,0,1,2,525706,0,12350.23,37.99384,1,12,1,9.491268,8.986333,0,0,0,18.4776,0,0,0,1,0,2,84,13.8,0,83,0,308.42,0,0,.6931472,5.731462,0,0,0,0,1,0,83,9.421511,.6931472,2.916559,1 11,5,0,1,3,525706,0,12350.23,38.99384,1,12,1,36.33218,2.730104,0,0,0,39.06228,0,0,0,2,0,2,84,13.8,0,83,0,308.42,0,0,.6931472,5.731462,0,0,0,0,1,0,83,9.421511,.6931472,3.665157,1 11,5,0,0,1,525715,0,8507.937,45.06503,1,9,1,14.58941,0,47.93664,0,0,62.52605,0,0,0,0,1,5,75.5,10.3,0,67.9,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67.9,9.048872,1.609438,4.135583,1 11,5,0,0,2,525715,0,8507.937,46.06503,1,9,1,7.501875,2.985746,0,0,0,10.48762,0,0,0,1,0,5,75.5,10.3,0,67.9,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67.9,9.048872,1.609438,2.350196,1 11,5,0,0,3,525715,0,8507.937,47.06503,1,9,1,88.97158,0,0,0,0,88.97158,0,0,0,4,0,5,75.5,10.3,0,67.9,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67.9,9.048872,1.609438,4.488317,1 11,5,0,0,1,525716,0,8507.937,12.05476,0,9,1,0,0,0,0,0,0,0,0,0,0,0,5,91.7,10.57626,.1442925,96.3,0,1189.12,1,0,1.609438,7.080969,0,0,0,1,0,0,96.3,9.048872,1.609438,,0 11,5,0,0,2,525716,0,8507.937,13.05476,0,9,1,23.44336,0,0,0,0,23.44336,0,0,0,1,0,5,91.7,10.57626,.1442925,96.3,0,1189.12,1,0,1.609438,7.080969,0,0,0,1,0,0,96.3,9.048872,1.609438,3.154587,1 11,5,0,0,3,525716,0,8507.937,14.05476,0,9,1,48.71448,4.803789,0,0,0,53.51827,0,0,0,1,0,5,91.7,10.57626,.1442925,96.3,0,1189.12,1,0,1.609438,7.080969,0,0,0,1,0,0,96.3,9.048872,1.609438,3.980023,1 11,5,0,0,1,525717,0,8507.937,44.58043,0,3,1,0,0,0,0,0,0,0,0,0,0,0,5,70.2,0,0,67,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67,9.048872,1.609438,,0 11,5,0,0,2,525717,0,8507.937,45.58043,0,3,1,31.88297,0,0,0,0,31.88297,0,0,0,1,0,5,70.2,0,0,67,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67,9.048872,1.609438,3.462072,1 11,5,0,0,3,525717,0,8507.937,46.58043,0,3,1,0,0,0,0,0,0,0,0,0,0,0,5,70.2,0,0,67,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,67,9.048872,1.609438,,0 11,5,0,0,1,525718,0,8507.937,16.09856,1,9,1,50.22926,9.983326,47.93664,0,513.1305,621.2797,1,0,0,4,1,5,88.3,3.4,0,84.1,0,1189.12,1,1,1.609438,7.080969,0,0,0,1,0,0,84.1,9.048872,1.609438,6.431781,1 11,5,0,0,2,525718,0,8507.937,17.09856,1,9,1,18.75469,.900225,0,0,0,19.65491,0,0,0,2,0,5,88.3,3.4,0,84.1,0,1189.12,1,1,1.609438,7.080969,0,0,0,1,0,0,84.1,9.048872,1.609438,2.978327,1 11,5,0,0,3,525718,0,8507.937,18.09856,1,9,1,21.98917,0,0,0,0,21.98917,0,0,0,3,0,5,88.3,3.4,0,84.1,0,1189.12,0,0,1.609438,7.080969,0,0,0,1,0,0,84.1,9.048872,1.609438,3.09055,1 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7,5,25,1,2,526187,0,15450.08,17.21082,0,12,1,3.777862,0,0,28.33396,0,3.777862,0,0,3,1,0,5,41.5,3.4,0,72.5,750,750,1,0,1.609438,6.620073,0,3.258096,8.006368,0,0,0,72.5,9.645434,1.609438,1.329158,1 7,5,25,1,3,526187,0,15450.08,18.21082,0,12,1,66.89537,0,0,0,0,66.89537,0,0,0,1,0,5,41.5,3.4,0,72.5,750,750,0,0,1.609438,6.620073,0,3.258096,8.006368,0,0,0,72.5,9.645434,1.609438,4.20313,1 11,5,0,1,1,526188,0,11392.73,34.33265,0,12,1,122.408,35.48032,0,0,177.7402,335.6284,1,0,0,5,0,4,75,20.7,0,60.2,0,0,0,0,1.386294,0,0,0,0,1,0,0,60.2,9.340818,1.386294,5.816005,1 11,5,0,1,2,526188,0,11392.73,35.33265,0,12,1,30.75171,9.335611,0,0,0,40.08732,0,0,0,1,1,4,75,20.7,0,60.2,0,0,0,0,1.386294,0,0,0,0,1,0,0,60.2,9.340818,1.386294,3.69106,1 11,5,0,1,3,526188,0,11392.73,36.33265,0,12,1,0,0,0,0,0,0,0,0,0,0,0,4,75,20.7,0,60.2,0,0,0,0,1.386294,0,0,0,0,1,0,0,60.2,9.340818,1.386294,,0 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11,5,0,0,1,526449,1,1998.976,17.57153,0,11,1,0,0,0,0,0,0,0,0,0,0,0,5,66.5,3.4,0,55.7,0,0,1,0,1.609438,0,0,0,0,0,0,0,55.7,7.600891,1.609438,,0 11,5,0,0,2,526449,1,1998.976,18.57153,0,11,1,0,0,0,0,0,0,0,0,0,0,0,5,66.5,3.4,0,55.7,0,0,0,0,1.609438,0,0,0,0,0,0,0,55.7,7.600891,1.609438,,0 11,5,0,0,3,526449,1,1998.976,19.57153,0,11,1,10.38062,0,0,0,0,10.38062,0,0,0,0,0,6,66.5,3.4,0,55.7,0,0,0,0,1.791759,0,0,0,0,0,0,0,55.7,7.600891,1.791759,2.339941,1 11,5,0,0,1,526450,1,1998.976,16.53114,1,11,1,0,0,0,0,0,0,0,0,0,0,0,5,73.4,3.4,0,81.8,0,0,1,1,1.609438,0,0,0,0,0,0,0,81.8,7.600891,1.609438,,0 11,5,0,0,2,526450,1,1998.976,17.53114,1,11,1,0,0,0,0,0,0,1,1,0,0,0,5,73.4,3.4,0,81.8,0,0,1,1,1.609438,0,0,0,0,0,0,0,81.8,7.600891,1.609438,,0 11,5,0,0,3,526450,1,1998.976,18.53114,1,11,1,92.04152,0,0,0,0,92.04152,0,0,0,5,0,6,73.4,3.4,0,81.8,0,0,0,0,1.791759,0,0,0,0,0,0,0,81.8,7.600891,1.791759,4.52224,1 11,5,0,0,1,526451,1,1998.976,45.02943,0,10,1,0,0,0,0,0,0,0,0,0,0,0,5,95.2,6.9,0,87.5,0,0,0,0,1.609438,0,0,0,0,0,0,0,87.5,7.600891,1.609438,,0 11,5,0,0,2,526451,1,1998.976,46.02943,0,10,1,0,0,0,0,0,0,0,0,0,0,0,5,95.2,6.9,0,87.5,0,0,0,0,1.609438,0,0,0,0,0,0,0,87.5,7.600891,1.609438,,0 11,5,0,0,3,526451,1,1998.976,47.02943,0,10,1,11.07266,0,0,0,0,11.07266,0,0,0,0,0,6,95.2,6.9,0,87.5,0,0,0,0,1.791759,0,0,0,0,0,0,0,87.5,7.600891,1.791759,2.40448,1 11,5,0,0,1,526452,1,1998.976,42.10267,1,11,1,0,0,0,0,0,0,0,0,0,0,0,5,80.3,13.8,0,72.7,0,0,0,0,1.609438,0,0,0,0,0,0,0,72.7,7.600891,1.609438,,0 11,5,0,0,2,526452,1,1998.976,43.10267,1,11,1,0,0,0,0,0,0,0,0,0,0,0,5,80.3,13.8,0,72.7,0,0,0,0,1.609438,0,0,0,0,0,0,0,72.7,7.600891,1.609438,,0 11,5,0,0,3,526452,1,1998.976,44.10267,1,11,1,0,0,0,0,0,0,0,0,0,0,0,6,80.3,13.8,0,72.7,0,0,0,0,1.791759,0,0,0,0,0,0,0,72.7,7.600891,1.791759,,0 6,5,25,0,1,526457,1,1484.895,48.95551,1,6,1,0,0,0,0,0,0,0,0,0,0,0,2,64.4,10.57626,1,62.5,190.4,0,0,0,.6931472,0,0,3.258096,6.635421,0,1,0,62.5,7.303772,.6931472,,0 6,5,25,0,2,526457,1,1484.895,49.95551,1,6,1,15.18603,0,0,0,0,15.18603,0,0,0,0,0,2,64.4,10.57626,1,62.5,190.4,0,0,0,.6931472,0,0,3.258096,6.635421,0,1,0,62.5,7.303772,.6931472,2.720376,1 6,5,25,0,3,526457,1,1484.895,50.95551,1,6,1,0,0,0,0,0,0,0,0,0,0,0,1,64.4,10.57626,1,62.5,190.4,0,0,0,0,0,0,3.258096,6.635421,0,1,0,62.5,7.303772,0,,0 10,5,50,0,1,526458,1,4537.243,40.77481,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,75.5,10.3,0,63.1,540,0,0,0,1.098612,0,0,3.931826,6.984716,1,0,0,63.1,8.420296,1.098612,,0 10,5,50,0,2,526458,1,4537.243,41.77481,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,75.5,10.3,0,63.1,540,0,0,0,1.098612,0,0,3.931826,6.984716,1,0,0,63.1,8.420296,1.098612,,0 10,5,50,0,3,526458,1,4537.243,42.77481,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,75.5,10.3,0,63.1,540,0,0,0,1.098612,0,0,3.931826,6.984716,1,0,0,63.1,8.420296,1.098612,,0 10,5,50,0,4,526458,1,4537.243,43.77481,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,75.5,10.3,0,63.1,540,0,0,0,1.098612,0,0,3.931826,6.984716,1,0,0,63.1,8.420296,1.098612,,0 10,5,50,0,5,526458,1,4537.243,44.77481,1,10,1,66.64411,0,0,0,2310.217,2376.861,1,0,0,1,0,3,75.5,10.3,0,63.1,540,0,0,0,1.098612,0,0,3.931826,6.984716,1,0,0,63.1,8.420296,1.098612,7.773536,1 10,5,50,0,1,526459,1,4537.243,13.32786,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,78.3,10.57626,0,74.1,540,0,1,0,1.098612,0,0,3.931826,6.984716,1,0,0,74.1,8.420296,1.098612,,0 10,5,50,0,2,526459,1,4537.243,14.32786,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,78.3,10.57626,0,74.1,540,0,1,0,1.098612,0,0,3.931826,6.984716,1,0,0,74.1,8.420296,1.098612,,0 10,5,50,0,3,526459,1,4537.243,15.32786,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,78.3,10.57626,0,74.1,540,0,1,0,1.098612,0,0,3.931826,6.984716,1,0,0,74.1,8.420296,1.098612,,0 10,5,50,0,4,526459,1,4537.243,16.32786,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,78.3,10.57626,0,74.1,540,0,1,0,1.098612,0,0,3.931826,6.984716,1,0,0,74.1,8.420296,1.098612,,0 10,5,50,0,5,526459,1,4537.243,17.32786,0,10,1,0,0,0,0,0,0,0,0,0,0,0,3,78.3,10.57626,0,74.1,540,0,1,0,1.098612,0,0,3.931826,6.984716,1,0,0,74.1,8.420296,1.098612,,0 10,5,50,0,1,526460,1,4537.243,11.78097,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,76.7,10.57626,0,88.9,540,0,1,1,1.098612,0,0,3.931826,6.984716,1,0,0,88.9,8.420296,1.098612,,0 10,5,50,0,2,526460,1,4537.243,12.78097,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,76.7,10.57626,0,88.9,540,0,1,1,1.098612,0,0,3.931826,6.984716,1,0,0,88.9,8.420296,1.098612,,0 10,5,50,0,3,526460,1,4537.243,13.78097,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,76.7,10.57626,0,88.9,540,0,1,1,1.098612,0,0,3.931826,6.984716,1,0,0,88.9,8.420296,1.098612,,0 10,5,50,0,4,526460,1,4537.243,14.78097,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,76.7,10.57626,0,88.9,540,0,1,1,1.098612,0,0,3.931826,6.984716,1,0,0,88.9,8.420296,1.098612,,0 10,5,50,0,5,526460,1,4537.243,15.78097,1,10,1,0,0,0,0,0,0,0,0,0,0,0,3,76.7,10.57626,0,88.9,540,0,1,1,1.098612,0,0,3.931826,6.984716,1,0,0,88.9,8.420296,1.098612,,0 13,5,0,0,1,526462,1,1185.868,14.36277,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,78.2,0,0,75,450,450,1,1,2.197225,6.109248,1,4.564348,6.160541,1,0,0,75,7.079073,2.197225,,0 13,5,0,0,2,526462,1,1185.868,15.36277,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,78.2,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,75,7.079073,1.791759,,0 13,5,0,0,3,526462,1,1185.868,16.36276,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,78.2,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,75,7.079073,1.791759,,0 13,5,0,0,1,526463,1,1185.868,40.42163,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,80.3,17.2,1,38.6,450,450,0,0,2.197225,6.109248,1,4.564348,6.160541,1,0,0,38.6,7.079073,2.197225,,0 13,5,0,0,2,526463,1,1185.868,41.42163,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,80.3,17.2,1,38.6,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,38.6,7.079073,1.791759,,0 13,5,0,0,3,526463,1,1185.868,42.42163,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,80.3,17.2,1,38.6,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,38.6,7.079073,1.791759,,0 13,5,0,0,1,526464,1,1185.868,9.995893,0,8,1,0,0,0,0,0,0,0,0,0,0,0,9,66.7,10.57626,0,70.4,450,450,1,0,2.197225,6.109248,1,4.564348,6.160541,1,0,0,70.4,7.079073,2.197225,,0 13,5,0,0,2,526464,1,1185.868,10.99589,0,8,1,0,0,0,0,0,0,0,0,0,0,0,6,66.7,10.57626,0,70.4,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,70.4,7.079073,1.791759,,0 13,5,0,0,3,526464,1,1185.868,11.99589,0,8,1,0,0,0,0,0,0,0,0,0,0,0,6,66.7,10.57626,0,70.4,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,70.4,7.079073,1.791759,,0 13,5,0,0,1,526465,1,1185.868,18.98973,1,9,1,0,0,0,0,0,0,0,0,0,0,0,9,61.7,6.9,0,63.6,450,450,0,0,2.197225,6.109248,1,4.564348,6.160541,1,0,0,63.6,7.079073,2.197225,,0 13,5,0,0,1,526466,1,1185.868,11.0883,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,85,10.57626,0,85.2,450,450,1,1,2.197225,6.109248,1,4.564348,6.160541,1,0,0,85.2,7.079073,2.197225,,0 13,5,0,0,2,526466,1,1185.868,12.0883,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,85,10.57626,0,85.2,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,85.2,7.079073,1.791759,,0 13,5,0,0,3,526466,1,1185.868,13.0883,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,85,10.57626,0,85.2,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,85.2,7.079073,1.791759,,0 13,5,0,0,1,526467,1,1185.868,15.44969,0,8,1,0,0,0,0,0,0,0,0,0,0,0,9,83,3.4,0,68.2,450,450,1,0,2.197225,6.109248,1,4.564348,6.160541,1,0,0,68.2,7.079073,2.197225,,0 13,5,0,0,2,526467,1,1185.868,16.44969,0,8,1,0,0,0,0,0,0,0,0,0,0,0,6,83,3.4,0,68.2,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,68.2,7.079073,1.791759,,0 13,5,0,0,3,526467,1,1185.868,17.44969,0,8,1,0,0,0,0,0,0,0,0,0,0,0,6,83,3.4,0,68.2,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,68.2,7.079073,1.791759,,0 13,5,0,0,1,526468,1,1185.868,12.8679,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,63.3,10.57626,0,81.5,450,450,1,1,2.197225,6.109248,1,4.564348,6.160541,1,0,0,81.5,7.079073,2.197225,,0 13,5,0,0,2,526468,1,1185.868,13.8679,1,8,1,2.250563,0,0,0,0,2.250563,0,0,0,1,0,6,63.3,10.57626,0,81.5,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,81.5,7.079073,1.791759,.8111802,1 13,5,0,0,3,526468,1,1185.868,14.8679,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,63.3,10.57626,0,81.5,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,81.5,7.079073,1.791759,,0 13,5,0,0,1,526469,1,1185.868,17.78234,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,59,3.4,0,43.8,450,450,1,1,2.197225,6.109248,1,4.564348,6.160541,1,0,0,43.8,7.079073,2.197225,,0 10,5,50,1,1,526470,1,4288.563,10.60643,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,1,59.3,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,59.3,8.36394,1.386294,,0 10,5,50,1,2,526470,1,4288.563,11.60643,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,1,59.3,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,59.3,8.36394,1.386294,,0 10,5,50,1,3,526470,1,4288.563,12.60643,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,1,59.3,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,59.3,8.36394,1.386294,,0 10,5,50,1,4,526470,1,4288.563,13.60643,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,1,59.3,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,59.3,8.36394,1.386294,,0 10,5,50,1,5,526470,1,4288.563,14.60643,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,1,59.3,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,59.3,8.36394,1.386294,,0 10,5,50,1,1,526472,1,4288.563,8.013689,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,0,70.4,960.6,960.6,1,0,1.386294,6.867558,0,3.931826,7.560705,0,0,0,70.4,8.36394,1.386294,,0 10,5,50,1,2,526472,1,4288.563,9.013689,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,0,70.4,960.6,960.6,1,0,1.386294,6.867558,0,3.931826,7.560705,0,0,0,70.4,8.36394,1.386294,,0 10,5,50,1,3,526472,1,4288.563,10.01369,0,10,1,11.42615,0,0,0,0,11.42615,0,0,0,0,0,4,83.3,10.57626,0,70.4,960.6,960.6,1,0,1.386294,6.867558,0,3.931826,7.560705,0,0,0,70.4,8.36394,1.386294,2.435905,1 10,5,50,1,4,526472,1,4288.563,11.01369,0,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,0,70.4,960.6,960.6,1,0,1.386294,6.867558,0,3.931826,7.560705,0,0,0,70.4,8.36394,1.386294,,0 10,5,50,1,5,526472,1,4288.563,12.01369,0,10,1,21.28028,0,0,0,0,21.28028,0,0,0,0,0,4,83.3,10.57626,0,70.4,960.6,960.6,1,0,1.386294,6.867558,0,3.931826,7.560705,0,0,0,70.4,8.36394,1.386294,3.057781,1 10,5,50,1,1,526473,1,4288.563,9.442847,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.3,10.57626,0,66.7,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,66.7,8.36394,1.386294,,0 10,5,50,1,2,526473,1,4288.563,10.44285,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.3,10.57626,0,66.7,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,66.7,8.36394,1.386294,,0 10,5,50,1,3,526473,1,4288.563,11.44285,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.3,10.57626,0,66.7,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,66.7,8.36394,1.386294,,0 10,5,50,1,4,526473,1,4288.563,12.44285,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.3,10.57626,0,66.7,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,66.7,8.36394,1.386294,,0 10,5,50,1,5,526473,1,4288.563,13.44285,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.3,10.57626,0,66.7,960.6,960.6,1,1,1.386294,6.867558,0,3.931826,7.560705,0,0,0,66.7,8.36394,1.386294,,0 10,5,50,1,1,526474,1,4288.563,30.08077,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,56.4,3.4,0,62.5,960.6,960.6,0,0,1.386294,6.867558,0,3.931826,7.560705,0,1,0,62.5,8.36394,1.386294,,0 10,5,50,1,2,526474,1,4288.563,31.08077,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,56.4,3.4,0,62.5,960.6,960.6,0,0,1.386294,6.867558,0,3.931826,7.560705,0,1,0,62.5,8.36394,1.386294,,0 10,5,50,1,3,526474,1,4288.563,32.08076,1,10,1,23.2755,0,0,0,0,23.2755,0,0,0,1,0,4,56.4,3.4,0,62.5,960.6,960.6,0,0,1.386294,6.867558,0,3.931826,7.560705,0,1,0,62.5,8.36394,1.386294,3.147401,1 10,5,50,1,4,526474,1,4288.563,33.08076,1,10,1,19.36219,0,0,0,0,19.36219,0,0,0,1,0,4,56.4,3.4,0,62.5,960.6,960.6,0,0,1.386294,6.867558,0,3.931826,7.560705,0,1,0,62.5,8.36394,1.386294,2.963322,1 10,5,50,1,5,526474,1,4288.563,34.08076,1,10,1,10.38062,0,0,0,0,10.38062,0,0,0,0,0,4,56.4,3.4,0,62.5,960.6,960.6,0,0,1.386294,6.867558,0,3.931826,7.560705,0,1,0,62.5,8.36394,1.386294,2.339941,1 11,5,0,1,1,526482,1,9806.964,59.19781,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,64.4,13.8,0,88.1,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,88.1,9.19095,1.098612,,0 11,5,0,1,2,526482,1,9806.964,60.19781,0,9,1,66.41222,0,0,0,0,66.41222,0,0,0,0,0,3,64.4,13.8,0,88.1,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,88.1,9.19095,1.098612,4.195881,1 11,5,0,1,3,526482,1,9806.964,61.19781,0,9,1,0,0,0,0,0,0,0,0,0,0,0,3,64.4,13.8,0,88.1,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,88.1,9.19095,1.098612,,0 11,5,0,0,1,526483,1,9806.964,32.15879,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,77.7,3.4,0,62.5,0,777.6,0,0,1.098612,6.656212,0,0,0,0,0,0,62.5,9.19095,1.098612,,0 11,5,0,0,2,526483,1,9806.964,33.15879,1,11,1,22.13741,6.240458,0,0,0,28.37786,0,0,0,3,0,3,77.7,3.4,0,62.5,0,777.6,0,0,1.098612,6.656212,0,0,0,0,0,0,62.5,9.19095,1.098612,3.345609,1 11,5,0,0,3,526483,1,9806.964,34.15879,1,11,1,26.82927,21.28223,0,0,0,48.1115,0,0,0,2,1,3,77.7,3.4,0,62.5,0,777.6,0,0,1.098612,6.656212,0,0,0,0,0,0,62.5,9.19095,1.098612,3.873521,1 11,5,0,0,1,526484,1,9806.964,50.9514,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,79.1,20.7,0,70.2,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,70.2,9.19095,1.098612,,0 11,5,0,0,2,526484,1,9806.964,51.9514,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,79.1,20.7,0,70.2,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,70.2,9.19095,1.098612,,0 11,5,0,0,3,526484,1,9806.964,52.9514,1,11,1,0,0,0,0,0,0,0,0,0,0,0,3,79.1,20.7,0,70.2,0,777.6,0,0,1.098612,6.656212,0,0,0,0,1,0,70.2,9.19095,1.098612,,0 13,5,0,1,1,526485,1,3130.205,18.55989,1,9,1,0,0,0,0,306.7393,306.7393,1,1,0,0,0,4,82.4,0,1,72.7,450,0,0,0,1.386294,0,1,4.564348,6.160541,1,0,0,72.7,8.049173,1.386294,5.725998,1 13,5,0,1,2,526485,1,3130.205,19.55989,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,82.4,0,1,72.7,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,72.7,8.049173,1.609438,,0 13,5,0,1,3,526485,1,3130.205,20.55989,1,9,1,0,0,0,0,0,0,0,0,0,0,0,5,82.4,0,1,72.7,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,72.7,8.049173,1.609438,,0 13,5,0,1,4,526485,1,3130.205,21.55989,1,9,1,10.9558,7.457499,0,0,208.538,226.9513,1,1,0,2,0,5,82.4,0,1,72.7,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,72.7,8.049173,1.609438,5.424735,1 13,5,0,1,5,526485,1,3130.205,22.55989,1,9,1,167.6844,1.876501,0,0,855.9177,1025.479,1,0,0,7,0,7,82.4,0,1,72.7,450,0,0,0,1.94591,0,1,4.564348,6.160541,1,0,0,72.7,8.049173,1.94591,6.932915,1 13,5,0,1,1,526486,1,3130.205,48.23272,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.5,13.8,0,84.1,450,0,0,0,1.386294,0,1,4.564348,6.160541,1,0,0,84.1,8.049173,1.386294,,0 13,5,0,1,2,526486,1,3130.205,49.23272,1,10,1,0,0,0,0,0,0,0,0,0,0,0,5,83.5,13.8,0,84.1,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,84.1,8.049173,1.609438,,0 13,5,0,1,3,526486,1,3130.205,50.23272,1,10,1,0,0,0,0,0,0,0,0,0,0,0,5,83.5,13.8,0,84.1,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,84.1,8.049173,1.609438,,0 13,5,0,1,4,526486,1,3130.205,51.23272,1,10,1,11.33358,0,0,0,0,11.33358,0,0,0,1,0,5,83.5,13.8,0,84.1,450,0,0,0,1.609438,0,1,4.564348,6.160541,1,0,0,84.1,8.049173,1.609438,2.42777,1 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18,5,25,1,1,528015,1,7266.257,41.12526,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,73.4,10.3,1,47.7,362.31,362.31,0,0,1.386294,5.8925,0,3.258096,7.278795,0,1,0,47.7,8.891134,1.386294,,0 18,5,25,1,2,528015,1,7266.257,42.12526,1,10,1,34.35115,45.17176,0,0,0,79.5229,0,0,0,0,0,4,73.4,10.3,1,47.7,362.31,362.31,0,0,1.386294,5.8925,0,3.258096,7.278795,0,1,0,47.7,8.891134,1.386294,4.376045,1 18,5,25,1,3,528015,1,7266.257,43.12526,1,10,1,0,8.170732,0,0,0,8.170732,0,0,0,0,0,4,73.4,10.3,1,47.7,362.31,362.31,0,0,1.386294,5.8925,0,3.258096,7.278795,0,1,0,47.7,8.891134,1.386294,2.100559,1 18,5,25,1,1,528018,1,7266.257,10.8282,1,10,1,8.081667,0,0,0,0,8.081667,0,0,0,0,0,4,83.3,10.57626,0,63,362.31,362.31,1,1,1.386294,5.8925,0,3.258096,7.278795,1,0,0,63,8.891134,1.386294,2.089598,1 18,5,25,1,2,528018,1,7266.257,11.8282,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,83.3,10.57626,0,63,362.31,362.31,1,1,1.386294,5.8925,0,3.258096,7.278795,1,0,0,63,8.891134,1.386294,,0 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18,6,25,1,1,627228,0,1896.569,18.42574,1,9,1,37.66399,39.91536,0,0,0,77.57935,0,0,0,4,0,5,31.9,6.9,0,73.9,173.5,173.5,0,0,1.609438,5.156178,0,3.258096,6.542472,1,0,0,73.9,7.548329,1.609438,4.351301,1 18,6,25,1,2,627228,0,1896.569,19.42574,1,9,1,18.98254,10.44039,0,0,609.0699,638.4928,1,0,0,0,0,5,31.9,6.9,0,73.9,173.5,173.5,0,0,1.609438,5.156178,0,3.258096,6.542472,1,0,0,73.9,7.548329,1.609438,6.45911,1 18,6,25,1,3,627228,0,1896.569,20.42574,1,9,1,6.228374,22.97578,0,0,0,29.20415,0,0,0,1,0,6,31.9,6.9,0,73.9,173.5,173.5,0,0,1.791759,5.156178,0,3.258096,6.542472,1,0,0,73.9,7.548329,1.791759,3.374311,1 18,6,25,1,1,627229,0,1896.569,50.48323,0,16,1,9.310199,137.7148,0,0,0,147.025,0,0,0,1,0,5,91,17.2,1,75,173.5,173.5,0,0,1.609438,5.156178,0,3.258096,6.542472,0,0,0,75,7.548329,1.609438,4.990602,1 18,6,25,1,2,627229,0,1896.569,51.48323,0,16,1,5.31511,133.6447,0,0,0,138.9598,0,0,0,1,0,5,91,17.2,1,75,173.5,173.5,0,0,1.609438,5.156178,0,3.258096,6.542472,0,0,0,75,7.548329,1.609438,4.934185,1 18,6,25,1,3,627229,0,1896.569,52.48323,0,16,1,21.79931,143.474,39.66436,0,0,204.9377,0,0,0,2,0,6,91,17.2,1,75,173.5,173.5,0,0,1.791759,5.156178,0,3.258096,6.542472,0,0,0,75,7.548329,1.791759,5.322706,1 16,6,95,0,1,627239,1,1725.55,26.29706,0,5,1,0,0,0,0,0,0,0,0,0,0,0,3,54.3,6.9,0,57.5,756,0,0,0,1.098612,0,0,4.564348,6.679335,1,0,0,57.5,7.453881,1.098612,,0 16,6,95,0,2,627239,1,1725.55,27.29706,0,5,1,0,0,0,0,0,0,0,0,0,0,0,4,54.3,6.9,0,57.5,756,0,0,0,1.386294,0,0,4.564348,6.679335,1,0,0,57.5,7.453881,1.386294,,0 16,6,95,0,3,627239,1,1725.55,28.29706,0,5,1,0,0,0,0,0,0,0,0,0,0,0,4,54.3,6.9,0,57.5,756,0,0,0,1.386294,0,0,4.564348,6.679335,1,0,0,57.5,7.453881,1.386294,,0 16,6,95,0,1,627240,1,1725.55,24.30664,1,12,1,0,0,0,0,295.3872,295.3872,1,0,0,0,0,3,38.8,17.2,0,59.1,756,0,0,0,1.098612,0,0,4.564348,6.679335,0,1,0,59.1,7.453881,1.098612,5.688287,1 16,6,95,0,2,627240,1,1725.55,25.30664,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,38.8,17.2,0,59.1,756,0,0,0,1.386294,0,0,4.564348,6.679335,0,1,0,59.1,7.453881,1.386294,,0 16,6,95,0,3,627240,1,1725.55,26.30664,1,12,1,0,1.449827,0,0,0,1.449827,0,0,0,0,0,4,38.8,17.2,0,59.1,756,0,0,0,1.386294,0,0,4.564348,6.679335,0,1,0,59.1,7.453881,1.386294,.3714442,1 16,6,95,0,1,627241,1,1725.55,3.195072,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,77.40034,10.57626,0,64.8,756,0,1,1,1.098612,0,0,4.564348,6.679335,0,0,0,64.8,7.453881,1.098612,,0 16,6,95,0,2,627241,1,1725.55,4.195072,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,77.40034,10.57626,0,64.8,756,0,1,1,1.386294,0,0,4.564348,6.679335,0,0,0,64.8,7.453881,1.386294,,0 16,6,95,0,3,627241,1,1725.55,5.195072,1,12,1,0,0,0,0,0,0,0,0,0,0,0,4,77.40034,10.57626,0,64.8,756,0,1,1,1.386294,0,0,4.564348,6.679335,0,0,0,64.8,7.453881,1.386294,,0 15,6,95,1,1,627247,1,9989.247,34.69405,0,12,1,222.4585,4.147171,0,0,0,226.6057,0,0,0,2,28,4,65.4,3.4,0,90.9,350,350,0,0,1.386294,5.857933,0,4.564348,5.909226,1,0,0,90.9,9.209365,1.386294,5.423212,1 15,6,95,1,1,627248,1,9989.247,11.75086,1,12,1,9.783071,0,0,0,0,9.783071,0,0,0,1,0,4,68.3,10.57626,1,51.9,350,350,1,1,1.386294,5.857933,0,4.564348,5.909226,1,0,0,51.9,9.209365,1.386294,2.280653,1 15,6,95,1,1,627249,1,9989.247,3.986311,0,12,1,12.76053,6.061251,0,0,0,18.82178,0,0,0,2,0,4,77.40034,10.57626,0,70.4,350,350,1,0,1.386294,5.857933,0,4.564348,5.909226,1,0,0,70.4,9.209365,1.386294,2.935014,1 15,6,95,1,1,627250,1,9989.247,33.21287,1,12,1,45.51255,8.430455,0,0,0,53.943,0,0,0,3,0,4,64.9,10.3,1,69.3,350,350,0,0,1.386294,5.857933,0,4.564348,5.909226,1,0,0,69.3,9.209365,1.386294,3.987928,1 13,6,0,0,1,627265,1,6735.316,22.54073,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,70.2,0,0,95.5,450,150,0,0,1.098612,5.010635,1,4.564348,6.160541,0,0,0,95.5,8.815269,1.098612,,0 13,6,0,0,2,627265,1,6735.316,23.54073,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,70.2,0,0,95.5,450,150,0,0,1.098612,5.010635,1,4.564348,6.160541,0,0,0,95.5,8.815269,1.098612,,0 13,6,0,0,3,627265,1,6735.316,24.54073,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,70.2,0,0,95.5,450,150,0,0,1.098612,5.010635,1,4.564348,6.160541,0,0,0,95.5,8.815269,1.098612,,0 13,6,0,0,1,627266,1,213.5177,16.33402,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,78.2,0,0,82.1,450,450,1,0,1.94591,6.109248,1,4.564348,6.160541,0,0,0,82.1,5.368392,1.94591,,0 13,6,0,0,2,627266,1,213.5177,17.33402,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,78.2,0,0,82.1,450,450,1,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,82.1,5.368392,2.079442,,0 13,6,0,0,3,627266,1,213.5177,18.33402,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,78.2,0,0,82.1,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,82.1,5.368392,2.079442,,0 13,6,0,0,1,627267,1,213.5177,43.57837,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,89.9,10.3,0,84.1,450,450,0,0,1.94591,6.109248,1,4.564348,6.160541,1,0,0,84.1,5.368392,1.94591,,0 13,6,0,0,2,627267,1,213.5177,44.57837,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,89.9,10.3,0,84.1,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,1,0,0,84.1,5.368392,2.079442,,0 13,6,0,0,3,627267,1,213.5177,45.57837,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,89.9,10.3,0,84.1,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,1,0,0,84.1,5.368392,2.079442,,0 13,6,0,0,1,627268,1,213.5177,41.78234,1,10,1,19.46678,0,30.89293,0,0,50.35971,0,0,0,1,1,7,88.3,6.9,0,,450,450,0,0,1.94591,6.109248,1,4.564348,6.160541,1,0,0,86.4,5.368392,1.94591,3.919191,1 13,6,0,0,2,627268,1,213.5177,42.78234,1,10,1,7.213364,0,0,0,0,7.213364,0,0,0,0,0,8,88.3,6.9,0,,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,1,0,0,86.4,5.368392,2.079442,1.975935,1 13,6,0,0,3,627268,1,213.5177,43.78234,1,10,1,8.650519,0,32.87197,0,0,41.52249,0,0,0,0,1,8,88.3,6.9,0,,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,1,0,0,86.4,5.368392,2.079442,3.726235,1 13,6,0,0,1,627269,1,213.5177,14.69678,0,10,1,0,0,0,0,0,0,0,0,0,0,0,7,100,10.57626,0,81.5,450,450,1,0,1.94591,6.109248,1,4.564348,6.160541,0,0,0,81.5,5.368392,1.94591,,0 13,6,0,0,2,627269,1,213.5177,15.69678,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,100,10.57626,0,81.5,450,450,1,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,81.5,5.368392,2.079442,,0 13,6,0,0,3,627269,1,213.5177,16.69678,0,10,1,0,0,0,0,0,0,0,0,0,0,0,8,100,10.57626,0,81.5,450,450,1,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,81.5,5.368392,2.079442,,0 13,6,0,0,1,627270,1,213.5177,13.14168,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,86.7,10.57626,0,51.9,450,450,1,1,1.94591,6.109248,1,4.564348,6.160541,0,0,0,51.9,5.368392,1.94591,,0 13,6,0,0,2,627270,1,213.5177,14.14168,1,10,1,0,0,0,0,0,0,0,0,0,0,0,8,86.7,10.57626,0,51.9,450,450,1,1,2.079442,6.109248,1,4.564348,6.160541,0,0,0,51.9,5.368392,2.079442,,0 13,6,0,0,3,627270,1,213.5177,15.14168,1,10,1,0,0,0,0,0,0,0,0,0,0,0,8,86.7,10.57626,0,51.9,450,450,1,1,2.079442,6.109248,1,4.564348,6.160541,0,0,0,51.9,5.368392,2.079442,,0 13,6,0,0,1,627271,1,213.5177,17.83984,1,10,1,0,0,0,0,0,0,0,0,0,0,0,7,72.3,0,0,84.1,450,450,1,1,1.94591,6.109248,1,4.564348,6.160541,0,0,0,84.1,5.368392,1.94591,,0 13,6,0,0,2,627271,1,213.5177,18.83984,1,10,1,0,0,0,0,0,0,0,0,0,0,0,8,72.3,0,0,84.1,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,84.1,5.368392,2.079442,,0 13,6,0,0,3,627271,1,213.5177,19.83984,1,10,1,32.17993,0,0,0,428.3737,460.5536,1,0,0,0,0,8,72.3,0,0,84.1,450,450,0,0,2.079442,6.109248,1,4.564348,6.160541,0,0,0,84.1,5.368392,2.079442,6.132429,1 13,6,0,0,1,627272,1,213.5177,19.49897,1,10,1,8.463818,0,0,0,0,8.463818,1,1,0,0,0,7,71.3,0,0,73.9,450,450,0,0,1.94591,6.109248,1,4.564348,6.160541,0,0,0,73.9,5.368392,1.94591,2.1358,1 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15,6,95,1,3,629653,1,10112.13,30.44901,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,62.3,13.8,0,48.9,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,48.9,9.22159,1.098612,,0 15,6,95,1,1,629654,1,10112.13,24.59411,1,12,1,41.04951,21.4854,0,0,497.2493,559.7842,1,0,0,2,0,2,46.3,20.7,0,47.7,1000,1000,0,0,.6931472,6.907755,0,4.564348,6.959049,1,0,0,47.7,9.22159,.6931472,6.327551,1 15,6,95,1,2,629654,1,10112.13,25.59411,1,12,1,14.42673,0,0,0,0,14.42673,0,0,0,1,0,3,46.3,20.7,0,47.7,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,47.7,9.22159,1.098612,2.669083,1 15,6,95,1,3,629654,1,10112.13,26.59411,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,46.3,20.7,0,47.7,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,47.7,9.22159,1.098612,,0 13,6,0,1,1,629667,1,0,4.717317,1,3,1,0,2.052476,0,0,0,2.052476,0,0,0,0,0,6,77.40034,10.57626,1,63,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,1,63,0,1.791759,.7190467,1 13,6,0,1,2,629667,1,0,5.717317,1,3,1,7.593014,0,0,0,0,7.593014,0,0,0,2,0,6,77.40034,10.57626,1,63,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,1,63,0,1.791759,2.027229,1 13,6,0,1,3,629667,1,0,6.717317,1,3,1,0,0,0,0,0,0,0,0,0,0,0,6,77.40034,10.57626,1,63,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,1,63,0,1.791759,,0 13,6,0,1,1,629668,1,0,16.55031,1,3,1,0,0,0,0,0,0,0,0,0,0,0,6,60.6,6.9,1,47.7,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,47.7,0,1.791759,,0 13,6,0,1,2,629668,1,0,17.55031,1,3,1,0,0,0,0,0,0,0,0,0,0,0,6,60.6,6.9,1,47.7,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,1,0,0,47.7,0,1.791759,,0 13,6,0,1,3,629668,1,0,18.55031,1,3,1,63.3218,6.903114,33.78201,0,0,104.0069,0,0,0,3,1,6,60.6,6.9,1,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,47.7,0,1.791759,4.644457,1 13,6,0,1,1,629669,1,0,18.74607,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,75.5,13.8,0,53.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,53.4,0,1.791759,,0 13,6,0,1,2,629669,1,0,19.74607,1,8,1,0,0,0,0,0,0,0,0,0,0,0,6,75.5,13.8,0,53.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,53.4,0,1.791759,,0 13,6,0,1,3,629669,1,0,20.74607,1,8,1,14.53287,0,0,0,0,14.53287,0,0,0,0,0,6,75.5,13.8,0,53.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,53.4,0,1.791759,2.676413,1 13,6,0,1,1,629670,1,0,25.89186,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,1,0,47.7,0,1.791759,,0 13,6,0,1,2,629670,1,0,26.89186,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,1,0,47.7,0,1.791759,,0 13,6,0,1,3,629670,1,0,27.89186,1,10,1,0,0,0,0,0,0,0,0,0,0,0,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,1,0,47.7,0,1.791759,,0 13,6,0,1,1,629671,1,0,47.07734,1,3,1,13.54211,10.57977,35.97122,0,0,60.0931,0,0,0,1,1,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,47.7,0,1.791759,4.095895,1 13,6,0,1,2,629671,1,0,48.07734,1,3,1,2.657555,4.536826,0,0,0,7.194381,0,0,0,1,0,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,47.7,0,1.791759,1.9733,1 13,6,0,1,3,629671,1,0,49.07734,1,3,1,0,0,0,0,0,0,0,0,0,0,0,6,63.8,6.9,0,47.7,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,47.7,0,1.791759,,0 13,6,0,1,1,629672,1,0,12.44627,0,3,1,31.31612,0,0,0,0,31.31612,0,0,0,1,0,6,78.3,10.57626,0,59.3,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,59.3,0,1.791759,3.444133,1 13,6,0,1,2,629672,1,0,13.44627,0,3,1,0,0,0,0,0,0,0,0,0,0,0,6,78.3,10.57626,0,59.3,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,59.3,0,1.791759,,0 13,6,0,1,3,629672,1,0,14.44627,0,3,1,0,0,0,0,0,0,0,0,0,0,0,6,78.3,10.57626,0,59.3,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,1,59.3,0,1.791759,,0 13,6,0,0,1,629689,1,6735.316,60.1013,0,7,1,0,0,0,0,0,0,0,0,0,0,0,1,72.4,0,0,45.5,150,274.64,0,0,0,5.615461,1,4.564348,5.061929,0,1,0,45.5,8.815269,0,,0 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14,6,95,1,2,629910,0,3860.727,15.93498,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,85.6,3.4,0,79.8,357,377.4,1,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,79.8,8.25887,1.386294,,0 14,6,95,1,3,629910,0,3860.727,16.93498,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,85.6,3.4,0,79.8,357,377.4,1,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,79.8,8.25887,1.386294,,0 14,6,95,1,1,629911,0,3860.727,41.21561,1,13,1,17.92414,0,0,0,0,17.92414,0,0,0,0,0,4,58,6.9,0,63.6,357,377.4,0,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,63.6,8.25887,1.386294,2.886148,1 14,6,95,1,2,629911,0,3860.727,42.21561,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,58,6.9,0,63.6,357,377.4,0,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,63.6,8.25887,1.386294,,0 14,6,95,1,3,629911,0,3860.727,43.21561,1,13,1,0,0,0,0,0,0,0,0,0,0,0,4,58,6.9,0,63.6,357,377.4,0,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,63.6,8.25887,1.386294,,0 14,6,95,1,1,629912,0,3860.727,14.02875,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,76.7,10.57626,0,55.6,357,377.4,1,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,55.6,8.25887,1.386294,,0 14,6,95,1,2,629912,0,3860.727,15.02875,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,76.7,10.57626,0,55.6,357,377.4,1,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,55.6,8.25887,1.386294,,0 14,6,95,1,3,629912,0,3860.727,16.02875,0,13,1,0,0,0,0,0,0,0,0,0,0,0,4,76.7,10.57626,0,55.6,357,377.4,1,0,1.386294,5.933306,0,4.564348,5.929029,0,0,0,55.6,8.25887,1.386294,,0 16,6,95,0,1,629920,0,2548.899,7.126626,1,12,1,21.87631,0,0,0,0,21.87631,0,0,0,3,0,7,73.3,10.57626,0,92.6,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,92.6,7.843809,1.94591,3.085404,1 16,6,95,0,2,629920,0,2548.899,8.126626,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,73.3,10.57626,0,92.6,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,92.6,7.843809,1.94591,,0 16,6,95,0,3,629920,0,2548.899,9.126626,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,73.3,10.57626,0,92.6,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,92.6,7.843809,1.94591,,0 16,6,95,0,1,629921,0,2548.899,11.29637,0,12,1,75.72571,2.734539,0,0,530.5006,608.9609,2,0,0,5,0,7,78.3,10.57626,0,81.5,1000,1000,1,0,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,6.411754,1 16,6,95,0,2,629921,0,2548.899,12.29637,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,78.3,10.57626,0,81.5,1000,1000,1,0,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,,0 16,6,95,0,3,629921,0,2548.899,13.29637,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,78.3,10.57626,0,81.5,1000,1000,1,0,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,,0 16,6,95,0,1,629922,0,2548.899,40.10951,0,12,1,8.413967,9.676063,0,0,0,18.09003,0,0,0,1,0,7,71.3,6.9,1,75,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,0,0,1,75,7.843809,1.94591,2.895361,1 16,6,95,0,2,629922,0,2548.899,41.10951,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,71.3,6.9,1,75,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,0,0,1,75,7.843809,1.94591,,0 16,6,95,0,3,629922,0,2548.899,42.10951,0,12,1,43.2247,0,39.45111,0,0,82.67581,0,0,0,1,8,7,71.3,6.9,1,75,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,0,0,1,75,7.843809,1.94591,4.414927,1 16,6,95,0,1,629923,0,2548.899,9.130733,1,12,1,10.51746,0,25.2419,0,0,35.75936,0,0,0,1,0,7,86.7,10.57626,0,81.5,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,3.576812,1 16,6,95,0,2,629923,0,2548.899,10.13073,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,86.7,10.57626,0,81.5,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,,0 16,6,95,0,3,629923,0,2548.899,11.13073,1,12,1,10.2916,0,30.87479,0,0,41.16638,0,0,0,1,0,7,86.7,10.57626,0,81.5,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,81.5,7.843809,1.94591,3.717622,1 16,6,95,0,1,629924,0,2548.899,17.95756,1,12,1,16.82793,0,33.65587,0,0,50.4838,0,0,0,2,0,7,71.3,17.2,0,55.7,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,55.7,7.843809,1.94591,3.921653,1 16,6,95,0,2,629924,0,2548.899,18.95756,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,71.3,17.2,0,55.7,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,0,0,0,55.7,7.843809,1.94591,,0 16,6,95,0,3,629924,0,2548.899,19.95756,1,12,1,80.61749,0,23.46484,0,0,104.0823,0,0,0,7,0,7,71.3,17.2,0,55.7,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,0,0,0,55.7,7.843809,1.94591,4.645182,1 16,6,95,0,1,629925,0,2548.899,15.91239,1,12,1,10.51746,14.72444,27.34539,0,0,52.5873,0,0,0,1,0,7,55.3,3.4,0,56.8,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,56.8,7.843809,1.94591,3.962475,1 16,6,95,0,2,629925,0,2548.899,16.91239,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,55.3,3.4,0,56.8,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,56.8,7.843809,1.94591,,0 16,6,95,0,3,629925,0,2548.899,17.91239,1,12,1,0,5.780446,28.34648,0,0,34.12693,0,0,0,0,0,7,55.3,3.4,0,56.8,1000,1000,1,1,1.94591,6.907755,0,4.564348,6.959049,0,0,0,56.8,7.843809,1.94591,3.530087,1 16,6,95,0,1,629926,0,2548.899,37.50034,1,12,1,198.5696,79.5162,29.44888,0,0,307.5347,0,0,0,4,14,7,43.6,3.4,0,64.8,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,1,0,0,64.8,7.843809,1.94591,5.728588,1 16,6,95,0,2,629926,0,2548.899,38.50034,1,12,1,20.77824,0,0,0,0,20.77824,0,0,0,3,0,7,43.6,3.4,0,64.8,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,1,0,0,64.8,7.843809,1.94591,3.033906,1 16,6,95,0,3,629926,0,2548.899,39.50034,1,12,1,270.6689,21.44082,25.72899,0,734.4769,1052.316,1,0,0,3,10,7,43.6,3.4,0,64.8,1000,1000,0,0,1.94591,6.907755,0,4.564348,6.959049,1,0,0,64.8,7.843809,1.94591,6.958748,1 16,6,95,0,1,629932,0,13601.13,44.76386,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,94.7,6.9,0,88.6,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,0,0,0,88.6,9.517982,1.098612,,0 16,6,95,0,2,629932,0,13601.13,45.76386,1,13,1,0,29.56107,0,0,0,29.56107,0,0,0,0,0,3,94.7,6.9,0,88.6,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,0,0,0,88.6,9.517982,1.098612,3.386458,1 16,6,95,0,3,629932,0,13601.13,46.76386,1,13,1,12.19512,10.49129,0,0,0,22.68641,0,0,0,1,0,3,94.7,6.9,0,88.6,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,0,0,0,88.6,9.517982,1.098612,3.121766,1 16,6,95,0,1,629933,0,13601.13,47.36756,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,78.2,13.8,0,77.3,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,77.3,9.517982,1.098612,,0 16,6,95,0,2,629933,0,13601.13,48.36756,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,78.2,13.8,0,77.3,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,77.3,9.517982,1.098612,,0 16,6,95,0,3,629933,0,13601.13,49.36756,0,12,1,0,0,0,0,0,0,0,0,0,0,0,3,78.2,13.8,0,77.3,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,1,0,0,77.3,9.517982,1.098612,,0 16,6,95,0,1,629934,0,13601.13,16.60506,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,82.4,6.9,0,76.1,1000,1000,1,1,1.098612,6.907755,0,4.564348,6.959049,0,0,0,76.1,9.517982,1.098612,,0 16,6,95,0,2,629934,0,13601.13,17.60506,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,82.4,6.9,0,76.1,1000,1000,1,1,1.098612,6.907755,0,4.564348,6.959049,0,0,0,76.1,9.517982,1.098612,,0 16,6,95,0,3,629934,0,13601.13,18.60506,1,13,1,0,0,0,0,0,0,0,0,0,0,0,3,82.4,6.9,0,76.1,1000,1000,0,0,1.098612,6.907755,0,4.564348,6.959049,0,0,0,76.1,9.517982,1.098612,,0 11,6,0,1,1,629944,0,3328.213,9.14716,1,8,1,15.56584,2.52419,0,0,354.6487,372.7387,1,0,0,2,0,3,86.7,10.57626,0,85.2,0,0,1,1,1.098612,0,0,0,0,1,0,0,85.2,8.110491,1.098612,5.920878,1 11,6,0,1,2,629944,0,3328.213,10.14716,1,8,1,0,9.161315,0,0,0,9.161315,0,0,0,0,0,3,86.7,10.57626,0,85.2,0,0,1,1,1.098612,0,0,0,0,1,0,0,85.2,8.110491,1.098612,2.21499,1 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11,6,0,1,2,630608,1,10933.14,43.40657,1,7,1,0,0,0,0,0,0,0,0,0,0,0,6,77.40034,10.3,.1442925,,0,424.32,0,0,1.791759,6.050488,0,0,0,0,1,0,70.68995,9.299645,1.791759,,0 11,6,0,1,3,630608,1,10933.14,44.40657,1,7,1,10.42101,0,42.10088,0,0,52.52188,0,0,0,0,1,7,77.40034,10.3,.1442925,,0,424.32,0,0,1.94591,6.050488,0,0,0,0,1,0,70.68995,9.299645,1.94591,3.96123,1 11,6,0,1,4,630608,1,10933.14,45.40657,1,7,1,78.3946,33.21455,0,0,0,111.6092,0,0,0,9,0,8,77.40034,10.3,.1442925,,0,424.32,0,0,2.079442,6.050488,0,0,0,0,1,0,70.68995,9.299645,2.079442,4.715003,1 11,6,0,1,5,630608,1,10933.14,46.40657,1,7,1,29.43166,105.8999,0,0,0,135.3315,0,0,0,3,0,8,77.40034,10.3,.1442925,,0,424.32,0,0,2.079442,6.050488,0,0,0,0,1,0,70.68995,9.299645,2.079442,4.907728,1 11,6,0,1,1,630609,1,10933.14,49.30322,0,3,1,18.67322,16.22604,0,0,0,34.89926,0,0,0,5,0,4,75,10.3,0,45.5,0,424.32,0,0,1.386294,6.050488,0,0,0,1,0,0,45.5,9.299645,1.386294,3.552466,1 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11,6,0,1,2,631291,0,9949.82,4.405886,1,11,1,94.82433,6.664148,0,0,0,101.4885,0,0,0,3,11,5,77.40034,10.57626,0,88.9,0,0,1,1,1.609438,0,0,0,0,0,0,0,88.9,9.20541,1.609438,4.619946,1 11,6,0,1,3,631291,0,9949.82,5.405886,1,11,1,79.58833,1.554031,0,0,0,81.14236,0,0,0,1,13,5,77.40034,10.57626,0,88.9,0,0,1,1,1.609438,0,0,0,0,0,0,0,88.9,9.20541,1.609438,4.396205,1 11,6,0,1,1,631296,1,6278.034,15.37303,1,11,1,30.89293,0,28.35379,0,0,59.24672,0,0,0,1,1,8,74.3,13.8,0,54.5,0,0,1,1,2.079442,0,0,0,0,1,0,0,54.5,8.744971,2.079442,4.08171,1 11,6,0,1,2,631296,1,6278.034,16.37303,1,11,1,17.08428,0,0,0,0,17.08428,0,0,0,3,0,8,74.3,13.8,0,54.5,0,0,1,1,2.079442,0,0,0,0,1,0,0,54.5,8.744971,2.079442,2.838159,1 11,6,0,1,3,631296,1,6278.034,17.37303,1,11,1,30.44983,2.058824,29.41176,0,0,61.92041,0,0,0,4,1,8,74.3,13.8,0,54.5,0,0,1,1,2.079442,0,0,0,0,1,0,0,54.5,8.744971,2.079442,4.12585,1 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11,6,0,1,2,631300,1,6278.034,15.44216,1,11,1,134.3964,0,25.05695,0,0,159.4533,0,0,0,9,1,8,73.4,6.9,1,69.3,0,0,1,1,2.079442,0,0,0,0,1,0,0,69.3,8.744971,2.079442,5.071751,1 11,6,0,1,3,631300,1,6278.034,16.44216,1,11,1,84.08305,29.25606,13.84083,0,0,127.1799,0,0,0,8,1,8,73.4,6.9,1,69.3,0,0,1,1,2.079442,0,0,0,0,1,0,0,69.3,8.744971,2.079442,4.845603,1 11,6,0,1,1,631301,1,6278.034,34.15469,1,11,1,0,0,0,0,0,0,0,0,0,0,0,8,76.1,10.3,0,51.1,0,0,0,0,2.079442,0,0,0,0,1,0,0,51.1,8.744971,2.079442,,0 11,6,0,1,2,631301,1,6278.034,35.15469,1,11,1,31.89066,0,0,0,0,31.89066,0,0,0,6,0,8,76.1,10.3,0,51.1,0,0,0,0,2.079442,0,0,0,0,1,0,0,51.1,8.744971,2.079442,3.462313,1 11,6,0,1,3,631301,1,6278.034,36.15469,1,11,1,15.22491,43.96194,0,0,0,59.18685,0,0,0,3,0,8,76.1,10.3,0,51.1,0,0,0,0,2.079442,0,0,0,0,1,0,0,51.1,8.744971,2.079442,4.080699,1 11,6,0,1,1,631302,1,6278.034,12.5859,1,11,1,14.81168,4.731274,0,0,0,19.54295,0,0,0,1,0,8,61.7,10.57626,0,77.8,0,0,1,1,2.079442,0,0,0,0,1,0,0,77.8,8.744971,2.079442,2.972615,1 11,6,0,1,2,631302,1,6278.034,13.5859,1,11,1,7.593014,0,26.57555,0,0,34.16856,0,0,0,0,1,8,61.7,10.57626,0,77.8,0,0,1,1,2.079442,0,0,0,0,1,0,0,77.8,8.744971,2.079442,3.531306,1 11,6,0,1,3,631302,1,6278.034,14.5859,1,11,1,38.75433,1.730104,0,0,0,40.48443,0,0,0,2,0,8,61.7,10.57626,0,77.8,0,0,1,1,2.079442,0,0,0,0,1,0,0,77.8,8.744971,2.079442,3.700917,1 13,6,0,1,1,631308,1,1989.247,3.173169,1,12,1,0,4.020313,0,0,109.6064,113.6267,1,1,0,0,0,3,77.40034,10.57626,0,85.2,300,300,1,1,1.098612,5.703783,1,4.564348,5.755076,0,0,0,85.2,7.596014,1.098612,4.732919,1 13,6,0,1,2,631308,1,1989.247,4.173169,1,12,.1530055,0,0,0,0,0,0,0,0,0,0,0,3,77.40034,10.57626,0,85.2,300,300,1,1,1.098612,5.703783,1,4.564348,5.755076,0,0,0,85.2,7.596014,1.098612,,0 13,6,0,1,1,631309,1,1989.247,24.57221,1,12,1,70.24968,3.118917,0,0,0,73.3686,0,0,0,4,0,3,42,6.9,1,63.6,300,300,0,0,1.098612,5.703783,1,4.564348,5.755076,1,0,0,63.6,7.596014,1.098612,4.295496,1 13,6,0,1,2,631309,1,1989.247,25.57221,1,12,1,72.13364,5.550494,27.33485,0,857.631,962.65,1,0,0,5,0,3,42,6.9,1,63.6,300,300,0,0,1.098612,5.703783,1,4.564348,5.755076,1,0,0,63.6,7.596014,1.098612,6.86969,1 13,6,0,1,3,631309,1,1989.247,26.57221,1,12,1,36.33218,13.3391,0,0,191.6955,241.3668,1,0,0,3,0,1,42,6.9,1,63.6,300,300,0,0,0,5.703783,1,4.564348,5.755076,1,0,0,63.6,7.596014,0,5.486318,1 11,6,0,1,1,631310,1,6735.316,20.47091,0,9,1,50.7829,2.32755,0,0,0,53.11045,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,3.972374,1 11,6,0,1,2,631310,1,6735.316,21.47091,0,9,1,0,1.708428,0,0,0,1.708428,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,.5355738,1 11,6,0,1,3,631310,1,6735.316,22.47091,0,9,1,0,0,0,0,0,0,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,,0 11,6,0,1,1,631311,1,6735.316,18.51335,1,8,1,0,0,0,0,0,0,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,,0 11,6,0,1,1,631312,1,6735.316,17.59617,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,77.1,0,0,55.7,0,0,1,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,,0 11,6,0,1,2,631312,1,6735.316,18.59617,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,,0 11,6,0,1,3,631312,1,6735.316,19.59617,0,0,1,31.14187,0,0,0,0,31.14187,0,0,0,0,0,9,77.1,0,0,55.7,0,0,0,0,2.197225,0,0,0,0,1,0,0,55.7,8.815269,2.197225,3.438553,1 11,6,0,1,1,631313,1,6735.316,12.02464,1,0,1,0,0,0,0,0,0,0,0,0,0,0,9,58.3,10.57626,1,70.4,0,0,1,1,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,,0 11,6,0,1,2,631313,1,6735.316,13.02464,1,0,1,0,0,0,0,0,0,0,0,0,0,0,9,58.3,10.57626,1,70.4,0,0,1,1,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,,0 11,6,0,1,3,631313,1,6735.316,14.02464,1,0,1,0,0,0,0,0,0,0,0,0,0,0,9,58.3,10.57626,1,70.4,0,0,1,1,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,,0 11,6,0,1,1,631314,1,6735.316,15.03901,0,0,1,12.69573,2.539145,0,0,0,15.23487,0,0,0,0,0,9,58.3,10.57626,1,70.4,0,0,1,0,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,2.723587,1 11,6,0,1,2,631314,1,6735.316,16.03901,0,0,1,0,0,0,56.94761,0,0,0,0,1,0,0,9,58.3,10.57626,1,70.4,0,0,1,0,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,,0 11,6,0,1,3,631314,1,6735.316,17.03901,0,0,1,0,0,0,0,0,0,0,0,0,0,0,9,58.3,10.57626,1,70.4,0,0,1,0,2.197225,0,0,0,0,1,0,0,70.4,8.815269,2.197225,,0 11,6,0,1,1,631315,1,6735.316,16.55852,1,0,1,50.35971,13.43631,0,0,315.7004,379.4964,1,0,0,0,0,9,77.1,3.4,0,51.1,0,0,1,1,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,5.938845,1 11,6,0,1,2,631315,1,6735.316,17.55852,1,0,1,52.01215,0,32.64996,0,0,84.66211,0,0,0,0,1,9,77.1,3.4,0,51.1,0,0,1,1,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,4.438668,1 11,6,0,1,3,631315,1,6735.316,18.55852,1,0,1,35.98616,2.49827,38.97232,0,0,77.45675,0,0,0,2,1,9,77.1,3.4,0,51.1,0,0,0,0,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,4.34972,1 11,6,0,0,1,631316,.5112414,6735.316,29.59617,1,7,1,32.93039,5.210505,0,0,0,38.14089,0,0,0,4,0,4,79.8,10.3,0,83,0,0,0,0,1.386294,0,0,0,0,1,0,0,83,8.815269,1.386294,3.641287,1 11,6,0,0,2,631316,.5112414,6735.316,30.59617,1,7,1,9.752439,4.793698,0,0,0,14.54614,0,0,0,0,0,4,79.8,10.3,0,83,0,0,0,0,1.386294,0,0,0,0,1,0,0,83,8.815269,1.386294,2.677325,1 11,6,0,0,3,631316,.5112414,6735.316,31.59617,1,7,1,0,0,0,0,0,0,0,0,0,0,0,4,79.8,10.3,0,83,0,0,0,0,1.386294,0,0,0,0,1,0,0,83,8.815269,1.386294,,0 11,6,0,1,1,631317,1,6735.316,50.05065,1,0,1,64.7482,16.57639,0,0,0,81.32458,0,0,0,5,0,9,67.6,10.3,0,40.9,0,0,0,0,2.197225,0,0,0,0,1,0,0,40.9,8.815269,2.197225,4.398448,1 11,6,0,1,2,631317,1,6735.316,51.05065,1,0,1,137.0539,41.22627,36.44647,0,0,214.7267,0,0,0,4,1,9,67.6,10.3,0,40.9,0,0,0,0,2.197225,0,0,0,0,1,0,0,40.9,8.815269,2.197225,5.369366,1 11,6,0,1,3,631317,1,6735.316,52.05065,1,0,1,47.05882,82.61938,0,0,0,129.6782,0,0,0,3,0,9,67.6,10.3,0,40.9,0,0,0,0,2.197225,0,0,0,0,1,0,0,40.9,8.815269,2.197225,4.865056,1 11,6,0,1,1,631318,1,6735.316,19.5729,0,9,1,0,0,0,0,0,0,0,0,0,0,0,9,77.1,0,0,51.1,0,0,0,0,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,,0 11,6,0,1,2,631318,1,6735.316,20.5729,0,9,1,0,14.2369,0,22.77904,1382.878,1397.115,3,0,1,0,0,9,77.1,0,0,51.1,0,0,0,0,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,7.242165,1 11,6,0,1,3,631318,1,6735.316,21.5729,0,9,1,11.24568,0,0,51.90311,181.6609,192.9066,1,0,2,0,0,9,77.1,0,0,51.1,0,0,0,0,2.197225,0,0,0,0,1,0,0,51.1,8.815269,2.197225,5.262206,1 13,6,0,1,1,631319,0,6735.316,49.62628,1,12,1,1.965602,33.04668,0,0,0,35.01228,0,0,0,1,0,1,70.7,31,0,73.9,150,150,0,0,0,5.010635,1,4.564348,5.061929,1,0,0,73.9,8.815269,0,3.555699,1 13,6,0,1,2,631319,0,6735.316,50.62628,1,12,1,33.72835,55.35551,0,0,0,89.08386,0,0,0,16,0,1,70.7,31,0,73.9,150,150,0,0,0,5.010635,1,4.564348,5.061929,1,0,0,73.9,8.815269,0,4.489578,1 13,6,0,1,3,631319,0,6735.316,51.62628,1,12,1,410.5877,19.9875,0,0,0,430.5752,0,0,0,16,28,1,70.7,31,0,73.9,150,150,0,0,0,5.010635,1,4.564348,5.061929,1,0,0,73.9,8.815269,0,6.065122,1 13,6,0,1,4,631319,0,6735.316,52.62628,1,12,1,163.1658,160.2776,0,0,0,323.4434,0,0,0,17,8,1,70.7,31,0,73.9,150,150,0,0,0,5.010635,1,4.564348,5.061929,1,0,0,73.9,8.815269,0,5.779024,1 13,6,0,1,5,631319,0,6735.316,53.62628,1,12,1,305.4804,220.3112,35.39581,0,0,561.1874,0,0,0,11,25,1,70.7,31,0,73.9,150,150,0,0,0,5.010635,1,4.564348,5.061929,1,0,0,73.9,8.815269,0,6.330055,1 11,6,0,1,1,631331,1,5319.648,29.19644,1,12,1,69.28747,8.230958,27.02703,0,0,104.5455,0,0,0,3,1,7,55.9,20.7,0,40.9,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,40.9,8.57935,1.94591,4.649622,1 11,6,0,1,2,631331,1,5319.648,30.19644,1,12,1,96.17138,14.81313,0,0,11.39471,122.3792,1,1,0,2,0,7,55.9,20.7,0,40.9,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,40.9,8.57935,1.94591,4.807125,1 11,6,0,1,3,631331,1,5319.648,31.19644,1,12,1,66.27762,23.28053,0,0,0,89.55815,0,0,0,4,0,7,55.9,20.7,0,40.9,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,40.9,8.57935,1.94591,4.494888,1 11,6,0,1,4,631331,1,5319.648,32.19644,1,12,1,64.14104,14.79745,28.50713,0,0,107.4456,0,0,0,2,1,8,55.9,20.7,0,40.9,0,206.44,0,0,2.079442,5.33001,0,0,0,1,0,0,40.9,8.57935,2.079442,4.676985,1 11,6,0,1,5,631331,1,5319.648,33.19644,1,12,1,84.23545,21.88769,0,0,0,106.1231,0,0,0,3,0,8,55.9,20.7,0,40.9,0,206.44,0,0,2.079442,5.33001,0,0,0,1,0,0,40.9,8.57935,2.079442,4.6646,1 11,6,0,1,1,631332,1,5319.648,6.743327,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,75,10.57626,0,59.3,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,59.3,8.57935,1.94591,,0 11,6,0,1,2,631332,1,5319.648,7.743327,1,12,1,36.00729,3.988149,0,0,0,39.99544,0,0,0,1,0,7,75,10.57626,0,59.3,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,59.3,8.57935,1.94591,3.688766,1 11,6,0,1,3,631332,1,5319.648,8.743326,1,12,1,95.45644,8.107545,0,0,743.6432,847.2072,2,0,0,8,0,7,75,10.57626,0,59.3,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,59.3,8.57935,1.94591,6.741945,1 11,6,0,1,4,631332,1,5319.648,9.743326,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,75,10.57626,0,59.3,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,59.3,8.57935,2.079442,,0 11,6,0,1,5,631332,1,5319.648,10.74333,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,75,10.57626,0,59.3,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,59.3,8.57935,2.079442,,0 11,6,0,1,1,631333,1,5319.648,11.41136,1,12,1,9.82801,0,27.02703,0,0,36.85504,0,0,0,0,1,7,66.7,10.57626,0,63,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,3.606992,1 11,6,0,1,2,631333,1,5319.648,12.41136,1,12,1,48.76937,2.620784,0,0,0,51.39016,0,0,0,3,0,7,66.7,10.57626,0,63,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,3.939447,1 11,6,0,1,3,631333,1,5319.648,13.41136,1,12,1,132.9721,19.61234,30.84619,0,4315.131,4498.562,2,0,0,7,0,7,66.7,10.57626,0,63,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,8.411513,1 11,6,0,1,4,631333,1,5319.648,14.41136,1,12,1,67.14178,15.71643,0,0,0,82.85822,0,0,0,5,0,8,66.7,10.57626,0,63,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,63,8.57935,2.079442,4.417131,1 11,6,0,1,5,631333,1,5319.648,15.41136,1,12,1,100.1353,44.82409,25.71042,0,0,170.6698,0,0,0,4,0,8,66.7,10.57626,0,63,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,63,8.57935,2.079442,5.139731,1 11,6,0,1,1,631334,1,5319.648,9.382615,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,68.3,10.57626,0,63,0,206.44,1,0,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,,0 11,6,0,1,2,631334,1,5319.648,10.38262,0,12,1,70.19143,4.216044,0,0,0,74.40748,0,0,0,5,0,7,68.3,10.57626,0,63,0,206.44,1,0,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,4.309556,1 11,6,0,1,3,631334,1,5319.648,11.38262,0,12,1,0,0,0,0,0,0,0,0,0,0,0,7,68.3,10.57626,0,63,0,206.44,1,0,1.94591,5.33001,0,0,0,1,0,0,63,8.57935,1.94591,,0 11,6,0,1,4,631334,1,5319.648,12.38262,0,12,1,60.76519,4.782445,0,0,0,65.54764,0,0,0,2,0,8,68.3,10.57626,0,63,0,206.44,1,0,2.079442,5.33001,0,0,0,1,0,0,63,8.57935,2.079442,4.182777,1 11,6,0,1,5,631334,1,5319.648,13.38262,0,12,1,17.59134,0,0,0,0,17.59134,0,0,0,1,0,8,68.3,10.57626,0,63,0,206.44,1,0,2.079442,5.33001,0,0,0,1,0,0,63,8.57935,2.079442,2.867407,1 11,6,0,1,1,631335,1,5319.648,32.31211,0,9,1,35.38084,1.149877,0,0,0,36.53071,0,0,0,1,1,7,78.7,3.4,0,68.2,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,68.2,8.57935,1.94591,3.598153,1 11,6,0,1,2,631335,1,5319.648,33.31211,0,9,1,19.59891,0,0,0,356.4266,376.0255,1,0,0,1,0,7,78.7,3.4,0,68.2,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,68.2,8.57935,1.94591,5.929657,1 11,6,0,1,3,631335,1,5319.648,34.31211,0,9,1,0,0,0,0,0,0,0,0,0,0,0,7,78.7,3.4,0,68.2,0,206.44,0,0,1.94591,5.33001,0,0,0,1,0,0,68.2,8.57935,1.94591,,0 11,6,0,1,4,631335,1,5319.648,35.31211,0,9,1,84.3961,18.9985,0,0,0,103.3946,0,0,0,2,0,8,78.7,3.4,0,68.2,0,206.44,0,0,2.079442,5.33001,0,0,0,1,0,0,68.2,8.57935,2.079442,4.638553,1 11,6,0,1,5,631335,1,5319.648,36.31211,0,9,1,49.05277,4.634641,0,0,263.1935,316.8809,1,0,0,2,0,8,78.7,3.4,0,68.2,0,206.44,0,0,2.079442,5.33001,0,0,0,1,0,0,68.2,8.57935,2.079442,5.758526,1 11,6,0,1,1,631336,1,5319.648,8.221766,1,12,1,20.14742,2.810811,0,0,0,22.95823,0,0,0,1,0,7,71.7,10.57626,0,55.6,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,55.6,8.57935,1.94591,3.133677,1 11,6,0,1,2,631336,1,5319.648,9.221766,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,71.7,10.57626,0,55.6,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,55.6,8.57935,1.94591,,0 11,6,0,1,3,631336,1,5319.648,10.22177,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,71.7,10.57626,0,55.6,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,55.6,8.57935,1.94591,,0 11,6,0,1,4,631336,1,5319.648,11.22177,1,12,1,0,0,0,0,0,0,0,0,0,0,0,8,71.7,10.57626,0,55.6,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,55.6,8.57935,2.079442,,0 11,6,0,1,5,631336,1,5319.648,12.22177,1,12,1,17.59134,0,0,0,0,17.59134,0,0,0,1,0,8,71.7,10.57626,0,55.6,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,55.6,8.57935,2.079442,2.867407,1 11,6,0,1,1,631337,1,5319.648,5.497604,1,12,1,32.43243,1.405405,0,0,345.946,379.7838,1,0,0,0,0,7,75,10.57626,0,74.1,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,74.1,8.57935,1.94591,5.939602,1 11,6,0,1,2,631337,1,5319.648,6.497604,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,75,10.57626,0,74.1,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,74.1,8.57935,1.94591,,0 11,6,0,1,3,631337,1,5319.648,7.497604,1,12,1,0,0,0,0,0,0,0,0,0,0,0,7,75,10.57626,0,74.1,0,206.44,1,1,1.94591,5.33001,0,0,0,1,0,0,74.1,8.57935,1.94591,,0 11,6,0,1,4,631337,1,5319.648,8.497604,1,12,1,43.88597,3.938485,0,0,0,47.82446,0,0,0,3,0,8,75,10.57626,0,74.1,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,74.1,8.57935,2.079442,3.867537,1 11,6,0,1,5,631337,1,5319.648,9.497604,1,12,1,29.76996,6.529093,0,0,0,36.29905,0,0,0,1,0,8,75,10.57626,0,74.1,0,206.44,1,1,2.079442,5.33001,0,0,0,1,0,0,74.1,8.57935,2.079442,3.591792,1 13,6,0,0,1,631338,1,9940.604,5.629021,1,10,1,10.51746,0,0,0,0,10.51746,0,0,0,0,0,4,90,10.57626,0,74.1,450,450,1,1,1.386294,6.109248,1,4.564348,6.160541,1,0,0,74.1,9.204484,1.386294,2.353037,1 13,6,0,0,2,631338,1,9940.604,6.629021,1,10,1,49.86777,0,0,0,800.5289,850.3967,1,0,0,4,0,4,90,10.57626,0,74.1,450,450,1,1,1.386294,6.109248,1,4.564348,6.160541,1,0,0,74.1,9.204484,1.386294,6.745703,1 13,6,0,0,3,631338,1,9940.604,7.629021,1,10,1,0,0,0,0,0,0,0,0,0,0,0,4,90,10.57626,0,74.1,450,450,1,1,1.386294,6.109248,1,4.564348,6.160541,1,0,0,74.1,9.204484,1.386294,,0 13,6,0,0,1,631340,1,9940.604,27.73717,0,9,1,28.18679,0,0,0,0,28.18679,0,0,0,1,0,4,80.9,3.4,0,69.3,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,69.3,9.204484,1.386294,3.338853,1 13,6,0,0,2,631340,1,9940.604,28.73717,0,9,1,69.89044,0,0,0,0,69.89044,0,0,0,3,0,4,80.9,3.4,0,69.3,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,69.3,9.204484,1.386294,4.246929,1 13,6,0,0,3,631340,1,9940.604,29.73717,0,9,1,81.3036,0,0,0,0,81.3036,0,0,0,4,0,4,80.9,3.4,0,69.3,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,69.3,9.204484,1.386294,4.39819,1 13,6,0,0,1,631341,1,9940.604,28.77481,1,10,1,116.1127,21.30837,0,0,0,137.4211,0,0,0,6,0,4,82.4,3.4,0,63.6,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,63.6,9.204484,1.386294,4.92305,1 13,6,0,0,2,631341,1,9940.604,29.77481,1,10,1,91.36758,0,0,0,0,91.36758,0,0,0,5,0,4,82.4,3.4,0,63.6,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,63.6,9.204484,1.386294,4.514891,1 13,6,0,0,3,631341,1,9940.604,30.77481,1,10,1,24.5283,2.024014,0,0,0,26.55231,0,0,0,2,0,4,82.4,3.4,0,63.6,450,450,0,0,1.386294,6.109248,1,4.564348,6.160541,1,0,0,63.6,9.204484,1.386294,3.279117,1 11,6,0,1,1,631367,0,10634.41,41.39631,0,11,1,0,0,0,0,0,0,0,0,0,0,0,3,85.1,6.9,0,81.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,81.8,9.271944,1.098612,,0 11,6,0,1,2,631367,0,10634.41,42.39631,0,11,1,11.33358,16.40347,0,0,0,27.73706,0,0,0,3,0,3,85.1,6.9,0,81.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,81.8,9.271944,1.098612,3.322769,1 11,6,0,1,3,631367,0,10634.41,43.39631,0,11,1,30.18868,11.80789,24.01372,0,0,66.01029,0,0,0,5,0,3,85.1,6.9,0,81.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,81.8,9.271944,1.098612,4.189811,1 11,6,0,1,1,631368,0,10634.41,39.15127,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,88.8,24.1,0,56.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,56.8,9.271944,1.098612,,0 11,6,0,1,2,631368,0,10634.41,40.15127,1,12,1,0,0,0,0,0,0,0,0,0,0,0,3,88.8,24.1,0,56.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,56.8,9.271944,1.098612,,0 11,6,0,1,3,631368,0,10634.41,41.15127,1,12,1,33.61921,10.18868,40.01029,0,0,83.81818,0,0,0,4,0,3,88.8,24.1,0,56.8,0,144.36,0,0,1.098612,4.97231,0,0,0,0,0,0,56.8,9.271944,1.098612,4.42865,1 11,6,0,1,1,631370,0,10634.41,12.71732,0,12,1,27.34539,0,0,0,0,27.34539,0,0,0,2,0,3,91.7,10.57626,0,92.6,0,144.36,1,0,1.098612,4.97231,0,0,0,0,0,0,92.6,9.271944,1.098612,3.308548,1 11,6,0,1,2,631370,0,10634.41,13.71732,0,12,1,25.31167,4.998111,0,0,0,30.30978,0,0,0,4,0,3,91.7,10.57626,0,92.6,0,144.36,1,0,1.098612,4.97231,0,0,0,0,0,0,92.6,9.271944,1.098612,3.411471,1 11,6,0,1,3,631370,0,10634.41,14.71732,0,12,1,13.72213,4.425386,0,0,0,18.14751,0,0,0,3,0,3,91.7,10.57626,0,92.6,0,144.36,1,0,1.098612,4.97231,0,0,0,0,0,0,92.6,9.271944,1.098612,2.898534,1 13,6,0,1,1,631379,1,6906.158,14.16016,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,75,8.840314,1.791759,,0 13,6,0,1,2,631379,1,6906.158,15.16016,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,75,8.840314,1.791759,,0 13,6,0,1,3,631379,1,6906.158,16.16016,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,75,8.840314,1.791759,,0 13,6,0,1,4,631379,1,6906.158,17.16016,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92,0,0,75,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,75,8.840314,1.791759,,0 13,6,0,1,5,631379,1,6906.158,18.16016,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,92,0,0,75,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,75,8.840314,1.791759,,0 13,6,0,1,1,631380,1,6906.158,16.70363,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,81.9,0,0,58,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,58,8.840314,1.791759,,0 13,6,0,1,2,631380,1,6906.158,17.70363,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,81.9,0,0,58,450,450,1,1,1.791759,6.109248,1,4.564348,6.160541,0,0,0,58,8.840314,1.791759,,0 13,6,0,1,3,631380,1,6906.158,18.70363,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,81.9,0,0,58,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,58,8.840314,1.791759,,0 13,6,0,1,4,631380,1,6906.158,19.70363,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,81.9,0,0,58,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,58,8.840314,1.791759,,0 13,6,0,1,5,631380,1,6906.158,20.70363,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,81.9,0,0,58,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,58,8.840314,1.791759,,0 13,6,0,1,1,631381,1,6906.158,50.141,0,4,1,0,0,0,0,0,0,0,0,0,0,0,6,70.7,10.3,1,59.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,59.1,8.840314,1.791759,,0 13,6,0,1,2,631381,1,6906.158,51.141,0,4,1,0,0,0,0,0,0,0,0,0,0,0,6,70.7,10.3,1,59.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,59.1,8.840314,1.791759,,0 13,6,0,1,3,631381,1,6906.158,52.141,0,4,1,0,0,0,0,0,0,0,0,0,0,0,6,70.7,10.3,1,59.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,59.1,8.840314,1.791759,,0 13,6,0,1,4,631381,1,6906.158,53.141,0,4,1,0,0,0,0,0,0,0,0,0,0,0,6,70.7,10.3,1,59.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,59.1,8.840314,1.791759,,0 13,6,0,1,5,631381,1,6906.158,54.141,0,4,1,0,0,0,0,0,0,0,0,0,0,0,6,70.7,10.3,1,59.1,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,59.1,8.840314,1.791759,,0 13,6,0,1,1,631382,1,6906.158,12.14784,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.3,10.57626,0,77.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,77.8,8.840314,1.791759,,0 13,6,0,1,2,631382,1,6906.158,13.14784,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.3,10.57626,0,77.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,77.8,8.840314,1.791759,,0 13,6,0,1,3,631382,1,6906.158,14.14784,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.3,10.57626,0,77.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,77.8,8.840314,1.791759,,0 13,6,0,1,4,631382,1,6906.158,15.14784,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.3,10.57626,0,77.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,77.8,8.840314,1.791759,,0 13,6,0,1,5,631382,1,6906.158,16.14784,0,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.3,10.57626,0,77.8,450,450,1,0,1.791759,6.109248,1,4.564348,6.160541,0,0,0,77.8,8.840314,1.791759,,0 13,6,0,1,1,631383,1,6906.158,43.71252,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.8,10.3,1,61.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,61.4,8.840314,1.791759,,0 13,6,0,1,2,631383,1,6906.158,44.71252,1,12,1,0,0,0,0,0,0,0,0,0,0,0,6,88.8,10.3,1,61.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,61.4,8.840314,1.791759,,0 13,6,0,1,3,631383,1,6906.158,45.71252,1,12,1,0,0,0,0,1636.932,1636.932,2,0,0,0,0,6,88.8,10.3,1,61.4,450,450,0,0,1.791759,6.109248,1,4.564348,6.160541,1,0,0,61.4,8.840314,1.791759,7.400579,1 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11,6,0,0,3,632031,0,7922.171,.5982204,1,15,1,54.32526,7.865052,0,0,0,62.19031,0,0,0,6,0,4,77.40034,10.57626,.1442925,,0,0,1,1,1.386294,0,0,0,0,0,0,0,70.68995,8.977547,1.386294,4.130199,1 11,6,0,0,3,632032,.5112414,6735.316,.4284737,0,12,1,92.68293,42.0662,0,0,0,134.7491,0,0,0,20,0,4,77.40034,10.57626,.1442925,,0,0,1,0,1.386294,0,0,0,0,0,0,0,70.68995,8.815269,1.386294,4.903415,1 19,6,25,0,3,632036,0,5479.263,.7734429,1,12,1,11.76471,0,0,0,0,11.76471,0,0,0,2,0,8,77.40034,10.57626,.1442925,,696.3,884,1,1,2.079442,6.784457,0,3.258096,7.932075,0,0,0,70.68995,8.608909,2.079442,2.465104,1 19,6,25,0,3,632037,0,5479.263,.6830938,0,12,1,5.882353,1.965398,0,0,0,7.847751,0,0,0,1,0,8,77.40034,10.57626,.1442925,,696.3,884,1,0,2.079442,6.784457,0,3.258096,7.932075,0,0,0,70.68995,8.608909,2.079442,2.060227,1 10,6,50,0,3,632038,1,1264.721,.6119096,0,10,1,36.70669,0,0,0,0,36.70669,0,0,0,3,0,7,77.40034,10.57626,.1442925,,795,0,1,0,1.94591,0,0,3.931826,7.37149,0,0,0,70.68995,7.143397,1.94591,3.602959,1 13,6,0,0,5,632045,1,6735.316,.3066393,0,11,1,0,0,0,0,0,0,0,0,0,0,0,7,77.40034,10.57626,.1442925,,450,450,1,0,1.94591,6.109248,1,4.564348,6.160541,0,0,0,70.68995,8.815269,1.94591,,0 17,6,25,0,5,632050,0,8273.9,.4544832,1,8,1,34.64837,0,0,0,0,34.64837,0,0,0,5,0,5,77.40034,10.57626,.1442925,,650,650,1,1,1.609438,6.476973,0,3.258096,7.863267,0,0,0,70.68995,9.020982,1.609438,3.545251,1 5,6,25,0,3,632051,0,8730.671,.275154,0,8,1,29.50257,4.51801,0,0,0,34.02058,0,0,0,0,0,5,77.40034,10.57626,.1442925,,670,670,1,0,1.609438,6.507277,0,3.258096,7.893572,0,0,0,70.68995,9.074712,1.609438,3.526966,1 14,6,95,0,3,632058,1,3400.922,.3737166,0,7,1,5.882353,0,0,0,0,5.882353,0,0,0,1,0,10,77.40034,10.57626,.1442925,,48.6,48.6,1,0,2.302585,3.883624,0,4.564348,3.934917,0,0,0,70.68995,8.132095,2.302585,1.771957,1 19,6,25,0,3,632064,1,6735.316,.5845311,1,8,1,0,0,0,0,0,0,0,0,0,0,0,4,77.40034,10.57626,.1442925,,524.7,524.7,1,1,1.386294,6.262826,0,3.258096,7.649121,0,0,0,70.68995,8.815269,1.386294,,0 11,6,0,0,2,632073,0,6735.316,.4031485,1,6,1,11.38952,7.517084,0,0,0,18.90661,0,0,0,2,0,7,77.40034,10.57626,.1442925,,0,216.48,1,1,1.94591,5.377498,0,0,0,0,0,0,70.68995,8.815269,1.94591,2.939512,1 11,6,0,0,3,632073,0,6735.316,1.403149,1,6,1,0,0,0,0,0,0,0,0,0,0,0,8,77.40034,10.57626,.1442925,,0,216.48,1,1,2.079442,5.377498,0,0,0,0,0,0,70.68995,8.815269,2.079442,,0 18,6,25,0,3,632075,0,7493.087,.3052704,0,12,1,98.96194,16.81661,0,0,467.4741,583.2526,1,0,0,8,0,5,77.40034,10.57626,.1442925,,750,967.6,1,0,1.609438,6.874819,0,3.258096,8.006368,0,0,0,70.68995,8.921869,1.609438,6.36862,1 18,6,25,0,3,632166,0,1896.569,.0561259,0,12,1,61.93772,4.865052,0,0,0,66.80276,0,0,0,8,0,6,77.40034,10.57626,.1442925,,173.5,173.5,1,0,1.791759,5.156178,0,3.258096,6.542472,0,0,0,70.68995,7.548329,1.791759,4.201745,1 6,6,25,0,3,632167,1,6735.316,.1054073,1,12,1,28.02768,6.813149,0,0,0,34.84083,0,0,0,6,0,4,77.40034,10.57626,.1442925,,750,750,1,1,1.386294,6.620073,0,3.258096,8.006368,0,0,0,70.68995,8.815269,1.386294,3.55079,1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/000077500000000000000000000000001224417117700241335ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/R_scotvote.s000066400000000000000000000006611224417117700264510ustar00rootroot00000000000000### SETUP ### d <- read.table("./scotvote.csv",sep=",", header=T) attach(d) ### MODEL ### m1 <- glm(YES ~ COUTAX * UNEMPF + MOR + ACT + GDP + AGE, family=Gamma) results <- summary.glm(m1) results results['coefficients'] logLik(m1) scale <- results$disp Y <- YES mu <- m1$fitted llf <- -1/scale * sum(Y/mu+log(mu)+(scale-1)*log(Y)+log(scale)+scale*lgamma(1/scale)) print(llf) print("This is the llf calculated with the formula") statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/__init__.py000066400000000000000000000000231224417117700262370ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/data.py000066400000000000000000000055361224417117700254270ustar00rootroot00000000000000"""Taxation Powers Vote for the Scottish Parliament 1997 dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission from the original author, who retains all rights.""" TITLE = "Taxation Powers Vote for the Scottish Parliamant 1997" SOURCE = """ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html """ DESCRSHORT = """Taxation Powers' Yes Vote for Scottish Parliamanet-1997""" DESCRLONG = """ This data is based on the example in Gill and describes the proportion of voters who voted Yes to grant the Scottish Parliament taxation powers. The data are divided into 32 council districts. This example's explanatory variables include the amount of council tax collected in pounds sterling as of April 1997 per two adults before adjustments, the female percentage of total claims for unemployment benefits as of January, 1998, the standardized mortality rate (UK is 100), the percentage of labor force participation, regional GDP, the percentage of children aged 5 to 15, and an interaction term between female unemployment and the council tax. The original source files and variable information are included in /scotland/src/ """ NOTE = """ Number of Observations - 32 (1 for each Scottish district) Number of Variables - 8 Variable name definitions:: YES - Proportion voting yes to granting taxation powers to the Scottish parliament. COUTAX - Amount of council tax collected in pounds steling as of April '97 UNEMPF - Female percentage of total unemployment benefits claims as of January 1998 MOR - The standardized mortality rate (UK is 100) ACT - Labor force participation (Short for active) GDP - GDP per county AGE - Percentage of children aged 5 to 15 in the county COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF Council district names are included in the data file, though are not returned by load. """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the Scotvote data and returns a Dataset instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Load the Scotvote data and returns a Dataset instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = np.recfromtxt(open(filepath + '/scotvote.csv',"rb"), delimiter=",", names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8)) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/scotvote.csv000066400000000000000000000035421224417117700265220ustar00rootroot00000000000000"COUNCILDIST","YES","COUTAX","UNEMPF","MOR","ACT","GDP","AGE","COUTAX_FEMALEUNEMP" "Aberdeen_City",60.3,712,21,105,82.4,13566,12.3,14952 "Aberdeenshire",52.3,643,26.5,97,80.2,13566,15.3,17039.5 "Angus",53.4,679,28.3,113,86.3,9611,13.9,19215.7 "Argyll_and_Bute",57,801,27.1,109,80.4,9483,13.6,21707.1 "Clackmannanshire",68.7,753,22,115,64.7,9265,14.6,16566 "Dumfries_and_Galloway",48.8,714,24.3,107,79,9555,13.8,17350.2 "Dundee_City",65.5,920,21.2,118,72.2,9611,13.3,19504 "East_Ayrshire",70.5,779,20.5,114,75.2,9483,14.5,15969.5 "East_Dunbartonshire",59.1,771,23.2,102,81.1,9483,14.2,17887.2 "East_Lothian",62.7,724,20.5,112,80.3,12656,13.7,14842 "East_Renfrewshire",51.6,682,23.8,96,83,9483,14.6,16231.6 "Edinburgh_City",62,837,22.1,111,74.5,12656,11.6,18497.7 "Eilean_Siar_(Western_Isles)",68.4,599,19.9,117,83.8,8298,15.1,11920.1 "Falkirk",69.2,680,21.5,121,77.6,9265,13.7,14620 "Fife",64.7,747,22.5,109,77.9,8314,14.4,16807.5 "Glasgow_City",75,982,19.4,137,65.3,9483,13.3,19050.8 "Highland",62.1,719,25.9,109,80.9,8298,14.9,18622.1 "Inverclyde",67.2,831,18.5,138,80.2,9483,14.6,15373.5 "Midlothian",67.7,858,19.4,119,84.8,12656,14.3,16645.2 "Moray",52.7,652,27.2,108,86.4,13566,14.6,17734.4 "North_Ayrshire",65.7,718,23.7,115,73.5,9483,15,17016.6 "North_Lanarkshire",72.2,787,20.8,126,74.7,9483,14.9,16369.6 "Orkney_Islands",47.4,515,26.8,106,87.8,8298,15.3,13802 "Perth_and_Kinross",51.3,732,23,103,86.6,9611,13.8,16836 "Renfrewshire",63.6,783,20.5,125,78.5,9483,14.1,16051.5 "Scottish_Borders_The",50.7,612,23.7,100,80.6,9033,13.3,14504.4 "Shetland_Islands",51.6,486,23.2,117,84.8,8298,15.9,11275.2 "South_Ayrshire",56.2,765,23.6,105,79.2,9483,13.7,18054 "South_Lanarkshire",67.6,793,21.7,125,78.4,9483,14.5,17208.1 "Stirling",58.9,776,23,110,77.2,9265,13.6,17848 "West_Dunbartonshire",74.7,978,19.3,130,71.5,9483,15.3,18875.4 "West_Lothian",67.3,792,21.2,126,82.2,12656,15.1,16790.4 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/000077500000000000000000000000001224417117700247225ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland.readme000066400000000000000000000026651224417117700277210ustar00rootroot00000000000000######################################################################################################### # # # This archive is part of the free distribution of data and statistical software code for # # "Generalized Linear Models: A Unified Approach", Jeff Gill, Sage QASS Series. You are # # free to use, modify, distribute, publish, etc. provided attribution. Please forward # # bugs, complaints, comments, and useful changes to: jgill@latte.harvard.edu. # # # ######################################################################################################### Electoral Politics in Scotland. These data are from the 1997 vote that established a Scottish Parliament with taxing powers. The data are culled from several different official UK documents provided by the Office for National Statistics, the General Register Office for Scotland, the Scottish Office: Education and Industry Department, the Scottish Department for Education and Employment, The Scottish Office Office: Development Department, and David Boothroyd (thank you). The files in this zip archive are: scotland.readme this file scotvote.dat the data file with a header indicating scotland_births.html scotland_changes.html scotland_devolution.html scotland_econ_summary.html scotland_economics.html scotland_education.html scotland_housing.html scotland_population.html these are html files with various details on the variables included. statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_births.html000066400000000000000000000350371224417117700310020ustar00rootroot00000000000000 Cross-sectional dataset viewer v1.1
    Home StatSearch Text Search StatStore FAQ  
    Dataset Display - Cross-Sectional
    Dataset Name: RT331602
    Title: Vital and social statistics: Scotland
    Description: Vital and social statistics: Scotland

    This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
    Source: Office for National Statistics; General Register Office for Scotland
    Time Frame: 1996
    Geographic Coverage: United Kingdom
    Universe: UK live births
    Measure: various
    Units: See table
    Scalar: various
    Formula: none
    Table Dimensions
    Please select at least one item from each list and "Display Selection" or choose "Display All"
    To select list items either hold down 'Ctrl' key and click each item required, or click the first item and hold down the mouse button whilst scrolling down the list.
    Region 4 Measure
     
    To change your selection, click in the appropriate box
    Table
    Live births per 1,000 population 19961Deaths per 1,000 population 19961Perinatal mortality rate, 3 year average, 1994-19962Infant mortality rate, 3 year average, 1994-19963Percentage of live births outside marriage 1996
    United Kingdom12.510.98.86.136.0
    Scotland11.611.89.36.236.0
    Aberdeen City11.010.47.75.735.0
    Aberdeenshire11.39.09.03.824.0
    Angus311.013.25.63.233.0
    Argyll and Bute10.513.88.67.033.0
    Clackmannanshire12.311.310.46.340.0
    Dumfries and Galloway10.912.88.87.834.0
    Dundee City11.513.18.66.851.0
    East Ayrshire311.411.612.36.540.0
    East Dunbartonshire10.59.28.17.219.0
    East Lothian12.312.67.65.229.0
    East Renfrewshire11.59.57.46.219.0
    Edinburgh, City of11.411.78.16.433.0
    Eilean Siar (Western Isles)239.714.911.25.719.0
    Falkirk11.711.77.94.834.0
    Fife11.011.48.77.137.0
    Glasgow City12.514.011.16.949.0
    Highland11.411.48.36.534.0
    Inverclyde11.714.511.58.045.0
    Midlothian11.210.710.86.035.0
    Moray12.411.09.87.426.0
    North Ayrshire2311.311.811.66.942.0
    North Lanarkshire12.511.111.68.538.0
    Orkney Islands10.911.67.51.430.0
    Perth and Kinross310.512.69.85.929.0
    Renfrewshire311.911.68.04.539.0
    Scottish Borders, The310.712.88.04.928.0
    Shetland Islands2311.710.99.96.528.0
    South Ayrshire10.112.76.24.333.0
    South Lanarkshire11.511.39.25.133.0
    Stirling11.111.87.74.933.0
    West Dunbartonshire12.512.711.78.742.0
    West Lothian13.19.58.74.633.0
    Footnotes
    1 -Births are on the basis of year of occurrence in England and Wales and year of registration in Scotland and Northern Ireland. Deaths relate to year of registration.
    2 -Still births and deaths of infants under 1 week of age per 1,000 live and still births. Figures for some Council areas should be treated with caution as the perinatal mortality rate was based on fewer than 20 deaths.
    3 -Deaths of infants under 1 year of age per 1,000 live births. Figures for some Council areas should be treated with caution as the infant mortality rate was based on fewer than 20 deaths.
    4 -New Councils for Scotland
    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_changes.html000066400000000000000000000265351224417117700311220ustar00rootroot00000000000000 GENUKI: Administrative Areas of Scotland

    GENUKI Home page
    Administrative Regions
    of the British Isles    		    Contents

    Administrative Areas of Scotland

    The first table below shows the historic counties and their administrative sub-divisions before the first round of changes and lists the successor regions for each, that is the post-change regions which contain some or all of the original county area. The second table shows the regions after the first round of changes and lists their successor unitary authorities. In all cases only the top-tier authority is shown - either the top-tier in a two-tier arrangement or a single tier authority (shown italicised).

    The tables also show the Chapman County Codes (CCC) for each county and region. These are unique 3 letter codes.

    For a brief description of the administrative changes in the United Kingdom see - Local Government Changes in the United Kingdom.

    The following abbreviations are used in these tables:

    Key
    (C) County of a City
    (U) Unitary Authority

    Single-tier local authorities are shown italicised.

    The links in the following table are to outline maps showing the location of each county.

    Scotland - changes of 1975
    Historic County CCC Administration until 1975 Successor Regions
    Aberdeenshire ABD Aberdeenshire
    Aberdeen (C)    		 Grampian
    Angus (1) ANS Angus
    Dundee (C)    		 Tayside
    Argyllshire (2) ARL Argyllshire Strathclyde
    Highland
    Ayrshire AYR Ayrshire Strathclyde
    Banffshire BAN Banffshire Grampian
    Berwickshire BEW Berwickshire Borders
    Bute (3) BUT Bute Strathclyde
    Caithness CAI Caithness Highland
    Clackmannanshire CLK Clackmannanshire Central
    Dunbartonshire DNB Dunbartonshire Strathclyde
    Dumfriesshire DFS Dumfriesshire Dumfries and Galloway
    East Lothian ELN East Lothian Lothian
    Fife FIF Fife Fife
    Inverness-shire (4) INV Inverness-shire Highland
    Western Isles
    Kincardineshire KCD Kincardineshire Grampian
    Kinross-shire KRS Kinross-shire Tayside
    Kirkcudbrightshire KKD Kirkcudbrightshire Dumfries and Galloway
    Lanarkshire LKS Lanarkshire
    Glasgow (C)    		 Strathclyde
    Midlothian MLN Midlothian
    Edinburgh (C)    		 Lothian
    Borders
    Moray MOR Moray Grampian
    Highland
    Nairnshire NAI Nairnshire Highland
    Orkney (5) OKI Orkney Orkney
    Peeblesshire PEE Peeblesshire Borders
    Perthshire PER Perthshire Tayside
    Central
    Renfrewshire RFW Renfrewshire Strathclyde
    Ross and Cromarty (6) ROC Ross and Cromarty Highland
    Western Isles
    Roxburghshire ROX Roxburghshire Borders
    Selkirkshire SEL Selkirkshire Borders
    Shetland (7) SHI Shetland Shetland
    Stirlingshire STI Stirlingshire Central
    Strathclyde
    Sutherland SUT Sutherland Highland
    West Lothian WLN West Lothian Lothian
    Central
    Wigtownshire WIG Wigtownshire Dumfries and Galloway

    The links in the following table are to maps provided by the Scottish Office.

    Scotland - changes of 1996
    Administration 1975-1996 CCC Successor Unitary Authorities
    Borders BOR The Scottish Borders (U)
    Central CEN Clackmannanshire (U)
    Falkirk (U)
    Stirling (U)
    Dumfries and Galloway DGY Dumfries and Galloway (U)
    Fife FIF Fife (U)
    Grampian GMP Aberdeenshire (U)
    Aberdeen City (U)
    Moray (U)
    Highland (8) HLD Highland (U)
    Lothian LTN City of Edinburgh (U)
    East Lothian (U)
    Midlothian (U)
    West Lothian (U)
    Orkney (5) OKI Orkney Islands (U)
    Shetland (7) SHI Shetland Islands (U)
    Strathclyde (9) STD Argyll and Bute (U)
    City of Glasgow (U)
    East Ayrshire (U)
    East Dunbartonshire (U)
    East Renfrewshire (U)
    Inverclyde (U)
    North Ayrshire (U)
    North Lanarkshire (U)
    Renfrewshire (U)
    South Ayrshire (U)
    South Lanarkshire (U)
    West Dunbartonshire (U)
    Tayside TAY Angus (U)
    Dundee City (U)
    Perth and Kinross (U)
    Western Isles (10) WIS Western Isles (U)

    Notes

    1. An old name for Angus is "Forfarshire".
    2. Includes islands: Islay, Jura and Mull.
    3. Consists of islands Arran and Bute.
    4. Includes islands: Lewis (part), North Uist, South Uist, and Skye.
    5. Also "Orkney Isles", or "Orkney Islands", but NOT "The Orkneys"!
    6. Includes part of the island of Lewis.
    7. Also "Shetland Isles", or "Shetland Islands", but NOT "The Shetlands"! Originally known as "Zetland".
    8. Includes the island of Skye.
    9. Includes islands: Arran, Bute, Islay, Jura and Mull.
    10. Includes islands: Lewis, North Uist and South Uist.
    Return to top of page

    © GENUKI and Contributors 1993, 1997

    Page created by Phil Lloyd in January 1993. Revised and updated in September 1997 by Brian Pears.

    [Last updated: 13th February 1999 - Brian Pears]

    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_devolution.html000066400000000000000000000321461224417117700316750ustar00rootroot00000000000000Devolution referendum 97 result

    Devolution referendum 97 result

    saltire shield'The reason we need a parliament in Scotland is partly so that we can repair some of the damage done by the last Government to, for example, the health service and our manufacturing industry, and partly to ensure that anti-democratic experiments like using Scotland to rehearse the poll tax can never happen again.'
    The Duke of Hamilton & Brandon, whose ancestors resisted the 1707 Treaty of Union, 9 th September 1997.     		Lion Rampant

    Devolution referendum 1997 - the results

    (See the note below concerning the Fife count by David Boothroyd).

    Ballot paper

    Final votes

    I agree that there should be a Scottish Parliament1,775,04574.3 %
    I do not agree that there should be a Scottish Parliament614,40025.7 %

    I agree that a Scottish Parliament should have tax-varying powers1,512,88963.5 %
    I do not agree that a Scottish Parliament should have tax-varying powers870,26336.5 %

    Votes by Unitary Authority

    I agree that there should be a Scottish Parliament

    AuthorityYes votesYes %No votesNo %
    Orkney4,74957.3 %3,54142.7 %
    Dumfries & Galloway44,61960.7 %28,86339.3 %
    Perthshire & Kinross40,34461.7 %24,99838.3 %
    East Renfrewshire28,25361.7 %17,57338.3 %
    Shetland5,43062.4 %3,27537.6 %
    Scottish Borders33,85562.8 %20,06037.2 %
    Aberdeenshire61,62163.9 %34,87836.1 %
    Angus33,57164.7 %18,35035.3 %
    South Ayrshire40,16166.9 %19,90933.1 %
    Moray24,82267.2 %12,12232.8 %
    Argyll & Bute30,45267.3 %14,79632.7 %
    Stirling29,19068.5 %13,44031.5 %
    East Dunbartonshire40,91769.8 %17,72530.2 %
    Aberdeen65,03571.8 %25,58028.2 %
    Edinburgh155,90071.9 %60,83228.1 %
    Highland72,55172.6 %27,43127.4 %
    East Lothian33,52574.2 %11,66525.8 %
    Dundee49,25276.0 %15,55324.0 %
    Fife125,66876.1 %39,51723.9 %
    North Ayrshire51,30476.3 %15,93123.7 %
    South Lanarkshire114,90877.8 %32,76222.2 %
    Inverclyde31,68078.0 %8,94522.0 %
    Renfrewshire68,71179.0 %18,21321.0 %
    Western Isles9,97779.4 %2,58920.6 %
    West Lothian56,92379.6 %14,61420.4 %
    Midlothian31,68179.9 %7,97920.1 %
    Clackmannanshire18,79080.0 %4,70620.0 %
    Falkirk55,64280.0 %13,95320.0 %
    East Ayrshire49,13181.1 %11,42618.9 %
    North Lanarkshire123,06382.6 %26,01017.4 %
    Glasgow204,26983.6 %40,10616.4 %
    West Dunbartonshire39,05184.7 %7,05815.3 %
    Scotland1,775,04574.3 %614,40025.7 %

    I agree that a Scottish Parliament should have tax-varying powers

    AuthorityYes votesYes %No votesNo %
    Orkney3,91747.4 %4,34452.6 %
    Dumfries & Galloway35,73748.8 %37,49951.2 %
    Scottish Borders27,28450.7 %26,49749.3 %
    Perthshire & Kinross33,39851.3 %31,70948.7 %
    East Renfrewshire23,58051.6 %22,15348.4 %
    Shetland4,47851.6 %4,19848.4 %
    Aberdeenshire50,29552.3 %45,92947.7 %
    Moray19,32652.7 %17,34447.3 %
    Angus27,64153.4 %24,08946.6 %
    South Ayrshire33,67956.2 %26,21743.8 %
    Argyll & Bute25,74657.0 %19,42943.0 %
    Stirling25,04458.9 %17,48741.1 %
    East Dunbartonshire34,57659.1 %23,91440.9 %
    Aberdeen54,32060.3 %35,70939.7 %
    Edinburgh133,84362.0 %82,18838.0 %
    Highland61,35962.1 %37,52537.9 %
    East Lothian28,15262.7 %16,76537.3 %
    Renfrewshire55,07563.6 %31,53736.4 %
    Fife108,02164.7 %58,98735.3 %
    Dundee42,30465.5 %22,28034.5 %
    North Ayrshire43,99065.7 %22,99134.3 %
    Inverclyde27,19467.2 %13,27732.8 %
    West Lothian47,99067.3 %23,35432.7 %
    South Lanarkshire99,58767.6 %47,70832.4 %
    Midlothian26,77667.7 %12,76232.3 %
    Western Isles8,55768.4 %3,94731.6 %
    Clackmannanshire16,11268.7 %7,35531.3 %
    Falkirk48,06469.2 %21,40330.8
    East Ayrshire42,55970.5 %17,82429.5 %
    North Lanarkshire107,28872.2 %41,37227.8 %
    West Dunbartonshire34,40874.7 %11,62825.3 %
    Glasgow182,58975.0 %60,84225.0 %
    Scotland1,512,88963.5 %870,26336.5 %

    How Scotland voted, region by region, in 1979

    Region/Islands areaYes Votes% votes% electorateNo Votes% votes% electorateTurnout
    Shetland Islands 2,02027145,466733650
    Orkney Islands2,1042815 5,439723954
    Borders 20,7464027 30,780604067
    Dumfries & Galloway 27,1624026 40,239603864
    Grampian 94,9444828101,485523058
    Tayside 91,482493193,325513263
    Lothian 187,2215033186,421503366
    Highland 44,9735133 43,274493265
    Fife86,2525435 74,436463065
    Strathclyde 596,5195434508,599462963
    Central 71,2965536 59,105453066
    Western Isles6,2185628 4,933442250
    Scotland1,230,9375233*1,153,5024831*64*
    *Percentage on register of 3,747,112 as adjusted by Secretary of State.

    Note by David Boothroyd concerning the Fife count

    I have been doing some work developing my website (which is now at http://www.election.demon.co.uk/election.html) and while preparing the results of the Scottish Parliament referendum I discovered a fairly big discrepancy in the count from Fife Council.

    The Scottish Office press release giving the results of the referendum (no. 1269/97) says that 166,554 people voted in Fife, which I presume represents the number marked on registers as voting. On the first question, the total number of votes (Yes, No and spoilt ballot papers) is 166,025.

    However on the second question, the total number of votes is 167,999 - 1,445 more than the number of ballot papers which should have been issued, and 1,974 more than the number of ballot papers counted on the first question.

    All sources of results give the same figures and so I wrote to the Scottish Office to ask them how this discrepancy might have come about. Their reply suggests it may have resulted from voters demanding only the ballot paper for the second question, though presiding officers were instructed to give all voters both ballot papers, and such people would be marked as voting and therefore included anyway.

    The Scottish Office verified that the results which were issued were those which were certified by the counting officer in Fife and so they represent the result of the referendum in spite of being inaccurate.

    If anyone can shed any light on this please contact David Boothroyd at david@election.demon.co.uk


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    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_econ_summary.html000066400000000000000000000751071224417117700322120ustar00rootroot00000000000000 Scottish Economic Bulletin: Economic Review
    Scottish Economic Bulletin 

     

    The Scottish Economy

    Gross Domestic Product

    Provisional estimates of GDP (income measure) for each UK Government Office Region/country are now available for 1996 with the publication of the Regional Accounts.7 Estimates for 1995 were also made available at county/former Scottish region level.

    Scottish GDP in 1996 was £54.43 billion, 8.6 per cent of UK GDP. GDP per head was £10,614, 99.1 per cent of the UK average. This was the fourth highest of the 12 UK Government Office Regions/countries - below only London, South East and Eastern - for the fifth successive year.

    GDP per head in Scotland relative to the UK increased strongly between 1989 and 1992, reflecting the stronger performance of the Scottish economy in the 1990-1992 UK recession. Since 1992, GDP per head has fluctuated around 99 per cent of UK GDP per head, reaching a peak of 100.2 per cent in 1995.

    Table 2 shows GDP per head in the former Scottish regions in 1995.8 It is instructive to look at trends and, accordingly, Table 2 also provides data for 1989. GDP per head was well above the UK average in both Grampian (133 per cent) and Lothian (124 per cent) in 1995. Although Grampian showed the smallest increase in GDP per head over the 1993-1995 period (and fell slightly relative to the UK), the level of GDP per head was third only to London and Berkshire across the UK, followed by Lothian. All other Scottish regions were below the UK average and GDP per head in the Highlands and Islands and in Fife was amongst the lowest in the UK.

    Table 2: GDP in the Scottish Regions, 1989 and 1995

     GDP per head 1995 (£)GDP per head, 1990=100
    19891995
    Borders9,00380.188.3
    Central9,26589.190.8
    Dumfries and Galloway9,55586.493.7
    Fife8,31484.081.5
    Grampian13,566119.4133.0
    Highlands and Islands8,29880.481.4
    Lothian12,656111.4124.1
    Strathclyde9,48387.993.0
    Tayside9,61188.894.2
    Scotland10,24493.8100.2
    UK10,199100.0100.0

    Source: Office for National Statistics

    The improvement in Scottish GDP per head, relative to the UK, from 1989 has been evident across most Scottish regions. Lothian, Borders and Grampian have seen particularly marked improvements and only Fife had a lower relative level of GDP per head in 1995 than in 1989. Relative GDP per head in the Highlands and Islands has increased slightly but levels have fallen since the peak (of 88.8 per cent ) in 1991.

    Index of Production and Construction

    The Scottish Office Education and Industry Department's quarterly Index of Production and Construction rose by 0.4 per cent in 1997 Q3. Excluding oil and gas, the Index rose by 0.5 per cent. At a broad sectoral level, output rose in manufacturing (0.9 per cent) and in electricity, gas and water supply (5.7 per cent), offset by falling output in construction (2.5 per cent) and mining and quarrying (1.8 per cent). The UK index (less oil and gas) rose by 0.7 per cent in 1997 Q3.

    An indication of the underlying trend in industrial output is obtained by comparing the last 4 quarters for which data are available (to 1997 Q3) with the previous 4 quarters (to 1996 Q3). Excluding oil and gas, the Index rose by 6.0 per cent over this period, as increases were recorded in manufacturing (7.4 per cent), construction (1.8 per cent), electricity, gas and water supply (5.7 per cent) and mining and quarrying (3.3 per cent). By comparison, the UK Index (less oil and gas) rose by 2.0 per cent over the same period.

    Since 1990, manufacturing output has increased by 25.6 per cent. Growth in UK manufacturing has been much more sluggish than in Scotland, growing by only 5.1 per cent over the same period. The influence of the electrical and instrument engineering sector (EIE) on Scottish manufacturing has been discussed in past editions of the Scottish Economic Bulletin and by outside commentators. Excluding EIE, manufacturing output in Scotland has declined by 7.7 per cent since 1990. UK manufacturing excluding EIE has increased by 1.8 per cent.

    In the year to 1997 Q3, the EIE sector continued to grow strongly - by 18.4 per cent. However, growth was also evident in 6 of the other 10 manufacturing sectors over the period. This is the continuation of a trend over the last year in which growth in the manufacturing sector has become more broadly based. Indeed as Chart 3 shows, manufacturing output excluding EIE has been increasing year-on-year in each quarter since 1996 Q4, a trend not seen since 1990 Q3. In the year to 1997 Q3, manufacturing output excluding EIE grew by 1.4 per cent, only slightly below the 1.5 per cent growth in the UK as a whole.

    CHART 3 HERE

    Exports

    The manufacturing sector accounts for most of Scotland's external trade with the rest of the world. Estimates from the 1994 Input-Output Tables99 indicate that around three quarters of trade is in manufacturing. The Scottish Council Development and Industry (SCDI) annual survey of Scottish Manufactured Exports for 1996 was published in December 1997. In current prices, the value of Scottish manufactured exports10 was estimated to have risen by 6.4 per cent in 1996 to £18.42 billion. This represents a slower rate of growth than in recent years (20.3 per cent in 1995 and 24.8 per cent in 1994) and can be compared with growth of 8.9 per cent in UK manufactured exports (to £155.18 billion) in 1996. For the first time since 1988, UK manufactured exports growth outpaced that of Scotland and Scotland's share of UK exports fell marginally from 12.1 per cent in 1995 to 11.9 per cent in 1996.

    As shown in Table 3, four sectors - Office Machinery, Radio/TV/Communication Equipment, Whisky and Chemicals - continued to dominate Scottish manufactured exports in 1996, accounting for 75 per cent of the total. The electronics sector11 had a more mixed export performance in 1996 than in recent years. Exports grew by 6.9 per cent to £10.21 billion (55.5 per cent of total manufactured exports). This compares with growth of over 42 per cent in 1995. Exports from the Office Machinery sector - the largest exporting sector - rose by 14.3 per cent in 1996 to £6.83 billion (37.1 per cent of total manufactured exports). While this rate of growth was considerably lower than in 1995, the sector still contributed over 77 per cent to the total growth in manufactured exports in 1996. Exports from the other major element of Scotland's electronics industry - the Radio/TV/Communication Equipment sector - declined by 7.3 per cent to £3.00 billion.

    Table 3: Top Exporting Sectors in Scotland, 1996

    Sector (SIC92)Value at current prices (£ million)Per cent of TotalNominal increase in value 1995-96: per centContribution to total export growth: per cent
    Office Machinery6,825.037.114.377.5
    Radio, Television & Communication3,003.816.3-7.3-21.6
    Equipment and Apparatus Whisky2,278.112.40.10.1
    Chemicals and Chemical Products1,706.49.39.213.1
    Machinery and Equipment nec802.24.418.411.4
    Other Food Products & Beverages446.02.4-10.7-4.8
    Fabricated Metal Products except Machinery and Equipment411.12.237.310.2
    Pulp, Paper and Paper Products387.02.1-2.0-0.7
    Coke, Refined Petroleum Products and Nuclear Fuel332.01.869.612.4
    Other Transport Equipment326.81.8-22.4-8.6
    Other sectors1,896.210.36.811.1
    All Manufacturing Industries18,414.6100.06.3100.0

    Source: Scottish Council Development and Industry

    Note: 1. Under SIC 92 Whisky is normally incorporated in the Food Products & Beverages sector.

    Exports from the whisky sector increased only marginally in 1996, up by 0.1 per cent to £2.28 billion (12.4 per cent of total manufactured exports). The Chemicals and Chemical Products sector experienced a further rise in exports in 1996, of 9.2 per cent to £1.71 billion (9.3 per cent of total manufactured exports). This follows growth of 9.0 per cent in 1995. An additional 19 industry sectors together represented 25 per cent of total manufactured exports in 1996. Export growth was recorded in 14 sectors.

    Overall the latest figures record a positive - and better than expected - performance by Scottish manufacturing in export markets during 1996. The SCDI quarterly index based on a selected panel survey of large exporters had provisionally estimated a fall of 6.8 per cent in manufactured exports. The rapid growth rates of recent years have slowed but export levels in most sectors continue to rise. Initial estimates from the SCDI quarterly index for 1997 suggest further growth of 12.0 per cent to £20.61 billion.

    Exports by Destination

    As shown in Table 4, the EU remained Scotland's main trading area in 1996 with a 58 per cent share of Scotland's exports. However, exports grew more modestly - by 2.9 per cent - in 1996. Six of the top ten individual country markets were in the EU, the others being the USA, Japan, Switzerland and Norway. The latest survey results confirm France as Scotland's largest export market for the fourth successive year, despite a drop in the actual value of exports of 5.4 per cent to £2.80 billion. (15.2 per cent of total Scottish manufactured exports).

    Table 4: Destination of Scottish Exports in 1996

     Value (£ million, current prices)Per cent of totalNominal percentage growth in 1996Contribution to overall growth: per cent
    European Union10,75658.42.927.5
    North America2,31812.636.856.8
    Other Asia Pacific1,5568.4-14.2-23.5
    EFTA1,0725.816.413.7
    Japan8124.46.34.4
    Middle East5132.823.38.8
    Latin America5102.84.52.0
    Eastern Europe3962.253.512.6
    Africa3111.7-0.6-0.2
    Australasia1710.9-11.9-2.1

    Source: Scottish Council Development and Industry

    Exports to the USA rose by 38.3 per cent in 1996 to £2.22 billion. The USA was responsible for nearly 50 per cent of the increase in total Scottish exports and overtook Germany as the second largest market. There was a strong upturn in sales across the Office Machinery, Radio/TV/Communication Equipment, Coke/Petroleum and Chemicals sectors; the strength of the US economy a causal factor. North America displaced Other Asia Pacific as Scotland's second largest trading area.

    Exports to Japan continued to increase and remained the 7th largest country market for Scottish goods. Total exports to the Other Asia Pacific countries fell by 14.2 per cent in 1996, compared with strong growth of 30.9 per cent in 1995. However, this was almost entirely due to a large drop in exports to Malaysia; there were significant rises in exports to Hong Kong, Singapore and Taiwan. Elsewhere, exports to most other regions showed significant growth with sales to Eastern Europe up 53.5 per cent and exports to the Middle East up 23.3 per cent. Growth in sales were also recorded to the EFTA countries, while exports to Latin America continued to grow modestly. There was a marginal decline in sales to Africa following last year's significant increase, while exports to Australasia continued to decline.

    The Sterling Exchange Rate and Exports

    Inevitably, the strength of sterling has put pressure on Scottish exports. As one would expect, the exposure to exchange rate movements varies by sector in Scotland. This is illustrated in Table 5 which shows, at the broad sectoral level, the proportion of total domestic (i.e. Scottish) output dependent on exports outwith the UK (i.e. to the rest of the world, ROW) and the import content of that output from the same source. The table also shows the corresponding proportions for Scotland's trade with the rest of the UK (RUK).

    Table 5: The External Orientation of Scottish Industry, 1994

    IndustryProportion of domestic output dependent on :Components of gross domestic output
    Exports to RUKExports to ROWImports from RUKImports from ROW
    Agriculture, Forestry and Fishing19.712.97.41.4
    Mining and Quarrying41.029.118.96.8
    Energy and Water Supply6.41.07.87.7
    Manufacturing26.741.818.818.2
    Construction6.00.017.73.9
    Transport and Communication20.08.48.92.5
    Distribution and Catering14.10.05.71.0
    Financial and Business Services12.35.810.81.9
    Other Services4.12.74.11.2
    Whole Economy16.516.312.07.3

    Source: The Scottish Office

    The manufacturing sector is clearly the most sensitive to the effects of exchange rate changes: over 40 per cent of output is exported to ROW and almost 20 per cent of inputs are imported from ROW. Within the sector (though not shown in the table), 2 industries - drink and electrical and instrument engineering - export more than two thirds of their output to ROW, while chemicals and electrical and instrument engineering also import more than a third of inputs. By contrast, the output of the service sector is much more dependent on the home market, relying less on exports to generate value added. The gross output of the service sector also embodies a lower import content.

    For manufacturing, available evidence from the SCDI for 1997 suggests that the strength of sterling is causing difficulties in terms of reduced margins and some job losses. However, as described above, it appears that it has not yet impacted upon the level of export sales, only profitability.

    Business survey evidence in Scotland does point to an adverse impact on exports resulting from sterling's strength but results are far from conclusive. The Scottish Chambers' Business Survey reported a decline in export orders and sales in 1997 Q4, as in Q3 and results from Scottish Engineering also revealed that export orders declined for the third successive quarter, falling in all sectors of the industry. By contrast, the CBI Industrial Trends Survey reported a return to growth in export orders and deliveries also increased significantly in the fourth quarter. However, optimism regarding export prospects fell markedly and, as one might expect, respondents continued to believe that prices would be the most important constraint on export orders over the coming months.

    One particular area in which the exchange rate may have been expected to affect activity levels is travel and tourism both to and from overseas. International Passenger Survey (IPS) evidence for the 12 months to November 1997 shows that the number of visitors to the UK rose by 3 per cent, compared with the year to November 1996. The number of visits from North America increased by 14 per cent, while the number of visits from Western Europe was broadly static. Visits from Other Areas rose by 4 per cent. The total number of UK residents' visits abroad during the 12 months ending November 1997 rose by 11 per cent compared with a year earlier. Visits to Western Europe increased by 12 per cent, while visits to North America and Other Areas increased by 2 per cent and 10 per cent respectively. Overseas earnings rose by 2 per cent in current prices in the year to November and expenditure by UK residents rose by 6 per cent. This resulted in an increase in the deficit on the travel account of the balance of payments from £3.8 billion to £4.6 billion over the period.

    The change in the composition of the tourism market appears to be consistent with the larger rise in sterling against the main European currencies over the last 18 months and has implications for Scotland. North America, Germany and France all account for higher proportions of overseas visits to Scotland than to the UK as a whole. However, a complicating factor is that US and French visitors tend to have a high propensity for travelling as part of a package holiday, paid for in advance with prices based on an exchange rate determined possibly months before the holiday is taken. Consequently, the impact of changes in exchange rates on visits from US and French residents may be delayed. By contrast, the principal types of Dutch and German holidaymakers to Scotland tend to travel independently and to holiday on an ad hoc basis at relatively short notice. The impact of the strength of sterling on these groups is likely to have been demonstrated relatively quickly.

    Some IPS data for Scotland are available to the third quarter of 1997. The total number of overnight visits from overseas tourists was broadly unchanged in the first 3 quarters of the year, compared with the same period in 1996. However, the total from Western Europe fell by 6 per cent and overnight visits from North America were broadly unchanged. By contrast, visits from Other Areas rose by 12 per cent. Evidence for Scotland from the United Kingdom Tourism Survey, covering the first 3 quarters of 1997, reported a 3 per cent fall in the number of tourist trips to Scotland by UK residents compared with the same period in 1996. This compares with growth rates of around 15 per cent in each of the previous 2 years. The value of these trips increased by 7 per cent in current prices, broadly equal to growth in the UK over the same period but lower than growth in 1995 and 1996.

    Labour Market

    Unemployment

    There are 2 main sources of unemployment data. An estimate of unemployment under the International Labour Office definition - ILO unemployment - is provided by the Labour Force Survey (LFS), a quarterly sample survey of households. The second measure of unemployment - the claimant count - is based on records of those claiming Jobseeker's Allowance and National Insurance Credits at Employment Service Offices. The Office for National Statistics announced on 3 February that (from April) its assessment of the labour market would give more weight than previously to the LFS, which is conducted according to internationally agreed definitions drawn up by the ILO.

    ILO unemployment (not seasonally adjusted) in Scotland fell by 32,000 in the year to Autumn (September to November) 1997 to 185,000. The rate of unemployment fell by 1.4 percentage points to 7.4 per cent of the workforce. ILO unemployment in the UK fell by 379,000 in the year to Autumn 1997 to 1,919,000 or 6.6 per cent, 0.8 percentage points below the Scottish rate. Unemployment fell in every Government Office Region (GOR) of the UK. Four GORs - Merseyside, North East, London and Northern Ireland - have higher ILO unemployment rates than Scotland.

    Claimant count unemployment (seasonally adjusted) in Scotland fell throughout 1997 but rose by 1,200 in January 1998 to 141,100, the first rise since April 1996. The rate of unemployment rose by 0.1 percentage point to 5.8 per cent of the workforce, 0.8 percentage points above the UK rate. Of the UK GORs, Merseyside, North East, and Northern Ireland have higher unemployment rates than Scotland, while London has the same rate.

    The claimant count measure of unemployment in Scotland remains significantly lower than the ILO measure. The difference between the ILO measure and the claimant count measure12 in Autumn 1997 was 42,000, a rise of 7,000 on Autumn 1996.

    In the July 1997 Budget, the Government set out a New Deal to help young people, the long term unemployed, lone parents and the disabled move from Welfare to Work. The New Deal for young claimants (aged 18-24) who have been unemployed for 6 months or more was launched in 12 "pathfinder" areas of the UK (including Tayside) in January and the programme will be launched nationally from April. The New Deal for long term unemployed adults (those aged 25 and over who have been unemployed for more than 2 years) will be launched in June.13

    Table 6 summarises the eligibility for the New Deal for these two groups in January 1998. It can be seen that, in Scotland there were 11,300 youth unemployed of over 6 months duration and 17,100 aged 25 and over who had been unemployed for 2 years or more in January 1998 (7.4 per cent and 11.3 per cent of total claimant unemployed, respectively). The total number in these 2 groups has fallen significantly over the last year - by 17,800 (38.5 per cent).

    Table 6: Claiment Count Unemployment for New Deal Target Groups

     Youth (18-24) Unemployment, over 6 months durationAdult (25+) Unemployment, over 2 years duration
    January 1997January 1998Percentage changeJanuary 1997January 1998Percentage change
    Scotland18,10011,300-37.728,10017,100-39.1
    Per cent of claimant count9.87.4..15.211.3..
    UK198,300118,400-40.3357,000216,300-39.4
    Scotland as a percentage of the UK9.19.5..7.97.9..

    Source: Office for National Statistics

    Note: 1. Percentages calculated with reference to unrounded figures.

    Employment

    There are two main official sources of quarterly employment data: the Workforce in Employment series, which is a survey of employers, and the Labour Force Survey.

    An increase of 43,000 in total employment (not seasonally adjusted) in Scotland was recorded by the LFS over the year to Autumn 1997 to reach a new (Autumn) peak of 2,305,000. This was due to increases of 24,000 in the number of employees, 14,000 in the number of self-employed and 5,000 in the number of people either on government supported training and employment programmes or who were unpaid family workers. Given the fall of 32,000 in the level of ILO unemployment, the number of people classed as economically active increased by 10,000 in the year to Autumn 1997. Increases in total employment were evident in most UK GORs, falling only in the North East, Merseyside and Wales. In the UK as a whole, total employment increased by 456,000.

    An increase of 23,000 in the civilian workforce (not seasonally adjusted) was recorded by the Workforce in Employment series over the year to September 1997 to reach a new peak of 2,277,000, (7,000 higher than the 1991 peak and 202,000 above the trough in 1983). This comprised increases of 19,000 in the number of self-employed and 6,000 in the number of employees (comprising increases across the service sector (14,000) and decreases in manufacturing (5,000) and other sectors (3,000)) over the year, partly offset by a fall of 2,000 in the number on work-related government training programmes. Increases in the civilian workforce were evident in all GB regions, except East Anglia and Yorkshire and Humberside. In Great Britain as a whole, the civilian workforce increased by 349,000.

    The growth in the number of employees has been due to the increase in part-time employment.14 In the year to September 1997, part-time employment rose by 32,000 (17,000 males and 15,000 females), offset by a fall of 26,000 in full-time employment (24,000 males and 2,000 females). This is a continuation of a trend over the past few years in which part-time employment has increased - in each year since 1992 (data are available from 1991) - to a level 104,000 higher (46,000 males and 58,000 females) in 1997 than 5 years earlier. By contrast, full-time employment has fallen consistently and in September 1997 was 90,000 lower than 1992 levels (86,000 males and 4,000 females).


     

    7Published in Economic Trends, February 1998.
    8GDP estimates of the Scottish regions measure the value of goods and services produced in an area; they do not measure the income of the residents in an area, as is the case for Government Office Regions/countries of the UK. There is a wide variation between areas in terms of size and population; in order to compare the economic performance of areas it is necessary to use an indicator such as GDP per head of population. Resident population is used as the denominator. The implication of using this in conjunction with the workplace-based GDP figures is that the productivity of urban areas into which workers commute will tend to be overstated by this indicator, while that of surrounding areas in which they live will be understated.
    9Input -Output Tables and Multipliers for Scotland, 1994, The Stationery Office.
    10It should be noted that the data presented by the SCDI for Scottish manufactured exports refer to gross output. They do not measure the level of (or changes in) the value-added component of Scottish manufactured exports (that is, the wages and profits accruing to domestic suppliers of labour and capital).
    11Electronics is classified by the SCDI as consisting of 4 industry groupings: Office Machinery, Electrical Machinery and Apparatus nec, Radio/TV/Communication Equipment and Apparatus and Medical, Precision and Optical Instruments, Watches and Clocks.
    12Average of September to November levels (not seasonally adjusted).
    13The New Deal for young people provides a period of advice and guidance -'the Gateway' - to find unsubsidised jobs. Thereafter, four options will be available: a subsidised job with an employer; a place on an Environment Task Force; a job in the voluntary sector; or full-time education or training. The first 3 options involve at least one day a week training and options 2 and 3 include top-ups to existing benefits. Long term unemployed adults under the New Deal will be able to benefit from two options: a subsidised job with an employer; or opportunities to study for up to 12 months in full-time employment-related courses designed to reach an accredited qualification.
    14Part-time employment is defined here as working less than 30 hours per week.
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    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_economics.html000066400000000000000000000444151224417117700314660ustar00rootroot00000000000000 Cross-sectional dataset viewer v1.1
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    Dataset Display - Cross-Sectional
    Dataset Name: RT331605
    Title: Labour market statistics: Scotland
    Description: Labour market statistics: Scotland

    This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
    Source: Office for National Statistics
    Time Frame: 1996-1998
    Geographic Coverage: United Kingdom
    Universe: Various
    Measure: Various
    Units: See table
    Scalar: none
    Formula: various
    Substitution Details:

    Value Meaning
    .. Not Available
    Table Dimensions
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    Table1
    Economically active 1996-97 (percentages)23Total in employment: 1996-97 (thousands)34In employment 1996-97 Manufacturing (percentages)13ILO unemployment rate 1996-97 (percentages)13Total claimant count: January 1998 (thousands)Claimant count: of which females: January 1998 (percentage)Claimant count: of which long-term unemployed January 1998 (percentages)5Average gross weekly all persons full-time earnings, April 1997 (£)16
    United Kingdom78.626462.019.18.01479.323.226.2366.3
    Scotland77.12277.017.18.7152.222.022.4336.8
    Aberdeen City82.4113.014.34.93.621.017.6404.8
    Aberdeenshire80.2112.014.5..2.826.516.3330.9
    Angus86.359.016.9..3.228.323.3320.0
    Argyll and Bute80.441.0..11.92.927.122.8305.2
    Clackmannanshire64.717.0....1.622.027.5..
    Dumfries and Galloway79.067.017.7..4.424.323.4300.2
    Dundee City72.260.014.69.36.221.226.3327.4
    East Ayrshire75.250.023.114.24.520.528.9307.6
    East Dunbartonshire81.153.0....2.223.217.3329.2
    East Lothian80.341.0....1.720.516.3310.3
    East Renfrewshire83.042.016.8..1.423.820.9..
    Edinburgh, City of74.5207.010.36.611.122.120.4362.8
    Eilean Siar (Western Isles)83.815.0....1.419.923.8..
    Falkirk77.666.023.4..4.521.519.9335.6
    Fife77.9147.021.79.311.122.522.7325.2
    Glasgow City65.3210.014.215.226.919.429.5341.5
    Highland80.9100.012.99.37.925.920.9296.2
    Inverclyde80.239.026.5..2.518.512.7323.4
    Midlothian84.839.0....1.619.413.7309.0
    Moray86.443.014.3..2.227.215.4285.0
    North Ayrshire73.558.027.49.15.123.719.2317.8
    North Lanarkshire74.7133.021.212.410.720.820.1336.7
    Orkney Islands87.810.0....0.426.824.4..
    Perth and Kinross86.666.011.3..2.823.017.8..
    Renfrewshire78.580.020.111.35.520.523.3336.1
    Scottish Borders, The80.649.020.6..2.123.712.3303.5
    Shetland Islands84.811.0....0.423.214.2..
    South Ayrshire79.248.022.910.33.623.623.4346.2
    South Lanarkshire78.4146.022.08.38.221.721.8319.1
    Stirling77.237.0....2.123.019.5346.6
    West Dunbartonshire71.536.0..13.64.219.327.3319.0
    West Lothian82.278.032.6..3.521.210.2335.5
    Footnotes
    1 -In some cases sample sizes are too small to provide reliable estimates.
    2 -Based on the population of working age.
    3 -Data are from the Labour Force Survey and relate to the period March 1996 - February 1997.
    4 -Includes those on government-supported employment and training programmes and unpaid family workers.
    5 -Persons who have been claiming for more than 12 months as a percentage of all claimants.
    6 -Average gross weekly earnings estimates have been derived from the New Earnings Survey and relate to full-time employees on adult rates whose pay for the survey pay-period was not affected by absence.
    7 -New Councils for Scotland
    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_education.html000066400000000000000000000465711224417117700314670ustar00rootroot00000000000000 Cross-sectional dataset viewer v1.1
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    Dataset Name: RT331603
    Title: Education and training: Scotland
    Description: Education and training: Scotland

    This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
    Source: The Scottish Office Home Department; The Scottish Office Education and Industry Department; Department for Education and Employment
    Time Frame: 1995-1997
    Geographic Coverage: United Kingdom
    Universe: various
    Measure: various
    Units: See table
    Scalar: none
    Formula: none
    Substitution Details:

    Value Meaning
    - Negligible (less than half the final digit shown)
    .. Not Available
    Table Dimensions
    Please select at least one item from each list and "Display Selection" or choose "Display All"
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    Table
    Day nursery places per 1,000 population aged under 5 years Nov. 19961Children under 5 in education (percentages) Jan. 19972Pupil/teacher ratio: primary schools (numbers) 1996/97Pupil/teacher ratio: secondary schools (numbers) 1996/97Pupils and students participating in post-compulsory education, (percentages) 1995/963Percentage of pupils in last year of compulsory schooling with no graded results 1995/9645Percentage of pupils in last year of compulsory schooling with 5 or more Grades 1-3 SCE Standard Grade (or equivalent) 1995/9645Percentage of employees of working age receiving job-related training 1996-9767
    United Kingdom..59.022.816.278.07.445.514.5
    Scotland80.639.019.613.093.03.653.612.5
    Aberdeen City126.050.019.912.9113.01.953.015.4
    Aberdeenshire34.434.018.613.881.09.258.211.4
    Angus70.238.019.113.089.0..62.0..
    Argyll and Bute38.813.017.312.481.08.654.1..
    Clackmannanshire90.947.021.213.478.0..63.6..
    Dumfries and Galloway21.344.018.912.793.02.760.2..
    Dundee City114.359.018.012.2117.00.646.9..
    East Ayrshire43.443.021.013.589.06.450.2..
    East Dunbartonshire76.211.022.213.994.0..71.2..
    East Lothian48.656.020.613.466.012.746.0..
    East Renfrewshire146.033.022.314.091.0..78.8..
    Edinburgh, City of132.350.020.713.4109.02.356.714.7
    Eilean Siar (Western Isles)12.6..13.09.5102.03.260.9..
    Falkirk81.840.021.313.691.04.049.4..
    Fife20.251.019.113.4106.05.352.113.5
    Glasgow City99.853.019.312.488.012.741.915.3
    Highland41.519.017.311.894.0..60.0..
    Inverclyde105.827.021.413.695.0..56.2..
    Midlothian49.054.019.913.677.03.853.0..
    Moray21.631.018.912.291.08.054.3..
    North Ayrshire115.323.021.213.472.010.545.6..
    North Lanarkshire64.425.020.213.494.02.347.511.8
    Orkney Islands31.852.015.110.997.0-69.3..
    Perth and Kinross93.245.018.712.578.010.253.9..
    Renfrewshire119.631.022.013.7103.0..55.915.5
    Scottish Borders, The56.922.018.512.192.01.361.7..
    Shetland Islands14.842.012.78.179.0..73.6..
    South Ayrshire70.436.021.013.699.0..61.9..
    South Lanarkshire103.915.020.713.792.03.651.513.7
    Stirling200.546.019.513.381.02.961.4..
    West Dunbartonshire81.947.020.314.0108.0..52.5..
    West Lothian53.651.020.313.580.06.346.8..
    Footnotes
    1 -Social Work Provision only (local authority and registered); includes Day Nurseries, Childrens Centres, Family Centres and Private Nursery Schools. Population data used mid-1996 estimates.
    2 -Figures relate to all pupils as a percentage of the three and four year old population.
    3 -In Scotland pupils in S5 at September 1995. The figure for the United Kingdom relates to 16 year olds in education at the beginning of the academic year. Some students in Scotland participate on short courses. They are counted for each course; hence there is double counting which results in some percentages being greater that 100.
    4 -Pupils in their last year of compulsory schooling as a percentage of the school population of the same age.
    5 -Figures relate to all schools.
    6 -Males aged 16-64 and females aged 16-59. Job-related education or training received in the four weeks before interview. In some cases sample sizes are too small to provide reliable estimates.
    7 -Data relate to the period March 1996 to February 1997. Figure for United Kingdom relates to Great Britain.
    8 -New Councils for Scotland
    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_housing.html000066400000000000000000000400701224417117700311540ustar00rootroot00000000000000 Cross-sectional dataset viewer v1.1
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    Dataset Name: RT331604
    Title: Housing and households: Scotland
    Description: Housing and households: Scotland

    This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
    Source: The Scottish Office Development Department
    Time Frame: 1996 and 1997
    Geographic Coverage: United Kingdom
    Universe: various
    Measure: various
    Units: See table
    Scalar: various
    Formula: none
    Substitution Details:

    Value Meaning
    . Not applicable
    .. Not available
    Table Dimensions
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    Table
    Housing starts: private enterprise (numbers) 19961Housing starts: housing associations, local authorities etc (numbers) 19962Stock of dwellings (thousands) 19963Households (thousands) 1996Local authority tenants: average weekly unrebated rent per dwelling (£) April 1997Council Tax (£) April 19974
    United Kingdom151826.031224.024607.024115.3...
    Scotland15759.04768.02232.02136.233.6783.0
    Aberdeen City1142.0136.0100.096.827.5712.0
    Aberdeenshire533.090.092.087.129.7643.0
    Angus272.0167.048.046.223.5679.0
    Argyll and Bute300.0266.043.037.735.0801.0
    Clackmannanshire125.037.021.020.029.6753.0
    Dumfries and Galloway388.0159.066.062.332.7714.0
    Dundee City182.0151.072.067.536.8920.0
    East Ayrshire262.030.051.050.126.9779.0
    East Dunbartonshire236.06.042.041.329.6771.0
    East Lothian469.0165.038.036.328.8724.0
    East Renfrewshire295.096.034.033.128.9682.0
    Edinburgh, City of1496.0525.0206.0198.243.8837.0
    Eilean Siar (Western Isles)75.010.013.011.636.5599.0
    Falkirk651.066.061.059.129.8680.0
    Fife202.0251.0152.0145.630.3747.0
    Glasgow City1884.01056.0286.0271.940.4982.0
    Highland664.0161.095.085.838.5719.0
    Inverclyde291.0126.039.038.034.6831.0
    Midlothian362.061.032.030.825.2858.0
    Moray327.00.037.034.928.0652.0
    North Ayrshire344.0157.060.057.730.2718.0
    North Lanarkshire1557.0175.0130.0128.531.3787.0
    Orkney Islands0.06.09.08.133.8515.0
    Perth and Kinross448.0147.059.055.028.2732.0
    Renfrewshire732.066.077.075.132.5783.0
    Scottish Borders, The245.098.049.044.929.7612.0
    Shetland Islands131.021.010.08.936.1486.0
    South Ayrshire182.080.049.047.630.7765.0
    South Lanarkshire488.098.0124.0122.335.3793.0
    Stirling341.066.034.033.133.6776.0
    West Dunbartonshire193.0139.042.040.433.4978.0
    West Lothian942.0156.061.060.328.3792.0
    Footnotes
    1 -Includes estimates for outstanding returns.
    2 -Based on incomplete returns.
    3 -Number of residential dwellings from the Council Tax Register.
    4 -Amounts shown for Council Tax are headline Council Tax for the area of each billing authority for B and D, 2 adults before transitional relief and benefit. The ratios of other bands are: A 6/9, B7/9, C 8/9, E 11/9, F 13/9, G 15/9 and H 18/9.
    5 -New Councils for Scotland
    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_population.csv000066400000000000000000000122631224417117700315240ustar00rootroot00000000000000Dataset Name:,"RT331601" Title:,"Area and population, 1996: Scotland" Description:,"Area and population, 1996: Scotland This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998. " Source:,"Office for National Statistics; General Register Office for Scotland" Time Frame:,"1996" Geographic Coverage:,"United Kingdom" Universe:,"UK population" Measure:,"various" Units:,"See table" Scalar:,"various" Formula:,"none" ==================================== Table ,"Area (sq km)","Persons per sq km","Population (thousands) Males","Population (thousands) Females","Population (thousands) Total","Total population percentage change 1981-1996","Total period fertility rate (TPFR)<1>","Standardised mortality ratio (UK=100) (SMR)<2>","Percentage of population aged under 5","Percentage of population aged 5-15","Percentage of population aged 16 up to pension age<3>","Percentage of population of pension age or over<4>", "United Kingdom","242910.00","242.00","28856.00","29946.00","58801.00","4.30","1.72","100.00","6.40","14.20","61.30","18.10", "Scotland","78133.00","66.00","2486.00","2642.00","5128.00","-1.00","1.55","116.00","6.10","13.90","63.10","17.80", "Aberdeen City","186.00","1169.00","106.00","111.00","217.00","2.20","1.35","105.00","5.80","12.30","65.80","17.10", "Aberdeenshire","6318.00","36.00","113.00","114.00","227.00","20.40","1.64","97.00","6.50","15.30","63.80","15.30", "Angus","2181.00","51.00","54.00","57.00","111.00","4.90","1.67","113.00","6.00","13.90","61.60","19.40", "Argyll and Bute","6930.00","13.00","45.00","46.00","91.00","-0.10","1.70","109.00","5.50","13.60","61.10","20.80", "Clackmannanshire","157.00","312.00","24.00","25.00","49.00","1.20","1.76","115.00","6.60","14.60","63.00","16.70", "Dumfries and Galloway","6439.00","23.00","72.00","76.00","148.00","1.40","1.78","107.00","5.90","13.80","60.30","21.20", "Dundee City","65.00","2306.00","72.00","79.00","150.00","-11.40","1.57","118.00","5.90","13.30","62.00","19.90", "East Ayrshire","1252.00","98.00","59.00","63.00","122.00","-3.90","1.64","114.00","6.30","14.50","61.90","18.30", "East Dunbartonshire","172.00","645.00","54.00","57.00","111.00","1.00","1.56","102.00","5.80","14.20","64.50","16.50", "East Lothian","678.00","130.00","43.00","45.00","88.00","9.20","1.77","112.00","6.40","13.70","61.50","19.50", "East Renfrewshire","173.00","510.00","43.00","45.00","88.00","9.80","1.67","96.00","6.20","14.60","63.00","17.10", "Edinburgh, City of","262.00","1711.00","217.00","232.00","449.00","0.60","1.34","111.00","5.70","11.60","65.80","17.90", "Eilean Siar (Western Isles)","3134.00","9.00","14.00","15.00","29.00","-8.50","1.65","117.00","5.50","15.10","59.80","20.70", "Falkirk","299.00","478.00","69.00","74.00","143.00","-1.50","1.58","121.00","6.20","13.70","63.80","17.40", "Fife","1323.00","264.00","169.00","180.00","349.00","2.30","1.55","109.00","6.00","14.40","62.30","18.30", "Glasgow City","175.00","3522.00","294.00","322.00","616.00","-13.50","1.48","137.00","6.30","13.30","63.20","18.10", "Highland","25784.00","8.00","102.00","106.00","209.00","7.10","1.77","109.00","6.20","14.90","61.80","18.10", "Inverclyde","162.00","538.00","42.00","45.00","87.00","-13.90","1.66","138.00","6.20","14.60","61.70","18.60", "Midlothian","356.00","225.00","39.00","41.00","80.00","-4.20","1.61","119.00","6.10","14.30","64.20","16.40", "Moray","2238.00","39.00","43.00","44.00","87.00","3.60","1.76","108.00","6.60","14.60","61.80","18.00", "North Ayrshire","884.00","158.00","67.00","72.00","140.00","1.60","1.63","115.00","6.20","15.00","62.20","17.60", "North Lanarkshire","474.00","688.00","158.00","168.00","326.00","-4.60","1.66","126.00","6.40","14.90","63.80","15.90", "Orkney Islands","992.00","20.00","10.00","10.00","20.00","3.20","1.78","106.00","6.00","15.30","61.20","18.50", "Perth and Kinross","5311.00","25.00","64.00","69.00","133.00","8.80","1.61","103.00","5.60","13.80","60.70","20.90", "Renfrewshire","261.00","683.00","86.00","92.00","179.00","-3.50","1.59","125.00","6.30","14.10","63.70","17.00", "Scottish Borders, The","4734.00","22.00","51.00","55.00","106.00","4.80","1.67","100.00","5.80","13.30","60.20","21.80", "Shetland Islands","1438.00","16.00","12.00","11.00","23.00","-12.60","1.77","117.00","7.00","15.90","62.90","14.90", "South Ayrshire","1202.00","95.00","55.00","60.00","115.00","1.30","1.55","105.00","5.50","13.70","61.10","20.90", "South Lanarkshire","1771.00","174.00","149.00","159.00","307.00","-0.80","1.55","125.00","6.30","14.50","63.80","16.50", "Stirling","2196.00","38.00","40.00","43.00","83.00","3.10","1.55","110.00","5.70","13.60","63.80","17.90", "West Dunbartonshire","162.00","590.00","46.00","50.00","96.00","-9.50","1.70","130.00","6.40","15.30","61.40","17.80", "West Lothian","425.00","355.00","74.00","77.00","151.00","8.30","1.63","126.00","6.80","15.10","65.80","13.20", ==================================== Footnotes "1 - The total period fertility rate (TPFR) is the average number of children which would be born to a woman if the current pattern of fertility persisted throughout her child-bearing years." "2 - Adjusted for the age structure of the population." "3 - Pension age is 65 for males and 60 for females." "4 - New Councils for Scotland" statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotland_population.html000066400000000000000000000534171224417117700317030ustar00rootroot00000000000000 Cross-sectional dataset viewer v1.1
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    Dataset Name: RT331601
    Title: Area and population, 1996: Scotland
    Description: Area and population, 1996: Scotland

    This dataset has been compiled from data published in Regional Trends 33, 1998 edition published on 25 June 1998.
    Source: Office for National Statistics; General Register Office for Scotland
    Time Frame: 1996
    Geographic Coverage: United Kingdom
    Universe: UK population
    Measure: various
    Units: See table
    Scalar: various
    Formula: none
    Table Dimensions
    Please select at least one item from each list and "Display Selection" or choose "Display All"
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    Table
    Area (sq km)Persons per sq kmPopulation (thousands) MalesPopulation (thousands) FemalesPopulation (thousands) TotalTotal population percentage change 1981-1996Total period fertility rate (TPFR)1Standardised mortality ratio (UK=100) (SMR)2Percentage of population aged under 5Percentage of population aged 5-15Percentage of population aged 16 up to pension age3Percentage of population of pension age or over4
    United Kingdom242910.00242.0028856.0029946.0058801.004.301.72100.006.4014.2061.3018.10
    Scotland78133.0066.002486.002642.005128.00-1.001.55116.006.1013.9063.1017.80
    Aberdeen City186.001169.00106.00111.00217.002.201.35105.005.8012.3065.8017.10
    Aberdeenshire6318.0036.00113.00114.00227.0020.401.6497.006.5015.3063.8015.30
    Angus2181.0051.0054.0057.00111.004.901.67113.006.0013.9061.6019.40
    Argyll and Bute6930.0013.0045.0046.0091.00-0.101.70109.005.5013.6061.1020.80
    Clackmannanshire157.00312.0024.0025.0049.001.201.76115.006.6014.6063.0016.70
    Dumfries and Galloway6439.0023.0072.0076.00148.001.401.78107.005.9013.8060.3021.20
    Dundee City65.002306.0072.0079.00150.00-11.401.57118.005.9013.3062.0019.90
    East Ayrshire1252.0098.0059.0063.00122.00-3.901.64114.006.3014.5061.9018.30
    East Dunbartonshire172.00645.0054.0057.00111.001.001.56102.005.8014.2064.5016.50
    East Lothian678.00130.0043.0045.0088.009.201.77112.006.4013.7061.5019.50
    East Renfrewshire173.00510.0043.0045.0088.009.801.6796.006.2014.6063.0017.10
    Edinburgh, City of262.001711.00217.00232.00449.000.601.34111.005.7011.6065.8017.90
    Eilean Siar (Western Isles)3134.009.0014.0015.0029.00-8.501.65117.005.5015.1059.8020.70
    Falkirk299.00478.0069.0074.00143.00-1.501.58121.006.2013.7063.8017.40
    Fife1323.00264.00169.00180.00349.002.301.55109.006.0014.4062.3018.30
    Glasgow City175.003522.00294.00322.00616.00-13.501.48137.006.3013.3063.2018.10
    Highland25784.008.00102.00106.00209.007.101.77109.006.2014.9061.8018.10
    Inverclyde162.00538.0042.0045.0087.00-13.901.66138.006.2014.6061.7018.60
    Midlothian356.00225.0039.0041.0080.00-4.201.61119.006.1014.3064.2016.40
    Moray2238.0039.0043.0044.0087.003.601.76108.006.6014.6061.8018.00
    North Ayrshire884.00158.0067.0072.00140.001.601.63115.006.2015.0062.2017.60
    North Lanarkshire474.00688.00158.00168.00326.00-4.601.66126.006.4014.9063.8015.90
    Orkney Islands992.0020.0010.0010.0020.003.201.78106.006.0015.3061.2018.50
    Perth and Kinross5311.0025.0064.0069.00133.008.801.61103.005.6013.8060.7020.90
    Renfrewshire261.00683.0086.0092.00179.00-3.501.59125.006.3014.1063.7017.00
    Scottish Borders, The4734.0022.0051.0055.00106.004.801.67100.005.8013.3060.2021.80
    Shetland Islands1438.0016.0012.0011.0023.00-12.601.77117.007.0015.9062.9014.90
    South Ayrshire1202.0095.0055.0060.00115.001.301.55105.005.5013.7061.1020.90
    South Lanarkshire1771.00174.00149.00159.00307.00-0.801.55125.006.3014.5063.8016.50
    Stirling2196.0038.0040.0043.0083.003.101.55110.005.7013.6063.8017.90
    West Dunbartonshire162.00590.0046.0050.0096.00-9.501.70130.006.4015.3061.4017.80
    West Lothian425.00355.0074.0077.00151.008.301.63126.006.8015.1065.8013.20
    Footnotes
    1 -The total period fertility rate (TPFR) is the average number of children which would be born to a woman if the current pattern of fertility persisted throughout her child-bearing years.
    2 -Adjusted for the age structure of the population.
    3 -Pension age is 65 for males and 60 for females.
    4 -New Councils for Scotland
    statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotvote.csv000066400000000000000000000153031224417117700273070ustar00rootroot00000000000000"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42" "Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3 "Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3 "Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4 "Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57 "Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7 "Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8 "Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5 "East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5 "East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1 "East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7 "East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6 "Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62 "Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4 "Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2 "Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7 "Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75 "Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1 "Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2 "Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7 "Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7 "North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7 "North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2 "Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4 "Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3 "Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6 "Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7 "Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6 "South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2 "South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6 "Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9 "West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7 "West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/scotland/src/scotvote.dat000066400000000000000000000153031224417117700272640ustar00rootroot00000000000000"PrivateHousingStarts" "PublicHousingStarts" "StockofDwellings" "Households" "LocalAuthorityRent" "CouncilTax" "Areas" "Density" "Males" "Females" "Population" "PopulationChange" "FertilityRate" "StdMortalityRatio" "PercentageUnder5" "Percentage5to15" "Percentage16topension" "PercentageOverPensionage" "InNursery" "InPreschool" "PrimaryPTRatio" "SecondaryPTRatio" "PostCompulsory" "NoGrade" "Grades" "InJobTraining" "Birthsper1000" "Deathsper1000" "PeriMortality" "InfantMortality" "PerBirthsOut" "Active" "TotalEmployment" "PerMfgEmployment" "PerUnemployment" "TotalClaimants" "PerClaimantFemale" "PerClaimLongT" "MeanWeekSal" "GDP" "Var.41" "Var.42" "Aberdeen_City" 1142 136 100 96.8 27.5 712 186 1169 106 111 217 2.2 1.35 105 5.8 12.3 65.8 17.1 126 50 19.9 12.9 113 1.9 53 15.4 11 10.4 7.7 5.7 35 82.4 113 14.3 4.9 3.6 21 17.6 404.8 13566 71.8 60.3 "Aberdeenshire" 533 90 92 87.1 29.7 643 6318 36 113 114 227 20.4 1.64 97 6.5 15.3 63.8 15.3 34.4 34 18.6 13.8 81 9.2 58.2 11.4 11.3 9 9 3.8 24 80.2 112 14.5 NA 2.8 26.5 16.3 330.9 13566 63.9 52.3 "Angus" 272 167 48 46.2 23.5 679 2181 51 54 57 111 4.9 1.67 113 6 13.9 61.6 19.4 70.2 38 19.1 13 89 NA 62 NA 11 13.2 5.6 3.2 33 86.3 59 16.9 NA 3.2 28.3 23.3 320 9611 64.7 53.4 "Argyll_and_Bute" 300 266 43 37.7 35 801 6930 13 45 46 91 -0.1 1.7 109 5.5 13.6 61.1 20.8 38.8 13 17.3 12.4 81 8.6 54.1 NA 10.5 13.8 8.6 7 33 80.4 41 NA 11.9 2.9 27.1 22.8 305.2 9483 67.3 57 "Clackmannanshire" 125 37 21 20 29.6 753 157 312 24 25 49 1.2 1.76 115 6.6 14.6 63 16.7 90.9 47 21.2 13.4 78 NA 63.6 NA 12.3 11.3 10.4 6.3 40 64.7 17 NA NA 1.6 22 27.5 NA 9265 80 68.7 "Dumfries_and_Galloway" 388 159 66 62.3 32.7 714 6439 23 72 76 148 1.4 1.78 107 5.9 13.8 60.3 21.2 21.3 44 18.9 12.7 93 2.7 60.2 NA 10.9 12.8 8.8 7.8 34 79 67 17.7 NA 4.4 24.3 23.4 300.2 9555 60.7 48.8 "Dundee_City" 182 151 72 67.5 36.8 920 65 2306 72 79 150 -11.4 1.57 118 5.9 13.3 62 19.9 114.3 59 18 12.2 117 0.6 46.9 NA 11.5 13.1 8.6 6.8 51 72.2 60 14.6 9.3 6.2 21.2 26.3 327.4 9611 76 65.5 "East_Ayrshire" 262 30 51 50.1 26.9 779 1252 98 59 63 122 -3.9 1.64 114 6.3 14.5 61.9 18.3 43.4 43 21 13.5 89 6.4 50.2 NA 11.4 11.6 12.3 6.5 40 75.2 50 23.1 14.2 4.5 20.5 28.9 307.6 9483 81.1 70.5 "East_Dunbartonshire" 236 6 42 41.3 29.6 771 172 645 54 57 111 1 1.56 102 5.8 14.2 64.5 16.5 76.2 11 22.2 13.9 94 NA 71.2 NA 10.5 9.2 8.1 7.2 19 81.1 53 NA NA 2.2 23.2 17.3 329.2 9483 69.8 59.1 "East_Lothian" 469 165 38 36.3 28.8 724 678 130 43 45 88 9.2 1.77 112 6.4 13.7 61.5 19.5 48.6 56 20.6 13.4 66 12.7 46 NA 12.3 12.6 7.6 5.2 29 80.3 41 NA NA 1.7 20.5 16.3 310.3 12656 74.2 62.7 "East_Renfrewshire" 295 96 34 33.1 28.9 682 173 510 43 45 88 9.8 1.67 96 6.2 14.6 63 17.1 146 33 22.3 14 91 NA 78.8 NA 11.5 9.5 7.4 6.2 19 83 42 16.8 NA 1.4 23.8 20.9 NA 9483 61.7 51.6 "Edinburgh_City" 1496 525 206 198.2 43.8 837 262 1711 217 232 449 0.6 1.34 111 5.7 11.6 65.8 17.9 132.3 50 20.7 13.4 109 2.3 56.7 14.7 11.4 11.7 8.1 6.4 33 74.5 207 10.3 6.6 11.1 22.1 20.4 362.8 12656 71.9 62 "Eilean_Siar_(Western_Isles)" 75 10 13 11.6 36.5 599 3134 9 14 15 29 -8.5 1.65 117 5.5 15.1 59.8 20.7 12.6 NA 13 9.5 102 3.2 60.9 NA 9.7 14.9 11.2 5.7 19 83.8 15 NA NA 1.4 19.9 23.8 NA 8298 79.4 68.4 "Falkirk" 651 66 61 59.1 29.8 680 299 478 69 74 143 -1.5 1.58 121 6.2 13.7 63.8 17.4 81.8 40 21.3 13.6 91 4 49.4 NA 11.7 11.7 7.9 4.8 34 77.6 66 23.4 NA 4.5 21.5 19.9 335.6 9265 80 69.2 "Fife" 202 251 152 145.6 30.3 747 1323 264 169 180 349 2.3 1.55 109 6 14.4 62.3 18.3 20.2 51 19.1 13.4 106 5.3 52.1 13.5 11 11.4 8.7 7.1 37 77.9 147 21.7 9.3 11.1 22.5 22.7 325.2 8314 76.1 64.7 "Glasgow_City" 1884 1056 286 271.9 40.4 982 175 3522 294 322 616 -13.5 1.48 137 6.3 13.3 63.2 18.1 99.8 53 19.3 12.4 88 12.7 41.9 15.3 12.5 14 11.1 6.9 49 65.3 210 14.2 15.2 26.9 19.4 29.5 341.5 9483 83.6 75 "Highland" 664 161 95 85.8 38.5 719 25784 8 102 106 209 7.1 1.77 109 6.2 14.9 61.8 18.1 41.5 19 17.3 11.8 94 NA 60 NA 11.4 11.4 8.3 6.5 34 80.9 100 12.9 9.3 7.9 25.9 20.9 296.2 8298 72.6 62.1 "Inverclyde" 291 126 39 38 34.6 831 162 538 42 45 87 -13.9 1.66 138 6.2 14.6 61.7 18.6 105.8 27 21.4 13.6 95 NA 56.2 NA 11.7 14.5 11.5 8 45 80.2 39 26.5 NA 2.5 18.5 12.7 323.4 9483 78 67.2 "Midlothian" 362 61 32 30.8 25.2 858 356 225 39 41 80 -4.2 1.61 119 6.1 14.3 64.2 16.4 49 54 19.9 13.6 77 3.8 53 NA 11.2 10.7 10.8 6 35 84.8 39 NA NA 1.6 19.4 13.7 309 12656 79.9 67.7 "Moray" 327 0 37 34.9 28 652 2238 39 43 44 87 3.6 1.76 108 6.6 14.6 61.8 18 21.6 31 18.9 12.2 91 8 54.3 NA 12.4 11 9.8 7.4 26 86.4 43 14.3 NA 2.2 27.2 15.4 285 13566 67.2 52.7 "North_Ayrshire" 344 157 60 57.7 30.2 718 884 158 67 72 140 1.6 1.63 115 6.2 15 62.2 17.6 115.3 23 21.2 13.4 72 10.5 45.6 NA 11.3 11.8 11.6 6.9 42 73.5 58 27.4 9.1 5.1 23.7 19.2 317.8 9483 76.3 65.7 "North_Lanarkshire" 1557 175 130 128.5 31.3 787 474 688 158 168 326 -4.6 1.66 126 6.4 14.9 63.8 15.9 64.4 25 20.2 13.4 94 2.3 47.5 11.8 12.5 11.1 11.6 8.5 38 74.7 133 21.2 12.4 10.7 20.8 20.1 336.7 9483 82.6 72.2 "Orkney_Islands" 0 6 9 8.1 33.8 515 992 20 10 10 20 3.2 1.78 106 6 15.3 61.2 18.5 31.8 52 15.1 10.9 97 0 69.3 NA 10.9 11.6 7.5 1.4 30 87.8 10 NA NA 0.4 26.8 24.4 NA 8298 57.3 47.4 "Perth_and_Kinross" 448 147 59 55 28.2 732 5311 25 64 69 133 8.8 1.61 103 5.6 13.8 60.7 20.9 93.2 45 18.7 12.5 78 10.2 53.9 NA 10.5 12.6 9.8 5.9 29 86.6 66 11.3 NA 2.8 23 17.8 NA 9611 61.7 51.3 "Renfrewshire" 732 66 77 75.1 32.5 783 261 683 86 92 179 -3.5 1.59 125 6.3 14.1 63.7 17 119.6 31 22 13.7 103 NA 55.9 15.5 11.9 11.6 8 4.5 39 78.5 80 20.1 11.3 5.5 20.5 23.3 336.1 9483 79 63.6 "Scottish_Borders_The" 245 98 49 44.9 29.7 612 4734 22 51 55 106 4.8 1.67 100 5.8 13.3 60.2 21.8 56.9 22 18.5 12.1 92 1.3 61.7 NA 10.7 12.8 8 4.9 28 80.6 49 20.6 NA 2.1 23.7 12.3 303.5 9033 62.8 50.7 "Shetland_Islands" 131 21 10 8.9 36.1 486 1438 16 12 11 23 -12.6 1.77 117 7 15.9 62.9 14.9 14.8 42 12.7 8.1 79 NA 73.6 NA 11.7 10.9 9.9 6.5 28 84.8 11 NA NA 0.4 23.2 14.2 NA 8298 62.4 51.6 "South_Ayrshire" 182 80 49 47.6 30.7 765 1202 95 55 60 115 1.3 1.55 105 5.5 13.7 61.1 20.9 70.4 36 21 13.6 99 NA 61.9 NA 10.1 12.7 6.2 4.3 33 79.2 48 22.9 10.3 3.6 23.6 23.4 346.2 9483 66.9 56.2 "South_Lanarkshire" 488 98 124 122.3 35.3 793 1771 174 149 159 307 -0.8 1.55 125 6.3 14.5 63.8 16.5 103.9 15 20.7 13.7 92 3.6 51.5 13.7 11.5 11.3 9.2 5.1 33 78.4 146 22 8.3 8.2 21.7 21.8 319.1 9483 77.8 67.6 "Stirling" 341 66 34 33.1 33.6 776 2196 38 40 43 83 3.1 1.55 110 5.7 13.6 63.8 17.9 200.5 46 19.5 13.3 81 2.9 61.4 NA 11.1 11.8 7.7 4.9 33 77.2 37 NA NA 2.1 23 19.5 346.6 9265 68.5 58.9 "West_Dunbartonshire" 193 139 42 40.4 33.4 978 162 590 46 50 96 -9.5 1.7 130 6.4 15.3 61.4 17.8 81.9 47 20.3 14 108 NA 52.5 NA 12.5 12.7 11.7 8.7 42 71.5 36 NA 13.6 4.2 19.3 27.3 319 9483 84.7 74.7 "West_Lothian" 942 156 61 60.3 28.3 792 425 355 74 77 151 8.3 1.63 126 6.8 15.1 65.8 13.2 53.6 51 20.3 13.5 80 6.3 46.8 NA 13.1 9.5 8.7 4.6 33 82.2 78 32.6 NA 3.5 21.2 10.2 335.5 12656 79.6 67.3 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/spector/000077500000000000000000000000001224417117700240035ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/spector/__init__.py000066400000000000000000000000231224417117700261070ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/spector/data.py000066400000000000000000000036551224417117700252770ustar00rootroot00000000000000"""Spector and Mazzeo (1980) - Program Effectiveness Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission of the original author, who retains all rights. """ TITLE = __doc__ SOURCE = """ http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm The raw data was downloaded from Bill Greene's Econometric Analysis web site, though permission was obtained from the original researcher, Dr. Lee Spector, Professor of Economics, Ball State University.""" DESCRSHORT = """Experimental data on the effectiveness of the personalized system of instruction (PSI) program""" DESCRLONG = DESCRSHORT NOTE = """ Number of Observations - 32 Number of Variables - 4 Variable name definitions:: Grade - binary variable indicating whether or not a student's grade improved. 1 indicates an improvement. TUCE - Test score on economics test PSI - participation in program GPA - Student's grade point average """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the Spector dataset and returns a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=3, dtype=float) def load_pandas(): """ Load the Spector dataset and returns a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=3, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/spector.csv',"rb"), delimiter=" ", names=True, dtype=float, usecols=(1,2,3,4)) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/spector/spector.csv000066400000000000000000000007611224417117700262030ustar00rootroot00000000000000'OBS' 'GPA' 'TUCE' 'PSI' 'GRADE' 1 2.66 20 0 0 2 2.89 22 0 0 3 3.28 24 0 0 4 2.92 12 0 0 5 4 21 0 1 6 2.86 17 0 0 7 2.76 17 0 0 8 2.87 21 0 0 9 3.03 25 0 0 10 3.92 29 0 1 11 2.63 20 0 0 12 3.32 23 0 0 13 3.57 23 0 0 14 3.26 25 0 1 15 3.53 26 0 0 16 2.74 19 0 0 17 2.75 25 0 0 18 2.83 19 0 0 19 3.12 23 1 0 20 3.16 25 1 1 21 2.06 22 1 0 22 3.62 28 1 1 23 2.89 14 1 0 24 3.51 26 1 0 25 3.54 24 1 1 26 2.83 27 1 1 27 3.39 17 1 1 28 2.67 24 1 0 29 3.65 21 1 1 30 4 23 1 1 31 3.1 21 1 0 32 2.39 19 1 1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/stackloss/000077500000000000000000000000001224417117700243325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/stackloss/R_stackloss.s000066400000000000000000000014061224417117700270060ustar00rootroot00000000000000### SETUP ### d <- read.table("./stackloss.csv",sep=",", header=T) attach(d) library(MASS) m1 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC) # psi.huber default m2 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC, psi = psi.hampel, init = "lts") m3 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC, psi = psi.bisquare) results1 <- summary(m1) results2 <- summary(m2) results3 <- summary(m3) m4 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC, scale.est="Huber") # psi.huber default m5 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC, scale.est="Huber", psi = psi.hampel, init = "lts") m6 <- rlm(STACKLOSS ~ AIRFLOW + WATERTEMP + ACIDCONC, scale.est="Huber", psi = psi.bisquare) results4 <- summary(m4) results5 <- summary(m5) results6 <- summary(m6) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/stackloss/__init__.py000066400000000000000000000000231224417117700264360ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/stackloss/data.py000066400000000000000000000035201224417117700256150ustar00rootroot00000000000000"""Stack loss data""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain. """ TITLE = __doc__ SOURCE = """ Brownlee, K. A. (1965), "Statistical Theory and Methodology in Science and Engineering", 2nd edition, New York:Wiley. """ DESCRSHORT = """Stack loss plant data of Brownlee (1965)""" DESCRLONG = """The stack loss plant data of Brownlee (1965) contains 21 days of measurements from a plant's oxidation of ammonia to nitric acid. The nitric oxide pollutants are captured in an absorption tower.""" NOTE = """ Number of Observations - 21 Number of Variables - 4 Variable name definitions:: STACKLOSS - 10 times the percentage of ammonia going into the plant that escapes from the absoroption column AIRFLOW - Rate of operation of the plant WATERTEMP - Cooling water temperature in the absorption tower ACIDCONC - Acid concentration of circulating acid minus 50 times 10. """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the stack loss data and returns a Dataset class instance. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Load the stack loss data and returns a Dataset class instance. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/stackloss.csv',"rb"), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/stackloss/stackloss.csv000066400000000000000000000004441224417117700270570ustar00rootroot00000000000000"STACKLOSS","AIRFLOW","WATERTEMP","ACIDCONC" 42,80,27,89 37,80,27,88 37,75,25,90 28,62,24,87 18,62,22,87 18,62,23,87 19,62,24,93 20,62,24,93 15,58,23,87 14,58,18,80 14,58,18,89 13,58,17,88 11,58,18,82 12,58,19,93 8,50,18,89 7,50,18,86 8,50,19,72 8,50,19,79 9,50,20,80 15,56,20,82 15,70,20,91 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/000077500000000000000000000000001224417117700234565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/__init__.py000066400000000000000000000000231224417117700255620ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/data.py000066400000000000000000000072501224417117700247450ustar00rootroot00000000000000"""Star98 Educational Testing dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """Used with express permission from the original author, who retains all rights.""" TITLE = "Star98 Educational Dataset" SOURCE = """ Jeff Gill's `Generalized Linear Models: A Unified Approach` http://jgill.wustl.edu/research/books.html """ DESCRSHORT = """Math scores for 303 student with 10 explanatory factors""" DESCRLONG = """ This data is on the California education policy and outcomes (STAR program results for 1998. The data measured standardized testing by the California Department of Education that required evaluation of 2nd - 11th grade students by the the Stanford 9 test on a variety of subjects. This dataset is at the level of the unified school district and consists of 303 cases. The binary response variable represents the number of 9th graders scoring over the national median value on the mathematics exam. The data used in this example is only a subset of the original source. """ NOTE = """ Number of Observations - 303 (counties in California). Number of Variables - 13 and 8 interaction terms. Definition of variables names:: NABOVE - Total number of students above the national median for the math section. NBELOW - Total number of students below the national median for the math section. LOWINC - Percentage of low income students PERASIAN - Percentage of Asian student PERBLACK - Percentage of black students PERHISP - Percentage of Hispanic students PERMINTE - Percentage of minority teachers AVYRSEXP - Sum of teachers' years in educational service divided by the number of teachers. AVSALK - Total salary budget including benefits divided by the number of full-time teachers (in thousands) PERSPENK - Per-pupil spending (in thousands) PTRATIO - Pupil-teacher ratio. PCTAF - Percentage of students taking UC/CSU prep courses PCTCHRT - Percentage of charter schools PCTYRRND - Percentage of year-round schools The below variables are interaction terms of the variables defined above. PERMINTE_AVYRSEXP PEMINTE_AVSAL AVYRSEXP_AVSAL PERSPEN_PTRATIO PERSPEN_PCTAF PTRATIO_PCTAF PERMINTE_AVTRSEXP_AVSAL PERSPEN_PTRATIO_PCTAF """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the star98 data and returns a Dataset class instance. Returns ------- Load instance: a class of the data with array attrbutes 'endog' and 'exog' """ data = _get_data() return du.process_recarray(data, endog_idx=[0, 1], dtype=float) def load_pandas(): data = _get_data() return du.process_recarray_pandas(data, endog_idx=['NABOVE', 'NBELOW'], dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### names = ["NABOVE","NBELOW","LOWINC","PERASIAN","PERBLACK","PERHISP", "PERMINTE","AVYRSEXP","AVSALK","PERSPENK","PTRATIO","PCTAF", "PCTCHRT","PCTYRRND","PERMINTE_AVYRSEXP","PERMINTE_AVSAL", "AVYRSEXP_AVSAL","PERSPEN_PTRATIO","PERSPEN_PCTAF","PTRATIO_PCTAF", "PERMINTE_AVYRSEXP_AVSAL","PERSPEN_PTRATIO_PCTAF"] data = recfromtxt(open(filepath + '/star98.csv',"rb"), delimiter=",", names=names, skip_header=1, dtype=float) # careful now nabove = data['NABOVE'].copy() nbelow = data['NBELOW'].copy() data['NABOVE'] = nbelow # successes data['NBELOW'] = nabove - nbelow # now failures return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/r_glm.s000066400000000000000000000037041224417117700247460ustar00rootroot00000000000000### SETUP #star.data <- as.matrix(read.csv("./star98.csv",header=T)) #star.factors3 <- data.frame( LOWINC=star.data[,3], PERASIAN=star.data[,4], PERBLACK=star.data[,5], # PERHISP=star.data[,6], PERMINTE=star.data[,7], AVYRSEXP=star.data[,8], AVSAL=star.data[,9], # PERSPEN=star.data[,10], PTRATIO=star.data[,11], PCTAF=star.data[,12], PCTCHRT=star.data[,13], # PCTYRRND=star.data[,14], PERMINTE.AVYRSEXP=star.data[,15], PERMINTE.AVSAL=star.data[,16], # AVYRSEXP.AVSAL=star.data[,17], PERSPEN.PTRATIO=star.data[,18], PERSPEN.PCTAF=star.data[,19], # PTRATIO.PCTAF=star.data[,20], PERMINTE.AVYRSEXP.AVSAL=star.data[,21], # PERSPEN.PTRATIO.PCTAF=star.data[,22], MATHTOT=star.data[,1], PR50M=star.data[,2] ) d <- read.table("./star98.csv", sep=",", header=T) attach(d) #attach(star.factors3) ### MATH MODEL m1 <- glm(cbind(PR50M,MATHTOT-PR50M) ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PERMINTE + AVYRSEXP + AVSALK + PERSPENK + PTRATIO + PCTAF + PCTCHRT + PCTYRRND + PERMINTE_AVYRSEXP + PERMINTE_AVSAL + AVYRSEXP_AVSAL + PERSPEN_PTRATIO + PERSPEN_PCTAF + PTRATIO_PCTAF + PERMINTE_AVYRSEXP_AVSAL + PERSPEN_PTRATIO_PCTAF, family=binomial) #as.numeric(m1$coef) #as.numeric(sqrt(diag(vcov(m1)))) results <- summary.glm(m1) #star.logit.fit3 <- glm(cbind(PR50M,MATHTOT-PR50M) ~ LOWINC + PERASIAN + PERBLACK + PERHISP + # PERMINTE + AVYRSEXP + AVSAL + PERSPEN + PTRATIO + PCTAF + PCTCHRT + PCTYRRND + # PERMINTE.AVYRSEXP + PERMINTE.AVSAL + AVYRSEXP.AVSAL + PERSPEN.PTRATIO + PERSPEN.PCTAF + # PTRATIO.PCTAF + PERMINTE.AVYRSEXP.AVSAL + PERSPEN.PTRATIO.PCTAF, # family = binomial(), data=star.factors3) #results <- summary.glm(star.logit.fit3) # WITH R STYLE INTERACTIONS #star.logit.fit4 <- glm(cbind(PR50M,MATHTOT-PR50M) ~ LOWINC + PERASIAN + PERBLACK + PERHISP + # PERMINTE + AVYRSEXP + AVSAL + PERSPEN + PTRATIO + PCTAF + PCTCHRT + PCTYRRND + # PERMINTE*AVYRSEXP*AVSAL + PERSPEN*PTRATIO*PCTAF, # family = binomial(), data=star.factors3) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/src/000077500000000000000000000000001224417117700242455ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/src/star.bi.dat000066400000000000000000001063551224417117700263130ustar00rootroot00000000000000161119 34.3973 23.29930 14.23528 11.41112 15.91837 14.70646 59157.32 4445.207 21.71025 57.03276 0 22.22222 805 467 807 452 161127 17.36507 29.32838 8.234897 9.314884 13.63636 16.08324 59503.97 5267.598 20.44278 64.62264 0 0 182 140 184 144 161143 32.64324 9.226386 42.40631 13.54372 28.83436 14.59559 60569.92 5482.922 18.95419 53.94191 0 0 566 357 571 337 161150 11.90953 13.88309 3.796973 11.44311 11.11111 14.38939 58334.11 4165.093 21.63539 49.06103 0 7.142857 573 424 573 395 161168 36.88889 12.1875 76.875 7.604167 43.58974 13.90568 63153.64 4324.902 18.77984 52.38095 0 0 62 15 65 8 161176 20.93149 28.02351 4.643221 13.80816 15.37849 14.97755 66970.55 3916.104 24.51914 44.91578 0 2.380952 2234 1452 2247 1348 161192 53.26898 8.447858 19.37483 37.90533 25.52553 14.67829 57621.95 4270.903 22.21278 32.28916 0 12.12121 1363 491 1364 477 161200 15.19009 3.665781 2.649680 13.09207 6.203008 13.66197 63447.4 4309.734 24.59026 30.45267 0 0 904 579 912 565 161234 28.21582 10.43042 6.786374 32.3343 13.46154 16.41760 57845.64 4527.603 21.74138 22.64574 0 0 519 218 525 205 161242 32.77897 17.17831 12.48493 28.32329 27.25989 12.51864 57801.41 4648.917 20.26010 26.07099 0 0 1071 514 1067 469 161259 59.97293 17.51736 50.94093 23.10134 52.34344 16.93283 57434.44 4693.069 21.31489 19.53216 3.296703 13.18681 2964 830 3016 784 161275 0 19.74886 1.864536 2.587519 7.407407 15.86979 52193.46 5248.693 18.51182 80.37975 0 0 235 204 235 209 161291 28.12111 12.69201 19.26582 26.84197 13.37209 14.42864 57240.45 3980.589 21.89844 31.65829 0 9.090909 547 241 556 195 161309 36.99047 10.03236 15.42302 30.74434 13.50763 13.29753 58413.74 4197.615 23.12943 31.15079 0 0 685 315 688 206 175093 12.92796 7.172996 4.825949 13.23840 6.185567 17.47108 65570.86 5382.009 20.68333 34.83146 0 0 259 179 252 136 175101 4.700895 8.736718 1.745657 6.932029 7.127883 16.18176 67367.55 4607.673 24.65106 39.63415 0 0 922 765 925 694 373981 20.25979 0.4857445 0.8025343 5.976769 2.439024 15.30949 51311.63 4025.593 24.82292 36.19403 0 91.66667 377 249 377 204 461408 42.66827 2.836879 0.9456265 23.87707 10.63830 11.83962 55990.32 5151.626 17.91209 24.44444 0 0 66 40 69 31 461424 33.12923 6.305412 2.766154 12.86616 6.17284 14.60144 58558.6 4004.401 22.73008 49.65517 4.347826 26.08696 1089 632 1092 601 461432 29.39297 1.071155 0.765111 9.716909 4.477612 14.04527 53772.3 4518.034 21.18506 51.02041 0 0 116 82 115 64 461473 52.99313 6.217371 0.3322259 38.72805 7.619048 14.17395 50695.12 4114.065 20.41136 23.27586 0 0 137 58 139 51 461531 35.08836 0.8292167 0.388144 4.622442 4.135338 14.30221 55278.74 4148.228 21.91947 25 23.07692 15.38462 439 241 449 220 561564 37.20441 0.5699482 1.113990 6.321244 5.357143 13.60247 50668.49 3975.448 24.86918 22.97872 0 8.333333 312 175 309 151 661598 54.26829 0.5497862 0.5497862 40.74527 7.407407 14.87105 52537 4205.362 19.44511 36.28319 0 0 115 46 116 36 661614 65.82397 0.2640845 3.873239 58.89085 6.557377 10.92652 46291.79 4469.798 19.52548 37.93103 0 0 80 29 81 15 661622 78.54077 1.282051 0 78.4188 20.83333 11.47788 49267.19 3893.337 21.12311 19.23077 0 0 66 7 66 5 761648 30.07553 3.626196 10.50386 20.94431 8.66575 13.69108 62341.2 4011.390 23.88905 23.85445 0 72.22222 1252 676 1259 541 761697 28.24601 6.893424 19.81859 16.78005 12.74510 15.91555 59176.53 4225.738 22.18379 18 0 0 188 94 190 86 761739 22.33375 2.523732 3.310952 14.03103 10.30928 16.15686 59230.72 4114.418 21.74332 34.42623 0 0 323 210 322 142 761754 22.62465 7.697888 4.902207 16.77688 9.421586 15.32368 57128.58 3928.696 22.14162 37.63838 0 0 2390 1482 2394 1365 761788 51.78493 4.598930 27.79679 35.10160 30.02481 15.97489 58788.93 4049.181 22.87300 85.11166 0 8.333333 646 239 648 201 761796 51.23848 13.02024 35.10118 25.52703 25.82688 17.18977 62664.1 4347.936 22.8705 36.5 0 8.474576 1909 668 1949 585 761804 1.895322 9.925228 1.633719 4.276349 4.503464 15.92369 59976.94 4035.189 23.34687 50.74503 0 0 1420 1235 1417 1247 861820 42.51143 6.252442 0.4103165 10.17976 8.076923 14.70948 58233.81 4733.28 20.67364 15.50388 0 0 396 210 403 165 961903 45.42344 1.501194 0.9041283 26.27090 5.338078 14.35703 55740.14 4323.754 21.53458 86.722 0 0 337 172 329 135 973783 30.16965 0.7973734 0.5159475 2.251407 1.904762 14.23233 51822.37 4030.978 21.47295 32.45614 0 0 188 132 193 106 1062117 23.43337 11.75065 2.871447 19.03101 15.94416 11.87890 53158.07 4242.702 22.70517 40.50633 0 0 2295 1584 2267 1496 1062125 57.9224 0.871731 0.6475716 68.74222 18.93491 11.19787 61085.44 4258.425 24.4786 14.83516 0 0 277 89 272 68 1062158 60.59908 7.196613 0.8466604 72.34243 24.76190 11.83632 51537.34 3807.79 21.07004 38.7931 0 0 146 50 144 55 1062166 62.01977 19.65177 11.31566 45.23962 23.40485 14.67996 61095.3 4685.643 22.25973 27.47559 0 31.11111 4973 1641 5018 1455 1062240 35.60339 2.518757 0.375134 67.73848 7.913669 15.34516 54702.22 6836.694 21.14435 41.57895 71.42857 0 195 109 196 84 1062265 63.17132 1.40625 0.6009615 71.35817 16.79198 14.46149 55184.78 4478.657 22.25652 16.78657 0 6.25 614 178 617 136 1062281 72.91667 1.238739 0.5630631 70.04505 3.921569 11.55763 39728.35 3899.132 18.23651 13.04348 0 0 62 12 64 6 1062364 91.60484 0.2143623 0.1071811 98.821 62.18487 11.92754 55527.29 4223.126 21.68654 13.38583 0 40 190 27 189 21 1062414 57.00249 4.322917 0.5859375 70.66406 23.48066 14.00443 52155.26 4382.061 21.71153 23.02326 0 0 550 198 552 132 1062430 69.6452 3.972303 1.020408 76.71283 27.6 13.01188 53531.64 4180.037 22.50103 14.69194 0 0 284 114 290 78 1073809 87.83718 0.4054054 1.216216 90.45045 32.40741 10.37561 51400.56 4317.728 20.49057 30.85106 0 0 143 26 146 16 1073965 49.25084 10.15299 7.874184 42.32374 16.53944 10.08090 54568.74 4006.118 22.59174 37.66667 0 76.92308 581 221 585 211 1073999 69.49807 5.350028 0.3130336 70.43256 22.64151 12.32314 54546.01 4283.101 22.15424 40 0 60 265 85 274 82 1075127 87.77725 0.5334627 0.5819593 97.76916 40 12.20753 60303.18 3933.777 25.6375 47.16981 0 0 151 14 155 16 1075234 86.54599 1.713710 0.2016129 92.2379 26.08696 14.58066 62758.26 5242.8 21.72113 19.17808 0 0 143 24 144 35 1075275 21.60468 0.5405405 0.2910603 7.484407 1.526718 17.11242 62522.82 5879.966 19.48512 26.37363 0 0 143 102 143 90 1075408 64.85075 0.6671114 3.068712 64.50967 14.92537 11.36918 47732.57 3511.9 21.48872 10.66667 0 0 67 24 67 23 1162661 53.125 21.75227 0.9063444 20.74522 4.651163 15.73608 60020.66 4325.154 24.48113 47.11538 0 0 138 68 138 76 1175481 44.97466 1.348412 0.8264463 33.18834 5.932203 16.25328 55653.8 4602.903 20.85538 49.19355 0 0 186 78 183 73 1262901 76.32121 0 0.6559767 2.842566 30 14.78554 65665.47 5770.566 19.64964 25.92593 0 0 90 37 88 38 1263040 33.91672 0.7147498 1.169591 2.988954 0 15.79261 56055.94 4427.872 21.31544 44.70588 0 0 114 67 109 23 1275374 11.71617 0.5 0 4.666667 0 15.51220 54645.67 4893.857 20.32154 34.14634 0 0 59 40 59 40 1363073 54.25667 0.5188067 0.2779322 80.21123 35.04274 14.95651 66530.25 4304.715 23.78075 33.89831 0 0 372 104 373 67 1363099 43.95482 1.432902 0.0551116 97.51998 70.09967 14.58775 67370.06 4266.618 23.57191 15.82569 0 0 495 89 506 91 1363107 74.30168 0.4219409 5.555556 71.58931 40.57971 12.21709 53519.74 4518.243 20.625 29.41176 0 0 99 32 98 36 1363123 62.10008 1.554243 2.9427 82.64429 33.41289 15.20630 66125.09 4453.164 23.32139 31.30288 0 0 560 162 578 133 1363149 63.91804 0.3990025 0.1496259 70.47382 26.88172 11.60701 56622.57 4245.811 21.68514 32.14286 0 0 158 41 162 19 1363164 39.94452 0.7492287 2.688409 60.77567 24.5283 9.224783 51167.03 3708.859 20.85687 34.92063 0 0 179 66 179 75 1363214 76.8279 0 3.005780 31.09827 20.40816 15.31852 60141.08 5355.161 18.99177 19.35484 0 0 49 14 50 4 1463255 25.58849 0.7623888 0.5082592 11.01228 5.454545 16.50992 59805.55 4349.222 23.45035 40.71856 0 0 171 106 170 126 1563404 68.64343 0.7808143 1.728946 76.8879 31.81818 12.21163 58861.69 3714.92 25.99096 14.03509 0 0 520 78 550 104 1563677 40.599 1.107011 14.87085 24.53875 7.627119 15.24427 63042.2 4572.615 24.41998 32.48408 0 37.5 190 63 187 45 1563685 31.86047 2.014723 12.43704 7.477722 9.848485 15.32667 63498 5767.594 20.30722 32.8 0 0 170 87 167 75 1563776 49.35103 1.697439 8.219178 31.98332 9.45946 13.08426 58159.2 4284.445 23.42700 15.49296 0 33.33333 216 97 219 68 1563800 51.46237 0.3612479 0.7553366 17.76683 2.739726 14.43697 62633.1 5763.701 21.05658 17.79141 0 0 195 80 193 58 1563826 24.99504 0.4204204 2.722723 17.39740 4.524887 14.79 60455.09 4308.128 22.87165 29.72973 0 0 322 213 324 156 1563842 65.89027 0.4718152 4.395332 78.91731 21.10092 14.93269 61803.9 4139.937 24.51174 14.01515 0 0 250 55 255 43 1573742 32.59873 2.233486 4.79962 10.89815 5.629139 14.35102 62453.15 4537.569 22.04157 38.66279 0 0 458 256 463 208 1573908 81.96457 0.3115265 0.2336449 92.01713 31.89655 13.13246 55369.86 4428.046 22.50856 23.68421 0 0 199 28 200 18 1575168 37.83202 0.3584229 0.860215 13.69176 8.333333 10.38143 55942.07 4098.994 24.92174 0 0 0 91 49 91 47 1663891 64.27918 0.4638219 4.050711 79.37539 17.36111 15.31545 66404.2 4605.861 22.67635 15.58442 0 0 191 61 197 45 1673932 86.49362 0 0.7340946 89.96737 15.59633 11.06066 57641.57 4543.492 21.75277 26.19048 0 0 153 28 161 14 1764014 45.57020 0.9541985 1.335878 22.08969 7.207207 14.75781 54040.6 4832.32 20.08333 50.9434 0 0 162 91 165 91 1764022 73.609 0.8296107 5.966816 9.60434 6.329114 13.7825 54439.95 4354.388 20.61290 10.60606 0 100 201 66 206 52 1764030 46.29523 0.6597031 1.869159 15.66795 3.448276 15.13131 58962.14 4361.361 21.41705 28.42105 0 0 146 88 147 85 1764055 26.79575 0.7237636 0.3618818 8.685163 1.204819 11.29516 50096.41 3932.39 19.15049 40.69767 0 0 120 76 116 77 1764063 52.91709 1.732926 2.854230 8.25688 6.25 14.98707 59995.4 5022.533 21.89956 37.70492 0 0 60 30 61 23 1864204 41.9708 0 0.5714286 9.142857 3.030303 17.90429 55176.85 5259.958 18.96552 18.18182 0 0 42 15 43 8 1964212 30.50552 32.46542 10.40141 32.65980 36.00406 15.25756 59289.09 4219.665 22.57679 49.73262 0 3.333333 1579 837 1599 879 1964261 8.362214 52.59396 1.213040 9.888444 8.900524 15.98201 64415.47 4166.218 24.27460 60.84011 0 27.27273 771 540 771 640 1964279 70.73827 0.9595613 3.152844 78.94106 25.95573 14.49885 64611.03 4379.243 23.98186 19.42446 0 11.11111 794 191 800 200 1964287 62.87168 4.72375 1.793829 85.46998 27.21202 15.06211 67839.96 3819.118 27.50542 26.77165 0 0 972 272 1029 268 1964295 79.6766 2.102924 2.102924 89.14344 45.6 14.39982 62084.61 4432.065 23.55966 20.98765 0 0 395 71 399 108 1964303 49.17215 5.575448 18.15857 38.48009 12.27437 13.04228 58550.52 3988.183 23.88522 17.99729 0 7.142857 927 287 927 260 1964311 9.733488 10.93985 3.571429 4.097744 10.81967 18.2 62455.76 6015.758 17.72469 61.88437 0 0 417 329 417 346 1964329 25.84395 5.866005 4.823821 27.58313 9.501188 16.89201 62021.76 4054.136 24.11424 51.76471 0 0 803 450 804 426 1964337 37.6798 5.688124 2.774695 36.95893 13.16667 14.46938 62400.43 4138.196 23.95766 32.4291 0 5.263158 995 517 992 436 1964378 23.94770 5.265554 4.871017 36.61608 11.76471 15.32254 61534.67 4024.875 24.12297 38.72549 0 0 459 271 464 209 1964394 20.80702 10.11185 12.00121 20.87364 11.70886 13.90775 61447.66 4401.148 21.54203 53.16973 0 0 482 323 483 338 1964436 37.64495 8.548446 5.798903 50.82998 14.25993 13.90046 62290.03 4265.807 24.2564 42.08443 0 0 955 458 958 450 1964444 33.51168 10.20237 17.91269 36.81218 18.65079 15.38502 62132.02 4282.612 22.95729 41.17647 0 0 386 174 388 144 1964451 42.84095 5.595576 4.80418 63.33705 14.59948 12.29348 64631.93 3979.219 25.14150 23.26733 0 0 1300 637 1312 538 1964469 61.40009 2.775971 13.20755 61.96053 30 16.06842 59303.74 4179.698 25.22527 15.01597 0 0 267 85 273 71 1964527 68.0851 0.683527 0.580998 94.48052 51.81452 16.27428 65653 4338.243 22.49503 33.67003 0 0 662 152 682 130 1964535 13.80008 6.320789 3.397881 14.87030 4.761905 11.92766 57362.05 4718.115 21.28874 38.99371 0 0 209 146 212 125 1964568 50.67402 12.12724 1.119947 23.8668 12.23958 13.22109 73039 4224.937 26.67020 26.38655 0 34.48276 2339 1123 2327 1326 1964576 14.82624 4.119138 1.292776 16.56527 6.329114 14.99266 63784.61 4130.35 24.90064 33.6 0 0 556 350 584 339 1964634 69.00829 0.1925546 43.09138 55.36819 63.43907 15.01983 63040.99 3748.33 27.40275 38.88889 0 57.89474 1095 296 1111 222 1964659 1.421801 26.20056 0.5414313 3.060264 8.205128 15.02579 63791.63 4705.506 21.59624 66.18182 0 0 326 293 326 287 1964683 2.971741 7.57133 1.366449 4.975937 7.024793 17.00701 64749.85 4414.05 24.23913 60.62323 0 0 825 693 828 671 1964725 64.2387 14.16632 20.33687 40.48983 29.10875 13.30980 59298.18 4210.309 22.98312 41.37823 2.325581 19.76744 5645 2145 5677 1930 1964733 73.18394 4.308746 13.79525 68.52417 45.46139 20.54574 73073.07 4365.276 23.42095 49.30618 2.321981 32.19814 38418 10373 38852 9324 1964774 59.29694 0.224649 12.05616 86.5273 52.48714 15.79301 67063.02 4064.954 27.05961 15.08475 0 50 951 171 939 113 1964790 56.89858 1.915122 14.06465 48.07722 17.54386 14.42588 57193.3 4017.302 22.55472 25.94752 0 11.11111 398 183 401 144 1964808 75.0343 4.879927 0.4471292 90.5185 59.52981 16.92341 67605.91 3810.285 27.99578 28.54167 0 25 2025 466 2093 377 1964840 44.02203 4.939493 5.01597 62.97629 29.22002 15.62108 60665.56 3616.505 25.62582 21.50866 0 7.142857 1425 513 1436 388 1964865 1.943602 30.02693 1.723209 3.941842 7.474227 18.94287 59821.21 3946.788 24.11441 89.13043 0 0 680 551 678 597 1964873 58.22835 1.783620 14.61458 75.7082 35.24845 11.80146 63190.71 4370.215 24.5058 5.430712 0 73.33333 1080 248 1088 185 1964881 61.8811 2.22193 32.00543 47.11193 42.18415 14.27299 64584.04 4443.599 23.86713 39.65517 0 0 1348 404 1352 365 1964907 73.5053 6.393463 11.23378 71.20447 48.69857 16.68337 65628.77 4007.186 26.04422 8.27719 0 23.68421 1986 616 1983 575 1964964 0.2653400 63.20234 0.4871712 3.345242 6.944444 15.90786 57543.38 4619.277 22.17736 78.1893 0 0 265 215 265 246 1964980 26.09065 5.531472 8.617999 27.07647 22.2664 15.87400 65963.43 5063.35 22.31528 65.10638 0 0 735 500 753 429 1965029 11.67883 32.08936 4.467806 18.21288 13.01775 16.19645 61169.62 4043.499 22.99094 64.07407 0 0 300 228 300 234 1965052 27.32198 38.10775 1.445466 20.36794 7.359307 13.7875 59417.3 4035.891 23.64895 31.56499 0 0 384 246 376 237 1965060 17.70193 28.47688 3.996755 15.83330 19.65726 13.31861 59555.29 3822.103 23.78323 45.95661 0 0 1726 1105 1732 1108 1965094 47.30717 9.43911 9.122654 58.16237 17.52022 13.90339 56841.37 3907.338 24.42266 29.06178 0 0 629 233 627 194 1973437 78.42391 0.05453864 35.72962 62.92736 80.17493 14.79315 58991.42 3743.125 28.09997 41.78905 0 2.564103 1810 308 1843 258 1973445 40.23607 16.39995 2.539347 67.97602 28.90365 17.39357 62325.3 4103.139 25.05607 38.67849 0 0 1636 622 1647 609 1973452 33.85063 18.78119 6.688608 55.35089 29.18239 16.1731 62277.31 4369.675 24.44574 30.26188 0 0 1204 554 1213 606 1973460 6.196674 42.31389 5.721689 18.18054 17.73649 14.82445 60555.24 3982.78 24.25086 57.78547 0 0 1162 802 1171 831 1975291 53.4557 36.40237 1.889589 43.47907 22.06573 14.12308 57663.26 3814.974 23.16667 0 11.11111 0 320 173 319 185 1975309 28.17235 1.443203 1.722533 11.91806 7.446809 11.72804 58880.17 3806.893 24.82222 0 0 0 191 124 192 109 1975333 7.197273 7.00382 1.819174 8.659269 7.936508 15.21719 64224.89 5093.928 21.58711 43.08943 0 0 296 263 299 242 1975341 25.63993 7.277444 5.188422 23.18405 10.24845 16.50521 68096.37 5006.776 22.47522 51.54062 0 0 488 303 489 298 2065193 59.28721 0.9413778 2.524604 41.63457 6.666667 12.20542 53265.82 3873.24 23.55149 25.40984 0 0 147 71 159 48 2065243 60.0239 1.152145 3.543626 67.3538 26.82927 14.05485 56274.59 4064.557 23.45833 31.99405 0 45 1101 374 1130 362 2165417 14.79229 4.893837 2.770585 11.10823 3.562341 16.62018 53794.99 4365.513 20.39636 48.08511 6.25 12.5 558 407 564 412 2165458 34.76768 7.884136 5.198542 36.17878 8.536585 15.19114 53336.38 4458.969 20.96682 53.59281 0 0 278 161 277 144 2173361 0 0.4801921 0.4801921 28.69148 7.407407 13.72615 60992.5 6459.384 16.40535 26 0 0 52 36 52 28 2265532 27.00072 0.5113221 0.2921841 6.208912 1.459854 15.59845 51692.82 4183.603 21.53846 29.80132 0 0 223 140 225 112 2365540 55.9322 0.8403361 1.344538 45.37815 0 12.756 48855.96 6406.862 14.31604 44.73684 0 0 38 23 38 21 2365565 37.29968 1.162791 0.4152824 19.68439 2.272727 17.13074 56336.78 4990.487 19.47287 25.89928 0 0 188 103 188 98 2365615 46.92754 0.969469 1.114166 22.81869 4.848485 17.4015 64535.71 4838.431 21.34488 33.33333 0 36.36364 492 241 489 240 2365623 46.53846 0.8461538 0.6923077 11.57692 2.985075 17.472 56568.13 4611.976 20.82808 26.38037 0 0 170 90 171 67 2373916 56.33117 0.5172414 0.6896552 3.965517 4.651163 12.49787 48867.6 6578.053 15.73298 34.21053 0 0 34 24 33 13 2465698 37.78899 1.586639 0.2922756 17.32777 11.71171 13.41706 58102.55 4361.619 22.78612 20.8 0 16.66667 181 63 183 60 2465722 41.43167 0.9155646 0 84.13021 25.58140 12.77143 55709.23 4140.527 22.18527 24.74227 0 0 46 4 48 14 2465755 50.92824 1.244953 4.862046 53.17968 12.1673 12.07816 55074.62 4075.188 21.77028 33.93502 0 50 454 141 455 123 2473619 52.40793 0.6143345 0.9556314 50.44369 8.333333 11.98961 53609.9 4080.966 21.15774 49.27536 0 0 106 29 108 23 2475317 60.91821 0.1568013 6.31125 61.70129 15.44118 11.49135 54802.7 4750.999 18.81218 19.59459 0 0 179 48 181 33 2573585 33.27773 0.75 0.75 10.41667 1.612903 11.68088 55854.74 4786.679 19.22953 32.78689 0 0 94 47 94 37 2573593 64.85084 0 0 52.38095 4.761905 12.95532 50840.07 4851.063 18.40095 45.71429 0 0 47 15 47 19 2673668 45.93373 0.3100775 0.7751938 14.10853 2.222222 14.03431 52320.31 6283.433 14.66970 76.92308 0 0 39 24 40 24 2673692 21.84007 0.5828476 0.08326395 21.81515 4.83871 14.98485 60159.44 4806.483 20.59423 42.85714 0 0 92 58 93 45 2765987 10.70682 2.977162 1.305057 7.544861 4.8 17.29423 69887.39 5676.651 20.67854 55.92105 0 0 202 156 201 149 2766092 40.13453 8.813424 15.63724 25.22573 17.65705 19.47192 61011.63 4496.243 20.60704 28.90792 0 95.2381 761 365 773 255 2766134 14.27955 4.711581 1.893439 7.089388 2.941176 17.54195 63909.55 4756.384 22.63210 48.17518 0 0 157 116 154 105 2773825 40.66667 1.844262 1.285395 49.23621 20.15810 14.68534 56619.21 4356.072 21.57957 60.59783 0 0 353 124 357 107 2775473 67.26937 0.2738788 0.4108182 86.51147 24.19355 11.88684 58893.12 4200.852 24.90864 11.30435 0 0 161 43 163 60 2866241 45.67023 0.6825939 0.3412969 47.09898 4.545455 13.51122 54344.89 4650.725 20.97387 50 0 0 62 31 61 21 2866266 32.70906 1.396074 1.776254 26.488 6.388527 15.47587 54785.13 3912.949 21.83841 29.25258 3.333333 0 1171 656 1198 587 2866290 34.43425 0.7380074 0.4305043 37.45387 14.63415 15.64778 51434.44 4386.959 21.20205 44.14414 0 0 113 62 112 58 3066449 16.15780 8.398994 2.179380 21.62615 4.897959 15.22727 57193.42 3825.952 25.22776 40.72022 0 0 449 323 455 300 3066464 17.33803 4.709514 1.580624 16.95375 9.391933 12.69992 60779.38 3802.974 23.00043 44.82558 0 10.52632 2686 1934 2692 1804 3066522 48.15608 28.71155 1.291070 44.05147 11.48612 15.67656 65184.42 3835.549 24.67322 20.3813 0 0 2986 1224 3007 1473 3066555 9.316273 1.883830 1.216641 9.22292 7.619048 16.90641 70274.37 4481.474 24.26357 74.28571 0 0 188 154 188 145 3066597 39.02544 4.683563 1.235117 34.12381 4.426788 16.00117 60202.04 4349.231 22.48276 42.87149 0 0 1186 759 1186 688 3066621 36.01998 10.88242 2.085541 36.82177 9.424084 16.81767 56415.11 3586.935 25.44882 47.33026 2.564103 12.82051 1989 1054 1991 1035 3066647 21.01388 8.124397 1.888460 25.92012 11.39742 15.15302 63101.53 4172.242 24.71715 36.56174 0 0 1873 1180 1875 1238 3066670 73.27723 4.408512 1.079825 90.79082 26.48752 14.97092 69824.54 4102.145 25.03219 21.72937 0 56.25 3331 666 3391 610 3073635 10.53450 8.401664 2.167491 14.81068 6.056435 14.97990 60372.2 3840.866 22.19101 53.79189 0 0 2363 1654 2363 1607 3073643 29.86897 8.276043 4.655274 41.6814 5.263158 13.01412 57831.15 3629.530 24.16947 39.59538 0 0 1053 569 1074 526 3073650 10.51650 25.16370 3.039764 7.41078 9.255725 16.09776 62550.98 4352.488 22.65816 0 0 9.67742 1795 1400 1799 1421 3073924 11.60025 8.99128 3.111006 11.53665 10.13333 15.09307 67284.94 4726.529 23.19879 49.59217 0 10 622 491 621 416 3166944 23.35644 0.627574 0.4314571 17.70935 6.617647 13.59359 49513.03 4638.098 19.67842 44.13146 0 0 357 243 358 204 3166951 28.66097 0.5628095 1.440792 17.42458 4.878049 13.33273 42658.39 3503.698 24.26859 27.46114 62.5 0 194 95 182 49 3175085 17.13624 3.538945 1.372581 6.664462 4.651163 11.19239 52654.82 3596.095 23.04330 0 0 0 438 346 445 276 3266969 36.66211 0.6635333 1.216478 6.856511 2.717391 16.15405 56500.12 4912.702 21.23209 33.19328 0 0 289 182 288 132 3366977 42.29029 4.021226 7.222877 47.15802 17.24138 13.00708 62390.7 3758.198 25.18195 41.66667 0 70.58824 1079 356 1078 280 3366985 75.52987 13.36851 13.85224 36.69745 20.29703 13.81733 60074.78 4456.322 21.89966 41.81818 0 12.5 316 95 335 54 3366993 58.74644 1.151316 3.426535 34.64912 9.027778 15.96616 59270.44 4070.74 24.75627 26.25 0 12.5 219 103 219 53 3367033 41.51139 3.604238 4.644538 41.77843 11.37321 13.54297 64063.46 4083.554 25.23114 34.63727 0 34.28571 2104 989 2122 849 3367058 55.99125 1.264684 2.467791 60.04168 15.80882 12.68737 64123.52 3937.206 25.2138 43.17181 4.347826 0 1428 486 1434 387 3367082 62.40778 1.435897 2.666667 29.23077 9.03328 15.50251 59735.05 3916.537 24.51756 21.42857 0 5.263158 1090 534 1085 456 3367090 50.21092 1.388811 5.304256 53.2545 23.78976 13.27531 62068.72 3826.458 24.66667 26.38353 0 4.166667 1177 424 1199 300 3367124 43.06891 3.265436 23.67600 35.35328 23.12358 14.01194 58401.16 4173.917 24.09220 25.59242 2.857143 22.85714 2269 885 2300 644 3367173 62.50497 1.157494 5.942876 53.49124 8.933333 13.07002 57242.97 4143.368 24.20104 13.75405 0 9.52381 1210 460 1226 319 3367181 55.91289 0.6329114 10.52215 52.08333 10.79545 14.11026 61885.48 4694.474 22.19581 20.93023 0 0 233 75 238 93 3367215 47.23435 3.598305 10.32666 40.11093 21.7477 10.97662 61445.65 4384.205 23.56853 33.77246 0 19.04762 2371 1091 2388 860 3367249 64.1791 1.007308 3.851471 50.81967 7.446809 13.11981 58040.66 3564.449 26.02162 11.93182 12.5 25 306 92 306 92 3373676 85.51459 0.1381215 0.2762431 96.16713 36.46532 11.92077 61385.59 4099.012 25.27964 13.54680 0 0 838 92 862 86 3375176 38.11647 1.790807 4.072428 30.11209 11.41869 13.05375 62326.66 3960.634 25.39694 25.38330 0 83.33333 994 457 990 307 3375192 18.16999 1.957028 3.825099 17.78432 8.097928 12.19485 64156.11 3664.265 25.5498 25.86806 6.25 81.25 1028 617 1029 587 3375200 18.21645 2.105698 3.437242 17.84682 15.19337 9.219296 59134.67 3774.601 25.86148 38.25666 0 100 669 408 674 290 3375242 54.84387 1.897597 23.57961 45.82304 21.23894 10.90779 58447.44 3799.078 25.54149 31.80212 0 0 647 214 656 177 3467314 36.94477 15.21258 19.09346 16.26987 17.40431 12.27789 58331.63 3762.583 23.11697 29.58015 0 33.33333 2805 1318 2817 1239 3467330 26.11432 5.308799 9.912578 8.671743 9.722222 14.26319 51104.65 3845.847 22.19777 27.70919 4.347826 30.43478 957 584 957 555 3467348 33.11538 1.770956 1.180638 35.72609 9.166667 11.77256 51742.2 4581.189 22.21333 23.98374 0 0 347 180 349 154 3467413 35.97884 1.407285 1.034768 36.92053 11.90476 14.73511 51080.37 4420.624 20.18018 45.3125 0 0 171 91 174 82 3467439 59.40643 23.87054 21.95251 23.50613 27.45747 14.0033 61997.03 4125.407 24.82678 38.74261 1.298701 10.38961 3322 1329 3398 1325 3467447 28.84975 4.59477 5.959822 8.972135 7.187223 12.61622 58872.75 4335.660 21.75074 39.28442 1.219512 6.097561 3282 2100 3310 1787 3473973 29.30606 7.97491 13.87097 10.28674 10.69959 13.23241 55944.13 3811.049 22.28951 78.96825 0 42.85714 388 244 391 266 3475283 37.95538 6.22615 29.74493 26.23017 12.23404 10.66611 57672.68 3312.515 22.42841 0 14.28571 14.28571 411 193 412 136 3575259 35.75375 2.109375 0.625 40.46875 16.66667 11.91818 61919.85 4272.916 22.22222 0 0 0 114 72 114 58 3667611 50.62411 1.101449 11.78261 38.13043 19.26910 14.40147 57100.62 4203.244 22.98675 12.98701 0 0 459 184 467 149 3667637 40.70337 0.449312 0.954788 11.42937 3.521127 13.88148 57824.88 3838.359 25.36429 35.42857 0 100 273 158 280 143 3667678 25.95567 6.023256 5.232558 38.20266 18.69852 15.21277 62270.05 3664.259 24.16969 29.01554 0 10 2137 1133 2132 1002 3667686 44.45212 2.019903 9.084639 63.68608 19.09425 11.89511 59625.26 3811.724 24.03023 34.44593 0 48 1302 430 1304 352 3667710 39.99806 1.101044 11.80547 65.77463 19.71081 12.96852 60468.72 3939.782 24.37131 18.2534 0 56.25 2213 575 2259 407 3667777 46.74229 1.503836 8.194373 13.34015 4.504505 13.40194 62286.07 4128.065 22.62928 13.5 5.882353 0 660 290 667 280 3667801 54.40152 1.161946 3.776325 17.28395 2.941176 16.79467 61234.99 4903.238 21.27721 40.35088 0 10 101 42 103 45 3667843 34.60404 8.123958 7.962554 30.9894 15.03356 14.84909 60477.27 3763.685 24.72111 24.77432 0 50 1262 681 1261 580 3667850 57.88502 2.875776 27.39844 51.59111 24.49393 12.17399 59096.31 3838.001 25.16211 13.23944 0 70.83333 1662 482 1750 368 3667868 24.70073 0.979605 1.268669 13.05605 10.27668 15.79196 62201.87 3995.416 24.43908 31.28655 0 0 457 279 488 229 3667876 70.23575 3.366044 19.61802 50.84942 25.21964 11.67938 61672.89 4147.682 23.98684 22.39521 0 42.62295 3036 911 3080 739 3667918 44.91996 1.618203 16.41763 36.17087 15.53398 10.97167 61492.85 4057.497 26.51144 13.97059 15 20 998 389 1011 354 3667959 27.57103 0.7626423 0.7743752 19.66444 6.060606 11.88049 63432.72 3784.39 25.07162 25 0 70 626 313 626 244 3673890 46.96416 2.107896 18.18507 12.04001 7.913669 11.09907 60618.41 5342.537 20.25289 18.34862 0 0 179 98 180 74 3673957 20.98413 0.8261886 2.088854 16.67966 4.054054 8.420029 53764.2 3823.18 22.52066 10.24845 9.090909 0 482 294 482 241 3675044 47.6781 0.955685 5.308634 31.19068 11.89711 12.19179 62220.1 3865.725 24.81302 13.34405 0 61.11111 1035 424 1057 328 3675051 62.31156 1.224847 2.274716 17.93526 5.263158 10.46923 59114.49 4587.305 20.48736 26.08696 0 0 81 32 81 17 3675069 30.64712 7.226302 11.49032 28.47964 11.45631 13.98121 59659.77 3922.567 23.51056 35.31353 6.666667 6.666667 905 507 910 473 3675077 43.73161 1.65699 17.27445 20.43186 5.168986 13.20700 62106.35 3058.089 24.52082 35.83916 13.33333 86.66667 889 471 880 352 3768031 12.89092 1.749271 2.587464 10.49563 8.661417 15.07955 59963.79 4346.546 22.36994 58.82353 0 0 199 163 200 152 3768098 43.17715 2.752435 1.797588 43.81089 8.999082 14.07094 58398.9 3882.451 23.42240 29.01186 4 44 1607 820 1610 757 3768114 49.18965 0.8987701 4.328288 36.21097 10.14085 16.56101 66966.72 4540.894 24.11054 22.46094 0 0 536 316 538 285 3768163 7.723036 0.1381215 0.6906077 9.254144 5 15.96932 62789.97 5092.907 19.09561 32.07547 0 0 54 48 54 40 3768213 48.69423 0.1632209 1.468988 25.29924 4.301075 12.16262 52084.4 4470.921 20.17877 40.86957 0 0 142 68 143 47 3768296 10.58816 8.755863 2.760139 7.792208 11.01512 14.19155 58198.62 3718.169 22.87571 46.68874 0 0 2347 1831 2349 1785 3768304 35.02365 0.7042254 0.8766887 19.61771 7.491857 13.51452 57168.13 3976.03 23.11592 39.73684 0 0 510 342 509 280 3768338 64.15235 9.492747 16.92434 35.27733 25.08497 12.29597 62782.7 4952.585 22.19313 31.24032 4.166667 28.57143 8470 3812 8479 3392 3768452 40.05977 2.53262 6.127555 37.38501 11.09091 12.81245 65164.45 4103.446 23.64449 34.33243 3.703704 81.48148 1691 846 1707 683 3773551 26.92308 2.594662 1.762880 26.13284 13.33333 15.28393 62997.11 4261.82 24.32617 43.44473 0 0 594 410 595 357 3773569 54.9774 1.804259 14.86001 41.52190 16.01344 13.17371 57811.14 3832.582 23.35025 44.7178 0 0 1303 573 1317 435 3773791 49.26504 2.389785 2.553705 44.13769 11.06472 13.66803 60657.16 4097.092 24.63998 29.65517 0 83.33333 734 338 749 330 3868478 64.55428 41.36902 16.19322 21.20412 43.73746 15.28547 52885.51 5230.051 17.93118 48.83066 2.654867 1.769912 3624 1740 3633 1925 3968502 36.41286 0.9198423 0.3285151 26.64258 5.673759 14.19245 53510.44 3846.512 23.32055 26.41509 0 0 197 126 200 114 3968569 44.12983 19.62769 10.69226 17.62653 14.7343 13.20137 53970.81 4022.264 21.56064 29.83539 0 0 629 327 634 279 3968577 40.58286 1.936027 0.3367003 34.21717 7.207207 16.12520 52198.86 3727.909 22.02652 39.28571 0 0 190 89 193 60 3968585 48.76863 19.79547 5.832491 23.64881 11.27119 15.08360 59465.23 4469.021 22.00292 30.33708 0 62.85714 1916 824 1932 638 3968593 35.13317 2.449629 4.936003 29.09547 11.32638 13.80978 56910.66 3589.197 24.38578 36.2426 0 0 1189 606 1207 591 3968650 13.77399 0.7308161 0.4872107 21.84328 2.564103 14.55039 51683 3786.43 20.59880 29.72973 0 0 173 119 173 130 3968676 68.72708 20.87249 13.34830 42.18544 33.74755 12.67768 66654.75 4527.226 22.94049 18.20926 0 21.95122 2218 554 2323 558 3975499 26.57033 4.436534 5.981941 26.72339 14.19624 10.59350 55631.9 3616.366 23.96260 26.28676 0 0 745 417 749 397 4068700 18.07366 1.065779 1.648626 9.342215 3.284672 14.97258 60464.24 4506.425 23.30425 45.45455 0 0 438 324 437 293 4068759 38.07467 1.538031 1.165175 30.20134 8.932039 13.48316 55684.26 3887.088 21.21927 25.90361 0 0 730 518 727 465 4068809 22.46579 2.346382 2.090835 12.61471 5.326877 16.19522 64397.68 4933.223 21.43327 25.63559 5.263158 0 669 482 676 453 4068841 17.99781 0.6178288 1.41218 9.223301 4.587156 11.08730 50372.47 3667.019 19.39394 35.95506 0 0 175 114 174 96 4075457 36.82691 1.330486 3.876478 27.18463 5.802048 14.22784 59653.03 4756.442 22.28147 39.81481 0 0 423 245 423 224 4075465 27.08779 1.005025 0.3015075 21.60804 9.23077 12.86419 46347.38 4792.461 17.37132 41.33333 0 0 65 45 66 34 4168890 19.30207 2.108511 0.5399846 25.09643 8.290155 13.32290 51768.27 4138.211 21.00933 48.80952 0 0 329 220 331 179 4169070 31.91489 8.753316 4.823657 34.79713 11.89931 17.10861 57489.22 3823.838 23.52320 38.95447 0 0 707 325 705 303 4269146 39.27727 1.385309 0.9664948 54.38144 21.32353 14.49774 60262.91 4158.069 22.21329 33.33333 0 0 232 123 235 108 4269229 35.70633 4.651992 8.920774 32.86695 9.057301 17.08317 53117.72 4325.819 21.67377 42.91498 0 13.33333 740 363 754 302 4369484 44.54203 2.308979 1.528165 62.94479 22.37197 15.48966 62890.81 4312.730 24.50787 20.19002 0 15.38462 562 242 590 171 4369583 23.6491 5.235658 2.399232 33.46129 11.25 17.79609 60283.85 4361.515 23.73711 37.80488 0 0 716 437 715 400 4369641 7.894435 17.389 4.916817 6.887782 13.52834 14.11575 66182.01 5970.823 18.74769 73.37526 0 0 598 514 605 538 4369666 42.80784 12.24502 3.270391 49.41048 22.36951 16.57729 80565.04 5038.4 28.21085 38.10758 0 0 2185 1071 2216 975 4369674 40.20545 18.39412 4.340957 25.20091 11.52 15.60997 65468.92 4317.319 23.7628 29.55112 0 0 898 476 908 463 4373387 28.89357 31.45988 5.952142 18.42952 20.22727 14.80637 60774.92 4090.016 22.11977 26.72065 0 0 710 426 713 399 4469799 56.34867 1.201376 0.587457 72.01376 19.71665 14.11421 59922.13 4282.146 22.53530 26.64756 4 20 1253 426 1269 393 4469807 14.50640 1.659053 1.226256 4.087521 3.333333 17.28916 58616.28 4262.088 24.33353 60.58091 14.28571 0 346 253 345 204 4569989 39.60894 0.7471264 0.2873563 8.908046 4.301075 16.49120 56131.94 4741.874 18.73638 28.82883 0 0 144 81 145 59 4575267 49.37712 0.8405323 1.074014 4.809713 4.090909 14.58583 55396.61 4509.515 20.73813 34.48276 0 0 338 169 335 131 4670177 32.10162 1.633166 0.6281407 8.354271 1.960784 14.40364 54122.02 2908.312 16.88889 43.24324 9.090909 0 121 88 121 80 4870524 11.44366 4.562111 7.805057 10.15024 10.16260 15.17562 61419.34 4303.057 22.27944 40.4908 0 0 433 359 435 326 4870532 40.81942 1.316920 1.774979 39.96565 12.64368 15.51333 57074.22 4262.563 20.02370 39.66480 0 16.66667 245 103 239 100 4870540 28.11086 6.548673 21.01071 18.01584 13.24153 15.96551 59268.99 3955.489 22.88891 26.11607 0 34.61538 1486 728 1496 688 4870565 22.71245 2.529475 15.34834 10.39657 18.22034 15.12085 62353.6 4900.856 20.22874 66.31016 0 0 320 189 323 155 4870573 23.01924 2.829361 8.373287 16.74657 9.129815 14.62274 53901.88 4110.539 22.38147 40.26144 5.263158 10.52632 1044 585 1052 515 4870581 35.46088 3.839582 35.21459 15.5493 23.87677 16.68085 68338.8 4364.927 25.67080 20.02275 0 56 1290 477 1324 450 4970656 39.70868 0.4294479 0.6748466 25.09202 6.329114 15.62941 56854.04 3942.010 20.42161 43.83562 0 0 117 67 120 55 4970953 30.76032 1.923440 0.7165755 22.40241 6.153846 15.85861 54122.34 4170.568 21.14767 23.37165 11.11111 11.11111 392 239 386 224 4973882 19.67584 3.863969 3.644564 12.84739 5.99455 15.71779 59484.68 4081.602 22.29298 14.45087 0 0 612 373 616 320 4975358 33.55805 1.379099 0.858106 26.50935 17.56757 11.02364 50731.47 3834.786 21.02305 0 0 0 236 132 234 108 4975390 21.46387 0.8783784 0.4391892 34.05405 14.28571 16.46453 56837.35 4968.968 20.91327 46.48438 0 0 231 136 232 151 5071043 51.20522 4.550344 3.431583 39.2642 10.04902 13.27732 56234.31 3767.504 23.17001 12.96703 0 41.66667 690 228 688 193 5071068 42.13075 0.7830854 0.2349256 25.92013 1.724138 13.38175 49550.74 3282.389 21.30662 29.87013 0 0 118 63 120 55 5071217 55.00726 0.673968 1.235608 64.5886 18.12081 13.32733 55535.15 4008.414 24.00409 13.07190 0 0 220 86 224 72 5071308 42.6007 4.702023 1.366867 32.55878 9.221311 14.96416 59645.21 3932.407 24.17863 21.40625 0 50 747 388 733 359 5073601 53.90428 1.221374 1.475827 53.43511 14.73684 12.70857 51297.37 4109.917 21.68662 28.86598 0 16.66667 120 44 120 31 5171399 72.2977 12.32356 0.597177 45.33116 6.521739 12.38267 52194.34 4350.793 21.44379 17.72152 0 0 131 43 135 39 5171464 49.25642 13.28312 2.939555 25.5282 14.10788 15.09745 57777.56 4165.932 23.06171 24.24242 0 25 767 360 776 303 5271498 49.15907 0.8762706 1.892744 29.75815 3.597122 13.24340 55372.46 3941.387 20.91108 36.42384 0 0 204 82 207 79 5271571 51.2931 0.1422475 0.56899 32.29018 2.564103 15.81222 53216.62 5045.576 19.64384 34.21053 0 0 40 18 41 19 5375028 60.14599 0.4807692 0 5.128205 5.555556 16.13205 63507.83 6482.47 17.90831 42.42424 0 0 41 25 43 23 5471860 84.09904 0.5628518 0.2412222 90.72635 18.78788 14.21739 58391.11 3952.910 22.75184 8.370044 0 0 258 46 259 91 5471878 74.57442 0.8955831 0.2442499 80.46 15.98174 12.97066 53304.9 3688.748 22.96475 16.46586 0 0 343 103 342 96 5471993 76.58037 1.721170 0.05737235 83.76363 23.68421 13.28494 58362.71 4115.279 22.61873 27.42857 0 0 243 51 250 40 5472256 44.83328 7.621228 1.954371 43.13517 12.71111 14.47325 58322.98 4057.255 22.31273 35.91731 3.125 28.125 1726 794 1750 578 5472272 72.66314 1.192504 0.2981261 79.25894 13.15789 14.81406 58001.31 4081.734 21.10603 36.13445 0 0 147 46 150 26 5475325 92.3345 0.933553 0.1098298 83.0313 17.10526 14.40238 61350.21 3792.456 22.91892 0 0 100 137 15 134 23 5575184 36.71642 0.4279601 0.8559201 4.707561 4.651163 13.26809 48160.47 4666.561 18.42667 70.27027 0 0 53 36 53 31 5672454 51.80654 0.5471956 0.4377565 76.90834 15.52795 15.02404 56925.98 3897.079 23.48475 30.60109 0 16.66667 264 82 266 59 5672520 23.28170 1.366251 0.8628955 18.64813 8.152174 14.22136 56808.01 3825.936 22.87717 35.54688 0 0 308 222 304 204 5672603 15.68154 5.522166 1.674629 17.43977 6.917476 15.00022 57203.55 3831.574 23.20986 37.68719 0 0 1328 837 1338 736 5672652 38.15821 2.905512 2.354871 33.44268 9.943978 15.35255 56413.61 3699.07 24.85112 34.36341 0 22.22222 1166 653 1170 526 5673759 12.32702 5.661354 1.485200 14.53115 5.405405 15.73222 62750.24 4265.133 23.81032 41.16638 0 0 1425 1126 1431 1088 5673874 1.247051 6.066291 1.000625 2.345216 4.72973 12.22485 55208.84 4079.327 20.98849 74.3421 0 0 249 212 248 186 5673940 23.45529 3.861647 1.819697 29.78191 10.36585 10.28723 54402.98 4155.084 21.65256 21.14094 0 0 510 332 516 284 5772678 17.89883 10.49096 3.372093 12.70026 14.92891 14.43766 49783.31 4254.717 20.06286 46.42032 0 0 583 461 591 455 5772686 49.23581 0.77951 1.670379 43.76392 4.166667 11.36091 50433.17 5039.203 19.54545 27.08333 0 0 51 24 59 17 5772694 62.23968 13.82086 3.956157 32.12879 15.88448 13.27089 52881.33 4324.174 20.98711 8 0 18.18182 331 116 342 96 5772702 46.16188 1.150575 0.8004002 45.62281 13.33333 11.80714 48426.15 3945.069 19.8583 53.77358 0 0 147 63 154 37 5772710 43.41431 3.648564 1.541204 46.31998 21.37767 14.40468 53796.16 3845.481 22.70884 29.89247 0 6.666667 593 255 595 208 5872736 70.62104 20.46172 2.989345 17.42305 11.03286 16.56420 61705.66 3970.984 24.88647 15.14286 0 13.63636 699 259 709 199 5872751 77.74827 5.998899 9.576225 11.11723 7.874016 16.91871 65099.57 6913.397 20.03252 23.52941 0 0 154 92 156 72 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/src/star98.dat000066400000000000000000005412161224417117700261020ustar00rootroot00000000000000CDS_CODE COUNTY DISTRICT LOWINC PERASIAN PERBLACK PERHISP PERMINTE AVYRSEXP AVSAL PERPSPEN PTRATIO PCT_AF PCTCHRT PCTYRRND READ MATH LANGUAGE LANGNCE MATHNCE READNCE READM11 READNC11 MATHM11 MATHNC11 LANGM11 LANGNC11 READM10 READNC10 MATHM10 MATHNC10 LANGM10 LANGNC10 READM9 READNCE9 MATHM9 MATHNCE9 LANGM9 LANGNCE9 READM8 READMCE8 MATHM8 MATHNCE8 LANGM8 LANGNCE8 READM7 READNCE7 MATHM7 MATHNCE7 LANGM7 LANGNCE7 READM6 READNCE6 MATHM6 MATHNCE6 LANGM6 LANGNCE6 READM5 READNCE5 MATHM5 MATHNCE5 LANGM5 LANGNCE5 READM4 READNCE4 MATHM4 MATHNCE4 LANGM4 LANGNCE4 READM3 READNCE3 MATHM3 MATHNCE3 LANGM3 LANGNCE3 READM2 READNCE2 MATHM2 MATHNCE2 LANGM2 LANGNCE2 161119 ALAMEDA ALAMEDA CITY UNIFIED 34.397299903568 23.2993035015743 14.2352828928537 11.4111248926629 15.9183673469388 14.7064601769912 59157.3224489796 4445.20742295583 21.7102488401518 57.0327552986513 0 22.2222222222222 661.66460833221 654.852837393022 649.464497916387 54.1977416319398 53.2739960500329 49.0258864770123 700.8 40 702.9 50 683.6 49 688.9 31 696.5 49 671.2 39 689.8 40 695.2 57 674.8 54 694.4 55 685.1 54 670.4 56 680 52 674.2 53 664.9 59 665.1 53 661.7 55 649.6 54 656.3 53 647 53 646.4 57 643.7 57 622 52 634.9 59 616.7 55 598.9 54 613.1 57 581.2 53 570.5 55 590.9 57 161127 ALAMEDA ALBANY CITY UNIFIED 17.3650687227623 29.3283833952076 8.2348970637867 9.31488356395545 13.6363636363636 16.0832386363636 59503.9675324675 5267.59838002025 20.4427792915531 64.622641509434 0 0 686.821559860904 682.097784342688 669.959373446047 72.8915962207857 76.2717872968981 71.290611028316 715.6 56 727.3 71 693.8 60 712.3 55 726.7 75 694.4 62 707.6 57 720.5 78 687.7 65 715.2 73 714.9 80 691.7 74 714.4 81 712.5 84 692.2 83 693.4 80 693.1 80 671.7 75 690.2 82 672.7 76 674.2 80 676.7 81 647.5 71 655.4 74 649.5 76 626 74 636.3 75 607.7 70 596.8 73 616.2 77 161143 ALAMEDA BERKELEY UNIFIED 32.6432354357246 9.22638614946307 42.4063116370809 13.5437212360289 28.8343558282209 14.5955882352941 60569.9202453988 5482.92209072978 18.9541918755402 53.9419087136929 0 0 671.640665091155 661.550763788391 653.599950413223 56.2609917355372 56.6483357452967 56.1053342336259 717.7 58 720 66 692.6 58 707.9 51 716.1 67 689.7 57 707.8 58 710.6 70 688.9 67 702.3 62 690.3 59 678.1 63 685.7 57 672.8 50 667.3 61 673.6 61 664 57 655.8 60 661.1 58 651.7 56 643.1 53 648.3 60 623.1 52 630.9 55 612.9 50 589.3 43 604.8 48 574.9 47 559.5 42 577.1 40 161150 ALAMEDA CASTRO VALLEY UNIFIED 11.9095333524191 13.8830897703549 3.79697286012526 11.4431106471816 11.1111111111111 14.3893861892583 58334.1081871345 4165.09303235908 21.6353887399464 49.0610328638498 0 7.14285714285714 676.513967201032 668.812306006938 660.962304147466 64.1185253456221 64.2161767390907 61.975677169707 716.3 57 713.7 61 691.9 58 708.3 51 705.5 54 685.9 54 704.7 54 705.1 66 684.6 63 709.9 69 701.2 69 683.7 68 697.9 68 691.1 68 676.8 70 677.8 66 675.9 67 660.5 65 665.9 62 659.8 63 653.1 63 654.4 65 637.1 61 644.6 65 629.4 61 615.8 65 622.2 63 600.4 64 589 66 607.4 69 175093 ALAMEDA DUBLIN UNIFIED 12.927964941112 7.17299578059072 4.82594936708861 13.2383966244726 6.18556701030929 17.4710762331839 65570.8608247423 5382.00870253165 20.6833333333333 34.8314606741573 0 0 672.820427644139 662.700114372856 657.977207001522 62.6826484018265 60.5428898208159 60.2913325696831 711.7 52 707.9 55 690.3 56 703 45 706.1 55 685.4 53 701.4 50 706.8 69 682 60 705 64 688.2 57 681.1 66 694.6 64 679.7 58 671.9 66 676.5 64 673.8 65 660 63 668.8 65 660.3 64 655.3 66 659 69 639.6 63 646.7 67 635.8 66 613.5 63 623.2 64 593.7 59 579.7 57 601.4 63 161168 ALAMEDA EMERY UNIFIED 36.8888888888889 12.1875 76.875 7.60416666666667 43.5897435897436 13.9056818181818 63153.641025641 4324.90208333333 18.7798408488064 52.3809523809524 0 0 634.917397881997 629.553768115942 624.377661169415 26.7661169415292 24.4666666666667 21.2692889561271 677.8 19 680.2 25 660.3 26 667.4 14 676.1 23 650.5 19 663.9 18 669.3 30 652.4 31 663.9 25 653 23 644.5 30 651.3 24 648.2 26 640.8 32 636.4 24 635.3 28 625.9 28 621.8 21 614.4 19 612.3 23 606.2 22 597.6 24 604.3 29 573.5 17 560.4 16 570.6 18 560.7 30 547.2 26 569.3 28 161176 ALAMEDA FREMONT UNIFIED 20.9314901814986 28.0235116408892 4.64322070075198 13.8081633993367 15.3784860557769 14.9775459688826 66970.5521912351 3916.10429185959 24.5191372040221 44.9157829070493 0 2.38095238095238 672.231665367151 667.751934900543 659.896057692308 64.1000915750916 64.6254972875226 59.4770903086731 713.3 54 720.7 67 695.4 61 708.2 51 715.9 64 688.8 56 699.6 49 707.6 68 683.5 61 701.3 61 694.5 62 679.9 64 693.9 64 689.7 66 676.7 70 679.4 67 682.7 72 666.9 70 668 64 661.2 64 658.7 68 652.3 63 636.5 61 646.2 67 624 56 609.1 59 619.8 60 601 64 586.7 64 601.3 63 161192 ALAMEDA HAYWARD UNIFIED 53.2689776217639 8.44785772029103 19.3748315817839 37.9053265067816 25.5255255255255 14.6782859680284 57621.947947948 4270.90330548819 22.2127790292853 32.289156626506 0 12.1212121212121 642.626397690466 636.33247468834 633.291838901472 39.561013590034 36.7048694813692 33.3479791578651 689.8 29 694 39 670.5 35 681.7 24 688.6 36 664.2 31 678.2 29 685.2 46 665.1 43 677.4 37 670.3 39 657 42 664.3 35 660 37 652.2 45 653.2 40 650.1 42 641.5 44 638.6 35 630.2 33 631.9 42 620.5 34 603.4 29 615.8 39 595 32 580.1 32 592.2 35 566.9 35 559.4 37 578.5 37 161200 ALAMEDA LIVERMORE VALLEY JOINT UNIFIED 15.1900897052542 3.6657808347663 2.6496795372831 13.0920744098796 6.20300751879699 13.6619730185497 63447.3966165414 4309.73393778334 24.5902624724504 30.4526748971193 0 0 668.662054255087 658.826442204589 652.29882914137 57.2864267129228 57.3267209950676 56.071866112448 713.8 54 712.7 60 691.6 57 706.3 48 707.3 57 685.7 53 704.3 54 706.4 68 687.7 65 701 60 694.2 62 674.3 59 688.2 58 682.1 59 666.8 61 667.7 55 667.6 59 650.8 54 663 59 652.8 56 648.7 59 647.9 59 625.8 50 633.7 56 626 58 603.4 54 614.7 56 590.3 55 574.2 51 593.5 54 161242 ALAMEDA NEW HAVEN UNIFIED 32.7789739401589 17.1783055654023 12.4849344204183 28.3232896136122 27.2598870056497 12.5186375321337 57801.4124293785 4648.91669620702 20.26010286554 26.0709914320685 0 0 654.289313771888 652.417934527718 647.614495897388 49.6735829482222 46.7383922708939 39.4493137718883 700.4 40 705.4 52 685.8 51 690.2 32 695.7 44 673.7 41 683.8 34 686 47 672.4 50 686.8 46 677.3 46 666.1 51 671.9 42 670.7 48 660.6 54 656.5 43 657.5 50 647.2 50 639.2 35 639.5 42 639.3 49 631.4 44 621.9 47 633.5 56 603.3 39 594.8 45 604.2 46 573.7 41 570.1 47 588.6 49 161234 ALAMEDA NEWARK UNIFIED 28.2158234660926 10.4304198574069 6.78637443886982 32.3343015579614 13.4615384615385 16.4176039119804 57845.6401098901 4527.60324795353 21.7413793103448 22.6457399103139 0 0 651.806326987682 646.560586617782 642.048435474912 46.2571745972968 43.4252978918424 39.2136991414707 693.5 33 696.5 42 677.8 43 687.1 29 693.1 41 671.5 39 686.3 36 690.7 52 672.7 51 684 43 676.6 45 661.3 46 673.1 43 665.8 43 659.6 53 657.4 44 657.4 50 648.3 52 646.6 43 639.3 42 640.2 50 631.6 44 617.4 42 625.9 49 603.3 38 586.7 37 597.6 40 570.3 38 563.6 41 581 40 161259 ALAMEDA OAKLAND UNIFIED 59.9729276290704 17.5173624075872 50.9409304756926 23.1013367186917 52.3434423001182 16.9328347826087 57434.4423001182 4693.06937495333 21.3148871700821 19.5321637426901 3.2967032967033 13.1868131868132 624.563806995628 620.398091796817 616.567219733771 28.6828906693584 28.4544450085108 24.1449718925671 683.7 24 689.8 35 670.7 35 673.7 18 683.6 31 658.8 26 669.3 21 677.1 38 657.9 36 669 29 663.1 32 647.5 32 651.6 24 651.8 29 640.7 32 636.6 24 631.9 25 625.3 28 626.8 25 621.6 25 617.5 28 606.4 22 594 22 602.8 28 582.5 23 573.1 26 579.8 24 556.9 28 552.3 31 566.7 26 161275 ALAMEDA PIEDMONT CITY UNIFIED 0 19.7488584474886 1.86453576864536 2.58751902587519 7.40740740740741 15.8697860962567 52193.4567901235 5248.69292237443 18.5118219749652 80.379746835443 0 0 704.578132444021 695.271888310459 688.142389758179 83.8003793266951 82.8760056791292 82.5393044306813 743.2 81 743.9 85 720.1 82 733.1 76 734.1 80 712.2 79 738.6 83 733.2 87 714.2 85 724.5 80 724.8 85 700.6 81 719.3 85 712.6 85 700.6 88 702.2 85 701 85 680.2 81 695.1 85 684.9 84 689.1 88 683.2 85 658.4 79 666.2 81 667.2 86 643.9 85 656.1 87 619.5 78 593.9 71 629.1 86 175101 ALAMEDA PLEASANTON UNIFIED 4.70089495996232 8.73671782762692 1.74565694046214 6.93202900995109 7.12788259958072 16.1817604355717 67367.5492662474 4607.67330072525 24.6510600706714 39.6341463414634 0 0 683.838015568723 678.03443586493 671.316753082152 73.9103583362139 73.4222657600553 69.0648309515511 718.5 59 718.9 65 697.9 64 716.1 59 717.2 66 696.5 64 712.5 61 715.3 75 695.9 72 714.1 72 705.2 72 694.2 77 703.2 72 698.1 74 686.5 79 682.7 70 685.8 75 670 73 680.2 75 673.8 76 671.9 79 669 76 652.1 75 656.4 75 646.5 74 631.8 78 638.8 77 607.4 70 600.2 76 616.3 77 161291 ALAMEDA SAN LEANDRO UNIFIED 28.1211115719618 12.6920072897683 19.2658161936996 26.8419682374382 13.3720930232558 14.428640776699 57240.4481686047 3980.58939468888 21.8984351933865 31.6582914572864 0 9.09090909090909 651.912278410151 642.209956553223 639.349656069902 43.8430935118052 39.3631426502534 39.3356969583878 693.6 33 695.4 40 678.3 43 685.4 27 689.7 38 668.9 36 682.1 32 685.9 47 666.9 45 684.7 44 672 40 657.5 42 673.9 44 666 44 661.2 54 657.4 44 653.1 45 643.3 46 649.5 46 632.8 35 637.7 48 629.4 42 606.2 32 623.1 46 602.7 38 579.5 31 594.8 37 573.9 41 563.8 41 581.1 40 161309 ALAMEDA SAN LORENZO UNIFIED 36.9904684648144 10.0323624595469 15.4230235783634 30.7443365695793 13.5076252723312 13.2975345167653 58413.7385620915 4197.61525658807 23.1294326241135 31.1507936507937 0 0 650.559694592323 638.287041796294 639.202783519361 43.6724541821689 35.5612065467334 37.6273465956851 691.3 31 691.2 37 672.9 38 685.6 28 690.8 39 666.7 34 680.2 30 683.6 45 666.5 44 685.2 44 666.5 35 661.1 46 677.5 47 665.6 43 662.1 56 655.8 43 642.8 35 645.4 49 643.1 39 628.4 31 635.5 46 628.1 40 604.1 30 622.6 45 598.1 34 575.6 28 592.3 35 570.8 38 555.8 33 581.4 41 373981 AMADOR AMADOR COUNTY UNIFIED 20.2597946783993 .485744456177402 .802534318901795 5.97676874340021 2.4390243902439 15.3094936708861 51311.6292682927 4025.59281942978 24.8229166666667 36.1940298507463 0 91.6666666666667 670.250681818182 655.3 651.925882352941 54.4072829131653 51.1426197729161 54.4082386363636 712.8 53 704.7 53 694.3 60 703.9 47 695.3 48 679.2 47 702.3 52 696.4 58 678.3 57 701.6 61 682.3 52 672.7 58 690 61 675.3 54 667 61 670.6 58 659.7 53 647.7 52 656.6 53 640.8 46 640.9 51 638.2 51 613.9 43 627.9 52 619.7 57 594.7 49 605.6 50 579.8 51 570.9 54 591.9 57 461408 BUTTE BIGGS UNIFIED 42.6682692307692 2.83687943262411 .945626477541371 23.8770685579196 10.6382978723404 11.8396226415094 55990.3191489362 5151.62647754137 17.9120879120879 24.4444444444444 0 0 654.831493506493 646.170872274143 638.868238993711 41.8977987421384 42.0809968847352 40.1103896103896 685.3 26 688.8 34 665.3 30 690.7 32 690.7 39 662 30 695.9 45 696.1 58 671 49 695.3 54 674.7 44 665.8 51 677.4 47 671.7 49 658.9 52 656.2 43 655.6 48 639.5 42 647.1 43 635.9 38 632.3 42 624.1 36 609.1 34 627.8 50 601.7 37 582.9 34 588.4 31 558.5 28 560.2 37 573.5 32 461424 BUTTE CHICO UNIFIED 33.1292320991212 6.30541173132846 2.76615362791687 12.8661607206185 6.17283950617285 14.6014383561644 58558.5972222222 4004.40095042202 22.7300762863172 49.6551724137931 4.34782608695652 26.0869565217391 663.164911595194 653.503262518968 647.277658142665 50.4598154201115 49.4008915022762 48.6200058610921 710.2 50 714.3 62 690.3 56 706.1 48 707.8 57 685.2 53 699.7 49 702.3 64 680.9 59 695 54 684.3 53 671 56 683.1 53 675.8 54 664.7 59 664.4 52 658.3 50 647.2 50 651.4 47 637.7 40 635.9 46 638.2 50 612 37 622.8 46 608.1 42 590.3 41 598.5 41 573.6 40 561.1 38 579.6 38 461432 BUTTE DURHAM UNIFIED 29.3929712460064 1.07115531752104 .765110941086458 9.71690895179801 4.47761194029852 14.0452702702703 53772.2985074627 4518.03442999235 21.1850649350649 51.0204081632653 0 0 675.541104294479 661.864705882353 662.146998982706 62.5757884028484 54.0425963488844 57.4836400817996 714.6 55 709.1 57 703.3 67 714 57 713.7 63 695.7 63 706.8 56 707.9 69 691.3 68 706.7 66 683.5 53 682.4 67 688.7 59 676.6 54 670.8 65 671.3 59 666.7 59 655.8 60 662.9 59 650.5 54 652.7 63 647.5 58 615.3 40 638 60 617.1 50 590.8 42 608.1 50 586.3 52 564.9 42 596.6 58 461473 BUTTE GRIDLEY UNION 52.9931305201178 6.21737066919791 .332225913621263 38.7280493592786 7.61904761904762 14.1739495798319 50695.1238095238 4114.06454674893 20.411361410382 23.2758620689655 0 0 647.268098591549 643.146611909651 636.80674002751 40.9401650618982 40.8555783709788 34.6556338028169 694.8 34 697 43 675.4 40 688 30 697.1 46 668.2 35 687.6 37 691.6 54 675.5 54 678.5 38 670.8 39 653.4 38 668.2 38 653.9 32 654.1 46 649 36 653.3 46 641.8 44 640.6 37 635.5 38 634.7 44 613.9 28 602.9 29 614.6 38 590.8 28 591.1 42 589 32 573.2 40 562.8 40 579.5 38 461531 BUTTE PARADISE UNIFIED 35.0883585352523 .829216654904728 .388143966125618 4.62244177840508 4.13533834586465 14.3022108843537 55278.7443609023 4148.22776993649 21.9194683346364 25 23.0769230769231 15.3846153846154 666.75602871998 653.663746369797 646.168135095448 48.0907978463045 47.7294288480155 50.525129982669 707.9 48 706.1 53 679.2 44 702.1 44 701.2 50 671.6 39 694.4 44 694.4 56 670.1 48 693.1 52 680.5 49 666.7 52 681.5 51 672.5 50 659.1 52 666.4 54 654.5 47 645.9 49 657.3 54 636.9 40 636.8 47 642 53 613.8 39 622.8 46 617.8 51 596.4 47 607.2 49 589.6 55 568.8 47 595.2 56 561564 CALAVERAS CALAVERAS UNIFIED 37.2044140830268 .569948186528497 1.1139896373057 6.32124352331606 5.35714285714286 13.6024725274725 50668.494047619 3975.44792746114 24.8691767708998 22.9787234042553 0 8.33333333333333 662.25800647715 649.149032711924 643.041403757533 46.6157390996101 45.047836792121 47.6523929471033 699.5 39 700.3 47 679.4 45 694.4 36 699.8 49 668 35 694.9 44 693.2 55 669.7 48 693.9 53 680.3 49 664.3 50 686 56 669.5 47 661 54 666.8 54 663 55 650.1 54 655.8 52 635.2 38 639.1 50 641.1 53 609.8 35 624.7 47 610.9 45 582.5 34 598.2 41 576.7 43 562 39 581.4 41 661598 COLUSA COLUSA UNIFIED 54.2682926829268 .549786194257789 .549786194257789 40.7452657299939 7.40740740740741 14.8710526315789 52537 4205.36224801466 19.4451145958987 36.283185840708 0 0 653.225547445255 646.493309545049 638.67279344859 38.926296633303 38.5388046387154 35.7308394160584 691.1 30 688.9 34 670.2 35 678.1 22 685.6 33 655.4 23 686.8 36 696.7 58 668.9 47 679.1 39 666.5 35 653.7 38 673.1 43 663 41 650.2 42 654.6 41 650.9 43 640.6 43 639 35 624.4 27 627.9 38 626.3 39 603.9 30 619.1 42 599 35 588.9 40 594.3 37 574.5 41 568.1 45 588 47 661614 COLUSA PIERCE JOINT UNIFIED 65.8239700374532 .264084507042254 3.87323943661972 58.8908450704225 6.55737704918032 10.9265151515152 46291.7868852459 4469.79753521127 19.5254833040422 37.9310344827586 0 0 639.172727272727 633.277030162413 623.559159859977 25.7491248541424 27.4095127610209 24.9306220095694 687.1 27 683.2 28 660 26 678.2 22 682.8 30 656.2 24 663.6 18 672.8 34 649 28 669.4 30 655.6 25 642.8 28 659 30 644.5 23 635.4 27 634.6 23 629.6 23 621.9 24 624.1 23 624.8 28 620 30 616.2 30 602.1 28 608.7 33 581.8 22 573.9 26 573.3 20 557.4 27 549.5 28 560.4 19 661622 COLUSA WILLIAMS UNIFIED 78.5407725321888 1.28205128205128 0 78.4188034188034 20.8333333333333 11.4778846153846 49267.1922916667 3893.3372008547 21.1231101511879 19.2307692307692 0 0 623.801777777778 624.58059914408 615.708849557522 19.3126843657817 21.2339514978602 15.0103703703704 661.9 10 676.5 22 648.2 16 664.9 13 679 26 639.6 13 652.6 11 665 26 639.1 20 645.7 13 651.1 21 627.6 16 640.4 16 647.8 25 628.5 20 628.7 18 634.6 28 618 21 610.6 14 603.4 12 602.1 16 592.5 14 581.3 13 594.7 22 573.5 16 558.5 15 572.4 19 556.7 26 545.9 24 576.9 34 761648 CONTRA COSTA ANTIOCH UNIFIED 30.0755277257763 3.62619624120835 10.5038625619739 20.9443099273608 8.66574965612105 13.6910780669145 62341.1939477304 4011.39040701026 23.8890485771774 23.8544474393531 0 72.2222222222222 654.988955223881 642.419637634059 642.558945199835 48.5542645241038 42.4466200844979 44.2719734660033 699.6 39 698.4 46 680.8 46 696.1 38 697.2 47 675.8 43 690.9 41 691.8 54 675.9 54 690.9 51 674.4 44 665.3 50 677.4 48 661.8 40 660 54 658.4 46 650.5 44 645.1 49 650.8 47 636.5 40 640.1 50 632.9 46 609.1 36 626.3 50 605.2 42 582.9 36 598.9 42 573.1 43 559.2 40 584.2 47 761697 CONTRA COSTA JOHN SWETT UNIFIED 28.246013667426 6.89342403628118 19.8185941043084 16.7800453514739 12.7450980392157 15.9155462184874 59176.5294117647 4225.73832199546 22.1837944664032 18 0 0 655.849803664921 645.640405904059 641.369559668156 44.1640076579451 42.0817958179582 40.6603403141361 701.6 41 695.7 43 682.3 48 689.9 33 693.4 47 672.5 41 689.3 40 688 50 673.1 52 687.5 48 678.3 47 664.4 49 672.4 44 669.3 48 657.4 50 657.2 44 656.5 50 642.1 45 640.7 38 626.1 31 629 39 624.7 38 605.1 34 616.7 41 601.9 41 582.2 37 593.7 38 562 36 545.8 29 573.1 36 761739 CONTRA COSTA MARTINEZ UNIFIED 22.3337515683814 2.52373234545034 3.31095160916879 14.0310257003936 10.3092783505155 16.1568584070796 59230.7216494845 4114.41768927993 21.7433155080214 34.4262295081967 0 0 666.986131386861 653.039726027397 651.011405568601 54.859443139886 49.3724722765819 53.342402123424 709.8 50 712.9 58 687.6 53 700.2 42 703.5 53 678.1 46 696.5 46 700.1 62 676.8 55 700.2 59 677.4 46 673.6 59 687.8 58 668.9 47 667.1 61 668.3 56 652.1 44 648 51 659 55 642 45 644.5 55 645 56 619 44 636.1 58 623.2 56 594.9 45 609.7 51 589.3 55 574 51 598.4 60 761754 CONTRA COSTA MT. DIABLO UNIFIED 22.6246525611209 7.69788789375297 4.90220696967161 16.7768756452108 9.42158616577221 15.3236785904965 57128.5825879547 3928.69554420915 22.1416213544241 37.6383763837638 0 0 663.753803895455 655.29439179166 651.228181140823 57.5844026823413 56.8430443382232 53.913434326619 707 47 709 57 687.9 53 702 45 706.7 60 683.5 51 699.4 50 703.6 65 682.9 62 698.4 58 688.6 58 675.1 60 687.6 59 680 58 669.1 63 670.3 58 663.6 57 655.3 59 660.9 58 650.9 56 649.8 60 646.3 58 625.9 54 634.6 58 613.8 51 595.7 50 607.9 51 581.4 52 571.4 55 592.7 57 761788 CONTRA COSTA PITTSBURG UNIFIED 51.7849321068769 4.59893048128342 27.7967914438503 35.1016042780749 30.0248138957816 15.9748903508772 58788.9305210918 4049.18085561497 22.8729963008631 85.1116625310174 0 8.33333333333333 638.057359342254 630.487298832439 628.793177419355 35.1459677419355 31.2234143262859 29.0448170240206 692.7 32 690.8 36 674.7 40 680.8 23 685 33 663.4 30 674.1 25 678.2 39 662.9 41 678.1 38 666.2 35 656.2 41 659.9 31 654.5 32 648.7 40 645.5 33 639.5 32 631.6 34 632.1 29 623.2 26 623.8 34 615.7 29 601 27 615.5 39 585.1 24 575.2 27 584.3 28 556.6 26 552.6 30 569 27 761804 CONTRA COSTA SAN RAMON VALLEY UNIFIED 1.89532160557704 9.92522790125986 1.63371914370583 4.27634948274096 4.50346420323325 15.9236921529175 59976.9376443418 4035.18872272867 23.3468734613491 50.7450331125828 0 0 687.436196062002 681.264960300878 673.262666202576 75.288827010094 75.4876027301853 71.8017036726714 720.5 61 724.5 71 701.7 68 719.4 63 722.3 71 700.5 68 716.3 65 719.3 79 700.1 76 717.3 75 719.1 82 695.6 78 708 76 709.8 83 689.1 81 690.4 77 690.4 79 674.7 77 684.4 78 674.4 77 672.6 79 670.5 77 652.6 75 656.3 75 646.9 74 622.9 71 636.1 75 608.6 71 589.8 67 613.3 75 761796 CONTRA COSTA WEST CONTRA COSTA UNIFIED 51.2384774565749 13.0202355783751 35.1011778918756 25.5270311084265 25.8268824771288 17.1897748592871 62664.0950035187 4347.93612201752 22.8705015585152 36.5 0 8.47457627118644 639.248799428299 632.602881497267 628.976394317857 36.1087329583373 34.3111275826925 31.5787994282992 690.4 30 694.8 39 674.4 39 682.4 25 688.6 36 663 30 677.9 28 679.4 40 663.4 41 674.5 34 665.7 34 652.4 37 658 29 654.6 32 644.3 35 650.5 37 646.9 39 636.4 39 637.6 34 629.3 32 627.6 37 617.2 30 602.6 28 613.3 37 595.4 32 582.9 34 590.8 33 566.8 34 555.8 33 576.2 34 861820 DEL NORTE DEL NORTE COUNTY UNIFIED 42.5114329268293 6.25244236029699 .41031652989449 10.1797577178585 8.07692307692308 14.7094827586207 58233.8115384615 4733.28038296209 20.6736353077816 15.5038759689922 0 0 654.110813174002 643.545233380481 636.959429065744 41.8500576701269 41.4144271570014 42.1262022733897 699 39 700.6 46 673.5 38 691.8 33 695.3 44 667.2 34 685.2 35 687.1 48 666.6 44 690.5 50 672.9 41 661.1 46 675.6 45 665.7 43 655.1 47 656.8 44 650.1 42 640.2 43 647.2 44 634.3 37 632.2 42 632.4 44 610.5 35 619.1 42 609.2 44 591.2 42 598.5 41 572.4 39 562.4 39 579.5 38 973783 EL DORADO BLACK OAK MINE UNIFIED 30.1696469509399 .797373358348968 .515947467166979 2.25140712945591 1.90476190476191 14.2323275862069 51822.3714285714 4030.97842401501 21.4729458917836 32.4561403508772 0 0 671.052793471438 662.392478421702 654.1465625 56.355 57.1011097410604 55.08223477715 705.8 46 707.6 55 681.9 47 697.9 40 697.6 47 670.9 38 698.2 47 700.9 63 676.7 55 701.4 61 687.5 57 679.5 65 683.2 53 672.5 50 663.4 58 670.9 59 675.1 67 653.5 57 667.6 64 661.2 65 654.8 65 652.4 63 631.3 55 643.1 64 629 60 609.6 59 620.7 61 593.5 58 574.4 52 591.6 52 961903 EL DORADO LAKE TAHOE UNIFIED 45.4234388366125 1.50119413169567 .904128283862163 26.2708973046742 5.33807829181495 14.3570287539936 55740.1387900356 4323.75400887069 21.5345821325648 86.7219917012448 0 0 655.848036826428 645.356022041459 644.127473404255 49.6300531914894 44.7874573602729 43.96452748443 695.8 35 697.4 43 675.1 40 697.6 39 696.4 45 675.4 42 692.2 41 695.5 57 676.1 54 690.4 50 673.4 42 671.7 57 675.7 46 665.6 43 665.1 59 658.7 46 653.6 46 650.5 54 652.7 49 641.8 45 643.8 54 632.9 45 615 40 629.9 52 607.4 42 589.4 40 598.8 41 579 45 569.8 47 585.3 44 1073965 FRESNO CENTRAL UNIFIED 49.2508395763369 10.1529902642559 7.8741842302343 42.3237402375094 16.5394402035623 10.0809012875536 54568.7404580153 4006.11832673585 22.5917372337696 37.6666666666667 0 76.9230769230769 645.428 641.522896312638 634.864938828075 40.9228911783645 41.6585250551529 34.9438367346939 683.8 24 688.6 35 665.9 31 680.9 24 683.7 33 654.2 23 673.7 26 680 41 658 37 681.1 41 669.9 39 660.8 46 665.5 37 663.1 41 653.5 47 652.1 39 651.7 45 642.2 46 638.1 35 632.4 37 629.1 39 624.4 39 614.8 44 620.7 46 597.8 38 592.8 47 594.7 39 571.8 45 566.7 51 586.8 52 1062117 FRESNO CLOVIS UNIFIED 23.433371722783 11.750645994832 2.87144702842377 19.031007751938 15.9441587068332 11.8789028213166 53158.0705363703 4242.7020994832 22.7051671732523 40.5063291139241 0 0 670.829070946691 669.983691062632 660.175030880537 62.7089730015881 64.6854327938072 55.8729220865118 710.2 50 711.5 59 690.5 56 703.4 45 704.1 53 681 48 699.3 49 701.6 63 682.2 60 703.5 63 695.2 64 685 69 691.8 62 686.1 64 676 70 673.7 61 683.2 73 662.4 66 661.2 57 660.4 64 652.9 63 644.3 55 638.2 62 639.1 61 624.2 57 622 71 623.1 64 595.7 60 597.9 74 609.4 70 1062125 FRESNO COALINGA/HURON JOINT UNIFIED 57.9224030037547 .87173100871731 .647571606475716 68.7422166874222 18.9349112426035 11.1978723404255 61085.4437869823 4258.42515566625 24.4786015672092 14.8351648351648 0 0 634.785217079297 631.830958230958 622.751865266208 28.8927924664976 32.6072306072306 26.6347326874776 687.3 27 689.2 35 666.3 31 673 18 682.7 32 650 20 670 23 673.3 34 653.7 33 673.4 34 661 31 649.2 34 659.9 31 651.6 30 640.7 32 641.8 29 636.7 30 627.2 30 631 29 626.8 30 620.1 30 617.5 32 610.5 38 610.1 35 575.7 20 579.1 33 579 24 550.5 25 551.9 33 562 23 1073809 FRESNO FIREBAUGH-LAS DELTAS UNIFIED 87.8371750858264 .405405405405405 1.21621621621622 90.4504504504505 32.4074074074074 10.3756097560976 51400.5555555556 4317.72837837838 20.4905660377359 30.8510638297872 0 0 629.51695945946 626.268102372035 615.682792207792 21.3207792207792 24.5274656679151 19.7959459459459 672.2 16 674.1 20 651.7 19 667 14 676.9 24 645.1 16 662.5 17 661.3 23 647.8 27 660.3 22 649.8 21 634.4 21 648.8 21 644.5 23 636.4 28 627.5 18 630.1 24 619.2 22 619.9 20 614.3 19 606.9 19 616.1 30 613.7 39 600.9 27 581 21 570.9 24 571 18 544.5 18 550.4 29 555.4 15 1062158 FRESNO FOWLER UNIFIED 60.5990783410138 7.19661335841957 .846660395108184 72.3424270931326 24.7619047619048 11.8363247863248 51537.3428571429 3807.78974600188 21.0700389105058 38.7931034482759 0 0 635.323164218959 638.899266177452 627.946421404682 32.0481605351171 36.1320880587058 25.084779706275 690 30 694.6 41 673 38 679.1 22 692.2 40 658.5 26 670.9 23 683 44 660.7 39 673.1 33 669.2 38 650.8 35 655.5 27 659.5 37 643.9 35 641.9 29 647.4 40 635.7 38 622 21 624.6 28 622.9 33 611.6 26 607.5 33 606.9 32 577.2 19 576.6 29 575.6 21 549.8 21 556 33 567.3 25 1062166 FRESNO FRESNO UNIFIED 62.0197740112994 19.6517667528081 11.3156615408234 45.2396182483433 23.4048481471162 14.6799561082663 61095.3050989134 4685.64291379884 22.2597299444003 27.4755927475593 0 31.1111111111111 632.640383670421 630.72546997266 623.040624146675 30.5068851180027 32.4933723013105 26.0484501837766 692 31 696.6 42 673.8 39 678.8 22 688.7 36 658.9 26 672.3 24 677.4 38 658.2 36 673.5 33 666 34 651.1 36 656.7 28 657 34 643.9 35 643.9 31 645.8 38 631.6 34 626.4 25 623.9 28 618.6 29 607.2 23 597.4 25 602.9 29 579.6 21 574.3 28 577.9 23 551.9 25 549.2 29 564.5 24 1075234 FRESNO GOLDEN PLAINS UNIFIED 86.545988258317 1.71370967741935 .201612903225806 92.2379032258065 26.0869565217391 14.5806603773585 62758.2608695652 5242.80040322581 21.7211328976035 19.1780821917808 0 0 617.92496382055 623.329073033708 614.588491779843 21.0793423874196 22.8026685393258 13.6685962373372 665.8 12 677.7 23 655.7 22 657.7 9 674.1 22 640.4 13 655.1 12 664.7 26 646.3 26 661.1 23 660.5 30 641.8 27 635.3 13 646.6 24 632.4 24 623 15 628.4 23 620.5 23 610.4 14 611.4 17 610.3 22 591.2 13 589.4 18 597 24 565.3 12 566.6 21 569.3 17 535.7 13 544.2 23 552.1 13 1073999 FRESNO KERMAN UNIFIED 69.4980694980695 5.35002845759818 .313033579965851 70.4325554923165 22.6415094339623 12.3231382978723 54546.0125786164 4283.10130904952 22.1542383683875 40 0 60 639.117610619469 631.208603832616 625.959092783505 27.9550515463918 27.4974579585452 24.4261061946903 680.9 21 682.1 27 662.2 28 668.7 15 676.4 24 649.9 19 668.4 21 670.5 32 654.5 33 673.2 33 663.2 32 645.7 30 661.4 33 659.3 36 646.3 38 641.3 28 641.6 34 627.5 30 630.1 28 619.5 23 620.2 30 607 22 592.9 21 599 25 574.7 17 571.6 25 577 22 552.3 23 539.2 20 563.8 22 1062265 FRESNO KINGS CANYON JOINT UNIFIED 63.1713244228433 1.40625 .600961538461539 71.3581730769231 16.7919799498747 14.4614893617021 55184.7769423559 4478.65733173077 22.2565194252262 16.7865707434053 0 6.25 638.653324854651 636.555282685512 628.19417703263 34.4696232197584 37.1314487632509 30.5952034883721 692.9 32 695.3 40 672.6 37 684.6 27 691.6 40 665.6 33 679.7 30 686.8 47 666.2 44 670.6 31 660.9 30 647.2 32 661.2 32 655.8 33 647.7 39 646.9 34 648.6 41 636.3 39 633.2 30 633.4 36 625.3 35 615 29 609.3 35 609 33 590.3 28 585.9 37 586.3 30 563 32 558.1 36 568 26 1062240 FRESNO KINGSBURG UNION 35.6033874382498 2.51875669882101 .37513397642015 67.7384780278671 7.91366906474819 15.3451612903226 54702.2225899281 6836.69390675241 21.1443530291698 41.5789473684211 71.4285714285714 0 659.55601965602 655.824398460058 646.9376953125 50.0771484375 50.7348411934552 45.3916461916462 684.7 25 690.8 36 667.3 32 685.3 27 692.4 41 660.8 28 682.4 32 689 50 667.3 45 696 55 677.3 46 669.6 55 683.5 54 672.6 50 662.9 57 663.8 51 660.4 53 651.2 54 654.3 51 643.1 46 641.3 51 637.9 50 630.8 56 634.8 57 614 48 613.3 63 611.8 53 597.7 62 589.2 66 606.2 68 1062281 FRESNO LATON JOINT UNIFIED 72.9166666666667 1.23873873873874 .563063063063063 70.045045045045 3.92156862745098 11.5576271186441 39728.3529411765 3899.13175675676 18.2365145228216 13.0434782608696 0 0 635.926148409894 633.431208053691 626.668181818182 29.2020202020202 27.7852348993289 22.6678445229682 677.3 19 683.7 29 659 25 673.8 18 674.7 22 655 23 662.3 17 668 29 653.2 31 663.5 24 654 24 644.3 29 656.1 27 652.4 30 646.5 38 640.5 28 642.2 35 631.5 34 628.4 26 630.6 33 620.1 30 603.8 20 595.4 22 593.5 21 581.9 22 570.6 24 580.3 25 556.2 26 553.8 31 574.4 33 1075127 FRESNO MENDOTA UNIFIED 87.7772534214252 .533462657613967 .5819592628516 97.7691561590689 40 12.2075268817204 60303.175 3933.77740058196 25.6375 47.1698113207547 0 0 622.616214335421 621.331699346405 613.684299191375 18.4973045822102 18.9797385620915 13.7035490605428 676.5 18 671.5 18 653.9 20 669 15 680.7 28 648 18 656.4 13 666.4 27 646.8 26 649.9 15 650.4 21 631.7 19 635.5 13 637.7 17 624.5 17 625.1 16 621.4 18 616.4 20 608.7 13 604.1 13 604.2 18 591.1 13 582.4 14 589.8 19 556.6 9 553.9 13 559.5 11 532.3 11 539 19 554.7 15 1062364 FRESNO PARLIER UNIFIED 91.604841858649 .214362272240086 .107181136120043 98.8210075026795 62.1848739495798 11.9275362318841 55527.2941176471 4223.12647374062 21.6865417376491 13.3858267716535 0 40 616.066551893753 620.616848599905 610.251985922574 19.1895424836601 22.1860465116279 13.9183472700443 672.7 16 681.9 26 651.7 19 660.9 11 675.8 23 642.5 14 653.9 12 665.1 26 643.9 24 652.1 17 651.2 22 636.9 23 637.6 14 642 21 626.7 19 630.3 20 637.5 30 624.3 27 611 14 612.6 18 606.3 19 588.5 12 589 18 591.7 20 562.6 11 560.4 16 561.7 12 535.6 13 545.9 24 554 14 1075408 FRESNO RIVERDALE JOINT UNIFIED 64.8507462686567 .66711140760507 3.06871247498332 64.5096731154103 14.9253731343284 11.3691780821918 47732.5671641791 3511.89993328886 21.4887218045113 10.6666666666667 0 0 644.658088235294 648.756526207606 634.010914760915 35.5135135135135 41.5817060637205 29.9044117647059 684 25 689.7 35 664.1 29 675.3 19 679.3 27 659.5 27 672.4 24 682.7 44 664.4 42 677.7 37 673.2 42 650.2 34 670.9 41 673.7 52 651.6 44 639.5 27 642.1 34 622.4 25 628.7 26 628.3 31 615.2 26 613 27 610.8 35 603.4 29 600.8 36 612.2 62 608.1 50 577.5 44 582.1 59 590.2 50 1062414 FRESNO SANGER UNIFIED 57.0024891916678 4.32291666666667 .5859375 70.6640625 23.4806629834254 14.0044289044289 52155.2569060773 4382.06119791667 21.7115330121848 23.0232558139535 0 0 637.416965285554 632.764367398802 627.134107534748 31.840160936357 30.8818297331639 26.3355729749907 684.5 24 688.6 34 664.3 29 677.5 21 688.4 36 655.2 23 668.7 21 676.6 37 655.4 34 675.1 35 662.4 31 654.5 39 659.7 30 653.5 31 645.4 37 646.6 33 642.3 35 637.1 39 631.3 28 625 28 625.4 35 609.5 24 596.3 23 608.1 33 584.5 23 575.5 28 580.4 25 553.5 24 548.4 27 566.1 24 1062430 FRESNO SELMA UNIFIED 69.6452036793693 3.97230320699708 1.02040816326531 76.7128279883382 27.6 13.0118794326241 53531.636 4180.03662536443 22.5010266940452 14.6919431279621 0 0 642.379013030219 638.60592632141 633.074566630553 38.6977248104009 38.217031500267 31.5431106182423 687.3 27 691.2 36 671.4 36 677.8 21 689.4 37 655.7 24 674.4 25 680.4 41 660.7 39 683.9 43 663.5 32 662.4 47 667.4 37 663.1 41 653.3 46 647.6 34 646.6 39 637.3 40 636.9 33 634.5 37 633 43 622.5 35 616.3 41 622.6 45 591.5 29 590.1 41 592.6 35 564 32 558.3 35 574.1 32 1075275 FRESNO SIERRA UNIFIED 21.604680317593 .540540540540541 .291060291060291 7.48440748440749 1.52671755725191 17.1124161073826 62522.8244274809 5879.9659043659 19.4851166532582 26.3736263736264 0 0 680.277309941521 669.70080552359 658.152347417841 51.9900234741784 53.2784810126582 53.2520467836257 711.1 51 707.9 55 683.2 49 701.1 43 698.3 48 673 40 700.3 50 692.4 54 673.5 52 702.6 62 689.3 59 677.1 63 691.9 62 677.3 56 666.5 61 669.9 57 666.6 59 650.9 55 661.6 57 653.6 57 644.7 55 646.7 58 620.3 45 631 54 627.6 60 606.6 57 615.6 57 579.7 46 566.6 44 586.4 46 1175481 GLENN ORLAND JOINT UNIFIED 44.9746621621622 1.34841235319704 .826446280991736 33.1883427577208 5.93220338983051 16.2532846715328 55653.8050847458 4602.90343627664 20.8553791887125 49.1935483870968 0 0 653.154290822408 643.173529411765 642.387649880096 45.0239808153477 39.0258823529412 39.0673420738975 703.1 43 699.9 47 688 54 687.7 29 690.1 38 669.3 36 687.7 37 687.2 49 673.3 51 682.9 42 671.7 41 666.3 52 667.7 38 654.9 32 657 50 651.7 39 645.4 38 646.8 50 639.5 36 625 28 623.7 34 633.9 46 611.1 36 623.7 46 605.6 41 590.4 41 594 37 572.7 40 560.9 38 580.8 40 1162661 GLENN WILLOWS UNIFIED 53.125 21.7522658610272 .906344410876133 20.7452165156093 4.65116279069767 15.7360824742268 60020.6627906977 4325.15357502518 24.4811320754717 47.1153846153846 0 0 652.248719723183 650.23700947226 643.737198067633 45.9889579020014 45.4945872801083 37.9148788927336 691.8 31 693.1 39 677.2 42 690.2 32 692.1 41 674.5 42 679.5 30 683.6 45 669.9 48 689.5 49 685.6 54 667.6 53 664.5 35 666.6 44 654 46 654.8 42 651.3 44 643.3 47 640.5 37 637.8 40 636.3 46 630.1 42 620.4 45 621 44 601.9 37 602 53 601.8 44 578.9 45 572.6 50 587.4 47 1275374 HUMBOLDT FERNDALE UNIFIED 11.7161716171617 .5 0 4.66666666666667 0 15.5121951219512 54645.6666666667 4893.85666666667 20.3215434083601 34.1463414634146 0 0 671.044444444444 664.697085201794 654.729306487696 57.5212527964206 59.7757847533632 55.0657596371882 718.1 59 723.3 72 689.6 56 712.5 55 711.6 63 692.2 60 698.7 48 703.4 66 668.9 47 698.4 58 693 62 676.2 62 694 63 695.4 73 672.1 67 668.4 56 671.7 65 651 55 661.4 58 656.1 60 655.5 65 640.2 52 611.8 37 632.9 55 620 53 606.1 55 609.5 50 576.4 43 565.7 42 591.7 53 1262901 HUMBOLDT KLAMATH-TRINITY JOINT UNIFIED 76.3212079615649 0 .65597667638484 2.84256559766764 30 14.7855421686747 65665.4714285714 5770.56559766764 19.6496350364964 25.9259259259259 0 0 647.671180124224 637.66 628.449749373434 33.7769423558897 36.3312883435583 34.6608695652174 684.3 24 691.1 37 660.4 26 683.6 26 689.4 37 656.8 24 674.3 25 681.1 42 655.3 34 683.1 42 678.6 47 661.6 47 676.2 46 670.1 47 658.5 52 650 37 646.1 38 629.6 32 637.3 34 628.7 31 617.1 28 631.9 44 603.1 29 614.1 38 599.6 36 583.4 35 590.9 34 557.8 27 544.6 23 561.2 20 1263040 HUMBOLDT SOUTHERN HUMBOLDT JOINT UNIFIE 33.9167169583585 .714749837556855 1.16959064327485 2.98895386614685 0 15.7926136363636 56055.9350649351 4427.87199480182 21.3154362416107 44.7058823529412 0 0 666.911310782241 645.515704154002 646.715384615385 48.0945945945946 39.0536980749747 49.9746300211417 715.8 57 707 55 692.5 59 709.5 51 691.3 40 681.4 49 693.9 43 688.8 50 668.7 47 693.2 53 660.6 30 656.8 41 673.1 43 657.6 35 657.8 50 672.8 60 656.2 49 653.5 58 652.4 48 632 34 634 44 646.3 57 616.6 41 631.5 53 614.1 48 576.9 29 599.2 42 578.7 45 554.2 32 582.4 41 1363073 IMPERIAL BRAWLEY UNION 54.2566709021601 .518806744487678 .277932184546971 80.2112284602557 35.042735042735 14.9565055762082 66530.2478632479 4304.7152121549 23.78075097 33.89830508 0 0 640.308263570078 636.234013245033 632.318937957193 35.1953400162558 32.2733774834437 28.2425060761545 688 28 684.8 30 668.7 33 682.4 25 684 31 661.9 29 674.2 25 676.8 37 658.1 36 671.3 32 660.1 29 651.3 36 658.3 29 656.3 34 646.7 38 647.1 34 648.9 41 638.9 41 632.7 30 628 31 627.8 38 611.5 26 602.9 28 618.7 42 585.2 24 575.4 27 585.1 28 561 30 556.7 34 574.1 33 1363099 IMPERIAL CALEXICO UNIFIED 43.954818017013 1.43290162579223 .0551116009920088 97.5199779553596 70.0996677740864 14.5877492877493 67370.0564784053 4266.61848994213 23.5719063545151 15.8256880733945 0 0 637.58567839196 641.629704962003 627.686793737236 24.8838211935557 28.0435851586947 18.5349474645957 674.8 17 684.6 30 657 23 666.5 13 681.6 29 648 18 665.1 18 675.4 36 653.7 32 662.4 24 658.6 28 643.7 29 645.1 19 647.2 25 635.2 26 632.7 21 635.1 28 625.9 28 621.3 21 622.2 25 613.4 25 598.1 17 598.7 25 598 24 571.7 16 572.5 25 573.9 20 549.8 21 546.7 25 561.1 20 1363107 IMPERIAL CALIPATRIA UNIFIED 74.3016759776536 .421940928270042 5.55555555555556 71.5893108298172 40.5797101449275 12.2170886075949 53519.7391304348 4518.24331926864 20.625 29.4117647058824 0 0 636.260303030303 641.081281281281 629.831552419355 32.9153225806452 35.8938938938939 24.4060606060606 680.7 21 679.3 24 661.7 27 666.9 14 678.7 26 648.1 18 665.8 19 672.9 34 656.8 35 676.1 36 669.9 39 655.9 41 658 29 657.7 34 651.2 43 635.4 24 642.6 35 630.5 33 623.4 22 627.8 30 624.6 35 605.7 22 613.3 38 611.1 35 593.5 30 605.5 56 592.3 35 554.7 25 563.8 41 565.6 24 1363123 IMPERIAL EL CENTRO UNION 62.1000820344545 1.55424308361828 2.94270023831727 82.6442855662626 33.4128878281623 15.2063025210084 66125.0906921241 4453.16423168584 23.3213859020311 31.3028764805415 0 0 646.318279404999 642.181461988304 635.553651897777 35.138363828822 34.3980994152047 28.9796043551603 687.7 27 686.7 32 668.1 33 680.1 23 682.8 30 656.5 24 675 26 676.6 37 658.3 36 671.6 32 660.7 30 651.1 36 656.6 28 654.7 32 645.5 37 649.6 36 653.1 45 639.8 42 629 26 627.6 30 623.4 33 617 31 609.2 34 618.3 41 591.7 29 583.8 35 589.7 33 569 36 562.3 39 580.7 39 1363149 IMPERIAL HOLTVILLE UNIFIED 63.9180409795103 .399002493765586 .149625935162095 70.4738154613466 26.8817204301075 11.6070093457944 56622.5698924731 4245.81097256858 21.6851441241685 32.1428571428571 0 0 646.915160142349 641.476949860724 635.499860432659 37.3028611304955 36.0898328690808 32.202846975089 694.8 34 691.8 38 671.9 37 684.5 27 687.4 35 657.8 25 678.1 29 681 42 661.6 40 665.8 27 652.6 23 645.8 30 650.7 23 644.3 22 639.3 30 653.6 41 644.9 37 640.5 43 640.6 37 634.2 37 634.5 44 621.9 35 604.3 30 621.7 45 591.5 29 590.9 42 594.6 37 575.8 43 580.2 57 584.4 44 1363164 IMPERIAL IMPERIAL UNIFIED 39.9445214979196 .749228735125606 2.68840899074482 60.7756721022477 24.5283018867924 9.22478260869565 51167.0283018868 3708.85940943147 20.8568738229755 34.9206349206349 0 0 648.104784978801 648.614979277679 637.713325401547 42.3599048185604 46.6820603907638 36.3301029678982 697 36 695.4 41 671.5 36 686.6 28 690.7 39 661.4 29 681.9 32 685.4 47 667.4 46 677.5 37 673 42 656.5 41 661.1 32 664.1 41 645.8 37 661.1 48 662.6 55 650 53 641.7 38 644.6 48 638.6 49 628.3 41 631.8 57 628.9 51 597.2 33 604.2 55 597 40 571 39 564.1 41 581.2 41 1363214 IMPERIAL SAN PASQUAL VALLEY UNIFIED 76.8278965129359 0 3.00578034682081 31.0982658959538 20.4081632653061 15.3185185185185 60141.0816326531 5355.16069364162 18.9917695473251 19.3548387096774 0 0 629.748180242634 614.521440536013 615.989964788732 20.7411971830986 16.5175879396985 20.155979202773 681.4 22 672 18 660.3 25 669.6 15 673.2 21 650.2 19 660.3 15 664.1 25 646.1 25 666.9 28 648.2 19 638.2 24 662.7 33 644.4 23 637.3 28 623.7 15 607.4 10 608.4 15 615.7 17 597.9 9 603.2 17 605.6 22 576.9 11 591.5 20 571.5 15 557.5 15 568.9 17 553.9 25 530.3 14 559.7 19 1463255 INYO BISHOP UNION 25.588491717524 .762388818297332 .508259212198221 11.0122829309615 5.45454545454545 16.5099173553719 59805.5454545455 4349.22151630665 23.4503510531595 40.7185628742515 0 0 665.129026548673 659.893040501997 647.360979827089 49.4184438040346 54.1928123217342 48.5286135693215 707.3 47 701.4 48 687.5 53 698.4 40 701 48 683.1 51 693.4 43 691.8 53 676.7 55 698 57 698.7 67 670.8 56 681 51 692 69 659.3 52 661.5 49 676.1 67 643.4 46 657.3 54 651.5 55 643.5 54 635.1 47 609.2 34 622.7 45 615 49 599.1 49 599.6 42 584 50 573.6 51 580.5 39 1563404 KERN DELANO UNION 68.6434302908726 .780814277746793 1.72894590072504 76.8878973786949 31.8181818181818 12.2116310160428 58861.6878787879 3714.92013385388 25.9909638554217 14.0350877192982 0 0 628.471576763485 630.4126899577 619.339649525057 22.7148706190632 27.0755130816231 16.9576417704011 677.2 19 685.1 30 660.5 26 666.8 14 680.2 28 644.8 15 661.6 16 672.3 33 649.1 28 655.4 19 658.3 27 638 24 643 18 650.9 28 631.8 23 631.7 21 639.6 32 625.9 28 615.6 17 619.6 24 613.2 24 593.1 14 590.2 19 594.6 22 565.4 13 565.1 20 570.9 18 549.1 21 550.2 29 560 19 1575168 KERN EL TEJON UNIFIED 37.8320172290022 .3584229390681 .860215053763441 13.6917562724014 8.33333333333334 10.3814285714286 55942.0666666667 4098.99426523297 24.9217391304348 0 0 0 656.297061704212 648.630488974113 642.803458213257 46.7867435158501 45.2895493767977 42.8873653281097 707.9 47 705.7 53 686 52 698.9 40 700.2 49 679.6 47 694.8 44 697.2 59 675.1 54 693.1 53 684.1 53 661.9 47 676.8 47 671.8 50 658.3 51 650.5 37 646.7 39 638.7 41 645.3 41 637.5 40 636.6 46 629.3 41 604.9 30 622.5 45 602.6 38 584.6 36 592.8 36 575.2 43 569.9 47 589.5 50 1573908 KERN MCFARLAND UNIFIED 81.9645732689211 .311526479750779 .233644859813084 92.0171339563863 31.8965517241379 13.1324626865672 55369.8620689655 4428.04595015576 22.5085616438356 23.6842105263158 0 0 624.363516609393 624.61632196758 615.951785714286 21.5478110599078 22.5846841811068 16.9203894616266 669 13 672.7 18 651.7 19 669.7 15 674.8 22 645.5 16 663.4 17 668.3 29 651 30 653.2 18 651.9 22 634.3 21 638 15 642.5 21 625.4 18 632.8 21 631 25 624.7 27 615.8 17 616.2 20 608.2 20 603.5 20 598.5 25 601.3 27 568.4 14 565.9 20 571.7 19 543.9 18 544.5 23 558.9 18 1563677 KERN MOJAVE UNIFIED 40.5990016638935 1.1070110701107 14.8708487084871 24.5387453874539 7.62711864406779 15.2442748091603 63042.2033898305 4572.61512915129 24.419983065199 32.484076433121 0 37.5 640.803693644758 630.078511087645 628.075648491265 34.137638962414 30.8104540654699 31.2036936447583 687.6 27 688.2 33 669.8 35 685.8 28 687.2 35 660 27 683 33 684.2 45 664.8 43 676.1 36 660.7 30 649.2 34 667.4 37 654.7 32 650.2 42 649.8 37 644.8 37 635.4 38 635.3 32 624.6 28 623.3 33 618.3 32 602.3 28 612.7 36 587.4 26 576.4 28 586.3 30 552.6 23 535.8 17 567.4 25 1563685 KERN MUROC JOINT UNIFIED 31.8604651162791 2.01472297559086 12.4370399070128 7.47772181325068 9.84848484848484 15.3266666666667 63498 5767.59356838435 20.3072196620584 32.8 0 0 663.702340772999 654.100107469103 645.626877682404 53.1512875536481 56.0972595378829 54.5666848121938 711.2 51 703.1 50 686.6 52 699.3 41 696.1 45 667.9 35 700.7 50 694.8 56 672.3 50 693.2 53 680 49 658.7 43 685.7 56 674.6 52 661 54 673.7 61 670.5 63 656.7 61 658.1 54 651 55 647.2 57 648.5 59 630.2 55 636.4 58 624.5 57 613.3 63 616.1 57 593.6 58 588.4 65 595.2 57 1573742 KERN SIERRA SANDS UNIFIED 32.5987279988168 2.2334864565183 4.79961983209251 10.898146681451 5.62913907284768 14.3510174418605 62453.1456953642 4537.56914303818 22.0415688903788 38.6627906976744 0 0 662.506528451698 650.898504875406 647.242869565218 51.0482608695652 47.435752979415 49.0284516982797 704.5 44 708.1 54 685.5 51 701 43 699.9 49 680.9 48 695 44 694.2 56 675.8 54 692.7 52 678.2 47 661.1 46 683.2 54 668.7 46 665 58 661.3 48 651 43 644.1 47 656.3 53 638.7 41 641.8 52 639.9 51 618.4 43 628.6 51 616.6 50 599.3 50 608.6 50 585.1 51 569.4 46 593 54 1563776 KERN SOUTHERN KERN UNIFIED 49.3510324483776 1.697438951757 8.21917808219178 31.9833234067898 9.45945945945947 13.0842592592593 58159.2027027027 4284.44460988684 23.4270047978067 15.4929577464789 0 33.3333333333333 648.160764044944 638.358244680851 633.247085002225 39.1441922563418 38.2047872340426 38.645393258427 698.4 38 690.8 36 679.3 44 693.3 35 690.6 39 668.8 36 681.1 31 682.2 43 664.3 42 684 44 670.8 39 653.6 38 673 43 661.5 39 652.7 45 662 49 656.4 49 644.3 47 645.3 41 633.4 36 630.7 41 621.8 35 605 30 615.9 39 596.9 33 583.4 35 588.3 32 569.6 37 560.1 37 572.8 31 1563800 KERN TAFT UNION 51.4623726585606 .361247947454844 .755336617405583 17.7668308702791 2.73972602739725 14.4369696969697 62633.095890411 5763.70114942529 21.0565780504431 17.7914110429448 0 0 644.816746411483 636.865050784857 627.733364355514 32.1526291298278 34.4307479224377 32.4698564593301 689.6 29 688.6 34 664.6 30 684.7 27 686.6 34 659 26 678.6 29 680.4 41 655.6 34 678.4 38 667.5 36 653 38 667 37 657.6 35 644.8 36 647.2 34 641.7 34 629.8 32 637.3 34 623.8 27 623.7 34 620.5 34 605.5 31 607.7 32 595.2 32 587 38 591.3 34 563.9 32 557 34 569.8 28 1563826 KERN TEHACHAPI UNIFIED 24.9950445986125 .42042042042042 2.72272272272272 17.3973973973974 4.52488687782805 14.79 60455.0904977376 4308.12812812813 22.8716528162512 29.7297297297297 0 0 664.144418872267 649.835591306802 645.632324660634 50.2760180995475 47.4959074230878 51.5356731875719 704.6 44 696.8 43 679.8 45 702.7 44 695.4 44 675.3 42 692.5 42 689.5 51 671.1 49 700.8 60 683.8 52 671.2 57 691 61 676.9 55 666.3 61 671.3 59 669 61 653.1 57 659.5 56 646.1 49 643.4 54 639.3 51 613.2 38 626.2 49 612.9 47 592.7 43 602.5 44 584 50 562.5 40 586 45 1563842 KERN WASCO UNION 65.8902691511387 .471815247082195 4.39533151229203 78.9173081698535 21.1009174311927 14.93269231 61803.9024390244 4139.93667742737 24.5117428924598 14.0151515151515 0 0 633.587433561124 631.382788990826 624.948731343284 27.0694029850746 25.8278899082569 20.7463933181473 679.6 21 680.3 25 666.2 31 673.9 18 679.5 27 658.3 26 663.8 18 672.7 33 657.2 35 667.7 28 657.5 27 642.2 27 655.2 27 656.4 34 641.3 33 637 25 635.6 29 631.3 33 621.8 21 617.8 22 616.3 27 602 19 590.4 19 601.3 27 575.4 18 570.1 23 571.3 18 540.1 15 539.3 19 553 14 1663891 KINGS CORCORAN JOINT UNIFIED 64.279183175861 .463821892393321 4.05071119356834 79.3753865182437 17.3611111111111 15.3154545454545 66404.1944444444 4605.86147186147 22.6763485477178 15.5844155844156 0 0 638.988273615635 633.939674152356 626.044679891795 29.0036068530207 29.4297666226332 25.7328990228013 678.1 19 683 28 663.7 29 671.3 16 681.1 28 651.4 20 666.9 20 674.1 35 654.7 33 672.1 32 662.2 31 651.2 36 660.4 31 652.7 30 639.9 31 646.7 34 649.6 42 634 36 628.9 26 616.1 20 614 25 607.8 23 595 22 604.6 30 592.1 30 580.1 31 584.4 28 561.2 30 552.5 30 567.8 26 1673932 KINGS REEF-SUNSET UNIFIED 86.4936207655081 0 .734094616639478 89.9673735725938 15.5963302752294 11.0606557377049 57641.5688073395 4543.49225122349 21.7527675276753 26.1904761904762 0 0 628.006722054381 614.202327044025 615.965977175464 19.9144079885877 16.6194968553459 16.5672205438066 671.1 15 672.3 19 652.6 19 661.4 11 669.5 18 643.8 15 659.7 15 663.4 24 644.7 24 659.5 22 650.5 21 637.1 23 645.6 19 637.8 17 634.2 25 626.4 17 617.3 15 615.5 19 619 19 607 14 606.4 19 597 16 584.5 15 593.9 21 559.1 10 551.5 12 561.8 13 547.8 20 528.6 13 558.6 18 1764014 LAKE KELSEYVILLE UNIFIED 45.5701953657428 .954198473282443 1.33587786259542 22.0896946564885 7.20720720720721 14.7578125 54040.6036036036 4832.31965648855 20.0833333333333 50.9433962264151 0 0 661.984046164291 654.591859695566 646.073990734613 48.5221707478491 49.3653209794838 46.0855397148676 701.9 41 696.4 42 679.7 45 694.1 36 697.2 46 674.5 42 692 41 689.5 51 673.1 51 696.1 55 682.8 52 675.9 60 673.1 43 662.8 40 655.7 48 668.8 56 667.2 59 655.9 60 651.8 48 648 51 636.7 47 642.5 53 626.2 51 627.2 50 605.6 40 597.3 48 594.3 37 583.2 49 575.4 53 584.8 45 1764022 LAKE KONOCTI UNIFIED 73.6089996959562 .829610721123165 5.96681557115507 9.60433950223357 6.32911392405063 13.7825 54439.9493670886 4354.3876834716 20.6129032258065 10.6060606060606 0 100 638.266855791962 624.112023328847 621.180950205573 28.9310187300137 28.0960071781068 31.0657210401891 696.7 36 691.8 39 673 38 687.6 30 684.4 34 662.3 30 678.4 30 677.7 39 657.4 37 674.2 34 660.2 30 644.9 30 655.3 28 647.7 26 633.3 25 649.5 37 641.5 35 625.4 28 635.7 33 615.7 21 617.2 28 617.9 32 591.2 21 604.2 30 585.7 27 563.7 21 578.6 24 552.7 27 541.7 24 563.5 25 1764030 LAKE LAKEPORT UNIFIED 46.2952326249282 .659703133589885 1.86915887850467 15.6679494227598 3.44827586206897 15.1313131313131 58962.1379310345 4361.36118746564 21.4170506912442 28.4210526315789 0 0 658.686214953271 647.881014604151 644.126870229008 48.1656488549618 45.1775557263643 45.417445482866 707.7 47 699.9 47 683.6 49 694.7 36 697 46 668.8 36 703 52 699.2 61 680.4 58 694.9 54 691.5 60 669.6 55 683.4 52 676.7 54 666.4 59 662.4 49 656.7 48 647.5 50 647 42 632.2 34 636.4 47 626.6 38 609.2 34 624.3 46 611.3 44 591.8 41 603.4 44 574.7 40 553.3 30 580.4 39 1764055 LAKE MIDDLETOWN UNIFIED 26.7957526545909 .723763570566948 .361881785283474 8.68516284680338 1.20481927710844 11.2951612903226 50096.4096385542 3932.38962605549 19.1504854368932 40.6976744186047 0 0 663.322495755518 657.100756938604 648.737091222031 52.0542168674699 52.7628259041211 48.4261460101868 701.7 41 705.9 53 681.2 47 695.3 37 698.3 47 670.4 37 693.1 42 698.8 61 672.8 50 696.2 55 692.1 61 674.5 60 679.8 50 669.1 47 663.3 57 667.8 55 670.5 62 655.6 60 648.6 45 642.9 46 639.5 50 643.8 55 621 45 634.7 57 618.4 52 599.4 50 606.5 48 586.1 52 580.5 58 594.3 56 1764063 LAKE UPPER LAKE UNION 52.9170931422723 1.7329255861366 2.85423037716616 8.25688073394496 6.25 14.9870689655172 59995.3958333333 5022.53312945974 21.8995633187773 37.7049180327869 0 0 656.359604519774 651.16495132128 638.096332863188 37.3497884344147 41.8817802503477 37.9901129943503 705.2 45 696.9 44 684.8 50 698.5 40 696 44 673.7 41 682.6 33 683.8 45 665 43 689.6 49 671.7 40 656.7 41 660.3 31 649.2 27 636.2 27 648 35 651.7 42 632.1 34 646.9 43 643 46 629.4 39 620.2 33 609 34 612 36 608.1 43 602.7 53 586.7 30 559.6 29 561.6 39 568.6 27 1864204 LASSEN WESTWOOD UNIFIED 41.970802919708 0 .571428571428571 9.14285714285714 3.03030303030303 17.9042857142857 55176.8484848485 5259.9580952381 18.9655172413793 18.1818181818182 0 0 652.683421052632 641.202067183463 631.90182767624 34.4229765013055 36.3927648578811 38.3631578947368 701.6 41 695.7 41 675.2 40 690.8 32 692.5 41 664.6 31 676.3 27 675.3 36 657.4 35 681.2 41 662.2 31 653.5 38 669.5 40 665 42 648.2 40 659.2 46 655.3 48 634.9 37 639.1 35 630.4 33 622.4 32 627.1 40 600.7 27 608.6 33 598.9 35 577.9 30 584.1 28 580.6 47 560.4 38 572.6 31 1964212 LOS ANGELES ABC UNIFIED 30.5055227095651 32.4654190398698 10.4014103607269 32.6597956785101 36.0040567951318 15.2575630252101 59289.0862068966 4219.66476810415 22.576788291074 49.7326203208556 0 3.33333333333333 659.923843327729 663.76048998045 650.499602854484 52.5440273037543 57.6390518084067 44.8664923096083 705.6 45 717.5 63 688.2 54 697 39 712.7 61 678.8 46 691.2 41 703.8 64 677.2 55 691.7 51 693.7 61 671.3 56 682.4 53 689.5 65 668.6 62 663.8 51 670.7 61 653.7 57 650.4 47 650.8 54 643.9 54 632.2 44 623.3 47 627.7 50 603.7 38 600.8 51 605 47 574.9 41 572.3 49 586.4 45 1975309 LOS ANGELES ACTON-AGUA DULCE UNIFIED 28.1723484848485 1.44320297951583 1.72253258845438 11.9180633147114 7.44680851063831 11.7280373831776 58880.170212766 3806.89338919926 24.8222222222222 0 0 0 663.988473520249 652.433478802993 652.670828105395 58.8782936010038 52.9339152119701 53.6859813084112 702.1 41 692.8 38 687.4 53 697 39 689.3 38 681.1 49 688.2 38 689.1 50 672.5 50 700.6 60 683.6 53 679.8 65 692.5 62 674.7 53 677.9 72 671.8 59 674.6 66 657.7 62 659.5 56 650.6 54 646 56 645 56 628.8 54 635.7 58 622.1 55 608.8 59 615.8 57 591.4 57 574.6 52 597.1 59 1964261 LOS ANGELES ARCADIA UNIFIED 8.36221352711012 52.593956460522 1.21304018195603 9.88844362612369 8.90052356020942 15.9820135746606 64415.4659685864 4166.21780569696 24.2746044515956 60.840108401084 0 27.2727272727273 682.071046962516 691.294912131519 676.073383867277 73.9323512585812 77.8511904761905 62.3396524486572 717 58 740.3 82 706 71 710.2 53 725.7 73 700.4 68 704.6 54 727.1 83 700.6 76 705.7 65 719.7 82 688.2 72 694.3 64 712.9 83 684.2 76 681.7 69 701.8 85 674.4 76 673 68 678.1 79 671.8 79 658.6 68 643.9 67 658.6 76 632.3 63 622.9 71 635.7 74 604.3 67 594.5 71 613.1 74 1964279 LOS ANGELES AZUSA UNIFIED 70.7382666782337 .959561343385881 3.15284441398218 78.9410555174777 25.9557344064386 14.4988495575221 64611.0342052314 4379.24306031528 23.9818631492168 19.4244604316547 0 11.1111111111111 630.515060402685 627.717457773394 619.018762369706 25.7844042749703 29.3741832079892 22.5122147651007 679 20 682.9 28 659.9 25 670.8 16 678.6 26 650.7 20 668.8 21 671.4 32 653.6 32 666.7 27 661.6 30 639.9 25 653.1 25 654.4 32 636.1 27 638.8 26 637.2 30 623.3 26 625.7 24 625.9 29 616.9 28 604.6 21 600.2 26 599.9 26 578.3 20 575.9 28 577.1 22 552.8 23 554.4 32 567.9 26 1964287 LOS ANGELES BALDWIN PARK UNIFIED 62.871681687205 4.72375029897154 1.79382922745755 85.4699832575939 27.212020033389 15.062106017192 67839.958263773 3819.11803396317 27.5054157640393 26.7716535433071 0 0 627.079255415984 625.502389252614 620.852864742304 28.1941222638877 26.5887254485163 21.1458219552157 680 21 683.3 28 663.7 29 667.7 14 675.8 23 652.8 21 664.8 18 668.8 30 655.5 34 671 31 663.1 32 650 35 656.1 27 653 30 643.7 35 642.2 29 645.7 38 634.4 37 621.9 21 621.8 25 620.2 30 603.9 21 596.1 23 605.4 30 571.2 15 568.8 22 573.2 20 542.6 17 537.9 19 556.9 16 1964295 LOS ANGELES BASSETT UNIFIED 79.6765957446809 2.10292357667977 2.10292357667977 89.1434433236451 45.6 14.3998207885305 62084.608 4432.06496837066 23.5596625150663 20.9876543209877 0 0 625.112745604964 627.803804077573 616.408769269649 23.1238311852413 26.8254599701641 18.82316442606 673.1 16 675.6 21 657.1 23 663 12 675.2 23 646.5 17 659.5 15 670.5 31 647.6 27 662.1 24 661.8 31 638.2 24 645 19 651.5 29 631.1 23 636.6 24 638.3 31 625 27 618.6 19 622.2 25 612.7 24 602.1 19 598.8 25 600.2 26 575.8 18 574.6 27 574.1 20 546.3 19 545.7 24 559.9 19 1964303 LOS ANGELES BELLFLOWER UNIFIED 49.1721460825477 5.57544757033248 18.1585677749361 38.4800876872488 12.2743682310469 13.0422764227642 58550.5216606498 3988.18260869565 23.8852218555189 17.9972936400541 0 7.14285714285714 641.814845076401 641.600951888257 632.726298875201 38.4131762185324 41.0006207966891 32.1771010186757 686.5 27 686.6 32 668.3 33 679.8 23 683.6 31 659.3 27 678.5 29 678.8 40 661.4 39 670.8 31 664.6 33 650 34 661.9 32 662 39 653.5 46 651.9 39 657.3 49 640.7 43 637.8 34 637.7 41 630.3 40 623.9 37 620.1 45 621.7 45 596.9 33 595.5 46 594.4 37 566.6 34 570.3 48 580 38 1964311 LOS ANGELES BEVERLY HILLS UNIFIED 9.73348783314021 10.9398496240602 3.57142857142857 4.09774436090226 10.8196721311475 18.2 62455.7573770492 6015.75751879699 17.7246909455396 61.8843683083512 0 0 690.567250257466 694.90896250642 681.280658436214 76.3996913580247 79.1928608115049 67.2659629248198 720.3 61 735.6 80 706 71 714.3 57 729.2 77 703.7 71 711.4 61 723.6 81 703.3 78 712.5 71 714.3 80 699.6 81 704.1 73 711.3 84 694.6 85 683.8 71 689.9 78 669.6 73 677.2 72 676.5 78 670.5 78 669.1 76 654.5 77 659.4 77 641 70 632.6 78 637 75 611.2 72 603.4 77 616.6 77 1964329 LOS ANGELES BONITA UNIFIED 25.843945825753 5.86600496277916 4.82382133995037 27.5831265508685 9.50118764845605 16.8920124481328 62021.7553444181 4054.13568238213 24.1142443962401 51.764705882353 0 0 663.099746396156 658.021031171906 650.03186929488 52.8662521497553 53.0515585952913 48.306727175654 706 46 704.3 51 683.8 49 699.9 42 698.4 47 678.1 45 694.5 44 694 56 677.8 56 692 51 684.9 53 669.1 54 680 50 678.1 56 663.6 57 666.2 54 669.3 61 652.6 56 653.9 50 647.7 51 643.3 54 637.5 49 623.3 48 629.2 52 614.2 48 601.5 52 606.9 49 582.9 49 577.9 55 595.2 56 1964337 LOS ANGELES BURBANK UNIFIED 37.6797976147452 5.6881243063263 2.77469478357381 36.9589345172031 13.1666666666667 14.4693832599119 62400.435 4138.19589345172 23.9576629974598 32.4290998766954 0 5.26315789473684 655.695169224715 652.230417919131 645.026701161769 49.5026847603241 49.3065063512072 43.0069854388036 701.1 40 701.4 48 682.2 47 694.7 36 701.4 50 675.9 43 684.1 34 687.9 49 667.8 46 688.5 48 678.8 47 668.1 53 676.8 47 668.2 46 662.2 56 661.1 48 663.3 55 649.3 53 651.1 47 645.2 48 643.4 54 636.1 48 627.9 53 629.6 52 605 40 596.6 47 600 42 575.3 42 573.2 50 588.4 48 1964378 LOS ANGELES CHARTER OAK UNIFIED 23.9477040816327 5.26555386949924 4.87101669195751 36.6160849772382 11.7647058823529 15.3225396825397 61534.6654411765 4024.87496206373 24.1229724632214 38.7254901960784 0 0 655.798924731183 648.840342904359 640.007804980122 44.9202762084118 47.0877917785582 44.1773139363272 704.8 44 699.6 46 679 44 697.8 39 699.4 49 671 38 693.9 43 694.8 57 669.7 48 693.7 53 680.1 49 669.2 54 680.8 51 674.1 52 658.7 52 661.1 48 659.9 52 643.7 47 646.8 43 638.3 41 633.1 43 630.3 42 620.3 44 621.6 45 603.3 39 590.3 41 594.3 37 574.6 41 565.4 42 582.4 42 1964394 LOS ANGELES CLAREMONT UNIFIED 20.8070229477899 10.1118500604595 12.0012091898428 20.8736396614268 11.7088607594937 13.9077464788732 61447.6582278481 4401.14782345828 21.542025148908 53.1697341513292 0 0 676.104095197256 668.467728327357 659.145744680851 59.1142553191489 59.8346340434508 57.3855060034305 718.3 59 724.6 70 695.3 61 713.2 56 716.2 66 691.2 59 702.4 52 709.3 70 682.9 60 702.2 61 699.9 68 680.5 66 685.8 56 686.1 64 669.1 63 680 67 672.9 64 662 66 664.9 61 646.3 50 649.6 59 648.1 59 622.8 47 630.7 53 622.2 55 594.2 45 611.5 53 582.5 48 568.8 46 588.6 49 1973437 LOS ANGELES COMPTON UNIFIED 78.4239149752959 .0545386372158026 35.7296247060027 62.9273613525582 80.1749271137026 14.7931484502447 58991.4207968902 3743.12516617241 28.0999707687811 41.7890520694259 0 2.56410256410256 612.667656386702 613.706413351527 605.890224260388 19.7304401319837 22.4384906056471 16.2775590551181 667.5 13 672.3 19 651.7 19 660.6 11 671.3 20 642.6 14 653.1 12 660.1 22 643.7 23 659 21 651.7 22 635.9 22 638 15 640.9 20 627.9 20 625.4 16 627.8 22 617.2 21 614.7 16 617.3 21 606.2 19 594.6 15 590.3 19 593.5 21 570.1 15 570.7 24 573.2 20 550 22 551.7 30 558.5 17 1964436 LOS ANGELES COVINA-VALLEY UNIFIED 37.6449529146237 8.54844606946984 5.79890310786106 50.8299817184644 14.2599277978339 13.9004615384615 62290.0270758123 4265.80702010969 24.2564009442528 42.0844327176781 0 0 653.860152895591 650.312752021288 641.89274360033 45.409372419488 46.8560024562481 40.6060320452403 701.5 41 701.1 48 680.3 45 691.6 34 692.5 42 669.9 37 684.8 35 687.6 49 667.8 47 687.3 47 680.2 49 661.1 46 672.2 43 669.4 47 653.3 46 657.4 44 658.3 51 645.5 49 644.3 41 641.4 44 637.2 47 631.7 44 620.7 45 625.1 48 603 38 598.8 50 602.2 44 572.7 40 566.4 44 585 45 1964444 LOS ANGELES CULVER CITY UNIFIED 33.5116818262886 10.2023749790935 17.9126944305068 36.8121759491554 18.6507936507936 15.3850174216028 62132.0198412699 4282.61247700284 22.9572925060435 41.1764705882353 0 0 659.061115169223 649.806527353839 644.15641634981 46.8624049429658 44.7816388823668 44.1195519844169 706 46 702.8 49 685 50 694.6 36 695.1 43 672.5 39 684.4 34 687.6 49 668.8 47 688.1 47 673.5 41 663.6 48 682.6 52 669.4 46 664.7 58 665.5 53 649.4 42 648.3 52 654 50 647.1 50 641.8 52 636.8 48 617.9 42 625.4 48 607.1 42 592.2 43 598.2 40 570.8 38 565.6 43 579.2 37 1964451 LOS ANGELES DOWNEY UNIFIED 42.8409459021704 5.5955762987013 4.8041801948052 63.3370535714286 14.5994832041344 12.2934807256236 64631.9341085271 3979.21940949675 25.1415032249572 23.2673267326733 0 0 651.33894611136 648.686241487882 645.34675671664 49.2248775419326 45.3423152961851 38.2581728616211 698.1 37 698.8 45 678.8 44 685.8 28 692 40 668.1 35 678.9 29 683.5 45 665.7 44 687.9 47 676.4 45 668.5 54 672.8 43 667.4 45 665.5 59 656.3 43 656.3 48 653.9 57 646.4 43 646.2 49 647 57 627.9 40 619.3 44 629.8 52 600.1 36 593.8 44 601.3 43 570.9 38 571.1 48 587.3 47 1964469 LOS ANGELES DUARTE UNIFIED 61.4000866926745 2.77597050531338 13.2075471698113 61.9605291693776 30 16.0684210526316 59303.7388888889 4179.69811320755 25.2252747252747 15.0159744408946 0 0 634.506660104987 631.105498721228 626.154217262882 33.6727929110601 32.9437340153453 27.7286745406824 679.6 21 685.1 30 664.3 29 669.8 16 679.6 27 651.7 20 669.4 22 677.7 39 656.4 35 671.7 32 664.7 33 649.7 34 662.8 33 654.5 32 646.9 39 650.7 37 646 38 639.5 42 632.9 30 629.2 32 625 35 616.1 30 609.9 35 616.6 40 584.7 24 581.7 33 586.2 30 557.7 27 551.2 29 569.6 28 1964527 LOS ANGELES EL RANCHO UNIFIED 68.0851063829787 .683526999316473 .580997949419002 94.4805194805195 51.8145161290323 16.2742753623188 65653 4338.24307928913 22.4950337703615 33.6700336700337 0 0 636.533907380608 629.92565662151 624.336384276997 28.9841067120761 28.1802598838817 25.4157742402316 682.5 23 684.1 29 667.2 32 673.8 18 684.4 32 656 24 669.6 22 676.7 37 659.1 37 668.9 29 658.3 28 646.5 31 653.9 25 646.9 25 635.3 27 640.3 28 635.3 28 624.4 27 628.2 26 620.6 24 617.6 28 609.2 24 595.8 23 602.6 28 589.8 28 576.1 28 585 29 560.8 30 550.6 29 570.1 28 1964535 LOS ANGELES EL SEGUNDO UNIFIED 13.8000809388911 6.32078918523931 3.39788089148703 14.8702959444647 4.76190476190477 11.9276595744681 57362.0476190476 4718.11472415053 21.2887438825449 38.9937106918239 0 0 677.041884280594 669.538985579314 659.628335832084 62.2778610694653 64.9607160616609 60.9467485919099 710.2 50 710.8 59 689.9 56 711.2 54 707.5 59 690.9 59 707.1 57 708.1 69 689.6 68 702.7 62 689.7 59 674.6 60 688.8 59 682.9 61 661.5 55 674.7 62 680.5 71 653.3 57 664.6 61 660.4 66 653.1 64 662.9 72 643.4 71 645.3 68 631.9 67 609.9 63 623 66 601.8 69 589.1 72 605.9 70 1964568 LOS ANGELES GLENDALE UNIFIED 50.674017547988 12.1272365805169 1.11994698475812 23.8667992047714 12.2395833333333 13.2210855949896 73039.0052083333 4224.93667992048 26.6702033598585 26.3865546218487 0 34.4827586206897 654.253990819555 657.112164532538 649.081694134326 52.2113680666312 53.7901683915103 40.357190605642 695.7 35 706.9 54 683 48 689.8 32 701.5 54 676.9 44 684.7 35 695.4 57 680.3 59 687.2 47 688.7 57 669.8 55 675.2 47 679.9 58 664.9 59 658 45 667.3 60 654.4 58 645.4 42 645.8 51 644.8 55 631.1 44 619.6 48 629.8 54 598.4 37 592.5 46 598.2 42 567.6 39 569.5 52 583.9 47 1964576 LOS ANGELES GLENDORA UNIFIED 14.826244465316 4.1191381495564 1.29277566539924 16.5652724968314 6.32911392405063 14.9926553672316 63784.6075949367 4130.34993662864 24.900641025641 33.6 0 0 669.393686181076 664.820381845587 655.879660447134 58.4019162884518 59.4938871210853 53.6786929884275 704.3 44 712.6 59 687.7 54 698.2 40 705.6 55 678.2 45 693.5 43 700.1 62 678.9 57 700.3 59 689.1 58 673.6 59 691.2 61 680.9 59 671.1 65 673.1 61 668.8 61 656.8 61 663.4 59 653.1 57 650.6 61 649.8 61 635.1 60 639.1 61 624.5 57 611.1 61 616.7 58 588.1 54 585.3 63 602 64 1973445 LOS ANGELES HACIENDA LA PUENTE UNIFIED 40.2360661714442 16.3999470969448 2.53934664726888 67.9760172816647 28.9036544850498 17.3935658153242 62325.3034330011 4103.13891460565 25.056066382597 38.6784850926672 0 0 642.964292922922 641.584377276038 632.63471999011 36.6834590184201 38.4453143966982 31.0369622808124 690.4 30 696 41 673.7 39 682.7 25 694.4 42 664.8 32 678.5 29 685.9 46 666.3 44 677.9 37 675 43 654.9 39 665.3 35 664.5 41 649.3 41 650.1 37 646.5 38 636.6 39 634.6 31 630.9 33 626.8 37 616.4 30 605.7 31 612 36 585.6 24 579 30 585.4 29 563.2 32 563.5 41 573.9 32 1964634 LOS ANGELES INGLEWOOD UNIFIED 69.0082898372736 .192554557124519 43.0913758898355 55.3681876531684 63.4390651085142 15.0198263386397 63040.9899833055 3748.32979344148 27.4027459954233 38.8888888888889 0 57.8947368421053 634.693625077045 632.165754127812 627.247653302921 35.6359329765769 35.4114124390453 29.0958879985912 676.7 18 675 21 659.8 25 668.1 14 674.2 22 649.1 18 662.9 17 664 25 649.5 28 670.1 30 658.6 28 647.9 33 654.3 26 648.3 26 642.8 34 643.1 30 642.5 35 633.7 36 639.9 36 640.3 43 636 46 617.4 31 612.5 37 616.4 40 595.7 32 596.3 47 598.9 41 574.3 41 573.3 50 580.8 39 1964659 LOS ANGELES LA CANADA UNIFIED 1.4218009478673 26.2005649717514 .541431261770245 3.06026365348399 8.20512820512821 15.0257918552036 63791.6307692308 4705.50564971751 21.5962441314554 66.1818181818182 0 0 695.106828057108 694.118644067797 679.942095416277 79.1699407545993 82.4656394453005 76.5555555555556 730.6 71 743.4 83 707.6 73 727.2 70 737.4 81 711.8 77 723.5 71 731.8 85 708.9 81 723.5 79 721.1 83 705 84 712.6 80 717.6 86 694.3 85 694.9 80 706.8 88 671.9 75 688 81 685.5 84 673.3 80 674.9 80 656 78 659.6 77 651 77 633.7 80 641.4 78 617.2 77 600.6 76 622.3 82 1964683 LOS ANGELES LAS VIRGENES UNIFIED 2.97174111212397 7.57133035407357 1.36644895152974 4.97593674802338 7.02479338842976 17.0070127504554 64749.8450413223 4414.05010312822 24.2391304347826 60.6232294617564 0 0 684.553328569727 678.410719754977 672.994556044217 75.3633662189469 73.8972788314289 70.318566154579 722.7 64 725.1 72 711.1 76 720.2 64 719.3 71 705.2 72 714.6 65 718.2 78 699.3 76 714.3 73 710 77 695 78 707.4 77 707.9 82 691.4 83 687.8 75 693.7 82 674.1 77 681.9 76 672.3 75 670.8 78 668.3 76 646.9 71 655.8 75 639.7 70 624.1 73 632.9 72 595.7 61 578.9 57 604 66 1964725 LOS ANGELES LONG BEACH UNIFIED 64.2386984600099 14.166317455883 20.3368720026074 40.4898263258369 29.1087489779231 13.3097951495173 59298.1777050968 4210.30880709596 22.9831165236645 41.3782252989302 2.32558139534884 19.7674418604651 639.333896392546 637.248315019538 631.351391051242 37.6159378596087 37.9077018135667 30.8294580270132 693 32 695.4 41 674.2 39 684.9 27 691.7 40 668.3 35 679.6 30 687 48 668.5 47 677.2 37 668.7 37 654.8 39 661.8 32 658.1 35 649.4 41 645.6 33 645.7 38 635.1 37 634.1 31 632 35 627.8 38 615.1 29 605.9 31 613.1 37 589 27 586.7 38 590 33 562.2 31 561.8 39 574.3 33 1964733 LOS ANGELES LOS ANGELES UNIFIED 73.183943472326 4.30874594006731 13.7952471231427 68.5241685404818 45.4613868482351 20.5457412962458 73073.0746819566 4365.27608718017 23.4209491853797 49.3061754442912 2.32198142414861 32.1981424148607 632.955948121759 630.926125049489 624.165265009434 30.2164675932538 30.9511268824142 24.1795656528519 689.1 29 692 37 671.9 37 678.5 22 686.2 33 659.5 27 670.4 22 676.2 37 657.5 35 667.4 28 661.8 30 647.1 32 651.1 23 652 29 639.3 30 637.3 25 637.9 30 627.4 30 626.5 24 625.1 28 618.7 29 608 23 601.3 27 605.3 30 581.6 21 578.9 30 583.1 27 557.3 27 554.4 32 570.2 28 1964774 LOS ANGELES LYNWOOD UNIFIED 59.296941237853 .224648985959438 12.0561622464899 86.5273010920437 52.4871355060034 15.7930124223603 67063.0171526587 4064.95432137286 27.0596092836868 15.0847457627119 0 50 618.290844222037 621.509246177233 613.061156723226 23.0633096150338 25.8221407493517 17.7372810675563 678.6 20 678 23 659.8 25 667.6 14 674.2 22 648.2 18 659.9 15 665.9 27 649.5 28 658.3 21 652.3 22 640.6 26 642.8 18 642.4 21 632.6 24 635.1 23 639.5 32 624.5 27 615.5 17 618.6 22 610.5 22 595.7 15 597.6 24 598.1 24 569 14 572.8 25 573.1 19 548.5 21 557.4 35 561.8 20 1975333 LOS ANGELES MANHATTAN BEACH UNIFIED 7.19727345629511 7.00382026559942 1.81917409496089 8.65926869201383 7.93650793650794 15.2171875 64224.8888888889 5093.92796070584 21.5871073031416 43.089430894309 0 0 690.056717557252 683.397692112605 674.556118036123 74.1541592470109 74.6817144306366 71.3648854961832 717.6 58 717.5 65 695.5 62 712.9 56 709.6 59 691.1 59 713.9 63 709.9 71 692.9 70 721.8 78 714.1 78 707.1 85 711.8 80 706.7 81 691.1 83 696.8 82 708.4 88 680.3 81 689.6 81 681.9 83 673.7 80 677.6 82 661.2 81 661.8 79 648.3 75 632.5 79 640.4 77 613.9 74 598.3 74 617 78 1964790 LOS ANGELES MONROVIA UNIFIED 56.8985750209556 1.91512180174659 14.064654512027 48.0772177110464 17.5438596491228 14.4258785942492 57193.298245614 4017.30228282519 22.5547183351274 25.9475218658892 0 11.1111111111111 645.345761124122 639.929743822263 635.35638225256 40.8038680318544 38.6701428247563 34.7861826697892 687.4 27 687.8 32 669.6 34 684.2 26 688.8 36 665.6 32 677.9 28 679.2 40 665.8 44 683.9 43 673.1 41 661.4 46 670.2 40 661.5 39 653.5 46 654 41 646.7 39 645 48 644.7 41 637.4 40 639.6 50 622.1 35 610.6 36 617.4 41 595.8 33 588.2 39 590.8 34 563.2 33 565.1 43 575.4 34 1964808 LOS ANGELES MONTEBELLO UNIFIED 75.0342977348252 4.8799265642119 .447129193686891 90.5184921974475 59.5298068849706 16.9234065934066 67605.9101595298 3810.28506706938 27.995777027027 28.5416666666667 0 25 633.256956384986 628.919973223583 622.355320469712 25.1499663752522 24.3407546982695 20.808764940239 684.5 24 686.5 31 666.3 31 673.2 18 681.1 28 651.6 20 664.1 18 669.4 30 651.4 30 665.3 26 656.4 26 643.6 29 649.9 23 648.5 26 638.1 29 636.2 24 632.3 26 625 27 620.1 20 614.7 19 612.6 24 607.4 23 592.3 20 600.8 26 573.3 16 563.9 19 571.7 18 542.2 16 536.5 18 556.4 16 1964840 LOS ANGELES NORWALK-LA MIRADA UNIFIED 44.0220253770649 4.93949345449638 5.01597012911062 62.9762922308696 29.2200232828871 15.6210774058577 60665.5646100116 3616.50474605245 25.6258156364931 21.5086646279307 0 7.14285714285714 639.250212154394 632.607114624506 627.880085696797 33.7937155682514 32.4326086956522 29.8602518477963 686.6 26 686.8 32 667.5 32 678.5 22 684.3 32 655.7 24 673.9 25 677.5 38 658.1 36 678.6 38 665.1 34 653.7 38 662.4 33 654.9 32 646 37 647.4 34 641.2 34 635.1 37 636.3 33 627.2 30 627.5 37 616.2 30 603.6 29 611.3 35 588.4 26 578.2 30 586.6 30 561 30 556.5 34 572.8 31 1964865 LOS ANGELES PALOS VERDES PENINSULA UNIFIED 1.94360184248961 30.0269251480883 1.72320947765213 3.94184168012924 7.4742268041237 18.9428733031674 59821.2139175258 3946.78772213247 24.1144056678037 89.1304347826087 0 0 691.220889080708 696.583954688844 679.690504153538 78.8099398453165 84.1756524232865 73.5451014242555 730.5 71 745.9 85 715.6 79 720.6 64 737.4 81 711.4 77 720 68 732.5 86 703.7 78 717.1 75 727.8 86 698.5 80 706.1 75 721 88 694.2 83 690 76 704.6 87 671.2 74 683.5 77 682.9 82 674.3 80 671.9 78 665.7 84 661.1 78 648.8 76 635.8 81 642.1 79 617.4 77 608.7 82 619.8 80 1964873 LOS ANGELES PARAMOUNT UNIFIED 58.2283516774964 1.78362031722521 14.6145775473678 75.7082021847806 35.2484472049689 11.8014563106796 63190.7142857143 4370.21483675863 24.5058004640371 5.43071161048689 0 73.3333333333333 621.628153616147 619.038806631794 616.064499361663 25.0995805216123 23.4514584626297 18.6787516352084 678 19 681.7 27 663.4 28 668.5 15 678.8 26 649.9 19 664.3 18 670.6 31 655.6 34 665 26 657.9 27 643.7 29 645 19 642.5 21 634.9 26 633.4 22 627.3 22 624.7 27 619.8 19 615.4 20 618 29 596.8 16 590.1 19 597.9 24 572.5 16 571.9 25 576.5 22 542.9 17 541.4 21 557.8 17 1964881 LOS ANGELES PASADENA UNIFIED 61.8810986628117 2.22193005521956 32.0054343062495 47.1119291787186 42.1841541755889 14.2729885057471 64584.0385438972 4443.59943903936 23.867133609254 39.6551724137931 0 0 640.261998178379 638.447005395069 630.055227304324 35.4722647017934 37.419791026429 30.9504659146641 692.5 32 687.4 33 674.8 40 680.3 23 682.4 30 661.7 29 675.6 26 675.8 37 661.1 39 671 31 663.6 32 646.6 31 656.9 28 656.7 34 643.9 35 647.7 35 651.4 43 636.6 39 637 34 636.9 39 626.5 36 616.8 30 609.7 34 609.9 34 594.5 31 592.1 42 592.3 34 569.3 37 569.4 46 580.4 38 1964907 LOS ANGELES POMONA UNIFIED 73.505306122449 6.3934630738523 11.2337824351297 71.2044660678643 48.6985726280437 16.6833691756272 65628.7707808564 4007.18603418164 26.0442219440968 8.2771896053898 0 23.6842105263158 633.616609392898 631.103751065644 625.687406073083 33.2647967061246 32.3901007973522 27.1438092262835 680.8 21 681.2 26 662.8 28 668.9 15 675.5 23 649.9 19 669.8 22 673.5 34 657 35 672.8 33 665.8 34 652.5 37 658.2 29 655 32 645.3 37 643.6 31 644 36 634.6 37 630.8 28 629 32 625.7 35 612.5 27 605.3 30 609.4 34 588 26 582 33 586.1 29 560.9 30 559.8 37 574.5 33 1975341 LOS ANGELES REDONDO BEACH UNIFIED 25.6399317406143 7.27744401966139 5.18842162752594 23.1840524303659 10.2484472049689 16.5052083333333 68096.3695652174 5006.77607864555 22.4752168525403 51.5406162464986 0 0 662.997025878718 658.485319712448 650.223375639326 56.3864368251563 58.2854710556186 52.5876786404017 703.3 43 705.6 52 683.2 48 696.6 38 704 53 676 43 693.9 43 699.2 61 674.9 53 700 59 693 61 677.1 62 685.5 55 684.5 62 669.4 63 667.8 55 673.2 64 654.3 58 664.3 60 655.2 59 652.4 63 646.8 58 629.9 54 635.2 57 621.5 54 606.2 57 614.4 56 589 54 581.4 59 596 57 1973452 LOS ANGELES ROWLAND UNIFIED 33.8506304558681 18.7811869625226 6.68860813249814 55.3508865059985 29.1823899371069 16.1730984340045 62277.3119496855 4369.67459390593 24.4457424714434 30.2618816682832 0 0 647.043888420569 652.661347437917 639.480387685291 44.3539338654504 50.7873808987921 36.0793930569392 695.9 35 704.1 51 676.8 42 686.1 28 699.8 48 667.6 34 682 32 694.1 55 667.6 46 684.7 44 685.8 54 661.7 47 670.1 40 674.6 52 657.3 50 656.3 43 661.5 53 648.2 51 638.7 35 645.1 48 634.9 45 622.8 35 621.1 45 624.2 47 598.4 34 604.5 54 600.4 42 567.9 35 572.3 49 581.5 40 1975291 LOS ANGELES SAN GABRIEL UNIFIED 53.4556990709268 36.4023712486106 1.8895887365691 43.4790663208596 22.0657276995305 14.1230769230769 57663.2582159624 3814.97406446832 23.1666666666667 0 11.1111111111111 0 652.373269403656 651.826455658406 643.521950492097 50.5377274082911 54.3069573006868 43.7374887623614 693 32 704.7 52 676.6 42 687.9 30 698 50 668.8 36 690.4 41 693.5 56 679.2 58 691 51 687 57 673.3 59 678.3 50 679.5 58 666.9 61 660.2 48 670.4 63 649.9 54 648.6 45 647.8 52 643.1 53 631.4 45 624.7 53 626.8 51 604.3 42 598.6 52 602.9 46 580.9 51 570.1 52 584.8 48 1964964 LOS ANGELES SAN MARINO UNIFIED .265339966832504 63.2023384215654 .48717115946736 3.34524196167587 6.94444444444444 15.9078616352201 57543.375 4619.27671321858 22.1773612112473 78.1893004115226 0 0 690.843953394938 703.648538245895 680.914090726616 78.169409875552 85.3384060873048 70.9003615910004 724 65 758 90 715 79 719.2 62 749.5 87 709.3 75 715.7 65 742.2 89 710.1 82 717.7 75 739.1 91 695.7 78 707.7 76 726.4 90 695.6 84 686.9 74 705.3 87 672.9 76 677.4 72 679.4 80 665.6 74 673.9 79 656.4 78 661.9 79 641 70 635.3 81 636.9 75 613.1 74 602.8 77 618.2 79 1964980 LOS ANGELES SANTA MONICA-MALIBU UNIFIED 26.0906515580737 5.53147216923877 8.61799895959771 27.0764695682331 22.2664015904573 15.8740034662045 65963.4274353877 5063.35009537021 22.315280853634 65.1063829787234 0 0 672.518027812895 666.633357933579 658.063282423635 60.9182523785679 60.80036900369 57.6074589127687 709.8 50 713.6 60 690.5 56 701.5 44 705.5 53 682.4 50 696.5 46 699 60 680.7 59 703.3 63 688.3 56 679.5 64 691.2 61 683.9 61 673.6 68 676.7 64 673 64 658.3 62 670.2 66 666 69 659.3 69 654.4 64 637.8 62 638.5 60 628.4 60 612.4 62 619.6 60 593.9 59 584.1 61 599.3 61 1965029 LOS ANGELES SOUTH PASADENA UNIFIED 11.6788321167883 32.0893561103811 4.46780551905388 18.2128777923785 13.0177514792899 16.1964467005076 61169.6213017752 4043.49855453351 22.9909365558912 64.0740740740741 0 0 684.429470771359 685.287564588357 669.604545454546 69.6936155447606 74.7888391319325 66.0823244552058 722.2 63 738.9 80 700.9 67 714.7 57 727 73 697.2 65 708 57 718.1 76 691.5 69 711.8 70 712.7 77 689.8 73 694.2 64 696.8 72 672.8 67 682 70 688.2 76 661.6 66 679.8 74 679.3 80 666.7 75 662.2 71 644.1 68 650.6 70 644.1 72 626.7 74 637.3 75 604.3 67 595.8 72 610.6 72 1965052 LOS ANGELES TEMPLE CITY UNIFIED 27.3219814241486 38.107752956636 1.44546649145861 20.3679369250986 7.35930735930735 13.7875 59417.303030303 4035.89093298292 23.6489504242966 31.5649867374005 0 0 668.867083854819 672.304788029925 658.75127245509 60.2450099800399 64.9730673316708 52.4027534418023 704.9 45 718 64 685.4 51 695.5 37 715 62 681.4 49 694.9 44 711 71 685 63 701.7 61 697.2 65 680.4 65 685.4 55 691.4 69 673.3 67 676.1 64 683.3 73 663.5 66 657.3 54 651.7 55 649.9 60 643.7 55 635.7 60 639.5 61 621.2 54 617 66 618.6 60 595.1 60 587 64 601.4 63 1965060 LOS ANGELES TORRANCE UNIFIED 17.7019320040674 28.4768777488364 3.99675477176651 15.8332977496904 19.6572580645161 13.3186108637578 59555.2883064516 3822.10277979418 23.7832310838446 45.956607495069 0 0 669.838492706645 669.997700028678 656.603946302511 58.3625737761833 63.3968454258675 53.6345797638342 706.4 46 713.2 60 686.6 52 700.6 42 709 58 681.9 49 699.2 48 711.1 71 682.8 61 700.4 60 696.8 65 676.2 61 690.1 60 688 65 669.2 63 669.5 57 675.1 66 653.9 57 661.4 58 659 62 651.8 62 648.5 59 640.2 64 641.2 63 620.3 53 612.2 62 615 56 590.4 56 583.8 61 599.3 61 1973460 LOS ANGELES WALNUT VALLEY UNIFIED 6.19667389732466 42.3138875271378 5.72168919392114 18.1805448560824 17.7364864864865 14.8244477172312 60555.2364864865 3982.78030674417 24.2508591065292 57.7854671280277 0 0 675.635385172445 678.79409180563 664.452948625181 63.8897431259045 68.21642460104 56.1614012854169 714.1 54 729.9 75 696.3 62 706.7 49 720.5 69 691.2 59 700 49 714.1 74 689.7 67 704.2 63 703.8 71 683 68 694.5 64 700.6 76 677.8 72 675.5 63 683.7 73 662.9 67 661.9 58 661.1 65 655.5 66 645.6 57 633.6 58 641.2 63 616 50 608 58 612.6 54 591.8 57 578.4 56 597.6 59 1965094 LOS ANGELES WEST COVINA UNIFIED 47.3071656777727 9.43910955914448 9.12265386294195 58.1623745089481 17.5202156334232 13.9033898305085 56841.371967655 3907.33773461371 24.4226579520697 29.0617848970252 0 0 646.576221907905 644.418671371587 637.665678554886 42.1999382430137 42.0662779993863 34.8972182932579 690.9 30 690 35 673.2 38 684.5 26 687.3 35 663.5 30 682.3 32 683.1 44 668.5 47 677.6 37 668.2 37 658.2 43 666.1 36 662.4 39 654.3 47 650.8 38 651.6 44 644.4 47 642 38 645.3 48 634.4 45 623 36 617 41 620.5 44 600.2 36 596.6 47 598.9 41 570.9 38 570.6 48 580.5 39 2065193 MADERA CHOWCHILLA UNION 59.2872117400419 .94137783483098 2.52460419341036 41.6345742404793 6.66666666666667 12.2054166666667 53265.8190476191 3873.24005134788 23.5514918190568 25.4098360655738 0 0 642.458759783263 637.68853686636 630.755444964871 33.6891100702576 33.3064516129032 29.3991571342565 685.5 26 688.7 34 671.1 36 684.2 26 692.9 41 660 27 680.1 30 685.3 46 662.4 40 686.5 46 666.2 35 656 41 663.5 34 653.1 31 649.1 41 644.8 32 648.5 41 635.7 38 637.8 34 632.9 35 632.6 43 610.1 25 598.7 25 609.1 33 580.6 21 566.5 21 576 22 553.6 24 543.9 23 563 21 2065243 MADERA MADERA UNIFIED 60.0239038812355 1.15214548172137 3.54362583297004 67.3538020800897 26.8292682926829 14.0548488664987 56274.5882352941 4064.55738930062 23.4583272301157 31.9940476190476 0 45 634.873307579102 632.891376291368 622.640314369921 29.2889166741091 33.3825949921205 26.8529249448124 684 24 688.3 33 660.7 26 676.9 21 682.6 30 648.5 18 674.2 25 680.3 41 653.2 32 674 34 666.9 35 650.4 35 657.4 29 659.6 37 643 34 643.7 31 647.6 41 630.8 33 626.9 25 626.9 31 618.7 29 608.6 25 600.3 29 604.2 30 583.9 25 575 29 582.4 27 553.8 28 547.3 29 566.1 27 2165417 MARIN NOVATO UNIFIED 14.7922867865925 4.89383738995339 2.77058518902123 11.1082340756085 3.56234096692111 16.6201793721973 53794.9872773537 4365.51268772657 20.3963577932512 48.0851063829787 6.25 12.5 676.327673869621 670.308662832495 664.362228654125 67.1979015918958 65.5445722501797 61.5907027419648 717.7 58 718 65 699.3 65 709.2 52 707.6 57 689.2 57 708.1 57 706.3 68 688 65 706.9 66 703.2 71 685.3 70 697.1 67 698.7 75 683.9 77 676.7 65 685.2 75 666.6 70 670.1 66 664 68 661.8 71 655 65 637.4 62 644.9 66 629.8 61 605.8 56 621.1 62 593.5 58 579.7 57 606.4 68 2165458 MARIN SAN RAFAEL CITY UN 34.7676767676768 7.88413581431038 5.19854210627278 36.1787838097065 8.53658536585365 15.1911371237458 53336.3807692308 4458.96873201611 20.9668168783286 53.5928143712575 0 0 670.195434083601 661.617473035439 655.652214080918 58.6352341510035 57.6804314329738 55.5993569131833 713.1 53 710.2 57 694.1 60 705.9 49 702.6 51 680.1 47 707 56 704.7 66 684.9 63 698.5 58 683.3 52 668.9 54 685.3 55 675.3 53 670.6 64 674.1 62 664.1 56 664.1 67 663.7 60 654 58 651.3 61 651.4 62 639.2 63 640.6 62 614.8 48 607.7 58 608.5 50 590.5 55 582.9 60 600.7 60 2173361 MARIN SHORELINE UNIFIED 0 .480192076830732 .480192076830732 28.6914765906363 7.40740740740741 13.7261538461538 60992.5 6459.38415366147 16.4053537284895 26 0 0 670.308067542214 656.187862318841 650.405056179775 53.2621722846442 50.6322463768116 54.078799249531 709 49 699 45 688.7 54 703.3 45 699.4 47 681.9 50 692.1 41 686.7 48 671.9 50 701.2 61 683.1 52 677.2 63 692.9 63 670.3 48 666.8 61 683.6 71 677.5 69 660.9 65 672.5 68 656.8 60 655.6 65 648.5 60 611 36 620 43 625.8 57 601.8 52 606.5 48 578.1 44 577.5 55 585.2 45 2265532 MARIPOSA MARIPOSA COUNTY UNIFIED 27.0007209805335 .511322132943755 .29218407596786 6.20891161431702 1.45985401459853 15.5984472049689 51692.8248175182 4183.60336011687 21.5384615384615 29.8013245033113 0 0 664.224974619289 650.738696939783 646.636458852868 51.3226932668329 49.3326752221125 51.4238578680203 706.6 47 703 50 681.6 48 702.4 46 694.9 48 673.7 42 696.2 47 690 53 672.9 53 698.9 59 679.9 50 676.3 62 684 56 666.1 45 663 57 663.2 51 660.9 56 649 54 655.9 53 636.9 43 638.9 50 641.1 55 618.6 49 628.8 54 614 53 591.5 47 599.2 44 571.7 46 566.3 52 581.5 47 2365540 MENDOCINO ANDERSON VALLEY UNIFIED 55.9322033898305 .840336134453782 1.34453781512605 45.3781512605042 0 12.756 48855.9574468085 6406.86218487395 14.3160377358491 44.7368421052632 0 0 663.290476190476 663.766666666667 649.817894736842 48.6394736842105 53.8087855297158 42.7169312169312 704.5 44 716.5 61 692.3 56 694.5 36 703.4 51 677.1 45 676.1 27 681.5 42 667.6 46 694.9 54 683.4 53 673.8 58 680.9 52 675.8 51 664.9 58 652.5 39 660 53 641.2 44 664.6 60 663.7 67 648.9 59 630.4 43 629.7 54 617.3 41 594.6 30 601 52 589.7 32 580.9 47 582.7 60 582.2 43 2365565 MENDOCINO FORT BRAGG UNIFIED 37.2996794871795 1.16279069767442 .415282392026578 19.6843853820598 2.27272727272727 17.1307432432432 56336.7803030303 4990.48712624585 19.4728682170543 25.8992805755396 0 0 662.634256472005 649.819894675249 645.872770795931 48.7175344105326 45.9631363370392 47.8657435279952 705.6 45 698.1 44 682.3 47 694.2 36 695.8 45 673.4 40 695.6 45 697.8 59 676.6 54 692.9 52 682.5 51 671 56 678.8 49 667.8 46 659.1 52 662.5 50 666.9 59 648.1 52 662.3 58 648.3 51 650.3 60 637.6 49 610.4 35 628.6 51 610.1 44 581.8 33 597.1 39 585.9 51 559 36 577.6 36 2373916 MENDOCINO LAYTONVILLE UNIFIED 56.3311688311688 .517241379310345 .689655172413793 3.96551724137931 4.65116279069767 12.4978723404255 48867.6046511628 6578.05344827586 15.7329842931937 34.2105263157895 0 0 658.700571428571 644.085294117647 641.450287356322 43.1494252873563 36.9117647058824 42.5542857142857 693.1 33 687.5 32 672.8 38 699.4 41 689.9 38 669 36 688.6 38 680.9 42 654.4 33 702 61 672.2 41 679.4 65 673.6 43 661.9 39 652.5 45 658.8 46 650.6 44 647.3 51 654.8 51 638 39 638.3 49 630.7 43 605.8 31 619.6 43 602.6 38 586.8 38 589 32 562.2 30 548.2 26 578.9 36 2365615 MENDOCINO UKIAH UNIFIED 46.9275405445618 .969468962523513 1.11416582260165 22.8186948343221 4.84848484848484 17.4014986376022 64535.7121212121 4838.43134133989 21.3448818897638 33.3333333333333 0 36.3636363636364 652.817643378519 645.442461600647 637.560484873601 39.9751346871115 40.8148746968472 38.4404588112617 705.6 46 706.9 54 685.2 51 698 40 696.4 45 674.5 41 688.2 37 689.1 50 669.4 47 686 45 679.2 48 660.3 45 667.4 38 668.8 46 650 42 655 42 653.5 45 637.1 40 644.8 41 633.3 36 630 40 624.3 37 602.9 28 610.3 34 596.8 33 580 32 589 32 557.1 27 547 25 571 29 2365623 MENDOCINO WILLITS UNIFIED 46.5384615384615 .846153846153846 .692307692307692 11.5769230769231 2.98507462686567 17.472 56568.1268656716 4611.97576923077 20.8280757097792 26.3803680981595 0 0 658.926685714286 644.182815057283 640.649972451791 44.7129476584022 41.1047463175123 44.8982857142857 705.7 45 700 46 680.1 45 694.9 37 694.6 43 672.9 40 697.7 47 698.6 60 676.6 55 693.9 53 674.9 44 666.9 52 680.3 50 661.3 39 659.6 53 663.1 50 653.4 45 647.2 50 656.8 53 639.3 42 639.5 50 636.3 48 615 40 622.5 45 602.6 38 580.9 32 589.8 33 557.4 27 544.2 23 566.8 25 2475317 MERCED DOS PALOS ORO LOMA JT. UNIFIED 60.9182098765432 .156801254410035 6.31125049000392 61.7012936103489 15.4411764705882 11.4913461538462 54802.6985294118 4750.99882399059 18.8121752041574 19.5945945945946 0 0 631.770659722222 629.861754780653 621.061990950226 26.993778280543 29.0989876265467 22.5873842592593 680.9 21 688 34 662.5 28 670.6 16 681.7 29 646.8 17 673.6 25 678.7 40 654.3 33 667.7 28 658.7 28 642.4 28 649.8 22 650.1 28 636.1 27 640.2 28 646.4 39 629.9 32 627.7 25 620.9 24 618.3 29 599.9 18 596.3 23 602 27 578.9 20 571.2 24 576.8 22 551.8 23 548.2 26 568.9 27 2473619 MERCED GUSTINE UNIFIED 52.4079320113314 .614334470989761 .955631399317406 50.4436860068259 8.33333333333334 11.9896103896104 53609.9027777778 4080.96587030717 21.1577424023155 49.2753623188406 0 0 640.392330978809 634.424028906956 624.696633303003 28.8653321201092 31.8925022583559 26.6801210898083 686.5 26 690.3 36 665.9 31 685.9 28 689.4 38 658.6 26 673.2 24 676.8 38 655.1 33 672.8 33 662 31 648.5 33 670.7 41 661.2 38 651.3 43 640.7 28 640.9 34 627.9 30 629.8 27 626 29 622.8 33 599.8 18 596.6 23 595.4 23 569.2 14 568.8 22 572.8 19 560.7 30 554.5 32 563 21 2465698 MERCED HILMAR UNIFIED 37.7889869693148 1.5866388308977 .292275574112735 17.3277661795407 11.7117117117117 13.4170634920635 58102.5495495496 4361.61920668058 22.7861163227017 20.8 0 16.6666666666667 651.254748941319 640.575577367206 636.812100058173 38.4729493891798 34.8556581986143 35.0725952813067 685.4 25 684 30 667.7 33 679.3 22 679.5 27 659.4 27 680.8 31 679.3 41 665.1 43 680.7 40 668 37 653 38 675.5 46 665.5 43 652.2 44 655.1 42 650.1 43 644.8 48 646 42 634.7 37 635.4 45 628.9 41 602.6 28 616.9 40 599.5 35 586.1 37 594.7 37 559.1 28 546.9 25 571.8 30 2465722 MERCED LE GRAND UNION 41.4316702819957 .915564598168871 0 84.1302136317396 25.5813953488372 12.7714285714286 55709.2325581395 4140.52695829095 22.1852731591449 24.7422680412371 0 0 643.677083333333 645.689630681818 632.859677419355 25.2463343108504 27.5411931818182 18.0059523809524 682.2 23 686.4 32 663.1 28 665.9 13 681.8 29 650 19 668.9 21 672.1 33 654.2 33 657.9 21 664.3 33 646.7 32 642.9 18 652.2 30 636.3 27 632.1 21 630.5 24 625.9 28 610.4 14 607.6 14 610.7 22 588 11 584.5 15 589.9 19 569.7 15 568.2 22 572 20 547.5 20 550.8 28 551.7 13 2465755 MERCED LOS BANOS UNIFIED 50.9282399143163 1.24495289367429 4.86204576043069 53.1796769851952 12.1673003802281 12.078156996587 55074.6235741445 4075.18842530283 21.7702805155421 33.9350180505415 0 50 638.536482875881 629.930409090909 625.650664451827 29.1734693877551 27.6852272727273 25.195530726257 685.1 25 687.3 32 665.9 31 674.2 18 679.9 27 654 22 669.2 21 674.5 35 652.4 31 671.6 32 663.4 32 647.2 32 663.2 34 658.4 36 648.2 39 643.1 30 640.1 33 634.3 37 624.6 23 617.6 22 616.8 27 611.7 26 597.8 24 606.8 31 584.5 23 565.8 20 576.3 22 546.8 19 537 18 559.8 19 2573585 MODOC MODOC JOINT UNIFIED 33.2777314428691 .75 .75 10.4166666666667 1.61290322580645 11.6808823529412 55854.7419354839 4786.67916666667 19.2295345104334 32.7868852459016 0 0 659.431176470588 650.901276102088 639.167674418605 41.9476744186047 46.4164733178654 45.2776470588235 709.4 50 706.6 54 679.3 44 699.2 41 693.3 42 670.9 37 690.6 40 689 51 660.9 39 689.5 49 675.2 44 658 43 670.3 40 660.5 38 647.3 39 660 47 654.6 47 639.5 42 647.7 44 643.8 47 624.7 35 646.1 57 628.7 53 631.6 54 611.2 45 595 45 598 40 575.3 42 568.5 45 586.6 47 2573593 MODOC TULELAKE BASIN JOINT UNIFIED 64.8508430609598 0 0 52.3809523809524 4.76190476190477 12.9553191489362 50840.0714285714 4851.06302521008 18.4009546539379 45.7142857142857 0 0 640.7754601227 638.625294117647 630.765029469548 35.9548133595285 37.8666666666667 30.2024539877301 693.7 33 691.1 37 675.7 41 687 29 695.7 44 666.9 34 671.6 23 679.2 40 656.1 34 671.8 32 674.2 43 653.6 38 666.5 37 675.6 54 659.2 52 648.6 36 650.7 43 640 42 627.6 25 631.1 34 623.6 34 610 24 594.7 22 605.6 30 598.8 34 585.2 36 590.6 33 562 30 550 28 567.5 26 2673668 MONO EASTERN SIERRA UNIFIED 45.933734939759 .310077519379845 .775193798449613 14.1085271317829 2.22222222222223 14.0343137254902 52320.3111111111 6283.43255813954 14.6697038724374 76.9230769230769 0 0 664.066666666667 656.607675906184 644.631556503198 51.6183368869936 56.9424307036247 50.8013698630137 709.7 50 698.3 45 685.1 51 696.6 38 685.6 34 674.7 42 697.1 46 698 60 678.3 57 697.7 57 694.9 62 669.9 54 685.3 55 689.6 67 663.1 57 661.5 49 669.6 61 639.7 42 651.9 48 647 51 634 44 648.5 60 631.2 56 631.4 54 616.2 50 600 50 608.5 50 585.6 50 591.1 68 599.7 62 2673692 MONO MAMMOTH UNIFIED 21.8400687876182 .582847626977519 .0832639467110741 21.8151540383014 4.83870967741935 14.9848484848485 60159.435483871 4806.48293089092 20.5942275042445 42.8571428571429 0 0 668.114354066986 654.781134259259 655.868557919622 59.5957446808511 53.09375 54.9138755980861 705.7 46 693.8 40 686.2 51 709.7 51 693.3 42 685.9 54 705 54 692.3 55 686.9 65 700.4 60 680.1 49 675.5 61 689.1 59 667.9 46 671 65 673.5 61 665.2 58 657 61 662 58 664.1 68 656.6 67 653.5 64 631.4 56 646.9 67 613.5 47 597 48 610.4 52 579.4 46 585.9 63 589.4 50 2765987 MONTEREY CARMEL UNIFIED 10.706817231284 2.97716150081566 1.30505709624796 7.54486133768352 4.80000000000001 17.2942307692308 69887.392 5676.6513050571 20.6785411365564 55.921052631579 0 0 685.661973333333 677.989883474576 667.65821845175 69.2104984093319 71.0222457627119 68.8314666666667 724.7 66 721.3 67 694.2 60 718.2 61 719 68 690.1 58 709.7 59 708.5 70 681.8 60 709.8 69 702.2 69 689 73 697.5 67 694.5 71 682.4 76 681 69 684.2 74 668 71 682 76 670.8 73 667.2 75 673.7 79 646.5 70 649 70 644.1 72 634.1 80 636.1 74 607.6 70 591.5 68 612.5 74 2775473 MONTEREY GONZALES UNIFIED 67.2693726937269 .273878808627182 .410818212940774 86.5114686751113 24.1935483870968 11.8868421052632 58893.1209677419 4200.85210544334 24.9086378737542 11.304347826087 0 0 641.562837480148 644.002448979592 631.401393908105 27.6288074341766 29.0015306122449 20.4552673372155 675.7 18 674.1 20 659 24 669.9 16 676.1 24 651.4 20 664.7 18 668.7 30 653 31 666.6 27 668.1 37 652.9 37 651.5 24 661 38 650.3 42 634.7 23 644.5 37 626.1 28 622.7 22 626.5 29 617.5 28 604.7 21 604.7 30 604 29 578.8 20 574.8 27 578.8 24 553.8 24 547.7 26 559.9 19 2766092 MONTEREY MONTEREY PENINSULA UNIFIED 40.1345291479821 8.8134238913304 15.6372353176189 25.2257291250499 17.6570458404075 19.471915820029 61011.6332767402 4496.24306831802 20.6070396191124 28.9079229122056 0 95.2380952380952 653.881037456875 642.261085381784 639.283652789647 44.9874252228055 41.4812967581047 43.0559388861508 699.1 39 692.5 38 674.8 39 689.2 31 689.3 37 667.3 34 685.5 35 684.3 45 666.7 44 687.2 47 669.3 38 660.7 46 678.7 49 665.9 43 660 53 660.4 47 651.4 44 644.8 48 649.5 46 641 44 637 47 635.3 47 615.2 40 625.7 48 608.7 43 591.6 42 601.3 43 577.1 44 565.1 42 585.5 45 2773825 MONTEREY NORTH MONTEREY COUNTY UNIFIED 40.6666666666667 1.84426229508197 1.28539493293592 49.2362146050671 20.1581027667984 14.6853448275862 56619.2134387352 4356.07228017884 21.5795724465558 60.5978260869565 0 0 646.961362367393 637.964190837625 633.754023307436 34.9500554938957 32.8462998102467 31.5178671133445 692 31 691.3 37 672.3 37 684.4 26 685.9 34 660.3 27 679.1 29 680.7 42 662.1 40 674.8 35 665.1 34 653.5 38 663.1 33 656.3 34 645.7 37 645.6 33 638.4 31 632.5 35 638.3 35 623.5 27 626.1 36 621.1 34 600.8 27 611.9 36 596.9 33 580.8 32 590.8 34 559.3 28 552.2 30 573.4 31 2766134 MONTEREY PACIFIC GROVE UNIFIED 14.2795513373598 4.71158080140907 1.89343901365037 7.08938793483047 2.94117647058823 17.5419491525424 63909.5490196079 4756.38441215324 22.6321036889332 48.1751824817518 0 0 681.073791821561 669.929643296433 661.348655256724 64.1760391198044 64.9292742927429 65.1474597273854 719.7 60 716.7 64 692.9 59 715.5 58 706.7 56 694.4 63 712.2 61 707.9 70 690.3 68 709.7 69 702.2 70 684 69 698.6 68 693.7 71 672.1 66 677.8 66 682 72 657.1 61 670.1 66 662.1 66 657.1 67 663.5 72 640.9 65 651.9 72 637.5 68 609.6 60 620 61 599.4 63 576.7 54 593.1 55 2866241 NAPA CALISTOGA JOINT UNIFIED 45.6702253855279 .68259385665529 .341296928327645 47.098976109215 4.54545454545455 13.5112244897959 54344.8863636364 4650.72468714448 20.9738717339667 50 0 0 651.626136363636 644.014660493827 642.586842105263 46.3684210526316 40.8425925925926 38.8019480519481 693.2 33 696.4 41 676.4 41 688.6 31 688.3 36 666.8 34 685.5 35 684.8 46 673 51 686.2 46 671.7 40 665.3 50 680.9 51 673 51 668.1 62 658.1 45 652.9 45 643.9 47 645.8 41 642.3 44 636.7 47 625.1 37 603.9 30 627.6 50 599.1 35 592.8 43 593.1 36 566.4 33 556.5 34 590.4 49 2866266 NAPA NAPA VALLEY UNIFIED 32.7090648979061 1.39607354315986 1.77625428482393 26.4880024929885 6.38852672750979 15.4758720930233 54785.1342894394 3912.94858211281 21.8384091225143 29.2525773195876 3.33333333333333 0 661.755781448539 653.602858646089 645.835317212456 49.862496653877 50.1478428621536 47.9921038300962 707.6 48 708.2 55 683.5 49 696.7 38 697.8 46 674.5 41 695.8 45 694.6 56 675.6 54 693.2 53 681.2 50 666.8 52 680.3 50 672 50 660.1 54 665.4 53 663.8 56 650.3 54 654.2 50 642.8 46 642.1 52 639.2 51 620.4 45 627.3 50 614.1 47 599.4 50 604.9 47 577.6 44 571.1 48 585.4 45 2866290 NAPA ST. HELENA UNIFIED 34.434250764526 .738007380073801 .43050430504305 37.4538745387454 14.6341463414634 15.6477777777778 51434.4390243902 4386.9594095941 21.2020460358056 44.1441441441441 0 0 671.938825591586 667.352487135506 655.775432525952 54.4567474048443 57.0694682675815 51.5530236634531 707.1 47 708.5 56 690.8 57 696.4 38 705.9 54 677.7 45 700.1 50 709.1 71 682.9 61 688.9 49 685 54 660.5 45 683.1 53 677 54 665.6 59 669.7 57 659.8 52 649.1 52 661.4 58 647.6 51 645.9 56 655.5 66 628.5 53 638.8 61 618.3 51 614.5 64 615.5 57 585.9 52 579.5 57 589.6 49 3066449 ORANGE BREA-OLINDA UNIFIED 16.1578044596913 8.39899413243923 2.17937971500419 21.6261525565801 4.89795918367348 15.2272727272727 57193.4244897959 3825.95222129086 25.2277564921243 40.7202216066482 0 0 678.08792520838 676.768032786885 664.696648793566 65.8469615728329 69.0533894550288 60.8702410452805 709.9 50 724.2 70 690.5 56 709.4 52 718 67 688.1 56 698.5 48 712.1 72 682.2 60 705.4 65 698.5 66 680.5 65 698.1 67 688.6 66 680.9 74 679.1 67 683.1 73 670.2 73 669.1 65 669.9 73 658.5 68 653.2 64 645.8 69 646.9 68 633.9 65 619 68 626.6 67 606 69 589.7 67 610.8 73 3066464 ORANGE CAPISTRANO UNIFIED 17.3380285722466 4.70951361577139 1.58062428436302 16.9537511823568 9.39193257074051 12.6999165275459 60779.3835039133 3802.97418728531 23.0004288952883 44.8255813953488 0 10.5263157894737 671.50029010734 664.568939854735 656.9490234375 63.8142361111111 65.2306343697449 61.3329344357412 721.1 62 721.2 69 698.7 65 713.7 57 714.3 66 692.3 60 708 58 710.6 71 689.5 68 706.2 65 697.8 66 680.6 66 693.5 63 687.5 65 672.8 67 676.1 64 679.9 71 657.4 61 665.7 62 657.6 61 650.5 61 652.4 63 633.8 59 641.4 63 626.8 59 611.4 62 618.9 60 595.2 60 587.1 65 605 67 3066522 ORANGE GARDEN GROVE UNIFIED 48.1560822795015 28.7115519049283 1.29106955609927 44.0514680181755 11.4861186717474 15.6765557163531 65184.4246053348 3835.54895578469 24.6732224738965 20.3812982296868 0 0 644.83656906429 646.905817561278 637.042428705735 42.3321215919774 45.7674837108284 34.1665499681731 690.9 30 702.4 49 675.9 41 683.4 26 698.8 47 667.4 34 677.7 28 692.7 54 670.5 49 682.1 41 682.2 50 663.3 48 669.5 40 673.4 51 659.3 52 655.2 42 660.3 52 644.7 48 639.1 35 636.8 39 631.6 42 622.1 35 615.5 40 618.9 42 591.8 29 588.9 40 591 34 568.7 36 563 40 577.5 36 3073650 ORANGE IRVINE UNIFIED 10.5164960182025 25.1636962837691 3.03976410389836 7.41078010493907 9.2557251908397 16.0977599323753 62550.9770992366 4352.4882702398 22.6581642900181 0 0 9.67741935483871 684.851803374055 685.089444252277 671.314190288104 72.8213289962825 76.8866020984665 68.4994764397906 720.5 61 735.6 79 702.4 68 715.9 59 725.7 73 696.1 64 713.9 63 725.4 82 697.8 74 714.4 72 711.5 77 692.2 75 702.3 71 704.2 78 684.8 78 689 76 701.2 84 673.5 76 676.7 72 675.6 77 666.4 75 665.2 73 649.9 73 653.9 73 638 68 626.3 74 632.4 71 607.9 70 596.1 72 612.9 74 3066555 ORANGE LAGUNA BEACH UNIFIED 9.31627349060376 1.88383045525903 1.21664050235479 9.22291993720565 7.61904761904762 16.9064102564103 70274.3714285714 4481.47409733124 24.2635658914729 74.2857142857143 0 0 684.004074889868 677.346978021978 663.808360836084 67.2827282728273 72.2983516483517 69.2643171806167 727.2 68 724.5 71 705.3 71 719 62 710.9 61 693.6 62 719.2 68 716.5 77 695.5 72 714.8 73 704.7 71 685 70 703.5 72 694.7 72 676.1 71 680.1 68 685 75 651.8 55 677.4 72 674.9 77 659.4 69 660.8 70 643.3 67 641.9 63 638.8 69 632.2 79 627.7 68 607.1 70 597 73 609.1 71 3073924 ORANGE LOS ALAMITOS UNIFIED 11.6002452483139 8.99127975489041 3.11100636342211 11.5366485976903 10.1333333333333 15.0930715935335 67284.9401866667 4726.52929766675 23.1987918725975 49.5921696574225 0 10 683.151620147578 676.998132780083 669.268828685259 69.4602390438247 68.4106287902968 64.971767725377 717.8 58 718.3 65 697.9 64 712.7 55 716.3 65 693.9 61 707.3 57 714.9 75 694.8 71 713.9 72 698.7 67 686.3 71 700.9 70 690.6 68 679 73 682.5 70 681.5 72 663.6 68 669.3 65 655.7 60 657.3 67 658.2 68 638 62 648 69 639.6 69 625.9 74 634.2 73 606 69 601 76 619.4 80 3066597 ORANGE NEWPORT-MESA UNIFIED 39.0254420008624 4.68356306506596 1.23511684205326 34.1238081122474 4.42678774120317 16.0011663286004 60202.0374574347 4349.23086804012 22.4827586206897 42.8714859437751 0 0 662.048115315852 657.721892473118 652.410960005849 57.3834174161 56.1364157706093 49.8311978545888 702.7 42 701.3 47 682.3 47 700.4 42 699.9 48 680.6 48 693.6 43 694.4 56 676.8 55 699 58 691.1 59 679.2 64 683.9 54 679.7 57 670.3 64 668.3 56 672.3 63 661.3 64 653.6 50 655.2 58 647.9 58 640.1 51 630.8 55 636.2 58 616.1 49 610.3 60 614 55 588.1 53 579 56 598.8 59 3066621 ORANGE ORANGE UNIFIED 36.019976669583 10.8824233307122 2.08554138603029 36.821771684502 9.42408376963351 16.8176700547303 56415.1108202443 3586.93545078464 25.4488217305271 47.3302570863547 2.56410256410256 12.8205128205128 657.097121487969 653.340421234853 646.768285012285 51.7346437346437 51.4449413348721 45.6142793878856 707.4 47 708.8 54 688.1 54 702.3 44 703.8 52 682.2 50 695.4 45 701.6 63 679.6 57 690.5 50 685.1 53 671.2 56 676.7 47 674.8 52 665.7 59 666.6 54 666.2 58 653.9 57 648.7 45 643.3 46 640.2 50 633.8 45 621.7 46 628.3 51 605.5 40 595 45 599.4 41 574.6 41 571.3 48 585.8 45 3066647 ORANGE PLACENTIA-YORBA LINDA UNIFIED 21.0138794854435 8.12439729990357 1.88846030215365 25.9201221472195 11.3974231912785 15.153023465704 63101.5312190288 4172.24176309868 24.7171532846715 36.5617433414044 0 0 669.720971327595 666.268838856005 656.515978973547 60.006330544879 62.7434308790175 56.1279712705923 713.6 54 717.9 65 692.3 58 707.3 50 711 60 687.4 55 698.4 48 700.9 63 681.2 59 700.3 60 699.1 66 677.5 62 689.3 59 691 68 672.4 66 672.2 60 677.9 69 657.8 61 660.6 57 656.9 60 651.4 61 647 58 633.6 58 636.7 59 622.3 55 609.2 59 615.9 57 594 59 583.3 60 599.5 61 3073635 ORANGE SADDLEBACK VALLEY UNIFIED 10.5344975854998 8.40166405402147 2.16749065476908 14.8106837091523 6.05643496214728 14.9799044585987 60372.2057811425 3840.86554925841 22.1910073196236 53.7918871252205 0 0 674.518041730873 671.646055990119 662.072027826377 66.466383404149 69.0351996706464 62.0291949523135 713.1 54 722.3 69 695 61 710.8 54 713.6 63 690 58 706.2 56 712 73 687.8 65 703.8 63 698.7 67 682 67 695.3 65 694 71 678.1 72 678.3 66 684.3 74 665.4 69 669.9 66 664.4 68 658 68 655.6 66 641.1 65 646.3 67 633.1 64 621.5 70 627.6 67 600.8 64 593.8 70 607.8 69 3066670 ORANGE SANTA ANA UNIFIED 73.2772340256227 4.4085122200539 1.07982529504693 90.7908186971471 26.4875239923225 14.970924927416 69824.5398272553 4102.14481925472 25.0321867794005 21.729365524986 0 56.25 620.712644461482 623.755428773726 616.861563590674 26.7030852994555 29.2057133520329 19.6294119317375 679.8 21 686.3 32 664.6 30 668 15 682.5 32 651.4 21 659.5 15 670.2 31 651.4 31 663.1 25 656.6 26 645.4 30 645.5 20 650 28 638.2 30 632.4 21 635.4 29 626.7 30 620.4 21 623.5 28 616.7 28 599.5 19 596 27 601.1 28 571.1 18 572.6 28 572.8 20 542.4 21 548.7 32 559 22 3073643 ORANGE TUSTIN UNIFIED 29.8689696247767 8.27604268971387 4.65527401296405 41.6813985464545 5.26315789473685 13.0141193595342 57831.146381579 3629.52956197211 24.1694687289845 39.5953757225434 0 0 653.79774335437 650.860966858654 643.780960137984 49.8789766193944 50.3713530355963 43.5576592082616 695.7 35 702.1 48 679.9 45 695 37 703.2 51 675 42 687.8 37 691.4 53 672.1 50 692.4 52 681.1 49 669.4 55 679 49 673.1 50 661.8 55 660.5 48 661.8 54 646.1 49 644.2 40 641.7 44 638.5 48 632.2 44 621.6 46 628.7 51 609.5 43 604.3 55 608.7 50 580.5 47 575.8 53 591.2 51 3175085 PLACER ROCKLIN UNIFIED 17.136240833655 3.53894493137093 1.3725814453448 6.66446171655366 4.65116279069767 11.1923875432526 52654.8217054264 3596.09525384488 23.0433039294306 0 0 0 673.707947320618 662.480751278062 657.31606741573 62.7487640449438 60.8290731273616 61.4452770208901 716 57 715.2 63 695.3 62 713.2 56 709.2 60 688.7 57 705 54 708.1 70 686.7 65 710.1 69 692.4 61 683.7 69 696.8 66 679.4 58 674.4 69 678.7 67 670 62 660.2 65 664.7 61 654.9 59 651.8 62 652.5 63 629.8 54 639.1 61 630 62 609.6 60 615.2 57 592.3 57 584.8 62 597 59 3166944 PLACER TAHOE-TRUCKEE UNIFIED 23.3564377231674 .627574034124338 .431457148460482 17.7093547754462 6.61764705882352 13.5935897435897 49513.0257352941 4638.09786232595 19.6784192173576 44.131455399061 0 0 666.170820668693 655.992460533479 648.804707520891 54.0721448467967 54.4324986390855 53.8347609836972 707.3 47 712.2 60 682.6 48 705.3 48 710 59 678.3 46 696.2 45 700 62 675.2 53 701.4 61 687 56 677.2 62 688.8 59 680.2 58 667.8 62 669.5 57 663 55 653.3 57 660.9 57 647.8 51 648.4 59 646.3 57 616.3 41 631.5 54 615.8 49 597.5 48 606.5 48 592.1 57 580.6 58 590.1 51 3166951 PLACER WESTERN PLACER UNIFIED 28.6609686609687 .562809545249887 1.44079243583971 17.4245835209365 4.8780487804878 13.3327272727273 42658.3902439024 3503.69765871229 24.2685851318945 27.4611398963731 62.5 0 656.233281893004 645.500357507661 640.171112229492 44.5485656768999 42.8406537282942 43.2489711934156 698.9 38 695.1 41 679.6 45 691.5 33 698.9 48 670.6 38 689.2 39 694.3 56 669.5 48 688.7 48 668 36 659 44 676.9 47 676.4 54 656.5 50 661.5 49 655.6 48 651.3 55 648.5 45 630.4 33 630.5 41 635.9 48 614.6 39 626.5 49 611.6 46 590.5 41 598.9 41 572.5 40 559.2 36 579 37 3266969 PLUMAS PLUMAS UNIFIED 36.6621067031464 .663533314901852 1.21647774398673 6.85651092065247 2.71739130434783 16.1540476190476 56500.125 4912.70168648051 21.2320916905444 33.1932773109244 0 0 668.071468144044 656.273361934477 650.500313479624 52.3761755485893 50.1907176287052 51.4273842500989 706.4 46 700.2 47 685.8 51 699.4 41 694.7 43 673.5 40 697.7 47 694.3 56 674.3 52 696.1 56 679.4 48 668.6 54 683.3 53 672.8 50 663.1 57 667.4 55 658.6 51 648.5 52 660.8 57 651.2 55 644.4 55 643.8 54 621.2 46 632.3 54 620.3 53 601.5 52 609.5 51 587.6 53 576.9 54 596.5 58 3366977 RIVERSIDE ALVORD UNIFIED 42.2902910247004 4.02122641509434 7.22287735849057 47.1580188679245 17.2413793103448 13.0070779220779 62390.7051124438 3758.19761084906 25.181954887218 41.6666666666667 0 70.5882352941177 638.111402394775 630.693225584594 627.071274732972 34.6095254771494 33.3498968363136 30.1157474600871 687.5 27 687.6 33 667 32 678.2 21 684.7 32 655.2 23 676.4 27 681.1 42 660.5 38 674.4 34 662.9 32 655 40 660 31 656.5 34 649.7 42 649.6 37 648.7 42 636.3 39 636.4 34 627.3 31 626.3 36 616.8 31 601.7 30 611.6 37 583.6 25 573.6 28 582 27 559.1 32 550.6 32 569.9 31 3366985 RIVERSIDE BANNING UNIFIED 75.5298651252409 13.3685136323659 13.8522427440633 36.6974494283201 20.2970297029703 13.8173333333333 60074.7807425743 4456.32191292876 21.8996590355577 41.8181818181818 0 12.5 634.098734577665 628.219981640147 620.879110554338 25.6636392107736 25.7264381884945 23.4349889275546 677.9 19 684.4 29 658.6 24 672.1 17 680.3 27 648.2 18 666.3 19 671.9 33 651.9 30 670.3 30 654.3 24 645.2 30 655.6 27 645.9 24 639.7 31 639.8 27 629.3 23 625.6 28 627.6 25 623 26 612 23 609.6 25 598 24 602.2 28 581.8 22 574.5 27 580.2 25 551.7 23 541 21 561 20 3366993 RIVERSIDE BEAUMONT UNIFIED 58.7464387464387 1.15131578947368 3.4265350877193 34.6491228070176 9.02777777777779 15.9661585365854 59270.4442361111 4070.73959429825 24.7562674094708 26.25 0 12.5 645.879280347964 637.157724827056 633.962882603642 41.2026346377373 38.8439661798616 37.7323052589957 698.4 38 692.6 39 678.5 44 690.5 32 684.8 33 666.2 33 687.9 37 684.8 46 670.7 49 685.2 44 665.1 34 656.8 42 674.9 45 659.5 37 657.3 50 656 43 655.6 48 646.5 50 640.8 37 630.9 34 634.1 44 623.7 37 608.6 34 614.5 38 599.3 35 592.7 43 592.5 35 562.3 31 562.4 39 573 31 3373676 RIVERSIDE COACHELLA VALLEY UNIFIED 85.5145886105639 .138121546961326 .276243093922652 96.1671270718232 36.4653243847875 11.9207692307692 61385.5887024609 4099.01191902624 25.2796420581656 13.5467980295567 0 0 612.505972615675 616.307526637951 605.373097282673 15.8368427243854 19.4258671503061 12.4275259678942 670.5 14 676.1 21 652.3 19 661 11 675.8 23 640.5 13 653.8 12 664.4 26 641.4 22 651.2 16 649 20 632.6 19 632.1 12 639.1 18 623.3 16 622 14 625.5 21 613.2 18 608.6 13 611.1 17 600.4 15 586.8 11 585.9 16 585 16 557.9 9 559.9 16 560.2 12 535.4 13 538 19 549.1 11 3367033 RIVERSIDE CORONA-NORCO UNIFIED 41.5113871635611 3.60423802054992 4.64453762683653 41.7784321884703 11.3732097725358 13.5429739776952 64063.4574557709 4083.55417560257 25.2311373152183 34.6372688477952 0 34.2857142857143 649.575955248793 642.475290069264 639.372187638951 44.6634059582037 40.9677063572595 38.9997744394821 695 34 693.3 39 673.8 39 686.5 28 687.8 36 665.2 32 685.8 35 684.6 46 669 47 685.8 45 673.7 42 662.5 47 673.9 44 666 43 657.6 50 659.7 47 659.1 51 648.4 52 643.2 39 637.8 40 636.9 47 628 40 610.6 35 623.9 47 600.8 36 587.4 38 597.9 40 573 40 563.3 40 584.9 44 3367058 RIVERSIDE DESERT SANDS UNIFIED 55.9912521615299 1.2646835922698 2.4677908298598 60.0416824554756 15.8088235294118 12.6873655913979 64123.518125 3937.20569865479 25.2138016019717 43.1718061674009 4.34782608695652 0 642.625749337795 640.398884858256 630.882184459367 35.1719884884199 37.7696493349456 31.2600027882337 692.2 32 690.1 35 669.9 35 682.6 25 686.7 34 659.8 27 680.7 31 684.9 46 663 41 675.3 35 664.5 33 650.9 35 662.7 33 663.9 41 647.5 39 649.2 36 647.7 40 635.3 38 635 32 636.5 39 630.3 40 618.9 32 609.7 35 613.7 37 587.8 26 584 35 584.3 28 563 31 561.6 39 574.1 32 3367082 RIVERSIDE HEMET UNIFIED 62.4077800134138 1.43589743589744 2.66666666666667 29.2307692307692 9.03328050713154 15.5025143678161 59735.0526624406 3916.53708397436 24.517557734894 21.4285714285714 0 5.26315789473684 650.938309754281 645.971246796045 636.643406695025 41.9052154614389 44.9794031490297 40.3741623231571 703.7 43 701.6 48 678.6 43 692.4 34 691.6 40 667.1 34 686.7 36 688.1 50 665 43 687.6 47 675.5 44 663.3 48 674.6 44 664.1 41 659.5 53 659.6 47 659.5 51 643.8 47 648.5 45 645.5 49 635.1 45 625 38 616 41 615.9 39 600 36 592.5 43 591.1 34 567.7 35 566.7 44 575.7 34 3367090 RIVERSIDE JURUPA UNIFIED 50.2109216989309 1.38881142283451 5.30425567516314 53.2545038764014 23.789764868603 13.2753071253071 62068.7191286307 3826.45784650566 24.6666666666667 26.3835263835264 0 4.16666666666667 638.791092144514 630.901365487503 627.674335051968 34.6180538505606 32.1523596582289 30.0636393768644 690.5 30 689.5 35 669 34 680.6 23 683.8 31 657.1 25 676.1 27 679.6 41 659.9 38 677.3 37 663.2 32 653.3 38 661.1 32 654.6 32 649.4 41 648.5 35 648.2 41 637.4 40 633.9 31 627.4 30 625.1 35 616.9 30 598.3 25 610 34 586.6 25 576.1 28 586.3 30 561 30 552.7 30 572.5 31 3375176 RIVERSIDE LAKE ELSINORE UNIFIED 38.1164650109672 1.79080718975924 4.07242820189693 30.1120912648405 11.4186851211073 13.05374617737 62326.655449827 3960.63418584599 25.3969359331476 25.3833049403748 0 83.3333333333333 649.218096224461 642.148974546786 634.754570195195 41.2464339339339 42.5663797839224 39.5877977591564 696.1 35 694.7 40 670.8 36 689.4 31 692.9 41 666 33 686 35 688.6 50 668.6 47 684.1 44 667.9 36 656.3 41 674 44 665.4 43 655.4 48 657.1 44 656.1 48 644.4 47 645.2 41 639.6 43 632.3 42 630.3 42 615.9 41 619.7 43 601.5 37 590.9 42 593 35 573.3 40 564.2 41 580.4 39 3367124 RIVERSIDE MORENO VALLEY UNIFIED 43.0689140716757 3.26543602800764 23.676002546149 35.3532781667728 23.1235784685368 14.0119418132612 58401.1608491281 4173.91743189052 24.0922034941892 25.5924170616114 2.85714285714286 22.8571428571429 642.660522821957 634.998522406493 629.402435943703 34.4060357271743 33.1140613973183 31.6017030627661 692.3 32 694.2 39 670.6 35 687 29 692.5 40 662.4 30 676.9 27 680.2 41 657.9 36 678.6 38 664.4 33 652.4 37 665.4 35 656 33 647.4 39 649.1 36 643.6 36 635 37 634.6 31 624.4 27 622.6 33 618.9 32 602.9 28 612.9 37 589.8 27 576.4 28 585.2 29 559.8 29 552.9 30 572.7 31 3375200 RIVERSIDE MURRIETA VALLEY UNIFIED 18.2164543398024 2.10569777043765 3.43724194880264 17.8468208092486 15.1933701657459 9.21929611650485 59134.6718232044 3774.6010125929 25.86148169174 38.2566585956416 0 100 664.968239211877 655.571895604396 650.502999299229 56.3379117028732 54.6623626373626 53.4476203690856 706 46 701.7 48 682.7 48 702.5 44 698.8 48 680.6 48 699.1 48 702.4 64 678.3 57 695.8 55 676.8 46 671 56 683.9 54 672.8 51 662.9 57 667.3 55 671.7 63 651.2 55 662.3 58 653.8 58 649.6 60 651.1 62 633.2 58 643.2 65 621.3 54 601 52 614.2 56 589.5 55 578.8 56 596.9 58 3367173 RIVERSIDE PALM SPRINGS UNIFIED 62.5049747000967 1.15749423932265 5.94287551578158 53.4912384116607 8.93333333333334 13.0700234192038 57242.9728133333 4143.36832699212 24.2010412494994 13.7540453074434 0 9.52380952380952 638.104933204571 636.219372751447 626.655804398614 33.8860871666801 38.3268418582825 30.6045388701111 690.7 30 689.8 35 669.8 34 682.3 25 687.3 35 658.3 26 677.2 28 680.4 41 658.5 36 676.8 36 663.8 33 651 36 664.6 35 657 34 647.8 39 645.2 32 640.1 33 632.1 34 635.8 32 637.2 40 628.3 38 617 30 612.7 38 611.4 35 588.7 27 593.5 44 587.1 30 561.9 30 568 45 572 30 3367181 RIVERSIDE PALO VERDE UNIFIED 55.9128893390732 .632911392405063 10.5221518987342 52.0833333333333 10.7954545454545 14.1102564102564 61885.4805113636 4694.47362869198 22.1958121109225 20.9302325581395 0 0 642.057746478873 637.300819061802 629.136989409985 33.160363086233 34.7956068503351 30.516939474686 692.1 31 689.2 35 671.5 36 681.8 24 684.2 32 656.7 24 672.7 24 675.1 36 656.2 34 674.2 34 669.1 38 650 34 663.7 34 660.9 38 646.3 38 647.7 35 643.4 36 634.4 37 632 29 625.7 29 621.5 32 617.8 31 603.5 29 610.5 35 593.3 30 580.5 32 589.3 33 564.7 33 564.6 42 570.8 29 3367215 RIVERSIDE RIVERSIDE UNIFIED 47.2343460276708 3.59830536819221 10.3266625787391 40.1109314900496 21.7477003942181 10.9766241299304 61445.6489947438 4384.20497268521 23.5685282566731 33.7724550898204 0 19.047619047619 652.423640417888 645.365187645035 639.211258767073 42.8254788564866 42.4636712811075 39.2414419622579 701.5 41 701.3 47 680.3 45 695 37 694.8 43 671.5 38 684.7 34 684.6 46 666.3 44 685.6 45 671.4 40 661.1 46 672.7 43 664.2 41 655.9 48 657.6 45 658.4 50 645.2 48 645.9 42 642.3 45 636.4 46 627.4 40 613.6 38 618.7 42 595.2 32 585.9 37 591.8 34 567.2 35 562.1 39 579.3 38 3367249 RIVERSIDE SAN JACINTO UNIFIED 64.1791044776119 1.00730792020541 3.85147145960893 50.8196721311475 7.44680851063831 13.1198113207547 58040.6569680851 3564.44928500889 26.0216216216216 11.9318181818182 12.5 25 633.898633323641 630.169063926941 625.056898550725 32.6866666666667 32.3090753424658 27.9002617039837 686.4 26 687.1 32 673 37 685.5 27 685.8 33 670.4 37 677.5 28 678.3 39 663.5 42 674.5 34 669.2 38 651.1 36 659.4 30 653 30 645.8 37 648.3 35 642.9 35 635.6 38 630.7 28 625.4 28 617.1 28 611.4 26 597.2 24 604.5 29 587.6 26 585.4 36 583.5 27 549.8 21 553.3 31 566.2 24 3375192 RIVERSIDE TEMECULA VALLEY UNIFIED 18.1699877999187 1.95702750786917 3.8250992199261 17.784316408923 8.09792843691149 12.194847972973 64156.1068361582 3664.26510606268 25.5497985031664 25.8680555555556 6.25 81.25 664.46675821574 654.871055332649 652.040092513924 58.8132729160767 56.1685173089484 54.7503551472677 709.6 50 702.7 50 687.3 53 703 45 699.2 51 681.1 49 697.3 47 699.7 62 680.9 60 695.8 55 686.4 55 671.6 57 687.3 58 677.3 56 668.1 62 670.5 58 667.7 60 657.1 61 661.2 58 653 57 653.2 63 647.9 59 628.8 55 638.8 61 620.7 55 603.2 55 612.7 55 591.2 58 577.4 57 599.9 63 3375242 RIVERSIDE VAL VERDE UNIFIED 54.8438652569462 1.89759712553335 23.5796092521895 45.823040646755 21.2389380530973 10.9077922077922 58447.4374631269 3799.07750392993 25.5414949970571 31.8021201413428 0 0 637.066459230892 632.573991655077 627.424335378323 34.5670913953123 34.0865399474579 30.0086165629488 690.2 30 688.6 34 668.3 33 679.3 22 682.8 30 654 22 675.2 26 678.6 39 656.5 35 674.2 34 663.7 32 649.5 34 659.4 30 652.4 30 644.5 36 642.8 30 635.5 29 633.1 35 637.3 34 635.1 38 631.9 41 614.9 29 609.4 34 613.8 37 588.5 27 582.3 34 587.5 31 567.2 35 562 39 578.9 37 3473973 SACRAMENTO CENTER JOINT UNIFIED 29.3060600267635 7.97491039426523 13.8709677419355 10.2867383512545 10.6995884773662 13.2324074074074 55944.1275720165 3811.04928315412 22.2895064288677 78.968253968254 0 42.8571428571429 662.739604722793 655.475031525851 650.780858585859 57.0383838383838 56.1669609079445 51.7995379876797 703.4 43 706 53 681.1 47 695.9 37 696.9 46 672.5 39 686.6 36 690.8 52 669.2 47 700.1 59 696.1 65 676.1 61 686.1 56 689.8 68 671.9 66 670.3 58 667.8 62 662.7 67 656.7 54 649.3 55 650.2 61 641.1 54 625.1 54 640.9 64 617.1 55 599.6 54 611.8 56 593.4 62 568 52 592.9 58 3467314 SACRAMENTO ELK GROVE UNIFIED 36.9447663190421 15.2125780530885 19.0934646864194 16.269870885887 17.4043062200957 12.2778861360042 58331.6309808612 3762.5834017464 23.1169705126637 29.5801526717557 0 33.3333333333333 654.782969037773 648.209210391211 643.323938438074 47.4667059516794 45.1266043741657 42.0050387462209 692.9 32 697.5 43 675.4 40 688.4 30 693.9 42 667.9 35 686.8 36 692.7 54 671.3 49 685.7 45 677.4 46 660.8 46 674.5 44 668.2 45 655.9 48 661.5 49 657.8 50 649.5 53 648.3 44 638.2 41 641.1 51 630.8 43 612.8 37 626.7 49 609.6 44 592.9 44 605.1 47 584.7 50 571.4 49 593.7 54 3467330 SACRAMENTO FOLSOM-CORDOVA UNIFIED 26.1143156790771 5.3087986463621 9.91257755217146 8.67174280879865 9.72222222222221 14.2631906077348 51104.6527777778 3845.84679921038 22.1977666289044 27.7091906721536 4.34782608695652 30.4347826086957 663.980873539868 658.512398760124 649.557049411291 54.2888195632485 56.1346865313469 51.1814118842052 703 43 703.8 50 683.1 48 697.9 40 700.6 49 674.3 41 693.8 43 700 61 675.2 53 697.6 57 688 57 676.5 61 682.1 52 679.3 57 669.5 64 669.5 57 672.5 64 655 59 661 57 654.6 58 647.3 57 642.7 54 627.1 52 630.5 53 617.4 50 605 55 609.4 51 590.7 56 580.3 57 592.9 53 3467348 SACRAMENTO GALT JOINT UNION 33.1153765028475 1.77095631641086 1.18063754427391 35.7260920897285 9.16666666666667 11.7725563909774 51742.2041666667 4581.18913813459 22.2133333333333 23.9837398373984 0 0 656.585923753666 650.842329994729 644.455909694555 46.5211155378486 45.3125988402741 41.4806718208478 702.8 42 699.8 46 682.6 48 691.5 33 690.4 39 668.8 35 683.9 34 684.9 46 668.3 46 686.8 46 675.5 44 664.5 50 676.6 47 667.6 45 659.5 53 663.9 51 666.8 59 653.6 57 642.5 39 635.2 38 633.8 44 627.6 40 612.2 37 622.2 45 608.8 43 600.1 50 604.3 46 576.5 43 572.3 50 585.2 44 3475283 SACRAMENTO NATOMAS UNIFIED 37.9553798362045 6.22614982928299 29.7449287005423 26.2301667001406 12.2340425531915 10.6661137440758 57672.6808510638 3312.51496284394 22.4284140969163 0 14.2857142857143 14.2857142857143 654.991544666866 642.894449262793 641.367069121642 43.5976456383942 38.1156403584851 39.4711682103376 694.8 34 688.5 35 673.4 38 684.6 27 684.9 32 660.9 28 680.5 31 679.1 40 663.6 41 686.6 46 670.9 39 663 48 674 44 661.6 39 660.7 54 658.8 46 651.4 44 647.6 50 648 44 637.1 40 636.7 47 626.2 38 607.8 33 623.7 47 607.7 42 591.8 43 599.5 41 575.1 42 557.4 35 581.3 40 3467413 SACRAMENTO RIVER DELTA JOINT UNIFIED 35.978835978836 1.40728476821192 1.03476821192053 36.9205298013245 11.9047619047619 14.7351063829787 51080.373015873 4420.62417218543 20.1801801801802 45.3125 0 0 652.409325771897 645.858159509202 639.278937259923 41.1651728553137 42.3993865030675 38.5519848771267 697.9 37 698.6 45 680.1 45 688.8 30 692.1 40 669.4 36 681.4 31 689.3 51 667.8 46 691.1 50 680.4 49 659.2 44 676 46 671.1 49 657.4 50 654.3 41 654.5 47 640.6 43 641.3 38 633.5 36 628.4 38 630.5 43 612.6 37 616.7 40 594.6 31 580.1 32 591.1 34 569.6 37 560.6 38 576 34 3467439 SACRAMENTO SACRAMENTO CITY UNIFIED 59.4064346159987 23.8705379883233 21.9525096978959 23.5061322048509 27.4574669187146 14.0032991803279 61997.025047259 4125.40662591591 24.8267842434226 38.742611499194 1.2987012987013 10.3896103896104 642.590368715732 637.104240392201 631.379925928053 37.3836812035256 37.4236729403809 33.106930463288 694.4 34 697.5 43 676.9 42 684.5 27 691.4 39 666.1 33 677.7 28 683 44 664.6 43 680.8 40 675 43 658 42 664 34 664.9 42 650.5 42 651.5 38 651.5 43 637.5 40 636 33 631.7 34 625.3 35 620.1 33 606.4 32 614.3 38 591.8 29 578.2 30 588.5 32 567.1 35 552 30 572.2 30 3467447 SACRAMENTO SAN JUAN UNIFIED 28.8497509510099 4.59476973890503 5.95982189518574 8.97213454020946 7.18722271517302 12.6162215239592 58872.7457852706 4335.65953132512 21.750739140323 39.2844235634261 1.21951219512195 6.09756097560976 670.17213159824 662.936219245407 655.298383336865 58.550422330538 58.9531301507387 55.3249022482894 708 48 710.7 57 687.1 53 703.8 46 706.8 56 683.1 50 700.2 49 702.2 64 681.2 59 700.1 59 686.8 55 675.6 61 687.6 57 678.8 57 669.9 63 676.4 64 676.7 68 661.4 65 662.8 59 655.1 58 650.5 61 648.4 59 632.1 57 636.4 58 624.2 56 610.2 60 616.8 58 591.1 56 579.7 57 595.9 57 3575259 SAN BENITO AROMAS/SAN JUAN UNIFIED 35.7537490134175 2.109375 .625 40.46875 16.6666666666667 11.9181818181818 61919.85 4272.915625 22.2222222222222 0 0 0 657.229098805646 644.65762886598 639.917678381257 48.8051118210863 48.5010309278351 49.1226927252986 702.3 42 694.2 39 678.6 44 691.7 33 696.3 43 667.8 35 690.2 39 695.3 56 668.4 47 700.6 60 680 49 676.8 62 677.5 47 673.1 51 663.5 57 674 61 674 66 653.3 57 651.1 47 644.2 47 635.3 45 636.1 48 609.9 35 619.4 43 609.5 44 596.5 47 598.4 41 591.7 57 571.3 49 587.3 47 3675077 SAN BERNARDINO APPLE VALLEY UNIFIED 43.7316116377901 1.65699001891839 17.2744471263618 20.431861178159 5.16898608349901 13.2070035460993 62106.3459244533 3058.08872072542 24.5208208806535 35.8391608391608 13.3333333333333 86.6666666666667 652.699094870935 642.722250164006 639.455594713656 44.5601321585903 40.9465340039361 41.3233880880545 705.5 45 700.8 46 682.6 48 694.6 36 688.1 36 669.9 37 690.4 40 685.6 47 672.7 51 690.2 50 673.3 42 667 52 673.8 44 660 37 656.9 50 654.1 41 652.3 45 643.3 46 646 42 637.7 40 634.8 45 632.8 45 616.5 41 622.3 45 599.4 35 587 38 591.7 34 568.8 36 562.1 39 579.8 39 3667611 SAN BERNARDINO BARSTOW UNIFIED 50.6241134751773 1.10144927536232 11.7826086956522 38.1304347826087 19.2691029900332 14.4014662756598 57100.6212624585 4203.24405797101 22.9867549668874 12.987012987013 0 0 644.092824226465 638.257651547266 630.222731201383 34.9842264477096 35.8380669775329 33.0118499012508 693.2 33 693.1 39 671.2 36 685.7 28 686.9 34 656.4 24 675.6 26 678.3 39 655.8 34 681.4 41 670.1 39 655 40 671.1 41 662.7 40 648.6 41 652.9 40 645 37 636.5 39 636.7 33 632.5 35 627.1 37 614.1 28 602 28 608.1 33 595.2 32 584.4 35 591.7 34 559.9 29 556.4 34 575.1 33 3667637 SAN BERNARDINO BEAR VALLEY UNIFIED 40.7033727298933 .44931199101376 .954787980904241 11.4293737714125 3.52112676056338 13.8814814814815 57824.8802816902 3838.35888795282 25.3642857142857 35.4285714285714 0 100 662.867092034029 653.319750755287 647.145671412082 51.0623316660254 50.2843655589124 48.7935034802784 706.7 46 704.6 52 686.9 53 695 37 701.4 50 670.7 38 689.2 39 692.1 54 670.4 49 694.2 54 681.1 50 662.8 48 680 50 667.7 45 656.9 50 661.2 48 655.4 48 645.4 48 655.7 52 643.5 47 642.7 53 645 56 623.2 48 635.8 58 625 57 608.5 58 619.6 60 580.2 47 574.4 52 591.7 53 3667678 SAN BERNARDINO CHINO UNIFIED 25.955670898506 6.02325581395349 5.23255813953488 38.202657807309 18.6985172981878 15.2127715355805 62270.0461285008 3664.25887043189 24.169686985173 29.0155440414508 0 10 655.456794936477 649.093457228997 645.536307072925 50.6846006038979 47.2450164244251 43.8672659725726 694.7 34 695.2 41 680 45 689.7 32 693.4 41 675.4 43 687.2 37 688.5 50 676.9 55 690.3 50 680.8 49 669 54 678.9 49 671.7 49 663.3 57 663.2 50 662.1 54 651.3 54 650.6 47 644.4 47 640.8 51 637.3 49 623.6 48 630.9 53 605.6 40 593.2 44 600.5 42 580.5 47 570.9 48 591.3 52 3667686 SAN BERNARDINO COLTON JOINT UNIFIED 44.4521203103412 2.01990343876244 9.0846388806779 63.6860774460538 19.094247246022 11.8951140065147 59625.2607099143 3811.72376588827 24.0302328416433 34.4459279038718 0 48 634.139532950423 631.313346569601 626.092709725316 33.9203414996288 33.8149863960585 27.9621372430472 691.4 31 690.1 35 671.1 36 682.5 25 685.7 33 661.8 29 673 24 676.4 37 657.7 36 674 34 663.3 32 651.7 36 658.2 29 652.5 30 645.1 36 648.4 35 653.9 46 640.4 43 632.2 29 631.8 34 628 38 613.8 28 605.7 31 611.6 35 583.8 23 580.3 32 583.6 27 553.6 24 551.7 30 568.2 26 3667710 SAN BERNARDINO FONTANA UNIFIED 39.998063204106 1.10104404176167 11.8054722188888 65.7746309852394 19.7108066971081 12.9685172647258 60468.7168949772 3939.78189127565 24.3713073776701 18.2534001431639 0 56.25 627.433972790867 625.397574910601 618.264922374043 25.880959431448 27.0517489415923 21.4247886356809 681.2 22 686.3 31 664.7 30 670.7 16 682.4 30 654.1 22 667.5 20 673.4 34 656.3 34 668.1 29 657.4 27 642.5 28 651.2 23 648.7 26 637.3 28 636 24 634.1 27 622.3 25 624 22 621.3 25 616.1 27 604.8 21 596.5 23 601.5 27 575.9 18 574.2 27 574.8 21 547.9 20 545.7 24 561.2 20 3675044 SAN BERNARDINO HESPERIA UNIFIED 47.6781020421918 .955685016691759 5.30863389408915 31.1906787981934 11.8971061093248 12.1917877906977 62220.1045016077 3865.72474962362 24.8130160271977 13.3440514469453 0 61.1111111111111 645.811905419082 636.345861854387 632.887036026201 37.3387008733625 34.2974486621033 34.072499769988 697.9 37 693.7 39 674.4 39 690.1 32 688 36 666.6 34 683.1 33 681.5 43 664.7 43 678.8 38 667.3 36 654.8 39 668.8 39 659 36 651.9 44 651.1 38 644.5 37 637.8 40 634.5 31 625.7 29 625.6 35 619.3 33 600.6 27 612.5 36 593.9 31 579.3 31 589.5 32 561.4 30 552.3 30 573.6 32 3675051 SAN BERNARDINO LUCERNE VALLEY UNIFIED 62.3115577889447 1.22484689413823 2.27471566054243 17.9352580927384 5.26315789473685 10.4692307692308 59114.4912280702 4587.3053368329 20.4873646209386 26.0869565217391 0 0 646.713190954774 636.121039903265 631.081157635468 35.2697044334975 34.2660217654172 33.7110552763819 684.9 25 677.6 23 665.5 30 694.1 35 684.9 33 665.9 33 683.4 33 682.1 44 663.3 41 676.3 36 660.9 30 648.1 33 653.2 25 646.6 24 634.8 26 648 35 643.4 36 625 27 640.7 37 636.1 39 631.1 41 632.8 44 613.1 38 623.5 46 594.1 31 577 29 592.5 35 567.1 35 562.6 40 577.1 36 3667777 SAN BERNARDINO MORONGO UNIFIED 46.7422949503062 1.50383631713555 8.19437340153453 13.3401534526854 4.5045045045045 13.4019387755102 62286.0720720721 4128.06496163683 22.6292806251436 13.5 5.88235294117647 0 649.351451800232 645.395127543709 636.913963963964 43.5964835803546 46.3238750358269 40.457462253194 703.7 44 700.6 47 681.6 47 697.6 39 696.6 46 670.3 37 687.4 37 688.6 50 666.8 45 683.1 43 675.2 44 656.8 41 670.2 40 665.6 43 650 42 655.8 43 659.2 51 644.7 47 643.8 40 638.9 42 630.8 41 630.2 42 617.5 42 624.3 47 604.8 40 596.2 47 599.6 42 570.1 38 573.6 51 585.7 46 3667801 SAN BERNARDINO NEEDLES UNIFIED 54.401519949335 1.16194625998548 3.7763253449528 17.283950617284 2.94117647058823 16.7946666666667 61234.9852941177 4903.2381989833 21.2772133526851 40.3508771929825 0 10 648.882936918304 640.701925025329 637.151745379877 44.7659137577002 42.2958459979737 40.3660806618408 698.2 37 694.5 40 680 45 688.5 30 690.3 39 669.2 36 683.9 34 680.9 42 668.6 47 683.2 43 675 44 659 44 675.1 45 669.9 48 655.2 48 656.5 43 648.3 41 644.1 47 639.3 36 628.8 32 632.7 43 632.9 45 620.2 45 625.8 49 597.3 33 588.7 40 593.6 36 588.4 53 573.2 50 590.9 52 3667843 SAN BERNARDINO REDLANDS UNIFIED 34.6040390288178 8.12395760477753 7.96255447355679 30.9894011943832 15.0335570469799 14.8490898058252 60477.2657718121 3763.68477968473 24.7211127943809 24.7743229689067 0 50 656.081160748377 651.376363772792 643.838623810985 47.177124884934 47.9456741952427 42.7080565101184 694.5 34 701 47 675.2 40 694.7 37 699.9 49 675.2 42 688.4 38 694 56 673.9 52 691.8 51 680.7 49 667.5 52 678 48 673.2 50 662.2 55 659.3 46 658 50 645.8 49 650.6 47 642.1 45 638.5 48 631.3 43 614.7 39 622.3 45 604.8 39 594.5 45 600.6 43 576.9 43 572.3 50 585.8 45 3667850 SAN BERNARDINO RIALTO UNIFIED 57.88501888376 2.87577590948377 27.3984442523768 51.5911055236898 24.4939271255061 12.1739946380697 59096.3066801619 3838.00078573112 25.1621099705255 13.2394366197183 0 70.8333333333333 630.819776598716 626.145838751625 623.00284021703 29.9329531613332 27.4568162115301 24.1199664898073 686.7 26 684.5 30 667.6 32 676.5 20 681.2 29 656.7 24 669.3 21 673 34 656.2 34 670.7 31 660 29 645.9 31 656.7 28 650.9 28 643.2 34 640.4 28 640.5 33 632.1 34 627.5 25 621.1 24 622.6 33 607.6 23 596.1 23 607.8 32 579.2 20 571.1 24 579.3 24 550 21 545.3 24 565.1 23 3667868 SAN BERNARDINO RIM OF THE WORLD UNIFIED 24.7007250042151 .979604946202023 1.26866870081901 13.0560462502007 10.2766798418972 15.791958041958 62201.8656126482 3995.41560944275 24.4390832328106 31.2865497076023 0 0 663.493173758865 657.51976744186 651.892599080359 55.119115393037 53.1539621016365 48.792109929078 707.5 47 703.1 49 689.4 55 698.5 40 694.3 43 680.3 48 692.6 42 690.6 52 678.2 57 695 55 678.4 48 668.3 54 686.3 58 678.5 57 668.7 63 662.6 50 666.3 60 653 57 651.6 48 648.3 54 645.2 56 638.6 52 629.5 58 633.1 57 613 51 601.3 55 608.9 53 575 47 573 57 587.6 52 3667876 SAN BERNARDINO SAN BERNARDINO CITY UNIFIED 70.2357454924486 3.36604410678485 19.6180225809855 50.849424923499 25.2196382428941 11.6793846153846 61672.8863049096 4147.68156589638 23.9868434683518 22.3952095808383 0 42.6229508196721 629.083041804347 626.491443928802 621.690639289877 29.5900654532413 29.2053692063679 23.6404037954346 688.8 28 692.9 39 672.1 37 675.9 20 685.5 33 657.5 25 669 21 675.5 36 656.6 35 669.5 30 660.8 30 648.5 33 653.6 25 652.2 29 641.8 33 638.9 26 638.9 32 629.1 31 624.2 23 623.2 26 618.5 29 606.5 22 597.1 24 605.1 30 580.3 21 574.1 26 581 25 551.8 23 546.6 25 565.5 23 3673890 SAN BERNARDINO SILVER VALLEY UNIFIED 46.9641550841258 2.10789567702751 18.1850660950339 12.0400142908182 7.91366906474819 11.0990683229814 60618.4100719425 5342.53733476242 20.2528901734104 18.348623853211 0 0 650.362307692308 643.62908301682 635.705941143809 43.6912826207662 46.2045577862181 43.4203296703297 698.9 38 693 38 682 46 697.3 39 693.5 42 678.1 46 689.3 39 683.3 45 668.8 47 692.4 52 673.6 42 666.4 51 674.7 44 659.8 37 650.6 43 656.9 44 652.5 45 637.2 39 647.9 44 644 47 634.1 44 634.5 47 623.5 48 624.2 47 604.6 40 599.6 50 596.4 39 576.9 44 581 58 580 39 3673957 SAN BERNARDINO SNOWLINE JOINT UNIFIED 20.984126984127 .826188620420889 2.08885424785659 16.6796570537802 4.05405405405406 8.42002881844381 53764.1993243243 3823.18035853468 22.5206611570248 10.2484472049689 9.09090909090909 0 662.535723570191 655.407981119931 648.334442523768 51.4716940363008 51.1527569191161 48.2629982668977 701.2 41 702.5 49 680.8 46 694.3 36 700.8 50 675.1 42 691.4 41 696.7 59 673.3 51 696.9 56 682.8 52 671.8 57 679.7 50 668.3 46 660.6 54 664.6 52 662.8 55 648.8 52 658.5 55 651.9 56 644.2 54 641.6 53 618.4 43 628.5 51 612.5 47 598.8 49 606.7 48 584.2 50 575.7 53 596.6 58 3675069 SAN BERNARDINO UPLAND UNIFIED 30.647120055517 7.22630173564753 11.4903204272363 28.4796395193591 11.4563106796116 13.9812068965517 59659.7650485437 3922.56667222964 23.5105551211884 35.3135313531353 6.66666666666667 6.66666666666667 644.531339823829 632.309168925023 634.454106132436 50.2338278024163 44.4777175549533 46.7111729253593 677.6 19 674.3 19 653.2 20 677.6 21 677.1 24 655.7 24 682.2 32 677.1 38 669.7 48 692.3 52 683.8 52 669.1 54 683.2 53 674.4 52 664.8 59 664.5 52 663.2 55 651.6 55 649.7 46 639.2 42 637.7 48 634 46 613.3 38 628.1 50 605 40 586.7 38 600.1 42 576.2 43 561.5 39 588.5 49 3667918 SAN BERNARDINO VICTOR VALLEY 44.9199607971251 1.61820323640647 16.4176328352657 36.1708723417447 15.5339805825243 10.971671388102 61492.8507936508 4057.49705499411 26.5114405414115 13.9705882352941 15 20 648.337447048856 642.959543184761 636.856736065727 40.8697600597517 40.029221379693 36.1985314882801 693.3 32 689.7 35 671.5 36 687.8 30 686.3 34 664.1 31 681.1 31 679.3 40 662.3 40 679.9 39 670.3 39 653.4 38 667.9 38 660.1 37 651.9 44 657 44 660.9 53 647.4 50 643.8 40 637.7 40 632.8 43 627.1 39 614.4 39 622.1 45 600 36 591.1 42 598 40 564.5 33 562.5 40 581.1 40 3667959 SAN BERNARDINO YUCAIPA-CALIMESA JT. UNIFIED 27.5710334422932 .762642262114279 .77437521999296 19.6644374046697 6.06060606060606 11.880487804878 63432.7181818182 3784.39035550862 25.0716245047242 25 0 70 660.963865401208 653.070776566758 645.047559298728 47.8502921966311 48.70810626703 45.8515962036238 710 50 705.8 53 684.8 50 695 36 696.8 46 670.2 37 690.4 40 695.5 57 670.2 48 688.9 48 674.3 43 661.5 46 679.1 49 675.3 53 662.9 56 663.9 51 664.7 57 650 54 654.8 51 643.2 46 640.3 50 638.4 50 621.3 46 628.5 51 607.6 42 589 40 597.3 40 577 44 570.1 47 588.2 48 3773551 SAN DIEGO CARLSBAD UNIFIED 26.9230769230769 2.59466170080695 1.76288019863439 26.1328367473619 13.3333333333333 15.2839285714286 62997.1121212121 4261.82011173184 24.3261749144102 43.4447300771208 0 0 675.131481481482 670.382800941493 660.844547008547 63.8950427350427 65.6696368527236 60.6031260618417 711.9 52 708.8 56 693.3 59 705.9 48 702.6 51 688.3 56 700.2 50 698.9 61 683.5 61 704.1 63 692.2 60 684.5 68 696 66 690.3 68 678 71 678.2 66 687.2 76 660.2 64 671.2 66 672.1 74 659.2 68 658.6 68 646.5 70 644 65 631.4 62 618.5 67 622.8 63 601.5 65 597.6 73 603.9 64 3768031 SAN DIEGO CORONADO UNIFIED 12.8909229595728 1.74927113702624 2.58746355685131 10.4956268221574 8.66141732283464 15.0795454545455 59963.7874015748 4346.54555393586 22.3699421965318 58.8235294117647 0 0 685.812943528236 678.442246520875 668.551767048283 70.2528621204579 71.7176938369781 69.2713643178411 725 66 722.8 70 702.7 68 722 65 713.7 64 697 65 710.7 60 707.6 69 687 65 714.5 73 706.3 73 687.4 72 702.7 72 696.4 73 677.4 72 686.9 74 681.6 72 671.1 74 677.2 72 670.6 74 663.4 72 663.6 72 647.6 71 649 69 638 68 627.3 75 625.1 66 610 72 603.6 78 621.7 81 3768098 SAN DIEGO ESCONDIDO UNION 43.1771475544595 2.75243544147209 1.7975877532086 43.8108860368022 8.99908172635445 14.0709437086093 58398.8970798898 3882.45098422762 23.4224049331963 29.0118577075099 4 44 654.3185102935 648.733153018358 641.556212530857 46.9538027506759 47.8680439661622 42.2829361983074 706 46 699.7 46 683.9 49 699.7 41 695.6 45 677 44 694.7 44 692 54 675.7 54 689.5 49 678.5 47 666 51 676.6 47 669.8 47 659.3 52 656.4 43 661.2 53 647 50 644.2 40 644.3 47 637.8 47 628.1 40 621 45 622.9 46 599.5 35 595.6 46 596.4 39 573 40 570.7 48 581.3 40 3768114 SAN DIEGO FALLBROOK UNION 49.1896468311563 .898770104068117 4.3282876064333 36.2109744560076 10.1408450704225 16.5610126582279 66966.7211267606 4540.89427625355 24.1105354058722 22.4609375 0 0 658.178589678079 649.598358753316 644.485851117459 47.8462443286843 45.9941976127321 44.0805654913984 703.3 43 699.2 45 681.5 47 696.7 39 697.6 46 677 44 692.1 41 694.3 56 675.4 53 693.2 53 684.4 53 670.8 56 685 55 679.4 57 667 61 664.2 52 668.4 60 653.5 57 648.3 44 639.3 42 638.2 48 635.7 47 611.9 37 622.9 46 603.1 38 582.6 34 594.6 37 563.8 32 556.8 34 574.6 33 3768163 SAN DIEGO JULIAN UNION 7.72303595206391 .138121546961326 .69060773480663 9.25414364640884 5 15.9693181818182 62789.975 5092.90745856354 19.0956072351421 32.0754716981132 0 0 674.98523364486 667.565988909427 655.901663585952 55.1293900184843 57.9334565619224 54.8616822429907 706 46 702.5 49 682.9 48 696.3 38 699 48 666.4 33 686.2 35 686.4 48 664.4 42 719.1 76 700.6 69 687.2 72 694.8 65 692 70 681.4 76 678.8 66 678.1 70 659.4 63 665 61 647.6 52 650.2 60 657.1 67 638.1 62 640.8 61 620.4 54 605.2 56 606.9 49 588.7 54 585.4 63 596.3 58 3768213 SAN DIEGO MOUNTAIN EMPIRE UNIFIED 48.6942328618063 .163220892274211 1.4689880304679 25.2992383025027 4.3010752688172 12.1626213592233 52084.3978494624 4470.92056583243 20.1787709497207 40.8695652173913 0 0 655.573507462687 648.464296296296 641.53604826546 44.1809954751131 44.2 41.234328358209 692.9 33 687.6 33 669.6 34 691 33 693.3 41 670.5 37 683.4 33 690.3 52 662.7 41 687.5 47 669.8 38 660 45 673.3 43 663.5 41 657.1 50 660.4 47 658.5 51 642.3 45 645.9 42 638.3 41 636.9 46 637.8 49 614 39 626.5 49 602 37 593 44 600.1 42 582.4 48 580.2 58 591.2 51 3773569 SAN DIEGO OCEANSIDE CITY UNIFIED 54.9773988083008 1.80425939219909 14.8600143575018 41.5218951902369 16.013437849944 13.1737055837563 57811.1422172453 3832.5821009811 23.3502479529466 44.7178002894356 0 0 640.598977391304 634.624421296296 630.728302937491 39.543366281498 37.8617919389978 34.5817739130435 690.5 30 690.9 36 671 36 684.5 26 688.2 36 662.9 30 678.3 29 680.1 41 662.8 41 684.3 44 669.9 38 663.5 49 667.3 37 659.9 37 652.7 45 653 40 652.3 44 641.6 44 639.4 36 633.6 36 630.8 41 622.9 35 610.4 35 618.8 42 594.4 31 583.1 34 591 34 567.1 35 563.4 41 576.4 34 3768296 SAN DIEGO POWAY UNIFIED 10.5881569750025 8.75586330131785 2.76013912377549 7.79220779220779 11.0151187904968 14.1915538362347 58198.62275018 3718.16908644181 22.8757095906782 46.6887417218543 0 0 681.429950602305 677.907482088464 667.726259878223 70.3265967094183 72.5113475481574 66.1724152872866 716.6 57 721.2 69 698.6 65 711.9 54 713.2 63 693 61 706.7 56 713.1 74 691 68 711.1 70 704.7 72 687.3 71 701.8 71 696.1 73 682.1 76 684.5 72 687.1 76 667.1 71 678.7 73 674.4 76 666.8 75 667.2 75 648.9 72 653.8 73 638.2 68 629 76 632 71 602 66 598 74 610.5 72 3768304 SAN DIEGO RAMONA CITY UNIFIED 35.0236546422235 .704225352112676 .876688703650474 19.6177062374246 7.49185667752444 13.5145161290323 57168.1302931596 3976.02960620868 23.1159175397094 39.7368421052632 0 0 670.183532751977 661.177163940965 652.80687851971 55.8986323411102 56.5917431192661 55.3364429121882 707 47 701.4 48 684.7 50 702 44 697.5 46 678.8 46 694.1 43 693.2 55 672.9 51 701.8 61 684.7 53 672.1 57 685.8 56 672.1 50 661.1 55 674.9 63 676.6 68 658.1 62 662.7 59 657.9 61 646 56 653.6 64 633.7 58 640.3 62 626.2 58 609.9 60 619.3 60 592.9 58 586.4 64 597.3 59 3768338 SAN DIEGO SAN DIEGO CITY UNIFIED 64.1523473458116 9.49274671088837 16.9243412604654 35.2773273262256 25.0849652047257 12.2959692898273 62782.7030263797 4952.58531878518 22.193133902188 31.2403234130397 4.16666666666667 28.5714285714286 650.53792231777 646.218283229981 639.276522675464 45.5376015212436 46.3890985890177 40.3608356899636 699.9 39 700.5 47 682.1 47 691.5 33 694.8 43 671.8 39 686.7 36 689.2 50 670.9 49 684.1 44 674.6 43 662.2 47 671.1 41 667.8 45 657.2 50 656.6 44 656.5 49 644.7 48 645.9 42 641.8 46 636.7 47 628.2 41 616.1 43 621.9 46 602.2 39 595.1 47 597.9 40 573.3 43 570.6 50 581.7 43 3773791 SAN DIEGO SAN MARCOS UNIFIED 49.2650415411303 2.38978517815547 2.55370546113364 44.1376930377017 11.0647181628393 13.668029739777 60657.1607515658 4097.09248554913 24.6399825973461 29.6551724137931 0 83.3333333333333 653.268111815711 651.195564163439 642.022429078014 48.4894883485309 51.2722728976634 43.4528277468617 702.4 42 699.2 46 681.9 47 688.3 30 693.6 42 669.6 36 681.7 32 689.4 50 668 46 684.6 44 677.1 46 662.2 47 670.4 40 673.8 51 658.4 51 663.5 51 671.2 63 651.3 55 653.3 50 648.9 52 642.3 52 638.9 50 626.2 51 631.3 54 613.2 47 606.7 57 607.6 49 578.4 44 573.8 51 585.4 45 3768452 SAN DIEGO VISTA UNIFIED 40.0597659168258 2.53261999153227 6.12755475154921 37.3850121242446 11.0909090909091 12.812450748621 65164.4454545455 4103.44648012009 23.6444866920152 34.3324250681199 3.7037037037037 81.4814814814815 653.530254976898 645.181577922439 640.063129506615 45.9587804462613 45.2737439492572 43.5388739946381 701.8 41 698.1 44 678.7 44 694.3 36 693.3 42 668.6 35 690.3 40 690.6 52 671.1 49 688 48 673.6 43 664 49 678.9 50 668.2 46 659.4 53 659.8 47 655.6 48 643.4 47 647.3 44 639.6 44 637 47 632.1 45 614.6 43 623.3 47 602 41 589.2 43 596.9 41 570.2 42 564.7 47 583.3 46 3868478 SAN FRANCISCO SAN FRANCISCO UNIFIED 64.5542826673561 41.3690232268428 16.1932237284246 21.2041241169046 43.7374605904271 15.2854677206851 52885.5067354543 5230.05073188323 17.9311759878063 48.8306565229642 2.65486725663717 1.76991150442478 658.739665223665 661.291423925138 647.996224272947 50.8723181275654 56.3745114262075 43.9237806637807 704 44 717.9 64 689.9 56 695.9 37 707.5 56 678.6 46 692.4 42 703.8 65 679.5 57 688.1 47 688.1 56 667.1 52 676.5 47 680.5 57 662.5 56 656.4 43 664.2 56 644.8 48 649.3 45 651.7 55 640.3 50 633.7 46 625.1 50 628 50 604.2 39 601.1 51 602.4 44 584.9 51 579.2 56 590.4 51 3968502 SAN JOAQUIN ESCALON UNIFIED 36.4128595600677 .919842312746386 .328515111695138 26.6425755584757 5.67375886524822 14.1924528301887 53510.4397163121 3846.51215505913 23.3205521472393 26.4150943396226 0 0 661.910785749146 654.90570754717 649.375467177767 52.7709631049353 50.8150943396226 47.4553440702782 700.7 40 700.3 47 680.1 46 690.4 32 694.5 43 670 37 695.8 45 692.5 54 677.1 55 698.4 57 684.9 54 673.9 59 678 48 667.7 46 661.4 55 666.6 54 663.9 56 653.1 57 658.4 55 647.8 51 648.8 59 640.4 52 629.4 54 632.9 55 605.9 41 598.9 49 599.2 41 585.9 51 576.8 54 600.1 63 3968569 SAN JOAQUIN LINCOLN UNIFIED 44.1298342541436 19.6276905177429 10.6922629435718 17.6265270506108 14.7342995169082 13.2013742071882 53970.809178744 4022.26410703898 21.5606361829026 29.8353909465021 0 0 653.120987453153 648.215219123506 643.269463793941 46.5541875911226 43.7188844621514 39.3379501385042 693.6 33 699 44 681.3 47 686.2 28 695.7 44 672.1 39 680.5 31 688.7 50 673.6 52 690.7 50 676.9 46 667.6 53 672.9 43 668.6 46 658.2 51 661.5 49 658.3 51 645.3 48 650.1 46 639.1 42 637.8 48 628.1 40 613.3 38 623.8 47 602.9 38 588.3 39 597.7 40 567 35 560.3 37 581.9 41 3968577 SAN JOAQUIN LINDEN UNIFIED 40.5828621139626 1.93602693602694 .336700336700337 34.2171717171717 7.20720720720721 16.1252032520325 52198.8558558559 3727.90909090909 22.0265151515152 39.2857142857143 0 0 652.678872403561 644.666724237191 639.678812572759 40.9796274738068 38.2941853770869 36.4160237388724 692.7 32 694.7 40 672.1 37 690.1 32 687.3 35 666.3 33 683.1 33 685.2 47 664.4 42 682.6 42 666.9 36 658 42 675.1 45 663.4 41 656.3 49 651.5 38 649.1 41 640 42 638.8 35 630.9 33 635.2 45 629.3 41 609.9 35 622.7 46 597.9 34 582.7 34 593 36 560.9 30 563.5 41 578.1 37 3968585 SAN JOAQUIN LODI UNIFIED 48.7686274509804 19.7954739870908 5.83249086243098 23.6488062835368 11.271186440678 15.083598265896 59465.2338983051 4469.02146356637 22.0029152019206 30.3370786516854 0 62.8571428571429 646.503272085196 639.40676819852 635.370433124489 39.6596022882049 37.8718249108046 34.3092203277603 693 33 697 44 675.4 40 685.1 28 693.5 44 664.9 32 683.1 34 687.4 49 668.3 47 682.2 42 669.2 39 659.8 45 667.1 39 662 40 653.5 47 651.9 39 645.5 39 639.1 42 635.7 33 627.1 32 627.5 38 619.5 34 602.9 32 616 41 588 29 573 29 586.2 31 555.8 31 548.3 32 570.1 33 3968593 SAN JOAQUIN MANTECA UNIFIED 35.1331654962237 2.4496294935391 4.93600342948129 29.0954743095107 11.3263785394933 13.8097754293263 56910.6646795827 3589.19701145202 24.385775862069 36.2426035502959 0 0 653.420248760546 648.740296094614 639.964641555286 45.0128449921889 47.2959244448226 41.5854570757589 698.5 38 701.1 48 676.5 41 691.2 33 697.3 46 666.7 34 690.1 39 695.3 57 670.2 48 690.4 50 681.4 50 663.5 48 676.4 46 674.2 52 658.5 52 657.7 44 662.3 54 645.4 49 647.4 44 639.9 43 637.6 48 629.5 42 619.4 44 621.9 45 604.5 40 591.6 42 598.1 40 570.8 38 562.4 39 583.1 43 3968650 SAN JOAQUIN RIPON UNIFIED 13.773987206823 .730816077953715 .48721071863581 21.8432805521721 2.56410256410257 14.5503875968992 51683 3786.43036946813 20.5988023952096 29.7297297297297 0 0 665.134521880065 664.102466487936 654.637203023758 59.0599352051836 61.3678284182306 52.5834683954619 699.4 39 696.3 43 674.3 39 695.8 37 695 44 671.6 39 689.1 39 692 54 670.1 48 703.2 62 701.8 70 683.2 68 687.2 57 690.3 68 670.8 65 664.4 52 676.8 68 661.1 65 657.4 54 654.2 58 650.6 61 650.9 62 638.3 63 641.3 63 625 57 619.4 68 625.4 65 597.2 61 594.1 70 609.5 69 3968676 SAN JOAQUIN STOCKTON CITY UNIFIED 68.7270837543667 20.8724926357133 13.3482956936457 42.1854397531211 33.7475474166122 12.677675070028 66654.75147155 4527.22617477907 22.9404855129209 18.2092555331992 0 21.9512195121951 626.294401667588 625.040340654415 618.696566775791 27.7514060270293 29.0440487347704 22.2195516229208 683.5 24 686.3 31 666.9 32 674.3 18 683.1 30 655.5 23 668.5 21 677.4 38 657.9 36 664.5 26 660.9 30 642.7 28 647.7 21 650.1 27 636.5 28 638.3 26 638 31 628.6 31 620.6 20 618 23 615 26 603.1 20 595 23 602.9 29 575.9 19 573.8 28 577.2 23 554.5 27 552.7 33 565.1 25 3975499 SAN JOAQUIN TRACY JOINT UNIFIED 26.5703275529865 4.43653412050703 5.98194130925508 26.7233894773398 14.19624217119 10.5934981684982 55631.8997912317 3616.36638305261 23.96259626 26.2867647 0 0 657.620744108147 652.641071428571 645.445892687559 48.9863485280152 48.9051926691729 43.6359612393827 700.1 39 701.8 48 679.5 45 694.9 36 696.3 45 672.4 40 690 39 693.5 55 673.3 51 693.1 52 685.9 54 666.6 52 678.2 48 675.3 53 661.4 55 664.1 51 662.1 54 648.9 52 653 49 645.6 49 646.7 57 633.6 45 620.3 45 628.4 51 607 41 593.8 44 603 45 572.7 40 567.4 44 584.4 44 4068700 SAN LUIS OBISPO ATASCADERO UNIFIED 18.0736636245111 1.06577851790175 1.64862614487927 9.34221482098251 3.28467153284672 14.9725806451613 60464.2408759124 4506.42464612823 23.304248861912 45.4545454545455 0 0 678.001860465116 667.072236267525 661.377217835578 63.4382257315374 61.4366812227074 61.55 717.6 58 709.6 57 698.5 64 711.2 54 703.9 54 693.1 61 706.4 56 704.8 67 687.5 65 706.3 65 696.8 65 679.6 65 694.3 64 680.7 59 669.7 64 680 68 675.9 68 662.8 67 668.5 64 655.1 59 651.6 62 654.6 65 634.7 59 642.5 64 628.8 61 609.9 60 618.5 59 597 61 588.6 66 601.4 63 4075465 SAN LUIS OBISPO COAST UNIFIED 27.0877944325482 1.00502512562814 .301507537688442 21.608040201005 9.23076923076923 12.8641891891892 46347.3846153846 4792.46130653266 17.3713235294118 41.3333333333333 0 0 675.78129395218 659.928201634877 656.442441054092 53.619972260749 46.7506811989101 52.4669479606188 709.9 50 705.2 52 682.7 48 698.4 40 696 44 673.6 41 698.5 48 693.3 54 674.4 52 701.2 61 681.6 51 681.1 66 687.4 57 664.2 42 670.9 65 679.2 67 668.5 61 664.2 68 669.3 65 640.7 42 647.6 58 649.9 61 610.4 35 629.9 53 617.3 50 598 48 606.7 48 566.6 35 554.9 32 587.2 48 4068759 SAN LUIS OBISPO LUCIA MAR UNIFIED 38.0746712736347 1.53803131991051 1.16517524235645 30.2013422818792 8.93203883495146 13.4831597222222 55684.2582524272 3887.0884601044 21.2192704203014 25.9036144578313 0 0 668.336747668833 660.265566037736 653.880173742469 57.6306571388539 56.7943951165372 54.3489686352077 707.1 47 704.4 51 686.4 52 702.3 44 699.7 49 680.5 48 699.2 49 700.5 63 678.3 57 705.7 65 694 63 679 64 687.6 58 682.6 61 669.8 64 674.2 62 673.1 65 657.9 62 659.8 56 651.7 55 648.7 59 644 55 625.2 50 634.1 56 616.7 50 599.2 50 609.7 51 590.3 55 581.7 59 600.4 61 4075457 SAN LUIS OBISPO PASO ROBLES JUSD 36.8269067436725 1.33048620236531 3.87647831800263 27.1846254927727 5.80204778156997 14.2278425655977 59653.0341296928 4756.44152431012 22.2814653608995 39.8148148148148 0 0 660.040743405276 650.914003759399 646.067205208585 49.2531950807813 47.344219924812 45.636690647482 705.3 45 700.3 48 683.6 49 701.2 44 697.8 50 679.4 47 694.3 44 694.7 57 676 55 695 55 681.4 51 668 54 677.5 48 672.1 50 659.2 53 662.2 50 656.8 50 645.3 49 649.2 46 638.8 43 636.4 46 633.6 46 610.3 38 625.9 50 603.8 41 589.8 43 599.7 43 566 38 561 42 583.4 46 4068809 SAN LUIS OBISPO SAN LUIS COASTAL UNIFIED 22.4657866053046 2.34638169357649 2.0908351724939 12.6147055407132 5.32687651331719 16.19522417154 64397.6755447942 4933.22348704844 21.4332659251769 25.635593220339 5.26315789473684 0 683.558106585091 673.451621621622 664.669967373573 66.636541598695 67.3852146263911 66.9017871518274 725.5 66 722 69 698.4 65 718.1 61 712.2 62 690.7 59 710.2 59 711.6 72 688.4 66 707.2 66 695.1 64 683.8 68 693.4 63 689.3 67 674.7 69 685.6 73 686.4 76 667.8 71 678.7 73 661.7 65 660.1 70 664.6 73 642.7 67 646.1 67 638.3 68 612.8 63 622.5 63 605.6 68 591.8 68 606.8 68 4068841 SAN LUIS OBISPO TEMPLETON UNIFIED 17.9978118161926 .617828773168579 1.41218005295675 9.22330097087379 4.58715596330275 11.0872950819672 50372.4678899083 3667.01941747573 19.3939393939394 35.9550561797753 0 0 673.663157894737 660.333891213389 658.17453565009 60.5056920311564 54.7489539748954 57.9606775559589 709.6 50 697.2 44 682.5 48 708.7 51 699.4 48 680.4 48 706 55 700.4 63 680 58 701.5 61 684.9 54 678.4 64 691.1 61 685.3 63 672.2 67 683.5 71 670.3 62 665.1 69 662.6 59 650.1 54 657.7 68 655.3 66 628.5 53 647.6 68 615.7 49 595.5 46 611.8 54 593.7 59 583.1 61 599.9 62 4168890 SAN MATEO CABRILLO UNIFIED 19.3020719738277 2.1085111853947 .539984571869375 25.0964258164052 8.29015544041451 13.3228971962617 51768.2660621762 4138.21131653381 21.0093252879868 48.8095238095238 0 0 670.851140684411 658.191318803103 654.942991356633 58.6384817737693 54.8485408200961 56.5441064638783 717.4 58 716.1 63 695.1 61 700.3 42 698.2 47 677.9 45 705.5 55 700.2 62 685.8 63 701.8 61 684.9 53 682.4 67 693.7 64 684.3 62 670.4 65 673.8 61 665 57 654.8 59 659.7 56 652.6 56 647.2 57 649 60 624.5 49 638.8 60 621.9 54 603.7 54 611.6 53 583.7 49 569 46 591.1 52 4169070 SAN MATEO SOUTH SAN FRANCISCO UNIFIED 31.9148936170213 8.75331564986738 4.82365654779448 34.7971313488555 11.8993135011442 17.1086138613861 57489.2173455378 3823.83839375184 23.5231970601746 38.9544688026981 0 0 657.938155779629 651.254546756409 645.310129153969 49.4857059933246 48.261205785479 44.5271079935701 699.2 38 699.5 46 681.2 47 697 39 701.6 50 680.4 48 692.1 41 693.5 55 679.2 57 683.6 43 675.7 44 658.8 44 674.8 45 670 47 657.1 50 662.1 49 658.5 51 647.2 51 653.9 50 646.9 50 643.9 54 633.1 45 618.6 43 626 49 609.2 44 604.1 54 606.4 48 584.1 50 565.9 43 586.6 47 4269146 SANTA BARBARA CARPINTERIA UNIFIED 39.2772711921648 1.38530927835052 .966494845360825 54.3814432989691 21.3235294117647 14.4977419354839 60262.9117647059 4158.06894329897 22.2132943754565 33.3333333333333 0 0 657.955866983373 652.616290842154 647.817581395349 50.293023255814 47.7096180395766 42.8688836104513 696.3 35 694.9 40 679 44 692.5 34 692.8 41 679.9 47 679.9 30 690.1 51 671.4 49 689.5 49 679.8 48 671.4 56 669.7 40 668 45 658.4 50 658.7 46 654.8 47 646.3 49 652.3 48 644.3 47 640.7 51 643.5 55 629.1 54 635.9 58 601.5 37 597.9 48 596 39 587.3 53 577.1 54 596.5 57 4269229 SANTA BARBARA LOMPOC UNIFIED 35.7063300538333 4.65199180108725 8.92077354959451 32.8669459049996 9.0573012939002 17.0831674958541 53117.7153419593 4325.81900008912 21.6737698566447 42.914979757085 0 13.3333333333333 652.84358281893 640.795576875156 636.877135995956 42.2170121334682 39.9328432594069 41.5646862139918 700.8 40 702.2 48 679.7 45 693.2 35 692.1 40 669.9 37 683.4 33 687.1 49 666.5 44 688.6 48 673.9 42 661.6 46 679.5 50 665.9 43 657.5 50 660.6 48 650.1 42 641.2 44 648.2 44 637.4 40 633.7 44 631.3 43 611.2 36 619.5 43 603.4 38 584.3 35 594.3 37 569.2 37 551 29 575.5 34 4369484 SANTA CLARA GILROY UNIFIED 44.5420326223338 2.30897936419409 1.5281650864473 62.9447852760736 22.3719676549865 15.4896551724138 62890.8086253369 4312.72950362521 24.507874015748 20.1900237529691 0 15.3846153846154 650.112128626354 639.755846077948 637.302743902439 42.6212737127371 39.8852162473277 39.2345333799371 696.2 35 697.8 43 677.1 42 689.4 31 693 41 669.1 36 684.7 34 690.3 52 670.3 48 682.4 42 668.5 37 657.6 42 668.8 39 660.9 38 650.9 43 660.5 48 658.6 51 647.7 51 644.2 40 634.9 37 633.4 43 626.8 39 609 34 617.6 41 599.2 35 582.8 34 594.6 37 579.7 46 559.4 36 583.3 42 4373387 SANTA CLARA MILPITAS UNIFIED 28.8935721812434 31.4598833701991 5.95214156444802 18.4295194047858 20.2272727272727 14.806374501992 60774.9181818182 4090.01588578323 22.1197740112994 26.7206477732794 0 0 662.136157941437 661.338135961734 648.919647918842 53.8408175443831 58.4918104073054 49.6240757172434 696.3 36 706.9 54 680.7 46 693.2 35 702.6 51 675.2 42 688.1 38 697.6 59 674.4 52 696.1 55 688.9 57 669.2 54 690.5 60 691.5 68 672.6 67 672.2 60 674.4 66 660.3 64 658 54 656.5 60 649 59 641.3 53 630.7 55 633.3 56 613.6 47 605.7 56 607.3 49 588.8 54 581.3 58 587.2 48 4369583 SANTA CLARA MORGAN HILL UNIFIED 23.6490993995997 5.23565792279804 2.39923224568138 33.4612923864363 11.25 17.7960850111857 60283.845 4361.51503518874 23.7371134020619 37.8048780487805 0 0 666.216356483583 659.320322915124 649.390683043739 52.9802276812463 55.3657235965042 51.7793917385384 707.5 47 710.7 58 684.1 50 696.6 38 703.4 52 673.1 40 701 50 707 68 678.9 57 698.3 58 688.3 57 673.5 58 684.9 55 677.9 56 663.2 57 668.9 57 668.2 59 651.2 55 653.9 50 645.1 48 641.2 51 644.9 56 625.6 50 632.6 55 618 51 604.1 54 610.1 52 588.8 54 573.5 51 593 53 4369641 SANTA CLARA PALO ALTO UNIFIED 7.89443488238669 17.3890007417612 4.91681678499523 6.8877821341528 13.528336380256 14.1157512116317 66182.0091407678 5970.82324891385 18.7476866132017 73.3752620545074 0 0 698.751069604087 698.750649145028 682.214690885915 81.8879859783302 86.2845155161495 81.1083971902937 739.9 79 754.8 89 719.9 82 730.8 74 745.8 86 710.1 76 729.9 77 743.6 90 708.6 81 724.1 80 731 87 706.7 85 714.6 81 719.1 87 695.7 85 702 85 711.2 89 681 81 697.4 86 694.1 88 682.4 85 686 86 662.7 82 663.7 80 660.4 82 639.3 82 647.4 82 622.7 80 613.5 84 624.1 82 4369666 SANTA CLARA SAN JOSE UNIFIED 42.8078358208955 12.2450216712636 3.27039068893402 49.4104810111236 22.3695111847556 16.5772932330827 80565.0364540182 5038.39963022459 28.2108543235523 38.1075826312378 0 0 655.468175303962 647.038183888913 641.538434616608 45.6173810281351 45.3295807963555 42.4182885673339 704.3 44 706.1 52 685 50 694.2 36 699 47 675.4 42 688.9 38 695.1 56 673.3 51 687.1 47 680 48 662.9 47 674 44 669.5 46 657.1 49 657.6 45 653.9 46 641.8 44 649.3 45 640.3 43 639.3 49 631.9 44 614.4 39 623.1 46 606.8 41 590.8 41 600.2 42 574 40 561.4 39 580 38 4369674 SANTA CLARA SANTA CLARA UNIFIED 40.20545013554 18.3941204753074 4.34095748334364 25.2009066556769 11.52 15.6099728629579 65468.92 4317.31904663782 23.7628014535844 29.5511221945137 0 0 655.844454600853 649.07458432304 643.911948427254 51.5725241747249 50.1679532858274 45.9975624619135 696.6 36 701.6 49 680.5 46 690.4 33 696.9 49 673.8 41 686 36 691.1 53 676.8 56 689.4 49 683.1 52 663.4 48 679.8 51 676.6 54 661.9 56 661.9 49 656.6 50 647.5 51 654.6 51 644.4 49 646.2 56 638.6 51 618.5 46 631.9 55 612.9 51 595.7 49 607.1 50 578.2 49 567.8 51 589.2 53 4469799 SANTA CRUZ PAJARO VALLEY JOINT UNIFIED 56.3486693730266 1.20137602540355 .587456999206139 72.0137602540355 19.7166469893743 14.11421107628 59922.1286894923 4282.14601746494 22.5352977286679 26.647564469914 4 20 640.186193505742 639.3257624461 628.568499879217 32.5221837507046 37.3257546060368 28.6157151119557 690.3 30 690.3 35 668.8 33 682.8 25 689.1 37 660.8 28 677.9 28 682.3 43 661.3 39 676 36 668 36 653.8 38 660 31 660.2 37 645.9 37 644.4 31 647.1 39 633.4 35 631.4 28 634.5 37 622.7 33 611.3 26 608.3 33 605.8 30 585 24 586.2 37 583.8 27 557.8 27 561.9 39 567.2 25 4469807 SANTA CRUZ SAN LORENZO VALLEY UNIFIED 14.506396331161 1.65905265688868 1.22625631161337 4.08752103871123 3.33333333333333 17.2891625615764 58616.2833333333 4262.08776148113 24.3335290663535 60.5809128630705 14.2857142857143 0 676.616056910569 662.310665329321 658.5132996633 60.0437710437711 55.7355399531929 59.2906504065041 714.7 55 706.8 54 689.2 55 704.1 46 696.8 46 679.9 47 701 50 698.3 61 681.6 59 707 66 687.2 56 681.2 66 692.1 62 666.9 45 673.7 67 680.7 68 676.7 68 661 65 672.6 68 659.9 63 657.3 67 654.8 65 627 51 638.8 61 627.1 59 611 61 612.5 54 590.3 55 573.8 51 597 59 4569989 SHASTA FALL RIVER JOINT UNIFIED 39.608938547486 .747126436781609 .28735632183908 8.9080459770115 4.3010752688172 16.4912037037037 56131.9429032258 4741.87406896552 18.7363834422658 28.8288288288288 0 0 662.603135313531 651.091162420382 643.225383993533 44.8334680679062 45.3734076433121 45.9117161716172 701 40 700.6 47 675.5 41 696.1 37 693 42 667.9 35 690 39 690.6 52 667.7 46 692.3 52 674.4 43 667.2 52 673.7 44 665.6 43 650.2 42 659.4 47 664.9 57 646.5 50 658.9 55 643.7 47 637.2 47 647.9 59 621.4 46 633 55 611.3 45 584.7 36 598.3 40 577.1 44 564.2 41 579.8 39 4575267 SHASTA GATEWAY UNIFIED 49.3771234428086 .84053233714686 1.07401354190988 4.80971281811814 4.09090909090909 14.5858299595142 55396.6149090909 4509.5146509456 20.7381288199342 34.4827586206897 0 0 653.934816312774 639.410946555055 634.704659498208 38.2245030954708 35.9034127495171 38.7081226828446 686.8 27 689.2 34 664.7 30 689.2 31 694.1 41 663.2 30 687.6 37 682.7 44 665.4 44 688.8 48 672.6 41 660.2 45 670 40 656.4 34 652.9 45 658.1 45 652 44 640.7 44 645 41 627.9 31 629 39 630.8 43 604.7 30 618.4 41 606 41 583.4 35 591.9 35 561.9 30 547.5 26 570.3 28 4670177 SIERRA SIERRA-PLUMAS JOINT UNIFIED 32.1016166281755 1.63316582914573 .628140703517588 8.35427135678392 1.9607843137255 14.4036363636364 54122.0196078431 2908.31155778895 16.8888888888889 43.2432432432432 9.09090909090909 0 672.277259752617 658.577871939736 653.042403846154 54.8115384615385 52.5866290018832 55.6641294005709 705.5 45 694.5 41 680.2 45 707 49 697 46 680.6 47 697.2 47 690.7 53 675.4 54 706.7 65 692.3 61 678.7 63 688.6 59 676.7 55 662.8 55 673.1 61 662.3 55 653.7 57 662.3 58 652.3 55 648 58 644.7 56 627.2 52 637.4 59 625 57 604.6 54 613.3 55 590.3 56 574.4 52 592 54 4870524 SOLANO BENICIA UNIFIED 11.443661971831 4.56211066324661 7.80505679736167 10.1502381824844 10.1626016260163 15.1756183745583 61419.337398374 4303.05734701356 22.2794428028704 40.4907975460123 0 0 681.573438639125 671.828107718202 664.123567802756 66.4998791394731 66.8773743688387 65.7105710814095 715.6 55 713.5 61 691.8 58 708.5 51 709.3 58 683.5 51 708.4 58 707.6 69 687.6 65 714.7 73 701.7 70 689.3 73 700.2 70 692.2 70 679 73 681.3 69 671.1 63 660.9 64 676.2 71 669.6 72 666.3 74 667.5 75 645.9 70 649 69 638.2 67 617 66 625.5 66 605.4 68 592.8 69 610.6 71 4870532 SOLANO DIXON UNIFIED 40.8194233687405 1.3169195533925 1.77497852848554 39.965645576868 12.6436781609195 15.5133333333333 57074.224137931 4262.56283996565 20.0236966824645 39.6648044692738 0 16.6666666666667 655.642246903033 647.059762100082 639.990394295302 42.4987416107383 43.0689089417555 40.5057667663392 696.4 36 699.5 46 672.7 37 689.9 32 695.7 44 664 31 687 36 687.6 49 664.3 42 679.2 39 674.1 43 657 41 670.8 41 670.6 48 654.9 47 661.6 49 655.5 47 647.6 51 647.9 44 634.6 37 634.3 44 632.9 45 613.6 38 623.5 46 607.8 42 589 40 600.1 42 574.8 41 561 39 583.5 42 4870540 SOLANO FAIRFIELD-SUISUN UNIFIED 28.1108624220201 6.54867256637168 21.0107126222636 18.0158360503027 13.2415254237288 15.9655090390105 59268.9862288136 3955.48863530508 22.8889128791968 26.1160714285714 0 34.6153846153846 653.017221528645 645.545050167224 640.046989921003 46.3172160174339 46.2223411371238 43.3465806630601 696.2 36 696.8 44 676.9 42 691.1 33 695.3 47 672.1 40 686.4 37 686.7 48 671.9 51 687.3 47 678.3 47 662.4 47 673.7 45 668.6 47 658.6 52 660.7 48 657.3 50 645.1 49 651.9 49 640.9 46 637.2 47 634.1 47 619.1 48 624.3 49 604.4 43 586.7 41 596.9 41 570.5 43 561.1 44 580 43 4870565 SOLANO TRAVIS UNIFIED 22.7124538143882 2.52947481243301 15.3483386923901 10.3965702036442 18.2203389830508 15.1208487084871 62353.6016949153 4900.85551982851 20.2287440656021 66.3101604278075 0 0 666.224245224892 651.059872804361 649.958239070621 55.8654845612962 50.3058752271351 55.0418977202711 705.8 46 700.1 46 685 50 698.3 40 695 44 671.8 39 701 50 697.9 60 675.7 54 697.6 57 681.6 50 665.7 50 690.7 61 679.7 58 665.2 59 672 60 661.9 54 655.3 59 662.8 59 644.2 47 653.5 64 649.3 60 624.4 49 641.7 63 620.9 54 596.9 48 613.8 55 592.9 58 569 46 598.4 60 4870573 SOLANO VACAVILLE UNIFIED 23.0192416525184 2.82936052400567 8.37328651495712 16.7465730299142 9.12981455064194 14.6227443609023 53901.8758915835 4110.53933418867 22.3814655172414 40.2614379084967 5.26315789473684 10.5263157894737 663.389330329715 654.597166998012 649.498064321 52.7524952112108 50.7088469184891 49.3357567069425 706 46 704.7 52 685.1 51 698.1 40 700.1 49 676.1 43 695.9 45 696.9 59 676.4 54 694.6 54 680.2 49 665.8 51 678.7 49 668.6 46 659.2 52 667.8 55 666.9 59 656.3 60 658.2 54 647.8 51 647.5 58 643.1 54 621.2 46 633.9 56 617.5 51 600.4 51 610.9 53 577.3 44 567.5 45 589.1 49 4870581 SOLANO VALLEJO CITY UNIFIED 35.4608814127758 3.83958186752437 35.2145944316012 15.5493014373304 23.8767650834403 16.6808544303798 68338.8023106547 4364.92657553523 25.6707959936742 20.0227531285552 0 56 644.687722460014 638.51153312395 632.704359794658 38.5993601666543 38.9284931706961 34.8631073064504 693.7 33 693.7 40 675.2 40 687.1 29 691.8 41 666.1 33 680.4 31 687 48 665.6 44 677.6 37 668.4 37 653.5 38 661.2 32 656.4 34 648 40 654.3 41 654.6 47 639.4 43 640.5 38 631.1 35 629.4 40 623.1 37 609 37 616.1 41 594.9 34 581.4 35 588.8 33 563.3 36 555.2 37 573 35 4970656 SONOMA CLOVERDALE UNIFIED 39.7086763774541 .429447852760736 .674846625766871 25.0920245398773 6.32911392405063 15.6294117647059 56854.0379746836 3942.01042944785 20.4216073781291 43.8356164383562 0 0 657.034640522876 649.894646098004 639.369549218031 43.8408463661454 47.7223230490018 44.2362278244631 708.5 48 702.7 50 677 42 703.2 45 696 45 674.8 42 693.4 42 696.2 58 673.4 52 689.4 49 678.9 48 664.6 50 679.8 49 671.4 49 653.2 45 663.9 51 674.4 66 648.5 52 644.5 41 641.3 44 628.6 39 635.8 48 613.7 38 624.9 48 600.9 36 588.5 39 592.7 35 566.5 34 562.9 40 576.7 35 4973882 SONOMA COTATI-ROHNERT PARK UNIFIED 19.6758386732002 3.86396879570941 3.64456362749878 12.8473915163335 5.99455040871935 15.7177884615385 59484.6839237057 4081.60153583618 22.2929762230118 14.4508670520231 0 0 663.548165536429 651.999588053553 648.170283428969 53.6576247609112 50.8764160659114 51.4564423578508 705.4 45 704.9 52 687.3 53 698.6 40 699.4 48 677.2 44 694.3 44 694.2 56 676.1 54 698.3 57 684.1 53 672.6 58 688.9 59 676.1 54 667.8 61 666.4 54 664.4 57 649.7 53 661.9 58 652.3 56 650.5 61 646.2 57 623 47 632.8 55 614.4 48 594.8 46 604.6 47 581.1 47 562.6 40 587.9 48 4975390 SONOMA HEALDSBURG UNIFIED 21.4638665843113 .878378378378378 .439189189189189 34.0540540540541 14.2857142857143 16.4645251396648 56837.3506493507 4968.96756756757 20.9132720105125 46.484375 0 0 667.173062015504 657.896311858077 648.388740458015 46.6307251908397 48.2278244631186 45.6545542635659 701.9 42 698.7 46 682.4 48 693.4 36 696.6 48 675 43 689.7 40 691.1 53 668.2 47 693.8 54 690 59 665.6 51 683.8 55 670.5 49 662.4 56 664.9 53 658.1 52 646.5 50 656.2 53 645.6 50 637.8 48 635 47 616.1 44 622.4 46 598.7 37 584.7 38 593.2 37 571.2 42 557.4 38 573.5 35 4970953 SONOMA SONOMA VALLEY UNIFIED 30.7603241991509 1.92343956251179 .716575523288705 22.4024137280784 6.15384615384616 15.8586092715232 54122.3423076923 4170.56835753347 21.1476725521669 23.3716475095785 11.1111111111111 11.1111111111111 664.450211416491 653.566589625064 648.412269300754 52.0428905640759 50.2734976887519 50.3742071881607 708.2 48 706.5 53 684 49 700.7 43 697.5 46 677.4 44 700 49 698.2 60 678.3 56 694.3 54 685.6 54 671.9 57 682.4 52 675.5 54 664.9 59 667.8 55 666 58 653.7 58 661.1 57 649.1 53 646.2 56 644.5 56 616.9 41 629.7 52 609 43 591.9 43 599.6 41 577.9 44 564.1 41 587 46 4975358 SONOMA WINDSOR UNIFIED 33.5580524344569 1.37909898866074 .858106037388906 26.5093472264787 17.5675675675676 11.0236363636364 50731.472972973 3834.78639288998 21.0230547550432 0 0 0 654.652495621716 641.131451275617 638.921030756442 48.0448877805486 45.0380593893768 46.2504378283713 703.4 43 692.3 38 682.7 48 697.6 39 694.9 44 675.6 43 696.2 45 692.8 54 676.9 55 695.5 55 678.5 47 668.7 54 680.6 50 670 48 659.7 53 663.9 51 660.4 53 644.8 48 649.6 46 636.3 39 636.4 46 631.2 43 617 41 623.4 46 608.4 42 592.7 43 599.2 41 578.4 45 566.4 43 588.1 48 5071308 STANILAUS TURLOCK UNION 42.6006966151648 4.70202296336796 1.36686714051394 32.5587752870421 9.22131147540983 14.9641577060932 59645.2069672131 3932.406689 24.1786317567568 21.40625 0 50 653.922410473623 649.459038583175 642.323309224718 44.8226113437381 45.3544592030361 39.6056988833269 699.6 39 698.2 44 682.7 48 690.7 32 692.8 41 674.5 41 684 34 686 47 669.2 47 687.1 46 681.2 50 662.8 47 669.3 39 668.3 45 653.1 46 657.8 45 659.4 52 645 48 642.3 39 637.8 41 635.1 45 630.6 43 616.2 42 621.3 45 601.2 38 591.4 44 596.5 39 573.1 42 567.7 47 581.6 42 5071043 STANISLAUS CERES UNIFIED 51.2052189296771 4.55034423407917 3.4315834767642 39.2641996557659 10.0490196078431 13.2773218142549 56234.306372549 3767.50398020654 23.1700146986771 12.967032967033 0 41.6666666666667 641.917007005515 635.964696577509 629.076138701146 33.683661475169 34.3288754673569 30.8339543896259 687.6 27 688.2 34 668.5 33 681.2 24 685.7 33 659.1 26 677.2 28 680.9 42 661.3 39 674.6 34 664.5 33 649.9 34 662.9 33 656.6 34 646 37 651 38 648.3 41 637.7 40 637.6 34 629.7 32 625.6 36 618.5 32 607.1 32 613.4 37 590.1 28 579.4 31 582.2 26 560.3 29 554.8 32 571.1 29 5071068 STANISLAUS DENAIR UNIFIED 42.1307506053269 .783085356303837 .234925606891151 25.920125293657 1.72413793103449 13.381746031746 49550.7413793104 3282.38919342208 21.3066202090592 29.8701298701299 0 0 658.764412416851 647.720043103448 644.830065359477 47.6655773420479 41.9946120689655 43.8968957871397 692.4 32 688.9 35 672.3 37 683.3 25 678 26 664.3 32 683.1 33 680.6 42 668.5 47 692.2 51 679.4 48 664.7 50 670.6 41 665.1 43 650.1 42 658.6 46 649.5 42 649.2 53 654.6 51 637.5 40 639.2 50 634 46 605.9 31 623.3 46 616.3 50 597.1 48 614.6 56 592.6 58 584 61 596.5 58 5073601 STANISLAUS NEWMAN-CROWS LANDING UNIFIED 53.904282115869 1.22137404580153 1.47582697201018 53.4351145038168 14.7368421052632 12.7085714285714 51297.3684210526 4109.91704834606 21.6866158868335 28.8659793814433 0 16.6666666666667 646.867903103709 644.301789709172 636.501729323308 40.2315789473684 40.7166293810589 34.1241483724451 688.8 28 695.2 41 671.8 37 681.7 24 687.2 35 662.8 30 673.1 24 679 40 660.1 38 679.7 39 663.2 32 657.3 43 666.6 37 658 35 651.9 44 646.6 33 648.6 41 634.8 37 632.7 30 630 33 623.6 34 621.8 35 615.2 40 616.1 39 596.2 33 596.7 47 599.4 41 596.1 61 588.6 65 598.9 61 5071217 STANISLAUS PATTERSON JOINT UNIFIED 55.0072568940494 .67396798652064 1.23560797528784 64.588598708228 18.1208053691275 13.3273255813954 55535.1476510067 4008.41449031171 24.0040927694407 13.0718954248366 0 0 645.663875278396 636.918853820598 634.555903083701 37.1092511013216 34.749584717608 31.6178173719376 686.7 26 689.7 35 670 35 681.7 24 683.5 31 659.6 27 676.1 27 679.7 41 664 42 682.5 42 669.5 38 655 40 669.9 40 662.5 40 654.3 47 646.1 33 642.2 35 637.1 39 636.5 33 625 28 632.5 42 612.3 27 603 29 613.3 37 592.1 30 574.3 27 582.8 27 566.8 35 565.5 43 575.6 34 5171399 SUTTER LIVE OAK UNIFIED 72.2976963969285 12.3235613463626 .597176981541802 45.3311617806732 6.52173913043478 12.3826732673267 52194.3369565217 4350.79261672096 21.4437869822485 17.7215189873418 0 0 640.02026295437 633.549270072993 626.57022556391 31.9917293233083 32.7766423357664 29.245939675174 686.9 27 688.7 34 663.4 29 682.6 25 687.5 36 657.9 26 669.9 22 677.1 38 650.6 29 673.9 34 664.9 34 651.3 36 657.5 28 655.3 33 642.3 34 643.2 30 640.4 33 630.6 33 634.7 32 619.8 24 623.9 34 624.9 38 608.9 34 619.4 42 585.6 24 580 32 580.9 25 565.4 33 553.5 31 573.2 32 5171464 SUTTER YUBA CITY UNIFIED 49.2564175428626 13.2831159287158 2.93955539224692 25.5282013595444 14.1078838174274 15.0974452554745 57777.5580912863 4165.93174719824 23.0617071597506 24.2424242424242 0 25 646.224358074926 636.887817258883 634.064643050297 38.9446998377501 35.8145872294951 34.6318226462747 688.3 28 697.8 44 674.6 39 685.1 27 692.1 40 666.9 34 681.6 32 688.3 50 669.3 47 683.9 44 674.4 43 662.3 47 668.1 39 658.2 35 652.9 46 650.8 38 646.1 39 636.1 39 639.8 36 625.2 28 627.2 37 622.3 35 604.2 30 615.4 39 594.3 32 573.7 27 587.5 31 562.3 32 548.9 28 572.1 31 5271498 TAHAMA CORNING UNION 49.1590749824807 .876270592358921 1.89274447949527 29.7581493165089 3.59712230215827 13.2433962264151 55372.4604316547 3941.38696109359 20.9110787172012 36.4238410596026 0 0 651.470603813559 639.559671805072 634.103220936084 36.3935581278309 34.4878170064644 35.4078389830508 698.1 37 694.3 40 673 38 684.8 27 689.4 37 662.6 30 681.6 32 684.4 46 665.8 44 680.9 40 670.8 39 654 39 660 31 651 28 642.2 33 651.4 38 641.9 34 635.3 38 639.6 36 626.1 29 620 30 627.9 40 600.6 27 613.7 37 600 36 577.1 29 590.3 33 571 38 557.1 35 581.9 41 5271571 TEHAMA LOS MOLINOS UNIFIED 51.2931034482759 .142247510668563 .568990042674253 32.2901849217639 2.56410256410257 15.8122222222222 53216.6153846154 5045.57610241821 19.6438356164384 34.2105263157895 0 0 648.061303462322 639.214003944773 634.328427419355 33.5907258064516 31.3510848126233 30.3401221995927 683.6 24 686.5 32 662.8 28 686.3 28 686.2 34 664.4 31 676.2 27 684.1 45 664.6 43 685 44 672.9 42 656.7 41 656.6 28 650.8 28 649.3 41 651.6 39 646 39 627.9 30 636 33 616.8 21 621.6 32 612.7 27 600.8 26 608.5 33 592.2 30 564.2 19 589 32 557.3 26 547.9 26 570.4 28 5375028 TRINITY MOUNTAIN VALLEY UNIFIED 60.1459854014599 .480769230769231 0 5.12820512820513 5.55555555555556 16.1320512820513 63507.8333333333 6482.46955128205 17.9083094555874 42.4242424242424 0 0 662.37205882353 650.207582938389 644.749373433584 42.4912280701754 42.6255924170616 44.5490196078431 702.5 42 704.1 50 685 50 699.4 41 689.2 37 676.9 43 691.4 41 686.5 48 667.5 46 698.1 57 680.5 50 667.7 53 672.6 43 662.4 40 647.2 39 648.8 36 632.8 26 624.3 27 650 46 628.5 31 629.8 40 643 54 617.7 43 625.2 48 609.7 45 608.3 59 596.6 40 578.2 46 569 46 580.3 40 5471860 TULARE CUTLER-OROSI JOINT UNIFIED 84.0990371389271 .562851782363978 .241222192441705 90.7263468239078 18.7878787878788 14.2173913043478 58391.1082424242 3952.91040471723 22.7518427518428 8.37004405286344 0 0 625.407986243791 629.289962962963 619.331384383252 25.2493398717465 28.1814814814815 18.533053114253 677.3 19 678.9 24 661.7 27 668.2 14 677 24 648.9 18 663.6 18 673.9 35 650.4 29 661.9 24 667.6 36 649.1 34 648 21 651.6 29 639.9 31 631.9 21 637.1 30 621.6 24 614.4 16 621.7 25 611.2 23 599.6 18 595.7 23 599.2 25 571.2 15 578 30 577.7 23 545.9 19 546.6 25 559.7 19 5471878 TULARE DINUBA UNION 74.5744216499345 .895583146753511 .244249949114594 80.4600040708325 15.9817351598174 12.970656637 53304.9005022831 3688.74829025036 22.9647513278609 16.4658634538153 0 0 636.254840613932 636.677197488584 626.026167956435 29.743766122098 32.8926940639269 24.8474025974026 682.8 23 686.7 32 663.8 29 671.7 17 681.4 29 651.5 20 668.3 21 676.5 37 653.9 32 671.8 32 666.6 35 647.9 33 652.7 25 657.3 34 643 34 648.6 35 653.2 45 637.7 40 627.9 26 628.1 31 620 30 612.1 26 605.4 31 608.8 33 577.9 19 571.6 24 576.4 22 557.3 27 554.8 32 569.1 27 5475325 TULARE FARMERSVILLE UNIFIED 92.3344947735192 .933552992861065 .109829763866008 83.0313014827018 17.1052631578947 14.402380952381 61350.2075 3792.45582646897 22.9189189189189 0 0 100 611.61476793249 605.792740046838 600.900717131474 15.8725099601594 16.3692427790788 13.3316455696203 665.2 13 676 23 643.7 15 656.9 14 664.4 25 643.7 23 651.1 16 653.8 24 631.2 19 644 18 641.1 20 630.2 22 622.6 14 621.1 17 612.8 17 609.2 13 605.1 13 597.8 14 592.6 14 577.6 11 586.3 17 558.4 9 544.6 9 559.5 11 530 10 526.7 12 544.8 9 5471993 TULARE LINDSAY UNIFIED 76.580373269115 1.72117039586919 .057372346528973 83.7636259323006 23.6842105263158 13.2849431818182 58362.7059868421 4115.27855708549 22.6187335092348 27.4285714285714 0 0 640.388823181549 640.34366359447 626.193461762989 26.1938120256859 30.7523041474654 23.0821998817268 685.3 25 684.6 30 663.6 29 672.6 17 682.5 30 650.2 19 669.4 22 678.4 39 655.6 34 664.8 26 655.1 25 637.4 23 645.7 19 645.9 24 631.3 23 633.6 22 634.2 28 621 24 628.3 26 633.3 36 620.6 31 608 24 598.6 25 602.4 28 584.7 24 583.3 34 580.5 25 559.8 30 568 45 570.9 29 5472256 TULARE VISALIA UNIFIED 44.8332783096732 7.62122822798266 1.95437075803418 43.1351704963611 12.7111111111111 14.4732521602514 58322.98376 4057.25547877995 22.3127332301811 35.9173126614987 3.125 28.125 648.229390179976 640.968154273093 635.425015825517 38.9894688381194 37.4061278812935 35.2702545285107 694.4 34 695.9 41 673.8 39 689.7 32 693.1 41 667.4 34 682.5 32 685.3 46 667 45 684.5 44 667.9 36 660.4 45 668.3 39 660.6 38 653.8 46 656.5 44 651.9 44 640.5 43 638.5 35 629.5 32 626.5 37 619.3 32 603.9 29 611.6 35 590.9 28 581.3 33 588.5 32 564.4 32 557.7 35 575.7 34 5472272 TULARE WOODLAKE UNION 72.663139329806 1.19250425894378 .298126064735946 79.2589437819421 13.1578947368421 14.8140625 58001.3071929825 4081.73411839864 21.1060329067642 36.1344537815126 0 0 640.209791122715 631.617446270544 627.961022364217 29.2447284345048 25.3400758533502 24.7630548302872 694.1 33 686.9 32 671 36 673.4 18 679.3 26 654.2 22 673.9 25 675.1 36 656.5 35 674.5 34 658.8 28 650.5 35 651.7 24 646.7 25 641.1 32 639.1 27 637.9 31 636.2 39 618.8 19 606.7 14 609.1 21 606.5 22 588.7 18 601.9 27 581.1 22 567.3 21 577.3 23 556 26 538.7 19 562.1 21 5575184 TUOLUMNE BIG OAK FLAT-GROVELAND UNIFIED 36.7164179104478 .427960057061341 .855920114122682 4.70756062767475 4.65116279069767 13.268085106383 48160.4651162791 4666.56062767475 18.4266666666667 70.2702702702703 0 0 670.61387283237 659.356621880998 649.060931174089 48.2085020242915 47.8541266794626 49.4836223506744 701.6 41 694.6 41 679.9 45 694.7 36 687.4 35 670.5 38 684.2 34 683.5 45 659.2 38 702.6 62 691.7 59 667.1 51 682.2 52 670.3 48 667.7 62 656.5 43 645.5 38 642.1 45 661.6 58 656.3 60 648 59 650.7 61 629.5 54 634 56 619.6 53 597.4 48 597.5 40 610.5 72 587.7 65 593.5 56 5673759 VENTURA CONEJO VALLEY UNIFIED 12.3270160618018 5.66135375698613 1.48519975160422 14.5311529703995 5.4054054054054 15.7322185061316 62750.2407862408 4265.1333057338 23.810318275154 41.1663807890223 0 0 682.02413435621 677.544326362702 667.723709777441 69.9877351340178 71.6343503599589 66.211486626581 721.7 62 725.9 72 700.8 67 715.4 58 717.1 66 695.7 63 711 60 716.7 77 691.1 68 713.5 72 708 74 689.6 73 702.2 71 699.3 75 682.8 76 684.1 71 688.9 78 671.2 74 670.9 67 666 69 660.3 70 660.1 69 640.4 65 648 69 635.5 66 620.5 69 628.3 68 604.9 67 595 71 610.3 72 5672454 VENTURA FILLMORE UNIFIED 51.8065433854908 .547195622435021 .437756497948016 76.9083447332421 15.527950310559 15.024043715847 56925.9751552795 3897.07879616963 23.4847501622323 30.6010928961749 0 16.6666666666667 635.765689381933 628.433169996231 624.119392073875 28.9003462870335 26.434602336977 24.655705229794 684.6 25 680.1 26 665.4 30 678.8 22 677 24 653 22 663.3 17 667.6 29 649.8 29 670.7 31 659.4 29 644.4 29 654.5 26 648.5 26 639.9 31 644.6 32 637.2 30 637.1 39 626.9 25 619.8 23 621.2 31 609.2 24 593.1 21 603.7 29 585.1 24 574.7 27 578.9 24 551.2 22 549.7 28 565.3 24 5673940 VENTURA MOORPARK UNIFIED 23.4552907241536 3.86164745103487 1.81969718016391 29.7819141547437 10.3658536585366 10.2872311827957 54402.9817073171 4155.08362272538 21.6525556600815 21.1409395973154 0 0 661.607069359756 652.657776530039 650.1753724307 56.6239864227796 52.736664795059 51.3782393292683 706.8 47 705.7 52 685.6 51 696 38 697.7 46 674.7 41 697.5 47 700.2 62 681.6 60 698.9 58 686 55 678.8 63 688.9 59 675.7 54 670.8 65 669.6 57 661.3 54 657.2 61 661.2 57 651.1 54 648.9 59 642.9 54 624.6 49 634.8 57 611.6 45 599.7 50 609.5 51 583.1 49 574.4 52 594.6 55 5673874 VENTURA OAK PARK UNIFIED 1.24705089315807 6.06629143214509 1.00062539086929 2.34521575984991 4.72972972972973 12.2248502994012 55208.8378378378 4079.32739212008 20.988490182803 74.3421052631579 0 0 682.161725394897 670.000522088354 672.424153225806 76.9189516129032 69.8827309236948 70.796678817335 720.7 61 715.6 63 704.4 70 719.3 62 714.3 64 701.3 68 714.1 63 720.6 80 701.4 77 716.9 74 705.7 72 698.2 80 709.7 78 697.1 74 694.8 85 684.3 72 691.1 80 673.3 76 680.2 74 665.5 69 676.5 82 667.3 75 640.4 65 658.8 77 643.5 72 611.4 61 636.6 75 608.3 71 594.6 71 615.4 76 5672520 VENTURA OJAI UNIFIED 23.2817037754114 1.36625119846596 .862895493767977 18.6481303930968 8.15217391304348 14.221359223301 56808.0108695652 3825.93600191755 22.8771733034212 35.546875 0 0 671.676079734219 660.902526246719 656.370046388337 58.2614314115308 55.6719160104987 55.8066445182724 705.2 45 707.1 54 682.6 48 706 48 708.6 58 682.5 50 697.6 47 699.6 62 674.6 53 708.4 67 696 65 684.8 69 691.6 61 680 58 671.2 65 678 66 670.2 62 663 67 663.6 59 645.4 48 653 63 649.3 60 626.3 51 637.7 59 617.1 50 598.3 49 609.8 51 583.4 49 570.5 48 591.1 51 5672603 VENTURA SIMI VALLEY UNIFIED 15.6815375530144 5.52216571634047 1.67462885909488 17.4397698669543 6.91747572815534 15.0002183406114 57203.5497572816 3831.57374017568 23.2098614007114 37.6871880199667 0 0 667.169897138059 658.573027866352 653.045612437846 57.1392254359549 55.2870511576321 54.030788608215 710 50 711.9 58 689.4 55 704.6 47 703.9 53 682.6 50 700.5 50 701.6 63 682.9 61 698.4 58 684.7 54 672.7 58 688.3 58 676.4 54 668.2 62 672.3 60 671.6 63 656.4 60 660.6 57 652.1 55 650.4 61 645.7 57 627.6 52 634.7 57 621.8 55 602.4 53 613.9 55 583 49 571.4 49 592.4 53 5672652 VENTURA VENTURA UNIFIED 38.1582125603865 2.90551227227462 2.35487083357741 33.4426805693867 9.94397759103642 15.3525516403402 56413.6120448179 3699.07023607287 24.8511202830189 34.3634116192831 0 22.2222222222222 663.126722987995 651.066852966466 648.690125295716 52.2804696398843 47.5606190885641 48.7767007558915 703.4 43 700.9 47 686.5 52 697.3 39 695.5 44 676.8 44 692.4 42 690.8 52 678.5 57 695.3 55 678.2 47 669.1 54 684.7 55 672.1 50 664.8 59 665.4 53 659.6 52 648.9 52 655.2 51 645.7 49 644.6 55 642.1 53 618.1 43 630.4 53 611.9 46 594 45 602.3 44 584.7 50 568.5 46 591.7 52 5772678 YOLO DAVIS JOINT UNIFIED 17.8988326848249 10.4909560723514 3.37209302325581 12.7002583979328 14.9289099526066 14.4376582278481 49783.3127962085 4254.71718346253 20.062860136197 46.4203233256351 0 0 688.106229626947 680.838578317557 668.965033207683 71.1364207503141 74.2502232541525 71.5622962694676 722.8 64 728.6 74 697.7 64 718.2 61 724 71 693.5 61 718.6 67 728.8 84 697.9 74 714.9 73 711.8 77 691.7 74 703.3 72 700.2 76 684.8 77 695.3 81 698.3 84 673.2 76 683.6 77 671 74 668.6 76 672 78 645.7 69 651.3 71 644.8 73 617 66 629.9 69 605.7 68 589.9 67 605.1 67 5772686 YOLO ESPARTO UNIFIED 49.235807860262 .779510022271715 1.67037861915368 43.7639198218263 4.16666666666666 11.3609090909091 50433.1666666667 5039.20267260579 19.5454545454545 27.0833333333333 0 0 640.121894409938 635.382379518072 625.547792998478 29.2648401826484 31.7394578313253 27.9472049689441 682.3 22 686.1 33 660.8 26 673.4 18 680.4 30 653.2 22 673.8 26 678.4 39 660 39 682.5 43 662 32 650.9 35 651.3 24 650.8 29 635.1 27 652.2 39 649.3 43 641.5 45 626.1 25 621.4 25 616.5 27 616.3 31 605.2 33 608.1 33 597.5 36 576.4 31 579.2 25 549.2 24 545.9 28 558.2 20 5772694 YOLO WASHINGTON UNIFIED 62.2396784800877 13.8208597362562 3.95615687617743 32.1287891762288 15.884476534296 13.2708860759494 52881.3321299639 4324.17365987327 20.9871086556169 8 0 18.1818181818182 639.1435538262 633.124164397128 627.616834677419 32.3477822580645 31.2792770487745 27.7229571984436 686.6 26 693.3 38 668.6 33 682.4 25 688 35 661.6 29 675.2 26 677.4 38 661.5 40 676.5 36 665.4 34 652.1 37 659.5 30 653.6 31 642.7 34 638.4 26 637.7 30 627 29 626.6 24 620.5 24 620.8 31 615.3 29 600.1 26 609.9 34 589.8 27 578.6 30 585.5 29 559.5 29 552.1 30 570.2 28 5772702 YOLO WINTERS JOINT UNIFIED 46.1618798955614 1.15057528764382 .80040020010005 45.6228114057029 13.3333333333333 11.8071428571429 48426.1523809524 3945.06903451726 19.8582995951417 53.7735849056604 0 0 650.807236842105 640.076315789474 634.960070175439 36.5312280701754 33.7548476454294 34.6834795321637 695.8 35 691.8 38 675.6 41 685.7 28 686.8 34 670.5 38 678.4 29 680.1 41 667.9 46 681.1 41 663.3 32 654.5 39 665.3 35 656.4 34 644.3 36 655 41 644.5 37 640.7 44 633.9 30 619.1 23 615.4 26 636.7 48 609.7 35 614.2 38 596.1 32 580.5 32 587.4 30 558.1 27 553 30 565.9 24 5772710 YOLO WOODLAND JOINT UNIFIED 43.4143080008915 3.64856364017614 1.54120360662613 46.3199832249948 21.3776722090261 14.4046843177189 53796.1638954869 3845.48081358775 22.7088422081094 29.8924731182796 0 6.66666666666667 650.685780525502 642.281801889866 636.105199735887 41.0627269725982 40.8595633756924 38.3171904516572 694.7 34 701.1 47 674.4 39 689.2 31 696.2 44 668.7 36 687.9 37 692.5 54 671.3 49 681.7 41 670.2 39 658.5 43 669.1 39 660.8 38 653.4 46 655.4 42 650.2 43 641.4 44 646.4 43 634.6 37 630.8 41 629.1 41 609.4 34 617.7 41 598.3 34 585.6 37 591 34 572.1 39 562.9 40 578.3 37 5872736 YUBA MARYSVILLE JOINT UNIFIED 70.6210416863598 20.4617205998422 2.98934490923441 17.423046566693 11.0328638497653 16.5641975308642 61705.6596244131 3970.9843133386 24.8864711447493 15.1428571428571 0 13.6363636363636 639.085190097259 631.172371396269 625.858513396716 30.7783059636992 30.1215375918598 28.466254052461 688.6 28 687.9 33 670.3 35 686.7 29 687.6 35 664 31 677.4 28 681.1 42 661.6 40 675.9 36 664.5 33 649.7 34 656.3 28 652.6 30 639.9 31 643.3 30 637.8 31 629.2 31 632.7 30 626.6 29 621.8 32 614.9 29 599.6 26 605.9 30 585.8 24 571.1 24 581.3 26 551.9 23 541.5 21 561.5 20 5872751 YUBA WHEATLAND UNION 77.7482740308019 5.99889928453495 9.57622454595487 11.1172261970281 7.8740157480315 16.9187050359712 65099.5669291339 6913.39680792515 20.0325203252033 23.5294117647059 0 0 663.065659500291 648.575580736544 645.966781214204 52.1821305841924 48.9008498583569 52.2887855897734 706.7 46 699 46 683.1 49 701 43 703.5 53 678.3 45 694.1 43 699.9 62 676.9 55 694.7 54 676.2 45 666.7 52 690.7 61 675.3 53 671.1 66 669.9 57 664.4 57 651.2 55 655.3 51 643.6 47 638.3 48 647 58 619.6 44 632.6 55 619.2 52 590.2 41 603.9 46 587.6 53 566.7 44 589.2 50 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/src/star98.names000066400000000000000000000170131224417117700264260ustar00rootroot00000000000000 18 variables and 303 cases Variable: CDS_CODE Type: Numeric County District Code Variable: COUNTY Type: String Variable: DISTRICT Type: String Variable: LOWINC Type: Numeric Percent Low Income Students This variable is the sum of students eligible for free or reduced lunch programs divided by the sum of students multiplied by 100. The district level percentages were aggregated from school level data Source: The National Center for Educational Statistics (NECS) Common Core of Data site, "http://nces.ed.gov/ccd/index.html" Variable: PERASIAN Type: Numeric Percent Asian Students This variable is the number of Asian Students divided by the the total number of students, multiplied by 100. Source: The California Department of Education Educational Demographics Unit site, file "ethdst97.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PERBLACK Type: Numeric Percent Black Students This variable is the number of Black Students divided by the the total number of students, multiplied by 100. Source: The California Department of Education Educational Demographics Unit site, file "ethdst97.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PERHISP Type: Numeric Percent Hispanic Students This variable is the number of Hispanic Students divided by the the total number of students, multiplied by 100. Source: The California Department of Education Educational Demographics Unit site, file "ethdst97.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PERMINTE Type: Numeric Percent Minority Teachers This variable is the number of minority (American Indians, Asians, Pacific Islanders, Filipinos, Hispanics And Blacks) teachers divided by the total number of teachers, then multiplied by 100. Source: The California Department of Education Educational Demographics Unit site, file "teaeth96.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: AVYRSEXP Type: Numeric Teachers' Experience This variable is the sum of years in educational service divided by the sum of teachers. The district level averages were aggregated from school level data. Source: The California Department of Education Educational Demographics Unit site, file "prcert96.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PTRATIO Type: Numeric Class Size This variable is enrollment divided By full-time equivalent teachers. Source: The California Department of Education Educational Demographics Unit site, file "cbeds96.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: AVSAL Type: Numeric Teacher Salary This variable is the total salary budget, including benefits, divided the number of full-time teachers. Source: School Business Services Division http://www.cde.ca.gov/ftpbranch/sbsdiv/ Variable: PERPSPEN Type: Numeric Per-pupil Spending This variable is the total spending divided by total number of students. Source: School Business Services Division "http://www.cde.ca.gov/ftpbranch/sbsdiv/" Variable: PCT_AF Type: Numeric Percent Students Taking UC/CSU Prep Courses This variable is the percentage of students taking courses that meet University of California and California State University entry requirements. Source: The California Department of Education Educational Demographics Unit site, file "cbeds96.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PCTCHRT Type: Numeric Percent Charter Schools Source: The California Department of Education Educational Demographics Unit site, file "schlname.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: PCTYRRND Type: Numeric Percent Year-round Schools Source: The California Department of Education Educational Demographics Unit site, file "schlname.exe" at "http://www.cde.ca.gov/ftpbranch/retdiv/demo/newcbeds/" Variable: LANGUAGE Type: Numeric Mean Language Score, Grades 2-11 This variable is the sum score for each grade(mean * test takers) divided by the sum of test takers for all grades. Source: The State of California Standardized Testing and Reporting site, "http://star.cde.ca.gov/index_index.html" Variable: MATH Type: Numeric Mean Math Score, Grades 2-11 This variable is the sum score for each grade(mean * test takers) divided by the sum of test takers for all grades. Source: The State of California Standardized Testing and Reporting site, "http://star.cde.ca.gov/index_index.html" Variable: READ Type: Numeric Mean Reading Score, Grades 2-11 This variable is the sum score for each grade(mean * test takers) divided by the sum of test takers for all grades. Source: The State of California Standardized Testing and Reporting site, "http://star.cde.ca.gov/index_index.html" Variable: LANGNCE Type: Numeric National Percentile Rank, Language National Percentile Rank is based on the mean NCE score for each district. Variable: MATHNCE Type: Numeric National Percentile Rank, Language National Percentile Rank is based on the mean NCE score for each district. Variable: READNCE Type: Numeric National Percentile Rank, Language National Percentile Rank is based on the mean NCE score for each district. Variables: READM(2-11) Mean reading score for individual grades. The grades are the number at the end of each variable name. Variables: MATHM(2-11) Mean math score for individual grades. The grades are the number at the end of each variable name. Variables: LANGM(2-11) Mean language score for individual grades. The grades are the number at the end of each variable name. Variables: READNCE(2-11) NCE percentile ranking for individual grades. The grades are the number at the end of each variable name. Variables: MATHCE(2-11) NCE percentile ranking for individual grades. The grades are the number at the end of each variable name. Variables: LANGNCE(2-11) NCE percentile ranking for individual grades. The grades are the number at the end of each variable name. statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/star98/star98.csv000066400000000000000000001773751224417117700253510ustar00rootroot00000000000000"MATHTOT","PR50M","LOWINC","PERASIAN","PERBLACK","PERHISP","PERMINTE","AVYRSEXP","AVSALK","PERSPENK","PTRATIO","PCTAF","PCTCHRT","PCTYRRND","PERMINTE_AVYRSEXP","PERMINTE_AVSAL","AVYRSEXP_AVSAL","PERSPEN_PTRATIO","PERSPEN_PCTAF","PTRATIO_PCTAF","PERMINTE_AVYRSEXP_AVSAL","PERSPEN_PTRATIO_PCTAF" 807.000000,452.000000,34.397300,23.299300,14.235280,11.411120,15.918370,14.706460,59.15732,4.445207,21.710250,57.032760,0.000000,22.222220,234.102872,941.68811,869.9948,96.50656,253.52242,1238.1955,13848.8985,5504.0352 184.000000,144.000000,17.365070,29.328380,8.234897,9.314884,13.636360,16.083240,59.50397,5.267598,20.442780,64.622640,0.000000,0.000000,219.316851,811.41756,957.0166,107.68435,340.40609,1321.0664,13050.2233,6958.8468 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156.000000,72.000000,77.748270,5.998899,9.576225,11.117230,7.874016,16.918710,65.09957,6.913397,20.032520,23.529410,0.000000,0.000000,133.218193,512.59506,1101.4007,138.49276,162.66815,471.3534,8672.4471,3258.6530 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/statecrime/000077500000000000000000000000001224417117700244645ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/statecrime/__init__.py000066400000000000000000000000231224417117700265700ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/statecrime/data.py000066400000000000000000000056511224417117700257560ustar00rootroot00000000000000#! /usr/bin/env python """Statewide Crime Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """Public domain.""" TITLE = """Statewide Crime Data 2009""" SOURCE = """ All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below. """ DESCRSHORT = """State crime data 2009""" DESCRLONG = DESCRSHORT #suggested notes NOTE = """ Number of observations: 51 Number of variables: 8 Variable name definitions: state All 50 states plus DC. violent Rate of violent crimes / 100,000 population. Includes murder, forcible rape, robbery, and aggravated assault. Numbers for Illinois and Minnesota do not include forcible rapes. Footnote included with the American Statistical Abstract table reads: "The data collection methodology for the offense of forcible rape used by the Illinois and the Minnesota state Uniform Crime Reporting (UCR) Programs (with the exception of Rockford, Illinois, and Minneapolis and St. Paul, Minnesota) does not comply with national UCR guidelines. Consequently, their state figures for forcible rape and violent crime (of which forcible rape is a part) are not published in this table." murder Rate of murders / 100,000 population. hs_grad Precent of population having graduated from high school or higher. poverty % of individuals below the poverty line white Percent of population that is one race - white only. From 2009 American Community Survey single Calculated from 2009 1-year American Community Survey obtained obtained from Census. Variable is Male householder, no wife present, family household combined with Female household, no husband prsent, family household, divided by the total number of Family households. urban % of population in Urbanized Areas as of 2010 Census. Urbanized Areas are area of 50,000 or more people.""" import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the statecrime data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray(data, endog_idx=2, exog_idx=[7, 4, 3, 5], dtype=float) def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=2, exog_idx=[7,4,3,5], dtype=float, index_idx=0) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/statecrime.csv', 'rb'), delimiter=",", names=True, dtype=None) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/statecrime/statecrime.csv000066400000000000000000000045011224417117700273410ustar00rootroot00000000000000state,violent,murder,hs_grad,poverty,single,white,urban Alabama,459.9,7.1,82.1,17.5,29.0,70,48.65 Alaska,632.6,3.2,91.4,9.0,25.5,68.3,44.46 Arizona,423.2,5.5,84.2,16.5,25.7,80,80.07 Arkansas,530.3,6.3,82.4,18.8,26.3,78.4,39.54 California,473.4,5.4,80.6,14.2,27.8,62.7,89.73 Colorado,340.9,3.2,89.3,12.9,21.4,84.6,76.86 Connecticut,300.5,3.0,88.6,9.4,25.0,79.1,84.83 Delaware,645.1,4.6,87.4,10.8,27.6,71.9,68.71 District of Columbia,1348.9,24.2,87.1,18.4,48.0,38.7,100 Florida,612.6,5.5,85.3,14.9,26.6,76.9,87.44 Georgia,432.6,6.0,83.9,16.5,29.3,61.9,65.38 Hawaii,274.1,1.8,90.4,10.4,26.3,26.9,71.46 Idaho,238.5,1.5,88.4,14.3,19.0,92.3,50.51 Illinois,618.2,8.4,86.4,13.3,26.0,72.5,79.97 Indiana,366.4,5.3,86.6,14.4,24.5,85.7,59.17 Iowa,294.5,1.3,90.5,11.8,20.3,92.3,41.66 Kansas ,412.0,4.7,89.7,13.4,22.8,86.3,50.17 Kentucky ,265.5,4.3,81.7,18.6,25.4,88.8,40.99 Louisiana,628.4,12.3,82.2,17.3,31.4,63.7,61.33 Maine,119.9,2.0,90.2,12.3,22.0,94.9,26.21 Maryland,590.0,7.7,88.2,9.1,27.3,60.2,83.53 Massachusetts,465.6,2.7,89.0,10.3,25.0,82.4,90.3 Michigan,504.4,6.3,87.9,16.2,25.6,79.9,66.37 Minnesota,214.2,1.5,91.5,11.0,20.2,87.4,58 Mississippi,306.7,6.9,80.4,21.9,32.8,59.6,27.62 Missouri,500.3,6.6,86.8,14.6,25.3,83.9,56.61 Montana ,283.9,3.2,90.8,15.1,20.3,89.4,26.49 Nebraska,305.5,2.5,89.8,12.3,20.9,88.1,53.78 Nevada,704.6,5.9,83.9,12.4,28.5,76.2,86.51 New Hampshire,169.5,0.9,91.3,8.5,19.5,94.5,47.34 New Jersey,311.3,3.7,87.4,9.4,25.8,70.7,92.24 New Mexico,652.8,10.0,82.8,18.0,29.1,72.5,53.75 New York,385.5,4.0,84.7,14.2,30.2,67.4,82.66 North Carolina,414.0,5.4,84.3,16.3,26.3,70.5,54.88 North Dakota,223.6,2.0,90.1,11.7,18.2,90.2,40 Ohio,358.1,5.0,87.6,15.2,26.3,84,65.31 Oklahoma,510.4,6.5,85.6,16.2,25.9,75.4,45.79 Oregon,261.2,2.3,89.1,14.3,22.7,85.6,62.47 Pennsylvania,388.9,5.4,87.9,12.5,24.5,83.5,70.68 Rhode Island,254.3,3.0,84.7,11.5,27.3,82.6,90.46 South Carolina,675.1,6.7,83.6,17.1,28.4,67.6,55.78 South Dakota,201.0,3.6,89.9,14.2,20.8,86.3,29.92 Tennessee,666.0,7.4,83.1,17.1,26.3,79.1,54.38 Texas,491.4,5.4,79.9,17.2,27.6,73.8,75.35 Utah,216.2,1.4,90.4,11.5,17.9,89.3,81.17 Vermont,135.1,1.3,91.0,11.4,21.3,95.8,17.38 Virginia,230.0,4.7,86.6,10.5,24.0,70.4,69.79 Washington,338.3,2.8,89.7,12.3,22.2,80.2,74.97 West Virginia,331.2,4.9,82.8,17.7,23.3,94.3,33.2 Wisconsin,259.7,2.6,89.8,12.4,22.2,88.4,55.8 Wyoming,219.3,2.0,91.8,9.8,18.9,91.3,24.51 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/strikes/000077500000000000000000000000001224417117700240105ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/strikes/__init__.py000066400000000000000000000000231224417117700261140ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/strikes/data.py000066400000000000000000000036101224417117700252730ustar00rootroot00000000000000#! /usr/bin/env python """U.S. Strike Duration Data""" __docformat__ = 'restructuredtext' COPYRIGHT = """This is public domain.""" TITLE = __doc__ SOURCE = """ This is a subset of the data used in Kennan (1985). It was originally published by the Bureau of Labor Statistics. :: Kennan, J. 1985. "The duration of contract strikes in US manufacturing. `Journal of Econometrics` 28.1, 5-28. """ DESCRSHORT = """Contains data on the length of strikes in US manufacturing and unanticipated industrial production.""" DESCRLONG = """Contains data on the length of strikes in US manufacturing and unanticipated industrial production. The data is a subset of the data originally used by Kennan. The data here is data for the months of June only to avoid seasonal issues.""" #suggested notes NOTE = """ Number of observations - 62 Number of variables - 2 Variable name definitions:: duration - duration of the strike in days iprod - unanticipated industrial production """ from numpy import recfromtxt, column_stack, array from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the strikes data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray(data, endog_idx=0, dtype=float) def load_pandas(): """ Load the strikes data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() return du.process_recarray_pandas(data, endog_idx=0, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/strikes.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/strikes/strikes.csv000066400000000000000000000013171224417117700262130ustar00rootroot00000000000000duration, iprod 7, .01138 9, .01138 13, .01138 14, .01138 26, .01138 29, .01138 52, .01138 130, .01138 9, .02299 37, .02299 41, .02299 49, .02299 52, .02299 119, .02299 3, -.03957 17, -.03957 19, -.03957 28, -.03957 72, -.03957 99, -.03957 104, -.03957 114, -.03957 152, -.03957 153, -.03957 216, -.03957 15, -.05467 61, -.05467 98, -.05467 2, .00535 25, .00535 85, .00535 3, .07427 10, .07427 1, .06450 2, .06450 2, .06450 3, .06450 3, .06450 4, .06450 8, .06450 11, .06450 22, .06450 23, .06450 27, .06450 32, .06450 33, .06450 35, .06450 43, .06450 43, .06450 44, .06450 100, .06450 5, -.10443 49, -.10443 2, -.00700 12, -.00700 12, -.00700 21, -.00700 21, -.00700 27, -.00700 38, -.00700 42, -.00700 117, -.00700 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/000077500000000000000000000000001224417117700242225ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/R_sunspots.s000066400000000000000000000006141224417117700265660ustar00rootroot00000000000000d <- read.table('./sunspots.csv', sep=',', header=T) attach(d) mod_ols <- ar(SUNACTIVITY, aic=FALSE, order.max=9, method="ols", intercept=FALSE) mod_yw <- ar(SUNACTIVITY, aic=FALSE, order.max=9, method="yw") mod_burg <- ar(SUNACTIVITY, aic=FALSE, order.max=9, method="burg") mod_mle <- ar(SUNACTIVITY, aic=FALSE, order.max=9, method="mle") select_ols <- ar(SUNACTIVITY, aic=TRUE, method="ols") statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/__init__.py000066400000000000000000000000231224417117700263260ustar00rootroot00000000000000from data import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/arima_mod.R000066400000000000000000000002021224417117700262670ustar00rootroot00000000000000dta <- read.csv('./sunspots.csv') attach(dta) arma_mod <- arima(SUNACTIVITY, order=c(9,0,0), xreg=rep(1,309), include.mean=FALSE) statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/data.py000066400000000000000000000037201224417117700255070ustar00rootroot00000000000000"""Yearly sunspots data 1700-2008""" __docformat__ = 'restructuredtext' COPYRIGHT = """This data is public domain.""" TITLE = __doc__ SOURCE = """ http://www.ngdc.noaa.gov/stp/SOLAR/ftpsunspotnumber.html The original dataset contains monthly data on sunspot activity in the file ./src/sunspots_yearly.dat. There is also sunspots_monthly.dat. """ DESCRSHORT = """Yearly (1700-2008) data on sunspots from the National Geophysical Data Center.""" DESCRLONG = DESCRSHORT NOTE = """ Number of Observations - 309 (Annual 1700 - 2008) Number of Variables - 1 Variable name definitions:: SUNACTIVITY - Number of sunspots for each year The data file contains a 'YEAR' variable that is not returned by load. """ from numpy import recfromtxt, column_stack, array from pandas import Series, DataFrame from statsmodels.datasets.utils import Dataset from os.path import dirname, abspath def load(): """ Load the yearly sunspot data and returns a data class. Returns -------- Dataset instance: See DATASET_PROPOSAL.txt for more information. Notes ----- This dataset only contains data for one variable, so the attributes data, raw_data, and endog are all the same variable. There is no exog attribute defined. """ data = _get_data() endog_name = 'SUNACTIVITY' endog = array(data[endog_name], dtype=float) dataset = Dataset(data=data, names=[endog_name], endog=endog, endog_name=endog_name) return dataset def load_pandas(): data = DataFrame(_get_data()) # TODO: time series endog = Series(data['SUNACTIVITY'], index=data['YEAR'].astype(int)) dataset = Dataset(data=data, names=list(data.columns), endog=endog, endog_name='volume') return dataset def _get_data(): filepath = dirname(abspath(__file__)) data = recfromtxt(open(filepath + '/sunspots.csv', 'rb'), delimiter=",", names=True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/src/000077500000000000000000000000001224417117700250115ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/src/sunspots_monthly.dat000066400000000000000000000513121224417117700311550ustar00rootroot00000000000000 MONTHLY MEAN SUNSPOT NUMBERS =============================================================================== Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ------------------------------------------------------------------------------- 1749 58.0 62.6 70.0 55.7 85.0 83.5 94.8 66.3 75.9 75.5 158.6 85.2 1750 73.3 75.9 89.2 88.3 90.0 100.0 85.4 103.0 91.2 65.7 63.3 75.4 1751 70.0 43.5 45.3 56.4 60.7 50.7 66.3 59.8 23.5 23.2 28.5 44.0 1752 35.0 50.0 71.0 59.3 59.7 39.6 78.4 29.3 27.1 46.6 37.6 40.0 1753 44.0 32.0 45.7 38.0 36.0 31.7 22.0 39.0 28.0 25.0 20.0 6.7 1754 0.0 3.0 1.7 13.7 20.7 26.7 18.8 12.3 8.2 24.1 13.2 4.2 1755 10.2 11.2 6.8 6.5 0.0 0.0 8.6 3.2 17.8 23.7 6.8 20.0 1756 12.5 7.1 5.4 9.4 12.5 12.9 3.6 6.4 11.8 14.3 17.0 9.4 1757 14.1 21.2 26.2 30.0 38.1 12.8 25.0 51.3 39.7 32.5 64.7 33.5 1758 37.6 52.0 49.0 72.3 46.4 45.0 44.0 38.7 62.5 37.7 43.0 43.0 1759 48.3 44.0 46.8 47.0 49.0 50.0 51.0 71.3 77.2 59.7 46.3 57.0 1760 67.3 59.5 74.7 58.3 72.0 48.3 66.0 75.6 61.3 50.6 59.7 61.0 1761 70.0 91.0 80.7 71.7 107.2 99.3 94.1 91.1 100.7 88.7 89.7 46.0 1762 43.8 72.8 45.7 60.2 39.9 77.1 33.8 67.7 68.5 69.3 77.8 77.2 1763 56.5 31.9 34.2 32.9 32.7 35.8 54.2 26.5 68.1 46.3 60.9 61.4 1764 59.7 59.7 40.2 34.4 44.3 30.0 30.0 30.0 28.2 28.0 26.0 25.7 1765 24.0 26.0 25.0 22.0 20.2 20.0 27.0 29.7 16.0 14.0 14.0 13.0 1766 12.0 11.0 36.6 6.0 26.8 3.0 3.3 4.0 4.3 5.0 5.7 19.2 1767 27.4 30.0 43.0 32.9 29.8 33.3 21.9 40.8 42.7 44.1 54.7 53.3 1768 53.5 66.1 46.3 42.7 77.7 77.4 52.6 66.8 74.8 77.8 90.6 111.8 1769 73.9 64.2 64.3 96.7 73.6 94.4 118.6 120.3 148.8 158.2 148.1 112.0 1770 104.0 142.5 80.1 51.0 70.1 83.3 109.8 126.3 104.4 103.6 132.2 102.3 1771 36.0 46.2 46.7 64.9 152.7 119.5 67.7 58.5 101.4 90.0 99.7 95.7 1772 100.9 90.8 31.1 92.2 38.0 57.0 77.3 56.2 50.5 78.6 61.3 64.0 1773 54.6 29.0 51.2 32.9 41.1 28.4 27.7 12.7 29.3 26.3 40.9 43.2 1774 46.8 65.4 55.7 43.8 51.3 28.5 17.5 6.6 7.9 14.0 17.7 12.2 1775 4.4 0.0 11.6 11.2 3.9 12.3 1.0 7.9 3.2 5.6 15.1 7.9 1776 21.7 11.6 6.3 21.8 11.2 19.0 1.0 24.2 16.0 30.0 35.0 40.0 1777 45.0 36.5 39.0 95.5 80.3 80.7 95.0 112.0 116.2 106.5 146.0 157.3 1778 177.3 109.3 134.0 145.0 238.9 171.6 153.0 140.0 171.7 156.3 150.3 105.0 1779 114.7 165.7 118.0 145.0 140.0 113.7 143.0 112.0 111.0 124.0 114.0 110.0 1780 70.0 98.0 98.0 95.0 107.2 88.0 86.0 86.0 93.7 77.0 60.0 58.7 1781 98.7 74.7 53.0 68.3 104.7 97.7 73.5 66.0 51.0 27.3 67.0 35.2 1782 54.0 37.5 37.0 41.0 54.3 38.0 37.0 44.0 34.0 23.2 31.5 30.0 1783 28.0 38.7 26.7 28.3 23.0 25.2 32.2 20.0 18.0 8.0 15.0 10.5 1784 13.0 8.0 11.0 10.0 6.0 9.0 6.0 10.0 10.0 8.0 17.0 14.0 1785 6.5 8.0 9.0 15.7 20.7 26.3 36.3 20.0 32.0 47.2 40.2 27.3 1786 37.2 47.6 47.7 85.4 92.3 59.0 83.0 89.7 111.5 112.3 116.0 112.7 1787 134.7 106.0 87.4 127.2 134.8 99.2 128.0 137.2 157.3 157.0 141.5 174.0 1788 138.0 129.2 143.3 108.5 113.0 154.2 141.5 136.0 141.0 142.0 94.7 129.5 1789 114.0 125.3 120.0 123.3 123.5 120.0 117.0 103.0 112.0 89.7 134.0 135.5 1790 103.0 127.5 96.3 94.0 93.0 91.0 69.3 87.0 77.3 84.3 82.0 74.0 1791 72.7 62.0 74.0 77.2 73.7 64.2 71.0 43.0 66.5 61.7 67.0 66.0 1792 58.0 64.0 63.0 75.7 62.0 61.0 45.8 60.0 59.0 59.0 57.0 56.0 1793 56.0 55.0 55.5 53.0 52.3 51.0 50.0 29.3 24.0 47.0 44.0 45.7 1794 45.0 44.0 38.0 28.4 55.7 41.5 41.0 40.0 11.1 28.5 67.4 51.4 1795 21.4 39.9 12.6 18.6 31.0 17.1 12.9 25.7 13.5 19.5 25.0 18.0 1796 22.0 23.8 15.7 31.7 21.0 6.7 26.9 1.5 18.4 11.0 8.4 5.1 1797 14.4 4.2 4.0 4.0 7.3 11.1 4.3 6.0 5.7 6.9 5.8 3.0 1798 2.0 4.0 12.4 1.1 0.0 0.0 0.0 3.0 2.4 1.5 12.5 9.9 1799 1.6 12.6 21.7 8.4 8.2 10.6 2.1 0.0 0.0 4.6 2.7 8.6 1800 6.9 9.3 13.9 0.0 5.0 23.7 21.0 19.5 11.5 12.3 10.5 40.1 1801 27.0 29.0 30.0 31.0 32.0 31.2 35.0 38.7 33.5 32.6 39.8 48.2 1802 47.8 47.0 40.8 42.0 44.0 46.0 48.0 50.0 51.8 38.5 34.5 50.0 1803 50.0 50.8 29.5 25.0 44.3 36.0 48.3 34.1 45.3 54.3 51.0 48.0 1804 45.3 48.3 48.0 50.6 33.4 34.8 29.8 43.1 53.0 62.3 61.0 60.0 1805 61.0 44.1 51.4 37.5 39.0 40.5 37.6 42.7 44.4 29.4 41.0 38.3 1806 39.0 29.6 32.7 27.7 26.4 25.6 30.0 26.3 24.0 27.0 25.0 24.0 1807 12.0 12.2 9.6 23.8 10.0 12.0 12.7 12.0 5.7 8.0 2.6 0.0 1808 0.0 4.5 0.0 12.3 13.5 13.5 6.7 8.0 11.7 4.7 10.5 12.3 1809 7.2 9.2 0.9 2.5 2.0 7.7 0.3 0.2 0.4 0.0 0.0 0.0 1810 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1811 0.0 0.0 0.0 0.0 0.0 0.0 6.6 0.0 2.4 6.1 0.8 1.1 1812 11.3 1.9 0.7 0.0 1.0 1.3 0.5 15.6 5.2 3.9 7.9 10.1 1813 0.0 10.3 1.9 16.6 5.5 11.2 18.3 8.4 15.3 27.8 16.7 14.3 1814 22.2 12.0 5.7 23.8 5.8 14.9 18.5 2.3 8.1 19.3 14.5 20.1 1815 19.2 32.2 26.2 31.6 9.8 55.9 35.5 47.2 31.5 33.5 37.2 65.0 1816 26.3 68.8 73.7 58.8 44.3 43.6 38.8 23.2 47.8 56.4 38.1 29.9 1817 36.4 57.9 96.2 26.4 21.2 40.0 50.0 45.0 36.7 25.6 28.9 28.4 1818 34.9 22.4 25.4 34.5 53.1 36.4 28.0 31.5 26.1 31.6 10.9 25.8 1819 32.8 20.7 3.7 20.2 19.6 35.0 31.4 26.1 14.9 27.5 25.1 30.6 1820 19.2 26.6 4.5 19.4 29.3 10.8 20.6 25.9 5.2 8.9 7.9 9.1 1821 21.5 4.2 5.7 9.2 1.7 1.8 2.5 4.8 4.4 18.8 4.4 0.2 1822 0.0 0.9 16.1 13.5 1.5 5.6 7.9 2.1 0.0 0.4 0.0 0.0 1823 0.0 0.0 0.6 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 20.4 1824 21.7 10.8 0.0 19.4 2.8 0.0 0.0 1.4 20.5 25.2 0.0 0.8 1825 5.0 15.5 22.4 3.8 15.5 15.4 30.9 25.7 15.7 15.6 11.7 22.0 1826 17.7 18.2 36.7 24.0 32.4 37.1 52.5 39.6 18.9 50.6 39.5 68.1 1827 34.6 47.4 57.8 46.0 56.3 56.7 42.3 53.7 49.6 56.1 48.2 46.1 1828 52.8 64.4 65.0 61.1 89.1 98.0 54.2 76.4 50.4 54.7 57.0 46.9 1829 43.0 49.4 72.3 95.0 67.4 73.9 90.8 77.6 52.8 57.2 67.6 56.5 1830 52.2 72.1 84.6 106.3 66.3 65.1 43.9 50.7 62.1 84.4 81.2 82.1 1831 47.5 50.1 93.4 54.5 38.1 33.4 45.2 55.0 37.9 46.3 43.5 28.9 1832 30.9 55.6 55.1 26.9 41.3 26.7 14.0 8.9 8.2 21.1 14.3 27.5 1833 11.3 14.9 11.8 2.8 12.9 1.0 7.0 5.7 11.6 7.5 5.9 9.9 1834 4.9 18.1 3.9 1.4 8.8 7.8 8.7 4.0 11.5 24.8 30.5 34.5 1835 7.5 24.5 19.7 61.5 43.6 33.2 59.8 59.0 100.8 95.2 100.0 77.5 1836 88.6 107.6 98.2 142.9 111.4 124.7 116.7 107.8 95.1 137.4 120.9 206.2 1837 188.0 175.6 134.6 138.2 111.7 158.0 162.8 134.0 96.3 123.7 107.0 129.8 1838 144.9 84.8 140.8 126.6 137.6 94.5 108.2 78.8 73.6 90.8 77.4 79.8 1839 105.6 102.5 77.7 61.8 53.8 54.6 84.8 131.2 132.7 90.9 68.8 63.7 1840 81.2 87.7 67.8 65.9 69.2 48.5 60.7 57.8 74.0 55.0 54.3 53.7 1841 24.1 29.9 29.7 40.2 67.5 55.7 30.8 39.3 36.5 28.5 19.8 38.8 1842 20.4 22.1 21.7 26.9 24.9 20.5 12.6 26.6 18.4 38.1 40.5 17.6 1843 13.3 3.5 8.3 9.5 21.1 10.5 9.5 11.8 4.2 5.3 19.1 12.7 1844 9.4 14.7 13.6 20.8 11.6 3.7 21.2 23.9 7.0 21.5 10.7 21.6 1845 25.7 43.6 43.3 57.0 47.8 31.1 30.6 32.3 29.6 40.7 39.4 59.7 1846 38.7 51.0 63.9 69.3 59.9 65.1 46.5 54.8 107.1 55.9 60.4 65.5 1847 62.6 44.9 85.7 44.7 75.4 85.3 52.2 140.6 160.9 180.4 138.9 109.6 1848 159.1 111.8 108.6 107.1 102.2 129.0 139.2 132.6 100.3 132.4 114.6 159.5 1849 157.0 131.7 96.2 102.5 80.6 81.1 78.0 67.7 93.7 71.5 99.0 97.0 1850 78.0 89.4 82.6 44.1 61.6 70.0 39.1 61.6 86.2 71.0 54.8 61.0 1851 75.5 105.4 64.6 56.5 62.6 63.2 36.1 57.4 67.9 62.5 51.0 71.4 1852 68.4 66.4 61.2 65.4 54.9 46.9 42.1 39.7 37.5 67.3 54.3 45.4 1853 41.1 42.9 37.7 47.6 34.7 40.0 45.9 50.4 33.5 42.3 28.8 23.4 1854 15.4 20.0 20.7 26.5 24.0 21.1 18.7 15.8 22.4 12.6 28.2 21.6 1855 12.3 11.4 17.4 4.4 9.1 5.3 0.4 3.1 0.0 9.6 4.2 3.1 1856 0.5 4.9 0.4 6.5 0.0 5.2 4.6 5.9 4.4 4.5 7.7 7.2 1857 13.7 7.4 5.2 11.1 28.6 16.0 22.2 16.9 42.4 40.6 31.4 37.2 1858 39.0 34.9 57.5 38.3 41.4 44.5 56.7 55.3 80.1 91.2 51.9 66.9 1859 83.7 87.6 90.3 85.7 91.0 87.1 95.2 106.8 105.8 114.6 97.2 81.0 1860 82.4 88.3 98.9 71.4 107.1 108.6 116.7 100.3 92.2 90.1 97.9 95.6 1861 62.3 77.7 101.0 98.5 56.8 88.1 78.0 82.5 79.9 67.2 53.7 80.5 1862 63.1 64.5 43.6 53.7 64.4 84.0 73.4 62.5 66.6 41.9 50.6 40.9 1863 48.3 56.7 66.4 40.6 53.8 40.8 32.7 48.1 22.0 39.9 37.7 41.2 1864 57.7 47.1 66.3 35.8 40.6 57.8 54.7 54.8 28.5 33.9 57.6 28.6 1865 48.7 39.3 39.5 29.4 34.5 33.6 26.8 37.8 21.6 17.1 24.6 12.8 1866 31.6 38.4 24.6 17.6 12.9 16.5 9.3 12.7 7.3 14.1 9.0 1.5 1867 0.0 0.7 9.2 5.1 2.9 1.5 5.0 4.8 9.8 13.5 9.6 25.2 1868 15.6 15.7 26.5 36.6 26.7 31.1 29.0 34.4 47.2 61.6 59.1 67.6 1869 60.9 59.9 52.7 41.0 103.9 108.4 59.2 79.6 80.6 59.3 78.1 104.3 1870 77.3 114.9 157.6 160.0 176.0 135.6 132.4 153.8 136.0 146.4 147.5 130.0 1871 88.3 125.3 143.2 162.4 145.5 91.7 103.0 110.1 80.3 89.0 105.4 90.4 1872 79.5 120.1 88.4 102.1 107.6 109.9 105.5 92.9 114.6 102.6 112.0 83.9 1873 86.7 107.0 98.3 76.2 47.9 44.8 66.9 68.2 47.1 47.1 55.4 49.2 1874 60.8 64.2 46.4 32.0 44.6 38.2 67.8 61.3 28.0 34.3 28.9 29.3 1875 14.6 21.5 33.8 29.1 11.5 23.9 12.5 14.6 2.4 12.7 17.7 9.9 1876 14.3 15.0 30.6 2.3 5.1 1.6 15.2 8.8 9.9 14.3 9.9 8.2 1877 24.4 8.7 11.9 15.8 21.6 14.2 6.0 6.3 16.9 6.7 14.2 2.2 1878 3.3 6.6 7.8 0.1 5.9 6.4 0.1 0.0 5.3 1.1 4.1 0.5 1879 1.0 0.6 0.0 6.2 2.4 4.8 7.5 10.7 6.1 12.3 13.1 7.3 1880 24.0 27.2 19.3 19.5 23.5 34.1 21.9 48.1 66.0 43.0 30.7 29.6 1881 36.4 53.2 51.5 51.6 43.5 60.5 76.9 58.4 53.2 64.4 54.8 47.3 1882 45.0 69.5 66.8 95.8 64.1 45.2 45.4 40.4 57.7 59.2 84.4 41.8 1883 60.6 46.9 42.8 82.1 31.5 76.3 80.6 46.0 52.6 83.8 84.5 75.9 1884 91.5 86.9 87.5 76.1 66.5 51.2 53.1 55.8 61.9 47.8 36.6 47.2 1885 42.8 71.8 49.8 55.0 73.0 83.7 66.5 50.0 39.6 38.7 30.9 21.7 1886 29.9 25.9 57.3 43.7 30.7 27.1 30.3 16.9 21.4 8.6 0.3 13.0 1887 10.3 13.2 4.2 6.9 20.0 15.7 23.3 21.4 7.4 6.6 6.9 20.7 1888 12.7 7.1 7.8 5.1 7.0 7.1 3.1 2.8 8.8 2.1 10.7 6.7 1889 0.8 8.5 6.7 4.3 2.4 6.4 9.4 20.6 6.5 2.1 0.2 6.7 1890 5.3 0.6 5.1 1.6 4.8 1.3 11.6 8.5 17.2 11.2 9.6 7.8 1891 13.5 22.2 10.4 20.5 41.1 48.3 58.8 33.0 53.8 51.5 41.9 32.5 1892 69.1 75.6 49.9 69.6 79.6 76.3 76.5 101.4 62.8 70.5 65.4 78.6 1893 75.0 73.0 65.7 88.1 84.7 89.9 88.6 129.2 77.9 80.0 75.1 93.8 1894 83.2 84.6 52.3 81.6 101.2 98.9 106.0 70.3 65.9 75.5 56.6 60.0 1895 63.3 67.2 61.0 76.9 67.5 71.5 47.8 68.9 57.7 67.9 47.2 70.7 1896 29.0 57.4 52.0 43.8 27.7 49.0 45.0 27.2 61.3 28.7 38.0 42.6 1897 40.6 29.4 29.1 31.0 20.0 11.3 27.6 21.8 48.1 14.3 8.4 33.3 1898 30.2 36.4 38.3 14.5 25.8 22.3 9.0 31.4 34.8 34.4 30.9 12.6 1899 19.5 9.2 18.1 14.2 7.7 20.5 13.5 2.9 8.4 13.0 7.8 10.5 1900 9.4 13.6 8.6 16.0 15.2 12.1 8.3 4.3 8.3 12.9 4.5 0.3 1901 0.2 2.4 4.5 0.0 10.2 5.8 0.7 1.0 0.6 3.7 3.8 0.0 1902 5.5 0.0 12.4 0.0 2.8 1.4 0.9 2.3 7.6 16.3 10.3 1.1 1903 8.3 17.0 13.5 26.1 14.6 16.3 27.9 28.8 11.1 38.9 44.5 45.6 1904 31.6 24.5 37.2 43.0 39.5 41.9 50.6 58.2 30.1 54.2 38.0 54.6 1905 54.8 85.8 56.5 39.3 48.0 49.0 73.0 58.8 55.0 78.7 107.2 55.5 1906 45.5 31.3 64.5 55.3 57.7 63.2 103.6 47.7 56.1 17.8 38.9 64.7 1907 76.4 108.2 60.7 52.6 42.9 40.4 49.7 54.3 85.0 65.4 61.5 47.3 1908 39.2 33.9 28.7 57.6 40.8 48.1 39.5 90.5 86.9 32.3 45.5 39.5 1909 56.7 46.6 66.3 32.3 36.0 22.6 35.8 23.1 38.8 58.4 55.8 54.2 1910 26.4 31.5 21.4 8.4 22.2 12.3 14.1 11.5 26.2 38.3 4.9 5.8 1911 3.4 9.0 7.8 16.5 9.0 2.2 3.5 4.0 4.0 2.6 4.2 2.2 1912 0.3 0.0 4.9 4.5 4.4 4.1 3.0 0.3 9.5 4.6 1.1 6.4 1913 2.3 2.9 0.5 0.9 0.0 0.0 1.7 0.2 1.2 3.1 0.7 3.8 1914 2.8 2.6 3.1 17.3 5.2 11.4 5.4 7.7 12.7 8.2 16.4 22.3 1915 23.0 42.3 38.8 41.3 33.0 68.8 71.6 69.6 49.5 53.5 42.5 34.5 1916 45.3 55.4 67.0 71.8 74.5 67.7 53.5 35.2 45.1 50.7 65.6 53.0 1917 74.7 71.9 94.8 74.7 114.1 114.9 119.8 154.5 129.4 72.2 96.4 129.3 1918 96.0 65.3 72.2 80.5 76.7 59.4 107.6 101.7 79.9 85.0 83.4 59.2 1919 48.1 79.5 66.5 51.8 88.1 111.2 64.7 69.0 54.7 52.8 42.0 34.9 1920 51.1 53.9 70.2 14.8 33.3 38.7 27.5 19.2 36.3 49.6 27.2 29.9 1921 31.5 28.3 26.7 32.4 22.2 33.7 41.9 22.8 17.8 18.2 17.8 20.3 1922 11.8 26.4 54.7 11.0 8.0 5.8 10.9 6.5 4.7 6.2 7.4 17.5 1923 4.5 1.5 3.3 6.1 3.2 9.1 3.5 0.5 13.2 11.6 10.0 2.8 1924 0.5 5.1 1.8 11.3 20.8 24.0 28.1 19.3 25.1 25.6 22.5 16.5 1925 5.5 23.2 18.0 31.7 42.8 47.5 38.5 37.9 60.2 69.2 58.6 98.6 1926 71.8 69.9 62.5 38.5 64.3 73.5 52.3 61.6 60.8 71.5 60.5 79.4 1927 81.6 93.0 69.6 93.5 79.1 59.1 54.9 53.8 68.4 63.1 67.2 45.2 1928 83.5 73.5 85.4 80.6 77.0 91.4 98.0 83.8 89.7 61.4 50.3 59.0 1929 68.9 62.8 50.2 52.8 58.2 71.9 70.2 65.8 34.4 54.0 81.1 108.0 1930 65.3 49.9 35.0 38.2 36.8 28.8 21.9 24.9 32.1 34.4 35.6 25.8 1931 14.6 43.1 30.0 31.2 24.6 15.3 17.4 13.0 19.0 10.0 18.7 17.8 1932 12.1 10.6 11.2 11.2 17.9 22.2 9.6 6.8 4.0 8.9 8.2 11.0 1933 12.3 22.2 10.1 2.9 3.2 5.2 2.8 0.2 5.1 3.0 0.6 0.3 1934 3.4 7.8 4.3 11.3 19.7 6.7 9.3 8.3 4.0 5.7 8.7 15.4 1935 18.6 20.5 23.1 12.2 27.3 45.7 33.9 30.1 42.1 53.2 64.2 61.5 1936 62.8 74.3 77.1 74.9 54.6 70.0 52.3 87.0 76.0 89.0 115.4 123.4 1937 132.5 128.5 83.9 109.3 116.7 130.3 145.1 137.7 100.7 124.9 74.4 88.8 1938 98.4 119.2 86.5 101.0 127.4 97.5 165.3 115.7 89.6 99.1 122.2 92.7 1939 80.3 77.4 64.6 109.1 118.3 101.0 97.6 105.8 112.6 88.1 68.1 42.1 1940 50.5 59.4 83.3 60.7 54.4 83.9 67.5 105.5 66.5 55.0 58.4 68.3 1941 45.6 44.5 46.4 32.8 29.5 59.8 66.9 60.0 65.9 46.3 38.4 33.7 1942 35.6 52.8 54.2 60.7 25.0 11.4 17.7 20.2 17.2 19.2 30.7 22.5 1943 12.4 28.9 27.4 26.1 14.1 7.6 13.2 19.4 10.0 7.8 10.2 18.8 1944 3.7 0.5 11.0 0.3 2.5 5.0 5.0 16.7 14.3 16.9 10.8 28.4 1945 18.5 12.7 21.5 32.0 30.6 36.2 42.6 25.9 34.9 68.8 46.0 27.4 1946 47.6 86.2 76.6 75.7 84.9 73.5 116.2 107.2 94.4 102.3 123.8 121.7 1947 115.7 133.4 129.8 149.8 201.3 163.9 157.9 188.8 169.4 163.6 128.0 116.5 1948 108.5 86.1 94.8 189.7 174.0 167.8 142.2 157.9 143.3 136.3 95.8 138.0 1949 119.1 182.3 157.5 147.0 106.2 121.7 125.8 123.8 145.3 131.6 143.5 117.6 1950 101.6 94.8 109.7 113.4 106.2 83.6 91.0 85.2 51.3 61.4 54.8 54.1 1951 59.9 59.9 55.9 92.9 108.5 100.6 61.5 61.0 83.1 51.6 52.4 45.8 1952 40.7 22.7 22.0 29.1 23.4 36.4 39.3 54.9 28.2 23.8 22.1 34.3 1953 26.5 3.9 10.0 27.8 12.5 21.8 8.6 23.5 19.3 8.2 1.6 2.5 1954 0.2 0.5 10.9 1.8 0.8 0.2 4.8 8.4 1.5 7.0 9.2 7.6 1955 23.1 20.8 4.9 11.3 28.9 31.7 26.7 40.7 42.7 58.5 89.2 76.9 1956 73.6 124.0 118.4 110.7 136.6 116.6 129.1 169.6 173.2 155.3 201.3 192.1 1957 165.0 130.2 157.4 175.2 164.6 200.7 187.2 158.0 235.8 253.8 210.9 239.4 1958 202.5 164.9 190.7 196.0 175.3 171.5 191.4 200.2 201.2 181.5 152.3 187.6 1959 217.4 143.1 185.7 163.3 172.0 168.7 149.6 199.6 145.2 111.4 124.0 125.0 1960 146.3 106.0 102.2 122.0 119.6 110.2 121.7 134.1 127.2 82.8 89.6 85.6 1961 57.9 46.1 53.0 61.4 51.0 77.4 70.2 55.8 63.6 37.7 32.6 39.9 1962 38.7 50.3 45.6 46.4 43.7 42.0 21.8 21.8 51.3 39.5 26.9 23.2 1963 19.8 24.4 17.1 29.3 43.0 35.9 19.6 33.2 38.8 35.3 23.4 14.9 1964 15.3 17.7 16.5 8.6 9.5 9.1 3.1 9.3 4.7 6.1 7.4 15.1 1965 17.5 14.2 11.7 6.8 24.1 15.9 11.9 8.9 16.8 20.1 15.8 17.0 1966 28.2 24.4 25.3 48.7 45.3 47.7 56.7 51.2 50.2 57.2 57.2 70.4 1967 110.9 93.6 111.8 69.5 86.5 67.3 91.5 107.2 76.8 88.2 94.3 126.4 1968 121.8 111.9 92.2 81.2 127.2 110.3 96.1 109.3 117.2 107.7 86.0 109.8 1969 104.4 120.5 135.8 106.8 120.0 106.0 96.8 98.0 91.3 95.7 93.5 97.9 1970 111.5 127.8 102.9 109.5 127.5 106.8 112.5 93.0 99.5 86.6 95.2 83.5 1971 91.3 79.0 60.7 71.8 57.5 49.8 81.0 61.4 50.2 51.7 63.2 82.2 1972 61.5 88.4 80.1 63.2 80.5 88.0 76.5 76.8 64.0 61.3 41.6 45.3 1973 43.4 42.9 46.0 57.7 42.4 37.5 23.1 25.6 59.3 30.7 23.9 23.3 1974 27.6 26.0 21.3 40.3 39.5 36.0 55.8 33.6 40.2 47.1 25.0 20.5 1975 18.9 11.5 11.5 5.1 9.0 11.4 28.2 39.7 13.9 9.1 19.4 7.8 1976 8.1 4.3 21.9 18.8 12.4 12.2 1.9 16.4 13.5 20.6 5.2 15.3 1977 16.4 23.1 8.7 12.9 18.6 38.5 21.4 30.1 44.0 43.8 29.1 43.2 1978 51.9 93.6 76.5 99.7 82.7 95.1 70.4 58.1 138.2 125.1 97.9 122.7 1979 166.6 137.5 138.0 101.5 134.4 149.5 159.4 142.2 188.4 186.2 183.3 176.3 1980 159.6 155.0 126.2 164.1 179.9 157.3 136.3 135.4 155.0 164.7 147.9 174.4 1981 114.0 141.3 135.5 156.4 127.5 90.9 143.8 158.7 167.3 162.4 137.5 150.1 1982 111.2 163.6 153.8 122.0 82.2 110.4 106.1 107.6 118.8 94.7 98.1 127.0 1983 84.3 51.0 66.5 80.7 99.2 91.1 82.2 71.8 50.3 55.8 33.3 33.4 1984 57.0 85.4 83.5 69.7 76.4 46.1 37.4 25.5 15.7 12.0 22.8 18.7 1985 16.5 15.9 17.2 16.2 27.5 24.2 30.7 11.1 3.9 18.6 16.2 17.3 1986 2.5 23.2 15.1 18.5 13.7 1.1 18.1 7.4 3.8 35.4 15.2 6.8 1987 10.4 2.4 14.7 39.6 33.0 17.4 33.0 38.7 33.9 60.6 39.9 27.1 1988 59.0 40.0 76.2 88.0 60.1 101.8 113.8 111.6 120.1 125.1 125.1 179.2 1989 161.3 165.1 131.4 130.6 138.5 196.2 126.9 168.9 176.7 159.4 173.0 165.5 1990 177.3 130.5 140.3 140.3 132.2 105.4 149.4 200.3 125.2 145.5 131.4 129.7 1991 136.9 167.5 141.9 140.0 121.3 169.7 173.7 176.3 125.3 144.1 108.2 144.4 1992 150.0 161.1 106.7 99.8 73.8 65.2 85.7 64.5 63.9 88.7 91.8 82.6 1993 59.3 91.0 69.8 62.2 61.3 49.8 57.9 42.2 22.4 56.4 35.6 48.9 1994 57.8 35.5 31.7 16.1 17.8 28.0 35.1 22.5 25.7 44.0 18.0 26.2 1995 24.2 29.9 31.1 14.0 14.5 15.6 14.5 14.3 11.8 21.1 9.0 10.0 1996 11.5 4.4 9.2 4.8 5.5 11.8 8.2 14.4 1.6 0.9 17.9 13.3 1997 5.7 7.6 8.7 15.5 18.5 12.7 10.4 24.4 51.3 23.8 39.0 41.2 1998 31.9 40.3 54.8 53.4 56.3 70.7 66.6 92.2 92.9 55.5 74.0 81.9 1999 62.0 66.3 68.8 63.7 106.4 137.7 113.5 93.7 71.5 116.7 133.2 84.6 2000 90.1 112.9 138.5 125.5 121.6 124.9 170.1 130.5 109.7 99.4 106.8 104.4 2001 95.6 80.6 113.5 107.7 96.6 134.0 81.8 106.4 150.7 125.5 106.5 132.2 2002 114.1 107.4 98.4 120.7 120.8 88.3 99.6 116.4 109.6 97.5 95.5 80.8 2003 79.7 46.0 61.1 60.0 54.6 77.4 83.3 72.7 48.7 65.5 67.3 46.5 2004 37.3 45.8 49.1 39.3 41.5 43.2 51.1 40.9 27.7 48.0 43.5 17.9 2005 31.3 29.2 24.5 24.2 42.7 39.3 40.1 36.4 21.9 8.7 18.0 41.1 2006 15.3 4.9 10.6 30.2 22.3 13.9 12.2 12.9 14.4 10.5 21.4 13.6 2007 16.8 10.7 4.5 3.4 11.7 12.1 9.7 6.0 2.4 0.9 1.7 10.1 2008 3.3 2.1 9.3 2.9 3.2 3.4 0.8 0.5 1.1 2.9 4.1 0.8 2009 1.5 1.4 0.7 1.2 2.9 2.6 ------------------------------------------------------------------------------- No observations were available during February 1824. The value shown was interpolated from the January and March monthly means of that year. Note: Data are preliminary after Dec 08. statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/src/sunspots_yearly.dat000066400000000000000000000063451224417117700307760ustar00rootroot000000000000001700 5 1701 11 1702 16 1703 23 1704 36 1705 58 1706 29 1707 20 1708 10 1709 8 1710 3 1711 0 1712 0 1713 2 1714 11 1715 27 1716 47 1717 63 1718 60 1719 39 1720 28 1721 26 1722 22 1723 11 1724 21 1725 40 1726 78 1727 122 1728 103 1729 73 1730 47 1731 35 1732 11 1733 5 1734 16 1735 34 1736 70 1737 81 1738 111 1739 101 1740 73 1741 40 1742 20 1743 16 1744 5 1745 11 1746 22 1747 40 1748 60 1749 80.9 1750 83.4 1751 47.7 1752 47.8 1753 30.7 1754 12.2 1755 9.6 1756 10.2 1757 32.4 1758 47.6 1759 54.0 1760 62.9 1761 85.9 1762 61.2 1763 45.1 1764 36.4 1765 20.9 1766 11.4 1767 37.8 1768 69.8 1769 106.1 1770 100.8 1771 81.6 1772 66.5 1773 34.8 1774 30.6 1775 7.0 1776 19.8 1777 92.5 1778 154.4 1779 125.9 1780 84.8 1781 68.1 1782 38.5 1783 22.8 1784 10.2 1785 24.1 1786 82.9 1787 132.0 1788 130.9 1789 118.1 1790 89.9 1791 66.6 1792 60.0 1793 46.9 1794 41.0 1795 21.3 1796 16.0 1797 6.4 1798 4.1 1799 6.8 1800 14.5 1801 34.0 1802 45.0 1803 43.1 1804 47.5 1805 42.2 1806 28.1 1807 10.1 1808 8.1 1809 2.5 1810 0.0 1811 1.4 1812 5.0 1813 12.2 1814 13.9 1815 35.4 1816 45.8 1817 41.1 1818 30.1 1819 23.9 1820 15.6 1821 6.6 1822 4.0 1823 1.8 1824 8.5 1825 16.6 1826 36.3 1827 49.6 1828 64.2 1829 67.0 1830 70.9 1831 47.8 1832 27.5 1833 8.5 1834 13.2 1835 56.9 1836 121.5 1837 138.3 1838 103.2 1839 85.7 1840 64.6 1841 36.7 1842 24.2 1843 10.7 1844 15.0 1845 40.1 1846 61.5 1847 98.5 1848 124.7 1849 96.3 1850 66.6 1851 64.5 1852 54.1 1853 39.0 1854 20.6 1855 6.7 1856 4.3 1857 22.7 1858 54.8 1859 93.8 1860 95.8 1861 77.2 1862 59.1 1863 44.0 1864 47.0 1865 30.5 1866 16.3 1867 7.3 1868 37.6 1869 74.0 1870 139.0 1871 111.2 1872 101.6 1873 66.2 1874 44.7 1875 17.0 1876 11.3 1877 12.4 1878 3.4 1879 6.0 1880 32.3 1881 54.3 1882 59.7 1883 63.7 1884 63.5 1885 52.2 1886 25.4 1887 13.1 1888 6.8 1889 6.3 1890 7.1 1891 35.6 1892 73.0 1893 85.1 1894 78.0 1895 64.0 1896 41.8 1897 26.2 1898 26.7 1899 12.1 1900 9.5 1901 2.7 1902 5.0 1903 24.4 1904 42.0 1905 63.5 1906 53.8 1907 62.0 1908 48.5 1909 43.9 1910 18.6 1911 5.7 1912 3.6 1913 1.4 1914 9.6 1915 47.4 1916 57.1 1917 103.9 1918 80.6 1919 63.6 1920 37.6 1921 26.1 1922 14.2 1923 5.8 1924 16.7 1925 44.3 1926 63.9 1927 69.0 1928 77.8 1929 64.9 1930 35.7 1931 21.2 1932 11.1 1933 5.7 1934 8.7 1935 36.1 1936 79.7 1937 114.4 1938 109.6 1939 88.8 1940 67.8 1941 47.5 1942 30.6 1943 16.3 1944 9.6 1945 33.2 1946 92.6 1947 151.6 1948 136.3 1949 134.7 1950 83.9 1951 69.4 1952 31.5 1953 13.9 1954 4.4 1955 38.0 1956 141.7 1957 190.2 1958 184.8 1959 159.0 1960 112.3 1961 53.9 1962 37.6 1963 27.9 1964 10.2 1965 15.1 1966 47.0 1967 93.8 1968 105.9 1969 105.5 1970 104.5 1971 66.6 1972 68.9 1973 38.0 1974 34.5 1975 15.5 1976 12.6 1977 27.5 1978 92.5 1979 155.4 1980 154.6 1981 140.4 1982 115.9 1983 66.6 1984 45.9 1985 17.9 1986 13.4 1987 29.4 1988 100.2 1989 157.6 1990 142.6 1991 145.7 1992 94.3 1993 54.6 1994 29.9 1995 17.5 1996 8.6 1997 21.5 1998 64.3 1999 93.3 2000 119.6 2001 111.0 2002 104.0 2003 63.7 2004 40.4 2005 29.8 2006 15.2 2007 7.5 2008 2.9 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/sunspots/sunspots.csv000066400000000000000000000056001224417117700266360ustar00rootroot00000000000000"YEAR","SUNACTIVITY" 1700,5 1701,11 1702,16 1703,23 1704,36 1705,58 1706,29 1707,20 1708,10 1709,8 1710,3 1711,0 1712,0 1713,2 1714,11 1715,27 1716,47 1717,63 1718,60 1719,39 1720,28 1721,26 1722,22 1723,11 1724,21 1725,40 1726,78 1727,122 1728,103 1729,73 1730,47 1731,35 1732,11 1733,5 1734,16 1735,34 1736,70 1737,81 1738,111 1739,101 1740,73 1741,40 1742,20 1743,16 1744,5 1745,11 1746,22 1747,40 1748,60 1749,80.9 1750,83.4 1751,47.7 1752,47.8 1753,30.7 1754,12.2 1755,9.6 1756,10.2 1757,32.4 1758,47.6 1759,54 1760,62.9 1761,85.9 1762,61.2 1763,45.1 1764,36.4 1765,20.9 1766,11.4 1767,37.8 1768,69.8 1769,106.1 1770,100.8 1771,81.6 1772,66.5 1773,34.8 1774,30.6 1775,7 1776,19.8 1777,92.5 1778,154.4 1779,125.9 1780,84.8 1781,68.1 1782,38.5 1783,22.8 1784,10.2 1785,24.1 1786,82.9 1787,132 1788,130.9 1789,118.1 1790,89.9 1791,66.6 1792,60 1793,46.9 1794,41 1795,21.3 1796,16 1797,6.4 1798,4.1 1799,6.8 1800,14.5 1801,34 1802,45 1803,43.1 1804,47.5 1805,42.2 1806,28.1 1807,10.1 1808,8.1 1809,2.5 1810,0 1811,1.4 1812,5 1813,12.2 1814,13.9 1815,35.4 1816,45.8 1817,41.1 1818,30.1 1819,23.9 1820,15.6 1821,6.6 1822,4 1823,1.8 1824,8.5 1825,16.6 1826,36.3 1827,49.6 1828,64.2 1829,67 1830,70.9 1831,47.8 1832,27.5 1833,8.5 1834,13.2 1835,56.9 1836,121.5 1837,138.3 1838,103.2 1839,85.7 1840,64.6 1841,36.7 1842,24.2 1843,10.7 1844,15 1845,40.1 1846,61.5 1847,98.5 1848,124.7 1849,96.3 1850,66.6 1851,64.5 1852,54.1 1853,39 1854,20.6 1855,6.7 1856,4.3 1857,22.7 1858,54.8 1859,93.8 1860,95.8 1861,77.2 1862,59.1 1863,44 1864,47 1865,30.5 1866,16.3 1867,7.3 1868,37.6 1869,74 1870,139 1871,111.2 1872,101.6 1873,66.2 1874,44.7 1875,17 1876,11.3 1877,12.4 1878,3.4 1879,6 1880,32.3 1881,54.3 1882,59.7 1883,63.7 1884,63.5 1885,52.2 1886,25.4 1887,13.1 1888,6.8 1889,6.3 1890,7.1 1891,35.6 1892,73 1893,85.1 1894,78 1895,64 1896,41.8 1897,26.2 1898,26.7 1899,12.1 1900,9.5 1901,2.7 1902,5 1903,24.4 1904,42 1905,63.5 1906,53.8 1907,62 1908,48.5 1909,43.9 1910,18.6 1911,5.7 1912,3.6 1913,1.4 1914,9.6 1915,47.4 1916,57.1 1917,103.9 1918,80.6 1919,63.6 1920,37.6 1921,26.1 1922,14.2 1923,5.8 1924,16.7 1925,44.3 1926,63.9 1927,69 1928,77.8 1929,64.9 1930,35.7 1931,21.2 1932,11.1 1933,5.7 1934,8.7 1935,36.1 1936,79.7 1937,114.4 1938,109.6 1939,88.8 1940,67.8 1941,47.5 1942,30.6 1943,16.3 1944,9.6 1945,33.2 1946,92.6 1947,151.6 1948,136.3 1949,134.7 1950,83.9 1951,69.4 1952,31.5 1953,13.9 1954,4.4 1955,38 1956,141.7 1957,190.2 1958,184.8 1959,159 1960,112.3 1961,53.9 1962,37.6 1963,27.9 1964,10.2 1965,15.1 1966,47 1967,93.8 1968,105.9 1969,105.5 1970,104.5 1971,66.6 1972,68.9 1973,38 1974,34.5 1975,15.5 1976,12.6 1977,27.5 1978,92.5 1979,155.4 1980,154.6 1981,140.4 1982,115.9 1983,66.6 1984,45.9 1985,17.9 1986,13.4 1987,29.4 1988,100.2 1989,157.6 1990,142.6 1991,145.7 1992,94.3 1993,54.6 1994,29.9 1995,17.5 1996,8.6 1997,21.5 1998,64.3 1999,93.3 2000,119.6 2001,111 2002,104 2003,63.7 2004,40.4 2005,29.8 2006,15.2 2007,7.5 2008,2.9 statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/template_data.py000066400000000000000000000031621224417117700255040ustar00rootroot00000000000000#! /usr/bin/env python """Name of dataset.""" __docformat__ = 'restructuredtext' COPYRIGHT = """E.g., This is public domain.""" TITLE = """Title of the dataset""" SOURCE = """ This section should provide a link to the original dataset if possible and attribution and correspondance information for the dataset's original author if so desired. """ DESCRSHORT = """A short description.""" DESCRLONG = """A longer description of the dataset.""" #suggested notes NOTE = """ Number of observations: Number of variables: Variable name definitions: Any other useful information that does not fit into the above categories. """ import numpy as np from statsmodels.datasets import utils as du from os.path import dirname, abspath def load(): """ Load the data and return a Dataset class instance. Returns ------- Dataset instance: See DATASET_PROPOSAL.txt for more information. """ data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray(data, endog_idx=0, exog_idx=None, dtype=float) def load_pandas(): data = _get_data() ##### SET THE INDICES ##### #NOTE: None for exog_idx is the complement of endog_idx return du.process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=float) def _get_data(): filepath = dirname(abspath(__file__)) ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv ##### data = np.recfromtxt(open(filepath + '/DatasetName.csv', 'rb'), delimiter=",", names = True, dtype=float) return data statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/tests/000077500000000000000000000000001224417117700234665ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/tests/__init__.py000066400000000000000000000000001224417117700255650ustar00rootroot00000000000000raw.github.com,vincentarelbundock,Rdatasets,master,csv,car,Duncan.csv.zip000066400000000000000000000011551224417117700414110ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/testsxœU“ÁŽ£0Dïó¹ÌŲÀ@p~cöºÇé Þ€ŒY6¿ÕfDAJ”çnwU¿>O'qJÏ‘ðå¼ ÿ ÛlMrÁã÷iJîN§ßþd¬ ³OƧ Â÷Iœ•Ðg¡èèú°ƒV‰ â²h;—È`#.J\ †sêBÜHR Äv4¸épUÍWå¢ÁyÚËT)4pËcàš¦CO^*<€7òéØS¢,Šµi¤1ĵébQÕ¢ŸhXù»ót¸®]Uk°Ùß(&ó8ÐÒkÑð0½YžÇ:̯…¾ð˜ÝsrÖÿ[<€ õß&’\B|kœU6\žÈÀŸÒâRŠ–5Ú౿^BзªE¥W”¢ùa?†W¢nVÿ3yÊ°øCçs!h-q`&9oîïrëMpàjüqÞZ±f¶ðÂãA4nöª »X±–Á¸^Z£Û(ä U êü4Gã-IÜš“Ç@Û†lSÙžâcïÝ`±9D&Ž¨Ê¯6EU¢bŸ¨G$ãk ëÁÀ¯/yØ:CJTäh¿æˆQ‡ÕºpÄ9H$ɸ8l}•ÊÏ3öópÝzÂ7Õ`\€»™ä”¼4)‘¿­¯)™#¤y{¦—ˆþVÞâS6Ù‚H” Unì7×|­(sbÌ?'oÑýÝÊÑö§sŠ³}¼C…«Ëj“K2`{fÍÎËÊB¨:ï=î¢Ê3ß©ŠõVó†´`Ÿ¦.œºƒŒû³Ìðx¯Ù¸¼È)Ü̶äÌžB0ZL²Ý®m`jÉÑøc¼Ûny^~ ÆÐ;K{^¬>‡i1n‹æµ²AŸcñ!ÿ®ž†õraw.github.com,vincentarelbundock,Rdatasets,master,datasets.csv.zip000066400000000000000000000403021224417117700404540ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/datasets/testsxœÕmsÛ¶¶ïßßOñ‹Óv&vâ¤ÝMΛ;²l'Þ±mËN¦g:s"! 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If you want a raw_data # attribute you must create this in the dataset's load function. try: # some datasets have string variables self.raw_data = self.data.view((float, len(self.names))) except: pass def __repr__(self): return str(self.__class__) def process_recarray(data, endog_idx=0, exog_idx=None, stack=True, dtype=None): names = list(data.dtype.names) if isinstance(endog_idx, int): endog = array(data[names[endog_idx]], dtype=dtype) endog_name = names[endog_idx] endog_idx = [endog_idx] else: endog_name = [names[i] for i in endog_idx] if stack: endog = np.column_stack(data[field] for field in endog_name) else: endog = data[endog_name] if exog_idx is None: exog_name = [names[i] for i in xrange(len(names)) if i not in endog_idx] else: exog_name = [names[i] for i in exog_idx] if stack: exog = np.column_stack(data[field] for field in exog_name) else: exog = data[exog_name] if dtype: endog = endog.astype(dtype) exog = exog.astype(dtype) dataset = Dataset(data=data, names=names, endog=endog, exog=exog, endog_name=endog_name, exog_name=exog_name) return dataset def process_recarray_pandas(data, endog_idx=0, exog_idx=None, dtype=None, index_idx=None): from pandas import DataFrame data = DataFrame(data, dtype=dtype) names = data.columns if isinstance(endog_idx, int): endog_name = names[endog_idx] endog = data[endog_name] if exog_idx is None: exog = data.drop([endog_name], axis=1) else: exog = data.filter(names[exog_idx]) else: endog = data.ix[:, endog_idx] endog_name = list(endog.columns) if exog_idx is None: exog = data.drop(endog_name, axis=1) elif isinstance(exog_idx, int): exog = data.filter([names[exog_idx]]) else: exog = data.filter(names[exog_idx]) if index_idx is not None: #NOTE: will have to be improved for dates from pandas import Index endog.index = Index(data.ix[:, index_idx]) exog.index = Index(data.ix[:, index_idx]) data = data.set_index(names[index_idx]) exog_name = list(exog.columns) dataset = Dataset(data=data, names=list(names), endog=endog, exog=exog, endog_name=endog_name, exog_name=exog_name) return dataset def _maybe_reset_index(data): """ All the Rdatasets have the integer row.labels from R if there is no real index. Strip this for a zero-based index """ from pandas import Index if data.index.equals(Index(range(1,len(data)+1))): data = data.reset_index(drop=True) return data def _get_cache(cache): if cache is False: # do not do any caching or load from cache cache = None elif cache is True: # use default dir for cache cache = get_data_home(None) else: cache = get_data_home(cache) return cache def _cache_it(data, cache_path): if sys.version_info[0] >= 3: # for some reason encode("zip") won't work for me in Python 3? import zlib # use protocol 2 so can open with python 2.x if cached in 3.x open(cache_path, "wb").write(zlib.compress(pickle.dumps(data, protocol=2))) else: open(cache_path, "wb").write(pickle.dumps(data).encode("zip")) def _open_cache(cache_path): if sys.version_info[0] >= 3: #NOTE: don't know why but decode('zip') doesn't work on my # Python 3 build import zlib data = zlib.decompress(open(cache_path, 'rb').read()) # return as bytes object encoded in utf-8 for cross-compat of cached data = pickle.loads(data).encode('utf-8') else: data = open(cache_path, 'rb').read().decode('zip') data = pickle.loads(data) return data def _urlopen_cached(url, cache): """ Tries to load data from cache location otherwise downloads it. If it downloads the data and cache is not None then it will put the downloaded data in the cache path. """ from_cache = False if cache is not None: cache_path = join(cache, url.split("://")[-1].replace('/', ',') +".zip") try: data = _open_cache(cache_path) from_cache = True except: pass # not using the cache or didn't find it in cache if not from_cache: data = urlopen(url).read() if cache is not None: # then put it in the cache _cache_it(data, cache_path) return data, from_cache def _get_data(base_url, dataname, cache, extension="csv"): url = base_url + (dataname + ".%s") % extension try: data, from_cache = _urlopen_cached(url, cache) except HTTPError, err: if '404' in str(err): raise ValueError("Dataset %s was not found." % dataname) else: raise err #Python 3, always decode as unicode if sys.version[0] == '3': # pragma: no cover data = data.decode('utf-8', errors='strict') return StringIO(data), from_cache def _get_dataset_meta(dataname, package, cache): # get the index, you'll probably want this cached because you have # to download info about all the data to get info about any of the data... index_url = ("https://raw.github.com/vincentarelbundock/Rdatasets/master/" "datasets.csv") data, _ = _urlopen_cached(index_url, cache) #Python 3 if sys.version[0] == '3': # pragma: no cover data = data.decode('ascii', errors='strict') index = read_csv(StringIO(data)) idx = np.logical_and(index.Item == dataname, index.Package == package) dataset_meta = index.ix[idx] return dataset_meta["Title"].item() def get_rdataset(dataname, package="datasets", cache=False): """download and return R dataset Parameters ---------- dataname : str The name of the dataset you want to download package : str The package in which the dataset is found. The default is the core 'datasets' package. cache : bool or str If True, will download this data into the STATSMODELS_DATA folder. The default location is a folder called statsmodels_data in the user home folder. Otherwise, you can specify a path to a folder to use for caching the data. If False, the data will not be cached. Returns ------- dataset : Dataset instance A `statsmodels.data.utils.Dataset` instance. This objects has attributes:: * data - A pandas DataFrame containing the data * title - The dataset title * package - The package from which the data came * from_cache - Whether not cached data was retrieved * __doc__ - The verbatim R documentation. Notes ----- If the R dataset has an integer index. This is reset to be zero-based. Otherwise the index is preserved. The caching facilities are dumb. That is, no download dates, e-tags, or otherwise identifying information is checked to see if the data should be downloaded again or not. If the dataset is in the cache, it's used. """ #NOTE: use raw github bc html site might not be most up to date data_base_url = ("https://raw.github.com/vincentarelbundock/Rdatasets/" "master/csv/"+package+"/") docs_base_url = ("https://raw.github.com/vincentarelbundock/Rdatasets/" "master/doc/"+package+"/rst/") cache = _get_cache(cache) data, from_cache = _get_data(data_base_url, dataname, cache) data = read_csv(data, index_col=0) data = _maybe_reset_index(data) title = _get_dataset_meta(dataname, package, cache) doc, _ = _get_data(docs_base_url, dataname, cache, "rst") return Dataset(data=data, __doc__=doc.read(), package=package, title=title, from_cache=from_cache) ### The below function were taken from sklearn def get_data_home(data_home=None): """Return the path of the statsmodels data dir. This folder is used by some large dataset loaders to avoid downloading the data several times. By default the data dir is set to a folder named 'statsmodels_data' in the user home folder. Alternatively, it can be set by the 'STATSMODELS_DATA' environment variable or programatically by giving an explit folder path. The '~' symbol is expanded to the user home folder. If the folder does not already exist, it is automatically created. """ if data_home is None: data_home = environ.get('STATSMODELS_DATA', join('~', 'statsmodels_data')) data_home = expanduser(data_home) if not exists(data_home): makedirs(data_home) return data_home def clear_data_home(data_home=None): """Delete all the content of the data home cache.""" data_home = get_data_home(data_home) shutil.rmtree(data_home) statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/000077500000000000000000000000001224417117700223165ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/__init__.py000066400000000000000000000001031224417117700244210ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/discrete_margins.py000066400000000000000000000614751224417117700262270ustar00rootroot00000000000000#Splitting out maringal effects to see if they can be generalized import numpy as np from scipy.stats import norm from statsmodels.tools.decorators import cache_readonly, resettable_cache #### margeff helper functions #### #NOTE: todo marginal effects for group 2 # group 2 oprobit, ologit, gologit, mlogit, biprobit def _check_margeff_args(at, method): """ Checks valid options for margeff """ if at not in ['overall','mean','median','zero','all']: raise ValueError("%s not a valid option for `at`." % at) if method not in ['dydx','eyex','dyex','eydx']: raise ValueError("method is not understood. Got %s" % method) def _check_discrete_args(at, method): """ Checks the arguments for margeff if the exogenous variables are discrete. """ if method in ['dyex','eyex']: raise ValueError("%s not allowed for discrete variables" % method) if at in ['median', 'zero']: raise ValueError("%s not allowed for discrete variables" % at) def _get_const_index(exog): """ Returns a boolean array of non-constant column indices in exog and an scalar array of where the constant is or None """ effects_idx = exog.var(0) != 0 if np.any(~effects_idx): const_idx = np.where(~effects_idx)[0] else: const_idx = None return effects_idx, const_idx def _isdummy(X): """ Given an array X, returns the column indices for the dummy variables. Parameters ---------- X : array-like A 1d or 2d array of numbers Examples -------- >>> X = np.random.randint(0, 2, size=(15,5)).astype(float) >>> X[:,1:3] = np.random.randn(15,2) >>> ind = _isdummy(X) >>> ind array([ True, False, False, True, True], dtype=bool) """ X = np.asarray(X) if X.ndim > 1: ind = np.zeros(X.shape[1]).astype(bool) max = (np.max(X, axis=0) == 1) min = (np.min(X, axis=0) == 0) remainder = np.all(X % 1. == 0, axis=0) ind = min & max & remainder if X.ndim == 1: ind = np.asarray([ind]) return np.where(ind)[0] def _get_dummy_index(X, const_idx): dummy_ind = _isdummy(X) dummy = True # adjust back for a constant because effects doesn't have one if const_idx is not None: dummy_ind[dummy_ind > const_idx] -= 1 if dummy_ind.size == 0: # don't waste your time dummy = False dummy_ind = None # this gets passed to stand err func return dummy_ind, dummy def _iscount(X): """ Given an array X, returns the column indices for count variables. Parameters ---------- X : array-like A 1d or 2d array of numbers Examples -------- >>> X = np.random.randint(0, 10, size=(15,5)).astype(float) >>> X[:,1:3] = np.random.randn(15,2) >>> ind = _iscount(X) >>> ind array([ True, False, False, True, True], dtype=bool) """ X = np.asarray(X) remainder = np.logical_and(np.logical_and(np.all(X % 1. == 0, axis = 0), X.var(0) != 0), np.all(X >= 0, axis=0)) dummy = _isdummy(X) remainder = np.where(remainder)[0].tolist() for idx in dummy: remainder.remove(idx) return np.array(remainder) def _get_count_index(X, const_idx): count_ind = _iscount(X) count = True # adjust back for a constant because effects doesn't have one if const_idx is not None: count_ind[count_ind > const_idx] -= 1 if count_ind.size == 0: # don't waste your time count = False count_ind = None # for stand err func return count_ind, count def _get_margeff_exog(exog, at, atexog, ind): if atexog is not None: # user supplied if isinstance(atexog, dict): # assumes values are singular or of len(exog) for key in atexog: exog[:,key] = atexog[key] elif isinstance(atexog, np.ndarray): #TODO: handle DataFrames if atexog.ndim == 1: k_vars = len(atexog) else: k_vars = atexog.shape[1] try: assert k_vars == exog.shape[1] except: raise ValueError("atexog does not have the same number " "of variables as exog") exog = atexog #NOTE: we should fill in atexog after we process at if at == 'mean': exog = np.atleast_2d(exog.mean(0)) elif at == 'median': exog = np.atleast_2d(np.median(exog, axis=0)) elif at == 'zero': exog = np.zeros((1,exog.shape[1])) exog[0,~ind] = 1 return exog def _get_count_effects(effects, exog, count_ind, method, model, params): """ If there's a count variable, the predicted difference is taken by subtracting one and adding one to exog then averaging the difference """ # this is the index for the effect and the index for count col in exog for i in count_ind: exog0 = exog.copy() exog0[:, i] -= 1 effect0 = model.predict(params, exog0) exog0[:, i] += 2 effect1 = model.predict(params, exog0) #NOTE: done by analogy with dummy effects but untested bc # stata doesn't handle both count and eydx anywhere if 'ey' in method: effect0 = np.log(effect0) effect1 = np.log(effect1) effects[:, i] = ((effect1 - effect0)/2) return effects def _get_dummy_effects(effects, exog, dummy_ind, method, model, params): """ If there's a dummy variable, the predicted difference is taken at 0 and 1 """ # this is the index for the effect and the index for dummy col in exog for i in dummy_ind: exog0 = exog.copy() # only copy once, can we avoid a copy? exog0[:,i] = 0 effect0 = model.predict(params, exog0) #fittedvalues0 = np.dot(exog0,params) exog0[:,i] = 1 effect1 = model.predict(params, exog0) if 'ey' in method: effect0 = np.log(effect0) effect1 = np.log(effect1) effects[:, i] = (effect1 - effect0) return effects def _effects_at(effects, at): if at == 'all': effects = effects elif at == 'overall': effects = effects.mean(0) else: effects = effects[0,:] return effects def _margeff_cov_params_dummy(model, cov_margins, params, exog, dummy_ind, method, J): """ Returns the Jacobian for discrete regressors for use in margeff_cov_params. For discrete regressors the marginal effect is \Delta F = F(XB) | d = 1 - F(XB) | d = 0 The row of the Jacobian for this variable is given by f(XB)*X | d = 1 - f(XB)*X | d = 0 Where F is the default prediction of the model. """ for i in dummy_ind: exog0 = exog.copy() exog1 = exog.copy() exog0[:,i] = 0 exog1[:,i] = 1 dfdb0 = model._derivative_predict(params, exog0, method) dfdb1 = model._derivative_predict(params, exog1, method) dfdb = (dfdb1 - dfdb0) if dfdb.ndim >= 2: # for overall dfdb = dfdb.mean(0) if J > 1: K = dfdb.shape[1] / (J-1) cov_margins[i::K, :] = dfdb else: cov_margins[i, :] = dfdb # how each F changes with change in B return cov_margins def _margeff_cov_params_count(model, cov_margins, params, exog, count_ind, method, J): """ Returns the Jacobian for discrete regressors for use in margeff_cov_params. For discrete regressors the marginal effect is \Delta F = F(XB) | d += 1 - F(XB) | d -= 1 The row of the Jacobian for this variable is given by (f(XB)*X | d += 1 - f(XB)*X | d -= 1) / 2 where F is the default prediction for the model. """ for i in count_ind: exog0 = exog.copy() exog0[:,i] -= 1 dfdb0 = model._derivative_predict(params, exog0, method) exog0[:,i] += 2 dfdb1 = model._derivative_predict(params, exog0, method) dfdb = (dfdb1 - dfdb0) if dfdb.ndim >= 2: # for overall dfdb = dfdb.mean(0) / 2 if J > 1: K = dfdb.shape[1] / (J-1) cov_margins[i::K, :] = dfdb else: cov_margins[i, :] = dfdb # how each F changes with change in B return cov_margins def margeff_cov_params(model, params, exog, cov_params, at, derivative, dummy_ind, count_ind, method, J): """ Computes the variance-covariance of marginal effects by the delta method. Parameters ---------- model : model instance The model that returned the fitted results. Its pdf method is used for computing the Jacobian of discrete variables in dummy_ind and count_ind params : array-like estimated model parameters exog : array-like exogenous variables at which to calculate the derivative cov_params : array-like The variance-covariance of the parameters at : str Options are: - 'overall', The average of the marginal effects at each observation. - 'mean', The marginal effects at the mean of each regressor. - 'median', The marginal effects at the median of each regressor. - 'zero', The marginal effects at zero for each regressor. - 'all', The marginal effects at each observation. Only overall has any effect here.you derivative : function or array-like If a function, it returns the marginal effects of the model with respect to the exogenous variables evaluated at exog. Expected to be called derivative(params, exog). This will be numerically differentiated. Otherwise, it can be the Jacobian of the marginal effects with respect to the parameters. dummy_ind : array-like Indices of the columns of exog that contain dummy variables count_ind : array-like Indices of the columns of exog that contain count variables Notes ----- For continuous regressors, the variance-covariance is given by Asy. Var[MargEff] = [d margeff / d params] V [d margeff / d params]' where V is the parameter variance-covariance. The outer Jacobians are computed via numerical differentiation if derivative is a function. """ if callable(derivative): from statsmodels.tools.numdiff import approx_fprime_cs params = params.ravel('F') # for Multinomial try: jacobian_mat = approx_fprime_cs(params, derivative, args=(exog,method)) except TypeError: # norm.cdf doesn't take complex values from statsmodels.tools.numdiff import approx_fprime jacobian_mat = approx_fprime(params, derivative, args=(exog,method)) if at == 'overall': jacobian_mat = np.mean(jacobian_mat, axis=1) else: jacobian_mat = jacobian_mat.squeeze() # exog was 2d row vector if dummy_ind is not None: jacobian_mat = _margeff_cov_params_dummy(model, jacobian_mat, params, exog, dummy_ind, method, J) if count_ind is not None: jacobian_mat = _margeff_cov_params_count(model, jacobian_mat, params, exog, count_ind, method, J) else: jacobian_mat = derivative #NOTE: this won't go through for at == 'all' return np.dot(np.dot(jacobian_mat, cov_params), jacobian_mat.T) def margeff_cov_with_se(model, params, exog, cov_params, at, derivative, dummy_ind, count_ind, method, J): """ See margeff_cov_params. Same function but returns both the covariance of the marginal effects and their standard errors. """ cov_me = margeff_cov_params(model, params, exog, cov_params, at, derivative, dummy_ind, count_ind, method, J) return cov_me, np.sqrt(np.diag(cov_me)) def margeff(): pass def _check_at_is_all(method): if method['at'] == 'all': raise NotImplementedError("Only margeff are available when `at` is " "all. Please input specific points if you would like to " "do inference.") _transform_names = dict(dydx='dy/dx', eyex='d(lny)/d(lnx)', dyex='dy/d(lnx)', eydx='d(lny)/dx') class Margins(object): """ Mostly a do nothing class. Lays out the methods expected of a sub-class. This is just a sketch of what we may want out of a general margins class. I (SS) need to look at details of other models. """ def __init__(self, results, get_margeff, derivative, dist=None, margeff_args=()): self._cache = resettable_cache() self.results = results self.dist = dist self.get_margeff(margeff_args) def _reset(self): self._cache = resettable_cache() def get_margeff(self, *args, **kwargs): self._reset() self.margeff = self.get_margeff(*args) @cache_readonly def tvalues(self): raise NotImplementedError @cache_readonly def cov_margins(self): raise NotImplementedError @cache_readonly def margins_se(self): raise NotImplementedError def summary_frame(self): raise NotImplementedError @cache_readonly def pvalues(self): raise NotImplementedError def conf_int(self, alpha=.05): raise NotImplementedError def summary(self, alpha=.05): raise NotImplementedError #class DiscreteMargins(Margins): class DiscreteMargins(object): """Get marginal effects of a Discrete Choice model. Parameters ---------- results : DiscreteResults instance The results instance of a fitted discrete choice model args : tuple Args are passed to `get_margeff`. This is the same as results.get_margeff. See there for more information. kwargs : dict Keyword args are passed to `get_margeff`. This is the same as results.get_margeff. See there for more information. """ def __init__(self, results, args, kwargs={}): self._cache = resettable_cache() self.results = results self.get_margeff(*args, **kwargs) def _reset(self): self._cache = resettable_cache() @cache_readonly def tvalues(self): _check_at_is_all(self.margeff_options) return self.margeff / self.margeff_se def summary_frame(self, alpha=.05): """ Returns a DataFrame summarizing the marginal effects. Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- frame : DataFrames A DataFrame summarizing the marginal effects. """ _check_at_is_all(self.margeff_options) from pandas import DataFrame names = [_transform_names[self.margeff_options['method']], 'Std. Err.', 'z', 'Pr(>|z|)', 'Conf. Int. Low', 'Cont. Int. Hi.'] ind = self.results.model.exog.var(0) != 0 # True if not a constant exog_names = self.results.model.exog_names var_names = [name for i,name in enumerate(exog_names) if ind[i]] table = np.column_stack((self.margeff, self.margeff_se, self.tvalues, self.pvalues, self.conf_int(alpha))) return DataFrame(table, columns=names, index=var_names) @cache_readonly def pvalues(self): _check_at_is_all(self.margeff_options) return norm.sf(np.abs(self.tvalues)) * 2 def conf_int(self, alpha=.05): """ Returns the confidence intervals of the marginal effects Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects. """ _check_at_is_all(self.margeff_options) me_se = self.margeff_se q = norm.ppf(1 - alpha / 2) lower = self.margeff - q * me_se upper = self.margeff + q * me_se return np.asarray(zip(lower, upper)) def summary(self, alpha=.05): """ Returns a summary table for marginal effects Parameters ---------- alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns ------- Summary : SummaryTable A SummaryTable instance """ _check_at_is_all(self.margeff_options) results = self.results model = results.model title = model.__class__.__name__ + " Marginal Effects" method = self.margeff_options['method'] top_left = [('Dep. Variable:', [model.endog_names]), ('Method:', [method]), ('At:', [self.margeff_options['at']]),] from statsmodels.iolib.summary import (Summary, summary_params, table_extend) exog_names = model.exog_names[:] # copy smry = Summary() # sigh, we really need to hold on to this in _data... _, const_idx = _get_const_index(model.exog) if const_idx is not None: exog_names.pop(const_idx) J = int(getattr(model, "J", 1)) if J > 1: yname, yname_list = results._get_endog_name(model.endog_names, None, all=True) else: yname = model.endog_names yname_list = [yname] smry.add_table_2cols(self, gleft=top_left, gright=[], yname=yname, xname=exog_names, title=title) #NOTE: add_table_params is not general enough yet for margeff # could use a refactor with getattr instead of hard-coded params # tvalues etc. table = [] conf_int = self.conf_int(alpha) margeff = self.margeff margeff_se = self.margeff_se tvalues = self.tvalues pvalues = self.pvalues if J > 1: for eq in range(J): restup = (results, margeff[:,eq], margeff_se[:,eq], tvalues[:,eq], pvalues[:,eq], conf_int[:,:,eq]) tble = summary_params(restup, yname=yname_list[eq], xname=exog_names, alpha=alpha, use_t=False, skip_header=True) tble.title = yname_list[eq] # overwrite coef with method name header = ['', _transform_names[method], 'std err', 'z', 'P>|z|', '[%3.1f%% Conf. Int.]' % (100-alpha*100)] tble.insert_header_row(0, header) #from IPython.core.debugger import Pdb; Pdb().set_trace() table.append(tble) table = table_extend(table, keep_headers=True) else: restup = (results, margeff, margeff_se, tvalues, pvalues, conf_int) table = summary_params(restup, yname=yname, xname=exog_names, alpha=alpha, use_t=False, skip_header=True) header = ['', _transform_names[method], 'std err', 'z', 'P>|z|', '[%3.1f%% Conf. Int.]' % (100-alpha*100)] table.insert_header_row(0, header) smry.tables.append(table) return smry def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Parameters ---------- at : str, optional Options are: - 'overall', The average of the marginal effects at each observation. - 'mean', The marginal effects at the mean of each regressor. - 'median', The marginal effects at the median of each regressor. - 'zero', The marginal effects at zero for each regressor. - 'all', The marginal effects at each observation. If `at` is all only margeff will be available. Note that if `exog` is specified, then marginal effects for all variables not specified by `exog` are calculated using the `at` option. method : str, optional Options are: - 'dydx' - dy/dx - No transformation is made and marginal effects are returned. This is the default. - 'eyex' - estimate elasticities of variables in `exog` -- d(lny)/d(lnx) - 'dyex' - estimate semielasticity -- dy/d(lnx) - 'eydx' - estimate semeilasticity -- d(lny)/dx Note that tranformations are done after each observation is calculated. Semi-elasticities for binary variables are computed using the midpoint method. 'dyex' and 'eyex' do not make sense for discrete variables. atexog : array-like, optional Optionally, you can provide the exogenous variables over which to get the marginal effects. This should be a dictionary with the key as the zero-indexed column number and the value of the dictionary. Default is None for all independent variables less the constant. dummy : bool, optional If False, treats binary variables (if present) as continuous. This is the default. Else if True, treats binary variables as changing from 0 to 1. Note that any variable that is either 0 or 1 is treated as binary. Each binary variable is treated separately for now. count : bool, optional If False, treats count variables (if present) as continuous. This is the default. Else if True, the marginal effect is the change in probabilities when each observation is increased by one. Returns ------- effects : ndarray the marginal effect corresponding to the input options Notes ----- When using after Poisson, returns the expected number of events per period, assuming that the model is loglinear. """ self._reset() # always reset the cache when this is called #TODO: if at is not all or overall, we can also put atexog values # in summary table head method = method.lower() at = at.lower() _check_margeff_args(at, method) self.margeff_options = dict(method=method, at=at) results = self.results model = results.model params = results.params exog = model.exog.copy() # copy because values are changed effects_idx, const_idx = _get_const_index(exog) if dummy: _check_discrete_args(at, method) dummy_idx, dummy = _get_dummy_index(exog, const_idx) else: dummy_idx = None if count: _check_discrete_args(at, method) count_idx, count = _get_count_index(exog, const_idx) else: count_idx = None # get the exogenous variables exog = _get_margeff_exog(exog, at, atexog, effects_idx) # get base marginal effects, handled by sub-classes effects = model._derivative_exog(params, exog, method, dummy_idx, count_idx) J = getattr(model, 'J', 1) effects_idx = np.tile(effects_idx, J) # adjust for multi-equation. effects = _effects_at(effects, at) if at == 'all': if J > 1: K = model.K - np.any(~effects_idx) # subtract constant self.margeff = effects[:, effects_idx].reshape(-1, K, J, order='F') else: self.margeff = effects[:, effects_idx] else: # Set standard error of the marginal effects by Delta method. margeff_cov, margeff_se = margeff_cov_with_se(model, params, exog, results.cov_params(), at, model._derivative_exog, dummy_idx, count_idx, method, J) # reshape for multi-equation if J > 1: K = model.K - np.any(~effects_idx) # subtract constant self.margeff = effects[effects_idx].reshape(K, J, order='F') self.margeff_se = margeff_se[effects_idx].reshape(K, J, order='F') self.margeff_cov = margeff_cov[effects_idx][:, effects_idx] else: # don't care about at constant self.margeff_cov = margeff_cov[effects_idx][:, effects_idx] self.margeff_se = margeff_se[effects_idx] self.margeff = effects[effects_idx] statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/discrete_model.py000066400000000000000000003041001224417117700256500ustar00rootroot00000000000000""" Limited dependent variable and qualitative variables. Includes binary outcomes, count data, (ordered) ordinal data and limited dependent variables. General References -------------------- A.C. Cameron and P.K. Trivedi. `Regression Analysis of Count Data`. Cambridge, 1998 G.S. Madalla. `Limited-Dependent and Qualitative Variables in Econometrics`. Cambridge, 1983. W. Greene. `Econometric Analysis`. Prentice Hall, 5th. edition. 2003. """ __all__ = ["Poisson", "Logit", "Probit", "MNLogit", "NegativeBinomial"] import numpy as np from scipy.special import gammaln from scipy import stats, special, optimize # opt just for nbin import statsmodels.tools.tools as tools from statsmodels.tools.decorators import (resettable_cache, cache_readonly) from statsmodels.regression.linear_model import OLS from scipy import stats, special, optimize # opt just for nbin from scipy.stats import nbinom from statsmodels.tools.sm_exceptions import PerfectSeparationError from statsmodels.tools.numdiff import (approx_fprime, approx_hess, approx_hess_cs, approx_fprime_cs) import statsmodels.base.model as base import statsmodels.regression.linear_model as lm import statsmodels.base.wrapper as wrap from statsmodels.base.l1_slsqp import fit_l1_slsqp try: import cvxopt have_cvxopt = True except ImportError: have_cvxopt = False #TODO: When we eventually get user-settable precision, we need to change # this FLOAT_EPS = np.finfo(float).eps #TODO: add options for the parameter covariance/variance # ie., OIM, EIM, and BHHH see Green 21.4 _discrete_models_docs = """ """ _discrete_results_docs = """ %(one_line_description)s Parameters ---------- model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns ------- *Attributes* aic : float Akaike information criterion. -2*(`llf` - p) where p is the number of regressors including the intercept. bic : float Bayesian information criterion. -2*`llf` + ln(`nobs`)*p where p is the number of regressors including the intercept. bse : array The standard errors of the coefficients. df_resid : float See model definition. df_model : float See model definition. fitted_values : array Linear predictor XB. llf : float Value of the loglikelihood llnull : float Value of the constant-only loglikelihood llr : float Likelihood ratio chi-squared statistic; -2*(`llnull` - `llf`) llr_pvalue : float The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom `df_model`. prsquared : float McFadden's pseudo-R-squared. 1 - (`llf`/`llnull`) %(extra_attr)s""" _l1_results_attr = """ nnz_params : Integer The number of nonzero parameters in the model. Train with trim_params == True or else numerical error will distort this. trimmed : Boolean array trimmed[i] == True if the ith parameter was trimmed from the model.""" #### Private Model Classes #### class DiscreteModel(base.LikelihoodModel): """ Abstract class for discrete choice models. This class does not do anything itself but lays out the methods and call signature expected of child classes in addition to those of statsmodels.model.LikelihoodModel. """ def __init__(self, endog, exog, **kwargs): super(DiscreteModel, self).__init__(endog, exog, **kwargs) self.raise_on_perfect_prediction = True def initialize(self): """ Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. """ self.df_model = float(tools.rank(self.exog) - 1) # assumes constant self.df_resid = float(self.exog.shape[0] - tools.rank(self.exog)) def cdf(self, X): """ The cumulative distribution function of the model. """ raise NotImplementedError def pdf(self, X): """ The probability density (mass) function of the model. """ raise NotImplementedError def _check_perfect_pred(self, params, *args): endog = self.endog fittedvalues = self.cdf(np.dot(self.exog, params[:self.exog.shape[1]])) if (self.raise_on_perfect_prediction and np.allclose(fittedvalues - endog, 0)): msg = "Perfect separation detected, results not available" raise PerfectSeparationError(msg) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): """ Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit """ if callback is None: callback = self._check_perfect_pred else: pass # make a function factory to have multiple call-backs mlefit = super(DiscreteModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) return mlefit # up to subclasses to wrap results fit.__doc__ += base.LikelihoodModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, qc_verbose=False, **kwargs): """ Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters ---------- start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : 'l1' or 'l1_cvxopt_cp' See notes for details. maxiter : Integer or 'defined_by_method' Maximum number of iterations to perform. If 'defined_by_method', then use method defaults (see notes). full_output : bool Set to True to have all available output in the Results object's mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. disp : bool Set to True to print convergence messages. fargs : tuple Extra arguments passed to the likelihood function, i.e., loglike(x,*args) callback : callable callback(xk) Called after each iteration, as callback(xk), where xk is the current parameter vector. retall : bool Set to True to return list of solutions at each iteration. Available in Results object's mle_retvals attribute. alpha : non-negative scalar or numpy array (same size as parameters) The weight multiplying the l1 penalty term trim_mode : 'auto, 'size', or 'off' If not 'off', trim (set to zero) parameters that would have been zero if the solver reached the theoretical minimum. If 'auto', trim params using the Theory above. If 'size', trim params if they have very small absolute value size_trim_tol : float or 'auto' (default = 'auto') For use when trim_mode == 'size' auto_trim_tol : float For sue when trim_mode == 'auto'. Use qc_tol : float Print warning and don't allow auto trim when (ii) (above) is violated by this much. qc_verbose : Boolean If true, print out a full QC report upon failure Notes ----- Optional arguments for the solvers (available in Results.mle_settings):: 'l1' acc : float (default 1e-6) Requested accuracy as used by slsqp 'l1_cvxopt_cp' abstol : float absolute accuracy (default: 1e-7). reltol : float relative accuracy (default: 1e-6). feastol : float tolerance for feasibility conditions (default: 1e-7). refinement : int number of iterative refinement steps when solving KKT equations (default: 1). Optimization methodology With :math:`L` the negative log likelihood, we solve the convex but non-smooth problem .. math:: \\min_\\beta L(\\beta) + \\sum_k\\alpha_k |\\beta_k| via the transformation to the smooth, convex, constrained problem in twice as many variables (adding the "added variables" :math:`u_k`) .. math:: \\min_{\\beta,u} L(\\beta) + \\sum_k\\alpha_k u_k, subject to .. math:: -u_k \\leq \\beta_k \\leq u_k. With :math:`\\partial_k L` the derivative of :math:`L` in the :math:`k^{th}` parameter direction, theory dictates that, at the minimum, exactly one of two conditions holds: (i) :math:`|\\partial_k L| = \\alpha_k` and :math:`\\beta_k \\neq 0` (ii) :math:`|\\partial_k L| \\leq \\alpha_k` and :math:`\\beta_k = 0` """ ### Set attributes based on method if method in ['l1', 'l1_cvxopt_cp']: cov_params_func = self.cov_params_func_l1 else: raise Exception( "argument method == %s, which is not handled" % method) ### Bundle up extra kwargs for the dictionary kwargs. These are ### passed through super(...).fit() as kwargs and unpacked at ### appropriate times alpha = np.array(alpha) assert alpha.min() >= 0 try: kwargs['alpha'] = alpha except TypeError: kwargs = dict(alpha=alpha) kwargs['alpha_rescaled'] = kwargs['alpha'] / float(self.endog.shape[0]) kwargs['trim_mode'] = trim_mode kwargs['size_trim_tol'] = size_trim_tol kwargs['auto_trim_tol'] = auto_trim_tol kwargs['qc_tol'] = qc_tol kwargs['qc_verbose'] = qc_verbose ### Define default keyword arguments to be passed to super(...).fit() if maxiter == 'defined_by_method': if method == 'l1': maxiter = 1000 elif method == 'l1_cvxopt_cp': maxiter = 70 ## Parameters to pass to super(...).fit() # For the 'extra' parameters, pass all that are available, # even if we know (at this point) we will only use one. extra_fit_funcs = {'l1': fit_l1_slsqp} if have_cvxopt and method == 'l1_cvxopt_cp': from statsmodels.base.l1_cvxopt import fit_l1_cvxopt_cp extra_fit_funcs['l1_cvxopt_cp'] = fit_l1_cvxopt_cp elif method.lower() == 'l1_cvxopt_cp': message = """Attempt to use l1_cvxopt_cp failed since cvxopt could not be imported""" if callback is None: callback = self._check_perfect_pred else: pass # make a function factory to have multiple call-backs mlefit = super(DiscreteModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, extra_fit_funcs=extra_fit_funcs, cov_params_func=cov_params_func, **kwargs) return mlefit # up to subclasses to wrap results def cov_params_func_l1(self, likelihood_model, xopt, retvals): """ Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero'd values set to np.nan. """ H = likelihood_model.hessian(xopt) trimmed = retvals['trimmed'] nz_idx = np.nonzero(trimmed == False)[0] nnz_params = (trimmed == False).sum() if nnz_params > 0: H_restricted = H[nz_idx[:, None], nz_idx] # Covariance estimate for the nonzero params H_restricted_inv = np.linalg.inv(-H_restricted) else: H_restricted_inv = np.zeros(0) cov_params = np.nan * np.ones(H.shape) cov_params[nz_idx[:, None], nz_idx] = H_restricted_inv return cov_params def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. """ raise NotImplementedError def _derivative_exog(self, params, exog=None, dummy_idx=None, count_idx=None): """ This should implement the derivative of the non-linear function """ raise NotImplementedError class BinaryModel(DiscreteModel): def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. Parameters ---------- params : array-like Fitted parameters of the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Returns ------- array Fitted values at exog. """ if exog is None: exog = self.exog if not linear: return self.cdf(np.dot(exog, params)) else: return np.dot(exog, params) def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): bnryfit = super(BinaryModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) if method in ['l1', 'l1_cvxopt_cp']: discretefit = L1BinaryResults(self, bnryfit) else: raise Exception( "argument method == %s, which is not handled" % method) return L1BinaryResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predict. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog dF = self.pdf(np.dot(exog, params))[:,None] * exog if 'ey' in transform: dF /= self.predict(params, exog)[:,None] return dF def _derivative_exog(self, params, exog=None, transform='dydx', dummy_idx=None, count_idx=None): """ For computing marginal effects returns dF(XB) / dX where F(.) is the predicted probabilities transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. """ #note, this form should be appropriate for ## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit if exog == None: exog = self.exog margeff = np.dot(self.pdf(np.dot(exog, params))[:,None], params[None,:]) if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:,None] if count_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_count_effects) margeff = _get_count_effects(margeff, exog, count_idx, transform, self, params) if dummy_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_dummy_effects) margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform, self, params) return margeff class MultinomialModel(BinaryModel): def initialize(self): """ Preprocesses the data for MNLogit. Turns the endogenous variable into an array of dummies and assigns J and K. """ super(MultinomialModel, self).initialize() #This is also a "whiten" method as used in other models (eg regression) wendog, ynames = tools.categorical(self.endog, drop=True, dictnames=True) self._ynames_map = ynames self.wendog = wendog # don't drop first category self.J = float(wendog.shape[1]) self.K = float(self.exog.shape[1]) self.df_model *= (self.J-1) # for each J - 1 equation. self.df_resid = self.exog.shape[0] - self.df_model - (self.J-1) def predict(self, params, exog=None, linear=False): """ Predict response variable of a model given exogenous variables. Parameters ---------- params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only have one regressor and would like to do prediction, you must provide a 2d array with shape[1] == 1. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Notes ----- Column 0 is the base case, the rest conform to the rows of params shifted up one for the base case. """ if exog is None: # do here to accomodate user-given exog exog = self.exog if exog.ndim == 1: exog = exog[None] pred = super(MultinomialModel, self).predict(params, exog, linear) if linear: pred = np.column_stack((np.zeros(len(exog)), pred)) return pred def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): if start_params is None: start_params = np.zeros((self.K * (self.J-1))) else: start_params = np.asarray(start_params) callback = lambda x : None # placeholder until check_perfect_pred # skip calling super to handle results from LikelihoodModel mnfit = base.LikelihoodModel.fit(self, start_params = start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) mnfit.params = mnfit.params.reshape(self.K, -1, order='F') mnfit = MultinomialResults(self, mnfit) return MultinomialResultsWrapper(mnfit) fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): if start_params is None: start_params = np.zeros((self.K * (self.J-1))) else: start_params = np.asarray(start_params) mnfit = DiscreteModel.fit_regularized( self, start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) mnfit.params = mnfit.params.reshape(self.K, -1, order='F') mnfit = L1MultinomialResults(self, mnfit) return L1MultinomialResultsWrapper(mnfit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predicted probabilities for each choice. dFdparams is of shape nobs x (J*K) x (J-1)*K. The zero derivatives for the base category are not included. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog if params.ndim == 1: # will get flatted from approx_fprime params = params.reshape(self.K, self.J-1, order='F') eXB = np.exp(np.dot(exog, params)) sum_eXB = (1 + eXB.sum(1))[:,None] J, K = map(int, [self.J, self.K]) repeat_eXB = np.repeat(eXB, J, axis=1) X = np.tile(exog, J-1) # this is the derivative wrt the base level F0 = -repeat_eXB * X / sum_eXB ** 2 # this is the derivative wrt the other levels when # dF_j / dParams_j (ie., own equation) #NOTE: this computes too much, any easy way to cut down? F1 = eXB.T[:,:,None]*X * (sum_eXB - repeat_eXB) / (sum_eXB**2) F1 = F1.transpose((1,0,2)) # put the nobs index first # other equation index other_idx = ~np.kron(np.eye(J-1), np.ones(K)).astype(bool) F1[:, other_idx] = (-eXB.T[:,:,None]*X*repeat_eXB / \ (sum_eXB**2)).transpose((1,0,2))[:, other_idx] dFdX = np.concatenate((F0[:, None,:], F1), axis=1) if 'ey' in transform: dFdX /= self.predict(params, exog)[:, :, None] return dFdX def _derivative_exog(self, params, exog=None, transform='dydx', dummy_idx=None, count_idx=None): """ For computing marginal effects returns dF(XB) / dX where F(.) is the predicted probabilities transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. For Multinomial models the marginal effects are P[j] * (params[j] - sum_k P[k]*params[k]) It is returned unshaped, so that each row contains each of the J equations. This makes it easier to take derivatives of this for standard errors. If you want average marginal effects you can do margeff.reshape(nobs, K, J, order='F).mean(0) and the marginal effects for choice J are in column J """ J = int(self.J) # number of alternative choices K = int(self.K) # number of variables #note, this form should be appropriate for ## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit if exog == None: exog = self.exog if params.ndim == 1: # will get flatted from approx_fprime params = params.reshape(K, J-1, order='F') zeroparams = np.c_[np.zeros(K), params] # add base in cdf = self.cdf(np.dot(exog, params)) margeff = np.array([cdf[:,[j]]* (zeroparams[:,j]-np.array([cdf[:,[i]]* zeroparams[:,i] for i in range(int(J))]).sum(0)) for j in range(J)]) margeff = np.transpose(margeff, (1,2,0)) # swap the axes to make sure margeff are in order nobs, K, J if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:,None,:] if count_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_count_effects) margeff = _get_count_effects(margeff, exog, count_idx, transform, self, params) if dummy_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_dummy_effects) margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform, self, params) return margeff.reshape(len(exog), -1, order='F') class CountModel(DiscreteModel): def __init__(self, endog, exog, offset=None, exposure=None, missing='none'): self._check_inputs(offset, exposure, endog) # attaches if needed super(CountModel, self).__init__(endog, exog, missing=missing, offset=self.offset, exposure=self.exposure) if offset is None: delattr(self, 'offset') if exposure is None: delattr(self, 'exposure') def _check_inputs(self, offset, exposure, endog): if offset is not None: offset = np.asarray(offset) if offset.shape[0] != endog.shape[0]: raise ValueError("offset is not the same length as endog") self.offset = offset if exposure is not None: exposure = np.log(exposure) if exposure.shape[0] != endog.shape[0]: raise ValueError("exposure is not the same length as endog") self.exposure = exposure #TODO: are these two methods only for Poisson? or also Negative Binomial? def predict(self, params, exog=None, exposure=None, offset=None, linear=False): """ Predict response variable of a count model given exogenous variables. Notes ----- If exposure is specified, then it will be logged by the method. The user does not need to log it first. """ #TODO: add offset tp if exog is None: exog = self.exog offset = getattr(self, 'offset', 0) exposure = getattr(self, 'exposure', 0) else: if exposure is None: exposure = 0 else: exposure = np.log(exposure) if offset is None: offset = 0 if not linear: return np.exp(np.dot(exog, params[:exog.shape[1]]) + exposure + offset) # not cdf else: return np.dot(exog, params[:exog.shape[1]]) + exposure + offset def _derivative_predict(self, params, exog=None, transform='dydx'): """ For computing marginal effects standard errors. This is used only in the case of discrete and count regressors to get the variance-covariance of the marginal effects. It returns [d F / d params] where F is the predict. Transform can be 'dydx' or 'eydx'. Checking is done in margeff computations for appropriate transform. """ if exog is None: exog = self.exog #NOTE: this handles offset and exposure dF = self.predict(params, exog)[:,None] * exog if 'ey' in transform: dF /= self.predict(params, exog)[:,None] return dF def _derivative_exog(self, params, exog=None, transform="dydx", dummy_idx=None, count_idx=None): """ For computing marginal effects. These are the marginal effects d F(XB) / dX For the Poisson model F(XB) is the predicted counts rather than the probabilities. transform can be 'dydx', 'dyex', 'eydx', or 'eyex'. Not all of these make sense in the presence of discrete regressors, but checks are done in the results in get_margeff. """ # group 3 poisson, nbreg, zip, zinb if exog == None: exog = self.exog margeff = self.predict(params, exog)[:,None] * params[None,:] if 'ex' in transform: margeff *= exog if 'ey' in transform: margeff /= self.predict(params, exog)[:,None] if count_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_count_effects) margeff = _get_count_effects(margeff, exog, count_idx, transform, self, params) if dummy_idx is not None: from statsmodels.discrete.discrete_margins import ( _get_dummy_effects) margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform, self, params) return margeff def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): cntfit = super(CountModel, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = CountResults(self, cntfit) return CountResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) if method in ['l1', 'l1_cvxopt_cp']: discretefit = L1CountResults(self, cntfit) else: raise Exception( "argument method == %s, which is not handled" % method) return L1CountResultsWrapper(discretefit) fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__ class OrderedModel(DiscreteModel): pass #### Public Model Classes #### class Poisson(CountModel): __doc__ = """ Poisson model for count data %(params)s %(extra_params)s Attributes ----------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def cdf(self, X): """ Poisson model cumulative distribution function Parameters ----------- X : array-like `X` is the linear predictor of the model. See notes. Returns ------- The value of the Poisson CDF at each point. Notes ----- The CDF is defined as .. math:: \\exp\left(-\\lambda\\right)\\sum_{i=0}^{y}\\frac{\\lambda^{i}}{i!} where :math:`\\lambda` assumes the loglinear model. I.e., .. math:: \\ln\\lambda_{i}=X\\beta The parameter `X` is :math:`X\\beta` in the above formula. """ y = self.endog return stats.poisson.cdf(y, np.exp(X)) def pdf(self, X): """ Poisson model probability mass function Parameters ----------- X : array-like `X` is the linear predictor of the model. See notes. Returns ------- pdf : ndarray The value of the Poisson probability mass function, PMF, for each point of X. Notes -------- The PMF is defined as .. math:: \\frac{e^{-\\lambda_{i}}\\lambda_{i}^{y_{i}}}{y_{i}!} where :math:`\\lambda` assumes the loglinear model. I.e., .. math:: \\ln\\lambda_{i}=x_{i}\\beta The parameter `X` is :math:`x_{i}\\beta` in the above formula. """ y = self.endog return np.exp(stats.poisson.logpmf(y, np.exp(X))) def loglike(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) XB = np.dot(self.exog, params) + offset + exposure endog = self.endog return np.sum(-np.exp(XB) + endog*XB - gammaln(endog+1)) def loglikeobs(self, params): """ Loglikelihood for observations of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes -------- .. math :: \\ln L_{i}=\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] for observations :math:`i=1,...,n` """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) XB = np.dot(self.exog, params) + offset + exposure endog = self.endog #np.sum(stats.poisson.logpmf(endog, np.exp(XB))) return -np.exp(XB) + endog*XB - gammaln(endog+1) def score(self, params): """ Poisson model score (gradient) vector of the log-likelihood Parameters ---------- params : array-like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\lambda_{i}\\right)x_{i} where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + offset + exposure) return np.dot(self.endog - L, X) def jac(self, params): """ Poisson model Jacobian of the log-likelihood for each observation Parameters ---------- params : array-like The parameters of the model Returns ------- score : ndarray (nobs, k_vars) The score vector of the model evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\lambda_{i}\\right)x_{i} for observations :math:`i=1,...,n` where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + offset + exposure) return (self.endog - L)[:,None] * X def hessian(self, params): """ Poisson model Hessian matrix of the loglikelihood Parameters ---------- params : array-like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i=1}^{n}\\lambda_{i}x_{i}x_{i}^{\\prime} where the loglinear model is assumed .. math:: \\ln\\lambda_{i}=x_{i}\\beta """ offset = getattr(self, "offset", 0) exposure = getattr(self, "exposure", 0) X = self.exog L = np.exp(np.dot(X,params) + exposure + offset) return -np.dot(L*X.T, X) class Logit(BinaryModel): __doc__ = """ Binary choice logit model %(params)s %(extra_params)s Attributes ----------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def cdf(self, X): """ The logistic cumulative distribution function Parameters ---------- X : array-like `X` is the linear predictor of the logit model. See notes. Returns ------- 1/(1 + exp(-X)) Notes ------ In the logit model, .. math:: \\Lambda\\left(x^{\\prime}\\beta\\right)=\\text{Prob}\\left(Y=1|x\\right)=\\frac{e^{x^{\\prime}\\beta}}{1+e^{x^{\\prime}\\beta}} """ X = np.asarray(X) return 1/(1+np.exp(-X)) def pdf(self, X): """ The logistic probability density function Parameters ----------- X : array-like `X` is the linear predictor of the logit model. See notes. Returns ------- pdf : ndarray The value of the Logit probability mass function, PMF, for each point of X. ``np.exp(-x)/(1+np.exp(-X))**2`` Notes ----- In the logit model, .. math:: \\lambda\\left(x^{\\prime}\\beta\\right)=\\frac{e^{-x^{\\prime}\\beta}}{\\left(1+e^{-x^{\\prime}\\beta}\\right)^{2}} """ X = np.asarray(X) return np.exp(-X)/(1+np.exp(-X))**2 def loglike(self, params): """ Log-likelihood of logit model. Parameters ----------- params : array-like The parameters of the logit model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ------ .. math:: \\ln L=\\sum_{i}\\ln\\Lambda\\left(q_{i}x_{i}^{\\prime}\\beta\\right) Where :math:`q=2y-1`. This simplification comes from the fact that the logistic distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.sum(np.log(self.cdf(q*np.dot(X,params)))) def loglikeobs(self, params): """ Log-likelihood of logit model for each observation. Parameters ----------- params : array-like The parameters of the logit model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ------ .. math:: \\ln L=\\sum_{i}\\ln\\Lambda\\left(q_{i}x_{i}^{\\prime}\\beta\\right) for observations :math:`i=1,...,n` where :math:`q=2y-1`. This simplification comes from the fact that the logistic distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.log(self.cdf(q*np.dot(X,params))) def score(self, params): """ Logit model score (gradient) vector of the log-likelihood Parameters ---------- params: array-like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left(y_{i}-\\Lambda_{i}\\right)x_{i} """ y = self.endog X = self.exog L = self.cdf(np.dot(X,params)) return np.dot(y - L,X) def jac(self, params): """ Logit model Jacobian of the log-likelihood for each observation Parameters ---------- params: array-like The parameters of the model Returns ------- jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left(y_{i}-\\Lambda_{i}\\right)x_{i} for observations :math:`i=1,...,n` """ y = self.endog X = self.exog L = self.cdf(np.dot(X, params)) return (y - L)[:,None] * X def hessian(self, params): """ Logit model Hessian matrix of the log-likelihood Parameters ---------- params : array-like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\\sum_{i}\\Lambda_{i}\\left(1-\\Lambda_{i}\\right)x_{i}x_{i}^{\\prime} """ X = self.exog L = self.cdf(np.dot(X,params)) return -np.dot(L*(1-L)*X.T,X) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): bnryfit = super(Logit, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = LogitResults(self, bnryfit) return BinaryResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ class Probit(BinaryModel): __doc__ = """ Binary choice Probit model %(params)s %(extra_params)s Attributes ----------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def cdf(self, X): """ Probit (Normal) cumulative distribution function Parameters ---------- X : array-like The linear predictor of the model (XB). Returns -------- cdf : ndarray The cdf evaluated at `X`. Notes ----- This function is just an alias for scipy.stats.norm.cdf """ return stats.norm._cdf(X) def pdf(self, X): """ Probit (Normal) probability density function Parameters ---------- X : array-like The linear predictor of the model (XB). Returns -------- pdf : ndarray The value of the normal density function for each point of X. Notes ----- This function is just an alias for scipy.stats.norm.pdf """ X = np.asarray(X) return stats.norm._pdf(X) def loglike(self, params): """ Log-likelihood of probit model (i.e., the normal distribution). Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- .. math:: \\ln L=\\sum_{i}\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right) Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.sum(np.log(np.clip(self.cdf(q*np.dot(X,params)), FLOAT_EPS, 1))) def loglikeobs(self, params): """ Log-likelihood of probit model for each observation Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ----- .. math:: \\ln L_{i}=\\ln\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right) for observations :math:`i=1,...,n` where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ q = 2*self.endog - 1 X = self.exog return np.log(np.clip(self.cdf(q*np.dot(X,params)), FLOAT_EPS, 1)) def score(self, params): """ Probit model score (gradient) vector Parameters ---------- params : array-like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta}=\\sum_{i=1}^{n}\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i} Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ y = self.endog X = self.exog XB = np.dot(X,params) q = 2*y - 1 # clip to get rid of invalid divide complaint L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS) return np.dot(L,X) def jac(self, params): """ Probit model Jacobian for each observation Parameters ---------- params : array-like The parameters of the model Returns ------- jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta}=\\left[\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}\\right]x_{i} for observations :math:`i=1,...,n` Where :math:`q=2y-1`. This simplification comes from the fact that the normal distribution is symmetric. """ y = self.endog X = self.exog XB = np.dot(X,params) q = 2*y - 1 # clip to get rid of invalid divide complaint L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS) return L[:,None] * X def hessian(self, params): """ Probit model Hessian matrix of the log-likelihood Parameters ---------- params : array-like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta\\partial\\beta^{\\prime}}=-\lambda_{i}\\left(\\lambda_{i}+x_{i}^{\\prime}\\beta\\right)x_{i}x_{i}^{\\prime} where .. math:: \\lambda_{i}=\\frac{q_{i}\\phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)}{\\Phi\\left(q_{i}x_{i}^{\\prime}\\beta\\right)} and :math:`q=2y-1` """ X = self.exog XB = np.dot(X,params) q = 2*self.endog - 1 L = q*self.pdf(q*XB)/self.cdf(q*XB) return np.dot(-L*(L+XB)*X.T,X) def fit(self, start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): bnryfit = super(Probit, self).fit(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) discretefit = ProbitResults(self, bnryfit) return BinaryResultsWrapper(discretefit) fit.__doc__ = DiscreteModel.fit.__doc__ class MNLogit(MultinomialModel): __doc__ = """ Multinomial logit model Parameters ---------- endog : array-like `endog` is an 1-d vector of the endogenous response. `endog` can contain strings, ints, or floats. Note that if it contains strings, every distinct string will be a category. No stripping of whitespace is done. exog : array-like A nobs x k array where `nobs` is the number of observations and `k` is the number of regressors. An interecept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`. %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. J : float The number of choices for the endogenous variable. Note that this is zero-indexed. K : float The actual number of parameters for the exogenous design. Includes the constant if the design has one. names : dict A dictionary mapping the column number in `wendog` to the variables in `endog`. wendog : array An n x j array where j is the number of unique categories in `endog`. Each column of j is a dummy variable indicating the category of each observation. See `names` for a dictionary mapping each column to its category. Notes ----- See developer notes for further information on `MNLogit` internals. """ % {'extra_params' : base._missing_param_doc} def pdf(self, eXB): """ NotImplemented """ raise NotImplementedError def cdf(self, X): """ Multinomial logit cumulative distribution function. Parameters ---------- X : array The linear predictor of the model XB. Returns -------- cdf : ndarray The cdf evaluated at `X`. Notes ----- In the multinomial logit model. .. math:: \\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)} """ eXB = np.column_stack((np.ones(len(X)), np.exp(X))) return eXB/eXB.sum(1)[:,None] def loglike(self, params): """ Log-likelihood of the multinomial logit model. Parameters ---------- params : array-like The parameters of the multinomial logit model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ------ .. math:: \\ln L=\\sum_{i=1}^{n}\\sum_{j=0}^{J}d_{ij}\\ln\\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right) where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0 if not. """ params = params.reshape(self.K, -1, order='F') d = self.wendog logprob = np.log(self.cdf(np.dot(self.exog,params))) return np.sum(d * logprob) def loglikeobs(self, params): """ Log-likelihood of the multinomial logit model for each observation. Parameters ---------- params : array-like The parameters of the multinomial logit model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ------ .. math:: \\ln L_{i}=\\sum_{j=0}^{J}d_{ij}\\ln\\left(\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right) for observations :math:`i=1,...,n` where :math:`d_{ij}=1` if individual `i` chose alternative `j` and 0 if not. """ params = params.reshape(self.K, -1, order='F') d = self.wendog logprob = np.log(self.cdf(np.dot(self.exog,params))) return d * logprob def score(self, params): """ Score matrix for multinomial logit model log-likelihood Parameters ---------- params : array The parameters of the multinomial logit model. Returns -------- score : ndarray, (K * (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at `params`. Notes ----- .. math:: \\frac{\\partial\\ln L}{\\partial\\beta_{j}}=\\sum_{i}\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i} for :math:`j=1,...,J` In the multinomial model the score matrix is K x J-1 but is returned as a flattened array to work with the solvers. """ params = params.reshape(self.K, -1, order='F') firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog, params))[:,1:] #NOTE: might need to switch terms if params is reshaped return np.dot(firstterm.T, self.exog).flatten() def jac(self, params): """ Jacobian matrix for multinomial logit model log-likelihood Parameters ---------- params : array The parameters of the multinomial logit model. Returns -------- jac : ndarray, (nobs, k_vars*(J-1)) The derivative of the loglikelihood for each observation evaluated at `params` . Notes ----- .. math:: \\frac{\\partial\\ln L_{i}}{\\partial\\beta_{j}}=\\left(d_{ij}-\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right)x_{i} for :math:`j=1,...,J`, for observations :math:`i=1,...,n` In the multinomial model the score vector is K x (J-1) but is returned as a flattened array. The Jacobian has the observations in rows and the flatteded array of derivatives in columns. """ params = params.reshape(self.K, -1, order='F') firstterm = self.wendog[:,1:] - self.cdf(np.dot(self.exog, params))[:,1:] #NOTE: might need to switch terms if params is reshaped return (firstterm[:,:,None] * self.exog[:,None,:]).reshape(self.exog.shape[0], -1) def hessian(self, params): """ Multinomial logit Hessian matrix of the log-likelihood Parameters ----------- params : array-like The parameters of the model Returns ------- hess : ndarray, (J*K, J*K) The Hessian, second derivative of loglikelihood function with respect to the flattened parameters, evaluated at `params` Notes ----- .. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta_{j}\\partial\\beta_{l}}=-\\sum_{i=1}^{n}\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\left[\\boldsymbol{1}\\left(j=l\\right)-\\frac{\\exp\\left(\\beta_{l}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right]x_{i}x_{l}^{\\prime} where :math:`\\boldsymbol{1}\\left(j=l\\right)` equals 1 if `j` = `l` and 0 otherwise. The actual Hessian matrix has J**2 * K x K elements. Our Hessian is reshaped to be square (J*K, J*K) so that the solvers can use it. This implementation does not take advantage of the symmetry of the Hessian and could probably be refactored for speed. """ params = params.reshape(self.K, -1, order='F') X = self.exog pr = self.cdf(np.dot(X,params)) partials = [] J = self.wendog.shape[1] - 1 K = self.exog.shape[1] for i in range(J): for j in range(J): # this loop assumes we drop the first col. if i == j: partials.append(\ -np.dot(((pr[:,i+1]*(1-pr[:,j+1]))[:,None]*X).T,X)) else: partials.append(-np.dot(((pr[:,i+1]*-pr[:,j+1])[:,None]*X).T,X)) H = np.array(partials) # the developer's notes on multinomial should clear this math up H = np.transpose(H.reshape(J,J,K,K), (0,2,1,3)).reshape(J*K,J*K) return H #TODO: Weibull can replaced by a survival analsysis function # like stat's streg (The cox model as well) #class Weibull(DiscreteModel): # """ # Binary choice Weibull model # # Notes # ------ # This is unfinished and untested. # """ ##TODO: add analytic hessian for Weibull # def initialize(self): # pass # # def cdf(self, X): # """ # Gumbell (Log Weibull) cumulative distribution function # """ ## return np.exp(-np.exp(-X)) # return stats.gumbel_r.cdf(X) # # these two are equivalent. # # Greene table and discussion is incorrect. # # def pdf(self, X): # """ # Gumbell (LogWeibull) probability distribution function # """ # return stats.gumbel_r.pdf(X) # # def loglike(self, params): # """ # Loglikelihood of Weibull distribution # """ # X = self.exog # cdf = self.cdf(np.dot(X,params)) # y = self.endog # return np.sum(y*np.log(cdf) + (1-y)*np.log(1-cdf)) # # def score(self, params): # y = self.endog # X = self.exog # F = self.cdf(np.dot(X,params)) # f = self.pdf(np.dot(X,params)) # term = (y*f/F + (1 - y)*-f/(1-F)) # return np.dot(term,X) # # def hessian(self, params): # hess = nd.Jacobian(self.score) # return hess(params) # # def fit(self, start_params=None, method='newton', maxiter=35, tol=1e-08): ## The example had problems with all zero start values, Hessian = 0 # if start_params is None: # start_params = OLS(self.endog, self.exog).fit().params # mlefit = super(Weibull, self).fit(start_params=start_params, # method=method, maxiter=maxiter, tol=tol) # return mlefit # class NegativeBinomial(CountModel): __doc__ = """ Negative Binomial Model for count data %(params)s %(extra_params)s Attributes ----------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. References ---------- References: Greene, W. 2008. "Functional forms for the negtive binomial model for count data". Economics Letters. Volume 99, Number 3, pp.585-590. Hilbe, J.M. 2011. "Negative binomial regression". Cambridge University Press. """ % {'params' : base._model_params_doc, 'extra_params' : """loglike_method : string Log-likelihood type. 'nb2','nb1', or 'geometric'. Fitted value :math:`\\mu` Heterogeneity parameter :math:`\\alpha` nb2: Variance equal to :math:`\\mu + \\alpha\\mu^2` (most common) nb1: Variance equal to :math:`\\mu + \\alpha\\mu` geometric: Variance equal to :math:`\\mu + \\mu^2` """ + base._missing_param_doc} def __init__(self, endog, exog, loglike_method='nb2', offset=None, exposure=None, missing='none'): super(NegativeBinomial, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing) self.loglike_method = loglike_method self._initialize() if loglike_method in ['nb2', 'nb1']: self.exog_names.append('alpha') def _initialize(self): if self.loglike_method == 'nb2': self.hessian = self._hessian_nb2 self.score = self._score_nbin self.loglikeobs = self._ll_nb2 self._transparams = True # transform lnalpha -> alpha in fit elif self.loglike_method == 'nb1': self.hessian = self._hessian_nb1 self.score = self._score_nb1 self.loglikeobs = self._ll_nb1 self._transparams = True # transform lnalpha -> alpha in fit elif self.loglike_method == 'geometric': self.hessian = self._hessian_geom self.score = self._score_geom self.loglikeobs = self._ll_geometric else: raise NotImplementedError("Likelihood type must nb1, nb2 or " "geometric") # Workaround to pickle instance methods def __getstate__(self): odict = self.__dict__.copy() # copy the dict since we change it del odict['hessian'] del odict['score'] del odict['loglikeobs'] return odict def __setstate__(self, indict): self.__dict__.update(indict) self._initialize() def _ll_nbin(self, params, alpha, Q=0): endog = self.endog mu = np.exp(np.dot(self.exog, params)) size = 1/alpha * mu**Q prob = size/(size+mu) coeff = (gammaln(size+endog) - gammaln(endog+1) - gammaln(size)) llf = coeff + size*np.log(prob) + endog*np.log(1-prob) return llf def _ll_nb2(self, params): if self._transparams: # got lnalpha during fit alpha = np.exp(params[-1]) else: alpha = params[-1] return self._ll_nbin(params[:-1], alpha, Q=0) def _ll_nb1(self, params): if self._transparams: # got lnalpha during fit alpha = np.exp(params[-1]) else: alpha = params[-1] return self._ll_nbin(params[:-1], alpha, Q=1) def _ll_geometric(self, params): # we give alpha of 1 because it's actually log(alpha) where alpha=0 return self._ll_nbin(params, 1, 0) def loglike(self, params): r""" Loglikelihood for negative binomial model Parameters ---------- params : array-like The parameters of the model. If `loglike_method` is nb1 or nb2, then the ancillary parameter is expected to be the last element. Returns ------- llf : float The loglikelihood value at `params` Notes ----- Following notation in Greene (2008), with negative binomial heterogeneity parameter :math:`\alpha`: .. math:: \lambda_i &= exp(X\beta) \\ \theta &= 1 / \alpha \\ g_i &= \theta \lambda_i^Q \\ w_i &= g_i/(g_i + \lambda_i) \\ r_i &= \theta / (\theta+\lambda_i) \\ ln \mathcal{L}_i &= ln \Gamma(y_i+g_i) - ln \Gamma(1+y_i) + g_iln (r_i) + y_i ln(1-r_i) where :math`Q=0` for NB2 and geometric and :math:`Q=1` for NB1. For the geometric, :math:`\alpha=0` as well. """ llf = np.sum(self.loglikeobs(params)) return llf def _score_geom(self, params): exog = self.exog y = self.endog[:,None] mu = np.exp(np.dot(exog, params))[:,None] dparams = exog * (y-mu)/(mu+1) return dparams.sum(0) def _score_nbin(self, params, Q=0): """ Score vector for NB2 model """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] exog = self.exog y = self.endog[:,None] mu = np.exp(np.dot(exog, params))[:,None] a1 = 1/alpha * mu**Q if Q: # nb1 dparams = exog*mu/alpha*(np.log(1/(alpha + 1)) + special.digamma(y + mu/alpha) - special.digamma(mu/alpha)) dalpha = ((alpha*(y - mu*np.log(1/(alpha + 1)) - mu*(special.digamma(y + mu/alpha) - special.digamma(mu/alpha) + 1)) - mu*(np.log(1/(alpha + 1)) + special.digamma(y + mu/alpha) - special.digamma(mu/alpha)))/ (alpha**2*(alpha + 1))).sum() else: # nb2 dparams = exog*a1 * (y-mu)/(mu+a1) da1 = -alpha**-2 dalpha = (special.digamma(a1+y) - special.digamma(a1) + np.log(a1) - np.log(a1+mu) - (a1+y)/(a1+mu) + 1).sum()*da1 #multiply above by constant outside sum to reduce rounding error return np.r_[dparams.sum(0), dalpha] def _score_nb1(self, params): return self._score_nbin(params, Q=1) def _hessian_geom(self, params): exog = self.exog y = self.endog[:,None] mu = np.exp(np.dot(exog, params))[:,None] # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim, dim)) const_arr = mu*(1+y)/(mu+1)**2 for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(-exog[:,i,None] * exog[:,j,None] * const_arr, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] return hess_arr def _hessian_nb1(self, params): """ Hessian of NB1 model. """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] params = params[:-1] exog = self.exog y = self.endog[:,None] mu = np.exp(np.dot(exog, params))[:,None] a1 = mu/alpha # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim+1,dim+1)) #const_arr = a1*mu*(a1+y)/(mu+a1)**2 # not all of dparams dparams = exog/alpha*(np.log(1/(alpha + 1)) + special.digamma(y + mu/alpha) - special.digamma(mu/alpha)) dmudb = exog*mu xmu_alpha = exog*mu/alpha trigamma = (special.polygamma(1, mu/alpha + y) - special.polygamma(1, mu/alpha)) for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(dparams[:,i,None] * dmudb[:,j,None] + xmu_alpha[:,i,None] * xmu_alpha[:,j,None] * trigamma, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] # for dl/dparams dalpha da1 = -alpha**-2 dldpda = np.sum(-mu/alpha * dparams + exog*mu/alpha * (-trigamma*mu/alpha**2 - 1/(alpha+1)), axis=0) hess_arr[-1,:-1] = dldpda hess_arr[:-1,-1] = dldpda # for dl/dalpha dalpha digamma_part = (special.digamma(y + mu/alpha) - special.digamma(mu/alpha)) log_alpha = np.log(1/(alpha+1)) alpha3 = alpha**3 alpha2 = alpha**2 mu2 = mu**2 dada = ((alpha3*mu*(2*log_alpha + 2*digamma_part + 3) - 2*alpha3*y + alpha2*mu2*trigamma + 4*alpha2*mu*(log_alpha + digamma_part) + alpha2 * (2*mu - y) + 2*alpha*mu2*trigamma + 2*alpha*mu*(log_alpha + digamma_part) + mu2*trigamma)/(alpha**4*(alpha2 + 2*alpha + 1))) hess_arr[-1,-1] = dada.sum() return hess_arr def _hessian_nb2(self, params): """ Hessian of NB2 model. """ if self._transparams: # lnalpha came in during fit alpha = np.exp(params[-1]) else: alpha = params[-1] a1 = 1/alpha params = params[:-1] exog = self.exog y = self.endog[:,None] mu = np.exp(np.dot(exog, params))[:,None] # for dl/dparams dparams dim = exog.shape[1] hess_arr = np.empty((dim+1,dim+1)) const_arr = a1*mu*(a1+y)/(mu+a1)**2 for i in range(dim): for j in range(dim): if j > i: continue hess_arr[i,j] = np.sum(-exog[:,i,None] * exog[:,j,None] * const_arr, axis=0) tri_idx = np.triu_indices(dim, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] # for dl/dparams dalpha da1 = -alpha**-2 dldpda = np.sum(mu*exog*(y-mu)*da1/(mu+a1)**2 , axis=0) hess_arr[-1,:-1] = dldpda hess_arr[:-1,-1] = dldpda # for dl/dalpha dalpha #NOTE: polygamma(1,x) is the trigamma function da2 = 2*alpha**-3 dalpha = da1 * (special.digamma(a1+y) - special.digamma(a1) + np.log(a1) - np.log(a1+mu) - (a1+y)/(a1+mu) + 1) dada = (da2 * dalpha/da1 + da1**2 * (special.polygamma(1, a1+y) - special.polygamma(1, a1) + 1/a1 - 1/(a1 + mu) + (y - mu)/(mu + a1)**2)).sum() hess_arr[-1,-1] = dada return hess_arr #TODO: replace this with analytic where is it used? def scoreobs(self, params): sc = approx_fprime_cs(params, self.loglikeobs) return sc def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, **kwargs): if self.loglike_method.startswith('nb') and method not in ['newton', 'ncg']: self._transparams = True # in case same Model instance is refit elif self.loglike_method.startswith('nb'): # method is newton/ncg self._transparams = False # because we need to step in alpha space if start_params == None: # Use poisson fit as first guess. start_params = Poisson(self.endog, self.exog).fit(disp=0).params if self.loglike_method.startswith('nb'): start_params = np.append(start_params, 0.1) mlefit = super(NegativeBinomial, self).fit(start_params=start_params, maxiter=maxiter, method=method, disp=disp, full_output=full_output, callback=lambda x:x, **kwargs) # TODO: Fix NBin _check_perfect_pred if self.loglike_method.startswith('nb'): # mlefit is a wrapped counts results self._transparams = False # don't need to transform anymore now # change from lnalpha to alpha if method not in ["newton", "ncg"]: mlefit._results.params[-1] = np.exp(mlefit._results.params[-1]) nbinfit = NegativeBinomialAncillaryResults(self, mlefit._results) return NegativeBinomialAncillaryResultsWrapper(nbinfit) else: return mlefit ### Results Class ### class DiscreteResults(base.LikelihoodModelResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for the discrete dependent variable models.", "extra_attr" : ""} def __init__(self, model, mlefit): #super(DiscreteResults, self).__init__(model, params, # np.linalg.inv(-hessian), scale=1.) self.model = model self.df_model = model.df_model self.df_resid = model.df_resid self._cache = resettable_cache() self.nobs = model.exog.shape[0] self.__dict__.update(mlefit.__dict__) def __getstate__(self): try: #remove unpicklable callback self.mle_settings['callback'] = None except (AttributeError, KeyError): pass return self.__dict__ @cache_readonly def prsquared(self): return 1 - self.llf/self.llnull @cache_readonly def llr(self): return -2*(self.llnull - self.llf) @cache_readonly def llr_pvalue(self): return stats.chisqprob(self.llr, self.df_model) @cache_readonly def llnull(self): model = self.model #TODO: what parameters to pass to fit? null = model.__class__(model.endog, np.ones(self.nobs)).fit(disp=0) return null.llf @cache_readonly def fittedvalues(self): return np.dot(self.model.exog, self.params[:self.model.exog.shape[1]]) @cache_readonly def aic(self): return -2*(self.llf - (self.df_model+1)) @cache_readonly def bic(self): return -2*self.llf + np.log(self.nobs)*(self.df_model+1) def _get_endog_name(self, yname, yname_list): if yname is None: yname = self.model.endog_names if yname_list is None: yname_list = self.model.endog_names return yname, yname_list def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Parameters ---------- at : str, optional Options are: - 'overall', The average of the marginal effects at each observation. - 'mean', The marginal effects at the mean of each regressor. - 'median', The marginal effects at the median of each regressor. - 'zero', The marginal effects at zero for each regressor. - 'all', The marginal effects at each observation. If `at` is all only margeff will be available from the returned object. Note that if `exog` is specified, then marginal effects for all variables not specified by `exog` are calculated using the `at` option. method : str, optional Options are: - 'dydx' - dy/dx - No transformation is made and marginal effects are returned. This is the default. - 'eyex' - estimate elasticities of variables in `exog` -- d(lny)/d(lnx) - 'dyex' - estimate semielasticity -- dy/d(lnx) - 'eydx' - estimate semeilasticity -- d(lny)/dx Note that tranformations are done after each observation is calculated. Semi-elasticities for binary variables are computed using the midpoint method. 'dyex' and 'eyex' do not make sense for discrete variables. atexog : array-like, optional Optionally, you can provide the exogenous variables over which to get the marginal effects. This should be a dictionary with the key as the zero-indexed column number and the value of the dictionary. Default is None for all independent variables less the constant. dummy : bool, optional If False, treats binary variables (if present) as continuous. This is the default. Else if True, treats binary variables as changing from 0 to 1. Note that any variable that is either 0 or 1 is treated as binary. Each binary variable is treated separately for now. count : bool, optional If False, treats count variables (if present) as continuous. This is the default. Else if True, the marginal effect is the change in probabilities when each observation is increased by one. Returns ------- DiscreteMargins : marginal effects instance Returns an object that holds the marginal effects, standard errors, confidence intervals, etc. See `statsmodels.discrete.discrete_margins.DiscreteMargins` for more information. Notes ----- When using after Poisson, returns the expected number of events per period, assuming that the model is loglinear. """ from statsmodels.discrete.discrete_margins import DiscreteMargins return DiscreteMargins(self, (at, method, atexog, dummy, count)) def margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): import warnings warnings.warn("This method is deprecated and will be removed in 0.6.0." " Use get_margeff instead", FutureWarning) return self.get_margeff(at, method, atexog, dummy, count) def summary(self, yname=None, xname=None, title=None, alpha=.05, yname_list=None): """Summarize the Regression Results Parameters ----------- yname : string, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Model:', [self.model.__class__.__name__]), ('Method:', ['MLE']), ('Date:', None), ('Time:', None), #('No. iterations:', ["%d" % self.mle_retvals['iterations']]), ('converged:', ["%s" % self.mle_retvals['converged']]) ] top_right = [('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None), ('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]), ('Log-Likelihood:', None), ('LL-Null:', ["%#8.5g" % self.llnull]), ('LLR p-value:', ["%#6.4g" % self.llr_pvalue]) ] if title is None: title = self.model.__class__.__name__ + ' ' + "Regression Results" #boiler plate from statsmodels.iolib.summary import Summary smry = Summary() yname, yname_list = self._get_endog_name(yname, yname_list) # for top of table smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[], yname=yname, xname=xname, title=title) # for parameters, etc smry.add_table_params(self, yname=yname_list, xname=xname, alpha=alpha, use_t=False) #diagnostic table not used yet #smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, # yname=yname, xname=xname, # title="") return smry def summary2(self, yname=None, xname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental function to summarize regression results Parameters ----------- xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ # Summary from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) return smry class CountResults(DiscreteResults): __doc__ = _discrete_results_docs % { "one_line_description" : "A results class for count data", "extra_attr" : ""} @cache_readonly def resid(self): """ Residuals Notes ----- The residuals for Count models are defined as .. math:: y - p where :math:`p = \\exp(X\\beta)`. Any exposure and offset variables are also handled. """ return self.model.endog - self.predict() class NegativeBinomialAncillaryResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description" : "A results class for NegativeBinomial 1 and 2", "extra_attr" : ""} def __init__(self, model, mlefit): super(NegativeBinomialAncillaryResults, self).__init__(model, mlefit) @cache_readonly def lnalpha(self): return np.log(self.params[-1]) @cache_readonly def lnalpha_std_err(self): return self.bse[-1] / self.params[-1] @cache_readonly def aic(self): # + 1 because we estimate alpha return -2*(self.llf - (self.df_model + self.k_constant + 1)) @cache_readonly def bic(self): # + 1 because we estimate alpha return -2*self.llf + np.log(self.nobs)*(self.df_model + self.k_constant + 1) class L1CountResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for count data fit by l1 regularization", "extra_attr" : _l1_results_attr} #discretefit = CountResults(self, cntfit) def __init__(self, model, cntfit): super(L1CountResults, self).__init__(model, cntfit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = cntfit.mle_retvals['trimmed'] self.nnz_params = (self.trimmed == False).sum() #update degrees of freedom self.model.df_model = self.nnz_params - 1 self.model.df_resid = float(self.model.endog.shape[0] - self.nnz_params) self.df_model = self.model.df_model self.df_resid = self.model.df_resid class OrderedResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for ordered discrete data." , "extra_attr" : ""} pass class BinaryResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for binary data", "extra_attr" : ""} def pred_table(self, threshold=.5): """ Prediction table Parameters ---------- threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes ------ pred_table[i,j] refers to the number of times "i" was observed and the model predicted "j". Correct predictions are along the diagonal. """ model = self.model actual = model.endog pred = np.array(self.predict() > threshold, dtype=float) return np.histogram2d(actual, pred, bins=2)[0] def summary(self, yname=None, xname=None, title=None, alpha=.05, yname_list=None): smry = super(BinaryResults, self).summary(yname, xname, title, alpha, yname_list) fittedvalues = self.model.cdf(self.fittedvalues) absprederror = np.abs(self.model.endog - fittedvalues) predclose_sum = (absprederror < 1e-4).sum() predclose_frac = predclose_sum / len(fittedvalues) #add warnings/notes etext = [] if predclose_sum == len(fittedvalues): #nobs? wstr = "Complete Separation: The results show that there is" wstr += "complete separation.\n" wstr += "In this case the Maximum Likelihood Estimator does " wstr += "not exist and the parameters\n" wstr += "are not identified." etext.append(wstr) elif predclose_frac > 0.1: # TODO: get better diagnosis wstr = "Possibly complete quasi-separation: A fraction " wstr += "%4.2f of observations can be\n" % predclose_frac wstr += "perfectly predicted. This might indicate that there " wstr += "is complete\nquasi-separation. In this case some " wstr += "parameters will not be identified." etext.append(wstr) if etext: smry.add_extra_txt(etext) return smry summary.__doc__ = DiscreteResults.summary.__doc__ @cache_readonly def resid(self): import warnings warnings.warn("This attribute is deprecated and will be removed in " "0.6.0. Use resid_dev instead.", FutureWarning) return self.resid_dev @cache_readonly def resid_dev(self): """ Deviance residuals Notes ----- Deviance residuals are defined .. math:: d_j = \\pm\\left(2\\left[Y_j\\ln\\left(\\frac{Y_j}{M_jp_j}\\right) + (M_j - Y_j\\ln\\left(\\frac{M_j-Y_j}{M_j(1-p_j)} \\right) \\right] \\right)^{1/2} where :math:`p_j = cdf(X\\beta)` and :math:`M_j` is the total number of observations sharing the covariate pattern :math:`j`. For now :math:`M_j` is always set to 1. """ #These are the deviance residuals #model = self.model endog = self.model.endog #exog = model.exog # M = # of individuals that share a covariate pattern # so M[i] = 2 for i = two share a covariate pattern M = 1 p = self.predict() #Y_0 = np.where(exog == 0) #Y_M = np.where(exog == M) #NOTE: Common covariate patterns are not yet handled res = -(1-endog)*np.sqrt(2*M*np.abs(np.log(1-p))) + \ endog*np.sqrt(2*M*np.abs(np.log(p))) return res @cache_readonly def resid_pearson(self): """ Pearson residuals Notes ----- Pearson residuals are defined to be .. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}} where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of observations sharing the covariate pattern :math:`j`. For now :math:`M_j` is always set to 1. """ # Pearson residuals #model = self.model endog = self.model.endog #exog = model.exog # M = # of individuals that share a covariate pattern # so M[i] = 2 for i = two share a covariate pattern # use unique row pattern? M = 1 p = self.predict() return (endog - M*p)/np.sqrt(M*p*(1-p)) @cache_readonly def resid_response(self): """ The response residuals Notes ----- Response residuals are defined to be .. math:: y - p where :math:`p=cdf(X\\beta)`. """ return self.model.endog - self.predict() class LogitResults(BinaryResults): __doc__ = _discrete_results_docs % { "one_line_description" : "A results class for Logit Model", "extra_attr" : ""} @cache_readonly def resid_generalized(self): """ Generalized residuals Notes ----- The generalized residuals for the Logit model are defined .. math:: y - p where :math:`p=cdf(X\\beta)`. This is the same as the `resid_response` for the Logit model. """ # Generalized residuals return self.model.endog - self.predict() class ProbitResults(BinaryResults): __doc__ = _discrete_results_docs % { "one_line_description" : "A results class for Probit Model", "extra_attr" : ""} @cache_readonly def resid_generalized(self): """ Generalized residuals Notes ----- The generalized residuals for the Probit model are defined .. math:: y\\frac{\phi(X\\beta)}{\\Phi(X\\beta)}-(1-y)\\frac{\\phi(X\\beta)}{1-\\Phi(X\\beta)} """ # generalized residuals model = self.model endog = model.endog XB = self.predict(linear=True) pdf = model.pdf(XB) cdf = model.cdf(XB) return endog * pdf/cdf - (1-endog)*pdf/(1-cdf) class L1BinaryResults(BinaryResults): __doc__ = _discrete_results_docs % {"one_line_description" : "Results instance for binary data fit by l1 regularization", "extra_attr" : _l1_results_attr} def __init__(self, model, bnryfit): super(L1BinaryResults, self).__init__(model, bnryfit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = bnryfit.mle_retvals['trimmed'] self.nnz_params = (self.trimmed == False).sum() self.model.df_model = self.nnz_params - 1 self.model.df_resid = float(self.model.endog.shape[0] - self.nnz_params) self.df_model = self.model.df_model self.df_resid = self.model.df_resid class MultinomialResults(DiscreteResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for multinomial data", "extra_attr" : ""} def _maybe_convert_ynames_int(self, ynames): # see if they're integers try: for i in ynames: if ynames[i] % 1 == 0: ynames[i] = str(int(ynames[i])) except TypeError: pass return ynames def _get_endog_name(self, yname, yname_list, all=False): """ If all is False, the first variable name is dropped """ model = self.model if yname is None: yname = model.endog_names if yname_list is None: ynames = model._ynames_map ynames = self._maybe_convert_ynames_int(ynames) # use range below to ensure sortedness ynames = [ynames[key] for key in range(int(model.J))] ynames = ['='.join([yname, name]) for name in ynames] if not all: yname_list = ynames[1:] # assumes first variable is dropped else: yname_list = ynames return yname, yname_list def pred_table(self): """ Returns the J x J prediction table. Notes ----- pred_table[i,j] refers to the number of times "i" was observed and the model predicted "j". Correct predictions are along the diagonal. """ J = self.model.J # these are the actual, predicted indices idx = zip(self.model.endog, self.predict().argmax(1)) return np.histogram2d(self.model.endog, self.predict().argmax(1), bins=J)[0] @cache_readonly def bse(self): bse = np.sqrt(np.diag(self.cov_params())) return bse.reshape(self.params.shape, order='F') @cache_readonly def aic(self): return -2*(self.llf - (self.df_model+self.model.J-1)) @cache_readonly def bic(self): return -2*self.llf + np.log(self.nobs)*(self.df_model+self.model.J-1) def conf_int(self, alpha=.05, cols=None): confint = super(DiscreteResults, self).conf_int(alpha=alpha, cols=cols) return confint.transpose(2,0,1) def margeff(self): raise NotImplementedError("Use get_margeff instead") @cache_readonly def resid_misclassified(self): """ Residuals indicating which observations are misclassified. Notes ----- The residuals for the multinomial model are defined as .. math:: argmax(y_i) \\neq argmax(p_i) where :math:`argmax(y_i)` is the index of the category for the endogenous variable and :math:`argmax(p_i)` is the index of the predicted probabilities for each category. That is, the residual is a binary indicator that is 0 if the category with the highest predicted probability is the same as that of the observed variable and 1 otherwise. """ # it's 0 or 1 - 0 for correct prediction and 1 for a missed one return (self.model.wendog.argmax(1) != self.predict().argmax(1)).astype(float) def summary2(self, alpha=0.05, float_format="%.4f"): """Experimental function to summarize regression results Parameters ----------- alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_dict(summary2.summary_model(self)) # One data frame per value of endog eqn = self.params.shape[1] confint = self.conf_int(alpha) for i in range(eqn): coefs = summary2.summary_params(self, alpha, self.params[:,i], self.bse[:,i], self.tvalues[:,i], self.pvalues[:,i], confint[i]) # Header must show value of endog level_str = self.model.endog_names + ' = ' + str(i) coefs[level_str] = coefs.index coefs = coefs.ix[:,[-1,0,1,2,3,4,5]] smry.add_df(coefs, index=False, header=True, float_format=float_format) smry.add_title(results=self) return smry class L1MultinomialResults(MultinomialResults): __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for multinomial data fit by l1 regularization", "extra_attr" : _l1_results_attr} def __init__(self, model, mlefit): super(L1MultinomialResults, self).__init__(model, mlefit) # self.trimmed is a boolean array with T/F telling whether or not that # entry in params has been set zero'd out. self.trimmed = mlefit.mle_retvals['trimmed'] self.nnz_params = (self.trimmed == False).sum() #Note: J-1 constants self.model.df_model = self.nnz_params - (self.model.J - 1) self.model.df_resid = float(self.model.endog.shape[0] - self.nnz_params) self.df_model = self.model.df_model self.df_resid = self.model.df_resid #### Results Wrappers #### class OrderedResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(OrderedResultsWrapper, OrderedResults) class CountResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(CountResultsWrapper, CountResults) class NegativeBinomialAncillaryResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(NegativeBinomialAncillaryResultsWrapper, NegativeBinomialAncillaryResults) class L1CountResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1CountResultsWrapper, L1CountResults) class BinaryResultsWrapper(lm.RegressionResultsWrapper): _attrs = {"resid_dev" : "rows", "resid_generalized" : "rows", "resid_pearson" : "rows", "resid_response" : "rows" } _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(BinaryResultsWrapper, BinaryResults) class L1BinaryResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1BinaryResultsWrapper, L1BinaryResults) class MultinomialResultsWrapper(lm.RegressionResultsWrapper): _attrs = {"resid_misclassified" : "rows"} _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(MultinomialResultsWrapper, MultinomialResults) class L1MultinomialResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1MultinomialResultsWrapper, L1MultinomialResults) if __name__=="__main__": import numpy as np import statsmodels.api as sm # Scratch work for negative binomial models # dvisits was written using an R package, I can provide the dataset # on request until the copyright is cleared up #TODO: request permission to use dvisits data2 = np.genfromtxt('../datasets/dvisits/dvisits.csv', names=True) # note that this has missing values for Accident endog = data2['doctorco'] exog = data2[['sex','age','agesq','income','levyplus','freepoor', 'freerepa','illness','actdays','hscore','chcond1', 'chcond2']].view(float).reshape(len(data2),-1) exog = sm.add_constant(exog, prepend=True) poisson_mod = Poisson(endog, exog) poisson_res = poisson_mod.fit() # nb2_mod = NegBinTwo(endog, exog) # nb2_res = nb2_mod.fit() # solvers hang (with no error and no maxiter warn...) # haven't derived hessian (though it will be block diagonal) to check # newton, note that Lawless (1987) has the derivations # appear to be something wrong with the score? # according to Lawless, traditionally the likelihood is maximized wrt to B # and a gridsearch on a to determin ahat? # or the Breslow approach, which is 2 step iterative. nb2_params = [-2.190,.217,-.216,.609,-.142,.118,-.497,.145,.214,.144, .038,.099,.190,1.077] # alpha is last # taken from Cameron and Trivedi # the below is from Cameron and Trivedi as well # endog2 = np.array(endog>=1, dtype=float) # skipped for now, binary poisson results look off? data = sm.datasets.randhie.load() nbreg = NegativeBinomial mod = nbreg(data.endog, data.exog.view((float,9))) #FROM STATA: params = np.asarray([-.05654133, -.21214282, .0878311, -.02991813, .22903632, .06210226, .06799715, .08407035, .18532336]) bse = [0.0062541, 0.0231818, 0.0036942, 0.0034796, 0.0305176, 0.0012397, 0.0198008, 0.0368707, 0.0766506] lnalpha = .31221786 mod.loglike(np.r_[params,np.exp(lnalpha)]) poiss_res = Poisson(data.endog, data.exog.view((float,9))).fit() func = lambda x: -mod.loglike(x) grad = lambda x: -mod.score(x) from scipy import optimize # res1 = optimize.fmin_l_bfgs_b(func, np.r_[poiss_res.params,.1], # approx_grad=True) res1 = optimize.fmin_bfgs(func, np.r_[poiss_res.params,.1], fprime=grad) from statsmodels.tools.numdiff import approx_hess_cs # np.sqrt(np.diag(-np.linalg.inv(approx_hess_cs(np.r_[params,lnalpha], mod.loglike)))) #NOTE: this is the hessian in terms of alpha _not_ lnalpha hess_arr = mod.hessian(res1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/000077500000000000000000000000001224417117700234605ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/__init__.py000066400000000000000000000000001224417117700255570ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/results/000077500000000000000000000000001224417117700251615ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/results/__init__.py000066400000000000000000000000001224417117700272600ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/results/mn_logit_summary.txt000066400000000000000000000074371224417117700313220ustar00rootroot00000000000000============================================================================== y=1 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -0.2794 0.612 -0.457 0.648 -1.479 0.920 x1 -0.0119 0.034 -0.350 0.727 -0.079 0.055 x2 0.2946 0.093 3.152 0.002 0.111 0.478 x3 -0.0257 0.007 -3.873 0.000 -0.039 -0.013 x4 0.0897 0.069 1.297 0.195 -0.046 0.225 ------------------------------------------------------------------------------ y=2 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -1.7392 0.720 -2.414 0.016 -3.151 -0.327 x1 -0.0941 0.039 -2.415 0.016 -0.170 -0.018 x2 0.3838 0.108 3.559 0.000 0.172 0.595 x3 -0.0230 0.008 -2.914 0.004 -0.038 -0.008 x4 0.2451 0.080 3.053 0.002 0.088 0.402 ------------------------------------------------------------------------------ y=3 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -3.0091 1.069 -2.815 0.005 -5.104 -0.914 x1 -0.1115 0.057 -1.965 0.049 -0.223 -0.000 x2 0.5641 0.159 3.557 0.000 0.253 0.875 x3 -0.0156 0.011 -1.391 0.164 -0.038 0.006 x4 0.0687 0.119 0.577 0.564 -0.165 0.302 ------------------------------------------------------------------------------ y=4 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -6.5607 0.870 -7.540 0.000 -8.266 -4.855 x1 -0.0949 0.043 -2.183 0.029 -0.180 -0.010 x2 1.2668 0.128 9.897 0.000 1.016 1.518 x3 -0.0109 0.008 -1.315 0.189 -0.027 0.005 x4 0.3067 0.088 3.475 0.001 0.134 0.480 ------------------------------------------------------------------------------ y=5 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -6.0760 0.774 -7.851 0.000 -7.593 -4.559 x1 -0.0967 0.039 -2.479 0.013 -0.173 -0.020 x2 1.3377 0.116 11.493 0.000 1.110 1.566 x3 -0.0198 0.008 -2.631 0.009 -0.035 -0.005 x4 0.3193 0.080 4.001 0.000 0.163 0.476 ------------------------------------------------------------------------------ y=6 coef std err z P>|z| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -10.5973 0.957 -11.076 0.000 -12.473 -8.722 x1 -0.1447 0.042 -3.475 0.001 -0.226 -0.063 x2 2.0395 0.141 14.479 0.000 1.763 2.316 x3 -0.0129 0.008 -1.612 0.107 -0.029 0.003 x4 0.4576 0.085 5.352 0.000 0.290 0.625 ============================================================================== 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-2.35, 8.38, -1.62, -2.26, -2.26, 4.26, 6.26, 3.39, 3.29, -2.71, -2.58, 18.58, -2.36, -0.54, 2.58, 5.58, 5.65, 8.00, -3.59, 3.00, 17.00, -0.44, -1.44, 1.11, -2.35, 2.58, -2.42, -1.36, -2.42, -1.63, -3.63, 5.56, 5.58, 3.58 statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/results/results_discrete.py000066400000000000000000001324731224417117700311300ustar00rootroot00000000000000""" Test Results for discrete models from Stata """ import os import numpy as np #### Discrete Model Tests #### # Note that there is a slight refactor of the classes, so that one dataset # might be used for more than one model cur_dir = os.path.abspath(os.path.dirname(__file__)) class Anes(): def __init__(self): """ Results are from Stata 11 (checked vs R nnet package). """ self.nobs = 944 def mnlogit_basezero(self): params = [-.01153598, .29771435, -.024945, .08249144, .00519655, -.37340167, -.08875065, .39166864, -.02289784, .18104276, .04787398, -2.2509132, -.1059667, .57345051, -.01485121, -.00715242, .05757516, -3.6655835, -.0915567, 1.2787718, -.00868135, .19982796, .08449838, -7.6138431, -.0932846, 1.3469616, -.01790407, .21693885, .08095841, -7.0604782, -.14088069, 2.0700801, -.00943265, .3219257, .10889408, -12.105751] self.params = np.reshape(params, (6,-1), order='F') bse = [.0342823657, .093626795, .0065248584, .0735865799, .0176336937, .6298376313, .0391615553, .1082386919, .0079144618, .0852893563, .0222809297, .7631899491, .0570382292, .1585481337, .0113313133, .1262913234, .0336142088, 1.156541492, .0437902764, .1288965854, .0084187486, .0941250559, .0261963632, .9575809602, .0393516553, .1171860107, .0076110152, .0850070091, .0229760791, .8443638283, .042138047, .1434089089, .0081338625, .0910979921, .025300888, 1.059954821] self.bse = np.reshape(bse, (6,-1), order='F') self.yhat = np.loadtxt(os.path.join(cur_dir,'yhat_mnlogit.csv')) self.phat = np.loadtxt(os.path.join(cur_dir,'phat_mnlogit.csv')) self.cov_params = None self.llf = -1461.922747312 self.llnull = -1750.34670999 self.llr = 576.8479253554 self.llr_pvalue = 1.8223179e-102 self.prsquared = .1647810465387 self.df_model = 30 self.df_resid = 944 - 36 self.J = 7 self.K = 6 self.aic = 2995.84549462 self.bic = 3170.45003661 z = [-.3364988051, 3.179798597, -3.823070772, 1.121012042, .2946945327, -.5928538661, -2.266269864, 3.618564069, -2.893164162, 2.122688754, 2.148652536, -2.949348555, -1.857818873, 3.616885888, -1.310634214, -.0566342868, 1.712822091, -3.169435381, -2.090799808, 9.920912816, -1.031191864, 2.123004903, 3.225576554, -7.951122047, -2.370538224, 11.49421878, -2.352389066, 2.552011323, 3.523595639, -8.361890935, -3.34331327, 14.43480847, -1.159676452, 3.533839715, 4.303962885, -11.42100649] self.z = np.reshape(z, (6,-1), order='F') pvalues = [0.7364947525, 0.0014737744, 0.0001317999, 0.2622827367, 0.7682272401, 0.5532789548, 0.0234348654, 0.0002962422, 0.0038138191, 0.0337799420, 0.0316619538, 0.0031844460, 0.0631947400, 0.0002981687, 0.1899813744, 0.9548365214, 0.0867452747, 0.0015273542, 0.0365460134, 3.37654e-23, 0.3024508550, 0.0337534410, 0.0012571921, 1.84830e-15, 0.0177622072, 1.41051e-30, 0.0186532528, 0.0107103038, 0.0004257334, 6.17209e-17, 0.0008278439, 3.12513e-47, 0.2461805610, 0.0004095694, 0.0000167770, 3.28408e-30] self.pvalues = np.reshape(pvalues, (6,-1), order='F') conf_int = [[[-0.0787282, 0.0556562], [0.1142092, 0.4812195], [-0.0377335, -0.0121565], [-0.0617356, 0.2267185], [-0.0293649, 0.0397580], [-1.6078610, 0.8610574]], [[-0.1655059, -0.0119954], [0.1795247, 0.6038126], [-0.0384099, -0.0073858], [0.0138787, 0.3482068], [0.0042042, 0.0915438], [-3.7467380, -0.7550884]], [[-0.2177596, 0.0058262], [0.2627019, 0.8841991], [-0.0370602, 0.0073578], [-0.2546789, 0.2403740], [-0.0083075, 0.1234578], [-5.9323630,-1.3988040]],[[-0.1773841, -0.0057293], [1.0261390, 1.5314040], [-0.0251818, 0.0078191], [0.0153462, 0.3843097], [0.0331544, 0.1358423], [-9.4906670, -5.7370190]], [[-0.1704124, -0.0161568], [1.1172810, 1.5766420], [-0.0328214, -0.0029868], [0.0503282, 0.3835495], [0.0359261, 0.1259907], [-8.7154010, -5.4055560]], [[-0.2234697, -0.0582916], [1.7890040, 2.3511560], [-0.0253747, 0.0065094], [0.1433769, 0.5004745], [0.0593053, 0.1584829], [-14.1832200, -10.0282800]]] self.conf_int = np.asarray(conf_int) # margins, dydx(*) predict(outcome(#)) self.margeff_dydx_overall = np.array([ [0.00868085993550, -0.09779854015456, 0.00272556969847, -0.01992376579372, -0.00603133322764], [0.00699386733148, -0.05022430802614, -0.00211003909752, -0.00536980000265, -0.00554366741814], [-0.00391040848820, -0.02824717135857, -0.00100551299310, 0.00664337806861, 0.00097987356999], [-0.00182580888015, -0.00573744730031, -0.00004249256428, -0.00546669558488, 0.00054101121854], [-0.00098558129923, 0.01985550937033, 0.00047972250012, 0.00172605778905, 0.00211291403209], [-0.00153469551647, 0.03755346502013, -0.00068531143399, 0.00472471794347, 0.00254733486106], [-0.00741820702809, 0.12459834487569, 0.00063806819375, 0.01766610701188, 0.00539385283759] ]).T self.margeff_dydx_overall_se = np.array([ [.0038581061, .0080471125, .0007068488, .0082318967, .0020261706], [.003904378, .0073600286, .000756431, .0084381578, .0020482238], [.003137126, .0056813182, .0006601377, .0068932588, .0018481806], [.0019427783, .0031904763, .0003865411, .004361789, .0011523221], [.0029863227, .0054076092, .0005886612, .0064426365, .0018886818], [.0035806552, .0069497362, .000722511, .0078287717, .0022352393], [.0033641608, .008376629, .0006774697, .0073505286, .0021660086] ]).T self.margeff_dydx_mean = np.array([ [0.01149887431225, -0.13784207091973, 0.00273313385873, -0.02542974260540, -0.00855346837482], [0.01114846831102, -0.09864273512889, -0.00222435063712, -0.01214617126321, -0.00903581444579], [-0.00381702868421, -0.05132297961269, -0.00116763216994, 0.00624203027060, 0.00021912081810], [-0.00233455327258, -0.00928554037343, -0.00000206561214, -0.00775415690571, 0.00060004460394], [-0.00352579921274, 0.06412187169362, 0.00073938948643, 0.00747778063206, 0.00459965010365], [-0.00574308219449, 0.11126535089794, -0.00057337915464, 0.01467424346725, 0.00641760846097], [-0.00722687818452, 0.12170608820238, 0.00049490419675, 0.01693601418978, 0.00575285798725]]).T self.margeff_dydx_mean_se = np.array([ [.0043729758, .0110343353, .0008149907, .0092551389, .0023752071], [.004875051, .0124746358, .0009613152, .0105665812, .0026524426], [.0040718954, .0103613938, .0008554615, .0089931297, .0024374625], [.0026430804, .0070845916, .0005364369, .0057654258, .0015988838], [.0037798151, .0103849291, .0007393481, .0082021938, .0023489261], [.0045654631, .0130329403, .0009128134, .0100053262, .0028048602], [.0027682389, .0113292677, .0005325113, .0061289353, .0017330763] ]).T self.margeff_dydx_dummy_overall = np.array([ [0.00549149574321, -0.05348235321783, 0.00298963549049, -0.01479461677951, -0.00332167981255, -0.26502967041815], [0.00345677928276, -0.00950322030929, -0.00189456107189, 0.00033893662061, -0.00314690167350, -0.21040878091828], [-0.00645089013284, 0.00401746940204, -0.00083948249351, 0.01114202556889, 0.00277069841472, -0.15967397659686], [-0.00215436802341, -0.00366545199370, -0.00000002297812, -0.00457368049644, 0.00065303026027, -0.00094772782001], [0.00058038428936, -0.00369080100124, 0.00035948233235, -0.00018863693013, 0.00079351293461, 0.12640653743480], [0.00217597030999, -0.01279456622853, -0.00091882392767, 0.00001651192759, -0.00037998290789, 0.27175070356670], [-0.00309932483642, 0.07911868907484, 0.00030378521102, 0.00805941631677, 0.00263129901425, 0.23790291475181]]).T self.margeff_dydx_dummy_overall_se = np.array([ [.0037314453, .0094102332, .000688838, .0079744554, .0019365971, .0243914836], [.0038215262, .0095938828, .0007410885, .008259353, .0019984087, .0317628806], [.0031045718, .00785814, .0006504353, .0067892866, .0018060332, 0.0262803561], [.0019756086, .0051031194, .0003862449, .0043621673, .0011796953, .0219999601], [.0029714074, .0081732018, .0005715192, .0064742872, .0019130195, .0331694192], [.0034443743, .0097296187, .0006774867, .0075996454, .0021993881, .038600835], [.0032003518, .0098741227, .0006335772, .0070902078, .0021003227, .0255727127]]).T self.margeff_eydx_dummy_overall = np.array([ [.03939188, -.65758371, .01750922, -.12131806, -.03613241, -3.2132513], [.02752366, -.383165, -.00830021, -.03652935, -.03286046, -1.8741853], [-.05006681, -.2719659, -.00626481, .06525323, .01012554, -2.0058029], [-.05239558, -.22549142, .00025015, -.13104416, .01114517, -.27052009], [-.00296374, .25627809, .00140513, .03358712, .02296041, 1.3302701], [.00328283, .2800168, -.0083912, .04332782, .01575863, 1.8441023], [-.03257068, .98346111, -.00122118, .10847807, .0406456, 2.9119099]]).T self.margeff_eydx_dummy_overall_se = np.array([ [.0272085605, .0777760394, .0052427952, .0584011446, .0148618012, .5796921383], [.0262290023, .0724479385, .005174736, .0567743614, .0144447083, .3015738731], [.0321415498, .0895589422, .0067480662, .0701460193, .0190451865, .3904138447], [.0511305319, .1420904068, .0102342163, .1129912244, .0308618233, .3693799595], [.0340186217, .0991711703, .0065812158, .0737441012, .0212966336, .2346982385], [.0289250212, .0840662279, .0056743561, .0631772185, .0177278895, .2089516714], [.0318251305, .1085637405, .0062400589, .0699123044, .0201045606, .3727166284]]).T # taken from gretl self.resid = np.loadtxt(os.path.join(cur_dir,'mnlogit_resid.csv'), delimiter=",") class DiscreteL1(object): def __init__(self): """ Special results for L1 models Uses the Spector data and a script to generate the baseline results """ pass def logit(self): """ Results generated with: data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) alpha = 3 * np.array([0, 1, 1, 1]) res2 = sm.Logit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, trim_mode='size', size_trim_tol=1e-5, acc=1e-10, maxiter=1000) """ nan = np.nan self.params = [-4.10271595, 0., 0.15493781, 0.] self.conf_int = [[-9.15205122, 0.94661932], [nan, nan], [-0.06539482, 0.37527044], [ nan, nan]] self.bse = [ 2.5762388 , nan, 0.11241668, nan] self.nnz_params = 2 self.aic = 42.091439368583671 self.bic = 45.022911174183122 self.cov_params = [[ 6.63700638, nan, -0.28636261, nan], [nan, nan, nan, nan], [-0.28636261, nan, 0.01263751, nan], [nan, nan, nan, nan]] def sweep(self): """ Results generated with params = np.zeros((3, 4)) alphas = np.array( [[0.1, 0.1, 0.1, 0.1], [0.4, 0.4, 0.5, 0.5], [0.5, 0.5, 1, 1]]) model = sm.Logit(data.endog, data.exog) for i in range(3): alpha = alphas[i, :] res2 = model.fit_regularized(method="l1", alpha=alpha, disp=0, acc=1e-10, maxiter=1000, trim_mode='off') params[i, :] = res2.params print params """ self.params = [[-10.37593611, 2.27080968, 0.06670638, 2.05723691], [ -5.32670811, 1.18216019, 0.01402395, 1.45178712], [ -3.92630318, 0.90126958, -0. , 1.09498178]] def probit(self): """ Results generated with data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) alpha = np.array([0.1, 0.2, 0.3, 10]) res2 = sm.Probit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, trim_mode='auto', auto_trim_tol=0.02, acc=1e-10, maxiter=1000) """ nan = np.nan self.params = [-5.40476992, 1.25018458, 0.04744558, 0. ] self.conf_int = [[-9.44077951, -1.36876033], [ 0.03716721, 2.46320194], [-0.09727571, 0.19216687], [ np.nan, np.nan]] self.bse = [ 2.05922641, 0.61889778, 0.07383875, np.nan] self.nnz_params = 3 self.aic = 38.399773877542927 self.bic = 42.796981585942106 self.cov_params = [[ 4.24041339, -0.83432592, -0.06827915, nan], [-0.83432592, 0.38303447, -0.01700249, nan], [-0.06827915, -0.01700249, 0.00545216, nan], [ nan, nan, nan, nan]] def mnlogit(self): """ Results generated with anes_data = sm.datasets.anes96.load() anes_exog = anes_data.exog anes_exog = sm.add_constant(anes_exog, prepend=False) mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog) alpha = 10 * np.ones((mlogit_mod.J - 1, mlogit_mod.K)) alpha[-1,:] = 0 mlogit_l1_res = mlogit_mod.fit_regularized( method='l1', alpha=alpha, trim_mode='auto', auto_trim_tol=0.02, acc=1e-10) """ self.params = [[ 0.00100163, -0.05864195, -0.06147822, -0.04769671, -0.05222987, -0.09522432], [ 0. , 0.03186139, 0.12048999, 0.83211915, 0.92330292, 1.5680646 ], [-0.0218185 , -0.01988066, -0.00808564, -0.00487463, -0.01400173, -0.00562079], [ 0. , 0.03306875, 0. , 0.02362861, 0.05486435, 0.14656966], [ 0. , 0.04448213, 0.03252651, 0.07661761, 0.07265266, 0.0967758 ], [ 0.90993803, -0.50081247, -2.08285102, -5.26132955, -4.86783179, -9.31537963]] self.conf_int = [[[ -0.0646223 , 0.06662556], [ np.nan, np.nan], [ -0.03405931, -0.00957768], [ np.nan, np.nan], [ np.nan, np.nan], [ 0.26697895, 1.55289711]], [[ -0.1337913 , 0.01650741], [ -0.14477255, 0.20849532], [ -0.03500303, -0.00475829], [ -0.11406121, 0.18019871], [ 0.00479741, 0.08416684], [ -1.84626136, 0.84463642]], [[ -0.17237962, 0.04942317], [ -0.15146029, 0.39244026], [ -0.02947379, 0.01330252], [ np.nan, np.nan], [ -0.02501483, 0.09006785], [ -3.90379391, -0.26190812]], [[ -0.12938296, 0.03398954], [ 0.62612955, 1.03810876], [ -0.02046322, 0.01071395], [ -0.13738534, 0.18464256], [ 0.03017236, 0.12306286], [ -6.91227465, -3.61038444]], [[ -0.12469773, 0.02023799], [ 0.742564 , 1.10404183], [ -0.02791975, -0.00008371], [ -0.08491561, 0.19464431], [ 0.0332926 , 0.11201273], [ -6.29331126, -3.44235233]], [[ -0.17165567, -0.01879296], [ 1.33994079, 1.79618841], [ -0.02027503, 0.00903345], [ -0.00267819, 0.29581751], [ 0.05343135, 0.14012026], [-11.10419107, -7.52656819]]] self.bse = [[ 0.03348221, 0.03834221, 0.05658338, 0.04167742, 0.03697408, 0.03899631], [ np.nan, 0.09012101, 0.13875269, 0.10509867, 0.09221543, 0.11639184], [ 0.00624543, 0.00771564, 0.01091253, 0.00795351, 0.00710116, 0.00747679], [ np.nan, 0.07506769, np.nan, 0.08215148, 0.07131762, 0.07614826], [ np.nan, 0.02024768, 0.02935837, 0.02369699, 0.02008204, 0.02211492], [ 0.32804638, 0.68646613, 0.92906957, 0.84233441, 0.72729881, 0.91267567]] self.nnz_params = 32 self.aic = 3019.4391360294126 self.bic = 3174.6431733460686 class Spector(): """ Results are from Stata 11 """ def __init__(self): self.nobs = 32 def logit(self): self.params = [2.82611297201, .0951576702557, 2.37868772835, -13.0213483201] self.cov_params = [[1.59502033639, -.036920566629, .427615725153, -4.57347950298], [-.036920566629, .0200375937069, .0149126464275, -.346255757562], [.427615725153 , .0149126464275, 1.13329715236, -2.35916128427], [-4.57347950298, -.346255757562, -2.35916128427, 24.3179625937]] self.bse = [1.26294114526, .141554207662, 1.06456430165, 4.93132462871] self.resid_pearson = [-.1652382, -.2515266, -.4800059, -.1630655, .8687437, -.1900454, -.165002, -.2331563, -.3535812, .6647838, -.1583799, -.4843181, -.689527, 2.043449, -.7516119, -.1764176, -.2380445, -.2003426, -1.199277, .7164842, -.255713, .3242821, -.5646816, -2.400189, .4392082, 1.038473, .75747, -.6659256, .4336657, .2404583, -1.060033, 2.829577] self.resid_dev = [-.2321102, -.3502712, -.6439626, -.2290982, 1.060478, -.2663844, -.2317827, -.3253788, -.4853875, .8555557, -.2225972, -.6491808, -.8819993, 1.813269, -.9463985, -.247583, -.3320177, -.2805444, -1.335131, .9103027, -.3559217, .4471892, -.744005, -1.955074, .5939538, 1.209638, .952332, -.8567857, .5870719, .335292, -1.227311, 2.096639] # from gretl self.resid_generalized = [-0.026578, -0.059501, -0.187260, -0.025902, 0.430107, -0.034858, -0.026504, -0.051559, -0.111127, 0.306489, -0.024470, -0.189997, -0.322240, 0.806789, -0.360990, -0.030184, -0.053626, -0.038588, -0.589872, 0.339214, -0.061376, 0.095153, -0.241772, -0.852091, 0.161709, 0.518867, 0.364579, -0.307219, 0.158296, 0.054660, -0.529117, 0.888969] self.phat = np.array([ .02657799236476, .05950126051903, .18725991249084, .02590163610876, .56989300251007, .03485824912786, .02650404907763, .05155897513032, .11112663894892, .69351142644882, .02447037212551, .18999740481377, .32223951816559, .1932111531496, .36098992824554, .03018374741077, .05362640321255, .03858831897378, .58987241983414, .66078591346741, .06137581542134, .90484726428986, .24177247285843, .85209089517593, .8382905125618, .48113295435905, .63542068004608, .30721867084503, .84170418977737, .94534027576447, .52911710739136, .1110308393836]) self.yhat = np.array([-3.6007342338562, -2.7604126930237, -1.4679137468338, -3.6272060871124, .28141465783119, -3.3209850788116, -3.6035962104797, -2.9120934009552, -2.0792844295502, .81658720970154, -3.6855175495148, -1.4500269889832, -.74349880218506, -1.429278254509, -.57107019424438, -3.4698030948639, -2.8705959320068, -3.2154531478882, .36343798041344, .66679841279984, -2.7273993492126, 2.2522828578949, -1.1429864168167, 1.7510952949524, 1.6455633640289, -.07550399750471, .55554306507111, -.81315463781357, 1.6709630489349, 2.8504176139832, .11660042405128, -2.0802545547485]) self.llf = -12.8896334653335 self.llnull = -20.5917296966173 self.df_model = 3 self.df_resid = 32 - 4 #TODO: is this right? not reported in stata self.llr = 15.4041924625676 self.prsquared = .374038332124624 self.llr_pvalue = .00150187761112892 self.aic = 33.779266930667 self.bic = 39.642210541866 self.z = [2.237723415, 0.6722348408, 2.234423721, -2.640537645] self.conf_int = [[.3507938,5.301432],[-.1822835,.3725988],[.29218, 4.465195],[-22.68657,-3.35613]] self.pvalues = [.0252390974, .5014342039, .0254552063, .0082774596] # taken from margins command self.margeff_nodummy_dydx = [.36258084688424,.01220841099085, .30517768382304] self.margeff_nodummy_dydx_se = [.1094412, .0177942, .0923796] self.margeff_nodummy_dydxmean = [.53385885781692,.01797548988961, .44933926079386] self.margeff_nodummy_dydxmean_se = [.237038, .0262369, .1967626] self.margeff_nodummy_dydxmedian = [.25009492465091,.00842091261329, .2105003352955] self.margeff_nodummy_dydxmedian_se = [.1546708, .0134314, .0928183] self.margeff_nodummy_dydxzero = [6.252993785e-06,2.105437138e-07, 5.263030788e-06] self.margeff_nodummy_dydxzero_se = [.0000288, 9.24e-07, .000025] self.margeff_nodummy_dyex = [1.1774000792198,.27896245178384, .16960002159996] self.margeff_nodummy_dyex_se = [.3616481, .4090679, .0635583] self.margeff_nodummy_dyexmean = [1.6641381583512,.39433730945339, .19658592659731] self.margeff_nodummy_dyexmean_se = [.7388917, .5755722, .0860836] #NOTE: PSI at median should be a NaN or 'omitted' self.margeff_nodummy_dyexmedian = [.76654095836557,.18947053379898,0] self.margeff_nodummy_dyexmedian_se = [ .4740659, .302207, 0] #NOTE: all should be NaN self.margeff_nodummy_dyexzero = [0,0,0] self.margeff_nodummy_dyexzero_se = [0,0,0] self.margeff_nodummy_eydx = [1.8546366266779,.06244722072812, 1.5610138123033] self.margeff_nodummy_eydx_se = [.847903, .0930901, .7146715] self.margeff_nodummy_eydxmean = [2.1116143062702,.0710998816585, 1.7773072368626] self.margeff_nodummy_eydxmean_se = [ 1.076109, .1081501, .9120842] self.margeff_nodummy_eydxmedian = [2.5488082240624,.0858205793373, 2.1452853812126] self.margeff_nodummy_eydxmedian_se = [1.255377, .1283771, 1.106872] self.margeff_nodummy_eydxzero = [2.8261067189993,.0951574597115, 2.3786824653103] self.margeff_nodummy_eydxzero_se = [1.262961, .1415544, 1.064574] self.margeff_nodummy_eyex = [5.4747106798973,1.3173389907576, .44600395466634] self.margeff_nodummy_eyex_se = [2.44682, 1.943525, .1567618] self.margeff_nodummy_eyexmean = [6.5822977203268,1.5597536538833, .77757191612739] self.margeff_nodummy_eyexmean_se = [3.354433, 2.372543, .3990368] self.margeff_nodummy_eyexmedian = [7.8120973525952,1.9309630350892,0] self.margeff_nodummy_eyexmedian_se = [3.847731951, 2.888485089, 0] self.margeff_nodummy_eyexzero = [0,0,0] self.margeff_nodummy_eyexzero_se = [0,0,0] # for below GPA = 2.0, psi = 1 self.margeff_nodummy_atexog1 = [.1456333017086,.00490359933927, .12257689308426] self.margeff_nodummy_atexog1_se = [.145633, .0111226, .1777101] # for below GPA at mean, tuce = 21, psi = 0 self.margeff_nodummy_atexog2 = [.25105129214546,.00845311433473, .2113052923675] self.margeff_nodummy_atexog2_se = [.1735778, .012017, .0971515] # must get this from older margeff or i.psi then margins self.margeff_dummy_dydx = [.36258084688424,.01220841099085, .35751515254729] self.margeff_dummy_dydx_se = [.1094412, .0177942, .1420034] self.margeff_dummy_dydxmean = [.53385885781692,.01797548988961, .4564984096959] self.margeff_dummy_dydxmean_se = [.237038, .0262369, .1810537] #self.margeff_dummy_dydxmedian # from margeff self.margeff_dummy_count_dydx_median = [0.250110487483923, 0.008426867847905, 0.441897738279663] self.margeff_dummy_count_dydx_median_se = [.1546736661, .0134551951, .1792363708] # estimate with i.psi for the below then use margins self.margeff_dummy_eydx = [1.8546366266779,.06244722072812, 1.5549034398832] self.margeff_dummy_eydx_se = [.847903, .0930901, .7283702] # ie # margins, eydx(*) at((mean) _all) self.margeff_dummy_eydxmean = [2.1116143062702,.0710998816585, 1.6631775707188] self.margeff_dummy_eydxmean_se = [1.076109, .1081501, .801205] # Factor variables not allowed in below # test raises #self.margeff_dummy_dydxzero #self.margeff_dummy_eydxmedian #self.margeff_dummy_eydxzero #self.margeff_dummy_dyex #self.margeff_dummy_dyexmean #self.margeff_dummy_dyexmedian #self.margeff_dummy_dyexzero #self.margeff_dummy_eyex #self.margeff_count_dummy_dydx_median #self.margeff_count_dummy_dydx_median_se #NOTE: need old version of margeff for nodisc but at option is broken # stata command is margeff, count nodisc # this can be replicated with the new results by margeff # and then using margins for the last value self.margeff_count_dydx = [.3625767598018, .0122068569914, .3051777] self.margeff_count_dydx_se = [.1094379569, .0177869773, .0923796] # middle value taken from margeff rest from margins self.margeff_count_dydxmean = [.5338588, 0.01797186545386, .4493393 ] self.margeff_count_dydxmean_se = [.237038, .0262211, .1967626] # with new version of margeff this is just a call to # margeff # mat list e(margeff_b), nonames format(%17.16g) self.margeff_count_dummy_dydxoverall = [.362576759801767, .012206856991439, .357515163621704] # AFAICT, an easy way to get se is # mata # V = st_matrix("e(margeff_V)") # se = diagonal(cholesky(diag(V))) # last SE taken from margins with i.psi, don't know how they # don't know why margeff is different, but trust official results self.margeff_count_dummy_dydxoverall_se = [.1094379569, .0177869773, .1420034] #.1574340751 ] # from new margeff self.margeff_count_dummy_dydxmean = [0.533849340033768, 0.017971865453858, 0.456498405282412] self.margeff_count_dummy_dydxmean_se = [.2370202503, .0262210796, .1810536852 ] # for below GPA = 2.0, psi = 1 self.margeff_dummy_atexog1 = [.1456333017086,.00490359933927, .0494715429937] self.margeff_dummy_atexog1_se = [.145633, .0111226, .0731368] # for below GPA at mean, tuce = 21, psi = 0 self.margeff_dummy_atexog2 = [.25105129214546,.00845311433473, .44265645632553] self.margeff_dummy_atexog2_se = [.1735778, .012017, .1811925] #The test for the prediction table was taken from Gretl #Gretl Output matched the Stata output here for params and SE self.pred_table = np.array([[18, 3], [3, 8]]) def probit(self): self.params = [1.62581025407, .051728948442, 1.42633236818, -7.45232041607] self.cov_params = [[.481472955383, -.01891350017, .105439226234, -1.1696681354], [-.01891350017, .00703757594, .002471864882, -.101172838897], [.105439226234, .002471864882, .354070126802, -.594791776765], [-1.1696681354, -.101172838897, -.594791776765, 6.46416639958]] self.bse = [.693882522754, .083890261293, .595037920474, 2.54247249731] self.llf = -12.8188033249334 self.llnull = -20.5917296966173 self.df_model = 3 self.df_resid = 32 - 4 self.llr = 15.5458527433678 self.prsquared = .377478069409622 self.llr_pvalue = .00140489496775855 self.aic = 33.637606649867 self.bic = 39.500550261066 self.z = [ 2.343062695, .6166263836, 2.397044489, -2.931131182] self.conf_int = [[.2658255,2.985795],[-.1126929,.2161508],[.2600795, 2.592585],[-12.43547,-2.469166]] self.pvalues = [.0191261688, .537481188, .0165279168, .0033773013] self.phat = [.0181707, .0530805, .1899263, .0185707, .5545748, .0272331, .0185033, .0445714, .1088081, .6631207, .0161024, .1935566, .3233282, .1951826, .3563406, .0219654, .0456943, .0308513, .5934023, .6571863, .0619288, .9045388, .2731908, .8474501, .8341947, .488726, .6424073, .3286732, .8400168, .9522446, .5399595, .123544] self.yhat = np.array([-2.0930860042572, -1.615691781044, -.87816804647446, -2.0842070579529, .13722851872444, -1.9231110811234, -2.0856919288635, -1.6999372243881, -1.2328916788101, .42099541425705, -2.1418602466583, -.86486464738846, -.45841211080551, -.85895526409149, -.36825761198997, -2.0147502422333, -1.6881184577942, -1.8684275150299, .23630557954311, .40479621291161, -1.538782119751, 1.3078554868698, -.60319095849991, 1.025558590889, .97087496519089, -.02826354466379, .36490100622177, -.44357979297638, .99452745914459, 1.6670187711716, .10033150017262, -1.1574513912201]) self.resid_dev = [-.191509, -.3302762, -.6490455, -.1936247, 1.085867, -.2349926, -.1932698, -.3019776, -.4799906, .9064196, -.1801855, -.6559291, -.8838201, 1.807661, -.9387071, -.2107617, -.3058469, -.2503485, -1.341589, .9162835, -.3575735, .447951, -.7988633, -1.939208, .6021435, 1.196623, .9407793, -.8927477, .59048, .3128364, -1.246147, 2.045071] # Stata doesn't have it, but I think it's just oversight self.resid_pearson = None # generalized residuals from gretl self.resid_generalized = [-0.045452, -0.114220, -0.334908, -0.046321, 0.712624, -0.064538, -0.046175, -0.098447, -0.209349, 0.550593, -0.040906, -0.340339, -0.530763, 1.413373, -0.579170, -0.053593, -0.100556, -0.071855, -0.954156, 0.559294, -0.130167, 0.187523, -0.457597, -1.545643, 0.298511, 0.815964, 0.581013, -0.538579, 0.289631, 0.104405, -0.862836, 1.652638] self.pred_table = np.array([[18, 3], [3, 8]]) class RandHIE(): """ Results obtained from Stata 11 """ def __init__(self): self.nobs = 20190 def poisson(self): self.params = [-.052535114675, -.247086797633, .035290201794, -.03457750643, .271713973711, .033941474461, -.012635035534, .054056326828, .206115121809, .700352877227] self.cov_params = None self.bse = [.00288398915279, .01061725196728, .00182833684966, .00161284852954, .01223913844387, .00056476496963, .00925061122826, .01530987068312, .02627928267502, .01116266712362] predict = np.loadtxt(os.path.join(cur_dir, 'yhat_poisson.csv'), delimiter=",") self.phat = predict[:,0] self.yhat = predict[:,1] self.llf = -62419.588535018 self.llnull = -66647.181687959 self.df_model = 9 self.df_resid = self.nobs - self.df_model - 1 self.llr = 8455.186305881856 self.prsquared = .0634324369893758 self.llr_pvalue = 0 self.aic = 124859.17707 self.bic = 124938.306497 self.z = [-18.21612769, -23.27219872, 19.30180524, -21.43878101, 22.20041672, 60.09840604, -1.36585953, 3.53081538, 7.84325525, 62.74063980] self.conf_int = [[ -.0581876, -.0468826],[-0.2678962, -0.2262774], [0.0317067, 0.0388737],[-0.0377386, -0.0314164], [0.2477257, 0.2957022], [0.0328346, 0.0350484],[-0.0307659, 0.0054958], [0.0240495, 0.0840631],[0.1546087, 0.2576216], [0.6784745, 0.7222313]] self.pvalues = [3.84415e-74, 8.4800e-120, 5.18652e-83, 5.8116e-102, 3.4028e-109, 0, .1719830562, .0004142808, 4.39014e-15, 0] # from stata # use margins and put i. in front of dummies self.margeff_dummy_overall = [-0.15027280560599, -0.66568074771099, 0.10094500919706, -0.09890639687842, 0.77721770295360, 0.09708707452600, -0.03608195237609, 0.15804581481115, 0.65104087597053] self.margeff_dummy_overall_se = [.008273103, .0269856266, .0052466639, .0046317555, .0351582169, .0016652181, .0263736472, .0457480115, .0913901155] # just use margins self.margeff_nodummy_overall = [-0.15027280560599, -0.70677348928158, 0.10094500919705, -0.09890639687842, 0.77721770295359, 0.09708707452600, -0.03614158359367, 0.15462412033340, 0.58957704430148] self.margeff_nodummy_overall_se = [.008273103, .0305119343, .0052466639, .0046317555, .0351582168, .0016652181, .0264611158, .0437974779, .0752099666] # taken from gretl self.resid = np.loadtxt(os.path.join(cur_dir,'poisson_resid.csv'), delimiter=",") def negativebinomial_nb2_bfgs(self): # R 2.15.1 MASS 7.3-22 glm.nb() self.params = [-0.0579469537244314, -0.267787718814838, 0.0412060770911646, -0.0381376804392121, 0.268915772213171, 0.0381637446219235, -0.0441338846217674, 0.0172521803400544, 0.177960787443151,0.663556087183864, # lnalpha from stata 1.292953339909746 ] # alpha and stderr from stata self.lnalpha_std_err = .0143932 self.lnalpha = 0.256929012449 self.bse = [0.00607085853920512, 0.0226125368090765, 0.00405542008282773, 0.00344455937127785, 0.0298855063286547, 0.00142421904710063, 0.0199374393307107, 0.0358416931939136, 0.0741013728607101, 0.0250354082637892, # from stata .0186098 ] self.z = [-9.54510030998327, -11.8424447940467, 10.1607419822296, -11.071860382846, 8.99820030672628, 26.7962605187844, -2.21361850384595, 0.481343898758222, 2.40158556546135, 26.5047040652267] self.pvalues = [1.35975947860026e-21, 2.35486776488278e-32, 2.96808970292151e-24, 1.71796558863781e-28, 2.2944789508802e-19, 3.57231639404726e-158, 0.0268550333379416, 0.630272102021494, 0.0163241908407114, 8.55476622951356e-155] self.fittedvalues = [0.892904166867786, 0.892904166867786, 0.892904166867786, 0.892904166867786, 0.892904166867786, 0.937038051489553, 0.937038051489553, 0.937038051489553, 0.937038051489553, 0.937038051489553] #self.aic = 86789.3241530713 # This is what R reports self.aic = 86789.32415307125484 # from Stata self.df_resid = 20180 self.df_model = 9 # R conf_int: 1.96 * bse, not profile likelihood via R's confint() self.conf_int = [ # from Stata [-.0698826, -.0460113], [-.3122654, -.2233101], [ .0330781, .049334], [-.0448006, -.0314748], [ .2102246, .3276069], [ .0352959, .0410316], [-.0834356, -.0048321], [-.0535908, .0880951], [ .0324115, .3235101], [ .6150055, .7121067], # from Stata [ 1.256989, 1.329947] ] self.bic = 86876.36652289562335 # stata self.llnull = -44199.27443563430279 # stata self.llr = 1631.224718197351 # stata self.llf = -43383.66207653563 # stata self.df_model = 9.0 self.llr_pvalue = 0.0 def negativebinomial_nb1_bfgs(self): # Unpublished implementation intended for R's COUNT package. Sent by # J.Hilbe (of Cambridge UP NBin book) and Andrew Robinson to Vincent # Arel-Bundock on 2012-12-06. #self.params = [-0.065309744899923, -0.296016207412261, # 0.0411724098925173, -0.0320460533573259, 0.19083354227553, # 0.0318566232844115, -0.0331972813313092, -0.0484691550721231, # 0.111971860837541, 0.757560143822609, # 3.73086958562569] # from Stata self.params = [-.065317260803762961, -.296023807893893376, .041187021258044826, -.032028789543547605, .19065933246421754, .031871625115758778, -.033250849053302826, -.04850769174426571, .111813637465757343, .757277086555503409, 3.731151380800305] # lnalpha and lnalpha_std_err are from stata self.lnalpha = 1.316716867203 self.lnalpha_std_err = .0168876692 self.bse = [0.00536019929563678, 0.0196998350459769, 0.00335779098766272, 0.00301145915122889, 0.0237984097096245, 0.00107360844112751, 0.0167174614755359, 0.0298037989274781, 0.0546838603596457,0.0214703279904911, 0.0630011409376052] self.z = [-12.1842008660173, -15.0263292419148, 12.2617548393554, -10.6413707601675, 8.0187518663633, 29.6724784046551, -1.98578482623631, -1.62627439508848, 2.04762173155154, 35.2840508145997, # From R, this is alpha/bse(alpha) 59.2190796881069 # taken from Stata even though they don't report it # lnalpha/bse(lnalpha) #77.968995 ] self.conf_int = [ [-0.075815736,-0.0548037543], [-0.334627884,-0.2574045307], [ 0.034591140, 0.0477536802], [-0.037948513,-0.0261435934], [ 0.144188659, 0.2374784253], [ 0.029752351, 0.0339608958], [-0.065963506,-0.0004310568], [-0.106884601, 0.0099462908], [ 0.004791495, 0.2191522271], [ 3.607387349, 3.8543518219], [ 0.715478301, 0.7996419867]] # from Stata self.llf = -43278.75612911823 self.llnull = -44199.2744356343 self.llr = 1841.036613032149 self.aic = 86579.51225823645655 self.bic = 86666.55462806082505 self.llr_pvalue = 0.0 self.df_model = 9.0 self.df_resid = 20180.0 # Smoke tests TODO: check against other stats package self.pvalues = [3.65557865e-034, 5.24431864e-051, 1.42921171e-034, 2.09797259e-026, 1.15949461e-015, 1.56785415e-193, 4.71746349e-002, 1.04731854e-001, 4.07534831e-002, 1.95504975e-272, 0.00000000e+000] self.conf_int = [[-.0758236, -.054811], [-.3346363, -.2574113], [ .0346053, .0477687], [-.0379314, -.0261261], [ .1440119, .2373067], [ .0297667, .0339766], [-.0660178, -.0004839], [-.1069241, .0099087], [ .0046266, .2190007], [ .7151889, .7993652], # from stata for alpha no lnalpha [ 3.609675, 3.856716]] #[ 1.28360034e+00, 1.34979803e+00]] self.fittedvalues = [ 0.8487497 , 0.8487497 , 0.8487497 , 0.8487497, 0.8487497 , 0.88201746, 0.88201746, 0.88201746, 0.88201746, 0.88201746] def negativebinomial_geometric_bfgs(self): # Smoke tests TODO: Cross check with other stats package self.params = [-0.05768894, -0.26646696, 0.04088528, -0.03795503, 0.26885821, 0.03802523, -0.04308456, 0.01931675, 0.18051684, 0.66469896] self.bse = [ 0.00553867, 0.02061988, 0.00375937, 0.0030924 , 0.02701658, 0.00132201, 0.01821646, 0.03271784, 0.06666231, 0.02250053] self.pvalues = [ 2.10310916e-025, 3.34666368e-038, 1.50697768e-027, 1.25468406e-034, 2.48155744e-023, 6.18745348e-182, 1.80230194e-002, 5.54919603e-001, 6.77044178e-003, 8.44913440e-192] self.z = [-10.41567024, -12.92281571, 10.8755779 , -12.27364916, 9.95160202, 28.76323587, -2.36514487, 0.59040434, 2.70792943, 29.54148082] self.aic = 87101.160011780419 self.bic = 87180.289438893495 self.df_model = 9.0 self.df_resid = 20180.0 self.llf = -43540.58000589021 self.llnull = -44199.27443567125 self.llr = 1317.3888595620811 self.llr_pvalue = 5.4288002863296022e-278 self.fittedvalues = [ 0.89348994, 0.89348994, 0.89348994, 0.89348994, 0.89348994, 0.9365745 , 0.9365745 , 0.9365745 , 0.9365745 , 0.9365745 ] self.conf_int = [[-0.06854453, -0.04683335], [-0.30688118, -0.22605273], [ 0.03351706, 0.04825351], [-0.04401602, -0.03189404], [ 0.21590669, 0.32180972], [ 0.03543415, 0.04061632], [-0.07878816, -0.00738096], [-0.04480903, 0.08344253], [ 0.04986111, 0.31117258], [ 0.62059873, 0.70879919]] 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statsmodels-0.5.0+git13-g8e07d34/statsmodels/discrete/tests/test_discrete.py000066400000000000000000001406261224417117700267040ustar00rootroot00000000000000""" Tests for discrete models Notes ----- DECIMAL_3 is used because it seems that there is a loss of precision in the Stata *.dta -> *.csv output, NOT the estimator for the Poisson tests. """ # pylint: disable-msg=E1101 import os import numpy as np from numpy.testing import (assert_, assert_raises, assert_almost_equal, assert_equal, assert_array_equal) from statsmodels.discrete.discrete_model import (Logit, Probit, MNLogit, Poisson, NegativeBinomial) from statsmodels.discrete.discrete_margins import _iscount, _isdummy import statsmodels.api as sm from nose import SkipTest from results.results_discrete import Spector, DiscreteL1 from statsmodels.tools.sm_exceptions import PerfectSeparationError try: import cvxopt has_cvxopt = True except ImportError: has_cvxopt = False try: from scipy.optimize import basinhopping has_basinhopping = True except ImportError: has_basinhopping = False DECIMAL_14 = 14 DECIMAL_10 = 10 DECIMAL_9 = 9 DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 DECIMAL_0 = 0 class CheckModelResults(object): """ res2 should be the test results from RModelWrap or the results as defined in model_results_data """ def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_conf_int(self): assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_4) def test_zstat(self): assert_almost_equal(self.res1.tvalues, self.res2.z, DECIMAL_4) def pvalues(self): assert_almost_equal(self.res1.pvalues, self.res2.pvalues, DECIMAL_4) # def test_cov_params(self): # assert_almost_equal(self.res1.cov_params(), self.res2.cov_params, # DECIMAL_4) def test_llf(self): assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_4) def test_llnull(self): assert_almost_equal(self.res1.llnull, self.res2.llnull, DECIMAL_4) def test_llr(self): assert_almost_equal(self.res1.llr, self.res2.llr, DECIMAL_3) def test_llr_pvalue(self): assert_almost_equal(self.res1.llr_pvalue, self.res2.llr_pvalue, DECIMAL_4) def test_normalized_cov_params(self): pass def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) def test_dof(self): assert_equal(self.res1.df_model, self.res2.df_model) assert_equal(self.res1.df_resid, self.res2.df_resid) def test_aic(self): assert_almost_equal(self.res1.aic, self.res2.aic, DECIMAL_3) def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic, DECIMAL_3) def test_predict(self): assert_almost_equal(self.res1.model.predict(self.res1.params), self.res2.phat, DECIMAL_4) def test_predict_xb(self): assert_almost_equal(self.res1.model.predict(self.res1.params, linear=True), self.res2.yhat, DECIMAL_4) def test_loglikeobs(self): #basic cross check llobssum = self.res1.model.loglikeobs(self.res1.params).sum() assert_almost_equal(llobssum, self.res1.llf, DECIMAL_14) def test_jac(self): #basic cross check jacsum = self.res1.model.jac(self.res1.params).sum(0) score = self.res1.model.score(self.res1.params) assert_almost_equal(jacsum, score, DECIMAL_9) #Poisson has low precision ? class CheckBinaryResults(CheckModelResults): def test_pred_table(self): assert_array_equal(self.res1.pred_table(), self.res2.pred_table) def test_resid_dev(self): assert_almost_equal(self.res1.resid_dev, self.res2.resid_dev, DECIMAL_4) def test_resid_generalized(self): assert_almost_equal(self.res1.resid_generalized, self.res2.resid_generalized, DECIMAL_4) def smoke_test_resid_response(self): self.res1.resid_response class CheckMargEff(object): """ Test marginal effects (margeff) and its options """ def test_nodummy_dydxoverall(self): me = self.res1.get_margeff() assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dydx, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dydx_se, DECIMAL_4) def test_nodummy_dydxmean(self): me = self.res1.get_margeff(at='mean') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dydxmean_se, DECIMAL_4) def test_nodummy_dydxmedian(self): me = self.res1.get_margeff(at='median') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dydxmedian, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dydxmedian_se, DECIMAL_4) def test_nodummy_dydxzero(self): me = self.res1.get_margeff(at='zero') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dydxzero, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dydxzero, DECIMAL_4) def test_nodummy_dyexoverall(self): me = self.res1.get_margeff(method='dyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dyex, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dyex_se, DECIMAL_4) def test_nodummy_dyexmean(self): me = self.res1.get_margeff(at='mean', method='dyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dyexmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dyexmean_se, DECIMAL_4) def test_nodummy_dyexmedian(self): me = self.res1.get_margeff(at='median', method='dyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dyexmedian, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dyexmedian_se, DECIMAL_4) def test_nodummy_dyexzero(self): me = self.res1.get_margeff(at='zero', method='dyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_dyexzero, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_dyexzero_se, DECIMAL_4) def test_nodummy_eydxoverall(self): me = self.res1.get_margeff(method='eydx') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eydx, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eydx_se, DECIMAL_4) def test_nodummy_eydxmean(self): me = self.res1.get_margeff(at='mean', method='eydx') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eydxmean_se, DECIMAL_4) def test_nodummy_eydxmedian(self): me = self.res1.get_margeff(at='median', method='eydx') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eydxmedian, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eydxmedian_se, DECIMAL_4) def test_nodummy_eydxzero(self): me = self.res1.get_margeff(at='zero', method='eydx') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eydxzero, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eydxzero_se, DECIMAL_4) def test_nodummy_eyexoverall(self): me = self.res1.get_margeff(method='eyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eyex, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eyex_se, DECIMAL_4) def test_nodummy_eyexmean(self): me = self.res1.get_margeff(at='mean', method='eyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eyexmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eyexmean_se, DECIMAL_4) def test_nodummy_eyexmedian(self): me = self.res1.get_margeff(at='median', method='eyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eyexmedian, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eyexmedian_se, DECIMAL_4) def test_nodummy_eyexzero(self): me = self.res1.get_margeff(at='zero', method='eyex') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_eyexzero, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_eyexzero_se, DECIMAL_4) def test_dummy_dydxoverall(self): me = self.res1.get_margeff(dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_dydx, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_dydx_se, DECIMAL_4) def test_dummy_dydxmean(self): me = self.res1.get_margeff(at='mean', dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_dydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_dydxmean_se, DECIMAL_4) def test_dummy_eydxoverall(self): me = self.res1.get_margeff(method='eydx', dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_eydx, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_eydx_se, DECIMAL_4) def test_dummy_eydxmean(self): me = self.res1.get_margeff(at='mean', method='eydx', dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_eydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_eydxmean_se, DECIMAL_4) def test_count_dydxoverall(self): me = self.res1.get_margeff(count=True) assert_almost_equal(me.margeff, self.res2.margeff_count_dydx, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_count_dydx_se, DECIMAL_4) def test_count_dydxmean(self): me = self.res1.get_margeff(count=True, at='mean') assert_almost_equal(me.margeff, self.res2.margeff_count_dydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_count_dydxmean_se, DECIMAL_4) def test_count_dummy_dydxoverall(self): me = self.res1.get_margeff(count=True, dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_count_dummy_dydxoverall, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_count_dummy_dydxoverall_se, DECIMAL_4) def test_count_dummy_dydxmean(self): me = self.res1.get_margeff(count=True, dummy=True, at='mean') assert_almost_equal(me.margeff, self.res2.margeff_count_dummy_dydxmean, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_count_dummy_dydxmean_se, DECIMAL_4) class TestProbitNewton(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) cls.res1 = Probit(data.endog, data.exog).fit(method="newton", disp=0) res2 = Spector() res2.probit() cls.res2 = res2 #def test_predict(self): # assert_almost_equal(self.res1.model.predict(self.res1.params), # self.res2.predict, DECIMAL_4) class TestProbitBFGS(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) cls.res1 = Probit(data.endog, data.exog).fit(method="bfgs", disp=0) res2 = Spector() res2.probit() cls.res2 = res2 class TestProbitNM(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.probit() cls.res2 = res2 cls.res1 = Probit(data.endog, data.exog).fit(method="nm", disp=0, maxiter=500) class TestProbitPowell(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.probit() cls.res2 = res2 cls.res1 = Probit(data.endog, data.exog).fit(method="powell", disp=0, ftol=1e-8) class TestProbitCG(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.probit() cls.res2 = res2 cls.res1 = Probit(data.endog, data.exog).fit(method="cg", disp=0, maxiter=500, gtol=1e-08) class TestProbitNCG(CheckBinaryResults): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.probit() cls.res2 = res2 cls.res1 = Probit(data.endog, data.exog).fit(method="ncg", disp=0, avextol=1e-8) class TestProbitBasinhopping(CheckBinaryResults): @classmethod def setupClass(cls): if not has_basinhopping: raise SkipTest("Skipped TestProbitBasinhopping since" " basinhopping solver is not available") data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.probit() cls.res2 = res2 fit = Probit(data.endog, data.exog).fit cls.res1 = fit(method="basinhopping", disp=0, niter=5, minimizer={'method' : 'L-BFGS-B', 'tol' : 1e-8}) class CheckLikelihoodModelL1(object): """ For testing results generated with L1 regularization """ def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_conf_int(self): assert_almost_equal( self.res1.conf_int(), self.res2.conf_int, DECIMAL_4) def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) def test_nnz_params(self): assert_almost_equal( self.res1.nnz_params, self.res2.nnz_params, DECIMAL_4) def test_aic(self): assert_almost_equal( self.res1.aic, self.res2.aic, DECIMAL_3) def test_bic(self): assert_almost_equal( self.res1.bic, self.res2.bic, DECIMAL_3) class TestProbitL1(CheckLikelihoodModelL1): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) alpha = np.array([0.1, 0.2, 0.3, 10]) #/ data.exog.shape[0] cls.res1 = Probit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, trim_mode='auto', auto_trim_tol=0.02, acc=1e-10, maxiter=1000) res2 = DiscreteL1() res2.probit() cls.res2 = res2 def test_cov_params(self): assert_almost_equal( self.res1.cov_params(), self.res2.cov_params, DECIMAL_4) class TestMNLogitL1(CheckLikelihoodModelL1): @classmethod def setupClass(cls): anes_data = sm.datasets.anes96.load() anes_exog = anes_data.exog anes_exog = sm.add_constant(anes_exog, prepend=False) mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog) alpha = 10. * np.ones((mlogit_mod.J - 1, mlogit_mod.K)) #/ anes_exog.shape[0] alpha[-1,:] = 0 cls.res1 = mlogit_mod.fit_regularized( method='l1', alpha=alpha, trim_mode='auto', auto_trim_tol=0.02, acc=1e-10, disp=0) res2 = DiscreteL1() res2.mnlogit() cls.res2 = res2 class TestLogitL1(CheckLikelihoodModelL1): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) cls.alpha = 3 * np.array([0., 1., 1., 1.]) #/ data.exog.shape[0] cls.res1 = Logit(data.endog, data.exog).fit_regularized( method="l1", alpha=cls.alpha, disp=0, trim_mode='size', size_trim_tol=1e-5, acc=1e-10, maxiter=1000) res2 = DiscreteL1() res2.logit() cls.res2 = res2 def test_cov_params(self): assert_almost_equal( self.res1.cov_params(), self.res2.cov_params, DECIMAL_4) class TestCVXOPT(object): @classmethod def setupClass(self): self.data = sm.datasets.spector.load() self.data.exog = sm.add_constant(self.data.exog, prepend=True) def test_cvxopt_versus_slsqp(self): #Compares resutls from cvxopt to the standard slsqp if has_cvxopt: self.alpha = 3. * np.array([0, 1, 1, 1.]) #/ self.data.endog.shape[0] res_slsqp = Logit(self.data.endog, self.data.exog).fit_regularized( method="l1", alpha=self.alpha, disp=0, acc=1e-10, maxiter=1000, trim_mode='auto') res_cvxopt = Logit(self.data.endog, self.data.exog).fit_regularized( method="l1_cvxopt_cp", alpha=self.alpha, disp=0, abstol=1e-10, trim_mode='auto', auto_trim_tol=0.01, maxiter=1000) assert_almost_equal(res_slsqp.params, res_cvxopt.params, DECIMAL_4) else: raise SkipTest("Skipped test_cvxopt since cvxopt is not available") class TestSweepAlphaL1(object): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) cls.model = Logit(data.endog, data.exog) cls.alphas = np.array( [[0.1, 0.1, 0.1, 0.1], [0.4, 0.4, 0.5, 0.5], [0.5, 0.5, 1, 1]]) #/ data.exog.shape[0] cls.res1 = DiscreteL1() cls.res1.sweep() def test_sweep_alpha(self): for i in range(3): alpha = self.alphas[i, :] res2 = self.model.fit_regularized( method="l1", alpha=alpha, disp=0, acc=1e-10, trim_mode='off', maxiter=1000) assert_almost_equal(res2.params, self.res1.params[i], DECIMAL_4) class CheckL1Compatability(object): """ Tests compatability between l1 and unregularized by setting alpha such that certain parameters should be effectively unregularized, and others should be ignored by the model. """ def test_params(self): m = self.m assert_almost_equal( self.res_unreg.params, self.res_reg.params[:m], DECIMAL_4) # The last entry should be close to zero assert_almost_equal(0, self.res_reg.params[m:], DECIMAL_4) def test_cov_params(self): m = self.m # The restricted cov_params should be equal assert_almost_equal( self.res_unreg.cov_params(), self.res_reg.cov_params()[:m, :m], DECIMAL_1) def test_df(self): assert_equal(self.res_unreg.df_model, self.res_reg.df_model) assert_equal(self.res_unreg.df_resid, self.res_reg.df_resid) def test_t_test(self): m = self.m kvars = self.kvars t_unreg = self.res_unreg.t_test(np.eye(m)) t_reg = self.res_reg.t_test(np.eye(kvars)) assert_almost_equal(t_unreg.effect, t_reg.effect[:m], DECIMAL_3) assert_almost_equal(t_unreg.sd, t_reg.sd[:m], DECIMAL_3) assert_almost_equal(np.nan, t_reg.sd[m]) assert_almost_equal(t_unreg.tvalue, t_reg.tvalue[:m], DECIMAL_3) assert_almost_equal(np.nan, t_reg.tvalue[m]) def test_f_test(self): m = self.m kvars = self.kvars f_unreg = self.res_unreg.f_test(np.eye(m)) f_reg = self.res_reg.f_test(np.eye(kvars)[:m]) assert_almost_equal(f_unreg.fvalue, f_reg.fvalue, DECIMAL_2) assert_almost_equal(f_unreg.pvalue, f_reg.pvalue, DECIMAL_3) def test_bad_r_matrix(self): kvars = self.kvars assert_raises(ValueError, self.res_reg.f_test, np.eye(kvars) ) class TestPoissonL1Compatability(CheckL1Compatability): @classmethod def setupClass(cls): cls.kvars = 10 # Number of variables cls.m = 7 # Number of unregularized parameters rand_data = sm.datasets.randhie.load() rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1) rand_exog = sm.add_constant(rand_exog, prepend=True) # Drop some columns and do an unregularized fit exog_no_PSI = rand_exog[:, :cls.m] cls.res_unreg = sm.Poisson( rand_data.endog, exog_no_PSI).fit(method="newton", disp=False) # Do a regularized fit with alpha, effectively dropping the last column alpha = 10 * len(rand_data.endog) * np.ones(cls.kvars) alpha[:cls.m] = 0 cls.res_reg = sm.Poisson(rand_data.endog, rand_exog).fit_regularized( method='l1', alpha=alpha, disp=False, acc=1e-10, maxiter=2000, trim_mode='auto') class TestLogitL1Compatability(CheckL1Compatability): @classmethod def setupClass(cls): cls.kvars = 4 # Number of variables cls.m = 3 # Number of unregularized parameters data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) # Do a regularized fit with alpha, effectively dropping the last column alpha = np.array([0, 0, 0, 10]) cls.res_reg = Logit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, acc=1e-15, maxiter=2000, trim_mode='auto') # Actually drop the last columnand do an unregularized fit exog_no_PSI = data.exog[:, :cls.m] cls.res_unreg = Logit(data.endog, exog_no_PSI).fit(disp=0, tol=1e-15) class TestMNLogitL1Compatability(CheckL1Compatability): @classmethod def setupClass(cls): cls.kvars = 4 # Number of variables cls.m = 3 # Number of unregularized parameters data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) alpha = np.array([0, 0, 0, 10]) cls.res_reg = MNLogit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, acc=1e-15, maxiter=2000, trim_mode='auto') # Actually drop the last columnand do an unregularized fit exog_no_PSI = data.exog[:, :cls.m] cls.res_unreg = MNLogit(data.endog, exog_no_PSI).fit( disp=0, tol=1e-15) def test_t_test(self): m = self.m kvars = self.kvars t_unreg = self.res_unreg.t_test(np.eye(m)) t_reg = self.res_reg.t_test(np.eye(kvars)) assert_almost_equal(t_unreg.effect, t_reg.effect[:m], DECIMAL_3) assert_almost_equal(t_unreg.sd, t_reg.sd[:m], DECIMAL_3) assert_almost_equal(np.nan, t_reg.sd[m]) assert_almost_equal(t_unreg.tvalue, t_reg.tvalue[:m, :m], DECIMAL_3) def test_f_test(self): raise SkipTest("Skipped test_f_test for MNLogit") class TestProbitL1Compatability(CheckL1Compatability): @classmethod def setupClass(cls): cls.kvars = 4 # Number of variables cls.m = 3 # Number of unregularized parameters data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) alpha = np.array([0, 0, 0, 10]) cls.res_reg = Probit(data.endog, data.exog).fit_regularized( method="l1", alpha=alpha, disp=0, acc=1e-15, maxiter=2000, trim_mode='auto') # Actually drop the last columnand do an unregularized fit exog_no_PSI = data.exog[:, :cls.m] cls.res_unreg = Probit(data.endog, exog_no_PSI).fit(disp=0, tol=1e-15) class CompareL1(object): """ For checking results for l1 regularization. Assumes self.res1 and self.res2 are two legitimate models to be compared. """ def test_basic_results(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) assert_almost_equal(self.res1.cov_params(), self.res2.cov_params(), DECIMAL_4) assert_almost_equal(self.res1.conf_int(), self.res2.conf_int(), DECIMAL_4) assert_almost_equal(self.res1.pvalues, self.res2.pvalues, DECIMAL_4) assert_almost_equal(self.res1.pred_table(), self.res2.pred_table(), DECIMAL_4) assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_4) assert_almost_equal(self.res1.aic, self.res2.aic, DECIMAL_4) assert_almost_equal(self.res1.bic, self.res2.bic, DECIMAL_4) assert_almost_equal(self.res1.pvalues, self.res2.pvalues, DECIMAL_4) class CompareL11D(CompareL1): """ Check t and f tests. This only works for 1-d results """ def test_tests(self): restrictmat = np.eye(len(self.res1.params.ravel())) assert_almost_equal(self.res1.t_test(restrictmat).pvalue, self.res2.t_test(restrictmat).pvalue, DECIMAL_4) assert_almost_equal(self.res1.f_test(restrictmat).pvalue, self.res2.f_test(restrictmat).pvalue, DECIMAL_4) class TestL1AlphaZeroLogit(CompareL11D): """ Compares l1 model with alpha = 0 to the unregularized model. """ @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) cls.res1 = Logit(data.endog, data.exog).fit_regularized( method="l1", alpha=0, disp=0, acc=1e-15, maxiter=1000, trim_mode='auto', auto_trim_tol=0.01) cls.res2 = Logit(data.endog, data.exog).fit(disp=0, tol=1e-15) class TestL1AlphaZeroProbit(CompareL11D): """ Compares l1 model with alpha = 0 to the unregularized model. """ @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=True) cls.res1 = Probit(data.endog, data.exog).fit_regularized( method="l1", alpha=0, disp=0, acc=1e-15, maxiter=1000, trim_mode='auto', auto_trim_tol=0.01) cls.res2 = Probit(data.endog, data.exog).fit(disp=0, tol=1e-15) class TestL1AlphaZeroMNLogit(CompareL1): @classmethod def setupClass(cls): data = sm.datasets.anes96.load() data.exog = sm.add_constant(data.exog, prepend=False) cls.res1 = MNLogit(data.endog, data.exog).fit_regularized( method="l1", alpha=0, disp=0, acc=1e-15, maxiter=1000, trim_mode='auto', auto_trim_tol=0.01) cls.res2 = MNLogit(data.endog, data.exog).fit(disp=0, tol=1e-15) class TestLogitNewton(CheckBinaryResults, CheckMargEff): @classmethod def setupClass(cls): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) cls.res1 = Logit(data.endog, data.exog).fit(method="newton", disp=0) res2 = Spector() res2.logit() cls.res2 = res2 def test_resid_pearson(self): assert_almost_equal(self.res1.resid_pearson, self.res2.resid_pearson, 5) def test_nodummy_exog1(self): me = self.res1.get_margeff(atexog={0 : 2.0, 2 : 1.}) assert_almost_equal(me.margeff, self.res2.margeff_nodummy_atexog1, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_atexog1_se, DECIMAL_4) def test_nodummy_exog2(self): me = self.res1.get_margeff(atexog={1 : 21., 2 : 0}, at='mean') assert_almost_equal(me.margeff, self.res2.margeff_nodummy_atexog2, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_atexog2_se, DECIMAL_4) def test_dummy_exog1(self): me = self.res1.get_margeff(atexog={0 : 2.0, 2 : 1.}, dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_atexog1, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_atexog1_se, DECIMAL_4) def test_dummy_exog2(self): me = self.res1.get_margeff(atexog={1 : 21., 2 : 0}, at='mean', dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_atexog2, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_atexog2_se, DECIMAL_4) class TestLogitBFGS(CheckBinaryResults, CheckMargEff): @classmethod def setupClass(cls): #import scipy #major, minor, micro = scipy.__version__.split('.')[:3] #if int(minor) < 9: # raise SkipTest #Skip this unconditionally for release 0.3.0 #since there are still problems with scipy 0.9.0 on some machines #Ralf on mailing list 2011-03-26 raise SkipTest data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) res2 = Spector() res2.logit() cls.res2 = res2 cls.res1 = Logit(data.endog, data.exog).fit(method="bfgs", disp=0) class TestPoissonNewton(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = Poisson(data.endog, exog).fit(method='newton', disp=0) res2 = RandHIE() res2.poisson() cls.res2 = res2 def test_margeff_overall(self): me = self.res1.get_margeff() assert_almost_equal(me.margeff, self.res2.margeff_nodummy_overall, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_nodummy_overall_se, DECIMAL_4) def test_margeff_dummy_overall(self): me = self.res1.get_margeff(dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dummy_overall, DECIMAL_4) assert_almost_equal(me.margeff_se, self.res2.margeff_dummy_overall_se, DECIMAL_4) def test_resid(self): assert_almost_equal(self.res1.resid, self.res2.resid, 2) class TestNegativeBinomialNB2Newton(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = NegativeBinomial(data.endog, exog, 'nb2').fit(method='newton', disp=0) res2 = RandHIE() res2.negativebinomial_nb2_bfgs() cls.res2 = res2 def test_jac(self): pass #NOTE: The bse is much closer precitions to stata def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_3) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_alpha(self): self.res1.bse # attaches alpha_std_err assert_almost_equal(self.res1.lnalpha, self.res2.lnalpha, DECIMAL_4) assert_almost_equal(self.res1.lnalpha_std_err, self.res2.lnalpha_std_err, DECIMAL_4) def test_conf_int(self): assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_3) def test_zstat(self): # Low precision because Z vs. t assert_almost_equal(self.res1.pvalues[:-1], self.res2.pvalues, DECIMAL_2) def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues[:10], self.res2.fittedvalues[:10], DECIMAL_3) def test_predict(self): assert_almost_equal(self.res1.predict()[:10], np.exp(self.res2.fittedvalues[:10]), DECIMAL_3) def test_predict_xb(self): assert_almost_equal(self.res1.predict(linear=True)[:10], self.res2.fittedvalues[:10], DECIMAL_3) def no_info(self): pass test_jac = no_info class TestNegativeBinomialNB1Newton(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = NegativeBinomial(data.endog, exog, 'nb1').fit( method="newton", maxiter=100, disp=0) res2 = RandHIE() res2.negativebinomial_nb1_bfgs() cls.res2 = res2 def test_zstat(self): assert_almost_equal(self.res1.tvalues, self.res2.z, DECIMAL_1) def test_lnalpha(self): self.res1.bse # attaches alpha_std_err assert_almost_equal(self.res1.lnalpha, self.res2.lnalpha, 3) assert_almost_equal(self.res1.lnalpha_std_err, self.res2.lnalpha_std_err, DECIMAL_4) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_conf_int(self): # the bse for alpha is not high precision from the hessian # approximation assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_2) def test_jac(self): pass def test_predict(self): pass def test_predict_xb(self): pass class TestNegativeBinomialNB2BFGS(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = NegativeBinomial(data.endog, exog, 'nb2').fit( method='bfgs', disp=0) res2 = RandHIE() res2.negativebinomial_nb2_bfgs() cls.res2 = res2 def test_jac(self): pass #NOTE: The bse is much closer precitions to stata def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_3) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_alpha(self): self.res1.bse # attaches alpha_std_err assert_almost_equal(self.res1.lnalpha, self.res2.lnalpha, DECIMAL_4) assert_almost_equal(self.res1.lnalpha_std_err, self.res2.lnalpha_std_err, DECIMAL_4) def test_conf_int(self): assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_3) def test_zstat(self): # Low precision because Z vs. t assert_almost_equal(self.res1.pvalues[:-1], self.res2.pvalues, DECIMAL_2) def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues[:10], self.res2.fittedvalues[:10], DECIMAL_3) def test_predict(self): assert_almost_equal(self.res1.predict()[:10], np.exp(self.res2.fittedvalues[:10]), DECIMAL_3) def test_predict_xb(self): assert_almost_equal(self.res1.predict(linear=True)[:10], self.res2.fittedvalues[:10], DECIMAL_3) def no_info(self): pass test_jac = no_info class TestNegativeBinomialNB1BFGS(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = NegativeBinomial(data.endog, exog, 'nb1').fit(method="bfgs", maxiter=100, disp=0) res2 = RandHIE() res2.negativebinomial_nb1_bfgs() cls.res2 = res2 def test_zstat(self): assert_almost_equal(self.res1.tvalues, self.res2.z, DECIMAL_1) def test_lnalpha(self): self.res1.bse # attaches alpha_std_err assert_almost_equal(self.res1.lnalpha, self.res2.lnalpha, 3) assert_almost_equal(self.res1.lnalpha_std_err, self.res2.lnalpha_std_err, DECIMAL_4) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) def test_conf_int(self): # the bse for alpha is not high precision from the hessian # approximation assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_2) def test_jac(self): pass def test_predict(self): pass def test_predict_xb(self): pass class TestNegativeBinomialGeometricBFGS(CheckModelResults): """ Cannot find another implementation of the geometric to cross-check results we only test fitted values because geometric has fewer parameters than nb1 and nb2 and we want to make sure that predict() np.dot(exog, params) works """ @classmethod def setupClass(cls): from results.results_discrete import RandHIE data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=False) cls.res1 = NegativeBinomial(data.endog, exog, 'geometric').fit(method='bfgs', disp=0) res2 = RandHIE() res2.negativebinomial_geometric_bfgs() cls.res2 = res2 def test_aic(self): assert_almost_equal(self.res1.aic, self.res2.aic, DECIMAL_1) def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic, DECIMAL_1) def test_conf_int(self): assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_3) def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues[:10], self.res2.fittedvalues[:10], DECIMAL_3) def test_jac(self): pass def test_predict(self): assert_almost_equal(self.res1.predict()[:10], np.exp(self.res2.fittedvalues[:10]), DECIMAL_3) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_3) def test_predict_xb(self): assert_almost_equal(self.res1.predict(linear=True)[:10], self.res2.fittedvalues[:10], DECIMAL_3) def test_zstat(self): # Low precision because Z vs. t assert_almost_equal(self.res1.tvalues, self.res2.z, DECIMAL_1) def no_info(self): pass def test_llf(self): assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_1) def test_llr(self): assert_almost_equal(self.res1.llr, self.res2.llr, DECIMAL_2) def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_3) test_jac = no_info class TestMNLogitNewtonBaseZero(CheckModelResults): @classmethod def setupClass(cls): from results.results_discrete import Anes data = sm.datasets.anes96.load() cls.data = data exog = data.exog exog = sm.add_constant(exog, prepend=False) cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0) res2 = Anes() res2.mnlogit_basezero() cls.res2 = res2 def test_margeff_overall(self): me = self.res1.get_margeff() assert_almost_equal(me.margeff, self.res2.margeff_dydx_overall, 6) assert_almost_equal(me.margeff_se, self.res2.margeff_dydx_overall_se, 6) def test_margeff_mean(self): me = self.res1.get_margeff(at='mean') assert_almost_equal(me.margeff, self.res2.margeff_dydx_mean, 7) assert_almost_equal(me.margeff_se, self.res2.margeff_dydx_mean_se, 7) def test_margeff_dummy(self): data = self.data vote = data.data['vote'] exog = np.column_stack((data.exog, vote)) exog = sm.add_constant(exog, prepend=False) res = MNLogit(data.endog, exog).fit(method="newton", disp=0) me = res.get_margeff(dummy=True) assert_almost_equal(me.margeff, self.res2.margeff_dydx_dummy_overall, 6) assert_almost_equal(me.margeff_se, self.res2.margeff_dydx_dummy_overall_se, 6) me = res.get_margeff(dummy=True, method="eydx") assert_almost_equal(me.margeff, self.res2.margeff_eydx_dummy_overall, 5) assert_almost_equal(me.margeff_se, self.res2.margeff_eydx_dummy_overall_se, 6) def test_j(self): assert_equal(self.res1.model.J, self.res2.J) def test_k(self): assert_equal(self.res1.model.K, self.res2.K) def test_endog_names(self): assert_equal(self.res1._get_endog_name(None,None)[1], ['y=1', 'y=2', 'y=3', 'y=4', 'y=5', 'y=6']) def test_pred_table(self): # fitted results taken from gretl pred = [6, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 6, 0, 1, 6, 0, 0, 1, 1, 6, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 6, 0, 0, 6, 6, 0, 0, 1, 1, 6, 1, 6, 0, 0, 0, 1, 0, 1, 0, 0, 0, 6, 0, 0, 6, 0, 0, 0, 1, 1, 0, 0, 6, 6, 6, 6, 1, 0, 5, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 6, 0, 6, 6, 1, 0, 1, 1, 6, 5, 1, 0, 0, 0, 5, 0, 0, 6, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 6, 6, 6, 6, 5, 0, 1, 1, 0, 1, 0, 6, 6, 0, 0, 0, 6, 0, 0, 0, 6, 6, 0, 5, 1, 0, 0, 0, 0, 6, 0, 5, 6, 6, 0, 0, 0, 0, 6, 1, 0, 0, 1, 0, 1, 6, 1, 1, 1, 1, 1, 0, 0, 0, 6, 0, 5, 1, 0, 6, 6, 6, 0, 0, 0, 0, 1, 6, 6, 0, 0, 0, 1, 1, 5, 6, 0, 6, 1, 0, 0, 1, 6, 0, 0, 1, 0, 6, 6, 0, 5, 6, 6, 0, 0, 6, 1, 0, 6, 0, 1, 0, 1, 6, 0, 1, 1, 1, 6, 0, 5, 0, 0, 6, 1, 0, 6, 5, 5, 0, 6, 1, 1, 1, 0, 0, 6, 0, 0, 5, 0, 0, 6, 6, 6, 6, 6, 0, 1, 0, 0, 6, 6, 0, 0, 1, 6, 0, 0, 6, 1, 6, 1, 1, 1, 0, 1, 6, 5, 0, 0, 1, 5, 0, 1, 6, 6, 1, 0, 0, 1, 6, 1, 5, 6, 1, 0, 0, 1, 1, 0, 6, 1, 6, 0, 1, 1, 5, 6, 6, 5, 1, 1, 1, 0, 6, 1, 6, 1, 0, 1, 0, 0, 1, 5, 0, 1, 1, 0, 5, 6, 0, 5, 1, 1, 6, 5, 0, 6, 0, 0, 0, 0, 0, 0, 1, 6, 1, 0, 5, 1, 0, 0, 1, 6, 0, 0, 6, 6, 6, 0, 2, 1, 6, 5, 6, 1, 1, 0, 5, 1, 1, 1, 6, 1, 6, 6, 5, 6, 0, 1, 0, 1, 6, 0, 6, 1, 6, 0, 0, 6, 1, 0, 6, 1, 0, 0, 0, 0, 6, 6, 6, 6, 5, 6, 6, 0, 0, 6, 1, 1, 6, 0, 0, 6, 6, 0, 6, 6, 0, 0, 6, 0, 0, 6, 6, 6, 1, 0, 6, 0, 0, 0, 6, 1, 1, 0, 1, 5, 0, 0, 5, 0, 0, 0, 1, 1, 6, 1, 0, 0, 0, 6, 6, 1, 1, 6, 5, 5, 0, 6, 6, 0, 1, 1, 0, 6, 6, 0, 6, 5, 5, 6, 5, 1, 0, 6, 0, 6, 1, 0, 1, 6, 6, 6, 1, 0, 6, 0, 5, 6, 6, 5, 0, 5, 1, 0, 6, 0, 6, 1, 5, 5, 0, 1, 5, 5, 2, 6, 6, 6, 5, 0, 0, 1, 6, 1, 0, 1, 6, 1, 0, 0, 1, 5, 6, 6, 0, 0, 0, 5, 6, 6, 6, 1, 5, 6, 1, 0, 0, 6, 5, 0, 1, 1, 1, 6, 6, 0, 1, 0, 0, 0, 5, 0, 0, 6, 1, 6, 0, 6, 1, 5, 5, 6, 5, 0, 0, 0, 0, 1, 1, 0, 5, 5, 0, 0, 0, 0, 1, 0, 6, 6, 1, 1, 6, 6, 0, 5, 5, 0, 0, 0, 6, 6, 1, 6, 0, 0, 5, 0, 1, 6, 5, 6, 6, 5, 5, 6, 6, 1, 0, 1, 6, 6, 1, 6, 0, 6, 0, 6, 5, 0, 6, 6, 0, 5, 6, 0, 6, 6, 5, 0, 1, 6, 6, 1, 0, 1, 0, 6, 6, 1, 0, 6, 6, 6, 0, 1, 6, 0, 1, 5, 1, 1, 5, 6, 6, 0, 1, 6, 6, 1, 5, 0, 5, 0, 6, 0, 1, 6, 1, 0, 6, 1, 6, 0, 6, 1, 0, 0, 0, 6, 6, 0, 1, 1, 6, 6, 6, 1, 6, 0, 5, 6, 0, 5, 6, 6, 5, 5, 5, 6, 0, 6, 0, 0, 0, 5, 0, 6, 1, 2, 6, 6, 6, 5, 1, 6, 0, 6, 0, 0, 0, 0, 6, 5, 0, 5, 1, 6, 5, 1, 6, 5, 1, 1, 0, 0, 6, 1, 1, 5, 6, 6, 0, 5, 2, 5, 5, 0, 5, 5, 5, 6, 5, 6, 6, 5, 2, 6, 5, 6, 0, 0, 6, 5, 0, 6, 0, 0, 6, 6, 6, 0, 5, 1, 1, 6, 6, 5, 2, 1, 6, 5, 6, 0, 6, 6, 1, 1, 5, 1, 6, 6, 6, 0, 0, 6, 1, 0, 5, 5, 1, 5, 6, 1, 6, 0, 1, 6, 5, 0, 0, 6, 1, 5, 1, 0, 6, 0, 6, 6, 5, 5, 6, 6, 6, 6, 2, 6, 6, 6, 5, 5, 5, 0, 1, 0, 0, 0, 6, 6, 1, 0, 6, 6, 6, 6, 6, 1, 0, 6, 1, 5, 5, 6, 6, 6, 6, 6, 5, 6, 1, 6, 2, 5, 5, 6, 5, 6, 6, 5, 6, 6, 5, 5, 6, 1, 5, 1, 6, 0, 2, 5, 0, 5, 0, 2, 1, 6, 0, 0, 6, 6, 1, 6, 0, 5, 5, 6, 6, 1, 6, 6, 6, 5, 6, 6, 1, 6, 5, 6, 1, 1, 0, 6, 6, 5, 1, 0, 0, 6, 6, 5, 6, 0, 1, 6, 0, 5, 6, 5, 2, 5, 2, 0, 0, 1, 6, 6, 1, 5, 6, 6, 0, 6, 6, 6, 6, 6, 5] assert_array_equal(self.res1.predict().argmax(1), pred) # the rows should add up for pred table assert_array_equal(self.res1.pred_table().sum(0), np.bincount(pred)) # note this is just a regression test, gretl doesn't have a prediction # table pred = [[ 126., 41., 2., 0., 0., 12., 19.], [ 77., 73., 3., 0., 0., 15., 12.], [ 37., 43., 2., 0., 0., 19., 7.], [ 12., 9., 1., 0., 0., 9., 6.], [ 19., 10., 2., 0., 0., 20., 43.], [ 22., 25., 1., 0., 0., 31., 71.], [ 9., 7., 1., 0., 0., 18., 140.]] assert_array_equal(self.res1.pred_table(), pred) def test_resid(self): assert_array_equal(self.res1.resid_misclassified, self.res2.resid) def test_perfect_prediction(): cur_dir = os.path.dirname(os.path.abspath(__file__)) iris_dir = os.path.join(cur_dir, '..', '..', 'genmod', 'tests', 'results') iris_dir = os.path.abspath(iris_dir) iris = np.genfromtxt(os.path.join(iris_dir, 'iris.csv'), delimiter=",", skip_header=1) y = iris[:,-1] X = iris[:,:-1] X = X[y != 2] y = y[y != 2] X = sm.add_constant(X, prepend=True) mod = Logit(y,X) assert_raises(PerfectSeparationError, mod.fit) #turn off raise PerfectSeparationError mod.raise_on_perfect_prediction = False mod.fit(disp=False) #should not raise def test_poisson_predict(): #GH: 175, make sure poisson predict works without offset and exposure data = sm.datasets.randhie.load() exog = sm.add_constant(data.exog, prepend=True) res = sm.Poisson(data.endog, exog).fit(method='newton', disp=0) pred1 = res.predict() pred2 = res.predict(exog) assert_almost_equal(pred1, pred2) #exta options pred3 = res.predict(exog, offset=0, exposure=1) assert_almost_equal(pred1, pred3) pred3 = res.predict(exog, offset=0, exposure=2) assert_almost_equal(2*pred1, pred3) pred3 = res.predict(exog, offset=np.log(2), exposure=1) assert_almost_equal(2*pred1, pred3) def test_poisson_newton(): #GH: 24, Newton doesn't work well sometimes nobs = 10000 np.random.seed(987689) x = np.random.randn(nobs, 3) x = sm.add_constant(x, prepend=True) y_count = np.random.poisson(np.exp(x.sum(1))) mod = sm.Poisson(y_count, x) res = mod.fit(start_params=-np.ones(4), method='newton', disp=0) assert_(not res.mle_retvals['converged']) def test_issue_339(): # make sure MNLogit summary works for J != K. data = sm.datasets.anes96.load() exog = data.exog # leave out last exog column exog = exog[:,:-1] exog = sm.add_constant(exog, prepend=True) res1 = sm.MNLogit(data.endog, exog).fit(method="newton", disp=0) # strip the header from the test smry = "\n".join(res1.summary().as_text().split('\n')[9:]) cur_dir = os.path.dirname(os.path.abspath(__file__)) test_case_file = os.path.join(cur_dir, 'results', 'mn_logit_summary.txt') test_case = open(test_case_file, 'r').read() np.testing.assert_(smry == test_case[:-1]) def test_issue_341(): data = sm.datasets.anes96.load() exog = data.exog # leave out last exog column exog = exog[:,:-1] exog = sm.add_constant(exog, prepend=True) res1 = sm.MNLogit(data.endog, exog).fit(method="newton", disp=0) x = exog[0] np.testing.assert_equal(res1.predict(x).shape, (1,7)) np.testing.assert_equal(res1.predict(x[None]).shape, (1,7)) def test_iscount(): X = np.random.random((50, 10)) X[:,2] = np.random.randint(1, 10, size=50) X[:,6] = np.random.randint(1, 10, size=50) X[:,4] = np.random.randint(0, 2, size=50) X[:,1] = np.random.randint(-10, 10, size=50) # not integers count_ind = _iscount(X) assert_equal(count_ind, [2, 6]) def test_isdummy(): X = np.random.random((50, 10)) X[:,2] = np.random.randint(1, 10, size=50) X[:,6] = np.random.randint(0, 2, size=50) X[:,4] = np.random.randint(0, 2, size=50) X[:,1] = np.random.randint(-10, 10, size=50) # not integers count_ind = _isdummy(X) assert_equal(count_ind, [4, 6]) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/000077500000000000000000000000001224417117700234165ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/__init__.py000066400000000000000000000002201224417117700255210ustar00rootroot00000000000000from empirical_distribution import ECDF, monotone_fn_inverter, StepFunction from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/empirical_distribution.py000066400000000000000000000116271224417117700305430ustar00rootroot00000000000000""" Empirical CDF Functions """ import numpy as np from scipy.interpolate import interp1d def _conf_set(F, alpha=.05): r""" Constructs a Dvoretzky-Kiefer-Wolfowitz confidence band for the eCDF. Parameters ---------- F : array-like The empirical distributions alpha : float Set alpha for a (1 - alpha) % confidence band. Notes ----- Based on the DKW inequality. ..math:: P \left( \sup_x \left| F(x) - \hat(F)_n(X) \right| > \epsilon \right) \leq 2e^{-2n\epsilon^2} References ---------- Wasserman, L. 2006. `All of Nonparametric Statistics`. Springer. """ nobs = len(F) epsilon = np.sqrt(np.log(2./alpha) / (2 * nobs)) lower = np.clip(F - epsilon, 0, 1) upper = np.clip(F + epsilon, 0, 1) return lower, upper class StepFunction(object): """ A basic step function. Values at the ends are handled in the simplest way possible: everything to the left of x[0] is set to ival; everything to the right of x[-1] is set to y[-1]. Parameters ---------- x : array-like y : array-like ival : float ival is the value given to the values to the left of x[0]. Default is 0. sorted : bool Default is False. side : {'left', 'right'}, optional Default is 'left'. Defines the shape of the intervals constituting the steps. 'right' correspond to [a, b) intervals and 'left' to (a, b]. Examples -------- >>> import numpy as np >>> from statsmodels.distributions.empirical_distribution import StepFunction >>> >>> x = np.arange(20) >>> y = np.arange(20) >>> f = StepFunction(x, y) >>> >>> print f(3.2) 3.0 >>> print f([[3.2,4.5],[24,-3.1]]) [[ 3. 4.] [ 19. 0.]] >>> f2 = StepFunction(x, y, side='right') >>> >>> print f(3.0) 2.0 >>> print f2(3.0) 3.0 """ def __init__(self, x, y, ival=0., sorted=False, side='left'): if side.lower() not in ['right', 'left']: msg = "side can take the values 'right' or 'left'" raise ValueError(msg) self.side = side _x = np.asarray(x) _y = np.asarray(y) if _x.shape != _y.shape: msg = "x and y do not have the same shape" raise ValueError(msg) if len(_x.shape) != 1: msg = 'x and y must be 1-dimensional' raise ValueError(msg) self.x = np.r_[-np.inf, _x] self.y = np.r_[ival, _y] if not sorted: asort = np.argsort(self.x) self.x = np.take(self.x, asort, 0) self.y = np.take(self.y, asort, 0) self.n = self.x.shape[0] def __call__(self, time): tind = np.searchsorted(self.x, time, self.side) - 1 return self.y[tind] class ECDF(StepFunction): """ Return the Empirical CDF of an array as a step function. Parameters ---------- x : array-like Observations side : {'left', 'right'}, optional Default is 'right'. Defines the shape of the intervals constituting the steps. 'right' correspond to [a, b) intervals and 'left' to (a, b]. Returns ------- Empirical CDF as a step function. Examples -------- >>> import numpy as np >>> from statsmodels.distributions.empirical_distribution import ECDF >>> >>> ecdf = ECDF([3, 3, 1, 4]) >>> >>> ecdf([3, 55, 0.5, 1.5]) array([ 0.75, 1. , 0. , 0.25]) """ def __init__(self, x, side='right'): step = True if step: #TODO: make this an arg and have a linear interpolation option? x = np.array(x, copy=True) x.sort() nobs = len(x) y = np.linspace(1./nobs,1,nobs) super(ECDF, self).__init__(x, y, side=side, sorted=True) else: return interp1d(x,y,drop_errors=False,fill_values=ival) def monotone_fn_inverter(fn, x, vectorized=True, **keywords): """ Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x. """ x = np.asarray(x) if vectorized: y = fn(x, **keywords) else: y = [] for _x in x: y.append(fn(_x, **keywords)) y = np.array(y) a = np.argsort(y) return interp1d(y[a], x[a]) if __name__ == "__main__": #TODO: Make sure everything is correctly aligned and make a plotting # function import matplotlib.pyplot as plt import urllib nerve_data = urllib.urlopen('http://www.statsci.org/data/general/nerve.txt') nerve_data = np.loadtxt(nerve_data) x = nerve_data / 50. # was in 1/50 seconds cdf = ECDF(x) x.sort() F = cdf(x) plt.step(x, F) lower, upper = _conf_set(F) plt.step(x, lower, 'r') plt.step(x, upper, 'r') plt.xlim(0, 1.5) plt.ylim(0, 1.05) plt.vlines(x, 0, .05) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/mixture_rvs.py000066400000000000000000000225141224417117700263630ustar00rootroot00000000000000import numpy as np def _make_index(prob,size): """ Returns a boolean index for given probabilities. Notes --------- prob = [.75,.25] means that there is a 75% chance of the first column being True and a 25% chance of the second column being True. The columns are mutually exclusive. """ rv = np.random.uniform(size=(size,1)) cumprob = np.cumsum(prob) return np.logical_and(np.r_[0,cumprob[:-1]] <= rv, rv < cumprob) def mixture_rvs(prob, size, dist, kwargs=None): """ Sample from a mixture of distributions. Parameters ---------- prob : array-like Probability of sampling from each distribution in dist size : int The length of the returned sample. dist : array-like An iterable of distributions objects from scipy.stats. kwargs : tuple of dicts, optional A tuple of dicts. Each dict in kwargs can have keys loc, scale, and args to be passed to the respective distribution in dist. If not provided, the distribution defaults are used. Examples -------- Say we want 5000 random variables from mixture of normals with two distributions norm(-1,.5) and norm(1,.5) and we want to sample from the first with probability .75 and the second with probability .25. >>> from scipy import stats >>> prob = [.75,.25] >>> Y = mixture_rvs(prob, 5000, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) """ if len(prob) != len(dist): raise ValueError("You must provide as many probabilities as distributions") if not np.allclose(np.sum(prob), 1): raise ValueError("prob does not sum to 1") if kwargs is None: kwargs = ({},)*len(prob) idx = _make_index(prob,size) sample = np.empty(size) for i in range(len(prob)): sample_idx = idx[...,i] sample_size = sample_idx.sum() loc = kwargs[i].get('loc',0) scale = kwargs[i].get('scale',1) args = kwargs[i].get('args',()) sample[sample_idx] = dist[i].rvs(*args, **dict(loc=loc,scale=scale, size=sample_size)) return sample class MixtureDistribution(object): '''univariate mixture distribution for simple case for now (unbound support) does not yet inherit from scipy.stats.distributions adding pdf to mixture_rvs, some restrictions on broadcasting Currently it does not hold any state, all arguments included in each method. ''' #def __init__(self, prob, size, dist, kwargs=None): def rvs(self, prob, size, dist, kwargs=None): return mixture_rvs(prob, size, dist, kwargs=kwargs) def pdf(self, x, prob, dist, kwargs=None): """ pdf a mixture of distributions. Parameters ---------- prob : array-like Probability of sampling from each distribution in dist dist : array-like An iterable of distributions objects from scipy.stats. kwargs : tuple of dicts, optional A tuple of dicts. Each dict in kwargs can have keys loc, scale, and args to be passed to the respective distribution in dist. If not provided, the distribution defaults are used. Examples -------- Say we want 5000 random variables from mixture of normals with two distributions norm(-1,.5) and norm(1,.5) and we want to sample from the first with probability .75 and the second with probability .25. >>> from scipy import stats >>> prob = [.75,.25] >>> Y = mixture.pdf(x, prob, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) """ if len(prob) != len(dist): raise ValueError("You must provide as many probabilities as distributions") if not np.allclose(np.sum(prob), 1): raise ValueError("prob does not sum to 1") if kwargs is None: kwargs = ({},)*len(prob) for i in range(len(prob)): loc = kwargs[i].get('loc',0) scale = kwargs[i].get('scale',1) args = kwargs[i].get('args',()) if i == 0: #assume all broadcast the same as the first dist pdf_ = prob[i] * dist[i].pdf(x, *args, loc=loc, scale=scale) else: pdf_ += prob[i] * dist[i].pdf(x, *args, loc=loc, scale=scale) return pdf_ def cdf(self, x, prob, dist, kwargs=None): """ cdf of a mixture of distributions. Parameters ---------- prob : array-like Probability of sampling from each distribution in dist size : int The length of the returned sample. dist : array-like An iterable of distributions objects from scipy.stats. kwargs : tuple of dicts, optional A tuple of dicts. Each dict in kwargs can have keys loc, scale, and args to be passed to the respective distribution in dist. If not provided, the distribution defaults are used. Examples -------- Say we want 5000 random variables from mixture of normals with two distributions norm(-1,.5) and norm(1,.5) and we want to sample from the first with probability .75 and the second with probability .25. >>> from scipy import stats >>> prob = [.75,.25] >>> Y = mixture.pdf(x, prob, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) """ if len(prob) != len(dist): raise ValueError("You must provide as many probabilities as distributions") if not np.allclose(np.sum(prob), 1): raise ValueError("prob does not sum to 1") if kwargs is None: kwargs = ({},)*len(prob) for i in range(len(prob)): loc = kwargs[i].get('loc',0) scale = kwargs[i].get('scale',1) args = kwargs[i].get('args',()) if i == 0: #assume all broadcast the same as the first dist cdf_ = prob[i] * dist[i].cdf(x, *args, loc=loc, scale=scale) else: cdf_ += prob[i] * dist[i].cdf(x, *args, loc=loc, scale=scale) return cdf_ def mv_mixture_rvs(prob, size, dist, nvars, **kwargs): """ Sample from a mixture of multivariate distributions. Parameters ---------- prob : array-like Probability of sampling from each distribution in dist size : int The length of the returned sample. dist : array-like An iterable of distributions instances with callable method rvs. nvargs : int dimension of the multivariate distribution, could be inferred instead kwargs : tuple of dicts, optional ignored Examples -------- Say we want 2000 random variables from mixture of normals with two multivariate normal distributions, and we want to sample from the first with probability .4 and the second with probability .6. import statsmodels.sandbox.distributions.mv_normal as mvd cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) mu = np.array([-1, 0.0, 2.0]) mu2 = np.array([4, 2.0, 2.0]) mvn3 = mvd.MVNormal(mu, cov3) mvn32 = mvd.MVNormal(mu2, cov3/2., 4) rvs = mix.mv_mixture_rvs([0.4, 0.6], 2000, [mvn3, mvn32], 3) """ if len(prob) != len(dist): raise ValueError("You must provide as many probabilities as distributions") if not np.allclose(np.sum(prob), 1): raise ValueError("prob does not sum to 1") if kwargs is None: kwargs = ({},)*len(prob) idx = _make_index(prob,size) sample = np.empty((size, nvars)) for i in range(len(prob)): sample_idx = idx[...,i] sample_size = sample_idx.sum() #loc = kwargs[i].get('loc',0) #scale = kwargs[i].get('scale',1) #args = kwargs[i].get('args',()) # use int to avoid numpy bug with np.random.multivariate_normal sample[sample_idx] = dist[i].rvs(size=int(sample_size)) return sample if __name__ == '__main__': from scipy import stats obs_dist = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta], kwargs=(dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5)))) nobs = 10000 mix = MixtureDistribution() ## mrvs = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm], ## kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.75))) mix_kwds = (dict(loc=-1,scale=.25),dict(loc=1,scale=.75)) mrvs = mix.rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm], kwargs=mix_kwds) grid = np.linspace(-4,4, 100) mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=mix_kwds) mcdf = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=mix_kwds) doplot = 1 if doplot: import matplotlib.pyplot as plt plt.figure() plt.hist(mrvs, bins=50, normed=True, color='red') plt.title('histogram of sample and pdf') plt.plot(grid, mpdf, lw=2, color='black') plt.figure() plt.hist(mrvs, bins=50, normed=True, cumulative=True, color='red') plt.title('histogram of sample and pdf') plt.plot(grid, mcdf, lw=2, color='black') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/tests/000077500000000000000000000000001224417117700245605ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/tests/__init__.py000066400000000000000000000000001224417117700266570ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/tests/test_ecdf.py000066400000000000000000000031501224417117700270710ustar00rootroot00000000000000import numpy as np import numpy.testing as npt from statsmodels.distributions import StepFunction, monotone_fn_inverter class TestDistributions(npt.TestCase): def test_StepFunction(self): x = np.arange(20) y = np.arange(20) f = StepFunction(x, y) npt.assert_almost_equal(f( np.array([[3.2,4.5],[24,-3.1],[3.0, 4.0]])), [[ 3, 4], [19, 0], [2, 3]]) def test_StepFunctionBadShape(self): x = np.arange(20) y = np.arange(21) self.assertRaises(ValueError, StepFunction, x, y) x = np.zeros((2, 2)) y = np.zeros((2, 2)) self.assertRaises(ValueError, StepFunction, x, y) def test_StepFunctionValueSideRight(self): x = np.arange(20) y = np.arange(20) f = StepFunction(x, y, side='right') npt.assert_almost_equal(f( np.array([[3.2,4.5],[24,-3.1],[3.0, 4.0]])), [[ 3, 4], [19, 0], [3, 4]]) def test_StepFunctionRepeatedValues(self): x = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5] y = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15] f = StepFunction(x, y) npt.assert_almost_equal(f([1, 2, 3, 4, 5]), [0, 7, 10, 13, 14]) f2 = StepFunction(x, y, side='right') npt.assert_almost_equal(f2([1, 2, 3, 4, 5]), [7, 10, 13, 14, 15]) def test_monotone_fn_inverter(self): x = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15] fn = lambda x : 1./x y = fn(np.array(x)) f = monotone_fn_inverter(fn, x) npt.assert_array_equal(f.y, x[::-1]) npt.assert_array_equal(f.x, y[::-1]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/distributions/tests/test_mixture.py000066400000000000000000000121271224417117700276710ustar00rootroot00000000000000# Copyright (c) 2013 Ana Martinez Pardo # License: BSD-3 [see LICENSE.txt] import numpy as np import numpy.testing as npt from statsmodels.distributions.mixture_rvs import (mv_mixture_rvs, MixtureDistribution) import statsmodels.sandbox.distributions.mv_normal as mvd from scipy import stats class TestMixtureDistributions(object): def test_mixture_rvs_random(self): # Test only medium small sample at 1 decimal np.random.seed(0) mix = MixtureDistribution() res = mix.rvs([.75,.25], 1000, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) npt.assert_almost_equal( np.array([res.std(),res.mean(),res.var()]), np.array([1,-0.5,1]), decimal=1) def test_mv_mixture_rvs_random(self): cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) mu = np.array([-1, 0.0, 2.0]) mu2 = np.array([4, 2.0, 2.0]) mvn3 = mvd.MVNormal(mu, cov3) mvn32 = mvd.MVNormal(mu2, cov3/2.) np.random.seed(0) res = mv_mixture_rvs([0.4, 0.6], 5000, [mvn3, mvn32], 3) npt.assert_almost_equal( np.array([res.std(),res.mean(),res.var()]), np.array([1.874,1.733,3.512]), decimal=1) def test_mixture_pdf(self): mix = MixtureDistribution() grid = np.linspace(-4,4, 10) res = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs= (dict(loc=-1,scale=.25),dict(loc=1,scale=.75))) npt.assert_almost_equal( res, np.array([ 7.92080017e-11, 1.05977272e-07, 3.82368500e-05, 2.21485447e-01, 1.00534607e-01, 2.69531536e-01, 3.21265627e-01, 9.39899015e-02, 6.74932493e-03, 1.18960201e-04])) def test_mixture_cdf(self): mix = MixtureDistribution() grid = np.linspace(-4,4, 10) res = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs= (dict(loc=-1,scale=.25),dict(loc=1,scale=.75))) npt.assert_almost_equal( res, np.array([ 8.72261646e-12, 1.40592960e-08, 5.95819161e-06, 3.10250226e-02, 3.46993159e-01, 4.86283549e-01, 7.81092904e-01, 9.65606734e-01, 9.98373155e-01, 9.99978886e-01])) def test_mixture_rvs_fixed(self): mix = MixtureDistribution() np.random.seed(1234) res = mix.rvs([.15,.85], 50, dist=[stats.norm, stats.norm], kwargs = (dict(loc=1,scale=.5),dict(loc=-1,scale=.5))) npt.assert_almost_equal( res, np.array([-0.5794956 , -1.72290504, -1.70098664, -1.0504591 , -1.27412122,-1.07230975, -0.82298983, -1.01775651, -0.71713085,-0.2271706 ,-1.48711817, -1.03517244, -0.84601557, -1.10424938, -0.48309963,-2.20022682, 0.01530181, 1.1238961 , -1.57131564, -0.89405831, -0.64763969, -1.39271761, 0.55142161, -0.76897013, -0.64788589,-0.73824602, -1.46312716, 0.00392148, -0.88651873, -1.57632955,-0.68401028, -0.98024366, -0.76780384, 0.93160258,-2.78175833,-0.33944719, -0.92368472, -0.91773523, -1.21504785, -0.61631563, 1.0091446 , -0.50754008, 1.37770699, -0.86458208, -0.3040069 ,-0.96007884, 1.10763429, -1.19998229, -1.51392528, -1.29235911])) def test_mv_mixture_rvs_fixed(self): np.random.seed(1234) cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) mu = np.array([-1, 0.0, 2.0]) mu2 = np.array([4, 2.0, 2.0]) mvn3 = mvd.MVNormal(mu, cov3) mvn32 = mvd.MVNormal(mu2, cov3/2) res = mv_mixture_rvs([0.2, 0.8], 10, [mvn3, mvn32], 3) npt.assert_almost_equal( res, np.array([[-0.23955497, 1.73426482, 0.36100243], [ 2.52063189, 1.0832677 , 1.89947131], [ 4.36755379, 2.14480498, 2.22003966], [ 3.1141545 , 1.21250505, 2.58511199], [ 4.1980202 , 2.50017561, 1.87324933], [ 3.48717503, 0.91847424, 2.14004598], [ 3.55904133, 2.74367622, 0.68619582], [ 3.60521933, 1.57316531, 0.82784584], [ 3.86102275, 0.6211812 , 1.33016426], [ 3.91074761, 2.037155 , 2.22247051]])) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/000077500000000000000000000000001224417117700221425ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/__init__.py000066400000000000000000000001031224417117700242450ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/aft_el.py000066400000000000000000000430471224417117700237560ustar00rootroot00000000000000""" Accelerated Failure Time (AFT) Model with empirical likelihood inference. AFT regression analysis is applicable when the researcher has access to a randomly right censored dependent variable, a matrix of exogenous variables and an indicatior variable (delta) that takes a value of 0 if the observation is censored and 1 otherwise. AFT References -------------- Stute, W. (1993). "Consistent Estimation Under Random Censorship when Covariables are Present." Journal of Multivariate Analysis. Vol. 45. Iss. 1. 89-103 EL and AFT References --------------------- Zhou, Kim And Bathke. "Empirical Likelihood Analysis for the Heteroskedastic Accelerated Failure Time Model." Manuscript: URL: www.ms.uky.edu/~mai/research/CasewiseEL20080724.pdf Zhou, M. (2005). Empirical Likelihood Ratio with Arbitrarily Censored/ Truncated Data by EM Algorithm. Journal of Computational and Graphical Statistics. 14:3, 643-656. """ import numpy as np from statsmodels.api import OLS, WLS, add_constant #from elregress import ElReg from scipy import optimize from scipy.stats import chi2 from descriptive import _OptFuncts # ^ this will change when descriptive gets merged import warnings class OptAFT(_OptFuncts): """ Provides optimization functions used in estimating and conducting inference in an AFT model. Methods ------ _opt_wtd_nuis_regress: Function optimized over nuisance parameters to compute the profile likelihood _EM_test: Uses the modified Em algorithm of Zhou 2005 to maximize the likelihood of a parameter vector. """ def __init__(_OptFuncts): pass def _opt_wtd_nuis_regress(self, test_vals): """ A function that is optimized over nuisance parameters to conduct a hypothesis test for the parameters of interest Parameters ---------- params: 1d array The regression coefficients of the model. This includes the nuisance and parameters of interests. Returns ------- llr: float -2 times the log likelihood of the nuisance parameters and the hypothesized value of the parameter(s) of interest. """ test_params = test_vals.reshape(self.model.nvar, 1) est_vect = self.model.uncens_exog * (self.model.uncens_endog - np.dot(self.model.uncens_exog, test_params)) eta_star = self._modif_newton(np.zeros(self.model.nvar), est_vect, self.model._fit_weights) self.eta_star = eta_star denom = np.sum(self.model._fit_weights) + np.dot(eta_star, est_vect.T) self.new_weights = self.model._fit_weights / denom return -1 * np.sum(np.log(self.new_weights)) def _EM_test(self, nuisance_params, params=None, param_nums=None, b0_vals=None, F=None, survidx=None, uncens_nobs=None, numcensbelow=None, km=None, uncensored=None, censored=None, maxiter=None, ftol=None): """ Uses EM algorithm to compute the maximum likelihood of a test Parameters --------- Nuisance Params: array Vector of values to be used as nuisance params. maxiter: int Number of iterations in the EM algorithm for a parameter vector Returns ------- -2 ''*'' log likelihood ratio at hypothesized values and nuisance params Notes ----- Optional parameters are provided by the test_beta function. """ iters = 0 params[param_nums] = b0_vals nuis_param_index = np.int_(np.delete(np.arange(self.model.nvar), param_nums)) params[nuis_param_index] = nuisance_params to_test = params.reshape(self.model.nvar, 1) opt_res = np.inf diff = np.inf while iters < maxiter and diff > ftol: F = F.flatten() death = np.cumsum(F[::-1]) survivalprob = death[::-1] surv_point_mat = np.dot(F.reshape(-1, 1), 1. / survivalprob[survidx].reshape(1, - 1)) surv_point_mat = add_constant(surv_point_mat) summed_wts = np.cumsum(surv_point_mat, axis=1) wts = summed_wts[np.int_(np.arange(uncens_nobs)), numcensbelow[uncensored]] # ^E step # See Zhou 2005, section 3. self.model._fit_weights = wts new_opt_res = self._opt_wtd_nuis_regress(to_test) # ^ Uncensored weights' contribution to likelihood value. F = self.new_weights # ^ M step diff = np.abs(new_opt_res - opt_res) opt_res = new_opt_res iters = iters + 1 death = np.cumsum(F.flatten()[::-1]) survivalprob = death[::-1] llike = -opt_res + np.sum(np.log(survivalprob[survidx])) wtd_km = km.flatten() / np.sum(km) survivalmax = np.cumsum(wtd_km[::-1])[::-1] llikemax = np.sum(np.log(wtd_km[uncensored])) + \ np.sum(np.log(survivalmax[censored])) if iters == maxiter: warnings.warn('The EM reached the maximum number of iterations') return -2 * (llike - llikemax) def _ci_limits_beta(self, b0, param_num=None): """ Returns the difference between the log likelihood for a parameter and some critical value. Parameters --------- b0: float Value of a regression parameter param_num: int Parameter index of b0 """ return self.test_beta([b0], [param_num])[0] - self.r0 class emplikeAFT(object): """ Class for estimating and conducting inference in an AFT model. Parameters --------- endog: nx1 array Response variables that are subject to random censoring exog: nxk array Matrix of covariates censors: nx1 array array with entries 0 or 1. 0 indicates a response was censored. Attributes ---------- nobs: float Number of observations endog: array Endog attay exog: array Exogenous variable matrix censors Censors array but sets the max(endog) to uncensored nvar: float Number of exogenous variables uncens_nobs: float Number of uncensored observations uncens_endog: array Uncensored response variables uncens_exog: array Exogenous variables of the uncensored observations Methods ------- params: Fits model parameters test_beta: Tests if beta = b0 for any vector b0. Notes ----- The data is immediately sorted in order of increasing endogenous variables The last observation is assumed to be uncensored which makes estimation and inference possible. """ def __init__(self, endog, exog, censors): self.nobs = float(np.shape(exog)[0]) self.endog = endog.reshape(self.nobs, 1) self.exog = exog.reshape(self.nobs, -1) self.censors = censors.reshape(self.nobs, 1) self.nvar = self.exog.shape[1] idx = np.lexsort((-self.censors[:, 0], self.endog[:, 0])) self.endog = self.endog[idx] self.exog = self.exog[idx] self.censors = self.censors[idx] self.censors[-1] = 1 # Sort in init, not in function self.uncens_nobs = np.sum(self.censors) mask = self.censors.ravel().astype(bool) self.uncens_endog = self.endog[mask, :].reshape(-1, 1) self.uncens_exog = self.exog[mask, :] def _is_tied(self, endog, censors): """ Indicated if an observation takes the same value as the next ordered observation. Parameters ---------- endog: array Models endogenous variable censors: array arrat indicating a censored array Returns ------- indic_ties: array ties[i]=1 if endog[i]==endog[i+1] and censors[i]=censors[i+1] """ nobs = int(self.nobs) endog_idx = endog[np.arange(nobs - 1)] == ( endog[np.arange(nobs - 1) + 1]) censors_idx = censors[np.arange(nobs - 1)] == ( censors[np.arange(nobs - 1) + 1]) indic_ties = endog_idx * censors_idx # Both true return np.int_(indic_ties) def _km_w_ties(self, tie_indic, untied_km): """ Computes KM estimator value at each observation, taking into acocunt ties in the data. Parameters: ---------- tie_indic: 1d array Indicates if the i'th observation is the same as the ith +1 untied_km: 1d array Km estimates at each observation assuming no ties. """ # TODO: Vectorize, even though it is only 1 pass through for any # function call num_same = 1 idx_nums = [] for obs_num in np.arange(int(self.nobs - 1))[::-1]: if tie_indic[obs_num] == 1: idx_nums.append(obs_num) num_same = num_same + 1 untied_km[obs_num] = untied_km[obs_num + 1] elif tie_indic[obs_num] == 0 and num_same > 1: idx_nums.append(max(idx_nums) + 1) idx_nums = np.asarray(idx_nums) untied_km[idx_nums] = untied_km[idx_nums] num_same = 1 idx_nums = [] return untied_km.reshape(self.nobs, 1) def _make_km(self, endog, censors): """ Computes the Kaplan-Meier estimate for the weights in the AFT model Parameters ---------- endog: nx1 array Array of response variables censors: nx1 array Censor-indicating variable Returns ------ Kaplan Meier estimate for each observation Notes ----- This function makes calls to _is_tied and km_w_ties to handle ties in the data.If a censored observation and an uncensored observation has the same value, it is assumed that the uncensored happened first. """ nobs = self.nobs num = (nobs - (np.arange(nobs) + 1.)) denom = ((nobs - (np.arange(nobs) + 1.) + 1.)) km = (num / denom).reshape(nobs, 1) km = km ** np.abs(censors - 1.) km = np.cumprod(km) # If no ties, this is kaplan-meier tied = self._is_tied(endog, censors) wtd_km = self._km_w_ties(tied, km) return (censors / wtd_km).reshape(nobs, 1) def fit(self): """ Fits an AFT model and returns results instance Parameters --------- None Returns ------- Results instance. Notes ----- To avoid dividing by zero, max(endog) is assumed to be uncensored. """ return AFTResults(self) def predict(self, params, endog=None): if endog is None: endog = self.endog return np.dot(endog, params) class AFTResults(OptAFT): def __init__(self, model): self.model = model def params(self): """ Fits an AFT model and returns parameters. Parameters --------- None Returns ------- Fitted params Notes ----- To avoid dividing by zero, max(endog) is assumed to be uncensored. """ self.model.modif_censors = np.copy(self.model.censors) self.model.modif_censors[-1] = 1 wts = self.model._make_km(self.model.endog, self.model.modif_censors) res = WLS(self.model.endog, self.model.exog, wts).fit() params = res.params return params def test_beta(self, b0_vals, param_nums, ftol=10 ** - 5, maxiter=30, print_weights=1): """ Returns the profile log likelihood for regression parameters 'param_num' at 'b0_vals.' Parameters ---------- b0_vals: list The value of parameters to be tested param_num: list Which parameters to be tested maxiter: int, optional How many iterations to use in the EM algorithm. Default is 30 ftol: float, optional The function tolerance for the EM optimization. Default is 10''**''-5 print_weights: bool If true, returns the weights tate maximize the profile log likelihood. Default is False Returns ------- test_results: tuple The log-likelihood and p-pvalue of the test. Notes ---- The function will warn if the EM reaches the maxiter. However, when optimizing over nuisance parameters, it is possible to reach a maximum number of inner iterations for a specific value for the nuisance parameters while the resultsof the function are still valid. This usually occurs when the optimization over the nuisance parameters selects paramater values that yield a log-likihood ratio close to infinity. Examples ------- import statsmodels.api as sm import numpy as np # Test parameter is .05 in one regressor no intercept model data=sm.datasets.heart.load() y = np.log10(data.endog) x = data.exog cens = data.censors model = sm.emplike.emplikeAFT(y, x, cens) res=model.test_beta([0], [0]) >>>res >>>(1.4657739632606308, 0.22601365256959183) #Test slope is 0 in model with intercept data=sm.datasets.heart.load() y = np.log10(data.endog) x = data.exog cens = data.censors model = sm.emplike.emplikeAFT(y, sm.add_constant(x), cens) res=model.test_beta([0], [1]) >>>res >>>(4.623487775078047, 0.031537049752572731) """ censors = self.model.censors endog = self.model.endog exog = self.model.exog uncensored = (censors == 1).flatten() censored = (censors == 0).flatten() uncens_endog = endog[uncensored] uncens_exog = exog[uncensored, :] reg_model = OLS(uncens_endog, uncens_exog).fit() llr, pval, new_weights = reg_model.el_test(b0_vals, param_nums, return_weights=True) # Needs to be changed km = self.model._make_km(endog, censors).flatten() # when merged uncens_nobs = self.model.uncens_nobs F = np.asarray(new_weights).reshape(uncens_nobs) # Step 0 ^ params = self.params() survidx = np.where(censors == 0) survidx = survidx[0] - np.arange(len(survidx[0])) numcensbelow = np.int_(np.cumsum(1 - censors)) if len(param_nums) == len(params): llr = self._EM_test([], F=F, params=params, param_nums=param_nums, b0_vals=b0_vals, survidx=survidx, uncens_nobs=uncens_nobs, numcensbelow=numcensbelow, km=km, uncensored=uncensored, censored=censored, ftol=ftol, maxiter=25) return llr, chi2.sf(llr, self.model.nvar) else: x0 = np.delete(params, param_nums) try: res = optimize.fmin(self._EM_test, x0, (params, param_nums, b0_vals, F, survidx, uncens_nobs, numcensbelow, km, uncensored, censored, maxiter, ftol), full_output=1, disp = 0) llr = res[1] return llr, chi2.sf(llr, len(param_nums)) except np.linalg.linalg.LinAlgError: return np.inf, 0 def ci_beta(self, param_num, beta_high, beta_low, sig=.05): """ Returns the confidence interval for a regression parameter in the AFT model. Parameters --------- param_num: int Parameter number of interest beta_high: float Upper bound for the confidence interval beta_low: Lower bound for the confidence interval sig: float, optional Significance level. Default is .05 Notes ---- If the function returns f(a) and f(b) must have different signs, consider widening the search area by adjusting beta_low and beta_high. Also note that this process is computational intensive. There are 4 levels of optimization/solving. From outer to inner: 1) Solving so that llr-critical value = 0 2) maximizing over nuisance parameters 3) Using EM at each value of nuisamce parameters 4) Using the _modified_Newton optimizer at each iteration of the EM algorithm. Also, for very unlikely nuisance parameters, it is possible for the EM algorithm to not converge. This is not an indicator that the solver did not find the correct solution. It just means for a specific iteration of the nuisance parameters, the optimizer was unable to converge. If the user desires to verify the success of the optimization, it is recommended to test the limits using test_beta. """ params = self.params() self.r0 = chi2.ppf(1 - sig, 1) ll = optimize.brentq(self._ci_limits_beta, beta_low, params[param_num], (param_num)) ul = optimize.brentq(self._ci_limits_beta, params[param_num], beta_high, (param_num)) return ll, ul statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/api.py000066400000000000000000000003401224417117700232620ustar00rootroot00000000000000""" api for empirical likelihood """ # pylint: disable=W0611 from .descriptive import DescStat, DescStatUV, DescStatMV from .originregress import ELOriginRegress from .elanova import ANOVA from .aft_el import emplikeAFT statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/descriptive.py000066400000000000000000001136501224417117700250430ustar00rootroot00000000000000""" Empirical likelihood inference on descriptive statistics This module conducts hypothesis tests and constructs confidence intervals for the mean, variance, skewness, kurtosis and correlation. If matplotlib is installed, this module can also generate multivariate confidence region plots as well as mean-variance contour plots. See _OptFuncts docstring for technical details and optimization variable definitions. General References: ------------------ Owen, A. (2001). "Empirical Likelihood." Chapman and Hall """ import numpy as np from scipy import optimize from scipy.stats import chi2, skew, kurtosis from statsmodels.base.model import _fit_mle_newton import itertools from statsmodels.graphics import utils def DescStat(endog): """ Returns an instance to conduct inference on descriptive statistics via empirical likelihood. See DescStatUV and DescStatMV for more information. Parameters ---------- endog : ndarray Array of data Returns : DescStat instance If k=1, the function returns a univariate instance, DescStatUV. If k>1, the function returns a multivariate instance, DescStatMV. """ if endog.ndim == 1: endog = endog.reshape(len(endog), 1) if endog.shape[1] == 1: return DescStatUV(endog) if endog.shape[1] > 1: return DescStatMV(endog) class _OptFuncts(object): """ A class that holds functions that are optimized/solved. The general setup of the class is simple. Any method that starts with _opt_ creates a vector of estimating equations named est_vect such that np.dot(p, (est_vect))=0 where p is the weight on each observation as a 1 x n array and est_vect is n x k. Then _modif_Newton is called to determine the optimal p by solving for the Lagrange multiplier (eta) in the profile likelihood maximization problem. In the presence of nuisance parameters, _opt_ functions are optimized over to profile out the nuisance parameters. Any method starting with _ci_limits calculates the log likelihood ratio for a specific value of a parameter and then subtracts a pre-specified critical value. This is solved so that llr - crit = 0. """ def __init__(self, endog): super(_OptFuncts, self).__init__(endog) def _log_star(self, eta, est_vect, weights, nobs): """ Transforms the log of observation probabilities in terms of the Lagrange multiplier to the log 'star' of the probabilities. Parameters ---------- eta : float Lagrange multiplier est_vect : ndarray (n,k) Estimating equations vector wts : nx1 array Observation weights Returns ------ data_star : array The weighted logstar of the estimting equations Notes ----- This function is only a placeholder for the _fit_mle_Newton. The function value is not used in optimization and the optimal value is disregarded when computing the log likelihood ratio. """ data_star = np.log(weights) + (np.sum(weights) +\ np.dot(est_vect, eta)) idx = data_star < 1. / nobs not_idx = ~idx nx = nobs * data_star[idx] data_star[idx] = np.log(1. / nobs) - 1.5 + nx * (2. - nx / 2) data_star[not_idx] = np.log(data_star[not_idx]) return data_star def _hess(self, eta, est_vect, weights, nobs): """ Calculates the hessian of a weighted empirical likelihood problem. Parameters ---------- eta : ndarray, (1,m) Lagrange multiplier in the profile likelihood maximization est_vect : ndarray (n,k) Estimating equations vector weights : 1darray Observation weights Returns ------- hess : m x m array Weighted hessian used in _wtd_modif_newton """ #eta = np.squeeze(eta) data_star_doub_prime = np.sum(weights) + np.dot(est_vect, eta) idx = data_star_doub_prime < 1. / nobs not_idx = ~idx data_star_doub_prime[idx] = - nobs ** 2 data_star_doub_prime[not_idx] = - (data_star_doub_prime[not_idx]) ** -2 wtd_dsdp = weights * data_star_doub_prime return np.dot(est_vect.T, wtd_dsdp[:, None] * est_vect) def _grad(self, eta, est_vect, weights, nobs): """ Calculates the gradient of a weighted empirical likelihood problem Parameters ---------- eta : ndarray, (1,m) Lagrange multiplier in the profile likelihood maximization est_vect : ndarray, (n,k) Estimating equations vector weights : 1darray Observation weights Returns ------- gradient : ndarray (m,1) The gradient used in _wtd_modif_newton """ #eta = np.squeeze(eta) data_star_prime = np.sum(weights) + np.dot(est_vect, eta) idx = data_star_prime < 1. / nobs not_idx = ~idx data_star_prime[idx] = nobs * (2 - nobs * data_star_prime[idx]) data_star_prime[not_idx] = 1. / data_star_prime[not_idx] return np.dot(weights * data_star_prime, est_vect) def _modif_newton(self, eta, est_vect, weights): """ Modified Newton's method for maximizing the log 'star' equation. This function calls _fit_mle_newton to find the optimal values of eta. Parameters ---------- eta : ndarray, (1,m) Lagrange multiplier in the profile likelihood maximization est_vect : ndarray, (n,k) Estimating equations vector weights : 1darray Observation weights Returns ------- params : 1xm array Lagrange multiplier that maximizes the log-likelihood """ nobs = len(est_vect) f = lambda x0: - np.sum(self._log_star(x0, est_vect, weights, nobs)) grad = lambda x0: - self._grad(x0, est_vect, weights, nobs) hess = lambda x0: - self._hess(x0, est_vect, weights, nobs) kwds = {'tol': 1e-8} eta = eta.squeeze() res = _fit_mle_newton(f, grad, eta, (), kwds, hess=hess, maxiter=50, \ disp=0) return res[0] def _find_eta(self, eta): """ Finding the root of sum(xi-h0)/(1+eta(xi-mu)) solves for eta when computing ELR for univariate mean. Parameters ---------- eta : float Lagrange multiplier in the empirical likelihood maximization Returns ------- llr : float n times the log likelihood value for a given value of eta """ return np.sum((self.endog - self.mu0) / \ (1. + eta * (self.endog - self.mu0))) def _ci_limits_mu(self, mu): """ Calculates the difference between the log likelihood of mu_test and a specified critical value. Parameters ---------- mu : float Hypothesized value of the mean. Returns ------- diff : float The difference between the log likelihood value of mu0 and a specified value. """ return self.test_mean(mu)[0] - self.r0 def _find_gamma(self, gamma): """ Finds gamma that satisfies sum(log(n * w(gamma))) - log(r0) = 0 Used for confidence intervals for the mean Parameters ---------- gamma : float Lagrange multiplier when computing confidence interval Returns ------- diff : float The difference between the log-liklihood when the Lagrange multiplier is gamma and a pre-specified value """ denom = np.sum((self.endog - gamma) ** -1) new_weights = (self.endog - gamma) ** -1 / denom return -2 * np.sum(np.log(self.nobs * new_weights)) - \ self.r0 def _opt_var(self, nuisance_mu, pval=False): """ This is the function to be optimized over a nuisance mean parameter to determine the likelihood ratio for the variance Parameters ---------- nuisance_mu : float Value of a nuisance mean parameter Returns ------- llr : float Log likelihood of a pre-specified variance holding the nuisance parameter constant """ endog = self.endog nobs = self.nobs sig_data = ((endog - nuisance_mu) ** 2 \ - self.sig2_0) mu_data = (endog - nuisance_mu) est_vect = np.column_stack((mu_data, sig_data)) eta_star = self._modif_newton(np.array([1. / nobs, 1. / nobs]), est_vect, np.ones(nobs) * (1. / nobs)) denom = 1 + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) if pval: # Used for contour plotting return chi2.sf(-2 * llr, 1) return -2 * llr def _ci_limits_var(self, var): """ Used to determine the confidence intervals for the variance. It calls test_var and when called by an optimizer, finds the value of sig2_0 that is chi2.ppf(significance-level) Parameters ---------- var_test : float Hypothesized value of the variance Returns ------- diff : float The difference between the log likelihood ratio at var_test and a pre-specified value. """ return self.test_var(var)[0] - self.r0 def _opt_skew(self, nuis_params): """ Called by test_skew. This function is optimized over nuisance parameters mu and sigma Parameters ---------- nuis_params : 1darray An array with a nuisance mean and variance parameter Returns ------- llr : float The log likelihood ratio of a pre-specified skewness holding the nuisance parameters constant. """ endog = self.endog nobs = self.nobs mu_data = endog - nuis_params[0] sig_data = ((endog - nuis_params[0]) ** 2) - nuis_params[1] skew_data = ((((endog - nuis_params[0]) ** 3) / (nuis_params[1] ** 1.5))) - self.skew0 est_vect = np.column_stack((mu_data, sig_data, skew_data)) eta_star = self._modif_newton(np.array([1. / nobs, 1. / nobs, 1. / nobs]), est_vect, np.ones(nobs) * (1. / nobs)) denom = 1. + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr def _opt_kurt(self, nuis_params): """ Called by test_kurt. This function is optimized over nuisance parameters mu and sigma Parameters ---------- nuis_params : 1darray An array with a nuisance mean and variance parameter Returns ------- llr : float The log likelihood ratio of a pre-speified kurtosis holding the nuisance parameters constant """ endog = self.endog nobs = self.nobs mu_data = endog - nuis_params[0] sig_data = ((endog - nuis_params[0]) ** 2) - nuis_params[1] kurt_data = (((((endog - nuis_params[0]) ** 4) / \ (nuis_params[1] ** 2))) - 3) - self.kurt0 est_vect = np.column_stack((mu_data, sig_data, kurt_data)) eta_star = self._modif_newton(np.array([1. / nobs, 1. / nobs, 1. / nobs]), est_vect, np.ones(nobs) * (1. / nobs)) denom = 1 + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr def _opt_skew_kurt(self, nuis_params): """ Called by test_joint_skew_kurt. This function is optimized over nuisance parameters mu and sigma Parameters ----------- nuis_params : 1darray An array with a nuisance mean and variance parameter Returns ------ llr : float The log likelihood ratio of a pre-speified skewness and kurtosis holding the nuisance parameters constant. """ endog = self.endog nobs = self.nobs mu_data = endog - nuis_params[0] sig_data = ((endog - nuis_params[0]) ** 2) - nuis_params[1] skew_data = ((((endog - nuis_params[0]) ** 3) / \ (nuis_params[1] ** 1.5))) - self.skew0 kurt_data = (((((endog - nuis_params[0]) ** 4) / \ (nuis_params[1] ** 2))) - 3) - self.kurt0 est_vect = np.column_stack((mu_data, sig_data, skew_data, kurt_data)) eta_star = self._modif_newton(np.array([1. / nobs, 1. / nobs, 1. / nobs, 1. / nobs]), est_vect, np.ones(nobs) * (1. / nobs)) denom = 1. + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr def _ci_limits_skew(self, skew): """ Parameters ---------- skew0 : float Hypothesized value of skewness Returns ------- diff : float The difference between the log likelihood ratio at skew and a pre-specified value. """ return self.test_skew(skew)[0] - self.r0 def _ci_limits_kurt(self, kurt): """ Parameters --------- skew0 : float Hypothesized value of kurtosis Returns ------- diff : float The difference between the log likelihood ratio at kurt and a pre-specified value. """ return self.test_kurt(kurt)[0] - self.r0 def _opt_correl(self, nuis_params, corr0, endog, nobs, x0, weights0): """ Parameters ---------- nuis_params : 1darray Array containing two nuisance means and two nuisance variances Returns ------- llr : float The log-likelihood of the correlation coefficient holding nuisance parameters constant """ mu1_data, mu2_data = (endog - nuis_params[::2]).T sig1_data = mu1_data ** 2 - nuis_params[1] sig2_data = mu2_data ** 2 - nuis_params[3] correl_data = ((mu1_data * mu2_data) - corr0 * (nuis_params[1] * nuis_params[3]) ** .5) est_vect = np.column_stack((mu1_data, sig1_data, mu2_data, sig2_data, correl_data)) eta_star = self._modif_newton(x0, est_vect, weights0) denom = 1. + np.dot(est_vect, eta_star) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr def _ci_limits_corr(self, corr): return self.test_corr(corr)[0] - self.r0 class DescStatUV(_OptFuncts): """ A class to compute confidence intervals and hypothesis tests involving mean, variance, kurtosis and skewness of a univariate random variable. Parameters ---------- endog : 1darray Data to be analyzed Attributes ---------- endog : 1darray Data to be analyzed nobs : float Number of observations """ def __init__(self, endog): self.endog = np.squeeze(endog) self.nobs = float(endog.shape[0]) def test_mean(self, mu0, return_weights=False): """ Returns - 2 x log-likelihood ratio, p-value and weights for a hypothesis test of the mean. Parameters ---------- mu0 : float Mean value to be tested return_weights : bool If return_weights is True the funtion returns the weights of the observations under the null hypothesis. Default is False Returns ------- test_results : tuple The log-likelihood ratio and p-value of mu0 """ self.mu0 = mu0 endog = self.endog nobs = self.nobs eta_min = (1. - (1. / nobs)) / (self.mu0 - max(endog)) eta_max = (1. - (1. / nobs)) / (self.mu0 - min(endog)) eta_star = optimize.brentq(self._find_eta, eta_min, eta_max) new_weights = (1. / nobs) * 1. / (1. + eta_star * (endog - self.mu0)) llr = -2 * np.sum(np.log(nobs * new_weights)) if return_weights: return llr, chi2.sf(llr, 1), new_weights else: return llr, chi2.sf(llr, 1) def ci_mean(self, sig=.05, method='gamma', epsilon=10 ** -8, gamma_low=-10 ** 10, gamma_high=10 ** 10): """ Returns the confidence interval for the mean. Parameters ---------- sig : float significance level. Default is .05 method : str Root finding method, Can be 'nested-brent' or 'gamma'. Default is 'gamma' 'gamma' Tries to solve for the gamma parameter in the Lagrange (see Owen pg 22) and then determine the weights. 'nested brent' uses brents method to find the confidence intervals but must maximize the likelihhod ratio on every iteration. gamma is generally much faster. If the optimizations does not converge, try expanding the gamma_high and gamma_low variable. gamma_low : float Lower bound for gamma when finding lower limit. If function returns f(a) and f(b) must have different signs, consider lowering gamma_low. gamma_high : float Upper bound for gamma when finding upper limit. If function returns f(a) and f(b) must have different signs, consider raising gamma_high. epsilon : float When using 'nested-brent', amount to decrease (increase) from the maximum (minimum) of the data when starting the search. This is to protect against the likelihood ratio being zero at the maximum (minimum) value of the data. If data is very small in absolute value (<10 ``**`` -6) consider shrinking epsilon When using 'gamma', amount to decrease (increase) the minimum (maximum) by to start the search for gamma. If fucntion returns f(a) and f(b) must have differnt signs, consider lowering epsilon. Returns ------- Interval : tuple Confidence interval for the mean """ endog = self.endog sig = 1 - sig if method == 'nested-brent': self.r0 = chi2.ppf(sig, 1) middle = np.mean(endog) epsilon_u = (max(endog) - np.mean(endog)) * epsilon epsilon_l = (np.mean(endog) - min(endog)) * epsilon ulim = optimize.brentq(self._ci_limits_mu, middle, max(endog) - epsilon_u) llim = optimize.brentq(self._ci_limits_mu, middle, min(endog) + epsilon_l) return llim, ulim if method == 'gamma': self.r0 = chi2.ppf(sig, 1) gamma_star_l = optimize.brentq(self._find_gamma, gamma_low, min(endog) - epsilon) gamma_star_u = optimize.brentq(self._find_gamma, \ max(endog) + epsilon, gamma_high) weights_low = ((endog - gamma_star_l) ** -1) / \ np.sum((endog - gamma_star_l) ** -1) weights_high = ((endog - gamma_star_u) ** -1) / \ np.sum((endog - gamma_star_u) ** -1) mu_low = np.sum(weights_low * endog) mu_high = np.sum(weights_high * endog) return mu_low, mu_high def test_var(self, sig2_0, return_weights=False): """ Returns -2 x log-likelihoog ratio and the p-value for the hypothesized variance Parameters ---------- sig2_0 : float Hypothesized variance to be tested return_weights : bool If True, returns the weights that maximize the likelihood of observing sig2_0. Default is False Returns -------- test_results : tuple The log-likelihood ratio and the p_value of sig2_0 Examples -------- >>> random_numbers = np.random.standard_normal(1000)*100 >>> el_analysis = sm.emplike.DescStat(random_numbers) >>> hyp_test = el_analysis.test_var(9500) """ self.sig2_0 = sig2_0 mu_max = max(self.endog) mu_min = min(self.endog) llr = optimize.fminbound(self._opt_var, mu_min, mu_max, \ full_output=1)[1] p_val = chi2.sf(llr, 1) if return_weights: return llr, p_val, self.new_weights.T else: return llr, p_val def ci_var(self, lower_bound=None, upper_bound=None, sig=.05): """ Returns the confidence interval for the variance. Parameters ---------- lower_bound : float The minimum value the lower confidence interval can take. The p-value from test_var(lower_bound) must be lower than 1 - significance level. Default is .99 confidence limit assuming normality upper_bound : float The maximum value the upper confidence interval can take. The p-value from test_var(upper_bound) must be lower than 1 - significance level. Default is .99 confidence limit assuming normality sig : float The significance level. Default is .05 Returns -------- Interval : tuple Confidence interval for the variance Examples -------- >>> random_numbers = np.random.standard_normal(100) >>> el_analysis = sm.emplike.DescStat(random_numbers) >>> el_analysis.ci_var() >>> 'f(a) and f(b) must have different signs' >>> el_analysis.ci_var(.5, 2) Notes ----- If the function returns the error f(a) and f(b) must have different signs, consider lowering lower_bound and raising upper_bound. """ endog = self.endog if upper_bound is None: upper_bound = ((self.nobs - 1) * endog.var()) / \ (chi2.ppf(.0001, self.nobs - 1)) if lower_bound is None: lower_bound = ((self.nobs - 1) * endog.var()) / \ (chi2.ppf(.9999, self.nobs - 1)) self.r0 = chi2.ppf(1 - sig, 1) llim = optimize.brentq(self._ci_limits_var, lower_bound, endog.var()) ulim = optimize.brentq(self._ci_limits_var, endog.var(), upper_bound) return llim, ulim def plot_contour(self, mu_low, mu_high, var_low, var_high, mu_step, var_step, levs=[.2, .1, .05, .01, .001]): """ Returns a plot of the confidence region for a univariate mean and variance. Parameters ---------- mu_low : float Lowest value of the mean to plot mu_high : float Highest value of the mean to plot var_low : float Lowest value of the variance to plot var_high : float Highest value of the variance to plot mu_step : float Increments to evaluate the mean var_step : float Increments to evaluate the mean levs : list Which values of significance the contour lines will be drawn. Default is [.2, .1, .05, .01, .001] Returns ------- fig : matplotlib figure instance The contour plot """ fig, ax = utils.create_mpl_ax() ax.set_ylabel('Variance') ax.set_xlabel('Mean') mu_vect = list(np.arange(mu_low, mu_high, mu_step)) var_vect = list(np.arange(var_low, var_high, var_step)) z = [] for sig0 in var_vect: self.sig2_0 = sig0 for mu0 in mu_vect: z.append(self._opt_var(mu0, pval=True)) z = np.asarray(z).reshape(len(var_vect), len(mu_vect)) ax.contour(mu_vect, var_vect, z, levels=levs) return fig def test_skew(self, skew0, return_weights=False): """ Returns -2 x log-likelihood and p-value for the hypothesized skewness. Parameters ---------- skew0 : float Skewness value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns -------- test_results : tuple The log-likelihood ratio and p_value of skew0 """ self.skew0 = skew0 start_nuisance = np.array([self.endog.mean(), self.endog.var()]) llr = optimize.fmin_powell(self._opt_skew, start_nuisance, full_output=1, disp=0)[1] p_val = chi2.sf(llr, 1) if return_weights: return llr, p_val, self.new_weights.T return llr, p_val def test_kurt(self, kurt0, return_weights=False): """ Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters ---------- kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns ------- test_results : tuple The log-likelihood ratio and p-value of kurt0 """ self.kurt0 = kurt0 start_nuisance = np.array([self.endog.mean(), self.endog.var()]) llr = optimize.fmin_powell(self._opt_kurt, start_nuisance, full_output=1, disp=0)[1] p_val = chi2.sf(llr, 1) if return_weights: return llr, p_val, self.new_weights.T return llr, p_val def test_joint_skew_kurt(self, skew0, kurt0, return_weights=False): """ Returns - 2 x log-likelihood and the p-value for the joint hypothesis test for skewness and kurtosis Parameters ---------- skew0 : float Skewness value to be tested kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns ------- test_results : tuple The log-likelihood ratio and p-value of the joint hypothesis test. """ self.skew0 = skew0 self.kurt0 = kurt0 start_nuisance = np.array([self.endog.mean(), self.endog.var()]) llr = optimize.fmin_powell(self._opt_skew_kurt, start_nuisance, full_output=1, disp=0)[1] p_val = chi2.sf(llr, 2) if return_weights: return llr, p_val, self.new_weights.T return llr, p_val def ci_skew(self, sig=.05, upper_bound=None, lower_bound=None): """ Returns the confidence interval for skewness. Parameters ---------- sig : float The significance level. Default is .05 upper_bound : float Maximum value of skewness the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of skewness the lower limit can be. Default is .99 confidence level assuming normality. Returns ------- Interval : tuple Confidence interval for the skewness Notes ----- If function returns f(a) and f(b) must have different signs, consider expanding lower and upper bounds """ nobs = self.nobs endog = self.endog if upper_bound is None: upper_bound = skew(endog) + \ 2.5 * ((6. * nobs * (nobs - 1.)) / \ ((nobs - 2.) * (nobs + 1.) * \ (nobs + 3.))) ** .5 if lower_bound is None: lower_bound = skew(endog) - \ 2.5 * ((6. * nobs * (nobs - 1.)) / \ ((nobs - 2.) * (nobs + 1.) * \ (nobs + 3.))) ** .5 self.r0 = chi2.ppf(1 - sig, 1) llim = optimize.brentq(self._ci_limits_skew, lower_bound, skew(endog)) ulim = optimize.brentq(self._ci_limits_skew, skew(endog), upper_bound) return llim, ulim def ci_kurt(self, sig=.05, upper_bound=None, lower_bound=None): """ Returns the confidence interval for kurtosis. Parameters ---------- sig : float The significance level. Default is .05 upper_bound : float Maximum value of kurtosis the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of kurtosis the lower limit can be. Default is .99 confidence limit assuming normality. Returns -------- Interval : tuple Lower and upper confidence limit Notes ----- For small n, upper_bound and lower_bound may have to be provided by the user. Consider using test_kurt to find values close to the desired significance level. If function returns f(a) and f(b) must have different signs, consider expanding the bounds. """ endog = self.endog nobs = self.nobs if upper_bound is None: upper_bound = kurtosis(endog) + \ (2.5 * (2. * ((6. * nobs * (nobs - 1.)) / \ ((nobs - 2.) * (nobs + 1.) * \ (nobs + 3.))) ** .5) * \ (((nobs ** 2.) - 1.) / ((nobs - 3.) *\ (nobs + 5.))) ** .5) if lower_bound is None: lower_bound = kurtosis(endog) - \ (2.5 * (2. * ((6. * nobs * (nobs - 1.)) / \ ((nobs - 2.) * (nobs + 1.) * \ (nobs + 3.))) ** .5) * \ (((nobs ** 2.) - 1.) / ((nobs - 3.) *\ (nobs + 5.))) ** .5) self.r0 = chi2.ppf(1 - sig, 1) llim = optimize.brentq(self._ci_limits_kurt, lower_bound, \ kurtosis(endog)) ulim = optimize.brentq(self._ci_limits_kurt, kurtosis(endog), \ upper_bound) return llim, ulim class DescStatMV(_OptFuncts): """ A class for conducting inference on multivariate means and correlation. Parameters ---------- endog : ndarray Data to be analyzed Attributes ---------- endog : ndarray Data to be analyzed nobs : float Number of observations """ def __init__(self, endog): self.endog = endog self.nobs = float(endog.shape[0]) def mv_test_mean(self, mu_array, return_weights=False): """ Returns -2 x log likelihood and the p-value for a multivariate hypothesis test of the mean Parameters ---------- mu_array : 1d array Hypothesized values for the mean. Must have same number of elements as columns in endog return_weights : bool If True, returns the weights that maximize the likelihood of mu_array. Default is False. Returns ------- test_results : tuple The log-likelihood ratio and p-value for mu_array """ endog = self.endog nobs = self.nobs if len(mu_array) != endog.shape[1]: raise Exception('mu_array must have the same number of \ elements as the columns of the data.') mu_array = mu_array.reshape(1, endog.shape[1]) means = np.ones((endog.shape[0], endog.shape[1])) means = mu_array * means est_vect = endog - means start_vals = 1 / nobs * np.ones(endog.shape[1]) eta_star = self._modif_newton(start_vals, est_vect, np.ones(nobs) * (1. / nobs)) denom = 1 + np.dot(eta_star, est_vect.T) self.new_weights = 1 / nobs * 1 / denom llr = -2 * np.sum(np.log(nobs * self.new_weights)) p_val = chi2.sf(llr, mu_array.shape[1]) if return_weights: return llr, p_val, self.new_weights.T else: return llr, p_val def mv_mean_contour(self, mu1_low, mu1_upp, mu2_low, mu2_upp, step1, step2, levs=[.2, .1, .05, .01, .001], var1_name=None, var2_name=None, plot_dta=False): """ Creates a confidence region plot for the mean of bivariate data Parameters ---------- m1_low : float Minimum value of the mean for variable 1 m1_upp : float Maximum value of the mean for variable 1 mu2_low : float Minimum value of the mean for variable 2 mu2_upp : float Maximum value of the mean for variable 2 step1 : float Increment of evaluations for variable 1 step2 : float Increment of evaluations for variable 2 levs : list Levels to be drawn on the contour plot. Default = [.2, .1 .05, .01, .001] plot_dta : bool If True, makes a scatter plot of the data on top of the contour plot. Defaultis False. var1_name : str Name of variable 1 to be plotted on the x-axis var2_name : str Name of variable 2 to be plotted on the y-axis Notes ----- The smaller the step size, the more accurate the intervals will be If the function returns optimization failed, consider narrowing the boundaries of the plot Examples -------- >>> two_rvs = np.random.standard_normal((20,2)) >>> el_analysis = sm.empllike.DescStat(two_rvs) >>> contourp = el_analysis.mv_mean_contour(-2, 2, -2, 2, .1, .1) >>> contourp.show() """ if self.endog.shape[1] != 2: raise Exception('Data must contain exactly two variables') fig, ax = utils.create_mpl_ax() if var2_name is None: ax.set_ylabel('Variable 2') else: ax.set_ylabel(var2_name) if var1_name is None: ax.set_xlabel('Variable 1') else: ax.set_xlabel(var1_name) x = (np.arange(mu1_low, mu1_upp, step1)) y = (np.arange(mu2_low, mu2_upp, step2)) pairs = itertools.product(x, y) z = [] for i in pairs: z.append(self.mv_test_mean(np.asarray(i))[0]) X, Y = np.meshgrid(x, y) z = np.asarray(z) z = z.reshape(X.shape[1], Y.shape[0]) ax.contour(x, y, z.T, levels=levs) if plot_dta: ax.plot(self.endog[:, 0], self.endog[:, 1], 'bo') return fig def test_corr(self, corr0, return_weights=0): """ Returns -2 x log-likelihood ratio and p-value for the correlation coefficient between 2 variables Parameters ---------- corr0 : float Hypothesized value to be tested return_weights : bool If true, returns the weights that maximize the log-likelihood at the hypothesized value """ nobs = self.nobs endog = self.endog if endog.shape[1] != 2: raise Exception('Correlation matrix not yet implemented') nuis0 = np.array([endog[:, 0].mean(), endog[:, 0].var(), endog[:, 1].mean(), endog[:, 1].var()]) x0 = np.zeros(5) weights0 = np.array([1. / nobs] * int(nobs)) args = (corr0, endog, nobs, x0, weights0) llr = optimize.fmin(self._opt_correl, nuis0, args=args, full_output=1, disp=0)[1] p_val = chi2.sf(llr, 1) if return_weights: return llr, p_val, self.new_weights.T return llr, p_val def ci_corr(self, sig=.05, upper_bound=None, lower_bound=None): """ Returns the confidence intervals for the correlation coefficient Parameters ---------- sig : float The significance level. Default is .05 upper_bound : float Maximum value the upper confidence limit can be. Default is 99% confidence limit assuming normality. lower_bound : float Minimum value the lower condidence limit can be. Default is 99% confidence limit assuming normality. Returns ------- interval : tuple Confidence interval for the correlation """ endog = self.endog nobs = self.nobs self.r0 = chi2.ppf(1 - sig, 1) point_est = np.corrcoef(endog[:, 0], endog[:, 1])[0, 1] if upper_bound is None: upper_bound = min(.999, point_est + \ 2.5 * ((1. - point_est ** 2.) / \ (nobs - 2.)) ** .5) if lower_bound is None: lower_bound = max(- .999, point_est - \ 2.5 * (np.sqrt((1. - point_est ** 2.) / \ (nobs - 2.)))) llim = optimize.brenth(self._ci_limits_corr, lower_bound, point_est) ulim = optimize.brenth(self._ci_limits_corr, point_est, upper_bound) return llim, ulim statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/elanova.py000066400000000000000000000071111224417117700241410ustar00rootroot00000000000000""" This script contains empirical likelihood ANOVA. Currently the script only contains one feature that allows the user to compare means of multiple groups General References ------------------ Owen, A. B. (2001). Empirical Likelihood. Chapman and Hall. """ import numpy as np from descriptive import _OptFuncts from scipy import optimize from scipy.stats import chi2 class _ANOVAOpt(_OptFuncts): """ Class containing functions that are optimized over when conducting ANOVA """ def _opt_common_mu(self, mu): """ Optimizes the likelihood under the null hypothesis that all groups have mean mu Parameters ---------- mu : float The common mean Returns ------- llr : float -2 times the llr ratio, which is the test statistic """ nobs = self.nobs endog = self.endog num_groups = self.num_groups endog_asarray = np.zeros((nobs, num_groups)) obs_num = 0 for arr_num in range(len(endog)): new_obs_num = obs_num + len(endog[arr_num]) endog_asarray[obs_num: new_obs_num, arr_num] = endog[arr_num] - \ mu obs_num = new_obs_num est_vect = endog_asarray wts = np.ones(est_vect.shape[0]) * (1. / (est_vect.shape[0])) eta_star = self._modif_newton(np.zeros(num_groups), est_vect, wts) denom = 1. + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr class ANOVA(_ANOVAOpt): """ A class for ANOVA and comparing means. Parameters ---------- endog : list of arrays endog should be a list containing 1 dimensional arrays. Each array is the data collected from a certain group. """ def __init__(self, endog): self.endog = endog self.num_groups = len(self.endog) self.nobs = 0 for i in self.endog: self.nobs = self.nobs + len(i) def compute_ANOVA(self, mu=None, mu_start=0, return_weights=0): """ Returns -2 log likelihood, the pvalue and the maximum likelihood estimate for a common mean. Parameters ---------- mu : float If a mu is specified, ANOVA is conducted with mu as the common mean. Otherwise, the common mean is the maximum empirical likelihood estimate of the common mean. Default is None. mu_start : float Starting value for commean mean if specific mu is not specified. Default = 0 return_weights : bool if TRUE, returns the weights on observations that maximize the likelihood. Default is FALSE Returns ------- res: tuple The log-likelihood, p-value and estimate for the common mean. """ if mu is not None: llr = self._opt_common_mu(mu) pval = 1 - chi2.cdf(llr, self.num_groups - 1) if return_weights: return llr, pval, mu, self.new_weights else: return llr, pval, mu else: res = optimize.fmin_powell(self._opt_common_mu, mu_start, full_output=1, disp=False) llr = res[1] mu_common = float(res[0]) pval = 1 - chi2.cdf(llr, self.num_groups - 1) if return_weights: return llr, pval, mu_common, self.new_weights else: return llr, pval, mu_common statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/elregress.py000066400000000000000000000053441224417117700245150ustar00rootroot00000000000000""" Empirical Likelihood Linear Regression Inference The script contains the function that is optimized over nuisance parameters to conduct inference on linear regression parameters. It is called by eltest in OLSResults. General References ----------------- Owen, A.B.(2001). Empirical Likelihood. Chapman and Hall """ import numpy as np from statsmodels.emplike.descriptive import _OptFuncts class _ELRegOpts(_OptFuncts): """ A class that holds functions to be optimized over when conducting hypothesis tests and calculating confidence intervals. Parameters ---------- OLSResults : Results instance A fitted OLS result """ def __init__(self): pass def _opt_nuis_regress(self, nuisance_params, param_nums=None, endog=None, exog=None, nobs=None, nvar=None, params=None, b0_vals=None, stochastic_exog=None): """ A function that is optimized over nuisance parameters to conduct a hypothesis test for the parameters of interest Parameters ---------- nuisance_params: 1darray Parameters to be optimized over Returns ------- llr : float -2 x the log-likelihood of the nuisance parameters and the hypothesized value of the parameter(s) of interest. """ params[param_nums] = b0_vals nuis_param_index = np.int_(np.delete(np.arange(nvar), param_nums)) params[nuis_param_index] = nuisance_params new_params = params.reshape(nvar, 1) self.new_params = new_params est_vect = exog * \ (endog - np.squeeze(np.dot(exog, new_params))).reshape(nobs, 1) if not stochastic_exog: exog_means = np.mean(exog, axis=0)[1:] exog_mom2 = (np.sum(exog * exog, axis=0))[1:]\ / nobs mean_est_vect = exog[:, 1:] - exog_means mom2_est_vect = (exog * exog)[:, 1:] - exog_mom2 regressor_est_vect = np.concatenate((mean_est_vect, mom2_est_vect), axis=1) est_vect = np.concatenate((est_vect, regressor_est_vect), axis=1) wts = np.ones(nobs) * (1. / nobs) x0 = np.zeros(est_vect.shape[1]).reshape(-1, 1) try: eta_star = self._modif_newton(x0, est_vect, wts) denom = 1. + np.dot(eta_star, est_vect.T) self.new_weights = 1. / nobs * 1. / denom llr = np.sum(np.log(nobs * self.new_weights)) return -2 * llr except np.linalg.linalg.LinAlgError: return np.inf statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/heartdata.csv000066400000000000000000000013121224417117700246110ustar00rootroot0000000000000015;1;54.3 3;1;40.4 624;1;51 46;1;42.5 127;1;48 64;1;54.6 1350;1;54.1 280;1;49.5 23;1;56.9 10;1;55.3 1024;1;43.4 39;1;42.8 730;1;58.4 136;1;52 1775;0;33.3 1;1;54.2 836;1;45 60;1;64.5 1536;0;49 1549;0;40.6 54;1;49 47;1;61.5 1;1;41.5 51;1;50.5 1367;0;48.6 1264;0;45.5 44;1;36.2 994;1;48.6 51;1;47.2 1106;0;36.8 897;1;46.1 253;1;48.8 147;1;47.5 51;1;52.5 875;0;38.9 322;1;48.1 838;0;41.6 65;1;49.1 815;0;32.7 551;1;48.9 66;1;51.3 228;1;19.7 65;1;45.2 660;0;48 25;1;53 589;0;47.5 592;0;26.7 63;1;56.4 12;1;29.2 499;0;52.2 305;0;49.3 29;1;54 456;0;46.5 439;0;52.9 48;1;53.4 297;1;42.8 389;0;48.9 50;1;46.4 339;0;54.4 68;1;51.4 26;1;52.5 30;0;45.8 237;0;47.8 161;1;43.8 14;1;40.3 167;0;26.7 110;0;23.7 13;0;28.9 1;0;35.2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/koul_and_mc.py000066400000000000000000000021721224417117700247710ustar00rootroot00000000000000import statsmodels.api as sm import numpy as np ################## #Monte Carlo test# ################## modrand1 = np.random.RandomState(5676576) modrand2 = np.random.RandomState(1543543) modrand3 = np.random.RandomState(5738276) X = modrand1.uniform(0, 5, (1000, 4)) X = sm.add_constant(X) beta = np.array([[10], [2], [3], [4], [5]]) y = np.dot(X, beta) params = [] for i in range(10000): yhat = y + modrand2.standard_normal((1000, 1)) cens_times = 50 + (modrand3.standard_normal((1000, 1)) * 5) yhat_observed = np.minimum(yhat, cens_times) censors = np.int_(yhat < cens_times) model = sm.emplike.emplikeAFT(yhat_observed, X, censors) new_params = model.fit().params params.append(new_params) mc_est = np.mean(params, axis=0) # Gives MC parameter estimate ################## #Koul replication# ################## koul_data = np.genfromtxt('/home/justin/rverify.csv', delimiter=';') # ^ Change path to where file is located. koul_y = np.log10(koul_data[:, 0]) koul_x = sm.add_constant(koul_data[:, 2]) koul_censors = koul_data[:, 1] koul_params = sm.emplike.emplikeAFT(koul_y, koul_x, koul_censors).fit().params statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/originregress.py000066400000000000000000000212771224417117700254070ustar00rootroot00000000000000""" This module implements empirical likelihood regression that is forced through the origin. This is different than regression not forced through the origin because the maximum empirical likelihood estimate is calculated with a vector of ones in the exogenous matrix but restricts the intercept parameter to be 0. This results in significantly more narrow confidence intervals and different parameter estimates. For notes on regression not forced through the origin, see empirical likelihood methods in the OLSResults class. General References ------------------ Owen, A.B. (2001). Empirical Likelihood. Chapman and Hall. p. 82. """ import numpy as np from scipy.stats import chi2 from scipy import optimize # When descriptive merged, this will be changed from statsmodels.tools.tools import add_constant from statsmodels.regression.linear_model import OLS, RegressionResults class ELOriginRegress(object): """ Empirical Likelihood inference and estimation for linear regression through the origin Parameters ---------- endog: nx1 array Array of response variables exog: nxk array Array of exogenous variables. Assumes no array of ones Attributes ---------- endog : nx1 array Array of response variables exog : nxk array Array of exogenous variables. Assumes no array of ones nobs : float Number of observations nvar : float Number of exogenous regressors """ def __init__(self, endog, exog): self.endog = endog self.exog = exog self.nobs = float(self.exog.shape[0]) try: self.nvar = float(exog.shape[1]) except IndexError: self.nvar = 1. def fit(self): """ Fits the model and provides regression results. Returns ------- Results: class Empirical likelihood regression class """ exog_with = add_constant(self.exog, prepend=True) restricted_model = OLS(self.endog, exog_with) restricted_fit = restricted_model.fit() restricted_el = restricted_fit.el_test( np.array([0]), np.array([0]), ret_params=1) params = np.squeeze(restricted_el[3]) beta_hat_llr = restricted_el[0] llf = np.sum(np.log(restricted_el[2])) return OriginResults(restricted_model, params, beta_hat_llr, llf) def predict(self, params, exog=None): if exog is None: exog = self.exog return np.dot(add_constant(exog, prepend=True), params) class OriginResults(RegressionResults): """ A Results class for empirical likelihood regression through the origin Parameters ---------- model : class An OLS model with an intercept params : 1darray Fitted parameters est_llr : float The log likelihood ratio of the model with the intercept restricted to 0 at the maximum likelihood estimates of the parameters. llr_restricted/llr_unrestricted llf_el : float The log likelihood of the fitted model with the intercept restricted to 0. Attributes ---------- model : class An OLS model with an intercept params : 1darray Fitted parameter llr : float The log likelihood ratio of the maximum empirical likelihood estimate llf_el : float The log likelihood of the fitted model with the intercept restricted to 0 Notes ----- IMPORTANT. Since EL estimation does not drop the intercept parameter but instead estimates the slope parameters conditional on the slope parameter being 0, the first element for params will be the intercept, which is restricted to 0. IMPORTANT. This class inherits from RegressionResults but inference is conducted via empirical likelihood. Therefore, any methods that require an estimate of the covariance matrix will not function. Instead use el_test and conf_int_el to conduct inference. Examples -------- >>> import statsmodels.api as sm >>> import numpy as np >>> data = sm.datasets.bc.load() >>> model = sm.emplike.OriginRegress(data.endog, data.exog) >>> fitted = model.fit() >>> fitted.params >>> array([ 0. , 0.00351813]) >>> # The 0 is the intercept term. >>> fitted.el_test(np.array([.0034]), np.array([1])) >>> (3.6696503297979302, 0.055411808127497755) >>> fitted.conf_int_el(1) >>> (0.0033971871114706867, 0.0036373150174892847 >>> fitted.conf_int() >>> TypeError: unsupported operand type(s) for *: 'instancemethod' and 'float' >>> # No covariance matrix so normal inference is not valid """ def __init__(self, model, params, est_llr, llf_el): self.model = model self.params = np.squeeze(params) self.llr = est_llr self.llf_el = llf_el def el_test(self, b0_vals, param_nums, method='nm', stochastic_exog=1, return_weights=0): """ Returns the llr and p-value for a hypothesized parameter value for a regression that goes through the origin Parameters ---------- b0_vals : 1darray The hypothesized value to be tested param_num : 1darray Which parameters to test. Note this uses python indexing but the '0' parameter refers to the intercept term, which is assumed 0. Therefore, param_num should be > 0. return_weights : bool If true, returns the weights that optimize the likelihood ratio at b0_vals. Default is False method : string Can either be 'nm' for Nelder-Mead or 'powell' for Powell. The optimization method that optimizes over nuisance parameters. Default is 'nm' stochastic_exog : bool When TRUE, the exogenous variables are assumed to be stochastic. When the regressors are nonstochastic, moment conditions are placed on the exogenous variables. Confidence intervals for stochastic regressors are at least as large as non-stochastic regressors. Default is TRUE Returns ------- res : tuple pvalue and likelihood ratio """ b0_vals = np.hstack((0, b0_vals)) param_nums = np.hstack((0, param_nums)) test_res = self.model.fit().el_test(b0_vals, param_nums, method=method, stochastic_exog=stochastic_exog, return_weights=return_weights) llr_test = test_res[0] llr_res = llr_test - self.llr pval = chi2.sf(llr_res, self.model.exog.shape[1] - 1) if return_weights: return llr_res, pval, test_res[2] else: return llr_res, pval def conf_int_el(self, param_num, upper_bound=None, lower_bound=None, sig=.05, method='nm', stochastic_exog=1): """ Returns the confidence interval for a regression parameter when the regression is forced through the origin Parameters ---------- param_num : int The parameter number to be tested. Note this uses python indexing but the '0' parameter refers to the intercept term upper_bound : float The maximum value the upper confidence limit can be. The closer this is to the confidence limit, the quicker the computation. Default is .00001 confidence limit under normality lower_bound : float The minimum value the lower confidence limit can be. Default is .00001 confidence limit under normality sig : float, optional The significance level. Default .05 method : str, optional Algorithm to optimize of nuisance params. Can be 'nm' or 'powell'. Default is 'nm'. Returns ------- ci: tuple The confidence interval for the parameter 'param_num' """ r0 = chi2.ppf(1 - sig, 1) param_num = np.array([param_num]) if upper_bound is None: upper_bound = (np.squeeze(self.model.fit(). conf_int(.0001)[param_num])[1]) if lower_bound is None: lower_bound = (np.squeeze(self.model.fit().conf_int(.00001) [param_num])[0]) f = lambda b0: self.el_test(np.array([b0]), param_num, method=method, stochastic_exog=stochastic_exog)[0] - r0 lowerl = optimize.brentq(f, lower_bound, self.params[param_num]) upperl = optimize.brentq(f, self.params[param_num], upper_bound) return (lowerl, upperl) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/000077500000000000000000000000001224417117700233045ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/__init__.py000066400000000000000000000000131224417117700254070ustar00rootroot00000000000000#init file statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/results/000077500000000000000000000000001224417117700250055ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/results/__init__.py000066400000000000000000000000231224417117700271110ustar00rootroot00000000000000# Init for results statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/results/el_results.py000066400000000000000000000450651224417117700275520ustar00rootroot00000000000000""" Results from Matlab and R """ import numpy as np class DescStatRes(object): """ The results were generated from Bruce Hansen's MATLAb package: Bruce E. Hansen Department of Economics Social Science Building University of Wisconsin Madison, WI 53706-1393 bhansen@ssc.wisc.edu http://www.ssc.wisc.edu/~bhansen/ The R results are from Mai Zhou's emplik package """ def __init__(self): self.ci_mean = (13.556709, 14.559394) self.test_mean_14 = (.080675, .776385) self.test_mean_weights = np.array([[0.01969213], [0.01911859], [0.01973982], [0.01982913], [0.02004183], [0.0195765], [0.01970423], [0.02015074], [0.01898431], [0.02067787], [0.01878104], [0.01920531], [0.01981207], [0.02031582], [0.01857329], [0.01907883], [0.01943674], [0.0210042], [0.0197373], [0.01997998], [0.0199233], [0.01986713], [0.02017751], [0.01962176], [0.0214596], [0.02118228], [0.02013767], [0.01918665], [0.01908886], [0.01943081], [0.01916251], [0.01868129], [0.01918334], [0.01969084], [0.01984322], [0.0198977], [0.02098504], [0.02132222], [0.02100581], [0.01970351], [0.01942184], [0.01979781], [0.02114276], [0.02096136], [0.01999804], [0.02044712], [0.02174404], [0.02189933], [0.02077078], [0.02082612]]).squeeze() self.test_var_3 = (.199385, .655218) self.ci_var = (2.290077, 4.423634) self.test_var_weights = np.array([[0.020965], [0.019686], [0.021011], [0.021073], [0.021089], [0.020813], [0.020977], [0.021028], [0.019213], [0.02017], [0.018397], [0.01996], [0.021064], [0.020854], [0.017463], [0.019552], [0.020555], [0.019283], [0.021009], [0.021103], [0.021102], [0.021089], [0.021007], [0.020879], [0.017796], [0.018726], [0.021038], [0.019903], [0.019587], [0.020543], [0.019828], [0.017959], [0.019893], [0.020963], [0.02108], [0.021098], [0.01934], [0.018264], [0.019278], [0.020977], [0.020523], [0.021055], [0.018853], [0.019411], [0.0211], [0.02065], [0.016803], [0.016259], [0.019939], [0.019793]]).squeeze() self.mv_test_mean = (.7002663, .7045943) self.mv_test_mean_wts = np.array([[0.01877015], [0.01895746], [0.01817092], [0.01904308], [0.01707106], [0.01602806], [0.0194296], [0.01692204], [0.01978322], [0.01881313], [0.02011785], [0.0226274], [0.01953733], [0.01874346], [0.01694224], [0.01611816], [0.02297437], [0.01943187], [0.01899873], [0.02113375], [0.02295293], [0.02043509], [0.02276583], [0.02208274], [0.02466621], [0.02287983], [0.0173136], [0.01905693], [0.01909335], [0.01982534], [0.01924093], [0.0179352], [0.01871907], [0.01916633], [0.02022359], [0.02228696], [0.02080555], [0.01725214], [0.02166185], [0.01798537], [0.02103582], [0.02052757], [0.03096074], [0.01966538], [0.02201629], [0.02094854], [0.02127771], [0.01961964], [0.02023756], [0.01774807]]).squeeze() self.test_skew = (2.498418, .113961) self.test_skew_wts = np.array([[0.016698], [0.01564], [0.01701], [0.017675], [0.019673], [0.016071], [0.016774], [0.020902], [0.016397], [0.027359], [0.019136], [0.015419], [0.01754], [0.022965], [0.027203], [0.015805], [0.015565], [0.028518], [0.016992], [0.019034], [0.018489], [0.01799], [0.021222], [0.016294], [0.022725], [0.027133], [0.020748], [0.015452], [0.015759], [0.01555], [0.015506], [0.021863], [0.015459], [0.01669], [0.017789], [0.018257], [0.028578], [0.025151], [0.028512], [0.01677], [0.015529], [0.01743], [0.027563], [0.028629], [0.019216], [0.024677], [0.017376], [0.014739], [0.028112], [0.02842]]).squeeze() self.test_kurt_0 = (1.904269, .167601) self.test_corr = (.012025, .912680,) self.test_corr_weights = np.array([[0.020037], [0.020108], [0.020024], [0.02001], [0.019766], [0.019971], [0.020013], [0.019663], [0.019944], [0.01982], [0.01983], [0.019436], [0.020005], [0.019897], [0.020768], [0.020468], [0.019521], [0.019891], [0.020024], [0.01997], [0.019824], [0.019976], [0.019979], [0.019753], [0.020814], [0.020474], [0.019751], [0.020085], [0.020087], [0.019977], [0.020057], [0.020435], [0.020137], [0.020025], [0.019982], [0.019866], [0.020151], [0.019046], [0.020272], [0.020034], [0.019813], [0.01996], [0.020657], [0.019947], [0.019931], [0.02008], [0.02035], [0.019823], [0.02005], [0.019497]]).squeeze() self.test_joint_skew_kurt = (8.753952, .012563) class RegressionResults(object): """ Results for EL Regression """ def __init__(self): self.source = 'Matlab' self.test_beta0 = (1.758104, .184961, np.array([ 0.04326392, 0.04736749, 0.03573865, 0.04770535, 0.04721684, 0.04718301, 0.07088816, 0.05631242, 0.04865098, 0.06572099, 0.04016013, 0.04438627, 0.04042288, 0.03938043, 0.04006474, 0.04845233, 0.01991985, 0.03623254, 0.03617999, 0.05607242, 0.0886806 ])) self.test_beta1 = (1.932529, .164482, np.array([ 0.033328, 0.051412, 0.03395 , 0.071695, 0.046433, 0.041303, 0.033329, 0.036413, 0.03246 , 0.037776, 0.043872, 0.037507, 0.04762 , 0.04881 , 0.05874 , 0.049553, 0.048898, 0.04512 , 0.041142, 0.048121, 0.11252 ])) self.test_beta2 = (.494593, .481866, np.array([ 0.046287, 0.048632, 0.048772, 0.034769, 0.048416, 0.052447, 0.053336, 0.050112, 0.056053, 0.049318, 0.053609, 0.055634, 0.042188, 0.046519, 0.048415, 0.047897, 0.048673, 0.047695, 0.047503, 0.047447, 0.026279])) self.test_beta3 = (3.537250, .060005, np.array([ 0.036327, 0.070483, 0.048965, 0.087399, 0.041685, 0.036221, 0.016752, 0.019585, 0.027467, 0.02957 , 0.069204, 0.060167, 0.060189, 0.030007, 0.067371, 0.046862, 0.069814, 0.053041, 0.053362, 0.041585, 0.033943])) self.test_ci_beta0 = (-52.77128837058528, -24.11607348661947) self.test_ci_beta1 = (0.41969831751229664, 0.9857167306604057) self.test_ci_beta2 = (0.6012045929381431, 2.1847079367275692) self.test_ci_beta3 = (-0.3804313225443794, 0.006934528877337928) class ANOVAResults(): """ Results for ANOVA """ def __init__(self): self.source = 'Matlab' self.compute_ANOVA = (.426163, .51387, np.array([9.582371]), np.array([ 0.018494, 0.01943 , 0.016624, 0.0172 , 0.016985, 0.01922 , 0.016532, 0.015985, 0.016769, 0.017631, 0.017677, 0.017984, 0.017049, 0.016721, 0.016382, 0.016566, 0.015642, 0.015894, 0.016282, 0.015704, 0.016272, 0.015678, 0.015651, 0.015648, 0.015618, 0.015726, 0.015981, 0.01635 , 0.01586 , 0.016443, 0.016126, 0.01683 , 0.01348 , 0.017391, 0.011225, 0.017282, 0.015568, 0.017543, 0.017009, 0.016325, 0.012841, 0.017648, 0.01558 , 0.015994, 0.017258, 0.017664, 0.017792, 0.017772, 0.017527, 0.017797, 0.017856, 0.017849, 0.017749, 0.017827, 0.017381, 0.017902, 0.016557, 0.015522, 0.017455, 0.017248])) class AFTRes(object): """ Results for the AFT model from package emplik in R written by Mai Zhou """ def __init__(self): self.test_params = np.array([3.77710799, -0.03281745]) self.test_beta0 = (.132511, 0.7158323) self.test_beta1 = (.297951, .5851693) self.test_joint = (11.8068, 0.002730147) class OriginResults(object): """ These results are from Bruce Hansen's Matlab package. To replicate the results, the exogenous variables were scaled down by 10**-2 and the results were then rescaled. These tests must also test likelihood functions because the llr when conducting hypothesis tests is the MLE while restricting the intercept to 0. Matlab's generic package always uses the unrestricted MLE. """ def __init__(self): self.test_params = np.array([0, .00351861]) self.test_llf = -1719.793173 # llf when testing param = .0034 self.test_llf_hat = -1717.95833 # llf when origin=0 self.test_llf_hypoth = -2*(self.test_llf-self.test_llf_hat) self.test_llf_conf = -1719.879077 # The likelihood function at conf_vals statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/test_aft.py000066400000000000000000000026621224417117700254750ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm from results.el_results import AFTRes class GenRes(object): def __init__(self): data = sm.datasets.heart.load() endog = np.log10(data.endog) exog = sm.add_constant(data.exog) self.mod1 = sm.emplike.emplikeAFT(endog, exog, data.censors) self.res1 = self.mod1.fit() self.res2 = AFTRes() class Test_AFTModel(GenRes): def __init__(self): super(Test_AFTModel, self).__init__() def test_params(self): assert_almost_equal(self.res1.params(), self.res2.test_params, decimal=4) def test_beta0(self): assert_almost_equal(self.res1.test_beta([4], [0]), self.res2.test_beta0, decimal=4) def test_beta1(self): assert_almost_equal(self.res1.test_beta([-.04], [1]), self.res2.test_beta1, decimal=4) def test_beta_vect(self): assert_almost_equal(self.res1.test_beta([3.5, -.035], [0, 1]), self.res2.test_joint, decimal=4) def test_betaci(self): ci = self.res1.ci_beta(1, -.06, 0) ll = ci[0] ul = ci[1] ll_pval = self.res1.test_beta([ll], [1])[1] ul_pval = self.res1.test_beta([ul], [1])[1] assert_almost_equal(ul_pval, .050000, decimal=4) assert_almost_equal(ll_pval, .05000, decimal=4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/test_anova.py000066400000000000000000000014151224417117700260220ustar00rootroot00000000000000from numpy.testing import assert_almost_equal import statsmodels.api as sm from results.el_results import ANOVAResults class TestANOVA(): """ Tests ANOVA difference in means """ def __init__(self): self.data = sm.datasets.star98.load().exog[:30, 1:3] self.res1 = sm.emplike.ANOVA([self.data[:, 0], self.data[:, 1]]) self.res2 = ANOVAResults() def test_anova(self): assert_almost_equal(self.res1.compute_ANOVA()[:2], self.res2.compute_ANOVA[:2], 4) assert_almost_equal(self.res1.compute_ANOVA()[2], self.res2.compute_ANOVA[2], 4) assert_almost_equal(self.res1.compute_ANOVA(return_weights=1)[3], self.res2.compute_ANOVA[3], 4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/test_descriptive.py000066400000000000000000000102561224417117700272420ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm from results.el_results import DescStatRes class GenRes(object): """ Reads in the data and creates class instance to be tested """ def __init__(self): data = sm.datasets.star98.load() desc_stat_data = data.exog[:50, 5] mv_desc_stat_data = data.exog[:50, 5:7] # mv = multivariate self.res1 = sm.emplike.DescStat(desc_stat_data) self.res2 = DescStatRes() self.mvres1 = sm.emplike.DescStat(mv_desc_stat_data) class TestDescriptiveStatistics(GenRes): def __init__(self): super(TestDescriptiveStatistics, self).__init__() def test_test_mean(self): assert_almost_equal(self.res1.test_mean(14), self.res2.test_mean_14, 4) def test_test_mean_weights(self): assert_almost_equal(self.res1.test_mean(14, return_weights=1)[2], self.res2.test_mean_weights, 4) def test_ci_mean(self): assert_almost_equal(self.res1.ci_mean(), self.res2.ci_mean, 4) def test_test_var(self): assert_almost_equal(self.res1.test_var(3), self.res2.test_var_3, 4) def test_test_var_weights(self): assert_almost_equal(self.res1.test_var(3, return_weights=1)[2], self.res2.test_var_weights, 4) def test_ci_var(self): assert_almost_equal(self.res1.ci_var(), self.res2.ci_var, 4) def test_mv_test_mean(self): assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56])), self.res2.mv_test_mean, 4) def test_mv_test_mean_weights(self): assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56]), return_weights=1)[2], self.res2.mv_test_mean_wts, 4) def test_test_skew(self): assert_almost_equal(self.res1.test_skew(0), self.res2.test_skew, 4) def test_ci_skew(self): """ This will be tested in a round about way since MATLAB fails when computing CI with multiple nuisance parameters. The process is: (1) Get CI for skewness from ci.skew() (2) In MATLAB test the hypotheis that skew=results of test_skew. (3) If p-value approx .05, test confirmed """ skew_ci = self.res1.ci_skew() lower_lim = skew_ci[0] upper_lim = skew_ci[1] ul_pval = self.res1.test_skew(lower_lim)[1] ll_pval = self.res1.test_skew(upper_lim)[1] assert_almost_equal(ul_pval, .050000, 4) assert_almost_equal(ll_pval, .050000, 4) def test_ci_skew_weights(self): assert_almost_equal(self.res1.test_skew(0, return_weights=1)[2], self.res2.test_skew_wts, 4) def test_test_kurt(self): assert_almost_equal(self.res1.test_kurt(0), self.res2.test_kurt_0, 4) def test_ci_kurt(self): """ Same strategy for skewness CI """ kurt_ci = self.res1.ci_kurt(upper_bound=.5, lower_bound=-1.5) lower_lim = kurt_ci[0] upper_lim = kurt_ci[1] ul_pval = self.res1.test_kurt(upper_lim)[1] ll_pval = self.res1.test_kurt(lower_lim)[1] assert_almost_equal(ul_pval, .050000, 4) assert_almost_equal(ll_pval, .050000, 4) def test_joint_skew_kurt(self): assert_almost_equal(self.res1.test_joint_skew_kurt(0, 0), self.res2.test_joint_skew_kurt, 4) def test_test_corr(self): assert_almost_equal(self.mvres1.test_corr(.5), self.res2.test_corr, 4) def test_ci_corr(self): corr_ci = self.mvres1.ci_corr() lower_lim = corr_ci[0] upper_lim = corr_ci[1] ul_pval = self.mvres1.test_corr(upper_lim)[1] ll_pval = self.mvres1.test_corr(lower_lim)[1] assert_almost_equal(ul_pval, .050000, 4) assert_almost_equal(ll_pval, .050000, 4) def test_test_corr_weights(self): assert_almost_equal(self.mvres1.test_corr(.5, return_weights=1)[2], self.res2.test_corr_weights, 4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/test_origin.py000066400000000000000000000024771224417117700262160ustar00rootroot00000000000000from numpy.testing import assert_almost_equal import statsmodels.api as sm from results.el_results import OriginResults import numpy as np class GenRes(object): """ Loads data and creates class instance ot be tested. """ def __init__(self): data = sm.datasets.cancer.load() self.res1 = sm.emplike.ELOriginRegress(data.endog, data.exog).fit() self.res2 = OriginResults() class TestOrigin(GenRes): """ See OriginResults for details on how tests were computed """ def __init__(self): super(TestOrigin, self).__init__() def test_params(self): assert_almost_equal(self.res1.params, self.res2.test_params, 4) def test_llf(self): assert_almost_equal(self.res1.llf_el, self.res2.test_llf_hat, 4) def test_hypothesis_beta1(self): assert_almost_equal(self.res1.el_test([.0034],[1])[0], self.res2.test_llf_hypoth,4) def test_ci_beta(self): ci = self.res1.conf_int_el(1) ll = ci[0] ul = ci[1] llf_low = np.sum(np.log(self.res1.el_test([ll],[1], return_weights=1)[2])) llf_high = np.sum(np.log(self.res1.el_test([ul],[1], return_weights=1)[2])) assert_almost_equal(llf_low, self.res2.test_llf_conf, 4) assert_almost_equal(llf_high, self.res2.test_llf_conf, 4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/emplike/tests/test_regression.py000066400000000000000000000127531224417117700271050ustar00rootroot00000000000000from numpy.testing import assert_almost_equal from numpy.testing.decorators import slow import statsmodels.api as sm from results.el_results import RegressionResults class GenRes(object): """ Loads data and creates class instance ot be tested """ def __init__(self): data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) self.res1 = sm.OLS(data.endog, data.exog).fit() self.res2 = RegressionResults() class TestRegressionPowell(GenRes): """ All confidence intervals are tested by conducting a hypothesis tests at the confidence interval values. See Also -------- test_descriptive.py, test_ci_skew """ def __init__(self): super(TestRegressionPowell, self).__init__() @slow def test_hypothesis_beta0(self): beta0res = self.res1.el_test([-30], [0], return_weights=1, method='powell') assert_almost_equal(beta0res[:2], self.res2.test_beta0[:2], 4) assert_almost_equal(beta0res[2], self.res2.test_beta0[2], 4) @slow def test_hypothesis_beta1(self): beta1res = self.res1.el_test([.5], [1], return_weights=1, method='powell') assert_almost_equal(beta1res[:2], self.res2.test_beta1[:2], 4) assert_almost_equal(beta1res[2], self.res2.test_beta1[2], 4) def test_hypothesis_beta2(self): beta2res = self.res1.el_test([1], [2], return_weights=1, method='powell') assert_almost_equal(beta2res[:2], self.res2.test_beta2[:2], 4) assert_almost_equal(beta2res[2], self.res2.test_beta2[2], 4) def test_hypothesis_beta3(self): beta3res = self.res1.el_test([0], [3], return_weights=1, method='powell') assert_almost_equal(beta3res[:2], self.res2.test_beta3[:2], 4) assert_almost_equal(beta3res[2], self.res2.test_beta3[2], 4) # Confidence interval results obtained through hypothesis testing in Matlab def test_ci_beta0(self): beta0ci = self.res1.conf_int_el(0, lower_bound=-52.9, upper_bound=-24.1, method='powell') assert_almost_equal(beta0ci, self.res2.test_ci_beta0, 3) # Slightly lower precision. CI was obtained from nm method. def test_ci_beta1(self): beta1ci = self.res1.conf_int_el(1, lower_bound=.418, upper_bound=.986, method='powell') assert_almost_equal(beta1ci, self.res2.test_ci_beta1, 4) @slow def test_ci_beta2(self): beta2ci = self.res1.conf_int_el(2, lower_bound=.59, upper_bound=2.2, method='powell') assert_almost_equal(beta2ci, self.res2.test_ci_beta2, 5) @slow def test_ci_beta3(self): beta3ci = self.res1.conf_int_el(3, lower_bound=-.39, upper_bound=.01, method='powell') assert_almost_equal(beta3ci, self.res2.test_ci_beta3, 6) class TestRegressionNM(GenRes): """ All confidence intervals are tested by conducting a hypothesis tests at the confidence interval values. See Also -------- test_descriptive.py, test_ci_skew """ def __init__(self): super(TestRegressionNM, self).__init__() def test_hypothesis_beta0(self): beta0res = self.res1.el_test([-30], [0], return_weights=1, method='nm') assert_almost_equal(beta0res[:2], self.res2.test_beta0[:2], 4) assert_almost_equal(beta0res[2], self.res2.test_beta0[2], 4) def test_hypothesis_beta1(self): beta1res = self.res1.el_test([.5], [1], return_weights=1, method='nm') assert_almost_equal(beta1res[:2], self.res2.test_beta1[:2], 4) assert_almost_equal(beta1res[2], self.res2.test_beta1[2], 4) @slow def test_hypothesis_beta2(self): beta2res = self.res1.el_test([1], [2], return_weights=1, method='nm') assert_almost_equal(beta2res[:2], self.res2.test_beta2[:2], 4) assert_almost_equal(beta2res[2], self.res2.test_beta2[2], 4) @slow def test_hypothesis_beta3(self): beta3res = self.res1.el_test([0], [3], return_weights=1, method='nm') assert_almost_equal(beta3res[:2], self.res2.test_beta3[:2], 4) assert_almost_equal(beta3res[2], self.res2.test_beta3[2], 4) # Confidence interval results obtained through hyp testing in Matlab @slow def test_ci_beta0(self): """ All confidence intervals are tested by conducting a hypothesis tests at the confidence interval values since el_test is already tested against Matlab See Also -------- test_descriptive.py, test_ci_skew """ beta0ci = self.res1.conf_int_el(0, method='nm') assert_almost_equal(beta0ci, self.res2.test_ci_beta0, 6) @slow def test_ci_beta1(self): beta1ci = self.res1.conf_int_el(1, method='nm') assert_almost_equal(beta1ci, self.res2.test_ci_beta1, 6) def test_ci_beta2(self): beta2ci = self.res1.conf_int_el(2, lower_bound=.59, upper_bound=2.2, method='nm') assert_almost_equal(beta2ci, self.res2.test_ci_beta2, 6) def test_ci_beta3(self): beta3ci = self.res1.conf_int_el(3, method='nm') assert_almost_equal(beta3ci, self.res2.test_ci_beta3, 6) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/000077500000000000000000000000001224417117700223325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/es_misc_poisson2.py000066400000000000000000000036301224417117700261640ustar00rootroot00000000000000 import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm from statsmodels.miscmodels.count import (PoissonGMLE, PoissonOffsetGMLE, PoissonZiGMLE) DEC = 3 class Dummy(object): pass self = Dummy() # generate artificial data np.random.seed(98765678) nobs = 200 rvs = np.random.randn(nobs,6) data_exog = rvs data_exog = sm.add_constant(data_exog, prepend=False) xbeta = 1 + 0.1*rvs.sum(1) data_endog = np.random.poisson(np.exp(xbeta)) #estimate discretemod.Poisson as benchmark from statsmodels.discrete.discrete_model import Poisson res_discrete = Poisson(data_endog, data_exog).fit() mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson()) res_glm = mod_glm.fit() #estimate generic MLE self.mod = PoissonGMLE(data_endog, data_exog) res = self.mod.fit() offset = res.params[0] * data_exog[:,0] #1d ??? mod1 = PoissonOffsetGMLE(data_endog, data_exog[:,1:], offset=offset) start_params = np.ones(6)/2. start_params = res.params[1:] res1 = mod1.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=1000) print 'mod2' mod2 = PoissonZiGMLE(data_endog, data_exog[:,1:], offset=offset) start_params = np.r_[np.ones(6)/2.,10] start_params = np.r_[res.params[1:], 20.] #-100] res2 = mod2.fit(start_params=start_params, method='bfgs', maxiter=1000, maxfun=2000) print 'mod3' mod3 = PoissonZiGMLE(data_endog, data_exog, offset=None) start_params = np.r_[np.ones(7)/2.,10] start_params = np.r_[res.params, 20.] res3 = mod3.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=2000) print 'mod4' data_endog2 = np.r_[data_endog, np.zeros(nobs)] data_exog2 = np.r_[data_exog, data_exog] mod4 = PoissonZiGMLE(data_endog2, data_exog2, offset=None) start_params = np.r_[np.ones(7)/2.,10] start_params = np.r_[res.params, 0.] res4 = mod4.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=1000) print res4.summary() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_arch_canada.py000066400000000000000000000207111224417117700256050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Dec 24 07:31:47 2011 Author: Josef Perktold """ import numpy as np import statsmodels.sandbox.stats.diagnostic as dia canada_raw = '''\ 405.36646642737 929.610513893698 7.52999999999884 386.136109062605 404.639833965913 929.803984550587 7.69999999999709 388.135759111711 403.814883043744 930.318387567177 7.47000000000116 390.540112911955 404.215773188006 931.427687420772 7.2699999999968 393.963817246136 405.046713585284 932.662005594273 7.37000000000262 396.764690917547 404.416738673847 933.550939726636 7.12999999999738 400.021701616327 402.81912737043 933.531526191785 7.40000000000146 400.751498688807 401.977334663103 933.076879439814 8.33000000000175 405.733473658807 402.089724946428 932.12375320915 8.83000000000175 409.05038628366 401.306688373207 930.635939140315 10.429999999993 411.398377747425 401.630171263522 929.097059933419 12.1999999999971 413.019421511595 401.56375463175 928.563335601161 12.7700000000041 415.166962884156 402.815698906973 929.069380060201 12.429999999993 414.662070678749 403.142107624713 930.265516098198 12.2299999999959 415.731936138368 403.078619166324 931.677031559203 11.6999999999971 416.231468866173 403.718785733801 932.138967575148 11.1999999999971 418.14392690728 404.866799027579 932.276686471608 11.2700000000041 419.735231229658 405.636186735378 932.832783118083 11.4700000000012 420.484186198549 405.136285378794 933.733419116009 11.3000000000029 420.930881402259 406.024639922986 934.177206176622 11.1699999999983 422.112404525291 406.412269729241 934.592839827856 11 423.627805811063 406.300932644569 935.606709830033 10.6300000000047 423.988686751336 406.335351723382 936.511085968336 10.2700000000041 424.190212657915 406.773695329549 937.420090112655 10.1999999999971 426.127043353785 405.152547649247 938.415921627889 9.66999999999825 426.857794216679 404.929830809648 938.999170021426 9.60000000000582 426.745717993024 404.576546350926 939.235354789206 9.60000000000582 426.885793656802 404.199492630983 939.679504234357 9.5 428.840253264144 405.94985619596 940.249674139969 9.5 430.122322107039 405.82209202516 941.435818685214 9.02999999999884 430.230679154048 406.446282537108 942.29809597644 8.69999999999709 430.392994893689 407.051247525876 943.532223256403 8.13000000000466 432.028420083791 407.946023990985 944.34896981513 7.87000000000262 433.388625934544 408.179584663105 944.821488789039 7.66999999999825 433.964091817787 408.599812740441 945.067136927327 7.80000000000291 434.484384354647 409.090560656008 945.80672616174 7.7300000000032 436.156879277168 408.704215141145 946.869661504613 7.56999999999971 438.265143944308 408.980275213206 946.876612143542 7.56999999999971 438.763587343863 408.328690037174 947.249692256472 7.33000000000175 439.949811558539 407.885696563307 947.651276093962 7.56999999999971 441.835856392131 407.260532233258 948.183970741596 7.62999999999738 443.176872656863 406.775150765526 948.349239264364 7.59999999999854 444.359199033223 406.179413590339 948.032170661406 8.16999999999825 444.523614807208 405.439793348166 947.106483115935 9.19999999999709 446.969404642587 403.279970790458 946.079554231134 10.1699999999983 450.158586973168 403.364855995771 946.183811678692 10.3300000000017 451.546427290378 403.380680430043 946.22579516585 10.3999999999942 452.298351499968 404.003182812546 945.997783938785 10.3699999999953 453.120066578834 404.47739841708 945.518279080208 10.6000000000058 453.999145996277 404.786782762866 945.351397570438 11 454.955176222477 405.271003921828 945.291785517556 11.3999999999942 455.482381155116 405.382993140508 945.400785900878 11.7299999999959 456.100929020225 405.156416006566 945.905809840959 11.070000000007 457.202696739531 406.470043094757 945.90347041344 11.6699999999983 457.388589594786 406.229308967752 946.319028746014 11.4700000000012 457.779898919191 406.726483850871 946.579621275764 11.3000000000029 457.553538085846 408.578504884277 946.780032223884 10.9700000000012 458.80240271533 409.67671010704 947.628284240641 10.6300000000047 459.05640335985 410.385763295936 948.622057553611 10.1000000000058 459.15782324686 410.539523677181 949.399183241404 9.66999999999825 459.703720275789 410.445258303139 949.948137966398 9.52999999999884 459.703720275789 410.625605270832 949.794494142446 9.47000000000116 460.025814162716 410.867239714014 949.953380175189 9.5 461.025722503696 411.235917829196 950.250239444989 9.27000000000407 461.30391443673 410.663655285725 950.538030883093 9.5 461.4030814421 410.808508412624 950.787128498243 9.42999999999302 462.927726133156 412.115961520089 950.869528648471 9.69999999999709 464.688777934061 412.999407129539 950.928132469716 9.89999999999418 465.071700094375 412.955056755303 951.845722481401 9.42999999999302 464.285125295526 412.82413309368 952.6004761952 9.30000000000291 464.034426099541 413.048874899 953.597552755418 8.86999999999534 463.453479461824 413.611017876145 954.143388344158 8.77000000000407 465.071700094375 413.604781916778 954.542593332134 8.60000000000582 466.088867474481 412.968388225217 955.263136106029 8.33000000000175 466.617120754625 412.265886525002 956.056052852469 8.16999999999825 465.747796561181 412.910594097915 956.79658640007 8.02999999999884 465.899527268299 413.829416419695 957.386480451857 7.90000000000146 466.409925351738 414.22415210314 958.06341570725 7.87000000000262 466.955244491812 415.1677707968 958.716592187518 7.52999999999884 467.628081344681 415.701580225863 959.488142422254 6.93000000000029 467.70256230891 416.867407108435 960.362493080892 6.80000000000291 469.134788222928 417.610399060359 960.783379042937 6.69999999999709 469.336419672322 418.002980476361 961.029029939624 6.93000000000029 470.011666329664 417.266680178544 961.765709811429 6.87000000000262 469.647234439539''' canada = np.array(canada_raw.split(), float).reshape(-1,4) k=2; resarch2 = dia.acorr_lm((canada[:,k]-canada[:,k].mean())**2, maxlag=2, autolag=None, store=1) print resarch2 resarch5 = dia.acorr_lm(canada[:,k]**2, maxlag=12, autolag=None, store=1) ss = '''\ ARCH LM-test; Null hypothesis: no ARCH effects Chi-squared = %(chi)-8.4f df = %(df)-4d p-value = %(pval)8.4g ''' resarch = resarch5 print print ss % dict(chi=resarch[2], df=resarch[-1].resols.df_model, pval=resarch[3]) #R:FinTS: ArchTest(as.vector(Canada[,3]), lag=5) ''' ARCH LM-test; Null hypothesis: no ARCH effects data: as.vector(Canada[, 3]) Chi-squared = 78.878, df = 5, p-value = 1.443e-15 ''' #from ss above ''' ARCH LM-test; Null hypothesis: no ARCH effects Chi-squared = 78.849 df = 5 p-value = 1.461e-15 ''' #k=2 #R ''' ARCH LM-test; Null hypothesis: no ARCH effects data: as.vector(Canada[, 4]) Chi-squared = 74.6028, df = 5, p-value = 1.121e-14 ''' #mine ''' ARCH LM-test; Null hypothesis: no ARCH effects Chi-squared = 74.6028 df = 5 p-value = 1.126e-14 ''' ''' > ArchTest(as.vector(Canada[,4]), lag=12) ARCH LM-test; Null hypothesis: no ARCH effects data: as.vector(Canada[, 4]) Chi-squared = 69.6359, df = 12, p-value = 3.747e-10 ''' #mine: ''' ARCH LM-test; Null hypothesis: no ARCH effects Chi-squared = 69.6359 df = 12 p-value = 3.747e-10 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_emplike_1.py000066400000000000000000000070441224417117700252530ustar00rootroot00000000000000""" This is a basic tutorial on how to conduct basic empirical likelihood inference for descriptive statistics. If matplotlib is installed it also generates plots. """ import numpy as np import statsmodels.api as sm print 'Welcome to El' np.random.seed(634) # No significance of the seed. # Let's first generate some univariate data. univariate = np.random.standard_normal(30) # Now let's play with it # Initiate an empirical likelihood descriptive statistics instance eldescriptive = sm.emplike.DescStat(univariate) # Empirical likelihood is (typically) a method of inference, # not estimation. Therefore, there is no attribute eldescriptive.mean # However, we can check the mean: eldescriptive_mean = eldescriptive.endog.mean() #.42 #Let's conduct a hypothesis test to see if the mean is 0 print 'Hypothesis test results for the mean:' print eldescriptive.test_mean(0) # The first value is is -2 *log-likelihood ratio, which is distributed #chi2. The second value is the p-value. # Let's see what the variance is: eldescriptive_var = eldescriptive.endog.var() # 1.01 #Let's test if the variance is 1: print 'Hypothesis test results for the variance:' print eldescriptive.test_var(1) # Let's test if Skewness and Kurtosis are 0 print 'Hypothesis test results for Skewness:' print eldescriptive.test_skew(0) print 'Hypothesis test results for the Kurtosis:' print eldescriptive.test_kurt(0) # Note that the skewness and Kurtosis take longer. This is because # we have to optimize over the nuisance parameters (mean, variance). # We can also test for the joint skewness and kurtoses print ' Joint Skewness-Kurtosis test' eldescriptive.test_joint_skew_kurt(0, 0) # Let's try and get some confidence intervals print 'Confidence interval for the mean' print eldescriptive.ci_mean() print 'Confidence interval for the variance' print eldescriptive.ci_var() print 'Confidence interval for skewness' print eldescriptive.ci_skew() print 'Confidence interval for kurtosis' print eldescriptive.ci_kurt() # if matplotlib is installed, we can get a contour plot for the mean # and variance. mean_variance_contour = eldescriptive.plot_contour(-.5, 1.2, .2, 2.5, .05, .05) # This returns a figure instance. Just type mean_var_contour.show() # to see the plot. # Once you close the plot, we can start some multivariate analysis. x1 = np.random.exponential(2, (30, 1)) x2 = 2 * x1 + np.random.chisquare(4, (30, 1)) mv_data = np.concatenate((x1, x2), axis=1) mv_elmodel = sm.emplike.DescStat(mv_data) # For multivariate data, the only methods are mv_test_mean, # mv mean contour and ci_corr and test_corr. # Let's test the hypthesis that x1 has a mean of 2 and x2 has a mean of 7 print 'Multivaraite mean hypothesis test' print mv_elmodel.mv_test_mean(np.array([2, 7])) # Now let's get the confidence interval for correlation print 'Correlation Coefficient CI' print mv_elmodel.ci_corr() # Note how this took much longer than previous functions. That is # because the function is optimizing over 4 nuisance parameters. # We can also do a hypothesis test for correlation print 'Hypothesis test for correlation' print mv_elmodel.test_corr(.7) # Finally, let's create a contour plot for the means of the data means_contour = mv_elmodel.mv_mean_contour(1, 3, 6,9, .15,.15, plot_dta=1) # This also returns a fig so we can type mean_contour.show() to see the figure # Sometimes, the data is very dispersed and we would like to see the confidence # intervals without the plotted data. Let's see the difference when we set # plot_dta=0 means_contour2 = mv_elmodel.mv_mean_contour(1, 3, 6,9, .05,.05, plot_dta=0) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_emplike_2.py000066400000000000000000000047131224417117700252540ustar00rootroot00000000000000""" This script is a basic tutorial on how to conduct empirical likelihood estimation and inference in linear regression models. """ import numpy as np import statsmodels.api as sm # Let's generate some regression data np.random.seed(100) # no significance of the seed X = np.random.standard_normal((40, 3)) X = sm.add_constant(X) beta = np.arange(1,5) y = np.dot(X, beta) + np.random.standard_normal(40) # There are no distributional assumptions on the error. I just chose # normal errors to demonstrate. print 'Lets play with EL Regression' # In a model with an intercept, access EL inference through OLS results. elmodel = sm.OLS(y, X) fitted = elmodel.fit() # Let's test if the intercept is 0 print 'Intercept test' test0_1 = fitted.el_test(np.array([0]), np.array([0])) print test0_1 # Let's test if beta3 is 4 print 'beta3 test' test1 = fitted.el_test(np.array([4]), np.array([3])) print test1 # Lets test the hypothesis that beta3=4 and beta2=3 print 'joint beta test' test2 = fitted.el_test(np.array([3, 4]), np.array([2, 3])) print test2 # Let's get the confidence intervals for the parameters print 'Confidence Interval for Beta1' ci_beta1 = fitted.conf_int_el(1) print ci_beta1 # Of course, we can still see the rest of the RegressionResults print 'R-squared' print fitted.rsquared print 'Params' print fitted.params # Now lets check out regression through the origin print 'Origin Regression' originx = np.random.standard_normal((30, 3)) originbeta = np.array([[1], [2], [3]]) originy = np.dot(originx, originbeta) + np.random.standard_normal((30, 1)) originmodel = sm.emplike.ELOriginRegress(originy, originx) # Since in this case, parameter estimates are different then in OLS, # we need to fit the model. originfit = originmodel.fit() print 'The fitted parameters' print originfit.params print 'The MSE' print originfit.mse_model print 'The R-squared' print originfit.rsquared # Note that the first element of param is 0 and there are 4 params. That is # because the first param is the intercept term. This is noted in the # documentation. # Now that the model is fitted, we can do some inference. print 'Test beta1 =1' test_beta1 = originfit.el_test([1], [1]) print test_beta1 # A confidence interval for Beta1. print 'confidence interval for beta1' ci_beta2 = originfit.conf_int_el(1) print ci_beta2 # Finally, since we initiated an EL model, normal inference is not available try: originfit.conf_int() except: print 'No normal inference available' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_emplike_3.py000066400000000000000000000014721224417117700252540ustar00rootroot00000000000000""" This script provides a tutorial on how to use estimate and conduct inference in an accelerated failure time model using empirical likelihood. We will be using the Stanford Heart Transplant data """ import statsmodels.api as sm import numpy as np data = sm.datasets.heart.load() # Note this data has endog, exog and censors # We will take the log (base 10) of the endogenous survival times model = sm.emplike.emplikeAFT(np.log10(data.endog), sm.add_constant(data.exog), data.censors) # We need to fit the model to get the parameters fitted = model.fit() print fitted.params() test1 = fitted.test_beta([4],[0]) # Test that the intercept is 4 print test1 test2 = fitted.test_beta([-.05], [1]) # Test that the slope is -.05 print test2 ci_beta1 = fitted.ci_beta(1, .1, -.1) print ci_beta1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_feasible_gls_het.py000066400000000000000000000101531224417117700266570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Examples for linear model with heteroscedasticity estimated by feasible GLS These are examples to check the results during developement. The assumptions: We have a linear model y = X*beta where the variance of an observation depends on some explanatory variable Z (`exog_var`). linear_model.WLS estimated the model for a given weight matrix here we want to estimate also the weight matrix by two step or iterative WLS Created on Wed Dec 21 12:28:17 2011 Author: Josef Perktold There might be something fishy with the example, but I don't see it. Or maybe it's supposed to be this way because in the first case I don't include a constant and in the second case I include some of the same regressors as in the main equation. """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.regression.linear_model import OLS, WLS, GLS from statsmodels.regression.feasible_gls import GLSHet, GLSHet2 examples = ['ex1'] if 'ex1' in examples: #from tut_ols_wls nsample = 1000 sig = 0.5 x1 = np.linspace(0, 20, nsample) X = np.c_[x1, (x1-5)**2, np.ones(nsample)] np.random.seed(0)#9876789) #9876543) beta = [0.5, -0.015, 1.] y_true2 = np.dot(X, beta) w = np.ones(nsample) w[nsample*6//10:] = 4 #Note this is the squared value #y2[:nsample*6/10] = y_true2[:nsample*6/10] + sig*1. * np.random.normal(size=nsample*6/10) #y2[nsample*6/10:] = y_true2[nsample*6/10:] + sig*4. * np.random.normal(size=nsample*4/10) y2 = y_true2 + sig*np.sqrt(w)* np.random.normal(size=nsample) X2 = X[:,[0,2]] X2 = X res_ols = OLS(y2, X2).fit() print 'OLS beta estimates' print res_ols.params print 'OLS stddev of beta' print res_ols.bse print '\nWLS' mod0 = GLSHet2(y2, X2, exog_var=w) res0 = mod0.fit() print 'new version' mod1 = GLSHet(y2, X2, exog_var=w) res1 = mod1.iterative_fit(2) print 'WLS beta estimates' print res1.params print res0.params print 'WLS stddev of beta' print res1.bse #compare with previous version GLSHet2, refactoring check #assert_almost_equal(res1.params, np.array([ 0.37642521, 1.51447662])) #this fails ??? more iterations? different starting weights? print res1.model.weights/res1.model.weights.max() #why is the error so small in the estimated weights ? assert_almost_equal(res1.model.weights/res1.model.weights.max(), 1./w, 14) print 'residual regression params' print res1.results_residual_regression.params print 'scale of model ?' print res1.scale print 'unweighted residual variance, note unweighted mean is not zero' print res1.resid.var() #Note weighted mean is zero: #(res1.model.weights * res1.resid).mean() doplots = False if doplots: import matplotlib.pyplot as plt plt.figure() plt.plot(x1, y2, 'o') plt.plot(x1, y_true2, 'b-', label='true') plt.plot(x1, res1.fittedvalues, 'r-', label='fwls') plt.plot(x1, res_ols.fittedvalues, '--', label='ols') plt.legend() #z = (w[:,None] == [1,4]).astype(float) #dummy variable z = (w[:,None] == np.unique(w)).astype(float) #dummy variable mod2 = GLSHet(y2, X2, exog_var=z) res2 = mod2.iterative_fit(2) print res2.params import statsmodels.api as sm z = sm.add_constant(w) mod3 = GLSHet(y2, X2, exog_var=z) res3 = mod3.iterative_fit(8) print res3.params print "np.array(res3.model.history['ols_params'])" print np.array(res3.model.history['ols_params']) print "np.array(res3.model.history['self_params'])" print np.array(res3.model.history['self_params']) print np.unique(res2.model.weights) #for discrete z only, only a few uniques print np.unique(res3.model.weights) if doplots: plt.figure() plt.plot(x1, y2, 'o') plt.plot(x1, y_true2, 'b-', label='true') plt.plot(x1, res1.fittedvalues, '-', label='fwls1') plt.plot(x1, res2.fittedvalues, '-', label='fwls2') plt.plot(x1, res3.fittedvalues, '-', label='fwls3') plt.plot(x1, res_ols.fittedvalues, '--', label='ols') plt.legend() plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_feasible_gls_het_0.py000066400000000000000000000143661224417117700271100ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Examples for linear model with heteroscedasticity estimated by feasible GLS These are examples to check the results during developement. The assumptions: We have a linear model y = X*beta where the variance of an observation depends on some explanatory variable Z (`exog_var`). linear_model.WLS estimated the model for a given weight matrix here we want to estimate also the weight matrix by two step or iterative WLS Created on Wed Dec 21 12:28:17 2011 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.regression.linear_model import OLS, WLS, GLS from statsmodels.regression.feasible_gls import GLSHet, GLSHet2 from statsmodels.tools.tools import add_constant examples = ['ex1'] if 'ex1' in examples: nsample = 300 #different pattern last graph with 100 or 200 or 500 sig = 0.5 np.random.seed(9876789) #9876543) X = np.random.randn(nsample, 3) X = np.column_stack((np.ones((nsample,1)), X)) beta = [1, 0.5, -0.5, 1.] y_true2 = np.dot(X, beta) x1 = np.linspace(0, 1, nsample) gamma = np.array([1, 3.]) #with slope 3 instead of two, I get negative weights, Not correct # - was misspecified, but the negative weights are still possible with identity link #gamma /= gamma.sum() #normalize assuming x1.max is 1 z_true = add_constant(x1) winv = np.dot(z_true, gamma) het_params = sig**2 * np.array([1, 3.]) # for squared sig2_het = sig**2 * winv weights_dgp = 1/winv weights_dgp /= weights_dgp.max() #should be already normalized - NOT check normalization #y2[:nsample*6/10] = y_true2[:nsample*6/10] + sig*1. * np.random.normal(size=nsample*6/10) z0 = np.zeros(nsample) z0[(nsample * 5)//10:] = 1 #dummy for 2 halfs of sample z0 = add_constant(z0) z1 = add_constant(x1) noise = np.sqrt(sig2_het) * np.random.normal(size=nsample) y2 = y_true2 + noise X2 = X[:,[0,2]] #misspecified, missing regressor in main equation X2 = X #correctly specigied res_ols = OLS(y2, X2).fit() print 'OLS beta estimates' print res_ols.params print 'OLS stddev of beta' print res_ols.bse print '\nWLS' mod0 = GLSHet2(y2, X2, exog_var=winv) res0 = mod0.fit() print 'new version' mod1 = GLSHet(y2, X2, exog_var=winv) res1 = mod1.iterative_fit(2) print 'WLS beta estimates' print res1.params print res0.params print 'WLS stddev of beta' print res1.bse #compare with previous version GLSHet2, refactoring check #assert_almost_equal(res1.params, np.array([ 0.37642521, 1.51447662])) #this fails ??? more iterations? different starting weights? print res1.model.weights/res1.model.weights.max() #why is the error so small in the estimated weights ? assert_almost_equal(res1.model.weights/res1.model.weights.max(), weights_dgp, 14) print 'residual regression params' print res1.results_residual_regression.params print 'scale of model ?' print res1.scale print 'unweighted residual variance, note unweighted mean is not zero' print res1.resid.var() #Note weighted mean is zero: #(res1.model.weights * res1.resid).mean() doplots = True #False if doplots: import matplotlib.pyplot as plt plt.figure() plt.plot(x1, y2, 'o') plt.plot(x1, y_true2, 'b-', label='true') plt.plot(x1, res1.fittedvalues, 'r-', label='fwls') plt.plot(x1, res_ols.fittedvalues, '--', label='ols') plt.legend() #the next only works if w has finite support, discrete/categorical #z = (w[:,None] == [1,4]).astype(float) #dummy variable #z = (w0[:,None] == np.unique(w0)).astype(float) #dummy variable #changed z0 contains dummy and constant mod2 = GLSHet(y2, X2, exog_var=z0) res2 = mod2.iterative_fit(3) print res2.params import statsmodels.api as sm #z = sm.add_constant(w, prepend=True) z = sm.add_constant(x1/x1.max()) mod3 = GLSHet(y2, X2, exog_var=z1)#, link=sm.families.links.log()) res3 = mod3.iterative_fit(20) error_var_3 = res3.mse_resid/res3.model.weights print res3.params print "np.array(res3.model.history['ols_params'])" print np.array(res3.model.history['ols_params']) print "np.array(res3.model.history['self_params'])" print np.array(res3.model.history['self_params']) #Models 2 and 3 are equivalent with different parameterization of Z print np.unique(res2.model.weights) #for discrete z only, only a few uniques print np.unique(res3.model.weights) print res3.summary() print '\n\nResults of estimation of weights' print '--------------------------------' print res3.results_residual_regression.summary() if doplots: plt.figure() plt.plot(x1, y2, 'o') plt.plot(x1, y_true2, 'b-', label='true') plt.plot(x1, res1.fittedvalues, '-', label='fwls1') plt.plot(x1, res2.fittedvalues, '-', label='fwls2') plt.plot(x1, res3.fittedvalues, '-', label='fwls3') plt.plot(x1, res_ols.fittedvalues, '--', label='ols') plt.legend() plt.figure() plt.ylim(0, 5) res_e2 = OLS(noise**2, z).fit() plt.plot(noise**2, 'bo', alpha=0.5, label='dgp error**2') plt.plot(res_e2.fittedvalues, lw=2, label='ols for noise**2') #plt.plot(res3.model.weights, label='GLSHet weights') plt.plot(error_var_3, lw=2, label='GLSHet error var') plt.plot(res3.resid**2, 'ro', alpha=0.5, label='resid squared') #plt.plot(weights_dgp, label='DGP weights') plt.plot(sig**2 * winv, lw=2, label='DGP error var') plt.legend() plt.show() '''Note these are close but maybe biased because of skewed distribution >>> res3.mse_resid/res3.model.weights[-10:] array([ 1.03115871, 1.03268209, 1.03420547, 1.03572885, 1.03725223, 1.03877561, 1.04029899, 1.04182237, 1.04334575, 1.04486913]) >>> res_e2.fittedvalues[-10:] array([ 1.0401953 , 1.04171386, 1.04323242, 1.04475098, 1.04626954, 1.0477881 , 1.04930666, 1.05082521, 1.05234377, 1.05386233]) >>> sig**2 * w[-10:] array([ 0.98647295, 0.98797595, 0.98947896, 0.99098196, 0.99248497, 0.99398798, 0.99549098, 0.99699399, 0.99849699, 1. ]) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_generic_mle.py000066400000000000000000000377741224417117700256730ustar00rootroot00000000000000 import numpy as np from scipy import stats import statsmodels.api as sm from statsmodels.base.model import GenericLikelihoodModel data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) # in this dir probit_mod = sm.Probit(data.endog, data.exog) probit_res = probit_mod.fit() loglike = probit_mod.loglike score = probit_mod.score mod = GenericLikelihoodModel(data.endog, data.exog*2, loglike, score) res = mod.fit(method="nm", maxiter = 500) def probitloglike(params, endog, exog): """ Log likelihood for the probit """ q = 2*endog - 1 X = exog return np.add.reduce(stats.norm.logcdf(q*np.dot(X,params))) mod = GenericLikelihoodModel(data.endog, data.exog, loglike=probitloglike) res = mod.fit(method="nm", fargs=(data.endog,data.exog), maxiter=500) print res #np.allclose(res.params, probit_res.params) print res.params, probit_res.params #datal = sm.datasets.longley.load() datal = sm.datasets.ccard.load() datal.exog = sm.add_constant(datal.exog, prepend=False) # Instance of GenericLikelihood model doesn't work directly, because loglike # cannot get access to data in self.endog, self.exog nobs = 5000 rvs = np.random.randn(nobs,6) datal.exog = rvs[:,:-1] datal.exog = sm.add_constant(datal.exog, prepend=False) datal.endog = 1 + rvs.sum(1) show_error = False show_error2 = 1#False if show_error: def loglike_norm_xb(self, params): beta = params[:-1] sigma = params[-1] xb = np.dot(self.exog, beta) return stats.norm.logpdf(self.endog, loc=xb, scale=sigma) mod_norm = GenericLikelihoodModel(datal.endog, datal.exog, loglike_norm_xb) res_norm = mod_norm.fit(method="nm", maxiter = 500) print res_norm.params if show_error2: def loglike_norm_xb(params, endog, exog): beta = params[:-1] sigma = params[-1] #print exog.shape, beta.shape xb = np.dot(exog, beta) #print xb.shape, stats.norm.logpdf(endog, loc=xb, scale=sigma).shape return stats.norm.logpdf(endog, loc=xb, scale=sigma).sum() mod_norm = GenericLikelihoodModel(datal.endog, datal.exog, loglike_norm_xb) res_norm = mod_norm.fit(start_params=np.ones(datal.exog.shape[1]+1), method="nm", maxiter = 5000, fargs=(datal.endog, datal.exog)) print res_norm.params class MygMLE(GenericLikelihoodModel): # just for testing def loglike(self, params): beta = params[:-1] sigma = params[-1] xb = np.dot(self.exog, beta) return stats.norm.logpdf(self.endog, loc=xb, scale=sigma).sum() def loglikeobs(self, params): beta = params[:-1] sigma = params[-1] xb = np.dot(self.exog, beta) return stats.norm.logpdf(self.endog, loc=xb, scale=sigma) mod_norm2 = MygMLE(datal.endog, datal.exog) #res_norm = mod_norm.fit(start_params=np.ones(datal.exog.shape[1]+1), method="nm", maxiter = 500) res_norm2 = mod_norm2.fit(start_params=[1.]*datal.exog.shape[1]+[1], method="nm", maxiter = 500) print res_norm2.params res2 = sm.OLS(datal.endog, datal.exog).fit() start_params = np.hstack((res2.params, np.sqrt(res2.mse_resid))) res_norm3 = mod_norm2.fit(start_params=start_params, method="nm", maxiter = 500, retall=0) print start_params print res_norm3.params print res2.bse #print res_norm3.bse # not available print 'llf', res2.llf, res_norm3.llf bse = np.sqrt(np.diag(np.linalg.inv(res_norm3.model.hessian(res_norm3.params)))) res_norm3.model.score(res_norm3.params) #fprime in fit option cannot be overwritten, set to None, when score is defined # exception is fixed, but I don't think score was supposed to be called ''' >>> mod_norm2.fit(start_params=start_params, method="bfgs", fprime=None, maxiter Traceback (most recent call last): File "", line 1, in File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s tatsmodels\model.py", line 316, in fit disp=disp, retall=retall, callback=callback) File "C:\Josef\_progs\Subversion\scipy-trunk_after\trunk\dist\scipy-0.9.0.dev6 579.win32\Programs\Python25\Lib\site-packages\scipy\optimize\optimize.py", line 710, in fmin_bfgs gfk = myfprime(x0) File "C:\Josef\_progs\Subversion\scipy-trunk_after\trunk\dist\scipy-0.9.0.dev6 579.win32\Programs\Python25\Lib\site-packages\scipy\optimize\optimize.py", line 103, in function_wrapper return function(x, *args) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s tatsmodels\model.py", line 240, in score = lambda params: -self.score(params) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s tatsmodels\model.py", line 480, in score return approx_fprime1(params, self.nloglike) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s tatsmodels\sandbox\regression\numdiff.py", line 81, in approx_fprime1 nobs = np.size(f0) #len(f0) TypeError: object of type 'numpy.float64' has no len() ''' res_bfgs = mod_norm2.fit(start_params=start_params, method="bfgs", fprime=None, maxiter = 500, retall=0) from statsmodels.tools.numdiff import approx_fprime, approx_hess hb=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) hf=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4) hh = (hf+hb)/2. print np.linalg.eigh(hh) grad = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) print grad gradb = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) gradf = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=1e-4) print (gradb+gradf)/2. print res_norm3.model.score(res_norm3.params) print res_norm3.model.score(start_params) mod_norm2.loglike(start_params/2.) print np.linalg.inv(-1*mod_norm2.hessian(res_norm3.params)) print np.sqrt(np.diag(res_bfgs.cov_params())) print res_norm3.bse print "MLE - OLS parameter estimates" print res_norm3.params[:-1] - res2.params print "bse diff in percent" print (res_norm3.bse[:-1] / res2.bse)*100. - 100 ''' C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect; please delete it from your matplotlibrc file warnings.warn('rcParams key "numerix" is obsolete and has no effect;\n' Optimization terminated successfully. Current function value: 12.818804 Iterations 6 Optimization terminated successfully. Current function value: 12.818804 Iterations: 439 Function evaluations: 735 Optimization terminated successfully. Current function value: 12.818804 Iterations: 439 Function evaluations: 735 [ 1.6258006 0.05172931 1.42632252 -7.45229732] [ 1.62581004 0.05172895 1.42633234 -7.45231965] Warning: Maximum number of function evaluations has been exceeded. [ -1.18109149 246.94438535 -16.21235536 24.05282629 -324.80867176 274.07378453] Warning: Maximum number of iterations has been exceeded [ 17.57107 -149.87528787 19.89079376 -72.49810777 -50.06067953 306.14170418] Optimization terminated successfully. Current function value: 506.488765 Iterations: 339 Function evaluations: 550 [ -3.08181404 234.34702702 -14.99684418 27.94090839 -237.1465136 284.75079529] [ -3.08181304 234.34701361 -14.99684381 27.94088692 -237.14649571 274.6857294 ] [ 5.51471653 80.36595035 7.46933695 82.92232357 199.35166485] llf -506.488764864 -506.488764864 Optimization terminated successfully. Current function value: 506.488765 Iterations: 9 Function evaluations: 13 Gradient evaluations: 13 (array([ 2.41772580e-05, 1.62492628e-04, 2.79438138e-04, 1.90996240e-03, 2.07117946e-01, 1.28747174e+00]), array([[ 1.52225754e-02, 2.01838216e-02, 6.90127235e-02, -2.57002471e-04, -5.25941060e-01, -8.47339404e-01], [ 2.39797491e-01, -2.32325602e-01, -9.36235262e-01, 3.02434938e-03, 3.95614029e-02, -1.02035585e-01], [ -2.11381471e-02, 3.01074776e-02, 7.97208277e-02, -2.94955832e-04, 8.49402362e-01, -5.20391053e-01], [ -1.55821981e-01, -9.66926643e-01, 2.01517298e-01, 1.52397702e-03, 4.13805882e-03, -1.19878714e-02], [ -9.57881586e-01, 9.87911166e-02, -2.67819451e-01, 1.55192932e-03, -1.78717579e-02, -2.55757014e-02], [ -9.96486655e-04, -2.03697290e-03, -2.98130314e-03, -9.99992985e-01, -1.71500426e-05, 4.70854949e-06]])) [[ -4.91007768e-05 -7.28732630e-07 -2.51941401e-05 -2.50111043e-08 -4.77484718e-08 -9.72022463e-08]] [[ -1.64845915e-08 -2.87059265e-08 -2.88764568e-07 -6.82121026e-09 2.84217094e-10 -1.70530257e-09]] [ -4.90678076e-05 -6.71320777e-07 -2.46166110e-05 -1.13686838e-08 -4.83169060e-08 -9.37916411e-08] [ -4.56753924e-05 -6.50857146e-07 -2.31756303e-05 -1.70530257e-08 -4.43378667e-08 -1.75592936e-02] [[ 2.99386348e+01 -1.24442928e+02 9.67254672e+00 -1.58968536e+02 -5.91960010e+02 -2.48738183e+00] [ -1.24442928e+02 5.62972166e+03 -5.00079203e+02 -7.13057475e+02 -7.82440674e+03 -1.05126925e+01] [ 9.67254672e+00 -5.00079203e+02 4.87472259e+01 3.37373299e+00 6.96960872e+02 7.69866589e-01] [ -1.58968536e+02 -7.13057475e+02 3.37373299e+00 6.82417837e+03 4.84485862e+03 3.21440021e+01] [ -5.91960010e+02 -7.82440674e+03 6.96960872e+02 4.84485862e+03 3.43753691e+04 9.37524459e+01] [ -2.48738183e+00 -1.05126925e+01 7.69866589e-01 3.21440021e+01 9.37524459e+01 5.23915258e+02]] >>> res_norm3.bse array([ 5.47162086, 75.03147114, 6.98192136, 82.60858536, 185.40595756, 22.88919522]) >>> print res_norm3.model.score(res_norm3.params) [ -4.90678076e-05 -6.71320777e-07 -2.46166110e-05 -1.13686838e-08 -4.83169060e-08 -9.37916411e-08] >>> print res_norm3.model.score(start_params) [ -4.56753924e-05 -6.50857146e-07 -2.31756303e-05 -1.70530257e-08 -4.43378667e-08 -1.75592936e-02] >>> mod_norm2.loglike(start_params/2.) -598.56178102781314 >>> print np.linalg.inv(-1*mod_norm2.hessian(res_norm3.params)) [[ 2.99386348e+01 -1.24442928e+02 9.67254672e+00 -1.58968536e+02 -5.91960010e+02 -2.48738183e+00] [ -1.24442928e+02 5.62972166e+03 -5.00079203e+02 -7.13057475e+02 -7.82440674e+03 -1.05126925e+01] [ 9.67254672e+00 -5.00079203e+02 4.87472259e+01 3.37373299e+00 6.96960872e+02 7.69866589e-01] [ -1.58968536e+02 -7.13057475e+02 3.37373299e+00 6.82417837e+03 4.84485862e+03 3.21440021e+01] [ -5.91960010e+02 -7.82440674e+03 6.96960872e+02 4.84485862e+03 3.43753691e+04 9.37524459e+01] [ -2.48738183e+00 -1.05126925e+01 7.69866589e-01 3.21440021e+01 9.37524459e+01 5.23915258e+02]] >>> print np.sqrt(np.diag(res_bfgs.cov_params())) [ 5.10032831 74.34988912 6.96522122 76.7091604 169.8117832 22.91695494] >>> print res_norm3.bse [ 5.47162086 75.03147114 6.98192136 82.60858536 185.40595756 22.88919522] >>> res_norm3.conf_int > >>> res_norm3.conf_int() Traceback (most recent call last): File "", line 1, in File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 993, in conf_int lower = self.params - dist.ppf(1-alpha/2,self.model.df_resid) *\ AttributeError: 'MygMLE' object has no attribute 'df_resid' >>> res_norm3.params array([ -3.08181304, 234.34701361, -14.99684381, 27.94088692, -237.14649571, 274.6857294 ]) >>> res2.params array([ -3.08181404, 234.34702702, -14.99684418, 27.94090839, -237.1465136 ]) >>> >>> res_norm3.params - res2.params Traceback (most recent call last): File "", line 1, in ValueError: shape mismatch: objects cannot be broadcast to a single shape >>> res_norm3.params[:-1] - res2.params array([ 9.96859735e-07, -1.34122981e-05, 3.72278400e-07, -2.14645839e-05, 1.78919019e-05]) >>> >>> res_norm3.bse[:-1] - res2.bse array([ -0.04309567, -5.33447922, -0.48741559, -0.31373822, -13.94570729]) >>> (res_norm3.bse[:-1] / res2.bse) - 1 array([-0.00781467, -0.06637735, -0.06525554, -0.00378352, -0.06995531]) >>> (res_norm3.bse[:-1] / res2.bse)*100. - 100 array([-0.7814667 , -6.6377355 , -6.52555369, -0.37835193, -6.99553089]) >>> np.sqrt(np.diag(np.linalg.inv(res_norm3.model.hessian(res_bfgs.params)))) array([ NaN, NaN, NaN, NaN, NaN, NaN]) >>> np.sqrt(np.diag(np.linalg.inv(-res_norm3.model.hessian(res_bfgs.params)))) array([ 5.10032831, 74.34988912, 6.96522122, 76.7091604 , 169.8117832 , 22.91695494]) >>> res_norm3.bse array([ 5.47162086, 75.03147114, 6.98192136, 82.60858536, 185.40595756, 22.88919522]) >>> res2.bse array([ 5.51471653, 80.36595035, 7.46933695, 82.92232357, 199.35166485]) >>> >>> bse_bfgs = np.sqrt(np.diag(np.linalg.inv(-res_norm3.model.hessian(res_bfgs.params)))) >>> (bse_bfgs[:-1] / res2.bse)*100. - 100 array([ -7.51422527, -7.4858335 , -6.74913633, -7.49275094, -14.8179759 ]) >>> hb=-approx_hess(res_bfgs.params, mod_norm2.loglike, epsilon=-1e-4) >>> hf=-approx_hess(res_bfgs.params, mod_norm2.loglike, epsilon=1e-4) >>> hh = (hf+hb)/2. >>> bse_bfgs = np.sqrt(np.diag(np.linalg.inv(-hh))) >>> bse_bfgs array([ NaN, NaN, NaN, NaN, NaN, NaN]) >>> bse_bfgs = np.sqrt(np.diag(np.linalg.inv(hh))) >>> np.diag(hh) array([ 9.81680159e-01, 1.39920076e-02, 4.98101826e-01, 3.60955710e-04, 9.57811608e-04, 1.90709670e-03]) >>> np.diag(np.inv(hh)) Traceback (most recent call last): File "", line 1, in AttributeError: 'module' object has no attribute 'inv' >>> np.diag(np.linalg.inv(hh)) array([ 2.64875153e+01, 5.91578496e+03, 5.13279911e+01, 6.11533345e+03, 3.33775960e+04, 5.24357391e+02]) >>> res2.bse**2 array([ 3.04120984e+01, 6.45868598e+03, 5.57909945e+01, 6.87611175e+03, 3.97410863e+04]) >>> bse_bfgs array([ 5.14660231, 76.91414015, 7.1643556 , 78.20059751, 182.69536402, 22.89885131]) >>> bse_bfgs - res_norm3.bse array([-0.32501855, 1.88266901, 0.18243424, -4.40798785, -2.71059354, 0.00965609]) >>> (bse_bfgs[:-1] / res2.bse)*100. - 100 array([-6.67512508, -4.29511526, -4.0831115 , -5.69415552, -8.35523538]) >>> (res_norm3.bse[:-1] / res2.bse)*100. - 100 array([-0.7814667 , -6.6377355 , -6.52555369, -0.37835193, -6.99553089]) >>> (bse_bfgs / res_norm3.bse)*100. - 100 array([-5.94007812, 2.50917247, 2.61295176, -5.33599242, -1.46197759, 0.04218624]) >>> bse_bfgs array([ 5.14660231, 76.91414015, 7.1643556 , 78.20059751, 182.69536402, 22.89885131]) >>> res_norm3.bse array([ 5.47162086, 75.03147114, 6.98192136, 82.60858536, 185.40595756, 22.88919522]) >>> res2.bse array([ 5.51471653, 80.36595035, 7.46933695, 82.92232357, 199.35166485]) >>> dir(res_bfgs) ['__class__', '__delattr__', '__dict__', '__doc__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', '__weakref__', 'bse', 'conf_int', 'cov_params', 'f_test', 'initialize', 'llf', 'mle_retvals', 'mle_settings', 'model', 'normalized_cov_params', 'params', 'scale', 't', 't_test'] >>> res_bfgs.scale 1.0 >>> res2.scale 81083.015420213851 >>> res2.mse_resid 81083.015420213851 >>> print np.sqrt(np.diag(np.linalg.inv(-1*mod_norm2.hessian(res_bfgs.params)))) [ 5.10032831 74.34988912 6.96522122 76.7091604 169.8117832 22.91695494] >>> print np.sqrt(np.diag(np.linalg.inv(-1*res_bfgs.model.hessian(res_bfgs.params)))) [ 5.10032831 74.34988912 6.96522122 76.7091604 169.8117832 22.91695494] Is scale a misnomer, actually scale squared, i.e. variance of error term ? ''' print res_norm3.model.jac(res_norm3.params).shape jac = res_norm3.model.jac(res_norm3.params) print np.sqrt(np.diag(np.dot(jac.T, jac)))/start_params jac2 = res_norm3.model.jac(res_norm3.params, centered=True) print np.sqrt(np.diag(np.linalg.inv(np.dot(jac.T, jac)))) print res_norm3.bse print res2.bse statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_generic_mle_t.py000066400000000000000000000250411224417117700261760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Jul 28 08:28:04 2010 Author: josef-pktd """ import numpy as np from scipy import stats, special import statsmodels.api as sm from statsmodels.base.model import GenericLikelihoodModel #redefine some shortcuts np_log = np.log np_pi = np.pi sps_gamln = special.gammaln def maxabs(arr1, arr2): return np.max(np.abs(arr1 - arr2)) def maxabsrel(arr1, arr2): return np.max(np.abs(arr2 / arr1 - 1)) class MyT(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' def loglike(self, params): return -self.nloglikeobs(params).sum(0) # copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ #print len(params), beta = params[:-2] df = params[-2] scale = params[-1] loc = np.dot(self.exog, beta) endog = self.endog x = (endog - loc)/scale #next part is stats.t._logpdf lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.) lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df) lPx -= np_log(scale) # correction for scale return -lPx #Example: np.random.seed(98765678) nobs = 1000 rvs = np.random.randn(nobs,5) data_exog = sm.add_constant(rvs, prepend=False) xbeta = 0.9 + 0.1*rvs.sum(1) data_endog = xbeta + 0.1*np.random.standard_t(5, size=nobs) #print data_endog modp = MyT(data_endog, data_exog) modp.start_value = np.ones(data_exog.shape[1]+2) modp.start_value[-2] = 10 modp.start_params = modp.start_value resp = modp.fit(start_params = modp.start_value) print resp.params print resp.bse from statsmodels.tools.numdiff import approx_fprime, approx_hess hb=-approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4) tmp = modp.loglike(modp.start_value) print tmp.shape ''' >>> tmp = modp.loglike(modp.start_value) 8 >>> tmp.shape (100,) >>> tmp.sum(0) -24220.877108016182 >>> tmp = modp.nloglikeobs(modp.start_value) 8 >>> tmp.shape (100, 100) >>> np.dot(modp.exog, beta).shape Traceback (most recent call last): File "", line 1, in NameError: name 'beta' is not defined >>> params = modp.start_value >>> beta = params[:-2] >>> beta.shape (6,) >>> np.dot(modp.exog, beta).shape (100,) >>> modp.endog.shape (100, 100) >>> xbeta.shape (100,) >>> ''' ''' C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect; please delete it from your matplotlibrc file warnings.warn('rcParams key "numerix" is obsolete and has no effect;\n' repr(start_params) array([ 1., 1., 1., 1., 1., 1., 1., 1.]) Optimization terminated successfully. Current function value: 91.897859 Iterations: 108 Function evaluations: 173 Gradient evaluations: 173 [ 1.58253308e-01 1.73188603e-01 1.77357447e-01 2.06707494e-02 -1.31174789e-01 8.79915580e-01 6.47663840e+03 6.73457641e+02] [ NaN NaN NaN NaN NaN 28.26906182 NaN NaN] () >>> resp.params array([ 1.58253308e-01, 1.73188603e-01, 1.77357447e-01, 2.06707494e-02, -1.31174789e-01, 8.79915580e-01, 6.47663840e+03, 6.73457641e+02]) >>> resp.bse array([ NaN, NaN, NaN, NaN, NaN, 28.26906182, NaN, NaN]) >>> resp.jac Traceback (most recent call last): File "", line 1, in AttributeError: 'GenericLikelihoodModelResults' object has no attribute 'jac' >>> resp.bsejac array([ 45243.35919908, 51997.80776897, 41418.33021984, 42763.46575168, 50101.91631612, 42804.92083525, 3005625.35649203, 13826948.68708931]) >>> resp.bsejhj array([ 1.51643931, 0.80229636, 0.27720185, 0.4711138 , 0.9028682 , 0.31673747, 0.00524426, 0.69729368]) >>> resp.covjac array([[ 2.04696155e+09, 1.46643494e+08, 7.59932781e+06, -2.39993397e+08, 5.62644255e+08, 2.34300598e+08, -3.07824799e+09, -1.93425470e+10], [ 1.46643494e+08, 2.70377201e+09, 1.06005712e+08, 3.76824011e+08, -1.21778986e+08, 5.38612723e+08, -2.12575784e+10, -1.69503271e+11], [ 7.59932781e+06, 1.06005712e+08, 1.71547808e+09, -5.94451158e+07, -1.44586401e+08, -5.41830441e+06, 1.25899515e+10, 1.06372065e+11], [ -2.39993397e+08, 3.76824011e+08, -5.94451158e+07, 1.82871400e+09, -5.66930891e+08, 3.75061111e+08, -6.84681772e+09, -7.29993789e+10], [ 5.62644255e+08, -1.21778986e+08, -1.44586401e+08, -5.66930891e+08, 2.51020202e+09, -4.67886982e+08, 1.78890380e+10, 1.75428694e+11], [ 2.34300598e+08, 5.38612723e+08, -5.41830441e+06, 3.75061111e+08, -4.67886982e+08, 1.83226125e+09, -1.27484996e+10, -1.12550321e+11], [ -3.07824799e+09, -2.12575784e+10, 1.25899515e+10, -6.84681772e+09, 1.78890380e+10, -1.27484996e+10, 9.03378378e+12, 2.15188047e+13], [ -1.93425470e+10, -1.69503271e+11, 1.06372065e+11, -7.29993789e+10, 1.75428694e+11, -1.12550321e+11, 2.15188047e+13, 1.91184510e+14]]) >>> hb array([[ 33.68732564, -2.33209221, -13.51255321, -1.60840159, -13.03920385, -9.3506543 , 4.86239173, -9.30409101], [ -2.33209221, 3.12512611, -6.08530968, -6.79232244, 3.66804898, 1.26497071, 5.10113409, -2.53482995], [ -13.51255321, -6.08530968, 31.14883498, -5.01514705, -10.48819911, -2.62533035, 3.82241581, -12.51046342], [ -1.60840159, -6.79232244, -5.01514705, 28.40141917, -8.72489636, -8.82449456, 5.47584023, -18.20500017], [ -13.03920385, 3.66804898, -10.48819911, -8.72489636, 9.03650914, 3.65206176, 6.55926726, -1.8233635 ], [ -9.3506543 , 1.26497071, -2.62533035, -8.82449456, 3.65206176, 21.41825348, -1.28610793, 4.28101146], [ 4.86239173, 5.10113409, 3.82241581, 5.47584023, 6.55926726, -1.28610793, 46.52354448, -32.23861427], [ -9.30409101, -2.53482995, -12.51046342, -18.20500017, -1.8233635 , 4.28101146, -32.23861427, 178.61978279]]) >>> np.linalg.eigh(hb) (array([ -10.50373649, 0.7460258 , 14.73131793, 29.72453087, 36.24103832, 41.98042979, 48.99815223, 190.04303734]), array([[-0.40303259, 0.10181305, 0.18164206, 0.48201456, 0.03916688, 0.00903695, 0.74620692, 0.05853619], [-0.3201713 , -0.88444855, -0.19867642, 0.02828812, 0.16733946, -0.21440765, -0.02927317, 0.01176904], [-0.41847094, 0.00170161, 0.04973298, 0.43276118, -0.55894304, 0.26454728, -0.49745582, 0.07251685], [-0.3508729 , -0.08302723, 0.25004884, -0.73495077, -0.38936448, 0.20677082, 0.24464779, 0.11448238], [-0.62065653, 0.44662675, -0.37388565, -0.19453047, 0.29084735, -0.34151809, -0.19088978, 0.00342713], [-0.15119802, -0.01099165, 0.84377273, 0.00554863, 0.37332324, -0.17917015, -0.30371283, -0.03635211], [ 0.15813581, 0.0293601 , 0.09882271, 0.03515962, -0.48768565, -0.81960996, 0.05248464, 0.22533642], [-0.06118044, -0.00549223, 0.03205047, -0.01782649, -0.21128588, -0.14391393, 0.05973658, -0.96226835]])) >>> np.linalg.eigh(np.linalg.inv(hb)) (array([-0.09520422, 0.00526197, 0.02040893, 0.02382062, 0.02759303, 0.03364225, 0.06788259, 1.34043621]), array([[-0.40303259, 0.05853619, 0.74620692, -0.00903695, -0.03916688, 0.48201456, 0.18164206, 0.10181305], [-0.3201713 , 0.01176904, -0.02927317, 0.21440765, -0.16733946, 0.02828812, -0.19867642, -0.88444855], [-0.41847094, 0.07251685, -0.49745582, -0.26454728, 0.55894304, 0.43276118, 0.04973298, 0.00170161], [-0.3508729 , 0.11448238, 0.24464779, -0.20677082, 0.38936448, -0.73495077, 0.25004884, -0.08302723], [-0.62065653, 0.00342713, -0.19088978, 0.34151809, -0.29084735, -0.19453047, -0.37388565, 0.44662675], [-0.15119802, -0.03635211, -0.30371283, 0.17917015, -0.37332324, 0.00554863, 0.84377273, -0.01099165], [ 0.15813581, 0.22533642, 0.05248464, 0.81960996, 0.48768565, 0.03515962, 0.09882271, 0.0293601 ], [-0.06118044, -0.96226835, 0.05973658, 0.14391393, 0.21128588, -0.01782649, 0.03205047, -0.00549223]])) >>> np.diag(np.linalg.inv(hb)) array([ 0.01991288, 1.0433882 , 0.00516616, 0.02642799, 0.24732871, 0.05281555, 0.02236704, 0.00643486]) >>> np.sqrt(np.diag(np.linalg.inv(hb))) array([ 0.14111302, 1.02146375, 0.07187597, 0.16256686, 0.49732154, 0.22981633, 0.14955616, 0.08021756]) >>> hess = modp.hessian(resp.params) >>> np.sqrt(np.diag(np.linalg.inv(hess))) array([ 231.3823423 , 117.79508218, 31.46595143, 53.44753106, 132.4855704 , NaN, 5.47881705, 90.75332693]) >>> hb=-approx_hess(resp.params, modp.loglike, epsilon=-1e-4) >>> np.sqrt(np.diag(np.linalg.inv(hb))) array([ 31.93524822, 22.0333515 , NaN, 29.90198792, 38.82615785, NaN, NaN, NaN]) >>> hb=-approx_hess(resp.params, modp.loglike, epsilon=-1e-8) >>> np.sqrt(np.diag(np.linalg.inv(hb))) Traceback (most recent call last): File "", line 1, in File "C:\Programs\Python25\lib\site-packages\numpy\linalg\linalg.py", line 423, in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) File "C:\Programs\Python25\lib\site-packages\numpy\linalg\linalg.py", line 306, in solve raise LinAlgError, 'Singular matrix' numpy.linalg.linalg.LinAlgError: Singular matrix >>> resp.params array([ 1.58253308e-01, 1.73188603e-01, 1.77357447e-01, 2.06707494e-02, -1.31174789e-01, 8.79915580e-01, 6.47663840e+03, 6.73457641e+02]) >>> ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_generic_mle_tdist.py000066400000000000000000001153211224417117700270630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Jul 28 08:28:04 2010 Author: josef-pktd """ import numpy as np from scipy import stats, special, optimize import statsmodels.api as sm from statsmodels.base.model import GenericLikelihoodModel #redefine some shortcuts np_log = np.log np_pi = np.pi sps_gamln = special.gammaln def maxabs(arr1, arr2): return np.max(np.abs(arr1 - arr2)) def maxabsrel(arr1, arr2): return np.max(np.abs(arr2 / arr1 - 1)) #global store_params = [] class MyT(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Linear Model with t-distributed errors This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' def loglike(self, params): return -self.nloglikeobs(params).sum(0) # copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ #print len(params), store_params.append(params) if not self.fixed_params is None: #print 'using fixed' params = self.expandparams(params) beta = params[:-2] df = params[-2] scale = params[-1] loc = np.dot(self.exog, beta) endog = self.endog x = (endog - loc)/scale #next part is stats.t._logpdf lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.) lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df) lPx -= np_log(scale) # correction for scale return -lPx #Example: np.random.seed(98765678) nobs = 1000 nvars = 6 df = 5 rvs = np.random.randn(nobs, nvars-1) data_exog = sm.add_constant(rvs, prepend=False) xbeta = 0.9 + 0.1*rvs.sum(1) data_endog = xbeta + 0.1*np.random.standard_t(df, size=nobs) print data_endog.var() res_ols = sm.OLS(data_endog, data_exog).fit() print res_ols.scale print np.sqrt(res_ols.scale) print res_ols.params kurt = stats.kurtosis(res_ols.resid) df_fromkurt = 6./kurt + 4 print stats.t.stats(df_fromkurt, moments='mvsk') print stats.t.stats(df, moments='mvsk') modp = MyT(data_endog, data_exog) start_value = 0.1*np.ones(data_exog.shape[1]+2) #start_value = np.zeros(data_exog.shape[1]+2) #start_value[:nvars] = sm.OLS(data_endog, data_exog).fit().params start_value[:nvars] = res_ols.params start_value[-2] = df_fromkurt #10 start_value[-1] = np.sqrt(res_ols.scale) #0.5 modp.start_params = start_value #adding fixed parameters fixdf = np.nan * np.zeros(modp.start_params.shape) fixdf[-2] = 100 fixone = 0 if fixone: modp.fixed_params = fixdf modp.fixed_paramsmask = np.isnan(fixdf) modp.start_params = modp.start_params[modp.fixed_paramsmask] else: modp.fixed_params = None modp.fixed_paramsmask = None resp = modp.fit(start_params = modp.start_params, disp=1, method='nm')#'newton') #resp = modp.fit(start_params = modp.start_params, disp=1, method='newton') print '\nestimation results t-dist' print resp.params print resp.bse resp2 = modp.fit(start_params = resp.params, method='Newton') print 'using Newton' print resp2.params print resp2.bse from statsmodels.tools.numdiff import approx_fprime, approx_hess hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4) tmp = modp.loglike(modp.start_params) print tmp.shape #np.linalg.eigh(np.linalg.inv(hb))[0] pp=np.array(store_params) print pp.min(0) print pp.max(0) ##################### Example: Pareto # estimating scale doesn't work yet, a bug somewhere ? # fit_ks works well, but no bse or other result statistics yet #import for kstest based estimation #should be replace import statsmodels.sandbox.distributions.sppatch class MyPareto(GenericLikelihoodModel): '''Maximum Likelihood Estimation pareto distribution first version: iid case, with constant parameters ''' #copied from stats.distribution def pdf(self, x, b): return b * x**(-b-1) def loglike(self, params): return -self.nloglikeobs(params).sum(0) def nloglikeobs(self, params): #print params.shape if not self.fixed_params is None: #print 'using fixed' params = self.expandparams(params) b = params[0] loc = params[1] scale = params[2] #loc = np.dot(self.exog, beta) endog = self.endog x = (endog - loc)/scale logpdf = np_log(b) - (b+1.)*np_log(x) #use np_log(1 + x) for Pareto II logpdf -= np.log(scale) #lb = loc + scale #logpdf[endog>> res_par.params array([ 7.42705803e+152, 2.17339053e+153]) >>> mod_par.loglike(mod_p.start_params) Traceback (most recent call last): File "", line 1, in NameError: name 'mod_p' is not defined >>> mod_par.loglike(mod_par.start_params) -1085.1993430947232 >>> np.log(mod_par.pdf(mod_par.start_params)) Traceback (most recent call last): File "", line 1, in TypeError: pdf() takes exactly 3 arguments (2 given) >>> np.log(mod_par.pdf(*mod_par.start_params)) 0.69314718055994529 >>> mod_par.loglike(*mod_par.start_params) Traceback (most recent call last): File "", line 1, in TypeError: loglike() takes exactly 2 arguments (3 given) >>> mod_par.loglike(mod_par.start_params) -1085.1993430947232 >>> np.log(stats.pareto.pdf(y[0],*mod_par.start_params)) -4.6414308627431353 >>> mod_par.loglike(mod_par.start_params) -1085.1993430947232 >>> mod_par.nloglikeobs(mod_par.start_params)[0] 0.29377232943845044 >>> mod_par.start_params array([ 1., 2.]) >>> np.log(stats.pareto.pdf(y[0],1,9.5,2)) -1.2806918394368461 >>> mod_par.fixed_params= None >>> mod_par.nloglikeobs(np.array([1., 10., 2.]))[0] 0.087533156771285828 >>> y[0] 12.182956907488885 >>> mod_para.endog[0] Traceback (most recent call last): File "", line 1, in NameError: name 'mod_para' is not defined >>> mod_par.endog[0] 12.182956907488885 >>> np.log(stats.pareto.pdf(y[0],1,10,2)) -0.86821349410251702 >>> np.log(stats.pareto.pdf(y[0],1.,10.,2.)) -0.86821349410251702 >>> stats.pareto.pdf(y[0],1.,10.,2.) 0.41970067762301644 >>> mod_par.loglikeobs(np.array([1., 10., 2.]))[0] -0.087533156771285828 >>> ''' ''' >>> mod_par.nloglikeobs(np.array([1., 10., 2.]))[0] 0.86821349410251691 >>> np.log(stats.pareto.pdf(y,1.,10.,2.)).sum() -2627.9403758026938 ''' #''' #C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect; # please delete it from your matplotlibrc file # warnings.warn('rcParams key "numerix" is obsolete and has no effect;\n' #0.0686702747648 #0.0164150896481 #0.128121386381 #[ 0.10370428 0.09921315 0.09676723 0.10457413 0.10201618 0.89964496] #(array(0.0), array(1.4552599885729827), array(0.0), array(2.5072143354058203)) #(array(0.0), array(1.6666666666666667), array(0.0), array(6.0)) #repr(start_params) array([ 0.10370428, 0.09921315, 0.09676723, 0.10457413, 0.10201618, # 0.89964496, 6.39309417, 0.12812139]) #Optimization terminated successfully. # Current function value: -679.951339 # Iterations: 398 # Function evaluations: 609 # #estimation results t-dist #[ 0.10400826 0.10111893 0.09725133 0.10507788 0.10086163 0.8996041 # 4.72131318 0.09825355] #[ 0.00365493 0.00356149 0.00349329 0.00362333 0.003732 0.00362716 # 0.72325227 0.00388822] #repr(start_params) array([ 0.10400826, 0.10111893, 0.09725133, 0.10507788, 0.10086163, # 0.8996041 , 4.72131318, 0.09825355]) #Optimization terminated successfully. # Current function value: -679.950443 # Iterations 3 #using Newton #[ 0.10395383 0.10106762 0.09720665 0.10503384 0.10080599 0.89954546 # 4.70918964 0.09815885] #[ 0.00365299 0.00355968 0.00349147 0.00362166 0.00373015 0.00362533 # 0.72014669 0.00388436] #() #[ 0.09992709 0.09786601 0.09387356 0.10229919 0.09756623 0.85466272 # 4.60459182 0.09661986] #[ 0.11308292 0.10828401 0.1028508 0.11268895 0.10934726 0.94462721 # 7.15412655 0.13452746] #repr(start_params) array([ 1., 2.]) #Warning: Maximum number of function evaluations has been exceeded. #repr(start_params) array([ 3.06504406e+302, 3.29325579e+303]) #Traceback (most recent call last): # File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\examples\ex_generic_mle_tdist.py", line 222, in # res_par2 = mod_par.fit(start_params=res_par.params, method='newton', maxfun=10000, maxiter=5000) # File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 547, in fit # disp=disp, callback=callback, **kwargs) # File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 262, in fit # newparams = oldparams - np.dot(np.linalg.inv(H), # File "C:\Programs\Python25\lib\site-packages\numpy\linalg\linalg.py", line 423, in inv # return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) # File "C:\Programs\Python25\lib\site-packages\numpy\linalg\linalg.py", line 306, in solve # raise LinAlgError, 'Singular matrix' #numpy.linalg.linalg.LinAlgError: Singular matrix # #>>> mod_par.fixed_params #array([ NaN, 10., NaN]) #>>> mod_par.start_params #array([ 1., 2.]) #>>> np.source(stats.pareto.fit_fr) #In file: c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\distributions_patch.py # #def fit_fr(self, data, *args, **kwds): # '''estimate distribution parameters by MLE taking some parameters as fixed # # Parameters # ---------- # data : array, 1d # data for which the distribution parameters are estimated, # args : list ? check # starting values for optimization # kwds : # # - 'frozen' : array_like # values for frozen distribution parameters and, for elements with # np.nan, the corresponding parameter will be estimated # # Returns # ------- # argest : array # estimated parameters # # # Examples # -------- # generate random sample # >>> np.random.seed(12345) # >>> x = stats.gamma.rvs(2.5, loc=0, scale=1.2, size=200) # # estimate all parameters # >>> stats.gamma.fit(x) # array([ 2.0243194 , 0.20395655, 1.44411371]) # >>> stats.gamma.fit_fr(x, frozen=[np.nan, np.nan, np.nan]) # array([ 2.0243194 , 0.20395655, 1.44411371]) # # keep loc fixed, estimate shape and scale parameters # >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, np.nan]) # array([ 2.45603985, 1.27333105]) # # keep loc and scale fixed, estimate shape parameter # >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) # array([ 3.00048828]) # >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, 1.2]) # array([ 2.57792969]) # # estimate only scale parameter for fixed shape and loc # >>> stats.gamma.fit_fr(x, frozen=[2.5, 0.0, np.nan]) # array([ 1.25087891]) # # Notes # ----- # self is an instance of a distribution class. This can be attached to # scipy.stats.distributions.rv_continuous # # *Todo* # # * check if docstring is correct # * more input checking, args is list ? might also apply to current fit method # # ''' # loc0, scale0 = map(kwds.get, ['loc', 'scale'],[0.0, 1.0]) # Narg = len(args) # # if Narg == 0 and hasattr(self, '_fitstart'): # x0 = self._fitstart(data) # elif Narg > self.numargs: # raise ValueError, "Too many input arguments." # else: # args += (1.0,)*(self.numargs-Narg) # # location and scale are at the end # x0 = args + (loc0, scale0) # # if 'frozen' in kwds: # frmask = np.array(kwds['frozen']) # if len(frmask) != self.numargs+2: # raise ValueError, "Incorrect number of frozen arguments." # else: # # keep starting values for not frozen parameters # x0 = np.array(x0)[np.isnan(frmask)] # else: # frmask = None # # #print x0 # #print frmask # return optimize.fmin(self.nnlf_fr, x0, # args=(np.ravel(data), frmask), disp=0) # #>>> stats.pareto.fit_fr(y, 1., frozen=[np.nan, loc, np.nan]) #Traceback (most recent call last): # File "", line 1, in #NameError: name 'loc' is not defined # #>>> stats.pareto.fit_fr(y, 1., frozen=[np.nan, 10., np.nan]) #array([ 1.0346268 , 2.00184808]) #>>> stats.pareto.fit_fr(y, (1.,2), frozen=[np.nan, 10., np.nan]) #Traceback (most recent call last): # File "", line 1, in # File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\distributions_patch.py", line 273, in fit_fr # x0 = np.array(x0)[np.isnan(frmask)] #ValueError: setting an array element with a sequence. # #>>> stats.pareto.fit_fr(y, [1.,2], frozen=[np.nan, 10., np.nan]) #Traceback (most recent call last): # File "", line 1, in # File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\distributions_patch.py", line 273, in fit_fr # x0 = np.array(x0)[np.isnan(frmask)] #ValueError: setting an array element with a sequence. # #>>> stats.pareto.fit_fr(y, frozen=[np.nan, 10., np.nan]) #array([ 1.03463526, 2.00184809]) #>>> stats.pareto.pdf(y, 1.03463526, 10, 2.00184809).sum() #173.33947284555239 #>>> mod_par(1.03463526, 10, 2.00184809) #Traceback (most recent call last): # File "", line 1, in #TypeError: 'MyPareto' object is not callable # #>>> mod_par.loglike(1.03463526, 10, 2.00184809) #Traceback (most recent call last): # File "", line 1, in #TypeError: loglike() takes exactly 2 arguments (4 given) # #>>> mod_par.loglike((1.03463526, 10, 2.00184809)) #-962.21623668859741 #>>> np.log(stats.pareto.pdf(y, 1.03463526, 10, 2.00184809)).sum() #-inf #>>> np.log(stats.pareto.pdf(y, 1.03463526, 9, 2.00184809)).sum() #-3074.5947476137271 #>>> np.log(stats.pareto.pdf(y, 1.03463526, 10., 2.00184809)).sum() #-inf #>>> np.log(stats.pareto.pdf(y, 1.03463526, 9.9, 2.00184809)).sum() #-2677.3867091635661 #>>> y.min() #12.001848089426717 #>>> np.log(stats.pareto.pdf(y, 1.03463526, loc=9.9, scale=2.00184809)).sum() #-2677.3867091635661 #>>> np.log(stats.pareto.pdf(y, 1.03463526, loc=10., scale=2.00184809)).sum() #-inf #>>> stats.pareto.logpdf(y, 1.03463526, loc=10., scale=2.00184809).sum() #-inf #>>> stats.pareto.logpdf(y, 1.03463526, loc=9.99, scale=2.00184809).sum() #-2631.6120098202355 #>>> mod_par.loglike((1.03463526, 9.99, 2.00184809)) #-963.2513896113644 #>>> maxabs(y, mod_par.endog) #0.0 #>>> np.source(stats.pareto.logpdf) #In file: C:\Josef\_progs\Subversion\scipy-trunk_after\trunk\dist\scipy-0.9.0.dev6579.win32\Programs\Python25\Lib\site-packages\scipy\stats\distributions.py # # def logpdf(self, x, *args, **kwds): # """ # Log of the probability density function at x of the given RV. # # This uses more numerically accurate calculation if available. # # Parameters # ---------- # x : array-like # quantiles # arg1, arg2, arg3,... : array-like # The shape parameter(s) for the distribution (see docstring of the # instance object for more information) # loc : array-like, optional # location parameter (default=0) # scale : array-like, optional # scale parameter (default=1) # # Returns # ------- # logpdf : array-like # Log of the probability density function evaluated at x # # """ # loc,scale=map(kwds.get,['loc','scale']) # args, loc, scale = self._fix_loc_scale(args, loc, scale) # x,loc,scale = map(arr,(x,loc,scale)) # args = tuple(map(arr,args)) # x = arr((x-loc)*1.0/scale) # cond0 = self._argcheck(*args) & (scale > 0) # cond1 = (scale > 0) & (x >= self.a) & (x <= self.b) # cond = cond0 & cond1 # output = empty(shape(cond),'d') # output.fill(NINF) # putmask(output,(1-cond0)*array(cond1,bool),self.badvalue) # goodargs = argsreduce(cond, *((x,)+args+(scale,))) # scale, goodargs = goodargs[-1], goodargs[:-1] # place(output,cond,self._logpdf(*goodargs) - log(scale)) # if output.ndim == 0: # return output[()] # return output # #>>> np.source(stats.pareto._logpdf) #In file: C:\Josef\_progs\Subversion\scipy-trunk_after\trunk\dist\scipy-0.9.0.dev6579.win32\Programs\Python25\Lib\site-packages\scipy\stats\distributions.py # # def _logpdf(self, x, *args): # return log(self._pdf(x, *args)) # #>>> np.source(stats.pareto._pdf) #In file: C:\Josef\_progs\Subversion\scipy-trunk_after\trunk\dist\scipy-0.9.0.dev6579.win32\Programs\Python25\Lib\site-packages\scipy\stats\distributions.py # # def _pdf(self, x, b): # return b * x**(-b-1) # #>>> stats.pareto.a #1.0 #>>> (1-loc)/scale #Traceback (most recent call last): # File "", line 1, in #NameError: name 'loc' is not defined # #>>> b, loc, scale = (1.03463526, 9.99, 2.00184809) #>>> (1-loc)/scale #-4.4908502522786327 #>>> (x-loc)/scale == 1 #Traceback (most recent call last): # File "", line 1, in #NameError: name 'x' is not defined # #>>> (lb-loc)/scale == 1 #Traceback (most recent call last): # File "", line 1, in #NameError: name 'lb' is not defined # #>>> lb = scale + loc #>>> lb #11.991848090000001 #>>> (lb-loc)/scale == 1 #False #>>> (lb-loc)/scale #1.0000000000000004 #>>> #''' ''' repr(start_params) array([ 1., 10., 2.]) Optimization terminated successfully. Current function value: 2626.436870 Iterations: 102 Function evaluations: 210 Optimization terminated successfully. Current function value: 0.016555 Iterations: 16 Function evaluations: 35 [ 1.03482659 10.00737039 1.9944777 ] (1.0596088578825995, 9.9043376069230007, 2.0975104813987118) >>> 9.9043376069230007 + 2.0975104813987118 12.001848088321712 >>> y.min() 12.001848089426717 ''' ''' C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect; please delete it from your matplotlibrc file warnings.warn('rcParams key "numerix" is obsolete and has no effect;\n' 0.0686702747648 0.0164150896481 0.128121386381 [ 0.10370428 0.09921315 0.09676723 0.10457413 0.10201618 0.89964496] (array(0.0), array(1.4552599885729829), array(0.0), array(2.5072143354058221)) (array(0.0), array(1.6666666666666667), array(0.0), array(6.0)) repr(start_params) array([ 0.10370428, 0.09921315, 0.09676723, 0.10457413, 0.10201618, 0.89964496, 6.39309417, 0.12812139]) Optimization terminated successfully. Current function value: -679.951339 Iterations: 398 Function evaluations: 609 estimation results t-dist [ 0.10400826 0.10111893 0.09725133 0.10507788 0.10086163 0.8996041 4.72131318 0.09825355] [ 0.00365493 0.00356149 0.00349329 0.00362333 0.003732 0.00362716 0.72329352 0.00388832] repr(start_params) array([ 0.10400826, 0.10111893, 0.09725133, 0.10507788, 0.10086163, 0.8996041 , 4.72131318, 0.09825355]) Optimization terminated successfully. Current function value: -679.950443 Iterations 3 using Newton [ 0.10395383 0.10106762 0.09720665 0.10503384 0.10080599 0.89954546 4.70918964 0.09815885] [ 0.00365299 0.00355968 0.00349147 0.00362166 0.00373015 0.00362533 0.7201488 0.00388437] () [ 0.09992709 0.09786601 0.09387356 0.10229919 0.09756623 0.85466272 4.60459182 0.09661986] [ 0.11308292 0.10828401 0.1028508 0.11268895 0.10934726 0.94462721 7.15412655 0.13452746] repr(start_params) array([ 1., 9., 2.]) Optimization terminated successfully. Current function value: 2636.129089 Iterations: 147 Function evaluations: 279 Optimization terminated successfully. Current function value: 0.016555 Iterations: 16 Function evaluations: 35 [ 0.84856418 10.2197801 1.78206799] (1.0596088578825995, 9.9043376069230007, 2.0975104813987118) 12.0018480891 12.0018480883 12.0018480894 repr(start_params) array([ 1., 2.]) Warning: Desired error not necessarily achieveddue to precision loss Current function value: 2643.549907 Iterations: 2 Function evaluations: 13 Gradient evaluations: 12 >>> res_parks2 = mod_par.fit_ks() repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2642.465273 Iterations: 92 Function evaluations: 172 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2636.639863 Iterations: 73 Function evaluations: 136 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2631.568778 Iterations: 75 Function evaluations: 133 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2627.821044 Iterations: 75 Function evaluations: 135 repr(start_params) array([ 1., 2.]) Warning: Maximum number of function evaluations has been exceeded. repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2631.568778 Iterations: 75 Function evaluations: 133 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.431596 Iterations: 58 Function evaluations: 109 repr(start_params) array([ 1., 2.]) Warning: Maximum number of function evaluations has been exceeded. repr(start_params) array([ 1., 2.]) Warning: Maximum number of function evaluations has been exceeded. repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.737426 Iterations: 60 Function evaluations: 109 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2627.821044 Iterations: 75 Function evaluations: 135 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.471666 Iterations: 48 Function evaluations: 94 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2627.196314 Iterations: 66 Function evaluations: 119 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.578538 Iterations: 56 Function evaluations: 103 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.471666 Iterations: 48 Function evaluations: 94 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.651702 Iterations: 67 Function evaluations: 122 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.737426 Iterations: 60 Function evaluations: 109 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.613505 Iterations: 73 Function evaluations: 141 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.578538 Iterations: 56 Function evaluations: 103 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.632218 Iterations: 64 Function evaluations: 119 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.651702 Iterations: 67 Function evaluations: 122 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.622789 Iterations: 63 Function evaluations: 114 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.613505 Iterations: 73 Function evaluations: 141 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.627465 Iterations: 59 Function evaluations: 109 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.632218 Iterations: 64 Function evaluations: 119 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.625104 Iterations: 59 Function evaluations: 108 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.629829 Iterations: 66 Function evaluations: 118 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.632218 Iterations: 64 Function evaluations: 119 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.632218 Iterations: 64 Function evaluations: 119 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.628642 Iterations: 67 Function evaluations: 122 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.631023 Iterations: 68 Function evaluations: 129 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.630430 Iterations: 57 Function evaluations: 108 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.629598 Iterations: 60 Function evaluations: 112 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.630430 Iterations: 57 Function evaluations: 108 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.630130 Iterations: 65 Function evaluations: 122 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.629536 Iterations: 62 Function evaluations: 111 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.630130 Iterations: 65 Function evaluations: 122 repr(start_params) array([ 1., 2.]) Optimization terminated successfully. Current function value: 2626.629984 Iterations: 67 Function evaluations: 123 Optimization terminated successfully. Current function value: 0.016560 Iterations: 18 Function evaluations: 38 >>> res_parks2 (1.0592352626264809, 9.9051580457572399, 2.0966900385041591) >>> res_parks (1.0596088578825995, 9.9043376069230007, 2.0975104813987118) >>> res_par.params array([ 0.84856418, 10.2197801 , 1.78206799]) >>> np.sqrt(np.diag(mod_par.hessian(res_par.params))) array([ NaN, NaN, NaN]) >>> mod_par.hessian(res_par.params ... ) array([[ NaN, NaN, NaN], [ NaN, NaN, NaN], [ NaN, NaN, NaN]]) >>> mod_par.hessian(res_parks) Traceback (most recent call last): File "", line 1, in File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 533, in hessian return approx_hess(params, self.loglike)[0] #need options for hess (epsilon) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\numdiff.py", line 118, in approx_hess xh = x + h TypeError: can only concatenate tuple (not "float") to tuple >>> mod_par.hessian(np.array(res_parks)) array([[ NaN, NaN, NaN], [ NaN, NaN, NaN], [ NaN, NaN, NaN]]) >>> mod_par.fixed_params array([ NaN, 9.90510677, NaN]) >>> mod_par.fixed_params=None >>> mod_par.hessian(np.array(res_parks)) array([[-890.48553491, NaN, NaN], [ NaN, NaN, NaN], [ NaN, NaN, NaN]]) >>> mod_par.loglike(np.array(res_parks)) -2626.6322080820569 >>> mod_par.bsejac Traceback (most recent call last): File "", line 1, in File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py", line 85, in __get__ _cachedval = self.fget(obj) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 592, in bsejac return np.sqrt(np.diag(self.covjac)) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py", line 85, in __get__ _cachedval = self.fget(obj) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 574, in covjac jacv = self.jacv File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py", line 85, in __get__ _cachedval = self.fget(obj) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 557, in jacv return self.jac(self._results.params) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 530, in jac return approx_fprime1(params, self.loglikeobs, **kwds) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\numdiff.py", line 80, in approx_fprime1 f0 = f(*((xk,)+args)) File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\model.py", line 522, in loglikeobs return -self.nloglikeobs(params) File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\examples\ex_generic_mle_tdist.py", line 184, in nloglikeobs scale = params[2] IndexError: index out of bounds >>> hasattr(self, 'start_params') Traceback (most recent call last): File "", line 1, in NameError: name 'self' is not defined >>> hasattr(mod_par, 'start_params') True >>> mod_par.start_params array([ 1., 2.]) >>> stats.pareto.stats(1., 9., 2., moments='mvsk') (array(1.#INF), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(1., 8., 2., moments='mvsk') (array(1.#INF), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(1., 8., 1., moments='mvsk') (array(1.#INF), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(1., moments='mvsk') (array(1.#INF), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(0.5., moments='mvsk') File "", line 1 stats.pareto.stats(0.5., moments='mvsk') ^ SyntaxError: invalid syntax >>> stats.pareto.stats(0.5, moments='mvsk') (array(1.#INF), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(2, moments='mvsk') (array(2.0), array(1.#INF), array(1.#QNAN), array(1.#QNAN)) >>> stats.pareto.stats(10, moments='mvsk') (array(1.1111111111111112), array(0.015432098765432098), array(2.8110568859997356), array(14.828571428571429)) >>> stats.pareto.rvs(10, size=10) array([ 1.07716265, 1.18977526, 1.07093 , 1.05157081, 1.15991232, 1.31015589, 1.06675107, 1.08082475, 1.19501243, 1.34967158]) >>> r = stats.pareto.rvs(10, size=1000) >>> plt Traceback (most recent call last): File "", line 1, in NameError: name 'plt' is not defined >>> import matplotlib.pyplot as plt >>> plt.hist(r) (array([962, 32, 3, 2, 0, 0, 0, 0, 0, 1]), array([ 1.00013046, 1.3968991 , 1.79366773, 2.19043637, 2.587205 , 2.98397364, 3.38074227, 3.77751091, 4.17427955, 4.57104818, 4.96781682]), ) >>> plt.show() ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_grangercausality.py000066400000000000000000000021341224417117700267440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Jul 06 15:44:57 2013 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.datasets import macrodata import statsmodels.tsa.stattools as tsa_stats # some example data mdata = macrodata.load().data mdata = mdata[['realgdp','realcons']] data = mdata.view((float,2)) data = np.diff(np.log(data), axis=0) #R: lmtest:grangertest r_result = [0.243097, 0.7844328, 195, 2] #f_test gr = tsa_stats.grangercausalitytests(data[:,1::-1], 2, verbose=False) assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7) assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'], decimal=7) lag = 2 print '\nTest Results for %d lags' % lag print print '\n'.join(['%-20s statistic: %f6.4 p-value: %f6.4' % (k, res[0], res[1]) for k, res in gr[lag][0].items() ]) print '\n Results for auxiliary restricted regression with two lags' print print gr[lag][1][0].summary() print '\n Results for auxiliary unrestricted regression with two lags' print print gr[lag][1][1].summary() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_inter_rater.py000066400000000000000000000061731224417117700257250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Dec 10 08:54:02 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.stats.inter_rater import fleiss_kappa, cohens_kappa, KappaResults table0 = np.asarray('''\ 1 0 0 0 0 14 1.000 2 0 2 6 4 2 0.253 3 0 0 3 5 6 0.308 4 0 3 9 2 0 0.440 5 2 2 8 1 1 0.330 6 7 7 0 0 0 0.462 7 3 2 6 3 0 0.242 8 2 5 3 2 2 0.176 9 6 5 2 1 0 0.286 10 0 2 2 3 7 0.286'''.split(), float).reshape(10,-1) Total = np.asarray("20 28 39 21 32".split('\t'), int) Pj = np.asarray("0.143 0.200 0.279 0.150 0.229".split('\t'), float) kappa_wp = 0.210 table1 = table0[:, 1:-1] print fleiss_kappa(table1) table4 = np.array([[20,5], [10, 15]]) print 'res', cohens_kappa(table4), 0.4 #wikipedia table5 = np.array([[45, 15], [25, 15]]) print 'res', cohens_kappa(table5), 0.1304 #wikipedia table6 = np.array([[25, 35], [5, 35]]) print 'res', cohens_kappa(table6), 0.2593 #wikipedia print 'res', cohens_kappa(table6, weights=np.arange(2)), 0.2593 #wikipedia t7 = np.array([[16, 18, 28], [10, 27, 13], [28, 20, 24]]) print cohens_kappa(t7, weights=[0, 1, 2]) table8 = np.array([[25, 35], [5, 35]]) print 'res', cohens_kappa(table8) #SAS example from http://www.john-uebersax.com/stat/saskappa.htm ''' Statistic Value ASE 95% Confidence Limits ------------------------------------------------------------ Simple Kappa 0.3333 0.0814 0.1738 0.4929 Weighted Kappa 0.2895 0.0756 0.1414 0.4376 ''' t9 = [[0, 0, 0], [5, 16, 3], [8, 12, 28]] res9 = cohens_kappa(t9) print 'res', res9 print 'res', cohens_kappa(t9, weights=[0, 1, 2]) #check max kappa, constructed by hand, same marginals table6a = np.array([[30, 30], [0, 40]]) res = cohens_kappa(table6a) assert res.kappa == res.kappa_max #print np.divide(*cohens_kappa(table6)[:2]) print res.kappa / res.kappa_max table10 = [[0, 4, 1], [0, 8, 0], [0, 1, 5]] res10 = cohens_kappa(table10) print 'res10', res10 '''SAS result for table10 Simple Kappa Coefficient -------------------------------- Kappa 0.4842 ASE 0.1380 95% Lower Conf Limit 0.2137 95% Upper Conf Limit 0.7547 Test of H0: Kappa = 0 ASE under H0 0.1484 Z 3.2626 One-sided Pr > Z 0.0006 Two-sided Pr > |Z| 0.0011 Weighted Kappa Coefficient -------------------------------- Weighted Kappa 0.4701 ASE 0.1457 95% Lower Conf Limit 0.1845 95% Upper Conf Limit 0.7558 Test of H0: Weighted Kappa = 0 ASE under H0 0.1426 Z 3.2971 One-sided Pr > Z 0.0005 Two-sided Pr > |Z| 0.0010 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression.py000066400000000000000000000033231224417117700271210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Jan 02 09:17:40 2013 Author: Josef Perktold based on test file by George Panterov """ import numpy as np import numpy.testing as npt import statsmodels.nonparametric.api as nparam #import statsmodels.api as sm #nparam = sm.nonparametric italy_gdp = \ [8.556, 12.262, 9.587, 8.119, 5.537, 6.796, 8.638, 6.483, 6.212, 5.111, 6.001, 7.027, 4.616, 3.922, 4.688, 3.957, 3.159, 3.763, 3.829, 5.242, 6.275, 8.518, 11.542, 9.348, 8.02, 5.527, 6.865, 8.666, 6.672, 6.289, 5.286, 6.271, 7.94, 4.72, 4.357, 4.672, 3.883, 3.065, 3.489, 3.635, 5.443, 6.302, 9.054, 12.485, 9.896, 8.33, 6.161, 7.055, 8.717, 6.95] italy_year = \ [1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1953, 1953, 1953, 1953, 1953, 1953, 1953, 1953] italy_year = np.asarray(italy_year, float) model = nparam.KernelReg(endog=[italy_gdp], exog=[italy_year], reg_type='lc', var_type='o', bw='cv_ls') sm_bw = model.bw R_bw = 0.1390096 sm_mean, sm_mfx = model.fit() sm_mean2 = sm_mean[0:5] sm_mfx = sm_mfx[0:5] R_mean = 6.190486 sm_R2 = model.r_squared() R_R2 = 0.1435323 npt.assert_allclose(sm_bw, R_bw, atol=1e-2) npt.assert_allclose(sm_mean2, R_mean, atol=1e-2) npt.assert_allclose(sm_R2, R_R2, atol=1e-2) import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(italy_year, italy_gdp, 'o') ax.plot(italy_year, sm_mean, '-') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression2.py000066400000000000000000000026761224417117700272150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Jan 02 13:43:44 2013 Author: Josef Perktold """ import numpy as np import numpy.testing as npt import statsmodels.nonparametric.api as nparam if __name__ == '__main__': np.random.seed(500) nobs = [250, 1000][0] sig_fac = 1 x = np.random.uniform(-2, 2, size=nobs) x.sort() y_true = np.sin(x*5)/x + 2*x y = y_true + sig_fac * (np.sqrt(np.abs(3+x))) * np.random.normal(size=nobs) model = nparam.KernelReg(endog=[y], exog=[x], reg_type='lc', var_type='c', bw='cv_ls', defaults=nparam.EstimatorSettings(efficient=True)) sm_bw = model.bw sm_mean, sm_mfx = model.fit() model1 = nparam.KernelReg(endog=[y], exog=[x], reg_type='lc', var_type='c', bw='cv_ls') mean1, mfx1 = model1.fit() model2 = nparam.KernelReg(endog=[y], exog=[x], reg_type='ll', var_type='c', bw='cv_ls') mean2, mfx2 = model2.fit() print model.bw print model1.bw print model2.bw import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(x, y, 'o', alpha=0.5) ax.plot(x, y_true, lw=2, label='DGP mean') ax.plot(x, sm_mean, lw=2, label='kernel mean') ax.plot(x, mean2, lw=2, label='kernel mean') ax.legend() plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression3.py000066400000000000000000000044441224417117700272110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """script to try out Censored kernel regression Created on Wed Jan 02 13:43:44 2013 Author: Josef Perktold """ import numpy as np import statsmodels.nonparametric.api as nparam if __name__ == '__main__': np.random.seed(500) nobs = [250, 1000][0] sig_fac = 1 x = np.random.uniform(-2, 2, size=nobs) x.sort() x2 = x**2 + 0.02 * np.random.normal(size=nobs) y_true = np.sin(x*5)/x + 2*x - 3 * x2 y = y_true + sig_fac * (np.sqrt(np.abs(3+x))) * np.random.normal(size=nobs) cens_side = ['left', 'right', 'random'][2] if cens_side == 'left': c_val = 0.5 y_cens = np.clip(y, c_val, 100) elif cens_side == 'right': c_val = 3.5 y_cens = np.clip(y, -100, c_val) elif cens_side == 'random': c_val = 3.5 + 3 * np.random.randn(nobs) y_cens = np.minimum(y, c_val) model = nparam.KernelCensoredReg(endog=[y_cens], #exog=[np.column_stack((x, x**2))], reg_type='lc', exog=[x, x2], reg_type='ll', var_type='cc', bw='aic', #'cv_ls', #[0.23, 434697.22], #'cv_ls', censor_val=c_val[:,None], #defaults=nparam.EstimatorSettings(efficient=True) ) sm_bw = model.bw sm_mean, sm_mfx = model.fit() # model1 = nparam.KernelReg(endog=[y], # exog=[x], reg_type='lc', # var_type='c', bw='cv_ls') # mean1, mfx1 = model1.fit() model2 = nparam.KernelReg(endog=[y_cens], exog=[x, x2], reg_type='ll', var_type='cc', bw='aic',# 'cv_ls' ) mean2, mfx2 = model2.fit() print model.bw #print model1.bw print model2.bw ix = np.argsort(y_cens) ix_rev = np.zeros(nobs, int) ix_rev[ix] = np.arange(nobs) ix_rev = model.sortix_rev import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(x, y, 'o', alpha=0.5) ax.plot(x, y_cens, 'o', alpha=0.5) ax.plot(x, y_true, lw=2, label='DGP mean') ax.plot(x, sm_mean[ix_rev], lw=2, label='model 0 mean') ax.plot(x, mean2, lw=2, label='model 2 mean') ax.legend() plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression_censored2.py000066400000000000000000000021611224417117700310640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """script to check KernelCensoredReg based on test file Created on Thu Jan 03 20:20:47 2013 Author: Josef Perktold """ import numpy as np import statsmodels.nonparametric.api as nparam if __name__ == '__main__': #example from test file nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) noise = 0.1 * np.random.normal(size=(nobs, )) y = 0.3 +1.2 * C1 - 0.9 * C2 + noise y[y>0] = 0 # censor the data model = nparam.KernelCensoredReg(endog=[y], exog=[C1, C2], reg_type='ll', var_type='cc', bw='cv_ls', censor_val=0) sm_mean, sm_mfx = model.fit() import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) sortidx = np.argsort(y) ax.plot(y[sortidx], 'o', alpha=0.5) #ax.plot(x, y_cens, 'o', alpha=0.5) #ax.plot(x, y_true, lw=2, label='DGP mean') ax.plot(sm_mean[sortidx], lw=2, label='model 0 mean') #ax.plot(x, mean2, lw=2, label='model 2 mean') ax.legend() plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression_dgp.py000066400000000000000000000022121224417117700277470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Jan 06 09:50:54 2013 Author: Josef Perktold """ if __name__ == '__main__': import numpy as np import matplotlib.pyplot as plt from statsmodels.nonparametric.api import KernelReg import statsmodels.sandbox.nonparametric.dgp_examples as dgp seed = np.random.randint(999999) seed = 430973 print seed np.random.seed(seed) funcs = [dgp.UnivariateFanGijbels1(), dgp.UnivariateFanGijbels2(), dgp.UnivariateFanGijbels1EU(), #dgp.UnivariateFanGijbels2(distr_x=stats.uniform(-2, 4)) dgp.UnivariateFunc1() ] res = [] fig = plt.figure() for i,func in enumerate(funcs): #f = func() f = func model = KernelReg(endog=[f.y], exog=[f.x], reg_type='ll', var_type='c', bw='cv_ls') mean, mfx = model.fit() ax = fig.add_subplot(2, 2, i+1) f.plot(ax=ax) ax.plot(f.x, mean, color='r', lw=2, label='est. mean') ax.legend(loc='upper left') res.append((model, mean, mfx)) fig.suptitle('Kernel Regression') fig.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_regression_sigtest.py000066400000000000000000000060511224417117700306640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Kernel Regression and Significance Test Warning: SLOW, 11 minutes on my computer Created on Thu Jan 03 20:20:47 2013 Author: Josef Perktold results - this version ---------------------- >>> execfile('ex_kernel_regression_censored1.py') bw [ 0.3987821 0.50933458] [0.39878209999999997, 0.50933457999999998] sig_test - default Not Significant pvalue 0.11 test statistic 0.000434305313291 bootstrap critical values [ 0.00043875 0.00046808 0.0005064 0.00054151] sig_test - pivot=True, nboot=200, nested_res=50 pvalue 0.01 test statistic 6.17877171579 bootstrap critical values [ 5.5658345 5.74761076 5.87386858 6.46012041] times: 8.34599995613 20.6909999847 666.373999834 """ import time import numpy as np import statsmodels.nonparametric.api as nparam import statsmodels.nonparametric.kernel_regression as smkr if __name__ == '__main__': t0 = time.time() #example from test file nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) noise = np.random.normal(size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 + noise #self.write2file('RegData.csv', (Y, C1, C2)) #CODE TO PRODUCE BANDWIDTH ESTIMATION IN R #library(np) #data <- read.csv('RegData.csv', header=FALSE) #bw <- npregbw(formula=data$V1 ~ data$V2 + data$V3, # bwmethod='cv.aic', regtype='lc') model = nparam.KernelReg(endog=[Y], exog=[C1, C2], reg_type='lc', var_type='cc', bw='aic') mean, marg = model.fit() #R_bw = [0.4017893, 0.4943397] # Bandwidth obtained in R bw_expected = [0.3987821, 0.50933458] #npt.assert_allclose(model.bw, bw_expected, rtol=1e-3) print 'bw' print model.bw print bw_expected print '\nsig_test - default' print model.sig_test([1], nboot=100) t1 = time.time() res0 = smkr.TestRegCoefC(model, [1]) print 'pvalue' print (res0.t_dist >= res0.test_stat).mean() print 'test statistic', res0.test_stat print 'bootstrap critical values' probs = np.array([0.9, 0.95, 0.975, 0.99]) bsort0 = np.sort(res0.t_dist) nrep0 = len(bsort0) print bsort0[(probs * nrep0).astype(int)] t2 = time.time() print '\nsig_test - pivot=True, nboot=200, nested_res=50' res1 = smkr.TestRegCoefC(model, [1], pivot=True, nboot=200, nested_res=50) print 'pvalue' print (res1.t_dist >= res1.test_stat).mean() print 'test statistic', res1.test_stat print 'bootstrap critical values' probs = np.array([0.9, 0.95, 0.975, 0.99]) bsort1 = np.sort(res1.t_dist) nrep1 = len(bsort1) print bsort1[(probs * nrep1).astype(int)] t3 = time.time() print 'times:', t1-t0, t2-t1, t3-t2 # import matplotlib.pyplot as plt # fig = plt.figure() # ax = fig.add_subplot(1,1,1) # ax.plot(x, y, 'o', alpha=0.5) # ax.plot(x, y_cens, 'o', alpha=0.5) # ax.plot(x, y_true, lw=2, label='DGP mean') # ax.plot(x, sm_mean, lw=2, label='model 0 mean') # ax.plot(x, mean2, lw=2, label='model 2 mean') # ax.legend() # # plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_semilinear_dgp.py000066400000000000000000000114641224417117700277300ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Jan 06 09:50:54 2013 Author: Josef Perktold """ if __name__ == '__main__': import numpy as np import matplotlib.pyplot as plt #from statsmodels.nonparametric.api import KernelReg import statsmodels.sandbox.nonparametric.kernel_extras as smke import statsmodels.sandbox.nonparametric.dgp_examples as dgp class UnivariateFunc1a(dgp.UnivariateFunc1): def het_scale(self, x): return 0.5 seed = np.random.randint(999999) #seed = 430973 #seed = 47829 seed = 648456 #good seed for het_scale = 0.5 print seed np.random.seed(seed) nobs, k_vars = 300, 3 x = np.random.uniform(-2, 2, size=(nobs, k_vars)) xb = x.sum(1) / 3 #beta = [1,1,1] k_vars_lin = 2 x2 = np.random.uniform(-2, 2, size=(nobs, k_vars_lin)) funcs = [#dgp.UnivariateFanGijbels1(), #dgp.UnivariateFanGijbels2(), #dgp.UnivariateFanGijbels1EU(), #dgp.UnivariateFanGijbels2(distr_x=stats.uniform(-2, 4)) UnivariateFunc1a(x=xb) ] res = [] fig = plt.figure() for i,func in enumerate(funcs): #f = func() f = func y = f.y + x2.sum(1) model = smke.SemiLinear(y, x2, x, 'ccc', k_vars_lin) mean, mfx = model.fit() ax = fig.add_subplot(1, 1, i+1) f.plot(ax=ax) xb_est = np.dot(model.exog, model.b) sortidx = np.argsort(xb_est) #f.x) ax.plot(f.x[sortidx], mean[sortidx], 'o', color='r', lw=2, label='est. mean') # ax.plot(f.x, mean0, color='g', lw=2, label='est. mean') ax.legend(loc='upper left') res.append((model, mean, mfx)) print 'beta', model.b print 'scale - est', (y - (xb_est+mean)).std() print 'scale - dgp realised, true', (y - (f.y_true + x2.sum(1))).std(), \ 2 * f.het_scale(1) fittedvalues = xb_est + mean resid = np.squeeze(model.endog) - fittedvalues print 'corrcoef(fittedvalues, resid)', np.corrcoef(fittedvalues, resid)[0,1] print 'variance of components, var and as fraction of var(y)' print 'fitted values', fittedvalues.var(), fittedvalues.var() / y.var() print 'linear ', xb_est.var(), xb_est.var() / y.var() print 'nonparametric', mean.var(), mean.var() / y.var() print 'residual ', resid.var(), resid.var() / y.var() print '\ncovariance decomposition fraction of var(y)' print np.cov(fittedvalues, resid) / model.endog.var(ddof=1) print 'sum', (np.cov(fittedvalues, resid) / model.endog.var(ddof=1)).sum() print '\ncovariance decomposition, xb, m, resid as fraction of var(y)' print np.cov(np.column_stack((xb_est, mean, resid)), rowvar=False) / model.endog.var(ddof=1) fig.suptitle('Kernel Regression') fig.show() alpha = 0.7 fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(f.x[sortidx], f.y[sortidx], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.x[sortidx], f.y_true[sortidx], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(f.x[sortidx], mean[sortidx], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') sortidx = np.argsort(xb_est + mean) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(f.x[sortidx], y[sortidx], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.x[sortidx], f.y_true[sortidx], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(f.x[sortidx], (xb_est + mean)[sortidx], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') ax.set_title('Semilinear Model - observed and total fitted') fig = plt.figure() # ax = fig.add_subplot(1, 2, 1) # ax.plot(f.x, f.y, 'o', color='b', lw=2, alpha=alpha, label='observed') # ax.plot(f.x, f.y_true, 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') # ax.plot(f.x, mean, 'o', color='r', lw=2, alpha=alpha, label='est. mean') # ax.legend(loc='upper left') sortidx0 = np.argsort(xb) ax = fig.add_subplot(1, 2, 1) ax.plot(f.y[sortidx0], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.y_true[sortidx0], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(mean[sortidx0], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') ax.set_title('Single Index Model (sorted by true xb)') ax = fig.add_subplot(1, 2, 2) ax.plot(y - xb_est, 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.y_true, 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(mean, 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') ax.set_title('Single Index Model (nonparametric)') plt.figure() plt.plot(y, xb_est+mean, '.') plt.title('observed versus fitted values') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_singleindex_dgp.py000066400000000000000000000065071224417117700301130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Jan 06 09:50:54 2013 Author: Josef Perktold """ if __name__ == '__main__': import numpy as np import matplotlib.pyplot as plt #from statsmodels.nonparametric.api import KernelReg import statsmodels.sandbox.nonparametric.kernel_extras as smke import statsmodels.sandbox.nonparametric.dgp_examples as dgp class UnivariateFunc1a(dgp.UnivariateFunc1): def het_scale(self, x): return 0.5 seed = np.random.randint(999999) #seed = 430973 #seed = 47829 seed = 648456 #good seed for het_scale = 0.5 print seed np.random.seed(seed) nobs, k_vars = 300, 3 x = np.random.uniform(-2, 2, size=(nobs, k_vars)) xb = x.sum(1) / 3 #beta = [1,1,1] funcs = [#dgp.UnivariateFanGijbels1(), #dgp.UnivariateFanGijbels2(), #dgp.UnivariateFanGijbels1EU(), #dgp.UnivariateFanGijbels2(distr_x=stats.uniform(-2, 4)) UnivariateFunc1a(x=xb) ] res = [] fig = plt.figure() for i,func in enumerate(funcs): #f = func() f = func # mod0 = smke.SingleIndexModel(endog=[f.y], exog=[xb], #reg_type='ll', # var_type='c')#, bw='cv_ls') # mean0, mfx0 = mod0.fit() model = smke.SingleIndexModel(endog=[f.y], exog=x, #reg_type='ll', var_type='ccc')#, bw='cv_ls') mean, mfx = model.fit() ax = fig.add_subplot(1, 1, i+1) f.plot(ax=ax) xb_est = np.dot(model.exog, model.b) sortidx = np.argsort(xb_est) #f.x) ax.plot(f.x[sortidx], mean[sortidx], 'o', color='r', lw=2, label='est. mean') # ax.plot(f.x, mean0, color='g', lw=2, label='est. mean') ax.legend(loc='upper left') res.append((model, mean, mfx)) fig.suptitle('Kernel Regression') fig.show() alpha = 0.7 fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(f.x[sortidx], f.y[sortidx], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.x[sortidx], f.y_true[sortidx], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(f.x[sortidx], mean[sortidx], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') fig = plt.figure() # ax = fig.add_subplot(1, 2, 1) # ax.plot(f.x, f.y, 'o', color='b', lw=2, alpha=alpha, label='observed') # ax.plot(f.x, f.y_true, 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') # ax.plot(f.x, mean, 'o', color='r', lw=2, alpha=alpha, label='est. mean') # ax.legend(loc='upper left') sortidx0 = np.argsort(xb) ax = fig.add_subplot(1, 2, 1) ax.plot(f.y[sortidx0], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.y_true[sortidx0], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(mean[sortidx0], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') ax.set_title('Single Index Model (sorted by true xb)') ax = fig.add_subplot(1, 2, 2) ax.plot(f.y[sortidx], 'o', color='b', lw=2, alpha=alpha, label='observed') ax.plot(f.y_true[sortidx], 'o', color='g', lw=2, alpha=alpha, label='dgp. mean') ax.plot(mean[sortidx], 'o', color='r', lw=2, alpha=alpha, label='est. mean') ax.legend(loc='upper left') ax.set_title('Single Index Model (sorted by estimated xb)') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_test_functional.py000066400000000000000000000042251224417117700301440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Jan 08 19:03:20 2013 Author: Josef Perktold """ if __name__ == '__main__': import numpy as np from statsmodels.regression.linear_model import OLS #from statsmodels.nonparametric.api import KernelReg import statsmodels.sandbox.nonparametric.kernel_extras as smke seed = np.random.randint(999999) #seed = 661176 print seed np.random.seed(seed) sig_e = 0.5 #0.1 nobs, k_vars = 200, 1 x = np.random.uniform(-2, 2, size=(nobs, k_vars)) x.sort() order = 3 exog = x**np.arange(order + 1) beta = np.array([1, 1, 0.1, 0.0])[:order+1] # 1. / np.arange(1, order + 2) y_true = np.dot(exog, beta) y = y_true + sig_e * np.random.normal(size=nobs) endog = y print 'DGP' print 'nobs=%d, beta=%r, sig_e=%3.1f' % (nobs, beta, sig_e) mod_ols = OLS(endog, exog[:,:2]) res_ols = mod_ols.fit() #'cv_ls'[1000, 0.5][0.01, 0.45] tst = smke.TestFForm(endog, exog[:,:2], bw=[0.01, 0.45], var_type='cc', fform=lambda x,p: mod_ols.predict(p,x), estimator=lambda y,x: OLS(y,x).fit().params, nboot=1000) print 'bw', tst.bw print 'tst.test_stat', tst.test_stat print tst.sig print 'tst.boots_results mean, min, max', (tst.boots_results.mean(), tst.boots_results.min(), tst.boots_results.max()) print 'lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean() print 'upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean() from scipy import stats print 'aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2 print 'aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat) do_plot=True if do_plot: import matplotlib.pyplot as plt plt.figure() plt.plot(x, y, '.') plt.plot(x, res_ols.fittedvalues) plt.title('OLS fit') plt.figure() plt.hist(tst.boots_results.ravel(), bins=20) plt.title('bootstrap histogram or test statistic') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_kernel_test_functional_li_wang.py000066400000000000000000000100161224417117700316370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example TestFForm with Li Wang DGP1 Created on Tue Jan 08 19:03:20 2013 Author: Josef Perktold trying to replicate some examples in Li, Q., and Suojin Wang. 1998. "A Simple Consistent Bootstrap Test for a Parametric Regression Function." Journal of Econometrics 87 (1) (November): 145-165. doi:10.1016/S0304-4076(98)00011-6. currently DGP1 Monte Carlo with 100 replications --------------------------------- results 598948 time 11.1642833312 [-0.72505981 0.26514944 0.45681704] [ 0.74884796 0.22005569 0.3004892 ] reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap) [[ 0.55 0.24 0.01] [ 0.29 0.16 0.06]] bw [ 0.11492364 0.11492364] tst.test_stat -1.40274609515 Not Significant tst.boots_results min, max -2.03386582198 2.32562183511 lower tail bootstrap p-value 0.077694235589 aymp.normal p-value (2-sided) 0.160692566481 mean and std in Li and Wang for n=1 are -0.764 and 0.621 results look reasonable now Power ----- true model: quadratic, estimated model: linear 498198 time 8.4588166674 [ 0.50374364 0.3991975 0.25373434] [ 1.21353172 0.28669981 0.25461368] reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap) [[ 0.66 0.78 0.82] [ 0.46 0.61 0.74]] bw [ 0.11492364 0.11492364] tst.test_stat 0.505426717024 Not Significant tst.boots_results min, max -1.67050998463 3.39835350718 lower tail bootstrap p-value 0.892230576441 upper tail bootstrap p-value 0.107769423559 aymp.normal p-value (2-sided) 0.613259157709 aymp.normal p-value (upper) 0.306629578855 """ if __name__ == '__main__': import time import numpy as np from scipy import stats from statsmodels.regression.linear_model import OLS #from statsmodels.nonparametric.api import KernelReg import statsmodels.sandbox.nonparametric.kernel_extras as smke seed = np.random.randint(999999) #seed = 661176 print seed np.random.seed(seed) sig_e = 0.1 #0.5 #0.1 nobs, k_vars = 100, 1 t0 = time.time() b_res = [] for i in range(100): x = np.random.uniform(0, 1, size=(nobs, k_vars)) x.sort(0) order = 2 exog = x**np.arange(1, order + 1) beta = np.array([2, -0.2])[:order+1-1] # 1. / np.arange(1, order + 2) y_true = np.dot(exog, beta) y = y_true + sig_e * np.random.normal(size=nobs) endog = y mod_ols = OLS(endog, exog[:,:1]) #res_ols = mod_ols.fit() #'cv_ls'[1000, 0.5] bw_lw = [1./np.sqrt(12.) * nobs**(-0.2)]*2 #(-1. / 5.) tst = smke.TestFForm(endog, exog[:,:1], bw=bw_lw, var_type='c', fform=lambda x,p: mod_ols.predict(p,x), estimator=lambda y,x: OLS(y,x).fit().params, nboot=399) b_res.append([tst.test_stat, stats.norm.sf(tst.test_stat), (tst.boots_results > tst.test_stat).mean()]) t1 = time.time() b_res = np.asarray(b_res) print 'time', (t1 - t0) / 60. print b_res.mean(0) print b_res.std(0) print 'reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap)' print (b_res[:,1:,None] >= [0.2, 0.1, 0.05]).mean(0) print 'bw', tst.bw print 'tst.test_stat', tst.test_stat print tst.sig print 'tst.boots_results min, max', tst.boots_results.min(), tst.boots_results.max() print 'lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean() print 'upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean() from scipy import stats print 'aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2 print 'aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat) res_ols = mod_ols.fit() do_plot=True if do_plot: import matplotlib.pyplot as plt plt.figure() plt.plot(x, y, '.') plt.plot(x, res_ols.fittedvalues) plt.title('OLS fit') plt.figure() plt.hist(tst.boots_results.ravel(), bins=20) plt.title('bootstrap histogram or test statistic') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_lowess.py000066400000000000000000000053431224417117700247210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Oct 31 15:26:06 2011 Author: Chris Jordan Squire extracted from test suite by josef-pktd """ import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm lowess = sm.nonparametric.lowess # this is just to check direct import import statsmodels.nonparametric.smoothers_lowess statsmodels.nonparametric.smoothers_lowess.lowess x = np.arange(20.) #standard normal noise noise = np.array([-0.76741118, -0.30754369, 0.39950921, -0.46352422, -1.67081778, 0.6595567 , 0.66367639, -2.04388585, 0.8123281 , 1.45977518, 1.21428038, 1.29296866, 0.78028477, -0.2402853 , -0.21721302, 0.24549405, 0.25987014, -0.90709034, -1.45688216, -0.31780505]) y = x + noise expected_lowess = np.array([[ 0. , -0.58337912], [ 1. , 0.61951246], [ 2. , 1.82221628], [ 3. , 3.02536876], [ 4. , 4.22667951], [ 5. , 5.42387723], [ 6. , 6.60834945], [ 7. , 7.7797691 ], [ 8. , 8.91824348], [ 9. , 9.94997506], [ 10. , 10.89697569], [ 11. , 11.78746276], [ 12. , 12.62356492], [ 13. , 13.41538492], [ 14. , 14.15745254], [ 15. , 14.92343948], [ 16. , 15.70019862], [ 17. , 16.48167846], [ 18. , 17.26380699], [ 19. , 18.0466769 ]]) actual_lowess = lowess(y, x) print actual_lowess print np.max(np.abs(actual_lowess-expected_lowess)) plt.plot(y, 'o') plt.plot(actual_lowess[:,1]) plt.plot(expected_lowess[:,1]) import os.path import statsmodels.nonparametric.tests.results rpath = os.path.split(statsmodels.nonparametric.tests.results.__file__)[0] rfile = os.path.join(rpath, 'test_lowess_frac.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) expected_lowess_23 = np.array([test_data['x'], test_data['out_2_3']]).T expected_lowess_15 = np.array([test_data['x'], test_data['out_1_5']]).T actual_lowess_23 = lowess(test_data['y'], test_data['x'] ,frac = 2./3) actual_lowess_15 = lowess(test_data['y'], test_data['x'] ,frac = 1./5) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_misc_tarma.py000066400000000000000000000034311224417117700255200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Jul 03 23:01:44 2013 Author: Josef Perktold """ import numpy as np from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess from statsmodels.miscmodels.tmodel import TArma from statsmodels.tsa.arima_model import ARMA nobs = 500 ar = [1, -0.6, -0.1] ma = [1, 0.7] dist = lambda n: np.random.standard_t(3, size=n) np.random.seed(8659567) x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist, burnin=500) mod = TArma(x) order = (2, 1) res = mod.fit(order=order) res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm') print res[0] proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]]) print ar, ma proc.nobs = nobs # TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__ print proc.ar, proc.ma print proc.ar_roots(), proc.ma_roots() from statsmodels.tsa.arma_mle import Arma modn = Arma(x) resn = modn.fit_mle(order=order) moda = ARMA(x, order=order) resa = moda.fit( trend='nc') print '\nparameter estimates' print 'ls ', res[0] print 'norm', resn.params print 't ', res2.params print 'A ', resa.params print '\nstandard deviation of parameter estimates' #print 'ls ', res[0] #TODO: not available yet print 'norm', resn.bse print 't ', res2.bse print 'A ', resa.bse print 'A/t-1', resa.bse / res2.bse[:3] - 1 print 'other bse' print resn.bsejac print resn.bsejhj print res2.bsejac print res2.bsejhj print res2.t_test(np.eye(len(res2.params))) # TArma has no fittedvalues and resid # TODO: check if lag is correct or if fitted `x-resid` is shifted resid = res2.model.geterrors(res2.params) fv = res[2]['fvec'] #resid returned from leastsq? import matplotlib.pyplot as plt plt.plot(x, 'o', alpha=0.5) plt.plot(x-resid) plt.plot(x-fv) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_misc_tmodel.py000066400000000000000000000045201224417117700257000ustar00rootroot00000000000000 import numpy as np from scipy import stats, special, optimize import statsmodels.api as sm from statsmodels.miscmodels import TLinearModel #Example: #np.random.seed(98765678) nobs = 50 nvars = 6 df = 3 rvs = np.random.randn(nobs, nvars-1) data_exog = sm.add_constant(rvs, prepend=False) xbeta = 0.9 + 0.1*rvs.sum(1) data_endog = xbeta + 0.1*np.random.standard_t(df, size=nobs) print 'variance of endog:', data_endog.var() print 'true parameters:', [0.1]*nvars + [0.9] res_ols = sm.OLS(data_endog, data_exog).fit() print '\nResults with ols' print '----------------' print res_ols.scale print np.sqrt(res_ols.scale) print res_ols.params print res_ols.bse kurt = stats.kurtosis(res_ols.resid) df_fromkurt = 6./kurt + 4 print 'df_fromkurt from ols residuals', df_fromkurt print stats.t.stats(df_fromkurt, moments='mvsk') print stats.t.stats(df, moments='mvsk') modp = TLinearModel(data_endog, data_exog) start_value = 0.1*np.ones(data_exog.shape[1]+2) #start_value = np.zeros(data_exog.shape[1]+2) #start_value[:nvars] = sm.OLS(data_endog, data_exog).fit().params start_value[:nvars] = res_ols.params start_value[-2] = df_fromkurt #10 start_value[-1] = np.sqrt(res_ols.scale) #0.5 modp.start_params = start_value #adding fixed parameters fixdf = np.nan * np.zeros(modp.start_params.shape) fixdf[-2] = 5 fixone = 0 if fixone: modp.fixed_params = fixdf modp.fixed_paramsmask = np.isnan(fixdf) modp.start_params = modp.start_params[modp.fixed_paramsmask] else: modp.fixed_params = None modp.fixed_paramsmask = None print '\nResults with TLinearModel' print '-------------------------' resp = modp.fit(start_params = modp.start_params, disp=1, method='nm', maxfun=10000, maxiter=5000)#'newton') #resp = modp.fit(start_params = modp.start_params, disp=1, method='newton') print 'using Nelder-Mead' print resp.params print resp.bse resp2 = modp.fit(start_params = resp.params, method='Newton') print 'using Newton' print resp2.params print resp2.bse from statsmodels.tools.numdiff import approx_fprime, approx_hess hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4) tmp = modp.loglike(modp.start_params) print tmp.shape print 'eigenvalues of numerical Hessian' print np.linalg.eigh(np.linalg.inv(hb))[0] #store_params is only available in original test script ##pp=np.array(store_params) ##print pp.min(0) ##print pp.max(0) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_multivar_kde.py000066400000000000000000000026711224417117700260740ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import axes3d import statsmodels.api as sm """ This example illustrates the nonparametric estimation of a bivariate bi-modal distribution that is a mixture of two normal distributions. author: George Panterov """ if __name__ == '__main__': np.random.seed(123456) # generate the data nobs = 500 BW = 'cv_ml' mu1 = [3, 4] mu2 = [6, 1] cov1 = np.asarray([[1, 0.7], [0.7, 1]]) cov2 = np.asarray([[1, -0.7], [-0.7, 1]]) ix = np.random.uniform(size=nobs) > 0.5 V = np.random.multivariate_normal(mu1, cov1, size=nobs) V[ix, :] = np.random.multivariate_normal(mu2, cov2, size=nobs)[ix, :] x = V[:, 0] y = V[:, 1] dens = sm.nonparametric.KDEMultivariate(data=[x, y], var_type='cc', bw=BW, defaults=sm.nonparametric.EstimatorSettings(efficient=True)) supportx = np.linspace(min(x), max(x), 60) supporty = np.linspace(min(y), max(y), 60) X, Y = np.meshgrid(supportx, supporty) edat = np.column_stack([X.ravel(), Y.ravel()]) Z = dens.pdf(edat).reshape(X.shape) # plot fig = plt.figure(1) ax = fig.gca(projection='3d') surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0, antialiased=False) fig.colorbar(surf, shrink=0.5, aspect=5) plt.figure(2) plt.imshow(Z) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_nearest_corr.py000066400000000000000000000064021224417117700260700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Find near positive definite correlation and covariance matrices Created on Sun Aug 19 15:25:07 2012 Author: Josef Perktold TODO: add examples for cov_nearest from script log Notes ----- We are looking at eigenvalues before and after the conversion to psd matrix. As distance measure for how close the change in the matrix is, we consider the sum of squared differences (Frobenious norm without taking the square root) """ import numpy as np from statsmodels.stats.correlation_tools import ( corr_nearest, corr_clipped, cov_nearest) examples = ['all'] if 'all' in examples: # x0 is positive definite x0 = np.array([[1, -0.2, -0.9], [-0.2, 1, -0.2], [-0.9, -0.2, 1]]) # x has negative eigenvalues, not definite x = np.array([[1, -0.9, -0.9], [-0.9, 1, -0.9], [-0.9, -0.9, 1]]) #x = np.array([[1, 0.2, 0.2], [0.2, 1, 0.2], [0.2, 0.2, 1]]) n_fact = 2 print 'evals original', np.linalg.eigvalsh(x) y = corr_nearest(x, n_fact=100) print 'evals nearest', np.linalg.eigvalsh(y) print y y = corr_nearest(x, n_fact=100, threshold=1e-16) print 'evals nearest', np.linalg.eigvalsh(y) print y y = corr_clipped(x, threshold=1e-16) print 'evals clipped', np.linalg.eigvalsh(y) print y np.set_printoptions(precision=4) print '\nMini Monte Carlo' # we are simulating a uniformly distributed symmetric matrix # and find close positive definite matrix # original can be far away from positive definite, # then original and converted matrices can be far apart in norm # results are printed for visual inspection of different cases k_vars = 5 diag_idx = np.arange(k_vars) for ii in range(10): print x = np.random.uniform(-1, 1, size=(k_vars, k_vars)) x = (x + x.T) * 0.5 x[diag_idx, diag_idx] = 1 #x_std = np.sqrt(np.diag(x)) #x = x / x_std / x_std[:,None] print print np.sort(np.linalg.eigvals(x)), 'original' yn = corr_nearest(x, threshold=1e-12, n_fact=200) print np.sort(np.linalg.eigvals(yn)), ((yn - x)**2).sum(), 'nearest' yc = corr_clipped(x, threshold=1e-12) print np.sort(np.linalg.eigvals(yc)), ((yc - x)**2).sum(), 'clipped' import time t0 = time.time() for _ in range(100): corr_nearest(x, threshold=1e-15, n_fact=100) t1 = time.time() for _ in range(1000): corr_clipped(x, threshold=1e-15) t2 = time.time() print '\ntime (nearest, clipped):', t1 - t0, t2 - t1 if 'all' in examples: # example for test case against R x2 = np.array([ 1, 0.477, 0.644, 0.478, 0.651, 0.826, 0.477, 1, 0.516, 0.233, 0.682, 0.75, 0.644, 0.516, 1, 0.599, 0.581, 0.742, 0.478, 0.233, 0.599, 1, 0.741, 0.8, 0.651, 0.682, 0.581, 0.741, 1, 0.798, 0.826, 0.75, 0.742, 0.8, 0.798, 1]).reshape(6,6) y1 = corr_nearest(x2, threshold=1e-15, n_fact=200) y2 = corr_clipped(x2, threshold=1e-15) print '\nmatrix 2' print np.sort(np.linalg.eigvals(x2)), 'original' print np.sort(np.linalg.eigvals(y1)), ((y1 - x2)**2).sum(), 'nearest' print np.sort(np.linalg.eigvals(y1)), ((y2 - x2)**2).sum(), 'clipped' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_outliers_influence.py000066400000000000000000000074211224417117700273020ustar00rootroot00000000000000 import numpy as np import statsmodels.stats.outliers_influence as oi if __name__ == '__main__': import statsmodels.api as sm data = np.array('''\ 64 57 8 71 59 10 53 49 6 67 62 11 55 51 8 58 50 7 77 55 10 57 48 9 56 42 10 51 42 6 76 61 12 68 57 9'''.split(), float).reshape(-1,3) varnames = 'weight height age'.split() endog = data[:,0] exog = sm.add_constant(data[:,2]) res_ols = sm.OLS(endog, exog).fit() hh = (res_ols.model.exog * res_ols.model.pinv_wexog.T).sum(1) x = res_ols.model.exog hh_check = np.diag(np.dot(x, np.dot(res_ols.model.normalized_cov_params, x.T))) from numpy.testing import assert_almost_equal assert_almost_equal(hh, hh_check, decimal=13) res = res_ols #alias #http://en.wikipedia.org/wiki/PRESS_statistic #predicted residuals, leave one out predicted residuals resid_press = res.resid / (1-hh) ess_press = np.dot(resid_press, resid_press) sigma2_est = np.sqrt(res.mse_resid) #can be replace by different estimators of sigma sigma_est = np.sqrt(sigma2_est) resid_studentized = res.resid / sigma_est / np.sqrt(1 - hh) #http://en.wikipedia.org/wiki/DFFITS: dffits = resid_studentized * np.sqrt(hh / (1 - hh)) nobs, k_vars = res.model.exog.shape #Belsley, Kuh and Welsch (1980) suggest a threshold for abs(DFFITS) dffits_threshold = 2 * np.sqrt(k_vars/nobs) res_ols.df_modelwc = res_ols.df_model + 1 n_params = res.model.exog.shape[1] #http://en.wikipedia.org/wiki/Cook%27s_distance cooks_d = res.resid**2 / sigma2_est / res_ols.df_modelwc * hh / (1 - hh)**2 #or #Eubank p.93, 94 cooks_d2 = resid_studentized**2 / res_ols.df_modelwc * hh / (1 - hh) #threshold if normal, also Wikipedia from scipy import stats alpha = 0.1 #df looks wrong print stats.f.isf(1-alpha, n_params, res.df_resid) print stats.f.sf(cooks_d, n_params, res.df_resid) print 'Cooks Distance' print cooks_d print cooks_d2 doplot = 0 if doplot: import matplotlib.pyplot as plt fig = plt.figure() # ax = fig.add_subplot(3,1,1) # plt.plot(andrew_results.weights, 'o', label='rlm weights') # plt.legend(loc='lower left') ax = fig.add_subplot(3,1,2) plt.plot(cooks_d, 'o', label="Cook's distance") plt.legend(loc='upper left') ax2 = fig.add_subplot(3,1,3) plt.plot(resid_studentized, 'o', label='studentized_resid') plt.plot(dffits, 'o', label='DFFITS') leg = plt.legend(loc='lower left', fancybox=True) leg.get_frame().set_alpha(0.5) #, fontsize='small') ltext = leg.get_texts() # all the text.Text instance in the legend plt.setp(ltext, fontsize='small') # the legend text fontsize print oi.reset_ramsey(res, degree=3) #note, constant in last column for i in range(1): print oi.variance_inflation_factor(res.model.exog, i) infl = oi.OLSInfluence(res_ols) print infl.resid_studentized_external print infl.resid_studentized_internal print infl.summary_table() print oi.summary_table(res, alpha=0.05)[0] ''' >>> res.resid array([ 4.28571429, 4. , 0.57142857, -3.64285714, -4.71428571, 1.92857143, 10. , -6.35714286, -11. , -1.42857143, 1.71428571, 4.64285714]) >>> infl.hat_matrix_diag array([ 0.10084034, 0.11764706, 0.28571429, 0.20168067, 0.10084034, 0.16806723, 0.11764706, 0.08403361, 0.11764706, 0.28571429, 0.33613445, 0.08403361]) >>> infl.resid_press array([ 4.76635514, 4.53333333, 0.8 , -4.56315789, -5.24299065, 2.31818182, 11.33333333, -6.94036697, -12.46666667, -2. , 2.58227848, 5.06880734]) >>> infl.ess_press 465.98646628086374 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_pairwise.py000066400000000000000000000121171224417117700252250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Mar 24 10:26:39 2013 Author: Josef Perktold """ from statsmodels.compatnp.py3k import BytesIO, asbytes import numpy as np from numpy.testing import assert_almost_equal, assert_equal ss = '''\ 43.9 1 1 39.0 1 2 46.7 1 3 43.8 1 4 44.2 1 5 47.7 1 6 43.6 1 7 38.9 1 8 43.6 1 9 40.0 1 10 89.8 2 1 87.1 2 2 92.7 2 3 90.6 2 4 87.7 2 5 92.4 2 6 86.1 2 7 88.1 2 8 90.8 2 9 89.1 2 10 68.4 3 1 69.3 3 2 68.5 3 3 66.4 3 4 70.0 3 5 68.1 3 6 70.6 3 7 65.2 3 8 63.8 3 9 69.2 3 10 36.2 4 1 45.2 4 2 40.7 4 3 40.5 4 4 39.3 4 5 40.3 4 6 43.2 4 7 38.7 4 8 40.9 4 9 39.7 4 10''' #idx Treatment StressReduction ss2 = '''\ 1 mental 2 2 mental 2 3 mental 3 4 mental 4 5 mental 4 6 mental 5 7 mental 3 8 mental 4 9 mental 4 10 mental 4 11 physical 4 12 physical 4 13 physical 3 14 physical 5 15 physical 4 16 physical 1 17 physical 1 18 physical 2 19 physical 3 20 physical 3 21 medical 1 22 medical 2 23 medical 2 24 medical 2 25 medical 3 26 medical 2 27 medical 3 28 medical 1 29 medical 3 30 medical 1''' ss3 = '''\ 1 24.5 1 23.5 1 26.4 1 27.1 1 29.9 2 28.4 2 34.2 2 29.5 2 32.2 2 30.1 3 26.1 3 28.3 3 24.3 3 26.2 3 27.8''' ss5 = '''\ 2 - 3 4.340 0.691 7.989 *** 2 - 1 4.600 0.951 8.249 *** 3 - 2 -4.340 -7.989 -0.691 *** 3 - 1 0.260 -3.389 3.909 - 1 - 2 -4.600 -8.249 -0.951 *** 1 - 3 -0.260 -3.909 3.389 ''' #accommodate recfromtxt for python 3.2, requires bytes ss = asbytes(ss) ss2 = asbytes(ss2) ss3 = asbytes(ss3) ss5 = asbytes(ss5) dta = np.recfromtxt(BytesIO(ss), names=("Rust","Brand","Replication")) dta2 = np.recfromtxt(BytesIO(ss2), names = ("idx", "Treatment", "StressReduction")) dta3 = np.recfromtxt(BytesIO(ss3), names = ("Brand", "Relief")) dta5 = np.recfromtxt(BytesIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t') sas_ = dta5[[1,3,2]] if __name__ == '__main__': import statsmodels.stats.multicomp as multi #incomplete refactoring mc = multi.MultiComparison(dta['Rust'], dta['Brand']) res = mc.tukeyhsd() print res[0] mc2 = multi.MultiComparison(dta2['StressReduction'], dta2['Treatment']) res2 = mc2.tukeyhsd() print res2[0] mc2s = multi.MultiComparison(dta2['StressReduction'][3:29], dta2['Treatment'][3:29]) res2s = mc2s.tukeyhsd() print res2s[0] res2s_001 = mc2s.tukeyhsd(alpha=0.01) #R result tukeyhsd2s = np.array([1.888889,0.8888889,-1,0.2658549,-0.5908785,-2.587133,3.511923,2.368656,0.5871331,0.002837638,0.150456,0.1266072]).reshape(3,4, order='F') assert_almost_equal(res2s_001[1][4], tukeyhsd2s[:,1:3], decimal=3) mc3 = multi.MultiComparison(dta3['Relief'], dta3['Brand']) res3 = mc3.tukeyhsd() print res3[0] # for mci in [mc, mc2, mc3]: # get_thsd(mci) from scipy import stats print mc2.allpairtest(stats.ttest_ind, method='b')[0] '''same as SAS: >>> np.var(mci.groupstats.groupdemean(), ddof=3) 4.6773333333333351 >>> var_ = np.var(mci.groupstats.groupdemean(), ddof=3) >>> tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=qsturng(0.95, 3, 12))[4] array([[ 0.95263648, 8.24736352], [-3.38736352, 3.90736352], [-7.98736352, -0.69263648]]) >>> tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=3.77278)[4] array([[ 0.95098508, 8.24901492], [-3.38901492, 3.90901492], [-7.98901492, -0.69098508]]) ''' ss5 = '''\ Comparisons significant at the 0.05 level are indicated by ***. BRAND Comparison Difference Between Means Simultaneous 95% Confidence Limits Sign. 2 - 3 4.340 0.691 7.989 *** 2 - 1 4.600 0.951 8.249 *** 3 - 2 -4.340 -7.989 -0.691 *** 3 - 1 0.260 -3.389 3.909 - 1 - 2 -4.600 -8.249 -0.951 *** 1 - 3 -0.260 -3.909 3.389 ''' ss5 = '''\ 2 - 3 4.340 0.691 7.989 *** 2 - 1 4.600 0.951 8.249 *** 3 - 2 -4.340 -7.989 -0.691 *** 3 - 1 0.260 -3.389 3.909 - 1 - 2 -4.600 -8.249 -0.951 *** 1 - 3 -0.260 -3.909 3.389 ''' import StringIO dta5 = np.recfromtxt(StringIO.StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t') sas_ = dta5[[1,3,2]] confint1 = res3[1][4] confint2 = sas_[['lower','upper']].view(float).reshape((3,2)) assert_almost_equal(confint1, confint2, decimal=2) reject1 = res3[1][1] reject2 = sas_['sig'] == '***' assert_equal(reject1, reject2) meandiff1 = res3[1][2] meandiff2 = sas_['mean'] assert_almost_equal(meandiff1, meandiff2, decimal=14) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_pandas.py000066400000000000000000000075321224417117700246550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Examples using Pandas """ from datetime import datetime import numpy as np from pandas import DataFrame, Series, datetools import statsmodels.api as sm import statsmodels.tsa.api as tsa data = sm.datasets.stackloss.load() X = DataFrame(data.exog, columns=data.exog_name) X['intercept'] = 1. Y = Series(data.endog) #Example: OLS model = sm.OLS(Y, X) results = model.fit() print results.summary() print results.params print results.cov_params() infl = results.get_influence() print infl.summary_table() #raise #Example RLM huber_t = sm.RLM(Y, X, M=sm.robust.norms.HuberT()) hub_results = huber_t.fit() print hub_results.params print hub_results.bcov_scaled print hub_results.summary() import matplotlib.pyplot as plt from matplotlib import cm import matplotlib as mpl def plot_acf_multiple(ys, lags=20): """ """ from statsmodels.tsa.stattools import acf # hack old_size = mpl.rcParams['font.size'] mpl.rcParams['font.size'] = 8 plt.figure(figsize=(10, 10)) xs = np.arange(lags + 1) acorr = np.apply_along_axis(lambda x: acf(x, nlags=lags), 0, ys) k = acorr.shape[1] for i in range(k): ax = plt.subplot(k, 1, i + 1) ax.vlines(xs, [0], acorr[:, i]) ax.axhline(0, color='k') ax.set_ylim([-1, 1]) # hack? ax.set_xlim([-1, xs[-1] + 1]) mpl.rcParams['font.size'] = old_size #Example TSA descriptive data = sm.datasets.macrodata.load() mdata = data.data df = DataFrame.from_records(mdata) quarter_end = datetools.BQuarterEnd() df.index = [quarter_end.rollforward(datetime(int(y), int(q) * 3, 1)) for y, q in zip(df.pop('year'), df.pop('quarter'))] logged = np.log(df.ix[:, ['m1', 'realgdp', 'cpi']]) logged.plot(subplots=True) log_difference = logged.diff().dropna() plot_acf_multiple(log_difference.values) #Example TSA VAR model = tsa.VAR(log_difference, freq='D') print model.select_order() res = model.fit(2) print res.summary() print res.is_stable() irf = res.irf(20) irf.plot() fevd = res.fevd() fevd.plot() #print res.test_whiteness() print res.test_causality('m1', 'realgdp') #print res.test_normality() # exception ''' Traceback (most recent call last): File "E:\Josef\eclipsegworkspace\statsmodels-git\statsmodels-josef\scikits\statsmodels\examples\ex_pandas.py", line 100, in print res.test_normality() File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all\scikits\statsmodels\tsa\vector_ar\var_model.py", line 1456, in test_normality summ = output.normality_summary(results) File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all\scikits\statsmodels\tsa\vector_ar\output.py", line 182, in normality_summary return hypothesis_test_table(results, title, null_hyp) File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all\scikits\statsmodels\tsa\vector_ar\output.py", line 190, in hypothesis_test_table results['crit_value'], KeyError: 'crit_value' ''' #Example TSA ARMA import numpy as np import statsmodels.api as sm # Generate some data from an ARMA process from statsmodels.tsa.arima_process import arma_generate_sample arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) # The conventions of the arma_generate function require that we specify a # 1 for the zero-lag of the AR and MA parameters and that the AR parameters # be negated. arparams = np.r_[1, -arparams] maparam = np.r_[1, maparams] nobs = 250 y = arma_generate_sample(arparams, maparams, nobs) plt.figure() plt.plot(y) #Now, optionally, we can add some dates information. For this example, # we'll use a pandas time series. dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) y = Series(y, index=dates) arma_mod = sm.tsa.ARMA(y, freq='M') #arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1) #fails #old pandas 0.4.0: AttributeError: 'TimeSeries' object has no attribute 'name' #arma_res.params plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_pareto_plot.py000066400000000000000000000014171224417117700257330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Aug 01 19:20:16 2010 Author: josef-pktd """ import numpy as np from scipy import stats import matplotlib.pyplot as plt nobs = 1000 r = stats.pareto.rvs(1, size=nobs) #rhisto = np.histogram(r, bins=20) rhisto, e = np.histogram(np.clip(r, 0 , 1000), bins=50) plt.figure() plt.loglog(e[:-1]+np.diff(e)/2, rhisto, '-o') plt.figure() plt.loglog(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o') ##plt.figure() ##plt.plot(e[:-1]+np.diff(e)/2, rhisto.cumsum(), '-o') ##plt.figure() ##plt.semilogx(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o') rsind = np.argsort(r) rs = r[rsind] rsf = nobs-rsind.argsort() plt.figure() plt.loglog(rs, nobs-np.arange(nobs), '-o') print stats.linregress(np.log(rs), np.log(nobs-np.arange(nobs))) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_proportion.py000066400000000000000000000034541224417117700256210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Apr 21 07:59:26 2013 Author: Josef Perktold """ import numpy as np import statsmodels.stats.proportion as sms import statsmodels.stats.weightstats as smw from numpy.testing import assert_almost_equal # Region, Eyes, Hair, Count ss = '''\ 1 blue fair 23 1 blue red 7 1 blue medium 24 1 blue dark 11 1 green fair 19 1 green red 7 1 green medium 18 1 green dark 14 1 brown fair 34 1 brown red 5 1 brown medium 41 1 brown dark 40 1 brown black 3 2 blue fair 46 2 blue red 21 2 blue medium 44 2 blue dark 40 2 blue black 6 2 green fair 50 2 green red 31 2 green medium 37 2 green dark 23 2 brown fair 56 2 brown red 42 2 brown medium 53 2 brown dark 54 2 brown black 13''' dta0 = np.array(ss.split()).reshape(-1,4) dta = np.array(map(tuple, dta0.tolist()), dtype=[('Region', int), ('Eyes', 'S6'), ('Hair', 'S6'), ('Count', int)]) xfair = np.repeat([1,0], [228, 762-228]) # comparing to SAS last output at # http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm # confidence interval for tost ci01 = smw.confint_ztest(xfair, alpha=0.1) assert_almost_equal(ci01, [0.2719, 0.3265], 4) res = smw.ztost(xfair, 0.18, 0.38) assert_almost_equal(res[1][0], 7.1865, 4) assert_almost_equal(res[2][0], -4.8701, 4) nn = np.arange(200, 351) pow_z = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05) pow_bin = sms.power_ztost_prop(0.5, 0.72, nn, 0.6, alpha=0.05, dist='binom') import matplotlib.pyplot as plt plt.plot(nn, pow_z[0], label='normal') plt.plot(nn, pow_bin[0], label='binomial') plt.legend(loc='lower right') plt.title('Proportion Equivalence Test: Power as function of sample size') plt.xlabel('Number of Observations') plt.ylabel('Power') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_regressionplots.py000066400000000000000000000105031224417117700266410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Examples for Regression Plots Author: Josef Perktold """ import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std import statsmodels.graphics.regressionplots as smrp #example from tut.ols with changes #fix a seed for these examples np.random.seed(9876789) # OLS non-linear curve but linear in parameters # --------------------------------------------- nsample = 100 sig = 0.5 x1 = np.linspace(0, 20, nsample) x2 = 5 + 3* np.random.randn(nsample) X = np.c_[x1, x2, np.sin(0.5*x1), (x2-5)**2, np.ones(nsample)] beta = [0.5, 0.5, 1, -0.04, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) #estimate only linear function, misspecified because of non-linear terms exog0 = sm.add_constant(np.c_[x1, x2], prepend=False) # plt.figure() # plt.plot(x1, y, 'o', x1, y_true, 'b-') res = sm.OLS(y, exog0).fit() #print res.params #print res.bse plot_old = 0 #True if plot_old: #current bug predict requires call to model.results #print res.model.predict prstd, iv_l, iv_u = wls_prediction_std(res) plt.plot(x1, res.fittedvalues, 'r-o') plt.plot(x1, iv_u, 'r--') plt.plot(x1, iv_l, 'r--') plt.title('blue: true, red: OLS') plt.figure() plt.plot(res.resid, 'o') plt.title('Residuals') fig2 = plt.figure() ax = fig2.add_subplot(2,1,1) #namestr = ' for %s' % self.name if self.name else '' plt.plot(x1, res.resid, 'o') ax.set_title('residuals versus exog')# + namestr) ax = fig2.add_subplot(2,1,2) plt.plot(x2, res.resid, 'o') fig3 = plt.figure() ax = fig3.add_subplot(2,1,1) #namestr = ' for %s' % self.name if self.name else '' plt.plot(x1, res.fittedvalues, 'o') ax.set_title('Fitted values versus exog')# + namestr) ax = fig3.add_subplot(2,1,2) plt.plot(x2, res.fittedvalues, 'o') fig4 = plt.figure() ax = fig4.add_subplot(2,1,1) #namestr = ' for %s' % self.name if self.name else '' plt.plot(x1, res.fittedvalues + res.resid, 'o') ax.set_title('Fitted values plus residuals versus exog')# + namestr) ax = fig4.add_subplot(2,1,2) plt.plot(x2, res.fittedvalues + res.resid, 'o') # see http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/partregr.htm fig5 = plt.figure() ax = fig5.add_subplot(2,1,1) #namestr = ' for %s' % self.name if self.name else '' res1a = sm.OLS(y, exog0[:,[0,2]]).fit() res1b = sm.OLS(x1, exog0[:,[0,2]]).fit() plt.plot(res1b.resid, res1a.resid, 'o') res1c = sm.OLS(res1a.resid, res1b.resid).fit() plt.plot(res1b.resid, res1c.fittedvalues, '-') ax.set_title('Partial Regression plot')# + namestr) ax = fig5.add_subplot(2,1,2) #plt.plot(x2, res.fittedvalues + res.resid, 'o') res2a = sm.OLS(y, exog0[:,[0,1]]).fit() res2b = sm.OLS(x2, exog0[:,[0,1]]).fit() plt.plot(res2b.resid, res2a.resid, 'o') res2c = sm.OLS(res2a.resid, res2b.resid).fit() plt.plot(res2b.resid, res2c.fittedvalues, '-') # see http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm fig6 = plt.figure() ax = fig6.add_subplot(2,1,1) #namestr = ' for %s' % self.name if self.name else '' x1beta = x1*res.params[1] x2beta = x2*res.params[2] plt.plot(x1, x1beta + res.resid, 'o') plt.plot(x1, x1beta, '-') ax.set_title('X_i beta_i plus residuals versus exog (CCPR)')# + namestr) ax = fig6.add_subplot(2,1,2) plt.plot(x2, x2beta + res.resid, 'o') plt.plot(x2, x2beta, '-') #print res.summary() doplots = 1 if doplots: fig1 = smrp.plot_fit(res, 0, y_true=None) smrp.plot_fit(res, 1, y_true=None) smrp.plot_partregress_grid(res, exog_idx=[0,1]) smrp.plot_regress_exog(res, exog_idx=0) smrp.plot_ccpr(res, exog_idx=0) smrp.plot_ccpr_grid(res, exog_idx=[0,1]) from statsmodels.graphics.tests.test_regressionplots import TestPlot tp = TestPlot() tp.test_plot_fit() fig1 = smrp.plot_partregress_grid(res, exog_idx=[0,1]) #add lowess ax = fig1.axes[0] y0 = ax.get_lines()[0]._y x0 = ax.get_lines()[0]._x lres = sm.nonparametric.lowess(y0, x0, frac=0.2) ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5) ax = fig1.axes[1] y0 = ax.get_lines()[0]._y x0 = ax.get_lines()[0]._x lres = sm.nonparametric.lowess(y0, x0, frac=0.2) ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_rootfinding.py000066400000000000000000000060331224417117700257240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Mar 23 13:35:51 2013 Author: Josef Perktold """ import numpy as np from statsmodels.tools.rootfinding import brentq_expanding # Warning: module.global, changing this will affect everyone #import statsmodels.tools.rootfinding as smroots #smroots.DEBUG = True DEBUG = False #True def func(x, a): f = (x - a)**3 if DEBUG: print 'evaluating at %g, fval = %f' % (x, f) return f def func_nan(x, a, b): x = np.atleast_1d(x) f = (x - 1.*a)**3 f[x < b] = np.nan if DEBUG: print 'evaluating at %f, fval = %f' % (x, f) return f def funcn(x, a): f = -(x - a)**3 if DEBUG: print 'evaluating at %g, fval = %g' % (x, f) return f def func2(x, a): f = (x - a)**3 print 'evaluating at %g, fval = %f' % (x, f) return f if __name__ == '__main__': run_all = False if run_all: print brentq_expanding(func, args=(0,), increasing=True) print brentq_expanding(funcn, args=(0,), increasing=False) print brentq_expanding(funcn, args=(-50,), increasing=False) print brentq_expanding(func, args=(20,)) print brentq_expanding(funcn, args=(20,)) print brentq_expanding(func, args=(500000,)) # one bound print brentq_expanding(func, args=(500000,), low=10000) print brentq_expanding(func, args=(-50000,), upp=-1000) print brentq_expanding(funcn, args=(500000,), low=10000) print brentq_expanding(funcn, args=(-50000,), upp=-1000) # both bounds # hits maxiter in brentq if bounds too wide print brentq_expanding(func, args=(500000,), low=300000, upp=700000) print brentq_expanding(func, args=(-50000,), low= -70000, upp=-1000) print brentq_expanding(funcn, args=(500000,), low=300000, upp=700000) print brentq_expanding(funcn, args=(-50000,), low= -70000, upp=-10000) print brentq_expanding(func, args=(1.234e30,), xtol=1e10, increasing=True, maxiter_bq=200) print brentq_expanding(func, args=(-50000,), start_low=-10000) try: print brentq_expanding(func, args=(-500,), start_upp=-100) except ValueError: print 'raised ValueError start_upp needs to be positive' ''' it still works raise ValueError('start_upp needs to be positive') -499.999996336 ''' ''' this doesn't work >>> print brentq_expanding(func, args=(-500,), start_upp=-1000) raise ValueError('start_upp needs to be positive') OverflowError: (34, 'Result too large') ''' try: print brentq_expanding(funcn, args=(-50000,), low= -40000, upp=-10000) except Exception, e: print e val, info = brentq_expanding(func, args=(500,), full_output=True) print val print vars(info) # print brentq_expanding(func_nan, args=(20,0), increasing=True) print brentq_expanding(func_nan, args=(20,0)) # In the next point 0 is minumum, below is nan print brentq_expanding(func_nan, args=(-20,0), increasing=True) print brentq_expanding(func_nan, args=(-20,0)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_sandwich.py000066400000000000000000000051721224417117700252050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """examples for sandwich estimators of covariance Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm import statsmodels.stats.sandwich_covariance as sw #import statsmodels.sandbox.panel.sandwich_covariance_generic as swg nobs = 100 kvars = 4 #including constant x = np.random.randn(nobs, kvars-1) exog = sm.add_constant(x) params_true = np.ones(kvars) y_true = np.dot(exog, params_true) sigma = 0.1 + np.exp(exog[:,-1]) endog = y_true + sigma * np.random.randn(nobs) self = sm.OLS(endog, exog).fit() print self.HC3_se print sw.se_cov(sw.cov_hc3(self)) #test standalone refactoring assert_almost_equal(sw.se_cov(sw.cov_hc0(self)), self.HC0_se, 15) assert_almost_equal(sw.se_cov(sw.cov_hc1(self)), self.HC1_se, 15) assert_almost_equal(sw.se_cov(sw.cov_hc2(self)), self.HC2_se, 15) assert_almost_equal(sw.se_cov(sw.cov_hc3(self)), self.HC3_se, 15) print self.HC0_se print sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=False)) #test White as HAC with nlags=0, same as nlags=1 ? bse_hac0 = sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=False)) assert_almost_equal(bse_hac0, self.HC0_se, 15) print bse_hac0 #test White as HAC with nlags=0, same as nlags=1 ? bse_hac0c = sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=True)) assert_almost_equal(bse_hac0c, self.HC1_se, 15) bse_w = sw.se_cov(sw.cov_white_simple(self, use_correction=False)) print bse_w #test White assert_almost_equal(bse_w, self.HC0_se, 15) bse_wc = sw.se_cov(sw.cov_white_simple(self, use_correction=True)) print bse_wc #test White assert_almost_equal(bse_wc, self.HC1_se, 15) groups = np.repeat(np.arange(5), 20) idx = np.nonzero(np.diff(groups))[0].tolist() groupidx = zip([0]+idx, idx+[len(groups)]) ngroups = len(groupidx) print sw.se_cov(sw.cov_cluster(self, groups)) #two strange looking corner cases BUG? print sw.se_cov(sw.cov_cluster(self, np.ones(len(endog), int), use_correction=False)) print sw.se_cov(sw.cov_crosssection_0(self, np.arange(len(endog)))) #these results are close to simple (no group) white, 50 groups 2 obs each groups = np.repeat(np.arange(50), 100//50) print sw.se_cov(sw.cov_cluster(self, groups)) #2 groups with 50 obs each, what was the interpretation again? groups = np.repeat(np.arange(2), 100//2) print sw.se_cov(sw.cov_cluster(self, groups)) "http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt" ''' test <- read.table( url(paste("http://www.kellogg.northwestern.edu/", "faculty/petersen/htm/papers/se/", "test_data.txt",sep="")), col.names=c("firmid", "year", "x", "y")) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_sandwich2.py000066400000000000000000000050111224417117700252570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Cluster robust standard errors for OLS Created on Fri Dec 16 12:52:13 2011 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm import statsmodels.stats.sandwich_covariance as sw #http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs.dta import statsmodels.iolib.foreign as dta try: srs = dta.genfromdta("srs.dta") print 'using local file' except IOError: import urllib urllib.urlretrieve('http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs.dta', 'srs.dta') print 'downloading file' srs = dta.genfromdta("srs.dta") # from statsmodels.tools.tools import webuse # srs = webuse('srs', 'http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/') # #does currently not cache file y = srs['api00'] #older numpy don't reorder #x = srs[['growth', 'emer', 'yr_rnd']].view(float).reshape(len(y), -1) #force sequence x = np.column_stack([srs[ii] for ii in ['growth', 'emer', 'yr_rnd']]) group = srs['dnum'] #xx = sm.add_constant(x, prepend=True) xx = sm.add_constant(x, prepend=False) #const at end for Stata compatibility #remove nan observation mask = (xx!=-999.0).all(1) #nan code in dta file mask.shape y = y[mask] xx = xx[mask] group = group[mask] #run OLS res_srs = sm.OLS(y, xx).fit() print 'params ', res_srs.params print 'bse_OLS ', res_srs.bse #get cluster robust standard errors and compare with STATA cov_cr = sw.cov_cluster(res_srs, group.astype(int)) bse_cr = sw.se_cov(cov_cr) print 'bse_rob ', bse_cr res_stata = np.rec.array( [ ('growth', '|', -0.1027121, 0.22917029999999999, -0.45000000000000001, 0.65500000000000003, -0.55483519999999997, 0.34941109999999997), ('emer', '|', -5.4449319999999997, 0.72939690000000001, -7.46, 0.0, -6.8839379999999997, -4.0059269999999998), ('yr_rnd', '|', -51.075690000000002, 22.83615, -2.2400000000000002, 0.027, -96.128439999999998, -6.0229350000000004), ('_cons', '|', 740.3981, 13.460760000000001, 55.0, 0.0, 713.84180000000003, 766.95439999999996)], dtype=[('exogname', '|S6'), ('del', '|S1'), ('params', 'float'), ('bse', 'float'), ('tvalues', 'float'), ('pvalues', 'float'), ('cilow', 'float'), ('ciupp', 'float')]) print 'diff Stata', bse_cr - res_stata.bse assert_almost_equal(bse_cr, res_stata.bse, decimal=6) #We see that in this case the robust standard errors of the parameter estimates #are larger than those of OLS by 8 to 35 % print 'reldiff to OLS', bse_cr/res_srs.bse - 1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_sandwich3.py000066400000000000000000000036151224417117700252700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Cluster Robust Standard Errors with Two Clusters Created on Sat Dec 17 08:39:16 2011 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm import statsmodels.stats.sandwich_covariance as sw #requires Petersen's test_data #http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt try: pet = np.genfromtxt("test_data.txt") print 'using local file' except IOError: import urllib urllib.urlretrieve('http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt', 'test_data.txt') print 'downloading file' pet = np.genfromtxt("test_data.txt") endog = pet[:,-1] group = pet[:,0].astype(int) time = pet[:,1].astype(int) exog = sm.add_constant(pet[:,2]) res = sm.OLS(endog, exog).fit() cov01, covg, covt = sw.cov_cluster_2groups(res, group, group2=time) #Reference number from Petersen #http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.htm bse_petw = [0.0284, 0.0284] bse_pet0 = [0.0670, 0.0506] bse_pet1 = [0.0234, 0.0334] #year bse_pet01 = [0.0651, 0.0536] #firm and year bse_0 = sw.se_cov(covg) bse_1 = sw.se_cov(covt) bse_01 = sw.se_cov(cov01) print 'OLS ', res.bse print 'het HC0 ', res.HC0_se, bse_petw - res.HC0_se print 'het firm ', bse_0, bse_0 - bse_pet0 print 'het year ', bse_1, bse_1 - bse_pet1 print 'het firm & year', bse_01, bse_01 - bse_pet01 print 'relative difference standard error het firm & year to OLS' print ' ', bse_01 / res.bse #From the last line we see that the cluster and year robust standard errors #are approximately twice those of OLS assert_almost_equal(bse_petw, res.HC0_se, decimal=4) assert_almost_equal(bse_0, bse_pet0, decimal=4) assert_almost_equal(bse_1, bse_pet1, decimal=4) assert_almost_equal(bse_01, bse_pet01, decimal=4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_scatter_ellipse.py000066400000000000000000000024461224417117700265700ustar00rootroot00000000000000'''example for grid of scatter plots with probability ellipses Author: Josef Perktold License: BSD-3 ''' import numpy as np import matplotlib.pyplot as plt from statsmodels.graphics.plot_grids import scatter_ellipse nvars = 6 mmean = np.arange(1.,nvars+1)/nvars * 1.5 rho = 0.5 #dcorr = rho*np.ones((nvars, nvars)) + (1-rho)*np.eye(nvars) r = np.random.uniform(-0.99, 0.99, size=(nvars, nvars)) ##from scipy import stats ##r = stats.rdist.rvs(1, size=(nvars, nvars)) r = (r + r.T) / 2. assert np.allclose(r, r.T) mcorr = r mcorr[range(nvars), range(nvars)] = 1 #dcorr = np.array([[1, 0.5, 0.1],[0.5, 1, -0.2], [0.1, -0.2, 1]]) mstd = np.arange(1.,nvars+1)/nvars mcov = mcorr * np.outer(mstd, mstd) evals = np.linalg.eigvalsh(mcov) assert evals.min > 0 #assert positive definite nobs = 100 data = np.random.multivariate_normal(mmean, mcov, size=nobs) dmean = data.mean(0) dcov = np.cov(data, rowvar=0) print dmean print dcov dcorr = np.corrcoef(data, rowvar=0) dcorr[np.triu_indices(nvars)] = 0 print dcorr #default #fig = scatter_ellipse(data, level=[0.5, 0.75, 0.95]) #used for checking #fig = scatter_ellipse(data, level=[0.5, 0.75, 0.95], add_titles=True, keep_ticks=True) #check varnames varnames = ['var%d' % i for i in range(nvars)] fig = scatter_ellipse(data, level=0.9, varnames=varnames) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_shrink_pickle.py000066400000000000000000000044001224417117700262230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 09 16:00:27 2012 Author: Josef Perktold """ import numpy as np import statsmodels.api as sm nobs = 10000 np.random.seed(987689) x = np.random.randn(nobs, 3) x = sm.add_constant(x) y = x.sum(1) + np.random.randn(nobs) xf = 0.25 * np.ones((2,4)) model = sm.OLS(y, x) #y_count = np.random.poisson(np.exp(x.sum(1)-x.mean())) #model = sm.Poisson(y_count, x)#, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default results = model.fit() #print results.predict(xf) print results.model.predict(results.params, xf) results.summary() shrinkit = 1 if shrinkit: results.remove_data() import pickle fname = 'try_shrink%d_ols.pickle' % shrinkit fh = open(fname, 'w') pickle.dump(results._results, fh) #pickling wrapper doesn't work fh.close() fh = open(fname, 'r') results2 = pickle.load(fh) fh.close() print results2.predict(xf) print results2.model.predict(results.params, xf) y_count = np.random.poisson(np.exp(x.sum(1)-x.mean())) model = sm.Poisson(y_count, x)#, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default results = model.fit(method='bfgs') results.summary() print results.model.predict(results.params, xf, exposure=1, offset=0) if shrinkit: results.remove_data() else: #work around pickling bug results.mle_settings['callback'] = None import pickle fname = 'try_shrink%d_poisson.pickle' % shrinkit fh = open(fname, 'w') pickle.dump(results._results, fh) #pickling wrapper doesn't work fh.close() fh = open(fname, 'r') results3 = pickle.load(fh) fh.close() print results3.predict(xf, exposure=1, offset=0) print results3.model.predict(results.params, xf, exposure=1, offset=0) def check_pickle(obj): import StringIO fh = StringIO.StringIO() pickle.dump(obj, fh) plen = fh.pos fh.seek(0,0) res = pickle.load(fh) fh.close() return res, plen def test_remove_data_pickle(results, xf): res, l = check_pickle(results) #Note: 10000 is just a guess for the limit on the length of the pickle np.testing.assert_(l < 10000, msg='pickle length not %d < %d' % (l, 10000)) pred1 = results.predict(xf, exposure=1, offset=0) pred2 = res.predict(xf, exposure=1, offset=0) np.testing.assert_equal(pred2, pred1) test_remove_data_pickle(results._results, xf) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/ex_univar_kde.py000066400000000000000000000117411224417117700255330ustar00rootroot00000000000000""" This example tests the nonparametric estimator for several popular univariate distributions with the different bandwidth selction methods - CV-ML; CV-LS; Scott's rule of thumb. Produces six different plots for each distribution 1) Beta 2) f 3) Pareto 4) Laplace 5) Weibull 6) Poisson """ import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt import statsmodels.api as sm KDEMultivariate = sm.nonparametric.KDEMultivariate np.random.seed(123456) # Beta distribution # Parameters a = 2 b = 5 nobs = 250 support = np.random.beta(a, b, size=nobs) rv = stats.beta(a, b) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='c', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='c', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='c', bw='cv_ml') plt.figure(1) plt.plot(support[ix], rv.pdf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of Beta Distributed " \ "Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) # f distribution df = 100 dn = 100 nobs = 250 support = np.random.f(dn, df, size=nobs) rv = stats.f(df, dn) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='c', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='c', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='c', bw='cv_ml') plt.figure(2) plt.plot(support[ix], rv.pdf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of f Distributed " \ "Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) # Pareto distribution a = 2 nobs = 150 support = np.random.pareto(a, size=nobs) rv = stats.pareto(a) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='c', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='c', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='c', bw='cv_ml') plt.figure(3) plt.plot(support[ix], rv.pdf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of Pareto " \ "Distributed Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) # Laplace Distribution mu = 0 s = 1 nobs = 250 support = np.random.laplace(mu, s, size=nobs) rv = stats.laplace(mu, s) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='c', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='c', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='c', bw='cv_ml') plt.figure(4) plt.plot(support[ix], rv.pdf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of Laplace " \ "Distributed Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) # Weibull Distribution a = 1 nobs = 250 support = np.random.weibull(a, size=nobs) rv = stats.weibull_min(a) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='c', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='c', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='c', bw='cv_ml') plt.figure(5) plt.plot(support[ix], rv.pdf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of Weibull " \ "Distributed Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) # Poisson Distribution a = 2 nobs = 250 support = np.random.poisson(a, size=nobs) rv = stats.poisson(a) ix = np.argsort(support) dens_normal = KDEMultivariate(data=[support], var_type='o', bw='normal_reference') dens_cvls = KDEMultivariate(data=[support], var_type='o', bw='cv_ls') dens_cvml = KDEMultivariate(data=[support], var_type='o', bw='cv_ml') plt.figure(6) plt.plot(support[ix], rv.pmf(support[ix]), label='Actual') plt.plot(support[ix], dens_normal.pdf()[ix], label='Scott') plt.plot(support[ix], dens_cvls.pdf()[ix], label='CV_LS') plt.plot(support[ix], dens_cvml.pdf()[ix], label='CV_ML') plt.title("Nonparametric Estimation of the Density of Poisson " \ "Distributed Random Variable") plt.legend(('Actual', 'Scott', 'CV_LS', 'CV_ML')) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_discrete_mnl.py000066400000000000000000000046001224417117700270670ustar00rootroot00000000000000"""Example: statsmodels.discretemod """ import numpy as np import statsmodels.api as sm anes_data = sm.datasets.anes96.load() anes_exog = anes_data.exog anes_exog = sm.add_constant(anes_exog, prepend=False) mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog) mlogit_res = mlogit_mod.fit() # The default method for the fit is Newton-Raphson # However, you can use other solvers mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100) # The below needs a lot of iterations to get it right? #TODO: Add a technical note on algorithms #mlogit_res = mlogit_mod.fit(method='ncg') # this takes forever from statsmodels.iolib.summary import ( summary_params_2d, summary_params_2dflat) exog_names = [anes_data.exog_name[i] for i in [0, 2]+range(5,8)] + ['const'] endog_names = [anes_data.endog_name+'_%d' % i for i in np.unique(mlogit_res.model.endog)[1:]] print '\n\nMultinomial' print summary_params_2d(mlogit_res, extras=['bse','tvalues'], endog_names=endog_names, exog_names=exog_names) tables, table_all = summary_params_2dflat(mlogit_res, endog_names=endog_names, exog_names=exog_names, keep_headers=True) tables, table_all = summary_params_2dflat(mlogit_res, endog_names=endog_names, exog_names=exog_names, keep_headers=False) print '\n\n' print table_all print '\n\n' print '\n'.join((str(t) for t in tables)) from statsmodels.iolib.summary import table_extend at = table_extend(tables) print at print '\n\n' print mlogit_res.summary() print mlogit_res.summary(yname='PID') #the following is supposed to raise ValueError #mlogit_res.summary(yname=['PID']) endog_names = [anes_data.endog_name+'=%d' % i for i in np.unique(mlogit_res.model.endog)[1:]] print mlogit_res.summary(yname='PID', yname_list=endog_names, xname=exog_names) ''' #trying pickle import pickle #, copy #copy.deepcopy(mlogit_res) #raises exception: AttributeError: 'ResettableCache' object has no attribute '_resetdict' mnl_res = mlogit_mod.fit(method='bfgs', maxiter=100) mnl_res.cov_params() #mnl_res.model.endog = None #mnl_res.model.exog = None pickle.dump(mnl_res, open('mnl_res.dump', 'w')) mnl_res_l = pickle.load(open('mnl_res.dump', 'r')) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_enhanced_boxplots.py000066400000000000000000000061041224417117700301170ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm # Necessary to make horizontal axis labels fit plt.rcParams['figure.subplot.bottom'] = 0.23 data = sm.datasets.anes96.load_pandas() party_ID = np.arange(7) labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", "Independent-Independent", "Independent-Republican", "Weak Republican", "Strong Republican"] # Group age by party ID. age = [data.exog['age'][data.endog == id] for id in party_ID] # Create a violin plot. fig = plt.figure() ax = fig.add_subplot(111) sm.graphics.violinplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") ax.set_title("US national election '96 - Age & Party Identification") # Create a bean plot. fig2 = plt.figure() ax = fig2.add_subplot(111) sm.graphics.beanplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") ax.set_title("US national election '96 - Age & Party Identification") # Create a jitter plot. fig3 = plt.figure() ax = fig3.add_subplot(111) plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30, 'violin_fc':(0.8, 0.8, 0.8), 'jitter_marker':'.', 'jitter_marker_size':3, 'bean_color':'#FF6F00', 'bean_mean_color':'#009D91'} sm.graphics.beanplot(age, ax=ax, labels=labels, jitter=True, plot_opts=plot_opts) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") ax.set_title("US national election '96 - Age & Party Identification") # Create an asymmetrical jitter plot. ix = data.exog['income'] < 16 # incomes < $30k age = data.exog['age'][ix] endog = data.endog[ix] age_lower_income = [age[endog == id] for id in party_ID] ix = data.exog['income'] >= 20 # incomes > $50k age = data.exog['age'][ix] endog = data.endog[ix] age_higher_income = [age[endog == id] for id in party_ID] fig = plt.figure() ax = fig.add_subplot(111) plot_opts['violin_fc'] = (0.5, 0.5, 0.5) plot_opts['bean_show_mean'] = False plot_opts['bean_show_median'] = False plot_opts['bean_legend_text'] = 'Income < \$30k' plot_opts['cutoff_val'] = 10 sm.graphics.beanplot(age_lower_income, ax=ax, labels=labels, side='left', jitter=True, plot_opts=plot_opts) plot_opts['violin_fc'] = (0.7, 0.7, 0.7) plot_opts['bean_color'] = '#009D91' plot_opts['bean_legend_text'] = 'Income > \$50k' sm.graphics.beanplot(age_higher_income, ax=ax, labels=labels, side='right', jitter=True, plot_opts=plot_opts) ax.set_xlabel("Party identification of respondent.") ax.set_ylabel("Age") ax.set_title("US national election '96 - Age & Party Identification") # Show all plots. plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_functional_plots.py000066400000000000000000000024611224417117700300050ustar00rootroot00000000000000'''Functional boxplots and rainbow plots see docstrings for an explanation Author: Ralf Gommers ''' import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm #Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea #surface temperature data. data = sm.datasets.elnino.load() #Create a functional boxplot: #We see that the years 1982-83 and 1997-98 are outliers; these are #the years where El Nino (a climate pattern characterized by warming #up of the sea surface and higher air pressures) occurred with unusual #intensity. fig = plt.figure() ax = fig.add_subplot(111) res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58, labels=data.raw_data[:, 0].astype(int), ax=ax) ax.set_xlabel("Month of the year") ax.set_ylabel("Sea surface temperature (C)") ax.set_xticks(np.arange(13, step=3) - 1) ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) ax.set_xlim([-0.2, 11.2]) #Create a rainbow plot: fig = plt.figure() ax = fig.add_subplot(111) res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax) ax.set_xlabel("Month of the year") ax.set_ylabel("Sea surface temperature (C)") ax.set_xticks(np.arange(13, step=3) - 1) ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) ax.set_xlim([-0.2, 11.2]) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_kde.py000066400000000000000000000020031224417117700251550ustar00rootroot00000000000000import numpy as np from scipy import stats from statsmodels.distributions.mixture_rvs import mixture_rvs from statsmodels.nonparametric.kde import kdensityfft import matplotlib.pyplot as plt np.random.seed(12345) obs_dist = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm], kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5))) #.. obs_dist = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta], #.. kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5)))) f_hat, grid, bw = kdensityfft(obs_dist, kernel="gauss", bw="scott") # Check the plot plt.figure() plt.hist(obs_dist, bins=50, normed=True, color='red') plt.plot(grid, f_hat, lw=2, color='black') plt.show() # do some timings # get bw first because they're not streamlined from statsmodels.nonparametric import bandwidths bw = bandwidths.bw_scott(obs_dist) #.. timeit kdensity(obs_dist, kernel="gauss", bw=bw, gridsize=2**10) #.. timeit kdensityfft(obs_dist, kernel="gauss", bw=bw, gridsize=2**10) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_ols_minimal_comp.py000066400000000000000000000012631224417117700277420ustar00rootroot00000000000000"""Example: minimal OLS add example for new compare methods """ import numpy as np import statsmodels.api as sm np.random.seed(765367) nsample = 100 x = np.linspace(0,10, 100) X = sm.add_constant(np.column_stack((x, x**2))) beta = np.array([10, 1, 0.01]) y = np.dot(X, beta) + np.random.normal(size=nsample) results = sm.OLS(y, X).fit() print results.summary() results2 = sm.OLS(y, X[:,:2]).fit() print results.compare_f_test(results2) print results.f_test([0,0,1]) print results.compare_lr_test(results2) ''' (1.841903749875428, 0.1778775592033047) (1.8810663357027693, 0.17021300121753191, 1.0) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/example_rpy.py000066400000000000000000000032751224417117700252400ustar00rootroot00000000000000'''Just two examples for using rpy These examples are mainly for developers. # example 1: OLS using LM # example 2: GLM with binomial family The second results isn't exactly correct since it assumes that each obvervation has the same number of trials see datasets/longley for an R script with the correct syntax. See rmodelwrap.py in the tests folder for a convenience wrapper to make rpy more like statsmodels. Note, however, that rmodelwrap was created in a very ad hoc manner and due to the idiosyncracies in R it does not work for all types of R models. There are also R scripts included with most of the datasets to run some basic models for comparisons of results to statsmodels. ''' from rpy import r import numpy as np import statsmodels.api as sm examples = [1, 2] if 1 in examples: data = sm.datasets.longley.load() y,x = data.endog, sm.add_constant(data.exog, prepend=False) des_cols = ['x.%d' % (i+1) for i in range(x.shape[1])] formula = r('y~%s-1' % '+'.join(des_cols)) frame = r.data_frame(y=y, x=x) results = r.lm(formula, data=frame) print results.keys() print results['coefficients'] if 2 in examples: data2 = sm.datasets.star98.load() y2,x2 = data2.endog, sm.add_constant(data2.exog, prepend=False) import rpy y2 = y2[:,0]/y2.sum(axis=1) des_cols2 = ['x.%d' % (i+1) for i in range(x2.shape[1])] formula2 = r('y~%s-1' % '+'.join(des_cols2)) frame2 = r.data_frame(y=y2, x=x2) results2 = r.glm(formula2, data=frame2, family='binomial') params_est = [results2['coefficients'][k] for k in sorted(results2['coefficients'])] print params_est print ', '.join(['%13.10f']*21) % tuple(params_est) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/l1_demo/000077500000000000000000000000001224417117700236525ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/l1_demo/demo.py000066400000000000000000000323151224417117700251540ustar00rootroot00000000000000from optparse import OptionParser import statsmodels.api as sm import scipy as sp from scipy import linalg from scipy import stats import pdb # pdb.set_trace() docstr = """ Demonstrates l1 regularization for likelihood models. Use different models by setting mode = mnlogit, logit, or probit. Examples ------- $ python demo.py --get_l1_slsqp_results logit >>> import demo >>> demo.run_demo('logit') The Story --------- The maximum likelihood (ML) solution works well when the number of data points is large and the noise is small. When the ML solution starts "breaking", the regularized solution should do better. The l1 Solvers -------------- The solvers are slower than standard Newton, and sometimes have convergence issues Nonetheless, the final solution makes sense and is often better than the ML solution. The standard l1 solver is fmin_slsqp and is included with scipy. It sometimes has trouble verifying convergence when the data size is large. The l1_cvxopt_cp solver is part of CVXOPT and this package needs to be installed separately. It works well even for larger data sizes. """ def main(): """ Provides a CLI for the demo. """ usage = "usage: %prog [options] mode" usage += '\n'+docstr parser = OptionParser(usage=usage) # base_alpha parser.add_option("-a", "--base_alpha", help="Size of regularization param (the param actully used will "\ "automatically scale with data size in this demo) "\ "[default: %default]", dest='base_alpha', action='store', type='float', default=0.01) # num_samples parser.add_option("-N", "--num_samples", help="Number of data points to generate for fit "\ "[default: %default]", dest='N', action='store', type='int', default=500) # get_l1_slsqp_results parser.add_option("--get_l1_slsqp_results", help="Do an l1 fit using slsqp. [default: %default]", \ action="store_true",dest='get_l1_slsqp_results', default=False) # get_l1_cvxopt_results parser.add_option("--get_l1_cvxopt_results", help="Do an l1 fit using cvxopt. [default: %default]", \ action="store_true",dest='get_l1_cvxopt_results', default=False) # num_nonconst_covariates parser.add_option("--num_nonconst_covariates", help="Number of covariates that are not constant "\ "(a constant will be prepended) [default: %default]", dest='num_nonconst_covariates', action='store', type='int', default=10) # noise_level parser.add_option("--noise_level", help="Level of the noise relative to signal [default: %default]", dest='noise_level', action='store', type='float', default=0.2) # cor_length parser.add_option("--cor_length", help="Correlation length of the (Gaussian) independent variables"\ "[default: %default]", dest='cor_length', action='store', type='float', default=2) # num_zero_params parser.add_option("--num_zero_params", help="Number of parameters equal to zero for every target in "\ "logistic regression examples. [default: %default]", dest='num_zero_params', action='store', type='int', default=8) # num_targets parser.add_option("-J", "--num_targets", help="Number of choices for the endogenous response in "\ "multinomial logit example [default: %default]", dest='num_targets', action='store', type='int', default=3) # print_summaries parser.add_option("-s", "--print_summaries", help="Print the full fit summary. [default: %default]", \ action="store_true",dest='print_summaries', default=False) # save_arrays parser.add_option("--save_arrays", help="Save exog/endog/true_params to disk for future use. "\ "[default: %default]", action="store_true",dest='save_arrays', default=False) # load_old_arrays parser.add_option("--load_old_arrays", help="Load exog/endog/true_params arrays from disk. "\ "[default: %default]", action="store_true",dest='load_old_arrays', default=False) (options, args) = parser.parse_args() assert len(args) == 1 mode = args[0].lower() run_demo(mode, **options.__dict__) def run_demo(mode, base_alpha=0.01, N=500, get_l1_slsqp_results=False, get_l1_cvxopt_results=False, num_nonconst_covariates=10, noise_level=0.2, cor_length=2, num_zero_params=8, num_targets=3, print_summaries=False, save_arrays=False, load_old_arrays=False): """ Run the demo and print results. Parameters ---------- mode : String either 'logit', 'mnlogit', or 'probit' base_alpha : Float Size of regularization param (the param actually used will automatically scale with data size in this demo) N : Integer Number of data points to generate for fit get_l1_slsqp_results : boolean, Do an l1 fit using slsqp. get_l1_cvxopt_results : boolean Do an l1 fit using cvxopt num_nonconst_covariates : Integer Number of covariates that are not constant (a constant will be prepended) noise_level : Float (non-negative) Level of the noise relative to signal cor_length : Float (non-negative) Correlation length of the (Gaussian) independent variables num_zero_params : Integer Number of parameters equal to zero for every target in logistic regression examples. num_targets : Integer Number of choices for the endogenous response in multinomial logit example print_summaries : Boolean print the full fit summary. save_arrays : Boolean Save exog/endog/true_params to disk for future use. load_old_arrays Load exog/endog/true_params arrays from disk. """ if mode != 'mnlogit': print "Setting num_targets to 2 since mode != 'mnlogit'" num_targets = 2 models = { 'logit': sm.Logit, 'mnlogit': sm.MNLogit, 'probit': sm.Probit} endog_funcs = { 'logit': get_logit_endog, 'mnlogit': get_logit_endog, 'probit': get_probit_endog} # The regularization parameter # Here we scale it with N for simplicity. In practice, you should # use cross validation to pick alpha alpha = base_alpha * N * sp.ones((num_nonconst_covariates+1, num_targets-1)) alpha[0,:] = 0 # Don't regularize the intercept #### Make the data and model exog = get_exog(N, num_nonconst_covariates, cor_length) exog = sm.add_constant(exog) true_params = sp.rand(num_nonconst_covariates+1, num_targets-1) if num_zero_params: true_params[-num_zero_params:, :] = 0 endog = endog_funcs[mode](true_params, exog, noise_level) endog, exog, true_params = save_andor_load_arrays( endog, exog, true_params, save_arrays, load_old_arrays) model = models[mode](endog, exog) #### Get the results and print results = run_solvers(model, true_params, alpha, get_l1_slsqp_results, get_l1_cvxopt_results, print_summaries) summary_str = get_summary_str(results, true_params, get_l1_slsqp_results, get_l1_cvxopt_results, print_summaries) print summary_str def run_solvers(model, true_params, alpha, get_l1_slsqp_results, get_l1_cvxopt_results, print_summaries): """ Runs the solvers using the specified settings and returns a result string. Works the same for any l1 penalized likelihood model. """ results = {} #### Train the models # Get ML results results['results_ML'] = model.fit(method='newton') # Get l1 results start_params = results['results_ML'].params.ravel(order='F') if get_l1_slsqp_results: results['results_l1_slsqp'] = model.fit_regularized( method='l1', alpha=alpha, maxiter=1000, start_params=start_params, retall=True) if get_l1_cvxopt_results: results['results_l1_cvxopt_cp'] = model.fit_regularized( method='l1_cvxopt_cp', alpha=alpha, maxiter=50, start_params=start_params, retall=True, feastol=1e-5) return results def get_summary_str(results, true_params, get_l1_slsqp_results, get_l1_cvxopt_results, print_summaries): """ Gets a string summarizing the results. """ #### Extract specific results results_ML = results['results_ML'] RMSE_ML = get_RMSE(results_ML, true_params) if get_l1_slsqp_results: results_l1_slsqp = results['results_l1_slsqp'] if get_l1_cvxopt_results: results_l1_cvxopt_cp = results['results_l1_cvxopt_cp'] #### Format summaries # Short summary print_str = '\n\n=========== Short Error Summary ============' print_str += '\n\n The maximum likelihood fit RMS error = %.4f'%RMSE_ML if get_l1_slsqp_results: RMSE_l1_slsqp = get_RMSE(results_l1_slsqp, true_params) print_str += '\n The l1_slsqp fit RMS error = %.4f'%RMSE_l1_slsqp if get_l1_cvxopt_results: RMSE_l1_cvxopt_cp = get_RMSE(results_l1_cvxopt_cp, true_params) print_str += '\n The l1_cvxopt_cp fit RMS error = %.4f'%RMSE_l1_cvxopt_cp # Parameters print_str += '\n\n\n============== Parameters =================' print_str += "\n\nTrue parameters: \n%s"%true_params # Full summary if print_summaries: print_str += '\n' + results_ML.summary().as_text() if get_l1_slsqp_results: print_str += '\n' + results_l1_slsqp.summary().as_text() if get_l1_cvxopt_results: print_str += '\n' + results_l1_cvxopt_cp.summary().as_text() else: print_str += '\n\nThe maximum likelihood params are \n%s'%results_ML.params if get_l1_slsqp_results: print_str += '\n\nThe l1_slsqp params are \n%s'%results_l1_slsqp.params if get_l1_cvxopt_results: print_str += '\n\nThe l1_cvxopt_cp params are \n%s'%\ results_l1_cvxopt_cp.params # Return return print_str def save_andor_load_arrays( endog, exog, true_params, save_arrays, load_old_arrays): if save_arrays: sp.save('endog.npy', endog) sp.save('exog.npy', exog) sp.save('true_params.npy', true_params) if load_old_arrays: endog = sp.load('endog.npy') exog = sp.load('exog.npy') true_params = sp.load('true_params.npy') return endog, exog, true_params def get_RMSE(results, true_params): """ Gets the (normalized) root mean square error. """ diff = results.params.reshape(true_params.shape) - true_params raw_RMSE = sp.sqrt(((diff)**2).sum()) param_norm = sp.sqrt((true_params**2).sum()) return raw_RMSE / param_norm def get_logit_endog(true_params, exog, noise_level): """ Gets an endogenous response that is consistent with the true_params, perturbed by noise at noise_level. """ N = exog.shape[0] ### Create the probability of entering the different classes, ### given exog and true_params Xdotparams = sp.dot(exog, true_params) noise = noise_level * sp.randn(*Xdotparams.shape) eXB = sp.column_stack((sp.ones(len(Xdotparams)), sp.exp(Xdotparams))) class_probabilities = eXB / eXB.sum(1)[:, None] ### Create the endog cdf = class_probabilities.cumsum(axis=1) endog = sp.zeros(N) for i in xrange(N): endog[i] = sp.searchsorted(cdf[i, :], sp.rand()) return endog def get_probit_endog(true_params, exog, noise_level): """ Gets an endogenous response that is consistent with the true_params, perturbed by noise at noise_level. """ N = exog.shape[0] ### Create the probability of entering the different classes, ### given exog and true_params Xdotparams = sp.dot(exog, true_params) noise = noise_level * sp.randn(*Xdotparams.shape) ### Create the endog cdf = stats.norm._cdf(-Xdotparams) endog = sp.zeros(N) for i in xrange(N): endog[i] = sp.searchsorted(cdf[i, :], sp.rand()) return endog def get_exog(N, num_nonconst_covariates, cor_length): """ Returns an exog array with correlations determined by cor_length. The covariance matrix of exog will have (asymptotically, as :math:'N\\to\\inf') .. math:: Cov[i,j] = \\exp(-|i-j| / cor_length) Higher cor_length makes the problem more ill-posed, and easier to screw up with noise. BEWARE: With very long correlation lengths, you often get a singular KKT matrix (during the l1_cvxopt_cp fit) """ ## Create the noiseless exog uncorrelated_exog = sp.randn(N, num_nonconst_covariates) if cor_length == 0: exog = uncorrelated_exog else: cov_matrix = sp.zeros((num_nonconst_covariates, num_nonconst_covariates)) j = sp.arange(num_nonconst_covariates) for i in xrange(num_nonconst_covariates): cov_matrix[i,:] = sp.exp(-sp.fabs(i-j) / cor_length) chol = linalg.cholesky(cov_matrix) # cov_matrix = sp.dot(chol.T, chol) exog = sp.dot(uncorrelated_exog, chol) ## Return return exog if __name__ == '__main__': main() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/l1_demo/short_demo.py000066400000000000000000000070741224417117700263770ustar00rootroot00000000000000""" You can fit your LikelihoodModel using l1 regularization by changing the method argument and adding an argument alpha. See code for details. The Story --------- The maximum likelihood (ML) solution works well when the number of data points is large and the noise is small. When the ML solution starts "breaking", the regularized solution should do better. The l1 Solvers -------------- The standard l1 solver is fmin_slsqp and is included with scipy. It sometimes has trouble verifying convergence when the data size is large. The l1_cvxopt_cp solver is part of CVXOPT and this package needs to be installed separately. It works well even for larger data sizes. """ import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np import pdb # pdb.set_trace() ## Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog) N = len(spector_data.endog) K = spector_data.exog.shape[1] ### Logit Model logit_mod = sm.Logit(spector_data.endog, spector_data.exog) ## Standard logistic regression logit_res = logit_mod.fit() ## Regularized regression # Set the reularization parameter to something reasonable alpha = 0.05 * N * np.ones(K) # Use l1, which solves via a built-in (scipy.optimize) solver logit_l1_res = logit_mod.fit_regularized(method='l1', alpha=alpha, acc=1e-6) # Use l1_cvxopt_cp, which solves with a CVXOPT solver logit_l1_cvxopt_res = logit_mod.fit_regularized( method='l1_cvxopt_cp', alpha=alpha) ## Print results print "============ Results for Logit =================" print "ML results" print logit_res.summary() print "l1 results" print logit_l1_res.summary() print logit_l1_cvxopt_res.summary() ### Multinomial Logit Example using American National Election Studies Data anes_data = sm.datasets.anes96.load() anes_exog = anes_data.exog anes_exog = sm.add_constant(anes_exog, prepend=False) mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog) mlogit_res = mlogit_mod.fit() ## Set the regularization parameter. alpha = 10 * np.ones((mlogit_mod.J - 1, mlogit_mod.K)) # Don't regularize the constant alpha[-1,:] = 0 mlogit_l1_res = mlogit_mod.fit_regularized(method='l1', alpha=alpha) print mlogit_l1_res.params #mlogit_l1_res = mlogit_mod.fit_regularized( # method='l1_cvxopt_cp', alpha=alpha, abstol=1e-10, trim_tol=1e-6) #print mlogit_l1_res.params ## Print results print "============ Results for MNLogit =================" print "ML results" print mlogit_res.summary() print "l1 results" print mlogit_l1_res.summary() # # #### Logit example with many params, sweeping alpha spector_data = sm.datasets.spector.load() X = spector_data.exog Y = spector_data.endog ## Fit N = 50 # number of points to solve at K = X.shape[1] logit_mod = sm.Logit(Y, X) coeff = np.zeros((N, K)) # Holds the coefficients alphas = 1 / np.logspace(-0.5, 2, N) ## Sweep alpha and store the coefficients # QC check doesn't always pass with the default options. # Use the options QC_verbose=True and disp=True # to to see what is happening. It just barely doesn't pass, so I decreased # acc and increased QC_tol to make it pass for n, alpha in enumerate(alphas): logit_res = logit_mod.fit_regularized( method='l1', alpha=alpha, trim_mode='off', QC_tol=0.1, disp=False, QC_verbose=True, acc=1e-15) coeff[n,:] = logit_res.params ## Plot plt.figure(1);plt.clf();plt.grid() plt.title('Regularization Path'); plt.xlabel('alpha'); plt.ylabel('Parameter value'); for i in xrange(K): plt.plot(alphas, coeff[:,i], label='X'+str(i), lw=3) plt.legend(loc='best') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/l1_demo/sklearn_compare.py000066400000000000000000000070441224417117700273760ustar00rootroot00000000000000""" For comparison with sklearn.linear_model.LogisticRegression Computes a regularzation path with both packages. The coefficient values in either path are related by a "constant" in the sense that for any fixed value of the constraint C and log likelihood, there exists an l1 regularization constant alpha such that the optimal solutions should be the same. Note that alpha(C) is a nonlinear function in general. Here we find alpha(C) by finding a reparameterization of the statsmodels path that makes the paths match up. An equation is available, but to use it I would need to hack the sklearn code to extract the gradient of the log likelihood. The results "prove" that the regularization paths are the same. Note that finding the reparameterization is non-trivial since the coefficient paths are NOT monotonic. As a result, the paths don't match up perfectly. """ from sklearn import linear_model from sklearn import datasets import statsmodels.api as sm import numpy as np import matplotlib.pyplot as plt import pdb # pdb.set_trace import sys ## Decide which dataset to use # Use either spector or anes96 use_spector = False #### Load data ## The Spector and Mazzeo (1980) data from statsmodels if use_spector: spector_data = sm.datasets.spector.load() X = spector_data.exog Y = spector_data.endog else: raise Exception( "The anes96 dataset is now loaded in as a short version that cannot "\ "be used here") anes96_data = sm.datasets.anes96.load_pandas() Y = anes96_data.exog.vote #### Fit and plot results N = 200 # number of points to solve at K = X.shape[1] ## Statsmodels logit_mod = sm.Logit(Y, X) sm_coeff = np.zeros((N, K)) # Holds the coefficients if use_spector: alphas = 1 / np.logspace(-1, 2, N) # for spector_data else: alphas = 1 / np.logspace(-3, 2, N) # for anes96_data for n, alpha in enumerate(alphas): logit_res = logit_mod.fit_regularized( method='l1', alpha=alpha, disp=False, trim_mode='off') sm_coeff[n,:] = logit_res.params ## Sklearn sk_coeff = np.zeros((N, K)) if use_spector: Cs = np.logspace(-0.45, 2, N) else: Cs = np.logspace(-2.6, 0, N) for n, C in enumerate(Cs): clf = linear_model.LogisticRegression( C=C, penalty='l1', fit_intercept=False) clf.fit(X, Y) sk_coeff[n, :] = clf.coef_ ## Get the reparametrization of sm_coeff that makes the paths equal # Do this by finding one single re-parameterization of the second coefficient # that makes the path for the second coefficient (almost) identical. This # same parameterization will work for the other two coefficients since the # the regularization coefficients (in sk and sm) are related by a constant. # # special_X is chosen since this coefficient becomes non-zero before the # other two...and is relatively monotonic...with both datasets. sk_special_X = np.fabs(sk_coeff[:,2]) sm_special_X = np.fabs(sm_coeff[:,2]) s = np.zeros(N) # Note that sk_special_X will not always be perfectly sorted... s = np.searchsorted(sk_special_X, sm_special_X) ## Plot plt.figure(2);plt.clf();plt.grid() plt.xlabel('Index in sklearn simulation') plt.ylabel('Coefficient value') plt.title('Regularization Paths') colors = ['b', 'r', 'k', 'g', 'm', 'c', 'y'] for coeff, name in [(sm_coeff, 'sm'), (sk_coeff, 'sk')]: if name == 'sk': ltype = 'x' # linetype t = range(N) # The 'time' parameter else: ltype = 'o' t = s for i in xrange(K): plt.plot(t, coeff[:,i], ltype+colors[i], label=name+'-X'+str(i)) plt.legend(loc='best') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/run_all.py000066400000000000000000000035361224417117700243470ustar00rootroot00000000000000'''run all examples to make sure we don't get an exception Note: If an example contaings plt.show(), then all plot windows have to be closed manually, at least in my setup. uncomment plt.show() to show all plot windows ''' import matplotlib.pyplot as plt #matplotlib is required for many examples stop_on_error = True filelist = ['example_glsar.py', 'example_wls.py', 'example_gls.py', 'example_glm.py', 'example_ols_tftest.py', #'example_rpy.py', 'example_ols.py', 'example_ols_minimal.py', 'example_rlm.py', 'example_discrete.py', 'example_predict.py', 'example_ols_table.py', 'tut_ols.py', 'tut_ols_rlm.py', 'tut_ols_wls.py'] use_glob = True if use_glob: import glob filelist = glob.glob('*.py') print zip(range(len(filelist)), filelist) for fname in ['run_all.py', 'example_rpy.py']: filelist.remove(fname) #filelist = filelist[15:] #temporarily disable show plt_show = plt.show def noop(*args): pass plt.show = noop cont = raw_input("""Are you sure you want to run all of the examples? This is done mainly to check that they are up to date. (y/n) >>> """) if 'y' in cont.lower(): has_errors = [] for run_all_f in filelist: try: print "\n\nExecuting example file", run_all_f print "-----------------------" + "-"*len(run_all_f) execfile(run_all_f) except: #f might be overwritten in the executed file print "**********************" + "*"*len(run_all_f) print "ERROR in example file", run_all_f print "**********************" + "*"*len(run_all_f) has_errors.append(run_all_f) if stop_on_error: raise print '\nModules that raised exception:' print has_errors #reenable show after closing windows plt.close('all') plt.show = plt_show plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/t_est_rlm.py000066400000000000000000000020731224417117700246760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Example from robust test_rlm, fails on Mac Created on Sun Mar 27 14:36:40 2011 """ import numpy as np import statsmodels.api as sm RLM = sm.RLM DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 from statsmodels.datasets.stackloss import load data = load() # class attributes for subclasses data.exog = sm.add_constant(data.exog, prepend=False) decimal_standarderrors = DECIMAL_1 decimal_scale = DECIMAL_3 results = RLM(data.endog, data.exog,\ M=sm.robust.norms.HuberT()).fit() # default M h2 = RLM(data.endog, data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled h3 = RLM(data.endog, data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled from statsmodels.robust.tests.results.results_rlm import Huber res2 = Huber() print "res2.h1" print res2.h1 print "results.bcov_scaled" print results.bcov_scaled print "res2.h1 - results.bcov_scaled" print res2.h1 - results.bcov_scaled from numpy.testing import assert_almost_equal assert_almost_equal(res2.h1, results.bcov_scaled, 4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_2regress.py000066400000000000000000000035041224417117700253400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """F test for null hypothesis that coefficients in two regressions are the same see discussion in http://mail.scipy.org/pipermail/scipy-user/2010-March/024851.html Created on Thu Mar 25 22:56:45 2010 Author: josef-pktd """ import numpy as np from numpy.testing import assert_almost_equal import statsmodels.api as sm np.random.seed(87654589) nobs = 10 #100 x1 = np.random.randn(nobs) y1 = 10 + 15*x1 + 2*np.random.randn(nobs) x1 = sm.add_constant(x1, prepend=False) assert_almost_equal(x1, np.vander(x1[:,0],2), 16) res1 = sm.OLS(y1, x1).fit() print res1.params print np.polyfit(x1[:,0], y1, 1) assert_almost_equal(res1.params, np.polyfit(x1[:,0], y1, 1), 14) print res1.summary(xname=['x1','const1']) #regression 2 x2 = np.random.randn(nobs) y2 = 19 + 17*x2 + 2*np.random.randn(nobs) #y2 = 10 + 15*x2 + 2*np.random.randn(nobs) # if H0 is true x2 = sm.add_constant(x2, prepend=False) assert_almost_equal(x2, np.vander(x2[:,0],2), 16) res2 = sm.OLS(y2, x2).fit() print res2.params print np.polyfit(x2[:,0], y2, 1) assert_almost_equal(res2.params, np.polyfit(x2[:,0], y2, 1), 14) print res2.summary(xname=['x2','const2']) # joint regression x = np.concatenate((x1,x2),0) y = np.concatenate((y1,y2)) dummy = np.arange(2*nobs)>nobs-1 x = np.column_stack((x,x*dummy[:,None])) res = sm.OLS(y, x).fit() print res.summary(xname=['x','const','x2','const2']) print '\nF test for equal coefficients in 2 regression equations' #effect of dummy times second regression is zero #is equivalent to 3rd and 4th coefficient are both zero print res.f_test([[0,0,1,0],[0,0,0,1]]) print '\nchecking coefficients individual versus joint' print res1.params, res2.params print res.params[:2], res.params[:2]+res.params[2:] assert_almost_equal(res1.params, res.params[:2], 13) assert_almost_equal(res2.params, res.params[:2]+res.params[2:], 13) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_gof_chisquare.py000066400000000000000000000062521224417117700264260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Feb 28 15:37:53 2013 Author: Josef Perktold """ import numpy as np from scipy import stats from statsmodels.stats.gof import (chisquare, chisquare_power, chisquare_effectsize) from numpy.testing import assert_almost_equal nobs = 30000 n_bins = 5 probs = 1./np.arange(2, n_bins + 2) probs /= probs.sum() #nicer probs = np.round(probs, 2) probs[-1] = 1 - probs[:-1].sum() print "probs", probs probs_d = probs.copy() delta = 0.01 probs_d[0] += delta probs_d[1] -= delta probs_cs = probs.cumsum() #rvs = np.random.multinomial(n_bins, probs, size=10) #rvs = np.round(np.random.randn(10), 2) rvs = np.argmax(np.random.rand(nobs,1) < probs_cs, 1) print probs print np.bincount(rvs) * (1. / nobs) freq = np.bincount(rvs) print stats.chisquare(freq, nobs*probs) print 'null', chisquare(freq, nobs*probs) print 'delta', chisquare(freq, nobs*probs_d) chisq_null, pval_null = chisquare(freq, nobs*probs) # effect size ? d_null = ((freq / float(nobs) - probs)**2 / probs).sum() print d_null d_delta = ((freq / float(nobs) - probs_d)**2 / probs_d).sum() print d_delta d_null_alt = ((probs - probs_d)**2 / probs_d).sum() print d_null_alt print '\nchisquare with value' chisq, pval = chisquare(freq, nobs*probs_d) print stats.ncx2.sf(chisq_null, n_bins, 0.001 * nobs) print stats.ncx2.sf(chisq, n_bins, 0.001 * nobs) print stats.ncx2.sf(chisq, n_bins, d_delta * nobs) print chisquare(freq, nobs*probs_d, value=np.sqrt(d_delta)) print chisquare(freq, nobs*probs_d, value=np.sqrt(chisq / nobs)) print assert_almost_equal(stats.chi2.sf(d_delta * nobs, n_bins - 1), chisquare(freq, nobs*probs_d)[1], decimal=13) crit = stats.chi2.isf(0.05, n_bins - 1) power = stats.ncx2.sf(crit, n_bins-1, 0.001**2 * nobs) #> library(pwr) #> tr = pwr.chisq.test(w =0.001, N =30000 , df = 5-1, sig.level = 0.05, power = NULL) assert_almost_equal(power, 0.05147563, decimal=7) effect_size = 0.001 power = chisquare_power(effect_size, nobs, n_bins, alpha=0.05) assert_almost_equal(power, 0.05147563, decimal=7) print chisquare(freq, nobs*probs, value=0, ddof=0) d_null_alt = ((probs - probs_d)**2 / probs).sum() print chisquare(freq, nobs*probs, value=np.sqrt(d_null_alt), ddof=0) #Monte Carlo to check correct size and power of test d_delta_r = chisquare_effectsize(probs, probs_d) n_rep = 10000 nobs = 3000 res_boots = np.zeros((n_rep, 6)) for i in range(n_rep): rvs = np.argmax(np.random.rand(nobs,1) < probs_cs, 1) freq = np.bincount(rvs) res1 = chisquare(freq, nobs*probs) res2 = chisquare(freq, nobs*probs_d) res3 = chisquare(freq, nobs*probs_d, value=d_delta_r) res_boots[i] = [res1[0], res2[0], res3[0], res1[1], res2[1], res3[1]] alpha = np.array([0.01, 0.05, 0.1, 0.25, 0.5]) chi2_power = chisquare_power(chisquare_effectsize(probs, probs_d), 3000, n_bins, alpha=[0.01, 0.05, 0.1, 0.25, 0.5]) print (res_boots[:, 3:] < 0.05).mean(0) reject_freq = (res_boots[:, 3:, None] < alpha).mean(0) reject = (res_boots[:, 3:, None] < alpha).sum(0) desired = np.column_stack((alpha, chi2_power, alpha)).T print 'relative difference Monte Carlo rejection and expected (in %)' print (reject_freq / desired - 1) * 100 statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_polytrend.py000066400000000000000000000026361224417117700256310ustar00rootroot00000000000000 import numpy as np #import statsmodels.linear_model.regression as smreg from scipy import special import statsmodels.api as sm from statsmodels.datasets.macrodata import data dta = data.load() gdp = np.log(dta.data['realgdp']) from numpy import polynomial from scipy import special maxorder = 20 polybase = special.chebyt polybase = special.legendre t = np.linspace(-1,1,len(gdp)) exog = np.column_stack([polybase(i)(t) for i in range(maxorder)]) fitted = [sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in range(2,maxorder)] print (np.corrcoef(exog[:,1:6], rowvar=0)*10000).astype(int) import matplotlib.pyplot as plt plt.figure() plt.plot(gdp, 'o') for i in range(maxorder-2): plt.plot(fitted[i]) plt.figure() #plt.plot(gdp, 'o') for i in range(maxorder-4, maxorder-2): #plt.figure() plt.plot(gdp - fitted[i]) plt.title(str(i+2)) plt.figure() plt.plot(gdp, '.') plt.plot(fitted[-1], lw=2, color='r') plt.plot(fitted[0], lw=2, color='g') plt.title('GDP and Polynomial Trend') plt.figure() plt.plot(gdp - fitted[-1], lw=2, color='r') plt.plot(gdp - fitted[0], lw=2, color='g') plt.title('Residual GDP minus Polynomial Trend (green: linear, red: legendre(20))') #orthonormalize an exog using QR ex2 = t[:,None]**np.arange(6) #np.vander has columns reversed q2,r2 = np.linalg.qr(ex2, mode='full') np.max(np.abs(np.dot(q2.T, q2)-np.eye(6))) plt.figure() plt.plot(q2, lw=2) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_power.py000066400000000000000000000047001224417117700247370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Mar 02 14:38:17 2013 Author: Josef Perktold """ import numpy as np import statsmodels.stats.power as smp import statsmodels.stats.proportion as smpr sigma=1; d=0.3; nobs=80; alpha=0.05 print smp.normal_power(d, nobs/2, 0.05) print smp.NormalIndPower().power(d, nobs, 0.05) print smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None) print 0.475100870572638, 'R' norm_pow = smp.normal_power(-0.01, nobs/2, 0.05) norm_pow_R = 0.05045832927039234 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided") print 'norm_pow', norm_pow, norm_pow - norm_pow_R norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05, alternative="larger") norm_pow_R = 0.056869534873146124 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater") print 'norm_pow', norm_pow, norm_pow - norm_pow_R # Note: negative effect size is same as switching one-sided alternative # TODO: should I switch to larger/smaller instead of "one-sided" options norm_pow = smp.NormalIndPower().power(-0.01, nobs, 0.05, alternative="larger") norm_pow_R = 0.0438089705093578 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less") print 'norm_pow', norm_pow, norm_pow - norm_pow_R #Note: I use n_bins and ddof instead of df # pwr.chisq.test(w=0.289,df=(4-1),N=100,sig.level=0.05) chi2_pow = smp.GofChisquarePower().power(0.289, 100, 4, 0.05) chi2_pow_R = 0.675077657003721 print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R chi2_pow = smp.GofChisquarePower().power(0.01, 100, 4, 0.05) chi2_pow_R = 0.0505845519208533 print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R chi2_pow = smp.GofChisquarePower().power(2, 100, 4, 0.05) chi2_pow_R = 1 print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R chi2_pow = smp.GofChisquarePower().power(0.9, 100, 4, 0.05) chi2_pow_R = 0.999999999919477 print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R, 'lower precision ?' chi2_pow = smp.GofChisquarePower().power(0.8, 100, 4, 0.05) chi2_pow_R = 0.999999968205591 print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R def cohen_es(*args, **kwds): print "You better check what's a meaningful effect size for your question." #BUG: after fixing 2.sided option, 2 rejection areas tt_pow = smp.TTestPower().power(effect_size=0.01, nobs=nobs, alpha=0.05) tt_pow_R = 0.05089485285965 # value from> pwr.t.test(d=0.01,n=80,sig.level=0.05,type="one.sample",alternative="two.sided") print 'tt_pow', tt_pow, tt_pow - tt_pow_R statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_power2.py000066400000000000000000000057001224417117700250220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Mar 13 13:06:14 2013 Author: Josef Perktold """ from statsmodels.stats.power import TTestPower, TTestIndPower, tt_solve_power if __name__ == '__main__': effect_size, alpha, power = 0.5, 0.05, 0.8 ttest_pow = TTestPower() print '\nroundtrip - root with respect to all variables' print '\n calculated, desired' nobs_p = ttest_pow.solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power) print 'nobs ', nobs_p print 'effect', ttest_pow.solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size print 'alpha ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha print 'power ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power print '\nroundtrip - root with respect to all variables' print '\n calculated, desired' print 'nobs ', tt_solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power), nobs_p print 'effect', tt_solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size print 'alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha print 'power ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power print '\none sided' nobs_p1 = tt_solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power, alternative='larger') print 'nobs ', nobs_p1 print 'effect', tt_solve_power(effect_size=None, nobs=nobs_p1, alpha=alpha, power=power, alternative='larger'), effect_size print 'alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=None, power=power, alternative='larger'), alpha print 'power ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=alpha, power=None, alternative='larger'), power #start_ttp = dict(effect_size=0.01, nobs1=10., alpha=0.15, power=0.6) ttind_solve_power = TTestIndPower().solve_power print '\nroundtrip - root with respect to all variables' print '\n calculated, desired' nobs_p2 = ttind_solve_power(effect_size=effect_size, nobs1=None, alpha=alpha, power=power) print 'nobs ', nobs_p2 print 'effect', ttind_solve_power(effect_size=None, nobs1=nobs_p2, alpha=alpha, power=power), effect_size print 'alpha ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=None, power=power), alpha print 'power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=None), power print 'ratio ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=power, ratio=None), 1 print '\ncheck ratio' print 'smaller power', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.7, ratio=None), '< 1' print 'larger power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.9, ratio=None), '> 1' statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/try_tukey_hsd.py000066400000000000000000000146571224417117700256160ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Mar 28 15:34:18 2012 Author: Josef Perktold """ import StringIO import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.stats.libqsturng import qsturng ss = '''\ 43.9 1 1 39.0 1 2 46.7 1 3 43.8 1 4 44.2 1 5 47.7 1 6 43.6 1 7 38.9 1 8 43.6 1 9 40.0 1 10 89.8 2 1 87.1 2 2 92.7 2 3 90.6 2 4 87.7 2 5 92.4 2 6 86.1 2 7 88.1 2 8 90.8 2 9 89.1 2 10 68.4 3 1 69.3 3 2 68.5 3 3 66.4 3 4 70.0 3 5 68.1 3 6 70.6 3 7 65.2 3 8 63.8 3 9 69.2 3 10 36.2 4 1 45.2 4 2 40.7 4 3 40.5 4 4 39.3 4 5 40.3 4 6 43.2 4 7 38.7 4 8 40.9 4 9 39.7 4 10''' #idx Treatment StressReduction ss2 = '''\ 1 mental 2 2 mental 2 3 mental 3 4 mental 4 5 mental 4 6 mental 5 7 mental 3 8 mental 4 9 mental 4 10 mental 4 11 physical 4 12 physical 4 13 physical 3 14 physical 5 15 physical 4 16 physical 1 17 physical 1 18 physical 2 19 physical 3 20 physical 3 21 medical 1 22 medical 2 23 medical 2 24 medical 2 25 medical 3 26 medical 2 27 medical 3 28 medical 1 29 medical 3 30 medical 1''' ss3 = '''\ 1 24.5 1 23.5 1 26.4 1 27.1 1 29.9 2 28.4 2 34.2 2 29.5 2 32.2 2 30.1 3 26.1 3 28.3 3 24.3 3 26.2 3 27.8''' cylinders = np.array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 6, 6, 6, 4, 4, 4, 4, 4, 4, 6, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 6, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 4, 4, 4, 4, 4, 8, 4, 6, 6, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4]) cyl_labels = np.array(['USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'France', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Japan', 'USA', 'USA', 'USA', 'Japan', 'Germany', 'France', 'Germany', 'Sweden', 'Germany', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'USA', 'USA', 'France', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'Japan', 'USA', 'Sweden', 'USA', 'France', 'Japan', 'Germany', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'Japan', 'USA', 'USA', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'USA', 'USA', 'USA', 'USA', 'Japan', 'USA', 'USA', 'USA', 'Germany', 'USA', 'USA', 'USA']) dta = np.recfromtxt(StringIO.StringIO(ss), names=("Rust","Brand","Replication")) dta2 = np.recfromtxt(StringIO.StringIO(ss2), names = ("idx", "Treatment", "StressReduction")) dta3 = np.recfromtxt(StringIO.StringIO(ss3), names = ("Brand", "Relief")) from statsmodels.sandbox.stats.multicomp import tukeyhsd import statsmodels.sandbox.stats.multicomp as multi #print tukeyhsd(dta['Brand'], dta['Rust']) def get_thsd(mci): var_ = np.var(mci.groupstats.groupdemean(), ddof=len(mci.groupsunique)) means = mci.groupstats.groupmean nobs = mci.groupstats.groupnobs resi = tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=qsturng(0.95, len(means), (nobs-1).sum())) print resi[4] var2 = (mci.groupstats.groupvarwithin() * (nobs - 1)).sum() \ / (nobs - 1).sum() assert_almost_equal(var_, var2, decimal=14) return resi mc = multi.MultiComparison(dta['Rust'], dta['Brand']) res = mc.tukeyhsd() print res mc2 = multi.MultiComparison(dta2['StressReduction'], dta2['Treatment']) res2 = mc2.tukeyhsd() print res2 mc2s = multi.MultiComparison(dta2['StressReduction'][3:29], dta2['Treatment'][3:29]) res2s = mc2s.tukeyhsd() print res2s res2s_001 = mc2s.tukeyhsd(alpha=0.01) #R result tukeyhsd2s = np.array([1.888889,0.8888889,-1,0.2658549,-0.5908785,-2.587133,3.511923,2.368656,0.5871331,0.002837638,0.150456,0.1266072]).reshape(3,4, order='F') assert_almost_equal(res2s_001.confint, tukeyhsd2s[:,1:3], decimal=3) mc3 = multi.MultiComparison(dta3['Relief'], dta3['Brand']) res3 = mc3.tukeyhsd() print res3 tukeyhsd4 = multi.MultiComparison(cylinders, cyl_labels, group_order=["Sweden", "Japan", "Germany", "France", "USA"]) res4 = tukeyhsd4.tukeyhsd() print res4 try: import matplotlib.pyplot as plt fig = res4.plot_simultaneous("USA") plt.show() except Exception as e: print e for mci in [mc, mc2, mc3]: get_thsd(mci) from scipy import stats print mc2.allpairtest(stats.ttest_ind, method='b')[0] '''same as SAS: >>> np.var(mci.groupstats.groupdemean(), ddof=3) 4.6773333333333351 >>> var_ = np.var(mci.groupstats.groupdemean(), ddof=3) >>> tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=qsturng(0.95, 3, 12))[4] array([[ 0.95263648, 8.24736352], [-3.38736352, 3.90736352], [-7.98736352, -0.69263648]]) >>> tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=3.77278)[4] array([[ 0.95098508, 8.24901492], [-3.38901492, 3.90901492], [-7.98901492, -0.69098508]]) ''' ss5 = '''\ Comparisons significant at the 0.05 level are indicated by ***. BRAND Comparison Difference Between Means Simultaneous 95% Confidence Limits Sign. 2 - 3 4.340 0.691 7.989 *** 2 - 1 4.600 0.951 8.249 *** 3 - 2 -4.340 -7.989 -0.691 *** 3 - 1 0.260 -3.389 3.909 - 1 - 2 -4.600 -8.249 -0.951 *** 1 - 3 -0.260 -3.909 3.389 ''' ss5 = '''\ 2 - 3 4.340 0.691 7.989 *** 2 - 1 4.600 0.951 8.249 *** 3 - 2 -4.340 -7.989 -0.691 *** 3 - 1 0.260 -3.389 3.909 - 1 - 2 -4.600 -8.249 -0.951 *** 1 - 3 -0.260 -3.909 3.389 ''' dta5 = np.recfromtxt(StringIO.StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t') sas_ = dta5[[1,3,2]] confint1 = res3.confint confint2 = sas_[['lower','upper']].view(float).reshape((3,2)) assert_almost_equal(confint1, confint2, decimal=2) reject1 = res3.reject reject2 = sas_['sig'] == '***' assert_equal(reject1, reject2) meandiff1 = res3.meandiffs meandiff2 = sas_['mean'] assert_almost_equal(meandiff1, meandiff2, decimal=14) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/000077500000000000000000000000001224417117700231215ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ar1cholesky.py000066400000000000000000000020131224417117700257140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Oct 21 15:42:18 2010 Author: josef-pktd """ import numpy as np from scipy import linalg def tiny2zero(x, eps = 1e-15): '''replace abs values smaller than eps by zero, makes copy ''' mask = np.abs(x.copy()) < eps x[mask] = 0 return x nobs = 5 autocov = 0.8**np.arange(nobs) #from statsmodels.tsa import arima_process as ap #autocov = ap.arma_acf([1, -0.8, 0.2], [1])[:10] autocov = np.array([ 3., 2., 1., 0.4, 0.12, 0.016, -0.0112, 0.016 , -0.0112 , -0.01216 , -0.007488 , -0.0035584])/3. autocov = autocov[:nobs] sigma = linalg.toeplitz(autocov) sigmainv = linalg.inv(sigma) c = linalg.cholesky(sigma, lower=True) ci = linalg.cholesky(sigmainv, lower=True) print sigma print tiny2zero(ci/ci.max()) "this is the text book transformation" print 'coefficient for first observation', np.sqrt(1-autocov[1]**2) ci2 = ci[::-1,::-1].T print tiny2zero(ci2/ci2.max()) print np.dot(ci/ci.max(), np.ones(nobs)) print np.dot(ci2/ci2.max(), np.ones(nobs)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/arma_plots.py000066400000000000000000000046561224417117700256470ustar00rootroot00000000000000'''Plot acf and pacf for some ARMA(1,1) ''' import numpy as np import matplotlib.pyplot as plt import statsmodels.tsa.arima_process as tsp from statsmodels.sandbox.tsa.fftarma import ArmaFft as FftArmaProcess import statsmodels.tsa.stattools as tss from statsmodels.graphics.tsaplots import plotacf np.set_printoptions(precision=2) arcoefs = [0.9, 0., -0.5] #[0.9, 0.5, 0.1, 0., -0.5] macoefs = [0.9, 0., -0.5] #[0.9, 0.5, 0.1, 0., -0.5] nsample = 1000 nburnin = 1000 sig = 1 fig = plt.figure(figsize=(8, 13)) fig.suptitle('ARMA: Autocorrelation (left) and Partial Autocorrelation (right)') subplotcount = 1 nrows = 4 for arcoef in arcoefs[:-1]: for macoef in macoefs[:-1]: ar = np.r_[1., -arcoef] ma = np.r_[1., macoef] #y = tsp.arma_generate_sample(ar,ma,nsample, sig, burnin) #armaprocess = FftArmaProcess(ar, ma, nsample) #TODO: make n optional #armaprocess.plot4() armaprocess = tsp.ArmaProcess(ar, ma) acf = armaprocess.acf(20)[:20] pacf = armaprocess.pacf(20)[:20] ax = fig.add_subplot(nrows, 2, subplotcount) plotacf(acf, ax=ax) ## ax.set_title('Autocorrelation \nar=%s, ma=%rs' % (ar, ma), ## size='xx-small') ax.text(0.7, 0.6, 'ar =%s \nma=%s' % (ar, ma), transform=ax.transAxes, horizontalalignment='left', #'right', size='xx-small') ax.set_xlim(-1,20) subplotcount +=1 ax = fig.add_subplot(nrows, 2, subplotcount) plotacf(pacf, ax=ax) ## ax.set_title('Partial Autocorrelation \nar=%s, ma=%rs' % (ar, ma), ## size='xx-small') ax.text(0.7, 0.6, 'ar =%s \nma=%s' % (ar, ma), transform=ax.transAxes, horizontalalignment='left', #'right', size='xx-small') ax.set_xlim(-1,20) subplotcount +=1 axs = fig.axes ### turn of the 2nd column y tick labels ##for ax in axs[1::2]:#[:,1].flat: ## for label in ax.get_yticklabels(): label.set_visible(False) # turn off all but the bottom xtick labels for ax in axs[:-2]:#[:-1,:].flat: for label in ax.get_xticklabels(): label.set_visible(False) # use a MaxNLocator on the first column y axis if you have a bunch of # rows to avoid bunching; example below uses at most 3 ticks import matplotlib.ticker as mticker for ax in axs: #[::2]:#[:,1].flat: ax.yaxis.set_major_locator( mticker.MaxNLocator(3 )) plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/compare_arma.py000066400000000000000000000042151224417117700261230ustar00rootroot00000000000000print "Battle of the dueling ARMAs" from time import time from statsmodels.tsa.arma_mle import Arma from statsmodels.tsa.api import ARMA import numpy as np y_arma22 = np.loadtxt(r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt') arma1 = Arma(y_arma22) arma2 = ARMA(y_arma22) print "The actual results from gretl exact mle are" params_mle = np.array([.826990, -.333986, .0362419, -.792825]) sigma_mle = 1.094011 llf_mle = -1510.233 print "params: ", params_mle print "sigma: ", sigma_mle print "llf: ", llf_mle print "The actual results from gretl css are" params_css = np.array([.824810, -.337077, .0407222, -.789792]) sigma_css = 1.095688 llf_css = -1507.301 results = [] results += ["gretl exact mle", params_mle, sigma_mle, llf_mle] results += ["gretl css", params_css, sigma_css, llf_css] t0 = time() print "Exact MLE - Kalman filter version using l_bfgs_b" arma2.fit(order=(2,2), trend='nc') t1 = time() print "params: ", arma2.params print "sigma: ", arma2.sigma2**.5 arma2.llf = arma2.loglike(arma2._invtransparams(arma2.params)) results += ["exact mle kalmanf", arma2.params, arma2.sigma2**.5, arma2.llf] print 'time used:', t1-t0 t1=time() print "CSS MLE - ARMA Class" arma2.fit(order=(2,2), trend='nc', method="css") t2=time() arma2.llf = arma2.loglike_css(arma2._invtransparams(arma2.params)) print "params: ", arma2.params print "sigma: ", arma2.sigma2**.5 results += ["css kalmanf", arma2.params, arma2.sigma2**.5, arma2.llf] print 'time used:', t2-t1 print "Arma.fit_mle results" # have to set nar and nma manually arma1.nar = 2 arma1.nma = 2 t2=time() ret = arma1.fit_mle() t3=time() print "params, first 4, sigma, last 1 ", ret.params results += ["Arma.fit_mle ", ret.params[:4], ret.params[-1], ret.llf] print 'time used:', t3-t2 print "Arma.fit method = \"ls\"" t3=time() ret2 = arma1.fit(order=(2,0,2), method="ls") t4=time() print ret2[0] results += ["Arma.fit ls", ret2[0]] print 'time used:', t4-t3 print "Arma.fit method = \"CLS\"" t4=time() ret3 = arma1.fit(order=(2,0,2), method="None") t5=time() print ret3 results += ["Arma.fit other", ret3[0]] print 'time used:', t5-t4 for i in results: print i statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ex_arma.py000066400000000000000000000056431224417117700251170ustar00rootroot00000000000000''' doesn't seem to work so well anymore even with nobs=1000 ??? works ok if noise variance is large ''' import numpy as np import statsmodels.api as sm from statsmodels.tsa.arima_process import arma_generate_sample from statsmodels.tsa.arma_mle import Arma as Arma from statsmodels.tsa.arima_process import ARIMA as ARIMA_old from statsmodels.sandbox.tsa.garch import Arma as Armamle_old from statsmodels.tsa.arima import ARMA as ARMA_kf print "\nExample 1" ar = [1.0, -0.6, 0.1] ma = [1.0, 0.5, 0.3] nobs = 1000 y22 = arma_generate_sample(ar, ma, nobs+1000, 0.5)[-nobs:] y22 -= y22.mean() start_params = [0.1, 0.1, 0.1, 0.1] start_params_lhs = [-0.1, -0.1, 0.1, 0.1] print 'truelhs', np.r_[ar[1:], ma[1:]] ###bug in current version, fixed in Skipper and 1 more ###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs ###ValueError: array dimensions are not compatible for copy ##arma22 = ARMA_kf(y22, constant=False, order=(2,2)) ##res = arma22.fit(start_params=start_params) ##print res.params print '\nARIMA new' arest2 = Arma(y22) naryw = 4 #= 30 resyw = sm.regression.yule_walker(y22, order=naryw, inv=True) arest2.nar = naryw arest2.nma = 0 e = arest2.geterrors(np.r_[1, -resyw[0]]) x=sm.tsa.tsatools.lagmat2ds(np.column_stack((y22,e)),3,dropex=1, trim='both') yt = x[:,0] xt = x[:,1:] res_ols = sm.OLS(yt, xt).fit() print 'hannan_rissannen' print res_ols.params start_params = res_ols.params start_params_mle = np.r_[-res_ols.params[:2], res_ols.params[2:], #res_ols.scale] #areste.var()] np.sqrt(res_ols.scale)] #need to iterate, ar1 too large ma terms too small #fix large parameters, if hannan_rissannen are too large start_params_mle[:-1] = (np.sign(start_params_mle[:-1]) * np.minimum(np.abs(start_params_mle[:-1]),0.75)) print 'conditional least-squares' #print rhohat2 print 'with mle' arest2.nar = 2 arest2.nma = 2 # res = arest2.fit_mle(start_params=start_params_mle, method='nm') #no order in fit print res.params rhohat2, cov_x2a, infodict, mesg, ier = arest2.fit((2,2)) print '\nARIMA_old' arest = ARIMA_old(y22) rhohat1, cov_x1, infodict, mesg, ier = arest.fit((2,0,2)) print rhohat1 print np.sqrt(np.diag(cov_x1)) err1 = arest.errfn(x=y22) print np.var(err1) print 'bse ls, formula not checked' print np.sqrt(np.diag(cov_x1))*err1.std() print 'bsejac for mle' #print arest2.bsejac #TODO:check bsejac raises singular matrix linalg error #in model.py line620: return np.linalg.inv(np.dot(jacv.T, jacv)) print '\nyule-walker' print sm.regression.yule_walker(y22, order=2, inv=True) print '\nArmamle_old' arma1 = Armamle_old(y22) arma1.nar = 2 arma1.nma = 2 #arma1res = arma1.fit(start_params=np.r_[-0.5, -0.1, 0.1, 0.1, 0.5], method='fmin') #maxfun=1000) arma1res = arma1.fit(start_params=res.params*0.7, method='fmin') print arma1res.params statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ex_arma_all.py000066400000000000000000000036151224417117700257440ustar00rootroot00000000000000 import numpy as np from numpy.testing import assert_almost_equal import matplotlib.pyplot as plt import statsmodels.sandbox.tsa.fftarma as fa from statsmodels.tsa.descriptivestats import TsaDescriptive from statsmodels.tsa.arma_mle import Arma x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) d = TsaDescriptive(x) d.plot4() #d.fit(order=(1,1)) d.fit((1,1), trend='nc') print d.res.params modc = Arma(x) resls = modc.fit(order=(1,1)) print resls[0] rescm = modc.fit_mle(order=(1,1), start_params=[-0.4,0.4, 1.]) print rescm.params #decimal 1 corresponds to threshold of 5% difference assert_almost_equal(resls[0] / d.res.params, 1, decimal=1) assert_almost_equal(rescm.params[:-1] / d.res.params, 1, decimal=1) #copied to tsa.tests plt.figure() plt.plot(x, 'b-o') plt.plot(modc.predicted(), 'r-') plt.figure() plt.plot(modc.error_estimate) #plt.show() from statsmodels.miscmodels.tmodel import TArma modct = TArma(x) reslst = modc.fit(order=(1,1)) print reslst[0] rescmt = modct.fit_mle(order=(1,1), start_params=[-0.4,0.4, 10, 1.],maxiter=500, maxfun=500) print rescmt.params from statsmodels.tsa.arima_model import ARMA mkf = ARMA(x) ##rkf = mkf.fit((1,1)) ##rkf.params rkf = mkf.fit((1,1), trend='nc') print rkf.params from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) y_arma22 = arma_generate_sample([1.,-.85,.35, -0.1],[1,.25,-.7], nsample=1000) ##arma22 = ARMA(y_arma22) ##res22 = arma22.fit(trend = 'nc', order=(2,2)) ##print 'kf ',res22.params ##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2)) ##print 'css', res22css.params mod22 = Arma(y_arma22) resls22 = mod22.fit(order=(2,2)) print 'ls ', resls22[0] resmle22 = mod22.fit_mle(order=(2,2), maxfun=2000) print 'mle', resmle22.params f = mod22.forecast() f3 = mod22.forecast3(start=900)[-20:] print y_arma22[-10:] print f[-20:] print f3[-109:-90] plt.show()statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ex_coint.py000066400000000000000000000002741224417117700253060ustar00rootroot00000000000000from statsmodels.tsa.tests.test_stattools import CheckCoint, TestCoint_t #test whether t-test for cointegration equals that produced by Stata tst = TestCoint_t() print tst.test_tstat() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ex_var.py000066400000000000000000000023251224417117700247610ustar00rootroot00000000000000 import numpy as np import statsmodels.api as sm from statsmodels.tsa.api import VAR # some example data mdata = sm.datasets.macrodata.load().data mdata = mdata[['realgdp','realcons','realinv']] names = mdata.dtype.names data = mdata.view((float,3)) use_growthrate = False #True #False if use_growthrate: data = 100 * 4 * np.diff(np.log(data), axis=0) model = VAR(data, names=names) res = model.fit(4) nobs_all = data.shape[0] #in-sample 1-step ahead forecasts fc_in = np.array([np.squeeze(res.forecast(model.y[t-20:t], 1)) for t in range(nobs_all-6,nobs_all)]) print fc_in - res.fittedvalues[-6:] #out-of-sample 1-step ahead forecasts fc_out = np.array([np.squeeze(VAR(data[:t]).fit(2).forecast(data[t-20:t], 1)) for t in range(nobs_all-6,nobs_all)]) print fc_out - data[nobs_all-6:nobs_all] print fc_out - res.fittedvalues[-6:] #out-of-sample h-step ahead forecasts h = 2 fc_out = np.array([VAR(data[:t]).fit(2).forecast(data[t-20:t], h)[-1] for t in range(nobs_all-6-h+1,nobs_all-h+1)]) print fc_out - data[nobs_all-6:nobs_all] #out-of-sample forecast error print fc_out - res.fittedvalues[-6:] import matplotlib.pyplot as plt res.plot_forecast(20) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/ex_var_reorder.py000066400000000000000000000002261224417117700265010ustar00rootroot00000000000000import statsmodels.api as sm from statsmodels.tsa.vector_ar.tests.test_var import TestVARResults test_VAR = TestVARResults() test_VAR.test_reorder() statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/lagpolynomial.py000066400000000000000000000020631224417117700263430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Oct 22 08:13:38 2010 Author: josef-pktd License: BSD (3-clause) """ import numpy as np from numpy import polynomial as npp class LagPolynomial(npp.Polynomial): #def __init__(self, maxlag): def pad(self, maxlag): return LagPolynomial(np.r_[self.coef, np.zeros(maxlag-len(self.coef))]) def padflip(self, maxlag): return LagPolynomial(np.r_[self.coef, np.zeros(maxlag-len(self.coef))][::-1]) def flip(self): '''reverse polynomial coefficients ''' return LagPolynomial(self.coef[::-1]) def div(self, other, maxlag=None): '''padded division, pads numerator with zeros to maxlag ''' if maxlag is None: maxlag = max(len(self.coef), len(other.coef)) + 1 return (self.padflip(maxlag) / other.flip()).flip() def filter(self, arr): return (self * arr).coef[:-len(self.coef)] #trim to end ar = LagPolynomial([1, -0.8]) arpad = ar.pad(10) ma = LagPolynomial([1, 0.1]) mapad = ma.pad(10) unit = LagPolynomial([1]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tsa/try_ar.py000066400000000000000000000047251224417117700250030ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Oct 21 21:45:24 2010 Author: josef-pktd """ import numpy as np from scipy import signal def armaloop(arcoefs, macoefs, x): '''get arma recursion in simple loop for simplicity assumes that ma polynomial is not longer than the ar-polynomial Parameters ---------- arcoefs : array_like autoregressive coefficients in right hand side parameterization macoefs : array_like moving average coefficients, without leading 1 Returns ------- y : ndarray predicted values, initial values are the same as the observed values e : ndarray predicted residuals, zero for initial observations Notes ----- Except for the treatment of initial observations this is the same as using scipy.signal.lfilter, which is much faster. Written for testing only ''' arcoefs_r = np.asarray(arcoefs) macoefs_r = np.asarray(macoefs) x = np.asarray(x) nobs = x.shape[0] #assume ar longer than ma arlag = arcoefs_r.shape[0] malag = macoefs_r.shape[0] maxlag = max(arlag, malag) print maxlag y = np.zeros(x.shape, float) e = np.zeros(x.shape, float) y[:maxlag] = x[:maxlag] #if malag > arlaga: for t in range(arlag, maxlag): y[t] = (x[t-arlag:t] * arcoefs_r).sum(0) + (e[:t] * macoefs_r[:t]).sum(0) e[t] = x[t] - y[t] for t in range(maxlag, nobs): #wrong broadcasting, 1d only y[t] = (x[t-arlag:t] * arcoefs_r).sum(0) + (e[t-malag:t] * macoefs_r).sum(0) e[t] = x[t] - y[t] return y, e arcoefs, macoefs = -np.array([1, -0.8, 0.2])[1:], np.array([1., 0.5, 0.1])[1:] print armaloop(arcoefs, macoefs, np.ones(10)) print armaloop([0.8], [], np.ones(10)) print armaloop([0.8], [], np.arange(2,10)) y, e = armaloop([0.1], [0.8], np.arange(2,10)) print e print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.arange(2,10)) y, e = armaloop([], [0.8], np.ones(10)) print e print signal.lfilter(np.array([1, -0.]), np.array([1., 0.8]), np.ones(10)) ic=signal.lfiltic(np.array([1, -0.1]), np.array([1., 0.8]), np.ones([0]), np.array([1])) print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=ic) zi = signal.lfilter_zi(np.array([1, -0.8, 0.2]), np.array([1., 0, 0])) print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=zi) print signal.filtfilt(np.array([1, -0.8]), np.array([1.]), np.ones(10)) #todo write examples/test across different versions statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tut_ols_ancova.py000066400000000000000000000045551224417117700257350ustar00rootroot00000000000000'''Examples OLS Note: uncomment plt.show() to display graphs Summary: ======== Relevant part of construction of design matrix xg includes group numbers/labels, x1 is continuous explanatory variable >>> dummy = (xg[:,None] == np.unique(xg)).astype(float) >>> X = np.c_[x1, dummy[:,1:], np.ones(nsample)] Estimate the model >>> res2 = sm.OLS(y, X).fit() >>> print res2.params [ 1.00901524 3.08466166 -2.84716135 9.94655423] >>> print res2.bse [ 0.07499873 0.71217506 1.16037215 0.38826843] >>> prstd, iv_l, iv_u = wls_prediction_std(res2) "Test hypothesis that all groups have same intercept" >>> R = [[0, 1, 0, 0], ... [0, 0, 1, 0]] >>> print res2.f_test(R) strongly rejected because differences in intercept are very large ''' import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std #fix a seed for these examples np.random.seed(98765789) #OLS with dummy variables, similar to ANCOVA #------------------------------------------- #construct simulated example: #3 groups common slope but different intercepts nsample = 50 x1 = np.linspace(0, 20, nsample) sig = 1. #suppose observations from 3 groups xg = np.zeros(nsample, int) xg[20:40] = 1 xg[40:] = 2 #print xg dummy = (xg[:,None] == np.unique(xg)).astype(float) #use group 0 as benchmark X = np.c_[x1, dummy[:,1:], np.ones(nsample)] beta = [1., 3, -3, 10] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) #estimate #~~~~~~~~ res2 = sm.OLS(y, X).fit() #print "estimated parameters: x d1-d0 d2-d0 constant" print res2.params #print "standard deviation of parameter estimates" print res2.bse prstd, iv_l, iv_u = wls_prediction_std(res2) #print res.summary() #plot #~~~~ plt.figure() plt.plot(x1, y, 'o', x1, y_true, 'b-') plt.plot(x1, res2.fittedvalues, 'r--.') plt.plot(x1, iv_u, 'r--') plt.plot(x1, iv_l, 'r--') plt.title('3 groups: different intercepts, common slope; blue: true, red: OLS') plt.show() #Test hypothesis that all groups have same intercept #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ R = [[0, 1, 0, 0], [0, 0, 1, 0]] # F test joint hypothesis R * beta = 0 # i.e. coefficient on both dummy variables equal zero print "Test hypothesis that all groups have same intercept" print res2.f_test(R) statsmodels-0.5.0+git13-g8e07d34/statsmodels/examples/tut_ols_rlm_short.py000066400000000000000000000031031224417117700264630ustar00rootroot00000000000000'''Examples: comparing OLS and RLM robust estimators and outliers RLM is less influenced by outliers than OLS and has estimated slope closer to true slope and not tilted like OLS. Note: uncomment plt.show() to display graphs ''' import numpy as np #from scipy import stats import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std #fix a seed for these examples np.random.seed(98765789) nsample = 50 x1 = np.linspace(0, 20, nsample) X = np.c_[x1, np.ones(nsample)] sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger beta = [0.5, 5.] y_true2 = np.dot(X, beta) y2 = y_true2 + sig*1. * np.random.normal(size=nsample) y2[[39,41,43,45,48]] -= 5 # add some outliers (10% of nsample) # Example: estimate linear function (true is linear) plt.figure() plt.plot(x1, y2, 'o', x1, y_true2, 'b-') res2 = sm.OLS(y2, X).fit() print "OLS: parameter estimates: slope, constant" print res2.params print "standard deviation of parameter estimates" print res2.bse prstd, iv_l, iv_u = wls_prediction_std(res2) plt.plot(x1, res2.fittedvalues, 'r-') plt.plot(x1, iv_u, 'r--') plt.plot(x1, iv_l, 'r--') #compare with robust estimator resrlm2 = sm.RLM(y2, X).fit() print "\nRLM: parameter estimates: slope, constant" print resrlm2.params print "standard deviation of parameter estimates" print resrlm2.bse plt.plot(x1, resrlm2.fittedvalues, 'g.-') plt.title('Data with Outliers; blue: true, red: OLS, green: RLM') # see also help(sm.RLM.fit) for more options and # module sm.robust.scale for scale options plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/000077500000000000000000000000001224417117700221615ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/__init__.py000066400000000000000000000001611224417117700242700ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test from formulatools import handle_formula_data statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/api.py000066400000000000000000000017721224417117700233130ustar00rootroot00000000000000from statsmodels.regression.linear_model import GLS gls = GLS.from_formula from statsmodels.regression.linear_model import WLS wls = WLS.from_formula from statsmodels.regression.linear_model import OLS ols = OLS.from_formula from statsmodels.regression.linear_model import GLSAR glsar = GLSAR.from_formula from statsmodels.genmod.generalized_linear_model import GLM glm = GLM.from_formula from statsmodels.robust.robust_linear_model import RLM rlm = RLM.from_formula from statsmodels.discrete.discrete_model import MNLogit mnlogit = MNLogit.from_formula from statsmodels.discrete.discrete_model import Logit logit = Logit.from_formula from statsmodels.discrete.discrete_model import Probit probit = Probit.from_formula from statsmodels.discrete.discrete_model import Poisson poisson = Poisson.from_formula from statsmodels.discrete.discrete_model import NegativeBinomial negativebinomial = NegativeBinomial.from_formula from statsmodels.regression.quantile_regression import QuantReg quantreg = QuantReg.from_formula statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/formulatools.py000066400000000000000000000050301224417117700252570ustar00rootroot00000000000000import statsmodels.tools.data as data_util from patsy import dmatrices # if users want to pass in a different formula framework, they can # add their handler here. how to do it interactively? # this is a mutable object, so editing it should show up in the below formula_handler = {} def handle_formula_data(Y, X, formula, depth=0): """ Returns endog, exog, and the model specification from arrays and formula Parameters ---------- Y : array-like Either endog (the LHS) of a model specification or all of the data. Y must define __getitem__ for now. X : array-like Either exog or None. If all the data for the formula is provided in Y then you must explicitly set X to None. formula : str or patsy.model_desc You can pass a handler by import formula_handler and adding a key-value pair where the key is the formula object class and the value is a function that returns endog, exog, formula object Returns ------- endog : array-like Should preserve the input type of Y,X exog : array-like Should preserve the input type of Y,X. Could be None. """ # half ass attempt to handle other formula objects if isinstance(formula, tuple(formula_handler.keys())): return formula_handler[type(formula)] if X is not None: if data_util._is_using_pandas(Y, X): return dmatrices(formula, (Y, X), 2, return_type='dataframe') else: return dmatrices(formula, (Y, X), 2, return_type='dataframe') else: if data_util._is_using_pandas(Y, None): return dmatrices(formula, Y, 2, return_type='dataframe') else: return dmatrices(formula, Y, 2, return_type='dataframe') def _remove_intercept_patsy(terms): """ Remove intercept from Patsy terms. """ from patsy.desc import INTERCEPT if INTERCEPT in terms: terms.remove(INTERCEPT) return terms def _has_intercept(design_info): from patsy.desc import INTERCEPT return INTERCEPT in design_info.terms def _intercept_idx(design_info): """ Returns boolean array index indicating which column holds the intercept """ from patsy.desc import INTERCEPT from numpy import array return array([INTERCEPT == i for i in design_info.terms]) def make_hypotheses_matrices(model_results, test_formula): """ """ from patsy.constraint import linear_constraint exog_names = model_results.model.exog_names LC = linear_constraint(test_formula, exog_names) return LC statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/tests/000077500000000000000000000000001224417117700233235ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/tests/__init__.py000066400000000000000000000000001224417117700254220ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/formula/tests/test_formula.py000066400000000000000000000107411224417117700264040ustar00rootroot00000000000000import warnings from statsmodels.formula.api import ols from statsmodels.formula.formulatools import make_hypotheses_matrices from statsmodels.tools import add_constant from statsmodels.datasets.longley import load, load_pandas import numpy.testing as npt from numpy.testing.utils import WarningManager longley_formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' class CheckFormulaOLS(object): @classmethod def setupClass(cls): cls.data = load() def test_endog_names(self): assert self.model.endog_names == 'TOTEMP' def test_exog_names(self): assert self.model.exog_names == ['Intercept', 'GNPDEFL', 'GNP', 'UNEMP', 'ARMED', 'POP', 'YEAR'] def test_design(self): npt.assert_equal(self.model.exog, add_constant(self.data.exog, prepend=True)) def test_endog(self): npt.assert_equal(self.model.endog, self.data.endog) def test_summary(self): # smoke test warn_ctx = WarningManager() warn_ctx.__enter__() try: warnings.filterwarnings("ignore", "kurtosistest only valid for n>=20") self.model.fit().summary() finally: warn_ctx.__exit__() class TestFormulaPandas(CheckFormulaOLS): @classmethod def setupClass(cls): data = load_pandas().data cls.model = ols(longley_formula, data) super(TestFormulaPandas, cls).setupClass() class TestFormulaDict(CheckFormulaOLS): @classmethod def setupClass(cls): data = dict((k, v.tolist()) for k, v in load_pandas().data.iteritems()) cls.model = ols(longley_formula, data) super(TestFormulaDict, cls).setupClass() class TestFormulaRecArray(CheckFormulaOLS): @classmethod def setupClass(cls): data = load().data cls.model = ols(longley_formula, data) super(TestFormulaRecArray, cls).setupClass() def test_tests(): formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' dta = load_pandas().data results = ols(formula, dta).fit() test_formula = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)' LC = make_hypotheses_matrices(results, test_formula) R = LC.coefs Q = LC.constants npt.assert_almost_equal(R, [[0, 1, -1, 0, 0, 0, 0], [0, 0 , 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1./1829]], 8) npt.assert_array_equal(Q, [[0],[2],[1]]) def test_formula_labels(): # make sure labels pass through patsy as expected # data(Duncan) from car in R from StringIO import StringIO dta = StringIO(""""type" "income" "education" "prestige"\n"accountant" "prof" 62 86 82\n"pilot" "prof" 72 76 83\n"architect" "prof" 75 92 90\n"author" "prof" 55 90 76\n"chemist" "prof" 64 86 90\n"minister" "prof" 21 84 87\n"professor" "prof" 64 93 93\n"dentist" "prof" 80 100 90\n"reporter" "wc" 67 87 52\n"engineer" "prof" 72 86 88\n"undertaker" "prof" 42 74 57\n"lawyer" "prof" 76 98 89\n"physician" "prof" 76 97 97\n"welfare.worker" "prof" 41 84 59\n"teacher" "prof" 48 91 73\n"conductor" "wc" 76 34 38\n"contractor" "prof" 53 45 76\n"factory.owner" "prof" 60 56 81\n"store.manager" "prof" 42 44 45\n"banker" "prof" 78 82 92\n"bookkeeper" "wc" 29 72 39\n"mail.carrier" "wc" 48 55 34\n"insurance.agent" "wc" 55 71 41\n"store.clerk" "wc" 29 50 16\n"carpenter" "bc" 21 23 33\n"electrician" "bc" 47 39 53\n"RR.engineer" "bc" 81 28 67\n"machinist" "bc" 36 32 57\n"auto.repairman" "bc" 22 22 26\n"plumber" "bc" 44 25 29\n"gas.stn.attendant" "bc" 15 29 10\n"coal.miner" "bc" 7 7 15\n"streetcar.motorman" "bc" 42 26 19\n"taxi.driver" "bc" 9 19 10\n"truck.driver" "bc" 21 15 13\n"machine.operator" "bc" 21 20 24\n"barber" "bc" 16 26 20\n"bartender" "bc" 16 28 7\n"shoe.shiner" "bc" 9 17 3\n"cook" "bc" 14 22 16\n"soda.clerk" "bc" 12 30 6\n"watchman" "bc" 17 25 11\n"janitor" "bc" 7 20 8\n"policeman" "bc" 34 47 41\n"waiter" "bc" 8 32 10""") from pandas import read_table dta = read_table(dta, sep=" ") model = ols("prestige ~ income + education", dta).fit() npt.assert_equal(model.fittedvalues.index, dta.index) def test_formula_predict(): from numpy import log formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED + POP + YEAR""" data = load_pandas() dta = load_pandas().data results = ols(formula, dta).fit() npt.assert_almost_equal(results.fittedvalues.values, results.predict(data.exog), 8) statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/000077500000000000000000000000001224417117700217655ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/__init__.py000066400000000000000000000001031224417117700240700ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/families/000077500000000000000000000000001224417117700235565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/families/__init__.py000066400000000000000000000010011224417117700256570ustar00rootroot00000000000000''' This module contains the one-parameter exponential families used for fitting GLMs and GAMs. These families are described in P. McCullagh and J. A. Nelder. "Generalized linear models." Monographs on Statistics and Applied Probability. Chapman & Hall, London, 1983. ''' #from statsmodels.family.family import Gaussian, Family, Poisson, Gamma, \ # InverseGaussian, Binomial, NegativeBinomial from family import Gaussian, Family, Poisson, Gamma, \ InverseGaussian, Binomial, NegativeBinomial statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/families/family.py000066400000000000000000001074541224417117700254240ustar00rootroot00000000000000''' The one parameter exponential family distributions used by GLM. ''' #TODO: quasi, quasibinomial, quasipoisson #see http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html # for comparison to R, and McCullagh and Nelder import numpy as np from scipy import special from scipy.stats import ss import links as L import varfuncs as V FLOAT_EPS = np.finfo(float).eps class Family(object): """ The parent class for one-parameter exponential families. Parameters ---------- link : a link function instance Link is the linear transformation function. See the individual families for available links. variance : a variance function Measures the variance as a function of the mean probabilities. See the individual families for the default variance function. """ #TODO: change these class attributes, use valid somewhere... valid = [-np.inf, np.inf] links = [] def _setlink(self, link): """ Helper method to set the link for a family. Raises a ValueError exception if the link is not available. Note that the error message might not be that informative because it tells you that the link should be in the base class for the link function. See glm.GLM for a list of appropriate links for each family but note that not all of these are currently available. """ #TODO: change the links class attribute in the families to hold meaningful # information instead of a list of links instances such as #[, # , # ] # for Poisson... self._link = link if not isinstance(link, L.Link): raise TypeError("The input should be a valid Link object.") if hasattr(self, "links"): validlink = link in self.links # validlink = max([isinstance(link, _.__class__) for _ in self.links]) validlink = max([isinstance(link, _) for _ in self.links]) if not validlink: errmsg = "Invalid link for family, should be in %s. (got %s)" raise ValueError(errmsg % (`self.links`, link)) def _getlink(self): """ Helper method to get the link for a family. """ return self._link #link property for each family #pointer to link instance link = property(_getlink, _setlink, doc="Link function for family") def __init__(self, link, variance): self.link = link() self.variance = variance def starting_mu(self, y): """ Starting value for mu in the IRLS algorithm. Parameters ---------- y : array The untransformed response variable. Returns ------- mu_0 : array The first guess on the transformed response variable. Notes ----- mu_0 = (endog + mean(endog))/2. Notes ----- Only the Binomial family takes a different initial value. """ return (y + y.mean())/2. def weights(self, mu): """ Weights for IRLS steps Parameters ---------- mu : array-like The transformed mean response variable in the exponential family Returns ------- w : array The weights for the IRLS steps Notes ----- `w` = 1 / (link'(`mu`)**2 * variance(`mu`)) """ return 1. / (self.link.deriv(mu)**2 * self.variance(mu)) def deviance(self, Y, mu, scale=1.): """ Deviance of (Y,mu) pair. Deviance is usually defined as twice the loglikelihood ratio. Parameters ---------- Y : array-like The endogenous response variable mu : array-like The inverse of the link function at the linear predicted values. scale : float, optional An optional scale argument Returns ------- DEV : array The value of deviance function defined below. Notes ----- DEV = (sum_i(2*loglike(Y_i,Y_i) - 2*loglike(Y_i,mu_i)) / scale The deviance functions are analytically defined for each family. """ raise NotImplementedError def resid_dev(self, Y, mu, scale=1.): """ The deviance residuals Parameters ---------- Y : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. scale : float, optional An optional argument to divide the residuals by scale Returns ------- Deviance residuals. Notes ----- The deviance residuals are defined for each family. """ raise NotImplementedError def fitted(self, eta): """ Fitted values based on linear predictors eta. Parameters ----------- eta : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns -------- mu : array The mean response variables given by the inverse of the link function. """ return self.link.inverse(eta) def predict(self, mu): """ Linear predictors based on given mu values. Parameters ---------- mu : array The mean response variables Returns ------- eta : array Linear predictors based on the mean response variables. The value of the link function at the given mu. """ return self.link(mu) def loglike(self, Y, mu, scale=1.): """ The loglikelihood function. Parameters ---------- `Y` : array Usually the endogenous response variable. `mu` : array Usually but not always the fitted mean response variable. Returns ------- llf : float The value of the loglikelihood evaluated at (Y,mu). Notes ----- This is defined for each family. Y and mu are not restricted to `Y` and `mu` respectively. For instance, the deviance function calls both loglike(Y,Y) and loglike(Y,mu) to get the likelihood ratio. """ raise NotImplementedError def resid_anscombe(self, Y, mu): """ The Anscome residuals. See also -------- statsmodels.families.family.Family docstring and the `resid_anscombe` for the individual families for more information. """ raise NotImplementedError class Poisson(Family): """ Poisson exponential family. Parameters ---------- link : a link instance, optional The default link for the Poisson family is the log link. Available links are log, identity, and sqrt. See statsmodels.family.links for more information. Attributes ---------- Poisson.link : a link instance The link function of the Poisson instance. Poisson.variance : varfuncs instance `variance` is an instance of statsmodels.genmod.families.family.varfuncs.mu See also -------- statsmodels.genmod.families.family.Family """ links = [L.log, L.identity, L.sqrt] variance = V.mu valid = [0, np.inf] def __init__(self, link=L.log): self.variance = Poisson.variance self.link = link() def resid_dev(self, Y, mu, scale=1.): """Poisson deviance residual Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns ------- resid_dev : array Deviance residuals as defined below Notes ----- resid_dev = sign(Y-mu)*sqrt(2*Y*log(Y/mu)-2*(Y-mu)) """ return np.sign(Y-mu) * np.sqrt(2*Y*np.log(Y/mu)-2*(Y-mu))/scale def deviance(self, Y, mu, scale=1.): ''' Poisson deviance function Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns ------- deviance : float The deviance function at (Y,mu) as defined below. Notes ----- If a constant term is included it is defined as :math:`deviance = 2*\\sum_{i}(Y*\\log(Y/\\mu))` ''' if np.any(Y==0): retarr = np.zeros(Y.shape) Ymu = Y/mu mask = Ymu != 0 YmuMasked = Ymu[mask] Ymasked = Y[mask] np.putmask(retarr, mask, Ymasked*np.log(YmuMasked)/scale) return 2*np.sum(retarr) else: return 2*np.sum(Y*np.log(Y/mu))/scale def loglike(self, Y, mu, scale=1.): """ Loglikelihood function for Poisson exponential family distribution. Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes ----- llf = scale * sum(-mu + Y*log(mu) - gammaln(Y+1)) where gammaln is the log gamma function """ return scale * np.sum(-mu + Y*np.log(mu)-special.gammaln(Y+1)) def resid_anscombe(self, Y, mu): """ Anscombe residuals for the Poisson exponential family distribution Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns ------- resid_anscombe : array The Anscome residuals for the Poisson family defined below Notes ----- resid_anscombe = :math:`(3/2.)*(Y^{2/3.} - \\mu**(2/3.))/\\mu^{1/6.}` """ return (3/2.)*(Y**(2/3.)-mu**(2/3.))/mu**(1/6.) class Gaussian(Family): """ Gaussian exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Gaussian family is the identity link. Available links are log, identity, and inverse. See statsmodels.family.links for more information. Attributes ---------- Gaussian.link : a link instance The link function of the Gaussian instance Gaussian.variance : varfunc instance `variance` is an instance of statsmodels.family.varfuncs.constant See also -------- statsmodels.genmod.families.family.Family """ links = [L.log, L.identity, L.inverse_power] variance = V.constant def __init__(self, link=L.identity): self.variance = Gaussian.variance self.link = link() def resid_dev(self, Y, mu, scale=1.): """ Gaussian deviance residuals Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns ------- resid_dev : array Deviance residuals as defined below Notes -------- `resid_dev` = (`Y` - `mu`)/sqrt(variance(`mu`)) """ return (Y - mu) / np.sqrt(self.variance(mu))/scale def deviance(self, Y, mu, scale=1.): """ Gaussian deviance function Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns ------- deviance : float The deviance function at (Y,mu) as defined below. Notes -------- `deviance` = sum((Y-mu)**2) """ return np.sum((Y-mu)**2)/scale def loglike(self, Y, mu, scale=1.): """ Loglikelihood function for Gaussian exponential family distribution. Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional Scales the loglikelihood function. The default is 1. Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes ----- If the link is the identity link function then the loglikelihood function is the same as the classical OLS model. llf = -(nobs/2)*(log(SSR) + (1 + log(2*pi/nobs))) where SSR = sum((Y-link^(-1)(mu))**2) If the links is not the identity link then the loglikelihood function is defined as llf = sum((`Y`*`mu`-`mu`**2/2)/`scale` - `Y`**2/(2*`scale`) - \ (1/2.)*log(2*pi*`scale`)) """ if isinstance(self.link, L.Power) and self.link.power == 1: # This is just the loglikelihood for classical OLS nobs2 = Y.shape[0]/2. SSR = ss(Y-self.fitted(mu)) llf = -np.log(SSR) * nobs2 llf -= (1+np.log(np.pi/nobs2))*nobs2 return llf else: # Return the loglikelihood for Gaussian GLM return np.sum((Y*mu-mu**2/2)/scale-Y**2/(2*scale)-\ .5*np.log(2*np.pi*scale)) def resid_anscombe(self, Y, mu): """ The Anscombe residuals for the Gaussian exponential family distribution Parameters ---------- Y : array Endogenous response variable mu : array Fitted mean response variable Returns ------- resid_anscombe : array The Anscombe residuals for the Gaussian family defined below Notes -------- `resid_anscombe` = `Y` - `mu` """ return Y-mu class Gamma(Family): """ Gamma exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Gamma family is the inverse link. Available links are log, identity, and inverse. See statsmodels.family.links for more information. Attributes ---------- Gamma.link : a link instance The link function of the Gamma instance Gamma.variance : varfunc instance `variance` is an instance of statsmodels.family.varfuncs.mu_squared See also -------- statsmodels.genmod.families.family.Family """ links = [L.log, L.identity, L.inverse_power] variance = V.mu_squared def __init__(self, link=L.inverse_power): self.variance = Gamma.variance self.link = link() #TODO: note the note def _clean(self, x): """ Helper function to trim the data so that is in (0,inf) Notes ----- The need for this function was discovered through usage and its possible that other families might need a check for validity of the domain. """ return np.clip(x, FLOAT_EPS, np.inf) def deviance(self, Y, mu, scale=1.): """ Gamma deviance function Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns ------- deviance : float Deviance function as defined below Notes ----- `deviance` = 2*sum((Y - mu)/mu - log(Y/mu)) """ Y_mu = self._clean(Y/mu) return 2 * np.sum((Y - mu)/mu - np.log(Y_mu)) def resid_dev(self, Y, mu, scale=1.): """ Gamma deviance residuals Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns ------- resid_dev : array Deviance residuals as defined below Notes ----- `resid_dev` = sign(Y - mu) * sqrt(-2*(-(Y-mu)/mu + log(Y/mu))) """ Y_mu = self._clean(Y/mu) return np.sign(Y-mu) * np.sqrt(-2*(-(Y-mu)/mu + np.log(Y_mu))) def loglike(self, Y, mu, scale=1.): """ Loglikelihood function for Gamma exponential family distribution. Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes -------- llf = -1/scale * sum(Y/mu + log(mu) + (scale-1)*log(Y) + log(scale) +\ scale*gammaln(1/scale)) where gammaln is the log gamma function. """ return - 1./scale * np.sum(Y/mu+np.log(mu)+(scale-1)*np.log(Y)\ +np.log(scale)+scale*special.gammaln(1./scale)) # in Stata scale is set to equal 1 for reporting llf # in R it's the dispersion, though there is a loss of precision vs. our # results due to an assumed difference in implementation def resid_anscombe(self, Y, mu): """ The Anscombe residuals for Gamma exponential family distribution Parameters ---------- Y : array Endogenous response variable mu : array Fitted mean response variable Returns ------- resid_anscombe : array The Anscombe residuals for the Gamma family defined below Notes ----- resid_anscombe = 3*(Y**(1/3.)-mu**(1/3.))/mu**(1/3.) """ return 3*(Y**(1/3.)-mu**(1/3.))/mu**(1/3.) class Binomial(Family): """ Binomial exponential family distribution. Parameters ---------- link : a link instance, optional The default link for the Binomial family is the logit link. Available links are logit, probit, cauchy, log, and cloglog. See statsmodels.family.links for more information. Attributes ---------- Binomial.link : a link instance The link function of the Binomial instance Binomial.variance : varfunc instance `variance` is an instance of statsmodels.family.varfuncs.binary See also -------- statsmodels.genmod.families.family.Family Notes ----- endog for Binomial can be specified in one of three ways. """ links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity] variance = V.binary # this is not used below in an effort to include n def __init__(self, link=L.logit): #, n=1.): #TODO: it *should* work for a constant n>1 actually, if data_weights is # equal to n self.n = 1 # overwritten by initialize if needed but # always used to initialize variance # since Y is assumed/forced to be (0,1) self.variance = V.Binomial(n=self.n) self.link = link() def starting_mu(self, y): """ The starting values for the IRLS algorithm for the Binomial family. A good choice for the binomial family is starting_mu = (y + .5)/2 """ return (y + .5)/2 def initialize(self, Y): ''' Initialize the response variable. Parameters ---------- Y : array Endogenous response variable Returns -------- If `Y` is binary, returns `Y` If `Y` is a 2d array, then the input is assumed to be in the format (successes, failures) and successes/(success + failures) is returned. And n is set to successes + failures. ''' if (Y.ndim > 1 and Y.shape[1] > 1): y = Y[:,0] self.n = Y.sum(1) # overwrite self.n for deviance below return y*1./self.n else: return Y def deviance(self, Y, mu, scale=1.): ''' Deviance function for either Bernoulli or Binomial data. Parameters ---------- Y : array-like Endogenous response variable (already transformed to a probability if appropriate). mu : array Fitted mean response variable scale : float, optional An optional scale argument Returns -------- deviance : float The deviance function as defined below Notes ----- If the endogenous variable is binary: `deviance` = -2*sum(I_one * log(mu) + (I_zero)*log(1-mu)) where I_one is an indicator function that evalueates to 1 if Y_i == 1. and I_zero is an indicator function that evaluates to 1 if Y_i == 0. If the model is ninomial: `deviance` = 2*sum(log(Y/mu) + (n-Y)*log((n-Y)/(n-mu))) where Y and n are as defined in Binomial.initialize. ''' if np.shape(self.n) == () and self.n == 1: one = np.equal(Y,1) return -2 * np.sum(one * np.log(mu+1e-200) + (1-one) * np.log(1-mu+1e-200)) else: return 2*np.sum(self.n*(Y*np.log(Y/mu+1e-200)+(1-Y)*np.log((1-Y)/(1-mu)+1e-200))) def resid_dev(self, Y, mu, scale=1.): """ Binomial deviance residuals Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns ------- resid_dev : array Deviance residuals as defined below Notes ----- If `Y` is binary: resid_dev = sign(Y-mu)*sqrt(-2*log(I_one*mu + I_zero*(1-mu))) where I_one is an indicator function that evaluates as 1 if Y == 1 and I_zero is an indicator function that evaluates as 1 if Y == 0. If `Y` is binomial: resid_dev = sign(Y-mu)*sqrt(2*n*(Y*log(Y/mu)+(1-Y)*log((1-Y)/(1-mu)))) where Y and n are as defined in Binomial.initialize. """ mu = self.link._clean(mu) if np.shape(self.n) == () and self.n == 1: one = np.equal(Y,1) return np.sign(Y-mu)*np.sqrt(-2*np.log(one*mu+(1-one)*(1-mu)))\ /scale else: return np.sign(Y-mu) * np.sqrt(2*self.n*(Y*np.log(Y/mu+1e-200)+(1-Y)*\ np.log((1-Y)/(1-mu)+1e-200)))/scale def loglike(self, Y, mu, scale=1.): """ Loglikelihood function for Binomial exponential family distribution. Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes -------- If `Y` is binary: `llf` = scale*sum(Y*log(mu/(1-mu))+log(1-mu)) If `Y` is binomial: `llf` = scale*sum(gammaln(n+1) - gammaln(y+1) - gammaln(n-y+1) +\ y*log(mu/(1-mu)) + n*log(1-mu) where gammaln is the log gamma function and y = Y*n with Y and n as defined in Binomial initialize. This simply makes y the original number of successes. """ if np.shape(self.n) == () and self.n == 1: return scale*np.sum(Y*np.log(mu/(1-mu)+1e-200)+np.log(1-mu)) else: y=Y*self.n #convert back to successes return scale * np.sum(special.gammaln(self.n+1)-\ special.gammaln(y+1)-special.gammaln(self.n-y+1)\ +y*np.log(mu/(1-mu))+self.n*np.log(1-mu)) def resid_anscombe(self, Y, mu): ''' The Anscombe residuals Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns ------- resid_anscombe : array The Anscombe residuals as defined below. Notes ----- sqrt(n)*(cox_snell(Y)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) where cox_snell is defined as cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) where betainc is the incomplete beta function The name 'cox_snell' is idiosyncratic and is simply used for convenience following the approach suggested in Cox and Snell (1968). Further note that cox_snell(x) = x**(2/3.)/(2/3.)*hyp2f1(2/3.,1/3.,5/3.,x) where hyp2f1 is the hypergeometric 2f1 function. The Anscombe residuals are sometimes defined in the literature using the hyp2f1 formulation. Both betainc and hyp2f1 can be found in scipy. References ---------- Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's paper." Journal of the Royal Statistical Society B. 15, 229-30. Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals." Journal of the Royal Statistical Society B. 30, 248-75. ''' cox_snell = lambda x: special.betainc(2/3., 2/3., x)\ *special.beta(2/3.,2/3.) return np.sqrt(self.n)*(cox_snell(Y)-cox_snell(mu))/\ (mu**(1/6.)*(1-mu)**(1/6.)) class InverseGaussian(Family): """ InverseGaussian exponential family. Parameters ---------- link : a link instance, optional The default link for the inverse Gaussian family is the inverse squared link. Available links are inverse_squared, inverse, log, and identity. See statsmodels.family.links for more information. Attributes ---------- InverseGaussian.link : a link instance The link function of the inverse Gaussian instance InverseGaussian.variance : varfunc instance `variance` is an instance of statsmodels.family.varfuncs.mu_cubed See also -------- statsmodels.genmod.families.family.Family Notes ----- The inverse Guassian distribution is sometimes referred to in the literature as the wald distribution. """ links = [L.inverse_squared, L.inverse_power, L.identity, L.log] variance = V.mu_cubed def __init__(self, link=L.inverse_squared): self.variance = InverseGaussian.variance self.link = link() def resid_dev(self, Y, mu, scale=1.): """ Returns the deviance residuals for the inverse Gaussian family. Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns ------- resid_dev : array Deviance residuals as defined below Notes ----- `dev_resid` = sign(Y-mu)*sqrt((Y-mu)**2/(Y*mu**2)) """ return np.sign(Y-mu) * np.sqrt((Y-mu)**2/(Y*mu**2))/scale def deviance(self, Y, mu, scale=1.): """ Inverse Gaussian deviance function Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns ------- deviance : float Deviance function as defined below Notes ----- `deviance` = sum((Y=mu)**2/(Y*mu**2)) """ return np.sum((Y-mu)**2/(Y*mu**2))/scale def loglike(self, Y, mu, scale=1.): """ Loglikelihood function for inverse Gaussian distribution. Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes ----- `llf` = -(1/2.)*sum((Y-mu)**2/(Y*mu**2*scale) + log(scale*Y**3)\ + log(2*pi)) """ return -.5 * np.sum((Y-mu)**2/(Y*mu**2*scale)\ + np.log(scale*Y**3) + np.log(2*np.pi)) def resid_anscombe(self, Y, mu): """ The Anscombe residuals for the inverse Gaussian distribution Parameters ---------- Y : array Endogenous response variable mu : array Fitted mean response variable Returns ------- resid_anscombe : array The Anscombe residuals for the inverse Gaussian distribution as defined below Notes ----- `resid_anscombe` = log(Y/mu)/sqrt(mu) """ return np.log(Y/mu)/np.sqrt(mu) class NegativeBinomial(Family): """ Negative Binomial exponential family. Parameters ---------- link : a link instance, optional The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.family.links for more information. alpha : float, optional The ancillary parameter for the negative binomial distribution. For now `alpha` is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be between .01 and 2. Attributes ---------- NegativeBinomial.link : a link instance The link function of the negative binomial instance NegativeBinomial.variance : varfunc instance `variance` is an instance of statsmodels.family.varfuncs.nbinom See also -------- statsmodels.genmod.families.family.Family Notes ----- Support for Power link functions is not yet supported. """ links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power] #TODO: add the ability to use the power links with an if test # similar to below variance = V.nbinom def __init__(self, link=L.log, alpha=1.): self.alpha = alpha self.variance = V.NegativeBinomial(alpha=self.alpha) if isinstance(link, L.NegativeBinomial): self.link = link(alpha=self.alpha) else: self.link = link() def _clean(self, x): """ Helper function to trim the data so that is in (0,inf) Notes ----- The need for this function was discovered through usage and its possible that other families might need a check for validity of the domain. """ return np.clip(x, FLOAT_EPS, np.inf) def deviance(self, Y, mu, scale=1.): """ Returns the value of the deviance function. Parameters ----------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns ------- deviance : float Deviance function as defined below Notes ----- `deviance` = sum(piecewise) where piecewise is defined as if :math:`Y_{i} == 0:` piecewise_i = :math:`2\\log\\left(1+\\alpha*\\mu\\right)/\\alpha` if :math:`Y_{i} > 0`: piecewise_i = :math:`2 Y \\log(Y/\\mu)-2/\\alpha(1+\\alpha Y)*\\log((1+\\alpha Y)/(1+\\alpha\\mu))` """ iszero = np.equal(Y,0) notzero = 1 - iszero tmp = np.zeros(len(Y)) Y_mu = self._clean(Y/mu) tmp = iszero*2*np.log(1+self.alpha*mu)/self.alpha tmp += notzero*(2*Y*np.log(Y_mu)-2/self.alpha*(1+self.alpha*Y)*\ np.log((1+self.alpha*Y)/(1+self.alpha*mu))) return np.sum(tmp)/scale def resid_dev(self, Y, mu, scale=1.): ''' Negative Binomial Deviance Residual Parameters ---------- Y : array-like `Y` is the response variable mu : array-like `mu` is the fitted value of the model scale : float, optional An optional argument to divide the residuals by scale Returns -------- resid_dev : array The array of deviance residuals Notes ----- `resid_dev` = sign(Y-mu) * sqrt(piecewise) where piecewise is defined as if :math:`Y_i = 0`: :math:`piecewise_i = 2*log(1+alpha*mu)/alpha` if :math:`Y_i > 0`: :math:`piecewise_i = 2*Y*log(Y/\\mu)-2/\\alpha*(1+\\alpha*Y)*log((1+\\alpha*Y)/(1+\\alpha*\\mu))` ''' iszero = np.equal(Y,0) notzero = 1 - iszero tmp=np.zeros(len(Y)) tmp = iszero*2*np.log(1+self.alpha*mu)/self.alpha tmp += notzero*(2*Y*np.log(Y/mu)-2/self.alpha*(1+self.alpha*Y)*\ np.log((1+self.alpha*Y)/(1+self.alpha*mu))) return np.sign(Y-mu)*np.sqrt(tmp)/scale def loglike(self, Y, fittedvalues=None): """ The loglikelihood function for the negative binomial family. Parameters ---------- Y : array-like Endogenous response variable fittedvalues : array-like The linear fitted values of the model. This is dot(exog,params). Returns ------- llf : float The value of the loglikelihood function evaluated at (Y,mu,scale) as defined below. Notes ----- sum(Y*log(alpha*exp(fittedvalues)/(1+alpha*exp(fittedvalues))) -\ log(1+alpha*exp(fittedvalues))/alpha + constant) where constant is defined as constant = gammaln(Y + 1/alpha) - gammaln(Y + 1) - gammaln(1/alpha) """ # don't need to specify mu if fittedvalues is None: raise AttributeError('The loglikelihood for the negative binomial \ requires that the fitted values be provided via the `fittedvalues` keyword \ argument.') constant = special.gammaln(Y + 1/self.alpha) - special.gammaln(Y+1)\ -special.gammaln(1/self.alpha) return np.sum(Y*np.log(self.alpha*np.exp(fittedvalues)/\ (1 + self.alpha*np.exp(fittedvalues))) - \ np.log(1+self.alpha*np.exp(fittedvalues))/self.alpha\ + constant) def resid_anscombe(self, Y, mu): """ The Anscombe residuals for the negative binomial family Parameters ---------- Y : array-like Endogenous response variable mu : array-like Fitted mean response variable Returns ------- resid_anscombe : array The Anscombe residuals as defined below. Notes ----- `resid_anscombe` = (hyp2f1(-alpha*Y)-hyp2f1(-alpha*mu)+\ 1.5*(Y**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.) where hyp2f1 is the hypergeometric 2f1 function parameterized as hyp2f1(x) = hyp2f1(2/3.,1/3.,5/3.,x) """ hyp2f1 = lambda x : special.hyp2f1(2/3.,1/3.,5/3.,x) return (hyp2f1(-self.alpha*Y)-hyp2f1(-self.alpha*mu)+1.5*(Y**(2/3.)-\ mu**(2/3.)))/(mu+self.alpha*mu**2)**(1/6.) statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/families/links.py000066400000000000000000000323221224417117700252520ustar00rootroot00000000000000''' Defines the link functions to be used with GLM families. ''' import numpy as np import scipy.stats FLOAT_EPS = np.finfo(float).eps #TODO: are the instance actually "aliases" # I used this terminology in varfuncs as well -ss class Link(object): """ A generic link function for one-parameter exponential family. `Link` does nothing, but lays out the methods expected of any subclass. """ def __call__(self, p): """ Return the value of the link function. This is just a placeholder. Parameters ---------- p : array-like Probabilities Returns ------- The value of the link function g(p) = z """ return NotImplementedError def inverse(self, z): """ Inverse of the link function. Just a placeholder. Parameters ---------- z : array-like `z` is usually the linear predictor of the transformed variable in the IRLS algorithm for GLM. Returns ------- The value of the inverse of the link function g^(-1)(z) = p """ return NotImplementedError def deriv(self, p): """ Derivative of the link function g'(p). Just a placeholder. Parameters ---------- p : array-like Returns ------- The value of the derivative of the link function g'(p) """ return NotImplementedError class Logit(Link): """ The logit transform Notes ----- call and derivative use a private method _clean to make trim p by machine epsilon so that p is in (0,1) Alias of Logit: logit = Logit() """ def _clean(self, p): """ Clip logistic values to range (eps, 1-eps) Parameters ----------- p : array-like Probabilities Returns -------- pclip : array Clipped probabilities """ return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS) def __call__(self, p): """ The logit transform Parameters ---------- p : array-like Probabilities Returns ------- z : array Logit transform of `p` Notes ----- g(p) = log(p / (1 - p)) """ p = self._clean(p) return np.log(p / (1. - p)) def inverse(self, z): """ Inverse of the logit transform Parameters ---------- z : array-like The value of the logit transform at `p` Returns ------- p : array Probabilities Notes ----- g^(-1)(z) = exp(z)/(1+exp(z)) """ t = np.exp(z) return t / (1. + t) def deriv(self, p): """ Derivative of the logit transform Parameters ---------- p: array-like Probabilities Returns ------- g'(p) : array Value of the derivative of logit transform at `p` Notes ----- g'(p) = 1 / (p * (1 - p)) Alias for `Logit`: logit = Logit() """ p = self._clean(p) return 1. / (p * (1 - p)) #logit = Logit() class logit(Logit): pass class Power(Link): """ The power transform Parameters ---------- power : float The exponent of the power transform Notes ----- Aliases of Power: inverse = Power(power=-1) sqrt = Power(power=.5) inverse_squared = Power(power=-2.) identity = Power(power=1.) """ def __init__(self, power=1.): self.power = power def __call__(self, p): """ Power transform link function Parameters ---------- p : array-like Mean parameters Returns ------- z : array-like Power transform of x Notes ----- g(p) = x**self.power """ return np.power(p, self.power) def inverse(self, z): """ Inverse of the power transform link function Parameters ---------- `z` : array-like Value of the transformed mean parameters at `p` Returns ------- `p` : array Mean parameters Notes ----- g^(-1)(z`) = `z`**(1/`power`) """ return np.power(z, 1. / self.power) def deriv(self, p): """ Derivative of the power transform Parameters ---------- p : array-like Mean parameters Returns -------- g'(p) : array Derivative of power transform of `p` Notes ----- g'(`p`) = `power` * `p`**(`power` - 1) """ return self.power * np.power(p, self.power - 1) #inverse = Power(power=-1.) class inverse_power(Power): """ The inverse transform Notes ----- g(p) = 1/p Alias of statsmodels.family.links.Power(power=-1.) """ def __init__(self): super(inverse_power, self).__init__(power=-1.) #sqrt = Power(power=0.5) class sqrt(Power): """ The square-root transform Notes ----- g(`p`) = sqrt(`p`) Alias of statsmodels.family.links.Power(power=.5) """ def __init__(self): super(sqrt, self).__init__(power=.5) class inverse_squared(Power): #inverse_squared = Power(power=-2.) """ The inverse squared transform Notes ----- g(`p`) = 1/(`p`\ \*\*2) Alias of statsmodels.family.links.Power(power=2.) """ def __init__(self): super(inverse_squared, self).__init__(power=-2.) class identity(Power): """ The identity transform Notes ----- g(`p`) = `p` Alias of statsmodels.family.links.Power(power=1.) """ def __init__(self): super(identity, self).__init__(power=1.) class Log(Link): """ The log transform Notes ----- call and derivative call a private method _clean to trim the data by machine epsilon so that p is in (0,1). log is an alias of Log. """ def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, p, **extra): """ Log transform link function Parameters ---------- x : array-like Mean parameters Returns ------- z : array log(x) Notes ----- g(p) = log(p) """ x = self._clean(p) return np.log(p) def inverse(self, z): """ Inverse of log transform link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- p : array The mean probabilities given the value of the inverse `z` Notes ----- g^{-1}(z) = exp(z) """ return np.exp(z) def deriv(self, p): """ Derivative of log transform link function Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array derivative of log transform of x Notes ----- g(x) = 1/x """ p = self._clean(p) return 1. / p class log(Log): """ The log transform Notes ----- log is a an alias of Log. """ pass #TODO: the CDFLink is untested class CDFLink(Logit): """ The use the CDF of a scipy.stats distribution CDFLink is a subclass of logit in order to use its _clean method for the link and its derivative. Parameters ---------- dbn : scipy.stats distribution Default is dbn=scipy.stats.norm Notes ----- The CDF link is untested. """ def __init__(self, dbn=scipy.stats.norm): self.dbn = dbn def __call__(self, p): """ CDF link function Parameters ---------- p : array-like Mean parameters Returns ------- z : array (ppf) inverse of CDF transform of p Notes ----- g(`p`) = `dbn`.ppf(`p`) """ p = self._clean(p) return self.dbn.ppf(p) def inverse(self, z): """ The inverse of the CDF link Parameters ---------- z : array-like The value of the inverse of the link function at `p` Returns ------- p : array Mean probabilities. The value of the inverse of CDF link of `z` Notes ----- g^(-1)(`z`) = `dbn`.cdf(`z`) """ return self.dbn.cdf(z) def deriv(self, p): """ Derivative of CDF link Parameters ---------- p : array-like mean parameters Returns ------- g'(p) : array The derivative of CDF transform at `p` Notes ----- g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`)) """ p = self._clean(p) return 1. / self.dbn.pdf(self.dbn.ppf(p)) #probit = CDFLink() class probit(CDFLink): """ The probit (standard normal CDF) transform Notes -------- g(p) = scipy.stats.norm.ppf(p) probit is an alias of CDFLink. """ pass class cauchy(CDFLink): """ The Cauchy (standard Cauchy CDF) transform Notes ----- g(p) = scipy.stats.cauchy.ppf(p) cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy """ def __init__(self): super(cauchy, self).__init__(dbn=scipy.stats.cauchy) #TODO: CLogLog is untested class CLogLog(Logit): """ The complementary log-log transform CLogLog inherits from Logit in order to have access to its _clean method for the link and its derivative. Notes ----- CLogLog is untested. """ def __call__(self, p): """ C-Log-Log transform link function Parameters ---------- p : array Mean parameters Returns ------- z : array The CLogLog transform of `p` Notes ----- g(p) = log(-log(1-p)) """ p = self._clean(p) return np.log(-np.log(1-p)) def inverse(self, z): """ Inverse of C-Log-Log transform link function Parameters ---------- z : array-like The value of the inverse of the CLogLog link function at `p` Returns ------- p : array Mean parameters Notes ----- g^(-1)(`z`) = 1-exp(-exp(`z`)) """ return 1-np.exp(-np.exp(z)) def deriv(self, p): """ Derivatve of C-Log-Log transform link function Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array The derivative of the CLogLog transform link function Notes ----- g'(p) = - 1 / (log(p) * p) """ p = self._clean(p) return 1. / ((p-1)*(np.log(1-p))) class cloglog(CLogLog): """ The CLogLog transform link function. Notes ----- g(`p`) = log(-log(1-`p`)) cloglog is an alias for CLogLog cloglog = CLogLog() """ pass class NegativeBinomial(object): ''' The negative binomial link function Parameters ---------- alpha : float, optional Alpha is the ancillary parameter of the Negative Binomial link function. It is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be in (.01,2). ''' def __init__(self, alpha=1.): self.alpha = alpha def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, x): ''' Negative Binomial transform link function Parameters ---------- p : array-like Mean parameters Returns ------- z : array The negative binomial transform of `p` Notes ----- g(p) = log(p/(p + 1/alpha)) ''' p = self._clean(p) return np.log(p/(p+1/self.alpha)) def inverse(self, z): ''' Inverse of the negative binomial transform Parameters ----------- z : array-like The value of the inverse of the negative binomial link at `p`. Returns ------- p : array Mean parameters Notes ----- g^(-1)(z) = exp(z)/(alpha*(1-exp(z))) ''' return np.exp(z)/(self.alpha*(1-np.exp(z))) def deriv(self,p): ''' Derivative of the negative binomial transform Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array The derivative of the negative binomial transform link function Notes ----- g'(x) = 1/(x+alpha*x^2) ''' return 1/(p+self.alpha*p**2) class nbinom(NegativeBinomial): """ The negative binomial link function. Notes ----- g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) """ pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/families/varfuncs.py000066400000000000000000000111371224417117700257620ustar00rootroot00000000000000""" Variance functions for use with the link functions in statsmodels.family.links """ __docformat__ = 'restructuredtext' import numpy as np FLOAT_EPS = np.finfo(float).eps class VarianceFunction(object): """ Relates the variance of a random variable to its mean. Defaults to 1. Methods ------- call Returns an array of ones that is the same shape as `mu` Notes ----- After a variance function is initialized, its call method can be used. Alias for VarianceFunction: constant = VarianceFunction() See also -------- statsmodels.family.family """ def __call__(self, mu): """ Default variance function Parameters ----------- mu : array-like mean parameters Returns ------- v : array ones(mu.shape) """ mu = np.asarray(mu) return np.ones(mu.shape, np.float64) constant = VarianceFunction() constant.__doc__ = """ The call method of constnat returns a constant variance, ie., a vector of ones. constant is an alias of VarianceFunction() """ class Power(object): """ Power variance function Parameters ---------- power : float exponent used in power variance function Methods ------- call Returns the power variance Formulas -------- V(mu) = numpy.fabs(mu)**power Notes ----- Aliases for Power: mu = Power() mu_squared = Power(power=2) mu_cubed = Power(power=3) """ def __init__(self, power=1.): self.power = power def __call__(self, mu): """ Power variance function Parameters ---------- mu : array-like mean parameters Returns ------- variance : array numpy.fabs(mu)**self.power """ return np.power(np.fabs(mu), self.power) mu = Power() mu.__doc__ = """ Returns np.fabs(mu) Notes ----- This is an alias of Power() """ mu_squared = Power(power=2) mu_squared.__doc__ = """ Returns np.fabs(mu)**2 Notes ----- This is an alias of statsmodels.family.links.Power(power=2) """ mu_cubed = Power(power=3) mu_cubed.__doc__ = """ Returns np.fabs(mu)**3 Notes ----- This is an alias of statsmodels.family.links.Power(power=3) """ class Binomial(object): """ Binomial variance function Parameters ---------- n : int, optional The number of trials for a binomial variable. The default is 1 for p in (0,1) Methods ------- call Returns the binomial variance Formulas -------- V(mu) = p * (1 - p) * n where p = mu / n Notes ----- Alias for Binomial: binary = Binomial() A private method _clean trims the data by machine epsilon so that p is in (0,1) """ def __init__(self, n=1): self.n = n def _clean(self, p): return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS) def __call__(self, mu): """ Binomial variance function Parameters ----------- mu : array-like mean parameters Returns ------- variance : array variance = mu/n * (1 - mu/n) * self.n """ p = self._clean(mu / self.n) return p * (1 - p) * self.n binary = Binomial() binary.__doc__ = """ The binomial variance function for n = 1 Notes ----- This is an alias of Binomial(n=1) """ class NegativeBinomial(object): ''' Negative binomial variance function Parameters ---------- alpha : float The ancillary parameter for the negative binomial variance function. `alpha` is assumed to be nonstochastic. The default is 1. Methods ------- call Returns the negative binomial variance Formulas -------- V(mu) = mu + alpha*mu**2 Notes ----- Alias for NegativeBinomial: nbinom = NegativeBinomial() A private method _clean trims the data by machine epsilon so that p is in (0,inf) ''' def __init__(self, alpha=1.): self.alpha = alpha def _clean(self, p): return np.clip(p, FLOAT_EPS, np.inf) def __call__(self, mu): """ Negative binomial variance function Parameters ---------- mu : array-like mean parameters Returns ------- variance : array variance = mu + alpha*mu**2 """ p = self._clean(mu) return p + self.alpha*p**2 nbinom = NegativeBinomial() nbinom.__doc__ = """ Negative Binomial variance function. Notes ----- This is an alias of NegativeBinomial(alpha=1.) """ statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/generalized_linear_model.py000066400000000000000000000727251224417117700273570ustar00rootroot00000000000000""" Generalized linear models currently supports estimation using the one-parameter exponential families References ---------- Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach. SAGE QASS Series. Green, PJ. 1984. "Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives." Journal of the Royal Statistical Society, Series B, 46, 149-192. Hardin, J.W. and Hilbe, J.M. 2007. "Generalized Linear Models and Extensions." 2nd ed. Stata Press, College Station, TX. McCullagh, P. and Nelder, J.A. 1989. "Generalized Linear Models." 2nd ed. Chapman & Hall, Boca Rotan. """ import numpy as np import families from statsmodels.tools.tools import rank from statsmodels.tools.decorators import (cache_readonly, resettable_cache) import statsmodels.base.model as base import statsmodels.regression.linear_model as lm import statsmodels.base.wrapper as wrap from statsmodels.tools.sm_exceptions import PerfectSeparationError __all__ = ['GLM'] def _check_convergence(criterion, iteration, tol, maxiter): return not ((np.fabs(criterion[iteration] - criterion[iteration-1]) > tol) and iteration <= maxiter) class GLM(base.LikelihoodModel): __doc__ = ''' Generalized Linear Models class GLM inherits from statsmodels.LikelihoodModel Parameters ----------- endog : array-like 1d array of endogenous response variable. This array can be 1d or 2d. Binomial family models accept a 2d array with two columns. If supplied, each observation is expected to be [success, failure]. exog : array-like A nobs x k array where `nobs` is the number of observations and `k` is the number of regressors. An interecept is not included by default and should be added by the user. See `statsmodels.tools.add_constant`. family : family class instance The default is Gaussian. To specify the binomial distribution family = sm.family.Binomial() Each family can take a link instance as an argument. See statsmodels.family.family for more information. %(extra_params)s Attributes ----------- df_model : float `p` - 1, where `p` is the number of regressors including the intercept. df_resid : float The number of observation `n` minus the number of regressors `p`. endog : array See Parameters. exog : array See Parameters. family : family class instance A pointer to the distribution family of the model. mu : array The estimated mean response of the transformed variable. normalized_cov_params : array `p` x `p` normalized covariance of the design / exogenous data. pinv_wexog : array For GLM this is just the pseudo inverse of the original design. scale : float The estimate of the scale / dispersion. Available after fit is called. scaletype : str The scaling used for fitting the model. Available after fit is called. weights : array The value of the weights after the last iteration of fit. Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.scotland.load() >>> data.exog = sm.add_constant(data.exog) Instantiate a gamma family model with the default link function. >>> gamma_model = sm.GLM(data.endog, data.exog, ... family=sm.families.Gamma()) >>> gamma_results = gamma_model.fit() >>> gamma_results.params array([-0.01776527, 0.00004962, 0.00203442, -0.00007181, 0.00011185, -0.00000015, -0.00051868, -0.00000243]) >>> gamma_results.scale 0.0035842831734919055 >>> gamma_results.deviance 0.087388516416999198 >>> gamma_results.pearson_chi2 0.086022796163805704 >>> gamma_results.llf -83.017202161073527 See also -------- statsmodels.families.* Notes ----- Only the following combinations make sense for family and link :: + ident log logit probit cloglog pow opow nbinom loglog logc Gaussian | x x x inv Gaussian | x x x binomial | x x x x x x x x x Poission | x x x neg binomial | x x x x gamma | x x x Not all of these link functions are currently available. Endog and exog are references so that if the data they refer to are already arrays and these arrays are changed, endog and exog will change. **Attributes** df_model : float Model degrees of freedom is equal to p - 1, where p is the number of regressors. Note that the intercept is not reported as a degree of freedom. df_resid : float Residual degrees of freedom is equal to the number of observation n minus the number of regressors p. endog : array See above. Note that endog is a reference to the data so that if data is already an array and it is changed, then `endog` changes as well. exposure : array-like Include ln(exposure) in model with coefficient constrained to 1. exog : array See above. Note that endog is a reference to the data so that if data is already an array and it is changed, then `endog` changes as well. iteration : int The number of iterations that fit has run. Initialized at 0. family : family class instance The distribution family of the model. Can be any family in statsmodels.families. Default is Gaussian. mu : array The mean response of the transformed variable. `mu` is the value of the inverse of the link function at eta, where eta is the linear predicted value of the WLS fit of the transformed variable. `mu` is only available after fit is called. See statsmodels.families.family.fitted of the distribution family for more information. normalized_cov_params : array The p x p normalized covariance of the design / exogenous data. This is approximately equal to (X.T X)^(-1) offset : array-like Include offset in model with coefficient constrained to 1. pinv_wexog : array The pseudoinverse of the design / exogenous data array. Note that GLM has no whiten method, so this is just the pseudo inverse of the design. The pseudoinverse is approximately equal to (X.T X)^(-1)X.T scale : float The estimate of the scale / dispersion of the model fit. Only available after fit is called. See GLM.fit and GLM.estimate_scale for more information. scaletype : str The scaling used for fitting the model. This is only available after fit is called. The default is None. See GLM.fit for more information. weights : array The value of the weights after the last iteration of fit. Only available after fit is called. See statsmodels.families.family for the specific distribution weighting functions. ''' % {'extra_params' : base._missing_param_doc} def __init__(self, endog, exog, family=None, offset=None, exposure=None, missing='none'): self._check_inputs(family, offset, exposure, endog) super(GLM, self).__init__(endog, exog, missing=missing, offset=self.offset, exposure=self.exposure) if offset is None: delattr(self, 'offset') if exposure is None: delattr(self, 'exposure') #things to remove_data self._data_attr.extend(['weights', 'pinv_wexog', 'mu', 'data_weights', ]) def initialize(self): """ Initialize a generalized linear model. """ #TODO: intended for public use? self.history = {'fittedvalues' : [], 'params' : [np.inf], 'deviance' : [np.inf]} self.pinv_wexog = np.linalg.pinv(self.exog) self.normalized_cov_params = np.dot(self.pinv_wexog, np.transpose(self.pinv_wexog)) self.df_model = rank(self.exog)-1 self.df_resid = self.exog.shape[0] - rank(self.exog) def _check_inputs(self, family, offset, exposure, endog): if family is None: family = families.Gaussian() self.family = family if offset is not None: offset = np.asarray(offset) if offset.shape[0] != endog.shape[0]: raise ValueError("offset is not the same length as endog") self.offset = offset if exposure is not None: exposure = np.log(exposure) if exposure.shape[0] != endog.shape[0]: raise ValueError("exposure is not the same length as endog") self.exposure = exposure def score(self, params): """ Score matrix. Not yet implemeneted """ raise NotImplementedError def loglike(self, *args): """ Loglikelihood function. Each distribution family has its own loglikelihood function. See statsmodels.families.family """ return self.family.loglike(*args) def information(self, params): """ Fisher information matrix. Not yet implemented. """ raise NotImplementedError def _update_history(self, tmp_result, mu, history): """ Helper method to update history during iterative fit. """ history['params'].append(tmp_result.params) history['deviance'].append(self.family.deviance(self.endog, mu)) return history def estimate_scale(self, mu): """ Estimates the dispersion/scale. Type of scale can be chose in the fit method. Parameters ---------- mu : array mu is the mean response estimate Returns ------- Estimate of scale Notes ----- The default scale for Binomial and Poisson families is 1. The default for the other families is Pearson's Chi-Square estimate. See also -------- statsmodels.glm.fit for more information """ if not self.scaletype: if isinstance(self.family, (families.Binomial, families.Poisson)): return 1. else: resid = self.endog - mu return ((np.power(resid, 2) / self.family.variance(mu)).sum() \ / self.df_resid) if isinstance(self.scaletype, float): return np.array(self.scaletype) if isinstance(self.scaletype, str): if self.scaletype.lower() == 'x2': resid = self.endog - mu return ((np.power(resid, 2) / self.family.variance(mu)).sum() \ / self.df_resid) elif self.scaletype.lower() == 'dev': return self.family.deviance(self.endog, mu)/self.df_resid else: raise ValueError("Scale %s with type %s not understood" %\ (self.scaletype,type(self.scaletype))) else: raise ValueError("Scale %s with type %s not understood" %\ (self.scaletype, type(self.scaletype))) def predict(self, params, exog=None, linear=False): """ Return predicted values for a design matrix Parameters ---------- params : array-like Parameters / coefficients of a GLM. exog : array-like, optional Design / exogenous data. Is exog is None, model exog is used. linear : bool If True, returns the linear predicted values. If False, returns the value of the inverse of the model's link function at the linear predicted values. Returns ------- An array of fitted values """ offset = getattr(self, 'offset', 0) exposure = getattr(self, 'exposure', 0) if exog is None: exog = self.exog if linear: return np.dot(exog, params) + offset + exposure else: return self.family.fitted(np.dot(exog, params) + exposure + \ offset) def fit(self, maxiter=100, method='IRLS', tol=1e-8, scale=None): ''' Fits a generalized linear model for a given family. parameters ---------- maxiter : int, optional Default is 100. method : string Default is 'IRLS' for iteratively reweighted least squares. This is currently the only method available for GLM fit. scale : string or float, optional `scale` can be 'X2', 'dev', or a float The default value is None, which uses `X2` for Gamma, Gaussian, and Inverse Gaussian. `X2` is Pearson's chi-square divided by `df_resid`. The default is 1 for the Binomial and Poisson families. `dev` is the deviance divided by df_resid tol : float Convergence tolerance. Default is 1e-8. ''' endog = self.endog if endog.ndim > 1 and endog.shape[1] == 2: data_weights = endog.sum(1) # weights are total trials else: data_weights = np.ones((endog.shape[0])) self.data_weights = data_weights if np.shape(self.data_weights) == () and self.data_weights>1: self.data_weights = self.data_weights *\ np.ones((endog.shape[0])) self.scaletype = scale if isinstance(self.family, families.Binomial): # this checks what kind of data is given for Binomial. # family will need a reference to endog if this is to be removed from # preprocessing self.endog = self.family.initialize(self.endog) if hasattr(self, 'offset'): offset = self.offset elif hasattr(self, 'exposure'): offset = self.exposure else: offset = 0 #TODO: would there ever be both and exposure and an offset? mu = self.family.starting_mu(self.endog) wlsexog = self.exog eta = self.family.predict(mu) dev = self.family.deviance(self.endog, mu) if np.isnan(dev): raise ValueError("The first guess on the deviance function " "returned a nan. This could be a boundary " " problem and should be reported.") # first guess on the deviance is assumed to be scaled by 1. # params are none to start, so they line up with the deviance history = dict(params = [None, None], deviance=[np.inf,dev]) iteration = 0 converged = 0 criterion = history['deviance'] while not converged: self.weights = data_weights*self.family.weights(mu) wlsendog = eta + self.family.link.deriv(mu) * (self.endog-mu) \ - offset wls_results = lm.WLS(wlsendog, wlsexog, self.weights).fit() eta = np.dot(self.exog, wls_results.params) + offset mu = self.family.fitted(eta) history = self._update_history(wls_results, mu, history) self.scale = self.estimate_scale(mu) iteration += 1 if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0): msg = "Perfect separation detected, results not available" raise PerfectSeparationError(msg) converged = _check_convergence(criterion, iteration, tol, maxiter) self.mu = mu glm_results = GLMResults(self, wls_results.params, wls_results.normalized_cov_params, self.scale) history['iteration'] = iteration glm_results.fit_history = history return GLMResultsWrapper(glm_results) class GLMResults(base.LikelihoodModelResults): ''' Class to contain GLM results. GLMResults inherits from statsmodels.LikelihoodModelResults Parameters ---------- See statsmodels.LikelihoodModelReesults Returns ------- **Attributes** aic : float Akaike Information Criterion -2 * `llf` + 2*(`df_model` + 1) bic : float Bayes Information Criterion `deviance` - `df_resid` * log(`nobs`) deviance : float See statsmodels.families.family for the distribution-specific deviance functions. df_model : float See GLM.df_model df_resid : float See GLM.df_resid fit_history : dict Contains information about the iterations. Its keys are `iterations`, `deviance` and `params`. fittedvalues : array Linear predicted values for the fitted model. dot(exog, params) llf : float Value of the loglikelihood function evalued at params. See statsmodels.families.family for distribution-specific loglikelihoods. model : class instance Pointer to GLM model instance that called fit. mu : array See GLM docstring. nobs : float The number of observations n. normalized_cov_params : array See GLM docstring null_deviance : float The value of the deviance function for the model fit with a constant as the only regressor. params : array The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data. pearson_chi2 : array Pearson's Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals. pinv_wexog : array See GLM docstring. pvalues : array The two-tailed p-values for the parameters. resid_anscombe : array Anscombe residuals. See statsmodels.families.family for distribution- specific Anscombe residuals. resid_deviance : array Deviance residuals. See statsmodels.families.family for distribution- specific deviance residuals. resid_pearson : array Pearson residuals. The Pearson residuals are defined as (`endog` - `mu`)/sqrt(VAR(`mu`)) where VAR is the distribution specific variance function. See statsmodels.families.family and statsmodels.families.varfuncs for more information. resid_response : array Respnose residuals. The response residuals are defined as `endog` - `fittedvalues` resid_working : array Working residuals. The working residuals are defined as `resid_response`/link'(`mu`). See statsmodels.family.links for the derivatives of the link functions. They are defined analytically. scale : float The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information. stand_errors : array The standard errors of the fitted GLM. #TODO still named bse See Also -------- statsmodels.LikelihoodModelResults ''' def __init__(self, model, params, normalized_cov_params, scale): super(GLMResults, self).__init__(model, params, normalized_cov_params=normalized_cov_params, scale=scale) self.family = model.family self._endog = model.endog self.nobs = model.endog.shape[0] self.mu = model.mu self._data_weights = model.data_weights self.df_resid = model.df_resid self.df_model = model.df_model self.pinv_wexog = model.pinv_wexog self._cache = resettable_cache() # are these intermediate results needed or can we just # call the model's attributes? @cache_readonly def resid_response(self): return self._data_weights * (self._endog-self.mu) @cache_readonly def resid_pearson(self): return np.sqrt(self._data_weights) * (self._endog-self.mu)/\ np.sqrt(self.family.variance(self.mu)) @cache_readonly def resid_working(self): val = (self.resid_response / self.family.link.deriv(self.mu)) val *= self._data_weights return val @cache_readonly def resid_anscombe(self): return self.family.resid_anscombe(self._endog, self.mu) @cache_readonly def resid_deviance(self): return self.family.resid_dev(self._endog, self.mu) @cache_readonly def pearson_chi2(self): chisq = (self._endog- self.mu)**2 / self.family.variance(self.mu) chisq *= self._data_weights chisqsum = np.sum(chisq) return chisqsum @cache_readonly def fittedvalues(self): return self.mu @cache_readonly def null(self): endog = self._endog model = self.model exog = np.ones((len(endog), 1)) if hasattr(model, 'offset'): return GLM(endog, exog, offset=model.offset, family=self.family).fit().mu elif hasattr(model, 'exposure'): return GLM(endog, exog, exposure=model.exposure, family=self.family).fit().mu else: wls_model = lm.WLS(endog, exog, weights=self._data_weights) return wls_model.fit().fittedvalues @cache_readonly def deviance(self): return self.family.deviance(self._endog, self.mu) @cache_readonly def null_deviance(self): return self.family.deviance(self._endog, self.null) @cache_readonly def llf(self): _modelfamily = self.family if isinstance(_modelfamily, families.NegativeBinomial): val = _modelfamily.loglike(self.model.endog, fittedvalues = np.dot(self.model.exog,self.params)) else: val = _modelfamily.loglike(self._endog, self.mu, scale=self.scale) return val @cache_readonly def aic(self): return -2 * self.llf + 2*(self.df_model+1) @cache_readonly def bic(self): return self.deviance - self.df_resid*np.log(self.nobs) def remove_data(self): #GLM has alias/reference in result instance self._data_attr.extend([i for i in self.model._data_attr if not '_data.' in i]) super(self.__class__, self).remove_data() #TODO: what are these in results? self._endog = None self._data_weights = None remove_data.__doc__ = base.LikelihoodModelResults.remove_data.__doc__ def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ----------- yname : string, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ top_left = [('Dep. Variable:', None), ('Model:', None), ('Model Family:', [self.family.__class__.__name__]), ('Link Function:', [self.family.link.__class__.__name__]), ('Method:', ['IRLS']), ('Date:', None), ('Time:', None), ('No. Iterations:', ["%d" % self.fit_history['iteration']]), ] top_right = [('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None), ('Scale:', [self.scale]), ('Log-Likelihood:', None), ('Deviance:', ["%#8.5g" % self.deviance]), ('Pearson chi2:', ["%#6.3g" % self.pearson_chi2]) ] if title is None: title = "Generalized Linear Model Regression Results" #create summary tables from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[], yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=True) #diagnostic table is not used yet: #smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, # yname=yname, xname=xname, # title="") return smry def summary2(self, yname=None, xname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental summary for regression Results Parameters ----------- yname : string Name of the dependent variable (optional) xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ self.method = 'IRLS' from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) return smry class GLMResultsWrapper(lm.RegressionResultsWrapper): _attrs = { 'resid_anscombe' : 'rows', 'resid_deviance' : 'rows', 'resid_pearson' : 'rows', 'resid_response' : 'rows', 'resid_working' : 'rows' } _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs, _attrs) wrap.populate_wrapper(GLMResultsWrapper, GLMResults) if __name__ == "__main__": import statsmodels.api as sm import numpy as np data = sm.datasets.longley.load() #data.exog = add_constant(data.exog) GLMmod = GLM(data.endog, data.exog).fit() GLMT = GLMmod.summary(returns='tables') ## GLMT[0].extend_right(GLMT[1]) ## print(GLMT[0]) ## print(GLMT[2]) GLMTp = GLMmod.summary(title='Test GLM') """ From Stata . webuse beetle . glm r i.beetle ldose, family(binomial n) link(cloglog) Iteration 0: log likelihood = -79.012269 Iteration 1: log likelihood = -76.94951 Iteration 2: log likelihood = -76.945645 Iteration 3: log likelihood = -76.945645 Generalized linear models No. of obs = 24 Optimization : ML Residual df = 20 Scale parameter = 1 Deviance = 73.76505595 (1/df) Deviance = 3.688253 Pearson = 71.8901173 (1/df) Pearson = 3.594506 Variance function: V(u) = u*(1-u/n) [Binomial] Link function : g(u) = ln(-ln(1-u/n)) [Complementary log-log] AIC = 6.74547 Log likelihood = -76.94564525 BIC = 10.20398 ------------------------------------------------------------------------------ | OIM r | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- beetle | 2 | -.0910396 .1076132 -0.85 0.398 -.3019576 .1198783 3 | -1.836058 .1307125 -14.05 0.000 -2.09225 -1.579867 | ldose | 19.41558 .9954265 19.50 0.000 17.46458 21.36658 _cons | -34.84602 1.79333 -19.43 0.000 -38.36089 -31.33116 ------------------------------------------------------------------------------ """ #NOTE: wfs dataset has been removed due to a licensing issue # example of using offset #data = sm.datasets.wfs.load() # get offset #offset = np.log(data.exog[:,-1]) #exog = data.exog[:,:-1] # convert dur to dummy #exog = sm.tools.categorical(exog, col=0, drop=True) # drop reference category # convert res to dummy #exog = sm.tools.categorical(exog, col=0, drop=True) # convert edu to dummy #exog = sm.tools.categorical(exog, col=0, drop=True) # drop reference categories and add intercept #exog = sm.add_constant(exog[:,[1,2,3,4,5,7,8,10,11,12]]) #endog = np.round(data.endog) #mod = sm.GLM(endog, exog, family=sm.families.Poisson()).fit() #res1 = GLM(endog, exog, family=sm.families.Poisson(), # offset=offset).fit(tol=1e-12, maxiter=250) #exposuremod = GLM(endog, exog, family=sm.families.Poisson(), # exposure = data.exog[:,-1]).fit(tol=1e-12, # maxiter=250) #assert(np.all(res1.params == exposuremod.params)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/000077500000000000000000000000001224417117700231275ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/__init__.py000066400000000000000000000000001224417117700252260ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/results/000077500000000000000000000000001224417117700246305ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/results/__init__.py000066400000000000000000000000001224417117700267270ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/results/glm_test_resids.py000066400000000000000000011634001224417117700303760ustar00rootroot00000000000000''' This file contains the residuals for testing GLM. All residuals were obtained with Stata. The residuals are column ordered as Pearson, Deviance, Working, Anscombe, and Response Residuals ''' import numpy as np lbw =[-.67369007, -.86512534, -.06703079, -.93506245, -.31217507, -.38301082, -.52323205, -.01427243, -.55879206, -.12793027, -.68788286, -.88025592, -.07003063, -.95205459, -.32119762, -.96932399, -1.1510657, -.12098923, -1.2626094, -.48442686, -1.017216, -1.1919416, -.12709645, -1.3106868, -.50853393, -.56992387, -.75002865, -.04537362, -.80685236, -.24517662, -.38236113, -.52240243, -.01419429, -.5578939, -.12755193, -.70541551, -.89874491, -.07371959, -.97286469, -.33226988, -.53129095, -.70516948, -.03779124, -.75734471, -.22013309, -.59687896, -.78068461, -.05087588, -.84082824, -.26268069, -.72944938, -.92372831, -.07872676, -1.0010676, -.34729955, -.48089188, -.64503581, -.02865125, -.6913447, -.18782188, -1.7509231, -1.6748694, -.13984667, -1.9107428, -.75404181, -1.0993874, -1.2588746, -.13558784, -1.3901949, -.54723527, -.73406378, -.9284774, -.0796795, -1.0064397, -.35016393, -.73406378, -.9284774, -.0796795, -1.0064397, -.35016393, -.91970083, -1.1071944, -.11376204, -1.2113902, -.45824406, -.58253395, -.76443611, -.04792993, -.8228052, -.25336683, -.87010277, -1.0617464, -.10565948, -1.1587271, -.43087357, -.55091811, -.72809506, -.04159148, -.78261562, -.23284101, -.50228984, -.67078749, -.03241138, -.71955949, -.20146616, -.56681231, -.74645574, -.04474827, -.80290024, -.24315597, -.16739043, -.23509228, -.00072263, -.24969795, -.02725586, -.53429779, -.70869969, -.03836573, -.76123202, -.22207692, -.81310524, -1.0074762, -.09536044, -1.0963421, -.39800383, -.24319644, -.339003, -.00294417, -.36060255, -.05584178, -.62985338, -.81746346, -.05776093, -.88175202, -.28403447, -.27289888, -.37902784, -.00447115, -.403468, -.06931188, -.73980785, -.9343678, -.08086098, -1.0131078, -.35371946, -.79896487, -.99366645, -.09266053, -1.0805507, -.3896279, -.79896487, -.99366645, -.09266053, -1.0805507, -.3896279, -.95715693, -1.140454, 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1.1961638, .45037299, .6236701, .81062745, .05646071, .87413186, .28003914, .71234937, .90599551, .07517106, .9810397, .3366244] lbw_resids = np.array(lbw).astype(float).reshape(-1,5) cpunish = [.29883413, .29637762, 62.478695, .29638095, 1.7736344, .280627, .27622019, 6.5853375, .27623151, .80342558, 4.0930531, 2.9777878, 6.1503069, 3.0157174, 4.6881034, .16338859, .16114971, 1.1563983, .16115474, .31370176, .63595872, .59618385, 1.9109264, .59656954, .91769973, .9059739, .8066189, .99577089, .80822353, .9349684, .0532905, .0529548, .14244545, .05295515, .07395759, -.26830664, -.2766384, -1.0082872, -.27668319, -.41714051, -.62341484, -.68349824, -1.5652763, -.68459104, -.84732188, -1.1015655, -1.2743561, -5.3400286, -1.279951, -1.8643241, -1.2006618, -1.4021282, -6.6206839, -1.40923, -2.1211989, -1.2797534, -1.505173, -7.8054171, -1.5136295, -2.3382067, -.960585, -1.0954134, -3.8587164, -1.0992211, -1.5269969, .10846917, .10649195, .0921891, .10649783, .1027458, .02088367, .02081086, 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-.03363248, 11219.87, -.03363212, -2.3151557] scotvote_resids = np.array(scotvote).astype(float).reshape(-1,5) star98 = [-1.3375372, -1.3342565, -3674.3805, -1.3343393, -18.732624, .97808463, .99272841, 197.05157, .99291658, 5.7338226, 4.2825696, 4.29447, 7304.117, 4.2983582, 51.167264, .20665475, .20689409, 283.85261, .20689456, 2.2971775, -3.4397844, -3.7418743, -184.10712, -3.7732602, -12.963164, -8.8955127, -8.7070478, -94493.01, -8.7156529, -195.54523, 1.3093612, 1.3040684, 6904.5348, 1.3041169, 22.790356, -2.3354095, -2.3171211, -6891.6853, -2.3175198, -33.497868, -4.9509734, -4.9716533, -7444.4706, -4.9782445, -56.720276, -4.3896461, -4.3936251, -19119.121, -4.3958412, -71.687315, 2.6246727, 2.6002292, 33800.262, 2.6004266, 61.52101, 1.0381778, 1.0655404, 147.40536, 1.0658131, 5.4160865, -5.371452, -5.4255371, -8735.5422, -5.433774, -63.167122, -7.5162302, -7.6600055, -16610.436, -7.6793193, -97.902488, -4.3246609, -4.217551, -1806.9456, -4.2260371, -32.330796, .71999176, 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-1.820410000000000084e-01,-2.191365999999999870e-01,-2.602996000000000087e+00,-2.178854000000000068e-01,-8.981559999999999544e-01 -2.005821000000000132e-01,-2.790536000000000127e-01,-1.629568000000000083e+01,-2.740469999999999851e-01,-2.806470000000000020e+00 -2.720534000000000008e-01,-4.635484999999999745e-01,-2.210215000000000174e+01,-4.423134000000000232e-01,-3.806470000000000020e+00 4.426596999999999893e-01,3.079182999999999781e-01,3.596255000000000024e+01,3.012597000000000191e-01,6.193529999999999980e+00 -2.005821000000000132e-01,-2.790536000000000127e-01,-1.629568000000000083e+01,-2.740469999999999851e-01,-2.806470000000000020e+00 -1.291107999999999978e-01,-1.555565999999999893e-01,-1.048920999999999992e+01,-1.546600999999999948e-01,-1.806470000000000020e+00 -1.291107999999999978e-01,-1.555565999999999893e-01,-1.048920999999999992e+01,-1.546600999999999948e-01,-1.806470000000000020e+00 -5.763950000000000323e-02,-6.211420000000000136e-02,-4.682743000000000322e+00,-6.205639999999999767e-02,-8.064698000000000144e-01 5.311487999999999765e-01,3.710235000000000061e-01,1.599070000000000036e+01,3.631816999999999962e-01,4.096442999999999834e+00 1.396043999999999896e-01,1.248781000000000058e-01,2.558501999999999832e+00,1.246199000000000057e-01,7.993759000000000281e-01 -1.043260000000000021e-01,-1.196085999999999955e-01,-6.612881999999999927e+00,-1.192368000000000039e-01,-1.257741999999999916e+00 2.155508999999999897e-01,1.777640000000000053e-01,1.066020999999999930e+01,1.766681999999999975e-01,2.239129999999999843e+00 -1.872730999999999979e-01,-2.479215999999999920e-01,-1.187062000000000062e+01,-2.446991000000000027e-01,-2.257741999999999916e+00 statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/results/results_glm.py000066400000000000000000010174631224417117700275560ustar00rootroot00000000000000""" Results for test_glm.py. Hard-coded from R or Stata. Note that some of the remaining discrepancy vs. Stata may be because Stata uses ML by default unless you specifically ask for IRLS. """ import numpy as np from statsmodels.compatnp.py3k import asbytes import glm_test_resids import os from statsmodels.api import add_constant, categorical # Test Precisions DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 DECIMAL_0 = 0 class Longley(object): """ Longley used for TestGlmGaussian Results are from Stata and R. """ def __init__(self): self.resids = np.array([[ 267.34002976, 267.34002976, 267.34002976, 267.34002976, 267.34002976], [ -94.0139424 , -94.0139424 , -94.0139424 , -94.0139424 , -94.0139424 ], [ 46.28716776, 46.28716776, 46.28716776, 46.28716776, 46.28716776], [-410.11462193, -410.11462193, -410.11462193, -410.11462193, -410.11462193], [ 309.71459076, 309.71459076, 309.71459076, 309.71459076, 309.71459076], [-249.31121533, -249.31121533, -249.31121533, -249.31121533, -249.31121533], [-164.0489564 , -164.0489564 , -164.0489564 , -164.0489564 , -164.0489564 ], [ -13.18035687, -13.18035687, -13.18035687, -13.18035687, -13.18035687], [ 14.3047726 , 14.3047726 , 14.3047726 , 14.3047726 , 14.3047726 ], [ 455.39409455, 455.39409455, 455.39409455, 455.39409455, 455.39409455], [ -17.26892711, -17.26892711, -17.26892711, -17.26892711, -17.26892711], [ -39.05504252, -39.05504252, -39.05504252, -39.05504252, -39.05504252], [-155.5499736 , -155.5499736 , -155.5499736 , -155.5499736 , -155.5499736 ], [ -85.67130804, -85.67130804, -85.67130804, -85.67130804, -85.67130804], [ 341.93151396, 341.93151396, 341.93151396, 341.93151396, 341.93151396], [-206.75782519, -206.75782519, -206.75782519, -206.75782519, -206.75782519]]) self.null_deviance = 185008826 # taken from R. self.params = np.array([ 1.50618723e+01, -3.58191793e-02, -2.02022980e+00, -1.03322687e+00, -5.11041057e-02, 1.82915146e+03, -3.48225863e+06]) self.bse = np.array([8.49149258e+01, 3.34910078e-02, 4.88399682e-01, 2.14274163e-01, 2.26073200e-01, 4.55478499e+02, 8.90420384e+05]) self.aic_R = 235.23486961695903 # R adds 2 for dof to AIC self.aic_Stata = 14.57717943930524 # stata divides by nobs self.deviance = 836424.0555058046 # from R self.scale = 92936.006167311629 self.llf = -109.61743480847952 self.null_deviance = 185008826 # taken from R. Rpy bug self.bic_Stata = 836399.1760177979 # no bic in R? self.df_model = 6 self.df_resid = 9 self.chi2 = 1981.711859508729 #TODO: taken from Stata not available # in sm yet # self.pearson_chi2 = 836424.1293162981 # from Stata (?) self.fittedvalues = np.array([60055.659970240202, 61216.013942398131, 60124.71283224225, 61597.114621930756, 62911.285409240052, 63888.31121532945, 65153.048956395127, 63774.180356866214, 66004.695227399934, 67401.605905447621, 68186.268927114084, 66552.055042522494, 68810.549973595422, 69649.67130804155, 68989.068486039061, 70757.757825193927]) class GaussianLog(object): """ Uses generated data. These results are from R and Stata. """ def __init__(self): # self.resids = np.genfromtxt('./glm_gaussian_log_resid.csv', ',') self.resids = np.array([[3.20800000e-04, 3.20800000e-04, 8.72100000e-04, 3.20800000e-04, 3.20800000e-04], [ 8.12100000e-04, 8.12100000e-04, 2.16350000e-03, 8.12100000e-04, 8.12100000e-04], [ -2.94800000e-04, -2.94800000e-04, -7.69700000e-04, -2.94800000e-04, -2.94800000e-04], [ 1.40190000e-03, 1.40190000e-03, 3.58560000e-03, 1.40190000e-03, 1.40190000e-03], [ -2.30910000e-03, -2.30910000e-03, -5.78490000e-03, -2.30910000e-03, -2.30910000e-03], [ 1.10380000e-03, 1.10380000e-03, 2.70820000e-03, 1.10380000e-03, 1.10380000e-03], [ -5.14000000e-06, -5.14000000e-06, -1.23000000e-05, -5.14000000e-06, -5.14000000e-06], [ -1.65500000e-04, -1.65500000e-04, -3.89200000e-04, -1.65500000e-04, -1.65500000e-04], [ -7.55400000e-04, -7.55400000e-04, -1.73870000e-03, -7.55400000e-04, -7.55400000e-04], [ -1.39800000e-04, -1.39800000e-04, -3.14800000e-04, -1.39800000e-04, -1.39800000e-04], [ -7.17000000e-04, -7.17000000e-04, -1.58000000e-03, -7.17000000e-04, -7.17000000e-04], [ -1.12200000e-04, -1.12200000e-04, -2.41900000e-04, -1.12200000e-04, -1.12200000e-04], [ 3.22100000e-04, 3.22100000e-04, 6.79000000e-04, 3.22100000e-04, 3.22100000e-04], [ -3.78000000e-05, -3.78000000e-05, -7.79000000e-05, -3.78000000e-05, -3.78000000e-05], [ 5.54500000e-04, 5.54500000e-04, 1.11730000e-03, 5.54500000e-04, 5.54500000e-04], [ 3.38400000e-04, 3.38400000e-04, 6.66300000e-04, 3.38400000e-04, 3.38400000e-04], [ 9.72000000e-05, 9.72000000e-05, 1.87000000e-04, 9.72000000e-05, 9.72000000e-05], [ -7.92900000e-04, -7.92900000e-04, -1.49070000e-03, -7.92900000e-04, -7.92900000e-04], [ 3.33000000e-04, 3.33000000e-04, 6.11500000e-04, 3.33000000e-04, 3.33000000e-04], [ -8.35300000e-04, -8.35300000e-04, -1.49790000e-03, -8.35300000e-04, -8.35300000e-04], [ -3.99700000e-04, -3.99700000e-04, -6.99800000e-04, -3.99700000e-04, -3.99700000e-04], [ 1.41300000e-04, 1.41300000e-04, 2.41500000e-04, 1.41300000e-04, 1.41300000e-04], [ -8.50700000e-04, -8.50700000e-04, -1.41920000e-03, -8.50700000e-04, -8.50700000e-04], [ 1.43000000e-06, 1.43000000e-06, 2.33000000e-06, 1.43000000e-06, 1.43000000e-06], [ -9.12000000e-05, -9.12000000e-05, -1.44900000e-04, -9.12000000e-05, -9.12000000e-05], [ 6.75500000e-04, 6.75500000e-04, 1.04650000e-03, 6.75500000e-04, 6.75500000e-04], [ 3.97900000e-04, 3.97900000e-04, 6.01100000e-04, 3.97900000e-04, 3.97900000e-04], [ 1.07000000e-05, 1.07000000e-05, 1.57000000e-05, 1.07000000e-05, 1.07000000e-05], [ -8.15200000e-04, -8.15200000e-04, -1.17060000e-03, -8.15200000e-04, -8.15200000e-04], [ -8.46400000e-04, -8.46400000e-04, -1.18460000e-03, -8.46400000e-04, -8.46400000e-04], [ 9.91200000e-04, 9.91200000e-04, 1.35180000e-03, 9.91200000e-04, 9.91200000e-04], [ -5.07400000e-04, -5.07400000e-04, -6.74200000e-04, -5.07400000e-04, -5.07400000e-04], [ 1.08520000e-03, 1.08520000e-03, 1.40450000e-03, 1.08520000e-03, 1.08520000e-03], [ 9.56100000e-04, 9.56100000e-04, 1.20500000e-03, 9.56100000e-04, 9.56100000e-04], [ 1.87500000e-03, 1.87500000e-03, 2.30090000e-03, 1.87500000e-03, 1.87500000e-03], [ -1.93920000e-03, -1.93920000e-03, -2.31650000e-03, -1.93920000e-03, -1.93920000e-03], [ 8.16000000e-04, 8.16000000e-04, 9.48700000e-04, 8.16000000e-04, 8.16000000e-04], [ 1.01520000e-03, 1.01520000e-03, 1.14860000e-03, 1.01520000e-03, 1.01520000e-03], [ 1.04150000e-03, 1.04150000e-03, 1.14640000e-03, 1.04150000e-03, 1.04150000e-03], [ -3.88200000e-04, -3.88200000e-04, -4.15600000e-04, -3.88200000e-04, -3.88200000e-04], [ 9.95900000e-04, 9.95900000e-04, 1.03690000e-03, 9.95900000e-04, 9.95900000e-04], [ -6.82800000e-04, -6.82800000e-04, -6.91200000e-04, -6.82800000e-04, -6.82800000e-04], [ -8.11400000e-04, -8.11400000e-04, -7.98500000e-04, -8.11400000e-04, -8.11400000e-04], [ -1.79050000e-03, -1.79050000e-03, -1.71250000e-03, -1.79050000e-03, -1.79050000e-03], [ 6.10000000e-04, 6.10000000e-04, 5.66900000e-04, 6.10000000e-04, 6.10000000e-04], [ 2.52600000e-04, 2.52600000e-04, 2.28100000e-04, 2.52600000e-04, 2.52600000e-04], [ -8.62500000e-04, -8.62500000e-04, -7.56400000e-04, -8.62500000e-04, -8.62500000e-04], [ -3.47300000e-04, -3.47300000e-04, -2.95800000e-04, -3.47300000e-04, -3.47300000e-04], [ -7.79000000e-05, -7.79000000e-05, -6.44000000e-05, -7.79000000e-05, -7.79000000e-05], [ 6.72000000e-04, 6.72000000e-04, 5.39400000e-04, 6.72000000e-04, 6.72000000e-04], [ -3.72100000e-04, -3.72100000e-04, -2.89900000e-04, -3.72100000e-04, -3.72100000e-04], [ -1.22900000e-04, -1.22900000e-04, -9.29000000e-05, -1.22900000e-04, -1.22900000e-04], [ -1.63470000e-03, -1.63470000e-03, -1.19900000e-03, -1.63470000e-03, -1.63470000e-03], [ 2.64400000e-04, 2.64400000e-04, 1.88100000e-04, 2.64400000e-04, 2.64400000e-04], [ 1.79230000e-03, 1.79230000e-03, 1.23650000e-03, 1.79230000e-03, 1.79230000e-03], [ -1.40500000e-04, -1.40500000e-04, -9.40000000e-05, -1.40500000e-04, -1.40500000e-04], [ -2.98500000e-04, -2.98500000e-04, -1.93600000e-04, -2.98500000e-04, -2.98500000e-04], [ -9.33100000e-04, -9.33100000e-04, -5.86400000e-04, -9.33100000e-04, -9.33100000e-04], [ 9.11200000e-04, 9.11200000e-04, 5.54900000e-04, 9.11200000e-04, 9.11200000e-04], [ -1.31840000e-03, -1.31840000e-03, -7.77900000e-04, -1.31840000e-03, -1.31840000e-03], [ -1.30200000e-04, -1.30200000e-04, -7.44000000e-05, -1.30200000e-04, -1.30200000e-04], [ 9.09300000e-04, 9.09300000e-04, 5.03200000e-04, 9.09300000e-04, 9.09300000e-04], [ -2.39500000e-04, -2.39500000e-04, -1.28300000e-04, -2.39500000e-04, -2.39500000e-04], [ 7.15300000e-04, 7.15300000e-04, 3.71000000e-04, 7.15300000e-04, 7.15300000e-04], [ 5.45000000e-05, 5.45000000e-05, 2.73000000e-05, 5.45000000e-05, 5.45000000e-05], [ 2.85310000e-03, 2.85310000e-03, 1.38600000e-03, 2.85310000e-03, 2.85310000e-03], [ 4.63400000e-04, 4.63400000e-04, 2.17800000e-04, 4.63400000e-04, 4.63400000e-04], [ 2.80900000e-04, 2.80900000e-04, 1.27700000e-04, 2.80900000e-04, 2.80900000e-04], [ 5.42000000e-05, 5.42000000e-05, 2.38000000e-05, 5.42000000e-05, 5.42000000e-05], [ -3.62300000e-04, -3.62300000e-04, -1.54000000e-04, -3.62300000e-04, -3.62300000e-04], [ -1.11900000e-03, -1.11900000e-03, -4.59800000e-04, -1.11900000e-03, -1.11900000e-03], [ 1.28900000e-03, 1.28900000e-03, 5.11900000e-04, 1.28900000e-03, 1.28900000e-03], [ -1.40820000e-03, -1.40820000e-03, -5.40400000e-04, -1.40820000e-03, -1.40820000e-03], [ -1.69300000e-04, -1.69300000e-04, -6.28000000e-05, -1.69300000e-04, -1.69300000e-04], [ -1.03620000e-03, -1.03620000e-03, -3.71000000e-04, -1.03620000e-03, -1.03620000e-03], [ 1.49150000e-03, 1.49150000e-03, 5.15800000e-04, 1.49150000e-03, 1.49150000e-03], [ -7.22000000e-05, -7.22000000e-05, -2.41000000e-05, -7.22000000e-05, -7.22000000e-05], [ 5.49000000e-04, 5.49000000e-04, 1.76900000e-04, 5.49000000e-04, 5.49000000e-04], [ -2.12320000e-03, -2.12320000e-03, -6.60400000e-04, -2.12320000e-03, -2.12320000e-03], [ 7.84000000e-06, 7.84000000e-06, 2.35000000e-06, 7.84000000e-06, 7.84000000e-06], [ 1.15580000e-03, 1.15580000e-03, 3.34700000e-04, 1.15580000e-03, 1.15580000e-03], [ 4.83400000e-04, 4.83400000e-04, 1.35000000e-04, 4.83400000e-04, 4.83400000e-04], [ -5.26100000e-04, -5.26100000e-04, -1.41700000e-04, -5.26100000e-04, -5.26100000e-04], [ -1.75100000e-04, -1.75100000e-04, -4.55000000e-05, -1.75100000e-04, -1.75100000e-04], [ -1.84600000e-03, -1.84600000e-03, -4.62100000e-04, -1.84600000e-03, -1.84600000e-03], [ 2.07200000e-04, 2.07200000e-04, 5.00000000e-05, 2.07200000e-04, 2.07200000e-04], [ -8.54700000e-04, -8.54700000e-04, -1.98700000e-04, -8.54700000e-04, -8.54700000e-04], [ -9.20000000e-05, -9.20000000e-05, -2.06000000e-05, -9.20000000e-05, -9.20000000e-05], [ 5.35700000e-04, 5.35700000e-04, 1.15600000e-04, 5.35700000e-04, 5.35700000e-04], [ -7.67300000e-04, -7.67300000e-04, -1.59400000e-04, -7.67300000e-04, -7.67300000e-04], [ -1.79710000e-03, -1.79710000e-03, -3.59500000e-04, -1.79710000e-03, -1.79710000e-03], [ 1.10910000e-03, 1.10910000e-03, 2.13500000e-04, 1.10910000e-03, 1.10910000e-03], [ -5.53800000e-04, -5.53800000e-04, -1.02600000e-04, -5.53800000e-04, -5.53800000e-04], [ 7.48000000e-04, 7.48000000e-04, 1.33400000e-04, 7.48000000e-04, 7.48000000e-04], [ 4.23000000e-04, 4.23000000e-04, 7.26000000e-05, 4.23000000e-04, 4.23000000e-04], [ -3.16400000e-04, -3.16400000e-04, -5.22000000e-05, -3.16400000e-04, -3.16400000e-04], [ -6.63200000e-04, -6.63200000e-04, -1.05200000e-04, -6.63200000e-04, -6.63200000e-04], [ 1.33540000e-03, 1.33540000e-03, 2.03700000e-04, 1.33540000e-03, 1.33540000e-03], [ -7.81200000e-04, -7.81200000e-04, -1.14600000e-04, -7.81200000e-04, -7.81200000e-04], [ 1.67880000e-03, 1.67880000e-03, 2.36600000e-04, 1.67880000e-03, 1.67880000e-03]]) self.null_deviance = 56.691617808182208 self.params = np.array([9.99964386e-01,-1.99896965e-02, -1.00027232e-04]) self.bse = np.array([1.42119293e-04, 1.20276468e-05, 1.87347682e-07]) self.aic_R = -1103.8187213072656 # adds 2 for dof for scale self.aic_Stata = -11.05818072104212 # divides by nobs for e(aic) self.deviance = 8.68876986288542e-05 self.scale = 8.9574946938163984e-07 # from R but e(phi) in Stata self.llf = 555.9093606536328 self.bic_Stata = -446.7014211525822 self.df_model = 2 self.df_resid = 97 self.chi2 = 33207648.86501769 # from Stata not in sm self.fittedvalues = np.array([2.7181850213327747, 2.664122305869506, 2.6106125414084405, 2.5576658143523567, 2.5052916730829535, 2.4534991313100165, 2.4022966718815781, 2.3516922510411282, 2.3016933031175575, 2.2523067456332542, 2.2035389848154616, 2.1553959214958001, 2.107882957382607, 2.0610050016905817, 2.0147664781120667, 1.969171332114154, 1.9242230385457144, 1.8799246095383746, 1.8362786026854092, 1.7932871294825108, 1.7509518640143886, 1.7092740518711942, 1.6682545192788105, 1.6278936824271399, 1.5881915569806042, 1.5491477677552221, 1.5107615585467538, 1.4730318020945796, 1.4359570101661721, 1.3995353437472129, 1.3637646233226499, 1.3286423392342188, 1.2941656621002184, 1.2603314532836074, 1.2271362753947765, 1.1945764028156565, 1.162647832232141, 1.1313462931621328, 1.1006672584668622, 1.0706059548334832, 1.0411573732173065, 1.0123162792324054, 0.98407722347970683, 0.95643455180206194, 0.92938241545618494, 0.90291478119174029, 0.87702544122826565, 0.85170802312101246, 0.82695599950720078, 0.80276269772458597, 0.77912130929465073, 0.75602489926313921, 0.73346641539106316, 0.71143869718971686, 0.68993448479364294, 0.66894642766589496, 0.64846709313034534, 0.62848897472617915, 0.60900450038011367, 0.5900060403922629, 0.57148591523195513, 0.55343640314018494, 0.5358497475357491, 0.51871816422248385, 0.50203384839536769, 0.48578898144361343, 0.46997573754920047, 0.45458629007964013, 0.4396128177740814, 0.42504751072218311, 0.41088257613548018, 0.39711024391126759, 0.38372277198930843, 0.37071245150195081, 0.35807161171849949, 0.34579262478494655, 0.33386791026040569, 0.32228993945183393, 0.31105123954884056, 0.30014439756060574, 0.28956206405712448, 0.27929695671718968, 0.26934186368570684, 0.25968964674310463, 0.25033324428976694, 0.24126567414856051, 0.23248003618867552, 0.22396951477412205, 0.21572738104035141, 0.20774699500257574, 0.20002180749946474, 0.19254536197598673, 0.18531129610924435, 0.17831334328122878, 0.17154533390247831, 0.16500119659068577, 0.15867495920834204, 0.15256074976354628, 0.14665279717814039, 0.14094543192735109]) class GaussianInverse(object): """ This test uses generated data. Results are from R and Stata. """ def __init__(self): self.resids = np.array([[-5.15300000e-04, -5.15300000e-04, 5.14800000e-04, -5.15300000e-04, -5.15300000e-04], [ -2.12500000e-04, -2.12500000e-04, 2.03700000e-04, -2.12500000e-04, -2.12500000e-04], [ -1.71400000e-04, -1.71400000e-04, 1.57200000e-04, -1.71400000e-04, -1.71400000e-04], [ 1.94020000e-03, 1.94020000e-03, -1.69710000e-03, 1.94020000e-03, 1.94020000e-03], [ -6.81100000e-04, -6.81100000e-04, 5.66900000e-04, -6.81100000e-04, -6.81100000e-04], [ 1.21370000e-03, 1.21370000e-03, -9.58800000e-04, 1.21370000e-03, 1.21370000e-03], [ -1.51090000e-03, -1.51090000e-03, 1.13070000e-03, -1.51090000e-03, -1.51090000e-03], [ 3.21500000e-04, 3.21500000e-04, -2.27400000e-04, 3.21500000e-04, 3.21500000e-04], [ -3.18500000e-04, -3.18500000e-04, 2.12600000e-04, -3.18500000e-04, -3.18500000e-04], [ 3.75600000e-04, 3.75600000e-04, -2.36300000e-04, 3.75600000e-04, 3.75600000e-04], [ 4.82300000e-04, 4.82300000e-04, -2.85500000e-04, 4.82300000e-04, 4.82300000e-04], [ -1.41870000e-03, -1.41870000e-03, 7.89300000e-04, -1.41870000e-03, -1.41870000e-03], [ 6.75000000e-05, 6.75000000e-05, -3.52000000e-05, 6.75000000e-05, 6.75000000e-05], [ 4.06300000e-04, 4.06300000e-04, -1.99100000e-04, 4.06300000e-04, 4.06300000e-04], [ -3.61500000e-04, -3.61500000e-04, 1.66000000e-04, -3.61500000e-04, -3.61500000e-04], [ -2.97400000e-04, -2.97400000e-04, 1.28000000e-04, -2.97400000e-04, -2.97400000e-04], [ -9.32700000e-04, -9.32700000e-04, 3.75800000e-04, -9.32700000e-04, -9.32700000e-04], [ 1.16270000e-03, 1.16270000e-03, -4.38500000e-04, 1.16270000e-03, 1.16270000e-03], [ 6.77900000e-04, 6.77900000e-04, -2.39200000e-04, 6.77900000e-04, 6.77900000e-04], [ -1.29330000e-03, -1.29330000e-03, 4.27000000e-04, -1.29330000e-03, -1.29330000e-03], [ 2.24500000e-04, 2.24500000e-04, -6.94000000e-05, 2.24500000e-04, 2.24500000e-04], [ 1.05510000e-03, 1.05510000e-03, -3.04900000e-04, 1.05510000e-03, 1.05510000e-03], [ 2.50400000e-04, 2.50400000e-04, -6.77000000e-05, 2.50400000e-04, 2.50400000e-04], [ 4.08600000e-04, 4.08600000e-04, -1.03400000e-04, 4.08600000e-04, 4.08600000e-04], [ -1.67610000e-03, -1.67610000e-03, 3.96800000e-04, -1.67610000e-03, -1.67610000e-03], [ 7.47600000e-04, 7.47600000e-04, -1.65700000e-04, 7.47600000e-04, 7.47600000e-04], [ 2.08200000e-04, 2.08200000e-04, -4.32000000e-05, 2.08200000e-04, 2.08200000e-04], [ -8.00800000e-04, -8.00800000e-04, 1.55700000e-04, -8.00800000e-04, -8.00800000e-04], [ 5.81200000e-04, 5.81200000e-04, -1.05900000e-04, 5.81200000e-04, 5.81200000e-04], [ 1.00980000e-03, 1.00980000e-03, -1.72400000e-04, 1.00980000e-03, 1.00980000e-03], [ 2.77400000e-04, 2.77400000e-04, -4.44000000e-05, 2.77400000e-04, 2.77400000e-04], [ -5.02800000e-04, -5.02800000e-04, 7.55000000e-05, -5.02800000e-04, -5.02800000e-04], [ 2.69800000e-04, 2.69800000e-04, -3.80000000e-05, 2.69800000e-04, 2.69800000e-04], [ 2.01300000e-04, 2.01300000e-04, -2.67000000e-05, 2.01300000e-04, 2.01300000e-04], [ -1.19690000e-03, -1.19690000e-03, 1.48900000e-04, -1.19690000e-03, -1.19690000e-03], [ -6.94200000e-04, -6.94200000e-04, 8.12000000e-05, -6.94200000e-04, -6.94200000e-04], [ 5.65500000e-04, 5.65500000e-04, -6.22000000e-05, 5.65500000e-04, 5.65500000e-04], [ 4.93100000e-04, 4.93100000e-04, -5.10000000e-05, 4.93100000e-04, 4.93100000e-04], [ 3.25000000e-04, 3.25000000e-04, -3.17000000e-05, 3.25000000e-04, 3.25000000e-04], [ -7.70200000e-04, -7.70200000e-04, 7.07000000e-05, -7.70200000e-04, -7.70200000e-04], [ 2.58000000e-05, 2.58000000e-05, -2.23000000e-06, 2.58000000e-05, 2.58000000e-05], [ -1.52800000e-04, -1.52800000e-04, 1.25000000e-05, -1.52800000e-04, -1.52800000e-04], [ 4.52000000e-05, 4.52000000e-05, -3.48000000e-06, 4.52000000e-05, 4.52000000e-05], [ -6.83900000e-04, -6.83900000e-04, 4.97000000e-05, -6.83900000e-04, -6.83900000e-04], [ -7.77600000e-04, -7.77600000e-04, 5.34000000e-05, -7.77600000e-04, -7.77600000e-04], [ 1.03170000e-03, 1.03170000e-03, -6.70000000e-05, 1.03170000e-03, 1.03170000e-03], [ 1.20000000e-03, 1.20000000e-03, -7.37000000e-05, 1.20000000e-03, 1.20000000e-03], [ -7.71600000e-04, -7.71600000e-04, 4.48000000e-05, -7.71600000e-04, -7.71600000e-04], [ -3.37000000e-04, -3.37000000e-04, 1.85000000e-05, -3.37000000e-04, -3.37000000e-04], [ 1.19880000e-03, 1.19880000e-03, -6.25000000e-05, 1.19880000e-03, 1.19880000e-03], [ -1.54610000e-03, -1.54610000e-03, 7.64000000e-05, -1.54610000e-03, -1.54610000e-03], [ 9.11600000e-04, 9.11600000e-04, -4.27000000e-05, 9.11600000e-04, 9.11600000e-04], [ -4.70800000e-04, -4.70800000e-04, 2.09000000e-05, -4.70800000e-04, -4.70800000e-04], [ -1.21550000e-03, -1.21550000e-03, 5.13000000e-05, -1.21550000e-03, -1.21550000e-03], [ 1.09160000e-03, 1.09160000e-03, -4.37000000e-05, 1.09160000e-03, 1.09160000e-03], [ -2.72000000e-04, -2.72000000e-04, 1.04000000e-05, -2.72000000e-04, -2.72000000e-04], [ -7.84500000e-04, -7.84500000e-04, 2.84000000e-05, -7.84500000e-04, -7.84500000e-04], [ 1.53330000e-03, 1.53330000e-03, -5.28000000e-05, 1.53330000e-03, 1.53330000e-03], [ -1.84450000e-03, -1.84450000e-03, 6.05000000e-05, -1.84450000e-03, -1.84450000e-03], [ 1.68550000e-03, 1.68550000e-03, -5.26000000e-05, 1.68550000e-03, 1.68550000e-03], [ -3.06100000e-04, -3.06100000e-04, 9.10000000e-06, -3.06100000e-04, -3.06100000e-04], [ 1.00950000e-03, 1.00950000e-03, -2.86000000e-05, 1.00950000e-03, 1.00950000e-03], [ 5.22000000e-04, 5.22000000e-04, -1.41000000e-05, 5.22000000e-04, 5.22000000e-04], [ -2.18000000e-05, -2.18000000e-05, 5.62000000e-07, -2.18000000e-05, -2.18000000e-05], [ -7.80600000e-04, -7.80600000e-04, 1.92000000e-05, -7.80600000e-04, -7.80600000e-04], [ 6.81400000e-04, 6.81400000e-04, -1.60000000e-05, 6.81400000e-04, 6.81400000e-04], [ -1.43800000e-04, -1.43800000e-04, 3.23000000e-06, -1.43800000e-04, -1.43800000e-04], [ 7.76000000e-04, 7.76000000e-04, -1.66000000e-05, 7.76000000e-04, 7.76000000e-04], [ 2.54900000e-04, 2.54900000e-04, -5.22000000e-06, 2.54900000e-04, 2.54900000e-04], [ 5.77500000e-04, 5.77500000e-04, -1.13000000e-05, 5.77500000e-04, 5.77500000e-04], [ 7.58100000e-04, 7.58100000e-04, -1.42000000e-05, 7.58100000e-04, 7.58100000e-04], [ -8.31000000e-04, -8.31000000e-04, 1.49000000e-05, -8.31000000e-04, -8.31000000e-04], [ -2.10340000e-03, -2.10340000e-03, 3.62000000e-05, -2.10340000e-03, -2.10340000e-03], [ -8.89900000e-04, -8.89900000e-04, 1.47000000e-05, -8.89900000e-04, -8.89900000e-04], [ 1.08570000e-03, 1.08570000e-03, -1.71000000e-05, 1.08570000e-03, 1.08570000e-03], [ -1.88600000e-04, -1.88600000e-04, 2.86000000e-06, -1.88600000e-04, -1.88600000e-04], [ 9.10000000e-05, 9.10000000e-05, -1.32000000e-06, 9.10000000e-05, 9.10000000e-05], [ 1.07700000e-03, 1.07700000e-03, -1.50000000e-05, 1.07700000e-03, 1.07700000e-03], [ 9.04100000e-04, 9.04100000e-04, -1.21000000e-05, 9.04100000e-04, 9.04100000e-04], [ -2.20000000e-04, -2.20000000e-04, 2.83000000e-06, -2.20000000e-04, -2.20000000e-04], [ -1.64030000e-03, -1.64030000e-03, 2.02000000e-05, -1.64030000e-03, -1.64030000e-03], [ 2.20600000e-04, 2.20600000e-04, -2.62000000e-06, 2.20600000e-04, 2.20600000e-04], [ -2.78300000e-04, -2.78300000e-04, 3.17000000e-06, -2.78300000e-04, -2.78300000e-04], [ -4.93000000e-04, -4.93000000e-04, 5.40000000e-06, -4.93000000e-04, -4.93000000e-04], [ -1.85000000e-04, -1.85000000e-04, 1.95000000e-06, -1.85000000e-04, -1.85000000e-04], [ -7.64000000e-04, -7.64000000e-04, 7.75000000e-06, -7.64000000e-04, -7.64000000e-04], [ 7.79600000e-04, 7.79600000e-04, -7.61000000e-06, 7.79600000e-04, 7.79600000e-04], [ 2.88400000e-04, 2.88400000e-04, -2.71000000e-06, 2.88400000e-04, 2.88400000e-04], [ 1.09370000e-03, 1.09370000e-03, -9.91000000e-06, 1.09370000e-03, 1.09370000e-03], [ 3.07000000e-04, 3.07000000e-04, -2.68000000e-06, 3.07000000e-04, 3.07000000e-04], [ -8.76000000e-04, -8.76000000e-04, 7.37000000e-06, -8.76000000e-04, -8.76000000e-04], [ -1.85300000e-04, -1.85300000e-04, 1.50000000e-06, -1.85300000e-04, -1.85300000e-04], [ 3.24700000e-04, 3.24700000e-04, -2.54000000e-06, 3.24700000e-04, 3.24700000e-04], [ 4.59600000e-04, 4.59600000e-04, -3.47000000e-06, 4.59600000e-04, 4.59600000e-04], [ -2.73300000e-04, -2.73300000e-04, 1.99000000e-06, -2.73300000e-04, -2.73300000e-04], [ 1.32180000e-03, 1.32180000e-03, -9.29000000e-06, 1.32180000e-03, 1.32180000e-03], [ -1.32620000e-03, -1.32620000e-03, 9.00000000e-06, -1.32620000e-03, -1.32620000e-03], [ 9.62000000e-05, 9.62000000e-05, -6.31000000e-07, 9.62000000e-05, 9.62000000e-05], [ -6.04400000e-04, -6.04400000e-04, 3.83000000e-06, -6.04400000e-04, -6.04400000e-04], [ -6.66300000e-04, -6.66300000e-04, 4.08000000e-06, -6.66300000e-04, -6.66300000e-04]]) self.null_deviance = 6.8088354977561 # from R, Rpy bug self.params = np.array([ 1.00045997, 0.01991666, 0.00100126]) self.bse = np.array([ 4.55214070e-04, 7.00529313e-05, 1.84478509e-06]) self.aic_R = -1123.1528237643774 self.aic_Stata = -11.25152876811373 self.deviance = 7.1612915365488368e-05 self.scale = 7.3827747608449547e-07 self.llf = 565.57641188218872 self.bic_Stata = -446.7014364279675 self.df_model = 2 self.df_resid = 97 self.chi2 = 2704006.698904491 self.fittedvalues = np.array([ 0.99954024, 0.97906956, 0.95758077, 0.93526008, 0.91228657, 0.88882978, 0.8650479 , 0.84108646, 0.81707757, 0.79313958, 0.76937709, 0.74588129, 0.72273051, 0.69999099, 0.67771773, 0.65595543, 0.63473944, 0.61409675, 0.59404691, 0.57460297, 0.55577231, 0.53755742, 0.51995663, 0.50296478, 0.48657379, 0.47077316, 0.4555505 , 0.44089187, 0.42678213, 0.41320529, 0.40014475, 0.38758348, 0.37550428, 0.36388987, 0.35272306, 0.34198684, 0.33166446, 0.32173953, 0.31219604, 0.30301842, 0.29419156, 0.28570085, 0.27753216, 0.26967189, 0.26210695, 0.25482476, 0.24781324, 0.2410608 , 0.23455636, 0.22828931, 0.22224947, 0.21642715, 0.21081306, 0.20539835, 0.20017455, 0.19513359, 0.19026777, 0.18556972, 0.18103243, 0.17664922, 0.1724137 , 0.16831977, 0.16436164, 0.16053377, 0.15683086, 0.15324789, 0.14978003, 0.1464227 , 0.14317153, 0.14002232, 0.13697109, 0.13401403, 0.1311475 , 0.12836802, 0.12567228, 0.1230571 , 0.12051944, 0.11805642, 0.11566526, 0.1133433 , 0.11108802, 0.10889699, 0.10676788, 0.10469847, 0.10268664, 0.10073034, 0.09882763, 0.09697663, 0.09517555, 0.09342267, 0.09171634, 0.09005498, 0.08843707, 0.08686116, 0.08532585, 0.08382979, 0.0823717 , 0.08095035, 0.07956453, 0.07821311]) class Star98(object): """ Star98 class used with TestGlmBinomial """ def __init__(self): self.params = (-0.0168150366, 0.0099254766, -0.0187242148, -0.0142385609, 0.2544871730, 0.2406936644, 0.0804086739, -1.9521605027, -0.3340864748, -0.1690221685, 0.0049167021, -0.0035799644, -0.0140765648, -0.0040049918, -0.0039063958, 0.0917143006, 0.0489898381, 0.0080407389, 0.0002220095, -0.0022492486, 2.9588779262) self.bse = (4.339467e-04, 6.013714e-04, 7.435499e-04, 4.338655e-04, 2.994576e-02, 5.713824e-02, 1.392359e-02, 3.168109e-01, 6.126411e-02, 3.270139e-02, 1.253877e-03, 2.254633e-04, 1.904573e-03, 4.739838e-04, 9.623650e-04, 1.450923e-02, 7.451666e-03, 1.499497e-03, 2.988794e-05, 3.489838e-04, 1.546712e+00) self.null_deviance = 34345.3688931 self.df_null = 302 self.deviance = 4078.76541772 self.df_resid = 282 self.df_model = 20 self.aic_R = 6039.22511799 self.aic_Stata = 19.93143846737438 self.bic_Stata = 2467.493504191302 self.llf = -2998.61255899391 # from R self.llf_Stata = -2998.612927807218 self.scale = 1. self.pearson_chi2 = 4051.921614 self.resids = glm_test_resids.star98_resids self.fittedvalues = np.array([ 0.5833118 , 0.75144661, 0.50058272, 0.68534524, 0.32251021, 0.68693601, 0.33299827, 0.65624766, 0.49851481, 0.506736, 0.23954874, 0.86631452, 0.46432936, 0.44171873, 0.66797935, 0.73988491, 0.51966014, 0.42442446, 0.5649369 , 0.59251634, 0.34798337, 0.56415024, 0.49974355, 0.3565539 , 0.20752309, 0.18269097, 0.44932642, 0.48025128, 0.59965277, 0.58848671, 0.36264203, 0.33333196, 0.74253352, 0.5081886 , 0.53421878, 0.56291445, 0.60205239, 0.29174423, 0.2954348 , 0.32220414, 0.47977903, 0.23687535, 0.11776464, 0.1557423 , 0.27854799, 0.22699533, 0.1819439 , 0.32554433, 0.22681989, 0.15785389, 0.15268609, 0.61094772, 0.20743222, 0.51649059, 0.46502006, 0.41031788, 0.59523288, 0.65733285, 0.27835336, 0.2371213 , 0.25137045, 0.23953942, 0.27854519, 0.39652413, 0.27023163, 0.61411863, 0.2212025 , 0.42005842, 0.55940397, 0.35413774, 0.45724563, 0.57399437, 0.2168918 , 0.58308738, 0.17181104, 0.49873249, 0.22832683, 0.14846056, 0.5028073 , 0.24513863, 0.48202096, 0.52823155, 0.5086262 , 0.46295993, 0.57869402, 0.78363217, 0.21144435, 0.2298366 , 0.17954825, 0.32232586, 0.8343015 , 0.56217006, 0.47367315, 0.52535649, 0.60350746, 0.43210701, 0.44712008, 0.35858239, 0.2521347 , 0.19787004, 0.63256553, 0.51386532, 0.64997027, 0.13402072, 0.81756174, 0.74543642, 0.30825852, 0.23988707, 0.17273125, 0.27880599, 0.17395893, 0.32052828, 0.80467697, 0.18726218, 0.23842081, 0.19020381, 0.85835388, 0.58703615, 0.72415106, 0.64433695, 0.68766653, 0.32923663, 0.16352185, 0.38868816, 0.44980444, 0.74810044, 0.42973792, 0.53762581, 0.72714996, 0.61229484, 0.30267667, 0.24713253, 0.65086008, 0.48957265, 0.54955545, 0.5697156 , 0.36406211, 0.48906545, 0.45919413, 0.4930565 , 0.39785555, 0.5078719 , 0.30159626, 0.28524393, 0.34687707, 0.22522042, 0.52947159, 0.29277287, 0.8585002 , 0.60800389, 0.75830521, 0.35648175, 0.69508796, 0.45518355, 0.21567675, 0.39682985, 0.49042948, 0.47615798, 0.60588234, 0.62910299, 0.46005639, 0.71755165, 0.48852156, 0.47940661, 0.60128813, 0.16589699, 0.68512861, 0.46305199, 0.68832227, 0.7006721 , 0.56564937, 0.51753941, 0.54261733, 0.56072214, 0.34545715, 0.30226104, 0.3572956 , 0.40996287, 0.33517519, 0.36248407, 0.33937041, 0.34140691, 0.2627528 , 0.29955161, 0.38581683, 0.24840026, 0.15414272, 0.40415991, 0.53936252, 0.52111887, 0.28060168, 0.45600958, 0.51110589, 0.43757523, 0.46891953, 0.39425249, 0.5834369 , 0.55817308, 0.32051259, 0.43567448, 0.34134195, 0.43016545, 0.4885413 , 0.28478325, 0.2650776 , 0.46784606, 0.46265983, 0.42655938, 0.18972234, 0.60448491, 0.211896 , 0.37886032, 0.50727577, 0.39782309, 0.50427121, 0.35882898, 0.39596807, 0.49160806, 0.35618002, 0.6819922 , 0.36871093, 0.43079679, 0.67985516, 0.41270595, 0.68952767, 0.52587734, 0.32042126, 0.39120123, 0.56870985, 0.32962349, 0.32168989, 0.54076251, 0.4592907 , 0.48480182, 0.4408386 , 0.431178 , 0.47078232, 0.55911605, 0.30331618, 0.50310393, 0.65036038, 0.45078895, 0.62354291, 0.56435463, 0.50034281, 0.52693538, 0.57217285, 0.49221472, 0.40707122, 0.44226533, 0.3475959 , 0.54746396, 0.86385832, 0.48402233, 0.54313657, 0.61586824, 0.27097185, 0.69717808, 0.52156974, 0.50401189, 0.56724181, 0.6577178 , 0.42732047, 0.44808396, 0.65435634, 0.54766225, 0.38160648, 0.49890847, 0.50879037, 0.5875452 , 0.45101593, 0.5709704 , 0.3175516 , 0.39813159, 0.28305688, 0.40521062, 0.30120578, 0.26400428, 0.44205496, 0.40545798, 0.39366599, 0.55288196, 0.14104184, 0.17550155, 0.1949095 , 0.40255144, 0.21016822, 0.09712017, 0.63151487, 0.25885514, 0.57323748, 0.61836898, 0.43268601, 0.67008878, 0.75801989, 0.50353406, 0.64222315, 0.29925757, 0.32592036, 0.39634977, 0.39582747, 0.41037006, 0.34174944]) class Lbw(object): ''' The LBW data can be found here http://www.stata-press.com/data/r9/rmain.html ''' def __init__(self): # data set up for data not in datasets filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_lbw_glm.csv") data=np.recfromcsv(open(filename, 'rb'), converters={4: lambda s: s.strip(asbytes("\""))}) data = categorical(data, col='race', drop=True) self.endog = data.low design = np.column_stack((data['age'], data['lwt'], data['race_black'], data['race_other'], data['smoke'], data['ptl'], data['ht'], data['ui'])) self.exog = add_constant(design, prepend=False) # Results for Canonical Logit Link self.params = (-.02710031, -.01515082, 1.26264728, .86207916, .92334482, .54183656, 1.83251780, .75851348, .46122388) self.bse = (0.036449917, 0.006925765, 0.526405169, 0.439146744, 0.400820976, 0.346246857, 0.691623875, 0.459373871, 1.204574885) self.aic_R = 219.447991133 self.aic_Stata = 1.161100482182551 self.deviance = 201.4479911325021 self.scale = 1 self.llf = -100.7239955662511 self.chi2 = 25.65329337867037 # from Stata not used by sm self.null_deviance = 234.671996193219 self.bic_Stata = -742.0664715782335 self.df_resid = 180 self.df_model = 8 self.df_null = 188 self.pearson_chi2 = 182.023342493558 self.resids = glm_test_resids.lbw_resids self.fittedvalues = np.array([ 0.31217507, 0.12793027, 0.32119762, 0.48442686, 0.50853393, 0.24517662, 0.12755193, 0.33226988, 0.22013309, 0.26268069, 0.34729955, 0.18782188, 0.75404181, 0.54723527, 0.35016393, 0.35016393, 0.45824406, 0.25336683, 0.43087357, 0.23284101, 0.20146616, 0.24315597, 0.02725586, 0.22207692, 0.39800383, 0.05584178, 0.28403447, 0.06931188, 0.35371946, 0.3896279 , 0.3896279 , 0.47812002, 0.60043853, 0.07144772, 0.29995988, 0.17910031, 0.22773411, 0.22691015, 0.06221253, 0.2384528 , 0.32633864, 0.05131047, 0.2954536 , 0.07364416, 0.57241299, 0.57241299, 0.08272435, 0.23298882, 0.12658158, 0.58967487, 0.46989562, 0.22455631, 0.2348285 , 0.29571887, 0.28212464, 0.31499013, 0.68340511, 0.14090647, 0.31448425, 0.28082972, 0.28082972, 0.24918728, 0.27018297, 0.08175784, 0.64808999, 0.38252574, 0.25550797, 0.09113411, 0.40736693, 0.32644055, 0.54367425, 0.29606968, 0.47028421, 0.39972155, 0.25079125, 0.09678472, 0.08807264, 0.27467837, 0.5675742 , 0.045619 , 0.10719293, 0.04826292, 0.23934092, 0.24179618, 0.23802197, 0.49196179, 0.31379451, 0.10605469, 0.04047396, 0.11620849, 0.09937016, 0.21822964, 0.29770265, 0.83912829, 0.25079125, 0.08548557, 0.06550308, 0.2046457 , 0.2046457 , 0.08110349, 0.13519643, 0.47862055, 0.38891913, 0.1383964 , 0.26176764, 0.31594589, 0.11418612, 0.06324112, 0.28468594, 0.21663702, 0.03827107, 0.27237604, 0.20246694, 0.19042999, 0.15019447, 0.18759474, 0.12308435, 0.19700616, 0.11564002, 0.36595033, 0.07765727, 0.14119063, 0.13584627, 0.11012759, 0.10102472, 0.10002166, 0.07439288, 0.27919958, 0.12491598, 0.06774594, 0.72513764, 0.17714986, 0.67373352, 0.80679436, 0.52908941, 0.15695938, 0.49722003, 0.41970014, 0.62375224, 0.53695622, 0.25474238, 0.79135707, 0.2503871 , 0.25352337, 0.33474211, 0.19308929, 0.24658944, 0.25495092, 0.30867144, 0.41240259, 0.59412526, 0.16811226, 0.48282791, 0.36566756, 0.09279325, 0.75337353, 0.57128885, 0.52974123, 0.44548504, 0.77748843, 0.3224082 , 0.40054277, 0.29522468, 0.19673553, 0.73781774, 0.57680312, 0.44545573, 0.30242355, 0.38720223, 0.16632904, 0.30804092, 0.56385194, 0.60012179, 0.48324821, 0.24636345, 0.26153216, 0.2348285 , 0.29023669, 0.41011454, 0.36472083, 0.65922069, 0.30476903, 0.09986775, 0.70658332, 0.30713075, 0.36096386, 0.54962701, 0.71996086, 0.6633756 ]) class Scotvote(object): """ Scotvot class is used with TestGlmGamma. """ def __init__(self): self.params = (4.961768e-05, 2.034423e-03, -7.181429e-05, 1.118520e-04, -1.467515e-07, -5.186831e-04, -2.42717498e-06, -1.776527e-02) self.bse = (1.621577e-05, 5.320802e-04, 2.711664e-05, 4.057691e-05, 1.236569e-07, 2.402534e-04, 7.460253e-07, 1.147922e-02) self.null_deviance = 0.536072 self.df_null = 31 self.deviance = 0.087388516417 self.df_resid = 24 self.df_model = 7 self.aic_R = 182.947045954721 self.aic_Stata = 10.72212 self.bic_Stata = -83.09027 self.llf = -163.5539382 # from Stata, same as ours with scale = 1 # self.llf = -82.47352 # Very close to ours as is self.scale = 0.003584283 self.pearson_chi2 = .0860228056 self.resids = glm_test_resids.scotvote_resids self.fittedvalues = np.array([57.80431482, 53.2733447, 50.56347993, 58.33003783, 70.46562169, 56.88801284, 66.81878401, 66.03410393, 57.92937473, 63.23216907, 53.9914785 , 61.28993391, 64.81036393, 63.47546816, 60.69696114, 74.83508176, 56.56991106, 72.01804172, 64.35676519, 52.02445881, 64.24933079, 71.15070332, 45.73479688, 54.93318588, 66.98031261, 52.02479973, 56.18413736, 58.12267471, 67.37947398, 60.49162862, 73.82609217, 69.61515621]) class Cancer(object): ''' The Cancer data can be found here http://www.stata-press.com/data/r10/rmain.html ''' def __init__(self): filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_cancer_glm.csv") data = np.recfromcsv(open(filename, 'rb')) self.endog = data.studytime design = np.column_stack((data.age,data.drug)) design = categorical(design, col=1, drop=True) design = np.delete(design, 1, axis=1) # drop first dummy self.exog = add_constant(design, prepend=False) class CancerLog(Cancer): """ CancerLog is used TestGlmGammaLog """ def __init__(self): super(CancerLog, self).__init__() self.resids = np.array([[-8.52598100e-01,-1.45739100e+00, -3.92408100e+01, -1.41526900e+00, -5.78417200e+00], [ -8.23683800e-01, -1.35040200e+00, -2.64957500e+01, -1.31777000e+00, -4.67162900e+00], [ -7.30450400e-01, -1.07754600e+00, -4.02136400e+01, -1.06208800e+00, -5.41978500e+00], [ -7.04471600e-01, -1.01441500e+00, -7.25951500e+01, -1.00172900e+00, -7.15130900e+00], [ -5.28668000e-01, -6.68617300e-01, -3.80758100e+01, -6.65304600e-01, -4.48658700e+00], [ -2.28658500e-01, -2.48859700e-01, -6.14913600e+00, -2.48707200e-01, -1.18577100e+00], [ -1.93939400e-01, -2.08119900e-01, -7.46226500e+00, -2.08031700e-01, -1.20300800e+00], [ -3.55635700e-01, -4.09525000e-01, -2.14132500e+01, -4.08815100e-01, -2.75958600e+00], [ -5.73360000e-02, -5.84700000e-02, -4.12946200e+00, -5.84681000e-02, -4.86586900e-01], [ 3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 3.06682000e-02, 2.40413800e-01], [ -2.11924300e-01, -2.29071300e-01, -2.18386100e+01, -2.28953000e-01, -2.15130900e+00], [ -3.10989000e-01, -3.50739300e-01, -4.19249500e+01, -3.50300400e-01, -3.61084500e+00], [ -9.22250000e-03, -9.25100000e-03, -1.13679700e+00, -9.25100000e-03, -1.02392100e-01], [ 2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 2.22493500e-01, 2.12475600e+00], [ 3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 3.31489000e-02, 3.89155400e-01], [ 8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 6.82689900e-01, 5.51291500e+00], [ 4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 3.66370400e-01, 4.38379500e+00], [ 4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 4.06561900e-01, 5.38915500e+00], [ 1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 1.18311900e+00, 1.38850500e+01], [ 1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 9.40244600e-01, 1.28486900e+01], [ -3.48532600e-01, -3.99988300e-01, -2.95638100e+01, -3.99328600e-01, -3.20997700e+00], [ -4.04340300e-01, -4.76960100e-01, -4.10254300e+01, -4.75818000e-01, -4.07286500e+00], [ -4.92057900e-01, -6.08818300e-01, -9.34509600e+01, -6.06357200e-01, -6.78109700e+00], [ -4.02876400e-01, -4.74878400e-01, -9.15226200e+01, -4.73751900e-01, -6.07225700e+00], [ -5.15056700e-01, -6.46013300e-01, -2.19014600e+02, -6.43043500e-01, -1.06209700e+01], [ -8.70423000e-02, -8.97043000e-02, -1.26361400e+01, -8.96975000e-02, -1.04875100e+00], [ 1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 1.23231000e-01, 1.47887800e+00], [ -2.39271900e-01, -2.61562100e-01, -9.30283300e+01, -2.61384400e-01, -4.71795100e+00], [ 7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 6.06409700e-01, 6.79002300e+00], [ -3.64110000e-02, -3.68626000e-02, -1.41565300e+01, -3.68621000e-02, -7.17951200e-01], [ 2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 2.47801000e-01, 4.23748400e+00], [ 5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 5.06180100e-01, 8.21890300e+00], [ 1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 1.96923000e-02, 4.47040700e-01], [ 7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 6.32303700e-01, 1.39711800e+01], [ -7.63925100e-01, -1.16591700e+00, -4.93461700e+02, -1.14588000e+00, -1.94156600e+01], [ -6.23771700e-01, -8.41174800e-01, -4.40679600e+02, -8.34266300e-01, -1.65796100e+01], [ -1.63272900e-01, -1.73115100e-01, -6.73975900e+01, -1.73064800e-01, -3.31725800e+00], [ -4.28562500e-01, -5.11932900e-01, -4.73787800e+02, -5.10507400e-01, -1.42494800e+01], [ 8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 7.80226000e-02, 1.77920500e+00], [ -2.13674400e-01, -2.31127400e-01, -2.15987000e+02, -2.31005700e-01, -6.79344600e+00], [ -1.63544000e-02, -1.64444000e-02, -1.05642100e+01, -1.64444000e-02, -4.15657600e-01], [ 2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 1.92309400e-01, 4.76156600e+00], [ -1.94758900e-01, -2.09067700e-01, -2.35484100e+02, -2.08978200e-01, -6.77219400e+00], [ 3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 2.88162100e-01, 7.69732400e+00], [ 6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 5.26448400e-01, 1.26827400e+01], [ 8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 6.69644800e-01, 1.54232100e+01], [ 6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 6.53710000e-03, 2.27805800e-01], [ 3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 3.08733300e-01, 9.93012900e+00]]) self.null_deviance = 27.92207137420696 # From R (bug in rpy) self.params = np.array([-0.04477778, 0.57437126, 1.05210726, 4.64604002]) self.bse = np.array([ 0.0147328 , 0.19694727, 0.19772507, 0.83534671]) self.aic_R = 331.89022395372069 self.aic_Stata = 7.403608467857651 self.deviance = 16.174635536991005 self.scale = 0.31805268736385695 # self.llf = -160.94511197686035 # From R self.llf = -173.6866032285836 # from Staa self.bic_Stata = -154.1582089453923 # from Stata self.df_model = 3 self.df_resid = 44 self.chi2 = 36.77821448266359 # from Stata not in sm self.fittedvalues = np.array([ 6.78419193, 5.67167253, 7.41979002, 10.15123371, 8.48656317, 5.18582263, 6.20304079, 7.75958258, 8.48656317, 7.75958258, 10.15123371, 11.61071755, 11.10228357, 8.87520908, 11.61071755, 6.48711178, 10.61611394, 11.61071755, 8.11493609, 10.15123371, 9.21009116, 10.07296716, 13.78112366, 15.07225103, 20.62079147, 12.04881666, 11.5211983 , 19.71780584, 9.21009116, 19.71780584, 15.76249142, 13.78112366, 22.55271436, 18.02872842, 25.41575239, 26.579678 , 20.31745227, 33.24937131, 22.22095589, 31.79337946, 25.41575239, 23.23857437, 34.77204095, 24.30279515, 20.31745227, 18.57700761, 34.77204095, 29.06987768]) class CancerIdentity(Cancer): """ CancerIdentity is used with TestGlmGammaIdentity """ def __init__(self): super(CancerIdentity, self).__init__() self.resids = np.array([[ -8.52598100e-01, -1.45739100e+00, -3.92408100e+01, -1.41526900e+00, -5.78417200e+00], [ -8.23683800e-01, -1.35040200e+00, -2.64957500e+01, -1.31777000e+00, -4.67162900e+00], [ -7.30450400e-01, -1.07754600e+00, -4.02136400e+01, -1.06208800e+00, -5.41978500e+00], [ -7.04471600e-01, -1.01441500e+00, -7.25951500e+01, -1.00172900e+00, -7.15130900e+00], [ -5.28668000e-01, -6.68617300e-01, -3.80758100e+01, -6.65304600e-01, -4.48658700e+00], [ -2.28658500e-01, -2.48859700e-01, -6.14913600e+00, -2.48707200e-01, -1.18577100e+00], [ -1.93939400e-01, -2.08119900e-01, -7.46226500e+00, -2.08031700e-01, -1.20300800e+00], [ -3.55635700e-01, -4.09525000e-01, -2.14132500e+01, -4.08815100e-01, -2.75958600e+00], [ -5.73360000e-02, -5.84700000e-02, -4.12946200e+00, -5.84681000e-02, -4.86586900e-01], [ 3.09828000e-02, 3.06685000e-02, 1.86551100e+00, 3.06682000e-02, 2.40413800e-01], [ -2.11924300e-01, -2.29071300e-01, -2.18386100e+01, -2.28953000e-01, -2.15130900e+00], [ -3.10989000e-01, -3.50739300e-01, -4.19249500e+01, -3.50300400e-01, -3.61084500e+00], [ -9.22250000e-03, -9.25100000e-03, -1.13679700e+00, -9.25100000e-03, -1.02392100e-01], [ 2.39402500e-01, 2.22589700e-01, 1.88577300e+01, 2.22493500e-01, 2.12475600e+00], [ 3.35166000e-02, 3.31493000e-02, 4.51842400e+00, 3.31489000e-02, 3.89155400e-01], [ 8.49829400e-01, 6.85180200e-01, 3.57627500e+01, 6.82689900e-01, 5.51291500e+00], [ 4.12934200e-01, 3.66785200e-01, 4.65392600e+01, 3.66370400e-01, 4.38379500e+00], [ 4.64148400e-01, 4.07123200e-01, 6.25726500e+01, 4.06561900e-01, 5.38915500e+00], [ 1.71104600e+00, 1.19474800e+00, 1.12676500e+02, 1.18311900e+00, 1.38850500e+01], [ 1.26571800e+00, 9.46389000e-01, 1.30431000e+02, 9.40244600e-01, 1.28486900e+01], [ -3.48532600e-01, -3.99988300e-01, -2.95638100e+01, -3.99328600e-01, -3.20997700e+00], [ -4.04340300e-01, -4.76960100e-01, -4.10254300e+01, -4.75818000e-01, -4.07286500e+00], [ -4.92057900e-01, -6.08818300e-01, -9.34509600e+01, -6.06357200e-01, -6.78109700e+00], [ -4.02876400e-01, -4.74878400e-01, -9.15226200e+01, -4.73751900e-01, -6.07225700e+00], [ -5.15056700e-01, -6.46013300e-01, -2.19014600e+02, -6.43043500e-01, -1.06209700e+01], [ -8.70423000e-02, -8.97043000e-02, -1.26361400e+01, -8.96975000e-02, -1.04875100e+00], [ 1.28362300e-01, 1.23247800e-01, 1.70383300e+01, 1.23231000e-01, 1.47887800e+00], [ -2.39271900e-01, -2.61562100e-01, -9.30283300e+01, -2.61384400e-01, -4.71795100e+00], [ 7.37246500e-01, 6.08186000e-01, 6.25359600e+01, 6.06409700e-01, 6.79002300e+00], [ -3.64110000e-02, -3.68626000e-02, -1.41565300e+01, -3.68621000e-02, -7.17951200e-01], [ 2.68833000e-01, 2.47933100e-01, 6.67934100e+01, 2.47801000e-01, 4.23748400e+00], [ 5.96389600e-01, 5.07237700e-01, 1.13265500e+02, 5.06180100e-01, 8.21890300e+00], [ 1.98218000e-02, 1.96923000e-02, 1.00820900e+01, 1.96923000e-02, 4.47040700e-01], [ 7.74936000e-01, 6.34305300e-01, 2.51883900e+02, 6.32303700e-01, 1.39711800e+01], [ -7.63925100e-01, -1.16591700e+00, -4.93461700e+02, -1.14588000e+00, -1.94156600e+01], [ -6.23771700e-01, -8.41174800e-01, -4.40679600e+02, -8.34266300e-01, -1.65796100e+01], [ -1.63272900e-01, -1.73115100e-01, -6.73975900e+01, -1.73064800e-01, -3.31725800e+00], [ -4.28562500e-01, -5.11932900e-01, -4.73787800e+02, -5.10507400e-01, -1.42494800e+01], [ 8.00693000e-02, 7.80269000e-02, 3.95353400e+01, 7.80226000e-02, 1.77920500e+00], [ -2.13674400e-01, -2.31127400e-01, -2.15987000e+02, -2.31005700e-01, -6.79344600e+00], [ -1.63544000e-02, -1.64444000e-02, -1.05642100e+01, -1.64444000e-02, -4.15657600e-01], [ 2.04900500e-01, 1.92372100e-01, 1.10651300e+02, 1.92309400e-01, 4.76156600e+00], [ -1.94758900e-01, -2.09067700e-01, -2.35484100e+02, -2.08978200e-01, -6.77219400e+00], [ 3.16727400e-01, 2.88367800e-01, 1.87065600e+02, 2.88162100e-01, 7.69732400e+00], [ 6.24234900e-01, 5.27632500e-01, 2.57678500e+02, 5.26448400e-01, 1.26827400e+01], [ 8.30241100e-01, 6.72002100e-01, 2.86513700e+02, 6.69644800e-01, 1.54232100e+01], [ 6.55140000e-03, 6.53710000e-03, 7.92130700e+00, 6.53710000e-03, 2.27805800e-01], [ 3.41595200e-01, 3.08985000e-01, 2.88667600e+02, 3.08733300e-01, 9.93012900e+00]]) self.params = np.array([ -0.5369833, 6.47296332, 16.20336802, 38.96617431]) self.bse = np.array([ 0.13341238, 2.1349966 , 3.87411875, 8.19235553]) self.aic_R = 328.39209118952965 #TODO: the below will fail self.aic_Stata = 7.381090276021671 self.deviance = 15.093762327607557 self.scale = 0.29512089119443752 self.null_deviance = 27.92207137420696 # from R bug in RPy #NOTE: our scale is Stata's dispers_p (pearson?) #NOTE: if scale is analagous to Stata's dispersion, then this might be #where the discrepancies come from? # self.llf = -159.19604559476483 # From R self.llf = -173.1461666245201 # From Stata self.bic_Stata = -155.2390821535193 self.df_model = 3 self.df_resid = 44 self.chi2 = 51.56632068622578 self.fittedvalues = np.array([ 6.21019277, 4.06225956, 7.28415938, 11.04304251, 8.89510929, 2.98829295, 5.13622616, 7.82114268, 8.89510929, 7.82114268, 11.04304251, 12.65399242, 12.11700911, 9.43209259, 12.65399242, 5.67320947, 11.58002581, 12.65399242, 8.35812599, 11.04304251, 9.46125627, 10.53522287, 14.294106 , 15.36807261, 19.12695574, 12.68315609, 12.14617279, 18.58997243, 9.46125627, 18.58997243, 15.90505591, 14.294106 , 20.20092234, 17.51600582, 25.63546061, 26.17244391, 22.95054409, 28.85736043, 24.0245107 , 28.32037713, 25.63546061, 24.561494 , 29.39434374, 25.09847731, 22.95054409, 21.87657748, 29.39434374, 27.24641052]) class Cpunish(object): ''' The following are from the R script in models.datasets.cpunish Slightly different than published results, but should be correct Probably due to rounding in cleaning? ''' def __init__(self): self.params = (2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01, 2.301183e+00, -1.872207e+01, -6.801480e+00) self.bse = (5.187132e-05, 7.940193e-02, 2.291926e-02, 4.375164e-01, 4.283826e-01, 4.283961e+00, 4.146850e+00) self.null_deviance = 136.57281747225 self.df_null = 16 self.deviance = 18.591641759528944 self.df_resid = 10 self.df_model = 6 self.aic_R = 77.8546573896503 # same as Stata self.aic_Stata = 4.579685683305706 self.bic_Stata = -9.740492454486446 self.chi2 = 128.8021169250578 # from Stata not in sm self.llf = -31.92732869482515 self.scale = 1 self.pearson_chi2 = 24.75374835 self.resids = glm_test_resids.cpunish_resids self.fittedvalues = np.array([35.2263655, 8.1965744, 1.3118966, 3.6862982, 2.0823003, 1.0650316, 1.9260424, 2.4171405, 1.8473219, 2.8643241, 3.1211989, 3.3382067, 2.5269969, 0.8972542, 0.9793332, 0.5346209, 1.9790936]) class InvGauss(object): ''' Usef Data was generated by Hardin and Hilbe using Stata. Note only the first 5000 observations are used because the models code currently uses np.eye. ''' # np.random.seed(54321) # x1 = np.abs(stats.norm.ppf((np.random.random(5000)))) # x2 = np.abs(stats.norm.ppf((np.random.random(5000)))) # X = np.column_stack((x1,x2)) # X = add_constant(X) # params = np.array([.5, -.25, 1]) # eta = np.dot(X, params) # mu = 1/np.sqrt(eta) # sigma = .5 # This isn't correct. Errors need to be normally distributed # But Y needs to be Inverse Gaussian, so we could build it up # by throwing out data? # Refs: Lai (2009) Generating inverse Gaussian random variates by # approximation # Atkinson (1982) The simulation of generalized inverse gaussian and # hyperbolic random variables seems to be the canonical ref # Y = np.dot(X,params) + np.random.wald(mu, sigma, 1000) # model = GLM(Y, X, family=models.family.InverseGaussian(link=\ # models.family.links.identity)) def __init__(self): # set up data # filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "inv_gaussian.csv") data=np.genfromtxt(open(filename, 'rb'), delimiter=",", dtype=float)[1:] self.endog = data[:5000,0] self.exog = data[:5000,1:] self.exog = add_constant(self.exog, prepend=False) #class InvGaussDefault(InvGauss) # def __init__(self): # super(InvGaussDefault, self).__init__() # Results #NOTE: loglikelihood difference in R vs. Stata vs. Models # is the same situation as gamma self.params = (0.4519770, -0.2508288, 1.0359574) self.bse = (0.03148291, 0.02237211, 0.03429943) self.null_deviance = 1520.673165475461 self.df_null = 4999 self.deviance = 1423.943980407997 self.df_resid = 4997 self.df_model = 2 self.aic_R = 5059.41911646446 self.aic_Stata = 1.552280060977946 self.bic_Stata = -41136.47039418921 self.llf = -3877.700354 # Stata is same as ours with scale set to 1 # self.llf = -2525.70955823223 # from R, close to ours self.scale = 0.2867266359127567 self.pearson_chi2 = 1432.771536 self.resids = glm_test_resids.invgauss_resids self.fittedvalues = np.array([ 1.0404339 , 0.96831526, 0.81265833, 0.9958362 , 1.05433442, 1.09866137, 0.95548191, 1.38082105, 0.98942888, 0.96521958, 1.02684056, 0.91412576, 0.91492102, 0.92639676, 0.96763425, 0.80250852, 0.85281816, 0.90962261, 0.95550299, 0.86386815, 0.94760134, 0.94269533, 0.98960509, 0.84787252, 0.78949111, 0.76873582, 0.98933453, 0.95105574, 0.8489395 , 0.88962971, 0.84856357, 0.88567313, 0.84505405, 0.84626147, 0.77250421, 0.90175601, 1.15436378, 0.98375558, 0.83539542, 0.82845381, 0.90703971, 0.85546165, 0.96707286, 0.84127197, 0.82096543, 1.1311227 , 0.87617029, 0.91194419, 1.05125511, 0.95330314, 0.75556148, 0.82573228, 0.80424982, 0.83800144, 0.8203644 , 0.84423807, 0.98348433, 0.93165089, 0.83968706, 0.79256287, 1.0302839 , 0.90982028, 0.99471562, 0.70931825, 0.85471721, 1.02668021, 1.11308301, 0.80497105, 1.02708486, 1.07671424, 0.821108 , 0.86373486, 0.99104964, 1.06840593, 0.94947784, 0.80982122, 0.95778065, 1.0254212 , 1.03480946, 0.83942363, 1.17194944, 0.91772559, 0.92368795, 1.10410916, 1.12558875, 1.11290791, 0.87816503, 1.04299294, 0.89631173, 1.02093004, 0.86331723, 1.13134858, 1.01807861, 0.98441692, 0.72567667, 1.42760495, 0.78987436, 0.72734482, 0.81750166, 0.86451854, 0.90564264, 0.81022323, 0.98720325, 0.98263709, 0.99364823, 0.7264445 , 0.81632452, 0.7627845 , 1.10726938, 0.79195664, 0.86836774, 1.01558149, 0.82673675, 0.99529548, 0.97155636, 0.980696 , 0.85460503, 1.00460782, 0.77395244, 0.81229831, 0.94078297, 1.05910564, 0.95921954, 0.97841172, 0.93093166, 0.93009865, 0.89888111, 1.18714408, 0.98964763, 1.03388898, 1.67554215, 0.82998876, 1.34100687, 0.86766346, 0.96392316, 0.91371033, 0.76589296, 0.92329051, 0.82560326, 0.96758148, 0.8412995 , 1.02550678, 0.74911108, 0.8751611 , 1.01389312, 0.87865556, 1.24095868, 0.90678261, 0.85973204, 1.05617845, 0.94163038, 0.88087351, 0.95699844, 0.86083491, 0.89669384, 0.78646825, 1.0014202 , 0.82399199, 1.05313139, 1.06458324, 0.88501766, 1.19043294, 0.8458026 , 1.00231535, 0.72464305, 0.94790753, 0.7829744 , 1.1953009 , 0.85574035, 0.95433052, 0.96341484, 0.91362908, 0.94097713, 0.87273804, 0.81126399, 0.72715262, 0.85526116, 0.76015834, 0.8403826 , 0.9831501 , 1.17104665, 0.78862494, 1.01054909, 0.91511601, 1.0990797 , 0.91352124, 1.13671162, 0.98793866, 1.0300545 , 1.04490115, 0.85778231, 0.94824343, 1.14510618, 0.81305136, 0.88085051, 0.94743792, 0.94875465, 0.96206997, 0.94493612, 0.93547218, 1.09212018, 0.86934651, 0.90532353, 1.07066001, 1.26197714, 0.93858662, 0.9685039 , 0.7946546 , 1.03052031, 0.75395899, 0.87527062, 0.82156476, 0.949774 , 1.01000235, 0.82613526, 1.0224591 , 0.91529149, 0.91608832, 1.09418385, 0.8228272 , 1.06337472, 1.05533176, 0.93513063, 1.00055806, 0.95474743, 0.91329368, 0.88711836, 0.95584926, 0.9825458 , 0.74954073, 0.96964967, 0.88779583, 0.95321846, 0.95390055, 0.95369029, 0.94326714, 1.31881201, 0.71512263, 0.84526602, 0.92323824, 1.01993108, 0.85155992, 0.81416851, 0.98749128, 1.00034192, 0.98763473, 1.05974138, 1.05912658, 0.89772172, 0.97905626, 1.1534306 , 0.92304181, 1.16450278, 0.7142307 , 0.99846981, 0.79861247, 0.73939835, 0.93776385, 1.0072242 , 0.89159707, 1.05514263, 1.05254569, 0.81005146, 0.95179784, 1.00278795, 1.04910398, 0.88427798, 0.74394266, 0.92941178, 0.83622845, 0.84064958, 0.93426956, 1.03619314, 1.22439347, 0.73510451, 0.82997071, 0.90828036, 0.80866989, 1.34078212, 0.85079169, 0.88346039, 0.76871666, 0.96763454, 0.66936914, 0.94175741, 0.97127617, 1.00844382, 0.83449557, 0.88095564, 1.17711652, 1.0547188 , 1.04525593, 0.93817487, 0.77978294, 1.36143199, 1.16127997, 1.03792952, 1.03151637, 0.83837387, 0.94326066, 1.0054787 , 0.99656841, 1.05575689, 0.97641643, 0.85108163, 0.82631589, 0.77407305, 0.90566132, 0.91308164, 0.95560906, 1.04523011, 1.03773723, 0.97378685, 0.83999133, 1.06926871, 1.01073982, 0.9804959 , 1.06473061, 1.25315673, 0.969175 , 0.63443508, 0.84574684, 1.06031239, 0.93834605, 1.01784925, 0.93488249, 0.80240225, 0.88757274, 0.9224097 , 0.99158962, 0.87412592, 0.76418199, 0.78044069, 1.03117412, 0.82042521, 1.10272129, 1.09673757, 0.89626935, 1.01678612, 0.84911824, 0.95821431, 0.99169558, 0.86853864, 0.92172772, 0.94046199, 0.89750517, 1.09599258, 0.92387291, 1.07770118, 0.98831383, 0.86352396, 0.83079533, 0.94431185, 1.12424626, 1.02553104, 0.8357513 , 0.97019669, 0.76816092, 1.34011343, 0.86489527, 0.82156358, 1.25529129, 0.86820218, 0.96970237, 0.85850546, 0.97429559, 0.84826078, 1.02498396, 0.72478517, 0.993497 , 0.76918521, 0.91079198, 0.80988325, 0.75431095, 1.02918073, 0.88884197, 0.82625507, 0.78564563, 0.91505355, 0.88896863, 0.85882361, 0.81538316, 0.67656235, 0.8564822 , 0.82473022, 0.92928331, 0.98068415, 0.82605685, 1.0150412 , 1.00631678, 0.92405101, 0.88909552, 0.94873568, 0.87657342, 0.8280683 , 0.77596382, 0.96598811, 0.78922426, 0.87637606, 0.98698735, 0.92207026, 0.71487846, 1.03845478, 0.70749745, 1.08603388, 0.92697779, 0.86470448, 0.70119494, 1.00596847, 0.91426549, 1.05318838, 0.79621712, 0.96169742, 0.88053405, 0.98963934, 0.94152997, 0.88413591, 0.75035344, 0.86007123, 0.83713514, 0.91234911, 0.79562744, 0.84099675, 1.0334279 , 1.00272243, 0.95359383, 0.84292969, 0.94234155, 0.90190899, 0.97302022, 1.1009829 , 1.0148975 , 0.99082987, 0.75916515, 0.9204784 , 0.94477378, 1.01108683, 1.00038149, 0.9259798 , 1.19400436, 0.80191877, 0.79565851, 0.81865924, 0.79003506, 0.8995508 , 0.73137983, 0.88336018, 0.7855268 , 1.04478073, 0.90857981, 1.16076951, 0.76096486, 0.90004113, 0.83819665, 0.95295365, 1.09911441, 0.78498197, 0.95094991, 0.94333419, 0.95131688, 0.82961049, 1.08001761, 1.06426458, 0.94291798, 1.04381938, 0.90380364, 0.74060138, 0.98701862, 0.72250236, 0.86125293, 0.76488061, 0.9858051 , 0.98099677, 0.96849209, 0.90053351, 0.88469597, 0.80688516, 1.06396217, 1.02446023, 0.911863 , 0.98837746, 0.91102987, 0.92810392, 1.13526335, 1.00419541, 1.00866175, 0.74352261, 0.91051641, 0.81868428, 0.93538014, 0.87822651, 0.93278572, 1.0356074 , 1.25158731, 0.98372647, 0.81335741, 1.06441863, 0.80305786, 0.95201148, 0.90283451, 1.17319519, 0.8984894 , 0.88911288, 0.91474736, 0.94512294, 0.92956283, 0.86682085, 1.08937227, 0.94825713, 0.9787145 , 1.16747163, 0.80863682, 0.98314119, 0.91052823, 0.80913225, 0.78503169, 0.78751737, 1.08932193, 0.86859845, 0.96847458, 0.93468839, 1.10769915, 1.1769249 , 0.84916138, 1.00556408, 0.84508585, 0.92617942, 0.93985886, 1.17303268, 0.81172495, 0.93482682, 1.04082486, 1.03209348, 0.97220394, 0.90274672, 0.93686291, 0.91116431, 1.14814563, 0.83279158, 0.95853283, 1.0261179 , 0.95779432, 0.86995883, 0.78164915, 0.89946906, 0.9194465 , 0.97919367, 0.92719039, 0.89063569, 0.80847805, 0.81192101, 0.75044535, 0.86819023, 1.03420014, 0.8899434 , 0.94899544, 0.9860773 , 1.10047297, 1.00243849, 0.82153972, 1.14289945, 0.8604684 , 0.87187524, 1.00415032, 0.78460709, 0.86319884, 0.92818335, 1.08892111, 1.06841003, 1.00735918, 1.20775251, 0.72613554, 1.25768191, 1.08573511, 0.89671127, 0.91259535, 1.01414208, 0.87422903, 0.82720677, 0.9568079 , 1.00450416, 0.91043845, 0.84095709, 1.08010574, 0.69848293, 0.90769214, 0.94713501, 1.14808251, 1.0605676 , 1.21734482, 0.78578521, 1.01516235, 0.94330326, 0.98363817, 0.99650084, 0.74280796, 0.96227123, 0.95741454, 1.00980406, 0.93468092, 1.10098591, 1.18175828, 0.8553791 , 0.81713219, 0.82912143, 0.87599518, 1.15006511, 1.03151163, 0.8751847 , 1.15701331, 0.73394166, 0.91426368, 0.96953458, 1.13901709, 0.83028721, 1.15742641, 0.9395442 , 0.98118552, 0.89585426, 0.74147117, 0.8902096 , 1.00212097, 0.97665858, 0.92624514, 0.98006601, 0.9507215 , 1.00889825, 1.2406772 , 0.88768719, 0.76587533, 1.0081044 , 0.89608494, 1.00083526, 0.85594415, 0.76425576, 1.0286636 , 1.13570272, 0.82020405, 0.81961271, 1.04586579, 1.26560245, 0.89721521, 1.19324037, 0.948205 , 0.79414261, 0.85157002, 0.95155101, 0.91969239, 0.87699126, 1.03452982, 0.97093572, 1.14355781, 0.85088592, 0.79032079, 0.84521733, 0.99547581, 0.87593455, 0.8776799 , 1.05531013, 0.94557017, 0.91538439, 0.79679863, 1.03398557, 0.88379021, 0.98850319, 1.05833423, 0.90055078, 0.92267584, 0.76273738, 0.98222632, 0.86392524, 0.78242646, 1.19417739, 0.89159895, 0.97565002, 0.85818308, 0.85334266, 1.85008011, 0.87199282, 0.77873231, 0.78036174, 0.96023918, 0.91574121, 0.89217979, 1.16421151, 1.29817786, 1.18683283, 0.96096225, 0.89964569, 1.00401442, 0.80758845, 0.89458758, 0.7994919 , 0.85889356, 0.73147252, 0.7777221 , 0.9148438 , 0.72388117, 0.91134001, 1.0892724 , 1.01736424, 0.86503014, 0.77344917, 1.04515616, 1.06677211, 0.93421936, 0.8821777 , 0.91860774, 0.96381507, 0.70913689, 0.82354748, 1.12416046, 0.85989778, 0.90588737, 1.22832895, 0.65955579, 0.93828405, 0.88946418, 0.92152859, 0.83168025, 0.93346887, 0.96456078, 0.9039245 , 1.03598695, 0.78405559, 1.21739525, 0.79019383, 0.84034646, 1.00273203, 0.96356393, 0.948103 , 0.90279217, 1.0187839 , 0.91630508, 1.15965854, 0.84203423, 0.98803156, 0.91604459, 0.90986512, 0.93384826, 0.76687038, 0.96251902, 0.80648134, 0.77336547, 0.85720164, 0.9351947 , 0.88004728, 0.91083961, 1.06225829, 0.90230812, 0.72383932, 0.8343425 , 0.8850996 , 1.19037918, 0.93595522, 0.85061223, 0.84330949, 0.82397482, 0.92075047, 0.86129584, 0.99296756, 0.84912251, 0.8569699 , 0.75252201, 0.80591772, 1.03902954, 1.04379139, 0.87360195, 0.97452318, 0.93240609, 0.85406409, 1.11717394, 0.95758536, 0.82772817, 0.67947416, 0.85957788, 0.93731268, 0.90349227, 0.79464185, 0.99148637, 0.8461071 , 0.95399991, 1.04320664, 0.87290871, 0.96780849, 0.99467159, 0.96421545, 0.80174643, 0.86475812, 0.74421362, 0.85230296, 0.89891758, 0.77589592, 0.98331957, 0.87387233, 0.92023388, 1.03037742, 0.83796515, 1.0296667 , 0.85891747, 1.02239978, 0.90958406, 1.09731875, 0.8032638 , 0.84482057, 0.8233118 , 0.86184709, 0.93105929, 0.99443502, 0.77442109, 0.98367982, 0.95786272, 0.81183444, 1.0526009 , 0.86993018, 0.985886 , 0.92016756, 1.00847155, 1.2309469 , 0.97732206, 0.83074957, 0.87406987, 0.95268492, 0.94189139, 0.87056443, 1.0135018 , 0.93051004, 1.5170931 , 0.80948763, 0.83737473, 1.05461331, 0.97501633, 1.01449333, 0.79760056, 1.05756482, 0.97300884, 0.92674035, 0.8933763 , 0.91624084, 1.13127607, 0.88115305, 0.9351562 , 0.91430431, 1.11668229, 1.10000526, 0.88171963, 0.74914744, 0.94610698, 1.13841497, 0.90551414, 0.89773592, 1.01696097, 0.85096063, 0.80935471, 0.68458106, 1.2718979 , 0.93550219, 0.96071403, 0.75434294, 0.95112257, 1.16233368, 0.73664915, 1.02195777, 1.07487625, 0.8937445 , 0.78006023, 0.89588994, 1.16354892, 1.02629448, 0.89208642, 1.02088244, 0.85385355, 0.88586061, 0.94571704, 0.89710576, 0.95191525, 0.99819848, 0.97117841, 1.13899808, 0.88414949, 0.90938883, 1.02937917, 0.92936684, 0.87323594, 0.8384819 , 0.87766945, 1.05869911, 0.91028734, 0.969953 , 1.11036647, 0.94996802, 1.01305483, 1.03697568, 0.9750155 , 1.04537837, 0.9314676 , 0.86589798, 1.17446667, 1.02564533, 0.82088708, 0.96481845, 0.86148642, 0.79174298, 1.18029919, 0.82132544, 0.92193776, 1.03669516, 0.96637464, 0.83725933, 0.88776321, 1.08395861, 0.91255709, 0.96884738, 0.89840008, 0.91168146, 0.99652569, 0.95693101, 0.83144932, 0.99886503, 1.02819927, 0.95273533, 0.95959945, 1.08515986, 0.70269432, 0.79529303, 0.93355669, 0.92597539, 1.0745695 , 0.87949758, 0.86133964, 0.95653873, 1.09161425, 0.91402143, 1.13895454, 0.89384443, 1.16281703, 0.8427015 , 0.7657266 , 0.92724079, 0.95383649, 0.86820891, 0.78942366, 1.11752711, 0.97902686, 0.87425286, 0.83944794, 1.12576718, 0.9196059 , 0.89844835, 1.10874172, 1.00396783, 0.9072041 , 1.63580253, 0.98327489, 0.68564426, 1.01007087, 0.92746473, 1.01328833, 0.99584546, 0.86381679, 1.0082541 , 0.85414132, 0.87620981, 1.22461203, 1.03935516, 0.86457326, 0.95165828, 0.84762138, 0.83080254, 0.84715241, 0.80323344, 1.09282941, 1.00902453, 1.02834261, 1.09810743, 0.86560231, 1.31568763, 1.03754782, 0.81298745, 1.14500629, 0.87364384, 0.89928367, 0.96118471, 0.83321743, 0.90590461, 0.98739499, 0.79408399, 1.18513754, 1.05619307, 0.99920088, 1.04347259, 1.07689022, 1.24916765, 0.74246274, 0.90949597, 0.87077335, 0.81233276, 1.05403934, 0.98333063, 0.77689527, 0.93181907, 0.98853585, 0.80700332, 0.89570662, 0.97102475, 0.69178123, 0.72950409, 0.89661719, 0.84821737, 0.8724469 , 0.96453177, 0.9690018 , 0.87132764, 0.91711564, 1.79521288, 0.75894855, 0.90733112, 0.86565687, 0.90433268, 0.83412618, 1.26779628, 1.06999114, 0.73181364, 0.90334838, 0.86634581, 0.76999285, 1.55403008, 0.74712547, 0.84702579, 0.72396203, 0.82292773, 0.73633208, 0.90524618, 0.9954355 , 0.85076517, 0.96097585, 1.21655611, 0.77658146, 0.81026686, 1.07540173, 0.94219623, 0.97472554, 0.72422803, 0.85055855, 0.85905477, 1.17391419, 0.87644114, 1.03573284, 1.16647944, 0.87810532, 0.89134419, 0.83531593, 0.93448128, 1.04967869, 1.00110843, 0.936784 , 1.00143426, 0.79714807, 0.82656251, 0.95057309, 0.93821813, 0.93469098, 0.99825205, 0.95384714, 1.07063008, 0.97603699, 0.816668 , 0.98286184, 0.86061483, 0.88166732, 0.93730982, 0.77633837, 0.87671549, 0.99192439, 0.86452825, 0.95880282, 0.7098419 , 1.12717149, 1.16707939, 0.84854333, 0.87486963, 0.9255293 , 1.06534197, 0.9888494 , 1.09931069, 1.21859221, 0.97489537, 0.82508579, 1.14868922, 0.98076133, 0.85524084, 0.69042079, 0.93012936, 0.96908499, 0.94284892, 0.80114327, 0.919846 , 0.95753354, 1.04536666, 0.77109284, 0.99942571, 0.79004323, 0.91820045, 0.97665489, 0.64689716, 0.89444405, 0.96106598, 0.74196857, 0.92905294, 0.70500318, 0.95074586, 0.98518665, 1.0794044 , 1.00364488, 0.96710486, 0.92429638, 0.94383006, 1.12554253, 0.95199191, 0.87380738, 0.72183594, 0.94453761, 0.98663804, 0.68247366, 1.02761427, 0.93255355, 0.85264705, 1.00341417, 1.07765999, 0.97396039, 0.90770805, 0.82750901, 0.73824542, 1.24491161, 0.83152629, 0.78656996, 0.99062838, 0.98276905, 0.98291014, 1.12795903, 0.98742704, 0.9579893 , 0.80451701, 0.87198344, 1.24746127, 0.95839155, 1.11708725, 0.97113877, 0.7721646 , 0.95781621, 0.67069168, 1.05509376, 0.96071852, 0.99768666, 0.83008521, 0.9156695 , 0.86314088, 1.23081412, 1.14723685, 0.8007289 , 0.81590842, 1.31857558, 0.7753396 , 1.11091566, 1.03560198, 1.01837739, 0.94882818, 0.82551111, 0.93188019, 0.99532255, 0.93848495, 0.77764975, 0.85192319, 0.79913938, 0.99495229, 0.96122733, 1.13845155, 0.95846389, 0.8891543 , 0.97979531, 0.87167192, 0.88119611, 0.79655111, 0.9298217 , 0.96399321, 1.02005428, 1.06936503, 0.86948022, 1.02560548, 0.9149464 , 0.83797207, 0.86175383, 0.92455994, 0.89218435, 0.81546463, 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1.03806277, 0.92000143, 0.91018767, 0.90555137, 0.89089532, 1.3530331 , 0.96933587, 0.82350429, 0.71549154, 1.13399156, 0.87838533, 0.99177078, 0.93296992, 1.43078263, 0.90278792, 0.85789581, 0.93531789, 0.84948314, 0.95778101, 0.80962713, 0.88865859, 1.15297165, 0.85695093, 0.88601982, 0.96665296, 0.9320964 , 1.04193558, 1.006005 , 0.78939639, 0.79344784, 0.87012624, 0.8532022 , 0.93351167, 0.91705323, 0.74384626, 0.84219843, 0.78265573, 1.07759963, 1.0236098 , 1.00202257, 1.18687122, 1.00869294, 0.8809502 , 0.76397598, 0.81845324, 0.97439912, 1.10466318, 1.10678275, 0.96692316, 0.84120323, 1.13151276, 0.72574077, 0.82457571, 0.8179266 , 1.01118196, 0.84303742, 0.86255339, 1.03927791, 0.82302701, 1.03586066, 0.75785864, 0.9186558 , 0.97139449, 0.92424514, 1.00415659, 1.08544681, 0.80940032, 0.9073428 , 0.83621672, 1.04027879, 0.79447936, 0.94829305, 1.16176292, 1.11185195, 0.88652664, 0.98676451, 0.89310091, 0.72272527, 0.79963233, 0.94651986, 0.91540761, 1.0498236 , 0.84938647, 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1.08396187, 0.69206719, 0.88113605, 0.96951674, 0.89248729, 0.909926 , 0.82966779, 0.8261611 , 0.9551228 , 0.79879533, 1.09416042, 1.01020839, 1.04133795, 1.09654304, 0.84060693, 1.02612223, 1.00177693, 0.90510435, 1.2091018 , 1.03290288, 0.80529305, 0.74332311, 1.04728164, 1.04647891, 0.83707027, 0.81648396, 1.07180239, 0.7926372 , 0.99855278, 1.16851397, 0.94566149, 0.75612408, 0.94975744, 0.92924923, 1.03215206, 0.82394984, 0.84142091, 0.88028348, 1.11036047, 0.82451341, 0.83694112, 0.84207459, 0.94095384, 1.00173733, 1.10241786, 0.86609134, 0.86859604, 1.1211537 , 0.84188088, 0.89023025, 0.99062899, 0.96828743, 0.80106184, 0.86745454, 0.99013196, 0.91838615, 0.86400837, 0.95679525, 0.78893711, 1.03753175, 0.97177648, 0.88685941, 0.9441012 , 0.69289996, 0.84219432, 1.01050959, 0.83578317, 0.79907595, 1.21281139, 0.91613925, 1.00202544, 0.95293036, 0.84583258, 0.84574886, 0.76470341, 1.23606485, 1.10063291, 0.93852084, 0.97201415, 0.68523403, 0.94560108, 0.81903039, 1.14332074, 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1.56288788, 0.87251203, 0.92808414, 1.03548911, 0.65226699, 0.81243827, 1.03103554, 1.11995602, 0.78956176, 0.96734427, 0.91600861, 0.8246106 , 1.09390498, 0.98187349, 0.8919928 , 0.98746862, 0.96298125, 0.93854424, 0.83060031, 0.74692856, 0.99757209, 0.78888849, 1.17517182, 1.06657933, 1.1244446 , 0.93608433, 0.88898472, 0.96823218, 0.87496056, 0.81776683, 0.98863687, 0.82962648, 1.02395766, 0.99622674, 1.07138771, 0.86669915, 0.98172208, 0.8787271 , 0.86125353, 0.79554881, 0.93382729, 1.00706175, 1.08386454, 0.69664542, 0.77316657, 0.79978147, 0.80764736, 0.9969375 , 0.83554928, 0.91017317, 0.95323454, 1.29872357, 1.08851275, 1.01673108, 0.79536208, 0.84878371, 0.95165619, 0.87733936, 0.86319684, 0.96758495, 0.87763237, 0.95094713, 1.00143077, 1.0596993 , 1.27278299, 0.82281481, 0.89765404, 0.94538181, 0.88161857, 0.77679456, 0.84274277, 0.89864342, 0.98705162, 0.95456512, 0.92712401, 0.77427128, 1.03292269, 0.87034158, 1.24316113, 0.98278702, 1.17325118, 1.18863971, 0.88678137, 0.90389731, 1.01740421, 0.80228624, 0.97742223, 0.82741518, 0.8359407 , 0.7177401 , 1.02297899, 0.81896048, 0.77127181, 0.83328601, 0.96939523, 0.94073198, 0.90356023, 1.12355064, 1.12811114, 0.92403138, 1.05423548, 0.70827734, 0.95891358, 0.89898027, 1.02318421, 0.93775375, 0.8245529 , 0.80604304, 0.77555283, 0.92112699, 0.85662169, 0.92725859, 0.93599147, 0.78971931, 0.8337306 , 0.93775212, 0.91025099, 0.75308822, 0.95391173, 0.96840576, 0.8394416 , 0.89087015, 0.73703219, 0.97812386, 0.8787356 , 0.93985266, 0.96406021, 0.88666152, 0.89242745, 0.97900374, 0.85697634, 0.8795755 , 0.78581812, 0.87138735, 0.74602994, 0.96158936, 0.84529806, 0.85333232, 1.06116542, 1.05929382, 1.09720986, 1.28959453, 0.91541148, 0.87657407, 1.06514793, 0.8668096 , 1.07325125, 0.85009534, 0.95542191, 0.86977409, 0.96249874, 0.97715908, 0.89360331, 0.98859647, 0.67560717, 0.90213348, 1.12051182, 0.99684949, 0.9863559 , 1.32246221, 0.84632664, 0.89707447, 1.00486846, 0.90843649, 1.02399424, 0.97899017, 0.95693977, 0.8384806 , 0.93927435, 0.79153251, 1.08694094, 1.01785553, 0.99674552, 0.898566 , 0.94116882, 0.95224977, 0.99859129, 0.81125029, 0.85985586, 1.14418875, 0.96306241, 1.31398561, 0.77961419, 1.01958366, 0.9575668 , 0.771084 , 1.04473363, 1.01569517, 1.04560744, 0.9648178 , 0.93466398, 1.09313672, 0.90349389, 1.00193114, 0.79991514, 0.91102351, 0.9795356 , 0.89285193, 1.04898573, 0.93031782, 0.95087069, 1.15644699, 0.91155375, 0.93005986, 0.70098757, 0.82751625, 0.85462106, 1.34969332, 0.93382692, 1.05558387, 1.25417819, 1.0546501 , 1.05217032, 0.86031346, 1.00864463, 0.73592482, 1.01899722, 1.00462831, 0.96882832, 0.81334751, 1.05102745, 0.82288113, 1.05798623, 0.77971966, 1.38584414, 1.0248193 , 0.78951056, 0.76171823, 0.78407227, 1.14808104, 0.97890501, 0.99870905, 0.96006489, 0.78442704, 0.99315422, 0.83653213, 0.95210661, 0.97233777, 0.78140495, 0.95996216, 0.76318841, 0.82333311, 0.87123204, 0.79531258, 0.82681452, 1.00492217, 0.93549261, 1.00240153, 1.02086339, 1.00424549, 0.87437775, 0.84675564, 0.98014462, 0.77262117, 1.02620976, 0.91162462, 1.0275041 , 1.1475431 , 0.78167746, 0.86273856, 0.84499552, 0.99712362, 0.9694771 , 0.94523806, 0.8450763 , 0.93068519, 1.29362523, 1.0249628 , 1.05522183, 1.13433408, 1.06981137, 0.85666419, 0.98203234, 0.75867592, 0.8844762 , 0.89708521, 0.75482121, 0.80137918, 0.90412883, 0.88815714, 1.11497471, 0.77441965, 0.93853353, 0.8962444 , 0.83055142, 0.99776183, 0.92581583, 0.78783745, 0.90934299, 0.81136457, 0.99000726, 0.9669203 , 1.2890399 , 1.01923088, 1.11076459, 1.01331706, 1.02470946, 0.92950448, 1.10298478, 1.03723287, 1.09129035, 0.95138186, 0.85764624, 0.86606803, 0.8141785 , 1.0129293 , 0.93267714, 0.95663734, 1.01940702, 0.8072268 , 1.0707215 , 0.90482063, 1.01546955, 0.84018308, 0.95938216, 0.96454054, 0.93114659, 1.09705112, 0.88720628, 0.81067916, 0.82667413, 0.89494027, 0.9173495 , 0.73326273, 1.00209461, 0.9560545 , 1.09126364, 0.95709908, 0.81314274, 0.8274943 , 1.37605062, 0.99097917, 1.02221806, 0.90277482, 1.01611791, 0.79663017, 1.16686882, 1.19669266, 0.88366356, 0.77661102, 0.73467145, 1.15438391, 0.91439204, 0.78280849, 1.07238853, 1.03588797, 1.0438292 , 0.75935005, 0.76200114, 0.81603429, 0.74402367, 1.1171573 , 0.90227791, 0.94762351, 0.92462278, 0.8847803 , 1.1343863 , 0.8662186 , 1.00410699, 1.05008842, 0.94783969, 0.89555844, 0.98278045, 0.80396855, 1.00483139, 0.82540491, 0.83284354, 0.93132265, 0.91191039, 0.95753995, 1.18260689, 0.84124197, 0.87429189, 0.67617592, 0.89495946, 0.92898357, 1.10528183, 1.06994417, 0.82259834, 0.74746328, 0.99070832, 1.07386274, 0.84007203, 0.89720099, 0.9670094 , 1.02728082, 0.78001838, 0.97709347, 0.90602469, 1.49985196, 0.80256976, 1.05905677, 0.98298874, 0.94679703, 0.94305923, 0.98720786, 0.82091251, 0.91644161, 0.79576881, 0.98942172, 0.92974761, 0.99307545, 0.86959859, 0.88549807, 1.09246144, 0.87265047, 1.01449921, 0.74353851, 0.95029192, 0.94385304, 0.84779449, 1.00690543, 0.79727923, 0.92285822, 0.83164749, 1.06508941, 1.09757529, 0.9059649 , 0.9146043 , 0.74474669, 0.71306438, 0.77989422, 0.84965464, 0.9424323 , 0.82492634, 0.85076686, 1.01110574, 1.01445751, 0.87929754, 0.8773275 , 0.72314196, 0.92285502, 1.18173931, 0.86460799, 0.91795108, 1.16580482, 0.79880497, 0.72734786, 0.97579653, 0.76967834, 0.97543732, 1.04996964, 1.16439594, 1.08656546, 1.15644902, 0.98333436, 1.24374723, 0.95810117, 0.8488915 , 1.06288523, 0.99055893, 0.75517736, 0.95856183, 0.85574796, 1.00426506, 1.25275675, 0.92735225, 0.83351314, 0.90216604, 0.87996386, 1.13312875, 1.00891523, 0.76513657, 0.85659621, 0.91142459, 1.05893495, 0.92253051, 0.87153684, 1.03190013, 0.92160845, 1.01768282, 0.80590054, 1.05172907, 0.92758177, 0.86902046, 0.93927127, 0.80389584, 0.96016014, 0.9720314 , 0.93255573, 0.85792534, 0.97826842, 0.80506149, 0.97170364, 1.08397772, 1.01866333, 1.18898045, 1.02855427, 0.94848891, 0.94336541, 0.93119013, 0.92907817, 1.11806635, 0.88409637, 0.88809707, 1.06735612, 0.98447974, 0.88816438, 1.00099784, 0.92443453, 1.00325146, 0.86977836, 0.84621801, 0.92361073, 0.85573903, 0.77309241, 0.86717528, 1.19892035, 1.07497019, 1.02178857, 0.8718756 , 0.90646803, 0.92912096, 1.04538692, 0.95245707, 0.99698525, 0.94583199, 0.92537599, 0.86720487, 0.89927054, 0.86111792, 0.94401208, 1.01130191, 1.03759681, 0.8177749 , 1.07784373, 0.79823294, 1.00839713, 1.39409602, 0.87146241, 1.21218822, 0.84895926, 1.01742432, 0.8044077 , 0.78632084, 1.07751744, 1.13147508, 0.90268302, 0.90024653, 0.92072578, 0.87763264, 1.00736787, 0.90978808, 0.90895492, 0.90766826, 0.98956566, 0.92075658, 0.77613105, 0.93815569, 0.95455546, 1.00607757, 0.82187828, 0.94197599, 0.867015 , 0.90709762, 0.75604815, 0.91312261, 0.9286002 , 0.74623204, 0.87368702, 0.83879278, 0.92224793, 0.81676402, 0.90355168, 0.92762955, 0.91784037, 0.82273304, 0.75947806, 0.92687078, 0.87971276, 1.15037445, 0.86707445, 0.8611453 , 0.91921763, 1.07088129, 1.05150864, 1.02162325, 0.90305964, 0.99912687, 0.87693204, 0.6186911 , 0.95526533, 1.15975655, 1.00061222, 0.74608861, 0.954568 , 0.84965574, 0.79177899, 0.9741051 , 1.0119514 , 0.79147502, 0.81367071, 0.87757421, 1.01270813, 0.86044808, 0.9689615 , 0.9577413 , 0.79480242, 0.76073002, 0.83131288, 0.96379259, 0.84679732, 0.82508685, 0.89977283, 0.86766439, 1.12231836, 0.93058445, 1.04584181, 0.88838751, 0.96615893, 0.98731619, 1.05517799, 1.02860493, 0.98881473, 0.85210319, 0.91497438, 0.9275787 , 0.97456134, 0.9011687 , 0.69417417, 0.89661214, 0.79038577, 1.08118303, 1.0509366 , 0.97813138, 0.85714945, 0.97330329, 0.83611871, 0.99772489, 0.83591193, 0.75592677, 0.85392601, 1.02734573, 0.72404609, 0.83534547, 0.91630472, 0.88463459, 1.12044562, 1.10991104, 0.96047701, 1.12342573, 0.72046647, 0.96852239, 0.89605698, 0.98310243, 0.92300659, 0.87794646, 0.83109321, 1.43297752, 0.80609029, 0.8692251 , 0.90254649, 0.81647796, 1.07521371, 1.03942973, 0.96156488, 1.25225334, 1.0265727 , 0.9518054 , 0.87765718, 1.15552582, 0.79577766, 0.66849239, 0.87236017, 1.03437641, 0.98567811, 0.78463682, 1.09573491, 0.89858959, 0.94056747, 1.16075317, 1.06296054, 0.85844006, 0.95475376, 0.67038747, 0.7924646 , 0.94009167, 0.88282093, 0.97711174, 0.9209607 , 1.03230176, 0.99981312, 1.12345314, 1.11705968, 1.02453864, 0.91724212, 0.98337942, 0.89195196, 0.83800177, 0.95044243, 0.76543521, 0.8613025 , 0.83907753, 0.69333275, 0.84411739, 0.68621941, 0.9847701 , 1.13328481, 1.1432074 , 0.97156328, 0.86464461, 0.74258211, 0.97319505, 1.11453917, 0.87344741, 0.91382664, 1.01635943, 1.38708812, 0.81377942, 1.3828856 , 0.74476285, 0.86657537, 1.1216954 , 0.91008346, 0.800862 , 0.98356936, 0.92409916, 1.13970543, 0.97547004, 0.99385865, 1.16476579, 0.78678084, 1.003947 , 0.81491463, 1.19724322, 0.9173622 , 0.93274116, 0.80047839, 0.86798029, 0.9433708 , 0.82376832, 1.01726905, 0.81914971, 0.73290844]) class Medpar1(object): ''' The medpar1 data can be found here. http://www.stata-press.com/data/hh2/medpar1 ''' def __init__(self): filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "stata_medpar1_glm.csv") data = np.recfromcsv(open(filename, 'rb'), converters ={1: lambda s: s.strip(asbytes("\""))}) self.endog = data.los design = np.column_stack((data.admitype, data.codes)) design = categorical(design, col=0, drop=True) design = np.delete(design, 1, axis=1) # drop first dummy self.exog = add_constant(design, prepend=False) class InvGaussLog(Medpar1): """ InvGaussLog is used with TestGlmInvgaussLog """ def __init__(self): super(InvGaussLog, self).__init__() filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "medparlogresids.csv") self.resids = np.genfromtxt(open(filename, 'rb'), delimiter=",") self.null_deviance = 335.1539777981053 # from R, Rpy bug self.params = np.array([ 0.09927544, -0.19161722, 1.05712336]) self.bse = np.array([ 0.00600728, 0.02632126, 0.04915765]) self.aic_R = 18545.836421595981 self.aic_Stata = 6.619000588187141 self.deviance = 304.27188306012789 self.scale = 0.10240599519220173 # self.llf = -9268.9182107979905 # from R self.llf = -12162.72308108797 # from Stata, big rounding diff with R self.bic_Stata = -29849.51723280784 self.chi2 = 398.5465213008323 # from Stata not in sm self.df_model = 2 self.df_resid = 3673 self.fittedvalues = np.array([ 7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001, 7.03292237, 7.03292237, 6.36826384, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.76642001, 7.03292237, 5.22145448, 7.03292237, 5.22145448, 4.72799187, 4.72799187, 7.03292237, 7.03292237, 6.36826384, 7.03292237, 5.76642001, 7.03292237, 4.28116479, 7.03292237, 7.03292237, 7.03292237, 5.76642001, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 3.87656588, 7.03292237, 7.03292237, 4.28116479, 7.03292237, 7.03292237, 4.72799187, 7.03292237, 7.03292237, 7.03292237, 5.22145448, 6.36826384, 6.36826384, 4.28116479, 4.72799187, 7.03292237, 7.03292237, 7.03292237, 7.03292237, 5.22145448, 7.03292237, 7.03292237, 6.36826384, 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3.53462742, 4.31095206, 5.25778406, 5.25778406, 3.20058132, 4.31095206, 4.31095206, 3.20058132, 4.31095206, 5.25778406, 4.31095206, 5.25778406, 3.90353806, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 5.80654132, 5.80654132, 5.80654132, 4.31095206, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 5.25778406, 3.90353806, 4.31095206, 4.76088805, 3.90353806, 5.80654132, 5.80654132, 5.80654132, 2.89810483, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 5.80654132, 3.90353806, 3.20058132, 5.25778406, 4.76088805, 5.25778406]) class InvGaussIdentity(Medpar1): """ Accuracy is different for R vs Stata ML vs Stata IRLS, we are close. """ def __init__(self): super(InvGaussIdentity, self).__init__() self.params = np.array([ 0.44538838, -1.05872706, 2.83947966]) self.bse = np.array([ 0.02586783, 0.13830023, 0.20834864]) filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "igaussident_resids.csv") self.resids = np.genfromtxt(open(filename, 'rb'), delimiter=",") self.null_deviance = 335.1539777981053 # from R, Rpy bug self.df_null = 3675 self.deviance = 305.33661191013988 self.df_resid = 3673 self.df_model = 2 self.aic_R = 18558.677276882016 self.aic_Stata = 6.619290231464371 self.bic_Stata = -29848.45250412075 self.llf_stata = -12163.25544543151 self.chi2 = 567.1229375785638 # in Stata not sm # self.llf = -9275.3386384410078 # from R self.llf = -12163.25545 # from Stata, big diff with R self.scale = 0.10115387793455666 self.pearson_chi2 = 371.5346609292967 # deviance_p in Stata self.fittedvalues = np.array([ 6.84797506, 6.84797506, 6.84797506, 6.84797506, 5.9571983 , 6.84797506, 6.84797506, 6.40258668, 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5.34385962, 3.56230611, 5.789248 , 5.789248 , 5.789248 , 5.34385962, 5.34385962, 5.789248 , 3.11691773, 5.789248 , 5.789248 , 3.56230611, 5.789248 , 5.789248 , 5.789248 , 4.45308287, 5.789248 , 4.89847125, 5.789248 , 5.789248 , 5.789248 , 4.00769449, 4.45308287, 5.34385962, 5.789248 , 3.56230611, 4.00769449, 5.34385962, 4.45308287, 5.789248 , 5.34385962, 5.34385962, 2.67152936, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.34385962, 5.789248 , 4.89847125, 5.789248 , 5.789248 , 5.789248 , 4.45308287, 5.789248 , 3.11691773, 4.00769449, 5.789248 , 5.789248 , 5.34385962, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 2.22614098, 5.789248 , 4.00769449, 5.34385962, 4.89847125, 5.789248 , 5.789248 , 4.00769449, 5.789248 , 3.56230611, 2.67152936, 5.789248 , 3.56230611, 2.67152936, 4.89847125, 5.789248 , 5.789248 , 5.789248 , 4.45308287, 5.789248 , 4.89847125, 4.00769449, 2.67152936, 4.89847125, 5.789248 , 2.22614098, 3.56230611, 4.45308287, 5.34385962, 5.34385962, 3.11691773, 4.45308287, 4.45308287, 3.11691773, 4.45308287, 5.34385962, 4.45308287, 5.34385962, 4.00769449, 4.45308287, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 4.00769449, 5.789248 , 5.789248 , 5.789248 , 4.45308287, 5.789248 , 5.789248 , 5.789248 , 4.00769449, 5.34385962, 4.00769449, 4.45308287, 4.89847125, 4.00769449, 5.789248 , 5.789248 , 5.789248 , 2.67152936, 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 5.789248 , 4.00769449, 3.11691773, 5.34385962, 4.89847125, 5.34385962]) class Committee(object): def __init__(self): self.resids = np.array([[ -5.04950800e-01, -6.29721800e-01, -8.35499100e+01, -1.30628500e+00, -6.62028600e+00], [ -2.34152200e-01, -2.55423500e-01, -2.16830700e+02, -7.58866000e-01, -7.18370200e+00], [ 1.02423700e+00, 7.98775800e-01, 4.83736300e+02, 2.50351500e+00, 2.25135300e+01], [ -2.85061700e-01, -3.17796600e-01, -7.04115100e+04, -2.37991800e+00, -1.41745600e+02], [ 2.09902500e-01, 1.96787700e-01, 2.24751400e+03, 9.51945500e-01, 2.17724200e+01], [ -4.03483500e-01, -4.75741500e-01, -1.95633600e+04, -2.63502600e+00, -8.89461400e+01], [ -1.64413400e-01, -1.74401100e-01, -1.73310300e+04, -1.16235500e+00, -5.34213500e+01], [ -4.29607700e-01, -5.13466700e-01, -5.30037000e+03, -2.24496200e+00, -4.78260300e+01], [ 3.23713000e-01, 2.94184600e-01, 4.11079400e+03, 1.48684400e+00, 3.65598400e+01], [ 1.50367200e-01, 1.43429400e-01, 7.28532100e+03, 8.85542900e-01, 3.31355000e+01], [ 4.21288600e-01, 3.73428000e-01, 1.37315700e+03, 1.52133200e+00, 2.41570200e+01], [ 4.50658700e-01, 3.96586700e-01, 1.70146900e+03, 1.66177900e+00, 2.78032600e+01], [ 2.43537500e-01, 2.26174000e-01, 3.18402300e+03, 1.13656200e+00, 2.79073400e+01], [ 1.05182900e+00, 8.16205400e-01, 6.00135200e+03, 3.89079700e+00, 7.97131300e+01], [ -5.54450300e-01, -7.12749000e-01, -2.09485200e+03, -2.45496500e+00, -3.42189900e+01], [ -6.05750600e-01, -8.06411100e-01, -2.74738200e+02, -1.90774400e+00, -1.30510500e+01], [ -3.41215700e-01, -3.90244600e-01, -6.31138000e+02, -1.27022900e+00, -1.47600100e+01], [ 2.21898500e-01, 2.07328700e-01, 6.91135800e+02, 8.16876400e-01, 1.24392900e+01], [ 2.45592500e-01, 2.26639200e-01, 1.99250600e-01, 2.57948300e-01, 2.74723700e-01], [ -7.58952600e-01, -1.15300800e+00, -2.56739000e+02, -2.40716600e+00, -1.41474200e+01]]) self.null_deviance = 27.81104693643434 # from R, Rpy bug self.params = np.array([-0.0268147 , 1.25103364, 2.91070663, -0.34799563, 0.00659808, -0.31303026, -6.44847076]) self.bse = np.array([ 1.99956263e-02, 4.76820254e-01, 6.48362654e-01, 4.17956107e-01, 1.41512690e-03, 1.07770186e-01, 1.99557656e+00]) self.aic_R = 216.66573352377935 self.aic_Stata = 10.83328660860436 self.deviance = 5.615520158267981 self.scale = 0.38528595746569905 self.llf = -101.33286676188968 # from R self.llf_Stata = -101.3328660860436 # same as R self.bic_Stata = -33.32900074962649 self.chi2 = 5.008550263545408 self.df_model = 6 self.df_resid = 13 self.fittedvalues = np.array([12.62019383, 30.18289514, 21.48377849, 496.74068604, 103.23024673, 219.94693494, 324.4301163 , 110.82526477, 112.44244488, 219.86056381, 56.84399998, 61.19840382, 114.09290269, 75.29071944, 61.21994387, 21.05130889, 42.75939828, 55.56133536, 0.72532053, 18.14664665]) class Wfs(object): """ Wfs used for TestGlmPoissonOffset Results are from Stata and R. """ def __init__(self): self.resids = glm_test_resids.wfs_resids self.null_deviance = 3731.85161919 # from R self.params = [.9969348, 1.3693953, 1.6137574, 1.7849111, 1.9764051, .11241858, .15166023, .02297282, -.10127377, -.31014953, -.11709716] self.bse = [.0527437, .0510688, .0511949, .0512138, .0500341, .0324963, .0283292, .0226563, .0309871, .0552107, .0549118] self.aic_R = 522.14215776 # R adds 2 for dof to AIC self.aic_Stata = 7.459173652869477 # stata divides by nobs # self.deviance = 70.6652992116034 # from Stata self.deviance = 70.665301270867 # from R self.scale = 1.0 self.llf = -250.0710778504317 # from Stata, ours with scale=1 self.bic_Stata = -179.9959200693088 # no bic in R? self.df_model = 10 self.df_resid = 59 self.chi2 = 2699.138063147485 #TODO: taken from Stata not available # in sm yet self.fittedvalues = [7.11599,19.11356,33.76075,33.26743,11.94399, 27.49849,35.07923,37.22563,64.18037,108.0408,100.0948,35.67896, 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4,"type3",5 9,"type3",9 5,"type3",9 6,"type3",9 2,"type3",2 3,"type3",9 2,"type3",9 12,"type3",9 3,"type3",9 4,"type3",9 4,"type3",9 5,"type3",9 8,"type3",5 4,"type3",3 4,"type3",8 7,"type3",7 3,"type3",8 statsmodels-0.5.0+git13-g8e07d34/statsmodels/genmod/tests/test_glm.py000066400000000000000000000445441224417117700253320ustar00rootroot00000000000000""" Test functions for models.GLM """ import os import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_raises from scipy import stats import statsmodels.api as sm from statsmodels.genmod.generalized_linear_model import GLM from statsmodels.tools.tools import add_constant from statsmodels.tools.sm_exceptions import PerfectSeparationError from nose import SkipTest # Test Precisions DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 DECIMAL_0 = 0 class CheckModelResultsMixin(object): ''' res2 should be either the results from RModelWrap or the results as defined in model_results_data ''' decimal_params = DECIMAL_4 def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, self.decimal_params) decimal_bse = DECIMAL_4 def test_standard_errors(self): assert_almost_equal(self.res1.bse, self.res2.bse, self.decimal_bse) decimal_resids = DECIMAL_4 def test_residuals(self): resids = np.column_stack((self.res1.resid_pearson, self.res1.resid_deviance, self.res1.resid_working, self.res1.resid_anscombe, self.res1.resid_response)) assert_almost_equal(resids, self.res2.resids, self.decimal_resids) decimal_aic_R = DECIMAL_4 def test_aic_R(self): # R includes the estimation of the scale as a lost dof # Doesn't with Gamma though if self.res1.scale != 1: dof = 2 else: dof = 0 assert_almost_equal(self.res1.aic+dof, self.res2.aic_R, self.decimal_aic_R) decimal_aic_Stata = DECIMAL_4 def test_aic_Stata(self): # Stata uses the below llf for aic definition for these families if isinstance(self.res1.model.family, (sm.families.Gamma, sm.families.InverseGaussian)): llf = self.res1.model.family.loglike(self.res1.model.endog, self.res1.mu, scale=1) aic = (-2*llf+2*(self.res1.df_model+1))/self.res1.nobs else: aic = self.res1.aic/self.res1.nobs assert_almost_equal(aic, self.res2.aic_Stata, self.decimal_aic_Stata) decimal_deviance = DECIMAL_4 def test_deviance(self): assert_almost_equal(self.res1.deviance, self.res2.deviance, self.decimal_deviance) decimal_scale = DECIMAL_4 def test_scale(self): assert_almost_equal(self.res1.scale, self.res2.scale, self.decimal_scale) decimal_loglike = DECIMAL_4 def test_loglike(self): # Stata uses the below llf for these families # We differ with R for them if isinstance(self.res1.model.family, (sm.families.Gamma, sm.families.InverseGaussian)): llf = self.res1.model.family.loglike(self.res1.model.endog, self.res1.mu, scale=1) else: llf = self.res1.llf assert_almost_equal(llf, self.res2.llf, self.decimal_loglike) decimal_null_deviance = DECIMAL_4 def test_null_deviance(self): assert_almost_equal(self.res1.null_deviance, self.res2.null_deviance, self.decimal_null_deviance) decimal_bic = DECIMAL_4 def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic_Stata, self.decimal_bic) def test_degrees(self): assert_equal(self.res1.model.df_resid,self.res2.df_resid) decimal_fittedvalues = DECIMAL_4 def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues, self.res2.fittedvalues, self.decimal_fittedvalues) def test_tpvalues(self): # test comparing tvalues and pvalues with normal implementation # make sure they use normal distribution (inherited in results class) params = self.res1.params tvalues = params / self.res1.bse pvalues = stats.norm.sf(np.abs(tvalues)) * 2 half_width = stats.norm.isf(0.025) * self.res1.bse conf_int = np.column_stack((params - half_width, params + half_width)) assert_almost_equal(self.res1.tvalues, tvalues) assert_almost_equal(self.res1.pvalues, pvalues) assert_almost_equal(self.res1.conf_int(), conf_int) class TestGlmGaussian(CheckModelResultsMixin): def __init__(self): ''' Test Gaussian family with canonical identity link ''' # Test Precisions self.decimal_resids = DECIMAL_3 self.decimal_params = DECIMAL_2 self.decimal_bic = DECIMAL_0 self.decimal_bse = DECIMAL_3 from statsmodels.datasets.longley import load self.data = load() self.data.exog = add_constant(self.data.exog, prepend=False) self.res1 = GLM(self.data.endog, self.data.exog, family=sm.families.Gaussian()).fit() from results.results_glm import Longley self.res2 = Longley() # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # Gauss = r.gaussian # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, family=Gauss) # self.res2.resids = np.array(self.res2.resid)[:,None]*np.ones((1,5)) # self.res2.null_deviance = 185008826 # taken from R. Rpy bug? class TestGaussianLog(CheckModelResultsMixin): def __init__(self): # Test Precision self.decimal_aic_R = DECIMAL_0 self.decimal_aic_Stata = DECIMAL_2 self.decimal_loglike = DECIMAL_0 self.decimal_null_deviance = DECIMAL_1 nobs = 100 x = np.arange(nobs) np.random.seed(54321) # y = 1.0 - .02*x - .001*x**2 + 0.001 * np.random.randn(nobs) self.X = np.c_[np.ones((nobs,1)),x,x**2] self.lny = np.exp(-(-1.0 + 0.02*x + 0.0001*x**2)) +\ 0.001 * np.random.randn(nobs) GaussLog_Model = GLM(self.lny, self.X, \ family=sm.families.Gaussian(sm.families.links.log)) self.res1 = GaussLog_Model.fit() from results.results_glm import GaussianLog self.res2 = GaussianLog() # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed" # GaussLogLink = r.gaussian(link = "log") # GaussLog_Res_R = RModel(self.lny, self.X, r.glm, family=GaussLogLink) # self.res2 = GaussLog_Res_R class TestGaussianInverse(CheckModelResultsMixin): def __init__(self): # Test Precisions self.decimal_bic = DECIMAL_1 self.decimal_aic_R = DECIMAL_1 self.decimal_aic_Stata = DECIMAL_3 self.decimal_loglike = DECIMAL_1 self.decimal_resids = DECIMAL_3 nobs = 100 x = np.arange(nobs) np.random.seed(54321) y = 1.0 + 2.0 * x + x**2 + 0.1 * np.random.randn(nobs) self.X = np.c_[np.ones((nobs,1)),x,x**2] self.y_inv = (1. + .02*x + .001*x**2)**-1 + .001 * np.random.randn(nobs) InverseLink_Model = GLM(self.y_inv, self.X, family=sm.families.Gaussian(sm.families.links.inverse_power)) InverseLink_Res = InverseLink_Model.fit() self.res1 = InverseLink_Res from results.results_glm import GaussianInverse self.res2 = GaussianInverse() # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # InverseLink = r.gaussian(link = "inverse") # InverseLink_Res_R = RModel(self.y_inv, self.X, r.glm, family=InverseLink) # self.res2 = InverseLink_Res_R class TestGlmBinomial(CheckModelResultsMixin): def __init__(self): ''' Test Binomial family with canonical logit link using star98 dataset. ''' self.decimal_resids = DECIMAL_1 self.decimal_bic = DECIMAL_2 from statsmodels.datasets.star98 import load from results.results_glm import Star98 data = load() data.exog = add_constant(data.exog, prepend=False) self.res1 = GLM(data.endog, data.exog, \ family=sm.families.Binomial()).fit() #NOTE: if you want to replicate with RModel #res2 = RModel(data.endog[:,0]/trials, data.exog, r.glm, # family=r.binomial, weights=trials) self.res2 = Star98() #TODO: #Non-Canonical Links for the Binomial family require the algorithm to be #slightly changed #class TestGlmBinomialLog(CheckModelResultsMixin): # pass #class TestGlmBinomialLogit(CheckModelResultsMixin): # pass #class TestGlmBinomialProbit(CheckModelResultsMixin): # pass #class TestGlmBinomialCloglog(CheckModelResultsMixin): # pass #class TestGlmBinomialPower(CheckModelResultsMixin): # pass #class TestGlmBinomialLoglog(CheckModelResultsMixin): # pass #class TestGlmBinomialLogc(CheckModelResultsMixin): #TODO: need include logc link # pass class TestGlmBernoulli(CheckModelResultsMixin): def __init__(self): from results.results_glm import Lbw self.res2 = Lbw() self.res1 = GLM(self.res2.endog, self.res2.exog, family=sm.families.Binomial()).fit() #class TestGlmBernoulliIdentity(CheckModelResultsMixin): # pass #class TestGlmBernoulliLog(CheckModelResultsMixin): # pass #class TestGlmBernoulliProbit(CheckModelResultsMixin): # pass #class TestGlmBernoulliCloglog(CheckModelResultsMixin): # pass #class TestGlmBernoulliPower(CheckModelResultsMixin): # pass #class TestGlmBernoulliLoglog(CheckModelResultsMixin): # pass #class test_glm_bernoulli_logc(CheckModelResultsMixin): # pass class TestGlmGamma(CheckModelResultsMixin): def __init__(self): ''' Tests Gamma family with canonical inverse link (power -1) ''' # Test Precisions self.decimal_aic_R = -1 #TODO: off by about 1, we are right with Stata self.decimal_resids = DECIMAL_2 from statsmodels.datasets.scotland import load from results.results_glm import Scotvote data = load() data.exog = add_constant(data.exog, prepend=False) res1 = GLM(data.endog, data.exog, \ family=sm.families.Gamma()).fit() self.res1 = res1 # res2 = RModel(data.endog, data.exog, r.glm, family=r.Gamma) res2 = Scotvote() res2.aic_R += 2 # R doesn't count degree of freedom for scale with gamma self.res2 = res2 class TestGlmGammaLog(CheckModelResultsMixin): def __init__(self): # Test Precisions self.decimal_resids = DECIMAL_3 self.decimal_aic_R = DECIMAL_0 self.decimal_fittedvalues = DECIMAL_3 from results.results_glm import CancerLog res2 = CancerLog() self.res1 = GLM(res2.endog, res2.exog, family=sm.families.Gamma(link=sm.families.links.log)).fit() self.res2 = res2 # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, # family=r.Gamma(link="log")) # self.res2.null_deviance = 27.92207137420696 # From R (bug in rpy) # self.res2.bic = -154.1582089453923 # from Stata class TestGlmGammaIdentity(CheckModelResultsMixin): def __init__(self): # Test Precisions self.decimal_resids = -100 #TODO Very off from Stata? self.decimal_params = DECIMAL_2 self.decimal_aic_R = DECIMAL_0 self.decimal_loglike = DECIMAL_1 from results.results_glm import CancerIdentity res2 = CancerIdentity() self.res1 = GLM(res2.endog, res2.exog, family=sm.families.Gamma(link=sm.families.links.identity)).fit() self.res2 = res2 # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, # family=r.Gamma(link="identity")) # self.res2.null_deviance = 27.92207137420696 # from R, Rpy bug class TestGlmPoisson(CheckModelResultsMixin): def __init__(self): ''' Tests Poisson family with canonical log link. Test results were obtained by R. ''' from results.results_glm import Cpunish from statsmodels.datasets.cpunish import load self.data = load() self.data.exog[:,3] = np.log(self.data.exog[:,3]) self.data.exog = add_constant(self.data.exog, prepend=False) self.res1 = GLM(self.data.endog, self.data.exog, family=sm.families.Poisson()).fit() self.res2 = Cpunish() #class TestGlmPoissonIdentity(CheckModelResultsMixin): # pass #class TestGlmPoissonPower(CheckModelResultsMixin): # pass class TestGlmInvgauss(CheckModelResultsMixin): def __init__(self): ''' Tests the Inverse Gaussian family in GLM. Notes ----- Used the rndivgx.ado file provided by Hardin and Hilbe to generate the data. Results are read from model_results, which were obtained by running R_ig.s ''' # Test Precisions self.decimal_aic_R = DECIMAL_0 self.decimal_loglike = DECIMAL_0 from results.results_glm import InvGauss res2 = InvGauss() res1 = GLM(res2.endog, res2.exog, \ family=sm.families.InverseGaussian()).fit() self.res1 = res1 self.res2 = res2 class TestGlmInvgaussLog(CheckModelResultsMixin): def __init__(self): # Test Precisions self.decimal_aic_R = -10 # Big difference vs R. self.decimal_resids = DECIMAL_3 from results.results_glm import InvGaussLog res2 = InvGaussLog() self.res1 = GLM(res2.endog, res2.exog, family=sm.families.InverseGaussian(link=\ sm.families.links.log)).fit() self.res2 = res2 # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, # family=r.inverse_gaussian(link="log")) # self.res2.null_deviance = 335.1539777981053 # from R, Rpy bug # self.res2.llf = -12162.72308 # from Stata, R's has big rounding diff class TestGlmInvgaussIdentity(CheckModelResultsMixin): def __init__(self): # Test Precisions self.decimal_aic_R = -10 #TODO: Big difference vs R self.decimal_fittedvalues = DECIMAL_3 self.decimal_params = DECIMAL_3 from results.results_glm import Medpar1 data = Medpar1() self.res1 = GLM(data.endog, data.exog, family=sm.families.InverseGaussian(link=\ sm.families.links.identity)).fit() from results.results_glm import InvGaussIdentity self.res2 = InvGaussIdentity() # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed." # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, # family=r.inverse_gaussian(link="identity")) # self.res2.null_deviance = 335.1539777981053 # from R, Rpy bug # self.res2.llf = -12163.25545 # from Stata, big diff with R class TestGlmNegbinomial(CheckModelResultsMixin): def __init__(self): ''' Test Negative Binomial family with canonical log link ''' # Test Precision self.decimal_resid = DECIMAL_1 self.decimal_params = DECIMAL_3 self.decimal_resids = -1 # 1 % mismatch at 0 self.decimal_fittedvalues = DECIMAL_1 from statsmodels.datasets.committee import load self.data = load() self.data.exog[:,2] = np.log(self.data.exog[:,2]) interaction = self.data.exog[:,2]*self.data.exog[:,1] self.data.exog = np.column_stack((self.data.exog,interaction)) self.data.exog = add_constant(self.data.exog, prepend=False) self.res1 = GLM(self.data.endog, self.data.exog, family=sm.families.NegativeBinomial()).fit() from results.results_glm import Committee res2 = Committee() res2.aic_R += 2 # They don't count a degree of freedom for the scale self.res2 = res2 # def setup(self): # if skipR: # raise SkipTest, "Rpy not installed" # r.library('MASS') # this doesn't work when done in rmodelwrap? # self.res2 = RModel(self.data.endog, self.data.exog, r.glm, # family=r.negative_binomial(1)) # self.res2.null_deviance = 27.8110469364343 #class TestGlmNegbinomial_log(CheckModelResultsMixin): # pass #class TestGlmNegbinomial_power(CheckModelResultsMixin): # pass #class TestGlmNegbinomial_nbinom(CheckModelResultsMixin): # pass #NOTE: hacked together version to test poisson offset class TestGlmPoissonOffset(CheckModelResultsMixin): @classmethod def setupClass(cls): from results.results_glm import Cpunish from statsmodels.datasets.cpunish import load data = load() data.exog[:,3] = np.log(data.exog[:,3]) data.exog = add_constant(data.exog, prepend=False) exposure = [100] * len(data.endog) cls.data = data cls.exposure = exposure cls.res1 = GLM(data.endog, data.exog, family=sm.families.Poisson(), exposure=exposure).fit() cls.res1.params[-1] += np.log(100) # add exposure back in to param # to make the results the same cls.res2 = Cpunish() def test_missing(self): # make sure offset is dropped correctly endog = self.data.endog.copy() endog[[2,4,6,8]] = np.nan mod = GLM(endog, self.data.exog, family=sm.families.Poisson(), exposure=self.exposure, missing='drop') assert_equal(mod.exposure.shape[0], 13) def test_prefect_pred(): cur_dir = os.path.dirname(os.path.abspath(__file__)) iris = np.genfromtxt(os.path.join(cur_dir, 'results', 'iris.csv'), delimiter=",", skip_header=1) y = iris[:,-1] X = iris[:,:-1] X = X[y != 2] y = y[y != 2] X = add_constant(X, prepend=True) glm = GLM(y, X, family=sm.families.Binomial()) assert_raises(PerfectSeparationError, glm.fit) def test_attribute_writable_resettable(): """ Regression test for mutables and class constructors. """ data = sm.datasets.longley.load() endog, exog = data.endog, data.exog glm_model = sm.GLM(endog, exog) assert_equal(glm_model.family.link.power, 1.0) glm_model.family.link.power = 2. assert_equal(glm_model.family.link.power, 2.0) glm_model2 = sm.GLM(endog, exog) assert_equal(glm_model2.family.link.power, 1.0) if __name__=="__main__": #run_module_suite() #taken from Fernando Perez: import nose nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/000077500000000000000000000000001224417117700223145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/__init__.py000066400000000000000000000001031224417117700244170ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/api.py000066400000000000000000000010251224417117700234350ustar00rootroot00000000000000from .functional import fboxplot, rainbowplot from .correlation import plot_corr, plot_corr_grid from .gofplots import qqplot from .boxplots import violinplot, beanplot from .regressionplots import (abline_plot, plot_regress_exog, plot_fit, plot_partregress, plot_partregress_grid, plot_ccpr, plot_ccpr_grid, influence_plot, plot_leverage_resid2) from .factorplots import interaction_plot from .plottools import rainbow import tsaplots as tsa statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/boxplots.py000066400000000000000000000367721224417117700245570ustar00rootroot00000000000000"""Variations on boxplots.""" # Author: Ralf Gommers # Based on code by Flavio Coelho and Teemu Ikonen. import numpy as np from scipy.stats import gaussian_kde from . import utils __all__ = ['violinplot', 'beanplot'] def violinplot(data, ax=None, labels=None, positions=None, side='both', show_boxplot=True, plot_opts={}): """Make a violin plot of each dataset in the `data` sequence. A violin plot is a boxplot combined with a kernel density estimate of the probability density function per point. Parameters ---------- data : sequence of ndarrays Data arrays, one array per value in `positions`. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. labels : list of str, optional Tick labels for the horizontal axis. If not given, integers ``1..len(data)`` are used. positions : array_like, optional Position array, used as the horizontal axis of the plot. If not given, spacing of the violins will be equidistant. side : {'both', 'left', 'right'}, optional How to plot the violin. Default is 'both'. The 'left', 'right' options can be used to create asymmetric violin plots. show_boxplot : bool, optional Whether or not to show normal box plots on top of the violins. Default is True. plot_opts : dict, optional A dictionary with plotting options. Any of the following can be provided, if not present in `plot_opts` the defaults will be used:: - 'violin_fc', MPL color. Fill color for violins. Default is 'y'. - 'violin_ec', MPL color. Edge color for violins. Default is 'k'. - 'violin_lw', scalar. Edge linewidth for violins. Default is 1. - 'violin_alpha', float. Transparancy of violins. Default is 0.5. - 'cutoff', bool. If True, limit violin range to data range. Default is False. - 'cutoff_val', scalar. Where to cut off violins if `cutoff` is True. Default is 1.5 standard deviations. - 'cutoff_type', {'std', 'abs'}. Whether cutoff value is absolute, or in standard deviations. Default is 'std'. - 'violin_width' : float. Relative width of violins. Max available space is 1, default is 0.8. - 'label_fontsize', MPL fontsize. Adjusts fontsize only if given. - 'label_rotation', scalar. Adjusts label rotation only if given. Specify in degrees. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- beanplot : Bean plot, builds on `violinplot`. matplotlib.pyplot.boxplot : Standard boxplot. Notes ----- The appearance of violins can be customized with `plot_opts`. If customization of boxplot elements is required, set `show_boxplot` to False and plot it on top of the violins by calling the Matplotlib `boxplot` function directly. For example:: violinplot(data, ax=ax, show_boxplot=False) ax.boxplot(data, sym='cv', whis=2.5) It can happen that the axis labels or tick labels fall outside the plot area, especially with rotated labels on the horizontal axis. With Matplotlib 1.1 or higher, this can easily be fixed by calling ``ax.tight_layout()``. With older Matplotlib one has to use ``plt.rc`` or ``plt.rcParams`` to fix this, for example:: plt.rc('figure.subplot', bottom=0.25) violinplot(data, ax=ax) References ---------- J.L. Hintze and R.D. Nelson, "Violin Plots: A Box Plot-Density Trace Synergism", The American Statistician, Vol. 52, pp.181-84, 1998. Examples -------- We use the American National Election Survey 1996 dataset, which has Party Identification of respondents as independent variable and (among other data) age as dependent variable. >>> data = sm.datasets.anes96.load_pandas() >>> party_ID = np.arange(7) >>> labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", ... "Independent-Indpendent", "Independent-Republican", ... "Weak Republican", "Strong Republican"] Group age by party ID, and create a violin plot with it: >>> plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible >>> age = [data.exog['age'][data.endog == id] for id in party_ID] >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> sm.graphics.violinplot(age, ax=ax, labels=labels, ... plot_opts={'cutoff_val':5, 'cutoff_type':'abs', ... 'label_fontsize':'small', ... 'label_rotation':30}) >>> ax.set_xlabel("Party identification of respondent.") >>> ax.set_ylabel("Age") >>> plt.show() .. plot:: plots/graphics_boxplot_violinplot.py """ fig, ax = utils.create_mpl_ax(ax) if positions is None: positions = np.arange(len(data)) + 1 # Determine available horizontal space for each individual violin. pos_span = np.max(positions) - np.min(positions) width = np.min([0.15 * np.max([pos_span, 1.]), plot_opts.get('violin_width', 0.8) / 2.]) # Plot violins. for pos_data, pos in zip(data, positions): xvals, violin = _single_violin(ax, pos, pos_data, width, side, plot_opts) if show_boxplot: ax.boxplot(data, notch=1, positions=positions, vert=1) # Set ticks and tick labels of horizontal axis. _set_ticks_labels(ax, data, labels, positions, plot_opts) return fig def _single_violin(ax, pos, pos_data, width, side, plot_opts): """""" def _violin_range(pos_data, plot_opts): """Return array with correct range, with which violins can be plotted.""" cutoff = plot_opts.get('cutoff', False) cutoff_type = plot_opts.get('cutoff_type', 'std') cutoff_val = plot_opts.get('cutoff_val', 1.5) s = 0.0 if not cutoff: if cutoff_type == 'std': s = cutoff_val * np.std(pos_data) else: s = cutoff_val x_lower = kde.dataset.min() - s x_upper = kde.dataset.max() + s return np.linspace(x_lower, x_upper, 100) pos_data = np.asarray(pos_data) # Kernel density estimate for data at this position. kde = gaussian_kde(pos_data) # Create violin for pos, scaled to the available space. xvals = _violin_range(pos_data, plot_opts) violin = kde.evaluate(xvals) violin = width * violin / violin.max() if side == 'both': envelope_l, envelope_r = (-violin + pos, violin + pos) elif side == 'right': envelope_l, envelope_r = (pos, violin + pos) elif side == 'left': envelope_l, envelope_r = (-violin + pos, pos) else: msg = "`side` parameter should be one of {'left', 'right', 'both'}." raise ValueError(msg) # Draw the violin. ax.fill_betweenx(xvals, envelope_l, envelope_r, facecolor=plot_opts.get('violin_fc', 'y'), edgecolor=plot_opts.get('violin_ec', 'k'), lw=plot_opts.get('violin_lw', 1), alpha=plot_opts.get('violin_alpha', 0.5)) return xvals, violin def _set_ticks_labels(ax, data, labels, positions, plot_opts): """Set ticks and labels on horizontal axis.""" # Set xticks and limits. ax.set_xlim([np.min(positions) - 0.5, np.max(positions) + 0.5]) ax.set_xticks(positions) label_fontsize = plot_opts.get('label_fontsize') label_rotation = plot_opts.get('label_rotation') if label_fontsize or label_rotation: from matplotlib.artist import setp if labels is not None: if not len(labels) == len(data): msg = "Length of `labels` should equal length of `data`." raise(ValueError, msg) xticknames = ax.set_xticklabels(labels) if label_fontsize: setp(xticknames, fontsize=label_fontsize) if label_rotation: setp(xticknames, rotation=label_rotation) return def beanplot(data, ax=None, labels=None, positions=None, side='both', jitter=False, plot_opts={}): """Make a bean plot of each dataset in the `data` sequence. A bean plot is a combination of a `violinplot` (kernel density estimate of the probability density function per point) with a line-scatter plot of all individual data points. Parameters ---------- data : sequence of ndarrays Data arrays, one array per value in `positions`. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. labels : list of str, optional Tick labels for the horizontal axis. If not given, integers ``1..len(data)`` are used. positions : array_like, optional Position array, used as the horizontal axis of the plot. If not given, spacing of the violins will be equidistant. side : {'both', 'left', 'right'}, optional How to plot the violin. Default is 'both'. The 'left', 'right' options can be used to create asymmetric violin plots. jitter : bool, optional If True, jitter markers within violin instead of plotting regular lines around the center. This can be useful if the data is very dense. plot_opts : dict, optional A dictionary with plotting options. All the options for `violinplot` can be specified, they will simply be passed to `violinplot`. Options specific to `beanplot` are: - 'bean_color', MPL color. Color of bean plot lines. Default is 'k'. Also used for jitter marker edge color if `jitter` is True. - 'bean_size', scalar. Line length as a fraction of maximum length. Default is 0.5. - 'bean_lw', scalar. Linewidth, default is 0.5. - 'bean_show_mean', bool. If True (default), show mean as a line. - 'bean_show_median', bool. If True (default), show median as a marker. - 'bean_mean_color', MPL color. Color of mean line. Default is 'b'. - 'bean_mean_lw', scalar. Linewidth of mean line, default is 2. - 'bean_median_color', MPL color. Color of median marker. Default is 'r'. - 'bean_median_marker', MPL marker. Marker type, default is '+'. - 'jitter_marker', MPL marker. Marker type for ``jitter=True``. Default is 'o'. - 'jitter_marker_size', int. Marker size. Default is 4. - 'jitter_fc', MPL color. Jitter marker face color. Default is None. - 'bean_legend_text', str. If given, add a legend with given text. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- violinplot : Violin plot, also used internally in `beanplot`. matplotlib.pyplot.boxplot : Standard boxplot. References ---------- P. Kampstra, "Beanplot: A Boxplot Alternative for Visual Comparison of Distributions", J. Stat. Soft., Vol. 28, pp. 1-9, 2008. Examples -------- We use the American National Election Survey 1996 dataset, which has Party Identification of respondents as independent variable and (among other data) age as dependent variable. >>> data = sm.datasets.anes96.load_pandas() >>> party_ID = np.arange(7) >>> labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", ... "Independent-Indpendent", "Independent-Republican", ... "Weak Republican", "Strong Republican"] Group age by party ID, and create a violin plot with it: >>> plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible >>> age = [data.exog['age'][data.endog == id] for id in party_ID] >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> sm.graphics.beanplot(age, ax=ax, labels=labels, ... plot_opts={'cutoff_val':5, 'cutoff_type':'abs', ... 'label_fontsize':'small', ... 'label_rotation':30}) >>> ax.set_xlabel("Party identification of respondent.") >>> ax.set_ylabel("Age") >>> plt.show() .. plot:: plots/graphics_boxplot_beanplot.py """ fig, ax = utils.create_mpl_ax(ax) if positions is None: positions = np.arange(len(data)) + 1 # Determine available horizontal space for each individual violin. pos_span = np.max(positions) - np.min(positions) width = np.min([0.15 * np.max([pos_span, 1.]), plot_opts.get('bean_size', 0.5) / 2.]) legend_txt = plot_opts.get('bean_legend_text', None) for pos_data, pos in zip(data, positions): # Draw violins. xvals, violin = _single_violin(ax, pos, pos_data, width, side, plot_opts) if jitter: # Draw data points at random coordinates within violin envelope. jitter_coord = pos + _jitter_envelope(pos_data, xvals, violin, side) ax.plot(jitter_coord, pos_data, ls='', marker=plot_opts.get('jitter_marker', 'o'), ms=plot_opts.get('jitter_marker_size', 4), mec=plot_opts.get('bean_color', 'k'), mew=1, mfc=plot_opts.get('jitter_fc', 'none'), label=legend_txt) else: # Draw bean lines. ax.hlines(pos_data, pos - width, pos + width, lw=plot_opts.get('bean_lw', 0.5), color=plot_opts.get('bean_color', 'k'), label=legend_txt) # Show legend if required. if legend_txt is not None: _show_legend(ax) legend_txt = None # ensure we get one entry per call to beanplot # Draw mean line. if plot_opts.get('bean_show_mean', True): ax.hlines(np.mean(pos_data), pos - width, pos + width, lw=plot_opts.get('bean_mean_lw', 2.), color=plot_opts.get('bean_mean_color', 'b')) # Draw median marker. if plot_opts.get('bean_show_median', True): ax.plot(pos, np.median(pos_data), marker=plot_opts.get('bean_median_marker', '+'), color=plot_opts.get('bean_median_color', 'r')) # Set ticks and tick labels of horizontal axis. _set_ticks_labels(ax, data, labels, positions, plot_opts) return fig def _jitter_envelope(pos_data, xvals, violin, side): """Determine envelope for jitter markers.""" if side == 'both': low, high = (-1., 1.) elif side == 'right': low, high = (0, 1.) elif side == 'left': low, high = (-1., 0) else: raise ValueError("`side` input incorrect: %s" % side) jitter_envelope = np.interp(pos_data, xvals, violin) jitter_coord = jitter_envelope * np.random.uniform(low=low, high=high, size=pos_data.size) return jitter_coord def _show_legend(ax): """Utility function to show legend.""" leg = ax.legend(loc=1, shadow=True, fancybox=True, labelspacing=0.2, borderpad=0.15) ltext = leg.get_texts() llines = leg.get_lines() frame = leg.get_frame() from matplotlib.artist import setp setp(ltext, fontsize='small') setp(llines, linewidth=1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/correlation.py000066400000000000000000000174331224417117700252170ustar00rootroot00000000000000'''correlation plots Author: Josef Perktold License: BSD-3 example for usage with different options in statsmodels\sandbox\examples\thirdparty\ex_ratereturn.py ''' import numpy as np from . import utils def plot_corr(dcorr, xnames=None, ynames=None, title=None, normcolor=False, ax=None, cmap='RdYlBu_r'): """Plot correlation of many variables in a tight color grid. Parameters ---------- dcorr : ndarray Correlation matrix, square 2-D array. xnames : list of str, optional Labels for the horizontal axis. If not given (None), then the matplotlib defaults (integers) are used. If it is an empty list, [], then no ticks and labels are added. ynames : list of str, optional Labels for the vertical axis. Works the same way as `xnames`. If not given, the same names as for `xnames` are re-used. title : str, optional The figure title. If None, the default ('Correlation Matrix') is used. If ``title=''``, then no title is added. normcolor : bool or tuple of scalars, optional If False (default), then the color coding range corresponds to the range of `dcorr`. If True, then the color range is normalized to (-1, 1). If this is a tuple of two numbers, then they define the range for the color bar. ax : Matplotlib AxesSubplot instance, optional If `ax` is None, then a figure is created. If an axis instance is given, then only the main plot but not the colorbar is created. cmap : str or Matplotlib Colormap instance, optional The colormap for the plot. Can be any valid Matplotlib Colormap instance or name. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. Examples -------- >>> import numpy as np >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm >>> hie_data = sm.datasets.randhie.load_pandas() >>> corr_matrix = np.corrcoef(hie_data.data.T) >>> sm.graphics.plot_corr(corr_matrix, xnames=hie_data.names) >>> plt.show() """ if ax is None: create_colorbar = True else: create_colorbar = False fig, ax = utils.create_mpl_ax(ax) import matplotlib as mpl from matplotlib import cm nvars = dcorr.shape[0] if ynames is None: ynames = xnames if title is None: title = 'Correlation Matrix' if isinstance(normcolor, tuple): vmin, vmax = normcolor elif normcolor: vmin, vmax = -1.0, 1.0 else: vmin, vmax = None, None axim = ax.imshow(dcorr, cmap=cmap, interpolation='nearest', extent=(0,nvars,0,nvars), vmin=vmin, vmax=vmax) # create list of label positions labelPos = np.arange(0, nvars) + 0.5 if ynames: ax.set_yticks(labelPos) ax.set_yticks(labelPos[:-1]+0.5, minor=True) ax.set_yticklabels(ynames[::-1], fontsize='small', horizontalalignment='right') elif ynames == []: ax.set_yticks([]) if xnames: ax.set_xticks(labelPos) ax.set_xticks(labelPos[:-1]+0.5, minor=True) ax.set_xticklabels(xnames, fontsize='small', rotation=45, horizontalalignment='right') elif xnames == []: ax.set_xticks([]) if not title == '': ax.set_title(title) if mpl.__version__ >= '1.1': # The tight_layout feature is not available before version 1.1 # It automatically pads the figure so labels do not get clipped. if create_colorbar: fig.colorbar(axim, use_gridspec=True) fig.tight_layout() else: if create_colorbar: fig.colorbar(axim) ax.tick_params(which='minor', length=0) ax.tick_params(direction='out', top=False, right=False) try: ax.grid(True, which='minor', linestyle='-', color='w', lw=1) except AttributeError: # Seems to fail for axes created with AxesGrid. MPL bug? pass return fig def plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r'): """Create a grid of correlation plots. The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once. Parameters ---------- dcorrs : list or iterable of ndarrays List of correlation matrices. titles : list of str, optional List of titles for the subplots. By default no title are shown. ncols : int, optional Number of columns in the subplot grid. If not given, the number of columns is determined automatically. normcolor : bool or tuple, optional If False (default), then the color coding range corresponds to the range of `dcorr`. If True, then the color range is normalized to (-1, 1). If this is a tuple of two numbers, then they define the range for the color bar. xnames : list of str, optional Labels for the horizontal axis. If not given (None), then the matplotlib defaults (integers) are used. If it is an empty list, [], then no ticks and labels are added. ynames : list of str, optional Labels for the vertical axis. Works the same way as `xnames`. If not given, the same names as for `xnames` are re-used. fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. cmap : str or Matplotlib Colormap instance, optional The colormap for the plot. Can be any valid Matplotlib Colormap instance or name. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. Examples -------- >>> import numpy as np >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm In this example we just reuse the same correlation matrix several times. Of course in reality one would show a different correlation (measuring a another type of correlation, for example Pearson (linear) and Spearman, Kendall (nonlinear) correlations) for the same variables. >>> hie_data = sm.datasets.randhie.load_pandas() >>> corr_matrix = np.corrcoef(hie_data.data.T) >>> sm.graphics.plot_corr_grid([corr_matrix] * 8, xnames=hie_data.names) >>> plt.show() """ if ynames is None: ynames = xnames if not titles: titles = ['']*len(dcorrs) n_plots = len(dcorrs) if ncols is not None: nrows = int(np.ceil(n_plots / float(ncols))) else: # Determine number of rows and columns, square if possible, otherwise # prefer a wide (more columns) over a high layout. if n_plots < 4: nrows, ncols = 1, n_plots else: nrows = int(np.sqrt(n_plots)) ncols = int(np.ceil(n_plots / float(nrows))) # Create a figure with the correct size aspect = min(ncols / float(nrows), 1.8) vsize = np.sqrt(nrows) * 5 fig = utils.create_mpl_fig(fig, figsize=(vsize * aspect + 1, vsize)) for i, c in enumerate(dcorrs): ax = fig.add_subplot(nrows, ncols, i+1) # Ensure to only plot labels on bottom row and left column _xnames = xnames if nrows * ncols - (i+1) < ncols else [] _ynames = ynames if (i+1) % ncols == 1 else [] plot_corr(c, xnames=_xnames, ynames=_ynames, title=titles[i], normcolor=normcolor, ax=ax, cmap=cmap) # Adjust figure margins and add a colorbar fig.subplots_adjust(bottom=0.1, left=0.09, right=0.9, top=0.9) cax = fig.add_axes([0.92, 0.1, 0.025, 0.8]) fig.colorbar(fig.axes[0].images[0], cax=cax) return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/factorplots.py000066400000000000000000000164311224417117700252330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Authors: Josef Perktold, Skipper Seabold, Denis A. Engemann """ import numpy as np from statsmodels.graphics.plottools import rainbow import utils def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b', xlabel=None, ylabel=None, colors=[], markers=[], linestyles=[], legendloc='best', legendtitle=None, **kwargs): """ Interaction plot for factor level statistics. Note. If categorial factors are supplied levels will be internally recoded to integers. This ensures matplotlib compatiblity. uses pandas.DataFrame to calculate an `aggregate` statistic for each level of the factor or group given by `trace`. Parameters ---------- x : array-like The `x` factor levels constitute the x-axis. If a `pandas.Series` is given its name will be used in `xlabel` if `xlabel` is None. trace : array-like The `trace` factor levels will be drawn as lines in the plot. If `trace` is a `pandas.Series` its name will be used as the `legendtitle` if `legendtitle` is None. response : array-like The reponse or dependent variable. If a `pandas.Series` is given its name will be used in `ylabel` if `ylabel` is None. func : function Anything accepted by `pandas.DataFrame.aggregate`. This is applied to the response variable grouped by the trace levels. plottype : str {'line', 'scatter', 'both'}, optional The type of plot to return. Can be 'l', 's', or 'b' ax : axes, optional Matplotlib axes instance xlabel : str, optional Label to use for `x`. Default is 'X'. If `x` is a `pandas.Series` it will use the series names. ylabel : str, optional Label to use for `response`. Default is 'func of response'. If `response` is a `pandas.Series` it will use the series names. colors : list, optional If given, must have length == number of levels in trace. linestyles : list, optional If given, must have length == number of levels in trace. markers : list, optional If given, must have length == number of lovels in trace kwargs These will be passed to the plot command used either plot or scatter. If you want to control the overall plotting options, use kwargs. Returns ------- fig : Figure The figure given by `ax.figure` or a new instance. Examples -------- >>> import numpy as np >>> np.random.seed(12345) >>> weight = np.random.randint(1,4,size=60) >>> duration = np.random.randint(1,3,size=60) >>> days = np.log(np.random.randint(1,30, size=60)) >>> fig = interaction_plot(weight, duration, days, ... colors=['red','blue'], markers=['D','^'], ms=10) >>> import matplotlib.pyplot as plt >>> plt.show() .. plot:: import numpy as np from statsmodels.graphics.factorplots import interaction_plot np.random.seed(12345) weight = np.random.randint(1,4,size=60) duration = np.random.randint(1,3,size=60) days = np.log(np.random.randint(1,30, size=60)) fig = interaction_plot(weight, duration, days, colors=['red','blue'], markers=['D','^'], ms=10) import matplotlib.pyplot as plt #plt.show() """ from pandas import DataFrame fig, ax = utils.create_mpl_ax(ax) response_name = ylabel or getattr(response, 'name', 'response') ylabel = '%s of %s' % (func.func_name, response_name) xlabel = xlabel or getattr(x, 'name', 'X') legendtitle = legendtitle or getattr(trace, 'name', 'Trace') ax.set_ylabel(ylabel) ax.set_xlabel(xlabel) x_values = x_levels = None if isinstance(x[0], str): x_levels = [l for l in np.unique(x)] x_values = xrange(len(x_levels)) x = _recode(x, dict(zip(x_levels, x_values))) data = DataFrame(dict(x=x, trace=trace, response=response)) plot_data = data.groupby(['trace', 'x']).aggregate(func).reset_index() # return data # check plot args n_trace = len(plot_data['trace'].unique()) if linestyles: try: assert len(linestyles) == n_trace except AssertionError, err: raise ValueError("Must be a linestyle for each trace level") else: # set a default linestyles = ['-'] * n_trace if markers: try: assert len(markers) == n_trace except AssertionError, err: raise ValueError("Must be a linestyle for each trace level") else: # set a default markers = ['.'] * n_trace if colors: try: assert len(colors) == n_trace except AssertionError, err: raise ValueError("Must be a linestyle for each trace level") else: # set a default #TODO: how to get n_trace different colors? colors = rainbow(n_trace) if plottype == 'both' or plottype == 'b': for i, (values, group) in enumerate(plot_data.groupby(['trace'])): # trace label label = str(group['trace'].values[0]) ax.plot(group['x'], group['response'], color=colors[i], marker=markers[i], label=label, linestyle=linestyles[i], **kwargs) elif plottype == 'line' or plottype == 'l': for i, (values, group) in enumerate(plot_data.groupby(['trace'])): # trace label label = str(group['trace'].values[0]) ax.plot(group['x'], group['response'], color=colors[i], label=label, linestyle=linestyles[i], **kwargs) elif plottype == 'scatter' or plottype == 's': for i, (values, group) in enumerate(plot_data.groupby(['trace'])): # trace label label = str(group['trace'].values[0]) ax.scatter(group['x'], group['response'], color=colors[i], label=label, marker=markers[i], **kwargs) else: raise ValueError("Plot type %s not understood" % plottype) ax.legend(loc=legendloc, title=legendtitle) ax.margins(.1) if all([x_levels, x_values]): ax.set_xticks(x_values) ax.set_xticklabels(x_levels) return fig def _recode(x, levels): """ Recode categorial data to int factor. Parameters ---------- x : array-like array like object supporting with numpy array methods of categorially coded data. levels : dict mapping of labels to integer-codings Returns ------- out : instance numpy.ndarray """ from pandas import Series name = None if isinstance(x, Series): name = x.name x = x.values if x.dtype.type not in [np.str_, np.object_]: raise ValueError('This is not a categorial factor.' ' Array of str type required.') elif not isinstance(levels, dict): raise ValueError('This is not a valid value for levels.' ' Dict required.') elif not (np.unique(x) == np.unique(levels.keys())).all(): raise ValueError('The levels do not match the array values.') else: out = np.empty(x.shape[0], dtype=np.int) for level, coding in levels.items(): out[x == level] = coding if name: out = Series(out) out.name = name return out statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/functional.py000066400000000000000000000342161224417117700250360ustar00rootroot00000000000000"""Module for functional boxplots.""" from statsmodels.compatnp.iter_compat import combinations import numpy as np from scipy import stats from scipy.misc import factorial from . import utils __all__ = ['fboxplot', 'rainbowplot', 'banddepth'] def fboxplot(data, xdata=None, labels=None, depth=None, method='MBD', wfactor=1.5, ax=None, plot_opts={}): """Plot functional boxplot. A functional boxplot is the analog of a boxplot for functional data. Functional data is any type of data that varies over a continuum, i.e. curves, probabillity distributions, seasonal data, etc. The data is first ordered, the order statistic used here is `banddepth`. Plotted are then the median curve, the envelope of the 50% central region, the maximum non-outlying envelope and the outlier curves. Parameters ---------- data : sequence of ndarrays or 2-D ndarray The vectors of functions to create a functional boxplot from. If a sequence of 1-D arrays, these should all be the same size. The first axis is the function index, the second axis the one along which the function is defined. So ``data[0, :]`` is the first functional curve. xdata : ndarray, optional The independent variable for the data. If not given, it is assumed to be an array of integers 0..N with N the length of the vectors in `data`. labels : sequence of scalar or str, optional The labels or identifiers of the curves in `data`. If given, outliers are labeled in the plot. depth : ndarray, optional A 1-D array of band depths for `data`, or equivalent order statistic. If not given, it will be calculated through `banddepth`. method : {'MBD', 'BD2'}, optional The method to use to calculate the band depth. Default is 'MBD'. wfactor : float, optional Factor by which the central 50% region is multiplied to find the outer region (analog of "whiskers" of a classical boxplot). ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. plot_opts : dict, optional A dictionary with plotting options. Any of the following can be provided, if not present in `plot_opts` the defaults will be used:: - 'cmap_outliers', a Matplotlib LinearSegmentedColormap instance. - 'c_inner', valid MPL color. Color of the central 50% region - 'c_outer', valid MPL color. Color of the non-outlying region - 'c_median', valid MPL color. Color of the median. - 'lw_outliers', scalar. Linewidth for drawing outlier curves. - 'lw_median', scalar. Linewidth for drawing the median curve. - 'draw_nonout', bool. If True, also draw non-outlying curves. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. depth : ndarray 1-D array containing the calculated band depths of the curves. ix_depth : ndarray 1-D array of indices needed to order curves (or `depth`) from most to least central curve. ix_outliers : ndarray 1-D array of indices of outlying curves in `data`. See Also -------- banddepth, rainbowplot Notes ----- The median curve is the curve with the highest band depth. Outliers are defined as curves that fall outside the band created by multiplying the central region by `wfactor`. Note that the range over which they fall outside this band doesn't matter, a single data point outside the band is enough. If the data is noisy, smoothing may therefore be required. The non-outlying region is defined as the band made up of all the non-outlying curves. References ---------- [1] Y. Sun and M.G. Genton, "Functional Boxplots", Journal of Computational and Graphical Statistics, vol. 20, pp. 1-19, 2011. [2] R.J. Hyndman and H.L. Shang, "Rainbow Plots, Bagplots, and Boxplots for Functional Data", vol. 19, pp. 29-25, 2010. Examples -------- Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea surface temperature data. >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm >>> data = sm.datasets.elnino.load() Create a functional boxplot. We see that the years 1982-83 and 1997-98 are outliers; these are the years where El Nino (a climate pattern characterized by warming up of the sea surface and higher air pressures) occurred with unusual intensity. >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58, ... labels=data.raw_data[:, 0].astype(int), ... ax=ax) >>> ax.set_xlabel("Month of the year") >>> ax.set_ylabel("Sea surface temperature (C)") >>> ax.set_xticks(np.arange(13, step=3) - 1) >>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) >>> ax.set_xlim([-0.2, 11.2]) >>> plt.show() .. plot:: plots/graphics_functional_fboxplot.py """ fig, ax = utils.create_mpl_ax(ax) if plot_opts.get('cmap_outliers') is None: from matplotlib.cm import rainbow_r plot_opts['cmap_outliers'] = rainbow_r data = np.asarray(data) if xdata is None: xdata = np.arange(data.shape[1]) # Calculate band depth if required. if depth is None: if method not in ['MBD', 'BD2']: raise ValueError("Unknown value for parameter `method`.") depth = banddepth(data, method=method) else: if depth.size != data.shape[0]: raise ValueError("Provided `depth` array is not of correct size.") # Inner area is 25%-75% region of band-depth ordered curves. ix_depth = np.argsort(depth)[::-1] median_curve = data[ix_depth[0], :] ix_IQR = data.shape[0] // 2 lower = data[ix_depth[0:ix_IQR], :].min(axis=0) upper = data[ix_depth[0:ix_IQR], :].max(axis=0) # Determine region for outlier detection inner_median = np.median(data[ix_depth[0:ix_IQR], :], axis=0) lower_fence = inner_median - (inner_median - lower) * wfactor upper_fence = inner_median + (upper - inner_median) * wfactor # Find outliers. ix_outliers = [] ix_nonout = [] for ii in range(data.shape[0]): if np.any(data[ii, :] > upper_fence) or np.any(data[ii, :] < lower_fence): ix_outliers.append(ii) else: ix_nonout.append(ii) ix_outliers = np.asarray(ix_outliers) # Plot envelope of all non-outlying data lower_nonout = data[ix_nonout, :].min(axis=0) upper_nonout = data[ix_nonout, :].max(axis=0) ax.fill_between(xdata, lower_nonout, upper_nonout, color=plot_opts.get('c_outer', (0.75,0.75,0.75))) # Plot central 50% region ax.fill_between(xdata, lower, upper, color=plot_opts.get('c_inner', (0.5,0.5,0.5))) # Plot median curve ax.plot(xdata, median_curve, color=plot_opts.get('c_median', 'k'), lw=plot_opts.get('lw_median', 2)) # Plot outliers cmap = plot_opts.get('cmap_outliers') for ii, ix in enumerate(ix_outliers): label = str(labels[ix]) if labels is not None else None ax.plot(xdata, data[ix, :], color=cmap(float(ii) / (len(ix_outliers)-1)), label=label, lw=plot_opts.get('lw_outliers', 1)) if plot_opts.get('draw_nonout', False): for ix in ix_nonout: ax.plot(xdata, data[ix, :], 'k-', lw=0.5) if labels is not None: ax.legend() return fig, depth, ix_depth, ix_outliers def rainbowplot(data, xdata=None, depth=None, method='MBD', ax=None, cmap=None): """Create a rainbow plot for a set of curves. A rainbow plot contains line plots of all curves in the dataset, colored in order of functional depth. The median curve is shown in black. Parameters ---------- data : sequence of ndarrays or 2-D ndarray The vectors of functions to create a functional boxplot from. If a sequence of 1-D arrays, these should all be the same size. The first axis is the function index, the second axis the one along which the function is defined. So ``data[0, :]`` is the first functional curve. xdata : ndarray, optional The independent variable for the data. If not given, it is assumed to be an array of integers 0..N with N the length of the vectors in `data`. depth : ndarray, optional A 1-D array of band depths for `data`, or equivalent order statistic. If not given, it will be calculated through `banddepth`. method : {'MBD', 'BD2'}, optional The method to use to calculate the band depth. Default is 'MBD'. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. cmap : Matplotlib LinearSegmentedColormap instance, optional The colormap used to color curves with. Default is a rainbow colormap, with red used for the most central and purple for the least central curves. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- banddepth, fboxplot References ---------- [1] R.J. Hyndman and H.L. Shang, "Rainbow Plots, Bagplots, and Boxplots for Functional Data", vol. 19, pp. 29-25, 2010. Examples -------- Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea surface temperature data. >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm >>> data = sm.datasets.elnino.load() Create a rainbow plot: >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax) >>> ax.set_xlabel("Month of the year") >>> ax.set_ylabel("Sea surface temperature (C)") >>> ax.set_xticks(np.arange(13, step=3) - 1) >>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"]) >>> ax.set_xlim([-0.2, 11.2]) >>> plt.show() .. plot:: plots/graphics_functional_rainbowplot.py """ fig, ax = utils.create_mpl_ax(ax) if cmap is None: from matplotlib.cm import rainbow_r cmap = rainbow_r data = np.asarray(data) if xdata is None: xdata = np.arange(data.shape[1]) # Calculate band depth if required. if depth is None: if method not in ['MBD', 'BD2']: raise ValueError("Unknown value for parameter `method`.") depth = banddepth(data, method=method) else: if depth.size != data.shape[0]: raise ValueError("Provided `depth` array is not of correct size.") ix_depth = np.argsort(depth)[::-1] # Plot all curves, colored by depth num_curves = data.shape[0] for ii in range(num_curves): ax.plot(xdata, data[ix_depth[ii], :], c=cmap(ii / (num_curves - 1.))) # Plot the median curve median_curve = data[ix_depth[0], :] ax.plot(xdata, median_curve, 'k-', lw=2) return fig def banddepth(data, method='MBD'): """Calculate the band depth for a set of functional curves. Band depth is an order statistic for functional data (see `fboxplot`), with a higher band depth indicating larger "centrality". In analog to scalar data, the functional curve with highest band depth is called the median curve, and the band made up from the first N/2 of N curves is the 50% central region. Parameters ---------- data : ndarray The vectors of functions to create a functional boxplot from. The first axis is the function index, the second axis the one along which the function is defined. So ``data[0, :]`` is the first functional curve. method : {'MBD', 'BD2'}, optional Whether to use the original band depth (with J=2) of [1]_ or the modified band depth. See Notes for details. Returns ------- depth : ndarray Depth values for functional curves. Notes ----- Functional band depth as an order statistic for functional data was proposed in [1]_ and applied to functional boxplots and bagplots in [2]_. The method 'BD2' checks for each curve whether it lies completely inside bands constructed from two curves. All permutations of two curves in the set of curves are used, and the band depth is normalized to one. Due to the complete curve having to fall within the band, this method yields a lot of ties. The method 'MBD' is similar to 'BD2', but checks the fraction of the curve falling within the bands. It therefore generates very few ties. References ---------- .. [1] S. Lopez-Pintado and J. Romo, "On the Concept of Depth for Functional Data", Journal of the American Statistical Association, vol. 104, pp. 718-734, 2009. .. [2] Y. Sun and M.G. Genton, "Functional Boxplots", Journal of Computational and Graphical Statistics, vol. 20, pp. 1-19, 2011. """ def _band2(x1, x2, curve): xb = np.vstack([x1, x2]) if np.any(curve < xb.min(axis=0)) or np.any(curve > xb.max(axis=0)): res = 0 else: res = 1 return res def _band_mod(x1, x2, curve): xb = np.vstack([x1, x2]) res = np.logical_and(curve >= xb.min(axis=0), curve <= xb.max(axis=0)) return np.sum(res) / float(res.size) if method == 'BD2': band = _band2 elif method == 'MBD': band = _band_mod else: raise ValueError("Unknown input value for parameter `method`.") num = data.shape[0] ix = np.arange(num) depth = [] for ii in range(num): res = 0 for ix1, ix2 in combinations(ix, 2): res += band(data[ix1, :], data[ix2, :], data[ii, :]) # Normalize by number of combinations to get band depth normfactor = factorial(num) / 2. / factorial(num - 2) depth.append(float(res) / normfactor) return np.asarray(depth) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/gofplots.py000066400000000000000000000622011224417117700245240ustar00rootroot00000000000000import numpy as np from scipy import stats from statsmodels.regression.linear_model import OLS from statsmodels.tools.tools import add_constant from . import utils __all__ = ['qqplot', 'qqplot_2samples', 'qqline', 'ProbPlot'] class ProbPlot(object): """ Class for convenient construction of Q-Q, P-P, and probability plots. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under kwargs.) Parameters ---------- data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.distributions.norm (a standard normal). distargs : tuple A tuple of arguments passed to dist to specify it fully so dist.ppf may be called. loc : float Location parameter for dist a : float Offset for the plotting position of an expected order statistic, for example. The plotting positions are given by (i - a)/(nobs - 2*a + 1) for i in range(0,nobs+1) scale : float Scale parameter for dist fit : boolean If fit is false, loc, scale, and distargs are passed to the distribution. If fit is True then the parameters for dist are fit automatically using dist.fit. The quantiles are formed from the standardized data, after subtracting the fitted loc and dividing by the fitted scale. See Also -------- scipy.stats.probplot Notes ----- 1) Depends on matplotlib. 2) If `fit` is True then the parameters are fit using the distribution's `fit()` method. 3) The call signatures for the `qqplot`, `ppplot`, and `probplot` methods are similar, so examples 1 through 4 apply to all three methods. 4) The three plotting methods are summarized below: ppplot : Probability-Probability plot Compares the sample and theoretical probabilities (percentiles). qqplot : Quantile-Quantile plot Compares the sample and theoretical quantiles probplot : Probability plot Same as a Q-Q plot, however probabilities are shown in the scale of the theoretical distribution (x-axis) and the y-axis contains unscaled quantiles of the sample data. Examples -------- >>> import statsmodels.api as sm >>> from matplotlib import pyplot as plt >>> # example 1 >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> model = sm.OLS(data.endog, data.exog) >>> mod_fit = model.fit() >>> res = mod_fit.resid # residuals >>> probplot = sm.ProbPlot(res) >>> probplot.qqplot() >>> plt.show() qqplot of the residuals against quantiles of t-distribution with 4 degrees of freedom: >>> # example 2 >>> import scipy.stats as stats >>> probplot = sm.ProbPlot(res, stats.t, distargs=(4,)) >>> fig = probplot.qqplot() >>> plt.show() qqplot against same as above, but with mean 3 and std 10: >>> # example 3 >>> probplot = sm.ProbPlot(res, stats.t, distargs=(4,), loc=3, scale=10) >>> fig = probplot.qqplot() >>> plt.show() Automatically determine parameters for t distribution including the loc and scale: >>> # example 4 >>> probplot = sm.ProbPlot(res, stats.t, fit=True) >>> fig = probplot.qqplot(line='45') >>> plt.show() A second `ProbPlot` object can be used to compare two seperate sample sets by using the `other` kwarg in the `qqplot` and `ppplot` methods. >>> # example 5 >>> import numpy as np >>> x = np.random.normal(loc=8.25, scale=2.75, size=37) >>> y = np.random.normal(loc=8.75, scale=3.25, size=37) >>> pp_x = sm.ProbPlot(x, fit=True) >>> pp_y = sm.ProbPlot(y, fit=True) >>> fig = pp_x.qqplot(line='45', other=pp_y) >>> plt.show() The following plot displays some options, follow the link to see the code. .. plot:: plots/graphics_gofplots_qqplot.py """ def __init__(self, data, dist=stats.norm, fit=False, distargs=(), a=0, loc=0, scale=1): self.data = data self.a = a self.nobs = data.shape[0] self.distargs = distargs self.fit = fit if isinstance(dist, basestring): dist = getattr(stats, dist) self.fit_params = dist.fit(data) if fit: self.loc = self.fit_params[-2] self.scale = self.fit_params[-1] if len(self.fit_params) > 2: self.dist = dist(*self.fit_params[:-2], **dict(loc = 0, scale = 1)) else: self.dist = dist(loc=0, scale=1) elif distargs or loc == 0 or scale == 1: self.dist = dist(*distargs, **dict(loc=loc, scale=scale)) self.loc = loc self.scale = scale else: self.dist = dist self.loc = loc self.scale = scale def theoretical_percentiles(self): return plotting_pos(self.nobs, self.a) def theoretical_quantiles(self): try: return self.dist.ppf(self.theoretical_percentiles()) except TypeError: print('%s requires more parameters to compute ppf' % \ (self.dist.name,)) except: print('failed to compute the ppf of %s' % \ (self.dist.name,)) def sorted_data(self): sorted_data = np.array(self.data, copy=True) sorted_data.sort() return sorted_data def sample_quantiles(self): if self.fit and self.loc != 0 and self.scale != 1: return (self.sorted_data() - self.loc)/self.scale else: return self.sorted_data() def sample_percentiles(self): qntls = (self.sorted_data() - self.fit_params[-2])/self.fit_params[-1] return self.dist.cdf(qntls) def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None): """ P-P plot of the percentiles (probabilities) of x versus the probabilities (percetiles) of a distribution. Parameters ---------- xlabel, ylabel : str or None User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg `other`. line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - by default no reference line is added to the plot. - If True a reference line is drawn on the graph. The default is to fit a line via OLS regression. other : `ProbPlot` instance, array-like, or None If provided, the sample quantiles of this `ProbPlot` instance are plotted against the sample quantiles of the `other` `ProbPlot` instance. If an array-like object is provided, it will be turned into a `ProbPlot` instance using default parameters. If not provided (default), the theoretical quantiles are used. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. """ if other is not None: check_other = isinstance(other, ProbPlot) if not check_other: other = ProbPlot(other) fig, ax = _do_plot(other.sample_percentiles(), self.sample_percentiles(), self.dist, ax=ax, line=line) if xlabel is None: xlabel = 'Probabilities of 2nd Sample' if ylabel is None: ylabel = 'Probabilities of 1st Sample' else: fig, ax = _do_plot(self.theoretical_percentiles(), self.sample_percentiles(), self.dist, ax=ax, line=line) if xlabel is None: xlabel = "Theoretical Probabilities" if ylabel is None: ylabel = "Sample Probabilities" ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_xlim([0.0, 1.0]) ax.set_ylim([0.0, 1.0]) return fig return fig def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None): """ Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution or the quantiles of another `ProbPlot` instance. Parameters ---------- xlabel, ylabel : str or None User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg `other`. line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - by default no reference line is added to the plot. - If True a reference line is drawn on the graph. The default is to fit a line via OLS regression. other : `ProbPlot` instance, array-like, or None If provided, the sample quantiles of this `ProbPlot` instance are plotted against the sample quantiles of the `other` `ProbPlot` instance. If an array-like object is provided, it will be turned into a `ProbPlot` instance using default parameters. If not provided (defualt), the theoretical quantiles are used. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. """ if other is not None: check_other = isinstance(other, ProbPlot) if not check_other: other = ProbPlot(other) fig, ax = _do_plot(other.sample_quantiles(), self.sample_quantiles(), self.dist, ax=ax, line=line) if xlabel is None: xlabel = 'Quantiles of 2nd Sample' if ylabel is None: ylabel = 'Quantiles of 1st Sample' else: fig, ax = _do_plot(self.theoretical_quantiles(), self.sample_quantiles(), self.dist, ax=ax, line=line) if xlabel is None: xlabel = "Theoretical Quantiles" if ylabel is None: ylabel = "Sample Quantiles" ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) return fig def probplot(self, line=None, ax=None, exceed=False): """ Probability plot of the unscaled quantiles of x versus the probabilities of a distibution (not to be confused with a P-P plot). The x-axis is scaled linearly with the quantiles, but the probabilities are used to label the axis. Parameters ---------- line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - by default no reference line is added to the plot. - If True a reference line is drawn on the graph. The default is to fit a line via OLS regression. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. excced : boolean - If False (default) the raw sample quantiles are plotted against the theoretical quantiles, show the probability that a sample will not exceed a given value - If True, the theoretical quantiles are flipped such that the figure displays the probability that a sample will exceed a given value. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. """ if exceed: fig, ax = _do_plot(self.theoretical_quantiles()[::-1], self.sorted_data(), self.dist, ax=ax, line=line) xlabel = 'Probability of Exceedance (%)' else: fig, ax = _do_plot(self.theoretical_quantiles(), self.sorted_data(), self.dist, ax=ax, line=line) xlabel = 'Non-exceedance Probability (%)' ax.set_xlabel(xlabel) ax.set_ylabel("Sample Quantiles") _fmt_probplot_axis(ax, self.dist, self.nobs) return fig def qqplot(data, dist=stats.norm, distargs=(), a=0, loc=0, scale=1, fit=False, line=False, ax=None): """ Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.) Parameters ---------- data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.distributions.norm (a standard normal). distargs : tuple A tuple of arguments passed to dist to specify it fully so dist.ppf may be called. loc : float Location parameter for dist a : float Offset for the plotting position of an expected order statistic, for example. The plotting positions are given by (i - a)/(nobs - 2*a + 1) for i in range(0,nobs+1) scale : float Scale parameter for dist fit : boolean If fit is false, loc, scale, and distargs are passed to the distribution. If fit is True then the parameters for dist are fit automatically using dist.fit. The quantiles are formed from the standardized data, after subtracting the fitted loc and dividing by the fitted scale. line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - by default no reference line is added to the plot. - If True a reference line is drawn on the graph. The default is to fit a line via OLS regression. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- scipy.stats.probplot Examples -------- >>> import statsmodels.api as sm >>> from matplotlib import pyplot as plt >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> mod_fit = sm.OLS(data.endog, data.exog).fit() >>> res = mod_fit.resid # residuals >>> fig = sm.qqplot(res) >>> plt.show() qqplot of the residuals against quantiles of t-distribution with 4 degrees of freedom: >>> import scipy.stats as stats >>> fig = sm.qqplot(res, stats.t, distargs=(4,)) >>> plt.show() qqplot against same as above, but with mean 3 and std 10: >>> fig = sm.qqplot(res, stats.t, distargs=(4,), loc=3, scale=10) >>> plt.show() Automatically determine parameters for t distribution including the loc and scale: >>> fig = sm.qqplot(res, stats.t, fit=True, line='45') >>> plt.show() The following plot displays some options, follow the link to see the code. .. plot:: plots/graphics_gofplots_qqplot.py Notes ----- Depends on matplotlib. If `fit` is True then the parameters are fit using the distribution's fit() method. """ probplot = ProbPlot(data, dist=dist, distargs=distargs, fit=fit, a=a, loc=loc, scale=scale) fig = probplot.qqplot(ax=ax, line=line) return fig def qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None): """ Q-Q Plot of two samples' quantiles. Can take either two `ProbPlot` instances or two array-like objects. In the case of the latter, both inputs will be converted to `ProbPlot` instances using only the default values - so use `ProbPlot` instances if finer-grained control of the quantile computations is required. Parameters ---------- data1, data2 : array-like (1d) or `ProbPlot` instances xlabel, ylabel : str or None User-provided labels for the x-axis and y-axis. If None (default), other values are used. line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - by default no reference line is added to the plot. - If True a reference line is drawn on the graph. The default is to fit a line via OLS regression. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- scipy.stats.probplot Examples -------- >>> x = np.random.normal(loc=8.5, scale=2.5, size=37) >>> y = np.random.normal(loc=8.0, scale=3.0, size=37) >>> pp_x = sm.ProbPlot(x) >>> pp_y = sm.ProbPlot(y) >>> qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None): Notes ----- 1) Depends on matplotlib. 2) If `data1` and `data2` are not `ProbPlot` instances, instances will be created using the default parameters. Therefore, it is recommended to use `ProbPlot` instance if fine-grained control is needed in the computation of the quantiles. """ check_data1 = isinstance(data1, ProbPlot) check_data2 = isinstance(data2, ProbPlot) if not check_data1 and not check_data2: data1 = ProbPlot(data1) data2 = ProbPlot(data2) fig = data1.qqplot(xlabel=xlabel, ylabel=ylabel, line=line, other=data2, ax=ax) return fig def qqline(ax, line, x=None, y=None, dist=None, fmt='r-'): """ Plot a reference line for a qqplot. Parameters ---------- ax : matplotlib axes instance The axes on which to plot the line line : str {'45','r','s','q'} Options for the reference line to which the data is compared.: - '45' - 45-degree line - 's' - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them - 'r' - A regression line is fit - 'q' - A line is fit through the quartiles. - None - By default no reference line is added to the plot. x : array X data for plot. Not needed if line is '45'. y : array Y data for plot. Not needed if line is '45'. dist : scipy.stats.distribution A scipy.stats distribution, needed if line is 'q'. Notes ----- There is no return value. The line is plotted on the given `ax`. """ if line == '45': end_pts = zip(ax.get_xlim(), ax.get_ylim()) end_pts[0] = min(end_pts[0]) end_pts[1] = max(end_pts[1]) ax.plot(end_pts, end_pts, fmt) ax.set_xlim(end_pts) ax.set_ylim(end_pts) return # does this have any side effects? if x is None and y is None: raise ValueError("If line is not 45, x and y cannot be None.") elif line == 'r': # could use ax.lines[0].get_xdata(), get_ydata(), # but don't know axes are 'clean' y = OLS(y, add_constant(x)).fit().fittedvalues ax.plot(x,y,fmt) elif line == 's': m,b = y.std(), y.mean() ref_line = x*m + b ax.plot(x, ref_line, fmt) elif line == 'q': _check_for_ppf(dist) q25 = stats.scoreatpercentile(y, 25) q75 = stats.scoreatpercentile(y, 75) theoretical_quartiles = dist.ppf([0.25, 0.75]) m = (q75 - q25) / np.diff(theoretical_quartiles) b = q25 - m*theoretical_quartiles[0] ax.plot(x, m*x + b, fmt) #about 10x faster than plotting_position in sandbox and mstats def plotting_pos(nobs, a): """ Generates sequence of plotting positions Parameters ---------- nobs : int Number of probability points to plot a : float Offset for the plotting position of an expected order statistic, for example. Returns ------- plotting_positions : array The plotting positions Notes ----- The plotting positions are given by (i - a)/(nobs - 2*a + 1) for i in range(0,nobs+1) See also -------- scipy.stats.mstats.plotting_positions """ return (np.arange(1.,nobs+1) - a)/(nobs- 2*a + 1) def _fmt_probplot_axis(ax, dist, nobs): """ Formats a theoretical quantile axis to display the corresponding probabilities on the quantiles' scale. Parameteters ------------ ax : Matplotlib AxesSubplot instance, optional The axis to be formatted nobs : scalar Numbero of observations in the sample dist : scipy.stats.distribution A scipy.stats distribution sufficiently specified to impletment its ppf() method. Returns ------- There is no return value. This operates on `ax` in place """ _check_for_ppf(dist) if nobs < 50: axis_probs = np.array([1,2,5,10,20,30,40,50,60, 70,80,90,95,98,99,])/100.0 elif nobs < 500: axis_probs = np.array([0.1,0.2,0.5,1,2,5,10,20,30,40,50,60,70, 80,90,95,98,99,99.5,99.8,99.9])/100.0 else: axis_probs = np.array([0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10, 20,30,40,50,60,70,80,90,95,98,99,99.5, 99.8,99.9,99.95,99.98,99.99])/100.0 axis_qntls = dist.ppf(axis_probs) ax.set_xticks(axis_qntls) ax.set_xticklabels(axis_probs*100, rotation=45, rotation_mode='anchor', horizontalalignment='right', verticalalignment='center') ax.set_xlim([axis_qntls.min(), axis_qntls.max()]) def _do_plot(x, y, dist=None, line=False, ax=None, fmt='bo'): """ Boiler plate plotting function for the `ppplot`, `qqplot`, and `probplot` methods of the `ProbPlot` class Parameteters ------------ x, y : array-like Data to be plotted dist : scipy.stats.distribution A scipy.stats distribution, needed if `line` is 'q'. line : str {'45', 's', 'r', q'} or None Options for the reference line to which the data is compared. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. fmt : str, optional matplotlib-compatible formatting string for the data markers Returns ------- fig : Matplotlib Figure instance ax : Matplotlib AxesSubplot instance (see Parameters) """ fig, ax = utils.create_mpl_ax(ax) ax.set_xmargin(0.02) ax.plot(x, y, fmt) if line: if line not in ['r','q','45','s']: msg = "%s option for line not understood" % line raise ValueError(msg) qqline(ax, line, x=x, y=y, dist=dist) return fig, ax def _check_for_ppf(dist): if not hasattr(dist, 'ppf'): raise ValueError("distribution must have a ppf method") statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/mosaicplot.py000066400000000000000000000636061224417117700250530ustar00rootroot00000000000000"""Create a mosaic plot from a contingency table. It allows to visualize multivariate categorical data in a rigorous and informative way. see the docstring of the mosaic function for more informations. """ # Author: Enrico Giampieri - 21 Jan 2013 from __future__ import division import numpy as np from statsmodels.compatnp.collections import OrderedDict from itertools import product from numpy import iterable, r_, cumsum, array from statsmodels.graphics import utils __all__ = ["mosaic"] def _normalize_split(proportion): """ return a list of proportions of the available space given the division if only a number is given, it will assume a split in two pieces """ if not iterable(proportion): if proportion == 0: proportion = array([0.0, 1.0]) elif proportion >= 1: proportion = array([1.0, 0.0]) elif proportion < 0: raise ValueError("proportions should be positive," "given value: {}".format(proportion)) else: proportion = array([proportion, 1.0 - proportion]) proportion = np.asarray(proportion, dtype=float) if np.any(proportion < 0): raise ValueError("proportions should be positive," "given value: {}".format(proportion)) if np.allclose(proportion, 0): raise ValueError("at least one proportion should be" "greater than zero".format(proportion)) # ok, data are meaningful, so go on if len(proportion) < 2: return array([0.0, 1.0]) left = r_[0, cumsum(proportion)] left /= left[-1] * 1.0 return left def _split_rect(x, y, width, height, proportion, horizontal=True, gap=0.05): """ Split the given rectangle in n segments whose proportion is specified along the given axis if a gap is inserted, they will be separated by a certain amount of space, retaining the relative proportion between them a gap of 1 correspond to a plot that is half void and the remaining half space is proportionally divided among the pieces. """ x, y, w, h = float(x), float(y), float(width), float(height) if (w < 0) or (h < 0): raise ValueError("dimension of the square less than" "zero w={} h=()".format(w, h)) proportions = _normalize_split(proportion) # extract the starting point and the dimension of each subdivision # in respect to the unit square starting = proportions[:-1] amplitude = proportions[1:] - starting # how much each extrema is going to be displaced due to gaps starting += gap * np.arange(len(proportions) - 1) # how much the squares plus the gaps are extended extension = starting[-1] + amplitude[-1] - starting[0] # normalize everything for fit again in the original dimension starting /= extension amplitude /= extension # bring everything to the original square starting = (x if horizontal else y) + starting * (w if horizontal else h) amplitude = amplitude * (w if horizontal else h) # create each 4-tuple for each new block results = [(s, y, a, h) if horizontal else (x, s, w, a) for s, a in zip(starting, amplitude)] return results def _reduce_dict(count_dict, partial_key): """ Make partial sum on a counter dict. Given a match for the beginning of the category, it will sum each value. """ L = len(partial_key) count = sum(v for k, v in count_dict.items() if k[:L] == partial_key) return count def _key_splitting(rect_dict, keys, values, key_subset, horizontal, gap): """ Given a dictionary where each entry is a rectangle, a list of key and value (count of elements in each category) it split each rect accordingly, as long as the key start with the tuple key_subset. The other keys are returned without modification. """ result = OrderedDict() L = len(key_subset) for name, (x, y, w, h) in rect_dict.items(): if key_subset == name[:L]: # split base on the values given divisions = _split_rect(x, y, w, h, values, horizontal, gap) for key, rect in zip(keys, divisions): result[name + (key,)] = rect else: result[name] = (x, y, w, h) return result def _tuplify(obj): """convert an object in a tuple of strings (even if it is not iterable, like a single integer number, but keep the string healthy) """ if np.iterable(obj) and not isinstance(obj, basestring): res = tuple(str(o) for o in obj) else: res = (str(obj),) return res def _categories_level(keys): """use the Ordered dict to implement a simple ordered set return each level of each category [[key_1_level_1,key_2_level_1],[key_1_level_2,key_2_level_2]] """ res = [] for i in zip(*(keys)): tuplefied = _tuplify(i) res.append(list(OrderedDict([(j, None) for j in tuplefied]))) return res def _hierarchical_split(count_dict, horizontal=True, gap=0.05): """ Split a square in a hierarchical way given a contingency table. Hierarchically split the unit square in alternate directions in proportion to the subdivision contained in the contingency table count_dict. This is the function that actually perform the tiling for the creation of the mosaic plot. If the gap array has been specified it will insert a corresponding amount of space (proportional to the unit lenght), while retaining the proportionality of the tiles. Parameters ---------- count_dict : dict Dictionary containing the contingency table. Each category should contain a non-negative number with a tuple as index. It expects that all the combination of keys to be representes; if that is not true, will automatically consider the missing values as 0 horizontal : bool The starting direction of the split (by default along the horizontal axis) gap : float or array of floats The list of gaps to be applied on each subdivision. If the lenght of the given array is less of the number of subcategories (or if it's a single number) it will extend it with exponentially decreasing gaps Returns ---------- base_rect : dict A dictionary containing the result of the split. To each key is associated a 4-tuple of coordinates that are required to create the corresponding rectangle: 0 - x position of the lower left corner 1 - y position of the lower left corner 2 - width of the rectangle 3 - height of the rectangle """ # this is the unit square that we are going to divide base_rect = OrderedDict([(tuple(), (0, 0, 1, 1))]) # get the list of each possible value for each level categories_levels = _categories_level(list(count_dict.keys())) L = len(categories_levels) # recreate the gaps vector starting from an int if not np.iterable(gap): gap = [gap / 1.5 ** idx for idx in range(L)] # extend if it's too short if len(gap) < L: last = gap[-1] gap = list(*gap) + [last / 1.5 ** idx for idx in range(L)] # trim if it's too long gap = gap[:L] # put the count dictionay in order for the keys # this will allow some code simplification count_ordered = OrderedDict([(k, count_dict[k]) for k in list(product(*categories_levels))]) for cat_idx, cat_enum in enumerate(categories_levels): # get the partial key up to the actual level base_keys = list(product(*categories_levels[:cat_idx])) for key in base_keys: # for each partial and each value calculate how many # observation we have in the counting dictionary part_count = [_reduce_dict(count_ordered, key + (partial,)) for partial in cat_enum] # reduce the gap for subsequents levels new_gap = gap[cat_idx] # split the given subkeys in the rectangle dictionary base_rect = _key_splitting(base_rect, cat_enum, part_count, key, horizontal, new_gap) horizontal = not horizontal return base_rect def _single_hsv_to_rgb(hsv): """Transform a color from the hsv space to the rgb.""" from matplotlib.colors import hsv_to_rgb return hsv_to_rgb(array(hsv).reshape(1, 1, 3)).reshape(3) def _create_default_properties(data): """"Create the default properties of the mosaic given the data first it will varies the color hue (first category) then the color saturation (second category) and then the color value (third category). If a fourth category is found, it will put decoration on the rectangle. Doesn't manage more than four level of categories """ categories_levels = _categories_level(list(data.keys())) Nlevels = len(categories_levels) # first level, the hue L = len(categories_levels[0]) # hue = np.linspace(1.0, 0.0, L+1)[:-1] hue = np.linspace(0.0, 1.0, L + 2)[:-2] # second level, the saturation L = len(categories_levels[1]) if Nlevels > 1 else 1 saturation = np.linspace(0.5, 1.0, L + 1)[:-1] # third level, the value L = len(categories_levels[2]) if Nlevels > 2 else 1 value = np.linspace(0.5, 1.0, L + 1)[:-1] # fourth level, the hatch L = len(categories_levels[3]) if Nlevels > 3 else 1 hatch = ['', '/', '-', '|', '+'][:L + 1] # convert in list and merge with the levels hue = list(zip(list(hue), categories_levels[0])) saturation = list(zip(list(saturation), categories_levels[1] if Nlevels > 1 else [''])) value = list(zip(list(value), categories_levels[2] if Nlevels > 2 else [''])) hatch = list(zip(list(hatch), categories_levels[3] if Nlevels > 3 else [''])) # create the properties dictionary properties = {} for h, s, v, t in product(hue, saturation, value, hatch): hv, hn = h sv, sn = s vv, vn = v tv, tn = t level = (hn,) + ((sn,) if sn else tuple()) level = level + ((vn,) if vn else tuple()) level = level + ((tn,) if tn else tuple()) hsv = array([hv, sv, vv]) prop = {'color': _single_hsv_to_rgb(hsv), 'hatch': tv, 'lw': 0} properties[level] = prop return properties def _normalize_data(data, index): """normalize the data to a dict with tuples of strings as keys right now it works with: 0 - dictionary (or equivalent mappable) 1 - pandas.Series with simple or hierarchical indexes 2 - numpy.ndarrays 3 - everything that can be converted to a numpy array 4 - pandas.DataFrame (via the _normalize_dataframe function) """ # if data is a dataframe we need to take a completely new road # before coming back here. Use the hasattr to avoid importing # pandas explicitly if hasattr(data, 'pivot') and hasattr(data, 'groupby'): data = _normalize_dataframe(data, index) index = None # can it be used as a dictionary? try: items = list(data.iteritems()) except AttributeError: # ok, I cannot use the data as a dictionary # Try to convert it to a numpy array, or die trying data = np.asarray(data) temp = OrderedDict() for idx in np.ndindex(data.shape): name = tuple(i for i in idx) temp[name] = data[idx] data = temp items = data.items() # make all the keys a tuple, even if simple numbers data = OrderedDict([_tuplify(k), v] for k, v in items) categories_levels = _categories_level(list(data.keys())) # fill the void in the counting dictionary indexes = product(*categories_levels) contingency = OrderedDict([(k, data.get(k, 0)) for k in indexes]) data = contingency # reorder the keys order according to the one specified by the user # or if the index is None convert it into a simple list # right now it doesn't do any check, but can be modified in the future index = list(range(len(categories_levels))) if index is None else index contingency = OrderedDict() for key, value in data.items(): new_key = tuple(key[i] for i in index) contingency[new_key] = value data = contingency return data def _normalize_dataframe(dataframe, index): """Take a pandas DataFrame and count the element present in the given columns, return a hierarchical index on those columns """ #groupby the given keys, extract the same columns and count the element # then collapse them with a mean data = dataframe[index].dropna() grouped = data.groupby(index, sort=False) counted = grouped[index].count() averaged = counted.mean(axis=1) return averaged def _statistical_coloring(data): """evaluate colors from the indipendence properties of the matrix It will encounter problem if one category has all zeros """ data = _normalize_data(data, None) categories_levels = _categories_level(list(data.keys())) Nlevels = len(categories_levels) total = 1.0 * sum(v for v in data.values()) # count the proportion of observation # for each level that has the given name # at each level levels_count = [] for level_idx in range(Nlevels): proportion = {} for level in categories_levels[level_idx]: proportion[level] = 0.0 for key, value in data.items(): if level == key[level_idx]: proportion[level] += value proportion[level] /= total levels_count.append(proportion) # for each key I obtain the expected value # and it's standard deviation from a binomial distribution # under the hipothesys of independence expected = {} for key, value in data.items(): base = 1.0 for i, k in enumerate(key): base *= levels_count[i][k] expected[key] = base * total, np.sqrt(total * base * (1.0 - base)) # now we have the standard deviation of distance from the # expected value for each tile. We create the colors from this sigmas = dict((k, (data[k] - m) / s) for k, (m, s) in expected.items()) props = {} for key, dev in sigmas.items(): red = 0.0 if dev < 0 else (dev / (1 + dev)) blue = 0.0 if dev > 0 else (dev / (-1 + dev)) green = (1.0 - red - blue) / 2.0 hatch = 'x' if dev > 2 else 'o' if dev < -2 else '' props[key] = {'color': [red, green, blue], 'hatch': hatch} return props def _create_labels(rects, horizontal, ax, rotation): """find the position of the label for each value of each category right now it supports only up to the four categories ax: the axis on which the label should be applied rotation: the rotation list for each side """ categories = _categories_level(list(rects.keys())) if len(categories) > 4: msg = ("maximum of 4 level supported for axes labeling..and 4" "is alreay a lot of level, are you sure you need them all?") raise NotImplementedError(msg) labels = {} #keep it fixed as will be used a lot of times items = list(rects.items()) vertical = not horizontal #get the axis ticks and labels locator to put the correct values! ax2 = ax.twinx() ax3 = ax.twiny() #this is the order of execution for horizontal disposition ticks_pos = [ax.set_xticks, ax.set_yticks, ax3.set_xticks, ax2.set_yticks] ticks_lab = [ax.set_xticklabels, ax.set_yticklabels, ax3.set_xticklabels, ax2.set_yticklabels] #for the vertical one, rotate it by one if vertical: ticks_pos = ticks_pos[1:] + ticks_pos[:1] ticks_lab = ticks_lab[1:] + ticks_lab[:1] #clean them for pos, lab in zip(ticks_pos, ticks_lab): pos([]) lab([]) #for each level, for each value in the level, take the mean of all #the sublevel that correspond to that partial key for level_idx, level in enumerate(categories): #this dictionary keep the labels only for this level level_ticks = dict() for value in level: #to which level it should refer to get the preceding #values of labels? it's rather a tricky question... #this is dependent on the side. It's a very crude management #but I couldn't think a more general way... if horizontal: if level_idx == 3: index_select = [-1, -1, -1] else: index_select = [+0, -1, -1] else: if level_idx == 3: index_select = [+0, -1, +0] else: index_select = [-1, -1, -1] #now I create the base key name and append the current value #It will search on all the rects to find the corresponding one #and use them to evaluate the mean position basekey = tuple(categories[i][index_select[i]] for i in range(level_idx)) basekey = basekey + (value,) subset = dict((k, v) for k, v in items if basekey == k[:level_idx + 1]) #now I extract the center of all the tiles and make a weighted #mean of all these center on the area of the tile #this should give me the (more or less) correct position #of the center of the category vals = list(subset.values()) W = sum(w * h for (x, y, w, h) in vals) x_lab = sum((x + w / 2.0) * w * h / W for (x, y, w, h) in vals) y_lab = sum((y + h / 2.0) * w * h / W for (x, y, w, h) in vals) #now base on the ordering, select which position to keep #needs to be written in a more general form of 4 level are enough? #should give also the horizontal and vertical alignment side = (level_idx + vertical) % 4 level_ticks[value] = y_lab if side % 2 else x_lab #now we add the labels of this level to the correct axis ticks_pos[level_idx](list(level_ticks.values())) ticks_lab[level_idx](list(level_ticks.keys()), rotation=rotation[level_idx]) return labels def mosaic(data, index=None, ax=None, horizontal=True, gap=0.005, properties=lambda key: None, labelizer=None, title='', statistic=False, axes_label=True, label_rotation=0.0): """Create a mosaic plot from a contingency table. It allows to visualize multivariate categorical data in a rigorous and informative way. Parameters ---------- data : dict, pandas.Series, np.ndarray, pandas.DataFrame The contingency table that contains the data. Each category should contain a non-negative number with a tuple as index. It expects that all the combination of keys to be representes; if that is not true, will automatically consider the missing values as 0. The order of the keys will be the same as the one of insertion. If a dict of a Series (or any other dict like object) is used, it will take the keys as labels. If a np.ndarray is provided, it will generate a simple numerical labels. index: list, optional Gives the preferred order for the category ordering. If not specified will default to the given order. It doesn't support named indexes for hierarchical Series. If a DataFrame is provided, it expects a list with the name of the columns. ax : matplotlib.Axes, optional The graph where display the mosaic. If not given, will create a new figure horizontal : bool, optional (default True) The starting direction of the split (by default along the horizontal axis) gap : float or array of floats The list of gaps to be applied on each subdivision. If the lenght of the given array is less of the number of subcategories (or if it's a single number) it will extend it with exponentially decreasing gaps labelizer : function (key) -> string, optional A function that generate the text to display at the center of each tile base on the key of that tile properties : function (key) -> dict, optional A function that for each tile in the mosaic take the key of the tile and returns the dictionary of properties of the generated Rectangle, like color, hatch or similar. A default properties set will be provided fot the keys whose color has not been defined, and will use color variation to help visually separates the various categories. It should return None to indicate that it should use the default property for the tile. A dictionary of the properties for each key can be passed, and it will be internally converted to the correct function statistic: bool, optional (default False) if true will use a crude statistical model to give colors to the plot. If the tile has a containt that is more than 2 standard deviation from the expected value under independence hipotesys, it will go from green to red (for positive deviations, blue otherwise) and will acquire an hatching when crosses the 3 sigma. title: string, optional The title of the axis axes_label: boolean, optional Show the name of each value of each category on the axis (default) or hide them. label_rotation: float or list of float the rotation of the axis label (if present). If a list is given each axis can have a different rotation Returns ---------- fig : matplotlib.Figure The generate figure rects : dict A dictionary that has the same keys of the original dataset, that holds a reference to the coordinates of the tile and the Rectangle that represent it See Also ---------- A Brief History of the Mosaic Display Michael Friendly, York University, Psychology Department Journal of Computational and Graphical Statistics, 2001 Mosaic Displays for Loglinear Models. Michael Friendly, York University, Psychology Department Proceedings of the Statistical Graphics Section, 1992, 61-68. Mosaic displays for multi-way contingecy tables. Michael Friendly, York University, Psychology Department Journal of the american statistical association March 1994, Vol. 89, No. 425, Theory and Methods Examples ---------- The most simple use case is to take a dictionary and plot the result >>> data = {'a': 10, 'b': 15, 'c': 16} >>> mosaic(data, title='basic dictionary') >>> pylab.show() A more useful example is given by a dictionary with multiple indices. In this case we use a wider gap to a better visual separation of the resulting plot >>> data = {('a', 'b'): 1, ('a', 'c'): 2, ('d', 'b'): 3, ('d', 'c'): 4} >>> mosaic(data, gap=0.05, title='complete dictionary') >>> pylab.show() The same data can be given as a simple or hierarchical indexed Series >>> rand = np.random.random >>> from itertools import product >>> >>> tuples = list(product(['bar', 'baz', 'foo', 'qux'], ['one', 'two'])) >>> index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) >>> data = pd.Series(rand(8), index=index) >>> mosaic(data, title='hierarchical index series') >>> pylab.show() The third accepted data structureis the np array, for which a very simple index will be created. >>> rand = np.random.random >>> data = 1+rand((2,2)) >>> mosaic(data, title='random non-labeled array') >>> pylab.show() If you need to modify the labeling and the coloring you can give a function tocreate the labels and one with the graphical properties starting from the key tuple >>> data = {'a': 10, 'b': 15, 'c': 16} >>> props = lambda key: {'color': 'r' if 'a' in key else 'gray'} >>> labelizer = lambda k: {('a',): 'first', ('b',): 'second', ('c',): 'third'}[k] >>> mosaic(data, title='colored dictionary', properties=props, labelizer=labelizer) >>> pylab.show() Using a DataFrame as source, specifying the name of the columns of interest >>> gender = ['male', 'male', 'male', 'female', 'female', 'female'] >>> pet = ['cat', 'dog', 'dog', 'cat', 'dog', 'cat'] >>> data = pandas.DataFrame({'gender': gender, 'pet': pet}) >>> mosaic(data, ['pet', 'gender']) >>> pylab.show() """ from pylab import Rectangle fig, ax = utils.create_mpl_ax(ax) # normalize the data to a dict with tuple of strings as keys data = _normalize_data(data, index) # split the graph into different areas rects = _hierarchical_split(data, horizontal=horizontal, gap=gap) # if there is no specified way to create the labels # create a default one if labelizer is None: labelizer = lambda k: "\n".join(k) if statistic: default_props = _statistical_coloring(data) else: default_props = _create_default_properties(data) if isinstance(properties, dict): color_dict = properties properties = lambda key: color_dict.get(key, None) for k, v in rects.items(): # create each rectangle and put a label on it x, y, w, h = v conf = properties(k) props = conf if conf else default_props[k] text = labelizer(k) Rect = Rectangle((x, y), w, h, label=text, **props) ax.add_patch(Rect) ax.text(x + w / 2, y + h / 2, text, ha='center', va='center', size='smaller') #creating the labels on the axis #o clearing it if axes_label: if np.iterable(label_rotation): rotation = label_rotation else: rotation = [label_rotation] * 4 labels = _create_labels(rects, horizontal, ax, rotation) else: ax.set_xticks([]) ax.set_xticklabels([]) ax.set_yticks([]) ax.set_yticklabels([]) ax.set_title(title) return fig, rectsstatsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/plot_grids.py000066400000000000000000000130431224417117700250350ustar00rootroot00000000000000'''create scatterplot with confidence ellipsis Author: Josef Perktold License: BSD-3 TODO: update script to use sharex, sharey, and visible=False see http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label for sharex I need to have the ax of the last_row when editing the earlier rows. Or you axes_grid1, imagegrid http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html ''' import numpy as np from scipy import stats from . import utils __all__ = ['scatter_ellipse'] def _make_ellipse(mean, cov, ax, level=0.95, color=None): """Support function for scatter_ellipse.""" from matplotlib.patches import Ellipse v, w = np.linalg.eigh(cov) u = w[0] / np.linalg.norm(w[0]) angle = np.arctan(u[1]/u[0]) angle = 180 * angle / np.pi # convert to degrees v = 2 * np.sqrt(v * stats.chi2.ppf(level, 2)) #get size corresponding to level ell = Ellipse(mean[:2], v[0], v[1], 180 + angle, facecolor='none', edgecolor=color, #ls='dashed', #for debugging lw=1.5) ell.set_clip_box(ax.bbox) ell.set_alpha(0.5) ax.add_artist(ell) def scatter_ellipse(data, level=0.9, varnames=None, ell_kwds=None, plot_kwds=None, add_titles=False, keep_ticks=False, fig=None): """Create a grid of scatter plots with confidence ellipses. ell_kwds, plot_kdes not used yet looks ok with 5 or 6 variables, too crowded with 8, too empty with 1 Parameters ---------- data : array_like Input data. level : scalar, optional Default is 0.9. varnames : list of str, optional Variable names. Used for y-axis labels, and if `add_titles` is True also for titles. If not given, integers 1..data.shape[1] are used. ell_kwds : dict, optional UNUSED plot_kwds : dict, optional UNUSED add_titles : bool, optional Whether or not to add titles to each subplot. Default is False. Titles are constructed from `varnames`. keep_ticks : bool, optional If False (default), remove all axis ticks. fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- fig : Matplotlib figure instance If `fig` is None, the created figure. Otherwise `fig` itself. """ fig = utils.create_mpl_fig(fig) import matplotlib.ticker as mticker data = np.asanyarray(data) #needs mean and cov nvars = data.shape[1] if varnames is None: #assuming single digit, nvars<=10 else use 'var%2d' varnames = ['var%d' % i for i in range(nvars)] plot_kwds_ = dict(ls='none', marker='.', color='k', alpha=0.5) if plot_kwds: plot_kwds_.update(plot_kwds) ell_kwds_= dict(color='k') if ell_kwds: ell_kwds_.update(ell_kwds) dmean = data.mean(0) dcov = np.cov(data, rowvar=0) for i in range(1, nvars): #print '---' ax_last=None for j in range(i): #print i,j, i*(nvars-1)+j+1 ax = fig.add_subplot(nvars-1, nvars-1, (i-1)*(nvars-1)+j+1) ## #sharey=ax_last) #sharey doesn't allow empty ticks? ## if j == 0: ## print 'new ax_last', j ## ax_last = ax ## ax.set_ylabel(varnames[i]) #TODO: make sure we have same xlim and ylim formatter = mticker.FormatStrFormatter('% 3.1f') ax.yaxis.set_major_formatter(formatter) ax.xaxis.set_major_formatter(formatter) idx = np.array([j,i]) ax.plot(*data[:,idx].T, **plot_kwds_) if np.isscalar(level): level = [level] for alpha in level: _make_ellipse(dmean[idx], dcov[idx[:,None], idx], ax, level=alpha, **ell_kwds_) if add_titles: ax.set_title('%s-%s' % (varnames[i], varnames[j])) if not ax.is_first_col(): if not keep_ticks: ax.set_yticks([]) else: ax.yaxis.set_major_locator(mticker.MaxNLocator(3)) else: ax.set_ylabel(varnames[i]) if ax.is_last_row(): ax.set_xlabel(varnames[j]) else: if not keep_ticks: ax.set_xticks([]) else: ax.xaxis.set_major_locator(mticker.MaxNLocator(3)) dcorr = np.corrcoef(data, rowvar=0) dc = dcorr[idx[:,None], idx] xlim = ax.get_xlim() ylim = ax.get_ylim() ## xt = xlim[0] + 0.1 * (xlim[1] - xlim[0]) ## yt = ylim[0] + 0.1 * (ylim[1] - ylim[0]) ## if dc[1,0] < 0 : ## yt = ylim[0] + 0.1 * (ylim[1] - ylim[0]) ## else: ## yt = ylim[1] - 0.2 * (ylim[1] - ylim[0]) yrangeq = ylim[0] + 0.4 * (ylim[1] - ylim[0]) if dc[1,0] < -0.25 or (dc[1,0] < 0.25 and dmean[idx][1] > yrangeq): yt = ylim[0] + 0.1 * (ylim[1] - ylim[0]) else: yt = ylim[1] - 0.2 * (ylim[1] - ylim[0]) xt = xlim[0] + 0.1 * (xlim[1] - xlim[0]) ax.text(xt, yt, '$\\rho=%0.2f$'% dc[1,0]) for ax in fig.axes: if ax.is_last_row(): # or ax.is_first_col(): ax.xaxis.set_major_locator(mticker.MaxNLocator(3)) if ax.is_first_col(): ax.yaxis.set_major_locator(mticker.MaxNLocator(3)) return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/plottools.py000066400000000000000000000011721224417117700247260ustar00rootroot00000000000000import numpy as np def rainbow(n): """ Returns a list of colors sampled at equal intervals over the spectrum. Parameters ---------- n : int The number of colors to return Returns ------- R : (n,3) array An of rows of RGB color values Notes ----- Converts from HSV coordinates (0, 1, 1) to (1, 1, 1) to RGB. Based on the Sage function of the same name. """ from matplotlib import colors R = np.ones((1,n,3)) R[0,:,0] = np.linspace(0, 1, n, endpoint=False) #Note: could iterate and use colorsys.hsv_to_rgb return colors.hsv_to_rgb(R).squeeze() statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/regressionplots.py000066400000000000000000000670751224417117700261470ustar00rootroot00000000000000'''Partial Regression plot and residual plots to find misspecification Author: Josef Perktold License: BSD-3 Created: 2011-01-23 update 2011-06-05 : start to convert example to usable functions 2011-10-27 : docstrings ''' import numpy as np from statsmodels.regression.linear_model import OLS from statsmodels.sandbox.regression.predstd import wls_prediction_std from statsmodels.graphics import utils from statsmodels.nonparametric.smoothers_lowess import lowess from statsmodels.tools.tools import maybe_unwrap_results __all__ = ['plot_fit', 'plot_regress_exog', 'plot_partregress', 'plot_ccpr', 'plot_regress_exog', 'plot_partregress_grid', 'plot_ccpr_grid', 'add_lowess', 'abline_plot', 'influence_plot', 'plot_leverage_resid2'] #TODO: consider moving to influence module def _high_leverage(results): #TODO: replace 1 with k_constant return 2. * (results.df_model + 1)/results.nobs def add_lowess(ax, lines_idx=0, frac=.2, **lowess_kwargs): """ Add Lowess line to a plot. Parameters ---------- ax : matplotlib Axes instance The Axes to which to add the plot lines_idx : int This is the line on the existing plot to which you want to add a smoothed lowess line. frac : float The fraction of the points to use when doing the lowess fit. lowess_kwargs Additional keyword arguments are passes to lowess. Returns ------- fig : matplotlib Figure instance The figure that holds the instance. """ y0 = ax.get_lines()[lines_idx]._y x0 = ax.get_lines()[lines_idx]._x lres = lowess(y0, x0, frac=frac, **lowess_kwargs) ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5) return ax.figure def plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs): """Plot fit against one regressor. This creates one graph with the scatterplot of observed values compared to fitted values. Parameters ---------- results : result instance result instance with resid, model.endog and model.exog as attributes x_var : int or str Name or index of regressor in exog matrix. y_true : array_like (optional) If this is not None, then the array is added to the plot ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. kwargs The keyword arguments are passed to the plot command for the fitted values points. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. Examples -------- Load the Statewide Crime data set and perform linear regression with `poverty` and `hs_grad` as variables and `murder` as the response >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import numpy as np >>> data = sm.datasets.statecrime.load_pandas().data >>> murder = data['murder'] >>> X = data[['poverty', 'hs_grad']] >>> X["constant"] = 1 >>> y = murder >>> model = sm.OLS(y, X) >>> results = model.fit() Create a plot just for the variable 'Poverty': >>> fig, ax = plt.subplots() >>> fig = sm.graphics.plot_fit(results, 0, ax=ax) >>> ax.set_ylabel("Murder Rate") >>> ax.set_xlabel("Poverty Level") >>> ax.set_title("Linear Regression") >>> plt.show() .. plot:: plots/graphics_plot_fit_ex.py """ fig, ax = utils.create_mpl_ax(ax) exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model) results = maybe_unwrap_results(results) #maybe add option for wendog, wexog y = results.model.endog x1 = results.model.exog[:, exog_idx] x1_argsort = np.argsort(x1) y = y[x1_argsort] x1 = x1[x1_argsort] ax.plot(x1, y, 'bo', label=results.model.endog_names) if not y_true is None: ax.plot(x1, y_true[x1_argsort], 'b-', label='True values') title = 'Fitted values versus %s' % exog_name prstd, iv_l, iv_u = wls_prediction_std(results) ax.plot(x1, results.fittedvalues[x1_argsort], 'D', color='r', label='fitted', **kwargs) ax.vlines(x1, iv_l[x1_argsort], iv_u[x1_argsort], linewidth=1, color='k', alpha=.7) #ax.fill_between(x1, iv_l[x1_argsort], iv_u[x1_argsort], alpha=0.1, # color='k') ax.set_title(title) ax.set_xlabel(exog_name) ax.set_ylabel(results.model.endog_names) ax.legend(loc='best', numpoints=1) return fig def plot_regress_exog(results, exog_idx, fig=None): """Plot regression results against one regressor. This plots four graphs in a 2 by 2 figure: 'endog versus exog', 'residuals versus exog', 'fitted versus exog' and 'fitted plus residual versus exog' Parameters ---------- results : result instance result instance with resid, model.endog and model.exog as attributes exog_idx : int index of regressor in exog matrix fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- fig : matplotlib figure instance """ fig = utils.create_mpl_fig(fig) exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model) results = maybe_unwrap_results(results) #maybe add option for wendog, wexog y_name = results.model.endog_names x1 = results.model.exog[:,exog_idx] prstd, iv_l, iv_u = wls_prediction_std(results) ax = fig.add_subplot(2,2,1) ax.plot(x1, results.model.endog, 'o', color='b', alpha=0.9, label=y_name) ax.plot(x1, results.fittedvalues, 'D', color='r', label='fitted', alpha=.5) ax.vlines(x1, iv_l, iv_u, linewidth=1, color='k', alpha=.7) ax.set_title('Y and Fitted vs. X', fontsize='large') ax.set_xlabel(exog_name) ax.set_ylabel(y_name) ax.legend(loc='best') ax = fig.add_subplot(2,2,2) ax.plot(x1, results.resid, 'o') ax.axhline(y=0, color='black') ax.set_title('Residuals versus %s' % exog_name, fontsize='large') ax.set_xlabel(exog_name) ax.set_ylabel("resid") ax = fig.add_subplot(2,2,3) exog_noti = np.ones(results.model.exog.shape[1], bool) exog_noti[exog_idx] = False exog_others = results.model.exog[:, exog_noti] from pandas import Series fig = plot_partregress(results.model.data.orig_endog, Series(x1, name=exog_name, index=results.model.data.row_labels), exog_others, obs_labels=False, ax=ax) ax.set_title('Partial regression plot', fontsize='large') #ax.set_ylabel("Fitted values") #ax.set_xlabel(exog_name) ax = fig.add_subplot(2,2,4) fig = plot_ccpr(results, exog_idx, ax=ax) ax.set_title('CCPR Plot', fontsize='large') #ax.set_xlabel(exog_name) #ax.set_ylabel("Fitted values + resids") fig.suptitle('Regression Plots for %s' % exog_name, fontsize="large") fig = utils.maybe_tight_layout(fig) fig.subplots_adjust(top=.90) return fig def _partial_regression(endog, exog_i, exog_others): """Partial regression. regress endog on exog_i conditional on exog_others uses OLS Parameters ---------- endog : array_like exog : array_like exog_others : array_like Returns ------- res1c : OLS results instance (res1a, res1b) : tuple of OLS results instances results from regression of endog on exog_others and of exog_i on exog_others """ #FIXME: This function doesn't appear to be used. res1a = OLS(endog, exog_others).fit() res1b = OLS(exog_i, exog_others).fit() res1c = OLS(res1a.resid, res1b.resid).fit() return res1c, (res1a, res1b) def plot_partregress(endog, exog_i, exog_others, data=None, title_kwargs={}, obs_labels=True, label_kwargs={}, ax=None, ret_coords=False, **kwargs): """Plot partial regression for a single regressor. Parameters ---------- endog : ndarray or string endogenous or response variable. If string is given, you can use a arbitrary translations as with a formula. exog_i : ndarray or string exogenous, explanatory variable. If string is given, you can use a arbitrary translations as with a formula. exog_others : ndarray or list of strings other exogenous, explanatory variables. If a list of strings is given, each item is a term in formula. You can use a arbitrary translations as with a formula. The effect of these variables will be removed by OLS regression. data : DataFrame, dict, or recarray Some kind of data structure with names if the other variables are given as strings. title_kwargs : dict Keyword arguments to pass on for the title. The key to control the fonts is fontdict. obs_labels : bool or array-like Whether or not to annotate the plot points with their observation labels. If obs_labels is a boolean, the point labels will try to do the right thing. First it will try to use the index of data, then fall back to the index of exog_i. Alternatively, you may give an array-like object corresponding to the obseveration numbers. labels_kwargs : dict Keyword arguments that control annotate for the observation labels. ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. ret_coords : bool If True will return the coordinates of the points in the plot. You can use this to add your own annotations. kwargs The keyword arguments passed to plot for the points. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. coords : list, optional If ret_coords is True, return a tuple of arrays (x_coords, y_coords). Notes ----- The slope of the fitted line is the that of `exog_i` in the full multiple regression. The individual points can be used to assess the influence of points on the estimated coefficient. See Also -------- plot_partregress_grid : Plot partial regression for a set of regressors. """ #NOTE: there is no interaction between possible missing data and #obs_labels yet, so this will need to be tweaked a bit for this case fig, ax = utils.create_mpl_ax(ax) if (isinstance(endog, basestring) or isinstance(exog_others, (basestring, list)) or isinstance(exog_i, basestring)): from patsy import dmatrix # strings, use patsy to transform to data if isinstance(endog, basestring): endog = dmatrix(endog + "-1", data) if isinstance(exog_others, basestring): RHS = dmatrix(RHS, data) elif isinstance(exog_others, list): RHS = "+".join(exog_others) RHS = dmatrix(RHS, data) else: RHS = exog_others if isinstance(exog_i, basestring): varname = exog_i exog_i = dmatrix(exog_i + "-1", data) # all arrays or pandas-like res_yaxis = OLS(endog, RHS).fit() res_xaxis = OLS(exog_i, RHS).fit() ax.plot(res_xaxis.resid, res_yaxis.resid, 'o', **kwargs) fitted_line = OLS(res_yaxis.resid, res_xaxis.resid).fit() fig = abline_plot(0, fitted_line.params[0], color='k', ax=ax) x_axis_endog_name = res_xaxis.model.endog_names if x_axis_endog_name == 'y': # for no names regression will just get a y x_axis_endog_name = 'x' # this is misleading, so use x ax.set_xlabel("e(%s | X)" % x_axis_endog_name) ax.set_ylabel("e(%s | X)" % res_yaxis.model.endog_names) ax.set_title('Partial Regression Plot', **title_kwargs) #NOTE: if we want to get super fancy, we could annotate if a point is #clicked using this widget #http://stackoverflow.com/questions/4652439/ #is-there-a-matplotlib-equivalent-of-matlabs-datacursormode/ #4674445#4674445 if obs_labels is True: if data is not None: obs_labels = data.index elif hasattr(exog_i, "index"): obs_labels = exog_i.index else: obs_labels = res_xaxis.model.data.row_labels #NOTE: row_labels can be None. #Maybe we should fix this to never be the case. if obs_labels is None: obs_labels = range(len(exog_i)) if obs_labels is not False: # could be array-like if len(obs_labels) != len(exog_i): raise ValueError("obs_labels does not match length of exog_i") label_kwargs.update(dict(ha="center", va="bottom")) ax = utils.annotate_axes(range(len(obs_labels)), obs_labels, zip(res_xaxis.resid, res_yaxis.resid), [(0, 5)] * len(obs_labels), "x-large", ax=ax, **label_kwargs) if ret_coords: return fig, (res_axis.resid, res_yaxis.resid) else: return fig def plot_partregress_grid(results, exog_idx=None, grid=None, fig=None): """Plot partial regression for a set of regressors. Parameters ---------- results : results instance A regression model results instance exog_idx : None, list of ints, list of strings (column) indices of the exog used in the plot, default is all. grid : None or tuple of int (nrows, ncols) If grid is given, then it is used for the arrangement of the subplots. If grid is None, then ncol is one, if there are only 2 subplots, and the number of columns is two otherwise. fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- fig : Matplotlib figure instance If `fig` is None, the created figure. Otherwise `fig` itself. Notes ----- A subplot is created for each explanatory variable given by exog_idx. The partial regression plot shows the relationship between the response and the given explanatory variable after removing the effect of all other explanatory variables in exog. See Also -------- plot_partregress : Plot partial regression for a single regressor. plot_ccpr References ---------- See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/partregr.htm """ import pandas fig = utils.create_mpl_fig(fig) exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model) #maybe add option for using wendog, wexog instead y = pandas.Series(results.model.endog, name=results.model.endog_names) exog = results.model.exog k_vars = exog.shape[1] #this function doesn't make sense if k_vars=1 if not grid is None: nrows, ncols = grid else: if len(exog_idx) > 2: nrows = int(np.ceil(len(exog_idx)/2.)) ncols = 2 title_kwargs = {"fontdict" : {"fontsize" : 'small'}} else: nrows = len(exog_idx) ncols = 1 title_kwargs = {} # for indexing purposes other_names = np.array(results.model.exog_names) for i,idx in enumerate(exog_idx): others = range(k_vars) others.pop(idx) exog_others = pandas.DataFrame(exog[:, others], columns=other_names[others]) ax = fig.add_subplot(nrows, ncols, i+1) plot_partregress(y, pandas.Series(exog[:, idx], name=other_names[idx]), exog_others, ax=ax, title_kwargs=title_kwargs, obs_labels=False) ax.set_title("") fig.suptitle("Partial Regression Plot", fontsize="large") fig = utils.maybe_tight_layout(fig) fig.subplots_adjust(top=.95) return fig def plot_ccpr(results, exog_idx, ax=None): """Plot CCPR against one regressor. Generates a CCPR (component and component-plus-residual) plot. Parameters ---------- results : result instance A regression results instance. exog_idx : int or string Exogenous, explanatory variable. If string is given, it should be the variable name that you want to use, and you can use arbitrary translations as with a formula. ax : Matplotlib AxesSubplot instance, optional If given, it is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- plot_ccpr_grid : Creates CCPR plot for multiple regressors in a plot grid. Notes ----- The CCPR plot provides a way to judge the effect of one regressor on the response variable by taking into account the effects of the other independent variables. The partial residuals plot is defined as Residuals + B_i*X_i versus X_i. The component adds the B_i*X_i versus X_i to show where the fitted line would lie. Care should be taken if X_i is highly correlated with any of the other independent variables. If this is the case, the variance evident in the plot will be an underestimate of the true variance. References ---------- http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm """ fig, ax = utils.create_mpl_ax(ax) exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model) results = maybe_unwrap_results(results) x1 = results.model.exog[:, exog_idx] #namestr = ' for %s' % self.name if self.name else '' x1beta = x1*results.params[exog_idx] ax.plot(x1, x1beta + results.resid, 'o') from statsmodels.tools.tools import add_constant mod = OLS(x1beta, add_constant(x1)).fit() params = mod.params fig = abline_plot(*params, **dict(ax=ax)) #ax.plot(x1, x1beta, '-') ax.set_title('Component and component plus residual plot') ax.set_ylabel("Residual + %s*beta_%d" % (exog_name, exog_idx)) ax.set_xlabel("%s" % exog_name) return fig def plot_ccpr_grid(results, exog_idx=None, grid=None, fig=None): """Generate CCPR plots against a set of regressors, plot in a grid. Generates a grid of CCPR (component and component-plus-residual) plots. Parameters ---------- results : result instance uses exog and params of the result instance exog_idx : None or list of int (column) indices of the exog used in the plot grid : None or tuple of int (nrows, ncols) If grid is given, then it is used for the arrangement of the subplots. If grid is None, then ncol is one, if there are only 2 subplots, and the number of columns is two otherwise. fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. Notes ----- Partial residual plots are formed as:: Res + Betahat(i)*Xi versus Xi and CCPR adds:: Betahat(i)*Xi versus Xi See Also -------- plot_ccpr : Creates CCPR plot for a single regressor. References ---------- See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm """ fig = utils.create_mpl_fig(fig) exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model) if grid is not None: nrows, ncols = grid else: if len(exog_idx) > 2: nrows = int(np.ceil(len(exog_idx)/2.)) ncols = 2 else: nrows = len(exog_idx) ncols = 1 seen_constant = 0 for i, idx in enumerate(exog_idx): if results.model.exog[:,idx].var() == 0: seen_constant = 1 continue ax = fig.add_subplot(nrows, ncols, i+1-seen_constant) fig = plot_ccpr(results, exog_idx=idx, ax=ax) ax.set_title("") fig.suptitle("Component-Component Plus Residual Plot", fontsize="large") fig = utils.maybe_tight_layout(fig) fig.subplots_adjust(top=.95) return fig def abline_plot(intercept=None, slope=None, horiz=None, vert=None, model_results=None, ax=None, **kwargs): """ Plots a line given an intercept and slope. intercept : float The intercept of the line slope : float The slope of the line horiz : float or array-like Data for horizontal lines on the y-axis vert : array-like Data for verterical lines on the x-axis model_results : statsmodels results instance Any object that has a two-value `params` attribute. Assumed that it is (intercept, slope) ax : axes, optional Matplotlib axes instance kwargs Options passed to matplotlib.pyplot.plt Returns ------- fig : Figure The figure given by `ax.figure` or a new instance. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> np.random.seed(12345) >>> X = sm.add_constant(np.random.normal(0, 20, size=30)) >>> y = np.dot(X, [25, 3.5]) + np.random.normal(0, 30, size=30) >>> mod = sm.OLS(y,X).fit() >>> fig = abline_plot(model_results=mod) >>> ax = fig.axes[0] >>> ax.scatter(X[:,1], y) >>> ax.margins(.1) >>> import matplotlib.pyplot as plt >>> plt.show() """ if ax is not None: # get axis limits first thing, don't change these x = ax.get_xlim() y = ax.get_ylim() else: x = None fig,ax = utils.create_mpl_ax(ax) if model_results: intercept, slope = model_results.params if x is None: x = [model_results.model.exog[:,1].min(), model_results.model.exog[:,1].max()] else: if not (intercept is not None and slope is not None): raise ValueError("specify slope and intercepty or model_results") if x is None: x = ax.get_xlim() data_y = [x[0]*slope+intercept, x[1]*slope+intercept] ax.set_xlim(x) #ax.set_ylim(y) from matplotlib.lines import Line2D class ABLine2D(Line2D): def update_datalim(self, ax): ax.set_autoscale_on(False) children = ax.get_children() abline = [children[i] for i in range(len(children)) if isinstance(children[i], ABLine2D)][0] x = ax.get_xlim() y = [x[0]*slope+intercept, x[1]*slope+intercept] abline.set_data(x,y) ax.figure.canvas.draw() #TODO: how to intercept something like a margins call and adjust? line = ABLine2D(x, data_y, **kwargs) ax.add_line(line) ax.callbacks.connect('xlim_changed', line.update_datalim) ax.callbacks.connect('ylim_changed', line.update_datalim) if horiz: ax.hline(horiz) if vert: ax.vline(vert) return fig def influence_plot(results, external=True, alpha=.05, criterion="cooks", size=48, plot_alpha=.75, ax=None, **kwargs): """ Plot of influence in regression. Plots studentized resids vs. leverage. Parameters ---------- results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha value to identify large studentized residuals. Large means abs(resid_studentized) > t.ppf(1-alpha/2, dof=results.df_resid) criterion : str {'DFFITS', 'Cooks'} Which criterion to base the size of the points on. Options are DFFITS or Cook's D. size : float The range of `criterion` is mapped to 10**2 - size**2 in points. plot_alpha : float The `alpha` of the plotted points. ax : matplotlib Axes instance An instance of a matplotlib Axes. Returns ------- fig : matplotlib figure The matplotlib figure that contains the Axes. Notes ----- Row labels for the observations in which the leverage, measured by the diagonal of the hat matrix, is high or the residuals are large, as the combination of large residuals and a high influence value indicates an influence point. The value of large residuals can be controlled using the `alpha` parameter. Large leverage points are identified as hat_i > 2 * (df_model + 1)/nobs. """ fig, ax = utils.create_mpl_ax(ax) infl = results.get_influence() if criterion.lower().startswith('dff'): psize = infl.cooks_distance[0] elif criterion.lower().startswith('coo'): psize = np.abs(infl.dffits[0]) else: raise ValueError("Criterion %s not understood" % criterion) # scale the variables #TODO: what is the correct scaling and the assumption here? #we want plots to be comparable across different plots #so we would need to use the expected distribution of criterion probably old_range = np.ptp(psize) new_range = size**2 - 8**2 psize = (psize - psize.min()) * new_range/old_range + 8**2 leverage = infl.hat_matrix_diag if external: resids = infl.resid_studentized_external else: resids = infl.resid_studentized_internal from scipy import stats cutoff = stats.t.ppf(1.-alpha/2, results.df_resid) large_resid = np.abs(resids) > cutoff large_leverage = leverage > _high_leverage(results) large_points = np.logical_or(large_resid, large_leverage) ax.scatter(leverage, resids, s=psize, alpha=plot_alpha) # add point labels labels = results.model.data.row_labels if labels is None: labels = range(len(resids)) ax = utils.annotate_axes(np.where(large_points)[0], labels, zip(leverage, resids), zip(-(psize/2)**.5, (psize/2)**.5), "x-large", ax) #TODO: make configurable or let people do it ex-post? font = {"fontsize" : 16, "color" : "black"} ax.set_ylabel("Studentized Residuals", **font) ax.set_xlabel("H Leverage", **font) ax.set_title("Influence Plot", **font) return fig def plot_leverage_resid2(results, alpha=.05, label_kwargs={}, ax=None, **kwargs): """ Plots leverage statistics vs. normalized residuals squared Parameters ---------- results : results instance A regression results instance alpha : float Specifies the cut-off for large-standardized residuals. Residuals are assumed to be distributed N(0, 1) with alpha=alpha. label_kwargs : dict The keywords to pass to annotate for the labels. ax : Axes instance Matplotlib Axes instance Returns ------- fig : matplotlib Figure A matplotlib figure instance. """ from scipy.stats import zscore, norm, t fig, ax = utils.create_mpl_ax(ax) infl = results.get_influence() leverage = infl.hat_matrix_diag resid = zscore(results.resid) ax.plot(resid**2, leverage, 'o', **kwargs) ax.set_xlabel("Normalized residuals**2") ax.set_ylabel("Leverage") ax.set_title("Leverage vs. Normalized residuals squared") large_leverage = leverage > _high_leverage(results) #norm or t here if standardized? cutoff = norm.ppf(1.-alpha/2) large_resid = np.abs(resid) > cutoff labels = results.model.data.row_labels if labels is None: labels = range(results.nobs) index = np.where(np.logical_or(large_leverage, large_resid))[0] ax = utils.annotate_axes(index, labels, zip(resid**2, leverage), [(0, 5)]*int(results.nobs), "large", ax=ax, ha="center", va="bottom") ax.margins(.075, .075) return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/000077500000000000000000000000001224417117700234565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/__init__.py000066400000000000000000000000001224417117700255550ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_boxplots.py000066400000000000000000000023551224417117700267460ustar00rootroot00000000000000import numpy as np from numpy.testing import dec from statsmodels.graphics.boxplots import violinplot, beanplot from statsmodels.datasets import anes96 try: import matplotlib.pyplot as plt have_matplotlib = True except: have_matplotlib = False @dec.skipif(not have_matplotlib) def test_violinplot_beanplot(): """Test violinplot and beanplot with the same dataset.""" data = anes96.load_pandas() party_ID = np.arange(7) labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat", "Independent-Independent", "Independent-Republican", "Weak Republican", "Strong Republican"] age = [data.exog['age'][data.endog == id] for id in party_ID] fig = plt.figure() ax = fig.add_subplot(111) violinplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) plt.close(fig) fig = plt.figure() ax = fig.add_subplot(111) beanplot(age, ax=ax, labels=labels, plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small', 'label_rotation':30}) plt.close(fig) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_correlation.py000066400000000000000000000021121224417117700274040ustar00rootroot00000000000000import numpy as np from numpy.testing import dec from statsmodels.graphics.correlation import plot_corr, plot_corr_grid from statsmodels.datasets import randhie try: import matplotlib.pyplot as plt have_matplotlib = True except: have_matplotlib = False @dec.skipif(not have_matplotlib) def test_plot_corr(): hie_data = randhie.load_pandas() corr_matrix = np.corrcoef(hie_data.data.T) fig = plot_corr(corr_matrix, xnames=hie_data.names) plt.close(fig) fig = plot_corr(corr_matrix, xnames=[], ynames=hie_data.names) plt.close(fig) fig = plot_corr(corr_matrix, normcolor=True, title='', cmap='jet') plt.close(fig) @dec.skipif(not have_matplotlib) def test_plot_corr_grid(): hie_data = randhie.load_pandas() corr_matrix = np.corrcoef(hie_data.data.T) fig = plot_corr_grid([corr_matrix] * 2, xnames=hie_data.names) plt.close(fig) fig = plot_corr_grid([corr_matrix] * 5, xnames=[], ynames=hie_data.names) plt.close(fig) fig = plot_corr_grid([corr_matrix] * 3, normcolor=True, titles='', cmap='jet') plt.close(fig) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_factorplots.py000066400000000000000000000030201224417117700274220ustar00rootroot00000000000000import numpy as np from nose import SkipTest from pandas import Series from statsmodels.graphics.factorplots import interaction_plot try: import matplotlib.pyplot as plt import matplotlib if matplotlib.__version__ < '1': raise have_matplotlib = True except: have_matplotlib = False class TestInteractionPlot(object): @classmethod def setupClass(cls): if not have_matplotlib: raise SkipTest('matplotlib not available') np.random.seed(12345) cls.weight = np.random.randint(1,4,size=60) cls.duration = np.random.randint(1,3,size=60) cls.days = np.log(np.random.randint(1,30, size=60)) def test_plot_both(self): fig = interaction_plot(self.weight, self.duration, self.days, colors=['red','blue'], markers=['D','^'], ms=10) plt.close(fig) def test_plot_rainbow(self): fig = interaction_plot(self.weight, self.duration, self.days, markers=['D','^'], ms=10) plt.close(fig) def test_plot_pandas(self): weight = Series(self.weight, name='Weight') duration = Series(self.duration, name='Duration') days = Series(self.days, name='Days') fig = interaction_plot(weight, duration, days, markers=['D','^'], ms=10) ax = fig.axes[0] trace = ax.get_legend().get_title().get_text() assert trace == 'Duration' assert ax.get_ylabel() == 'mean of Days' assert ax.get_xlabel() == 'Weight' plt.close(fig) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_functional.py000066400000000000000000000053771224417117700272450ustar00rootroot00000000000000import numpy as np from numpy.testing import dec, assert_equal, assert_almost_equal from statsmodels.graphics.functional import \ banddepth, fboxplot, rainbowplot try: import matplotlib.pyplot as plt import matplotlib if matplotlib.__version__ < '1': raise have_matplotlib = True except: have_matplotlib = False def test_banddepth_BD2(): xx = np.arange(500) / 150. y1 = 1 + 0.5 * np.sin(xx) y2 = 0.3 + np.sin(xx + np.pi/6) y3 = -0.5 + np.sin(xx + np.pi/6) y4 = -1 + 0.3 * np.cos(xx + np.pi/6) data = np.asarray([y1, y2, y3, y4]) depth = banddepth(data, method='BD2') expected_depth = [0.5, 5./6, 5./6, 0.5] assert_almost_equal(depth, expected_depth) ## Plot to visualize why we expect this output #fig = plt.figure() #ax = fig.add_subplot(111) #for ii, yy in enumerate([y1, y2, y3, y4]): # ax.plot(xx, yy, label="y%s" % ii) #ax.legend() #plt.show() def test_banddepth_MBD(): xx = np.arange(5001) / 5000. y1 = np.zeros(xx.shape) y2 = 2 * xx - 1 y3 = np.ones(xx.shape) * 0.5 y4 = np.ones(xx.shape) * -0.25 data = np.asarray([y1, y2, y3, y4]) depth = banddepth(data, method='MBD') expected_depth = [5./6, (2*(0.75-3./8)+3)/6, 3.5/6, (2*3./8+3)/6] assert_almost_equal(depth, expected_depth, decimal=4) @dec.skipif(not have_matplotlib) def test_fboxplot_rainbowplot(): """Test fboxplot and rainbowplot together, is much faster.""" def harmfunc(t): """Test function, combination of a few harmonic terms.""" # Constant, 0 with p=0.9, 1 with p=1 - for creating outliers ci = int(np.random.random() > 0.9) a1i = np.random.random() * 0.05 a2i = np.random.random() * 0.05 b1i = (0.15 - 0.1) * np.random.random() + 0.1 b2i = (0.15 - 0.1) * np.random.random() + 0.1 func = (1 - ci) * (a1i * np.sin(t) + a2i * np.cos(t)) + \ ci * (b1i * np.sin(t) + b2i * np.cos(t)) return func np.random.seed(1234567) # Some basic test data, Model 6 from Sun and Genton. t = np.linspace(0, 2 * np.pi, 250) data = [] for ii in range(20): data.append(harmfunc(t)) # fboxplot test fig = plt.figure() ax = fig.add_subplot(111) _, depth, ix_depth, ix_outliers = fboxplot(data, wfactor=2, ax=ax) ix_expected = np.array([13, 4, 15, 19, 8, 6, 3, 16, 9, 7, 1, 5, 2, 12, 17, 11, 14, 10, 0, 18]) assert_equal(ix_depth, ix_expected) ix_expected2 = np.array([2, 11, 17, 18]) assert_equal(ix_outliers, ix_expected2) plt.close(fig) # rainbowplot test (re-uses depth variable) xdata = np.arange(data[0].size) fig = rainbowplot(data, xdata=xdata, depth=depth, cmap=plt.cm.rainbow) plt.close(fig) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_gofplots.py000066400000000000000000000054051224417117700267300ustar00rootroot00000000000000import numpy as np from numpy.testing import dec import statsmodels.api as sm from statsmodels.graphics.gofplots import qqplot, qqline, ProbPlot from scipy import stats try: import matplotlib.pyplot as plt import matplotlib if matplotlib.__version__ < '1': raise have_matplotlib = True except: have_matplotlib = False @dec.skipif(not have_matplotlib) def test_qqplot(): #just test that it runs data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=False) mod_fit = sm.OLS(data.endog, data.exog).fit() res = mod_fit.resid fig = sm.qqplot(res, line='r') plt.close('all') @dec.skipif(not have_matplotlib) def test_ProbPlot(): #just test that it runs data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=False) mod_fit = sm.OLS(data.endog, data.exog).fit() res = sm.ProbPlot(mod_fit.resid, stats.t, distargs=(4,)) # basic tests modeled after example in docstring fig1 = res.qqplot(line='r') fig2 = res.ppplot(line='r') fig3 = res.probplot(line='r') plt.close('all') @dec.skipif(not have_matplotlib) def test_ProbPlot_comparison(): # two fake samples for comparison x = np.random.normal(loc=8.25, scale=3.25, size=37) y = np.random.normal(loc=8.25, scale=3.25, size=37) pp_x = sm.ProbPlot(x) pp_y = sm.ProbPlot(y) # test `other` kwarg with `ProbPlot` instance fig4 = pp_x.qqplot(other=pp_y) fig5 = pp_x.ppplot(other=pp_y) plt.close('all') @dec.skipif(not have_matplotlib) def test_ProbPlot_comparison_arrays(): # two fake samples for comparison x = np.random.normal(loc=8.25, scale=3.25, size=37) y = np.random.normal(loc=8.25, scale=3.25, size=37) pp_x = sm.ProbPlot(x) pp_y = sm.ProbPlot(y) # test `other` kwarg with array fig6 = pp_x.qqplot(other=y) fig7 = pp_x.ppplot(other=y) plt.close('all') @dec.skipif(not have_matplotlib) def test_qqplot_2samples(): #just test that it runs x = np.random.normal(loc=8.25, scale=3.25, size=37) y = np.random.normal(loc=8.25, scale=3.25, size=37) pp_x = sm.ProbPlot(x) pp_y = sm.ProbPlot(y) # also tests all values for line for line in ['r', 'q', '45', 's']: # test with `ProbPlot` instances fig2 = sm.qqplot_2samples(pp_x, pp_y, line=line) plt.close('all') @dec.skipif(not have_matplotlib) def test_qqplot_2samples_arrays(): #just test that it runs x = np.random.normal(loc=8.25, scale=3.25, size=37) y = np.random.normal(loc=8.25, scale=3.25, size=37) pp_x = sm.ProbPlot(x) pp_y = sm.ProbPlot(y) # also tests all values for line for line in ['r', 'q', '45', 's']: # test with arrays fig1 = sm.qqplot_2samples(x, y, line=line) plt.close('all') statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_mosaicplot.py000066400000000000000000000416631224417117700272530ustar00rootroot00000000000000from __future__ import division from numpy.testing import assert_, assert_raises, dec from numpy.testing import run_module_suite # utilities for the tests from statsmodels.compatnp.collections import OrderedDict from statsmodels.api import datasets import numpy as np from itertools import product try: import matplotlib.pyplot as pylab have_matplotlib = True except: have_matplotlib = False import pandas pandas_old = int(pandas.__version__.split('.')[1]) < 9 # the main drawing function from statsmodels.graphics.mosaicplot import mosaic # other functions to be tested for accuracy from statsmodels.graphics.mosaicplot import _hierarchical_split from statsmodels.graphics.mosaicplot import _reduce_dict from statsmodels.graphics.mosaicplot import _key_splitting from statsmodels.graphics.mosaicplot import _normalize_split from statsmodels.graphics.mosaicplot import _split_rect @dec.skipif(not have_matplotlib or pandas_old) def test_data_conversion(): # It will not reorder the elements # so the dictionary will look odd # as it key order has the c and b # keys swapped import pandas fig, ax = pylab.subplots(4, 4) data = {'ax': 1, 'bx': 2, 'cx': 3} mosaic(data, ax=ax[0, 0], title='basic dict', axes_label=False) data = pandas.Series(data) mosaic(data, ax=ax[0, 1], title='basic series', axes_label=False) data = [1, 2, 3] mosaic(data, ax=ax[0, 2], title='basic list', axes_label=False) data = np.asarray(data) mosaic(data, ax=ax[0, 3], title='basic array', axes_label=False) data = {('ax', 'cx'): 1, ('bx', 'cx'): 2, ('ax', 'dx'): 3, ('bx', 'dx'): 4} mosaic(data, ax=ax[1, 0], title='compound dict', axes_label=False) mosaic(data, ax=ax[2, 0], title='inverted keys dict', index=[1, 0], axes_label=False) data = pandas.Series(data) mosaic(data, ax=ax[1, 1], title='compound series', axes_label=False) mosaic(data, ax=ax[2, 1], title='inverted keys series', index=[1, 0]) data = [[1, 2], [3, 4]] mosaic(data, ax=ax[1, 2], title='compound list', axes_label=False) mosaic(data, ax=ax[2, 2], title='inverted keys list', index=[1, 0]) data = np.array([[1, 2], [3, 4]]) mosaic(data, ax=ax[1, 3], title='compound array', axes_label=False) mosaic(data, ax=ax[2, 3], title='inverted keys array', index=[1, 0], axes_label=False) gender = ['male', 'male', 'male', 'female', 'female', 'female'] pet = ['cat', 'dog', 'dog', 'cat', 'dog', 'cat'] data = pandas.DataFrame({'gender': gender, 'pet': pet}) mosaic(data, ['gender'], ax=ax[3, 0], title='dataframe by key 1', axes_label=False) mosaic(data, ['pet'], ax=ax[3, 1], title='dataframe by key 2', axes_label=False) mosaic(data, ['gender', 'pet'], ax=ax[3, 2], title='both keys', axes_label=False) mosaic(data, ['pet', 'gender'], ax=ax[3, 3], title='keys inverted', axes_label=False) pylab.suptitle('testing data conversion (plot 1 of 4)') #pylab.show() @dec.skipif(not have_matplotlib) def test_mosaic_simple(): # display a simple plot of 4 categories of data, splitted in four # levels with increasing size for each group # creation of the levels key_set = (['male', 'female'], ['old', 'adult', 'young'], ['worker', 'unemployed'], ['healty', 'ill']) # the cartesian product of all the categories is # the complete set of categories keys = list(product(*key_set)) data = OrderedDict(zip(keys, range(1, 1 + len(keys)))) # which colours should I use for the various categories? # put it into a dict props = {} #males and females in blue and red props[('male',)] = {'color': 'b'} props[('female',)] = {'color': 'r'} # all the groups corresponding to ill groups have a different color for key in keys: if 'ill' in key: if 'male' in key: props[key] = {'color': 'BlueViolet' , 'hatch': '+'} else: props[key] = {'color': 'Crimson' , 'hatch': '+'} # mosaic of the data, with given gaps and colors mosaic(data, gap=0.05, properties=props, axes_label=False) pylab.suptitle('syntetic data, 4 categories (plot 2 of 4)') #pylab.show() @dec.skipif(not have_matplotlib or pandas_old) def test_mosaic(): # make the same analysis on a known dataset # load the data and clean it a bit affairs = datasets.fair.load_pandas() datas = affairs.exog # any time greater than 0 is cheating datas['cheated'] = affairs.endog > 0 # sort by the marriage quality and give meaningful name # [rate_marriage, age, yrs_married, children, # religious, educ, occupation, occupation_husb] datas = datas.sort(['rate_marriage', 'religious']) num_to_desc = {1: 'awful', 2: 'bad', 3: 'intermediate', 4: 'good', 5: 'wonderful'} datas['rate_marriage'] = datas['rate_marriage'].map(num_to_desc) num_to_faith = {1: 'non religious', 2: 'poorly religious', 3: 'religious', 4: 'very religious'} datas['religious'] = datas['religious'].map(num_to_faith) num_to_cheat = {False: 'faithful', True: 'cheated'} datas['cheated'] = datas['cheated'].map(num_to_cheat) # finished cleaning fig, ax = pylab.subplots(2, 2) mosaic(datas, ['rate_marriage', 'cheated'], ax=ax[0, 0], title='by marriage happiness') mosaic(datas, ['religious', 'cheated'], ax=ax[0, 1], title='by religiosity') mosaic(datas, ['rate_marriage', 'religious', 'cheated'], ax=ax[1, 0], title='by both', labelizer=lambda k:'') ax[1, 0].set_xlabel('marriage rating') ax[1, 0].set_ylabel('religion status') mosaic(datas, ['religious', 'rate_marriage'], ax=ax[1, 1], title='inter-dependence', axes_label=False) pylab.suptitle("extramarital affairs (plot 3 of 4)") #pylab.show() @dec.skipif(not have_matplotlib) def test_mosaic_very_complex(): # make a scattermatrix of mosaic plots to show the correlations between # each pair of variable in a dataset. Could be easily converted into a # new function that does this automatically based on the type of data key_name = ['gender', 'age', 'health', 'work'] key_base = (['male', 'female'], ['old', 'young'], ['healty', 'ill'], ['work', 'unemployed']) keys = list(product(*key_base)) data = OrderedDict(zip(keys, range(1, 1 + len(keys)))) props = {} props[('male', 'old')] = {'color': 'r'} props[('female',)] = {'color': 'pink'} L = len(key_base) fig, axes = pylab.subplots(L, L) for i in range(L): for j in range(L): m = set(range(L)).difference(set((i, j))) if i == j: axes[i, i].text(0.5, 0.5, key_name[i], ha='center', va='center') axes[i, i].set_xticks([]) axes[i, i].set_xticklabels([]) axes[i, i].set_yticks([]) axes[i, i].set_yticklabels([]) else: ji = max(i, j) ij = min(i, j) temp_data = OrderedDict([((k[ij], k[ji]) + tuple(k[r] for r in m), v) for k, v in data.items()]) keys = temp_data.keys() for k in keys: value = _reduce_dict(temp_data, k[:2]) temp_data[k[:2]] = value del temp_data[k] mosaic(temp_data, ax=axes[i, j], axes_label=False, properties=props, gap=0.05, horizontal=i > j) pylab.suptitle('old males should look bright red, (plot 4 of 4)') #pylab.show() @dec.skipif(not have_matplotlib) def test_axes_labeling(): from numpy.random import rand key_set = (['male', 'female'], ['old', 'adult', 'young'], ['worker', 'unemployed'], ['yes', 'no']) # the cartesian product of all the categories is # the complete set of categories keys = list(product(*key_set)) data = OrderedDict(zip(keys, rand(len(keys)))) lab = lambda k: ''.join(s[0] for s in k) fig, (ax1, ax2) = pylab.subplots(1, 2, figsize=(16, 8)) mosaic(data, ax=ax1, labelizer=lab, horizontal=True, label_rotation=45) mosaic(data, ax=ax2, labelizer=lab, horizontal=False, label_rotation=[0, 45, 90, 0]) #fig.tight_layout() fig.suptitle("correct alignment of the axes labels") #pylab.show() eq = lambda x, y: assert_(np.allclose(x, y)) def test_recursive_split(): keys = list(product('mf')) data = OrderedDict(zip(keys, [1] * len(keys))) res = _hierarchical_split(data, gap=0) assert_(res.keys() == keys) res[('m',)] = (0.0, 0.0, 0.5, 1.0) res[('f',)] = (0.5, 0.0, 0.5, 1.0) keys = list(product('mf', 'yao')) data = OrderedDict(zip(keys, [1] * len(keys))) res = _hierarchical_split(data, gap=0) assert_(res.keys() == keys) res[('m', 'y')] = (0.0, 0.0, 0.5, 1 / 3) res[('m', 'a')] = (0.0, 1 / 3, 0.5, 1 / 3) res[('m', 'o')] = (0.0, 2 / 3, 0.5, 1 / 3) res[('f', 'y')] = (0.5, 0.0, 0.5, 1 / 3) res[('f', 'a')] = (0.5, 1 / 3, 0.5, 1 / 3) res[('f', 'o')] = (0.5, 2 / 3, 0.5, 1 / 3) def test__reduce_dict(): data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), [1] * 8)) eq(_reduce_dict(data, ('m',)), 4) eq(_reduce_dict(data, ('m', 'o')), 2) eq(_reduce_dict(data, ('m', 'o', 'w')), 1) data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), range(8))) eq(_reduce_dict(data, ('m',)), 6) eq(_reduce_dict(data, ('m', 'o')), 1) eq(_reduce_dict(data, ('m', 'o', 'w')), 0) def test__key_splitting(): # subdivide starting with an empty tuple base_rect = {tuple(): (0, 0, 1, 1)} res = _key_splitting(base_rect, ['a', 'b'], [1, 1], tuple(), True, 0) assert_(res.keys() == [('a',), ('b',)]) eq(res[('a',)], (0, 0, 0.5, 1)) eq(res[('b',)], (0.5, 0, 0.5, 1)) # subdivide a in two sublevel res_bis = _key_splitting(res, ['c', 'd'], [1, 1], ('a',), False, 0) assert_(res_bis.keys() == [('a', 'c'), ('a', 'd'), ('b',)]) eq(res_bis[('a', 'c')], (0.0, 0.0, 0.5, 0.5)) eq(res_bis[('a', 'd')], (0.0, 0.5, 0.5, 0.5)) eq(res_bis[('b',)], (0.5, 0, 0.5, 1)) # starting with a non empty tuple and uneven distribution base_rect = {('total',): (0, 0, 1, 1)} res = _key_splitting(base_rect, ['a', 'b'], [1, 2], ('total',), True, 0) assert_(res.keys() == [('total',) + (e,) for e in ['a', 'b']]) eq(res[('total', 'a')], (0, 0, 1 / 3, 1)) eq(res[('total', 'b')], (1 / 3, 0, 2 / 3, 1)) def test_proportion_normalization(): # extremes should give the whole set, as well # as if 0 is inserted eq(_normalize_split(0.), [0.0, 0.0, 1.0]) eq(_normalize_split(1.), [0.0, 1.0, 1.0]) eq(_normalize_split(2.), [0.0, 1.0, 1.0]) # negative values should raise ValueError assert_raises(ValueError, _normalize_split, -1) assert_raises(ValueError, _normalize_split, [1., -1]) assert_raises(ValueError, _normalize_split, [1., -1, 0.]) # if everything is zero it will complain assert_raises(ValueError, _normalize_split, [0.]) assert_raises(ValueError, _normalize_split, [0., 0.]) # one-element array should return the whole interval eq(_normalize_split([0.5]), [0.0, 1.0]) eq(_normalize_split([1.]), [0.0, 1.0]) eq(_normalize_split([2.]), [0.0, 1.0]) # simple division should give two pieces for x in [0.3, 0.5, 0.9]: eq(_normalize_split(x), [0., x, 1.0]) # multiple division should split as the sum of the components for x, y in [(0.25, 0.5), (0.1, 0.8), (10., 30.)]: eq(_normalize_split([x, y]), [0., x / (x + y), 1.0]) for x, y, z in [(1., 1., 1.), (0.1, 0.5, 0.7), (10., 30., 40)]: eq(_normalize_split( [x, y, z]), [0., x / (x + y + z), (x + y) / (x + y + z), 1.0]) def test_false_split(): # if you ask it to be divided in only one piece, just return the original # one pure_square = [0., 0., 1., 1.] conf_h = dict(proportion=[1], gap=0.0, horizontal=True) conf_v = dict(proportion=[1], gap=0.0, horizontal=False) eq(_split_rect(*pure_square, **conf_h), pure_square) eq(_split_rect(*pure_square, **conf_v), pure_square) conf_h = dict(proportion=[1], gap=0.5, horizontal=True) conf_v = dict(proportion=[1], gap=0.5, horizontal=False) eq(_split_rect(*pure_square, **conf_h), pure_square) eq(_split_rect(*pure_square, **conf_v), pure_square) # identity on a void rectangle should not give anything strange null_square = [0., 0., 0., 0.] conf = dict(proportion=[1], gap=0.0, horizontal=True) eq(_split_rect(*null_square, **conf), null_square) conf = dict(proportion=[1], gap=1.0, horizontal=True) eq(_split_rect(*null_square, **conf), null_square) # splitting a negative rectangle should raise error neg_square = [0., 0., -1., 0.] conf = dict(proportion=[1], gap=0.0, horizontal=True) assert_raises(ValueError, _split_rect, *neg_square, **conf) conf = dict(proportion=[1, 1], gap=0.0, horizontal=True) assert_raises(ValueError, _split_rect, *neg_square, **conf) conf = dict(proportion=[1], gap=0.5, horizontal=True) assert_raises(ValueError, _split_rect, *neg_square, **conf) conf = dict(proportion=[1, 1], gap=0.5, horizontal=True) assert_raises(ValueError, _split_rect, *neg_square, **conf) def test_rect_pure_split(): pure_square = [0., 0., 1., 1.] # division in two equal pieces from the perfect square h_2split = [(0.0, 0.0, 0.5, 1.0), (0.5, 0.0, 0.5, 1.0)] conf_h = dict(proportion=[1, 1], gap=0.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) v_2split = [(0.0, 0.0, 1.0, 0.5), (0.0, 0.5, 1.0, 0.5)] conf_v = dict(proportion=[1, 1], gap=0.0, horizontal=False) eq(_split_rect(*pure_square, **conf_v), v_2split) # division in two non-equal pieces from the perfect square h_2split = [(0.0, 0.0, 1 / 3, 1.0), (1 / 3, 0.0, 2 / 3, 1.0)] conf_h = dict(proportion=[1, 2], gap=0.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) v_2split = [(0.0, 0.0, 1.0, 1 / 3), (0.0, 1 / 3, 1.0, 2 / 3)] conf_v = dict(proportion=[1, 2], gap=0.0, horizontal=False) eq(_split_rect(*pure_square, **conf_v), v_2split) # division in three equal pieces from the perfect square h_2split = [(0.0, 0.0, 1 / 3, 1.0), (1 / 3, 0.0, 1 / 3, 1.0), (2 / 3, 0.0, 1 / 3, 1.0)] conf_h = dict(proportion=[1, 1, 1], gap=0.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) v_2split = [(0.0, 0.0, 1.0, 1 / 3), (0.0, 1 / 3, 1.0, 1 / 3), (0.0, 2 / 3, 1.0, 1 / 3)] conf_v = dict(proportion=[1, 1, 1], gap=0.0, horizontal=False) eq(_split_rect(*pure_square, **conf_v), v_2split) # division in three non-equal pieces from the perfect square h_2split = [(0.0, 0.0, 1 / 4, 1.0), (1 / 4, 0.0, 1 / 2, 1.0), (3 / 4, 0.0, 1 / 4, 1.0)] conf_h = dict(proportion=[1, 2, 1], gap=0.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) v_2split = [(0.0, 0.0, 1.0, 1 / 4), (0.0, 1 / 4, 1.0, 1 / 2), (0.0, 3 / 4, 1.0, 1 / 4)] conf_v = dict(proportion=[1, 2, 1], gap=0.0, horizontal=False) eq(_split_rect(*pure_square, **conf_v), v_2split) # splitting on a void rectangle should give multiple void null_square = [0., 0., 0., 0.] conf = dict(proportion=[1, 1], gap=0.0, horizontal=True) eq(_split_rect(*null_square, **conf), [null_square, null_square]) conf = dict(proportion=[1, 2], gap=1.0, horizontal=True) eq(_split_rect(*null_square, **conf), [null_square, null_square]) def test_rect_deformed_split(): non_pure_square = [1., -1., 1., 0.5] # division in two equal pieces from the perfect square h_2split = [(1.0, -1.0, 0.5, 0.5), (1.5, -1.0, 0.5, 0.5)] conf_h = dict(proportion=[1, 1], gap=0.0, horizontal=True) eq(_split_rect(*non_pure_square, **conf_h), h_2split) v_2split = [(1.0, -1.0, 1.0, 0.25), (1.0, -0.75, 1.0, 0.25)] conf_v = dict(proportion=[1, 1], gap=0.0, horizontal=False) eq(_split_rect(*non_pure_square, **conf_v), v_2split) # division in two non-equal pieces from the perfect square h_2split = [(1.0, -1.0, 1 / 3, 0.5), (1 + 1 / 3, -1.0, 2 / 3, 0.5)] conf_h = dict(proportion=[1, 2], gap=0.0, horizontal=True) eq(_split_rect(*non_pure_square, **conf_h), h_2split) v_2split = [(1.0, -1.0, 1.0, 1 / 6), (1.0, 1 / 6 - 1, 1.0, 2 / 6)] conf_v = dict(proportion=[1, 2], gap=0.0, horizontal=False) eq(_split_rect(*non_pure_square, **conf_v), v_2split) def test_gap_split(): pure_square = [0., 0., 1., 1.] # null split conf_h = dict(proportion=[1], gap=1.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), pure_square) # equal split h_2split = [(0.0, 0.0, 0.25, 1.0), (0.75, 0.0, 0.25, 1.0)] conf_h = dict(proportion=[1, 1], gap=1.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) # disequal split h_2split = [(0.0, 0.0, 1 / 6, 1.0), (0.5 + 1 / 6, 0.0, 1 / 3, 1.0)] conf_h = dict(proportion=[1, 2], gap=1.0, horizontal=True) eq(_split_rect(*pure_square, **conf_h), h_2split) if __name__ == '__main__': run_module_suite() statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_regressionplots.py000066400000000000000000000104661224417117700303400ustar00rootroot00000000000000'''Tests for regressionplots, entire module is skipped ''' import numpy as np import nose import statsmodels.api as sm from statsmodels.graphics.regressionplots import (plot_fit, plot_ccpr, plot_partregress, plot_regress_exog, abline_plot, plot_partregress_grid, plot_ccpr_grid, add_lowess) from pandas import Series, DataFrame try: import matplotlib.pyplot as plt #makes plt available for test functions have_matplotlib = True except: have_matplotlib = False def setup(): if not have_matplotlib: raise nose.SkipTest('No tests here') def teardown_module(): plt.close('all') class TestPlot(object): def __init__(self): self.setup() #temp: for testing without nose def setup(self): nsample = 100 sig = 0.5 x1 = np.linspace(0, 20, nsample) x2 = 5 + 3* np.random.randn(nsample) X = np.c_[x1, x2, np.sin(0.5*x1), (x2-5)**2, np.ones(nsample)] beta = [0.5, 0.5, 1, -0.04, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) exog0 = sm.add_constant(np.c_[x1, x2], prepend=False) res = sm.OLS(y, exog0).fit() self.res = res def test_plot_fit(self): res = self.res fig = plot_fit(res, 0, y_true=None) x0 = res.model.exog[:, 0] yf = res.fittedvalues y = res.model.endog px1, px2 = fig.axes[0].get_lines()[0].get_data() np.testing.assert_equal(x0, px1) np.testing.assert_equal(y, px2) px1, px2 = fig.axes[0].get_lines()[1].get_data() np.testing.assert_equal(x0, px1) np.testing.assert_equal(yf, px2) plt.close(fig) def test_plot_oth(self): #just test that they run res = self.res endog = res.model.endog exog = res.model.exog plot_fit(res, 0, y_true=None) plot_partregress_grid(res, exog_idx=[0,1]) plot_regress_exog(res, exog_idx=0) plot_ccpr(res, exog_idx=0) plot_ccpr_grid(res, exog_idx=[0]) fig = plot_ccpr_grid(res, exog_idx=[0,1]) for ax in fig.axes: add_lowess(ax) plt.close('all') class TestPlotPandas(TestPlot): def setup(self): nsample = 100 sig = 0.5 x1 = np.linspace(0, 20, nsample) x2 = 5 + 3* np.random.randn(nsample) X = np.c_[x1, x2, np.sin(0.5*x1), (x2-5)**2, np.ones(nsample)] beta = [0.5, 0.5, 1, -0.04, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) exog0 = sm.add_constant(np.c_[x1, x2], prepend=False) exog0 = DataFrame(exog0, columns=["const", "var1", "var2"]) y = Series(y, name="outcome") res = sm.OLS(y, exog0).fit() self.res = res class TestABLine(object): @classmethod def setupClass(cls): np.random.seed(12345) X = sm.add_constant(np.random.normal(0, 20, size=30)) y = np.dot(X, [25, 3.5]) + np.random.normal(0, 30, size=30) mod = sm.OLS(y,X).fit() cls.X = X cls.y = y cls.mod = mod def test_abline_model(self): fig = abline_plot(model_results=self.mod) ax = fig.axes[0] ax.scatter(self.X[:,1], self.y) plt.close(fig) def test_abline_model_ax(self): fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(self.X[:,1], self.y) fig = abline_plot(model_results=self.mod, ax=ax) plt.close(fig) def test_abline_ab(self): mod = self.mod intercept, slope = mod.params fig = abline_plot(intercept=intercept, slope=slope) plt.close(fig) def test_abline_ab_ax(self): mod = self.mod intercept, slope = mod.params fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(self.X[:,1], self.y) fig = abline_plot(intercept=intercept, slope=slope, ax=ax) plt.close(fig) class TestABLinePandas(TestABLine): @classmethod def setupClass(cls): np.random.seed(12345) X = sm.add_constant(np.random.normal(0, 20, size=30)) y = np.dot(X, [25, 3.5]) + np.random.normal(0, 30, size=30) cls.X = X cls.y = y X = DataFrame(X, columns=["const", "someX"]) y = Series(y, name="outcome") mod = sm.OLS(y,X).fit() cls.mod = mod statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tests/test_tsaplots.py000066400000000000000000000011211224417117700267330ustar00rootroot00000000000000import numpy as np from numpy.testing import dec import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf import statsmodels.tsa.arima_process as tsp try: import matplotlib.pyplot as plt have_matplotlib = True except: have_matplotlib = False @dec.skipif(not have_matplotlib) def test_plot_acf(): # Just test that it runs. fig = plt.figure() ax = fig.add_subplot(111) ar = np.r_[1., -0.9] ma = np.r_[1., 0.9] armaprocess = tsp.ArmaProcess(ar, ma) acf = armaprocess.acf(20)[:20] plot_acf(acf, ax=ax) plt.close(fig) statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tsaplots.py000066400000000000000000000132421224417117700245410ustar00rootroot00000000000000"""Correlation plot functions.""" import numpy as np from statsmodels.graphics import utils from statsmodels.tsa.stattools import acf, pacf def plot_acf(x, ax=None, lags=None, alpha=.05, use_vlines=True, unbiased=False, fft=False, **kwargs): """Plot the autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters ---------- x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional Array of lag values, used on horizontal axis. If not given, ``lags=np.arange(len(corr))`` is used. alpha : scalar, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett's formula. If None, no confidence intervals are plotted. use_vlines : bool, optional If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is 'o'; it can be overridden with a ``marker`` kwarg. unbiased : bool If True, then denominators for autocovariance are n-k, otherwise n fft : bool, optional If True, computes the ACF via FFT. **kwargs : kwargs, optional Optional keyword arguments that are directly passed on to the Matplotlib ``plot`` and ``axhline`` functions. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- matplotlib.pyplot.xcorr matplotlib.pyplot.acorr mpl_examples/pylab_examples/xcorr_demo.py Notes ----- Adapted from matplotlib's `xcorr`. Data are plotted as ``plot(lags, corr, **kwargs)`` """ fig, ax = utils.create_mpl_ax(ax) if lags is None: lags = np.arange(len(x)) nlags = len(lags) - 1 else: nlags = lags lags = np.arange(lags + 1) # +1 for zero lag acf_x, confint = acf(x, nlags=nlags, alpha=alpha, fft=fft, unbiased=unbiased) if use_vlines: ax.vlines(lags, [0], acf_x, **kwargs) ax.axhline(**kwargs) # center the confidence interval TODO: do in acf? confint = confint - confint.mean(1)[:,None] kwargs.setdefault('marker', 'o') kwargs.setdefault('markersize', 5) kwargs.setdefault('linestyle', 'None') ax.margins(.05) ax.plot(lags, acf_x, **kwargs) ax.fill_between(lags, confint[:,0], confint[:,1], alpha=.25) ax.set_title("Autocorrelation") return fig def plot_pacf(x, ax=None, lags=None, alpha=.05, method='ywm', use_vlines=True, **kwargs): """Plot the partial autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters ---------- x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional Array of lag values, used on horizontal axis. If not given, ``lags=np.arange(len(corr))`` is used. alpha : scalar, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x)) method : 'ywunbiased' (default) or 'ywmle' or 'ols' specifies which method for the calculations to use: - yw or ywunbiased : yule walker with bias correction in denominator for acovf - ywm or ywmle : yule walker without bias correction - ols - regression of time series on lags of it and on constant - ld or ldunbiased : Levinson-Durbin recursion with bias correction - ldb or ldbiased : Levinson-Durbin recursion without bias correction use_vlines : bool, optional If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is 'o'; it can be overridden with a ``marker`` kwarg. **kwargs : kwargs, optional Optional keyword arguments that are directly passed on to the Matplotlib ``plot`` and ``axhline`` functions. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- matplotlib.pyplot.xcorr matplotlib.pyplot.acorr mpl_examples/pylab_examples/xcorr_demo.py Notes ----- Adapted from matplotlib's `xcorr`. Data are plotted as ``plot(lags, corr, **kwargs)`` """ fig, ax = utils.create_mpl_ax(ax) if lags is None: lags = np.arange(len(x)) nlags = len(lags) - 1 else: nlags = lags lags = np.arange(lags + 1) # +1 for zero lag acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method) if use_vlines: ax.vlines(lags, [0], acf_x, **kwargs) ax.axhline(**kwargs) # center the confidence interval TODO: do in acf? confint = confint - confint.mean(1)[:,None] kwargs.setdefault('marker', 'o') kwargs.setdefault('markersize', 5) kwargs.setdefault('linestyle', 'None') ax.margins(.05) ax.plot(lags, acf_x, **kwargs) ax.fill_between(lags, confint[:,0], confint[:,1], alpha=.25) ax.set_title("Partial Autocorrelation") return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/tukeyplot.py000066400000000000000000000045751224417117700247410ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker as mticker import matplotlib.lines as lines def tukeyplot(results, dim=None, yticklabels=None): npairs = len(results) fig = plt.figure() fsp = fig.add_subplot(111) fsp.axis([-50,50,0.5,10.5]) fsp.set_title('95 % family-wise confidence level') fsp.title.set_y(1.025) fsp.set_yticks(np.arange(1,11)) fsp.set_yticklabels(['V-T','V-S','T-S','V-P','T-P','S-P','V-M', 'T-M','S-M','P-M']) #fsp.yaxis.set_major_locator(mticker.MaxNLocator(npairs)) fsp.yaxis.grid(True, linestyle='-', color='gray') fsp.set_xlabel('Differences in mean levels of Var', labelpad=8) fsp.xaxis.tick_bottom() fsp.yaxis.tick_left() xticklines = fsp.get_xticklines() for xtickline in xticklines: xtickline.set_marker(lines.TICKDOWN) xtickline.set_markersize(10) xlabels = fsp.get_xticklabels() for xlabel in xlabels: xlabel.set_y(-.04) yticklines = fsp.get_yticklines() for ytickline in yticklines: ytickline.set_marker(lines.TICKLEFT) ytickline.set_markersize(10) ylabels = fsp.get_yticklabels() for ylabel in ylabels: ylabel.set_x(-.04) for pair in range(npairs): data = .5+results[pair]/100. #fsp.axhline(y=npairs-pair, xmin=data[0], xmax=data[1], linewidth=1.25, fsp.axhline(y=npairs-pair, xmin=data.mean(), xmax=data[1], linewidth=1.25, color='blue', marker="|", markevery=1) fsp.axhline(y=npairs-pair, xmin=data[0], xmax=data.mean(), linewidth=1.25, color='blue', marker="|", markevery=1) #for pair in range(npairs): # data = .5+results[pair]/100. # data = results[pair] # data = np.r_[data[0],data.mean(),data[1]] # l = plt.plot(data, [npairs-pair]*len(data), color='black', # linewidth=.5, marker="|", markevery=1) fsp.axvline(x=0, linestyle="--", color='black') fig.subplots_adjust(bottom=.125) results = np.array([[-10.04391794, 26.34391794], [-21.45225794, 14.93557794], [ 5.61441206, 42.00224794], [-13.40225794, 22.98557794], [-29.60225794, 6.78557794], [ -2.53558794, 33.85224794], [-21.55225794, 14.83557794], [ 8.87275206, 45.26058794], [-10.14391794, 26.24391794], [-37.21058794, -0.82275206]]) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/graphics/utils.py000066400000000000000000000104141224417117700240260ustar00rootroot00000000000000"""Helper functions for graphics with Matplotlib.""" __all__ = ['create_mpl_ax', 'create_mpl_fig'] def _import_mpl(): """This function is not needed outside this utils module.""" try: import matplotlib.pyplot as plt except: raise ImportError("Matplotlib is not found.") return plt def create_mpl_ax(ax=None): """Helper function for when a single plot axis is needed. Parameters ---------- ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. ax : Matplotlib AxesSubplot instance The created axis if `ax` is None, otherwise the axis that was passed in. Notes ----- This function imports `matplotlib.pyplot`, which should only be done to create (a) figure(s) with ``plt.figure``. All other functionality exposed by the pyplot module can and should be imported directly from its Matplotlib module. See Also -------- create_mpl_fig Examples -------- A plotting function has a keyword ``ax=None``. Then calls: >>> from statsmodels.graphics import utils >>> fig, ax = utils.create_mpl_ax(ax) """ if ax is None: plt = _import_mpl() fig = plt.figure() ax = fig.add_subplot(111) else: fig = ax.figure return fig, ax def create_mpl_fig(fig=None, figsize=None): """Helper function for when multiple plot axes are needed. Those axes should be created in the functions they are used in, with ``fig.add_subplot()``. Parameters ---------- fig : Matplotlib figure instance, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- fig : Matplotlib figure instance If `fig` is None, the created figure. Otherwise the input `fig` is returned. See Also -------- create_mpl_ax """ if fig is None: plt = _import_mpl() fig = plt.figure(figsize=figsize) return fig def maybe_name_or_idx(idx, model): """ Give a name or an integer and return the name and integer location of the column in a design matrix. """ if idx is None: idx = range(model.exog.shape[1]) if isinstance(idx, int): exog_name = model.exog_names[idx] exog_idx = idx # anticipate index as list and recurse elif isinstance(idx, (tuple, list)): exog_name = [] exog_idx = [] for item in idx: exog_name_item, exog_idx_item = maybe_name_or_idx(item, model) exog_name.append(exog_name_item) exog_idx.append(exog_idx_item) else: # assume we've got a string variable exog_name = idx exog_idx = model.exog_names.index(idx) return exog_name, exog_idx def get_data_names(series_or_dataframe): """ Input can be an array or pandas-like. Will handle 1d array-like but not 2d. Returns a str for 1d data or a list of strings for 2d data. """ names = getattr(series_or_dataframe, 'name', None) if not names: names = getattr(series_or_dataframe, 'columns', None) if not names: shape = getattr(series_or_dataframe, 'shape', [1]) nvars = 1 if len(shape) == 1 else series.shape[1] names = ["X%d" for names in range(nvars)] if nvars == 1: names = names[0] else: names = names.tolist() return names def annotate_axes(index, labels, points, offset_points, size, ax, **kwargs): """ Annotate Axes with labels, points, offset_points according to the given index. """ for i in index: label = labels[i] point = points[i] offset = offset_points[i] ax.annotate(label, point, xytext=offset, textcoords="offset points", size=size, **kwargs) return ax def maybe_tight_layout(fig): import matplotlib as mpl if mpl.__version__ >= '1.1': # The tight_layout feature is not available before version 1.1 # It automatically pads the figure so labels do not get clipped. fig.tight_layout() return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/info.py000066400000000000000000000012021224417117700220140ustar00rootroot00000000000000""" Statistical models - standard `regression` models - `GLS` (generalized least squares regression) - `OLS` (ordinary least square regression) - `WLS` (weighted least square regression) - `GLASAR` (GLS with autoregressive errors model) - `GLM` (generalized linear models) - robust statistical models - `RLM` (robust linear models using M estimators) - `robust.norms` estimates - `robust.scale` estimates (MAD, Huber's proposal 2). - sandbox models - `mixed` effects models - `gam` (generalized additive models) """ __docformat__ = 'restructuredtext en' depends = ['numpy', 'scipy'] postpone_import = True statsmodels-0.5.0+git13-g8e07d34/statsmodels/interface/000077500000000000000000000000001224417117700224545ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/interface/__init__.py000066400000000000000000000000001224417117700245530ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/000077500000000000000000000000001224417117700216125ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/__init__.py000066400000000000000000000003151224417117700237220ustar00rootroot00000000000000from foreign import StataReader, genfromdta, savetxt from table import SimpleTable, csv2st from smpickle import save_pickle, load_pickle from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/api.py000066400000000000000000000002271224417117700227360ustar00rootroot00000000000000from foreign import StataReader, genfromdta, savetxt, StataWriter from table import SimpleTable, csv2st from smpickle import save_pickle, load_pickle statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/foreign.py000066400000000000000000001237221224417117700236240ustar00rootroot00000000000000""" Input/Output tools for working with binary data. The Stata input tools were originally written by Joe Presbrey as part of PyDTA. You can find more information here http://presbrey.mit.edu/PyDTA See also --------- numpy.lib.io """ from struct import unpack, calcsize, pack from struct import error as struct_error import datetime import sys import numpy as np from numpy.lib._iotools import _is_string_like, easy_dtype from statsmodels.compatnp.py3k import asbytes, asstr import statsmodels.tools.data as data_util from pandas import isnull def is_py3(): import sys if sys.version_info[0] == 3: return True return False PY3 = is_py3() _date_formats = ["%tc", "%tC", "%td", "%tw", "%tm", "%tq", "%th", "%ty"] def _datetime_to_stata_elapsed(date, fmt): """ Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime Parameters ---------- date : datetime.datetime The date to convert to the Stata Internal Format given by fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty """ if not isinstance(date, datetime.datetime): raise ValueError("date should be datetime.datetime format") stata_epoch = datetime.datetime(1960, 1, 1) if fmt in ["%tc", "tc"]: delta = date - stata_epoch return (delta.days * 86400000 + delta.seconds*1000 + delta.microseconds/1000) elif fmt in ["%tC", "tC"]: from warnings import warn warn("Stata Internal Format tC not supported.") return date elif fmt in ["%td", "td"]: return (date- stata_epoch).days elif fmt in ["%tw", "tw"]: return (52*(date.year-stata_epoch.year) + (date - datetime.datetime(date.year, 1, 1)).days / 7) elif fmt in ["%tm", "tm"]: return (12 * (date.year - stata_epoch.year) + date.month - 1) elif fmt in ["%tq", "tq"]: return 4*(date.year-stata_epoch.year) + int((date.month - 1)/3) elif fmt in ["%th", "th"]: return 2 * (date.year - stata_epoch.year) + int(date.month > 6) elif fmt in ["%ty", "ty"]: return date.year else: raise ValueError("fmt %s not understood" % fmt) def _stata_elapsed_date_to_datetime(date, fmt): """ Convert from SIF to datetime. http://www.stata.com/help.cgi?datetime Parameters ---------- date : int The Stata Internal Format date to convert to datetime according to fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty Examples -------- >>> _stata_elapsed_date_to_datetime(52, "%tw") datetime.datetime(1961, 1, 1, 0, 0) Notes ----- datetime/c - tc milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day datetime/C - tC - NOT IMPLEMENTED milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds date - td days since 01jan1960 (01jan1960 = 0) weekly date - tw weeks since 1960w1 This assumes 52 weeks in a year, then adds 7 * remainder of the weeks. The datetime value is the start of the week in terms of days in the year, not ISO calendar weeks. monthly date - tm months since 1960m1 quarterly date - tq quarters since 1960q1 half-yearly date - th half-years since 1960h1 yearly date - ty years since 0000 If you don't have pandas with datetime support, then you can't do milliseconds accurately. """ #NOTE: we could run into overflow / loss of precision situations here # casting to int, but I'm not sure what to do. datetime won't deal with # numpy types and numpy datetime isn't mature enough / we can't rely on # pandas version > 0.7.1 #TODO: IIRC relative delta doesn't play well with np.datetime? date = int(date) stata_epoch = datetime.datetime(1960, 1, 1) if fmt in ["%tc", "tc"]: from dateutil.relativedelta import relativedelta return stata_epoch + relativedelta(microseconds=date*1000) elif fmt in ["%tC", "tC"]: from warnings import warn warn("Encountered %tC format. Leaving in Stata Internal Format.") return date elif fmt in ["%td", "td"]: return stata_epoch + datetime.timedelta(int(date)) elif fmt in ["%tw", "tw"]: # does not count leap days - 7 days is a week year = datetime.datetime(stata_epoch.year + date // 52, 1, 1) day_delta = (date % 52 ) * 7 return year + datetime.timedelta(int(day_delta)) elif fmt in ["%tm", "tm"]: year = stata_epoch.year + date // 12 month_delta = (date % 12 ) + 1 return datetime.datetime(year, month_delta, 1) elif fmt in ["%tq", "tq"]: year = stata_epoch.year + date // 4 month_delta = (date % 4) * 3 + 1 return datetime.datetime(year, month_delta, 1) elif fmt in ["%th", "th"]: year = stata_epoch.year + date // 2 month_delta = (date % 2) * 6 + 1 return datetime.datetime(year, month_delta, 1) elif fmt in ["%ty", "ty"]: if date > 0: return datetime.datetime(date, 1, 1) else: # don't do negative years bc can't mix dtypes in column raise ValueError("Year 0 and before not implemented") else: raise ValueError("Date fmt %s not understood" % fmt) ### Helper classes for StataReader ### class _StataMissingValue(object): """ An observation's missing value. Parameters ----------- offset value Attributes ---------- string value Notes ----- More information: """ def __init__(self, offset, value): self._value = value if type(value) is int or type(value) is long: self._str = value-offset is 1 and \ '.' or ('.' + chr(value-offset+96)) else: self._str = '.' string = property(lambda self: self._str, doc="The Stata representation of \ the missing value: '.', '.a'..'.z'") value = property(lambda self: self._value, doc='The binary representation \ of the missing value.') def __str__(self): return self._str __str__.__doc__ = string.__doc__ class _StataVariable(object): """ A dataset variable. Not intended for public use. Parameters ---------- variable_data Attributes ----------- format : str Stata variable format. See notes for more information. index : int Zero-index column index of variable. label : str Data Label name : str Variable name type : str Stata data type. See notes for more information. value_format : str Value format. Notes ----- More information: http://www.stata.com/help.cgi?format """ def __init__(self, variable_data): self._data = variable_data def __int__(self): return self.index def __str__(self): return self.name index = property(lambda self: self._data[0], doc='the variable\'s index \ within an observation') type = property(lambda self: self._data[1], doc='the data type of \ variable\n\nPossible types are:\n{1..244:string, b:byte, h:int, l:long, \ f:float, d:double)') name = property(lambda self: self._data[2], doc='the name of the variable') format = property(lambda self: self._data[4], doc='the variable\'s Stata \ format') value_format = property(lambda self: self._data[5], doc='the variable\'s \ value format') label = property(lambda self: self._data[6], doc='the variable\'s label') __int__.__doc__ = index.__doc__ __str__.__doc__ = name.__doc__ class StataReader(object): """ Stata .dta file reader. Provides methods to return the metadata of a Stata .dta file and a generator for the data itself. Parameters ---------- file : file-like A file-like object representing a Stata .dta file. missing_values : bool If missing_values is True, parse missing_values and return a Missing Values object instead of None. encoding : string, optional Used for Python 3 only. Encoding to use when reading the .dta file. Defaults to `locale.getpreferredencoding` See also -------- statsmodels.lib.io.genfromdta Notes ----- This is known only to work on file formats 113 (Stata 8/9), 114 (Stata 10/11), and 115 (Stata 12). Needs to be tested on older versions. Known not to work on format 104, 108. If you have the documentation for older formats, please contact the developers. For more information about the .dta format see http://www.stata.com/help.cgi?dta http://www.stata.com/help.cgi?dta_113 """ _header = {} _data_location = 0 _col_sizes = () _has_string_data = False _missing_values = False #type code #-------------------- #str1 1 = 0x01 #str2 2 = 0x02 #... #str244 244 = 0xf4 #byte 251 = 0xfb (sic) #int 252 = 0xfc #long 253 = 0xfd #float 254 = 0xfe #double 255 = 0xff #-------------------- #NOTE: the byte type seems to be reserved for categorical variables # with a label, but the underlying variable is -127 to 100 # we're going to drop the label and cast to int DTYPE_MAP = dict(zip(range(1,245), ['a' + str(i) for i in range(1,245)]) + \ [(251, np.int16),(252, np.int32),(253, int), (254, np.float32), (255, np.float64)]) TYPE_MAP = range(251)+list('bhlfd') #NOTE: technically, some of these are wrong. there are more numbers # that can be represented. it's the 27 ABOVE and BELOW the max listed # numeric data type in [U] 12.2.2 of the 11.2 manual MISSING_VALUES = { 'b': (-127,100), 'h': (-32767, 32740), 'l': (-2147483647, 2147483620), 'f': (-1.701e+38, +1.701e+38), 'd': (-1.798e+308, +8.988e+307) } def __init__(self, fname, missing_values=False, encoding=None): if encoding == None: import locale self._encoding = locale.getpreferredencoding() else: self._encoding = encoding self._missing_values = missing_values self._parse_header(fname) def file_headers(self): """ Returns all .dta file headers. out: dict Has keys typlist, data_label, lbllist, varlist, nvar, filetype, ds_format, nobs, fmtlist, vlblist, time_stamp, srtlist, byteorder """ return self._header def file_format(self): """ Returns the file format. Returns ------- out : int Notes ----- Format 113: Stata 8/9 Format 114: Stata 10/11 Format 115: Stata 12 """ return self._header['ds_format'] def file_label(self): """ Returns the dataset's label. Returns ------- out: string """ return self._header['data_label'] def file_timestamp(self): """ Returns the date and time Stata recorded on last file save. Returns ------- out : str """ return self._header['time_stamp'] def variables(self): """ Returns a list of the dataset's StataVariables objects. """ return map(_StataVariable, zip(range(self._header['nvar']), self._header['typlist'], self._header['varlist'], self._header['srtlist'], self._header['fmtlist'], self._header['lbllist'], self._header['vlblist'])) def dataset(self, as_dict=False): """ Returns a Python generator object for iterating over the dataset. Parameters ---------- as_dict : bool, optional If as_dict is True, yield each row of observations as a dict. If False, yields each row of observations as a list. Returns ------- Generator object for iterating over the dataset. Yields each row of observations as a list by default. Notes ----- If missing_values is True during instantiation of StataReader then observations with _StataMissingValue(s) are not filtered and should be handled by your applcation. """ try: self._file.seek(self._data_location) except Exception: pass if as_dict: vars = map(str, self.variables()) for i in range(len(self)): yield dict(zip(vars, self._next())) else: for i in range(self._header['nobs']): yield self._next() ### Python special methods def __len__(self): """ Return the number of observations in the dataset. This value is taken directly from the header and includes observations with missing values. """ return self._header['nobs'] def __getitem__(self, k): """ Seek to an observation indexed k in the file and return it, ordered by Stata's output to the .dta file. k is zero-indexed. Prefer using R.data() for performance. """ if not (type(k) is int or type(k) is long) or k < 0 or k > len(self)-1: raise IndexError(k) loc = self._data_location + sum(self._col_size()) * k if self._file.tell() != loc: self._file.seek(loc) return self._next() ### Private methods def _null_terminate(self, s, encoding): if PY3: # have bytes not strings, so must decode null_byte = asbytes('\x00') try: s = s.lstrip(null_byte)[:s.index(null_byte)] except: pass return s.decode(encoding) else: null_byte = asbytes('\x00') try: return s.lstrip(null_byte)[:s.index(null_byte)] except: return s def _parse_header(self, file_object): self._file = file_object encoding = self._encoding # parse headers self._header['ds_format'] = unpack('b', self._file.read(1))[0] if self._header['ds_format'] not in [113, 114, 115]: raise ValueError("Only file formats >= 113 (Stata >= 9)" " are supported. Got format %s. Please report " "if you think this error is incorrect." % self._header['ds_format']) byteorder = self._header['byteorder'] = unpack('b', self._file.read(1))[0]==0x1 and '>' or '<' self._header['filetype'] = unpack('b', self._file.read(1))[0] self._file.read(1) nvar = self._header['nvar'] = unpack(byteorder+'h', self._file.read(2))[0] self._header['nobs'] = unpack(byteorder+'i', self._file.read(4))[0] self._header['data_label'] = self._null_terminate(self._file.read(81), encoding) self._header['time_stamp'] = self._null_terminate(self._file.read(18), encoding) # parse descriptors typlist =[ord(self._file.read(1)) for i in range(nvar)] self._header['typlist'] = [self.TYPE_MAP[typ] for typ in typlist] self._header['dtyplist'] = [self.DTYPE_MAP[typ] for typ in typlist] self._header['varlist'] = [self._null_terminate(self._file.read(33), encoding) for i in range(nvar)] self._header['srtlist'] = unpack(byteorder+('h'*(nvar+1)), self._file.read(2*(nvar+1)))[:-1] if self._header['ds_format'] <= 113: self._header['fmtlist'] = \ [self._null_terminate(self._file.read(12), encoding) \ for i in range(nvar)] else: self._header['fmtlist'] = \ [self._null_terminate(self._file.read(49), encoding) \ for i in range(nvar)] self._header['lbllist'] = [self._null_terminate(self._file.read(33), encoding) for i in range(nvar)] self._header['vlblist'] = [self._null_terminate(self._file.read(81), encoding) for i in range(nvar)] # ignore expansion fields # When reading, read five bytes; the last four bytes now tell you the # size of the next read, which you discard. You then continue like # this until you read 5 bytes of zeros. while True: data_type = unpack(byteorder+'b', self._file.read(1))[0] data_len = unpack(byteorder+'i', self._file.read(4))[0] if data_type == 0: break self._file.read(data_len) # other state vars self._data_location = self._file.tell() self._has_string_data = len(filter(lambda x: type(x) is int, self._header['typlist'])) > 0 self._col_size() def _calcsize(self, fmt): return type(fmt) is int and fmt or \ calcsize(self._header['byteorder']+fmt) def _col_size(self, k = None): """Calculate size of a data record.""" if len(self._col_sizes) == 0: self._col_sizes = map(lambda x: self._calcsize(x), self._header['typlist']) if k == None: return self._col_sizes else: return self._col_sizes[k] def _unpack(self, fmt, byt): d = unpack(self._header['byteorder']+fmt, byt)[0] if fmt[-1] in self.MISSING_VALUES: nmin, nmax = self.MISSING_VALUES[fmt[-1]] if d < nmin or d > nmax: if self._missing_values: return _StataMissingValue(nmax, d) else: return None return d def _next(self): typlist = self._header['typlist'] if self._has_string_data: data = [None]*self._header['nvar'] for i in range(len(data)): if type(typlist[i]) is int: data[i] = self._null_terminate(self._file.read(typlist[i]), self._encoding) else: data[i] = self._unpack(typlist[i], self._file.read(self._col_size(i))) return data else: return map(lambda i: self._unpack(typlist[i], self._file.read(self._col_size(i))), range(self._header['nvar'])) def _open_file_binary_write(fname, encoding): if hasattr(fname, 'write'): #if 'b' not in fname.mode: return fname if PY3: return open(fname, "wb", encoding=encoding) else: return open(fname, "wb") def _set_endianness(endianness): if endianness.lower() in ["<", "little"]: return "<" elif endianness.lower() in [">", "big"]: return ">" else: # pragma : no cover raise ValueError("Endianness %s not understood" % endianness) def _dtype_to_stata_type(dtype): """ Converts dtype types to stata types. Returns the byte of the given ordinal. See TYPE_MAP and comments for an explanation. This is also explained in the dta spec. 1 - 244 are strings of this length 251 - chr(251) - for int8 and int16, byte 252 - chr(252) - for int32, int 253 - chr(253) - for int64, long 254 - chr(254) - for float32, float 255 - chr(255) - double, double If there are dates to convert, then dtype will already have the correct type inserted. """ #TODO: expand to handle datetime to integer conversion if dtype.type == np.string_: return chr(dtype.itemsize) elif dtype.type == np.object_: # try to coerce it to the biggest string # not memory efficient, what else could we do? return chr(244) elif dtype == np.float64: return chr(255) elif dtype == np.float32: return chr(254) elif dtype == np.int64: return chr(253) elif dtype == np.int32: return chr(252) elif dtype == np.int8 or dtype == np.int16: # ok to assume bytes? return chr(251) else: # pragma : no cover raise ValueError("Data type %s not currently understood. " "Please report an error to the developers." % dtype) def _dtype_to_default_stata_fmt(dtype): """ Maps numpy dtype to stata's default format for this type. Not terribly important since users can change this in Stata. Semantics are string -> "%DDs" where DD is the length of the string float64 -> "%10.0g" float32 -> "%9.0g" int64 -> "%9.0g" int32 -> "%9.0g" int16 -> "%9.0g" int8 -> "%8.0g" """ #TODO: expand this to handle a default datetime format? if dtype.type == np.string_: return "%" + str(dtype.itemsize) + "s" elif dtype.type == np.object_: return "%244s" elif dtype == np.float64: return "%10.0g" elif dtype == np.float32: return "%9.0g" elif dtype == np.int64: return "%9.0g" elif dtype == np.int32: return "%8.0g" elif dtype == np.int8 or dtype == np.int16: # ok to assume bytes? return "%8.0g" else: # pragma : no cover raise ValueError("Data type %s not currently understood. " "Please report an error to the developers." % dtype) def _pad_bytes(name, length): """ Takes a char string and pads it wih null bytes until it's length chars """ return name + "\x00" * (length - len(name)) def _default_names(nvar): """ Returns default Stata names v1, v2, ... vnvar """ return ["v%d" % i for i in range(1,nvar+1)] def _convert_datetime_to_stata_type(fmt): """ Converts from one of the stata date formats to a type in TYPE_MAP """ if fmt in ["tc", "%tc", "td", "%td", "tw", "%tw", "tm", "%tm", "tq", "%tq", "th", "%th", "ty", "%ty"]: return np.float64 # Stata expects doubles for SIFs else: raise ValueError("fmt %s not understood" % fmt) def _maybe_convert_to_int_keys(convert_dates, varlist): new_dict = {} for key in convert_dates: if not convert_dates[key].startswith("%"): # make sure proper fmts convert_dates[key] = "%" + convert_dates[key] if key in varlist: new_dict.update({varlist.index(key) : convert_dates[key]}) else: if not isinstance(key, int): raise ValueError("convery_dates key is not in varlist " "and is not an int") new_dict.update({key : convert_dates[key]}) return new_dict _type_converters = {253 : np.long, 252 : int} class StataWriter(object): """ A class for writing Stata binary dta files from array-like objects Parameters ---------- fname : file path or buffer Where to save the dta file. data : array-like Array-like input to save. Pandas objects are also accepted. convert_dates : dict Dictionary mapping column of datetime types to the stata internal format that you want to use for the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either a number or a name. encoding : str Default is latin-1. Note that Stata does not support unicode. byteorder : str Can be ">", "<", "little", or "big". The default is None which uses `sys.byteorder` Returns ------- writer : StataWriter instance The StataWriter instance has a write_file method, which will write the file to the given `fname`. Examples -------- >>> writer = StataWriter('./data_file.dta', data) >>> writer.write_file() Or with dates >>> writer = StataWriter('./date_data_file.dta', date, {2 : 'tw'}) >>> writer.write_file() """ #type code #-------------------- #str1 1 = 0x01 #str2 2 = 0x02 #... #str244 244 = 0xf4 #byte 251 = 0xfb (sic) #int 252 = 0xfc #long 253 = 0xfd #float 254 = 0xfe #double 255 = 0xff #-------------------- #NOTE: the byte type seems to be reserved for categorical variables # with a label, but the underlying variable is -127 to 100 # we're going to drop the label and cast to int DTYPE_MAP = dict(zip(range(1,245), ['a' + str(i) for i in range(1,245)]) + \ [(251, np.int16),(252, np.int32),(253, int), (254, np.float32), (255, np.float64)]) TYPE_MAP = range(251)+list('bhlfd') MISSING_VALUES = { 'b': 101, 'h': 32741, 'l' : 2147483621, 'f': 1.7014118346046923e+38, 'd': 8.98846567431158e+307} def __init__(self, fname, data, convert_dates=None, encoding="latin-1", byteorder=None): self._convert_dates = convert_dates # attach nobs, nvars, data, varlist, typlist if data_util._is_using_pandas(data, None): self._prepare_pandas(data) elif data_util._is_array_like(data, None): data = np.asarray(data) if data_util._is_structured_ndarray(data): self._prepare_structured_array(data) else: if convert_dates is not None: raise ValueError("Not able to convert dates in a plain" " ndarray.") self._prepare_ndarray(data) else: # pragma : no cover raise ValueError("Type %s for data not understood" % type(data)) if byteorder is None: byteorder = sys.byteorder self._byteorder = _set_endianness(byteorder) self._encoding = encoding self._file = _open_file_binary_write(fname, encoding) def _write(self, to_write): """ Helper to call asbytes before writing to file for Python 3 compat. """ self._file.write(asbytes(to_write)) def _prepare_structured_array(self, data): self.nobs = len(data) self.nvar = len(data.dtype) self.data = data self.datarows = iter(data) dtype = data.dtype descr = dtype.descr if dtype.names is None: varlist = _default_names(nvar) else: varlist = dtype.names # check for datetime and change the type convert_dates = self._convert_dates if convert_dates is not None: convert_dates = _maybe_convert_to_int_keys(convert_dates, varlist) self._convert_dates = convert_dates for key in convert_dates: descr[key] = ( descr[key][0], _convert_datetime_to_stata_type(convert_dates[key]) ) dtype = np.dtype(descr) self.varlist = varlist self.typlist = [_dtype_to_stata_type(dtype[i]) for i in range(self.nvar)] self.fmtlist = [_dtype_to_default_stata_fmt(dtype[i]) for i in range(self.nvar)] # set the given format for the datetime cols if convert_dates is not None: for key in convert_dates: self.fmtlist[key] = convert_dates[key] def _prepare_ndarray(self, data): if data.ndim == 1: data = data[:,None] self.nobs, self.nvar = data.shape self.data = data self.datarows = iter(data) #TODO: this should be user settable dtype = data.dtype self.varlist = _default_names(self.nvar) self.typlist = [_dtype_to_stata_type(dtype) for i in range(self.nvar)] self.fmtlist = [_dtype_to_default_stata_fmt(dtype) for i in range(self.nvar)] def _prepare_pandas(self, data): #NOTE: we might need a different API / class for pandas objects so # we can set different semantics - handle this with a PR to pandas.io class DataFrameRowIter(object): def __init__(self, data): self.data = data def __iter__(self): for i, row in data.iterrows(): yield row data = data.reset_index() self.datarows = DataFrameRowIter(data) self.nobs, self.nvar = data.shape self.data = data self.varlist = data.columns.tolist() dtypes = data.dtypes convert_dates = self._convert_dates if convert_dates is not None: convert_dates = _maybe_convert_to_int_keys(convert_dates, self.varlist) self._convert_dates = convert_dates for key in convert_dates: new_type = _convert_datetime_to_stata_type(convert_dates[key]) dtypes[key] = np.dtype(new_type) self.typlist = [_dtype_to_stata_type(dt) for dt in dtypes] self.fmtlist = [_dtype_to_default_stata_fmt(dt) for dt in dtypes] # set the given format for the datetime cols if convert_dates is not None: for key in convert_dates: self.fmtlist[key] = convert_dates[key] def write_file(self): self._write_header() self._write_descriptors() self._write_variable_labels() # write 5 zeros for expansion fields self._write(_pad_bytes("", 5)) if self._convert_dates is None: self._write_data_nodates() else: self._write_data_dates() #self._write_value_labels() def _write_header(self, data_label=None, time_stamp=None): byteorder = self._byteorder # ds_format - just use 114 self._write(pack("b", 114)) # byteorder self._write(byteorder == ">" and "\x01" or "\x02") # filetype self._write("\x01") # unused self._write("\x00") # number of vars, 2 bytes self._write(pack(byteorder+"h", self.nvar)[:2]) # number of obs, 4 bytes self._write(pack(byteorder+"i", self.nobs)[:4]) # data label 81 bytes, char, null terminated if data_label is None: self._write(self._null_terminate(_pad_bytes("", 80), self._encoding)) else: self._write(self._null_terminate(_pad_bytes(data_label[:80], 80), self._encoding)) # time stamp, 18 bytes, char, null terminated # format dd Mon yyyy hh:mm if time_stamp is None: time_stamp = datetime.datetime.now() elif not isinstance(time_stamp, datetime): raise ValueError("time_stamp should be datetime type") self._write(self._null_terminate( time_stamp.strftime("%d %b %Y %H:%M"), self._encoding)) def _write_descriptors(self, typlist=None, varlist=None, srtlist=None, fmtlist=None, lbllist=None): nvar = self.nvar # typlist, length nvar, format byte array for typ in self.typlist: self._write(typ) # varlist, length 33*nvar, char array, null terminated for name in self.varlist: name = self._null_terminate(name, self._encoding) name = _pad_bytes(asstr(name[:32]), 33) self._write(name) # srtlist, 2*(nvar+1), int array, encoded by byteorder srtlist = _pad_bytes("", (2*(nvar+1))) self._write(srtlist) # fmtlist, 49*nvar, char array for fmt in self.fmtlist: self._write(_pad_bytes(fmt, 49)) # lbllist, 33*nvar, char array #NOTE: this is where you could get fancy with pandas categorical type for i in range(nvar): self._write(_pad_bytes("", 33)) def _write_variable_labels(self, labels=None): nvar = self.nvar if labels is None: for i in range(nvar): self._write(_pad_bytes("", 81)) def _write_data_nodates(self): data = self.datarows byteorder = self._byteorder TYPE_MAP = self.TYPE_MAP typlist = self.typlist for row in data: #row = row.squeeze().tolist() # needed for structured arrays for i,var in enumerate(row): typ = ord(typlist[i]) if typ <= 244: # we've got a string if len(var) < typ: var = _pad_bytes(asstr(var), len(var) + 1) self._write(var) else: try: self._write(pack(byteorder+TYPE_MAP[typ], var)) except struct_error: # have to be strict about type pack won't do any # kind of casting self._write(pack(byteorder+TYPE_MAP[typ], _type_converters[typ](var))) def _write_data_dates(self): convert_dates = self._convert_dates data = self.datarows byteorder = self._byteorder TYPE_MAP = self.TYPE_MAP MISSING_VALUES = self.MISSING_VALUES typlist = self.typlist for row in data: #row = row.squeeze().tolist() # needed for structured arrays for i,var in enumerate(row): typ = ord(typlist[i]) #NOTE: If anyone finds this terribly slow, there is # a vectorized way to convert dates, see genfromdta for going # from int to datetime and reverse it. will copy data though if i in convert_dates: var = _datetime_to_stata_elapsed(var, self.fmtlist[i]) if typ <= 244: # we've got a string if isnull(var): var = "" # missing string if len(var) < typ: var = _pad_bytes(var, len(var) + 1) self._write(var) else: if isnull(var): # this only matters for floats var = MISSING_VALUES[typ] self._write(pack(byteorder+TYPE_MAP[typ], var)) def _null_terminate(self, s, encoding): null_byte = '\x00' if PY3: s += null_byte return s.encode(encoding) else: s += null_byte return s def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False, convert_dates=True): """ Returns an ndarray or DataFrame from a Stata .dta file. Parameters ---------- fname : str or filehandle Stata .dta file. missing_flt : numeric The numeric value to replace missing values with. Will be used for any numeric value. encoding : string, optional Used for Python 3 only. Encoding to use when reading the .dta file. Defaults to `locale.getpreferredencoding` pandas : bool Optionally return a DataFrame instead of an ndarray convert_dates : bool If convert_dates is True, then Stata formatted dates will be converted to datetime types according to the variable's format. """ if isinstance(fname, basestring): fhd = StataReader(open(fname, 'rb'), missing_values=False, encoding=encoding) elif not hasattr(fname, 'read'): raise TypeError("The input should be a string or a filehandle. "\ "(got %s instead)" % type(fname)) else: fhd = StataReader(fname, missing_values=False, encoding=encoding) # validate_names = np.lib._iotools.NameValidator(excludelist=excludelist, # deletechars=deletechars, # case_sensitive=case_sensitive) #TODO: This needs to handle the byteorder? header = fhd.file_headers() types = header['dtyplist'] nobs = header['nobs'] numvars = header['nvar'] varnames = header['varlist'] fmtlist = header['fmtlist'] dataname = header['data_label'] labels = header['vlblist'] # labels are thrown away unless DataArray # type is used data = np.zeros((nobs,numvars)) stata_dta = fhd.dataset() dt = np.dtype(zip(varnames, types)) data = np.zeros((nobs), dtype=dt) # init final array for rownum,line in enumerate(stata_dta): # doesn't handle missing value objects, just casts # None will only work without missing value object. if None in line: for i,val in enumerate(line): #NOTE: This will only be scalar types because missing strings # are empty not None in Stata if val is None: line[i] = missing_flt data[rownum] = tuple(line) if pandas: from pandas import DataFrame data = DataFrame.from_records(data) if convert_dates: cols = np.where(map(lambda x : x in _date_formats, fmtlist))[0] for col in cols: i = col col = data.columns[col] data[col] = data[col].apply(_stata_elapsed_date_to_datetime, args=(fmtlist[i],)) elif convert_dates: #date_cols = np.where(map(lambda x : x in _date_formats, # fmtlist))[0] # make the dtype for the datetime types cols = np.where(map(lambda x : x in _date_formats, fmtlist))[0] dtype = data.dtype.descr dtype = [(dt[0], object) if i in cols else dt for i,dt in enumerate(dtype)] data = data.astype(dtype) # have to copy for col in cols: def convert(x): return _stata_elapsed_date_to_datetime(x, fmtlist[col]) data[data.dtype.names[col]] = map(convert, data[data.dtype.names[col]]) return data def savetxt(fname, X, names=None, fmt='%.18e', delimiter=' '): """ Save an array to a text file. This is just a copy of numpy.savetxt patched to support structured arrays or a header of names. Does not include py3 support now in savetxt. Parameters ---------- fname : filename or file handle If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. X : array_like Data to be saved to a text file. names : list, optional If given names will be the column header in the text file. If None and X is a structured or recarray then the names are taken from X.dtype.names. fmt : str or sequence of strs A single format (%10.5f), a sequence of formats, or a multi-format string, e.g. 'Iteration %d -- %10.5f', in which case `delimiter` is ignored. delimiter : str Character separating columns. See Also -------- save : Save an array to a binary file in NumPy ``.npy`` format savez : Save several arrays into a ``.npz`` compressed archive Notes ----- Further explanation of the `fmt` parameter (``%[flag]width[.precision]specifier``): flags: ``-`` : left justify ``+`` : Forces to preceed result with + or -. ``0`` : Left pad the number with zeros instead of space (see width). width: Minimum number of characters to be printed. The value is not truncated if it has more characters. precision: - For integer specifiers (eg. ``d,i,o,x``), the minimum number of digits. - For ``e, E`` and ``f`` specifiers, the number of digits to print after the decimal point. - For ``g`` and ``G``, the maximum number of significant digits. - For ``s``, the maximum number of characters. specifiers: ``c`` : character ``d`` or ``i`` : signed decimal integer ``e`` or ``E`` : scientific notation with ``e`` or ``E``. ``f`` : decimal floating point ``g,G`` : use the shorter of ``e,E`` or ``f`` ``o`` : signed octal ``s`` : string of characters ``u`` : unsigned decimal integer ``x,X`` : unsigned hexadecimal integer This explanation of ``fmt`` is not complete, for an exhaustive specification see [1]_. References ---------- .. [1] `Format Specification Mini-Language `_, Python Documentation. Examples -------- >>> savetxt('test.out', x, delimiter=',') # x is an array >>> savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays >>> savetxt('test.out', x, fmt='%1.4e') # use exponential notation """ if _is_string_like(fname): if fname.endswith('.gz'): import gzip fh = gzip.open(fname, 'wb') else: fh = file(fname, 'w') elif hasattr(fname, 'seek'): fh = fname else: raise ValueError('fname must be a string or file handle') X = np.asarray(X) # Handle 1-dimensional arrays if X.ndim == 1: # Common case -- 1d array of numbers if X.dtype.names is None: X = np.atleast_2d(X).T ncol = 1 # Complex dtype -- each field indicates a separate column else: ncol = len(X.dtype.descr) else: ncol = X.shape[1] # `fmt` can be a string with multiple insertion points or a list of formats. # E.g. '%10.5f\t%10d' or ('%10.5f', '$10d') if type(fmt) in (list, tuple): if len(fmt) != ncol: raise AttributeError('fmt has wrong shape. %s' % str(fmt)) format = delimiter.join(fmt) elif type(fmt) is str: if fmt.count('%') == 1: fmt = [fmt, ]*ncol format = delimiter.join(fmt) elif fmt.count('%') != ncol: raise AttributeError('fmt has wrong number of %% formats. %s' % fmt) else: format = fmt # handle names if names is None and X.dtype.names: names = X.dtype.names if names is not None: fh.write(delimiter.join(names) + '\n') for row in X: fh.write(format % tuple(row) + '\n') if __name__ == "__main__": import os curdir = os.path.dirname(os.path.abspath(__file__)) res1 = genfromdta(curdir+'/../../datasets/macrodata/macrodata.dta') statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/notes_table_update.txt000066400000000000000000000006241224417117700262160ustar00rootroot00000000000000updating table.py from econpy ============================= table.py : reformat tabs to 4 spaces adjust import path in docstrings currently insufficient tests for backwards compatibility, might break silently test_table.py : renamed to test_table_econpy.py in parallel to test_table.py change import paths in test_table_econpy.py currently too many differences to maintain mergestatsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/smpickle.py000066400000000000000000000015601224417117700237750ustar00rootroot00000000000000'''Helper files for pickling''' def _get_file_obj(fname, mode): """ Light wrapper to handle strings and let files (anything else) pass through """ try: fh = open(fname, mode) except (IOError, TypeError): fh = fname return fh def save_pickle(obj, fname): """ Save the object to file via pickling. Parameters ---------- fname : str Filename to pickle to """ import cPickle as pickle fout = _get_file_obj(fname, 'wb') pickle.dump(obj, fout, protocol=-1) def load_pickle(fname): """ Load a previously saved object from file Parameters ---------- fname : str Filename to unpickle Notes ----- This method can be used to load *both* models and results. """ import cPickle as pickle fin = _get_file_obj(fname, 'rb') return pickle.load(fin) statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/stata_summary_examples.py000066400000000000000000000110061224417117700267510ustar00rootroot00000000000000 """. regress totemp gnpdefl gnp unemp armed pop year Source | SS df MS Number of obs = 16 -------------+------------------------------ F( 6, 9) = 330.29 Model | 184172402 6 30695400.3 Prob > F = 0.0000 Residual | 836424.129 9 92936.0144 R-squared = 0.9955 -------------+------------------------------ Adj R-squared = 0.9925 Total | 185008826 15 12333921.7 Root MSE = 304.85 ------------------------------------------------------------------------------ totemp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gnpdefl | 15.06167 84.91486 0.18 0.863 -177.0291 207.1524 gnp | -.0358191 .033491 -1.07 0.313 -.111581 .0399428 unemp | -2.020229 .4883995 -4.14 0.003 -3.125065 -.9153928 armed | -1.033227 .2142741 -4.82 0.001 -1.517948 -.5485049 pop | -.0511045 .2260731 -0.23 0.826 -.5625173 .4603083 year | 1829.151 455.4785 4.02 0.003 798.7873 2859.515 _cons | -3482258 890420.3 -3.91 0.004 -5496529 -1467987 ------------------------------------------------------------------------------ """ #From Stata using Longley dataset as in the test and example for GLM """ . glm totemp gnpdefl gnp unemp armed pop year Iteration 0: log likelihood = -109.61744 Generalized linear models No. of obs = 16 Optimization : ML Residual df = 9 Scale parameter = 92936.01 Deviance = 836424.1293 (1/df) Deviance = 92936.01 Pearson = 836424.1293 (1/df) Pearson = 92936.01 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 14.57718 Log likelihood = -109.6174355 BIC = 836399.2 ------------------------------------------------------------------------------ | OIM totemp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- gnpdefl | 15.06167 84.91486 0.18 0.859 -151.3684 181.4917 gnp | -.0358191 .033491 -1.07 0.285 -.1014603 .029822 unemp | -2.020229 .4883995 -4.14 0.000 -2.977475 -1.062984 armed | -1.033227 .2142741 -4.82 0.000 -1.453196 -.6132571 pop | -.0511045 .2260731 -0.23 0.821 -.4941996 .3919906 year | 1829.151 455.4785 4.02 0.000 936.4298 2721.873 _cons | -3482258 890420.3 -3.91 0.000 -5227450 -1737066 ------------------------------------------------------------------------------ """ #RLM Example """ . rreg stackloss airflow watertemp acidconc Huber iteration 1: maximum difference in weights = .48402478 Huber iteration 2: maximum difference in weights = .07083248 Huber iteration 3: maximum difference in weights = .03630349 Biweight iteration 4: maximum difference in weights = .2114744 Biweight iteration 5: maximum difference in weights = .04709559 Biweight iteration 6: maximum difference in weights = .01648123 Biweight iteration 7: maximum difference in weights = .01050023 Biweight iteration 8: maximum difference in weights = .0027233 Robust regression Number of obs = 21 F( 3, 17) = 74.15 Prob > F = 0.0000 ------------------------------------------------------------------------------ stackloss | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- airflow | .8526511 .1223835 6.97 0.000 .5944446 1.110858 watertemp | .8733594 .3339811 2.61 0.018 .1687209 1.577998 acidconc | -.1224349 .1418364 -0.86 0.400 -.4216836 .1768139 _cons | -41.6703 10.79559 -3.86 0.001 -64.447 -18.89361 ------------------------------------------------------------------------------ """statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/summary.py000066400000000000000000000763331224417117700236750ustar00rootroot00000000000000 import numpy as np from statsmodels.compatnp.iter_compat import zip_longest from statsmodels.iolib.table import SimpleTable from statsmodels.iolib.tableformatting import (gen_fmt, fmt_2, fmt_params, fmt_base, fmt_2cols) #from statsmodels.iolib.summary2d import summary_params_2dflat #from summary2d import summary_params_2dflat def forg(x, prec=3): if prec == 3: #for 3 decimals if (abs(x) >= 1e4) or (abs(x) < 1e-4): return '%9.3g' % x else: return '%9.3f' % x elif prec == 4: if (abs(x) >= 1e4) or (abs(x) < 1e-4): return '%10.4g' % x else: return '%10.4f' % x else: raise NotImplementedError def summary(self, yname=None, xname=None, title=0, alpha=.05, returns='text', model_info=None): """ Parameters ----------- yname : string optional, Default is `Y` xname : list of strings optional, Default is `X.#` for # in p the number of regressors Confidance interval : (0,1) not implimented title : string optional, Defualt is 'Generalized linear model' returns : string 'text', 'table', 'csv', 'latex', 'html' Returns ------- Defualt : returns='print' Prints the summarirized results Option : returns='text' Prints the summarirized results Option : returns='table' SimpleTable instance : summarizing the fit of a linear model. Option : returns='csv' returns a string of csv of the results, to import into a spreadsheet Option : returns='latex' Not implimented yet Option : returns='HTML' Not implimented yet Examples (needs updating) -------- >>> import statsmodels as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> ols_results = sm.OLS(data.endog, data.exog).results >>> print ols_results.summary() ... Notes ----- conf_int calculated from normal dist. """ import time as time #TODO Make sure all self.model.__class__.__name__ are listed model_types = {'OLS' : 'Ordinary least squares', 'GLS' : 'Generalized least squares', 'GLSAR' : 'Generalized least squares with AR(p)', 'WLS' : 'Weigthed least squares', 'RLM' : 'Robust linear model', 'GLM' : 'Generalized linear model' } model_methods = {'OLS' : 'Least Squares', 'GLS' : 'Least Squares', 'GLSAR' : 'Least Squares', 'WLS' : 'Least Squares', 'RLM' : '?', 'GLM' : '?' } if title==0: title = model_types[self.model.__class__.__name__] if yname is None: try: yname = self.model.endog_names except AttributeError: yname = 'y' if xname is None: try: xname = self.model.exog_names except AttributeError: xname = ['var_%d' % i for i in range(len(self.params))] time_now = time.localtime() time_of_day = [time.strftime("%H:%M:%S", time_now)] date = time.strftime("%a, %d %b %Y", time_now) modeltype = self.model.__class__.__name__ #dist_family = self.model.family.__class__.__name__ nobs = self.nobs df_model = self.df_model df_resid = self.df_resid #General part of the summary table, Applicable to all? models #------------------------------------------------------------ #TODO: define this generically, overwrite in model classes #replace definition of stubs data by single list #e.g. gen_left = [('Model type:', [modeltype]), ('Date:', [date]), ('Dependent Variable:', yname), #What happens with multiple names? ('df model', [df_model]) ] gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col gen_title = title gen_header = None ## gen_stubs_left = ('Model type:', ## 'Date:', ## 'Dependent Variable:', ## 'df model' ## ) ## gen_data_left = [[modeltype], ## [date], ## yname, #What happens with multiple names? ## [df_model] ## ] gen_table_left = SimpleTable(gen_data_left, gen_header, gen_stubs_left, title = gen_title, txt_fmt = gen_fmt ) gen_stubs_right = ('Method:', 'Time:', 'Number of Obs:', 'df resid' ) gen_data_right = ([modeltype], #was dist family need to look at more time_of_day, [nobs], [df_resid] ) gen_table_right = SimpleTable(gen_data_right, gen_header, gen_stubs_right, title = gen_title, txt_fmt = gen_fmt ) gen_table_left.extend_right(gen_table_right) general_table = gen_table_left #Parameters part of the summary table #------------------------------------ #Note: this is not necessary since we standardized names, only t versus normal tstats = {'OLS' : self.t(), 'GLS' : self.t(), 'GLSAR' : self.t(), 'WLS' : self.t(), 'RLM' : self.t(), 'GLM' : self.t() } prob_stats = {'OLS' : self.pvalues, 'GLS' : self.pvalues, 'GLSAR' : self.pvalues, 'WLS' : self.pvalues, 'RLM' : self.pvalues, 'GLM' : self.pvalues } #Dictionary to store the header names for the parameter part of the #summary table. look up by modeltype alp = str((1-alpha)*100)+'%' param_header = { 'OLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], 'GLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], 'GLSAR' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], 'WLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], 'GLM' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], #glm uses t-distribution 'RLM' : ['coef', 'std err', 'z', 'P>|z|', alp + ' Conf. Interval'] #checke z } params_stubs = xname params = self.params conf_int = self.conf_int(alpha) std_err = self.bse exog_len = xrange(len(xname)) tstat = tstats[modeltype] prob_stat = prob_stats[modeltype] # Simpletable should be able to handle the formating params_data = zip(["%#6.4g" % (params[i]) for i in exog_len], ["%#6.4f" % (std_err[i]) for i in exog_len], ["%#6.4f" % (tstat[i]) for i in exog_len], ["%#6.4f" % (prob_stat[i]) for i in exog_len], ["(%#5g, %#5g)" % tuple(conf_int[i]) for i in \ exog_len] ) parameter_table = SimpleTable(params_data, param_header[modeltype], params_stubs, title = None, txt_fmt = fmt_2, #gen_fmt, ) #special table #------------- #TODO: exists in linear_model, what about other models #residual diagnostics #output options #-------------- #TODO: JP the rest needs to be fixed, similar to summary in linear_model def ols_printer(): """ print summary table for ols models """ table = str(general_table)+'\n'+str(parameter_table) return table def ols_to_csv(): """ exports ols summary data to csv """ pass def glm_printer(): table = str(general_table)+'\n'+str(parameter_table) return table pass printers = {'OLS': ols_printer, 'GLM' : glm_printer } if returns=='print': try: return printers[modeltype]() except KeyError: return printers['OLS']() def _getnames(self, yname=None, xname=None): '''extract names from model or construct names ''' if yname is None: if hasattr(self.model, 'endog_names') and ( not self.model.endog_names is None): yname = self.model.endog_names else: yname = 'y' if xname is None: if hasattr(self.model, 'exog_names') and ( not self.model.exog_names is None): xname = self.model.exog_names else: xname = ['var_%d' % i for i in range(len(self.params))] return yname, xname def summary_top(results, title=None, gleft=None, gright=None, yname=None, xname=None): '''generate top table(s) TODO: this still uses predefined model_methods ? allow gleft, gright to be 1 element tuples instead of filling with None? ''' #change of names ? gen_left, gen_right = gleft, gright #time and names are always included import time time_now = time.localtime() time_of_day = [time.strftime("%H:%M:%S", time_now)] date = time.strftime("%a, %d %b %Y", time_now) yname, xname = _getnames(results, yname=yname, xname=xname) #create dictionary with default #use lambdas because some values raise exception if they are not available #alternate spellings are commented out to force unique labels default_items = dict([ ('Dependent Variable:', lambda: [yname]), ('Dep. Variable:', lambda: [yname]), ('Model:', lambda: [results.model.__class__.__name__]), #('Model type:', lambda: [results.model.__class__.__name__]), ('Date:', lambda: [date]), ('Time:', lambda: time_of_day), ('Number of Obs:', lambda: [results.nobs]), #('No. of Observations:', lambda: ["%#6d" % results.nobs]), ('No. Observations:', lambda: ["%#6d" % results.nobs]), #('Df model:', lambda: [results.df_model]), ('Df Model:', lambda: ["%#6d" % results.df_model]), #TODO: check when we have non-integer df ('Df Residuals:', lambda: ["%#6d" % results.df_resid]), #('Df resid:', lambda: [results.df_resid]), #('df resid:', lambda: [results.df_resid]), #check capitalization ('Log-Likelihood:', lambda: ["%#8.5g" % results.llf]) #doesn't exist for RLM - exception #('Method:', lambda: [???]), #no default for this ]) if title is None: title = results.model.__class__.__name__ + 'Regression Results' if gen_left is None: #default: General part of the summary table, Applicable to all? models gen_left = [('Dep. Variable:', None), ('Model type:', None), ('Date:', None), ('No. Observations:', None), ('Df model:', None), ('Df resid:', None)] try: llf = results.llf gen_left.append(('Log-Likelihood', None)) except: #AttributeError, NotImplementedError pass gen_right = [] gen_title = title gen_header = None #needed_values = [k for k,v in gleft + gright if v is None] #not used anymore #replace missing (None) values with default values gen_left_ = [] for item, value in gen_left: if value is None: value = default_items[item]() #let KeyErrors raise exception gen_left_.append((item, value)) gen_left = gen_left_ if gen_right: gen_right_ = [] for item, value in gen_right: if value is None: value = default_items[item]() #let KeyErrors raise exception gen_right_.append((item, value)) gen_right = gen_right_ #check missing_values = [k for k,v in gen_left + gen_right if v is None] assert missing_values == [], missing_values #pad both tables to equal number of rows if gen_right: if len(gen_right) < len(gen_left): #fill up with blank lines to same length gen_right += [(' ', ' ')] * (len(gen_left) - len(gen_right)) elif len(gen_right) > len(gen_left): #fill up with blank lines to same length, just to keep it symmetric gen_left += [(' ', ' ')] * (len(gen_right) - len(gen_left)) #padding in SimpleTable doesn't work like I want #force extra spacing and exact string length in right table gen_right = [('%-21s' % (' '+k), v) for k,v in gen_right] gen_stubs_right, gen_data_right = zip_longest(*gen_right) #transpose row col gen_table_right = SimpleTable(gen_data_right, gen_header, gen_stubs_right, title = gen_title, txt_fmt = fmt_2cols #gen_fmt ) else: gen_table_right = [] #because .extend_right seems works with [] #moved below so that we can pad if needed to match length of gen_right #transpose rows and columns, `unzip` gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col gen_table_left = SimpleTable(gen_data_left, gen_header, gen_stubs_left, title = gen_title, txt_fmt = fmt_2cols ) gen_table_left.extend_right(gen_table_right) general_table = gen_table_left return general_table #, gen_table_left, gen_table_right def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True, skip_header=False): '''create a summary table for the parameters Parameters ---------- res : results instance some required information is directly taken from the result instance yname : string or None optional name for the endogenous variable, default is "y" xname : list of strings or None optional names for the exogenous variables, default is "var_xx" alpha : float significance level for the confidence intervals use_t : bool indicator whether the p-values are based on the Student-t distribution (if True) or on the normal distribution (if False) skip_headers : bool If false (default), then the header row is added. If true, then no header row is added. Returns ------- params_table : SimpleTable instance ''' #Parameters part of the summary table #------------------------------------ #Note: this is not necessary since we standardized names, only t versus normal if isinstance(results, tuple): #for multivariate endog #TODO: check whether I don't want to refactor this #we need to give parameter alpha to conf_int results, params, std_err, tvalues, pvalues, conf_int = results else: params = results.params std_err = results.bse tvalues = results.tvalues #is this sometimes called zvalues pvalues = results.pvalues conf_int = results.conf_int(alpha) #Dictionary to store the header names for the parameter part of the #summary table. look up by modeltype alp = str((1-alpha)*100)+'%' if use_t: param_header = ['coef', 'std err', 't', 'P>|t|', '[' + alp + ' Conf. Int.]'] else: param_header = ['coef', 'std err', 'z', 'P>|z|', '[' + alp + ' Conf. Int.]'] if skip_header: param_header = None _, xname = _getnames(results, yname=yname, xname=xname) params_stubs = xname exog_idx = xrange(len(xname)) #center confidence intervals if they are unequal lengths # confint = ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \ # exog_idx] confint = ["%s %s" % tuple(map(forg, conf_int[i])) for i in \ exog_idx] len_ci = map(len, confint) max_ci = max(len_ci) min_ci = min(len_ci) if min_ci < max_ci: confint = [ci.center(max_ci) for ci in confint] #explicit f/g formatting, now uses forg, f or g depending on values # params_data = zip(["%#6.4g" % (params[i]) for i in exog_idx], # ["%#6.4f" % (std_err[i]) for i in exog_idx], # ["%#6.3f" % (tvalues[i]) for i in exog_idx], # ["%#6.3f" % (pvalues[i]) for i in exog_idx], # confint ## ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \ ## exog_idx] # ) params_data = zip([forg(params[i], prec=4) for i in exog_idx], [forg(std_err[i]) for i in exog_idx], [forg(tvalues[i]) for i in exog_idx], ["%#6.3f" % (pvalues[i]) for i in exog_idx], confint # ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \ # exog_idx] ) parameter_table = SimpleTable(params_data, param_header, params_stubs, title = None, txt_fmt = fmt_params #gen_fmt #fmt_2, #gen_fmt, ) return parameter_table def summary_params_2d(result, extras=None, endog_names=None, exog_names=None, title=None): '''create summary table of regression parameters with several equations This allows interleaving of parameters with bse and/or tvalues Parameters ---------- result : result instance the result instance with params and attributes in extras extras : list of strings additional attributes to add below a parameter row, e.g. bse or tvalues endog_names : None or list of strings names for rows of the parameter array (multivariate endog) exog_names : None or list of strings names for columns of the parameter array (exog) alpha : float level for confidence intervals, default 0.95 title : None or string Returns ------- tables : list of SimpleTable this contains a list of all seperate Subtables table_all : SimpleTable the merged table with results concatenated for each row of the parameter array ''' if endog_names is None: #TODO: note the [1:] is specific to current MNLogit endog_names = ['endog_%d' % i for i in np.unique(result.model.endog)[1:]] if exog_names is None: exog_names = ['var%d' %i for i in range(len(result.params))] #TODO: check formatting options with different values #res_params = [['%10.4f'%item for item in row] for row in result.params] res_params = [[forg(item, prec=4) for item in row] for row in result.params] if extras: #not None or non-empty #maybe this should be a simple triple loop instead of list comprehension? #below_list = [[['%10s' % ('('+('%10.3f'%v).strip()+')') extras_list = [[['%10s' % ('(' + forg(v, prec=3).strip() + ')') for v in col] for col in getattr(result, what)] for what in extras ] data = zip(res_params, *extras_list) data = [i for j in data for i in j] #flatten stubs = zip(endog_names, *[['']*len(endog_names)]*len(extras)) stubs = [i for j in stubs for i in j] #flatten #return SimpleTable(data, headers=exog_names, stubs=stubs) else: data = res_params stubs = endog_names # return SimpleTable(data, headers=exog_names, stubs=stubs, # data_fmts=['%10.4f']) import copy txt_fmt = copy.deepcopy(fmt_params) txt_fmt.update(dict(data_fmts = ["%s"]*result.params.shape[1])) return SimpleTable(data, headers=exog_names, stubs=stubs, title=title, # data_fmts = ["%s"]), txt_fmt = txt_fmt) def summary_params_2dflat(result, endog_names=None, exog_names=None, alpha=0.05, use_t=True, keep_headers=True, endog_cols=False): #skip_headers2=True): '''summary table for parameters that are 2d, e.g. multi-equation models Parameters ---------- result : result instance the result instance with params, bse, tvalues and conf_int endog_names : None or list of strings names for rows of the parameter array (multivariate endog) exog_names : None or list of strings names for columns of the parameter array (exog) alpha : float level for confidence intervals, default 0.95 use_t : bool indicator whether the p-values are based on the Student-t distribution (if True) or on the normal distribution (if False) keep_headers : bool If true (default), then sub-tables keep their headers. If false, then only the first headers are kept, the other headerse are blanked out endog_cols : bool If false (default) then params and other result statistics have equations by rows. If true, then equations are assumed to be in columns. Not implemented yet. Returns ------- tables : list of SimpleTable this contains a list of all seperate Subtables table_all : SimpleTable the merged table with results concatenated for each row of the parameter array ''' res = result params = res.params if params.ndim == 2: # we've got multiple equations n_equ = params.shape[1] if not len(endog_names) == params.shape[1]: raise ValueError('endog_names has wrong length') else: if not len(endog_names) == len(params): raise ValueError('endog_names has wrong length') n_equ = 1 #VAR doesn't have conf_int #params = res.params.T # this is a convention for multi-eq models if not isinstance(endog_names, list): #this might be specific to multinomial logit type, move? if endog_names is None: endog_basename = 'endog' else: endog_basename = endog_names #TODO: note, the [1:] is specific to current MNLogit endog_names = res.model.endog_names[1:] #check if we have the right length of names tables = [] for eq in range(n_equ): restup = (res, res.params[:,eq], res.bse[:,eq], res.tvalues[:,eq], res.pvalues[:,eq], res.conf_int(alpha)[eq]) #not used anymore in current version # if skip_headers2: # skiph = (row != 0) # else: # skiph = False skiph = False tble = summary_params(restup, yname=endog_names[eq], xname=exog_names, alpha=alpha, use_t=use_t, skip_header=skiph) tables.append(tble) #add titles, they will be moved to header lines in table_extend for i in range(len(endog_names)): tables[i].title = endog_names[i] table_all = table_extend(tables, keep_headers=keep_headers) return tables, table_all def table_extend(tables, keep_headers=True): '''extend a list of SimpleTables, adding titles to header of subtables This function returns the merged table as a deepcopy, in contrast to the SimpleTable extend method. Parameters ---------- tables : list of SimpleTable instances keep_headers : bool If true, then all headers are kept. If falls, then the headers of subtables are blanked out. Returns ------- table_all : SimpleTable merged tables as a single SimpleTable instance ''' from copy import deepcopy for ii, t in enumerate(tables[:]): #[1:]: t = deepcopy(t) #move title to first cell of header #TODO: check if we have multiline headers if t[0].datatype == 'header': t[0][0].data = t.title t[0][0]._datatype = None t[0][0].row = t[0][1].row if not keep_headers and (ii > 0): for c in t[0][1:]: c.data = '' #add separating line and extend tables if ii == 0: table_all = t else: r1 = table_all[-1] r1.add_format('txt', row_dec_below='-') table_all.extend(t) table_all.title = None return table_all def summary_return(tables, return_fmt='text'): ######## Return Summary Tables ######## # join table parts then print if return_fmt == 'text': strdrop = lambda x: str(x).rsplit('\n',1)[0] #convert to string drop last line return '\n'.join(map(strdrop, tables[:-1]) + [str(tables[-1])]) elif return_fmt == 'tables': return tables elif return_fmt == 'csv': return '\n'.join(map(lambda x: x.as_csv(), tables)) elif return_fmt == 'latex': #TODO: insert \hline after updating SimpleTable import copy table = copy.deepcopy(tables[0]) del table[-1] for part in tables[1:]: table.extend(part) return table.as_latex_tabular() elif return_fmt == 'html': return "\n".join(table.as_html() for table in tables) else: raise ValueError('available output formats are text, csv, latex, html') class Summary(object): '''class to hold tables for result summary presentation Construction does not take any parameters. Tables and text can be added with the add_... methods. Attributes ---------- tables : list of tables Contains the list of SimpleTable instances, horizontally concatenated tables are not saved separately. extra_txt : string extra lines that are added to the text output, used for warnings and explanations. ''' def __init__(self): self.tables = [] self.extra_txt = None def __str__(self): return self.as_text() def __repr__(self): #return '<' + str(type(self)) + '>\n"""\n' + self.__str__() + '\n"""' return str(type(self)) + '\n"""\n' + self.__str__() + '\n"""' def _repr_html_(self): '''Display as HTML in IPython notebook.''' return self.as_html() def add_table_2cols(self, res, title=None, gleft=None, gright=None, yname=None, xname=None): '''add a double table, 2 tables with one column merged horizontally Parameters ---------- res : results instance some required information is directly taken from the result instance title : string or None if None, then a default title is used. ?how did I do no title? gleft : list of tuples elements for the left table, tuples are (name, value) pairs If gleft is None, then a default table is created gright : list of tuples or None elements for the right table, tuples are (name, value) pairs yname : string or None optional name for the endogenous variable, default is "y" xname : list of strings or None optional names for the exogenous variables, default is "var_xx" Returns ------- None : tables are attached ''' table = summary_top(res, title=title, gleft=gleft, gright=gright, yname=yname, xname=xname) self.tables.append(table) def add_table_params(self, res, yname=None, xname=None, alpha=.05, use_t=True): '''create and add a table for the parameter estimates Parameters ---------- res : results instance some required information is directly taken from the result instance yname : string or None optional name for the endogenous variable, default is "y" xname : list of strings or None optional names for the exogenous variables, default is "var_xx" alpha : float significance level for the confidence intervals use_t : bool indicator whether the p-values are based on the Student-t distribution (if True) or on the normal distribution (if False) Returns ------- None : table is attached ''' if res.params.ndim == 1: table = summary_params(res, yname=yname, xname=xname, alpha=alpha, use_t=use_t) elif res.params.ndim == 2: # _, table = summary_params_2dflat(res, yname=yname, xname=xname, # alpha=alpha, use_t=use_t) _, table = summary_params_2dflat(res, endog_names=yname, exog_names=xname, alpha=alpha, use_t=use_t) else: raise ValueError('params has to be 1d or 2d') self.tables.append(table) def add_extra_txt(self, etext): '''add additional text that will be added at the end in text format Parameters ---------- etext : string string with lines that are added to the text output. ''' self.extra_txt = '\n'.join(etext) def as_text(self): '''return tables as string Returns ------- txt : string summary tables and extra text as one string ''' txt = summary_return(self.tables, return_fmt='text') if not self.extra_txt is None: txt = txt + '\n\n' + self.extra_txt return txt def as_latex(self): '''return tables as string Returns ------- latex : string summary tables and extra text as string of Latex Notes ----- This currently merges tables with different number of columns. It is recommended to use `as_latex_tabular` directly on the individual tables. ''' return summary_return(self.tables, return_fmt='latex') def as_csv(self): '''return tables as string Returns ------- csv : string concatenated summary tables in comma delimited format ''' return summary_return(self.tables, return_fmt='csv') def as_html(self): '''return tables as string Returns ------- html : string concatenated summary tables in HTML format ''' return summary_return(self.tables, return_fmt='html') if __name__ == "__main__": import statsmodels.api as sm data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog) res = sm.OLS(data.endog, data.exog).fit() #summary( statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/summary2.py000066400000000000000000000445541224417117700237570ustar00rootroot00000000000000import numpy as np import pandas as pd import datetime import copy #import collections # OrderedDict requires python >= 2.7 from statsmodels.compatnp.collections import OrderedDict import StringIO import textwrap from table import SimpleTable from tableformatting import fmt_latex, fmt_txt class Summary(object): def __init__(self): self.tables = [] self.settings = [] self.extra_txt = [] self.title = None def __str__(self): return self.as_text() def __repr__(self): return str(type(self)) + '\n"""\n' + self.__str__() + '\n"""' def _repr_html_(self): '''Display as HTML in IPython notebook.''' return self.as_html() def add_df(self, df, index=True, header=True, float_format='%.4f', align='r'): '''Add the contents of a DataFrame to summary table Parameters ---------- df : DataFrame header: bool Reproduce the DataFrame column labels in summary table index: bool Reproduce the DataFrame row labels in summary table float_format: string Formatting to float data columns align : string Data alignment (l/c/r) ''' settings = {'index':index, 'header':header, 'float_format':float_format, 'align':align} self.tables.append(df) self.settings.append(settings) def add_array(self, array, align='r', float_format="%.4f"): '''Add the contents of a Numpy array to summary table Parameters ---------- array : numpy array (2D) float_format: string Formatting to array if type is float align : string Data alignment (l/c/r) ''' table = pd.DataFrame(array) self.add_df(table, index=False, header=False, float_format=float_format, align=align) def add_dict(self, d, ncols=2, align='l', float_format="%.4f"): '''Add the contents of a Dict to summary table Parameters ---------- d : dict Keys and values are automatically coerced to strings with str(). Users are encouraged to format them before using add_dict. ncols: int Number of columns of the output table align : string Data alignment (l/c/r) ''' keys = [_formatter(x, float_format) for x in d.keys()] vals = [_formatter(x, float_format) for x in d.values()] data = np.array(zip(keys, vals)) if data.shape[0] % ncols != 0: pad = ncols - (data.shape[0] % ncols) data = np.vstack([data, np.array(pad * [['','']])]) data = np.split(data, ncols) data = reduce(lambda x,y: np.hstack([x,y]), data) self.add_array(data, align=align) def add_text(self, string): '''Append a note to the bottom of the summary table. In ASCII tables, the note will be wrapped to table width. Notes are not indendented. ''' self.extra_txt.append(string) def add_title(self, title=None, results=None): '''Insert a title on top of the summary table. If a string is provided in the title argument, that string is printed. If no title string is provided but a results instance is provided, statsmodels attempts to construct a useful title automatically. ''' if type(title) == str: self.title = title else: try: model = results.model.__class__.__name__ if model in _model_types: model = _model_types[model] self.title = 'Results: ' + model except: self.title = '' def add_base(self, results, alpha=0.05, float_format="%.4f", title=None, xname=None, yname=None): '''Try to construct a basic summary instance. Parameters ---------- results : Model results instance alpha : float significance level for the confidence intervals (optional) float_formatting: string Float formatting for summary of parameters (optional) title : string Title of the summary table (optional) xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) ''' param = summary_params(results, alpha=alpha) info = summary_model(results) if xname != None: param.index = xname if yname != None: info['Dependent Variable:'] = yname self.add_dict(info, align='l') self.add_df(param, float_format=float_format) self.add_title(title=title, results=results) def as_text(self): '''Generate ASCII Summary Table ''' tables = self.tables settings = self.settings title = self.title extra_txt = self.extra_txt pad_col, pad_index, widest = _measure_tables(tables, settings) rule_equal = widest * '=' rule_dash = widest * '-' simple_tables = _simple_tables(tables, settings, pad_col, pad_index) tab = [x.as_text() for x in simple_tables] tab = '\n'.join(tab) tab = tab.split('\n') tab[0] = rule_equal tab.append(rule_equal) tab = '\n'.join(tab) if title != None: title = title if len(title) < widest: title = ' ' * int(widest/2 - len(title)/2) + title else: title = '' txt = [textwrap.wrap(x, widest) for x in extra_txt] txt = ['\n'.join(x) for x in txt] txt = '\n'.join(txt) out = '\n'.join([title, tab, txt]) return out def as_html(self): '''Generate HTML Summary Table ''' tables = self.tables settings = self.settings title = self.title simple_tables = _simple_tables(tables, settings) tab = [x.as_html() for x in simple_tables] tab = '\n'.join(tab) return tab def as_latex(self): '''Generate LaTeX Summary Table ''' tables = self.tables settings = self.settings title = self.title if title != None: title = '\\caption{' + title + '} \\\\' else: title = '\\caption{}' simple_tables = _simple_tables(tables, settings) tab = [x.as_latex_tabular() for x in simple_tables] tab = '\n\\hline\n'.join(tab) out = '\\begin{table}', title, tab, '\\end{table}' out = '\n'.join(out) return out def _measure_tables(tables, settings): '''Compare width of ascii tables in a list and calculate padding values. We add space to each col_sep to get us as close as possible to the width of the largest table. Then, we add a few spaces to the first column to pad the rest. ''' simple_tables = _simple_tables(tables, settings) tab = [x.as_text() for x in simple_tables] length = [len(x.splitlines()[0]) for x in tab] len_max = max(length) pad_sep = [] pad_index = [] for i in range(len(tab)): nsep = tables[i].shape[1] - 1 pad = int((len_max - length[i]) / nsep) pad_sep.append(pad) len_new = length[i] + nsep * pad pad_index.append(len_max - len_new) return pad_sep, pad_index, max(length) # Useful stuff _model_types = {'OLS' : 'Ordinary least squares', 'GLS' : 'Generalized least squares', 'GLSAR' : 'Generalized least squares with AR(p)', 'WLS' : 'Weigthed least squares', 'RLM' : 'Robust linear model', 'NBin': 'Negative binomial model', 'GLM' : 'Generalized linear model' } def summary_model(results): '''Create a dict with information about the model ''' def time_now(**kwrds): now = datetime.datetime.now() return now.strftime('%Y-%m-%d %H:%M') info = OrderedDict() info['Model:'] = lambda x: x.model.__class__.__name__ info['Model Family:'] = lambda x: x.family.__class.__name__ info['Link Function:'] = lambda x: x.family.link.__class__.__name__ info['Dependent Variable:'] = lambda x: x.model.endog_names info['Date:'] = time_now() info['No. Observations:'] = lambda x: "%#6d" % x.nobs info['Df Model:'] = lambda x: "%#6d" % x.df_model info['Df Residuals:'] = lambda x: "%#6d" % x.df_resid info['Converged:'] = lambda x: x.mle_retvals['converged'] info['No. Iterations:'] = lambda x: x.mle_retvals['iterations'] info['Method:'] = lambda x: x.method info['Norm:'] = lambda x: x.fit_options['norm'] info['Scale Est.:'] = lambda x: x.fit_options['scale_est'] info['Cov. Type:'] = lambda x: x.fit_options['cov'] info['R-squared:'] = lambda x: "%#8.3f" % x.rsquared info['Adj. R-squared:'] = lambda x: "%#8.3f" % x.rsquared_adj info['Pseudo R-squared:'] = lambda x: "%#8.3f" % x.prsquared info['AIC:'] = lambda x: "%8.4f" % x.aic info['BIC:'] = lambda x: "%8.4f" % x.bic info['Log-Likelihood:'] = lambda x: "%#8.5g" % x.llf info['LL-Null:'] = lambda x: "%#8.5g" % x.llnull info['LLR p-value:'] = lambda x: "%#8.5g" % x.llr_pvalue info['Deviance:'] = lambda x: "%#8.5g" % x.deviance info['Pearson chi2:'] = lambda x: "%#6.3g" % x.pearson_chi2 info['F-statistic:'] = lambda x: "%#8.4g" % x.fvalue info['Prob (F-statistic):'] = lambda x: "%#6.3g" % x.f_pvalue info['Scale:'] = lambda x: "%#8.5g" % x.scale out = OrderedDict() for key in info.keys(): try: out[key] = info[key](results) except: pass return out def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True, skip_header=False, float_format="%.4f"): '''create a summary table of parameters from results instance Parameters ---------- res : results instance some required information is directly taken from the result instance yname : string or None optional name for the endogenous variable, default is "y" xname : list of strings or None optional names for the exogenous variables, default is "var_xx" alpha : float significance level for the confidence intervals use_t : bool indicator whether the p-values are based on the Student-t distribution (if True) or on the normal distribution (if False) skip_headers : bool If false (default), then the header row is added. If true, then no header row is added. float_format : string float formatting options (e.g. ".3g") Returns ------- params_table : SimpleTable instance ''' if isinstance(results, tuple): results, params, std_err, tvalues, pvalues, conf_int = results else: params = results.params bse = results.bse tvalues = results.tvalues pvalues = results.pvalues conf_int = results.conf_int(alpha) data = np.array([params, bse, tvalues, pvalues]).T data = np.hstack([data, conf_int]) data = pd.DataFrame(data) if use_t: data.columns = ['Coef.', 'Std.Err.', 't', 'P>|t|', '[' + str(alpha/2), str(1-alpha/2) + ']'] else: data.columns = ['Coef.', 'Std.Err.', 'z', 'P>|z|', '[' + str(alpha/2), str(1-alpha/2) + ']'] if not xname: data.index = results.model.exog_names else: data.index = xname return data # Vertical summary instance for multiple models def _col_params(result, float_format='%.4f', stars=True): '''Stack coefficients and standard errors in single column ''' # Extract parameters res = summary_params(result) # Format float for col in res.columns[:2]: res[col] = res[col].apply(lambda x: float_format % x) # Std.Errors in parentheses res.ix[:,1] = '(' + res.ix[:,1] + ')' # Significance stars if stars: idx = res.ix[:,3] < .1 res.ix[:,0][idx] = res.ix[:,0][idx] + '*' idx = res.ix[:,3] < .05 res.ix[:,0][idx] = res.ix[:,0][idx] + '*' idx = res.ix[:,3] < .01 res.ix[:,0][idx] = res.ix[:,0][idx] + '*' # Stack Coefs and Std.Errors res = res.ix[:,:2] res = res.stack() res = pd.DataFrame(res) res.columns = [str(result.model.endog_names)] return res def _col_info(result, info_dict=None): '''Stack model info in a column ''' if info_dict == None: info_dict = {} out = [] index = [] for i in info_dict: if isinstance(info_dict[i], dict): # this is a specific model info_dict, but not for this result... continue try: out.append(info_dict[i](result)) except: out.append('') index.append(i) out = pd.DataFrame({str(result.model.endog_names):out}, index=index) return out def _make_unique(list_of_names): if len(set(list_of_names)) == len(list_of_names): return list_of_names # pandas does not like it if multiple columns have the same names from collections import defaultdict name_counter = defaultdict(str) header = [] for _name in list_of_names: name_counter[_name] += "I" header.append(_name+" " +name_counter[_name]) return header def summary_col(results, float_format='%.4f', model_names=[], stars=False, info_dict=None, regressor_order=[]): '''Summarize multiple results instances side-by-side (coefs and SEs) Parameters ---------- results : statsmodels results instance or list of result instances float_format : string float format for coefficients and standard errors Default : '%.4f' model_names : list of strings of length len(results) if the names are not unique, a roman number will be appended to all model names stars : bool print significance stars info_dict : dict dict of lambda functions to be applied to results instances to retrieve model info. To use specific information for different models, add a (nested) info_dict with model name as the key. Example: `info_dict = {"N":..., "R2": ..., "OLS":{"R2":...}}` would only show `R2` for OLS regression models, but additionally `N` for all other results. Default : None (use the info_dict specified in result.default_model_infos, if this property exists) regressor_order : list of strings list of names of the regressors in the desired order. All regressors not specified will be appended to the end of the list. ''' if type(results) != list: results = [results] cols = [_col_params(x, stars=stars, float_format=float_format) for x in results] # Unique column names (pandas has problems merging otherwise) if model_names: colnames = _make_unique(model_names) else: colnames = _make_unique([x.columns[0] for x in cols]) for i in range(len(cols)): cols[i].columns = [colnames[i]] merg = lambda x,y: x.merge(y, how='outer', right_index=True, left_index=True) summ = reduce(merg, cols) if regressor_order: varnames = summ.index.get_level_values(0).tolist() ordered = [x for x in regressor_order if x in varnames] unordered = [x for x in varnames if x not in regressor_order + ['']] order = ordered + list(np.unique(unordered)) f = lambda idx: sum([[x + 'coef', x + 'stde'] for x in idx], []) summ.index = f(np.unique(varnames)) summ = summ.reindex(f(order)) summ.index = [x[:-4] for x in summ.index] idx = pd.Series(range(summ.shape[0])) %2 == 1 summ.index = np.where(idx, '', summ.index.get_level_values(0)) # add infos about the models. if info_dict: cols = [_col_info(x, info_dict.get(x.model.__class__.__name__, info_dict)) for x in results] else: cols = [_col_info(x, getattr(x, "default_model_infos", None)) for x in results] # use unique column names, otherwise the merge will not succeed for df , name in zip(cols, _make_unique([df.columns[0] for df in cols])): df.columns = [name] merg = lambda x,y: x.merge(y, how='outer', right_index=True, left_index=True) info = reduce(merg, cols) dat = pd.DataFrame(np.vstack([summ,info])) # pd.concat better, but error dat.columns = summ.columns dat.index = pd.Index(summ.index.tolist() + info.index.tolist()) summ = dat summ = summ.fillna('') smry = Summary() smry.add_df(summ, header=True, align='l') smry.add_text('Standard errors in parentheses.') if stars: smry.add_text('* p<.1, ** p<.05, ***p<.01') return smry def _formatter(element, float_format='%.4f'): try: out = float_format % element except: out = str(element) return out.strip() def _df_to_simpletable(df, align='r', float_format="%.4f", header=True, index=True, table_dec_above='-', table_dec_below=None, header_dec_below='-', pad_col=0, pad_index=0): dat = df.copy() dat = dat.applymap(lambda x: _formatter(x, float_format)) if header: headers = [str(x) for x in dat.columns.tolist()] else: headers = None if index: stubs = [str(x) + int(pad_index) * ' ' for x in dat.index.tolist()] else: dat.ix[:,0] = [str(x) + int(pad_index) * ' ' for x in dat.ix[:,0]] stubs = None st = SimpleTable(np.array(dat), headers=headers, stubs=stubs, ltx_fmt=fmt_latex, txt_fmt=fmt_txt) st.output_formats['latex']['data_aligns'] = align st.output_formats['txt']['data_aligns'] = align st.output_formats['txt']['table_dec_above'] = table_dec_above st.output_formats['txt']['table_dec_below'] = table_dec_below st.output_formats['txt']['header_dec_below'] = header_dec_below st.output_formats['txt']['colsep'] = ' ' * int(pad_col + 1) return st def _simple_tables(tables, settings, pad_col=None, pad_index=None): simple_tables = [] float_format = '%.4f' if pad_col == None: pad_col = [0] * len(tables) if pad_index == None: pad_index = [0] * len(tables) for i,v in enumerate(tables): index = settings[i]['index'] header = settings[i]['header'] align = settings[i]['align'] simple_tables.append(_df_to_simpletable(v, align=align, float_format=float_format, header=header, index=index, pad_col=pad_col[i], pad_index=pad_index[i])) return simple_tables statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/table.py000066400000000000000000001005521224417117700232560ustar00rootroot00000000000000""" Provides a simple table class. A SimpleTable is essentially a list of lists plus some formatting functionality. Dependencies: the Python 2.5+ standard library. Installation: just copy this module into your working directory (or anywhere in your pythonpath). Basic use:: mydata = [[11,12],[21,22]] # data MUST be 2-dimensional myheaders = [ "Column 1", "Column 2" ] mystubs = [ "Row 1", "Row 2" ] tbl = SimpleTable(mydata, myheaders, mystubs, title="Title") print( tbl ) print( tbl.as_csv() ) A SimpleTable is inherently (but not rigidly) rectangular. You should create it from a *rectangular* (2d!) iterable of data. Each item in your rectangular iterable will become the data of a single Cell. In principle, items can be any object, not just numbers and strings. However, default conversion during table production is by simple string interpolation. (So you cannot have a tuple as a data item *and* rely on the default conversion.) A SimpleTable allows only one column (the first) of stubs at initilization, concatenation of tables allows you to produce tables with interior stubs. (You can also assign the datatype 'stub' to the cells in any column, or use ``insert_stubs``.) A SimpleTable can be concatenated with another SimpleTable or extended by another SimpleTable. :: table1.extend_right(table2) table1.extend(table2) A SimpleTable can be initialized with `datatypes`: a list of ints that provide indexes into `data_fmts` and `data_aligns`. Each data cell is assigned a datatype, which will control formatting. If you do not specify the `datatypes` list, it will be set to ``range(ncols)`` where `ncols` is the number of columns in the data. (I.e., cells in a column have their own datatype.) This means that you can just specify `data_fmts` without bothering to provide a `datatypes` list. If ``len(datatypes)'] if self.title: title = '%s' % self.title formatted_rows.append(title) formatted_rows.extend( row.as_string('html', **fmt) for row in self ) formatted_rows.append('') return '\n'.join(formatted_rows) def as_latex_tabular(self, **fmt_dict): '''Return string, the table as a LaTeX tabular environment. Note: will require the booktabs package.''' #fetch the text format, override with fmt_dict fmt = self._get_fmt('latex', **fmt_dict) formatted_rows = ["\\begin{center}"] table_dec_above = fmt['table_dec_above'] or '' table_dec_below = fmt['table_dec_below'] or '' prev_aligns = None last = None for row in self + [last]: if row == last: aligns = None else: aligns = row.get_aligns('latex', **fmt) if aligns != prev_aligns: # When the number/type of columns changes... if prev_aligns: # ... if there is a tabular to close, close it... formatted_rows.append(table_dec_below) formatted_rows.append( r'\end{tabular}' ) if aligns: # ... and if there are more lines, open a new one: formatted_rows.append( r'\begin{tabular}{%s}' % aligns ) if not prev_aligns: # (with a nice line if it's the top of the whole table) formatted_rows.append(table_dec_above) if row != last: formatted_rows.append( row.as_string(output_format='latex', **fmt) ) prev_aligns = aligns #tabular does not support caption, but make it available for figure environment if self.title: title = r'%%\caption{%s}' % self.title formatted_rows.append(title) formatted_rows.append( "\\end{center}" ) return '\n'.join(formatted_rows) """ if fmt_dict['strip_backslash']: ltx_stubs = [stub.replace('\\',r'$\backslash$') for stub in self.stubs] ltx_headers = [header.replace('\\',r'$\backslash$') for header in self.headers] ltx_headers = self.format_headers(fmt_dict, ltx_headers) else: ltx_headers = self.format_headers(fmt_dict) ltx_stubs = self.format_stubs(fmt_dict, ltx_stubs) """ def extend_right(self, table): """Return None. Extend each row of `self` with corresponding row of `table`. Does **not** import formatting from ``table``. This generally makes sense only if the two tables have the same number of rows, but that is not enforced. :note: To extend append a table below, just use `extend`, which is the ordinary list method. This generally makes sense only if the two tables have the same number of columns, but that is not enforced. """ for row1, row2 in zip(self, table): row1.extend(row2) def label_cells(self, func): """Return None. Labels cells based on `func`. If ``func(cell) is None`` then its datatype is not changed; otherwise it is set to ``func(cell)``. """ for row in self: for cell in row: label = func(cell) if label is not None: cell.datatype = label @property def data(self): return [row.data for row in self] #END: class SimpleTable def pad(s, width, align): """Return string padded with spaces, based on alignment parameter.""" if align == 'l': s = s.ljust(width) elif align == 'r': s = s.rjust(width) else: s = s.center(width) return s class Row(list): """Provides a table row as a list of cells. A row can belong to a SimpleTable, but does not have to. """ def __init__(self, seq, datatype='data', table=None, celltype=None, dec_below='row_dec_below', **fmt_dict): """ Parameters ---------- seq : sequence of data or cells table : SimpleTable datatype : str ('data' or 'header') dec_below : str (e.g., 'header_dec_below' or 'row_dec_below') decoration tag, identifies the decoration to go below the row. (Decoration is repeated as needed for text formats.) """ self.datatype = datatype self.table = table if celltype is None: if table is None: celltype = Cell else: celltype = table._Cell self._Cell = celltype self._fmt = fmt_dict self.special_fmts = dict() #special formatting for any output format self.dec_below = dec_below list.__init__(self, (celltype(cell,row=self) for cell in seq)) def add_format(self, output_format, **fmt_dict): """ Return None. Adds row-instance specific formatting for the specified output format. Example: myrow.add_format('txt', row_dec_below='+-') """ output_format = get_output_format(output_format) if output_format not in self.special_fmts: self.special_fmts[output_format] = dict() self.special_fmts[output_format].update(fmt_dict) def insert_stub(self, loc, stub): """Return None. Inserts a stub cell in the row at `loc`. """ _Cell = self._Cell if not isinstance(stub, _Cell): stub = stub stub = _Cell(stub, datatype='stub', row=self) self.insert(loc, stub) def _get_fmt(self, output_format, **fmt_dict): """Return dict, the formatting options. """ output_format = get_output_format(output_format) #first get the default formatting try: fmt = default_fmts[output_format].copy() except KeyError: raise ValueError('Unknown format: %s' % output_format) #second get table specific formatting (if possible) try: fmt.update(self.table.output_formats[output_format]) except AttributeError: pass #finally, add formatting for this row and this call fmt.update(self._fmt) fmt.update(fmt_dict) special_fmt = self.special_fmts.get(output_format, None) if special_fmt is not None: fmt.update(special_fmt) return fmt def get_aligns(self, output_format, **fmt_dict): """Return string, sequence of column alignments. Ensure comformable data_aligns in `fmt_dict`.""" fmt = self._get_fmt(output_format, **fmt_dict) return ''.join( cell.alignment(output_format, **fmt) for cell in self ) def as_string(self, output_format='txt', **fmt_dict): """Return string: the formatted row. This is the default formatter for rows. Override this to get different formatting. A row formatter must accept as arguments a row (self) and an output format, one of ('html', 'txt', 'csv', 'latex'). """ fmt = self._get_fmt(output_format, **fmt_dict) #get column widths try: colwidths = self.table.get_colwidths(output_format, **fmt) except AttributeError: colwidths = fmt.get('colwidths') if colwidths is None: colwidths = (0,) * len(self) colsep = fmt['colsep'] row_pre = fmt.get('row_pre','') row_post = fmt.get('row_post','') formatted_cells = [] for cell, width in zip(self, colwidths): content = cell.format(width, output_format=output_format, **fmt) formatted_cells.append(content) formatted_row = row_pre + colsep.join(formatted_cells) + row_post formatted_row = self._decorate_below(formatted_row, output_format, **fmt) return formatted_row def _decorate_below(self, row_as_string, output_format, **fmt_dict): """This really only makes sense for the text and latex output formats.""" dec_below = fmt_dict.get(self.dec_below, None) if dec_below is None: result = row_as_string else: output_format = get_output_format(output_format) if output_format == 'txt': row0len = len(row_as_string) dec_len = len (dec_below) repeat, addon = divmod(row0len, dec_len) result = row_as_string + "\n" + (dec_below * repeat + dec_below[:addon]) elif output_format == 'latex': result = row_as_string + "\n" + dec_below else: raise ValueError("I can't decorate a %s header."%output_format) return result @property def data(self): return [cell.data for cell in self] #END class Row class Cell(object): """Provides a table cell. A cell can belong to a Row, but does not have to. """ def __init__(self, data='', datatype=None, row=None, **fmt_dict): try: #might have passed a Cell instance self.data = data.data self._datatype = data.datatype self._fmt = data._fmt except (AttributeError, TypeError): #passed ordinary data self.data = data self._datatype = datatype self._fmt = dict() self._fmt.update(fmt_dict) self.row = row def __str__(self): return '%s' % self.data def _get_fmt(self, output_format, **fmt_dict): """Return dict, the formatting options. """ output_format = get_output_format(output_format) #first get the default formatting try: fmt = default_fmts[output_format].copy() except KeyError: raise ValueError('Unknown format: %s' % output_format) #then get any table specific formtting try: fmt.update(self.row.table.output_formats[output_format]) except AttributeError: pass #then get any row specific formtting try: fmt.update(self.row._fmt) except AttributeError: pass #finally add formatting for this instance and call fmt.update(self._fmt) fmt.update(fmt_dict) return fmt def alignment(self, output_format, **fmt_dict): fmt = self._get_fmt(output_format, **fmt_dict) datatype = self.datatype data_aligns = fmt.get('data_aligns','c') if isinstance(datatype, int): align = data_aligns[datatype % len(data_aligns)] elif datatype == 'stub': #still support deprecated `stubs_align` align = fmt.get('stubs_align') or fmt.get('stub_align','l') elif datatype in fmt: label_align = '%s_align' % datatype align = fmt.get(label_align,'c') else: raise ValueError('Unknown cell datatype: %s'%datatype) return align def format(self, width, output_format='txt', **fmt_dict): """Return string. This is the default formatter for cells. Override this to get different formating. A cell formatter must accept as arguments a cell (self) and an output format, one of ('html', 'txt', 'csv', 'latex'). It will generally respond to the datatype, one of (int, 'header', 'stub'). """ fmt = self._get_fmt(output_format, **fmt_dict) data = self.data datatype = self.datatype data_fmts = fmt.get('data_fmts') if data_fmts is None: #chk allow for deprecated use of data_fmt data_fmt = fmt.get('data_fmt') if data_fmt is None: data_fmt = '%s' data_fmts = [data_fmt] data_aligns = fmt.get('data_aligns','c') if isinstance(datatype, int): datatype = datatype % len(data_fmts) #constrain to indexes content = data_fmts[datatype] % (data,) elif datatype in fmt: if "replacements" in fmt: if isinstance( data, str ): for repl in fmt["replacements"]: data = data.replace( repl, fmt["replacements"][repl] ) dfmt = fmt.get(datatype) try: content = dfmt % (data,) except TypeError: #dfmt is not a substitution string content = dfmt else: raise ValueError('Unknown cell datatype: %s'%datatype) align = self.alignment(output_format, **fmt) return pad(content, width, align) def get_datatype(self): if self._datatype == None: dtype = self.row.datatype else: dtype = self._datatype return dtype def set_datatype(self, val): #TODO: add checking self._datatype = val datatype = property(get_datatype, set_datatype) #END class Cell ######### begin: default formats for SimpleTable ############## """ Some formatting suggestions: - if you want rows to have no extra spacing, set colwidths=0 and colsep=''. (Naturally the columns will not align.) - if you want rows to have minimal extra spacing, set colwidths=1. The columns will align. - to get consistent formatting, you should leave all field width handling to SimpleTable: use 0 as the field width in data_fmts. E.g., :: data_fmts = ["%#0.6g","%#0.6g","%#0.4g","%#0.4g"], colwidths = 14, data_aligns = "r", """ default_txt_fmt = dict( fmt = 'txt', #basic table formatting table_dec_above='=', table_dec_below='-', title_align='c', #basic row formatting row_pre = '', row_post = '', header_dec_below = '-', row_dec_below = None, colwidths = None, colsep=' ', data_aligns = "c", #data formats #data_fmt = "%s", #deprecated; use data_fmts data_fmts = ["%s"], #labeled alignments #stubs_align = 'l', #deprecated; use data_fmts stub_align = 'l', header_align = 'c', #labeled formats header_fmt = '%s', #deprecated; just use 'header' stub_fmt = '%s', #deprecated; just use 'stub' header='%s', stub='%s', empty_cell = '', #deprecated; just use 'empty' empty = '', missing='--', ) default_csv_fmt = dict( fmt = 'csv', table_dec_above = None, #'', table_dec_below = None, #'', #basic row formatting row_pre = '', row_post = '', header_dec_below = None, #'', row_dec_below = None, title_align = '', data_aligns = "l", colwidths = None, colsep = ',', #data formats data_fmt = '%s', #deprecated; use data_fmts data_fmts = ['%s'], #labeled alignments #stubs_align = 'l', #deprecated; use data_fmts stub_align = "l", header_align = 'c', #labeled formats header_fmt = '"%s"', #deprecated; just use 'header' stub_fmt = '"%s"', #deprecated; just use 'stub' empty_cell = '', #deprecated; just use 'empty' header='%s', stub='%s', empty = '', missing='--', ) default_html_fmt = dict( #basic table formatting table_dec_above=None, table_dec_below=None, header_dec_below=None, row_dec_below = None, title_align='c', #basic row formatting colwidths = None, colsep=' ', row_pre = '\n ', row_post = '\n', data_aligns = "c", #data formats data_fmts = ['%s'], data_fmt = "%s", #deprecated; use data_fmts #labeled alignments #stubs_align = 'l', #deprecated; use data_fmts stub_align = 'l', header_align = 'c', #labeled formats header_fmt = '%s', #deprecated; just use `header` stub_fmt = '%s', #deprecated; just use `stub` empty_cell = '', #deprecated; just use `empty` header='%s', stub='%s', empty = '', missing='--', ) default_latex_fmt = dict( fmt = 'ltx', #basic table formatting table_dec_above = r'\toprule', table_dec_below = r'\bottomrule', header_dec_below = r'\midrule', row_dec_below = None, strip_backslash = True, # NotImplemented #row formatting row_post = r' \\', data_aligns = 'c', colwidths = None, colsep = ' & ', #data formats data_fmts = ['%s'], data_fmt = '%s', #deprecated; use data_fmts #labeled alignments #stubs_align = 'l', #deprecated; use data_fmts stub_align = 'l', header_align = 'c', empty_align = 'l', #labeled formats header_fmt = r'\textbf{%s}', #deprecated; just use 'header' stub_fmt = r'\textbf{%s}', #deprecated; just use 'stub' empty_cell = '', #deprecated; just use 'empty' header = r'\textbf{%s}', stub = r'\textbf{%s}', empty = '', missing = '--', replacements = {"%" : "\%", ">" : "$>$", "|" : "$|$"} ) default_fmts = dict( html= default_html_fmt, txt=default_txt_fmt, latex=default_latex_fmt, csv=default_csv_fmt ) output_format_translations = dict( htm='html', text='txt', ltx='latex' ) def get_output_format(output_format): if output_format not in ('html', 'txt', 'latex', 'csv'): try: output_format = output_format_translations[output_format] except KeyError: raise ValueError('unknown output format %s'%output_format) return output_format ######### end: default formats ############## statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tableformatting.py000066400000000000000000000067531224417117700253610ustar00rootroot00000000000000""" Summary Table formating This is here to help keep the formating consistent across the different models """ gen_fmt = dict( data_fmts = ["%s", "%s", "%s", "%s", "%s"], empty_cell = '', colwidths = 7, #17, colsep=' ', row_pre = ' ', row_post = ' ', table_dec_above='=', table_dec_below=None, header_dec_below=None, header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = "r", stubs_align = "l", fmt = 'txt' ) # Note table_1l_fmt over rides the below formating unless it is not # appended to table_1l fmt_1_right = dict( data_fmts = ["%s", "%s", "%s", "%s", "%s"], empty_cell = '', colwidths = 16, colsep=' ', row_pre = '', row_post = '', table_dec_above='=', table_dec_below=None, header_dec_below=None, header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = "r", stubs_align = "l", fmt = 'txt' ) fmt_2 = dict( data_fmts = ["%s", "%s", "%s", "%s"], empty_cell = '', colwidths = 10, colsep=' ', row_pre = ' ', row_post = ' ', table_dec_above='=', table_dec_below='=', header_dec_below='-', header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = 'r', stubs_align = 'l', fmt = 'txt' ) # new version fmt_base = dict( data_fmts = ["%s", "%s", "%s", "%s", "%s"], empty_cell = '', colwidths = 10, colsep=' ', row_pre = '', row_post = '', table_dec_above='=', table_dec_below='=', #TODO need '=' at the last subtable header_dec_below='-', header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = 'r', stubs_align = 'l', fmt = 'txt' ) import copy fmt_2cols = copy.deepcopy(fmt_base) fmt2 = dict( data_fmts = ["%18s", "-%19s", "%18s", "%19s"], #TODO: colsep=' ', colwidths = 18, stub_fmt = '-%21s', ) fmt_2cols.update(fmt2) fmt_params = copy.deepcopy(fmt_base) fmt3 = dict( data_fmts = ["%s", "%s", "%8s", "%s", "%23s"], ) fmt_params.update(fmt3) """ Summary Table formating This is here to help keep the formating consistent across the different models """ fmt_latex = {'colsep': ' & ', 'colwidths': None, 'data_aligns': 'r', 'data_fmt': '%s', 'data_fmts': ['%s'], 'empty': '', 'empty_cell': '', 'fmt': 'ltx', 'header': '%s', 'header_align': 'c', 'header_dec_below': '\\hline', 'header_fmt': '%s', 'missing': '--', 'row_dec_below': None, 'row_post': ' \\\\', 'strip_backslash': True, 'stub': '%s', 'stub_align': 'l', 'stub_fmt': '%s', 'table_dec_above': '\\hline', 'table_dec_below': '\\hline'} fmt_txt = {'colsep': ' ', 'colwidths': None, 'data_aligns': 'r', 'data_fmts': ['%s'], 'empty': '', 'empty_cell': '', 'fmt': 'txt', 'header': '%s', 'header_align': 'c', 'header_dec_below': '-', 'header_fmt': '%s', 'missing': '--', 'row_dec_below': None, 'row_post': '', 'row_pre': '', 'stub': '%s', 'stub_align': 'l', 'stub_fmt': '%s', 'table_dec_above': '-', 'table_dec_below': None, 'title_align': 'c'} statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/000077500000000000000000000000001224417117700227545ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/__init__.py000066400000000000000000000000001224417117700250530ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/gen_dates.do000066400000000000000000000004421224417117700252310ustar00rootroot00000000000000insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/iolib/tests/stata_dates.csv" format datetime_c %tc format datetime_big_c %tC format date %td format weekly_date %tw format monthly_date %tm format quarterly_date %tq format half_yearly_date %th format yearly_date %ty statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/000077500000000000000000000000001224417117700244555ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/__init__.py000066400000000000000000000000001224417117700265540ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/data_missing.dta000066400000000000000000000021521224417117700276110ustar00rootroot00000000000000r€Ú ß€Ú ßÑ É@çH @ æÈ@4 U@iy@ 9 Oct 2012 11:02þÿûüýfloat_missdouble_missbyte_misssint_misslong_miss%9.0g%10.0g%8.0g%8.0g%12.0g~Cß ÍC ÍC ÍCàeååÿÿC~Cß ÍC ÍC ÍCàeååÿÿC~Cß ÍC ÍC ÍCàeååÿÿC~Cß ÍC ÍC ÍCàeååÿÿC~Cß ÍC ÍC ÍCàeååÿÿCàeååÿÿstatsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/macrodata.npy_000066400000000000000000000550401224417117700273030ustar00rootroot00000000000000“NUMPYF{'descr': [('year', 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@ÍÌÌÌÌÌ@åÐ"Ûù r@ÍÌÌÌÌÌ@Âõ(\þ?XŸ@@`åÐ";QÉ@€¥Á@9´Èv~ª¡@ÁÊ¡E¶æ‹@ffff¦ÈÂ@fffffVi@ÍÌÌÌÌd•@Ház®G@ÍÌÌÌÌÌ@ÇK7‰A¬r@Ãõ(\Â@333333ë?XŸ@@‘í|?õRÉ@š™™™YÁÁ@ƒÀÊ¡Ek¡@¼t“à‹@@ßÂ@ÍÌÌÌÌÉ@š™™™™üÁ@åÐ"ÛùY˜@žï§ÆK"@33333cÃ@¶óýÔx•j@33333ã˜@)\Âõ(Ì?333333 @˜nƒÀ(s@®Gázî?¸…ëQ¸æ¿dŸ@@˜nƒÀ2É@€òÁ@ÁÊ¡E¶Â–@NbX9ü@À®Ã@^ºI Ïj@fffffÖ™@ ×£p= Ç?ffffff"@V-²3s@ö(\Âõ @…ëQ¸… ÀdŸ@@øSã¥+_É@Â@;ßO—9—@1¬ZP@ÍÌÌÌLœÃ@¸…ëQ k@š™™™™'š@¸…ëQ¸¾?333333#@‘í|?5@s@{®Gáz @…ëQ¸… Àstatsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/macrodata.py000066400000000000000000000552021224417117700267660ustar00rootroot00000000000000from numpy import array macrodata_result = array( [ (1959.0, 1.0, 2710.349, 1707.4, 286.898, 470.045, 1886.9, 28.98, 139.7, 2.82, 5.8, 177.146, 0.0, 0.0), (1959.0, 2.0, 2778.801, 1733.7, 310.859, 481.301, 1919.7, 29.15, 141.7, 3.08, 5.1, 177.83, 2.34, 0.74), (1959.0, 3.0, 2775.488, 1751.8, 289.226, 491.26, 1916.4, 29.35, 140.5, 3.82, 5.3, 178.657, 2.74, 1.09), (1959.0, 4.0, 2785.204, 1753.7, 299.356, 484.052, 1931.3, 29.37, 140.0, 4.33, 5.6, 179.386, 0.27, 4.06), (1960.0, 1.0, 2847.699, 1770.5, 331.722, 462.199, 1955.5, 29.54, 139.6, 3.5, 5.2, 180.007, 2.31, 1.19), (1960.0, 2.0, 2834.39, 1792.9, 298.152, 460.4, 1966.1, 29.55, 140.2, 2.68, 5.2, 180.671, 0.14, 2.55), (1960.0, 3.0, 2839.022, 1785.8, 296.375, 474.676, 1967.8, 29.75, 140.9, 2.36, 5.6, 181.528, 2.7, -0.34), (1960.0, 4.0, 2802.616, 1788.2, 259.764, 476.434, 1966.6, 29.84, 141.1, 2.29, 6.3, 182.287, 1.21, 1.08), (1961.0, 1.0, 2819.264, 1787.7, 266.405, 475.854, 1984.5, 29.81, 142.1, 2.37, 6.8, 182.992, -0.4, 2.77), (1961.0, 2.0, 2872.005, 1814.3, 286.246, 480.328, 2014.4, 29.92, 142.9, 2.29, 7.0, 183.691, 1.47, 0.81), (1961.0, 3.0, 2918.419, 1823.1, 310.227, 493.828, 2041.9, 29.98, 144.1, 2.32, 6.8, 184.524, 0.8, 1.52), (1961.0, 4.0, 2977.83, 1859.6, 315.463, 502.521, 2082.0, 30.04, 145.2, 2.6, 6.2, 185.242, 0.8, 1.8), (1962.0, 1.0, 3031.241, 1879.4, 334.271, 520.96, 2101.7, 30.21, 146.4, 2.73, 5.6, 185.874, 2.26, 0.47), (1962.0, 2.0, 3064.709, 1902.5, 331.039, 523.066, 2125.2, 30.22, 146.5, 2.78, 5.5, 186.538, 0.13, 2.65), (1962.0, 3.0, 3093.047, 1917.9, 336.962, 538.838, 2137.0, 30.38, 146.7, 2.78, 5.6, 187.323, 2.11, 0.67), (1962.0, 4.0, 3100.563, 1945.1, 325.65, 535.912, 2154.6, 30.44, 148.3, 2.87, 5.5, 188.013, 0.79, 2.08), (1963.0, 1.0, 3141.087, 1958.2, 343.721, 522.917, 2172.5, 30.48, 149.7, 2.9, 5.8, 188.58, 0.53, 2.38), (1963.0, 2.0, 3180.447, 1976.9, 348.73, 518.108, 2193.1, 30.69, 151.3, 3.03, 5.7, 189.242, 2.75, 0.29), (1963.0, 3.0, 3240.332, 2003.8, 360.102, 546.893, 2217.9, 30.75, 152.6, 3.38, 5.5, 190.028, 0.78, 2.6), (1963.0, 4.0, 3264.967, 2020.6, 364.534, 532.383, 2254.6, 30.94, 153.7, 3.52, 5.6, 190.668, 2.46, 1.06), (1964.0, 1.0, 3338.246, 2060.5, 379.523, 529.686, 2299.6, 30.95, 154.8, 3.51, 5.5, 191.245, 0.13, 3.38), (1964.0, 2.0, 3376.587, 2096.7, 377.778, 526.175, 2362.1, 31.02, 156.8, 3.47, 5.2, 191.889, 0.9, 2.57), (1964.0, 3.0, 3422.469, 2135.2, 386.754, 522.008, 2392.7, 31.12, 159.2, 3.53, 5.0, 192.631, 1.29, 2.25), (1964.0, 4.0, 3431.957, 2141.2, 389.91, 514.603, 2420.4, 31.28, 160.7, 3.76, 5.0, 193.223, 2.05, 1.71), (1965.0, 1.0, 3516.251, 2188.8, 429.145, 508.006, 2447.4, 31.38, 162.0, 3.93, 4.9, 193.709, 1.28, 2.65), (1965.0, 2.0, 3563.96, 2213.0, 429.119, 508.931, 2474.5, 31.58, 163.1, 3.84, 4.7, 194.303, 2.54, 1.3), (1965.0, 3.0, 3636.285, 2251.0, 444.444, 529.446, 2542.6, 31.65, 166.0, 3.93, 4.4, 194.997, 0.89, 3.04), (1965.0, 4.0, 3724.014, 2314.3, 446.493, 544.121, 2594.1, 31.88, 169.1, 4.35, 4.1, 195.539, 2.9, 1.46), (1966.0, 1.0, 3815.423, 2348.5, 484.244, 556.593, 2618.4, 32.28, 171.8, 4.62, 3.9, 195.999, 4.99, -0.37), (1966.0, 2.0, 3828.124, 2354.5, 475.408, 571.371, 2624.7, 32.45, 170.3, 4.65, 3.8, 196.56, 2.1, 2.55), (1966.0, 3.0, 3853.301, 2381.5, 470.697, 594.514, 2657.8, 32.85, 171.2, 5.23, 3.8, 197.207, 4.9, 0.33), (1966.0, 4.0, 3884.52, 2391.4, 472.957, 599.528, 2688.2, 32.9, 171.9, 5.0, 3.7, 197.736, 0.61, 4.39), (1967.0, 1.0, 3918.74, 2405.3, 460.007, 640.682, 2728.4, 33.1, 174.2, 4.22, 3.8, 198.206, 2.42, 1.8), (1967.0, 2.0, 3919.556, 2438.1, 440.393, 631.43, 2750.8, 33.4, 178.1, 3.78, 3.8, 198.712, 3.61, 0.17), (1967.0, 3.0, 3950.826, 2450.6, 453.033, 641.504, 2777.1, 33.7, 181.6, 4.42, 3.8, 199.311, 3.58, 0.84), (1967.0, 4.0, 3980.97, 2465.7, 462.834, 640.234, 2797.4, 34.1, 184.3, 4.9, 3.9, 199.808, 4.72, 0.18), (1968.0, 1.0, 4063.013, 2524.6, 472.907, 651.378, 2846.2, 34.4, 186.6, 5.18, 3.7, 200.208, 3.5, 1.67), (1968.0, 2.0, 4131.998, 2563.3, 492.026, 646.145, 2893.5, 34.9, 190.5, 5.5, 3.5, 200.706, 5.77, -0.28), (1968.0, 3.0, 4160.267, 2611.5, 476.053, 640.615, 2899.3, 35.3, 194.0, 5.21, 3.5, 201.29, 4.56, 0.65), (1968.0, 4.0, 4178.293, 2623.5, 480.998, 636.729, 2918.4, 35.7, 198.7, 5.85, 3.4, 201.76, 4.51, 1.34), (1969.0, 1.0, 4244.1, 2652.9, 512.686, 633.224, 2923.4, 36.3, 200.7, 6.08, 3.4, 202.161, 6.67, -0.58), (1969.0, 2.0, 4256.46, 2669.8, 508.601, 623.16, 2952.9, 36.8, 201.7, 6.49, 3.4, 202.677, 5.47, 1.02), 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1044.088, 10040.6, 216.385, 1673.9, 0.12, 9.6, 308.013, 3.56, -3.44)], dtype=[('year', 'i4'), ('quarter', 'i2'), ('realgdp', 'f4'), ('realcons', 'f4'), ('realinv', 'f4'), ('realgovt', 'f4'), ('realdpi', 'f4'), ('cpi', 'f4'), ('m1', 'f4'), ('tbilrate', 'f4'), ('unemp', 'f4'), ('pop', 'f4'), ('infl', 'f4'), ('realint', 'f4')]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/results/time_series_examples.dta000066400000000000000000000033401224417117700313550ustar00rootroot00000000000000r2âå C³5@`É@çH @ æÈ@4 U@iy@ 8 Oct 2012 18:34ÿÿüüüûûüdatetime_cdatetime_big_cdateweekly_datemonthly_datequarterly_datehalf_yearly_dateyearly_date%tc0g%tC0g%tdg%twg%tmg%tqg%thg%tyg>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s>°è>°€ gB€ gBome/skipper/s(Ùí‡uB€¡êì‡uBjG) X:dÚ˜kÁ¦“6Á÷§ýÄÿîöstatsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/stata_dates.csv000066400000000000000000000003071224417117700257650ustar00rootroot00000000000000datetime_c,datetime_big_c,date,weekly_date,monthly_date,quarterly_date,half_yearly_date,yearly_date 1479597200000,1479596223000,18282,2601,600,58,100,2010 -14200000,-1479590,-2282,-601,-60,-18,-10,2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_data.csv000066400000000000000000000003121224417117700254350ustar00rootroot00000000000000"year","quarter","realgdp","realcons","realinv","realgovt","realdpi","cpi","m1","tbilrate","unemp","pop","infl","realint" 1959,1,2710.349,1707.4,286.898,470.045,1886.9,28.980,139.7,2.82,5.8,177.146,0,0 statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_foreign.py000066400000000000000000000163661224417117700260320ustar00rootroot00000000000000""" Tests for iolib/foreign.py """ import os import warnings from datetime import datetime from numpy.testing import * import numpy as np from pandas import DataFrame, isnull import pandas.util.testing as ptesting from statsmodels.compatnp.py3k import BytesIO, asbytes import statsmodels.api as sm from statsmodels.iolib.foreign import (StataWriter, genfromdta, _datetime_to_stata_elapsed, _stata_elapsed_date_to_datetime) from statsmodels.datasets import macrodata import pandas pandas_old = int(pandas.__version__.split('.')[1]) < 9 # Test precisions DECIMAL_4 = 4 DECIMAL_3 = 3 curdir = os.path.dirname(os.path.abspath(__file__)) def test_genfromdta(): #Test genfromdta vs. results/macrodta.npy created with genfromtxt. #NOTE: Stata handles data very oddly. Round tripping from csv to dta # to ndarray 2710.349 (csv) -> 2510.2491 (stata) -> 2710.34912109375 # (dta/ndarray) #res2 = np.load(curdir+'/results/macrodata.npy') #res2 = res2.view((float,len(res2[0]))) from results.macrodata import macrodata_result as res2 res1 = genfromdta(curdir+'/../../datasets/macrodata/macrodata.dta') #res1 = res1.view((float,len(res1[0]))) assert_array_equal(res1 == res2, True) def test_genfromdta_pandas(): from pandas.util.testing import assert_frame_equal dta = macrodata.load_pandas().data curdir = os.path.dirname(os.path.abspath(__file__)) res1 = sm.iolib.genfromdta(curdir+'/../../datasets/macrodata/macrodata.dta', pandas=True) res1 = res1.astype(float) assert_frame_equal(res1, dta) def test_stata_writer_structured(): buf = BytesIO() dta = macrodata.load().data dtype = dta.dtype dta = dta.astype(np.dtype([('year', int), ('quarter', int)] + dtype.descr[2:])) writer = StataWriter(buf, dta) writer.write_file() buf.seek(0) dta2 = genfromdta(buf) assert_array_equal(dta, dta2) def test_stata_writer_array(): buf = BytesIO() dta = macrodata.load().data dta = DataFrame.from_records(dta) dta.columns = ["v%d" % i for i in range(1,15)] writer = StataWriter(buf, dta.values) writer.write_file() buf.seek(0) dta2 = genfromdta(buf) dta = dta.to_records(index=False) assert_array_equal(dta, dta2) def test_missing_roundtrip(): buf = BytesIO() dta = np.array([(np.nan, np.inf, "")], dtype=[("double_miss", float), ("float_miss", np.float32), ("string_miss", "a1")]) writer = StataWriter(buf, dta) writer.write_file() buf.seek(0) dta = genfromdta(buf, missing_flt=np.nan) assert_(isnull(dta[0][0])) assert_(isnull(dta[0][1])) assert_(dta[0][2] == asbytes("")) dta = genfromdta(os.path.join(curdir, "results/data_missing.dta"), missing_flt=-999) assert_(np.all([dta[0][i] == -999 for i in range(5)])) def test_stata_writer_pandas(): buf = BytesIO() dta = macrodata.load().data dtype = dta.dtype #as of 0.9.0 pandas only supports i8 and f8 dta = dta.astype(np.dtype([('year', 'i8'), ('quarter', 'i8')] + dtype.descr[2:])) dta4 = dta.astype(np.dtype([('year', 'i4'), ('quarter', 'i4')] + dtype.descr[2:])) dta = DataFrame.from_records(dta) dta4 = DataFrame.from_records(dta4) # dta is int64 'i8' given to Stata writer writer = StataWriter(buf, dta) writer.write_file() buf.seek(0) dta2 = genfromdta(buf) dta5 = DataFrame.from_records(dta2) # dta2 is int32 'i4' returned from Stata reader if dta5.dtypes[1] is np.dtype('int64'): ptesting.assert_frame_equal(dta.reset_index(), dta5) else: # don't check index because it has different size, int32 versus int64 ptesting.assert_frame_equal(dta4, dta5[dta5.columns[1:]]) def test_stata_writer_unicode(): # make sure to test with characters outside the latin-1 encoding pass @dec.skipif(pandas_old) def test_genfromdta_datetime(): results = [(datetime(2006, 11, 19, 23, 13, 20), 1479596223000, datetime(2010, 1, 20), datetime(2010, 1, 8), datetime(2010, 1, 1), datetime(1974, 7, 1), datetime(2010, 1, 1), datetime(2010, 1, 1)), (datetime(1959, 12, 31, 20, 3, 20), -1479590, datetime(1953, 10, 2), datetime(1948, 6, 10), datetime(1955, 1, 1), datetime(1955, 7, 1), datetime(1955, 1, 1), datetime(2, 1, 1))] with warnings.catch_warnings(record=True) as w: dta = genfromdta(os.path.join(curdir, "results/time_series_examples.dta")) assert_(len(w) == 1) # should get a warning for that format. assert_array_equal(dta[0].tolist(), results[0]) assert_array_equal(dta[1].tolist(), results[1]) with warnings.catch_warnings(record=True): dta = genfromdta(os.path.join(curdir, "results/time_series_examples.dta"), pandas=True) assert_array_equal(dta.irow(0).tolist(), results[0]) assert_array_equal(dta.irow(1).tolist(), results[1]) def test_date_converters(): ms = [-1479597200000, -1e6, -1e5, -100, 1e5, 1e6, 1479597200000] days = [-1e5, -1200, -800, -365, -50, 0, 50, 365, 800, 1200, 1e5] weeks = [-1e4, -1e2, -53, -52, -51, 0, 51, 52, 53, 1e2, 1e4] months = [-1e4, -1e3, -100, -13, -12, -11, 0, 11, 12, 13, 100, 1e3, 1e4] quarter = [-100, -50, -5, -4, -3, 0, 3, 4, 5, 50, 100] half = [-50, 40, 30, 10, 3, 2, 1, 0, 1, 2, 3, 10, 30, 40, 50] year = [1, 50, 500, 1000, 1500, 1975, 2075] for i in ms: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "tc"), "tc"), i) for i in days: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "td"), "td"), i) for i in weeks: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "tw"), "tw"), i) for i in months: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "tm"), "tm"), i) for i in quarter: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "tq"), "tq"), i) for i in half: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "th"), "th"), i) for i in year: assert_equal(_datetime_to_stata_elapsed( _stata_elapsed_date_to_datetime(i, "ty"), "ty"), i) @dec.skipif(pandas_old) def test_datetime_roundtrip(): dta = np.array([(1, datetime(2010, 1, 1), 2), (2, datetime(2010, 2, 1), 3), (4, datetime(2010, 3, 1), 5)], dtype=[('var1', float), ('var2', object), ('var3', float)]) buf = BytesIO() writer = StataWriter(buf, dta, {"var2" : "tm"}) writer.write_file() buf.seek(0) dta2 = genfromdta(buf) assert_equal(dta, dta2) dta = DataFrame.from_records(dta) buf = BytesIO() writer = StataWriter(buf, dta, {"var2" : "tm"}) writer.write_file() buf.seek(0) dta2 = genfromdta(buf, pandas=True) ptesting.assert_frame_equal(dta, dta2.drop('index', axis=1)) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_pickle.py000066400000000000000000000013131224417117700256320ustar00rootroot00000000000000 from statsmodels.iolib.smpickle import save_pickle, load_pickle def test_pickle(): import tempfile from numpy.testing import assert_equal tmpdir = tempfile.mkdtemp(prefix='pickle') a = range(10) save_pickle(a, tmpdir+'/res.pkl') b = load_pickle(tmpdir+'/res.pkl') assert_equal(a, b) #cleanup, tested on Windows try: import os os.remove(tmpdir+'/res.pkl') os.rmdir(tmpdir) except (OSError, IOError): pass assert not os.path.exists(tmpdir) #test with file handle from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() save_pickle(a, fh) fh.seek(0,0) c = load_pickle(fh) fh.close() assert_equal(a,b) statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_summary.py000066400000000000000000000027161224417117700260700ustar00rootroot00000000000000'''examples to check summary, not converted to tests yet ''' if __name__ == '__main__': from statsmodels.regression.tests.test_regression import TestOLS #def mytest(): aregression = TestOLS() TestOLS.setupClass() results = aregression.res1 r_summary = str(results.summary_old()) print r_summary olsres = results print '\n\n' r_summary = str(results.summary()) print r_summary print '\n\n' from statsmodels.discrete.tests.test_discrete import TestProbitNewton aregression = TestProbitNewton() TestProbitNewton.setupClass() results = aregression.res1 r_summary = str(results.summary()) print r_summary print '\n\n' probres = results from statsmodels.robust.tests.test_rlm import TestHampel aregression = TestHampel() #TestHampel.setupClass() results = aregression.res1 r_summary = str(results.summary()) print r_summary rlmres = results print '\n\n' from statsmodels.genmod.tests.test_glm import TestGlmBinomial aregression = TestGlmBinomial() #TestGlmBinomial.setupClass() results = aregression.res1 r_summary = str(results.summary()) print r_summary #print results.summary2(return_fmt='latex') #print results.summary2(return_fmt='csv') smry = olsres.summary() print smry.as_csv() # import matplotlib.pyplot as plt # plt.plot(rlmres.model.endog,'o') # plt.plot(rlmres.fittedvalues,'-') # # plt.show()statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_summary_old.py000066400000000000000000000067011224417117700267240ustar00rootroot00000000000000import warnings def _est_regression_summary(): #little luck getting this test to pass (It should?), can be used for #visual testing of the regression.summary table #fixed, might fail at minute changes from statsmodels.regression.tests.test_regression import TestOLS #from test_regression import TestOLS import time from string import Template t = time.localtime() desired = Template( ''' Summary of Regression Results ======================================= | Dependent Variable: y| | Model: OLS| | Method: Least Squares| | Date: $XXcurrentXdateXX| | Time: $XXtimeXXX| | # obs: 16.0| | Df residuals: 9.0| | Df model: 6.0| ============================================================================== | coefficient std. error t-statistic prob. | ------------------------------------------------------------------------------ | x1 15.06 84.91 0.1774 0.8631 | | x2 -0.03582 0.03349 -1.0695 0.3127 | | x3 -2.020 0.4884 -4.1364 0.0025 | | x4 -1.033 0.2143 -4.8220 0.0009 | | x5 -0.05110 0.2261 -0.2261 0.8262 | | x6 1829. 455.5 4.0159 0.0030 | | const -3.482e+06 8.904e+05 -3.9108 0.0036 | ============================================================================== | Models stats Residual stats | ------------------------------------------------------------------------------ | R-squared: 0.9955 Durbin-Watson: 2.559 | | Adjusted R-squared: 0.9925 Omnibus: 0.7486 | | F-statistic: 330.3 Prob(Omnibus): 0.6878 | | Prob (F-statistic): 4.984e-10 JB: 0.6841 | | Log likelihood: -109.6 Prob(JB): 0.7103 | | AIC criterion: 233.2 Skew: 0.4200 | | BIC criterion: 238.6 Kurtosis: 2.434 | ------------------------------------------------------------------------------''' ).substitute(XXcurrentXdateXX = str(time.strftime("%a, %d %b %Y",t)), XXtimeXXX = str(time.strftime("%H:%M:%S",t))) desired = str(desired) aregression = TestOLS() TestOLS.setupClass() results = aregression.res1 # be quiet! original_filters = warnings.filters[:] # copy original warnings.simplefilter("ignore") try: r_summary = str(results.summary_old()) finally: warnings.filters = original_filters # restore filters ## print('###') ## print(r_summary) ## print('###') ## print(desired) ## print('###') actual = r_summary import numpy as np actual = '\n'.join((line.rstrip() for line in actual.split('\n'))) # print len(actual), len(desired) # print repr(actual) # print repr(desired) # counter = 0 # for c1,c2 in zip(actual, desired): # if not c1==c2 and counter<20: # print c1,c2 # counter += 1 np.testing.assert_(actual == desired) if __name__ == '__main__': test_regression_summary() statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_table.py000066400000000000000000000153061224417117700254610ustar00rootroot00000000000000import numpy as np import unittest from statsmodels.iolib.table import SimpleTable, default_txt_fmt from statsmodels.iolib.table import default_latex_fmt from statsmodels.iolib.table import default_html_fmt import pandas from statsmodels.regression.linear_model import OLS ltx_fmt1 = default_latex_fmt.copy() html_fmt1 = default_html_fmt.copy() class TestSimpleTable(unittest.TestCase): def test_SimpleTable_1(self): """Basic test, test_SimpleTable_1""" desired = ''' ===================== header1 header2 --------------------- stub1 1.30312 2.73999 stub2 1.95038 2.65765 --------------------- ''' test1data = [[1.30312, 2.73999],[1.95038, 2.65765]] test1stubs = ('stub1', 'stub2') test1header = ('header1', 'header2') actual = SimpleTable(test1data, test1header, test1stubs, txt_fmt=default_txt_fmt) actual = '\n%s\n' % actual.as_text() self.assertEqual(desired, str(actual)) def test_SimpleTable_2(self): """ Test SimpleTable.extend_right()""" desired = ''' ============================================================= header s1 header d1 header s2 header d2 ------------------------------------------------------------- stub R1 C1 10.30312 10.73999 stub R1 C2 50.95038 50.65765 stub R2 C1 90.30312 90.73999 stub R2 C2 40.95038 40.65765 ------------------------------------------------------------- ''' data1 = [[10.30312, 10.73999], [90.30312, 90.73999]] data2 = [[50.95038, 50.65765], [40.95038, 40.65765]] stubs1 = ['stub R1 C1', 'stub R2 C1'] stubs2 = ['stub R1 C2', 'stub R2 C2'] header1 = ['header s1', 'header d1'] header2 = ['header s2', 'header d2'] actual1 = SimpleTable(data1, header1, stubs1, txt_fmt=default_txt_fmt) actual2 = SimpleTable(data2, header2, stubs2, txt_fmt=default_txt_fmt) actual1.extend_right(actual2) actual = '\n%s\n' % actual1.as_text() self.assertEqual(desired, str(actual)) def test_SimpleTable_3(self): """ Test SimpleTable.extend() as in extend down""" desired = ''' ============================== header s1 header d1 ------------------------------ stub R1 C1 10.30312 10.73999 stub R2 C1 90.30312 90.73999 header s2 header d2 ------------------------------ stub R1 C2 50.95038 50.65765 stub R2 C2 40.95038 40.65765 ------------------------------ ''' data1 = [[10.30312, 10.73999], [90.30312, 90.73999]] data2 = [[50.95038, 50.65765], [40.95038, 40.65765]] stubs1 = ['stub R1 C1', 'stub R2 C1'] stubs2 = ['stub R1 C2', 'stub R2 C2'] header1 = ['header s1', 'header d1'] header2 = ['header s2', 'header d2'] actual1 = SimpleTable(data1, header1, stubs1, txt_fmt=default_txt_fmt) actual2 = SimpleTable(data2, header2, stubs2, txt_fmt=default_txt_fmt) actual1.extend(actual2) actual = '\n%s\n' % actual1.as_text() self.assertEqual(desired, str(actual)) def test_SimpleTable_4(self): """Basic test, test_SimpleTable_4 test uses custom txt_fmt""" txt_fmt1 = dict(data_fmts = ['%3.2f', '%d'], empty_cell = ' ', colwidths = 1, colsep=' * ', row_pre = '* ', row_post = ' *', table_dec_above='*', table_dec_below='*', header_dec_below='*', header_fmt = '%s', stub_fmt = '%s', title_align='r', header_align = 'r', data_aligns = "r", stubs_align = "l", fmt = 'txt' ) ltx_fmt1 = default_latex_fmt.copy() html_fmt1 = default_html_fmt.copy() cell0data = 0.0000 cell1data = 1 row0data = [cell0data, cell1data] row1data = [2, 3.333] table1data = [ row0data, row1data ] test1stubs = ('stub1', 'stub2') test1header = ('header1', 'header2') tbl = SimpleTable(table1data, test1header, test1stubs,txt_fmt=txt_fmt1, ltx_fmt=ltx_fmt1, html_fmt=html_fmt1) def test_txt_fmt1(self): """Limited test of custom txt_fmt""" desired = """ ***************************** * * header1 * header2 * ***************************** * stub1 * 0.00 * 1 * * stub2 * 2.00 * 3 * ***************************** """ actual = '\n%s\n' % tbl.as_text() #print(actual) #print(desired) self.assertEqual(actual, desired) def test_ltx_fmt1(self): """Limited test of custom ltx_fmt""" desired = r""" \begin{tabular}{lcc} \toprule & \textbf{header1} & \textbf{header2} \\ \midrule \textbf{stub1} & 0.0 & 1 \\ \textbf{stub2} & 2 & 3.333 \\ \bottomrule \end{tabular} """ actual = '\n%s\n' % tbl.as_latex_tabular() #print(actual) #print(desired) self.assertEqual(actual, desired) def test_html_fmt1(self): """Limited test of custom html_fmt""" desired = """
    header1 header2
    stub1 0.0 1
    stub2 2 3.333
    """ actual = '\n%s\n' % tbl.as_html() self.assertEqual(actual, desired) def test_regression_with_tuples(self): i = pandas.Series( [1,2,3,4]*10 , name="i") y = pandas.Series( [1,2,3,4,5]*8, name="y") x = pandas.Series( [1,2,3,4,5,6,7,8]*5, name="x") df = pandas.DataFrame( index=i.index ) df = df.join( i ) endo = df.join( y ) exo = df.join( x ) endo_groups = endo.groupby( ("i",) ) exo_groups = exo.groupby( ("i",) ) exo_Df = exo_groups.agg( [np.sum, np.max] ) endo_Df = endo_groups.agg( [np.sum, np.max] ) reg = OLS(exo_Df[[("x", "sum")]],endo_Df).fit() interesting_lines = [] for line in str( reg.summary() ).splitlines(): if "('" in line: interesting_lines.append( line[:38] ) desired = ["Dep. Variable: ('x', 'sum') ", "('y', 'sum') 1.4595 0.209 ", "('y', 'amax') 0.2432 0.035 "] self.assertEqual(sorted(desired), sorted(interesting_lines) ) if __name__ == "__main__": #unittest.main() pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/iolib/tests/test_table_econpy.py000066400000000000000000000106231224417117700270330ustar00rootroot00000000000000''' Unit tests table.py. :see: http://docs.python.org/lib/minimal-example.html for an intro to unittest :see: http://agiletesting.blogspot.com/2005/01/python-unit-testing-part-1-unittest.html :see: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/305292 ''' from __future__ import absolute_import import unittest try: import numpy as np has_numpy = True except ImportError: has_numpy = False __docformat__ = "restructuredtext en" from statsmodels.iolib.table import Cell, Row, SimpleTable from statsmodels.iolib.table import default_latex_fmt from statsmodels.iolib.table import default_html_fmt ltx_fmt1 = default_latex_fmt.copy() html_fmt1 = default_html_fmt.copy() txt_fmt1 = dict( data_fmts = ['%0.2f', '%d'], empty_cell = ' ', colwidths = 1, colsep=' * ', row_pre = '* ', row_post = ' *', table_dec_above='*', table_dec_below='*', header_dec_below='*', header_fmt = '%s', stub_fmt = '%s', title_align='r', header_align = 'r', data_aligns = "r", stubs_align = "l", fmt = 'txt' ) cell0data = 0.0000 cell1data = 1 row0data = [cell0data, cell1data] row1data = [2, 3.333] table1data = [ row0data, row1data ] test1stubs = ('stub1', 'stub2') test1header = ('header1', 'header2') #test1header = ('header1\nheader1a', 'header2\nheader2a') tbl = SimpleTable(table1data, test1header, test1stubs, txt_fmt=txt_fmt1, ltx_fmt=ltx_fmt1, html_fmt=html_fmt1) def custom_labeller(cell): if cell.data is np.nan: return 'missing' class test_Cell(unittest.TestCase): def test_celldata(self): celldata = cell0data, cell1data, row1data[0], row1data[1] cells = [Cell(datum, datatype=i%2) for i, datum in enumerate(celldata)] for cell, datum in zip(cells, celldata): self.assertEqual(cell.data, datum) class test_SimpleTable(unittest.TestCase): def test_txt_fmt1(self): """Limited test of custom txt_fmt""" desired = """ ***************************** * * header1 * header2 * ***************************** * stub1 * 0.00 * 1 * * stub2 * 2.00 * 3 * ***************************** """ actual = '\n%s\n' % tbl.as_text() #print('actual') #print(actual) #print('desired') #print(desired) self.assertEqual(actual, desired) def test_ltx_fmt1(self): """Limited test of custom ltx_fmt""" desired = r""" \begin{center} \begin{tabular}{lcc} \toprule & \textbf{header1} & \textbf{header2} \\ \midrule \textbf{stub1} & 0.0 & 1 \\ \textbf{stub2} & 2 & 3.333 \\ \bottomrule \end{tabular} \end{center} """ actual = '\n%s\n' % tbl.as_latex_tabular() #print(actual) #print(desired) self.assertEqual(actual, desired) def test_html_fmt1(self): """Limited test of custom html_fmt""" desired = """
    header1 header2
    stub1 0.0 1
    stub2 2 3.333
    """ #the previous has significant trailing whitespace that got removed #desired = '''\n\n\n \n\n\n \n\n\n \n\n
    header1 header2
    stub1 0.0 1
    stub2 2 3.333
    \n''' actual = '\n%s\n' % tbl.as_html() actual = '\n'.join((line.rstrip() for line in actual.split('\n'))) #print(actual) #print(desired) #print len(actual), len(desired) self.assertEqual(actual, desired) def test_customlabel(self): """Limited test of custom custom labeling""" if has_numpy: tbl = SimpleTable(table1data, test1header, test1stubs, txt_fmt=txt_fmt1) tbl[1][1].data = np.nan tbl.label_cells(custom_labeller) #print([[c.datatype for c in row] for row in tbl]) desired = """ ***************************** * * header1 * header2 * ***************************** * stub1 * -- * 1 * * stub2 * 2.00 * 3 * ***************************** """ actual = '\n%s\n' % tbl.as_text(missing='--') #print(actual) #print(desired) self.assertEqual(actual, desired) if __name__=="__main__": unittest.main() statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/000077500000000000000000000000001224417117700226535ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/__init__.py000066400000000000000000000001041224417117700247570ustar00rootroot00000000000000 from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/api.py000066400000000000000000000002411224417117700237730ustar00rootroot00000000000000from .tmodel import TLinearModel from .count import (PoissonGMLE, PoissonOffsetGMLE, PoissonZiGMLE, #NonlinearDeltaCov ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/count.py000066400000000000000000000250041224417117700243560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Jul 26 08:34:59 2010 Author: josef-pktd changes: added offset and zero-inflated version of Poisson - kind of ok, need better test cases, - a nan in ZIP bse, need to check hessian calculations - found error in ZIP loglike - all tests pass with Issues ------ * If true model is not zero-inflated then numerical Hessian for ZIP has zeros for the inflation probability and is not invertible. -> hessian inverts and bse look ok if row and column are dropped, pinv also works * GenericMLE: still get somewhere (where?) "CacheWriteWarning: The attribute 'bse' cannot be overwritten" * bfgs is too fragile, doesn't come back * `nm` is slow but seems to work * need good start_params and their use in genericmle needs to be checked for consistency, set as attribute or method (called as attribute) * numerical hessian needs better scaling * check taking parts out of the loop, e.g. factorial(endog) could be precalculated """ import numpy as np from scipy import stats from scipy.misc import factorial import statsmodels.api as sm from statsmodels.base.model import GenericLikelihoodModel def maxabs(arr1, arr2): return np.max(np.abs(arr1 - arr2)) def maxabsrel(arr1, arr2): return np.max(np.abs(arr2 / arr1 - 1)) class NonlinearDeltaCov(object): '''Asymptotic covariance by Deltamethod the function is designed for 2d array, with rows equal to the number of equations and columns equal to the number of parameters. 1d params work by chance ? fun: R^{m*k) -> R^{m} where m is number of equations and k is the number of parameters. equations follow Greene ''' def __init__(self, fun, params, cov_params): self.fun = fun self.params = params self.cov_params = cov_params def grad(self, params=None, **kwds): if params is None: params = self.params kwds.setdefault('epsilon', 1e-4) from statsmodels.tools.numdiff import approx_fprime return approx_fprime(params, self.fun, **kwds) def cov(self): g = self.grad() covar = np.dot(np.dot(g, self.cov_params), g.T) return covar def expected(self): # rename: misnomer, this is the MLE of the fun return self.fun(self.params) def wald(self, value): m = self.expected() v = self.cov() df = np.size(m) diff = m - value lmstat = np.dot(np.dot(diff.T, np.linalg.inv(v)), diff) return lmstat, stats.chi2.sf(lmstat, df) class PoissonGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' # copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ XB = np.dot(self.exog, params) endog = self.endog return np.exp(XB) - endog*XB + np.log(factorial(endog)) def predict_distribution(self, exog): '''return frozen scipy.stats distribution with mu at estimated prediction ''' if not hasattr(self, result): raise else: mu = np.exp(np.dot(exog, params)) return stats.poisson(mu, loc=0) class PoissonOffsetGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. ''' def __init__(self, endog, exog=None, offset=None, missing='none', **kwds): # let them be none in case user wants to use inheritance if not offset is None: if offset.ndim == 1: offset = offset[:,None] #need column self.offset = offset.ravel() else: self.offset = 0. super(PoissonOffsetGMLE, self).__init__(endog, exog, missing=missing, **kwds) #this was added temporarily for bug-hunting, but shouldn't be needed # def loglike(self, params): # return -self.nloglikeobs(params).sum(0) # original copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ XB = self.offset + np.dot(self.exog, params) endog = self.endog nloglik = np.exp(XB) - endog*XB + np.log(factorial(endog)) return nloglik class PoissonZiGMLE(GenericLikelihoodModel): '''Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation. There are numerical problems if there is no zero-inflation. ''' def __init__(self, endog, exog=None, offset=None, missing='none', **kwds): # let them be none in case user wants to use inheritance super(PoissonZiGMLE, self).__init__(endog, exog, missing=missing, **kwds) if not offset is None: if offset.ndim == 1: offset = offset[:,None] #need column self.offset = offset.ravel() #which way? else: self.offset = 0. #TODO: it's not standard pattern to use default exog if exog is None: self.exog = np.ones((self.nobs,1)) self.nparams = self.exog.shape[1] #what's the shape in regression for exog if only constant self.start_params = np.hstack((np.ones(self.nparams), 0)) self.cloneattr = ['start_params'] #needed for t_test and summary self.exog_names.append('zi') # original copied from discretemod.Poisson def nloglikeobs(self, params): """ Loglikelihood of Poisson model Parameters ---------- params : array-like The parameters of the model. Returns ------- The log likelihood of the model evaluated at `params` Notes -------- .. math :: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right] """ beta = params[:-1] gamm = 1 / (1 + np.exp(params[-1])) #check this # replace with np.dot(self.exogZ, gamma) #print np.shape(self.offset), self.exog.shape, beta.shape XB = self.offset + np.dot(self.exog, beta) endog = self.endog nloglik = -np.log(1-gamm) + np.exp(XB) - endog*XB + np.log(factorial(endog)) nloglik[endog==0] = - np.log(gamm + np.exp(-nloglik[endog==0])) return nloglik if __name__ == '__main__': #Example: np.random.seed(98765678) nobs = 1000 rvs = np.random.randn(nobs,6) data_exog = rvs data_exog = sm.add_constant(data_exog, prepend=False) xbeta = 1 + 0.1*rvs.sum(1) data_endog = np.random.poisson(np.exp(xbeta)) #print data_endog modp = MyPoisson(data_endog, data_exog) resp = modp.fit() print resp.params print resp.bse from statsmodels.discretemod import Poisson resdp = Poisson(data_endog, data_exog).fit() print '\ncompare with discretemod' print 'compare params' print resdp.params - resp.params print 'compare bse' print resdp.bse - resp.bse gmlp = sm.GLM(data_endog, data_exog, family=sm.families.Poisson()) resgp = gmlp.fit() ''' this creates a warning, bug bse is double defined ??? c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py:105: CacheWriteWarning: The attribute 'bse' cannot be overwritten warnings.warn(errmsg, CacheWriteWarning) ''' print '\ncompare with GLM' print 'compare params' print resgp.params - resp.params print 'compare bse' print resgp.bse - resp.bse lam = np.exp(np.dot(data_exog, resp.params)) '''mean of Poisson distribution''' predmean = stats.poisson.stats(lam,moments='m') print np.max(np.abs(predmean - lam)) fun = lambda params: np.exp(np.dot(data_exog.mean(0), params)) lamcov = NonlinearDeltaCov(fun, resp.params, resdp.cov_params()) print lamcov.cov().shape print lamcov.cov() print 'analytical' xm = data_exog.mean(0) print np.dot(np.dot(xm, resdp.cov_params()), xm.T) * \ np.exp(2*np.dot(data_exog.mean(0), resp.params)) ''' cov of linear transformation of params >>> np.dot(np.dot(xm, resdp.cov_params()), xm.T) 0.00038904130127582825 >>> resp.cov_params(xm) 0.00038902428119179394 >>> np.dot(np.dot(xm, resp.cov_params()), xm.T) 0.00038902428119179394 ''' print lamcov.wald(1.) print lamcov.wald(2.) print lamcov.wald(2.6) do_bootstrap = False if do_bootstrap: m,s,r = resp.bootstrap(method='newton') print m print s print resp.bse print '\ncomparison maxabs, masabsrel' print 'discr params', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params) print 'discr bse ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse) print 'discr bsejac', maxabs(resdp.bse, resp.bsejac), maxabsrel(resdp.bse, resp.bsejac) print 'discr bsejhj', maxabs(resdp.bse, resp.bsejhj), maxabsrel(resdp.bse, resp.bsejhj) print print 'glm params ', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params) print 'glm bse ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse) statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/nonlinls.py000066400000000000000000000221441224417117700250640ustar00rootroot00000000000000'''Non-linear least squares Author: Josef Perktold based on scipy.optimize.curve_fit ''' import numpy as np from scipy import optimize from statsmodels.base.model import Model class Results(object): '''just a dummy placeholder for now most results from RegressionResults can be used here ''' pass ##def getjaccov(retval, n): ## '''calculate something and raw covariance matrix from return of optimize.leastsq ## ## I cannot figure out how to recover the Jacobian, or whether it is even ## possible ## ## this is a partial copy of scipy.optimize.leastsq ## ''' ## info = retval[-1] ## #n = len(x0) #nparams, where do I get this ## cov_x = None ## if info in [1,2,3,4]: ## from numpy.dual import inv ## from numpy.linalg import LinAlgError ## perm = np.take(np.eye(n), retval[1]['ipvt']-1,0) ## r = np.triu(np.transpose(retval[1]['fjac'])[:n,:]) ## R = np.dot(r, perm) ## try: ## cov_x = inv(np.dot(np.transpose(R),R)) ## except LinAlgError: ## print 'cov_x not available' ## pass ## return r, R, cov_x ## ##def _general_function(params, xdata, ydata, function): ## return function(xdata, *params) - ydata ## ##def _weighted_general_function(params, xdata, ydata, function, weights): ## return weights * (function(xdata, *params) - ydata) ## class NonlinearLS(Model): #or subclass a model '''Base class for estimation of a non-linear model with least squares This class is supposed to be subclassed, and the subclass has to provide a method `_predict` that defines the non-linear function `f(params) that is predicting the endogenous variable. The model is assumed to be :math: y = f(params) + error and the estimator minimizes the sum of squares of the estimated error. :math: min_parmas \sum (y - f(params))**2 f has to return the prediction for each observation. Exogenous or explanatory variables should be accessed as attributes of the class instance, and can be given as arguments when the instance is created. Warning: Weights are not correctly handled yet in the results statistics, but included when estimating the parameters. similar to scipy.optimize.curve_fit API difference: params are array_like not split up, need n_params information includes now weights similar to curve_fit no general sigma yet (OLS and WLS, but no GLS) This is currently holding on to intermediate results that are not necessary but useful for testing. Fit returns and instance of RegressionResult, in contrast to the linear model, results in this case are based on a local approximation, essentially y = f(X, params) is replaced by y = grad * params where grad is the Gradient or Jacobian with the shape (nobs, nparams). See for example Greene Examples -------- class Myfunc(NonlinearLS): def _predict(self, params): x = self.exog a, b, c = params return a*np.exp(-b*x) + c Ff we have data (y, x), we can create an instance and fit it with mymod = Myfunc(y, x) myres = mymod.fit(nparams=3) and use the non-linear regression results, for example myres.params myres.bse myres.tvalues ''' #NOTE: This needs to call super for data checking def __init__(self, endog=None, exog=None, weights=None, sigma=None, missing='none'): self.endog = endog self.exog = exog if not sigma is None: sigma = np.asarray(sigma) if sigma.ndim < 2: self.sigma = sigma self.weights = 1./sigma else: raise ValueError('correlated errors are not handled yet') else: self.weights = None def predict(self, exog, params=None): #copied from GLS, Model has different signature return self._predict(params) def _predict(self, params): pass def start_value(self): return None def geterrors(self, params, weights=None): if weights is None: if self.weights is None: return self.endog - self._predict(params) else: weights = self.weights return weights * (self.endog - self._predict(params)) def errorsumsquares(self, params): return (self.geterrors(params)**2).sum() def fit(self, start_value=None, nparams=None, **kw): #if hasattr(self, 'start_value'): #I added start_value even if it's empty, not sure about it #but it makes a visible placeholder if not start_value is None: p0 = start_value else: #nesting so that start_value is only calculated if it is needed p0 = self.start_value() if not p0 is None: pass elif not nparams is None: p0 = 0.1 * np.ones(nparams) else: raise ValueError('need information about start values for' + 'optimization') func = self.geterrors res = optimize.leastsq(func, p0, full_output=1, **kw) (popt, pcov, infodict, errmsg, ier) = res if ier not in [1,2,3,4]: msg = "Optimal parameters not found: " + errmsg raise RuntimeError(msg) err = infodict['fvec'] ydata = self.endog if (len(ydata) > len(p0)) and pcov is not None: #this can use the returned errors instead of recalculating s_sq = (err**2).sum()/(len(ydata)-len(p0)) pcov = pcov * s_sq else: pcov = None self.df_resid = len(ydata)-len(p0) self.df_model = len(p0) fitres = Results() fitres.params = popt fitres.pcov = pcov fitres.rawres = res self.wendog = self.endog #add weights self.wexog = self.jac_predict(popt) pinv_wexog = np.linalg.pinv(self.wexog) self.normalized_cov_params = np.dot(pinv_wexog, np.transpose(pinv_wexog)) #TODO: check effect of `weights` on result statistics #I think they are correctly included in cov_params #maybe not anymore, I'm not using pcov of leastsq #direct calculation with jac_predict misses the weights ## if not weights is None ## fitres.wexogw = self.weights * self.jacpredict(popt) from statsmodels.regression import RegressionResults results = RegressionResults beta = popt lfit = RegressionResults(self, beta, normalized_cov_params=self.normalized_cov_params) lfit.fitres = fitres #mainly for testing self._results = lfit return lfit def fit_minimal(self, start_value): '''minimal fitting with no extra calculations''' func = self.geterrors res = optimize.leastsq(func, start_value, full_output=0, **kw) return res def fit_random(self, ntries=10, rvs_generator=None, nparams=None): '''fit with random starting values this could be replaced with a global fitter ''' if nparams is None: nparams = self.nparams if rvs_generator is None: rvs = np.random.uniform(low=-10, high=10, size=(ntries, nparams)) else: rvs = rvs_generator(size=(ntries, nparams)) results = np.array([np.r_[self.fit_minimal(rv), rv] for rv in rvs]) #selct best results and check how many solutions are within 1e-6 of best #not sure what leastsq returns return results def jac_predict(self, params): '''jacobian of prediction function using complex step derivative This assumes that the predict function does not use complex variable but is designed to do so. ''' from statsmodels.tools.numdiff import approx_fprime_cs jaccs_err = approx_fprime_cs(params, self._predict) return jaccs_err class Myfunc(NonlinearLS): #predict model.Model has a different signature ## def predict(self, params, exog=None): ## if not exog is None: ## x = exog ## else: ## x = self.exog ## a, b, c = params ## return a*np.exp(-b*x) + c def _predict(self, params): x = self.exog a, b, c = params return a*np.exp(-b*x) + c if __name__ == '__main__': def func0(x, a, b, c): return a*np.exp(-b*x) + c def func(params, x): a, b, c = params return a*np.exp(-b*x) + c def error(params, x, y): return y - func(params, x) def error2(params, x, y): return (y - func(params, x))**2 x = np.linspace(0,4,50) params = np.array([2.5, 1.3, 0.5]) y0 = func(params, x) y = y0 + 0.2*np.random.normal(size=len(x)) res = optimize.leastsq(error, params, args=(x, y), full_output=True) ## r, R, c = getjaccov(res[1:], 3) mod = Myfunc(y, x) resmy = mod.fit(nparams=3) cf_params, cf_pcov = optimize.curve_fit(func0, x, y) cf_bse = np.sqrt(np.diag(cf_pcov)) print res[0] print cf_params print resmy.params print cf_bse print resmy.bse statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/tests/000077500000000000000000000000001224417117700240155ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/tests/__init__.py000066400000000000000000000000001224417117700261140ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/tests/results_tmodel.py000066400000000000000000000273321224417117700274430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Jun 30 23:14:36 2013 Author: Josef Perktold """ import numpy as np class Holder(): pass res_t_dfest = Holder() #> tfit3 <- tlm(m.marietta ~ CRSP, data = mm, start = list(dof = 3),estDof = TRUE) #> cat_items(tfit3, "res_t_dfest.") res_t_dfest.random = np.array([ 0.6242843, 1.349205, 1.224172, 1.272655, 1.323455, 1.091313, 1.227218, 0.0316284, 0.7202973, 1.038392, 1.091907, 0.7966355, 0.784222, 0.5042926, 0.1964543, 1.172123, 1.017338, 0.8799186, 0.7849335, 0.790158, 0.8121724, 1.286998, 0.7286052, 1.330104, 1.054037, 1.299656, 1.285306, 1.271166, 1.106877, 1.303909, 0.4250416, 1.277096, 1.160106, 0.1871806, 1.074168, 1.197795, 1.046638, 1.104423, 1.301670, 1.333217, 0.8156778, 1.309934, 1.142454, 1.347481, 0.6605017, 1.035725, 1.172666, 1.281746, 0.8796436, 0.9597098, 0.6221453, 1.149490, 1.291864, 1.207619, 1.239625, 1.351065, 1.248711, 0.3532520, 0.6067273, 0.8180234 ]) res_t_dfest.dof = 2.837183 res_t_dfest.dofse = 1.175296 res_t_dfest.iter = 7 res_t_dfest.logLik = 71.81292 res_t_dfest.endTime = 0.01 loc_fit = Holder() #> cat_items(tfit3$loc.fit, "loc_fit.") loc_fit.coefficients = np.array([-0.007248461, 1.263751]) loc_fit.residuals = np.array([ -0.09133902, 0.004151492, -0.02737765, 0.02117769, 0.01251936, -0.0413709, -0.02701702, 0.5465314, -0.07922967, -0.04651135, -0.04131256, 0.07064283, -0.07199043, -0.1096804, 0.2051536, 0.0331728, 0.04853971, 0.06197657, 0.07191273, -0.07134392, 0.06897908, -0.01907315, -0.0782573, -0.01096341, 0.04500034, -0.01704652, -0.01933079, 0.02138696, 0.03983612, -0.01631880, 0.1249257, -0.02054422, -0.03443716, 0.2110156, -0.04304691, 0.03038995, 0.04571555, 0.04007908, -0.01670529, 0.01015959, 0.06860706, -0.01523742, 0.03625959, 0.005138716, -0.08656302, -0.04676856, -0.03311507, -0.01986418, -0.06200429, 0.05410242, 0.09163019, 0.03553772, 0.01831594, -0.02928904, 0.02551524, 0.002713425, 0.02437713, -0.1422379, 0.09376145, -0.06835877 ]) loc_fit.fitted_values = np.array([ -0.04516098, -0.0810515, -0.03012235, 0.03142231, -0.05741936, -0.0445291, -0.04718298, 0.1413686, 0.002229668, 0.1315113, 0.04431256, 0.004757169, 0.03079043, 0.02068043, 0.02674643, 0.0755272, -0.01103971, 0.03382343, -0.05451273, -0.001056083, 0.00602092, -0.03972685, 0.0162573, -0.02683659, -0.02810034, -0.06285348, 0.004630794, -0.01078696, -0.08193612, 0.01271880, -0.03732572, 0.1230442, -0.01546284, -0.01571559, -0.02835309, 0.01651005, 0.08538445, 0.00602092, -0.01609471, -0.01975959, 0.05859294, 0.00753742, -0.01975959, -0.02013872, -0.06133698, 0.04026856, 0.07211507, 0.04216418, -0.006995711, 0.07969758, 0.05416981, -0.02923772, 0.05088406, 0.005389044, -0.08231524, 0.07868658, -0.1132771, 0.05353794, 0.009938546, -0.04794123 ]) loc_fit.effects = np.array([ -0.4809681, 6.645774, -0.6803134, 0.8423367, 0.5333795, -0.9748358, -0.6818408, 0.6716256, -1.239349, -0.9454051, -0.919986, 1.546571, -1.206974, -1.185012, 1.167586, 1.162168, 1.367571, 1.521386, 1.514811, -1.224234, 1.541103, -0.4658513, -1.229607, -0.2132594, 1.309335, -0.4211133, -0.4462804, 0.8224875, 1.194665, -0.3537337, 1.443046, -0.4086050, -0.8295162, 1.122057, -0.9918254, 1.065403, 1.388378, 1.252209, -0.3826760, 0.4819511, 1.571469, -0.3244968, 1.166818, 0.3190928, -1.280510, -1.004244, -0.7489287, -0.4386115, -1.182836, 1.485040, 1.594513, 1.146952, 0.773323, -0.7043392, 0.888233, 0.2990213, 0.8402323, -1.040679, 1.564756, -1.241512 ]) loc_fit.weights = np.array([ 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666, 260.7666 ]) loc_fit.qr = '''structure(list(qr = structure(c(-125.083961390618, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, 0.129099444873581, -1.09802870774065, 5.25877087979451, 0.068920957251135, -0.0806236351850806, 0.135248743629416, 0.103927288950783, 0.110375823737560, -0.34777721920871, -0.00968975253053064, -0.323825518572109, -0.111945089863713, -0.0158312142322233, -0.0790882697596574, -0.0545224229528869, -0.0692619310369492, -0.187792141879617, 0.0225529214033557, -0.0864580238016886, 0.128186062672469, -0.00170585231833022, -0.0189019450830696, 0.092258511717567, -0.0437748649749248, 0.0609370570389346, 0.064007787889781, 0.148452886288055, -0.0155241411471386, 0.0219387752331864, 0.194820922135834, -0.0351768185925551, 0.0864241231009591, -0.303251621871439, 0.0333004793813178, 0.0339146255514870, 0.0646219340599502, -0.044389011145094, -0.211743842516218, -0.0189019450830696, 0.0348358448067410, 0.0437409642741953, -0.146644348478276, -0.0225868221040852, 0.0437409642741953, 0.0446621835294492, 0.144768009267039, -0.102118751141005, -0.179501168582332, -0.106724847417274, 0.0127265826806475, -0.19792555368741, -0.135896790500314, 0.0667714456555426, -0.127912890288114, -0.0173665796576464, 0.195742141391088, -0.195468969006733, 0.270975047236823, -0.134361425074891, -0.0284212107206932, 0.112218262248068), assign = 0:1, .Dim = c(60L, 2L), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60"), c("(Intercept)", "CRSP"))), qraux = c(1.12909944487358, 1.19267141054024), pivot = 1:2, tol = 1e-07, rank = 2L), .Names = c("qr", "qraux", "pivot", "tol", "rank"), class = "qr")''' loc_fit.method = 'maximum likelihood' loc_fit.formula = '''m.marietta ~ CRSP''' loc_fit.terms = '''m.marietta ~ CRSP''' loc_fit.iter = 7 loc_fit.call = '''tlm(lform = m.marietta ~ CRSP, data = mm, start = list(dof = 3), estDof = TRUE)''' #> s = summary(tfit3) #> cat_items(s$loc.summary, prefix="loc_fit.") # renamed coefficient -> table loc_fit.table = np.array([ -0.007248461, 1.263751, 0.008167043, 0.1901585, -0.8875258, 6.645774, 0.3784616, 1.150536e-08 ]).reshape(2,4, order='F') loc_fit.table_rownames = ['(Intercept)', 'CRSP', ] loc_fit.table_colnames = ['Estimate', 'Std. Error', 't value', 'Pr(>|t|)', ] loc_fit.dispersion = 1 loc_fit.cov_unscaled = np.array([ 6.670059e-05, -0.0003174268, -0.0003174268, 0.03616026 ]).reshape(2,2, order='F') loc_fit.cov_unscaled_rownames = ['(Intercept)', 'CRSP', ] loc_fit.cov_unscaled_colnames = ['(Intercept)', 'CRSP', ] loc_fit.cov_scaled = np.array([ 6.670059e-05, -0.0003174268, -0.0003174268, 0.03616026 ]).reshape(2,2, order='F') loc_fit.cov_scaled_rownames = ['(Intercept)', 'CRSP', ] loc_fit.cov_scaled_colnames = ['(Intercept)', 'CRSP', ] scale_fit = Holder() #> cat_items(tfit3$scale.fit, "scale_fit.") scale_fit.coefficients = -5.983115 scale_fit.residuals = np.array([ 2.193327, -2.038408, -1.308573, -1.591579, -1.888103, -0.5330418, -1.326357, 5.653531, 1.632833, -0.2240806, -0.5364947, 1.187192, 1.259662, 2.893897, 4.691185, -1.004751, -0.1011940, 0.7009929, 1.255535, 1.224993, 1.096490, -1.675302, 1.584339, -1.926915, -0.3154245, -1.749184, -1.665424, -1.582882, -0.6238652, -1.774011, 3.356624, -1.617496, -0.9346065, 4.74536, -0.4329547, -1.154602, -0.2722567, -0.6095562, -1.76094, -1.945082, 1.076003, -1.809181, -0.8315554, -2.028347, 1.98188, -0.2085298, -1.007913, -1.644641, 0.702596, 0.2351866, 2.205829, -0.8726262, -1.703705, -1.211949, -1.398769, -2.049267, -1.451807, 3.775749, 2.295868, 1.062300 ]) scale_fit.fitted_values = np.array([ 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962, 0.002520962 ]) scale_fit.effects = np.array([ 32.31074, -1.595974, -1.087148, -1.284454, -1.491184, -0.5464648, -1.099547, 3.766681, 0.9635367, -0.3310637, -0.5488721, 0.6528456, 0.70337, 1.842724, 3.095754, -0.8753306, -0.2453897, 0.3138777, 0.7004929, 0.6791996, 0.5896097, -1.342824, 0.9297278, -1.518243, -0.3947467, -1.394333, -1.335937, -1.278391, -0.6097849, -1.411642, 2.165327, -1.302523, -0.826427, 3.133524, -0.4766862, -0.979803, -0.364651, -0.599809, -1.402529, -1.530909, 0.5753267, -1.436162, -0.754582, -1.588959, 1.206885, -0.3202220, -0.8775351, -1.321448, 0.3149953, -0.01087243, 1.363017, -0.7832157, -1.362626, -1.019784, -1.150031, -1.603544, -1.187008, 2.457531, 1.425790, 0.5657731 ]) scale_fit.weights = np.array([ 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579, 0.4860579 ]) scale_fit.formula = '''m.marietta ~ 1''' scale_fit.terms = '''m.marietta ~ 1''' scale_fit.iter = 7 scale_fit.call = '''tlm(lform = m.marietta ~ CRSP, data = mm, start = list(dof = 3), estDof = TRUE)''' res_t_dfest.loc_fit = loc_fit res_t_dfest.scale_fit = scale_fit statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/tests/test_generic_mle.py000066400000000000000000000105711224417117700277030ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Jun 28 14:19:26 2013 Author: Josef Perktold """ import numpy as np from scipy import stats from statsmodels.base.model import GenericLikelihoodModel from numpy.testing import assert_array_less, assert_almost_equal, assert_allclose class MyPareto(GenericLikelihoodModel): '''Maximum Likelihood Estimation pareto distribution first version: iid case, with constant parameters ''' def initialize(self): #TODO needed or not super(MyPareto, self).initialize() #start_params needs to be attribute self.start_params = np.array([1.5, self.endog.min() - 1.5, 1.]) #copied from stats.distribution def pdf(self, x, b): return b * x**(-b-1) def loglike(self, params): return -self.nloglikeobs(params).sum(0) # TODO: design start_params needs to be an attribute, # so it can be overwritten # @property # def start_params(self): # return np.array([1.5, self.endog.min() - 1.5, 1.]) def nloglikeobs(self, params): #print params.shape if not self.fixed_params is None: #print 'using fixed' params = self.expandparams(params) b = params[0] loc = params[1] scale = params[2] #loc = np.dot(self.exog, beta) endog = self.endog x = (endog - loc)/scale logpdf = np.log(b) - (b+1.)*np.log(x) #use np_log(1 + x) for Pareto II logpdf -= np.log(scale) #lb = loc + scale #logpdf[endog fit, fit -> fit_ls def fit_mle(self, order, start_params=None, method='nm', maxiter=5000, tol=1e-08, **kwds): nar, nma = order if start_params is not None: if len(start_params) != nar + nma + 2: raise ValueError('start_param need sum(order) + 2 elements') else: start_params = np.concatenate((0.05*np.ones(nar + nma), [5, 1])) res = super(TArma, self).fit_mle(order=order, start_params=start_params, method=method, maxiter=maxiter, tol=tol, **kwds) return res statsmodels-0.5.0+git13-g8e07d34/statsmodels/miscmodels/try_mlecov.py000066400000000000000000000163101224417117700254110ustar00rootroot00000000000000'''Multivariate Normal Model with full covariance matrix toeplitz structure is not exploited, need cholesky or inv for toeplitz Author: josef-pktd ''' import numpy as np #from scipy import special #, stats from scipy import linalg from scipy.linalg import norm, toeplitz import statsmodels.api as sm from statsmodels.base.model import (GenericLikelihoodModel, LikelihoodModel) from statsmodels.tsa.arima_process import arma_acovf, arma_generate_sample def mvn_loglike_sum(x, sigma): '''loglike multivariate normal copied from GLS and adjusted names not sure why this differes from mvn_loglike ''' nobs = len(x) nobs2 = nobs / 2.0 SSR = (x**2).sum() llf = -np.log(SSR) * nobs2 # concentrated likelihood llf -= (1+np.log(np.pi/nobs2))*nobs2 # with likelihood constant if np.any(sigma) and sigma.ndim == 2: #FIXME: robust-enough check? unneeded if _det_sigma gets defined llf -= .5*np.log(np.linalg.det(sigma)) return llf def mvn_loglike(x, sigma): '''loglike multivariate normal assumes x is 1d, (nobs,) and sigma is 2d (nobs, nobs) brute force from formula no checking of correct inputs use of inv and log-det should be replace with something more efficient ''' #see numpy thread #Sturla: sqmahal = (cx*cho_solve(cho_factor(S),cx.T).T).sum(axis=1) sigmainv = linalg.inv(sigma) logdetsigma = np.log(np.linalg.det(sigma)) nobs = len(x) llf = - np.dot(x, np.dot(sigmainv, x)) llf -= nobs * np.log(2 * np.pi) llf -= logdetsigma llf *= 0.5 return llf def mvn_loglike_chol(x, sigma): '''loglike multivariate normal assumes x is 1d, (nobs,) and sigma is 2d (nobs, nobs) brute force from formula no checking of correct inputs use of inv and log-det should be replace with something more efficient ''' #see numpy thread #Sturla: sqmahal = (cx*cho_solve(cho_factor(S),cx.T).T).sum(axis=1) sigmainv = np.linalg.inv(sigma) cholsigmainv = np.linalg.cholesky(sigmainv).T x_whitened = np.dot(cholsigmainv, x) logdetsigma = np.log(np.linalg.det(sigma)) nobs = len(x) from scipy import stats print 'scipy.stats' print np.log(stats.norm.pdf(x_whitened)).sum() llf = - np.dot(x_whitened.T, x_whitened) llf -= nobs * np.log(2 * np.pi) llf -= logdetsigma llf *= 0.5 return llf, logdetsigma, 2 * np.sum(np.log(np.diagonal(cholsigmainv))) #0.5 * np.dot(x_whitened.T, x_whitened) + nobs * np.log(2 * np.pi) + logdetsigma) def mvn_nloglike_obs(x, sigma): '''loglike multivariate normal assumes x is 1d, (nobs,) and sigma is 2d (nobs, nobs) brute force from formula no checking of correct inputs use of inv and log-det should be replace with something more efficient ''' #see numpy thread #Sturla: sqmahal = (cx*cho_solve(cho_factor(S),cx.T).T).sum(axis=1) #Still wasteful to calculate pinv first sigmainv = np.linalg.inv(sigma) cholsigmainv = np.linalg.cholesky(sigmainv).T #2 * np.sum(np.log(np.diagonal(np.linalg.cholesky(A)))) #Dag mailinglist # logdet not needed ??? #logdetsigma = 2 * np.sum(np.log(np.diagonal(cholsigmainv))) x_whitened = np.dot(cholsigmainv, x) #sigmainv = linalg.cholesky(sigma) logdetsigma = np.log(np.linalg.det(sigma)) sigma2 = 1. # error variance is included in sigma llike = 0.5 * (np.log(sigma2) - 2.* np.log(np.diagonal(cholsigmainv)) + (x_whitened**2)/sigma2 + np.log(2*np.pi)) return llike def invertibleroots(ma): import numpy.polynomial as poly pr = poly.polyroots(ma) insideroots = np.abs(pr)<1 if insideroots.any(): pr[np.abs(pr)<1] = 1./pr[np.abs(pr)<1] pnew = poly.Polynomial.fromroots(pr) mainv = pn.coef/pnew.coef[0] wasinvertible = False else: mainv = ma wasinvertible = True return mainv, wasinvertible def getpoly(self, params): ar = np.r_[[1], -params[:self.nar]] ma = np.r_[[1], params[-self.nma:]] import numpy.polynomial as poly return poly.Polynomial(ar), poly.Polynomial(ma) class MLEGLS(GenericLikelihoodModel): '''ARMA model with exact loglikelhood for short time series Inverts (nobs, nobs) matrix, use only for nobs <= 200 or so. This class is a pattern for small sample GLS-like models. Intended use for loglikelihood of initial observations for ARMA. TODO: This might be missing the error variance. Does it assume error is distributed N(0,1) Maybe extend to mean handling, or assume it is already removed. ''' def _params2cov(self, params, nobs): '''get autocovariance matrix from ARMA regression parameter ar parameters are assumed to have rhs parameterization ''' ar = np.r_[[1], -params[:self.nar]] ma = np.r_[[1], params[-self.nma:]] #print 'ar', ar #print 'ma', ma #print 'nobs', nobs autocov = arma_acovf(ar, ma, nobs=nobs) #print 'arma_acovf(%r, %r, nobs=%d)' % (ar, ma, nobs) #print autocov.shape #something is strange fixed in aram_acovf autocov = autocov[:nobs] sigma = toeplitz(autocov) return sigma def loglike(self, params): sig = self._params2cov(params[:-1], self.nobs) sig = sig * params[-1]**2 loglik = mvn_loglike(self.endog, sig) return loglik def fit_invertible(self, *args, **kwds): res = self.fit(*args, **kwds) ma = np.r_[[1], res.params[self.nar: self.nar+self.nma]] mainv, wasinvertible = invertibleroots(ma) if not wasinvertible: start_params = res.params.copy() start_params[self.nar: self.nar+self.nma] = mainv[1:] #need to add args kwds res = self.fit(start_params=start_params) return res if __name__ == '__main__': nobs = 50 ar = [1.0, -0.8, 0.1] ma = [1.0, 0.1, 0.2] #ma = [1] np.random.seed(9875789) y = arma_generate_sample(ar,ma,nobs,2) y -= y.mean() #I haven't checked treatment of mean yet, so remove mod = MLEGLS(y) mod.nar, mod.nma = 2, 2 #needs to be added, no init method mod.nobs = len(y) res = mod.fit(start_params=[0.1, -0.8, 0.2, 0.1, 1.]) print 'DGP', ar, ma print res.params from statsmodels.regression import yule_walker print yule_walker(y, 2) #resi = mod.fit_invertible(start_params=[0.1,0,0.2,0, 0.5]) #print resi.params arpoly, mapoly = getpoly(mod, res.params[:-1]) data = sm.datasets.sunspots.load() #ys = data.endog[-100:] ## ys = data.endog[12:]-data.endog[:-12] ## ys -= ys.mean() ## mods = MLEGLS(ys) ## mods.nar, mods.nma = 13, 1 #needs to be added, no init method ## mods.nobs = len(ys) ## ress = mods.fit(start_params=np.r_[0.4, np.zeros(12), [0.2, 5.]],maxiter=200) ## print ress.params ## #from statsmodels.sandbox.tsa import arima as tsaa ## #tsaa ## import matplotlib.pyplot as plt ## plt.plot(data.endog[1]) ## #plt.show() sigma = mod._params2cov(res.params[:-1], nobs) * res.params[-1]**2 print mvn_loglike(y, sigma) llo = mvn_nloglike_obs(y, sigma) print llo.sum(), llo.shape print mvn_loglike_chol(y, sigma) print mvn_loglike_sum(y, sigma) statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/000077500000000000000000000000001224417117700233565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/__init__.py000066400000000000000000000003351224417117700254700ustar00rootroot00000000000000""" Tools for nonparametric statistics, mainly density estimation and regression. For an overview of this module, see docs/source/nonparametric.rst """ from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/_kernel_base.py000066400000000000000000000430261224417117700263460ustar00rootroot00000000000000""" Module containing the base object for multivariate kernel density and regression, plus some utilities. """ import copy import numpy as np from scipy import optimize from scipy.stats.mstats import mquantiles try: import joblib has_joblib = True except ImportError: has_joblib = False import kernels kernel_func = dict(wangryzin=kernels.wang_ryzin, aitchisonaitken=kernels.aitchison_aitken, gaussian=kernels.gaussian, aitchison_aitken_reg = kernels.aitchison_aitken_reg, wangryzin_reg = kernels.wang_ryzin_reg, gauss_convolution=kernels.gaussian_convolution, wangryzin_convolution=kernels.wang_ryzin_convolution, aitchisonaitken_convolution=kernels.aitchison_aitken_convolution, gaussian_cdf=kernels.gaussian_cdf, aitchisonaitken_cdf=kernels.aitchison_aitken_cdf, wangryzin_cdf=kernels.wang_ryzin_cdf, d_gaussian=kernels.d_gaussian) def _compute_min_std_IQR(data): """Compute minimum of std and IQR for each variable.""" s1 = np.std(data, axis=0) q75 = mquantiles(data, 0.75, axis=0).data[0] q25 = mquantiles(data, 0.25, axis=0).data[0] s2 = (q75 - q25) / 1.349 # IQR dispersion = np.minimum(s1, s2) return dispersion def _compute_subset(class_type, data, bw, co, do, n_cvars, ix_ord, ix_unord, n_sub, class_vars, randomize, bound): """"Compute bw on subset of data. Called from ``GenericKDE._compute_efficient_*``. Notes ----- Needs to be outside the class in order for joblib to be able to pickle it. """ if randomize: np.random.shuffle(data) sub_data = data[:n_sub, :] else: sub_data = data[bound[0]:bound[1], :] if class_type == 'KDEMultivariate': from kernel_density import KDEMultivariate var_type = class_vars[0] sub_model = KDEMultivariate(sub_data, var_type, bw=bw, defaults=EstimatorSettings(efficient=False)) elif class_type == 'KDEMultivariateConditional': from kernel_density import KDEMultivariateConditional k_dep, dep_type, indep_type = class_vars endog = sub_data[:, :k_dep] exog = sub_data[:, k_dep:] sub_model = KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw=bw, defaults=EstimatorSettings(efficient=False)) elif class_type == 'KernelReg': from kernel_regression import KernelReg var_type, k_vars, reg_type = class_vars endog = _adjust_shape(sub_data[:, 0], 1) exog = _adjust_shape(sub_data[:, 1:], k_vars) sub_model = KernelReg(endog=endog, exog=exog, reg_type=reg_type, var_type=var_type, bw=bw, defaults=EstimatorSettings(efficient=False)) else: raise ValueError("class_type not recognized, should be one of " \ "{KDEMultivariate, KDEMultivariateConditional, KernelReg}") # Compute dispersion in next 4 lines if class_type == 'KernelReg': sub_data = sub_data[:, 1:] dispersion = _compute_min_std_IQR(sub_data) fct = dispersion * n_sub**(-1. / (n_cvars + co)) fct[ix_unord] = n_sub**(-2. / (n_cvars + do)) fct[ix_ord] = n_sub**(-2. / (n_cvars + do)) sample_scale_sub = sub_model.bw / fct #TODO: check if correct bw_sub = sub_model.bw return sample_scale_sub, bw_sub class GenericKDE (object): """ Base class for density estimation and regression KDE classes. """ def _compute_bw(self, bw): """ Computes the bandwidth of the data. Parameters ---------- bw: array_like or str If array_like: user-specified bandwidth. If a string, should be one of: - cv_ml: cross validation maximum likelihood - normal_reference: normal reference rule of thumb - cv_ls: cross validation least squares Notes ----- The default values for bw is 'normal_reference'. """ self.bw_func = dict(normal_reference=self._normal_reference, cv_ml=self._cv_ml, cv_ls=self._cv_ls) if bw is None: bwfunc = self.bw_func['normal_reference'] return bwfunc() if not isinstance(bw, basestring): self._bw_method = "user-specified" res = np.asarray(bw) else: # The user specified a bandwidth selection method self._bw_method = bw bwfunc = self.bw_func[bw] res = bwfunc() return res def _compute_dispersion(self, data): """ Computes the measure of dispersion. The minimum of the standard deviation and interquartile range / 1.349 Notes ----- Reimplemented in `KernelReg`, because the first column of `data` has to be removed. References ---------- See the user guide for the np package in R. In the notes on bwscaling option in npreg, npudens, npcdens there is a discussion on the measure of dispersion """ return _compute_min_std_IQR(data) def _get_class_vars_type(self): """Helper method to be able to pass needed vars to _compute_subset. Needs to be implemented by subclasses.""" pass def _compute_efficient(self, bw): """ Computes the bandwidth by estimating the scaling factor (c) in n_res resamples of size ``n_sub`` (in `randomize` case), or by dividing ``nobs`` into as many ``n_sub`` blocks as needed (if `randomize` is False). References ---------- See p.9 in socserv.mcmaster.ca/racine/np_faq.pdf """ nobs = self.nobs n_sub = self.n_sub data = copy.deepcopy(self.data) n_cvars = self.data_type.count('c') co = 4 # 2*order of continuous kernel do = 4 # 2*order of discrete kernel _, ix_ord, ix_unord = _get_type_pos(self.data_type) # Define bounds for slicing the data if self.randomize: # randomize chooses blocks of size n_sub, independent of nobs bounds = [None] * self.n_res else: bounds = [(i * n_sub, (i+1) * n_sub) for i in range(nobs // n_sub)] if nobs % n_sub > 0: bounds.append((nobs - nobs % n_sub, nobs)) n_blocks = self.n_res if self.randomize else len(bounds) sample_scale = np.empty((n_blocks, self.k_vars)) only_bw = np.empty((n_blocks, self.k_vars)) class_type, class_vars = self._get_class_vars_type() if has_joblib: # `res` is a list of tuples (sample_scale_sub, bw_sub) res = joblib.Parallel(n_jobs=self.n_jobs) \ (joblib.delayed(_compute_subset) \ (class_type, data, bw, co, do, n_cvars, ix_ord, ix_unord, \ n_sub, class_vars, self.randomize, bounds[i]) \ for i in range(n_blocks)) else: res = [] for i in xrange(n_blocks): res.append(_compute_subset(class_type, data, bw, co, do, n_cvars, ix_ord, ix_unord, n_sub, class_vars, self.randomize, bounds[i])) for i in xrange(n_blocks): sample_scale[i, :] = res[i][0] only_bw[i, :] = res[i][1] s = self._compute_dispersion(data) order_func = np.median if self.return_median else np.mean m_scale = order_func(sample_scale, axis=0) # TODO: Check if 1/5 is correct in line below! bw = m_scale * s * nobs**(-1. / (n_cvars + co)) bw[ix_ord] = m_scale[ix_ord] * nobs**(-2./ (n_cvars + do)) bw[ix_unord] = m_scale[ix_unord] * nobs**(-2./ (n_cvars + do)) if self.return_only_bw: bw = np.median(only_bw, axis=0) return bw def _set_defaults(self, defaults): """Sets the default values for the efficient estimation""" self.n_res = defaults.n_res self.n_sub = defaults.n_sub self.randomize = defaults.randomize self.return_median = defaults.return_median self.efficient = defaults.efficient self.return_only_bw = defaults.return_only_bw self.n_jobs = defaults.n_jobs def _normal_reference(self): """ Returns Scott's normal reference rule of thumb bandwidth parameter. Notes ----- See p.13 in [2] for an example and discussion. The formula for the bandwidth is .. math:: h = 1.06n^{-1/(4+q)} where ``n`` is the number of observations and ``q`` is the number of variables. """ X = np.std(self.data, axis=0) return 1.06 * X * self.nobs ** (- 1. / (4 + self.data.shape[1])) def _set_bw_bounds(self, bw): """ Sets bandwidth lower bound to effectively zero )1e-10), and for discrete values upper bound to 1. """ bw[bw < 0] = 1e-10 _, ix_ord, ix_unord = _get_type_pos(self.data_type) bw[ix_ord] = np.minimum(bw[ix_ord], 1.) bw[ix_unord] = np.minimum(bw[ix_unord], 1.) return bw def _cv_ml(self): """ Returns the cross validation maximum likelihood bandwidth parameter. Notes ----- For more details see p.16, 18, 27 in Ref. [1] (see module docstring). Returns the bandwidth estimate that maximizes the leave-out-out likelihood. The leave-one-out log likelihood function is: .. math:: \ln L=\sum_{i=1}^{n}\ln f_{-i}(X_{i}) The leave-one-out kernel estimator of :math:`f_{-i}` is: .. math:: f_{-i}(X_{i})=\frac{1}{(n-1)h} \sum_{j=1,j\neq i}K_{h}(X_{i},X_{j}) where :math:`K_{h}` represents the Generalized product kernel estimator: .. math:: K_{h}(X_{i},X_{j})=\prod_{s=1}^ {q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right) """ # the initial value for the optimization is the normal_reference h0 = self._normal_reference() bw = optimize.fmin(self.loo_likelihood, x0=h0, args=(np.log, ), maxiter=1e3, maxfun=1e3, disp=0, xtol=1e-3) bw = self._set_bw_bounds(bw) # bound bw if necessary return bw def _cv_ls(self): """ Returns the cross-validation least squares bandwidth parameter(s). Notes ----- For more details see pp. 16, 27 in Ref. [1] (see module docstring). Returns the value of the bandwidth that maximizes the integrated mean square error between the estimated and actual distribution. The integrated mean square error (IMSE) is given by: .. math:: \int\left[\hat{f}(x)-f(x)\right]^{2}dx This is the general formula for the IMSE. The IMSE differs for conditional (``KDEMultivariateConditional``) and unconditional (``KDEMultivariate``) kernel density estimation. """ h0 = self._normal_reference() bw = optimize.fmin(self.imse, x0=h0, maxiter=1e3, maxfun=1e3, disp=0, xtol=1e-3) bw = self._set_bw_bounds(bw) # bound bw if necessary return bw def loo_likelihood(self): raise NotImplementedError class EstimatorSettings(object): """ Object to specify settings for density estimation or regression. `EstimatorSettings` has several proporties related to how bandwidth estimation for the `KDEMultivariate`, `KDEMultivariateConditional`, `KernelReg` and `CensoredKernelReg` classes behaves. Parameters ---------- efficient: bool, optional If True, the bandwidth estimation is to be performed efficiently -- by taking smaller sub-samples and estimating the scaling factor of each subsample. This is useful for large samples (nobs >> 300) and/or multiple variables (k_vars > 3). If False (default), all data is used at the same time. randomize: bool, optional If True, the bandwidth estimation is to be performed by taking `n_res` random resamples (with replacement) of size `n_sub` from the full sample. If set to False (default), the estimation is performed by slicing the full sample in sub-samples of size `n_sub` so that all samples are used once. n_sub: int, optional Size of the sub-samples. Default is 50. n_res: int, optional The number of random re-samples used to estimate the bandwidth. Only has an effect if ``randomize == True``. Default value is 25. return_median: bool, optional If True (default), the estimator uses the median of all scaling factors for each sub-sample to estimate the bandwidth of the full sample. If False, the estimator uses the mean. return_only_bw: bool, optional If True, the estimator is to use the bandwidth and not the scaling factor. This is *not* theoretically justified. Should be used only for experimenting. n_jobs : int, optional The number of jobs to use for parallel estimation with ``joblib.Parallel``. Default is -1, meaning ``n_cores - 1``, with ``n_cores`` the number of available CPU cores. See the `joblib documentation `_ for more details. Examples -------- >>> settings = EstimatorSettings(randomize=True, n_jobs=3) >>> k_dens = KDEMultivariate(data, var_type, defaults=settings) """ def __init__(self, efficient=False, randomize=False, n_res=25, n_sub=50, return_median=True, return_only_bw=False, n_jobs=-1): self.efficient = efficient self.randomize = randomize self.n_res = n_res self.n_sub = n_sub self.return_median = return_median self.return_only_bw = return_only_bw # TODO: remove this? self.n_jobs = n_jobs class LeaveOneOut(object): """ Generator to give leave-one-out views on X. Parameters ---------- X : array-like 2-D array. Examples -------- >>> X = np.random.normal(0, 1, [10,2]) >>> loo = LeaveOneOut(X) >>> for x in loo: ... print x Notes ----- A little lighter weight than sklearn LOO. We don't need test index. Also passes views on X, not the index. """ def __init__(self, X): self.X = np.asarray(X) def __iter__(self): X = self.X nobs, k_vars = np.shape(X) for i in xrange(nobs): index = np.ones(nobs, dtype=np.bool) index[i] = False yield X[index, :] def _get_type_pos(var_type): ix_cont = np.array([c == 'c' for c in var_type]) ix_ord = np.array([c == 'o' for c in var_type]) ix_unord = np.array([c == 'u' for c in var_type]) return ix_cont, ix_ord, ix_unord def _adjust_shape(dat, k_vars): """ Returns an array of shape (nobs, k_vars) for use with `gpke`.""" dat = np.asarray(dat) if dat.ndim > 2: dat = np.squeeze(dat) if dat.ndim == 1 and k_vars > 1: # one obs many vars nobs = 1 elif dat.ndim == 1 and k_vars == 1: # one obs one var nobs = len(dat) else: if np.shape(dat)[0] == k_vars and np.shape(dat)[1] != k_vars: dat = dat.T nobs = np.shape(dat)[0] # ndim >1 so many obs many vars dat = np.reshape(dat, (nobs, k_vars)) return dat def gpke(bw, data, data_predict, var_type, ckertype='gaussian', okertype='wangryzin', ukertype='aitchisonaitken', tosum=True): """ Returns the non-normalized Generalized Product Kernel Estimator Parameters ---------- bw: 1-D ndarray The user-specified bandwidth parameters. data: 1D or 2-D ndarray The training data. data_predict: 1-D ndarray The evaluation points at which the kernel estimation is performed. var_type: str, optional The variable type (continuous, ordered, unordered). ckertype: str, optional The kernel used for the continuous variables. okertype: str, optional The kernel used for the ordered discrete variables. ukertype: str, optional The kernel used for the unordered discrete variables. tosum : bool, optional Whether or not to sum the calculated array of densities. Default is True. Returns ------- dens: array-like The generalized product kernel density estimator. Notes ----- The formula for the multivariate kernel estimator for the pdf is: .. math:: f(x)=\frac{1}{nh_{1}...h_{q}}\sum_{i=1}^ {n}K\left(\frac{X_{i}-x}{h}\right) where .. math:: K\left(\frac{X_{i}-x}{h}\right) = k\left( \frac{X_{i1}-x_{1}}{h_{1}}\right)\times k\left( \frac{X_{i2}-x_{2}}{h_{2}}\right)\times...\times k\left(\frac{X_{iq}-x_{q}}{h_{q}}\right) """ kertypes = dict(c=ckertype, o=okertype, u=ukertype) #Kval = [] #for ii, vtype in enumerate(var_type): # func = kernel_func[kertypes[vtype]] # Kval.append(func(bw[ii], data[:, ii], data_predict[ii])) #Kval = np.column_stack(Kval) Kval = np.empty(data.shape) for ii, vtype in enumerate(var_type): func = kernel_func[kertypes[vtype]] Kval[:, ii] = func(bw[ii], data[:, ii], data_predict[ii]) iscontinuous = np.array([c == 'c' for c in var_type]) dens = Kval.prod(axis=1) / np.prod(bw[iscontinuous]) if tosum: return dens.sum(axis=0) else: return dens statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/_smoothers_lowess.pyx000066400000000000000000000477741224417117700277210ustar00rootroot00000000000000#cython: boundscheck = False #cython: wraparound = False #cython: cdivision = True ''' Univariate lowess function, like in R. References ---------- Hastie, Tibshirani, Friedman. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition: Chapter 6. Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836. ''' cimport numpy as np import numpy as np from cpython cimport bool cimport cython from libc.math cimport fabs # there's no fmax in math.h with windows SDK apparently cdef inline double fmax(double x, double y): return x if x >= y else y DTYPE = np.double ctypedef np.double_t DTYPE_t def lowess(np.ndarray[DTYPE_t, ndim = 1] endog, np.ndarray[DTYPE_t, ndim = 1] exog, double frac = 2.0 / 3.0, Py_ssize_t it = 3, double delta = 0.0): '''lowess(endog, exog, frac=2.0/3.0, it=3, delta=0.0) LOWESS (Locally Weighted Scatterplot Smoothing) A lowess function that outs smoothed estimates of endog at the given exog values from points (exog, endog) Parameters ---------- endog: 1-D numpy array The y-values of the observed points exog: 1-D numpy array The x-values of the observed points. exog has to be increasing. frac: float Between 0 and 1. The fraction of the data used when estimating each y-value. it: int The number of residual-based reweightings to perform. delta: float Distance within which to use linear-interpolation instead of weighted regression. Returns ------- out: numpy array A numpy array with two columns. The first column is the sorted x values and the second column the associated estimated y-values. Notes ----- This lowess function implements the algorithm given in the reference below using local linear estimates. Suppose the input data has N points. The algorithm works by estimating the `smooth` y_i by taking the frac*N closest points to (x_i,y_i) based on their x values and estimating y_i using a weighted linear regression. The weight for (x_j,y_j) is tricube function applied to |x_i-x_j|. If it > 1, then further weighted local linear regressions are performed, where the weights are the same as above times the _lowess_bisquare function of the residuals. Each iteration takes approximately the same amount of time as the original fit, so these iterations are expensive. They are most useful when the noise has extremely heavy tails, such as Cauchy noise. Noise with less heavy-tails, such as t-distributions with df>2, are less problematic. The weights downgrade the influence of points with large residuals. In the extreme case, points whose residuals are larger than 6 times the median absolute residual are given weight 0. delta can be used to save computations. For each x_i, regressions are skipped for points closer than delta. The next regression is fit for the farthest point within delta of x_i and all points in between are estimated by linearly interpolating between the two regression fits. Judicious choice of delta can cut computation time considerably for large data (N > 5000). A good choice is delta = 0.01 * range(exog). Some experimentation is likely required to find a good choice of frac and iter for a particular dataset. References ---------- Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836. Examples -------- The below allows a comparison between how different the fits from lowess for different values of frac can be. >>> import numpy as np >>> import statsmodels.api as sm >>> lowess = sm.nonparametric.lowess >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500) >>> y = np.sin(x) + np.random.normal(size=len(x)) >>> z = lowess(y, x) >>> w = lowess(y, x, frac=1./3) This gives a similar comparison for when it is 0 vs not. >>> import numpy as np >>> import scipy.stats as stats >>> import statsmodels.api as sm >>> lowess = sm.nonparametric.lowess >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500) >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x)) >>> z = lowess(y, x, frac= 1./3, it=0) >>> w = lowess(y, x, frac=1./3) ''' cdef: Py_ssize_t n int k Py_ssize_t robiter, i, left_end, right_end int last_fit_i, np.ndarray[DTYPE_t, ndim = 1] x, y np.ndarray[DTYPE_t, ndim = 1] y_fit np.ndarray[DTYPE_t, ndim = 1] weights np.ndarray[DTYPE_t, ndim = 1] resid_weights y = endog # now just alias x = exog n = x.shape[0] # The number of neighbors in each regression. # round up if close to integer k = int(frac * n + 1e-10) # frac should be set, so that 2 <= k <= n. # Conform them instead of throwing error. if k < 2: k = 2 if k > n: k = n y_fit = np.zeros(n, dtype = DTYPE) resid_weights = np.zeros(n, dtype = DTYPE) it += 1 # Add one to it for initial run. for robiter in xrange(it): i = 0 last_fit_i = -1 left_end = 0 right_end = k y_fit = np.zeros(n, dtype = DTYPE) # 'do' Fit y[i]'s 'until' the end of the regression while True: # Re-initialize the weights for each point x[i]. weights = np.zeros(n, dtype = DTYPE) # Describe the neighborhood around the current x[i]. left_end, right_end, radius = update_neighborhood(x, i, n, left_end, right_end) # Calculate the weights for the regression in this neighborhood. # Determine if at least some weights are positive, so a regression # is ok. reg_ok = calculate_weights(x, weights, resid_weights, i, left_end, right_end, radius, robiter > 0) # If ok, run the regression calculate_y_fit(x, y, i, y_fit, weights, left_end, right_end, reg_ok) # If we skipped some points (because of how delta was set), go back # and fit them by linear interpolation. if last_fit_i < (i - 1): interpolate_skipped_fits(x, y_fit, i, last_fit_i) # Update the last fit counter to indicate we've now fit this point. # Find the next i for which we'll run a regression. i, last_fit_i = update_indices(x, y_fit, delta, i, n, last_fit_i) if last_fit_i >= n-1: break # Calculate residual weights, but don't bother on the last iteration. if robiter < it - 1: resid_weights = calculate_residual_weights(y, y_fit) return np.array([x, y_fit]).T def update_neighborhood(np.ndarray[DTYPE_t, ndim = 1] x, Py_ssize_t i, Py_ssize_t n, Py_ssize_t left_end, Py_ssize_t right_end): ''' Find the indices bounding the k-nearest-neighbors of the current point. Parameters ---------- x: 1-D numpy array The input x-values i: indexing integer The index of the point currently being fit. n: indexing integer The length of the input vectors, x and y. left_end: indexing integer The index of the left-most point in the neighborhood of x[i-1] (the previously-fit point). right_end: indexing integer The index of the right-most point in the neighborhood of x[i-1]. Non-inclusive, s.t. the neighborhood is x[left_end] <= x < x[right_end]. radius: float The radius of the current neighborhood. The larger of distances between x[i] and its left-most or right-most neighbor. Returns ------- left_end: indexing integer The index of the left-most point in the neighborhood of x[i] (the current point). right_end: indexing integer The index of the right-most point in the neighborhood of x[i]. Non-inclusive, s.t. the neighborhood is x[left_end] <= x < x[right_end]. radius: float The radius of the current neighborhood. The larger of distances between x[i] and its left-most or right-most neighbor. ''' cdef double radius # A subtle loop. Start from the current neighborhood range: # [left_end, right_end). Shift both ends rightwards by one # (so that the neighborhood still contains k points), until # the current point is in the center (or just to the left of # the center) of the neighborhood. This neighborhood will # contain the k-nearest neighbors of x[i]. # # Once the right end hits the end of the data, hold the # neighborhood the same for the remaining x[i]s. while True: if right_end < n: if (x[i] > (x[left_end] + x[right_end]) / 2.0): left_end += 1 right_end += 1 else: break else: break radius = fmax(x[i] - x[left_end], x[right_end-1] - x[i]) return left_end, right_end, radius cdef bool calculate_weights(np.ndarray[DTYPE_t, ndim = 1] x, np.ndarray[DTYPE_t, ndim = 1] weights, np.ndarray[DTYPE_t, ndim = 1] resid_weights, Py_ssize_t i, Py_ssize_t left_end, Py_ssize_t right_end, double radius, bool use_resid_weights): ''' Parameters ---------- x: 1-D vector The input x-values. weights: 1-D numpy array The vector of regression weights. resid_weights: 1-D numpy array The vector of residual weights from the last iteration. i: indexing integer The index of the point currently being fit. left_end: indexing integer The index of the left-most point in the neighborhood of x[i]. right_end: indexing integer The index of the right-most point in the neighborhood of x[i]. Non-inclusive, s.t. the neighborhood is x[left_end] <= x < x[right_end]. radius: float The radius of the current neighborhood. The larger of distances between x[i] and its left-most or right-most neighbor. use_resid_weights: boolean If True, multiply the x-distance weights by the residual weights from the last iteration of regressions. Set to False on the first iteration (since there are no residuals yet) and True on the subsequent ``robustifying`` iterations. Returns ------- reg_ok: boolean If True, at least some points have positive weight, and the regression will be run. If False, the regression is skipped and y_fit[i] is set to equal y[i]. Also, changes elements of weights in-place. ''' cdef: np.ndarray[DTYPE_t, ndim = 1] x_j = x[left_end:right_end] np.ndarray[DTYPE_t, ndim = 1] dist_i_j = np.abs(x_j - x[i]) / radius bool reg_ok = True double sum_weights # Assign the distance measure to the weights, then apply the tricube # function to change in-place. # use_resid_weights will be False on the first iteration, then True # on the subsequent ones, after some residuals have been calculated. weights[left_end:right_end] = dist_i_j if use_resid_weights == False: tricube(weights[left_end:right_end]) if use_resid_weights == True: tricube(weights[left_end:right_end]) weights[left_end:right_end] = (weights[left_end:right_end] * resid_weights[left_end:right_end]) sum_weights = np.sum(weights[left_end:right_end]) if sum_weights <= 0.0: reg_ok = False else: weights[left_end:right_end] = weights[left_end:right_end] / sum_weights return reg_ok cdef void calculate_y_fit(np.ndarray[DTYPE_t, ndim = 1] x, np.ndarray[DTYPE_t, ndim = 1] y, Py_ssize_t i, np.ndarray[DTYPE_t, ndim = 1] y_fit, np.ndarray[DTYPE_t, ndim = 1] weights, Py_ssize_t left_end, Py_ssize_t right_end, bool reg_ok): ''' Calculate smoothed/fitted y-value by weighted regression. Parameters ---------- x: 1-D numpy array The vector of input x-values. y: 1-D numpy array The vector of input y-values. i: indexing integer The index of the point currently being fit. y_fit: 1-D numpy array The vector of fitted y-values. weights: 1-D numpy array The vector of regression weights. left_end: indexing integer The index of the left-most point in the neighborhood of x[i]. right_end: indexing integers The index of the right-most point in the neighborhood of x[i]. Non-inclusive, s.t. the neighborhood is x[left_end] <= x < x[right_end]. reg_ok: boolean If True, at least some points have positive weight, and the regression will be run. If False, the regression is skipped and y_fit[i] is set to equal y[i]. Returns ------- Nothing. Changes y_fit[i] in-place. Notes ----- No regression function (e.g. lstsq) is called. Instead "projection vector" p_i_j is calculated, and y_fit[i] = sum(p_i_j * y[j]) = y_fit[i] for j s.t. x[j] is in the neighborhood of x[i]. p_i_j is a function of the weights, x[i], and its neighbors. ''' cdef: double sum_weighted_x = 0, weighted_sqdev_x = 0, p_i_j if reg_ok == False: y_fit[i] = y[i] else: for j in xrange(left_end, right_end): sum_weighted_x += weights[j] * x[j] for j in xrange(left_end, right_end): weighted_sqdev_x += weights[j] * (x[j] - sum_weighted_x) ** 2 for j in xrange(left_end, right_end): p_i_j = weights[j] * (1.0 + (x[i] - sum_weighted_x) * (x[j] - sum_weighted_x) / weighted_sqdev_x) y_fit[i] += p_i_j * y[j] cdef void interpolate_skipped_fits(np.ndarray[DTYPE_t, ndim = 1] x, np.ndarray[DTYPE_t, ndim = 1] y_fit, Py_ssize_t i, Py_ssize_t last_fit_i): ''' Calculate smoothed/fitted y by linear interpolation between the current and previous y fitted by weighted regression. Called only if delta > 0. Parameters ---------- x: 1-D numpy array The vector of input x-values. y_fit: 1-D numpy array The vector of fitted y-values i: indexing integer The index of the point currently being fit by weighted regression. last_fit_i: indexing integer The index of the last point fit by weighted regression. Returns ------- Nothing: changes elements of y_fit in-place. ''' cdef np.ndarray[DTYPE_t, ndim = 1] a a = x[(last_fit_i + 1): i] - x[last_fit_i] a = a / (x[i] - x[last_fit_i]) y_fit[(last_fit_i + 1): i] = a * y_fit[i] + (1.0 - a) * y_fit[last_fit_i] def update_indices(np.ndarray[DTYPE_t, ndim = 1] x, np.ndarray[DTYPE_t, ndim = 1] y_fit, double delta, Py_ssize_t i, Py_ssize_t n, Py_ssize_t last_fit_i): ''' Update the counters of the local regression. Parameters ---------- x: 1-D numpy array The vector of input x-values. y_fit: 1-D numpy array The vector of fitted y-values delta: float Indicates the range of x values within which linear interpolation should be used to estimate y_fit instead of weighted regression. i: indexing integer The index of the current point being fit. n: indexing integer The length of the input vectors, x and y. last_fit_i: indexing integer The last point at which y_fit was calculated. Returns ------- i: indexing integer The next point at which to run a weighted regression. last_fit_i: indexing integer The updated last point at which y_fit was calculated Notes ----- The relationship between the outputs is s.t. x[i+1] > x[last_fit_i] + delta. ''' cdef: Py_ssize_t k double cutpoint last_fit_i = i k = last_fit_i # For most points within delta of the current point, we skip the # weighted linear regression (which save much computation of # weights and fitted points). Instead, we'll jump to the last # point within delta, fit the weighted regression at that point, # and linearly interpolate in between. # This loop increments until we fall just outside of delta distance, # copying the results for any repeated x's along the way. cutpoint = x[last_fit_i] + delta for k in range(last_fit_i + 1, n): if x[k] > cutpoint: break if x[k] == x[last_fit_i]: # if tied with previous x-value, just use the already # fitted y, and update the last-fit counter. y_fit[k] = y_fit[last_fit_i] last_fit_i = k # i, which indicates the next point to fit the regression at, is # either one prior to k (since k should be the first point outside # of delta) or is just incremented + 1 if k = i+1. This insures we # always step forward. i = max(k-1, last_fit_i + 1) return i, last_fit_i def calculate_residual_weights(np.ndarray[DTYPE_t, ndim = 1] y, np.ndarray[DTYPE_t, ndim = 1] y_fit): ''' Calculate residual weights for the next `robustifying` iteration. Parameters ---------- y: 1-D numpy array The vector of actual input y-values. y_fit: 1-D numpy array The vector of fitted y-values from the current iteration. Returns ------- resid_weights: 1-D numpy array The vector of residual weights, to be used in the next iteration of regressions. ''' std_resid = np.abs(y - y_fit) std_resid /= 6.0 * np.median(std_resid) # Some trimming of outlier residuals. std_resid[std_resid >= 1.0] = 1.0 #std_resid[std_resid >= 0.999] = 1.0 #std_resid[std_resid <= 0.001] = 0.0 resid_weights = bisquare(std_resid) return resid_weights cdef void tricube(np.ndarray[DTYPE_t, ndim = 1] x): ''' The tri-cubic function (1 - x**3)**3. Used to weight neighboring points along the x-axis based on their distance to the current point. Parameters ---------- x: 1-D numpy array A vector of neighbors` distances from the current point, in units of the neighborhood radius. Returns ------- Nothing. Changes array elements in-place ''' # fast_array_cube is an elementwise, in-place cubed-power # operator. fast_array_cube(x) x[:] = np.negative(x) x += 1 fast_array_cube(x) cdef void fast_array_cube(np.ndarray[DTYPE_t, ndim = 1] x): ''' A fast, elementwise, in-place cube operator. Called by the tricube function. Parameters ---------- x: 1-D numpy array Returns ------- Nothing. Changes array elements in-place. ''' x2 = x*x x *= x2 def bisquare(np.ndarray[DTYPE_t, ndim = 1] x): ''' The bi-square function (1 - x**2)**2. Used to weight the residuals in the `robustifying` iterations. Called by the calculate_residual_weights function. Parameters ---------- x: 1-D numpy array A vector of absolute regression residuals, in units of 6 times the median absolute residual. Returns ------- A 1-D numpy array of residual weights. ''' return (1.0 - x**2)**2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/api.py000066400000000000000000000003721224417117700245030ustar00rootroot00000000000000from .kde import KDE, KDEUnivariate from .smoothers_lowess import lowess import bandwidths from .kernel_density import \ KDEMultivariate, KDEMultivariateConditional, EstimatorSettings from .kernel_regression import KernelReg, KernelCensoredReg statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/bandwidths.py000066400000000000000000000052671224417117700260710ustar00rootroot00000000000000import numpy as np from scipy.stats import scoreatpercentile as sap #from scipy.stats import norm def _select_sigma(X): """ Returns the smaller of std(X, ddof=1) or normalized IQR(X) over axis 0. References ---------- Silverman (1986) p.47 """ # normalize = norm.ppf(.75) - norm.ppf(.25) normalize = 1.349 # IQR = np.subtract.reduce(percentile(X, [75,25], # axis=axis), axis=axis)/normalize IQR = (sap(X, 75) - sap(X, 25))/normalize return np.minimum(np.std(X, axis=0, ddof=1), IQR) ## Univariate Rule of Thumb Bandwidths ## def bw_scott(x): """ Scott's Rule of Thumb Parameters ---------- x : array-like Array for which to get the bandwidth Returns ------- bw : float The estimate of the bandwidth Notes ----- Returns 1.059 * A * n ** (-1/5.) where :: A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References ---------- Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualization. """ A = _select_sigma(x) n = len(x) return 1.059 * A * n ** -.2 def bw_silverman(x): """ Silverman's Rule of Thumb Parameters ---------- x : array-like Array for which to get the bandwidth Returns ------- bw : float The estimate of the bandwidth Notes ----- Returns .9 * A * n ** (-1/5.) where :: A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References ---------- Silverman, B.W. (1986) `Density Estimation.` """ A = _select_sigma(x) n = len(x) return .9 * A * n ** -.2 ## Plug-In Methods ## ## Least Squares Cross-Validation ## ## Helper Functions ## bandwidth_funcs = dict(scott=bw_scott,silverman=bw_silverman) def select_bandwidth(x, bw, kernel): """ Selects bandwidth for a selection rule bw this is a wrapper around existing bandwidth selection rules Parameters ---------- x : array-like Array for which to get the bandwidth bw : string name of bandwidth selection rule, currently "scott" and "silverman" are supported kernel : not used yet Returns ------- bw : float The estimate of the bandwidth """ bw = bw.lower() if bw not in ["scott","silverman"]: raise ValueError("Bandwidth %s not understood" % bw) #TODO: uncomment checks when we have non-rule of thumb bandwidths for diff. kernels # if kernel == "gauss": return bandwidth_funcs[bw](x) # else: # raise ValueError("Only Gaussian Kernels are currently supported") statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/kde.py000066400000000000000000000435401224417117700245010ustar00rootroot00000000000000""" Univariate Kernel Density Estimators References ---------- Racine, Jeff. (2008) "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. http://dx.doi.org/10.1561/0800000009 http://en.wikipedia.org/wiki/Kernel_%28statistics%29 Silverman, B.W. Density Estimation for Statistics and Data Analysis. """ from __future__ import absolute_import # for 2to3 with extensions import warnings import numpy as np from scipy import integrate, stats from statsmodels.sandbox.nonparametric import kernels from statsmodels.tools.decorators import (cache_readonly, resettable_cache) from . import bandwidths from .kdetools import (forrt, revrt, silverman_transform, counts) from .linbin import fast_linbin #### Kernels Switch for estimators #### kernel_switch = dict(gau=kernels.Gaussian, epa=kernels.Epanechnikov, uni=kernels.Uniform, tri=kernels.Triangular, biw=kernels.Biweight, triw=kernels.Triweight, cos=kernels.Cosine) def _checkisfit(self): try: self.density except: raise ValueError("Call fit to fit the density first") #### Kernel Density Estimator Class ### class KDEUnivariate(object): """ Univariate Kernel Density Estimator. Parameters ---------- endog : array-like The variable for which the density estimate is desired. Notes ----- If cdf, sf, cumhazard, or entropy are computed, they are computed based on the definition of the kernel rather than the FFT approximation, even if the density is fit with FFT = True. `KDEUnivariate` is much faster than `KDEMultivariate`, due to its FFT-based implementation. It should be preferred for univariate, continuous data. `KDEMultivariate` also supports mixed data. See Also -------- KDEMultivariate kdensity, kdensityfft Examples -------- >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> nobs = 300 >>> np.random.seed(1234) # Seed random generator >>> dens = sm.nonparametric.KDEUnivariate(np.random.normal(size=nobs)) >>> dens.fit() >>> plt.plot(dens.cdf) >>> plt.show() """ def __init__(self, endog): self.endog = np.asarray(endog) def fit(self, kernel="gau", bw="scott", fft=True, weights=None, gridsize=None, adjust=1, cut=3, clip=(-np.inf, np.inf)): """ Attach the density estimate to the KDEUnivariate class. Parameters ---------- kernel : str The Kernel to be used. Choices are: - "biw" for biweight - "cos" for cosine - "epa" for Epanechnikov - "gau" for Gaussian. - "tri" for triangular - "triw" for triweight - "uni" for uniform bw : str, float The bandwidth to use. Choices are: - "scott" - 1.059 * A * nobs ** (-1/5.), where A is `min(std(X),IQR/1.34)` - "silverman" - .9 * A * nobs ** (-1/5.), where A is `min(std(X),IQR/1.34)` - If a float is given, it is the bandwidth. fft : bool Whether or not to use FFT. FFT implementation is more computationally efficient. However, only the Gaussian kernel is implemented. If FFT is False, then a 'nobs' x 'gridsize' intermediate array is created. gridsize : int If gridsize is None, max(len(X), 50) is used. cut : float Defines the length of the grid past the lowest and highest values of X so that the kernel goes to zero. The end points are -/+ cut*bw*{min(X) or max(X)} adjust : float An adjustment factor for the bw. Bandwidth becomes bw * adjust. """ try: bw = float(bw) self.bw_method = "user-given" except: self.bw_method = bw endog = self.endog if fft: if kernel != "gau": msg = "Only gaussian kernel is available for fft" raise NotImplementedError(msg) if weights is not None: msg = "Weights are not implemented for fft" raise NotImplementedError(msg) density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw, adjust=adjust, weights=weights, gridsize=gridsize, clip=clip, cut=cut) else: density, grid, bw = kdensity(endog, kernel=kernel, bw=bw, adjust=adjust, weights=weights, gridsize=gridsize, clip=clip, cut=cut) self.density = density self.support = grid self.bw = bw self.kernel = kernel_switch[kernel](h=bw) # we instantiate twice, # should this passed to funcs? # put here to ensure empty cache after re-fit with new options self._cache = resettable_cache() @cache_readonly def cdf(self): """ Returns the cumulative distribution function evaluated at the support. Notes ----- Will not work if fit has not been called. """ _checkisfit(self) density = self.density kern = self.kernel if kern.domain is None: # TODO: test for grid point at domain bound a,b = -np.inf,np.inf else: a,b = kern.domain func = lambda x,s: kern.density(s,x) support = self.support support = np.r_[a,support] gridsize = len(support) endog = self.endog probs = [integrate.quad(func, support[i-1], support[i], args=endog)[0] for i in xrange(1,gridsize)] return np.cumsum(probs) @cache_readonly def cumhazard(self): """ Returns the hazard function evaluated at the support. Notes ----- Will not work if fit has not been called. """ _checkisfit(self) return -np.log(self.sf) @cache_readonly def sf(self): """ Returns the survival function evaluated at the support. Notes ----- Will not work if fit has not been called. """ _checkisfit(self) return 1 - self.cdf @cache_readonly def entropy(self): """ Returns the differential entropy evaluated at the support Notes ----- Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called. """ _checkisfit(self) def entr(x,s): pdf = kern.density(s,x) return pdf*np.log(pdf+1e-12) pdf = self.density kern = self.kernel if kern.domain is not None: a,b = self.domain else: a,b = -np.inf,np.inf endog = self.endog #TODO: below could run into integr problems, cf. stats.dist._entropy return -integrate.quad(entr, a,b, args=(endog,))[0] @cache_readonly def icdf(self): """ Inverse Cumulative Distribution (Quantile) Function Notes ----- Will not work if fit has not been called. Uses `scipy.stats.mstats.mquantiles`. """ _checkisfit(self) gridsize = len(self.density) return stats.mstats.mquantiles(self.endog, np.linspace(0,1, gridsize)) def evaluate(self, point): """ Evaluate density at a single point. Parameters ---------- point : float Point at which to evaluate the density. """ _checkisfit(self) return self.kernel.density(self.endog, point) class KDE(KDEUnivariate): def __init__(self, endog): self.endog = np.asarray(endog) warnings.warn("KDE is deprecated and will be removed in 0.6, " "use KDEUnivariate instead", FutureWarning) #### Kernel Density Estimator Functions #### def kdensity(X, kernel="gau", bw="scott", weights=None, gridsize=None, adjust=1, clip=(-np.inf,np.inf), cut=3, retgrid=True): """ Rosenblatt-Parzen univariate kernel density estimator. Parameters ---------- X : array-like The variable for which the density estimate is desired. kernel : str The Kernel to be used. Choices are - "biw" for biweight - "cos" for cosine - "epa" for Epanechnikov - "gau" for Gaussian. - "tri" for triangular - "triw" for triweight - "uni" for uniform bw : str, float "scott" - 1.059 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34) "silverman" - .9 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34) If a float is given, it is the bandwidth. weights : array or None Optional weights. If the X value is clipped, then this weight is also dropped. gridsize : int If gridsize is None, max(len(X), 50) is used. adjust : float An adjustment factor for the bw. Bandwidth becomes bw * adjust. clip : tuple Observations in X that are outside of the range given by clip are dropped. The number of observations in X is then shortened. cut : float Defines the length of the grid past the lowest and highest values of X so that the kernel goes to zero. The end points are -/+ cut*bw*{min(X) or max(X)} retgrid : bool Whether or not to return the grid over which the density is estimated. Returns ------- density : array The densities estimated at the grid points. grid : array, optional The grid points at which the density is estimated. Notes ----- Creates an intermediate (`gridsize` x `nobs`) array. Use FFT for a more computationally efficient version. """ X = np.asarray(X) if X.ndim == 1: X = X[:,None] clip_x = np.logical_and(X>clip[0], X z_high) k = kern(k) # estimate density k[domain_mask] = 0 else: k = kern(k) # estimate density k[k<0] = 0 # get rid of any negative values, do we need this? dens = np.dot(k,weights)/(q*bw) if retgrid: return dens, grid, bw else: return dens, bw def kdensityfft(X, kernel="gau", bw="scott", weights=None, gridsize=None, adjust=1, clip=(-np.inf,np.inf), cut=3, retgrid=True): """ Rosenblatt-Parzen univariate kernel density estimator Parameters ---------- X : array-like The variable for which the density estimate is desired. kernel : str ONLY GAUSSIAN IS CURRENTLY IMPLEMENTED. "bi" for biweight "cos" for cosine "epa" for Epanechnikov, default "epa2" for alternative Epanechnikov "gau" for Gaussian. "par" for Parzen "rect" for rectangular "tri" for triangular bw : str, float "scott" - 1.059 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34) "silverman" - .9 * A * nobs ** (-1/5.), where A is min(std(X),IQR/1.34) If a float is given, it is the bandwidth. weights : array or None WEIGHTS ARE NOT CURRENTLY IMPLEMENTED. Optional weights. If the X value is clipped, then this weight is also dropped. gridsize : int If gridsize is None, min(len(X), 512) is used. Note that the provided number is rounded up to the next highest power of 2. adjust : float An adjustment factor for the bw. Bandwidth becomes bw * adjust. clip : tuple Observations in X that are outside of the range given by clip are dropped. The number of observations in X is then shortened. cut : float Defines the length of the grid past the lowest and highest values of X so that the kernel goes to zero. The end points are -/+ cut*bw*{X.min() or X.max()} retgrid : bool Whether or not to return the grid over which the density is estimated. Returns ------- density : array The densities estimated at the grid points. grid : array, optional The grid points at which the density is estimated. Notes ----- Only the default kernel is implemented. Weights aren't implemented yet. This follows Silverman (1982) with changes suggested by Jones and Lotwick (1984). However, the discretization step is replaced by linear binning of Fan and Marron (1994). This should be extended to accept the parts that are dependent only on the data to speed things up for cross-validation. References ---------- :: Fan, J. and J.S. Marron. (1994) `Fast implementations of nonparametric curve estimators`. Journal of Computational and Graphical Statistics. 3.1, 35-56. Jones, M.C. and H.W. Lotwick. (1984) `Remark AS R50: A Remark on Algorithm AS 176. Kernal Density Estimation Using the Fast Fourier Transform`. Journal of the Royal Statistical Society. Series C. 33.1, 120-2. Silverman, B.W. (1982) `Algorithm AS 176. Kernel density estimation using the Fast Fourier Transform. Journal of the Royal Statistical Society. Series C. 31.2, 93-9. """ X = np.asarray(X) X = X[np.logical_and(X>clip[0], X0: # there are points of X in the grid here # Xingrid = X[j:j+count[k]] # get all these points # # get weights at grid[k],grid[k+1] # binned[k] += np.sum(grid[k+1]-Xingrid) # binned[k+1] += np.sum(Xingrid-grid[k]) # j += count[k] # binned /= (nobs)*delta**2 # normalize binned to sum to 1/delta #NOTE: THE ABOVE IS WRONG, JUST TRY WITH LINEAR BINNING binned = fast_linbin(X,a,b,gridsize)/(delta*nobs) # step 2 compute FFT of the weights, using Munro (1976) FFT convention y = forrt(binned) # step 3 and 4 for optimal bw compute zstar and the density estimate f # don't have to redo the above if just changing bw, ie., for cross val #NOTE: silverman_transform is the closed form solution of the FFT of the #gaussian kernel. Not yet sure how to generalize it. zstar = silverman_transform(bw, gridsize, RANGE)*y # 3.49 in Silverman # 3.50 w Gaussian kernel f = revrt(zstar) if retgrid: return f, grid, bw else: return f, bw if __name__ == "__main__": import numpy as np np.random.seed(12345) xi = np.random.randn(100) f,grid, bw1 = kdensity(xi, kernel="gau", bw=.372735, retgrid=True) f2, bw2 = kdensityfft(xi, kernel="gau", bw="silverman",retgrid=False) # do some checking vs. silverman algo. # you need denes.f, http://lib.stat.cmu.edu/apstat/176 #NOTE: I (SS) made some changes to the Fortran # and the FFT stuff from Munro http://lib.stat.cmu.edu/apstat/97o # then compile everything and link to denest with f2py #Make pyf file as usual, then compile shared object #f2py denest.f -m denest2 -h denest.pyf #edit pyf #-c flag makes it available to other programs, fPIC builds a shared library #/usr/bin/gfortran -Wall -c -fPIC fft.f #f2py -c denest.pyf ./fft.o denest.f try: from denest2 import denest # @UnresolvedImport a = -3.4884382032045504 b = 4.3671504686785605 RANGE = b - a bw = bandwidths.bw_silverman(xi) ft,smooth,ifault,weights,smooth1 = denest(xi,a,b,bw,np.zeros(512),np.zeros(512),0, np.zeros(512), np.zeros(512)) # We use a different binning algo, so only accurate up to 3 decimal places np.testing.assert_almost_equal(f2, smooth, 3) #NOTE: for debugging # y2 = forrt(weights) # RJ = np.arange(512/2+1) # FAC1 = 2*(np.pi*bw/RANGE)**2 # RJFAC = RJ**2*FAC1 # BC = 1 - RJFAC/(6*(bw/((b-a)/M))**2) # FAC = np.exp(-RJFAC)/BC # SMOOTH = np.r_[FAC,FAC[1:-1]] * y2 # dens = revrt(SMOOTH) except: # ft = np.loadtxt('./ft_silver.csv') # smooth = np.loadtxt('./smooth_silver.csv') print "Didn't get the estimates from the Silverman algorithm" statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/kdetools.py000066400000000000000000000024411224417117700255550ustar00rootroot00000000000000#### Convenience Functions to be moved to kerneltools #### import numpy as np def forrt(X,m=None): """ RFFT with order like Munro (1976) FORTT routine. """ if m is None: m = len(X) y = np.fft.rfft(X,m)/m return np.r_[y.real,y[1:-1].imag] def revrt(X,m=None): """ Inverse of forrt. Equivalent to Munro (1976) REVRT routine. """ if m is None: m = len(X) y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j return np.fft.irfft(y)*m def silverman_transform(bw, M, RANGE): """ FFT of Gaussian kernel following to Silverman AS 176. Notes ----- Underflow is intentional as a dampener. """ J = np.arange(M/2+1) FAC1 = 2*(np.pi*bw/RANGE)**2 JFAC = J**2*FAC1 BC = 1 - 1./3 * (J*1./M*np.pi)**2 FAC = np.exp(-JFAC)/BC kern_est = np.r_[FAC,FAC[1:-1]] return kern_est def counts(x,v): """ Counts the number of elements of x that fall within the grid points v Notes ----- Using np.digitize and np.bincount """ idx = np.digitize(x,v) try: # numpy 1.6 return np.bincount(idx, minlength=len(v)) except: bc = np.bincount(idx) return np.r_[bc,np.zeros(len(v)-len(bc))] def kdesum(x,axis=0): return np.asarray([np.sum(x[i] - x, axis) for i in range(len(x))]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/kernel_density.py000066400000000000000000000602561224417117700267600ustar00rootroot00000000000000""" Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types. References ---------- [1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007) [2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) http://dx.doi.org/10.1561/0800000009 [3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions with Categorical and Continuous Data." Working Paper. (2000) [4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional Distributions Annals of Economics and Finance 5, 211-235 (2004) [5] Liu, R., Yang, L. "Kernel estimation of multivariate cumulative distribution function." Journal of Nonparametric Statistics (2008) [6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF with Categorical and Continuous Data." Working Paper [7] Li, Q., Racine, J. "Cross-validated local linear nonparametric regression" Statistica Sinica 14(2004), pp. 485-512 [8] Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics [9] Racine, J., Hart, J., Li, Q., "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models", 2006, Econometric Reviews 25, 523-544 """ # TODO: make default behavior efficient=True above a certain n_obs import numpy as np import kernels from _kernel_base import GenericKDE, EstimatorSettings, gpke, \ LeaveOneOut, _adjust_shape __all__ = ['KDEMultivariate', 'KDEMultivariateConditional', 'EstimatorSettings'] class KDEMultivariate(GenericKDE): """ Multivariate kernel density estimator. This density estimator can handle univariate as well as multivariate data, including mixed continuous / ordered discrete / unordered discrete data. It also provides cross-validated bandwidth selection methods (least squares, maximum likelihood). Parameters ---------- data: list of ndarrays or 2-D ndarray The training data for the Kernel Density Estimation, used to determine the bandwidth(s). If a 2-D array, should be of shape (num_observations, num_variables). If a list, each list element is a separate observation. var_type: str The type of the variables: - c : continuous - u : unordered (discrete) - o : ordered (discrete) The string should contain a type specifier for each variable, so for example ``var_type='ccuo'``. bw: array_like or str, optional If an array, it is a fixed user-specified bandwidth. If a string, should be one of: - normal_reference: normal reference rule of thumb (default) - cv_ml: cross validation maximum likelihood - cv_ls: cross validation least squares defaults: EstimatorSettings instance, optional The default values for (efficient) bandwidth estimation. Attributes ---------- bw: array_like The bandwidth parameters. See Also -------- KDEMultivariateConditional Examples -------- >>> import statsmodels.api as sm >>> nobs = 300 >>> np.random.seed(1234) # Seed random generator >>> c1 = np.random.normal(size=(nobs,1)) >>> c2 = np.random.normal(2, 1, size=(nobs,1)) Estimate a bivariate distribution and display the bandwidth found: >>> dens_u = sm.nonparametric.KDEMultivariate(data=[c1,c2], ... var_type='cc', bw='normal_reference') >>> dens_u.bw array([ 0.39967419, 0.38423292]) """ def __init__(self, data, var_type, bw=None, defaults=EstimatorSettings()): self.var_type = var_type self.k_vars = len(self.var_type) self.data = _adjust_shape(data, self.k_vars) self.data_type = var_type self.nobs, self.k_vars = np.shape(self.data) if self.nobs <= self.k_vars: raise ValueError("The number of observations must be larger " \ "than the number of variables.") self._set_defaults(defaults) if not self.efficient: self.bw = self._compute_bw(bw) else: self.bw = self._compute_efficient(bw) def __repr__(self): """Provide something sane to print.""" rpr = "KDE instance\n" rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" rpr += "Number of samples: nobs = " + str(self.nobs) + "\n" rpr += "Variable types: " + self.var_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" return rpr def loo_likelihood(self, bw, func=lambda x: x): r""" Returns the leave-one-out likelihood function. The leave-one-out likelihood function for the unconditional KDE. Parameters ---------- bw: array_like The value for the bandwidth parameter(s). func: callable, optional Function to transform the likelihood values (before summing); for the log likelihood, use ``func=np.log``. Default is ``f(x) = x``. Notes ----- The leave-one-out kernel estimator of :math:`f_{-i}` is: .. math:: f_{-i}(X_{i})=\frac{1}{(n-1)h} \sum_{j=1,j\neq i}K_{h}(X_{i},X_{j}) where :math:`K_{h}` represents the generalized product kernel estimator: .. math:: K_{h}(X_{i},X_{j}) = \prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right) """ LOO = LeaveOneOut(self.data) L = 0 for i, X_not_i in enumerate(LOO): f_i = gpke(bw, data=-X_not_i, data_predict=-self.data[i, :], var_type=self.var_type) L += func(f_i) return -L def pdf(self, data_predict=None): r""" Evaluate the probability density function. Parameters ---------- data_predict: array_like, optional Points to evaluate at. If unspecified, the training data is used. Returns ------- pdf_est: array_like Probability density function evaluated at `data_predict`. Notes ----- The probability density is given by the generalized product kernel estimator: .. math:: K_{h}(X_{i},X_{j}) = \prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right) """ if data_predict is None: data_predict = self.data else: data_predict = _adjust_shape(data_predict, self.k_vars) pdf_est = [] for i in xrange(np.shape(data_predict)[0]): pdf_est.append(gpke(self.bw, data=self.data, data_predict=data_predict[i, :], var_type=self.var_type) / self.nobs) pdf_est = np.squeeze(pdf_est) return pdf_est def cdf(self, data_predict=None): r""" Evaluate the cumulative distribution function. Parameters ---------- data_predict: array_like, optional Points to evaluate at. If unspecified, the training data is used. Returns ------- cdf_est: array_like The estimate of the cdf. Notes ----- See http://en.wikipedia.org/wiki/Cumulative_distribution_function For more details on the estimation see Ref. [5] in module docstring. The multivariate CDF for mixed data (continuous and ordered/unordered discrete) is estimated by: ..math:: F(x^{c},x^{d})=n^{-1}\sum_{i=1}^{n}\left[G( \frac{x^{c}-X_{i}}{h})\sum_{u\leq x^{d}}L(X_{i}^{d},x_{i}^{d}, \lambda)\right] where G() is the product kernel CDF estimator for the continuous and L() for the discrete variables. Used bandwidth is ``self.bw``. """ if data_predict is None: data_predict = self.data else: data_predict = _adjust_shape(data_predict, self.k_vars) cdf_est = [] for i in xrange(np.shape(data_predict)[0]): cdf_est.append(gpke(self.bw, data=self.data, data_predict=data_predict[i, :], var_type=self.var_type, ckertype="gaussian_cdf", ukertype="aitchisonaitken_cdf", okertype='wangryzin_cdf') / self.nobs) cdf_est = np.squeeze(cdf_est) return cdf_est def imse(self, bw): r""" Returns the Integrated Mean Square Error for the unconditional KDE. Parameters ---------- bw: array_like The bandwidth parameter(s). Returns ------ CV: float The cross-validation objective function. Notes ----- See p. 27 in [1] For details on how to handle the multivariate estimation with mixed data types see p.6 in [3] The formula for the cross-validation objective function is: .. math:: CV=\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{N} \bar{K}_{h}(X_{i},X_{j})-\frac{2}{n(n-1)}\sum_{i=1}^{n} \sum_{j=1,j\neq i}^{N}K_{h}(X_{i},X_{j}) Where :math:`\bar{K}_{h}` is the multivariate product convolution kernel (consult [3] for mixed data types). """ #F = 0 #for i in range(self.nobs): # k_bar_sum = gpke(bw, data=-self.data, # data_predict=-self.data[i, :], # var_type=self.var_type, # ckertype='gauss_convolution', # okertype='wangryzin_convolution', # ukertype='aitchisonaitken_convolution') # F += k_bar_sum ## there is a + because loo_likelihood returns the negative #return (F / self.nobs**2 + self.loo_likelihood(bw) * \ # 2 / ((self.nobs) * (self.nobs - 1))) # The code below is equivalent to the commented-out code above. It's # about 20% faster due to some code being moved outside the for-loops # and shared by gpke() and loo_likelihood(). F = 0 kertypes = dict(c=kernels.gaussian_convolution, o=kernels.wang_ryzin_convolution, u=kernels.aitchison_aitken_convolution) nobs = self.nobs data = -self.data var_type = self.var_type ix_cont = np.array([c == 'c' for c in var_type]) _bw_cont_product = bw[ix_cont].prod() Kval = np.empty(data.shape) for i in range(nobs): for ii, vtype in enumerate(var_type): Kval[:, ii] = kertypes[vtype](bw[ii], data[:, ii], data[i, ii]) dens = Kval.prod(axis=1) / _bw_cont_product k_bar_sum = dens.sum(axis=0) F += k_bar_sum # sum of prod kernel over nobs kertypes = dict(c=kernels.gaussian, o=kernels.wang_ryzin, u=kernels.aitchison_aitken) LOO = LeaveOneOut(self.data) L = 0 # leave-one-out likelihood Kval = np.empty((data.shape[0]-1, data.shape[1])) for i, X_not_i in enumerate(LOO): for ii, vtype in enumerate(var_type): Kval[:, ii] = kertypes[vtype](bw[ii], -X_not_i[:, ii], data[i, ii]) dens = Kval.prod(axis=1) / _bw_cont_product L += dens.sum(axis=0) # CV objective function, eq. (2.4) of Ref. [3] return (F / nobs**2 - 2 * L / (nobs * (nobs - 1))) def _get_class_vars_type(self): """Helper method to be able to pass needed vars to _compute_subset.""" class_type = 'KDEMultivariate' class_vars = (self.var_type, ) return class_type, class_vars class KDEMultivariateConditional(GenericKDE): """ Conditional multivariate kernel density estimator. Calculates ``P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m)``. The conditional density is by definition the ratio of the two densities, see [1]_. Parameters ---------- endog: list of ndarrays or 2-D ndarray The training data for the dependent variables, used to determine the bandwidth(s). If a 2-D array, should be of shape (num_observations, num_variables). If a list, each list element is a separate observation. exog: list of ndarrays or 2-D ndarray The training data for the independent variable; same shape as `endog`. dep_type: str The type of the dependent variables: c : Continuous u : Unordered (Discrete) o : Ordered (Discrete) The string should contain a type specifier for each variable, so for example ``dep_type='ccuo'``. indep_type: str The type of the independent variables; specifed like `dep_type`. bw: array_like or str, optional If an array, it is a fixed user-specified bandwidth. If a string, should be one of: - normal_reference: normal reference rule of thumb (default) - cv_ml: cross validation maximum likelihood - cv_ls: cross validation least squares defaults: Instance of class EstimatorSettings The default values for the efficient bandwidth estimation Attributes --------- bw: array_like The bandwidth parameters See Also -------- KDEMultivariate References ---------- .. [1] http://en.wikipedia.org/wiki/Conditional_probability_distribution Examples -------- >>> import statsmodels.api as sm >>> nobs = 300 >>> c1 = np.random.normal(size=(nobs,1)) >>> c2 = np.random.normal(2,1,size=(nobs,1)) >>> dens_c = sm.nonparametric.KDEMultivariateConditional(endog=[c1], ... exog=[c2], dep_type='c', indep_type='c', bw='normal_reference') >>> dens_c.bw # show computed bandwidth array([ 0.41223484, 0.40976931]) """ def __init__(self, endog, exog, dep_type, indep_type, bw, defaults=EstimatorSettings()): self.dep_type = dep_type self.indep_type = indep_type self.data_type = dep_type + indep_type self.k_dep = len(self.dep_type) self.k_indep = len(self.indep_type) self.endog = _adjust_shape(endog, self.k_dep) self.exog = _adjust_shape(exog, self.k_indep) self.nobs, self.k_dep = np.shape(self.endog) self.data = np.column_stack((self.endog, self.exog)) self.k_vars = np.shape(self.data)[1] self._set_defaults(defaults) if not self.efficient: self.bw = self._compute_bw(bw) else: self.bw = self._compute_efficient(bw) def __repr__(self): """Provide something sane to print.""" rpr = "KDEMultivariateConditional instance\n" rpr += "Number of independent variables: k_indep = " + \ str(self.k_indep) + "\n" rpr += "Number of dependent variables: k_dep = " + \ str(self.k_dep) + "\n" rpr += "Number of observations: nobs = " + str(self.nobs) + "\n" rpr += "Independent variable types: " + self.indep_type + "\n" rpr += "Dependent variable types: " + self.dep_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" return rpr def loo_likelihood(self, bw, func=lambda x: x): """ Returns the leave-one-out conditional likelihood of the data. If `func` is not equal to the default, what's calculated is a function of the leave-one-out conditional likelihood. Parameters ---------- bw: array_like The bandwidth parameter(s). func: callable, optional Function to transform the likelihood values (before summing); for the log likelihood, use ``func=np.log``. Default is ``f(x) = x``. Returns ------- L: float The value of the leave-one-out function for the data. Notes ----- Similar to ``KDE.loo_likelihood`, but substitute ``f(y|x)=f(x,y)/f(y)`` for ``f(x)``. """ yLOO = LeaveOneOut(self.data) xLOO = LeaveOneOut(self.exog).__iter__() L = 0 for i, Y_j in enumerate(yLOO): X_not_i = xLOO.next() f_yx = gpke(bw, data=-Y_j, data_predict=-self.data[i, :], var_type=(self.dep_type + self.indep_type)) f_x = gpke(bw[self.k_dep:], data=-X_not_i, data_predict=-self.exog[i, :], var_type=self.indep_type) f_i = f_yx / f_x L += func(f_i) return -L def pdf(self, endog_predict=None, exog_predict=None): r""" Evaluate the probability density function. Parameters ---------- endog_predict: array_like, optional Evaluation data for the dependent variables. If unspecified, the training data is used. exog_predict: array_like, optional Evaluation data for the independent variables. Returns ------- pdf: array_like The value of the probability density at `endog_predict` and `exog_predict`. Notes ----- The formula for the conditional probability density is: .. math:: f(X|Y)=\frac{f(X,Y)}{f(Y)} with .. math:: f(X)=\prod_{s=1}^{q}h_{s}^{-1}k \left(\frac{X_{is}-X_{js}}{h_{s}}\right) where :math:`k` is the appropriate kernel for each variable. """ if endog_predict is None: endog_predict = self.endog else: endog_predict = _adjust_shape(endog_predict, self.k_dep) if exog_predict is None: exog_predict = self.exog else: exog_predict = _adjust_shape(exog_predict, self.k_indep) pdf_est = [] data_predict = np.column_stack((endog_predict, exog_predict)) for i in xrange(np.shape(data_predict)[0]): f_yx = gpke(self.bw, data=self.data, data_predict=data_predict[i, :], var_type=(self.dep_type + self.indep_type)) f_x = gpke(self.bw[self.k_dep:], data=self.exog, data_predict=exog_predict[i, :], var_type=self.indep_type) pdf_est.append(f_yx / f_x) return np.squeeze(pdf_est) def cdf(self, endog_predict=None, exog_predict=None): r""" Cumulative distribution function for the conditional density. Parameters ---------- endog_predict: array_like, optional The evaluation dependent variables at which the cdf is estimated. If not specified the training dependent variables are used. exog_predict: array_like, optional The evaluation independent variables at which the cdf is estimated. If not specified the training independent variables are used. Returns ------- cdf_est: array_like The estimate of the cdf. Notes ----- For more details on the estimation see [5], and p.181 in [1]. The multivariate conditional CDF for mixed data (continuous and ordered/unordered discrete) is estimated by: ..math:: F(y|x)=\frac{n^{-1}\sum_{i=1}^{n}G(\frac{y-Y_{i}}{h_{0}}) W_{h}(X_{i},x)}{\widehat{\mu}(x)} where G() is the product kernel CDF estimator for the dependent (y) variable(s) and W() is the product kernel CDF estimator for the independent variable(s). """ if endog_predict is None: endog_predict = self.endog else: endog_predict = _adjust_shape(endog_predict, self.k_dep) if exog_predict is None: exog_predict = self.exog else: exog_predict = _adjust_shape(exog_predict, self.k_indep) N_data_predict = np.shape(exog_predict)[0] cdf_est = np.empty(N_data_predict) for i in xrange(N_data_predict): mu_x = gpke(self.bw[self.k_dep:], data=self.exog, data_predict=exog_predict[i, :], var_type=self.indep_type) / self.nobs mu_x = np.squeeze(mu_x) cdf_endog = gpke(self.bw[0:self.k_dep], data=self.endog, data_predict=endog_predict[i, :], var_type=self.dep_type, ckertype="gaussian_cdf", ukertype="aitchisonaitken_cdf", okertype='wangryzin_cdf', tosum=False) cdf_exog = gpke(self.bw[self.k_dep:], data=self.exog, data_predict=exog_predict[i, :], var_type=self.indep_type, tosum=False) S = (cdf_endog * cdf_exog).sum(axis=0) cdf_est[i] = S / (self.nobs * mu_x) return cdf_est def imse(self, bw): r""" The integrated mean square error for the conditional KDE. Parameters ---------- bw: array_like The bandwidth parameter(s). Returns ------- CV: float The cross-validation objective function. Notes ----- For more details see pp. 156-166 in [1]. For details on how to handle the mixed variable types see [3]. The formula for the cross-validation objective function for mixed variable types is: .. math:: CV(h,\lambda)=\frac{1}{n}\sum_{l=1}^{n} \frac{G_{-l}(X_{l})}{\left[\mu_{-l}(X_{l})\right]^{2}}- \frac{2}{n}\sum_{l=1}^{n}\frac{f_{-l}(X_{l},Y_{l})}{\mu_{-l}(X_{l})} where .. math:: G_{-l}(X_{l}) = n^{-2}\sum_{i\neq l}\sum_{j\neq l} K_{X_{i},X_{l}} K_{X_{j},X_{l}}K_{Y_{i},Y_{j}}^{(2)} where :math:`K_{X_{i},X_{l}}` is the multivariate product kernel and :math:`\mu_{-l}(X_{l})` is the leave-one-out estimator of the pdf. :math:`K_{Y_{i},Y_{j}}^{(2)}` is the convolution kernel. The value of the function is minimized by the ``_cv_ls`` method of the `GenericKDE` class to return the bw estimates that minimize the distance between the estimated and "true" probability density. """ zLOO = LeaveOneOut(self.data) CV = 0 nobs = float(self.nobs) expander = np.ones((self.nobs - 1, 1)) for ii, Z in enumerate(zLOO): X = Z[:, self.k_dep:] Y = Z[:, :self.k_dep] Ye_L = np.kron(Y, expander) Ye_R = np.kron(expander, Y) Xe_L = np.kron(X, expander) Xe_R = np.kron(expander, X) K_Xi_Xl = gpke(bw[self.k_dep:], data=Xe_L, data_predict=self.exog[ii, :], var_type=self.indep_type, tosum=False) K_Xj_Xl = gpke(bw[self.k_dep:], data=Xe_R, data_predict=self.exog[ii, :], var_type=self.indep_type, tosum=False) K2_Yi_Yj = gpke(bw[0:self.k_dep], data=Ye_L, data_predict=Ye_R, var_type=self.dep_type, ckertype='gauss_convolution', okertype='wangryzin_convolution', ukertype='aitchisonaitken_convolution', tosum=False) G = (K_Xi_Xl * K_Xj_Xl * K2_Yi_Yj).sum() / nobs**2 f_X_Y = gpke(bw, data=-Z, data_predict=-self.data[ii, :], var_type=(self.dep_type + self.indep_type)) / nobs m_x = gpke(bw[self.k_dep:], data=-X, data_predict=-self.exog[ii, :], var_type=self.indep_type) / nobs CV += (G / m_x ** 2) - 2 * (f_X_Y / m_x) return CV / nobs def _get_class_vars_type(self): """Helper method to be able to pass needed vars to _compute_subset.""" class_type = 'KDEMultivariateConditional' class_vars = (self.k_dep, self.dep_type, self.indep_type) return class_type, class_vars statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/kernel_regression.py000066400000000000000000000777421224417117700274710ustar00rootroot00000000000000""" Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types References ---------- [1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007) [2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) http://dx.doi.org/10.1561/0800000009 [3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions with Categorical and Continuous Data." Working Paper. (2000) [4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional Distributions Annals of Economics and Finance 5, 211-235 (2004) [5] Liu, R., Yang, L. "Kernel estimation of multivariate cumulative distribution function." Journal of Nonparametric Statistics (2008) [6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF with Categorical and Continuous Data." Working Paper [7] Li, Q., Racine, J. "Cross-validated local linear nonparametric regression" Statistica Sinica 14(2004), pp. 485-512 [8] Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics [9] Racine, J., Hart, J., Li, Q., "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models", 2006, Econometric Reviews 25, 523-544 """ # TODO: make default behavior efficient=True above a certain n_obs import copy import numpy as np from scipy import optimize from scipy.stats.mstats import mquantiles from _kernel_base import GenericKDE, EstimatorSettings, gpke, \ LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR __all__ = ['KernelReg', 'KernelCensoredReg'] class KernelReg(GenericKDE): """ Nonparametric kernel regression class. Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``. Note that the "local constant" type of regression provided here is also known as Nadaraya-Watson kernel regression; "local linear" is an extension of that which suffers less from bias issues at the edge of the support. Parameters ---------- endog: list with one element which is array_like This is the dependent variable. exog: list The training data for the independent variable(s) Each element in the list is a separate variable var_type: str The type of the variables, one character per variable: - c: continuous - u: unordered (discrete) - o: ordered (discrete) reg_type: {'lc', 'll'}, optional Type of regression estimator. 'lc' means local constant and 'll' local Linear estimator. Default is 'll' bw: str or array_like, optional Either a user-specified bandwidth or the method for bandwidth selection. If a string, valid values are 'cv_ls' (least-squares cross-validation) and 'aic' (AIC Hurvich bandwidth estimation). Default is 'cv_ls'. defaults: EstimatorSettings instance, optional The default values for the efficient bandwidth estimation. Attributes --------- bw: array_like The bandwidth parameters. **Methods** r-squared : calculates the R-Squared coefficient for the model. fit : calculates the conditional mean and marginal effects. """ def __init__(self, endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=EstimatorSettings()): self.var_type = var_type self.data_type = var_type self.reg_type = reg_type self.k_vars = len(self.var_type) self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, self.k_vars) self.data = np.column_stack((self.endog, self.exog)) self.nobs = np.shape(self.exog)[0] self.bw_func = dict(cv_ls=self.cv_loo, aic=self.aic_hurvich) self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) self._set_defaults(defaults) if not self.efficient: self.bw = self._compute_reg_bw(bw) else: self.bw = self._compute_efficient(bw) def _compute_reg_bw(self, bw): if not isinstance(bw, basestring): self._bw_method = "user-specified" return np.asarray(bw) else: # The user specified a bandwidth selection method e.g. 'cv_ls' self._bw_method = bw res = self.bw_func[bw] X = np.std(self.exog, axis=0) h0 = 1.06 * X * \ self.nobs ** (- 1. / (4 + np.size(self.exog, axis=1))) func = self.est[self.reg_type] bw_estimated = optimize.fmin(res, x0=h0, args=(func, ), maxiter=1e3, maxfun=1e3, disp=0) return bw_estimated def _est_loc_linear(self, bw, endog, exog, data_predict): """ Local linear estimator of g(x) in the regression ``y = g(x) + e``. Parameters ---------- bw: array_like Vector of bandwidth value(s). endog: 1D array_like The dependent variable. exog: 1D or 2D array_like The independent variable(s). data_predict: 1D array_like of length K, where K is the number of variables. The point at which the density is estimated. Returns ------- D_x: array_like The value of the conditional mean at `data_predict`. Notes ----- See p. 81 in [1] and p.38 in [2] for the formulas. Unlike other methods, this one requires that `data_predict` be 1D. """ nobs, k_vars = exog.shape ker = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, #ukertype='aitchison_aitken_reg', #okertype='wangryzin_reg', tosum=False) / float(nobs) # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij # See also p. 38 in [2] #ix_cont = np.arange(self.k_vars) # Use all vars instead of continuous only # Note: because ix_cont was defined here such that it selected all # columns, I removed the indexing with it from exog/data_predict. # Convert ker to a 2-D array to make matrix operations below work ker = ker[:, np.newaxis] M12 = exog - data_predict M22 = np.dot(M12.T, M12 * ker) M12 = (M12 * ker).sum(axis=0) M = np.empty((k_vars + 1, k_vars + 1)) M[0, 0] = ker.sum() M[0, 1:] = M12 M[1:, 0] = M12 M[1:, 1:] = M22 ker_endog = ker * endog V = np.empty((k_vars + 1, 1)) V[0, 0] = ker_endog.sum() V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) mean_mfx = np.dot(np.linalg.pinv(M), V) mean = mean_mfx[0] mfx = mean_mfx[1:, :] return mean, mfx def _est_loc_constant(self, bw, endog, exog, data_predict): """ Local constant estimator of g(x) in the regression y = g(x) + e Parameters ---------- bw : array_like Array of bandwidth value(s). endog : 1D array_like The dependent variable. exog : 1D or 2D array_like The independent variable(s). data_predict : 1D or 2D array_like The point(s) at which the density is estimated. Returns ------- G : ndarray The value of the conditional mean at `data_predict`. B_x : ndarray The marginal effects. """ ker_x = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, #ukertype='aitchison_aitken_reg', #okertype='wangryzin_reg', tosum=False) ker_x = np.reshape(ker_x, np.shape(endog)) G_numer = (ker_x * endog).sum(axis=0) G_denom = ker_x.sum(axis=0) G = G_numer / G_denom nobs = exog.shape[0] f_x = G_denom / float(nobs) ker_xc = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, ckertype='d_gaussian', #okertype='wangryzin_reg', tosum=False) ker_xc = ker_xc[:, np.newaxis] d_mx = -(endog * ker_xc).sum(axis=0) / float(nobs) #* np.prod(bw[:, ix_cont])) d_fx = -ker_xc.sum(axis=0) / float(nobs) #* np.prod(bw[:, ix_cont])) B_x = d_mx / f_x - G * d_fx / f_x B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2) #B_x = (f_x * d_mx - m_x * d_fx) / (f_x ** 2) return G, B_x def aic_hurvich(self, bw, func=None): """ Computes the AIC Hurvich criteria for the estimation of the bandwidth. Parameters ---------- bw : str or array_like See the ``bw`` parameter of `KernelReg` for details. Returns ------- aic : ndarray The AIC Hurvich criteria, one element for each variable. func : None Unused here, needed in signature because it's used in `cv_loo`. References ---------- See ch.2 in [1] and p.35 in [2]. """ H = np.empty((self.nobs, self.nobs)) for j in range(self.nobs): H[:, j] = gpke(bw, data=self.exog, data_predict=self.exog[j,:], var_type=self.var_type, tosum=False) denom = H.sum(axis=1) H = H / denom gx = KernelReg(endog=self.endog, exog=self.exog, var_type=self.var_type, reg_type=self.reg_type, bw=bw, defaults=EstimatorSettings(efficient=False)).fit()[0] gx = np.reshape(gx, (self.nobs, 1)) sigma = ((self.endog - gx)**2).sum(axis=0) / float(self.nobs) frac = (1 + np.trace(H) / float(self.nobs)) / \ (1 - (np.trace(H) + 2) / float(self.nobs)) #siga = np.dot(self.endog.T, (I - H).T) #sigb = np.dot((I - H), self.endog) #sigma = np.dot(siga, sigb) / float(self.nobs) aic = np.log(sigma) + frac return aic def cv_loo(self, bw, func): """ The cross-validation function with leave-one-out estimator. Parameters ---------- bw: array_like Vector of bandwidth values. func: callable function Returns the estimator of g(x). Can be either ``_est_loc_constant`` (local constant) or ``_est_loc_linear`` (local_linear). Returns ------- L: float The value of the CV function. Notes ----- Calculates the cross-validation least-squares function. This function is minimized by compute_bw to calculate the optimal value of `bw`. For details see p.35 in [2] ..math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) and :math:`h` is the vector of bandwidths """ LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() L = 0 for ii, X_not_i in enumerate(LOO_X): Y = LOO_Y.next() G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :])[0] L += (self.endog[ii] - G) ** 2 # Note: There might be a way to vectorize this. See p.72 in [1] return L / self.nobs def r_squared(self): r""" Returns the R-Squared for the nonparametric regression. Notes ----- For more details see p.45 in [2] The R-Squared is calculated by: .. math:: R^{2}=\frac{\left[\sum_{i=1}^{n} (Y_{i}-\bar{y})(\hat{Y_{i}}-\bar{y}\right]^{2}}{\sum_{i=1}^{n} (Y_{i}-\bar{y})^{2}\sum_{i=1}^{n}(\hat{Y_{i}}-\bar{y})^{2}}, where :math:`\hat{Y_{i}}` is the mean calculated in `fit` at the exog points. """ Y = np.squeeze(self.endog) Yhat = self.fit()[0] Y_bar = np.mean(Yhat) R2_numer = (((Y - Y_bar) * (Yhat - Y_bar)).sum())**2 R2_denom = ((Y - Y_bar)**2).sum(axis=0) * \ ((Yhat - Y_bar)**2).sum(axis=0) return R2_numer / R2_denom def fit(self, data_predict=None): """ Returns the mean and marginal effects at the `data_predict` points. Parameters ---------- data_predict : array_like, optional Points at which to return the mean and marginal effects. If not given, ``data_predict == exog``. Returns ------- mean : ndarray The regression result for the mean (i.e. the actual curve). mfx : ndarray The marginal effects, i.e. the partial derivatives of the mean. """ func = self.est[self.reg_type] if data_predict is None: data_predict = self.exog else: data_predict = _adjust_shape(data_predict, self.k_vars) N_data_predict = np.shape(data_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.k_vars)) for i in xrange(N_data_predict): mean_mfx = func(self.bw, self.endog, self.exog, data_predict=data_predict[i, :]) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx def sig_test(self, var_pos, nboot=50, nested_res=25, pivot=False): """ Significance test for the variables in the regression. Parameters ---------- var_pos: sequence The position of the variable in exog to be tested. Returns ------- sig: str The level of significance: - `*` : at 90% confidence level - `**` : at 95% confidence level - `***` : at 99* confidence level - "Not Significant" : if not significant """ var_pos = np.asarray(var_pos) ix_cont, ix_ord, ix_unord = _get_type_pos(self.var_type) if np.any(ix_cont[var_pos]): if np.any(ix_ord[var_pos]) or np.any(ix_unord[var_pos]): raise "Discrete variable in hypothesis. Must be continuous" Sig = TestRegCoefC(self, var_pos, nboot, nested_res, pivot) else: Sig = TestRegCoefD(self, var_pos, nboot) return Sig.sig def __repr__(self): """Provide something sane to print.""" rpr = "KernelReg instance\n" rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" rpr += "Number of samples: N = " + str(self.nobs) + "\n" rpr += "Variable types: " + self.var_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" rpr += "Estimator type: " + self.reg_type + "\n" return rpr def _get_class_vars_type(self): """Helper method to be able to pass needed vars to _compute_subset.""" class_type = 'KernelReg' class_vars = (self.var_type, self.k_vars, self.reg_type) return class_type, class_vars def _compute_dispersion(self, data): """ Computes the measure of dispersion. The minimum of the standard deviation and interquartile range / 1.349 References ---------- See the user guide for the np package in R. In the notes on bwscaling option in npreg, npudens, npcdens there is a discussion on the measure of dispersion """ data = data[:, 1:] return _compute_min_std_IQR(data) class KernelCensoredReg(KernelReg): """ Nonparametric censored regression. Calculates the condtional mean ``E[y|X]`` where ``y = g(X) + e``, where y is left-censored. Left censored variable Y is defined as ``Y = min {Y', L}`` where ``L`` is the value at which ``Y`` is censored and ``Y'`` is the true value of the variable. Parameters ---------- endog: list with one element which is array_like This is the dependent variable. exog: list The training data for the independent variable(s) Each element in the list is a separate variable dep_type: str The type of the dependent variable(s) c: Continuous u: Unordered (Discrete) o: Ordered (Discrete) reg_type: str Type of regression estimator lc: Local Constant Estimator ll: Local Linear Estimator bw: array_like Either a user-specified bandwidth or the method for bandwidth selection. cv_ls: cross-validaton least squares aic: AIC Hurvich Estimator censor_val: float Value at which the dependent variable is censored defaults: EstimatorSettings instance, optional The default values for the efficient bandwidth estimation Attributes --------- bw: array_like The bandwidth parameters *Methods* r-squared : calculates the R-Squared coefficient for the model. fit : calculates the conditional mean and marginal effects. """ def __init__(self, endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=EstimatorSettings()): self.var_type = var_type self.data_type = var_type self.reg_type = reg_type self.k_vars = len(self.var_type) self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, self.k_vars) self.data = np.column_stack((self.endog, self.exog)) self.nobs = np.shape(self.exog)[0] self.bw_func = dict(cv_ls=self.cv_loo, aic=self.aic_hurvich) self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) self._set_defaults(defaults) self.censor_val = censor_val if self.censor_val is not None: self.censored(censor_val) else: self.W_in = np.ones((self.nobs, 1)) if not self.efficient: self.bw = self._compute_reg_bw(bw) else: self.bw = self._compute_efficient(bw) def censored(self, censor_val): # see pp. 341-344 in [1] self.d = (self.endog != censor_val) * 1. ix = np.argsort(np.squeeze(self.endog)) self.sortix = ix self.sortix_rev = np.zeros(ix.shape, int) self.sortix_rev[ix] = np.arange(len(ix)) self.endog = np.squeeze(self.endog[ix]) self.endog = _adjust_shape(self.endog, 1) self.exog = np.squeeze(self.exog[ix]) self.d = np.squeeze(self.d[ix]) self.W_in = np.empty((self.nobs, 1)) for i in xrange(1, self.nobs + 1): P=1 for j in xrange(1, i): P *= ((self.nobs - j)/(float(self.nobs)-j+1))**self.d[j-1] self.W_in[i-1,0] = P * self.d[i-1] / (float(self.nobs) - i + 1 ) def __repr__(self): """Provide something sane to print.""" rpr = "KernelCensoredReg instance\n" rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" rpr += "Number of samples: nobs = " + str(self.nobs) + "\n" rpr += "Variable types: " + self.var_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" rpr += "Estimator type: " + self.reg_type + "\n" return rpr def _est_loc_linear(self, bw, endog, exog, data_predict, W): """ Local linear estimator of g(x) in the regression ``y = g(x) + e``. Parameters ---------- bw: array_like Vector of bandwidth value(s) endog: 1D array_like The dependent variable exog: 1D or 2D array_like The independent variable(s) data_predict: 1D array_like of length K, where K is the number of variables. The point at which the density is estimated Returns ------- D_x: array_like The value of the conditional mean at data_predict Notes ----- See p. 81 in [1] and p.38 in [2] for the formulas Unlike other methods, this one requires that data_predict be 1D """ nobs, k_vars = exog.shape ker = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, ukertype='aitchison_aitken_reg', okertype='wangryzin_reg', tosum=False) # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij # See also p. 38 in [2] # Convert ker to a 2-D array to make matrix operations below work ker = W * ker[:, np.newaxis] M12 = exog - data_predict M22 = np.dot(M12.T, M12 * ker) M12 = (M12 * ker).sum(axis=0) M = np.empty((k_vars + 1, k_vars + 1)) M[0, 0] = ker.sum() M[0, 1:] = M12 M[1:, 0] = M12 M[1:, 1:] = M22 ker_endog = ker * endog V = np.empty((k_vars + 1, 1)) V[0, 0] = ker_endog.sum() V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) mean_mfx = np.dot(np.linalg.pinv(M), V) mean = mean_mfx[0] mfx = mean_mfx[1:, :] return mean, mfx def cv_loo(self, bw, func): """ The cross-validation function with leave-one-out estimator Parameters ---------- bw: array_like Vector of bandwidth values func: callable function Returns the estimator of g(x). Can be either ``_est_loc_constant`` (local constant) or ``_est_loc_linear`` (local_linear). Returns ------- L: float The value of the CV function Notes ----- Calculates the cross-validation least-squares function. This function is minimized by compute_bw to calculate the optimal value of bw For details see p.35 in [2] ..math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) and :math:`h` is the vector of bandwidths """ LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() LOO_W = LeaveOneOut(self.W_in).__iter__() L = 0 for ii, X_not_i in enumerate(LOO_X): Y = LOO_Y.next() w = LOO_W.next() G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :], W=w)[0] L += (self.endog[ii] - G) ** 2 # Note: There might be a way to vectorize this. See p.72 in [1] return L / self.nobs def fit(self, data_predict=None): """ Returns the marginal effects at the data_predict points. """ func = self.est[self.reg_type] if data_predict is None: data_predict = self.exog else: data_predict = _adjust_shape(data_predict, self.k_vars) N_data_predict = np.shape(data_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.k_vars)) for i in xrange(N_data_predict): mean_mfx = func(self.bw, self.endog, self.exog, data_predict=data_predict[i, :], W=self.W_in) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx class TestRegCoefC(object): """ Significance test for continuous variables in a nonparametric regression. The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis is ``dE(Y|X)/dX_not_i != 0``. Parameters ---------- model: KernelReg instance This is the nonparametric regression model whose elements are tested for significance. test_vars: tuple, list of integers, array_like index of position of the continuous variables to be tested for significance. E.g. (1,3,5) jointly tests variables at position 1,3 and 5 for significance. nboot: int Number of bootstrap samples used to determine the distribution of the test statistic in a finite sample. Default is 400 nested_res: int Number of nested resamples used to calculate lambda. Must enable the pivot option pivot: bool Pivot the test statistic by dividing by its standard error Significantly increases computational time. But pivot statistics have more desirable properties (See references) Attributes ---------- sig: str The significance level of the variable(s) tested "Not Significant": Not significant at the 90% confidence level Fails to reject the null "*": Significant at the 90% confidence level "**": Significant at the 95% confidence level "***": Significant at the 99% confidence level Notes ----- This class allows testing of joint hypothesis as long as all variables are continuous. References ---------- Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business \& Economics Statistics. Chapter 12 in [1]. """ # Significance of continuous vars in nonparametric regression # Racine: Consistent Significance Testing for Nonparametric Regression # Journal of Business & Economics Statistics def __init__(self, model, test_vars, nboot=400, nested_res=400, pivot=False): self.nboot = nboot self.nres = nested_res self.test_vars = test_vars self.model = model self.bw = model.bw self.var_type = model.var_type self.k_vars = len(self.var_type) self.endog = model.endog self.exog = model.exog self.gx = model.est[model.reg_type] self.test_vars = test_vars self.pivot = pivot self.run() def run(self): self.test_stat = self._compute_test_stat(self.endog, self.exog) self.sig = self._compute_sig() def _compute_test_stat(self, Y, X): """ Computes the test statistic. See p.371 in [8]. """ lam = self._compute_lambda(Y, X) t = lam if self.pivot: se_lam = self._compute_se_lambda(Y, X) t = lam / float(se_lam) return t def _compute_lambda(self, Y, X): """Computes only lambda -- the main part of the test statistic""" n = np.shape(X)[0] Y = _adjust_shape(Y, 1) X = _adjust_shape(X, self.k_vars) b = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)).fit()[1] b = b[:, self.test_vars] b = np.reshape(b, (n, len(self.test_vars))) #fct = np.std(b) # Pivot the statistic by dividing by SE fct = 1. # Don't Pivot -- Bootstrapping works better if Pivot lam = ((b / fct) ** 2).sum() / float(n) return lam def _compute_se_lambda(self, Y, X): """ Calculates the SE of lambda by nested resampling Used to pivot the statistic. Bootstrapping works better with estimating pivotal statistics but slows down computation significantly. """ n = np.shape(Y)[0] lam = np.empty(shape=(self.nres, )) for i in xrange(self.nres): ind = np.random.random_integers(0, n-1, size=(n,1)) Y1 = Y[ind, 0] X1 = X[ind, :] lam[i] = self._compute_lambda(Y1, X1) se_lambda = np.std(lam) return se_lambda def _compute_sig(self): """ Computes the significance value for the variable(s) tested. The empirical distribution of the test statistic is obtained through bootstrapping the sample. The null hypothesis is rejected if the test statistic is larger than the 90, 95, 99 percentiles. """ t_dist = np.empty(shape=(self.nboot, )) Y = self.endog X = copy.deepcopy(self.exog) n = np.shape(Y)[0] X[:, self.test_vars] = np.mean(X[:, self.test_vars], axis=0) # Calculate the restricted mean. See p. 372 in [8] M = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)).fit()[0] M = np.reshape(M, (n, 1)) e = Y - M e = e - np.mean(e) # recenter residuals for i in xrange(self.nboot): ind = np.random.random_integers(0, n-1, size=(n,1)) e_boot = e[ind, 0] Y_boot = M + e_boot t_dist[i] = self._compute_test_stat(Y_boot, self.exog) self.t_dist = t_dist sig = "Not Significant" if self.test_stat > mquantiles(t_dist, 0.9): sig = "*" if self.test_stat > mquantiles(t_dist, 0.95): sig = "**" if self.test_stat > mquantiles(t_dist, 0.99): sig = "***" return sig class TestRegCoefD(TestRegCoefC): """ Significance test for the categorical variables in a nonparametric regression. Parameters ---------- model: Instance of KernelReg class This is the nonparametric regression model whose elements are tested for significance. test_vars: tuple, list of one element index of position of the discrete variable to be tested for significance. E.g. (3) tests variable at position 3 for significance. nboot: int Number of bootstrap samples used to determine the distribution of the test statistic in a finite sample. Default is 400 Attributes ---------- sig: str The significance level of the variable(s) tested "Not Significant": Not significant at the 90% confidence level Fails to reject the null "*": Significant at the 90% confidence level "**": Significant at the 95% confidence level "***": Significant at the 99% confidence level Notes ----- This class currently doesn't allow joint hypothesis. Only one variable can be tested at a time References ---------- See [9] and chapter 12 in [1]. """ def _compute_test_stat(self, Y, X): """Computes the test statistic""" dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) n = np.shape(X)[0] model = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)) X1 = copy.deepcopy(X) X1[:, self.test_vars] = 0 m0 = model.fit(data_predict=X1)[0] m0 = np.reshape(m0, (n, 1)) I = np.zeros((n, 1)) for i in dom_x[1:] : X1[:, self.test_vars] = i m1 = model.fit(data_predict=X1)[0] m1 = np.reshape(m1, (n, 1)) I += (m1 - m0) ** 2 I = I.sum(axis=0) / float(n) return I def _compute_sig(self): """Calculates the significance level of the variable tested""" m = self._est_cond_mean() Y = self.endog X = self.exog n = np.shape(X)[0] u = Y - m u = u - np.mean(u) # center fct1 = (1 - 5**0.5) / 2. fct2 = (1 + 5**0.5) / 2. u1 = fct1 * u u2 = fct2 * u r = fct2 / (5 ** 0.5) I_dist = np.empty((self.nboot,1)) for j in xrange(self.nboot): u_boot = copy.deepcopy(u2) prob = np.random.uniform(0,1, size = (n,1)) ind = prob < r u_boot[ind] = u1[ind] Y_boot = m + u_boot I_dist[j] = self._compute_test_stat(Y_boot, X) sig = "Not Significant" if self.test_stat > mquantiles(I_dist, 0.9): sig = "*" if self.test_stat > mquantiles(I_dist, 0.95): sig = "**" if self.test_stat > mquantiles(I_dist, 0.99): sig = "***" return sig def _est_cond_mean(self): """ Calculates the expected conditional mean m(X, Z=l) for all possible l """ self.dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) X = copy.deepcopy(self.exog) m=0 for i in self.dom_x: X[:, self.test_vars] = i m += self.model.fit(data_predict = X)[0] m = m / float(len(self.dom_x)) m = np.reshape(m, (np.shape(self.exog)[0], 1)) return m statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/kernels.py000066400000000000000000000143351224417117700254010ustar00rootroot00000000000000""" Module of kernels that are able to handle continuous as well as categorical variables (both ordered and unordered). This is a slight deviation from the current approach in statsmodels.nonparametric.kernels where each kernel is a class object. Having kernel functions rather than classes makes extension to a multivariate kernel density estimation much easier. NOTE: As it is, this module does not interact with the existing API """ import numpy as np from scipy.special import erf #TODO: # - make sure we only receive int input for wang-ryzin and aitchison-aitken # - Check for the scalar Xi case everywhere def aitchison_aitken(h, Xi, x, num_levels=None): """ The Aitchison-Aitken kernel, used for unordered discrete random variables. Parameters ---------- h : 1-D ndarray, shape (K,) The bandwidths used to estimate the value of the kernel function. Xi : 2-D ndarray of ints, shape (nobs, K) The value of the training set. x: 1-D ndarray, shape (K,) The value at which the kernel density is being estimated. num_levels: bool, optional Gives the user the option to specify the number of levels for the random variable. If False, the number of levels is calculated from the data. Returns ------- kernel_value : ndarray, shape (nobs, K) The value of the kernel function at each training point for each var. Notes ----- See p.18 of [2]_ for details. The value of the kernel L if :math:`X_{i}=x` is :math:`1-\lambda`, otherwise it is :math:`\frac{\lambda}{c-1}`. Here :math:`c` is the number of levels plus one of the RV. References ---------- .. [1] J. Aitchison and C.G.G. Aitken, "Multivariate binary discrimination by the kernel method", Biometrika, vol. 63, pp. 413-420, 1976. .. [2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88., 2008. """ Xi = Xi.reshape(Xi.size) # seems needed in case Xi is scalar if num_levels is None: num_levels = np.asarray(np.unique(Xi).size) kernel_value = np.ones(Xi.size) * h / (num_levels - 1) idx = Xi == x kernel_value[idx] = (idx * (1 - h))[idx] return kernel_value def wang_ryzin(h, Xi, x): """ The Wang-Ryzin kernel, used for ordered discrete random variables. Parameters ---------- h : scalar or 1-D ndarray, shape (K,) The bandwidths used to estimate the value of the kernel function. Xi : ndarray of ints, shape (nobs, K) The value of the training set. x : scalar or 1-D ndarray of shape (K,) The value at which the kernel density is being estimated. Returns ------- kernel_value : ndarray, shape (nobs, K) The value of the kernel function at each training point for each var. Notes ----- See p. 19 in [1]_ for details. The value of the kernel L if :math:`X_{i}=x` is :math:`1-\lambda`, otherwise it is :math:`\frac{1-\lambda}{2}\lambda^{|X_{i}-x|}`, where :math:`\lambda` is the bandwidth. References ---------- .. [1] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88., 2008. http://dx.doi.org/10.1561/0800000009 .. [2] M.-C. Wang and J. van Ryzin, "A class of smooth estimators for discrete distributions", Biometrika, vol. 68, pp. 301-309, 1981. """ Xi = Xi.reshape(Xi.size) # seems needed in case Xi is scalar kernel_value = 0.5 * (1 - h) * (h ** abs(Xi - x)) idx = Xi == x kernel_value[idx] = (idx * (1 - h))[idx] return kernel_value def gaussian(h, Xi, x): """ Gaussian Kernel for continuous variables Parameters ---------- h : 1-D ndarray, shape (K,) The bandwidths used to estimate the value of the kernel function. Xi : 1-D ndarray, shape (K,) The value of the training set. x : 1-D ndarray, shape (K,) The value at which the kernel density is being estimated. Returns ------- kernel_value : ndarray, shape (nobs, K) The value of the kernel function at each training point for each var. """ return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.)) def gaussian_convolution(h, Xi, x): """ Calculates the Gaussian Convolution Kernel """ return (1. / np.sqrt(4 * np.pi)) * np.exp(- (Xi - x)**2 / (h**2 * 4.)) def wang_ryzin_convolution(h, Xi, Xj): # This is the equivalent of the convolution case with the Gaussian Kernel # However it is not exactly convolution. Think of a better name # References ordered = np.zeros(Xi.size) for x in np.unique(Xi): ordered += wang_ryzin(h, Xi, x) * wang_ryzin(h, Xj, x) return ordered def aitchison_aitken_convolution(h, Xi, Xj): Xi_vals = np.unique(Xi) ordered = np.zeros(Xi.size) num_levels = Xi_vals.size for x in Xi_vals: ordered += aitchison_aitken(h, Xi, x, num_levels=num_levels) * \ aitchison_aitken(h, Xj, x, num_levels=num_levels) return ordered def gaussian_cdf(h, Xi, x): return 0.5 * h * (1 + erf((x - Xi) / (h * np.sqrt(2)))) def aitchison_aitken_cdf(h, Xi, x_u): x_u = int(x_u) Xi_vals = np.unique(Xi) ordered = np.zeros(Xi.size) num_levels = Xi_vals.size for x in Xi_vals: if x <= x_u: #FIXME: why a comparison for unordered variables? ordered += aitchison_aitken(h, Xi, x, num_levels=num_levels) return ordered def wang_ryzin_cdf(h, Xi, x_u): ordered = np.zeros(Xi.size) for x in np.unique(Xi): if x <= x_u: ordered += wang_ryzin(h, Xi, x) return ordered def d_gaussian(h, Xi, x): # The derivative of the Gaussian Kernel return 2 * (Xi - x) * gaussian(h, Xi, x) / h**2 def aitchison_aitken_reg(h, Xi, x): """ A version for the Aitchison-Aitken kernel for nonparametric regression. Suggested by Li and Racine. """ kernel_value = np.ones(Xi.size) ix = Xi != x inDom = ix * h kernel_value[ix] = inDom[ix] return kernel_value def wang_ryzin_reg(h, Xi, x): """ A version for the Wang-Ryzin kernel for nonparametric regression. Suggested by Li and Racine in [1] ch.4 """ return h ** abs(Xi - x) statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/linbin.pyx000066400000000000000000000021731224417117700253760ustar00rootroot00000000000000#cython profile=True """ cython -a fast_linbin.pyx gcc -shared -pthread -fPIC -fwrapv -O2 -Wall -fno-strict-aliasing -I/usr/include/python2.7 -I/usr/local/lib/python2.7/dist-packages/numpy/core/include/ -o fast_linbin.so fast_linbin.c """ cimport cython cimport numpy as np import numpy as np ctypedef np.float64_t DOUBLE ctypedef np.int_t INT @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def fast_linbin(np.ndarray[DOUBLE] X, double a, double b, int M, int trunc=1): """ Linear Binning as described in Fan and Marron (1994) """ cdef: Py_ssize_t i, li_i int nobs = X.shape[0] double delta = (b - a)/(M - 1) np.ndarray[DOUBLE] gcnts = np.zeros(M, np.float) np.ndarray[DOUBLE] lxi = (X - a)/delta np.ndarray[INT] li = lxi.astype(int) np.ndarray[DOUBLE] rem = lxi - li for i in range(nobs): li_i = li[i] if li_i > 1 and li_i < M: gcnts[li_i] = gcnts[li_i] + 1 - rem[i] gcnts[li_i+1] = gcnts[li_i+1] + rem[i] if li_i > M and trunc == 0: gcnts[M] = gcnts[M] + 1 return gcnts statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/smoothers_lowess.py000066400000000000000000000154411224417117700273540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Lowess - wrapper for cythonized extension Author : Chris Jordan-Squire Author : Carl Vogel Author : Josef Perktold """ import numpy as np from ._smoothers_lowess import lowess as _lowess def lowess(endog, exog, frac=2.0/3.0, it=3, delta=0.0, is_sorted=False, missing='drop', return_sorted=True): '''LOWESS (Locally Weighted Scatterplot Smoothing) A lowess function that outs smoothed estimates of endog at the given exog values from points (exog, endog) Parameters ---------- endog: 1-D numpy array The y-values of the observed points exog: 1-D numpy array The x-values of the observed points frac: float Between 0 and 1. The fraction of the data used when estimating each y-value. it: int The number of residual-based reweightings to perform. delta: float Distance within which to use linear-interpolation instead of weighted regression. is_sorted : bool If False (default), then the data will be sorted by exog before calculating lowess. If True, then it is assumed that the data is already sorted by exog. missing : str Available options are 'none', 'drop', and 'raise'. If 'none', no nan checking is done. If 'drop', any observations with nans are dropped. If 'raise', an error is raised. Default is 'drop'. return_sorted : bool If True (default), then the returned array is sorted by exog and has missing (nan or infinite) observations removed. If False, then the returned array is in the same length and the same sequence of observations as the input array. Returns ------- out: ndarray, float The returned array is two-dimensional if return_sorted is True, and one dimensional if return_sorted is False. If return_sorted is True, then a numpy array with two columns. The first column contains the sorted x (exog) values and the second column the associated estimated y (endog) values. If return_sorted is False, then only the fitted values are returned, and the observations will be in the same order as the input arrays. Notes ----- This lowess function implements the algorithm given in the reference below using local linear estimates. Suppose the input data has N points. The algorithm works by estimating the `smooth` y_i by taking the frac*N closest points to (x_i,y_i) based on their x values and estimating y_i using a weighted linear regression. The weight for (x_j,y_j) is tricube function applied to abs(x_i-x_j). If it > 1, then further weighted local linear regressions are performed, where the weights are the same as above times the _lowess_bisquare function of the residuals. Each iteration takes approximately the same amount of time as the original fit, so these iterations are expensive. They are most useful when the noise has extremely heavy tails, such as Cauchy noise. Noise with less heavy-tails, such as t-distributions with df>2, are less problematic. The weights downgrade the influence of points with large residuals. In the extreme case, points whose residuals are larger than 6 times the median absolute residual are given weight 0. `delta` can be used to save computations. For each `x_i`, regressions are skipped for points closer than `delta`. The next regression is fit for the farthest point within delta of `x_i` and all points in between are estimated by linearly interpolating between the two regression fits. Judicious choice of delta can cut computation time considerably for large data (N > 5000). A good choice is ``delta = 0.01 * range(exog)``. Some experimentation is likely required to find a good choice of `frac` and `iter` for a particular dataset. References ---------- Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836. Examples -------- The below allows a comparison between how different the fits from lowess for different values of frac can be. >>> import numpy as np >>> import statsmodels.api as sm >>> lowess = sm.nonparametric.lowess >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500) >>> y = np.sin(x) + np.random.normal(size=len(x)) >>> z = lowess(y, x) >>> w = lowess(y, x, frac=1./3) This gives a similar comparison for when it is 0 vs not. >>> import numpy as np >>> import scipy.stats as stats >>> import statsmodels.api as sm >>> lowess = sm.nonparametric.lowess >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500) >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x)) >>> z = lowess(y, x, frac= 1./3, it=0) >>> w = lowess(y, x, frac=1./3) ''' endog = np.asarray(endog, float) exog = np.asarray(exog, float) # Inputs should be vectors (1-D arrays) of the # same length. if exog.ndim != 1: raise ValueError('exog must be a vector') if endog.ndim != 1: raise ValueError('endog must be a vector') if endog.shape[0] != exog.shape[0] : raise ValueError('exog and endog must have same length') if missing in ['drop', 'raise']: # Cut out missing values mask_valid = (np.isfinite(exog) & np.isfinite(endog)) all_valid = np.all(mask_valid) if all_valid: y = endog x = exog else: if missing == 'drop': x = exog[mask_valid] y = endog[mask_valid] else: raise ValueError('nan or inf found in data') elif missing == 'none': y = endog x = exog all_valid = True # we assume it's true if missing='none' else: raise ValueError("missing can only be 'none', 'drop' or 'raise'") if not is_sorted: # Sort both inputs according to the ascending order of x values sort_index = np.argsort(x) x = np.array(x[sort_index]) y = np.array(y[sort_index]) res = _lowess(y, x, frac=frac, it=it, delta=delta) _, yfitted = res.T if return_sorted: return res else: # rebuild yfitted with original indices # a bit messy: y might have been selected twice if not is_sorted: yfitted_ = np.empty_like(y) yfitted_.fill(np.nan) yfitted_[sort_index] = yfitted yfitted = yfitted_ else: yfitted = yfitted if not all_valid: yfitted_ = np.empty_like(endog) yfitted_.fill(np.nan) yfitted_[mask_valid] = yfitted yfitted = yfitted_ # we don't need to return exog anymore return yfitted statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/smoothers_lowess_old.py000066400000000000000000000242041224417117700302070ustar00rootroot00000000000000""" Univariate lowess function, like in R. References ---------- Hastie, Tibshirani, Friedman. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition: Chapter 6. Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836. """ import numpy as np from scipy.linalg import lstsq def lowess(endog, exog, frac=2./3, it=3): """ LOWESS (Locally Weighted Scatterplot Smoothing) A lowess function that outs smoothed estimates of endog at the given exog values from points (exog, endog) Parameters ---------- endog: 1-D numpy array The y-values of the observed points exog: 1-D numpy array The x-values of the observed points frac: float Between 0 and 1. The fraction of the data used when estimating each y-value. it: int The number of residual-based reweightings to perform. Returns ------- out: numpy array A numpy array with two columns. The first column is the sorted x values and the second column the associated estimated y-values. Notes ----- This lowess function implements the algorithm given in the reference below using local linear estimates. Suppose the input data has N points. The algorithm works by estimating the true ``y_i`` by taking the frac*N closest points to ``(x_i,y_i)`` based on their x values and estimating ``y_i`` using a weighted linear regression. The weight for ``(x_j,y_j)`` is `_lowess_tricube` function applied to ``|x_i-x_j|``. If ``iter > 0``, then further weighted local linear regressions are performed, where the weights are the same as above times the `_lowess_bisquare` function of the residuals. Each iteration takes approximately the same amount of time as the original fit, so these iterations are expensive. They are most useful when the noise has extremely heavy tails, such as Cauchy noise. Noise with less heavy-tails, such as t-distributions with ``df > 2``, are less problematic. The weights downgrade the influence of points with large residuals. In the extreme case, points whose residuals are larger than 6 times the median absolute residual are given weight 0. Some experimentation is likely required to find a good choice of frac and iter for a particular dataset. References ---------- Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836. Examples -------- The below allows a comparison between how different the fits from `lowess` for different values of frac can be. >>> import numpy as np >>> import statsmodels.api as sm >>> lowess = sm.nonparametric.lowess >>> x = np.random.uniform(low=-2*np.pi, high=2*np.pi, size=500) >>> y = np.sin(x) + np.random.normal(size=len(x)) >>> z = lowess(y, x) >>> w = lowess(y, x, frac=1./3) This gives a similar comparison for when it is 0 vs not. >>> import scipy.stats as stats >>> x = np.random.uniform(low=-2*np.pi, high=2*np.pi, size=500) >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x)) >>> z = lowess(y, x, frac= 1./3, it=0) >>> w = lowess(y, x, frac=1./3) """ x = exog if exog.ndim != 1: raise ValueError('exog must be a vector') if endog.ndim != 1: raise ValueError('endog must be a vector') if endog.shape[0] != x.shape[0] : raise ValueError('exog and endog must have same length') n = exog.shape[0] fitted = np.zeros(n) k = int(frac * n) index_array = np.argsort(exog) x_copy = np.array(exog[index_array]) #, dtype ='float32') y_copy = endog[index_array] fitted, weights = _lowess_initial_fit(x_copy, y_copy, k, n) for i in xrange(it): _lowess_robustify_fit(x_copy, y_copy, fitted, weights, k, n) out = np.array([x_copy, fitted]).T out.shape = (n,2) return out def _lowess_initial_fit(x_copy, y_copy, k, n): """ The initial weighted local linear regression for lowess. Parameters ---------- x_copy : 1-d ndarray The x-values/exogenous part of the data being smoothed y_copy : 1-d ndarray The y-values/ endogenous part of the data being smoothed k : int The number of data points which affect the linear fit for each estimated point n : int The total number of points Returns ------- fitted : 1-d ndarray The fitted y-values weights : 2-d ndarray An n by k array. The contribution to the weights in the local linear fit coming from the distances between the x-values """ weights = np.zeros((n,k), dtype = x_copy.dtype) nn_indices = [0,k] X = np.ones((k,2)) fitted = np.zeros(n) for i in xrange(n): #note: all _lowess functions are inplace, no return left_width = x_copy[i] - x_copy[nn_indices[0]] right_width = x_copy[nn_indices[1]-1] - x_copy[i] width = max(left_width, right_width) _lowess_wt_standardize(weights[i,:], x_copy[nn_indices[0]:nn_indices[1]], x_copy[i], width) _lowess_tricube(weights[i,:]) weights[i,:] = np.sqrt(weights[i,:]) X[:,1] = x_copy[nn_indices[0]:nn_indices[1]] y_i = weights[i,:] * y_copy[nn_indices[0]:nn_indices[1]] beta = lstsq(weights[i,:].reshape(k,1) * X, y_i)[0] fitted[i] = beta[0] + beta[1]*x_copy[i] _lowess_update_nn(x_copy, nn_indices, i+1) return fitted, weights def _lowess_wt_standardize(weights, new_entries, x_copy_i, width): """ The initial phase of creating the weights. Subtract the current x_i and divide by the width. Parameters ---------- weights : ndarray The memory where (new_entries - x_copy_i)/width will be placed new_entries : ndarray The x-values of the k closest points to x[i] x_copy_i : float x[i], the i'th point in the (sorted) x values width : float The maximum distance between x[i] and any point in new_entries Returns ------- Nothing. The modifications are made to weight in place. """ weights[:] = new_entries weights -= x_copy_i weights /= width def _lowess_robustify_fit(x_copy, y_copy, fitted, weights, k, n): """ Additional weighted local linear regressions, performed if iter>0. They take into account the sizes of the residuals, to eliminate the effect of extreme outliers. Parameters ---------- x_copy : 1-d ndarray The x-values/exogenous part of the data being smoothed y_copy : 1-d ndarray The y-values/ endogenous part of the data being smoothed fitted : 1-d ndarray The fitted y-values from the previous iteration weights : 2-d ndarray An n by k array. The contribution to the weights in the local linear fit coming from the distances between the x-values k : int The number of data points which affect the linear fit for each estimated point n : int The total number of points Returns ------- Nothing. The fitted values are modified in place. """ nn_indices = [0,k] X = np.ones((k,2)) residual_weights = np.copy(y_copy) residual_weights.shape = (n,) residual_weights -= fitted residual_weights = np.absolute(residual_weights)#, out=residual_weights) s = np.median(residual_weights) residual_weights /= (6*s) too_big = residual_weights>=1 _lowess_bisquare(residual_weights) residual_weights[too_big] = 0 for i in xrange(n): total_weights = weights[i,:] * np.sqrt(residual_weights[nn_indices[0]: nn_indices[1]]) X[:,1] = x_copy[nn_indices[0]:nn_indices[1]] y_i = total_weights * y_copy[nn_indices[0]:nn_indices[1]] total_weights.shape = (k,1) beta = lstsq(total_weights * X, y_i)[0] fitted[i] = beta[0] + beta[1] * x_copy[i] _lowess_update_nn(x_copy, nn_indices, i+1) def _lowess_update_nn(x, cur_nn,i): """ Update the endpoints of the nearest neighbors to the ith point. Parameters ---------- x : iterable The sorted points of x-values cur_nn : list of length 2 The two current indices between which are the k closest points to x[i]. (The actual value of k is irrelevant for the algorithm. i : int The index of the current value in x for which the k closest points are desired. Returns ------- Nothing. It modifies cur_nn in place. """ while True: if cur_nn[1]> data (Italy) self.Italy_gdp = \ [8.556, 12.262, 9.587, 8.119, 5.537, 6.796, 8.638, 6.483, 6.212, 5.111, 6.001, 7.027, 4.616, 3.922, 4.688, 3.957, 3.159, 3.763, 3.829, 5.242, 6.275, 8.518, 11.542, 9.348, 8.02, 5.527, 6.865, 8.666, 6.672, 6.289, 5.286, 6.271, 7.94, 4.72, 4.357, 4.672, 3.883, 3.065, 3.489, 3.635, 5.443, 6.302, 9.054, 12.485, 9.896, 8.33, 6.161, 7.055, 8.717, 6.95] self.Italy_year = \ [1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1953, 1953, 1953, 1953, 1953, 1953, 1953, 1953] # OECD panel data from NP R>> data(oecdpanel) self.growth = \ [-0.0017584, 0.00740688, 0.03424461, 0.03848719, 0.02932506, 0.03769199, 0.0466038, 0.00199456, 0.03679607, 0.01917304, -0.00221, 0.00787269, 0.03441118, -0.0109228, 0.02043064, -0.0307962, 0.02008947, 0.00580313, 0.00344502, 0.04706358, 0.03585851, 0.01464953, 0.04525762, 0.04109222, -0.0087903, 0.04087915, 0.04551403, 0.036916, 0.00369293, 0.0718669, 0.02577732, -0.0130759, -0.01656641, 0.00676429, 0.08833017, 0.05092105, 0.02005877, 0.00183858, 0.03903173, 0.05832116, 0.0494571, 0.02078484, 0.09213897, 0.0070534, 0.08677202, 0.06830603, -0.00041, 0.0002856, 0.03421225, -0.0036825] self.oecd = \ [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] class TestKDEMultivariate(MyTest): @dec.slow def test_pdf_mixeddata_CV_LS(self): dens_u = nparam.KDEMultivariate(data=[self.c1, self.o, self.o2], var_type='coo', bw='cv_ls') npt.assert_allclose(dens_u.bw, [0.70949447, 0.08736727, 0.09220476], atol=1e-6) # Matches R to 3 decimals; results seem more stable than with R. # Can be checked with following code: ## import rpy2.robjects as robjects ## from rpy2.robjects.packages import importr ## NP = importr('np') ## r = robjects.r ## D = {"S1": robjects.FloatVector(c1), "S2":robjects.FloatVector(c2), ## "S3":robjects.FloatVector(c3), "S4":robjects.FactorVector(o), ## "S5":robjects.FactorVector(o2)} ## df = robjects.DataFrame(D) ## formula = r('~S1+ordered(S4)+ordered(S5)') ## r_bw = NP.npudensbw(formula, data=df, bwmethod='cv.ls') def test_pdf_mixeddata_LS_vs_ML(self): dens_ls = nparam.KDEMultivariate(data=[self.c1, self.o, self.o2], var_type='coo', bw='cv_ls') dens_ml = nparam.KDEMultivariate(data=[self.c1, self.o, self.o2], var_type='coo', bw='cv_ml') npt.assert_allclose(dens_ls.bw, dens_ml.bw, atol=0, rtol=0.5) def test_pdf_mixeddata_CV_ML(self): # Test ML cross-validation dens_ml = nparam.KDEMultivariate(data=[self.c1, self.o, self.c2], var_type='coc', bw='cv_ml') R_bw = [1.021563, 2.806409e-14, 0.5142077] npt.assert_allclose(dens_ml.bw, R_bw, atol=0.1, rtol=0.1) @dec.slow def test_pdf_continuous(self): # Test for only continuous data dens = nparam.KDEMultivariate(data=[self.growth, self.Italy_gdp], var_type='cc', bw='cv_ls') # take the first data points from the training set sm_result = np.squeeze(dens.pdf()[0:5]) R_result = [1.6202284, 0.7914245, 1.6084174, 2.4987204, 1.3705258] ## CODE TO REPRODUCE THE RESULTS IN R ## library(np) ## data(oecdpanel) ## data (Italy) ## bw <-npudensbw(formula = ~oecdpanel$growth[1:50] + Italy$gdp[1:50], ## bwmethod ='cv.ls') ## fhat <- fitted(npudens(bws=bw)) ## fhat[1:5] npt.assert_allclose(sm_result, R_result, atol=1e-3) def test_pdf_ordered(self): # Test for only ordered data dens = nparam.KDEMultivariate(data=[self.oecd], var_type='o', bw='cv_ls') sm_result = np.squeeze(dens.pdf()[0:5]) R_result = [0.7236395, 0.7236395, 0.2763605, 0.2763605, 0.7236395] # lower tol here. only 2nd decimal npt.assert_allclose(sm_result, R_result, atol=1e-1) @dec.slow def test_unordered_CV_LS(self): dens = nparam.KDEMultivariate(data=[self.growth, self.oecd], var_type='cu', bw='cv_ls') R_result = [0.0052051, 0.05835941] npt.assert_allclose(dens.bw, R_result, atol=1e-2) def test_continuous_cdf(self, data_predict=None): dens = nparam.KDEMultivariate(data=[self.Italy_gdp, self.growth], var_type='cc', bw='cv_ml') sm_result = dens.cdf()[0:5] R_result = [0.192180770, 0.299505196, 0.557303666, 0.513387712, 0.210985350] npt.assert_allclose(sm_result, R_result, atol=1e-3) def test_mixeddata_cdf(self, data_predict=None): dens = nparam.KDEMultivariate(data=[self.Italy_gdp, self.oecd], var_type='cu', bw='cv_ml') sm_result = dens.cdf()[0:5] R_result = [0.54700010, 0.65907039, 0.89676865, 0.74132941, 0.25291361] npt.assert_allclose(sm_result, R_result, atol=1e-3) @dec.slow def test_continuous_cvls_efficient(self): nobs = 400 np.random.seed(12345) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 dens_efficient = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ls', defaults=nparam.EstimatorSettings(efficient=True, n_sub=100)) #dens = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ls', # defaults=nparam.EstimatorSettings(efficient=False)) #bw = dens.bw bw = np.array([0.3404, 0.1666]) npt.assert_allclose(bw, dens_efficient.bw, atol=0.1, rtol=0.2) @dec.slow def test_continuous_cvml_efficient(self): nobs = 400 np.random.seed(12345) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 dens_efficient = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ml', defaults=nparam.EstimatorSettings(efficient=True, n_sub=100)) #dens = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ml', # defaults=nparam.EstimatorSettings(efficient=False)) #bw = dens.bw bw = np.array([0.4471, 0.2861]) npt.assert_allclose(bw, dens_efficient.bw, atol=0.1, rtol = 0.2) @dec.slow def test_efficient_notrandom(self): nobs = 400 np.random.seed(12345) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 dens_efficient = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ml', defaults=nparam.EstimatorSettings(efficient=True, randomize=False, n_sub=100)) dens = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ml') npt.assert_allclose(dens.bw, dens_efficient.bw, atol=0.1, rtol = 0.2) class TestKDEMultivariateConditional(MyTest): @dec.slow def test_mixeddata_CV_LS(self): dens_ls = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.Italy_year], dep_type='c', indep_type='o', bw='cv_ls') # R result: [1.6448, 0.2317373] npt.assert_allclose(dens_ls.bw, [1.01203728, 0.31905144], atol=1e-5) def test_continuous_CV_ML(self): dens_ml = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.growth], dep_type='c', indep_type='c', bw='cv_ml') # Results from R npt.assert_allclose(dens_ml.bw, [0.5341164, 0.04510836], atol=1e-3) @dec.slow def test_unordered_CV_LS(self): dens_ls = nparam.KDEMultivariateConditional(endog=[self.oecd], exog=[self.growth], dep_type='u', indep_type='c', bw='cv_ls') # TODO: assert missing def test_pdf_continuous(self): # Hardcode here the bw that will be calculated is we had used # ``bw='cv_ml'``. That calculation is slow, and tested in other tests. bw_cv_ml = np.array([0.010043, 12095254.7]) # TODO: odd numbers (?!) dens = nparam.KDEMultivariateConditional(endog=[self.growth], exog=[self.Italy_gdp], dep_type='c', indep_type='c', bw=bw_cv_ml) sm_result = np.squeeze(dens.pdf()[0:5]) R_result = [11.97964, 12.73290, 13.23037, 13.46438, 12.22779] npt.assert_allclose(sm_result, R_result, atol=1e-3) @dec.slow def test_pdf_mixeddata(self): dens = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.Italy_year], dep_type='c', indep_type='o', bw='cv_ls') sm_result = np.squeeze(dens.pdf()[0:5]) #R_result = [0.08469226, 0.01737731, 0.05679909, 0.09744726, 0.15086674] expected = [0.08592089, 0.0193275, 0.05310327, 0.09642667, 0.171954] ## CODE TO REPRODUCE IN R ## library(np) ## data (Italy) ## bw <- npcdensbw(formula = ## Italy$gdp[1:50]~ordered(Italy$year[1:50]),bwmethod='cv.ls') ## fhat <- fitted(npcdens(bws=bw)) ## fhat[1:5] npt.assert_allclose(sm_result, expected, atol=0, rtol=1e-5) def test_continuous_normal_ref(self): # test for normal reference rule of thumb with continuous data dens_nm = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.growth], dep_type='c', indep_type='c', bw='normal_reference') sm_result = dens_nm.bw R_result = [1.283532, 0.01535401] # TODO: here we need a smaller tolerance.check! npt.assert_allclose(sm_result, R_result, atol=1e-1) def test_continuous_cdf(self): dens_nm = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.growth], dep_type='c', indep_type='c', bw='normal_reference') sm_result = dens_nm.cdf()[0:5] R_result = [0.81304920, 0.95046942, 0.86878727, 0.71961748, 0.38685423] npt.assert_allclose(sm_result, R_result, atol=1e-3) @dec.slow def test_mixeddata_cdf(self): dens = nparam.KDEMultivariateConditional(endog=[self.Italy_gdp], exog=[self.Italy_year], dep_type='c', indep_type='o', bw='cv_ls') sm_result = dens.cdf()[0:5] #R_result = [0.8118257, 0.9724863, 0.8843773, 0.7720359, 0.4361867] expected = [0.83378885, 0.97684477, 0.90655143, 0.79393161, 0.43629083] npt.assert_allclose(sm_result, expected, atol=0, rtol=1e-5) @dec.slow def test_continuous_cvml_efficient(self): nobs = 500 np.random.seed(12345) O = np.random.binomial(2, 0.5, size=(nobs, )) C1 = np.random.normal(size=(nobs, )) noise = np.random.normal(size=(nobs, )) b0 = 3 b1 = 1.2 b2 = 3.7 # regression coefficients Y = b0+ b1 * C1 + b2*O + noise dens_efficient = nparam.KDEMultivariateConditional(endog=[Y], exog=[C1], dep_type='c', indep_type='c', bw='cv_ml', defaults=nparam.EstimatorSettings(efficient=True, n_sub=50)) #dens = nparam.KDEMultivariateConditional(endog=[Y], exog=[C1], # dep_type='c', indep_type='c', bw='cv_ml') #bw = dens.bw bw_expected = np.array([0.73387, 0.43715]) npt.assert_allclose(dens_efficient.bw, bw_expected, atol=0, rtol=1e-3) statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/tests/test_kernel_regression.py000066400000000000000000000301171224417117700316530ustar00rootroot00000000000000import numpy as np import numpy.testing as npt import numpy.testing.decorators as dec import statsmodels.api as sm nparam = sm.nonparametric class MyTest(object): def setUp(self): nobs = 60 np.random.seed(123456) self.o = np.random.binomial(2, 0.7, size=(nobs, 1)) self.o2 = np.random.binomial(3, 0.7, size=(nobs, 1)) self.c1 = np.random.normal(size=(nobs, 1)) self.c2 = np.random.normal(10, 1, size=(nobs, 1)) self.c3 = np.random.normal(10, 2, size=(nobs, 1)) self.noise = np.random.normal(size=(nobs, 1)) b0 = 0.3 b1 = 1.2 b2 = 3.7 # regression coefficients self.y = b0 + b1 * self.c1 + b2 * self.c2 + self.noise self.y2 = b0 + b1 * self.c1 + b2 * self.c2 + self.o + self.noise # Italy data from R's np package (the first 50 obs) R>> data (Italy) self.Italy_gdp = \ [8.556, 12.262, 9.587, 8.119, 5.537, 6.796, 8.638, 6.483, 6.212, 5.111, 6.001, 7.027, 4.616, 3.922, 4.688, 3.957, 3.159, 3.763, 3.829, 5.242, 6.275, 8.518, 11.542, 9.348, 8.02, 5.527, 6.865, 8.666, 6.672, 6.289, 5.286, 6.271, 7.94, 4.72, 4.357, 4.672, 3.883, 3.065, 3.489, 3.635, 5.443, 6.302, 9.054, 12.485, 9.896, 8.33, 6.161, 7.055, 8.717, 6.95] self.Italy_year = \ [1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1953, 1953, 1953, 1953, 1953, 1953, 1953, 1953] # OECD panel data from NP R>> data(oecdpanel) self.growth = \ [-0.0017584, 0.00740688, 0.03424461, 0.03848719, 0.02932506, 0.03769199, 0.0466038, 0.00199456, 0.03679607, 0.01917304, -0.00221, 0.00787269, 0.03441118, -0.0109228, 0.02043064, -0.0307962, 0.02008947, 0.00580313, 0.00344502, 0.04706358, 0.03585851, 0.01464953, 0.04525762, 0.04109222, -0.0087903, 0.04087915, 0.04551403, 0.036916, 0.00369293, 0.0718669, 0.02577732, -0.0130759, -0.01656641, 0.00676429, 0.08833017, 0.05092105, 0.02005877, 0.00183858, 0.03903173, 0.05832116, 0.0494571, 0.02078484, 0.09213897, 0.0070534, 0.08677202, 0.06830603, -0.00041, 0.0002856, 0.03421225, -0.0036825] self.oecd = \ [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] def write2file(self, file_name, data): """Write some data to a csv file. Only use for debugging!""" import csv data_file = csv.writer(open(file_name, "w")) data = np.column_stack(data) nobs = max(np.shape(data)) K = min(np.shape(data)) data = np.reshape(data, (nobs,K)) for i in range(nobs): data_file.writerow(list(data[i, :])) class TestKernelReg(MyTest): def test_ordered_lc_cvls(self): model = nparam.KernelReg(endog=[self.Italy_gdp], exog=[self.Italy_year], reg_type='lc', var_type='o', bw='cv_ls') sm_bw = model.bw R_bw = 0.1390096 sm_mean, sm_mfx = model.fit() sm_mean = sm_mean[0:5] sm_mfx = sm_mfx[0:5] R_mean = 6.190486 sm_R2 = model.r_squared() R_R2 = 0.1435323 ## CODE TO REPRODUCE IN R ## library(np) ## data(Italy) ## attach(Italy) ## bw <- npregbw(formula=gdp[1:50]~ordered(year[1:50])) npt.assert_allclose(sm_bw, R_bw, atol=1e-2) npt.assert_allclose(sm_mean, R_mean, atol=1e-2) npt.assert_allclose(sm_R2, R_R2, atol=1e-2) def test_continuousdata_lc_cvls(self): model = nparam.KernelReg(endog=[self.y], exog=[self.c1, self.c2], reg_type='lc', var_type='cc', bw='cv_ls') # Bandwidth sm_bw = model.bw R_bw = [0.6163835, 0.1649656] # Conditional Mean sm_mean, sm_mfx = model.fit() sm_mean = sm_mean[0:5] sm_mfx = sm_mfx[0:5] R_mean = [31.49157, 37.29536, 43.72332, 40.58997, 36.80711] # R-Squared sm_R2 = model.r_squared() R_R2 = 0.956381720885 npt.assert_allclose(sm_bw, R_bw, atol=1e-2) npt.assert_allclose(sm_mean, R_mean, atol=1e-2) npt.assert_allclose(sm_R2, R_R2, atol=1e-2) def test_continuousdata_ll_cvls(self): model = nparam.KernelReg(endog=[self.y], exog=[self.c1, self.c2], reg_type='ll', var_type='cc', bw='cv_ls') sm_bw = model.bw R_bw = [1.717891, 2.449415] sm_mean, sm_mfx = model.fit() sm_mean = sm_mean[0:5] sm_mfx = sm_mfx[0:5] R_mean = [31.16003, 37.30323, 44.49870, 40.73704, 36.19083] sm_R2 = model.r_squared() R_R2 = 0.9336019 npt.assert_allclose(sm_bw, R_bw, atol=1e-2) npt.assert_allclose(sm_mean, R_mean, atol=1e-2) npt.assert_allclose(sm_R2, R_R2, atol=1e-2) def test_continuous_mfx_ll_cvls(self, file_name='RegData.csv'): nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) C3 = np.random.beta(0.5,0.2, size=(nobs,)) noise = np.random.normal(size=(nobs, )) b0 = 3 b1 = 1.2 b2 = 3.7 # regression coefficients b3 = 2.3 Y = b0+ b1 * C1 + b2*C2+ b3 * C3 + noise bw_cv_ls = np.array([0.96075, 0.5682, 0.29835]) model = nparam.KernelReg(endog=[Y], exog=[C1, C2, C3], reg_type='ll', var_type='ccc', bw=bw_cv_ls) sm_mean, sm_mfx = model.fit() sm_mean = sm_mean[0:5] npt.assert_allclose(sm_mfx[0,:], [b1,b2,b3], rtol=2e-1) def test_mixed_mfx_ll_cvls(self, file_name='RegData.csv'): nobs = 200 np.random.seed(1234) O = np.random.binomial(2, 0.5, size=(nobs, )) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) noise = np.random.normal(size=(nobs, )) b0 = 3 b1 = 1.2 b2 = 3.7 # regression coefficients b3 = 2.3 Y = b0+ b1 * C1 + b2*C2+ b3 * O + noise bw_cv_ls = np.array([1.04726, 1.67485, 0.39852]) model = nparam.KernelReg(endog=[Y], exog=[C1, C2, O], reg_type='ll', var_type='cco', bw=bw_cv_ls) sm_mean, sm_mfx = model.fit() sm_R2 = model.r_squared() # TODO: add expected result npt.assert_allclose(sm_mfx[0,:], [b1,b2,b3], rtol=2e-1) @dec.skipif(True, "Test doesn't make much sense. " "It would pass with very small bw.") def test_mfx_nonlinear_ll_cvls(self, file_name='RegData.csv'): #FIXME nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) C3 = np.random.beta(0.5,0.2, size=(nobs,)) noise = np.random.normal(size=(nobs, )) b0 = 3 b1 = 1.2 b3 = 2.3 Y = b0+ b1 * C1 * C2 + b3 * C3 + noise model = nparam.KernelReg(endog=[Y], exog=[C1, C2, C3], reg_type='ll', var_type='ccc', bw='cv_ls') sm_bw = model.bw sm_mean, sm_mfx = model.fit() sm_R2 = model.r_squared() # Theoretical marginal effects mfx1 = b1 * C2 mfx2 = b1 * C1 #npt.assert_allclose(sm_mfx[:,0], mfx1, rtol=2e-1) #npt.assert_allclose(sm_mfx[0:10,1], mfx2[0:10], rtol=2e-1) npt.assert_allclose(sm_mean, Y, rtol = 2e-1) @dec.slow def test_continuous_cvls_efficient(self): nobs = 500 np.random.seed(12345) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) b0 = 3 b1 = 1.2 b2 = 3.7 # regression coefficients Y = b0+ b1 * C1 + b2*C2 model_efficient = nparam.KernelReg(endog=[Y], exog=[C1], reg_type='lc', var_type='c', bw='cv_ls', defaults=nparam.EstimatorSettings(efficient=True, n_sub=100)) model = nparam.KernelReg(endog=[Y], exog=[C1], reg_type='ll', var_type='c', bw='cv_ls') npt.assert_allclose(model.bw, model_efficient.bw, atol=5e-2, rtol=1e-1) @dec.slow def test_censored_ll_cvls(self): nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) noise = np.random.normal(size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 + noise Y[Y>0] = 0 # censor the data model = nparam.KernelCensoredReg(endog=[Y], exog=[C1, C2], reg_type='ll', var_type='cc', bw='cv_ls', censor_val=0) sm_mean, sm_mfx = model.fit() npt.assert_allclose(sm_mfx[0,:], [1.2, -0.9], rtol = 2e-1) @dec.slow def test_continuous_lc_aic(self): nobs = 200 np.random.seed(1234) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) noise = np.random.normal(size=(nobs, )) Y = 0.3 +1.2 * C1 - 0.9 * C2 + noise #self.write2file('RegData.csv', (Y, C1, C2)) #CODE TO PRODUCE BANDWIDTH ESTIMATION IN R #library(np) #data <- read.csv('RegData.csv', header=FALSE) #bw <- npregbw(formula=data$V1 ~ data$V2 + data$V3, # bwmethod='cv.aic', regtype='lc') model = nparam.KernelReg(endog=[Y], exog=[C1, C2], reg_type='lc', var_type='cc', bw='aic') #R_bw = [0.4017893, 0.4943397] # Bandwidth obtained in R bw_expected = [0.3987821, 0.50933458] npt.assert_allclose(model.bw, bw_expected, rtol=1e-3) @dec.slow def test_significance_continuous(self): nobs = 250 np.random.seed(12345) C1 = np.random.normal(size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) C3 = np.random.beta(0.5,0.2, size=(nobs,)) noise = np.random.normal(size=(nobs, )) b1 = 1.2 b2 = 3.7 # regression coefficients Y = b1 * C1 + b2 * C2 + noise # This is the cv_ls bandwidth estimated earlier bw=[11108137.1087194, 1333821.85150218] model = nparam.KernelReg(endog=[Y], exog=[C1, C3], reg_type='ll', var_type='cc', bw=bw) nboot = 45 # Number of bootstrap samples sig_var12 = model.sig_test([0,1], nboot=nboot) # H0: b1 = 0 and b2 = 0 npt.assert_equal(sig_var12 == 'Not Significant', False) sig_var1 = model.sig_test([0], nboot=nboot) # H0: b1 = 0 npt.assert_equal(sig_var1 == 'Not Significant', False) sig_var2 = model.sig_test([1], nboot=nboot) # H0: b2 = 0 npt.assert_equal(sig_var2 == 'Not Significant', True) @dec.slow def test_significance_discrete(self): nobs = 200 np.random.seed(12345) O = np.random.binomial(2, 0.5, size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) C3 = np.random.beta(0.5,0.2, size=(nobs,)) noise = np.random.normal(size=(nobs, )) b1 = 1.2 b2 = 3.7 # regression coefficients Y = b1 * O + b2 * C2 + noise bw= [3.63473198e+00, 1.21404803e+06] # This is the cv_ls bandwidth estimated earlier # The cv_ls bandwidth was estimated earlier to save time model = nparam.KernelReg(endog=[Y], exog=[O, C3], reg_type='ll', var_type='oc', bw=bw) # This was also tested with local constant estimator nboot = 45 # Number of bootstrap samples sig_var1 = model.sig_test([0], nboot=nboot) # H0: b1 = 0 npt.assert_equal(sig_var1 == 'Not Significant', False) sig_var2 = model.sig_test([1], nboot=nboot) # H0: b2 = 0 npt.assert_equal(sig_var2 == 'Not Significant', True) statsmodels-0.5.0+git13-g8e07d34/statsmodels/nonparametric/tests/test_lowess.py000066400000000000000000000154241224417117700274530ustar00rootroot00000000000000''' Lowess testing suite. Expected outcomes are generated by R's lowess function given the same arguments. The R script test_lowess_r_outputs.R can be used to generate the expected outcomes. The delta tests utilize Silverman's motorcycle collision data, available in R's MASS package. ''' import os import numpy as np from numpy.testing import (assert_almost_equal, assert_, assert_raises, assert_equal) #import statsmodels.api as sm from statsmodels.nonparametric.smoothers_lowess import lowess # Number of decimals to test equality with. # The default is 7. testdec = 7 curdir = os.path.dirname(os.path.abspath(__file__)) rpath = os.path.join(curdir, 'results') class TestLowess(object): def test_import(self): #this doesn't work #from statsmodels.api.nonparametric import lowess as lowess1 import statsmodels.api as sm lowess1 = sm.nonparametric.lowess assert_(lowess is lowess1) def test_simple(self): rfile = os.path.join(rpath, 'test_lowess_simple.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) expected_lowess = np.array([test_data['x'], test_data['out']]).T actual_lowess = lowess(test_data['y'], test_data['x']) assert_almost_equal(expected_lowess, actual_lowess, decimal = testdec) def test_iter(self): rfile = os.path.join(rpath, 'test_lowess_iter.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) expected_lowess_no_iter = np.array([test_data['x'], test_data['out_0']]).T expected_lowess_3_iter = np.array([test_data['x'], test_data['out_3']]).T actual_lowess_no_iter = lowess(test_data['y'], test_data['x'], it = 0) actual_lowess_3_iter = lowess(test_data['y'], test_data['x'], it = 3) assert_almost_equal(expected_lowess_no_iter, actual_lowess_no_iter, decimal = testdec) assert_almost_equal(expected_lowess_3_iter, actual_lowess_3_iter, decimal = testdec) def test_frac(self): rfile = os.path.join(rpath, 'test_lowess_frac.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) expected_lowess_23 = np.array([test_data['x'], test_data['out_2_3']]).T expected_lowess_15 = np.array([test_data['x'], test_data['out_1_5']]).T actual_lowess_23 = lowess(test_data['y'], test_data['x'] ,frac = 2./3) actual_lowess_15 = lowess(test_data['y'], test_data['x'] ,frac = 1./5) assert_almost_equal(expected_lowess_23, actual_lowess_23, decimal = testdec-1) assert_almost_equal(expected_lowess_15, actual_lowess_15, decimal = testdec) def test_delta(self): rfile = os.path.join(rpath, 'test_lowess_delta.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) expected_lowess_del0 = np.array([test_data['x'], test_data['out_0']]).T expected_lowess_delRdef = np.array([test_data['x'], test_data['out_Rdef']]).T expected_lowess_del1 = np.array([test_data['x'], test_data['out_1']]).T actual_lowess_del0 = lowess(test_data['y'], test_data['x'], frac=0.1) actual_lowess_delRdef = lowess(test_data['y'], test_data['x'], frac=0.1, delta = 0.01 * np.ptp(test_data['x'])) actual_lowess_del1 = lowess(test_data['y'], test_data['x'], frac = 0.1, delta = 1.0 + 1e-10) assert_almost_equal(expected_lowess_del0, actual_lowess_del0, decimal = testdec) assert_almost_equal(expected_lowess_delRdef, actual_lowess_delRdef, decimal = testdec) assert_almost_equal(expected_lowess_del1, actual_lowess_del1, decimal = 10) #testdec) def test_options(self): rfile = os.path.join(rpath, 'test_lowess_simple.csv') test_data = np.genfromtxt(open(rfile, 'rb'), delimiter = ',', names = True) y, x = test_data['y'], test_data['x'] res1_fitted = test_data['out'] expected_lowess = np.array([test_data['x'], test_data['out']]).T # check skip sorting actual_lowess1 = lowess(y, x, is_sorted=True) assert_almost_equal(actual_lowess1, expected_lowess, decimal=13) # check skip missing actual_lowess = lowess(y, x, is_sorted=True, missing='none') assert_almost_equal(actual_lowess, actual_lowess1, decimal=13) # check order/index, returns yfitted only actual_lowess = lowess(y[::-1], x[::-1], return_sorted=False) assert_almost_equal(actual_lowess, actual_lowess1[::-1, 1], decimal=13) # check returns yfitted only actual_lowess = lowess(y, x, return_sorted=False, missing='none', is_sorted=True) assert_almost_equal(actual_lowess, actual_lowess1[:, 1], decimal=13) # check integer input actual_lowess = lowess(np.round(y).astype(int), x, is_sorted=True) actual_lowess1 = lowess(np.round(y), x, is_sorted=True) assert_almost_equal(actual_lowess, actual_lowess1, decimal=13) assert_(actual_lowess.dtype is np.dtype(float)) # this will also have duplicate x actual_lowess = lowess(y, np.round(x).astype(int), is_sorted=True) actual_lowess1 = lowess(y, np.round(x), is_sorted=True) assert_almost_equal(actual_lowess, actual_lowess1, decimal=13) assert_(actual_lowess.dtype is np.dtype(float)) # check with nans, this changes the arrays y[[5, 6]] = np.nan x[3] = np.nan mask_valid = np.isfinite(x) & np.isfinite(y) #actual_lowess1[[3, 5, 6], 1] = np.nan actual_lowess = lowess(y, x, is_sorted=True) actual_lowess1 = lowess(y[mask_valid], x[mask_valid], is_sorted=True) assert_almost_equal(actual_lowess, actual_lowess1, decimal=13) assert_raises(ValueError, lowess, y, x, missing='raise') perm_idx = np.arange(len(x)) np.random.shuffle(perm_idx) yperm = y[perm_idx] xperm = x[perm_idx] actual_lowess2 = lowess(yperm, xperm, is_sorted=False) assert_almost_equal(actual_lowess, actual_lowess2, decimal=13) actual_lowess3 = lowess(yperm, xperm, is_sorted=False, return_sorted=False) mask_valid = np.isfinite(xperm) & np.isfinite(yperm) assert_equal(np.isnan(actual_lowess3), ~mask_valid) # get valid sorted smoothed y from actual_lowess3 sort_idx = np.argsort(xperm) yhat = actual_lowess3[sort_idx] yhat = yhat[np.isfinite(yhat)] assert_almost_equal(yhat, actual_lowess2[:,1], decimal=13) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/000077500000000000000000000000001224417117700226745ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/__init__.py000066400000000000000000000001511224417117700250020ustar00rootroot00000000000000from linear_model import yule_walker from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/feasible_gls.py000066400000000000000000000157441224417117700257000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Dec 20 20:24:20 2011 Author: Josef Perktold License: BSD-3 """ import numpy as np import statsmodels.base.model as base from statsmodels.regression.linear_model import OLS, GLS, WLS, RegressionResults def atleast_2dcols(x): x = np.asarray(x) if x.ndim == 1: x = x[:,None] return x class GLSHet2(GLS): '''WLS with heteroscedasticity that depends on explanatory variables note: mixing GLS sigma and weights for heteroscedasticity might not make sense I think rewriting following the pattern of GLSAR is better stopping criteria: improve in GLSAR also, e.g. change in rho ''' def __init__(self, endog, exog, exog_var, sigma=None): self.exog_var = atleast_2dcols(exog_var) super(self.__class__, self).__init__(endog, exog, sigma=sigma) def fit(self, lambd=1.): #maybe iterate #preliminary estimate res_gls = GLS(self.endog, self.exog, sigma=self.sigma).fit() res_resid = OLS(res_gls.resid**2, self.exog_var).fit() #or log-link #res_resid = OLS(np.log(res_gls.resid**2), self.exog_var).fit() #here I could use whiten and current instance instead of delegating #but this is easier #see pattern of GLSAR, calls self.initialize and self.fit res_wls = WLS(self.endog, self.exog, weights=1./res_resid.fittedvalues).fit() res_wls._results.results_residual_regression = res_resid return res_wls class GLSHet(WLS): """ A regression model with an estimated heteroscedasticity. A subclass of WLS, that additionally estimates the weight matrix as a function of additional explanatory variables. Parameters ---------- endog : array_like exog : array_like exog_var : array_like, 1d or 2d regressors, explanatory variables for the variance weights : array_like or None If weights are given, then they are used in the first step estimation. link : link function or None If None, then the variance is assumed to be a linear combination of the exog_var. If given, then ... not tested yet *extra attributes* history : dict contains the parameter estimates in both regression for each iteration result instance has results_residual_regression : OLS result instance result of heteroscedasticity estimation except for fit_iterative all methods are inherited from WLS. Notes ----- GLSHet is considered to be experimental. `fit` is just standard WLS fit for fixed weights `fit_iterative` updates the estimate for weights, see its docstring The two alternative for handling heteroscedasticity in the data are to use heteroscedasticity robust standard errors or estimating the heteroscedasticity Estimating heteroscedasticity and using weighted least squares produces smaller confidence intervals for the estimated parameters then the heteroscedasticity robust standard errors if the heteroscedasticity is correctly specified. If the heteroscedasticity is incorrectly specified then the estimated covariance is inconsistent. Stock and Watson for example argue in favor of using OLS with heteroscedasticity robust standard errors instead of GLSHet sind we are seldom sure enough about the correct specification (in economics). GLSHet has asymptotically the same distribution as WLS if the true weights are know. In both cases the asymptotic distribution of the parameter estimates is the normal distribution. The assumption of the model: y = X*beta + u, with E(u) = 0, E(X*u)=0, var(u_i) = z_i*gamma or for vector of all observations Sigma = diag(Z*gamma) where y : endog (nobs) X : exog (nobs, k_vars) Z : exog_var (nobs, k_vars2) beta, gamma estimated parameters If a link is specified, then the heteroscedasticity is var(u_i) = link.inverse(z_i*gamma), or link(var(u_i)) = z_i*gamma for example for log-linkg var(u_i) = exp(z_i*gamma) Usage : see example .... TODO: test link option """ def __init__(self, endog, exog, exog_var=None, weights=None, link=None): self.exog_var = atleast_2dcols(exog_var) if weights is None: weights = np.ones(endog.shape) if link is not None: self.link = link self.linkinv = link.inverse #as defined in families.links else: self.link = lambda x: x #no transformation self.linkinv = lambda x: x super(self.__class__, self).__init__(endog, exog, weights=weights) def iterative_fit(self, maxiter=3): """ Perform an iterative two-step procedure to estimate a WLS model. The model is assumed to have heteroscedastic errors. The variance is estimated by OLS regression of the link transformed squared residuals on Z, i.e.:: link(sigma_i) = x_i*gamma. Parameters ---------- maxiter : integer, optional the number of iterations Notes ----- maxiter=1: returns the estimated based on given weights maxiter=2: performs a second estimation with the updated weights, this is 2-step estimation maxiter>2: iteratively estimate and update the weights TODO: possible extension stop iteration if change in parameter estimates is smaller than x_tol Repeated calls to fit_iterative, will do one redundant pinv_wexog calculation. Calling fit_iterative(maxiter) ones does not do any redundant recalculations (whitening or calculating pinv_wexog). """ import collections self.history = collections.defaultdict(list) #not really necessary res_resid = None #if maxiter < 2 no updating for i in range(maxiter): #pinv_wexog is cached if hasattr(self, 'pinv_wexog'): del self.pinv_wexog #self.initialize() #print 'wls self', results = self.fit() self.history['self_params'].append(results.params) if not i == maxiter-1: #skip for last iteration, could break instead #print 'ols', self.results_old = results #for debugging #estimate heteroscedasticity res_resid = OLS(self.link(results.resid**2), self.exog_var).fit() self.history['ols_params'].append(res_resid.params) #update weights self.weights = 1./self.linkinv(res_resid.fittedvalues) self.weights /= self.weights.max() #not required self.weights[self.weights < 1e-14] = 1e-14 #clip #print 'in iter', i, self.weights.var() #debug, do weights change self.initialize() #note results is the wrapper, results._results is the results instance results._results.results_residual_regression = res_resid return results if __name__ == '__main__': pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/linear_model.py000066400000000000000000001674031224417117700257130ustar00rootroot00000000000000""" This module implements some standard regression models: Generalized Least Squares (GLS), Ordinary Least Squares (OLS), and Weighted Least Squares (WLS), as well as an GLS model with autoregressive error terms GLSAR(p) Models are specified with an endogenous response variable and an exogenous design matrix and are fit using their `fit` method. Subclasses that have more complicated covariance matrices should write over the 'whiten' method as the fit method prewhitens the response by calling 'whiten'. General reference for regression models: D. C. Montgomery and E.A. Peck. "Introduction to Linear Regression Analysis." 2nd. Ed., Wiley, 1992. Econometrics references for regression models: R. Davidson and J.G. MacKinnon. "Econometric Theory and Methods," Oxford, 2004. W. Green. "Econometric Analysis," 5th ed., Pearson, 2003. """ __docformat__ = 'restructuredtext en' __all__ = ['GLS', 'WLS', 'OLS', 'GLSAR'] import numpy as np from scipy.linalg import toeplitz from scipy import stats from scipy.stats.stats import ss from statsmodels.tools.tools import (add_constant, rank, recipr, chain_dot) from statsmodels.tools.decorators import (resettable_cache, cache_readonly, cache_writable) import statsmodels.base.model as base import statsmodels.base.wrapper as wrap from statsmodels.emplike.elregress import _ELRegOpts from scipy import optimize from scipy.stats import chi2 def _get_sigma(sigma, nobs): """ Returns sigma for GLS and the inverse of its Cholesky decomposition. Handles dimensions and checks integrity. If sigma is None, returns None, None. Otherwise returns sigma, cholsigmainv. """ if sigma is None: return None, None sigma = np.asarray(sigma).squeeze() if sigma.ndim == 0: sigma = np.repeat(sigma, nobs) if sigma.ndim == 1: if sigma.shape != (nobs,): raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d " "array of shape %s x %s" % (nobs, nobs)) cholsigmainv = np.diag(1/sigma**.5) sigma = np.diag(sigma) else: if sigma.shape != (nobs, nobs): raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d " "array of shape %s x %s" % (nobs, nobs)) cholsigmainv = np.linalg.cholesky(np.linalg.pinv(sigma)).T return sigma, cholsigmainv class RegressionModel(base.LikelihoodModel): """ Base class for linear regression models not used by users. Intended for subclassing. """ def __init__(self, endog, exog, **kwargs): super(RegressionModel, self).__init__(endog, exog, **kwargs) self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights']) def initialize(self): #print "calling initialize, now whitening" #for debugging self.wexog = self.whiten(self.exog) self.wendog = self.whiten(self.endog) # overwrite nobs from class Model: self.nobs = float(self.wexog.shape[0]) self.rank = rank(self.exog) self.df_model = float(self.rank - self.k_constant) self.df_resid = self.nobs - self.rank self.df_model = float(rank(self.exog) - self.k_constant) def fit(self, method="pinv", **kwargs): """ Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters ---------- method : str Can be "pinv", "qr". "pinv" uses the Moore-Penrose pseudoinverse to solve the least squares problem. "qr" uses the QR factorization. Returns ------- A RegressionResults class instance. See Also --------- regression.RegressionResults Notes ----- The fit method uses the pseudoinverse of the design/exogenous variables to solve the least squares minimization. """ exog = self.wexog endog = self.wendog if method == "pinv": if ((not hasattr(self, 'pinv_wexog')) or (not hasattr(self, 'normalized_cov_params'))): #print "recalculating pinv" #for debugging self.pinv_wexog = pinv_wexog = np.linalg.pinv(self.wexog) self.normalized_cov_params = np.dot(pinv_wexog, np.transpose(pinv_wexog)) beta = np.dot(self.pinv_wexog, endog) elif method == "qr": if ((not hasattr(self, 'exog_Q')) or (not hasattr(self, 'normalized_cov_params'))): Q, R = np.linalg.qr(exog) self.exog_Q, self.exog_R = Q, R self.normalized_cov_params = np.linalg.inv(np.dot(R.T, R)) else: Q, R = self.exog_Q, self.exog_R # used in ANOVA self.effects = effects = np.dot(Q.T, endog) beta = np.linalg.solve(R, effects) # no upper triangular solve routine in numpy/scipy? if isinstance(self, OLS): lfit = OLSResults(self, beta, normalized_cov_params=self.normalized_cov_params) else: lfit = RegressionResults(self, beta, normalized_cov_params=self.normalized_cov_params) return RegressionResultsWrapper(lfit) def predict(self, params, exog=None): """ Return linear predicted values from a design matrix. Parameters ---------- params : array-like, optional after fit has been called Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns ------- An array of fitted values Notes ----- If the model as not yet been fit, params is not optional. """ #JP: this doesn't look correct for GLMAR #SS: it needs its own predict method if exog is None: exog = self.exog return np.dot(exog, params) class GLS(RegressionModel): __doc__ = """ Generalized least squares model with a general covariance structure. %(params)s sigma : scalar or array `sigma` is the weighting matrix of the covariance. The default is None for no scaling. If `sigma` is a scalar, it is assumed that `sigma` is an n x n diagonal matrix with the given scalar, `sigma` as the value of each diagonal element. If `sigma` is an n-length vector, then `sigma` is assumed to be a diagonal matrix with the given `sigma` on the diagonal. This should be the same as WLS. %(extra_params)s Attributes ---------- pinv_wexog : array `pinv_wexog` is the p x n Moore-Penrose pseudoinverse of `wexog`. cholsimgainv : array The transpose of the Cholesky decomposition of the pseudoinverse. df_model : float p - 1, where p is the number of regressors including the intercept. of freedom. df_resid : float Number of observations n less the number of parameters p. llf : float The value of the likelihood function of the fitted model. nobs : float The number of observations n. normalized_cov_params : array p x p array :math:`(X^{T}\Sigma^{-1}X)^{-1}` results : RegressionResults instance A property that returns the RegressionResults class if fit. sigma : array `sigma` is the n x n covariance structure of the error terms. wexog : array Design matrix whitened by `cholsigmainv` wendog : array Response variable whitened by `cholsigmainv` Notes ----- If sigma is a function of the data making one of the regressors a constant, then the current postestimation statistics will not be correct. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load() >>> data.exog = sm.add_constant(data.exog) >>> ols_resid = sm.OLS(data.endog, data.exog).fit().resid >>> res_fit = sm.OLS(ols_resid[1:], ols_resid[:-1]).fit() >>> rho = res_fit.params `rho` is a consistent estimator of the correlation of the residuals from an OLS fit of the longley data. It is assumed that this is the true rho of the AR process data. >>> from scipy.linalg import toeplitz >>> order = toeplitz(np.arange(16)) >>> sigma = rho**order `sigma` is an n x n matrix of the autocorrelation structure of the data. >>> gls_model = sm.GLS(data.endog, data.exog, sigma=sigma) >>> gls_results = gls_model.fit() >>> print gls_results.summary() """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog, sigma=None, missing='none', hasconst=None): #TODO: add options igls, for iterative fgls if sigma is None #TODO: default is sigma is none should be two-step GLS sigma, cholsigmainv = _get_sigma(sigma, len(endog)) super(GLS, self).__init__(endog, exog, missing=missing, hasconst=hasconst, sigma=sigma, cholsigmainv=cholsigmainv) #store attribute names for data arrays self._data_attr.extend(['sigma', 'cholsigmainv']) def whiten(self, X): """ GLS whiten method. Parameters ----------- X : array-like Data to be whitened. Returns ------- np.dot(cholsigmainv,X) See Also -------- regression.GLS """ X = np.asarray(X) if np.any(self.sigma) and not self.sigma.shape == (): return np.dot(self.cholsigmainv, X) else: return X def loglike(self, params): """ Returns the value of the gaussian loglikelihood function at params. Given the whitened design matrix, the loglikelihood is evaluated at the parameter vector `params` for the dependent variable `endog`. Parameters ---------- params : array-like The parameter estimates Returns ------- loglike : float The value of the loglikelihood function for a GLS Model. Notes ----- The loglikelihood function for the normal distribution is .. math:: -\\frac{n}{2}\\log\\left(Y-\\hat{Y}\\right)-\\frac{n}{2}\\left(1+\\log\\left(\\frac{2\\pi}{n}\\right)\\right)-\\frac{1}{2}\\log\\left(\\left|\\Sigma\\right|\\right) Y and Y-hat are whitened. """ #TODO: combine this with OLS/WLS loglike and add _det_sigma argument nobs2 = self.nobs / 2.0 SSR = ss(self.wendog - np.dot(self.wexog,params)) llf = -np.log(SSR) * nobs2 # concentrated likelihood llf -= (1+np.log(np.pi/nobs2))*nobs2 # with likelihood constant if np.any(self.sigma) and self.sigma.ndim == 2: #FIXME: robust-enough check? unneeded if _det_sigma gets defined llf -= .5*np.log(np.linalg.det(self.sigma)) # with error covariance matrix return llf class WLS(RegressionModel): __doc__ = """ A regression model with diagonal but non-identity covariance structure. The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W. %(params)s weights : array-like, optional 1d array of weights. If you supply 1/W then the variables are pre- multiplied by 1/sqrt(W). If no weights are supplied the default value is 1 and WLS reults are the same as OLS. %(extra_params)s Attributes ---------- weights : array The stored weights supplied as an argument. See regression.GLS Examples --------- >>> import numpy as np >>> import statsmodels.api as sm >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> wls_model = sm.WLS(Y,X, weights=range(1,8)) >>> results = wls_model.fit() >>> results.params array([ 2.91666667, 0.0952381 ]) >>> results.tvalues array([ 2.0652652 , 0.35684428]) >>> print results.t_test([1, 0]) >>> print results.f_test([0, 1]) Notes ----- If the weights are a function of the data, then the postestimation statistics such as fvalue and mse_model might not be correct, as the package does not yet support no-constant regression. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog, weights=1., missing='none', hasconst=None): weights = np.array(weights) if weights.shape == (): weights = np.repeat(weights, len(endog)) # handle case that endog might be of len == 1 if len(weights) == 1: weights = np.array([weights.squeeze()]) else: weights = weights.squeeze() super(WLS, self).__init__(endog, exog, missing=missing, weights=weights, hasconst=hasconst) nobs = self.exog.shape[0] weights = self.weights if len(weights) != nobs and weights.size == nobs: raise ValueError('Weights must be scalar or same length as design') def whiten(self, X): """ Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters ---------- X : array-like Data to be whitened Returns ------- sqrt(weights)*X """ #print self.weights.var() X = np.asarray(X) if X.ndim == 1: return X * np.sqrt(self.weights) elif X.ndim == 2: return np.sqrt(self.weights)[:,None]*X def loglike(self, params): """ Returns the value of the gaussian loglikelihood function at params. Given the whitened design matrix, the loglikelihood is evaluated at the parameter vector `params` for the dependent variable `Y`. Parameters ---------- params : array-like The parameter estimates. Returns ------- The value of the loglikelihood function for a WLS Model. Notes -------- .. math:: -\\frac{n}{2}\\log\\left(Y-\\hat{Y}\\right)-\\frac{n}{2}\\left(1+\\log\\left(\\frac{2\\pi}{n}\\right)\\right)-\\frac{1}{2}log\\left(\\left|W\\right|\\right) where :math:`W` is a diagonal matrix """ nobs2 = self.nobs / 2.0 SSR = ss(self.wendog - np.dot(self.wexog,params)) llf = -np.log(SSR) * nobs2 # concentrated likelihood llf -= (1+np.log(np.pi/nobs2))*nobs2 # with constant return llf class OLS(WLS): __doc__ = """ A simple ordinary least squares model. %(params)s %(extra_params)s Attributes ---------- weights : scalar Has an attribute weights = array(1.0) due to inheritance from WLS. See regression.GLS Examples -------- >>> import numpy as np >>> >>> import statsmodels.api as sm >>> >>> Y = [1,3,4,5,2,3,4] >>> X = range(1,8) >>> X = sm.add_constant(X) >>> >>> model = sm.OLS(Y,X) >>> results = model.fit() >>> results.params array([ 2.14285714, 0.25 ]) >>> results.tvalues array([ 1.87867287, 0.98019606]) >>> print results.t_test([1, 0]) >>> print results.f_test(np.identity(2)) Notes ----- No constant is added by the model unless you are using formulas. """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc + base._extra_param_doc} #TODO: change example to use datasets. This was the point of datasets! def __init__(self, endog, exog=None, missing='none', hasconst=None): super(OLS, self).__init__(endog, exog, missing=missing, hasconst=hasconst) def loglike(self, params): ''' The likelihood function for the clasical OLS model. Parameters ---------- params : array-like The coefficients with which to estimate the loglikelihood. Returns ------- The concentrated likelihood function evaluated at params. ''' nobs2 = self.nobs/2. return -nobs2*np.log(2*np.pi)-nobs2*np.log(1/(2*nobs2) *\ np.dot(np.transpose(self.endog - np.dot(self.exog, params)), (self.endog - np.dot(self.exog,params)))) -\ nobs2 def whiten(self, Y): """ OLS model whitener does nothing: returns Y. """ return Y class GLSAR(GLS): __doc__ = """ A regression model with an AR(p) covariance structure. %(params)s rho : int Order of the autoregressive covariance %(extra_params)s Examples -------- >>> import statsmodels.api as sm >>> X = range(1,8) >>> X = sm.add_constant(X) >>> Y = [1,3,4,5,8,10,9] >>> model = sm.GLSAR(Y, X, rho=2) >>> for i in range(6): ... results = model.fit() ... print "AR coefficients:", model.rho ... rho, sigma = sm.regression.yule_walker(results.resid, ... order=model.order) ... model = sm.GLSAR(Y, X, rho) ... AR coefficients: [ 0. 0.] AR coefficients: [-0.52571491 -0.84496178] AR coefficients: [-0.6104153 -0.86656458] AR coefficients: [-0.60439494 -0.857867 ] AR coefficients: [-0.6048218 -0.85846157] AR coefficients: [-0.60479146 -0.85841922] >>> results.params array([-0.66661205, 1.60850853]) >>> results.tvalues array([ -2.10304127, 21.8047269 ]) >>> print results.t_test([1, 0]) >>> print results.f_test(np.identity(2)) Or, equivalently >>> model2 = sm.GLSAR(Y, X, rho=2) >>> res = model2.iterative_fit(maxiter=6) >>> model2.rho array([-0.60479146, -0.85841922]) Notes ----- GLSAR is considered to be experimental. The linear autoregressive process of order p--AR(p)--is defined as: TODO """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc + base._extra_param_doc} def __init__(self, endog, exog=None, rho=1, missing='none'): #this looks strange, interpreting rho as order if it is int if isinstance(rho, np.int): self.order = rho self.rho = np.zeros(self.order, np.float64) else: self.rho = np.squeeze(np.asarray(rho)) if len(self.rho.shape) not in [0,1]: raise ValueError("AR parameters must be a scalar or a vector") if self.rho.shape == (): self.rho.shape = (1,) self.order = self.rho.shape[0] if exog is None: #JP this looks wrong, should be a regression on constant #results for rho estimate now identical to yule-walker on y #super(AR, self).__init__(endog, add_constant(endog)) super(GLSAR, self).__init__(endog, np.ones((endog.shape[0],1)), missing=missing) else: super(GLSAR, self).__init__(endog, exog, missing=missing) def iterative_fit(self, maxiter=3): """ Perform an iterative two-stage procedure to estimate a GLS model. The model is assumed to have AR(p) errors, AR(p) parameters and regression coefficients are estimated iteratively. Parameters ---------- maxiter : integer, optional the number of iterations """ #TODO: update this after going through example. for i in range(maxiter-1): if hasattr(self, 'pinv_wexog'): del self.pinv_wexog self.initialize() results = self.fit() self.rho, _ = yule_walker(results.resid, order=self.order, df=None) #why not another call to self.initialize if hasattr(self, 'pinv_wexog'): del self.pinv_wexog self.initialize() results = self.fit() #final estimate return results # add missing return def whiten(self, X): """ Whiten a series of columns according to an AR(p) covariance structure. This drops initial p observations. Parameters ---------- X : array-like The data to be whitened, Returns ------- whitened array """ #TODO: notation for AR process X = np.asarray(X, np.float64) _X = X.copy() #the following loops over the first axis, works for 1d and nd for i in range(self.order): _X[(i+1):] = _X[(i+1):] - self.rho[i] * X[0:-(i+1)] return _X[self.order:] def yule_walker(X, order=1, method="unbiased", df=None, inv=False, demean=True): """ Estimate AR(p) parameters from a sequence X using Yule-Walker equation. Unbiased or maximum-likelihood estimator (mle) See, for example: http://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters ---------- X : array-like 1d array order : integer, optional The order of the autoregressive process. Default is 1. method : string, optional Method can be "unbiased" or "mle" and this determines denominator in estimate of autocorrelation function (ACF) at lag k. If "mle", the denominator is n=X.shape[0], if "unbiased" the denominator is n-k. The default is unbiased. df : integer, optional Specifies the degrees of freedom. If `df` is supplied, then it is assumed the X has `df` degrees of freedom rather than `n`. Default is None. inv : bool If inv is True the inverse of R is also returned. Default is False. demean : bool True, the mean is subtracted from `X` before estimation. Returns ------- rho The autoregressive coefficients sigma TODO Examples -------- >>> import statsmodels.api as sm >>> from statsmodels.datasets.sunspots import load >>> data = load() >>> rho, sigma = sm.regression.yule_walker(data.endog, order=4, method="mle") >>> rho array([ 1.28310031, -0.45240924, -0.20770299, 0.04794365]) >>> sigma 16.808022730464351 """ #TODO: define R better, look back at notes and technical notes on YW. #First link here is useful #http://www-stat.wharton.upenn.edu/~steele/Courses/956/ResourceDetails/YuleWalkerAndMore.htm method = str(method).lower() if method not in ["unbiased", "mle"]: raise ValueError("ACF estimation method must be 'unbiased' or 'MLE'") X = np.array(X) if demean: X -= X.mean() # automatically demean's X n = df or X.shape[0] if method == "unbiased": # this is df_resid ie., n - p denom = lambda k: n - k else: denom = lambda k: n if X.ndim > 1 and X.shape[1] != 1: raise ValueError("expecting a vector to estimate AR parameters") r = np.zeros(order+1, np.float64) r[0] = (X**2).sum() / denom(0) for k in range(1,order+1): r[k] = (X[0:-k]*X[k:]).sum() / denom(k) R = toeplitz(r[:-1]) rho = np.linalg.solve(R, r[1:]) sigmasq = r[0] - (r[1:]*rho).sum() if inv == True: return rho, np.sqrt(sigmasq), np.linalg.inv(R) else: return rho, np.sqrt(sigmasq) class RegressionResults(base.LikelihoodModelResults): """ This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Returns ------- **Attributes** aic Aikake's information criteria. For a model with a constant :math:`-2llf + 2(df_model + 1)`. For a model without a constant :math:`-2llf + 2(df_model)`. bic Bayes' information criteria For a model with a constant :math:`-2llf + \log(n)(df_model+1)`. For a model without a constant :math:`-2llf + \log(n)(df_model)` bse The standard errors of the parameter estimates. pinv_wexog See specific model class docstring centered_tss The total (weighted) sum of squares centered about the mean. cov_HC0 See HC0_se below. Only available after calling HC0_se. cov_HC1 See HC1_se below. Only available after calling HC1_se. cov_HC2 See HC2_se below. Only available after calling HC2_se. cov_HC3 See HC3_se below. Only available after calling HC3_se. df_model : Model degress of freedom. The number of regressors `p`. Does not include the constant if one is present df_resid Residual degrees of freedom. `n - p - 1`, if a constant is present. `n - p` if a constant is not included. ess Explained sum of squares. If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used. fvalue F-statistic of the fully specified model. Calculated as the mean squared error of the model divided by the mean squared error of the residuals. f_pvalue p-value of the F-statistic fittedvalues The predicted the values for the original (unwhitened) design. het_scale Only available if HC#_se is called. See HC#_se for more information. HC0_se White's (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC0, which is the full heteroskedasticity consistent covariance matrix and also `het_scale`, which is in this case just resid**2. HCCM matrices are only appropriate for OLS. HC1_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC1, which is the full HCCM and also `het_scale`, which is in this case n/(n-p)*resid**2. HCCM matrices are only appropriate for OLS. HC2_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC2, which is the full HCCM and also `het_scale`, which is in this case is resid^(2)/(1-h_ii). HCCM matrices are only appropriate for OLS. HC3_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC3, which is the full HCCM and also `het_scale`, which is in this case is resid^(2)/(1-h_ii)^(2). HCCM matrices are only appropriate for OLS. model A pointer to the model instance that called fit() or results. mse_model Mean squared error the model. This is the explained sum of squares divided by the model degrees of freedom. mse_resid Mean squared error of the residuals. The sum of squared residuals divided by the residual degrees of freedom. mse_total Total mean squared error. Defined as the uncentered total sum of squares divided by n the number of observations. nobs Number of observations n. normalized_cov_params See specific model class docstring params The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model. pvalues The two-tailed p values for the t-stats of the params. resid The residuals of the model. rsquared R-squared of a model with an intercept. This is defined here as 1 - `ssr`/`centered_tss` if the constant is included in the model and 1 - `ssr`/`uncentered_tss` if the constant is omitted. rsquared_adj Adjusted R-squared. This is defined here as 1 - (`nobs`-1)/`df_resid` * (1-`rsquared`) if a constant is included and 1 - `nobs`/`df_resid` * (1-`rsquared`) if no constant is included. scale A scale factor for the covariance matrix. Default value is ssr/(n-p). Note that the square root of `scale` is often called the standard error of the regression. ssr Sum of squared (whitened) residuals. uncentered_tss Uncentered sum of squares. Sum of the squared values of the (whitened) endogenous response variable. wresid The residuals of the transformed/whitened regressand and regressor(s) """ # For robust covariance matrix properties _HC0_se = None _HC1_se = None _HC2_se = None _HC3_se = None _cache = {} # needs to be a class attribute for scale setter? def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(RegressionResults, self).__init__(model, params, normalized_cov_params, scale) self._cache = resettable_cache() def __str__(self): self.summary() def conf_int(self, alpha=.05, cols=None): """ Returns the confidence interval of the fitted parameters. Parameters ---------- alpha : float, optional The `alpha` level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. cols : array-like, optional `cols` specifies which confidence intervals to return Notes ----- The confidence interval is based on Student's t-distribution. """ bse = self.bse params = self.params dist = stats.t q = dist.ppf(1 - alpha / 2, self.df_resid) if cols is None: lower = self.params - q * bse upper = self.params + q * bse else: cols = np.asarray(cols) lower = params[cols] - q * bse[cols] upper = params[cols] + q * bse[cols] return np.asarray(zip(lower, upper)) @cache_readonly def df_resid(self): return self.model.df_resid @cache_readonly def df_model(self): return self.model.df_model @cache_readonly def nobs(self): return float(self.model.wexog.shape[0]) @cache_readonly def fittedvalues(self): return self.model.predict(self.params, self.model.exog) @cache_readonly def wresid(self): return self.model.wendog - self.model.predict(self.params, self.model.wexog) @cache_readonly def resid(self): return self.model.endog - self.model.predict(self.params, self.model.exog) #TODO: fix writable example @cache_writable() def scale(self): wresid = self.wresid return np.dot(wresid, wresid) / self.df_resid @cache_readonly def ssr(self): wresid = self.wresid return np.dot(wresid, wresid) @cache_readonly def centered_tss(self): model = self.model weights = getattr(model, 'weights', None) if weights is not None: return np.sum(weights*(model.endog - np.average(model.endog, weights=weights))**2) else: # this is probably broken for GLS centered_endog = model.wendog - model.wendog.mean() return np.dot(centered_endog, centered_endog) @cache_readonly def uncentered_tss(self): wendog = self.model.wendog return np.dot(wendog, wendog) @cache_readonly def ess(self): if self.k_constant: return self.centered_tss - self.ssr else: return self.uncentered_tss - self.ssr @cache_readonly def rsquared(self): if self.k_constant: return 1 - self.ssr/self.centered_tss else: return 1 - self.ssr/self.uncentered_tss @cache_readonly def rsquared_adj(self): return 1 - np.divide(self.nobs - self.k_constant, self.df_resid) * (1 - self.rsquared) @cache_readonly def mse_model(self): return self.ess/self.df_model @cache_readonly def mse_resid(self): return self.ssr/self.df_resid @cache_readonly def mse_total(self): if self.k_constant: return self.centered_tss / (self.df_resid + self.df_model) else: return self.uncentered_tss/ (self.df_resid + self.df_model) @cache_readonly def fvalue(self): return self.mse_model/self.mse_resid @cache_readonly def f_pvalue(self): return stats.f.sf(self.fvalue, self.df_model, self.df_resid) @cache_readonly def bse(self): return np.sqrt(np.diag(self.cov_params())) @cache_readonly def pvalues(self): return stats.t.sf(np.abs(self.tvalues), self.df_resid)*2 @cache_readonly def aic(self): return -2 * self.llf + 2 * (self.df_model + self.k_constant) @cache_readonly def bic(self): return (-2 * self.llf + np.log(self.nobs) * (self.df_model + self.k_constant)) #TODO: make these properties reset bse def _HCCM(self, scale): H = np.dot(self.model.pinv_wexog, scale[:,None]*self.model.pinv_wexog.T) return H @property def HC0_se(self): """ See statsmodels.RegressionResults """ if self._HC0_se is None: self.het_scale = self.resid**2 # or whitened residuals? only OLS? self.cov_HC0 = self._HCCM(self.het_scale) self._HC0_se = np.sqrt(np.diag(self.cov_HC0)) return self._HC0_se @property def HC1_se(self): """ See statsmodels.RegressionResults """ if self._HC1_se is None: self.het_scale = self.nobs/(self.df_resid)*(self.resid**2) self.cov_HC1 = self._HCCM(self.het_scale) self._HC1_se = np.sqrt(np.diag(self.cov_HC1)) return self._HC1_se @property def HC2_se(self): """ See statsmodels.RegressionResults """ if self._HC2_se is None: # probably could be optimized h = np.diag(chain_dot(self.model.exog, self.normalized_cov_params, self.model.exog.T)) self.het_scale = self.resid**2/(1-h) self.cov_HC2 = self._HCCM(self.het_scale) self._HC2_se = np.sqrt(np.diag(self.cov_HC2)) return self._HC2_se @property def HC3_se(self): """ See statsmodels.RegressionResults """ if self._HC3_se is None: # above probably could be optimized to only calc the diag h = np.diag(chain_dot(self.model.exog, self.normalized_cov_params, self.model.exog.T)) self.het_scale=(self.resid/(1-h))**2 self.cov_HC3 = self._HCCM(self.het_scale) self._HC3_se = np.sqrt(np.diag(self.cov_HC3)) return self._HC3_se #TODO: this needs a test def norm_resid(self): """ Residuals, normalized to have unit length and unit variance. Returns ------- An array wresid/sqrt(scale) Notes ----- This method is untested """ if not hasattr(self, 'resid'): raise ValueError('need normalized residuals to estimate standard ' 'deviation') return self.wresid * recipr(np.sqrt(self.scale)) def compare_f_test(self, restricted): '''use F test to test whether restricted model is correct Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. Returns ------- f_value : float test statistic, F distributed p_value : float p-value of the test statistic df_diff : int degrees of freedom of the restriction, i.e. difference in df between models Notes ----- See mailing list discussion October 17, ''' ssr_full = self.ssr ssr_restr = restricted.ssr df_full = self.df_resid df_restr = restricted.df_resid df_diff = (df_restr - df_full) f_value = (ssr_restr - ssr_full) / df_diff / ssr_full * df_full p_value = stats.f.sf(f_value, df_diff, df_full) return f_value, p_value, df_diff def compare_lr_test(self, restricted): ''' Likelihood ratio test to test whether restricted model is correct Parameters ---------- restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, `ssr`, residual degrees of freedom, `df_resid`. Returns ------- lr_stat : float likelihood ratio, chisquare distributed with df_diff degrees of freedom p_value : float p-value of the test statistic df_diff : int degrees of freedom of the restriction, i.e. difference in df between models Notes ----- .. math:: D=-2\\log\\left(\\frac{\\mathcal{L}_{null}} {\\mathcal{L}_{alternative}}\\right) where :math:`\mathcal{L}` is the likelihood of the model. With :math:`D` distributed as chisquare with df equal to difference in number of parameters or equivalently difference in residual degrees of freedom TODO: put into separate function, needs tests ''' # See mailing list discussion October 17, llf_full = self.llf llf_restr = restricted.llf df_full = self.df_resid df_restr = restricted.df_resid lrdf = (df_restr - df_full) lrstat = -2*(llf_restr - llf_full) lr_pvalue = stats.chi2.sf(lrstat, lrdf) return lrstat, lr_pvalue, lrdf def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ----------- yname : string, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ #TODO: import where we need it (for now), add as cached attributes from statsmodels.stats.stattools import (jarque_bera, omni_normtest, durbin_watson) jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) #TODO: reuse condno from somewhere else ? #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog)) wexog = self.model.wexog eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog)) eigvals = np.sort(eigvals) #in increasing order condno = np.sqrt(eigvals[-1]/eigvals[0]) self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis, omni=omni, omnipv=omnipv, condno=condno, mineigval=eigvals[0]) #TODO not used yet #diagn_left_header = ['Models stats'] #diagn_right_header = ['Residual stats'] #TODO: requiring list/iterable is a bit annoying #need more control over formatting #TODO: default don't work if it's not identically spelled top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Least Squares']), ('Date:', None), ('Time:', None), ('No. Observations:', None), ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling ('Df Model:', None), #[self.df_model]) ] top_right = [('R-squared:', ["%#8.3f" % self.rsquared]), ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]), ('F-statistic:', ["%#8.4g" % self.fvalue] ), ('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]), ('Log-Likelihood:', None), #["%#6.4g" % self.llf]), ('AIC:', ["%#8.4g" % self.aic]), ('BIC:', ["%#8.4g" % self.bic]) ] diagn_left = [('Omnibus:', ["%#6.3f" % omni]), ('Prob(Omnibus):', ["%#6.3f" % omnipv]), ('Skew:', ["%#6.3f" % skew]), ('Kurtosis:', ["%#6.3f" % kurtosis]) ] diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]), ('Jarque-Bera (JB):', ["%#8.3f" % jb]), ('Prob(JB):', ["%#8.3g" % jbpv]), ('Cond. No.', ["%#8.3g" % condno]) ] if title is None: title = self.model.__class__.__name__ + ' ' + "Regression Results" #create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=True) smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, yname=yname, xname=xname, title="") #add warnings/notes, added to text format only etext =[] if self.model.exog.shape[0] < self.model.exog.shape[1]: wstr = "The input rank is higher than the number of observations." etext.append(wstr) if eigvals[0] < 1e-10: wstr = "The smallest eigenvalue is %6.3g. This might indicate " wstr += "that there are\n" wstr += "strong multicollinearity problems or that the design " wstr += "matrix is singular." wstr = wstr % eigvals[0] etext.append(wstr) elif condno > 1000: #TODO: what is recommended wstr = "The condition number is large, %6.3g. This might " wstr += "indicate that there are\n" wstr += "strong multicollinearity or other numerical " wstr += "problems." wstr = wstr % condno etext.append(wstr) if etext: etext = ["[{0}] {1}".format(i + 1, text) for i, text in enumerate(etext)] etext.insert(0, "Warnings:") smry.add_extra_txt(etext) return smry #top = summary_top(self, gleft=topleft, gright=diagn_left, #[], # yname=yname, xname=xname, # title=self.model.__class__.__name__ + ' ' + # "Regression Results") #par = summary_params(self, yname=yname, xname=xname, alpha=.05, # use_t=False) # #diagn = summary_top(self, gleft=diagn_left, gright=diagn_right, # yname=yname, xname=xname, # title="Linear Model") # #return summary_return([top, par, diagn], return_fmt=return_fmt) def summary2(self, yname=None, xname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental summary function to summarize the regression results Parameters ----------- xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ # Diagnostics from statsmodels.stats.stattools import (jarque_bera, omni_normtest, durbin_watson) from numpy.linalg import (cond, eigvalsh) from statsmodels.compatnp.collections import OrderedDict jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) dw = durbin_watson(self.wresid) condno = cond(self.model.wexog) eigvals = eigvalsh(np.dot(self.model.wexog.T, self.model.wexog)) eigvals = np.sort(eigvals) #in increasing order diagnostic = OrderedDict([ ('Omnibus:', "%.3f" % omni), ('Prob(Omnibus):', "%.3f" % omnipv), ('Skew:', "%.3f" % skew), ('Kurtosis:', "%.3f" % kurtosis), ('Durbin-Watson:', "%.3f" % dw), ('Jarque-Bera (JB):', "%.3f" % jb), ('Prob(JB):', "%.3f" % jbpv), ('Condition No.:', "%.0f" % condno) ]) # Summary from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) smry.add_dict(diagnostic) # Warnings if eigvals[0] < 1e-10: warn = "The smallest eigenvalue is %6.3g. This might indicate that\ there are strong multicollinearity problems or that the design\ matrix is singular." % eigvals[0] smry.add_text(warn) if condno > 1000: warn = "* The condition number is large (%.g). This might indicate \ strong multicollinearity or other numerical problems." % condno smry.add_text(warn) return smry class OLSResults(RegressionResults): """ Results class for for an OLS model. Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are: - get_influence - outlier_test - el_test - conf_int_el See Also -------- RegressionResults """ def get_influence(self): """ get an instance of Influence with influence and outlier measures Returns ------- infl : Influence instance the instance has methods to calculate the main influence and outlier measures for the OLS regression """ from statsmodels.stats.outliers_influence import OLSInfluence return OLSInfluence(self) def outlier_test(self, method='bonf', alpha=.05): """ Test observations for outliers according to method Parameters ---------- method : str - `bonferroni` : one-step correction - `sidak` : one-step correction - `holm-sidak` : - `holm` : - `simes-hochberg` : - `hommel` : - `fdr_bh` : Benjamini/Hochberg - `fdr_by` : Benjamini/Yekutieli See `statsmodels.stats.multitest.multipletests` for details. alpha : float familywise error rate Returns ------- table : ndarray or DataFrame Returns either an ndarray or a DataFrame if labels is not None. Will attempt to get labels from model_results if available. The columns are the Studentized residuals, the unadjusted p-value, and the corrected p-value according to method. Notes ----- The unadjusted p-value is stats.t.sf(abs(resid), df) where df = df_resid - 1. """ from statsmodels.stats.outliers_influence import outlier_test return outlier_test(self, method, alpha) def el_test(self, b0_vals, param_nums, return_weights=0, ret_params=0, method='nm', stochastic_exog=1, return_params=0): """ Tests single or joint hypotheses of the regression parameters. Parameters ---------- b0_vals : 1darray The hypthesized value of the parameter to be tested param_nums : 1darray The parameter number to be tested print_weights : bool If true, returns the weights that optimize the likelihood ratio at b0_vals. Default is False ret_params : bool If true, returns the parameter vector that maximizes the likelihood ratio at b0_vals. Also returns the weights. Default is False method : string Can either be 'nm' for Nelder-Mead or 'powell' for Powell. The optimization method that optimizes over nuisance parameters. Default is 'nm' stochastic_exog : bool When TRUE, the exogenous variables are assumed to be stochastic. When the regressors are nonstochastic, moment conditions are placed on the exogenous variables. Confidence intervals for stochastic regressors are at least as large as non-stochastic regressors. Default = TRUE Returns ------- res : tuple The p-value and -2 times the log likelihood ratio for the hypothesized values. Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.stackloss.load() >>> endog = data.endog >>> exog = sm.add_constant(data.exog) >>> model = sm.OLS(endog, exog) >>> fitted = model.fit() >>> fitted.params >>> array([-39.91967442, 0.7156402 , 1.29528612, -0.15212252]) >>> fitted.rsquared >>> 0.91357690446068196 >>> # Test that the slope on the first variable is 0 >>> fitted.test_beta([0], [1]) >>> (1.7894660442330235e-07, 27.248146353709153) """ params = np.copy(self.params) opt_fun_inst = _ELRegOpts() # to store weights if len(param_nums) == len(params): llr = opt_fun_inst._opt_nuis_regress(b0_vals, param_nums=param_nums, endog=self.model.endog, exog=self.model.exog, nobs=self.model.nobs, nvar=self.model.exog.shape[1], params=params, b0_vals=b0_vals, stochastic_exog=stochastic_exog) pval = 1 - chi2.cdf(llr, len(param_nums)) if return_weights: return llr, pval, opt_fun_inst.new_weights else: return llr, pval x0 = np.delete(params, param_nums) args = (param_nums, self.model.endog, self.model.exog, self.model.nobs, self.model.exog.shape[1], params, b0_vals, stochastic_exog) if method == 'nm': llr = optimize.fmin(opt_fun_inst._opt_nuis_regress, x0, maxfun=10000, maxiter=10000, full_output=1, disp=0, args=args)[1] if method == 'powell': llr = optimize.fmin_powell(opt_fun_inst._opt_nuis_regress, x0, full_output=1, disp=0, args=args)[1] pval = 1 - chi2.cdf(llr, len(param_nums)) if ret_params: return llr, pval, opt_fun_inst.new_weights, opt_fun_inst.new_params elif return_weights: return llr, pval, opt_fun_inst.new_weights else: return llr, pval def conf_int_el(self, param_num, sig=.05, upper_bound=None, lower_bound=None, method='nm', stochastic_exog=1): """ Computes the confidence interval for the parameter given by param_num Parameters ---------- param_num : float The parameter thats confidence interval is desired sig : float The significance level. Default is .05 upper_bound : float Tha mximum value the upper limit can be. Default is the 99.9% confidence value under OLS assumptions. lower_bound : float The minimum value the lower limit can be. Default is the 99.9% confidence value under OLS assumptions. method : string Can either be 'nm' for Nelder-Mead or 'powell' for Powell. The optimization method that optimizes over nuisance parameters. Default is 'nm' Returns ------- ci : tuple The confidence interval See Also -------- el_test Notes ----- This function uses brentq to find the value of beta where test_beta([beta], param_num)[1] is equal to the critical value. The function returns the results of each iteration of brentq at each value of beta. The current function value of the last printed optimization should be the critical value at the desired significance level. For alpha=.05, the value is 3.841459. To ensure optimization terminated successfully, it is suggested to do test_beta([lower_limit], [param_num]) If the optimization does not terminate successfully, consider switching optimization algorithms. If optimization is still not successful, try changing the values of start_int_params. If the current function value repeatedly jumps from a number between 0 and the critical value and a very large number (>50), the starting parameters of the interior minimization need to be changed. """ r0 = chi2.ppf(1 - sig, 1) if upper_bound is None: upper_bound = self.conf_int(.01)[param_num][1] if lower_bound is None: lower_bound = self.conf_int(.01)[param_num][0] f = lambda b0: self.el_test(np.array([b0]), np.array([param_num]), method=method, stochastic_exog=stochastic_exog)[0]-r0 lowerl = optimize.brenth(f, lower_bound, self.params[param_num]) upperl = optimize.brenth(f, self.params[param_num], upper_bound) # ^ Seems to be faster than brentq in most cases return (lowerl, upperl) class RegressionResultsWrapper(wrap.ResultsWrapper): _attrs = { 'chisq' : 'columns', 'sresid' : 'rows', 'weights' : 'rows', 'wresid' : 'rows', 'bcov_unscaled' : 'cov', 'bcov_scaled' : 'cov', 'HC0_se' : 'columns', 'HC1_se' : 'columns', 'HC2_se' : 'columns', 'HC3_se' : 'columns' } _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._attrs, _attrs) _methods = { 'norm_resid' : 'rows', } _wrap_methods = wrap.union_dicts( base.LikelihoodResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(RegressionResultsWrapper, RegressionResults) if __name__ == "__main__": import statsmodels.api as sm data = sm.datasets.longley.load() data.exog = add_constant(data.exog, prepend=False) ols_results = OLS(data.endog, data.exog).fit() #results gls_results = GLS(data.endog, data.exog).fit() #results print(ols_results.summary()) tables = ols_results.summary(returns='tables') csv = ols_results.summary(returns='csv') """ Summary of Regression Results ======================================= | Dependent Variable: ['y']| | Model: OLS| | Method: Least Squares| | Date: Tue, 29 Jun 2010| | Time: 22:32:21| | # obs: 16.0| | Df residuals: 9.0| | Df model: 6.0| =========================================================================== | coefficient std. error t-statistic prob.| --------------------------------------------------------------------------- | x1 15.0619 84.9149 0.1774 0.8631| | x2 -0.0358 0.0335 -1.0695 0.3127| | x3 -2.0202 0.4884 -4.1364 0.002535| | x4 -1.0332 0.2143 -4.8220 0.0009444| | x5 -0.0511 0.2261 -0.2261 0.8262| | x6 1829.1515 455.4785 4.0159 0.003037| | const -3482258.6346 890420.3836 -3.9108 0.003560| =========================================================================== | Models stats Residual stats | --------------------------------------------------------------------------- | R-squared: 0.995479 Durbin-Watson: 2.55949 | | Adjusted R-squared: 0.992465 Omnibus: 0.748615 | | F-statistic: 330.285 Prob(Omnibus): 0.687765 | | Prob (F-statistic): 4.98403e-10 JB: 0.352773 | | Log likelihood: -109.617 Prob(JB): 0.838294 | | AIC criterion: 233.235 Skew: 0.419984 | | BIC criterion: 238.643 Kurtosis: 2.43373 | --------------------------------------------------------------------------- """ statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/quantile_regression.py000066400000000000000000000353351224417117700273410ustar00rootroot00000000000000#!/usr/bin/env python ''' Quantile regression model Model parameters are estimated using iterated reweighted least squares. The asymptotic covariance matrix estimated using kernel density estimation. Author: Vincent Arel-Bundock License: BSD-3 Created: 2013-03-19 The original IRLS function was written for Matlab by Shapour Mohammadi, University of Tehran, 2008 (shmohammadi@gmail.com), with some lines based on code written by James P. Lesage in Applied Econometrics Using MATLAB(1999).PP. 73-4. Translated to python with permission from original author by Christian Prinoth (christian at prinoth dot name). ''' import numpy as np import warnings import scipy.stats as stats from scipy.linalg import pinv from scipy.stats import norm from statsmodels.tools.tools import chain_dot from statsmodels.tools.decorators import cache_readonly from statsmodels.regression.linear_model import (RegressionModel, RegressionResults, RegressionResultsWrapper) class QuantReg(RegressionModel): '''Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters ---------- endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes ----- The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit method). The asymptotic covariance matrix is estimated following the procedure in Greene (2008, p.407-408), using either the logistic or gaussian kernels (kernel argument of the fit method). References ---------- General: * Birkes, D. and Y. Dodge(1993). Alternative Methods of Regression, John Wiley and Sons. * Green,W. H. (2008). Econometric Analysis. Sixth Edition. International Student Edition. * Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press. * LeSage, J. P.(1999). Applied Econometrics Using MATLAB, Kernels (used by the fit method): * Green (2008) Table 14.2 Bandwidth selection (used by the fit method): * Bofinger, E. (1975). Estimation of a density function using order statistics. Australian Journal of Statistics 17: 1-17. * Chamberlain, G. (1994). Quantile regression, censoring, and the structure of wages. In Advances in Econometrics, Vol. 1: Sixth World Congress, ed. C. A. Sims, 171-209. Cambridge: Cambridge University Press. * Hall, P., and S. Sheather. (1988). On the distribution of the Studentized quantile. Journal of the Royal Statistical Society, Series B 50: 381-391. Keywords: Least Absolute Deviation(LAD) Regression, Quantile Regression, Regression, Robust Estimation. ''' def __init__(self, endog, exog): super(QuantReg, self).__init__(endog, exog) def whiten(self, data): """ QuantReg model whitener does nothing: returns data. """ return data def fit(self, q=.5, vcov='robust', kernel='epa', bandwidth='hsheather', max_iter=1000, p_tol=1e-6, **kwargs): '''Solve by Iterative Weighted Least Squares Parameters ---------- q : float Quantile must be between 0 and 1 vcov : string, method used to calculate the variance-covariance matrix of the parameters. Default is ``robust``: - robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition) - iid : iid errors (as in Stata 12) kernel : string, kernel to use in the kernel density estimation for the asymptotic covariance matrix: - epa: Epanechnikov - cos: Cosine - gau: Gaussian - par: Parzene bandwidth: string, Bandwidth selection method in kernel density estimation for asymptotic covariance estimate (full references in QuantReg docstring): - hsheather: Hall-Sheather (1988) - bofinger: Bofinger (1975) - chamberlain: Chamberlain (1994) ''' if q < 0 or q > 1: raise Exception('p must be between 0 and 1') kern_names = ['biw', 'cos', 'epa', 'gau', 'par'] if kernel not in kern_names: raise Exception("kernel must be one of " + ', '.join(kern_names)) else: kernel = kernels[kernel] if bandwidth == 'hsheather': bandwidth = hall_sheather elif bandwidth == 'bofinger': bandwidth = bofinger elif bandwidth == 'chamberlain': bandwidth = chamberlain else: raise Exception("bandwidth must be in 'hsheather', 'bofinger', 'chamberlain'") endog = self.endog exog = self.exog nobs = self.nobs rank = self.rank n_iter = 0 xstar = exog beta = np.ones(rank) # TODO: better start, initial beta is used only for convergence check # Note the following doesn't work yet, # the iteration loop always starts with OLS as initial beta # if start_params is not None: # if len(start_params) != rank: # raise ValueError('start_params has wrong length') # beta = start_params # else: # # start with OLS # beta = np.dot(np.linalg.pinv(exog), endog) diff = 10 cycle = False history = dict(params = [], mse=[]) while n_iter < max_iter and diff > p_tol and not cycle: n_iter += 1 beta0 = beta xtx = np.dot(xstar.T, exog) xty = np.dot(xstar.T, endog) beta = np.dot(pinv(xtx), xty) resid = endog - np.dot(exog, beta) mask = np.abs(resid) < .000001 resid[mask] = np.sign(resid[mask]) * .000001 resid = np.where(resid < 0, q * resid, (1-q) * resid) resid = np.abs(resid) xstar = exog / resid[:, np.newaxis] diff = np.max(np.abs(beta - beta0)) history['params'].append(beta) history['mse'].append(np.mean(resid*resid)) if (n_iter >= 300) and (n_iter % 100 == 0): # check for convergence circle, shouldn't happen for ii in range(2, 10): if np.all(beta == history['params'][-ii]): cycle = True break warnings.warn("Convergence cycle detected") if n_iter == max_iter: warnings.warn("Maximum number of iterations (1000) reached.") e = endog - np.dot(exog, beta) # Greene (2008, p.407) writes that Stata 6 uses this bandwidth: # h = 0.9 * np.std(e) / (nobs**0.2) # Instead, we calculate bandwidth as in Stata 12 iqre = stats.scoreatpercentile(e, 75) - stats.scoreatpercentile(e, 25) h = bandwidth(nobs, q) h = min(np.std(endog), iqre / 1.34) * (norm.ppf(q + h) - norm.ppf(q - h)) fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h)) if vcov == 'robust': d = np.where(e > 0, (q/fhat0)**2, ((1-q)/fhat0)**2) xtxi = pinv(np.dot(exog.T, exog)) xtdx = np.dot(exog.T * d[np.newaxis, :], exog) vcov = chain_dot(xtxi, xtdx, xtxi) elif vcov == 'iid': vcov = (1. / fhat0)**2 * q * (1 - q) * pinv(np.dot(exog.T, exog)) else: raise Exception("vcov must be 'robust' or 'iid'") lfit = QuantRegResults(self, beta, normalized_cov_params=vcov) lfit.q = q lfit.iterations = n_iter lfit.sparsity = 1. / fhat0 lfit.bandwidth = h lfit.history = history return RegressionResultsWrapper(lfit) def _parzen(u): z = np.where(np.abs(u) <= .5, 4./3 - 8. * u**2 + 8. * np.abs(u)**3, 8. * (1 - np.abs(u))**3 / 3.) z[np.abs(u) > 1] = 0 return z kernels = {} kernels['biw'] = lambda u: 15. / 16 * (1 - u**2)**2 * np.where(np.abs(u) <= 1, 1, 0) kernels['cos'] = lambda u: np.where(np.abs(u) <= .5, 1 + np.cos(2 * np.pi * u), 0) kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0) kernels['gau'] = lambda u: norm.pdf(u) kernels['par'] = _parzen #kernels['bet'] = lambda u: np.where(np.abs(u) <= 1, .75 * (1 - u) * (1 + u), 0) #kernels['log'] = lambda u: logistic.pdf(u) * (1 - logistic.pdf(u)) #kernels['tri'] = lambda u: np.where(np.abs(u) <= 1, 1 - np.abs(u), 0) #kernels['trw'] = lambda u: 35. / 32 * (1 - u**2)**3 * np.where(np.abs(u) <= 1, 1, 0) #kernels['uni'] = lambda u: 1. / 2 * np.where(np.abs(u) <= 1, 1, 0) def hall_sheather(n, q, alpha=.05): z = norm.ppf(q) num = 1.5 * norm.pdf(z)**2. den = 2. * z**2. + 1. h = n**(-1. / 3) * norm.ppf(1. - alpha / 2.)**(2./3) * (num / den)**(1./3) return h def bofinger(n, q): num = 9. / 2 * norm.pdf(2 * norm.ppf(q))**4 den = (2 * norm.ppf(q)**2 + 1)**2 h = n**(-1. / 5) * (num / den)**(1. / 5) return h def chamberlain(n, q, alpha=.05): return norm.ppf(1 - alpha / 2) * np.sqrt(q*(1 - q) / n) class QuantRegResults(RegressionResults): '''Results instance for the QuantReg model''' @cache_readonly def prsquared(self): q = self.q endog = self.model.endog e = self.resid e = np.where(e < 0, (1 - q) * e, q * e) e = np.abs(e) ered = endog - stats.scoreatpercentile(endog, q * 100) ered = np.where(ered < 0, (1 - q) * ered, q * ered) ered = np.abs(ered) return 1 - np.sum(e) / np.sum(ered) #@cache_readonly def scale(self): return 1. @cache_readonly def bic(self): return np.nan @cache_readonly def aic(self): return np.nan @cache_readonly def llf(self): return np.nan @cache_readonly def rsquared(self): return np.nan @cache_readonly def rsquared_adj(self): return np.nan @cache_readonly def mse(self): return np.nan @cache_readonly def mse_model(self): return np.nan @cache_readonly def mse_total(self): return np.nan @cache_readonly def centered_tss(self): return np.nan @cache_readonly def uncentered_tss(self): return np.nan @cache_readonly def HC0_se(self): raise NotImplementedError @cache_readonly def HC1_se(self): raise NotImplementedError @cache_readonly def HC2_se(self): raise NotImplementedError @cache_readonly def HC3_se(self): raise NotImplementedError def summary(self, yname=None, xname=None, title=None, alpha=.05): """Summarize the Regression Results Parameters ----------- yname : string, optional Default is `y` xname : list of strings, optional Default is `var_##` for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ #TODO: import where we need it (for now), add as cached attributes from statsmodels.stats.stattools import (jarque_bera, omni_normtest, durbin_watson) jb, jbpv, skew, kurtosis = jarque_bera(self.wresid) omni, omnipv = omni_normtest(self.wresid) #TODO: reuse condno from somewhere else ? #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog)) wexog = self.model.wexog eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog)) eigvals = np.sort(eigvals) #in increasing order condno = np.sqrt(eigvals[-1]/eigvals[0]) self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis, omni=omni, omnipv=omnipv, condno=condno, mineigval=eigvals[0]) top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['Least Squares']), ('Date:', None), ('Time:', None) ] top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]), ('Bandwidth:', ["%#8.4g" % self.bandwidth]), ('Sparsity:', ["%#8.4g" % self.sparsity]), ('No. Observations:', None), ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling ('Df Model:', None) #[self.df_model]) ] diagn_left = [('Omnibus:', ["%#6.3f" % omni]), ('Prob(Omnibus):', ["%#6.3f" % omnipv]), ('Skew:', ["%#6.3f" % skew]), ('Kurtosis:', ["%#6.3f" % kurtosis]) ] diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]), ('Jarque-Bera (JB):', ["%#8.3f" % jb]), ('Prob(JB):', ["%#8.3g" % jbpv]), ('Cond. No.', ["%#8.3g" % condno]) ] if title is None: title = self.model.__class__.__name__ + ' ' + "Regression Results" #create summary table instance from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=.05, use_t=True) # smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, #yname=yname, xname=xname, #title="") #add warnings/notes, added to text format only etext =[] if eigvals[0] < 1e-10: wstr = "The smallest eigenvalue is %6.3g. This might indicate " wstr += "that there are\n" wstr = "strong multicollinearity problems or that the design " wstr += "matrix is singular." wstr = wstr % eigvals[0] etext.append(wstr) elif condno > 1000: #TODO: what is recommended wstr = "The condition number is large, %6.3g. This might " wstr += "indicate that there are\n" wstr += "strong multicollinearity or other numerical " wstr += "problems." wstr = wstr % condno etext.append(wstr) if etext: smry.add_extra_txt(etext) return smry statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/000077500000000000000000000000001224417117700240365ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/__init__.py000066400000000000000000000000001224417117700261350ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results/000077500000000000000000000000001224417117700255375ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results/__init__.py000066400000000000000000000000001224417117700276360ustar00rootroot00000000000000leverage_influence_ols_nostars.txt000066400000000000000000000343171224417117700345010ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results residual leverage influence DFFITS u 0<=h<=1 u h/(1-h) 1959:2 -2.0742 0.026 -0.054583 -0.032 1959:3 -16.829 0.010 -0.17499 -0.162 1959:4 17.807 0.006 0.11026 0.132 1960:1 14.214 0.023 0.33301 0.206 1960:2 -24.269 0.015 -0.36715 -0.284 1960:3 5.7992 0.008 0.049617 0.050 1960:4 -20.886 0.034 -0.73335 -0.375 1961:1 9.8798 0.005 0.05179 0.067 1961:2 7.4863 0.014 0.10314 0.083 1961:3 14.11 0.010 0.13739 0.131 1961:4 -18.151 0.015 -0.27316 -0.211 1962:1 2.6465 0.011 0.030731 0.027 1962:2 -13.329 0.006 -0.083492 -0.099 1962:3 2.0981 0.006 0.013157 0.016 1962:4 -8.0123 0.007 -0.056953 -0.063 1963:1 9.6415 0.007 0.06812 0.076 1963:2 -5.0585 0.007 -0.035805 -0.040 1963:3 -10.143 0.014 -0.13973 -0.112 1963:4 2.7192 0.006 0.016533 0.020 1964:1 -12.585 0.019 -0.23723 -0.163 1964:2 -10.268 0.009 -0.088872 -0.090 1964:3 -3.1627 0.008 -0.025489 -0.027 1964:4 9.2701 0.007 0.067079 0.074 1965:1 6.4275 0.022 0.14784 0.092 1965:2 -12.496 0.008 -0.10223 -0.106 1965:3 -10.836 0.015 -0.16227 -0.125 1965:4 -28.524 0.023 -0.67634 -0.422 1966:1 0.41525 0.022 0.0095312 0.006 1966:2 -3.9265 0.008 -0.032508 -0.033 1966:3 -4.4059 0.006 -0.026898 -0.032 1966:4 -2.5184 0.006 -0.014469 -0.018 1967:1 -14.274 0.011 -0.16548 -0.145 1967:2 -7.2071 0.009 -0.064055 -0.064 1967:3 7.0015 0.006 0.041966 0.051 1967:4 5.2597 0.005 0.027186 0.035 1968:1 -17.488 0.016 -0.29267 -0.214 1968:2 -3.098 0.010 -0.032174 -0.030 1968:3 -15.821 0.007 -0.10969 -0.124 1968:4 6.4495 0.006 0.039111 0.047 1969:1 8.4822 0.009 0.076508 0.076 1969:2 0.83748 0.009 0.007586 0.007 1969:3 8.2205 0.005 0.042642 0.055 1969:4 -3.4261 0.015 -0.05238 -0.040 1970:1 0.28305 0.011 0.0030162 0.003 1970:2 7.8525 0.008 0.060957 0.065 1970:3 2.2956 0.006 0.013765 0.017 1970:4 4.9034 0.027 0.13486 0.077 1971:1 10.614 0.031 0.34456 0.182 1971:2 12.805 0.005 0.068279 0.088 1971:3 0.68069 0.007 0.0045619 0.005 1971:4 -7.0127 0.007 -0.047212 -0.054 1972:1 6.3863 0.012 0.074474 0.065 1972:2 -7.3721 0.021 -0.16064 -0.103 1972:3 -0.95255 0.005 -0.0050441 -0.006 1972:4 -16.627 0.010 -0.17016 -0.159 1973:1 -9.8533 0.026 -0.25905 -0.151 1973:2 5.6197 0.021 0.12265 0.078 1973:3 4.5097 0.017 0.080032 0.057 1973:4 5.179 0.029 0.15387 0.084 1974:1 -3.1457 0.033 -0.10821 -0.055 1974:2 1.08 0.021 0.022905 0.015 1974:3 2.4398 0.032 0.080559 0.042 1974:4 16.85 0.045 0.79075 0.350 1975:1 -48.281 0.043 -2.1902 -1.037 1975:2 -17.841 0.006 -0.10532 -0.129 1975:3 11.599 0.018 0.2142 0.149 1975:4 -2.5892 0.009 -0.023227 -0.023 1976:1 8.8047 0.024 0.2192 0.131 1976:2 14.779 0.006 0.094697 0.111 1976:3 1.0038 0.010 0.0097456 0.009 1976:4 -1.156 0.010 -0.01127 -0.011 1977:1 7.9332 0.010 0.077599 0.074 1977:2 2.2751 0.037 0.086679 0.042 1977:3 -0.45568 0.013 -0.0061948 -0.005 1977:4 -1.2479 0.009 -0.011785 -0.011 1978:1 10.833 0.010 0.10458 0.100 1978:2 -32.23 0.072 -2.5075 -0.892 1978:3 2.6192 0.020 0.053266 0.035 1978:4 -5.7424 0.015 -0.088262 -0.067 1979:1 6.9597 0.008 0.052818 0.057 1979:2 5.2803 0.019 0.10193 0.069 1979:3 -13.413 0.026 -0.35491 -0.207 1979:4 -3.6896 0.012 -0.043521 -0.038 1980:1 -0.70094 0.018 -0.012507 -0.009 1980:2 7.3702 0.060 0.46678 0.178 1980:3 -19.233 0.013 -0.25082 -0.208 1980:4 16.33 0.013 0.217 0.178 1981:1 13.303 0.017 0.23446 0.167 1981:2 7.2551 0.033 0.24794 0.127 1981:3 15.318 0.014 0.21378 0.171 1981:4 19.5 0.051 1.0428 0.435 1982:1 -1.2325 0.068 -0.090284 -0.032 1982:2 6.0687 0.064 0.4129 0.153 1982:3 12.515 0.014 0.17407 0.139 1982:4 -25.737 0.022 -0.56739 -0.365 1983:1 7.234 0.045 0.33773 0.149 1983:2 10.245 0.025 0.26194 0.155 1983:3 4.2063 0.021 0.090006 0.058 1983:4 16.804 0.024 0.41352 0.250 1984:1 17.996 0.017 0.31173 0.224 1984:2 -4.2825 0.018 -0.079641 -0.055 1984:3 5.9567 0.026 0.16111 0.093 1984:4 -7.4108 0.023 -0.172 -0.107 1985:1 -16.758 0.019 -0.33137 -0.223 1985:2 3.6384 0.008 0.028555 0.030 1985:3 -19.283 0.016 -0.30668 -0.230 1985:4 14.153 0.011 0.15085 0.138 1986:1 -6.8394 0.005 -0.037411 -0.047 1986:2 0.21312 0.071 0.016406 0.006 1986:3 -18.126 0.007 -0.13387 -0.147 1986:4 3.3595 0.007 0.023536 0.026 1987:1 12.522 0.005 0.066831 0.086 1987:2 -7.8712 0.006 -0.044228 -0.055 1987:3 -4.1257 0.005 -0.021252 -0.028 1987:4 11.053 0.011 0.11837 0.107 1988:1 -21.337 0.006 -0.12956 -0.157 1988:2 -2.2261 0.007 -0.014779 -0.017 1988:3 4.3039 0.006 0.024997 0.031 1988:4 -6.9168 0.008 -0.054036 -0.057 1989:1 10.457 0.009 0.094882 0.093 1989:2 -6.7649 0.006 -0.039373 -0.048 1989:3 -6.8402 0.009 -0.059272 -0.060 1989:4 4.5364 0.016 0.0739 0.054 1990:1 -4.1645 0.005 -0.023026 -0.029 1990:2 4.7673 0.009 0.043593 0.043 1990:3 1.6749 0.010 0.017602 0.016 1990:4 -2.1491 0.027 -0.060722 -0.034 1991:1 3.0644 0.017 0.052763 0.038 1991:2 -1.4489 0.013 -0.01868 -0.015 1991:3 13.369 0.006 0.086494 0.101 1991:4 19.045 0.007 0.12493 0.145 1992:1 -18.029 0.006 -0.10394 -0.129 1992:2 16.942 0.006 0.09862 0.122 1992:3 -4.1448 0.006 -0.02545 -0.030 1992:4 3.3586 0.008 0.02638 0.028 1993:1 15.572 0.008 0.13114 0.134 1993:2 1.5315 0.006 0.0094249 0.011 1993:3 0.67663 0.005 0.0036734 0.005 1993:4 7.0721 0.008 0.059242 0.061 1994:1 10.12 0.005 0.053258 0.069 1994:2 8.9972 0.007 0.065707 0.072 1994:3 -8.4192 0.005 -0.043756 -0.057 1994:4 9.6114 0.006 0.057062 0.069 1995:1 10.836 0.008 0.086906 0.091 1995:2 -3.7803 0.008 -0.029172 -0.031 1995:3 -6.9312 0.008 -0.055583 -0.058 1995:4 10.256 0.007 0.0707 0.080 1996:1 4.0309 0.005 0.021694 0.028 1996:2 0.4955 0.011 0.0053216 0.005 1996:3 15.719 0.006 0.1017 0.119 1996:4 -9.3751 0.006 -0.054944 -0.067 1997:1 6.5172 0.005 0.034166 0.044 1997:2 10.854 0.012 0.13379 0.113 1997:3 -2.9846 0.010 -0.030983 -0.028 1997:4 3.8535 0.006 0.021563 0.027 1998:1 14.065 0.010 0.13561 0.130 1998:2 -8.2154 0.012 -0.10053 -0.085 1998:3 -0.60661 0.008 -0.0046736 -0.005 1998:4 -6.1779 0.012 -0.074119 -0.063 1999:1 7.8563 0.006 0.045995 0.056 1999:2 -4.6266 0.005 -0.023074 -0.031 1999:3 -1.1028 0.008 -0.0083415 -0.009 1999:4 -6.8508 0.011 -0.079265 -0.069 2000:1 0.73293 0.007 0.0054868 0.006 2000:2 3.6035 0.014 0.049842 0.040 2000:3 2.708 0.008 0.022145 0.023 2000:4 1.3636 0.008 0.010847 0.011 2001:1 -5.3679 0.013 -0.070967 -0.058 2001:2 -1.6618 0.006 -0.010209 -0.012 2001:3 6.5455 0.012 0.07979 0.068 2001:4 -19.04 0.007 -0.1294 -0.148 2002:1 8.6579 0.005 0.043551 0.057 2002:2 3.8596 0.012 0.048695 0.041 2002:3 1.6741 0.006 0.010859 0.013 2002:4 8.1865 0.012 0.10312 0.086 2003:1 1.2353 0.013 0.016361 0.013 2003:2 -2.2006 0.007 -0.01465 -0.017 2003:3 -5.4225 0.012 -0.064693 -0.056 2003:4 7.3162 0.012 0.085825 0.074 2004:1 -2.0091 0.013 -0.027416 -0.022 2004:2 13.225 0.010 0.13921 0.128 2004:3 0.34955 0.015 0.0053195 0.004 2004:4 1.3304 0.013 0.017246 0.014 2005:1 0.58582 0.006 0.0037749 0.004 2005:2 -6.0779 0.011 -0.068898 -0.061 2005:3 1.3355 0.005 0.0066849 0.009 2005:4 11.099 0.040 0.45714 0.215 2006:1 -5.3245 0.010 -0.054333 -0.050 2006:2 3.7492 0.006 0.023831 0.028 2006:3 3.9062 0.009 0.034228 0.034 2006:4 -10.87 0.024 -0.26209 -0.160 2007:1 -0.97833 0.006 -0.0063771 -0.007 2007:2 1.3714 0.006 0.0078089 0.010 2007:3 -3.9713 0.005 -0.021069 -0.027 2007:4 -7.4003 0.006 -0.043073 -0.053 2008:1 2.8908 0.026 0.077049 0.045 2008:2 -8.5902 0.011 -0.093244 -0.084 2008:3 10.043 0.064 0.6827 0.253 2008:4 8.6286 0.042 0.37961 0.173 2009:1 -26.221 0.086 -2.4808 -0.800 2009:2 -14.74 0.014 -0.20334 -0.163 2009:3 3.5952 0.020 0.071804 0.048 ('*' indicates a leverage point) Cross-validation criterion = 23908.4 statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results/macro_gr_corc_stata.py000066400000000000000000000560251224417117700321140ustar00rootroot00000000000000import numpy as np est = dict( N = 201, df_m = 2, df_r = 198, F = 221.0377347263228, r2 = .6906614775140222, rmse = 10.66735221013527, mss = 50304.8300537672, rss = 22530.89582866539, r2_a = .6875368459737599, ll = -759.5001027340874, N_gaps = 0, tol = 1.00000000000e-06, max_ic = 100, ic = 4, dw = 1.993977855026291, dw_0 = 2.213805016982909, rho = -.1080744185979703, rank = 3, cmd = "prais", title = "Prais-Winsten AR(1) regression", cmdline = "prais g_realinv g_realgdp L.realint, corc rhotype(tscorr)", tranmeth = "corc", method = "iterated", depvar = "g_realinv", predict = "prais_p", rhotype = "tscorr", vce = "ols", properties = "b V", ) params_table = np.array([ 4.3704012379033, .20815070994319, 20.996331163589, 2.939551581e-52, 3.9599243998713, 4.7808780759353, 198, 1.9720174778363, 0, -.5792713864578, .26801792119756, -2.1613158697355, .03187117882819, -1.1078074114328, -.05073536148285, 198, 1.9720174778363, 0, -9.509886614971, .99049648344574, -9.6011311235432, 3.656321106e-18, -11.463162992061, -7.5566102378806, 198, 1.9720174778363, 0]).reshape(3,9) params_table_colnames = 'b se t pvalue ll ul df crit eform'.split() params_table_rownames = 'g_realgdp L.realint _cons'.split() cov = np.array([ .04609356125016, -.00228616599156, -.13992917065996, -.00228616599156, .08103590074551, -.10312637237487, -.13992917065996, -.10312637237487, 1.1416832888557]).reshape(3,3) cov_colnames = 'g_realgdp L.realint _cons'.split() cov_rownames = 'g_realgdp L.realint _cons'.split() fittedvalues = np.array([ 34.092961143383, -12.024019193439, -4.0322884907855, 26.930266763436, -18.388570551209, -8.1324920108371, -31.875322937642, .21814034941388, 21.286872048596, 18.046781817358, 24.839977757054, 20.524927286892, 9.4135589847345, 5.0452309081846, -5.6551802066401, 11.985460837387, 10.881000973745, 22.932381778173, 2.2243337561549, 28.678018086877, 8.4960232938048, 12.595931232461, -5.9735838194583, 31.918171668207, 12.51486507374, 24.858076725235, 30.404510147102, 32.036251557873, -3.4858432808681, .47271857077911, 4.4052813399748, 3.2797897853075, -10.188593201973, 4.2830255779438, 3.2910087767189, 26.047109629787, 18.955198396277, 2.5715957164729, -2.3281798982217, 17.032381944743, -4.0901785042795, .91987313367478, -18.683722252157, -12.984352504073, -6.6160771495293, 4.5273942020195, -28.749156193466, 38.148138299645, -.57670949517535, 4.4996010868259, -5.6725761358388, 20.923003763987, 31.094336738501, 6.5964070665898, 18.667129468109, 34.425102067117, 12.495888351027, -20.391896794875, 9.6404042884163, -23.337183583421, -3.2089713311258, -25.745423471535, -13.275374642526, -29.102203184727, 3.6718187868943, 20.796143264795, 13.375486730466, 30.503754833333, 1.9832701043709, -.33573283324142, 3.8454619057923, 11.198841650909, 27.300615912881, 21.617271919636, -10.213387774549, -3.041115945304, 58.662013692103, 9.3875351174185, 14.62429232726, -7.0289661733807, -6.3287417283568, 5.3688078321969, -3.9071766792954, -2.3350903711443, -45.218293861742, -12.518622374718, 22.354531985202, 24.642689788549, -26.596828143888, 8.8860015710942, -35.108835611339, -42.53255546273, -6.0981548538498, -17.177799997821, -11.40329039155, 7.0180843697455, 26.705585327159, 21.928461900264, 23.355257443029, 21.894505946705, 17.657379506229, 3.4294676089343, .97440677130275, 3.509217298751, 3.2828913970637, 14.944972085155, 1.2923006012963, 6.0435554866624, -8.8442213362579, 5.4509233390479, -2.6794059247137, -.48161809935521, 8.4201161664985, 4.549532431433, 18.996304264647, -1.8170413137623, 11.849281819014, -1.7066851102854, 12.218564198008, 4.6715818362824, 2.108627710813, 2.1972303599907, -8.5249858114578, 8.0728543937531, -4.5685185423019, -11.135138151837, -24.047910391406, -19.615146607087, -.44428684605231, -3.4877810606396, -3.9736701841582, 9.0239662841874, 8.5557600343168, 8.2271736548708, 9.0458357352972, -6.3116081663522, 1.5636968490473, -.95723547143789, 13.44821815082, 6.7721586886424, 13.649238514998, 1.1579094135284, 8.6396143031575, -6.7325794361447, -7.0767574403351, 3.1400395218988, .91586368309611, 1.2368141501238, 19.694940608963, 4.0145204498294, 8.3107956501685, 2.7428657521209, 13.988676494758, 10.116898229369, 2.524027931198, 4.6794023157157, 3.5155627033849, 11.945878601415, 18.880925278224, 4.5865622289454, 3.2393307465763, 11.062953867859, 20.793966231154, -6.3076444941097, 23.178864105791, -8.9873099715132, -1.107322743866, -16.332040882272, .42734142108626, -15.050781890016, -4.6612306163595, 4.565652288529, .80725599873503, -.87706444767528, -8.5407936802022, -1.3521383036839, 4.4765977604986, 19.623863498831, 7.1113956960438, 3.9798487855641, 3.6934842863203, 4.6801104799091, 6.7218162617593, 7.7832579175778, -1.2290990957424, 3.0474310004174, 2.7567736850761, 11.188206993423, -4.3306276498455, -9.5365114805844, -.53338170341178, -5.206342794124, 4.1154674910376, 4.7884361973806, -.64799653797949, -10.743852791188, -2.461403042047, -17.431541988995, -36.151189705211, -43.711601400093, -12.334881925913, 4.3341943478598]) fittedvalues_colnames = 'fittedvalues'.split() fittedvalues_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32 r33 r34 r35 r36 r37 r38 r39 r40 r41 r42 r43 r44 r45 r46 r47 r48 r49 r50 r51 r52 r53 r54 r55 r56 r57 r58 r59 r60 r61 r62 r63 r64 r65 r66 r67 r68 r69 r70 r71 r72 r73 r74 r75 r76 r77 r78 r79 r80 r81 r82 r83 r84 r85 r86 r87 r88 r89 r90 r91 r92 r93 r94 r95 r96 r97 r98 r99 r100 r101 r102 r103 r104 r105 r106 r107 r108 r109 r110 r111 r112 r113 r114 r115 r116 r117 r118 r119 r120 r121 r122 r123 r124 r125 r126 r127 r128 r129 r130 r131 r132 r133 r134 r135 r136 r137 r138 r139 r140 r141 r142 r143 r144 r145 r146 r147 r148 r149 r150 r151 r152 r153 r154 r155 r156 r157 r158 r159 r160 r161 r162 r163 r164 r165 r166 r167 r168 r169 r170 r171 r172 r173 r174 r175 r176 r177 r178 r179 r180 r181 r182 r183 r184 r185 r186 r187 r188 r189 r190 r191 r192 r193 r194 r195 r196 r197 r198 r199 r200 r201 r202'.split() fittedvalues_se = np.array([ 1.6473872957314, 1.0113850707964, .7652190209006, 1.534040692487, 1.2322657893516, .91158310011358, 1.8788908534927, .69811681139453, 1.1767226952576, .98858641944986, 1.2396486964702, 1.0822616701665, .7753481322096, .76860948102754, .82419167032501, .82704859414698, .82559058693092, 1.1856589638289, .75525402187835, 1.3922568629515, .91202394595876, .88520111465725, .83302439819375, 1.5372725435314, .89177714550085, 1.2372408530959, 1.5528101417195, 1.5366212693726, .89043286423929, .75705684478985, .73518329909511, 1.0572392569996, .93211401184553, .75196863280241, .69360947719119, 1.3106012580927, 1.0209999290642, .81133440956099, .75559203443239, .94831739014224, .93322729630976, .69417535741881, 1.2398628905688, 1.0261547060202, .86379439641561, .75032871325094, 1.6696098440084, 1.8282957404356, .70460484132802, .79812152719782, .8016314317639, 1.0872600753814, 1.4987341895633, .70529145955969, 1.0149968053074, 1.6467690495895, 1.4677194036031, 1.3384921199391, 1.7046600039586, 1.8412203199988, 1.4367363864341, 1.8103264820977, 2.1242535073081, 2.1101077640804, .745443770473, 1.3651585515035, .94103291060948, 1.5981517654886, .77654854940188, .96643611834989, .97013991129283, .97997241479455, 1.9501042753844, 1.1758285978818, .9603215961611, .96398854110297, 2.7840981863809, 1.413576103565, 1.2364541532613, .85434724833597, 1.3715636637071, 1.6066244823944, 1.0686828612843, 1.3176342918943, 2.5021204574497, 1.1330278834213, 1.1636905462155, 1.3340487203567, 1.8517041947194, 1.1639745295786, 2.304428659791, 2.6786336953086, 2.5354570978302, 1.1805089049206, 1.4742162139537, 2.1143611509083, 1.5986085606212, 1.4576906503482, 1.5624993432094, 1.3158885079997, 1.3538646144937, 1.619414838601, 1.5015020934095, 1.3855500128549, .86402762365746, 1.2492579410465, 1.0127848313981, .7158840034754, 2.6885176805365, .83774886448436, .81595294371756, .70504062914926, .73023061883128, .69228489060199, 1.0360869522995, .75590017576772, .80137295502042, .73813943198212, .87064014103306, .93205385237951, .73787688995958, .90978243354856, 1.2648707813937, .72349602267222, .93927915871284, 1.0172563832871, 1.6776109713009, 1.3175046180712, 1.1171209329088, .78301185980687, .78918856044216, .74112029520399, .74451851348435, .76567972163601, .87244845911018, .90049784898067, .76120214907789, .71166085065751, .90870901548966, .70296598825213, .84621726620383, .69493630035748, .75059818136305, .87986442867509, .86277076414594, .87380573440987, .80727321528666, .70768449827207, 1.040570485976, .78088040381913, .74507562843308, .69792915007094, 1.0988781063157, 1.0027100639643, .72261812289435, .96179349200945, 1.0868653716913, .86400879397201, 1.094197020208, .74093235691985, .67984744050458, .8543509596351, 1.0818096940792, .84834713756928, 1.1852779873098, .89126223599075, .87108400982232, 1.1499249237911, .75958251908371, 1.1014705573844, .80471862911078, .68374044074715, 1.1058743822208, .78261190454279, 1.1081858646506, 1.1333032178191, .79529240253276, 1.0984357956297, 1.0696598625923, 1.1524457583105, 1.0091448954756, 1.2183914737714, 1.1249902630054, .78498039737187, 1.04667102358, .68135274836726, 1.9934991355412, .995276050357, .77642057869112, .92353879856007, 1.5308870921603, .78757917918954, .73146117757714, .70335029981046, .73869949489316, 1.6122142696964, 1.023758116961, 2.5353634753616, 2.1057022389201, 3.0080233992196, 1.1648858140616, 1.3964393384387]) fittedvalues_se_colnames = 'fittedvalues_se'.split() fittedvalues_se_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32 r33 r34 r35 r36 r37 r38 r39 r40 r41 r42 r43 r44 r45 r46 r47 r48 r49 r50 r51 r52 r53 r54 r55 r56 r57 r58 r59 r60 r61 r62 r63 r64 r65 r66 r67 r68 r69 r70 r71 r72 r73 r74 r75 r76 r77 r78 r79 r80 r81 r82 r83 r84 r85 r86 r87 r88 r89 r90 r91 r92 r93 r94 r95 r96 r97 r98 r99 r100 r101 r102 r103 r104 r105 r106 r107 r108 r109 r110 r111 r112 r113 r114 r115 r116 r117 r118 r119 r120 r121 r122 r123 r124 r125 r126 r127 r128 r129 r130 r131 r132 r133 r134 r135 r136 r137 r138 r139 r140 r141 r142 r143 r144 r145 r146 r147 r148 r149 r150 r151 r152 r153 r154 r155 r156 r157 r158 r159 r160 r161 r162 r163 r164 r165 r166 r167 r168 r169 r170 r171 r172 r173 r174 r175 r176 r177 r178 r179 r180 r181 r182 r183 r184 r185 r186 r187 r188 r189 r190 r191 r192 r193 r194 r195 r196 r197 r198 r199 r200 r201 r202'.split() class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']): self[att] = self.params_table[:,i] results = Bunch( params_table=params_table, params_table_colnames=params_table_colnames, params_table_rownames=params_table_rownames, cov=cov, cov_colnames=cov_colnames, cov_rownames=cov_rownames, fittedvalues=fittedvalues, fittedvalues_colnames=fittedvalues_colnames, fittedvalues_rownames=fittedvalues_rownames, fittedvalues_se=fittedvalues_se, fittedvalues_se_colnames=fittedvalues_se_colnames, fittedvalues_se_rownames=fittedvalues_se_rownames, **est ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results/results_regression.py000066400000000000000000000175461224417117700320670ustar00rootroot00000000000000""" Hard-coded results for test_regression """ ### REGRESSION MODEL RESULTS : OLS, GLS, WLS, AR### import numpy as np class Longley(object): ''' The results for the Longley dataset were obtained from NIST http://www.itl.nist.gov/div898/strd/general/dataarchive.html Other results were obtained from Stata ''' def __init__(self): self.params = ( 15.0618722713733, -0.358191792925910E-01, -2.02022980381683, -1.03322686717359, -0.511041056535807E-01, 1829.15146461355, -3482258.63459582) self.bse = (84.9149257747669, 0.334910077722432E-01, 0.488399681651699, 0.214274163161675, 0.226073200069370, 455.478499142212, 890420.383607373) self.scale = 92936.0061673238 self.rsquared = 0.995479004577296 self.rsquared_adj = 0.99246501 self.df_model = 6 self.df_resid = 9 self.ess = 184172401.944494 self.ssr = 836424.055505915 self.mse_model = 30695400.3240823 self.mse_resid = 92936.0061673238 self.mse_total = (self.ess + self.ssr) / (self.df_model + self.df_resid) self.fvalue = 330.285339234588 self.llf = -109.6174 self.aic = 233.2349 self.bic = 238.643 self.pvalues = np.array([ 0.86314083, 0.31268106, 0.00253509, 0.00094437, 0.8262118 , 0.0030368 , 0.0035604 ]) #pvalues from rmodelwrap self.resid = np.array((267.34003, -94.01394, 46.28717, -410.11462, 309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409, -17.26893, -39.05504, -155.54997, -85.67131, 341.93151, -206.75783)) def conf_int(self): # a method to be consistent with sm return [(-177.0291,207.1524), (-.111581,.0399428),(-3.125065, -.9153928),(-1.517948,-.5485049),(-.5625173,.4603083), (798.7873,2859.515),(-5496529,-1467987)] HC0_se=(51.22035, 0.02458, 0.38324, 0.14625, 0.15821, 428.38438, 832212) HC1_se=(68.29380, 0.03277, 0.51099, 0.19499, 0.21094, 571.17917, 1109615) HC2_se=(67.49208, 0.03653, 0.55334, 0.20522, 0.22324, 617.59295, 1202370) HC3_se=(91.11939, 0.05562, 0.82213, 0.29879, 0.32491, 922.80784, 1799477) class LongleyGls(object): ''' The following results were obtained from running the test script with R. ''' def __init__(self): self.params = (6.73894832e-02, -4.74273904e-01, 9.48988771e+04) self.bse = (1.07033903e-02, 1.53385472e-01, 1.39447723e+04) self.llf = -121.4294962954981 self.fittedvalues = [59651.8255, 60860.1385, 60226.5336, 61467.1268, 63914.0846, 64561.9553, 64935.9028, 64249.1684, 66010.0426, 66834.7630, 67612.9309, 67018.8998, 68918.7758, 69310.1280, 69181.4207, 70598.8734] self.resid = [671.174465, 261.861502, -55.533603, -280.126803, -693.084618, -922.955349, 53.097212, -488.168351, 8.957367, 1022.236970, 556.069099, -505.899787, -263.775842, 253.871965, 149.579309, -47.873374] self.scale = 542.443043098**2 self.tvalues = [6.296088, -3.092039, 6.805337] self.pvalues = [2.761673e-05, 8.577197e-03, 1.252284e-05] self.bic = 253.118790021 self.aic = 250.858992591 class CCardWLS(object): def __init__(self): self.params = [-2.6941851611, 158.426977524, -7.24928987289, 60.4487736936, -114.10886935] self.bse = [3.807306306, 76.39115431, 9.724337321, 58.55088753, 139.6874965] #NOTE: we compute the scale differently than they do for analytic #weights self.scale = 189.0025755829012 ** 2 self.rsquared = .2549143871187359 self.rsquared_adj = .2104316639616448 self.df_model = 4 self.df_resid = 67 self.ess = 818838.8079468152 self.ssr = 2393372.229657007 self.mse_model = 818838.8079468152 / 4 self.mse_resid = 2393372.229657007 / 67 self.mse_total = (self.ess + self.ssr) / 71. self.fvalue = 5.730638077585917 self.llf = -476.9792946562806 self.aic = 963.95858931256 self.bic = 975.34191990764 # pvalues from R self.pvalues = [0.4816259843354, 0.0419360764848, 0.4585895209814, 0.3055904431658, 0.4168883565685] self.resid = [-286.964904785, -128.071563721, -405.860900879, -20.1363945007, -169.824432373, -82.6842575073, -283.314300537, -52.1719360352, 433.822174072, -190.607543945, -118.839683533, -133.97076416, -85.5728149414, 66.8180847168, -107.571769714, -149.883285522, -140.972610474, 75.9255981445, -135.979736328, -415.701263428, 130.080032349, 25.2313785553, 1042.14013672, -75.6622238159, 177.336639404, 315.870544434, -8.72801017761, 240.823760986, 54.6106033325, 65.6312484741, -40.9218444824, 24.6115856171, -131.971786499, 36.1587944031, 92.5052108765, -136.837036133, 242.73274231, -65.0315093994, 20.1536407471, -15.8874826431, 27.3513431549, -173.861785889, -113.121154785, -37.1303443909, 1510.31530762, 582.916931152, -17.8628063202, -132.77381897, -108.896934509, 12.4665794373, -122.014572144, -158.986968994, -175.798873901, 405.886505127, 99.3692703247, 85.3450698853, -179.15007019, -34.1245117188, -33.4909172058, -20.7287139893, -116.217689514, 53.8837738037, -52.1533050537, -100.632293701, 34.9342498779, -96.6685943604, -367.32925415, -40.1300048828, -72.8692245483, -60.8728256226, -35.9937324524, -222.944747925] def conf_int(self): # a method to be consistent with sm return [( -10.2936, 4.90523), ( 5.949595, 310.9044), (-26.65915, 12.16057), (-56.41929, 177.3168), (-392.9263, 164.7085)] class LongleyRTO(object): def __init__(self): # Regression Through the Origin model # from Stata, make sure you force double to replicate self.params = [-52.993523, .07107319, -.42346599, -.57256869, -.41420348, 48.417859] self.bse = [129.5447812, .0301663805, .4177363573, .2789908665, .3212848136, 17.68947719] self.scale = 475.1655079819532**2 self.rsquared = .9999670130705958 self.rsquared_adj = .9999472209129532 self.df_model = 6 self.df_resid = 10 self.ess = 68443718827.40025 self.ssr = 2257822.599757476 self.mse_model = 68443718827.40025 / 6 self.mse_resid = 2257822.599757476 / 10 self.mse_total = (self.ess + self.ssr) / 16. self.fvalue = 50523.39573737409 self.llf = -117.5615983965251 self.aic = 247.123196793 self.bic = 251.758729126 self.pvalues = [0.6911082828354, 0.0402241925699, 0.3346175334102, 0.0672506018552, 0.2263470345100, 0.0209367642585] self.resid = [279.902740479, -130.324661255, 90.7322845459, -401.312530518, -440.467681885, -543.54510498, 201.321121216, 215.908889771, 73.0936813354, 913.216918945, 424.824859619, -8.56475830078, -361.329742432, 27.3456058502, 151.28956604, -492.499359131] def conf_int(self): return [(-341.6373, 235.6502), ( .0038583, .1382881), (-1.354241, .5073086), (-1.194199, .0490617), (-1.130071, .3016637), ( 9.003248, 87.83247)] statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/results_quantile_regression.py000066400000000000000000000671151224417117700322650ustar00rootroot00000000000000import numpy as np class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self epanechnikov_hsheather_q75 = Bunch() epanechnikov_hsheather_q75.table = np.array([ [.6440143 , .0122001 , 52.79 , 0.000 , .6199777 , .6680508], [62.39648 , 13.5509 , 4.60 , 0.000 , 35.69854 , 89.09443] ]) epanechnikov_hsheather_q75.psrsquared = 0.6966 epanechnikov_hsheather_q75.rank = 2 epanechnikov_hsheather_q75.sparsity = 223.784434936344 epanechnikov_hsheather_q75.bwidth = .1090401129546568 #epanechnikov_hsheather_q75.kbwidth = 59.62067927472172 # Stata 12 results epanechnikov_hsheather_q75.kbwidth = 59.30 # TODO: why do we need lower tolerance? epanechnikov_hsheather_q75.df_m = 1 epanechnikov_hsheather_q75.df_r = 233 epanechnikov_hsheather_q75.f_r = .0044685860313942 epanechnikov_hsheather_q75.N = 235 epanechnikov_hsheather_q75.q_v = 745.2352905273438 epanechnikov_hsheather_q75.q = .75 epanechnikov_hsheather_q75.sum_rdev = 43036.06956481934 epanechnikov_hsheather_q75.sum_adev = 13058.50008841318 epanechnikov_hsheather_q75.convcode = 0 biweight_bofinger = Bunch() biweight_bofinger.table = np.array([ [ .5601805 , .0136491 , 41.04 , 0.000 , .533289 , .5870719], [ 81.48233 , 15.1604 , 5.37 , 0.000 , 51.61335 , 111.3513] ]) biweight_bofinger.psrsquared = 0.6206 biweight_bofinger.rank = 2 biweight_bofinger.sparsity = 216.8218989750115 biweight_bofinger.bwidth = .2173486679767846 biweight_bofinger.kbwidth = 91.50878448104551 biweight_bofinger.df_m = 1 biweight_bofinger.df_r = 233 biweight_bofinger.f_r = .0046120802590851 biweight_bofinger.N = 235 biweight_bofinger.q_v = 582.541259765625 biweight_bofinger.q = .5 biweight_bofinger.sum_rdev = 46278.05667114258 biweight_bofinger.sum_adev = 17559.93220318131 biweight_bofinger.convcode = 0 biweight_hsheather = Bunch() biweight_hsheather.table = np.array([ [.5601805 , .0128449 , 43.61 , 0.000 , .5348735 , .5854875], [81.48233 , 14.26713 , 5.71 , 0.000 , 53.37326 , 109.5914] ]) biweight_hsheather.psrsquared = 0.6206 biweight_hsheather.rank = 2 biweight_hsheather.sparsity = 204.0465407204423 biweight_hsheather.bwidth = .1574393314202373 biweight_hsheather.kbwidth = 64.53302151153288 biweight_hsheather.df_m = 1 biweight_hsheather.df_r = 233 biweight_hsheather.f_r = .0049008427022052 biweight_hsheather.N = 235 biweight_hsheather.q_v = 582.541259765625 biweight_hsheather.q = .5 biweight_hsheather.sum_rdev = 46278.05667114258 biweight_hsheather.sum_adev = 17559.93220318131 biweight_hsheather.convcode = 0 biweight_chamberlain = Bunch() biweight_chamberlain.table = np.array([ [ .5601805 , .0114969 , 48.72 , 0.000 , .5375294 , .5828315], [ 81.48233 , 12.76983 , 6.38 , 0.000 , 56.32325 , 106.6414] ]) biweight_chamberlain.psrsquared = 0.6206 biweight_chamberlain.rank = 2 biweight_chamberlain.sparsity = 182.6322495257494 biweight_chamberlain.bwidth = .063926976464458 biweight_chamberlain.kbwidth = 25.61257055690209 biweight_chamberlain.df_m = 1 biweight_chamberlain.df_r = 233 biweight_chamberlain.f_r = .005475484218131 biweight_chamberlain.N = 235 biweight_chamberlain.q_v = 582.541259765625 biweight_chamberlain.q = .5 biweight_chamberlain.sum_rdev = 46278.05667114258 biweight_chamberlain.sum_adev = 17559.93220318131 biweight_chamberlain.convcode = 0 epanechnikov_bofinger = Bunch() epanechnikov_bofinger.table = np.array([ [ .5601805 , .0209663 , 26.72 , 0.000 , .5188727 , .6014882], [ 81.48233 , 23.28774 , 3.50 , 0.001 , 35.60088 , 127.3638] ]) epanechnikov_bofinger.psrsquared = 0.6206 epanechnikov_bofinger.rank = 2 epanechnikov_bofinger.sparsity = 333.0579553401614 epanechnikov_bofinger.bwidth = .2173486679767846 epanechnikov_bofinger.kbwidth = 91.50878448104551 epanechnikov_bofinger.df_m = 1 epanechnikov_bofinger.df_r = 233 epanechnikov_bofinger.f_r = .0030024804511235 epanechnikov_bofinger.N = 235 epanechnikov_bofinger.q_v = 582.541259765625 epanechnikov_bofinger.q = .5 epanechnikov_bofinger.sum_rdev = 46278.05667114258 epanechnikov_bofinger.sum_adev = 17559.93220318131 epanechnikov_bofinger.convcode = 0 epanechnikov_hsheather = Bunch() epanechnikov_hsheather.table = np.array([ [.5601805 , .0170484 , 32.86 , 0.000 , .5265918 , .5937692], [81.48233 , 18.93605 , 4.30 , 0.000 , 44.17457 , 118.7901] ]) epanechnikov_hsheather.psrsquared = 0.6206 epanechnikov_hsheather.rank = 2 epanechnikov_hsheather.sparsity = 270.8207209067576 epanechnikov_hsheather.bwidth = .1574393314202373 epanechnikov_hsheather.kbwidth = 64.53302151153288 epanechnikov_hsheather.df_m = 1 epanechnikov_hsheather.df_r = 233 epanechnikov_hsheather.f_r = .0036924796472434 epanechnikov_hsheather.N = 235 epanechnikov_hsheather.q_v = 582.541259765625 epanechnikov_hsheather.q = .5 epanechnikov_hsheather.sum_rdev = 46278.05667114258 epanechnikov_hsheather.sum_adev = 17559.93220318131 epanechnikov_hsheather.convcode = 0 epanechnikov_chamberlain = Bunch() epanechnikov_chamberlain.table = np.array([ [.5601805 , .0130407 , 42.96 , 0.000 , .5344876 , .5858733], [81.48233 , 14.48467 , 5.63 , 0.000 , 52.94468 , 110.02] ]) epanechnikov_chamberlain.psrsquared = 0.6206 epanechnikov_chamberlain.rank = 2 epanechnikov_chamberlain.sparsity = 207.1576340635951 epanechnikov_chamberlain.bwidth = .063926976464458 epanechnikov_chamberlain.kbwidth = 25.61257055690209 epanechnikov_chamberlain.df_m = 1 epanechnikov_chamberlain.df_r = 233 epanechnikov_chamberlain.f_r = .0048272418466269 epanechnikov_chamberlain.N = 235 epanechnikov_chamberlain.q_v = 582.541259765625 epanechnikov_chamberlain.q = .5 epanechnikov_chamberlain.sum_rdev = 46278.05667114258 epanechnikov_chamberlain.sum_adev = 17559.93220318131 epanechnikov_chamberlain.convcode = 0 epan2_bofinger = Bunch() epan2_bofinger.table = np.array([ [.5601805 , .0143484 , 39.04 , 0.000 , .5319113 , .5884496], [81.48233 , 15.93709 , 5.11 , 0.000 , 50.08313 , 112.8815] ]) epan2_bofinger.psrsquared = 0.6206 epan2_bofinger.rank = 2 epan2_bofinger.sparsity = 227.9299402797656 epan2_bofinger.bwidth = .2173486679767846 epan2_bofinger.kbwidth = 91.50878448104551 epan2_bofinger.df_m = 1 epan2_bofinger.df_r = 233 epan2_bofinger.f_r = .0043873130435281 epan2_bofinger.N = 235 epan2_bofinger.q_v = 582.541259765625 epan2_bofinger.q = .5 epan2_bofinger.sum_rdev = 46278.05667114258 epan2_bofinger.sum_adev = 17559.93220318131 epan2_bofinger.convcode = 0 epan2_hsheather = Bunch() epan2_hsheather.table = np.array([ [.5601805 , .0131763 , 42.51 , 0.000 , .5342206 , .5861403], [81.48233 , 14.63518 , 5.57 , 0.000 , 52.64815 , 110.3165] ]) epan2_hsheather.psrsquared = 0.6206 epan2_hsheather.rank = 2 epan2_hsheather.sparsity = 209.3102085912557 epan2_hsheather.bwidth = .1574393314202373 epan2_hsheather.kbwidth = 64.53302151153288 epan2_hsheather.df_m = 1 epan2_hsheather.df_r = 233 epan2_hsheather.f_r = .0047775978378236 epan2_hsheather.N = 235 epan2_hsheather.q_v = 582.541259765625 epan2_hsheather.q = .5 epan2_hsheather.sum_rdev = 46278.05667114258 epan2_hsheather.sum_adev = 17559.93220318131 epan2_hsheather.convcode = 0 epan2_chamberlain = Bunch() epan2_chamberlain.table = np.array([ [.5601805 , .0117925 , 47.50 , 0.000 , .5369469 , .583414], [81.48233 , 13.0982 , 6.22 , 0.000 , 55.67629 , 107.2884] ]) epan2_chamberlain.psrsquared = 0.6206 epan2_chamberlain.rank = 2 epan2_chamberlain.sparsity = 187.3286437436797 epan2_chamberlain.bwidth = .063926976464458 epan2_chamberlain.kbwidth = 25.61257055690209 epan2_chamberlain.df_m = 1 epan2_chamberlain.df_r = 233 epan2_chamberlain.f_r = .0053382119253919 epan2_chamberlain.N = 235 epan2_chamberlain.q_v = 582.541259765625 epan2_chamberlain.q = .5 epan2_chamberlain.sum_rdev = 46278.05667114258 epan2_chamberlain.sum_adev = 17559.93220318131 epan2_chamberlain.convcode = 0 rectangle_bofinger = Bunch() rectangle_bofinger.table = np.array([ [.5601805 , .0158331 , 35.38 , 0.000 , .5289861 , .5913748], [81.48233 , 17.5862 , 4.63 , 0.000 , 46.83404 , 116.1306] ]) rectangle_bofinger.psrsquared = 0.6206 rectangle_bofinger.rank = 2 rectangle_bofinger.sparsity = 251.515372550242 rectangle_bofinger.bwidth = .2173486679767846 rectangle_bofinger.kbwidth = 91.50878448104551 rectangle_bofinger.df_m = 1 rectangle_bofinger.df_r = 233 rectangle_bofinger.f_r = .0039759001203803 rectangle_bofinger.N = 235 rectangle_bofinger.q_v = 582.541259765625 rectangle_bofinger.q = .5 rectangle_bofinger.sum_rdev = 46278.05667114258 rectangle_bofinger.sum_adev = 17559.93220318131 rectangle_bofinger.convcode = 0 rectangle_hsheather = Bunch() rectangle_hsheather.table = np.array([ [.5601805 , .0137362 , 40.78 , 0.000 , .5331174 , .5872435], [81.48233 , 15.25712 , 5.34 , 0.000 , 51.42279 , 111.5419] ]) rectangle_hsheather.psrsquared = 0.6206 rectangle_hsheather.rank = 2 rectangle_hsheather.sparsity = 218.2051806505069 rectangle_hsheather.bwidth = .1574393314202373 rectangle_hsheather.kbwidth = 64.53302151153288 rectangle_hsheather.df_m = 1 rectangle_hsheather.df_r = 233 rectangle_hsheather.f_r = .004582842611797 rectangle_hsheather.N = 235 rectangle_hsheather.q_v = 582.541259765625 rectangle_hsheather.q = .5 rectangle_hsheather.sum_rdev = 46278.05667114258 rectangle_hsheather.sum_adev = 17559.93220318131 rectangle_hsheather.convcode = 0 rectangle_chamberlain = Bunch() rectangle_chamberlain.table = np.array([ [.5601805 , .0118406 , 47.31 , 0.000 , .5368522 , .5835087], [81.48233 , 13.1516 , 6.20 , 0.000 , 55.57108 , 107.3936] ]) rectangle_chamberlain.psrsquared = 0.6206 rectangle_chamberlain.rank = 2 rectangle_chamberlain.sparsity = 188.0923150272497 rectangle_chamberlain.bwidth = .063926976464458 rectangle_chamberlain.kbwidth = 25.61257055690209 rectangle_chamberlain.df_m = 1 rectangle_chamberlain.df_r = 233 rectangle_chamberlain.f_r = .0053165383171297 rectangle_chamberlain.N = 235 rectangle_chamberlain.q_v = 582.541259765625 rectangle_chamberlain.q = .5 rectangle_chamberlain.sum_rdev = 46278.05667114258 rectangle_chamberlain.sum_adev = 17559.93220318131 rectangle_chamberlain.convcode = 0 triangle_bofinger = Bunch() triangle_bofinger.table = np.array([ [.5601805 , .0138712 , 40.38 , 0.000 , .5328515 , .5875094], [81.48233 , 15.40706 , 5.29 , 0.000 , 51.12738 , 111.8373] ]) triangle_bofinger.psrsquared = 0.6206 triangle_bofinger.rank = 2 triangle_bofinger.sparsity = 220.3495620604223 triangle_bofinger.bwidth = .2173486679767846 triangle_bofinger.kbwidth = 91.50878448104551 triangle_bofinger.df_m = 1 triangle_bofinger.df_r = 233 triangle_bofinger.f_r = .0045382436463649 triangle_bofinger.N = 235 triangle_bofinger.q_v = 582.541259765625 triangle_bofinger.q = .5 triangle_bofinger.sum_rdev = 46278.05667114258 triangle_bofinger.sum_adev = 17559.93220318131 triangle_bofinger.convcode = 0 triangle_hsheather = Bunch() triangle_hsheather.table = np.array([ [.5601805 , .0128874 , 43.47 , 0.000 , .5347898 , .5855711], [81.48233 , 14.31431 , 5.69 , 0.000 , 53.2803 , 109.6844] ]) triangle_hsheather.psrsquared = 0.6206 triangle_hsheather.rank = 2 triangle_hsheather.sparsity = 204.7212998199564 triangle_hsheather.bwidth = .1574393314202373 triangle_hsheather.kbwidth = 64.53302151153288 triangle_hsheather.df_m = 1 triangle_hsheather.df_r = 233 triangle_hsheather.f_r = .004884689579831 triangle_hsheather.N = 235 triangle_hsheather.q_v = 582.541259765625 triangle_hsheather.q = .5 triangle_hsheather.sum_rdev = 46278.05667114258 triangle_hsheather.sum_adev = 17559.93220318131 triangle_hsheather.convcode = 0 triangle_chamberlain = Bunch() triangle_chamberlain.table = np.array([ [.5601805 , .0115725 , 48.41 , 0.000 , .5373803 , .5829806], [81.48233 , 12.85389 , 6.34 , 0.000 , 56.15764 , 106.807] ]) triangle_chamberlain.psrsquared = 0.6206 triangle_chamberlain.rank = 2 triangle_chamberlain.sparsity = 183.8344452913298 triangle_chamberlain.bwidth = .063926976464458 triangle_chamberlain.kbwidth = 25.61257055690209 triangle_chamberlain.df_m = 1 triangle_chamberlain.df_r = 233 triangle_chamberlain.f_r = .0054396769790083 triangle_chamberlain.N = 235 triangle_chamberlain.q_v = 582.541259765625 triangle_chamberlain.q = .5 triangle_chamberlain.sum_rdev = 46278.05667114258 triangle_chamberlain.sum_adev = 17559.93220318131 triangle_chamberlain.convcode = 0 gaussian_bofinger = Bunch() gaussian_bofinger.table = np.array([ [.5601805 , .0197311 , 28.39 , 0.000 , .5213062 , .5990547], [81.48233 , 21.91582 , 3.72 , 0.000 , 38.30383 , 124.6608] ]) gaussian_bofinger.psrsquared = 0.6206 gaussian_bofinger.rank = 2 gaussian_bofinger.sparsity = 313.4370075776719 gaussian_bofinger.bwidth = .2173486679767846 gaussian_bofinger.kbwidth = 91.50878448104551 gaussian_bofinger.df_m = 1 gaussian_bofinger.df_r = 233 gaussian_bofinger.f_r = .0031904337261521 gaussian_bofinger.N = 235 gaussian_bofinger.q_v = 582.541259765625 gaussian_bofinger.q = .5 gaussian_bofinger.sum_rdev = 46278.05667114258 gaussian_bofinger.sum_adev = 17559.93220318131 gaussian_bofinger.convcode = 0 gaussian_hsheather = Bunch() gaussian_hsheather.table = np.array([ [.5601805 , .016532 , 33.88 , 0.000 , .5276092 , .5927518], [81.48233 , 18.36248 , 4.44 , 0.000 , 45.30462 , 117.66] ]) gaussian_hsheather.psrsquared = 0.6206 gaussian_hsheather.rank = 2 gaussian_hsheather.sparsity = 262.6175743002715 gaussian_hsheather.bwidth = .1574393314202373 gaussian_hsheather.kbwidth = 64.53302151153288 gaussian_hsheather.df_m = 1 gaussian_hsheather.df_r = 233 gaussian_hsheather.f_r = .0038078182797341 gaussian_hsheather.N = 235 gaussian_hsheather.q_v = 582.541259765625 gaussian_hsheather.q = .5 gaussian_hsheather.sum_rdev = 46278.05667114258 gaussian_hsheather.sum_adev = 17559.93220318131 gaussian_hsheather.convcode = 0 gaussian_chamberlain = Bunch() gaussian_chamberlain.table = np.array([ [.5601805 , .0128123 , 43.72 , 0.000 , .5349378 , .5854232], [81.48233 , 14.23088 , 5.73 , 0.000 , 53.44468 , 109.52] ]) gaussian_chamberlain.psrsquared = 0.6206 gaussian_chamberlain.rank = 2 gaussian_chamberlain.sparsity = 203.5280962791137 gaussian_chamberlain.bwidth = .063926976464458 gaussian_chamberlain.kbwidth = 25.61257055690209 gaussian_chamberlain.df_m = 1 gaussian_chamberlain.df_r = 233 gaussian_chamberlain.f_r = .004913326554328 gaussian_chamberlain.N = 235 gaussian_chamberlain.q_v = 582.541259765625 gaussian_chamberlain.q = .5 gaussian_chamberlain.sum_rdev = 46278.05667114258 gaussian_chamberlain.sum_adev = 17559.93220318131 gaussian_chamberlain.convcode = 0 cosine_bofinger = Bunch() cosine_bofinger.table = np.array([ [.5601805 , .0121011 , 46.29 , 0.000 , .536339 , .5840219], [81.48233 , 13.44092 , 6.06 , 0.000 , 55.00106 , 107.9636] ]) cosine_bofinger.psrsquared = 0.6206 cosine_bofinger.rank = 2 cosine_bofinger.sparsity = 192.2302014415605 cosine_bofinger.bwidth = .2173486679767846 cosine_bofinger.kbwidth = 91.50878448104551 cosine_bofinger.df_m = 1 cosine_bofinger.df_r = 233 cosine_bofinger.f_r = .0052020961976883 cosine_bofinger.N = 235 cosine_bofinger.q_v = 582.541259765625 cosine_bofinger.q = .5 cosine_bofinger.sum_rdev = 46278.05667114258 cosine_bofinger.sum_adev = 17559.93220318131 cosine_bofinger.convcode = 0 cosine_hsheather = Bunch() cosine_hsheather.table = np.array([ [.5601805 , .0116679 , 48.01 , 0.000 , .5371924 , .5831685], [81.48233 , 12.9598 , 6.29 , 0.000 , 55.94897 , 107.0157] ]) cosine_hsheather.psrsquared = 0.6206 cosine_hsheather.rank = 2 cosine_hsheather.sparsity = 185.349198428224 cosine_hsheather.bwidth = .1574393314202373 cosine_hsheather.kbwidth = 64.53302151153288 cosine_hsheather.df_m = 1 cosine_hsheather.df_r = 233 cosine_hsheather.f_r = .0053952216059205 cosine_hsheather.N = 235 cosine_hsheather.q_v = 582.541259765625 cosine_hsheather.q = .5 cosine_hsheather.sum_rdev = 46278.05667114258 cosine_hsheather.sum_adev = 17559.93220318131 cosine_hsheather.convcode = 0 cosine_chamberlain = Bunch() cosine_chamberlain.table = np.array([ [.5601805 , .0106479 , 52.61 , 0.000 , .539202 , .5811589], [81.48233 , 11.82688 , 6.89 , 0.000 , 58.18104 , 104.7836] ]) cosine_chamberlain.psrsquared = 0.6206 cosine_chamberlain.rank = 2 cosine_chamberlain.sparsity = 169.1463943762948 cosine_chamberlain.bwidth = .063926976464458 cosine_chamberlain.kbwidth = 25.61257055690209 cosine_chamberlain.df_m = 1 cosine_chamberlain.df_r = 233 cosine_chamberlain.f_r = .0059120385254878 cosine_chamberlain.N = 235 cosine_chamberlain.q_v = 582.541259765625 cosine_chamberlain.q = .5 cosine_chamberlain.sum_rdev = 46278.05667114258 cosine_chamberlain.sum_adev = 17559.93220318131 cosine_chamberlain.convcode = 0 parzen_bofinger = Bunch() parzen_bofinger.table = np.array([ [.5601805 , .012909 , 43.39 , 0.000 , .5347471 , .5856138], [81.48233 , 14.33838 , 5.68 , 0.000 , 53.23289 , 109.7318] ]) parzen_bofinger.psrsquared = 0.6206 parzen_bofinger.rank = 2 parzen_bofinger.sparsity = 205.0654663067616 parzen_bofinger.bwidth = .2173486679767846 parzen_bofinger.kbwidth = 91.50878448104551 parzen_bofinger.df_m = 1 parzen_bofinger.df_r = 233 parzen_bofinger.f_r = .0048764914834762 parzen_bofinger.N = 235 parzen_bofinger.q_v = 582.541259765625 parzen_bofinger.q = .5 parzen_bofinger.sum_rdev = 46278.05667114258 parzen_bofinger.sum_adev = 17559.93220318131 parzen_bofinger.convcode = 0 parzen_hsheather = Bunch() parzen_hsheather.table = np.array([ [.5601805 , .0122688 , 45.66 , 0.000 , .5360085 , .5843524], [81.48233 , 13.62723 , 5.98 , 0.000 , 54.63401 , 108.3307] ]) parzen_hsheather.psrsquared = 0.6206 parzen_hsheather.rank = 2 parzen_hsheather.sparsity = 194.8946558099188 parzen_hsheather.bwidth = .1574393314202373 parzen_hsheather.kbwidth = 64.53302151153288 parzen_hsheather.df_m = 1 parzen_hsheather.df_r = 233 parzen_hsheather.f_r = .0051309770185556 parzen_hsheather.N = 235 parzen_hsheather.q_v = 582.541259765625 parzen_hsheather.q = .5 parzen_hsheather.sum_rdev = 46278.05667114258 parzen_hsheather.sum_adev = 17559.93220318131 parzen_hsheather.convcode = 0 parzen_chamberlain = Bunch() parzen_chamberlain.table = np.array([ [.5601805 , .0110507 , 50.69 , 0.000 , .5384084 , .5819526], [81.48233 , 12.2743 , 6.64 , 0.000 , 57.29954 , 105.6651] ]) parzen_chamberlain.psrsquared = 0.6206 parzen_chamberlain.rank = 2 parzen_chamberlain.sparsity = 175.5452813763412 parzen_chamberlain.bwidth = .063926976464458 parzen_chamberlain.kbwidth = 25.61257055690209 parzen_chamberlain.df_m = 1 parzen_chamberlain.df_r = 233 parzen_chamberlain.f_r = .0056965359146063 parzen_chamberlain.N = 235 parzen_chamberlain.q_v = 582.541259765625 parzen_chamberlain.q = .5 parzen_chamberlain.sum_rdev = 46278.05667114258 parzen_chamberlain.sum_adev = 17559.93220318131 parzen_chamberlain.convcode = 0 Rquantreg = Bunch() Rquantreg.fittedvalues = np.array([ 278.946531823426, 327.662259651587, 472.195784028597, 366.902127539958, 411.817682123087, 490.131199885949, 443.36524597881, 503.536477958636, 636.406081281679, 709.736288922034, 312.165058899648, 357.917286612496, 427.907157504212, 333.474578745265, 396.777813086185, 447.125068738706, 325.117049130677, 349.771067249961, 481.598886608367, 306.106158691415, 388.420502955027, 511.05437589194, 313.836609745169, 372.960145596262, 485.358358918327, 284.379882747628, 346.21761302202, 470.314386890694, 292.735362869831, 345.174109237497, 431.875199165716, 312.003504742171, 396.809344806674, 474.141604734191, 463.93526593027, 430.280150030025, 453.602705891221, 579.151509166254, 320.586493222875, 379.637682965454, 261.63071606774, 452.262394881918, 560.558633285135, 361.453261675451, 433.779038355879, 334.560374744198, 465.46340752116, 615.361560833631, 934.235038902725, 699.263962186247, 403.470311834164, 431.875199165716, 610.619729234566, 592.662336978523, 365.021855935831, 494.3226340628, 571.610146001548, 820.79435094965, 1244.13870203673, 480.7121636877, 1031.46758784013, 362.238578597709, 467.705425607653, 577.073721157792, 591.087071047729, 323.397708458532, 569.0193777981, 547.264281981988, 304.861831677351, 727.261856258699, 382.899300447742, 380.080019200391, 387.548077543462, 455.939284414524, 461.007422390708, 469.504909066202, 571.628605113602, 482.528691661418, 447.239662606701, 443.405235218729, 617.975233440748, 888.754433927184, 390.908239427091, 479.934781986261, 872.520684097057, 468.389025193617, 467.583874563025, 600.891141536243, 328.94286096288, 524.54251098332, 697.904660068404, 443.782036008026, 501.879849409525, 647.703493205875, 458.730704476452, 401.721440320659, 507.49016247142, 722.834547664808, 380.206920088912, 481.131145133749, 299.904199685339, 347.488751302111, 488.171723133137, 368.620444647759, 1135.31179977255, 831.440717300088, 578.509405496803, 437.83023460596, 618.056493326843, 550.238043601025, 289.910800377653, 583.813030650723, 502.185524461957, 519.079736225537, 280.441976767315, 334.638935071642, 489.10661171308, 651.842445716676, 1050.52148262534, 346.050058523346, 729.252186278533, 558.451123252156, 529.859949563712, 668.51276154189, 1113.66644452647, 747.093352541566, 858.247029841287, 917.230577685989, 390.29683477012, 339.650965997876, 350.536705877117, 295.394850812194, 502.711215489629, 465.29644549865, 398.753783734842, 328.660789278315, 747.883662637697, 2102.02108846944, 499.727307212846, 278.882398125705, 335.933513242034, 387.198765462081, 672.019128653175, 439.917678218736, 461.755563134383, 669.580269655421, 614.886877182468, 657.691667171562, 913.373865558921, 596.066409235456, 562.142375003233, 844.328711460744, 517.593907165227, 463.853902738299, 1087.39298812569, 583.363256443676, 601.337926899021, 720.657238588974, 386.421327566452, 493.072243052398, 493.072243052398, 493.072243052398, 487.416418391789, 625.768736956952, 717.442211022949, 649.545102325439, 315.764483006998, 387.279123698926, 725.770230770932, 534.438688677752, 265.753297513836, 265.753297513836, 275.266770483504, 444.714192650306, 357.226955834115, 466.694950404995, 521.878226495306, 514.492832530541, 401.82351113515, 308.781536524607, 410.920025494164, 506.942199600954, 426.771590593594, 444.329655290455, 567.951437149727, 314.044484679614, 341.637888287124, 390.011222091572, 371.617953454683, 491.650387255751, 309.86707973756, 339.215312155678, 401.313514123063, 274.198577993648, 366.737853359169, 602.621623875386, 397.5527766612, 431.743315417498, 502.103203876367, 758.493699184236, 354.616793574895, 495.299694296035, 445.994704272822, 461.179388784645, 492.398877810457, 300.177654706392, 351.604018662754, 397.001476569931, 443.325261537312, 495.754449130328, 597.467889923833, 495.384438403965, 563.913157758648, 890.789267794131, 326.949960612346, 296.399786939819, 336.191062827908, 406.145865402583, 678.81933687482, 997.557647439337, 365.6649642219, 415.242904378452, 543.581822640472, 310.924419326878, 519.951702825405, 751.022676264054, 422.151172626615, 604.684498888169, 836.51478813311, 277.051441764913, 287.126391607546, 327.57744312456, 343.712072517474, 408.684566875422, 535.079861284096 ]) Rquantreg.residuals = np.array([ -23.1071072288498, -16.7035925924416, 13.4842301424879, 36.0952280042049, 83.7430928106889, 143.666615246444, 187.391321726407, 196.904426307396, 194.552540223671, 105.623928368427, 25.8363284299391, 54.4440518161423, 92.0934604769094, 118.926894162067, 115.9422447507, 211.714461564889, 67.4824476183351, 93.7875665496341, 158.517491105043, 27.7332282843317, 78.537815573821, 32.3425283712905, 3.88323401131549, 51.360750756311, 33.6032971350797, 53.6215045819588, 73.423561609355, 6.00566204286901, 93.6248004402887, 78.1042405680565, 71.4819720245138, 42.635363803128, 100.508824022316, 114.377859224531, 190.661878666847, 120.44727756794, 74.7742708230821, 61.3298388891191, 80.7338623237625, 56.3613389354645, 14.9298935780977, 136.086423177107, 103.639168918795, 83.406904735325, 29.1204765079443, 43.2188638018142, 87.68702809519, 195.534593927816, 133.719017238015, 350.614829501073, 119.230897841979, 140.205463451968, 296.7772153462, 218.915256717207, 62.7756642652107, 155.675830060721, 288.990008558354, 322.6267348787, 788.540488171593, 109.906163322267, 538.923550476946, 121.241446063999, 132.774973479468, 119.128384587689, 183.709108010048, 67.2007219409504, 43.5425230980599, 161.49786961119, -7.94264590491014, 344.200840210711, 113.698279589573, 123.317422174319, -29.9069666643728, -25.6016916976065, 163.691619008174, 113.036341875648, 8.59293713333579, 61.3520511228119, 141.397517015987, 184.594660239251, 94.1259408216085, 79.6405061341515, 91.6733481942127, 113.234603595283, 161.045070172193, 225.290448255665, 226.095598886257, 160.387924286533, 32.4551953759993, 103.909707216036, 73.543904350086, 313.336630439861, 319.717144678425, 374.616677214425, 220.710022883649, 137.027675366142, 172.507934775539, 254.168723294869, 180.994568087336, 247.26860148984, 72.4144228992704, 14.0322012670994, 29.7478676180694, 91.1972063421171, -271.391948301552, -1.13686837721616e-13, -43.7483617057195, -45.7799925843755, 316.918702117259, 263.070052700293, -26.2008042070246, 185.270818370404, 128.400761958987, 126.90766445377, 39.11640958216, 13.8128950328002, 125.400191450337, 10.1671163220818, 453.849292287238, 60.1679571929374, -37.0832869573327, 29.6859273527971, -18.5990635840888, 32.0472277998125, 187.478653019356, 131.972664872163, 54.6380484088667, 592.550594160211, 93.7637141660844, 60.0193510971532, 93.5633996863289, -46.5847395899585, 25.0901440883857, 35.3349035566588, 38.0569510057356, 46.1382548463761, -21.4915275364559, -274.821124029843, 23.7637774954742, 56.1174237129451, 137.267370647069, 194.004180243594, 257.734838779675, 151.279738459292, 175.792712085095, 5.37066578891586, 161.87201799729, 301.825301306357, 337.590468355399, 141.753670144823, 248.534867350933, 138.672153434971, 191.302922030722, 169.266111420092, 337.411668163301, 247.595365061674, 324.241547334534, 441.345158140916, -2.96336484193375, 128.045086149367, 128.045086149367, 128.045086149367, 61.183812841238, 119.466557499153, 120.358284525061, 145.795139311381, 102.833084929086, 121.51832618868, 157.507780852421, 208.08888035649, -23.4330955930956, -23.4330955930956, -9.26578861762408, 170.044609958764, 28.0914420512634, 48.9250186205624, 186.600476930648, 219.742798279756, 31.1774726408891, 18.6372355131512, 18.1199081442213, 112.698628091063, -25.9726125403696, 176.470984835994, 252.045003846191, 46.8335329758523, 54.1229163256514, 51.9888300300891, 32.4204776768067, 179.14892185478, -12.2969284011601, 14.2728509760057, -17.3759292437432, 10.6022252751131, 64.362111397076, 198.730134737533, 50.898482335236, 146.167755331756, 68.4178084237137, 106.826836694182, 89.9409707510827, 185.120132493866, 130.283190180665, 170.618786347098, 116.24297250849, 0.822265604206791, 26.3943953963694, 0, 145.19420242141, 186.007125906439, 209.892380176326, 201.416658681816, 247.283084092749, 414.930873548859, 115.050091509315, 57.2015105045924, 131.809734683313, 120.611481397197, 211.419693391706, 321.245634797383, -34.6644264712713, 1.15862100546684, 53.2587319600932, 97.5747986822971, 255.069199707627, 387.139369759176, 63.3685935821613, 168.076643464964, 157.44822913826, 28.387531965303, 19.3926869633912, -28.3781151570223, 124.288724993746, 113.917339005042, 215.240302135105 ]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/test_cov.py000066400000000000000000000023301224417117700262340ustar00rootroot00000000000000"""Example: minimal OLS """ import numpy as np import statsmodels.api as sm from numpy.testing import assert_almost_equal def test_HC_use(): np.random.seed(0) nsample = 100 x = np.linspace(0,10, 100) X = sm.add_constant(np.column_stack((x, x**2)), prepend=False) beta = np.array([1, 0.1, 10]) y = np.dot(X, beta) + np.random.normal(size=nsample) results = sm.OLS(y, X).fit() #test cov_params idx = np.array([1,2]) #need to call HC0_se to have cov_HC0 available results.HC0_se cov12 = results.cov_params(column=[1,2], cov_p=results.cov_HC0) assert_almost_equal(cov12, results.cov_HC0[idx[:,None], idx], decimal=15) #test t_test tvals = results.params/results.HC0_se ttest = results.t_test(np.eye(3), cov_p=results.cov_HC0) assert_almost_equal(ttest.tvalue, tvals, decimal=14) assert_almost_equal(ttest.sd, results.HC0_se, decimal=14) #test f_test ftest = results.f_test(np.eye(3)[:-1], cov_p=results.cov_HC0) slopes = results.params[:-1] idx = np.array([0,1]) cov_slopes = results.cov_HC0[idx[:,None], idx] fval = np.dot(slopes, np.dot(np.linalg.inv(cov_slopes), slopes))/len(idx) assert_almost_equal(ftest.fvalue, fval, decimal=12) statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/test_glsar_gretl.py000066400000000000000000000622631224417117700277650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests of GLSAR and diagnostics against Gretl Created on Thu Feb 02 21:15:47 2012 Author: Josef Perktold License: BSD-3 """ import os import numpy as np from numpy.testing import (assert_almost_equal, assert_equal, assert_approx_equal, assert_array_less) from statsmodels.regression.linear_model import OLS, GLSAR from statsmodels.tools.tools import add_constant from statsmodels.datasets import macrodata import statsmodels.stats.sandwich_covariance as sw import statsmodels.stats.diagnostic as smsdia #import statsmodels.sandbox.stats.diagnostic as smsdia import statsmodels.stats.outliers_influence as oi def compare_ftest(contrast_res, other, decimal=(5,4)): assert_almost_equal(contrast_res.fvalue, other[0], decimal=decimal[0]) assert_almost_equal(contrast_res.pvalue, other[1], decimal=decimal[1]) assert_equal(contrast_res.df_num, other[2]) assert_equal(contrast_res.df_denom, other[3]) assert_equal("f", other[4]) class TestGLSARGretl(object): def test_all(self): d = macrodata.load().data #import datasetswsm.greene as g #d = g.load('5-1') #growth rates gs_l_realinv = 400 * np.diff(np.log(d['realinv'])) gs_l_realgdp = 400 * np.diff(np.log(d['realgdp'])) #simple diff, not growthrate, I want heteroscedasticity later for testing endogd = np.diff(d['realinv']) exogd = add_constant(np.c_[np.diff(d['realgdp']), d['realint'][:-1]]) endogg = gs_l_realinv exogg = add_constant(np.c_[gs_l_realgdp, d['realint'][:-1]]) res_ols = OLS(endogg, exogg).fit() #print res_ols.params mod_g1 = GLSAR(endogg, exogg, rho=-0.108136) res_g1 = mod_g1.fit() #print res_g1.params mod_g2 = GLSAR(endogg, exogg, rho=-0.108136) #-0.1335859) from R res_g2 = mod_g2.iterative_fit(maxiter=5) #print res_g2.params rho = -0.108136 # coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL partable = np.array([ [-9.50990, 0.990456, -9.602, 3.65e-018, -11.4631, -7.55670], # *** [ 4.37040, 0.208146, 21.00, 2.93e-052, 3.95993, 4.78086], # *** [-0.579253, 0.268009, -2.161, 0.0319, -1.10777, -0.0507346]]) # ** #Statistics based on the rho-differenced data: result_gretl_g1 = dict( endog_mean = ("Mean dependent var", 3.113973), endog_std = ("S.D. dependent var", 18.67447), ssr = ("Sum squared resid", 22530.90), mse_resid_sqrt = ("S.E. of regression", 10.66735), rsquared = ("R-squared", 0.676973), rsquared_adj = ("Adjusted R-squared", 0.673710), fvalue = ("F(2, 198)", 221.0475), f_pvalue = ("P-value(F)", 3.56e-51), resid_acf1 = ("rho", -0.003481), dw = ("Durbin-Watson", 1.993858)) #fstatistic, p-value, df1, df2 reset_2_3 = [5.219019, 0.00619, 2, 197, "f"] reset_2 = [7.268492, 0.00762, 1, 198, "f"] reset_3 = [5.248951, 0.023, 1, 198, "f"] #LM-statistic, p-value, df arch_4 = [7.30776, 0.120491, 4, "chi2"] #multicollinearity vif = [1.002, 1.002] cond_1norm = 6862.0664 determinant = 1.0296049e+009 reciprocal_condition_number = 0.013819244 #Chi-square(2): test-statistic, pvalue, df normality = [20.2792, 3.94837e-005, 2] #tests res = res_g1 #with rho from Gretl #basic assert_almost_equal(res.params, partable[:,0], 4) assert_almost_equal(res.bse, partable[:,1], 6) assert_almost_equal(res.tvalues, partable[:,2], 2) assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2) #assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl #assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL #assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5) assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=4) assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=2) #assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO #arch #sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None) sm_arch = smsdia.het_arch(res.wresid, maxlag=4) assert_almost_equal(sm_arch[0], arch_4[0], decimal=4) assert_almost_equal(sm_arch[1], arch_4[1], decimal=6) #tests res = res_g2 #with estimated rho #estimated lag coefficient assert_almost_equal(res.model.rho, rho, decimal=3) #basic assert_almost_equal(res.params, partable[:,0], 4) assert_almost_equal(res.bse, partable[:,1], 3) assert_almost_equal(res.tvalues, partable[:,2], 2) assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2) #assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl #assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=7) #FAIL #assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=7) #FAIL assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5) assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=0) assert_almost_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], decimal=6) #assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO c = oi.reset_ramsey(res, degree=2) compare_ftest(c, reset_2, decimal=(2,4)) c = oi.reset_ramsey(res, degree=3) compare_ftest(c, reset_2_3, decimal=(2,4)) #arch #sm_arch = smsdia.acorr_lm(res.wresid**2, maxlag=4, autolag=None) sm_arch = smsdia.het_arch(res.wresid, maxlag=4) assert_almost_equal(sm_arch[0], arch_4[0], decimal=1) assert_almost_equal(sm_arch[1], arch_4[1], decimal=2) ''' Performing iterative calculation of rho... ITER RHO ESS 1 -0.10734 22530.9 2 -0.10814 22530.9 Model 4: Cochrane-Orcutt, using observations 1959:3-2009:3 (T = 201) Dependent variable: ds_l_realinv rho = -0.108136 coefficient std. error t-ratio p-value ------------------------------------------------------------- const -9.50990 0.990456 -9.602 3.65e-018 *** ds_l_realgdp 4.37040 0.208146 21.00 2.93e-052 *** realint_1 -0.579253 0.268009 -2.161 0.0319 ** Statistics based on the rho-differenced data: Mean dependent var 3.113973 S.D. dependent var 18.67447 Sum squared resid 22530.90 S.E. of regression 10.66735 R-squared 0.676973 Adjusted R-squared 0.673710 F(2, 198) 221.0475 P-value(F) 3.56e-51 rho -0.003481 Durbin-Watson 1.993858 ''' ''' RESET test for specification (squares and cubes) Test statistic: F = 5.219019, with p-value = P(F(2,197) > 5.21902) = 0.00619 RESET test for specification (squares only) Test statistic: F = 7.268492, with p-value = P(F(1,198) > 7.26849) = 0.00762 RESET test for specification (cubes only) Test statistic: F = 5.248951, with p-value = P(F(1,198) > 5.24895) = 0.023: ''' ''' Test for ARCH of order 4 coefficient std. error t-ratio p-value -------------------------------------------------------- alpha(0) 97.0386 20.3234 4.775 3.56e-06 *** alpha(1) 0.176114 0.0714698 2.464 0.0146 ** alpha(2) -0.0488339 0.0724981 -0.6736 0.5014 alpha(3) -0.0705413 0.0737058 -0.9571 0.3397 alpha(4) 0.0384531 0.0725763 0.5298 0.5968 Null hypothesis: no ARCH effect is present Test statistic: LM = 7.30776 with p-value = P(Chi-square(4) > 7.30776) = 0.120491: ''' ''' Variance Inflation Factors Minimum possible value = 1.0 Values > 10.0 may indicate a collinearity problem ds_l_realgdp 1.002 realint_1 1.002 VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient between variable j and the other independent variables Properties of matrix X'X: 1-norm = 6862.0664 Determinant = 1.0296049e+009 Reciprocal condition number = 0.013819244 ''' ''' Test for ARCH of order 4 - Null hypothesis: no ARCH effect is present Test statistic: LM = 7.30776 with p-value = P(Chi-square(4) > 7.30776) = 0.120491 Test of common factor restriction - Null hypothesis: restriction is acceptable Test statistic: F(2, 195) = 0.426391 with p-value = P(F(2, 195) > 0.426391) = 0.653468 Test for normality of residual - Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 20.2792 with p-value = 3.94837e-005: ''' #no idea what this is ''' Augmented regression for common factor test OLS, using observations 1959:3-2009:3 (T = 201) Dependent variable: ds_l_realinv coefficient std. error t-ratio p-value --------------------------------------------------------------- const -10.9481 1.35807 -8.062 7.44e-014 *** ds_l_realgdp 4.28893 0.229459 18.69 2.40e-045 *** realint_1 -0.662644 0.334872 -1.979 0.0492 ** ds_l_realinv_1 -0.108892 0.0715042 -1.523 0.1294 ds_l_realgdp_1 0.660443 0.390372 1.692 0.0923 * realint_2 0.0769695 0.341527 0.2254 0.8219 Sum of squared residuals = 22432.8 Test of common factor restriction Test statistic: F(2, 195) = 0.426391, with p-value = 0.653468 ''' ################ with OLS, HAC errors #Model 5: OLS, using observations 1959:2-2009:3 (T = 202) #Dependent variable: ds_l_realinv #HAC standard errors, bandwidth 4 (Bartlett kernel) #coefficient std. error t-ratio p-value 95% CONFIDENCE INTERVAL #for confidence interval t(199, 0.025) = 1.972 partable = np.array([ [-9.48167, 1.17709, -8.055, 7.17e-014, -11.8029, -7.16049], # *** [4.37422, 0.328787, 13.30, 2.62e-029, 3.72587, 5.02258], #*** [-0.613997, 0.293619, -2.091, 0.0378, -1.19300, -0.0349939]]) # ** result_gretl_g1 = dict( endog_mean = ("Mean dependent var", 3.257395), endog_std = ("S.D. dependent var", 18.73915), ssr = ("Sum squared resid", 22799.68), mse_resid_sqrt = ("S.E. of regression", 10.70380), rsquared = ("R-squared", 0.676978), rsquared_adj = ("Adjusted R-squared", 0.673731), fvalue = ("F(2, 199)", 90.79971), f_pvalue = ("P-value(F)", 9.53e-29), llf = ("Log-likelihood", -763.9752), aic = ("Akaike criterion", 1533.950), bic = ("Schwarz criterion", 1543.875), hqic = ("Hannan-Quinn", 1537.966), resid_acf1 = ("rho", -0.107341), dw = ("Durbin-Watson", 2.213805)) linear_logs = [1.68351, 0.430953, 2, "chi2"] #for logs: dropping 70 nan or incomplete observations, T=133 #(res_ols.model.exog <=0).any(1).sum() = 69 ?not 70 linear_squares = [7.52477, 0.0232283, 2, "chi2"] #Autocorrelation, Breusch-Godfrey test for autocorrelation up to order 4 lm_acorr4 = [1.17928, 0.321197, 4, 195, "F"] lm2_acorr4 = [4.771043, 0.312, 4, "chi2"] acorr_ljungbox4 = [5.23587, 0.264, 4, "chi2"] #break cusum_Harvey_Collier = [0.494432, 0.621549, 198, "t"] #stats.t.sf(0.494432, 198)*2 #see cusum results in files break_qlr = [3.01985, 0.1, 3, 196, "maxF"] #TODO check this, max at 2001:4 break_chow = [13.1897, 0.00424384, 3, "chi2"] # break at 1984:1 arch_4 = [3.43473, 0.487871, 4, "chi2"] normality = [23.962, 0.00001, 2, "chi2"] het_white = [33.503723, 0.000003, 5, "chi2"] het_breush_pagan = [1.302014, 0.521520, 2, "chi2"] #TODO: not available het_breush_pagan_konker = [0.709924, 0.701200, 2, "chi2"] reset_2_3 = [5.219019, 0.00619, 2, 197, "f"] reset_2 = [7.268492, 0.00762, 1, 198, "f"] reset_3 = [5.248951, 0.023, 1, 198, "f"] #not available cond_1norm = 5984.0525 determinant = 7.1087467e+008 reciprocal_condition_number = 0.013826504 vif = [1.001, 1.001] names = 'date residual leverage influence DFFITS'.split() cur_dir = os.path.abspath(os.path.dirname(__file__)) fpath = os.path.join(cur_dir, 'results/leverage_influence_ols_nostars.txt') lev = np.genfromtxt(fpath, skip_header=3, skip_footer=1, converters={0:lambda s: s}) #either numpy 1.6 or python 3.2 changed behavior if np.isnan(lev[-1]['f1']): lev = np.genfromtxt(fpath, skip_header=3, skip_footer=2, converters={0:lambda s: s}) lev.dtype.names = names res = res_ols #for easier copying cov_hac = sw.cov_hac_simple(res, nlags=4, use_correction=False) bse_hac = sw.se_cov(cov_hac) assert_almost_equal(res.params, partable[:,0], 5) assert_almost_equal(bse_hac, partable[:,1], 5) #TODO assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2) #assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=6) #FAIL assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=6) #FAIL assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5) #f-value is based on cov_hac I guess #assert_almost_equal(res.fvalue, result_gretl_g1['fvalue'][1], decimal=0) #FAIL #assert_approx_equal(res.f_pvalue, result_gretl_g1['f_pvalue'][1], significant=1) #FAIL #assert_almost_equal(res.durbin_watson, result_gretl_g1['dw'][1], decimal=7) #TODO c = oi.reset_ramsey(res, degree=2) compare_ftest(c, reset_2, decimal=(6,5)) c = oi.reset_ramsey(res, degree=3) compare_ftest(c, reset_2_3, decimal=(6,5)) linear_sq = smsdia.linear_lm(res.resid, res.model.exog) assert_almost_equal(linear_sq[0], linear_squares[0], decimal=6) assert_almost_equal(linear_sq[1], linear_squares[1], decimal=7) hbpk = smsdia.het_breushpagan(res.resid, res.model.exog) assert_almost_equal(hbpk[0], het_breush_pagan_konker[0], decimal=6) assert_almost_equal(hbpk[1], het_breush_pagan_konker[1], decimal=6) hw = smsdia.het_white(res.resid, res.model.exog) assert_almost_equal(hw[:2], het_white[:2], 6) #arch #sm_arch = smsdia.acorr_lm(res.resid**2, maxlag=4, autolag=None) sm_arch = smsdia.het_arch(res.resid, maxlag=4) assert_almost_equal(sm_arch[0], arch_4[0], decimal=5) assert_almost_equal(sm_arch[1], arch_4[1], decimal=6) vif2 = [oi.variance_inflation_factor(res.model.exog, k) for k in [1,2]] infl = oi.OLSInfluence(res_ols) #print np.max(np.abs(lev['DFFITS'] - infl.dffits[0])) #print np.max(np.abs(lev['leverage'] - infl.hat_matrix_diag)) #print np.max(np.abs(lev['influence'] - infl.influence)) #just added this based on Gretl #just rough test, low decimal in Gretl output, assert_almost_equal(lev['residual'], res.resid, decimal=3) assert_almost_equal(lev['DFFITS'], infl.dffits[0], decimal=3) assert_almost_equal(lev['leverage'], infl.hat_matrix_diag, decimal=3) assert_almost_equal(lev['influence'], infl.influence, decimal=4) def test_GLSARlag(): #test that results for lag>1 is close to lag=1, and smaller ssr from statsmodels.datasets import macrodata d2 = macrodata.load().data g_gdp = 400*np.diff(np.log(d2['realgdp'])) g_inv = 400*np.diff(np.log(d2['realinv'])) exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False) mod1 = GLSAR(g_inv, exogg, 1) res1 = mod1.iterative_fit(5) mod4 = GLSAR(g_inv, exogg, 4) res4 = mod4.iterative_fit(10) assert_array_less(np.abs(res1.params / res4.params - 1), 0.03) assert_array_less(res4.ssr, res1.ssr) assert_array_less(np.abs(res4.bse / res1.bse) - 1, 0.015) assert_array_less(np.abs((res4.fittedvalues / res1.fittedvalues - 1).mean()), 0.015) assert_equal(len(mod4.rho), 4) if __name__ == '__main__': t = TestGLSARGretl() t.test_all() ''' Model 5: OLS, using observations 1959:2-2009:3 (T = 202) Dependent variable: ds_l_realinv HAC standard errors, bandwidth 4 (Bartlett kernel) coefficient std. error t-ratio p-value ------------------------------------------------------------- const -9.48167 1.17709 -8.055 7.17e-014 *** ds_l_realgdp 4.37422 0.328787 13.30 2.62e-029 *** realint_1 -0.613997 0.293619 -2.091 0.0378 ** Mean dependent var 3.257395 S.D. dependent var 18.73915 Sum squared resid 22799.68 S.E. of regression 10.70380 R-squared 0.676978 Adjusted R-squared 0.673731 F(2, 199) 90.79971 P-value(F) 9.53e-29 Log-likelihood -763.9752 Akaike criterion 1533.950 Schwarz criterion 1543.875 Hannan-Quinn 1537.966 rho -0.107341 Durbin-Watson 2.213805 QLR test for structural break - Null hypothesis: no structural break Test statistic: max F(3, 196) = 3.01985 at observation 2001:4 (10 percent critical value = 4.09) Non-linearity test (logs) - Null hypothesis: relationship is linear Test statistic: LM = 1.68351 with p-value = P(Chi-square(2) > 1.68351) = 0.430953 Non-linearity test (squares) - Null hypothesis: relationship is linear Test statistic: LM = 7.52477 with p-value = P(Chi-square(2) > 7.52477) = 0.0232283 LM test for autocorrelation up to order 4 - Null hypothesis: no autocorrelation Test statistic: LMF = 1.17928 with p-value = P(F(4,195) > 1.17928) = 0.321197 CUSUM test for parameter stability - Null hypothesis: no change in parameters Test statistic: Harvey-Collier t(198) = 0.494432 with p-value = P(t(198) > 0.494432) = 0.621549 Chow test for structural break at observation 1984:1 - Null hypothesis: no structural break Asymptotic test statistic: Chi-square(3) = 13.1897 with p-value = 0.00424384 Test for ARCH of order 4 - Null hypothesis: no ARCH effect is present Test statistic: LM = 3.43473 with p-value = P(Chi-square(4) > 3.43473) = 0.487871: #ANOVA Analysis of Variance: Sum of squares df Mean square Regression 47782.7 2 23891.3 Residual 22799.7 199 114.571 Total 70582.3 201 351.156 R^2 = 47782.7 / 70582.3 = 0.676978 F(2, 199) = 23891.3 / 114.571 = 208.528 [p-value 1.47e-049] #LM-test autocorrelation Breusch-Godfrey test for autocorrelation up to order 4 OLS, using observations 1959:2-2009:3 (T = 202) Dependent variable: uhat coefficient std. error t-ratio p-value ------------------------------------------------------------ const 0.0640964 1.06719 0.06006 0.9522 ds_l_realgdp -0.0456010 0.217377 -0.2098 0.8341 realint_1 0.0511769 0.293136 0.1746 0.8616 uhat_1 -0.104707 0.0719948 -1.454 0.1475 uhat_2 -0.00898483 0.0742817 -0.1210 0.9039 uhat_3 0.0837332 0.0735015 1.139 0.2560 uhat_4 -0.0636242 0.0737363 -0.8629 0.3893 Unadjusted R-squared = 0.023619 Test statistic: LMF = 1.179281, with p-value = P(F(4,195) > 1.17928) = 0.321 Alternative statistic: TR^2 = 4.771043, with p-value = P(Chi-square(4) > 4.77104) = 0.312 Ljung-Box Q' = 5.23587, with p-value = P(Chi-square(4) > 5.23587) = 0.264: RESET test for specification (squares and cubes) Test statistic: F = 5.219019, with p-value = P(F(2,197) > 5.21902) = 0.00619 RESET test for specification (squares only) Test statistic: F = 7.268492, with p-value = P(F(1,198) > 7.26849) = 0.00762 RESET test for specification (cubes only) Test statistic: F = 5.248951, with p-value = P(F(1,198) > 5.24895) = 0.023 #heteroscedasticity White White's test for heteroskedasticity OLS, using observations 1959:2-2009:3 (T = 202) Dependent variable: uhat^2 coefficient std. error t-ratio p-value ------------------------------------------------------------- const 104.920 21.5848 4.861 2.39e-06 *** ds_l_realgdp -29.7040 6.24983 -4.753 3.88e-06 *** realint_1 -6.93102 6.95607 -0.9964 0.3203 sq_ds_l_realg 4.12054 0.684920 6.016 8.62e-09 *** X2_X3 2.89685 1.38571 2.091 0.0379 ** sq_realint_1 0.662135 1.10919 0.5970 0.5512 Unadjusted R-squared = 0.165860 Test statistic: TR^2 = 33.503723, with p-value = P(Chi-square(5) > 33.503723) = 0.000003: #heteroscedasticity Breusch-Pagan (original) Breusch-Pagan test for heteroskedasticity OLS, using observations 1959:2-2009:3 (T = 202) Dependent variable: scaled uhat^2 coefficient std. error t-ratio p-value ------------------------------------------------------------- const 1.09468 0.192281 5.693 4.43e-08 *** ds_l_realgdp -0.0323119 0.0386353 -0.8363 0.4040 realint_1 0.00410778 0.0512274 0.08019 0.9362 Explained sum of squares = 2.60403 Test statistic: LM = 1.302014, with p-value = P(Chi-square(2) > 1.302014) = 0.521520 #heteroscedasticity Breusch-Pagan Koenker Breusch-Pagan test for heteroskedasticity OLS, using observations 1959:2-2009:3 (T = 202) Dependent variable: scaled uhat^2 (Koenker robust variant) coefficient std. error t-ratio p-value ------------------------------------------------------------ const 10.6870 21.7027 0.4924 0.6230 ds_l_realgdp -3.64704 4.36075 -0.8363 0.4040 realint_1 0.463643 5.78202 0.08019 0.9362 Explained sum of squares = 33174.2 Test statistic: LM = 0.709924, with p-value = P(Chi-square(2) > 0.709924) = 0.701200 ########## forecast #forecast mean y For 95% confidence intervals, t(199, 0.025) = 1.972 Obs ds_l_realinv prediction std. error 95% interval 2008:3 -7.134492 -17.177905 2.946312 -22.987904 - -11.367905 2008:4 -27.665860 -36.294434 3.036851 -42.282972 - -30.305896 2009:1 -70.239280 -44.018178 4.007017 -51.919841 - -36.116516 2009:2 -27.024588 -12.284842 1.427414 -15.099640 - -9.470044 2009:3 8.078897 4.483669 1.315876 1.888819 - 7.078520 Forecast evaluation statistics Mean Error -3.7387 Mean Squared Error 218.61 Root Mean Squared Error 14.785 Mean Absolute Error 12.646 Mean Percentage Error -7.1173 Mean Absolute Percentage Error -43.867 Theil's U 0.4365 Bias proportion, UM 0.06394 Regression proportion, UR 0.13557 Disturbance proportion, UD 0.80049 #forecast actual y For 95% confidence intervals, t(199, 0.025) = 1.972 Obs ds_l_realinv prediction std. error 95% interval 2008:3 -7.134492 -17.177905 11.101892 -39.070353 - 4.714544 2008:4 -27.665860 -36.294434 11.126262 -58.234939 - -14.353928 2009:1 -70.239280 -44.018178 11.429236 -66.556135 - -21.480222 2009:2 -27.024588 -12.284842 10.798554 -33.579120 - 9.009436 2009:3 8.078897 4.483669 10.784377 -16.782652 - 25.749991 Forecast evaluation statistics Mean Error -3.7387 Mean Squared Error 218.61 Root Mean Squared Error 14.785 Mean Absolute Error 12.646 Mean Percentage Error -7.1173 Mean Absolute Percentage Error -43.867 Theil's U 0.4365 Bias proportion, UM 0.06394 Regression proportion, UR 0.13557 Disturbance proportion, UD 0.80049 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/test_glsar_stata.py000066400000000000000000000037351224417117700277630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Testing GLSAR against STATA Created on Wed May 30 09:25:24 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_allclose from statsmodels.regression.linear_model import GLSAR from statsmodels.tools.tools import add_constant from statsmodels.datasets import macrodata class CheckStataResultsMixin(object): def test_params_table(self): res, results = self.res, self.results assert_almost_equal(res.params, results.params, 3) assert_almost_equal(res.bse, results.bse, 3) #assert_almost_equal(res.tvalues, results.tvalues, 3) 0.0003 assert_allclose(res.tvalues, results.tvalues, atol=0, rtol=0.004) assert_allclose(res.pvalues, results.pvalues, atol=1e-7, rtol=0.004) class CheckStataResultsPMixin(CheckStataResultsMixin): def test_predicted(self): res, results = self.res, self.results assert_allclose(res.fittedvalues, results.fittedvalues, rtol=0.002) predicted = res.predict(res.model.exog) #should be equal assert_allclose(predicted, results.fittedvalues, rtol=0.0016) #not yet #assert_almost_equal(res.fittedvalues_se, results.fittedvalues_se, 4) class TestGLSARCorc(CheckStataResultsPMixin): @classmethod def setup_class(self): d2 = macrodata.load().data g_gdp = 400*np.diff(np.log(d2['realgdp'])) g_inv = 400*np.diff(np.log(d2['realinv'])) exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False) mod1 = GLSAR(g_inv, exogg, 1) self.res = mod1.iterative_fit(5) from results.macro_gr_corc_stata import results self.results = results def test_rho(self): assert_almost_equal(self.res.model.rho, self.results.rho, 3) # pylint: disable-msg=E1101 if __name__=="__main__": import nose nose.runmodule(argv=[__file__,'-vvs','-x',#'--pdb', '--pdb-failure' ], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/test_quantile_regression.py000066400000000000000000000171251224417117700315370ustar00rootroot00000000000000 import scipy.stats import numpy as np import statsmodels.api as sm from numpy.testing import assert_allclose, assert_equal, assert_almost_equal from patsy import dmatrices # pylint: disable=E0611 from statsmodels.regression.quantile_regression import QuantReg from results_quantile_regression import ( biweight_chamberlain, biweight_hsheather, biweight_bofinger, cosine_chamberlain, cosine_hsheather, cosine_bofinger, gaussian_chamberlain, gaussian_hsheather, gaussian_bofinger, epan2_chamberlain, epan2_hsheather, epan2_bofinger, parzen_chamberlain, parzen_hsheather, parzen_bofinger, #rectangle_chamberlain, rectangle_hsheather, rectangle_bofinger, #triangle_chamberlain, triangle_hsheather, triangle_bofinger, #epanechnikov_chamberlain, epanechnikov_hsheather, epanechnikov_bofinger, epanechnikov_hsheather_q75, Rquantreg) idx = ['income', 'Intercept'] class CheckModelResultsMixin(object): def test_params(self): assert_allclose(np.ravel(self.res1.params.ix[idx]), self.res2.table[:,0], rtol=1e-3) def test_bse(self): assert_equal(self.res1.scale, 1) assert_allclose(np.ravel(self.res1.bse.ix[idx]), self.res2.table[:,1], rtol=1e-3) def test_tvalues(self): assert_allclose(np.ravel(self.res1.tvalues.ix[idx]), self.res2.table[:,2], rtol=1e-2) def test_pvalues(self): pvals_stata = scipy.stats.t.sf(self.res2.table[:, 2] , self.res2.df_r) assert_allclose(np.ravel(self.res1.pvalues.ix[idx]), pvals_stata, rtol=1.1) # test that we use the t distribution for the p-values pvals_t = scipy.stats.t.sf(self.res1.tvalues , self.res2.df_r) * 2 assert_allclose(np.ravel(self.res1.pvalues), pvals_t, rtol=1e-9, atol=1e-10) def test_conf_int(self): assert_allclose(self.res1.conf_int().ix[idx], self.res2.table[:,-2:], rtol=1e-3) def test_nobs(self): assert_allclose(self.res1.nobs, self.res2.N, rtol=1e-3) def test_df_model(self): assert_allclose(self.res1.df_model, self.res2.df_m, rtol=1e-3) def test_df_resid(self): assert_allclose(self.res1.df_resid, self.res2.df_r, rtol=1e-3) def test_prsquared(self): assert_allclose(self.res1.prsquared, self.res2.psrsquared, rtol=1e-3) def test_sparsity(self): assert_allclose(np.array(self.res1.sparsity), self.res2.sparsity, rtol=1e-3) def test_bandwidth(self): assert_allclose(np.array(self.res1.bandwidth), self.res2.kbwidth, rtol=1e-3) d = {('biw','bofinger'): biweight_bofinger, ('biw','chamberlain'): biweight_chamberlain, ('biw','hsheather'): biweight_hsheather, ('cos','bofinger'): cosine_bofinger, ('cos','chamberlain'): cosine_chamberlain, ('cos','hsheather'): cosine_hsheather, ('gau','bofinger'): gaussian_bofinger, ('gau','chamberlain'): gaussian_chamberlain, ('gau','hsheather'): gaussian_hsheather, ('par','bofinger'): parzen_bofinger, ('par','chamberlain'): parzen_chamberlain, ('par','hsheather'): parzen_hsheather, #('rec','bofinger'): rectangle_bofinger, #('rec','chamberlain'): rectangle_chamberlain, #('rec','hsheather'): rectangle_hsheather, #('tri','bofinger'): triangle_bofinger, #('tri','chamberlain'): triangle_chamberlain, #('tri','hsheather'): triangle_hsheather, ('epa', 'bofinger'): epan2_bofinger, ('epa', 'chamberlain'): epan2_chamberlain, ('epa', 'hsheather'): epan2_hsheather #('epa2', 'bofinger'): epan2_bofinger, #('epa2', 'chamberlain'): epan2_chamberlain, #('epa2', 'hsheather'): epan2_hsheather } def setup_fun(kernel='gau', bandwidth='bofinger'): data = sm.datasets.engel.load_pandas().data y, X = dmatrices('foodexp ~ income', data, return_type='dataframe') statsm = QuantReg(y, X).fit(vcov='iid', kernel=kernel, bandwidth=bandwidth) stata = d[(kernel, bandwidth)] return statsm, stata def test_fitted_residuals(): data = sm.datasets.engel.load_pandas().data y, X = dmatrices('foodexp ~ income', data, return_type='dataframe') res = QuantReg(y, X).fit(q=.1) # Note: maxabs relative error with fitted is 1.789e-09 assert_almost_equal(np.array(res.fittedvalues), Rquantreg.fittedvalues, 5) assert_almost_equal(np.array(res.predict()), Rquantreg.fittedvalues, 5) assert_almost_equal(np.array(res.resid), Rquantreg.residuals, 5) class TestEpanechnikovHsheatherQ75(CheckModelResultsMixin): # Vincent Arel-Bundock also spot-checked q=.1 @classmethod def setUp(cls): data = sm.datasets.engel.load_pandas().data y, X = dmatrices('foodexp ~ income', data, return_type='dataframe') cls.res1 = QuantReg(y, X).fit(q=.75, vcov='iid', kernel='epa', bandwidth='hsheather') cls.res2 = epanechnikov_hsheather_q75 class TestEpanechnikovBofinger(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('epa', 'bofinger') class TestEpanechnikovChamberlain(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('epa', 'chamberlain') class TestEpanechnikovHsheather(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('epa', 'hsheather') class TestGaussianBofinger(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('gau', 'bofinger') class TestGaussianChamberlain(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('gau', 'chamberlain') class TestGaussianHsheather(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('gau', 'hsheather') class TestBiweightBofinger(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('biw', 'bofinger') class TestBiweightChamberlain(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('biw', 'chamberlain') class TestBiweightHsheather(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('biw', 'hsheather') class TestCosineBofinger(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('cos', 'bofinger') class TestCosineChamberlain(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('cos', 'chamberlain') class TestCosineHsheather(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('cos', 'hsheather') class TestParzeneBofinger(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('par', 'bofinger') class TestParzeneChamberlain(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('par', 'chamberlain') class TestParzeneHsheather(CheckModelResultsMixin): @classmethod def setUp(cls): cls.res1, cls.res2 = setup_fun('par', 'hsheather') #class TestTriangleBofinger(CheckModelResultsMixin): #@classmethod #def setUp(cls): #cls.res1, cls.res2 = setup_fun('tri', 'bofinger') #class TestTriangleChamberlain(CheckModelResultsMixin): #@classmethod #def setUp(cls): #cls.res1, cls.res2 = setup_fun('tri', 'chamberlain') #class TestTriangleHsheather(CheckModelResultsMixin): #@classmethod #def setUp(cls): #cls.res1, cls.res2 = setup_fun('tri', 'hsheather') statsmodels-0.5.0+git13-g8e07d34/statsmodels/regression/tests/test_regression.py000066400000000000000000000615441224417117700276410ustar00rootroot00000000000000""" Test functions for models.regression """ import warnings import pandas import numpy as np from numpy.testing import (assert_almost_equal, assert_approx_equal, assert_raises, assert_equal) from scipy.linalg import toeplitz from statsmodels.tools.tools import add_constant, categorical from statsmodels.regression.linear_model import OLS, WLS, GLS, yule_walker from statsmodels.datasets import longley from scipy.stats import t as student_t DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 DECIMAL_7 = 7 DECIMAL_0 = 0 class CheckRegressionResults(object): ''' res2 contains results from Rmodelwrap or were obtained from a statistical packages such as R, Stata, or SAS and were written to model_results ''' decimal_params = DECIMAL_4 def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, self.decimal_params) decimal_standarderrors = DECIMAL_4 def test_standarderrors(self): assert_almost_equal(self.res1.bse,self.res2.bse, self.decimal_standarderrors) decimal_confidenceintervals = DECIMAL_4 def test_confidenceintervals(self): #NOTE: stata rounds residuals (at least) to sig digits so approx_equal conf1 = self.res1.conf_int() conf2 = self.res2.conf_int() for i in range(len(conf1)): assert_approx_equal(conf1[i][0], conf2[i][0], self.decimal_confidenceintervals) assert_approx_equal(conf1[i][1], conf2[i][1], self.decimal_confidenceintervals) decimal_conf_int_subset = DECIMAL_4 def test_conf_int_subset(self): if len(self.res1.params) > 1: ci1 = self.res1.conf_int(cols=(1,2)) ci2 = self.res1.conf_int()[1:3] assert_almost_equal(ci1, ci2, self.decimal_conf_int_subset) else: pass decimal_scale = DECIMAL_4 def test_scale(self): assert_almost_equal(self.res1.scale, self.res2.scale, self.decimal_scale) decimal_rsquared = DECIMAL_4 def test_rsquared(self): assert_almost_equal(self.res1.rsquared, self.res2.rsquared, self.decimal_rsquared) decimal_rsquared_adj = DECIMAL_4 def test_rsquared_adj(self): assert_almost_equal(self.res1.rsquared_adj, self.res2.rsquared_adj, self.decimal_rsquared_adj) def test_degrees(self): assert_equal(self.res1.model.df_model, self.res2.df_model) assert_equal(self.res1.model.df_resid, self.res2.df_resid) decimal_ess = DECIMAL_4 def test_ess(self): #Explained Sum of Squares assert_almost_equal(self.res1.ess, self.res2.ess, self.decimal_ess) decimal_ssr = DECIMAL_4 def test_sumof_squaredresids(self): assert_almost_equal(self.res1.ssr, self.res2.ssr, self.decimal_ssr) decimal_mse_resid = DECIMAL_4 def test_mse_resid(self): #Mean squared error of residuals assert_almost_equal(self.res1.mse_model, self.res2.mse_model, self.decimal_mse_resid) decimal_mse_model = DECIMAL_4 def test_mse_model(self): assert_almost_equal(self.res1.mse_resid, self.res2.mse_resid, self.decimal_mse_model) decimal_mse_total = DECIMAL_4 def test_mse_total(self): assert_almost_equal(self.res1.mse_total, self.res2.mse_total, self.decimal_mse_total) decimal_fvalue = DECIMAL_4 def test_fvalue(self): #didn't change this, not sure it should complain -inf not equal -inf #if not (np.isinf(self.res1.fvalue) and np.isinf(self.res2.fvalue)): assert_almost_equal(self.res1.fvalue, self.res2.fvalue, self.decimal_fvalue) decimal_loglike = DECIMAL_4 def test_loglike(self): assert_almost_equal(self.res1.llf, self.res2.llf, self.decimal_loglike) decimal_aic = DECIMAL_4 def test_aic(self): assert_almost_equal(self.res1.aic, self.res2.aic, self.decimal_aic) decimal_bic = DECIMAL_4 def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic, self.decimal_bic) decimal_pvalues = DECIMAL_4 def test_pvalues(self): assert_almost_equal(self.res1.pvalues, self.res2.pvalues, self.decimal_pvalues) decimal_wresid = DECIMAL_4 def test_wresid(self): assert_almost_equal(self.res1.wresid, self.res2.wresid, self.decimal_wresid) decimal_resids = DECIMAL_4 def test_resids(self): assert_almost_equal(self.res1.resid, self.res2.resid, self.decimal_resids) #TODO: test fittedvalues and what else? class TestOLS(CheckRegressionResults): @classmethod def setupClass(cls): from results.results_regression import Longley data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() res2 = Longley() res2.wresid = res1.wresid # workaround hack cls.res1 = res1 cls.res2 = res2 res_qr = OLS(data.endog, data.exog).fit(method="qr") cls.res_qr = res_qr # Robust error tests. Compare values computed with SAS def test_HC0_errors(self): #They are split up because the copied results do not have any DECIMAL_4 #places for the last place. assert_almost_equal(self.res1.HC0_se[:-1], self.res2.HC0_se[:-1], DECIMAL_4) assert_approx_equal(np.round(self.res1.HC0_se[-1]), self.res2.HC0_se[-1]) def test_HC1_errors(self): assert_almost_equal(self.res1.HC1_se[:-1], self.res2.HC1_se[:-1], DECIMAL_4) assert_approx_equal(self.res1.HC1_se[-1], self.res2.HC1_se[-1]) def test_HC2_errors(self): assert_almost_equal(self.res1.HC2_se[:-1], self.res2.HC2_se[:-1], DECIMAL_4) assert_approx_equal(self.res1.HC2_se[-1], self.res2.HC2_se[-1]) def test_HC3_errors(self): assert_almost_equal(self.res1.HC3_se[:-1], self.res2.HC3_se[:-1], DECIMAL_4) assert_approx_equal(self.res1.HC3_se[-1], self.res2.HC3_se[-1]) def test_qr_params(self): assert_almost_equal(self.res1.params, self.res_qr.params, 6) def test_qr_normalized_cov_params(self): #todo: need assert_close assert_almost_equal(np.ones_like(self.res1.normalized_cov_params), self.res1.normalized_cov_params / self.res_qr.normalized_cov_params, 5) def test_missing(self): data = longley.load() data.exog = add_constant(data.exog, prepend=False) data.endog[[3, 7, 14]] = np.nan mod = OLS(data.endog, data.exog, missing='drop') assert_equal(mod.endog.shape[0], 13) assert_equal(mod.exog.shape[0], 13) def test_rsquared_adj_overfit(self): # Test that if df_resid = 0, rsquared_adj = 0. # This is a regression test for user issue: # https://github.com/statsmodels/statsmodels/issues/868 with warnings.catch_warnings(record=True): x = np.random.randn(5) y = np.random.randn(5, 6) results = OLS(x, y).fit() rsquared_adj = results.rsquared_adj assert_equal(rsquared_adj, np.nan) class TestRTO(CheckRegressionResults): @classmethod def setupClass(cls): from results.results_regression import LongleyRTO data = longley.load() res1 = OLS(data.endog, data.exog).fit() res2 = LongleyRTO() res2.wresid = res1.wresid # workaround hack cls.res1 = res1 cls.res2 = res2 res_qr = OLS(data.endog, data.exog).fit(method="qr") cls.res_qr = res_qr class TestFtest(object): """ Tests f_test vs. RegressionResults """ @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) cls.res1 = OLS(data.endog, data.exog).fit() R = np.identity(7)[:-1,:] cls.Ftest = cls.res1.f_test(R) def test_F(self): assert_almost_equal(self.Ftest.fvalue, self.res1.fvalue, DECIMAL_4) def test_p(self): assert_almost_equal(self.Ftest.pvalue, self.res1.f_pvalue, DECIMAL_4) def test_Df_denom(self): assert_equal(self.Ftest.df_denom, self.res1.model.df_resid) def test_Df_num(self): assert_equal(self.Ftest.df_num, 6) class TestFTest2(object): ''' A joint test that the coefficient on GNP = the coefficient on UNEMP and that the coefficient on POP = the coefficient on YEAR for the Longley dataset. Ftest1 is from statsmodels. Results are from Rpy using R's car library. ''' @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() R2 = [[0,1,-1,0,0,0,0],[0, 0, 0, 0, 1, -1, 0]] cls.Ftest1 = res1.f_test(R2) hyp = 'x2 = x3, x5 = x6' cls.NewFtest1 = res1.f_test(hyp) def test_new_ftest(self): assert_equal(self.NewFtest1.fvalue, self.Ftest1.fvalue) def test_fvalue(self): assert_almost_equal(self.Ftest1.fvalue, 9.7404618732968196, DECIMAL_4) def test_pvalue(self): assert_almost_equal(self.Ftest1.pvalue, 0.0056052885317493459, DECIMAL_4) def test_df_denom(self): assert_equal(self.Ftest1.df_denom, 9) def test_df_num(self): assert_equal(self.Ftest1.df_num, 2) class TestFtestQ(object): """ A joint hypothesis test that Rb = q. Coefficient tests are essentially made up. Test values taken from Stata. """ @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() R = np.array([[0,1,1,0,0,0,0], [0,1,0,1,0,0,0], [0,1,0,0,0,0,0], [0,0,0,0,1,0,0], [0,0,0,0,0,1,0]]) q = np.array([0,0,0,1,0]) cls.Ftest1 = res1.f_test((R,q)) def test_fvalue(self): assert_almost_equal(self.Ftest1.fvalue, 70.115557, 5) def test_pvalue(self): assert_almost_equal(self.Ftest1.pvalue, 6.229e-07, 10) def test_df_denom(self): assert_equal(self.Ftest1.df_denom, 9) def test_df_num(self): assert_equal(self.Ftest1.df_num, 5) class TestTtest(object): ''' Test individual t-tests. Ie., are the coefficients significantly different than zero. ''' @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) cls.res1 = OLS(data.endog, data.exog).fit() R = np.identity(7) cls.Ttest = cls.res1.t_test(R) hyp = 'x1 = 0, x2 = 0, x3 = 0, x4 = 0, x5 = 0, x6 = 0, const = 0' cls.NewTTest = cls.res1.t_test(hyp) def test_new_tvalue(self): assert_equal(self.NewTTest.tvalue, self.Ttest.tvalue) def test_tvalue(self): assert_almost_equal(self.Ttest.tvalue, self.res1.tvalues, DECIMAL_4) def test_sd(self): assert_almost_equal(self.Ttest.sd, self.res1.bse, DECIMAL_4) def test_pvalue(self): assert_almost_equal(self.Ttest.pvalue, student_t.sf( np.abs(self.res1.tvalues), self.res1.model.df_resid)*2, DECIMAL_4) def test_df_denom(self): assert_equal(self.Ttest.df_denom, self.res1.model.df_resid) def test_effect(self): assert_almost_equal(self.Ttest.effect, self.res1.params) class TestTtest2(object): ''' Tests the hypothesis that the coefficients on POP and YEAR are equal. Results from RPy using 'car' package. ''' @classmethod def setupClass(cls): R = np.zeros(7) R[4:6] = [1,-1] data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() cls.Ttest1 = res1.t_test(R) def test_tvalue(self): assert_almost_equal(self.Ttest1.tvalue, -4.0167754636397284, DECIMAL_4) def test_sd(self): assert_almost_equal(self.Ttest1.sd, 455.39079425195314, DECIMAL_4) def test_pvalue(self): assert_almost_equal(self.Ttest1.pvalue, 2*0.0015163772380932246, DECIMAL_4) def test_df_denom(self): assert_equal(self.Ttest1.df_denom, 9) def test_effect(self): assert_almost_equal(self.Ttest1.effect, -1829.2025687186533, DECIMAL_4) class TestGLS(object): ''' These test results were obtained by replication with R. ''' @classmethod def setupClass(cls): from results.results_regression import LongleyGls data = longley.load() exog = add_constant(np.column_stack((data.exog[:,1], data.exog[:,4])), prepend=False) tmp_results = OLS(data.endog, exog).fit() rho = np.corrcoef(tmp_results.resid[1:], tmp_results.resid[:-1])[0][1] # by assumption order = toeplitz(np.arange(16)) sigma = rho**order GLS_results = GLS(data.endog, exog, sigma=sigma).fit() cls.res1 = GLS_results cls.res2 = LongleyGls() # attach for test_missing cls.sigma = sigma cls.exog = exog cls.endog = data.endog def test_aic(self): assert_approx_equal(self.res1.aic+2, self.res2.aic, 3) def test_bic(self): assert_approx_equal(self.res1.bic, self.res2.bic, 2) def test_loglike(self): assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_0) def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_1) def test_resid(self): assert_almost_equal(self.res1.resid, self.res2.resid, DECIMAL_4) def test_scale(self): assert_almost_equal(self.res1.scale, self.res2.scale, DECIMAL_4) def test_tvalues(self): assert_almost_equal(self.res1.tvalues, self.res2.tvalues, DECIMAL_4) def test_standarderrors(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues, self.res2.fittedvalues, DECIMAL_4) def test_pvalues(self): assert_almost_equal(self.res1.pvalues, self.res2.pvalues, DECIMAL_4) def test_missing(self): endog = self.endog.copy() # copy or changes endog for other methods endog[[4,7,14]] = np.nan mod = GLS(endog, self.exog, sigma=self.sigma, missing='drop') assert_equal(mod.endog.shape[0], 13) assert_equal(mod.exog.shape[0], 13) assert_equal(mod.sigma.shape, (13,13)) class TestGLS_nosigma(CheckRegressionResults): ''' Test that GLS with no argument is equivalent to OLS. ''' @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) ols_res = OLS(data.endog, data.exog).fit() gls_res = GLS(data.endog, data.exog).fit() cls.res1 = gls_res cls.res2 = ols_res # self.res2.conf_int = self.res2.conf_int() # def check_confidenceintervals(self, conf1, conf2): # assert_almost_equal(conf1, conf2, DECIMAL_4) class TestWLSExogWeights(CheckRegressionResults): #Test WLS with Greene's credit card data #reg avgexp age income incomesq ownrent [aw=1/incomesq] def __init__(self): from results.results_regression import CCardWLS from statsmodels.datasets.ccard import load dta = load() dta.exog = add_constant(dta.exog, prepend=False) nobs = 72. weights = 1/dta.exog[:,2] # for comparison with stata analytic weights scaled_weights = ((weights * nobs)/weights.sum()) self.res1 = WLS(dta.endog, dta.exog, weights=scaled_weights).fit() self.res2 = CCardWLS() self.res2.wresid = scaled_weights ** .5 * self.res2.resid def test_wls_example(): #example from the docstring, there was a note about a bug, should #be fixed now Y = [1,3,4,5,2,3,4] X = range(1,8) X = add_constant(X, prepend=False) wls_model = WLS(Y,X, weights=range(1,8)).fit() #taken from R lm.summary assert_almost_equal(wls_model.fvalue, 0.127337843215, 6) assert_almost_equal(wls_model.scale, 2.44608530786**2, 6) def test_wls_tss(): y = np.array([22, 22, 22, 23, 23, 23]) X = [[1, 0], [1, 0], [1, 1], [0, 1], [0, 1], [0, 1]] ols_mod = OLS(y, add_constant(X, prepend=False)).fit() yw = np.array([22, 22, 23.]) Xw = [[1,0],[1,1],[0,1]] w = np.array([2, 1, 3.]) wls_mod = WLS(yw, add_constant(Xw, prepend=False), weights=w).fit() assert_equal(ols_mod.centered_tss, wls_mod.centered_tss) class TestWLSScalarVsArray(CheckRegressionResults): @classmethod def setupClass(cls): from statsmodels.datasets.longley import load dta = load() dta.exog = add_constant(dta.exog, prepend=True) wls_scalar = WLS(dta.endog, dta.exog, weights=1./3).fit() weights = [1/3.] * len(dta.endog) wls_array = WLS(dta.endog, dta.exog, weights=weights).fit() cls.res1 = wls_scalar cls.res2 = wls_array #class TestWLS_GLS(CheckRegressionResults): # @classmethod # def setupClass(cls): # from statsmodels.datasets.ccard import load # data = load() # cls.res1 = WLS(data.endog, data.exog, weights = 1/data.exog[:,2]).fit() # cls.res2 = GLS(data.endog, data.exog, sigma = data.exog[:,2]).fit() # # def check_confidenceintervals(self, conf1, conf2): # assert_almost_equal(conf1, conf2(), DECIMAL_4) def test_wls_missing(): from statsmodels.datasets.ccard import load data = load() endog = data.endog endog[[10, 25]] = np.nan mod = WLS(data.endog, data.exog, weights = 1/data.exog[:,2], missing='drop') assert_equal(mod.endog.shape[0], 70) assert_equal(mod.exog.shape[0], 70) assert_equal(mod.weights.shape[0], 70) class TestWLS_OLS(CheckRegressionResults): @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) cls.res1 = OLS(data.endog, data.exog).fit() cls.res2 = WLS(data.endog, data.exog).fit() def check_confidenceintervals(self, conf1, conf2): assert_almost_equal(conf1, conf2(), DECIMAL_4) class TestGLS_OLS(CheckRegressionResults): @classmethod def setupClass(cls): data = longley.load() data.exog = add_constant(data.exog, prepend=False) cls.res1 = GLS(data.endog, data.exog).fit() cls.res2 = OLS(data.endog, data.exog).fit() def check_confidenceintervals(self, conf1, conf2): assert_almost_equal(conf1, conf2(), DECIMAL_4) #TODO: test AR # why the two-stage in AR? #class test_ar(object): # from statsmodels.datasets.sunspots import load # data = load() # model = AR(data.endog, rho=4).fit() # R_res = RModel(data.endog, aic="FALSE", order_max=4) # def test_params(self): # assert_almost_equal(self.model.rho, # pass # def test_order(self): # In R this can be defined or chosen by minimizing the AIC if aic=True # pass class TestYuleWalker(object): @classmethod def setupClass(cls): from statsmodels.datasets.sunspots import load data = load() cls.rho, cls.sigma = yule_walker(data.endog, order=4, method="mle") cls.R_params = [1.2831003105694765, -0.45240924374091945, -0.20770298557575195, 0.047943648089542337] def test_params(self): assert_almost_equal(self.rho, self.R_params, DECIMAL_4) class TestDataDimensions(CheckRegressionResults): @classmethod def setupClass(cls): np.random.seed(54321) cls.endog_n_ = np.random.uniform(0,20,size=30) cls.endog_n_one = cls.endog_n_[:,None] cls.exog_n_ = np.random.uniform(0,20,size=30) cls.exog_n_one = cls.exog_n_[:,None] cls.degen_exog = cls.exog_n_one[:-1] cls.mod1 = OLS(cls.endog_n_one, cls.exog_n_one) cls.mod1.df_model += 1 #cls.mod1.df_resid -= 1 cls.res1 = cls.mod1.fit() # Note that these are created for every subclass.. # A little extra overhead probably cls.mod2 = OLS(cls.endog_n_one, cls.exog_n_one) cls.mod2.df_model += 1 cls.res2 = cls.mod2.fit() def check_confidenceintervals(self, conf1, conf2): assert_almost_equal(conf1, conf2(), DECIMAL_4) class TestNxNx(TestDataDimensions): @classmethod def setupClass(cls): super(TestNxNx, cls).setupClass() cls.mod2 = OLS(cls.endog_n_, cls.exog_n_) cls.mod2.df_model += 1 cls.res2 = cls.mod2.fit() class TestNxOneNx(TestDataDimensions): @classmethod def setupClass(cls): super(TestNxOneNx, cls).setupClass() cls.mod2 = OLS(cls.endog_n_one, cls.exog_n_) cls.mod2.df_model += 1 cls.res2 = cls.mod2.fit() class TestNxNxOne(TestDataDimensions): @classmethod def setupClass(cls): super(TestNxNxOne, cls).setupClass() cls.mod2 = OLS(cls.endog_n_, cls.exog_n_one) cls.mod2.df_model += 1 cls.res2 = cls.mod2.fit() def test_bad_size(): np.random.seed(54321) data = np.random.uniform(0,20,31) assert_raises(ValueError, OLS, data, data[1:]) def test_const_indicator(): np.random.seed(12345) X = np.random.randint(0, 3, size=30) X = categorical(X, drop=True) y = np.dot(X, [1., 2., 3.]) + np.random.normal(size=30) modc = OLS(y, add_constant(X[:,1:], prepend=True)).fit() mod = OLS(y, X, hasconst=True).fit() assert_almost_equal(modc.rsquared, mod.rsquared, 12) def test_706(): # make sure one regressor pandas Series gets passed to DataFrame # for conf_int. y = pandas.Series(np.random.randn(10)) x = pandas.Series(np.ones(10)) res = OLS(y,x).fit() conf_int = res.conf_int() np.testing.assert_equal(conf_int.shape, (1, 2)) np.testing.assert_(isinstance(conf_int, pandas.DataFrame)) def test_summary(): # test 734 import re dta = longley.load_pandas() X = dta.exog X["constant"] = 1 y = dta.endog with warnings.catch_warnings(record=True): res = OLS(y, X).fit() table = res.summary().as_latex() # replace the date and time table = re.sub("(?<=\n\\\\textbf\{Date:\} &).+?&", " Sun, 07 Apr 2013 &", table) table = re.sub("(?<=\n\\\\textbf\{Time:\} &).+?&", " 13:46:07 &", table) expected = """\\begin{center} \\begin{tabular}{lclc} \\toprule \\textbf{Dep. Variable:} & TOTEMP & \\textbf{ R-squared: } & 0.995 \\\\ \\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.992 \\\\ \\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 330.3 \\\\ \\textbf{Date:} & Sun, 07 Apr 2013 & \\textbf{ Prob (F-statistic):} & 4.98e-10 \\\\ \\textbf{Time:} & 13:46:07 & \\textbf{ Log-Likelihood: } & -109.62 \\\\ \\textbf{No. Observations:} & 16 & \\textbf{ AIC: } & 233.2 \\\\ \\textbf{Df Residuals:} & 9 & \\textbf{ BIC: } & 238.6 \\\\ \\bottomrule \\end{tabular} \\begin{tabular}{lccccc} & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$>$$|$t$|$} & \\textbf{[95.0\\% Conf. Int.]} \\\\ \\midrule \\textbf{GNPDEFL} & 15.0619 & 84.915 & 0.177 & 0.863 & -177.029 207.153 \\\\ \\textbf{GNP} & -0.0358 & 0.033 & -1.070 & 0.313 & -0.112 0.040 \\\\ \\textbf{UNEMP} & -2.0202 & 0.488 & -4.136 & 0.003 & -3.125 -0.915 \\\\ \\textbf{ARMED} & -1.0332 & 0.214 & -4.822 & 0.001 & -1.518 -0.549 \\\\ \\textbf{POP} & -0.0511 & 0.226 & -0.226 & 0.826 & -0.563 0.460 \\\\ \\textbf{YEAR} & 1829.1515 & 455.478 & 4.016 & 0.003 & 798.788 2859.515 \\\\ \\textbf{constant} & -3.482e+06 & 8.9e+05 & -3.911 & 0.004 & -5.5e+06 -1.47e+06 \\\\ \\bottomrule \\end{tabular} \\begin{tabular}{lclc} \\textbf{Omnibus:} & 0.749 & \\textbf{ Durbin-Watson: } & 2.559 \\\\ \\textbf{Prob(Omnibus):} & 0.688 & \\textbf{ Jarque-Bera (JB): } & 0.684 \\\\ \\textbf{Skew:} & 0.420 & \\textbf{ Prob(JB): } & 0.710 \\\\ \\textbf{Kurtosis:} & 2.434 & \\textbf{ Cond. No. } & 4.86e+09 \\\\ \\bottomrule \\end{tabular} %\\caption{OLS Regression Results} \\end{center}""" assert_equal(table, expected) if __name__=="__main__": import nose # run_module_suite() nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'], exit=False) # nose.runmodule(argv=[__file__,'-vvs','-x'], exit=False) #, '--pdb' statsmodels-0.5.0+git13-g8e07d34/statsmodels/resampling/000077500000000000000000000000001224417117700226555ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/resampling/__init__.py000066400000000000000000000000001224417117700247540ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/000077500000000000000000000000001224417117700220325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/__init__.py000066400000000000000000000002661224417117700241470ustar00rootroot00000000000000""" Robust statistical models """ import norms from .scale import mad, stand_mad, Huber, HuberScale, hubers_scale from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/norms.py000066400000000000000000000463601224417117700235530ustar00rootroot00000000000000import numpy as np #TODO: add plots to weighting functions for online docs. class RobustNorm(object): """ The parent class for the norms used for robust regression. Lays out the methods expected of the robust norms to be used by statsmodels.RLM. Parameters ---------- None : Some subclasses have optional tuning constants. References ---------- PJ Huber. 'Robust Statistics' John Wiley and Sons, Inc., New York, 1981. DC Montgomery, EA Peck. 'Introduction to Linear Regression Analysis', John Wiley and Sons, Inc., New York, 2001. R Venables, B Ripley. 'Modern Applied Statistics in S' Springer, New York, 2002. See Also -------- statsmodels.rlm for more information on how the estimators are used and the inputs for the methods of RobustNorm and subclasses. Notes ----- Currently only M-estimators are available. """ def rho(self, z): """ The robust criterion estimator function. Abstract method: -2 loglike used in M-estimator """ raise NotImplementedError def psi(self, z): """ Derivative of rho. Sometimes referred to as the influence function. Abstract method: psi = rho' """ raise NotImplementedError def weights(self, z): """ Returns the value of psi(z) / z Abstract method: psi(z) / z """ raise NotImplementedError def psi_deriv(self, z): ''' Deriative of psi. Used to obtain robust covariance matrix. See statsmodels.rlm for more information. Abstract method: psi_derive = psi' ''' raise NotImplementedError def __call__(self, z): """ Returns the value of estimator rho applied to an input """ return self.rho(z) class LeastSquares(RobustNorm): """ Least squares rho for M-estimation and its derived functions. See also -------- statsmodels.robust.norms.RobustNorm for the methods. """ def rho(self, z): """ The least squares estimator rho function Parameters ----------- z : array 1d array Returns ------- rho : array rho(z) = (1/2.)*z**2 """ return z**2 * 0.5 def psi(self, z): """ The psi function for the least squares estimator The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z """ return np.asarray(z) def weights(self, z): """ The least squares estimator weighting function for the IRLS algorithm. The psi function scaled by the input z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = np.ones(z.shape) """ z = np.asarray(z) return np.ones(z.shape, np.float64) def psi_deriv(self, z): """ The derivative of the least squares psi function. Returns ------- psi_deriv : array ones(z.shape) Notes ----- Used to estimate the robust covariance matrix. """ return np.ones(z.shape, np.float64) class HuberT(RobustNorm): """ Huber's T for M estimation. Parameters ---------- t : float, optional The tuning constant for Huber's t function. The default value is 1.345. See also -------- statsmodels.robust.norms.RobustNorm """ def __init__(self, t=1.345): self.t = t def _subset(self, z): """ Huber's T is defined piecewise over the range for z """ z = np.asarray(z) return np.less_equal(np.fabs(z), self.t) def rho(self, z): """ The robust criterion function for Huber's t. Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = .5*z**2 for \|z\| <= t rho(z) = \|z\|*t - .5*t**2 for \|z\| > t """ z = np.asarray(z) test = self._subset(z) return (test * 0.5 * z**2 + (1 - test) * (np.fabs(z) * self.t - 0.5 * self.t**2)) def psi(self, z): """ The psi function for Huber's t estimator The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z for \|z\| <= t psi(z) = sign(z)*t for \|z\| > t """ z = np.asarray(z) test = self._subset(z) return test * z + (1 - test) * self.t * np.sign(z) def weights(self, z): """ Huber's t weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = 1 for \|z\| <= t weights(z) = t/\|z\| for \|z\| > t """ z = np.asarray(z) test = self._subset(z) absz = np.fabs(z) absz[test] = 1.0 return test + (1 - test) * self.t / absz def psi_deriv(self, z): """ The derivative of Huber's t psi function Notes ----- Used to estimate the robust covariance matrix. """ return np.less_equal(np.fabs(z), self.t) #TODO: untested, but looks right. RamsayE not available in R or SAS? class RamsayE(RobustNorm): """ Ramsay's Ea for M estimation. Parameters ---------- a : float, optional The tuning constant for Ramsay's Ea function. The default value is 0.3. See also -------- statsmodels.robust.norms.RobustNorm """ def __init__(self, a = .3): self.a = a def rho(self, z): """ The robust criterion function for Ramsay's Ea. Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = a**-2 * (1 - exp(-a*\|z\|)*(1 + a*\|z\|)) """ z = np.asarray(z) return (1 - np.exp(-self.a * np.fabs(z)) * (1 + self.a * np.fabs(z))) / self.a**2 def psi(self, z): """ The psi function for Ramsay's Ea estimator The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z*exp(-a*\|z\|) """ z = np.asarray(z) return z * np.exp(-self.a * np.fabs(z)) def weights(self, z): """ Ramsay's Ea weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = exp(-a*\|z\|) """ z = np.asarray(z) return np.exp(-self.a * np.fabs(z)) def psi_deriv(self, z): """ The derivative of Ramsay's Ea psi function. Notes ----- Used to estimate the robust covariance matrix. """ return np.exp(-self.a * np.fabs(z)) + z**2*\ np.exp(-self.a*np.fabs(z))*-self.a/np.fabs(z) class AndrewWave(RobustNorm): """ Andrew's wave for M estimation. Parameters ---------- a : float, optional The tuning constant for Andrew's Wave function. The default value is 1.339. See also -------- statsmodels.robust.norms.RobustNorm """ def __init__(self, a = 1.339): self.a = a def _subset(self, z): """ Andrew's wave is defined piecewise over the range of z. """ z = np.asarray(z) return np.less_equal(np.fabs(z), self.a * np.pi) def rho(self, z): """ The robust criterion function for Andrew's wave. Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = a*(1-cos(z/a)) for \|z\| <= a*pi rho(z) = 2*a for \|z\| > a*pi """ a = self.a z = np.asarray(z) test = self._subset(z) return (test * a * (1 - np.cos(z / a)) + (1 - test) * 2 * a) def psi(self, z): """ The psi function for Andrew's wave The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = sin(z/a) for \|z\| <= a*pi psi(z) = 0 for \|z\| > a*pi """ a = self.a z = np.asarray(z) test = self._subset(z) return test * np.sin(z / a) def weights(self, z): """ Andrew's wave weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = sin(z/a)/(z/a) for \|z\| <= a*pi weights(z) = 0 for \|z\| > a*pi """ a = self.a z = np.asarray(z) test = self._subset(z) return test * np.sin(z / a) / (z / a) def psi_deriv(self, z): """ The derivative of Andrew's wave psi function Notes ----- Used to estimate the robust covariance matrix. """ test = self._subset(z) return test*np.cos(z / self.a)/self.a #TODO: this is untested class TrimmedMean(RobustNorm): """ Trimmed mean function for M-estimation. Parameters ---------- c : float, optional The tuning constant for Ramsay's Ea function. The default value is 2.0. See also -------- statsmodels.robust.norms.RobustNorm """ def __init__(self, c=2.): self.c = c def _subset(self, z): """ Least trimmed mean is defined piecewise over the range of z. """ z = np.asarray(z) return np.less_equal(np.fabs(z), self.c) def rho(self, z): """ The robust criterion function for least trimmed mean. Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = (1/2.)*z**2 for \|z\| <= c rho(z) = 0 for \|z\| > c """ z = np.asarray(z) test = self._subset(z) return test * z**2 * 0.5 def psi(self, z): """ The psi function for least trimmed mean The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z for \|z\| <= c psi(z) = 0 for \|z\| > c """ z = np.asarray(z) test = self._subset(z) return test * z def weights(self, z): """ Least trimmed mean weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = 1 for \|z\| <= c weights(z) = 0 for \|z\| > c """ z = np.asarray(z) test = self._subset(z) return test def psi_deriv(self, z): """ The derivative of least trimmed mean psi function Notes ----- Used to estimate the robust covariance matrix. """ test = self._subset(z) return test class Hampel(RobustNorm): """ Hampel function for M-estimation. Parameters ---------- a : float, optional b : float, optional c : float, optional The tuning constants for Hampel's function. The default values are a,b,c = 2, 4, 8. See also -------- statsmodels.robust.norms.RobustNorm """ def __init__(self, a = 2., b = 4., c = 8.): self.a = a self.b = b self.c = c def _subset(self, z): """ Hampel's function is defined piecewise over the range of z """ z = np.fabs(np.asarray(z)) t1 = np.less_equal(z, self.a) t2 = np.less_equal(z, self.b) * np.greater(z, self.a) t3 = np.less_equal(z, self.c) * np.greater(z, self.b) return t1, t2, t3 def rho(self, z): """ The robust criterion function for Hampel's estimator Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = (1/2.)*z**2 for \|z\| <= a rho(z) = a*\|z\| - 1/2.*a**2 for a < \|z\| <= b rho(z) = a*(c*\|z\|-(1/2.)*z**2)/(c-b) for b < \|z\| <= c rho(z) = a*(b + c - a) for \|z\| > c """ z = np.fabs(z) a = self.a; b = self.b; c = self.c t1, t2, t3 = self._subset(z) v = (t1 * z**2 * 0.5 + t2 * (a * z - a**2 * 0.5) + t3 * (a * (c * z - z**2 * 0.5) / (c - b) - 7 * a**2 / 6.) + (1 - t1 + t2 + t3) * a * (b + c - a)) return v def psi(self, z): """ The psi function for Hampel's estimator The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z for \|z\| <= a psi(z) = a*sign(z) for a < \|z\| <= b psi(z) = a*sign(z)*(c - \|z\|)/(c-b) for b < \|z\| <= c psi(z) = 0 for \|z\| > c """ z = np.asarray(z) a = self.a; b = self.b; c = self.c t1, t2, t3 = self._subset(z) s = np.sign(z) z = np.fabs(z) v = s * (t1 * z + t2 * a*s + t3 * a*s * (c - z) / (c - b)) return v def weights(self, z): """ Hampel weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array weights(z) = 1 for \|z\| <= a weights(z) = a/\|z\| for a < \|z\| <= b weights(z) = a*(c - \|z\|)/(\|z\|*(c-b)) for b < \|z\| <= c weights(z) = 0 for \|z\| > c """ z = np.asarray(z) a = self.a; b = self.b; c = self.c t1, t2, t3 = self._subset(z) v = (t1 + t2 * a/np.fabs(z) + t3 * a*(c-np.fabs(z))/(np.fabs(z)*(c-b))) v[np.where(np.isnan(v))]=1. # for some reason 0 returns a nan? return v def psi_deriv(self, z): t1, t2, t3 = self._subset(z) return t1 + t3 * (self.a*np.sign(z)*z)/(np.fabs(z)*(self.c-self.b)) class TukeyBiweight(RobustNorm): """ Tukey's biweight function for M-estimation. Parameters ---------- c : float, optional The tuning constant for Tukey's Biweight. The default value is c = 4.685. Notes ----- Tukey's biweight is sometime's called bisquare. """ def __init__(self, c = 4.685): self.c = c def _subset(self, z): """ Tukey's biweight is defined piecewise over the range of z """ z = np.fabs(np.asarray(z)) return np.less_equal(z, self.c) def rho(self, z): """ The robust criterion function for Tukey's biweight estimator Parameters ---------- z : array-like 1d array Returns ------- rho : array rho(z) = -(1 - (z/c)**2)**3 * c**2/6. for \|z\| <= R rho(z) = 0 for \|z\| > R """ subset = self._subset(z) return -(1 - (z / self.c)**2)**3 * subset * self.c**2 / 6. def psi(self, z): """ The psi function for Tukey's biweight estimator The analytic derivative of rho Parameters ---------- z : array-like 1d array Returns ------- psi : array psi(z) = z*(1 - (z/c)**2)**2 for \|z\| <= R psi(z) = 0 for \|z\| > R """ z = np.asarray(z) subset = self._subset(z) return z * (1 - (z / self.c)**2)**2 * subset def weights(self, z): """ Tukey's biweight weighting function for the IRLS algorithm The psi function scaled by z Parameters ---------- z : array-like 1d array Returns ------- weights : array psi(z) = (1 - (z/c)**2)**2 for \|z\| <= R psi(z) = 0 for \|z\| > R """ subset = self._subset(z) return (1 - (z / self.c)**2)**2 * subset def psi_deriv(self, z): """ The derivative of Tukey's biweight psi function Notes ----- Used to estimate the robust covariance matrix. """ subset = self._subset(z) return subset*((1 - (z/self.c)**2)**2 - (4*z**2/self.c**2) *\ (1-(z/self.c)**2)) def estimate_location(a, scale, norm=None, axis=0, initial=None, maxiter=30, tol=1.0e-06): """ M-estimator of location using self.norm and a current estimator of scale. This iteratively finds a solution to norm.psi((a-mu)/scale).sum() == 0 Parameters ---------- a : array Array over which the location parameter is to be estimated scale : array Scale parameter to be used in M-estimator norm : RobustNorm, optional Robust norm used in the M-estimator. The default is HuberT(). axis : int, optional Axis along which to estimate the location parameter. The default is 0. initial : array, optional Initial condition for the location parameter. Default is None, which uses the median of a. niter : int, optional Maximum number of iterations. The default is 30. tol : float, optional Toleration for convergence. The default is 1e-06. Returns -------- mu : array Estimate of location """ if norm is None: norm = HuberT() if initial is None: mu = np.median(a, axis) else: mu = initial for iter in range(maxiter): W = norm.weights((a-mu)/scale) nmu = np.sum(W*a, axis) / np.sum(W, axis) if np.alltrue(np.less(np.fabs(mu - nmu), scale * tol)): return nmu else: mu = nmu raise ValueError("location estimator failed to converge in %d iterations"\ % maxiter) statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/robust_linear_model.py000066400000000000000000000612101224417117700264340ustar00rootroot00000000000000""" Robust linear models with support for the M-estimators listed under :ref:`norms `. References ---------- PJ Huber. 'Robust Statistics' John Wiley and Sons, Inc., New York. 1981. PJ Huber. 1973, 'The 1972 Wald Memorial Lectures: Robust Regression: Asymptotics, Conjectures, and Monte Carlo.' The Annals of Statistics, 1.5, 799-821. R Venables, B Ripley. 'Modern Applied Statistics in S' Springer, New York, 2002. """ import numpy as np import scipy.stats as stats from statsmodels.tools.decorators import (cache_readonly, resettable_cache) from statsmodels.tools.tools import rank import statsmodels.regression.linear_model as lm import statsmodels.robust.norms as norms import statsmodels.robust.scale as scale import statsmodels.base.model as base import statsmodels.base.wrapper as wrap __all__ = ['RLM'] def _check_convergence(criterion, iteration, tol, maxiter): return not (np.any(np.fabs(criterion[iteration] - criterion[iteration-1]) > tol) and iteration < maxiter) class RLM(base.LikelihoodModel): __doc__ = """ Robust Linear Models Estimate a robust linear model via iteratively reweighted least squares given a robust criterion estimator. %(params)s M : statsmodels.robust.norms.RobustNorm, optional The robust criterion function for downweighting outliers. The current options are LeastSquares, HuberT, RamsayE, AndrewWave, TrimmedMean, Hampel, and TukeyBiweight. The default is HuberT(). See statsmodels.robust.norms for more information. %(extra_params)s Notes ----- **Attributes** df_model : float The degrees of freedom of the model. The number of regressors p less one for the intercept. Note that the reported model degrees of freedom does not count the intercept as a regressor, though the model is assumed to have an intercept. df_resid : float The residual degrees of freedom. The number of observations n less the number of regressors p. Note that here p does include the intercept as using a degree of freedom. endog : array See above. Note that endog is a reference to the data so that if data is already an array and it is changed, then `endog` changes as well. exog : array See above. Note that endog is a reference to the data so that if data is already an array and it is changed, then `endog` changes as well. M : statsmodels.robust.norms.RobustNorm See above. Robust estimator instance instantiated. nobs : float The number of observations n pinv_wexog : array The pseudoinverse of the design / exogenous data array. Note that RLM has no whiten method, so this is just the pseudo inverse of the design. normalized_cov_params : array The p x p normalized covariance of the design / exogenous data. This is approximately equal to (X.T X)^(-1) Examples --------- >>> import statsmodels.api as sm >>> data = sm.datasets.stackloss.load() >>> data.exog = sm.add_constant(data.exog) >>> rlm_model = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()) >>> rlm_results = rlm_model.fit() >>> rlm_results.params array([ 0.82938433, 0.92606597, -0.12784672, -41.02649835]) >>> rlm_results.bse array([ 0.11100521, 0.30293016, 0.12864961, 9.79189854]) >>> rlm_results_HC2 = rlm_model.fit(cov="H2") >>> rlm_results_HC2.params array([ 0.82938433, 0.92606597, -0.12784672, -41.02649835]) >>> rlm_results_HC2.bse array([ 0.11945975, 0.32235497, 0.11796313, 9.08950419]) >>> >>> rlm_hamp_hub = sm.RLM(data.endog, data.exog, M=sm.robust.norms.Hampel()).fit( sm.robust.scale.HuberScale()) >>> rlm_hamp_hub.params array([ 0.73175452, 1.25082038, -0.14794399, -40.27122257]) """ % {'params' : base._model_params_doc, 'extra_params' : base._missing_param_doc} def __init__(self, endog, exog, M=norms.HuberT(), missing='none'): self.M = M super(base.LikelihoodModel, self).__init__(endog, exog, missing=missing) self._initialize() #things to remove_data self._data_attr.extend(['weights', 'pinv_wexog']) def _initialize(self): """ Initializes the model for the IRLS fit. Resets the history and number of iterations. """ self.pinv_wexog = np.linalg.pinv(self.exog) self.normalized_cov_params = np.dot(self.pinv_wexog, np.transpose(self.pinv_wexog)) self.df_resid = np.float(self.exog.shape[0] - rank(self.exog)) self.df_model = np.float(rank(self.exog)-1) self.nobs = float(self.endog.shape[0]) def score(self, params): raise NotImplementedError def information(self, params): raise NotImplementedError def predict(self, params, exog=None): """ Return linear predicted values from a design matrix. Parameters ---------- params : array-like, optional after fit has been called Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns ------- An array of fitted values Notes ----- If the model as not yet been fit, params is not optional. """ #copied from linear_model if exog is None: exog = self.exog return np.dot(exog, params) def loglike(self, params): raise NotImplementedError def deviance(self, tmp_results): """ Returns the (unnormalized) log-likelihood from the M estimator. """ return self.M((self.endog - tmp_results.fittedvalues) / tmp_results.scale).sum() def _update_history(self, tmp_results, history, conv): history['params'].append(tmp_results.params) history['scale'].append(tmp_results.scale) if conv == 'dev': history['deviance'].append(self.deviance(tmp_results)) elif conv == 'sresid': history['sresid'].append(tmp_results.resid/tmp_results.scale) elif conv == 'weights': history['weights'].append(tmp_results.model.weights) return history def _estimate_scale(self, resid): """ Estimates the scale based on the option provided to the fit method. """ if isinstance(self.scale_est, str): if self.scale_est.lower() == 'mad': return scale.mad(resid) if self.scale_est.lower() == 'stand_mad': return scale.stand_mad(resid) elif isinstance(self.scale_est, scale.HuberScale): return self.scale_est(self.df_resid, self.nobs, resid) else: return scale.scale_est(self, resid)**2 def fit(self, maxiter=50, tol=1e-8, scale_est='mad', init=None, cov='H1', update_scale=True, conv='dev'): """ Fits the model using iteratively reweighted least squares. The IRLS routine runs until the specified objective converges to `tol` or `maxiter` has been reached. Parameters ---------- conv : string Indicates the convergence criteria. Available options are "coefs" (the coefficients), "weights" (the weights in the iteration), "sresid" (the standardized residuals), and "dev" (the un-normalized log-likelihood for the M estimator). The default is "dev". cov : string, optional 'H1', 'H2', or 'H3' Indicates how the covariance matrix is estimated. Default is 'H1'. See rlm.RLMResults for more information. init : string Specifies method for the initial estimates of the parameters. Default is None, which means that the least squares estimate is used. Currently it is the only available choice. maxiter : int The maximum number of iterations to try. Default is 50. scale_est : string or HuberScale() 'mad', 'stand_mad', or HuberScale() Indicates the estimate to use for scaling the weights in the IRLS. The default is 'mad' (median absolute deviation. Other options are use 'stand_mad' for the median absolute deviation standardized around the median and 'HuberScale' for Huber's proposal 2. Huber's proposal 2 has optional keyword arguments d, tol, and maxiter for specifying the tuning constant, the convergence tolerance, and the maximum number of iterations. See models.robust.scale for more information. tol : float The convergence tolerance of the estimate. Default is 1e-8. update_scale : Bool If `update_scale` is False then the scale estimate for the weights is held constant over the iteration. Otherwise, it is updated for each fit in the iteration. Default is True. Returns ------- results : object statsmodels.rlm.RLMresults """ if not cov.upper() in ["H1","H2","H3"]: raise ValueError("Covariance matrix %s not understood" % cov) else: self.cov = cov.upper() conv = conv.lower() if not conv in ["weights","coefs","dev","sresid"]: raise ValueError("Convergence argument %s not understood" \ % conv) self.scale_est = scale_est wls_results = lm.WLS(self.endog, self.exog).fit() if not init: self.scale = self._estimate_scale(wls_results.resid) history = dict(params = [np.inf], scale = []) if conv == 'coefs': criterion = history['params'] elif conv == 'dev': history.update(dict(deviance = [np.inf])) criterion = history['deviance'] elif conv == 'sresid': history.update(dict(sresid = [np.inf])) criterion = history['sresid'] elif conv == 'weights': history.update(dict(weights = [np.inf])) criterion = history['weights'] # done one iteration so update history = self._update_history(wls_results, history, conv) iteration = 1 converged = 0 while not converged: self.weights = self.M.weights(wls_results.resid/self.scale) wls_results = lm.WLS(self.endog, self.exog, weights=self.weights).fit() if update_scale is True: self.scale = self._estimate_scale(wls_results.resid) history = self._update_history(wls_results, history, conv) iteration += 1 converged = _check_convergence(criterion, iteration, tol, maxiter) results = RLMResults(self, wls_results.params, self.normalized_cov_params, self.scale) history['iteration'] = iteration results.fit_history = history results.fit_options = dict(cov=cov.upper(), scale_est=scale_est, norm=self.M.__class__.__name__, conv=conv) #norm is not changed in fit, no old state #doing the next causes exception #self.cov = self.scale_est = None #reset for additional fits #iteration and history could contain wrong state with repeated fit return RLMResultsWrapper(results) class RLMResults(base.LikelihoodModelResults): """ Class to contain RLM results Returns ------- **Attributes** bcov_scaled : array p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as ``k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1)`` where ``k = 1 + (df_model +1)/nobs * var_psiprime/m**2`` where ``m = mean(M.psi_deriv(sresid))`` and ``var_psiprime = var(M.psi_deriv(sresid))`` H2 is defined as ``k * (1/df_resid) * sum(M.psi(sresid)**2) *scale**2/ ((1/nobs)*sum(M.psi_deriv(sresid)))*W_inv`` H3 is defined as ``1/k * (1/df_resid * sum(M.psi(sresid)**2)*scale**2 * (W_inv X.T X W_inv))`` where `k` is defined as above and ``W_inv = (M.psi_deriv(sresid) exog.T exog)^(-1)`` See the technical documentation for cleaner formulae. bcov_unscaled : array The usual p x p covariance matrix with scale set equal to 1. It is then just equivalent to normalized_cov_params. bse : array An array of the standard errors of the parameters. The standard errors are taken from the robust covariance matrix specified in the argument to fit. chisq : array An array of the chi-squared values of the paramter estimates. df_model See RLM.df_model df_resid See RLM.df_resid fit_history : dict Contains information about the iterations. Its keys are `deviance`, `params`, `iteration` and the convergence criteria specified in `RLM.fit`, if different from `deviance` or `params`. fit_options : dict Contains the options given to fit. fittedvalues : array The linear predicted values. dot(exog, params) model : statsmodels.rlm.RLM A reference to the model instance nobs : float The number of observations n normalized_cov_params : array See RLM.normalized_cov_params params : array The coefficients of the fitted model pinv_wexog : array See RLM.pinv_wexog pvalues : array The p values associated with `tvalues`. Note that `tvalues` are assumed to be distributed standard normal rather than Student's t. resid : array The residuals of the fitted model. endog - fittedvalues scale : float The type of scale is determined in the arguments to the fit method in RLM. The reported scale is taken from the residuals of the weighted least squares in the last IRLS iteration if update_scale is True. If update_scale is False, then it is the scale given by the first OLS fit before the IRLS iterations. sresid : array The scaled residuals. tvalues : array The "t-statistics" of params. These are defined as params/bse where bse are taken from the robust covariance matrix specified in the argument to fit. weights : array The reported weights are determined by passing the scaled residuals from the last weighted least squares fit in the IRLS algortihm. See also -------- statsmodels.model.LikelihoodModelResults """ def __init__(self, model, params, normalized_cov_params, scale): super(RLMResults, self).__init__(model, params, normalized_cov_params, scale) self.model = model self.df_model = model.df_model self.df_resid = model.df_resid self.nobs = model.nobs self._cache = resettable_cache() #for remove_data self.data_in_cache = ['sresid'] #TODO: "pvals" should come from chisq on bse? @cache_readonly def fittedvalues(self): return np.dot(self.model.exog, self.params) @cache_readonly def resid(self): return self.model.endog - self.fittedvalues # before bcov @cache_readonly def sresid(self): return self.resid/self.scale @cache_readonly def bcov_unscaled(self): return self.cov_params(scale=1.) @cache_readonly def weights(self): return self.model.weights @cache_readonly def bcov_scaled(self): model = self.model m = np.mean(model.M.psi_deriv(self.sresid)) var_psiprime = np.var(model.M.psi_deriv(self.sresid)) k = 1 + (self.df_model+1)/self.nobs * var_psiprime/m**2 if model.cov == "H1": return k**2 * (1/self.df_resid*\ np.sum(model.M.psi(self.sresid)**2)*self.scale**2)\ /((1/self.nobs*np.sum(model.M.psi_deriv(self.sresid)))**2)\ *model.normalized_cov_params else: W = np.dot(model.M.psi_deriv(self.sresid)*model.exog.T, model.exog) W_inv = np.linalg.inv(W) # [W_jk]^-1 = [SUM(psi_deriv(Sr_i)*x_ij*x_jk)]^-1 # where Sr are the standardized residuals if model.cov == "H2": # These are correct, based on Huber (1973) 8.13 return k*(1/self.df_resid)*np.sum(\ model.M.psi(self.sresid)**2)*self.scale**2\ /((1/self.nobs)*np.sum(\ model.M.psi_deriv(self.sresid)))*W_inv elif model.cov == "H3": return k**-1*1/self.df_resid*np.sum(\ model.M.psi(self.sresid)**2)*self.scale**2\ *np.dot(np.dot(W_inv, np.dot(model.exog.T,model.exog)),\ W_inv) @cache_readonly def pvalues(self): return stats.norm.sf(np.abs(self.tvalues))*2 @cache_readonly def bse(self): return np.sqrt(np.diag(self.bcov_scaled)) @cache_readonly def chisq(self): return (self.params/self.bse)**2 def remove_data(self): super(self.__class__, self).remove_data() #self.model.history['sresid'] = None #self.model.history['weights'] = None remove_data.__doc__ = base.LikelihoodModelResults.remove_data.__doc__ def summary(self, yname=None, xname=None, title=0, alpha=.05, return_fmt='text'): """ This is for testing the new summary setup """ from statsmodels.iolib.summary import (summary_top, summary_params, summary_return) ## left = [(i, None) for i in ( ## 'Dependent Variable:', ## 'Model type:', ## 'Method:', ## 'Date:', ## 'Time:', ## 'Number of Obs:', ## 'df resid', ## 'df model', ## )] top_left = [('Dep. Variable:', None), ('Model:', None), ('Method:', ['IRLS']), ('Norm:', [self.fit_options['norm']]), ('Scale Est.:', [self.fit_options['scale_est']]), ('Cov Type:', [self.fit_options['cov']]), ('Date:', None), ('Time:', None), ('No. Iterations:', ["%d" % self.fit_history['iteration']]) ] top_right = [('No. Observations:', None), ('Df Residuals:', None), ('Df Model:', None) ] if not title is None: title = "Robust linear Model Regression Results" #boiler plate from statsmodels.iolib.summary import Summary smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[], yname=yname, xname=xname, title=title) smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=False) #diagnostic table is not used yet # smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right, # yname=yname, xname=xname, # title="") #add warnings/notes, added to text format only etext =[] wstr = \ '''If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .''' etext.append(wstr) if etext: smry.add_extra_txt(etext) return smry def summary2(self, xname=None, yname=None, title=None, alpha=.05, float_format="%.4f"): """Experimental summary function for regression results Parameters ----------- xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results """ # Summary from statsmodels.iolib import summary2 smry = summary2.Summary() smry.add_base(results=self, alpha=alpha, float_format=float_format, xname=xname, yname=yname, title=title) return smry class RLMResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(RLMResultsWrapper, RLMResults) if __name__=="__main__": #NOTE: This is to be removed #Delivery Time Data is taken from Montgomery and Peck import statsmodels.api as sm #delivery time(minutes) endog = np.array([16.68, 11.50, 12.03, 14.88, 13.75, 18.11, 8.00, 17.83, 79.24, 21.50, 40.33, 21.00, 13.50, 19.75, 24.00, 29.00, 15.35, 19.00, 9.50, 35.10, 17.90, 52.32, 18.75, 19.83, 10.75]) #number of cases, distance (Feet) exog = np.array([[7, 3, 3, 4, 6, 7, 2, 7, 30, 5, 16, 10, 4, 6, 9, 10, 6, 7, 3, 17, 10, 26, 9, 8, 4], [560, 220, 340, 80, 150, 330, 110, 210, 1460, 605, 688, 215, 255, 462, 448, 776, 200, 132, 36, 770, 140, 810, 450, 635, 150]]) exog = exog.T exog = sm.add_constant(exog) # model_ols = models.regression.OLS(endog, exog) # results_ols = model_ols.fit() # model_huber = RLM(endog, exog, M=norms.HuberT(t=2.)) # results_huber = model_huber.fit(scale_est="stand_mad", update_scale=False) # model_ramsaysE = RLM(endog, exog, M=norms.RamsayE()) # results_ramsaysE = model_ramsaysE.fit(update_scale=False) # model_andrewWave = RLM(endog, exog, M=norms.AndrewWave()) # results_andrewWave = model_andrewWave.fit(update_scale=False) # model_hampel = RLM(endog, exog, M=norms.Hampel(a=1.7,b=3.4,c=8.5)) # convergence problems with scale changed, not with 2,4,8 though? # results_hampel = model_hampel.fit(update_scale=False) ####################### ### Stack Loss Data ### ####################### from statsmodels.datasets.stackloss import load data = load() data.exog = sm.add_constant(data.exog) ############# ### Huber ### ############# # m1_Huber = RLM(data.endog, data.exog, M=norms.HuberT()) # results_Huber1 = m1_Huber.fit() # m2_Huber = RLM(data.endog, data.exog, M=norms.HuberT()) # results_Huber2 = m2_Huber.fit(cov="H2") # m3_Huber = RLM(data.endog, data.exog, M=norms.HuberT()) # results_Huber3 = m3_Huber.fit(cov="H3") ############## ### Hampel ### ############## # m1_Hampel = RLM(data.endog, data.exog, M=norms.Hampel()) # results_Hampel1 = m1_Hampel.fit() # m2_Hampel = RLM(data.endog, data.exog, M=norms.Hampel()) # results_Hampel2 = m2_Hampel.fit(cov="H2") # m3_Hampel = RLM(data.endog, data.exog, M=norms.Hampel()) # results_Hampel3 = m3_Hampel.fit(cov="H3") ################ ### Bisquare ### ################ # m1_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight()) # results_Bisquare1 = m1_Bisquare.fit() # m2_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight()) # results_Bisquare2 = m2_Bisquare.fit(cov="H2") # m3_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight()) # results_Bisquare3 = m3_Bisquare.fit(cov="H3") ############################################## # Huber's Proposal 2 scaling # ############################################## ################ ### Huber'sT ### ################ m1_Huber_H = RLM(data.endog, data.exog, M=norms.HuberT()) results_Huber1_H = m1_Huber_H.fit(scale_est=scale.HuberScale()) # m2_Huber_H # m3_Huber_H # m4 = RLM(data.endog, data.exog, M=norms.HuberT()) # results4 = m1.fit(scale_est="Huber") # m5 = RLM(data.endog, data.exog, M=norms.Hampel()) # results5 = m2.fit(scale_est="Huber") # m6 = RLM(data.endog, data.exog, M=norms.TukeyBiweight()) # results6 = m3.fit(scale_est="Huber") # print """Least squares fit #%s #Huber Params, t = 2. #%s #Ramsay's E Params #%s #Andrew's Wave Params #%s #Hampel's 17A Function #%s #""" % (results_ols.params, results_huber.params, results_ramsaysE.params, # results_andrewWave.params, results_hampel.params) statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/scale.py000066400000000000000000000201621224417117700234740ustar00rootroot00000000000000""" Support and standalone functions for Robust Linear Models References ---------- PJ Huber. 'Robust Statistics' John Wiley and Sons, Inc., New York, 1981. R Venables, B Ripley. 'Modern Applied Statistics in S' Springer, New York, 2002. """ import numpy as np from scipy.stats import norm as Gaussian import norms from statsmodels.tools import tools def mad(a, c=Gaussian.ppf(3/4.), axis=0): # c \approx .6745 """ The Median Absolute Deviation along given axis of an array Parameters ---------- a : array-like Input array. c : float, optional The normalization constant. Defined as scipy.stats.norm.ppf(3/4.), which is approximately .6745. axis : int, optional The defaul is 0. Returns ------- mad : float `mad` = median(abs(`a`))/`c` """ a = np.asarray(a) return np.median((np.fabs(a))/c, axis=axis) def stand_mad(a, c=Gaussian.ppf(3/4.), axis=0): """ The standardized Median Absolute Deviation along given axis of an array. Parameters ---------- a : array-like Input array. c : float, optional The normalization constant. Defined as scipy.stats.norm.ppf(3/4.), which is approximately .6745. axis : int, optional The defaul is 0. Returns ------- mad : float `mad` = median(abs(`a`-median(`a`))/`c` """ a = np.asarray(a) d = np.median(a, axis = axis) d = tools.unsqueeze(d, axis, a.shape) return np.median(np.fabs(a - d)/c, axis = axis) class Huber(object): """ Huber's proposal 2 for estimating location and scale jointly. Parameters ---------- c : float, optional Threshold used in threshold for chi=psi**2. Default value is 1.5. tol : float, optional Tolerance for convergence. Default value is 1e-08. maxiter : int, optional0 Maximum number of iterations. Default value is 30. norm : statsmodels.robust.norms.RobustNorm, optional A robust norm used in M estimator of location. If None, the location estimator defaults to a one-step fixed point version of the M-estimator using Huber's T. call Return joint estimates of Huber's scale and location. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> chem_data = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03, ... 3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7, 3.7, 3.7, 3.7, ... 3.77, 5.28, 28.95]) >>> sm.robust.scale.huber(chem_data) (array(3.2054980819923693), array(0.67365260010478967)) """ def __init__(self, c=1.5, tol=1.0e-08, maxiter=30, norm=None): self.c = c self.maxiter = maxiter self.tol = tol self.norm = norm tmp = 2 * Gaussian.cdf(c) - 1 self.gamma = tmp + c**2 * (1 - tmp) - 2 * c * Gaussian.pdf(c) def __call__(self, a, mu=None, initscale=None, axis=0): """ Compute Huber's proposal 2 estimate of scale, using an optional initial value of scale and an optional estimate of mu. If mu is supplied, it is not reestimated. Parameters ---------- a : array 1d array mu : float or None, optional If the location mu is supplied then it is not reestimated. Default is None, which means that it is estimated. initscale : float or None, optional A first guess on scale. If initscale is None then the standardized median absolute deviation of a is used. Notes ----- `Huber` minimizes the function sum(psi((a[i]-mu)/scale)**2) as a function of (mu, scale), where psi(x) = np.clip(x, -self.c, self.c) """ a = np.asarray(a) if mu is None: n = a.shape[0] - 1 mu = np.median(a, axis=axis) est_mu = True else: n = a.shape[0] mu = mu est_mu = False if initscale is None: scale = stand_mad(a, axis=axis) else: scale = initscale scale = tools.unsqueeze(scale, axis, a.shape) mu = tools.unsqueeze(mu, axis, a.shape) return self._estimate_both(a, scale, mu, axis, est_mu, n) def _estimate_both(self, a, scale, mu, axis, est_mu, n): """ Estimate scale and location simultaneously with the following pseudo_loop: while not_converged: mu, scale = estimate_location(a, scale, mu), estimate_scale(a, scale, mu) where estimate_location is an M-estimator and estimate_scale implements the check used in Section 5.5 of Venables & Ripley """ for _ in range(self.maxiter): # Estimate the mean along a given axis if est_mu: if self.norm is None: # This is a one-step fixed-point estimator # if self.norm == norms.HuberT # It should be faster than using norms.HuberT nmu = np.clip(a, mu-self.c*scale, mu+self.c*scale).sum(axis) / a.shape[axis] else: nmu = norms.estimate_location(a, scale, self.norm, axis, mu, self.maxiter, self.tol) else: # Effectively, do nothing nmu = mu.squeeze() nmu = tools.unsqueeze(nmu, axis, a.shape) subset = np.less_equal(np.fabs((a - mu)/scale), self.c) card = subset.sum(axis) nscale = np.sqrt(np.sum(subset * (a - nmu)**2, axis) \ / (n * self.gamma - (a.shape[axis] - card) * self.c**2)) nscale = tools.unsqueeze(nscale, axis, a.shape) test1 = np.alltrue(np.less_equal(np.fabs(scale - nscale), nscale * self.tol)) test2 = np.alltrue(np.less_equal(np.fabs(mu - nmu), nscale*self.tol)) if not (test1 and test2): mu = nmu; scale = nscale else: return nmu.squeeze(), nscale.squeeze() raise ValueError('joint estimation of location and scale failed to converge in %d iterations' % self.maxiter) huber = Huber() class HuberScale(object): """ Huber's scaling for fitting robust linear models. Huber's scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the `Huber` class. Parameters ---------- d : float, optional d is the tuning constant for Huber's scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Methods ------- call Return's Huber's scale computed as below Notes -------- Huber's scale is the iterative solution to scale_(i+1)**2 = 1/(n*h)*sum(chi(r/sigma_i)*sigma_i**2 where the Huber function is chi(x) = (x**2)/2 for \|x\| < d chi(x) = (d**2)/2 for \|x\| >= d and the Huber constant h = (n-p)/n*(d**2 + (1-d**2)*\ scipy.stats.norm.cdf(d) - .5 - d*sqrt(2*pi)*exp(-0.5*d**2) """ def __init__(self, d=2.5, tol=1e-08, maxiter=30): self.d = d self.tol = tol self.maxiter = maxiter def __call__(self, df_resid, nobs, resid): h = (df_resid)/nobs*(self.d**2 + (1-self.d**2)*\ Gaussian.cdf(self.d)-.5 - self.d/(np.sqrt(2*np.pi))*\ np.exp(-.5*self.d**2)) s = stand_mad(resid) subset = lambda x: np.less(np.fabs(resid/x),self.d) chi = lambda s: subset(s)*(resid/s)**2/2+(1-subset(s))*(self.d**2/2) scalehist = [np.inf,s] niter = 1 while (np.abs(scalehist[niter-1] - scalehist[niter])>self.tol \ and niter < self.maxiter): nscale = np.sqrt(1/(nobs*h)*np.sum(chi(scalehist[-1]))*\ scalehist[-1]**2) scalehist.append(nscale) niter += 1 #if niter == self.maxiter: # raise ValueError("Huber's scale failed to converge") return scalehist[-1] hubers_scale = HuberScale() statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/000077500000000000000000000000001224417117700231745ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/__init__.py000066400000000000000000000000001224417117700252730ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/results/000077500000000000000000000000001224417117700246755ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/results/__init__.py000066400000000000000000000000001224417117700267740ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/results/results_rlm.py000066400000000000000000000451421224417117700276300ustar00rootroot00000000000000### RLM MODEL RESULTS ### import numpy as np def _shift_intercept(arr): """ A convenience function to make the SAS covariance matrix compatible with statsmodels.rlm covariance """ arr = np.asarray(arr) side = np.sqrt(len(arr)) return np.roll(np.roll(arr.reshape(side,side),-1, axis =1), -1, axis=0) class Huber(object): """ """ def __init__(self): self.params = np.array([ 0.82937387, 0.92610818, -0.12784916, -41.02653105]) self.bse = np.array([ 0.11118035, 0.3034081 , 0.12885259, 9.8073472 ]) self.scale = 2.4407137948148447 self.weights = np.array([ 1., 1., 0.7858871, 0.50494094, 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.36814106]) self.resid = np.array([ 3.05027584, -2.07757332, 4.17721071, 6.50163171, -1.64615192, -2.57226011, -1.73127333, -0.73127333, -2.25476463, 0.48083217,1.63147461, 1.42973363, -2.26346951, -0.78323693, 2.26646556, 0.88291808, -0.83307835, 0.06186577, 0.26360675, 1.54306186, -8.91752986]) self.df_model = 3 self.df_resid = 17 self.bcov_unscaled = np.array([[ 1.72887367e-03, -3.47079127e-03, -6.79080082e-04, 2.73387119e-02], [ -3.47079127e-03, 1.28754242e-02, 9.95952051e-07, -6.19611175e-02], [ -6.79080082e-04, 9.95952051e-07, 2.32216722e-03, -1.59355028e-01], [ 2.73387119e-02, -6.19611175e-02, -1.59355028e-01, 1.34527267e+01]]) # From R self.fittedvalues = np.array([ 38.94972416, 39.07757332, 32.82278929, 21.49836829, 19.64615192, 20.57226011, 20.73127333, 20.73127333, 17.25476463, 13.51916783, 12.36852539, 11.57026637, 13.26346951, 12.78323693, 5.73353444, 6.11708192, 8.83307835, 7.93813423, 8.73639325, 13.45693814, 23.91752986]) self.tvalues = np.array([ 7.45971657, 3.0523516 , -0.99221261, -4.18324448]) # from R this is equivalent to # self.res1.params/np.sqrt(np.diag(self.res1.bcov_scaled)) # def conf_int(self): # method to be consistent with sm # return # The below are taken from SAS huber_h1 = [95.8813, 0.19485, -0.44161, -1.13577, 0.1949, 0.01232, -0.02474, -0.00484, -0.4416, -0.02474, 0.09177, 0.00001, -1.1358, -0.00484, 0.00001, 0.01655] h1 = _shift_intercept(huber_h1) huber_h2 = [82.6191, 0.07942, -0.23915, -0.95604, 0.0794, 0.01427, -0.03013, -0.00344, -0.2392, -0.03013, 0.10391, -0.00166, -0.9560, -0.00344, -0.00166, 0.01392] h2 = _shift_intercept(huber_h2) huber_h3 = [70.1633, -0.04533, -0.00790, -0.78618, -0.0453, 0.01656, -0.03608, -0.00203, -0.0079, -0.03608, 0.11610, -0.00333, -0.7862, -0.00203, -0.00333, 0.01138] h3 = _shift_intercept(huber_h3) class Hampel(object): """ """ def __init__(self): self.params = np.array([ 0.74108304, 1.22507934, -0.14552506, -40.47473236]) self.bse = np.array([ 0.13482596, 0.36793632, 0.1562567 , 11.89315426]) self.scale = 3.0882646556217064 self.weights = np.array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.80629719]) self.resid = np.array([ 3.06267708, -2.08284798, 4.36377602, 5.78635972, -1.7634816 , -2.98856094, -2.34048993, -1.34048993, -3.02422878, 1.08249252, 2.39221804, 2.47177232, -1.62645737, -0.25076107, 2.32088237, 0.88430719, -1.37812296, -0.35944755, -0.43900184, 1.40555003, -7.65988702]) self.df_model = 3 self.df_resid = 17 self.bcov_unscaled = np.array([[ 1.72887367e-03, -3.47079127e-03, -6.79080082e-04, 2.73387119e-02], [ -3.47079127e-03, 1.28754242e-02, 9.95952051e-07, -6.19611175e-02], [ -6.79080082e-04, 9.95952051e-07, 2.32216722e-03, -1.59355028e-01], [ 2.73387119e-02, -6.19611175e-02, -1.59355028e-01, 1.34527267e+01]]) self.fittedvalues = np.array([ 38.93732292, 39.08284798, 32.63622398, 22.21364028, 19.7634816 , 20.98856094, 21.34048993, 21.34048993, 18.02422878, 12.91750748, 11.60778196, 10.52822768, 12.62645737, 12.25076107, 5.67911763, 6.11569281, 9.37812296, 8.35944755, 9.43900184, 13.59444997, 22.65988702]) self.tvalues = np.array([ 5.49659011, 3.32959607, -0.93132046, -3.40319578]) hampel_h1 = [141.309, 0.28717, -0.65085, -1.67388, 0.287, 0.01816, -0.03646, -0.00713, -0.651, -0.03646, 0.13524, 0.00001, -1.674, -0.00713, 0.00001, 0.02439] h1 = _shift_intercept(hampel_h1) hampel_h2 = [135.248, 0.18207, -0.36884, -1.60217, 0.182, 0.02120, -0.04563, -0.00567, -0.369, -0.04563, 0.15860, -0.00290, -1.602, -0.00567, -0.00290, 0.02329] h2 = _shift_intercept(hampel_h2) hampel_h3 = [128.921, 0.05409, -0.02445, -1.52732, 0.054, 0.02514, -0.05732, -0.00392, -0.024, -0.05732, 0.18871, -0.00652, -1.527, -0.00392, -0.00652, 0.02212] h3 = _shift_intercept(hampel_h3) class BiSquare(object): def __init__(self): self.params = np.array([ 0.9275471 , 0.65073222, -0.11233103, -42.28525369]) self.bse = np.array([ 0.10805398, 0.29487634, 0.12522928, 9.5315672 ]) self.scale = 2.2818858795649497 self.weights = np.array([ 0.89283149, 0.88496132, 0.79040651, 0.3358111 , 0.94617358, 0.90040725, 0.96630596, 0.99729171, 0.94968061, 0.99900087, 0.98959903, 0.99831448, 0.84731833, 0.96455873, 0.91767906, 0.98724523, 0.99762848, 0.99694419, 0.98650731, 0.95897484, 0.00222999]) self.resid = np.array([ 2.50917802, -2.60315301, 3.56070896, 6.93256033, -1.76597524, -2.41670746, -1.39345348, -0.39345348, -1.70651907, -0.23917521, 0.77180408, 0.31020526, -3.01451315, -1.42960401, 2.19218084, 0.85518774, -0.36817892, 0.4181383 , 0.87973712, 1.53911661, -10.43556344]) self.df_model = 3 self.df_resid = 17 self.bcov_unscaled = np.array([[ 1.72887367e-03, -3.47079127e-03, -6.79080082e-04, 2.73387119e-02], [ -3.47079127e-03, 1.28754242e-02, 9.95952051e-07, -6.19611175e-02], [ -6.79080082e-04, 9.95952051e-07, 2.32216722e-03, -1.59355028e-01], [ 2.73387119e-02, -6.19611175e-02, -1.59355028e-01, 1.34527267e+01]]) self.fittedvalues = np.array([ 39.49082198, 39.60315301, 33.43929104, 21.06743967, 19.76597524, 20.41670746, 20.39345348, 20.39345348, 16.70651907, 14.23917521, 13.22819592, 12.68979474, 14.01451315, 13.42960401, 5.80781916, 6.14481226, 8.36817892, 7.5818617 , 8.12026288, 13.46088339, 25.43556344]) self.tvalues = np.array([ 8.58410823, 2.20679698, -0.8970029 , -4.43633799]) bisquare_h1 = [90.3354, 0.18358, -0.41607, -1.07007, 0.1836, 0.01161, -0.02331, -0.00456, -0.4161, -0.02331, 0.08646, 0.00001, -1.0701, -0.00456, 0.00001, 0.01559] h1 = _shift_intercept(bisquare_h1) bisquare_h2 = [67.82521, 0.091288, -0.29038, -0.78124, 0.091288, 0.013849, -0.02914, -0.00352, -0.29038, -0.02914, 0.101088, -0.001, -0.78124, -0.00352, -0.001, 0.011766] h2 = _shift_intercept(bisquare_h2) bisquare_h3 = [48.8983, 0.000442, -0.15919, -0.53523, 0.000442, 0.016113, -0.03461, -0.00259, -0.15919, -0.03461, 0.112728, -0.00164, -0.53523, -0.00259, -0.00164, 0.008414] h3 = _shift_intercept(bisquare_h3) class Andrews(object): def __init__(self): self.params = [0.9282, 0.6492, -.1123,-42.2930] self.bse = [.1061, .2894, .1229, 9.3561] self.scale = 2.2801 self.df_model = 3. self.df_resid = 17. # self.bcov_unscaled = [] # not given as part of SAS self.resid = [2.503338458, -2.608934536, 3.5548678338, 6.9333705014, -1.768179527, -2.417404513, -1.392991531, -0.392991531, -1.704759385,-0.244545418, 0.7659115325, 0.3028635237, -3.019999429,-1.434221475,2.1912017882, 0.8543828047, -0.366664104,0.4192468573,0.8822948661,1.5378731634, -10.44592783] self.sresids = [1.0979293816, -1.144242351, 1.5591155202, 3.040879735, -0.775498914, -1.06023995, -0.610946684, -0.172360612, -0.747683723, -0.107254214, 0.3359181307, 0.1328317233, -1.324529688, -0.629029563, 0.9610305856, 0.3747203984, -0.160813769, 0.1838758324, 0.3869622398, 0.6744897502, -4.581438458] self.weights = [0.8916509101, 0.8826581922, 0.7888664106, 0.3367252734, 0.9450252405, 0.8987321912, 0.9656622, 0.9972406688, 0.948837669, 0.9989310017, 0.9895434667, 0.998360628, 0.8447116551, 0.9636222149, 0.916330067, 0.9869982597, 0.9975977354, 0.9968600162, 0.9861384742, 0.9582432444, 0] def conf_int(self): # method to be consistent with sm return [(0.7203,1.1360),(.0819,1.2165),(-.3532,.1287), (-60.6305,-23.9555)] andrews_h1 = [87.5357, 0.177891, -0.40318, -1.03691, 0.177891, 0.01125, -0.02258, -0.00442, -0.40318, -0.02258, 0.083779, 6.481E-6, -1.03691, -0.00442, 6.481E-6, 0.01511] h1 = _shift_intercept(andrews_h1) andrews_h2 = [66.50472, 0.10489, -0.3246, -0.76664, 0.10489, 0.012786, -0.02651, -0.0036, -0.3246, -0.02651, 0.09406, -0.00065, -0.76664, -0.0036, -0.00065, 0.011567] h2 = _shift_intercept(andrews_h2) andrews_h3 = [48.62157, 0.034949, -0.24633, -0.53394, 0.034949, 0.014088, -0.02956, -0.00287, -0.24633, -0.02956, 0.100628, -0.00104, -0.53394, -0.00287, -0.00104, 0.008441] h3 = _shift_intercept(andrews_h3) ### RLM Results with Huber's Proposal 2 ### ### Obtained from SAS ### class HuberHuber(object): def __init__(self): self.h1 = [114.4936, 0.232675, -0.52734, -1.35624, 0.232675, 0.014714, -0.02954, -0.00578, -0.52734, -0.02954, 0.10958, 8.476E-6, -1.35624, -0.00578, 8.476E-6, 0.019764] self.h1 = _shift_intercept(self.h1) self.h2 = [103.2876, 0.152602, -0.33476, -1.22084, 0.152602, 0.016904, -0.03766, -0.00434, -0.33476, -0.03766, 0.132043, -0.00214, -1.22084, -0.00434, -0.00214, 0.017739] self.h2 = _shift_intercept(self.h2) self.h3 = [ 91.7544, 0.064027, -0.11379, -1.08249, 0.064027, 0.019509, -0.04702, -0.00278, -0.11379, -0.04702, 0.157872, -0.00462, -1.08249, -0.00278, -0.00462, 0.015677] self.h3 = _shift_intercept(self.h3) self.resid = [2.909155172, -2.225912162, 4.134132661, 6.163172632, -1.741815737, -2.789321552, -2.02642336, -1.02642336, -2.593402734, 0.698655, 1.914261011, 1.826699492, -2.031210331, -0.592975466, 2.306098648, 0.900896645, -1.037551854, -0.092080512, -0.004518993, 1.471737448, -8.498372406] self.sresids = [0.883018497, -0.675633129, 1.25483702, 1.870713355, -0.528694904, -0.84664529, -0.615082113, -0.311551209, -0.787177874, 0.212063383, 0.581037374, 0.554459746, -0.616535106, -0.179986379, 0.699972205, 0.273449972, -0.314929051, -0.027949281, -0.001371654, 0.446717797, -2.579518651] self.weights = [1, 1, 1, 0.718977066, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.52141511] self.params = (.7990,1.0475,-0.1351,-41.0892) self.bse = (.1213,.3310,.1406,10.7002) self.scale = 3.2946 self.df_model = 3 self.df_resid = 17 def conf_int(self): # method for consistency with sm return [(0.5612,1.0367),(.3987,1.6963), (-.4106,.1405),(-62.0611,-20.1172)] class HampelHuber(object): def __init__(self): self.h1 = [147.4727, 0.299695, -0.67924, -1.7469, 0.299695, 0.018952, -0.03805, -0.00744, -0.67924, -0.03805, 0.141144, 0.000011, -1.7469, -0.00744, 0.000011, 0.025456] self.h1 = _shift_intercept(self.h1) self.h2 = [141.148, 0.190007, -0.38493, -1.67206, 0.190007, 0.02213, -0.04762, -0.00592, -0.38493, -0.04762, 0.165518, -0.00303, -1.67206, -0.00592, -0.00303, 0.024301] self.h2 = _shift_intercept(self.h2) self.h3 = [134.5444, 0.05645, -0.02552, -1.59394, 0.05645, 0.026232, -0.05982, -0.00409, -0.02552, -0.05982, 0.196946, -0.0068, -1.59394, -0.00409, -0.0068, 0.023083] self.h3 = _shift_intercept(self.h3) self.resid = [3.125725599, -2.022218392, 4.434082972, 5.753880172, -1.744479058, -2.995299443, -2.358455878, -1.358455878, -3.068281354, 1.150212629, 2.481708553, 2.584584946, -1.553899388, -0.177335865, 2.335744732, 0.891912757, -1.43012351, -0.394515569, -0.497391962, 1.407968887, -7.505098501] self.sresids = [0.952186413, -0.616026205, 1.350749906, 1.752798302, -0.531418771, -0.912454834, -0.718453867, -0.413824947, -0.934687235, 0.350388031, 0.756000196, 0.787339321, -0.473362692, -0.054021633, 0.711535395, 0.27170242, -0.43565698, -0.120180852, -0.151519976, 0.428908041, -2.28627005] self.weights = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.874787298] self.params = (.7318,1.2508,-0.1479,-40.2712) self.bse = (.1377, .3757, .1596, 12.1438) self.scale = 3.2827 self.df_model = 3 self.df_resid = 17 def conf_int(self): return [(0.4619,1.0016),(.5145,1.9872), (-.4607,.1648),(-64.0727,-16.4697)] class BisquareHuber(object): def __init__(self): self.h1 = [129.9556, 0.264097, -0.59855, -1.5394, 0.264097, 0.016701, -0.03353, -0.00656, -0.59855, -0.03353, 0.124379, 9.621E-6, -1.5394, -0.00656, 9.621E-6, 0.022433] self.h1 = _shift_intercept(self.h1) self.h2 = [109.7685, 0.103038, -0.25926, -1.28355, 0.103038, 0.0214, -0.04688, -0.00453, -0.25926, -0.04688, 0.158535, -0.00327, -1.28355, -0.00453, -0.00327, 0.018892] self.h2 = _shift_intercept(self.h2) self.h3 = [91.80527, -0.09171, 0.171716, -1.05244, -0.09171, 0.027999, -0.06493, -0.00223, 0.171716, -0.06493, 0.203254, -0.0071, -1.05244, -0.00223, -0.0071, 0.015584] self.h3 = _shift_intercept(self.h3) self.resid = [3.034895447, -2.09863887, 4.229870063, 6.18871385, -1.715906134, -2.763596142, -2.010080245, -1.010080245, -2.590747917, 0.712961901, 1.914770759, 1.82892645, -2.019969464, -0.598781979, 2.260467209, 0.859864256, -1.057306197, -0.122565974, -0.036721665, 1.471074632, -8.432085298] self.sresids = [0.918227061, -0.634956635, 1.279774287, 1.872435025, -0.519158394, -0.836143718, -0.608162656, -0.305606249, -0.78384738, 0.215711191, 0.579326161, 0.553353415, -0.611154703, -0.181165324, 0.683918836, 0.26015744, -0.319894764, -0.037083121, -0.011110375, 0.445083055, -2.551181429] self.weights = [0.924649089, 0.963600796, 0.856330585, 0.706048833, 0.975591792, 0.937309703, 0.966582366, 0.991507994, 0.944798311, 0.995764589, 0.969652425, 0.972293856, 0.966255569, 0.997011618, 0.957833493, 0.993842376, 0.990697247, 0.9998747, 0.999988752, 0.982030803, 0.494874977] self.params = (.7932, 1.0477, -0.1335, -40.8949) self.bse = (.1292, .3527, .1498, 11.3998) self.scale = 3.3052 self.df_model = 3 self.df_resid = 17 def conf_int(self): return [(0.5399,1.0465),(.3565,1.7389), (-.4271,.1600),(-63.2381,-18.5517)] class AndrewsHuber(object): def __init__(self): self.h1 = [129.9124, 0.264009, -0.59836, -1.53888, 0.264009, 0.016696, -0.03352, -0.00656, -0.59836, -0.03352, 0.124337, 9.618E-6, -1.53888, -0.00656, 9.618E-6, 0.022425] self.h1 = _shift_intercept(self.h1) self.h2 = [109.7595, 0.105022, -0.26535, -1.28332, .105022, 0.021321, -0.04664, -0.00456, -0.26535, -0.04664, 0.157885, -0.00321, -1.28332, -0.00456, -0.00321, 0.018895] self.h2 = _shift_intercept(self.h2) self.h3 = [91.82518, -0.08649, 0.155965, -1.05238, -0.08649, 0.027785, -0.06427, -0.0023, 0.155965, -0.06427, 0.201544, -0.00693, -1.05238, -0.0023, -0.00693, 0.015596] self.h3 = _shift_intercept(self.h3) self.resid = [3.040515104, -2.093093543, 4.235081748, 6.188729166, -1.714119676, -2.762695255, -2.009618953, -1.009618953, -2.591649784, 0.715967584, 1.918445405, 1.833412337, -2.016815123, -0.595695587, 2.260536347, 0.859710406, -1.059386228, -0.1241257, -0.039092633, 1.471556455, -8.424624872] self.sresids = [0.919639919, -0.633081011, 1.280950793, 1.871854667, -0.518455862, -0.835610004, -0.607833129, -0.305371248, -0.783875269, 0.216552902, 0.580256606, 0.554537345, -0.610009696, -0.180175208, 0.683726076, 0.260029627, -0.320423952, -0.037543293, -0.011824031, 0.445089734, -2.548127888] self.weights = [0.923215335, 0.963157359, 0.854300342, 0.704674258, 0.975199805, 0.936344742, 0.9660077, 0.991354016, 0.943851708, 0.995646409, 0.968993767, 0.971658421, 0.965766352, 0.99698502, 0.957106815, 0.993726436, 0.990483134, 0.999868981, 0.999987004, 0.981686004, 0.496752113] self.params = (.7928, 1.0486, -0.1336, -40.8818) self.bse = (.1292, .3526, .1498, 11.3979) self.scale = 3.3062 self.df_model = 3 self.df_resid = 17 def conf_int(self): return [(0.5395,1.0460),(.3575,1.7397), (-.4271,.1599),(-63.2213,-18.5423)] statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/test_rlm.py000066400000000000000000000261551224417117700254100ustar00rootroot00000000000000""" Test functions for sm.rlm """ import numpy as np from numpy.testing import assert_almost_equal, assert_allclose from scipy import stats import statsmodels.api as sm from statsmodels.robust.robust_linear_model import RLM from nose import SkipTest DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 class CheckRlmResultsMixin(object): ''' res2 contains results from Rmodelwrap or were obtained from a statistical packages such as R, Stata, or SAS and written to results.results_rlm Covariance matrices were obtained from SAS and are imported from results.results_rlm ''' def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4) decimal_standarderrors = DECIMAL_4 def test_standarderrors(self): assert_almost_equal(self.res1.bse, self.res2.bse, self.decimal_standarderrors) #TODO: get other results from SAS, though if it works for one... def test_confidenceintervals(self): if not hasattr(self.res2, 'conf_int'): raise SkipTest("Results from R") else: assert_almost_equal(self.res1.conf_int(), self.res2.conf_int(), DECIMAL_4) decimal_scale = DECIMAL_4 def test_scale(self): assert_almost_equal(self.res1.scale, self.res2.scale, self.decimal_scale) def test_weights(self): assert_almost_equal(self.res1.weights, self.res2.weights, DECIMAL_4) def test_residuals(self): assert_almost_equal(self.res1.resid, self.res2.resid, DECIMAL_4) def test_degrees(self): assert_almost_equal(self.res1.model.df_model, self.res2.df_model, DECIMAL_4) assert_almost_equal(self.res1.model.df_resid, self.res2.df_resid, DECIMAL_4) def test_bcov_unscaled(self): if not hasattr(self.res2, 'bcov_unscaled'): raise SkipTest("No unscaled cov matrix from SAS") else: assert_almost_equal(self.res1.bcov_unscaled, self.res2.bcov_unscaled, DECIMAL_4) decimal_bcov_scaled = DECIMAL_4 def test_bcov_scaled(self): assert_almost_equal(self.res1.bcov_scaled, self.res2.h1, self.decimal_bcov_scaled) assert_almost_equal(self.res1.h2, self.res2.h2, self.decimal_bcov_scaled) assert_almost_equal(self.res1.h3, self.res2.h3, self.decimal_bcov_scaled) def test_tvalues(self): if not hasattr(self.res2, 'tvalues'): raise SkipTest("No tvalues in benchmark") else: assert_allclose(self.res1.tvalues, self.res2.tvalues, rtol=0.003) def test_tpvalues(self): # test comparing tvalues and pvalues with normal implementation # make sure they use normal distribution (inherited in results class) params = self.res1.params tvalues = params / self.res1.bse pvalues = stats.norm.sf(np.abs(tvalues)) * 2 half_width = stats.norm.isf(0.025) * self.res1.bse conf_int = np.column_stack((params - half_width, params + half_width)) assert_almost_equal(self.res1.tvalues, tvalues) assert_almost_equal(self.res1.pvalues, pvalues) assert_almost_equal(self.res1.conf_int(), conf_int) class TestRlm(CheckRlmResultsMixin): from statsmodels.datasets.stackloss import load data = load() # class attributes for subclasses data.exog = sm.add_constant(data.exog, prepend=False) def __init__(self): # Test precisions self.decimal_standarderrors = DECIMAL_1 self.decimal_scale = DECIMAL_3 results = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit() # default M h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): # r.library('MASS') # self.res2 = RModel(self.data.endog, self.data.exog, # r.rlm, psi="psi.huber") from results.results_rlm import Huber self.res2 = Huber() def test_summary(self): # smoke test that summary at least returns something self.res1.summary() class TestHampel(TestRlm): def __init__(self): # Test precisions self.decimal_standarderrors = DECIMAL_2 self.decimal_scale = DECIMAL_3 self.decimal_bcov_scaled = DECIMAL_3 results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.Hampel()).fit() h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.Hampel()).fit(cov="H2").bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.Hampel()).fit(cov="H3").bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): # self.res2 = RModel(self.data.endog[:,None], self.data.exog, # r.rlm, psi="psi.hampel") #, init="lts") from results.results_rlm import Hampel self.res2 = Hampel() class TestRlmBisquare(TestRlm): def __init__(self): # Test precisions self.decimal_standarderrors = DECIMAL_1 results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.TukeyBiweight()).fit() h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.TukeyBiweight()).fit(cov=\ "H2").bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.TukeyBiweight()).fit(cov=\ "H3").bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): # self.res2 = RModel(self.data.endog, self.data.exog, # r.rlm, psi="psi.bisquare") from results.results_rlm import BiSquare self.res2 = BiSquare() class TestRlmAndrews(TestRlm): def __init__(self): results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit() h2 = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit(cov=\ "H2").bcov_scaled h3 = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit(cov=\ "H3").bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): from results.results_rlm import Andrews self.res2 = Andrews() ### tests with Huber scaling class TestRlmHuber(CheckRlmResultsMixin): from statsmodels.datasets.stackloss import load data = load() data.exog = sm.add_constant(data.exog, prepend=False) def __init__(self): results = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(scale_est=\ sm.robust.scale.HuberScale()) h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H2", scale_est=sm.robust.scale.HuberScale()).bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H3", scale_est=sm.robust.scale.HuberScale()).bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): from results.results_rlm import HuberHuber self.res2 = HuberHuber() class TestHampelHuber(TestRlm): def __init__(self): results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.Hampel()).fit(scale_est=\ sm.robust.scale.HuberScale()) h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.Hampel()).fit(cov="H2", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.Hampel()).fit(cov="H3", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): from results.results_rlm import HampelHuber self.res2 = HampelHuber() class TestRlmBisquareHuber(TestRlm): def __init__(self): results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.TukeyBiweight()).fit(\ scale_est=\ sm.robust.scale.HuberScale()) h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.TukeyBiweight()).fit(cov=\ "H2", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.TukeyBiweight()).fit(cov=\ "H3", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): from results.results_rlm import BisquareHuber self.res2 = BisquareHuber() class TestRlmAndrewsHuber(TestRlm): def __init__(self): results = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit(scale_est=\ sm.robust.scale.HuberScale()) h2 = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit(cov=\ "H2", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled h3 = RLM(self.data.endog, self.data.exog, M=sm.robust.norms.AndrewWave()).fit(cov=\ "H3", scale_est=\ sm.robust.scale.HuberScale()).bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): from results.results_rlm import AndrewsHuber self.res2 = AndrewsHuber() class TestRlmSresid(CheckRlmResultsMixin): #Check GH:187 from statsmodels.datasets.stackloss import load data = load() # class attributes for subclasses data.exog = sm.add_constant(data.exog, prepend=False) def __init__(self): # Test precisions self.decimal_standarderrors = DECIMAL_1 self.decimal_scale = DECIMAL_3 results = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(conv='sresid') # default M h2 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled h3 = RLM(self.data.endog, self.data.exog,\ M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled self.res1 = results self.res1.h2 = h2 self.res1.h3 = h3 def setup(self): # r.library('MASS') # self.res2 = RModel(self.data.endog, self.data.exog, # r.rlm, psi="psi.huber") from results.results_rlm import Huber self.res2 = Huber() statsmodels-0.5.0+git13-g8e07d34/statsmodels/robust/tests/test_scale.py000066400000000000000000000064301224417117700256770ustar00rootroot00000000000000""" Test functions for models.robust.scale """ import numpy as np from numpy.random import standard_normal from numpy.testing import * # Example from Section 5.5, Venables & Ripley (2002) import statsmodels.robust.scale as scale DECIMAL = 4 #TODO: Can replicate these tests using stackloss data and R if this # data is a problem class TestChem(object): def __init__(self): self.chem = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03, 3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7, 3.7, 3.7, 3.7, 3.77, 5.28, 28.95]) def test_mean(self): assert_almost_equal(np.mean(self.chem), 4.2804, DECIMAL) def test_median(self): assert_almost_equal(np.median(self.chem), 3.385, DECIMAL) def test_stand_mad(self): assert_almost_equal(scale.stand_mad(self.chem), 0.52632, DECIMAL) def test_huber_scale(self): assert_almost_equal(scale.huber(self.chem)[0], 3.20549, DECIMAL) def test_huber_location(self): assert_almost_equal(scale.huber(self.chem)[1], 0.67365, DECIMAL) def test_huber_huberT(self): n = scale.norms.HuberT() n.t = 1.5 h = scale.Huber(norm=n) assert_almost_equal(scale.huber(self.chem)[0], h(self.chem)[0], DECIMAL) assert_almost_equal(scale.huber(self.chem)[1], h(self.chem)[1], DECIMAL) def test_huber_Hampel(self): hh = scale.Huber(norm=scale.norms.Hampel()) assert_almost_equal(hh(self.chem)[0], 3.17434, DECIMAL) assert_almost_equal(hh(self.chem)[1], 0.66782, DECIMAL) class TestMad(object): def __init__(self): np.random.seed(54321) self.X = standard_normal((40,10)) def test_stand_mad(self): m = scale.stand_mad(self.X) assert_equal(m.shape, (10,)) def test_mad(self): n = scale.mad(self.X) assert_equal(n.shape, (10,)) class TestMadAxes(): def __init__(self): np.random.seed(54321) self.X = standard_normal((40,10,30)) def test_axis0(self): m = scale.stand_mad(self.X, axis=0) assert_equal(m.shape, (10,30)) def test_axis1(self): m = scale.stand_mad(self.X, axis=1) assert_equal(m.shape, (40,30)) def test_axis2(self): m = scale.stand_mad(self.X, axis=2) assert_equal(m.shape, (40,10)) def test_axisneg1(self): m = scale.stand_mad(self.X, axis=-1) assert_equal(m.shape, (40,10)) class TestHuber(): def __init__(self): np.random.seed(54321) self.X = standard_normal((40,10)) def basic_functionality(self): h = scale.Huber(maxiter=100) m, s = h(self.X) assert_equal(m.shape, (10,)) class TestHuberAxes(object): def __init__(self): np.random.seed(54321) self.X = standard_normal((40,10,30)) self.h = scale.Huber(maxiter=1000, tol=1.0e-05) def test_default(self): m, s = self.h(self.X, axis=0) assert_equal(m.shape, (10,30)) def test_axis1(self): m, s = self.h(self.X, axis=1) assert_equal(m.shape, (40,30)) def test_axis2(self): m, s = self.h(self.X, axis=2) assert_equal(m.shape, (40,10)) def test_axisneg1(self): m, s = self.h(self.X, axis=-1) assert_equal(m.shape, (40,10)) if __name__=="__main__": run_module_suite() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/000077500000000000000000000000001224417117700221525ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/__init__.py000066400000000000000000000000351224417117700242610ustar00rootroot00000000000000'''This is sandbox code ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/archive/000077500000000000000000000000001224417117700235735ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/archive/__init__.py000066400000000000000000000000001224417117700256720ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/archive/linalg_covmat.py000066400000000000000000000210001224417117700267550ustar00rootroot00000000000000 import math import numpy as np from scipy import linalg, stats from linalg_decomp_1 import tiny2zero #univariate standard normal distribution #following from scipy.stats.distributions with adjustments sqrt2pi = math.sqrt(2 * np.pi) logsqrt2pi = math.log(sqrt2pi) class StandardNormal(object): '''Distribution of vector x, with independent distribution N(0,1) this is the same as univariate normal for pdf and logpdf other methods not checked/adjusted yet ''' def rvs(self, size): return np.random.standard_normal(size) def pdf(self, x): return exp(-x**2 * 0.5) / sqrt2pi def logpdf(self, x): return -x**2 * 0.5 - logsqrt2pi def _cdf(self, x): return special.ndtr(x) def _logcdf(self, x): return log(special.ndtr(x)) def _ppf(self, q): return special.ndtri(q) class AffineTransform(object): '''affine full rank transformation of a multivariate distribution no dimension checking, assumes everything broadcasts correctly first version without bound support provides distribution of y given distribution of x y = const + tmat * x ''' def __init__(self, const, tmat, dist): self.const = const self.tmat = tmat self.dist = dist self.nrv = len(const) if not np.equal(self.nrv, tmat.shape).all(): raise ValueError('dimension of const and tmat do not agree') #replace the following with a linalgarray class self.tmatinv = linalg.inv(tmat) self.absdet = np.abs(np.linalg.det(self.tmat)) self.logabsdet = np.log(np.abs(np.linalg.det(self.tmat))) self.dist def rvs(self, size): #size can only be integer not yet tuple print (size,)+(self.nrv,) return self.transform(self.dist.rvs(size=(size,)+(self.nrv,))) def transform(self, x): #return np.dot(self.tmat, x) + self.const return np.dot(x, self.tmat) + self.const def invtransform(self, y): return np.dot(self.tmatinv, y - self.const) def pdf(self, x): return 1. / self.absdet * self.dist.pdf(self.invtransform(x)) def logpdf(self, x): return - self.logabsdet + self.dist.logpdf(self.invtransform(x)) from linalg_decomp_1 import SvdArray, OneTimeProperty class MultivariateNormal(object): '''multivariate normal distribution with plain linalg ''' def __init__(mean, sigma): self.mean = mean self.sigma = sigma self.sigmainv = sigmainv class MultivariateNormalChol(object): '''multivariate normal distribution with cholesky decomposition of sigma ignoring mean at the beginning, maybe needs testing for broadcasting to contemporaneously but not intertemporaly correlated random variable, which axis?, maybe swapaxis or rollaxis if x.ndim != mean.ndim == (sigma.ndim - 1) initially 1d is ok, 2d should work with iid in axis 0 and mvn in axis 1 ''' def __init__(self, mean, sigma): self.mean = mean self.sigma = sigma self.sigmainv = sigmainv self.cholsigma = linalg.cholesky(sigma) #the following makes it lower triangular with increasing time self.cholsigmainv = linalg.cholesky(sigmainv)[::-1,::-1] #todo: this might be a trick todo backward instead of forward filtering def whiten(self, x): return np.dot(cholsigmainv, x) def logpdf_obs(self, x): x = x - self.mean x_whitened = self.whiten(x) #sigmainv = linalg.cholesky(sigma) logdetsigma = np.log(np.linalg.det(sigma)) sigma2 = 1. # error variance is included in sigma llike = 0.5 * (np.log(sigma2) - 2.* np.log(np.diagonal(self.cholsigmainv)) + (x_whitened**2)/sigma2 + np.log(2*np.pi)) return llike def logpdf(self, x): return self.logpdf_obs(x).sum(-1) def pdf(self, x): return np.exp(self.logpdf(x)) class MultivariateNormal(object): def __init__(self, mean, sigma): self.mean = mean self.sigma = SvdArray(sigma) def loglike_ar1(x, rho): '''loglikelihood of AR(1) process, as a test case sigma_u partially hard coded Greene chapter 12 eq. (12-31) ''' x = np.asarray(x) u = np.r_[x[0], x[1:] - rho * x[:-1]] sigma_u2 = 2*(1-rho**2) loglik = 0.5*(-(u**2).sum(0) / sigma_u2 + np.log(1-rho**2) - x.shape[0] * (np.log(2*np.pi) + np.log(sigma_u2))) return loglik def ar2transform(x, arcoefs): ''' (Greene eq 12-30) ''' a1, a2 = arcoefs y = np.zeros_like(x) y[0] = np.sqrt((1+a2) * ((1-a2)**2 - a1**2) / (1-a2)) * x[0] y[1] = np.sqrt(1-a2**2) * x[2] - a1 * np.sqrt(1-a1**2)/(1-a2) * x[1] #TODO:wrong index in x y[2:] = x[2:] - a1 * x[1:-1] - a2 * x[:-2] return y def mvn_loglike(x, sigma): '''loglike multivariate normal assumes x is 1d, (nobs,) and sigma is 2d (nobs, nobs) brute force from formula no checking of correct inputs use of inv and log-det should be replace with something more efficient ''' #see numpy thread #Sturla: sqmahal = (cx*cho_solve(cho_factor(S),cx.T).T).sum(axis=1) sigmainv = linalg.inv(sigma) logdetsigma = np.log(np.linalg.det(sigma)) nobs = len(x) llf = - np.dot(x, np.dot(sigmainv, x)) llf -= nobs * np.log(2 * np.pi) llf -= logdetsigma llf *= 0.5 return llf def mvn_nloglike_obs(x, sigma): '''loglike multivariate normal assumes x is 1d, (nobs,) and sigma is 2d (nobs, nobs) brute force from formula no checking of correct inputs use of inv and log-det should be replace with something more efficient ''' #see numpy thread #Sturla: sqmahal = (cx*cho_solve(cho_factor(S),cx.T).T).sum(axis=1) #Still wasteful to calculate pinv first sigmainv = linalg.inv(sigma) cholsigmainv = linalg.cholesky(sigmainv) #2 * np.sum(np.log(np.diagonal(np.linalg.cholesky(A)))) #Dag mailinglist # logdet not needed ??? #logdetsigma = 2 * np.sum(np.log(np.diagonal(cholsigmainv))) x_whitened = np.dot(cholsigmainv, x) #sigmainv = linalg.cholesky(sigma) logdetsigma = np.log(np.linalg.det(sigma)) sigma2 = 1. # error variance is included in sigma llike = 0.5 * (np.log(sigma2) - 2.* np.log(np.diagonal(cholsigmainv)) + (x_whitened**2)/sigma2 + np.log(2*np.pi)) return llike, (x_whitened**2) nobs = 10 x = np.arange(nobs) autocov = 2*0.8**np.arange(nobs)# +0.01 * np.random.randn(nobs) sigma = linalg.toeplitz(autocov) #sigma = np.diag(1+np.random.randn(10)**2) cholsigma = linalg.cholesky(sigma).T#, lower=True) sigmainv = linalg.inv(sigma) cholsigmainv = linalg.cholesky(sigmainv) #2 * np.sum(np.log(np.diagonal(np.linalg.cholesky(A)))) #Dag mailinglist # logdet not needed ??? #logdetsigma = 2 * np.sum(np.log(np.diagonal(cholsigmainv))) x_whitened = np.dot(cholsigmainv, x) #sigmainv = linalg.cholesky(sigma) logdetsigma = np.log(np.linalg.det(sigma)) sigma2 = 1. # error variance is included in sigma llike = 0.5 * (np.log(sigma2) - 2.* np.log(np.diagonal(cholsigmainv)) + (x_whitened**2)/sigma2 + np.log(2*np.pi)) ll, ls = mvn_nloglike_obs(x, sigma) #the following are all the same for diagonal sigma print ll.sum(), 'll.sum()' print llike.sum(), 'llike.sum()' print np.log(stats.norm._pdf(x_whitened)).sum() - 0.5 * logdetsigma, print 'stats whitened' print np.log(stats.norm.pdf(x,scale=np.sqrt(np.diag(sigma)))).sum(), print 'stats scaled' print 0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma, lower=False)[0].T, False),x.T), x) + nobs*np.log(2*np.pi) - 2.* np.log(np.diagonal(cholsigmainv)).sum()) print 0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma)[0].T, False),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum()) print 0.5*(np.dot(linalg.cho_solve(linalg.cho_factor(sigma),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum()) print mvn_loglike(x, sigma) normtransf = AffineTransform(np.zeros(nobs), cholsigma, StandardNormal()) print normtransf.logpdf(x_whitened).sum() #print normtransf.rvs(5) print loglike_ar1(x, 0.8) mch = MultivariateNormalChol(np.zeros(nobs), sigma) print mch.logpdf(x) #print tiny2zero(mch.cholsigmainv / mch.cholsigmainv[-1,-1]) xw = mch.whiten(x) print 'xSigmax', np.dot(xw,xw) print 'xSigmax', np.dot(x,linalg.cho_solve(linalg.cho_factor(mch.sigma),x)) print 'xSigmax', np.dot(x,linalg.cho_solve((mch.cholsigma, False),x)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/archive/linalg_decomp_1.py000066400000000000000000000213621224417117700271660ustar00rootroot00000000000000'''Recipes for more efficient work with linalg using classes intended for use for multivariate normal and linear regression calculations x is the data (nobs, nvars) m is the moment matrix (x'x) or a covariance matrix Sigma examples: x'sigma^{-1}x z = Px where P=Sigma^{-1/2} or P=Sigma^{1/2} Initially assume positive definite, then add spectral cutoff and regularization of moment matrix, and extend to PCA maybe extend to sparse if some examples work out (transformation matrix P for random effect and for toeplitz) Author: josef-pktd Created on 2010-10-20 ''' import numpy as np from scipy import linalg #this has been copied from nitime a long time ago #TODO: ceck whether class has changed in nitime class OneTimeProperty(object): """A descriptor to make special properties that become normal attributes. This is meant to be used mostly by the auto_attr decorator in this module. Author: Fernando Perez, copied from nitime """ def __init__(self,func): """Create a OneTimeProperty instance. Parameters ---------- func : method The method that will be called the first time to compute a value. Afterwards, the method's name will be a standard attribute holding the value of this computation. """ self.getter = func self.name = func.func_name def __get__(self,obj,type=None): """This will be called on attribute access on the class or instance. """ if obj is None: # Being called on the class, return the original function. This way, # introspection works on the class. #return func print 'class access' return self.getter val = self.getter(obj) #print "** auto_attr - loading '%s'" % self.name # dbg setattr(obj, self.name, val) return val class PlainMatrixArray(object): '''Class that defines linalg operation on an array simplest version as benchmark linear algebra recipes for multivariate normal and linear regression calculations ''' def __init__(self, data=None, sym=None): if not data is None: if sym is None: self.x = np.asarray(data) self.m = np.dot(self.x.T, self.x) else: raise ValueError('data and sym cannot be both given') elif not sym is None: self.m = np.asarray(sym) self.x = np.eye(*self.m.shape) #default else: raise ValueError('either data or sym need to be given') @OneTimeProperty def minv(self): return np.linalg.inv(self.m) @OneTimeProperty def m_y(self, y): return np.dot(self.m, y) def minv_y(self, y): return np.dot(self.minv, y) @OneTimeProperty def mpinv(self): return linalg.pinv(self.m) @OneTimeProperty def xpinv(self): return linalg.pinv(self.x) def yt_m_y(self, y): return np.dot(y.T, np.dot(self.m, y)) def yt_minv_y(self, y): return np.dot(y.T, np.dot(self.minv, y)) #next two are redundant def y_m_yt(self, y): return np.dot(y, np.dot(self.m, y.T)) def y_minv_yt(self, y): return np.dot(y, np.dot(self.minv, y.T)) @OneTimeProperty def mdet(self): return linalg.det(self.m) @OneTimeProperty def mlogdet(self): return np.log(linalg.det(self.m)) @OneTimeProperty def meigh(self): evals, evecs = linalg.eigh(self.m) sortind = np.argsort(evals)[::-1] return evals[sortind], evecs[:,sortind] @OneTimeProperty def mhalf(self): evals, evecs = self.meigh return np.dot(np.diag(evals**0.5), evecs.T) #return np.dot(evecs, np.dot(np.diag(evals**0.5), evecs.T)) #return np.dot(evecs, 1./np.sqrt(evals) * evecs.T)) @OneTimeProperty def minvhalf(self): evals, evecs = self.meigh return np.dot(evecs, 1./np.sqrt(evals) * evecs.T) class SvdArray(PlainMatrixArray): '''Class that defines linalg operation on an array svd version, where svd is taken on original data array, if or when it matters no spectral cutoff in first version ''' def __init__(self, data=None, sym=None): super(SvdArray, self).__init__(data=data, sym=sym) u, s, v = np.linalg.svd(self.x, full_matrices=1) self.u, self.s, self.v = u, s, v self.sdiag = linalg.diagsvd(s, *x.shape) self.sinvdiag = linalg.diagsvd(1./s, *x.shape) def _sdiagpow(self, p): return linalg.diagsvd(np.power(self.s, p), *x.shape) @OneTimeProperty def minv(self): sinvv = np.dot(self.sinvdiag, self.v) return np.dot(sinvv.T, sinvv) @OneTimeProperty def meigh(self): evecs = self.v.T evals = self.s**2 return evals, evecs @OneTimeProperty def mdet(self): return self.meigh[0].prod() @OneTimeProperty def mlogdet(self): return np.log(self.meigh[0]).sum() @OneTimeProperty def mhalf(self): return np.dot(np.diag(self.s), self.v) @OneTimeProperty def xxthalf(self): return np.dot(self.u, self.sdiag) @OneTimeProperty def xxtinvhalf(self): return np.dot(self.u, self.sinvdiag) class CholArray(PlainMatrixArray): '''Class that defines linalg operation on an array cholesky version, where svd is taken on original data array, if or when it matters plan: use cholesky factor and cholesky solve nothing implemented yet ''' def __init__(self, data=None, sym=None): super(SvdArray, self).__init__(data=data, sym=sym) def yt_minv_y(self, y): '''xSigmainvx doesn't use stored cholesky yet ''' return np.dot(x,linalg.cho_solve(linalg.cho_factor(self.m),x)) #same as #lower = False #if cholesky(sigma) is used, default is upper #np.dot(x,linalg.cho_solve((self.cholsigma, lower),x)) def testcompare(m1, m2): from numpy.testing import assert_almost_equal, assert_approx_equal decimal = 12 #inv assert_almost_equal(m1.minv, m2.minv, decimal=decimal) #matrix half and invhalf #fix sign in test, should this be standardized s1 = np.sign(m1.mhalf.sum(1))[:,None] s2 = np.sign(m2.mhalf.sum(1))[:,None] scorr = s1/s2 assert_almost_equal(m1.mhalf, m2.mhalf * scorr, decimal=decimal) assert_almost_equal(m1.minvhalf, m2.minvhalf, decimal=decimal) #eigenvalues, eigenvectors evals1, evecs1 = m1.meigh evals2, evecs2 = m2.meigh assert_almost_equal(evals1, evals2, decimal=decimal) #normalization can be different: evecs in columns s1 = np.sign(evecs1.sum(0)) s2 = np.sign(evecs2.sum(0)) scorr = s1/s2 assert_almost_equal(evecs1, evecs2 * scorr, decimal=decimal) #determinant assert_approx_equal(m1.mdet, m2.mdet, significant=13) assert_approx_equal(m1.mlogdet, m2.mlogdet, significant=13) ####### helper function for interactive work def tiny2zero(x, eps = 1e-15): '''replace abs values smaller than eps by zero, makes copy ''' mask = np.abs(x.copy()) < eps x[mask] = 0 return x def maxabs(x): return np.max(np.abs(x)) if __name__ == '__main__': n = 5 y = np.arange(n) x = np.random.randn(100,n) autocov = 2*0.8**np.arange(n) +0.01 * np.random.randn(n) sigma = linalg.toeplitz(autocov) mat = PlainMatrixArray(sym=sigma) print tiny2zero(mat.mhalf) mih = mat.minvhalf print tiny2zero(mih) #for nicer printing mat2 = PlainMatrixArray(data=x) print maxabs(mat2.yt_minv_y(np.dot(x.T, x)) - mat2.m) print tiny2zero(mat2.minv_y(mat2.m)) mat3 = SvdArray(data=x) print mat3.meigh[0] print mat2.meigh[0] testcompare(mat2, mat3) ''' m = np.dot(x.T, x) u,s,v = np.linalg.svd(x, full_matrices=1) Sig = linalg.diagsvd(s,*x.shape) >>> np.max(np.abs(np.dot(u, np.dot(Sig, v)) - x)) 3.1086244689504383e-015 >>> np.max(np.abs(np.dot(u.T, u) - np.eye(100))) 3.3306690738754696e-016 >>> np.max(np.abs(np.dot(v.T, v) - np.eye(5))) 6.6613381477509392e-016 >>> np.max(np.abs(np.dot(Sig.T, Sig) - np.diag(s**2))) 5.6843418860808015e-014 >>> evals,evecs = linalg.eigh(np.dot(x.T, x)) >>> evals[::-1] array([ 123.36404464, 112.17036442, 102.04198468, 76.60832278, 74.70484487]) >>> s**2 array([ 123.36404464, 112.17036442, 102.04198468, 76.60832278, 74.70484487]) >>> np.max(np.abs(np.dot(v.T, np.dot(np.diag(s**2), v)) - m)) 1.1368683772161603e-013 >>> us = np.dot(u, Sig) >>> np.max(np.abs(np.dot(us, us.T) - np.dot(x, x.T))) 1.0658141036401503e-014 >>> sv = np.dot(Sig, v) >>> np.max(np.abs(np.dot(sv.T, sv) - np.dot(x.T, x))) 1.1368683772161603e-013 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/archive/tsa.py000066400000000000000000000027251224417117700247420ustar00rootroot00000000000000'''Collection of alternative implementations for time series analysis ''' ''' >>> signal.fftconvolve(x,x[::-1])[len(x)-1:len(x)+10]/x.shape[0] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) >>> sm.tsa.stattools.acovf(X, fft=True)[:order+1] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) >>> import nitime.utils as ut >>> ut.autocov(s)[:order+1] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) ''' def acovf_fft(x, demean=True): '''autocovariance function with call to fftconvolve, biased Parameters ---------- x : array_like timeseries, signal demean : boolean If true, then demean time series Returns ------- acovf : array autocovariance for data, same length as x might work for nd in parallel with time along axis 0 ''' from scipy import signal x = np.asarray(x) if demean: x = x - x.mean() signal.fftconvolve(x,x[::-1])[len(x)-1:len(x)+10]/x.shape[0] statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/bspline.py000066400000000000000000000475451224417117700241770ustar00rootroot00000000000000''' Bspines and smoothing splines. General references: Craven, P. and Wahba, G. (1978) "Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized cross-validation." Numerische Mathematik, 31(4), 377-403. Hastie, Tibshirani and Friedman (2001). "The Elements of Statistical Learning." Springer-Verlag. 536 pages. Hutchison, M. and Hoog, F. "Smoothing noisy data with spline functions." Numerische Mathematik, 47(1), 99-106. ''' import numpy as np import numpy.linalg as L from scipy.linalg import solveh_banded from scipy.optimize import golden from models import _hbspline #removed because this was segfaulting # Issue warning regarding heavy development status of this module import warnings _msg = "The bspline code is technology preview and requires significant work\ on the public API and documentation. The API will likely change in the future" warnings.warn(_msg, UserWarning) def _band2array(a, lower=0, symmetric=False, hermitian=False): """ Take an upper or lower triangular banded matrix and return a numpy array. INPUTS: a -- a matrix in upper or lower triangular banded matrix lower -- is the matrix upper or lower triangular? symmetric -- if True, return the original result plus its transpose hermitian -- if True (and symmetric False), return the original result plus its conjugate transposed """ n = a.shape[1] r = a.shape[0] _a = 0 if not lower: for j in range(r): _b = np.diag(a[r-1-j],k=j)[j:(n+j),j:(n+j)] _a += _b if symmetric and j > 0: _a += _b.T elif hermitian and j > 0: _a += _b.conjugate().T else: for j in range(r): _b = np.diag(a[j],k=j)[0:n,0:n] _a += _b if symmetric and j > 0: _a += _b.T elif hermitian and j > 0: _a += _b.conjugate().T _a = _a.T return _a def _upper2lower(ub): """ Convert upper triangular banded matrix to lower banded form. INPUTS: ub -- an upper triangular banded matrix OUTPUTS: lb lb -- a lower triangular banded matrix with same entries as ub """ lb = np.zeros(ub.shape, ub.dtype) nrow, ncol = ub.shape for i in range(ub.shape[0]): lb[i,0:(ncol-i)] = ub[nrow-1-i,i:ncol] lb[i,(ncol-i):] = ub[nrow-1-i,0:i] return lb def _lower2upper(lb): """ Convert lower triangular banded matrix to upper banded form. INPUTS: lb -- a lower triangular banded matrix OUTPUTS: ub ub -- an upper triangular banded matrix with same entries as lb """ ub = np.zeros(lb.shape, lb.dtype) nrow, ncol = lb.shape for i in range(lb.shape[0]): ub[nrow-1-i,i:ncol] = lb[i,0:(ncol-i)] ub[nrow-1-i,0:i] = lb[i,(ncol-i):] return ub def _triangle2unit(tb, lower=0): """ Take a banded triangular matrix and return its diagonal and the unit matrix: the banded triangular matrix with 1's on the diagonal, i.e. each row is divided by the corresponding entry on the diagonal. INPUTS: tb -- a lower triangular banded matrix lower -- if True, then tb is assumed to be lower triangular banded, in which case return value is also lower triangular banded. OUTPUTS: d, b d -- diagonal entries of tb b -- unit matrix: if lower is False, b is upper triangular banded and its rows of have been divided by d, else lower is True, b is lower triangular banded and its columns have been divieed by d. """ if lower: d = tb[0].copy() else: d = tb[-1].copy() if lower: return d, (tb / d) else: l = _upper2lower(tb) return d, _lower2upper(l / d) def _trace_symbanded(a, b, lower=0): """ Compute the trace(ab) for two upper or banded real symmetric matrices stored either in either upper or lower form. INPUTS: a, b -- two banded real symmetric matrices (either lower or upper) lower -- if True, a and b are assumed to be the lower half OUTPUTS: trace trace -- trace(ab) """ if lower: t = _zero_triband(a * b, lower=1) return t[0].sum() + 2 * t[1:].sum() else: t = _zero_triband(a * b, lower=0) return t[-1].sum() + 2 * t[:-1].sum() def _zero_triband(a, lower=0): """ Explicitly zero out unused elements of a real symmetric banded matrix. INPUTS: a -- a real symmetric banded matrix (either upper or lower hald) lower -- if True, a is assumed to be the lower half """ nrow, ncol = a.shape if lower: for i in range(nrow): a[i,(ncol-i):] = 0. else: for i in range(nrow): a[i,0:i] = 0. return a class BSpline(object): ''' Bsplines of a given order and specified knots. Implementation is based on description in Chapter 5 of Hastie, Tibshirani and Friedman (2001). "The Elements of Statistical Learning." Springer-Verlag. 536 pages. INPUTS: knots -- a sorted array of knots with knots[0] the lower boundary, knots[1] the upper boundary and knots[1:-1] the internal knots. order -- order of the Bspline, default is 4 which yields cubic splines M -- number of additional boundary knots, if None it defaults to order coef -- an optional array of real-valued coefficients for the Bspline of shape (knots.shape + 2 * (M - 1) - order,). x -- an optional set of x values at which to evaluate the Bspline to avoid extra evaluation in the __call__ method ''' # FIXME: update parameter names, replace single character names # FIXME: `order` should be actual spline order (implemented as order+1) ## FIXME: update the use of spline order in extension code (evaluate is recursively called) # FIXME: eliminate duplicate M and m attributes (m is order, M is related to tau size) def __init__(self, knots, order=4, M=None, coef=None, x=None): knots = np.squeeze(np.unique(np.asarray(knots))) if knots.ndim != 1: raise ValueError('expecting 1d array for knots') self.m = order if M is None: M = self.m self.M = M self.tau = np.hstack([[knots[0]]*(self.M-1), knots, [knots[-1]]*(self.M-1)]) self.K = knots.shape[0] - 2 if coef is None: self.coef = np.zeros((self.K + 2 * self.M - self.m), np.float64) else: self.coef = np.squeeze(coef) if self.coef.shape != (self.K + 2 * self.M - self.m): raise ValueError('coefficients of Bspline have incorrect shape') if x is not None: self.x = x def _setx(self, x): self._x = x self._basisx = self.basis(self._x) def _getx(self): return self._x x = property(_getx, _setx) def __call__(self, *args): """ Evaluate the BSpline at a given point, yielding a matrix B and return B * self.coef INPUTS: args -- optional arguments. If None, it returns self._basisx, the BSpline evaluated at the x values passed in __init__. Otherwise, return the BSpline evaluated at the first argument args[0]. OUTPUTS: y y -- value of Bspline at specified x values BUGS: If self has no attribute x, an exception will be raised because self has no attribute _basisx. """ if not args: b = self._basisx.T else: x = args[0] b = np.asarray(self.basis(x)).T return np.squeeze(np.dot(b, self.coef)) def basis_element(self, x, i, d=0): """ Evaluate a particular basis element of the BSpline, or its derivative. INPUTS: x -- x values at which to evaluate the basis element i -- which element of the BSpline to return d -- the order of derivative OUTPUTS: y y -- value of d-th derivative of the i-th basis element of the BSpline at specified x values """ x = np.asarray(x, np.float64) _shape = x.shape if _shape == (): x.shape = (1,) x.shape = (np.product(_shape,axis=0),) if i < self.tau.shape[0] - 1: ## TODO: OWNDATA flags... v = _hbspline.evaluate(x, self.tau, self.m, d, i, i+1) else: return np.zeros(x.shape, np.float64) if (i == self.tau.shape[0] - self.m): v = np.where(np.equal(x, self.tau[-1]), 1, v) v.shape = _shape return v def basis(self, x, d=0, lower=None, upper=None): """ Evaluate the basis of the BSpline or its derivative. If lower or upper is specified, then only the [lower:upper] elements of the basis are returned. INPUTS: x -- x values at which to evaluate the basis element i -- which element of the BSpline to return d -- the order of derivative lower -- optional lower limit of the set of basis elements upper -- optional upper limit of the set of basis elements OUTPUTS: y y -- value of d-th derivative of the basis elements of the BSpline at specified x values """ x = np.asarray(x) _shape = x.shape if _shape == (): x.shape = (1,) x.shape = (np.product(_shape,axis=0),) if upper is None: upper = self.tau.shape[0] - self.m if lower is None: lower = 0 upper = min(upper, self.tau.shape[0] - self.m) lower = max(0, lower) d = np.asarray(d) if d.shape == (): v = _hbspline.evaluate(x, self.tau, self.m, int(d), lower, upper) else: if d.shape[0] != 2: raise ValueError("if d is not an integer, expecting a jx2 \ array with first row indicating order \ of derivative, second row coefficient in front.") v = 0 for i in range(d.shape[1]): v += d[1,i] * _hbspline.evaluate(x, self.tau, self.m, d[0,i], lower, upper) v.shape = (upper-lower,) + _shape if upper == self.tau.shape[0] - self.m: v[-1] = np.where(np.equal(x, self.tau[-1]), 1, v[-1]) return v def gram(self, d=0): """ Compute Gram inner product matrix, storing it in lower triangular banded form. The (i,j) entry is G_ij = integral b_i^(d) b_j^(d) where b_i are the basis elements of the BSpline and (d) is the d-th derivative. If d is a matrix then, it is assumed to specify a differential operator as follows: the first row represents the order of derivative with the second row the coefficient corresponding to that order. For instance: [[2, 3], [3, 1]] represents 3 * f^(2) + 1 * f^(3). INPUTS: d -- which derivative to apply to each basis element, if d is a matrix, it is assumed to specify a differential operator as above OUTPUTS: gram gram -- the matrix of inner products of (derivatives) of the BSpline elements """ d = np.squeeze(d) if np.asarray(d).shape == (): self.g = _hbspline.gram(self.tau, self.m, int(d), int(d)) else: d = np.asarray(d) if d.shape[0] != 2: raise ValueError("if d is not an integer, expecting a jx2 \ array with first row indicating order \ of derivative, second row coefficient in front.") if d.shape == (2,): d.shape = (2,1) self.g = 0 for i in range(d.shape[1]): for j in range(d.shape[1]): self.g += d[1,i]* d[1,j] * _hbspline.gram(self.tau, self.m, int(d[0,i]), int(d[0,j])) self.g = self.g.T self.d = d return np.nan_to_num(self.g) class SmoothingSpline(BSpline): penmax = 30. method = "target_df" target_df = 5 default_pen = 1.0e-03 optimize = True ''' A smoothing spline, which can be used to smooth scatterplots, i.e. a list of (x,y) tuples. See fit method for more information. ''' def fit(self, y, x=None, weights=None, pen=0.): """ Fit the smoothing spline to a set of (x,y) pairs. INPUTS: y -- response variable x -- if None, uses self.x weights -- optional array of weights pen -- constant in front of Gram matrix OUTPUTS: None The smoothing spline is determined by self.coef, subsequent calls of __call__ will be the smoothing spline. ALGORITHM: Formally, this solves a minimization: fhat = ARGMIN_f SUM_i=1^n (y_i-f(x_i))^2 + pen * int f^(2)^2 int is integral. pen is lambda (from Hastie) See Chapter 5 of Hastie, Tibshirani and Friedman (2001). "The Elements of Statistical Learning." Springer-Verlag. 536 pages. for more details. TODO: Should add arbitrary derivative penalty instead of just second derivative. """ banded = True if x is None: x = self._x bt = self._basisx.copy() else: bt = self.basis(x) if pen == 0.: # can't use cholesky for singular matrices banded = False if x.shape != y.shape: raise ValueError('x and y shape do not agree, by default x are \ the Bspline\'s internal knots') if pen >= self.penmax: pen = self.penmax if weights is not None: self.weights = weights else: self.weights = 1. _w = np.sqrt(self.weights) bt *= _w # throw out rows with zeros (this happens at boundary points!) mask = np.flatnonzero(1 - np.alltrue(np.equal(bt, 0), axis=0)) bt = bt[:,mask] y = y[mask] self.df_total = y.shape[0] bty = np.squeeze(np.dot(bt, _w * y)) self.N = y.shape[0] if not banded: self.btb = np.dot(bt, bt.T) _g = _band2array(self.g, lower=1, symmetric=True) self.coef, _, self.rank = L.lstsq(self.btb + pen*_g, bty)[0:3] self.rank = min(self.rank, self.btb.shape[0]) del(_g) else: self.btb = np.zeros(self.g.shape, np.float64) nband, nbasis = self.g.shape for i in range(nbasis): for k in range(min(nband, nbasis-i)): self.btb[k,i] = (bt[i] * bt[i+k]).sum() bty.shape = (1,bty.shape[0]) self.pen = pen self.chol, self.coef = solveh_banded(self.btb + pen*self.g, bty, lower=1) self.coef = np.squeeze(self.coef) self.resid = y * self.weights - np.dot(self.coef, bt) self.pen = pen del(bty); del(mask); del(bt) def smooth(self, y, x=None, weights=None): if self.method == "target_df": if hasattr(self, 'pen'): self.fit(y, x=x, weights=weights, pen=self.pen) else: self.fit_target_df(y, x=x, weights=weights, df=self.target_df) elif self.method == "optimize_gcv": self.fit_optimize_gcv(y, x=x, weights=weights) def gcv(self): """ Generalized cross-validation score of current fit. Craven, P. and Wahba, G. "Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized cross-validation." Numerische Mathematik, 31(4), 377-403. """ norm_resid = (self.resid**2).sum() return norm_resid / (self.df_total - self.trace()) def df_resid(self): """ Residual degrees of freedom in the fit. self.N - self.trace() where self.N is the number of observations of last fit. """ return self.N - self.trace() def df_fit(self): """ How many degrees of freedom used in the fit? self.trace() """ return self.trace() def trace(self): """ Trace of the smoothing matrix S(pen) TODO: addin a reference to Wahba, and whoever else I used. """ if self.pen > 0: _invband = _hbspline.invband(self.chol.copy()) tr = _trace_symbanded(_invband, self.btb, lower=1) return tr else: return self.rank def fit_target_df(self, y, x=None, df=None, weights=None, tol=1.0e-03, apen=0, bpen=1.0e-03): """ Fit smoothing spline with approximately df degrees of freedom used in the fit, i.e. so that self.trace() is approximately df. Uses binary search strategy. In general, df must be greater than the dimension of the null space of the Gram inner product. For cubic smoothing splines, this means that df > 2. INPUTS: y -- response variable x -- if None, uses self.x df -- target degrees of freedom weights -- optional array of weights tol -- (relative) tolerance for convergence apen -- lower bound of penalty for binary search bpen -- upper bound of penalty for binary search OUTPUTS: None The smoothing spline is determined by self.coef, subsequent calls of __call__ will be the smoothing spline. """ df = df or self.target_df olddf = y.shape[0] - self.m if hasattr(self, "pen"): self.fit(y, x=x, weights=weights, pen=self.pen) curdf = self.trace() if np.fabs(curdf - df) / df < tol: return if curdf > df: apen, bpen = self.pen, 2 * self.pen else: apen, bpen = 0., self.pen while True: curpen = 0.5 * (apen + bpen) self.fit(y, x=x, weights=weights, pen=curpen) curdf = self.trace() if curdf > df: apen, bpen = curpen, 2 * curpen else: apen, bpen = apen, curpen if apen >= self.penmax: raise ValueError("penalty too large, try setting penmax \ higher or decreasing df") if np.fabs(curdf - df) / df < tol: break def fit_optimize_gcv(self, y, x=None, weights=None, tol=1.0e-03, brack=(-100,20)): """ Fit smoothing spline trying to optimize GCV. Try to find a bracketing interval for scipy.optimize.golden based on bracket. It is probably best to use target_df instead, as it is sometimes difficult to find a bracketing interval. INPUTS: y -- response variable x -- if None, uses self.x df -- target degrees of freedom weights -- optional array of weights tol -- (relative) tolerance for convergence brack -- an initial guess at the bracketing interval OUTPUTS: None The smoothing spline is determined by self.coef, subsequent calls of __call__ will be the smoothing spline. """ def _gcv(pen, y, x): self.fit(y, x=x, pen=np.exp(pen)) a = self.gcv() return a a = golden(_gcv, args=(y,x), brack=bracket, tol=tol) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/contrast_old.py000066400000000000000000000110721224417117700252200ustar00rootroot00000000000000import copy import numpy as np from numpy.linalg import pinv from statsmodels.sandbox import utils_old as utils class ContrastResults(object): """ Results from looking at a particular contrast of coefficients in a parametric model. The class does nothing, it is a container for the results from T and F contrasts. """ def __init__(self, t=None, F=None, sd=None, effect=None, df_denom=None, df_num=None): if F is not None: self.F = F self.df_denom = df_denom self.df_num = df_num else: self.t = t self.sd = sd self.effect = effect self.df_denom = df_denom def __array__(self): if hasattr(self, "F"): return self.F else: return self.t def __str__(self): if hasattr(self, 'F'): return '' % \ (`self.F`, self.df_denom, self.df_num) else: return '' % \ (`self.effect`, `self.sd`, `self.t`, self.df_denom) class Contrast(object): """ This class is used to construct contrast matrices in regression models. They are specified by a (term, formula) pair. The term, T, is a linear combination of columns of the design matrix D=formula(). The matrix attribute is a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D. Further, the matrix Tnew = dot(C, D) is full rank. The rank attribute is the rank of dot(D, dot(pinv(D), T)) In a regression model, the contrast tests that E(dot(Tnew, Y)) = 0 for each column of Tnew. """ def __init__(self, term, formula, name=''): self.term = term self.formula = formula if name is '': self.name = str(term) else: self.name = name def __str__(self): return '' % \ `{'term':str(self.term), 'formula':str(self.formula)}` def compute_matrix(self, *args, **kw): """ Construct a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D=self.D=self.formula(). If the design, self.D is already set, then evaldesign can be set to False. """ t = copy.copy(self.term) t.namespace = self.formula.namespace T = np.transpose(np.array(t(*args, **kw))) if T.ndim == 1: T.shape = (T.shape[0], 1) self.T = utils.clean0(T) self.D = self.formula.design(*args, **kw) self._matrix = contrastfromcols(self.T, self.D) try: self.rank = self.matrix.shape[1] except: self.rank = 1 def _get_matrix(self): """ This will fail if the formula needs arguments to construct the design. """ if not hasattr(self, "_matrix"): self.compute_matrix() return self._matrix matrix = property(_get_matrix) def contrastfromcols(L, D, pseudo=None): """ From an n x p design matrix D and a matrix L, tries to determine a p x q contrast matrix C which determines a contrast of full rank, i.e. the n x q matrix dot(transpose(C), pinv(D)) is full rank. L must satisfy either L.shape[0] == n or L.shape[1] == p. If L.shape[0] == n, then L is thought of as representing columns in the column space of D. If L.shape[1] == p, then L is thought of as what is known as a contrast matrix. In this case, this function returns an estimable contrast corresponding to the dot(D, L.T) Note that this always produces a meaningful contrast, not always with the intended properties because q is always non-zero unless L is identically 0. That is, it produces a contrast that spans the column space of L (after projection onto the column space of D). """ L = np.asarray(L) D = np.asarray(D) n, p = D.shape if L.shape[0] != n and L.shape[1] != p: raise ValueError, 'shape of L and D mismatched' if pseudo is None: pseudo = pinv(D) if L.shape[0] == n: C = np.dot(pseudo, L).T else: C = L C = np.dot(pseudo, np.dot(D, C.T)).T Lp = np.dot(D, C.T) if len(Lp.shape) == 1: Lp.shape = (n, 1) if utils.rank(Lp) != Lp.shape[1]: Lp = utils.fullrank(Lp) C = np.dot(pseudo, Lp).T return np.squeeze(C) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/cox.py000066400000000000000000000232361224417117700233230ustar00rootroot00000000000000'''Cox proportional hazards regression model. some dimension problems fixed import errors currently produces parameter estimate but then raises exception for other results finally, after running the script several times, I get a OSError with too many open file handles updates and changes : as of 2010-05-15 AttributeError: 'CoxPH' object has no attribute 'cachedir' Traceback (most recent call last): File "C:\...\scikits\statsmodels\sandbox\cox.py", line 244, in res = c.newton([0.4]) AttributeError: 'CoxPH' object has no attribute 'newton' replaced newton by call to new fit method for mle with bfgs feels very slow need testcase before trying to fix ''' import shutil import tempfile import numpy as np from statsmodels.base import model import survival class Discrete(object): """ A simple little class for working with discrete random vectors. Note: assumes x is 2-d and observations are in 0 axis, variables in 1 axis """ def __init__(self, x, w=None): self.x = np.squeeze(x) if self.x.shape == (): self.x = np.array([self.x]) ## #JP added and removed again b/c still broadcast error ## if self.x.ndim == 1: ## self.x = self.x[:,None] self.n = self.x.shape[0] if w is None: w = np.ones(self.n, np.float64) else: if w.shape[0] != self.n: raise ValueError('incompatible shape for weights w') if np.any(np.less(w, 0)): raise ValueError('weights should be non-negative') self.w = w*1.0 / w.sum() def mean(self, f=None): #JP: this is expectation, "expect" in mine if f is None: fx = self.x else: fx = f(self.x) return (fx * self.w).sum() def cov(self): mu = self.mean() #JP: call to method (confusing name) dx = self.x - mu#np.multiply.outer(mu, self.x.shape[1]) return np.dot(dx, np.transpose(dx)) ## if dx.ndim == 1: ## dx = dx[:,None] ## return np.dot(dx.T, dx) class Observation(survival.RightCensored): def __getitem__(self, item): if self.namespace is not None: return self.namespace[item] else: return getattr(self, item) def __init__(self, time, delta, namespace=None): self.namespace = namespace survival.RightCensored.__init__(self, time, delta) def __call__(self, formula, time=None, **extra): return formula(namespace=self, time=time, **extra) class CoxPH(model.LikelihoodModel): """Cox proportional hazards regression model.""" def __init__(self, subjects, formula, time_dependent=False): self.subjects, self.formula = subjects, formula self.time_dependent = time_dependent self.initialize(self.subjects) def initialize(self, subjects): print 'called initialize' self.failures = {} for i in range(len(subjects)): s = subjects[i] if s.delta: if s.time not in self.failures: self.failures[s.time] = [i] else: self.failures[s.time].append(i) self.failure_times = self.failures.keys() self.failure_times.sort() def cache(self): if self.time_dependent: self.cachedir = tempfile.mkdtemp() self.design = {} self.risk = {} first = True for t in self.failures.keys(): if self.time_dependent: d = np.array([s(self.formula, time=t) for s in self.subjects]).astype(float)[:,None] dshape = d.shape dfile = file(tempfile.mkstemp(dir=self.cachedir)[1], 'w') d.tofile(dfile) dfile.close() del(d) self.design[t] = np.memmap(dfile.name, dtype=np.dtype(float), shape=dshape) elif first: d = np.array([s(self.formula, time=t) for s in self.subjects]).astype(np.float64) self.design[t] = d else: self.design[t] = d self.risk[t] = np.compress([s.atrisk(t) for s in self.subjects], np.arange(self.design[t].shape[0]),axis=-1) # this raised exception on exit, def __del__(self): try: shutil.rmtree(self.cachedir, ignore_errors=True) except AttributeError: print "AttributeError: 'CoxPH' object has no attribute 'cachedir'" pass def loglike(self, b, ties='breslow'): logL = 0 for t in self.failures.keys(): fail = self.failures[t] d = len(fail) risk = self.risk[t] Zb = np.dot(self.design[t], b) logL += Zb[fail].sum() if ties == 'breslow': s = np.exp(Zb[risk]).sum() logL -= np.log(np.exp(Zb[risk]).sum()) * d elif ties == 'efron': s = np.exp(Zb[risk]).sum() r = np.exp(Zb[fail]).sum() for j in range(d): logL -= np.log(s - j * r / d) elif ties == 'cox': raise NotImplementedError('Cox tie breaking method not \ implemented') else: raise NotImplementedError('tie breaking method not recognized') return logL def score(self, b, ties='breslow'): score = 0 for t in self.failures.keys(): fail = self.failures[t] d = len(fail) risk = self.risk[t] Z = self.design[t] score += Z[fail].sum() if ties == 'breslow': w = np.exp(np.dot(Z, b)) rv = Discrete(Z[risk], w=w[risk]) score -= rv.mean() * d elif ties == 'efron': w = np.exp(np.dot(Z, b)) score += Z[fail].sum() for j in range(d): efron_w = w efron_w[fail] -= i * w[fail] / float(d) rv = Discrete(Z[risk], w=efron_w[risk]) score -= rv.mean() elif ties == 'cox': raise NotImplementedError('Cox tie breaking method not \ implemented') else: raise NotImplementedError('tie breaking method not recognized') return np.array([score]) def information(self, b, ties='breslow'): info = 0 #np.zeros((len(b),len(b))) #0 score = 0 for t in self.failures.keys(): fail = self.failures[t] d = len(fail) risk = self.risk[t] Z = self.design[t] if ties == 'breslow': w = np.exp(np.dot(Z, b)) rv = Discrete(Z[risk], w=w[risk]) info += rv.cov() elif ties == 'efron': w = np.exp(np.dot(Z, b)) score += Z[fail].sum() for j in range(d): efron_w = w efron_w[fail] -= i * w[fail] / d rv = Discrete(Z[risk], w=efron_w[risk]) info += rv.cov() elif ties == 'cox': raise NotImplementedError('Cox tie breaking method not \ implemented') else: raise NotImplementedError('tie breaking method not recognized') return score if __name__ == '__main__': import numpy.random as R n = 100 X = np.array([0]*n + [1]*n) b = 0.4 lin = 1 + b*X Y = R.standard_exponential((2*n,)) / lin delta = R.binomial(1, 0.9, size=(2*n,)) subjects = [Observation(Y[i], delta[i]) for i in range(2*n)] for i in range(2*n): subjects[i].X = X[i] import statsmodels.sandbox.formula as F x = F.Quantitative('X') f = F.Formula(x) c = CoxPH(subjects, f) # c.cache() # temp file cleanup doesn't work on windows c = CoxPH(subjects, f, time_dependent=True) c.cache() #this creates tempfile cache, # no tempfile cache is created in normal use of CoxPH #res = c.newton([0.4]) #doesn't work anymore res=c.fit([0.4],method="bfgs") print res.params print dir(c) #print c.fit(Y) #c.information(res.params) #raises exception ''' Note: Replacement for c.newton >>> c.fit() Traceback (most recent call last): File "", line 1, in c.fit() File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental\scikits\statsmodels\model.py", line 132, in fit start_params = [0]*self.exog.shape[1] # will fail for shape (K,) AttributeError: 'CoxPH' object has no attribute 'exog' >>> c.fit([0.4]) Traceback (most recent call last): File "", line 1, in c.fit([0.4]) File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental\scikits\statsmodels\model.py", line 148, in fit H = self.hessian(history[-1]) File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental\scikits\statsmodels\model.py", line 115, in hessian raise NotImplementedError NotImplementedError >>> c.fit([0.4],method="bfgs") Optimization terminated successfully. Current function value: 802.354181 Iterations: 3 Function evaluations: 5 Gradient evaluations: 5 >>> res=c.fit([0.4],method="bfgs") Optimization terminated successfully. Current function value: 802.354181 Iterations: 3 Function evaluations: 5 Gradient evaluations: 5 >>> res.params array([ 0.34924421]) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/datarich/000077500000000000000000000000001224417117700237315ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/datarich/__init__.py000066400000000000000000000143221224417117700260440ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/datarich/factormodels.py000066400000000000000000000155361224417117700267770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Nov 14 08:21:41 2010 Author: josef-pktd License: BSD (3-clause) """ import numpy as np from numpy.testing import assert_array_almost_equal import statsmodels.api as sm from statsmodels.sandbox.tools import pca from statsmodels.sandbox.tools.cross_val import LeaveOneOut #converting example Principal Component Regression to a class #from sandbox/example_pca_regression.py class FactorModelUnivariate(object): ''' Todo: check treatment of const, make it optional ? add hasconst (0 or 1), needed when selecting nfact+hasconst options are arguments in calc_factors, should be more public instead cross-validation is slow for large number of observations ''' def __init__(self, endog, exog): #do this in a superclass? self.endog = np.asarray(endog) self.exog = np.asarray(exog) def calc_factors(self, x=None, keepdim=0, addconst=True): '''get factor decomposition of exogenous variables This uses principal component analysis to obtain the factors. The number of factors kept is the maximum that will be considered in the regression. ''' if x is None: x = self.exog else: x = np.asarray(x) xred, fact, evals, evecs = pca(x, keepdim=keepdim, normalize=1) self.exog_reduced = xred #self.factors = fact if addconst: self.factors = sm.add_constant(fact, prepend=True) self.hasconst = 1 #needs to be int else: self.factors = fact self.hasconst = 0 #needs to be int self.evals = evals self.evecs = evecs def fit_fixed_nfact(self, nfact): if not hasattr(self, 'factors_wconst'): self.calc_factors() return sm.OLS(self.endog, self.factors[:,:nfact+1]).fit() def fit_find_nfact(self, maxfact=None, skip_crossval=True, cv_iter=None): '''estimate the model and selection criteria for up to maxfact factors The selection criteria that are calculated are AIC, BIC, and R2_adj. and additionally cross-validation prediction error sum of squares if `skip_crossval` is false. Cross-validation is not used by default because it can be time consuming to calculate. By default the cross-validation method is Leave-one-out on the full dataset. A different cross-validation sample can be specified as an argument to cv_iter. Results are attached in `results_find_nfact` ''' #print 'OLS on Factors' if not hasattr(self, 'factors'): self.calc_factors() hasconst = self.hasconst if maxfact is None: maxfact = self.factors.shape[1] - hasconst if (maxfact+hasconst) < 1: raise ValueError('nothing to do, number of factors (incl. constant) should ' + 'be at least 1') #temporary safety maxfact = min(maxfact, 10) y0 = self.endog results = [] #xred, fact, eva, eve = pca(x0, keepdim=0, normalize=1) for k in range(1, maxfact+hasconst): #k includes now the constnat #xred, fact, eva, eve = pca(x0, keepdim=k, normalize=1) # this is faster and same result fact = self.factors[:,:k] res = sm.OLS(y0, fact).fit() ## print 'k =', k ## print res.params ## print 'aic: ', res.aic ## print 'bic: ', res.bic ## print 'llf: ', res.llf ## print 'R2 ', res.rsquared ## print 'R2 adj', res.rsquared_adj if not skip_crossval: if cv_iter is None: cv_iter = LeaveOneOut(len(y0)) prederr2 = 0. for inidx, outidx in cv_iter: res_l1o = sm.OLS(y0[inidx], fact[inidx,:]).fit() #print data.endog[outidx], res.model.predict(data.exog[outidx,:]), prederr2 += (y0[outidx] - res_l1o.model.predict(res_l1o.params, fact[outidx,:]))**2. else: prederr2 = np.nan results.append([k, res.aic, res.bic, res.rsquared_adj, prederr2]) self.results_find_nfact = results = np.array(results) self.best_nfact = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0), np.argmin(results[:,-1],0))] def summary_find_nfact(self): '''provides a summary for the selection of the number of factors Returns ------- sumstr : string summary of the results for selecting the number of factors ''' if not hasattr(self, 'results_find_nfact'): self.fit_find_nfact() results = self.results_find_nfact sumstr = '' sumstr += '\n' + 'Best result for k, by AIC, BIC, R2_adj, L1O' # best = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0), # np.argmin(results[:,-1],0))] sumstr += '\n' + ' '*19 + '%5d %4d %6d %5d' % tuple(self.best_nfact) from statsmodels.iolib.table import (SimpleTable, default_txt_fmt, default_latex_fmt, default_html_fmt) headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ') numformat = ['%6d'] + ['%10.3f']*4 #'%10.4f' txt_fmt1 = dict(data_fmts = numformat) tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1) sumstr += '\n' + "PCA regression on simulated data," sumstr += '\n' + "DGP: 2 factors and 4 explanatory variables" sumstr += '\n' + tabl.__str__() sumstr += '\n' + "Notes: k is number of components of PCA," sumstr += '\n' + " constant is added additionally" sumstr += '\n' + " k=0 means regression on constant only" sumstr += '\n' + " L1O: sum of squared prediction errors for leave-one-out" return sumstr if __name__ == '__main__': examples = [1] if 1 in examples: nobs = 500 f0 = np.c_[np.random.normal(size=(nobs,2)), np.ones((nobs,1))] f2xcoef = np.c_[np.repeat(np.eye(2),2,0),np.arange(4)[::-1]].T f2xcoef = np.array([[ 1., 1., 0., 0.], [ 0., 0., 1., 1.], [ 3., 2., 1., 0.]]) f2xcoef = np.array([[ 0.1, 3., 1., 0.], [ 0., 0., 1.5, 0.1], [ 3., 2., 1., 0.]]) x0 = np.dot(f0, f2xcoef) x0 += 0.1*np.random.normal(size=x0.shape) ytrue = np.dot(f0,[1., 1., 1.]) y0 = ytrue + 0.1*np.random.normal(size=ytrue.shape) mod = FactorModelUnivariate(y0, x0) print mod.summary_find_nfact() print "with cross validation - slower" mod.fit_find_nfact(maxfact=None, skip_crossval=False, cv_iter=None) print mod.summary_find_nfact() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/dataset_notes.rst000066400000000000000000000051321224417117700255420ustar00rootroot00000000000000Adding a dataset. Main Steps 1) Obtain permission to use the data. 1) Obtain permission! This is really important! I usually look up an e-mail address and politely (and briefly) explain why I would like to use the data. Most people get back to me almost immediately, and I have never had anyone say no. After all, I think most academics are sympathetic to the idea that information wants to be free... 2) Make a directory in the datasets folder. For this example I will be using the Spector and Mazzeo data from Greene's Econometric Analysis, so I make a folder called statsmodels/datasets/spector 3) Copy the template_data.py file over to the new directory, but rename it data.py. It contains all the meta information for the datasets. So we now have datasets/spector/data.py 4) Put the raw data into this folder and convert it. Sometimes the data used for examples is different than the raw data. If this is the case then the datasets/spector directory should contain a folder named src for the original data. In this case, the data is clean, so I just put a file name spector.csv into datasets/spector. This file is just an ascii file with spaces as delimiters. If the file requires a little cleaning, then put the raw data in src and create a file called spector.csv in the spector folder for the cleaned data. After this is done, we use the convert function in scikits.statsmodels.datasets.data_utils to convert the data into the format needed. In the folder with our .csv file, just do. from scikits.statsmodels.datasets.data_utils import convert convert('./spector.csv', delimiter=" ") This creates a spector.py file, which contains all of the variables as lists of strings. 5) Edit data.py to reflect the correct meta information. Usually, this will require editing the COPYRIGHT, TITLE, SOURCE, DESCRSHORT (and/or DESCRLONG), and "NOTE" 6) Edit the Load class of data.py to load the newly created dataset. In this case, we change the following lines to read from spector import __dict__, names self.endog = np.array(self._d[self._names[4]], dtype=float) self.exog = np.column_stack(self._d[i] \ for i in self._names[1:4]).astype(np.float) This is probably not the best way to handle the datasets class, and will probably change in the future as the datasets package becomes more robust. Suggetions are very welcome. 7) Create an __init__.py in the new folder The __init__.py file should contain from data import * 8) Edit the datasets.__init__.py to import the new directory 9) Make sure everything is correct, and you've saved everything, and put the directory under version control. bzr add spector statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/descstats.py000066400000000000000000000154411224417117700245260ustar00rootroot00000000000000''' Glue for returning descriptive statistics. ''' import numpy as np from scipy import stats import os from statsmodels.stats.descriptivestats import sign_test ############################################# # #============================================ # Univariate Descriptive Statistics #============================================ # def descstats(data, cols=None, axis=0): ''' Prints descriptive statistics for one or multiple variables. Parameters ------------ data: numpy array `x` is the data v: list, optional A list of the column number or field names (for a recarray) of variables. Default is all columns. axis: 1 or 0 axis order of data. Default is 0 for column-ordered data. Examples -------- >>> descstats(data.exog,v=['x_1','x_2','x_3']) ''' x = np.array(data) # or rather, the data we're interested in if cols is None: # if isinstance(x, np.recarray): # cols = np.array(len(x.dtype.names)) if not isinstance(x, np.recarray) and x.ndim == 1: x = x[:,None] if x.shape[1] == 1: desc = ''' --------------------------------------------- Univariate Descriptive Statistics --------------------------------------------- Var. Name %(name)12s ---------- Obs. %(nobs)22i Range %(range)22s Sum of Wts. %(sum)22s Coeff. of Variation %(coeffvar)22.4g Mode %(mode)22.4g Skewness %(skewness)22.4g Repeats %(nmode)22i Kurtosis %(kurtosis)22.4g Mean %(mean)22.4g Uncorrected SS %(uss)22.4g Median %(median)22.4g Corrected SS %(ss)22.4g Variance %(variance)22.4g Sum Observations %(sobs)22.4g Std. Dev. %(stddev)22.4g ''' % {'name': cols, 'sum': 'N/A', 'nobs': len(x), 'mode': \ stats.mode(x)[0][0], 'nmode': stats.mode(x)[1][0], \ 'mean': x.mean(), 'median': np.median(x), 'range': \ '('+str(x.min())+', '+str(x.max())+')', 'variance': \ x.var(), 'stddev': x.std(), 'coeffvar': \ stats.variation(x), 'skewness': stats.skew(x), \ 'kurtosis': stats.kurtosis(x), 'uss': stats.ss(x),\ 'ss': stats.ss(x-x.mean()), 'sobs': np.sum(x)} # ''' % {'name': cols[0], 'sum': 'N/A', 'nobs': len(x[cols[0]]), 'mode': \ # stats.mode(x[cols[0]])[0][0], 'nmode': stats.mode(x[cols[0]])[1][0], \ # 'mean': x[cols[0]].mean(), 'median': np.median(x[cols[0]]), 'range': \ # '('+str(x[cols[0]].min())+', '+str(x[cols[0]].max())+')', 'variance': \ # x[cols[0]].var(), 'stddev': x[cols[0]].std(), 'coeffvar': \ # stats.variation(x[cols[0]]), 'skewness': stats.skew(x[cols[0]]), \ # 'kurtosis': stats.kurtosis(x[cols[0]]), 'uss': stats.ss(x[cols[0]]),\ # 'ss': stats.ss(x[cols[0]]-x[cols[0]].mean()), 'sobs': np.sum(x[cols[0]])} desc+= ''' Percentiles ------------- 1 %% %12.4g 5 %% %12.4g 10 %% %12.4g 25 %% %12.4g 50 %% %12.4g 75 %% %12.4g 90 %% %12.4g 95 %% %12.4g 99 %% %12.4g ''' % tuple([stats.scoreatpercentile(x,per) for per in (1,5,10,25, 50,75,90,95,99)]) t,p_t=stats.ttest_1samp(x,0) M,p_M=sign_test(x) S,p_S=stats.wilcoxon(np.squeeze(x)) desc+= ''' Tests of Location (H0: Mu0=0) ----------------------------- Test Statistic Two-tailed probability -----------------+----------------------------------------- Student's t | t %7.5f Pr > |t| <%.4f Sign | M %8.2f Pr >= |M| <%.4f Signed Rank | S %8.2f Pr >= |S| <%.4f ''' % (t,p_t,M,p_M,S,p_S) # Should this be part of a 'descstats' # in any event these should be split up, so that they can be called # individually and only returned together if someone calls summary # or something of the sort elif x.shape[1] > 1: desc =''' Var. Name | Obs. Mean Std. Dev. Range ------------+--------------------------------------------------------'''+\ os.linesep # for recarrays with columns passed as names # if isinstance(cols[0],str): # for var in cols: # desc += "%(name)15s %(obs)9i %(mean)12.4g %(stddev)12.4g \ #%(range)20s" % {'name': var, 'obs': len(x[var]), 'mean': x[var].mean(), # 'stddev': x[var].std(), 'range': '('+str(x[var].min())+', '\ # +str(x[var].max())+')'+os.linesep} # else: for var in range(x.shape[1]): desc += "%(name)15s %(obs)9i %(mean)12.4g %(stddev)12.4g \ %(range)20s" % {'name': var, 'obs': len(x[:,var]), 'mean': x[:,var].mean(), 'stddev': x[:,var].std(), 'range': '('+str(x[:,var].min())+', '+\ str(x[:,var].max())+')'+os.linesep} else: raise ValueError, "data not understood" return desc #if __name__=='__main__': # test descstats # import os # loc='http://eagle1.american.edu/~js2796a/data/handguns_data.csv' # relpath=(load_dataset(loc)) # dta=np.recfromcsv(relpath) # descstats(dta,['stpop']) # raw_input('Hit enter for multivariate test') # descstats(dta,['stpop','avginc','vio']) # with plain arrays # import string2dummy as s2d # dts=s2d.string2dummy(dta) # ndts=np.vstack(dts[col] for col in dts.dtype.names) # observations in columns and data in rows # is easier for the call to stats # what to make of # ndts=np.column_stack(dts[col] for col in dts.dtype.names) # ntda=ntds.swapaxis(1,0) # ntda is ntds returns false? # or now we just have detailed information about the different strings # would this approach ever be inappropriate for a string typed variable # other than dates? # descstats(ndts, [1]) # raw_input("Enter to try second part") # descstats(ndts, [1,20,3]) if __name__ == '__main__': import statsmodels.api as sm import os data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=False) sum1 = descstats(data.exog) sum1a = descstats(data.exog[:,:1]) # loc='http://eagle1.american.edu/~js2796a/data/handguns_data.csv' # dta=np.recfromcsv(loc) # summary2 = descstats(dta,['stpop']) # summary3 = descstats(dta,['stpop','avginc','vio']) #TODO: needs a by argument # summary4 = descstats(dta) this fails # this is a bug # p = dta[['stpop']] # p.view(dtype = np.float, type = np.ndarray) # this works # p.view(dtype = np.int, type = np.ndarray) ### This is *really* slow ### if os.path.isfile('./Econ724_PS_I_Data.csv'): data2 = np.recfromcsv('./Econ724_PS_I_Data.csv') sum2 = descstats(data2.ahe) sum3 = descstats(np.column_stack((data2.ahe,data2.yrseduc))) sum4 = descstats(np.column_stack(([data2[_] for \ _ in data2.dtype.names]))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/000077500000000000000000000000001224417117700250545ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/__init__.py000066400000000000000000000012301224417117700271610ustar00rootroot00000000000000'''temporary location for enhancements to scipy.stats includes ^^^^^^^^ * Per Brodtkorb's estimation enhancements to scipy.stats.distributions - distributions_per.py is copy of scipy.stats.distributions.py with changes - distributions_profile.py partially extracted classes and functions to separate code into more managable pieces * josef's extra distribution and helper functions - moment helpers - goodness of fit test - fitting distributions with some fixed parameters - find best distribution that fits data: working script * example and test folders to keep all together status ^^^^^^ mixed status : from not-working to well-tested ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/copula.py000066400000000000000000000202371224417117700267150ustar00rootroot00000000000000''' Which Archimedean is Best? Extreme Value copulas formulas are based on Genest 2009 References ---------- Genest, C., 2009. Rank-based inference for bivariate extreme-value copulas. The Annals of Statistics, 37(5), pp.2990-3022. ''' import numpy as np from scipy.special import expm1, log1p def copula_bv_indep(u,v): '''independent bivariate copula ''' return u*v def copula_bv_min(u,v): '''comonotonic bivariate copula ''' return np.minimum(u, v) def copula_bv_max(u, v): '''countermonotonic bivariate copula ''' return np.maximum(u + v - 1, 0) def copula_bv_clayton(u, v, theta): '''Clayton or Cook, Johnson bivariate copula ''' if not theta > 0: raise ValueError('theta needs to be strictly positive') return np.power(np.power(u, -theta) + np.power(v, -theta) - 1, -theta) def copula_bv_frank(u, v, theta): '''Cook, Johnson bivariate copula ''' if not theta > 0: raise ValueError('theta needs to be strictly positive') cdfv = -np.log(1 + expm1(-theta*u) * expm1(-theta*v) / expm1(-theta))/theta cdfv = np.minimum(cdfv, 1) #necessary for example if theta=100 return cdfv def copula_bv_gauss(u, v, rho): raise NotImplementedError def copula_bv_t(u, v, rho, df): raise NotImplementedError #not used yet class Transforms(object): def __init__(self): pass class TransfFrank(object): def evaluate(self, t, theta): return - (np.log(-expm1(-theta*t)) - np.log(-expm1(-theta))) #return - np.log(expm1(-theta*t) / expm1(-theta)) def inverse(self, phi, theta): return -np.log1p(np.exp(-phi) * expm1(-theta)) / theta class TransfClayton(object): def _checkargs(theta): return theta > 0 def evaluate(self, t, theta): return np.power(t, -theta) - 1. def inverse(self, phi, theta): return np.power(1 + phi, -theta) class TransfGumbel(object): ''' requires theta >=1 ''' def _checkargs(theta): return theta >= 1 def evaluate(self, t, theta): return np.power(-np.log(t), theta) def inverse(self, phi, theta): return np.exp(-np.power(phi, 1. / theta)) class TransfIndep(object): def evaluate(self, t): return -np.log(t) def inverse(self, phi): return np.exp(-phi) def copula_bv_archimedean(u, v, transform, args=()): ''' ''' phi = transform.evaluate phi_inv = transform.inverse cdfv = phi_inv(phi(u, *args) + phi(v, *args), *args) return cdfv def copula_mv_archimedean(u, transform, args=(), axis=-1): '''generic multivariate Archimedean copula ''' phi = transform.evaluate phi_inv = transform.inverse cdfv = phi_inv(phi(u, *args).sum(axis), *args) return cdfv def copula_bv_ev(u, v, transform, args=()): '''generic bivariate extreme value copula ''' return np.exp(np.log(u * v) * (transform(np.log(v)/np.log(u*v), *args))) def transform_tawn(t, a1, a2, theta): '''asymmetric logistic model of Tawn 1988 special case: a1=a2=1 : Gumbel restrictions: - theta in (0,1] - a1, a2 in [0,1] ''' def _check_args(a1, a2, theta): condth = (theta > 0) and (theta <= 1) conda1 = (a1 >= 0) and (a1 <= 1) conda2 = (a2 >= 0) and (a2 <= 1) return condth and conda1 and conda2 if not np.all(_check_args(a1, a2, theta)): raise ValueError('invalid args') transf = (1 - a1) * (1-t) transf += (1 - a2) * t transf += ((a1 * t)**(1./theta) + (a2 * (1-t))**(1./theta))**theta return transf def transform_joe(t, a1, a2, theta): '''asymmetric negative logistic model of Joe 1990 special case: a1=a2=1 : symmetric negative logistic of Galambos 1978 restrictions: - theta in (0,inf) - a1, a2 in (0,1] ''' def _check_args(a1, a2, theta): condth = (theta > 0) conda1 = (a1 > 0) and (a1 <= 1) conda2 = (a2 > 0) and (a2 <= 1) return condth and conda1 and conda2 if not np.all(_check_args(a1, a2, theta)): raise ValueError('invalid args') transf = 1 - ((a1 * (1-t))**(-1./theta) + (a2 * t)**(-1./theta))**(-theta) return transf def transform_tawn2(t, theta, k): '''asymmetric mixed model of Tawn 1988 special case: k=0, theta in [0,1] : symmetric mixed model of Tiago de Oliveira 1980 restrictions: - theta > 0 - theta + 3*k > 0 - theta + k <= 1 - theta + 2*k <= 1 ''' def _check_args(theta, k): condth = (theta >= 0) cond1 = (theta + 3*k > 0) and (theta + k <= 1) and (theta + 2*k <= 1) return condth and cond1 if not np.all(_check_args(theta, k)): raise ValueError('invalid args') transf = 1 - (theta + k) * t + theta * t*t + k * t**3 return transf def transform_bilogistic(t, beta, delta): '''bilogistic model of Coles and Tawn 1994, Joe, Smith and Weissman 1992 restrictions: - (beta, delta) in (0,1)^2 or - (beta, delta) in (-inf,0)^2 not vectorized because of numerical integration ''' def _check_args(beta, delta): cond1 = (beta > 0) and (beta <= 1) and (delta > 0) and (delta <= 1) cond2 = (beta < 0) and (delta < 0) return cond1 | cond2 if not np.all(_check_args(beta, delta)): raise ValueError('invalid args') def _integrant(w): term1 = (1 - beta) * np.power(w, -beta) * (1-t) term2 = (1 - delta) * np.power(1-w, -delta) * t np.maximum(term1, term2) from scipy.integrate import quad transf = quad(_integrant, 0, 1) return transf def transform_hr(t, lamda): '''model of Huesler Reiss 1989 special case: a1=a2=1 : symmetric negative logistic of Galambos 1978 restrictions: - lambda in (0,inf) ''' def _check_args(lamda): cond = (lamda > 0) return cond if not np.all(_check_args(lamda)): raise ValueError('invalid args') term = np.log((1. - t) / t) * 0.5 / lamda from scipy.stats import norm #use special if I want to avoid stats import transf = (1 - t) * norm._cdf(lamda + term) + t * norm._cdf(lamda - term) return transf def transform_tev(t, rho, x): '''t-EV model of Demarta and McNeil 2005 restrictions: - rho in (-1,1) - x > 0 ''' def _check_args(rho, x): cond1 = (x > 0) cond2 = (rho > 0) and (rho < 1) return cond1 and cond2 if not np.all(_check_args(rho, x)): raise ValueError('invalid args') from scipy.stats import t as stats_t #use special if I want to avoid stats import z = np.sqrt(1. + x) * (np.power(t/(1.-t), 1./x) - rho) z /= np.sqrt(1 - rho*rho) transf = (1 - t) * stats_t._cdf(z, x+1) + t * stats_t._cdf(z, x+1) return transf #define dictionary of copulas by names and aliases copulanames = {'indep' : copula_bv_indep, 'i' : copula_bv_indep, 'min' : copula_bv_min, 'max' : copula_bv_max, 'clayton' : copula_bv_clayton, 'cookjohnson' : copula_bv_clayton, 'cj' : copula_bv_clayton, 'frank' : copula_bv_frank, 'gauss' : copula_bv_gauss, 'normal' : copula_bv_gauss, 't' : copula_bv_frank} class CopulaBivariate(object): '''bivariate copula class Instantiation needs the arguments, cop_args, that are required for copula ''' def __init__(self, marginalcdfs, copula, copargs=()): if copula in copulanames: self.copula = copulanames[copula] else: #see if we can call it as a copula function try: tmp = copula(0.5, 0.5, *copargs) except: #blanket since we throw again raise ValueError('copula needs to be a copula name or callable') self.copula = copula #no checking done on marginals self.marginalcdfs = marginalcdfs self.copargs = copargs def cdf(self, xy, args=None): '''xx needs to be iterable, instead of x,y for extension to multivariate ''' x, y = xy if args is None: args = self.copargs return self.copula(self.marginalcdfs[0](x), self.marginalcdfs[1](y), *args) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/estimators.py000066400000000000000000000603621224417117700276270ustar00rootroot00000000000000'''estimate distribution parameters by various methods method of moments or matching quantiles, and Maximum Likelihood estimation based on binned data and Maximum Product-of-Spacings Warning: I'm still finding cut-and-paste and refactoring errors, e.g. hardcoded variables from outer scope in functions some results don't seem to make sense for Pareto case, looks better now after correcting some name errors initially loosely based on a paper and blog for quantile matching by John D. Cook formula for gamma quantile (ppf) matching by him (from paper) http://www.codeproject.com/KB/recipes/ParameterPercentile.aspx http://www.johndcook.com/blog/2010/01/31/parameters-from-percentiles/ this is what I actually used (in parts): http://www.bepress.com/mdandersonbiostat/paper55/ quantile based estimator ^^^^^^^^^^^^^^^^^^^^^^^^ only special cases for number or parameters so far Is there a literature for GMM estimation of distribution parameters? check found one: Wu/Perloff 2007 binned estimator ^^^^^^^^^^^^^^^^ * I added this also * use it for chisquare tests with estimation distribution parameters * move this to distribution_extras (next to gof tests powerdiscrepancy and continuous) or add to distribution_patch example: t-distribution * works with quantiles if they contain tail quantiles * results with momentcondquant don't look as good as mle estimate TODOs * rearange and make sure I don't use module globals (as I did initially) DONE make two version exactly identified method of moments with fsolve and GMM (?) version with fmin and maybe the special cases of JD Cook update: maybe exact (MM) version is not so interesting compared to GMM * add semifrozen version of moment and quantile based estimators, e.g. for beta (both loc and scale fixed), or gamma (loc fixed) * add beta example to the semifrozen MLE, fitfr, code -> added method of moment estimator to _fitstart for beta * start a list of how well different estimators, especially current mle work for the different distributions * need general GMM code (with optimal weights ?), looks like a good example for it * get example for binned data estimation, mailing list a while ago * any idea when these are better than mle ? * check language: I use quantile to mean the value of the random variable, not quantile between 0 and 1. * for GMM: move moment conditions to separate function, so that they can be used for further analysis, e.g. covariance matrix of parameter estimates * question: Are GMM properties different for matching quantiles with cdf or ppf? Estimate should be the same, but derivatives of moment conditions differ. * add maximum spacings estimator, Wikipedia, Per Brodtkorb -> basic version Done * add parameter estimation based on empirical characteristic function (Carrasco/Florens), especially for stable distribution * provide a model class based on estimating all distributions, and collect all distribution specific information References ---------- Ximing Wu, Jeffrey M. Perloff, GMM estimation of a maximum entropy distribution with interval data, Journal of Econometrics, Volume 138, Issue 2, 'Information and Entropy Econometrics' - A Volume in Honor of Arnold Zellner, June 2007, Pages 532-546, ISSN 0304-4076, DOI: 10.1016/j.jeconom.2006.05.008. http://www.sciencedirect.com/science/article/B6VC0-4K606TK-4/2/78bc07c6245546374490f777a6bdbbcc http://escholarship.org/uc/item/7jf5w1ht (working paper) Johnson, Kotz, Balakrishnan: Volume 2 Author : josef-pktd License : BSD created : 2010-04-20 changes: added Maximum Product-of-Spacings 2010-05-12 ''' import numpy as np from scipy import stats, optimize, special cache = {} #module global storage for temp results, not used # the next two use distfn from module scope - not anymore def gammamomentcond(distfn, params, mom2, quantile=None): '''estimate distribution parameters based method of moments (mean, variance) for distributions with 1 shape parameter and fixed loc=0. Returns ------- cond : function Notes ----- first test version, quantile argument not used ''' def cond(params): alpha, scale = params mom2s = distfn.stats(alpha, 0.,scale) #quantil return np.array(mom2)-mom2s return cond def gammamomentcond2(distfn, params, mom2, quantile=None): '''estimate distribution parameters based method of moments (mean, variance) for distributions with 1 shape parameter and fixed loc=0. Returns ------- difference : array difference between theoretical and empirical moments Notes ----- first test version, quantile argument not used The only difference to previous function is return type. ''' alpha, scale = params mom2s = distfn.stats(alpha, 0.,scale) return np.array(mom2)-mom2s ######### fsolve doesn't move in small samples, fmin not very accurate def momentcondunbound(distfn, params, mom2, quantile=None): '''moment conditions for estimating distribution parameters using method of moments, uses mean, variance and one quantile for distributions with 1 shape parameter. Returns ------- difference : array difference between theoretical and empirical moments and quantiles ''' shape, loc, scale = params mom2diff = np.array(distfn.stats(shape, loc,scale)) - mom2 if not quantile is None: pq, xq = quantile #ppfdiff = distfn.ppf(pq, alpha) cdfdiff = distfn.cdf(xq, shape, loc, scale) - pq return np.concatenate([mom2diff, cdfdiff[:1]]) return mom2diff ###### loc scale only def momentcondunboundls(distfn, params, mom2, quantile=None, shape=None): '''moment conditions for estimating loc and scale of a distribution with method of moments using either 2 quantiles or 2 moments (not both). Returns ------- difference : array difference between theoretical and empirical moments or quantiles ''' loc, scale = params mom2diff = np.array(distfn.stats(shape, loc, scale)) - mom2 if not quantile is None: pq, xq = quantile #ppfdiff = distfn.ppf(pq, alpha) cdfdiff = distfn.cdf(xq, shape, loc, scale) - pq #return np.concatenate([mom2diff, cdfdiff[:1]]) return cdfdiff return mom2diff ######### try quantile GMM with identity weight matrix #(just a guess that's what it is def momentcondquant(distfn, params, mom2, quantile=None, shape=None): '''moment conditions for estimating distribution parameters by matching quantiles, defines as many moment conditions as quantiles. Returns ------- difference : array difference between theoretical and empirical quantiles Notes ----- This can be used for method of moments or for generalized method of moments. ''' #this check looks redundant/unused know if len(params) == 2: loc, scale = params elif len(params) == 3: shape, loc, scale = params else: #raise NotImplementedError pass #see whether this might work, seems to work for beta with 2 shape args #mom2diff = np.array(distfn.stats(*params)) - mom2 #if not quantile is None: pq, xq = quantile #ppfdiff = distfn.ppf(pq, alpha) cdfdiff = distfn.cdf(xq, *params) - pq #return np.concatenate([mom2diff, cdfdiff[:1]]) return cdfdiff #return mom2diff def fitquantilesgmm(distfn, x, start=None, pquant=None, frozen=None): if pquant is None: pquant = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99]) if start is None: if hasattr(distfn, '_fitstart'): start = distfn._fitstart(x) else: start = [1]*distfn.numargs + [0.,1.] #TODO: vectorize this: xqs = [stats.scoreatpercentile(x, p) for p in pquant*100] mom2s = None parest = optimize.fmin(lambda params:np.sum( momentcondquant(distfn, params, mom2s,(pquant,xqs), shape=None)**2), start) return parest def fitbinned(distfn, freq, binedges, start, fixed=None): '''estimate parameters of distribution function for binned data using MLE Parameters ---------- distfn : distribution instance needs to have cdf method, as in scipy.stats freq : array, 1d frequency count, e.g. obtained by histogram binedges : array, 1d binedges including lower and upper bound start : tuple or array_like ? starting values, needs to have correct length Returns ------- paramest : array estimated parameters Notes ----- todo: add fixed parameter option added factorial ''' if not fixed is None: raise NotImplementedError nobs = np.sum(freq) lnnobsfact = special.gammaln(nobs+1) def nloglike(params): '''negative loglikelihood function of binned data corresponds to multinomial ''' prob = np.diff(distfn.cdf(binedges, *params)) return -(lnnobsfact + np.sum(freq*np.log(prob)- special.gammaln(freq+1))) return optimize.fmin(nloglike, start) def fitbinnedgmm(distfn, freq, binedges, start, fixed=None, weightsoptimal=True): '''estimate parameters of distribution function for binned data using GMM Parameters ---------- distfn : distribution instance needs to have cdf method, as in scipy.stats freq : array, 1d frequency count, e.g. obtained by histogram binedges : array, 1d binedges including lower and upper bound start : tuple or array_like ? starting values, needs to have correct length fixed : None not used yet weightsoptimal : boolean If true, then the optimal weighting matrix for GMM is used. If false, then the identity matrix is used Returns ------- paramest : array estimated parameters Notes ----- todo: add fixed parameter option added factorial ''' if not fixed is None: raise NotImplementedError nobs = np.sum(freq) if weightsoptimal: weights = freq/float(nobs) else: weights = np.ones(len(freq)) freqnormed = freq/float(nobs) # skip turning weights into matrix diag(freq/float(nobs)) def gmmobjective(params): '''negative loglikelihood function of binned data corresponds to multinomial ''' prob = np.diff(distfn.cdf(binedges, *params)) momcond = freqnormed - prob return np.dot(momcond*weights, momcond) return optimize.fmin(gmmobjective, start) #Addition from try_maxproductspacings: """Estimating Parameters of Log-Normal Distribution with Maximum Likelihood and Maximum Product-of-Spacings MPS definiton from JKB page 233 Created on Tue May 11 13:52:50 2010 Author: josef-pktd License: BSD """ def hess_ndt(fun, pars, args, options): import numdifftools as ndt if not ('stepMax' in options or 'stepFix' in options): options['stepMax'] = 1e-5 f = lambda params: fun(params, *args) h = ndt.Hessian(f, **options) return h(pars), h def logmps(params, xsorted, dist): '''calculate negative log of Product-of-Spacings Parameters ---------- params : array_like, tuple ? parameters of the distribution funciton xsorted : array_like data that is already sorted dist : instance of a distribution class only cdf method is used Returns ------- mps : float negative log of Product-of-Spacings Notes ----- MPS definiton from JKB page 233 ''' xcdf = np.r_[0., dist.cdf(xsorted, *params), 1.] D = np.diff(xcdf) return -np.log(D).mean() def getstartparams(dist, data): '''get starting values for estimation of distribution parameters Parameters ---------- dist : distribution instance the distribution instance needs to have either a method fitstart or an attribute numargs data : ndarray data for which preliminary estimator or starting value for parameter estimation is desired Returns ------- x0 : ndarray preliminary estimate or starting value for the parameters of the distribution given the data, including loc and scale ''' if hasattr(dist, 'fitstart'): #x0 = getattr(dist, 'fitstart')(data) x0 = dist.fitstart(data) else: if np.isfinite(dist.a): x0 = np.r_[[1.]*dist.numargs, (data.min()-1), 1.] else: x0 = np.r_[[1.]*dist.numargs, (data.mean()-1), 1.] return x0 def fit_mps(dist, data, x0=None): '''Estimate distribution parameters with Maximum Product-of-Spacings Parameters ---------- params : array_like, tuple ? parameters of the distribution funciton xsorted : array_like data that is already sorted dist : instance of a distribution class only cdf method is used Returns ------- x : ndarray estimates for the parameters of the distribution given the data, including loc and scale ''' xsorted = np.sort(data) if x0 is None: x0 = getstartparams(dist, xsorted) args = (xsorted, dist) print x0 #print args return optimize.fmin(logmps, x0, args=args) if __name__ == '__main__': #Example: gamma - distribution #----------------------------- print '\n\nExample: gamma Distribution' print '---------------------------' alpha = 2 xq = [0.5, 4] pq = [0.1, 0.9] print stats.gamma.ppf(pq, alpha) xq = stats.gamma.ppf(pq, alpha) print np.diff((stats.gamma.ppf(pq, np.linspace(0.01,4,10)[:,None])*xq[::-1])) #optimize.bisect(lambda alpha: np.diff((stats.gamma.ppf(pq, alpha)*xq[::-1]))) print optimize.fsolve(lambda alpha: np.diff((stats.gamma.ppf(pq, alpha)*xq[::-1])), 3.) distfn = stats.gamma mcond = gammamomentcond(distfn, [5.,10], mom2=stats.gamma.stats(alpha, 0.,1.), quantile=None) print optimize.fsolve(mcond, [1.,2.]) mom2 = stats.gamma.stats(alpha, 0.,1.) print optimize.fsolve(lambda params:gammamomentcond2(distfn, params, mom2), [1.,2.]) grvs = stats.gamma.rvs(alpha, 0.,2., size=1000) mom2 = np.array([grvs.mean(), grvs.var()]) alphaestq = optimize.fsolve(lambda params:gammamomentcond2(distfn, params, mom2), [1.,3.]) print alphaestq print 'scale = ', xq/stats.gamma.ppf(pq, alphaestq) #Example beta - distribution #--------------------------- #Warning: this example had cut-and-paste errors print '\n\nExample: beta Distribution' print '--------------------------' #monkey patching : ## if hasattr(stats.beta, '_fitstart'): ## del stats.beta._fitstart #bug in _fitstart #raises AttributeError: _fitstart #stats.distributions.beta_gen._fitstart = lambda self, data : np.array([1,1,0,1]) #_fitstart seems to require a tuple stats.distributions.beta_gen._fitstart = lambda self, data : (5,5,0,1) pq = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99]) #rvsb = stats.beta.rvs(0.5,0.15,size=200) rvsb = stats.beta.rvs(10,15,size=2000) print 'true params', 10, 15, 0, 1 print stats.beta.fit(rvsb) xqsb = [stats.scoreatpercentile(rvsb, p) for p in pq*100] mom2s = np.array([rvsb.mean(), rvsb.var()]) betaparest_gmmquantile = optimize.fmin(lambda params:np.sum(momentcondquant(stats.beta, params, mom2s,(pq,xqsb), shape=None)**2), [10,10, 0., 1.], maxiter=2000) print 'betaparest_gmmquantile', betaparest_gmmquantile #result sensitive to initial condition #Example t - distribution #------------------------ print '\n\nExample: t Distribution' print '-----------------------' nobs = 1000 distfn = stats.t pq = np.array([0.1,0.9]) paramsdgp = (5, 0, 1) trvs = distfn.rvs(5, 0, 1, size=nobs) xqs = [stats.scoreatpercentile(trvs, p) for p in pq*100] mom2th = distfn.stats(*paramsdgp) mom2s = np.array([trvs.mean(), trvs.var()]) tparest_gmm3quantilefsolve = optimize.fsolve(lambda params:momentcondunbound(distfn,params, mom2s,(pq,xqs)), [10,1.,2.]) print 'tparest_gmm3quantilefsolve', tparest_gmm3quantilefsolve tparest_gmm3quantile = optimize.fmin(lambda params:np.sum(momentcondunbound(distfn,params, mom2s,(pq,xqs))**2), [10,1.,2.]) print 'tparest_gmm3quantile', tparest_gmm3quantile print distfn.fit(trvs) ## ##distfn = stats.t ##pq = np.array([0.1,0.9]) ##paramsdgp = (5, 0, 1) ##trvs = distfn.rvs(5, 0, 1, size=nobs) ##xqs = [stats.scoreatpercentile(trvs, p) for p in pq*100] ##mom2th = distfn.stats(*paramsdgp) ##mom2s = np.array([trvs.mean(), trvs.var()]) print optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,shape=5), [1.,2.]) print optimize.fmin(lambda params:np.sum(momentcondunboundls(distfn, params, mom2s,shape=5)**2), [1.,2.]) print distfn.fit(trvs) #loc, scale, based on quantiles print optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,(pq,xqs),shape=5), [1.,2.]) ## pq = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99]) #paramsdgp = (5, 0, 1) xqs = [stats.scoreatpercentile(trvs, p) for p in pq*100] tparest_gmmquantile = optimize.fmin(lambda params:np.sum(momentcondquant(distfn, params, mom2s,(pq,xqs), shape=None)**2), [10, 1.,2.]) print 'tparest_gmmquantile', tparest_gmmquantile tparest_gmmquantile2 = fitquantilesgmm(distfn, trvs, start=[10, 1.,2.], pquant=None, frozen=None) print 'tparest_gmmquantile2', tparest_gmmquantile2 ## #use trvs from before bt = stats.t.ppf(np.linspace(0,1,21),5) ft,bt = np.histogram(trvs,bins=bt) print 'fitbinned t-distribution' tparest_mlebinew = fitbinned(stats.t, ft, bt, [10, 0, 1]) tparest_gmmbinewidentity = fitbinnedgmm(stats.t, ft, bt, [10, 0, 1]) tparest_gmmbinewoptimal = fitbinnedgmm(stats.t, ft, bt, [10, 0, 1], weightsoptimal=False) print paramsdgp #Note: this can be used for chisquare test and then has correct asymptotic # distribution for a distribution with estimated parameters, find ref again #TODO combine into test with binning included, check rule for number of bins #bt2 = stats.t.ppf(np.linspace(trvs.,1,21),5) ft2,bt2 = np.histogram(trvs,bins=50) 'fitbinned t-distribution' tparest_mlebinel = fitbinned(stats.t, ft2, bt2, [10, 0, 1]) tparest_gmmbinelidentity = fitbinnedgmm(stats.t, ft2, bt2, [10, 0, 1]) tparest_gmmbineloptimal = fitbinnedgmm(stats.t, ft2, bt2, [10, 0, 1], weightsoptimal=False) tparest_mle = stats.t.fit(trvs) np.set_printoptions(precision=6) print 'sample size', nobs print 'true (df, loc, scale) ', paramsdgp print 'parest_mle ', tparest_mle print print 'tparest_mlebinel ', tparest_mlebinel print 'tparest_gmmbinelidentity ', tparest_gmmbinelidentity print 'tparest_gmmbineloptimal ', tparest_gmmbineloptimal print print 'tparest_mlebinew ', tparest_mlebinew print 'tparest_gmmbinewidentity ', tparest_gmmbinewidentity print 'tparest_gmmbinewoptimal ', tparest_gmmbinewoptimal print print 'tparest_gmmquantileidentity', tparest_gmmquantile print 'tparest_gmm3quantilefsolve ', tparest_gmm3quantilefsolve print 'tparest_gmm3quantile ', tparest_gmm3quantile ''' example results: standard error for df estimate looks large note: iI don't impose that df is an integer, (b/c not necessary) need Monte Carlo to check variance of estimators sample size 1000 true (df, loc, scale) (5, 0, 1) parest_mle [ 4.571405 -0.021493 1.028584] tparest_mlebinel [ 4.534069 -0.022605 1.02962 ] tparest_gmmbinelidentity [ 2.653056 0.012807 0.896958] tparest_gmmbineloptimal [ 2.437261 -0.020491 0.923308] tparest_mlebinew [ 2.999124 -0.0199 0.948811] tparest_gmmbinewidentity [ 2.900939 -0.020159 0.93481 ] tparest_gmmbinewoptimal [ 2.977764 -0.024925 0.946487] tparest_gmmquantileidentity [ 3.940797 -0.046469 1.002001] tparest_gmm3quantilefsolve [ 10. 1. 2.] tparest_gmm3quantile [ 6.376101 -0.029322 1.112403] ''' #Example with Maximum Product of Spacings Estimation #=================================================== #Example: Lognormal Distribution #------------------------------- #tough problem for MLE according to JKB #but not sure for which parameters print '\n\nExample: Lognormal Distribution' print '-------------------------------' sh = np.exp(10) sh = 0.01 print sh x = stats.lognorm.rvs(sh,loc=100, scale=10,size=200) print x.min() print stats.lognorm.fit(x, 1.,loc=x.min()-1,scale=1) xsorted = np.sort(x) x0 = [1., x.min()-1, 1] args = (xsorted, stats.lognorm) print optimize.fmin(logmps,x0,args=args) #Example: Lomax, Pareto, Generalized Pareto Distributions #-------------------------------------------------------- #partially a follow-up to the discussion about numpy.random.pareto #Reference: JKB #example Maximum Product of Spacings Estimation # current results: # doesn't look very good yet sensitivity to starting values # Pareto and Generalized Pareto look like a tough estimation problemprint '\n\nExample: Lognormal Distribution' print '\n\nExample: Lomax, Pareto, Generalized Pareto Distributions' print '--------------------------------------------------------' #p2rvs = np.random.pareto(2,size=500)# + 1 p2rvs = stats.genpareto.rvs(2, size=500) #Note: is Lomax without +1; and classical Pareto with +1 p2rvssorted = np.sort(p2rvs) argsp = (p2rvssorted, stats.pareto) x0p = [1., p2rvs.min()-5, 1] print optimize.fmin(logmps,x0p,args=argsp) print stats.pareto.fit(p2rvs, 0.5, loc=-20, scale=0.5) print 'gpdparest_ mle', stats.genpareto.fit(p2rvs) parsgpd = fit_mps(stats.genpareto, p2rvs) print 'gpdparest_ mps', parsgpd argsgpd = (p2rvssorted, stats.genpareto) options = dict(stepFix=1e-7) #hess_ndt(fun, pars, argsgdp, options) #the results for the following look strange, maybe refactoring error he, h = hess_ndt(logmps, parsgpd, argsgpd, options) print np.linalg.eigh(he)[0] f = lambda params: logmps(params, *argsgpd) print f(parsgpd) #add binned fp2, bp2 = np.histogram(p2rvs, bins=50) 'fitbinned t-distribution' gpdparest_mlebinel = fitbinned(stats.genpareto, fp2, bp2, x0p) gpdparest_gmmbinelidentity = fitbinnedgmm(stats.genpareto, fp2, bp2, x0p) print 'gpdparest_mlebinel', gpdparest_mlebinel print 'gpdparest_gmmbinelidentity', gpdparest_gmmbinelidentity gpdparest_gmmquantile2 = fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=None, frozen=None) print 'gpdparest_gmmquantile2', gpdparest_gmmquantile2 #something wrong : something hard coded ? ''' >>> fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=np.linspace(0.5,0.95,10), frozen=None) Traceback (most recent call last): File "", line 1, in fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=np.linspace(0.5,0.95,10), frozen=None) File "C:\...\scikits\statsmodels\sandbox\stats\distribution_estimators.py", line 224, in fitquantilesgmm parest = optimize.fmin(lambda params:np.sum(momentcondquant(distfn, params, mom2s,(pq,xqs), shape=None)**2), start) File "c:\...\scipy-trunk_after\trunk\dist\scipy-0.8.0.dev6156.win32\programs\python25\lib\site-packages\scipy\optimize\optimize.py", line 183, in fmin fsim[0] = func(x0) File "c:\...\scipy-trunk_after\trunk\dist\scipy-0.8.0.dev6156.win32\programs\python25\lib\site-packages\scipy\optimize\optimize.py", line 103, in function_wrapper return function(x, *args) File "C:\...\scikits\statsmodels\sandbox\stats\distribution_estimators.py", line 224, in parest = optimize.fmin(lambda params:np.sum(momentcondquant(distfn, params, mom2s,(pq,xqs), shape=None)**2), start) File "C:\...\scikits\statsmodels\sandbox\stats\distribution_estimators.py", line 210, in momentcondquant cdfdiff = distfn.cdf(xq, *params) - pq ValueError: shape mismatch: objects cannot be broadcast to a single shape ''' print fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=np.linspace(0.01,0.99,10), frozen=None) fp2, bp2 = np.histogram(p2rvs, bins=stats.genpareto(2).ppf(np.linspace(0,0.99,10))) print 'fitbinnedgmm equal weight bins', print fitbinnedgmm(stats.genpareto, fp2, bp2, x0p) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/000077500000000000000000000000001224417117700266725ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/__init__.py000066400000000000000000000000021224417117700307730ustar00rootroot00000000000000# statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/ex_fitfr.py000066400000000000000000000016301224417117700310520ustar00rootroot00000000000000'''Example for estimating distribution parameters when some are fixed. This uses currently a patched version of the distributions, two methods are added to the continuous distributions. This has no side effects. It also adds bounds to vonmises, which changes the behavior of it for some methods. ''' import numpy as np from scipy import stats #Note the following import attaches methods to scipy.stats.distributions # and adds bounds to stats.vonmises from statsmodels.sandbox.distributions import sppatch np.random.seed(12345) x = stats.gamma.rvs(2.5, loc=0, scale=1.2, size=200) #estimate all parameters print stats.gamma.fit(x) print stats.gamma.fit_fr(x, frozen=[np.nan, np.nan, np.nan]) #estimate shape parameter only print stats.gamma.fit_fr(x, frozen=[np.nan, 0., 1.2]) np.random.seed(12345) x = stats.lognorm.rvs(2, loc=0, scale=2, size=200) print stats.lognorm.fit_fr(x, frozen=[np.nan, 0., np.nan]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/ex_gof.py000066400000000000000000000006321224417117700305140ustar00rootroot00000000000000 import numpy as np from scipy import stats from statsmodels.stats import gof poissrvs = stats.poisson.rvs(0.6, size = 200) freq, expfreq, histsupp = gof.gof_binning_discrete(poissrvs, stats.poisson, (0.6,), nsupp=20) (chi2val, pval) = stats.chisquare(freq, expfreq) print chi2val, pval print gof.gof_chisquare_discrete(stats.poisson, (0.6,), poissrvs, 0.05, 'Poisson') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py000066400000000000000000000117571224417117700324400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """examples for multivariate normal and t distributions Created on Fri Jun 03 16:00:26 2011 @author: josef for comparison I used R mvtnorm version 0.9-96 """ import numpy as np import statsmodels.sandbox.distributions.mv_normal as mvd from numpy.testing import assert_array_almost_equal cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) mu = np.array([-1, 0.0, 2.0]) #************** multivariate normal distribution *************** mvn3 = mvd.MVNormal(mu, cov3) #compare with random sample x = mvn3.rvs(size=1000000) xli = [[2., 1., 1.5], [0., 2., 1.5], [1.5, 1., 2.5], [0., 1., 1.5]] xliarr = np.asarray(xli).T[None,:, :] #from R session #pmvnorm(lower=-Inf,upper=(x[0,.]-mu)/sqrt(diag(cov3)),mean=rep(0,3),corr3) r_cdf = [0.3222292, 0.3414643, 0.5450594, 0.3116296] r_cdf_errors = [1.715116e-05, 1.590284e-05, 5.356471e-05, 3.567548e-05] n_cdf = [mvn3.cdf(a) for a in xli] assert_array_almost_equal(r_cdf, n_cdf, decimal=4) print n_cdf print print (x>> np.random.seed(464239857) >>> rvstsq = squaretg.rvs(10,size=100000) >>> squaretg.moment(4,10) 2734.3750000000009 >>> (rvstsq**4).mean() 2739.672765170933 >>> squaretg.moment(3,10) 78.124999999997044 >>> (rvstsq**3).mean() 84.13950048850549 >>> squaretg.stats(10, moments='mvsk') (array(1.2500000000000022), array(4.6874999999630909), array(5.7735026919777912), array(106.00000000170148)) >>> stats.describe(rvstsq) (100000, (3.2953470738423724e-009, 92.649615690914473), 1.2534924690963247, 4.7741427958594098, 6.1562177957041895, 100.99331166052181) ''' # checking the distribution # fraction of observations in each decile dec = squaretg.ppf(np.linspace(0.,1,11),10) freq,edges = np.histogram(rvstsq, bins=dec) print freq/float(len(rvstsq)) import matplotlib.pyplot as plt freq,edges,_ = plt.hist(rvstsq, bins=50, range=(0,4),normed=True) edges += (edges[1]-edges[0])/2.0 plt.plot(edges[:-1], squaretg.pdf(edges[:-1], 10), 'r') #plt.show() #plt.close() ''' >>> plt.plot(edges[:-1], squaretg.pdf(edges[:-1], 10), 'r') [] >>> plt.fill(edges[4:8], squaretg.pdf(edges[4:8], 10), 'r') [] >>> plt.show() >>> plt.fill_between(edges[4:8], squaretg.pdf(edges[4:8], 10), y2=0, 'r') SyntaxError: non-keyword arg after keyword arg (, line 1) >>> plt.fill_between(edges[4:8], squaretg.pdf(edges[4:8], 10), 0, 'r') Traceback (most recent call last): AttributeError: 'module' object has no attribute 'fill_between' >>> fig = figure() Traceback (most recent call last): NameError: name 'figure' is not defined >>> ax1 = fig.add_subplot(311) Traceback (most recent call last): NameError: name 'fig' is not defined >>> fig = plt.figure() >>> ax1 = fig.add_subplot(111) >>> ax1.fill_between(edges[4:8], squaretg.pdf(edges[4:8], 10), 0, 'r') Traceback (most recent call last): AttributeError: 'AxesSubplot' object has no attribute 'fill_between' >>> ax1.fill(edges[4:8], squaretg.pdf(edges[4:8], 10), 0, 'r') Traceback (most recent call last): ''' import nose nose.runmodule(argv=['__main__','-vvs','-x'],#,'--pdb', '--pdb-failure'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/examples/matchdist.py000066400000000000000000000230531224417117700312270ustar00rootroot00000000000000'''given a 1D sample of observation, find a matching distribution * estimate maximum likelihood paramater for each distribution * rank estimated distribution by Kolmogorov-Smirnov and Anderson-Darling test statistics Author: Josef Pktd License: Simplified BSD original December 2008 TODO: * refactor to result class * split estimation by support, add option and choose automatically * ''' from scipy import stats import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt #stats.distributions.beta_gen._fitstart = lambda self, data : (5,5,0,1) def plothist(x,distfn, args, loc, scale, right=1): plt.figure() # the histogram of the data n, bins, patches = plt.hist(x, 25, normed=1, facecolor='green', alpha=0.75) maxheight = max([p.get_height() for p in patches]) print maxheight axlim = list(plt.axis()) #print axlim axlim[-1] = maxheight*1.05 #plt.axis(tuple(axlim)) ## print bins ## print 'args in plothist', args # add a 'best fit' line #yt = stats.norm.pdf( bins, loc=loc, scale=scale) yt = distfn.pdf( bins, loc=loc, scale=scale, *args) yt[yt>maxheight]=maxheight lt = plt.plot(bins, yt, 'r--', linewidth=1) ys = stats.t.pdf( bins, 10,scale=10,)*right ls = plt.plot(bins, ys, 'b-', linewidth=1) plt.xlabel('Smarts') plt.ylabel('Probability') plt.title(r'$\mathrm{Testing: %s :}\ \mu=%f,\ \sigma=%f$'%(distfn.name,loc,scale)) #plt.axis([bins[0], bins[-1], 0, 0.134+0.05]) plt.grid(True) plt.draw() #plt.show() #plt.close() #targetdist = ['norm','t','truncnorm','johnsonsu','johnsonsb', targetdist = ['norm','alpha', 'anglit', 'arcsine', 'beta', 'betaprime', 'bradford', 'burr', 'fisk', 'cauchy', 'chi', 'chi2', 'cosine', 'dgamma', 'dweibull', 'erlang', 'expon', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'frechet_r', 'weibull_min', 'frechet_l', 'weibull_max', 'genlogistic', 'genpareto', 'genexpon', 'genextreme', 'gamma', 'gengamma', 'genhalflogistic', 'gompertz', 'gumbel_r', 'gumbel_l', 'halfcauchy', 'halflogistic', 'halfnorm', 'hypsecant', 'gausshyper', 'invgamma', 'invnorm', 'invweibull', 'johnsonsb', 'johnsonsu', 'laplace', 'levy', 'levy_l', 'logistic', 'loggamma', 'loglaplace', 'lognorm', 'gilbrat', 'maxwell', 'mielke', 'nakagami', 'ncx2', 'ncf', 't', 'nct', 'pareto', 'lomax', 'powerlaw', 'powerlognorm', 'powernorm', 'rdist', 'rayleigh', 'reciprocal', 'rice', 'recipinvgauss', 'semicircular', 'triang', 'truncexpon', 'truncnorm', 'tukeylambda', 'uniform', 'vonmises', 'wald', 'wrapcauchy', 'binom', 'bernoulli', 'nbinom', 'geom', 'hypergeom', 'logser', 'poisson', 'planck', 'boltzmann', 'randint', 'zipf', 'dlaplace'] left = [] right = [] finite = [] unbound = [] other = [] contdist = [] discrete = [] categ = {('open','open'):'unbound', ('0','open'):'right',('open','0',):'left', ('finite','finite'):'finite',('oth','oth'):'other'} categ = {('open','open'):unbound, ('0','open'):right,('open','0',):left, ('finite','finite'):finite,('oth','oth'):other} categ2 = { ('open', '0') : ['frechet_l', 'weibull_max', 'levy_l'], ('finite', 'finite') : ['anglit', 'cosine', 'rdist', 'semicircular'], ('0', 'open') : ['alpha', 'burr', 'fisk', 'chi', 'chi2', 'erlang', 'expon', 'exponweib', 'exponpow', 'fatiguelife', 'foldcauchy', 'f', 'foldnorm', 'frechet_r', 'weibull_min', 'genpareto', 'genexpon', 'gamma', 'gengamma', 'genhalflogistic', 'gompertz', 'halfcauchy', 'halflogistic', 'halfnorm', 'invgamma', 'invnorm', 'invweibull', 'levy', 'loglaplace', 'lognorm', 'gilbrat', 'maxwell', 'mielke', 'nakagami', 'ncx2', 'ncf', 'lomax', 'powerlognorm', 'rayleigh', 'rice', 'recipinvgauss', 'truncexpon', 'wald'], ('open', 'open') : ['cauchy', 'dgamma', 'dweibull', 'genlogistic', 'genextreme', 'gumbel_r', 'gumbel_l', 'hypsecant', 'johnsonsu', 'laplace', 'logistic', 'loggamma', 't', 'nct', 'powernorm', 'reciprocal', 'truncnorm', 'tukeylambda', 'vonmises'], ('0', 'finite') : ['arcsine', 'beta', 'betaprime', 'bradford', 'gausshyper', 'johnsonsb', 'powerlaw', 'triang', 'uniform', 'wrapcauchy'], ('finite', 'open') : ['pareto'] } #Note: weibull_max == frechet_l right_incorrect = ['genextreme'] right_all = categ2[('0', 'open')] + categ2[('0', 'finite')] + categ2[('finite', 'open')]\ + right_incorrect for distname in targetdist: distfn = getattr(stats,distname) if hasattr(distfn,'_pdf'): if np.isinf(distfn.a): low = 'open' elif distfn.a == 0: low = '0' else: low = 'finite' if np.isinf(distfn.b): high = 'open' elif distfn.b == 0: high = '0' else: high = 'finite' contdist.append(distname) categ.setdefault((low,high),[]).append(distname) not_good = ['genextreme', 'reciprocal', 'vonmises'] # 'genextreme' is right (or left?), 'reciprocal' requires 00] rightfactor = 1 rvs_right = rvs_pos print '='*50 print 'samplesize = ', n for distname in targetdist: distfn = getattr(stats,distname) if distname in right_all: rvs = rvs_right rind = rightfactor else: rvs = rvs_orig rind = 1 print '-'*30 print 'target = %s' % distname sm = rvs.mean() sstd = np.sqrt(rvs.var()) ssupp = (rvs.min(), rvs.max()) if distname in ['truncnorm','betaprime','reciprocal']: par0 = (sm-2*sstd,sm+2*sstd) par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd,*par0)) elif distname == 'norm': par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd)) elif distname == 'genextreme': par_est = tuple(distfn.fit(rvs,-5,loc=sm,scale=sstd)) elif distname == 'wrapcauchy': par_est = tuple(distfn.fit(rvs,0.5,loc=0,scale=sstd)) elif distname == 'f':\ par_est = tuple(distfn.fit(rvs,10,15,loc=0,scale=1)) elif distname in right: sm = rvs.mean() sstd = np.sqrt(rvs.var()) par_est = tuple(distfn.fit(rvs,loc=0,scale=1)) else: sm = rvs.mean() sstd = np.sqrt(rvs.var()) par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd)) print 'fit', par_est arg_est = par_est[:-2] loc_est = par_est[-2] scale_est = par_est[-1] rvs_normed = (rvs-loc_est)/scale_est ks_stat, ks_pval = stats.kstest(rvs_normed,distname, arg_est) print 'kstest', ks_stat, ks_pval quant = 0.1 crit = distfn.ppf(1-quant*float(rind), loc=loc_est, scale=scale_est,*par_est) tail_prob = stats.t.sf(crit,dgp_arg,scale=dgp_scale) print 'crit, prob', quant, crit, tail_prob #if distname == 'norm': #plothist(rvs,loc_est,scale_est) #args = tuple() results.append([distname,ks_stat, ks_pval,arg_est,loc_est,scale_est,crit,tail_prob ]) #plothist(rvs,distfn,arg_est,loc_est,scale_est) #plothist(rvs,distfn,arg_est,loc_est,scale_est) #plt.show() #plt.close() #TODO: collect results and compare tail quantiles from operator import itemgetter res_sort = sorted(results, key = itemgetter(2)) res_sort.reverse() #kstest statistic: smaller is better, pval larger is better print 'number of distributions', len(res_sort) imagedir = 'matchresults' import os if not os.path.exists(imagedir): os.makedirs(imagedir) for ii,di in enumerate(res_sort): distname,ks_stat, ks_pval,arg_est,loc_est,scale_est,crit,tail_prob = di[:] distfn = getattr(stats,distname) if distname in right_all: rvs = rvs_right rind = rightfactor ri = 'r' else: rvs = rvs_orig ri = '' rind = 1 print '%s ks-stat = %f, ks-pval = %f tail_prob = %f)' % \ (distname, ks_stat, ks_pval, tail_prob) ## print 'arg_est = %s, loc_est = %f scale_est = %f)' % \ ## (repr(arg_est),loc_est,scale_est) plothist(rvs,distfn,arg_est,loc_est,scale_est,right = rind) plt.savefig(os.path.join(imagedir,'%s%s%02d_%s.png'% (prefix, ri,ii, distname))) ##plt.show() ##plt.close() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/extras.py000066400000000000000000001226231224417117700267420ustar00rootroot00000000000000'''Various extensions to distributions * skew normal and skew t distribution by Azzalini, A. & Capitanio, A. * Gram-Charlier expansion distribution (using 4 moments), * distributions based on non-linear transformation - Transf_gen - ExpTransf_gen, LogTransf_gen - TransfTwo_gen (defines as examples: square, negative square and abs transformations) - this versions are without __new__ * mnvormcdf, mvstdnormcdf : cdf, rectangular integral for multivariate normal distribution TODO: * Where is Transf_gen for general monotonic transformation ? found and added it * write some docstrings, some parts I don't remember * add Box-Cox transformation, parameterized ? this is only partially cleaned, still includes test examples as functions main changes * add transf_gen (2010-05-09) * added separate example and tests (2010-05-09) * collect transformation function into classes Example ------- >>> logtg = Transf_gen(stats.t, np.exp, np.log, numargs = 1, a=0, name = 'lnnorm', longname = 'Exp transformed normal', extradoc = '\ndistribution of y = exp(x), with x standard normal' 'precision for moment andstats is not very high, 2-3 decimals') >>> logtg.cdf(5, 6) 0.92067704211191848 >>> stats.t.cdf(np.log(5), 6) 0.92067704211191848 >>> logtg.pdf(5, 6) 0.021798547904239293 >>> stats.t.pdf(np.log(5), 6) 0.10899273954837908 >>> stats.t.pdf(np.log(5), 6)/5. #derivative 0.021798547909675815 Author: josef-pktd License: BSD ''' #note copied from distr_skewnorm_0.py from scipy import stats, special, integrate # integrate is for scipy 0.6.0 ??? from scipy.stats import distributions from statsmodels.stats.moment_helpers import mvsk2mc, mc2mvsk import numpy as np class SkewNorm_gen(distributions.rv_continuous): '''univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ ''' def __init__(self): #super(SkewNorm_gen,self).__init__( distributions.rv_continuous.__init__(self, name = 'Skew Normal distribution', shapes = 'alpha', extradoc = ''' ''' ) def _argcheck(self, alpha): return 1 #(alpha >= 0) def _rvs(self, alpha): # see http://azzalini.stat.unipd.it/SN/faq.html delta = alpha/np.sqrt(1+alpha**2) u0 = stats.norm.rvs(size=self._size) u1 = delta*u0 + np.sqrt(1-delta**2)*stats.norm.rvs(size=self._size) return np.where(u0>0, u1, -u1) def _munp(self, n, alpha): # use pdf integration with _mom0_sc if only _pdf is defined. # default stats calculation uses ppf, which is much slower return self._mom0_sc(n, alpha) def _pdf(self,x,alpha): # 2*normpdf(x)*normcdf(alpha*x) return 2.0/np.sqrt(2*np.pi)*np.exp(-x**2/2.0) * special.ndtr(alpha*x) def _stats_skip(self,x,alpha,moments='mvsk'): #skip for now to force moment integration as check pass skewnorm = SkewNorm_gen() def example_n(): print skewnorm.pdf(1,0), stats.norm.pdf(1), skewnorm.pdf(1,0) - stats.norm.pdf(1) print skewnorm.pdf(1,1000), stats.chi.pdf(1,1), skewnorm.pdf(1,1000) - stats.chi.pdf(1,1) print skewnorm.pdf(-1,-1000), stats.chi.pdf(1,1), skewnorm.pdf(-1,-1000) - stats.chi.pdf(1,1) rvs = skewnorm.rvs(0,size=500) print 'sample mean var: ', rvs.mean(), rvs.var() print 'theoretical mean var', skewnorm.stats(0) rvs = skewnorm.rvs(5,size=500) print 'sample mean var: ', rvs.mean(), rvs.var() print 'theoretical mean var', skewnorm.stats(5) print skewnorm.cdf(1,0), stats.norm.cdf(1), skewnorm.cdf(1,0) - stats.norm.cdf(1) print skewnorm.cdf(1,1000), stats.chi.cdf(1,1), skewnorm.cdf(1,1000) - stats.chi.cdf(1,1) print skewnorm.sf(0.05,1000), stats.chi.sf(0.05,1), skewnorm.sf(0.05,1000) - stats.chi.sf(0.05,1) # generated the same way as distributions in stats.distributions class SkewNorm2_gen(distributions.rv_continuous): '''univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern ''' def _argcheck(self, alpha): return 1 #where(alpha>=0, 1, 0) def _pdf(self,x,alpha): # 2*normpdf(x)*normcdf(alpha*x return 2.0/np.sqrt(2*np.pi)*np.exp(-x**2/2.0) * special.ndtr(alpha*x) skewnorm2 = SkewNorm2_gen(name = 'Skew Normal distribution', shapes = 'alpha', extradoc = ''' -inf < alpha < inf''') class ACSkewT_gen(distributions.rv_continuous): '''univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ ''' def __init__(self): #super(SkewT_gen,self).__init__( distributions.rv_continuous.__init__(self, name = 'Skew T distribution', shapes = 'df, alpha', extradoc = ''' Skewed T distribution by Azzalini, A. & Capitanio, A. (2003)_ the pdf is given by: pdf(x) = 2.0 * t.pdf(x, df) * t.cdf(df+1, alpha*x*np.sqrt((1+df)/(x**2+df))) with alpha >=0 Note: different from skewed t distribution by Hansen 1999 .._ Azzalini, A. & Capitanio, A. (2003), Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution, appears in J.Roy.Statist.Soc, series B, vol.65, pp.367-389 ''' ) def _argcheck(self, df, alpha): return (alpha == alpha)*(df>0) ## def _arg_check(self, alpha): ## return np.where(alpha>=0, 0, 1) ## def _argcheck(self, alpha): ## return np.where(alpha>=0, 1, 0) def _rvs(self, df, alpha): # see http://azzalini.stat.unipd.it/SN/faq.html #delta = alpha/np.sqrt(1+alpha**2) V = stats.chi2.rvs(df, size=self._size) z = skewnorm.rvs(alpha, size=self._size) return z/np.sqrt(V/df) def _munp(self, n, df, alpha): # use pdf integration with _mom0_sc if only _pdf is defined. # default stats calculation uses ppf return self._mom0_sc(n, df, alpha) def _pdf(self, x, df, alpha): # 2*normpdf(x)*normcdf(alpha*x) return 2.0*distributions.t._pdf(x, df) * special.stdtr(df+1, alpha*x*np.sqrt((1+df)/(x**2+df))) def example_T(): skewt = ACSkewT_gen() rvs = skewt.rvs(10,0,size=500) print 'sample mean var: ', rvs.mean(), rvs.var() print 'theoretical mean var', skewt.stats(10,0) print 't mean var', stats.t.stats(10) print skewt.stats(10,1000) # -> folded t distribution, as alpha -> inf rvs = np.abs(stats.t.rvs(10,size=1000)) print rvs.mean(), rvs.var() ## ##def mvsk2cm(*args): ## mu,sig,sk,kur = args ## # Get central moments ## cnt = [None]*4 ## cnt[0] = mu ## cnt[1] = sig #*sig ## cnt[2] = sk * sig**1.5 ## cnt[3] = (kur+3.0) * sig**2.0 ## return cnt ## ## ##def mvsk2m(args): ## mc, mc2, skew, kurt = args#= self._stats(*args,**mdict) ## mnc = mc ## mnc2 = mc2 + mc*mc ## mc3 = skew*(mc2**1.5) # 3rd central moment ## mnc3 = mc3+3*mc*mc2+mc**3 # 3rd non-central moment ## mc4 = (kurt+3.0)*(mc2**2.0) # 4th central moment ## mnc4 = mc4+4*mc*mc3+6*mc*mc*mc2+mc**4 ## return (mc, mc2, mc3, mc4), (mnc, mnc2, mnc3, mnc4) ## ##def mc2mvsk(args): ## mc, mc2, mc3, mc4 = args ## skew = mc3 / mc2**1.5 ## kurt = mc4 / mc2**2.0 - 3.0 ## return (mc, mc2, skew, kurt) ## ##def m2mc(args): ## mnc, mnc2, mnc3, mnc4 = args ## mc = mnc ## mc2 = mnc2 - mnc*mnc ## #mc3 = skew*(mc2**1.5) # 3rd central moment ## mc3 = mnc3 - (3*mc*mc2+mc**3) # 3rd central moment ## #mc4 = (kurt+3.0)*(mc2**2.0) # 4th central moment ## mc4 = mnc4 - (4*mc*mc3+6*mc*mc*mc2+mc**4) ## return (mc, mc2, mc3, mc4) from numpy import poly1d,sqrt, exp import scipy def _hermnorm(N): # return the negatively normalized hermite polynomials up to order N-1 # (inclusive) # using the recursive relationship # p_n+1 = p_n(x)' - x*p_n(x) # and p_0(x) = 1 plist = [None]*N plist[0] = poly1d(1) for n in range(1,N): plist[n] = plist[n-1].deriv() - poly1d([1,0])*plist[n-1] return plist def pdf_moments_st(cnt): """Return the Gaussian expanded pdf function given the list of central moments (first one is mean). version of scipy.stats, any changes ? the scipy.stats version has a bug and returns normal distribution """ N = len(cnt) if N < 2: raise ValueError, "At least two moments must be given to" + \ "approximate the pdf." totp = poly1d(1) sig = sqrt(cnt[1]) mu = cnt[0] if N > 2: Dvals = _hermnorm(N+1) for k in range(3,N+1): # Find Ck Ck = 0.0 for n in range((k-3)/2): m = k-2*n if m % 2: # m is odd momdiff = cnt[m-1] else: momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1) Ck += Dvals[k][m] / sig**m * momdiff # Add to totp raise print Dvals print Ck totp = totp + Ck*Dvals[k] def thisfunc(x): xn = (x-mu)/sig return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*np.pi)/sig return thisfunc, totp def pdf_mvsk(mvsk): """Return the Gaussian expanded pdf function given the list of 1st, 2nd moment and skew and Fisher (excess) kurtosis. Parameters ---------- mvsk : list of mu, mc2, skew, kurt distribution is matched to these four moments Returns ------- pdffunc : function function that evaluates the pdf(x), where x is the non-standardized random variable. Notes ----- Changed so it works only if four arguments are given. Uses explicit formula, not loop. This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. In the Gram-Charlier distribution it is possible that the density becomes negative. This is the case when the deviation from the normal distribution is too large. References ---------- http://en.wikipedia.org/wiki/Edgeworth_series Johnson N.L., S. Kotz, N. Balakrishnan: Continuous Univariate Distributions, Volume 1, 2nd ed., p.30 """ N = len(mvsk) if N < 4: raise ValueError, "Four moments must be given to" + \ "approximate the pdf." mu, mc2, skew, kurt = mvsk totp = poly1d(1) sig = sqrt(mc2) if N > 2: Dvals = _hermnorm(N+1) C3 = skew/6.0 C4 = kurt/24.0 # Note: Hermite polynomial for order 3 in _hermnorm is negative # instead of positive totp = totp - C3*Dvals[3] + C4*Dvals[4] def pdffunc(x): xn = (x-mu)/sig return totp(xn)*np.exp(-xn*xn/2.0)/np.sqrt(2*np.pi)/sig return pdffunc def pdf_moments(cnt): """Return the Gaussian expanded pdf function given the list of central moments (first one is mean). Changed so it works only if four arguments are given. Uses explicit formula, not loop. Notes ----- This implements a Gram-Charlier expansion of the normal distribution where the first 2 moments coincide with those of the normal distribution but skew and kurtosis can deviate from it. In the Gram-Charlier distribution it is possible that the density becomes negative. This is the case when the deviation from the normal distribution is too large. References ---------- http://en.wikipedia.org/wiki/Edgeworth_series Johnson N.L., S. Kotz, N. Balakrishnan: Continuous Univariate Distributions, Volume 1, 2nd ed., p.30 """ N = len(cnt) if N < 2: raise ValueError, "At least two moments must be given to" + \ "approximate the pdf." mc, mc2, mc3, mc4 = cnt skew = mc3 / mc2**1.5 kurt = mc4 / mc2**2.0 - 3.0 # Fisher kurtosis, excess kurtosis totp = poly1d(1) sig = sqrt(cnt[1]) mu = cnt[0] if N > 2: Dvals = _hermnorm(N+1) ## for k in range(3,N+1): ## # Find Ck ## Ck = 0.0 ## for n in range((k-3)/2): ## m = k-2*n ## if m % 2: # m is odd ## momdiff = cnt[m-1] ## else: ## momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1) ## Ck += Dvals[k][m] / sig**m * momdiff ## # Add to totp ## raise ## print Dvals ## print Ck ## totp = totp + Ck*Dvals[k] C3 = skew/6.0 C4 = kurt/24.0 totp = totp - C3*Dvals[3] + C4*Dvals[4] def thisfunc(x): xn = (x-mu)/sig return totp(xn)*np.exp(-xn*xn/2.0)/np.sqrt(2*np.pi)/sig return thisfunc class NormExpan_gen(distributions.rv_continuous): '''Gram-Charlier Expansion of Normal distribution class follows scipy.stats.distributions pattern but with __init__ ''' def __init__(self,args, **kwds): #todo: replace with super call distributions.rv_continuous.__init__(self, name = 'Normal Expansion distribution', shapes = 'alpha', extradoc = ''' The distribution is defined as the Gram-Charlier expansion of the normal distribution using the first four moments. The pdf is given by pdf(x) = (1+ skew/6.0 * H(xc,3) + kurt/24.0 * H(xc,4))*normpdf(xc) where xc = (x-mu)/sig is the standardized value of the random variable and H(xc,3) and H(xc,4) are Hermite polynomials Note: This distribution has to be parameterized during initialization and instantiation, and does not have a shape parameter after instantiation (similar to frozen distribution except for location and scale.) Location and scale can be used as with other distributions, however note, that they are relative to the initialized distribution. ''' ) #print args, kwds mode = kwds.get('mode', 'sample') if mode == 'sample': mu,sig,sk,kur = stats.describe(args)[2:] self.mvsk = (mu,sig,sk,kur) cnt = mvsk2mc((mu,sig,sk,kur)) elif mode == 'mvsk': cnt = mvsk2mc(args) self.mvsk = args elif mode == 'centmom': cnt = args self.mvsk = mc2mvsk(cnt) else: raise ValueError, "mode must be 'mvsk' or centmom" self.cnt = cnt #self.mvsk = (mu,sig,sk,kur) #self._pdf = pdf_moments(cnt) self._pdf = pdf_mvsk(self.mvsk) def _munp(self,n): # use pdf integration with _mom0_sc if only _pdf is defined. # default stats calculation uses ppf return self._mom0_sc(n) def _stats_skip(self): # skip for now to force numerical integration of pdf for testing return self.mvsk def examples_normexpand(): skewnorm = SkewNorm_gen() rvs = skewnorm.rvs(5,size=100) normexpan = NormExpan_gen(rvs, mode='sample') smvsk = stats.describe(rvs)[2:] print 'sample: mu,sig,sk,kur' print smvsk dmvsk = normexpan.stats(moments='mvsk') print 'normexpan: mu,sig,sk,kur' print dmvsk print 'mvsk diff distribution - sample' print np.array(dmvsk) - np.array(smvsk) print 'normexpan attributes mvsk' print mc2mvsk(normexpan.cnt) print normexpan.mvsk from statsmodels.stats.momenthelpers import mvsk2mnc, mnc2mc mnc = mvsk2mnc(dmvsk) mc = mnc2mc(mnc) print 'central moments' print mc print 'non-central moments' print mnc pdffn = pdf_moments(mc) print '\npdf approximation from moments' print 'pdf at', mc[0]-1,mc[0]+1 print pdffn([mc[0]-1,mc[0]+1]) print normexpan.pdf([mc[0]-1,mc[0]+1]) ## copied from nonlinear_transform_gen.py ''' A class for the distribution of a non-linear monotonic transformation of a continuous random variable simplest usage: example: create log-gamma distribution, i.e. y = log(x), where x is gamma distributed (also available in scipy.stats) loggammaexpg = Transf_gen(stats.gamma, np.log, np.exp) example: what is the distribution of the discount factor y=1/(1+x) where interest rate x is normally distributed with N(mux,stdx**2)')? (just to come up with a story that implies a nice transformation) invnormalg = Transf_gen(stats.norm, inversew, inversew_inv, decr=True, a=-np.inf) This class does not work well for distributions with difficult shapes, e.g. 1/x where x is standard normal, because of the singularity and jump at zero. Note: I'm working from my version of scipy.stats.distribution. But this script runs under scipy 0.6.0 (checked with numpy: 1.2.0rc2 and python 2.4) This is not yet thoroughly tested, polished or optimized TODO: * numargs handling is not yet working properly, numargs needs to be specified (default = 0 or 1) * feeding args and kwargs to underlying distribution is untested and incomplete * distinguish args and kwargs for the transformed and the underlying distribution - currently all args and no kwargs are transmitted to underlying distribution - loc and scale only work for transformed, but not for underlying distribution - possible to separate args for transformation and underlying distribution parameters * add _rvs as method, will be faster in many cases Created on Tuesday, October 28, 2008, 12:40:37 PM Author: josef-pktd License: BSD ''' from scipy import integrate # for scipy 0.6.0 from scipy import stats, info from scipy.stats import distributions def get_u_argskwargs(**kwargs): #Todo: What's this? wrong spacing, used in Transf_gen TransfTwo_gen u_kwargs = dict((k.replace('u_','',1),v) for k,v in kwargs.items() if k.startswith('u_')) u_args = u_kwargs.pop('u_args',None) return u_args, u_kwargs class Transf_gen(distributions.rv_continuous): '''a class for non-linear monotonic transformation of a continuous random variable ''' def __init__(self, kls, func, funcinv, *args, **kwargs): #print args #print kwargs self.func = func self.funcinv = funcinv #explicit for self.__dict__.update(kwargs) #need to set numargs because inspection does not work self.numargs = kwargs.pop('numargs', 0) #print self.numargs name = kwargs.pop('name','transfdist') longname = kwargs.pop('longname','Non-linear transformed distribution') extradoc = kwargs.pop('extradoc',None) a = kwargs.pop('a', -np.inf) b = kwargs.pop('b', np.inf) self.decr = kwargs.pop('decr', False) #defines whether it is a decreasing (True) # or increasing (False) monotonic transformation self.u_args, self.u_kwargs = get_u_argskwargs(**kwargs) self.kls = kls #(self.u_args, self.u_kwargs) # possible to freeze the underlying distribution super(Transf_gen,self).__init__(a=a, b=b, name = name, longname = longname, extradoc = extradoc) def _rvs(self, *args, **kwargs): self.kls._size = self._size return self.funcinv(self.kls._rvs(*args)) def _cdf(self,x,*args, **kwargs): #print args if not self.decr: return self.kls._cdf(self.funcinv(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self.kls._cdf(self.funcinv(x),*args, **kwargs) def _ppf(self, q, *args, **kwargs): if not self.decr: return self.func(self.kls._ppf(q,*args, **kwargs)) else: return self.func(self.kls._ppf(1-q,*args, **kwargs)) def inverse(x): return np.divide(1.0,x) mux, stdx = 0.05, 0.1 mux, stdx = 9.0, 1.0 def inversew(x): return 1.0/(1+mux+x*stdx) def inversew_inv(x): return (1.0/x - 1.0 - mux)/stdx #.np.divide(1.0,x)-10 def identit(x): return x invdnormalg = Transf_gen(stats.norm, inversew, inversew_inv, decr=True, #a=-np.inf, numargs = 0, name = 'discf', longname = 'normal-based discount factor', extradoc = '\ndistribution of discount factor y=1/(1+x)) with x N(0.05,0.1**2)') lognormalg = Transf_gen(stats.norm, np.exp, np.log, numargs = 2, a=0, name = 'lnnorm', longname = 'Exp transformed normal', extradoc = '\ndistribution of y = exp(x), with x standard normal' 'precision for moment andstats is not very high, 2-3 decimals') loggammaexpg = Transf_gen(stats.gamma, np.log, np.exp, numargs=1) ## copied form nonlinear_transform_short.py '''univariate distribution of a non-linear monotonic transformation of a random variable ''' from scipy import stats from scipy.stats import distributions import numpy as np class ExpTransf_gen(distributions.rv_continuous): '''Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable ''' def __init__(self, kls, *args, **kwargs): #print args #print kwargs #explicit for self.__dict__.update(kwargs) if 'numargs' in kwargs: self.numargs = kwargs['numargs'] else: self.numargs = 1 if 'name' in kwargs: name = kwargs['name'] else: name = 'Log transformed distribution' if 'a' in kwargs: a = kwargs['a'] else: a = 0 super(ExpTransf_gen,self).__init__(a=0, name = name) self.kls = kls def _cdf(self,x,*args): pass #print args return self.kls.cdf(np.log(x),*args) def _ppf(self, q, *args): return np.exp(self.kls.ppf(q,*args)) class LogTransf_gen(distributions.rv_continuous): '''Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable ''' def __init__(self, kls, *args, **kwargs): #explicit for self.__dict__.update(kwargs) if 'numargs' in kwargs: self.numargs = kwargs['numargs'] else: self.numargs = 1 if 'name' in kwargs: name = kwargs['name'] else: name = 'Log transformed distribution' if 'a' in kwargs: a = kwargs['a'] else: a = 0 super(LogTransf_gen,self).__init__(a=a, name = name) self.kls = kls def _cdf(self,x, *args): #print args return self.kls._cdf(np.exp(x),*args) def _ppf(self, q, *args): return np.log(self.kls._ppf(q,*args)) def examples_transf(): ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal') ##print lognormal.cdf(1) ##print stats.lognorm.cdf(1,1) ##print lognormal.stats() ##print stats.lognorm.stats(1) ##print lognormal.rvs(size=10) print 'Results for lognormal' lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general') print lognormalg.cdf(1) print stats.lognorm.cdf(1,1) print lognormalg.stats() print stats.lognorm.stats(1) print lognormalg.rvs(size=5) ##print 'Results for loggamma' ##loggammag = ExpTransf_gen(stats.gamma) ##print loggammag._cdf(1,10) ##print stats.loggamma.cdf(1,10) print 'Results for expgamma' loggammaexpg = LogTransf_gen(stats.gamma) print loggammaexpg._cdf(1,10) print stats.loggamma.cdf(1,10) print loggammaexpg._cdf(2,15) print stats.loggamma.cdf(2,15) # this requires change in scipy.stats.distribution #print loggammaexpg.cdf(1,10) print 'Results for loglaplace' loglaplaceg = LogTransf_gen(stats.laplace) print loglaplaceg._cdf(2) print stats.loglaplace.cdf(2,1) loglaplaceexpg = ExpTransf_gen(stats.laplace) print loglaplaceexpg._cdf(2) stats.loglaplace.cdf(3,3) #0.98148148148148151 loglaplaceexpg._cdf(3,0,1./3) #0.98148148148148151 ## copied from transformtwo.py ''' Created on Apr 28, 2009 @author: Josef Perktold ''' ''' A class for the distribution of a non-linear u-shaped or hump shaped transformation of a continuous random variable This is a companion to the distributions of non-linear monotonic transformation to the case when the inverse mapping is a 2-valued correspondence, for example for absolute value or square simplest usage: example: create squared distribution, i.e. y = x**2, where x is normal or t distributed This class does not work well for distributions with difficult shapes, e.g. 1/x where x is standard normal, because of the singularity and jump at zero. This verifies for normal - chi2, normal - halfnorm, foldnorm, and t - F TODO: * numargs handling is not yet working properly, numargs needs to be specified (default = 0 or 1) * feeding args and kwargs to underlying distribution works in t distribution example * distinguish args and kwargs for the transformed and the underlying distribution - currently all args and no kwargs are transmitted to underlying distribution - loc and scale only work for transformed, but not for underlying distribution - possible to separate args for transformation and underlying distribution parameters * add _rvs as method, will be faster in many cases ''' class TransfTwo_gen(distributions.rv_continuous): '''Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it's inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, derivminus. Currently no numerical derivatives or inverse are calculated This can be used to generate distribution instances similar to the distributions in scipy.stats. ''' #a class for non-linear non-monotonic transformation of a continuous random variable def __init__(self, kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs): #print args #print kwargs self.func = func self.funcinvplus = funcinvplus self.funcinvminus = funcinvminus self.derivplus = derivplus self.derivminus = derivminus #explicit for self.__dict__.update(kwargs) #need to set numargs because inspection does not work self.numargs = kwargs.pop('numargs', 0) #print self.numargs name = kwargs.pop('name','transfdist') longname = kwargs.pop('longname','Non-linear transformed distribution') extradoc = kwargs.pop('extradoc',None) a = kwargs.pop('a', -np.inf) # attached to self in super b = kwargs.pop('b', np.inf) # self.a, self.b would be overwritten self.shape = kwargs.pop('shape', False) #defines whether it is a `u` shaped or `hump' shaped # transformation self.u_args, self.u_kwargs = get_u_argskwargs(**kwargs) self.kls = kls #(self.u_args, self.u_kwargs) # possible to freeze the underlying distribution super(TransfTwo_gen,self).__init__(a=a, b=b, name = name, shapes = kls.shapes, longname = longname, extradoc = extradoc) def _rvs(self, *args): self.kls._size = self._size #size attached to self, not function argument return self.func(self.kls._rvs(*args)) def _pdf(self,x,*args, **kwargs): #print args if self.shape == 'u': signpdf = 1 elif self.shape == 'hump': signpdf = -1 else: raise ValueError, 'shape can only be `u` or `hump`' return signpdf * (self.derivplus(x)*self.kls._pdf(self.funcinvplus(x),*args, **kwargs) - self.derivminus(x)*self.kls._pdf(self.funcinvminus(x),*args, **kwargs)) #note scipy _cdf only take *args not *kwargs def _cdf(self,x,*args, **kwargs): #print args if self.shape == 'u': return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \ self.kls._cdf(self.funcinvminus(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self._sf(x,*args, **kwargs) def _sf(self,x,*args, **kwargs): #print args if self.shape == 'hump': return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \ self.kls._cdf(self.funcinvminus(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self._cdf(x, *args, **kwargs) def _munp(self, n,*args, **kwargs): return self._mom0_sc(n,*args) # ppf might not be possible in general case? # should be possible in symmetric case # def _ppf(self, q, *args, **kwargs): # if self.shape == 'u': # return self.func(self.kls._ppf(q,*args, **kwargs)) # elif self.shape == 'hump': # return self.func(self.kls._ppf(1-q,*args, **kwargs)) #TODO: rename these functions to have unique names class SquareFunc(object): '''class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, if we want extension to parameterized function ''' def inverseplus(self, x): return np.sqrt(x) def inverseminus(self, x): return 0.0 - np.sqrt(x) def derivplus(self, x): return 0.5/np.sqrt(x) def derivminus(self, x): return 0.0 - 0.5/np.sqrt(x) def squarefunc(self, x): return np.power(x,2) sqfunc = SquareFunc() squarenormalg = TransfTwo_gen(stats.norm, sqfunc.squarefunc, sqfunc.inverseplus, sqfunc.inverseminus, sqfunc.derivplus, sqfunc.derivminus, shape='u', a=0.0, b=np.inf, numargs = 0, name = 'squarenorm', longname = 'squared normal distribution', extradoc = '\ndistribution of the square of a normal random variable' +\ ' y=x**2 with x N(0.0,1)') #u_loc=l, u_scale=s) squaretg = TransfTwo_gen(stats.t, sqfunc.squarefunc, sqfunc.inverseplus, sqfunc.inverseminus, sqfunc.derivplus, sqfunc.derivminus, shape='u', a=0.0, b=np.inf, numargs = 1, name = 'squarenorm', longname = 'squared t distribution', extradoc = '\ndistribution of the square of a t random variable' +\ ' y=x**2 with x t(dof,0.0,1)') def inverseplus(x): return np.sqrt(-x) def inverseminus(x): return 0.0 - np.sqrt(-x) def derivplus(x): return 0.0 - 0.5/np.sqrt(-x) def derivminus(x): return 0.5/np.sqrt(-x) def negsquarefunc(x): return -np.power(x,2) negsquarenormalg = TransfTwo_gen(stats.norm, negsquarefunc, inverseplus, inverseminus, derivplus, derivminus, shape='hump', a=-np.inf, b=0.0, numargs = 0, name = 'negsquarenorm', longname = 'negative squared normal distribution', extradoc = '\ndistribution of the negative square of a normal random variable' +\ ' y=-x**2 with x N(0.0,1)') #u_loc=l, u_scale=s) def inverseplus(x): return x def inverseminus(x): return 0.0 - x def derivplus(x): return 1.0 def derivminus(x): return 0.0 - 1.0 def absfunc(x): return np.abs(x) absnormalg = TransfTwo_gen(stats.norm, np.abs, inverseplus, inverseminus, derivplus, derivminus, shape='u', a=0.0, b=np.inf, numargs = 0, name = 'absnorm', longname = 'absolute of normal distribution', extradoc = '\ndistribution of the absolute value of a normal random variable' +\ ' y=abs(x) with x N(0,1)') #copied from mvncdf.py '''multivariate normal probabilities and cumulative distribution function a wrapper for scipy.stats.kde.mvndst SUBROUTINE MVNDST( N, LOWER, UPPER, INFIN, CORREL, MAXPTS, & ABSEPS, RELEPS, ERROR, VALUE, INFORM ) * * A subroutine for computing multivariate normal probabilities. * This subroutine uses an algorithm given in the paper * "Numerical Computation of Multivariate Normal Probabilities", in * J. of Computational and Graphical Stat., 1(1992), pp. 141-149, by * Alan Genz * Department of Mathematics * Washington State University * Pullman, WA 99164-3113 * Email : AlanGenz@wsu.edu * * Parameters * * N INTEGER, the number of variables. * LOWER REAL, array of lower integration limits. * UPPER REAL, array of upper integration limits. * INFIN INTEGER, array of integration limits flags: * if INFIN(I) < 0, Ith limits are (-infinity, infinity); * if INFIN(I) = 0, Ith limits are (-infinity, UPPER(I)]; * if INFIN(I) = 1, Ith limits are [LOWER(I), infinity); * if INFIN(I) = 2, Ith limits are [LOWER(I), UPPER(I)]. * CORREL REAL, array of correlation coefficients; the correlation * coefficient in row I column J of the correlation matrix * should be stored in CORREL( J + ((I-2)*(I-1))/2 ), for J < I. * THe correlation matrix must be positive semidefinite. * MAXPTS INTEGER, maximum number of function values allowed. This * parameter can be used to limit the time. A sensible * strategy is to start with MAXPTS = 1000*N, and then * increase MAXPTS if ERROR is too large. * ABSEPS REAL absolute error tolerance. * RELEPS REAL relative error tolerance. * ERROR REAL estimated absolute error, with 99% confidence level. * VALUE REAL estimated value for the integral * INFORM INTEGER, termination status parameter: * if INFORM = 0, normal completion with ERROR < EPS; * if INFORM = 1, completion with ERROR > EPS and MAXPTS * function vaules used; increase MAXPTS to * decrease ERROR; * if INFORM = 2, N > 500 or N < 1. * >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[10.0,10.0],[0,0],[0.5]) (2e-016, 1.0, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[100.0,100.0],[0,0],[0.0]) (2e-016, 1.0, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[1.0,1.0],[0,0],[0.0]) (2e-016, 0.70786098173714096, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.001,1.0],[0,0],[0.0]) (2e-016, 0.42100802096993045, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.001,10.0],[0,0],[0.0]) (2e-016, 0.50039894221391101, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.001,100.0],[0,0],[0.0]) (2e-016, 0.50039894221391101, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.01,100.0],[0,0],[0.0]) (2e-016, 0.5039893563146316, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.1,100.0],[0,0],[0.0]) (2e-016, 0.53982783727702899, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.1,100.0],[2,2],[0.0]) (2e-016, 0.019913918638514494, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.0,0.0],[0,0],[0.0]) (2e-016, 0.25, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.0,0.0],[-1,0],[0.0]) (2e-016, 0.5, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.0,0.0],[-1,0],[0.5]) (2e-016, 0.5, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.0,0.0],[0,0],[0.5]) (2e-016, 0.33333333333333337, 0) >>> scipy.stats.kde.mvn.mvndst([0.0,0.0],[0.0,0.0],[0,0],[0.99]) (2e-016, 0.47747329317779391, 0) ''' #from scipy.stats import kde informcode = {0: 'normal completion with ERROR < EPS', 1: '''completion with ERROR > EPS and MAXPTS function values used; increase MAXPTS to decrease ERROR;''', 2: 'N > 500 or N < 1'} def mvstdnormcdf(lower, upper, corrcoef, **kwds): '''standardized multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a standardized multivariate normal distribution. This function assumes standardized scale, that is the variance in each dimension is one, but correlation can be arbitrary, covariance = correlation matrix Parameters ---------- lower, upper : array_like, 1d lower and upper integration limits with length equal to the number of dimensions of the multivariate normal distribution. It can contain -np.inf or np.inf for open integration intervals corrcoef : float or array_like specifies correlation matrix in one of three ways, see notes optional keyword parameters to influence integration * maxpts : int, maximum number of function values allowed. This parameter can be used to limit the time. A sensible strategy is to start with `maxpts` = 1000*N, and then increase `maxpts` if ERROR is too large. * abseps : float absolute error tolerance. * releps : float relative error tolerance. Returns ------- cdfvalue : float value of the integral Notes ----- The correlation matrix corrcoef can be given in 3 different ways If the multivariate normal is two-dimensional than only the correlation coefficient needs to be provided. For general dimension the correlation matrix can be provided either as a one-dimensional array of the upper triangular correlation coefficients stacked by rows, or as full square correlation matrix See Also -------- mvnormcdf : cdf of multivariate normal distribution without standardization Examples -------- >>> print mvstdnormcdf([-np.inf,-np.inf], [0.0,np.inf], 0.5) 0.5 >>> corr = [[1.0, 0, 0.5],[0,1,0],[0.5,0,1]] >>> print mvstdnormcdf([-np.inf,-np.inf,-100.0], [0.0,0.0,0.0], corr, abseps=1e-6) 0.166666399198 >>> print mvstdnormcdf([-np.inf,-np.inf,-100.0],[0.0,0.0,0.0],corr, abseps=1e-8) something wrong completion with ERROR > EPS and MAXPTS function values used; increase MAXPTS to decrease ERROR; 1.048330348e-006 0.166666546218 >>> print mvstdnormcdf([-np.inf,-np.inf,-100.0],[0.0,0.0,0.0], corr, maxpts=100000, abseps=1e-8) 0.166666588293 ''' n = len(lower) #don't know if converting to array is necessary, #but it makes ndim check possible lower = np.array(lower) upper = np.array(upper) corrcoef = np.array(corrcoef) correl = np.zeros(n*(n-1)/2.0) #dtype necessary? if (lower.ndim != 1) or (upper.ndim != 1): raise ValueError, 'can handle only 1D bounds' if len(upper) != n: raise ValueError, 'bounds have different lengths' if n==2 and corrcoef.size==1: correl = corrcoef #print 'case scalar rho', n elif corrcoef.ndim == 1 and len(corrcoef) == n*(n-1)/2.0: #print 'case flat corr', corrcoeff.shape correl = corrcoef elif corrcoef.shape == (n,n): #print 'case square corr', correl.shape correl = corrcoef[np.tril_indices(n, -1)] # for ii in range(n): # for jj in range(ii): # correl[ jj + ((ii-2)*(ii-1))/2] = corrcoef[ii,jj] else: raise ValueError, 'corrcoef has incorrect dimension' if not 'maxpts' in kwds: if n >2: kwds['maxpts'] = 10000*n lowinf = np.isneginf(lower) uppinf = np.isposinf(upper) infin = 2.0*np.ones(n) np.putmask(infin,lowinf,0)# infin.putmask(0,lowinf) np.putmask(infin,uppinf,1) #infin.putmask(1,uppinf) #this has to be last np.putmask(infin,lowinf*uppinf,-1) ## #remove infs ## np.putmask(lower,lowinf,-100)# infin.putmask(0,lowinf) ## np.putmask(upper,uppinf,100) #infin.putmask(1,uppinf) #print lower,',',upper,',',infin,',',correl #print correl.shape #print kwds.items() error, cdfvalue, inform = scipy.stats.kde.mvn.mvndst(lower,upper,infin,correl,**kwds) if inform: print 'something wrong', informcode[inform], error return cdfvalue def mvnormcdf(upper, mu, cov, lower=None, **kwds): '''multivariate normal cumulative distribution function This is a wrapper for scipy.stats.kde.mvn.mvndst which calculates a rectangular integral over a multivariate normal distribution. Parameters ---------- lower, upper : array_like, 1d lower and upper integration limits with length equal to the number of dimensions of the multivariate normal distribution. It can contain -np.inf or np.inf for open integration intervals mu : array_lik, 1d list or array of means cov : array_like, 2d specifies covariance matrix optional keyword parameters to influence integration * maxpts : int, maximum number of function values allowed. This parameter can be used to limit the time. A sensible strategy is to start with `maxpts` = 1000*N, and then increase `maxpts` if ERROR is too large. * abseps : float absolute error tolerance. * releps : float relative error tolerance. Returns ------- cdfvalue : float value of the integral Notes ----- This function normalizes the location and scale of the multivariate normal distribution and then uses `mvstdnormcdf` to call the integration. See Also -------- mvstdnormcdf : location and scale standardized multivariate normal cdf ''' upper = np.array(upper) if lower is None: lower = -np.ones(upper.shape) * np.inf else: lower = np.array(lower) cov = np.array(cov) stdev = np.sqrt(np.diag(cov)) # standard deviation vector #do I need to make sure stdev is float and not int? #is this correct to normalize to corr? lower = (lower - mu)/stdev upper = (upper - mu)/stdev divrow = np.atleast_2d(stdev) corr = cov/divrow/divrow.T #v/np.sqrt(np.atleast_2d(np.diag(covv)))/np.sqrt(np.atleast_2d(np.diag(covv))).T return mvstdnormcdf(lower, upper, corr, **kwds) if __name__ == '__main__': examples_transf() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/genpareto.py000066400000000000000000000241521224417117700274160ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Aug 12 14:59:03 2010 Warning: not tried out or tested yet, Done Author: josef-pktd """ import numpy as np from scipy import stats from scipy import comb from scipy.stats.distributions import rv_continuous from numpy import where, inf from numpy import abs as np_abs ## Generalized Pareto with reversed sign of c as in literature class genpareto2_gen(rv_continuous): def _argcheck(self, c): c = np.asarray(c) self.b = where(c > 0, 1.0/np_abs(c), inf) return where(c==0, 0, 1) def _pdf(self, x, c): Px = np.power(1-c*x,-1.0+1.0/c) return Px def _logpdf(self, x, c): return (-1.0+1.0/c) * np.log1p(-c*x) def _cdf(self, x, c): return 1.0 - np.power(1-c*x,1.0/c) def _ppf(self, q, c): vals = -1.0/c * (np.power(1-q, c)-1) return vals def _munp(self, n, c): k = np.arange(0,n+1) val = (1.0/c)**n * np.sum(comb(n,k)*(-1)**k / (1.0+c*k),axis=0) return where(c*n > -1, val, inf) def _entropy(self, c): if (c < 0): return 1-c else: self.b = 1.0 / c return rv_continuous._entropy(self, c) genpareto2 = genpareto2_gen(a=0.0,name='genpareto', longname="A generalized Pareto", shapes='c',extradoc=""" Generalized Pareto distribution genpareto2.pdf(x,c) = (1+c*x)**(-1-1/c) for c != 0, and for x >= 0 for all c, and x < 1/abs(c) for c < 0. """ ) shape, loc, scale = 0.5, 0, 1 rv = np.arange(5) quant = [0.01, 0.1, 0.5, 0.9, 0.99] for method, x in [('pdf', rv), ('cdf', rv), ('sf', rv), ('ppf', quant), ('isf', quant)]: print getattr(genpareto2, method)(x, shape, loc, scale) print getattr(stats.genpareto, method)(x, -shape, loc, scale) print genpareto2.stats(shape, loc, scale, moments='mvsk') print stats.genpareto.stats(-shape, loc, scale, moments='mvsk') print genpareto2.entropy(shape, loc, scale) print stats.genpareto.entropy(-shape, loc, scale) def paramstopot(thresh, shape, scale): '''transform shape scale for peak over threshold y = x-u|x>u ~ GPD(k, sigma-k*u) if x ~ GPD(k, sigma) notation of de Zea Bermudez, Kotz k, sigma is shape, scale ''' return shape, scale - shape*thresh def paramsfrompot(thresh, shape, scalepot): return shape, scalepot + shape*thresh def warnif(cond, msg): if not cond: print msg, 'does not hold' def meanexcess(thresh, shape, scale): '''mean excess function of genpareto assert are inequality conditions in de Zea Bermudez, Kotz ''' warnif(shape > -1, 'shape > -1') warnif(thresh >= 0, 'thresh >= 0') #make it weak inequality warnif((scale - shape*thresh) > 0, '(scale - shape*thresh) > 0') return (scale - shape*thresh) / (1 + shape) def meanexcess_plot(data, params=None, lidx=100, uidx=10, method='emp', plot=0): if method == 'est': #doesn't make much sense yet, #estimate the parameters and use theoretical meanexcess if params is None: raise NotImplementedError else: pass #estimate parames elif method == 'emp': #calculate meanexcess from data datasorted = np.sort(data) meanexcess = (datasorted[::-1].cumsum())/np.arange(1,len(data)+1) - datasorted[::-1] meanexcess = meanexcess[::-1] if plot: plt.plot(datasorted[:-uidx], meanexcess[:-uidx]) if not params is None: shape, scale = params plt.plot(datasorted[:-uidx], (scale - datasorted[:-uidx] * shape) / (1. + shape)) return datasorted, meanexcess print meanexcess(5, -0.5, 10) print meanexcess(5, -2, 10) import matplotlib.pyplot as plt data = genpareto2.rvs(-0.75, scale=5, size=1000) #data = np.random.uniform(50, size=1000) #data = stats.norm.rvs(0, np.sqrt(50), size=1000) #data = stats.pareto.rvs(1.5, np.sqrt(50), size=1000) tmp = meanexcess_plot(data, params=(-0.75, 5), plot=1) print tmp[1][-20:] print tmp[0][-20:] #plt.show() def meanexcess_emp(data): datasorted = np.sort(data).astype(float) meanexcess = (datasorted[::-1].cumsum())/np.arange(1,len(data)+1) - datasorted[::-1] meancont = (datasorted[::-1].cumsum())/np.arange(1,len(data)+1) meanexcess = meanexcess[::-1] return datasorted, meanexcess, meancont[::-1] def meanexcess_dist(self, lb, *args, **kwds): #default function in expect is identity # need args in call if np.ndim(lb) == 0: return self.expect(lb=lb, conditional=True) else: return np.array([self.expect(lb=lbb, conditional=True) for lbb in lb]) ds, me, mc = meanexcess_emp(1.*np.arange(1,10)) print ds print me print mc print meanexcess_dist(stats.norm, lb=0.5) print meanexcess_dist(stats.norm, lb=[-np.inf, -0.5, 0, 0.5]) rvs = stats.norm.rvs(size=100000) rvs = rvs - rvs.mean() print rvs.mean(), rvs[rvs>-0.5].mean(), rvs[rvs>0].mean(), rvs[rvs>0.5].mean() ''' C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect; please delete it from your matplotlibrc file warnings.warn('rcParams key "numerix" is obsolete and has no effect;\n' [ 1. 0.5 0. 0. 0. ] [ 1. 0.5 0. 0. 0. ] [ 0. 0.75 1. 1. 1. ] [ 0. 0.75 1. 1. 1. ] [ 1. 0.25 0. 0. 0. ] [ 1. 0.25 0. 0. 0. ] [ 0.01002513 0.1026334 0.58578644 1.36754447 1.8 ] [ 0.01002513 0.1026334 0.58578644 1.36754447 1.8 ] [ 1.8 1.36754447 0.58578644 0.1026334 0.01002513] [ 1.8 1.36754447 0.58578644 0.1026334 0.01002513] (array(0.66666666666666674), array(0.22222222222222243), array(0.56568542494923058), array(-0.60000000000032916)) (array(0.66666666666666674), array(0.22222222222222243), array(0.56568542494923058), array(-0.60000000000032916)) 0.5 0.5 25.0 shape > -1 does not hold -20 [ 41.4980671 42.83145298 44.24197578 45.81622844 47.57145212 49.52692287 51.70553275 54.0830766 56.61358997 59.53409167 62.8970042 66.73494156 71.04227973 76.24015612 82.71835988 89.79611663 99.4252195 106.2372462 94.83432424 0. ] [ 15.79736355 16.16373531 17.44204268 17.47968055 17.73264951 18.23939099 19.02638455 20.79746264 23.7169161 24.48807136 25.90496638 28.35556795 32.27623618 34.65714495 37.37093362 47.32957609 51.27970515 78.98913941 129.04309012 189.66864848] >>> np.arange(10) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> meanexcess_emp(np.arange(10)) (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5, 4])) >>> meanexcess_emp(1*np.arange(10)) (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5, 4])) >>> meanexcess_emp(1.*np.arange(10)) (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]), array([ 4.5 , 4.88888889, 5.25 , 5.57142857, 5.83333333, 6. , 6. , 5.66666667, 4.5 , 0. ]), array([ 9. , 8.5, 8. , 7.5, 7. , 6.5, 6. , 5.5, 5. , 4.5])) >>> meanexcess_emp(0.5**np.arange(10)) (array([ 0.00195313, 0.00390625, 0.0078125 , 0.015625 , 0.03125 , 0.0625 , 0.125 , 0.25 , 0.5 , 1. ]), array([ 0.19960938, 0.22135417, 0.24804688, 0.28125 , 0.32291667, 0.375 , 0.4375 , 0.5 , 0.5 , 0. ]), array([ 1. , 0.75 , 0.58333333, 0.46875 , 0.3875 , 0.328125 , 0.28348214, 0.24902344, 0.22178819, 0.19980469])) >>> meanexcess_emp(np.arange(10)**0.5) (array([ 0. , 1. , 1.41421356, 1.73205081, 2. , 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ]), array([ 1.93060005, 2.03400006, 2.11147337, 2.16567659, 2.19328936, 2.18473364, 2.11854461, 1.94280904, 1.5 , 0. ]), array([ 3. , 2.91421356, 2.82472615, 2.73091704, 2.63194723, 2.52662269, 2.41311242, 2.28825007, 2.14511117, 1.93060005])) >>> meanexcess_emp(np.arange(10)**-2) (array([-2147483648, 0, 0, 0, 0, 0, 0, 0, 0, 1]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([ 1, 0, 0, 0, 0, 0, 0, 0, 0, -214748365])) >>> meanexcess_emp(np.arange(10)**(-0.5)) (array([ 0.33333333, 0.35355339, 0.37796447, 0.40824829, 0.4472136 , 0.5 , 0.57735027, 0.70710678, 1. , Inf]), array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, NaN]), array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf])) >>> np.arange(10)**(-0.5) array([ Inf, 1. , 0.70710678, 0.57735027, 0.5 , 0.4472136 , 0.40824829, 0.37796447, 0.35355339, 0.33333333]) >>> meanexcess_emp(np.arange(1,10)**(-0.5)) (array([ 0.33333333, 0.35355339, 0.37796447, 0.40824829, 0.4472136 , 0.5 , 0.57735027, 0.70710678, 1. ]), array([ 0.4857152 , 0.50223543, 0.51998842, 0.53861177, 0.55689141, 0.57111426, 0.56903559, 0.5 , 0. ]), array([ 1. , 0.85355339, 0.76148568, 0.69611426, 0.64633413, 0.60665316, 0.57398334, 0.5464296 , 0.52275224])) >>> meanexcess_emp(np.arange(1,10)) (array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5])) >>> meanexcess_emp(1.*np.arange(1,10)) (array([ 1., 2., 3., 4., 5., 6., 7., 8., 9.]), array([ 4.88888889, 5.25 , 5.57142857, 5.83333333, 6. , 6. , 5.66666667, 4.5 , 0. ]), array([ 9. , 8.5, 8. , 7.5, 7. , 6.5, 6. , 5.5, 5. ])) >>> datasorted = np.sort(1.*np.arange(1,10)) >>> (datasorted[::-1].cumsum()-datasorted[::-1]) array([ 0., 9., 17., 24., 30., 35., 39., 42., 44.]) >>> datasorted[::-1].cumsum() array([ 9., 17., 24., 30., 35., 39., 42., 44., 45.]) >>> datasorted[::-1] array([ 9., 8., 7., 6., 5., 4., 3., 2., 1.]) >>> ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/gof_new.py000066400000000000000000000532411224417117700270570ustar00rootroot00000000000000'''More Goodness of fit tests contains GOF : 1 sample gof tests based on Stephens 1970, plus AD A^2 bootstrap : vectorized bootstrap p-values for gof test with fitted parameters Created : 2011-05-21 Author : Josef Perktold parts based on ks_2samp and kstest from scipy.stats.stats (license: Scipy BSD, but were completely rewritten by Josef Perktold) References ---------- ''' import numpy as np from scipy.stats import distributions from statsmodels.tools.decorators import cache_readonly from scipy.special import kolmogorov as ksprob #from scipy.stats unchanged def ks_2samp(data1, data2): """ Computes the Kolmogorov-Smirnof statistic on 2 samples. This is a two-sided test for the null hypothesis that 2 independent samples are drawn from the same continuous distribution. Parameters ---------- a, b : sequence of 1-D ndarrays two arrays of sample observations assumed to be drawn from a continuous distribution, sample sizes can be different Returns ------- D : float KS statistic p-value : float two-tailed p-value Notes ----- This tests whether 2 samples are drawn from the same distribution. Note that, like in the case of the one-sample K-S test, the distribution is assumed to be continuous. This is the two-sided test, one-sided tests are not implemented. The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution. If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same. Examples -------- >>> from scipy import stats >>> import numpy as np >>> from scipy.stats import ks_2samp >>> #fix random seed to get the same result >>> np.random.seed(12345678); >>> n1 = 200 # size of first sample >>> n2 = 300 # size of second sample different distribution we can reject the null hypothesis since the pvalue is below 1% >>> rvs1 = stats.norm.rvs(size=n1,loc=0.,scale=1); >>> rvs2 = stats.norm.rvs(size=n2,loc=0.5,scale=1.5) >>> ks_2samp(rvs1,rvs2) (0.20833333333333337, 4.6674975515806989e-005) slightly different distribution we cannot reject the null hypothesis at a 10% or lower alpha since the pvalue at 0.144 is higher than 10% >>> rvs3 = stats.norm.rvs(size=n2,loc=0.01,scale=1.0) >>> ks_2samp(rvs1,rvs3) (0.10333333333333333, 0.14498781825751686) identical distribution we cannot reject the null hypothesis since the pvalue is high, 41% >>> rvs4 = stats.norm.rvs(size=n2,loc=0.0,scale=1.0) >>> ks_2samp(rvs1,rvs4) (0.07999999999999996, 0.41126949729859719) """ data1, data2 = map(np.asarray, (data1, data2)) n1 = data1.shape[0] n2 = data2.shape[0] n1 = len(data1) n2 = len(data2) data1 = np.sort(data1) data2 = np.sort(data2) data_all = np.concatenate([data1,data2]) #reminder: searchsorted inserts 2nd into 1st array cdf1 = np.searchsorted(data1,data_all,side='right')/(1.0*n1) cdf2 = (np.searchsorted(data2,data_all,side='right'))/(1.0*n2) d = np.max(np.absolute(cdf1-cdf2)) #Note: d absolute not signed distance en = np.sqrt(n1*n2/float(n1+n2)) try: prob = ksprob((en+0.12+0.11/en)*d) except: prob = 1.0 return d, prob #from scipy.stats unchanged def kstest(rvs, cdf, args=(), N=20, alternative = 'two_sided', mode='approx',**kwds): """ Perform the Kolmogorov-Smirnov test for goodness of fit This performs a test of the distribution G(x) of an observed random variable against a given distribution F(x). Under the null hypothesis the two distributions are identical, G(x)=F(x). The alternative hypothesis can be either 'two_sided' (default), 'less' or 'greater'. The KS test is only valid for continuous distributions. Parameters ---------- rvs : string or array or callable string: name of a distribution in scipy.stats array: 1-D observations of random variables callable: function to generate random variables, requires keyword argument `size` cdf : string or callable string: name of a distribution in scipy.stats, if rvs is a string then cdf can evaluate to `False` or be the same as rvs callable: function to evaluate cdf args : tuple, sequence distribution parameters, used if rvs or cdf are strings N : int sample size if rvs is string or callable alternative : 'two_sided' (default), 'less' or 'greater' defines the alternative hypothesis (see explanation) mode : 'approx' (default) or 'asymp' defines the distribution used for calculating p-value 'approx' : use approximation to exact distribution of test statistic 'asymp' : use asymptotic distribution of test statistic Returns ------- D : float KS test statistic, either D, D+ or D- p-value : float one-tailed or two-tailed p-value Notes ----- In the one-sided test, the alternative is that the empirical cumulative distribution function of the random variable is "less" or "greater" than the cumulative distribution function F(x) of the hypothesis, G(x)<=F(x), resp. G(x)>=F(x). Examples -------- >>> from scipy import stats >>> import numpy as np >>> from scipy.stats import kstest >>> x = np.linspace(-15,15,9) >>> kstest(x,'norm') (0.44435602715924361, 0.038850142705171065) >>> np.random.seed(987654321) # set random seed to get the same result >>> kstest('norm','',N=100) (0.058352892479417884, 0.88531190944151261) is equivalent to this >>> np.random.seed(987654321) >>> kstest(stats.norm.rvs(size=100),'norm') (0.058352892479417884, 0.88531190944151261) Test against one-sided alternative hypothesis: >>> np.random.seed(987654321) Shift distribution to larger values, so that cdf_dgp(x)< norm.cdf(x): >>> x = stats.norm.rvs(loc=0.2, size=100) >>> kstest(x,'norm', alternative = 'less') (0.12464329735846891, 0.040989164077641749) Reject equal distribution against alternative hypothesis: less >>> kstest(x,'norm', alternative = 'greater') (0.0072115233216311081, 0.98531158590396395) Don't reject equal distribution against alternative hypothesis: greater >>> kstest(x,'norm', mode='asymp') (0.12464329735846891, 0.08944488871182088) Testing t distributed random variables against normal distribution: With 100 degrees of freedom the t distribution looks close to the normal distribution, and the kstest does not reject the hypothesis that the sample came from the normal distribution >>> np.random.seed(987654321) >>> stats.kstest(stats.t.rvs(100,size=100),'norm') (0.072018929165471257, 0.67630062862479168) With 3 degrees of freedom the t distribution looks sufficiently different from the normal distribution, that we can reject the hypothesis that the sample came from the normal distribution at a alpha=10% level >>> np.random.seed(987654321) >>> stats.kstest(stats.t.rvs(3,size=100),'norm') (0.131016895759829, 0.058826222555312224) """ if isinstance(rvs, basestring): #cdf = getattr(stats, rvs).cdf if (not cdf) or (cdf == rvs): cdf = getattr(distributions, rvs).cdf rvs = getattr(distributions, rvs).rvs else: raise AttributeError('if rvs is string, cdf has to be the same distribution') if isinstance(cdf, basestring): cdf = getattr(distributions, cdf).cdf if callable(rvs): kwds = {'size':N} vals = np.sort(rvs(*args,**kwds)) else: vals = np.sort(rvs) N = len(vals) cdfvals = cdf(vals, *args) if alternative in ['two_sided', 'greater']: Dplus = (np.arange(1.0, N+1)/N - cdfvals).max() if alternative == 'greater': return Dplus, distributions.ksone.sf(Dplus,N) if alternative in ['two_sided', 'less']: Dmin = (cdfvals - np.arange(0.0, N)/N).max() if alternative == 'less': return Dmin, distributions.ksone.sf(Dmin,N) if alternative == 'two_sided': D = np.max([Dplus,Dmin]) if mode == 'asymp': return D, distributions.kstwobign.sf(D*np.sqrt(N)) if mode == 'approx': pval_two = distributions.kstwobign.sf(D*np.sqrt(N)) if N > 2666 or pval_two > 0.80 - N*0.3/1000.0 : return D, distributions.kstwobign.sf(D*np.sqrt(N)) else: return D, distributions.ksone.sf(D,N)*2 #TODO: split into modification and pvalue functions separately ? # for separate testing and combining different pieces def dplus_st70_upp(stat, nobs): mod_factor = np.sqrt(nobs) + 0.12 + 0.11 / np.sqrt(nobs) stat_modified = stat * mod_factor pval = np.exp(-2 * stat_modified**2) digits = np.sum(stat > np.array([0.82, 0.82, 1.00])) #repeat low to get {0,2,3} return stat_modified, pval, digits dminus_st70_upp = dplus_st70_upp def d_st70_upp(stat, nobs): mod_factor = np.sqrt(nobs) + 0.12 + 0.11 / np.sqrt(nobs) stat_modified = stat * mod_factor pval = 2 * np.exp(-2 * stat_modified**2) digits = np.sum(stat > np.array([0.91, 0.91, 1.08])) #repeat low to get {0,2,3} return stat_modified, pval, digits def v_st70_upp(stat, nobs): mod_factor = np.sqrt(nobs) + 0.155 + 0.24 / np.sqrt(nobs) #repeat low to get {0,2,3} stat_modified = stat * mod_factor zsqu = stat_modified**2 pval = (8 * zsqu - 2) * np.exp(-2 * zsqu) digits = np.sum(stat > np.array([1.06, 1.06, 1.26])) return stat_modified, pval, digits def wsqu_st70_upp(stat, nobs): nobsinv = 1. / nobs stat_modified = (stat - 0.4 * nobsinv + 0.6 * nobsinv**2) * (1 + nobsinv) pval = 0.05 * np.exp(2.79 - 6 * stat_modified) digits = np.nan # some explanation in txt #repeat low to get {0,2,3} return stat_modified, pval, digits def usqu_st70_upp(stat, nobs): nobsinv = 1. / nobs stat_modified = (stat - 0.1 * nobsinv + 0.1 * nobsinv**2) stat_modified *= (1 + 0.8 * nobsinv) pval = 2 * np.exp(- 2 * stat_modified * np.pi**2) digits = np.sum(stat > np.array([0.29, 0.29, 0.34])) #repeat low to get {0,2,3} return stat_modified, pval, digits def a_st70_upp(stat, nobs): nobsinv = 1. / nobs stat_modified = (stat - 0.7 * nobsinv + 0.9 * nobsinv**2) stat_modified *= (1 + 1.23 * nobsinv) pval = 1.273 * np.exp(- 2 * stat_modified / 2. * np.pi**2) digits = np.sum(stat > np.array([0.11, 0.11, 0.452])) #repeat low to get {0,2,3} return stat_modified, pval, digits gof_pvals = {} gof_pvals['stephens70upp'] = { 'd_plus' : dplus_st70_upp, 'd_minus' : dplus_st70_upp, 'd' : d_st70_upp, 'v' : v_st70_upp, 'wsqu' : wsqu_st70_upp, 'usqu' : usqu_st70_upp, 'a' : a_st70_upp } def pval_kstest_approx(D, N): pval_two = distributions.kstwobign.sf(D*np.sqrt(N)) if N > 2666 or pval_two > 0.80 - N*0.3/1000.0 : return D, distributions.kstwobign.sf(D*np.sqrt(N)), np.nan else: return D, distributions.ksone.sf(D,N)*2, np.nan gof_pvals['scipy'] = { 'd_plus' : lambda Dplus, N: (Dplus, distributions.ksone.sf(Dplus, N), np.nan), 'd_minus' : lambda Dmin, N: (Dmin, distributions.ksone.sf(Dmin,N), np.nan), 'd' : lambda D, N: (D, distributions.kstwobign.sf(D*np.sqrt(N)), np.nan) } gof_pvals['scipy_approx'] = { 'd' : pval_kstest_approx } class GOF(object): '''One Sample Goodness of Fit tests includes Kolmogorov-Smirnov D, D+, D-, Kuiper V, Cramer-von Mises W^2, U^2 and Anderson-Darling A, A^2. The p-values for all tests except for A^2 are based on the approximatiom given in Stephens 1970. A^2 has currently no p-values. For the Kolmogorov-Smirnov test the tests as given in scipy.stats are also available as options. design: I might want to retest with different distributions, to calculate data summary statistics only once, or add separate class that holds summary statistics and data (sounds good). ''' def __init__(self, rvs, cdf, args=(), N=20): if isinstance(rvs, basestring): #cdf = getattr(stats, rvs).cdf if (not cdf) or (cdf == rvs): cdf = getattr(distributions, rvs).cdf rvs = getattr(distributions, rvs).rvs else: raise AttributeError('if rvs is string, cdf has to be the same distribution') if isinstance(cdf, basestring): cdf = getattr(distributions, cdf).cdf if callable(rvs): kwds = {'size':N} vals = np.sort(rvs(*args,**kwds)) else: vals = np.sort(rvs) N = len(vals) cdfvals = cdf(vals, *args) self.nobs = N self.vals_sorted = vals self.cdfvals = cdfvals @cache_readonly def d_plus(self): nobs = self.nobs cdfvals = self.cdfvals return (np.arange(1.0, nobs+1)/nobs - cdfvals).max() @cache_readonly def d_minus(self): nobs = self.nobs cdfvals = self.cdfvals return (cdfvals - np.arange(0.0, nobs)/nobs).max() @cache_readonly def d(self): return np.max([self.d_plus, self.d_minus]) @cache_readonly def v(self): '''Kuiper''' return self.d_plus + self.d_minus @cache_readonly def wsqu(self): '''Cramer von Mises''' nobs = self.nobs cdfvals = self.cdfvals #use literal formula, TODO: simplify with arange(,,2) wsqu = ((cdfvals - (2. * np.arange(1., nobs+1) - 1)/nobs/2.)**2).sum() \ + 1./nobs/12. return wsqu @cache_readonly def usqu(self): nobs = self.nobs cdfvals = self.cdfvals #use literal formula, TODO: simplify with arange(,,2) usqu = self.wsqu - nobs * (cdfvals.mean() - 0.5)**2 return usqu @cache_readonly def a(self): nobs = self.nobs cdfvals = self.cdfvals #one loop instead of large array msum = 0 for j in xrange(1,nobs): mj = cdfvals[j] - cdfvals[:j] mask = (mj > 0.5) mj[mask] = 1 - mj[mask] msum += mj.sum() a = nobs / 4. - 2. / nobs * msum return a @cache_readonly def asqu(self): '''Stephens 1974, doesn't have p-value formula for A^2''' nobs = self.nobs cdfvals = self.cdfvals asqu = -((2. * np.arange(1., nobs+1) - 1) * (np.log(cdfvals) + np.log(1-cdfvals[::-1]) )).sum()/nobs - nobs return asqu def get_test(self, testid='d', pvals='stephens70upp'): ''' ''' #print gof_pvals[pvals][testid] stat = getattr(self, testid) if pvals == 'stephens70upp': return gof_pvals[pvals][testid](stat, self.nobs), stat else: return gof_pvals[pvals][testid](stat, self.nobs) def gof_mc(randfn, distr, nobs=100): #print '\nIs it correctly sized?' from collections import defaultdict results = defaultdict(list) for i in xrange(1000): rvs = randfn(nobs) goft = GOF(rvs, distr) for ti in all_gofs: results[ti].append(goft.get_test(ti, 'stephens70upp')[0][1]) resarr = np.array([results[ti] for ti in all_gofs]) print ' ', ' '.join(all_gofs) print 'at 0.01:', (resarr < 0.01).mean(1) print 'at 0.05:', (resarr < 0.05).mean(1) print 'at 0.10:', (resarr < 0.1).mean(1) def asquare(cdfvals, axis=0): '''vectorized Anderson Darling A^2, Stephens 1974''' ndim = len(cdfvals.shape) nobs = cdfvals.shape[axis] slice_reverse = [slice(None)] * ndim #might make copy if not specific axis??? islice = [None] * ndim islice[axis] = slice(None) slice_reverse[axis] = slice(None, None, -1) asqu = -((2. * np.arange(1., nobs+1)[islice] - 1) * (np.log(cdfvals) + np.log(1-cdfvals[slice_reverse]))/nobs).sum(axis) \ - nobs return asqu #class OneSGOFFittedVec(object): # '''for vectorized fitting''' # currently I use the bootstrap as function instead of full class #note: kwds loc and scale are a pain # I would need to overwrite rvs, fit and cdf depending on fixed parameters #def bootstrap(self, distr, args=(), kwds={}, nobs=200, nrep=1000, def bootstrap(distr, args=(), nobs=200, nrep=100, value=None, batch_size=None): '''Monte Carlo (or parametric bootstrap) p-values for gof currently hardcoded for A^2 only assumes vectorized fit_vec method, builds and analyses (nobs, nrep) sample in one step rename function to less generic this works also with nrep=1 ''' #signature similar to kstest ? #delegate to fn ? #rvs_kwds = {'size':(nobs, nrep)} #rvs_kwds.update(kwds) #it will be better to build a separate batch function that calls bootstrap #keep batch if value is true, but batch iterate from outside if stat is returned if (not batch_size is None): if value is None: raise ValueError('using batching requires a value') n_batch = int(np.ceil(nrep/float(batch_size))) count = 0 for irep in xrange(n_batch): rvs = distr.rvs(args, **{'size':(batch_size, nobs)}) params = distr.fit_vec(rvs, axis=1) params = map(lambda x: np.expand_dims(x, 1), params) cdfvals = np.sort(distr.cdf(rvs, params), axis=1) stat = asquare(cdfvals, axis=1) count += (stat >= value).sum() return count / float(n_batch * batch_size) else: #rvs = distr.rvs(args, **kwds) #extension to distribution kwds ? rvs = distr.rvs(args, **{'size':(nrep, nobs)}) params = distr.fit_vec(rvs, axis=1) params = map(lambda x: np.expand_dims(x, 1), params) cdfvals = np.sort(distr.cdf(rvs, params), axis=1) stat = asquare(cdfvals, axis=1) if value is None: #return all bootstrap results stat_sorted = np.sort(stat) return stat_sorted else: #calculate and return specific p-value return (stat >= value).mean() def bootstrap2(value, distr, args=(), nobs=200, nrep=100): '''Monte Carlo (or parametric bootstrap) p-values for gof currently hardcoded for A^2 only non vectorized, loops over all parametric bootstrap replications and calculates and returns specific p-value, rename function to less generic ''' #signature similar to kstest ? #delegate to fn ? #rvs_kwds = {'size':(nobs, nrep)} #rvs_kwds.update(kwds) count = 0 for irep in xrange(nrep): #rvs = distr.rvs(args, **kwds) #extension to distribution kwds ? rvs = distr.rvs(args, **{'size':nobs}) params = distr.fit_vec(rvs) cdfvals = np.sort(distr.cdf(rvs, params)) stat = asquare(cdfvals, axis=0) count += (stat >= value) return count * 1. / nrep class NewNorm(object): '''just a holder for modified distributions ''' def fit_vec(self, x, axis=0): return x.mean(axis), x.std(axis) def cdf(self, x, args): return distributions.norm.cdf(x, loc=args[0], scale=args[1]) def rvs(self, args, size): loc=args[0] scale=args[1] return loc + scale * distributions.norm.rvs(size=size) if __name__ == '__main__': from scipy import stats #rvs = np.random.randn(1000) rvs = stats.t.rvs(3, size=200) print 'scipy kstest' print kstest(rvs, 'norm') goft = GOF(rvs, 'norm') print goft.get_test() all_gofs = ['d', 'd_plus', 'd_minus', 'v', 'wsqu', 'usqu', 'a'] for ti in all_gofs: print ti, goft.get_test(ti, 'stephens70upp') print '\nIs it correctly sized?' from collections import defaultdict results = defaultdict(list) nobs = 200 for i in xrange(100): rvs = np.random.randn(nobs) goft = GOF(rvs, 'norm') for ti in all_gofs: results[ti].append(goft.get_test(ti, 'stephens70upp')[0][1]) resarr = np.array([results[ti] for ti in all_gofs]) print ' ', ' '.join(all_gofs) print 'at 0.01:', (resarr < 0.01).mean(1) print 'at 0.05:', (resarr < 0.05).mean(1) print 'at 0.10:', (resarr < 0.1).mean(1) gof_mc(lambda nobs: stats.t.rvs(3, size=nobs), 'norm', nobs=200) nobs = 200 nrep = 100 bt = bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=nrep, value=None) quantindex = np.floor(nrep * np.array([0.99, 0.95, 0.9])).astype(int) print bt[quantindex] #the bootstrap results match Stephens pretty well for nobs=100, but not so well for #large (1000) or small (20) nobs ''' >>> np.array([15.0, 10.0, 5.0, 2.5, 1.0])/100. #Stephens array([ 0.15 , 0.1 , 0.05 , 0.025, 0.01 ]) >>> nobs = 100 >>> [bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=10000, value=c/ (1 + 4./nobs - 25./nobs**2)) for c in [0.576, 0.656, 0.787, 0.918, 1.092]] [0.1545, 0.10009999999999999, 0.049000000000000002, 0.023, 0.0104] >>> ''' #test equality of loop, vectorized, batch-vectorized np.random.seed(8765679) resu1 = bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=100, value=0.576/(1 + 4./nobs - 25./nobs**2)) np.random.seed(8765679) tmp = [bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=1) for _ in range(100)] resu2 = (np.array(tmp) > 0.576/(1 + 4./nobs - 25./nobs**2)).mean() np.random.seed(8765679) tmp = [bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=1, value=0.576/ (1 + 4./nobs - 25./nobs**2), batch_size=10) for _ in range(10)] resu3 = np.array(resu).mean() from numpy.testing import assert_almost_equal, assert_array_almost_equal assert_array_almost_equal(resu1, resu2, 15) assert_array_almost_equal(resu2, resu3, 15) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/multivariate.py000066400000000000000000000113771224417117700301450ustar00rootroot00000000000000'''Multivariate Distribution Probability of a multivariate t distribution Now also mvstnormcdf has tests against R mvtnorm Still need non-central t, extra options, and convenience function for location, scale version. Author: Josef Perktold License: BSD (3-clause) Reference: Genz and Bretz for formula ''' import numpy as np from scipy import integrate, stats, special from scipy.stats import chi,chi2 from extras import mvnormcdf, mvstdnormcdf, mvnormcdf from numpy import exp as np_exp from numpy import log as np_log from scipy.special import gamma as sps_gamma from scipy.special import gammaln as sps_gammaln def chi2_pdf(self, x, df): '''pdf of chi-square distribution''' #from scipy.stats.distributions Px = x**(df/2.0-1)*np.exp(-x/2.0) Px /= special.gamma(df/2.0)* 2**(df/2.0) return Px def chi_pdf(x, df): tmp = (df-1.)*np_log(x) + (-x*x*0.5) - (df*0.5-1)*np_log(2.0) \ - sps_gammaln(df*0.5) return np_exp(tmp) #return x**(df-1.)*np_exp(-x*x*0.5)/(2.0)**(df*0.5-1)/sps_gamma(df*0.5) def chi_logpdf(x, df): tmp = (df-1.)*np_log(x) + (-x*x*0.5) - (df*0.5-1)*np_log(2.0) \ - sps_gammaln(df*0.5) return tmp def funbgh(s, a, b, R, df): sqrt_df = np.sqrt(df+0.5) ret = chi_logpdf(s,df) ret += np_log(mvstdnormcdf(s*a/sqrt_df, s*b/sqrt_df, R, maxpts=1000000, abseps=1e-6)) ret = np_exp(ret) return ret def funbgh2(s, a, b, R, df): n = len(a) sqrt_df = np.sqrt(df) #np.power(s, df-1) * np_exp(-s*s*0.5) return np_exp((df-1)*np_log(s)-s*s*0.5) \ * mvstdnormcdf(s*a/sqrt_df, s*b/sqrt_df, R[np.tril_indices(n, -1)], maxpts=1000000, abseps=1e-4) def bghfactor(df): return np.power(2.0, 1-df*0.5) / sps_gamma(df*0.5) def mvstdtprob(a, b, R, df, ieps=1e-5, quadkwds=None, mvstkwds=None): '''probability of rectangular area of standard t distribution assumes mean is zero and R is correlation matrix Notes ----- This function does not calculate the estimate of the combined error between the underlying multivariate normal probability calculations and the integration. ''' kwds = dict(args=(a,b,R,df), epsabs=1e-4, epsrel=1e-2, limit=150) if not quadkwds is None: kwds.update(quadkwds) #print kwds res, err = integrate.quad(funbgh2, *chi.ppf([ieps,1-ieps], df), **kwds) prob = res * bghfactor(df) return prob #written by Enzo Michelangeli, style changes by josef-pktd # Student's T random variable def multivariate_t_rvs(m, S, df=np.inf, n=1): '''generate random variables of multivariate t distribution Parameters ---------- m : array_like mean of random variable, length determines dimension of random variable S : array_like square array of covariance matrix df : int or float degrees of freedom n : int number of observations, return random array will be (n, len(m)) Returns ------- rvs : ndarray, (n, len(m)) each row is an independent draw of a multivariate t distributed random variable ''' m = np.asarray(m) d = len(m) if df == np.inf: x = 1. else: x = np.random.chisquare(df, n)/df z = np.random.multivariate_normal(np.zeros(d),S,(n,)) return m + z/np.sqrt(x)[:,None] # same output format as random.multivariate_normal if __name__ == '__main__': corr = np.asarray([[1.0, 0, 0.5],[0,1,0],[0.5,0,1]]) corr_indep = np.asarray([[1.0, 0, 0],[0,1,0],[0,0,1]]) corr_equal = np.asarray([[1.0, 0.5, 0.5],[0.5,1,0.5],[0.5,0.5,1]]) R = corr_equal a = np.array([-np.inf,-np.inf,-100.0]) a = np.array([-0.96,-0.96,-0.96]) b = np.array([0.0,0.0,0.0]) b = np.array([0.96,0.96, 0.96]) a[:] = -1 b[:] = 3 df = 10. sqrt_df = np.sqrt(df) print mvstdnormcdf(a, b, corr, abseps=1e-6) #print integrate.quad(funbgh, 0, np.inf, args=(a,b,R,df)) print (stats.t.cdf(b[0], df) - stats.t.cdf(a[0], df))**3 s = 1 print mvstdnormcdf(s*a/sqrt_df, s*b/sqrt_df, R) df=4 print mvstdtprob(a, b, R, df) S = np.array([[1.,.5],[.5,1.]]) print multivariate_t_rvs([10.,20.], S, 2, 5) nobs = 10000 rvst = multivariate_t_rvs([10.,20.], S, 2, nobs) print np.sum((rvst<[10.,20.]).all(1),0) * 1. / nobs print mvstdtprob(-np.inf*np.ones(2), np.zeros(2), R[:2,:2], 2) ''' > lower <- -1 > upper <- 3 > df <- 4 > corr <- diag(3) > delta <- rep(0, 3) > pmvt(lower=lower, upper=upper, delta=delta, df=df, corr=corr) [1] 0.5300413 attr(,"error") [1] 4.321136e-05 attr(,"msg") [1] "Normal Completion" > (pt(upper, df) - pt(lower, df))**3 [1] 0.4988254 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/mv_measures.py000066400000000000000000000140561224417117700277620ustar00rootroot00000000000000'''using multivariate dependence and divergence measures The standard correlation coefficient measures only linear dependence between random variables. kendall's tau measures any monotonic relationship also non-linear. mutual information measures any kind of dependence, but does not distinguish between positive and negative relationship mutualinfo_kde and mutualinfo_binning follow Khan et al. 2007 Shiraj Khan, Sharba Bandyopadhyay, Auroop R. Ganguly, Sunil Saigal, David J. Erickson, III, Vladimir Protopopescu, and George Ostrouchov, Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data, Phys. Rev. E 76, 026209 (2007) http://pre.aps.org/abstract/PRE/v76/i2/e026209 ''' import numpy as np from scipy import stats from scipy.stats import gaussian_kde import statsmodels.sandbox.infotheo as infotheo def mutualinfo_kde(y, x, normed=True): '''mutual information of two random variables estimated with kde ''' nobs = len(x) if not len(y) == nobs: raise ValueError('both data arrays need to have the same size') x = np.asarray(x, float) y = np.asarray(y, float) yx = np.vstack((y,x)) kde_x = gaussian_kde(x)(x) kde_y = gaussian_kde(y)(y) kde_yx = gaussian_kde(yx)(yx) mi_obs = np.log(kde_yx) - np.log(kde_x) - np.log(kde_y) mi = mi_obs.sum() / nobs if normed: mi_normed = np.sqrt(1. - np.exp(-2 * mi)) return mi_normed else: return mi def mutualinfo_kde_2sample(y, x, normed=True): '''mutual information of two random variables estimated with kde ''' nobs = len(x) x = np.asarray(x, float) y = np.asarray(y, float) #yx = np.vstack((y,x)) kde_x = gaussian_kde(x.T)(x.T) kde_y = gaussian_kde(y.T)(x.T) #kde_yx = gaussian_kde(yx)(yx) mi_obs = np.log(kde_x) - np.log(kde_y) if len(mi_obs) != nobs: raise mi = mi_obs.mean() if normed: mi_normed = np.sqrt(1. - np.exp(-2 * mi)) return mi_normed else: return mi def mutualinfo_binned(y, x, bins, normed=True): '''mutual information of two random variables estimated with kde Notes ----- bins='auto' selects the number of bins so that approximately 5 observations are expected to be in each bin under the assumption of independence. This follows roughly the description in Kahn et al. 2007 ''' nobs = len(x) if not len(y) == nobs: raise ValueError('both data arrays need to have the same size') x = np.asarray(x, float) y = np.asarray(y, float) #yx = np.vstack((y,x)) ## fyx, binsy, binsx = np.histogram2d(y, x, bins=bins) ## fx, binsx_ = np.histogram(x, bins=binsx) ## fy, binsy_ = np.histogram(y, bins=binsy) if bins == 'auto': ys = np.sort(y) xs = np.sort(x) #quantiles = np.array([0,0.25, 0.4, 0.6, 0.75, 1]) qbin_sqr = np.sqrt(5./nobs) quantiles = np.linspace(0, 1, 1./qbin_sqr) quantile_index = ((nobs-1)*quantiles).astype(int) #move edges so that they don't coincide with an observation shift = 1e-6 + np.ones(quantiles.shape) shift[0] -= 2*1e-6 binsy = ys[quantile_index] + shift binsx = xs[quantile_index] + shift elif np.size(bins) == 1: binsy = bins binsx = bins elif (len(bins) == 2): binsy, binsx = bins ## if np.size(bins[0]) == 1: ## binsx = bins[0] ## if np.size(bins[1]) == 1: ## binsx = bins[1] fx, binsx = np.histogram(x, bins=binsx) fy, binsy = np.histogram(y, bins=binsy) fyx, binsy, binsx = np.histogram2d(y, x, bins=(binsy, binsx)) pyx = fyx * 1. / nobs px = fx * 1. / nobs py = fy * 1. / nobs mi_obs = pyx * (np.log(pyx+1e-10) - np.log(py)[:,None] - np.log(px)) mi = mi_obs.sum() if normed: mi_normed = np.sqrt(1. - np.exp(-2 * mi)) return mi_normed, (pyx, py, px, binsy, binsx), mi_obs else: return mi if __name__ == '__main__': import statsmodels.api as sm funtype = ['linear', 'quadratic'][1] nobs = 200 sig = 2#5. #x = np.linspace(-3, 3, nobs) + np.random.randn(nobs) x = np.sort(3*np.random.randn(nobs)) exog = sm.add_constant(x, prepend=True) #y = 0 + np.log(1+x**2) + sig * np.random.randn(nobs) if funtype == 'quadratic': y = 0 + x**2 + sig * np.random.randn(nobs) if funtype == 'linear': y = 0 + x + sig * np.random.randn(nobs) print 'correlation' print np.corrcoef(y,x)[0, 1] print 'pearsonr', stats.pearsonr(y,x) print 'spearmanr', stats.spearmanr(y,x) print 'kendalltau', stats.kendalltau(y,x) pxy, binsx, binsy = np.histogram2d(x,y, bins=5) px, binsx_ = np.histogram(x, bins=binsx) py, binsy_ = np.histogram(y, bins=binsy) print 'mutualinfo', infotheo.mutualinfo(px*1./nobs, py*1./nobs, 1e-15+pxy*1./nobs, logbase=np.e) print 'mutualinfo_kde normed', mutualinfo_kde(y,x) print 'mutualinfo_kde ', mutualinfo_kde(y,x, normed=False) mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \ mutualinfo_binned(y, x, 5, normed=True) print 'mutualinfo_binned normed', mi_normed print 'mutualinfo_binned ', mi_obs.sum() mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \ mutualinfo_binned(y, x, 'auto', normed=True) print 'auto' print 'mutualinfo_binned normed', mi_normed print 'mutualinfo_binned ', mi_obs.sum() ys = np.sort(y) xs = np.sort(x) by = ys[((nobs-1)*np.array([0, 0.25, 0.4, 0.6, 0.75, 1])).astype(int)] bx = xs[((nobs-1)*np.array([0, 0.25, 0.4, 0.6, 0.75, 1])).astype(int)] mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \ mutualinfo_binned(y, x, (by,bx), normed=True) print 'quantiles' print 'mutualinfo_binned normed', mi_normed print 'mutualinfo_binned ', mi_obs.sum() doplot = 1#False if doplot: import matplotlib.pyplot as plt plt.plot(x, y, 'o') olsres = sm.OLS(y, exog).fit() plt.plot(x, olsres.fittedvalues) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/mv_normal.py000066400000000000000000001133761224417117700274330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Multivariate Normal and t distributions Created on Sat May 28 15:38:23 2011 @author: Josef Perktold TODO: * renaming, - after adding t distribution, cov doesn't make sense for Sigma DONE - should mean also be renamed to mu, if there will be distributions with mean != mu * not sure about corner cases - behavior with (almost) singular sigma or transforms - df <= 2, is everything correct if variance is not finite or defined ? * check to return possibly univariate distribution for marginals or conditional distributions, does univariate special case work? seems ok for conditional * are all the extra transformation methods useful outside of testing ? - looks like I have some mixup in definitions of standardize, normalize * new methods marginal, conditional, ... just added, typos ? - largely tested for MVNormal, not yet for MVT DONE * conditional: reusing, vectorizing, should we reuse a projection matrix or allow for a vectorized, conditional_mean similar to OLS.predict * add additional things similar to LikelihoodModelResults? quadratic forms, F distribution, and others ??? * add Delta method for nonlinear functions here, current function is hidden somewhere in miscmodels * raise ValueErrors for wrong input shapes, currently only partially checked * quantile method (ppf for equal bounds for multiple testing) is missing http://svitsrv25.epfl.ch/R-doc/library/mvtnorm/html/qmvt.html seems to use just a root finder for inversion of cdf * normalize has ambiguous definition, and mixing it up in different versions std from sigma or std from cov ? I would like to get what I need for mvt-cdf, or not univariate standard t distribution has scale=1 but std>1 FIXED: add std_sigma, and normalize uses std_sigma * more work: bivariate distributions, inherit from multivariate but overwrite some methods for better efficiency, e.g. cdf and expect I kept the original MVNormal0 class as reference, can be deleted See Also -------- sandbox/examples/ex_mvelliptical.py Examples -------- Note, several parts of these examples are random and the numbers will not be (exactly) the same. >>> import numpy as np >>> import statsmodels.sandbox.distributions.mv_normal as mvd >>> >>> from numpy.testing import assert_array_almost_equal >>> >>> cov3 = np.array([[ 1. , 0.5 , 0.75], ... [ 0.5 , 1.5 , 0.6 ], ... [ 0.75, 0.6 , 2. ]]) >>> mu = np.array([-1, 0.0, 2.0]) multivariate normal distribution -------------------------------- >>> mvn3 = mvd.MVNormal(mu, cov3) >>> mvn3.rvs(size=3) array([[-0.08559948, -1.0319881 , 1.76073533], [ 0.30079522, 0.55859618, 4.16538667], [-1.36540091, -1.50152847, 3.87571161]]) >>> mvn3.std array([ 1. , 1.22474487, 1.41421356]) >>> a = [0.0, 1.0, 1.5] >>> mvn3.pdf(a) 0.013867410439318712 >>> mvn3.cdf(a) 0.31163181123730122 Monte Carlo integration >>> mvn3.expect_mc(lambda x: (x>> mvn3.expect_mc(lambda x: (x>> mvt3 = mvd.MVT(mu, cov3, 4) >>> mvt3.rvs(size=4) array([[-0.94185437, 0.3933273 , 2.40005487], [ 0.07563648, 0.06655433, 7.90752238], [ 1.06596474, 0.32701158, 2.03482886], [ 3.80529746, 7.0192967 , 8.41899229]]) >>> mvt3.pdf(a) 0.010402959362646937 >>> mvt3.cdf(a) 0.30269483623249821 >>> mvt3.expect_mc(lambda x: (x>> mvt3.cov array([[ 2. , 1. , 1.5], [ 1. , 3. , 1.2], [ 1.5, 1.2, 4. ]]) >>> mvt3.corr array([[ 1. , 0.40824829, 0.53033009], [ 0.40824829, 1. , 0.34641016], [ 0.53033009, 0.34641016, 1. ]]) get normalized distribution >>> mvt3n = mvt3.normalized() >>> mvt3n.sigma array([[ 1. , 0.40824829, 0.53033009], [ 0.40824829, 1. , 0.34641016], [ 0.53033009, 0.34641016, 1. ]]) >>> mvt3n.cov array([[ 2. , 0.81649658, 1.06066017], [ 0.81649658, 2. , 0.69282032], [ 1.06066017, 0.69282032, 2. ]]) What's currently there? >>> [i for i in dir(mvn3) if not i[0]=='_'] ['affine_transformed', 'cdf', 'cholsigmainv', 'conditional', 'corr', 'cov', 'expect_mc', 'extra_args', 'logdetsigma', 'logpdf', 'marginal', 'mean', 'normalize', 'normalized', 'normalized2', 'nvars', 'pdf', 'rvs', 'sigma', 'sigmainv', 'standardize', 'standardized', 'std', 'std_sigma', 'whiten'] >>> [i for i in dir(mvt3) if not i[0]=='_'] ['affine_transformed', 'cdf', 'cholsigmainv', 'corr', 'cov', 'df', 'expect_mc', 'extra_args', 'logdetsigma', 'logpdf', 'marginal', 'mean', 'normalize', 'normalized', 'normalized2', 'nvars', 'pdf', 'rvs', 'sigma', 'sigmainv', 'standardize', 'standardized', 'std', 'std_sigma', 'whiten'] """ import numpy as np from statsmodels.sandbox.distributions.multivariate import ( mvstdtprob, mvstdnormcdf, mvnormcdf) def expect_mc(dist, func=lambda x: 1, size=50000): '''calculate expected value of function by Monte Carlo integration Parameters ---------- dist : distribution instance needs to have rvs defined as a method for drawing random numbers func : callable function for which expectation is calculated, this function needs to be vectorized, integration is over axis=0 size : int number of random samples to use in the Monte Carlo integration, Notes ----- this doesn't batch Returns ------- expected value : ndarray return of function func integrated over axis=0 by MonteCarlo, this will have the same shape as the return of func without axis=0 Examples -------- integrate probability that both observations are negative >>> mvn = mve.MVNormal([0,0],2.) >>> mve.expect_mc(mvn, lambda x: (x>> c = stats.norm.isf(0.05, scale=np.sqrt(2.)) >>> expect_mc(mvn, lambda x: (np.abs(x)>np.array([c, c])), size=100000) array([ 0.09969, 0.0986 ]) or calling the method >>> mvn.expect_mc(lambda x: (np.abs(x)>np.array([c, c])), size=100000) array([ 0.09937, 0.10075]) ''' def fun(x): return func(x) # * dist.pdf(x) rvs = dist.rvs(size=size) return fun(rvs).mean(0) def expect_mc_bounds(dist, func=lambda x: 1, size=50000, lower=None, upper=None, conditional=False, overfact=1.2): '''calculate expected value of function by Monte Carlo integration Parameters ---------- dist : distribution instance needs to have rvs defined as a method for drawing random numbers func : callable function for which expectation is calculated, this function needs to be vectorized, integration is over axis=0 size : int minimum number of random samples to use in the Monte Carlo integration, the actual number used can be larger because of oversampling. lower : None or array_like lower integration bounds, if None, then it is set to -inf upper : None or array_like upper integration bounds, if None, then it is set to +inf conditional : bool If true, then the expectation is conditional on being in within [lower, upper] bounds, otherwise it is unconditional overfact : float oversampling factor, the actual number of random variables drawn in each attempt are overfact * remaining draws. Extra draws are also used in the integration. Notes ----- this doesn't batch Returns ------- expected value : ndarray return of function func integrated over axis=0 by MonteCarlo, this will have the same shape as the return of func without axis=0 Examples -------- >>> mvn = mve.MVNormal([0,0],2.) >>> mve.expect_mc_bounds(mvn, lambda x: np.ones(x.shape[0]), lower=[-10,-10],upper=[0,0]) 0.24990416666666668 get 3 marginal moments with one integration >>> mvn = mve.MVNormal([0,0],1.) >>> mve.expect_mc_bounds(mvn, lambda x: np.dstack([x, x**2, x**3, x**4]), lower=[-np.inf,-np.inf], upper=[np.inf,np.inf]) array([[ 2.88629497e-03, 9.96706297e-01, -2.51005344e-03, 2.95240921e+00], [ -5.48020088e-03, 9.96004409e-01, -2.23803072e-02, 2.96289203e+00]]) >>> from scipy import stats >>> [stats.norm.moment(i) for i in [1,2,3,4]] [0.0, 1.0, 0.0, 3.0] ''' #call rvs once to find length of random vector rvsdim = dist.rvs(size=1).shape[-1] if lower is None: lower = -np.inf * np.ones(rvsdim) else: lower = np.asarray(lower) if upper is None: upper = np.inf * np.ones(rvsdim) else: upper = np.asarray(upper) def fun(x): return func(x) # * dist.pdf(x) rvsli = [] used = 0 #remain = size #inplace changes size total = 0 while True: remain = size - used #just a temp variable rvs = dist.rvs(size=int(remain * overfact)) total += int(size * overfact) rvsok = rvs[((rvs >= lower) & (rvs <= upper)).all(-1)] #if rvsok.ndim == 1: #possible shape problems if only 1 random vector rvsok = np.atleast_2d(rvsok) used += rvsok.shape[0] rvsli.append(rvsok) #[:remain]) use extras instead print used if used >= size: break rvs = np.vstack(rvsli) print rvs.shape assert used == rvs.shape[0] #saftey check mean_conditional = fun(rvs).mean(0) if conditional: return mean_conditional else: return mean_conditional * (used * 1. / total) def bivariate_normal(x, mu, cov): """ Bivariate Gaussian distribution for equal shape *X*, *Y*. See `bivariate normal `_ at mathworld. """ X, Y = np.transpose(x) mux, muy = mu sigmax, sigmaxy, tmp, sigmay = np.ravel(cov) sigmax, sigmay = np.sqrt(sigmax), np.sqrt(sigmay) Xmu = X-mux Ymu = Y-muy rho = sigmaxy/(sigmax*sigmay) z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay) denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2) return np.exp( -z/(2*(1-rho**2))) / denom class BivariateNormal(object): #TODO: make integration limits more flexible # or normalize before integration def __init__(self, mean, cov): self.mean = mu self.cov = cov self.sigmax, self.sigmaxy, tmp, self.sigmay = np.ravel(cov) self.nvars = 2 def rvs(self, size=1): return np.random.multivariate_normal(self.mean, self.cov, size=size) def pdf(self, x): return bivariate_normal(x, self.mean, self.cov) def logpdf(self, x): #TODO: replace this return np.log(self.pdf(x)) def cdf(self, x): return self.expect(upper=x) def expect(self, func=lambda x: 1, lower=(-10,-10), upper=(10,10)): def fun(x, y): x = np.column_stack((x,y)) return func(x) * self.pdf(x) from scipy.integrate import dblquad return dblquad(fun, lower[0], upper[0], lambda y: lower[1], lambda y: upper[1]) def kl(self, other): '''Kullback-Leibler divergence between this and another distribution int f(x) (log f(x) - log g(x)) dx where f is the pdf of self, and g is the pdf of other uses double integration with scipy.integrate.dblquad limits currently hardcoded ''' fun = lambda x : self.logpdf(x) - other.logpdf(x) return self.expect(fun) def kl_mc(self, other, size=500000): fun = lambda x : self.logpdf(x) - other.logpdf(x) rvs = self.rvs(size=size) return fun(rvs).mean() class MVElliptical(object): '''Base Class for multivariate elliptical distributions, normal and t contains common initialization, and some common methods subclass needs to implement at least rvs and logpdf methods ''' #getting common things between normal and t distribution def __init__(self, mean, sigma, *args, **kwds): '''initialize instance Parameters ---------- mean : array_like parameter mu (might be renamed), for symmetric distributions this is the mean sigma : array_like, 2d dispersion matrix, covariance matrix in normal distribution, but only proportional to covariance matrix in t distribution args : list distribution specific arguments, e.g. df for t distribution kwds : dict currently not used ''' self.extra_args = [] self.mean = np.asarray(mean) self.sigma = sigma = np.asarray(sigma) sigma = np.squeeze(sigma) self.nvars = nvars = len(mean) #self.covchol = np.linalg.cholesky(sigma) #in the following sigma is original, self.sigma is full matrix if sigma.shape == (): #iid self.sigma = np.eye(nvars) * sigma self.sigmainv = np.eye(nvars) / sigma self.cholsigmainv = np.eye(nvars) / np.sqrt(sigma) elif (sigma.ndim == 1) and (len(sigma) == nvars): #independent heteroscedastic self.sigma = np.diag(sigma) self.sigmainv = np.diag(1. / sigma) self.cholsigmainv = np.diag( 1. / np.sqrt(sigma)) elif sigma.shape == (nvars, nvars): #python tuple comparison #general self.sigmainv = np.linalg.pinv(sigma) self.cholsigmainv = np.linalg.cholesky(self.sigmainv).T else: raise ValueError('sigma has invalid shape') #store logdetsigma for logpdf self.logdetsigma = np.log(np.linalg.det(self.sigma)) def rvs(self, size=1): '''random variable Parameters ---------- size : int or tuple the number and shape of random variables to draw. Returns ------- rvs : ndarray the returned random variables with shape given by size and the dimension of the multivariate random vector as additional last dimension ''' raise NotImplementedError def logpdf(self, x): '''logarithm of probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- logpdf : float or array probability density value of each random vector this should be made to work with 2d x, with multivariate normal vector in each row and iid across rows doesn't work now because of dot in whiten ''' raise NotImplementedError def cdf(self, x, **kwds): '''cumulative distribution function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector kwds : dict contains options for the numerical calculation of the cdf Returns ------- cdf : float or array probability density value of each random vector ''' raise NotImplementedError def affine_transformed(self, shift, scale_matrix): '''affine transformation define in subclass because of distribution specific restrictions''' #implemented in subclass at least for now raise NotImplementedError def whiten(self, x): """ whiten the data by linear transformation Parameters ----------- x : array-like, 1d or 2d Data to be whitened, if 2d then each row contains an independent sample of the multivariate random vector Returns ------- np.dot(x, self.cholsigmainv.T) Notes ----- This only does rescaling, it doesn't subtract the mean, use standardize for this instead See Also -------- standardize : subtract mean and rescale to standardized random variable. """ x = np.asarray(x) return np.dot(x, self.cholsigmainv.T) def pdf(self, x): '''probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- pdf : float or array probability density value of each random vector ''' return np.exp(self.logpdf(x)) def standardize(self, x): '''standardize the random variable, i.e. subtract mean and whiten Parameters ----------- x : array-like, 1d or 2d Data to be whitened, if 2d then each row contains an independent sample of the multivariate random vector Returns ------- np.dot(x - self.mean, self.cholsigmainv.T) Notes ----- See Also -------- whiten : rescale random variable, standardize without subtracting mean. ''' return self.whiten(x - self.mean) def standardized(self): '''return new standardized MVNormal instance ''' return self.affine_transformed(-self.mean, self.cholsigmainv) def normalize(self, x): '''normalize the random variable, i.e. subtract mean and rescale The distribution will have zero mean and sigma equal to correlation Parameters ----------- x : array-like, 1d or 2d Data to be whitened, if 2d then each row contains an independent sample of the multivariate random vector Returns ------- (x - self.mean)/std_sigma Notes ----- See Also -------- whiten : rescale random variable, standardize without subtracting mean. ''' std_ = np.atleast_2d(self.std_sigma) return (x - self.mean)/std_ #/std_.T def normalized(self, demeaned=True): '''return a normalized distribution where sigma=corr if demeaned is True, then mean will be set to zero ''' if demeaned: mean_new = np.zeros_like(self.mean) else: mean_new = self.mean / self.std_sigma sigma_new = self.corr args = [getattr(self, ea) for ea in self.extra_args] return self.__class__(mean_new, sigma_new, *args) def normalized2(self, demeaned=True): '''return a normalized distribution where sigma=corr second implementation for testing affine transformation ''' if demeaned: shift = -self.mean else: shift = self.mean * (1. / self.std_sigma - 1.) return self.affine_transformed(shift, np.diag(1. / self.std_sigma)) #the following "standardizes" cov instead #return self.affine_transformed(shift, self.cholsigmainv) @property def std(self): '''standard deviation, square root of diagonal elements of cov ''' return np.sqrt(np.diag(self.cov)) @property def std_sigma(self): '''standard deviation, square root of diagonal elements of sigma ''' return np.sqrt(np.diag(self.sigma)) @property def corr(self): '''correlation matrix''' return self.cov / np.outer(self.std, self.std) expect_mc = expect_mc def marginal(self, indices): '''return marginal distribution for variables given by indices this should be correct for normal and t distribution Parameters ---------- indices : array_like, int list of indices of variables in the marginal distribution Returns ------- mvdist : instance new instance of the same multivariate distribution class that contains the marginal distribution of the variables given in indices ''' indices = np.asarray(indices) mean_new = self.mean[indices] sigma_new = self.sigma[indices[:,None], indices] args = [getattr(self, ea) for ea in self.extra_args] return self.__class__(mean_new, sigma_new, *args) #parts taken from linear_model, but heavy adjustments class MVNormal0(object): '''Class for Multivariate Normal Distribution original full version, kept for testing, new version inherits from MVElliptical uses Cholesky decomposition of covariance matrix for the transformation of the data ''' def __init__(self, mean, cov): self.mean = mean self.cov = cov = np.asarray(cov) cov = np.squeeze(cov) self.nvars = nvars = len(mean) #in the following cov is original, self.cov is full matrix if cov.shape == (): #iid self.cov = np.eye(nvars) * cov self.covinv = np.eye(nvars) / cov self.cholcovinv = np.eye(nvars) / np.sqrt(cov) elif (cov.ndim == 1) and (len(cov) == nvars): #independent heteroscedastic self.cov = np.diag(cov) self.covinv = np.diag(1. / cov) self.cholcovinv = np.diag( 1. / np.sqrt(cov)) elif cov.shape == (nvars, nvars): #python tuple comparison #general self.covinv = np.linalg.pinv(cov) self.cholcovinv = np.linalg.cholesky(self.covinv).T else: raise ValueError('cov has invalid shape') #store logdetcov for logpdf self.logdetcov = np.log(np.linalg.det(self.cov)) def whiten(self, x): """ whiten the data by linear transformation Parameters ----------- X : array-like, 1d or 2d Data to be whitened, if 2d then each row contains an independent sample of the multivariate random vector Returns ------- np.dot(x, self.cholcovinv.T) Notes ----- This only does rescaling, it doesn't subtract the mean, use standardize for this instead See Also -------- standardize : subtract mean and rescale to standardized random variable. """ x = np.asarray(x) if np.any(self.cov): #return np.dot(self.cholcovinv, x) return np.dot(x, self.cholcovinv.T) else: return x def rvs(self, size=1): '''random variable Parameters ---------- size : int or tuple the number and shape of random variables to draw. Returns ------- rvs : ndarray the returned random variables with shape given by size and the dimension of the multivariate random vector as additional last dimension Notes ----- uses numpy.random.multivariate_normal directly ''' return np.random.multivariate_normal(self.mean, self.cov, size=size) def pdf(self, x): '''probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- pdf : float or array probability density value of each random vector ''' return np.exp(self.logpdf(x)) def logpdf(self, x): '''logarithm of probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- logpdf : float or array probability density value of each random vector this should be made to work with 2d x, with multivariate normal vector in each row and iid across rows doesn't work now because of dot in whiten ''' x = np.asarray(x) x_whitened = self.whiten(x - self.mean) SSR = np.sum(x_whitened**2, -1) llf = -SSR llf -= self.nvars * np.log(2. * np.pi) llf -= self.logdetcov llf *= 0.5 return llf expect_mc = expect_mc class MVNormal(MVElliptical): '''Class for Multivariate Normal Distribution uses Cholesky decomposition of covariance matrix for the transformation of the data ''' __name__ == 'Multivariate Normal Distribution' def rvs(self, size=1): '''random variable Parameters ---------- size : int or tuple the number and shape of random variables to draw. Returns ------- rvs : ndarray the returned random variables with shape given by size and the dimension of the multivariate random vector as additional last dimension Notes ----- uses numpy.random.multivariate_normal directly ''' return np.random.multivariate_normal(self.mean, self.sigma, size=size) def logpdf(self, x): '''logarithm of probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- logpdf : float or array probability density value of each random vector this should be made to work with 2d x, with multivariate normal vector in each row and iid across rows doesn't work now because of dot in whiten ''' x = np.asarray(x) x_whitened = self.whiten(x - self.mean) SSR = np.sum(x_whitened**2, -1) llf = -SSR llf -= self.nvars * np.log(2. * np.pi) llf -= self.logdetsigma llf *= 0.5 return llf def cdf(self, x, **kwds): '''cumulative distribution function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector kwds : dict contains options for the numerical calculation of the cdf Returns ------- cdf : float or array probability density value of each random vector ''' #lower = -np.inf * np.ones_like(x) #return mvstdnormcdf(lower, self.standardize(x), self.corr, **kwds) return mvnormcdf(x, self.mean, self.cov, **kwds) @property def cov(self): '''covariance matrix''' return self.sigma def affine_transformed(self, shift, scale_matrix): '''return distribution of an affine transform for full rank scale_matrix only Parameters ---------- shift : array_like shift of mean scale_matrix : array_like linear transformation matrix Returns ------- mvt : instance of MVT instance of multivariate t distribution given by affine transformation Notes ----- the affine transformation is defined by y = a + B x where a is shift, B is a scale matrix for the linear transformation Notes ----- This should also work to select marginal distributions, but not tested for this case yet. currently only tested because it's called by standardized ''' B = scale_matrix #tmp variable mean_new = np.dot(B, self.mean) + shift sigma_new = np.dot(np.dot(B, self.sigma), B.T) return MVNormal(mean_new, sigma_new) def conditional(self, indices, values): '''return conditional distribution indices are the variables to keep, the complement is the conditioning set values are the values of the conditioning variables \bar{\mu} = \mu_1 + \Sigma_{12} \Sigma_{22}^{-1} \left( a - \mu_2 \right) and covariance matrix \overline{\Sigma} = \Sigma_{11} - \Sigma_{12} \Sigma_{22}^{-1} \Sigma_{21}.T Parameters ---------- indices : array_like, int list of indices of variables in the marginal distribution given : array_like values of the conditioning variables Returns ------- mvn : instance of MVNormal new instance of the MVNormal class that contains the conditional distribution of the variables given in indices for given values of the excluded variables. ''' #indices need to be nd arrays for broadcasting keep = np.asarray(indices) given = np.asarray([i for i in range(self.nvars) if not i in keep]) sigmakk = self.sigma[keep[:, None], keep] sigmagg = self.sigma[given[:, None], given] sigmakg = self.sigma[keep[:, None], given] sigmagk = self.sigma[given[:, None], keep] sigma_new = sigmakk - np.dot(sigmakg, np.linalg.solve(sigmagg, sigmagk)) mean_new = self.mean[keep] + \ np.dot(sigmakg, np.linalg.solve(sigmagg, values-self.mean[given])) # #or # sig = np.linalg.solve(sigmagg, sigmagk).T # mean_new = self.mean[keep] + np.dot(sigmakg, values-self.mean[given]) # sigma_new = sigmakk - np.dot(sigmakg, sig) return MVNormal(mean_new, sigma_new) from scipy import special #redefine some shortcuts np_log = np.log np_pi = np.pi sps_gamln = special.gammaln class MVT(MVElliptical): __name__ == 'Multivariate Student T Distribution' def __init__(self, mean, sigma, df): '''initialize instance Parameters ---------- mean : array_like parameter mu (might be renamed), for symmetric distributions this is the mean sigma : array_like, 2d dispersion matrix, covariance matrix in normal distribution, but only proportional to covariance matrix in t distribution args : list distribution specific arguments, e.g. df for t distribution kwds : dict currently not used ''' super(MVT, self).__init__(mean, sigma) self.extra_args = ['df'] #overwrites extra_args of super self.df = df def rvs(self, size=1): '''random variables with Student T distribution Parameters ---------- size : int or tuple the number and shape of random variables to draw. Returns ------- rvs : ndarray the returned random variables with shape given by size and the dimension of the multivariate random vector as additional last dimension - TODO: Not sure if this works for size tuples with len>1. Notes ----- generated as a chi-square mixture of multivariate normal random variables. does this require df>2 ? ''' from multivariate import multivariate_t_rvs return multivariate_t_rvs(self.mean, self.sigma, df=self.df, n=size) def logpdf(self, x): '''logarithm of probability density function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector Returns ------- logpdf : float or array probability density value of each random vector ''' x = np.asarray(x) df = self.df nvars = self.nvars x_whitened = self.whiten(x - self.mean) #should be float llf = - nvars * np_log(df * np_pi) llf -= self.logdetsigma llf -= (df + nvars) * np_log(1 + np.sum(x_whitened**2,-1) / df) llf *= 0.5 llf += sps_gamln((df + nvars) / 2.) - sps_gamln(df / 2.) return llf def cdf(self, x, **kwds): '''cumulative distribution function Parameters ---------- x : array_like can be 1d or 2d, if 2d, then each row is taken as independent multivariate random vector kwds : dict contains options for the numerical calculation of the cdf Returns ------- cdf : float or array probability density value of each random vector ''' lower = -np.inf * np.ones_like(x) #std_sigma = np.sqrt(np.diag(self.sigma)) upper = (x - self.mean)/self.std_sigma return mvstdtprob(lower, upper, self.corr, self.df, **kwds) #mvstdtcdf doesn't exist yet #return mvstdtcdf(lower, x, self.corr, df, **kwds) @property def cov(self): '''covariance matrix The covariance matrix for the t distribution does not exist for df<=2, and is equal to sigma * df/(df-2) for df>2 ''' if self.df <= 2: return np.nan * np.ones_like(self.sigma) else: return self.df / (self.df - 2.) * self.sigma def affine_transformed(self, shift, scale_matrix): '''return distribution of a full rank affine transform for full rank scale_matrix only Parameters ---------- shift : array_like shift of mean scale_matrix : array_like linear transformation matrix Returns ------- mvt : instance of MVT instance of multivariate t distribution given by affine transformation Notes ----- This checks for eigvals<=0, so there are possible problems for cases with positive eigenvalues close to zero. see: http://www.statlect.com/mcdstu1.htm I'm not sure about general case, non-full rank transformation are not multivariate t distributed. y = a + B x where a is shift, B is full rank scale matrix with same dimension as sigma ''' #full rank method could also be in elliptical and called with super #after the rank check B = scale_matrix #tmp variable as shorthand if not B.shape == (self.nvars, self.nvars): if (np.linalg.eigvals(B) <= 0).any(): raise ValueError('affine transform has to be full rank') mean_new = np.dot(B, self.mean) + shift sigma_new = np.dot(np.dot(B, self.sigma), B.T) return MVT(mean_new, sigma_new, self.df) def quad2d(func=lambda x: 1, lower=(-10,-10), upper=(10,10)): def fun(x, y): x = np.column_stack((x,y)) return func(x) from scipy.integrate import dblquad return dblquad(fun, lower[0], upper[0], lambda y: lower[1], lambda y: upper[1]) if __name__ == '__main__': from numpy.testing import assert_almost_equal, assert_array_almost_equal examples = ['mvn'] mu = (0,0) covx = np.array([[1.0, 0.5], [0.5, 1.0]]) mu3 = [-1, 0., 2.] cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) if 'mvn' in examples: bvn = BivariateNormal(mu, covx) rvs = bvn.rvs(size=1000) print rvs.mean(0) print np.cov(rvs, rowvar=0) print bvn.expect() print bvn.cdf([0,0]) bvn1 = BivariateNormal(mu, np.eye(2)) bvn2 = BivariateNormal(mu, 4*np.eye(2)) fun = lambda(x) : np.log(bvn1.pdf(x)) - np.log(bvn.pdf(x)) print bvn1.expect(fun) print bvn1.kl(bvn2), bvn1.kl_mc(bvn2) print bvn2.kl(bvn1), bvn2.kl_mc(bvn1) print bvn1.kl(bvn), bvn1.kl_mc(bvn) mvn = MVNormal(mu, covx) mvn.pdf([0,0]) mvn.pdf(np.zeros((2,2))) #np.dot(mvn.cholcovinv.T, mvn.cholcovinv) - mvn.covinv cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) mu3 = [-1, 0., 2.] mvn3 = MVNormal(mu3, cov3) mvn3.pdf((0., 2., 3.)) mvn3.logpdf((0., 2., 3.)) #comparisons with R mvtnorm::dmvnorm #decimal=14 # mvn3.logpdf(cov3) - [-7.667977543898155, -6.917977543898155, -5.167977543898155] # #decimal 18 # mvn3.pdf(cov3) - [0.000467562492721686, 0.000989829804859273, 0.005696077243833402] # #cheating new mean, same cov # mvn3.mean = np.array([0,0,0]) # #decimal= 16 # mvn3.pdf(cov3) - [0.02914269740502042, 0.02269635555984291, 0.01767593948287269] #as asserts r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155] assert_array_almost_equal( mvn3.logpdf(cov3), r_val, decimal = 14) #decimal 18 r_val = [0.000467562492721686, 0.000989829804859273, 0.005696077243833402] assert_array_almost_equal( mvn3.pdf(cov3), r_val, decimal = 17) #cheating new mean, same cov, too dangerous, got wrong instance in tests #mvn3.mean = np.array([0,0,0]) mvn3c = MVNormal(np.array([0,0,0]), cov3) r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269] assert_array_almost_equal( mvn3c.pdf(cov3), r_val, decimal = 16) mvn3b = MVNormal((0,0,0), 1) fun = lambda(x) : np.log(mvn3.pdf(x)) - np.log(mvn3b.pdf(x)) print mvn3.expect_mc(fun) print mvn3.expect_mc(fun, size=200000) mvt = MVT((0,0), 1, 5) assert_almost_equal(mvt.logpdf(np.array([0.,0.])), -1.837877066409345, decimal=15) assert_almost_equal(mvt.pdf(np.array([0.,0.])), 0.1591549430918953, decimal=15) mvt.logpdf(np.array([1.,1.]))-(-3.01552989458359) mvt1 = MVT((0,0), 1, 1) mvt1.logpdf(np.array([1.,1.]))-(-3.48579549941151) #decimal=16 rvs = mvt.rvs(100000) assert_almost_equal(np.cov(rvs, rowvar=0), mvt.cov, decimal=1) mvt31 = MVT(mu3, cov3, 1) assert_almost_equal(mvt31.pdf(cov3), [0.0007276818698165781, 0.0009980625182293658, 0.0027661422056214652], decimal=18) mvt = MVT(mu3, cov3, 3) assert_almost_equal(mvt.pdf(cov3), [0.000863777424247410, 0.001277510788307594, 0.004156314279452241], decimal=17) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/otherdist.py000066400000000000000000000235701224417117700274420ustar00rootroot00000000000000'''Parametric Mixture Distributions Created on Sat Jun 04 2011 Author: Josef Perktold Notes: Compound Poisson has mass point at zero http://en.wikipedia.org/wiki/Compound_Poisson_distribution and would need special treatment need a distribution that has discrete mass points and contiuous range, e.g. compound Poisson, Tweedie (for some parameter range), pdf of Tobit model (?) - truncation with clipping Question: Metaclasses and class factories for generating new distributions from existing distributions by transformation, mixing, compounding ''' import numpy as np from scipy import stats class ParametricMixtureD(object): '''mixtures with a discrete distribution The mixing distribution is a discrete distribution like scipy.stats.poisson. All distribution in the mixture of the same type and parameterized by the outcome of the mixing distribution and have to be a continuous distribution (or have a pdf method). As an example, a mixture of normal distributed random variables with Poisson as the mixing distribution. assumes vectorized shape, loc and scale as in scipy.stats.distributions assume mixing_dist is frozen initialization looks fragile for all possible cases of lower and upper bounds of the distributions. ''' def __init__(self, mixing_dist, base_dist, bd_args_func, bd_kwds_func, cutoff=1e-3): '''create a mixture distribution Parameters ---------- mixing_dist : discrete frozen distribution mixing distribution base_dist : continuous distribution parameterized distributions in the mixture bd_args_func : callable function that builds the tuple of args for the base_dist. The function obtains as argument the values in the support of the mixing distribution and should return an empty tuple or a tuple of arrays. bd_kwds_func : callable function that builds the dictionary of kwds for the base_dist. The function obtains as argument the values in the support of the mixing distribution and should return an empty dictionary or a dictionary with arrays as values. cutoff : float If the mixing distribution has infinite support, then the distribution is truncated with approximately (subject to integer conversion) the cutoff probability in the missing tail. Random draws that are outside the truncated range are clipped, that is assigned to the highest or lowest value in the truncated support. ''' self.mixing_dist = mixing_dist self.base_dist = base_dist #self.bd_args = bd_args if not np.isneginf(mixing_dist.dist.a): lower = mixing_dist.dist.a else: lower = mixing_dist.ppf(1e-4) if not np.isposinf(mixing_dist.dist.b): upper = mixing_dist.dist.b else: upper = mixing_dist.isf(1e-4) self.ma = lower self.mb = upper mixing_support = np.arange(lower, upper+1) self.mixing_probs = mixing_dist.pmf(mixing_support) self.bd_args = bd_args_func(mixing_support) self.bd_kwds = bd_kwds_func(mixing_support) def rvs(self, size=1): mrvs = self.mixing_dist.rvs(size) #TODO: check strange cases ? this assumes continous integers mrvs_idx = (np.clip(mrvs, self.ma, self.mb) - self.ma).astype(int) bd_args = tuple(md[mrvs_idx] for md in self.bd_args) bd_kwds = dict((k, self.bd_kwds[k][mrvs_idx]) for k in self.bd_kwds) kwds = {'size':size} kwds.update(bd_kwds) rvs = self.base_dist.rvs(*self.bd_args, **kwds) return rvs, mrvs_idx def pdf(self, x): x = np.asarray(x) if np.size(x) > 1: x = x[...,None] #[None, ...] bd_probs = self.base_dist.pdf(x, *self.bd_args, **self.bd_kwds) prob = (bd_probs * self.mixing_probs).sum(-1) return prob, bd_probs def cdf(self, x): x = np.asarray(x) if np.size(x) > 1: x = x[...,None] #[None, ...] bd_probs = self.base_dist.cdf(x, *self.bd_args, **self.bd_kwds) prob = (bd_probs * self.mixing_probs).sum(-1) return prob, bd_probs #try: class ClippedContinuous(object): '''clipped continuous distribution with a masspoint at clip_lower Notes ----- first version, to try out possible designs insufficient checks for valid arguments and not clear whether it works for distributions that have compact support clip_lower is fixed and independent of the distribution parameters. The clip_lower point in the pdf has to be interpreted as a mass point, i.e. different treatment in integration and expect function, which means none of the generic methods for this can be used. maybe this will be better designed as a mixture between a degenerate or discrete and a continuous distribution Warning: uses equality to check for clip_lower values in function arguments, since these are floating points, the comparison might fail if clip_lower values are not exactly equal. We could add a check whether the values are in a small neighborhood, but it would be expensive (need to search and check all values). ''' def __init__(self, base_dist, clip_lower): self.base_dist = base_dist self.clip_lower = clip_lower def _get_clip_lower(self, kwds): '''helper method to get clip_lower from kwds or attribute ''' if not 'clip_lower' in kwds: clip_lower = self.clip_lower else: clip_lower = kwds.pop('clip_lower') return clip_lower, kwds def rvs(self, *args, **kwds): clip_lower, kwds = self._get_clip_lower(kwds) rvs_ = self.base_dist.rvs(*args, **kwds) #same as numpy.clip ? rvs_[rvs_ < clip_lower] = clip_lower return rvs_ def pdf(self, x, *args, **kwds): x = np.atleast_1d(x) if not 'clip_lower' in kwds: clip_lower = self.clip_lower else: #allow clip_lower to be a possible parameter clip_lower = kwds.pop('clip_lower') pdf_raw = np.atleast_1d(self.base_dist.pdf(x, *args, **kwds)) clip_mask = (x == self.clip_lower) if np.any(clip_mask): clip_prob = self.base_dist.cdf(clip_lower, *args, **kwds) pdf_raw[clip_mask] = clip_prob #the following will be handled by sub-classing rv_continuous pdf_raw[x < clip_lower] = 0 return pdf_raw def cdf(self, x, *args, **kwds): if not 'clip_lower' in kwds: clip_lower = self.clip_lower else: #allow clip_lower to be a possible parameter clip_lower = kwds.pop('clip_lower') cdf_raw = self.base_dist.cdf(x, *args, **kwds) #not needed if equality test is used ## clip_mask = (x == self.clip_lower) ## if np.any(clip_mask): ## clip_prob = self.base_dist.cdf(clip_lower, *args, **kwds) ## pdf_raw[clip_mask] = clip_prob #the following will be handled by sub-classing rv_continuous #if self.a is defined cdf_raw[x < clip_lower] = 0 return cdf_raw def sf(self, x, *args, **kwds): if not 'clip_lower' in kwds: clip_lower = self.clip_lower else: #allow clip_lower to be a possible parameter clip_lower = kwds.pop('clip_lower') sf_raw = self.base_dist.sf(x, *args, **kwds) sf_raw[x <= clip_lower] = 1 return sf_raw def ppf(self, x, *args, **kwds): raise NotImplementedError def plot(self, x, *args, **kwds): clip_lower, kwds = self._get_clip_lower(kwds) mass = self.pdf(clip_lower, *args, **kwds) xr = np.concatenate(([clip_lower+1e-6], x[x>clip_lower])) import matplotlib.pyplot as plt #x = np.linspace(-4, 4, 21) #plt.figure() plt.xlim(clip_lower-0.1, x.max()) #remove duplicate calculation xpdf = self.pdf(x, *args, **kwds) plt.ylim(0, max(mass, xpdf.max())*1.1) plt.plot(xr, self.pdf(xr, *args, **kwds)) #plt.vline(clip_lower, self.pdf(clip_lower, *args, **kwds)) plt.stem([clip_lower], [mass], linefmt='b-', markerfmt='bo', basefmt='r-') return if __name__ == '__main__': doplots = 1 #*********** Poisson-Normal Mixture mdist = stats.poisson(2.) bdist = stats.norm bd_args_fn = lambda x: () #bd_kwds_fn = lambda x: {'loc': np.atleast_2d(10./(1+x))} bd_kwds_fn = lambda x: {'loc': x, 'scale': 0.1*np.ones_like(x)} #10./(1+x)} pd = ParametricMixtureD(mdist, bdist, bd_args_fn, bd_kwds_fn) print pd.pdf(1) p, bp = pd.pdf(np.linspace(0,20,21)) pc, bpc = pd.cdf(np.linspace(0,20,21)) print pd.rvs() rvs, m = pd.rvs(size=1000) if doplots: import matplotlib.pyplot as plt plt.hist(rvs, bins = 100) plt.title('poisson mixture of normal distributions') #********** clipped normal distribution (Tobit) bdist = stats.norm clip_lower_ = 0. #-0.5 cnorm = ClippedContinuous(bdist, clip_lower_) x = np.linspace(1e-8, 4, 11) print cnorm.pdf(x) print cnorm.cdf(x) if doplots: #plt.figure() #cnorm.plot(x) plt.figure() cnorm.plot(x = np.linspace(-1, 4, 51), loc=0.5, scale=np.sqrt(2)) plt.title('clipped normal distribution') fig = plt.figure() for i, loc in enumerate([0., 0.5, 1.,2.]): fig.add_subplot(2,2,i+1) cnorm.plot(x = np.linspace(-1, 4, 51), loc=loc, scale=np.sqrt(2)) plt.title('clipped normal, loc = %3.2f' % loc) loc = 1.5 rvs = cnorm.rvs(loc=loc, size=2000) plt.figure() plt.hist(rvs, bins=50) plt.title('clipped normal rvs, loc = %3.2f' % loc) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/quantize.py000066400000000000000000000101321224417117700272630ustar00rootroot00000000000000'''Quantizing a continuous distribution in 2d Author: josef-pktd ''' import numpy as np def prob_bv_rectangle(lower, upper, cdf): '''helper function for probability of a rectangle in a bivariate distribution Parameters ---------- lower : array_like tuple of lower integration bounds upper : array_like tuple of upper integration bounds cdf : callable cdf(x,y), cumulative distribution function of bivariate distribution how does this generalize to more than 2 variates ? ''' probuu = cdf(*upper) probul = cdf(upper[0], lower[1]) problu = cdf(lower[0], upper[1]) probll = cdf(*lower) return probuu - probul - problu + probll def prob_mv_grid(bins, cdf, axis=-1): '''helper function for probability of a rectangle grid in a multivariate distribution how does this generalize to more than 2 variates ? bins : tuple tuple of bin edges, currently it is assumed that they broadcast correctly ''' if not isinstance(bins, np.ndarray): bins = map(np.asarray, bins) n_dim = len(bins) bins_ = [] #broadcast if binedges are 1d if all(map(np.ndim, bins) == np.ones(n_dim)): for d in xrange(n_dim): sl = [None]*n_dim sl[d] = slice(None) bins_.append(bins[d][sl]) else: #assume it is already correctly broadcasted n_dim = bins.shape[0] bins_ = bins print len(bins) cdf_values = cdf(bins_) probs = cdf_values.copy() for d in xrange(n_dim): probs = np.diff(probs, axis=d) return probs def prob_quantize_cdf(binsx, binsy, cdf): '''quantize a continuous distribution given by a cdf Parameters ---------- binsx : array_like, 1d binedges ''' binsx = np.asarray(binsx) binsy = np.asarray(binsy) nx = len(binsx) - 1 ny = len(binsy) - 1 probs = np.nan * np.ones((nx, ny)) #np.empty(nx,ny) cdf_values = cdf(binsx[:,None], binsy) cdf_func = lambda x, y: cdf_values[x,y] for xind in range(1, nx+1): for yind in range(1, ny+1): upper = (xind, yind) lower = (xind-1, yind-1) #print upper,lower, probs[xind-1,yind-1] = prob_bv_rectangle(lower, upper, cdf_func) assert not np.isnan(probs).any() return probs def prob_quantize_cdf_old(binsx, binsy, cdf): '''quantize a continuous distribution given by a cdf old version without precomputing cdf values Parameters ---------- binsx : array_like, 1d binedges ''' binsx = np.asarray(binsx) binsy = np.asarray(binsy) nx = len(binsx) - 1 ny = len(binsy) - 1 probs = np.nan * np.ones((nx, ny)) #np.empty(nx,ny) for xind in range(1, nx+1): for yind in range(1, ny+1): upper = (binsx[xind], binsy[yind]) lower = (binsx[xind-1], binsy[yind-1]) #print upper,lower, probs[xind-1,yind-1] = prob_bv_rectangle(lower, upper, cdf) assert not np.isnan(probs).any() return probs if __name__ == '__main__': from numpy.testing import assert_almost_equal unif_2d = lambda x,y: x*y assert_almost_equal(prob_bv_rectangle([0,0], [1,0.5], unif_2d), 0.5, 14) assert_almost_equal(prob_bv_rectangle([0,0], [0.5,0.5], unif_2d), 0.25, 14) arr1b = np.array([[ 0.05, 0.05, 0.05, 0.05], [ 0.05, 0.05, 0.05, 0.05], [ 0.05, 0.05, 0.05, 0.05], [ 0.05, 0.05, 0.05, 0.05], [ 0.05, 0.05, 0.05, 0.05]]) arr1a = prob_quantize_cdf(np.linspace(0,1,6), np.linspace(0,1,5), unif_2d) assert_almost_equal(arr1a, arr1b, 14) arr2b = np.array([[ 0.25], [ 0.25], [ 0.25], [ 0.25]]) arr2a = prob_quantize_cdf(np.linspace(0,1,5), np.linspace(0,1,2), unif_2d) assert_almost_equal(arr2a, arr2b, 14) arr3b = np.array([[ 0.25, 0.25, 0.25, 0.25]]) arr3a = prob_quantize_cdf(np.linspace(0,1,2), np.linspace(0,1,5), unif_2d) assert_almost_equal(arr3a, arr3b, 14) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/sppatch.py000066400000000000000000000557421224417117700271050ustar00rootroot00000000000000'''patching scipy to fit distributions and expect method This adds new methods to estimate continuous distribution parameters with some fixed/frozen parameters. It also contains functions that calculate the expected value of a function for any continuous or discrete distribution It temporarily also contains Bootstrap and Monte Carlo function for testing the distribution fit, but these are neither general nor verified. Author: josef-pktd License: Simplified BSD ''' import numpy as np from scipy import stats, optimize, integrate ########## patching scipy #vonmises doesn't define finite bounds, because it is intended for circular #support which does not define a proper pdf on the real line stats.distributions.vonmises.a = -np.pi stats.distributions.vonmises.b = np.pi #the next 3 functions are for fit with some fixed parameters #As they are written, they do not work as functions, only as methods def _fitstart(self, x): '''example method, method of moment estimator as starting values Parameters ---------- x : array data for which the parameters are estimated Returns ------- est : tuple preliminary estimates used as starting value for fitting, not necessarily a consistent estimator Notes ----- This needs to be written and attached to each individual distribution This example was written for the gamma distribution, but not verified with literature ''' loc = np.min([x.min(),0]) a = 4/stats.skew(x)**2 scale = np.std(x) / np.sqrt(a) return (a, loc, scale) def _fitstart_beta(self, x, fixed=None): '''method of moment estimator as starting values for beta distribution Parameters ---------- x : array data for which the parameters are estimated fixed : None or array_like sequence of numbers and np.nan to indicate fixed parameters and parameters to estimate Returns ------- est : tuple preliminary estimates used as starting value for fitting, not necessarily a consistent estimator Notes ----- This needs to be written and attached to each individual distribution References ---------- for method of moment estimator for known loc and scale http://en.wikipedia.org/wiki/Beta_distribution#Parameter_estimation http://www.itl.nist.gov/div898/handbook/eda/section3/eda366h.htm NIST reference also includes reference to MLE in Johnson, Kotz, and Balakrishan, Volume II, pages 221-235 ''' #todo: separate out this part to be used for other compact support distributions # e.g. rdist, vonmises, and truncnorm # but this might not work because it might still be distribution specific a, b = x.min(), x.max() eps = (a-b)*0.01 if fixed is None: #this part not checked with books loc = a - eps scale = (a - b) * (1 + 2*eps) else: if np.isnan(fixed[-2]): #estimate loc loc = a - eps else: loc = fixed[-2] if np.isnan(fixed[-1]): #estimate scale scale = (b + eps) - loc else: scale = fixed[-1] #method of moment for known loc scale: scale = float(scale) xtrans = (x - loc)/scale xm = xtrans.mean() xv = xtrans.var() tmp = (xm*(1-xm)/xv - 1) p = xm * tmp q = (1 - xm) * tmp return (p, q, loc, scale) #check return type and should fixed be returned ? def _fitstart_poisson(self, x, fixed=None): '''maximum likelihood estimator as starting values for Poisson distribution Parameters ---------- x : array data for which the parameters are estimated fixed : None or array_like sequence of numbers and np.nan to indicate fixed parameters and parameters to estimate Returns ------- est : tuple preliminary estimates used as starting value for fitting, not necessarily a consistent estimator Notes ----- This needs to be written and attached to each individual distribution References ---------- MLE : http://en.wikipedia.org/wiki/Poisson_distribution#Maximum_likelihood ''' #todo: separate out this part to be used for other compact support distributions # e.g. rdist, vonmises, and truncnorm # but this might not work because it might still be distribution specific a = x.min() eps = 0 # is this robust ? if fixed is None: #this part not checked with books loc = a - eps else: if np.isnan(fixed[-1]): #estimate loc loc = a - eps else: loc = fixed[-1] #MLE for standard (unshifted, if loc=0) Poisson distribution xtrans = (x - loc) lambd = xtrans.mean() #second derivative d loglike/ dlambd Not used #dlldlambd = 1/lambd # check return (lambd, loc) #check return type and should fixed be returned ? def nnlf_fr(self, thetash, x, frmask): # new frozen version # - sum (log pdf(x, theta),axis=0) # where theta are the parameters (including loc and scale) # try: if frmask != None: theta = frmask.copy() theta[np.isnan(frmask)] = thetash else: theta = thetash loc = theta[-2] scale = theta[-1] args = tuple(theta[:-2]) except IndexError: raise ValueError, "Not enough input arguments." if not self._argcheck(*args) or scale <= 0: return np.inf x = np.array((x-loc) / scale) cond0 = (x <= self.a) | (x >= self.b) if (np.any(cond0)): return np.inf else: N = len(x) #raise ValueError return self._nnlf(x, *args) + N*np.log(scale) def fit_fr(self, data, *args, **kwds): '''estimate distribution parameters by MLE taking some parameters as fixed Parameters ---------- data : array, 1d data for which the distribution parameters are estimated, args : list ? check starting values for optimization kwds : - 'frozen' : array_like values for frozen distribution parameters and, for elements with np.nan, the corresponding parameter will be estimated Returns ------- argest : array estimated parameters Examples -------- generate random sample >>> np.random.seed(12345) >>> x = stats.gamma.rvs(2.5, loc=0, scale=1.2, size=200) estimate all parameters >>> stats.gamma.fit(x) array([ 2.0243194 , 0.20395655, 1.44411371]) >>> stats.gamma.fit_fr(x, frozen=[np.nan, np.nan, np.nan]) array([ 2.0243194 , 0.20395655, 1.44411371]) keep loc fixed, estimate shape and scale parameters >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, np.nan]) array([ 2.45603985, 1.27333105]) keep loc and scale fixed, estimate shape parameter >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) array([ 3.00048828]) >>> stats.gamma.fit_fr(x, frozen=[np.nan, 0.0, 1.2]) array([ 2.57792969]) estimate only scale parameter for fixed shape and loc >>> stats.gamma.fit_fr(x, frozen=[2.5, 0.0, np.nan]) array([ 1.25087891]) Notes ----- self is an instance of a distribution class. This can be attached to scipy.stats.distributions.rv_continuous *Todo* * check if docstring is correct * more input checking, args is list ? might also apply to current fit method ''' loc0, scale0 = map(kwds.get, ['loc', 'scale'],[0.0, 1.0]) Narg = len(args) if Narg == 0 and hasattr(self, '_fitstart'): x0 = self._fitstart(data) elif Narg > self.numargs: raise ValueError, "Too many input arguments." else: args += (1.0,)*(self.numargs-Narg) # location and scale are at the end x0 = args + (loc0, scale0) if 'frozen' in kwds: frmask = np.array(kwds['frozen']) if len(frmask) != self.numargs+2: raise ValueError, "Incorrect number of frozen arguments." else: # keep starting values for not frozen parameters x0 = np.array(x0)[np.isnan(frmask)] else: frmask = None #print x0 #print frmask return optimize.fmin(self.nnlf_fr, x0, args=(np.ravel(data), frmask), disp=0) #The next two functions/methods calculate expected value of an arbitrary #function, however for the continuous functions intquad is use, which might #require continuouity or smoothness in the function. #TODO: add option for Monte Carlo integration def expect(self, fn=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False): '''calculate expected value of a function with respect to the distribution location and scale only tested on a few examples Parameters ---------- all parameters are keyword parameters fn : function (default: identity mapping) Function for which integral is calculated. Takes only one argument. args : tuple argument (parameters) of the distribution lb, ub : numbers lower and upper bound for integration, default is set to the support of the distribution conditional : boolean (False) If true then the integral is corrected by the conditional probability of the integration interval. The return value is the expectation of the function, conditional on being in the given interval. Returns ------- expected value : float Notes ----- This function has not been checked for it's behavior when the integral is not finite. The integration behavior is inherited from scipy.integrate.quad. ''' if fn is None: def fun(x, *args): return x*self.pdf(x, loc=loc, scale=scale, *args) else: def fun(x, *args): return fn(x)*self.pdf(x, loc=loc, scale=scale, *args) if lb is None: lb = loc + self.a * scale #(self.a - loc)/(1.0*scale) if ub is None: ub = loc + self.b * scale #(self.b - loc)/(1.0*scale) if conditional: invfac = (self.sf(lb, loc=loc, scale=scale, *args) - self.sf(ub, loc=loc, scale=scale, *args)) else: invfac = 1.0 return integrate.quad(fun, lb, ub, args=args)[0]/invfac def expect_v2(self, fn=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False): '''calculate expected value of a function with respect to the distribution location and scale only tested on a few examples Parameters ---------- all parameters are keyword parameters fn : function (default: identity mapping) Function for which integral is calculated. Takes only one argument. args : tuple argument (parameters) of the distribution lb, ub : numbers lower and upper bound for integration, default is set using quantiles of the distribution, see Notes conditional : boolean (False) If true then the integral is corrected by the conditional probability of the integration interval. The return value is the expectation of the function, conditional on being in the given interval. Returns ------- expected value : float Notes ----- This function has not been checked for it's behavior when the integral is not finite. The integration behavior is inherited from scipy.integrate.quad. The default limits are lb = self.ppf(1e-9, *args), ub = self.ppf(1-1e-9, *args) For some heavy tailed distributions, 'alpha', 'cauchy', 'halfcauchy', 'levy', 'levy_l', and for 'ncf', the default limits are not set correctly even when the expectation of the function is finite. In this case, the integration limits, lb and ub, should be chosen by the user. For example, for the ncf distribution, ub=1000 works in the examples. There are also problems with numerical integration in some other cases, for example if the distribution is very concentrated and the default limits are too large. ''' #changes: 20100809 #correction and refactoring how loc and scale are handled #uses now _pdf #needs more testing for distribution with bound support, e.g. genpareto if fn is None: def fun(x, *args): return (loc + x*scale)*self._pdf(x, *args) else: def fun(x, *args): return fn(loc + x*scale)*self._pdf(x, *args) if lb is None: #lb = self.a try: lb = self.ppf(1e-9, *args) #1e-14 quad fails for pareto except ValueError: lb = self.a else: lb = max(self.a, (lb - loc)/(1.0*scale)) #transform to standardized if ub is None: #ub = self.b try: ub = self.ppf(1-1e-9, *args) except ValueError: ub = self.b else: ub = min(self.b, (ub - loc)/(1.0*scale)) if conditional: invfac = self._sf(lb,*args) - self._sf(ub,*args) else: invfac = 1.0 return integrate.quad(fun, lb, ub, args=args, limit=500)[0]/invfac ### for discrete distributions #TODO: check that for a distribution with finite support the calculations are # done with one array summation (np.dot) #based on _drv2_moment(self, n, *args), but streamlined def expect_discrete(self, fn=None, args=(), loc=0, lb=None, ub=None, conditional=False): '''calculate expected value of a function with respect to the distribution for discrete distribution Parameters ---------- (self : distribution instance as defined in scipy stats) fn : function (default: identity mapping) Function for which integral is calculated. Takes only one argument. args : tuple argument (parameters) of the distribution optional keyword parameters lb, ub : numbers lower and upper bound for integration, default is set to the support of the distribution, lb and ub are inclusive (ul<=k<=ub) conditional : boolean (False) If true then the expectation is corrected by the conditional probability of the integration interval. The return value is the expectation of the function, conditional on being in the given interval (k such that ul<=k<=ub). Returns ------- expected value : float Notes ----- * function is not vectorized * accuracy: uses self.moment_tol as stopping criterium for heavy tailed distribution e.g. zipf(4), accuracy for mean, variance in example is only 1e-5, increasing precision (moment_tol) makes zipf very slow * suppnmin=100 internal parameter for minimum number of points to evaluate could be added as keyword parameter, to evaluate functions with non-monotonic shapes, points include integers in (-suppnmin, suppnmin) * uses maxcount=1000 limits the number of points that are evaluated to break loop for infinite sums (a maximum of suppnmin+1000 positive plus suppnmin+1000 negative integers are evaluated) ''' #moment_tol = 1e-12 # increase compared to self.moment_tol, # too slow for only small gain in precision for zipf #avoid endless loop with unbound integral, eg. var of zipf(2) maxcount = 1000 suppnmin = 100 #minimum number of points to evaluate (+ and -) if fn is None: def fun(x): #loc and args from outer scope return (x+loc)*self._pmf(x, *args) else: def fun(x): #loc and args from outer scope return fn(x+loc)*self._pmf(x, *args) # used pmf because _pmf does not check support in randint # and there might be problems(?) with correct self.a, self.b at this stage # maybe not anymore, seems to work now with _pmf self._argcheck(*args) # (re)generate scalar self.a and self.b if lb is None: lb = (self.a) else: lb = lb - loc if ub is None: ub = (self.b) else: ub = ub - loc if conditional: invfac = self.sf(lb,*args) - self.sf(ub+1,*args) else: invfac = 1.0 tot = 0.0 low, upp = self._ppf(0.001, *args), self._ppf(0.999, *args) low = max(min(-suppnmin, low), lb) upp = min(max(suppnmin, upp), ub) supp = np.arange(low, upp+1, self.inc) #check limits #print 'low, upp', low, upp tot = np.sum(fun(supp)) diff = 1e100 pos = upp + self.inc count = 0 #handle cases with infinite support while (pos <= ub) and (diff > self.moment_tol) and count <= maxcount: diff = fun(pos) tot += diff pos += self.inc count += 1 if self.a < 0: #handle case when self.a = -inf diff = 1e100 pos = low - self.inc while (pos >= lb) and (diff > self.moment_tol) and count <= maxcount: diff = fun(pos) tot += diff pos -= self.inc count += 1 if count > maxcount: # replace with proper warning print 'sum did not converge' return tot/invfac stats.distributions.rv_continuous.fit_fr = fit_fr stats.distributions.rv_continuous.nnlf_fr = nnlf_fr stats.distributions.rv_continuous.expect = expect stats.distributions.rv_discrete.expect = expect_discrete stats.distributions.beta_gen._fitstart = _fitstart_beta #not tried out yet stats.distributions.poisson_gen._fitstart = _fitstart_poisson #not tried out yet ########## end patching scipy def distfitbootstrap(sample, distr, nrepl=100): '''run bootstrap for estimation of distribution parameters hard coded: only one shape parameter is allowed and estimated, loc=0 and scale=1 are fixed in the estimation Parameters ---------- sample : array original sample data for bootstrap distr : distribution instance with fit_fr method nrepl : integer number of bootstrap replications Returns ------- res : array (nrepl,) parameter estimates for all bootstrap replications ''' nobs = len(sample) res = np.zeros(nrepl) for ii in xrange(nrepl): rvsind = np.random.randint(nobs, size=nobs) x = sample[rvsind] res[ii] = distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) return res def distfitmc(sample, distr, nrepl=100, distkwds={}): '''run Monte Carlo for estimation of distribution parameters hard coded: only one shape parameter is allowed and estimated, loc=0 and scale=1 are fixed in the estimation Parameters ---------- sample : array original sample data, in Monte Carlo only used to get nobs, distr : distribution instance with fit_fr method nrepl : integer number of Monte Carlo replications Returns ------- res : array (nrepl,) parameter estimates for all Monte Carlo replications ''' arg = distkwds.pop('arg') nobs = len(sample) res = np.zeros(nrepl) for ii in xrange(nrepl): x = distr.rvs(arg, size=nobs, **distkwds) res[ii] = distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) return res def printresults(sample, arg, bres, kind='bootstrap'): '''calculate and print Bootstrap or Monte Carlo result Parameters ---------- sample : array original sample data arg : float (for general case will be array) bres : array parameter estimates from Bootstrap or Monte Carlo run kind : {'bootstrap', 'montecarlo'} output is printed for Mootstrap (default) or Monte Carlo Returns ------- None, currently only printing Notes ----- still a bit a mess because it is used for both Bootstrap and Monte Carlo made correction: reference point for bootstrap is estimated parameter not clear: I'm not doing any ddof adjustment in estimation of variance, do we need ddof>0 ? todo: return results and string instead of printing ''' print 'true parameter value' print arg print 'MLE estimate of parameters using sample (nobs=%d)'% (nobs) argest = distr.fit_fr(sample, frozen=[np.nan, 0.0, 1.0]) print argest if kind == 'bootstrap': #bootstrap compares to estimate from sample argorig = arg arg = argest print '%s distribution of parameter estimate (nrepl=%d)'% (kind, nrepl) print 'mean = %f, bias=%f' % (bres.mean(0), bres.mean(0)-arg) print 'median', np.median(bres, axis=0) print 'var and std', bres.var(0), np.sqrt(bres.var(0)) bmse = ((bres - arg)**2).mean(0) print 'mse, rmse', bmse, np.sqrt(bmse) bressorted = np.sort(bres) print '%s confidence interval (90%% coverage)' % kind print bressorted[np.floor(nrepl*0.05)], bressorted[np.floor(nrepl*0.95)] print '%s confidence interval (90%% coverage) normal approximation' % kind print stats.norm.ppf(0.05, loc=bres.mean(), scale=bres.std()), print stats.norm.isf(0.05, loc=bres.mean(), scale=bres.std()) print 'Kolmogorov-Smirnov test for normality of %s distribution' % kind print ' - estimated parameters, p-values not really correct' print stats.kstest(bres, 'norm', (bres.mean(), bres.std())) if __name__ == '__main__': examplecases = ['largenumber', 'bootstrap', 'montecarlo'][:] if 'largenumber' in examplecases: print '\nDistribution: vonmises' for nobs in [200]:#[20000, 1000, 100]: x = stats.vonmises.rvs(1.23, loc=0, scale=1, size=nobs) print '\nnobs:', nobs print 'true parameter' print '1.23, loc=0, scale=1' print 'unconstraint' print stats.vonmises.fit(x) print stats.vonmises.fit_fr(x, frozen=[np.nan, np.nan, np.nan]) print 'with fixed loc and scale' print stats.vonmises.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) print '\nDistribution: gamma' distr = stats.gamma arg, loc, scale = 2.5, 0., 20. for nobs in [200]:#[20000, 1000, 100]: x = distr.rvs(arg, loc=loc, scale=scale, size=nobs) print '\nnobs:', nobs print 'true parameter' print '%f, loc=%f, scale=%f' % (arg, loc, scale) print 'unconstraint' print distr.fit(x) print distr.fit_fr(x, frozen=[np.nan, np.nan, np.nan]) print 'with fixed loc and scale' print distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0]) print 'with fixed loc' print distr.fit_fr(x, frozen=[np.nan, 0.0, np.nan]) ex = ['gamma', 'vonmises'][0] if ex == 'gamma': distr = stats.gamma arg, loc, scale = 2.5, 0., 1 elif ex == 'vonmises': distr = stats.vonmises arg, loc, scale = 1.5, 0., 1 else: raise ValueError('wrong example') nobs = 100 nrepl = 1000 sample = distr.rvs(arg, loc=loc, scale=scale, size=nobs) print '\nDistribution:', distr if 'bootstrap' in examplecases: print '\nBootstrap' bres = distfitbootstrap(sample, distr, nrepl=nrepl ) printresults(sample, arg, bres) if 'montecarlo' in examplecases: print '\nMonteCarlo' mcres = distfitmc(sample, distr, nrepl=nrepl, distkwds=dict(arg=arg, loc=loc, scale=scale)) printresults(sample, arg, mcres, kind='montecarlo') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/000077500000000000000000000000001224417117700262165ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/__init__.py000066400000000000000000000143221224417117700303310ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/_est_fit.py000066400000000000000000000050601224417117700303650ustar00rootroot00000000000000# NOTE: contains only one test, _est_cont_fit, that is renamed so that # nose doesn't run it # I put this here for the record and for the case when someone wants to # verify the quality of fit # with current parameters: relatively small sample size, default starting values # Ran 84 tests in 401.797s # FAILED (failures=15) import numpy.testing as npt import numpy as np from scipy import stats from distparams import distcont # this is not a proper statistical test for convergence, but only # verifies that the estimate and true values don't differ by too much n_repl1 = 1000 # sample size for first run n_repl2 = 5000 # sample size for second run, if first run fails thresh_percent = 0.25 # percent of true parameters for fail cut-off thresh_min = 0.75 # minimum difference estimate - true to fail test #distcont = [['genextreme', (3.3184017469423535,)]] def _est_cont_fit(): # this tests the closeness of the estimated parameters to the true # parameters with fit method of continuous distributions # Note: is slow, some distributions don't converge with sample size <= 10000 for distname, arg in distcont: yield check_cont_fit, distname,arg def check_cont_fit(distname,arg): distfn = getattr(stats, distname) rvs = distfn.rvs(size=n_repl1,*arg) est = distfn.fit(rvs) #,*arg) # start with default values truearg = np.hstack([arg,[0.0,1.0]]) diff = est-truearg txt = '' diffthreshold = np.max(np.vstack([truearg*thresh_percent, np.ones(distfn.numargs+2)*thresh_min]),0) # threshold for location diffthreshold[-2] = np.max([np.abs(rvs.mean())*thresh_percent,thresh_min]) if np.any(np.isnan(est)): raise AssertionError('nan returned in fit') else: if np.any((np.abs(diff) - diffthreshold) > 0.0): ## txt = 'WARNING - diff too large with small sample' ## print 'parameter diff =', diff - diffthreshold, txt rvs = np.concatenate([rvs,distfn.rvs(size=n_repl2-n_repl1,*arg)]) est = distfn.fit(rvs) #,*arg) truearg = np.hstack([arg,[0.0,1.0]]) diff = est-truearg if np.any((np.abs(diff) - diffthreshold) > 0.0): txt = 'parameter: %s\n' % str(truearg) txt += 'estimated: %s\n' % str(est) txt += 'diff : %s\n' % str(diff) raise AssertionError('fit not very good in %s\n' % distfn.name + txt) if __name__ == "__main__": import nose #nose.run(argv=['', __file__]) nose.runmodule(argv=[__file__,'-s'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/check_moments.py000066400000000000000000000124241224417117700314120ustar00rootroot00000000000000'''script to test expect and moments in distributions.stats method not written as a test, prints results, renamed to prevent nose from running it ''' import numpy as np from scipy import stats #from statsmodels.stats.moment_helpers import mnc2mvsk from statsmodels.sandbox.distributions.sppatch import expect_v2 from distparams import distcont, distdiscrete#, distslow specialcases = {'ncf':{'ub':1000} # diverges if it's too large, checked for mean } #next functions are copies from sm.stats.moment_helpers def mc2mvsk(args): '''convert central moments to mean, variance, skew, kurtosis ''' mc, mc2, mc3, mc4 = args skew = np.divide(mc3, mc2**1.5) kurt = np.divide(mc4, mc2**2.0) - 3.0 return (mc, mc2, skew, kurt) def mnc2mvsk(args): '''convert central moments to mean, variance, skew, kurtosis ''' #convert four non-central moments to central moments mnc, mnc2, mnc3, mnc4 = args mc = mnc mc2 = mnc2 - mnc*mnc mc3 = mnc3 - (3*mc*mc2+mc**3) # 3rd central moment mc4 = mnc4 - (4*mc*mc3+6*mc*mc*mc2+mc**4) return mc2mvsk((mc, mc2, mc3, mc4)) def mom_nc0(x): return 1. def mom_nc1(x): return x def mom_nc2(x): return x*x def mom_nc3(x): return x*x*x def mom_nc4(x): return np.power(x,4) res = {} distex = [] distlow = [] distok = [] distnonfinite = [] def check_cont_basic(): #results saved in module global variable for distname, distargs in distcont[:]: #if distname not in distex_0: continue distfn = getattr(stats, distname) ## np.random.seed(765456) ## sn = 1000 ## rvs = distfn.rvs(size=sn,*arg) ## sm = rvs.mean() ## sv = rvs.var() ## skurt = stats.kurtosis(rvs) ## sskew = stats.skew(rvs) m,v,s,k = distfn.stats(*distargs, **dict(moments='mvsk')) st = np.array([m,v,s,k]) mask = np.isfinite(st) if mask.sum() < 4: distnonfinite.append(distname) print distname #print 'stats ', m,v,s,k expect = distfn.expect expect = lambda *args, **kwds : expect_v2(distfn, *args, **kwds) special_kwds = specialcases.get(distname, {}) mnc0 = expect(mom_nc0, args=distargs, **special_kwds) mnc1 = expect(args=distargs, **special_kwds) mnc2 = expect(mom_nc2, args=distargs, **special_kwds) mnc3 = expect(mom_nc3, args=distargs, **special_kwds) mnc4 = expect(mom_nc4, args=distargs, **special_kwds) mnc1_lc = expect(args=distargs, loc=1, scale=2, **special_kwds) #print mnc1, mnc2, mnc3, mnc4 try: me, ve, se, ke = mnc2mvsk((mnc1, mnc2, mnc3, mnc4)) except: print 'exception', mnc1, mnc2, mnc3, mnc4, st me, ve, se, ke = [np.nan]*4 if mask.size > 0: distex.append(distname) #print 'expect', me, ve, se, ke, #print mnc1, mnc2, mnc3, mnc4 em = np.array([me, ve, se, ke]) diff = st[mask] - em[mask] print diff, mnc1_lc - (1 + 2*mnc1) if np.size(diff)>0 and np.max(np.abs(diff)) > 1e-3: distlow.append(distname) else: distok.append(distname) res[distname] = [mnc0, st, em, diff, mnc1_lc] def nct_kurt_bug(): '''test for incorrect kurtosis of nct D. Hogben, R. S. Pinkham, M. B. Wilk: The Moments of the Non-Central t-DistributionAuthor(s): Biometrika, Vol. 48, No. 3/4 (Dec., 1961), pp. 465-468 ''' from numpy.testing import assert_almost_equal mvsk_10_1 = (1.08372, 1.325546, 0.39993, 1.2499424941142943) assert_almost_equal(stats.nct.stats(10, 1, moments='mvsk'), mvsk_10_1, decimal=6) c1=np.array([1.08372]) c2=np.array([.0755460, 1.25000]) c3 = np.array([.0297802, .580566]) c4 = np.array([0.0425458, 1.17491, 6.25]) #calculation for df=10, for arbitrary nc nc = 1 mc1 = c1.item() mc2 = (c2*nc**np.array([2,0])).sum() mc3 = (c3*nc**np.array([3,1])).sum() mc4 = c4=np.array([0.0425458, 1.17491, 6.25]) mvsk_nc = mc2mvsk((mc1,mc2,mc3,mc4)) if __name__ == '__main__': check_cont_basic() #print [(k, v[0]) for k,v in res.items() if np.abs(v[0]-1)>1e-3] #print [(k, v[2][0], 1+2*v[2][0]) for k,v in res.items() if np.abs(v[-1]-(1+2*v[2][0]))>1e-3] mean_ = [(k, v[1][0], v[2][0]) for k,v in res.items() if np.abs(v[1][0] - v[2][0])>1e-6 and np.isfinite(v[1][0])] var_ = [(k, v[1][1], v[2][1]) for k,v in res.items() if np.abs(v[1][1] - v[2][1])>1e-2 and np.isfinite(v[1][1])] skew = [(k, v[1][2], v[2][2]) for k,v in res.items() if np.abs(v[1][2] - v[2][2])>1e-2 and np.isfinite(v[1][1])] kurt = [(k, v[1][3], v[2][3]) for k,v in res.items() if np.abs(v[1][3] - v[2][3])>1e-2 and np.isfinite(v[1][1])] from statsmodels.iolib import SimpleTable if len(mean_) > 0: print '\nMean difference at least 1e-6' print SimpleTable(mean_, headers=['distname', 'diststats', 'expect']) print '\nVariance difference at least 1e-2' print SimpleTable(var_, headers=['distname', 'diststats', 'expect']) print '\nSkew difference at least 1e-2' print SimpleTable(skew, headers=['distname', 'diststats', 'expect']) print '\nKurtosis difference at least 1e-2' print SimpleTable(kurt, headers=['distname', 'diststats', 'expect']) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/distparams.py000066400000000000000000000120071224417117700307370ustar00rootroot00000000000000 distcont = [ ['alpha', (3.5704770516650459,)], ['anglit', ()], ['arcsine', ()], ['beta', (2.3098496451481823, 0.62687954300963677)], ['betaprime', (5, 6)], # avoid unbound error in entropy with (100, 86)], ['bradford', (0.29891359763170633,)], ['burr', (10.5, 4.3)], #incorrect mean and var for(0.94839838075366045, 4.3820284068855795)], ['cauchy', ()], ['chi', (78,)], ['chi2', (55,)], ['cosine', ()], ['dgamma', (1.1023326088288166,)], ['dweibull', (2.0685080649914673,)], ['erlang', (20,)], #correction numargs = 1 ['expon', ()], ['exponpow', (2.697119160358469,)], ['exponweib', (2.8923945291034436, 1.9505288745913174)], ['f', (29, 18)], #['fatiguelife', (29,)], #correction numargs = 1, variance very large ['fatiguelife', (2,)], ['fisk', (3.0857548622253179,)], ['foldcauchy', (4.7164673455831894,)], ['foldnorm', (1.9521253373555869,)], ['frechet_l', (3.6279911255583239,)], ['frechet_r', (1.8928171603534227,)], ['gamma', (1.9932305483800778,)], ['gausshyper', (13.763771604130699, 3.1189636648681431, 2.5145980350183019, 5.1811649903971615)], #veryslow ['genexpon', (9.1325976465418908, 16.231956600590632, 3.2819552690843983)], ['genextreme', (-0.1,)], # sample mean test fails for (3.3184017469423535,)], ['gengamma', (4.4162385429431925, 3.1193091679242761)], ['genhalflogistic', (0.77274727809929322,)], ['genlogistic', (0.41192440799679475,)], ['genpareto', (0.1,)], # use case with finite moments ['gilbrat', ()], ['gompertz', (0.94743713075105251,)], ['gumbel_l', ()], ['gumbel_r', ()], ['halfcauchy', ()], ['halflogistic', ()], ['halfnorm', ()], ['hypsecant', ()], #['invgamma', (2.0668996136993067,)], #convergence problem with expect #['invgamma', (3.0,)], ['invgamma', (5.0,)], #kurtosis requires alpha > 4 ['invnorm', (0.14546264555347513,)], ['invweibull', (10.58,)], # sample mean test fails at(0.58847112119264788,)] ['johnsonsb', (4.3172675099141058, 3.1837781130785063)], ['johnsonsu', (2.554395574161155, 2.2482281679651965)], ['ksone', (1000,)], #replace 22 by 100 to avoid failing range, ticket 956 ['kstwobign', ()], ['laplace', ()], ['levy', ()], ['levy_l', ()], # ['levy_stable', (0.35667405469844993, # -0.67450531578494011)], #NotImplementedError # rvs not tested ['loggamma', (0.41411931826052117,)], ['logistic', ()], ['loglaplace', (3.2505926592051435,)], ['lognorm', (0.95368226960575331,)], ['lomax', (1.8771398388773268,)], #this has infinite variance ['lomax', (10,)], #first 4 moments are finite ['maxwell', ()], ['mielke', (10.4, 3.6)], # sample mean test fails for (4.6420495492121487, 0.59707419545516938)], # mielke: good results if 2nd parameter >2, weird mean or var below ['nakagami', (4.9673794866666237,)], ['ncf', (27, 27, 0.41578441799226107)], ['nct', (14, 0.24045031331198066)], ['ncx2', (21, 1.0560465975116415)], ['norm', ()], ['pareto', (2.621716532144454,)], ['powerlaw', (1.6591133289905851,)], ['powerlognorm', (2.1413923530064087, 0.44639540782048337)], ['powernorm', (4.4453652254590779,)], ['rayleigh', ()], ['rdist', (0.9,)], # feels also slow # ['rdist', (3.8266985793976525,)], #veryslow, especially rvs #['rdist', (541.0,)], # from ticket #758 #veryslow ['recipinvgauss', (0.63004267809369119,)], ['reciprocal', (0.0062309367010521255, 1.0062309367010522)], ['rice', (0.7749725210111873,)], ['semicircular', ()], ['t', (2.7433514990818093,)], ['triang', (0.15785029824528218,)], ['truncexpon', (4.6907725456810478,)], ['truncnorm', (-1.0978730080013919, 2.7306754109031979)], ['tukeylambda', (3.1321477856738267,)], ['uniform', ()], ['vonmises', (3.9939042581071398,)], ['wald', ()], ['weibull_max', (2.8687961709100187,)], ['weibull_min', (1.7866166930421596,)], ['wrapcauchy', (0.031071279018614728,)]] distdiscrete = [ ['bernoulli',(0.3,)], ['binom', (5, 0.4)], ['boltzmann',(1.4, 19)], ['dlaplace', (0.8,)], #0.5 ['geom', (0.5,)], ['hypergeom',(30, 12, 6)], ['hypergeom',(21,3,12)], #numpy.random (3,18,12) numpy ticket:921 ['hypergeom',(21,18,11)], #numpy.random (18,3,11) numpy ticket:921 ['logser', (0.6,)], # reenabled, numpy ticket:921 ['nbinom', (5, 0.5)], ['nbinom', (0.4, 0.4)], #from tickets: 583 ['planck', (0.51,)], #4.1 ['poisson', (0.6,)], ['randint', (7, 31)], ['skellam', (15, 8)], ['zipf', (4,)] ] # arg=4 is ok, # Zipf broken for arg = 2, e.g. weird .stats # looking closer, mean, var should be inf for arg=2 distslow = ['rdist', 'gausshyper', 'recipinvgauss', 'ksone', 'genexpon', 'vonmises', 'rice', 'mielke', 'semicircular', 'cosine', 'invweibull', 'powerlognorm', 'johnsonsu', 'kstwobign'] statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/test_extras.py000066400000000000000000000113251224417117700311370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Apr 17 22:13:36 2011 @author: josef """ import numpy as np from numpy.testing import assert_, assert_almost_equal from statsmodels.sandbox.distributions.extras import (skewnorm, skewnorm2, ACSkewT_gen) def test_skewnorm(): #library("sn") #dsn(c(-2,-1,0,1,2), shape=10) #psn(c(-2,-1,0,1,2), shape=10) #noquote(sprintf("%.15e,", snp)) pdf_r = np.array([2.973416551551523e-90, 3.687562713971017e-24, 3.989422804014327e-01, 4.839414490382867e-01, 1.079819330263761e-01]) pdf_sn = skewnorm.pdf([-2,-1,0,1,2], 10) #res = (snp-snp_r)/snp assert_(np.allclose(pdf_sn, pdf_r,rtol=1e-13, atol=0)) pdf_sn2 = skewnorm2.pdf([-2,-1,0,1,2], 10) assert_(np.allclose(pdf_sn2, pdf_r, rtol=1e-13, atol=0)) cdf_r = np.array([0.000000000000000e+00, 0.000000000000000e+00, 3.172551743055357e-02, 6.826894921370859e-01, 9.544997361036416e-01]) cdf_sn = skewnorm.cdf([-2,-1,0,1,2], 10) maxabs = np.max(np.abs(cdf_sn - cdf_r)) maxrel = np.max(np.abs(cdf_sn - cdf_r)/(cdf_r+1e-50)) msg = "maxabs=%15.13g, maxrel=%15.13g\n%r\n%r" % (maxabs, maxrel, cdf_sn, cdf_r) #assert_(np.allclose(cdf_sn, cdf_r, rtol=1e-13, atol=1e-25), msg=msg) assert_almost_equal(cdf_sn, cdf_r, decimal=10) cdf_sn2 = skewnorm2.cdf([-2,-1,0,1,2], 10) maxabs = np.max(np.abs(cdf_sn2 - cdf_r)) maxrel = np.max(np.abs(cdf_sn2 - cdf_r)/(cdf_r+1e-50)) msg = "maxabs=%15.13g, maxrel=%15.13g" % (maxabs, maxrel) #assert_(np.allclose(cdf_sn2, cdf_r, rtol=1e-13, atol=1e-25), msg=msg) assert_almost_equal(cdf_sn2, cdf_r, decimal=10, err_msg=msg) def test_skewt(): skewt = ACSkewT_gen() x = [-2, -1, -0.5, 0, 1, 2] #noquote(sprintf("%.15e,", dst(c(-2,-1, -0.5,0,1,2), shape=10))) #default in R:sn is df=inf pdf_r = np.array([2.973416551551523e-90, 3.687562713971017e-24, 2.018401586422970e-07, 3.989422804014327e-01, 4.839414490382867e-01, 1.079819330263761e-01]) pdf_st = skewt.pdf(x, 1000000, 10) pass np.allclose(pdf_st, pdf_r, rtol=0, atol=1e-6) np.allclose(pdf_st, pdf_r, rtol=1e-1, atol=0) #noquote(sprintf("%.15e,", pst(c(-2,-1, -0.5,0,1,2), shape=10))) cdf_r = np.array([0.000000000000000e+00, 0.000000000000000e+00, 3.729478836866917e-09, 3.172551743055357e-02, 6.826894921370859e-01, 9.544997361036416e-01]) cdf_st = skewt.cdf(x, 1000000, 10) np.allclose(cdf_st, cdf_r, rtol=0, atol=1e-6) np.allclose(cdf_st, cdf_r, rtol=1e-1, atol=0) #assert_(np.allclose(cdf_st, cdf_r, rtol=1e-13, atol=1e-15)) #noquote(sprintf("%.15e,", dst(c(-2,-1, -0.5,0,1,2), shape=10, df=5))) pdf_r = np.array([2.185448836190663e-07, 1.272381597868587e-05, 5.746937644959992e-04, 3.796066898224945e-01, 4.393468708859825e-01, 1.301804021075493e-01]) pdf_st = skewt.pdf(x, 5, 10) #args = (df, alpha) assert_(np.allclose(pdf_st, pdf_r, rtol=1e-13, atol=1e-25)) #noquote(sprintf("%.15e,", pst(c(-2,-1, -0.5,0,1,2), shape=10, df=5))) cdf_r = np.array([8.822783669199699e-08, 2.638467463775795e-06, 6.573106017198583e-05, 3.172551743055352e-02, 6.367851708183412e-01, 8.980606093979784e-01]) cdf_st = skewt.cdf(x, 5, 10) #args = (df, alpha) assert_(np.allclose(cdf_st, cdf_r, rtol=1e-10, atol=0)) #noquote(sprintf("%.15e,", dst(c(-2,-1, -0.5,0,1,2), shape=10, df=1))) pdf_r = np.array([3.941955996757291e-04, 1.568067236862745e-03, 6.136996029432048e-03, 3.183098861837907e-01, 3.167418189469279e-01, 1.269297588738406e-01]) pdf_st = skewt.pdf(x, 1, 10) #args = (df, alpha) = (1, 10)) assert_(np.allclose(pdf_st, pdf_r, rtol=1e-13, atol=1e-25)) #noquote(sprintf("%.15e,", pst(c(-2,-1, -0.5,0,1,2), shape=10, df=1))) cdf_r = np.array([7.893671370544414e-04, 1.575817262600422e-03, 3.128720749105560e-03, 3.172551743055351e-02, 5.015758172626005e-01, 7.056221318361879e-01]) cdf_st = skewt.cdf(x, 1, 10) #args = (df, alpha) = (1, 10) assert_(np.allclose(cdf_st, cdf_r, rtol=1e-13, atol=1e-25)) if __name__ == '__main__': import nose nose.runmodule(argv=['__main__','-vvs','-x','--pdb', '--pdb-failure'], exit=False) print ('Done') ''' >>> skewt.pdf([-2,-1,0,1,2], 10000000, 10) array([ 2.98557345e-90, 3.68850289e-24, 3.98942271e-01, 4.83941426e-01, 1.07981952e-01]) >>> skewt.pdf([-2,-1,0,1,2], np.inf, 10) array([ nan, nan, nan, nan, nan]) ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/test_multivariate.py000066400000000000000000000142731224417117700323440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Apr 16 15:02:13 2011 @author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_array_almost_equal from statsmodels.sandbox.distributions.multivariate import ( mvstdtprob, mvstdnormcdf) from statsmodels.sandbox.distributions.mv_normal import MVT, MVNormal class Test_MVN_MVT_prob(object): #test for block integratal, cdf, of multivariate t and normal #comparison results from R def __init__(self): self.corr_equal = np.asarray([[1.0, 0.5, 0.5],[0.5,1,0.5],[0.5,0.5,1]]) self.a = -1 * np.ones(3) self.b = 3 * np.ones(3) self.df = 4 corr2 = self.corr_equal.copy() corr2[2,1] = -0.5 self.corr2 = corr2 def test_mvn_mvt_1(self): a, b = self.a, self.b df = self.df corr_equal = self.corr_equal #result from R, mvtnorm with option #algorithm = GenzBretz(maxpts = 100000, abseps = 0.000001, releps = 0) # or higher probmvt_R = 0.60414 #report, ed error approx. 7.5e-06 probmvn_R = 0.673970 #reported error approx. 6.4e-07 assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr_equal, df), 4) assert_almost_equal(probmvn_R, mvstdnormcdf(a, b, corr_equal, abseps=1e-5), 4) mvn_high = mvstdnormcdf(a, b, corr_equal, abseps=1e-8, maxpts=10000000) assert_almost_equal(probmvn_R, mvn_high, 5) #this still barely fails sometimes at 6 why?? error is -7.2627419411830374e-007 #>>> 0.67396999999999996 - 0.67397072627419408 #-7.2627419411830374e-007 #>>> assert_almost_equal(0.67396999999999996, 0.67397072627419408, 6) #Fail def test_mvn_mvt_2(self): a, b = self.a, self.b df = self.df corr2 = self.corr2 probmvn_R = 0.6472497 #reported error approx. 7.7e-08 probmvt_R = 0.5881863 #highest reported error up to approx. 1.99e-06 assert_almost_equal(probmvt_R, mvstdtprob(a, b, corr2, df), 4) assert_almost_equal(probmvn_R, mvstdnormcdf(a, b, corr2, abseps=1e-5), 4) def test_mvn_mvt_3(self): a, b = self.a, self.b df = self.df corr2 = self.corr2 #from -inf #print 'from -inf' a2 = a.copy() a2[:] = -np.inf probmvn_R = 0.9961141 #using higher precision in R, error approx. 6.866163e-07 probmvt_R = 0.9522146 #using higher precision in R, error approx. 1.6e-07 assert_almost_equal(probmvt_R, mvstdtprob(a2, b, corr2, df), 4) assert_almost_equal(probmvn_R, mvstdnormcdf(a2, b, corr2, maxpts=100000, abseps=1e-5), 4) def test_mvn_mvt_4(self): a, bl = self.a, self.b df = self.df corr2 = self.corr2 #from 0 to inf #print '0 inf' a2 = a.copy() a2[:] = -np.inf probmvn_R = 0.1666667 #error approx. 6.1e-08 probmvt_R = 0.1666667 #error approx. 8.2e-08 assert_almost_equal(probmvt_R, mvstdtprob(np.zeros(3), -a2, corr2, df), 4) assert_almost_equal(probmvn_R, mvstdnormcdf(np.zeros(3), -a2, corr2, maxpts=100000, abseps=1e-5), 4) def test_mvn_mvt_5(self): a, bl = self.a, self.b df = self.df corr2 = self.corr2 #unequal integration bounds #print "ue" a3 = np.array([0.5, -0.5, 0.5]) probmvn_R = 0.06910487 #using higher precision in R, error approx. 3.5e-08 probmvt_R = 0.05797867 #using higher precision in R, error approx. 5.8e-08 assert_almost_equal(mvstdtprob(a3, a3+1, corr2, df), probmvt_R, 4) assert_almost_equal(probmvn_R, mvstdnormcdf(a3, a3+1, corr2, maxpts=100000, abseps=1e-5), 4) class TestMVDistributions(object): #this is not well organized def __init__(self): covx = np.array([[1.0, 0.5], [0.5, 1.0]]) mu3 = [-1, 0., 2.] cov3 = np.array([[ 1. , 0.5 , 0.75], [ 0.5 , 1.5 , 0.6 ], [ 0.75, 0.6 , 2. ]]) self.mu3 = mu3 self.cov3 = cov3 mvn3 = MVNormal(mu3, cov3) mvn3c = MVNormal(np.array([0,0,0]), cov3) self.mvn3 = mvn3 self.mvn3c = mvn3c def test_mvn_pdf(self): cov3 = self.cov3 mvn3 = self.mvn3 mvn3c = self.mvn3c r_val = [-7.667977543898155, -6.917977543898155, -5.167977543898155] assert_array_almost_equal( mvn3.logpdf(cov3), r_val, decimal = 14) #decimal 18 r_val = [0.000467562492721686, 0.000989829804859273, 0.005696077243833402] assert_array_almost_equal( mvn3.pdf(cov3), r_val, decimal = 17) #cheating new mean, same cov, too dangerous, got wrong instance in tests #mvn3.mean = np.array([0,0,0]) mvn3b = MVNormal(np.array([0,0,0]), cov3) r_val = [0.02914269740502042, 0.02269635555984291, 0.01767593948287269] assert_array_almost_equal( mvn3b.pdf(cov3), r_val, decimal = 16) def test_mvt_pdf(self): cov3 = self.cov3 mu3 = self.mu3 mvt = MVT((0,0), 1, 5) assert_almost_equal(mvt.logpdf(np.array([0.,0.])), -1.837877066409345, decimal=15) assert_almost_equal(mvt.pdf(np.array([0.,0.])), 0.1591549430918953, decimal=15) mvt.logpdf(np.array([1.,1.]))-(-3.01552989458359) mvt1 = MVT((0,0), 1, 1) mvt1.logpdf(np.array([1.,1.]))-(-3.48579549941151) #decimal=16 rvs = mvt.rvs(100000) assert_almost_equal(np.cov(rvs, rowvar=0), mvt.cov, decimal=1) mvt31 = MVT(mu3, cov3, 1) assert_almost_equal(mvt31.pdf(cov3), [0.0007276818698165781, 0.0009980625182293658, 0.0027661422056214652], decimal=17) mvt = MVT(mu3, cov3, 3) assert_almost_equal(mvt.pdf(cov3), [0.000863777424247410, 0.001277510788307594, 0.004156314279452241], decimal=17) if __name__ == '__main__': import nose nose.runmodule(argv=['__main__','-vvs','-x'],#,'--pdb', '--pdb-failure'], exit=False) print ('Done') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/tests/testtransf.py000066400000000000000000000143431224417117700307720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun May 09 22:35:21 2010 Author: josef-pktd License: BSD todo: change moment calculation, (currently uses default _ppf method - I think) >>> lognormalg.moment(4) Warning: The algorithm does not converge. Roundoff error is detected in the extrapolation table. It is assumed that the requested tolerance cannot be achieved, and that the returned result (if full_output = 1) is the best which can be obtained. array(2981.0032380193438) """ import warnings # for silencing, see above... import numpy as np from numpy.testing import assert_almost_equal from scipy import stats, special from statsmodels.sandbox.distributions.extras import ( lognormalg, squarenormalg, absnormalg, negsquarenormalg, squaretg) #some patches to scipy.stats.distributions so tests work and pass #patch frozen distributions with a name stats.distributions.rv_frozen.name = property(lambda self: self.dist.name) #patch f distribution, correct skew and maybe kurtosis def f_stats(self, dfn, dfd): arr, where, inf, sqrt, nan = np.array, np.where, np.inf, np.sqrt, np.nan v2 = arr(dfd*1.0) v1 = arr(dfn*1.0) mu = where (v2 > 2, v2 / arr(v2 - 2), inf) mu2 = 2*v2*v2*(v2+v1-2)/(v1*(v2-2)**2 * (v2-4)) mu2 = where(v2 > 4, mu2, inf) #g1 = 2*(v2+2*v1-2)/(v2-6)*sqrt((2*v2-4)/(v1*(v2+v1-2))) g1 = 2*(v2+2*v1-2.)/(v2-6.)*np.sqrt(2*(v2-4.)/(v1*(v2+v1-2.))) g1 = where(v2 > 6, g1, nan) #g2 = 3/(2*v2-16)*(8+g1*g1*(v2-6)) g2 = 3/(2.*v2-16)*(8+g1*g1*(v2-6.)) g2 = where(v2 > 8, g2, nan) return mu, mu2, g1, g2 stats.distributions.f_gen._stats = f_stats #correct kurtosis by subtracting 3 (Fisher) #after this it matches halfnorm for arg close to zero def foldnorm_stats(self, c): arr, where, inf, sqrt, nan = np.array, np.where, np.inf, np.sqrt, np.nan exp = np.exp pi = np.pi fac = special.erf(c/sqrt(2)) mu = sqrt(2.0/pi)*exp(-0.5*c*c)+c*fac mu2 = c*c + 1 - mu*mu c2 = c*c g1 = sqrt(2/pi)*exp(-1.5*c2)*(4-pi*exp(c2)*(2*c2+1.0)) g1 += 2*c*fac*(6*exp(-c2) + 3*sqrt(2*pi)*c*exp(-c2/2.0)*fac + \ pi*c*(fac*fac-1)) g1 /= pi*mu2**1.5 g2 = c2*c2+6*c2+3+6*(c2+1)*mu*mu - 3*mu**4 g2 -= 4*exp(-c2/2.0)*mu*(sqrt(2.0/pi)*(c2+2)+c*(c2+3)*exp(c2/2.0)*fac) g2 /= mu2**2.0 g2 -= 3. return mu, mu2, g1, g2 stats.distributions.foldnorm_gen._stats = foldnorm_stats #----------------------------- DECIMAL = 5 class Test_Transf2(object): def __init__(self): self.dist_equivalents = [ #transf, stats.lognorm(1)) #The below fails on the SPARC box with scipy 10.1 #(lognormalg, stats.lognorm(1)), #transf2 (squarenormalg, stats.chi2(1)), (absnormalg, stats.halfnorm), (absnormalg, stats.foldnorm(1e-5)), #try frozen #(negsquarenormalg, 1-stats.chi2), # won't work as distribution (squaretg(10), stats.f(1, 10))] #try both frozen l,s = 0.0, 1.0 self.ppfq = [0.1,0.5,0.9] self.xx = [0.95,1.0,1.1] self.nxx = [-0.95,-1.0,-1.1] def test_equivalent(self): xx, ppfq = self.xx, self.ppfq for d1,d2 in self.dist_equivalents: ## print d1.name assert_almost_equal(d1.cdf(xx), d2.cdf(xx), err_msg='cdf'+d1.name) assert_almost_equal(d1.pdf(xx), d2.pdf(xx), err_msg='pdf '+d1.name+d2.name) assert_almost_equal(d1.sf(xx), d2.sf(xx), err_msg='sf '+d1.name+d2.name) assert_almost_equal(d1.ppf(ppfq), d2.ppf(ppfq), err_msg='ppq '+d1.name+d2.name) assert_almost_equal(d1.isf(ppfq), d2.isf(ppfq), err_msg='isf '+d1.name+d2.name) self.d1 = d1 self.d2 = d2 ## print d1, d2 ## print d1.moment(3) ## print d2.moment(3) #work around bug#1293 if hasattr(d2, 'dist'): d2mom = d2.dist.moment(3, *d2.args) else: d2mom = d2.moment(3) assert_almost_equal(d1.moment(3), d2mom, DECIMAL, err_msg='moment '+d1.name+d2.name) # silence warnings in scipy, works for versions # after print changed to warning in scipy orig_filter = warnings.filters[:] warnings.simplefilter('ignore') try: s1 = d1.stats(moments='mvsk') s2 = d2.stats(moments='mvsk') finally: warnings.filters = orig_filter #stats(moments='k') prints warning for lognormalg assert_almost_equal(s1[:2], s2[:2], err_msg='stats '+d1.name+d2.name) assert_almost_equal(s1[2:], s2[2:], decimal=2, #lognorm for kurtosis err_msg='stats '+d1.name+d2.name) def test_equivalent_negsq(self): #special case negsquarenormalg #negsquarenormalg.cdf(x) == stats.chi2(1).cdf(-x), for x<=0 xx, nxx, ppfq = self.xx, self.nxx, self.ppfq d1,d2 = (negsquarenormalg, stats.chi2(1)) #print d1.name assert_almost_equal(d1.cdf(nxx), 1-d2.cdf(xx), err_msg='cdf'+d1.name) assert_almost_equal(d1.pdf(nxx), d2.pdf(xx)) assert_almost_equal(d1.sf(nxx), 1-d2.sf(xx)) assert_almost_equal(d1.ppf(ppfq), -d2.ppf(ppfq)[::-1]) assert_almost_equal(d1.isf(ppfq), -d2.isf(ppfq)[::-1]) assert_almost_equal(d1.moment(3), -d2.moment(3)) ch2oddneg = [v*(-1)**(i+1) for i,v in enumerate(d2.stats(moments='mvsk'))] assert_almost_equal(d1.stats(moments='mvsk'), ch2oddneg, err_msg='stats '+d1.name+d2.name) if __name__ == '__main__': tt = Test_Transf2() tt.test_equivalent() tt.test_equivalent_negsq() debug = 0 if debug: print negsquarenormalg.ppf([0.1,0.5,0.9]) print stats.chi2.ppf([0.1,0.5,0.9],1) print negsquarenormalg.a print negsquarenormalg.b print absnormalg.stats( moments='mvsk') print stats.foldnorm(1e-10).stats( moments='mvsk') print stats.halfnorm.stats( moments='mvsk') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/transform_functions.py000066400000000000000000000071111224417117700315310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Nonlinear Transformation classes Created on Sat Apr 16 16:06:11 2011 Author: Josef Perktold License : BSD """ import numpy as np class TransformFunction(object): def __call__(self, x): self.func(x) ## Hump and U-shaped functions class SquareFunc(TransformFunction): '''class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, if we want extension to parameterized function ''' def func(self, x): return np.power(x, 2.) def inverseplus(self, x): return np.sqrt(x) def inverseminus(self, x): return 0.0 - np.sqrt(x) def derivplus(self, x): return 0.5/np.sqrt(x) def derivminus(self, x): return 0.0 - 0.5/np.sqrt(x) class NegSquareFunc(TransformFunction): '''negative quadratic function ''' def func(self, x): return -np.power(x,2) def inverseplus(self, x): return np.sqrt(-x) def inverseminus(self, x): return 0.0 - np.sqrt(-x) def derivplus(self, x): return 0.0 - 0.5/np.sqrt(-x) def derivminus(self, x): return 0.5/np.sqrt(-x) class AbsFunc(TransformFunction): '''class for absolute value transformation ''' def func(self, x): return np.abs(x) def inverseplus(self, x): return x def inverseminus(self, x): return 0.0 - x def derivplus(self, x): return 1.0 def derivminus(self, x): return 0.0 - 1.0 ## monotonic functions # more monotone functions in families.links, some for restricted domains class LogFunc(TransformFunction): def func(self, x): return np.log(x) def inverse(self, y): return np.exp(y) def deriv(self, x): return 1./x class ExpFunc(TransformFunction): def func(self, x): return np.exp(x) def inverse(self, y): return np.log(y) def deriv(self, x): return np.exp(x) class BoxCoxNonzeroFunc(TransformFunction): def __init__(self, lamda): self.lamda = lamda def func(self, x): return (np.power(x, self.lamda) - 1)/self.lamda def inverse(self, y): return (self.lamda * y + 1)/self.lamda def deriv(self, x): return np.power(x, self.lamda - 1) class AffineFunc(TransformFunction): def __init__(self, constant, slope): self.constant = constant self.slope = slope def func(self, x): return self.constant + self.slope * x def inverse(self, y): return (y - self.constant) / self.slope def deriv(self, x): return self.slope class ChainFunc(TransformFunction): def __init__(self, finn, fout): self.finn = finn self.fout = fout def func(self, x): return self.fout.func(self.finn.func(x)) def inverse(self, y): return self.f1.inverse(self.fout.inverse(y)) def deriv(self, x): z = self.finn.func(x) return self.fout.deriv(z) * self.finn.deriv(x) #def inverse(x): # return np.divide(1.0,x) # #mux, stdx = 0.05, 0.1 #mux, stdx = 9.0, 1.0 #def inversew(x): # return 1.0/(1+mux+x*stdx) #def inversew_inv(x): # return (1.0/x - 1.0 - mux)/stdx #.np.divide(1.0,x)-10 # #def identit(x): # return x if __name__ == '__main__': absf = AbsFunc() absf.func(5) == 5 absf.func(-5) == 5 absf.inverseplus(5) == 5 absf.inverseminus(5) == -5 chainf = ChainFunc(AffineFunc(1,2), BoxCoxNonzeroFunc(2)) print chainf.func(3.) chainf2 = ChainFunc(BoxCoxNonzeroFunc(2), AffineFunc(1,2)) print chainf.func(3.) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/transformed.py000066400000000000000000000377731224417117700277730ustar00rootroot00000000000000 ## copied from nonlinear_transform_gen.py ''' A class for the distribution of a non-linear monotonic transformation of a continuous random variable simplest usage: example: create log-gamma distribution, i.e. y = log(x), where x is gamma distributed (also available in scipy.stats) loggammaexpg = Transf_gen(stats.gamma, np.log, np.exp) example: what is the distribution of the discount factor y=1/(1+x) where interest rate x is normally distributed with N(mux,stdx**2)')? (just to come up with a story that implies a nice transformation) invnormalg = Transf_gen(stats.norm, inversew, inversew_inv, decr=True, a=-np.inf) This class does not work well for distributions with difficult shapes, e.g. 1/x where x is standard normal, because of the singularity and jump at zero. Note: I'm working from my version of scipy.stats.distribution. But this script runs under scipy 0.6.0 (checked with numpy: 1.2.0rc2 and python 2.4) This is not yet thoroughly tested, polished or optimized TODO: * numargs handling is not yet working properly, numargs needs to be specified (default = 0 or 1) * feeding args and kwargs to underlying distribution is untested and incomplete * distinguish args and kwargs for the transformed and the underlying distribution - currently all args and no kwargs are transmitted to underlying distribution - loc and scale only work for transformed, but not for underlying distribution - possible to separate args for transformation and underlying distribution parameters * add _rvs as method, will be faster in many cases Created on Tuesday, October 28, 2008, 12:40:37 PM Author: josef-pktd License: BSD ''' from scipy import integrate # for scipy 0.6.0 from scipy import stats, info from scipy.stats import distributions import numpy as np def get_u_argskwargs(**kwargs): #Todo: What's this? wrong spacing, used in Transf_gen TransfTwo_gen u_kwargs = dict((k.replace('u_','',1),v) for k,v in kwargs.items() if k.startswith('u_')) u_args = u_kwargs.pop('u_args',None) return u_args, u_kwargs class Transf_gen(distributions.rv_continuous): '''a class for non-linear monotonic transformation of a continuous random variable ''' def __init__(self, kls, func, funcinv, *args, **kwargs): #print args #print kwargs self.func = func self.funcinv = funcinv #explicit for self.__dict__.update(kwargs) #need to set numargs because inspection does not work self.numargs = kwargs.pop('numargs', 0) #print self.numargs name = kwargs.pop('name','transfdist') longname = kwargs.pop('longname','Non-linear transformed distribution') extradoc = kwargs.pop('extradoc',None) a = kwargs.pop('a', -np.inf) b = kwargs.pop('b', np.inf) self.decr = kwargs.pop('decr', False) #defines whether it is a decreasing (True) # or increasing (False) monotonic transformation self.u_args, self.u_kwargs = get_u_argskwargs(**kwargs) self.kls = kls #(self.u_args, self.u_kwargs) # possible to freeze the underlying distribution super(Transf_gen,self).__init__(a=a, b=b, name = name, shapes=kls.shapes, longname = longname, extradoc = extradoc) def _cdf(self,x,*args, **kwargs): #print args if not self.decr: return self.kls._cdf(self.funcinv(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self.kls._cdf(self.funcinv(x),*args, **kwargs) def _ppf(self, q, *args, **kwargs): if not self.decr: return self.func(self.kls._ppf(q,*args, **kwargs)) else: return self.func(self.kls._ppf(1-q,*args, **kwargs)) def inverse(x): return np.divide(1.0,x) mux, stdx = 0.05, 0.1 mux, stdx = 9.0, 1.0 def inversew(x): return 1.0/(1+mux+x*stdx) def inversew_inv(x): return (1.0/x - 1.0 - mux)/stdx #.np.divide(1.0,x)-10 def identit(x): return x invdnormalg = Transf_gen(stats.norm, inversew, inversew_inv, decr=True, #a=-np.inf, numargs = 0, name = 'discf', longname = 'normal-based discount factor', extradoc = '\ndistribution of discount factor y=1/(1+x)) with x N(0.05,0.1**2)') lognormalg = Transf_gen(stats.norm, np.exp, np.log, numargs = 2, a=0, name = 'lnnorm', longname = 'Exp transformed normal', extradoc = '\ndistribution of y = exp(x), with x standard normal' 'precision for moment andstats is not very high, 2-3 decimals') loggammaexpg = Transf_gen(stats.gamma, np.log, np.exp, numargs=1) ## copied form nonlinear_transform_short.py '''univariate distribution of a non-linear monotonic transformation of a random variable ''' from scipy import stats from scipy.stats import distributions import numpy as np class ExpTransf_gen(distributions.rv_continuous): '''Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable ''' def __init__(self, kls, *args, **kwargs): #print args #print kwargs #explicit for self.__dict__.update(kwargs) if 'numargs' in kwargs: self.numargs = kwargs['numargs'] else: self.numargs = 1 if 'name' in kwargs: name = kwargs['name'] else: name = 'Log transformed distribution' if 'a' in kwargs: a = kwargs['a'] else: a = 0 super(ExpTransf_gen,self).__init__(a=a, name=name) self.kls = kls def _cdf(self,x,*args): #print args return self.kls._cdf(np.log(x),*args) def _ppf(self, q, *args): return np.exp(self.kls._ppf(q,*args)) class LogTransf_gen(distributions.rv_continuous): '''Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable ''' def __init__(self, kls, *args, **kwargs): #explicit for self.__dict__.update(kwargs) if 'numargs' in kwargs: self.numargs = kwargs['numargs'] else: self.numargs = 1 if 'name' in kwargs: name = kwargs['name'] else: name = 'Log transformed distribution' if 'a' in kwargs: a = kwargs['a'] else: a = 0 super(LogTransf_gen,self).__init__(a=a, name = name) self.kls = kls def _cdf(self,x, *args): #print args return self.kls._cdf(np.exp(x),*args) def _ppf(self, q, *args): return np.log(self.kls._ppf(q,*args)) def examples_transf(): ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal') ##print lognormal.cdf(1) ##print stats.lognorm.cdf(1,1) ##print lognormal.stats() ##print stats.lognorm.stats(1) ##print lognormal.rvs(size=10) print 'Results for lognormal' lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general') print lognormalg.cdf(1) print stats.lognorm.cdf(1,1) print lognormalg.stats() print stats.lognorm.stats(1) print lognormalg.rvs(size=5) ##print 'Results for loggamma' ##loggammag = ExpTransf_gen(stats.gamma) ##print loggammag._cdf(1,10) ##print stats.loggamma.cdf(1,10) print 'Results for expgamma' loggammaexpg = LogTransf_gen(stats.gamma) print loggammaexpg._cdf(1,10) print stats.loggamma.cdf(1,10) print loggammaexpg._cdf(2,15) print stats.loggamma.cdf(2,15) # this requires change in scipy.stats.distribution #print loggammaexpg.cdf(1,10) print 'Results for loglaplace' loglaplaceg = LogTransf_gen(stats.laplace) print loglaplaceg._cdf(2,10) print stats.loglaplace.cdf(2,10) loglaplaceexpg = ExpTransf_gen(stats.laplace) print loglaplaceexpg._cdf(2,10) ## copied from transformtwo.py ''' Created on Apr 28, 2009 @author: Josef Perktold ''' ''' A class for the distribution of a non-linear u-shaped or hump shaped transformation of a continuous random variable This is a companion to the distributions of non-linear monotonic transformation to the case when the inverse mapping is a 2-valued correspondence, for example for absolute value or square simplest usage: example: create squared distribution, i.e. y = x**2, where x is normal or t distributed This class does not work well for distributions with difficult shapes, e.g. 1/x where x is standard normal, because of the singularity and jump at zero. This verifies for normal - chi2, normal - halfnorm, foldnorm, and t - F TODO: * numargs handling is not yet working properly, numargs needs to be specified (default = 0 or 1) * feeding args and kwargs to underlying distribution works in t distribution example * distinguish args and kwargs for the transformed and the underlying distribution - currently all args and no kwargs are transmitted to underlying distribution - loc and scale only work for transformed, but not for underlying distribution - possible to separate args for transformation and underlying distribution parameters * add _rvs as method, will be faster in many cases ''' class TransfTwo_gen(distributions.rv_continuous): '''Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it's inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, derivminus. Currently no numerical derivatives or inverse are calculated This can be used to generate distribution instances similar to the distributions in scipy.stats. ''' #a class for non-linear non-monotonic transformation of a continuous random variable def __init__(self, kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs): #print args #print kwargs self.func = func self.funcinvplus = funcinvplus self.funcinvminus = funcinvminus self.derivplus = derivplus self.derivminus = derivminus #explicit for self.__dict__.update(kwargs) #need to set numargs because inspection does not work self.numargs = kwargs.pop('numargs', 0) #print self.numargs name = kwargs.pop('name','transfdist') longname = kwargs.pop('longname','Non-linear transformed distribution') extradoc = kwargs.pop('extradoc',None) a = kwargs.pop('a', -np.inf) # attached to self in super b = kwargs.pop('b', np.inf) # self.a, self.b would be overwritten self.shape = kwargs.pop('shape', False) #defines whether it is a `u` shaped or `hump' shaped # transformation self.u_args, self.u_kwargs = get_u_argskwargs(**kwargs) self.kls = kls #(self.u_args, self.u_kwargs) # possible to freeze the underlying distribution super(TransfTwo_gen,self).__init__(a=a, b=b, name = name, shapes=kls.shapes, longname = longname, extradoc = extradoc) def _rvs(self, *args): self.kls._size = self._size #size attached to self, not function argument return self.func(self.kls._rvs(*args)) def _pdf(self,x,*args, **kwargs): #print args if self.shape == 'u': signpdf = 1 elif self.shape == 'hump': signpdf = -1 else: raise ValueError, 'shape can only be `u` or `hump`' return signpdf * (self.derivplus(x)*self.kls._pdf(self.funcinvplus(x),*args, **kwargs) - self.derivminus(x)*self.kls._pdf(self.funcinvminus(x),*args, **kwargs)) #note scipy _cdf only take *args not *kwargs def _cdf(self,x,*args, **kwargs): #print args if self.shape == 'u': return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \ self.kls._cdf(self.funcinvminus(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self._sf(x,*args, **kwargs) def _sf(self,x,*args, **kwargs): #print args if self.shape == 'hump': return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \ self.kls._cdf(self.funcinvminus(x),*args, **kwargs) #note scipy _cdf only take *args not *kwargs else: return 1.0 - self._cdf(x, *args, **kwargs) def _munp(self, n,*args, **kwargs): return self._mom0_sc(n,*args) # ppf might not be possible in general case? # should be possible in symmetric case # def _ppf(self, q, *args, **kwargs): # if self.shape == 'u': # return self.func(self.kls._ppf(q,*args, **kwargs)) # elif self.shape == 'hump': # return self.func(self.kls._ppf(1-q,*args, **kwargs)) #TODO: rename these functions to have unique names class SquareFunc(object): '''class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, if we want extension to parameterized function ''' def inverseplus(self, x): return np.sqrt(x) def inverseminus(self, x): return 0.0 - np.sqrt(x) def derivplus(self, x): return 0.5/np.sqrt(x) def derivminus(self, x): return 0.0 - 0.5/np.sqrt(x) def squarefunc(self, x): return np.power(x,2) sqfunc = SquareFunc() squarenormalg = TransfTwo_gen(stats.norm, sqfunc.squarefunc, sqfunc.inverseplus, sqfunc.inverseminus, sqfunc.derivplus, sqfunc.derivminus, shape='u', a=0.0, b=np.inf, numargs = 0, name = 'squarenorm', longname = 'squared normal distribution', extradoc = '\ndistribution of the square of a normal random variable' +\ ' y=x**2 with x N(0.0,1)') #u_loc=l, u_scale=s) squaretg = TransfTwo_gen(stats.t, sqfunc.squarefunc, sqfunc.inverseplus, sqfunc.inverseminus, sqfunc.derivplus, sqfunc.derivminus, shape='u', a=0.0, b=np.inf, numargs = 1, name = 'squarenorm', longname = 'squared t distribution', extradoc = '\ndistribution of the square of a t random variable' +\ ' y=x**2 with x t(dof,0.0,1)') def inverseplus(x): return np.sqrt(-x) def inverseminus(x): return 0.0 - np.sqrt(-x) def derivplus(x): return 0.0 - 0.5/np.sqrt(-x) def derivminus(x): return 0.5/np.sqrt(-x) def negsquarefunc(x): return -np.power(x,2) negsquarenormalg = TransfTwo_gen(stats.norm, negsquarefunc, inverseplus, inverseminus, derivplus, derivminus, shape='hump', a=-np.inf, b=0.0, numargs = 0, name = 'negsquarenorm', longname = 'negative squared normal distribution', extradoc = '\ndistribution of the negative square of a normal random variable' +\ ' y=-x**2 with x N(0.0,1)') #u_loc=l, u_scale=s) def inverseplus(x): return x def inverseminus(x): return 0.0 - x def derivplus(x): return 1.0 def derivminus(x): return 0.0 - 1.0 def absfunc(x): return np.abs(x) absnormalg = TransfTwo_gen(stats.norm, np.abs, inverseplus, inverseminus, derivplus, derivminus, shape='u', a=0.0, b=np.inf, numargs = 0, name = 'absnorm', longname = 'absolute of normal distribution', extradoc = '\ndistribution of the absolute value of a normal random variable' +\ ' y=abs(x) with x N(0,1)') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/try_max.py000066400000000000000000000045721224417117700271210ustar00rootroot00000000000000''' adjusted from Denis on pystatsmodels mailing list there might still be problems with loc and scale, ''' from __future__ import division import numpy as np from scipy import stats __date__ = "2010-12-29 dec" class MaxDist(stats.rv_continuous): """ max of n of scipy.stats normal expon ... Example: maxnormal10 = RVmax( scipy.stats.norm, 10 ) sample = maxnormal10( size=1000 ) sample.cdf = cdf ^ n, ppf ^ (1/n) """ def __init__( self, dist, n ): self.dist = dist self.n = n extradoc = 'maximumdistribution is the distribution of the '\ + 'maximum of n i.i.d. random variable' super(MaxDist, self).__init__(name='maxdist', a=dist.a, b=dist.b, longname = 'A maximumdistribution', extradoc = extradoc) def _pdf(self, x, *args, **kw): return self.n * self.dist.pdf(x, *args, **kw) \ * self.dist.cdf(x, *args, **kw )**(self.n-1) def _cdf(self, x, *args, **kw): return self.dist.cdf(x, *args, **kw)**self.n def _ppf(self, q, *args, **kw): # y = F(x) ^ n <=> x = F-1( y ^ 1/n) return self.dist.ppf(q**(1./self.n), *args, **kw) ## def rvs( self, *args, **kw ): ## size = kw.pop( "size", 1 ) ## u = np.random.uniform( size=size, **kw ) ** (1 / self.n) ## return self.dist.ppf( u, **kw ) maxdistr = MaxDist(stats.norm, 10) print maxdistr.rvs(size=10) print maxdistr.stats(moments = 'mvsk') ''' >>> print maxdistr.stats(moments = 'mvsk') (array(1.5387527308351818), array(0.34434382328492852), array(0.40990510188513779), array(0.33139861783918922)) >>> rvs = np.random.randn(1000,10) >>> stats.describe(rvs.max(1)) (1000, (-0.028558517753519492, 3.6134958002753685), 1.5560520428553426, 0.34965234046170773, 0.48504309950278557, 0.17691859056779258) >>> rvs2 = maxdistr.rvs(size=1000) >>> stats.describe(rvs2) (1000, (-0.015290995091401905, 3.3227019151170931), 1.5248146840651813, 0.32827518543128631, 0.23998620901199566, -0.080555658370268013) >>> rvs2 = maxdistr.rvs(size=10000) >>> stats.describe(rvs2) (10000, (-0.15855091764294812, 4.1898138060896937), 1.532862047388899, 0.34361316060467512, 0.43128838106936973, 0.41115043864619061) >>> maxdistr.pdf(1.5) 0.69513824417156755 #integrating the pdf >>> maxdistr.expect() 1.5387527308351729 >>> maxdistr.expect(lambda x:1) 0.99999999999999956 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/distributions/try_pot.py000066400000000000000000000043531224417117700271330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed May 04 06:09:18 2011 @author: josef """ import numpy as np def mean_residual_life(x, frac=None, alpha=0.05): '''emprirical mean residual life or expected shortfall Parameters ---------- todo: check formula for std of mean doesn't include case for all observations last observations std is zero vectorize loop using cumsum frac doesn't work yet ''' axis = 0 #searchsorted is 1d only x = np.asarray(x) nobs = x.shape[axis] xsorted = np.sort(x, axis=axis) if frac is None: xthreshold = xsorted else: xthreshold = xsorted[np.floor(nobs * frac).astype(int)] #use searchsorted instead of simple index in case of ties xlargerindex = np.searchsorted(xsorted, xthreshold, side='right') #replace loop with cumsum ? result = [] for i in range(len(xthreshold)-1): k_ind = xlargerindex[i] rmean = x[k_ind:].mean() rstd = x[k_ind:].std() #this doesn't work for last observations, nans rmstd = rstd/np.sqrt(nobs-k_ind) #std error of mean, check formula result.append((k_ind, xthreshold[i], rmean, rmstd)) res = np.array(result) crit = 1.96 # todo: without loading stats, crit = -stats.t.ppf(0.05) confint = res[:,1:2] + crit * res[:,-1:] * np.array([[-1,1]]) return np.column_stack((res, confint)) expected_shortfall = mean_residual_life #alias if __name__ == "__main__": rvs = np.random.standard_t(5, size= 10) res = mean_residual_life(rvs) print res rmean = [rvs[i:].mean() for i in range(len(rvs))] print res[:,2] - rmean[1:] ''' >>> mean_residual_life(rvs, frac= 0.5) Traceback (most recent call last): File "", line 1, in File "E:\Josef\eclipsegworkspace\statsmodels-josef-experimental-030\scikits\statsmodels\sandbox\distributions\try_pot.py", line 35, in mean_residual_life for i in range(len(xthreshold)-1): TypeError: object of type 'numpy.float64' has no len() >>> mean_residual_life(rvs, frac= [0.5]) array([[ 1. , -1.16904459, 0.35165016, 0.41090978, -1.97442776, -0.36366142], [ 1. , -1.16904459, 0.35165016, 0.41090978, -1.97442776, -0.36366142], [ 1. , -1.1690445 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/000077500000000000000000000000001224417117700237705ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/bayesprior.py000066400000000000000000000155251224417117700265310ustar00rootroot00000000000000# # This script examines the predictive prior densities of two local level # models given the same priors for parameters that appear to be the same. # Reference: Del Negro and Schorfheide. try: import pymc pymc_installed = 1 except: print "pymc not imported" pymc_installed = 0 from scipy.stats import gamma, beta, invgamma import numpy as np from matplotlib import pyplot as plt from scipy import stats from scipy.stats import rv_continuous from scipy.special import gammaln, gammaincinv, gamma, gammainc from numpy import log,exp #np.random.seed(12345) class igamma_gen(rv_continuous): def _pdf(self, x, a, b): return exp(self._logpdf(x,a,b)) def _logpdf(self, x, a, b): return a*log(b) - gammaln(a) -(a+1)*log(x) - b/x def _cdf(self, x, a, b): return 1.0-gammainc(a,b/x) # why is this different than the wiki? def _ppf(self, q, a, b): return b/gammaincinv(a,1-q) #NOTE: should be correct, work through invgamma example and 2 param inv gamma #CDF def _munp(self, n, a, b): args = (a,b) super(igamma_gen, self)._munp(self, n, *args) #TODO: is this robust for differential entropy in this case? closed form or #shortcuts in special? def _entropy(self, *args): def integ(x): val = self._pdf(x, *args) return val*log(val) entr = -integrate.quad(integ, self.a, self.b)[0] if not np.isnan(entr): return entr else: raise ValueError("Problem with integration. Returned nan.") igamma = igamma_gen(a=0.0, name='invgamma', longname="An inverted gamma", shapes = 'a,b', extradoc=""" Inverted gamma distribution invgamma.pdf(x,a,b) = b**a*x**(-a-1)/gamma(a) * exp(-b/x) for x > 0, a > 0, b>0. """ ) #NOTE: the above is unnecessary. B takes the same role as the scale parameter # in inverted gamma palpha = np.random.gamma(400.,.005, size=10000) print "First moment: %s\nSecond moment: %s" % (palpha.mean(),palpha.std()) palpha = palpha[0] prho = np.random.beta(49.5,49.5, size=1e5) print "Beta Distribution" print "First moment: %s\nSecond moment: %s" % (prho.mean(),prho.std()) prho = prho[0] psigma = igamma.rvs(1.,4.**2/2, size=1e5) print "Inverse Gamma Distribution" print "First moment: %s\nSecond moment: %s" % (psigma.mean(),psigma.std()) # First do the univariate case # y_t = theta_t + epsilon_t # epsilon ~ N(0,1) # Where theta ~ N(mu,lambda**2) # or the model # y_t = theta2_t + theta1_t * y_t-1 + epsilon_t # Prior 1: # theta1 ~ uniform(0,1) # theta2|theta1 ~ N(mu,lambda**2) # Prior 2: # theta1 ~ U(0,1) # theta2|theta1 ~ N(mu(1-theta1),lambda**2(1-theta1)**2) draws = 400 # prior beliefs, from JME paper mu_, lambda_ = 1.,2. # Model 1 y1y2 = np.zeros((draws,2)) for draw in range(draws): theta = np.random.normal(mu_,lambda_**2) y1 = theta + np.random.normal() y2 = theta + np.random.normal() y1y2[draw] = y1,y2 # log marginal distribution lnp1p2_mod1 = stats.norm.pdf(y1,loc=mu_, scale=lambda_**2+1)*\ stats.norm.pdf(y2,mu_,scale=lambda_**2+1) # Model 2 pmu_pairsp1 = np.zeros((draws,2)) y1y2pairsp1 = np.zeros((draws,2)) # prior 1 for draw in range(draws): theta1 = np.random.uniform(0,1) theta2 = np.random.normal(mu_, lambda_**2) # mu = theta2/(1-theta1) #don't do this to maintain independence theta2 is the _location_ # y1 = np.random.normal(mu_, lambda_**2) y1 = theta2 # pmu_pairsp1[draw] = mu, theta1 pmu_pairsp1[draw] = theta2, theta1 # mean, autocorr y2 = theta2 + theta1 * y1 + np.random.normal() y1y2pairsp1[draw] = y1,y2 # for a = 0, b = 1 - epsilon = .99999 # mean of u is .5*.99999 # variance is 1./12 * .99999**2 # Model 2 pmu_pairsp2 = np.zeros((draws,2)) y1y2pairsp2 = np.zeros((draws,2)) # prior 2 theta12_2 = [] for draw in range(draws): # y1 = np.random.uniform(-4,6) theta1 = np.random.uniform(0,1) theta2 = np.random.normal(mu_*(1-theta1), lambda_**2*(1-theta1)**2) theta12_2.append([theta1,theta2]) mu = theta2/(1-theta1) y1 = np.random.normal(mu_,lambda_**2) y2 = theta2 + theta1 * y1 + np.random.normal() pmu_pairsp2[draw] = mu, theta1 y1y2pairsp2[draw] = y1,y2 fig = plt.figure() fsp = fig.add_subplot(221) fsp.scatter(pmu_pairsp1[:,0], pmu_pairsp1[:,1], color='b', facecolor='none') fsp.set_ylabel('Autocorrelation (Y)') fsp.set_xlabel('Mean (Y)') fsp.set_title('Model 2 (P1)') fsp.axis([-20,20,0,1]) fsp = fig.add_subplot(222) fsp.scatter(pmu_pairsp2[:,0],pmu_pairsp2[:,1], color='b', facecolor='none') fsp.set_title('Model 2 (P2)') fsp.set_ylabel('Autocorrelation (Y)') fsp.set_xlabel('Mean (Y)') fsp.set_title('Model 2 (P2)') fsp.axis([-20,20,0,1]) fsp = fig.add_subplot(223) fsp.scatter(y1y2pairsp1[:,0], y1y2pairsp1[:,1], color='b', marker='o', facecolor='none') fsp.scatter(y1y2[:,0], y1y2[:,1], color ='g', marker='+') fsp.set_title('Model 1 vs. Model 2 (P1)') fsp.set_ylabel('Y(2)') fsp.set_xlabel('Y(1)') fsp.axis([-20,20,-20,20]) fsp = fig.add_subplot(224) fsp.scatter(y1y2pairsp2[:,0], y1y2pairsp2[:,1], color='b', marker='o') fsp.scatter(y1y2[:,0], y1y2[:,1], color='g', marker='+') fsp.set_title('Model 1 vs. Model 2 (P2)') fsp.set_ylabel('Y(2)') fsp.set_xlabel('Y(1)') fsp.axis([-20,20,-20,20]) #plt.show() #TODO: this doesn't look the same as the working paper? #NOTE: but it matches the language? I think mine is right! # Contour plots. # on the basis of observed data. ie., the mgrid #np.mgrid[6:-4:10j,-4:6:10j] # Example 2: # 2 NK Phillips Curves # Structural form # M1: y_t = 1/alpha *E_t[y_t+1] + mu_t # mu_t = p1 * mu_t-1 + epsilon_t # epsilon_t ~ N(0,sigma2) # Reduced form Law of Motion # M1: y_t = p1*y_t-1 + 1/(1-p1/alpha)*epsilon_t # specify prior for M1 # for i = 1,2 # theta_i = [alpha # p_i # sigma] # truncate effective priors by the determinancy region # for determinancy we need alpha > 1 # p in [0,1) # palpha ~ Gamma(2.00,.10) # mean = 2.00 # std = .1 which implies k = 400, theta = .005 palpha = np.random.gamma(400,.005) # pi ~ Beta(.5,.05) pi = np.random.beta(49.5, 49.5) # psigma ~ InvGamma(1.00,4.00) #def invgamma(a,b): # return np.sqrt(b*a**2/np.sum(np.random.random(b,1)**2, axis=1)) #NOTE: Use inverse gamma distribution igamma psigma = igamma.rvs(1.,4.0, size=1e6) #TODO: parameterization is not correct vs. # Del Negro and Schorfheide if pymc_installed: psigma2 = pymc.rinverse_gamma(1.,4.0, size=1e6) else: psigma2 = stats.invgamma.rvs(1., scale=4.0, size=1e6) nsims = 500 y = np.zeros((nsims)) #for i in range(1,nsims): # y[i] = .9*y[i-1] + 1/(1-p1/alpha) + np.random.normal() #Are these supposed to be sampled jointly? # InvGamma(sigma|v,s) propto sigma**(-v-1)*e**(-vs**2/2*sigma**2) #igamma = # M2: y_t = 1/alpha * E_t[y_t+1] + p2*y_t-1 + mu_t # mu_t ~ epsilon_t # epsilon_t ~ n(0,sigma2) # Reduced form Law of Motion # y_t = 1/2 (alpha-sqrt(alpha**2-4*p2*alpha)) * y_t-1 + 2*alpha/(alpha + \ # sqrt(alpha**2 - 4*p2*alpha)) * epsilon_t statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/dji_table.csv000066400000000000000000043342411224417117700264350ustar00rootroot00000000000000Date,Open,High,Low,Close,Volume,Adj Close 2010-02-12,10137.23,10140.18,9962.13,10099.14,4160680000,10099.14 2010-02-11,10037.85,10184.85,9963.19,10144.19,4400870000,10144.19 2010-02-10,10055.46,10120.15,9946.26,10038.38,4251450000,10038.38 2010-02-09,9910.28,10154.24,9910.28,10058.64,5114260000,10058.64 2010-02-08,10005.43,10059.24,9882.85,9908.39,4089820000,9908.39 2010-02-05,10003.69,10078.89,9822.84,10012.23,6438900000,10012.23 2010-02-04,10273.12,10273.12,9984.35,10002.18,5859690000,10002.18 2010-02-03,10291.73,10356.86,10192.03,10270.55,4285450000,10270.55 2010-02-02,10186.13,10333.35,10138.75,10296.85,4749540000,10296.85 2010-02-01,10068.99,10227.24,10068.99,10185.53,4077610000,10185.53 2010-01-29,10122.04,10272.29,10014.35,10067.33,5412850000,10067.33 2010-01-28,10236.92,10310.68,10023.80,10120.46,5452400000,10120.46 2010-01-27,10194.29,10294.13,10060.98,10236.16,5319120000,10236.16 2010-01-26,10195.35,10323.00,10102.17,10194.29,4731910000,10194.29 2010-01-25,10175.10,10316.65,10135.95,10196.86,4481390000,10196.86 2010-01-22,10389.58,10450.04,10133.15,10172.98,6208650000,10172.98 2010-01-21,10603.91,10651.14,10334.18,10389.88,6874289600,10389.88 2010-01-20,10719.69,10719.69,10492.36,10603.15,4810560000,10603.15 2010-01-19,10608.37,10763.45,10555.47,10725.43,4724830000,10725.43 2010-01-15,10706.99,10736.54,10529.09,10609.65,4758730000,10609.65 2010-01-14,10680.16,10767.15,10619.02,10710.55,3915200000,10710.55 2010-01-13,10628.09,10747.12,10569.07,10680.77,4170360000,10680.77 2010-01-12,10662.86,10701.48,10523.35,10627.26,4716160000,10627.26 2010-01-11,10620.31,10739.87,10538.91,10663.99,4255780000,10663.99 2010-01-08,10606.40,10653.11,10509.74,10618.19,4389590000,10618.19 2010-01-07,10571.11,10655.60,10471.73,10606.86,5270680000,10606.86 2010-01-06,10564.72,10655.22,10488.28,10573.68,4972660000,10573.68 2010-01-05,10584.56,10647.14,10468.86,10572.02,2491020000,10572.02 2010-01-04,10430.69,10641.62,10430.69,10583.96,3991400000,10583.96 2009-12-31,10548.51,10578.74,10420.56,10428.05,2076990000,10428.05 2009-12-30,10544.36,10583.28,10470.75,10548.51,2277300000,10548.51 2009-12-29,10547.83,10605.65,10518.59,10545.41,2491020000,10545.41 2009-12-28,10517.91,10550.78,10517.91,10547.07,2716400000,10547.07 2009-12-24,10467.12,10541.26,10450.95,10520.10,1267710000,10520.10 2009-12-23,10464.32,10520.93,10409.00,10466.44,3166870000,10466.44 2009-12-22,10414.67,10511.56,10399.33,10464.93,3641130000,10464.93 2009-12-21,10330.10,10489.41,10330.10,10414.14,3977340000,10414.14 2009-12-18,10309.39,10412.55,10237.75,10328.89,6325890000,10328.89 2009-12-17,10439.99,10439.99,10279.39,10308.26,7615070400,10308.26 2009-12-16,10449.81,10552.75,10401.90,10441.12,4829820000,10441.12 2009-12-15,10499.31,10542.09,10380.96,10452.00,5045100000,10452.00 2009-12-14,10471.28,10566.88,10431.60,10501.05,4548490000,10501.05 2009-12-11,10403.41,10516.47,10385.42,10471.50,3791090000,10471.50 2009-12-10,10336.00,10479.06,10332.14,10405.83,3996490000,10405.83 2009-12-09,10282.85,10377.11,10207.29,10337.05,4115410000,10337.05 2009-12-08,10385.42,10385.42,10216.44,10285.97,4748030000,10285.97 2009-12-07,10386.86,10478.23,10321.11,10390.11,4103360000,10390.11 2009-12-04,10368.57,10549.04,10285.44,10388.90,5781140000,10388.90 2009-12-03,10455.63,10533.55,10338.49,10366.15,4810030000,10366.15 2009-12-02,10470.44,10537.63,10386.03,10452.68,3941340000,10452.68 2009-12-01,10343.82,10537.03,10343.82,10471.58,4249310000,10471.58 2009-11-30,10309.77,10394.34,10238.05,10344.84,3895520000,10344.84 2009-11-27,10452.23,10452.23,10179.33,10309.92,2362910000,10309.92 2009-11-25,10432.96,10513.60,10385.65,10464.40,3036350000,10464.40 2009-11-24,10451.25,10488.66,10335.62,10433.71,3700820000,10433.71 2009-11-23,10320.13,10524.40,10320.13,10450.95,3827920000,10450.95 2009-11-20,10327.91,10377.41,10237.60,10318.16,3751230000,10318.16 2009-11-19,10425.33,10425.33,10226.41,10332.44,4178030000,10332.44 2009-11-18,10426.27,10471.28,10330.33,10426.31,4293340000,10426.31 2009-11-17,10404.77,10465.76,10318.69,10437.42,3824070000,10437.42 2009-11-16,10267.53,10465.83,10267.53,10406.96,4565850000,10406.96 2009-11-13,10197.85,10332.29,10162.93,10270.47,3792610000,10270.47 2009-11-12,10289.82,10341.21,10157.64,10197.47,4160250000,10197.47 2009-11-11,10247.42,10357.38,10217.19,10291.26,4286700000,10291.26 2009-11-10,10223.01,10300.33,10148.12,10246.97,4394770000,10246.97 2009-11-09,10020.62,10248.93,10020.62,10226.94,4460030000,10226.94 2009-11-06,10001.35,10077.08,9898.49,10023.42,4277130000,10023.42 2009-11-05,9807.80,10043.75,9807.80,10005.96,4848350000,10005.96 2009-11-04,9767.30,9962.35,9745.76,9802.14,5635510000,9802.14 2009-11-03,9787.47,9844.84,9649.78,9771.91,5487500000,9771.91 2009-11-02,9712.13,9883.68,9647.06,9789.44,6202640000,9789.44 2009-10-30,9961.52,9980.19,9664.89,9712.73,6512420000,9712.73 2009-10-29,9762.91,9996.67,9762.91,9962.58,5595040000,9962.58 2009-10-28,9881.11,9940.89,9723.31,9762.69,6600350000,9762.69 2009-10-27,9868.34,9994.55,9802.36,9882.17,5337380000,9882.17 2009-10-26,9972.33,10107.99,9817.55,9867.96,6363380000,9867.96 2009-10-23,10099.90,10138.59,9908.70,9972.18,4767460000,9972.18 2009-10-22,9946.18,10133.08,9879.07,10081.31,5192410000,10081.31 2009-10-21,10038.84,10157.94,9909.83,9949.36,5616290000,9949.36 2009-10-20,10092.42,10157.26,9952.98,10041.48,5396930000,10041.48 2009-10-19,9996.67,10146.61,9967.49,10092.19,4619240000,10092.19 2009-10-16,10061.36,10072.62,9884.51,9995.91,4894740000,9995.91 2009-10-15,10014.88,10087.43,9916.93,10062.94,5369780000,10062.94 2009-10-14,9873.55,10064.98,9873.55,10015.86,5406420000,10015.86 2009-10-13,9883.98,9935.53,9780.90,9871.06,4320480000,9871.06 2009-10-12,9865.24,9978.07,9814.45,9885.80,3710430000,9885.80 2009-10-09,9786.04,9890.41,9731.32,9864.94,3763780000,9864.94 2009-10-08,9728.22,9872.50,9709.78,9786.87,4988400000,9786.87 2009-10-07,9725.69,9782.56,9634.96,9725.58,4238220000,9725.58 2009-10-06,9601.26,9793.37,9601.26,9731.25,5029840000,9731.25 2009-10-05,9488.73,9640.33,9449.81,9599.75,4313310000,9599.75 2009-10-02,9507.62,9571.71,9378.77,9487.67,5583240000,9487.67 2009-10-01,9711.60,9714.70,9482.98,9509.28,5791450000,9509.28 2009-09-30,9741.83,9817.17,9583.04,9712.28,5998860000,9712.28 2009-09-29,9789.74,9861.99,9705.10,9742.20,4949900000,9742.20 2009-09-28,9663.23,9861.39,9658.09,9789.36,3726950000,9789.36 2009-09-25,9706.68,9781.73,9605.19,9665.19,4507090000,9665.19 2009-09-24,9749.99,9836.82,9637.53,9707.44,5505610000,9707.44 2009-09-23,9830.63,9937.72,9724.90,9748.55,5531930000,9748.55 2009-09-22,9779.61,9890.71,9742.96,9829.87,5246600000,9829.87 2009-09-21,9818.61,9846.12,9688.40,9778.86,4615280000,9778.86 2009-09-18,9784.75,9898.57,9751.27,9820.20,5607970000,9820.20 2009-09-17,9789.82,9896.38,9706.23,9783.92,6668110000,9783.92 2009-09-16,9683.71,9837.05,9648.95,9791.71,6793529600,9791.71 2009-09-15,9626.42,9745.91,9553.80,9683.41,6185620000,9683.41 2009-09-14,9598.08,9662.10,9492.96,9626.80,4979610000,9626.80 2009-09-11,9625.44,9698.67,9532.11,9605.41,4922600000,9605.41 2009-09-10,9546.54,9666.55,9479.20,9627.48,5191380000,9627.48 2009-09-09,9496.59,9604.43,9435.45,9547.22,5202550000,9547.22 2009-09-08,9440.13,9564.45,9402.80,9497.34,5235160000,9497.34 2009-09-04,9345.36,9465.37,9302.28,9441.27,4097370000,9441.27 2009-09-03,9282.03,9350.27,9252.93,9344.61,4624280000,9344.61 2009-09-02,9306.21,9378.77,9223.08,9280.67,5842730000,9280.67 2009-09-01,9492.32,9573.67,9275.15,9310.60,6862360000,9310.60 2009-08-31,9542.91,9552.97,9389.27,9496.28,5004560000,9496.28 2009-08-28,9582.74,9666.71,9476.63,9544.20,5785780000,9544.20 2009-08-27,9541.63,9629.98,9440.43,9580.63,5785880000,9580.63 2009-08-26,9538.61,9613.65,9446.71,9543.52,5080060000,9543.52 2009-08-25,9509.21,9646.53,9485.70,9539.29,5768740000,9539.29 2009-08-24,9506.18,9625.89,9442.17,9509.28,6302450000,9509.28 2009-08-21,9347.86,9549.19,9347.86,9505.96,5885550000,9505.96 2009-08-20,9278.55,9385.72,9237.52,9350.05,4893160000,9350.05 2009-08-19,9208.68,9333.34,9099.14,9279.16,4257000000,9279.16 2009-08-18,9134.36,9262.08,9124.08,9217.94,4198970000,9217.94 2009-08-17,9313.85,9313.85,9078.28,9135.34,4854970000,9135.34 2009-08-14,9398.04,9425.17,9214.47,9321.40,4940750000,9321.40 2009-08-13,9362.29,9448.97,9269.26,9398.19,5250660000,9398.19 2009-08-12,9236.06,9442.47,9199.80,9361.61,5498170000,9361.61 2009-08-11,9334.33,9351.86,9180.23,9241.45,5773160000,9241.45 2009-08-10,9368.41,9420.56,9249.99,9337.95,5406080000,9337.95 2009-08-07,9258.45,9466.89,9258.45,9370.07,6827089600,9370.07 2009-08-06,9277.19,9378.01,9168.44,9256.26,6753380000,9256.26 2009-08-05,9315.36,9374.38,9173.20,9280.97,7242120000,9280.97 2009-08-04,9285.05,9370.30,9207.21,9320.19,5713700000,9320.19 2009-08-03,9173.65,9342.11,9162.09,9286.56,5603440000,9286.56 2009-07-31,9154.61,9264.65,9081.30,9171.61,5139070000,9171.61 2009-07-30,9072.84,9298.13,9072.84,9154.46,6035180000,9154.46 2009-07-29,9092.34,9141.23,8967.26,9070.72,5178770000,9070.72 2009-07-28,9106.92,9154.76,8980.03,9096.72,5490350000,9096.72 2009-07-27,9093.09,9154.23,8996.58,9108.51,4631290000,9108.51 2009-07-24,9066.11,9144.48,8955.77,9093.24,4458300000,9093.24 2009-07-23,8882.31,9143.05,8837.95,9069.29,5761650000,9069.29 2009-07-22,8912.39,8993.48,8802.13,8881.26,4634100000,8881.26 2009-07-21,8848.15,8991.07,8780.82,8915.94,5309300000,8915.94 2009-07-20,8746.05,8884.43,8717.26,8848.15,4853150000,8848.15 2009-07-17,8711.89,8797.97,8638.81,8743.94,5141380000,8743.94 2009-07-16,8612.66,8750.28,8543.97,8711.82,4898640000,8711.82 2009-07-15,8363.95,8643.04,8363.95,8616.21,5238830000,8616.21 2009-07-14,8331.37,8407.48,8255.27,8359.49,4149030000,8359.49 2009-07-13,8146.82,8348.08,8106.16,8331.68,4499440000,8331.68 2009-07-10,8182.49,8216.65,8057.57,8146.52,3912080000,8146.52 2009-07-09,8179.01,8273.48,8117.27,8183.17,4347170000,8183.17 2009-07-08,8157.02,8259.05,8057.94,8178.41,5721780000,8178.41 2009-07-07,8324.95,8355.48,8138.51,8163.60,4673300000,8163.60 2009-07-06,8279.30,8364.02,8156.49,8324.87,4712580000,8324.87 2009-07-02,8503.00,8503.00,8260.41,8280.74,3931000000,8280.74 2009-07-01,8447.53,8610.32,8447.00,8504.06,3919400000,8504.06 2009-06-30,8528.93,8584.17,8369.99,8447.00,4627570000,8447.00 2009-06-29,8440.13,8569.59,8406.57,8529.38,4211760000,8529.38 2009-06-26,8468.54,8509.73,8364.17,8438.39,6076660000,8438.39 2009-06-25,8299.25,8512.60,8236.07,8472.40,4911240000,8472.40 2009-06-24,8323.51,8456.83,8246.20,8299.86,4636720000,8299.86 2009-06-23,8340.44,8413.22,8239.17,8322.91,5071020000,8322.91 2009-06-22,8538.52,8538.52,8306.66,8339.01,4903940000,8339.01 2009-06-19,8556.96,8665.26,8476.02,8539.73,5713390000,8539.73 2009-06-18,8496.73,8634.28,8438.61,8555.60,4684010000,8555.60 2009-06-17,8504.36,8602.99,8421.46,8497.18,5523650000,8497.18 2009-06-16,8612.44,8688.69,8483.58,8504.67,4951200000,8504.67 2009-06-15,8798.50,8798.50,8540.87,8612.13,4697880000,8612.13 2009-06-12,8770.01,8850.95,8671.61,8799.26,4528120000,8799.26 2009-06-11,8736.23,8911.11,8697.99,8770.92,5500840000,8770.92 2009-06-10,8763.66,8871.36,8625.21,8739.02,5379420000,8739.02 2009-06-09,8764.83,8854.80,8688.99,8763.06,4439950000,8763.06 2009-06-08,8759.35,8832.13,8593.84,8764.49,4483430000,8764.49 2009-06-05,8751.75,8900.48,8673.41,8763.13,5277910000,8763.13 2009-06-04,8665.72,8802.59,8609.17,8750.24,5352890000,8750.24 2009-06-03,8740.07,8750.83,8556.90,8675.24,5323770000,8675.24 2009-06-02,8721.60,8832.16,8635.25,8740.87,5987340000,8740.87 2009-06-01,8501.53,8797.58,8501.53,8721.44,6370440000,8721.44 2009-05-29,8404.04,8541.27,8323.91,8500.33,6050420000,8500.33 2009-05-28,8300.50,8463.70,8221.65,8403.80,5738980000,8403.80 2009-05-27,8473.65,8534.66,8280.82,8300.02,5698800000,8300.02 2009-05-26,8275.33,8523.59,8194.33,8473.49,5667050000,8473.49 2009-05-22,8292.21,8415.75,8218.86,8277.32,5155320000,8277.32 2009-05-21,8416.07,8416.07,8185.25,8292.13,6019840000,8292.13 2009-05-20,8471.82,8645.85,8376.40,8422.04,8205060000,8422.04 2009-05-19,8502.48,8594.16,8402.61,8474.85,6616270000,8474.85 2009-05-18,8270.15,8534.66,8270.15,8504.08,5702150000,8504.08 2009-05-15,8326.22,8422.28,8206.67,8268.64,5439720000,8268.64 2009-05-14,8285.92,8427.93,8218.94,8331.32,6134870000,8331.32 2009-05-13,8461.80,8461.80,8208.74,8284.89,7091820000,8284.89 2009-05-12,8419.17,8574.88,8306.47,8469.11,6871750400,8469.11 2009-05-11,8569.23,8569.23,8347.41,8418.77,6150600000,8418.77 2009-05-08,8410.73,8657.96,8388.11,8574.65,8163280000,8574.65 2009-05-07,8513.56,8651.51,8296.04,8409.85,9120100000,8409.85 2009-05-06,8403.48,8608.26,8350.12,8512.28,8555040000,8512.28 2009-05-05,8425.55,8520.80,8321.37,8410.65,6882860000,8410.65 2009-05-04,8213.60,8488.87,8213.60,8426.74,7038840000,8426.74 2009-05-01,8167.41,8278.28,8047.54,8212.41,5312170000,8212.41 2009-04-30,8188.51,8383.81,8083.62,8168.12,6862540000,8168.12 2009-04-29,8018.31,8278.12,8018.31,8185.73,6101620000,8185.73 2009-04-28,8023.56,8136.74,7898.75,8016.95,6328000000,8016.95 2009-04-27,8073.82,8152.27,7920.42,8025.00,5613460000,8025.00 2009-04-24,7957.45,8182.30,7905.60,8076.29,7114440000,8076.29 2009-04-23,7886.81,8015.36,7762.80,7957.06,6563100000,7957.06 2009-04-22,7964.78,8111.02,7802.46,7886.57,7327860000,7886.57 2009-04-21,7841.73,8027.54,7699.79,7969.56,7436489600,7969.56 2009-04-20,8128.94,8128.94,7801.58,7841.73,6973960000,7841.73 2009-04-17,8125.43,8251.20,8024.92,8131.33,7352009600,8131.33 2009-04-16,8029.14,8201.81,7933.08,8125.43,6598670000,8125.43 2009-04-15,7914.92,8069.92,7808.19,8029.62,6241100000,8029.62 2009-04-14,8057.41,8076.05,7840.53,7920.18,7569840000,7920.18 2009-04-13,8082.02,8146.86,7888.96,8057.81,6434890000,8057.81 2009-04-09,7839.89,8150.44,7839.89,8083.38,7600710400,8083.38 2009-04-08,7788.68,7925.36,7715.09,7837.11,5938460000,7837.11 2009-04-07,7968.92,7968.92,7733.56,7789.56,5155580000,7789.56 2009-04-06,8016.16,8037.42,7830.66,7975.85,6210000000,7975.85 2009-04-03,7980.63,8090.71,7850.33,8017.59,5855640000,8017.59 2009-04-02,7763.99,8129.33,7763.99,7978.08,7542809600,7978.08 2009-04-01,7606.13,7804.77,7450.74,7761.60,6034140000,7761.60 2009-03-31,7523.77,7744.24,7502.98,7608.92,6089100000,7608.92 2009-03-30,7773.31,7773.31,7406.85,7522.02,5912660000,7522.02 2009-03-27,7922.57,7922.57,7695.97,7776.18,5600210000,7776.18 2009-03-26,7752.36,7969.00,7709.19,7924.56,6992960000,7924.56 2009-03-25,7659.81,7897.48,7539.54,7749.81,7687180000,7749.81 2009-03-24,7773.47,7837.11,7585.98,7660.21,6767980000,7660.21 2009-03-23,7279.25,7789.24,7279.25,7775.86,7715769600,7775.86 2009-03-20,7402.31,7524.81,7215.77,7278.38,7643720000,7278.38 2009-03-19,7489.68,7624.45,7325.13,7400.80,9033870400,7400.80 2009-03-18,7395.70,7592.03,7218.24,7486.58,9098449600,7486.58 2009-03-17,7218.00,7407.41,7129.60,7395.70,6156800000,7395.70 2009-03-16,7225.33,7428.75,7171.41,7216.97,7883540000,7216.97 2009-03-13,7219.20,7241.98,7106.34,7223.98,6787089600,7223.98 2009-03-12,6932.39,7198.25,6840.79,7170.06,7326630400,7170.06 2009-03-11,6923.13,7078.22,6804.55,6930.40,7287809600,6930.40 2009-03-10,6547.01,6951.50,6547.01,6926.49,8618329600,6926.49 2009-03-09,6625.74,6758.44,6440.08,6547.05,7277320000,6547.05 2009-03-06,6595.16,6776.44,6443.27,6626.94,7331830400,6626.94 2009-03-05,6874.01,6874.01,6531.28,6594.44,7507249600,6594.44 2009-03-04,6726.50,7012.19,6715.11,6875.84,7673620000,6875.84 2009-03-03,6764.81,6922.59,6661.74,6726.02,7583230400,6726.02 2009-03-02,7056.48,7056.48,6736.69,6763.29,7868289600,6763.29 2009-02-27,7180.97,7244.61,6952.06,7062.93,8926480000,7062.93 2009-02-26,7269.06,7451.13,7135.25,7182.08,7599969600,7182.08 2009-02-25,7349.58,7442.13,7123.94,7270.89,7483640000,7270.89 2009-02-24,7115.34,7396.34,7077.35,7350.94,7234489600,7350.94 2009-02-23,7365.99,7477.10,7092.64,7114.78,6509300000,7114.78 2009-02-20,7461.49,7500.44,7226.29,7365.67,8210590400,7365.67 2009-02-19,7555.23,7679.01,7420.63,7465.95,5746940000,7465.95 2009-02-18,7546.35,7661.56,7451.37,7555.63,5740710000,7555.63 2009-02-17,7845.63,7845.63,7502.59,7552.60,5907820000,7552.60 2009-02-13,7933.00,8005.96,7811.38,7850.41,5296650000,7850.41 2009-02-12,7931.97,7956.02,7662.04,7932.76,6476460000,7932.76 2009-02-11,7887.05,8042.36,7820.14,7939.53,5926460000,7939.53 2009-02-10,8269.36,8293.17,7835.83,7888.88,6770169600,7888.88 2009-02-09,8281.38,8376.56,8137.70,8270.87,5574370000,8270.87 2009-02-06,8056.38,8360.07,8044.03,8280.59,6484100000,8280.59 2009-02-05,7954.83,8138.65,7811.70,8063.07,6624030000,8063.07 2009-02-04,8070.32,8197.04,7899.79,7956.66,6420450000,7956.66 2009-02-03,7936.99,8157.13,7855.19,8078.36,5886310000,8078.36 2009-02-02,8000.62,8053.43,7796.17,7936.83,5673270000,7936.83 2009-01-30,8149.01,8243.95,7924.88,8000.86,5350580000,8000.86 2009-01-29,8373.06,8373.06,8092.14,8149.01,5067060000,8149.01 2009-01-28,8175.93,8446.33,8175.93,8375.45,6199180000,8375.45 2009-01-27,8117.39,8264.10,8042.60,8174.73,5353260000,8174.73 2009-01-26,8078.04,8278.12,7971.15,8116.03,6039940000,8116.03 2009-01-23,8108.79,8187.88,7856.86,8077.56,5832160000,8077.56 2009-01-22,8224.43,8239.33,7925.75,8122.80,5843830000,8122.80 2009-01-21,7949.17,8286.40,7890.63,8228.10,6467830000,8228.10 2009-01-20,8279.63,8309.02,7920.66,7949.09,6375230000,7949.09 2009-01-16,8215.67,8424.59,8086.01,8281.22,6786040000,8281.22 2009-01-15,8196.24,8326.06,7949.65,8212.49,7807350400,8212.49 2009-01-14,8446.01,8446.01,8097.95,8200.14,5407880000,8200.14 2009-01-13,8474.61,8584.68,8325.59,8448.56,5567460000,8448.56 2009-01-12,8599.26,8653.97,8391.85,8473.97,4725050000,8473.97 2009-01-09,8738.80,8800.45,8541.75,8599.18,4716500000,8599.18 2009-01-08,8769.94,8807.14,8593.52,8742.46,4991550000,8742.46 2009-01-07,8996.94,8996.94,8690.45,8769.70,4704940000,8769.70 2009-01-06,8954.57,9175.19,8868.07,9015.10,5392620000,9015.10 2009-01-05,9027.13,9093.47,8841.70,8952.89,5413910000,8952.89 2009-01-02,8772.25,9080.57,8725.10,9034.69,4048270000,9034.69 2008-12-31,8666.48,8862.65,8634.06,8776.39,4172940000,8776.39 2008-12-30,8487.51,8700.89,8463.70,8668.39,3627800000,8668.39 2008-12-29,8515.87,8575.60,8349.24,8483.93,3323430000,8483.93 2008-12-26,8468.71,8581.58,8434.94,8515.55,1880050000,8515.55 2008-12-24,8428.17,8498.26,8417.02,8468.48,1546550000,8468.48 2008-12-23,8518.65,8647.60,8376.80,8419.49,4051970000,8419.49 2008-12-22,8573.37,8672.06,8351.79,8519.69,4869850000,8519.69 2008-12-19,8606.50,8823.78,8499.06,8579.11,6705310000,8579.11 2008-12-18,8823.94,8946.36,8516.02,8604.99,5675000000,8604.99 2008-12-17,8921.91,9001.96,8701.13,8824.34,5907380000,8824.34 2008-12-16,8565.65,8985.63,8534.03,8924.14,6009780000,8924.14 2008-12-15,8628.81,8738.40,8431.04,8564.53,4982390000,8564.53 2008-12-12,8563.10,8705.43,8272.22,8629.68,5959590000,8629.68 2008-12-11,8750.13,8861.86,8480.18,8565.09,5513840000,8565.09 2008-12-10,8693.00,8942.46,8589.86,8761.42,5942130000,8761.42 2008-12-09,8934.10,8978.14,8591.69,8691.33,5693110000,8691.33 2008-12-08,8637.65,9151.61,8637.65,8934.18,6553600000,8934.18 2008-12-05,8376.08,8722.47,8084.25,8635.42,6165370000,8635.42 2008-12-04,8587.07,8705.98,8222.84,8376.24,5860390000,8376.24 2008-12-03,8409.14,8654.77,8170.19,8591.69,6221880000,8591.69 2008-12-02,8153.75,8490.62,8072.47,8419.09,6170100000,8419.09 2008-12-01,8826.89,8826.89,8123.04,8149.09,6052010000,8149.09 2008-11-28,8690.24,8840.33,8687.05,8829.04,2740860000,8829.04 2008-11-26,8464.49,8760.46,8250.80,8726.61,5793260000,8726.61 2008-11-25,8445.14,8682.09,8244.43,8479.47,6952700000,8479.47 2008-11-24,8048.09,8624.27,8023.32,8443.39,7879440000,8443.39 2008-11-21,7552.37,8121.45,7392.27,8046.42,9495900000,8046.42 2008-11-20,7995.53,8224.35,7464.51,7552.29,9093740000,7552.29 2008-11-19,8420.69,8534.34,7967.33,7997.28,6548600000,7997.28 2008-11-18,8273.34,8540.08,8075.81,8424.75,6679470000,8424.75 2008-11-17,8494.84,8596.31,8197.12,8273.58,4927490000,8273.58 2008-11-14,8822.19,8980.93,8421.08,8497.31,5881030000,8497.31 2008-11-13,8281.14,8898.41,7947.74,8835.25,7849120000,8835.25 2008-11-12,8684.52,8684.52,8235.66,8282.66,5764180000,8282.66 2008-11-11,8864.32,8892.20,8499.62,8693.96,4998340000,8693.96 2008-11-10,8946.60,9212.94,8735.61,8870.54,4572000000,8870.54 2008-11-07,8696.03,9032.54,8661.22,8943.81,4931640000,8943.81 2008-11-06,9134.01,9216.37,8607.14,8695.79,6102230000,8695.79 2008-11-05,9616.60,9628.15,9086.06,9139.27,5426640000,9139.27 2008-11-04,9323.89,9711.46,9323.89,9625.28,5531290000,9625.28 2008-11-03,9326.04,9488.92,9175.03,9319.83,4492280000,9319.83 2008-10-31,9179.09,9498.48,9014.78,9325.01,6394350000,9325.01 2008-10-30,9004.66,9380.36,8916.81,9180.69,6175830000,9180.69 2008-10-29,9062.33,9405.05,8800.61,8990.96,7077800000,8990.96 2008-10-28,8178.72,9112.51,8153.79,9065.12,7096950400,9065.12 2008-10-27,8375.92,8639.64,8085.37,8175.77,5558050000,8175.77 2008-10-24,8683.21,8683.21,8088.63,8378.95,6550050000,8378.95 2008-10-23,8519.77,8864.48,8200.06,8691.25,7189900000,8691.25 2008-10-22,9027.84,9027.84,8324.07,8519.21,6147980000,8519.21 2008-10-21,9179.11,9293.07,9017.30,9045.21,5121830000,9045.21 2008-10-20,8852.30,9305.89,8799.49,9265.43,5175640000,9265.43 2008-10-17,8975.35,9304.38,8640.83,8852.22,6581780000,8852.22 2008-10-16,8577.04,9073.64,8176.17,8979.26,7984500000,8979.26 2008-10-15,9301.91,9301.91,8516.50,8577.91,6542330000,8577.91 2008-10-14,9388.97,9924.28,9050.06,9310.99,8161990400,9310.99 2008-10-13,8462.42,9501.91,8462.42,9387.61,7263369600,9387.61 2008-10-10,8568.67,8989.13,7773.71,8451.19,11456230400,8451.19 2008-10-09,9261.69,9522.77,8523.27,8579.19,8285670400,8579.19 2008-10-08,9437.23,9778.04,9042.97,9258.10,8716329600,9258.10 2008-10-07,9955.42,10205.04,9391.67,9447.11,7069209600,9447.11 2008-10-06,10322.52,10322.52,9503.10,9955.50,7956020000,9955.50 2008-10-03,10483.96,10844.69,10261.75,10325.38,6716120000,10325.38 2008-10-02,10825.54,10843.10,10368.08,10482.85,6285640000,10482.85 2008-10-01,10847.40,11022.06,10495.99,10831.07,5782130000,10831.07 2008-09-30,10371.58,10922.03,10371.58,10850.66,6065000000,10850.66 2008-09-29,11139.62,11139.62,10266.76,10365.45,7305060000,10365.45 2008-09-26,11019.04,11218.48,10781.37,11143.13,5383610000,11143.13 2008-09-25,10827.17,11206.05,10799.77,11022.06,5877640000,11022.06 2008-09-24,10850.02,11041.02,10696.38,10825.17,4820360000,10825.17 2008-09-23,11015.69,11214.65,10763.77,10854.17,5185730000,10854.17 2008-09-22,11394.42,11450.81,10956.43,11015.69,5368130000,11015.69 2008-09-19,11027.51,11415.48,11027.51,11388.44,9387169600,11388.44 2008-09-18,10609.01,11149.07,10403.75,11019.69,10082689600,11019.69 2008-09-17,11056.58,11068.87,10521.81,10609.66,9431870400,10609.66 2008-09-16,10905.62,11193.12,10604.70,11059.02,9459830400,11059.02 2008-09-15,11416.37,11416.37,10849.85,10917.51,8279510400,10917.51 2008-09-12,11429.32,11532.72,11191.08,11421.99,6273260000,11421.99 2008-09-11,11264.44,11461.15,11018.72,11433.71,6869249600,11433.71 2008-09-10,11233.91,11453.50,11135.64,11268.92,6543440000,11268.92 2008-09-09,11514.73,11623.50,11209.81,11230.73,7380630400,11230.73 2008-09-08,11224.87,11656.64,11224.87,11510.74,7351340000,11510.74 2008-09-05,11185.63,11301.73,10998.77,11220.96,5017080000,11220.96 2008-09-04,11532.48,11532.48,11130.26,11188.23,5212500000,11188.23 2008-09-03,11506.01,11629.69,11328.84,11532.88,5056980000,11532.88 2008-09-02,11545.63,11831.29,11444.79,11516.92,4783560000,11516.92 2008-08-29,11713.23,11730.49,11508.78,11543.55,3288120000,11543.55 2008-08-28,11499.87,11756.46,11493.72,11715.18,3854280000,11715.18 2008-08-27,11412.46,11575.14,11349.69,11502.51,3499610000,11502.51 2008-08-26,11383.56,11483.62,11284.47,11412.87,3587570000,11412.87 2008-08-25,11626.19,11626.19,11336.82,11386.25,3420600000,11386.25 2008-08-22,11426.79,11684.00,11426.79,11628.06,3741070000,11628.06 2008-08-21,11415.23,11501.29,11263.63,11430.21,4032590000,11430.21 2008-08-20,11345.94,11511.06,11240.18,11417.43,4555030000,11417.43 2008-08-19,11478.09,11501.45,11260.53,11348.55,4159760000,11348.55 2008-08-18,11659.65,11744.49,11410.18,11479.39,3829290000,11479.39 2008-08-15,11611.21,11776.41,11540.05,11659.90,4041820000,11659.90 2008-08-14,11532.07,11744.33,11399.84,11615.93,4064000000,11615.93 2008-08-13,11632.81,11689.05,11377.37,11532.96,4787600000,11532.96 2008-08-12,11781.70,11830.39,11541.43,11642.47,4711290000,11642.47 2008-08-11,11729.67,11933.55,11580.19,11782.35,5067310000,11782.35 2008-08-08,11432.09,11808.49,11344.23,11734.32,4966810000,11734.32 2008-08-07,11655.42,11680.50,11355.63,11431.43,5319380000,11431.43 2008-08-06,11603.64,11745.71,11454.64,11656.07,4873420000,11656.07 2008-08-05,11286.02,11652.24,11286.02,11615.77,1219310000,11615.77 2008-08-04,11326.32,11449.67,11144.59,11284.15,4562280000,11284.15 2008-08-01,11379.89,11512.61,11205.41,11326.32,4684870000,11326.32 2008-07-31,11577.99,11631.16,11317.69,11378.02,5346050000,11378.02 2008-07-30,11397.56,11681.47,11328.68,11583.69,5631330000,11583.69 2008-07-29,11133.44,11444.05,11086.13,11397.56,5414240000,11397.56 2008-07-28,11369.47,11439.25,11094.76,11131.08,4282960000,11131.08 2008-07-25,11341.14,11540.78,11252.47,11370.69,4672560000,11370.69 2008-07-24,11630.34,11714.21,11288.79,11349.28,6127980000,11349.28 2008-07-23,11603.39,11820.21,11410.02,11632.38,6705830000,11632.38 2008-07-22,11457.90,11692.79,11273.32,11602.50,6180230000,11602.50 2008-07-21,11495.02,11663.40,11339.02,11467.34,4630640000,11467.34 2008-07-18,11436.56,11599.57,11290.50,11496.57,5653280000,11496.57 2008-07-17,11238.39,11538.50,11118.46,11446.66,7365209600,11446.66 2008-07-16,10961.89,11308.41,10831.61,11239.28,6738630400,11239.28 2008-07-15,11050.80,11201.67,10731.96,10962.54,7363640000,10962.54 2008-07-14,11103.64,11299.70,10972.63,11055.19,5434860000,11055.19 2008-07-11,11226.17,11292.04,10908.64,11100.54,6742200000,11100.54 2008-07-10,11148.01,11351.24,11006.01,11229.02,5840430000,11229.02 2008-07-09,11381.93,11505.12,11115.61,11147.44,5181000000,11147.44 2008-07-08,11225.03,11459.52,11101.19,11384.21,6034110000,11384.21 2008-07-07,11289.19,11477.52,11094.44,11231.96,5265420000,11231.96 2008-07-03,11297.33,11336.49,11158.02,11288.53,3247590000,11288.53 2008-07-02,11382.34,11510.41,11180.58,11215.51,5276090000,11215.51 2008-07-01,11344.64,11465.79,11106.65,11382.26,5846290000,11382.26 2008-06-30,11345.70,11504.55,11226.34,11350.01,5032330000,11350.01 2008-06-27,11452.85,11556.33,11248.48,11346.51,6208260000,11346.51 2008-06-26,11808.57,11808.57,11431.92,11453.42,5231280000,11453.42 2008-06-25,11805.31,12008.70,11683.75,11811.83,4825640000,11811.83 2008-06-24,11842.36,11962.37,11668.53,11807.43,4705050000,11807.43 2008-06-23,11843.83,11986.96,11731.06,11842.36,4186370000,11842.36 2008-06-20,12062.19,12078.23,11785.04,11842.69,5324900000,11842.69 2008-06-19,12022.54,12188.31,11881.03,12063.09,4811670000,12063.09 2008-06-18,12158.68,12212.33,11947.07,12029.06,4573570000,12029.06 2008-06-17,12269.65,12378.67,12114.14,12160.30,3801960000,12160.30 2008-06-16,12306.86,12381.44,12139.79,12269.08,3706940000,12269.08 2008-06-13,12144.59,12376.72,12096.23,12307.35,4080420000,12307.35 2008-06-12,12089.63,12337.72,12041.43,12141.58,4734240000,12141.58 2008-06-11,12286.34,12317.20,12029.46,12083.77,4779980000,12083.77 2008-06-10,12277.71,12425.98,12116.58,12289.76,4635070000,12289.76 2008-06-09,12210.13,12406.36,12102.50,12280.32,4404570000,12280.32 2008-06-06,12602.74,12602.74,12180.50,12209.81,4771660000,12209.81 2008-06-05,12388.81,12652.81,12358.07,12604.45,4350790000,12604.45 2008-06-04,12391.86,12540.37,12283.74,12390.48,4338640000,12390.48 2008-06-03,12503.20,12620.98,12317.61,12402.85,4396380000,12402.85 2008-06-02,12637.67,12645.40,12385.76,12503.82,3714320000,12503.82 2008-05-30,12647.36,12750.84,12555.60,12638.32,3845630000,12638.32 2008-05-29,12593.87,12760.21,12493.47,12646.22,3894440000,12646.22 2008-05-28,12542.90,12693.77,12437.38,12594.03,3927240000,12594.03 2008-05-27,12479.63,12626.84,12397.56,12548.35,3588860000,12548.35 2008-05-23,12620.90,12637.43,12420.20,12479.63,3516380000,12479.63 2008-05-22,12597.69,12743.68,12515.78,12625.62,3955960000,12625.62 2008-05-21,12824.94,12926.71,12550.39,12601.19,4517990000,12601.19 2008-05-20,13026.04,13026.04,12742.29,12828.68,3854320000,12828.68 2008-05-19,12985.41,13170.97,12899.19,13028.16,3683970000,13028.16 2008-05-16,12992.74,13069.52,12860.60,12986.80,3842590000,12986.80 2008-05-15,12891.29,13028.16,12798.39,12992.66,3836480000,12992.66 2008-05-14,12825.12,13037.44,12806.21,12898.38,3979370000,12898.38 2008-05-13,12872.08,12957.65,12716.16,12832.18,4018590000,12832.18 2008-05-12,12768.38,12903.33,12746.36,12876.05,3370630000,12876.05 2008-05-09,12860.68,12871.75,12648.09,12745.88,3518620000,12745.88 2008-05-08,12814.84,12965.95,12727.56,12866.78,3827550000,12866.78 2008-05-07,13010.82,13097.77,12756.14,12814.35,4075860000,12814.35 2008-05-06,12968.89,13071.07,12817.53,13020.83,3924100000,13020.83 2008-05-05,13056.57,13105.75,12896.50,12969.54,3410090000,12969.54 2008-05-02,13012.53,13191.49,12931.35,13058.20,3953030000,13058.20 2008-05-01,12818.34,13079.94,12721.94,13010.00,4448780000,13010.00 2008-04-30,12831.45,13052.91,12746.45,12820.13,4508890000,12820.13 2008-04-29,12870.37,12970.27,12737.82,12831.94,3815320000,12831.94 2008-04-28,12890.76,13015.62,12791.55,12871.75,3607000000,12871.75 2008-04-25,12848.38,12987.29,12703.70,12891.86,3891150000,12891.86 2008-04-24,12764.68,12979.88,12651.51,12848.95,4461660000,12848.95 2008-04-23,12721.45,12883.80,12627.00,12763.22,4103610000,12763.22 2008-04-22,12825.02,12870.86,12604.53,12720.23,3821900000,12720.23 2008-04-21,12850.91,12902.69,12666.08,12825.02,3420570000,12825.02 2008-04-18,12626.76,12965.47,12626.76,12849.36,4222380000,12849.36 2008-04-17,12617.40,12725.93,12472.71,12620.49,3713880000,12620.49 2008-04-16,12371.51,12670.56,12371.51,12619.27,4260370000,12619.27 2008-04-15,12303.60,12459.36,12223.97,12362.47,3581230000,12362.47 2008-04-14,12324.77,12430.86,12208.42,12302.06,3565020000,12302.06 2008-04-11,12579.78,12579.78,12280.89,12325.42,3723790000,12325.42 2008-04-10,12526.78,12705.90,12447.96,12581.98,3686150000,12581.98 2008-04-09,12574.65,12686.93,12416.53,12527.26,3556670000,12527.26 2008-04-08,12602.66,12664.38,12440.55,12576.44,3602500000,12576.44 2008-04-07,12612.59,12786.83,12550.22,12612.43,3747780000,12612.43 2008-04-04,12626.35,12738.30,12489.40,12609.42,3703100000,12609.42 2008-04-03,12604.69,12734.97,12455.04,12626.03,3920100000,12626.03 2008-04-02,12651.67,12790.28,12488.22,12608.92,4320440000,12608.92 2008-04-01,12266.64,12693.93,12266.64,12654.36,4745120000,12654.36 2008-03-31,12215.92,12384.84,12095.18,12262.89,4188990000,12262.89 2008-03-28,12303.92,12441.67,12164.22,12216.40,3686980000,12216.40 2008-03-27,12421.88,12528.13,12264.76,12302.46,4037930000,12302.46 2008-03-26,12531.79,12531.79,12309.62,12422.86,4055670000,12422.86 2008-03-25,12547.34,12639.82,12397.62,12532.60,4145120000,12532.60 2008-03-24,12361.97,12687.61,12346.17,12548.64,4499000000,12548.64 2008-03-20,12102.43,12434.34,12024.68,12361.32,6145220000,12361.32 2008-03-19,12391.52,12525.19,12077.27,12099.66,5358550000,12099.66 2008-03-18,11975.92,12411.63,11975.92,12392.66,5335630000,12392.66 2008-03-17,11946.45,12119.69,11650.44,11972.25,5683010000,11972.25 2008-03-14,12146.39,12249.86,11781.43,11951.09,5153780000,11951.09 2008-03-13,12096.49,12242.29,11832.88,12145.74,5073360000,12145.74 2008-03-12,12148.61,12360.58,12037.79,12110.24,4414280000,12110.24 2008-03-11,11741.33,12205.98,11741.33,12156.81,5109080000,12156.81 2008-03-10,11893.04,11993.75,11691.47,11740.15,4261240000,11740.15 2008-03-07,12039.09,12131.33,11778.66,11893.69,4565410000,11893.69 2008-03-06,12254.59,12267.86,12010.03,12040.39,4323460000,12040.39 2008-03-05,12204.93,12392.74,12105.36,12254.99,4277710000,12254.99 2008-03-04,12259.14,12291.22,11991.06,12213.80,4757180000,12213.80 2008-03-03,12264.36,12344.71,12101.29,12258.90,4117570000,12258.90 2008-02-29,12579.58,12579.58,12210.30,12266.39,4426730000,12266.39 2008-02-28,12689.28,12713.99,12463.32,12582.18,3938580000,12582.18 2008-02-27,12683.54,12815.59,12527.64,12694.28,3904700000,12694.28 2008-02-26,12569.48,12771.14,12449.08,12684.92,4096060000,12684.92 2008-02-25,12380.77,12612.47,12292.03,12570.22,3866350000,12570.22 2008-02-22,12281.09,12429.05,12116.92,12381.02,3572660000,12381.02 2008-02-21,12426.85,12545.79,12225.36,12284.30,3696660000,12284.30 2008-02-20,12333.31,12489.29,12159.42,12427.26,3870520000,12427.26 2008-02-19,12349.59,12571.11,12276.81,12337.22,3613550000,12337.22 2008-02-15,12376.66,12441.20,12216.68,12348.21,3583300000,12348.21 2008-02-14,12551.51,12611.26,12332.03,12376.98,3644760000,12376.98 2008-02-13,12368.12,12627.76,12354.22,12552.24,3856420000,12552.24 2008-02-12,12241.56,12524.12,12207.90,12373.41,4044640000,12373.41 2008-02-11,12181.89,12332.76,12006.79,12240.01,3593140000,12240.01 2008-02-08,12248.47,12330.97,12058.01,12182.13,3768490000,12182.13 2008-02-07,12196.20,12366.99,12045.00,12247.00,4589160000,12247.00 2008-02-06,12257.25,12436.33,12142.14,12200.10,4008120000,12200.10 2008-02-05,12631.85,12631.85,12234.97,12265.13,4315740000,12265.13 2008-02-04,12743.11,12810.34,12557.61,12635.16,3495780000,12635.16 2008-02-01,12638.17,12841.88,12510.05,12743.19,4650770000,12743.19 2008-01-31,12438.28,12734.74,12197.09,12650.36,4970290000,12650.36 2008-01-30,12480.14,12715.96,12311.55,12442.83,4742760000,12442.83 2008-01-29,12385.19,12604.92,12262.29,12480.30,4232960000,12480.30 2008-01-28,12205.71,12423.81,12061.42,12383.89,4100930000,12383.89 2008-01-25,12391.70,12590.69,12103.61,12207.17,4882250000,12207.17 2008-01-24,12272.69,12522.82,12114.83,12378.61,5735300000,12378.61 2008-01-23,11969.08,12339.10,11530.12,12270.17,3241680000,12270.17 2008-01-22,12092.72,12167.42,11508.74,11971.19,6544690000,11971.19 2008-01-18,12159.94,12441.85,11953.71,12099.30,6004840000,12099.30 2008-01-17,12467.05,12597.85,12089.38,12159.21,5303130000,12159.21 2008-01-16,12476.81,12699.05,12294.48,12466.16,5440620000,12466.16 2008-01-15,12777.50,12777.50,12425.92,12501.11,4601640000,12501.11 2008-01-14,12613.78,12866.10,12596.95,12778.15,3682090000,12778.15 2008-01-11,12850.74,12863.34,12495.91,12606.30,4495840000,12606.30 2008-01-10,12733.11,12931.29,12632.15,12853.09,5170490000,12853.09 2008-01-09,12590.21,12814.97,12431.53,12735.31,5351030000,12735.31 2008-01-08,12820.90,12998.11,12511.03,12589.07,4705390000,12589.07 2008-01-07,12801.15,12984.95,12640.44,12827.49,4221260000,12827.49 2008-01-04,13046.56,13049.65,12740.51,12800.18,4166000000,12800.18 2008-01-03,13044.12,13197.43,12968.44,13056.72,3429500000,13056.72 2008-01-02,13261.82,13338.23,12969.42,13043.96,3452650000,13043.96 2007-12-31,13364.16,13423.91,13197.35,13264.82,2440880000,13264.82 2007-12-28,13361.23,13494.30,13272.14,13365.87,2420510000,13365.87 2007-12-27,13549.17,13551.53,13325.71,13359.61,2365770000,13359.61 2007-12-26,13547.95,13614.53,13440.16,13551.69,2010500000,13551.69 2007-12-24,13487.12,13562.72,13451.35,13550.04,2200000,13550.04 2007-12-21,13241.66,13518.20,13241.66,13450.65,4508590000,13450.65 2007-12-20,13206.46,13354.00,13112.98,13245.64,3526890000,13245.64 2007-12-19,13231.98,13368.79,13097.77,13207.27,3401300000,13207.27 2007-12-18,13168.66,13346.84,13059.32,13232.47,3723690000,13232.47 2007-12-17,13339.20,13378.38,13111.92,13167.20,3569030000,13167.20 2007-12-14,13515.11,13557.54,13284.66,13339.85,3401050000,13339.85 2007-12-13,13473.98,13586.73,13281.00,13517.96,3635170000,13517.96 2007-12-12,13434.80,13778.98,13299.61,13473.90,4482120000,13473.90 2007-12-11,13726.87,13850.92,13374.89,13432.77,4080180000,13432.77 2007-12-10,13623.55,13807.02,13582.50,13727.03,2911760000,13727.03 2007-12-07,13618.27,13744.02,13514.22,13625.58,3177710000,13625.58 2007-12-06,13445.85,13652.49,13362.37,13619.89,3568570000,13619.89 2007-12-05,13244.01,13513.00,13244.01,13444.96,3663660000,13444.96 2007-12-04,13311.24,13395.21,13139.56,13248.73,3343620000,13248.73 2007-12-03,13368.22,13490.24,13207.60,13314.57,3323250000,13314.57 2007-11-30,13314.25,13570.31,13225.32,13371.72,4422200000,13371.72 2007-11-29,13287.91,13399.03,13150.21,13311.73,3524730000,13311.73 2007-11-28,12958.04,13353.51,12958.04,13289.45,4508020000,13289.45 2007-11-27,12744.78,13040.38,12711.98,12958.44,4320720000,12958.44 2007-11-26,12979.99,13104.44,12707.26,12743.44,3706470000,12743.44 2007-11-23,12889.45,12981.56,12796.29,12980.88,1612720000,12980.88 2007-11-21,13006.65,13055.59,12725.39,12799.04,4076230000,12799.04 2007-11-20,12955.92,13179.23,12800.74,13010.14,4875150000,13010.14 2007-11-19,13176.30,13195.48,12871.14,12958.44,4119650000,12958.44 2007-11-16,13109.48,13293.44,12987.22,13176.79,4168870000,13176.79 2007-11-15,13230.68,13333.59,13007.95,13110.05,3941010000,13110.05 2007-11-14,13305.47,13465.20,13159.88,13231.01,4031470000,13231.01 2007-11-13,12975.11,13357.57,12975.11,13307.09,4141310000,13307.09 2007-11-12,13039.16,13238.73,12910.40,12987.55,4192520000,12987.55 2007-11-09,13261.17,13321.81,12920.65,13042.74,4587050000,13042.74 2007-11-08,13299.70,13463.66,13001.93,13266.29,5439720000,13266.29 2007-11-07,13646.72,13646.72,13269.46,13300.02,4353160000,13300.02 2007-11-06,13542.34,13716.55,13460.73,13660.94,3879160000,13660.94 2007-11-05,13592.58,13666.15,13393.67,13543.40,3819330000,13543.40 2007-11-02,13569.90,13708.58,13381.64,13595.10,4285990000,13595.10 2007-11-01,13924.16,13924.16,13522.75,13567.87,4241470000,13567.87 2007-10-31,13792.06,13990.65,13711.59,13930.01,3953070000,13930.01 2007-10-30,13869.04,13930.91,13719.80,13792.47,3212520000,13792.47 2007-10-29,13807.35,13966.18,13748.33,13870.26,3124480000,13870.26 2007-10-26,13675.66,13885.95,13622.01,13806.70,3612120000,13806.70 2007-10-25,13677.85,13819.78,13471.87,13671.92,4183960000,13671.92 2007-10-24,13675.58,13751.50,13423.74,13675.25,4003300000,13675.25 2007-10-23,13568.93,13754.91,13494.95,13676.23,3309120000,13676.23 2007-10-22,13521.62,13636.80,13337.90,13566.97,3471830000,13566.97 2007-10-19,13888.47,13888.47,13478.94,13522.02,4160970000,13522.02 2007-10-18,13887.90,13984.39,13746.22,13888.96,3203210000,13888.96 2007-10-17,13920.66,14075.84,13738.66,13892.54,3638070000,13892.54 2007-10-16,13986.34,14061.37,13810.68,13912.94,3234560000,13912.94 2007-10-15,14092.43,14157.38,13877.82,13984.80,3139290000,13984.80 2007-10-12,14016.34,14168.51,13949.85,14093.08,2788690000,14093.08 2007-10-11,14079.10,14279.96,13917.82,14015.12,3911260000,14015.12 2007-10-10,14165.02,14225.66,13963.26,14078.69,3044760000,14078.69 2007-10-09,14043.73,14198.83,13980.90,14164.53,2932040000,14164.53 2007-10-08,14065.36,14134.05,13747.41,14043.73,2040650000,14043.73 2007-10-05,13969.07,14169.49,13965.05,14066.01,2919030000,14066.01 2007-10-04,13967.89,14074.54,13894.98,13974.31,2690430000,13974.31 2007-10-03,14038.86,14090.48,13883.43,13968.05,3065320000,13968.05 2007-10-02,14087.14,14166.16,13951.72,14047.31,3101910000,14047.31 2007-10-01,13895.71,14147.30,13869.86,14087.55,3281990000,14087.55 2007-09-28,13912.94,13994.64,13802.96,13895.63,2925350000,13895.63 2007-09-27,13879.53,13991.63,13811.17,13912.94,2872180000,13912.94 2007-09-26,13779.30,13962.61,13741.26,13878.15,3237390000,13878.15 2007-09-25,13757.84,13847.10,13629.16,13778.65,3187770000,13778.65 2007-09-24,13821.57,13930.74,13702.89,13759.06,3131310000,13759.06 2007-09-21,13768.33,13948.95,13740.61,13820.19,3679460000,13820.19 2007-09-20,13813.52,13893.02,13680.21,13766.70,2957700000,13766.70 2007-09-19,13740.61,13936.68,13689.80,13815.56,3846750000,13815.56 2007-09-18,13403.18,13772.15,13379.68,13739.39,3708940000,13739.39 2007-09-17,13441.95,13514.71,13306.69,13403.42,2598390000,13403.42 2007-09-14,13421.39,13507.55,13273.68,13442.52,2641740000,13442.52 2007-09-13,13292.38,13519.91,13292.38,13424.88,2877080000,13424.88 2007-09-12,13298.31,13408.62,13195.40,13291.65,2885720000,13291.65 2007-09-11,13129.40,13369.77,13124.68,13308.39,3015330000,13308.39 2007-09-10,13116.39,13280.67,12992.02,13127.85,2835720000,13127.85 2007-09-07,13360.74,13360.74,13059.16,13113.38,3191080000,13113.38 2007-09-06,13306.44,13464.79,13217.11,13363.35,2459590000,13363.35 2007-09-05,13442.85,13442.85,13203.86,13305.47,2991600000,13305.47 2007-09-04,13358.39,13521.86,13248.57,13448.86,2766600000,13448.86 2007-08-31,13240.84,13472.35,13240.84,13357.74,2731610000,13357.74 2007-08-30,13287.91,13355.46,13126.39,13238.73,2582960000,13238.73 2007-08-29,13043.07,13336.93,13020.63,13289.29,2824070000,13289.29 2007-08-28,13318.43,13319.61,13024.29,13041.85,3078090000,13041.85 2007-08-27,13377.16,13438.46,13248.32,13322.13,2406180000,13322.13 2007-08-24,13231.78,13402.20,13174.27,13378.87,2541400000,13378.87 2007-08-23,13237.27,13358.22,13127.69,13235.88,3084390000,13235.88 2007-08-22,13088.26,13304.33,13075.34,13236.13,3309120000,13236.13 2007-08-21,13120.05,13228.57,12975.68,13090.86,3012150000,13090.86 2007-08-20,13078.51,13245.80,12938.77,13121.35,3321340000,13121.35 2007-08-17,12848.05,13289.70,12847.24,13079.08,3570040000,13079.08 2007-08-16,12859.52,12996.73,12455.92,12845.78,6509300000,12845.78 2007-08-15,13021.93,13184.51,12800.83,12861.47,4290930000,12861.47 2007-08-14,13235.72,13309.04,12974.30,13028.92,3814630000,13028.92 2007-08-13,13238.24,13440.08,13163.54,13236.53,3696280000,13236.53 2007-08-10,13270.59,13386.43,12958.04,13239.54,5345780000,13239.54 2007-08-09,13652.33,13675.66,13196.05,13270.68,5889600000,13270.68 2007-08-08,13497.23,13769.63,13386.92,13657.86,5499560000,13657.86 2007-08-07,13467.72,13635.09,13282.38,13504.30,4909390000,13504.30 2007-08-06,13183.13,13501.86,13077.05,13468.78,5067200000,13468.78 2007-08-03,13462.25,13539.50,13156.79,13181.91,4272110000,13181.91 2007-08-02,13357.82,13547.47,13272.79,13463.33,4368850000,13463.33 2007-08-01,13211.09,13431.06,13041.77,13362.37,5256780000,13362.37 2007-07-31,13360.66,13579.41,13182.15,13211.99,4524520000,13211.99 2007-07-30,13266.21,13445.12,13143.87,13358.31,4128780000,13358.31 2007-07-27,13472.68,13589.17,13228.57,13265.47,4784650000,13265.47 2007-07-26,13783.12,13793.61,13307.74,13473.57,4472550000,13473.57 2007-07-25,13718.25,13919.77,13607.70,13785.07,4283200000,13785.07 2007-07-24,13940.90,13967.65,13661.51,13716.95,4115830000,13716.95 2007-07-23,13851.73,14039.59,13819.54,13943.42,3102700000,13943.42 2007-07-20,14000.73,14039.67,13745.65,13851.08,3745780000,13851.08 2007-07-19,13918.79,14121.04,13860.18,14000.41,3251450000,14000.41 2007-07-18,13955.05,14020.89,13768.73,13918.22,3609220000,13918.22 2007-07-17,13951.96,14095.60,13880.67,13971.55,3007140000,13971.55 2007-07-16,13907.09,14053.57,13834.33,13950.98,2704110000,13950.98 2007-07-13,13859.86,13982.93,13784.83,13907.25,2801120000,13907.25 2007-07-12,13579.33,13889.45,13579.33,13861.73,3489600000,13861.73 2007-07-11,13500.40,13638.75,13435.45,13577.87,3082920000,13577.87 2007-07-10,13648.59,13685.90,13463.57,13501.70,3244280000,13501.70 2007-07-09,13612.66,13739.06,13563.89,13649.97,2715330000,13649.97 2007-07-06,13559.01,13670.46,13501.54,13611.68,2441520000,13611.68 2007-07-05,13576.24,13637.78,13459.84,13565.84,2622950000,13565.84 2007-07-03,13556.87,13592.07,13531.83,13577.30,1560790000,13577.30 2007-07-02,13409.60,13586.97,13406.59,13535.43,2648990000,13535.43 2007-06-29,13422.61,13556.16,13291.32,13408.62,3165410000,13408.62 2007-06-28,13427.48,13537.47,13342.05,13422.28,3006710000,13422.28 2007-06-27,13336.93,13455.36,13205.08,13427.73,3398150000,13427.73 2007-06-26,13352.37,13491.70,13272.79,13337.66,3398530000,13337.66 2007-06-25,13360.09,13519.34,13273.68,13352.05,3287250000,13352.05 2007-06-22,13545.03,13564.13,13323.51,13360.26,4284320000,13360.26 2007-06-21,13486.66,13596.56,13368.79,13545.84,3161110000,13545.84 2007-06-20,13636.56,13735.08,13469.43,13489.42,3286900000,13489.42 2007-06-19,13611.68,13705.41,13527.14,13635.42,2873590000,13635.42 2007-06-18,13639.00,13720.29,13560.15,13612.98,2480240000,13612.98 2007-06-15,13556.65,13741.18,13556.65,13639.48,3406030000,13639.48 2007-06-14,13482.43,13622.66,13444.07,13553.73,2813630000,13553.73 2007-06-13,13287.62,13502.76,13287.62,13482.35,3077930000,13482.35 2007-06-12,13424.39,13474.12,13264.05,13295.01,3056200000,13295.01 2007-06-11,13423.74,13519.88,13335.16,13424.96,2525280000,13424.96 2007-06-08,13267.14,13445.19,13207.73,13424.39,2993460000,13424.39 2007-06-07,13463.48,13517.85,13236.34,13266.73,3538470000,13266.73 2007-06-06,13590.66,13606.75,13403.10,13465.67,2964190000,13465.67 2007-06-05,13673.19,13689.40,13523.70,13595.46,2939450000,13595.46 2007-06-04,13667.21,13723.37,13575.14,13676.32,2738930000,13676.32 2007-06-01,13628.69,13756.69,13562.54,13668.11,2927020000,13668.11 2007-05-31,13633.00,13718.82,13564.49,13627.64,3335530000,13627.64 2007-05-30,13517.89,13650.64,13403.26,13633.08,2980210000,13633.08 2007-05-29,13507.28,13603.26,13428.86,13521.34,2571790000,13521.34 2007-05-25,13441.94,13571.48,13410.00,13507.28,2316250000,13507.28 2007-05-24,13522.60,13645.52,13391.56,13441.13,3365530000,13441.13 2007-05-23,13540.84,13648.69,13476.72,13525.65,3084260000,13525.65 2007-05-22,13544.99,13632.03,13466.57,13539.95,2860500000,13539.95 2007-05-21,13556.53,13636.98,13473.31,13542.88,3465360000,13542.88 2007-05-18,13476.40,13611.95,13454.46,13556.53,2959050000,13556.53 2007-05-17,13486.96,13558.24,13384.89,13476.72,2868640000,13476.72 2007-05-16,13374.13,13526.54,13325.49,13487.53,2915350000,13487.53 2007-05-15,13346.05,13518.33,13302.65,13383.84,3071020000,13383.84 2007-05-14,13325.81,13432.84,13265.02,13346.78,2776130000,13346.78 2007-05-11,13212.20,13373.35,13192.62,13326.22,2720780000,13326.22 2007-05-10,13359.05,13376.20,13161.08,13215.13,3031240000,13215.13 2007-05-09,13300.62,13410.17,13229.92,13362.87,2935550000,13362.87 2007-05-08,13309.40,13359.05,13192.45,13309.07,2795720000,13309.07 2007-05-07,13264.13,13385.06,13218.87,13312.97,2545090000,13312.97 2007-05-04,13243.08,13340.60,13176.61,13264.62,2761930000,13264.62 2007-05-03,13206.65,13306.55,13131.42,13241.38,3007970000,13241.38 2007-05-02,13133.94,13291.60,13105.34,13211.88,3189800000,13211.88 2007-05-01,13062.75,13188.96,12993.02,13136.14,3400350000,13136.14 2007-04-30,13120.21,13226.99,13003.91,13062.91,3093420000,13062.91 2007-04-27,13104.04,13195.05,13002.37,13120.94,2732810000,13120.94 2007-04-26,13088.84,13197.49,13016.43,13105.50,3211800000,13105.50 2007-04-25,12951.42,13142.31,12929.80,13089.89,3252590000,13089.89 2007-04-24,12919.64,13033.66,12845.12,12953.94,3119750000,12953.94 2007-04-23,12961.49,13029.59,12867.96,12919.40,2575020000,12919.40 2007-04-20,12811.15,13035.77,12811.15,12961.98,3329940000,12961.98 2007-04-19,12799.77,12889.17,12677.47,12808.63,2913610000,12808.63 2007-04-18,12771.08,12871.21,12691.20,12803.84,2971330000,12803.84 2007-04-17,12719.56,12837.40,12669.50,12773.04,2920570000,12773.04 2007-04-16,12611.64,12770.60,12596.36,12720.46,2870140000,12720.46 2007-04-13,12551.91,12654.47,12504.04,12612.13,2690020000,12612.13 2007-04-12,12483.64,12580.92,12407.50,12552.96,2770570000,12552.96 2007-04-11,12573.12,12618.63,12432.53,12484.62,2950190000,12484.62 2007-04-10,12568.49,12641.87,12496.48,12573.85,2510110000,12573.85 2007-04-09,12562.64,12641.22,12505.83,12569.14,2349410000,12569.14 2007-04-05,12505.73,12573.02,12501.25,12560.83,2357230000,12560.83 2007-04-04,12511.36,12591.81,12444.55,12530.05,2616320000,12530.05 2007-04-03,12431.28,12534.49,12378.94,12510.93,2921760000,12510.93 2007-04-02,12354.52,12450.81,12284.54,12382.30,2875880000,12382.30 2007-03-30,12348.91,12442.40,12237.87,12354.35,2903960000,12354.35 2007-03-29,12301.48,12424.77,12251.89,12348.75,2854710000,12348.75 2007-03-28,12396.49,12415.56,12234.50,12300.36,3000440000,12300.36 2007-03-27,12468.59,12484.54,12336.89,12397.29,2673040000,12397.29 2007-03-26,12480.37,12526.27,12339.53,12469.07,2754660000,12469.07 2007-03-23,12460.50,12550.07,12396.89,12481.01,2619020000,12481.01 2007-03-22,12446.72,12524.03,12364.21,12461.14,3129970000,12461.14 2007-03-21,12288.98,12489.02,12220.49,12447.52,3184770000,12447.52 2007-03-20,12226.81,12324.31,12172.66,12288.10,2795940000,12288.10 2007-03-19,12110.41,12273.52,12110.41,12226.17,2777180000,12226.17 2007-03-16,12160.16,12226.01,12053.05,12110.41,3393640000,12110.41 2007-03-15,12133.16,12228.42,12060.10,12159.68,2821900000,12159.68 2007-03-14,12074.52,12187.88,11926.79,12133.40,3758350000,12133.40 2007-03-13,12307.49,12319.66,12049.85,12075.96,3485570000,12075.96 2007-03-12,12275.68,12385.44,12205.58,12318.62,2664000000,12318.62 2007-03-09,12262.06,12379.51,12200.62,12276.32,2623050000,12276.32 2007-03-08,12193.33,12355.47,12183.79,12260.70,3014850000,12260.70 2007-03-07,12204.46,12315.18,12122.11,12192.45,3141350000,12192.45 2007-03-06,12051.17,12252.61,12051.17,12207.59,3358160000,12207.59 2007-03-05,12111.61,12220.16,11973.58,12050.41,3480520000,12050.41 2007-03-02,12233.78,12293.15,12064.91,12114.10,3312260000,12114.10 2007-03-01,12265.59,12338.89,11996.17,12234.34,3874910000,12234.34 2007-02-28,12214.92,12396.81,12122.03,12268.63,3925250000,12268.63 2007-02-27,12628.90,12628.90,12078.85,12216.24,4065230000,12216.24 2007-02-26,12647.88,12746.34,12562.72,12632.26,2822170000,12632.26 2007-02-23,12679.89,12726.79,12578.51,12647.48,2579950000,12647.48 2007-02-22,12735.77,12792.97,12621.93,12686.02,1950770000,12686.02 2007-02-21,12782.87,12813.88,12662.79,12738.41,2606980000,12738.41 2007-02-20,12766.85,12845.76,12675.04,12786.64,2337860000,12786.64 2007-02-16,12764.13,12829.42,12685.86,12767.57,2399450000,12767.57 2007-02-15,12741.70,12828.38,12681.85,12765.01,2490920000,12765.01 2007-02-14,12651.29,12793.29,12623.21,12741.86,2699290000,12741.86 2007-02-13,12549.19,12702.36,12549.19,12654.85,2652150000,12654.85 2007-02-12,12595.90,12605.11,12536.77,12552.55,2395680000,12552.55 2007-02-09,12638.03,12725.59,12518.58,12580.83,2951810000,12580.83 2007-02-08,12639.16,12666.88,12576.59,12637.63,2816180000,12637.63 2007-02-07,12656.86,12748.99,12589.56,12666.87,2618820000,12666.87 2007-02-06,12661.66,12738.41,12586.44,12666.31,2608710000,12666.31 2007-02-05,12641.08,12681.06,12630.50,12661.74,2439430000,12661.74 2007-02-02,12673.84,12740.65,12582.99,12653.49,2569450000,12653.49 2007-02-01,12617.20,12741.30,12563.85,12673.68,2914890000,12673.68 2007-01-31,12520.03,12685.54,12461.30,12621.69,2976690000,12621.69 2007-01-30,12484.70,12538.06,12463.07,12523.31,2706250000,12523.31 2007-01-29,12487.10,12599.74,12422.93,12490.78,2730480000,12490.78 2007-01-26,12503.28,12582.67,12391.44,12487.02,2626620000,12487.02 2007-01-25,12621.77,12670.48,12461.54,12502.56,2994330000,12502.56 2007-01-24,12534.37,12659.42,12489.98,12621.77,2783180000,12621.77 2007-01-23,12467.96,12553.44,12467.96,12533.80,2975070000,12533.80 2007-01-22,12566.33,12619.04,12389.68,12477.16,2540120000,12477.16 2007-01-19,12567.93,12649.89,12462.50,12565.53,2777480000,12565.53 2007-01-18,12575.06,12674.16,12487.90,12567.93,2822430000,12567.93 2007-01-17,12563.53,12613.28,12550.95,12577.15,2690270000,12577.15 2007-01-16,12555.84,12638.27,12489.90,12582.59,2599530000,12582.59 2007-01-12,12514.66,12616.08,12432.30,12556.08,2686480000,12556.08 2007-01-11,12442.96,12586.12,12413.72,12514.98,2857870000,12514.98 2007-01-10,12417.00,12487.18,12313.01,12442.16,2764660000,12442.16 2007-01-09,12424.77,12516.66,12337.85,12416.60,3038380000,12416.60 2007-01-08,12393.93,12445.37,12337.53,12423.49,2763340000,12423.49 2007-01-05,12480.05,12504.40,12326.79,12398.01,2919400000,12398.01 2007-01-04,12467.32,12510.26,12405.47,12480.69,3004460000,12480.69 2007-01-03,12459.54,12630.34,12373.82,12474.52,3429160000,12474.52 2006-12-29,12500.48,12560.16,12423.81,12463.15,1678200000,12463.15 2006-12-28,12510.57,12566.17,12440.23,12501.52,1508570000,12501.52 2006-12-27,12463.46,12518.34,12407.62,12510.57,1667370000,12510.57 2006-12-26,12341.94,12439.19,12301.40,12407.63,1310310000,12407.63 2006-12-22,12407.87,12417.96,12341.77,12343.21,1647590000,12343.21 2006-12-21,12461.62,12526.59,12369.97,12421.25,2322410000,12421.25 2006-12-20,12471.32,12549.35,12393.45,12463.87,2387630000,12463.87 2006-12-19,12439.51,12517.78,12348.50,12471.32,2717060000,12471.32 2006-12-18,12446.24,12545.74,12372.30,12441.27,2568140000,12441.27 2006-12-15,12417.96,12536.37,12377.35,12445.52,3229580000,12445.52 2006-12-14,12317.50,12472.76,12271.44,12416.76,2729700000,12416.76 2006-12-13,12312.71,12411.55,12263.19,12317.50,2552260000,12317.50 2006-12-12,12328.24,12396.01,12222.65,12315.58,2738170000,12315.58 2006-12-11,12306.21,12399.54,12245.32,12328.48,2289900000,12328.48 2006-12-08,12256.21,12332.16,12243.31,12307.48,2440460000,12307.48 2006-12-07,12310.13,12396.33,12233.06,12278.41,2743150000,12278.41 2006-12-06,12328.72,12390.88,12239.95,12309.25,2725280000,12309.25 2006-12-05,12283.69,12398.57,12218.24,12331.60,2755700000,12331.60 2006-12-04,12195.57,12349.87,12149.27,12283.85,2766320000,12283.85 2006-12-01,12220.97,12289.30,12070.52,12194.13,2800980000,12194.13 2006-11-30,12226.73,12317.10,12118.42,12221.93,4006230000,12221.93 2006-11-29,12134.40,12283.05,12119.70,12226.73,2790970000,12226.73 2006-11-28,12095.27,12148.78,12073.40,12136.44,2639750000,12136.44 2006-11-27,12279.13,12303.32,12079.01,12121.71,2711210000,12121.71 2006-11-24,12321.71,12340.89,12219.28,12280.17,832550000,12280.17 2006-11-22,12321.91,12403.54,12238.43,12326.95,2237710000,12326.95 2006-11-21,12312.13,12409.31,12233.94,12321.59,2597940000,12321.59 2006-11-20,12340.71,12400.10,12257.34,12316.54,2546710000,12316.54 2006-11-17,12293.49,12342.55,12278.20,12342.55,2726100000,12342.55 2006-11-16,12250.05,12375.37,12204.00,12305.82,2835730000,12305.82 2006-11-15,12214.37,12326.07,12156.37,12251.71,2831130000,12251.71 2006-11-14,12132.44,12261.15,12051.68,12218.01,3027480000,12218.01 2006-11-13,12084.89,12164.22,12084.89,12131.88,2386340000,12131.88 2006-11-10,12102.74,12173.08,12074.01,12108.43,2290200000,12108.43 2006-11-09,12174.70,12236.10,12039.59,12103.30,3012050000,12103.30 2006-11-08,12147.38,12233.54,12051.60,12176.54,2814820000,12176.54 2006-11-07,12104.75,12239.94,12065.20,12156.77,2636390000,12156.77 2006-11-06,11985.16,12146.45,11973.23,12105.55,2533550000,12105.55 2006-11-03,12018.30,12095.30,11928.97,11986.04,2419730000,11986.04 2006-11-02,12023.98,12070.25,11938.89,12018.54,2646180000,12018.54 2006-11-01,12080.25,12160.70,11972.99,12031.02,2821160000,12031.02 2006-10-31,12086.18,12160.46,11986.84,12080.73,2803030000,12080.73 2006-10-30,12074.01,12117.07,12050.23,12086.49,2770440000,12086.49 2006-10-27,12164.78,12202.72,12024.78,12090.26,2458450000,12090.26 2006-10-26,12134.84,12236.10,12037.99,12163.66,2793350000,12163.66 2006-10-25,12127.24,12212.16,12017.66,12134.68,2953540000,12134.68 2006-10-24,12116.51,12204.80,12028.14,12127.88,2876890000,12127.88 2006-10-23,12001.33,12177.35,11940.41,12116.91,2480430000,12116.91 2006-10-20,12013.01,12087.38,11881.34,12002.37,2526410000,12002.37 2006-10-19,11988.92,12082.33,11911.44,12011.73,2619830000,12011.73 2006-10-18,11947.62,12108.91,11900.79,11992.68,2658840000,11992.68 2006-10-17,11977.40,12024.54,11849.16,11950.02,2519620000,11950.02 2006-10-16,11957.70,11996.92,11945.70,11980.59,2305920000,11980.59 2006-10-13,11947.22,12009.97,11862.29,11960.51,2482920000,11960.51 2006-10-12,11896.63,11959.14,11852.12,11947.70,2514350000,11947.70 2006-10-11,11865.49,11907.60,11762.72,11852.13,2521000000,11852.13 2006-10-10,11857.73,11930.33,11778.09,11867.17,2376140000,11867.17 2006-10-09,11849.56,11923.53,11759.36,11857.81,1935170000,11857.81 2006-10-06,11865.49,11921.04,11743.35,11850.21,2523000000,11850.21 2006-10-05,11832.51,11869.33,11821.55,11866.69,2817240000,11866.69 2006-10-04,11722.94,11879.18,11654.02,11850.61,3019880000,11850.61 2006-10-03,11670.11,11794.41,11608.23,11727.34,2682690000,11727.34 2006-10-02,11678.99,11773.60,11608.79,11670.35,2154480000,11670.35 2006-09-29,11718.05,11782.49,11642.17,11679.07,2273430000,11679.07 2006-09-28,11689.40,11775.36,11625.92,11718.45,2397820000,11718.45 2006-09-27,11670.19,11775.60,11595.75,11689.24,2749190000,11689.24 2006-09-26,11575.73,11723.74,11517.54,11669.39,2673350000,11669.39 2006-09-25,11536.67,11616.23,11486.00,11575.81,2710240000,11575.81 2006-09-22,11532.91,11588.62,11423.57,11508.10,2162880000,11508.10 2006-09-21,11611.67,11677.39,11471.76,11533.23,2627440000,11533.23 2006-09-20,11542.28,11680.19,11514.50,11613.19,2543070000,11613.19 2006-09-19,11554.60,11605.67,11450.30,11540.91,2390850000,11540.91 2006-09-18,11538.35,11588.22,11528.43,11555.00,2325080000,11555.00 2006-09-15,11528.75,11661.38,11504.57,11560.77,3198030000,11560.77 2006-09-14,11508.82,11548.84,11495.77,11527.39,2351220000,11527.39 2006-09-13,11487.69,11605.59,11423.73,11543.32,2597220000,11543.32 2006-09-12,11412.52,11512.74,11396.83,11498.09,2791580000,11498.09 2006-09-11,11389.87,11468.88,11295.90,11396.84,2506430000,11396.84 2006-09-08,11332.24,11448.30,11295.18,11392.11,2132890000,11392.11 2006-09-07,11405.16,11443.66,11273.89,11331.44,2325850000,11331.44 2006-09-06,11421.33,11469.27,11395.15,11406.20,2329870000,11406.20 2006-09-05,11461.83,11533.87,11385.95,11469.28,2114480000,11469.28 2006-09-01,11427.41,11476.40,11381.14,11464.15,1800520000,11464.15 2006-08-31,11383.47,11451.03,11326.80,11381.15,1974540000,11381.15 2006-08-30,11365.98,11452.95,11309.99,11382.91,2060690000,11382.91 2006-08-29,11352.65,11432.38,11255.88,11369.94,2093720000,11369.94 2006-08-28,11285.33,11411.24,11240.91,11352.01,1834920000,11352.01 2006-08-25,11301.22,11350.09,11218.66,11284.05,1667580000,11284.05 2006-08-24,11297.82,11386.59,11232.83,11304.46,1930320000,11304.46 2006-08-23,11337.12,11394.75,11238.67,11297.90,1893670000,11297.90 2006-08-22,11344.41,11426.13,11279.25,11339.84,1908740000,11339.84 2006-08-21,11353.29,11381.46,11322.31,11345.04,1759240000,11345.04 2006-08-18,11333.76,11437.66,11273.49,11381.47,2033910000,11381.47 2006-08-17,11321.19,11372.34,11298.54,11334.96,2458340000,11334.96 2006-08-16,11224.91,11373.78,11207.85,11327.12,2554570000,11327.12 2006-08-15,11098.03,11271.25,11098.03,11230.26,2334100000,11230.26 2006-08-14,11089.07,11242.83,11049.68,11097.87,2118020000,11097.87 2006-08-11,11103.55,11121.40,11042.88,11088.02,2004540000,11088.02 2006-08-10,11073.14,11176.47,10998.06,11124.37,2402190000,11124.37 2006-08-09,11168.47,11296.22,11044.64,11076.18,2555180000,11076.18 2006-08-08,11218.18,11319.51,11117.80,11173.59,2457840000,11173.59 2006-08-07,11239.47,11294.14,11143.02,11219.38,2045660000,11219.38 2006-08-04,11244.59,11367.94,11165.11,11240.35,2530970000,11240.35 2006-08-03,11195.28,11304.78,11101.55,11242.59,2728440000,11242.59 2006-08-02,11167.91,11228.98,11124.52,11199.92,2610750000,11199.92 2006-08-01,11184.80,11210.65,11035.92,11125.73,2527690000,11125.73 2006-07-31,11218.90,11265.80,11115.24,11185.68,2461300000,11185.68 2006-07-28,11102.11,11282.05,11102.11,11219.70,2480420000,11219.70 2006-07-27,11104.19,11245.47,11040.80,11100.43,2776710000,11100.43 2006-07-26,11102.91,11208.09,10987.41,11102.51,2667710000,11102.51 2006-07-25,11037.59,11133.73,11000.05,11103.71,2563930000,11103.71 2006-07-24,10868.70,11096.35,10868.70,11051.05,2312720000,11051.05 2006-07-21,10937.94,10995.73,10778.58,10868.38,2704090000,10868.38 2006-07-20,11007.26,11098.75,10884.47,10928.10,2345580000,10928.10 2006-07-19,10854.22,11038.16,10796.74,11011.42,2701980000,11011.42 2006-07-18,10745.84,10867.02,10658.35,10799.23,2481750000,10799.23 2006-07-17,10739.35,10858.22,10668.35,10747.36,2146410000,10747.36 2006-07-14,10846.53,10892.64,10664.43,10739.35,2467120000,10739.35 2006-07-13,11012.62,11015.19,10790.82,10846.29,2545760000,10846.29 2006-07-12,11133.97,11181.12,10973.37,11013.18,2250450000,11013.18 2006-07-11,11102.59,11186.32,10987.33,11134.77,2310850000,11134.77 2006-07-10,11130.53,11174.47,11090.10,11103.55,1854590000,11103.55 2006-07-07,11224.18,11227.62,11040.32,11090.67,1988150000,11090.67 2006-07-06,11147.12,11301.58,11117.16,11225.30,2009160000,11225.30 2006-07-05,11225.06,11239.47,11083.94,11151.82,2165070000,11151.82 2006-07-03,11149.34,11277.09,11146.06,11228.02,1114470000,11228.02 2006-06-30,11190.80,11288.86,11100.11,11150.22,3049560000,11150.22 2006-06-29,10974.36,11225.06,10974.36,11190.80,2621250000,11190.80 2006-06-28,10925.30,11026.07,10869.02,10973.56,2085490000,10973.56 2006-06-27,11048.24,11064.09,10920.73,10924.74,2203130000,10924.74 2006-06-26,10990.29,11089.07,10937.06,11045.28,1878580000,11045.28 2006-06-23,11019.19,11098.67,10932.82,10989.09,2017270000,10989.09 2006-06-22,11077.78,11127.73,10954.83,11019.11,2148180000,11019.11 2006-06-21,10975.24,11165.91,10952.43,11079.46,2361230000,11079.46 2006-06-20,10942.03,11066.73,10895.28,10974.84,2232950000,10974.84 2006-06-19,11014.87,11098.99,10886.63,10942.11,2517200000,10942.11 2006-06-16,11009.10,11045.04,10984.29,11014.54,2783390000,11014.54 2006-06-15,10817.48,11049.85,10788.34,11015.19,2775480000,11015.19 2006-06-14,10713.10,10816.99,10699.25,10816.91,2667990000,10816.91 2006-06-13,10783.14,10893.04,10653.23,10706.14,3215770000,10706.14 2006-06-12,10892.00,10969.32,10767.61,10792.58,2247010000,10792.58 2006-06-09,10939.14,11015.67,10842.89,10891.92,2214000000,10891.92 2006-06-08,10929.70,11032.15,10726.15,10938.82,3543790000,10938.82 2006-06-07,11002.06,11107.48,10897.76,10930.90,2644170000,10930.90 2006-06-06,11048.24,11140.45,10890.24,11002.14,2697650000,11002.14 2006-06-05,11247.55,11259.96,11025.75,11048.72,2313470000,11048.72 2006-06-02,11260.52,11329.28,11158.06,11247.87,2295540000,11247.87 2006-06-01,11169.03,11290.86,11115.40,11260.28,2360160000,11260.28 2006-05-31,11091.15,11225.78,11050.40,11168.31,2692160000,11168.31 2006-05-30,11277.25,11277.25,11071.70,11094.43,2176190000,11094.43 2006-05-26,11211.69,11329.36,11177.43,11278.61,1814020000,11278.61 2006-05-25,11114.96,11258.12,11089.55,11211.05,2372730000,11211.05 2006-05-24,11100.11,11241.15,10980.29,11117.32,2999030000,11117.32 2006-05-23,11126.29,11254.68,11068.49,11098.35,2605250000,11098.35 2006-05-22,11092.90,11175.03,11040.16,11125.32,2773010000,11125.32 2006-05-19,11124.37,11254.60,11009.98,11144.06,2982300000,11144.06 2006-05-18,11206.17,11301.26,11096.35,11128.29,2537490000,11128.29 2006-05-17,11410.13,11412.28,11139.17,11205.61,2830200000,11205.61 2006-05-16,11428.21,11520.42,11334.64,11419.89,2386210000,11419.89 2006-05-15,11380.43,11485.61,11273.65,11428.77,2505660000,11428.77 2006-05-12,11500.01,11551.40,11336.96,11380.99,2567970000,11380.99 2006-05-11,11639.29,11660.58,11449.74,11500.73,2531520000,11500.73 2006-05-10,11630.48,11709.09,11545.64,11642.65,2268550000,11642.65 2006-05-09,11584.62,11684.28,11535.71,11639.77,2157290000,11639.77 2006-05-08,11576.37,11665.14,11504.09,11584.54,2151300000,11584.54 2006-05-05,11440.62,11616.16,11440.62,11577.74,2294760000,11577.74 2006-05-04,11401.80,11512.18,11366.82,11438.86,2431450000,11438.86 2006-05-03,11414.69,11472.96,11308.55,11400.28,2395230000,11400.28 2006-05-02,11372.74,11427.43,11343.28,11416.44,2403470000,11416.44 2006-05-01,11367.78,11476.96,11304.30,11343.29,2437040000,11343.29 2006-04-28,11358.33,11462.95,11278.13,11367.14,2419920000,11367.14 2006-04-27,11349.53,11465.75,11220.74,11382.51,2772010000,11382.51 2006-04-26,11283.25,11428.77,11256.12,11354.49,2502690000,11354.49 2006-04-25,11336.56,11401.32,11213.05,11283.25,2366380000,11283.25 2006-04-24,11346.81,11420.05,11246.35,11336.32,2117330000,11336.32 2006-04-21,11343.45,11468.16,11272.61,11347.45,2392630000,11347.45 2006-04-20,11278.53,11429.25,11221.30,11342.89,2512920000,11342.89 2006-04-19,11265.40,11379.79,11181.84,11278.77,2447310000,11278.77 2006-04-18,11074.58,11302.30,11064.01,11268.77,2595440000,11268.77 2006-04-17,11137.33,11203.13,11017.43,11073.78,1794650000,11073.78 2006-04-13,11130.13,11210.73,11053.29,11137.65,1891940000,11137.65 2006-04-12,11089.47,11194.00,11052.17,11129.97,1938100000,11129.97 2006-04-11,11141.33,11220.98,11017.99,11089.63,2232880000,11089.63 2006-04-10,11119.88,11211.37,11083.06,11141.33,1898320000,11141.33 2006-04-07,11228.10,11268.92,11108.75,11120.04,2082470000,11120.04 2006-04-06,11233.01,11294.30,11137.57,11216.50,2281680000,11216.50 2006-04-05,11203.21,11290.30,11141.82,11239.55,2420020000,11239.55 2006-04-04,11142.54,11269.17,11094.03,11203.85,2147660000,11203.85 2006-04-03,11113.00,11287.02,11101.07,11144.94,2494080000,11144.94 2006-03-31,11151.34,11229.47,11069.78,11109.32,2236710000,11109.32 2006-03-30,11195.36,11259.08,11118.12,11150.70,2294560000,11150.70 2006-03-29,11154.94,11283.97,11117.48,11215.70,2143540000,11215.70 2006-03-28,11250.11,11312.31,11132.05,11154.54,2148580000,11154.54 2006-03-27,11280.13,11314.96,11194.07,11250.11,2029700000,11250.11 2006-03-24,11270.61,11353.21,11197.21,11279.97,2326070000,11279.97 2006-03-23,11317.35,11363.46,11207.85,11270.29,1980940000,11270.29 2006-03-22,11234.51,11358.01,11200.22,11317.43,2039810000,11317.43 2006-03-21,11275.89,11364.34,11188.56,11235.47,2147370000,11235.47 2006-03-20,11278.93,11350.73,11208.33,11274.53,1976830000,11274.53 2006-03-17,11294.94,11294.94,11253.23,11279.65,2549620000,11279.65 2006-03-16,11210.97,11324.80,11176.07,11253.24,2292180000,11253.24 2006-03-15,11149.76,11258.28,11097.23,11209.77,2293000000,11209.77 2006-03-14,11076.02,11190.96,11030.23,11151.34,2165270000,11151.34 2006-03-13,11067.61,11157.82,11019.75,11076.02,2070330000,11076.02 2006-03-10,10972.92,11125.41,10948.43,11076.34,2123450000,11076.34 2006-03-09,11005.66,11093.39,10923.86,10972.28,2140110000,10972.28 2006-03-08,10977.08,11065.61,10885.99,11005.74,2442870000,11005.74 2006-03-07,10957.31,11032.31,10885.35,10980.69,2268050000,10980.69 2006-03-06,11022.47,11084.66,10899.76,10958.59,2280190000,10958.59 2006-03-03,11024.23,11125.01,10942.99,11021.59,2152950000,11021.59 2006-03-02,11052.57,11090.91,10951.71,11025.51,2494590000,11025.51 2006-03-01,10993.25,11115.80,10960.60,11053.53,2308320000,11053.53 2006-02-28,11096.75,11115.24,10947.07,10993.41,2370860000,10993.41 2006-02-27,11062.81,11180.48,11038.72,11097.55,1975320000,11097.55 2006-02-24,11068.33,11085.38,11010.46,11061.85,1933010000,11061.85 2006-02-23,11133.52,11167.83,11017.27,11069.22,2144210000,11069.22 2006-02-22,11086.98,11159.02,11064.25,11137.17,2222380000,11137.17 2006-02-21,11115.48,11182.68,11011.18,11069.06,2104320000,11069.06 2006-02-17,11119.56,11178.80,11035.44,11115.32,2128260000,11115.32 2006-02-16,11059.05,11154.14,10997.66,11120.68,2251490000,11120.68 2006-02-15,11025.67,11115.56,10940.18,11058.97,2317590000,11058.97 2006-02-14,10890.72,11071.54,10873.62,11028.39,2437940000,11028.39 2006-02-13,10915.21,10982.93,10824.60,10892.32,1850080000,10892.32 2006-02-10,10883.51,10972.76,10787.62,10919.05,2290050000,10919.05 2006-02-09,10859.42,11003.50,10800.91,10883.35,2441920000,10883.35 2006-02-08,10742.16,10897.44,10712.30,10858.62,2456860000,10858.62 2006-02-07,10796.42,10874.79,10691.97,10749.76,2366370000,10749.76 2006-02-06,10793.30,10868.62,10725.91,10798.27,2132360000,10798.27 2006-02-03,10849.57,10905.28,10725.35,10793.62,2282210000,10793.62 2006-02-02,10950.11,10987.73,10800.35,10851.98,2565300000,10851.98 2006-02-01,10862.14,11001.82,10815.39,10953.95,2589410000,10953.95 2006-01-31,10900.40,10970.28,10807.55,10864.86,2708310000,10864.86 2006-01-30,10913.13,10930.34,10887.67,10899.92,2282730000,10899.92 2006-01-27,10815.32,10988.29,10766.01,10907.21,2623620000,10907.21 2006-01-26,10768.17,10827.96,10709.73,10809.47,2856780000,10809.47 2006-01-25,10713.26,10832.93,10615.20,10709.74,2617060000,10709.74 2006-01-24,10690.21,10804.75,10624.49,10712.22,2608720000,10712.22 2006-01-23,10668.75,10783.70,10607.36,10688.77,2256070000,10688.77 2006-01-20,10880.71,10890.08,10637.21,10667.39,2845810000,10667.39 2006-01-19,10855.18,10965.00,10796.66,10880.71,2444020000,10880.71 2006-01-18,10890.08,10934.90,10778.33,10854.86,2233200000,10854.86 2006-01-17,10957.55,10977.01,10841.17,10896.32,2179970000,10896.32 2006-01-13,10961.48,11033.04,10888.88,10959.87,2206510000,10959.87 2006-01-12,11043.12,11070.10,10918.09,10962.36,2318350000,10962.36 2006-01-11,11011.66,11099.15,10939.86,11043.44,2406130000,11043.44 2006-01-10,11010.46,11054.49,10902.96,11011.58,2373080000,11011.58 2006-01-09,10959.47,11053.93,10906.33,11011.90,2301490000,11011.90 2006-01-06,10875.45,11005.98,10846.21,10959.31,2446560000,10959.31 2006-01-05,10880.39,10951.39,10797.55,10882.15,2433340000,10882.15 2006-01-04,10843.97,10946.27,10772.89,10880.15,2515330000,10880.15 2006-01-03,10718.30,10888.40,10650.18,10847.41,2554570000,10847.41 2005-12-30,10783.86,10801.87,10675.64,10717.50,1443500000,10717.50 2005-12-29,10795.70,10870.71,10747.76,10784.82,1382540000,10784.82 2005-12-28,10778.25,10858.07,10750.40,10796.26,1422360000,10796.26 2005-12-27,10883.75,10956.99,10754.16,10777.77,1540470000,10777.77 2005-12-23,10901.68,10904.40,10869.98,10883.27,1285810000,10883.27 2005-12-22,10831.56,10928.34,10785.93,10889.44,1888500000,10889.44 2005-12-21,10805.63,10933.70,10776.01,10833.73,2065170000,10833.73 2005-12-20,10836.93,10905.28,10754.56,10805.55,1996690000,10805.55 2005-12-19,10875.51,10970.04,10781.70,10836.53,2208810000,10836.53 2005-12-16,10881.67,10978.21,10830.84,10875.59,2584190000,10875.59 2005-12-15,10883.43,10985.01,10803.55,10881.67,2180590000,10881.67 2005-12-14,10821.32,10953.07,10786.34,10883.51,2145520000,10883.51 2005-12-13,10765.69,10902.64,10694.29,10823.72,2390020000,10823.72 2005-12-12,10778.66,10857.02,10707.18,10767.77,1876550000,10767.77 2005-12-09,10751.76,10845.33,10694.05,10778.58,1896290000,10778.58 2005-12-08,10808.43,10871.11,10705.17,10755.12,2178300000,10755.12 2005-12-07,10856.86,10916.89,10737.27,10810.91,2093830000,10810.91 2005-12-06,10835.41,10956.13,10809.15,10856.86,2110740000,10856.86 2005-12-05,10876.95,10923.37,10766.57,10835.01,2325840000,10835.01 2005-12-02,10912.01,10952.83,10818.36,10877.51,2125580000,10877.51 2005-12-01,10806.03,10969.97,10806.03,10912.57,2614830000,10912.57 2005-11-30,10883.91,10959.80,10789.38,10805.87,2374690000,10805.87 2005-11-29,10888.48,10994.85,10850.29,10888.16,2268340000,10888.16 2005-11-28,10932.74,10992.39,10839.37,10890.72,2016900000,10890.72 2005-11-25,10915.13,10997.50,10883.55,10931.62,724940000,10931.62 2005-11-23,10865.90,10950.59,10855.42,10916.09,1985400000,10916.09 2005-11-22,10815.96,10907.77,10729.99,10871.43,2291420000,10871.43 2005-11-21,10766.33,10871.11,10708.86,10820.28,2117350000,10820.28 2005-11-18,10719.34,10865.58,10663.79,10766.33,2453290000,10766.33 2005-11-17,10677.00,10778.50,10619.61,10720.22,2298040000,10720.22 2005-11-16,10697.81,10710.78,10653.14,10674.76,2121580000,10674.76 2005-11-15,10697.01,10783.62,10610.08,10686.44,2359370000,10686.44 2005-11-14,10686.60,10756.88,10618.89,10697.17,1899780000,10697.17 2005-11-11,10641.30,10725.99,10595.89,10686.04,1773140000,10686.04 2005-11-10,10550.61,10655.22,10519.71,10640.10,2378460000,10640.10 2005-11-09,10539.24,10637.78,10466.24,10546.21,2214460000,10546.21 2005-11-08,10574.18,10606.80,10478.49,10539.72,1965050000,10539.72 2005-11-07,10531.24,10632.34,10488.74,10586.23,1987580000,10586.23 2005-11-04,10523.23,10593.51,10441.59,10530.76,2050510000,10530.76 2005-11-03,10470.49,10613.84,10421.98,10522.59,2716630000,10522.59 2005-11-02,10406.29,10527.32,10347.70,10472.73,2648090000,10472.73 2005-11-01,10437.51,10510.03,10352.42,10406.77,2457850000,10406.77 2005-10-31,10403.17,10539.16,10372.67,10440.07,2567470000,10440.07 2005-10-28,10231.15,10433.51,10213.14,10402.77,2379400000,10402.77 2005-10-27,10334.81,10348.66,10229.95,10229.95,2395370000,10229.95 2005-10-26,10377.39,10474.18,10283.10,10344.98,2467750000,10344.98 2005-10-25,10383.88,10457.52,10282.78,10377.87,2312470000,10377.87 2005-10-24,10219.15,10411.57,10219.15,10385.00,2197790000,10385.00 2005-10-21,10282.22,10354.02,10161.60,10215.22,2470920000,10215.22 2005-10-20,10411.73,10483.21,10230.27,10281.10,2617250000,10281.10 2005-10-19,10277.18,10444.15,10173.52,10414.13,2703590000,10414.13 2005-10-18,10349.14,10412.85,10233.47,10285.26,2197010000,10285.26 2005-10-17,10287.42,10419.58,10213.06,10348.10,2054570000,10348.10 2005-10-14,10216.59,10327.21,10165.12,10287.34,2188940000,10287.34 2005-10-13,10216.91,10309.20,10098.18,10216.59,2351150000,10216.59 2005-10-12,10247.40,10308.23,10186.17,10216.90,2491280000,10216.90 2005-10-11,10239.16,10361.15,10195.13,10253.17,2299040000,10253.17 2005-10-10,10292.95,10378.19,10184.09,10238.76,2195990000,10238.76 2005-10-07,10287.42,10387.48,10221.47,10292.31,2126080000,10292.31 2005-10-06,10317.36,10425.98,10200.81,10287.10,2792030000,10287.10 2005-10-05,10434.81,10477.21,10299.27,10317.36,2546780000,10317.36 2005-10-04,10534.36,10618.41,10409.89,10441.11,2341420000,10441.11 2005-10-03,10569.50,10637.00,10486.17,10535.48,2097490000,10535.48 2005-09-30,10540.51,10569.81,10526.34,10568.70,2097520000,10568.70 2005-09-29,10472.61,10583.43,10389.01,10552.78,2176120000,10552.78 2005-09-28,10456.61,10560.02,10390.05,10473.08,2106980000,10473.08 2005-09-27,10444.58,10534.31,10376.83,10456.21,1976270000,10456.21 2005-09-26,10420.22,10544.98,10381.53,10443.63,2022220000,10443.63 2005-09-23,10421.81,10494.42,10328.59,10419.59,1973020000,10419.59 2005-09-22,10376.20,10489.64,10303.51,10422.05,2424720000,10422.05 2005-09-21,10484.23,10512.02,10335.28,10378.03,2548150000,10378.03 2005-09-20,10558.19,10642.26,10453.98,10481.52,2319250000,10481.52 2005-09-19,10641.87,10656.75,10497.29,10557.63,2076540000,10557.63 2005-09-16,10560.50,10696.24,10539.64,10641.94,3152470000,10641.94 2005-09-15,10545.85,10627.85,10488.05,10558.75,2079340000,10558.75 2005-09-14,10545.85,10627.85,10488.05,10558.75,1986750000,10558.75 2005-09-13,10673.71,10701.57,10561.61,10597.44,2082360000,10597.44 2005-09-12,10678.41,10743.77,10618.13,10682.94,1938050000,10682.94 2005-09-09,10594.10,10727.53,10573.47,10678.56,1992560000,10678.56 2005-09-08,10633.11,10670.60,10530.01,10595.93,1955380000,10595.93 2005-09-07,10588.68,10667.10,10534.23,10633.50,2067700000,10633.50 2005-09-06,10447.69,10621.96,10447.69,10589.24,1932090000,10589.24 2005-09-02,10460.67,10536.14,10400.88,10447.37,1640160000,10447.37 2005-09-01,10481.44,10557.47,10382.09,10459.63,2229860000,10459.63 2005-08-31,10415.84,10484.55,10357.65,10481.60,2365510000,10481.60 2005-08-30,10461.54,10476.83,10329.15,10412.82,1916470000,10412.82 2005-08-29,10396.90,10508.35,10321.42,10463.05,1599450000,10463.05 2005-08-26,10450.95,10480.01,10355.02,10397.29,1541090000,10397.29 2005-08-25,10434.39,10506.60,10391.71,10450.63,1571110000,10450.63 2005-08-24,10519.34,10584.30,10407.56,10434.87,1930800000,10434.87 2005-08-23,10571.01,10604.29,10475.63,10519.58,1678620000,10519.58 2005-08-22,10559.78,10669.81,10509.07,10569.89,1621330000,10569.89 2005-08-19,10552.70,10656.59,10503.90,10559.23,1558790000,10559.23 2005-08-18,10531.12,10592.34,10517.67,10554.92,1808170000,10554.92 2005-08-17,10505.60,10625.86,10472.45,10550.71,1859150000,10550.71 2005-08-16,10631.59,10650.14,10489.24,10513.45,1820410000,10513.45 2005-08-15,10599.19,10687.72,10532.48,10634.38,1562880000,10634.38 2005-08-12,10682.70,10688.68,10549.19,10600.30,1709300000,10600.30 2005-08-11,10591.83,10721.56,10549.43,10685.89,1941560000,10685.89 2005-08-10,10606.52,10746.87,10553.81,10594.41,2172320000,10594.41 2005-08-09,10537.65,10662.80,10537.01,10615.67,1897520000,10615.67 2005-08-08,10557.24,10635.65,10497.45,10536.93,1804140000,10536.93 2005-08-05,10610.34,10643.46,10512.49,10558.03,1930280000,10558.03 2005-08-04,10696.80,10709.77,10568.70,10610.10,1981220000,10610.10 2005-08-03,10681.51,10735.17,10600.02,10697.59,1999980000,10697.59 2005-08-02,10623.79,10729.60,10600.62,10683.74,2043120000,10683.74 2005-08-01,10641.78,10713.51,10578.97,10623.15,1716870000,10623.15 2005-07-29,10705.16,10754.60,10608.27,10640.91,1789600000,10640.91 2005-07-28,10633.50,10745.68,10603.49,10705.55,2001680000,10705.55 2005-07-27,10579.45,10689.31,10530.50,10637.09,1945800000,10637.09 2005-07-26,10597.60,10667.90,10535.58,10579.77,1934180000,10579.77 2005-07-25,10651.66,10709.69,10565.12,10596.48,1717580000,10596.48 2005-07-22,10624.19,10702.21,10552.54,10651.18,1766990000,10651.18 2005-07-21,10690.03,10735.33,10567.98,10627.77,2129840000,10627.77 2005-07-20,10629.52,10726.81,10535.10,10689.15,2063340000,10689.15 2005-07-19,10576.90,10718.69,10573.00,10646.56,2041280000,10646.56 2005-07-18,10640.19,10681.99,10533.59,10574.99,1582100000,10574.99 2005-07-15,10629.44,10698.07,10559.46,10640.83,1716400000,10640.83 2005-07-14,10559.86,10696.96,10559.86,10628.88,2048710000,10628.88 2005-07-13,10513.36,10596.98,10481.36,10557.39,1812500000,10557.39 2005-07-12,10519.49,10583.02,10444.46,10513.89,1932010000,10513.89 2005-07-11,10449.60,10570.67,10425.97,10519.72,1846300000,10519.72 2005-07-08,10302.90,10486.50,10279.65,10449.14,1900810000,10449.14 2005-07-07,10269.76,10337.84,10142.24,10302.29,1952440000,10302.29 2005-07-06,10366.52,10413.23,10242.21,10270.68,1883470000,10270.68 2005-07-05,10292.62,10388.91,10282.64,10371.80,1805820000,10371.80 2005-07-01,10273.59,10380.78,10239.91,10303.44,1593820000,10303.44 2005-06-30,10374.18,10458.19,10253.49,10274.97,2109490000,10274.97 2005-06-29,10405.94,10472.46,10332.52,10374.48,1769280000,10374.48 2005-06-28,10291.01,10434.18,10285.86,10405.63,1772410000,10405.63 2005-06-27,10298.07,10377.55,10229.40,10290.78,1738620000,10290.78 2005-06-24,10422.28,10452.82,10266.30,10297.83,2418800000,10297.83 2005-06-23,10587.09,10617.39,10401.49,10421.44,2029920000,10421.44 2005-06-22,10599.36,10676.24,10543.28,10587.93,1823250000,10587.93 2005-06-21,10608.88,10670.56,10545.89,10599.67,1720700000,10599.67 2005-06-20,10621.54,10656.66,10539.21,10609.10,1714530000,10609.10 2005-06-17,10580.41,10710.38,10561.00,10623.07,2407370000,10623.07 2005-06-16,10566.76,10632.20,10501.92,10578.65,1776040000,10578.65 2005-06-15,10548.65,10628.67,10471.69,10566.37,1840440000,10566.37 2005-06-14,10521.95,10617.01,10473.92,10547.57,1698150000,10547.57 2005-06-13,10503.57,10611.10,10437.32,10522.56,1661350000,10522.56 2005-06-10,10503.02,10581.13,10410.50,10512.63,1664180000,10512.63 2005-06-09,10477.75,10556.90,10410.80,10503.02,1824120000,10503.02 2005-06-08,10484.84,10575.81,10439.77,10476.86,1715490000,10476.86 2005-06-07,10466.00,10603.15,10454.18,10483.07,1851370000,10483.07 2005-06-06,10461.64,10519.79,10410.28,10467.03,1547120000,10467.03 2005-06-03,10552.82,10572.18,10427.35,10460.97,1627520000,10460.97 2005-06-02,10548.83,10590.07,10478.26,10553.49,1813790000,10553.49 2005-06-01,10462.86,10616.15,10433.48,10549.87,1810100000,10549.87 2005-05-31,10541.89,10574.92,10437.77,10467.48,1840680000,10467.48 2005-05-27,10537.08,10579.94,10489.35,10542.55,1381430000,10542.55 2005-05-26,10458.68,10581.87,10450.55,10537.60,1654110000,10537.60 2005-05-25,10503.17,10516.17,10396.46,10457.80,1742180000,10457.80 2005-05-24,10522.68,10550.24,10433.78,10503.68,1681000000,10503.68 2005-05-23,10472.80,10589.92,10438.36,10523.56,1681170000,10523.56 2005-05-20,10492.75,10535.24,10400.60,10471.91,1631750000,10471.91 2005-05-19,10464.45,10538.71,10394.39,10493.19,1775860000,10493.19 2005-05-18,10323.19,10518.17,10323.19,10464.45,2266320000,10464.45 2005-05-17,10247.49,10357.30,10175.81,10331.88,1887260000,10331.88 2005-05-16,10139.61,10274.39,10118.32,10252.29,1856860000,10252.29 2005-05-13,10188.23,10268.85,10062.76,10140.12,2188590000,10140.12 2005-05-12,10299.74,10357.37,10155.86,10189.48,1995290000,10189.48 2005-05-11,10272.91,10355.31,10172.86,10300.25,1834970000,10300.25 2005-05-10,10382.94,10389.96,10230.35,10281.11,1889660000,10281.11 2005-05-09,10345.40,10416.56,10288.95,10384.34,1857020000,10384.34 2005-05-06,10339.71,10454.40,10300.70,10345.40,1707200000,10345.40 2005-05-05,10384.49,10447.08,10276.75,10340.38,1997100000,10340.38 2005-05-04,10255.25,10412.20,10239.95,10384.64,2306480000,10384.64 2005-05-03,10251.04,10327.37,10169.39,10256.95,2167020000,10256.95 2005-05-02,10192.00,10309.86,10149.06,10251.70,1980040000,10251.70 2005-04-29,10073.47,10231.31,10021.23,10192.51,2362360000,10192.51 2005-04-28,10194.58,10229.09,10036.74,10070.37,2182270000,10070.37 2005-04-27,10150.32,10250.23,10048.27,10198.80,2151520000,10198.80 2005-04-26,10240.99,10298.85,10108.79,10151.13,1959740000,10151.13 2005-04-25,10158.52,10305.87,10148.25,10242.47,1795030000,10242.47 2005-04-22,10216.68,10266.48,10055.29,10157.71,2045880000,10157.71 2005-04-21,10010.51,10250.30,10010.51,10218.60,2308560000,10218.60 2005-04-20,10131.18,10232.34,9978.74,10012.36,2217050000,10012.36 2005-04-19,10071.55,10220.21,10021.08,10127.41,2142700000,10127.41 2005-04-18,10088.54,10183.50,9961.52,10071.25,2180670000,10071.25 2005-04-15,10276.61,10311.26,10059.36,10087.51,2689960000,10087.51 2005-04-14,10403.71,10457.06,10248.23,10278.75,2355040000,10278.75 2005-04-13,10507.45,10567.38,10355.16,10403.93,2049740000,10403.93 2005-04-12,10448.63,10552.60,10331.21,10507.97,1979830000,10507.97 2005-04-11,10462.08,10530.58,10393.36,10448.56,1525310000,10448.56 2005-04-08,10546.32,10584.60,10445.31,10461.34,1661330000,10461.34 2005-04-07,10485.88,10589.99,10434.30,10546.32,1900620000,10546.32 2005-04-06,10453.45,10557.18,10434.22,10486.02,1797400000,10486.02 2005-04-05,10421.14,10530.14,10372.59,10458.46,1870800000,10458.46 2005-04-04,10401.71,10496.44,10307.64,10421.14,2079770000,10421.14 2005-04-01,10504.57,10600.56,10349.02,10404.30,2168690000,10404.30 2005-03-31,10541.59,10586.15,10448.19,10503.76,2214230000,10503.76 2005-03-30,10405.77,10564.80,10395.21,10540.93,2097110000,10540.93 2005-03-29,10486.10,10565.46,10351.76,10405.70,2223250000,10405.70 2005-03-28,10444.13,10568.05,10412.79,10485.65,1746220000,10485.65 2005-03-24,10457.06,10554.60,10415.16,10442.87,1721720000,10442.87 2005-03-23,10470.58,10558.51,10384.34,10456.02,2246870000,10456.02 2005-03-22,10565.39,10651.41,10446.78,10470.51,2114470000,10470.51 2005-03-21,10629.90,10662.93,10503.46,10565.39,1819440000,10565.39 2005-03-18,10627.83,10709.85,10520.60,10629.67,2344370000,10629.67 2005-03-17,10633.30,10707.86,10561.10,10626.35,1581930000,10626.35 2005-03-16,10741.63,10764.16,10569.30,10633.07,1653190000,10633.07 2005-03-15,10804.29,10884.76,10716.87,10745.10,1513530000,10745.10 2005-03-14,10773.92,10859.12,10709.26,10804.51,1437430000,10804.51 2005-03-11,10845.30,10897.68,10728.62,10774.36,1449820000,10774.36 2005-03-10,10806.28,10907.45,10757.36,10851.51,1604020000,10851.51 2005-03-09,10912.32,10949.20,10768.66,10805.62,1704970000,10805.62 2005-03-08,10935.60,10996.56,10863.40,10912.62,1523090000,10912.62 2005-03-07,10940.55,11027.15,10886.09,10936.86,1488830000,10936.86 2005-03-04,10834.51,10996.93,10834.51,10940.55,1636820000,10940.55 2005-03-03,10812.27,10904.34,10744.14,10833.03,1616240000,10833.03 2005-03-02,10825.68,10896.66,10736.16,10811.97,1568540000,10811.97 2005-03-01,10794.98,10849.14,10769.04,10830.00,1708060000,10830.00 2005-02-28,10842.05,10877.07,10709.41,10766.23,1795480000,10766.23 2005-02-25,10748.42,10871.53,10698.32,10841.60,1523680000,10841.60 2005-02-24,10672.24,10779.39,10612.24,10748.79,1518750000,10748.79 2005-02-23,10609.28,10735.42,10583.64,10673.79,1501090000,10673.79 2005-02-22,10783.38,10806.36,10596.79,10611.20,1744940000,10611.20 2005-02-18,10755.15,10854.98,10682.29,10785.22,1551200000,10785.22 2005-02-17,10835.03,10873.60,10729.95,10754.26,1580120000,10754.26 2005-02-16,10832.03,10889.78,10746.21,10834.88,1490100000,10834.88 2005-02-15,10791.06,10886.31,10745.10,10837.32,1527080000,10837.32 2005-02-14,10795.72,10853.80,10722.63,10791.13,1290180000,10791.13 2005-02-11,10742.92,10865.69,10680.15,10796.01,1562300000,10796.01 2005-02-10,10665.00,10803.92,10633.67,10749.61,1491670000,10749.61 2005-02-09,10717.76,10781.38,10621.84,10664.11,1511040000,10664.11 2005-02-08,10712.51,10783.97,10647.85,10724.63,1416170000,10724.63 2005-02-07,10715.76,10774.80,10650.59,10715.76,1347270000,10715.76 2005-02-04,10593.17,10750.94,10553.93,10716.13,1648160000,10716.13 2005-02-03,10592.21,10640.69,10511.96,10593.10,1554460000,10593.10 2005-02-02,10551.05,10638.25,10501.02,10596.79,1561740000,10596.79 2005-02-01,10489.72,10609.73,10464.38,10551.94,1681980000,10551.94 2005-01-31,10428.76,10559.77,10414.49,10489.94,1679800000,10489.94 2005-01-28,10470.58,10520.61,10331.58,10427.20,1641800000,10427.20 2005-01-27,10498.14,10535.16,10379.47,10467.40,1600600000,10467.40 2005-01-26,10463.19,10571.66,10433.56,10498.59,1635900000,10498.59 2005-01-25,10369.42,10534.72,10369.42,10461.56,1610400000,10461.56 2005-01-24,10393.58,10491.71,10317.10,10368.61,1494600000,10368.61 2005-01-21,10471.98,10541.30,10370.97,10392.99,1643500000,10392.99 2005-01-20,10538.90,10582.09,10414.72,10471.47,1692000000,10471.47 2005-01-19,10626.05,10668.40,10519.20,10539.97,1498700000,10539.97 2005-01-18,10554.23,10665.44,10456.61,10628.79,1596800000,10628.79 2005-01-14,10506.71,10606.47,10475.01,10558.00,1335400000,10558.00 2005-01-13,10617.41,10650.96,10463.26,10505.83,1510300000,10505.83 2005-01-12,10561.32,10648.52,10481.37,10617.78,1562100000,10617.78 2005-01-11,10619.77,10632.63,10504.72,10556.22,1488800000,10556.22 2005-01-10,10603.44,10696.85,10544.99,10621.03,1490400000,10621.03 2005-01-07,10624.80,10697.81,10545.80,10603.96,1477900000,10603.96 2005-01-06,10593.19,10708.37,10555.48,10622.88,1569100000,10622.88 2005-01-05,10629.53,10736.16,10561.03,10597.83,1738900000,10597.83 2005-01-04,10727.81,10803.62,10587.48,10630.78,1720200000,10630.78 2005-01-03,10783.75,10892.67,10694.18,10729.43,1505900000,10729.43 2004-12-31,10800.30,10849.81,10759.94,10783.01,786900000,10783.01 2004-12-30,10829.12,10880.03,10781.67,10800.30,829800000,10800.30 2004-12-29,10853.72,10872.71,10771.26,10829.19,924900000,10829.19 2004-12-28,10776.06,10890.82,10774.51,10854.54,984200000,10854.54 2004-12-27,10828.01,10892.52,10755.67,10776.13,922000000,10776.13 2004-12-23,10815.00,10895.10,10780.12,10827.12,956100000,10827.12 2004-12-22,10752.34,10861.33,10709.78,10815.89,1390800000,10815.89 2004-12-21,10661.89,10789.66,10646.60,10759.43,1483700000,10759.43 2004-12-20,10652.14,10769.13,10622.20,10661.60,1422800000,10661.60 2004-12-17,10704.83,10766.01,10562.76,10649.92,2335000000,10649.92 2004-12-16,10691.71,10776.43,10612.31,10705.64,1793900000,10705.64 2004-12-15,10675.71,10749.53,10601.74,10691.45,1695800000,10691.45 2004-12-14,10640.53,10715.76,10571.67,10676.45,1544400000,10676.45 2004-12-13,10543.44,10678.52,10520.61,10638.32,1436100000,10638.32 2004-12-10,10552.16,10616.23,10469.62,10543.22,1443700000,10543.22 2004-12-09,10492.45,10579.94,10389.81,10552.82,1624700000,10552.82 2004-12-08,10438.77,10550.16,10395.05,10494.23,1525200000,10494.23 2004-12-07,10546.80,10612.90,10423.21,10440.58,1533900000,10440.58 2004-12-06,10591.32,10614.82,10490.31,10547.06,1354400000,10547.06 2004-12-03,10597.90,10670.47,10515.73,10592.21,1566700000,10592.21 2004-12-02,10590.44,10670.02,10485.21,10585.12,1774900000,10585.12 2004-12-01,10425.80,10618.59,10421.66,10590.22,1772800000,10590.22 2004-11-30,10475.27,10530.80,10380.58,10428.02,1553500000,10428.02 2004-11-29,10520.64,10590.73,10390.92,10475.90,1378500000,10475.90 2004-11-26,10518.69,10574.70,10479.52,10522.23,504580000,10522.23 2004-11-24,10493.86,10565.76,10451.66,10520.31,1149600000,10520.31 2004-11-23,10486.69,10547.28,10407.18,10492.60,1428300000,10492.60 2004-11-22,10455.73,10535.46,10380.95,10489.42,1392700000,10489.42 2004-11-19,10571.63,10588.29,10419.89,10456.91,1526600000,10456.91 2004-11-18,10549.20,10639.43,10499.62,10572.55,1456700000,10572.55 2004-11-17,10481.83,10655.09,10470.21,10549.57,1684200000,10549.57 2004-11-16,10549.79,10570.56,10445.31,10487.65,1364400000,10487.65 2004-11-15,10541.89,10612.31,10463.34,10550.24,1453300000,10550.24 2004-11-12,10469.21,10565.48,10419.77,10539.01,1531600000,10539.01 2004-11-11,10386.95,10513.42,10364.10,10469.84,1393000000,10469.84 2004-11-10,10378.59,10470.21,10330.54,10385.48,1504300000,10385.48 2004-11-09,10387.62,10466.81,10327.00,10386.37,1450800000,10386.37 2004-11-08,10385.15,10452.58,10313.51,10391.31,1358700000,10391.31 2004-11-05,10317.05,10458.10,10279.96,10387.54,1724400000,10387.54 2004-11-04,10132.48,10352.81,10076.36,10314.76,1782700000,10314.76 2004-11-03,10137.05,10253.27,10062.80,10137.05,1767500000,10137.05 2004-11-02,10053.87,10180.85,9976.29,10035.73,1659000000,10035.73 2004-11-01,10028.73,10124.44,9953.29,10054.39,1395900000,10054.39 2004-10-29,10004.69,10083.44,9936.77,10027.47,1500800000,10027.47 2004-10-28,9998.94,10074.89,9900.42,10004.54,1628200000,10004.54 2004-10-27,9888.25,10051.96,9807.80,10002.03,1741900000,10002.03 2004-10-26,9750.59,9914.65,9718.94,9888.48,1685400000,9888.48 2004-10-25,9757.22,9827.20,9660.18,9749.99,1380500000,9749.99 2004-10-22,9863.85,9920.99,9732.81,9757.81,1469600000,9757.81 2004-10-21,9884.65,9969.07,9769.16,9865.76,1673000000,9865.76 2004-10-20,9895.19,9957.05,9766.95,9886.93,1685700000,9886.93 2004-10-19,9958.38,10064.20,9854.19,9897.62,1737500000,9897.62 2004-10-18,9932.98,10002.18,9816.73,9956.32,1373300000,9956.32 2004-10-15,9895.63,10022.31,9845.56,9933.38,1645100000,9933.38 2004-10-14,10002.54,10048.48,9837.22,9894.45,1489500000,9894.45 2004-10-13,10085.21,10157.11,9935.08,10002.33,1546200000,10002.33 2004-10-12,10080.42,10121.49,9985.44,10077.18,1320100000,10077.18 2004-10-11,10056.09,10142.74,10017.67,10081.97,943800000,10081.97 2004-10-08,10124.11,10177.53,10013.91,10055.20,1291600000,10055.20 2004-10-07,10239.84,10267.57,10086.83,10125.40,1447500000,10125.40 2004-10-06,10170.70,10267.14,10112.20,10239.92,1416700000,10239.92 2004-10-05,10216.76,10253.93,10123.19,10177.68,1418400000,10177.68 2004-10-04,10191.40,10313.81,10153.79,10216.54,1534000000,10216.54 2004-10-01,10082.04,10237.05,10081.38,10192.65,1582200000,10192.65 2004-09-30,10136.38,10155.49,9987.36,10080.27,1748000000,10080.27 2004-09-29,10078.06,10160.50,10002.57,10136.24,1402900000,10136.24 2004-09-28,9989.73,10132.04,9950.71,10077.40,1396600000,10077.40 2004-09-27,10046.65,10077.32,9952.78,9988.54,1263500000,9988.54 2004-09-24,10039.42,10111.39,9993.92,10047.24,1255400000,10047.24 2004-09-23,10108.29,10134.84,9999.67,10038.90,1286300000,10038.90 2004-09-22,10244.05,10244.05,10075.33,10109.18,1379900000,10109.18 2004-09-21,10204.52,10291.39,10159.54,10244.93,1325000000,10244.93 2004-09-20,10283.87,10293.23,10152.90,10204.89,1197600000,10204.89 2004-09-17,10245.82,10352.37,10219.71,10284.46,1422600000,10284.46 2004-09-16,10231.59,10315.72,10194.35,10244.49,1113900000,10244.49 2004-09-15,10316.90,10324.87,10195.60,10231.36,1256000000,10231.36 2004-09-14,10315.13,10374.79,10260.93,10318.16,1204500000,10318.16 2004-09-13,10306.65,10380.47,10250.61,10314.76,1299800000,10314.76 2004-09-10,10289.47,10348.68,10196.19,10313.07,1261200000,10313.07 2004-09-09,10313.36,10337.33,10269.49,10289.10,1371300000,10289.10 2004-09-08,10342.42,10390.64,10267.94,10313.36,1246300000,10313.36 2004-09-07,10261.52,10388.95,10255.62,10341.16,1214400000,10341.16 2004-09-03,10277.82,10346.99,10229.52,10260.20,924170000,10260.20 2004-09-02,10168.39,10312.99,10129.01,10290.28,1118400000,10290.28 2004-09-01,10170.12,10230.48,10092.37,10168.46,1142100000,10168.46 2004-08-31,10121.97,10198.03,10056.09,10173.92,1138200000,10173.92 2004-08-30,10193.83,10226.87,10110.43,10122.52,843100000,10122.52 2004-08-27,10174.07,10235.49,10143.91,10195.01,845400000,10195.01 2004-08-26,10181.07,10225.61,10124.88,10173.41,1023600000,10173.41 2004-08-25,10098.49,10224.29,10041.93,10181.74,1192200000,10181.74 2004-08-24,10074.89,10165.07,10044.66,10098.63,1092500000,10098.63 2004-08-23,10111.10,10159.54,10046.72,10073.05,1021900000,10073.05 2004-08-20,10040.81,10143.76,9989.65,10110.14,1199900000,10110.14 2004-08-19,10082.78,10111.91,9972.39,10040.82,1249400000,10040.82 2004-08-18,9964.22,10097.01,9910.82,10083.15,1282500000,10083.15 2004-08-17,9955.50,10053.21,9916.05,9972.83,1267800000,9972.83 2004-08-16,9825.35,9987.58,9807.44,9954.55,1206200000,9954.55 2004-08-13,9814.11,9897.34,9746.60,9825.35,1175100000,9825.35 2004-08-12,9936.48,9940.02,9780.52,9814.59,1405100000,9814.59 2004-08-11,9931.24,9981.61,9804.63,9938.32,1410400000,9938.32 2004-08-10,9815.55,9961.70,9798.44,9944.67,1245600000,9944.67 2004-08-09,9816.14,9902.49,9773.74,9814.66,1086000000,9814.66 2004-08-06,9960.67,9963.47,9767.54,9815.33,1521000000,9815.33 2004-08-05,10127.10,10158.44,9945.84,9963.03,1397400000,9963.03 2004-08-04,10117.96,10186.16,10029.02,10126.51,1369200000,10126.51 2004-08-03,10178.27,10228.49,10064.57,10120.24,1338300000,10120.24 2004-08-02,10138.45,10224.29,10063.75,10179.16,1276000000,10179.16 2004-07-30,10129.12,10194.13,10045.76,10139.71,1298200000,10139.71 2004-07-29,10115.52,10213.08,10049.74,10129.24,1530100000,10129.24 2004-07-28,10084.03,10170.31,9966.34,10117.07,1554300000,10117.07 2004-07-27,9963.54,10133.95,9942.75,10085.14,1610800000,10085.14 2004-07-26,9964.71,10054.32,9874.38,9961.92,1413400000,9961.92 2004-07-23,10045.46,10069.51,9893.93,9962.22,1337500000,9962.22 2004-07-22,10047.60,10114.41,9906.62,10050.33,1680800000,10050.33 2004-07-21,10156.30,10279.96,10027.92,10046.13,1679500000,10046.13 2004-07-20,10094.43,10186.90,10031.24,10149.07,1445800000,10149.07 2004-07-19,10140.95,10211.60,10027.33,10094.06,1319900000,10094.06 2004-07-16,10162.34,10289.40,10095.32,10139.78,1450300000,10139.78 2004-07-15,10208.20,10275.27,10115.81,10163.16,1408700000,10163.16 2004-07-14,10232.84,10313.95,10129.31,10208.80,1462000000,10208.80 2004-07-13,10238.37,10297.58,10188.45,10247.59,1199700000,10247.59 2004-07-12,10211.75,10284.39,10130.42,10238.22,1114600000,10238.22 2004-07-09,10173.12,10277.16,10150.32,10213.22,1186300000,10213.22 2004-07-08,10238.52,10297.73,10134.99,10171.56,1401100000,10171.56 2004-07-07,10211.92,10300.53,10156.45,10240.29,1328600000,10240.29 2004-07-06,10280.26,10308.43,10163.60,10219.34,1283300000,10219.34 2004-07-02,10334.00,10371.40,10228.71,10282.83,1085000000,10282.83 2004-07-01,10434.00,10473.23,10255.55,10334.16,1495700000,10334.16 2004-06-30,10413.43,10489.16,10348.98,10435.48,1473800000,10435.48 2004-06-29,10356.35,10460.25,10315.65,10413.43,1375000000,10413.43 2004-06-28,10377.52,10505.16,10317.64,10357.09,1354600000,10357.09 2004-06-25,10444.24,10514.67,10329.88,10371.84,1812900000,10371.84 2004-06-24,10477.43,10530.01,10398.16,10443.81,1394900000,10443.81 2004-06-23,10395.14,10498.67,10323.32,10479.57,1444200000,10479.57 2004-06-22,10370.21,10431.05,10284.24,10395.07,1382300000,10395.07 2004-06-21,10417.82,10471.76,10336.44,10371.47,1123900000,10371.47 2004-06-18,10375.82,10471.84,10328.20,10416.41,1500600000,10416.41 2004-06-17,10378.59,10417.69,10308.25,10377.52,1296700000,10377.52 2004-06-16,10380.23,10433.52,10320.60,10379.58,1168400000,10379.58 2004-06-15,10336.51,10464.04,10319.89,10380.43,1345900000,10380.43 2004-06-14,10401.23,10403.50,10283.48,10334.73,1179400000,10334.73 2004-06-10,10367.80,10448.00,10333.94,10410.10,1160600000,10410.10 2004-06-09,10431.10,10466.59,10325.07,10368.44,1276800000,10368.44 2004-06-08,10389.41,10462.97,10323.94,10432.52,1190300000,10432.52 2004-06-07,10243.31,10410.81,10243.31,10391.08,1211800000,10391.08 2004-06-04,10196.83,10327.84,10196.83,10242.82,1115300000,10242.82 2004-06-03,10261.85,10309.46,10163.40,10195.91,1232400000,10195.91 2004-06-02,10199.78,10310.10,10170.57,10262.97,1251700000,10262.97 2004-06-01,10187.18,10254.17,10104.07,10202.65,1238000000,10202.65 2004-05-28,10205.83,10250.27,10137.14,10188.45,1172600000,10188.45 2004-05-27,10109.89,10267.66,10106.13,10205.20,1447500000,10205.20 2004-05-26,10116.84,10175.75,10034.16,10109.89,1369400000,10109.89 2004-05-25,9958.08,10139.27,9895.41,10117.62,1545700000,10117.62 2004-05-24,9968.02,10084.91,9891.22,9958.43,1227500000,9958.43 2004-05-21,9939.34,10058.50,9910.81,9966.74,1258600000,9966.74 2004-05-20,9939.12,10014.50,9867.73,9937.64,1211000000,9937.64 2004-05-19,9962.55,10124.79,9919.90,9937.71,1548600000,9937.71 2004-05-18,9906.71,10028.27,9895.77,9968.51,1353000000,9968.51 2004-05-17,10009.92,10009.92,9827.21,9906.91,1430100000,9906.91 2004-05-14,10008.43,10096.69,9912.45,10012.87,1335900000,10012.87 2004-05-13,10044.31,10100.24,9924.94,10010.74,1411100000,10010.74 2004-05-12,10011.52,10089.87,9822.10,10045.16,1697600000,10045.16 2004-05-11,9989.24,10092.78,9928.91,10019.47,1533800000,10019.47 2004-05-10,10116.28,10116.28,9881.86,9990.02,1918400000,9990.02 2004-05-07,10240.62,10302.93,10086.94,10117.34,1653600000,10117.34 2004-05-06,10308.20,10332.10,10147.21,10241.26,1509300000,10241.26 2004-05-05,10316.98,10382.98,10249.63,10310.95,1469000000,10310.95 2004-05-04,10314.32,10403.14,10232.31,10317.20,1662100000,10317.20 2004-05-03,10227.27,10365.74,10199.67,10314.00,1571600000,10314.00 2004-04-30,10273.06,10374.61,10198.39,10225.57,1634700000,10225.57 2004-04-29,10339.41,10443.81,10199.31,10272.27,1859000000,10272.27 2004-04-28,10476.67,10479.58,10301.65,10342.60,1855600000,10342.60 2004-04-27,10445.38,10570.92,10410.52,10478.16,1518000000,10478.16 2004-04-26,10472.91,10540.26,10396.75,10444.73,1290600000,10444.73 2004-04-23,10463.11,10543.95,10362.97,10472.84,1396100000,10472.84 2004-04-22,10314.99,10529.12,10255.88,10461.20,1826700000,10461.20 2004-04-21,10311.87,10398.53,10200.38,10317.27,1738100000,10317.27 2004-04-20,10437.85,10530.61,10297.39,10314.50,1508500000,10314.50 2004-04-19,10451.62,10501.79,10351.97,10437.85,1194900000,10437.85 2004-04-16,10398.32,10500.57,10343.74,10451.97,1487800000,10451.97 2004-04-15,10377.95,10481.21,10279.37,10397.46,1568700000,10397.46 2004-04-14,10378.10,10453.39,10259.35,10377.95,1547700000,10377.95 2004-04-13,10516.05,10572.13,10343.17,10381.28,1423200000,10381.28 2004-04-12,10444.38,10559.28,10439.27,10515.56,1102400000,10515.56 2004-04-08,10482.77,10590.15,10383.84,10442.03,1199800000,10442.03 2004-04-07,10569.26,10580.51,10422.74,10480.15,1458800000,10480.15 2004-04-06,10553.76,10596.37,10467.26,10570.81,1397700000,10570.81 2004-04-05,10470.59,10582.22,10423.33,10558.37,1413700000,10558.37 2004-04-02,10375.33,10548.74,10375.33,10470.59,1629200000,10470.59 2004-04-01,10357.52,10449.33,10299.48,10373.33,1560700000,10373.33 2004-03-31,10380.89,10428.59,10287.11,10357.70,1560700000,10357.70 2004-03-30,10327.63,10411.41,10264.15,10381.70,1332400000,10381.70 2004-03-29,10212.91,10389.93,10212.91,10329.63,1405500000,10329.63 2004-03-26,10218.37,10306.22,10145.63,10212.97,1319100000,10212.97 2004-03-25,10049.56,10246.15,10049.56,10218.82,1471700000,10218.82 2004-03-24,10065.41,10140.23,9975.86,10048.23,1527800000,10048.23 2004-03-23,10066.67,10177.04,10020.75,10063.64,1458200000,10063.64 2004-03-22,10185.93,10185.93,9985.19,10064.75,1452300000,10064.75 2004-03-19,10295.85,10355.41,10163.71,10186.60,1457400000,10186.60 2004-03-18,10298.96,10355.04,10187.78,10295.78,1369200000,10295.78 2004-03-17,10184.30,10356.59,10184.30,10300.30,1490100000,10300.30 2004-03-16,10103.41,10253.26,10085.34,10184.67,1500700000,10184.67 2004-03-15,10238.45,10252.68,10066.08,10102.89,1600600000,10102.89 2004-03-12,10130.67,10281.63,10097.04,10240.08,1388500000,10240.08 2004-03-11,10288.85,10356.22,10102.75,10128.38,1889900000,10128.38 2004-03-10,10457.59,10523.11,10259.34,10296.89,1648400000,10296.89 2004-03-09,10529.52,10567.03,10391.48,10456.96,1499400000,10456.96 2004-03-08,10595.37,10677.85,10505.85,10529.48,1254400000,10529.48 2004-03-05,10582.59,10681.40,10497.11,10595.55,1398200000,10595.55 2004-03-04,10593.48,10645.33,10522.59,10588.00,1265800000,10588.00 2004-03-03,10588.59,10651.03,10506.66,10593.11,1334500000,10593.11 2004-03-02,10678.36,10713.92,10539.40,10591.48,1476000000,10591.48 2004-03-01,10582.25,10720.14,10568.74,10678.14,1497100000,10678.14 2004-02-27,10581.55,10689.55,10519.03,10583.92,1540400000,10583.92 2004-02-26,10598.14,10652.96,10493.70,10580.14,1383900000,10580.14 2004-02-25,10566.59,10660.73,10509.40,10601.62,1360700000,10601.62 2004-02-24,10609.55,10681.40,10479.33,10566.37,1543600000,10566.37 2004-02-23,10619.55,10711.84,10508.89,10609.62,1380400000,10609.62 2004-02-20,10666.29,10722.77,10559.11,10619.03,1479600000,10619.03 2004-02-19,10674.59,10794.95,10626.44,10664.73,1562800000,10664.73 2004-02-18,10706.68,10764.36,10623.62,10671.99,1382400000,10671.99 2004-02-17,10628.88,10762.07,10628.88,10714.88,1396500000,10714.88 2004-02-13,10696.22,10755.47,10578.66,10627.85,1329200000,10627.85 2004-02-12,10735.18,10775.03,10636.44,10694.07,1464300000,10694.07 2004-02-11,10605.48,10779.40,10561.55,10737.70,1699300000,10737.70 2004-02-10,10578.74,10667.03,10511.18,10613.85,1403900000,10613.85 2004-02-09,10592.00,10634.81,10433.70,10579.03,1303500000,10579.03 2004-02-06,10494.89,10634.81,10433.70,10593.03,1477600000,10593.03 2004-02-05,10469.33,10566.37,10399.92,10495.55,1566600000,10495.55 2004-02-04,10503.11,10567.85,10394.81,10470.74,1634800000,10470.74 2004-02-03,10499.48,10571.48,10414.15,10505.18,1476900000,10505.18 2004-02-02,10487.78,10614.44,10395.55,10499.18,1599200000,10499.18 2004-01-30,10510.22,10551.03,10385.56,10488.07,1635000000,10488.07 2004-01-29,10467.41,10611.56,10369.92,10510.29,1921900000,10510.29 2004-01-28,10610.07,10703.25,10412.44,10468.37,1842000000,10468.37 2004-01-27,10701.10,10748.81,10579.33,10609.92,1673100000,10609.92 2004-01-26,10568.00,10725.18,10510.44,10702.51,1480600000,10702.51 2004-01-23,10625.25,10691.77,10490.14,10568.29,1561200000,10568.29 2004-01-22,10624.22,10717.40,10545.03,10623.18,1693700000,10623.18 2004-01-21,10522.77,10665.70,10453.11,10623.62,1757600000,10623.62 2004-01-20,10601.40,10676.96,10447.92,10528.66,1698200000,10528.66 2004-01-16,10556.37,10666.88,10503.70,10600.51,1721100000,10600.51 2004-01-15,10534.52,10639.03,10454.52,10553.85,1695000000,10553.85 2004-01-14,10428.67,10573.85,10426.89,10538.37,1514600000,10538.37 2004-01-13,10485.18,10539.25,10341.19,10427.18,1595900000,10427.18 2004-01-12,10461.55,10543.03,10389.85,10485.18,1510200000,10485.18 2004-01-09,10589.25,10603.48,10420.52,10458.89,1720700000,10458.89 2004-01-08,10530.07,10651.99,10480.59,10592.44,1868400000,10592.44 2004-01-07,10535.46,10587.55,10432.00,10529.03,1704900000,10529.03 2004-01-06,10543.85,10584.07,10454.37,10538.66,1494500000,10538.66 2004-01-05,10411.85,10575.92,10411.85,10544.07,1578200000,10544.07 2004-01-02,10452.74,10554.96,10367.41,10409.85,1153200000,10409.85 2003-12-31,10426.30,10494.44,10382.89,10453.92,1027500000,10453.92 2003-12-30,10449.70,10493.11,10374.52,10425.04,1012600000,10425.04 2003-12-29,10321.35,10457.78,10319.70,10450.00,1058800000,10450.00 2003-12-26,10305.85,10368.89,10282.22,10324.67,356070000,10324.67 2003-12-24,10341.41,10365.63,10263.04,10305.19,518060000,10305.19 2003-12-23,10337.56,10421.48,10265.71,10341.26,1145300000,10341.26 2003-12-22,10276.48,10371.11,10216.08,10338.00,1251700000,10338.00 2003-12-19,10249.48,10345.44,10189.63,10278.22,1657300000,10278.22 2003-12-18,10141.41,10278.82,10117.78,10248.08,1579900000,10248.08 2003-12-17,10128.75,10186.67,10040.08,10145.26,1441700000,10145.26 2003-12-16,10023.34,10173.19,9986.23,10129.56,1547900000,10129.56 2003-12-15,10046.53,10180.97,9994.38,10022.82,1520800000,10022.82 2003-12-12,10008.75,10091.71,9946.60,10042.16,1223100000,10042.16 2003-12-11,9922.45,10056.97,9896.16,10008.16,1441100000,10008.16 2003-12-10,9922.38,10000.53,9848.97,9921.86,1444000000,9921.86 2003-12-09,9966.45,10048.75,9887.57,9923.42,1465500000,9923.42 2003-12-08,9862.01,9997.34,9824.83,9965.27,1218900000,9965.27 2003-12-05,9923.27,9954.16,9819.05,9862.68,1265900000,9862.68 2003-12-04,9874.83,9978.08,9814.97,9930.82,1463100000,9930.82 2003-12-03,9851.94,9974.45,9824.31,9873.42,1441700000,9873.42 2003-12-02,9899.64,9936.97,9798.83,9853.64,1383200000,9853.64 2003-12-01,9785.35,9943.34,9777.64,9899.05,1375000000,9899.05 2003-11-28,9779.72,9832.16,9733.20,9782.46,487220000,9782.46 2003-11-26,9763.49,9838.01,9689.05,9779.57,1097700000,9779.57 2003-11-25,9748.68,9821.94,9679.13,9763.94,1333700000,9763.94 2003-11-24,9629.87,9788.60,9629.87,9747.79,1302800000,9747.79 2003-11-21,9622.02,9692.53,9556.68,9628.53,1273800000,9628.53 2003-11-20,9688.46,9756.09,9576.91,9619.42,1326700000,9619.42 2003-11-19,9620.68,9731.94,9584.31,9690.46,1326200000,9690.46 2003-11-18,9711.44,9792.16,9601.35,9624.16,1354300000,9624.16 2003-11-17,9765.64,9775.35,9603.57,9710.83,1374300000,9710.83 2003-11-14,9836.46,9919.19,9709.13,9768.68,1356100000,9768.68 2003-11-13,9846.97,9895.71,9740.53,9837.94,1383000000,9837.94 2003-11-12,9729.50,9882.60,9700.02,9848.83,1349300000,9848.83 2003-11-11,9756.53,9801.21,9673.64,9737.79,1162500000,9737.79 2003-11-10,9807.49,9861.05,9702.46,9756.53,1243600000,9756.53 2003-11-07,9857.12,9945.71,9770.31,9809.79,1440500000,9809.79 2003-11-06,9820.68,9888.90,9738.53,9856.97,1453900000,9856.97 2003-11-05,9837.64,9883.86,9746.98,9820.83,1401800000,9820.83 2003-11-04,9857.49,9905.94,9770.46,9838.83,1417600000,9838.83 2003-11-03,9802.46,9936.31,9792.60,9858.46,1378200000,9858.46 2003-10-31,9786.75,9880.08,9745.94,9801.12,1498900000,9801.12 2003-10-30,9772.01,9882.97,9719.20,9786.61,1629700000,9786.61 2003-10-29,9747.05,9830.23,9663.27,9774.53,1562600000,9774.53 2003-10-28,9609.72,9769.57,9592.54,9748.31,1629200000,9748.31 2003-10-27,9584.54,9696.98,9553.80,9608.16,1371800000,9608.16 2003-10-24,9600.98,9631.50,9463.28,9582.46,1420300000,9582.46 2003-10-23,9597.20,9661.05,9517.50,9613.13,1604300000,9613.13 2003-10-22,9741.98,9741.98,9551.65,9598.24,1647200000,9598.24 2003-10-21,9778.31,9824.90,9697.94,9747.64,1498000000,9747.64 2003-10-20,9721.50,9816.97,9665.35,9777.94,1172600000,9777.94 2003-10-17,9791.86,9833.12,9679.72,9721.79,1352000000,9721.79 2003-10-16,9798.68,9838.53,9679.13,9791.72,1417700000,9791.72 2003-10-15,9824.09,9901.64,9731.57,9803.05,1521100000,9803.05 2003-10-14,9763.27,9833.42,9696.90,9812.98,1271900000,9812.98 2003-10-13,9675.72,9814.97,9675.72,9764.38,1040500000,9764.38 2003-10-10,9682.90,9743.12,9622.31,9674.68,1108100000,9674.68 2003-10-09,9633.35,9798.08,9617.35,9680.01,1578700000,9680.01 2003-10-08,9653.33,9699.35,9571.35,9630.90,1262500000,9630.90 2003-10-07,9593.28,9672.68,9512.76,9654.61,1279500000,9654.61 2003-10-06,9572.39,9656.09,9524.39,9594.98,1025800000,9594.98 2003-10-03,9492.54,9701.87,9492.54,9572.31,1570500000,9572.31 2003-10-02,9464.76,9539.20,9397.87,9487.80,1269300000,9487.80 2003-10-01,9275.06,9472.69,9275.06,9469.20,1566300000,9469.20 2003-09-30,9378.10,9393.35,9199.43,9275.06,1590500000,9275.06 2003-09-29,9314.42,9431.54,9259.68,9380.24,1366500000,9380.24 2003-09-26,9342.43,9407.89,9257.99,9313.08,1472500000,9313.08 2003-09-25,9425.58,9493.44,9311.47,9343.96,1530000000,9343.96 2003-09-24,9575.97,9612.53,9400.67,9425.51,1556000000,9425.51 2003-09-23,9535.76,9623.34,9479.41,9576.04,1301700000,9576.04 2003-09-22,9641.80,9641.87,9467.83,9535.41,1278800000,9535.41 2003-09-19,9661.80,9719.49,9582.64,9644.82,1518600000,9644.82 2003-09-18,9544.04,9691.55,9513.37,9659.13,1498800000,9659.13 2003-09-17,9566.08,9641.45,9510.28,9545.65,1338210000,9545.65 2003-09-16,9449.16,9587.90,9426.84,9567.34,1403200000,9567.34 2003-09-15,9471.19,9541.02,9396.03,9448.81,1151300000,9448.81 2003-09-12,9456.88,9517.37,9348.59,9471.55,1236700000,9471.55 2003-09-11,9415.05,9540.60,9374.49,9459.76,1335900000,9459.76 2003-09-10,9504.88,9545.51,9373.08,9420.46,1582100000,9420.46 2003-09-09,9584.92,9617.02,9459.85,9507.20,1414800000,9507.20 2003-09-08,9503.41,9638.57,9492.25,9586.29,1299300000,9586.29 2003-09-05,9589.52,9602.22,9441.93,9503.34,1465200000,9503.34 2003-09-04,9569.09,9661.80,9492.74,9587.90,1453900000,9587.90 2003-09-03,9521.86,9632.75,9465.72,9568.46,1675600000,9568.46 2003-09-02,9416.67,9563.83,9357.44,9523.27,1470500000,9523.27 2003-08-29,9373.33,9444.25,9320.52,9415.82,945100000,9415.82 2003-08-28,9334.35,9415.68,9246.48,9374.21,1165200000,9374.21 2003-08-27,9339.82,9387.82,9280.03,9333.79,1051400000,9333.79 2003-08-26,9316.03,9372.45,9203.54,9340.45,1178700000,9340.45 2003-08-25,9349.44,9381.51,9242.56,9317.64,971700000,9317.64 2003-08-22,9436.04,9535.97,9323.26,9348.87,1308900000,9348.87 2003-08-21,9399.96,9517.37,9345.71,9423.68,1407100000,9423.68 2003-08-20,9420.13,9454.49,9324.03,9397.51,1210800000,9397.51 2003-08-19,9412.17,9490.60,9315.25,9428.90,1300600000,9428.90 2003-08-18,9320.66,9466.79,9309.16,9412.45,1127600000,9412.45 2003-08-15,9308.52,9360.27,9253.20,9321.69,636370000,9321.69 2003-08-14,9272.25,9356.63,9193.33,9310.56,1186800000,9310.56 2003-08-13,9303.69,9350.19,9213.43,9271.76,1208800000,9271.76 2003-08-12,9218.12,9329.04,9163.29,9310.06,1132300000,9310.06 2003-08-11,9189.62,9275.61,9124.00,9217.35,1022200000,9217.35 2003-08-08,9127.36,9230.37,9097.74,9191.09,1086600000,9191.09 2003-08-07,9060.48,9171.13,9003.63,9126.45,1389300000,9126.45 2003-08-06,9032.96,9151.38,8964.13,9061.74,1491000000,9061.74 2003-08-05,9185.27,9210.21,9024.77,9036.32,1351700000,9036.32 2003-08-04,9154.18,9231.84,9033.66,9186.04,1318700000,9186.04 2003-08-01,9232.68,9266.37,9100.12,9153.97,1390600000,9153.97 2003-07-31,9199.35,9398.02,9183.80,9233.80,1608000000,9233.80 2003-07-30,9204.39,9272.88,9131.70,9200.05,1391900000,9200.05 2003-07-29,9268.19,9327.36,9135.62,9204.46,1508900000,9204.46 2003-07-28,9284.92,9356.77,9182.96,9266.51,1328600000,9266.51 2003-07-25,9113.85,9296.62,9062.38,9284.57,1397500000,9284.57 2003-07-24,9196.56,9313.99,9092.98,9112.51,1559000000,9112.51 2003-07-23,9159.08,9249.91,9064.69,9194.24,1362700000,9194.24 2003-07-22,9098.65,9221.83,9008.60,9158.45,1439700000,9158.45 2003-07-21,9187.80,9219.87,9025.12,9096.69,1254200000,9096.69 2003-07-18,9052.29,9212.31,9034.36,9188.15,1365200000,9188.15 2003-07-17,9089.34,9160.27,8968.82,9050.82,1661400000,9050.82 2003-07-16,9133.70,9185.63,9005.23,9094.59,1662000000,9094.59 2003-07-15,9179.18,9260.42,9054.60,9128.97,1518600000,9128.97 2003-07-14,9126.45,9316.44,9126.45,9177.15,1448900000,9177.15 2003-07-11,9036.39,9176.59,9009.01,9119.59,1212700000,9119.59 2003-07-10,9154.53,9154.53,8978.13,9036.04,1465700000,9036.04 2003-07-09,9221.90,9270.22,9077.91,9156.21,1618000000,9156.21 2003-07-08,9213.48,9281.77,9122.24,9223.09,1565700000,9223.09 2003-07-07,9073.92,9289.90,9073.92,9216.79,1429100000,9216.79 2003-07-03,9141.16,9164.48,9014.06,9070.21,775900000,9070.21 2003-07-02,9043.54,9168.82,9025.12,9142.84,1519300000,9142.84 2003-07-01,8983.66,9068.05,8843.61,9040.95,1460200000,9040.95 2003-06-30,8990.74,9100.12,8934.71,8985.44,1587200000,8985.44 2003-06-27,9079.74,9135.91,8938.26,8989.05,1267800000,8989.05 2003-06-26,9011.06,9118.82,8953.83,9079.04,1387400000,9079.04 2003-06-25,9109.57,9195.20,8984.29,9011.53,1459200000,9011.53 2003-06-24,9071.48,9179.25,9020.08,9109.85,1388300000,9109.85 2003-06-23,9199.49,9215.32,9003.83,9072.95,1398100000,9072.95 2003-06-20,9180.93,9306.36,9149.21,9200.75,1698000000,9200.75 2003-06-19,9293.80,9346.34,9134.50,9179.53,1530100000,9179.53 2003-06-18,9322.67,9363.15,9185.17,9293.80,1488900000,9293.80 2003-06-17,9319.66,9406.50,9234.78,9323.02,1479700000,9323.02 2003-06-16,9117.56,9335.49,9117.56,9318.96,1345900000,9318.96 2003-06-13,9197.88,9234.23,9042.63,9117.12,1271600000,9117.12 2003-06-12,9184.29,9270.64,9089.38,9196.55,1553100000,9196.55 2003-06-11,9048.85,9202.08,9010.13,9183.22,1520000000,9183.22 2003-06-10,8980.79,9111.11,8946.06,9054.89,1275400000,9054.89 2003-06-09,9061.18,9109.43,8909.78,8980.00,1307000000,8980.00 2003-06-06,9045.57,9248.71,9008.87,9062.79,1837200000,9062.79 2003-06-05,9036.67,9106.70,8905.37,9041.30,1693100000,9041.30 2003-06-04,8919.94,9076.94,8882.75,9038.98,1618700000,9038.98 2003-06-03,8898.23,8973.02,8823.51,8922.95,1450200000,8922.95 2003-06-02,8851.45,9040.46,8833.66,8897.81,1662500000,8897.81 2003-05-30,8711.46,8897.04,8711.46,8850.26,1688800000,8850.26 2003-05-29,8787.23,8895.99,8657.96,8711.18,1685800000,8711.18 2003-05-28,8781.84,8885.34,8721.90,8793.12,1559000000,8793.12 2003-05-27,8600.54,8812.30,8523.64,8781.35,1532000000,8781.35 2003-05-23,8594.02,8668.47,8513.98,8601.38,1201000000,8601.38 2003-05-22,8516.99,8650.33,8495.35,8594.02,1448500000,8594.02 2003-05-21,8485.62,8577.64,8389.68,8516.43,1457800000,8516.43 2003-05-20,8494.09,8593.61,8394.51,8491.36,1505300000,8491.36 2003-05-19,8676.52,8676.52,8455.72,8493.39,1375700000,8493.39 2003-05-16,8710.36,8766.36,8614.33,8678.97,1505500000,8678.97 2003-05-15,8649.22,8766.58,8613.84,8713.14,1508700000,8713.14 2003-05-14,8673.29,8744.94,8575.96,8647.82,1401800000,8647.82 2003-05-13,8722.88,8757.40,8611.18,8679.25,1418100000,8679.25 2003-05-12,8603.90,8764.12,8544.24,8726.73,1378800000,8726.73 2003-05-09,8492.69,8637.58,8482.89,8604.60,1326100000,8604.60 2003-05-08,8558.67,8591.01,8435.61,8491.22,1379600000,8491.22 2003-05-07,8585.42,8650.82,8494.87,8560.63,1531900000,8560.63 2003-05-06,8531.28,8658.18,8486.88,8588.36,1649600000,8588.36 2003-05-05,8583.17,8643.11,8472.67,8531.57,1446300000,8531.57 2003-05-02,8453.48,8612.86,8389.40,8582.68,1554300000,8582.68 2003-05-01,8478.48,8519.09,8328.55,8454.25,1397500000,8454.25 2003-04-30,8501.38,8557.62,8402.36,8480.09,1788510000,8480.09 2003-04-29,8472.88,8578.55,8414.61,8502.99,1525600000,8502.99 2003-04-28,8306.84,8515.80,8290.46,8471.61,1273000000,8471.61 2003-04-25,8438.98,8453.06,8258.80,8306.35,1335800000,8306.35 2003-04-24,8512.44,8527.99,8363.28,8440.04,1648100000,8440.04 2003-04-23,8484.92,8587.72,8398.65,8515.66,1667200000,8515.66 2003-04-22,8326.38,8509.43,8238.14,8484.99,1631200000,8484.99 2003-04-21,8336.67,8423.09,8270.56,8328.90,1118700000,8328.90 2003-04-17,8255.79,8388.28,8172.52,8337.65,1430600000,8337.65 2003-04-16,8405.72,8459.15,8217.27,8257.61,1587600000,8257.61 2003-04-15,8347.66,8443.11,8251.38,8402.36,1460200000,8402.36 2003-04-14,8203.97,8375.19,8175.60,8351.10,1131000000,8351.10 2003-04-11,8223.22,8360.76,8156.77,8203.41,1141600000,8203.41 2003-04-10,8198.99,8274.56,8109.85,8221.33,1275300000,8221.33 2003-04-09,8299.28,8404.25,8174.90,8197.94,1293700000,8197.94 2003-04-08,8299.12,8382.43,8222.24,8298.92,1235400000,8298.92 2003-04-07,8284.15,8550.40,8272.24,8300.41,1494000000,8300.41 2003-04-04,8240.59,8348.29,8176.66,8277.15,1241200000,8277.15 2003-04-03,8285.76,8378.76,8199.49,8240.38,1351500000,8240.38 2003-04-02,8070.98,8342.27,8070.98,8285.06,1589800000,8285.06 2003-04-01,7992.83,8133.03,7947.38,8069.86,1461600000,8069.86 2003-03-31,8142.83,8142.83,7903.97,7992.13,1495500000,7992.13 2003-03-28,8198.85,8224.83,8070.77,8145.77,1227000000,8145.77 2003-03-27,8226.39,8272.03,8085.40,8201.45,1232900000,8201.45 2003-03-26,8279.88,8323.01,8165.52,8229.88,1319700000,8229.88 2003-03-25,8216.85,8355.93,8155.65,8280.23,1333400000,8280.23 2003-03-24,8514.82,8514.82,8166.78,8214.68,1293000000,8214.68 2003-03-21,8290.38,8552.08,8290.38,8521.97,1883710000,8521.97 2003-03-20,8264.68,8335.20,8122.59,8286.60,1439100000,8286.60 2003-03-19,8193.04,8304.04,8116.01,8265.45,1473400000,8265.45 2003-03-18,8142.69,8253.48,8057.54,8194.23,1555100000,8194.23 2003-03-17,7857.96,8163.56,7763.56,8141.92,1700420000,8141.92 2003-03-14,7822.17,7961.60,7761.25,7859.71,1541900000,7859.71 2003-03-13,7555.29,7847.03,7555.29,7821.75,1816300000,7821.75 2003-03-12,7517.76,7582.96,7397.31,7552.07,1620000000,7552.07 2003-03-11,7568.53,7673.98,7488.00,7524.06,1427700000,7524.06 2003-03-10,7739.40,7739.40,7545.77,7568.18,1255000000,7568.18 2003-03-07,7671.75,7780.57,7536.18,7740.03,1368500000,7740.03 2003-03-06,7774.76,7795.07,7624.97,7673.99,1299200000,7673.99 2003-03-05,7702.35,7801.79,7639.82,7775.60,1332700000,7775.60 2003-03-04,7838.14,7859.08,7688.98,7704.87,1256600000,7704.87 2003-03-03,7890.24,7997.66,7809.05,7837.86,1208900000,7837.86 2003-02-28,7886.11,7987.80,7826.72,7891.08,1373300000,7891.08 2003-02-27,7807.96,7950.46,7774.34,7884.99,1287800000,7884.99 2003-02-26,7907.39,7944.93,7767.62,7806.98,1374400000,7806.98 2003-02-25,7856.42,7931.14,7700.53,7909.50,1483700000,7909.50 2003-02-24,8017.34,8017.34,7828.06,7858.24,1229200000,7858.24 2003-02-21,7915.52,8055.79,7843.81,8018.11,1398200000,8018.11 2003-02-20,8002.70,8051.37,7858.24,7914.96,1194100000,7914.96 2003-02-19,8033.80,8079.11,7913.70,8000.60,1075600000,8000.60 2003-02-18,7909.30,8114.82,7909.30,8041.15,1250800000,8041.15 2003-02-14,7750.90,7936.56,7704.96,7908.80,1404600000,7908.80 2003-02-13,7756.55,7817.88,7602.81,7749.87,1489300000,7749.87 2003-02-12,7836.36,7892.96,7720.39,7758.17,1260500000,7758.17 2003-02-11,7920.93,8010.89,7798.69,7843.11,1307000000,7843.11 2003-02-10,7865.74,7963.03,7777.98,7920.11,1238200000,7920.11 2003-02-07,7932.45,8019.46,7811.10,7864.23,1276800000,7864.23 2003-02-06,7981.95,8031.66,7855.87,7929.30,1430900000,7929.30 2003-02-05,8014.45,8162.96,7950.42,7985.18,1450800000,7985.18 2003-02-04,8104.61,8104.61,7915.93,8013.29,1451600000,8013.29 2003-02-03,8053.74,8189.35,8032.90,8109.82,1258500000,8109.82 2003-01-31,7939.72,8121.37,7884.66,8053.81,1578530000,8053.81 2003-01-30,8109.14,8169.81,7918.19,7945.13,1510300000,7945.13 2003-01-29,8087.95,8173.31,7916.89,8110.71,1595400000,8110.71 2003-01-28,7991.07,8153.36,7954.95,8088.84,1459100000,8088.84 2003-01-27,8128.54,8167.41,7929.16,7989.56,1435900000,7989.56 2003-01-24,8367.89,8367.89,8092.00,8131.01,1574800000,8131.01 2003-01-23,8320.72,8437.69,8215.61,8369.47,1744550000,8369.47 2003-01-22,8439.54,8493.50,8270.94,8318.73,1560800000,8318.73 2003-01-21,8586.26,8650.43,8421.03,8442.90,1335200000,8442.90 2003-01-17,8695.82,8695.82,8523.60,8586.74,1358200000,8586.74 2003-01-16,8721.12,8837.47,8634.46,8697.87,1534600000,8697.87 2003-01-15,8843.64,8865.65,8674.43,8723.18,1432100000,8723.18 2003-01-14,8787.22,8865.41,8717.56,8842.62,1379400000,8842.62 2003-01-13,8787.83,8896.09,8721.12,8785.98,1396300000,8785.98 2003-01-10,8776.04,8846.25,8654.21,8784.89,1485400000,8784.89 2003-01-09,8596.68,8814.09,8596.68,8776.18,1560300000,8776.18 2003-01-08,8735.93,8749.44,8549.72,8595.31,1467600000,8595.31 2003-01-07,8775.84,8843.37,8661.20,8740.59,1545200000,8740.59 2003-01-06,8602.78,8826.71,8578.86,8773.57,1435900000,8773.57 2003-01-03,8607.38,8669.91,8503.71,8601.69,1130800000,8601.69 2003-01-02,8342.38,8633.02,8342.38,8607.52,1229200000,8607.52 2002-12-31,8332.24,8400.46,8216.44,8341.63,1088500000,8341.63 2002-12-30,8304.06,8405.67,8214.93,8332.85,1057800000,8332.85 2002-12-27,8429.28,8470.05,8272.31,8303.78,758400000,8303.78 2002-12-26,8448.86,8587.98,8392.23,8432.61,721100000,8432.61 2002-12-24,8491.99,8522.98,8407.45,8448.11,458310000,8448.11 2002-12-23,8511.39,8573.92,8418.90,8493.29,1112100000,8493.29 2002-12-20,8367.41,8557.47,8367.41,8511.32,1782730000,8511.32 2002-12-19,8441.94,8530.25,8302.41,8364.80,1385900000,8364.80 2002-12-18,8531.31,8553.76,8367.89,8447.35,1446200000,8447.35 2002-12-17,8626.99,8672.38,8495.21,8535.39,1251800000,8535.39 2002-12-16,8436.59,8649.06,8421.58,8627.40,1271600000,8627.40 2002-12-13,8536.07,8563.16,8374.68,8433.71,1330800000,8433.71 2002-12-12,8590.99,8662.71,8466.21,8538.40,1255300000,8538.40 2002-12-11,8571.52,8669.91,8452.84,8589.14,1285100000,8589.14 2002-12-10,8473.61,8624.93,8419.86,8574.26,1286600000,8574.26 2002-12-09,8643.99,8643.99,8438.24,8473.41,1320800000,8473.41 2002-12-06,8620.88,8707.21,8469.16,8645.77,1241100000,8645.77 2002-12-05,8740.66,8796.68,8572.00,8623.28,1250200000,8623.28 2002-12-04,8734.22,8834.04,8600.93,8737.85,1588900000,8737.85 2002-12-03,8861.13,8882.04,8649.89,8742.93,1488400000,8742.93 2002-12-02,8902.95,9076.35,8757.60,8862.57,1612000000,8862.57 2002-11-29,8933.67,8995.78,8847.42,8896.09,643460000,8896.09 2002-11-27,8678.96,8975.08,8678.96,8931.68,1350300000,8931.68 2002-11-26,8844.12,8845.01,8634.53,8676.42,1543600000,8676.42 2002-11-25,8804.97,8918.03,8717.63,8849.40,1574000000,8849.40 2002-11-22,8842.41,8944.16,8732.23,8804.84,1626800000,8804.84 2002-11-21,8625.48,8910.83,8619.58,8845.15,2415100000,8845.15 2002-11-20,8469.57,8675.12,8402.72,8623.01,1517300000,8623.01 2002-11-19,8484.93,8566.45,8355.62,8474.78,1337400000,8474.78 2002-11-18,8579.74,8671.41,8444.26,8486.57,1282600000,8486.57 2002-11-15,8535.64,8622.30,8421.10,8579.09,1400100000,8579.09 2002-11-14,8403.69,8596.50,8396.27,8542.13,1488100000,8542.13 2002-11-13,8380.32,8523.47,8237.65,8398.49,1463400000,8398.49 2002-11-12,8356.73,8557.25,8286.13,8386.00,1377100000,8386.00 2002-11-11,8535.81,8541.09,8315.40,8358.95,1113000000,8358.95 2002-11-08,8585.75,8688.96,8455.01,8537.13,1446500000,8537.13 2002-11-07,8766.08,8766.08,8517.78,8586.24,1466900000,8586.24 2002-11-06,8677.17,8841.61,8561.48,8771.01,1623700000,8771.01 2002-11-05,8568.76,8730.92,8497.60,8678.27,1354100000,8678.27 2002-11-04,8521.60,8787.30,8510.08,8571.60,1645900000,8571.60 2002-11-01,8395.64,8569.24,8271.77,8517.64,1450400000,8517.64 2002-10-31,8427.34,8538.17,8293.17,8397.03,1641300000,8397.03 2002-10-30,8363.95,8502.32,8273.99,8427.41,1422300000,8427.41 2002-10-29,8367.28,8456.19,8161.01,8368.94,1529700000,8368.94 2002-10-28,8448.98,8601.01,8281.34,8368.04,1382600000,8368.04 2002-10-25,8317.48,8474.92,8210.74,8443.99,1340400000,8443.99 2002-10-24,8495.38,8607.04,8253.60,8317.34,1700570000,8317.34 2002-10-23,8448.56,8546.57,8256.79,8494.27,1593900000,8494.27 2002-10-22,8534.08,8543.65,8303.33,8450.16,1549200000,8450.16 2002-10-21,8320.74,8580.13,8191.87,8538.24,1447000000,8538.24 2002-10-18,8287.72,8383.16,8115.72,8322.40,1423100000,8322.40 2002-10-17,8038.31,8395.99,8038.31,8275.04,1780390000,8275.04 2002-10-16,8232.10,8232.10,7958.34,8036.03,1585000000,8036.03 2002-10-15,7883.23,8304.58,7883.23,8255.68,1956000000,8255.68 2002-10-14,7848.21,7948.91,7725.23,7877.40,1200300000,7877.40 2002-10-11,7540.74,7919.57,7540.74,7850.29,1854130000,7850.29 2002-10-10,7286.34,7588.25,7181.47,7533.95,2090230000,7533.95 2002-10-09,7499.96,7500.03,7215.39,7286.27,1885030000,7286.27 2002-10-08,7425.82,7680.57,7294.53,7501.49,1938430000,7501.49 2002-10-07,7528.68,7685.42,7368.46,7422.84,1576500000,7422.84 2002-10-04,7719.34,7817.06,7428.32,7528.40,1835930000,7528.40 2002-10-03,7753.46,7943.64,7638.47,7717.19,1674500000,7717.19 2002-10-02,7936.57,7996.77,7696.66,7755.61,1668900000,7755.61 2002-10-01,7593.04,7964.24,7558.36,7938.79,1780900000,7938.79 2002-09-30,7698.81,7729.53,7422.28,7591.93,1721870000,7591.93 2002-09-27,7996.01,7997.12,7664.89,7701.45,1507300000,7701.45 2002-09-26,7844.62,8086.86,7800.97,7997.12,1650000000,7997.12 2002-09-25,7687.16,7939.90,7641.87,7841.82,1651500000,7841.82 2002-09-24,7871.23,7893.36,7606.77,7683.13,1670240000,7683.13 2002-09-23,7984.77,7984.77,7738.06,7872.15,1381100000,7872.15 2002-09-20,7945.93,8081.04,7868.60,7986.02,1792800000,7986.02 2002-09-19,8170.65,8170.65,7904.87,7942.39,1524000000,7942.39 2002-09-18,8203.07,8283.49,8013.42,8172.45,1501000000,8172.45 2002-09-17,8386.35,8508.61,8169.88,8207.55,1448600000,8207.55 2002-09-16,8311.79,8435.04,8214.27,8380.18,1001400000,8380.18 2002-09-13,8377.68,8414.79,8175.85,8312.69,1271000000,8312.69 2002-09-12,8574.94,8574.94,8334.82,8379.41,1191600000,8379.41 2002-09-11,8604.27,8767.82,8545.11,8581.17,846600000,8581.17 2002-09-10,8520.14,8660.94,8447.73,8602.61,1186400000,8602.61 2002-09-09,8425.88,8584.30,8288.28,8519.38,1130600000,8519.38 2002-09-06,8296.46,8526.80,8296.46,8427.20,1184500000,8427.20 2002-09-05,8420.20,8420.20,8173.56,8283.70,1401300000,8283.70 2002-09-04,8308.53,8495.03,8216.98,8425.12,1372100000,8425.12 2002-09-03,8659.27,8659.27,8282.87,8308.05,1323400000,8308.05 2002-08-30,8669.26,8811.72,8572.85,8663.50,929900000,8663.50 2002-08-29,8690.69,8769.34,8514.10,8670.99,1271100000,8670.99 2002-08-28,8823.93,8832.18,8610.79,8694.09,1146600000,8694.09 2002-08-27,8917.49,9040.04,8747.77,8824.41,1307700000,8824.41 2002-08-26,8873.93,8981.23,8723.43,8919.01,1016900000,8919.01 2002-08-23,9051.49,9051.49,8806.38,8872.96,1071500000,8872.96 2002-08-22,8961.18,9129.10,8860.34,9053.64,1373000000,9053.64 2002-08-21,8866.14,9033.52,8768.51,8957.23,1353100000,8957.23 2002-08-20,8986.50,8989.69,8789.13,8872.07,1308500000,8872.07 2002-08-19,8777.09,9037.38,8720.81,8990.79,1299800000,8990.79 2002-08-16,8813.07,8899.42,8644.59,8778.06,1265300000,8778.06 2002-08-15,8745.04,8914.37,8620.98,8818.14,1505100000,8818.14 2002-08-14,8479.14,8778.89,8295.34,8743.31,1533800000,8743.31 2002-08-13,8683.15,8801.87,8445.91,8482.39,1297700000,8482.39 2002-08-12,8741.92,8753.14,8528.15,8688.89,1036500000,8688.89 2002-08-09,8707.24,8824.92,8538.46,8745.45,1294900000,8745.45 2002-08-08,8456.29,8755.70,8364.29,8712.02,1646700000,8712.02 2002-08-07,8282.25,8520.95,8171.70,8456.15,1490400000,8456.15 2002-08-06,8049.93,8472.28,8049.93,8274.09,1514100000,8274.09 2002-08-05,8312.92,8371.00,7991.43,8043.63,1425500000,8043.63 2002-08-02,8504.96,8566.36,8179.80,8313.13,1538100000,8313.13 2002-08-01,8732.58,8758.40,8430.68,8506.62,1672200000,8506.62 2002-07-31,8678.65,8793.36,8463.21,8736.59,2049360000,8736.59 2002-07-30,8707.03,8806.86,8484.05,8680.03,1826090000,8680.03 2002-07-29,8267.99,8749.12,8267.99,8711.88,1778650000,8711.88 2002-07-26,8192.61,8350.10,8039.89,8264.39,1796100000,8264.39 2002-07-25,8185.89,8390.39,7893.34,8186.31,2424700000,8186.31 2002-07-24,7698.46,8243.07,7489.53,8191.29,2775560000,8191.29 2002-07-23,7785.55,8007.91,7590.75,7702.34,2441020000,7702.34 2002-07-22,8015.04,8173.08,7668.35,7784.58,2248060000,7784.58 2002-07-19,8356.74,8356.74,7940.83,8019.26,2654100000,8019.26 2002-07-18,8540.47,8683.84,8350.72,8409.49,1736300000,8409.49 2002-07-17,8476.21,8765.39,8401.12,8542.48,2566500000,8542.48 2002-07-16,8635.31,8697.69,8346.29,8473.11,1843700000,8473.11 2002-07-15,8681.28,8720.18,8220.78,8639.19,2574800000,8639.19 2002-07-12,8805.33,8903.00,8555.15,8684.53,1607400000,8684.53 2002-07-11,8812.12,8937.87,8557.84,8801.53,2080480000,8801.53 2002-07-10,9098.16,9188.71,8772.94,8813.50,1816900000,8813.50 2002-07-09,9273.38,9357.35,9065.88,9096.09,1348900000,9096.09 2002-07-08,9375.70,9433.08,9184.90,9274.90,1184400000,9274.90 2002-07-05,9061.54,9399.65,9054.97,9379.50,699400000,9379.50 2002-07-03,9006.37,9140.32,8832.89,9054.97,1527800000,9054.97 2002-07-02,9104.95,9185.88,8918.11,9007.75,1823000000,9007.75 2002-07-01,9239.25,9381.37,9059.88,9109.79,1425500000,9109.79 2002-06-28,9270.33,9435.44,9131.49,9243.26,2117000000,9243.26 2002-06-27,9122.12,9342.33,8992.32,9269.92,1908600000,9269.92 2002-06-26,9108.22,9207.07,8831.92,9120.11,336570000,9120.11 2002-06-25,9285.56,9457.38,9089.17,9126.82,1513700000,9126.82 2002-06-24,9252.47,9417.23,9046.04,9281.82,1552600000,9281.82 2002-06-21,9430.66,9456.76,9186.71,9253.79,1497200000,9253.79 2002-06-20,9561.64,9628.11,9390.10,9431.77,1389700000,9431.77 2002-06-19,9702.00,9760.20,9514.29,9561.57,1336100000,9561.57 2002-06-18,9684.52,9775.83,9588.29,9706.12,1193100000,9706.12 2002-06-17,9476.50,9736.58,9462.30,9687.42,1236600000,9687.42 2002-06-14,9498.92,9538.93,9229.63,9474.21,1549000000,9474.21 2002-06-13,9612.87,9671.64,9454.41,9502.80,1405500000,9502.80 2002-06-12,9515.12,9682.37,9380.82,9617.71,1795720000,9617.71 2002-06-11,9647.62,9794.17,9487.91,9517.26,1212400000,9517.26 2002-06-10,9587.38,9744.33,9509.91,9645.40,1226200000,9645.40 2002-06-07,9592.38,9668.18,9416.33,9589.67,1341300000,9589.67 2002-06-06,9795.70,9820.41,9552.85,9624.64,1601500000,9624.64 2002-06-05,9688.53,9860.91,9636.82,9796.80,1300100000,9796.80 2002-06-04,9710.34,9798.74,9541.36,9687.84,1466600000,9687.84 2002-06-03,9923.94,10016.04,9685.49,9709.79,1324300000,9709.79 2002-05-31,9915.15,10074.16,9865.89,9925.25,1277300000,9925.25 2002-05-30,9915.01,9995.76,9769.64,9911.69,1286600000,9911.69 2002-05-29,9976.94,10056.31,9860.42,9923.04,1081800000,9923.04 2002-05-28,10106.54,10144.53,9917.64,9981.58,996500000,9981.58 2002-05-24,10211.92,10255.93,10054.58,10104.26,885400000,10104.26 2002-05-23,10158.30,10268.87,10044.20,10216.08,1192900000,10216.08 2002-05-22,10098.58,10200.78,10004.41,10157.88,1136300000,10157.88 2002-05-21,10229.08,10322.15,10060.46,10105.71,1169200000,10105.71 2002-05-20,10348.93,10357.44,10164.39,10229.50,989800000,10229.50 2002-05-17,10291.05,10400.62,10209.49,10353.08,1274400000,10353.08 2002-05-16,10242.11,10374.05,10168.64,10289.21,1256600000,10289.21 2002-05-15,10288.56,10382.97,10152.97,10243.68,1420200000,10243.68 2002-05-14,10119.34,10346.59,10119.34,10298.14,1414500000,10298.14 2002-05-13,9938.82,10148.71,9892.73,10109.66,1088600000,10109.66 2002-05-10,10040.25,10115.23,9891.04,9939.92,1171900000,9939.92 2002-05-09,10137.96,10185.08,9966.63,10037.42,1153000000,10037.42 2002-05-08,9847.96,10203.76,9847.96,10141.83,1502000000,10141.83 2002-05-07,9810.53,9985.32,9749.73,9836.55,1354700000,9836.55 2002-05-06,10005.80,10081.98,9780.29,9808.04,1122600000,9808.04 2002-05-03,10091.73,10130.97,9891.49,10006.63,1284500000,10006.63 2002-05-02,10057.62,10182.94,9970.92,10091.87,1364000000,10091.87 2002-05-01,9944.90,10121.21,9778.42,10059.63,1451400000,10059.63 2002-04-30,9818.90,10063.64,9775.10,9946.22,1628600000,9946.22 2002-04-29,9910.52,10012.16,9767.15,9819.87,1314700000,9819.87 2002-04-26,10037.42,10127.85,9875.44,9910.72,1374200000,9910.72 2002-04-25,10028.70,10103.64,9864.08,10035.06,1517400000,10035.06 2002-04-24,10090.07,10209.64,9984.00,10030.43,1373200000,10030.43 2002-04-23,10137.20,10243.06,10003.84,10089.24,1388500000,10089.24 2002-04-22,10256.00,10299.39,10056.45,10136.43,1181800000,10136.43 2002-04-19,10212.69,10338.83,10151.52,10257.11,1185000000,10257.11 2002-04-18,10219.47,10334.67,10010.22,10205.28,1359300000,10205.28 2002-04-17,10299.66,10379.51,10137.47,10220.78,1376900000,10220.78 2002-04-16,10100.38,10365.12,10100.38,10301.32,1341300000,10301.32 2002-04-15,10189.57,10261.05,10037.90,10093.67,1120400000,10093.67 2002-04-12,10178.57,10312.26,10083.92,10190.82,1282100000,10190.82 2002-04-11,10378.89,10425.87,10116.37,10176.08,1505600000,10176.08 2002-04-10,10210.40,10437.43,10175.18,10381.73,1447900000,10381.73 2002-04-09,10249.84,10362.98,10156.57,10208.67,1235400000,10208.67 2002-04-08,10258.91,10300.78,10049.94,10249.08,1095300000,10249.08 2002-04-05,10235.80,10403.66,10169.79,10271.64,1110200000,10271.64 2002-04-04,10199.54,10301.53,10118.10,10235.17,1283800000,10235.17 2002-04-03,10311.81,10377.09,10116.02,10198.29,1219700000,10198.29 2002-04-02,10352.46,10394.94,10204.66,10313.71,1176700000,10313.71 2002-04-01,10402.07,10434.52,10226.59,10362.70,1050900000,10362.70 2002-03-28,10429.68,10537.48,10341.59,10403.94,1147600000,10403.94 2002-03-27,10351.28,10490.15,10300.29,10426.91,1180100000,10426.91 2002-03-26,10280.51,10475.00,10233.65,10353.36,1223600000,10353.36 2002-03-25,10428.43,10497.76,10255.59,10281.67,1057900000,10281.67 2002-03-22,10477.70,10537.62,10324.23,10427.67,1243300000,10427.67 2002-03-21,10501.99,10577.82,10326.99,10479.84,1339200000,10479.84 2002-03-20,10626.85,10634.84,10455.56,10501.57,1304900000,10501.57 2002-03-19,10578.38,10722.78,10530.63,10635.25,1255000000,10635.25 2002-03-18,10608.54,10707.01,10488.84,10577.75,1169500000,10577.75 2002-03-15,10516.45,10663.69,10452.10,10607.23,1493900000,10607.23 2002-03-14,10501.29,10615.74,10421.17,10517.14,1208800000,10517.14 2002-03-13,10620.17,10648.68,10427.67,10501.85,1354000000,10501.85 2002-03-12,10604.32,10682.72,10462.34,10632.35,1304400000,10632.35 2002-03-11,10570.07,10679.68,10470.85,10611.24,1210200000,10611.24 2002-03-08,10531.67,10728.87,10480.33,10572.49,1412000000,10572.49 2002-03-07,10578.10,10663.82,10405.95,10525.37,1517400000,10525.37 2002-03-06,10431.96,10637.19,10393.84,10574.29,1541300000,10574.29 2002-03-05,10591.38,10639.96,10349.90,10433.41,1549300000,10433.41 2002-03-04,10368.10,10656.50,10313.01,10586.82,1594300000,10586.82 2002-03-01,10111.04,10397.09,10086.51,10368.86,1456500000,10368.86 2002-02-28,10130.28,10283.89,10055.34,10106.13,1392200000,10106.13 2002-02-27,10117.65,10315.72,10025.86,10127.58,1393800000,10127.58 2002-02-26,10145.86,10241.89,9986.84,10115.26,1309200000,10115.26 2002-02-25,9969.75,10204.04,9934.94,10145.71,1367400000,10145.71 2002-02-22,9834.89,10032.45,9726.67,9968.15,1411000000,9968.15 2002-02-21,9933.56,10072.98,9788.11,9834.68,1381600000,9834.68 2002-02-20,9742.37,9990.78,9674.91,9941.17,1438900000,9941.17 2002-02-19,9899.24,9923.39,9704.03,9745.14,1189900000,9745.14 2002-02-15,10000.83,10065.37,9843.54,9903.04,1359200000,9903.04 2002-02-14,9989.67,10092.29,9905.95,10001.99,1272500000,10001.99 2002-02-13,9856.99,10056.24,9839.18,9989.67,1215900000,9989.67 2002-02-12,9880.35,9943.24,9766.18,9863.74,1094200000,9863.74 2002-02-11,9739.81,9933.07,9668.34,9884.78,1159400000,9884.78 2002-02-08,9627.65,9795.38,9503.52,9744.24,1371900000,9744.24 2002-02-07,9650.97,9799.67,9562.12,9625.44,1441600000,9625.44 2002-02-06,9682.04,9801.33,9558.18,9653.39,1665800000,9653.39 2002-02-05,9684.74,9842.77,9553.96,9685.43,1778300000,9685.43 2002-02-04,9905.46,9940.96,9648.55,9687.09,1437600000,9687.09 2002-02-01,9923.04,10022.82,9795.45,9907.26,1367200000,9907.26 2002-01-31,9763.20,9963.79,9701.76,9920.00,1557000000,9920.00 2002-01-30,9619.14,9821.81,9443.32,9762.86,2019600000,9762.86 2002-01-29,9865.54,9952.59,9576.65,9618.24,1812000000,9618.24 2002-01-28,9843.05,9959.44,9746.66,9865.75,1186800000,9865.75 2002-01-25,9793.23,9949.54,9697.47,9840.08,1345100000,9840.08 2002-01-24,9734.21,9926.71,9670.99,9796.07,1552800000,9796.07 2002-01-23,9710.96,9853.64,9588.49,9730.96,1479200000,9730.96 2002-01-22,9772.34,9905.26,9652.01,9713.80,1311600000,9713.80 2002-01-18,9830.94,9873.98,9673.11,9771.85,1333300000,9771.85 2002-01-17,9712.21,9910.11,9684.39,9850.04,1380100000,9850.04 2002-01-16,9916.54,9923.32,9661.00,9712.27,1482500000,9712.27 2002-01-15,9892.73,10038.94,9805.33,9924.15,1386900000,9924.15 2002-01-14,9985.38,10038.87,9831.98,9891.42,1286400000,9891.42 2002-01-11,10069.52,10163.77,9938.12,9987.53,1211900000,9987.53 2002-01-10,10092.50,10174.91,9956.67,10067.86,1299000000,10067.86 2002-01-09,10153.18,10311.98,10049.25,10094.09,1452000000,10094.09 2002-01-08,10195.76,10270.53,10063.43,10150.55,1258800000,10150.55 2002-01-07,10261.33,10345.40,10137.61,10197.05,1308300000,10197.05 2002-01-04,10176.84,10341.87,10132.14,10259.74,1513000000,10259.74 2002-01-03,10073.88,10227.36,10002.54,10172.14,1398900000,10172.14 2002-01-02,10021.71,10125.85,9889.69,10073.40,1171000000,10073.40 2001-12-31,10136.36,10178.71,10002.96,10021.57,943600000,10021.57 2001-12-28,10133.94,10220.78,10067.17,10136.99,917400000,10136.99 2001-12-27,10088.71,10187.71,10036.10,10131.31,876300000,10131.31 2001-12-26,10035.55,10203.28,10014.10,10088.14,791100000,10088.14 2001-12-24,10036.59,10114.91,9987.39,10035.34,439670000,10035.34 2001-12-21,9986.84,10148.13,9935.70,10035.34,1694000000,10035.34 2001-12-20,10064.13,10141.21,9912.76,9985.18,1490500000,9985.18 2001-12-19,9994.59,10142.95,9876.96,10070.49,1484900000,10070.49 2001-12-18,9893.22,10066.27,9876.19,9998.39,1354000000,9998.39 2001-12-17,9809.42,9996.25,9747.77,9891.97,1260400000,9891.97 2001-12-14,9764.72,9888.44,9661.14,9811.15,1306800000,9811.15 2001-12-13,9889.13,9927.95,9691.30,9766.45,1511500000,9766.45 2001-12-12,9887.27,9985.59,9745.42,9894.81,1449700000,9894.81 2001-12-11,9925.60,10063.98,9794.48,9888.37,1367200000,9888.37 2001-12-10,10047.04,10123.78,9868.03,9921.45,1218700000,9921.45 2001-12-07,10099.14,10160.24,9938.54,10049.46,1248200000,10049.46 2001-12-06,10113.53,10220.23,9997.98,10099.14,1487900000,10099.14 2001-12-05,9891.35,10195.04,9875.92,10114.29,1765300000,10114.29 2001-12-04,9765.55,9937.29,9700.24,9893.84,1318500000,9893.84 2001-12-03,9848.93,9861.94,9651.87,9763.96,1202900000,9763.96 2001-11-30,9828.80,9945.80,9752.26,9851.56,1343600000,9851.56 2001-11-29,9710.34,9873.29,9629.72,9829.42,1375700000,9829.42 2001-11-28,9867.06,9889.13,9662.80,9711.86,1423700000,9711.86 2001-11-27,9980.33,10021.48,9776.07,9872.60,1288000000,9872.60 2001-11-26,9961.58,10054.58,9862.22,9982.75,1129800000,9982.75 2001-11-23,9833.09,9983.24,9804.37,9959.71,410300000,9959.71 2001-11-21,9894.19,9932.31,9746.45,9834.68,1029300000,9834.68 2001-11-20,9968.64,10023.37,9825.06,9901.38,1330200000,9901.38 2001-11-19,9870.45,10040.46,9826.96,9976.46,1316800000,9976.46 2001-11-16,9871.51,9967.94,9754.07,9866.99,1337400000,9866.99 2001-11-15,9824.65,9967.46,9745.43,9872.39,1454500000,9872.39 2001-11-14,9751.13,9943.18,9683.97,9823.61,1443400000,9823.61 2001-11-13,9551.43,9811.29,9551.43,9750.95,1370100000,9750.95 2001-11-12,9606.13,9642.25,9347.76,9554.37,991600000,9554.37 2001-11-09,9586.96,9692.35,9478.75,9608.00,1093800000,9608.00 2001-11-08,9558.39,9765.00,9506.91,9587.52,1517500000,9587.52 2001-11-07,9584.68,9695.67,9457.99,9554.37,1411300000,9554.37 2001-11-06,9437.09,9627.44,9315.79,9591.12,1356000000,9591.12 2001-11-05,9326.59,9534.58,9326.59,9441.03,1267700000,9441.03 2001-11-02,9264.52,9406.93,9152.91,9323.54,1121900000,9323.54 2001-11-01,9087.45,9320.77,8987.61,9263.90,1317400000,9263.90 2001-10-31,9123.64,9281.68,9018.26,9075.14,1352500000,9075.14 2001-10-30,9264.52,9265.34,9011.96,9121.98,1297400000,9121.98 2001-10-29,9543.37,9543.37,9232.83,9269.50,1106100000,9269.50 2001-10-26,9462.28,9626.54,9369.35,9545.17,1244500000,9545.17 2001-10-25,9342.29,9491.48,9143.09,9462.90,1364400000,9462.90 2001-10-24,9341.40,9456.40,9218.29,9345.62,1336200000,9345.62 2001-10-23,9379.17,9499.78,9249.02,9340.08,1317300000,9340.08 2001-10-22,9203.91,9438.75,9101.08,9377.03,1105700000,9377.03 2001-10-19,9162.81,9278.36,9027.74,9204.11,1294900000,9204.11 2001-10-18,9230.75,9310.33,9061.02,9163.22,1262900000,9163.22 2001-10-17,9389.76,9539.22,9199.89,9232.97,1452200000,9232.97 2001-10-16,9346.31,9479.37,9239.68,9384.23,1210500000,9384.23 2001-10-15,9340.84,9417.51,9181.07,9347.62,1024700000,9347.62 2001-10-12,9409.07,9426.30,9146.34,9344.16,1331400000,9344.16 2001-10-11,9242.63,9522.61,9204.04,9410.45,1704580000,9410.45 2001-10-10,9052.30,9305.97,8975.15,9240.86,1312400000,9240.86 2001-10-09,9066.56,9168.42,8927.34,9052.44,1227800000,9052.44 2001-10-08,9115.75,9187.85,8937.86,9067.94,979000000,9067.94 2001-10-05,9058.83,9208.41,8894.47,9119.77,1301700000,9119.77 2001-10-04,9127.24,9259.61,8982.28,9060.88,1609100000,9060.88 2001-10-03,8946.02,9193.32,8800.99,9123.78,1650600000,9123.78 2001-10-02,8836.69,9001.03,8737.61,8950.59,1289800000,8950.59 2001-10-01,8845.97,8931.70,8659.90,8836.83,1175600000,8836.83 2001-09-28,8679.07,8945.68,8633.75,8847.56,1727400000,8847.56 2001-09-27,8567.46,8757.47,8398.14,8681.42,1467000000,8681.42 2001-09-26,8660.06,8766.81,8457.37,8567.39,1519100000,8567.39 2001-09-25,8605.59,8778.23,8435.56,8659.97,1613800000,8659.97 2001-09-24,8242.32,8733.39,8242.32,8603.86,1746600000,8603.86 2001-09-21,8356.56,8484.22,7926.93,8235.81,2317300000,8235.81 2001-09-20,8375.72,8711.38,8304.45,8376.21,2004800000,8376.21 2001-09-19,8903.54,8990.37,8453.01,8759.13,2120550000,8759.13 2001-09-18,8922.70,9126.89,8743.91,8903.40,1650410000,8903.40 2001-09-17,9294.55,9294.55,8755.46,8920.70,2330830000,8920.70 2001-09-10,9603.36,9740.44,9431.07,9605.51,1276600000,9605.51 2001-09-07,9841.25,9842.08,9507.04,9605.85,1424300000,9605.85 2001-09-06,10028.35,10053.73,9762.03,9840.84,1359700000,9840.84 2001-09-05,9998.12,10140.79,9820.98,10033.27,1384500000,10033.27 2001-09-04,9946.98,10238.50,9858.34,9997.49,1178300000,9997.49 2001-08-31,9918.96,10072.22,9846.72,9949.75,920100000,9949.75 2001-08-30,10077.07,10149.10,9829.35,9919.58,1157000000,9919.58 2001-08-29,10224.45,10292.60,10030.43,10090.90,963700000,10090.90 2001-08-28,10382.56,10405.88,10175.60,10222.03,987100000,10222.03 2001-08-27,10422.76,10498.03,10334.88,10382.35,842600000,10382.35 2001-08-24,10232.48,10487.52,10190.34,10423.17,1043600000,10423.17 2001-08-23,10276.41,10357.09,10142.66,10229.15,986200000,10229.15 2001-08-22,10170.30,10340.76,10099.07,10276.90,1110800000,10276.90 2001-08-21,10320.07,10436.39,10132.92,10174.14,1041600000,10174.14 2001-08-20,10239.33,10388.23,10146.05,10320.07,897100000,10320.07 2001-08-17,10385.46,10418.68,10143.49,10240.78,974300000,10240.78 2001-08-16,10342.10,10460.82,10198.15,10392.52,1055400000,10392.52 2001-08-15,10407.05,10530.36,10289.01,10345.95,1065600000,10345.95 2001-08-14,10416.95,10513.68,10333.29,10412.17,964600000,10412.17 2001-08-13,10411.90,10504.82,10314.95,10415.91,837600000,10415.91 2001-08-10,10296.89,10473.34,10164.67,10416.25,960900000,10416.25 2001-08-09,10291.15,10361.52,10160.51,10298.56,1104200000,10298.56 2001-08-08,10456.18,10509.80,10245.68,10293.50,1124600000,10293.50 2001-08-07,10399.03,10520.11,10324.50,10458.74,1012000000,10458.74 2001-08-06,10504.13,10549.59,10337.23,10401.31,811700000,10401.31 2001-08-03,10550.01,10592.98,10381.10,10512.78,939900000,10512.78 2001-08-02,10513.47,10663.07,10454.53,10551.18,1218300000,10551.18 2001-08-01,10527.38,10659.33,10423.31,10510.01,1340300000,10510.01 2001-07-31,10403.18,10639.40,10364.84,10522.81,1129200000,10522.81 2001-07-30,10418.68,10513.26,10301.05,10401.72,909100000,10401.72 2001-07-27,10451.89,10516.38,10316.27,10416.67,1015300000,10416.67 2001-07-26,10403.46,10498.73,10237.46,10455.63,1213900000,10455.63 2001-07-25,10241.75,10466.28,10159.34,10405.67,1280700000,10405.67 2001-07-24,10423.80,10469.40,10170.82,10241.12,1198700000,10241.12 2001-07-23,10576.92,10644.73,10374.81,10424.42,986900000,10424.42 2001-07-20,10606.19,10668.33,10456.25,10576.65,1170900000,10576.65 2001-07-19,10574.33,10758.14,10480.54,10610.00,1343500000,10610.00 2001-07-18,10594.54,10676.83,10374.55,10569.83,1316300000,10569.83 2001-07-17,10468.62,10683.76,10363.51,10606.39,1238100000,10606.39 2001-07-16,10537.98,10649.02,10374.55,10472.12,1039800000,10472.12 2001-07-13,10478.39,10615.42,10374.01,10539.06,1121700000,10539.06 2001-07-12,10269.31,10542.02,10249.58,10478.99,1394000000,10478.99 2001-07-11,10174.70,10355.83,10049.38,10241.02,1384100000,10241.02 2001-07-10,10300.82,10406.87,10104.06,10175.64,1263800000,10175.64 2001-07-09,10253.62,10389.91,10166.55,10299.40,1045700000,10299.40 2001-07-06,10476.73,10483.82,10176.26,10252.68,1056700000,10252.68 2001-07-05,10566.23,10617.47,10403.57,10479.86,934900000,10479.86 2001-07-03,10588.89,10648.00,10479.99,10571.11,622110000,10571.11 2001-07-02,10504.95,10707.24,10397.20,10593.72,1128300000,10593.72 2001-06-29,10565.27,10729.18,10374.32,10502.40,1832360000,10502.40 2001-06-28,10438.73,10736.43,10429.74,10566.21,1327300000,10566.21 2001-06-27,10470.35,10608.48,10351.10,10434.84,1162100000,10434.84 2001-06-26,10497.30,10600.90,10313.40,10472.48,1198900000,10472.48 2001-06-25,10607.88,10711.19,10417.93,10504.22,1050100000,10504.22 2001-06-22,10716.50,10753.27,10513.61,10604.59,1189200000,10604.59 2001-06-21,10646.39,10848.47,10512.67,10715.43,1546820000,10715.43 2001-06-20,10593.79,10770.88,10480.20,10647.33,1350100000,10647.33 2001-06-19,10654.30,10793.46,10514.74,10596.67,1184900000,10596.67 2001-06-18,10622.50,10781.45,10531.78,10645.38,1111600000,10645.38 2001-06-15,10690.13,10792.25,10495.69,10623.64,1635550000,10623.64 2001-06-14,10868.27,10874.91,10604.45,10690.13,1242900000,10690.13 2001-06-13,10942.00,11065.92,10817.21,10871.62,1063600000,10871.62 2001-06-12,10914.67,11009.93,10744.99,10948.38,1136500000,10948.38 2001-06-11,10974.79,11038.56,10819.29,10922.09,870100000,10922.09 2001-06-08,11095.62,11096.46,10882.92,10977.00,726200000,10977.00 2001-06-07,11069.58,11169.34,10940.11,11090.74,1089600000,11090.74 2001-06-06,11177.73,11236.68,10998.48,11070.24,1061900000,11070.24 2001-06-05,11061.39,11234.98,10973.10,11175.84,1116800000,11175.84 2001-06-04,10991.77,11125.99,10898.80,11061.52,836500000,11061.52 2001-06-01,10913.57,11063.61,10793.46,10990.41,1015000000,10990.41 2001-05-31,10873.23,11023.25,10798.53,10911.94,1226600000,10911.94 2001-05-30,11032.96,11089.89,10819.74,10872.64,1158600000,10872.64 2001-05-29,11004.66,11162.96,10913.44,11039.14,1026000000,11039.14 2001-05-25,11122.03,11166.92,10949.01,11005.37,828100000,11005.37 2001-05-24,11107.07,11248.19,10977.20,11122.42,1100700000,11122.42 2001-05-23,11257.76,11308.44,11033.16,11105.51,1134800000,11105.51 2001-05-22,11339.80,11411.63,11162.83,11257.24,1260400000,11257.24 2001-05-21,11299.14,11436.42,11149.82,11337.92,1174900000,11337.92 2001-05-18,11245.78,11374.87,11123.01,11301.74,1130800000,11301.74 2001-05-17,11218.65,11413.46,11104.34,11248.58,1355600000,11248.58 2001-05-16,10864.74,11258.21,10779.66,11215.92,1405300000,11215.92 2001-05-15,10877.46,10979.35,10752.73,10872.97,1071800000,10872.97 2001-05-14,10819.55,10930.09,10730.74,10877.33,858200000,10877.33 2001-05-11,10908.30,10969.39,10716.16,10821.31,906200000,10821.31 2001-05-10,10868.87,11049.39,10826.90,10910.44,1056700000,10910.44 2001-05-09,10875.96,10964.84,10739.78,10866.98,1132400000,10866.98 2001-05-08,10936.66,11001.66,10755.07,10883.51,1006300000,10883.51 2001-05-07,10952.35,11059.51,10822.74,10935.17,949000000,10935.17 2001-05-04,10793.20,10989.95,10638.48,10951.24,1082100000,10951.24 2001-05-03,10872.32,10911.06,10657.99,10796.65,1137900000,10796.65 2001-05-02,10902.77,11024.31,10726.77,10876.68,1342200000,10876.68 2001-05-01,10734.05,10966.07,10669.32,10898.34,1181300000,10898.34 2001-04-30,10814.41,10973.15,10666.13,10734.97,1226000000,10734.97 2001-04-27,10694.95,10894.60,10632.36,10810.05,1091300000,10810.05 2001-04-26,10633.01,10820.72,10533.98,10692.35,1345200000,10692.35 2001-04-25,10453.43,10675.95,10373.14,10625.20,1203600000,10625.20 2001-04-24,10529.75,10694.61,10401.45,10454.34,1216500000,10454.34 2001-04-23,10571.00,10669.71,10393.05,10532.23,1012600000,10532.23 2001-04-20,10690.33,10755.46,10445.43,10579.85,1338700000,10579.85 2001-04-19,10616.09,10768.28,10468.79,10693.71,1486800000,10693.71 2001-04-18,10226.88,10806.41,10215.69,10615.83,1918900000,10615.83 2001-04-17,10151.73,10286.61,9980.22,10216.73,1109600000,10216.73 2001-04-16,10118.16,10282.38,9991.14,10158.56,913900000,10158.56 2001-04-12,10013.08,10178.22,9862.71,10126.94,1102000000,10126.94 2001-04-11,10109.05,10246.59,9898.77,10013.47,1290300000,10013.47 2001-04-10,9850.35,10226.85,9850.35,10102.74,1349600000,10102.74 2001-04-09,9793.58,9999.35,9699.92,9845.15,1062800000,9845.15 2001-04-06,9913.94,9951.73,9600.91,9791.09,1266800000,9791.09 2001-04-05,9527.21,9969.92,9527.21,9918.05,1368000000,9918.05 2001-04-04,9480.95,9693.05,9303.48,9515.42,1425590000,9515.42 2001-04-03,9774.78,9779.74,9385.43,9485.71,1386100000,9485.71 2001-04-02,9877.16,10043.02,9638.35,9777.93,1204200000,9777.93 2001-03-30,9799.47,9998.49,9685.07,9878.78,1280800000,9878.78 2001-03-29,9784.94,9950.22,9583.67,9799.06,1234500000,9799.06 2001-03-28,9939.68,9939.68,9607.06,9785.35,1333400000,9785.35 2001-03-27,9687.93,10012.98,9584.29,9947.54,1314200000,9947.54 2001-03-26,9509.25,9820.50,9489.75,9687.53,1114000000,9687.53 2001-03-23,9395.58,9631.80,9249.63,9504.78,1364900000,9504.78 2001-03-22,9490.66,9565.40,9047.56,9389.48,1723950000,9389.48 2001-03-21,9717.46,9807.08,9391.42,9487.00,1346300000,9487.00 2001-03-20,9961.14,10130.45,9675.51,9720.76,1235900000,9720.76 2001-03-19,9820.05,10059.08,9720.94,9959.11,1126200000,9959.11 2001-03-16,10023.55,10119.44,9720.17,9823.41,1543560000,9823.41 2001-03-15,9982.92,10190.80,9887.68,10031.28,1259500000,10031.28 2001-03-14,10279.42,10279.42,9817.74,9973.46,1397400000,9973.46 2001-03-13,10206.89,10397.83,10021.60,10290.80,1360900000,10290.80 2001-03-12,10638.52,10638.63,10138.90,10208.25,1229000000,10208.25 2001-03-09,10850.11,10874.15,10520.42,10644.62,1085900000,10644.62 2001-03-08,10727.16,10940.45,10625.95,10858.25,1114100000,10858.25 2001-03-07,10591.86,10822.23,10524.76,10729.60,1132200000,10729.60 2001-03-06,10570.17,10759.40,10508.43,10591.22,1091800000,10591.22 2001-03-05,10468.93,10659.52,10393.59,10562.30,929200000,10562.30 2001-03-02,10438.04,10645.57,10239.81,10466.31,1294000000,10466.31 2001-03-01,10493.25,10605.23,10236.92,10450.14,1294900000,10450.14 2001-02-28,10639.32,10750.23,10374.62,10495.28,1225300000,10495.28 2001-02-27,10638.44,10787.29,10463.92,10636.88,1114100000,10636.88 2001-02-26,10447.59,10701.92,10347.59,10642.53,1130800000,10642.53 2001-02-23,10529.25,10595.01,10225.14,10441.90,1231300000,10441.90 2001-02-22,10527.80,10694.50,10278.93,10526.81,1365900000,10526.81 2001-02-21,10721.29,10828.48,10468.32,10526.58,1208500000,10526.58 2001-02-20,10800.23,10988.29,10612.25,10730.88,1112200000,10730.88 2001-02-16,10884.11,10946.11,10652.33,10799.82,1257200000,10799.82 2001-02-15,10800.65,11023.44,10694.43,10891.02,1153700000,10891.02 2001-02-14,10899.42,10989.61,10683.39,10795.41,1150300000,10795.41 2001-02-13,10950.18,11114.44,10774.98,10903.32,1075200000,10903.32 2001-02-12,10779.42,11024.92,10727.02,10946.77,1039100000,10946.77 2001-02-09,10878.51,10979.12,10682.77,10781.45,1075500000,10781.45 2001-02-08,10940.62,11080.42,10776.04,10880.55,1107200000,10880.55 2001-02-07,10948.95,11140.09,10794.29,10946.72,1158300000,10946.72 2001-02-06,10965.03,11117.80,10820.28,10957.42,1059600000,10957.42 2001-02-05,10860.44,11061.42,10759.98,10965.85,1013000000,10965.85 2001-02-02,10982.71,11093.01,10786.69,10864.10,1048400000,10864.10 2001-02-01,10884.82,11063.95,10759.85,10983.63,1118800000,10983.63 2001-01-31,10882.25,11072.28,10705.23,10887.36,1295300000,10887.36 2001-01-30,10702.19,10950.38,10609.77,10881.20,1149800000,10881.20 2001-01-29,10657.13,10832.56,10515.99,10702.19,1053100000,10702.19 2001-01-26,10727.08,10874.28,10506.26,10659.98,1098000000,10659.98 2001-01-25,10644.53,10882.42,10520.90,10729.52,1258000000,10729.52 2001-01-24,10651.85,10795.80,10483.49,10646.97,1309000000,10646.97 2001-01-23,10575.80,10773.94,10459.91,10649.81,1232600000,10649.81 2001-01-22,10581.90,10749.44,10371.66,10578.24,1164000000,10578.24 2001-01-19,10686.00,10792.14,10448.93,10587.59,1407800000,10587.59 2001-01-18,10584.57,10834.43,10466.01,10678.28,1445000000,10678.28 2001-01-17,10660.95,10817.35,10442.83,10584.34,1349100000,10584.34 2001-01-16,10525.78,10751.48,10362.72,10652.66,1205700000,10652.66 2001-01-12,10608.74,10743.75,10339.94,10525.38,1276000000,10525.38 2001-01-11,10600.20,10808.00,10400.94,10609.55,1411200000,10609.55 2001-01-10,10568.48,10728.30,10325.71,10604.27,1296500000,10604.27 2001-01-09,10625.21,10801.09,10387.12,10572.55,1191300000,10572.55 2001-01-08,10658.73,10818.98,10407.85,10621.35,1115500000,10621.35 2001-01-05,10912.81,10990.59,10492.84,10662.01,1430800000,10662.01 2001-01-04,10944.94,11224.41,10672.58,10912.41,216940000,10912.41 2001-01-03,10637.42,11212.62,10367.19,10945.75,188070000,10945.75 2001-01-02,10790.92,10916.98,10450.55,10646.15,1129400000,10646.15 2000-12-29,10868.76,11031.05,10675.75,10787.99,1035500000,10787.99 2000-12-28,10795.20,11009.44,10645.42,10868.76,1015300000,10868.76 2000-12-27,10690.10,10944.60,10551.00,10803.16,1092700000,10803.16 2000-12-26,10638.21,10813.78,10479.71,10692.44,806500000,10692.44 2000-12-22,10495.26,10772.07,10364.06,10635.56,1087100000,10635.56 2000-12-21,10314.38,10651.96,10158.16,10487.29,1449900000,10487.29 2000-12-20,10580.97,10604.08,10197.59,10318.93,1421600000,10318.93 2000-12-19,10643.14,10865.35,10441.41,10584.37,1324900000,10584.37 2000-12-18,10433.34,10783.82,10417.43,10645.42,1189900000,10645.42 2000-12-15,10647.98,10706.84,10324.24,10434.96,156110000,10434.96 2000-12-14,10794.82,10864.49,10508.53,10674.99,1061300000,10674.99 2000-12-13,10777.95,11002.23,10653.76,10794.44,1195100000,10794.44 2000-12-12,10722.77,10968.77,10582.09,10768.27,1083400000,10768.27 2000-12-11,10719.36,10931.33,10521.04,10725.80,1202400000,10725.80 2000-12-08,10632.14,10896.82,10534.69,10712.91,1358300000,10712.91 2000-12-07,10644.66,10791.40,10448.99,10617.36,1128000000,10617.36 2000-12-06,10896.14,10995.41,10513.84,10664.38,1399300000,10664.38 2000-12-05,10576.78,11044.70,10504.36,10898.72,900300000,10898.72 2000-12-04,10377.33,10701.35,10227.17,10560.95,1103000000,10560.95 2000-12-01,10416.76,10645.42,10238.54,10373.54,1195200000,10373.54 2000-11-30,10610.53,10690.16,10204.80,10414.49,1186530000,10414.49 2000-11-29,10502.74,10746.66,10383.02,10629.11,402100000,10629.11 2000-11-28,10537.86,10730.35,10356.47,10507.58,1028200000,10507.58 2000-11-27,10479.33,10758.04,10411.08,10546.07,946100000,10546.07 2000-11-24,10403.87,10596.50,10354.20,10470.23,404870000,10470.23 2000-11-22,10484.26,10589.67,10251.06,10399.32,963200000,10399.32 2000-11-21,10465.57,10676.13,10303.39,10494.50,1137100000,10494.50 2000-11-20,10624.18,10707.22,10331.45,10462.65,955800000,10462.65 2000-11-17,10657.13,10824.77,10462.55,10629.87,1070400000,10629.87 2000-11-16,10705.33,10857.38,10536.30,10656.03,956300000,10656.03 2000-11-15,10681.21,10863.83,10544.17,10707.60,1066800000,10707.60 2000-11-14,10528.25,10809.61,10484.64,10681.06,1118800000,10681.06 2000-11-13,10595.35,10701.54,10273.24,10517.25,1129300000,10517.25 2000-11-10,10813.78,10886.20,10497.53,10602.95,962500000,10602.95 2000-11-09,10902.11,10989.34,10576.40,10834.25,1111000000,10834.25 2000-11-08,10954.34,11152.02,10779.27,10907.06,909300000,10907.06 2000-11-07,10978.72,11105.75,10825.15,10952.18,880900000,10952.18 2000-11-06,10820.60,11092.10,10741.73,10977.21,930900000,10977.21 2000-11-03,10883.17,10996.17,10650.72,10817.95,997700000,10817.95 2000-11-02,10903.17,11071.06,10731.49,10880.51,1167700000,10880.51 2000-11-01,10966.21,11103.10,10736.42,10899.47,1206800000,10899.47 2000-10-31,10835.39,11108.79,10681.06,10971.14,1366400000,10971.14 2000-10-30,10588.06,10944.98,10506.25,10835.77,1186500000,10835.77 2000-10-27,10381.60,10696.43,10296.70,10590.62,1086300000,10590.62 2000-10-26,10330.18,10563.25,10128.18,10380.12,1303800000,10380.12 2000-10-25,10395.66,10563.99,10170.08,10326.48,1315600000,10326.48 2000-10-24,10273.57,10583.96,10136.69,10393.07,1158600000,10393.07 2000-10-23,10230.29,10496.28,10078.24,10271.72,1046800000,10271.72 2000-10-20,10141.13,10406.76,9925.82,10226.59,1177400000,10226.59 2000-10-19,10014.61,10317.23,9901.77,10142.98,1297900000,10142.98 2000-10-18,10085.99,10171.47,9571.40,9975.02,1441700000,9975.02 2000-10-17,10242.87,10402.32,9924.34,10089.71,1161500000,10089.71 2000-10-16,10184.78,10428.95,10033.84,10238.80,1005400000,10238.80 2000-10-13,10031.62,10325.37,9883.27,10192.18,1223900000,10192.18 2000-10-12,10424.14,10462.25,9873.66,10034.58,1388600000,10034.58 2000-10-11,10521.07,10647.23,10228.44,10413.79,1387500000,10413.79 2000-10-10,10569.17,10744.52,10377.16,10524.40,1044000000,10524.40 2000-10-09,10596.91,10762.10,10438.94,10568.43,716600000,10568.43 2000-10-06,10726.76,10871.42,10440.05,10596.54,1150100000,10596.54 2000-10-05,10783.72,10940.23,10570.28,10724.92,1176100000,10724.92 2000-10-04,10723.33,10972.41,10596.54,10784.48,1167400000,10784.48 2000-10-03,10709.84,10976.11,10561.03,10719.74,1098100000,10719.74 2000-10-02,10659.06,10876.23,10479.27,10700.13,1051200000,10700.13 2000-09-29,10821.40,10923.21,10552.15,10650.92,1197100000,10650.92 2000-09-28,10629.84,10948.00,10539.48,10824.06,1206200000,10824.06 2000-09-27,10634.45,10821.10,10439.31,10628.36,1174700000,10628.36 2000-09-26,10806.30,10915.44,10499.61,10631.32,1106600000,10631.32 2000-09-25,10847.37,11039.47,10664.24,10808.15,982400000,10808.15 2000-09-22,10678.30,10936.53,10505.16,10847.37,1185500000,10847.37 2000-09-21,10680.52,10902.12,10548.08,10765.52,1105400000,10765.52 2000-09-20,10794.47,10906.93,10500.35,10687.92,1104000000,10687.92 2000-09-19,10812.22,10960.95,10645.38,10789.29,1024900000,10789.29 2000-09-18,10926.42,11053.80,10693.84,10808.52,962500000,10808.52 2000-09-15,11087.84,11203.26,10857.73,10927.00,1268400000,10927.00 2000-09-14,11189.58,11285.39,10986.47,11087.47,1014000000,11087.47 2000-09-13,11225.03,11350.87,11020.14,11182.18,1068300000,11182.18 2000-09-12,11197.71,11351.98,11015.70,11233.23,991200000,11233.23 2000-09-11,11219.54,11367.15,11043.07,11195.49,899300000,11195.49 2000-09-08,11261.72,11381.95,11059.72,11220.65,961000000,11220.65 2000-09-07,11316.01,11444.84,11124.83,11259.87,985500000,11259.87 2000-09-06,11253.58,11518.83,11186.25,11310.64,995100000,11310.64 2000-09-05,11221.76,11382.69,11094.50,11260.61,838500000,11260.61 2000-09-01,11219.54,11406.74,11130.01,11238.78,767700000,11238.78 2000-08-31,11105.23,11415.99,11040.85,11215.10,1056600000,11215.10 2000-08-30,11209.01,11282.06,11034.57,11103.01,818400000,11103.01 2000-08-29,11249.27,11356.42,11100.79,11215.10,795600000,11215.10 2000-08-28,11194.48,11410.44,11123.35,11252.84,733600000,11252.84 2000-08-25,11180.54,11301.41,11073.59,11192.63,685600000,11192.63 2000-08-24,11143.91,11302.51,11009.12,11182.74,837100000,11182.74 2000-08-23,11130.55,11253.06,10990.44,11144.65,871000000,11144.65 2000-08-22,11082.20,11274.67,11000.33,11139.15,818800000,11139.15 2000-08-21,11058.85,11193.73,10945.39,11079.81,731600000,11079.81 2000-08-18,11051.20,11180.54,10933.30,11046.48,821400000,11046.48 2000-08-17,11010.95,11180.91,10899.24,11055.64,922400000,11055.64 2000-08-16,11068.83,11171.02,10888.62,11008.39,929800000,11008.39 2000-08-15,11175.05,11227.06,10965.54,11067.00,895900000,11067.00 2000-08-14,11027.07,11232.92,10928.91,11176.14,783800000,11176.14 2000-08-11,10905.98,11131.83,10841.37,11027.80,835500000,11027.80 2000-08-10,10901.06,11069.93,10779.47,10908.76,940800000,10908.76 2000-08-09,10970.94,11097.03,10780.02,10905.83,1054000000,10905.83 2000-08-08,10865.15,11083.11,10741.38,10976.89,992200000,10976.89 2000-08-07,10773.98,10973.23,10657.50,10867.01,854800000,10867.01 2000-08-04,10713.36,10873.97,10555.68,10767.75,956000000,10767.75 2000-08-03,10679.37,10844.30,10518.68,10706.58,1095600000,10706.58 2000-08-02,10609.15,10818.66,10514.29,10687.53,986300000,10687.53 2000-08-01,10523.81,10728.92,10428.58,10606.95,938700000,10606.95 2000-07-31,10514.29,10727.09,10374.00,10521.98,952600000,10521.98 2000-07-28,10594.97,10732.14,10367.28,10511.17,980000000,10511.17 2000-07-27,10516.83,10745.93,10450.01,10586.13,1156400000,10586.13 2000-07-26,10689.36,10790.13,10447.18,10516.48,1235800000,10516.48 2000-07-25,10689.28,10867.20,10557.49,10699.97,969400000,10699.97 2000-07-24,10731.44,10895.84,10545.47,10685.12,880300000,10685.12 2000-07-21,10843.51,10949.58,10614.06,10733.56,968300000,10733.56 2000-07-20,10700.68,10980.34,10671.33,10843.87,1064600000,10843.87 2000-07-19,10724.15,10907.15,10587.89,10696.08,909400000,10696.08 2000-07-18,10799.16,10895.57,10613.00,10739.92,908300000,10739.92 2000-07-17,10812.40,10969.38,10653.30,10804.27,906000000,10804.27 2000-07-14,10793.31,10935.44,10661.43,10812.75,960600000,10812.75 2000-07-13,10774.92,10963.02,10643.40,10788.71,1026800000,10788.71 2000-07-12,10722.24,10930.84,10639.51,10783.76,1001200000,10783.76 2000-07-11,10649.06,10877.46,10544.76,10727.19,980500000,10727.19 2000-07-10,10627.14,10792.25,10520.01,10646.58,838700000,10646.58 2000-07-07,10483.33,10742.04,10419.25,10635.98,931700000,10635.98 2000-07-06,10481.45,10644.11,10303.28,10481.47,947300000,10481.47 2000-07-05,10538.23,10674.16,10362.33,10483.60,1019300000,10483.60 2000-07-03,10450.36,10610.17,10353.66,10560.67,451900000,10560.67 2000-06-30,10393.09,10626.79,10161.51,10447.89,1459700000,10447.89 2000-06-29,10523.90,10582.94,10279.24,10398.04,1110900000,10398.04 2000-06-28,10506.39,10712.70,10399.10,10527.79,1095100000,10527.79 2000-06-27,10541.58,10741.69,10384.60,10504.46,1042500000,10504.46 2000-06-26,10403.69,10680.26,10365.15,10542.99,889000000,10542.99 2000-06-23,10376.47,10555.37,10283.13,10404.75,847600000,10404.75 2000-06-22,10495.97,10596.73,10256.97,10376.12,1022700000,10376.12 2000-06-21,10446.83,10607.69,10312.48,10497.74,1009600000,10497.74 2000-06-20,10558.90,10632.09,10318.84,10435.16,1031500000,10435.16 2000-06-19,10448.40,10733.56,10322.02,10557.84,921700000,10557.84 2000-06-16,10717.76,10784.47,10393.44,10449.30,1250800000,10449.30 2000-06-15,10689.63,10889.48,10552.89,10714.82,1011400000,10714.82 2000-06-14,10632.46,10860.84,10542.99,10687.95,929700000,10687.95 2000-06-13,10562.31,10751.86,10395.56,10621.84,935900000,10621.84 2000-06-12,10615.12,10757.60,10476.88,10564.21,774100000,10564.21 2000-06-09,10678.47,10848.38,10515.52,10614.06,786000000,10614.06 2000-06-08,10818.78,10887.72,10524.92,10668.72,854300000,10668.72 2000-06-07,10733.48,10974.07,10588.64,10812.86,854600000,10812.86 2000-06-06,10822.61,10916.97,10592.82,10735.57,950100000,10735.57 2000-06-05,10793.11,10951.79,10629.03,10815.30,838600000,10815.30 2000-06-02,10660.09,11013.05,10600.46,10794.76,1162400000,10794.76 2000-06-01,10532.27,10780.37,10422.95,10652.20,960100000,10652.20 2000-05-31,10528.28,10692.73,10377.37,10522.33,960500000,10522.33 2000-05-30,10302.31,10596.00,10287.94,10527.13,844200000,10527.13 2000-05-26,10322.89,10487.72,10163.20,10299.24,722600000,10299.24 2000-05-25,10529.87,10644.32,10207.75,10323.92,984500000,10323.92 2000-05-24,10420.90,10679.53,10240.99,10535.35,1152300000,10535.35 2000-05-23,10539.12,10671.74,10325.63,10422.27,869900000,10422.27 2000-05-22,10624.79,10718.00,10308.15,10542.55,869000000,10542.55 2000-05-19,10764.22,10821.83,10468.18,10626.85,853700000,10626.85 2000-05-18,10771.80,10938.34,10669.00,10777.28,807900000,10777.28 2000-05-17,10930.64,10947.25,10648.78,10769.74,820500000,10769.74 2000-05-16,10816.01,11086.72,10723.48,10934.57,955500000,10934.57 2000-05-15,10606.97,10902.36,10509.31,10807.78,854600000,10807.78 2000-05-12,10549.06,10780.37,10444.54,10609.37,858200000,10609.37 2000-05-11,10369.27,10676.88,10315.69,10545.97,953600000,10545.97 2000-05-10,10533.09,10649.95,10169.77,10367.78,1006400000,10367.78 2000-05-09,10607.54,10765.75,10435.96,10536.75,896600000,10536.75 2000-05-08,10571.31,10744.22,10400.40,10603.63,787600000,10603.63 2000-05-05,10409.70,10688.61,10312.91,10577.86,805500000,10577.86 2000-05-04,10478.89,10631.53,10293.05,10412.49,925800000,10412.49 2000-05-03,10732.21,10754.39,10345.17,10480.13,991600000,10480.13 2000-05-02,10805.58,10932.47,10580.65,10731.12,1011500000,10731.12 2000-05-01,10749.42,11001.34,10622.22,10811.78,966300000,10811.78 2000-04-28,10892.76,11005.07,10632.46,10733.91,984600000,10733.91 2000-04-27,10941.91,11024.61,10650.14,10888.10,1111000000,10888.10 2000-04-26,11127.92,11246.75,10816.44,10945.50,999600000,10945.50 2000-04-25,10916.65,11265.67,10764.62,11124.82,1071100000,11124.82 2000-04-24,10822.33,11060.29,10579.10,10906.10,868700000,10906.10 2000-04-20,10668.19,10941.47,10582.51,10844.05,896200000,10844.05 2000-04-19,10748.97,10909.20,10503.40,10674.96,1001400000,10674.96 2000-04-18,10584.02,10941.78,10424.90,10767.42,1109400000,10767.42 2000-04-17,10303.29,10721.50,10128.62,10582.51,1204700000,10582.51 2000-04-14,10922.85,10922.85,10173.92,10305.77,1279700000,10305.77 2000-04-13,11132.58,11290.80,10806.51,10923.55,1032000000,10923.55 2000-04-12,11283.05,11600.43,11026.47,11125.13,1175900000,11125.13 2000-04-11,11180.98,11459.58,11024.22,11287.08,971400000,11287.08 2000-04-10,11114.89,11404.35,10955.43,11186.56,853700000,11186.56 2000-04-07,11122.03,11317.79,10932.78,11111.48,891600000,11111.48 2000-04-06,11029.56,11303.83,10921.07,11114.27,1008000000,11114.27 2000-04-05,11163.29,11326.79,10894.00,11033.92,1110300000,11033.92 2000-04-04,11225.34,11531.24,10682.72,11164.84,1515460000,11164.84 2000-04-03,10863.28,11344.17,10821.71,11221.93,1021700000,11221.93 2000-03-31,10993.28,11244.58,10801.23,10921.92,1227400000,10921.92 2000-03-30,11008.17,11258.23,10796.58,10980.25,1193400000,10980.25 2000-03-29,10939.05,11214.48,10792.24,11018.72,1061900000,11018.72 2000-03-28,11023.68,11192.76,10804.65,10936.11,959100000,10936.11 2000-03-27,11093.25,11274.98,10881.90,11025.85,901000000,11025.85 2000-03-24,11107.45,11311.28,10901.44,11112.72,1052200000,11112.72 2000-03-23,10884.38,11224.72,10737.63,11119.86,1078300000,11119.86 2000-03-22,10916.96,11054.71,10671.86,10866.70,1075000000,10866.70 2000-03-21,10680.24,11012.20,10515.50,10907.34,1065900000,10907.34 2000-03-20,10594.75,10866.08,10456.86,10680.24,920800000,10680.24 2000-03-17,10629.98,10849.32,10399.15,10595.23,1295100000,10595.23 2000-03-16,10139.58,10716.23,10139.58,10630.60,1482300000,10630.60 2000-03-15,9808.15,10294.60,9676.90,10131.41,1302800000,10131.41 2000-03-14,9957.67,10149.41,9747.33,9811.24,1094000000,9811.24 2000-03-13,9911.22,10111.25,9670.07,9947.13,1016100000,9947.13 2000-03-10,10008.55,10211.77,9792.93,9928.82,1138800000,9928.82 2000-03-09,9855.29,10097.28,9667.28,10010.73,1123000000,10010.73 2000-03-08,9800.69,10037.41,9611.75,9856.53,1203000000,9856.53 2000-03-07,10197.61,10208.66,9651.77,9796.03,1314100000,9796.03 2000-03-06,10358.96,10518.91,10038.65,10170.50,1029000000,10170.50 2000-03-03,10171.12,10581.89,10148.16,10367.20,1150300000,10367.20 2000-03-02,10135.44,10361.61,9986.53,10164.92,1198600000,10164.92 2000-03-01,10128.11,10355.72,9935.96,10137.93,1274100000,10137.93 2000-02-29,10039.58,10332.14,9926.65,10128.31,1204300000,10128.31 2000-02-28,9854.66,10228.52,9760.36,10038.65,1026500000,10038.65 2000-02-25,10090.77,10196.02,9767.80,9862.12,1065200000,9862.12 2000-02-24,10242.48,10321.90,9877.94,10092.63,1215000000,10092.63 2000-02-23,10294.82,10443.21,10077.74,10225.73,993700000,10225.73 2000-02-22,10219.83,10446.62,10011.66,10304.84,980000000,10304.84 2000-02-18,10514.57,10562.34,10129.24,10219.52,1042300000,10219.52 2000-02-17,10565.76,10768.66,10348.66,10514.57,1034800000,10514.57 2000-02-16,10711.82,10831.64,10468.65,10561.41,1018800000,10561.41 2000-02-15,10520.15,10821.09,10377.44,10718.09,1092100000,10718.09 2000-02-14,10431.65,10674.96,10327.56,10519.84,927300000,10519.84 2000-02-11,10638.64,10763.38,10301.12,10425.21,1025700000,10425.21 2000-02-10,10697.92,10853.67,10491.30,10643.63,1058800000,10643.63 2000-02-09,10948.82,11016.54,10647.97,10699.16,1050500000,10699.16 2000-02-08,10904.26,11139.40,10826.67,10957.60,1047700000,10957.60 2000-02-07,10965.97,11097.52,10732.67,10905.79,918100000,10905.79 2000-02-04,11014.37,11200.83,10847.54,10963.80,1045100000,10963.80 2000-02-03,11010.48,11207.97,10799.68,11013.44,1146500000,11013.44 2000-02-02,11037.64,11228.44,10876.00,11003.20,1038600000,11003.20 2000-02-01,10937.74,11187.18,10798.44,11041.05,981000000,11041.05 2000-01-31,10735.77,11059.67,10610.43,10940.53,993800000,10940.53 2000-01-28,11024.92,11115.20,10649.21,10738.87,1095800000,10738.87 2000-01-27,11035.55,11274.36,10818.30,11028.02,1129500000,11028.02 2000-01-26,11025.85,11280.87,10870.73,11032.99,1117300000,11032.99 2000-01-25,11010.96,11228.75,10779.83,11029.89,1073700000,11029.89 2000-01-24,11251.94,11501.15,10849.01,11008.17,1115800000,11008.17 2000-01-21,11356.26,11513.87,11113.65,11251.71,1209800000,11251.71 2000-01-20,11490.29,11654.72,11194.32,11351.30,1100700000,11351.30 2000-01-19,11535.24,11710.57,11320.28,11489.36,1087800000,11489.36 2000-01-18,11719.19,11834.67,11397.22,11560.72,1056700000,11560.72 2000-01-14,11619.35,11908.50,11506.42,11722.98,1085900000,11722.98 2000-01-13,11558.24,11761.14,11421.42,11582.43,1030400000,11582.43 2000-01-12,11506.73,11751.83,11385.74,11551.10,974600000,11551.10 2000-01-11,11568.47,11748.11,11398.30,11511.08,1014000000,11511.08 2000-01-10,11532.48,11765.17,11427.00,11572.20,1064800000,11572.20 2000-01-07,11247.06,11655.65,11168.26,11522.56,1225200000,11522.56 2000-01-06,11113.37,11447.79,10963.18,11253.26,1092300000,11253.26 2000-01-05,10989.37,11337.65,10862.66,11122.65,1085500000,11122.65 2000-01-04,11349.75,11358.44,10907.03,10997.93,1009000000,10997.93 2000-01-03,11501.85,11641.07,11180.98,11357.51,931800000,11357.51 1999-12-31,11453.48,11598.26,11368.05,11497.12,374050000,11497.12 1999-12-30,11484.36,11640.33,11388.94,11452.86,554680000,11452.86 1999-12-29,11472.88,11658.68,11367.53,11484.66,567860000,11484.66 1999-12-28,11389.24,11613.72,11302.08,11476.71,655400000,11476.71 1999-12-27,11410.65,11603.02,11253.15,11391.08,722600000,11391.08 1999-12-23,11202.07,11506.38,11202.07,11405.76,728600000,11405.76 1999-12-22,11199.45,11375.17,11075.76,11203.60,850000000,11203.60 1999-12-21,11142.43,11336.03,10973.61,11200.54,963500000,11200.54 1999-12-20,11254.50,11417.38,11026.22,11144.27,904600000,11144.27 1999-12-17,11259.26,11497.20,11103.59,11257.43,1349800000,11257.43 1999-12-16,11224.40,11396.89,11016.13,11244.89,1070300000,11244.89 1999-12-15,11158.78,11400.48,11014.60,11225.32,1033900000,11225.32 1999-12-14,11195.34,11336.33,11027.13,11160.17,1027800000,11160.17 1999-12-13,11217.46,11379.46,11024.38,11192.59,977600000,11192.59 1999-12-10,11137.85,11358.66,11042.12,11224.70,987200000,11224.70 1999-12-09,11073.62,11327.46,10962.60,11134.79,1122100000,11134.79 1999-12-08,11107.26,11273.33,10957.41,11068.12,957000000,11068.12 1999-12-07,11221.65,11351.32,10993.49,11106.65,1085800000,11106.65 1999-12-06,11286.79,11412.79,11100.53,11225.01,916800000,11225.01 1999-12-03,11046.10,11423.50,11046.10,11286.18,1006400000,11286.18 1999-12-02,10995.02,11183.72,10896.24,11039.06,900700000,11039.06 1999-12-01,10876.47,11111.24,10798.07,10998.39,884000000,10998.39 1999-11-30,10945.49,11142.13,10785.22,10877.81,951500000,10877.81 1999-11-29,10985.73,11059.25,10813.97,10947.92,866100000,10947.92 1999-11-26,11007.26,11115.83,10938.44,10988.91,312120000,10988.91 1999-11-24,11000.22,11131.12,10859.23,11008.17,734800000,11008.17 1999-11-23,11090.29,11178.83,10899.60,10995.63,926100000,10995.63 1999-11-22,11008.78,11195.34,10887.67,11089.52,873500000,11089.52 1999-11-19,11033.25,11146.41,10871.77,11003.89,893800000,11003.89 1999-11-18,10887.67,11147.02,10859.92,11035.70,1022800000,11035.70 1999-11-17,10929.00,11040.90,10774.21,10883.09,960000000,10883.09 1999-11-16,10762.89,10992.88,10691.94,10932.33,942200000,10932.33 1999-11-15,10764.73,10899.22,10626.80,10760.75,795700000,10760.75 1999-11-12,10593.51,10845.16,10513.03,10769.32,900200000,10769.32 1999-11-11,10603.25,10716.71,10485.20,10595.30,891300000,10595.30 1999-11-10,10611.94,10713.04,10449.42,10597.74,984700000,10597.74 1999-11-09,10715.10,10806.02,10506.61,10617.32,854300000,10617.32 1999-11-08,10668.42,10838.74,10548.20,10718.85,806800000,10718.85 1999-11-05,10639.95,10885.53,10636.59,10704.48,1007300000,10704.48 1999-11-04,10612.42,10817.95,10523.43,10639.64,981700000,10639.64 1999-11-03,10582.54,10759.22,10485.50,10609.06,914400000,10609.06 1999-11-02,10653.71,10816.11,10508.14,10581.84,904500000,10581.84 1999-11-01,10730.78,10828.96,10568.08,10648.51,861000000,10648.51 1999-10-29,10629.18,10883.10,10580.11,10729.86,1120500000,10729.86 1999-10-28,10397.67,10726.70,10397.67,10622.53,1135100000,10622.53 1999-10-27,10302.76,10515.21,10172.32,10394.89,950100000,10394.89 1999-10-26,10350.88,10486.39,10208.73,10302.13,878300000,10302.13 1999-10-25,10468.98,10518.37,10225.82,10349.93,777000000,10349.93 1999-10-22,10581.69,10581.69,10301.18,10470.25,959200000,10470.25 1999-10-21,10390.71,10414.52,10047.57,10297.69,1012500000,10297.69 1999-10-20,10203.31,10485.13,10144.46,10392.36,928800000,10392.36 1999-10-19,10117.54,10418.01,10093.80,10204.93,905700000,10204.93 1999-10-18,10018.45,10221.08,9884.20,10116.28,818700000,10116.28 1999-10-15,10286.61,10286.61,9911.43,10019.71,912600000,10019.71 1999-10-14,10230.89,10416.74,10071.64,10286.61,892300000,10286.61 1999-10-13,10412.31,10495.89,10173.58,10232.16,821500000,10232.16 1999-10-12,10648.81,10659.58,10366.08,10417.06,778300000,10417.06 1999-10-11,10649.76,10781.16,10545.60,10648.18,655900000,10648.18 1999-10-08,10534.52,10743.48,10423.71,10649.76,897300000,10649.76 1999-10-07,10588.34,10687.12,10430.99,10537.05,827800000,10537.05 1999-10-06,10399.77,10655.46,10340.75,10588.34,895200000,10588.34 1999-10-05,10401.23,10578.21,10250.20,10400.59,965700000,10400.59 1999-10-04,10274.58,10538.32,10220.44,10401.23,803300000,10401.23 1999-10-01,10335.69,10425.92,10108.05,10273.00,896200000,10273.00 1999-09-30,10214.11,10456.32,10156.85,10336.95,1017600000,10336.95 1999-09-29,10274.27,10408.51,10119.76,10213.48,856000000,10213.48 1999-09-28,10300.51,10386.03,10055.17,10275.53,885400000,10275.53 1999-09-27,10279.33,10498.74,10201.76,10303.39,780600000,10303.39 1999-09-24,10311.94,10428.14,10128.63,10279.33,872800000,10279.33 1999-09-23,10524.07,10643.75,10260.33,10318.59,890800000,10318.59 1999-09-22,10599.42,10688.39,10407.56,10524.07,822200000,10524.07 1999-09-21,10822.95,10822.95,10517.10,10598.47,817300000,10598.47 1999-09-20,10801.87,10918.88,10715.93,10823.90,568000000,10823.90 1999-09-17,10745.38,10946.74,10690.61,10803.63,861900000,10803.63 1999-09-16,10795.77,10876.46,10592.46,10737.46,739000000,10737.46 1999-09-15,10910.40,11049.96,10757.41,10801.42,787300000,10801.42 1999-09-14,11028.74,11061.04,10815.67,10910.33,734500000,10910.33 1999-09-13,11027.40,11146.52,10886.90,11030.33,657900000,11030.33 1999-09-10,11087.00,11218.39,10965.74,11028.43,808500000,11028.43 1999-09-09,11031.28,11172.17,10900.83,11079.40,773900000,11079.40 1999-09-08,11034.13,11164.89,10898.62,11036.34,791200000,11036.34 1999-09-07,11079.08,11191.80,10910.65,11034.13,715300000,11034.13 1999-09-03,10922.36,11155.70,10922.36,11078.45,663200000,11078.45 1999-09-02,10923.95,10923.95,10669.71,10843.21,687100000,10843.21 1999-09-01,10828.44,11013.55,10784.32,10937.88,708200000,10937.88 1999-08-31,10909.70,11079.08,10725.43,10829.28,861700000,10829.28 1999-08-30,11087.69,11176.60,10869.81,10914.13,597900000,10914.13 1999-08-27,11198.45,11295.33,11021.14,11090.17,570050000,11090.17 1999-08-26,11326.04,11393.48,11133.86,11198.45,719000000,11198.45 1999-08-25,11282.38,11428.94,11106.31,11326.04,864600000,11326.04 1999-08-24,11297.54,11404.87,11135.76,11283.30,732700000,11283.30 1999-08-23,11099.66,11344.09,11087.95,11299.76,682600000,11299.76 1999-08-20,10963.84,11155.07,10916.35,11100.61,661200000,11100.61 1999-08-19,10988.85,11037.29,10809.65,10963.84,684200000,10963.84 1999-08-18,11109.66,11153.49,10913.50,10991.38,682800000,10991.38 1999-08-17,11049.64,11180.08,10963.52,11117.08,691500000,11117.08 1999-08-16,10980.11,11105.05,10853.98,11046.79,583550000,11046.79 1999-08-13,10822.00,11049.64,10822.00,10973.65,691700000,10973.65 1999-08-12,10785.65,10967.64,10688.07,10789.39,745600000,10789.39 1999-08-11,10650.59,10878.99,10596.26,10787.80,792300000,10787.80 1999-08-10,10704.22,10812.82,10487.34,10655.15,836200000,10655.15 1999-08-09,10714.03,10854.61,10571.56,10707.70,684300000,10707.70 1999-08-06,10792.58,10896.08,10584.54,10714.03,698900000,10714.03 1999-08-05,10675.41,10876.77,10509.19,10793.82,859300000,10793.82 1999-08-04,10675.66,10901.78,10585.17,10674.77,789300000,10674.77 1999-08-03,10645.96,10812.82,10538.00,10677.31,739600000,10677.31 1999-08-02,10654.83,10849.23,10551.30,10645.96,649550000,10645.96 1999-07-30,10791.29,10897.98,10594.99,10655.15,736800000,10655.15 1999-07-29,10920.15,10920.15,10672.24,10791.29,770100000,10791.29 1999-07-28,10979.04,11090.48,10851.13,10972.07,690900000,10972.07 1999-07-27,10863.16,11079.40,10834.35,10979.04,723800000,10979.04 1999-07-26,10910.96,11014.50,10748.86,10863.16,613450000,10863.16 1999-07-23,10979.67,11069.59,10813.77,10910.96,630580000,10910.96 1999-07-22,11002.78,11140.51,10796.67,10969.22,771700000,10969.22 1999-07-21,10998.94,11142.72,10865.69,11002.78,785500000,11002.78 1999-07-20,11130.06,11245.62,10914.13,10996.13,754800000,10996.13 1999-07-19,11209.84,11321.61,11069.90,11187.68,642330000,11187.68 1999-07-16,11188.63,11300.08,11069.27,11209.84,714100000,11209.84 1999-07-15,11200.03,11313.37,11072.75,11186.41,818800000,11186.41 1999-07-14,11202.24,11289.95,11051.86,11148.10,756100000,11148.10 1999-07-13,11106.31,11277.60,11013.55,11175.02,736000000,11175.02 1999-07-12,11234.22,11314.64,11087.95,11200.98,685300000,11200.98 1999-07-09,11142.72,11284.25,11050.59,11193.70,701000000,11193.70 1999-07-08,11118.66,11290.58,10985.68,11126.89,830600000,11126.89 1999-07-07,11129.74,11268.42,11008.16,11187.36,791200000,11187.36 1999-07-06,11119.93,11291.21,11021.78,11135.12,722900000,11135.12 1999-07-02,11089.53,11205.41,10995.50,11139.24,613570000,11139.24 1999-07-01,10972.39,11156.65,10841.63,11066.42,843400000,11066.42 1999-06-30,10805.22,11120.24,10657.36,10970.80,1117000000,10970.80 1999-06-29,10673.83,10855.56,10589.61,10815.35,820100000,10815.35 1999-06-28,10633.93,10792.24,10535.15,10655.15,652910000,10655.15 1999-06-25,10586.44,10719.42,10477.21,10552.56,623460000,10552.56 1999-06-24,10620.32,10736.83,10404.07,10534.83,690400000,10534.83 1999-06-23,10702.32,10791.29,10542.75,10666.86,731800000,10666.86 1999-06-22,10768.49,10879.94,10639.63,10721.63,716500000,10721.63 1999-06-21,10865.06,10960.36,10684.91,10815.98,686600000,10815.98 1999-06-18,10885.00,10978.09,10751.39,10855.56,914500000,10855.56 1999-06-17,10732.71,10936.61,10642.80,10841.63,700300000,10841.63 1999-06-16,10709.92,10883.74,10608.92,10784.95,806800000,10784.95 1999-06-15,10612.09,10740.00,10500.01,10594.99,696600000,10594.99 1999-06-14,10490.83,10691.24,10414.52,10563.33,669400000,10563.33 1999-06-11,10619.69,10744.11,10386.98,10490.51,698200000,10490.51 1999-06-10,10621.58,10736.20,10452.52,10621.27,716500000,10621.27 1999-06-09,10780.84,10871.39,10602.59,10690.29,662000000,10690.29 1999-06-08,10878.35,10954.66,10671.93,10765.64,685900000,10765.64 1999-06-07,10805.20,11016.40,10718.47,10909.38,664300000,10909.38 1999-06-04,10702.64,10892.60,10558.26,10799.84,694500000,10799.84 1999-06-03,10629.18,10767.86,10515.52,10663.69,719600000,10663.69 1999-06-02,10591.82,10694.09,10388.24,10577.89,728000000,10577.89 1999-06-01,10549.08,10717.20,10334.42,10596.26,683800000,10596.26 1999-05-28,10489.56,10690.07,10389.94,10559.74,649960000,10559.74 1999-05-27,10643.04,10734.21,10372.96,10466.93,811400000,10466.93 1999-05-26,10580.94,10818.08,10416.06,10702.16,870800000,10702.16 1999-05-25,10651.73,10818.96,10494.52,10531.09,826700000,10531.09 1999-05-24,10829.87,10924.26,10568.85,10654.67,754700000,10654.67 1999-05-21,10879.13,10974.69,10728.12,10829.28,686600000,10829.28 1999-05-20,10892.40,11020.70,10773.83,10866.74,752200000,10866.74 1999-05-19,10836.67,10988.85,10722.81,10887.39,801100000,10887.39 1999-05-18,10895.94,11017.75,10678.86,10836.95,753400000,10836.95 1999-05-17,10864.35,10976.32,10677.64,10853.47,665500000,10853.47 1999-05-14,10950.26,11076.55,10787.03,10913.32,727800000,10913.32 1999-05-13,11087.71,11244.36,10931.36,11107.19,796900000,11107.19 1999-05-12,11002.38,11146.71,10759.54,11000.37,825500000,11000.37 1999-05-11,11059.65,11185.08,10882.11,11026.15,836100000,11026.15 1999-05-10,11029.25,11173.05,10876.38,11007.25,773300000,11007.25 1999-05-07,10967.16,11135.54,10838.29,11031.59,814900000,11031.59 1999-05-06,10909.02,11093.44,10730.33,10946.82,875400000,10946.82 1999-05-05,10902.72,11055.36,10711.15,10955.41,913500000,10955.41 1999-05-04,11012.11,11140.12,10787.32,10886.11,933100000,10886.11 1999-05-03,10788.75,11083.13,10701.12,11014.69,811400000,11014.69 1999-04-30,10933.94,11072.25,10603.76,10789.04,936500000,10789.04 1999-04-29,10864.64,11052.49,10710.00,10878.38,1003600000,10878.38 1999-04-28,10806.22,11050.77,10665.90,10845.45,951700000,10845.45 1999-04-27,10781.88,10977.46,10647.29,10831.71,891700000,10831.71 1999-04-26,10721.17,10854.33,10540.47,10718.59,712000000,10718.59 1999-04-23,10646.71,10858.34,10530.45,10689.67,744900000,10689.67 1999-04-22,10735.77,10896.42,10517.85,10727.18,927900000,10727.18 1999-04-21,10462.58,10702.56,10309.09,10581.42,920000000,10581.42 1999-04-20,10440.53,10610.06,10212.87,10448.55,985400000,10448.55 1999-04-19,10566.07,10879.76,10284.39,10440.53,1214400000,10440.53 1999-04-16,10497.26,10632.90,10299.55,10493.89,1002300000,10493.89 1999-04-15,10411.66,10673.90,10201.31,10462.72,1089800000,10462.72 1999-04-14,10436.91,10692.49,10196.87,10411.66,952000000,10411.66 1999-04-13,10340.62,10512.11,10239.33,10395.01,810900000,10395.01 1999-04-12,10094.19,10384.46,10042.02,10339.51,810800000,10339.51 1999-04-09,10166.62,10298.71,10035.09,10173.84,716100000,10173.84 1999-04-08,10083.93,10282.62,9980.69,10197.70,850500000,10197.70 1999-04-07,10023.43,10166.62,9859.98,10085.31,816400000,10085.31 1999-04-06,9985.69,10097.25,9862.48,9963.49,787500000,9963.49 1999-04-05,9893.84,10080.87,9826.40,10007.33,695800000,10007.33 1999-04-01,9825.29,9927.69,9707.91,9832.51,703000000,9832.51 1999-03-31,9968.21,10025.10,9744.82,9786.16,924300000,9786.16 1999-03-30,10003.84,10035.36,9812.53,9913.26,729000000,9913.26 1999-03-29,9879.41,10089.48,9837.78,10006.78,747900000,10006.78 1999-03-26,9795.88,9923.53,9700.69,9822.24,707200000,9822.24 1999-03-25,9735.66,9916.59,9666.28,9836.39,784200000,9836.39 1999-03-24,9702.08,9803.09,9547.23,9666.84,761900000,9666.84 1999-03-23,9846.66,9897.72,9593.85,9671.83,811300000,9671.83 1999-03-22,9901.88,10005.95,9796.99,9890.51,658200000,9890.51 1999-03-19,10107.79,10158.57,9856.37,9903.55,914700000,9903.55 1999-03-18,9958.59,10060.62,9776.17,9997.62,831000000,9997.62 1999-03-17,9939.90,10024.54,9792.55,9879.41,752300000,9879.41 1999-03-16,9969.32,10062.84,9857.48,9930.47,751900000,9930.47 1999-03-15,9880.24,10027.59,9807.53,9958.77,727200000,9958.77 1999-03-12,9927.14,10042.58,9779.78,9876.35,825800000,9876.35 1999-03-11,9815.86,9992.35,9742.32,9897.44,904800000,9897.44 1999-03-10,9735.10,9849.71,9624.94,9772.84,841900000,9772.84 1999-03-09,9703.47,9856.65,9586.08,9693.76,803700000,9693.76 1999-03-08,9728.72,9825.02,9616.33,9727.61,714600000,9727.61 1999-03-05,9636.65,9799.92,9558.41,9736.08,834900000,9736.08 1999-03-04,9339.18,9549.99,9272.08,9467.40,770900000,9467.40 1999-03-03,9311.74,9397.86,9163.41,9275.88,751700000,9275.88 1999-03-02,9382.10,9494.57,9216.66,9297.61,753600000,9297.61 1999-03-01,9315.27,9419.32,9167.76,9324.78,699500000,9324.78 1999-02-26,9382.64,9459.52,9177.54,9306.58,784600000,9306.58 1999-02-25,9395.66,9446.32,9200.27,9366.34,740500000,9366.34 1999-02-24,9547.88,9662.77,9357.81,9399.67,782000000,9399.67 1999-02-23,9549.21,9659.58,9425.26,9544.42,781100000,9544.42 1999-02-22,9346.62,9596.13,9289.04,9552.68,718500000,9552.68 1999-02-19,9284.51,9430.59,9218.13,9339.95,700000000,9339.95 1999-02-18,9239.72,9368.48,9145.09,9298.63,742400000,9298.63 1999-02-17,9240.79,9409.53,9124.03,9195.47,735100000,9195.47 1999-02-16,9333.34,9458.46,9187.10,9297.03,653760000,9297.03 1999-02-12,9346.47,9437.35,9161.35,9274.89,691500000,9274.89 1999-02-11,9176.28,9423.19,9088.49,9363.46,815800000,9363.46 1999-02-10,9135.86,9267.68,9025.41,9177.31,721400000,9177.31 1999-02-09,9268.46,9361.92,9076.13,9133.03,736000000,9133.03 1999-02-08,9318.66,9427.83,9148.99,9291.11,705400000,9291.11 1999-02-05,9317.12,9457.43,9141.27,9304.24,872000000,9304.24 1999-02-04,9389.72,9511.50,9178.60,9304.50,854400000,9304.50 1999-02-03,9275.41,9454.09,9176.80,9366.81,876500000,9366.81 1999-02-02,9318.40,9394.10,9146.16,9274.12,845500000,9274.12 1999-02-01,9405.43,9513.05,9266.91,9345.70,799400000,9345.70 1999-01-29,9317.37,9457.18,9172.16,9358.83,917000000,9358.83 1999-01-28,9220.57,9384.83,9134.57,9281.33,848800000,9281.33 1999-01-27,9378.65,9461.04,9135.86,9200.23,893800000,9200.23 1999-01-26,9210.78,9408.77,9118.35,9324.58,896400000,9324.58 1999-01-25,9117.58,9273.09,8994.26,9203.32,723900000,9203.32 1999-01-22,9149.51,9289.31,8998.89,9120.67,785900000,9120.67 1999-01-21,9371.96,9479.83,9150.28,9264.08,871800000,9264.08 1999-01-20,9395.64,9555.01,9230.35,9335.91,905700000,9335.91 1999-01-19,9408.52,9499.14,9165.73,9355.22,785500000,9355.22 1999-01-15,9200.74,9381.74,9124.02,9340.55,798100000,9340.55 1999-01-14,9315.06,9380.20,9052.44,9120.93,797200000,9120.93 1999-01-13,9213.62,9485.24,9134.06,9349.56,931500000,9349.56 1999-01-12,9599.55,9680.40,9394.87,9474.68,800200000,9474.68 1999-01-11,9613.97,9751.46,9446.36,9619.89,818000000,9619.89 1999-01-08,9612.17,9759.44,9447.91,9643.32,937800000,9643.32 1999-01-07,9445.33,9616.29,9369.12,9537.76,863000000,9537.76 1999-01-06,9399.25,9608.05,9331.02,9544.97,986900000,9544.97 1999-01-05,9201.00,9389.46,9137.66,9311.19,775000000,9311.19 1999-01-04,9212.84,9393.84,9089.00,9184.27,877000000,9184.27 1998-12-31,9271.55,9343.64,9106.77,9181.43,719200000,9181.43 1998-12-30,9310.17,9390.75,9211.30,9274.64,594220000,9274.64 1998-12-29,9222.63,9375.30,9152.34,9320.98,586490000,9320.98 1998-12-28,9251.21,9330.50,9133.54,9226.75,531560000,9226.75 1998-12-24,9202.29,9289.57,9146.42,9217.99,246980000,9217.99 1998-12-23,9085.91,9255.84,9022.58,9202.03,697500000,9202.03 1998-12-22,9000.95,9122.99,8909.29,9044.46,680500000,9044.46 1998-12-21,8903.89,9150.54,8874.28,8988.85,744800000,8988.85 1998-12-18,8902.86,9012.28,8789.31,8903.63,839600000,8903.63 1998-12-17,8787.55,8959.76,8725.21,8875.82,739400000,8875.82 1998-12-16,8843.38,8922.94,8675.52,8790.60,725500000,8790.60 1998-12-15,8725.72,8878.40,8614.75,8823.30,777900000,8823.30 1998-12-14,8767.95,8868.10,8610.63,8695.60,741800000,8695.60 1998-12-11,8814.29,8916.24,8680.41,8821.76,688900000,8821.76 1998-12-10,9008.42,9034.42,8795.24,8841.58,748600000,8841.58 1998-12-09,9025.41,9138.18,8881.23,9009.19,694200000,9009.19 1998-12-08,9038.03,9153.63,8903.37,9027.98,727700000,9027.98 1998-12-07,9031.59,9146.93,8946.11,9070.47,671200000,9070.47 1998-12-04,8964.91,9078.70,8873.76,9016.14,709700000,9016.14 1998-12-03,9031.33,9112.69,8839.78,8879.68,799100000,8879.68 1998-12-02,9039.57,9154.40,8922.94,9064.54,727400000,9064.54 1998-12-01,9039.57,9214.65,8934.52,9133.54,789200000,9133.54 1998-11-30,9292.40,9348.53,9074.07,9116.55,687900000,9116.55 1998-11-27,9333.85,9403.62,9263.82,9333.08,256950000,9333.08 1998-11-25,9292.91,9387.66,9199.20,9314.28,583580000,9314.28 1998-11-24,9327.93,9457.95,9219.28,9301.15,766200000,9301.15 1998-11-23,9225.72,9425.77,9137.92,9374.27,774100000,9374.27 1998-11-20,9129.94,9215.93,9048.32,9159.55,721200000,9159.55 1998-11-19,9081.02,9147.96,8967.22,9056.05,671000000,9056.05 1998-11-18,8988.85,9095.70,8897.96,9041.11,652510000,9041.11 1998-11-17,8991.17,9158.26,8870.42,8986.28,705200000,8986.28 1998-11-16,8921.14,9093.12,8885.09,9011.25,615580000,9011.25 1998-11-13,8863.21,8982.16,8782.62,8919.59,602270000,8919.59 1998-11-12,8823.04,8951.26,8733.70,8829.74,662300000,8829.74 1998-11-11,8922.42,8980.35,8759.19,8823.82,715700000,8823.82 1998-11-10,8895.17,9020.00,8760.99,8863.98,671300000,8863.98 1998-11-09,8980.87,9024.89,8813.00,8897.96,592990000,8897.96 1998-11-06,8902.86,9042.40,8831.02,8975.46,683100000,8975.46 1998-11-05,8782.11,8943.79,8676.55,8915.47,770200000,8915.47 1998-11-04,8801.93,8933.49,8660.33,8783.14,861100000,8783.14 1998-11-03,8708.99,8819.18,8601.88,8706.15,704300000,8706.15 1998-11-02,8645.65,8804.51,8573.56,8706.15,753800000,8706.15 1998-10-30,8591.58,8718.25,8481.39,8592.10,785000000,8592.10 1998-10-29,8371.22,8539.83,8305.54,8495.03,699400000,8495.03 1998-10-28,8372.74,8504.04,8271.55,8371.97,677500000,8371.97 1998-10-27,8503.79,8586.69,8309.92,8366.04,764500000,8366.04 1998-10-26,8483.96,8564.03,8349.57,8432.21,609910000,8432.21 1998-10-23,8531.34,8589.01,8366.04,8452.29,637640000,8452.29 1998-10-22,8519.03,8629.17,8334.12,8533.14,754900000,8533.14 1998-10-21,8504.67,8641.02,8380.72,8519.23,745100000,8519.23 1998-10-20,8540.09,8713.62,8419.08,8505.85,958200000,8505.85 1998-10-19,8415.48,8591.07,8321.50,8466.45,738600000,8466.45 1998-10-16,8384.84,8521.29,8244.26,8416.76,1042200000,8416.76 1998-10-15,7953.07,8375.57,7885.62,8299.36,937600000,8299.36 1998-10-14,7925.01,8107.29,7812.75,7968.78,791200000,7968.78 1998-10-13,7982.68,8093.90,7805.29,7938.14,733300000,7938.14 1998-10-12,8038.03,8162.65,7931.19,8001.47,691100000,8001.47 1998-10-09,7806.57,7976.76,7628.15,7899.52,878100000,7899.52 1998-10-08,7734.48,7822.02,7399.78,7731.91,1114600000,7731.91 1998-10-07,7754.82,7913.94,7558.89,7741.69,977000000,7741.69 1998-10-06,7733.97,7951.53,7646.43,7742.98,845700000,7742.98 1998-10-05,7760.75,7866.82,7507.14,7726.24,817500000,7726.24 1998-10-02,7631.50,7866.31,7496.84,7784.69,902900000,7784.69 1998-10-01,7749.42,7856.26,7540.87,7632.53,899700000,7632.53 1998-09-30,8025.42,8097.51,7775.42,7842.62,800100000,7842.62 1998-09-29,8141.28,8253.79,7946.63,8080.52,760100000,8080.52 1998-09-28,8114.76,8239.37,7986.80,8108.84,690500000,8108.84 1998-09-25,7911.10,8127.89,7849.06,8028.77,736800000,8028.77 1998-09-24,8117.33,8227.53,7906.73,8001.99,805900000,8001.99 1998-09-23,7988.60,8198.43,7891.79,8154.41,899700000,8154.41 1998-09-22,7987.83,8038.29,7816.87,7897.20,694900000,7897.20 1998-09-21,7739.12,8008.43,7653.64,7933.25,609880000,7933.25 1998-09-18,7942.77,8012.03,7759.97,7895.66,794700000,7895.66 1998-09-17,7905.96,8001.99,7795.50,7873.77,694500000,7873.77 1998-09-16,8061.46,8159.30,7923.46,8089.78,797500000,8089.78 1998-09-15,7918.83,8087.21,7840.30,8024.39,724600000,8024.39 1998-09-14,7936.08,8081.29,7848.03,7945.35,714400000,7945.35 1998-09-11,7583.61,7866.31,7497.10,7795.50,819100000,7795.50 1998-09-10,7680.42,7761.00,7469.04,7615.54,880300000,7615.54 1998-09-09,7995.04,8094.16,7796.79,7865.02,704300000,7865.02 1998-09-08,7964.91,8103.69,7779.03,8020.78,814800000,8020.78 1998-09-04,7737.32,7831.29,7450.24,7640.25,780300000,7640.25 1998-09-03,7679.13,7841.59,7499.68,7682.22,880500000,7682.22 1998-09-02,7901.06,8036.23,7710.54,7782.37,894600000,7782.37 1998-09-01,7583.09,7937.37,7379.70,7827.43,1216600000,7827.43 1998-08-31,8078.97,8149.00,7517.70,7539.07,917500000,7539.07 1998-08-28,8193.54,8301.68,7951.27,8051.68,840300000,8051.68 1998-08-27,8377.89,8448.69,8062.24,8165.99,938600000,8165.99 1998-08-26,8492.97,8639.47,8396.68,8523.35,674100000,8523.35 1998-08-25,8632.00,8740.91,8510.22,8602.65,664900000,8602.65 1998-08-24,8584.63,8680.66,8452.29,8566.61,558100000,8566.61 1998-08-21,8600.34,8600.34,8307.60,8533.65,725700000,8533.65 1998-08-20,8639.47,8726.75,8538.29,8611.41,621630000,8611.41 1998-08-19,8745.80,8797.81,8635.61,8693.28,633630000,8693.28 1998-08-18,8593.90,8767.69,8551.93,8714.65,690600000,8714.65 1998-08-17,8404.66,8613.98,8350.34,8574.85,584380000,8574.85 1998-08-14,8531.85,8635.87,8342.10,8425.00,644030000,8425.00 1998-08-13,8535.46,8656.46,8399.51,8459.50,660700000,8459.50 1998-08-12,8528.76,8621.71,8437.10,8552.96,711700000,8552.96 1998-08-11,8431.44,8538.29,8263.32,8462.85,774400000,8462.85 1998-08-10,8585.40,8689.16,8491.17,8574.85,579180000,8574.85 1998-08-07,8610.63,8745.03,8490.40,8598.02,759100000,8598.02 1998-08-06,8513.83,8671.14,8415.22,8577.68,768400000,8577.68 1998-08-05,8493.75,8641.79,8316.10,8546.78,851600000,8546.78 1998-08-04,8859.86,8896.68,8463.37,8487.31,852600000,8487.31 1998-08-03,8868.10,8948.17,8729.58,8786.74,620400000,8786.74 1998-07-31,9024.64,9109.34,8810.94,8883.29,645910000,8883.29 1998-07-30,8962.85,9113.20,8884.83,9026.95,687400000,9026.95 1998-07-29,8976.49,9039.57,8831.28,8914.96,644350000,8914.96 1998-07-28,8982.16,9065.57,8786.48,8934.78,703600000,8934.78 1998-07-27,8905.17,9084.63,8806.05,9028.24,619990000,9028.24 1998-07-24,9002.24,9077.67,8814.29,8937.36,698600000,8937.36 1998-07-23,9130.46,9196.11,8892.56,8932.98,741600000,8932.98 1998-07-22,9185.71,9264.85,8996.83,9128.91,739800000,9128.91 1998-07-21,9296.26,9369.12,9145.65,9190.19,659700000,9190.19 1998-07-20,9340.55,9408.26,9206.15,9295.75,560580000,9295.75 1998-07-17,9328.19,9412.64,9259.96,9337.97,618030000,9337.97 1998-07-16,9233.23,9368.35,9173.19,9328.19,677800000,9328.19 1998-07-15,9246.57,9388.69,9165.99,9234.47,723900000,9234.47 1998-07-14,9100.07,9314.54,9087.20,9245.54,700300000,9245.54 1998-07-13,9107.03,9186.58,9020.52,9096.21,574880000,9096.21 1998-07-10,9098.53,9194.82,8976.23,9105.74,576080000,9105.74 1998-07-09,9135.36,9217.07,9014.30,9089.78,663600000,9089.78 1998-07-08,9107.96,9231.27,9041.70,9174.97,607230000,9174.97 1998-07-07,9094.76,9209.85,9001.84,9085.04,624890000,9085.04 1998-07-06,9017.53,9137.11,8965.97,9091.77,514750000,9091.77 1998-07-02,9037.71,9094.01,8956.25,9025.26,510210000,9025.26 1998-07-01,9011.56,9103.23,8918.64,9048.67,701600000,9048.67 1998-06-30,9010.81,9085.79,8868.57,8952.02,757200000,8952.02 1998-06-29,9002.34,9099.24,8930.84,8997.36,564350000,8997.36 1998-06-26,8948.28,9047.93,8879.78,8944.54,520050000,8944.54 1998-06-25,8950.52,9079.56,8863.58,8935.58,669900000,8935.58 1998-06-24,8828.46,8970.20,8740.52,8923.87,714900000,8923.87 1998-06-23,8789.35,8877.78,8724.83,8828.46,657100000,8828.46 1998-06-22,8705.65,8805.04,8639.38,8711.13,531550000,8711.13 1998-06-19,8844.90,8885.01,8664.79,8712.87,715500000,8712.87 1998-06-18,8825.72,8900.20,8740.77,8813.01,590440000,8813.01 1998-06-17,8745.51,8932.34,8695.43,8829.46,744400000,8829.46 1998-06-16,8666.04,8751.24,8524.55,8665.29,664600000,8665.29 1998-06-15,8723.34,8818.99,8588.32,8627.93,595820000,8627.93 1998-06-12,8812.77,8891.24,8660.56,8834.94,633300000,8834.94 1998-06-11,8975.43,9015.54,8774.40,8811.77,627470000,8811.77 1998-06-10,9031.98,9145.08,8891.98,8971.70,609410000,8971.70 1998-06-09,9074.58,9130.13,8966.72,9049.92,563610000,9049.92 1998-06-08,9052.66,9155.04,8994.12,9069.60,543390000,9069.60 1998-06-05,8920.88,9057.39,8866.57,9037.71,558440000,9037.71 1998-06-04,8798.07,8916.64,8719.85,8870.56,577470000,8870.56 1998-06-03,8909.42,8970.20,8769.67,8803.80,584480000,8803.80 1998-06-02,8961.48,9014.05,8838.92,8891.24,590930000,8891.24 1998-06-01,8907.93,9023.01,8805.04,8922.37,537660000,8922.37 1998-05-29,9004.83,9060.13,8859.60,8899.95,556780000,8899.95 1998-05-28,8947.53,9040.70,8861.59,8970.20,588900000,8970.20 1998-05-27,8902.45,9015.04,8760.95,8936.57,682040000,8936.57 1998-05-26,9167.00,9201.63,8949.78,8963.73,541410000,8963.73 1998-05-22,9153.05,9209.35,9040.70,9114.44,444070000,9114.44 1998-05-21,9173.48,9248.96,9058.64,9132.37,551970000,9132.37 1998-05-20,9101.98,9219.06,9010.56,9171.48,587240000,9171.48 1998-05-19,9097.00,9164.51,8997.36,9054.65,566020000,9054.65 1998-05-18,9088.53,9171.23,8972.20,9050.91,519100000,9050.91 1998-05-15,9161.52,9240.74,9051.91,9096.00,621990000,9096.00 1998-05-14,9136.86,9266.89,9077.07,9172.23,578380000,9172.23 1998-05-13,9178.06,9283.09,9119.67,9211.84,600010000,9211.84 1998-05-12,9098.00,9214.08,9023.51,9161.77,604420000,9161.77 1998-05-11,9125.65,9231.52,9045.43,9091.52,560840000,9091.52 1998-05-08,8981.91,9143.09,8951.27,9055.15,567890000,9055.15 1998-05-07,9044.44,9102.98,8914.90,8976.68,582240000,8976.68 1998-05-06,9174.72,9223.80,9017.28,9054.65,606540000,9054.65 1998-05-05,9189.92,9251.95,9062.37,9147.57,583630000,9147.57 1998-05-04,9201.38,9311.98,9134.86,9192.66,551700000,9192.66 1998-05-01,9106.47,9211.84,9015.54,9147.07,581970000,9147.07 1998-04-30,9018.28,9169.99,8970.95,9063.37,695600000,9063.37 1998-04-29,8899.21,9036.47,8857.85,8951.52,638790000,8951.52 1998-04-28,8920.88,9051.16,8828.71,8898.96,678600000,8898.96 1998-04-27,8937.07,9026.25,8796.08,8917.64,685960000,8917.64 1998-04-24,9143.83,9206.36,8983.65,9064.62,633890000,9064.62 1998-04-23,9176.96,9269.88,9061.88,9143.33,653190000,9143.33 1998-04-22,9184.93,9287.32,9083.80,9176.72,696740000,9176.72 1998-04-21,9137.85,9250.20,9051.41,9184.94,675640000,9184.94 1998-04-20,9166.75,9245.22,9027.25,9141.84,595190000,9141.84 1998-04-17,9077.57,9213.58,9010.06,9167.50,672290000,9167.50 1998-04-16,9162.27,9204.12,8978.92,9076.57,699570000,9076.57 1998-04-15,9110.45,9249.95,9017.53,9162.27,685020000,9162.27 1998-04-14,9014.30,9177.96,8971.45,9110.20,613730000,9110.20 1998-04-13,8997.11,9107.21,8870.31,9012.30,564480000,9012.30 1998-04-09,8891.48,9062.12,8891.48,8994.86,548940000,8994.86 1998-04-08,8951.52,9023.51,8821.73,8891.48,616330000,8891.48 1998-04-07,8995.36,9072.34,8862.34,8956.50,670760000,8956.50 1998-04-06,9051.16,9170.74,8958.49,9033.23,625810000,9033.23 1998-04-03,8986.64,9085.79,8896.22,8983.41,653880000,8983.41 1998-04-02,8900.20,9025.26,8829.21,8986.64,674340000,8986.64 1998-04-01,8818.50,8935.33,8715.61,8868.32,677310000,8868.32 1998-03-31,8852.12,8937.57,8726.32,8799.81,674930000,8799.81 1998-03-30,8802.30,8877.04,8701.66,8782.12,497400000,8782.12 1998-03-27,8835.68,8941.31,8741.77,8796.08,582190000,8796.08 1998-03-26,8847.89,8931.09,8769.92,8846.89,606770000,8846.89 1998-03-25,8943.05,8997.11,8780.88,8872.80,676550000,8872.80 1998-03-24,8887.00,8962.48,8815.01,8904.44,605720000,8904.44 1998-03-23,8839.42,8939.31,8758.71,8816.25,631350000,8816.25 1998-03-20,8814.01,8957.25,8766.43,8906.43,717310000,8906.43 1998-03-19,8776.89,8855.11,8708.64,8803.05,598240000,8803.05 1998-03-18,8749.24,8825.72,8655.83,8775.40,632690000,8775.40 1998-03-17,8706.64,8812.52,8633.41,8749.99,680960000,8749.99 1998-03-16,8647.36,8760.45,8573.37,8718.85,548980000,8718.85 1998-03-13,8669.28,8733.30,8563.90,8602.52,597800000,8602.52 1998-03-12,8622.20,8725.58,8576.86,8659.56,594940000,8659.56 1998-03-11,8682.73,8740.52,8599.28,8675.75,655260000,8675.75 1998-03-10,8623.19,8688.71,8550.45,8643.12,631920000,8643.12 1998-03-09,8601.02,8661.56,8482.45,8567.14,624700000,8567.14 1998-03-06,8505.86,8610.49,8432.62,8569.39,665500000,8569.39 1998-03-05,8480.20,8536.80,8377.32,8444.33,648270000,8444.33 1998-03-04,8524.55,8609.99,8454.79,8539.24,644280000,8539.24 1998-03-03,8532.77,8632.16,8464.26,8584.83,612360000,8584.83 1998-03-02,8528.78,8649.35,8427.89,8550.45,591470000,8550.45 1998-02-27,8532.77,8616.72,8426.15,8545.72,574480000,8545.72 1998-02-26,8471.48,8541.98,8377.82,8490.67,646280000,8490.67 1998-02-25,8455.29,8510.60,8362.37,8457.78,611350000,8457.78 1998-02-24,8370.10,8457.78,8303.83,8370.10,589880000,8370.10 1998-02-23,8381.31,8490.92,8331.98,8410.20,550730000,8410.20 1998-02-20,8350.67,8449.06,8291.88,8413.94,594300000,8413.94 1998-02-19,8379.56,8463.26,8327.25,8375.58,581820000,8375.58 1998-02-18,8419.17,8503.12,8326.75,8451.06,606000000,8451.06 1998-02-17,8439.35,8483.94,8335.97,8398.50,605890000,8398.50 1998-02-13,8346.68,8416.43,8269.21,8370.10,531940000,8370.10 1998-02-12,8260.49,8412.20,8206.93,8369.60,611480000,8369.60 1998-02-11,8298.60,8367.61,8223.87,8314.55,599300000,8314.55 1998-02-10,8241.81,8368.10,8149.88,8295.61,642800000,8295.61 1998-02-09,8185.26,8256.75,8104.55,8180.52,524810000,8180.52 1998-02-06,8178.28,8255.26,8102.30,8189.49,569650000,8189.49 1998-02-05,8129.71,8215.65,8037.54,8117.25,703980000,8117.25 1998-02-04,8160.35,8188.00,8050.99,8129.71,695420000,8129.71 1998-02-03,8107.78,8193.23,8010.63,8160.35,692120000,8160.35 1998-02-02,7987.46,8157.86,7987.46,8107.78,724320000,8107.78 1998-01-30,7973.02,8023.83,7850.95,7906.50,613380000,7906.50 1998-01-29,7915.47,8015.12,7883.09,7973.02,750760000,7973.02 1998-01-28,7815.08,7985.97,7761.52,7915.47,708470000,7915.47 1998-01-27,7712.94,7890.56,7677.07,7815.08,679140000,7815.08 1998-01-26,7700.74,7813.59,7629.99,7712.94,555080000,7712.94 1998-01-23,7730.88,7814.08,7609.31,7700.74,635770000,7700.74 1998-01-22,7794.40,7833.26,7637.71,7730.88,646570000,7730.88 1998-01-21,7873.12,7874.87,7707.96,7794.40,626160000,7794.40 1998-01-20,7753.55,7908.00,7696.01,7873.12,644790000,7873.12 1998-01-16,7691.77,7846.47,7687.04,7753.55,670080000,7753.55 1998-01-15,7784.69,7803.87,7651.66,7691.77,569050000,7691.77 1998-01-14,7732.13,7836.25,7669.10,7784.69,603280000,7784.69 1998-01-13,7647.18,7791.17,7610.31,7732.13,646740000,7732.13 1998-01-12,7580.42,7706.22,7391.59,7647.18,705450000,7647.18 1998-01-09,7802.62,7815.08,7513.41,7580.42,746420000,7580.42 1998-01-08,7902.27,7935.40,7733.12,7802.62,652140000,7802.62 1998-01-07,7906.25,7943.87,7751.56,7902.27,667390000,7902.27 1998-01-06,7978.99,7999.17,7832.52,7906.25,618360000,7906.25 1998-01-05,7965.04,8072.91,7865.40,7978.99,628070000,7978.99 1998-01-02,7908.25,8001.41,7845.47,7965.04,366730000,7965.04 1997-12-31,7915.97,7995.93,7833.76,7908.25,467280000,7908.25 1997-12-30,7792.41,7958.32,7787.68,7915.97,499500000,7915.97 1997-12-29,7712.94,7835.01,7712.94,7792.41,443160000,7792.41 1997-12-26,7660.13,7743.34,7634.72,7679.31,154900000,7679.31 1997-12-24,7691.77,7767.75,7624.26,7660.13,265980000,7660.13 1997-12-23,7819.31,7874.12,7667.11,7691.77,515070000,7691.77 1997-12-22,7756.29,7892.05,7710.45,7819.31,530670000,7819.31 1997-12-19,7846.50,7878.60,7563.23,7756.29,793200000,7756.29 1997-12-18,7957.41,7991.48,7781.35,7846.50,618870000,7846.50 1997-12-17,7976.31,8068.81,7879.08,7957.41,618900000,7957.41 1997-12-16,7922.59,8069.06,7889.52,7976.31,623320000,7976.31 1997-12-15,7838.30,7992.72,7807.96,7922.59,597150000,7922.59 1997-12-12,7848.99,7947.21,7756.73,7838.30,579280000,7838.30 1997-12-11,7961.64,7961.64,7778.37,7848.99,631770000,7848.99 1997-12-10,8049.66,8077.27,7881.07,7978.79,602290000,7978.79 1997-12-09,8110.84,8142.67,7990.98,8049.66,539130000,8049.66 1997-12-08,8149.13,8195.88,8057.37,8110.84,490320000,8110.84 1997-12-05,8050.16,8209.56,7980.04,8149.13,563590000,8149.13 1997-12-04,8032.01,8159.58,7978.30,8050.16,633470000,8050.16 1997-12-03,8018.83,8118.30,7915.63,8032.01,624610000,8032.01 1997-12-02,8013.11,8096.66,7935.03,8018.83,576120000,8018.83 1997-12-01,7823.62,8048.92,7823.62,8013.11,590300000,8013.11 1997-11-28,7794.78,7872.86,7786.08,7823.13,189070000,7823.13 1997-11-26,7808.95,7893.50,7749.77,7794.78,487750000,7794.78 1997-11-25,7767.92,7880.32,7706.00,7808.95,587890000,7808.95 1997-11-24,7881.07,7887.28,7723.41,7767.92,514920000,7767.92 1997-11-21,7826.61,7934.53,7758.47,7881.07,611000000,7881.07 1997-11-20,7724.74,7889.77,7718.19,7826.61,602610000,7826.61 1997-11-19,7650.82,7779.75,7599.50,7724.74,542720000,7724.74 1997-11-18,7698.22,7772.62,7607.11,7650.82,521380000,7650.82 1997-11-17,7613.49,7781.71,7613.49,7698.22,576540000,7698.22 1997-11-14,7487.76,7634.61,7431.03,7572.48,635760000,7572.48 1997-11-13,7401.32,7546.45,7334.77,7487.76,653960000,7487.76 1997-11-12,7558.73,7573.22,7359.32,7401.32,585340000,7401.32 1997-11-11,7552.59,7599.74,7518.21,7558.73,435660000,7558.73 1997-11-10,7581.32,7687.90,7511.83,7552.59,464140000,7552.59 1997-11-07,7646.89,7646.89,7460.99,7581.32,569980000,7581.32 1997-11-06,7692.57,7758.63,7597.78,7683.24,522890000,7683.24 1997-11-05,7689.13,7791.78,7628.96,7692.57,565680000,7692.57 1997-11-04,7674.39,7757.15,7583.29,7689.13,541590000,7689.13 1997-11-03,7443.07,7674.39,7443.07,7674.39,564740000,7674.39 1997-10-31,7381.67,7544.24,7329.61,7442.08,638070000,7442.08 1997-10-30,7506.67,7569.78,7340.42,7381.67,712230000,7381.67 1997-10-29,7498.32,7664.08,7409.67,7506.67,777660000,7506.67 1997-10-28,7161.15,7553.57,6936.45,7498.32,1202550000,7498.32 1997-10-27,7715.41,7717.37,7150.10,7161.15,693730000,7161.15 1997-10-24,7847.77,7975.47,7645.91,7715.41,677630000,7715.41 1997-10-23,7957.05,7957.05,7767.22,7847.77,673270000,7847.77 1997-10-22,8060.44,8124.78,7941.33,8034.65,613490000,8034.65 1997-10-21,7921.44,8090.40,7909.41,8060.44,582310000,8060.44 1997-10-20,7847.03,7966.63,7774.83,7921.44,483880000,7921.44 1997-10-17,7938.88,7953.61,7731.12,7847.03,624980000,7847.03 1997-10-16,8057.98,8146.14,7880.18,7938.88,597010000,7938.88 1997-10-15,8096.29,8127.48,7997.57,8057.98,505310000,8057.98 1997-10-14,8072.22,8168.24,7990.20,8096.29,510330000,8096.29 1997-10-13,8045.21,8150.31,8026.30,8072.22,354800000,8072.22 1997-10-10,8061.42,8091.38,7961.96,8045.21,500680000,8045.21 1997-10-09,8095.06,8127.72,7980.87,8061.42,551840000,8061.42 1997-10-08,8178.31,8208.52,8019.92,8095.06,573110000,8095.06 1997-10-07,8100.22,8218.34,8058.96,8178.31,551970000,8178.31 1997-10-06,8038.58,8160.63,8017.71,8100.22,495620000,8100.22 1997-10-03,8027.53,8183.22,7937.89,8038.58,623370000,8038.58 1997-10-02,8015.50,8087.45,7943.79,8027.53,474760000,8027.53 1997-10-01,7945.26,8081.06,7909.41,8015.50,598660000,8015.50 1997-09-30,7991.43,8045.70,7900.08,7945.26,587500000,7945.26 1997-09-29,7922.18,8030.48,7862.99,7991.43,477100000,7991.43 1997-09-26,7853.66,7970.80,7853.66,7922.18,505340000,7922.18 1997-09-25,7906.71,7962.45,7802.34,7848.01,524880000,7848.01 1997-09-24,7970.06,8065.84,7866.43,7906.71,639460000,7906.71 1997-09-23,7996.83,8048.16,7885.34,7970.06,522930000,7970.06 1997-09-22,7917.27,8078.36,7889.27,7996.83,490900000,7996.83 1997-09-19,7922.72,7972.29,7821.69,7917.27,631040000,7917.27 1997-09-18,7886.44,8049.36,7858.93,7922.72,566830000,7922.72 1997-09-17,7895.92,7981.54,7816.47,7886.44,590550000,7886.44 1997-09-16,7721.14,7951.18,7709.75,7895.92,636380000,7895.92 1997-09-15,7742.97,7832.86,7680.95,7721.14,468030000,7721.14 1997-09-12,7660.98,7779.67,7584.56,7742.97,544150000,7742.97 1997-09-11,7719.28,7745.29,7556.23,7660.98,575020000,7660.98 1997-09-10,7851.91,7868.16,7686.30,7719.28,517620000,7719.28 1997-09-09,7835.18,7922.05,7768.52,7851.91,502200000,7851.91 1997-09-08,7822.41,7921.35,7795.00,7835.18,466430000,7835.18 1997-09-05,7867.24,7952.01,7765.27,7822.41,536400000,7822.41 1997-09-04,7894.64,7936.45,7788.50,7867.24,559310000,7867.24 1997-09-03,7879.78,7972.22,7828.45,7894.64,549060000,7894.64 1997-09-02,7650.99,7903.47,7650.99,7879.78,491870000,7879.78 1997-08-29,7694.43,7729.73,7580.85,7622.42,413910000,7622.42 1997-08-28,7787.33,7831.70,7634.50,7694.43,486300000,7694.43 1997-08-27,7782.22,7850.51,7666.55,7787.33,492150000,7787.33 1997-08-26,7859.57,7891.62,7734.61,7782.22,449110000,7782.22 1997-08-25,7887.91,7974.08,7791.75,7859.57,388990000,7859.57 1997-08-22,7893.95,7904.86,7695.36,7887.91,460160000,7887.91 1997-08-21,8021.23,8043.06,7834.02,7893.95,499000000,7893.95 1997-08-20,7918.10,8037.95,7870.95,8021.23,521270000,8021.23 1997-08-19,7803.36,7947.37,7776.65,7918.10,545630000,7918.10 1997-08-18,7694.66,7847.49,7588.28,7803.36,514330000,7803.36 1997-08-15,7919.26,7919.26,7685.14,7694.66,537820000,7694.66 1997-08-14,7928.32,8027.73,7843.54,7942.03,530460000,7942.03 1997-08-13,7960.84,8075.58,7848.19,7928.32,587210000,7928.32 1997-08-12,8062.11,8132.49,7926.46,7960.84,499310000,7960.84 1997-08-11,8031.22,8122.27,7922.52,8062.11,480340000,8062.11 1997-08-08,8170.58,8170.58,7961.07,8031.22,563420000,8031.22 1997-08-07,8259.31,8340.14,8158.73,8188.00,576030000,8188.00 1997-08-06,8187.54,8302.05,8130.63,8259.31,565200000,8259.31 1997-08-05,8198.45,8251.88,8129.93,8187.54,525710000,8187.54 1997-08-04,8194.04,8261.40,8097.65,8198.45,456000000,8198.45 1997-08-01,8222.61,8287.65,8062.11,8194.04,513750000,8194.04 1997-07-31,8254.89,8328.99,8160.36,8222.61,547830000,8222.61 1997-07-30,8174.53,8313.19,8156.88,8254.89,568470000,8254.89 1997-07-29,8121.11,8232.36,8049.34,8174.53,544540000,8174.53 1997-07-28,8113.44,8221.68,8051.89,8121.11,466920000,8121.11 1997-07-25,8116.93,8200.31,8037.95,8113.44,521510000,8113.44 1997-07-24,8088.36,8174.53,7968.51,8116.93,571020000,8116.93 1997-07-23,8061.65,8199.15,8024.95,8088.36,616930000,8088.36 1997-07-22,7906.72,8093.93,7870.72,8061.65,579590000,8061.65 1997-07-21,7890.46,7981.51,7783.85,7906.72,459500000,7906.72 1997-07-18,8020.77,8057.23,7829.61,7890.46,589710000,7890.46 1997-07-17,8038.88,8136.67,7910.67,8020.77,629250000,8020.77 1997-07-16,7975.71,8108.10,7919.73,8038.88,647390000,8038.88 1997-07-15,7922.98,8037.03,7843.78,7975.71,598370000,7975.71 1997-07-14,7921.82,8001.26,7831.00,7922.98,485960000,7922.98 1997-07-11,7886.76,7984.02,7837.68,7921.82,500050000,7921.82 1997-07-10,7842.43,7953.94,7777.06,7886.76,551340000,7886.76 1997-07-09,7962.31,8027.68,7775.48,7842.43,589110000,7842.43 1997-07-08,7858.49,8000.08,7824.33,7962.31,526010000,7962.31 1997-07-07,7895.81,7983.79,7805.34,7858.49,518780000,7858.49 1997-07-03,7805.79,7952.35,7805.79,7895.81,374680000,7895.81 1997-07-02,7722.33,7814.38,7648.14,7795.38,526970000,7795.38 1997-07-01,7672.79,7801.04,7613.53,7722.33,544190000,7722.33 1997-06-30,7687.72,7768.01,7571.46,7672.79,561540000,7672.79 1997-06-27,7654.25,7793.12,7627.33,7687.72,472540000,7687.72 1997-06-26,7689.98,7754.22,7581.64,7654.25,499780000,7654.25 1997-06-25,7758.06,7832.48,7615.57,7689.98,603040000,7689.98 1997-06-24,7787.01,7787.01,7602.90,7758.06,542650000,7758.06 1997-06-23,7796.51,7816.87,7592.04,7604.26,492940000,7604.26 1997-06-20,7777.06,7868.44,7733.64,7796.51,653110000,7796.51 1997-06-19,7718.71,7834.51,7697.45,7777.06,536940000,7777.06 1997-06-18,7760.78,7789.28,7651.30,7718.71,491740000,7718.71 1997-06-17,7772.09,7834.97,7680.71,7760.78,543010000,7760.78 1997-06-16,7782.04,7829.99,7712.37,7772.09,414280000,7772.09 1997-06-13,7711.47,7829.08,7683.42,7782.04,575810000,7782.04 1997-06-12,7575.83,7741.42,7569.66,7711.47,592730000,7711.47 1997-06-11,7539.27,7633.96,7476.29,7575.83,513740000,7575.83 1997-06-10,7478.50,7599.61,7459.12,7539.27,526980000,7539.27 1997-06-09,7435.78,7548.52,7412.44,7478.50,465810000,7478.50 1997-06-06,7305.29,7473.98,7290.26,7435.78,488940000,7435.78 1997-06-05,7269.66,7376.97,7241.33,7305.29,452610000,7305.29 1997-06-04,7312.15,7350.36,7214.29,7269.66,466690000,7269.66 1997-06-03,7289.40,7379.98,7225.02,7312.15,527120000,7312.15 1997-06-02,7331.04,7383.84,7244.76,7289.40,435950000,7289.40 1997-05-30,7330.18,7385.99,7203.99,7331.04,537200000,7331.04 1997-05-29,7357.23,7404.44,7272.24,7330.18,462600000,7330.18 1997-05-28,7383.41,7430.20,7287.26,7357.23,487340000,7357.23 1997-05-27,7345.91,7421.32,7276.68,7383.41,436150000,7383.41 1997-05-23,7260.61,7382.58,7260.61,7345.91,417030000,7345.91 1997-05-22,7290.69,7348.79,7210.33,7258.13,426940000,7258.13 1997-05-21,7303.46,7385.47,7218.99,7290.69,540730000,7290.69 1997-05-20,7228.88,7336.02,7123.38,7303.46,450850000,7303.46 1997-05-19,7194.67,7274.21,7140.69,7228.88,345140000,7228.88 1997-05-16,7332.31,7332.31,7192.61,7194.67,486780000,7194.67 1997-05-15,7286.16,7363.63,7225.99,7333.55,458180000,7333.55 1997-05-14,7274.21,7381.76,7238.35,7286.16,504960000,7286.16 1997-05-13,7292.75,7350.85,7211.16,7274.21,489760000,7274.21 1997-05-12,7169.53,7317.47,7156.35,7292.75,459370000,7292.75 1997-05-09,7136.62,7235.76,7079.63,7169.53,455690000,7169.53 1997-05-08,7085.65,7236.96,7040.30,7136.62,534120000,7136.62 1997-05-07,7225.32,7246.60,7064.78,7085.65,500580000,7085.65 1997-05-06,7213.68,7299.98,7139.43,7225.32,603680000,7225.32 1997-05-05,7071.20,7220.91,7029.46,7213.68,549410000,7213.68 1997-05-02,6976.48,7104.51,6935.54,7071.20,499770000,7071.20 1997-05-01,7008.99,7041.90,6891.39,6976.48,460380000,6976.48 1997-04-30,6962.03,7081.23,6912.26,7008.99,562830000,7008.99 1997-04-29,6820.75,6998.15,6820.75,6962.03,547690000,6962.03 1997-04-28,6738.87,6826.37,6691.51,6783.02,404470000,6783.02 1997-04-25,6792.25,6826.77,6681.08,6738.87,414350000,6738.87 1997-04-24,6812.72,6923.90,6742.89,6792.25,493640000,6792.25 1997-04-23,6833.59,6902.23,6746.50,6812.72,489350000,6812.72 1997-04-22,6660.21,6858.08,6628.50,6833.59,507500000,6833.59 1997-04-21,6703.55,6768.17,6594.38,6660.21,397300000,6660.21 1997-04-18,6658.60,6749.71,6616.46,6703.55,468940000,6703.55 1997-04-17,6679.87,6750.91,6600.80,6658.60,503760000,6658.60 1997-04-16,6587.16,6708.77,6506.08,6679.87,498820000,6679.87 1997-04-15,6457.92,6626.09,6457.92,6587.16,507370000,6587.16 1997-04-14,6391.69,6487.22,6315.84,6451.90,406800000,6451.90 1997-04-11,6529.48,6529.48,6358.42,6391.69,444380000,6391.69 1997-04-10,6563.84,6626.91,6495.48,6540.05,421790000,6540.05 1997-04-09,6609.16,6662.41,6521.16,6563.84,451500000,6563.84 1997-04-08,6555.91,6638.62,6491.71,6609.16,450790000,6609.16 1997-04-07,6526.07,6639.00,6508.70,6555.91,453790000,6555.91 1997-04-04,6477.35,6561.57,6376.90,6526.07,544580000,6526.07 1997-04-03,6517.01,6540.43,6379.16,6477.35,498010000,6477.35 1997-04-02,6611.05,6636.73,6476.60,6517.01,478210000,6517.01 1997-04-01,6583.48,6667.70,6482.64,6611.05,515770000,6611.05 1997-03-31,6740.59,6743.23,6532.49,6583.48,555880000,6583.48 1997-03-27,6880.70,6918.47,6654.86,6740.59,476790000,6740.59 1997-03-26,6876.17,6954.72,6811.21,6880.70,506670000,6880.70 1997-03-25,6905.25,6984.94,6832.74,6876.17,487520000,6876.17 1997-03-24,6804.79,6933.58,6746.63,6905.25,451970000,6905.25 1997-03-21,6820.28,6890.52,6742.85,6804.79,544830000,6804.79 1997-03-20,6877.68,6901.47,6765.14,6820.28,497480000,6820.28 1997-03-19,6896.56,6944.15,6794.22,6877.68,539200000,6877.68 1997-03-18,6955.48,6997.40,6841.05,6896.56,467490000,6896.56 1997-03-17,6935.46,7004.20,6820.28,6955.48,495260000,6955.48 1997-03-14,6878.89,6996.27,6851.19,6935.46,491540000,6935.46 1997-03-13,7023.59,7023.59,6852.72,6878.89,507560000,6878.89 1997-03-12,7085.16,7111.72,6988.96,7039.37,490200000,7039.37 1997-03-11,7079.39,7158.28,7026.28,7085.16,493250000,7085.16 1997-03-10,7000.89,7103.25,6970.10,7079.39,468780000,7079.39 1997-03-07,6944.70,7047.07,6927.00,7000.89,508270000,7000.89 1997-03-06,6945.85,7017.05,6896.60,6944.70,540310000,6944.70 1997-03-05,6852.72,6966.64,6836.95,6945.85,532500000,6945.85 1997-03-04,6918.92,6983.95,6814.63,6852.72,537890000,6852.72 1997-03-03,6877.74,6957.01,6798.46,6918.92,437220000,6918.92 1997-02-28,6925.07,6978.18,6820.78,6877.74,508280000,6877.74 1997-02-27,6983.18,7028.59,6885.44,6925.07,467190000,6925.07 1997-02-26,7037.83,7076.31,6878.12,6983.18,573920000,6983.18 1997-02-25,7008.20,7099.79,6935.08,7037.83,527450000,7037.83 1997-02-24,6931.62,7049.76,6868.50,7008.20,462450000,7008.20 1997-02-21,6927.38,6984.72,6869.27,6931.62,478450000,6931.62 1997-02-20,7020.13,7035.91,6890.82,6927.38,492220000,6927.38 1997-02-19,7067.46,7112.87,6980.87,7020.13,519350000,7020.13 1997-02-18,6988.96,7084.39,6946.62,7067.46,474110000,7067.46 1997-02-14,7022.44,7074.77,6935.85,6988.96,491540000,6988.96 1997-02-13,6961.63,7074.39,6924.30,7022.44,593710000,7022.44 1997-02-12,6858.11,6976.64,6844.26,6961.63,563890000,6961.63 1997-02-11,6806.54,6887.36,6756.90,6858.11,483090000,6858.11 1997-02-10,6855.80,6908.14,6771.14,6806.54,471590000,6806.54 1997-02-07,6773.06,6906.22,6747.28,6855.80,540910000,6855.80 1997-02-06,6746.90,6821.17,6683.40,6773.06,519660000,6773.06 1997-02-05,6833.48,6913.14,6704.56,6746.90,580520000,6746.90 1997-02-04,6806.16,6859.65,6738.43,6833.48,506530000,6833.48 1997-02-03,6813.09,6858.11,6733.04,6806.16,463600000,6806.16 1997-01-31,6823.86,6912.37,6769.99,6813.09,578550000,6813.09 1997-01-30,6740.74,6845.03,6719.96,6823.86,524160000,6823.86 1997-01-29,6656.08,6766.91,6627.98,6740.74,498390000,6740.74 1997-01-28,6660.69,6823.48,6612.20,6656.08,541580000,6656.08 1997-01-27,6696.48,6748.82,6598.73,6660.69,445760000,6660.69 1997-01-24,6755.75,6798.08,6629.91,6696.48,542920000,6696.48 1997-01-23,6850.03,6953.55,6724.19,6755.75,685070000,6755.75 1997-01-22,6883.90,6913.14,6801.16,6850.03,587300000,6850.03 1997-01-21,6843.87,6934.69,6771.14,6883.90,571280000,6883.90 1997-01-20,6833.10,6893.13,6777.30,6843.87,440470000,6843.87 1997-01-17,6765.37,6863.88,6732.66,6833.10,534640000,6833.10 1997-01-16,6726.88,6818.47,6688.40,6765.37,537290000,6765.37 1997-01-15,6762.29,6800.77,6669.93,6726.88,524990000,6726.88 1997-01-14,6709.18,6816.17,6689.94,6762.29,531600000,6762.29 1997-01-13,6703.79,6773.45,6647.99,6709.18,445400000,6709.18 1997-01-10,6625.67,6725.35,6530.62,6703.79,545860000,6703.79 1997-01-09,6549.48,6677.24,6520.23,6625.67,555370000,6625.67 1997-01-08,6600.66,6650.30,6509.84,6549.48,557510000,6549.48 1997-01-07,6567.18,6621.82,6481.75,6600.66,538220000,6600.66 1997-01-06,6544.09,6647.22,6508.30,6567.18,531350000,6567.18 1997-01-03,6442.49,6586.42,6437.10,6544.09,452970000,6544.09 1997-01-02,6448.27,6511.38,6318.96,6442.49,463230000,6442.49 1996-12-31,6549.37,6580.90,6421.74,6448.27,399760000,6448.27 1996-12-30,6560.91,6623.96,6518.23,6549.37,339060000,6549.37 1996-12-27,6546.68,6607.81,6510.93,6560.91,253810000,6560.91 1996-12-26,6522.85,6594.74,6495.94,6546.68,254630000,6546.68 1996-12-24,6489.02,6546.68,6467.10,6522.85,165140000,6522.85 1996-12-23,6484.40,6539.76,6428.27,6489.02,343280000,6489.02 1996-12-20,6473.64,6597.43,6445.19,6484.40,654340000,6484.40 1996-12-19,6352.15,6499.40,6352.15,6473.64,526410000,6473.64 1996-12-18,6308.33,6399.06,6272.96,6346.77,500490000,6346.77 1996-12-17,6268.35,6354.46,6206.83,6308.33,519840000,6308.33 1996-12-16,6304.87,6388.68,6227.98,6268.35,447560000,6268.35 1996-12-13,6303.71,6377.14,6227.59,6304.87,458540000,6304.87 1996-12-12,6402.52,6465.57,6283.72,6303.71,492920000,6303.71 1996-12-11,6457.88,6457.88,6317.94,6402.52,494210000,6402.52 1996-12-10,6463.94,6545.56,6427.04,6473.25,446120000,6473.25 1996-12-09,6381.94,6496.73,6375.98,6463.94,381570000,6463.94 1996-12-06,6437.10,6441.20,6274.24,6381.94,500860000,6381.94 1996-12-05,6422.94,6491.14,6363.31,6437.10,483710000,6437.10 1996-12-04,6442.69,6482.20,6339.46,6422.94,498240000,6422.94 1996-12-03,6521.70,6577.23,6422.94,6442.69,516160000,6442.69 1996-12-02,6521.70,6553.01,6439.34,6521.70,412520000,6521.70 1996-11-29,6499.34,6551.15,6490.40,6521.70,151550000,6521.70 1996-11-27,6528.41,6566.43,6457.97,6499.34,377780000,6499.34 1996-11-26,6547.79,6606.30,6475.49,6528.41,527380000,6528.41 1996-11-25,6471.76,6565.68,6443.44,6547.79,475260000,6547.79 1996-11-22,6418.47,6498.22,6402.44,6471.76,525210000,6471.76 1996-11-21,6430.02,6463.56,6370.02,6418.47,464430000,6418.47 1996-11-20,6397.60,6482.20,6370.02,6430.02,497900000,6430.02 1996-11-19,6346.91,6428.16,6323.06,6397.60,461980000,6397.60 1996-11-18,6348.03,6407.66,6297.34,6346.91,388520000,6346.91 1996-11-15,6313.00,6414.37,6277.22,6348.03,529100000,6348.03 1996-11-14,6274.24,6350.64,6232.50,6313.00,480350000,6313.00 1996-11-13,6266.04,6317.10,6206.78,6274.24,429840000,6274.24 1996-11-12,6255.60,6314.49,6199.70,6266.04,472940000,6266.04 1996-11-11,6219.82,6292.87,6198.58,6255.60,353960000,6255.60 1996-11-08,6206.04,6247.40,6151.62,6219.82,402320000,6219.82 1996-11-07,6177.71,6238.83,6130.38,6206.04,502530000,6206.04 1996-11-06,6081.18,6200.44,6060.69,6177.71,509600000,6177.71 1996-11-05,6041.68,6125.53,6029.01,6081.18,492860000,6081.18 1996-11-04,6021.93,6083.05,5981.30,6041.68,398790000,6041.68 1996-11-01,6029.38,6082.67,5975.34,6021.93,465510000,6021.93 1996-10-31,5993.23,6063.67,5955.59,6029.38,488500000,6029.38 1996-10-30,6007.02,6055.84,5956.71,5993.23,437770000,5993.23 1996-10-29,5972.73,6054.72,5938.82,6007.02,443890000,6007.02 1996-10-28,6007.02,6063.67,5952.61,5972.73,383630000,5972.73 1996-10-25,5992.48,6047.64,5954.47,6007.02,367640000,6007.02 1996-10-24,6036.46,6080.07,5953.72,5992.48,418970000,5992.48 1996-10-23,6061.80,6081.93,5965.65,6036.46,442170000,6036.46 1996-10-22,6090.87,6114.73,6015.59,6061.80,410790000,6061.80 1996-10-21,6094.23,6162.80,6027.89,6090.87,414630000,6090.87 1996-10-18,6059.20,6119.57,6011.49,6094.23,473020000,6094.23 1996-10-17,6020.81,6101.31,5994.72,6059.20,478550000,6059.20 1996-10-16,6004.78,6056.21,5943.66,6020.81,441410000,6020.81 1996-10-15,6010.00,6077.83,5951.12,6004.78,458980000,6004.78 1996-10-14,5969.38,6043.91,5952.61,6010.00,322000000,6010.00 1996-10-11,5921.67,5999.56,5912.36,5969.38,396050000,5969.38 1996-10-10,5930.62,5956.71,5876.58,5921.67,394950000,5921.67 1996-10-09,5966.77,5985.03,5892.23,5930.62,408450000,5930.62 1996-10-08,5979.81,6032.36,5930.24,5966.77,435070000,5966.77 1996-10-07,5992.86,6025.28,5945.15,5979.81,380750000,5979.81 1996-10-04,5932.85,6023.79,5917.20,5992.86,463940000,5992.86 1996-10-03,5933.97,5972.36,5887.76,5932.85,386500000,5932.85 1996-10-02,5904.90,5966.02,5888.88,5933.97,440130000,5933.97 1996-10-01,5882.17,5942.17,5833.72,5904.90,421550000,5904.90 1996-09-30,5845.18,5934.32,5845.18,5882.17,394260000,5882.17 1996-09-27,5868.85,5903.99,5819.65,5872.92,414760000,5872.92 1996-09-26,5877.36,5928.04,5828.90,5868.85,500870000,5868.85 1996-09-25,5874.03,5928.77,5827.42,5877.36,451710000,5877.36 1996-09-24,5894.74,5952.08,5831.86,5874.03,460150000,5874.03 1996-09-23,5888.46,5913.24,5820.39,5894.74,297760000,5894.74 1996-09-20,5867.74,5925.82,5840.74,5888.46,519420000,5888.46 1996-09-19,5877.36,5908.80,5818.17,5867.74,398580000,5867.74 1996-09-18,5888.83,5919.90,5838.15,5877.36,396600000,5877.36 1996-09-17,5889.20,5929.51,5830.38,5888.83,449850000,5888.83 1996-09-16,5838.52,5920.64,5823.72,5889.20,430080000,5889.20 1996-09-13,5786.73,5871.44,5786.73,5838.52,488360000,5838.52 1996-09-12,5754.92,5815.59,5739.76,5771.94,398820000,5771.94 1996-09-11,5727.18,5777.49,5696.85,5754.92,376880000,5754.92 1996-09-10,5733.84,5767.87,5681.68,5727.18,372960000,5727.18 1996-09-09,5662.08,5756.40,5662.08,5733.84,311530000,5733.84 1996-09-06,5606.96,5696.48,5585.51,5659.86,348710000,5659.86 1996-09-05,5656.90,5669.47,5592.53,5606.96,361430000,5606.96 1996-09-04,5648.39,5682.05,5608.81,5656.90,351290000,5656.90 1996-09-03,5616.21,5667.99,5550.37,5648.39,345740000,5648.39 1996-08-30,5647.65,5659.86,5571.45,5616.21,258380000,5616.21 1996-08-29,5712.38,5716.82,5615.47,5647.65,321120000,5647.65 1996-08-28,5711.27,5747.89,5678.72,5712.38,296440000,5712.38 1996-08-27,5693.89,5735.32,5670.58,5711.27,310520000,5711.27 1996-08-26,5722.74,5742.71,5665.41,5693.89,281430000,5693.89 1996-08-23,5733.47,5761.58,5679.09,5722.74,308010000,5722.74 1996-08-22,5689.82,5761.95,5662.45,5733.47,354950000,5733.47 1996-08-21,5721.26,5734.95,5648.76,5689.82,348820000,5689.82 1996-08-20,5699.44,5747.52,5670.21,5721.26,334960000,5721.26 1996-08-19,5689.45,5728.66,5664.30,5699.44,294080000,5699.44 1996-08-16,5665.78,5722.74,5646.54,5689.45,337650000,5689.45 1996-08-15,5666.88,5701.66,5633.22,5665.78,323950000,5665.78 1996-08-14,5647.28,5695.00,5614.36,5666.88,343470000,5666.88 1996-08-13,5618.06,5713.86,5618.06,5647.28,362470000,5647.28 1996-08-12,5681.31,5746.04,5625.83,5704.98,312170000,5704.98 1996-08-09,5713.49,5747.15,5654.31,5681.31,327280000,5681.31 1996-08-08,5718.67,5746.41,5661.34,5713.49,334570000,5713.49 1996-08-07,5696.11,5754.55,5659.12,5718.67,394340000,5718.67 1996-08-06,5674.28,5716.45,5630.26,5696.11,347290000,5696.11 1996-08-05,5679.83,5719.04,5639.14,5674.28,307240000,5674.28 1996-08-02,5601.41,5703.51,5601.41,5679.83,442080000,5679.83 1996-08-01,5528.91,5628.79,5507.83,5594.75,439110000,5594.75 1996-07-31,5481.93,5558.13,5460.48,5528.91,403560000,5528.91 1996-07-30,5434.59,5503.73,5403.52,5481.93,341090000,5481.93 1996-07-29,5473.06,5497.47,5414.61,5434.59,281560000,5434.59 1996-07-26,5422.01,5503.02,5397.97,5473.06,349900000,5473.06 1996-07-25,5355.80,5469.36,5355.80,5422.01,405390000,5422.01 1996-07-24,5346.55,5402.04,5244.83,5354.69,463030000,5354.69 1996-07-23,5390.94,5447.90,5313.63,5346.55,421900000,5346.55 1996-07-22,5426.82,5446.05,5346.92,5390.94,327300000,5390.94 1996-07-19,5464.18,5490.44,5393.53,5426.82,408070000,5426.82 1996-07-18,5376.88,5500.43,5352.10,5464.18,474460000,5464.18 1996-07-17,5358.76,5465.29,5325.10,5376.88,513830000,5376.88 1996-07-16,5349.51,5438.66,5170.11,5358.76,68290000,5358.76 1996-07-15,5510.56,5527.93,5326.98,5349.51,419020000,5349.51 1996-07-12,5520.54,5562.65,5455.16,5510.56,397790000,5510.56 1996-07-11,5600.33,5600.33,5447.77,5520.54,520470000,5520.54 1996-07-10,5581.86,5628.03,5515.00,5603.65,421350000,5603.65 1996-07-09,5550.83,5622.49,5540.12,5581.86,379200000,5581.86 1996-07-08,5588.14,5622.12,5519.80,5550.83,367560000,5550.83 1996-07-05,5657.58,5657.58,5570.77,5588.14,181470000,5588.14 1996-07-03,5720.38,5749.93,5656.47,5703.02,336260000,5703.02 1996-07-02,5729.98,5769.88,5668.29,5720.38,388000000,5720.38 1996-07-01,5654.63,5749.19,5637.26,5729.98,345750000,5729.98 1996-06-28,5677.53,5724.44,5625.81,5654.63,470460000,5654.63 1996-06-27,5682.70,5715.21,5616.21,5677.53,405580000,5677.53 1996-06-26,5719.27,5739.22,5652.04,5682.70,386520000,5682.70 1996-06-25,5717.79,5760.27,5668.66,5719.27,391900000,5719.27 1996-06-24,5705.23,5770.61,5688.61,5717.79,333840000,5717.79 1996-06-21,5659.43,5722.22,5639.11,5705.23,520340000,5705.23 1996-06-20,5648.35,5709.30,5606.97,5659.43,441060000,5659.43 1996-06-19,5628.03,5693.78,5600.70,5648.35,383610000,5648.35 1996-06-18,5652.78,5683.44,5607.71,5628.03,373290000,5628.03 1996-06-17,5649.45,5689.72,5614.36,5652.78,298410000,5652.78 1996-06-14,5657.95,5687.13,5609.93,5649.45,390630000,5649.45 1996-06-13,5668.29,5714.84,5622.86,5657.95,397620000,5657.95 1996-06-12,5668.66,5725.55,5637.63,5668.29,397190000,5668.29 1996-06-11,5687.87,5747.71,5637.63,5668.66,405390000,5668.66 1996-06-10,5697.11,5727.03,5643.54,5687.87,337480000,5687.87 1996-06-07,5667.19,5708.93,5559.69,5697.11,445710000,5697.11 1996-06-06,5697.48,5750.67,5639.48,5667.19,464800000,5667.19 1996-06-05,5665.71,5718.53,5631.35,5697.48,380360000,5697.48 1996-06-04,5624.71,5697.84,5616.58,5665.71,386040000,5665.71 1996-06-03,5643.18,5662.75,5587.40,5624.71,318470000,5624.71 1996-05-31,5693.41,5712.62,5614.36,5643.18,351750000,5643.18 1996-05-30,5673.83,5734.41,5625.07,5693.41,381960000,5693.41 1996-05-29,5709.67,5743.65,5632.46,5673.83,346730000,5673.83 1996-05-28,5762.86,5798.69,5680.48,5709.67,341480000,5709.67 1996-05-24,5762.12,5815.68,5722.59,5762.86,329150000,5762.86 1996-05-23,5778.00,5833.04,5704.12,5762.12,431850000,5762.12 1996-05-22,5736.26,5803.49,5687.87,5778.00,423670000,5778.00 1996-05-21,5748.82,5794.63,5698.21,5736.26,409610000,5736.26 1996-05-20,5687.50,5778.74,5657.58,5748.82,385000000,5748.82 1996-05-17,5635.05,5729.61,5631.35,5687.50,429140000,5687.50 1996-05-16,5625.44,5677.53,5577.42,5635.05,392070000,5635.05 1996-05-15,5624.71,5691.57,5590.72,5625.44,447790000,5625.44 1996-05-14,5582.60,5663.12,5566.71,5624.71,460440000,5624.71 1996-05-13,5518.14,5601.80,5495.42,5582.60,394180000,5582.60 1996-05-10,5478.03,5559.68,5478.03,5518.14,428370000,5518.14 1996-05-09,5474.06,5534.76,5419.87,5475.14,404310000,5475.14 1996-05-08,5420.95,5486.34,5327.74,5474.06,495460000,5474.06 1996-05-07,5464.31,5486.34,5392.05,5420.95,410770000,5420.95 1996-05-06,5478.03,5528.97,5397.83,5464.31,375820000,5464.31 1996-05-03,5498.27,5555.71,5439.74,5478.03,434010000,5478.03 1996-05-02,5575.22,5578.11,5455.27,5498.27,442960000,5498.27 1996-05-01,5569.08,5619.29,5523.56,5575.22,404620000,5575.22 1996-04-30,5573.41,5606.29,5522.83,5569.08,393390000,5569.08 1996-04-29,5567.99,5611.71,5515.25,5573.41,344030000,5573.41 1996-04-26,5566.91,5618.57,5517.78,5567.99,402530000,5567.99 1996-04-25,5553.90,5601.23,5490.68,5566.91,461120000,5566.91 1996-04-24,5588.59,5619.29,5512.36,5553.90,494220000,5553.90 1996-04-23,5564.74,5621.82,5534.76,5588.59,452690000,5588.59 1996-04-22,5535.48,5610.62,5516.33,5564.74,397460000,5564.74 1996-04-19,5551.74,5593.64,5504.41,5535.48,435690000,5535.48 1996-04-18,5549.93,5600.51,5493.93,5551.74,415150000,5551.74 1996-04-17,5620.02,5653.25,5500.07,5549.93,465200000,5549.93 1996-04-16,5592.92,5656.14,5554.99,5620.02,453310000,5620.02 1996-04-15,5532.59,5614.60,5517.41,5592.92,346370000,5592.92 1996-04-12,5487.07,5577.39,5445.16,5532.59,413270000,5532.59 1996-04-11,5485.98,5540.17,5382.66,5487.07,519710000,5487.07 1996-04-10,5560.41,5601.23,5452.02,5485.98,475150000,5485.98 1996-04-09,5594.37,5644.58,5530.06,5560.41,426790000,5560.41 1996-04-08,5624.71,5624.71,5518.14,5594.37,411810000,5594.37 1996-04-04,5689.74,5737.07,5633.02,5682.88,383400000,5682.88 1996-04-03,5671.68,5712.50,5619.29,5689.74,386620000,5689.74 1996-04-02,5637.72,5706.72,5597.26,5671.68,406640000,5671.68 1996-04-01,5587.14,5670.23,5562.57,5637.72,392120000,5637.72 1996-03-29,5630.85,5682.88,5550.29,5587.14,413510000,5587.14 1996-03-28,5626.88,5664.45,5564.74,5630.85,370750000,5630.85 1996-03-27,5670.60,5703.47,5596.53,5626.88,406280000,5626.88 1996-03-26,5643.86,5701.30,5595.81,5670.60,400090000,5670.60 1996-03-25,5636.64,5712.14,5592.92,5643.86,336700000,5643.86 1996-03-22,5626.88,5681.07,5589.67,5636.64,329390000,5636.64 1996-03-21,5655.42,5696.25,5590.03,5626.88,367180000,5626.88 1996-03-20,5669.51,5724.06,5590.39,5655.42,409780000,5655.42 1996-03-19,5683.60,5755.86,5619.66,5669.51,438300000,5669.51 1996-03-18,5584.97,5706.00,5573.41,5683.60,437100000,5683.60 1996-03-15,5586.06,5628.69,5523.56,5584.97,529970000,5584.97 1996-03-14,5568.72,5662.29,5536.56,5586.06,492630000,5586.06 1996-03-13,5583.89,5626.52,5513.80,5568.72,413030000,5568.72 1996-03-12,5581.00,5622.55,5464.67,5583.89,454980000,5583.89 1996-03-11,5470.45,5605.20,5425.29,5581.00,449500000,5581.00 1996-03-08,5612.79,5612.79,5395.30,5470.45,546550000,5470.45 1996-03-07,5629.77,5667.34,5572.33,5641.69,425790000,5641.69 1996-03-06,5642.42,5700.22,5585.70,5629.77,428220000,5629.77 1996-03-05,5600.15,5655.78,5558.24,5642.42,445700000,5642.42 1996-03-04,5536.56,5646.39,5511.99,5600.15,417270000,5600.15 1996-03-01,5485.62,5573.41,5424.20,5536.56,471480000,5536.56 1996-02-29,5506.21,5562.21,5440.46,5485.62,453170000,5485.62 1996-02-28,5549.21,5625.07,5487.07,5506.21,447790000,5506.21 1996-02-27,5565.10,5597.26,5478.76,5549.21,431340000,5549.21 1996-02-26,5630.49,5647.83,5530.42,5565.10,399330000,5565.10 1996-02-23,5608.46,5693.36,5546.32,5630.49,443130000,5630.49 1996-02-22,5515.97,5638.44,5511.63,5608.46,494750000,5608.46 1996-02-21,5458.53,5548.12,5440.82,5515.97,431220000,5515.97 1996-02-20,5503.32,5515.97,5393.50,5458.53,395910000,5458.53 1996-02-16,5551.37,5563.30,5470.45,5503.32,445570000,5503.32 1996-02-15,5579.55,5623.27,5514.88,5551.37,415320000,5551.37 1996-02-14,5601.23,5633.02,5534.39,5579.55,421790000,5579.55 1996-02-13,5600.15,5644.94,5530.06,5601.23,441540000,5601.23 1996-02-12,5541.62,5643.50,5531.14,5600.15,401490000,5600.15 1996-02-09,5539.45,5603.76,5474.06,5541.62,477640000,5541.62 1996-02-08,5492.12,5559.68,5441.55,5539.45,474970000,5539.45 1996-02-07,5459.61,5520.30,5423.84,5492.12,462730000,5492.12 1996-02-06,5407.59,5483.82,5371.82,5459.61,465940000,5459.61 1996-02-05,5373.99,5433.96,5319.43,5407.59,377760000,5407.59 1996-02-02,5405.06,5442.99,5338.94,5373.99,420020000,5373.99 1996-02-01,5395.30,5438.29,5335.69,5405.06,461610000,5405.06 1996-01-31,5381.21,5433.24,5314.38,5395.30,472210000,5395.30 1996-01-30,5304.98,5409.75,5288.73,5381.21,464350000,5381.21 1996-01-29,5271.75,5338.22,5246.82,5304.98,363330000,5304.98 1996-01-26,5216.83,5293.78,5187.21,5271.75,385700000,5271.75 1996-01-25,5242.84,5289.45,5173.84,5216.83,453270000,5216.83 1996-01-24,5192.27,5297.04,5176.73,5242.84,476380000,5242.84 1996-01-23,5219.36,5241.76,5141.33,5192.27,416910000,5192.27 1996-01-22,5184.68,5254.77,5133.02,5219.36,398040000,5219.36 1996-01-19,5124.35,5214.66,5087.49,5184.68,497720000,5184.68 1996-01-18,5066.90,5166.98,5032.58,5124.35,450410000,5124.35 1996-01-17,5088.22,5133.38,5028.61,5066.90,458720000,5066.90 1996-01-16,5043.78,5116.04,5000.79,5088.22,425220000,5088.22 1996-01-15,5061.12,5099.42,5012.71,5043.78,306180000,5043.78 1996-01-12,5065.10,5114.23,5000.07,5061.12,383400000,5061.12 1996-01-11,5032.94,5099.78,5002.96,5065.10,408800000,5065.10 1996-01-10,5130.13,5151.69,5000.07,5032.94,496830000,5032.94 1996-01-09,5197.70,5239.23,5100.86,5130.13,417400000,5130.13 1996-01-08,5181.43,5207.08,5179.98,5197.70,130360000,5197.70 1996-01-05,5173.84,5217.55,5107.00,5181.43,437110000,5181.43 1996-01-04,5194.07,5259.46,5114.23,5173.84,512580000,5173.84 1996-01-03,5177.45,5252.60,5143.13,5194.07,468950000,5194.07 1996-01-02,5117.12,5208.16,5087.13,5177.45,364180000,5177.45 1995-12-29,5095.80,5144.58,5063.65,5117.12,319680000,5117.12 1995-12-28,5105.92,5132.65,5062.21,5095.80,288470000,5095.80 1995-12-27,5110.26,5146.74,5077.74,5105.92,252270000,5105.92 1995-12-26,5097.97,5149.27,5072.32,5110.26,217030000,5110.26 1995-12-22,5096.53,5146.74,5064.01,5097.97,289600000,5097.97 1995-12-21,5059.32,5135.91,5031.50,5096.53,415780000,5096.53 1995-12-20,5109.89,5173.48,5047.39,5059.32,437650000,5059.32 1995-12-19,5075.21,5146.02,5016.68,5109.89,18440000,5109.89 1995-12-18,5159.39,5159.39,5042.70,5075.21,426230000,5075.21 1995-12-15,5182.15,5217.55,5135.54,5176.73,636780000,5176.73 1995-12-14,5216.47,5266.69,5148.55,5182.15,461960000,5182.15 1995-12-13,5174.92,5246.10,5144.22,5216.47,414680000,5216.47 1995-12-12,5184.32,5226.23,5132.29,5174.92,349410000,5174.92 1995-12-11,5156.86,5225.50,5122.18,5184.32,342050000,5184.32 1995-12-08,5159.39,5200.21,5115.31,5156.86,327500000,5156.86 1995-12-07,5199.13,5211.05,5124.35,5159.39,379260000,5159.39 1995-12-06,5177.45,5234.53,5147.47,5199.13,417750000,5199.13 1995-12-05,5139.52,5216.83,5108.45,5177.45,434000000,5177.45 1995-12-04,5087.13,5157.58,5057.87,5139.52,405080000,5139.52 1995-12-01,5074.49,5133.02,5038.36,5087.13,392650000,5087.13 1995-11-30,5105.56,5143.13,5033.17,5074.49,439470000,5074.49 1995-11-29,5078.10,5126.51,5047.03,5105.56,398230000,5105.56 1995-11-28,5070.88,5105.56,5006.21,5078.10,408860000,5078.10 1995-11-27,5048.84,5114.23,5034.03,5070.88,356180000,5070.88 1995-11-24,5041.61,5072.68,5028.61,5048.84,125870000,5048.84 1995-11-22,5023.55,5096.17,4982.72,5041.61,404900000,5041.61 1995-11-21,4983.09,5050.28,4948.04,5023.55,405860000,5023.55 1995-11-20,4989.95,5030.77,4946.60,4983.09,333110000,4983.09 1995-11-17,4969.36,5022.10,4934.31,4989.95,429990000,4989.95 1995-11-16,4922.75,5003.68,4902.16,4969.36,418430000,4969.36 1995-11-15,4871.81,4939.73,4835.69,4922.75,373090000,4922.75 1995-11-14,4872.90,4923.11,4826.29,4871.81,353650000,4871.81 1995-11-13,4870.37,4909.75,4821.23,4872.90,292390000,4872.90 1995-11-10,4864.23,4896.02,4820.51,4870.37,297950000,4870.37 1995-11-09,4852.67,4902.52,4811.84,4864.23,379500000,4864.23 1995-11-08,4797.03,4877.59,4775.71,4852.67,359770000,4852.67 1995-11-07,4814.01,4839.30,4770.66,4797.03,365720000,4797.03 1995-11-06,4825.57,4862.78,4779.33,4814.01,309090000,4814.01 1995-11-03,4808.59,4858.81,4774.63,4825.57,344360000,4825.57 1995-11-02,4766.68,4833.16,4745.00,4808.59,396150000,4808.59 1995-11-01,4755.48,4801.73,4719.72,4766.68,377930000,4766.68 1995-10-31,4756.57,4821.96,4735.97,4755.48,377350000,4755.48 1995-10-30,4741.75,4798.84,4716.46,4756.57,318840000,4756.57 1995-10-27,4703.82,4766.68,4653.24,4741.75,378820000,4741.75 1995-10-26,4753.68,4777.88,4655.05,4703.82,464190000,4703.82 1995-10-25,4783.66,4824.49,4708.88,4753.68,433570000,4753.68 1995-10-24,4755.48,4819.07,4730.92,4783.66,415400000,4783.66 1995-10-23,4794.86,4805.70,4724.41,4755.48,329720000,4755.48 1995-10-20,4802.45,4836.41,4758.37,4794.86,388140000,4794.86 1995-10-19,4777.52,4830.27,4744.28,4802.45,406620000,4802.45 1995-10-18,4795.95,4837.49,4747.53,4777.52,411270000,4777.52 1995-10-17,4784.38,4831.35,4747.90,4795.95,352940000,4795.95 1995-10-16,4793.78,4823.04,4751.51,4784.38,299520000,4784.38 1995-10-13,4764.88,4845.08,4749.70,4793.78,373970000,4793.78 1995-10-12,4735.25,4790.16,4709.24,4764.88,343930000,4764.88 1995-10-11,4720.80,4773.91,4686.43,4735.25,343060000,4735.25 1995-10-10,4726.22,4745.73,4638.43,4720.80,412670000,4720.80 1995-10-09,4769.21,4781.13,4691.54,4726.22,275280000,4726.22 1995-10-06,4762.71,4805.70,4731.64,4769.21,314170000,4769.21 1995-10-05,4740.67,4794.86,4705.63,4762.71,367410000,4762.71 1995-10-04,4749.70,4787.27,4689.01,4740.67,339350000,4740.67 1995-10-03,4761.26,4791.61,4702.74,4749.70,385890000,4749.70 1995-10-02,4789.08,4817.98,4732.72,4761.26,304990000,4761.26 1995-09-29,4787.64,4834.96,4757.65,4789.08,337670000,4789.08 1995-09-28,4762.35,4810.03,4731.64,4787.64,367610000,4787.64 1995-09-27,4765.60,4790.16,4692.98,4762.35,410970000,4762.35 1995-09-26,4769.93,4816.54,4738.86,4765.60,362550000,4765.60 1995-09-25,4764.15,4804.62,4728.02,4769.93,273110000,4769.93 1995-09-22,4767.40,4789.44,4706.35,4764.15,370750000,4764.15 1995-09-21,4792.69,4820.15,4728.75,4767.40,367060000,4767.40 1995-09-20,4767.04,4828.46,4746.45,4792.69,402530000,4792.69 1995-09-19,4780.41,4798.11,4734.53,4767.04,371080000,4767.04 1995-09-18,4797.57,4814.01,4729.47,4780.41,328630000,4780.41 1995-09-15,4801.80,4839.48,4762.35,4797.57,459370000,4797.57 1995-09-14,4765.52,4830.32,4731.36,4801.80,382860000,4801.80 1995-09-13,4747.21,4801.09,4697.55,4765.52,381450000,4765.52 1995-09-12,4704.94,4762.35,4689.45,4747.21,342580000,4747.21 1995-09-11,4700.72,4744.04,4672.54,4704.94,296820000,4704.94 1995-09-08,4669.72,4722.91,4648.59,4700.72,317260000,4700.72 1995-09-07,4683.81,4710.58,4640.14,4669.72,321710000,4669.72 1995-09-06,4670.08,4716.21,4648.95,4683.81,369510000,4683.81 1995-09-05,4647.54,4697.90,4625.70,4670.08,332660000,4670.08 1995-09-01,4610.56,4672.19,4594.71,4647.54,255710000,4647.54 1995-08-31,4604.57,4634.51,4582.38,4610.56,300890000,4610.56 1995-08-30,4608.44,4639.79,4582.38,4604.57,329780000,4604.57 1995-08-29,4594.00,4625.35,4554.21,4608.44,311290000,4608.44 1995-08-28,4601.40,4632.39,4566.53,4594.00,267860000,4594.00 1995-08-25,4580.62,4629.93,4569.70,4601.40,255990000,4601.40 1995-08-24,4584.85,4618.30,4552.80,4580.62,299200000,4580.62 1995-08-23,4620.42,4636.27,4571.81,4584.85,291380000,4584.85 1995-08-22,4614.78,4646.13,4580.62,4620.42,290350000,4620.42 1995-08-21,4617.60,4676.42,4593.65,4614.78,303200000,4614.78 1995-08-18,4630.63,4665.15,4597.88,4617.60,312030000,4617.60 1995-08-17,4639.08,4659.16,4587.66,4630.63,354460000,4630.63 1995-08-16,4640.84,4673.60,4593.30,4639.08,390070000,4639.08 1995-08-15,4659.86,4672.89,4602.10,4640.84,330040000,4640.84 1995-08-14,4618.30,4677.12,4601.75,4659.86,263750000,4659.86 1995-08-11,4643.66,4659.51,4584.85,4618.30,267850000,4618.30 1995-08-10,4671.49,4698.96,4621.47,4643.66,306660000,4643.66 1995-08-09,4693.32,4712.34,4649.30,4671.49,303390000,4671.49 1995-08-08,4693.32,4722.55,4654.23,4693.32,305150000,4693.32 1995-08-07,4683.46,4727.84,4667.26,4693.32,276040000,4693.32 1995-08-04,4701.42,4728.54,4660.57,4683.46,314730000,4683.46 1995-08-03,4690.15,4720.44,4632.04,4701.42,353110000,4701.42 1995-08-02,4700.37,4772.56,4667.96,4690.15,374310000,4690.15 1995-08-01,4708.47,4742.98,4648.59,4700.37,334770000,4700.37 1995-07-31,4715.51,4753.55,4671.13,4708.47,291950000,4708.47 1995-07-28,4732.77,4762.70,4686.28,4715.51,311580000,4715.51 1995-07-27,4707.06,4767.99,4691.56,4732.77,356570000,4732.77 1995-07-26,4714.45,4750.73,4665.15,4707.06,393460000,4707.06 1995-07-25,4668.67,4743.69,4651.76,4714.45,373210000,4714.45 1995-07-24,4641.55,4701.42,4620.42,4668.67,315170000,4668.67 1995-07-21,4641.55,4676.06,4596.82,4641.55,427770000,4641.55 1995-07-20,4628.87,4684.52,4578.51,4641.55,383380000,4641.55 1995-07-19,4686.28,4698.60,4530.26,4628.87,489850000,4628.87 1995-07-18,4734.53,4734.53,4648.24,4686.28,372230000,4686.28 1995-07-17,4708.82,4767.63,4681.35,4736.29,322540000,4736.29 1995-07-14,4727.48,4736.99,4664.79,4708.82,312930000,4708.82 1995-07-13,4727.29,4766.58,4676.06,4727.48,387470000,4727.48 1995-07-12,4680.60,4747.00,4654.66,4727.29,416000000,4727.29 1995-07-11,4702.39,4724.52,4647.74,4680.60,376770000,4680.60 1995-07-10,4702.73,4747.69,4670.91,4702.39,409700000,4702.39 1995-07-07,4664.00,4740.77,4628.38,4702.73,466540000,4702.73 1995-07-06,4615.23,4681.98,4587.91,4664.00,420470000,4664.00 1995-07-05,4585.15,4652.24,4561.63,4615.23,357810000,4615.23 1995-07-03,4556.10,4594.83,4536.04,4585.15,117870000,4585.15 1995-06-30,4550.56,4606.24,4513.90,4556.10,311320000,4556.10 1995-06-29,4556.79,4592.76,4510.45,4550.56,313060000,4550.56 1995-06-28,4542.61,4587.22,4512.87,4556.79,368060000,4556.79 1995-06-27,4551.25,4592.41,4511.48,4542.61,346950000,4542.61 1995-06-26,4585.84,4600.02,4536.38,4551.25,296720000,4551.25 1995-06-23,4589.64,4609.70,4549.18,4585.84,321530000,4585.84 1995-06-22,4547.10,4614.20,4535.35,4589.64,420950000,4589.64 1995-06-21,4550.56,4583.07,4517.02,4547.10,398210000,4547.10 1995-06-20,4553.68,4587.22,4497.99,4550.56,382290000,4550.56 1995-06-19,4510.79,4577.19,4498.69,4553.68,322620000,4553.68 1995-06-16,4496.27,4533.96,4482.43,4510.79,442740000,4510.79 1995-06-15,4491.08,4527.74,4466.87,4496.27,334640000,4496.27 1995-06-14,4484.51,4510.79,4449.23,4491.08,330750000,4491.08 1995-06-13,4446.46,4504.91,4440.24,4484.51,339620000,4484.51 1995-06-12,4423.99,4482.09,4415.69,4446.46,287830000,4446.46 1995-06-09,4458.57,4466.87,4394.59,4423.99,328960000,4423.99 1995-06-08,4462.03,4492.12,4431.59,4458.57,289880000,4458.57 1995-06-07,4485.20,4497.65,4432.29,4462.03,327790000,4462.03 1995-06-06,4476.55,4520.47,4446.46,4485.20,340470000,4485.20 1995-06-05,4444.39,4510.45,4417.76,4476.55,337520000,4476.55 1995-06-02,4472.75,4496.27,4406.35,4444.39,366000000,4444.39 1995-06-01,4465.14,4499.72,4421.56,4472.75,345890000,4472.75 1995-05-31,4378.68,4467.91,4360.70,4465.14,358160000,4465.14 1995-05-30,4369.00,4412.92,4334.41,4378.68,283020000,4378.68 1995-05-26,4412.23,4423.64,4338.91,4369.00,290730000,4369.00 1995-05-25,4427.44,4451.31,4375.91,4412.23,341820000,4412.23 1995-05-24,4436.44,4480.70,4390.78,4438.16,391750000,4438.16 1995-05-23,4395.63,4451.65,4372.46,4436.44,362690000,4436.44 1995-05-22,4341.33,4416.03,4337.87,4395.63,285600000,4395.63 1995-05-19,4340.64,4368.65,4287.38,4341.33,354010000,4341.33 1995-05-18,4422.60,4426.41,4330.95,4340.64,351850000,4340.64 1995-05-17,4435.05,4452.69,4386.63,4422.60,347930000,4422.60 1995-05-16,4437.47,4464.10,4408.08,4435.05,366180000,4435.05 1995-05-15,4430.56,4468.94,4398.74,4437.47,316240000,4437.47 1995-05-12,4411.19,4463.07,4371.07,4430.56,361000000,4430.56 1995-05-11,4404.62,4446.12,4365.19,4411.19,339900000,4411.19 1995-05-10,4390.78,4440.24,4347.55,4404.62,381990000,4404.62 1995-05-09,4383.87,4421.22,4360.00,4390.78,361430000,4390.78 1995-05-08,4343.40,4407.04,4316.43,4383.87,291810000,4383.87 1995-05-05,4359.66,4389.75,4310.55,4343.40,342380000,4343.40 1995-05-04,4373.15,4426.41,4329.92,4359.66,434920000,4359.66 1995-05-03,4328.88,4392.51,4312.28,4373.15,392290000,4373.15 1995-05-02,4316.08,4348.25,4288.42,4328.88,300660000,4328.88 1995-05-01,4321.27,4352.74,4278.73,4316.08,296830000,4316.08 1995-04-28,4314.70,4348.94,4270.78,4321.27,320270000,4321.27 1995-04-27,4299.83,4335.10,4269.05,4314.70,350850000,4314.70 1995-04-26,4300.17,4324.73,4267.32,4299.83,350790000,4299.83 1995-04-25,4303.98,4337.18,4267.32,4300.17,351790000,4300.17 1995-04-24,4270.09,4328.88,4239.65,4303.98,325760000,4303.98 1995-04-21,4230.66,4296.37,4214.41,4270.09,403210000,4270.09 1995-04-20,4207.49,4263.86,4186.39,4230.66,368440000,4230.66 1995-04-19,4179.13,4232.39,4143.51,4207.49,377520000,4207.49 1995-04-18,4195.38,4221.67,4152.15,4179.13,344680000,4179.13 1995-04-17,4208.18,4260.06,4174.63,4195.38,333930000,4195.38 1995-04-13,4197.81,4243.46,4173.94,4208.18,301510000,4208.18 1995-04-12,4187.08,4221.67,4163.57,4197.81,327880000,4197.81 1995-04-11,4198.15,4229.97,4168.06,4187.08,309710000,4187.08 1995-04-10,4192.62,4219.94,4163.91,4198.15,260980000,4198.15 1995-04-07,4205.41,4226.86,4154.23,4192.62,314750000,4192.62 1995-04-06,4200.57,4239.31,4179.13,4205.41,320460000,4205.41 1995-04-05,4201.61,4229.62,4166.33,4200.57,315170000,4200.57 1995-04-04,4168.41,4220.98,4154.23,4201.61,330550000,4201.61 1995-04-03,4157.69,4202.30,4129.68,4168.41,287380000,4168.41 1995-03-31,4172.56,4194.00,4100.28,4157.69,352940000,4157.69 1995-03-30,4160.80,4213.37,4128.29,4172.56,362020000,4172.56 1995-03-29,4151.81,4213.71,4121.72,4160.80,385910000,4160.80 1995-03-28,4157.34,4176.02,4120.68,4151.81,320360000,4151.81 1995-03-27,4138.67,4177.40,4113.08,4157.34,296160000,4157.34 1995-03-24,4087.83,4155.27,4087.14,4138.67,358370000,4138.67 1995-03-23,4082.99,4107.89,4046.33,4087.83,318490000,4087.83 1995-03-22,4072.61,4107.54,4039.07,4082.99,313120000,4082.99 1995-03-21,4083.68,4122.41,4046.67,4072.61,367110000,4072.61 1995-03-20,4073.65,4115.15,4051.17,4083.68,301720000,4083.68 1995-03-17,4069.15,4101.66,4044.94,4073.65,415810000,4073.65 1995-03-16,4038.37,4095.78,4021.77,4069.15,336330000,4069.15 1995-03-15,4048.75,4070.19,4008.63,4038.37,309540000,4038.37 1995-03-14,4025.23,4087.14,4014.86,4048.75,346140000,4048.75 1995-03-13,4035.61,4060.85,3999.29,4025.23,275280000,4025.23 1995-03-10,3983.39,4054.97,3978.20,4035.61,383000000,4035.61 1995-03-09,3979.23,4011.40,3953.99,3983.39,319100000,3983.39 1995-03-08,3962.63,4002.41,3944.65,3979.23,349700000,3979.23 1995-03-07,3997.56,4009.67,3935.31,3962.63,355550000,3962.63 1995-03-06,3989.61,4015.89,3944.31,3997.56,298850000,3997.56 1995-03-03,3979.93,4004.83,3946.03,3989.61,330840000,3989.61 1995-03-02,3994.80,4009.67,3947.07,3979.93,329430000,3979.93 1995-03-01,4011.05,4033.19,3960.56,3994.80,362590000,3994.80 1995-02-28,3988.57,4021.43,3967.48,4011.05,317120000,4011.05 1995-02-27,4011.74,4033.19,3966.78,3988.57,285790000,3988.57 1995-02-24,4003.33,4033.61,3974.06,4011.74,302850000,4011.74 1995-02-23,3979.45,4034.62,3979.45,4003.33,394100000,4003.33 1995-02-22,3963.97,4002.32,3944.46,3973.05,339140000,3973.05 1995-02-21,3953.54,3989.88,3930.66,3963.97,308060000,3963.97 1995-02-17,3987.52,4001.99,3937.05,3953.54,350560000,3953.54 1995-02-16,3986.17,4011.07,3948.16,3987.52,360990000,3987.52 1995-02-15,3958.25,4019.15,3937.73,3986.17,377940000,3986.17 1995-02-14,3954.21,3984.83,3930.66,3958.25,300720000,3958.25 1995-02-13,3939.07,3974.74,3920.57,3954.21,256210000,3954.21 1995-02-10,3932.68,3962.96,3908.46,3939.07,295550000,3939.07 1995-02-09,3935.37,3967.33,3904.76,3932.68,325570000,3932.68 1995-02-08,3937.39,3967.67,3909.13,3935.37,317910000,3935.37 1995-02-07,3937.73,3960.27,3907.45,3937.39,317110000,3937.39 1995-02-06,3928.64,3964.31,3900.04,3937.73,325660000,3937.73 1995-02-03,3876.16,3959.60,3876.16,3928.64,440950000,3928.64 1995-02-02,3847.56,3884.23,3828.38,3870.77,322030000,3870.77 1995-02-01,3843.86,3886.25,3809.21,3847.56,395300000,3847.56 1995-01-31,3832.08,3872.12,3809.54,3843.86,411570000,3843.86 1995-01-30,3857.99,3881.20,3794.40,3832.08,318530000,3832.08 1995-01-27,3870.44,3914.18,3827.37,3857.99,339490000,3857.99 1995-01-26,3871.45,3903.41,3839.15,3870.44,304650000,3870.44 1995-01-25,3862.70,3908.79,3832.08,3871.45,342240000,3871.45 1995-01-24,3867.41,3898.36,3840.16,3862.70,315410000,3862.70 1995-01-23,3869.43,3885.58,3815.26,3867.41,325810000,3867.41 1995-01-20,3882.21,3898.70,3826.70,3869.43,376870000,3869.43 1995-01-19,3928.98,3929.99,3866.74,3882.21,297220000,3882.21 1995-01-18,3930.66,3950.85,3889.95,3928.98,343100000,3928.98 1995-01-17,3932.34,3953.88,3895.33,3930.66,330890000,3930.66 1995-01-16,3908.46,3955.56,3889.95,3932.34,315800000,3932.34 1995-01-13,3859.00,3924.27,3852.61,3908.46,336740000,3908.46 1995-01-12,3862.03,3886.59,3831.41,3859.00,313040000,3859.00 1995-01-11,3866.74,3899.71,3824.68,3862.03,346310000,3862.03 1995-01-10,3861.35,3912.49,3844.87,3866.74,352440000,3866.74 1995-01-09,3867.41,3889.28,3834.44,3861.35,278710000,3861.35 1995-01-06,3850.92,3902.40,3823.67,3867.41,308070000,3867.41 1995-01-05,3857.65,3876.83,3825.35,3850.92,309140000,3850.92 1995-01-04,3838.48,3876.83,3815.26,3857.65,319510000,3857.65 1995-01-03,3834.44,3864.72,3805.50,3838.48,262450000,3838.48 1994-12-30,3833.43,3874.48,3812.91,3834.44,256260000,3834.44 1994-12-29,3839.49,3867.41,3812.91,3833.43,250650000,3833.43 1994-12-28,3861.69,3871.45,3816.94,3839.49,246260000,3839.49 1994-12-27,3833.43,3882.21,3832.08,3861.69,211180000,3861.69 1994-12-23,3814.92,3860.01,3808.53,3833.43,196540000,3833.43 1994-12-22,3801.80,3850.59,3780.61,3814.92,339670000,3814.92 1994-12-21,3767.15,3836.46,3761.77,3801.80,378790000,3801.80 1994-12-20,3790.70,3807.19,3752.35,3767.15,325510000,3767.15 1994-12-19,3807.19,3815.26,3764.46,3790.70,271850000,3790.70 1994-12-16,3765.47,3817.95,3758.07,3807.19,481860000,3807.19 1994-12-15,3746.29,3794.07,3729.13,3765.47,332790000,3765.47 1994-12-14,3715.34,3772.20,3700.53,3746.29,355000000,3746.29 1994-12-13,3718.37,3749.32,3690.78,3715.34,307110000,3715.34 1994-12-12,3691.11,3734.52,3667.90,3718.37,285730000,3718.37 1994-12-09,3685.73,3713.32,3638.97,3691.11,336440000,3691.11 1994-12-08,3735.52,3757.39,3670.25,3685.73,362280000,3685.73 1994-12-07,3745.95,3758.07,3700.87,3735.52,283490000,3735.52 1994-12-06,3741.92,3762.10,3703.56,3745.95,298890000,3745.95 1994-12-05,3745.62,3779.93,3715.67,3741.92,258480000,3741.92 1994-12-02,3700.87,3755.04,3680.68,3745.62,284740000,3745.62 1994-12-01,3739.23,3755.37,3680.68,3700.87,285920000,3700.87 1994-11-30,3738.55,3783.30,3718.37,3739.23,300800000,3739.23 1994-11-29,3739.56,3755.04,3703.90,3738.55,286580000,3738.55 1994-11-28,3708.27,3759.41,3693.13,3739.56,265020000,3739.56 1994-11-25,3675.97,3722.40,3675.97,3708.27,118290000,3708.27 1994-11-23,3677.99,3704.91,3612.05,3674.63,430730000,3674.63 1994-11-22,3769.51,3787.34,3669.58,3677.99,387270000,3677.99 1994-11-21,3815.26,3845.20,3758.74,3769.51,293030000,3769.51 1994-11-18,3828.05,3844.87,3773.21,3815.26,356730000,3815.26 1994-11-17,3845.20,3868.08,3797.77,3828.05,323190000,3828.05 1994-11-16,3826.36,3864.38,3803.82,3845.20,296980000,3845.20 1994-11-15,3829.73,3871.45,3794.40,3826.36,336330000,3826.36 1994-11-14,3801.47,3849.91,3795.07,3829.73,260380000,3829.73 1994-11-11,3821.99,3830.06,3779.93,3801.47,220800000,3801.47 1994-11-10,3831.75,3873.47,3821.99,3821.99,280910000,3821.99 1994-11-09,3830.74,3882.89,3798.44,3831.75,341500000,3831.75 1994-11-08,3808.87,3857.65,3798.75,3830.74,290860000,3830.74 1994-11-07,3807.52,3830.40,3781.62,3808.87,255030000,3808.87 1994-11-04,3845.88,3879.52,3802.81,3807.52,280560000,3807.52 1994-11-03,3837.13,3872.46,3814.25,3845.88,284480000,3845.88 1994-11-02,3863.37,3888.94,3820.31,3837.13,331360000,3837.13 1994-11-01,3908.12,3919.90,3836.79,3863.37,314940000,3863.37 1994-10-31,3930.66,3956.90,3889.28,3908.12,302810000,3908.12 1994-10-28,3875.15,3953.54,3863.37,3930.66,381450000,3930.66 1994-10-27,3848.23,3893.99,3834.44,3875.15,327790000,3875.15 1994-10-26,3850.59,3891.97,3816.61,3848.23,322560000,3848.23 1994-10-25,3855.30,3883.56,3803.49,3850.59,326100000,3850.59 1994-10-24,3891.30,3925.61,3840.16,3855.30,282800000,3855.30 1994-10-21,3911.15,3921.58,3861.35,3891.30,309410000,3891.30 1994-10-20,3936.04,3954.89,3873.13,3911.15,326100000,3911.15 1994-10-19,3917.54,3958.25,3887.93,3936.04,317030000,3936.04 1994-10-18,3923.93,3945.46,3892.64,3917.54,259730000,3917.54 1994-10-17,3910.47,3949.84,3883.22,3923.93,238490000,3923.93 1994-10-14,3889.95,3927.97,3862.03,3910.47,251770000,3910.47 1994-10-13,3878.18,3944.12,3878.18,3889.95,337900000,3889.95 1994-10-12,3876.83,3903.07,3844.87,3875.15,269550000,3875.15 1994-10-11,3828.96,3898.36,3828.96,3876.83,355540000,3876.83 1994-10-10,3797.43,3843.19,3789.36,3821.32,213110000,3821.32 1994-10-07,3775.56,3826.03,3754.37,3797.43,284230000,3797.43 1994-10-06,3787.34,3810.89,3752.01,3775.56,272620000,3775.56 1994-10-05,3801.13,3811.56,3736.20,3787.34,359670000,3787.34 1994-10-04,3846.89,3873.47,3788.35,3801.13,325620000,3801.13 1994-10-03,3843.19,3872.79,3809.21,3846.89,269130000,3846.89 1994-09-30,3854.63,3891.30,3825.69,3843.19,292060000,3843.19 1994-09-29,3878.18,3892.64,3834.78,3854.63,302280000,3854.63 1994-09-28,3863.04,3904.76,3854.29,3878.18,330020000,3878.18 1994-09-27,3849.24,3891.63,3828.72,3863.04,290330000,3863.04 1994-09-26,3831.75,3869.43,3804.50,3849.24,272530000,3849.24 1994-09-23,3837.13,3872.46,3806.51,3831.75,300060000,3831.75 1994-09-22,3851.60,3882.55,3818.29,3837.13,305210000,3837.13 1994-09-21,3869.09,3886.92,3806.85,3851.60,355330000,3851.60 1994-09-20,3932.01,3932.01,3859.67,3869.09,326050000,3869.09 1994-09-19,3933.35,3972.72,3909.80,3936.72,272920000,3936.72 1994-09-16,3953.88,3955.22,3894.66,3933.35,410750000,3933.35 1994-09-15,3895.33,3959.93,3883.90,3953.88,281890000,3953.88 1994-09-14,3879.86,3918.55,3856.64,3895.33,297480000,3895.33 1994-09-13,3860.34,3905.76,3854.29,3879.86,293370000,3879.86 1994-09-12,3874.81,3897.02,3843.86,3860.34,244680000,3860.34 1994-09-09,3902.06,3902.06,3844.20,3874.81,293330000,3874.81 1994-09-08,3886.25,3926.96,3872.46,3908.46,295010000,3908.46 1994-09-07,3898.70,3920.90,3860.68,3886.25,292110000,3886.25 1994-09-06,3885.58,3918.55,3860.01,3898.70,199670000,3898.70 1994-09-02,3901.44,3928.64,3871.98,3885.58,216150000,3885.58 1994-09-01,3913.42,3927.99,3871.66,3901.44,285670000,3901.44 1994-08-31,3917.30,3954.54,3878.78,3913.42,354650000,3913.42 1994-08-30,3898.85,3934.14,3875.87,3917.30,294520000,3917.30 1994-08-29,3881.05,3927.34,3869.39,3898.85,266080000,3898.85 1994-08-26,3836.69,3903.38,3836.69,3881.05,305120000,3881.05 1994-08-25,3846.73,3872.63,3810.47,3829.89,284200000,3829.89 1994-08-24,3775.83,3852.56,3767.41,3846.73,309780000,3846.73 1994-08-23,3751.22,3804.64,3745.07,3775.83,307240000,3775.83 1994-08-22,3755.11,3771.94,3722.41,3751.22,235870000,3751.22 1994-08-19,3755.43,3785.22,3727.59,3755.11,276630000,3755.11 1994-08-18,3776.48,3793.63,3736.98,3755.43,287330000,3755.43 1994-08-17,3784.57,3812.09,3754.46,3776.48,309250000,3776.48 1994-08-16,3760.29,3804.64,3732.45,3784.57,306640000,3784.57 1994-08-15,3768.71,3798.17,3742.16,3760.29,223210000,3760.29 1994-08-12,3750.90,3792.66,3738.27,3768.71,249270000,3768.71 1994-08-11,3766.76,3785.86,3728.56,3750.90,275690000,3750.90 1994-08-10,3755.76,3793.97,3738.27,3766.76,279470000,3766.76 1994-08-09,3753.81,3773.89,3728.56,3755.76,259130000,3755.76 1994-08-08,3747.02,3780.68,3732.77,3753.81,217670000,3753.81 1994-08-05,3765.79,3769.68,3725.00,3747.02,230270000,3747.02 1994-08-04,3792.66,3807.23,3752.52,3765.79,289150000,3765.79 1994-08-03,3796.22,3815.00,3762.88,3792.66,283840000,3792.66 1994-08-02,3798.17,3829.25,3776.48,3796.22,294740000,3796.22 1994-08-01,3764.50,3809.50,3749.28,3798.17,258180000,3798.17 1994-07-29,3730.83,3782.63,3726.30,3764.50,269560000,3764.50 1994-07-28,3720.47,3757.05,3701.69,3730.83,247560000,3730.83 1994-07-27,3735.68,3753.17,3693.27,3720.47,251680000,3720.47 1994-07-26,3741.84,3764.50,3706.22,3735.68,232670000,3735.68 1994-07-25,3735.04,3762.56,3713.67,3741.84,213470000,3741.84 1994-07-22,3732.45,3765.15,3707.19,3735.04,261090000,3735.04 1994-07-21,3727.27,3763.20,3684.86,3732.45,292080000,3732.45 1994-07-20,3748.31,3763.85,3706.22,3727.27,267740000,3727.27 1994-07-19,3755.43,3777.45,3726.30,3748.31,251530000,3748.31 1994-07-18,3753.81,3779.39,3725.00,3755.43,227480000,3755.43 1994-07-15,3739.25,3769.35,3712.37,3753.81,275860000,3753.81 1994-07-14,3704.28,3763.53,3697.48,3739.25,319950000,3739.25 1994-07-13,3702.66,3738.27,3683.89,3704.28,265840000,3704.28 1994-07-12,3702.99,3725.32,3660.90,3702.66,252250000,3702.66 1994-07-11,3709.14,3735.36,3671.26,3702.99,222580000,3702.99 1994-07-08,3688.42,3728.56,3660.90,3709.14,236520000,3709.14 1994-07-07,3674.50,3714.32,3656.04,3688.42,258500000,3688.42 1994-07-06,3652.48,3698.13,3631.11,3674.50,233640000,3674.50 1994-07-05,3646.65,3680.65,3621.73,3652.48,193030000,3652.48 1994-07-01,3624.96,3675.79,3611.04,3646.65,199030000,3646.65 1994-06-30,3667.05,3683.56,3610.72,3624.96,294110000,3624.96 1994-06-29,3669.64,3711.73,3645.68,3667.05,263890000,3667.05 1994-06-28,3685.50,3705.58,3630.47,3669.64,265880000,3669.64 1994-06-27,3636.94,3694.89,3603.92,3685.50,250110000,3685.50 1994-06-24,3699.09,3704.80,3616.65,3636.94,261260000,3636.94 1994-06-23,3724.77,3752.04,3685.14,3699.09,256480000,3699.09 1994-06-22,3707.97,3748.87,3694.65,3724.77,251110000,3724.77 1994-06-21,3741.90,3756.80,3674.68,3707.97,298730000,3707.97 1994-06-20,3771.07,3771.07,3716.21,3741.90,229520000,3741.90 1994-06-17,3811.34,3821.48,3764.09,3776.78,373400000,3776.78 1994-06-16,3790.41,3830.68,3771.39,3811.34,254880000,3811.34 1994-06-15,3814.83,3838.29,3772.02,3790.41,269740000,3790.41 1994-06-14,3783.12,3839.88,3769.17,3814.83,288550000,3814.83 1994-06-13,3773.45,3808.17,3741.26,3783.12,242810000,3783.12 1994-06-10,3753.14,3798.68,3739.60,3773.45,222480000,3773.45 1994-06-09,3749.45,3776.83,3721.45,3753.14,252870000,3753.14 1994-06-08,3755.91,3780.83,3721.45,3749.45,256000000,3749.45 1994-06-07,3768.52,3788.52,3733.14,3755.91,234680000,3755.91 1994-06-06,3772.22,3812.83,3749.75,3768.52,259080000,3768.52 1994-06-03,3758.99,3792.22,3732.52,3772.22,271490000,3772.22 1994-06-02,3760.83,3791.60,3732.83,3758.99,271150000,3758.99 1994-06-01,3758.37,3785.14,3717.76,3760.83,279910000,3760.83 1994-05-31,3757.14,3783.29,3725.45,3758.37,216700000,3758.37 1994-05-27,3753.46,3772.18,3723.68,3757.14,186430000,3757.14 1994-05-26,3755.30,3785.07,3722.76,3753.46,255740000,3753.46 1994-05-25,3745.17,3768.19,3710.79,3755.30,254410000,3755.30 1994-05-24,3742.41,3786.30,3707.11,3745.17,280040000,3745.17 1994-05-23,3766.35,3770.65,3711.40,3742.41,249390000,3742.41 1994-05-20,3758.98,3788.76,3721.84,3766.35,295180000,3766.35 1994-05-19,3732.89,3782.92,3707.72,3758.98,303680000,3758.98 1994-05-18,3720.61,3761.74,3683.16,3732.89,339280000,3732.89 1994-05-17,3671.50,3735.04,3653.70,3720.61,311280000,3720.61 1994-05-16,3659.68,3694.21,3628.52,3671.50,234680000,3671.50 1994-05-13,3652.84,3690.32,3626.37,3659.68,252070000,3659.68 1994-05-12,3629.04,3682.58,3624.58,3652.84,272770000,3652.84 1994-05-11,3656.41,3678.42,3609.71,3629.04,277400000,3629.04 1994-05-10,3629.04,3684.67,3627.56,3656.41,297660000,3656.41 1994-05-09,3669.50,3676.93,3612.39,3629.04,250870000,3629.04 1994-05-06,3695.97,3702.51,3625.77,3669.50,291910000,3669.50 1994-05-05,3697.75,3731.07,3675.45,3695.97,255690000,3695.97 1994-05-04,3714.41,3730.17,3670.09,3697.75,267940000,3697.75 1994-05-03,3701.02,3736.12,3669.79,3714.41,288270000,3714.41 1994-05-02,3681.69,3734.04,3648.97,3701.02,296130000,3701.02 1994-04-29,3668.31,3707.87,3642.13,3681.69,293970000,3681.69 1994-04-28,3699.54,3723.93,3640.35,3668.31,325200000,3668.31 1994-04-26,3705.78,3733.15,3669.79,3699.54,288110000,3699.54 1994-04-25,3648.68,3721.25,3635.29,3705.78,262220000,3705.78 1994-04-22,3652.54,3690.32,3621.01,3648.68,295710000,3648.68 1994-04-21,3598.71,3673.96,3582.94,3652.54,378760000,3652.54 1994-04-20,3619.82,3662.66,3546.65,3598.71,366540000,3598.71 1994-04-19,3620.42,3670.39,3567.18,3619.82,323280000,3619.82 1994-04-18,3661.47,3679.61,3593.35,3620.42,271450000,3620.42 1994-04-15,3663.25,3699.24,3631.13,3661.47,309550000,3661.47 1994-04-14,3661.47,3693.59,3625.77,3663.25,275130000,3663.25 1994-04-13,3681.69,3704.30,3616.85,3661.47,278030000,3661.47 1994-04-12,3688.83,3722.14,3661.17,3681.69,256250000,3681.69 1994-04-11,3674.26,3720.66,3651.35,3688.83,243180000,3688.83 1994-04-08,3693.26,3717.15,3644.03,3674.26,264090000,3674.26 1994-04-07,3679.73,3712.83,3642.59,3693.26,289280000,3693.26 1994-04-06,3675.41,3722.34,3643.16,3679.73,302000000,3679.73 1994-04-05,3625.02,3698.15,3625.02,3675.41,366890000,3675.41 1994-04-04,3633.08,3633.08,3520.80,3593.35,344390000,3593.35 1994-03-31,3626.75,3673.10,3544.12,3635.96,403580000,3635.96 1994-03-30,3699.02,3718.88,3612.36,3626.75,390520000,3626.75 1994-03-29,3762.35,3771.86,3689.23,3699.02,301630000,3699.02 1994-03-28,3774.73,3793.45,3719.74,3762.35,287360000,3762.35 1994-03-25,3821.09,3845.85,3764.66,3774.73,249670000,3774.73 1994-03-24,3865.42,3865.42,3792.58,3821.09,303800000,3821.09 1994-03-23,3862.55,3901.41,3839.80,3869.46,284600000,3869.46 1994-03-22,3864.85,3896.23,3840.66,3862.55,283090000,3862.55 1994-03-21,3895.65,3898.25,3838.65,3864.85,247390000,3864.85 1994-03-18,3865.14,3911.78,3838.65,3895.65,462240000,3895.65 1994-03-17,3848.15,3891.34,3821.66,3865.14,303950000,3865.14 1994-03-16,3849.59,3879.53,3819.94,3848.15,306820000,3848.15 1994-03-15,3862.98,3888.46,3826.85,3849.59,329260000,3849.59 1994-03-14,3862.70,3894.21,3835.96,3862.98,260160000,3862.98 1994-03-11,3830.62,3872.83,3806.69,3862.70,303250000,3862.70 1994-03-10,3853.41,3865.51,3801.63,3830.62,369370000,3830.62 1994-03-09,3851.72,3874.52,3817.95,3853.41,309810000,3853.41 1994-03-08,3856.22,3881.55,3822.45,3851.72,298110000,3851.72 1994-03-07,3832.30,3882.40,3824.71,3856.22,285580000,3856.22 1994-03-04,3824.42,3868.04,3800.50,3832.30,311850000,3832.30 1994-03-03,3831.74,3862.13,3784.74,3824.42,291960000,3824.42 1994-03-02,3809.23,3845.25,3741.69,3831.74,361130000,3831.74 1994-03-01,3832.02,3848.34,3772.93,3809.23,304450000,3809.23 1994-02-28,3838.78,3874.52,3817.95,3832.02,267610000,3832.02 1994-02-25,3839.90,3868.89,3811.76,3838.78,273680000,3838.78 1994-02-24,3891.68,3895.62,3823.86,3839.90,342940000,3839.90 1994-02-23,3911.66,3931.36,3872.83,3891.68,309910000,3891.68 1994-02-22,3887.46,3928.83,3873.39,3911.66,270900000,3911.66 1994-02-18,3922.64,3931.92,3869.73,3887.46,293210000,3887.46 1994-02-17,3937.27,3975.82,3894.21,3922.64,340030000,3922.64 1994-02-16,3928.27,3964.85,3906.32,3937.27,295450000,3937.27 1994-02-15,3904.06,3950.50,3891.40,3928.27,306790000,3928.27 1994-02-14,3894.78,3935.86,3873.67,3904.06,263190000,3904.06 1994-02-11,3895.34,3920.95,3855.94,3894.78,213740000,3894.78 1994-02-10,3931.92,3953.59,3882.96,3895.34,327250000,3895.34 1994-02-09,3906.03,3956.97,3887.74,3931.92,332670000,3931.92 1994-02-08,3906.32,3951.34,3873.67,3906.03,318180000,3906.03 1994-02-07,3871.42,3923.48,3840.75,3906.32,348270000,3906.32 1994-02-04,3967.66,3979.76,3857.63,3871.42,378380000,3871.42 1994-02-03,3975.54,3995.52,3932.77,3967.66,318350000,3967.66 1994-02-02,3964.01,3997.78,3937.27,3975.54,328960000,3975.54 1994-02-01,3978.36,3998.06,3938.12,3964.01,322510000,3964.01 1994-01-31,3945.43,4002.84,3937.27,3978.36,322870000,3978.36 1994-01-28,3926.30,3971.89,3919.54,3945.43,313140000,3945.43 1994-01-27,3908.00,3951.34,3876.77,3926.30,346500000,3926.30 1994-01-26,3895.34,3934.74,3863.82,3908.00,304660000,3908.00 1994-01-25,3912.79,3937.55,3863.82,3895.34,326120000,3895.34 1994-01-24,3914.48,3947.68,3882.11,3912.79,296900000,3912.79 1994-01-21,3891.96,3933.33,3875.36,3914.48,346220000,3914.48 1994-01-20,3884.37,3915.60,3857.91,3891.96,310450000,3891.96 1994-01-19,3870.29,3909.97,3839.34,3884.37,311370000,3884.37 1994-01-18,3870.29,3905.19,3840.75,3870.29,309730000,3870.29 1994-01-17,3867.20,3896.75,3833.71,3870.29,233980000,3870.29 1994-01-14,3842.43,3891.96,3830.62,3867.20,304910000,3867.20 1994-01-13,3848.63,3864.67,3808.38,3842.43,277970000,3842.43 1994-01-12,3850.31,3876.49,3809.79,3848.63,310690000,3848.63 1994-01-11,3865.51,3885.21,3823.02,3850.31,305490000,3850.31 1994-01-10,3820.77,3874.52,3804.72,3865.51,319490000,3865.51 1994-01-07,3803.88,3842.15,3778.83,3820.77,324920000,3820.77 1994-01-06,3798.82,3843.84,3771.52,3803.88,367880000,3803.88 1994-01-05,3783.90,3821.33,3750.41,3798.82,400030000,3798.82 1994-01-04,3756.60,3798.25,3718.89,3783.90,326600000,3783.90 1994-01-03,3754.09,3790.70,3715.24,3756.60,270140000,3756.60 1993-12-31,3775.88,3804.11,3745.15,3754.09,168590000,3754.09 1993-12-30,3794.33,3806.34,3759.96,3775.88,195860000,3775.88 1993-12-29,3793.77,3818.92,3764.43,3794.33,269570000,3794.33 1993-12-28,3792.93,3813.33,3766.10,3793.77,200960000,3793.77 1993-12-27,3757.72,3804.95,3750.46,3792.93,171200000,3792.93 1993-12-23,3762.19,3791.81,3739.56,3757.72,227240000,3757.72 1993-12-22,3745.15,3780.64,3725.86,3762.19,272440000,3762.19 1993-12-21,3755.21,3774.77,3719.72,3745.15,273370000,3745.15 1993-12-20,3751.57,3777.84,3728.66,3755.21,255900000,3755.21 1993-12-17,3726.14,3768.90,3706.30,3751.57,363750000,3751.57 1993-12-16,3716.92,3756.60,3694.29,3726.14,284620000,3726.14 1993-12-15,3742.63,3776.44,3689.82,3716.92,331770000,3716.92 1993-12-14,3764.43,3785.94,3726.70,3742.63,275050000,3742.63 1993-12-13,3740.67,3780.64,3711.33,3764.43,256580000,3764.43 1993-12-10,3729.78,3758.84,3704.91,3740.67,245620000,3740.67 1993-12-09,3734.53,3764.43,3709.94,3729.78,287570000,3729.78 1993-12-08,3718.88,3756.60,3693.73,3734.53,314460000,3734.53 1993-12-07,3710.21,3746.82,3689.26,3718.88,285690000,3718.88 1993-12-06,3704.07,3738.44,3687.30,3710.21,292370000,3710.21 1993-12-03,3702.11,3726.98,3677.80,3704.07,268780000,3704.07 1993-12-02,3697.08,3725.30,3674.17,3702.11,256370000,3702.11 1993-12-01,3683.95,3731.73,3673.33,3697.08,293870000,3697.08 1993-11-30,3677.80,3713.57,3654.88,3683.95,290250000,3683.95 1993-11-29,3683.95,3721.67,3654.33,3677.80,272710000,3677.80 1993-11-26,3687.58,3703.23,3660.75,3683.95,90220000,3683.95 1993-11-24,3674.17,3709.94,3654.33,3687.58,230630000,3687.58 1993-11-23,3670.25,3702.67,3649.57,3674.17,261590000,3674.17 1993-11-22,3694.01,3697.92,3627.78,3670.25,280130000,3670.25 1993-11-19,3685.34,3711.05,3640.07,3694.01,302970000,3694.01 1993-11-18,3704.35,3721.95,3649.57,3685.34,313480000,3685.34 1993-11-17,3710.77,3749.90,3660.19,3704.35,319560000,3704.35 1993-11-16,3677.52,3723.91,3653.77,3710.77,303980000,3710.77 1993-11-15,3684.51,3705.74,3653.49,3677.52,251030000,3677.52 1993-11-12,3662.43,3707.42,3648.74,3684.51,326240000,3684.51 1993-11-11,3663.55,3700.43,3638.40,3662.43,287110000,3662.43 1993-11-10,3640.07,3683.39,3616.60,3663.55,283450000,3663.55 1993-11-09,3647.90,3689.82,3623.03,3640.07,278290000,3640.07 1993-11-08,3643.43,3674.17,3621.91,3647.90,232340000,3647.90 1993-11-05,3624.98,3663.83,3585.86,3643.43,336900000,3643.43 1993-11-04,3661.87,3681.99,3609.61,3624.98,323430000,3624.98 1993-11-03,3697.64,3714.69,3638.40,3661.87,342110000,3661.87 1993-11-02,3692.61,3724.75,3656.00,3697.64,304780000,3697.64 1993-11-01,3680.59,3708.26,3656.84,3692.61,256030000,3692.61 1993-10-29,3687.86,3712.45,3656.84,3680.59,270570000,3680.59 1993-10-28,3664.66,3713.57,3657.96,3687.86,301220000,3687.86 1993-10-27,3672.49,3692.33,3634.76,3664.66,279830000,3664.66 1993-10-26,3673.61,3701.55,3636.16,3672.49,284530000,3672.49 1993-10-25,3649.30,3694.01,3618.28,3673.61,260310000,3673.61 1993-10-22,3636.16,3697.92,3612.13,3649.30,301440000,3649.30 1993-10-21,3645.10,3678.36,3610.45,3636.16,288820000,3636.16 1993-10-20,3635.32,3667.74,3611.85,3645.10,305670000,3645.10 1993-10-19,3642.31,3669.97,3601.79,3635.32,304400000,3635.32 1993-10-18,3629.73,3675.84,3595.92,3642.31,329570000,3642.31 1993-10-15,3621.63,3664.66,3596.20,3629.73,366110000,3629.73 1993-10-14,3603.19,3652.09,3574.40,3621.63,353820000,3621.63 1993-10-13,3593.13,3628.34,3574.12,3603.19,290930000,3603.19 1993-10-12,3593.41,3619.39,3575.24,3593.13,263970000,3593.13 1993-10-11,3584.74,3611.01,3565.46,3593.41,183190000,3593.41 1993-10-08,3583.63,3610.45,3550.37,3584.74,243590000,3584.74 1993-10-07,3598.99,3616.32,3564.90,3583.63,255210000,3583.63 1993-10-06,3587.26,3620.79,3570.21,3598.99,277070000,3598.99 1993-10-05,3577.76,3616.88,3553.72,3587.26,294570000,3587.26 1993-10-04,3581.11,3603.19,3550.65,3577.76,229380000,3577.76 1993-10-01,3555.12,3605.14,3541.71,3581.11,256870000,3581.11 1993-09-30,3566.30,3583.63,3528.57,3555.12,282740000,3555.12 1993-09-29,3566.02,3596.48,3538.91,3566.30,277690000,3566.30 1993-09-28,3567.70,3589.49,3542.27,3566.02,243320000,3566.02 1993-09-27,3543.11,3589.49,3539.47,3567.70,244920000,3567.70 1993-09-24,3539.75,3568.54,3507.90,3543.11,248270000,3543.11 1993-09-23,3547.02,3567.70,3513.48,3539.75,276660000,3539.75 1993-09-22,3537.24,3577.20,3516.56,3547.02,288960000,3547.02 1993-09-21,3575.80,3592.57,3501.47,3537.24,301740000,3537.24 1993-09-20,3613.25,3632.81,3567.98,3575.80,228040000,3575.80 1993-09-17,3630.85,3637.28,3580.83,3613.25,369600000,3613.25 1993-09-16,3633.65,3651.25,3601.79,3630.85,229700000,3630.85 1993-09-15,3615.76,3644.82,3573.84,3633.65,294410000,3633.65 1993-09-14,3634.21,3639.51,3590.61,3615.76,258620000,3615.76 1993-09-13,3621.63,3655.16,3607.66,3634.21,244970000,3634.21 1993-09-10,3589.49,3636.16,3576.64,3621.63,269950000,3621.63 1993-09-09,3588.93,3616.04,3556.52,3589.49,258070000,3589.49 1993-09-08,3607.10,3623.31,3561.27,3588.93,283100000,3588.93 1993-09-07,3633.93,3649.02,3592.57,3607.10,229500000,3607.10 1993-09-03,3626.10,3651.53,3603.75,3633.93,197160000,3633.93 1993-09-02,3645.10,3660.19,3610.17,3626.10,259870000,3626.10 1993-09-01,3651.25,3665.50,3619.67,3645.10,245040000,3645.10 1993-08-31,3643.99,3662.99,3619.67,3651.25,252830000,3651.25 1993-08-30,3640.63,3667.46,3621.63,3643.99,194180000,3643.99 1993-08-27,3648.18,3656.28,3606.54,3640.63,196140000,3640.63 1993-08-26,3652.09,3681.71,3620.23,3648.18,247800000,3648.18 1993-08-25,3638.96,3674.17,3620.51,3652.09,301640000,3652.09 1993-08-24,3605.98,3648.18,3590.89,3638.96,270700000,3638.96 1993-08-23,3615.48,3630.57,3578.87,3605.98,212500000,3605.98 1993-08-20,3612.13,3633.37,3580.55,3615.48,276800000,3615.48 1993-08-19,3604.86,3632.25,3581.11,3612.13,293330000,3612.13 1993-08-18,3586.98,3638.96,3577.48,3604.86,312940000,3604.86 1993-08-17,3579.15,3611.29,3554.00,3586.98,261320000,3586.98 1993-08-16,3569.65,3606.54,3547.86,3579.15,229190000,3579.15 1993-08-13,3569.09,3594.52,3547.58,3569.65,214370000,3569.65 1993-08-12,3583.35,3607.66,3537.24,3569.09,278530000,3569.09 1993-08-11,3572.73,3607.94,3550.93,3583.35,268330000,3583.35 1993-08-10,3576.08,3599.83,3547.02,3572.73,255520000,3572.73 1993-08-09,3560.43,3599.83,3541.15,3576.08,232750000,3576.08 1993-08-06,3548.97,3583.35,3536.12,3560.43,221150000,3560.43 1993-08-05,3552.05,3574.12,3523.54,3548.97,249650000,3548.97 1993-08-04,3561.27,3579.99,3530.25,3552.05,230040000,3552.05 1993-08-03,3560.99,3588.10,3530.81,3561.27,254540000,3561.27 1993-08-02,3539.47,3578.32,3524.94,3560.99,230380000,3560.99 1993-07-30,3567.42,3581.11,3515.72,3539.47,254420000,3539.47 1993-07-29,3553.45,3593.41,3531.37,3567.42,261240000,3567.42 1993-07-28,3565.46,3588.38,3524.38,3553.45,270530000,3553.45 1993-07-27,3567.70,3604.86,3533.60,3565.46,256750000,3565.46 1993-07-26,3546.74,3584.74,3541.43,3567.70,223280000,3567.70 1993-07-23,3525.22,3569.37,3508.17,3546.74,222170000,3546.74 1993-07-22,3555.40,3566.86,3514.32,3525.22,249630000,3525.22 1993-07-21,3544.78,3573.84,3516.84,3555.40,278590000,3555.40 1993-07-20,3535.28,3567.98,3500.63,3544.78,275130000,3544.78 1993-07-19,3528.29,3563.23,3502.87,3535.28,216370000,3535.28 1993-07-16,3550.93,3571.61,3505.94,3528.29,263100000,3528.29 1993-07-15,3542.55,3573.01,3509.85,3550.93,277810000,3550.93 1993-07-14,3515.44,3562.39,3507.34,3542.55,285890000,3542.55 1993-07-13,3524.38,3544.22,3491.13,3515.44,236720000,3515.44 1993-07-12,3521.06,3545.97,3495.04,3524.38,202310000,3524.38 1993-07-09,3514.42,3544.03,3490.89,3521.06,235210000,3521.06 1993-07-08,3475.67,3532.41,3462.38,3514.42,282910000,3514.42 1993-07-07,3449.93,3495.32,3443.28,3475.67,253170000,3475.67 1993-07-06,3483.97,3502.52,3443.28,3449.93,233420000,3449.93 1993-07-02,3510.54,3511.65,3468.47,3483.97,220750000,3483.97 1993-07-01,3516.08,3542.10,3487.29,3510.54,292040000,3510.54 1993-06-30,3518.85,3543.48,3493.11,3516.08,281120000,3516.08 1993-06-29,3530.20,3544.87,3491.44,3518.85,276310000,3518.85 1993-06-28,3490.89,3542.10,3486.74,3530.20,242090000,3530.20 1993-06-25,3490.61,3512.76,3473.18,3490.89,210430000,3490.89 1993-06-24,3466.81,3504.73,3449.10,3490.61,267450000,3490.61 1993-06-23,3497.53,3514.14,3445.77,3466.81,278260000,3466.81 1993-06-22,3510.82,3530.47,3474.28,3497.53,259530000,3497.53 1993-06-21,3494.77,3531.86,3477.05,3510.82,223650000,3510.82 1993-06-18,3521.89,3539.88,3478.44,3494.77,300500000,3494.77 1993-06-17,3511.65,3539.61,3488.68,3521.89,239810000,3521.89 1993-06-16,3492.00,3527.43,3460.17,3511.65,264500000,3511.65 1993-06-15,3514.69,3531.86,3471.24,3492.00,234110000,3492.00 1993-06-14,3505.01,3538.22,3486.74,3514.69,210440000,3514.69 1993-06-11,3491.72,3536.01,3479.27,3505.01,255200000,3505.01 1993-06-10,3511.93,3538.22,3461.83,3491.72,231760000,3491.72 1993-06-09,3510.54,3541.54,3490.89,3511.93,249030000,3511.93 1993-06-08,3532.13,3543.48,3494.21,3510.54,238170000,3510.54 1993-06-07,3545.14,3570.33,3510.82,3532.13,236930000,3532.13 1993-06-04,3544.87,3563.13,3516.63,3545.14,226440000,3545.14 1993-06-03,3553.45,3569.50,3516.08,3544.87,285770000,3544.87 1993-06-02,3552.34,3576.14,3530.47,3553.45,287120000,3553.45 1993-06-01,3527.43,3577.25,3518.02,3552.34,229690000,3552.34 1993-05-28,3554.83,3563.41,3503.62,3527.43,207820000,3527.43 1993-05-27,3540.16,3582.23,3523.28,3554.83,300810000,3554.83 1993-05-26,3516.63,3558.98,3498.64,3540.16,274230000,3540.16 1993-05-25,3507.78,3534.35,3486.46,3516.63,222090000,3516.63 1993-05-24,3492.83,3526.60,3472.35,3507.78,197990000,3507.78 1993-05-21,3523.28,3532.96,3468.47,3492.83,279120000,3492.83 1993-05-20,3500.03,3539.88,3475.39,3523.28,289160000,3523.28 1993-05-19,3444.39,3511.10,3405.09,3500.03,342420000,3500.03 1993-05-18,3449.93,3468.47,3409.79,3444.39,264300000,3444.39 1993-05-17,3443.01,3465.98,3421.42,3449.93,227580000,3449.93 1993-05-14,3447.99,3472.62,3421.14,3443.01,252910000,3443.01 1993-05-13,3482.31,3486.74,3428.89,3447.99,289860000,3447.99 1993-05-12,3468.75,3501.69,3441.07,3482.31,255680000,3482.31 1993-05-11,3443.28,3481.76,3421.42,3468.75,218480000,3468.75 1993-05-10,3437.19,3485.36,3420.31,3443.28,235580000,3443.28 1993-05-07,3441.90,3468.47,3413.67,3437.19,223570000,3437.19 1993-05-06,3449.10,3475.67,3418.93,3441.90,255460000,3441.90 1993-05-05,3446.19,3478.44,3420.86,3449.10,270960000,3449.10 1993-05-04,3446.46,3477.26,3424.04,3446.19,268310000,3446.19 1993-05-03,3427.55,3462.94,3402.42,3446.46,220860000,3446.46 1993-04-30,3425.12,3466.18,3405.39,3427.55,247460000,3427.55 1993-04-29,3413.50,3444.57,3377.57,3425.12,249760000,3425.12 1993-04-28,3415.93,3445.65,3372.16,3413.50,267980000,3413.50 1993-04-27,3398.37,3433.76,3364.06,3415.93,284140000,3415.93 1993-04-26,3413.77,3442.14,3369.46,3398.37,281180000,3398.37 1993-04-23,3429.17,3448.08,3385.67,3413.77,259810000,3413.77 1993-04-22,3439.44,3488.33,3399.99,3429.17,310390000,3429.17 1993-04-21,3443.49,3480.77,3401.34,3439.44,287300000,3439.44 1993-04-20,3466.99,3486.71,3400.26,3443.49,317990000,3443.49 1993-04-19,3478.61,3499.41,3436.73,3466.99,244710000,3466.99 1993-04-16,3455.92,3498.60,3437.54,3478.61,305160000,3478.61 1993-04-15,3455.64,3484.28,3419.17,3455.92,259500000,3455.92 1993-04-14,3444.03,3482.93,3424.85,3455.64,257340000,3455.64 1993-04-13,3428.09,3469.42,3408.37,3444.03,286690000,3444.03 1993-04-12,3396.48,3452.67,3391.08,3428.09,259610000,3428.09 1993-04-08,3397.02,3429.44,3357.58,3396.48,282470000,3396.48 1993-04-07,3377.57,3417.55,3355.41,3397.02,296290000,3397.02 1993-04-06,3379.19,3417.55,3339.75,3377.57,289640000,3377.57 1993-04-05,3370.81,3411.61,3338.39,3379.19,238630000,3379.19 1993-04-02,3439.44,3439.44,3344.88,3370.81,323330000,3370.81 1993-04-01,3435.11,3466.45,3419.98,3439.44,231950000,3439.44 1993-03-31,3457.27,3484.82,3424.85,3435.11,275710000,3435.11 1993-03-30,3455.10,3481.58,3422.14,3457.27,233330000,3457.27 1993-03-29,3439.98,3487.52,3431.87,3455.10,199890000,3455.10 1993-03-26,3461.32,3484.28,3428.63,3439.98,219130000,3439.98 1993-03-25,3445.38,3485.36,3429.44,3461.32,250040000,3461.32 1993-03-24,3461.86,3489.14,3417.55,3445.38,271980000,3445.38 1993-03-23,3463.48,3488.60,3438.08,3461.86,231900000,3461.86 1993-03-22,3471.58,3483.20,3429.71,3463.48,231900000,3463.48 1993-03-19,3465.64,3493.20,3446.46,3471.58,295510000,3471.58 1993-03-18,3427.01,3486.17,3427.01,3465.64,240490000,3465.64 1993-03-17,3442.95,3458.35,3408.91,3426.74,237850000,3426.74 1993-03-16,3442.41,3466.72,3423.77,3442.95,217730000,3442.95 1993-03-15,3427.82,3460.51,3412.96,3442.41,192690000,3442.41 1993-03-12,3447.81,3447.81,3385.40,3427.82,244740000,3427.82 1993-03-11,3478.34,3493.74,3437.27,3457.00,250720000,3457.00 1993-03-10,3472.12,3497.25,3432.68,3478.34,255190000,3478.34 1993-03-09,3469.42,3495.63,3443.76,3472.12,290120000,3472.12 1993-03-08,3404.58,3476.99,3401.34,3469.42,274560000,3469.42 1993-03-05,3398.91,3447.81,3373.25,3404.58,247130000,3404.58 1993-03-04,3404.04,3423.77,3372.43,3398.91,230780000,3398.91 1993-03-03,3400.53,3437.00,3368.92,3404.04,272290000,3404.04 1993-03-02,3355.41,3406.21,3334.07,3400.53,269640000,3400.53 1993-03-01,3370.81,3404.58,3336.23,3355.41,232090000,3355.41 1993-02-26,3365.14,3396.21,3336.23,3370.81,234160000,3370.81 1993-02-25,3356.50,3387.02,3326.78,3365.14,252790000,3365.14 1993-02-24,3323.27,3371.89,3311.92,3356.50,298940000,3356.50 1993-02-23,3342.99,3373.52,3296.79,3323.27,321050000,3323.27 1993-02-22,3322.18,3367.30,3297.06,3342.99,311510000,3342.99 1993-02-19,3302.19,3347.04,3270.04,3322.18,307960000,3322.18 1993-02-18,3312.19,3362.98,3262.48,3302.19,309570000,3302.19 1993-02-17,3309.49,3338.12,3273.56,3312.19,287240000,3312.19 1993-02-16,3391.62,3391.62,3285.98,3309.49,325350000,3309.49 1993-02-12,3422.69,3436.19,3378.11,3392.43,215750000,3392.43 1993-02-11,3412.42,3455.64,3395.94,3422.69,252780000,3422.69 1993-02-10,3414.58,3438.08,3379.19,3412.42,248480000,3412.42 1993-02-09,3437.54,3449.16,3394.86,3414.58,233670000,3414.58 1993-02-08,3442.14,3472.94,3408.91,3437.54,240350000,3437.54 1993-02-05,3416.74,3463.21,3391.62,3442.14,320960000,3442.14 1993-02-04,3373.79,3441.33,3367.03,3416.74,346480000,3416.74 1993-02-03,3328.67,3397.83,3322.73,3373.79,342530000,3373.79 1993-02-02,3332.18,3355.68,3300.30,3328.67,269250000,3328.67 1993-02-01,3310.03,3343.80,3300.30,3332.18,238260000,3332.18 1993-01-29,3306.25,3331.10,3287.06,3310.03,247010000,3310.03 1993-01-28,3291.39,3327.86,3270.58,3306.25,255550000,3306.25 1993-01-27,3298.95,3318.67,3260.05,3291.39,276880000,3291.39 1993-01-26,3292.20,3331.91,3272.47,3298.95,311450000,3298.95 1993-01-25,3256.81,3324.89,3243.84,3292.20,288010000,3292.20 1993-01-22,3253.02,3292.74,3225.74,3256.81,293250000,3256.81 1993-01-21,3241.95,3269.77,3219.25,3253.02,257350000,3253.02 1993-01-20,3255.99,3278.96,3231.41,3241.95,267740000,3241.95 1993-01-19,3274.91,3299.49,3239.52,3255.99,283110000,3255.99 1993-01-18,3271.12,3296.52,3244.65,3274.91,195980000,3274.91 1993-01-15,3267.88,3300.03,3238.70,3271.12,306140000,3271.12 1993-01-14,3263.56,3297.06,3230.87,3267.88,280020000,3267.88 1993-01-13,3264.64,3283.01,3225.47,3263.56,245020000,3263.56 1993-01-12,3262.75,3287.06,3229.79,3264.64,239250000,3264.64 1993-01-11,3251.67,3284.09,3228.98,3262.75,217150000,3262.75 1993-01-08,3268.96,3280.31,3221.68,3251.67,262620000,3251.67 1993-01-07,3305.16,3333.51,3250.30,3268.96,303140000,3268.96 1993-01-06,3307.87,3330.29,3276.53,3305.16,282850000,3305.16 1993-01-05,3309.22,3338.12,3279.23,3307.87,240550000,3307.87 1993-01-04,3301.11,3335.69,3282.74,3309.22,199680000,3309.22 1992-12-31,3321.10,3340.29,3294.36,3301.11,165910000,3301.11 1992-12-30,3310.84,3340.54,3298.93,3321.10,183870000,3321.10 1992-12-29,3333.26,3364.87,3295.71,3310.84,213660000,3310.84 1992-12-28,3326.24,3352.17,3297.33,3333.26,143800000,3333.26 1992-12-24,3313.54,3339.48,3303.27,3326.24,95240000,3326.24 1992-12-23,3321.10,3350.55,3293.01,3313.54,227980000,3313.54 1992-12-22,3312.46,3347.04,3286.52,3321.10,249670000,3321.10 1992-12-21,3313.27,3335.15,3280.85,3312.46,224680000,3312.46 1992-12-18,3269.23,3325.97,3261.94,3313.27,371080000,3313.27 1992-12-17,3255.18,3289.22,3229.79,3269.23,248590000,3269.23 1992-12-16,3284.36,3303.00,3238.70,3255.18,241910000,3255.18 1992-12-15,3292.20,3313.27,3260.32,3284.36,225880000,3284.36 1992-12-14,3304.08,3324.89,3279.50,3292.20,183510000,3292.20 1992-12-11,3312.19,3324.89,3280.85,3304.08,164450000,3304.08 1992-12-10,3323.81,3335.69,3285.17,3312.19,240570000,3312.19 1992-12-09,3322.18,3345.42,3295.98,3323.81,229980000,3323.81 1992-12-08,3307.33,3331.64,3288.41,3322.18,234300000,3322.18 1992-12-07,3288.68,3320.56,3274.37,3307.33,217700000,3307.33 1992-12-04,3276.53,3312.46,3261.40,3288.68,227410000,3288.68 1992-12-03,3286.25,3302.73,3252.48,3276.53,238200000,3276.53 1992-12-02,3294.36,3310.84,3265.18,3286.25,241860000,3286.25 1992-12-01,3305.16,3321.64,3268.15,3294.36,259020000,3294.36 1992-11-30,3282.20,3326.51,3270.31,3305.16,230190000,3305.16 1992-11-27,3266.26,3304.62,3259.24,3282.20,106010000,3282.20 1992-11-25,3248.70,3285.71,3243.57,3266.26,206700000,3266.26 1992-11-24,3223.04,3277.88,3209.26,3248.70,241520000,3248.70 1992-11-23,3227.36,3251.13,3196.83,3223.04,190490000,3223.04 1992-11-20,3209.53,3249.78,3197.64,3227.36,256340000,3227.36 1992-11-19,3207.37,3231.14,3190.35,3209.53,218700000,3209.53 1992-11-18,3193.32,3227.09,3176.84,3207.37,218660000,3207.37 1992-11-17,3205.74,3223.31,3178.19,3193.32,185460000,3193.32 1992-11-16,3233.03,3241.41,3191.16,3205.74,173500000,3205.74 1992-11-13,3239.79,3252.48,3211.69,3233.03,192320000,3233.03 1992-11-12,3240.33,3264.91,3214.39,3239.79,224580000,3239.79 1992-11-11,3225.47,3258.70,3203.31,3240.33,240160000,3240.33 1992-11-10,3240.87,3268.15,3212.50,3225.47,221990000,3225.47 1992-11-09,3240.06,3268.69,3220.33,3240.87,197490000,3240.87 1992-11-06,3243.84,3264.64,3216.82,3240.06,204870000,3240.06 1992-11-05,3223.04,3259.78,3203.58,3243.84,219580000,3243.84 1992-11-04,3252.48,3274.10,3211.15,3223.04,193980000,3223.04 1992-11-03,3262.21,3285.44,3226.82,3252.48,209740000,3252.48 1992-11-02,3226.28,3274.64,3211.42,3262.21,201620000,3262.21 1992-10-30,3246.27,3260.59,3208.45,3226.28,196740000,3226.28 1992-10-29,3251.40,3268.96,3224.39,3246.27,206300000,3246.27 1992-10-28,3235.73,3262.48,3210.34,3251.40,203040000,3251.40 1992-10-27,3244.11,3270.31,3210.88,3235.73,201650000,3235.73 1992-10-26,3207.64,3256.00,3190.08,3244.11,187950000,3244.11 1992-10-23,3200.88,3233.57,3175.76,3207.64,197400000,3207.64 1992-10-22,3187.10,3221.14,3162.79,3200.88,216080000,3200.88 1992-10-21,3186.02,3211.69,3163.33,3187.10,218990000,3187.10 1992-10-20,3188.45,3223.31,3166.30,3186.02,258220000,3186.02 1992-10-19,3174.41,3211.15,3154.14,3188.45,218670000,3188.45 1992-10-16,3174.68,3203.85,3123.89,3174.41,234480000,3174.41 1992-10-15,3195.48,3207.37,3147.66,3174.68,206990000,3174.68 1992-10-14,3201.42,3225.74,3167.11,3195.48,175440000,3195.48 1992-10-13,3174.41,3222.77,3159.28,3201.42,184120000,3201.42 1992-10-12,3140.10,3186.29,3140.10,3174.41,126590000,3174.41 1992-10-09,3176.03,3176.57,3120.37,3136.58,178910000,3136.58 1992-10-08,3152.25,3195.48,3135.77,3176.03,203780000,3176.03 1992-10-07,3178.19,3200.88,3136.85,3152.25,183100000,3152.25 1992-10-06,3179.00,3205.20,3147.93,3178.19,196660000,3178.19 1992-10-05,3197.10,3197.10,3087.41,3179.00,284380000,3179.00 1992-10-02,3254.37,3259.51,3193.59,3200.61,187400000,3200.61 1992-10-01,3271.66,3291.39,3237.35,3254.37,203670000,3254.37 1992-09-30,3266.80,3291.66,3247.89,3271.66,184220000,3271.66 1992-09-29,3276.26,3295.98,3247.08,3266.80,170350000,3266.80 1992-09-28,3250.32,3283.01,3226.55,3276.26,157480000,3276.26 1992-09-25,3287.87,3292.20,3226.82,3250.32,213650000,3250.32 1992-09-24,3278.69,3310.84,3267.07,3287.87,187770000,3287.87 1992-09-23,3280.85,3302.19,3252.48,3278.69,204350000,3278.69 1992-09-22,3320.83,3327.05,3270.58,3280.85,186990000,3280.85 1992-09-21,3327.05,3341.91,3295.71,3320.83,153900000,3320.83 1992-09-18,3315.70,3340.56,3297.87,3327.05,236080000,3327.05 1992-09-17,3319.21,3347.04,3292.20,3315.70,188270000,3315.70 1992-09-16,3327.32,3353.52,3288.95,3319.21,229380000,3319.21 1992-09-15,3372.98,3372.98,3317.86,3327.32,200220000,3327.32 1992-09-14,3326.51,3391.35,3326.51,3376.22,250900000,3376.22 1992-09-11,3305.16,3327.32,3285.44,3305.70,180640000,3305.70 1992-09-10,3271.39,3318.94,3261.67,3305.16,221970000,3305.16 1992-09-09,3260.59,3284.09,3244.92,3271.39,172890000,3271.39 1992-09-08,3281.93,3292.20,3245.73,3260.59,160930000,3260.59 1992-09-04,3292.20,3306.23,3265.16,3281.93,124340000,3281.93 1992-09-03,3290.31,3325.95,3268.94,3292.20,212540000,3292.20 1992-09-02,3266.26,3301.90,3249.76,3290.31,187010000,3290.31 1992-09-01,3257.35,3279.21,3239.77,3266.26,172650000,3266.26 1992-08-31,3267.61,3287.04,3244.90,3257.35,160230000,3257.35 1992-08-28,3254.64,3280.56,3237.88,3267.61,152230000,3267.61 1992-08-27,3246.81,3285.69,3237.06,3254.64,178490000,3254.64 1992-08-26,3232.22,3261.38,3217.07,3246.81,171840000,3246.81 1992-08-25,3228.17,3253.00,3200.86,3232.22,201630000,3232.22 1992-08-24,3254.09,3258.41,3207.08,3228.17,165130000,3228.17 1992-08-21,3304.89,3330.27,3243.55,3254.09,202640000,3254.09 1992-08-20,3307.06,3326.22,3285.69,3304.89,183390000,3304.89 1992-08-19,3329.48,3347.56,3302.44,3307.06,186710000,3307.06 1992-08-18,3324.89,3343.80,3309.22,3329.48,169880000,3329.48 1992-08-17,3328.94,3350.28,3310.03,3324.89,152750000,3324.89 1992-08-14,3313.27,3342.16,3300.82,3328.94,166180000,3328.94 1992-08-13,3320.83,3347.29,3294.07,3313.27,185700000,3313.27 1992-08-12,3331.10,3349.72,3304.88,3320.83,176440000,3320.83 1992-08-11,3337.58,3356.48,3304.34,3331.10,173810000,3331.10 1992-08-10,3332.18,3350.26,3305.14,3337.58,142440000,3337.58 1992-08-07,3340.56,3378.38,3318.40,3332.18,188340000,3332.18 1992-08-06,3365.14,3374.33,3327.05,3340.56,181390000,3340.56 1992-08-05,3384.32,3391.35,3347.58,3365.14,172410000,3365.14 1992-08-04,3395.40,3405.12,3367.03,3384.32,166740000,3384.32 1992-08-03,3393.78,3413.23,3365.14,3395.40,164360000,3395.40 1992-07-31,3391.89,3414.85,3371.89,3393.78,172920000,3393.78 1992-07-30,3379.19,3409.16,3351.07,3391.89,193280000,3391.89 1992-07-29,3345.13,3399.70,3345.13,3379.19,275760000,3379.19 1992-07-28,3282.20,3345.67,3277.59,3334.07,217950000,3334.07 1992-07-27,3285.71,3311.38,3265.99,3282.20,164680000,3282.20 1992-07-24,3290.04,3305.68,3261.94,3285.71,163870000,3285.71 1992-07-23,3277.61,3308.39,3255.43,3290.04,175460000,3290.04 1992-07-22,3308.41,3310.28,3262.19,3277.61,190130000,3277.61 1992-07-21,3303.00,3335.94,3292.45,3308.41,173800000,3308.41 1992-07-20,3318.38,3318.38,3271.11,3303.00,165730000,3303.00 1992-07-17,3359.74,3359.74,3308.14,3331.64,187520000,3331.64 1992-07-16,3345.42,3375.66,3318.65,3361.63,206040000,3361.63 1992-07-15,3358.39,3381.87,3319.73,3345.42,206550000,3345.42 1992-07-14,3337.31,3371.88,3315.95,3358.39,195310000,3358.39 1992-07-13,3330.56,3357.55,3315.41,3337.31,148820000,3337.31 1992-07-10,3324.08,3355.93,3304.88,3330.56,164750000,3330.56 1992-07-09,3293.28,3345.40,3292.45,3324.08,207970000,3324.08 1992-07-08,3295.17,3322.98,3258.70,3293.28,200020000,3293.28 1992-07-07,3339.21,3360.80,3285.69,3295.17,218740000,3295.17 1992-07-06,3330.29,3367.57,3295.96,3339.21,186410000,3339.21 1992-07-02,3354.10,3385.43,3303.30,3330.29,220130000,3330.29 1992-07-01,3318.52,3366.43,3305.45,3354.10,214240000,3354.10 1992-06-30,3319.86,3347.71,3295.01,3318.52,195520000,3318.52 1992-06-29,3282.41,3330.32,3278.16,3319.86,176710000,3319.86 1992-06-26,3284.01,3303.04,3254.89,3282.41,154430000,3282.41 1992-06-25,3290.70,3319.62,3261.31,3284.01,182310000,3284.01 1992-06-24,3285.62,3317.75,3262.11,3290.70,193850000,3290.70 1992-06-23,3280.80,3312.13,3263.45,3285.62,189170000,3285.62 1992-06-22,3285.35,3298.22,3242.32,3280.80,169340000,3280.80 1992-06-19,3274.12,3313.20,3264.79,3285.35,233440000,3285.35 1992-06-18,3287.76,3313.74,3245.53,3274.12,215080000,3274.12 1992-06-17,3329.49,3347.98,3271.21,3287.76,227650000,3287.76 1992-06-16,3354.90,3379.01,3321.76,3329.49,178850000,3329.49 1992-06-15,3354.36,3371.79,3327.11,3354.90,159100000,3354.90 1992-06-12,3351.51,3397.68,3339.62,3354.36,181830000,3354.36 1992-06-11,3343.22,3374.61,3301.51,3351.51,192940000,3351.51 1992-06-10,3369.92,3385.76,3328.99,3343.22,206380000,3343.22 1992-06-09,3404.14,3412.98,3350.25,3369.92,190780000,3369.92 1992-06-08,3398.69,3422.83,3370.21,3404.14,161130000,3404.14 1992-06-05,3399.73,3424.64,3356.98,3398.69,199050000,3398.69 1992-06-04,3406.99,3428.02,3376.43,3399.73,204360000,3399.73 1992-06-03,3396.10,3431.13,3378.76,3406.99,215730000,3406.99 1992-06-02,3413.21,3435.27,3380.84,3396.10,200660000,3396.10 1992-06-01,3396.88,3424.39,3365.02,3413.21,180780000,3413.21 1992-05-29,3398.43,3433.98,3384.20,3396.88,202730000,3396.88 1992-05-28,3370.44,3408.31,3354.91,3398.43,195230000,3398.43 1992-05-27,3364.21,3396.65,3337.29,3370.44,179980000,3370.44 1992-05-26,3386.77,3410.91,3338.84,3364.21,196950000,3364.21 1992-05-22,3378.71,3408.49,3364.08,3386.77,146640000,3386.77 1992-05-21,3393.84,3402.88,3354.80,3378.71,182190000,3378.71 1992-05-20,3397.99,3421.42,3376.04,3393.84,198170000,3393.84 1992-05-19,3376.03,3413.37,3350.41,3397.99,187030000,3397.99 1992-05-18,3353.09,3398.24,3349.93,3376.03,149830000,3376.03 1992-05-15,3368.88,3380.30,3330.43,3353.09,192040000,3353.09 1992-05-14,3391.98,3397.46,3347.59,3368.88,187700000,3368.88 1992-05-13,3385.12,3414.17,3362.93,3391.98,171610000,3391.98 1992-05-12,3397.58,3414.81,3361.51,3385.12,192810000,3385.12 1992-05-11,3369.41,3410.11,3363.82,3397.58,155730000,3397.58 1992-05-08,3363.37,3388.41,3344.59,3369.41,168670000,3369.41 1992-05-07,3369.41,3390.65,3343.25,3363.37,168910000,3363.37 1992-05-06,3359.35,3398.70,3346.60,3369.41,199070000,3369.41 1992-05-05,3378.13,3393.78,3346.15,3359.35,198860000,3359.35 1992-05-04,3340.79,3394.68,3340.79,3378.13,174520000,3378.13 1992-05-01,3359.12,3375.67,3316.64,3336.09,177360000,3336.09 1992-04-30,3333.18,3377.01,3314.40,3359.12,223160000,3359.12 1992-04-29,3307.92,3353.75,3295.39,3333.18,199740000,3333.18 1992-04-28,3304.56,3329.83,3273.03,3307.92,186740000,3307.92 1992-04-27,3324.46,3340.79,3284.66,3304.56,172820000,3304.56 1992-04-24,3348.61,3381.48,3305.23,3324.46,198740000,3324.46 1992-04-23,3338.77,3376.34,3298.52,3348.61,235740000,3348.61 1992-04-22,3343.25,3366.72,3309.48,3338.77,218790000,3338.77 1992-04-21,3336.31,3366.50,3305.68,3343.25,214400000,3343.25 1992-04-20,3366.50,3375.67,3300.98,3336.31,191910000,3336.31 1992-04-16,3353.76,3387.97,3321.33,3366.50,232480000,3366.50 1992-04-15,3306.13,3370.08,3296.51,3353.76,228400000,3353.76 1992-04-14,3269.90,3336.54,3259.39,3306.13,232040000,3306.13 1992-04-13,3255.37,3293.60,3230.77,3269.90,143100000,3269.90 1992-04-10,3227.64,3280.86,3227.64,3255.37,199450000,3255.37 1992-04-09,3181.35,3245.08,3171.06,3224.96,231330000,3224.96 1992-04-08,3213.55,3221.38,3141.77,3181.35,249210000,3181.35 1992-04-07,3275.49,3291.81,3207.51,3213.55,205110000,3213.55 1992-04-06,3249.11,3295.84,3238.37,3275.49,179330000,3275.49 1992-04-03,3234.12,3271.91,3201.92,3249.11,188530000,3249.11 1992-04-02,3249.33,3277.06,3209.08,3234.12,185110000,3234.12 1992-04-01,3235.47,3262.52,3202.37,3249.33,186260000,3249.33 1992-03-31,3235.24,3272.81,3216.46,3235.47,182200000,3235.47 1992-03-30,3231.44,3260.06,3215.79,3235.24,133980000,3235.24 1992-03-27,3267.67,3272.58,3218.92,3231.44,166140000,3231.44 1992-03-26,3259.39,3288.91,3245.08,3267.67,176690000,3267.67 1992-03-25,3260.96,3290.47,3238.15,3259.39,192080000,3259.39 1992-03-24,3272.14,3296.96,3242.84,3260.96,191580000,3260.96 1992-03-23,3276.39,3288.68,3249.33,3272.14,156620000,3272.14 1992-03-20,3261.40,3292.48,3240.83,3276.39,246170000,3276.39 1992-03-19,3254.25,3286.22,3240.38,3261.40,197240000,3261.40 1992-03-18,3256.04,3281.98,3230.10,3254.25,190800000,3254.25 1992-03-17,3236.36,3272.58,3222.27,3256.04,187240000,3256.04 1992-03-16,3235.91,3249.11,3201.25,3236.36,151280000,3236.36 1992-03-13,3208.63,3252.23,3203.71,3235.91,176000000,3235.91 1992-03-12,3208.63,3232.33,3176.21,3208.63,180240000,3208.63 1992-03-11,3230.99,3242.17,3189.85,3208.63,185320000,3208.63 1992-03-10,3215.12,3259.17,3207.29,3230.99,194530000,3230.99 1992-03-09,3221.60,3242.62,3188.95,3215.12,160620000,3215.12 1992-03-06,3241.50,3262.97,3204.61,3221.60,185040000,3221.60 1992-03-05,3268.56,3283.54,3226.74,3241.50,205730000,3241.50 1992-03-04,3290.25,3318.42,3256.04,3268.56,206770000,3268.56 1992-03-03,3275.27,3313.28,3258.50,3290.25,200860000,3290.25 1992-03-02,3267.67,3298.97,3242.39,3275.27,180380000,3275.27 1992-02-28,3269.45,3304.11,3247.09,3267.67,202130000,3267.67 1992-02-27,3283.32,3300.54,3251.79,3269.45,215050000,3269.45 1992-02-26,3257.83,3305.01,3246.20,3283.32,240690000,3283.32 1992-02-25,3282.42,3293.60,3226.74,3257.83,208400000,3257.83 1992-02-24,3280.19,3305.01,3253.58,3282.42,177470000,3282.42 1992-02-21,3280.64,3307.47,3242.17,3280.19,258700000,3280.19 1992-02-20,3230.32,3304.34,3224.28,3280.64,270550000,3280.64 1992-02-19,3224.73,3259.17,3199.02,3230.32,232900000,3230.32 1992-02-18,3245.97,3280.19,3195.66,3224.73,234270000,3224.73 1992-02-14,3246.65,3271.91,3208.63,3245.97,214840000,3245.97 1992-02-13,3276.83,3296.73,3226.07,3246.65,229360000,3246.65 1992-02-12,3251.57,3291.37,3233.68,3276.83,230230000,3276.83 1992-02-11,3245.08,3277.28,3222.49,3251.57,200110000,3251.57 1992-02-10,3225.40,3269.00,3208.85,3245.08,184390000,3245.08 1992-02-07,3255.59,3283.09,3199.91,3225.40,230950000,3225.40 1992-02-06,3257.60,3285.55,3224.95,3255.59,238210000,3255.59 1992-02-05,3272.81,3299.87,3233.68,3257.60,261960000,3257.60 1992-02-04,3234.12,3283.09,3208.63,3272.81,231530000,3272.81 1992-02-03,3223.39,3261.40,3193.42,3234.12,182150000,3234.12 1992-01-31,3244.86,3274.37,3207.96,3223.39,197060000,3223.39 1992-01-30,3224.96,3269.68,3200.58,3244.86,194670000,3244.86 1992-01-29,3272.14,3313.51,3204.16,3224.96,248900000,3224.96 1992-01-28,3240.61,3298.30,3230.99,3272.14,217070000,3272.14 1992-01-27,3232.78,3267.44,3216.23,3240.61,190430000,3240.61 1992-01-24,3226.74,3264.31,3203.71,3232.78,213600000,3232.78 1992-01-23,3255.81,3279.52,3205.95,3226.74,229680000,3226.74 1992-01-22,3223.39,3272.80,3190.96,3255.81,225030000,3255.81 1992-01-21,3254.03,3275.49,3194.09,3223.39,218650000,3223.39 1992-01-20,3264.98,3283.32,3230.99,3254.03,180870000,3254.03 1992-01-17,3249.55,3297.63,3224.28,3264.98,284560000,3264.98 1992-01-16,3258.50,3289.13,3200.80,3249.55,333750000,3249.55 1992-01-15,3246.20,3299.19,3207.73,3258.50,312400000,3258.50 1992-01-14,3185.60,3255.81,3167.93,3246.20,264540000,3246.20 1992-01-13,3199.46,3215.56,3161.67,3185.60,200270000,3185.60 1992-01-10,3209.53,3235.91,3165.92,3199.46,236060000,3199.46 1992-01-09,3203.94,3246.64,3173.75,3209.53,291780000,3209.53 1992-01-08,3204.83,3245.53,3164.58,3203.94,289690000,3203.94 1992-01-07,3200.13,3224.73,3165.25,3204.83,251280000,3204.83 1992-01-06,3201.48,3230.32,3166.37,3200.13,241980000,3200.13 1992-01-03,3172.41,3221.38,3156.30,3201.48,219170000,3201.48 1992-01-02,3168.83,3184.70,3119.86,3172.41,203610000,3172.41 1991-12-31,3163.91,3204.61,3121.20,3168.83,247670000,3168.83 1991-12-30,3101.52,3174.64,3090.79,3163.91,245450000,3163.91 1991-12-27,3082.96,3121.65,3063.73,3101.52,157940000,3101.52 1991-12-26,3050.98,3101.97,3036.23,3082.96,149200000,3082.96 1991-12-24,3022.58,3087.88,3015.65,3050.98,162300000,3050.98 1991-12-23,2934.48,3037.57,2919.05,3022.58,228730000,3022.58 1991-12-20,2914.36,2965.79,2907.65,2934.48,313810000,2934.48 1991-12-19,2908.09,2927.10,2881.48,2914.36,199830000,2914.36 1991-12-18,2902.28,2921.29,2870.30,2908.09,192350000,2908.09 1991-12-17,2919.05,2936.27,2886.40,2902.28,191280000,2902.28 1991-12-16,2914.36,2946.33,2900.49,2919.05,173010000,2919.05 1991-12-13,2895.13,2936.72,2893.34,2914.36,195790000,2914.36 1991-12-12,2865.38,2911.67,2861.14,2895.13,192930000,2895.13 1991-12-11,2863.82,2889.53,2832.29,2865.38,207410000,2865.38 1991-12-10,2871.65,2891.10,2841.23,2863.82,191800000,2863.82 1991-12-09,2886.40,2913.46,2856.22,2871.65,174700000,2871.65 1991-12-06,2889.09,2924.87,2854.65,2886.40,198170000,2886.40 1991-12-05,2911.67,2923.75,2874.77,2889.09,165660000,2889.09 1991-12-04,2929.56,2945.66,2892.22,2911.67,186380000,2911.67 1991-12-03,2935.38,2958.18,2904.74,2929.56,187160000,2929.56 1991-12-02,2894.68,2941.64,2855.32,2935.38,188050000,2935.38 1991-11-29,2900.04,2913.46,2877.46,2894.68,77420000,2894.68 1991-11-27,2916.14,2929.11,2883.72,2900.04,159790000,2900.04 1991-11-26,2902.06,2942.31,2861.14,2916.14,213650000,2916.14 1991-11-25,2902.73,2921.96,2870.30,2902.06,173520000,2902.06 1991-11-22,2932.69,2940.30,2880.81,2902.73,188110000,2902.73 1991-11-21,2930.01,2953.94,2902.28,2932.69,195060000,2932.69 1991-11-20,2931.57,2973.61,2912.12,2930.01,192670000,2930.01 1991-11-19,2960.20,2960.20,2883.72,2931.57,243880000,2931.57 1991-11-18,2943.20,2990.61,2923.30,2972.72,238170000,2972.72 1991-11-15,3063.51,3069.54,2936.05,2943.20,236000000,2943.20 1991-11-14,3065.30,3084.97,3037.34,3063.51,200030000,3063.51 1991-11-13,3054.11,3075.58,3023.70,3065.30,184080000,3065.30 1991-11-12,3042.26,3078.71,3028.62,3054.11,198600000,3054.11 1991-11-11,3045.62,3057.69,3025.27,3042.26,128910000,3042.26 1991-11-08,3054.11,3080.95,3034.66,3045.62,183260000,3045.62 1991-11-07,3038.46,3066.19,3020.35,3054.11,205440000,3054.11 1991-11-06,3031.31,3056.57,3010.51,3038.46,167370000,3038.46 1991-11-05,3045.62,3062.84,3015.88,3031.31,171950000,3031.31 1991-11-04,3056.35,3061.27,3019.01,3045.62,155530000,3045.62 1991-11-01,3069.10,3091.91,3031.75,3056.35,205750000,3056.35 1991-10-31,3071.78,3091.01,3045.62,3069.10,179490000,3069.10 1991-10-30,3061.94,3090.12,3038.24,3071.78,195340000,3071.78 1991-10-29,3045.62,3077.82,3020.13,3061.94,192700000,3061.94 1991-10-28,3004.92,3055.23,3001.57,3045.62,160220000,3045.62 1991-10-25,3016.32,3034.44,2983.01,3004.92,167260000,3004.92 1991-10-24,3040.92,3047.63,2991.73,3016.32,178990000,3016.32 1991-10-23,3039.80,3065.52,3015.21,3040.92,185340000,3040.92 1991-10-22,3060.38,3084.53,3020.57,3039.80,194120000,3039.80 1991-10-21,3077.15,3085.20,3042.49,3060.38,153770000,3060.38 1991-10-18,3053.00,3089.45,3045.62,3077.15,204030000,3077.15 1991-10-17,3061.72,3077.15,3027.06,3053.00,202780000,3053.00 1991-10-16,3041.37,3082.29,3016.10,3061.72,225290000,3061.72 1991-10-15,3019.45,3057.69,3000.22,3041.37,213440000,3041.37 1991-10-14,2983.68,3026.39,2975.85,3019.45,130110000,3019.45 1991-10-11,2976.52,3000.89,2957.51,2983.68,146560000,2983.68 1991-10-10,2946.33,2985.47,2930.23,2976.52,164120000,2976.52 1991-10-09,2963.77,2984.79,2925.54,2946.33,186590000,2946.33 1991-10-08,2942.75,2983.68,2927.77,2963.77,170000000,2963.77 1991-10-07,2961.76,2973.17,2926.21,2942.75,148430000,2942.75 1991-10-04,2984.79,3007.16,2956.17,2961.76,163930000,2961.76 1991-10-03,3012.52,3021.24,2972.50,2984.79,174230000,2984.79 1991-10-02,3018.34,3040.25,2992.40,3012.52,166290000,3012.52 1991-10-01,3016.77,3043.60,3002.46,3018.34,163520000,3018.34 1991-09-30,3006.04,3032.87,2982.78,3016.77,146740000,3016.77 1991-09-27,3017.22,3040.70,2989.49,3006.04,160430000,3006.04 1991-09-26,3021.02,3040.70,2996.87,3017.22,160190000,3017.22 1991-09-25,3029.07,3048.52,3004.92,3021.02,153800000,3021.02 1991-09-24,3010.51,3043.38,2995.97,3029.07,170300000,3029.07 1991-09-23,3019.23,3037.79,2997.76,3010.51,145530000,3010.51 1991-09-20,3024.37,3045.39,3002.46,3019.23,241340000,3019.23 1991-09-19,3017.89,3050.76,3002.91,3024.37,210770000,3024.37 1991-09-18,3013.19,3025.49,2993.52,3017.89,141280000,3017.89 1991-09-17,3015.21,3036.00,2992.62,3013.19,168310000,3013.19 1991-09-16,2985.69,3023.03,2973.61,3015.21,171960000,3015.21 1991-09-13,3007.83,3021.47,2963.10,2985.69,167800000,2985.69 1991-09-12,2987.03,3023.26,2977.64,3007.83,160310000,3007.83 1991-09-11,2982.56,3004.70,2964.67,2987.03,147910000,2987.03 1991-09-10,3007.16,3012.97,2967.80,2982.56,146730000,2982.56 1991-09-09,3011.63,3025.49,2987.70,3007.16,109250000,3007.16 1991-09-06,3008.50,3033.32,2987.48,3011.63,166410000,3011.63 1991-09-05,3008.50,3031.98,2989.71,3008.50,162250000,3008.50 1991-09-04,3017.67,3034.66,2990.83,3008.50,156770000,3008.50 1991-09-03,3043.60,3066.64,3013.64,3017.67,153550000,3017.67 1991-08-30,3049.64,3059.26,3024.82,3043.60,125830000,3043.60 1991-08-29,3055.23,3068.65,3030.86,3049.64,150180000,3049.64 1991-08-28,3026.16,3064.85,3018.11,3055.23,169590000,3055.23 1991-08-27,3039.36,3047.41,3007.83,3026.16,143920000,3026.16 1991-08-26,3040.25,3057.02,3020.80,3039.36,128400000,3039.36 1991-08-23,3007.38,3065.97,2999.11,3040.25,187010000,3040.25 1991-08-22,3001.79,3029.96,2989.94,3007.38,172900000,3007.38 1991-08-21,2941.64,3009.17,2941.64,3001.79,231720000,3001.79 1991-08-20,2898.03,2937.39,2886.85,2913.69,184090000,2913.69 1991-08-19,2924.42,2924.42,2836.31,2898.03,229590000,2898.03 1991-08-16,2998.43,3013.64,2945.89,2968.02,188360000,2968.02 1991-08-15,3005.37,3029.96,2985.24,2998.43,174310000,2998.43 1991-08-14,3008.72,3036.67,2988.60,3005.37,195890000,3005.37 1991-08-13,3001.34,3041.14,2989.27,3008.72,211890000,3008.72 1991-08-12,2996.20,3013.42,2974.06,3001.34,145280000,3001.34 1991-08-09,3013.86,3036.45,2986.81,2996.20,143610000,2996.20 1991-08-08,3026.61,3045.17,2995.30,3013.86,163620000,3013.86 1991-08-07,3027.28,3050.54,3008.50,3026.61,170820000,3026.61 1991-08-06,2989.04,3034.88,2970.48,3027.28,174340000,3027.28 1991-08-05,3006.26,3019.68,2978.76,2989.04,125790000,2989.04 1991-08-02,3017.67,3042.26,2994.41,3006.26,162050000,3006.26 1991-08-01,3024.82,3037.12,2997.76,3017.67,169920000,3017.67 1991-07-31,3016.32,3039.58,2999.11,3024.82,166650000,3024.82 1991-07-30,2985.24,3029.07,2983.68,3016.32,168950000,3016.32 1991-07-29,2972.50,2996.20,2957.07,2985.24,135240000,2985.24 1991-07-26,2980.10,2994.86,2953.26,2972.50,127690000,2972.50 1991-07-25,2966.23,2999.11,2947.23,2980.10,145510000,2980.10 1991-07-24,2983.23,3003.13,2950.80,2966.23,158530000,2966.23 1991-07-23,3012.97,3038.24,2972.94,2983.23,160010000,2983.23 1991-07-22,3016.32,3034.44,2990.83,3012.97,148880000,3012.97 1991-07-19,3016.32,3036.23,2989.49,3016.32,187290000,3016.32 1991-07-18,2978.76,3026.61,2970.93,3016.32,199470000,3016.32 1991-07-17,2983.90,3011.18,2961.54,2978.76,194150000,2978.76 1991-07-16,2990.61,3012.52,2962.88,2983.90,182740000,2983.90 1991-07-15,2980.77,3007.38,2965.79,2990.61,159340000,2990.61 1991-07-12,2959.75,3003.35,2942.98,2980.77,173060000,2980.77 1991-07-11,2944.77,2975.63,2933.36,2959.75,157680000,2959.75 1991-07-10,2947.23,2990.83,2927.77,2944.77,178170000,2944.77 1991-07-09,2961.99,2980.10,2934.03,2947.23,151430000,2947.23 1991-07-08,2932.47,2966.23,2897.36,2961.99,138210000,2961.99 1991-07-05,2934.70,2953.26,2917.71,2932.47,69790000,2932.47 1991-07-03,2957.07,2957.07,2915.03,2934.70,139290000,2934.70 1991-07-02,2958.41,2987.03,2939.62,2972.72,157140000,2972.72 1991-07-01,2911.67,2971.15,2911.67,2958.41,166880000,2958.41 1991-06-28,2934.03,2934.03,2879.25,2906.75,162670000,2906.75 1991-06-27,2913.01,2946.56,2909.21,2934.93,155820000,2934.93 1991-06-26,2910.11,2934.03,2879.25,2913.01,186830000,2913.01 1991-06-25,2913.01,2938.51,2887.97,2910.11,155450000,2910.11 1991-06-24,2957.29,2957.29,2904.29,2913.01,137700000,2913.01 1991-06-21,2953.94,2977.64,2937.84,2965.56,193110000,2965.56 1991-06-20,2955.50,2969.81,2927.33,2953.94,163780000,2953.94 1991-06-19,2978.31,2978.31,2933.59,2955.50,156070000,2955.50 1991-06-18,2993.96,3022.14,2970.48,2986.81,154900000,2986.81 1991-06-17,3000.45,3021.69,2977.86,2993.96,133910000,2993.96 1991-06-14,2967.58,3014.98,2967.58,3000.45,167530000,3000.45 1991-06-13,2961.99,2982.11,2942.53,2965.12,144870000,2965.12 1991-06-12,2985.91,2987.25,2928.89,2961.99,165720000,2961.99 1991-06-11,2975.40,3012.08,2963.10,2985.91,161240000,2985.91 1991-06-10,2976.74,2992.84,2958.41,2975.40,127420000,2975.40 1991-06-07,2994.86,3005.14,2954.61,2976.74,167830000,2976.74 1991-06-06,3005.37,3027.06,2975.63,2994.86,168050000,2994.86 1991-06-05,3027.95,3042.93,2989.49,3005.37,186300000,3005.37 1991-06-04,3035.33,3042.04,2992.62,3027.95,180170000,3027.95 1991-06-03,3027.50,3057.47,3000.67,3035.33,173710000,3035.33 1991-05-31,3000.45,3044.50,2972.94,3027.50,231680000,3027.50 1991-05-30,2969.59,3022.81,2950.58,3000.45,234090000,3000.45 1991-05-29,2958.86,2987.92,2940.74,2969.59,188150000,2969.59 1991-05-28,2913.91,2965.12,2903.85,2958.86,161950000,2958.86 1991-05-24,2900.04,2931.13,2893.78,2913.91,124500000,2913.91 1991-05-23,2910.33,2935.38,2881.04,2900.04,173080000,2900.04 1991-05-22,2906.08,2930.01,2881.93,2910.33,159310000,2910.33 1991-05-21,2892.22,2931.80,2875.00,2906.08,186860000,2906.08 1991-05-20,2886.63,2910.78,2872.76,2892.22,109510000,2892.22 1991-05-17,2894.01,2908.54,2860.02,2886.63,174210000,2886.63 1991-05-16,2865.38,2905.64,2863.82,2894.01,154460000,2894.01 1991-05-15,2886.85,2901.83,2834.53,2865.38,193110000,2865.38 1991-05-14,2922.85,2922.85,2870.08,2886.85,207910000,2886.85 1991-05-13,2920.17,2945.44,2896.24,2924.42,129620000,2924.42 1991-05-10,2971.15,2986.14,2906.08,2920.17,172730000,2920.17 1991-05-09,2930.90,2983.68,2926.88,2971.15,180210000,2971.15 1991-05-08,2917.49,2945.21,2898.70,2930.90,157240000,2930.90 1991-05-07,2941.64,2962.66,2906.53,2917.49,153290000,2917.49 1991-05-06,2938.86,2956.84,2911.45,2941.64,128360000,2941.64 1991-05-03,2938.61,2959.90,2906.93,2938.86,158160000,2938.86 1991-05-02,2930.20,2966.34,2917.33,2938.61,187090000,2938.61 1991-05-01,2887.87,2945.30,2882.43,2930.20,181900000,2930.20 1991-04-30,2876.98,2929.21,2859.41,2887.87,204930000,2887.87 1991-04-29,2912.38,2941.09,2869.55,2876.98,149860000,2876.98 1991-04-26,2921.04,2934.90,2888.61,2912.38,154550000,2912.38 1991-04-25,2949.51,2958.42,2905.20,2921.04,166940000,2921.04 1991-04-24,2930.45,2965.10,2913.37,2949.51,166800000,2949.51 1991-04-23,2927.72,2957.92,2905.94,2930.45,167840000,2930.45 1991-04-22,2962.13,2962.13,2911.63,2927.72,164410000,2927.72 1991-04-19,2999.26,3000.25,2943.56,2965.59,195510000,2965.59 1991-04-18,3004.46,3027.72,2976.24,2999.26,217410000,2999.26 1991-04-17,2986.88,3030.45,2963.12,3004.46,246930000,3004.46 1991-04-16,2933.17,2995.79,2912.13,2986.88,214480000,2986.88 1991-04-15,2920.79,2957.18,2896.29,2933.17,161800000,2933.17 1991-04-12,2905.45,2946.53,2884.16,2920.79,198610000,2920.79 1991-04-11,2877.72,2937.62,2877.72,2905.45,196570000,2905.45 1991-04-10,2873.02,2902.48,2848.51,2874.50,167940000,2874.50 1991-04-09,2918.56,2932.42,2863.36,2873.02,169940000,2873.02 1991-04-08,2896.78,2929.21,2877.72,2918.56,138580000,2918.56 1991-04-05,2924.50,2949.26,2876.98,2896.78,187410000,2896.78 1991-04-04,2926.73,2959.41,2899.75,2924.50,198120000,2924.50 1991-04-03,2945.05,2970.54,2915.84,2926.73,213720000,2926.73 1991-04-02,2881.19,2951.98,2878.22,2945.05,189530000,2945.05 1991-04-01,2913.86,2919.31,2868.32,2881.19,144010000,2881.19 1991-03-28,2917.57,2942.08,2893.32,2913.86,150750000,2913.86 1991-03-27,2914.85,2956.93,2886.14,2917.57,201830000,2917.57 1991-03-26,2865.84,2924.01,2848.02,2914.85,198720000,2914.85 1991-03-25,2858.91,2897.52,2837.87,2865.84,153920000,2865.84 1991-03-22,2855.45,2879.95,2829.21,2858.91,160890000,2858.91 1991-03-21,2872.03,2907.92,2841.58,2855.45,199830000,2855.45 1991-03-20,2867.82,2897.03,2840.84,2872.03,196810000,2872.03 1991-03-19,2908.17,2908.17,2840.84,2867.82,177070000,2867.82 1991-03-18,2948.27,2960.64,2899.75,2929.95,163100000,2929.95 1991-03-15,2952.23,2966.58,2918.81,2948.27,237660000,2948.27 1991-03-14,2955.20,3000.00,2925.74,2952.23,231000000,2952.23 1991-03-13,2922.52,2967.82,2910.89,2955.20,176000000,2955.20 1991-03-12,2939.36,2962.13,2906.68,2922.52,176440000,2922.52 1991-03-11,2955.20,2975.50,2923.27,2939.36,161600000,2939.36 1991-03-08,2963.37,3003.47,2934.90,2955.20,206850000,2955.20 1991-03-07,2973.27,2998.51,2948.51,2963.37,197060000,2963.37 1991-03-06,2972.52,3017.82,2957.67,2973.27,262290000,2973.27 1991-03-05,2914.11,2995.54,2913.86,2972.52,253700000,2972.52 1991-03-04,2909.90,2950.74,2897.03,2914.11,199830000,2914.11 1991-03-01,2882.18,2923.51,2846.78,2909.90,221510000,2909.90 1991-02-28,2889.11,2923.51,2858.42,2882.18,223010000,2882.18 1991-02-27,2864.60,2915.35,2839.85,2889.11,211410000,2889.11 1991-02-26,2887.87,2895.30,2840.10,2864.60,164170000,2864.60 1991-02-25,2889.36,2935.89,2865.35,2887.87,193820000,2887.87 1991-02-22,2891.83,2945.79,2860.40,2889.36,218760000,2889.36 1991-02-21,2899.01,2927.72,2871.29,2891.83,180770000,2891.83 1991-02-20,2923.76,2923.76,2879.46,2899.01,185680000,2899.01 1991-02-19,2934.65,2955.20,2895.30,2932.18,189900000,2932.18 1991-02-15,2877.23,2947.28,2868.07,2934.65,222370000,2934.65 1991-02-14,2909.16,2942.08,2852.48,2877.23,230750000,2877.23 1991-02-13,2874.75,2918.32,2848.51,2909.16,209960000,2909.16 1991-02-12,2902.23,2923.02,2848.27,2874.75,256160000,2874.75 1991-02-11,2830.69,2910.64,2822.52,2902.23,265930000,2902.23 1991-02-08,2810.64,2847.28,2787.13,2830.69,187830000,2830.69 1991-02-07,2830.94,2875.50,2790.59,2810.64,292190000,2810.64 1991-02-06,2788.37,2844.80,2760.15,2830.94,276940000,2830.94 1991-02-05,2772.28,2805.20,2742.82,2788.37,290570000,2788.37 1991-02-04,2730.69,2791.83,2714.85,2772.28,250750000,2772.28 1991-02-01,2736.39,2764.60,2694.31,2730.69,246670000,2730.69 1991-01-31,2713.12,2747.28,2691.58,2736.39,204240000,2736.39 1991-01-30,2662.62,2725.99,2654.70,2713.12,226790000,2713.12 1991-01-29,2654.46,2677.23,2627.23,2662.62,155740000,2662.62 1991-01-28,2659.41,2688.61,2635.15,2654.46,141270000,2654.46 1991-01-25,2643.07,2679.46,2623.27,2659.41,194350000,2659.41 1991-01-24,2619.06,2667.33,2615.10,2643.07,223150000,2643.07 1991-01-23,2603.22,2645.54,2584.65,2619.06,169440000,2619.06 1991-01-22,2629.21,2645.05,2586.39,2603.22,175590000,2603.22 1991-01-21,2646.29,2646.29,2602.97,2629.21,136290000,2629.21 1991-01-18,2623.51,2663.37,2600.25,2646.78,226770000,2646.78 1991-01-17,2571.78,2635.40,2571.78,2623.51,318890000,2623.51 1991-01-16,2490.59,2525.25,2475.99,2508.91,134560000,2508.91 1991-01-15,2483.91,2504.46,2470.05,2490.59,109980000,2490.59 1991-01-14,2500.99,2500.99,2447.03,2483.91,120830000,2483.91 1991-01-11,2498.76,2516.09,2472.77,2501.49,123050000,2501.49 1991-01-10,2470.30,2512.13,2467.08,2498.76,124510000,2498.76 1991-01-09,2509.41,2562.38,2456.19,2470.30,191100000,2470.30 1991-01-08,2522.77,2544.06,2493.07,2509.41,143390000,2509.41 1991-01-07,2563.37,2563.37,2514.11,2522.77,130610000,2522.77 1991-01-04,2573.51,2601.98,2540.84,2566.09,140820000,2566.09 1991-01-03,2610.64,2622.28,2567.57,2573.51,141450000,2573.51 1991-01-02,2633.66,2651.73,2600.99,2610.64,126280000,2610.64 1990-12-31,2629.21,2641.09,2611.14,2633.66,114130000,2633.66 1990-12-28,2625.50,2639.11,2607.92,2629.21,111030000,2629.21 1990-12-27,2637.13,2652.23,2616.58,2625.50,102900000,2625.50 1990-12-26,2621.29,2653.96,2613.37,2637.13,78730000,2637.13 1990-12-24,2633.66,2638.12,2609.65,2621.29,57200000,2621.29 1990-12-21,2629.46,2662.62,2619.55,2633.66,233400000,2633.66 1990-12-20,2626.73,2645.81,2591.58,2629.46,174700000,2629.46 1990-12-19,2626.73,2648.51,2602.48,2626.73,180380000,2626.73 1990-12-18,2593.32,2639.36,2583.17,2626.73,176460000,2626.73 1990-12-17,2593.81,2600.74,2563.61,2593.32,118560000,2593.32 1990-12-14,2614.36,2617.82,2572.03,2593.81,150880000,2593.81 1990-12-13,2622.28,2640.59,2598.76,2614.36,162110000,2614.36 1990-12-12,2586.14,2630.69,2578.22,2622.28,182270000,2622.28 1990-12-11,2596.78,2606.93,2565.59,2586.14,145330000,2586.14 1990-12-10,2590.10,2609.40,2566.58,2596.78,138650000,2596.78 1990-12-07,2602.48,2618.07,2571.78,2590.10,164950000,2590.10 1990-12-06,2610.40,2656.44,2589.36,2602.48,256380000,2602.48 1990-12-05,2579.70,2615.84,2558.42,2610.40,205820000,2610.40 1990-12-04,2565.59,2592.08,2534.65,2579.70,185820000,2579.70 1990-12-03,2559.65,2589.60,2543.56,2565.59,177010000,2565.59 1990-11-30,2518.81,2577.23,2498.51,2559.65,192350000,2559.65 1990-11-29,2535.15,2544.31,2501.23,2518.81,140920000,2518.81 1990-11-28,2543.81,2564.60,2521.04,2535.15,145490000,2535.15 1990-11-27,2533.17,2562.62,2516.09,2543.81,147590000,2543.81 1990-11-26,2527.23,2541.58,2489.85,2533.17,131540000,2533.17 1990-11-23,2539.36,2557.92,2521.04,2527.23,63350000,2527.23 1990-11-21,2530.20,2552.23,2502.72,2539.36,140660000,2539.36 1990-11-20,2565.35,2574.50,2524.01,2530.20,161170000,2530.20 1990-11-19,2550.25,2580.69,2543.07,2565.35,140950000,2565.35 1990-11-16,2545.05,2570.79,2522.28,2550.25,165440000,2550.25 1990-11-15,2559.65,2567.33,2528.71,2545.05,151370000,2545.05 1990-11-14,2535.40,2581.19,2522.77,2559.65,179310000,2559.65 1990-11-13,2540.35,2559.90,2512.38,2535.40,160240000,2535.40 1990-11-12,2490.10,2550.99,2490.10,2540.35,161390000,2540.35 1990-11-09,2443.81,2500.74,2438.61,2488.61,145160000,2488.61 1990-11-08,2440.84,2468.81,2415.84,2443.81,155570000,2443.81 1990-11-07,2485.15,2490.35,2430.94,2440.84,149130000,2440.84 1990-11-06,2502.23,2516.83,2471.53,2485.15,141130000,2485.15 1990-11-05,2490.84,2519.06,2472.77,2502.23,147510000,2502.23 1990-11-02,2454.95,2501.73,2447.28,2490.84,168700000,2490.84 1990-11-01,2442.33,2473.02,2415.59,2454.95,159270000,2454.95 1990-10-31,2448.02,2475.99,2419.55,2442.33,156060000,2442.33 1990-10-30,2430.20,2462.87,2400.50,2448.02,153450000,2448.02 1990-10-29,2436.14,2470.30,2407.67,2430.20,133980000,2430.20 1990-10-26,2482.18,2482.18,2429.21,2436.14,130190000,2436.14 1990-10-25,2504.21,2525.99,2464.11,2484.41,141460000,2484.41 1990-10-24,2494.06,2523.02,2470.54,2504.21,149290000,2504.21 1990-10-23,2516.09,2527.97,2480.20,2494.06,146300000,2494.06 1990-10-22,2520.79,2535.40,2476.73,2516.09,152650000,2516.09 1990-10-19,2453.47,2536.88,2453.47,2520.79,221480000,2520.79 1990-10-18,2391.83,2461.63,2391.83,2452.72,204110000,2452.72 1990-10-17,2381.19,2418.56,2358.17,2387.87,161260000,2387.87 1990-10-16,2416.34,2434.41,2366.83,2381.19,149570000,2381.19 1990-10-15,2398.02,2451.24,2354.95,2416.34,164980000,2416.34 1990-10-12,2365.10,2428.71,2349.75,2398.02,187940000,2398.02 1990-10-11,2407.92,2427.23,2344.31,2365.10,180060000,2365.10 1990-10-10,2445.54,2472.03,2388.36,2407.92,167890000,2407.92 1990-10-09,2514.60,2514.60,2437.87,2445.54,145610000,2445.54 1990-10-08,2510.64,2548.02,2507.18,2523.76,99470000,2523.76 1990-10-05,2516.83,2544.55,2453.47,2510.64,153380000,2510.64 1990-10-04,2489.36,2528.22,2464.85,2516.83,145410000,2516.83 1990-10-03,2505.20,2534.65,2470.79,2489.36,135490000,2489.36 1990-10-02,2515.84,2565.35,2487.62,2505.20,188360000,2505.20 1990-10-01,2452.48,2534.65,2446.53,2515.84,202210000,2515.84 1990-09-28,2427.48,2465.59,2367.82,2452.48,201010000,2452.48 1990-09-27,2459.65,2492.33,2396.29,2427.48,182690000,2427.48 1990-09-26,2485.64,2496.04,2435.40,2459.65,155570000,2459.65 1990-09-25,2452.97,2501.24,2437.38,2485.64,155940000,2485.64 1990-09-24,2498.02,2498.02,2438.12,2452.97,162210000,2452.97 1990-09-21,2518.32,2538.37,2479.95,2512.38,201050000,2512.38 1990-09-20,2549.01,2549.01,2495.54,2518.32,145100000,2518.32 1990-09-19,2571.29,2594.55,2534.41,2557.43,147530000,2557.43 1990-09-18,2567.33,2587.87,2524.26,2571.29,141130000,2571.29 1990-09-17,2564.11,2585.89,2537.13,2567.33,110600000,2567.33 1990-09-14,2582.67,2585.15,2545.54,2564.11,133390000,2564.11 1990-09-13,2625.74,2629.21,2570.54,2582.67,123370000,2582.67 1990-09-12,2612.62,2639.11,2592.33,2625.74,129890000,2625.74 1990-09-11,2615.59,2635.64,2586.88,2612.62,113220000,2612.62 1990-09-10,2619.55,2665.35,2600.99,2615.59,119730000,2615.59 1990-09-07,2596.29,2635.89,2577.72,2619.55,123800000,2619.55 1990-09-06,2628.22,2633.17,2575.99,2596.29,125620000,2596.29 1990-09-05,2613.37,2643.07,2588.61,2628.22,120610000,2628.22 1990-09-04,2614.36,2618.07,2573.02,2613.37,92940000,2613.37 1990-08-31,2593.32,2624.50,2569.31,2614.36,96480000,2614.36 1990-08-30,2632.43,2641.34,2578.47,2593.32,120890000,2593.32 1990-08-29,2614.85,2651.24,2596.53,2632.43,134240000,2632.43 1990-08-28,2611.63,2629.95,2582.92,2614.85,127660000,2614.85 1990-08-27,2590.59,2645.05,2590.59,2611.63,160150000,2611.63 1990-08-24,2483.42,2555.69,2480.45,2532.92,199040000,2532.92 1990-08-23,2539.60,2539.60,2459.41,2483.42,250440000,2483.42 1990-08-22,2603.96,2630.69,2553.22,2560.15,175550000,2560.15 1990-08-21,2645.53,2645.53,2566.09,2603.96,194630000,2603.96 1990-08-20,2644.80,2679.21,2632.43,2656.44,129630000,2656.44 1990-08-17,2679.21,2679.21,2598.02,2644.80,212560000,2644.80 1990-08-16,2746.53,2746.53,2676.24,2681.44,138850000,2681.44 1990-08-15,2747.77,2778.71,2736.38,2748.27,135210000,2748.27 1990-08-14,2746.78,2773.02,2720.54,2747.77,130320000,2747.77 1990-08-13,2716.58,2752.23,2676.73,2746.78,122820000,2746.78 1990-08-10,2758.91,2763.61,2692.82,2716.58,145340000,2716.58 1990-08-09,2734.90,2774.26,2716.58,2758.91,155810000,2758.91 1990-08-08,2710.64,2763.37,2691.34,2734.90,190400000,2734.90 1990-08-07,2716.34,2763.86,2676.24,2710.64,231580000,2710.64 1990-08-06,2758.42,2758.42,2683.17,2716.34,240400000,2716.34 1990-08-03,2863.86,2863.86,2722.03,2809.65,292360000,2809.65 1990-08-02,2899.26,2903.71,2833.17,2864.60,253090000,2864.60 1990-08-01,2905.20,2931.19,2875.00,2899.26,176810000,2899.26 1990-07-31,2917.33,2940.10,2878.47,2905.20,173810000,2905.20 1990-07-30,2898.51,2922.52,2861.14,2917.33,146470000,2917.33 1990-07-27,2920.79,2938.36,2877.72,2898.51,149070000,2898.51 1990-07-26,2930.94,2945.79,2888.12,2920.79,155040000,2920.79 1990-07-25,2922.52,2946.53,2896.78,2930.94,163530000,2930.94 1990-07-24,2904.70,2939.60,2866.83,2922.52,181920000,2922.52 1990-07-23,2961.14,2961.39,2833.17,2904.70,209030000,2904.70 1990-07-20,2993.81,3019.31,2953.71,2961.14,177810000,2961.14 1990-07-19,2981.68,3006.19,2948.27,2993.81,161990000,2993.81 1990-07-18,2999.75,3009.65,2963.61,2981.68,168760000,2981.68 1990-07-17,2999.75,3024.26,2970.05,2999.75,176790000,2999.75 1990-07-16,2980.20,3017.08,2971.78,2999.75,149450000,2999.75 1990-07-13,2969.80,3012.38,2956.68,2980.20,215600000,2980.20 1990-07-12,2932.67,2980.69,2917.82,2969.80,211510000,2969.80 1990-07-11,2890.84,2942.33,2890.10,2932.67,162220000,2932.67 1990-07-10,2914.11,2928.22,2882.18,2890.84,147630000,2890.84 1990-07-09,2904.95,2927.97,2890.84,2914.11,119390000,2914.11 1990-07-06,2879.21,2919.80,2867.57,2904.95,111730000,2904.95 1990-07-05,2906.44,2906.44,2863.61,2879.21,128320000,2879.21 1990-07-03,2899.26,2925.74,2891.09,2911.63,130050000,2911.63 1990-07-02,2880.69,2908.66,2869.80,2899.26,130200000,2899.26 1990-06-29,2878.71,2901.98,2865.84,2880.69,145510000,2880.69 1990-06-28,2862.13,2896.04,2850.50,2878.71,136120000,2878.71 1990-06-27,2842.33,2878.96,2821.53,2862.13,146620000,2862.13 1990-06-26,2845.05,2882.92,2832.67,2842.33,141420000,2842.33 1990-06-25,2857.18,2882.18,2834.90,2845.05,133100000,2845.05 1990-06-22,2901.73,2931.93,2848.02,2857.18,172570000,2857.18 1990-06-21,2895.30,2916.58,2870.05,2901.73,138570000,2901.73 1990-06-20,2893.56,2914.60,2868.81,2895.30,137420000,2895.30 1990-06-19,2882.18,2907.92,2866.58,2893.56,134930000,2893.56 1990-06-18,2930.45,2930.45,2877.48,2882.18,133470000,2882.18 1990-06-15,2928.22,2947.77,2902.72,2935.89,205130000,2935.89 1990-06-14,2929.95,2943.56,2902.97,2928.22,135770000,2928.22 1990-06-13,2933.42,2956.93,2910.15,2929.95,158910000,2929.95 1990-06-12,2892.57,2947.52,2877.97,2933.42,157100000,2933.42 1990-06-11,2862.38,2901.98,2852.97,2892.57,119550000,2892.57 1990-06-08,2897.33,2908.50,2850.73,2862.38,142600000,2862.38 1990-06-07,2911.65,2930.58,2880.34,2897.33,160360000,2897.33 1990-06-06,2925.00,2936.65,2891.99,2911.65,164030000,2911.65 1990-06-05,2935.19,2956.55,2911.89,2925.00,199720000,2925.00 1990-06-04,2900.97,2943.69,2883.98,2935.19,175520000,2935.19 1990-06-01,2876.66,2919.90,2868.69,2900.97,187860000,2900.97 1990-05-31,2878.56,2900.62,2854.64,2876.66,165690000,2876.66 1990-05-30,2870.49,2908.21,2853.89,2878.56,199540000,2878.56 1990-05-29,2820.92,2876.19,2812.62,2870.49,137410000,2870.49 1990-05-25,2855.55,2856.50,2806.69,2820.92,120250000,2820.92 1990-05-24,2856.26,2878.08,2828.04,2855.55,155140000,2855.55 1990-05-23,2852.23,2869.07,2824.95,2856.26,172330000,2856.26 1990-05-22,2844.68,2877.13,2828.51,2852.23,203350000,2852.23 1990-05-21,2819.91,2859.72,2802.08,2844.68,166280000,2844.68 1990-05-18,2831.71,2838.43,2804.63,2819.91,162520000,2819.91 1990-05-17,2819.68,2856.25,2811.57,2831.71,164770000,2831.71 1990-05-16,2822.45,2841.44,2800.23,2819.68,159810000,2819.68 1990-05-15,2821.53,2840.74,2798.15,2822.45,165730000,2822.45 1990-05-14,2801.58,2857.87,2793.75,2821.53,225410000,2821.53 1990-05-11,2743.02,2810.36,2743.02,2801.58,234040000,2801.58 1990-05-10,2732.88,2755.63,2716.44,2738.51,158460000,2738.51 1990-05-09,2733.56,2745.50,2710.14,2732.88,152220000,2732.88 1990-05-08,2721.62,2739.41,2710.36,2733.56,144230000,2733.56 1990-05-07,2710.36,2739.19,2698.87,2721.62,132760000,2721.62 1990-05-04,2696.17,2718.92,2682.43,2710.36,140550000,2710.36 1990-05-03,2689.64,2716.22,2682.88,2696.17,145560000,2696.17 1990-05-02,2668.92,2697.30,2659.01,2689.64,141610000,2689.64 1990-05-01,2656.76,2687.39,2651.35,2668.92,149020000,2668.92 1990-04-30,2645.05,2667.79,2627.70,2656.76,122750000,2656.76 1990-04-27,2676.58,2687.39,2639.19,2645.05,130630000,2645.05 1990-04-26,2666.44,2691.89,2650.22,2676.58,141330000,2676.58 1990-04-25,2654.50,2685.36,2649.32,2666.44,133480000,2666.44 1990-04-24,2666.67,2686.04,2643.69,2654.50,137360000,2654.50 1990-04-23,2690.77,2690.77,2650.90,2666.67,136150000,2666.67 1990-04-20,2711.94,2722.07,2668.47,2695.95,174260000,2695.95 1990-04-19,2732.88,2742.12,2700.00,2711.94,152930000,2711.94 1990-04-18,2765.77,2775.68,2722.75,2732.88,147130000,2732.88 1990-04-17,2763.06,2774.77,2738.29,2765.77,127990000,2765.77 1990-04-16,2751.80,2793.47,2748.87,2763.06,142810000,2763.06 1990-04-12,2729.73,2763.96,2729.50,2751.80,142470000,2751.80 1990-04-11,2731.08,2751.35,2713.29,2729.73,141080000,2729.73 1990-04-10,2722.07,2741.67,2707.88,2731.08,136020000,2731.08 1990-04-09,2717.12,2737.39,2701.35,2722.07,114970000,2722.07 1990-04-06,2721.17,2732.43,2695.50,2717.12,137490000,2717.12 1990-04-05,2719.37,2746.17,2709.01,2721.17,144170000,2721.17 1990-04-04,2736.71,2755.86,2708.56,2719.37,159540000,2719.37 1990-04-03,2700.45,2747.30,2699.55,2736.71,154310000,2736.71 1990-04-02,2707.21,2708.78,2668.69,2700.45,124360000,2700.45 1990-03-30,2727.70,2736.71,2692.34,2707.21,139340000,2707.21 1990-03-29,2743.69,2753.38,2711.49,2727.70,132190000,2727.70 1990-03-28,2736.94,2755.63,2716.21,2743.69,142300000,2743.69 1990-03-27,2707.66,2738.96,2691.89,2736.94,131610000,2736.94 1990-03-26,2704.28,2735.81,2697.97,2707.66,116110000,2707.66 1990-03-23,2695.72,2722.75,2688.06,2704.28,132070000,2704.28 1990-03-22,2727.93,2734.01,2674.32,2695.72,175930000,2695.72 1990-03-21,2738.74,2759.68,2718.24,2727.93,130990000,2727.93 1990-03-20,2755.63,2775.00,2724.10,2738.74,177320000,2738.74 1990-03-19,2741.22,2761.71,2706.53,2755.63,142300000,2755.63 1990-03-16,2699.32,2745.50,2699.32,2741.22,222520000,2741.22 1990-03-15,2687.84,2714.86,2677.93,2695.72,144410000,2695.72 1990-03-14,2674.55,2705.18,2662.39,2687.84,145060000,2687.84 1990-03-13,2686.71,2699.55,2657.88,2674.55,145440000,2674.55 1990-03-12,2683.33,2698.65,2661.94,2686.71,114790000,2686.71 1990-03-09,2696.17,2705.63,2665.54,2683.33,150410000,2683.33 1990-03-08,2669.59,2705.18,2661.26,2696.17,170900000,2696.17 1990-03-07,2676.80,2696.85,2656.08,2669.59,163580000,2669.59 1990-03-06,2649.55,2684.46,2638.06,2676.80,143640000,2676.80 1990-03-05,2660.36,2675.22,2636.71,2649.55,140110000,2649.55 1990-03-02,2635.59,2669.82,2630.18,2660.36,164330000,2660.36 1990-03-01,2627.25,2655.63,2607.88,2635.59,157930000,2635.59 1990-02-28,2617.12,2652.48,2603.60,2627.25,184410000,2627.25 1990-02-27,2602.48,2637.84,2592.12,2617.12,152590000,2617.12 1990-02-26,2564.19,2606.30,2560.13,2602.48,148910000,2602.48 1990-02-23,2574.77,2591.44,2540.99,2564.19,148490000,2564.19 1990-02-22,2583.56,2622.30,2568.02,2574.77,184320000,2574.77 1990-02-21,2596.85,2602.48,2556.31,2583.56,159240000,2583.56 1990-02-20,2619.14,2619.14,2579.05,2596.85,147300000,2596.85 1990-02-16,2649.55,2669.37,2623.65,2635.59,166840000,2635.59 1990-02-15,2624.32,2658.78,2614.86,2649.55,174630000,2649.55 1990-02-14,2624.10,2643.69,2608.11,2624.32,138530000,2624.32 1990-02-13,2619.14,2639.19,2592.57,2624.10,144490000,2624.10 1990-02-12,2648.20,2652.48,2610.59,2619.14,118390000,2619.14 1990-02-09,2644.37,2666.67,2628.15,2648.20,146910000,2648.20 1990-02-08,2640.09,2674.32,2622.30,2644.37,176240000,2644.37 1990-02-07,2606.31,2651.35,2579.28,2640.09,186710000,2640.09 1990-02-06,2622.52,2629.50,2588.29,2606.31,134070000,2606.31 1990-02-05,2602.70,2633.56,2592.12,2622.52,130950000,2622.52 1990-02-02,2586.26,2623.65,2577.03,2602.70,164400000,2602.70 1990-02-01,2590.54,2611.49,2571.85,2586.26,154580000,2586.26 1990-01-31,2546.85,2600.90,2546.85,2590.54,189660000,2590.54 1990-01-30,2553.38,2576.13,2513.06,2543.24,186030000,2543.24 1990-01-29,2559.23,2583.56,2522.97,2553.38,150770000,2553.38 1990-01-26,2561.04,2591.67,2516.89,2559.23,198190000,2559.23 1990-01-25,2604.50,2628.83,2546.17,2561.04,172270000,2561.04 1990-01-24,2615.32,2619.59,2534.23,2604.50,207830000,2604.50 1990-01-23,2600.45,2639.64,2584.01,2615.32,179300000,2615.32 1990-01-22,2677.90,2683.33,2595.27,2600.45,148380000,2600.45 1990-01-19,2666.38,2696.89,2657.85,2677.90,185590000,2677.90 1990-01-18,2659.13,2678.11,2625.85,2666.38,178590000,2666.38 1990-01-17,2692.62,2711.39,2643.77,2659.13,170470000,2659.13 1990-01-16,2669.37,2698.38,2634.17,2692.62,186070000,2692.62 1990-01-15,2689.21,2698.11,2656.36,2669.37,140590000,2669.37 1990-01-12,2735.92,2735.92,2675.98,2689.21,183880000,2689.21 1990-01-11,2750.64,2783.27,2748.29,2760.67,154390000,2760.67 1990-01-10,2766.00,2772.82,2725.47,2750.64,175990000,2750.64 1990-01-09,2794.37,2810.79,2760.03,2766.00,155210000,2766.00 1990-01-08,2773.25,2803.97,2753.41,2794.37,140110000,2794.37 1990-01-05,2796.08,2810.15,2758.11,2773.25,158530000,2773.25 1990-01-04,2809.73,2821.46,2766.42,2796.08,177000000,2796.08 1990-01-03,2810.15,2834.04,2786.26,2809.73,192330000,2809.73 1990-01-02,2753.20,2811.65,2732.51,2810.15,162070000,2810.15 1989-12-29,2732.30,2763.01,2726.96,2753.20,145940000,2753.20 1989-12-28,2724.40,2742.53,2709.04,2732.30,128030000,2732.30 1989-12-27,2709.26,2739.12,2700.30,2724.40,133740000,2724.40 1989-12-26,2711.39,2728.88,2694.75,2709.26,77610000,2709.26 1989-12-22,2691.13,2721.20,2682.59,2711.39,120980000,2711.39 1989-12-21,2687.93,2714.16,2671.72,2691.13,175150000,2691.13 1989-12-20,2695.61,2719.07,2667.02,2687.93,176520000,2687.93 1989-12-19,2697.53,2720.14,2658.70,2695.61,186060000,2695.61 1989-12-18,2739.55,2755.55,2679.82,2697.53,184750000,2697.53 1989-12-15,2753.63,2763.65,2703.92,2739.55,240390000,2739.55 1989-12-14,2761.09,2771.33,2732.08,2753.63,178700000,2753.63 1989-12-13,2752.13,2784.77,2737.20,2761.09,184660000,2761.09 1989-12-12,2728.24,2764.51,2716.51,2752.13,176820000,2752.13 1989-12-11,2731.44,2742.75,2705.63,2728.24,147130000,2728.24 1989-12-08,2720.78,2750.85,2716.51,2731.44,144910000,2731.44 1989-12-07,2736.77,2756.83,2702.00,2720.78,161980000,2720.78 1989-12-06,2741.68,2754.91,2722.70,2736.77,145850000,2736.77 1989-12-05,2753.63,2773.89,2731.23,2741.68,154640000,2741.68 1989-12-04,2747.65,2772.18,2731.02,2753.63,150360000,2753.63 1989-12-01,2706.27,2763.87,2705.42,2747.65,199200000,2747.65 1989-11-30,2688.78,2718.22,2681.95,2706.27,153200000,2706.27 1989-11-29,2702.01,2713.10,2677.47,2688.78,147270000,2688.78 1989-11-28,2694.97,2715.02,2677.26,2702.01,153770000,2702.01 1989-11-27,2675.55,2713.95,2663.40,2694.97,149390000,2694.97 1989-11-24,2657.00,2686.01,2657.00,2675.55,86290000,2675.55 1989-11-22,2639.29,2668.30,2629.91,2656.78,145730000,2656.78 1989-11-21,2632.04,2654.22,2614.97,2639.29,147900000,2639.29 1989-11-20,2652.66,2659.56,2615.40,2632.04,128170000,2632.04 1989-11-17,2635.66,2665.37,2623.15,2652.66,151020000,2652.66 1989-11-16,2632.58,2650.61,2613.52,2635.66,148370000,2635.66 1989-11-15,2610.25,2641.60,2600.00,2632.58,155130000,2632.58 1989-11-14,2626.43,2640.57,2597.13,2610.25,143170000,2610.25 1989-11-13,2625.61,2648.36,2602.05,2626.43,140750000,2626.43 1989-11-10,2603.69,2635.86,2603.07,2625.61,131800000,2625.61 1989-11-09,2623.36,2634.84,2595.90,2603.69,143390000,2603.69 1989-11-08,2597.13,2644.26,2594.67,2623.36,170150000,2623.36 1989-11-07,2582.17,2608.81,2563.11,2597.13,163000000,2597.13 1989-11-06,2621.72,2621.72,2574.59,2582.17,135480000,2582.17 1989-11-03,2631.56,2650.82,2612.91,2629.51,131500000,2629.51 1989-11-02,2645.90,2653.69,2607.58,2631.56,152440000,2631.56 1989-11-01,2645.08,2665.78,2622.95,2645.90,154240000,2645.90 1989-10-31,2606.97,2662.09,2606.97,2645.08,176100000,2645.08 1989-10-30,2596.72,2627.05,2588.11,2603.48,126630000,2603.48 1989-10-27,2613.73,2621.11,2573.56,2596.72,170330000,2596.72 1989-10-26,2653.28,2657.99,2597.74,2613.73,175240000,2613.73 1989-10-25,2659.22,2684.84,2627.66,2653.28,155650000,2653.28 1989-10-24,2662.91,2680.74,2570.28,2659.22,237960000,2659.22 1989-10-23,2689.14,2704.71,2648.77,2662.91,135860000,2662.91 1989-10-20,2683.20,2703.07,2660.25,2689.14,164830000,2689.14 1989-10-19,2650.61,2707.58,2650.61,2683.20,198120000,2683.20 1989-10-18,2638.73,2664.34,2610.66,2643.65,166900000,2643.65 1989-10-17,2657.38,2665.37,2588.73,2638.73,224070000,2638.73 1989-10-16,2569.26,2667.42,2496.93,2657.38,416290000,2657.38 1989-10-13,2759.84,2773.36,2545.49,2569.26,251170000,2569.26 1989-10-12,2744.06,2785.25,2744.06,2759.84,160120000,2759.84 1989-10-11,2785.33,2790.98,2750.82,2773.36,164070000,2773.36 1989-10-10,2791.41,2809.08,2766.53,2785.33,147560000,2785.33 1989-10-09,2785.52,2804.52,2766.32,2791.41,86810000,2791.41 1989-10-06,2773.56,2808.13,2765.58,2785.52,172520000,2785.52 1989-10-05,2771.09,2797.11,2747.15,2773.56,177890000,2773.56 1989-10-04,2754.56,2785.52,2738.79,2771.09,194590000,2771.09 1989-10-03,2713.72,2765.96,2708.97,2754.56,182550000,2754.56 1989-10-02,2692.82,2724.16,2677.05,2713.72,127410000,2713.72 1989-09-29,2694.91,2721.12,2675.72,2692.82,155300000,2692.82 1989-09-28,2673.06,2709.16,2657.86,2694.91,164240000,2694.91 1989-09-27,2663.94,2685.03,2636.78,2673.06,158400000,2673.06 1989-09-26,2659.19,2688.64,2652.93,2663.94,158350000,2663.94 1989-09-25,2681.61,2685.41,2647.61,2659.19,121130000,2659.19 1989-09-22,2680.28,2696.62,2667.55,2681.61,133350000,2681.61 1989-09-21,2683.89,2707.83,2665.65,2680.28,146930000,2680.28 1989-09-20,2687.31,2703.46,2672.49,2683.89,136640000,2683.89 1989-09-19,2687.50,2709.35,2675.91,2687.31,141610000,2687.31 1989-09-18,2674.58,2694.91,2662.61,2687.50,136940000,2687.50 1989-09-15,2664.89,2695.67,2643.05,2674.58,234870000,2674.58 1989-09-14,2679.52,2690.16,2647.61,2664.89,149250000,2664.89 1989-09-13,2707.26,2725.87,2671.35,2679.52,175330000,2679.52 1989-09-12,2704.41,2726.06,2689.59,2707.26,142140000,2707.26 1989-09-11,2709.54,2720.74,2677.05,2704.41,126020000,2704.41 1989-09-08,2706.88,2728.72,2679.33,2709.54,154090000,2709.54 1989-09-07,2719.79,2748.10,2692.06,2706.88,160160000,2706.88 1989-09-06,2744.68,2752.09,2697.95,2719.79,161800000,2719.79 1989-09-05,2752.09,2768.24,2731.76,2744.68,145180000,2744.68 1989-09-01,2737.27,2766.91,2726.82,2752.09,133300000,2752.09 1989-08-31,2728.15,2744.68,2717.90,2737.27,144820000,2737.27 1989-08-30,2726.63,2753.61,2705.36,2728.15,174350000,2728.15 1989-08-29,2743.36,2757.60,2704.98,2726.63,175210000,2726.63 1989-08-28,2732.36,2748.10,2710.74,2743.36,131180000,2743.36 1989-08-25,2734.64,2758.73,2715.48,2732.36,165930000,2732.36 1989-08-24,2678.11,2738.43,2672.23,2734.64,225520000,2734.64 1989-08-23,2650.99,2683.61,2641.20,2678.11,159640000,2678.11 1989-08-22,2647.00,2659.71,2619.71,2650.99,141930000,2650.99 1989-08-21,2687.97,2693.29,2640.74,2647.00,136800000,2647.00 1989-08-18,2679.63,2697.29,2665.97,2687.97,145810000,2687.97 1989-08-17,2693.29,2703.72,2663.13,2679.63,157560000,2679.63 1989-08-16,2687.78,2712.25,2680.58,2693.29,150060000,2693.29 1989-08-15,2677.92,2705.24,2665.02,2687.78,148770000,2687.78 1989-08-14,2683.99,2702.96,2654.97,2677.92,142010000,2677.92 1989-08-11,2712.63,2747.53,2668.06,2683.99,197550000,2683.99 1989-08-10,2686.08,2732.93,2669.58,2712.63,198660000,2712.63 1989-08-09,2699.17,2725.15,2678.11,2686.08,209900000,2686.08 1989-08-08,2694.99,2718.51,2676.97,2699.17,200340000,2699.17 1989-08-07,2653.45,2701.82,2643.59,2694.99,197580000,2694.99 1989-08-04,2661.61,2679.44,2629.17,2653.45,169750000,2653.45 1989-08-03,2657.44,2676.97,2640.74,2661.61,168690000,2661.61 1989-08-02,2641.12,2668.44,2626.33,2657.44,181760000,2657.44 1989-08-01,2660.66,2687.03,2628.98,2641.12,225280000,2641.12 1989-07-31,2635.24,2668.25,2615.52,2660.66,166650000,2660.66 1989-07-28,2635.43,2656.68,2615.71,2635.24,180610000,2635.24 1989-07-27,2613.05,2649.28,2604.70,2635.43,213680000,2635.43 1989-07-26,2583.08,2621.21,2563.73,2613.05,188270000,2613.05 1989-07-25,2584.98,2610.39,2565.63,2583.08,179270000,2583.08 1989-07-24,2607.36,2607.55,2574.36,2584.98,136260000,2584.98 1989-07-21,2575.49,2613.81,2558.23,2607.36,174880000,2607.36 1989-07-20,2584.41,2616.46,2566.58,2575.49,204590000,2575.49 1989-07-19,2546.47,2590.48,2546.47,2584.41,215740000,2584.41 1989-07-18,2553.49,2560.70,2531.68,2544.76,152350000,2544.76 1989-07-17,2554.82,2565.82,2535.85,2553.49,131960000,2553.49 1989-07-14,2538.32,2563.16,2512.52,2554.82,183480000,2554.82 1989-07-13,2532.63,2553.30,2517.83,2538.32,153820000,2538.32 1989-07-12,2514.61,2543.25,2504.93,2532.63,160550000,2532.63 1989-07-11,2503.41,2537.37,2503.41,2514.61,171590000,2514.61 1989-07-10,2487.86,2513.47,2482.74,2502.66,131870000,2502.66 1989-07-07,2462.44,2503.22,2456.56,2487.86,166430000,2487.86 1989-07-06,2456.56,2475.53,2446.51,2462.44,140450000,2462.44 1989-07-05,2452.77,2467.18,2431.53,2456.56,127710000,2456.56 1989-07-03,2440.06,2461.49,2434.37,2452.77,68870000,2452.77 1989-06-30,2458.27,2466.05,2412.94,2440.06,170490000,2440.06 1989-06-29,2500.00,2500.00,2451.25,2458.27,167100000,2458.27 1989-06-28,2526.37,2531.87,2485.77,2504.74,158470000,2504.74 1989-06-27,2511.38,2544.95,2507.40,2526.37,171090000,2526.37 1989-06-26,2531.87,2536.99,2500.57,2511.38,143600000,2511.38 1989-06-23,2490.14,2536.04,2490.14,2531.87,198720000,2531.87 1989-06-22,2464.91,2489.75,2452.77,2481.98,176510000,2481.98 1989-06-21,2472.88,2489.38,2450.87,2464.91,168830000,2464.91 1989-06-20,2479.89,2496.02,2463.96,2472.88,167650000,2472.88 1989-06-19,2486.38,2497.15,2465.10,2479.89,130720000,2479.89 1989-06-16,2475.00,2495.52,2461.57,2486.38,244510000,2486.38 1989-06-15,2503.36,2505.97,2460.82,2475.00,179480000,2475.00 1989-06-14,2503.54,2520.71,2486.01,2503.36,170540000,2503.36 1989-06-13,2518.84,2520.71,2484.70,2503.54,164870000,2503.54 1989-06-12,2513.42,2528.17,2484.70,2518.84,151460000,2518.84 1989-06-09,2516.91,2533.64,2494.85,2513.42,173240000,2513.42 1989-06-08,2512.32,2534.38,2499.81,2516.91,212310000,2516.91 1989-06-07,2496.32,2526.65,2493.38,2512.32,213710000,2512.32 1989-06-06,2480.70,2507.54,2471.32,2496.32,187570000,2496.32 1989-06-05,2517.83,2523.71,2476.84,2480.70,163420000,2480.70 1989-06-02,2492.46,2530.88,2492.46,2517.83,229140000,2517.83 1989-06-01,2480.15,2504.23,2469.67,2490.63,223160000,2490.63 1989-05-31,2475.55,2497.06,2462.68,2480.15,162530000,2480.15 1989-05-30,2493.77,2510.66,2459.74,2475.55,151780000,2475.55 1989-05-26,2482.59,2501.83,2473.06,2493.77,143120000,2493.77 1989-05-25,2483.87,2498.72,2467.56,2482.59,154470000,2482.59 1989-05-24,2478.01,2494.68,2458.03,2483.87,178600000,2483.87 1989-05-23,2501.83,2501.83,2467.19,2478.01,187690000,2478.01 1989-05-22,2501.10,2521.63,2481.30,2502.02,185010000,2502.02 1989-05-19,2470.12,2509.34,2468.29,2501.10,242410000,2501.10 1989-05-18,2462.43,2482.77,2448.86,2470.12,177480000,2470.12 1989-05-17,2453.45,2478.74,2439.70,2462.43,191210000,2462.43 1989-05-16,2463.89,2469.76,2439.88,2453.45,173100000,2453.45 1989-05-15,2439.70,2474.89,2432.37,2463.89,179350000,2463.89 1989-05-12,2408.54,2447.95,2408.54,2439.70,221490000,2439.70 1989-05-11,2374.45,2393.51,2365.83,2382.88,151620000,2382.88 1989-05-10,2371.33,2387.83,2361.25,2374.45,146000000,2374.45 1989-05-09,2376.47,2390.03,2356.85,2371.33,150090000,2371.33 1989-05-08,2381.96,2387.28,2356.30,2376.47,135130000,2376.47 1989-05-05,2384.90,2415.69,2371.88,2381.96,180810000,2381.96 1989-05-04,2393.70,2399.01,2373.35,2384.90,153130000,2384.90 1989-05-03,2402.86,2410.56,2382.51,2393.70,171690000,2393.70 1989-05-02,2414.96,2434.38,2394.61,2402.86,172560000,2402.86 1989-05-01,2418.80,2420.45,2390.03,2414.96,138050000,2414.96 1989-04-28,2418.99,2430.17,2403.59,2418.80,158390000,2418.80 1989-04-27,2389.10,2433.10,2387.83,2418.99,191170000,2418.99 1989-04-26,2386.85,2404.14,2373.90,2389.10,146090000,2389.10 1989-04-25,2402.68,2421.55,2378.85,2386.85,165430000,2386.85 1989-04-24,2409.46,2416.06,2385.08,2402.68,142100000,2402.68 1989-04-21,2377.38,2412.57,2373.35,2409.46,187310000,2409.46 1989-04-20,2386.91,2399.19,2356.85,2377.38,175970000,2377.38 1989-04-19,2379.40,2398.46,2369.50,2386.91,191510000,2386.91 1989-04-18,2351.36,2386.00,2351.36,2379.40,208650000,2379.40 1989-04-17,2337.06,2348.42,2324.23,2337.79,128540000,2337.79 1989-04-14,2308.83,2342.93,2308.83,2337.06,169780000,2337.06 1989-04-13,2319.65,2321.85,2289.59,2296.00,141590000,2296.00 1989-04-12,2311.58,2332.84,2304.44,2319.65,165200000,2319.65 1989-04-11,2301.87,2321.48,2295.27,2311.58,146830000,2311.58 1989-04-10,2304.80,2316.35,2293.62,2301.87,123990000,2301.87 1989-04-07,2291.97,2314.88,2282.07,2304.80,156950000,2304.80 1989-04-06,2304.80,2305.72,2282.81,2291.97,146530000,2291.97 1989-04-05,2298.20,2315.80,2286.84,2304.80,165880000,2304.80 1989-04-04,2304.80,2314.88,2282.44,2298.20,160680000,2298.20 1989-04-03,2293.62,2317.45,2284.09,2304.80,164660000,2304.80 1989-03-31,2281.34,2303.34,2277.49,2293.62,170960000,2293.62 1989-03-30,2281.52,2296.92,2264.30,2281.34,159950000,2281.34 1989-03-29,2275.54,2293.62,2262.65,2281.52,144240000,2281.52 1989-03-28,2258.93,2287.86,2258.93,2275.54,146420000,2275.54 1989-03-27,2243.04,2264.29,2234.46,2257.86,112960000,2257.86 1989-03-23,2263.21,2274.82,2235.89,2243.04,153750000,2243.04 1989-03-22,2266.25,2276.61,2247.14,2263.21,146570000,2263.21 1989-03-21,2262.50,2284.82,2259.64,2266.25,142010000,2266.25 1989-03-20,2286.25,2286.25,2249.46,2262.50,151260000,2262.50 1989-03-17,2307.68,2307.68,2267.86,2292.14,242900000,2292.14 1989-03-16,2320.54,2351.07,2315.36,2340.71,196040000,2340.71 1989-03-15,2306.25,2332.86,2300.71,2320.54,167070000,2320.54 1989-03-14,2306.25,2322.68,2296.79,2306.25,139970000,2306.25 1989-03-13,2282.68,2319.64,2282.68,2306.25,140460000,2306.25 1989-03-10,2291.43,2295.54,2261.61,2282.14,146830000,2282.14 1989-03-09,2295.54,2306.61,2282.86,2291.43,143160000,2291.43 1989-03-08,2290.71,2316.79,2280.00,2295.54,167620000,2295.54 1989-03-07,2294.82,2308.57,2278.93,2290.71,172500000,2290.71 1989-03-06,2274.29,2302.50,2270.36,2294.82,168880000,2294.82 1989-03-03,2265.71,2281.79,2255.00,2274.29,151790000,2274.29 1989-03-02,2243.04,2274.82,2239.82,2265.71,161980000,2265.71 1989-03-01,2258.39,2277.50,2234.82,2243.04,177210000,2243.04 1989-02-28,2250.36,2269.82,2242.68,2258.39,147430000,2258.39 1989-02-27,2245.54,2260.18,2232.14,2250.36,139900000,2250.36 1989-02-24,2289.46,2291.79,2242.68,2245.54,160680000,2245.54 1989-02-23,2283.93,2295.71,2266.96,2289.46,150370000,2289.46 1989-02-22,2326.43,2327.50,2276.43,2283.93,163140000,2283.93 1989-02-21,2324.82,2340.89,2311.79,2326.43,141950000,2326.43 1989-02-17,2311.43,2339.11,2301.96,2324.82,159520000,2324.82 1989-02-16,2303.93,2326.07,2293.04,2311.43,177450000,2311.43 1989-02-15,2281.25,2309.64,2275.71,2303.93,154220000,2303.93 1989-02-14,2282.50,2308.04,2274.82,2281.25,150610000,2281.25 1989-02-13,2286.07,2295.71,2266.07,2282.50,143520000,2282.50 1989-02-10,2322.32,2322.32,2278.93,2286.07,173560000,2286.07 1989-02-09,2343.21,2347.68,2308.39,2323.04,224220000,2323.04 1989-02-08,2347.14,2369.29,2332.86,2343.21,189420000,2343.21 1989-02-07,2321.07,2363.75,2314.64,2347.14,217260000,2347.14 1989-02-06,2331.25,2338.04,2307.67,2321.07,150980000,2321.07 1989-02-03,2333.75,2348.75,2319.29,2331.25,172980000,2331.25 1989-02-02,2338.21,2352.86,2319.64,2333.75,183430000,2333.75 1989-02-01,2342.32,2355.00,2319.29,2338.21,215640000,2338.21 1989-01-31,2324.11,2350.18,2302.68,2342.32,194050000,2342.32 1989-01-30,2322.86,2336.61,2304.82,2324.11,167830000,2324.11 1989-01-27,2295.18,2349.64,2295.18,2322.86,254870000,2322.86 1989-01-26,2265.89,2303.57,2252.86,2291.07,212250000,2291.07 1989-01-25,2256.43,2277.68,2242.32,2265.89,183610000,2265.89 1989-01-24,2218.39,2264.82,2214.82,2256.43,189620000,2256.43 1989-01-23,2235.36,2254.64,2214.29,2218.39,141640000,2218.39 1989-01-20,2239.11,2249.46,2221.25,2235.36,166120000,2235.36 1989-01-19,2238.75,2254.64,2225.71,2239.11,192030000,2239.11 1989-01-18,2214.64,2247.32,2202.50,2238.75,187450000,2238.75 1989-01-17,2224.64,2227.50,2203.75,2214.64,143930000,2214.64 1989-01-16,2226.07,2236.25,2215.36,2224.64,117380000,2224.64 1989-01-13,2222.32,2235.00,2210.54,2226.07,132320000,2226.07 1989-01-12,2206.43,2239.11,2200.71,2222.32,183000000,2222.32 1989-01-11,2193.21,2211.96,2185.71,2206.43,148950000,2206.43 1989-01-10,2199.46,2213.75,2182.32,2193.21,140420000,2193.21 1989-01-09,2194.29,2209.11,2185.00,2199.46,163180000,2199.46 1989-01-06,2190.54,2213.75,2182.32,2194.29,161330000,2194.29 1989-01-05,2177.68,2205.18,2173.04,2190.54,174040000,2190.54 1989-01-04,2146.61,2183.39,2146.61,2177.68,149700000,2177.68 1989-01-03,2168.39,2168.39,2127.14,2144.64,128500000,2144.64 1988-12-30,2182.68,2193.75,2162.50,2168.57,127210000,2168.57 1988-12-29,2166.43,2193.04,2165.18,2182.68,131290000,2182.68 1988-12-28,2162.68,2179.64,2153.57,2166.43,110630000,2166.43 1988-12-27,2168.93,2179.29,2156.43,2162.68,87490000,2162.68 1988-12-23,2160.36,2176.79,2157.86,2168.93,81760000,2168.93 1988-12-22,2164.64,2176.43,2151.43,2160.36,150510000,2160.36 1988-12-21,2166.07,2178.57,2146.07,2164.64,147250000,2164.64 1988-12-20,2172.68,2192.68,2161.25,2166.07,161090000,2166.07 1988-12-19,2150.71,2179.64,2141.07,2172.68,162250000,2172.68 1988-12-16,2133.00,2155.00,2131.25,2150.71,196480000,2150.71 1988-12-15,2134.25,2146.87,2120.91,2133.00,136820000,2133.00 1988-12-14,2143.49,2149.71,2121.44,2134.25,132350000,2134.25 1988-12-13,2139.58,2151.14,2119.31,2143.49,132340000,2143.49 1988-12-12,2143.49,2165.18,2133.89,2139.58,124160000,2139.58 1988-12-09,2141.71,2157.00,2131.40,2143.49,133770000,2143.49 1988-12-08,2153.63,2162.70,2133.18,2141.71,124150000,2141.71 1988-12-07,2149.36,2167.50,2136.38,2153.63,148360000,2153.63 1988-12-06,2123.76,2158.96,2117.18,2149.36,158340000,2149.36 1988-12-05,2092.64,2135.49,2092.64,2123.76,144660000,2123.76 1988-12-02,2101.88,2105.80,2075.39,2092.28,124610000,2092.28 1988-12-01,2114.51,2120.02,2095.48,2101.88,129380000,2101.88 1988-11-30,2101.53,2126.60,2095.48,2114.51,157810000,2114.51 1988-11-29,2081.44,2112.73,2072.37,2101.53,127420000,2101.53 1988-11-28,2074.68,2095.48,2062.23,2081.44,123480000,2081.44 1988-11-25,2085.53,2085.53,2062.77,2074.68,72090000,2074.68 1988-11-23,2077.70,2103.49,2071.48,2092.28,112010000,2092.28 1988-11-22,2065.97,2086.77,2052.63,2077.70,127000000,2077.70 1988-11-21,2062.41,2070.06,2036.81,2065.97,120430000,2065.97 1988-11-18,2052.45,2070.41,2047.12,2062.41,119320000,2062.41 1988-11-17,2038.58,2064.19,2033.25,2052.45,141280000,2052.45 1988-11-16,2077.17,2081.43,2026.67,2038.58,161710000,2038.58 1988-11-15,2065.08,2088.02,2060.28,2077.17,115170000,2077.17 1988-11-14,2067.03,2084.81,2050.85,2065.08,142900000,2065.08 1988-11-11,2107.04,2107.04,2063.48,2067.03,135500000,2067.03 1988-11-10,2118.24,2132.82,2103.31,2114.69,128920000,2114.69 1988-11-09,2127.49,2134.25,2100.82,2118.24,153140000,2118.24 1988-11-08,2124.64,2146.87,2119.67,2127.49,141660000,2127.49 1988-11-07,2143.14,2143.14,2113.62,2124.64,133870000,2124.64 1988-11-04,2170.34,2173.90,2139.58,2145.80,143580000,2145.80 1988-11-03,2156.83,2185.99,2153.45,2170.34,152980000,2170.34 1988-11-02,2150.96,2167.67,2131.40,2156.83,161300000,2156.83 1988-11-01,2148.65,2165.36,2132.82,2150.96,151250000,2150.96 1988-10-31,2149.89,2161.63,2125.36,2148.65,143460000,2148.65 1988-10-28,2140.83,2166.43,2135.67,2149.89,146300000,2149.89 1988-10-27,2165.18,2165.54,2120.90,2140.83,196540000,2140.83 1988-10-26,2173.36,2182.61,2148.47,2165.18,181550000,2165.18 1988-10-25,2170.34,2188.66,2156.83,2173.36,155190000,2173.36 1988-10-24,2183.50,2195.06,2162.16,2170.34,170590000,2170.34 1988-10-21,2181.19,2193.28,2152.56,2183.50,195410000,2183.50 1988-10-20,2137.27,2183.68,2129.80,2181.19,189580000,2181.19 1988-10-19,2159.85,2173.01,2110.95,2137.27,186350000,2137.27 1988-10-18,2140.47,2163.41,2124.29,2159.85,162500000,2159.85 1988-10-17,2133.18,2150.60,2120.02,2140.47,119290000,2140.47 1988-10-14,2133.36,2153.80,2118.77,2133.18,160240000,2133.18 1988-10-13,2126.24,2149.54,2114.33,2133.36,154530000,2133.36 1988-10-12,2146.34,2146.34,2113.44,2126.24,154840000,2126.24 1988-10-11,2158.96,2164.83,2137.09,2156.47,140900000,2156.47 1988-10-10,2150.25,2167.85,2139.05,2158.96,124660000,2158.96 1988-10-07,2112.02,2157.72,2112.02,2150.25,216390000,2150.25 1988-10-06,2106.51,2118.07,2092.64,2107.75,153570000,2107.75 1988-10-05,2102.06,2119.84,2082.33,2106.51,175130000,2106.51 1988-10-04,2105.26,2119.49,2090.68,2102.06,157760000,2102.06 1988-10-03,2112.91,2116.82,2083.39,2105.26,130380000,2105.26 1988-09-30,2119.31,2140.29,2104.02,2112.91,175750000,2112.91 1988-09-29,2085.53,2123.76,2084.46,2119.31,155790000,2119.31 1988-09-28,2082.33,2096.37,2070.23,2085.53,113720000,2085.53 1988-09-27,2085.17,2095.30,2069.88,2082.33,113010000,2082.33 1988-09-26,2090.68,2098.33,2075.75,2085.17,116420000,2085.17 1988-09-23,2080.01,2097.79,2070.41,2090.68,145100000,2090.68 1988-09-22,2090.50,2097.08,2066.68,2080.01,150670000,2080.01 1988-09-21,2087.48,2103.31,2078.77,2090.50,127400000,2090.50 1988-09-20,2081.08,2097.26,2073.26,2087.48,142220000,2087.48 1988-09-19,2096.55,2096.55,2066.86,2081.08,135770000,2081.08 1988-09-16,2092.28,2113.26,2077.70,2098.15,211110000,2098.15 1988-09-15,2100.64,2112.55,2084.28,2092.28,161210000,2092.28 1988-09-14,2083.04,2111.66,2079.13,2100.64,177220000,2100.64 1988-09-13,2072.37,2088.19,2055.30,2083.04,162490000,2083.04 1988-09-12,2068.81,2088.55,2057.43,2072.37,114880000,2072.37 1988-09-09,2063.12,2088.73,2038.94,2068.81,141540000,2068.81 1988-09-08,2065.79,2079.30,2052.10,2063.12,149380000,2063.12 1988-09-07,2065.26,2084.10,2052.98,2065.79,139590000,2065.79 1988-09-06,2054.59,2074.68,2046.41,2065.26,122250000,2065.26 1988-09-02,2022.40,2064.72,2022.40,2054.59,159840000,2054.59 1988-09-01,2027.56,2027.56,1988.44,2002.31,144090000,2002.31 1988-08-31,2038.23,2052.45,2022.94,2031.65,130480000,2031.65 1988-08-30,2041.43,2052.63,2028.63,2038.23,108720000,2038.23 1988-08-29,2019.56,2047.47,2019.56,2041.43,99280000,2041.43 1988-08-26,2010.85,2027.38,2002.13,2017.43,89240000,2017.43 1988-08-25,2022.05,2022.05,1990.40,2010.85,127640000,2010.85 1988-08-24,1990.04,2028.98,1990.04,2026.67,127800000,2026.67 1988-08-23,1990.22,2008.36,1978.66,1989.33,119540000,1989.33 1988-08-22,2016.00,2027.56,1987.02,1990.22,122250000,1990.22 1988-08-19,2027.03,2048.89,2011.38,2016.00,122370000,2016.00 1988-08-18,2025.96,2046.59,2014.22,2027.03,139820000,2027.03 1988-08-17,2021.51,2042.50,2003.38,2025.96,169500000,2025.96 1988-08-16,2004.27,2043.56,1993.78,2021.51,162790000,2021.51 1988-08-15,2037.52,2040.72,1998.76,2004.27,128560000,2004.27 1988-08-12,2039.30,2046.41,2019.38,2037.52,176960000,2037.52 1988-08-11,2034.14,2050.68,2014.05,2039.30,173000000,2039.30 1988-08-10,2078.41,2078.41,2023.65,2034.14,200950000,2034.14 1988-08-09,2107.40,2110.95,2061.17,2079.13,200710000,2079.13 1988-08-08,2119.13,2134.60,2103.13,2107.40,148800000,2107.40 1988-08-05,2126.60,2128.73,2105.44,2119.13,113400000,2119.13 1988-08-04,2134.07,2147.23,2117.71,2126.60,157240000,2126.60 1988-08-03,2131.22,2147.94,2115.40,2134.07,203590000,2134.07 1988-08-02,2130.51,2151.85,2112.20,2131.22,166660000,2131.22 1988-08-01,2128.73,2146.16,2111.84,2130.51,138170000,2130.51 1988-07-29,2084.28,2133.01,2084.28,2128.73,192340000,2128.73 1988-07-28,2053.70,2089.26,2047.30,2082.33,154570000,2082.33 1988-07-27,2073.97,2089.97,2047.30,2053.70,135890000,2053.70 1988-07-26,2071.83,2089.79,2059.57,2073.97,121960000,2073.97 1988-07-25,2060.99,2082.68,2048.54,2071.83,215140000,2071.83 1988-07-22,2086.59,2092.46,2050.32,2060.99,148880000,2060.99 1988-07-21,2110.60,2113.62,2077.17,2086.59,149460000,2086.59 1988-07-20,2097.26,2119.31,2091.39,2110.60,151990000,2110.60 1988-07-19,2117.89,2127.67,2081.08,2097.26,144110000,2097.26 1988-07-18,2129.45,2132.47,2099.40,2117.89,156210000,2117.89 1988-07-15,2113.62,2140.11,2099.57,2129.45,199710000,2129.45 1988-07-14,2104.37,2125.71,2089.26,2113.62,172410000,2113.62 1988-07-13,2092.64,2114.15,2073.44,2104.37,218930000,2104.37 1988-07-12,2111.31,2121.44,2077.52,2092.64,161650000,2092.64 1988-07-11,2106.15,2126.07,2098.68,2111.31,123300000,2111.31 1988-07-08,2122.69,2133.53,2099.40,2106.15,136070000,2106.15 1988-07-07,2130.16,2136.02,2101.35,2122.69,156100000,2122.69 1988-07-06,2158.61,2169.45,2107.40,2130.16,189630000,2130.16 1988-07-05,2131.58,2162.52,2117.71,2158.61,171790000,2158.61 1988-07-01,2141.71,2151.14,2118.95,2131.58,238330000,2131.58 1988-06-30,2121.98,2147.22,2112.38,2141.71,227410000,2141.71 1988-06-29,2130.87,2144.91,2106.15,2121.98,159590000,2121.98 1988-06-28,2108.46,2139.94,2100.11,2130.87,152370000,2130.87 1988-06-27,2142.96,2147.76,2101.17,2108.46,264410000,2108.46 1988-06-24,2148.29,2165.36,2133.53,2142.96,179880000,2142.96 1988-06-23,2152.20,2169.10,2131.58,2148.29,185770000,2148.29 1988-06-22,2131.05,2160.10,2131.05,2152.20,217510000,2152.20 1988-06-21,2083.93,2115.75,2072.55,2109.17,155060000,2109.17 1988-06-20,2100.11,2100.11,2072.72,2083.93,116750000,2083.93 1988-06-17,2094.24,2116.47,2070.06,2104.02,343920000,2104.02 1988-06-16,2120.73,2120.73,2081.97,2094.24,161550000,2094.24 1988-06-15,2124.47,2137.45,2109.53,2131.40,150260000,2131.40 1988-06-14,2111.13,2148.12,2111.13,2124.47,227150000,2124.47 1988-06-13,2101.71,2114.15,2084.64,2099.40,125310000,2099.40 1988-06-10,2093.35,2123.58,2084.81,2101.71,155710000,2101.71 1988-06-09,2102.95,2119.31,2081.79,2093.35,235160000,2093.35 1988-06-08,2055.83,2113.62,2055.83,2102.95,310030000,2102.95 1988-06-07,2075.21,2082.86,2042.85,2054.59,168710000,2054.59 1988-06-06,2071.31,2083.93,2049.08,2075.21,152460000,2075.21 1988-06-03,2052.45,2080.73,2040.00,2071.31,189600000,2071.31 1988-06-02,2064.01,2073.26,2030.94,2052.45,193540000,2052.45 1988-06-01,2031.12,2076.28,2021.34,2064.01,234560000,2064.01 1988-05-31,1959.38,2034.50,1959.38,2031.12,247610000,2031.12 1988-05-27,1966.70,1970.84,1944.35,1956.44,133590000,1956.44 1988-05-26,1961.37,1981.15,1951.28,1966.70,164260000,1966.70 1988-05-25,1962.53,1984.58,1952.42,1961.37,138310000,1961.37 1988-05-24,1941.48,1967.67,1938.16,1962.53,139930000,1962.53 1988-05-23,1952.59,1962.37,1927.22,1941.48,102640000,1941.48 1988-05-20,1958.72,1978.78,1940.48,1952.59,120600000,1952.59 1988-05-19,1951.09,1963.86,1921.58,1958.72,165160000,1958.72 1988-05-18,1986.41,1992.87,1941.31,1951.09,209420000,1951.09 1988-05-17,2007.63,2026.69,1982.10,1986.41,133850000,1986.41 1988-05-16,1990.55,2012.60,1980.27,2007.63,155010000,2007.63 1988-05-13,1971.65,2001.49,1971.65,1990.55,147240000,1990.55 1988-05-12,1965.85,1986.07,1960.54,1968.00,143880000,1968.00 1988-05-11,1988.56,1988.56,1950.27,1965.85,176720000,1965.85 1988-05-10,1997.35,2020.23,1984.42,2003.64,131200000,2003.64 1988-05-09,2007.46,2013.59,1984.08,1997.35,166320000,1997.35 1988-05-06,2013.59,2013.59,1997.84,2007.46,129080000,2007.46 1988-05-05,2036.31,2038.46,2008.95,2020.23,171840000,2020.23 1988-05-04,2058.36,2067.14,2029.51,2036.31,141320000,2036.31 1988-05-03,2043.27,2071.95,2036.64,2058.36,176920000,2058.36 1988-05-02,2032.33,2050.40,2019.89,2043.27,136470000,2043.27 1988-04-29,2041.28,2047.91,2014.09,2032.33,135620000,2032.33 1988-04-28,2047.91,2057.53,2026.03,2041.28,128680000,2041.28 1988-04-27,2044.76,2066.64,2033.16,2047.91,133810000,2047.91 1988-04-26,2035.97,2062.83,2023.04,2044.76,152300000,2044.76 1988-04-25,2015.09,2048.57,2011.60,2035.97,156950000,2035.97 1988-04-22,1987.40,2029.01,1985.58,2015.09,152520000,2015.09 1988-04-21,1985.41,2030.50,1968.00,1987.40,168440000,1987.40 1988-04-20,1999.50,2015.42,1975.46,1985.41,147590000,1985.41 1988-04-19,2008.12,2047.08,1990.55,1999.50,161910000,1999.50 1988-04-18,2013.93,2023.04,1985.74,2008.12,144650000,2008.12 1988-04-15,2005.64,2031.33,1969.99,2013.93,234160000,2013.93 1988-04-14,2083.22,2083.22,1998.18,2005.64,211810000,2005.64 1988-04-13,2110.08,2121.35,2082.56,2107.10,185120000,2107.10 1988-04-12,2095.99,2120.69,2085.38,2110.08,146400000,2110.08 1988-04-11,2090.19,2110.08,2075.27,2095.99,146370000,2095.99 1988-04-08,2062.17,2103.78,2051.06,2090.19,169300000,2090.19 1988-04-07,2061.67,2084.22,2042.94,2062.17,177840000,2062.17 1988-04-06,1997.51,2067.47,1987.67,2061.67,189760000,2061.67 1988-04-05,1980.60,2011.27,1969.99,1997.51,135290000,1997.51 1988-04-04,1988.06,2013.26,1969.50,1980.60,182240000,1980.60 1988-03-31,1978.12,2000.66,1957.89,1988.06,139870000,1988.06 1988-03-30,1998.34,2017.57,1973.81,1978.12,151810000,1978.12 1988-03-29,1979.77,2018.57,1979.61,1998.34,152680000,1998.34 1988-03-28,1978.95,1990.88,1951.26,1979.77,142820000,1979.77 1988-03-25,2023.87,2031.00,1973.81,1978.95,163170000,1978.95 1988-03-24,2052.22,2052.22,2011.44,2023.87,184910000,2023.87 1988-03-23,2066.15,2085.05,2047.41,2067.64,167370000,2067.64 1988-03-22,2067.14,2082.89,2052.06,2066.15,142000000,2066.15 1988-03-21,2081.23,2081.23,2050.56,2067.14,128830000,2067.14 1988-03-18,2086.04,2110.91,2067.64,2087.37,245750000,2087.37 1988-03-17,2064.32,2093.17,2057.03,2086.04,211920000,2086.04 1988-03-16,2047.41,2070.62,2027.02,2064.32,153590000,2064.32 1988-03-15,2050.07,2062.17,2030.17,2047.41,133170000,2047.41 1988-03-14,2034.98,2056.70,2023.04,2050.07,131890000,2050.07 1988-03-11,2026.03,2049.07,1996.52,2034.98,200020000,2034.98 1988-03-10,2074.27,2081.40,2020.72,2026.03,197260000,2026.03 1988-03-09,2081.07,2094.16,2064.49,2074.27,210900000,2074.27 1988-03-08,2056.37,2090.02,2055.04,2081.07,237680000,2081.07 1988-03-07,2057.86,2069.30,2034.15,2056.37,152980000,2056.37 1988-03-04,2063.49,2076.76,2033.49,2057.86,201410000,2057.86 1988-03-03,2071.29,2085.38,2048.41,2063.49,203310000,2063.49 1988-03-02,2070.47,2094.50,2058.19,2071.29,199630000,2071.29 1988-03-01,2071.62,2086.87,2048.57,2070.47,199990000,2070.47 1988-02-29,2023.21,2074.60,2017.24,2071.62,236050000,2071.62 1988-02-26,2017.57,2034.81,2005.14,2023.21,158060000,2023.21 1988-02-25,2039.95,2078.08,2006.96,2017.57,213490000,2017.57 1988-02-24,2039.12,2061.34,2022.88,2039.95,212730000,2039.95 1988-02-23,2040.29,2057.03,2017.24,2039.12,192260000,2039.12 1988-02-22,2014.59,2051.72,1998.67,2040.29,178930000,2040.29 1988-02-19,1986.41,2018.90,1973.31,2014.59,180300000,2014.59 1988-02-18,2000.99,2011.94,1972.81,1986.41,151430000,1986.41 1988-02-17,2005.97,2026.19,1982.59,2000.99,176830000,2000.99 1988-02-16,1983.26,2010.44,1969.83,2005.97,135380000,2005.97 1988-02-12,1961.54,1995.69,1958.06,1983.26,177190000,1983.26 1988-02-11,1962.04,1982.43,1942.81,1961.54,200760000,1961.54 1988-02-10,1916.78,1967.84,1916.78,1962.04,187980000,1962.04 1988-02-09,1895.72,1921.58,1885.78,1914.46,162350000,1914.46 1988-02-08,1910.48,1911.14,1878.15,1895.72,168850000,1895.72 1988-02-05,1923.57,1940.32,1906.32,1910.48,161310000,1910.48 1988-02-04,1924.57,1941.48,1902.85,1923.57,186490000,1923.57 1988-02-03,1952.92,1971.98,1907.00,1924.57,237270000,1924.57 1988-02-02,1944.63,1965.02,1921.09,1952.92,164920000,1952.92 1988-02-01,1958.22,1985.41,1935.84,1944.63,210660000,1944.63 1988-01-29,1930.04,1967.67,1921.09,1958.22,211880000,1958.22 1988-01-28,1911.14,1944.13,1904.34,1930.04,166430000,1930.04 1988-01-27,1920.59,1951.76,1889.42,1911.14,176360000,1911.14 1988-01-26,1946.45,1957.73,1906.50,1920.59,138380000,1920.59 1988-01-25,1903.51,1960.54,1897.05,1946.45,275250000,1946.45 1988-01-22,1879.31,1911.80,1876.16,1903.51,147050000,1903.51 1988-01-21,1879.14,1903.35,1845.99,1879.31,158080000,1879.31 1988-01-20,1934.85,1934.85,1858.59,1879.14,181660000,1879.14 1988-01-19,1963.86,1977.29,1923.41,1936.34,153550000,1936.34 1988-01-18,1956.07,1977.45,1938.99,1963.86,135100000,1963.86 1988-01-15,1942.47,1988.40,1942.47,1956.07,197940000,1956.07 1988-01-14,1924.73,1940.15,1894.06,1916.11,140570000,1916.11 1988-01-13,1928.55,1959.55,1884.95,1924.73,154020000,1924.73 1988-01-12,1945.13,1950.76,1877.98,1928.55,165730000,1928.55 1988-01-11,1911.31,1985.25,1886.94,1945.13,158980000,1945.13 1988-01-08,2051.89,2058.69,1898.04,1911.31,197300000,1911.31 1988-01-07,2037.80,2061.51,2004.64,2051.89,175360000,2051.89 1988-01-06,2031.50,2058.19,2012.77,2037.80,169730000,2037.80 1988-01-05,2021.39,2075.27,2021.39,2031.50,209520000,2031.50 1988-01-04,1950.76,2030.01,1950.76,2015.25,181810000,2015.25 1987-12-31,1950.10,1951.26,1912.63,1938.83,170140000,1938.83 1987-12-30,1926.89,1966.18,1925.73,1950.10,149230000,1950.10 1987-12-29,1942.97,1951.76,1918.10,1926.89,111580000,1926.89 1987-12-28,1966.18,1966.18,1921.92,1942.97,131220000,1942.97 1987-12-24,2005.64,2013.10,1985.74,1999.67,108800000,1999.67 1987-12-23,1978.45,2019.56,1977.62,2005.64,203110000,2005.64 1987-12-22,1990.38,1999.67,1949.27,1978.45,192650000,1978.45 1987-12-21,1975.30,2008.45,1959.56,1990.38,161790000,1990.38 1987-12-18,1924.73,1982.92,1924.73,1975.30,276220000,1975.30 1987-12-17,1974.47,1987.23,1917.44,1924.40,191780000,1924.40 1987-12-16,1941.48,1984.08,1918.44,1974.47,193820000,1974.47 1987-12-15,1932.86,1969.00,1916.28,1941.48,214970000,1941.48 1987-12-14,1867.04,1942.31,1860.41,1932.86,187680000,1932.86 1987-12-11,1855.44,1887.93,1838.36,1867.04,151680000,1867.04 1987-12-10,1902.52,1923.57,1835.71,1855.44,188960000,1855.44 1987-12-09,1868.37,1924.90,1846.65,1902.52,231430000,1902.52 1987-12-08,1812.17,1870.69,1794.60,1868.37,227310000,1868.37 1987-12-07,1766.74,1818.14,1765.75,1812.17,146660000,1812.17 1987-12-04,1776.53,1794.60,1733.92,1766.74,184800000,1766.74 1987-12-03,1848.97,1859.08,1772.21,1776.53,204160000,1776.53 1987-12-02,1842.34,1871.52,1825.27,1848.97,148890000,1848.97 1987-12-01,1833.55,1877.98,1829.08,1842.34,149870000,1842.34 1987-11-30,1867.37,1867.37,1795.26,1833.55,268910000,1833.55 1987-11-27,1946.95,1955.24,1907.00,1910.48,86360000,1910.48 1987-11-25,1963.53,1982.59,1935.68,1946.95,139780000,1946.95 1987-11-24,1936.84,1985.25,1936.84,1963.53,199520000,1963.53 1987-11-23,1913.63,1931.86,1889.26,1923.08,143160000,1923.08 1987-11-20,1895.39,1926.39,1853.61,1913.63,189170000,1913.63 1987-11-19,1939.16,1941.98,1881.80,1895.39,157140000,1895.39 1987-11-18,1922.25,1951.92,1888.93,1939.16,158260000,1939.16 1987-11-17,1939.99,1939.99,1888.76,1922.25,148240000,1922.25 1987-11-16,1935.01,1979.28,1921.75,1949.10,164340000,1949.10 1987-11-13,1960.21,1973.81,1923.08,1935.01,174920000,1935.01 1987-11-12,1934.84,1983.92,1934.84,1960.21,206280000,1960.21 1987-11-11,1881.96,1927.72,1881.96,1899.20,147850000,1899.20 1987-11-10,1900.20,1911.97,1845.99,1878.15,184310000,1878.15 1987-11-09,1941.81,1941.81,1881.96,1900.20,160690000,1900.20 1987-11-06,1985.41,2023.54,1938.83,1959.05,228290000,1959.05 1987-11-05,1945.29,2011.94,1920.42,1985.41,226000000,1985.41 1987-11-04,1963.53,1980.60,1913.50,1945.29,202500000,1945.29 1987-11-03,1988.40,1988.40,1891.74,1963.53,227800000,1963.53 1987-11-02,1993.53,2027.52,1957.23,2014.09,176000000,2014.09 1987-10-30,1965.68,2049.07,1965.68,1993.53,303400000,1993.53 1987-10-29,1849.30,1971.98,1849.30,1938.33,258100000,1938.33 1987-10-28,1846.49,1904.51,1767.74,1846.82,279400000,1846.82 1987-10-27,1806.70,1904.68,1806.70,1846.49,260200000,1846.49 1987-10-26,1881.80,1881.80,1774.04,1793.93,308800000,1793.93 1987-10-23,1950.43,1993.87,1898.54,1950.76,245600000,1950.76 1987-10-22,2004.97,2004.97,1837.86,1950.43,392200000,1950.43 1987-10-21,1951.76,2081.07,1951.76,2027.85,449600000,2027.85 1987-10-20,1738.74,2067.47,1616.21,1841.01,608100000,1841.01 1987-10-19,2164.16,2164.16,1677.55,1738.74,604300000,1738.74 1987-10-16,2355.09,2396.21,2207.73,2246.73,338500000,2246.73 1987-10-15,2412.70,2439.78,2345.63,2355.09,263200000,2355.09 1987-10-14,2485.15,2485.15,2400.46,2412.70,207400000,2412.70 1987-10-13,2471.44,2528.39,2456.92,2508.16,172900000,2508.16 1987-10-12,2482.21,2504.90,2433.91,2471.44,141900000,2471.44 1987-10-09,2516.64,2531.66,2472.26,2482.21,158300000,2482.21 1987-10-08,2551.08,2561.36,2491.51,2516.64,198700000,2516.64 1987-10-07,2548.63,2570.99,2511.75,2551.08,186300000,2551.08 1987-10-06,2632.83,2632.83,2542.59,2548.63,175600000,2548.63 1987-10-05,2640.99,2658.78,2610.97,2640.18,159700000,2640.18 1987-10-02,2639.20,2662.37,2622.06,2640.99,189100000,2640.99 1987-10-01,2596.28,2648.99,2593.18,2639.20,193200000,2639.20 1987-09-30,2590.57,2613.58,2563.64,2596.28,183100000,2596.28 1987-09-29,2601.50,2627.61,2569.03,2590.57,173500000,2590.57 1987-09-28,2574.58,2634.95,2574.58,2601.50,188100000,2601.50 1987-09-25,2566.42,2584.04,2543.73,2570.17,138000000,2570.17 1987-09-24,2585.67,2603.95,2552.06,2566.42,162200000,2566.42 1987-09-23,2568.05,2602.15,2554.83,2585.67,220300000,2585.67 1987-09-22,2492.82,2572.62,2468.99,2568.05,209500000,2568.05 1987-09-21,2524.64,2559.73,2479.77,2492.82,170100000,2492.82 1987-09-18,2527.90,2554.83,2514.03,2524.64,188100000,2524.64 1987-09-17,2530.19,2557.28,2508.00,2527.90,150700000,2527.90 1987-09-16,2566.58,2585.18,2522.52,2530.19,195700000,2530.19 1987-09-15,2610.48,2610.48,2560.22,2566.58,136200000,2566.58 1987-09-14,2608.74,2634.57,2587.85,2613.04,154400000,2613.04 1987-09-11,2576.05,2625.95,2575.41,2608.74,178000000,2608.74 1987-09-10,2549.43,2595.50,2549.43,2576.05,179800000,2576.05 1987-09-09,2545.12,2570.63,2522.80,2549.27,164900000,2549.27 1987-09-08,2561.38,2571.43,2493.78,2545.12,242900000,2545.12 1987-09-04,2599.49,2617.19,2556.28,2561.38,129100000,2561.38 1987-09-03,2602.04,2642.22,2560.11,2599.49,165200000,2599.49 1987-09-02,2610.97,2631.06,2567.76,2602.04,199900000,2602.04 1987-09-01,2662.95,2695.47,2594.07,2610.97,193500000,2610.97 1987-08-31,2639.35,2679.53,2623.57,2662.95,165800000,2662.95 1987-08-28,2675.06,2680.32,2632.02,2639.35,156300000,2639.35 1987-08-27,2701.85,2705.52,2661.35,2675.06,163600000,2675.06 1987-08-26,2722.42,2742.03,2686.54,2701.85,196200000,2701.85 1987-08-25,2697.07,2746.65,2694.83,2722.42,213500000,2722.42 1987-08-24,2709.50,2730.55,2678.89,2697.07,149400000,2697.07 1987-08-21,2706.79,2735.49,2689.09,2709.50,189600000,2709.50 1987-08-20,2665.97,2714.60,2665.97,2706.79,196600000,2706.79 1987-08-19,2654.66,2676.18,2628.19,2665.82,180900000,2665.82 1987-08-18,2691.01,2691.01,2626.59,2654.66,198400000,2654.66 1987-08-17,2685.43,2713.97,2674.43,2700.57,166100000,2700.57 1987-08-14,2691.49,2713.33,2660.08,2685.43,196100000,2685.43 1987-08-13,2669.32,2714.92,2656.57,2691.49,217100000,2691.49 1987-08-12,2680.48,2700.73,2648.60,2669.32,235800000,2669.32 1987-08-11,2635.84,2691.96,2635.52,2680.48,278100000,2680.48 1987-08-10,2592.00,2640.15,2587.85,2635.84,187200000,2635.84 1987-08-07,2594.23,2611.93,2576.21,2592.00,212700000,2592.00 1987-08-06,2566.65,2600.92,2551.82,2594.23,192000000,2594.23 1987-08-05,2546.72,2586.58,2540.98,2566.65,192700000,2566.65 1987-08-04,2557.08,2568.72,2525.99,2546.72,166500000,2546.72 1987-08-03,2572.07,2585.30,2536.67,2557.08,207800000,2557.08 1987-07-31,2567.44,2588.33,2551.02,2572.07,181900000,2572.07 1987-07-30,2539.54,2576.85,2527.42,2567.44,208000000,2567.44 1987-07-29,2519.77,2548.47,2501.59,2539.54,196200000,2539.54 1987-07-28,2493.94,2537.15,2489.32,2519.77,172600000,2519.77 1987-07-27,2485.33,2511.64,2468.75,2493.94,152000000,2493.94 1987-07-24,2471.94,2494.90,2462.53,2485.33,158400000,2485.33 1987-07-23,2470.18,2492.67,2447.07,2471.94,163700000,2471.94 1987-07-22,2467.95,2488.68,2448.50,2470.18,174700000,2470.18 1987-07-21,2487.82,2500.16,2444.67,2467.95,186600000,2467.95 1987-07-20,2510.04,2512.28,2476.88,2487.82,168100000,2487.82 1987-07-17,2496.97,2533.64,2485.65,2510.04,210000000,2510.04 1987-07-16,2483.74,2511.48,2471.78,2496.97,210900000,2496.97 1987-07-15,2481.35,2502.23,2455.20,2483.74,202300000,2483.74 1987-07-14,2452.97,2492.98,2442.60,2481.35,185900000,2481.35 1987-07-13,2455.99,2470.34,2427.14,2452.97,152500000,2452.97 1987-07-10,2451.21,2470.50,2435.91,2455.99,172100000,2455.99 1987-07-09,2463.97,2478.48,2441.17,2451.21,195400000,2451.21 1987-07-08,2449.78,2476.24,2432.56,2463.97,207500000,2463.97 1987-07-07,2429.53,2467.63,2425.06,2449.78,200700000,2449.78 1987-07-06,2436.70,2460.30,2412.95,2429.53,155000000,2429.53 1987-07-02,2415.66,2446.27,2415.66,2436.70,154900000,2436.70 1987-07-01,2418.53,2430.80,2391.26,2409.76,157000000,2409.76 1987-06-30,2446.91,2456.31,2401.15,2418.53,165500000,2418.53 1987-06-29,2436.86,2460.30,2423.79,2446.91,142500000,2446.91 1987-06-26,2451.05,2453.92,2423.63,2436.86,150500000,2436.86 1987-06-25,2430.33,2460.14,2430.33,2451.05,173500000,2451.05 1987-06-24,2439.73,2454.08,2415.34,2428.41,153800000,2428.41 1987-06-23,2445.51,2466.04,2421.72,2439.73,194200000,2439.73 1987-06-22,2420.85,2457.29,2417.40,2445.51,178200000,2445.51 1987-06-19,2408.13,2431.85,2395.26,2420.85,220500000,2420.85 1987-06-18,2407.35,2419.91,2386.46,2408.13,168600000,2408.13 1987-06-17,2407.35,2427.29,2390.70,2407.35,184700000,2407.35 1987-06-16,2391.54,2415.36,2376.10,2407.35,157800000,2407.35 1987-06-15,2377.73,2409.44,2374.39,2391.54,156900000,2391.54 1987-06-12,2360.74,2399.42,2360.74,2377.73,175100000,2377.73 1987-06-11,2353.61,2380.46,2341.93,2360.13,138900000,2360.13 1987-06-10,2352.70,2389.71,2328.58,2353.61,197400000,2353.61 1987-06-09,2351.64,2366.05,2331.77,2352.70,164200000,2352.70 1987-06-08,2326.15,2362.71,2306.89,2351.64,136400000,2351.64 1987-06-05,2337.08,2346.33,2312.35,2326.15,129100000,2326.15 1987-06-04,2320.69,2343.45,2308.71,2337.08,140300000,2337.08 1987-06-03,2283.37,2330.10,2283.37,2320.69,164200000,2320.69 1987-06-02,2288.23,2306.43,2260.62,2278.22,153400000,2278.22 1987-06-01,2291.57,2319.78,2277.00,2288.23,149300000,2288.23 1987-05-29,2310.68,2332.39,2281.10,2291.57,153500000,2291.57 1987-05-28,2295.81,2323.57,2265.93,2310.68,153800000,2310.68 1987-05-27,2297.94,2320.24,2274.27,2295.81,171400000,2295.81 1987-05-26,2252.88,2305.52,2252.88,2297.94,152500000,2297.94 1987-05-22,2225.77,2258.42,2220.15,2243.20,135800000,2243.20 1987-05-21,2215.87,2256.06,2212.77,2225.77,164800000,2225.77 1987-05-20,2221.28,2248.08,2188.53,2215.87,206800000,2215.87 1987-05-19,2258.66,2280.46,2205.40,2221.28,175400000,2221.28 1987-05-18,2272.52,2282.91,2227.05,2258.66,174200000,2258.66 1987-05-15,2324.48,2324.48,2266.60,2272.52,180800000,2272.52 1987-05-14,2329.68,2349.31,2307.16,2325.49,152000000,2325.49 1987-05-13,2322.60,2346.85,2302.11,2329.68,171000000,2329.68 1987-05-12,2307.30,2333.14,2289.84,2322.60,155300000,2322.60 1987-05-11,2322.30,2380.77,2297.92,2307.30,203700000,2307.30 1987-05-08,2334.66,2357.24,2303.13,2322.30,161900000,2322.30 1987-05-07,2342.19,2368.61,2315.77,2334.66,215200000,2334.66 1987-05-06,2338.07,2369.89,2309.94,2342.19,196600000,2342.19 1987-05-05,2294.60,2348.01,2294.60,2338.07,192300000,2338.07 1987-05-04,2280.40,2305.96,2255.26,2286.22,140600000,2286.22 1987-05-01,2286.36,2312.22,2256.68,2280.40,160100000,2280.40 1987-04-30,2254.26,2314.20,2250.00,2286.36,183100000,2286.36 1987-04-29,2231.96,2283.24,2228.27,2254.26,173600000,2254.26 1987-04-28,2230.54,2270.74,2214.06,2231.96,180100000,2231.96 1987-04-27,2235.37,2264.35,2180.54,2230.54,222700000,2230.54 1987-04-24,2279.55,2279.55,2221.59,2235.37,178000000,2235.37 1987-04-23,2285.94,2318.61,2255.82,2280.97,173900000,2280.97 1987-04-22,2337.07,2353.13,2282.10,2285.94,185900000,2285.94 1987-04-21,2270.60,2341.05,2240.91,2337.07,191300000,2337.07 1987-04-20,2275.99,2300.57,2252.56,2270.60,139100000,2270.60 1987-04-16,2282.95,2320.88,2264.63,2275.99,189600000,2275.99 1987-04-15,2252.98,2307.53,2243.32,2282.95,198200000,2282.95 1987-04-14,2287.07,2293.89,2213.64,2252.98,266500000,2252.98 1987-04-13,2338.78,2355.68,2278.41,2287.07,181000000,2287.07 1987-04-10,2339.20,2358.66,2305.11,2338.78,169500000,2338.78 1987-04-09,2372.16,2383.81,2320.60,2339.20,180300000,2339.20 1987-04-08,2360.94,2396.16,2342.61,2372.16,179800000,2372.16 1987-04-07,2405.54,2428.41,2352.84,2360.94,186400000,2360.94 1987-04-06,2390.34,2425.00,2384.80,2405.54,173700000,2405.54 1987-04-03,2320.45,2402.41,2307.67,2390.34,213400000,2390.34 1987-04-02,2316.05,2347.59,2299.29,2320.45,183000000,2320.45 1987-04-01,2304.69,2325.14,2271.16,2316.05,182600000,2316.05 1987-03-31,2278.41,2315.48,2271.31,2304.69,171800000,2304.69 1987-03-30,2303.41,2303.41,2230.26,2278.41,208400000,2278.41 1987-03-27,2372.59,2386.65,2326.99,2335.80,184400000,2335.80 1987-03-26,2363.49,2399.57,2359.38,2372.59,196000000,2372.59 1987-03-25,2369.18,2383.52,2344.46,2363.49,171300000,2363.49 1987-03-24,2363.78,2387.36,2348.01,2369.18,189900000,2369.18 1987-03-23,2333.52,2373.01,2322.44,2363.78,189100000,2363.78 1987-03-20,2299.57,2342.05,2295.03,2333.52,234000000,2333.52 1987-03-19,2286.93,2310.65,2270.17,2299.57,166100000,2299.57 1987-03-18,2284.80,2315.20,2262.22,2286.93,198100000,2286.93 1987-03-17,2248.44,2287.78,2238.21,2284.80,177300000,2284.80 1987-03-16,2258.66,2263.64,2224.86,2248.44,134900000,2248.44 1987-03-13,2267.34,2282.39,2245.03,2258.66,150900000,2258.66 1987-03-12,2268.98,2288.15,2250.14,2267.34,174500000,2267.34 1987-03-11,2280.09,2302.17,2256.47,2268.98,186900000,2268.98 1987-03-10,2260.12,2289.93,2246.48,2280.09,174800000,2280.09 1987-03-09,2279.67,2279.67,2238.61,2260.12,165400000,2260.12 1987-03-06,2276.43,2298.23,2256.47,2280.23,181600000,2280.23 1987-03-05,2257.45,2295.70,2253.94,2276.43,205400000,2276.43 1987-03-04,2226.52,2270.11,2226.24,2257.45,198400000,2257.45 1987-03-03,2220.47,2240.44,2209.93,2226.52,149200000,2226.52 1987-03-02,2223.99,2242.55,2209.08,2220.47,156700000,2220.47 1987-02-27,2216.68,2237.91,2204.87,2223.99,142800000,2223.99 1987-02-26,2226.24,2239.31,2196.71,2216.68,165800000,2216.68 1987-02-25,2223.28,2248.59,2209.36,2226.24,184100000,2226.24 1987-02-24,2216.54,2244.09,2202.62,2223.28,151300000,2223.28 1987-02-23,2235.24,2249.86,2186.30,2216.54,170500000,2216.54 1987-02-20,2244.09,2260.12,2220.89,2235.24,175800000,2235.24 1987-02-19,2237.63,2263.22,2212.32,2244.09,181500000,2244.09 1987-02-18,2237.49,2266.45,2213.16,2237.63,218200000,2237.63 1987-02-17,2184.90,2242.41,2184.90,2237.49,187800000,2237.49 1987-02-13,2165.78,2201.35,2148.76,2183.35,184400000,2183.35 1987-02-12,2171.96,2203.18,2146.09,2165.78,200400000,2165.78 1987-02-11,2158.04,2182.51,2145.53,2171.96,172400000,2171.96 1987-02-10,2176.74,2183.63,2134.42,2158.04,168300000,2158.04 1987-02-09,2186.87,2195.73,2157.90,2176.74,143300000,2176.74 1987-02-06,2201.49,2213.30,2176.32,2186.87,184100000,2186.87 1987-02-05,2191.23,2223.28,2174.63,2201.49,256700000,2201.49 1987-02-04,2168.45,2198.40,2154.67,2191.23,222400000,2191.23 1987-02-03,2179.42,2201.21,2159.73,2168.45,198100000,2168.45 1987-02-02,2158.04,2193.62,2142.15,2179.42,177400000,2179.42 1987-01-30,2160.01,2173.51,2127.67,2158.04,163400000,2158.04 1987-01-29,2163.39,2193.34,2139.20,2160.01,205300000,2160.01 1987-01-28,2150.45,2181.10,2136.39,2163.39,195800000,2163.39 1987-01-27,2110.24,2160.85,2110.24,2150.45,192300000,2150.45 1987-01-26,2101.52,2125.42,2079.72,2107.28,138900000,2107.28 1987-01-23,2145.67,2214.57,2062.85,2101.52,302400000,2101.52 1987-01-22,2094.07,2151.86,2083.66,2145.67,188700000,2145.67 1987-01-21,2104.47,2123.88,2079.02,2094.07,184200000,2094.07 1987-01-20,2102.50,2131.61,2082.54,2104.47,224800000,2104.47 1987-01-19,2076.63,2112.49,2051.32,2102.50,162800000,2102.50 1987-01-16,2070.73,2099.69,2054.56,2076.63,218400000,2076.63 1987-01-15,2035.01,2091.25,2030.79,2070.73,253100000,2070.73 1987-01-14,2012.94,2046.26,2003.94,2035.01,214200000,2035.01 1987-01-13,2009.42,2027.14,1994.52,2012.94,170900000,2012.94 1987-01-12,2005.91,2022.08,1987.63,2009.42,184200000,2009.42 1987-01-09,2002.25,2021.65,1988.61,2005.91,193000000,2005.91 1987-01-08,1993.95,2014.20,1975.96,2002.25,194500000,2002.25 1987-01-07,1974.83,2003.09,1965.69,1993.95,190900000,1993.95 1987-01-06,1971.32,1993.81,1961.90,1974.83,189300000,1974.83 1987-01-05,1936.59,1980.74,1936.59,1971.32,181900000,1971.32 1987-01-02,1897.36,1933.49,1897.36,1927.31,91880000,1927.31 1986-12-31,1908.61,1920.42,1885.40,1895.95,139200000,1895.95 1986-12-30,1912.12,1920.70,1894.69,1908.61,126200000,1908.61 1986-12-29,1930.40,1931.38,1904.53,1912.12,99800000,1912.12 1986-12-26,1926.88,1939.12,1919.71,1930.40,48860000,1930.40 1986-12-24,1914.37,1937.57,1910.01,1926.88,95410000,1926.88 1986-12-23,1926.18,1933.77,1902.28,1914.37,188700000,1914.37 1986-12-22,1928.85,1938.84,1907.76,1926.18,157600000,1926.18 1986-12-19,1912.82,1935.18,1899.89,1928.85,244700000,1928.85 1986-12-18,1918.31,1927.02,1900.17,1912.82,155400000,1912.82 1986-12-17,1936.16,1937.57,1909.45,1918.31,148800000,1918.31 1986-12-16,1922.81,1942.91,1912.40,1936.16,157000000,1936.16 1986-12-15,1912.26,1925.34,1887.09,1922.81,148200000,1922.81 1986-12-12,1923.65,1932.09,1904.39,1912.26,126600000,1912.26 1986-12-11,1932.93,1947.41,1904.81,1923.65,136000000,1923.65 1986-12-10,1916.90,1943.05,1908.32,1932.93,139700000,1932.93 1986-12-09,1930.26,1939.82,1909.87,1916.90,128700000,1916.90 1986-12-08,1925.06,1946.71,1906.92,1930.26,159000000,1930.26 1986-12-05,1939.68,1950.22,1911.70,1925.06,139800000,1925.06 1986-12-04,1947.27,1963.30,1930.26,1939.68,156900000,1939.68 1986-12-03,1955.57,1971.74,1933.91,1947.27,200100000,1947.27 1986-12-02,1914.93,1958.80,1914.93,1955.57,230400000,1955.57 1986-12-01,1914.23,1915.91,1880.48,1912.54,133800000,1912.54 1986-11-28,1916.76,1923.51,1903.82,1914.23,93530000,1914.23 1986-11-26,1912.12,1926.46,1895.11,1916.76,152000000,1916.76 1986-11-25,1906.07,1922.81,1890.04,1912.12,154600000,1912.12 1986-11-24,1893.56,1921.40,1879.08,1906.07,150800000,1906.07 1986-11-21,1860.66,1901.86,1853.07,1893.56,200700000,1893.56 1986-11-20,1826.63,1866.28,1823.12,1860.66,158100000,1860.66 1986-11-19,1817.21,1838.02,1797.10,1826.63,183300000,1826.63 1986-11-18,1860.52,1864.31,1806.52,1817.21,185300000,1817.21 1986-11-17,1873.59,1879.92,1846.74,1860.52,133300000,1860.52 1986-11-14,1862.20,1883.57,1848.28,1873.59,172100000,1873.59 1986-11-13,1893.70,1901.01,1857.28,1862.20,164000000,1862.20 1986-11-12,1895.95,1909.31,1876.97,1893.70,162200000,1893.70 1986-11-11,1892.29,1904.95,1882.45,1895.95,118500000,1895.95 1986-11-10,1886.53,1901.01,1871.20,1892.29,120200000,1892.29 1986-11-07,1891.59,1899.47,1870.78,1886.53,142300000,1886.53 1986-11-06,1899.04,1907.48,1868.95,1891.59,165300000,1891.59 1986-11-05,1892.44,1911.14,1876.55,1899.04,183200000,1899.04 1986-11-04,1894.26,1902.00,1873.88,1892.44,163200000,1892.44 1986-11-03,1877.71,1902.28,1870.78,1894.26,138200000,1894.26 1986-10-31,1878.37,1891.73,1858.13,1877.71,147200000,1877.71 1986-10-30,1862.77,1894.69,1862.77,1878.37,194200000,1878.37 1986-10-29,1845.47,1863.33,1834.51,1851.80,164400000,1851.80 1986-10-28,1841.82,1863.47,1830.99,1845.47,145900000,1845.47 1986-10-27,1832.36,1850.11,1817.49,1841.82,133200000,1841.82 1986-10-24,1834.93,1851.66,1821.71,1832.36,137500000,1832.36 1986-10-23,1808.35,1845.05,1806.95,1834.93,150900000,1834.93 1986-10-22,1805.68,1821.29,1793.59,1808.35,114000000,1808.35 1986-10-21,1811.02,1822.98,1792.04,1805.68,110000000,1805.68 1986-10-20,1826.63,1826.63,1793.87,1811.02,109000000,1811.02 1986-10-17,1836.19,1844.91,1817.07,1837.04,124100000,1837.04 1986-10-16,1831.69,1852.64,1819.18,1836.19,156900000,1836.19 1986-10-15,1800.20,1835.77,1792.18,1831.69,144300000,1831.69 1986-10-14,1798.37,1816.51,1783.32,1800.20,116800000,1800.20 1986-10-13,1793.17,1806.95,1781.50,1798.37,54990000,1798.37 1986-10-10,1796.82,1806.38,1780.93,1793.17,105100000,1793.17 1986-10-09,1803.85,1824.24,1785.85,1796.82,153400000,1796.82 1986-10-08,1784.45,1812.43,1772.22,1803.85,141700000,1803.85 1986-10-07,1784.45,1799.35,1765.19,1784.45,125100000,1784.45 1986-10-06,1774.18,1794.85,1763.36,1784.45,88250000,1784.45 1986-10-03,1781.21,1806.38,1761.25,1774.18,128100000,1774.18 1986-10-02,1782.90,1796.54,1767.72,1781.21,128100000,1781.21 1986-10-01,1767.58,1805.82,1762.65,1782.90,143600000,1782.90 1986-09-30,1755.20,1790.35,1750.14,1767.58,124900000,1767.58 1986-09-29,1768.98,1768.98,1732.99,1755.20,115600000,1755.20 1986-09-26,1768.56,1788.10,1750.14,1769.69,115300000,1769.69 1986-09-25,1803.15,1803.15,1753.80,1768.56,134300000,1768.56 1986-09-24,1797.81,1820.02,1786.28,1803.29,134600000,1803.29 1986-09-23,1793.45,1809.76,1778.54,1797.81,132600000,1797.81 1986-09-22,1762.65,1797.81,1761.67,1793.45,126100000,1793.45 1986-09-19,1774.18,1783.04,1747.61,1762.65,153900000,1762.65 1986-09-18,1769.40,1789.93,1748.03,1774.18,132200000,1774.18 1986-09-17,1778.54,1799.35,1757.87,1769.40,141000000,1769.40 1986-09-16,1767.58,1785.01,1739.60,1778.54,131200000,1778.54 1986-09-15,1758.72,1786.98,1746.20,1767.58,155600000,1767.58 1986-09-12,1792.89,1805.40,1733.55,1758.72,240500000,1758.72 1986-09-11,1868.67,1868.67,1780.79,1792.89,237600000,1792.89 1986-09-10,1884.14,1897.50,1863.05,1879.50,140300000,1879.50 1986-09-09,1888.64,1909.45,1874.02,1884.14,137500000,1884.14 1986-09-08,1899.75,1907.06,1866.84,1888.64,153300000,1888.64 1986-09-05,1919.71,1933.35,1890.19,1899.75,180600000,1899.75 1986-09-04,1881.33,1927.87,1877.25,1919.71,189400000,1919.71 1986-09-03,1870.36,1888.22,1852.78,1881.33,154300000,1881.33 1986-09-02,1898.34,1913.39,1857.42,1870.36,135500000,1870.36 1986-08-29,1900.17,1913.53,1883.44,1898.34,125300000,1898.34 1986-08-28,1904.53,1912.26,1882.73,1900.17,125100000,1900.17 1986-08-27,1904.25,1918.59,1887.80,1904.53,143300000,1904.53 1986-08-26,1871.77,1911.28,1870.50,1904.25,156600000,1904.25 1986-08-25,1887.80,1893.42,1860.52,1871.77,104400000,1871.77 1986-08-22,1881.19,1899.04,1868.11,1887.80,118100000,1887.80 1986-08-21,1881.33,1899.47,1864.31,1881.19,135200000,1881.19 1986-08-20,1862.91,1889.34,1849.97,1881.33,156600000,1881.33 1986-08-19,1869.52,1880.20,1846.88,1862.91,109300000,1862.91 1986-08-18,1855.60,1875.14,1842.66,1869.52,112800000,1869.52 1986-08-15,1844.91,1863.61,1829.02,1855.60,123500000,1855.60 1986-08-14,1844.49,1859.81,1832.54,1844.91,123800000,1844.91 1986-08-13,1835.49,1860.66,1822.98,1844.49,156400000,1844.49 1986-08-12,1811.16,1840.55,1804.42,1835.49,131700000,1835.49 1986-08-11,1782.62,1824.66,1779.25,1811.16,125600000,1811.16 1986-08-08,1786.28,1799.78,1769.69,1782.62,106300000,1782.62 1986-08-07,1779.53,1802.02,1766.17,1786.28,122400000,1786.28 1986-08-06,1777.00,1788.67,1759.84,1779.53,127500000,1779.53 1986-08-05,1769.97,1800.20,1766.87,1777.00,153100000,1777.00 1986-08-04,1763.64,1785.15,1730.60,1769.97,130000000,1769.97 1986-08-01,1775.31,1788.80,1753.09,1763.64,114900000,1763.64 1986-07-31,1779.39,1794.29,1764.20,1775.31,112700000,1775.31 1986-07-30,1766.87,1794.15,1741.56,1779.39,146700000,1779.39 1986-07-29,1773.90,1788.95,1752.95,1766.87,115700000,1766.87 1986-07-28,1810.04,1815.24,1760.26,1773.90,128000000,1773.90 1986-07-25,1791.62,1821.57,1791.06,1810.04,132000000,1810.04 1986-07-24,1798.37,1806.81,1778.68,1791.62,134700000,1791.62 1986-07-23,1795.13,1813.70,1780.09,1798.37,133300000,1798.37 1986-07-22,1779.11,1810.46,1769.12,1795.13,138500000,1795.13 1986-07-21,1777.98,1792.60,1763.92,1779.11,106300000,1779.11 1986-07-18,1781.78,1807.37,1754.64,1777.98,149700000,1777.98 1986-07-17,1774.18,1802.73,1762.09,1781.78,132400000,1781.78 1986-07-16,1768.70,1794.29,1759.70,1774.18,160800000,1774.18 1986-07-15,1793.45,1805.54,1760.12,1768.70,184000000,1768.70 1986-07-14,1816.65,1816.65,1785.29,1793.45,123200000,1793.45 1986-07-11,1831.83,1840.97,1810.88,1821.43,124500000,1821.43 1986-07-10,1826.07,1842.94,1799.07,1831.83,146200000,1831.83 1986-07-09,1820.73,1838.58,1807.65,1826.07,142900000,1826.07 1986-07-08,1834.36,1834.36,1794.99,1820.73,174100000,1820.73 1986-07-07,1895.53,1895.53,1834.08,1838.86,138200000,1838.86 1986-07-03,1909.03,1918.59,1887.23,1900.87,108300000,1900.87 1986-07-02,1903.54,1922.67,1891.31,1909.03,150000000,1909.03 1986-07-01,1892.72,1911.28,1878.52,1903.54,147700000,1903.54 1986-06-30,1885.26,1908.62,1876.12,1892.72,135100000,1892.72 1986-06-27,1880.20,1898.90,1871.34,1885.26,123800000,1885.26 1986-06-26,1885.05,1897.22,1867.41,1880.20,134100000,1880.20 1986-06-25,1875.55,1903.36,1870.32,1885.05,161800000,1885.05 1986-06-24,1864.26,1891.80,1853.94,1875.55,140600000,1875.55 1986-06-23,1879.54,1883.40,1853.39,1864.26,123800000,1864.26 1986-06-20,1855.86,1882.85,1847.64,1879.54,149100000,1879.54 1986-06-19,1868.94,1883.12,1851.46,1855.86,129000000,1855.86 1986-06-18,1865.78,1881.33,1843.75,1868.94,117000000,1868.94 1986-06-17,1871.77,1886.98,1853.94,1865.78,123100000,1865.78 1986-06-16,1874.19,1890.61,1863.29,1871.77,112100000,1871.77 1986-06-13,1843.92,1883.21,1843.92,1874.19,141200000,1874.19 1986-06-12,1846.07,1851.86,1826.29,1838.13,109100000,1838.13 1986-06-11,1837.19,1854.68,1825.75,1846.07,127400000,1846.07 1986-06-10,1840.15,1852.80,1816.07,1837.19,125000000,1837.19 1986-06-09,1880.25,1880.25,1833.83,1840.15,123300000,1840.15 1986-06-06,1879.44,1894.38,1866.12,1885.90,110900000,1885.90 1986-06-05,1863.29,1880.79,1851.18,1879.44,110900000,1879.44 1986-06-04,1870.43,1882.00,1846.34,1863.29,117000000,1863.29 1986-06-03,1861.95,1875.54,1845.67,1870.43,114700000,1870.43 1986-06-02,1876.71,1886.17,1845.53,1861.95,120600000,1861.95 1986-05-30,1882.35,1898.22,1857.95,1876.71,151200000,1876.71 1986-05-29,1878.28,1892.97,1857.42,1882.35,135700000,1882.35 1986-05-28,1853.03,1891.79,1851.92,1878.28,159600000,1878.28 1986-05-27,1823.48,1856.56,1823.48,1853.03,121200000,1853.03 1986-05-23,1806.30,1842.14,1804.69,1823.29,130200000,1823.29 1986-05-22,1775.17,1813.74,1774.18,1806.30,144900000,1806.30 1986-05-21,1783.98,1798.61,1766.62,1775.17,117100000,1775.17 1986-05-20,1758.18,1788.44,1752.48,1783.98,113000000,1783.98 1986-05-19,1759.80,1769.84,1746.53,1758.18,85840000,1758.18 1986-05-16,1774.68,1775.67,1749.88,1759.80,113500000,1759.80 1986-05-15,1808.28,1808.78,1766.99,1774.68,131600000,1774.68 1986-05-14,1785.34,1815.23,1775.55,1808.28,132100000,1808.28 1986-05-13,1787.33,1804.07,1772.32,1785.34,119200000,1785.34 1986-05-12,1789.43,1804.32,1774.80,1787.33,125400000,1787.33 1986-05-09,1786.21,1797.12,1767.73,1789.43,137400000,1789.43 1986-05-08,1775.30,1797.87,1768.48,1786.21,136000000,1786.21 1986-05-07,1787.95,1790.05,1755.21,1775.30,129900000,1775.30 1986-05-06,1793.77,1803.32,1775.92,1787.95,121200000,1787.95 1986-05-05,1774.68,1800.35,1770.96,1793.77,102400000,1793.77 1986-05-02,1777.78,1795.88,1763.14,1774.68,126300000,1774.68 1986-05-01,1783.98,1796.88,1761.04,1777.78,146500000,1777.78 1986-04-30,1825.89,1831.23,1777.90,1783.98,147500000,1783.98 1986-04-29,1843.75,1856.15,1813.12,1825.89,148800000,1825.89 1986-04-28,1835.57,1852.66,1817.71,1843.75,123900000,1843.75 1986-04-25,1831.72,1848.46,1813.74,1835.57,142300000,1835.57 1986-04-24,1829.61,1848.46,1818.95,1831.72,146600000,1831.72 1986-04-23,1830.98,1841.27,1803.94,1829.61,149700000,1829.61 1986-04-22,1855.90,1866.44,1815.97,1830.98,161500000,1830.98 1986-04-21,1840.40,1864.21,1829.86,1855.90,136100000,1855.90 1986-04-18,1855.03,1870.16,1830.98,1840.40,153600000,1840.40 1986-04-17,1847.97,1870.16,1831.72,1855.03,161400000,1855.03 1986-04-16,1814.73,1859.62,1814.73,1847.97,173800000,1847.97 1986-04-15,1805.31,1822.05,1789.68,1809.65,123700000,1809.65 1986-04-14,1790.18,1811.38,1781.00,1805.31,106700000,1805.31 1986-04-11,1794.30,1812.50,1779.14,1790.18,139400000,1790.18 1986-04-10,1778.62,1807.83,1768.20,1794.30,184800000,1794.30 1986-04-09,1769.76,1807.71,1754.19,1778.62,156300000,1778.62 1986-04-08,1737.07,1779.57,1737.07,1769.76,146300000,1769.76 1986-04-07,1739.22,1749.04,1712.52,1735.51,129800000,1735.51 1986-04-04,1766.40,1772.87,1728.45,1739.22,147300000,1739.22 1986-04-03,1795.26,1809.27,1753.71,1766.40,148200000,1766.40 1986-04-02,1790.11,1803.64,1769.76,1795.26,145300000,1795.26 1986-04-01,1818.61,1829.02,1777.54,1790.11,167400000,1790.11 1986-03-31,1821.72,1841.83,1803.16,1818.61,134400000,1818.61 1986-03-27,1810.70,1849.74,1806.51,1821.72,178100000,1821.72 1986-03-26,1778.50,1817.41,1775.98,1810.70,161500000,1810.70 1986-03-25,1782.93,1794.30,1762.09,1778.50,139300000,1778.50 1986-03-24,1768.56,1796.22,1763.29,1782.93,143800000,1782.93 1986-03-21,1804.24,1821.24,1764.85,1768.56,199100000,1768.56 1986-03-20,1787.95,1820.76,1784.36,1804.24,148000000,1804.24 1986-03-19,1789.87,1806.99,1773.47,1787.95,150000000,1787.95 1986-03-18,1776.86,1806.75,1763.65,1789.87,148000000,1789.87 1986-03-17,1790.59,1790.59,1758.38,1776.86,137500000,1776.86 1986-03-14,1753.71,1799.93,1745.21,1792.74,181900000,1792.74 1986-03-13,1745.45,1768.80,1729.53,1753.71,171500000,1753.71 1986-03-12,1746.05,1773.35,1733.48,1745.45,210300000,1745.45 1986-03-11,1704.26,1752.17,1704.26,1746.05,187300000,1746.05 1986-03-10,1699.83,1715.16,1688.70,1702.95,129900000,1702.95 1986-03-07,1696.60,1713.48,1682.83,1699.83,163200000,1699.83 1986-03-06,1686.66,1711.21,1681.75,1696.60,159000000,1696.60 1986-03-05,1686.42,1695.40,1663.07,1686.66,154600000,1686.66 1986-03-04,1696.67,1718.63,1679.72,1686.42,174500000,1686.42 1986-03-03,1709.06,1714.33,1684.63,1696.67,142700000,1696.67 1986-02-28,1713.99,1732.57,1687.50,1709.06,191700000,1709.06 1986-02-27,1696.90,1728.90,1683.37,1713.99,181700000,1713.99 1986-02-26,1692.66,1714.79,1679.59,1696.90,158000000,1696.90 1986-02-25,1698.28,1709.52,1674.08,1692.66,148000000,1692.66 1986-02-24,1697.71,1709.75,1678.67,1698.28,144700000,1698.28 1986-02-21,1672.82,1702.75,1668.69,1697.71,177600000,1697.71 1986-02-20,1658.26,1675.46,1641.97,1672.82,139700000,1672.82 1986-02-19,1678.78,1689.22,1651.03,1658.26,152000000,1658.26 1986-02-18,1664.45,1685.55,1649.43,1678.78,160200000,1678.78 1986-02-14,1645.07,1668.69,1636.12,1664.45,155600000,1664.45 1986-02-13,1629.93,1649.31,1616.86,1645.07,136500000,1645.07 1986-02-12,1622.82,1640.48,1611.70,1629.93,136400000,1629.93 1986-02-11,1626.38,1637.50,1613.30,1622.82,141300000,1622.82 1986-02-10,1613.42,1633.14,1605.16,1626.38,129900000,1626.38 1986-02-07,1600.69,1622.82,1576.83,1613.42,144400000,1613.42 1986-02-06,1593.12,1616.51,1587.50,1600.69,146100000,1600.69 1986-02-05,1593.23,1601.83,1576.26,1593.12,134300000,1593.12 1986-02-04,1594.27,1611.47,1575.46,1593.23,175700000,1593.23 1986-02-03,1570.99,1600.11,1564.33,1594.27,145300000,1594.27 1986-01-31,1552.18,1582.91,1548.17,1570.99,143500000,1570.99 1986-01-30,1558.94,1572.59,1545.76,1552.18,125300000,1552.18 1986-01-29,1556.42,1578.10,1548.05,1558.94,193800000,1558.94 1986-01-28,1537.61,1561.35,1530.50,1556.42,145700000,1556.42 1986-01-27,1529.93,1548.17,1524.43,1537.61,122900000,1537.61 1986-01-24,1511.24,1537.39,1507.68,1529.93,128900000,1529.93 1986-01-23,1502.29,1519.39,1491.74,1511.24,130300000,1511.24 1986-01-22,1514.45,1525.57,1494.84,1502.29,131200000,1502.29 1986-01-21,1529.13,1535.89,1502.87,1514.45,128300000,1514.45 1986-01-20,1536.01,1536.01,1516.86,1529.13,85340000,1529.13 1986-01-17,1541.63,1552.52,1519.72,1536.70,132100000,1536.70 1986-01-16,1527.29,1547.71,1517.09,1541.63,130500000,1541.63 1986-01-15,1519.04,1535.32,1512.96,1527.29,122400000,1527.29 1986-01-14,1520.53,1530.62,1508.03,1519.04,113900000,1519.04 1986-01-13,1513.53,1526.83,1502.29,1520.53,108700000,1520.53 1986-01-10,1518.23,1530.96,1500.57,1513.53,122800000,1513.53 1986-01-09,1526.61,1530.85,1495.64,1518.23,176500000,1518.23 1986-01-08,1565.71,1578.10,1516.63,1526.61,180300000,1526.61 1986-01-07,1547.59,1573.74,1544.50,1565.71,153000000,1565.71 1986-01-06,1549.20,1557.00,1537.04,1547.59,99610000,1547.59 1986-01-03,1537.73,1557.11,1534.98,1549.20,105000000,1549.20 1986-01-02,1546.67,1551.95,1523.74,1537.73,98960000,1537.73 1985-12-31,1550.46,1562.84,1537.50,1546.67,112700000,1546.67 1985-12-30,1543.00,1557.34,1534.98,1550.46,91970000,1550.46 1985-12-27,1527.29,1548.85,1527.29,1543.00,81560000,1543.00 1985-12-26,1519.15,1532.57,1514.68,1526.49,62050000,1526.49 1985-12-24,1528.78,1532.45,1508.83,1519.15,78300000,1519.15 1985-12-23,1543.00,1547.82,1516.63,1528.78,107900000,1528.78 1985-12-20,1543.92,1564.56,1535.67,1543.00,170300000,1543.00 1985-12-19,1542.43,1552.98,1532.00,1543.92,130200000,1543.92 1985-12-18,1544.50,1560.44,1528.44,1542.43,137900000,1542.43 1985-12-17,1553.10,1568.81,1536.35,1544.50,155200000,1544.50 1985-12-16,1535.21,1570.87,1528.90,1553.10,176000000,1553.10 1985-12-13,1511.24,1546.44,1502.75,1535.21,177900000,1535.21 1985-12-12,1511.70,1523.51,1496.79,1511.24,170500000,1511.24 1985-12-11,1499.20,1520.30,1492.09,1511.70,178500000,1511.70 1985-12-10,1497.02,1514.91,1483.94,1499.20,156500000,1499.20 1985-12-09,1478.10,1504.13,1478.10,1497.02,144000000,1497.02 1985-12-06,1482.91,1488.30,1464.79,1477.18,125500000,1477.18 1985-12-05,1484.40,1504.01,1477.98,1482.91,181000000,1482.91 1985-12-04,1459.06,1488.76,1457.45,1484.40,153200000,1484.40 1985-12-03,1457.91,1466.40,1450.34,1459.06,109700000,1459.06 1985-12-02,1472.13,1476.26,1451.26,1457.91,103500000,1457.91 1985-11-29,1475.69,1486.93,1463.99,1472.13,84060000,1472.13 1985-11-27,1456.77,1483.83,1454.01,1475.69,143700000,1475.69 1985-11-26,1456.65,1470.07,1447.25,1456.77,123100000,1456.77 1985-11-25,1464.33,1467.09,1449.43,1456.65,91710000,1456.65 1985-11-22,1462.27,1475.57,1451.61,1464.33,133800000,1464.33 1985-11-21,1439.79,1466.06,1439.79,1462.27,150300000,1462.27 1985-11-20,1438.99,1451.49,1429.24,1439.22,105100000,1439.22 1985-11-19,1440.02,1452.18,1430.05,1438.99,126100000,1438.99 1985-11-18,1435.09,1447.25,1422.25,1440.02,108400000,1440.02 1985-11-15,1439.22,1450.57,1425.69,1435.09,130200000,1435.09 1985-11-14,1427.75,1443.81,1420.64,1439.22,124900000,1439.22 1985-11-13,1433.60,1439.79,1419.38,1427.75,109700000,1427.75 1985-11-12,1431.88,1446.10,1421.10,1433.60,170800000,1433.60 1985-11-11,1404.36,1432.57,1402.41,1431.88,126500000,1431.88 1985-11-08,1399.54,1410.44,1391.06,1404.36,115000000,1404.36 1985-11-07,1403.44,1410.11,1389.91,1399.54,119000000,1399.54 1985-11-06,1396.67,1411.01,1386.47,1403.44,129500000,1403.44 1985-11-05,1389.68,1402.52,1380.28,1396.67,119200000,1396.67 1985-11-04,1390.25,1398.51,1377.06,1389.68,104900000,1389.68 1985-11-01,1374.31,1393.58,1367.78,1390.25,129400000,1390.25 1985-10-31,1375.57,1383.60,1362.61,1374.31,121500000,1374.31 1985-10-30,1368.73,1384.63,1361.01,1375.57,120400000,1375.57 1985-10-29,1359.99,1377.69,1356.18,1368.73,110600000,1368.73 1985-10-28,1356.52,1364.02,1346.55,1359.99,97880000,1359.99 1985-10-25,1362.34,1366.71,1351.59,1356.52,101800000,1356.52 1985-10-24,1367.16,1374.55,1355.29,1362.34,123100000,1362.34 1985-10-23,1364.36,1373.43,1357.08,1367.16,121700000,1367.16 1985-10-22,1364.14,1374.66,1356.18,1364.36,111300000,1364.36 1985-10-21,1368.84,1375.67,1357.75,1364.14,95680000,1364.14 1985-10-18,1369.29,1383.40,1358.87,1368.84,107100000,1368.84 1985-10-17,1368.50,1378.36,1356.85,1369.29,140500000,1369.29 1985-10-16,1350.81,1371.30,1346.21,1368.50,117400000,1368.50 1985-10-15,1354.73,1364.14,1343.53,1350.81,110400000,1350.81 1985-10-14,1339.94,1356.18,1336.58,1354.73,78540000,1354.73 1985-10-11,1328.07,1343.41,1323.70,1339.94,96370000,1339.94 1985-10-10,1326.72,1333.33,1318.44,1328.07,90910000,1328.07 1985-10-09,1325.49,1336.81,1320.23,1326.72,99140000,1326.72 1985-10-08,1324.37,1333.89,1316.20,1325.49,97170000,1325.49 1985-10-07,1328.74,1335.91,1315.19,1324.37,95550000,1324.37 1985-10-04,1333.11,1335.01,1321.12,1328.74,101200000,1328.74 1985-10-03,1333.67,1345.43,1323.81,1333.11,127500000,1333.11 1985-10-02,1340.95,1351.59,1327.96,1333.67,147300000,1333.67 1985-10-01,1328.63,1343.97,1323.48,1340.95,130200000,1340.95 1985-09-30,1320.79,1336.81,1318.55,1328.63,103600000,1328.63 1985-09-26,1312.05,1325.27,1301.97,1320.79,106100000,1320.79 1985-09-25,1321.12,1330.20,1306.00,1312.05,92120000,1312.05 1985-09-24,1316.31,1334.57,1301.86,1321.12,97870000,1321.12 1985-09-23,1305.78,1323.59,1305.78,1316.31,104800000,1316.31 1985-09-20,1306.79,1313.40,1294.47,1297.94,101400000,1297.94 1985-09-19,1300.40,1312.95,1291.22,1306.79,100300000,1306.79 1985-09-18,1298.16,1307.57,1283.71,1300.40,105700000,1300.40 1985-09-17,1309.14,1314.52,1291.22,1298.16,111900000,1298.16 1985-09-16,1307.68,1315.75,1301.08,1309.14,66700000,1309.14 1985-09-13,1312.39,1320.12,1296.71,1307.68,111400000,1307.68 1985-09-12,1319.44,1327.40,1307.01,1312.39,107100000,1312.39 1985-09-11,1333.45,1334.01,1314.85,1319.44,100400000,1319.44 1985-09-10,1339.27,1346.55,1324.82,1333.45,104700000,1333.45 1985-09-09,1335.69,1348.23,1326.61,1339.27,89850000,1339.27 1985-09-06,1325.83,1341.40,1323.92,1335.69,95040000,1335.69 1985-09-05,1326.72,1333.00,1318.66,1325.83,94480000,1325.83 1985-09-04,1329.19,1334.12,1318.21,1326.72,85510000,1326.72 1985-09-03,1334.01,1337.14,1322.80,1329.19,81190000,1329.19 1985-08-30,1335.13,1342.41,1325.27,1334.01,81620000,1334.01 1985-08-29,1331.09,1340.05,1325.16,1335.13,85660000,1335.13 1985-08-28,1322.47,1334.79,1316.98,1331.09,88530000,1331.09 1985-08-27,1317.65,1328.63,1315.19,1322.47,82140000,1322.47 1985-08-26,1318.32,1324.26,1309.81,1317.65,70290000,1317.65 1985-08-23,1318.10,1322.92,1310.48,1318.32,75270000,1318.32 1985-08-22,1329.53,1334.68,1313.51,1318.10,90600000,1318.10 1985-08-21,1323.70,1333.67,1319.11,1329.53,94880000,1329.53 1985-08-20,1312.50,1328.07,1311.83,1323.70,91230000,1323.70 1985-08-19,1312.72,1319.22,1307.57,1312.50,67930000,1312.50 1985-08-16,1317.76,1321.57,1305.89,1312.72,87910000,1312.72 1985-08-15,1316.98,1324.71,1308.13,1317.76,86100000,1317.76 1985-08-14,1315.30,1326.05,1311.38,1316.98,85780000,1316.98 1985-08-13,1314.29,1325.49,1303.65,1315.30,80300000,1315.30 1985-08-12,1320.79,1324.37,1309.14,1314.29,77340000,1314.29 1985-08-09,1329.86,1331.88,1317.65,1320.79,81750000,1320.79 1985-08-08,1325.04,1334.79,1318.32,1329.86,102900000,1329.86 1985-08-07,1325.16,1331.21,1316.31,1325.04,100000000,1325.04 1985-08-06,1346.89,1350.02,1322.92,1325.16,104000000,1325.16 1985-08-05,1352.71,1352.71,1336.92,1346.89,79610000,1346.89 1985-08-02,1355.62,1360.10,1345.43,1353.05,87860000,1353.05 1985-08-01,1347.45,1361.90,1342.97,1355.62,121500000,1355.62 1985-07-31,1346.10,1357.41,1340.05,1347.45,124200000,1347.45 1985-07-30,1343.86,1352.60,1334.90,1346.10,102300000,1346.10 1985-07-29,1357.08,1358.09,1338.93,1343.86,95960000,1343.86 1985-07-26,1353.61,1365.59,1345.21,1357.08,107000000,1357.08 1985-07-25,1348.90,1359.88,1338.60,1353.61,123300000,1353.61 1985-07-24,1351.81,1359.21,1335.69,1348.90,128600000,1348.90 1985-07-23,1357.64,1372.20,1344.76,1351.81,143600000,1351.81 1985-07-22,1359.54,1362.12,1346.89,1357.64,93540000,1357.64 1985-07-19,1350.92,1363.35,1346.44,1359.54,114800000,1359.54 1985-07-18,1357.97,1363.26,1345.32,1350.92,131400000,1350.92 1985-07-17,1347.89,1365.93,1346.21,1357.97,159900000,1357.97 1985-07-16,1335.46,1351.93,1331.54,1347.89,132500000,1347.89 1985-07-15,1338.60,1347.67,1326.95,1335.46,103900000,1335.46 1985-07-12,1337.70,1345.99,1326.39,1338.60,120300000,1338.60 1985-07-11,1332.89,1343.64,1326.16,1337.70,122800000,1337.70 1985-07-10,1321.91,1336.81,1315.30,1332.89,108200000,1332.89 1985-07-09,1328.41,1333.67,1313.40,1321.91,99060000,1321.91 1985-07-08,1334.45,1336.47,1318.88,1328.41,83670000,1328.41 1985-07-05,1326.39,1340.61,1324.93,1334.45,62450000,1334.45 1985-07-03,1334.01,1337.59,1320.23,1326.39,98410000,1326.39 1985-07-02,1337.14,1343.75,1329.19,1334.01,111100000,1334.01 1985-07-01,1335.46,1342.18,1322.58,1337.14,96080000,1337.14 1985-06-28,1332.21,1341.17,1326.16,1335.46,105200000,1335.46 1985-06-27,1323.81,1338.71,1321.80,1332.21,106700000,1332.21 1985-06-26,1323.03,1329.19,1314.74,1323.81,94130000,1323.81 1985-06-25,1320.56,1338.82,1314.07,1323.03,115700000,1323.03 1985-06-24,1324.48,1326.72,1307.80,1320.56,96040000,1320.56 1985-06-21,1299.73,1326.05,1293.79,1324.48,125400000,1324.48 1985-06-20,1297.38,1305.44,1287.75,1299.73,87500000,1299.73 1985-06-19,1304.77,1312.72,1293.57,1297.38,108300000,1297.38 1985-06-18,1298.39,1312.39,1293.68,1304.77,106900000,1304.77 1985-06-17,1300.96,1303.99,1288.75,1298.39,82170000,1298.39 1985-06-14,1290.10,1305.44,1288.88,1300.96,93090000,1300.96 1985-06-13,1306.34,1308.13,1285.39,1290.10,107000000,1290.10 1985-06-12,1313.84,1318.21,1300.74,1306.34,97700000,1306.34 1985-06-11,1318.44,1323.81,1308.02,1313.84,102100000,1313.84 1985-06-10,1316.42,1322.58,1307.24,1318.44,87940000,1318.44 1985-06-07,1327.28,1331.99,1312.95,1316.42,99630000,1316.42 1985-06-06,1320.56,1330.53,1311.94,1327.28,117200000,1327.28 1985-06-05,1315.30,1331.77,1312.61,1320.56,143900000,1320.56 1985-06-04,1310.93,1321.24,1304.44,1315.30,115400000,1315.30 1985-06-03,1315.41,1325.04,1304.21,1310.93,125000000,1310.93 1985-05-31,1305.78,1320.79,1298.72,1315.41,134100000,1315.41 1985-05-30,1302.98,1314.18,1293.91,1305.78,108300000,1305.78 1985-05-29,1301.52,1308.36,1292.79,1302.98,96540000,1302.98 1985-05-28,1301.97,1312.84,1293.23,1301.52,90600000,1301.52 1985-05-24,1296.71,1305.67,1291.11,1301.97,85970000,1301.97 1985-05-23,1303.76,1307.80,1290.99,1296.71,101000000,1296.71 1985-05-22,1308.92,1308.92,1294.35,1303.76,101400000,1303.76 1985-05-21,1304.88,1316.08,1297.49,1309.70,130200000,1309.70 1985-05-20,1292.23,1310.37,1292.23,1304.88,146300000,1304.88 1985-05-17,1278.05,1295.83,1271.42,1285.34,124600000,1285.34 1985-05-16,1273.52,1285.11,1270.10,1278.05,99420000,1278.05 1985-05-15,1273.30,1283.57,1265.46,1273.52,106100000,1273.52 1985-05-14,1277.50,1288.65,1267.67,1273.30,97360000,1273.30 1985-05-13,1274.18,1283.57,1268.77,1277.50,85830000,1277.50 1985-05-10,1261.59,1282.46,1261.59,1274.18,140300000,1274.18 1985-05-09,1249.78,1265.57,1246.58,1260.27,111000000,1260.27 1985-05-08,1252.75,1258.94,1239.51,1249.78,101300000,1249.78 1985-05-07,1247.79,1261.37,1243.15,1252.75,100200000,1252.75 1985-05-06,1247.24,1256.40,1241.39,1247.79,85650000,1247.79 1985-05-03,1242.27,1253.86,1238.30,1247.24,94870000,1247.24 1985-05-02,1242.05,1251.21,1235.53,1242.27,107700000,1242.27 1985-05-01,1258.06,1262.81,1239.07,1242.05,101600000,1242.05 1985-04-30,1259.72,1266.23,1245.80,1258.06,111800000,1258.06 1985-04-29,1275.18,1277.94,1257.40,1259.72,88860000,1259.72 1985-04-26,1284.78,1290.30,1270.54,1275.18,86570000,1275.18 1985-04-25,1278.49,1289.20,1272.53,1284.78,108600000,1284.78 1985-04-24,1278.71,1286.44,1269.88,1278.49,99600000,1278.49 1985-04-23,1266.56,1282.91,1261.15,1278.71,108900000,1278.71 1985-04-22,1266.56,1273.63,1257.62,1266.56,79930000,1266.56 1985-04-19,1265.13,1272.53,1258.50,1266.56,81110000,1266.56 1985-04-18,1272.31,1281.91,1260.82,1265.13,100600000,1265.13 1985-04-17,1269.55,1277.50,1263.47,1272.31,96020000,1272.31 1985-04-16,1266.78,1276.61,1256.96,1269.55,98480000,1269.55 1985-04-15,1265.68,1272.42,1257.84,1266.78,80660000,1266.78 1985-04-12,1263.69,1270.43,1257.29,1265.68,86220000,1265.68 1985-04-11,1259.94,1274.85,1257.18,1263.69,108400000,1263.69 1985-04-10,1253.86,1269.10,1252.10,1259.94,108200000,1259.94 1985-04-09,1252.98,1262.04,1246.58,1253.86,83980000,1253.86 1985-04-08,1259.05,1267.01,1246.02,1252.98,79960000,1252.98 1985-04-04,1258.06,1264.24,1248.34,1259.05,86910000,1259.05 1985-04-03,1265.68,1268.11,1251.55,1258.06,95480000,1258.06 1985-04-02,1272.75,1280.15,1261.48,1265.68,101700000,1265.68 1985-04-01,1266.78,1276.28,1260.49,1272.75,89900000,1272.75 1985-03-29,1260.71,1270.65,1255.85,1266.78,101400000,1266.78 1985-03-28,1264.91,1275.51,1256.63,1260.71,99780000,1260.71 1985-03-27,1259.72,1270.87,1255.96,1264.91,101000000,1264.91 1985-03-26,1259.94,1267.56,1252.10,1259.72,89930000,1259.72 1985-03-25,1267.45,1268.33,1252.87,1259.94,74040000,1259.94 1985-03-22,1268.22,1277.39,1261.70,1267.45,99250000,1267.45 1985-03-21,1265.24,1278.93,1257.62,1268.22,95930000,1268.22 1985-03-20,1271.09,1277.39,1257.84,1265.24,107500000,1265.24 1985-03-19,1249.67,1274.18,1245.69,1271.09,119200000,1271.09 1985-03-18,1247.35,1260.93,1242.82,1249.67,94020000,1249.67 1985-03-15,1260.05,1266.89,1245.14,1247.35,105200000,1247.35 1985-03-14,1261.70,1269.21,1254.20,1260.05,103400000,1260.05 1985-03-13,1271.75,1278.27,1257.62,1261.70,101700000,1261.70 1985-03-12,1268.55,1279.48,1262.48,1271.75,92840000,1271.75 1985-03-11,1269.66,1277.05,1260.93,1268.55,84110000,1268.55 1985-03-08,1271.53,1280.81,1264.13,1269.66,96390000,1269.66 1985-03-07,1280.37,1283.24,1265.46,1271.53,112100000,1271.53 1985-03-06,1291.85,1296.93,1275.51,1280.37,116900000,1280.37 1985-03-05,1289.53,1300.02,1281.03,1291.85,116400000,1291.85 1985-03-04,1299.36,1304.55,1283.13,1289.53,102100000,1289.53 1985-03-01,1284.01,1309.96,1282.69,1299.36,139900000,1299.36 1985-02-28,1281.03,1288.32,1271.42,1284.01,100700000,1284.01 1985-02-27,1286.11,1295.27,1274.40,1281.03,107700000,1281.03 1985-02-26,1277.50,1293.18,1272.31,1286.11,114200000,1286.11 1985-02-25,1275.84,1283.13,1263.91,1277.50,89740000,1277.50 1985-02-22,1279.04,1286.55,1269.99,1275.84,93680000,1275.84 1985-02-21,1283.13,1287.10,1272.64,1279.04,104000000,1279.04 1985-02-20,1280.59,1292.51,1272.53,1283.13,118200000,1283.13 1985-02-19,1282.02,1287.43,1273.52,1280.59,90400000,1280.59 1985-02-15,1287.88,1296.27,1275.07,1282.02,106500000,1282.02 1985-02-14,1297.92,1307.53,1283.68,1287.88,139700000,1287.88 1985-02-13,1276.61,1304.66,1274.51,1297.92,142500000,1297.92 1985-02-12,1276.06,1283.13,1266.34,1276.61,111100000,1276.61 1985-02-11,1289.97,1291.74,1268.66,1276.06,104000000,1276.06 1985-02-08,1290.08,1297.15,1281.36,1289.97,116500000,1289.97 1985-02-07,1280.59,1297.04,1279.81,1290.08,151700000,1290.08 1985-02-06,1285.23,1294.50,1275.07,1280.59,141000000,1280.59 1985-02-05,1290.08,1301.13,1278.60,1285.23,143900000,1285.23 1985-02-04,1277.72,1294.94,1268.99,1290.08,113700000,1290.08 1985-02-01,1286.11,1286.11,1269.77,1277.72,105400000,1277.72 1985-01-31,1287.88,1293.40,1272.64,1286.77,132500000,1286.77 1985-01-30,1292.62,1305.10,1278.93,1287.88,170000000,1287.88 1985-01-29,1277.83,1295.49,1266.89,1292.62,115700000,1292.62 1985-01-28,1276.06,1290.97,1264.24,1277.83,128400000,1277.83 1985-01-25,1270.43,1284.67,1262.04,1276.06,122400000,1276.06 1985-01-24,1274.73,1287.99,1264.58,1270.43,160700000,1270.43 1985-01-23,1259.50,1278.27,1252.54,1274.73,144400000,1274.73 1985-01-22,1261.37,1277.27,1252.87,1259.50,174800000,1259.50 1985-01-21,1227.36,1266.45,1226.70,1261.37,146800000,1261.37 1985-01-18,1228.69,1237.63,1219.52,1227.36,104700000,1227.36 1985-01-17,1230.68,1234.65,1219.74,1228.69,113600000,1228.69 1985-01-16,1230.79,1241.17,1220.74,1230.68,135500000,1230.68 1985-01-15,1234.54,1243.71,1223.06,1230.79,155300000,1230.79 1985-01-14,1218.09,1238.18,1210.36,1234.54,124900000,1234.54 1985-01-11,1223.50,1229.35,1210.91,1218.09,107600000,1218.09 1985-01-10,1202.74,1225.93,1195.56,1223.50,124700000,1223.50 1985-01-09,1191.70,1209.47,1190.26,1202.74,99230000,1202.74 1985-01-08,1190.59,1199.54,1184.08,1191.70,92110000,1191.70 1985-01-07,1184.96,1198.32,1182.86,1190.59,86190000,1190.59 1985-01-04,1189.82,1192.69,1178.67,1184.96,77480000,1184.96 1985-01-03,1198.87,1207.93,1185.18,1189.82,88880000,1189.82 1985-01-02,1211.57,1212.46,1194.35,1198.87,67820000,1198.87 1984-12-31,1204.17,1215.55,1199.20,1211.57,80260000,1211.57 1984-12-28,1202.52,1210.47,1196.11,1204.17,77070000,1204.17 1984-12-27,1208.92,1214.22,1199.09,1202.52,70100000,1202.52 1984-12-26,1210.14,1213.69,1201.74,1208.92,46700000,1208.92 1984-12-24,1199.20,1214.44,1199.20,1210.14,55550000,1210.14 1984-12-21,1203.29,1210.14,1186.84,1198.98,101200000,1198.98 1984-12-20,1208.04,1219.52,1198.87,1203.29,93220000,1203.29 1984-12-19,1211.57,1223.61,1201.52,1208.04,139600000,1208.04 1984-12-18,1182.31,1213.23,1182.31,1211.57,169000000,1211.57 1984-12-17,1175.91,1182.31,1164.53,1176.79,89490000,1176.79 1984-12-14,1168.84,1187.28,1165.64,1175.91,95060000,1175.91 1984-12-13,1175.13,1183.52,1162.65,1168.84,80850000,1168.84 1984-12-12,1178.33,1183.97,1170.38,1175.13,78710000,1175.13 1984-12-11,1172.26,1183.86,1167.51,1178.33,80240000,1178.33 1984-12-10,1163.21,1179.00,1154.70,1172.26,81140000,1172.26 1984-12-07,1170.49,1177.78,1160.00,1163.21,81000000,1163.21 1984-12-06,1171.60,1178.67,1159.23,1170.49,96560000,1170.49 1984-12-05,1185.07,1187.17,1166.85,1171.60,88700000,1171.60 1984-12-04,1182.42,1192.58,1178.67,1185.07,81250000,1185.07 1984-12-03,1188.94,1192.25,1173.37,1182.42,95300000,1182.42 1984-11-30,1193.46,1197.77,1181.10,1188.94,77580000,1188.94 1984-11-29,1205.39,1206.60,1188.16,1193.46,75860000,1193.46 1984-11-28,1220.19,1224.71,1203.51,1205.39,86300000,1205.39 1984-11-27,1212.35,1226.92,1204.62,1220.19,95470000,1220.19 1984-11-26,1220.30,1224.49,1206.16,1212.35,76520000,1212.35 1984-11-23,1204.28,1224.49,1204.28,1220.30,73910000,1220.30 1984-11-21,1195.12,1207.05,1185.07,1201.52,81620000,1201.52 1984-11-20,1185.29,1202.41,1184.30,1195.12,83240000,1195.12 1984-11-19,1187.94,1197.44,1180.76,1185.29,69730000,1185.29 1984-11-16,1206.16,1213.01,1186.73,1187.94,83140000,1187.94 1984-11-15,1206.93,1214.22,1198.54,1206.16,81530000,1206.16 1984-11-14,1206.60,1215.88,1198.98,1206.93,73940000,1206.93 1984-11-13,1219.19,1222.17,1203.07,1206.60,69790000,1206.60 1984-11-12,1218.97,1223.72,1209.25,1219.19,55610000,1219.19 1984-11-09,1228.69,1238.63,1216.65,1218.97,83620000,1218.97 1984-11-08,1233.22,1237.52,1220.30,1228.69,88580000,1228.69 1984-11-07,1244.15,1245.69,1223.72,1233.22,110800000,1233.22 1984-11-06,1229.24,1247.46,1227.03,1244.15,101200000,1244.15 1984-11-05,1216.65,1233.11,1212.57,1229.24,84730000,1229.24 1984-11-02,1217.09,1225.82,1208.26,1216.65,96810000,1216.65 1984-11-01,1207.38,1224.38,1204.51,1217.09,107300000,1217.09 1984-10-31,1217.31,1219.63,1201.52,1207.38,91890000,1207.38 1984-10-30,1201.41,1224.60,1200.42,1217.31,95200000,1217.31 1984-10-29,1204.95,1207.49,1195.01,1201.41,63200000,1201.41 1984-10-26,1211.02,1213.45,1197.77,1204.95,83900000,1204.95 1984-10-25,1216.43,1223.61,1206.05,1211.02,92760000,1211.02 1984-10-24,1213.01,1223.06,1205.17,1216.43,91620000,1216.43 1984-10-23,1217.20,1226.70,1206.71,1213.01,92260000,1213.01 1984-10-22,1225.93,1230.34,1210.80,1217.20,81020000,1217.20 1984-10-19,1225.38,1246.02,1213.56,1225.93,186900000,1225.93 1984-10-18,1195.89,1226.70,1185.73,1225.38,149500000,1225.38 1984-10-17,1197.77,1208.59,1185.62,1195.89,99740000,1195.89 1984-10-16,1202.96,1209.36,1192.25,1197.77,82930000,1197.77 1984-10-15,1190.70,1209.58,1186.73,1202.96,87590000,1202.96 1984-10-12,1183.08,1198.43,1181.10,1190.70,92190000,1190.70 1984-10-11,1177.23,1189.93,1170.61,1183.08,87020000,1183.08 1984-10-10,1175.13,1182.97,1158.24,1177.23,94270000,1177.23 1984-10-09,1177.89,1188.16,1171.93,1175.13,76840000,1175.13 1984-10-08,1182.53,1183.08,1170.49,1177.89,46360000,1177.89 1984-10-05,1187.39,1196.33,1176.57,1182.53,82950000,1182.53 1984-10-04,1182.86,1195.01,1181.10,1187.39,76700000,1187.39 1984-10-03,1191.36,1195.78,1174.69,1182.86,92400000,1182.86 1984-10-02,1198.98,1207.38,1188.60,1191.36,89360000,1191.36 1984-10-01,1206.71,1208.15,1192.36,1198.98,73630000,1198.98 1984-09-28,1216.76,1217.98,1200.09,1206.71,78950000,1206.71 1984-09-27,1212.12,1225.27,1211.35,1216.76,88880000,1216.76 1984-09-26,1207.16,1222.06,1199.98,1212.12,100200000,1212.12 1984-09-25,1205.06,1216.87,1192.80,1207.16,86250000,1207.16 1984-09-24,1201.74,1216.43,1196.00,1205.06,76380000,1205.06 1984-09-21,1216.54,1231.01,1197.00,1201.74,120600000,1201.74 1984-09-20,1213.01,1221.73,1202.52,1216.54,92030000,1216.54 1984-09-19,1226.26,1234.10,1206.05,1213.01,119900000,1213.01 1984-09-18,1237.08,1239.95,1221.18,1226.26,107700000,1226.26 1984-09-17,1237.52,1248.01,1226.26,1237.08,88790000,1237.08 1984-09-14,1228.25,1248.23,1227.92,1237.52,137400000,1237.52 1984-09-13,1200.31,1232.11,1196.00,1228.25,110500000,1228.25 1984-09-12,1197.99,1206.60,1189.82,1200.31,77980000,1200.31 1984-09-11,1202.52,1221.07,1196.33,1197.99,101300000,1197.99 1984-09-10,1207.38,1214.33,1191.81,1202.52,74410000,1202.52 1984-09-07,1218.86,1226.59,1201.63,1207.38,84110000,1207.38 1984-09-06,1209.03,1226.04,1208.70,1218.86,91920000,1218.86 1984-09-05,1212.35,1215.88,1199.65,1209.03,69250000,1209.03 1984-09-04,1222.39,1222.39,1206.93,1212.35,62110000,1212.35 1984-08-31,1223.28,1226.70,1213.89,1224.38,57460000,1224.38 1984-08-30,1226.92,1231.89,1218.64,1223.28,70840000,1223.28 1984-08-29,1232.11,1243.04,1222.50,1226.92,90660000,1226.92 1984-08-28,1227.92,1236.20,1220.85,1232.11,70560000,1232.11 1984-08-27,1234.76,1234.76,1219.08,1227.92,57660000,1227.92 1984-08-24,1232.44,1240.72,1227.14,1236.53,69640000,1236.53 1984-08-23,1231.78,1241.94,1221.51,1232.44,83130000,1232.44 1984-08-22,1239.73,1250.55,1225.82,1231.78,116000000,1231.78 1984-08-21,1216.98,1247.35,1216.65,1239.73,128100000,1239.73 1984-08-20,1211.90,1221.18,1203.62,1216.98,75450000,1216.98 1984-08-17,1209.14,1222.06,1205.94,1211.90,71500000,1211.90 1984-08-16,1198.98,1218.42,1196.78,1209.14,93610000,1209.14 1984-08-15,1214.11,1219.74,1195.56,1198.98,91880000,1198.98 1984-08-14,1220.08,1230.45,1208.70,1214.11,81470000,1214.11 1984-08-13,1218.09,1226.15,1204.39,1220.08,77960000,1220.08 1984-08-10,1224.05,1253.75,1214.66,1218.09,171000000,1218.09 1984-08-09,1196.11,1231.45,1189.49,1224.05,131100000,1224.05 1984-08-08,1204.62,1215.44,1191.70,1196.11,121200000,1196.11 1984-08-07,1202.96,1216.98,1184.96,1204.62,127900000,1204.62 1984-08-06,1202.08,1230.90,1191.14,1202.96,203000000,1202.96 1984-08-03,1176.13,1213.34,1176.13,1202.08,236500000,1202.08 1984-08-02,1138.14,1171.38,1138.14,1166.08,172800000,1166.08 1984-08-01,1115.28,1139.58,1114.95,1134.61,127500000,1134.61 1984-07-31,1109.98,1123.56,1104.90,1115.28,86910000,1115.28 1984-07-30,1114.62,1121.69,1103.25,1109.98,72330000,1109.98 1984-07-27,1107.55,1124.23,1101.59,1114.62,101350000,1114.62 1984-07-26,1096.95,1113.18,1093.42,1107.55,90410000,1107.55 1984-07-25,1086.57,1106.45,1078.95,1096.95,90520000,1096.95 1984-07-24,1096.62,1103.91,1083.59,1086.57,74370000,1086.57 1984-07-23,1101.37,1102.58,1083.37,1096.62,77990000,1096.62 1984-07-20,1102.92,1109.10,1092.42,1101.37,79090000,1101.37 1984-07-19,1111.64,1114.07,1098.28,1102.92,85230000,1102.92 1984-07-18,1122.90,1125.44,1106.01,1111.64,76640000,1111.64 1984-07-17,1116.83,1128.53,1110.87,1122.90,82890000,1122.90 1984-07-16,1109.87,1120.25,1100.82,1116.83,73420000,1116.83 1984-07-13,1104.57,1118.04,1101.92,1109.87,75480000,1109.87 1984-07-12,1108.55,1116.72,1096.18,1104.57,86050000,1104.57 1984-07-11,1126.88,1127.87,1106.23,1108.55,89540000,1108.55 1984-07-10,1134.05,1139.47,1119.92,1126.88,74010000,1126.88 1984-07-09,1122.57,1138.69,1112.63,1134.05,74830000,1134.05 1984-07-06,1124.56,1127.10,1112.19,1122.57,65850000,1122.57 1984-07-05,1134.28,1138.47,1122.24,1124.56,66100000,1124.56 1984-07-03,1130.08,1139.13,1124.01,1134.28,69960000,1134.28 1984-07-02,1132.40,1137.81,1120.25,1130.08,69230000,1130.08 1984-06-29,1126.55,1143.33,1124.56,1132.40,90770000,1132.40 1984-06-28,1116.72,1131.62,1112.74,1126.55,77660000,1126.55 1984-06-27,1122.79,1130.08,1109.98,1116.72,78400000,1116.72 1984-06-26,1130.52,1133.61,1116.83,1122.79,82600000,1122.79 1984-06-25,1131.07,1140.68,1124.01,1130.52,72850000,1130.52 1984-06-22,1127.21,1139.02,1118.93,1131.07,98400000,1131.07 1984-06-21,1131.63,1143.44,1115.17,1127.21,123380000,1127.21 1984-06-20,1115.83,1135.16,1097.73,1131.63,99090000,1131.63 1984-06-19,1109.65,1123.12,1105.79,1115.83,98000000,1115.83 1984-06-18,1086.90,1114.29,1079.39,1109.65,94900000,1109.65 1984-06-15,1097.61,1106.34,1083.59,1086.90,85460000,1086.90 1984-06-14,1110.53,1113.96,1089.55,1097.61,79120000,1097.61 1984-06-13,1110.53,1120.91,1103.80,1110.53,67510000,1110.53 1984-06-12,1115.61,1119.92,1101.92,1110.53,84660000,1110.53 1984-06-11,1130.96,1130.96,1112.63,1115.61,69050000,1115.61 1984-06-08,1132.44,1137.82,1123.81,1131.25,67840000,1131.25 1984-06-07,1133.84,1139.12,1125.11,1132.44,82120000,1132.44 1984-06-06,1124.89,1144.83,1117.89,1133.84,83440000,1133.84 1984-06-05,1131.57,1133.62,1117.13,1124.89,84840000,1124.89 1984-06-04,1125.32,1143.53,1125.32,1131.57,96740000,1131.57 1984-06-01,1106.25,1127.26,1106.25,1124.35,96040000,1124.35 1984-05-31,1102.59,1113.90,1095.15,1104.85,81890000,1104.85 1984-05-30,1101.24,1116.59,1083.19,1102.59,105660000,1102.59 1984-05-29,1107.10,1113.80,1093.80,1101.24,69060000,1101.24 1984-05-25,1103.43,1114.32,1096.84,1107.10,78190000,1107.10 1984-05-24,1113.80,1117.15,1096.31,1103.43,99040000,1103.43 1984-05-23,1116.62,1125.63,1109.19,1113.80,82690000,1113.80 1984-05-22,1125.31,1126.99,1105.95,1116.62,88030000,1116.62 1984-05-21,1133.79,1139.13,1121.23,1125.31,73380000,1125.31 1984-05-18,1142.27,1147.82,1125.73,1133.79,81270000,1133.79 1984-05-17,1153.16,1154.10,1134.84,1142.27,90310000,1142.27 1984-05-16,1150.86,1159.86,1146.88,1153.16,89210000,1153.16 1984-05-15,1151.07,1160.80,1143.43,1150.86,88250000,1150.86 1984-05-14,1157.14,1160.49,1141.96,1151.07,64900000,1151.07 1984-05-11,1167.19,1168.24,1145.31,1157.14,82780000,1157.14 1984-05-10,1165.52,1176.61,1158.71,1167.19,101810000,1167.19 1984-05-09,1176.30,1182.47,1157.45,1165.52,100590000,1165.52 1984-05-08,1166.56,1181.11,1159.97,1176.30,81610000,1176.30 1984-05-07,1165.31,1170.96,1157.87,1166.56,72760000,1166.56 1984-05-04,1181.53,1185.41,1158.92,1165.31,98580000,1165.31 1984-05-03,1186.56,1192.73,1174.10,1181.53,91910000,1181.53 1984-05-02,1183.00,1194.41,1174.10,1186.56,107080000,1186.56 1984-05-01,1170.75,1188.86,1167.40,1183.00,110550000,1183.00 1984-04-30,1169.07,1177.45,1160.07,1170.75,72740000,1170.75 1984-04-27,1175.25,1180.49,1163.32,1169.07,88530000,1169.07 1984-04-26,1163.53,1182.06,1162.79,1175.25,98000000,1175.25 1984-04-25,1162.90,1170.33,1153.69,1163.53,83520000,1163.53 1984-04-24,1149.50,1170.54,1144.37,1162.90,87060000,1162.90 1984-04-23,1158.08,1164.78,1146.04,1149.50,73080000,1149.50 1984-04-19,1156.51,1162.79,1145.41,1158.08,75860000,1158.08 1984-04-18,1164.57,1167.71,1151.07,1156.51,85040000,1156.51 1984-04-17,1160.28,1173.99,1156.83,1164.57,98150000,1164.57 1984-04-16,1150.13,1162.90,1139.55,1160.28,73870000,1160.28 1984-04-13,1157.14,1169.28,1144.58,1150.13,99620000,1150.13 1984-04-12,1130.97,1159.55,1121.02,1157.14,96330000,1157.14 1984-04-11,1138.30,1145.10,1127.20,1130.97,80280000,1130.97 1984-04-10,1133.90,1147.93,1131.28,1138.30,78990000,1138.30 1984-04-09,1132.22,1141.86,1123.64,1133.90,71570000,1133.90 1984-04-06,1130.55,1138.18,1117.25,1132.22,86620000,1132.22 1984-04-05,1148.56,1157.87,1128.35,1130.55,101750000,1130.55 1984-04-04,1148.76,1156.83,1141.02,1148.56,92860000,1148.56 1984-04-03,1153.16,1159.13,1140.39,1148.76,87980000,1148.76 1984-04-02,1164.89,1175.36,1145.94,1153.16,85680000,1153.16 1984-03-30,1170.75,1173.68,1157.87,1164.89,71590000,1164.89 1984-03-29,1174.62,1184.25,1165.52,1170.75,81470000,1170.75 1984-03-28,1154.31,1176.61,1151.80,1174.62,104870000,1174.62 1984-03-27,1152.95,1162.27,1145.62,1154.31,73670000,1154.31 1984-03-26,1154.84,1161.96,1146.25,1152.95,69070000,1152.95 1984-03-23,1155.88,1161.54,1145.31,1154.84,79760000,1154.84 1984-03-22,1170.85,1170.85,1150.75,1155.88,87340000,1155.88 1984-03-21,1175.77,1181.95,1165.62,1170.85,87170000,1170.85 1984-03-20,1171.38,1184.15,1166.25,1175.77,86460000,1175.77 1984-03-19,1179.02,1179.02,1163.00,1171.38,64060000,1171.38 1984-03-16,1176.19,1197.24,1176.19,1184.36,118000000,1184.36 1984-03-15,1166.04,1177.24,1159.23,1167.40,79520000,1167.40 1984-03-14,1164.78,1173.68,1155.67,1166.04,77250000,1166.04 1984-03-13,1156.09,1176.51,1156.09,1164.78,102600000,1164.78 1984-03-12,1139.76,1160.80,1136.52,1155.36,84470000,1155.36 1984-03-09,1147.09,1149.29,1132.54,1139.76,73170000,1139.76 1984-03-08,1143.63,1155.46,1136.10,1147.09,80630000,1147.09 1984-03-07,1152.53,1153.27,1131.70,1143.63,90080000,1143.63 1984-03-06,1165.20,1171.59,1148.24,1152.53,83590000,1152.53 1984-03-05,1171.48,1174.31,1158.50,1165.20,69870000,1165.20 1984-03-02,1163.21,1181.01,1163.21,1171.48,108270000,1171.48 1984-03-01,1154.63,1166.46,1146.25,1159.44,82010000,1159.44 1984-02-29,1157.14,1171.38,1146.46,1154.63,92810000,1154.63 1984-02-28,1177.45,1177.45,1150.34,1157.14,91010000,1157.14 1984-02-27,1165.10,1186.66,1156.41,1179.96,99140000,1179.96 1984-02-24,1137.04,1167.09,1137.04,1165.10,102620000,1165.10 1984-02-23,1134.21,1142.38,1114.95,1134.63,100220000,1134.63 1984-02-22,1139.34,1145.83,1128.66,1134.21,90080000,1134.21 1984-02-21,1148.87,1153.79,1134.00,1139.34,71890000,1139.34 1984-02-17,1154.94,1164.36,1141.23,1148.87,76600000,1148.87 1984-02-16,1158.71,1164.99,1143.74,1154.94,81750000,1154.94 1984-02-15,1163.84,1175.67,1154.31,1158.71,94870000,1158.71 1984-02-14,1150.13,1170.54,1147.09,1163.84,91800000,1163.84 1984-02-13,1160.70,1164.05,1139.97,1150.13,78460000,1150.13 1984-02-10,1152.74,1167.92,1149.92,1160.70,92220000,1160.70 1984-02-09,1156.30,1167.29,1139.03,1152.74,128190000,1152.74 1984-02-08,1180.49,1188.76,1151.28,1156.30,96890000,1156.30 1984-02-07,1174.31,1187.29,1161.12,1180.49,107640000,1180.49 1984-02-06,1193.15,1193.15,1167.92,1174.31,109090000,1174.31 1984-02-03,1213.88,1226.55,1192.00,1197.03,109100000,1197.03 1984-02-02,1212.31,1223.93,1198.49,1213.88,111330000,1213.88 1984-02-01,1220.58,1226.34,1203.31,1212.31,107100000,1212.31 1984-01-31,1221.52,1231.68,1209.59,1220.58,113510000,1220.58 1984-01-30,1230.00,1239.64,1212.42,1221.52,103120000,1221.52 1984-01-27,1229.69,1238.69,1218.91,1230.00,103720000,1230.00 1984-01-26,1231.89,1242.57,1222.78,1229.69,111100000,1229.69 1984-01-25,1242.88,1255.44,1227.39,1231.89,113470000,1231.89 1984-01-24,1244.45,1251.57,1234.61,1242.88,103050000,1242.88 1984-01-23,1259.11,1259.42,1240.05,1244.45,82010000,1244.45 1984-01-20,1266.02,1270.94,1252.30,1259.11,93360000,1259.11 1984-01-19,1269.37,1276.17,1259.32,1266.02,98340000,1266.02 1984-01-18,1271.46,1280.36,1261.93,1269.37,109010000,1269.37 1984-01-17,1267.59,1275.75,1260.89,1271.46,92750000,1271.46 1984-01-16,1270.10,1275.96,1259.53,1267.59,93790000,1267.59 1984-01-13,1279.31,1289.57,1262.98,1270.10,101790000,1270.10 1984-01-12,1277.32,1287.90,1271.78,1279.31,99410000,1279.31 1984-01-11,1278.48,1284.13,1267.59,1277.32,98660000,1277.32 1984-01-10,1286.22,1295.44,1273.97,1278.48,109570000,1278.48 1984-01-09,1286.64,1295.33,1275.86,1286.22,107100000,1286.22 1984-01-06,1282.24,1293.66,1271.78,1286.64,137590000,1286.64 1984-01-05,1269.05,1290.72,1268.74,1282.24,159990000,1282.24 1984-01-04,1252.74,1271.36,1246.55,1269.05,112980000,1269.05 1984-01-03,1258.64,1264.13,1247.46,1252.74,71340000,1252.74 1983-12-30,1260.16,1265.24,1253.15,1258.64,71840000,1258.64 1983-12-29,1263.21,1273.88,1256.30,1260.16,86560000,1260.16 1983-12-28,1263.72,1268.90,1254.17,1263.21,85660000,1263.21 1983-12-27,1250.51,1267.28,1249.80,1263.72,63800000,1263.72 1983-12-23,1253.66,1259.25,1241.97,1250.51,62710000,1250.51 1983-12-22,1254.98,1265.24,1246.65,1253.66,106260000,1253.66 1983-12-21,1241.97,1260.26,1240.24,1254.98,108080000,1254.98 1983-12-20,1244.61,1251.93,1236.79,1241.97,83740000,1241.97 1983-12-19,1242.17,1254.88,1238.21,1244.61,75180000,1244.61 1983-12-16,1236.79,1248.27,1231.40,1242.17,81030000,1242.17 1983-12-15,1246.65,1250.20,1231.20,1236.79,88300000,1236.79 1983-12-14,1255.89,1261.38,1241.77,1246.65,85430000,1246.65 1983-12-13,1261.59,1267.17,1250.81,1255.89,93500000,1255.89 1983-12-12,1260.06,1269.11,1248.78,1261.59,77340000,1261.59 1983-12-09,1261.89,1268.09,1252.03,1260.06,98280000,1260.06 1983-12-08,1273.78,1278.15,1256.91,1261.89,96530000,1261.89 1983-12-07,1269.31,1282.42,1263.21,1273.78,105670000,1273.78 1983-12-06,1270.53,1281.30,1262.80,1269.31,89690000,1269.31 1983-12-05,1265.24,1275.41,1256.40,1270.53,88330000,1270.53 1983-12-02,1275.10,1281.30,1259.76,1265.24,93960000,1265.24 1983-12-01,1276.02,1285.47,1265.45,1275.10,106970000,1275.10 1983-11-30,1287.19,1296.95,1271.04,1276.02,120130000,1276.02 1983-11-29,1269.82,1290.95,1264.22,1287.19,100460000,1287.19 1983-11-28,1277.44,1279.78,1263.11,1269.82,78210000,1269.82 1983-11-25,1275.61,1282.22,1268.80,1277.44,57820000,1277.44 1983-11-23,1275.81,1284.55,1265.04,1275.61,108080000,1275.61 1983-11-22,1268.80,1287.80,1262.91,1275.81,117550000,1275.81 1983-11-21,1251.02,1272.36,1248.17,1268.80,97740000,1268.80 1983-11-18,1254.67,1260.77,1240.35,1251.02,88280000,1251.02 1983-11-17,1251.32,1262.09,1243.19,1254.67,80740000,1254.67 1983-11-16,1247.95,1260.47,1242.07,1251.32,83380000,1251.32 1983-11-15,1254.07,1260.87,1242.28,1247.95,77840000,1247.95 1983-11-14,1250.20,1265.04,1244.61,1254.07,86880000,1254.07 1983-11-11,1235.87,1253.56,1230.79,1250.20,74270000,1250.20 1983-11-10,1232.52,1245.83,1226.83,1235.87,88730000,1235.87 1983-11-09,1214.94,1235.47,1212.20,1232.52,83100000,1232.52 1983-11-08,1214.84,1222.05,1208.74,1214.94,64900000,1214.94 1983-11-07,1218.29,1226.52,1209.35,1214.84,69400000,1214.84 1983-11-04,1227.13,1227.74,1211.89,1218.29,72080000,1218.29 1983-11-03,1237.30,1242.68,1222.76,1227.13,85350000,1227.13 1983-11-02,1229.27,1244.11,1220.83,1237.30,95210000,1237.30 1983-11-01,1225.20,1233.94,1210.47,1229.27,84460000,1229.27 1983-10-31,1223.48,1236.48,1214.63,1225.20,79460000,1225.20 1983-10-28,1242.07,1248.68,1217.07,1223.48,81180000,1223.48 1983-10-27,1243.80,1249.09,1230.49,1242.07,79570000,1242.07 1983-10-26,1252.44,1259.15,1238.62,1243.80,79570000,1243.80 1983-10-25,1248.98,1263.41,1245.22,1252.44,82530000,1252.44 1983-10-24,1248.88,1252.54,1227.74,1248.98,85420000,1248.98 1983-10-21,1251.52,1260.77,1231.61,1248.88,91640000,1248.88 1983-10-20,1246.75,1258.74,1241.06,1251.52,86000000,1251.52 1983-10-19,1250.81,1255.28,1229.37,1246.75,107790000,1246.75 1983-10-18,1268.70,1272.76,1245.53,1250.81,91080000,1250.81 1983-10-17,1263.52,1278.66,1256.20,1268.70,77730000,1268.70 1983-10-14,1261.38,1270.12,1251.83,1263.52,71600000,1263.52 1983-10-13,1259.65,1270.63,1250.51,1261.38,67750000,1261.38 1983-10-12,1265.14,1273.07,1252.95,1259.65,75630000,1259.65 1983-10-11,1284.65,1285.26,1261.89,1265.14,79510000,1265.14 1983-10-10,1272.15,1286.69,1261.59,1284.65,67050000,1284.65 1983-10-07,1268.80,1280.28,1262.60,1272.15,103630000,1272.15 1983-10-06,1250.20,1272.15,1246.95,1268.80,118270000,1268.80 1983-10-05,1236.69,1254.88,1228.96,1250.20,101710000,1250.20 1983-10-04,1231.30,1247.56,1225.71,1236.69,90270000,1236.69 1983-10-03,1233.13,1237.70,1216.26,1231.30,77230000,1231.30 1983-09-30,1240.14,1242.89,1224.59,1233.13,70860000,1233.13 1983-09-29,1241.97,1252.03,1236.08,1240.14,73730000,1240.14 1983-09-28,1247.97,1253.45,1234.35,1241.97,75820000,1241.97 1983-09-27,1259.25,1259.25,1239.33,1247.97,81100000,1247.97 1983-09-26,1255.59,1270.73,1247.56,1260.77,86400000,1260.77 1983-09-23,1257.52,1265.55,1246.24,1255.59,93180000,1255.59 1983-09-22,1243.29,1261.28,1236.79,1257.52,97050000,1257.52 1983-09-21,1249.19,1255.89,1236.79,1243.29,91280000,1243.29 1983-09-20,1233.94,1257.01,1233.64,1249.19,103050000,1249.19 1983-09-19,1225.71,1242.68,1221.24,1233.94,85630000,1233.94 1983-09-16,1215.04,1232.72,1210.16,1225.71,75530000,1225.71 1983-09-15,1229.47,1232.83,1213.01,1215.04,70420000,1215.04 1983-09-14,1224.09,1233.94,1218.80,1229.47,73370000,1229.47 1983-09-13,1229.07,1233.43,1214.63,1224.09,73970000,1224.09 1983-09-12,1239.74,1262.80,1225.00,1229.07,114020000,1229.07 1983-09-09,1246.14,1252.74,1233.33,1239.74,77990000,1239.74 1983-09-08,1244.11,1252.95,1232.83,1246.14,79250000,1246.14 1983-09-07,1238.72,1253.96,1229.27,1244.11,94240000,1244.11 1983-09-06,1222.05,1244.11,1222.05,1238.72,87500000,1238.72 1983-09-02,1206.81,1221.34,1204.17,1215.45,59300000,1215.45 1983-09-01,1216.16,1221.24,1197.36,1206.81,76120000,1206.81 1983-08-31,1196.04,1219.82,1191.16,1216.16,80800000,1216.16 1983-08-30,1194.11,1204.37,1187.20,1196.04,62370000,1196.04 1983-08-29,1192.07,1198.37,1178.15,1194.11,53030000,1194.11 1983-08-26,1185.06,1196.95,1174.59,1192.07,61650000,1192.07 1983-08-25,1184.25,1195.22,1173.27,1185.06,70140000,1185.06 1983-08-24,1192.89,1199.80,1179.67,1184.25,72200000,1184.25 1983-08-23,1203.15,1205.49,1186.38,1192.89,66800000,1192.89 1983-08-22,1194.21,1216.26,1191.36,1203.15,76420000,1203.15 1983-08-19,1192.48,1200.51,1183.64,1194.21,58950000,1194.21 1983-08-18,1206.54,1215.45,1188.52,1192.48,82280000,1192.48 1983-08-17,1190.45,1212.09,1186.28,1206.54,87800000,1206.54 1983-08-16,1193.50,1199.70,1180.49,1190.45,71780000,1190.45 1983-08-15,1186.69,1208.74,1186.69,1193.50,83200000,1193.50 1983-08-12,1174.39,1189.63,1169.82,1182.83,71840000,1182.83 1983-08-11,1175.98,1185.87,1165.55,1174.39,70630000,1174.39 1983-08-10,1168.27,1182.19,1157.25,1175.98,82900000,1175.98 1983-08-09,1163.06,1177.28,1152.14,1168.27,81420000,1168.27 1983-08-08,1180.19,1180.19,1157.55,1163.06,71460000,1163.06 1983-08-05,1183.09,1191.91,1173.58,1183.29,67850000,1183.29 1983-08-04,1197.82,1200.52,1166.57,1183.09,100870000,1183.09 1983-08-03,1188.00,1203.73,1179.29,1197.82,80370000,1197.82 1983-08-02,1194.21,1202.52,1183.89,1188.00,74460000,1188.00 1983-08-01,1199.22,1204.33,1183.79,1194.21,77210000,1194.21 1983-07-29,1216.35,1216.75,1188.10,1199.22,95240000,1199.22 1983-07-28,1230.47,1236.58,1211.74,1216.35,78410000,1216.35 1983-07-27,1243.69,1258.51,1225.86,1230.47,99290000,1230.47 1983-07-26,1232.87,1250.00,1226.46,1243.69,91280000,1243.69 1983-07-25,1231.17,1239.78,1217.45,1232.87,73680000,1232.87 1983-07-22,1229.37,1238.18,1220.45,1231.17,68850000,1231.17 1983-07-21,1227.86,1238.48,1217.35,1229.37,101830000,1229.37 1983-07-20,1206.03,1230.57,1206.03,1227.86,109310000,1227.86 1983-07-19,1189.90,1205.13,1188.40,1197.12,74030000,1197.12 1983-07-18,1192.31,1197.62,1179.79,1189.90,69110000,1189.90 1983-07-15,1204.33,1205.73,1187.10,1192.31,63160000,1192.31 1983-07-14,1197.82,1215.04,1195.11,1204.33,83500000,1204.33 1983-07-13,1198.52,1205.83,1189.40,1197.82,68900000,1197.82 1983-07-12,1215.54,1218.35,1194.11,1198.52,70220000,1198.52 1983-07-11,1207.23,1221.96,1205.53,1215.54,61610000,1215.54 1983-07-08,1210.44,1218.95,1201.52,1207.23,66520000,1207.23 1983-07-07,1220.65,1227.26,1203.33,1210.44,97130000,1210.44 1983-07-06,1208.53,1224.46,1202.72,1220.65,85670000,1220.65 1983-07-05,1217.25,1217.25,1200.22,1208.53,67320000,1208.53 1983-07-01,1221.96,1231.07,1213.94,1225.26,65110000,1225.26 1983-06-30,1213.84,1229.27,1209.54,1221.96,76310000,1221.96 1983-06-29,1209.23,1222.16,1201.22,1213.84,81580000,1213.84 1983-06-28,1229.47,1237.68,1205.83,1209.23,82730000,1209.23 1983-06-27,1241.69,1246.29,1221.85,1229.47,69360000,1229.47 1983-06-24,1241.79,1250.20,1229.87,1241.69,80810000,1241.69 1983-06-23,1245.69,1250.60,1231.67,1241.79,89590000,1241.79 1983-06-22,1247.40,1258.21,1234.27,1245.69,110270000,1245.69 1983-06-21,1239.18,1253.00,1228.27,1247.40,102880000,1247.40 1983-06-20,1242.19,1258.61,1230.77,1239.18,84270000,1239.18 1983-06-17,1248.30,1260.72,1232.87,1242.19,93630000,1242.19 1983-06-16,1237.28,1259.82,1236.08,1248.30,124560000,1248.30 1983-06-15,1227.26,1240.18,1216.85,1237.28,93410000,1237.28 1983-06-14,1220.55,1237.28,1214.64,1227.26,97710000,1227.26 1983-06-13,1199.12,1223.46,1199.12,1220.55,90700000,1220.55 1983-06-10,1189.00,1204.53,1187.30,1196.11,78470000,1196.11 1983-06-09,1185.50,1196.71,1176.78,1189.00,87440000,1189.00 1983-06-08,1194.91,1199.52,1179.59,1185.50,96600000,1185.50 1983-06-07,1214.24,1218.15,1193.21,1194.91,88550000,1194.91 1983-06-06,1213.04,1223.26,1203.83,1214.24,87670000,1214.24 1983-06-03,1211.44,1222.46,1204.23,1213.04,83110000,1213.04 1983-06-02,1202.21,1218.15,1195.01,1211.44,89750000,1211.44 1983-06-01,1199.98,1208.30,1187.69,1202.21,84460000,1202.21 1983-05-31,1216.14,1217.59,1191.95,1199.98,73910000,1199.98 1983-05-27,1223.49,1229.20,1209.95,1216.14,76290000,1216.14 1983-05-26,1229.01,1237.04,1215.17,1223.49,94980000,1223.49 1983-05-25,1219.04,1234.04,1207.62,1229.01,121050000,1229.01 1983-05-24,1200.56,1224.07,1199.21,1219.04,109850000,1219.04 1983-05-23,1190.02,1205.40,1174.25,1200.56,84960000,1200.56 1983-05-20,1191.37,1197.27,1179.08,1190.02,73150000,1190.02 1983-05-19,1203.56,1208.49,1186.15,1191.37,83260000,1191.37 1983-05-18,1205.79,1225.04,1196.98,1203.56,99780000,1203.56 1983-05-17,1202.98,1211.88,1193.30,1205.79,79510000,1205.79 1983-05-16,1209.95,1209.95,1190.21,1202.98,76250000,1202.98 1983-05-13,1214.40,1228.04,1209.17,1218.75,83110000,1218.75 1983-05-12,1219.72,1226.59,1204.82,1214.40,84060000,1214.40 1983-05-11,1229.68,1234.42,1210.72,1219.72,99820000,1219.72 1983-05-10,1228.23,1240.03,1220.49,1229.68,104010000,1229.68 1983-05-09,1232.59,1241.29,1219.04,1228.23,93670000,1228.23 1983-05-06,1219.72,1244.49,1217.69,1232.59,128200000,1232.59 1983-05-05,1212.65,1225.81,1202.40,1219.72,107860000,1219.72 1983-05-04,1208.01,1227.94,1198.63,1212.65,101690000,1212.65 1983-05-03,1204.33,1214.11,1188.37,1208.01,89550000,1208.01 1983-05-02,1225.43,1225.43,1197.27,1204.33,88170000,1204.33 1983-04-29,1219.52,1235.49,1208.59,1226.20,105750000,1226.20 1983-04-28,1208.40,1226.97,1201.04,1219.52,94410000,1219.52 1983-04-27,1209.46,1225.04,1197.37,1208.40,118140000,1208.40 1983-04-26,1187.21,1210.82,1177.92,1209.46,91210000,1209.46 1983-04-25,1196.30,1206.75,1183.24,1187.21,90150000,1187.21 1983-04-22,1188.27,1204.62,1184.21,1196.30,92270000,1196.30 1983-04-21,1191.47,1202.11,1180.92,1188.27,106170000,1188.27 1983-04-20,1174.54,1197.56,1170.47,1191.47,110240000,1191.47 1983-04-19,1183.24,1187.98,1166.22,1174.54,91210000,1174.54 1983-04-18,1171.34,1187.89,1164.57,1183.24,88560000,1183.24 1983-04-15,1165.25,1177.05,1159.06,1171.34,89590000,1171.34 1983-04-14,1156.64,1171.05,1146.67,1165.25,90160000,1165.25 1983-04-13,1145.32,1165.34,1142.22,1156.64,100520000,1156.64 1983-04-12,1141.83,1151.80,1134.09,1145.32,79900000,1145.32 1983-04-11,1127.23,1144.54,1127.23,1141.83,81440000,1141.83 1983-04-08,1117.65,1128.87,1109.52,1124.71,67710000,1124.71 1983-04-07,1113.49,1123.36,1106.71,1117.65,69480000,1117.65 1983-04-06,1120.16,1122.48,1102.17,1113.49,77140000,1113.49 1983-04-05,1127.61,1137.58,1116.39,1120.16,76810000,1120.16 1983-04-04,1130.03,1133.03,1115.13,1127.61,66010000,1127.61 1983-03-31,1143.29,1158.48,1125.29,1130.03,100570000,1130.03 1983-03-30,1131.19,1146.67,1129.64,1143.29,75800000,1143.29 1983-03-29,1133.32,1142.80,1125.39,1131.19,65300000,1131.19 1983-03-28,1140.09,1145.80,1126.84,1133.32,58510000,1133.32 1983-03-25,1145.90,1153.83,1132.64,1140.09,77330000,1140.09 1983-03-24,1140.87,1154.41,1134.48,1145.90,92340000,1145.90 1983-03-23,1122.97,1147.83,1121.81,1140.87,94980000,1140.87 1983-03-22,1125.29,1135.74,1118.61,1122.97,79610000,1122.97 1983-03-21,1117.74,1130.32,1109.33,1125.29,72160000,1125.29 1983-03-18,1116.97,1128.00,1109.71,1117.74,75110000,1117.74 1983-03-17,1116.00,1123.55,1108.07,1116.97,70290000,1116.97 1983-03-16,1124.52,1134.77,1111.46,1116.00,83570000,1116.00 1983-03-15,1114.45,1126.84,1109.62,1124.52,62410000,1124.52 1983-03-14,1117.74,1124.61,1105.07,1114.45,61890000,1114.45 1983-03-11,1120.94,1127.61,1107.97,1117.74,67240000,1117.74 1983-03-10,1132.64,1145.22,1116.49,1120.94,95410000,1120.94 1983-03-09,1119.78,1136.42,1112.04,1132.64,84250000,1132.64 1983-03-08,1141.74,1143.00,1117.55,1119.78,79410000,1119.78 1983-03-07,1140.96,1149.67,1128.19,1141.74,84020000,1141.74 1983-03-04,1138.06,1146.28,1125.58,1140.96,90930000,1140.96 1983-03-03,1135.06,1151.12,1128.39,1138.06,114440000,1138.06 1983-03-02,1130.71,1144.45,1121.90,1135.06,112600000,1135.06 1983-03-01,1112.16,1139.22,1110.20,1130.71,103750000,1130.71 1983-02-28,1120.94,1128.58,1105.26,1112.16,83750000,1112.16 1983-02-25,1121.81,1135.26,1115.13,1120.94,100970000,1120.94 1983-02-24,1100.62,1126.74,1100.62,1121.81,113220000,1121.81 1983-02-23,1080.40,1100.14,1077.69,1096.94,84100000,1096.94 1983-02-22,1092.82,1097.72,1074.01,1080.40,84080000,1080.40 1983-02-18,1088.91,1099.42,1079.99,1092.82,77420000,1092.82 1983-02-17,1087.43,1096.35,1075.52,1088.91,74930000,1088.91 1983-02-16,1093.10,1103.70,1083.43,1087.43,82100000,1087.43 1983-02-15,1097.10,1107.61,1086.22,1093.10,89040000,1093.10 1983-02-14,1086.50,1103.33,1080.64,1097.10,72640000,1097.10 1983-02-11,1087.75,1100.82,1080.17,1086.50,86700000,1086.50 1983-02-10,1067.88,1094.83,1067.88,1087.75,93510000,1087.75 1983-02-09,1075.33,1081.59,1058.13,1067.42,84520000,1067.42 1983-02-08,1087.10,1091.70,1068.71,1075.33,76580000,1075.33 1983-02-07,1077.91,1097.04,1073.77,1087.10,86030000,1087.10 1983-02-04,1064.66,1081.13,1056.75,1077.91,87000000,1077.91 1983-02-03,1062.64,1075.06,1057.03,1064.66,78890000,1064.66 1983-02-02,1059.79,1070.09,1046.82,1062.64,77220000,1062.64 1983-02-01,1075.70,1082.78,1057.58,1059.79,82750000,1059.79 1983-01-31,1064.75,1079.75,1055.83,1075.70,67140000,1075.70 1983-01-28,1063.65,1080.57,1056.66,1064.75,89490000,1064.75 1983-01-27,1037.99,1068.16,1037.35,1063.65,88120000,1063.65 1983-01-26,1042.03,1050.31,1027.78,1037.99,73720000,1037.99 1983-01-25,1030.17,1048.93,1028.61,1042.03,79740000,1042.03 1983-01-24,1039.28,1039.28,1013.43,1030.17,90800000,1030.17 1983-01-21,1070.46,1070.46,1045.16,1052.98,77110000,1052.98 1983-01-20,1068.06,1080.39,1060.15,1070.82,82790000,1070.82 1983-01-19,1079.65,1082.97,1057.12,1068.06,80900000,1068.06 1983-01-18,1084.81,1089.22,1068.06,1079.65,78380000,1079.65 1983-01-17,1080.85,1098.60,1077.35,1084.81,89210000,1084.81 1983-01-14,1073.95,1089.04,1070.36,1080.85,86480000,1080.85 1983-01-13,1083.61,1091.06,1068.80,1073.95,77030000,1073.95 1983-01-12,1083.79,1105.13,1075.88,1083.61,109850000,1083.61 1983-01-11,1092.35,1097.50,1076.07,1083.79,98250000,1083.79 1983-01-10,1076.07,1099.98,1065.12,1092.35,101890000,1092.35 1983-01-07,1070.92,1091.34,1063.19,1076.07,127290000,1076.07 1983-01-06,1044.89,1081.95,1042.40,1070.92,129410000,1070.92 1983-01-05,1046.08,1056.75,1033.94,1044.89,95390000,1044.89 1983-01-04,1027.04,1047.46,1020.24,1046.08,75530000,1046.08 1983-01-03,1046.54,1057.21,1022.17,1027.04,59080000,1027.04 1982-12-31,1047.37,1055.56,1041.84,1046.54,42110000,1046.54 1982-12-30,1059.60,1066.13,1043.60,1047.37,56380000,1047.37 1982-12-29,1058.87,1069.35,1050.68,1059.60,54810000,1059.60 1982-12-28,1070.55,1077.91,1053.44,1058.87,58610000,1058.87 1982-12-27,1045.07,1076.07,1043.60,1070.55,64690000,1070.55 1982-12-23,1035.04,1051.51,1032.19,1045.07,62880000,1045.07 1982-12-22,1030.26,1047.19,1021.62,1035.04,83470000,1035.04 1982-12-21,1004.51,1036.33,998.53,1030.26,78010000,1030.26 1982-12-20,1011.50,1022.63,998.44,1004.51,62210000,1004.51 1982-12-17,990.25,1018.12,989.24,1011.50,76010000,1011.50 1982-12-16,992.64,1004.42,983.35,990.25,73680000,990.25 1982-12-15,1009.38,1013.52,985.74,992.64,81030000,992.64 1982-12-14,1024.28,1047.28,1006.99,1009.38,98380000,1009.38 1982-12-13,1018.76,1030.91,1013.34,1024.28,63140000,1024.28 1982-12-10,1027.96,1038.36,1009.47,1018.76,86430000,1018.76 1982-12-09,1047.09,1049.39,1020.79,1027.96,90320000,1027.96 1982-12-08,1056.94,1069.72,1041.30,1047.09,97430000,1047.09 1982-12-07,1055.65,1071.10,1045.07,1056.94,111620000,1056.94 1982-12-06,1031.36,1061.26,1020.88,1055.65,83880000,1055.65 1982-12-03,1033.11,1046.82,1027.04,1031.36,71540000,1031.36 1982-12-02,1031.09,1047.37,1027.04,1033.11,77600000,1033.11 1982-12-01,1039.28,1055.00,1025.57,1031.09,107850000,1031.09 1982-11-30,1002.85,1042.40,998.34,1039.28,93470000,1039.28 1982-11-29,1007.36,1014.72,993.84,1002.85,61080000,1002.85 1982-11-26,1000.00,1012.60,998.53,1007.36,38810000,1007.36 1982-11-24,990.99,1007.36,988.23,1000.00,67220000,1000.00 1982-11-23,1000.00,1007.63,986.66,990.99,72920000,990.99 1982-11-22,1021.25,1024.01,997.42,1000.00,74960000,1000.00 1982-11-19,1032.10,1042.86,1017.11,1021.25,70310000,1021.25 1982-11-18,1027.50,1040.47,1020.05,1032.10,77620000,1032.10 1982-11-17,1012.05,1032.74,1012.05,1027.50,84440000,1027.50 1982-11-16,1019.68,1019.68,993.10,1008.00,102910000,1008.00 1982-11-15,1032.56,1032.56,1012.60,1021.43,78900000,1021.43 1982-11-12,1054.73,1060.52,1035.60,1039.92,95080000,1039.92 1982-11-11,1044.52,1057.03,1030.35,1054.73,78410000,1054.73 1982-11-10,1060.25,1078.27,1037.71,1044.52,113240000,1044.52 1982-11-09,1037.44,1065.86,1034.86,1060.25,111220000,1060.25 1982-11-08,1048.57,1048.57,1029.25,1037.44,75240000,1037.44 1982-11-05,1050.22,1061.17,1040.65,1051.78,96550000,1051.78 1982-11-04,1065.49,1078.46,1042.59,1050.22,149350000,1050.22 1982-11-03,1022.08,1068.16,1021.71,1065.49,137010000,1065.49 1982-11-02,1007.45,1036.98,1007.45,1022.08,104770000,1022.08 1982-11-01,991.72,1009.01,983.72,1005.70,73530000,1005.70 1982-10-29,990.99,999.45,977.92,991.72,74830000,991.72 1982-10-28,1006.35,1011.87,989.33,990.99,73590000,990.99 1982-10-27,1006.07,1019.87,996.87,1006.35,81670000,1006.35 1982-10-26,995.13,1011.13,975.90,1006.07,102080000,1006.07 1982-10-25,1018.76,1018.76,991.91,995.13,83720000,995.13 1982-10-22,1036.98,1051.88,1024.37,1031.46,101120000,1031.46 1982-10-21,1034.12,1049.12,1018.40,1036.98,122460000,1036.98 1982-10-20,1013.80,1035.23,1005.98,1034.12,98680000,1034.12 1982-10-19,1019.22,1031.09,1001.66,1013.80,100850000,1013.80 1982-10-18,993.10,1023.45,991.63,1019.22,83790000,1019.22 1982-10-15,996.87,1003.86,981.88,993.10,80290000,993.10 1982-10-14,1015.18,1020.97,991.17,996.87,107530000,996.87 1982-10-13,1003.68,1033.57,991.63,1015.18,139800000,1015.18 1982-10-12,1012.79,1026.95,992.00,1003.68,126310000,1003.68 1982-10-11,991.54,1027.59,991.54,1012.79,138530000,1012.79 1982-10-08,965.97,992.92,961.64,986.85,122250000,986.85 1982-10-07,950.24,972.87,950.24,965.97,147070000,965.97 1982-10-06,907.74,946.38,907.74,944.26,93570000,944.26 1982-10-05,903.61,917.77,899.47,907.19,69770000,907.19 1982-10-04,907.74,908.57,892.02,903.61,55650000,903.61 1982-10-01,896.25,910.87,892.02,907.74,65000000,907.74 1982-09-30,906.27,908.20,891.28,896.25,62610000,896.25 1982-09-29,919.33,920.71,901.12,906.27,62550000,906.27 1982-09-28,920.90,930.74,916.02,919.33,65900000,919.33 1982-09-27,919.52,925.04,909.86,920.90,44840000,920.90 1982-09-24,925.77,926.42,913.45,919.52,54600000,919.52 1982-09-23,927.61,933.50,915.38,925.77,68260000,925.77 1982-09-22,934.79,951.16,924.67,927.61,113150000,927.61 1982-09-21,916.30,937.09,913.91,934.79,82920000,934.79 1982-09-20,916.94,921.45,904.71,916.30,58520000,916.30 1982-09-17,927.80,931.29,912.71,916.94,63950000,916.94 1982-09-16,930.46,938.47,922.46,927.80,78900000,927.80 1982-09-15,923.01,933.13,915.56,930.46,69680000,930.46 1982-09-14,918.69,933.13,914.92,923.01,83070000,923.01 1982-09-13,906.82,921.08,898.36,918.69,59520000,918.69 1982-09-10,912.53,912.90,897.08,906.82,71080000,906.82 1982-09-09,915.75,925.31,906.27,912.53,73090000,912.53 1982-09-08,914.28,927.34,906.82,915.75,77960000,915.75 1982-09-07,925.13,928.35,906.92,914.28,68960000,914.28 1982-09-03,913.54,940.49,913.54,925.13,130910000,925.13 1982-09-02,895.05,913.17,888.70,909.40,74740000,909.40 1982-09-01,901.31,911.33,890.91,895.05,82830000,895.05 1982-08-31,893.30,910.96,887.97,901.31,86360000,901.31 1982-08-30,883.47,895.42,872.42,893.30,59560000,893.30 1982-08-27,892.41,893.84,874.14,883.47,74410000,883.47 1982-08-26,884.89,908.96,882.61,892.41,137330000,892.41 1982-08-25,874.90,900.97,867.87,884.89,106200000,884.89 1982-08-24,891.17,893.36,869.01,874.90,121650000,874.90 1982-08-23,869.29,894.22,861.68,891.17,110310000,891.17 1982-08-20,840.85,871.19,840.85,869.29,95890000,869.29 1982-08-19,829.43,848.93,824.49,838.57,78270000,838.57 1982-08-18,831.24,858.54,826.10,829.43,132690000,829.43 1982-08-17,796.14,832.76,796.14,831.24,92860000,831.24 1982-08-16,788.53,803.84,788.53,792.43,55420000,792.43 1982-08-13,776.92,790.62,774.54,788.05,44720000,788.05 1982-08-12,777.21,786.15,773.59,776.92,50080000,776.92 1982-08-11,779.30,783.96,772.17,777.21,49040000,777.21 1982-08-10,780.35,789.10,775.68,779.30,52680000,779.30 1982-08-09,784.34,784.34,769.98,780.35,54560000,780.35 1982-08-06,795.85,798.99,781.77,784.34,48660000,784.34 1982-08-05,803.46,804.41,789.76,795.85,54700000,795.85 1982-08-04,815.35,815.35,800.89,803.46,53440000,803.46 1982-08-03,822.11,831.43,815.26,816.40,60480000,816.40 1982-08-02,808.60,824.11,807.84,822.11,53460000,822.11 1982-07-30,812.21,817.64,806.13,808.60,39270000,808.60 1982-07-29,811.83,817.54,801.46,812.21,55680000,812.21 1982-07-28,822.77,823.44,807.65,811.83,53830000,811.83 1982-07-27,825.44,831.05,818.02,822.77,45740000,822.77 1982-07-26,830.57,833.24,820.59,825.44,37740000,825.44 1982-07-23,832.00,838.47,824.68,830.57,47280000,830.57 1982-07-22,832.19,839.71,823.71,832.00,53870000,832.00 1982-07-21,833.43,843.80,828.20,832.19,66770000,832.19 1982-07-20,826.10,837.71,820.02,833.43,61060000,833.43 1982-07-19,828.67,836.66,822.68,826.10,53030000,826.10 1982-07-16,827.34,837.33,819.92,828.67,58740000,828.67 1982-07-15,828.39,836.57,821.25,827.34,61090000,827.34 1982-07-14,824.20,831.62,815.26,828.39,58160000,828.39 1982-07-13,824.87,832.95,816.97,824.20,66170000,824.20 1982-07-12,814.69,830.10,814.69,824.87,74690000,824.87 1982-07-09,804.98,818.59,803.37,814.12,65870000,814.12 1982-07-08,799.66,807.17,787.39,804.98,63270000,804.98 1982-07-07,798.90,804.70,793.19,799.66,46920000,799.66 1982-07-06,796.99,802.89,789.19,798.90,44350000,798.90 1982-07-02,803.18,803.18,792.52,796.99,43760000,796.99 1982-07-01,811.93,814.88,800.04,803.27,47900000,803.27 1982-06-30,812.21,821.63,807.84,811.93,65280000,811.93 1982-06-29,811.93,818.40,805.18,812.21,46990000,812.21 1982-06-28,803.08,816.69,800.51,811.93,40700000,811.93 1982-06-25,810.41,812.12,800.61,803.08,38740000,803.08 1982-06-24,813.17,821.63,805.56,810.41,55860000,810.41 1982-06-23,799.66,815.35,795.76,813.17,62710000,813.17 1982-06-22,789.95,801.46,787.67,799.66,55290000,799.66 1982-06-21,788.62,797.85,784.25,789.95,50370000,789.95 1982-06-18,791.48,793.57,784.53,788.62,53800000,788.62 1982-06-17,796.90,797.56,787.01,791.48,49230000,791.48 1982-06-16,801.27,809.17,793.85,796.90,56280000,796.90 1982-06-15,801.85,805.18,793.38,801.27,44970000,801.27 1982-06-14,809.74,810.22,798.61,801.85,40100000,801.85 1982-06-11,803.37,815.92,803.37,809.74,68610000,809.74 1982-06-10,795.57,803.37,790.53,798.71,50950000,798.71 1982-06-09,802.23,804.03,789.76,795.57,55770000,795.57 1982-06-08,804.03,809.84,799.28,802.23,46820000,802.23 1982-06-07,804.98,809.93,797.09,804.03,44630000,804.03 1982-06-04,816.50,818.87,802.42,804.98,44110000,804.98 1982-06-03,816.88,824.30,810.79,816.50,48450000,816.50 1982-06-02,814.97,822.39,810.88,816.88,49220000,816.88 1982-06-01,819.54,822.96,811.92,814.97,41650000,814.97 1982-05-28,824.96,828.86,815.54,819.54,43900000,819.54 1982-05-27,828.77,832.19,821.06,824.96,44730000,824.96 1982-05-26,834.57,836.85,824.20,828.77,51250000,828.77 1982-05-25,836.38,845.32,832.38,834.57,44010000,834.57 1982-05-24,835.90,840.09,830.10,836.38,38510000,836.38 1982-05-21,832.48,841.23,829.24,835.90,45260000,835.90 1982-05-20,835.90,839.71,827.62,832.48,48330000,832.48 1982-05-19,840.85,845.41,832.57,835.90,48840000,835.90 1982-05-18,845.32,848.08,836.66,840.85,48970000,840.85 1982-05-17,857.21,857.21,842.94,845.32,45600000,845.32 1982-05-14,859.11,864.54,853.88,857.78,49900000,857.78 1982-05-13,865.77,869.86,855.88,859.11,58230000,859.11 1982-05-12,865.87,874.52,858.92,865.77,59210000,865.77 1982-05-11,860.92,870.62,857.31,865.87,54680000,865.87 1982-05-10,869.20,870.24,857.97,860.92,46300000,860.92 1982-05-07,863.20,876.52,859.78,869.20,67130000,869.20 1982-05-06,856.35,867.96,856.35,863.20,67540000,863.20 1982-05-05,854.45,861.97,848.74,854.45,58860000,854.45 1982-05-04,849.03,860.16,848.17,854.45,58720000,854.45 1982-05-03,848.36,852.74,837.52,849.03,46490000,849.03 1982-04-30,844.94,852.55,841.70,848.36,48200000,848.36 1982-04-29,852.64,852.93,840.09,844.94,51330000,844.94 1982-04-28,857.50,861.49,845.99,852.64,50530000,852.64 1982-04-27,865.58,868.34,853.50,857.50,56480000,857.50 1982-04-26,862.16,871.19,856.45,865.58,60500000,865.58 1982-04-23,853.12,865.37,851.79,862.16,71840000,862.16 1982-04-22,843.70,857.02,843.70,853.12,64470000,853.12 1982-04-21,840.56,847.70,835.81,843.42,57820000,843.42 1982-04-20,846.08,848.93,836.38,840.56,54610000,840.56 1982-04-19,843.42,854.26,838.66,846.08,58470000,846.08 1982-04-16,839.61,848.36,836.85,843.42,55890000,843.42 1982-04-15,838.09,842.85,832.38,839.61,45700000,839.61 1982-04-14,841.04,844.46,831.91,838.09,45150000,838.09 1982-04-13,841.32,847.89,836.19,841.04,48660000,841.04 1982-04-12,842.94,847.32,836.38,841.32,46520000,841.32 1982-04-08,836.35,847.51,833.33,842.94,60190000,842.94 1982-04-07,839.33,845.03,832.48,836.35,53130000,836.35 1982-04-06,835.33,842.85,827.91,839.33,43200000,839.33 1982-04-05,838.57,843.89,830.96,835.33,46900000,835.33 1982-04-02,833.24,843.23,831.53,838.57,59800000,838.57 1982-04-01,822.77,836.66,820.49,833.24,57100000,833.24 1982-03-31,824.49,830.57,818.40,822.77,43300000,822.77 1982-03-30,823.82,829.72,816.97,824.49,43900000,824.49 1982-03-29,817.92,826.77,813.17,823.82,37100000,823.82 1982-03-26,827.44,827.44,814.12,817.92,42400000,817.92 1982-03-25,823.34,833.71,818.59,827.63,51970000,827.63 1982-03-24,826.67,831.81,819.92,823.34,49380000,823.34 1982-03-23,819.54,830.67,817.54,826.67,67130000,826.67 1982-03-22,805.65,824.01,804.79,819.54,57610000,819.54 1982-03-19,805.27,811.64,799.75,805.65,46250000,805.65 1982-03-18,795.85,808.41,795.00,805.27,54270000,805.27 1982-03-17,798.33,802.70,791.29,795.85,48900000,795.85 1982-03-16,800.99,808.41,795.57,798.33,48900000,798.33 1982-03-15,797.37,805.18,789.38,800.99,43370000,800.99 1982-03-12,805.56,806.32,792.05,797.37,49600000,797.37 1982-03-11,804.89,814.88,800.51,805.56,52960000,805.56 1982-03-10,803.84,815.26,797.85,804.89,59440000,804.89 1982-03-09,795.47,810.79,786.15,803.84,76060000,803.84 1982-03-08,807.36,819.82,794.52,795.47,67330000,795.47 1982-03-05,807.55,814.97,799.28,807.36,67440000,807.36 1982-03-04,815.16,819.82,800.70,807.55,74340000,807.55 1982-03-03,825.82,826.01,808.69,815.16,70230000,815.16 1982-03-02,828.39,840.66,821.54,825.82,63800000,825.82 1982-03-01,824.39,834.28,818.21,828.39,53010000,828.39 1982-02-26,825.82,831.24,817.83,824.39,43840000,824.39 1982-02-25,826.77,837.42,820.78,825.82,54160000,825.82 1982-02-24,812.98,829.72,806.51,826.77,64800000,826.77 1982-02-23,811.26,817.73,802.23,812.98,60100000,812.98 1982-02-22,824.30,835.81,809.55,811.26,58310000,811.26 1982-02-19,828.96,833.33,818.59,824.30,51340000,824.30 1982-02-18,827.63,836.95,824.01,828.96,60810000,828.96 1982-02-17,831.34,836.95,822.68,827.63,47660000,827.63 1982-02-16,833.81,834.67,817.26,831.34,48880000,831.34 1982-02-12,834.67,841.32,829.24,833.81,37070000,833.81 1982-02-11,836.66,841.42,827.25,834.67,46730000,834.67 1982-02-10,830.57,842.09,829.34,836.66,46620000,836.66 1982-02-09,833.43,838.47,824.11,830.57,54420000,830.57 1982-02-08,849.70,849.70,830.86,833.43,48500000,833.43 1982-02-05,847.03,858.26,842.94,851.03,53350000,851.03 1982-02-04,845.03,852.74,835.71,847.03,53300000,847.03 1982-02-03,852.55,858.26,841.89,845.03,49560000,845.03 1982-02-02,851.69,860.83,846.46,852.55,45020000,852.55 1982-02-01,866.34,866.34,848.08,851.69,47720000,851.69 1982-01-29,864.25,876.71,859.78,871.10,73400000,871.10 1982-01-28,845.32,867.20,845.32,864.25,66690000,864.25 1982-01-27,841.51,848.08,832.67,842.66,50060000,842.66 1982-01-26,842.75,849.79,836.76,841.51,44870000,841.51 1982-01-25,845.03,846.75,832.57,842.75,43170000,842.75 1982-01-22,848.27,853.31,839.80,845.03,44370000,845.03 1982-01-21,845.89,856.64,842.85,848.27,48610000,848.27 1982-01-20,847.41,853.50,838.95,845.89,48860000,845.89 1982-01-19,855.12,861.97,845.22,847.41,45070000,847.41 1982-01-18,847.60,857.78,838.47,855.12,44920000,855.12 1982-01-15,842.28,852.64,840.37,847.60,43310000,847.60 1982-01-14,838.95,848.36,834.09,842.28,42940000,842.28 1982-01-13,847.70,855.31,835.14,838.95,49130000,838.95 1982-01-12,850.46,856.07,842.66,847.70,49800000,847.70 1982-01-11,866.53,872.43,848.55,850.46,51900000,850.46 1982-01-08,861.78,872.15,858.92,866.53,42050000,866.53 1982-01-07,861.02,865.39,851.22,861.78,43410000,861.78 1982-01-06,865.30,868.72,853.41,861.02,51510000,861.02 1982-01-05,882.52,882.61,862.82,865.30,47510000,865.30 1982-01-04,875.00,887.37,871.86,882.52,36760000,882.52 1981-12-31,873.10,880.33,868.91,875.00,40780000,875.00 1981-12-30,868.25,879.76,865.96,873.10,42960000,873.10 1981-12-29,870.34,875.67,864.44,868.25,35300000,868.25 1981-12-28,873.38,878.04,867.77,870.34,28320000,870.34 1981-12-24,869.67,876.14,866.15,873.38,23940000,873.38 1981-12-23,871.96,877.28,865.39,869.67,42910000,869.67 1981-12-22,873.10,878.81,867.77,871.96,48320000,871.96 1981-12-21,875.76,879.57,868.63,873.10,41290000,873.10 1981-12-18,870.53,880.71,867.39,875.76,50940000,875.76 1981-12-17,868.72,874.71,862.82,870.53,47230000,870.53 1981-12-16,875.95,878.42,865.11,868.72,42770000,868.72 1981-12-15,871.48,880.33,867.77,875.95,44130000,875.95 1981-12-14,882.52,882.52,868.72,871.48,44740000,871.48 1981-12-11,892.03,897.45,883.37,886.51,45850000,886.51 1981-12-10,888.22,897.74,885.18,892.03,47020000,892.03 1981-12-09,881.75,891.46,879.19,888.22,44810000,888.22 1981-12-08,886.99,889.36,875.29,881.75,45140000,881.75 1981-12-07,892.69,897.17,883.37,886.99,45720000,886.99 1981-12-04,885.75,898.50,885.75,892.69,55040000,892.69 1981-12-03,882.61,888.32,875.86,883.85,43770000,883.85 1981-12-02,890.22,892.41,879.28,882.61,44510000,882.61 1981-12-01,888.98,896.12,881.37,890.22,53980000,890.22 1981-11-30,885.94,892.79,878.14,888.98,47580000,888.98 1981-11-27,878.14,890.89,875.76,885.94,32770000,885.94 1981-11-25,870.72,884.99,870.72,878.14,58570000,878.14 1981-11-24,851.79,873.29,850.17,870.24,53200000,870.24 1981-11-23,852.93,860.92,848.17,851.79,45250000,851.79 1981-11-20,844.75,858.16,841.80,852.93,52010000,852.93 1981-11-19,844.08,850.08,834.57,844.75,48890000,844.75 1981-11-18,850.17,854.26,839.99,844.08,49980000,844.08 1981-11-17,845.03,853.50,841.23,850.17,43190000,850.17 1981-11-16,853.79,853.79,839.04,845.03,43740000,845.03 1981-11-13,860.54,865.20,851.79,855.88,45550000,855.88 1981-11-12,857.12,869.01,854.07,860.54,55720000,860.54 1981-11-11,853.98,860.83,849.03,857.12,41920000,857.12 1981-11-10,855.21,865.58,849.12,853.98,53940000,853.98 1981-11-09,852.45,861.49,844.65,855.21,48310000,855.21 1981-11-06,859.11,861.21,847.89,852.45,43270000,852.45 1981-11-05,866.82,874.14,856.93,859.11,50860000,859.11 1981-11-04,868.72,876.05,859.30,866.82,53450000,866.82 1981-11-03,866.82,873.19,859.87,868.72,54620000,868.72 1981-11-02,855.97,872.53,855.97,866.82,65100000,866.82 1981-10-30,832.95,854.83,826.96,852.55,59570000,852.55 1981-10-29,837.61,842.47,826.58,832.95,40070000,832.95 1981-10-28,838.38,847.89,832.29,837.61,48100000,837.61 1981-10-27,830.96,846.94,828.01,838.38,53030000,838.38 1981-10-26,837.99,837.99,823.63,830.96,38210000,830.96 1981-10-23,847.60,847.60,833.14,837.99,41990000,837.99 1981-10-22,851.03,854.07,840.85,848.27,40630000,848.27 1981-10-21,851.88,861.97,846.46,851.03,48490000,851.03 1981-10-20,847.13,860.06,844.75,851.88,51530000,851.88 1981-10-19,851.69,853.88,840.09,847.13,41590000,847.13 1981-10-16,856.26,860.73,846.37,851.69,37800000,851.69 1981-10-15,850.65,861.49,845.61,856.26,42830000,856.26 1981-10-14,865.58,866.53,848.46,850.65,40260000,850.65 1981-10-13,869.48,876.05,859.59,865.58,43360000,865.58 1981-10-12,873.00,877.76,862.25,869.48,30030000,869.48 1981-10-09,878.14,885.84,866.34,873.00,50060000,873.00 1981-10-08,868.72,881.66,862.63,878.14,47090000,878.14 1981-10-07,856.26,871.58,853.98,868.72,50030000,868.72 1981-10-06,859.87,870.81,848.74,856.26,45460000,856.26 1981-10-05,860.73,873.95,854.83,859.87,51290000,859.87 1981-10-02,852.26,866.34,848.65,860.73,54540000,860.73 1981-10-01,849.98,855.88,840.47,852.26,41600000,852.26 1981-09-30,847.89,854.36,839.61,849.98,40700000,849.98 1981-09-29,842.56,858.83,837.80,847.89,49800000,847.89 1981-09-28,824.01,844.65,807.46,842.56,61320000,842.56 1981-09-25,834.47,834.47,815.37,824.01,54390000,824.01 1981-09-24,840.94,850.65,832.00,835.14,48880000,835.14 1981-09-23,845.70,847.51,827.25,840.94,52700000,840.94 1981-09-22,846.56,855.40,838.18,845.70,46830000,845.70 1981-09-21,836.19,851.69,829.43,846.56,44570000,846.56 1981-09-18,840.09,845.51,829.43,836.19,47350000,836.19 1981-09-17,851.59,858.26,837.33,840.09,48300000,840.09 1981-09-16,858.35,858.44,845.98,851.59,43660000,851.59 1981-09-15,866.15,872.53,856.74,858.35,38580000,858.35 1981-09-14,872.81,876.24,860.06,866.15,34040000,866.15 1981-09-11,862.44,875.48,857.21,872.81,42170000,872.81 1981-09-10,853.88,868.82,853.12,862.44,47430000,862.44 1981-09-09,851.12,861.30,844.94,853.88,43910000,853.88 1981-09-08,861.68,863.39,843.13,851.12,47340000,851.12 1981-09-04,867.01,869.96,856.16,861.68,42760000,861.68 1981-09-03,884.23,888.70,865.11,867.01,41730000,867.01 1981-09-02,882.71,892.41,879.28,884.23,37570000,884.23 1981-09-01,881.47,889.36,873.10,882.71,45110000,882.71 1981-08-31,892.22,900.88,879.09,881.47,40360000,881.47 1981-08-28,889.08,898.78,884.80,892.22,38020000,892.22 1981-08-27,899.26,900.49,883.66,889.08,43900000,889.08 1981-08-26,901.83,908.39,893.65,899.26,39980000,899.26 1981-08-25,900.11,904.30,887.46,901.83,54600000,901.83 1981-08-24,917.43,917.43,896.97,900.11,46750000,900.11 1981-08-21,928.37,930.65,917.14,920.57,37670000,920.57 1981-08-20,926.46,935.31,923.52,928.37,38270000,928.37 1981-08-19,924.37,932.08,918.38,926.46,39390000,926.46 1981-08-18,926.75,932.74,916.38,924.37,47270000,924.37 1981-08-17,936.93,939.40,924.37,926.75,40840000,926.75 1981-08-14,944.35,947.77,933.79,936.93,42580000,936.93 1981-08-13,945.21,952.91,938.55,944.35,42460000,944.35 1981-08-12,949.30,955.86,942.26,945.21,53650000,945.21 1981-08-11,943.68,955.48,939.50,949.30,52600000,949.30 1981-08-10,942.54,948.82,935.88,943.68,38370000,943.68 1981-08-07,952.91,954.15,938.45,942.54,38370000,942.54 1981-08-06,953.58,961.47,947.30,952.91,52070000,952.91 1981-08-05,945.97,958.81,942.16,953.58,54290000,953.58 1981-08-04,946.25,951.39,937.40,945.97,39460000,945.97 1981-08-03,952.34,955.48,940.45,946.25,39650000,946.25 1981-07-31,945.11,956.72,943.40,952.34,43480000,952.34 1981-07-30,937.40,948.25,934.36,945.11,41560000,945.11 1981-07-29,939.40,947.30,933.50,937.40,37610000,937.40 1981-07-28,945.87,948.15,934.65,939.40,38160000,939.40 1981-07-27,937.12,950.82,937.12,945.87,39610000,945.87 1981-07-24,928.56,941.78,926.94,936.74,38880000,936.74 1981-07-23,924.66,932.84,918.66,928.56,41790000,928.56 1981-07-22,934.46,942.07,922.37,924.66,47500000,924.66 1981-07-21,940.54,944.35,927.23,934.46,47280000,934.46 1981-07-20,954.81,954.81,937.02,940.54,40240000,940.54 1981-07-17,955.48,964.80,949.30,958.90,42780000,958.90 1981-07-16,954.15,960.52,947.11,955.48,39010000,955.48 1981-07-15,948.25,960.33,945.49,954.15,48950000,954.15 1981-07-14,954.34,956.53,941.50,948.25,45230000,948.25 1981-07-13,955.67,962.42,949.87,954.34,38100000,954.34 1981-07-10,959.00,963.38,950.15,955.67,39950000,955.67 1981-07-09,953.48,963.18,950.06,959.00,45510000,959.00 1981-07-08,954.15,961.28,944.06,953.48,46000000,953.48 1981-07-07,949.30,962.90,943.49,954.15,53560000,954.15 1981-07-06,957.95,957.95,941.97,949.30,44590000,949.30 1981-07-02,967.66,972.70,954.72,959.19,45100000,959.19 1981-07-01,976.88,978.98,963.28,967.66,49080000,967.66 1981-06-30,984.59,988.01,972.69,976.88,41550000,976.88 1981-06-29,992.87,996.39,981.16,984.59,37930000,984.59 1981-06-26,996.77,1001.81,988.49,992.87,39240000,992.87 1981-06-25,999.33,1007.99,992.58,996.77,43920000,996.77 1981-06-24,1006.66,1008.09,993.15,999.33,46650000,999.33 1981-06-23,994.20,1008.94,990.01,1006.66,51840000,1006.66 1981-06-22,996.19,1004.19,988.96,994.20,41790000,994.20 1981-06-19,995.15,1002.85,988.77,996.19,46430000,996.19 1981-06-18,1006.56,1011.80,990.96,995.15,48400000,995.15 1981-06-17,1003.33,1010.84,992.01,1006.56,55470000,1006.56 1981-06-16,1011.99,1017.50,997.90,1003.33,57780000,1003.33 1981-06-15,1006.85,1023.02,1006.85,1011.99,63350000,1011.99 1981-06-12,1007.42,1017.22,996.77,1006.28,60790000,1006.28 1981-06-11,993.88,1010.57,989.89,1007.42,59530000,1007.42 1981-06-10,994.44,1003.71,985.91,993.88,53200000,993.88 1981-06-09,995.64,1004.45,987.76,994.44,44600000,994.44 1981-06-08,993.79,1003.52,990.54,995.64,41580000,995.64 1981-06-05,986.74,999.44,981.18,993.79,47180000,993.79 1981-06-04,989.71,996.66,980.25,986.74,48940000,986.74 1981-06-03,987.48,993.14,976.82,989.71,54700000,989.71 1981-06-02,997.96,1003.15,984.70,987.48,53930000,987.48 1981-06-01,991.75,1010.48,988.97,997.96,62170000,997.96 1981-05-29,994.25,1003.15,985.44,991.75,51580000,991.75 1981-05-28,993.14,1002.97,982.66,994.25,59500000,994.25 1981-05-27,983.96,998.70,978.76,993.14,58730000,993.14 1981-05-26,971.72,987.20,965.69,983.96,42760000,983.96 1981-05-22,976.59,982.62,966.59,971.72,40710000,971.72 1981-05-21,976.86,984.06,969.11,976.59,46820000,976.59 1981-05-20,980.01,984.87,971.36,976.86,42370000,976.86 1981-05-19,985.77,988.29,972.26,980.01,42220000,980.01 1981-05-18,985.95,994.42,978.84,985.77,42510000,985.77 1981-05-15,973.07,990.09,971.72,985.95,45460000,985.95 1981-05-14,967.76,979.02,963.08,973.07,42750000,973.07 1981-05-13,970.82,978.12,961.82,967.76,42600000,967.76 1981-05-12,963.44,973.70,956.68,970.82,40440000,970.82 1981-05-11,976.40,979.47,959.83,963.44,37640000,963.44 1981-05-08,978.39,983.88,972.71,976.40,41860000,976.40 1981-05-07,973.34,981.99,969.29,978.39,42590000,978.39 1981-05-06,972.44,983.16,969.47,973.34,47100000,973.34 1981-05-05,979.11,979.38,964.52,972.44,49000000,972.44 1981-05-04,985.41,985.41,971.90,979.11,40430000,979.11 1981-05-01,997.75,1005.22,987.12,995.59,48360000,995.59 1981-04-30,1004.32,1010.90,991.89,997.75,47970000,997.75 1981-04-29,1016.93,1018.37,996.58,1004.32,53340000,1004.32 1981-04-28,1024.05,1029.63,1009.37,1016.93,58210000,1016.93 1981-04-27,1020.35,1030.98,1013.87,1024.05,51080000,1024.05 1981-04-24,1010.27,1024.68,1004.32,1020.35,60000000,1020.35 1981-04-23,1007.02,1022.33,1001.89,1010.27,64200000,1010.27 1981-04-22,1005.94,1013.96,996.13,1007.02,60660000,1007.02 1981-04-21,1015.94,1020.98,1001.44,1005.94,60280000,1005.94 1981-04-20,1005.58,1020.71,995.14,1015.94,51020000,1015.94 1981-04-16,1001.71,1012.79,995.23,1005.58,52950000,1005.58 1981-04-15,989.10,1004.41,987.03,1001.71,56040000,1001.71 1981-04-14,993.16,998.56,981.81,989.10,48350000,989.10 1981-04-13,1000.27,1003.51,987.66,993.16,49860000,993.16 1981-04-10,998.83,1009.01,990.63,1000.27,58130000,1000.27 1981-04-09,993.43,1004.50,984.87,998.83,59520000,998.83 1981-04-08,992.89,1000.45,987.84,993.43,48000000,993.43 1981-04-07,994.24,1004.41,987.03,992.89,44540000,992.89 1981-04-06,1004.59,1004.59,986.67,994.24,43190000,994.24 1981-04-03,1009.01,1017.02,1000.72,1007.11,48680000,1007.11 1981-04-02,1014.14,1020.62,1002.07,1009.01,52570000,1009.01 1981-04-01,1003.87,1020.62,1002.16,1014.14,54880000,1014.14 1981-03-31,996.58,1011.44,996.58,1003.87,50980000,1003.87 1981-03-30,994.78,1005.04,987.21,992.16,33500000,992.16 1981-03-27,1005.76,1008.29,990.00,994.78,46930000,994.78 1981-03-26,1015.22,1021.79,1000.81,1005.76,60370000,1005.76 1981-03-25,996.13,1016.48,992.71,1015.22,56320000,1015.22 1981-03-24,1004.23,1015.58,992.35,996.13,66400000,996.13 1981-03-23,992.80,1008.92,988.56,1004.23,57880000,1004.23 1981-03-20,986.58,1002.07,981.27,992.80,61980000,992.80 1981-03-19,994.06,1001.71,981.18,986.58,62440000,986.58 1981-03-18,992.53,1003.33,984.15,994.06,55740000,994.06 1981-03-17,1002.79,1011.08,986.22,992.53,65920000,992.53 1981-03-16,985.77,1005.40,979.38,1002.79,49940000,1002.79 1981-03-13,989.82,1002.70,981.09,985.77,68290000,985.77 1981-03-12,967.67,991.35,966.05,989.82,54640000,989.82 1981-03-11,972.67,978.30,959.11,967.67,47390000,967.67 1981-03-10,976.42,987.60,968.47,972.67,56610000,972.67 1981-03-09,964.62,981.22,962.79,976.42,46180000,976.42 1981-03-06,964.62,972.13,955.71,964.62,43940000,964.62 1981-03-05,971.44,977.11,961.39,964.62,45380000,964.62 1981-03-04,966.02,980.70,959.47,971.44,47260000,971.44 1981-03-03,977.99,982.01,961.65,966.02,48730000,966.02 1981-03-02,974.58,983.75,964.36,977.99,47710000,977.99 1981-02-27,966.81,980.87,960.60,974.58,53210000,974.58 1981-02-26,954.40,972.22,953.00,966.81,60300000,966.81 1981-02-25,946.10,958.51,932.22,954.40,45710000,954.40 1981-02-24,945.23,955.63,938.77,946.10,43960000,946.10 1981-02-23,936.09,952.22,930.64,945.23,39590000,945.23 1981-02-20,933.36,942.41,926.11,936.09,41900000,936.09 1981-02-19,947.10,952.05,930.29,933.36,41630000,933.36 1981-02-18,939.68,950.34,935.15,947.10,40410000,947.10 1981-02-17,931.57,944.28,930.38,939.68,37940000,939.68 1981-02-13,936.60,940.19,926.45,931.57,33360000,931.57 1981-02-12,942.49,947.53,933.02,936.60,34700000,936.60 1981-02-11,948.63,954.44,938.99,942.49,37770000,942.49 1981-02-10,947.18,955.12,940.02,948.63,40820000,948.63 1981-02-09,952.30,958.53,943.26,947.18,38330000,947.18 1981-02-06,946.76,959.98,944.20,952.30,45820000,952.30 1981-02-05,941.98,952.22,936.09,946.76,45320000,946.76 1981-02-04,941.38,948.21,933.28,941.98,45520000,941.98 1981-02-03,932.25,944.37,925.77,941.38,45950000,941.38 1981-02-02,947.10,947.10,923.89,932.25,44070000,932.25 1981-01-30,948.89,960.24,940.61,947.27,41160000,947.27 1981-01-29,942.58,955.63,937.12,948.89,38170000,948.89 1981-01-28,949.49,956.91,939.68,942.58,36690000,942.58 1981-01-27,938.91,954.69,935.92,949.49,42260000,949.49 1981-01-26,940.19,947.70,930.89,938.91,35380000,938.91 1981-01-23,940.44,947.10,934.30,940.19,37220000,940.19 1981-01-22,946.25,951.28,934.13,940.44,39880000,940.44 1981-01-21,950.68,954.01,938.48,946.25,39190000,946.25 1981-01-20,970.99,975.94,948.55,950.68,41750000,950.68 1981-01-19,973.29,980.38,965.36,970.99,36470000,970.99 1981-01-16,969.97,980.29,964.85,973.29,43260000,973.29 1981-01-15,966.47,975.17,959.30,969.97,39640000,969.97 1981-01-14,965.10,978.07,960.75,966.47,41390000,966.47 1981-01-13,968.77,971.25,956.48,965.10,40890000,965.10 1981-01-12,968.69,983.87,964.76,968.77,48760000,968.77 1981-01-09,965.70,977.05,957.76,968.69,50190000,968.69 1981-01-08,980.89,986.01,959.13,965.70,55350000,965.70 1981-01-07,986.35,986.35,962.63,980.89,92890000,980.89 1981-01-06,992.66,1013.14,986.26,1004.69,67400000,1004.69 1981-01-05,976.28,996.93,976.28,992.66,58710000,992.66 1981-01-02,963.99,975.68,958.70,972.78,28870000,972.78 1980-12-31,962.03,971.50,955.55,963.99,41210000,963.99 1980-12-30,960.58,969.54,952.99,962.03,39750000,962.03 1980-12-29,966.38,974.57,956.40,960.58,36060000,960.58 1980-12-26,963.05,969.37,958.45,966.38,16130000,966.38 1980-12-24,958.28,968.17,950.85,963.05,29490000,963.05 1980-12-23,958.79,971.25,949.49,958.28,55260000,958.28 1980-12-22,937.20,962.17,934.64,958.79,51950000,958.79 1980-12-19,930.20,945.31,924.57,937.20,50770000,937.20 1980-12-18,928.50,948.38,925.00,930.20,69570000,930.20 1980-12-17,918.09,933.96,913.05,928.50,50800000,928.50 1980-12-16,911.60,922.35,902.47,918.09,41630000,918.09 1980-12-15,917.15,927.65,907.85,911.60,39700000,911.60 1980-12-12,908.45,923.12,906.48,917.15,39530000,917.15 1980-12-11,916.21,918.52,894.45,908.45,60220000,908.45 1980-12-10,934.04,943.52,914.16,916.21,49860000,916.21 1980-12-09,933.70,944.28,921.59,934.04,53220000,934.04 1980-12-08,951.19,951.19,928.33,933.70,53390000,933.70 1980-12-05,970.48,972.61,951.45,956.23,51990000,956.23 1980-12-04,972.27,984.22,963.31,970.48,51170000,970.48 1980-12-03,974.40,981.57,963.57,972.27,43430000,972.27 1980-12-02,969.45,979.69,954.95,974.40,52340000,974.40 1980-12-01,991.13,991.13,966.38,969.45,48180000,969.45 1980-11-28,989.68,998.72,980.38,993.34,34240000,993.34 1980-11-26,982.68,1000.77,979.86,989.68,55340000,989.68 1980-11-25,978.75,993.52,973.04,982.68,55840000,982.68 1980-11-24,988.14,988.14,967.66,978.75,51120000,978.75 1980-11-21,1000.17,1004.69,984.30,989.93,55950000,989.93 1980-11-20,991.04,1004.61,984.90,1000.17,60180000,1000.17 1980-11-19,997.95,1009.39,983.36,991.04,69230000,991.04 1980-11-18,986.26,1005.20,985.92,997.95,70380000,997.95 1980-11-17,986.35,990.78,968.94,986.26,50260000,986.26 1980-11-14,982.42,997.35,973.21,986.35,71630000,986.35 1980-11-13,964.93,984.64,962.20,982.42,69340000,982.42 1980-11-12,944.03,968.86,943.77,964.93,58500000,964.93 1980-11-11,933.79,951.19,933.02,944.03,41520000,944.03 1980-11-10,932.42,940.44,926.71,933.79,35720000,933.79 1980-11-07,935.41,940.53,926.11,932.42,40070000,932.42 1980-11-06,950.43,950.43,932.08,935.41,48890000,935.41 1980-11-05,950.34,982.59,950.34,953.16,84080000,953.16 1980-11-03,924.49,941.30,924.49,937.20,35820000,937.20 1980-10-31,917.75,929.35,911.60,924.49,40110000,924.49 1980-10-30,929.18,932.51,915.02,917.75,39060000,917.75 1980-10-29,932.59,941.64,925.68,929.18,37200000,929.18 1980-10-28,931.74,937.54,922.78,932.59,40300000,932.59 1980-10-27,943.60,944.62,930.12,931.74,34430000,931.74 1980-10-24,939.51,947.35,931.06,943.60,41050000,943.60 1980-10-23,955.12,958.28,936.69,939.51,49200000,939.51 1980-10-22,954.44,961.77,947.87,955.12,43060000,955.12 1980-10-21,960.84,968.69,949.91,954.44,51220000,954.44 1980-10-20,956.14,964.42,946.59,960.84,40910000,960.84 1980-10-17,958.70,966.04,946.42,956.14,43920000,956.14 1980-10-16,972.44,987.29,956.57,958.70,65450000,958.70 1980-10-15,962.20,975.94,959.73,972.44,48260000,972.44 1980-10-14,959.90,971.67,957.08,962.20,48830000,962.20 1980-10-13,950.68,963.65,946.50,959.90,31360000,959.90 1980-10-10,958.96,965.70,946.25,950.68,44040000,950.68 1980-10-09,963.99,971.08,953.07,958.96,43980000,958.96 1980-10-08,960.67,971.42,955.29,963.99,46580000,963.99 1980-10-07,965.70,973.04,955.55,960.67,50310000,960.67 1980-10-06,950.85,969.62,950.85,965.70,50130000,965.70 1980-10-03,942.24,957.85,938.40,950.68,47510000,950.68 1980-10-02,939.42,948.21,931.31,942.24,46160000,942.24 1980-10-01,932.42,945.14,923.04,939.42,48720000,939.42 1980-09-30,921.93,937.20,921.59,932.42,40290000,932.42 1980-09-29,934.81,934.81,918.00,921.93,46410000,921.93 1980-09-26,952.65,952.65,934.98,940.10,49460000,940.10 1980-09-25,964.76,972.61,953.07,955.97,49510000,955.97 1980-09-24,962.03,972.78,952.73,964.76,56860000,964.76 1980-09-23,974.57,980.72,957.94,962.03,64390000,962.03 1980-09-22,963.74,977.05,955.29,974.57,53140000,974.57 1980-09-19,956.48,972.70,950.77,963.74,53780000,963.74 1980-09-18,961.26,972.01,950.26,956.48,63390000,956.48 1980-09-17,945.90,966.98,943.69,961.26,63990000,961.26 1980-09-16,937.63,953.07,935.32,945.90,57290000,945.90 1980-09-15,936.52,942.58,926.96,937.63,44630000,937.63 1980-09-12,941.30,945.48,932.51,936.52,47180000,936.52 1980-09-11,938.48,947.78,934.39,941.30,44770000,941.30 1980-09-10,934.73,946.16,930.63,938.48,51430000,938.48 1980-09-09,928.58,936.09,919.03,934.73,44460000,934.73 1980-09-08,940.96,945.48,925.34,928.58,42050000,928.58 1980-09-05,948.81,950.68,937.03,940.96,37990000,940.96 1980-09-04,953.16,964.85,942.92,948.81,59030000,948.81 1980-09-03,940.78,955.20,939.85,953.16,52370000,953.16 1980-09-02,932.59,944.80,928.16,940.78,35290000,940.78 1980-08-29,930.38,936.43,923.04,932.59,33510000,932.59 1980-08-28,943.09,944.03,927.22,930.38,39890000,930.38 1980-08-27,953.24,953.24,939.68,943.09,44000000,943.09 1980-08-26,956.23,964.59,949.91,953.41,41700000,953.41 1980-08-25,958.19,961.35,947.70,956.23,35400000,956.23 1980-08-22,955.03,969.45,952.73,958.19,58210000,958.19 1980-08-21,945.31,958.53,943.94,955.03,50770000,955.03 1980-08-20,939.85,948.98,934.47,945.31,42560000,945.31 1980-08-19,948.63,952.13,937.37,939.85,41930000,939.85 1980-08-18,961.95,961.95,944.97,948.63,41890000,948.63 1980-08-15,962.63,972.35,956.31,966.72,47780000,966.72 1980-08-14,949.23,965.36,943.69,962.63,47700000,962.63 1980-08-13,952.39,958.79,944.45,949.23,44350000,949.23 1980-08-12,964.08,969.97,948.72,952.39,52050000,952.39 1980-08-11,954.69,966.89,950.43,964.08,44690000,964.08 1980-08-08,950.94,965.87,949.74,954.69,58860000,954.69 1980-08-07,938.23,953.75,935.84,950.94,61820000,950.94 1980-08-06,929.78,941.04,923.63,938.23,45050000,938.23 1980-08-05,931.06,938.82,924.23,929.78,45510000,929.78 1980-08-04,931.48,935.24,918.52,931.06,41550000,931.06 1980-08-01,935.32,940.02,924.83,931.48,46440000,931.48 1980-07-31,936.18,939.51,918.77,935.32,54610000,935.32 1980-07-30,931.91,946.93,926.54,936.18,58060000,936.18 1980-07-29,925.43,935.15,921.33,931.91,44840000,931.91 1980-07-28,918.09,927.73,911.69,925.43,35330000,925.43 1980-07-25,925.34,925.34,913.23,918.09,36250000,918.09 1980-07-24,928.58,933.79,919.97,926.11,42420000,926.11 1980-07-23,927.30,937.71,920.99,928.58,45890000,928.58 1980-07-22,928.67,940.78,922.18,927.30,52230000,927.30 1980-07-21,923.98,933.28,916.38,928.67,42750000,928.67 1980-07-18,915.10,930.80,914.42,923.98,58040000,923.98 1980-07-17,904.44,916.64,902.05,915.10,48850000,915.10 1980-07-16,901.54,912.97,898.21,904.44,49140000,904.44 1980-07-15,905.55,916.04,899.23,901.54,60920000,901.54 1980-07-14,891.13,908.28,887.20,905.55,45500000,905.55 1980-07-11,885.92,895.22,880.80,891.13,38310000,891.13 1980-07-10,897.27,900.34,883.28,885.92,43730000,885.92 1980-07-09,897.35,908.87,892.58,897.27,52010000,897.27 1980-07-08,898.21,904.86,891.38,897.35,45830000,897.35 1980-07-07,888.91,900.77,886.86,898.21,42540000,898.21 1980-07-03,876.02,890.78,874.83,888.91,47230000,888.91 1980-07-02,872.27,880.46,867.06,876.02,42950000,876.02 1980-07-01,867.92,876.02,862.63,872.27,34340000,872.27 1980-06-30,880.89,880.89,864.33,867.92,29910000,867.92 1980-06-27,883.45,889.08,874.49,881.83,33110000,881.83 1980-06-26,887.54,896.33,880.03,883.45,45110000,883.45 1980-06-25,877.30,892.49,874.91,887.54,46500000,887.54 1980-06-24,873.81,881.31,867.15,877.30,37730000,877.30 1980-06-23,869.71,879.61,864.85,873.81,34180000,873.81 1980-06-20,870.90,875.09,863.31,869.71,36530000,869.71 1980-06-19,881.91,886.01,869.62,870.90,38280000,870.90 1980-06-18,879.27,885.32,870.14,881.91,41960000,881.91 1980-06-17,877.73,887.63,872.35,879.27,41990000,879.27 1980-06-16,876.37,883.19,868.77,877.73,36190000,877.73 1980-06-13,872.61,883.96,866.81,876.37,41880000,876.37 1980-06-12,872.70,881.40,863.05,872.61,47300000,872.61 1980-06-11,863.99,876.88,860.24,872.70,43800000,872.70 1980-06-10,860.67,870.73,855.20,863.99,42030000,863.99 1980-06-09,861.52,867.66,855.72,860.67,36820000,860.67 1980-06-06,858.70,867.06,853.07,861.52,37230000,861.52 1980-06-05,858.02,868.60,853.50,858.70,49070000,858.70 1980-06-04,843.77,860.49,842.75,858.02,44180000,858.02 1980-06-03,847.35,851.96,840.70,843.77,33150000,843.77 1980-06-02,850.85,857.94,842.92,847.35,32710000,847.35 1980-05-30,846.25,853.24,835.07,850.85,34820000,850.85 1980-05-29,860.32,863.65,844.71,846.25,42000000,846.25 1980-05-28,857.76,866.13,850.09,860.32,38580000,860.32 1980-05-27,854.10,864.42,851.28,857.76,40810000,857.76 1980-05-23,843.34,858.62,843.34,854.10,45790000,854.10 1980-05-22,831.06,848.63,828.84,842.92,41040000,842.92 1980-05-21,832.51,836.43,821.50,831.06,34830000,831.06 1980-05-20,830.89,837.12,825.60,832.51,31800000,832.51 1980-05-19,826.88,835.75,820.65,830.89,30970000,830.89 1980-05-16,822.53,829.52,819.11,826.88,31710000,826.88 1980-05-15,819.62,829.35,816.04,822.53,41120000,822.53 1980-05-14,816.89,827.73,813.74,819.62,40840000,819.62 1980-05-13,805.20,820.39,803.07,816.89,35460000,816.89 1980-05-12,805.80,810.32,795.82,805.20,28220000,805.20 1980-05-09,815.10,815.10,802.47,805.80,30280000,805.80 1980-05-08,821.25,827.47,811.01,815.19,39280000,815.19 1980-05-07,816.04,828.67,810.49,821.25,42600000,821.25 1980-05-06,816.30,827.65,808.28,816.04,40160000,816.04 1980-05-05,810.92,819.20,803.58,816.30,34090000,816.30 1980-05-02,808.79,815.70,802.99,810.92,28040000,810.92 1980-05-01,817.06,820.22,803.24,808.79,32480000,808.79 1980-04-30,811.09,818.52,801.02,817.06,30850000,817.06 1980-04-29,805.46,816.21,802.65,811.09,27940000,811.09 1980-04-28,803.58,814.68,798.72,805.46,30600000,805.46 1980-04-25,797.10,806.74,786.35,803.58,28590000,803.58 1980-04-24,789.25,804.95,785.24,797.10,35790000,797.10 1980-04-23,789.85,801.11,784.13,789.25,42620000,789.25 1980-04-22,771.33,793.43,771.33,789.85,47920000,789.85 1980-04-21,763.40,769.62,751.37,759.13,27560000,759.13 1980-04-18,768.86,775.94,760.32,763.40,26880000,763.40 1980-04-17,771.25,776.37,762.12,768.86,32770000,768.86 1980-04-16,783.36,794.88,769.80,771.25,39730000,771.25 1980-04-15,784.90,792.41,778.84,783.36,26670000,783.36 1980-04-14,791.55,791.81,779.52,784.90,23060000,784.90 1980-04-11,791.47,800.85,786.52,791.55,29960000,791.55 1980-04-10,785.92,796.42,783.02,791.47,33940000,791.47 1980-04-09,775.00,790.02,772.44,785.92,33020000,785.92 1980-04-08,768.34,779.61,759.81,775.00,31700000,775.00 1980-04-07,784.13,784.39,765.44,768.34,29130000,768.34 1980-04-03,787.80,791.98,778.24,784.13,27970000,784.13 1980-04-02,784.47,796.40,779.95,787.80,35210000,787.80 1980-04-01,785.75,792.15,776.28,784.47,32230000,784.47 1980-03-31,777.65,790.87,772.18,785.75,35840000,785.75 1980-03-28,759.98,784.39,756.57,777.65,46720000,777.65 1980-03-27,762.12,767.41,729.95,759.98,63680000,759.98 1980-03-26,767.83,781.66,758.79,762.12,37370000,762.12 1980-03-25,765.44,777.47,758.36,767.83,43790000,767.83 1980-03-24,784.98,784.98,761.69,765.44,39230000,765.44 1980-03-21,789.08,794.71,779.52,785.15,32220000,785.15 1980-03-20,800.94,804.61,785.92,789.08,32580000,789.08 1980-03-19,801.62,812.12,794.97,800.94,36520000,800.94 1980-03-18,788.65,806.66,780.97,801.62,47340000,801.62 1980-03-17,811.26,811.26,784.98,788.65,37020000,788.65 1980-03-14,809.56,819.11,800.68,811.69,35180000,811.69 1980-03-13,819.54,825.26,806.57,809.56,33070000,809.56 1980-03-12,826.45,829.86,808.70,819.54,37990000,819.54 1980-03-11,818.94,831.91,817.58,826.45,41350000,826.45 1980-03-10,820.56,828.84,807.25,818.94,43750000,818.94 1980-03-07,828.07,832.51,813.23,820.56,50950000,820.56 1980-03-06,844.88,848.12,822.44,828.07,49610000,828.07 1980-03-05,856.48,868.69,840.78,844.88,49240000,844.88 1980-03-04,854.35,859.90,842.75,856.48,44310000,856.48 1980-03-03,863.14,868.26,851.37,854.35,38690000,854.35 1980-02-29,854.44,866.55,850.85,863.14,38810000,863.14 1980-02-28,855.12,865.10,847.27,854.44,40330000,854.44 1980-02-27,864.25,875.26,850.09,855.12,46430000,855.12 1980-02-26,859.81,869.45,853.24,864.25,40000000,864.25 1980-02-25,868.77,868.94,854.61,859.81,39140000,859.81 1980-02-22,868.52,877.47,855.80,868.77,48210000,868.77 1980-02-21,886.86,891.13,863.99,868.52,51530000,868.52 1980-02-20,876.02,891.30,872.44,886.86,44340000,886.86 1980-02-19,883.96,883.96,869.37,876.02,39480000,876.02 1980-02-15,892.92,892.92,875.09,884.98,46680000,884.98 1980-02-14,903.84,912.03,886.86,893.77,50540000,893.77 1980-02-13,898.98,918.17,896.59,903.84,65230000,903.84 1980-02-12,889.59,901.37,880.97,898.98,48090000,898.98 1980-02-11,895.73,902.39,884.04,889.59,58660000,889.59 1980-02-08,885.49,901.11,879.86,895.73,57860000,895.73 1980-02-07,881.83,897.27,878.24,885.49,57690000,885.49 1980-02-06,876.62,888.05,867.92,881.83,51950000,881.83 1980-02-05,875.09,880.89,868.17,876.62,41880000,876.62 1980-02-04,881.48,887.37,870.48,875.09,43070000,875.09 1980-02-01,875.85,886.64,866.30,881.48,46610000,881.48 1980-01-31,881.91,897.87,873.04,875.85,65900000,875.85 1980-01-30,874.40,886.09,870.14,881.91,51170000,881.91 1980-01-29,878.50,885.67,864.59,874.40,55480000,874.40 1980-01-28,876.11,884.56,866.64,878.50,53620000,878.50 1980-01-25,879.95,882.25,869.03,876.11,47100000,876.11 1980-01-24,877.56,891.38,874.15,879.95,59070000,879.95 1980-01-23,866.21,883.02,859.39,877.56,50730000,877.56 1980-01-22,872.78,879.95,861.95,866.21,50620000,866.21 1980-01-21,867.15,880.03,863.65,872.78,48040000,872.78 1980-01-18,863.57,873.21,855.29,867.15,47150000,867.15 1980-01-17,865.19,872.95,857.00,863.57,54170000,863.57 1980-01-16,868.60,881.14,862.12,865.19,67700000,865.19 1980-01-15,863.57,873.81,855.89,868.60,52320000,868.60 1980-01-14,858.53,870.65,854.95,863.57,52930000,863.57 1980-01-11,858.96,868.17,848.89,858.53,52890000,858.53 1980-01-10,850.09,866.72,848.89,858.96,55980000,858.96 1980-01-09,851.71,865.70,846.76,850.09,65260000,850.09 1980-01-08,832.00,853.67,828.84,851.71,53390000,851.71 1980-01-07,828.84,839.85,824.74,832.00,44500000,832.00 1980-01-04,820.31,833.53,819.03,828.84,39130000,828.84 1980-01-03,824.57,827.73,809.04,820.31,50480000,820.31 1980-01-02,838.74,841.21,822.35,824.57,40610000,824.57 1979-12-31,838.91,843.17,834.39,838.74,31530000,838.74 1979-12-28,840.10,843.43,834.64,838.91,34430000,838.91 1979-12-27,838.14,842.83,834.47,840.10,31410000,840.10 1979-12-26,839.16,843.09,833.74,838.14,24960000,838.14 1979-12-24,838.91,842.32,833.19,839.16,19150000,839.16 1979-12-21,843.34,847.35,834.56,838.91,36160000,838.91 1979-12-20,838.91,848.55,835.32,843.34,40380000,843.34 1979-12-19,838.65,842.75,830.46,838.91,41780000,838.91 1979-12-18,844.62,849.15,835.32,838.65,43310000,838.65 1979-12-17,842.75,851.54,838.05,844.62,43830000,844.62 1979-12-14,836.09,846.93,833.28,842.75,41800000,842.75 1979-12-13,835.67,840.53,830.03,836.09,36690000,836.09 1979-12-12,833.70,840.87,830.20,835.67,34630000,835.67 1979-12-11,833.87,841.47,828.41,833.70,36160000,833.70 1979-12-10,833.19,837.29,826.62,833.87,32270000,833.87 1979-12-07,835.07,844.71,829.35,833.19,42370000,833.19 1979-12-06,828.41,837.46,826.19,835.07,37510000,835.07 1979-12-05,824.91,837.37,824.15,828.41,39300000,828.41 1979-12-04,819.62,828.33,818.09,824.91,33510000,824.91 1979-12-03,822.35,825.51,814.76,819.62,29030000,819.62 1979-11-30,830.80,830.80,819.88,822.35,30480000,822.35 1979-11-29,830.46,838.57,826.45,831.74,33550000,831.74 1979-11-28,825.85,835.92,818.09,830.46,39690000,830.46 1979-11-27,828.75,837.29,820.73,825.85,45140000,825.85 1979-11-26,813.82,833.11,813.82,828.75,47940000,828.75 1979-11-23,807.42,815.96,804.86,811.77,23300000,811.77 1979-11-21,809.22,810.49,793.60,807.42,37020000,807.42 1979-11-20,815.27,820.14,805.12,809.22,35010000,809.22 1979-11-19,815.70,822.95,810.07,815.27,33090000,815.27 1979-11-16,821.33,822.61,812.54,815.70,30060000,815.70 1979-11-15,816.55,827.56,813.57,821.33,32380000,821.33 1979-11-14,814.08,823.04,805.03,816.55,30970000,816.55 1979-11-13,821.93,825.85,811.52,814.08,29240000,814.08 1979-11-12,806.48,823.72,805.63,821.93,26640000,821.93 1979-11-09,799.91,812.29,799.91,806.48,30060000,806.48 1979-11-08,796.67,804.27,792.24,797.61,26270000,797.61 1979-11-07,805.20,805.20,793.43,796.67,30830000,796.67 1979-11-06,812.63,813.40,804.61,806.48,21960000,806.48 1979-11-05,818.94,819.37,808.45,812.63,20470000,812.63 1979-11-02,820.14,824.74,814.42,818.94,23670000,818.94 1979-11-01,815.70,823.63,809.73,820.14,25880000,820.14 1979-10-31,823.81,826.88,812.88,815.70,27780000,815.70 1979-10-30,808.62,824.66,805.80,823.81,28890000,823.81 1979-10-29,809.30,815.53,804.86,808.62,22720000,808.62 1979-10-26,808.46,814.68,801.62,809.30,29660000,809.30 1979-10-25,808.36,816.38,803.07,808.46,28440000,808.46 1979-10-24,806.83,816.64,803.24,808.36,31480000,808.36 1979-10-23,809.13,816.38,801.96,806.83,32910000,806.83 1979-10-22,813.91,813.91,795.99,809.13,45240000,809.13 1979-10-19,829.18,829.18,812.46,814.68,42430000,814.68 1979-10-18,830.72,838.74,826.71,830.12,29590000,830.12 1979-10-17,829.52,840.53,827.39,830.72,29650000,830.72 1979-10-16,831.06,838.51,825.68,829.52,33770000,829.52 1979-10-15,838.99,840.36,823.89,831.06,34850000,831.06 1979-10-12,844.62,852.90,835.67,838.99,36390000,838.99 1979-10-11,849.32,854.69,834.98,844.62,47530000,844.62 1979-10-10,855.03,855.03,826.54,849.32,81620000,849.32 1979-10-09,879.35,879.35,854.10,857.59,55560000,857.59 1979-10-08,897.61,900.26,882.59,884.04,32610000,884.04 1979-10-05,890.10,904.86,889.59,897.61,48250000,897.61 1979-10-04,885.15,895.82,882.25,890.10,38800000,890.10 1979-10-03,885.32,891.98,879.35,885.15,36470000,885.15 1979-10-02,872.95,889.16,868.77,885.32,38310000,885.32 1979-10-01,877.73,877.73,866.55,872.95,24980000,872.95 1979-09-28,887.46,889.93,875.00,878.58,35950000,878.58 1979-09-27,886.35,892.32,880.55,887.46,33110000,887.46 1979-09-26,886.18,898.63,883.02,886.35,37700000,886.35 1979-09-25,885.84,889.68,875.00,886.18,32410000,886.18 1979-09-24,893.94,896.59,882.85,885.84,33790000,885.84 1979-09-21,893.69,902.13,886.52,893.94,52380000,893.94 1979-09-20,876.45,894.62,870.99,893.69,45100000,893.69 1979-09-19,874.15,883.36,870.39,876.45,35370000,876.45 1979-09-18,881.31,882.00,869.45,874.15,38750000,874.15 1979-09-17,879.10,890.02,877.39,881.31,37610000,881.31 1979-09-14,870.73,884.56,868.60,879.10,41980000,879.10 1979-09-13,870.90,876.37,864.93,870.73,35240000,870.73 1979-09-12,869.71,874.91,862.97,870.90,39350000,870.90 1979-09-11,876.88,880.80,864.93,869.71,42530000,869.71 1979-09-10,874.15,881.06,870.14,876.88,32980000,876.88 1979-09-07,867.32,877.39,863.32,874.15,34360000,874.15 1979-09-06,866.13,874.74,862.63,867.32,30330000,867.32 1979-09-05,871.08,871.08,857.85,866.13,41650000,866.13 1979-09-04,887.63,887.63,871.16,872.61,33350000,872.61 1979-08-31,883.70,890.10,881.14,887.63,26370000,887.63 1979-08-30,884.90,887.63,879.52,883.70,29300000,883.70 1979-08-29,884.64,888.23,879.52,884.90,30810000,884.90 1979-08-28,885.41,889.68,881.40,884.64,29430000,884.64 1979-08-27,880.20,891.81,877.47,885.41,32050000,885.41 1979-08-24,880.38,883.87,872.18,880.20,32730000,880.20 1979-08-23,885.84,888.40,877.22,880.38,35710000,880.38 1979-08-22,886.01,890.02,879.44,885.84,38450000,885.84 1979-08-21,886.52,892.32,880.63,886.01,38860000,886.01 1979-08-20,883.36,890.27,878.07,886.52,32300000,886.52 1979-08-17,884.04,888.31,877.05,883.36,31630000,883.36 1979-08-16,885.84,893.60,878.75,884.04,47000000,884.04 1979-08-15,876.71,888.23,871.50,885.84,46130000,885.84 1979-08-14,875.26,880.72,868.69,876.71,40910000,876.71 1979-08-13,867.15,878.58,867.15,875.26,41980000,875.26 1979-08-10,858.28,869.54,852.30,867.06,36740000,867.06 1979-08-09,863.14,864.51,855.12,858.28,34630000,858.28 1979-08-08,859.81,870.14,857.42,863.14,44970000,863.14 1979-08-07,848.55,863.99,848.04,859.81,45410000,859.81 1979-08-06,846.16,850.77,839.08,848.55,27190000,848.55 1979-08-03,847.95,850.51,843.32,846.16,28160000,846.16 1979-08-02,850.34,855.03,844.28,847.95,37720000,847.95 1979-08-01,846.42,852.30,841.38,850.34,36570000,850.34 1979-07-31,838.74,849.06,837.80,846.42,34360000,846.42 1979-07-30,839.76,842.49,833.45,838.74,28640000,838.74 1979-07-27,839.76,842.66,833.28,839.76,27760000,839.76 1979-07-26,839.51,843.17,833.96,839.76,32270000,839.76 1979-07-25,829.78,840.78,829.18,839.51,34890000,839.51 1979-07-24,825.51,833.28,822.61,829.78,29690000,829.78 1979-07-23,828.07,829.69,819.97,825.51,26860000,825.51 1979-07-20,827.30,832.08,822.70,828.07,26360000,828.07 1979-07-19,828.58,834.04,822.78,827.30,26780000,827.30 1979-07-18,828.50,830.97,818.00,828.58,35950000,828.58 1979-07-17,834.90,836.35,824.66,828.50,34270000,828.50 1979-07-16,833.53,838.57,828.92,834.90,26620000,834.90 1979-07-13,836.86,837.88,827.47,833.53,33080000,833.53 1979-07-12,843.86,844.62,834.22,836.86,31780000,836.86 1979-07-11,849.40,849.40,839.16,843.86,36650000,843.86 1979-07-10,852.99,857.00,845.73,850.34,39730000,850.34 1979-07-09,846.16,856.57,844.28,852.99,42460000,852.99 1979-07-06,835.75,847.27,834.22,846.16,38570000,846.16 1979-07-05,835.58,840.02,830.97,835.75,30290000,835.75 1979-07-03,834.04,838.99,829.27,835.58,31670000,835.58 1979-07-02,840.61,840.61,830.46,834.04,32060000,834.04 1979-06-29,843.04,848.12,837.20,841.98,34690000,841.98 1979-06-28,840.52,848.84,836.97,843.04,38470000,843.04 1979-06-27,837.66,846.15,834.20,840.52,36720000,840.52 1979-06-26,844.25,849.19,834.46,837.66,34680000,837.66 1979-06-25,849.10,850.83,839.66,844.25,31330000,844.25 1979-06-22,843.64,853.06,842.34,849.10,36410000,849.10 1979-06-21,839.83,847.80,837.40,843.64,36490000,843.64 1979-06-20,839.40,843.56,836.11,839.83,33790000,839.83 1979-06-19,839.40,843.99,835.85,839.40,30780000,839.40 1979-06-18,843.30,845.20,836.28,839.40,30970000,839.40 1979-06-15,842.34,847.11,837.40,843.30,40740000,843.30 1979-06-14,842.17,844.07,834.29,842.34,37850000,842.34 1979-06-13,845.29,850.31,840.00,842.17,40740000,842.17 1979-06-12,837.58,851.61,837.14,845.29,45450000,845.29 1979-06-11,835.15,839.66,830.73,837.58,28270000,837.58 1979-06-08,836.97,839.48,831.25,835.15,31470000,835.15 1979-06-07,835.50,842.78,833.16,836.97,43380000,836.97 1979-06-06,831.34,841.04,829.26,835.50,39830000,835.50 1979-06-05,821.90,834.46,820.25,831.34,35050000,831.34 1979-06-04,821.21,825.36,817.83,821.90,24040000,821.90 1979-06-01,822.33,826.14,817.74,821.21,24560000,821.21 1979-05-31,822.16,827.01,815.14,822.33,30300000,822.33 1979-05-30,832.47,832.47,820.17,822.16,29250000,822.16 1979-05-29,836.28,837.75,829.26,832.55,27040000,832.55 1979-05-25,837.66,840.26,831.86,836.28,27810000,836.28 1979-05-24,837.40,842.69,832.38,837.66,25710000,837.66 1979-05-23,845.37,851.26,836.19,837.40,30390000,837.40 1979-05-22,842.43,848.41,837.23,845.37,30400000,845.37 1979-05-21,841.91,847.80,836.28,842.43,25550000,842.43 1979-05-18,842.95,848.32,837.40,841.91,26590000,841.91 1979-05-17,828.48,845.03,827.44,842.95,30550000,842.95 1979-05-16,825.88,832.47,821.03,828.48,28350000,828.48 1979-05-15,825.02,832.12,821.55,825.88,26190000,825.88 1979-05-14,830.56,834.03,823.02,825.02,22450000,825.02 1979-05-11,828.92,834.11,824.76,830.56,24010000,830.56 1979-05-10,838.62,839.92,827.53,828.92,25230000,828.92 1979-05-09,834.89,842.26,829.09,838.62,27670000,838.62 1979-05-08,833.42,838.10,823.63,834.89,32720000,834.89 1979-05-07,846.07,846.07,832.03,833.42,30480000,833.42 1979-05-04,857.59,859.23,845.89,847.54,30630000,847.54 1979-05-03,855.51,863.22,852.74,857.59,30870000,857.59 1979-05-02,855.51,860.27,849.97,855.51,30510000,855.51 1979-05-01,854.90,861.40,850.83,855.51,31040000,855.51 1979-04-30,856.64,859.49,847.28,854.90,26440000,854.90 1979-04-27,860.79,860.79,852.39,856.64,29610000,856.64 1979-04-26,867.46,869.02,858.71,860.97,32400000,860.97 1979-04-25,866.86,872.31,863.22,867.46,31750000,867.46 1979-04-24,860.10,873.35,858.54,866.86,35540000,866.86 1979-04-23,856.98,863.31,852.39,860.10,25610000,860.10 1979-04-20,855.25,861.57,848.93,856.98,28830000,856.98 1979-04-19,860.27,865.73,852.91,855.25,31150000,855.25 1979-04-18,857.93,865.90,856.38,860.27,29510000,860.27 1979-04-17,860.45,865.64,853.95,857.93,29260000,857.93 1979-04-16,868.42,868.42,856.81,860.45,28050000,860.45 1979-04-12,871.71,875.78,865.21,870.50,26780000,870.50 1979-04-11,878.72,884.62,868.68,871.71,32900000,871.71 1979-04-10,873.70,882.54,869.80,878.72,31900000,878.72 1979-04-09,875.69,879.07,869.46,873.70,27230000,873.70 1979-04-06,877.60,884.01,871.88,875.69,34710000,875.69 1979-04-05,869.80,880.02,866.68,877.60,34520000,877.60 1979-04-04,868.33,878.38,867.12,869.80,41940000,869.80 1979-04-03,855.25,870.67,854.21,868.33,33530000,868.33 1979-04-02,859.93,859.93,849.27,855.25,28990000,855.25 1979-03-30,866.77,870.58,858.11,862.18,29970000,862.18 1979-03-29,866.25,873.53,860.19,866.77,28510000,866.77 1979-03-28,871.36,876.99,863.83,866.25,39920000,866.25 1979-03-27,854.82,872.49,852.91,871.36,32940000,871.36 1979-03-26,859.75,860.01,851.52,854.82,23430000,854.82 1979-03-23,861.31,867.20,855.08,859.75,33570000,859.75 1979-03-22,857.76,867.46,856.03,861.31,34380000,861.31 1979-03-21,850.31,859.23,844.68,857.76,31120000,857.76 1979-03-20,857.59,858.89,847.45,850.31,27180000,850.31 1979-03-19,852.82,864.95,852.65,857.59,34620000,857.59 1979-03-16,847.02,856.72,843.21,852.82,31770000,852.82 1979-03-15,845.37,853.17,841.39,847.02,29370000,847.02 1979-03-14,846.93,852.56,841.74,845.37,24630000,845.37 1979-03-13,844.68,855.60,840.78,846.93,31170000,846.93 1979-03-12,842.86,846.33,833.85,844.68,25740000,844.68 1979-03-09,844.85,851.44,840.00,842.86,33410000,842.86 1979-03-08,834.29,846.41,831.69,844.85,32000000,844.85 1979-03-07,826.58,841.04,825.28,834.29,28930000,834.29 1979-03-06,827.36,831.34,821.55,826.58,24490000,826.58 1979-03-05,818.00,832.55,818.00,827.36,25690000,827.36 1979-03-02,815.84,820.69,811.50,815.75,23130000,815.75 1979-03-01,808.82,818.17,807.26,815.84,23830000,815.84 1979-02-28,807.00,811.76,802.23,808.82,25090000,808.82 1979-02-27,821.12,821.12,803.53,807.00,31470000,807.00 1979-02-26,823.28,826.40,818.35,821.12,22620000,821.12 1979-02-23,828.57,828.83,819.99,823.28,22750000,823.28 1979-02-22,834.55,834.98,825.10,828.57,26290000,828.57 1979-02-21,834.55,841.56,830.91,834.55,26050000,834.55 1979-02-20,827.01,836.19,824.15,834.55,22010000,834.55 1979-02-16,829.09,831.69,823.54,827.01,21110000,827.01 1979-02-15,829.78,831.25,822.68,829.09,22550000,829.09 1979-02-14,830.21,836.88,825.71,829.78,27220000,829.78 1979-02-13,826.14,836.19,826.14,830.21,28470000,830.21 1979-02-12,822.23,827.62,816.61,824.84,20610000,824.84 1979-02-09,818.87,826.49,817.05,822.23,24320000,822.23 1979-02-08,816.01,823.02,812.89,818.87,23360000,818.87 1979-02-07,822.85,823.46,810.81,816.01,28450000,816.01 1979-02-06,823.98,828.48,819.21,822.85,23570000,822.85 1979-02-05,830.65,830.65,819.13,823.98,26490000,823.98 1979-02-02,840.87,843.38,832.47,834.63,25350000,834.63 1979-02-01,839.22,843.99,832.55,840.87,27930000,840.87 1979-01-31,851.78,853.52,835.67,839.22,30330000,839.22 1979-01-30,855.77,861.83,848.75,851.78,26910000,851.78 1979-01-29,859.75,862.96,851.61,855.77,24170000,855.77 1979-01-26,854.64,865.04,853.26,859.75,34230000,859.75 1979-01-25,846.41,858.11,843.90,854.64,31440000,854.64 1979-01-24,846.85,856.81,840.61,846.41,31730000,846.41 1979-01-23,838.53,851.35,835.67,846.85,30130000,846.85 1979-01-22,837.49,841.74,829.95,838.53,24390000,838.53 1979-01-19,839.14,846.67,833.94,837.49,26800000,837.49 1979-01-18,834.20,843.73,829.35,839.14,27260000,839.14 1979-01-17,835.59,839.05,825.80,834.20,25310000,834.20 1979-01-16,848.06,848.06,833.25,835.59,30340000,835.59 1979-01-15,836.28,851.00,831.69,848.67,27520000,848.67 1979-01-12,830.04,843.38,830.04,836.28,37120000,836.28 1979-01-11,824.93,829.44,817.31,828.05,24580000,828.05 1979-01-10,831.43,833.68,821.47,824.93,24990000,824.93 1979-01-09,828.14,836.80,825.80,831.43,27340000,831.43 1979-01-08,830.73,832.12,821.38,828.14,21440000,828.14 1979-01-05,826.14,837.23,823.89,830.73,28890000,830.73 1979-01-04,817.39,832.55,815.92,826.14,33290000,826.14 1979-01-03,811.42,822.68,811.42,817.39,29180000,817.39 1979-01-02,805.01,813.06,798.51,811.42,18340000,811.42 1978-12-29,805.96,812.20,800.50,805.01,30030000,805.01 1978-12-28,808.56,813.06,802.75,805.96,25440000,805.96 1978-12-27,815.32,815.32,804.57,808.56,23580000,808.56 1978-12-26,808.47,819.13,804.66,816.01,21470000,816.01 1978-12-22,795.05,811.16,795.05,808.47,23790000,808.47 1978-12-21,793.66,801.63,790.11,794.79,28670000,794.79 1978-12-20,789.85,797.90,785.69,793.66,26520000,793.66 1978-12-19,787.51,795.30,782.92,789.85,25960000,789.85 1978-12-18,794.87,794.87,781.01,787.51,32900000,787.51 1978-12-15,812.54,813.76,801.80,805.35,23620000,805.35 1978-12-14,809.86,814.97,804.40,812.54,20840000,812.54 1978-12-13,814.97,819.21,806.48,809.86,22480000,809.86 1978-12-12,817.65,821.90,811.50,814.97,22210000,814.97 1978-12-11,811.85,821.29,808.73,817.65,21000000,817.65 1978-12-08,816.09,819.56,807.95,811.85,18560000,811.85 1978-12-07,821.90,826.49,812.98,816.09,21170000,816.09 1978-12-06,820.51,829.87,815.23,821.90,29680000,821.90 1978-12-05,806.83,821.81,805.01,820.51,25670000,820.51 1978-12-04,811.50,814.80,802.23,806.83,22020000,806.83 1978-12-01,801.98,815.06,801.98,811.50,26830000,811.50 1978-11-30,790.11,800.94,786.64,799.03,19900000,799.03 1978-11-29,801.89,801.89,788.63,790.11,21160000,790.11 1978-11-28,813.84,817.91,802.67,804.14,22740000,804.14 1978-11-27,810.12,817.22,806.31,813.84,19790000,813.84 1978-11-24,807.00,812.98,803.36,810.12,14590000,810.12 1978-11-22,804.05,810.98,799.46,807.00,20010000,807.00 1978-11-21,805.61,810.55,799.98,804.05,20750000,804.05 1978-11-20,797.73,811.24,797.47,805.61,24440000,805.61 1978-11-17,794.18,804.49,792.19,797.73,25170000,797.73 1978-11-16,785.60,797.38,785.26,794.18,21340000,794.18 1978-11-15,785.26,798.08,782.66,785.60,26280000,785.60 1978-11-14,792.01,795.22,779.11,785.26,30610000,785.26 1978-11-13,806.83,806.83,790.71,792.01,20960000,792.01 1978-11-10,803.97,812.02,799.55,807.09,16750000,807.09 1978-11-09,807.61,815.75,798.86,803.97,23320000,803.97 1978-11-08,800.07,809.60,790.97,807.61,23560000,807.61 1978-11-07,808.13,808.13,793.92,800.07,25320000,800.07 1978-11-06,823.11,825.88,812.63,814.88,20450000,814.88 1978-11-03,816.96,829.00,809.51,823.11,25990000,823.11 1978-11-02,827.79,831.34,811.07,816.96,41030000,816.96 1978-11-01,805.53,831.69,805.53,827.79,50450000,827.79 1978-10-31,811.85,818.26,789.67,792.45,42720000,792.45 1978-10-30,806.05,814.36,782.05,811.85,59480000,811.85 1978-10-27,821.12,825.54,804.49,806.05,40360000,806.05 1978-10-26,830.21,835.41,816.61,821.12,31990000,821.12 1978-10-25,832.55,842.86,822.85,830.21,31380000,830.21 1978-10-24,839.66,846.93,830.35,832.55,28880000,832.55 1978-10-23,838.01,845.89,825.80,839.66,36090000,839.66 1978-10-20,846.41,847.37,830.47,838.01,43670000,838.01 1978-10-19,859.67,862.87,844.42,846.41,31810000,846.41 1978-10-18,866.34,871.19,853.60,859.67,32940000,859.67 1978-10-17,872.49,872.49,858.11,866.34,37870000,866.34 1978-10-16,894.84,894.84,874.39,875.17,24600000,875.17 1978-10-13,896.74,902.20,891.55,897.09,21920000,897.09 1978-10-12,901.42,909.39,892.84,896.74,30170000,896.74 1978-10-11,891.63,902.29,885.14,901.42,21740000,901.42 1978-10-10,893.19,899.26,887.56,891.63,25470000,891.63 1978-10-09,880.02,894.92,877.86,893.19,19720000,893.19 1978-10-06,876.47,885.14,873.79,880.02,27380000,880.02 1978-10-05,873.96,883.32,869.63,876.47,27820000,876.47 1978-10-04,867.90,875.52,860.10,873.96,25090000,873.96 1978-10-03,871.36,876.30,865.56,867.90,22540000,867.90 1978-10-02,865.82,874.48,863.13,871.36,18700000,871.36 1978-09-29,861.31,870.84,858.89,865.82,23610000,865.82 1978-09-28,860.19,864.43,854.90,861.31,24390000,861.31 1978-09-27,868.16,875.43,858.45,860.19,28370000,860.19 1978-09-26,862.35,872.66,859.67,868.16,26330000,868.16 1978-09-25,862.44,865.47,856.20,862.35,20970000,862.35 1978-09-22,861.14,868.16,855.42,862.44,27960000,862.44 1978-09-21,857.16,865.38,850.92,861.14,33640000,861.14 1978-09-20,861.57,870.50,853.95,857.16,35080000,857.16 1978-09-19,870.15,873.87,859.06,861.57,31660000,861.57 1978-09-18,878.55,884.88,866.86,870.15,35860000,870.15 1978-09-15,886.17,886.17,874.05,878.55,37290000,878.55 1978-09-14,899.60,900.21,885.48,887.04,37400000,887.04 1978-09-13,906.44,913.29,896.83,899.60,43340000,899.60 1978-09-12,907.74,910.26,899.43,906.44,34400000,906.44 1978-09-11,907.74,917.24,904.80,907.74,39670000,907.74 1978-09-08,895.62,909.91,895.62,907.74,42170000,907.74 1978-09-07,895.79,903.15,889.55,893.71,40310000,893.71 1978-09-06,887.91,902.03,887.91,895.79,42600000,895.79 1978-09-05,879.33,889.73,876.91,886.61,32170000,886.61 1978-09-01,876.82,884.88,871.36,879.33,35070000,879.33 1978-08-31,880.72,883.66,871.36,876.82,33850000,876.82 1978-08-30,880.20,888.17,873.61,880.72,37750000,880.72 1978-08-29,884.88,887.13,875.95,880.20,33780000,880.20 1978-08-28,895.53,895.70,882.62,884.88,31760000,884.88 1978-08-25,897.35,902.55,890.59,895.53,36190000,895.53 1978-08-24,897.00,904.80,890.94,897.35,38500000,897.35 1978-08-23,892.41,904.28,890.94,897.00,39630000,897.00 1978-08-22,888.95,896.05,881.15,892.41,29620000,892.41 1978-08-21,896.83,900.38,884.88,888.95,29440000,888.95 1978-08-18,900.12,909.22,892.93,896.83,34650000,896.83 1978-08-17,894.66,911.21,894.66,900.12,45270000,900.12 1978-08-16,887.13,898.30,884.88,894.58,36120000,894.58 1978-08-15,888.17,891.37,879.16,887.13,29760000,887.13 1978-08-14,890.85,899.17,884.27,888.17,32320000,888.17 1978-08-11,885.48,895.70,880.20,890.85,33550000,890.85 1978-08-10,891.63,898.39,880.20,885.48,39760000,885.48 1978-08-09,889.21,903.67,886.61,891.63,48800000,891.63 1978-08-08,885.05,891.55,877.51,889.21,34290000,889.21 1978-08-07,888.43,896.22,881.41,885.05,33350000,885.05 1978-08-04,886.87,895.79,879.33,888.43,37910000,888.43 1978-08-03,883.49,905.15,881.84,886.87,66370000,886.87 1978-08-02,860.71,884.88,857.50,883.49,47470000,883.49 1978-08-01,862.27,868.16,855.34,860.71,34810000,860.71 1978-07-31,856.29,866.34,852.56,862.27,33990000,862.27 1978-07-28,850.57,860.27,844.77,856.29,33390000,856.29 1978-07-27,847.19,857.16,843.04,850.57,33970000,850.57 1978-07-26,839.57,852.47,839.05,847.19,36830000,847.19 1978-07-25,831.60,841.65,827.18,839.57,25400000,839.57 1978-07-24,833.42,835.07,823.46,831.60,23280000,831.60 1978-07-21,838.62,842.00,828.83,833.42,26060000,833.42 1978-07-20,840.70,849.45,835.24,838.62,33350000,838.62 1978-07-19,829.00,842.52,828.05,840.70,30850000,840.70 1978-07-18,839.05,840.00,826.75,829.00,22860000,829.00 1978-07-17,839.83,848.58,835.59,839.05,29180000,839.05 1978-07-14,824.76,841.22,823.02,839.83,28370000,839.83 1978-07-13,824.93,827.88,818.09,824.76,23620000,824.76 1978-07-12,821.29,829.18,819.39,824.93,26640000,824.93 1978-07-11,816.79,826.32,814.62,821.29,27470000,821.29 1978-07-10,812.46,819.56,808.13,816.79,22470000,816.79 1978-07-07,807.17,816.18,805.79,812.46,23480000,812.46 1978-07-06,805.79,810.72,800.94,807.17,24990000,807.17 1978-07-05,812.62,812.62,802.15,805.79,23730000,805.79 1978-07-03,818.95,818.95,809.71,812.89,11560000,812.89 1978-06-30,821.64,823.20,815.06,818.95,18100000,818.95 1978-06-29,819.91,827.88,816.79,821.64,21660000,821.64 1978-06-28,817.31,824.24,811.59,819.91,23260000,819.91 1978-06-27,812.28,820.95,807.95,817.31,29280000,817.31 1978-06-26,823.02,824.15,810.03,812.28,29250000,812.28 1978-06-23,827.70,832.55,821.12,823.02,28530000,823.02 1978-06-22,824.93,831.77,822.16,827.70,27160000,827.70 1978-06-21,830.04,830.82,820.34,824.93,29100000,824.93 1978-06-20,838.62,840.44,828.40,830.04,27920000,830.04 1978-06-19,836.97,841.04,829.87,838.62,25500000,838.62 1978-06-16,844.25,846.85,834.98,836.97,27690000,836.97 1978-06-15,854.21,854.21,842.17,844.25,29280000,844.25 1978-06-14,856.98,865.82,850.57,854.56,37290000,854.56 1978-06-13,856.72,859.75,848.15,856.98,30760000,856.98 1978-06-12,859.23,866.94,853.34,856.72,24440000,856.72 1978-06-09,862.09,867.20,854.30,859.23,32470000,859.23 1978-06-08,861.92,871.88,856.46,862.09,39380000,862.09 1978-06-07,866.51,868.94,855.51,861.92,33060000,861.92 1978-06-06,863.83,879.33,863.22,866.51,51970000,866.51 1978-06-05,848.41,865.47,848.41,863.83,39580000,863.83 1978-06-02,840.70,849.79,838.62,847.54,31860000,847.54 1978-06-01,840.61,845.81,835.24,840.70,28750000,840.70 1978-05-31,834.20,846.85,832.99,840.61,29070000,840.61 1978-05-30,831.69,837.32,826.40,834.20,21040000,834.20 1978-05-26,835.41,836.37,827.79,831.69,21410000,831.69 1978-05-25,837.92,843.04,830.99,835.41,28410000,835.41 1978-05-24,842.86,842.86,829.87,837.92,31450000,837.92 1978-05-23,855.42,856.12,842.17,845.29,33230000,845.29 1978-05-22,846.85,857.76,842.34,855.42,28680000,855.42 1978-05-19,850.92,855.16,840.87,846.85,34360000,846.85 1978-05-18,858.37,863.57,848.67,850.92,42270000,850.92 1978-05-17,854.30,865.64,847.89,858.37,45490000,858.37 1978-05-16,848.15,861.05,848.15,854.30,48170000,854.30 1978-05-15,840.70,848.84,834.72,846.76,33890000,846.76 1978-05-12,834.20,847.54,834.11,840.70,46600000,840.70 1978-05-11,822.16,835.33,821.12,834.20,36630000,834.20 1978-05-10,822.07,829.61,817.22,822.16,33330000,822.16 1978-05-09,824.58,829.44,817.65,822.07,30860000,822.07 1978-05-08,829.09,837.14,822.77,824.58,34680000,824.58 1978-05-05,824.41,837.32,820.86,829.09,42680000,829.09 1978-05-04,828.83,829.09,813.84,824.41,37520000,824.41 1978-05-03,840.18,841.82,826.58,828.83,37560000,828.83 1978-05-02,844.33,847.54,833.42,840.18,41400000,840.18 1978-05-01,837.32,849.45,832.81,844.33,37020000,844.33 1978-04-28,826.92,838.79,822.16,837.32,32850000,837.32 1978-04-27,836.37,836.37,822.77,826.92,35470000,826.92 1978-04-26,833.59,846.15,828.48,836.97,44430000,836.97 1978-04-25,828.83,845.81,828.83,833.59,55800000,833.59 1978-04-24,812.80,827.36,810.38,826.06,34510000,826.06 1978-04-21,814.54,818.95,807.17,812.80,31540000,812.80 1978-04-20,810.38,825.10,810.38,814.54,43230000,814.54 1978-04-19,803.27,812.72,797.56,808.04,35060000,808.04 1978-04-18,810.12,814.97,798.15,803.27,38950000,803.27 1978-04-17,803.45,824.24,803.45,810.12,63510000,810.12 1978-04-14,780.41,797.73,780.41,795.13,52280000,795.13 1978-04-13,766.29,777.29,764.47,775.21,31580000,775.21 1978-04-12,770.18,772.96,764.30,766.29,26210000,766.29 1978-04-11,773.65,775.38,766.20,770.18,24300000,770.18 1978-04-10,769.58,776.16,766.11,773.65,25740000,773.65 1978-04-07,763.95,772.00,760.83,769.58,25160000,769.58 1978-04-06,763.08,768.10,759.62,763.95,27360000,763.95 1978-04-05,755.37,764.47,753.21,763.08,27260000,763.08 1978-04-04,751.04,757.19,748.79,755.37,20130000,755.37 1978-04-03,756.67,756.67,747.05,751.04,20230000,751.04 1978-03-31,759.62,761.78,753.12,757.36,20130000,757.36 1978-03-30,761.78,764.03,756.15,759.62,20460000,759.62 1978-03-29,758.84,765.42,755.80,761.78,25450000,761.78 1978-03-28,753.21,760.22,749.91,758.84,21600000,758.84 1978-03-27,756.50,758.14,750.52,753.21,18870000,753.21 1978-03-23,757.54,760.91,752.69,756.50,21290000,756.50 1978-03-22,762.82,764.38,754.50,757.54,21950000,757.54 1978-03-21,773.82,774.69,760.91,762.82,24410000,762.82 1978-03-20,768.88,777.81,768.88,773.82,28360000,773.82 1978-03-17,762.82,770.79,760.31,768.71,28470000,768.71 1978-03-16,758.58,764.03,754.42,762.82,25400000,762.82 1978-03-15,762.56,763.60,754.24,758.58,23340000,758.58 1978-03-14,759.96,764.99,752.86,762.56,24300000,762.56 1978-03-13,758.58,766.63,756.41,759.96,24070000,759.96 1978-03-10,750.09,760.91,750.09,758.58,27090000,758.58 1978-03-09,750.87,755.46,746.45,750.00,21820000,750.00 1978-03-08,746.79,752.86,743.76,750.87,22030000,750.87 1978-03-07,742.72,748.70,739.78,746.79,19900000,746.79 1978-03-06,746.97,746.97,740.12,742.72,17230000,742.72 1978-03-03,746.45,751.30,742.72,747.31,20120000,747.31 1978-03-02,743.33,749.13,739.78,746.45,20280000,746.45 1978-03-01,742.12,747.92,736.75,743.33,21010000,743.33 1978-02-28,748.35,748.61,739.17,742.12,19750000,742.12 1978-02-27,756.24,761.26,746.62,748.35,19990000,748.35 1978-02-24,750.95,760.40,750.78,756.24,22510000,756.24 1978-02-23,749.05,752.51,742.98,750.95,18720000,750.95 1978-02-22,749.31,753.55,746.71,749.05,18450000,749.05 1978-02-21,752.69,753.90,745.24,749.31,21890000,749.31 1978-02-17,753.29,759.96,748.18,752.69,18500000,752.69 1978-02-16,760.14,760.14,749.65,753.29,21570000,753.29 1978-02-15,765.16,767.07,758.32,761.69,20170000,761.69 1978-02-14,773.91,773.91,762.65,765.16,20470000,765.16 1978-02-13,775.99,777.37,770.44,774.43,16810000,774.43 1978-02-10,777.81,781.01,772.61,775.99,19480000,775.99 1978-02-09,782.66,783.00,774.43,777.81,17940000,777.81 1978-02-08,778.85,787.42,776.68,782.66,21300000,782.66 1978-02-07,768.62,780.23,768.62,778.85,14730000,778.85 1978-02-06,770.96,772.96,764.21,768.62,11630000,768.62 1978-02-03,775.38,776.59,766.98,770.96,19400000,770.96 1978-02-02,774.34,781.88,770.88,775.38,23050000,775.38 1978-02-01,769.92,777.89,764.81,774.34,22240000,774.34 1978-01-31,772.44,778.59,762.91,769.92,19870000,769.92 1978-01-30,764.12,774.95,761.09,772.44,17400000,772.44 1978-01-27,763.34,768.54,759.44,764.12,17600000,764.12 1978-01-26,772.44,775.81,761.00,763.34,19600000,763.34 1978-01-25,771.57,777.72,768.02,772.44,18690000,772.44 1978-01-24,770.70,776.42,765.68,771.57,18690000,771.57 1978-01-23,776.94,777.98,766.55,770.70,19380000,770.70 1978-01-20,778.67,780.15,772.52,776.94,7580000,776.94 1978-01-19,786.30,790.02,777.03,778.67,21500000,778.67 1978-01-18,779.02,788.20,776.25,786.30,21390000,786.30 1978-01-17,771.74,781.26,770.62,779.02,19360000,779.02 1978-01-16,775.73,777.81,767.41,771.74,18760000,771.74 1978-01-13,778.15,784.04,773.65,775.73,18010000,775.73 1978-01-12,775.90,785.00,772.78,778.15,22730000,778.15 1978-01-11,781.53,785.95,771.74,775.90,22880000,775.90 1978-01-10,784.56,790.89,777.55,781.53,25180000,781.53 1978-01-09,790.89,790.89,778.41,784.56,27990000,784.56 1978-01-06,803.36,803.36,788.29,793.49,26150000,793.49 1978-01-05,813.58,822.77,802.58,804.92,23570000,804.92 1978-01-04,817.48,817.48,804.92,813.58,24090000,813.58 1978-01-03,830.47,830.47,815.06,817.74,17720000,817.74 1977-12-30,830.39,835.15,825.80,831.17,23560000,831.17 1977-12-29,829.70,834.37,823.63,830.39,23610000,830.39 1977-12-28,829.70,833.33,822.77,829.70,19630000,829.70 1977-12-27,829.87,833.77,823.46,829.70,16750000,829.70 1977-12-23,822.16,832.64,822.16,829.87,20080000,829.87 1977-12-22,815.32,825.62,815.32,821.81,28100000,821.81 1977-12-21,806.22,817.83,805.35,813.93,24510000,813.93 1977-12-20,807.95,810.46,800.42,806.22,23250000,806.22 1977-12-19,815.32,817.31,806.13,807.95,21150000,807.95 1977-12-16,817.91,821.55,812.37,815.32,20270000,815.32 1977-12-15,822.68,825.10,815.23,817.91,21610000,817.91 1977-12-14,815.23,823.63,811.42,822.68,22110000,822.68 1977-12-13,815.75,818.61,809.94,815.23,19190000,815.23 1977-12-12,815.23,820.51,811.76,815.75,18180000,815.75 1977-12-09,806.91,819.04,806.65,815.23,19210000,815.23 1977-12-08,807.43,815.49,804.14,806.91,20400000,806.91 1977-12-07,806.91,812.54,802.06,807.43,21050000,807.43 1977-12-06,819.91,819.91,804.23,806.91,23770000,806.91 1977-12-05,823.98,826.84,818.78,821.03,19160000,821.03 1977-12-02,825.71,830.56,819.30,823.98,21160000,823.98 1977-12-01,829.70,832.81,823.02,825.71,24220000,825.71 1977-11-30,827.27,831.77,821.29,829.70,22670000,829.70 1977-11-29,839.48,839.48,824.24,827.27,22950000,827.27 1977-11-28,844.42,846.67,836.71,839.57,21570000,839.57 1977-11-25,843.30,847.63,838.70,844.42,17910000,844.42 1977-11-23,842.52,847.11,836.28,843.30,29150000,843.30 1977-11-22,836.11,846.15,834.63,842.52,28600000,842.52 1977-11-21,835.76,840.18,829.18,836.11,20110000,836.11 1977-11-18,831.86,840.26,829.18,835.76,23930000,835.76 1977-11-17,837.06,838.88,827.53,831.86,25110000,831.86 1977-11-16,842.78,846.24,834.03,837.06,24950000,837.06 1977-11-15,838.36,846.85,831.86,842.78,27740000,842.78 1977-11-14,845.89,849.01,834.46,838.36,23220000,838.36 1977-11-11,837.66,850.05,837.66,845.89,35260000,845.89 1977-11-10,818.43,836.11,814.10,832.55,31980000,832.55 1977-11-09,816.27,821.29,810.20,818.43,21330000,818.43 1977-11-08,816.44,821.29,810.81,816.27,19210000,816.27 1977-11-07,809.94,820.17,808.56,816.44,21270000,816.44 1977-11-04,802.67,814.02,802.41,809.94,21700000,809.94 1977-11-03,800.85,805.87,794.53,802.67,18090000,802.67 1977-11-02,806.91,809.51,797.73,800.85,20760000,800.85 1977-11-01,816.01,816.01,804.40,806.91,17170000,806.91 1977-10-31,822.68,824.93,813.93,818.35,17070000,818.35 1977-10-28,818.61,826.49,814.54,822.68,18050000,822.68 1977-10-27,813.41,825.71,809.42,818.61,21920000,818.61 1977-10-26,801.54,816.01,795.56,813.41,24860000,813.41 1977-10-25,802.32,805.35,792.79,801.54,23590000,801.54 1977-10-24,808.30,810.90,800.85,802.32,19210000,802.32 1977-10-21,814.80,815.75,804.57,808.30,20230000,808.30 1977-10-20,812.20,819.65,804.66,814.80,20520000,814.80 1977-10-19,820.51,823.20,809.08,812.20,22030000,812.20 1977-10-18,820.34,826.84,815.84,820.51,20130000,820.51 1977-10-17,821.64,824.93,813.76,820.34,17340000,820.34 1977-10-14,818.17,826.66,814.02,821.64,20410000,821.64 1977-10-13,823.98,824.67,811.42,818.17,23870000,818.17 1977-10-12,830.65,830.65,818.60,823.98,22440000,823.98 1977-10-11,840.26,841.39,830.47,832.29,17870000,832.29 1977-10-10,840.35,844.33,834.46,840.26,10580000,840.26 1977-10-07,842.08,845.98,836.11,840.35,16250000,840.35 1977-10-06,837.32,845.20,834.98,842.08,18490000,842.08 1977-10-05,842.00,843.73,832.38,837.32,18300000,837.32 1977-10-04,851.96,855.57,839.57,842.00,20850000,842.00 1977-10-03,847.11,853.60,842.08,851.96,19460000,851.96 1977-09-30,840.09,848.84,839.14,847.11,21170000,847.11 1977-09-29,834.72,843.56,832.55,840.09,21160000,840.09 1977-09-28,835.85,841.56,830.30,834.72,17960000,834.72 1977-09-27,841.65,845.55,831.95,835.85,19080000,835.85 1977-09-26,839.14,844.16,831.51,841.65,18230000,841.65 1977-09-23,839.41,844.77,834.81,839.14,18760000,839.14 1977-09-22,840.96,844.25,833.16,839.41,16660000,839.41 1977-09-21,851.78,856.29,838.79,840.96,22200000,840.96 1977-09-20,851.52,855.16,846.67,851.78,19030000,851.78 1977-09-19,856.81,857.42,848.23,851.52,16890000,851.52 1977-09-16,860.79,865.38,853.52,856.81,18340000,856.81 1977-09-15,858.71,866.16,855.51,860.79,18230000,860.79 1977-09-14,854.56,861.49,850.49,858.71,17330000,858.71 1977-09-13,854.38,859.58,848.49,854.56,14900000,854.56 1977-09-12,857.04,860.71,847.71,854.38,18700000,854.38 1977-09-09,865.47,865.47,853.17,857.04,18100000,857.04 1977-09-08,876.39,879.76,866.25,869.16,18290000,869.16 1977-09-07,873.27,878.98,869.80,876.39,18070000,876.39 1977-09-06,872.31,877.43,866.77,873.27,16130000,873.27 1977-09-02,864.86,873.79,862.44,872.31,15620000,872.31 1977-09-01,861.49,869.80,858.71,864.86,18820000,864.86 1977-08-31,858.89,862.87,850.92,861.49,19080000,861.49 1977-08-30,864.09,867.55,855.51,858.89,18220000,858.89 1977-08-29,855.77,866.77,855.77,864.09,15280000,864.09 1977-08-26,854.12,857.76,844.42,855.42,18480000,855.42 1977-08-25,862.87,863.13,851.18,854.12,19400000,854.12 1977-08-24,865.56,869.63,858.80,862.87,18170000,862.87 1977-08-23,867.29,875.61,862.70,865.56,20290000,865.56 1977-08-22,863.48,871.45,856.55,867.29,17870000,867.29 1977-08-19,864.26,870.06,856.64,863.48,20800000,863.48 1977-08-18,864.69,873.96,860.36,864.26,21040000,864.26 1977-08-17,869.28,872.75,859.58,864.69,20920000,864.69 1977-08-16,874.13,877.17,865.99,869.28,19340000,869.28 1977-08-15,871.10,877.60,864.09,874.13,15750000,874.13 1977-08-12,877.43,879.24,866.86,871.10,16870000,871.10 1977-08-11,887.04,891.46,876.04,877.43,21740000,877.43 1977-08-10,879.42,887.65,875.69,887.04,18280000,887.04 1977-08-09,879.42,884.53,873.70,879.42,19900000,879.42 1977-08-08,888.17,888.17,877.43,879.42,15870000,879.42 1977-08-05,888.17,894.84,884.36,888.69,19940000,888.69 1977-08-04,886.00,891.89,880.11,888.17,18870000,888.17 1977-08-03,887.39,888.95,877.25,886.00,21710000,886.00 1977-08-02,891.81,893.97,884.27,887.39,17910000,887.39 1977-08-01,890.07,900.03,886.17,891.81,17920000,891.81 1977-07-29,889.99,892.24,878.81,890.07,20350000,890.07 1977-07-28,888.43,893.71,879.76,889.99,26340000,889.99 1977-07-27,907.31,907.31,884.27,888.43,26440000,888.43 1977-07-26,914.24,914.33,902.98,908.18,21390000,908.18 1977-07-25,923.42,923.94,911.73,914.24,20430000,914.24 1977-07-22,921.78,927.84,916.75,923.42,23110000,923.42 1977-07-21,920.48,926.20,914.67,921.78,26880000,921.78 1977-07-20,919.27,927.75,915.45,920.48,29380000,920.48 1977-07-19,910.60,921.86,908.26,919.27,31930000,919.27 1977-07-18,905.95,915.02,901.25,910.60,29890000,910.60 1977-07-15,902.99,910.62,899.68,905.95,29120000,905.95 1977-07-13,903.41,906.80,896.29,902.99,23160000,902.99 1977-07-12,905.53,906.26,898.58,903.41,22470000,903.41 1977-07-11,907.99,910.96,900.27,905.53,19790000,905.53 1977-07-08,909.51,914.94,904.17,907.99,23820000,907.99 1977-07-07,907.73,913.42,903.49,909.51,21740000,909.51 1977-07-06,913.59,915.37,904.94,907.73,21230000,907.73 1977-07-05,912.65,919.01,907.90,913.59,16850000,913.59 1977-07-01,916.30,918.25,907.48,912.65,18160000,912.65 1977-06-30,913.33,920.88,909.09,916.30,19410000,916.30 1977-06-29,915.62,917.73,906.63,913.33,19000000,913.33 1977-06-28,924.10,926.98,914.01,915.62,22670000,915.62 1977-06-27,929.70,931.73,920.12,924.10,19870000,924.10 1977-06-24,925.37,933.77,924.02,929.70,27490000,929.70 1977-06-23,926.31,930.46,922.45,925.37,24330000,925.37 1977-06-22,928.60,930.89,921.13,926.31,25070000,926.31 1977-06-21,924.27,934.36,923.51,928.60,29730000,928.60 1977-06-20,920.45,926.98,916.89,924.27,22950000,924.27 1977-06-17,920.45,924.78,915.79,920.45,21960000,920.45 1977-06-16,917.57,924.53,910.53,920.45,24310000,920.45 1977-06-15,922.57,925.03,914.43,917.57,22640000,917.57 1977-06-14,912.40,924.27,912.14,922.57,25390000,922.57 1977-06-13,910.79,917.15,906.21,912.40,20250000,912.40 1977-06-10,909.85,915.20,904.77,910.79,20630000,910.79 1977-06-09,912.99,914.69,904.34,909.85,19940000,909.85 1977-06-08,908.67,918.25,906.21,912.99,22200000,912.99 1977-06-07,903.04,910.79,896.79,908.67,21110000,908.67 1977-06-06,912.23,916.13,901.03,903.04,18930000,903.04 1977-06-03,903.15,915.88,901.03,912.23,20330000,912.23 1977-06-02,906.55,912.91,899.42,903.15,18620000,903.15 1977-06-01,898.66,909.35,896.46,906.55,18320000,906.55 1977-05-31,898.83,904.77,892.55,898.66,17800000,898.66 1977-05-27,908.07,909.60,896.29,898.83,15730000,898.83 1977-05-26,903.24,910.45,899.17,908.07,18620000,908.07 1977-05-25,912.40,916.72,901.46,903.24,20710000,903.24 1977-05-24,917.06,917.83,906.55,912.40,20050000,912.40 1977-05-23,928.17,928.17,915.03,917.06,18290000,917.06 1977-05-20,936.48,937.16,925.71,930.46,18950000,930.46 1977-05-19,941.91,945.13,933.34,936.48,21280000,936.48 1977-05-18,936.48,947.34,935.46,941.91,27800000,941.91 1977-05-17,932.50,939.45,925.20,936.48,22290000,936.48 1977-05-16,928.34,938.43,926.73,932.50,21170000,932.50 1977-05-13,925.54,932.58,923.17,928.34,19780000,928.34 1977-05-12,926.90,930.04,917.74,925.54,21980000,925.54 1977-05-11,936.14,937.84,923.85,926.90,18980000,926.90 1977-05-10,933.09,941.40,930.55,936.14,21090000,936.14 1977-05-09,936.74,938.52,928.77,933.09,15230000,933.09 1977-05-06,943.27,943.27,932.24,936.74,19370000,936.74 1977-05-05,940.72,949.46,934.53,943.44,23450000,943.44 1977-05-04,934.19,944.96,929.44,940.72,23330000,940.72 1977-05-03,931.22,939.70,929.27,934.19,21950000,934.19 1977-05-02,926.90,934.02,923.00,931.22,17970000,931.22 1977-04-29,927.32,931.99,922.32,926.90,18330000,926.90 1977-04-28,923.76,931.14,919.78,927.32,18370000,927.32 1977-04-27,915.62,928.34,913.08,923.76,20590000,923.76 1977-04-26,914.60,922.74,910.36,915.62,20040000,915.62 1977-04-25,924.02,924.02,910.45,914.60,20440000,914.60 1977-04-22,935.13,935.13,923.59,927.07,20700000,927.07 1977-04-21,942.59,949.37,933.43,935.80,22740000,935.80 1977-04-20,938.77,948.69,933.43,942.59,25090000,942.59 1977-04-19,942.76,944.28,934.79,938.77,19510000,938.77 1977-04-18,947.76,951.32,939.28,942.76,17830000,942.76 1977-04-15,947.00,953.10,941.74,947.76,20230000,947.76 1977-04-14,943.69,956.07,943.69,947.00,30490000,947.00 1977-04-13,937.16,942.76,927.49,938.18,21800000,938.18 1977-04-12,924.69,940.98,924.69,937.16,23760000,937.16 1977-04-11,918.88,928.85,916.47,924.10,17650000,924.10 1977-04-07,914.73,921.21,910.57,918.88,17260000,918.88 1977-04-06,916.14,922.12,910.07,914.73,16600000,914.73 1977-04-05,915.56,920.88,909.74,916.14,18330000,916.14 1977-04-04,927.36,929.36,913.48,915.56,16250000,915.56 1977-04-01,919.55,930.27,919.55,927.36,17050000,927.36 1977-03-31,921.21,926.86,914.15,919.13,16510000,919.13 1977-03-30,932.01,935.84,917.64,921.21,18810000,921.21 1977-03-29,926.11,936.67,925.78,932.01,17030000,932.01 1977-03-28,928.86,931.27,920.71,926.11,16710000,926.11 1977-03-25,935.67,937.50,925.12,928.86,16550000,928.86 1977-03-24,942.32,944.48,931.93,935.67,19650000,935.67 1977-03-23,950.96,954.37,940.16,942.32,19360000,942.32 1977-03-22,953.54,955.95,944.65,950.96,18660000,950.96 1977-03-21,961.02,961.44,949.47,953.54,18040000,953.54 1977-03-18,964.84,967.50,956.95,961.02,19840000,961.02 1977-03-17,968.00,970.08,958.78,964.84,20700000,964.84 1977-03-16,965.01,971.58,961.44,968.00,22140000,968.00 1977-03-15,958.36,970.33,958.19,965.01,23940000,965.01 1977-03-14,947.72,960.19,944.73,958.36,19290000,958.36 1977-03-11,946.73,952.96,942.90,947.72,18230000,947.72 1977-03-10,942.90,948.89,938.00,946.73,18620000,946.73 1977-03-09,950.96,950.96,938.66,942.90,19680000,942.90 1977-03-08,955.12,960.36,949.47,952.04,19520000,952.04 1977-03-07,953.46,958.61,949.22,955.12,17410000,955.12 1977-03-04,948.64,956.28,947.39,953.46,18950000,953.46 1977-03-03,942.07,951.88,940.16,948.64,17560000,948.64 1977-03-02,944.73,949.97,938.50,942.07,18010000,942.07 1977-03-01,936.42,948.47,936.25,944.73,19480000,944.73 1977-02-28,933.43,938.50,928.61,936.42,16220000,936.42 1977-02-25,932.60,938.75,926.20,933.43,17610000,933.43 1977-02-24,938.25,940.24,927.69,932.60,19730000,932.60 1977-02-23,939.91,943.82,934.59,938.25,18240000,938.25 1977-02-22,940.24,946.06,933.84,939.91,17730000,939.91 1977-02-18,943.73,944.73,935.09,940.24,18040000,940.24 1977-02-17,948.30,950.71,939.58,943.73,19040000,943.73 1977-02-16,944.32,957.28,941.24,948.30,23430000,948.30 1977-02-15,938.33,948.39,937.08,944.32,21620000,944.32 1977-02-14,931.52,939.91,926.11,938.33,19230000,938.33 1977-02-11,937.92,940.91,926.03,931.52,20510000,931.52 1977-02-10,933.84,943.57,930.60,937.92,22340000,937.92 1977-02-09,942.24,944.98,928.27,933.84,23640000,933.84 1977-02-08,946.31,952.79,938.66,942.24,24040000,942.24 1977-02-07,947.89,954.70,941.82,946.31,20700000,946.31 1977-02-04,947.14,955.70,941.90,947.89,23130000,947.89 1977-02-03,952.79,954.95,941.99,947.14,23790000,947.14 1977-02-02,958.36,963.76,949.97,952.79,25700000,952.79 1977-02-01,954.37,962.02,950.05,958.36,23700000,958.36 1977-01-31,957.53,958.03,944.90,954.37,22920000,954.37 1977-01-28,954.54,961.85,949.38,957.53,22700000,957.53 1977-01-27,958.53,963.93,950.47,954.54,24360000,954.54 1977-01-26,965.92,968.33,953.13,958.53,27840000,958.53 1977-01-25,963.60,973.57,959.36,965.92,26340000,965.92 1977-01-24,962.43,968.83,956.87,963.60,22890000,963.60 1977-01-21,959.03,967.42,953.87,962.43,23930000,962.43 1977-01-20,968.67,973.82,954.70,959.03,26520000,959.03 1977-01-19,962.43,972.16,959.69,968.67,27120000,968.67 1977-01-18,967.25,970.08,958.53,962.43,24380000,962.43 1977-01-17,972.16,972.99,961.10,967.25,21060000,967.25 1977-01-14,976.15,979.22,967.00,972.16,24480000,972.16 1977-01-13,968.25,979.55,966.51,976.15,24780000,976.15 1977-01-12,976.23,976.23,962.52,968.25,22670000,968.25 1977-01-11,986.87,991.11,972.16,976.65,24100000,976.65 1977-01-10,983.13,990.69,979.31,986.87,20860000,986.87 1977-01-07,979.89,987.03,975.40,983.13,21720000,983.13 1977-01-06,978.06,989.03,974.07,979.89,23920000,979.89 1977-01-05,987.87,990.69,974.07,978.06,25010000,978.06 1977-01-04,999.75,1001.99,985.70,987.87,22740000,987.87 1977-01-03,1004.65,1007.81,994.18,999.75,21280000,999.75 1976-12-31,999.09,1006.32,997.34,1004.65,19170000,1004.65 1976-12-30,994.93,1005.49,991.69,999.09,23700000,999.09 1976-12-29,1000.08,1003.49,990.94,994.93,21910000,994.93 1976-12-28,996.09,1006.82,992.77,1000.08,25790000,1000.08 1976-12-27,985.62,997.92,982.71,996.09,20130000,996.09 1976-12-23,984.54,991.61,978.47,985.62,24560000,985.62 1976-12-22,978.39,993.35,977.73,984.54,26970000,984.54 1976-12-21,972.41,981.05,966.17,978.39,24390000,978.39 1976-12-20,979.06,981.96,969.00,972.41,20690000,972.41 1976-12-17,981.30,990.53,976.23,979.06,23870000,979.06 1976-12-16,983.79,988.20,973.90,981.30,23920000,981.30 1976-12-15,980.63,989.11,974.82,983.79,28300000,983.79 1976-12-14,974.24,983.13,966.92,980.63,25130000,980.63 1976-12-13,973.15,980.14,967.50,974.24,24830000,974.24 1976-12-10,970.74,978.31,965.59,973.15,25960000,973.15 1976-12-09,963.26,976.31,962.35,970.74,31800000,970.74 1976-12-08,960.69,966.01,953.54,963.26,24560000,963.26 1976-12-07,961.77,968.67,957.03,960.69,26140000,960.69 1976-12-06,950.55,966.59,948.72,961.77,24830000,961.77 1976-12-03,946.64,956.28,943.07,950.55,22640000,950.55 1976-12-02,949.38,956.62,944.07,946.64,23300000,946.64 1976-12-01,947.22,954.12,942.24,949.38,21960000,949.38 1976-11-30,950.05,952.13,941.41,947.22,17030000,947.22 1976-11-29,956.62,959.86,947.14,950.05,18750000,950.05 1976-11-26,950.96,959.28,947.97,956.62,15000000,956.62 1976-11-24,949.30,956.20,941.74,950.96,20420000,950.96 1976-11-23,955.87,958.94,945.98,949.30,19090000,949.30 1976-11-22,948.80,959.94,945.81,955.87,20930000,955.87 1976-11-19,950.13,957.86,942.49,948.80,24550000,948.80 1976-11-18,938.08,953.46,935.51,950.13,24000000,950.13 1976-11-17,935.34,943.90,930.44,938.08,19900000,938.08 1976-11-16,935.42,946.73,931.77,935.34,21020000,935.34 1976-11-15,927.69,937.25,921.63,935.42,16710000,935.42 1976-11-12,931.43,933.93,920.21,927.69,15550000,927.69 1976-11-11,924.04,932.85,917.97,931.43,13230000,931.43 1976-11-10,930.77,936.92,917.89,924.04,18890000,924.04 1976-11-09,933.68,939.33,924.45,930.77,19210000,930.77 1976-11-08,941.41,941.41,928.94,933.68,16520000,933.68 1976-11-05,960.44,962.93,940.24,943.07,20780000,943.07 1976-11-04,956.53,967.75,951.30,960.44,21700000,960.44 1976-11-03,960.44,960.44,944.73,956.53,19350000,956.53 1976-11-01,964.93,971.99,957.61,966.09,18390000,966.09 1976-10-29,952.63,966.26,947.47,964.93,17030000,964.93 1976-10-28,956.12,962.35,948.72,952.63,16920000,952.63 1976-10-27,948.14,959.77,944.56,956.12,15790000,956.12 1976-10-26,938.00,951.63,937.17,948.14,15490000,948.14 1976-10-25,938.75,942.65,932.51,938.00,13310000,938.00 1976-10-22,944.90,946.56,932.26,938.75,17870000,938.75 1976-10-21,954.87,960.77,942.74,944.90,17980000,944.90 1976-10-20,949.97,958.86,944.40,954.87,15860000,954.87 1976-10-19,946.56,953.87,938.66,949.97,16200000,949.97 1976-10-18,937.00,950.13,936.59,946.56,15710000,946.56 1976-10-15,935.92,942.74,928.27,937.00,16210000,937.00 1976-10-14,947.56,947.56,931.02,935.92,18610000,935.92 1976-10-13,932.35,950.38,932.01,948.30,21690000,948.30 1976-10-12,940.82,946.31,928.27,932.35,18210000,932.35 1976-10-11,948.88,948.88,934.26,940.82,14620000,940.82 1976-10-08,965.09,969.25,949.72,952.38,16740000,952.38 1976-10-07,959.69,968.50,951.71,965.09,19830000,965.09 1976-10-06,966.76,968.33,949.88,959.69,20870000,959.69 1976-10-05,977.98,979.97,960.69,966.76,19200000,966.76 1976-10-04,979.89,983.21,971.83,977.98,12630000,977.98 1976-10-01,990.19,995.60,974.40,979.89,20620000,979.89 1976-09-30,991.19,995.84,983.79,990.19,14700000,990.19 1976-09-29,994.93,1001.25,985.54,991.19,18090000,991.19 1976-09-28,1013.13,1014.38,992.11,994.93,20440000,994.93 1976-09-27,1009.31,1016.54,1004.40,1013.13,17430000,1013.13 1976-09-24,1010.80,1014.13,1001.75,1009.31,17400000,1009.31 1976-09-23,1014.05,1019.78,1005.32,1010.80,24210000,1010.80 1976-09-22,1014.79,1026.26,1009.56,1014.05,32970000,1014.05 1976-09-21,994.51,1016.54,993.60,1014.79,30300000,1014.79 1976-09-20,995.10,1002.74,990.69,994.51,21730000,994.51 1976-09-17,987.95,1000.50,986.62,995.10,28270000,995.10 1976-09-16,979.31,989.44,974.82,987.95,19620000,987.95 1976-09-15,978.64,983.96,971.24,979.31,17570000,979.31 1976-09-14,983.29,983.96,973.57,978.64,15550000,978.64 1976-09-13,988.36,994.02,980.30,983.29,16100000,983.29 1976-09-10,986.87,991.86,981.05,988.36,16930000,988.36 1976-09-09,992.94,993.43,983.05,986.87,16540000,986.87 1976-09-08,996.59,1001.41,987.87,992.94,19750000,992.94 1976-09-07,989.11,998.67,985.12,996.59,16310000,996.59 1976-09-03,984.79,991.19,979.22,989.11,13280000,989.11 1976-09-02,985.95,993.10,980.14,984.79,18920000,984.79 1976-09-01,973.74,987.95,971.24,985.95,18640000,985.95 1976-08-31,968.92,979.97,966.84,973.74,15480000,973.74 1976-08-30,963.93,972.91,961.44,968.92,11140000,968.92 1976-08-27,960.44,967.50,954.12,963.93,12120000,963.93 1976-08-26,970.83,975.90,958.28,960.44,15270000,960.44 1976-08-25,962.93,973.82,956.37,970.83,17400000,970.83 1976-08-24,971.49,977.14,960.77,962.93,16740000,962.93 1976-08-23,974.07,975.73,962.18,971.49,15450000,971.49 1976-08-20,982.63,982.63,970.99,974.07,14920000,974.07 1976-08-19,995.01,995.84,979.31,983.88,17230000,983.88 1976-08-18,999.34,1004.74,992.60,995.01,17150000,995.01 1976-08-17,992.77,1002.41,990.44,999.34,18500000,999.34 1976-08-16,990.19,997.51,986.95,992.77,16210000,992.77 1976-08-13,987.12,994.35,983.05,990.19,13930000,990.19 1976-08-12,986.79,992.10,979.22,987.12,15560000,987.12 1976-08-11,993.43,1000.00,984.46,986.79,18710000,986.79 1976-08-10,983.46,995.68,981.13,993.43,16690000,993.43 1976-08-09,986.00,987.53,979.06,983.46,11700000,983.46 1976-08-06,986.68,989.90,978.70,986.00,13930000,986.00 1976-08-05,992.28,995.76,983.28,986.68,15530000,986.68 1976-08-04,990.33,998.73,986.42,992.28,20650000,992.28 1976-08-03,982.26,992.96,978.70,990.33,18500000,990.33 1976-08-02,984.64,988.97,977.43,982.26,13870000,982.26 1976-07-30,979.29,987.95,974.71,984.64,14830000,984.64 1976-07-29,981.33,985.74,974.20,979.29,13330000,979.29 1976-07-28,984.13,984.89,974.71,981.33,16000000,981.33 1976-07-27,991.51,994.99,981.92,984.13,15580000,984.13 1976-07-26,990.91,997.20,985.91,991.51,13530000,991.51 1976-07-23,991.08,997.71,985.43,990.91,15870000,990.91 1976-07-22,989.44,995.99,982.97,991.08,15600000,991.08 1976-07-21,988.29,996.77,984.20,989.44,18350000,989.44 1976-07-20,990.83,994.43,983.06,988.29,18810000,988.29 1976-07-19,993.21,999.10,986.08,990.83,18200000,990.83 1976-07-16,997.30,997.30,985.43,993.21,20450000,993.21 1976-07-15,1005.16,1006.79,993.21,997.46,20400000,997.46 1976-07-14,1006.06,1012.20,997.95,1005.16,23840000,1005.16 1976-07-13,1011.21,1017.93,1001.64,1006.06,27550000,1006.06 1976-07-12,1003.11,1015.72,998.94,1011.21,23750000,1011.21 1976-07-09,991.98,1007.53,989.60,1003.11,23500000,1003.11 1976-07-08,991.16,998.85,987.15,991.98,21710000,991.98 1976-07-07,991.81,994.43,982.56,991.16,18470000,991.16 1976-07-06,999.84,1003.85,989.19,991.81,16130000,991.81 1976-07-02,994.84,1003.85,991.00,999.84,16730000,999.84 1976-07-01,1002.78,1009.00,990.26,994.84,21130000,994.84 1976-06-30,1000.65,1011.79,995.99,1002.78,23830000,1002.78 1976-06-29,997.38,1004.50,992.47,1000.65,19620000,1000.65 1976-06-28,999.84,1006.30,993.45,997.38,17490000,997.38 1976-06-25,1003.77,1008.35,995.58,999.84,17830000,999.84 1976-06-24,996.56,1009.09,994.52,1003.77,19850000,1003.77 1976-06-23,997.63,1002.05,987.23,996.56,17530000,996.56 1976-06-22,1007.45,1011.87,995.74,997.63,21150000,997.63 1976-06-21,1001.88,1010.97,997.54,1007.45,18930000,1007.45 1976-06-18,1003.19,1012.93,996.97,1001.88,25720000,1001.88 1976-06-17,988.62,1007.86,988.38,1003.19,27810000,1003.19 1976-06-16,985.92,995.66,979.94,988.62,21620000,988.62 1976-06-15,991.24,994.19,981.42,985.92,18440000,985.92 1976-06-14,979.78,995.17,979.78,991.24,21250000,991.24 1976-06-11,964.39,980.52,963.98,978.80,19470000,978.80 1976-06-10,958.09,967.99,956.12,964.39,16100000,964.39 1976-06-09,959.97,965.54,954.98,958.09,14560000,958.09 1976-06-08,958.09,968.48,956.45,959.97,16660000,959.97 1976-06-07,963.90,965.62,951.70,958.09,14510000,958.09 1976-06-04,973.80,973.97,961.28,963.90,15960000,963.90 1976-06-03,975.93,983.14,971.02,973.80,18900000,973.80 1976-06-02,973.13,979.37,968.57,975.93,16120000,975.93 1976-06-01,975.23,981.26,969.27,973.13,13880000,973.13 1976-05-28,965.57,978.60,962.92,975.23,16860000,975.23 1976-05-27,968.63,970.48,955.84,965.57,15310000,965.57 1976-05-26,971.69,976.43,963.48,968.63,16750000,968.63 1976-05-25,971.53,976.99,961.95,971.69,18770000,971.69 1976-05-24,988.82,988.82,969.03,971.53,16560000,971.53 1976-05-21,997.27,999.03,986.41,990.75,18730000,990.75 1976-05-20,988.90,1001.05,985.76,997.27,22560000,997.27 1976-05-19,989.45,996.14,983.99,988.90,18450000,988.90 1976-05-18,987.64,994.80,984.41,989.45,17410000,989.45 1976-05-17,992.60,993.47,982.05,987.64,14720000,987.64 1976-05-14,1000.24,1000.24,988.74,992.60,16800000,992.60 1976-05-13,1005.67,1008.66,997.48,1001.10,16730000,1001.10 1976-05-12,1006.61,1012.52,999.53,1005.67,18510000,1005.67 1976-05-11,1007.48,1015.82,1001.42,1006.61,23590000,1006.61 1976-05-10,996.22,1010.39,994.10,1007.48,22760000,1007.48 1976-05-07,989.53,999.53,984.97,996.22,17810000,996.22 1976-05-06,986.46,995.43,981.50,989.53,16200000,989.53 1976-05-05,993.70,997.09,982.53,986.46,14970000,986.46 1976-05-04,990.32,998.03,982.21,993.70,17240000,993.70 1976-05-03,995.83,995.83,981.74,990.32,15180000,990.32 1976-04-30,1002.13,1007.01,993.31,996.85,14530000,996.85 1976-04-29,1000.70,1010.39,996.62,1002.13,17740000,1002.13 1976-04-28,995.51,1002.36,987.09,1000.70,15790000,1000.70 1976-04-27,1002.76,1008.42,993.15,995.51,17760000,995.51 1976-04-26,1000.71,1005.20,991.03,1002.76,15520000,1002.76 1976-04-23,1007.71,1008.89,995.36,1000.71,17000000,1000.71 1976-04-22,1011.02,1017.71,1003.70,1007.71,20220000,1007.71 1976-04-21,1003.46,1016.85,1000.31,1011.02,26600000,1011.02 1976-04-20,998.51,1008.19,998.51,1003.46,23500000,1003.46 1976-04-19,980.48,991.18,979.16,988.11,16500000,988.11 1976-04-15,974.65,984.81,969.30,980.48,15100000,980.48 1976-04-14,984.26,990.24,972.76,974.65,18440000,974.65 1976-04-13,971.27,985.99,969.54,984.26,15990000,984.26 1976-04-12,968.28,978.90,963.00,971.27,16030000,971.27 1976-04-09,977.09,981.58,964.34,968.28,19050000,968.28 1976-04-08,986.22,987.80,968.99,977.09,20860000,977.09 1976-04-07,1001.65,1004.80,983.08,986.22,20190000,986.22 1976-04-06,1004.09,1015.35,998.90,1001.65,24170000,1001.65 1976-04-05,992.99,1008.74,992.99,1004.09,21940000,1004.09 1976-04-02,994.10,996.54,982.13,991.58,17420000,991.58 1976-04-01,999.45,1003.31,987.72,994.10,17910000,994.10 1976-03-31,992.13,1000.79,988.35,999.45,17520000,999.45 1976-03-30,997.40,1000.31,985.04,992.13,17930000,992.13 1976-03-29,1003.46,1007.40,993.78,997.40,16100000,997.40 1976-03-26,1002.13,1009.68,997.64,1003.46,18510000,1003.46 1976-03-25,1009.21,1014.72,997.72,1002.13,22510000,1002.13 1976-03-24,1001.65,1018.03,1001.65,1009.21,32610000,1009.21 1976-03-23,982.29,997.01,978.12,995.43,22450000,995.43 1976-03-22,979.85,988.27,977.64,982.29,19410000,982.29 1976-03-19,979.85,985.36,970.80,979.85,18090000,979.85 1976-03-18,985.99,988.98,971.35,979.85,20330000,979.85 1976-03-17,983.47,995.59,981.03,985.99,26190000,985.99 1976-03-16,974.50,986.62,970.09,983.47,22780000,983.47 1976-03-15,986.85,986.85,969.69,974.50,19570000,974.50 1976-03-12,1003.31,1006.61,984.49,987.64,26020000,987.64 1976-03-11,995.28,1008.42,992.76,1003.31,27300000,1003.31 1976-03-10,993.70,1003.15,987.17,995.28,24900000,995.28 1976-03-09,988.74,1005.67,987.88,993.70,31770000,993.70 1976-03-08,973.08,991.73,973.08,988.74,25060000,988.74 1976-03-05,970.64,981.03,965.37,972.92,23030000,972.92 1976-03-04,978.83,984.26,967.18,970.64,24410000,970.64 1976-03-03,985.12,988.99,973.00,978.83,25450000,978.83 1976-03-02,975.36,991.11,974.26,985.12,25590000,985.12 1976-03-01,972.61,981.19,964.18,975.36,22070000,975.36 1976-02-27,978.83,980.71,962.30,972.61,26940000,972.61 1976-02-26,994.57,1000.94,975.99,978.83,34320000,978.83 1976-02-25,993.55,1002.83,983.39,994.57,34680000,994.57 1976-02-24,985.28,1001.65,983.00,993.55,34380000,993.55 1976-02-23,987.80,993.39,977.25,985.28,31460000,985.28 1976-02-20,977.80,996.93,977.80,987.80,44510000,987.80 1976-02-19,961.74,979.85,961.74,975.76,39210000,975.76 1976-02-18,950.57,965.68,946.16,960.09,29900000,960.09 1976-02-17,958.36,963.48,946.63,950.57,25460000,950.57 1976-02-13,966.78,968.99,953.56,958.36,23870000,958.36 1976-02-12,971.90,978.12,963.00,966.78,28610000,966.78 1976-02-11,968.75,980.24,965.76,971.90,32300000,971.90 1976-02-10,957.18,971.74,953.09,968.75,27660000,968.75 1976-02-09,954.90,967.18,948.44,957.18,25340000,957.18 1976-02-06,964.81,965.60,948.84,954.90,27360000,954.90 1976-02-05,976.62,980.71,961.98,964.81,33780000,964.81 1976-02-04,972.61,981.66,966.94,976.62,38270000,976.62 1976-02-03,971.35,979.69,960.80,972.61,34080000,972.61 1976-02-02,975.28,976.54,962.06,971.35,24000000,971.35 1976-01-30,968.75,985.99,966.07,975.28,38510000,975.28 1976-01-29,951.35,970.80,948.13,968.75,29800000,968.75 1976-01-28,957.81,963.08,942.38,951.35,27370000,951.35 1976-01-27,961.51,973.16,950.80,957.81,32070000,957.81 1976-01-26,953.95,969.22,953.87,961.51,34470000,961.51 1976-01-23,943.48,958.38,940.57,953.95,33640000,953.95 1976-01-22,946.24,949.62,933.96,943.48,27420000,943.48 1976-01-21,949.86,954.97,934.35,946.24,34470000,946.24 1976-01-20,943.72,954.03,935.77,949.86,36690000,949.86 1976-01-19,929.63,946.08,923.80,943.72,29450000,943.72 1976-01-16,924.51,935.22,917.98,929.63,25940000,929.63 1976-01-15,929.63,940.26,921.21,924.51,38450000,924.51 1976-01-14,912.94,932.62,908.22,929.63,30340000,929.63 1976-01-13,922.39,930.26,909.40,912.94,34530000,912.94 1976-01-12,911.13,925.93,905.38,922.39,30440000,922.39 1976-01-09,907.98,916.88,903.73,911.13,26510000,911.13 1976-01-08,898.69,916.64,893.89,907.98,29030000,907.98 1976-01-07,890.82,908.69,886.41,898.69,33170000,898.69 1976-01-06,878.70,894.99,878.70,890.82,31270000,890.82 1976-01-05,858.71,879.80,858.63,877.83,21960000,877.83 1976-01-02,852.41,860.44,848.63,858.71,10300000,858.71 1975-12-31,852.41,859.65,848.71,852.41,16970000,852.41 1975-12-30,856.66,860.75,847.13,852.41,16040000,852.41 1975-12-29,859.81,866.11,853.59,856.66,17070000,856.66 1975-12-26,851.94,860.91,850.20,859.81,10020000,859.81 1975-12-24,845.01,854.93,845.01,851.94,11150000,851.94 1975-12-23,838.63,847.84,833.36,843.75,17750000,843.75 1975-12-22,844.38,847.29,835.33,838.63,15340000,838.63 1975-12-19,852.09,854.30,841.39,844.38,17720000,844.38 1975-12-18,846.27,857.13,843.12,852.09,18040000,852.09 1975-12-17,844.30,852.17,840.37,846.27,16560000,846.27 1975-12-16,836.59,849.73,833.91,844.30,18350000,844.30 1975-12-15,832.81,841.23,828.72,836.59,13960000,836.59 1975-12-12,832.73,836.74,825.53,832.81,13100000,832.81 1975-12-11,833.99,840.13,828.87,832.73,15300000,832.73 1975-12-10,824.15,835.72,820.92,833.99,15680000,833.99 1975-12-09,821.63,827.53,814.47,824.15,16040000,824.15 1975-12-08,818.80,826.43,812.81,821.63,14150000,821.63 1975-12-05,829.11,833.60,816.99,818.80,14050000,818.80 1975-12-04,825.49,833.67,818.17,829.11,16380000,829.11 1975-12-03,836.67,836.67,822.10,825.49,21320000,825.49 1975-12-02,856.34,856.66,841.70,843.20,17930000,843.20 1975-12-01,860.67,865.95,853.04,856.34,16050000,856.34 1975-11-28,858.55,865.24,854.06,860.67,12870000,860.67 1975-11-26,855.40,863.90,851.07,858.55,18780000,858.55 1975-11-25,845.64,859.10,845.56,855.40,17490000,855.40 1975-11-24,840.76,849.10,833.83,845.64,13930000,845.64 1975-11-21,843.51,848.39,834.62,840.76,14110000,840.76 1975-11-20,848.24,853.35,838.87,843.51,16460000,843.51 1975-11-19,855.24,857.53,842.25,848.24,16820000,848.24 1975-11-18,856.66,865.79,851.78,855.24,20760000,855.24 1975-11-17,853.67,862.56,847.61,856.66,17660000,856.66 1975-11-14,851.23,857.92,845.32,853.67,16460000,853.67 1975-11-13,852.25,863.11,848.47,851.23,25070000,851.23 1975-11-12,838.55,855.64,838.40,852.25,23960000,852.25 1975-11-11,835.48,843.28,831.63,838.55,14640000,838.55 1975-11-10,835.80,840.99,824.62,835.48,14910000,835.48 1975-11-07,840.92,843.83,830.29,835.80,15930000,835.80 1975-11-06,836.27,845.40,827.93,840.92,18600000,840.92 1975-11-05,830.13,843.91,829.50,836.27,17390000,836.27 1975-11-04,825.72,834.07,821.08,830.13,11570000,830.13 1975-11-03,836.04,836.82,822.26,825.72,11400000,825.72 1975-10-31,839.42,842.49,829.97,836.04,12910000,836.04 1975-10-30,838.63,847.29,832.97,839.42,15080000,839.42 1975-10-29,850.83,850.83,834.86,838.63,16110000,838.63 1975-10-28,838.48,853.67,837.53,851.46,17060000,851.46 1975-10-27,840.52,845.17,831.31,838.48,13100000,838.48 1975-10-24,855.16,857.76,837.22,840.52,18120000,840.52 1975-10-23,849.57,859.57,844.46,855.16,17900000,855.16 1975-10-22,846.82,855.09,840.37,849.57,16060000,849.57 1975-10-21,842.25,855.71,841.23,846.82,20800000,846.82 1975-10-20,832.18,844.14,828.24,842.25,13250000,842.25 1975-10-17,837.85,841.23,824.46,832.18,15650000,832.18 1975-10-16,837.22,851.46,833.60,837.85,18910000,837.85 1975-10-15,835.25,843.91,828.01,837.22,14440000,837.22 1975-10-14,837.77,852.41,830.13,835.25,19960000,835.25 1975-10-13,823.91,838.95,818.48,837.77,12020000,837.77 1975-10-10,824.54,832.57,817.14,823.91,14880000,823.91 1975-10-09,823.91,834.93,817.30,824.54,17770000,824.54 1975-10-08,816.51,829.35,809.82,823.91,17800000,823.91 1975-10-07,819.66,821.79,806.12,816.51,13530000,816.51 1975-10-06,813.21,826.83,811.32,819.66,15470000,819.66 1975-10-03,794.71,816.12,794.71,813.21,16360000,813.21 1975-10-02,784.16,799.28,781.72,794.55,14290000,794.55 1975-10-01,793.88,799.35,780.54,784.16,14070000,784.16 1975-09-30,804.44,804.44,789.66,793.88,12520000,793.88 1975-09-29,818.60,820.56,803.43,805.23,10580000,805.23 1975-09-26,820.24,827.28,811.33,818.60,12570000,818.60 1975-09-25,826.19,828.85,812.89,820.24,12890000,820.24 1975-09-24,820.24,836.83,820.24,826.19,16060000,826.19 1975-09-23,820.40,824.31,807.96,819.85,12800000,819.85 1975-09-22,829.79,835.65,817.58,820.40,14750000,820.40 1975-09-19,816.72,834.72,816.72,829.79,20830000,829.79 1975-09-18,799.05,817.19,797.64,814.61,14560000,814.61 1975-09-17,795.13,804.52,792.01,799.05,12190000,799.05 1975-09-16,803.19,809.76,792.79,795.13,13090000,795.13 1975-09-15,809.23,811.25,799.05,803.19,8670000,803.19 1975-09-12,812.66,823.69,806.63,809.23,12230000,809.23 1975-09-11,817.66,820.32,808.12,812.66,11100000,812.66 1975-09-10,825.33,825.33,809.53,817.66,14780000,817.66 1975-09-09,840.11,849.03,826.58,827.75,15790000,827.75 1975-09-08,835.97,843.95,830.18,840.11,11500000,840.11 1975-09-05,838.31,843.63,830.26,835.97,11680000,835.97 1975-09-04,832.29,843.55,827.21,838.31,12810000,838.31 1975-09-03,823.69,834.64,815.94,832.29,12260000,832.29 1975-09-02,835.34,840.19,821.26,823.69,11460000,823.69 1975-08-29,829.47,844.02,828.69,835.34,15480000,835.34 1975-08-28,809.92,831.59,809.92,829.47,14530000,829.47 1975-08-27,803.11,809.61,797.09,807.20,11100000,807.20 1975-08-26,812.34,815.55,799.98,803.11,11350000,803.11 1975-08-25,804.76,817.43,803.19,812.34,11250000,812.34 1975-08-22,791.69,806.95,789.97,804.76,13050000,804.76 1975-08-21,793.26,801.31,785.75,791.69,16610000,791.69 1975-08-20,805.46,805.46,789.58,793.26,18630000,793.26 1975-08-19,822.75,824.23,806.56,808.51,14990000,808.51 1975-08-18,825.64,833.31,819.38,822.75,10810000,822.75 1975-08-15,817.04,830.73,814.06,825.64,10610000,825.64 1975-08-14,820.56,824.08,812.73,817.04,12460000,817.04 1975-08-13,828.54,831.20,817.43,820.56,12000000,820.56 1975-08-12,823.76,838.63,823.29,828.54,14510000,828.54 1975-08-11,817.74,825.33,809.92,823.76,12350000,823.76 1975-08-08,815.79,826.19,812.66,817.74,11660000,817.74 1975-08-07,813.67,824.86,809.84,815.79,12390000,815.79 1975-08-06,810.15,820.40,804.60,813.67,16280000,813.67 1975-08-05,818.05,824.94,806.95,810.15,15470000,810.15 1975-08-04,826.50,826.81,813.52,818.05,12620000,818.05 1975-08-01,831.51,833.78,822.20,826.50,13320000,826.50 1975-07-31,831.66,843.09,828.77,831.51,14540000,831.51 1975-07-30,824.86,837.69,819.15,831.66,16150000,831.66 1975-07-29,827.83,840.74,820.32,824.86,19000000,824.86 1975-07-28,834.09,836.91,821.96,827.83,14850000,827.83 1975-07-25,840.27,846.92,829.40,834.09,15110000,834.09 1975-07-24,836.67,847.78,829.24,840.27,20550000,840.27 1975-07-23,846.76,853.41,834.64,836.67,20150000,836.67 1975-07-22,854.74,854.97,838.70,846.76,20660000,846.76 1975-07-21,862.41,867.88,852.00,854.74,16690000,854.74 1975-07-18,864.28,867.02,854.27,862.41,16870000,862.41 1975-07-17,872.11,877.35,858.73,864.28,21420000,864.28 1975-07-16,881.81,888.53,868.59,872.11,25250000,872.11 1975-07-15,875.86,888.85,874.14,881.81,28340000,881.81 1975-07-14,871.09,880.71,865.22,875.86,21900000,875.86 1975-07-11,871.87,881.49,862.80,871.09,22210000,871.09 1975-07-10,871.87,884.46,868.19,871.87,28880000,871.87 1975-07-09,858.65,875.00,858.65,871.87,26350000,871.87 1975-07-08,861.08,863.97,850.52,857.79,18990000,857.79 1975-07-07,871.79,873.36,859.04,861.08,15850000,861.08 1975-07-03,870.38,877.42,863.50,871.79,19000000,871.79 1975-07-02,876.25,876.25,861.62,870.38,18530000,870.38 1975-07-01,878.99,884.86,869.60,877.42,20390000,877.42 1975-06-30,873.12,884.62,867.96,878.99,19430000,878.99 1975-06-27,874.14,880.48,867.18,873.12,18820000,873.12 1975-06-26,872.73,883.37,866.55,874.14,24560000,874.14 1975-06-25,869.06,878.05,861.94,872.73,21610000,872.73 1975-06-24,864.83,878.44,861.00,869.06,26620000,869.06 1975-06-23,855.44,867.10,846.92,864.83,20720000,864.83 1975-06-20,847.86,864.67,847.86,855.44,26260000,855.44 1975-06-19,827.83,848.64,826.11,845.35,21450000,845.35 1975-06-18,828.61,834.01,818.99,827.83,15590000,827.83 1975-06-17,834.56,843.09,824.00,828.61,19440000,828.61 1975-06-16,824.47,837.77,821.34,834.56,16660000,834.56 1975-06-13,819.31,829.01,811.23,824.47,16300000,824.47 1975-06-12,824.55,831.43,815.39,819.31,15970000,819.31 1975-06-11,822.12,833.54,819.85,824.55,18230000,824.55 1975-06-10,829.08,829.08,813.75,822.12,21130000,822.12 1975-06-09,839.64,844.49,827.99,830.10,20670000,830.10 1975-06-06,842.15,849.73,833.15,839.64,22230000,839.64 1975-06-05,839.96,845.04,829.94,842.15,21610000,842.15 1975-06-04,846.14,850.83,834.17,839.96,24900000,839.96 1975-06-03,846.61,855.44,839.57,846.14,26560000,846.14 1975-06-02,835.73,853.49,835.73,846.61,28240000,846.61 1975-05-30,817.43,836.20,817.43,832.29,22670000,832.29 1975-05-29,817.04,823.53,808.12,815.00,18570000,815.00 1975-05-28,826.11,832.84,812.81,817.04,21850000,817.04 1975-05-27,831.90,837.53,820.09,826.11,17050000,826.11 1975-05-23,818.91,835.50,818.28,831.90,17870000,831.90 1975-05-22,818.68,828.54,807.96,818.91,17610000,818.91 1975-05-21,830.26,830.26,815.24,818.68,17640000,818.68 1975-05-20,837.69,843.79,827.52,830.49,18310000,830.49 1975-05-19,837.61,843.71,823.22,837.69,17870000,837.69 1975-05-16,848.80,849.03,831.35,837.61,16630000,837.61 1975-05-15,858.73,868.58,847.15,848.80,27690000,848.80 1975-05-14,850.13,866.00,846.84,858.73,29050000,858.73 1975-05-13,847.47,857.87,840.19,850.13,24950000,850.13 1975-05-12,850.13,857.32,841.99,847.47,22410000,847.47 1975-05-09,840.50,856.77,839.41,850.13,28440000,850.13 1975-05-08,836.44,845.20,828.07,840.50,22980000,840.50 1975-05-07,834.72,842.69,822.67,836.44,22250000,836.44 1975-05-06,855.60,860.06,832.68,834.72,25410000,834.72 1975-05-05,848.48,860.06,839.72,855.60,22370000,855.60 1975-05-02,832.21,853.25,832.21,848.48,25210000,848.48 1975-05-01,821.34,837.22,817.82,830.96,20660000,830.96 1975-04-30,803.04,823.22,796.93,821.34,18060000,821.34 1975-04-29,810.00,817.04,797.79,803.04,17740000,803.04 1975-04-28,811.80,820.56,803.50,810.00,17850000,810.00 1975-04-25,803.66,819.77,800.77,811.80,20260000,811.80 1975-04-24,802.49,810.15,792.32,803.66,19050000,803.66 1975-04-23,814.14,815.94,798.97,802.49,20040000,802.49 1975-04-22,815.86,828.93,811.17,814.14,26120000,814.14 1975-04-21,808.43,821.57,803.74,815.86,23960000,815.86 1975-04-18,819.46,822.12,802.25,808.43,26610000,808.43 1975-04-17,815.71,835.18,813.75,819.46,32650000,819.46 1975-04-16,815.08,819.77,800.30,815.71,22970000,815.71 1975-04-15,806.95,822.43,801.31,815.08,29620000,815.08 1975-04-14,790.83,811.48,790.83,806.95,26800000,806.95 1975-04-11,781.29,792.94,775.81,789.50,20160000,789.50 1975-04-10,769.40,788.25,769.40,781.29,24990000,781.29 1975-04-09,749.22,771.04,748.44,767.99,18120000,767.99 1975-04-08,742.88,755.38,741.00,749.22,14320000,749.22 1975-04-07,747.26,749.84,736.62,742.88,13860000,742.88 1975-04-04,752.19,755.40,740.69,747.26,14170000,747.26 1975-04-03,760.56,763.22,749.06,752.19,13920000,752.19 1975-04-02,761.58,770.96,754.77,760.56,15600000,760.56 1975-04-01,768.15,771.12,756.49,761.58,14480000,761.58 1975-03-31,770.26,779.72,764.39,768.15,16270000,768.15 1975-03-27,766.19,778.63,763.45,770.26,18300000,770.26 1975-03-26,750.94,769.97,750.94,766.19,18580000,766.19 1975-03-25,743.43,753.29,731.46,747.89,18500000,747.89 1975-03-24,752.50,752.50,737.48,743.43,17810000,743.43 1975-03-21,764.00,768.93,753.13,763.06,15940000,763.06 1975-03-20,769.48,779.65,758.29,764.00,20960000,764.00 1975-03-19,776.83,776.83,761.19,769.48,19030000,769.48 1975-03-18,786.53,796.93,776.60,779.41,29180000,779.41 1975-03-17,773.47,789.42,771.35,786.53,26780000,786.53 1975-03-14,762.98,779.57,761.81,773.47,24840000,773.47 1975-03-13,763.69,766.97,752.58,762.98,18620000,762.98 1975-03-12,770.89,774.33,756.73,763.69,21560000,763.69 1975-03-11,776.13,784.73,765.64,770.89,31280000,770.89 1975-03-10,770.10,779.41,761.58,776.13,25890000,776.13 1975-03-07,761.81,775.66,759.62,770.10,25930000,770.10 1975-03-06,761.81,764.71,761.81,761.81,21780000,761.81 1975-03-05,757.74,766.97,745.54,752.82,24120000,752.82 1975-03-04,753.83,773.23,753.83,757.74,34140000,757.74 1975-03-03,739.05,756.49,737.64,753.13,24100000,753.13 1975-02-28,731.15,741.94,724.50,739.05,17560000,739.05 1975-02-27,728.10,738.50,725.13,731.15,16430000,731.15 1975-02-26,719.18,731.77,713.70,728.10,18790000,728.10 1975-02-25,732.79,732.79,714.57,719.18,20910000,719.18 1975-02-24,749.77,750.16,733.49,736.94,19150000,736.94 1975-02-21,745.38,757.35,742.49,749.77,24440000,749.77 1975-02-20,736.39,749.53,731.38,745.38,22260000,745.38 1975-02-19,731.30,740.61,721.92,736.39,21930000,736.39 1975-02-18,734.20,742.10,722.39,731.30,23990000,731.30 1975-02-14,726.92,739.52,719.81,734.20,23290000,734.20 1975-02-13,718.48,738.27,718.48,726.92,35160000,726.92 1975-02-12,707.60,716.83,700.64,715.03,19790000,715.03 1975-02-11,708.39,711.67,697.83,707.60,16470000,707.60 1975-02-10,711.91,717.85,702.75,708.39,16120000,708.39 1975-02-07,714.17,715.97,697.51,711.91,19060000,711.91 1975-02-06,717.85,731.54,710.81,714.17,32020000,714.17 1975-02-05,708.07,721.45,699.78,717.85,25830000,717.85 1975-02-04,711.44,713.47,695.24,708.07,25040000,708.07 1975-02-03,703.69,717.62,696.50,711.44,25400000,711.44 1975-01-31,696.42,709.56,690.16,703.69,24640000,703.69 1975-01-30,705.96,717.30,693.13,696.42,29740000,696.42 1975-01-29,694.77,712.22,686.95,705.96,27410000,705.96 1975-01-28,692.66,705.10,689.69,694.77,31760000,694.77 1975-01-27,678.43,698.69,678.43,692.66,32130000,692.66 1975-01-24,656.76,671.46,652.69,666.61,20670000,666.61 1975-01-23,652.61,666.61,647.45,656.76,17960000,656.76 1975-01-22,641.90,654.02,634.39,652.61,15330000,652.61 1975-01-21,647.45,656.60,637.91,641.90,14780000,641.90 1975-01-20,644.63,650.19,635.09,647.45,13450000,647.45 1975-01-17,655.74,657.70,641.90,644.63,14260000,644.63 1975-01-16,653.39,660.12,648.08,655.74,17110000,655.74 1975-01-15,648.70,657.15,640.25,653.39,16580000,653.39 1975-01-14,654.18,657.93,644.01,648.70,16610000,648.70 1975-01-13,658.79,669.27,651.36,654.18,19780000,654.18 1975-01-10,650.11,666.69,650.11,658.79,25890000,658.79 1975-01-09,635.40,646.90,627.58,645.26,16340000,645.26 1975-01-08,641.19,646.28,632.12,635.40,15600000,635.40 1975-01-07,637.20,645.42,630.24,641.19,14890000,641.19 1975-01-06,634.54,646.43,631.88,637.20,17550000,637.20 1975-01-03,632.04,642.60,623.67,634.54,15270000,634.54 1975-01-02,619.13,637.12,619.13,632.04,14800000,632.04 1974-12-31,603.25,619.84,602.86,616.24,20970000,616.24 1974-12-30,602.16,607.24,595.04,603.25,18520000,603.25 1974-12-27,604.74,608.26,597.47,602.16,13060000,602.16 1974-12-26,598.40,610.37,597.07,604.74,11810000,604.74 1974-12-24,589.64,602.16,589.57,598.40,9540000,598.40 1974-12-23,598.48,598.95,583.70,589.64,18040000,589.64 1974-12-20,604.43,607.01,594.49,598.48,15840000,598.48 1974-12-19,603.49,611.00,596.21,604.43,15900000,604.43 1974-12-18,597.62,610.84,597.62,603.49,18050000,603.49 1974-12-17,586.83,599.42,582.45,597.54,16880000,597.54 1974-12-16,592.77,598.09,584.25,586.83,15370000,586.83 1974-12-13,596.37,602.00,586.67,592.77,14000000,592.77 1974-12-12,595.35,604.27,586.67,596.37,15390000,596.37 1974-12-11,593.87,606.15,589.17,595.35,15700000,595.35 1974-12-10,582.21,602.08,582.21,593.87,15690000,593.87 1974-12-09,577.60,587.14,570.01,579.94,14660000,579.94 1974-12-06,587.06,588.00,572.12,577.60,15500000,577.60 1974-12-05,598.64,604.11,585.11,587.06,12890000,587.06 1974-12-04,596.61,606.77,592.07,598.64,12580000,598.64 1974-12-03,603.02,603.33,590.82,596.61,13620000,596.61 1974-12-02,616.08,616.08,599.58,603.02,11140000,603.02 1974-11-29,619.29,623.90,610.92,618.66,7400000,618.66 1974-11-27,617.26,633.45,612.64,619.29,14810000,619.29 1974-11-26,611.94,625.08,606.85,617.26,13600000,617.26 1974-11-25,615.30,620.70,604.43,611.94,11300000,611.94 1974-11-22,609.59,625.31,609.59,615.30,13020000,615.30 1974-11-21,609.59,616.55,599.81,608.57,13820000,608.57 1974-11-20,614.05,622.81,605.76,609.59,12430000,609.59 1974-11-19,624.92,627.42,609.75,614.05,15720000,614.05 1974-11-18,641.11,641.11,621.71,624.92,15230000,624.92 1974-11-15,658.40,660.04,642.60,647.61,12480000,647.61 1974-11-14,659.18,671.31,635.55,658.40,13540000,658.40 1974-11-13,659.18,666.22,647.29,659.18,16040000,659.18 1974-11-12,672.64,675.06,656.45,659.18,15040000,659.18 1974-11-11,667.16,675.69,660.51,672.64,13220000,672.64 1974-11-08,671.93,676.55,661.53,667.16,15890000,667.16 1974-11-07,669.12,681.79,660.75,671.93,17150000,671.93 1974-11-06,674.75,692.82,666.07,669.12,23930000,669.12 1974-11-05,657.23,676.63,651.83,674.75,15960000,674.75 1974-11-04,664.19,664.19,647.37,657.23,12740000,657.23 1974-11-01,665.52,673.03,655.74,665.28,13470000,665.28 1974-10-31,673.03,685.62,659.18,665.52,18840000,665.52 1974-10-30,659.34,681.40,655.19,673.03,20130000,673.03 1974-10-29,638.77,661.53,638.77,659.34,15610000,659.34 1974-10-28,636.19,639.63,624.06,633.84,10540000,633.84 1974-10-25,636.26,648.00,629.93,636.19,12650000,636.19 1974-10-24,643.85,643.85,624.30,636.26,14910000,636.26 1974-10-23,660.51,660.51,637.83,645.03,14200000,645.03 1974-10-22,669.82,680.07,658.56,662.86,18930000,662.86 1974-10-21,654.88,674.12,645.65,669.82,14500000,669.82 1974-10-18,651.44,669.51,646.04,654.88,16460000,654.88 1974-10-17,642.29,657.31,635.48,651.44,14470000,651.44 1974-10-16,658.40,660.51,636.97,642.29,14790000,642.29 1974-10-15,673.50,679.21,652.38,658.40,17390000,658.40 1974-10-14,659.73,689.30,659.73,673.50,19770000,673.50 1974-10-11,648.08,665.91,638.14,658.17,20090000,658.17 1974-10-10,634.00,664.03,634.00,648.08,26360000,648.08 1974-10-09,602.63,633.37,591.91,631.02,18820000,631.02 1974-10-08,607.56,615.93,596.21,602.63,15460000,602.63 1974-10-07,587.77,610.45,587.77,607.56,15000000,607.56 1974-10-04,587.61,593.71,573.22,584.56,15910000,584.56 1974-10-03,600.75,600.75,582.21,587.61,13150000,587.61 1974-10-02,604.82,613.89,596.84,601.53,12230000,601.53 1974-10-01,607.87,613.27,589.57,604.82,16890000,604.82 1974-09-30,619.37,619.37,598.80,607.87,15000000,607.87 1974-09-27,637.98,643.62,619.37,621.95,12320000,621.95 1974-09-26,648.39,648.39,633.60,637.98,9060000,637.98 1974-09-25,654.10,671.46,643.77,649.95,17620000,649.95 1974-09-24,662.08,662.08,648.78,654.10,9840000,654.10 1974-09-23,670.76,678.19,657.78,663.72,12130000,663.72 1974-09-20,674.05,680.54,659.34,670.76,16250000,670.76 1974-09-19,659.34,679.83,659.34,674.05,17000000,674.05 1974-09-18,648.78,654.57,635.09,651.91,11760000,651.91 1974-09-17,642.76,662.16,642.76,648.78,13730000,648.78 1974-09-16,627.19,645.89,617.73,639.79,18370000,639.79 1974-09-13,641.74,643.77,624.37,627.19,16070000,627.19 1974-09-12,654.72,655.04,637.44,641.74,16920000,641.74 1974-09-11,658.17,665.60,650.58,654.72,11820000,654.72 1974-09-10,662.94,669.51,652.22,658.17,11980000,658.17 1974-09-09,676.71,676.71,660.59,662.94,11160000,662.94 1974-09-06,670.76,685.00,663.56,677.88,15130000,677.88 1974-09-05,649.95,673.50,649.95,670.76,14210000,670.76 1974-09-04,657.46,657.46,638.38,648.00,16930000,648.00 1974-09-03,678.58,686.48,662.23,663.33,12750000,663.33 1974-08-30,661.14,681.71,661.14,678.58,16230000,678.58 1974-08-29,666.61,669.51,651.60,656.84,13690000,656.84 1974-08-28,671.54,681.79,663.56,666.61,16670000,666.61 1974-08-27,688.13,690.86,668.02,671.54,12970000,671.54 1974-08-26,686.80,697.12,671.46,688.13,14630000,688.13 1974-08-23,704.63,710.73,683.90,686.80,13590000,686.80 1974-08-22,711.59,713.94,694.15,704.63,15690000,704.63 1974-08-21,726.85,730.05,709.25,711.59,11650000,711.59 1974-08-20,721.84,736.31,717.46,726.85,13820000,726.85 1974-08-19,731.15,731.15,715.35,721.84,11670000,721.84 1974-08-16,737.88,744.45,728.65,731.54,10510000,731.54 1974-08-15,740.54,749.14,732.24,737.88,11130000,737.88 1974-08-14,755.01,755.01,735.37,740.54,11750000,740.54 1974-08-13,767.29,768.54,750.55,756.44,10140000,756.44 1974-08-12,777.30,780.04,762.83,767.29,7780000,767.29 1974-08-09,784.89,787.47,771.90,777.30,10160000,777.30 1974-08-08,797.56,803.43,779.02,784.89,16060000,784.89 1974-08-07,773.78,798.89,770.81,797.56,13380000,797.56 1974-08-06,766.97,790.99,766.97,773.78,15770000,773.78 1974-08-05,752.58,768.46,747.97,760.40,11230000,760.40 1974-08-02,751.10,758.45,745.31,752.58,10110000,752.58 1974-08-01,757.43,764.16,745.62,751.10,11470000,751.10 1974-07-31,765.57,767.44,754.38,757.43,10960000,757.43 1974-07-30,770.89,775.03,758.84,765.57,11360000,765.57 1974-07-29,780.74,780.74,764.55,770.89,11560000,770.89 1974-07-26,795.68,798.03,781.52,784.57,10420000,784.57 1974-07-25,805.77,807.65,790.05,795.68,13310000,795.68 1974-07-24,797.72,809.76,791.54,805.77,12870000,805.77 1974-07-23,790.36,805.62,789.82,797.72,12910000,797.72 1974-07-22,787.94,795.53,779.49,790.36,9290000,790.36 1974-07-19,789.19,796.62,780.43,787.94,11080000,787.94 1974-07-18,784.97,803.04,781.45,789.19,13980000,789.19 1974-07-17,775.97,788.56,765.80,784.97,11320000,784.97 1974-07-16,785.20,785.20,771.82,775.97,9920000,775.97 1974-07-15,787.23,800.92,777.85,786.61,13560000,786.61 1974-07-12,770.03,790.68,770.03,787.23,17770000,787.23 1974-07-11,762.12,769.76,753.13,759.62,14640000,759.62 1974-07-10,772.29,779.18,759.54,762.12,13490000,762.12 1974-07-09,770.57,782.85,764.63,772.29,15580000,772.29 1974-07-08,788.41,788.41,765.33,770.57,15510000,770.57 1974-07-05,792.87,796.31,786.22,791.77,7400000,791.77 1974-07-03,790.68,798.58,785.12,792.87,13430000,792.87 1974-07-02,806.24,808.75,788.25,790.68,13460000,790.68 1974-07-01,802.41,811.95,797.48,806.24,10270000,806.24 1974-06-28,803.66,808.20,796.46,802.41,12010000,802.41 1974-06-27,816.96,817.27,800.77,803.66,12650000,803.66 1974-06-26,828.85,830.41,815.47,816.96,11410000,816.96 1974-06-25,817.66,832.53,817.66,828.85,11920000,828.85 1974-06-24,815.39,822.43,808.90,816.33,9960000,816.33 1974-06-21,820.79,822.59,810.15,815.39,11830000,815.39 1974-06-20,826.11,831.20,818.13,820.79,11990000,820.79 1974-06-19,830.26,833.54,821.26,826.11,10550000,826.11 1974-06-18,833.23,838.55,826.50,830.26,10110000,830.26 1974-06-17,843.09,843.24,829.47,833.23,9680000,833.23 1974-06-14,850.67,850.67,837.84,843.09,10030000,843.09 1974-06-13,848.56,861.00,843.79,852.08,11540000,852.08 1974-06-12,852.08,853.80,837.92,848.56,11150000,848.56 1974-06-11,859.67,865.54,848.09,852.08,12380000,852.08 1974-06-10,853.72,864.60,846.14,859.67,13540000,859.67 1974-06-07,845.59,863.27,845.59,853.72,19020000,853.72 1974-06-06,830.18,847.54,824.08,845.35,13360000,845.35 1974-06-05,828.69,839.41,819.23,830.18,13680000,830.18 1974-06-04,821.81,837.06,821.81,828.69,16040000,828.69 1974-06-03,802.17,822.36,799.83,821.26,12490000,821.26 1974-05-31,803.58,808.04,792.32,802.17,10810000,802.17 1974-05-30,795.37,808.28,788.80,803.58,13580000,803.58 1974-05-29,814.30,817.27,793.02,795.37,12300000,795.37 1974-05-28,816.65,824.55,809.14,814.30,10580000,814.30 1974-05-24,806.24,824.31,806.24,816.65,13740000,816.65 1974-05-23,802.57,811.33,796.15,805.23,14770000,805.23 1974-05-22,809.53,818.84,800.14,802.57,15450000,802.57 1974-05-21,812.42,822.67,803.04,809.53,12190000,809.53 1974-05-20,818.84,826.19,807.49,812.42,10550000,812.42 1974-05-17,833.31,833.31,813.75,818.84,13870000,818.84 1974-05-16,846.06,853.41,833.15,835.34,12090000,835.34 1974-05-15,847.86,853.02,839.25,846.06,11240000,846.06 1974-05-14,845.59,856.07,842.30,847.86,10880000,847.86 1974-05-13,850.44,854.82,838.70,845.59,11290000,845.59 1974-05-10,865.77,870.38,846.84,850.44,15270000,850.44 1974-05-09,850.99,870.07,849.19,865.77,14710000,865.77 1974-05-08,847.15,856.93,843.01,850.99,11850000,850.99 1974-05-07,844.88,855.84,840.50,847.15,10710000,847.15 1974-05-06,845.90,847.47,834.32,844.88,9450000,844.88 1974-05-03,851.06,852.78,839.88,845.90,11080000,845.90 1974-05-02,853.88,865.85,845.90,851.06,13620000,851.06 1974-05-01,836.75,861.08,832.77,853.88,15120000,853.88 1974-04-30,835.42,844.49,829.63,836.75,10980000,836.75 1974-04-29,834.64,839.80,824.70,835.42,10170000,835.42 1974-04-26,827.68,841.29,823.22,834.64,13250000,834.64 1974-04-25,832.37,835.11,818.68,827.68,15870000,827.68 1974-04-24,845.98,847.23,828.22,832.37,16010000,832.37 1974-04-23,858.57,859.51,843.55,845.98,14110000,845.98 1974-04-22,859.90,864.28,852.39,858.57,10520000,858.57 1974-04-19,868.90,868.90,856.15,859.90,10710000,859.90 1974-04-18,867.41,875.94,862.64,869.92,12470000,869.92 1974-04-17,861.23,874.45,856.38,867.41,14020000,867.41 1974-04-16,845.74,863.58,845.74,861.23,14530000,861.23 1974-04-15,844.81,852.00,839.33,843.79,10130000,843.79 1974-04-11,843.71,849.97,837.77,844.81,9970000,844.81 1974-04-10,846.84,855.52,840.27,843.71,11160000,843.71 1974-04-09,839.96,852.55,836.12,846.84,11330000,846.84 1974-04-08,847.23,847.23,834.25,839.96,10740000,839.96 1974-04-05,858.57,858.57,842.46,847.54,11670000,847.54 1974-04-04,858.03,865.46,852.32,858.89,11650000,858.89 1974-04-03,846.61,860.45,844.65,858.03,11500000,858.03 1974-04-02,843.48,853.18,839.17,846.61,12010000,846.61 1974-04-01,846.68,856.38,839.72,843.48,11470000,843.48 1974-03-29,854.35,858.57,842.38,846.68,12150000,846.68 1974-03-28,867.18,867.18,850.59,854.35,14940000,854.35 1974-03-27,883.68,887.83,869.76,871.17,11690000,871.17 1974-03-26,881.02,890.18,875.16,883.68,11840000,883.68 1974-03-25,878.13,884.78,866.79,881.02,10540000,881.02 1974-03-22,875.47,884.31,868.43,878.13,11930000,878.13 1974-03-21,872.34,887.12,870.23,875.47,12950000,875.47 1974-03-20,867.57,878.60,863.03,872.34,12960000,872.34 1974-03-19,874.22,875.70,861.86,867.57,12800000,867.57 1974-03-18,887.83,889.86,870.38,874.22,14010000,874.22 1974-03-15,889.78,893.38,878.68,887.83,14500000,887.83 1974-03-14,891.66,904.02,885.17,889.78,19770000,889.78 1974-03-13,887.12,900.81,881.88,891.66,16820000,891.66 1974-03-12,888.45,895.57,877.50,887.12,17250000,887.12 1974-03-11,878.05,893.30,865.93,888.45,18470000,888.45 1974-03-08,869.06,880.94,858.81,878.05,16210000,878.05 1974-03-07,879.85,880.24,865.07,869.06,14500000,869.06 1974-03-06,872.42,885.25,866.47,879.85,19140000,879.85 1974-03-05,860.40,880.40,860.40,872.42,21980000,872.42 1974-03-04,851.92,854.97,841.76,853.18,12270000,853.18 1974-03-01,860.53,861.62,845.90,851.92,12880000,851.92 1974-02-28,863.42,868.19,851.85,860.53,13680000,860.53 1974-02-27,859.51,871.40,855.76,863.42,18730000,863.42 1974-02-26,851.38,861.78,843.09,859.51,15860000,859.51 1974-02-25,855.99,859.20,843.79,851.38,12900000,851.38 1974-02-22,846.84,862.41,843.01,855.99,16360000,855.99 1974-02-21,831.04,849.66,830.18,846.84,13930000,846.84 1974-02-20,819.54,834.87,814.22,831.04,11670000,831.04 1974-02-19,820.32,840.50,816.65,819.54,15940000,819.54 1974-02-15,809.92,825.72,808.12,820.32,12640000,820.32 1974-02-14,806.87,816.57,802.17,809.92,12230000,809.92 1974-02-13,806.63,816.18,803.04,806.87,10990000,806.87 1974-02-12,803.90,811.72,795.68,806.63,12920000,806.63 1974-02-11,820.40,821.26,802.24,803.90,12930000,803.90 1974-02-08,828.46,831.90,817.82,820.40,12990000,820.40 1974-02-07,824.62,833.93,819.38,828.46,11750000,828.46 1974-02-06,820.64,831.35,817.51,824.62,11610000,824.62 1974-02-05,821.50,829.87,811.95,820.64,12820000,820.64 1974-02-04,838.63,838.63,816.49,821.50,14380000,821.50 1974-02-01,855.55,856.55,838.02,843.94,12480000,843.94 1974-01-31,862.32,868.85,852.01,855.55,14020000,855.55 1974-01-30,853.09,869.77,853.09,862.32,16790000,862.32 1974-01-29,853.01,858.86,844.48,852.32,12850000,852.32 1974-01-28,859.39,862.16,847.32,853.01,13410000,853.01 1974-01-25,863.08,869.16,850.71,859.39,14860000,859.39 1974-01-24,871.00,875.85,853.47,863.08,15980000,863.08 1974-01-23,863.47,878.69,859.01,871.00,16890000,871.00 1974-01-22,854.63,871.62,850.71,863.47,17330000,863.47 1974-01-21,855.47,858.86,835.79,854.63,15630000,854.63 1974-01-18,872.16,873.15,850.86,855.47,16470000,855.47 1974-01-17,856.32,878.54,856.32,872.16,21040000,872.16 1974-01-16,846.40,861.16,842.48,856.09,14930000,856.09 1974-01-15,840.18,855.78,834.64,846.40,13250000,846.40 1974-01-14,841.48,855.63,833.26,840.18,14610000,840.18 1974-01-11,823.11,847.63,819.88,841.48,15140000,841.48 1974-01-10,834.79,845.17,816.57,823.11,16120000,823.11 1974-01-09,853.01,853.01,831.41,834.79,18070000,834.79 1974-01-08,876.85,880.77,857.70,861.78,18080000,861.78 1974-01-07,880.23,883.99,866.31,876.85,19070000,876.85 1974-01-04,880.69,890.38,868.08,880.23,21700000,880.23 1974-01-03,858.86,886.69,858.86,880.69,24850000,880.69 1974-01-02,850.86,859.16,841.41,855.32,12060000,855.32 1973-12-31,848.02,857.09,836.18,850.86,23470000,850.86 1973-12-28,851.01,857.63,839.94,848.02,21310000,848.02 1973-12-27,839.02,858.93,839.02,851.01,22720000,851.01 1973-12-26,820.19,844.10,820.19,837.56,18620000,837.56 1973-12-24,818.73,820.73,805.73,814.81,11540000,814.81 1973-12-21,828.11,830.72,809.81,818.73,18680000,818.73 1973-12-20,829.57,841.64,820.50,828.11,17340000,828.11 1973-12-19,829.49,847.86,820.96,829.57,20670000,829.57 1973-12-18,811.12,834.72,807.12,829.49,19490000,829.49 1973-12-17,815.65,821.42,804.20,811.12,12930000,811.12 1973-12-14,800.43,823.57,793.51,815.65,20000000,815.65 1973-12-13,810.73,821.57,794.43,800.43,18130000,800.43 1973-12-12,828.64,828.64,805.27,810.73,18190000,810.73 1973-12-11,851.14,861.09,830.80,834.18,20100000,834.18 1973-12-10,838.05,857.31,829.17,851.14,18590000,851.14 1973-12-07,815.47,846.63,815.47,838.05,23230000,838.05 1973-12-06,788.31,816.68,786.50,814.12,23260000,814.12 1973-12-05,803.21,806.82,783.56,788.31,19180000,788.31 1973-12-04,806.52,817.73,795.68,803.21,19030000,803.21 1973-12-03,820.51,820.51,798.31,806.52,17900000,806.52 1973-11-30,835.11,836.02,819.39,822.25,15380000,822.25 1973-11-29,839.78,846.93,824.28,835.11,18870000,835.11 1973-11-28,817.73,843.47,814.80,839.78,19990000,839.78 1973-11-27,824.95,833.16,810.36,817.73,19750000,817.73 1973-11-26,844.15,844.15,816.60,824.95,19830000,824.95 1973-11-23,854.98,861.45,847.46,854.00,11470000,854.00 1973-11-21,844.90,869.21,839.78,854.98,24260000,854.98 1973-11-20,862.43,862.43,836.17,844.90,23960000,844.90 1973-11-19,889.75,889.75,860.78,862.66,16700000,862.66 1973-11-16,874.55,905.10,869.21,891.33,22510000,891.33 1973-11-15,869.88,886.82,859.27,874.55,24530000,874.55 1973-11-14,891.03,899.53,865.37,869.88,22710000,869.88 1973-11-13,897.65,902.47,876.88,891.03,20310000,891.03 1973-11-12,908.41,910.82,885.54,897.65,19250000,897.65 1973-11-09,932.65,932.95,902.92,908.41,17320000,908.41 1973-11-08,923.92,946.79,923.92,932.65,19650000,932.65 1973-11-07,913.08,929.64,909.69,920.08,16570000,920.08 1973-11-06,919.40,933.77,909.69,913.08,16430000,913.08 1973-11-05,932.87,932.87,912.55,919.40,17150000,919.40 1973-11-02,948.83,951.31,929.11,935.28,16340000,935.28 1973-11-01,956.58,963.80,941.83,948.83,16920000,948.83 1973-10-31,968.54,973.13,951.31,956.58,17890000,956.58 1973-10-30,984.80,985.93,963.65,968.54,17580000,968.54 1973-10-29,987.06,997.59,979.98,984.80,17960000,984.80 1973-10-26,974.49,992.62,972.61,987.06,17800000,987.06 1973-10-25,971.85,981.26,957.10,974.49,15580000,974.49 1973-10-24,966.51,977.50,960.79,971.85,15840000,971.85 1973-10-23,960.57,976.44,944.46,966.51,17230000,966.51 1973-10-22,963.73,970.42,951.08,960.57,14290000,960.57 1973-10-19,959.74,974.04,954.55,963.73,17880000,963.73 1973-10-18,962.52,974.34,950.11,959.74,19210000,959.74 1973-10-17,967.41,976.67,956.35,962.52,18600000,962.52 1973-10-16,967.04,971.63,951.76,967.41,18780000,967.41 1973-10-15,977.80,977.80,960.19,967.04,16160000,967.04 1973-10-12,976.07,991.80,972.00,978.63,22730000,978.63 1973-10-11,960.57,981.19,957.71,976.07,20740000,976.07 1973-10-10,974.19,980.28,953.79,960.57,19010000,960.57 1973-10-09,977.65,984.65,964.63,974.19,19440000,974.19 1973-10-08,971.25,983.22,955.82,977.65,18990000,977.65 1973-10-05,955.90,975.92,950.78,971.25,18820000,971.25 1973-10-04,964.55,969.30,949.50,955.90,19730000,955.90 1973-10-03,956.80,971.78,952.14,964.55,22040000,964.55 1973-10-02,948.83,961.54,943.93,956.80,20770000,956.80 1973-10-01,947.10,954.09,937.69,948.83,15830000,948.83 1973-09-28,953.27,954.92,937.54,947.10,16300000,947.10 1973-09-27,949.50,964.55,942.50,953.27,23660000,953.27 1973-09-26,940.55,954.55,935.43,949.50,21130000,949.50 1973-09-25,936.71,947.02,927.53,940.55,21530000,940.55 1973-09-24,927.90,940.70,924.37,936.71,19490000,936.71 1973-09-21,920.53,934.38,911.72,927.90,23760000,927.90 1973-09-20,910.37,925.42,909.54,920.53,25960000,920.53 1973-09-19,891.26,915.41,891.26,910.37,24570000,910.37 1973-09-18,892.99,900.14,882.38,891.26,16400000,891.26 1973-09-17,886.36,900.29,884.86,892.99,15100000,892.99 1973-09-14,880.57,889.15,873.34,886.36,13760000,886.36 1973-09-13,881.32,888.09,874.17,880.57,11670000,880.57 1973-09-12,885.76,889.75,874.77,881.32,12040000,881.32 1973-09-11,891.33,892.46,878.61,885.76,12690000,885.76 1973-09-10,898.63,903.45,888.62,891.33,11620000,891.33 1973-09-07,901.04,905.70,892.61,898.63,14930000,898.63 1973-09-06,899.08,908.04,894.04,901.04,15670000,901.04 1973-09-05,895.39,903.52,887.94,899.08,14580000,899.08 1973-09-04,887.57,900.25,885.69,895.39,14210000,895.39 1973-08-31,882.53,890.95,876.51,887.57,10530000,887.57 1973-08-30,883.43,892.31,877.48,882.53,12100000,882.53 1973-08-29,872.07,890.35,870.56,883.43,15690000,883.43 1973-08-28,870.71,877.63,865.37,872.07,11810000,872.07 1973-08-27,863.49,874.32,860.02,870.71,9740000,870.71 1973-08-24,864.46,873.12,856.56,863.49,11200000,863.49 1973-08-23,853.48,869.81,853.48,864.46,11390000,864.46 1973-08-22,857.84,860.55,845.50,851.90,10770000,851.90 1973-08-21,867.40,869.96,855.51,857.84,11480000,857.84 1973-08-20,871.84,875.08,862.73,867.40,8970000,867.40 1973-08-17,872.74,877.18,864.69,871.84,11110000,871.84 1973-08-16,874.17,886.44,867.62,872.74,12990000,872.74 1973-08-15,870.71,879.82,863.71,874.17,12040000,874.17 1973-08-14,883.20,887.42,867.70,870.71,11740000,870.71 1973-08-13,892.38,892.91,878.09,883.20,11330000,883.20 1973-08-10,901.49,903.52,887.12,892.38,10870000,892.38 1973-08-09,902.02,910.60,893.96,901.49,12880000,901.49 1973-08-08,911.95,913.23,897.43,902.02,12440000,902.02 1973-08-07,912.78,920.38,905.25,911.95,13510000,911.95 1973-08-06,908.87,918.50,900.96,912.78,12320000,912.78 1973-08-03,910.14,913.91,900.36,908.87,9940000,908.87 1973-08-02,912.18,917.37,899.46,910.14,16080000,910.14 1973-08-01,924.37,924.37,907.28,912.18,13530000,912.18 1973-07-31,933.77,941.15,923.39,926.40,13530000,926.40 1973-07-30,936.71,942.35,926.32,933.77,11170000,933.77 1973-07-27,934.53,942.28,925.65,936.71,12910000,936.71 1973-07-26,933.02,944.08,924.74,934.53,18410000,934.53 1973-07-25,918.72,942.58,917.22,933.02,22220000,933.02 1973-07-24,913.15,922.19,903.67,918.72,16280000,918.72 1973-07-23,910.90,921.88,907.36,913.15,15580000,913.15 1973-07-20,906.68,918.05,902.54,910.90,16300000,910.90 1973-07-19,905.40,916.47,893.66,906.68,18650000,906.68 1973-07-18,898.03,911.20,889.90,905.40,17020000,905.40 1973-07-17,897.58,911.80,892.84,898.03,18750000,898.03 1973-07-16,885.99,900.44,881.32,897.58,12920000,897.58 1973-07-13,901.94,902.77,883.05,885.99,11390000,885.99 1973-07-12,908.19,910.82,895.39,901.94,16400000,901.94 1973-07-11,889.90,911.80,889.90,908.19,18730000,908.19 1973-07-10,879.06,894.64,879.06,888.32,15090000,888.32 1973-07-09,870.11,879.74,863.94,877.26,11560000,877.26 1973-07-06,874.32,878.09,865.82,870.11,9980000,870.11 1973-07-05,874.17,879.74,867.10,874.32,10500000,874.32 1973-07-03,880.57,882.53,868.75,874.17,10560000,874.17 1973-07-02,890.20,890.20,876.66,880.57,9830000,880.57 1973-06-29,894.64,900.59,886.59,891.71,10770000,891.71 1973-06-28,884.63,897.88,880.87,894.64,12760000,894.64 1973-06-27,879.44,888.09,872.44,884.63,12660000,884.63 1973-06-26,869.13,881.85,864.46,879.44,14040000,879.44 1973-06-25,879.82,880.72,865.67,869.13,11670000,869.13 1973-06-22,875.23,897.58,875.23,879.82,18470000,879.82 1973-06-21,884.71,887.49,871.84,873.65,11630000,873.65 1973-06-20,881.55,890.65,876.28,884.71,10600000,884.71 1973-06-19,875.08,889.52,868.08,881.55,12970000,881.55 1973-06-18,887.34,887.34,869.36,875.08,11460000,875.08 1973-06-15,899.68,899.68,882.60,888.55,11970000,888.55 1973-06-14,915.49,922.49,899.61,902.92,13210000,902.92 1973-06-13,927.00,935.58,912.48,915.49,15700000,915.49 1973-06-12,915.11,930.01,912.85,927.00,13840000,927.00 1973-06-11,920.00,924.67,911.05,915.11,9940000,915.11 1973-06-08,909.62,926.85,908.19,920.00,14050000,920.00 1973-06-07,898.18,913.68,896.37,909.62,14160000,909.62 1973-06-06,900.81,908.87,891.71,898.18,13080000,898.18 1973-06-05,885.91,905.63,883.43,900.81,14080000,900.81 1973-06-04,893.96,894.42,880.72,885.91,11230000,885.91 1973-06-01,901.41,902.32,887.27,893.96,10410000,893.96 1973-05-31,908.87,912.03,895.62,901.41,12190000,901.41 1973-05-30,924.37,924.37,906.01,908.87,11730000,908.87 1973-05-29,930.84,934.08,921.36,925.57,11300000,925.57 1973-05-25,924.44,938.82,915.64,930.84,19270000,930.84 1973-05-24,895.02,926.48,890.35,924.44,17310000,924.44 1973-05-23,892.46,904.80,884.18,895.02,14950000,895.02 1973-05-22,886.51,905.93,884.86,892.46,18020000,892.46 1973-05-21,894.79,894.79,875.45,886.51,20690000,886.51 1973-05-18,908.79,908.79,889.00,895.17,17080000,895.17 1973-05-17,917.14,919.93,907.59,911.72,13060000,911.72 1973-05-16,917.44,927.83,907.66,917.14,13800000,917.14 1973-05-15,909.69,920.83,894.49,917.44,18530000,917.44 1973-05-14,925.27,925.27,906.31,909.69,13520000,909.69 1973-05-11,939.12,939.12,924.14,927.98,12980000,927.98 1973-05-10,949.05,950.63,934.75,939.34,13520000,939.34 1973-05-09,956.58,965.16,945.29,949.05,16050000,949.05 1973-05-08,950.71,961.24,940.92,956.58,13730000,956.58 1973-05-07,953.87,956.80,941.98,950.71,12500000,950.71 1973-05-04,945.67,962.67,944.31,953.87,19510000,953.87 1973-05-03,932.34,950.03,915.04,945.67,17760000,945.67 1973-05-02,921.21,938.37,920.23,932.34,14380000,932.34 1973-05-01,921.43,929.03,905.40,921.21,15380000,921.21 1973-04-30,922.19,929.18,907.51,921.43,14820000,921.43 1973-04-27,937.76,941.38,918.05,922.19,13730000,922.19 1973-04-26,930.54,944.24,919.78,937.76,16210000,937.76 1973-04-25,940.77,941.60,925.42,930.54,15960000,930.54 1973-04-24,955.37,958.68,938.37,940.77,13830000,940.77 1973-04-23,963.20,966.51,951.23,955.37,12580000,955.37 1973-04-19,958.31,970.95,953.79,963.20,14560000,963.20 1973-04-18,953.43,961.47,944.39,958.31,13890000,958.31 1973-04-17,956.73,959.29,947.62,953.43,12830000,953.43 1973-04-16,959.36,965.53,950.03,956.73,11350000,956.73 1973-04-13,964.03,967.56,950.71,959.36,14390000,959.36 1973-04-12,967.41,975.32,956.73,964.03,16360000,964.03 1973-04-11,960.49,970.50,953.94,967.41,14890000,967.41 1973-04-10,950.71,966.74,950.71,960.49,16770000,960.49 1973-04-09,931.07,950.63,927.45,947.55,13740000,947.55 1973-04-06,923.46,937.31,921.43,931.07,13890000,931.07 1973-04-05,925.05,929.03,914.81,923.46,12750000,923.46 1973-04-04,927.75,935.66,919.55,925.05,11890000,925.05 1973-04-03,936.18,936.71,920.45,927.75,12910000,927.75 1973-04-02,951.01,951.84,932.72,936.18,10640000,936.18 1973-03-30,959.14,961.69,944.84,951.01,13740000,951.01 1973-03-29,948.00,962.82,941.75,959.14,16050000,959.14 1973-03-28,944.91,955.82,936.94,948.00,15850000,948.00 1973-03-27,930.16,948.52,930.16,944.91,17500000,944.91 1973-03-26,922.71,931.74,914.28,927.90,14980000,927.90 1973-03-23,925.20,934.53,911.12,922.71,18470000,922.71 1973-03-22,937.46,937.46,919.25,925.20,17130000,925.20 1973-03-21,949.43,958.84,934.90,938.37,16080000,938.37 1973-03-20,952.06,956.80,941.15,949.43,13250000,949.43 1973-03-19,962.45,962.45,946.79,952.06,12460000,952.06 1973-03-16,969.82,973.21,957.41,963.05,15130000,963.05 1973-03-15,978.85,982.47,966.21,969.82,14450000,969.82 1973-03-14,976.07,982.31,972.00,978.85,14460000,978.85 1973-03-13,969.75,980.13,966.29,976.07,14210000,976.07 1973-03-12,972.23,979.00,965.68,969.75,13810000,969.75 1973-03-09,976.44,978.03,963.58,972.23,14070000,972.23 1973-03-08,979.98,985.25,973.59,976.44,15100000,976.44 1973-03-07,979.00,984.80,966.74,979.98,19310000,979.98 1973-03-06,966.89,982.39,966.44,979.00,17710000,979.00 1973-03-05,961.32,972.00,955.67,966.89,13720000,966.89 1973-03-02,949.65,962.97,937.69,961.32,17710000,961.32 1973-03-01,955.07,967.04,947.17,949.65,18210000,949.65 1973-02-28,947.92,958.31,938.82,955.07,17950000,955.07 1973-02-27,953.79,963.12,943.26,947.92,16130000,947.92 1973-02-26,959.89,962.90,943.78,953.79,15860000,953.79 1973-02-23,971.78,973.81,957.10,959.89,15450000,959.89 1973-02-22,974.34,978.63,963.05,971.78,14570000,971.78 1973-02-21,983.59,988.79,970.27,974.34,14880000,974.34 1973-02-20,979.23,991.04,975.69,983.59,14020000,983.59 1973-02-16,973.13,981.86,965.76,979.23,13320000,979.23 1973-02-15,979.91,984.12,966.29,973.13,13940000,973.13 1973-02-14,996.76,997.97,974.11,979.91,16520000,979.91 1973-02-13,991.65,1019.94,991.65,996.76,25320000,996.76 1973-02-12,980.81,996.24,980.81,991.57,16130000,991.57 1973-02-09,967.19,983.14,964.93,979.46,19260000,979.46 1973-02-08,968.32,973.81,954.17,967.19,18440000,967.19 1973-02-07,979.91,989.16,965.53,968.32,17960000,968.32 1973-02-06,978.40,986.00,972.23,979.91,15720000,979.91 1973-02-05,980.81,987.06,974.11,978.40,14580000,978.40 1973-02-02,985.78,992.32,975.02,980.81,17470000,980.81 1973-02-01,999.02,1008.58,983.14,985.78,20670000,985.78 1973-01-31,992.93,1004.06,988.11,999.02,14870000,999.02 1973-01-30,996.47,1005.34,989.09,992.93,15270000,992.93 1973-01-29,1003.54,1008.80,988.18,996.47,14680000,996.47 1973-01-26,1004.59,1008.50,989.46,1003.54,21130000,1003.54 1973-01-24,1018.66,1025.74,998.57,1004.59,20870000,1004.59 1973-01-23,1018.81,1024.68,1007.22,1018.66,19060000,1018.66 1973-01-22,1026.19,1034.69,1014.52,1018.81,15570000,1018.81 1973-01-19,1029.12,1031.68,1014.00,1026.19,17020000,1026.19 1973-01-18,1029.12,1039.96,1024.01,1029.12,17810000,1029.12 1973-01-17,1024.31,1036.35,1020.32,1029.12,17680000,1029.12 1973-01-16,1025.59,1033.34,1014.37,1024.31,19170000,1024.31 1973-01-15,1039.36,1053.28,1022.88,1025.59,21520000,1025.59 1973-01-12,1051.70,1059.90,1033.41,1039.36,22230000,1039.36 1973-01-11,1046.06,1067.20,1039.28,1051.70,25050000,1051.70 1973-01-10,1047.11,1053.28,1040.94,1046.06,20880000,1046.06 1973-01-09,1047.86,1053.21,1040.49,1047.11,16830000,1047.11 1973-01-08,1047.49,1053.58,1040.86,1047.86,16840000,1047.86 1973-01-05,1039.81,1053.43,1037.40,1047.49,19330000,1047.49 1973-01-04,1043.80,1047.86,1027.62,1039.81,20230000,1039.81 1973-01-03,1032.21,1049.59,1032.21,1043.80,20620000,1043.80 1973-01-02,1022.88,1038.98,1022.88,1031.68,17090000,1031.68 1972-12-29,1008.58,1027.39,1008.58,1020.02,27550000,1020.02 1972-12-27,1006.70,1014.22,1001.13,1007.68,19100000,1007.68 1972-12-26,1004.21,1010.91,999.85,1006.70,11120000,1006.70 1972-12-22,1000.00,1010.01,996.84,1004.21,12540000,1004.21 1972-12-21,1004.82,1010.91,996.09,1000.00,18290000,1000.00 1972-12-20,1009.18,1014.98,1001.88,1004.82,18490000,1004.82 1972-12-19,1013.25,1017.99,1004.06,1009.18,17000000,1009.18 1972-12-18,1022.05,1022.05,1004.29,1013.25,17540000,1013.25 1972-12-15,1025.06,1034.69,1018.59,1027.24,18300000,1027.24 1972-12-14,1030.48,1035.45,1020.09,1025.06,17930000,1025.06 1972-12-13,1033.19,1036.65,1025.14,1030.48,16540000,1030.48 1972-12-12,1036.27,1042.44,1029.65,1033.19,17040000,1033.19 1972-12-11,1033.19,1041.32,1029.80,1036.27,17230000,1036.27 1972-12-08,1033.26,1039.21,1027.17,1033.19,18030000,1033.19 1972-12-07,1027.54,1037.85,1025.36,1033.26,19320000,1033.26 1972-12-06,1022.95,1031.16,1018.29,1027.54,18610000,1027.54 1972-12-05,1027.02,1030.85,1017.46,1022.95,17800000,1022.95 1972-12-04,1023.93,1033.19,1021.45,1027.02,19730000,1027.02 1972-12-01,1018.21,1031.53,1016.56,1023.93,22570000,1023.93 1972-11-30,1018.81,1025.81,1010.54,1018.21,19340000,1018.21 1972-11-29,1019.34,1023.63,1011.89,1018.81,17380000,1018.81 1972-11-28,1017.76,1026.79,1012.57,1019.34,19210000,1019.34 1972-11-27,1025.21,1026.87,1008.65,1017.76,18190000,1017.76 1972-11-24,1020.54,1029.73,1014.15,1025.21,15760000,1025.21 1972-11-22,1013.25,1026.87,1009.93,1020.54,24510000,1020.54 1972-11-21,1005.04,1017.61,1002.86,1013.25,22110000,1013.25 1972-11-20,1005.57,1011.14,997.14,1005.04,16680000,1005.04 1972-11-17,1003.69,1012.34,998.57,1005.57,20220000,1005.57 1972-11-16,998.42,1008.13,991.57,1003.69,19580000,1003.69 1972-11-15,1003.16,1013.55,993.08,998.42,23270000,998.42 1972-11-14,997.07,1006.92,991.12,1003.16,20200000,1003.16 1972-11-13,995.26,1004.89,988.49,997.07,17210000,997.07 1972-11-10,988.26,1007.15,986.08,995.26,24360000,995.26 1972-11-09,983.74,992.32,973.89,988.26,17040000,988.26 1972-11-08,984.80,998.42,978.63,983.74,24620000,983.74 1972-11-06,984.12,993.38,977.05,984.80,21330000,984.80 1972-11-03,973.06,988.94,969.22,984.12,22510000,984.12 1972-11-02,968.54,977.80,961.77,973.06,20690000,973.06 1972-11-01,955.52,975.24,955.30,968.54,21360000,968.54 1972-10-31,946.42,958.53,944.39,955.52,15450000,955.52 1972-10-30,946.42,950.71,936.56,946.42,11820000,946.42 1972-10-27,950.56,956.20,941.07,946.42,15470000,946.42 1972-10-26,951.38,962.45,946.27,950.56,20790000,950.56 1972-10-25,952.51,958.46,944.99,951.38,17430000,951.38 1972-10-24,951.31,957.10,941.30,952.51,15240000,952.51 1972-10-23,945.21,958.31,945.21,951.31,14190000,951.31 1972-10-20,932.12,946.72,927.98,942.81,15740000,942.81 1972-10-19,932.34,939.27,926.70,932.12,13850000,932.12 1972-10-18,926.48,940.85,925.80,932.34,17290000,932.34 1972-10-17,921.66,931.29,917.07,926.48,13410000,926.48 1972-10-16,930.46,934.53,919.25,921.66,10940000,921.66 1972-10-13,937.46,940.17,923.77,930.46,12870000,930.46 1972-10-12,946.42,947.10,932.42,937.46,13130000,937.46 1972-10-11,951.84,956.05,941.60,946.42,11900000,946.42 1972-10-10,948.75,960.49,946.34,951.84,13310000,951.84 1972-10-09,945.36,952.21,941.00,948.75,7940000,948.75 1972-10-06,941.30,954.39,930.39,945.36,16630000,945.36 1972-10-05,951.31,955.67,937.54,941.30,17730000,941.30 1972-10-04,954.47,964.10,947.85,951.31,16640000,951.31 1972-10-03,953.27,959.96,948.22,954.47,13090000,954.47 1972-10-02,953.27,959.59,945.44,953.27,12440000,953.27 1972-09-29,955.15,965.08,949.05,953.27,16250000,953.27 1972-09-28,947.25,956.88,939.87,955.15,14710000,955.15 1972-09-27,936.56,949.05,932.95,947.25,14620000,947.25 1972-09-26,935.73,942.13,927.15,936.56,13150000,936.56 1972-09-25,943.03,947.47,933.10,935.73,10920000,935.73 1972-09-22,939.49,948.98,934.38,943.03,12570000,943.03 1972-09-21,940.25,945.44,932.72,939.49,11940000,939.49 1972-09-20,943.18,946.12,934.75,940.25,11980000,940.25 1972-09-19,945.36,952.44,939.12,943.18,13330000,943.18 1972-09-18,947.32,951.16,938.67,945.36,8880000,945.36 1972-09-15,947.55,952.29,940.62,947.32,11690000,947.32 1972-09-14,949.88,955.30,942.05,947.55,12500000,947.55 1972-09-13,946.04,954.09,941.30,949.88,13070000,949.88 1972-09-12,955.00,957.86,940.62,946.04,13560000,946.04 1972-09-11,961.24,964.55,951.08,955.00,10710000,955.00 1972-09-08,962.45,968.24,957.33,961.24,10980000,961.24 1972-09-07,963.43,968.02,957.41,962.45,11090000,962.45 1972-09-06,969.37,970.35,958.91,963.43,12010000,963.43 1972-09-05,970.05,977.35,964.63,969.37,10630000,969.37 1972-09-01,963.73,975.62,962.30,970.05,11600000,970.05 1972-08-31,957.86,966.89,953.57,963.73,12340000,963.73 1972-08-30,954.70,964.03,951.16,957.86,12470000,957.86 1972-08-29,956.95,961.62,945.29,954.70,12300000,954.70 1972-08-28,959.36,964.33,952.59,956.95,10720000,956.95 1972-08-25,958.38,964.86,949.80,959.36,13840000,959.36 1972-08-24,970.35,974.56,955.98,958.38,18280000,958.38 1972-08-23,973.51,980.36,962.60,970.35,18670000,970.35 1972-08-22,967.19,979.76,963.88,973.51,18560000,973.51 1972-08-21,965.83,974.19,958.16,967.19,14290000,967.19 1972-08-18,961.39,972.23,957.48,965.83,16150000,965.83 1972-08-17,964.25,971.25,955.60,961.39,14360000,961.39 1972-08-16,969.97,974.94,958.46,964.25,14950000,964.25 1972-08-15,973.51,978.33,962.30,969.97,16670000,969.97 1972-08-14,964.18,980.21,964.18,973.51,18870000,973.51 1972-08-11,952.89,966.59,948.75,964.18,16570000,964.18 1972-08-10,951.16,958.84,945.29,952.89,15260000,952.89 1972-08-09,952.44,958.99,943.86,951.16,15730000,951.16 1972-08-08,953.12,958.01,944.31,952.44,14550000,952.44 1972-08-07,951.76,959.36,945.67,953.12,13220000,953.12 1972-08-04,947.70,957.10,942.28,951.76,15700000,951.76 1972-08-03,941.15,953.19,939.12,947.70,19970000,947.70 1972-08-02,931.07,944.61,931.07,941.15,17920000,941.15 1972-08-01,924.74,935.36,922.03,930.46,15540000,930.46 1972-07-31,926.70,932.72,917.37,924.74,11120000,924.74 1972-07-28,926.85,933.93,920.15,926.70,13050000,926.70 1972-07-27,932.57,938.37,923.62,926.85,13870000,926.85 1972-07-26,934.45,940.92,927.60,932.57,14130000,932.57 1972-07-25,935.36,946.79,930.24,934.45,17180000,934.45 1972-07-24,922.19,941.98,922.19,935.36,18020000,935.36 1972-07-21,910.45,923.24,903.90,920.45,14010000,920.45 1972-07-20,916.69,919.40,905.55,910.45,15050000,910.45 1972-07-19,911.72,927.00,909.69,916.69,17880000,916.69 1972-07-18,914.96,917.97,900.06,911.72,16820000,911.72 1972-07-17,922.26,928.43,912.70,914.96,13170000,914.96 1972-07-14,916.99,927.83,911.88,922.26,13910000,922.26 1972-07-13,923.69,926.02,912.93,916.99,14740000,916.99 1972-07-12,925.87,934.15,919.33,923.69,16150000,923.69 1972-07-11,932.27,934.90,921.81,925.87,12830000,925.87 1972-07-10,938.06,943.03,928.73,932.27,11700000,932.27 1972-07-07,942.13,948.15,932.34,938.06,12900000,938.06 1972-07-06,934.90,955.45,934.90,942.13,19520000,942.13 1972-07-05,928.66,939.64,926.25,933.47,14710000,933.47 1972-07-03,929.03,935.05,922.71,928.66,8140000,928.66 1972-06-30,926.25,935.81,921.21,929.03,12860000,929.03 1972-06-29,930.84,932.95,917.75,926.25,14610000,926.25 1972-06-28,935.28,938.14,925.80,930.84,12140000,930.84 1972-06-27,936.41,943.33,928.96,935.28,13750000,935.28 1972-06-26,942.50,942.50,928.13,936.41,12720000,936.41 1972-06-23,950.71,957.25,939.27,944.69,13940000,944.69 1972-06-22,951.61,956.58,940.02,950.71,13410000,950.71 1972-06-21,948.22,959.96,943.63,951.61,15510000,951.61 1972-06-20,941.83,952.14,940.10,948.22,14970000,948.22 1972-06-19,945.06,947.62,935.43,941.83,11660000,941.83 1972-06-16,945.97,949.88,936.94,945.06,13010000,945.06 1972-06-15,946.79,956.05,940.25,945.97,16940000,945.97 1972-06-14,938.29,954.24,936.11,946.79,18320000,946.79 1972-06-13,936.71,943.33,928.88,938.29,15710000,938.29 1972-06-12,934.45,943.63,930.39,936.71,13390000,936.71 1972-06-09,941.30,942.88,930.01,934.45,12790000,934.45 1972-06-08,944.08,953.19,938.82,941.30,13820000,941.30 1972-06-07,951.46,952.51,938.52,944.08,15220000,944.08 1972-06-06,954.39,961.02,946.87,951.46,15980000,951.46 1972-06-05,961.39,964.48,948.52,954.39,13450000,954.39 1972-06-02,960.72,967.72,954.24,961.39,15400000,961.39 1972-06-01,960.72,966.36,954.85,960.72,14910000,960.72 1972-05-31,970.88,970.88,955.52,960.72,15230000,960.72 1972-05-30,971.25,979.46,966.51,971.18,15810000,971.18 1972-05-26,969.07,977.42,963.80,971.25,15730000,971.25 1972-05-25,965.46,975.54,960.57,969.07,16480000,969.07 1972-05-24,962.30,973.06,958.76,965.46,17870000,965.46 1972-05-23,965.31,970.05,956.20,962.30,16410000,962.30 1972-05-22,961.54,972.00,958.76,965.31,16030000,965.31 1972-05-19,951.23,967.56,950.11,961.54,19580000,961.54 1972-05-18,941.15,955.67,939.95,951.23,17370000,951.23 1972-05-17,939.27,944.61,932.95,941.15,13600000,941.15 1972-05-16,942.20,947.17,934.83,939.27,14070000,939.27 1972-05-15,941.83,948.45,936.63,942.20,13600000,942.20 1972-05-12,934.83,947.40,934.23,941.83,13990000,941.83 1972-05-11,931.07,941.15,925.87,934.83,12900000,934.83 1972-05-10,925.12,938.06,922.03,931.07,13870000,931.07 1972-05-09,937.54,937.54,917.37,925.12,19910000,925.12 1972-05-08,941.23,943.18,930.01,937.84,11250000,937.84 1972-05-05,937.31,948.30,932.57,941.23,13210000,941.23 1972-05-04,933.47,942.43,927.60,937.31,14790000,937.31 1972-05-03,935.20,947.32,927.90,933.47,15900000,933.47 1972-05-02,942.28,947.32,930.61,935.20,15370000,935.20 1972-05-01,954.17,956.95,938.59,942.28,12880000,942.28 1972-04-28,945.97,959.74,944.46,954.17,14160000,954.17 1972-04-27,946.94,954.92,940.70,945.97,15740000,945.97 1972-04-26,946.49,954.39,938.29,946.94,17710000,946.94 1972-04-25,957.48,959.14,943.63,946.49,17030000,946.49 1972-04-24,963.80,966.59,950.93,957.48,14650000,957.48 1972-04-21,966.29,974.64,959.21,963.80,18200000,963.80 1972-04-20,964.78,971.33,954.17,966.29,18190000,966.29 1972-04-19,968.92,975.92,959.36,964.78,19180000,964.78 1972-04-18,966.59,977.72,961.62,968.92,19410000,968.92 1972-04-17,967.72,972.16,959.36,966.59,15390000,966.59 1972-04-14,965.53,973.06,959.59,967.72,17460000,967.72 1972-04-13,966.96,973.28,960.42,965.53,17990000,965.53 1972-04-12,962.60,976.44,960.64,966.96,24690000,966.96 1972-04-11,958.08,967.79,951.23,962.60,19930000,962.60 1972-04-10,962.60,970.57,953.42,958.08,19470000,958.08 1972-04-07,959.44,965.91,950.26,962.60,19900000,962.60 1972-04-06,954.55,968.24,951.76,959.44,22830000,959.44 1972-04-05,943.41,958.99,943.33,954.55,22960000,954.55 1972-04-04,940.92,948.52,933.62,943.41,18110000,943.41 1972-04-03,940.70,948.75,935.66,940.92,14990000,940.92 1972-03-30,933.02,943.78,929.79,940.70,14360000,940.70 1972-03-29,937.01,940.17,925.87,933.02,13860000,933.02 1972-03-28,939.72,947.02,932.65,937.01,15380000,937.01 1972-03-27,942.28,946.57,933.77,939.72,12180000,939.72 1972-03-24,944.69,950.03,937.91,942.28,15390000,942.28 1972-03-23,933.93,949.20,932.34,944.69,18380000,944.69 1972-03-22,934.00,938.59,926.78,933.93,15400000,933.93 1972-03-21,940.32,940.32,925.95,934.00,18610000,934.00 1972-03-20,942.88,951.84,937.31,941.15,16420000,941.15 1972-03-17,936.71,949.88,931.52,942.88,16040000,942.88 1972-03-16,937.31,942.88,927.68,936.71,16700000,936.71 1972-03-15,934.00,945.44,929.18,937.31,19460000,937.31 1972-03-14,928.66,938.82,925.35,934.00,22370000,934.00 1972-03-13,939.87,941.23,924.22,928.66,16730000,928.66 1972-03-10,942.81,948.60,932.95,939.87,19690000,939.87 1972-03-09,945.59,950.78,937.61,942.81,21460000,942.81 1972-03-08,946.87,953.57,937.09,945.59,21290000,945.59 1972-03-07,950.18,956.20,940.40,946.87,22640000,946.87 1972-03-06,942.43,957.03,940.47,950.18,21000000,950.18 1972-03-03,933.77,948.00,930.69,942.43,20420000,942.43 1972-03-02,935.43,943.78,927.90,933.77,22200000,933.77 1972-03-01,928.13,943.03,924.14,935.43,23670000,935.43 1972-02-29,924.29,932.34,916.24,928.13,20320000,928.13 1972-02-28,922.79,930.31,917.14,924.29,18200000,924.29 1972-02-25,912.70,927.75,909.39,922.79,18180000,922.79 1972-02-24,911.88,919.25,906.61,912.70,16000000,912.70 1972-02-23,913.46,919.85,906.98,911.88,16770000,911.88 1972-02-22,917.52,922.86,909.17,913.46,16670000,913.46 1972-02-18,922.03,925.35,911.12,917.52,16590000,917.52 1972-02-17,922.94,933.25,916.84,922.03,22330000,922.03 1972-02-16,914.51,928.58,911.65,922.94,20670000,922.94 1972-02-15,910.90,920.98,906.46,914.51,17770000,914.51 1972-02-14,917.59,921.96,906.01,910.90,15840000,910.90 1972-02-11,921.28,924.82,910.52,917.59,17850000,917.59 1972-02-10,918.72,931.89,914.89,921.28,23460000,921.28 1972-02-09,907.13,921.81,905.18,918.72,19850000,918.72 1972-02-08,903.97,910.97,898.18,907.13,17390000,907.13 1972-02-07,906.68,913.61,898.93,903.97,16930000,903.97 1972-02-04,903.15,912.10,897.50,906.68,17890000,906.68 1972-02-03,905.85,910.97,896.30,903.15,19880000,903.15 1972-02-02,901.79,913.76,896.15,905.85,24070000,905.85 1972-02-01,902.17,906.23,894.34,901.79,19600000,901.79 1972-01-31,906.38,911.20,897.05,902.17,18250000,902.17 1972-01-28,899.83,913.23,896.60,906.38,25000000,906.38 1972-01-27,889.15,902.92,887.87,899.83,20360000,899.83 1972-01-26,894.72,897.65,883.43,889.15,14940000,889.15 1972-01-25,896.82,902.17,887.49,894.72,17570000,894.72 1972-01-24,907.44,911.20,893.81,896.82,15640000,896.82 1972-01-21,910.30,913.91,898.93,907.44,18810000,907.44 1972-01-20,914.96,922.19,906.76,910.30,20210000,910.30 1972-01-19,917.22,922.34,904.58,914.96,18800000,914.96 1972-01-18,911.12,923.99,908.94,917.22,21070000,917.22 1972-01-17,906.68,914.51,902.77,911.12,15860000,911.12 1972-01-14,905.18,910.82,899.16,906.68,14960000,906.68 1972-01-13,910.82,912.93,899.98,905.18,16410000,905.18 1972-01-12,912.10,922.03,906.01,910.82,20970000,910.82 1972-01-11,907.96,919.02,903.67,912.10,17970000,912.10 1972-01-10,910.37,914.43,898.48,907.96,15320000,907.96 1972-01-07,908.49,916.47,903.37,910.37,17140000,910.37 1972-01-06,904.43,913.83,901.87,908.49,21100000,908.49 1972-01-05,893.06,910.07,893.06,904.43,21350000,904.43 1972-01-04,889.30,897.73,882.75,892.23,15190000,892.23 1972-01-03,890.20,898.71,884.63,889.30,12570000,889.30 1971-12-31,889.07,895.77,884.48,890.20,14040000,890.20 1971-12-30,893.66,898.78,883.05,889.07,13810000,889.07 1971-12-29,889.98,902.47,886.59,893.66,17150000,893.66 1971-12-28,881.47,892.84,877.18,889.98,15090000,889.98 1971-12-27,881.17,889.83,875.68,881.47,11890000,881.47 1971-12-23,884.86,889.98,873.34,881.17,16000000,881.17 1971-12-22,888.32,893.81,878.54,884.86,18930000,884.86 1971-12-21,885.01,896.22,875.30,888.32,20460000,888.32 1971-12-20,876.28,895.17,876.28,885.01,23810000,885.01 1971-12-17,871.39,879.29,865.29,873.80,18270000,873.80 1971-12-16,863.76,878.69,863.26,871.39,21070000,871.39 1971-12-15,855.14,867.84,849.81,863.76,16890000,863.76 1971-12-14,858.79,864.92,851.12,855.14,16070000,855.14 1971-12-13,856.75,868.28,852.22,858.79,17020000,858.79 1971-12-10,852.15,861.86,847.69,856.75,17510000,856.75 1971-12-09,854.85,858.43,844.55,852.15,14710000,852.15 1971-12-08,857.40,861.71,847.91,854.85,16650000,854.85 1971-12-07,855.72,862.44,845.28,857.40,15250000,857.40 1971-12-06,859.59,872.59,852.58,855.72,17480000,855.72 1971-12-03,848.79,864.05,845.58,859.59,16760000,859.59 1971-12-02,846.01,854.70,837.47,848.79,17780000,848.79 1971-12-01,831.34,851.85,831.12,846.01,21040000,846.01 1971-11-30,829.73,837.03,819.22,831.34,18320000,831.34 1971-11-29,816.59,838.57,815.79,829.73,18910000,829.73 1971-11-26,799.87,818.12,799.87,816.59,10870000,816.59 1971-11-24,797.97,807.90,793.88,798.63,11870000,798.63 1971-11-23,803.15,807.10,790.67,797.97,16840000,797.97 1971-11-22,810.67,817.03,800.89,803.15,11390000,803.15 1971-11-19,815.35,817.98,804.83,810.67,12420000,810.67 1971-11-18,822.14,829.66,813.81,815.35,13010000,815.35 1971-11-17,818.71,825.79,812.13,822.14,12840000,822.14 1971-11-16,810.53,823.89,806.59,818.71,13300000,818.71 1971-11-15,812.94,820.68,806.66,810.53,9370000,810.53 1971-11-12,814.91,821.12,802.21,812.94,14540000,812.94 1971-11-11,826.15,827.91,810.82,814.91,13310000,814.91 1971-11-10,837.91,842.22,821.85,826.15,13410000,826.15 1971-11-09,837.54,845.72,832.21,837.91,12080000,837.91 1971-11-08,840.39,843.60,832.51,837.54,8520000,837.54 1971-11-05,843.17,845.94,832.94,840.39,10780000,840.39 1971-11-04,842.58,855.21,838.49,843.17,15750000,843.17 1971-11-03,828.34,845.87,828.34,842.58,14590000,842.58 1971-11-02,825.86,833.53,814.69,827.98,13330000,827.98 1971-11-01,839.00,840.25,823.21,825.86,10960000,825.86 1971-10-29,837.62,843.03,831.85,839.00,11710000,839.00 1971-10-28,836.38,844.63,827.83,837.62,15530000,837.62 1971-10-27,845.36,846.31,832.29,836.38,13480000,836.38 1971-10-26,848.50,857.26,842.95,845.36,13390000,845.36 1971-10-25,852.37,854.63,843.38,848.50,7340000,848.50 1971-10-22,854.85,863.90,848.57,852.37,14560000,852.37 1971-10-21,855.65,861.05,846.45,854.85,14990000,854.85 1971-10-20,868.43,873.39,851.20,855.65,16340000,855.65 1971-10-19,872.44,874.34,860.11,868.43,13040000,868.43 1971-10-18,874.58,880.40,868.87,872.44,10420000,872.44 1971-10-15,878.36,881.64,868.50,874.58,13120000,874.58 1971-10-14,888.80,889.16,875.22,878.36,12870000,878.36 1971-10-13,893.55,897.12,885.22,888.80,13540000,888.80 1971-10-12,891.94,899.61,888.29,893.55,14340000,893.55 1971-10-11,893.91,896.39,886.32,891.94,7800000,891.94 1971-10-08,901.80,902.82,890.55,893.91,13870000,893.91 1971-10-07,900.55,910.56,896.10,901.80,17780000,901.80 1971-10-06,891.14,902.38,886.90,900.55,15630000,900.55 1971-10-05,895.66,898.66,885.81,891.14,12360000,891.14 1971-10-04,893.98,904.42,892.01,895.66,14570000,895.66 1971-10-01,887.19,898.44,884.49,893.98,13400000,893.98 1971-09-30,883.83,894.13,878.14,887.19,13490000,887.19 1971-09-29,884.42,890.41,877.70,883.83,8580000,883.83 1971-09-28,883.47,891.50,877.77,884.42,11250000,884.42 1971-09-27,889.31,891.72,877.48,883.47,10220000,883.47 1971-09-24,891.28,900.26,886.24,889.31,13460000,889.31 1971-09-23,893.55,899.39,884.64,891.28,13250000,891.28 1971-09-22,903.40,904.94,891.72,893.55,14250000,893.55 1971-09-21,905.15,907.78,897.78,903.40,10640000,903.40 1971-09-20,908.22,911.29,900.19,905.15,9540000,905.15 1971-09-17,903.11,912.16,901.07,908.22,11020000,908.22 1971-09-16,904.86,908.81,898.66,903.11,10550000,903.11 1971-09-15,901.65,907.71,896.25,904.86,11080000,904.86 1971-09-14,909.39,911.73,898.58,901.65,11410000,901.65 1971-09-13,911.00,915.67,903.91,909.39,10000000,909.39 1971-09-10,915.89,916.84,904.42,911.00,11380000,911.00 1971-09-09,920.93,925.23,912.38,915.89,15790000,915.89 1971-09-08,916.47,925.67,911.87,920.93,14230000,920.93 1971-09-07,912.75,925.67,909.75,916.47,17080000,916.47 1971-09-03,900.63,914.06,899.53,912.75,14040000,912.75 1971-09-02,899.02,905.37,893.84,900.63,10690000,900.63 1971-09-01,898.07,906.25,894.20,899.02,10770000,899.02 1971-08-31,901.43,903.84,892.01,898.07,10430000,898.07 1971-08-30,908.15,912.53,898.51,901.43,11140000,901.43 1971-08-27,906.10,915.45,902.02,908.15,12490000,908.15 1971-08-26,908.37,914.06,898.88,906.10,13990000,906.10 1971-08-25,904.13,917.79,901.80,908.37,18280000,908.37 1971-08-24,892.38,909.02,892.09,904.13,18700000,904.13 1971-08-23,880.99,897.20,880.99,892.38,13040000,892.38 1971-08-20,880.77,887.27,874.93,880.91,11890000,880.91 1971-08-19,886.17,889.68,873.90,880.77,14190000,880.77 1971-08-18,899.90,901.36,882.08,886.17,20680000,886.17 1971-08-17,888.95,908.66,885.29,899.90,26790000,899.90 1971-08-16,882.59,906.32,882.59,888.95,31730000,888.95 1971-08-13,859.01,864.41,851.64,856.02,9960000,856.02 1971-08-12,848.57,863.46,848.57,859.01,15910000,859.01 1971-08-11,839.59,851.12,836.96,846.38,11370000,846.38 1971-08-10,842.65,846.82,834.92,839.59,9460000,839.59 1971-08-09,850.61,853.46,839.52,842.65,8110000,842.65 1971-08-06,849.45,855.94,844.70,850.61,9490000,850.61 1971-08-05,844.92,855.72,841.19,849.45,12100000,849.45 1971-08-04,850.03,857.70,840.10,844.92,15410000,844.92 1971-08-03,864.92,865.87,845.94,850.03,13490000,850.03 1971-08-02,858.43,871.86,857.70,864.92,11870000,864.92 1971-07-30,861.42,868.50,854.41,858.43,12970000,858.43 1971-07-29,872.01,873.03,856.97,861.42,14570000,861.42 1971-07-28,880.70,882.16,868.36,872.01,13940000,872.01 1971-07-27,888.87,891.28,877.04,880.70,11560000,880.70 1971-07-26,887.78,895.15,882.96,888.87,9930000,888.87 1971-07-23,886.68,893.03,880.33,887.78,12370000,887.78 1971-07-22,890.84,893.55,882.74,886.68,12570000,886.68 1971-07-21,892.30,896.54,886.61,890.84,11920000,890.84 1971-07-20,886.39,896.90,885.15,892.30,12540000,892.30 1971-07-19,888.51,893.47,879.23,886.39,11430000,886.39 1971-07-16,888.87,898.73,884.71,888.51,13870000,888.51 1971-07-15,891.21,901.43,885.00,888.87,13080000,888.87 1971-07-14,892.38,895.88,882.52,891.21,14360000,891.21 1971-07-13,903.40,906.47,888.80,892.38,13540000,892.38 1971-07-12,901.80,908.81,896.47,903.40,12020000,903.40 1971-07-09,900.99,908.73,896.69,901.80,12640000,901.80 1971-07-08,895.88,906.10,894.20,900.99,13920000,900.99 1971-07-07,892.30,903.18,888.87,895.88,14520000,895.88 1971-07-06,890.19,896.83,885.29,892.30,10440000,892.30 1971-07-02,893.03,895.52,885.73,890.19,9960000,890.19 1971-07-01,891.14,899.39,886.24,893.03,13090000,893.03 1971-06-30,882.30,895.66,881.28,891.14,15410000,891.14 1971-06-29,873.10,887.63,871.20,882.30,14460000,882.30 1971-06-28,876.68,879.53,866.82,873.10,9810000,873.10 1971-06-25,877.26,882.45,870.04,876.68,10580000,876.68 1971-06-24,879.45,885.44,872.01,877.26,11360000,877.26 1971-06-23,874.42,886.61,870.04,879.45,12640000,879.45 1971-06-22,876.53,884.56,866.31,874.42,15200000,874.42 1971-06-21,889.16,890.48,872.74,876.53,16490000,876.53 1971-06-18,906.25,906.47,887.63,889.16,15040000,889.16 1971-06-17,908.59,915.08,902.16,906.25,13980000,906.25 1971-06-16,907.20,915.52,901.29,908.59,14300000,908.59 1971-06-15,907.71,915.23,899.39,907.20,13550000,907.20 1971-06-14,916.47,917.79,904.13,907.71,11530000,907.71 1971-06-11,915.96,923.70,909.68,916.47,12270000,916.47 1971-06-10,912.46,921.00,907.78,915.96,12450000,915.96 1971-06-09,915.01,919.54,905.74,912.46,14250000,912.46 1971-06-08,923.06,925.89,911.95,915.01,13610000,915.01 1971-06-07,922.15,928.54,917.44,923.06,13800000,923.06 1971-06-04,921.30,926.78,915.40,922.15,14400000,922.15 1971-06-03,919.62,929.60,914.63,921.30,18790000,921.30 1971-06-02,913.65,925.31,911.96,919.62,17740000,919.62 1971-06-01,907.81,918.49,903.74,913.65,11930000,913.65 1971-05-28,905.78,912.24,900.58,907.81,11760000,907.81 1971-05-27,906.41,911.96,899.94,905.78,12610000,905.78 1971-05-26,906.69,914.91,901.07,906.41,13550000,906.41 1971-05-25,913.15,913.86,898.61,906.69,16050000,906.69 1971-05-24,921.87,923.69,911.05,913.15,12060000,913.15 1971-05-21,923.41,927.84,917.30,921.87,12090000,921.87 1971-05-20,920.04,931.42,917.93,923.41,11740000,923.41 1971-05-19,918.56,926.64,913.93,920.04,17640000,920.04 1971-05-18,921.30,926.64,909.99,918.56,17640000,918.56 1971-05-17,935.15,935.15,916.60,921.30,15980000,921.30 1971-05-14,936.34,942.03,930.37,936.06,16430000,936.06 1971-05-13,937.46,944.63,928.12,936.34,17640000,936.34 1971-05-12,937.25,943.86,930.65,937.46,15140000,937.46 1971-05-11,932.55,943.79,927.63,937.25,17730000,937.25 1971-05-10,936.97,938.87,925.24,932.55,12810000,932.55 1971-05-07,937.39,941.96,927.35,936.97,16490000,936.97 1971-05-06,939.92,948.85,933.53,937.39,19300000,937.39 1971-05-05,938.45,943.02,929.45,939.92,17270000,939.92 1971-05-04,932.41,942.38,927.42,938.45,17310000,938.45 1971-05-03,941.75,942.17,923.83,932.41,16120000,932.41 1971-04-30,948.15,951.31,936.20,941.75,17490000,941.75 1971-04-29,950.82,957.35,941.75,948.15,20340000,948.15 1971-04-28,947.09,958.12,941.82,950.82,24820000,950.82 1971-04-27,944.00,953.63,935.22,947.09,21250000,947.09 1971-04-26,947.79,953.20,937.46,944.00,18860000,944.00 1971-04-23,940.63,952.92,934.87,947.79,20150000,947.79 1971-04-22,941.33,947.65,932.97,940.63,19270000,940.63 1971-04-21,944.42,949.20,933.39,941.33,17040000,941.33 1971-04-20,948.85,952.43,940.49,944.42,17880000,944.42 1971-04-19,940.21,953.49,939.01,948.85,17730000,948.85 1971-04-16,938.17,945.55,932.55,940.21,18280000,940.21 1971-04-15,932.55,945.69,930.44,938.17,22540000,938.17 1971-04-14,927.28,937.96,921.51,932.55,19440000,932.55 1971-04-13,926.64,935.99,921.51,927.28,23200000,927.28 1971-04-12,920.39,932.83,917.02,926.64,19410000,926.64 1971-04-08,918.49,925.94,911.68,920.39,17590000,920.39 1971-04-07,912.73,925.59,908.94,918.49,22270000,918.49 1971-04-06,905.07,917.79,901.77,912.73,19990000,912.73 1971-04-05,903.04,909.99,897.48,905.07,16040000,905.07 1971-04-02,903.88,910.06,898.96,903.04,14520000,903.04 1971-04-01,904.37,910.06,898.75,903.88,13470000,903.88 1971-03-31,903.39,910.98,898.33,904.37,17610000,904.37 1971-03-30,903.48,909.99,896.78,903.39,15430000,903.39 1971-03-29,903.48,909.78,896.29,903.48,13650000,903.48 1971-03-26,900.81,910.32,895.95,903.48,15560000,903.48 1971-03-25,899.37,903.96,889.03,900.81,15870000,900.81 1971-03-24,908.89,910.05,896.77,899.37,15770000,899.37 1971-03-23,910.60,915.32,902.31,908.89,16470000,908.89 1971-03-22,912.92,917.78,905.53,910.60,14290000,910.60 1971-03-19,916.83,921.69,908.61,912.92,15150000,912.92 1971-03-18,914.02,922.30,910.46,916.83,17910000,916.83 1971-03-17,914.64,918.06,904.16,914.02,17070000,914.02 1971-03-16,908.20,920.93,908.06,914.64,22270000,914.64 1971-03-15,898.34,911.21,895.33,908.20,18920000,908.20 1971-03-12,899.44,903.14,892.25,898.34,14680000,898.34 1971-03-11,895.88,906.76,890.81,899.44,19830000,899.44 1971-03-10,899.10,902.59,891.50,895.88,17220000,895.88 1971-03-09,898.62,906.15,892.25,899.10,20490000,899.10 1971-03-08,898.00,905.19,891.43,898.62,19340000,898.62 1971-03-05,891.36,903.61,889.24,898.00,22430000,898.00 1971-03-04,882.39,894.92,878.01,891.36,17350000,891.36 1971-03-03,883.01,888.55,876.85,882.39,14680000,882.39 1971-03-02,882.53,887.94,875.27,883.01,14870000,883.01 1971-03-01,878.83,887.46,873.22,882.53,13020000,882.53 1971-02-26,881.98,886.36,870.55,878.83,17250000,878.83 1971-02-25,875.62,887.53,871.92,881.98,16200000,881.98 1971-02-24,870.00,881.37,868.29,875.62,15930000,875.62 1971-02-23,868.98,875.62,861.99,870.00,15080000,870.00 1971-02-22,877.05,877.05,863.29,868.98,15840000,868.98 1971-02-19,885.06,886.50,875.00,878.56,17860000,878.56 1971-02-18,887.87,891.16,877.67,885.06,16650000,885.06 1971-02-17,890.06,894.78,879.38,887.87,18720000,887.87 1971-02-16,888.83,898.14,882.26,890.06,21350000,890.06 1971-02-12,885.34,894.03,881.30,888.83,18470000,888.83 1971-02-11,881.09,891.02,878.77,885.34,19260000,885.34 1971-02-10,879.79,884.99,869.04,881.09,19040000,881.09 1971-02-09,882.12,889.17,874.79,879.79,28250000,879.79 1971-02-08,876.57,885.47,870.41,882.12,25590000,882.12 1971-02-05,874.79,882.67,868.22,876.57,20480000,876.57 1971-02-04,876.23,880.07,868.29,874.79,20860000,874.79 1971-02-03,874.59,880.61,866.51,876.23,21680000,876.23 1971-02-02,877.81,882.39,868.09,874.59,22030000,874.59 1971-02-01,868.50,882.19,866.03,877.81,20650000,877.81 1971-01-29,865.14,873.49,860.15,868.50,20960000,868.50 1971-01-28,860.83,869.11,853.92,865.14,18840000,865.14 1971-01-27,866.79,867.47,853.85,860.83,20640000,860.83 1971-01-26,865.62,873.84,859.19,866.79,21380000,866.79 1971-01-25,861.31,870.48,855.28,865.62,19050000,865.62 1971-01-22,854.74,866.58,854.46,861.31,21680000,861.31 1971-01-21,849.95,859.60,845.08,854.74,19060000,854.74 1971-01-20,849.47,856.24,842.96,849.95,18330000,849.95 1971-01-19,847.82,853.78,841.87,849.47,15800000,849.47 1971-01-18,845.70,854.39,841.25,847.82,15400000,847.82 1971-01-15,843.31,853.37,838.72,845.70,18010000,845.70 1971-01-14,841.11,849.26,832.97,843.31,17600000,843.31 1971-01-13,844.19,853.44,835.57,841.11,19070000,841.11 1971-01-12,837.21,849.19,834.68,844.19,17820000,844.19 1971-01-11,837.01,841.46,828.31,837.21,14720000,837.21 1971-01-08,837.83,843.31,831.94,837.01,14100000,837.01 1971-01-07,837.97,843.78,832.42,837.83,16460000,837.83 1971-01-06,835.77,843.92,831.67,837.97,16960000,837.97 1971-01-05,830.57,840.09,827.35,835.77,12600000,835.77 1971-01-04,838.92,839.06,826.53,830.57,10010000,830.57 1970-12-31,841.32,844.95,832.56,838.92,13390000,838.92 1970-12-30,842.00,848.23,836.05,841.32,19140000,841.32 1970-12-29,830.91,844.19,828.04,842.00,17750000,842.00 1970-12-28,828.38,834.75,824.07,830.91,12290000,830.91 1970-12-24,823.11,831.67,820.65,828.38,12140000,828.38 1970-12-23,822.77,830.16,816.54,823.11,15400000,823.11 1970-12-22,821.54,828.93,815.65,822.77,14510000,822.77 1970-12-21,822.77,828.45,815.31,821.54,12690000,821.54 1970-12-18,822.15,828.79,815.51,822.77,14360000,822.77 1970-12-17,819.07,826.94,815.85,822.15,13660000,822.15 1970-12-16,819.62,823.52,810.17,819.07,14240000,819.07 1970-12-15,823.18,826.12,813.25,819.62,13420000,819.62 1970-12-14,825.92,832.28,818.66,823.18,13810000,823.18 1970-12-11,821.06,831.67,818.18,825.92,15790000,825.92 1970-12-10,815.24,824.75,811.68,821.06,14610000,821.06 1970-12-09,815.10,819.96,806.27,815.24,13550000,815.24 1970-12-08,818.66,822.49,809.69,815.10,14370000,815.10 1970-12-07,816.06,823.59,810.24,818.66,15530000,818.66 1970-12-04,808.53,819.07,801.34,816.06,15980000,816.06 1970-12-03,802.64,816.88,801.48,808.53,20480000,808.53 1970-12-02,794.29,805.72,787.86,802.64,17960000,802.64 1970-12-01,794.09,805.65,787.45,794.29,20170000,794.29 1970-11-30,781.35,797.51,780.26,794.09,17700000,794.09 1970-11-27,774.71,783.61,771.15,781.35,10130000,781.35 1970-11-25,772.73,780.94,768.48,774.71,13490000,774.71 1970-11-24,767.52,775.60,762.60,772.73,12560000,772.73 1970-11-23,761.57,773.41,759.79,767.52,12720000,767.52 1970-11-20,755.82,764.10,752.19,761.57,10920000,761.57 1970-11-19,754.24,760.47,749.52,755.82,9280000,755.82 1970-11-18,760.47,761.77,751.78,754.24,9850000,754.24 1970-11-17,760.13,768.48,756.37,760.47,9450000,760.47 1970-11-16,759.79,764.99,752.46,760.13,9160000,760.13 1970-11-13,767.94,767.94,755.61,759.79,11890000,759.79 1970-11-12,779.50,781.76,765.88,768.00,12520000,768.00 1970-11-11,777.38,788.75,776.29,779.50,13520000,779.50 1970-11-10,777.66,783.00,773.34,777.38,12030000,777.38 1970-11-09,771.97,782.38,769.85,777.66,10890000,777.66 1970-11-06,771.56,777.38,765.81,771.97,9970000,771.97 1970-11-05,770.81,775.94,765.68,771.56,10800000,771.56 1970-11-04,768.07,777.25,764.92,770.81,12180000,770.81 1970-11-03,758.01,771.50,756.02,768.07,11760000,768.07 1970-11-02,755.61,762.25,750.68,758.01,9470000,758.01 1970-10-30,753.56,759.38,748.36,755.61,10520000,755.61 1970-10-29,755.96,760.34,749.45,753.56,10440000,753.56 1970-10-28,754.45,759.31,746.71,755.96,10660000,755.96 1970-10-27,756.43,759.38,750.21,754.45,9680000,754.45 1970-10-26,759.38,764.24,753.42,756.43,9200000,756.43 1970-10-23,757.87,764.03,753.90,759.38,10270000,759.38 1970-10-22,759.65,763.28,753.22,757.87,9000000,757.87 1970-10-21,758.83,769.24,756.64,759.65,11330000,759.65 1970-10-20,756.50,764.03,750.82,758.83,10630000,758.83 1970-10-19,763.35,766.09,753.29,756.50,9890000,756.50 1970-10-16,767.87,771.70,759.10,763.35,11300000,763.35 1970-10-15,762.73,773.27,760.88,767.87,11250000,767.87 1970-10-14,760.06,767.25,754.72,762.73,9920000,762.73 1970-10-13,764.24,767.11,754.38,760.06,9500000,760.06 1970-10-12,768.69,770.47,758.42,764.24,8570000,764.24 1970-10-09,777.04,780.46,764.72,768.69,13980000,768.69 1970-10-08,783.68,789.29,773.48,777.04,14500000,777.04 1970-10-07,782.45,788.47,771.22,783.68,15610000,783.68 1970-10-06,776.70,791.07,773.62,782.45,20240000,782.45 1970-10-05,766.16,780.53,763.76,776.70,19760000,776.70 1970-10-02,760.68,769.72,756.43,766.16,15420000,766.16 1970-10-01,760.68,764.79,754.38,760.68,9700000,760.68 1970-09-30,760.88,766.91,754.79,760.68,14830000,760.68 1970-09-29,758.97,766.09,749.66,760.88,17880000,760.88 1970-09-28,761.77,764.65,750.68,758.97,14390000,758.97 1970-09-25,759.31,767.39,754.65,761.77,20470000,761.77 1970-09-24,754.38,764.99,749.04,759.31,21340000,759.31 1970-09-23,747.47,762.32,744.25,754.38,16940000,754.38 1970-09-22,751.92,753.15,741.51,747.47,12110000,747.47 1970-09-21,758.49,763.96,748.22,751.92,12540000,751.92 1970-09-18,757.67,766.29,751.92,758.49,15900000,758.49 1970-09-17,754.31,766.29,751.57,757.67,15530000,757.67 1970-09-16,750.55,759.45,744.18,754.31,12090000,754.31 1970-09-15,757.05,757.05,745.14,750.55,9830000,750.55 1970-09-14,761.84,766.84,751.37,757.12,11900000,757.12 1970-09-11,760.75,767.32,755.48,761.84,12140000,761.84 1970-09-10,766.43,768.69,754.65,760.75,11900000,760.75 1970-09-09,773.14,776.97,759.31,766.43,16250000,766.43 1970-09-08,771.15,778.27,759.99,773.14,17110000,773.14 1970-09-04,765.27,775.12,761.91,771.15,15360000,771.15 1970-09-03,756.64,770.33,755.89,765.27,14110000,765.27 1970-09-02,758.15,760.13,748.43,756.64,9710000,756.64 1970-09-01,764.58,766.16,754.93,758.15,10960000,758.15 1970-08-31,765.81,770.67,759.86,764.58,10740000,764.58 1970-08-28,759.79,772.04,756.37,765.81,13820000,765.81 1970-08-27,760.47,766.84,752.53,759.79,12440000,759.79 1970-08-26,758.97,769.78,755.34,760.47,15970000,760.47 1970-08-25,759.58,764.17,747.47,758.97,17520000,758.97 1970-08-24,747.19,765.68,747.19,759.58,18910000,759.58 1970-08-21,729.60,748.84,728.78,745.41,13420000,745.41 1970-08-20,723.99,732.82,717.76,729.60,10170000,729.60 1970-08-19,716.66,728.92,715.09,723.99,9870000,723.99 1970-08-18,709.47,721.11,709.47,716.66,9500000,716.66 1970-08-17,710.84,713.92,704.41,709.06,6940000,709.06 1970-08-14,707.35,715.84,704.75,710.84,7850000,710.84 1970-08-13,710.64,713.51,702.83,707.35,8640000,707.35 1970-08-12,712.55,716.87,707.83,710.64,7440000,710.64 1970-08-11,713.92,716.73,706.39,712.55,7330000,712.55 1970-08-10,725.01,725.01,709.95,713.92,7580000,713.92 1970-08-07,722.82,731.93,718.03,725.70,9370000,725.70 1970-08-06,724.81,728.92,716.86,722.82,7560000,722.82 1970-08-05,725.90,732.06,719.67,724.81,7660000,724.81 1970-08-04,722.96,728.85,714.88,725.90,8310000,725.90 1970-08-03,734.12,734.67,718.65,722.96,7650000,722.96 1970-07-31,734.73,743.36,728.23,734.12,11640000,734.12 1970-07-30,735.56,740.07,728.78,734.73,10430000,734.73 1970-07-29,731.45,742.40,729.39,735.56,12580000,735.56 1970-07-28,730.08,735.49,724.40,731.45,9040000,731.45 1970-07-27,730.22,735.01,724.53,730.08,7460000,730.08 1970-07-24,732.68,736.58,722.96,730.22,9520000,730.22 1970-07-23,724.67,737.88,718.44,732.68,12460000,732.68 1970-07-22,722.07,735.01,716.25,724.67,12460000,724.67 1970-07-21,733.91,734.12,717.96,722.07,9940000,722.07 1970-07-20,735.08,743.36,728.64,733.91,11660000,733.91 1970-07-17,725.49,739.46,725.49,735.08,13870000,735.08 1970-07-16,712.14,730.76,712.14,723.44,12200000,723.44 1970-07-15,703.04,716.32,699.48,711.66,8860000,711.66 1970-07-14,702.22,707.69,696.40,703.04,7360000,703.04 1970-07-13,700.10,709.34,696.19,702.22,7450000,702.22 1970-07-10,692.77,704.41,689.69,700.10,10160000,700.10 1970-07-09,682.09,697.63,681.48,692.77,12820000,692.77 1970-07-08,669.36,684.08,666.00,682.09,10970000,682.09 1970-07-07,675.66,679.90,665.32,669.36,10470000,669.36 1970-07-06,689.14,689.90,671.69,675.66,9340000,675.66 1970-07-02,687.64,697.02,684.76,689.14,8440000,689.14 1970-07-01,683.53,692.02,678.26,687.64,8610000,687.64 1970-06-30,682.91,692.36,679.08,683.53,9280000,683.53 1970-06-29,687.84,691.13,679.15,682.91,8770000,682.91 1970-06-26,693.59,700.51,686.06,687.84,9160000,687.84 1970-06-25,692.29,701.60,686.40,693.59,8200000,693.59 1970-06-24,698.11,705.37,685.31,692.29,12630000,692.29 1970-06-23,716.11,716.66,696.40,698.11,10790000,698.11 1970-06-22,720.43,723.17,707.15,716.11,8700000,716.11 1970-06-19,713.65,728.23,713.65,720.43,10980000,720.43 1970-06-18,704.68,718.92,696.95,712.69,8870000,712.69 1970-06-17,706.26,715.36,699.55,704.68,9870000,704.68 1970-06-16,687.36,711.80,686.88,706.26,11330000,706.26 1970-06-15,684.21,694.82,679.83,687.36,6920000,687.36 1970-06-12,684.42,688.66,674.90,684.21,8890000,684.21 1970-06-11,694.35,695.44,677.85,684.42,7770000,684.42 1970-06-10,700.16,703.24,690.03,694.35,7240000,694.35 1970-06-09,700.23,705.64,695.17,700.16,7050000,700.16 1970-06-08,695.03,709.75,689.83,700.23,8040000,700.23 1970-06-05,706.53,707.56,687.91,695.03,12450000,695.03 1970-06-04,713.86,723.92,704.41,706.53,14380000,706.53 1970-06-03,709.61,721.73,702.22,713.86,16600000,713.86 1970-06-02,710.36,719.13,698.32,709.61,13480000,709.61 1970-06-01,700.44,715.64,694.14,710.36,15020000,710.36 1970-05-29,684.15,703.86,673.06,700.44,14630000,700.44 1970-05-28,666.96,690.92,666.96,684.15,18910000,684.15 1970-05-27,631.98,668.81,631.98,663.20,17460000,663.20 1970-05-26,641.36,649.64,627.46,631.16,17030000,631.16 1970-05-25,660.80,660.80,639.10,641.36,12660000,641.36 1970-05-22,665.25,675.11,653.27,662.17,12170000,662.17 1970-05-21,676.55,676.62,654.37,665.25,16710000,665.25 1970-05-20,690.72,690.72,674.56,676.55,13020000,676.55 1970-05-19,702.81,704.20,689.62,691.40,9480000,691.40 1970-05-18,702.22,709.74,695.35,702.81,8280000,702.81 1970-05-15,684.79,705.58,680.37,702.22,14570000,702.22 1970-05-14,693.57,693.57,673.51,684.79,13920000,684.79 1970-05-13,703.80,703.80,686.64,693.84,10720000,693.84 1970-05-12,710.07,715.15,699.31,704.59,10850000,704.59 1970-05-11,717.73,721.03,707.04,710.07,6650000,710.07 1970-05-08,723.07,725.51,713.04,717.73,6930000,717.73 1970-05-07,718.39,730.33,713.37,723.07,9530000,723.07 1970-05-06,709.74,731.19,707.76,718.39,14380000,718.39 1970-05-05,714.56,720.63,704.46,709.74,10580000,709.74 1970-05-04,732.38,732.38,710.40,714.56,11450000,714.56 1970-05-01,736.07,740.76,723.60,733.63,8290000,733.63 1970-04-30,737.39,745.58,728.48,736.07,9880000,736.07 1970-04-29,724.33,740.03,716.74,737.39,15800000,737.39 1970-04-28,735.15,740.76,720.70,724.33,12620000,724.33 1970-04-27,747.29,750.46,731.72,735.15,10240000,735.15 1970-04-24,750.59,754.42,742.21,747.29,10410000,747.29 1970-04-23,760.63,760.63,745.45,750.59,11050000,750.59 1970-04-22,772.51,774.35,758.51,762.61,10780000,762.61 1970-04-21,775.87,783.00,769.87,772.51,8490000,772.51 1970-04-20,775.94,780.56,769.67,775.87,8280000,775.87 1970-04-17,775.87,781.22,766.96,775.94,10990000,775.94 1970-04-16,782.60,787.22,773.03,775.87,10250000,775.87 1970-04-15,780.56,788.87,777.06,782.60,9410000,782.60 1970-04-14,785.90,787.35,772.90,780.56,10840000,780.56 1970-04-13,790.46,793.82,782.07,785.90,8810000,785.90 1970-04-10,792.50,797.25,785.04,790.46,10020000,790.46 1970-04-09,791.64,798.84,786.96,792.50,9060000,792.50 1970-04-08,791.64,796.00,787.42,791.64,9070000,791.64 1970-04-07,791.18,797.12,785.70,791.64,8490000,791.64 1970-04-06,791.84,796.07,786.30,791.18,8380000,791.18 1970-04-03,792.37,796.40,787.02,791.84,9920000,791.84 1970-04-02,792.04,797.91,787.02,792.37,10520000,792.37 1970-04-01,785.57,795.67,784.05,792.04,9810000,792.04 1970-03-31,784.65,789.20,778.38,785.57,8370000,785.57 1970-03-30,791.05,792.37,779.17,784.65,9600000,784.65 1970-03-26,790.13,797.45,785.24,791.05,11350000,791.05 1970-03-25,775.67,803.26,775.67,790.13,17500000,790.13 1970-03-24,764.52,776.99,764.52,773.76,8840000,773.76 1970-03-23,763.66,768.41,757.99,763.60,7330000,763.60 1970-03-20,764.98,768.55,758.91,763.66,7910000,763.66 1970-03-19,767.95,773.76,761.55,764.98,8930000,764.98 1970-03-18,767.42,775.67,763.93,767.95,9790000,767.95 1970-03-17,765.05,773.36,759.17,767.42,9090000,767.42 1970-03-16,772.11,772.84,760.16,765.05,8910000,765.05 1970-03-13,776.47,779.30,768.02,772.11,9560000,772.11 1970-03-12,778.12,781.55,771.77,776.47,9140000,776.47 1970-03-11,779.70,785.84,774.29,778.12,9180000,778.12 1970-03-10,778.31,785.37,772.18,779.70,9450000,779.70 1970-03-09,784.12,785.57,773.56,778.31,9760000,778.31 1970-03-06,787.55,789.93,779.37,784.12,10980000,784.12 1970-03-05,788.15,795.54,783.13,787.55,11370000,787.55 1970-03-04,787.42,795.54,782.27,788.15,11850000,788.15 1970-03-03,780.23,790.52,775.08,787.42,11700000,787.42 1970-03-02,777.59,790.52,774.49,780.23,12270000,780.23 1970-02-27,764.45,784.19,762.61,777.59,12890000,777.59 1970-02-26,768.28,770.53,755.48,764.45,11540000,764.45 1970-02-25,754.42,772.70,749.47,768.28,13210000,768.28 1970-02-24,757.46,761.68,748.88,754.42,10810000,754.42 1970-02-20,757.92,764.45,749.34,757.46,10790000,757.46 1970-02-19,756.80,766.90,751.58,757.92,12890000,757.92 1970-02-18,747.43,762.61,745.45,756.80,11950000,756.80 1970-02-17,753.70,757.13,741.09,747.43,10140000,747.43 1970-02-16,753.30,761.09,748.02,753.70,19780000,753.70 1970-02-13,755.61,758.65,745.97,753.30,11060000,753.30 1970-02-12,757.33,762.28,748.88,755.61,10010000,755.61 1970-02-11,746.63,760.69,739.90,757.33,12260000,757.33 1970-02-10,755.68,758.51,743.20,746.63,10110000,746.63 1970-02-09,752.77,764.92,750.40,755.68,10830000,755.68 1970-02-06,750.26,758.71,744.98,752.77,10150000,752.77 1970-02-05,754.49,756.73,743.33,750.26,9430000,750.26 1970-02-04,757.46,765.97,748.09,754.49,11040000,754.49 1970-02-03,746.44,764.45,738.78,757.46,16050000,757.46 1970-02-02,744.06,756.47,739.37,746.44,13440000,746.44 1970-01-30,748.35,756.20,739.11,744.06,12320000,744.06 1970-01-29,758.84,760.69,743.73,748.35,12210000,748.35 1970-01-28,763.99,769.87,755.74,758.84,10510000,758.84 1970-01-27,768.88,773.03,757.92,763.99,9630000,763.99 1970-01-26,775.54,777.52,762.54,768.88,10670000,768.88 1970-01-23,786.10,789.40,774.35,775.54,11000000,775.54 1970-01-22,782.27,793.16,777.39,786.10,11050000,786.10 1970-01-21,777.85,788.61,773.96,782.27,9880000,782.27 1970-01-20,776.07,783.13,770.72,777.85,11050000,777.85 1970-01-19,782.60,784.52,772.18,776.07,9500000,776.07 1970-01-16,785.04,791.97,778.25,782.60,11940000,782.60 1970-01-15,787.16,791.25,777.98,785.04,11120000,785.04 1970-01-14,788.01,796.73,780.36,787.16,10380000,787.16 1970-01-13,790.52,796.79,782.47,788.01,9870000,788.01 1970-01-12,798.11,799.23,786.30,790.52,8900000,790.52 1970-01-09,802.07,805.70,794.35,798.11,9380000,798.11 1970-01-08,801.81,809.33,796.92,802.07,10670000,802.07 1970-01-07,803.66,808.47,796.20,801.81,10010000,801.81 1970-01-06,811.31,814.08,798.71,803.66,11460000,803.66 1970-01-05,809.20,819.23,804.84,811.31,11490000,811.31 1970-01-02,800.36,813.56,797.32,809.20,8050000,809.20 1969-12-31,794.68,806.69,792.24,800.36,19380000,800.36 1969-12-30,792.37,799.63,786.30,794.68,15790000,794.68 1969-12-29,797.65,803.19,787.62,792.37,12500000,792.37 1969-12-26,794.15,802.47,790.85,797.65,6750000,797.65 1969-12-24,783.79,799.63,782.67,794.15,11670000,794.15 1969-12-23,785.97,789.66,776.27,783.79,13890000,783.79 1969-12-22,789.86,796.13,781.48,785.97,12680000,785.97 1969-12-19,783.79,798.18,780.76,789.86,15420000,789.86 1969-12-18,769.93,788.87,764.45,783.79,15950000,783.79 1969-12-17,773.83,780.16,765.71,769.93,12840000,769.93 1969-12-16,784.05,787.42,770.46,773.83,11880000,773.83 1969-12-15,786.69,791.97,779.63,784.05,11100000,784.05 1969-12-12,783.53,794.61,780.23,786.69,11630000,786.69 1969-12-11,783.99,791.25,778.38,783.53,10430000,783.53 1969-12-10,783.79,791.51,773.76,783.99,12590000,783.99 1969-12-09,785.04,794.42,778.18,783.79,12290000,783.79 1969-12-08,793.03,797.32,781.48,785.04,9990000,785.04 1969-12-05,796.53,804.05,788.94,793.03,11150000,793.03 1969-12-04,793.36,801.15,783.86,796.53,13230000,796.53 1969-12-03,801.35,804.78,789.93,793.36,11300000,793.36 1969-12-02,805.04,808.34,794.94,801.35,9940000,801.35 1969-12-01,812.30,816.39,802.20,805.04,9950000,805.04 1969-11-28,810.52,818.57,805.37,812.30,8550000,812.30 1969-11-26,807.29,814.15,800.36,810.52,10630000,810.52 1969-11-25,812.90,818.37,803.92,807.29,11560000,807.29 1969-11-24,823.13,825.96,808.01,812.90,10940000,812.90 1969-11-21,831.18,832.43,820.35,823.13,9840000,823.13 1969-11-20,839.96,840.28,823.98,831.18,12010000,831.18 1969-11-19,845.17,848.60,835.93,839.96,11240000,839.96 1969-11-18,842.53,848.67,836.99,845.17,11010000,845.17 1969-11-17,849.26,851.04,837.45,842.53,10120000,842.53 1969-11-14,849.85,854.74,842.00,849.26,10580000,849.26 1969-11-13,855.99,858.90,844.64,849.85,12090000,849.85 1969-11-12,859.75,863.98,851.70,855.99,12480000,855.99 1969-11-11,863.05,866.62,853.62,859.75,10080000,859.75 1969-11-10,860.48,871.77,856.65,863.05,12490000,863.05 1969-11-07,855.20,867.28,850.38,860.48,13280000,860.48 1969-11-06,854.08,859.82,846.49,855.20,11110000,855.20 1969-11-05,853.48,862.59,847.54,854.08,12110000,854.08 1969-11-04,854.54,859.82,841.67,853.48,12340000,853.48 1969-11-03,855.99,860.68,847.41,854.54,11140000,854.54 1969-10-31,850.51,860.61,845.04,855.99,13100000,855.99 1969-10-30,848.34,854.28,839.82,850.51,12820000,850.51 1969-10-29,855.86,858.83,845.37,848.34,12380000,848.34 1969-10-28,860.28,865.23,851.90,855.86,12410000,855.86 1969-10-27,862.26,866.95,852.56,860.28,12160000,860.28 1969-10-24,855.73,868.00,851.31,862.26,15430000,862.26 1969-10-23,860.35,863.38,845.23,855.73,14780000,855.73 1969-10-22,846.88,864.37,844.90,860.35,19320000,860.35 1969-10-21,839.23,850.78,834.94,846.88,16460000,846.88 1969-10-20,836.06,844.18,829.59,839.23,13540000,839.23 1969-10-17,838.77,845.23,829.73,836.06,13740000,836.06 1969-10-16,830.06,848.14,825.50,838.77,19500000,838.77 1969-10-15,832.43,837.12,823.13,830.06,15740000,830.06 1969-10-14,821.21,839.49,821.21,832.43,19950000,832.43 1969-10-13,806.96,820.88,804.51,819.30,13620000,819.30 1969-10-10,803.79,813.23,799.89,806.96,12210000,806.96 1969-10-09,802.20,808.94,793.95,803.79,10420000,803.79 1969-10-08,806.23,809.79,797.65,802.20,10370000,802.20 1969-10-07,809.40,813.23,803.13,806.23,10050000,806.23 1969-10-06,808.41,814.94,802.73,809.40,9180000,809.40 1969-10-03,811.84,819.76,803.59,808.41,12410000,808.41 1969-10-02,806.89,815.87,797.78,811.84,11430000,811.84 1969-10-01,813.09,814.48,803.37,806.89,9090000,806.89 1969-09-30,818.04,821.67,808.54,813.09,9180000,813.09 1969-09-29,824.18,826.03,811.05,818.04,10170000,818.04 1969-09-26,829.92,832.83,820.09,824.18,9680000,824.18 1969-09-25,834.68,838.50,826.49,829.92,10690000,829.92 1969-09-24,834.81,840.48,829.53,834.68,11320000,834.68 1969-09-23,831.77,841.80,827.28,834.81,13030000,834.81 1969-09-22,830.39,835.80,823.79,831.77,9280000,831.77 1969-09-19,831.57,837.32,825.30,830.39,12270000,830.39 1969-09-18,826.56,836.66,822.80,831.57,11170000,831.57 1969-09-17,831.64,835.47,821.61,826.56,10980000,826.56 1969-09-16,830.45,836.85,824.84,831.64,11160000,831.64 1969-09-15,824.25,835.73,821.28,830.45,10680000,830.45 1969-09-12,825.77,830.65,818.37,824.25,10800000,824.25 1969-09-11,828.01,838.50,821.94,825.77,12370000,825.77 1969-09-10,815.67,831.71,813.69,828.01,11490000,828.01 1969-09-09,811.84,822.20,804.45,815.67,10980000,815.67 1969-09-08,819.50,820.55,808.67,811.84,8310000,811.84 1969-09-05,825.30,826.82,816.46,819.50,8890000,819.50 1969-09-04,835.67,835.67,822.20,825.30,9380000,825.30 1969-09-03,837.78,841.61,829.86,835.67,8760000,835.67 1969-09-02,836.72,845.96,831.31,837.78,8560000,837.78 1969-08-29,828.41,839.96,825.37,836.72,8850000,836.72 1969-08-28,824.78,832.23,822.14,828.41,7730000,828.41 1969-08-27,823.52,830.39,819.43,824.78,9100000,824.78 1969-08-26,830.39,830.39,818.84,823.52,8910000,823.52 1969-08-25,837.25,841.54,828.01,831.44,8410000,831.44 1969-08-22,834.87,843.72,829.33,837.25,10140000,837.25 1969-08-21,833.22,839.69,828.60,834.87,8420000,834.87 1969-08-20,833.69,837.91,827.55,833.22,9680000,833.22 1969-08-19,827.68,841.28,824.97,833.69,12640000,833.69 1969-08-18,820.88,830.39,816.46,827.68,9420000,827.68 1969-08-15,813.23,825.17,810.52,820.88,10210000,820.88 1969-08-14,809.13,818.11,805.37,813.23,9690000,813.23 1969-08-13,812.96,815.07,800.62,809.13,9910000,809.13 1969-08-12,819.83,821.54,810.39,812.96,7870000,812.96 1969-08-11,824.46,826.76,816.33,819.83,6680000,819.83 1969-08-08,826.27,830.27,819.87,824.46,8760000,824.46 1969-08-07,825.88,832.02,820.26,826.27,9450000,826.27 1969-08-06,821.23,831.63,817.67,825.88,11100000,825.88 1969-08-05,822.58,827.30,813.40,821.23,8940000,821.23 1969-08-04,826.59,835.06,817.61,822.58,10700000,822.58 1969-08-01,815.60,833.63,815.60,826.59,15070000,826.59 1969-07-31,803.58,819.87,800.67,815.47,14160000,815.47 1969-07-30,801.96,809.40,788.07,803.58,15580000,803.58 1969-07-29,806.23,817.86,798.41,801.96,13630000,801.96 1969-07-28,818.06,819.48,803.26,806.23,11800000,806.23 1969-07-25,826.53,830.08,814.83,818.06,9800000,818.06 1969-07-24,827.95,834.22,821.10,826.53,9750000,826.53 1969-07-23,834.02,836.48,821.35,827.95,11680000,827.95 1969-07-22,845.92,851.54,831.76,834.02,9780000,834.02 1969-07-18,853.09,853.54,839.06,845.92,8590000,845.92 1969-07-17,849.34,863.04,845.79,853.09,10450000,853.09 1969-07-16,841.13,854.64,840.29,849.34,10470000,849.34 1969-07-15,843.14,847.60,832.79,841.13,11110000,841.13 1969-07-14,852.25,855.93,841.07,843.14,8310000,843.14 1969-07-11,847.79,858.91,844.49,852.25,11730000,852.25 1969-07-10,861.04,861.04,844.30,847.79,11450000,847.79 1969-07-09,870.35,871.06,857.94,861.62,9320000,861.62 1969-07-08,880.82,880.82,866.47,870.35,9320000,870.35 1969-07-07,886.12,893.87,879.27,883.21,9970000,883.21 1969-07-03,880.69,891.55,877.71,886.12,10110000,886.12 1969-07-02,875.90,884.69,872.87,880.69,11350000,880.69 1969-07-01,873.19,881.27,867.83,875.90,9890000,875.90 1969-06-30,869.76,880.69,867.18,873.19,8640000,873.19 1969-06-27,870.28,876.49,863.56,869.76,9020000,869.76 1969-06-26,874.10,876.16,862.78,870.28,10310000,870.28 1969-06-25,877.20,884.69,870.41,874.10,10490000,874.10 1969-06-24,870.86,883.73,869.25,877.20,11460000,877.20 1969-06-23,876.16,880.75,862.46,870.86,12900000,870.86 1969-06-20,882.37,888.77,873.32,876.16,11360000,876.16 1969-06-19,887.09,889.54,877.46,882.37,11160000,882.37 1969-06-18,885.73,897.10,882.30,887.09,11290000,887.09 1969-06-17,891.16,893.29,876.55,885.73,12210000,885.73 1969-06-16,894.84,902.79,888.83,891.16,10400000,891.16 1969-06-13,892.58,902.28,886.89,894.84,13070000,894.84 1969-06-12,904.60,906.22,890.71,892.58,11790000,892.58 1969-06-11,912.49,913.39,899.24,904.60,13640000,904.60 1969-06-10,918.05,922.83,909.32,912.49,10660000,912.49 1969-06-09,924.77,925.48,911.91,918.05,10650000,918.05 1969-06-06,930.71,936.14,921.41,924.77,12520000,924.77 1969-06-05,928.84,937.24,925.35,930.71,12350000,930.71 1969-06-04,930.78,936.47,925.87,928.84,10840000,928.84 1969-06-03,933.17,937.56,925.74,930.78,11190000,930.78 1969-06-02,937.56,938.73,927.68,933.17,9180000,933.17 1969-05-29,936.92,944.29,931.62,937.56,11770000,937.56 1969-05-28,938.66,941.96,928.58,936.92,11330000,936.92 1969-05-27,946.94,950.23,934.33,938.66,10580000,938.66 1969-05-26,947.45,954.05,942.22,946.94,9030000,946.94 1969-05-23,950.04,953.92,942.48,947.45,10900000,947.45 1969-05-22,951.78,963.09,946.10,950.04,13710000,950.04 1969-05-21,949.26,956.18,941.31,951.78,12100000,951.78 1969-05-20,959.02,959.86,944.35,949.26,10280000,949.26 1969-05-19,967.30,968.98,954.56,959.02,9790000,959.02 1969-05-16,965.16,971.56,959.80,967.30,12280000,967.30 1969-05-15,968.85,973.18,958.51,965.16,11930000,965.16 1969-05-14,962.97,974.92,959.86,968.85,14360000,968.85 1969-05-13,957.86,967.62,954.37,962.97,12910000,962.97 1969-05-12,961.61,964.97,951.14,957.86,10550000,957.86 1969-05-09,963.68,971.11,955.86,961.61,12530000,961.61 1969-05-08,959.60,970.14,954.76,963.68,13050000,963.68 1969-05-07,962.06,967.36,953.08,959.60,14030000,959.60 1969-05-06,958.95,968.23,954.05,962.06,14700000,962.06 1969-05-05,957.17,965.43,951.07,958.95,13300000,958.95 1969-05-02,949.22,961.68,945.41,957.17,13070000,957.17 1969-05-01,950.18,958.69,942.74,949.22,14380000,949.22 1969-04-30,934.10,956.28,933.97,950.18,19350000,950.18 1969-04-29,925.08,937.60,922.28,934.10,14730000,934.10 1969-04-28,924.00,930.73,917.13,925.08,11120000,925.08 1969-04-25,921.20,929.65,916.24,924.00,12480000,924.00 1969-04-24,917.64,925.65,911.60,921.20,11340000,921.20 1969-04-23,918.59,927.43,913.13,917.64,12220000,917.64 1969-04-22,917.51,922.09,908.81,918.59,10250000,918.59 1969-04-21,924.82,928.19,913.76,917.51,10010000,917.51 1969-04-18,924.12,932.26,916.94,924.82,10850000,924.82 1969-04-17,923.49,930.80,917.90,924.12,9360000,924.12 1969-04-16,931.94,932.89,920.69,923.49,9680000,923.49 1969-04-15,932.64,937.28,924.63,931.94,9610000,931.94 1969-04-14,933.46,938.36,926.73,932.64,8990000,932.64 1969-04-11,932.89,939.12,927.17,933.46,10650000,933.46 1969-04-10,929.97,941.28,927.87,932.89,12200000,932.89 1969-04-09,923.17,933.59,920.63,929.97,12530000,929.97 1969-04-08,918.78,927.81,913.89,923.17,9360000,923.17 1969-04-07,926.09,926.09,911.03,918.78,9430000,918.78 1969-04-03,930.92,933.66,921.39,927.30,10300000,927.30 1969-04-02,933.08,936.90,924.57,930.92,10110000,930.92 1969-04-01,935.48,942.23,928.38,933.08,12360000,933.08 1969-03-28,930.88,941.38,928.02,935.48,12430000,935.48 1969-03-27,923.30,936.16,921.74,930.88,11900000,930.88 1969-03-26,917.08,926.84,914.72,923.30,11030000,923.30 1969-03-25,917.08,923.05,911.30,917.08,9820000,917.08 1969-03-24,920.00,924.04,912.48,917.08,8110000,917.08 1969-03-21,920.13,926.16,913.35,920.00,9830000,920.00 1969-03-20,912.11,924.79,911.24,920.13,10260000,920.13 1969-03-19,907.38,917.52,903.53,912.11,9740000,912.11 1969-03-18,904.03,913.97,902.16,907.38,11210000,907.38 1969-03-17,904.28,909.00,895.76,904.03,9150000,904.03 1969-03-14,907.14,911.36,899.55,904.28,8640000,904.28 1969-03-13,916.77,916.77,903.97,907.14,10030000,907.14 1969-03-12,920.93,926.22,912.67,917.52,8720000,917.52 1969-03-11,917.14,928.77,914.53,920.93,9870000,920.93 1969-03-10,911.18,920.87,906.51,917.14,8920000,917.14 1969-03-07,913.54,914.59,900.67,911.18,10830000,911.18 1969-03-06,923.11,923.79,908.94,913.54,9670000,913.54 1969-03-05,919.51,929.89,916.65,923.11,11370000,923.11 1969-03-04,908.63,922.55,908.50,919.51,9320000,919.51 1969-03-03,905.21,914.04,902.04,908.63,8260000,908.63 1969-02-28,903.03,911.74,900.17,905.21,8990000,905.21 1969-02-27,905.77,908.88,897.07,903.03,9670000,903.03 1969-02-26,899.80,912.11,895.39,905.77,9540000,905.77 1969-02-25,903.97,914.41,896.32,899.80,9540000,899.80 1969-02-24,916.65,918.95,900.17,903.97,12730000,903.97 1969-02-20,925.10,928.46,913.04,916.65,10990000,916.65 1969-02-19,930.82,936.91,923.23,925.10,10390000,925.10 1969-02-18,937.53,937.53,923.11,930.82,12490000,930.82 1969-02-17,951.95,954.87,935.11,937.72,11670000,937.72 1969-02-14,952.70,959.16,945.67,951.95,11460000,951.95 1969-02-13,949.09,957.73,942.57,952.70,12010000,952.70 1969-02-12,948.97,954.00,942.81,949.09,11530000,949.09 1969-02-11,947.85,954.00,941.94,948.97,12320000,948.97 1969-02-07,946.67,954.00,940.39,947.85,12780000,947.85 1969-02-06,945.98,954.07,939.46,946.67,12570000,946.67 1969-02-05,945.11,952.32,938.53,945.98,13750000,945.98 1969-02-04,946.85,951.27,938.59,945.11,12550000,945.11 1969-02-03,946.05,953.01,936.41,946.85,12510000,946.85 1969-01-31,942.13,950.71,937.28,946.05,12020000,946.05 1969-01-30,938.09,947.23,934.36,942.13,13010000,942.13 1969-01-29,938.40,943.62,931.94,938.09,11470000,938.09 1969-01-28,937.47,944.49,930.88,938.40,12070000,938.40 1969-01-27,938.59,944.37,932.00,937.47,11020000,937.47 1969-01-24,940.20,946.85,932.93,938.59,12520000,938.59 1969-01-23,934.17,945.98,932.56,940.20,13140000,940.20 1969-01-22,929.82,938.46,925.16,934.17,11480000,934.17 1969-01-21,931.25,936.29,923.92,929.82,10910000,929.82 1969-01-20,935.54,939.40,927.09,931.25,10950000,931.25 1969-01-17,938.59,944.06,930.20,935.54,11590000,935.54 1969-01-16,931.75,945.55,930.07,938.59,13120000,938.59 1969-01-15,928.33,940.51,924.23,931.75,11810000,931.75 1969-01-14,923.11,931.56,918.32,928.33,10700000,928.33 1969-01-13,925.53,930.63,917.14,923.11,11160000,923.11 1969-01-10,927.46,937.22,921.31,925.53,12680000,925.53 1969-01-09,921.25,935.67,918.64,927.46,12100000,927.46 1969-01-08,925.72,932.19,915.53,921.25,13840000,921.25 1969-01-07,936.66,939.33,915.84,925.72,15740000,925.72 1969-01-06,951.89,956.05,932.93,936.66,12720000,936.66 1969-01-03,947.73,960.16,942.94,951.89,12750000,951.89 1969-01-02,943.75,956.36,940.39,947.73,9800000,947.73 1968-12-31,945.11,950.46,936.66,943.75,13130000,943.75 1968-12-30,952.51,955.87,938.90,945.11,12080000,945.11 1968-12-27,954.25,961.96,947.54,952.51,11200000,952.51 1968-12-26,952.32,961.52,948.47,954.25,9670000,954.25 1968-12-24,953.75,961.28,945.11,952.32,11540000,952.32 1968-12-23,966.99,969.05,949.47,953.75,12970000,953.75 1968-12-20,975.14,983.90,962.83,966.99,15910000,966.99 1968-12-19,970.91,980.11,954.62,975.14,19630000,975.14 1968-12-17,976.32,978.80,962.46,970.91,14700000,970.91 1968-12-16,981.29,989.12,970.60,976.32,15950000,976.32 1968-12-13,977.13,990.99,972.03,981.29,16740000,981.29 1968-12-12,977.69,987.20,969.05,977.13,18160000,977.13 1968-12-10,979.36,986.01,971.06,977.69,14500000,977.69 1968-12-09,978.24,987.01,971.41,979.36,15800000,979.36 1968-12-06,977.69,986.39,970.10,978.24,15320000,978.24 1968-12-05,985.21,989.31,969.67,977.69,19330000,977.69 1968-12-03,983.34,990.99,973.83,985.21,15460000,985.21 1968-12-02,985.08,994.65,974.76,983.34,15390000,983.34 1968-11-29,976.32,989.56,971.59,985.08,14390000,985.08 1968-11-27,979.49,984.96,969.17,976.32,16550000,976.32 1968-11-26,971.35,986.20,969.11,979.49,16360000,979.49 1968-11-25,967.06,977.13,961.46,971.35,14490000,971.35 1968-11-22,965.13,973.89,958.23,967.06,15420000,967.06 1968-11-21,966.75,971.35,953.69,965.13,18320000,965.13 1968-11-19,963.70,972.40,957.48,966.75,15120000,966.75 1968-11-18,965.88,970.85,957.11,963.70,14390000,963.70 1968-11-15,963.89,972.59,956.24,965.88,15040000,965.88 1968-11-14,967.43,972.90,957.86,963.89,14900000,963.89 1968-11-13,964.20,973.83,958.98,967.43,15660000,967.43 1968-11-12,958.98,972.15,953.75,964.20,17250000,964.20 1968-11-08,950.65,965.07,947.41,958.98,14250000,958.98 1968-11-07,949.47,957.55,938.59,950.65,11660000,950.65 1968-11-06,946.23,959.97,941.01,949.47,12640000,949.47 1968-11-04,948.41,952.01,936.54,946.23,10930000,946.23 1968-11-01,952.39,961.15,941.88,948.41,14480000,948.41 1968-10-31,951.08,965.00,944.06,952.39,17650000,952.39 1968-10-29,957.73,961.96,945.67,951.08,12340000,951.08 1968-10-28,961.28,966.50,951.45,957.73,11740000,957.73 1968-10-25,956.68,966.81,950.96,961.28,14150000,961.28 1968-10-24,963.14,967.37,949.34,956.68,18300000,956.68 1968-10-22,967.49,970.41,957.92,963.14,13970000,963.14 1968-10-21,967.49,974.27,959.54,967.49,14380000,967.49 1968-10-18,958.91,971.72,955.06,967.49,15130000,967.49 1968-10-17,955.31,969.29,952.51,958.91,21060000,958.91 1968-10-15,949.96,960.47,946.05,955.31,13410000,955.31 1968-10-14,949.59,955.37,942.50,949.96,11980000,949.96 1968-10-11,949.78,955.18,942.96,949.59,12650000,949.59 1968-10-10,956.24,958.66,944.24,949.78,17000000,949.78 1968-10-08,956.68,963.33,950.58,956.24,14000000,956.24 1968-10-07,952.95,961.03,946.98,956.68,12420000,956.68 1968-10-04,949.47,957.30,943.93,952.95,15350000,952.95 1968-10-03,942.32,954.81,940.39,949.47,21110000,949.47 1968-10-01,935.79,948.66,932.93,942.32,15560000,942.32 1968-09-30,933.80,941.45,926.59,935.79,13610000,935.79 1968-09-27,933.24,938.40,926.09,933.80,13860000,933.80 1968-09-26,938.28,945.86,928.08,933.24,18950000,933.24 1968-09-24,930.45,942.69,926.22,938.28,15210000,938.28 1968-09-23,924.42,934.05,920.93,930.45,11550000,930.45 1968-09-20,923.98,930.01,917.14,924.42,14190000,924.42 1968-09-19,923.05,933.43,916.27,923.98,17910000,923.98 1968-09-17,921.37,929.08,916.77,923.05,13920000,923.05 1968-09-16,917.21,927.46,912.98,921.37,13260000,921.37 1968-09-13,915.65,923.36,909.12,917.21,13070000,917.21 1968-09-12,919.38,923.26,908.07,915.65,14630000,915.65 1968-09-10,924.98,931.32,914.41,919.38,11430000,919.38 1968-09-09,921.25,932.31,916.89,924.98,11890000,924.98 1968-09-06,917.52,928.27,912.54,921.25,13180000,921.25 1968-09-05,906.95,921.87,904.03,917.52,12980000,917.52 1968-09-04,900.36,911.18,896.26,906.95,10040000,906.95 1968-09-03,896.01,904.96,892.59,900.36,8620000,900.36 1968-08-30,894.33,902.35,891.29,896.01,8190000,896.01 1968-08-29,893.65,900.73,886.69,894.33,10940000,894.33 1968-08-27,896.13,901.48,888.18,893.65,9710000,893.65 1968-08-26,892.34,902.23,890.04,896.13,9740000,896.13 1968-08-23,888.30,899.37,883.83,892.34,9890000,892.34 1968-08-22,888.67,894.95,878.85,888.30,15140000,888.30 1968-08-20,887.68,894.64,879.97,888.67,10640000,888.67 1968-08-19,885.89,893.15,880.66,887.68,9900000,887.68 1968-08-16,879.51,889.44,877.17,885.89,9940000,885.89 1968-08-15,884.68,892.32,874.76,879.51,12710000,879.51 1968-08-13,881.02,890.82,877.71,884.68,12730000,884.68 1968-08-12,869.65,884.20,867.66,881.02,10420000,881.02 1968-08-09,870.37,876.26,863.33,869.65,8390000,869.65 1968-08-08,876.92,882.82,866.58,870.37,12920000,870.37 1968-08-06,872.53,881.38,868.86,876.92,9620000,876.92 1968-08-05,871.27,879.33,865.86,872.53,8850000,872.53 1968-08-02,878.07,879.27,865.19,871.27,9860000,871.27 1968-08-01,883.00,892.02,874.94,878.07,14380000,878.07 1968-07-30,883.36,890.64,876.99,883.00,10250000,883.00 1968-07-29,888.47,893.59,877.23,883.36,10940000,883.36 1968-07-26,885.47,895.21,878.55,888.47,11690000,888.47 1968-07-25,898.10,901.29,880.41,885.47,16140000,885.47 1968-07-23,900.32,904.48,889.68,898.10,13570000,898.10 1968-07-22,913.92,914.52,894.31,900.32,13530000,900.32 1968-07-19,917.95,924.21,907.60,913.92,14620000,913.92 1968-07-18,921.20,926.55,911.93,917.95,17420000,917.95 1968-07-16,923.72,928.84,913.56,921.20,13380000,921.20 1968-07-15,922.46,929.32,915.24,923.72,13390000,923.72 1968-07-12,922.82,929.92,914.88,922.46,14810000,922.46 1968-07-11,920.42,933.83,915.78,922.82,20290000,922.82 1968-07-09,912.60,924.87,908.63,920.42,16540000,920.42 1968-07-08,903.51,920.00,901.89,912.60,16860000,912.60 1968-07-03,896.84,907.18,893.29,903.51,14390000,903.51 1968-07-02,896.35,902.97,889.62,896.84,13350000,896.84 1968-07-01,897.80,904.05,889.68,896.35,11280000,896.35 1968-06-28,898.76,905.14,892.26,897.80,12040000,897.80 1968-06-27,901.41,909.23,891.78,898.76,15370000,898.76 1968-06-25,901.83,908.08,895.09,901.41,13200000,901.41 1968-06-24,900.93,908.14,895.09,901.83,12320000,901.83 1968-06-21,898.28,907.96,891.48,900.93,13450000,900.93 1968-06-20,900.20,909.29,892.63,898.28,16290000,898.28 1968-06-18,903.45,909.17,895.09,900.20,13630000,900.20 1968-06-17,913.62,917.35,898.40,903.45,12570000,903.45 1968-06-14,913.86,918.91,904.48,913.62,14690000,913.62 1968-06-13,917.95,924.93,908.93,913.86,21350000,913.86 1968-06-11,913.38,924.81,909.41,917.95,15700000,917.95 1968-06-10,914.88,922.94,907.54,913.38,14640000,913.38 1968-06-07,910.13,920.84,906.28,914.88,17320000,914.88 1968-06-06,907.42,918.01,901.71,910.13,16130000,910.13 1968-06-05,916.63,922.10,904.05,907.42,15590000,907.42 1968-06-04,905.38,923.00,902.43,916.63,18030000,916.63 1968-06-03,899.00,912.12,896.23,905.38,14970000,905.38 1968-05-31,895.21,907.96,891.72,899.00,13090000,899.00 1968-05-29,896.78,903.69,889.44,895.21,14100000,895.21 1968-05-28,891.60,901.95,886.97,896.78,13850000,896.78 1968-05-27,895.28,898.76,885.53,891.60,12720000,891.60 1968-05-24,893.15,900.49,886.67,895.28,13300000,895.28 1968-05-23,896.79,901.47,888.23,893.15,12840000,893.15 1968-05-22,896.32,906.73,891.82,896.79,14200000,896.79 1968-05-21,894.19,902.39,886.33,896.32,13160000,896.32 1968-05-20,898.98,902.10,887.54,894.19,11180000,894.19 1968-05-17,903.72,905.92,891.18,898.98,11830000,898.98 1968-05-16,907.82,913.08,898.17,903.72,13030000,903.72 1968-05-15,908.06,914.64,900.08,907.82,13180000,907.82 1968-05-14,909.96,915.28,900.89,908.06,13160000,908.06 1968-05-13,912.91,918.34,903.90,909.96,11860000,909.96 1968-05-10,911.35,920.48,906.38,912.91,11700000,912.91 1968-05-09,918.86,921.81,904.82,911.35,12890000,911.35 1968-05-08,919.90,927.42,911.52,918.86,13120000,918.86 1968-05-07,914.53,926.78,910.54,919.90,13920000,919.90 1968-05-06,919.21,922.79,903.61,914.53,12160000,914.53 1968-05-03,918.05,935.68,913.49,919.21,17990000,919.21 1968-05-02,913.20,923.43,906.90,918.05,14260000,918.05 1968-05-01,912.22,919.85,902.51,913.20,14440000,913.20 1968-04-30,908.34,918.11,901.70,912.22,14380000,912.22 1968-04-29,906.03,914.01,900.72,908.34,12030000,908.34 1968-04-26,905.57,912.27,898.00,906.03,13500000,906.03 1968-04-25,898.46,912.56,895.98,905.57,14430000,905.57 1968-04-24,897.48,904.65,889.68,898.46,14810000,898.46 1968-04-23,891.99,904.47,890.43,897.48,14010000,897.48 1968-04-22,897.65,897.88,881.24,891.99,11720000,891.99 1968-04-19,908.40,908.40,890.72,897.65,14560000,897.65 1968-04-18,908.17,916.78,901.24,909.21,15890000,909.21 1968-04-17,906.78,912.74,896.84,908.17,14090000,908.17 1968-04-16,910.19,917.36,899.33,906.78,15680000,906.78 1968-04-15,905.69,916.03,893.61,910.19,14220000,910.19 1968-04-11,892.63,908.29,886.56,905.69,14230000,905.69 1968-04-10,884.42,905.74,884.07,892.63,20410000,892.63 1968-04-08,865.81,888.35,865.18,884.42,13010000,884.42 1968-04-05,872.52,877.31,862.11,865.81,12570000,865.81 1968-04-04,869.11,879.68,862.46,872.52,14340000,872.52 1968-04-03,863.96,883.38,859.45,869.11,19290000,869.11 1968-04-02,861.25,869.63,852.12,863.96,14520000,863.96 1968-04-01,849.23,870.90,849.23,861.25,17730000,861.25 1968-03-29,835.12,845.87,831.48,840.67,9000000,840.67 1968-03-28,836.57,841.08,830.67,835.12,8000000,835.12 1968-03-27,831.54,841.42,830.33,836.57,8970000,836.57 1968-03-26,827.27,836.17,823.62,831.54,8670000,831.54 1968-03-25,826.05,831.25,819.87,827.27,6700000,827.27 1968-03-22,825.13,831.48,817.61,826.05,9900000,826.05 1968-03-21,830.85,836.40,822.58,825.13,8580000,825.13 1968-03-20,832.99,836.63,826.34,830.85,7390000,830.85 1968-03-19,840.09,842.35,829.63,832.99,7410000,832.99 1968-03-18,837.55,854.25,836.28,840.09,10800000,840.09 1968-03-15,830.91,843.39,825.01,837.55,11210000,837.55 1968-03-14,841.54,841.54,824.26,830.91,11640000,830.91 1968-03-13,843.22,847.72,836.22,842.23,8990000,842.23 1968-03-12,843.04,847.55,834.72,843.22,9250000,843.22 1968-03-11,835.24,846.63,833.56,843.04,9520000,843.04 1968-03-08,836.22,840.38,828.59,835.24,7410000,835.24 1968-03-07,837.21,843.62,830.33,836.22,8630000,836.22 1968-03-06,827.03,841.37,826.46,837.21,9900000,837.21 1968-03-05,830.56,837.44,821.72,827.03,11440000,827.03 1968-03-04,840.44,842.75,828.02,830.56,10590000,830.56 1968-03-01,840.50,845.24,836.05,840.44,8610000,840.44 1968-02-29,844.72,847.03,835.76,840.50,7700000,840.50 1968-02-28,846.68,853.33,841.71,844.72,8020000,844.72 1968-02-27,841.77,848.59,835.93,846.68,7600000,846.68 1968-02-26,849.80,850.79,836.40,841.77,7810000,841.77 1968-02-23,849.23,857.78,844.43,849.80,8810000,849.80 1968-02-21,843.10,855.05,840.50,849.23,9170000,849.23 1968-02-20,838.65,847.67,836.05,843.10,8800000,843.10 1968-02-19,836.34,843.97,830.50,838.65,7270000,838.65 1968-02-16,839.23,841.66,830.85,836.34,9070000,836.34 1968-02-15,837.38,846.16,833.62,839.23,9770000,839.23 1968-02-14,831.77,841.94,826.46,837.38,11390000,837.38 1968-02-13,840.04,843.68,828.36,831.77,10830000,831.77 1968-02-09,850.32,851.65,834.84,840.04,11850000,840.04 1968-02-08,859.92,863.27,847.84,850.32,9660000,850.32 1968-02-07,861.25,867.78,855.93,859.92,8380000,859.92 1968-02-06,861.13,867.31,854.83,861.25,8560000,861.25 1968-02-05,863.56,867.66,854.77,861.13,8980000,861.13 1968-02-02,861.36,871.30,856.22,863.56,10120000,863.56 1968-02-01,855.47,864.89,850.55,861.36,10590000,861.36 1968-01-31,859.57,863.90,850.50,855.47,9410000,855.47 1968-01-30,863.67,867.95,852.92,859.57,10110000,859.57 1968-01-29,865.06,873.04,858.53,863.67,9950000,863.67 1968-01-26,864.25,873.79,859.51,865.06,9980000,865.06 1968-01-25,862.23,871.24,853.44,864.25,12410000,864.25 1968-01-24,864.77,870.20,854.83,862.23,10570000,862.23 1968-01-23,871.71,875.06,860.15,864.77,11030000,864.77 1968-01-22,880.32,881.24,866.68,871.71,10630000,871.71 1968-01-19,882.80,889.27,876.21,880.32,11950000,880.32 1968-01-18,883.78,893.49,879.33,882.80,13840000,882.80 1968-01-17,887.14,890.89,877.48,883.78,12910000,883.78 1968-01-16,892.74,896.56,882.34,887.14,12340000,887.14 1968-01-15,898.98,903.03,888.93,892.74,12640000,892.74 1968-01-12,899.79,906.58,891.07,898.98,13080000,898.98 1968-01-11,903.95,911.35,895.28,899.79,13220000,899.79 1968-01-10,908.29,911.87,897.94,903.95,11670000,903.95 1968-01-09,908.92,921.87,903.26,908.29,13720000,908.29 1968-01-08,901.24,915.63,896.27,908.92,14260000,908.92 1968-01-05,899.39,908.06,893.55,901.24,11880000,901.24 1968-01-04,904.13,908.23,889.51,899.39,13440000,899.39 1968-01-03,906.84,916.15,899.56,904.13,12650000,904.13 1968-01-02,905.11,914.30,897.54,906.84,11080000,906.84 1967-12-29,897.83,910.37,893.44,905.11,14950000,905.11 1967-12-28,894.94,902.97,886.73,897.83,12530000,897.83 1967-12-27,888.12,900.14,884.59,894.94,12690000,894.94 1967-12-26,887.37,893.96,881.93,888.12,9150000,888.12 1967-12-22,888.35,894.94,880.95,887.37,9570000,887.37 1967-12-21,886.90,897.71,883.78,888.35,11010000,888.35 1967-12-20,881.36,892.86,876.33,886.90,11390000,886.90 1967-12-19,881.65,886.96,875.69,881.36,10610000,881.36 1967-12-18,880.61,889.91,874.94,881.65,10320000,881.65 1967-12-15,883.44,890.43,875.87,880.61,11530000,880.61 1967-12-14,882.34,891.47,878.53,883.44,12310000,883.44 1967-12-13,881.30,887.77,881.14,882.34,12480000,882.34 1967-12-12,882.05,887.54,873.32,881.30,10860000,881.30 1967-12-11,887.25,892.16,876.96,882.05,10500000,882.05 1967-12-08,892.22,895.92,883.55,887.25,10710000,887.25 1967-12-07,892.28,901.76,887.94,892.22,12490000,892.22 1967-12-06,888.12,896.50,884.19,892.28,11940000,892.28 1967-12-05,883.50,895.80,881.24,888.12,12940000,888.12 1967-12-04,879.16,888.35,876.33,883.50,11740000,883.50 1967-12-01,875.81,882.69,868.76,879.16,9740000,879.16 1967-11-30,883.15,887.48,872.63,875.81,8860000,875.81 1967-11-29,884.88,894.88,878.24,883.15,11400000,883.15 1967-11-28,882.11,890.43,876.62,884.88,11040000,884.88 1967-11-27,877.60,887.77,873.21,882.11,10040000,882.11 1967-11-24,874.02,882.80,867.26,877.60,9470000,877.60 1967-11-22,870.95,884.54,865.87,874.02,12180000,874.02 1967-11-21,857.78,876.68,857.49,870.95,12300000,870.95 1967-11-20,862.11,862.58,839.40,857.78,12750000,857.78 1967-11-17,859.74,869.28,853.96,862.11,10050000,862.11 1967-11-16,855.18,867.49,852.12,859.74,10570000,859.74 1967-11-15,852.40,860.44,845.01,855.18,10000000,855.18 1967-11-14,859.74,864.60,849.23,852.40,10350000,852.40 1967-11-13,862.81,872.98,855.06,859.74,10130000,859.74 1967-11-10,856.97,868.93,854.83,862.81,9960000,862.81 1967-11-09,849.57,862.86,846.28,856.97,8890000,856.97 1967-11-08,855.29,870.67,847.61,849.57,12630000,849.57 1967-11-06,856.62,860.61,844.89,855.29,10320000,855.29 1967-11-03,864.83,867.95,853.10,856.62,8800000,856.62 1967-11-02,867.08,878.87,860.55,864.83,10760000,864.83 1967-11-01,879.74,880.95,863.44,867.08,10930000,867.08 1967-10-31,886.62,890.72,876.21,879.74,12020000,879.74 1967-10-30,888.18,894.07,880.37,886.62,10250000,886.62 1967-10-27,890.89,896.15,882.98,888.18,9880000,888.18 1967-10-26,886.73,896.04,881.99,890.89,9920000,890.89 1967-10-25,888.18,893.44,877.31,886.73,10300000,886.73 1967-10-24,894.65,903.84,885.52,888.18,11110000,888.18 1967-10-23,896.73,900.43,883.03,894.65,9680000,894.65 1967-10-20,903.72,906.96,891.35,896.73,9510000,896.73 1967-10-19,903.49,914.41,899.68,903.72,11620000,903.72 1967-10-18,904.36,910.48,896.15,903.49,10500000,903.49 1967-10-17,908.52,912.97,896.73,904.36,10290000,904.36 1967-10-16,918.17,922.97,905.46,908.52,9080000,908.52 1967-10-13,913.20,924.41,908.58,918.17,9040000,918.17 1967-10-12,920.25,922.56,909.79,913.20,7770000,913.20 1967-10-11,926.61,929.73,916.61,920.25,11230000,920.25 1967-10-10,933.31,940.07,921.35,926.61,12000000,926.61 1967-10-09,928.74,940.36,924.47,933.31,11180000,933.31 1967-10-06,927.13,935.74,921.58,928.74,9830000,928.74 1967-10-05,921.29,931.46,917.88,927.13,8490000,927.13 1967-10-04,924.47,932.33,916.32,921.29,11520000,921.29 1967-10-03,921.00,930.77,916.49,924.47,10320000,924.47 1967-10-02,926.66,930.42,915.92,921.00,9240000,921.00 1967-09-29,929.38,933.77,919.85,926.66,9710000,926.66 1967-09-28,933.14,937.30,925.05,929.38,10470000,929.38 1967-09-27,937.18,942.61,927.88,933.14,8810000,933.14 1967-09-26,943.08,951.57,934.12,937.18,10940000,937.18 1967-09-25,934.35,949.66,931.75,943.08,10910000,943.08 1967-09-22,930.48,940.48,923.95,934.35,11160000,934.35 1967-09-21,929.79,938.22,924.12,930.48,11290000,930.48 1967-09-20,930.07,936.89,922.50,929.79,10980000,929.79 1967-09-19,938.74,944.64,926.95,930.07,11540000,930.07 1967-09-18,933.48,944.58,929.73,938.74,11620000,938.74 1967-09-15,929.44,938.51,923.25,933.48,10270000,933.48 1967-09-14,923.77,939.38,923.72,929.44,12220000,929.44 1967-09-13,911.75,928.40,911.64,923.77,12400000,923.77 1967-09-12,909.62,916.84,905.05,911.75,9930000,911.75 1967-09-11,907.54,916.20,902.68,909.62,9170000,909.62 1967-09-08,908.17,914.41,901.24,907.54,9300000,907.54 1967-09-07,906.96,913.49,901.64,908.17,8910000,908.17 1967-09-06,904.13,913.55,899.27,906.96,9550000,906.96 1967-09-05,901.18,909.91,895.69,904.13,8320000,904.13 1967-09-01,901.29,906.84,894.19,901.18,7460000,901.18 1967-08-31,893.72,906.38,893.09,901.29,8840000,901.29 1967-08-30,894.76,900.72,888.64,893.72,7200000,893.72 1967-08-29,894.71,900.20,887.94,894.76,6350000,894.76 1967-08-28,894.07,902.16,887.83,894.71,6270000,894.71 1967-08-25,898.46,900.49,888.81,894.07,7250000,894.07 1967-08-24,905.11,910.54,896.09,898.46,7740000,898.46 1967-08-23,907.48,910.14,896.44,905.11,8760000,905.11 1967-08-22,912.27,916.15,903.61,907.48,7940000,907.48 1967-08-21,919.04,921.64,907.48,912.27,8600000,912.27 1967-08-18,918.23,925.51,912.79,919.04,8250000,919.04 1967-08-17,915.68,923.89,912.85,918.23,8790000,918.23 1967-08-16,919.15,921.64,910.83,915.68,8220000,915.68 1967-08-15,916.32,924.93,912.85,919.15,8710000,919.15 1967-08-14,920.65,922.16,909.96,916.32,7990000,916.32 1967-08-11,925.22,928.11,915.57,920.65,8250000,920.65 1967-08-10,926.72,933.14,920.77,925.22,9040000,925.22 1967-08-09,922.45,931.75,919.09,926.72,10100000,926.72 1967-08-08,920.37,927.88,916.67,922.45,8970000,922.45 1967-08-07,923.77,930.65,915.34,920.37,10160000,920.37 1967-08-04,921.98,931.06,917.65,923.77,11130000,923.77 1967-08-03,922.27,929.67,908.11,921.98,13440000,921.98 1967-08-02,913.55,931.29,913.55,922.27,13510000,922.27 1967-08-01,904.24,917.19,900.25,912.97,12290000,912.97 1967-07-31,901.53,912.85,896.67,904.24,10330000,904.24 1967-07-28,903.14,910.31,896.15,901.53,10900000,901.53 1967-07-27,903.14,912.79,896.96,903.14,12400000,903.14 1967-07-26,901.29,908.17,895.75,903.14,11160000,903.14 1967-07-25,904.53,909.56,895.52,901.29,9890000,901.29 1967-07-24,909.56,912.68,897.48,904.53,9580000,904.53 1967-07-21,908.69,918.69,903.03,909.56,11710000,909.56 1967-07-20,903.32,913.83,896.79,908.69,11160000,908.69 1967-07-19,896.09,908.75,892.63,903.32,12850000,903.32 1967-07-18,882.74,899.79,879.39,896.09,12060000,896.09 1967-07-17,882.05,891.30,875.35,882.74,10390000,882.74 1967-07-14,878.53,889.62,872.52,882.05,10880000,882.05 1967-07-13,878.70,885.81,873.04,878.53,10730000,878.53 1967-07-12,879.45,885.40,870.55,878.70,11240000,878.70 1967-07-11,875.52,886.15,870.90,879.45,12400000,879.45 1967-07-10,869.05,880.37,866.04,875.52,12130000,875.52 1967-07-07,864.02,874.42,860.32,869.05,11540000,869.05 1967-07-06,864.94,871.24,858.70,864.02,10170000,864.02 1967-07-05,859.69,871.01,857.43,864.94,9170000,864.94 1967-07-03,860.26,864.60,853.21,859.69,6040000,859.69 1967-06-30,861.94,867.83,855.58,860.26,7850000,860.26 1967-06-29,868.87,873.67,859.45,861.94,9940000,861.94 1967-06-28,869.39,875.17,864.14,868.87,9310000,868.87 1967-06-27,872.11,875.87,864.14,869.39,8780000,869.39 1967-06-26,877.37,884.30,866.91,872.11,9040000,872.11 1967-06-23,875.69,883.32,870.95,877.37,9130000,877.37 1967-06-22,877.66,881.13,868.82,875.69,9550000,875.69 1967-06-21,880.61,886.90,872.52,877.66,9760000,877.66 1967-06-20,884.54,890.72,876.44,880.61,10350000,880.61 1967-06-19,885.00,890.03,877.43,884.54,8570000,884.54 1967-06-16,883.26,892.80,879.68,885.00,10740000,885.00 1967-06-15,880.61,891.24,873.32,883.26,11240000,883.26 1967-06-14,886.15,890.78,876.39,880.61,10960000,880.61 1967-06-13,878.93,892.68,875.75,886.15,11570000,886.15 1967-06-12,874.89,884.77,870.49,878.93,10230000,878.93 1967-06-09,873.20,881.94,865.08,874.89,9650000,874.89 1967-06-08,869.19,875.17,861.64,873.20,8300000,873.20 1967-06-07,862.71,877.14,860.57,869.19,10170000,869.19 1967-06-06,848.33,868.46,848.33,862.71,9230000,862.71 1967-06-05,858.41,858.41,836.92,847.77,11110000,847.77 1967-06-02,864.98,870.82,857.46,863.31,8070000,863.31 1967-06-01,852.56,869.60,850.56,864.98,9040000,864.98 1967-05-31,864.64,864.64,850.39,852.56,8870000,852.56 1967-05-29,870.32,871.60,860.24,864.98,6590000,864.98 1967-05-26,870.71,875.22,863.31,870.32,7810000,870.32 1967-05-25,862.42,876.50,860.36,870.71,8960000,870.71 1967-05-24,868.71,870.49,857.18,862.42,10290000,862.42 1967-05-23,871.05,875.72,862.03,868.71,9810000,868.71 1967-05-22,874.55,876.61,863.75,871.05,9600000,871.05 1967-05-19,877.34,881.24,867.59,874.55,10560000,874.55 1967-05-18,882.24,885.13,873.27,877.34,10290000,877.34 1967-05-17,885.80,891.37,878.40,882.24,9560000,882.24 1967-05-16,882.41,891.76,877.90,885.80,10700000,885.80 1967-05-15,890.03,892.26,878.67,882.41,8320000,882.41 1967-05-12,896.21,900.67,885.86,890.03,10470000,890.03 1967-05-11,894.10,901.56,888.25,896.21,10320000,896.21 1967-05-10,899.89,902.23,888.47,894.10,10410000,894.10 1967-05-09,909.63,915.87,897.05,899.89,10830000,899.89 1967-05-08,905.96,915.31,899.44,909.63,10330000,909.63 1967-05-05,901.95,910.91,898.05,905.96,10630000,905.96 1967-05-04,896.77,906.57,892.65,901.95,12850000,901.95 1967-05-03,891.65,900.95,886.41,896.77,11550000,896.77 1967-05-02,892.93,898.39,886.19,891.65,10260000,891.65 1967-05-01,897.05,900.50,887.81,892.93,9410000,892.93 1967-04-28,894.82,903.73,891.20,897.05,11200000,897.05 1967-04-27,889.03,898.33,882.74,894.82,10250000,894.82 1967-04-26,891.20,898.50,883.96,889.03,10560000,889.03 1967-04-25,887.53,894.93,880.73,891.20,10420000,891.20 1967-04-24,883.18,894.82,877.84,887.53,10250000,887.53 1967-04-21,878.62,888.30,873.61,883.18,10210000,883.18 1967-04-20,873.94,882.18,869.04,878.62,9690000,878.62 1967-04-19,873.00,882.07,868.76,873.94,10860000,873.94 1967-04-18,866.59,876.73,861.92,873.00,10500000,873.00 1967-04-17,859.74,874.83,857.91,866.59,9070000,866.59 1967-04-14,848.83,865.03,847.05,859.74,8810000,859.74 1967-04-13,844.65,852.84,840.81,848.83,7610000,848.83 1967-04-12,847.66,852.67,841.48,844.65,7750000,844.65 1967-04-11,842.43,852.39,838.98,847.66,7710000,847.66 1967-04-10,852.84,852.84,839.76,842.43,8110000,842.43 1967-04-07,861.25,866.59,850.22,853.34,9090000,853.34 1967-04-06,861.19,867.59,856.51,861.25,9470000,861.25 1967-04-05,859.19,867.26,855.23,861.19,8810000,861.19 1967-04-04,859.97,865.65,852.17,859.19,8750000,859.19 1967-04-03,865.98,868.43,854.45,859.97,8530000,859.97 1967-03-31,869.99,874.28,861.19,865.98,8130000,865.98 1967-03-30,871.10,875.39,863.92,869.99,8340000,869.99 1967-03-29,875.28,880.40,866.31,871.10,8430000,871.10 1967-03-28,873.72,882.13,868.37,875.28,8940000,875.28 1967-03-27,876.67,883.41,869.15,873.72,9260000,873.72 1967-03-23,870.55,882.29,868.82,876.67,9500000,876.67 1967-03-22,866.59,875.11,857.68,870.55,8820000,870.55 1967-03-21,870.43,879.18,861.25,866.59,9820000,866.59 1967-03-20,869.77,877.28,862.36,870.43,9040000,870.43 1967-03-17,868.49,876.50,861.80,869.77,10020000,869.77 1967-03-16,854.51,872.33,854.51,868.49,12170000,868.49 1967-03-15,844.27,856.90,842.65,854.06,10830000,854.06 1967-03-14,844.82,850.33,836.86,844.27,10260000,844.27 1967-03-13,848.50,853.56,840.59,844.82,9910000,844.82 1967-03-10,845.43,864.25,845.43,848.50,14900000,848.50 1967-03-09,843.32,850.72,836.53,844.15,10480000,844.15 1967-03-08,841.76,851.00,836.36,843.32,11070000,843.32 1967-03-07,842.20,847.10,835.30,841.76,9810000,841.76 1967-03-06,846.60,851.45,837.86,842.20,10400000,842.20 1967-03-03,846.71,853.51,841.04,846.60,11100000,846.60 1967-03-02,843.49,852.62,840.87,846.71,11900000,846.71 1967-03-01,839.37,852.12,836.47,843.49,11510000,843.49 1967-02-28,836.64,844.27,827.95,839.37,9970000,839.37 1967-02-27,847.33,849.44,830.46,836.64,10210000,836.64 1967-02-24,846.77,854.79,839.64,847.33,9830000,847.33 1967-02-23,844.10,851.67,837.31,846.77,10010000,846.77 1967-02-21,847.88,851.39,840.03,844.10,9030000,844.10 1967-02-20,850.84,853.90,839.81,847.88,8640000,847.88 1967-02-17,851.56,856.35,845.88,850.84,8530000,850.84 1967-02-16,855.79,863.42,846.60,851.56,8490000,851.56 1967-02-15,856.90,863.47,850.22,855.79,10480000,855.79 1967-02-14,853.34,862.64,848.39,856.90,9760000,856.90 1967-02-13,855.73,860.52,848.72,853.34,7570000,853.34 1967-02-10,857.52,861.36,847.72,855.73,8850000,855.73 1967-02-09,860.97,871.71,854.06,857.52,10970000,857.52 1967-02-08,852.51,865.37,850.11,860.97,11220000,860.97 1967-02-07,855.12,857.68,847.27,852.51,6400000,852.51 1967-02-06,857.46,862.53,849.72,855.12,10680000,855.12 1967-02-03,853.12,864.09,849.78,857.46,12010000,857.46 1967-02-02,848.39,857.68,842.20,853.12,10720000,853.12 1967-02-01,849.89,854.51,842.54,848.39,9580000,848.39 1967-01-31,848.11,857.02,844.82,849.89,11540000,849.89 1967-01-30,844.04,854.57,840.48,848.11,10250000,848.11 1967-01-27,838.70,850.17,836.14,844.04,9690000,844.04 1967-01-26,840.59,848.27,830.18,838.70,10630000,838.70 1967-01-25,847.72,852.67,837.69,840.59,10260000,840.59 1967-01-24,847.72,853.12,836.30,847.72,10430000,847.72 1967-01-23,847.16,856.68,841.43,847.72,10830000,847.72 1967-01-20,846.44,851.39,838.59,847.16,9530000,847.16 1967-01-19,847.49,853.73,840.81,846.44,10230000,846.44 1967-01-18,843.65,853.34,839.14,847.49,11390000,847.49 1967-01-17,833.24,848.72,830.57,843.65,11590000,843.65 1967-01-16,835.13,842.48,828.12,833.24,10280000,833.24 1967-01-13,829.95,838.47,821.15,835.13,10000000,835.13 1967-01-12,822.49,839.25,819.76,829.95,12830000,829.95 1967-01-11,814.14,827.11,798.71,822.49,13230000,822.49 1967-01-10,813.47,820.93,808.18,814.14,8120000,814.14 1967-01-09,808.74,818.87,803.34,813.47,9180000,813.47 1967-01-06,805.51,816.53,801.67,808.74,7830000,808.74 1967-01-05,791.64,807.90,791.64,805.51,7320000,805.51 1967-01-04,786.41,795.54,776.16,791.14,6150000,791.14 1967-01-03,785.69,800.55,782.34,786.41,6100000,786.41 1966-12-30,786.35,794.59,779.34,785.69,11330000,785.69 1966-12-29,788.58,792.70,781.51,786.35,7900000,786.35 1966-12-28,792.20,799.38,785.96,788.58,7160000,788.58 1966-12-27,799.10,802.61,788.25,792.20,6280000,792.20 1966-12-23,801.67,806.51,793.09,799.10,7350000,799.10 1966-12-22,797.43,807.18,793.87,801.67,8560000,801.67 1966-12-21,794.59,802.00,789.69,797.43,7690000,797.43 1966-12-20,798.99,800.66,789.42,794.59,6830000,794.59 1966-12-19,807.18,807.46,792.70,798.99,7340000,798.99 1966-12-16,809.18,813.02,802.22,807.18,6980000,807.18 1966-12-15,817.98,820.82,804.45,809.18,7150000,809.18 1966-12-14,816.70,823.66,810.63,817.98,7470000,817.98 1966-12-13,820.54,827.22,812.08,816.70,9650000,816.70 1966-12-12,813.02,826.67,810.52,820.54,9530000,820.54 1966-12-09,812.80,818.20,804.95,813.02,7650000,813.02 1966-12-08,808.01,818.81,805.95,812.80,8370000,812.80 1966-12-07,797.43,812.41,795.43,808.01,8980000,808.01 1966-12-06,791.59,800.94,788.25,797.43,7670000,797.43 1966-12-05,789.47,795.54,784.35,791.59,6470000,791.59 1966-12-02,789.75,794.82,783.51,789.47,6230000,789.47 1966-12-01,791.59,797.93,785.63,789.75,8480000,789.75 1966-11-30,795.26,796.21,784.79,791.59,7230000,791.59 1966-11-29,801.16,803.28,791.59,795.26,7320000,795.26 1966-11-28,803.34,808.24,793.59,801.16,7630000,801.16 1966-11-25,796.82,807.68,793.87,803.34,6810000,803.34 1966-11-23,794.98,804.78,789.36,796.82,7350000,796.82 1966-11-22,798.16,799.55,785.46,794.98,6430000,794.98 1966-11-21,807.73,807.73,791.09,798.16,7450000,798.16 1966-11-18,816.03,817.81,804.95,809.40,6900000,809.40 1966-11-17,820.87,825.61,808.90,816.03,8900000,816.03 1966-11-16,815.31,827.33,812.69,820.87,10350000,820.87 1966-11-15,813.75,819.48,808.12,815.31,7190000,815.31 1966-11-14,819.09,823.10,808.29,813.75,6540000,813.75 1966-11-11,816.87,822.32,810.46,819.09,6690000,819.09 1966-11-10,809.91,822.38,807.18,816.87,8870000,816.87 1966-11-09,802.22,815.64,799.72,809.91,8390000,809.91 1966-11-07,805.06,809.85,796.38,802.22,6120000,802.22 1966-11-04,804.34,809.57,793.54,805.06,6530000,805.06 1966-11-03,807.29,812.24,798.32,804.34,5860000,804.34 1966-11-02,809.63,815.92,801.61,807.29,6740000,807.29 1966-11-01,807.07,814.30,800.33,809.63,6480000,809.63 1966-10-31,807.96,812.91,797.04,807.07,5860000,807.07 1966-10-28,809.57,816.64,801.44,807.96,6420000,807.96 1966-10-27,801.11,814.97,800.77,809.57,6670000,809.57 1966-10-26,793.09,809.29,792.31,801.11,6760000,801.11 1966-10-25,787.75,796.60,780.34,793.09,6190000,793.09 1966-10-24,787.30,796.10,781.73,787.75,5780000,787.75 1966-10-21,783.68,791.36,777.17,787.30,5690000,787.30 1966-10-20,785.35,796.04,778.11,783.68,6840000,783.68 1966-10-19,791.87,799.22,779.84,785.35,6460000,785.35 1966-10-18,778.89,795.65,777.44,791.87,7180000,791.87 1966-10-17,771.71,789.14,770.82,778.89,5570000,778.89 1966-10-14,772.93,782.90,765.14,771.71,5610000,771.71 1966-10-13,778.17,791.09,768.81,772.93,8680000,772.93 1966-10-12,758.63,780.12,752.89,778.17,6910000,778.17 1966-10-11,754.51,771.43,752.44,758.63,8430000,758.63 1966-10-10,744.32,758.07,735.74,754.51,9630000,754.51 1966-10-07,749.61,756.84,739.64,744.32,8140000,744.32 1966-10-06,755.45,759.52,744.09,749.61,8110000,749.61 1966-10-05,763.19,768.04,751.72,755.45,5880000,755.45 1966-10-04,757.96,767.53,750.33,763.19,8910000,763.19 1966-10-03,774.22,778.11,756.79,757.96,6490000,757.96 1966-09-30,772.66,779.34,763.91,774.22,6170000,774.22 1966-09-29,780.95,783.18,768.26,772.66,6110000,772.66 1966-09-28,794.09,795.60,777.33,780.95,5990000,780.95 1966-09-27,792.70,805.45,789.53,794.09,6300000,794.09 1966-09-26,790.97,797.32,784.46,792.70,4960000,792.70 1966-09-23,797.77,800.11,787.36,790.97,4560000,790.97 1966-09-22,793.59,801.83,785.69,797.77,5760000,797.77 1966-09-21,806.01,806.73,792.53,793.59,5360000,793.59 1966-09-20,810.85,813.08,801.22,806.01,4560000,806.01 1966-09-19,814.30,818.54,804.00,810.85,4920000,810.85 1966-09-16,814.30,822.93,808.35,814.30,5150000,814.30 1966-09-15,806.23,821.26,804.62,814.30,6140000,814.30 1966-09-14,795.48,809.02,789.58,806.23,6250000,806.23 1966-09-13,790.59,803.11,789.25,795.48,6870000,795.48 1966-09-12,775.61,795.32,775.61,790.59,6780000,790.59 1966-09-09,774.88,781.01,767.92,775.55,5280000,775.55 1966-09-08,777.39,782.46,763.36,774.88,6660000,774.88 1966-09-07,782.34,787.30,771.77,777.39,5530000,777.39 1966-09-06,787.69,795.15,778.84,782.34,4350000,782.34 1966-09-02,792.09,796.26,776.72,787.69,6080000,787.69 1966-09-01,788.41,798.94,782.12,792.09,6250000,792.09 1966-08-31,777.44,796.49,777.44,788.41,8690000,788.41 1966-08-30,767.03,781.62,759.52,775.72,11230000,775.72 1966-08-29,780.56,781.06,763.97,767.03,10900000,767.03 1966-08-26,792.37,792.53,776.22,780.56,8190000,780.56 1966-08-25,799.55,806.23,789.08,792.37,6760000,792.37 1966-08-24,790.20,805.45,790.20,799.55,7050000,799.55 1966-08-23,792.03,801.78,781.90,790.14,9830000,790.14 1966-08-22,804.62,807.79,786.85,792.03,8690000,792.03 1966-08-19,810.74,816.75,801.50,804.62,7070000,804.62 1966-08-18,819.59,821.15,804.73,810.74,7000000,810.74 1966-08-17,823.83,827.28,813.69,819.59,6630000,819.59 1966-08-16,834.79,834.79,820.04,823.83,6130000,823.83 1966-08-15,840.53,845.65,831.79,834.85,5680000,834.85 1966-08-12,837.91,847.66,834.68,840.53,6230000,840.53 1966-08-11,838.53,843.65,830.56,837.91,5700000,837.91 1966-08-10,844.82,846.71,835.02,838.53,5290000,838.53 1966-08-09,849.05,853.11,841.09,844.82,6270000,844.82 1966-08-08,852.39,854.78,842.42,849.05,4900000,849.05 1966-08-05,851.50,860.57,845.71,852.39,5500000,852.39 1966-08-04,841.70,858.57,841.20,851.50,6880000,851.50 1966-08-03,832.90,847.99,832.90,841.70,6220000,841.70 1966-08-02,835.18,840.97,827.28,832.57,5710000,832.57 1966-08-01,846.60,846.60,830.84,835.18,5880000,835.18 1966-07-29,854.06,857.46,843.76,847.38,5150000,847.38 1966-07-28,856.23,862.75,850.50,854.06,5680000,854.06 1966-07-27,852.17,863.30,850.55,856.23,6070000,856.23 1966-07-26,852.83,861.19,846.88,852.17,7610000,852.17 1966-07-25,869.15,870.43,850.50,852.83,7050000,852.83 1966-07-22,873.99,877.50,864.97,869.15,6540000,869.15 1966-07-21,874.49,880.34,866.98,873.99,6200000,873.99 1966-07-20,884.07,887.08,872.60,874.49,5470000,874.49 1966-07-19,888.41,889.81,876.55,884.07,5960000,884.07 1966-07-18,889.36,892.98,882.18,888.41,5110000,888.41 1966-07-15,887.80,896.65,883.74,889.36,6090000,889.36 1966-07-14,881.40,892.48,878.17,887.80,5950000,887.80 1966-07-13,886.19,887.58,875.33,881.40,5580000,881.40 1966-07-12,893.09,896.65,882.46,886.19,5180000,886.19 1966-07-11,894.04,901.16,888.64,893.09,6200000,893.09 1966-07-08,891.64,898.44,885.57,894.04,6100000,894.04 1966-07-07,888.86,898.21,883.90,891.64,7200000,891.64 1966-07-06,875.27,893.20,872.43,888.86,6860000,888.86 1966-07-05,877.06,884.13,869.09,875.27,4610000,875.27 1966-07-01,870.10,884.02,869.93,877.06,5200000,877.06 1966-06-30,871.60,876.33,858.90,870.10,7250000,870.10 1966-06-29,880.90,884.29,869.15,871.60,6020000,871.60 1966-06-28,888.97,890.81,874.83,880.90,6280000,880.90 1966-06-27,897.16,904.06,887.47,888.97,5330000,888.97 1966-06-24,896.43,904.17,888.41,897.16,7140000,897.16 1966-06-23,901.00,908.51,893.48,896.43,7930000,896.43 1966-06-22,894.98,904.62,892.59,901.00,7800000,901.00 1966-06-21,892.76,900.44,889.36,894.98,6860000,894.98 1966-06-20,894.26,898.21,887.24,892.76,5940000,892.76 1966-06-17,897.16,900.55,889.75,894.26,6580000,894.26 1966-06-16,901.11,903.61,890.92,897.16,6870000,897.16 1966-06-15,903.17,910.35,897.82,901.11,8520000,901.11 1966-06-14,897.60,907.18,891.53,903.17,7600000,903.17 1966-06-13,891.75,904.67,891.64,897.60,7600000,897.60 1966-06-10,882.62,896.77,880.84,891.75,8240000,891.75 1966-06-09,879.34,887.30,874.05,882.62,5810000,882.62 1966-06-08,877.33,883.68,872.16,879.34,4580000,879.34 1966-06-07,881.68,882.62,870.32,877.33,5040000,877.33 1966-06-06,887.86,888.95,876.22,881.68,4260000,881.68 1966-06-03,882.73,890.86,879.73,887.86,4430000,887.86 1966-06-02,883.63,890.64,878.34,882.73,5080000,882.73 1966-06-01,884.07,888.53,877.33,883.63,5290000,883.63 1966-05-31,897.04,899.61,881.73,884.07,5770000,884.07 1966-05-27,891.75,899.77,885.85,897.04,4790000,897.04 1966-05-26,890.42,900.33,886.80,891.75,6080000,891.75 1966-05-25,888.41,894.98,881.29,890.42,5820000,890.42 1966-05-24,882.46,898.38,882.12,888.41,7210000,888.41 1966-05-23,876.89,889.86,873.49,882.46,7080000,882.46 1966-05-20,872.99,880.06,863.53,876.89,6430000,876.89 1966-05-19,878.50,887.86,869.65,872.99,8640000,872.99 1966-05-18,864.42,883.96,864.42,878.50,9310000,878.50 1966-05-17,867.53,876.83,859.13,864.14,9870000,864.14 1966-05-16,876.11,881.73,862.86,867.53,9260000,867.53 1966-05-13,884.85,884.85,868.65,876.11,8970000,876.11 1966-05-12,895.43,900.00,878.84,885.57,8210000,885.57 1966-05-11,895.48,908.40,892.70,895.43,7470000,895.43 1966-05-10,886.80,904.39,886.35,895.48,9050000,895.48 1966-05-09,902.83,903.39,883.18,886.80,9290000,886.80 1966-05-06,899.77,909.18,882.90,902.83,13110000,902.83 1966-05-05,914.86,919.65,897.55,899.77,10100000,899.77 1966-05-04,921.77,923.38,906.23,914.86,9740000,914.86 1966-05-03,931.95,932.12,919.20,921.77,8020000,921.77 1966-05-02,933.68,939.64,926.61,931.95,7070000,931.95 1966-04-29,937.41,942.09,930.23,933.68,7220000,933.68 1966-04-28,944.54,945.54,927.22,937.41,8310000,937.41 1966-04-27,947.21,951.28,937.58,944.54,7950000,944.54 1966-04-26,950.55,955.56,943.20,947.21,7540000,947.21 1966-04-25,949.83,957.51,942.09,950.55,7270000,950.55 1966-04-22,954.73,957.23,944.09,949.83,8650000,949.83 1966-04-21,951.28,961.91,946.82,954.73,9560000,954.73 1966-04-20,941.75,957.01,941.75,951.28,10530000,951.28 1966-04-19,941.98,946.99,934.02,941.64,8820000,941.64 1966-04-18,947.77,953.22,936.91,941.98,9150000,941.98 1966-04-15,945.48,957.40,941.70,947.77,10270000,947.77 1966-04-14,938.36,953.67,936.52,945.48,12980000,945.48 1966-04-13,937.24,945.32,930.01,938.36,10440000,938.36 1966-04-12,942.42,947.10,932.51,937.24,10500000,937.24 1966-04-11,945.76,950.77,937.69,942.42,9310000,942.42 1966-04-07,945.26,952.39,939.53,945.76,9650000,945.76 1966-04-06,944.71,951.22,937.58,945.26,9040000,945.26 1966-04-05,937.86,951.44,935.91,944.71,10560000,944.71 1966-04-04,931.29,945.48,930.51,937.86,9360000,937.86 1966-04-01,924.77,936.30,921.77,931.29,9050000,931.29 1966-03-31,919.76,929.12,916.31,924.77,6690000,924.77 1966-03-30,928.00,928.00,915.31,919.76,7980000,919.76 1966-03-29,932.62,937.19,922.88,929.39,8300000,929.39 1966-03-28,929.95,940.36,928.61,932.62,8640000,932.62 1966-03-25,928.61,936.52,925.38,929.95,7750000,929.95 1966-03-24,929.00,933.35,923.37,928.61,7880000,928.61 1966-03-23,934.52,935.91,924.88,929.00,6720000,929.00 1966-03-22,929.28,942.92,929.28,934.52,8910000,934.52 1966-03-21,922.88,935.52,921.65,929.17,7230000,929.17 1966-03-18,919.32,928.95,914.58,922.88,6450000,922.88 1966-03-17,916.03,922.99,912.58,919.32,5460000,919.32 1966-03-16,911.08,922.27,909.13,916.03,7330000,916.03 1966-03-15,917.09,919.15,905.40,911.08,9440000,911.08 1966-03-14,927.95,930.01,914.36,917.09,7400000,917.09 1966-03-11,929.23,937.30,924.72,927.95,7000000,927.95 1966-03-10,929.84,941.42,924.10,929.23,10310000,929.23 1966-03-09,919.98,933.51,918.98,929.84,7980000,929.84 1966-03-08,917.78,928.95,909.57,919.98,10120000,919.98 1966-03-07,932.34,932.46,914.81,917.78,9370000,917.78 1966-03-04,936.35,943.31,928.89,932.34,9000000,932.34 1966-03-03,932.01,941.25,922.27,936.35,9900000,936.35 1966-03-02,938.19,943.09,927.78,932.01,10470000,932.01 1966-03-01,951.89,955.17,935.52,938.19,11030000,938.19 1966-02-28,953.00,960.41,948.21,951.89,9910000,951.89 1966-02-25,950.66,962.30,946.10,953.00,8140000,953.00 1966-02-24,960.13,962.19,946.60,950.66,7860000,950.66 1966-02-23,966.48,967.87,955.12,960.13,8080000,960.13 1966-02-21,975.22,977.11,962.80,966.48,8510000,966.48 1966-02-18,975.27,982.07,966.31,975.22,8470000,975.22 1966-02-17,982.40,985.85,969.93,975.27,9330000,975.27 1966-02-16,981.57,987.80,976.22,982.40,9180000,982.40 1966-02-15,987.69,990.81,977.61,981.57,8750000,981.57 1966-02-14,989.03,995.60,983.24,987.69,8360000,987.69 1966-02-11,990.81,997.99,984.96,989.03,8150000,989.03 1966-02-10,995.15,1000.27,986.74,990.81,9790000,990.81 1966-02-09,991.03,1001.11,987.63,995.15,9760000,995.15 1966-02-08,989.69,998.71,979.28,991.03,10560000,991.03 1966-02-07,986.35,996.71,981.84,989.69,8000000,989.69 1966-02-04,981.23,991.53,976.00,986.35,7560000,986.35 1966-02-03,982.29,991.59,976.33,981.23,8160000,981.23 1966-02-02,975.89,985.35,967.81,982.29,8130000,982.29 1966-02-01,983.51,987.47,969.43,975.89,9090000,975.89 1966-01-31,985.35,992.53,979.67,983.51,7800000,983.51 1966-01-28,990.36,994.59,979.95,985.35,9000000,985.35 1966-01-27,990.92,997.49,985.02,990.36,8970000,990.36 1966-01-26,991.64,999.16,984.13,990.92,9910000,990.92 1966-01-25,991.42,998.21,986.52,991.64,9300000,991.64 1966-01-24,988.14,999.22,984.63,991.42,8780000,991.42 1966-01-21,987.80,993.20,980.62,988.14,9180000,988.14 1966-01-20,991.14,997.77,983.90,987.80,8670000,987.80 1966-01-19,994.20,1000.55,985.30,991.14,10230000,991.14 1966-01-18,989.75,1000.50,984.68,994.20,9790000,994.20 1966-01-17,987.30,996.60,984.02,989.75,9430000,989.75 1966-01-14,985.69,994.09,982.07,987.30,9210000,987.30 1966-01-13,983.96,992.65,980.23,985.69,8860000,985.69 1966-01-12,986.85,992.59,980.45,983.96,8530000,983.96 1966-01-11,985.41,993.04,980.34,986.85,8910000,986.85 1966-01-10,986.13,991.92,980.56,985.41,7720000,985.41 1966-01-07,985.46,991.59,978.61,986.13,7600000,986.13 1966-01-06,981.62,992.26,979.00,985.46,7880000,985.46 1966-01-05,969.26,984.85,968.76,981.62,9650000,981.62 1966-01-04,968.54,978.50,963.02,969.26,7540000,969.26 1966-01-03,969.26,974.55,961.91,968.54,5950000,968.54 1965-12-31,963.69,976.61,962.58,969.26,7240000,969.26 1965-12-30,960.30,969.76,957.62,963.69,7060000,963.69 1965-12-29,957.96,969.15,953.67,960.30,7610000,960.30 1965-12-28,959.79,966.09,950.05,957.96,7280000,957.96 1965-12-27,966.36,972.49,958.29,959.79,5950000,959.79 1965-12-23,965.86,972.93,959.35,966.36,6870000,966.36 1965-12-22,959.46,974.16,956.34,965.86,9720000,965.86 1965-12-21,952.22,964.86,950.05,959.46,8230000,959.46 1965-12-20,957.85,960.69,947.16,952.22,7350000,952.22 1965-12-17,959.13,966.09,952.61,957.85,9490000,957.85 1965-12-16,958.74,967.14,953.78,959.13,9950000,959.13 1965-12-15,954.06,964.81,947.99,958.74,9560000,958.74 1965-12-14,951.55,959.85,948.05,954.06,9920000,954.06 1965-12-13,952.72,959.52,946.54,951.55,8660000,951.55 1965-12-10,949.55,957.51,945.71,952.72,8740000,952.72 1965-12-09,946.60,955.67,942.59,949.55,9150000,949.55 1965-12-08,951.33,959.63,943.42,946.60,10120000,946.60 1965-12-07,939.53,958.63,938.80,951.33,9340000,951.33 1965-12-06,946.10,946.15,924.44,939.53,11440000,939.53 1965-12-03,944.59,950.94,939.36,946.10,8160000,946.10 1965-12-02,947.60,951.11,938.80,944.59,9070000,944.59 1965-12-01,946.71,953.00,941.81,947.60,10140000,947.60 1965-11-30,946.93,950.89,937.24,946.71,8990000,946.71 1965-11-29,948.16,954.84,942.48,946.93,8760000,946.93 1965-11-26,948.94,953.56,943.76,948.16,6970000,948.16 1965-11-24,948.94,956.06,942.98,948.94,7870000,948.94 1965-11-23,946.38,953.28,941.87,948.94,7150000,948.94 1965-11-22,952.72,954.95,941.48,946.38,6370000,946.38 1965-11-19,950.50,958.51,946.88,952.72,6850000,952.72 1965-11-18,956.57,958.24,945.26,950.50,7040000,950.50 1965-11-17,956.51,963.14,950.66,956.57,9120000,956.57 1965-11-16,955.90,962.08,950.27,956.51,8380000,956.51 1965-11-15,956.29,963.91,951.39,955.90,8310000,955.90 1965-11-12,953.28,961.80,949.44,956.29,7780000,956.29 1965-11-11,951.22,955.84,946.15,953.28,5430000,953.28 1965-11-10,951.72,957.34,946.60,951.22,4860000,951.22 1965-11-09,953.95,959.13,945.76,951.72,6680000,951.72 1965-11-08,959.46,961.74,948.60,953.95,7000000,953.95 1965-11-05,961.85,967.42,953.89,959.46,7310000,959.46 1965-11-04,961.13,969.98,956.23,961.85,8380000,961.85 1965-11-03,958.96,965.97,953.06,961.13,7520000,961.13 1965-11-01,960.82,964.36,953.61,958.96,6340000,958.96 1965-10-29,959.11,966.47,955.38,960.82,7240000,960.82 1965-10-28,959.50,965.21,952.75,959.11,7230000,959.11 1965-10-27,956.32,967.46,954.83,959.50,7670000,959.50 1965-10-26,948.14,959.44,945.95,956.32,6750000,956.32 1965-10-25,952.42,958.95,944.30,948.14,7090000,948.14 1965-10-22,950.28,959.39,946.71,952.42,8960000,952.42 1965-10-21,948.47,957.36,944.90,950.28,9170000,950.28 1965-10-20,947.76,953.57,941.72,948.47,8200000,948.47 1965-10-19,945.84,955.49,943.15,947.76,8620000,947.76 1965-10-18,940.68,952.31,939.31,945.84,8180000,945.84 1965-10-15,937.50,945.07,934.15,940.68,7470000,940.68 1965-10-14,941.01,946.22,933.87,937.50,8580000,937.50 1965-10-13,941.12,945.84,935.74,941.01,9470000,941.01 1965-10-12,942.65,947.92,936.73,941.12,9470000,941.12 1965-10-11,938.32,948.30,936.40,942.65,9600000,942.65 1965-10-08,934.42,944.08,932.50,938.32,7670000,938.32 1965-10-07,936.84,941.83,930.75,934.42,6670000,934.42 1965-10-06,938.70,942.49,927.29,936.84,6010000,936.84 1965-10-05,930.86,942.49,930.14,938.70,6980000,938.70 1965-10-04,929.65,935.57,924.05,930.86,5590000,930.86 1965-10-01,930.58,936.07,923.23,929.65,7470000,929.65 1965-09-30,932.39,939.20,927.40,930.58,8670000,930.58 1965-09-29,935.85,944.96,927.89,932.39,10600000,932.39 1965-09-28,937.88,943.42,928.66,935.85,8750000,935.85 1965-09-27,929.54,944.74,927.89,937.88,6820000,937.88 1965-09-24,927.45,933.65,918.89,929.54,7810000,929.54 1965-09-23,931.62,938.37,922.95,927.45,9990000,927.45 1965-09-22,926.52,936.62,922.35,931.62,8290000,931.62 1965-09-21,931.18,936.73,923.45,926.52,7750000,926.52 1965-09-20,928.99,937.00,924.27,931.18,7040000,931.18 1965-09-17,931.18,934.59,922.57,928.99,6610000,928.99 1965-09-16,922.95,935.46,922.73,931.18,7410000,931.18 1965-09-15,916.59,927.89,913.41,922.95,6220000,922.95 1965-09-14,920.92,928.22,913.57,916.59,7830000,916.59 1965-09-13,918.95,927.62,913.41,920.92,7020000,920.92 1965-09-10,917.47,924.00,911.65,918.95,6650000,918.95 1965-09-09,913.68,923.12,911.81,917.47,7360000,917.47 1965-09-08,910.11,918.45,905.67,913.68,6240000,913.68 1965-09-07,907.97,915.22,904.30,910.11,5750000,910.11 1965-09-03,900.46,910.28,900.46,907.97,6010000,907.97 1965-09-02,893.60,903.03,892.17,900.40,6470000,900.40 1965-09-01,893.10,897.93,886.96,893.60,5890000,893.60 1965-08-31,895.63,900.84,888.22,893.10,5170000,893.10 1965-08-30,895.96,899.69,890.52,895.63,4400000,895.63 1965-08-27,896.18,901.88,891.95,895.96,5570000,895.96 1965-08-26,890.85,899.25,886.74,896.18,6010000,896.18 1965-08-25,887.12,895.79,884.38,890.85,6240000,890.85 1965-08-24,887.07,893.05,883.50,887.12,4740000,887.12 1965-08-23,889.92,893.65,883.94,887.07,4470000,887.07 1965-08-20,891.79,894.97,884.71,889.92,4170000,889.92 1965-08-19,894.37,900.13,889.43,891.79,5000000,891.79 1965-08-18,894.26,901.39,890.30,894.37,5850000,894.37 1965-08-17,891.13,898.37,888.11,894.26,4520000,894.26 1965-08-16,888.82,896.89,886.41,891.13,5270000,891.13 1965-08-13,881.96,891.95,880.48,888.82,5430000,888.82 1965-08-12,881.47,886.90,876.86,881.96,5160000,881.96 1965-08-11,878.89,885.42,876.48,881.47,5030000,881.47 1965-08-10,879.77,883.39,875.43,878.89,4690000,878.89 1965-08-09,882.51,887.40,876.31,879.77,4540000,879.77 1965-08-06,881.63,886.57,875.38,882.51,4200000,882.51 1965-08-05,883.88,886.96,878.34,881.63,4920000,881.63 1965-08-04,881.20,888.11,878.40,883.88,4830000,883.88 1965-08-03,881.85,885.04,874.78,881.20,4640000,881.20 1965-08-02,881.74,888.11,876.48,881.85,4220000,881.85 1965-07-30,875.49,885.31,875.49,881.74,5200000,881.74 1965-07-29,867.92,877.85,864.62,874.23,4690000,874.23 1965-07-28,863.53,873.18,858.42,867.92,4760000,867.92 1965-07-27,867.26,872.09,860.78,863.53,4190000,863.53 1965-07-26,863.97,870.66,858.75,867.26,3790000,867.26 1965-07-23,861.77,869.40,859.58,863.97,3600000,863.97 1965-07-22,865.01,868.08,857.71,861.77,3310000,861.77 1965-07-21,868.79,872.58,860.95,865.01,4350000,865.01 1965-07-20,880.26,882.02,866.82,868.79,4670000,868.79 1965-07-19,880.43,884.54,876.09,880.26,3220000,880.26 1965-07-16,880.98,885.91,875.98,880.43,3520000,880.43 1965-07-15,883.23,889.98,878.62,880.98,4420000,880.98 1965-07-14,876.97,886.24,874.23,883.23,4100000,883.23 1965-07-13,877.96,881.03,872.47,876.97,3260000,876.97 1965-07-12,879.49,883.99,874.39,877.96,3690000,877.96 1965-07-09,877.85,885.04,874.72,879.49,4800000,879.49 1965-07-08,870.77,880.59,867.20,877.85,4380000,877.85 1965-07-07,873.18,874.61,866.71,870.77,3020000,870.77 1965-07-06,875.16,879.60,870.00,873.18,3400000,873.18 1965-07-02,871.59,878.40,866.76,875.16,4260000,875.16 1965-07-01,868.03,873.84,861.88,871.59,4520000,871.59 1965-06-30,856.94,873.35,856.94,868.03,6930000,868.03 1965-06-29,840.59,856.45,832.74,851.40,10450000,851.40 1965-06-28,854.36,858.86,838.78,840.59,7650000,840.59 1965-06-25,857.76,861.99,849.75,854.36,5790000,854.36 1965-06-24,870.22,870.93,854.64,857.76,5840000,857.76 1965-06-23,875.43,879.38,868.25,870.22,3580000,870.22 1965-06-22,874.12,882.18,871.48,875.43,3330000,875.43 1965-06-21,879.17,881.47,868.79,874.12,3280000,874.12 1965-06-18,883.06,888.22,875.10,879.17,4330000,879.17 1965-06-17,878.07,888.44,876.20,883.06,5220000,883.06 1965-06-16,874.57,886.13,873.40,878.07,6290000,878.07 1965-06-15,868.71,876.75,859.13,874.57,8450000,874.57 1965-06-14,881.70,885.43,863.87,868.71,5920000,868.71 1965-06-11,876.49,886.55,874.52,881.70,5350000,881.70 1965-06-10,879.84,887.56,869.35,876.49,7470000,876.49 1965-06-09,889.05,893.79,875.90,879.84,7070000,879.84 1965-06-08,902.15,903.16,886.71,889.05,4660000,889.05 1965-06-07,900.87,907.47,889.90,902.15,4680000,902.15 1965-06-04,899.22,905.02,894.69,900.87,4530000,900.87 1965-06-03,904.06,913.11,896.40,899.22,5720000,899.22 1965-06-02,908.53,910.08,894.75,904.06,6790000,904.06 1965-06-01,918.04,921.85,907.04,908.53,4830000,908.53 1965-05-28,913.22,920.69,910.89,918.04,4270000,918.04 1965-05-27,917.16,918.20,904.82,913.22,5520000,913.22 1965-05-26,921.00,927.48,915.09,917.16,5330000,917.16 1965-05-25,914.21,924.58,912.55,921.00,4950000,921.00 1965-05-24,922.01,924.27,910.89,914.21,4790000,914.21 1965-05-21,927.27,928.02,918.66,922.01,4660000,922.01 1965-05-20,932.12,934.17,922.41,927.27,5750000,927.27 1965-05-19,930.62,936.87,926.97,932.12,5860000,932.12 1965-05-18,930.67,932.82,924.36,930.62,5130000,930.62 1965-05-17,939.62,941.52,928.07,930.67,4980000,930.67 1965-05-14,938.87,944.82,933.82,939.62,5860000,939.62 1965-05-13,934.17,942.37,933.52,938.87,6460000,938.87 1965-05-12,930.92,938.12,928.72,934.17,6310000,934.17 1965-05-11,931.47,934.87,925.62,930.92,5150000,930.92 1965-05-10,932.52,937.22,926.62,931.47,5600000,931.47 1965-05-07,933.52,937.07,927.07,932.52,5820000,932.52 1965-05-06,932.22,938.12,928.02,933.52,6340000,933.52 1965-05-05,928.22,936.37,924.91,932.22,6350000,932.22 1965-05-04,922.11,931.07,919.71,928.22,5720000,928.22 1965-05-03,922.31,926.97,916.66,922.11,5340000,922.11 1965-04-30,918.71,925.42,915.36,922.31,5190000,922.31 1965-04-29,918.86,923.01,914.91,918.71,5510000,918.71 1965-04-28,918.16,923.56,914.01,918.86,5680000,918.86 1965-04-27,916.86,924.96,915.76,918.16,6310000,918.16 1965-04-26,916.41,920.11,910.21,916.86,5410000,916.86 1965-04-23,915.06,921.51,912.26,916.41,5860000,916.41 1965-04-22,910.71,917.51,908.31,915.06,5990000,915.06 1965-04-21,911.96,915.31,903.81,910.71,5590000,910.71 1965-04-20,912.76,919.01,908.01,911.96,6480000,911.96 1965-04-19,911.91,917.41,907.91,912.76,5700000,912.76 1965-04-15,912.86,917.01,905.71,911.91,5830000,911.91 1965-04-14,908.01,916.51,905.96,912.86,6580000,912.86 1965-04-13,906.36,912.41,902.21,908.01,6690000,908.01 1965-04-12,901.29,909.51,899.90,906.36,6040000,906.36 1965-04-09,897.90,905.77,895.93,901.29,6580000,901.29 1965-04-08,892.94,900.41,890.72,897.90,5770000,897.90 1965-04-07,891.90,895.84,888.27,892.94,4430000,892.94 1965-04-06,893.23,896.13,888.51,891.90,4610000,891.90 1965-04-05,893.38,898.69,889.00,893.23,4920000,893.23 1965-04-02,890.33,897.31,888.71,893.38,5060000,893.38 1965-04-01,889.05,894.95,886.84,890.33,4890000,890.33 1965-03-31,889.05,893.72,885.51,889.05,4470000,889.05 1965-03-30,887.82,892.99,885.27,889.05,4270000,889.05 1965-03-29,891.66,896.43,884.68,887.82,4590000,887.82 1965-03-26,898.34,900.60,888.51,891.66,5020000,891.66 1965-03-25,900.56,904.88,895.74,898.34,5460000,898.34 1965-03-24,898.69,904.39,896.13,900.56,5420000,900.56 1965-03-23,896.12,902.32,893.82,898.69,4820000,898.69 1965-03-22,895.79,901.14,892.87,896.12,4920000,896.12 1965-03-19,896.55,900.43,892.20,895.79,5040000,895.79 1965-03-18,899.37,902.67,893.35,896.55,4990000,896.55 1965-03-17,898.90,904.01,894.45,899.37,5120000,899.37 1965-03-16,899.85,904.58,894.83,898.90,5480000,898.90 1965-03-15,900.33,907.88,896.89,899.85,6000000,899.85 1965-03-12,896.51,905.16,894.50,900.33,6370000,900.33 1965-03-11,892.39,900.71,890.24,896.51,5770000,896.51 1965-03-10,894.07,898.13,888.81,892.39,5100000,892.39 1965-03-09,896.84,900.71,890.72,894.07,5210000,894.07 1965-03-08,895.98,901.81,891.30,896.84,5250000,896.84 1965-03-05,897.75,898.61,886.28,895.98,6120000,895.98 1965-03-04,900.76,905.25,894.45,897.75,7300000,897.75 1965-03-03,901.91,906.95,896.27,900.76,6600000,900.76 1965-03-02,899.76,905.73,896.17,901.91,5730000,901.91 1965-03-01,903.48,907.40,895.55,899.76,5780000,899.76 1965-02-26,899.90,908.26,897.08,903.48,5800000,903.48 1965-02-25,897.84,904.82,894.64,899.90,6680000,899.90 1965-02-24,891.96,900.76,891.30,897.84,7160000,897.84 1965-02-23,885.61,895.84,884.60,891.96,5880000,891.96 1965-02-19,883.69,890.24,880.25,885.61,5560000,885.61 1965-02-18,882.93,888.52,878.53,883.69,6060000,883.69 1965-02-17,881.35,889.10,877.86,882.93,5510000,882.93 1965-02-16,885.32,887.90,877.48,881.35,5000000,881.35 1965-02-15,888.47,895.26,882.60,885.32,5760000,885.32 1965-02-12,881.88,891.15,881.45,888.47,4960000,888.47 1965-02-11,892.92,897.70,879.97,881.88,5800000,881.88 1965-02-10,901.24,905.54,890.77,892.92,7210000,892.92 1965-02-09,897.89,905.40,896.12,901.24,5690000,901.24 1965-02-08,901.57,901.86,889.96,897.89,6010000,897.89 1965-02-05,904.06,906.93,897.70,901.57,5690000,901.57 1965-02-04,906.30,911.80,900.33,904.06,6230000,904.06 1965-02-03,903.77,909.75,898.99,906.30,6130000,906.30 1965-02-02,903.68,907.21,898.61,903.77,5460000,903.77 1965-02-01,902.86,908.26,897.99,903.68,5690000,903.68 1965-01-29,900.95,907.79,898.32,902.86,6940000,902.86 1965-01-28,899.52,906.26,895.79,900.95,6730000,900.95 1965-01-27,897.84,904.44,894.26,899.52,6010000,899.52 1965-01-26,896.46,901.86,892.30,897.84,5760000,897.84 1965-01-25,893.59,900.71,890.82,896.46,5370000,896.46 1965-01-22,893.26,898.70,888.43,893.59,5430000,893.59 1965-01-21,895.31,897.46,887.81,893.26,4780000,893.26 1965-01-20,896.27,901.43,891.92,895.31,5550000,895.31 1965-01-19,895.21,902.10,891.10,896.27,5550000,896.27 1965-01-18,891.15,898.90,889.86,895.21,5550000,895.21 1965-01-15,887.18,895.12,884.65,891.15,5340000,891.15 1965-01-14,886.85,892.30,881.59,887.18,5810000,887.18 1965-01-13,885.89,891.77,883.22,886.85,6160000,886.85 1965-01-12,883.22,889.34,880.97,885.89,5400000,885.89 1965-01-11,882.60,887.42,877.43,883.22,5440000,883.22 1965-01-08,884.36,888.62,878.87,882.60,5340000,882.60 1965-01-07,879.68,887.52,876.95,884.36,5080000,884.36 1965-01-06,875.86,885.08,875.04,879.68,4850000,879.68 1965-01-05,869.78,879.87,868.69,875.86,4110000,875.86 1965-01-04,874.13,877.19,865.10,869.78,3930000,869.78 1964-12-31,868.78,879.78,868.78,874.13,6470000,874.13 1964-12-30,862.18,871.51,861.56,868.69,5610000,868.69 1964-12-29,867.01,868.78,858.26,862.18,4450000,862.18 1964-12-28,868.16,871.98,863.09,867.01,3990000,867.01 1964-12-24,868.02,872.89,863.57,868.16,3600000,868.16 1964-12-23,870.36,876.05,863.19,868.02,4470000,868.02 1964-12-22,869.74,876.38,865.86,870.36,4520000,870.36 1964-12-21,868.73,875.62,866.10,869.74,4470000,869.74 1964-12-18,863.57,872.22,862.95,868.73,4630000,868.73 1964-12-17,860.08,867.30,857.50,863.57,4850000,863.57 1964-12-16,857.45,865.34,854.87,860.08,4610000,860.08 1964-12-15,860.65,862.71,850.19,857.45,5340000,857.45 1964-12-14,864.34,868.78,857.40,860.65,4340000,860.65 1964-12-11,863.14,869.31,860.22,864.34,4530000,864.34 1964-12-10,863.81,869.16,858.46,863.14,4790000,863.14 1964-12-09,870.69,873.13,861.61,863.81,5120000,863.81 1964-12-08,873.99,878.44,867.35,870.69,4990000,870.69 1964-12-07,871.17,881.35,871.17,873.99,4770000,873.99 1964-12-04,870.79,875.09,866.44,870.93,4340000,870.93 1964-12-03,867.25,876.86,867.25,870.79,4250000,870.79 1964-12-02,864.43,871.84,861.08,867.16,4930000,867.16 1964-12-01,875.43,877.96,863.04,864.43,4940000,864.43 1964-11-30,882.12,885.22,871.46,875.43,4890000,875.43 1964-11-27,882.40,886.80,874.47,882.12,4070000,882.12 1964-11-25,887.61,890.00,880.16,882.40,4800000,882.40 1964-11-24,889.29,891.49,881.11,887.61,5070000,887.61 1964-11-23,890.72,894.83,882.88,889.29,4860000,889.29 1964-11-20,888.71,896.41,884.51,890.72,5210000,890.72 1964-11-19,891.71,892.97,882.07,888.71,5570000,888.71 1964-11-18,885.39,897.00,884.69,891.71,6560000,891.71 1964-11-17,880.19,892.18,880.19,885.39,5920000,885.39 1964-11-16,874.11,883.52,870.22,880.10,4870000,880.10 1964-11-13,874.62,878.46,870.73,874.11,4860000,874.11 1964-11-12,873.59,880.00,870.41,874.62,5250000,874.62 1964-11-11,870.64,876.17,866.80,873.59,3790000,873.59 1964-11-10,874.57,878.32,867.60,870.64,5020000,870.64 1964-11-09,876.87,880.00,871.34,874.57,4560000,874.57 1964-11-06,873.54,879.35,869.33,876.87,4810000,876.87 1964-11-05,873.82,879.26,869.10,873.54,4380000,873.54 1964-11-04,875.51,881.27,870.41,873.82,4720000,873.82 1964-11-02,873.08,878.13,867.64,875.51,4430000,875.51 1964-10-30,871.86,876.91,868.91,873.08,4120000,873.08 1964-10-29,871.16,876.87,867.83,871.86,4390000,871.86 1964-10-28,875.98,879.58,867.79,871.16,4890000,871.16 1964-10-27,877.01,880.33,872.75,875.98,4470000,875.98 1964-10-26,877.62,883.33,873.45,877.01,5230000,877.01 1964-10-23,877.01,879.91,872.14,877.62,3830000,877.62 1964-10-22,879.72,883.33,872.51,877.01,4670000,877.01 1964-10-21,881.50,886.28,876.49,879.72,5170000,879.72 1964-10-20,876.21,884.87,873.97,881.50,5140000,881.50 1964-10-19,873.54,879.82,870.41,876.21,5010000,876.21 1964-10-16,868.44,877.71,866.80,873.54,5140000,873.54 1964-10-15,875.18,876.40,861.42,868.44,6500000,868.44 1964-10-14,876.21,880.00,872.00,875.18,4530000,875.18 1964-10-13,877.57,881.88,871.95,876.21,5400000,876.21 1964-10-12,878.08,881.50,874.76,877.57,4110000,877.57 1964-10-09,874.90,881.78,872.65,878.08,5290000,878.08 1964-10-08,873.78,879.40,869.56,874.90,5060000,874.90 1964-10-07,875.14,879.02,871.34,873.78,5090000,873.78 1964-10-06,877.15,881.17,872.05,875.14,4820000,875.14 1964-10-05,872.65,882.39,871.48,877.15,4850000,877.15 1964-10-02,872.00,876.21,867.36,872.65,4370000,872.65 1964-10-01,875.37,878.23,869.80,872.00,4470000,872.00 1964-09-30,875.74,880.24,872.19,875.37,4720000,875.37 1964-09-29,875.46,880.14,871.30,875.74,5070000,875.74 1964-09-28,874.71,879.40,869.24,875.46,4810000,875.46 1964-09-25,872.98,879.26,868.72,874.71,6170000,874.71 1964-09-24,871.95,876.96,867.04,872.98,5840000,872.98 1964-09-23,872.47,876.77,867.88,871.95,5920000,871.95 1964-09-22,871.58,877.43,868.67,872.47,5250000,872.47 1964-09-21,866.47,875.00,866.47,871.58,5310000,871.58 1964-09-18,868.67,873.92,860.20,865.12,6160000,865.12 1964-09-17,864.18,873.17,862.87,868.67,6380000,868.67 1964-09-16,862.54,867.64,857.35,864.18,4230000,864.18 1964-09-15,866.24,871.30,860.95,862.54,5690000,862.54 1964-09-14,867.13,872.84,862.35,866.24,5370000,866.24 1964-09-11,859.50,870.64,857.06,867.13,5630000,867.13 1964-09-10,855.57,863.06,852.66,859.50,5470000,859.50 1964-09-09,851.91,860.01,850.56,855.57,5690000,855.57 1964-09-08,848.31,855.61,847.61,851.91,4090000,851.91 1964-09-04,846.02,850.84,843.58,848.31,4210000,848.31 1964-09-03,845.08,848.97,841.24,846.02,4310000,846.02 1964-09-02,844.00,849.62,840.96,845.08,4800000,845.08 1964-09-01,838.48,846.06,836.56,844.00,4650000,844.00 1964-08-31,839.09,842.83,834.59,838.48,3340000,838.48 1964-08-28,835.25,841.94,833.98,839.09,3760000,839.09 1964-08-27,829.21,837.40,826.91,835.25,3560000,835.25 1964-08-26,832.20,834.87,826.59,829.21,3300000,829.21 1964-08-25,837.31,839.09,830.29,832.20,3780000,832.20 1964-08-24,838.62,843.21,834.83,837.31,3790000,837.31 1964-08-21,838.71,842.60,835.39,838.62,3620000,838.62 1964-08-20,841.76,844.19,835.44,838.71,3840000,838.71 1964-08-19,842.83,847.19,838.24,841.76,4160000,841.76 1964-08-18,840.21,845.64,837.17,842.83,4180000,842.83 1964-08-17,838.81,844.77,836.18,840.21,3780000,840.21 1964-08-14,838.52,844.00,834.92,838.81,4080000,838.81 1964-08-13,834.08,842.41,833.14,838.52,4600000,838.52 1964-08-12,828.08,837.54,827.29,834.08,4140000,834.08 1964-08-11,829.35,832.95,825.56,828.08,3450000,828.08 1964-08-10,829.16,834.08,825.18,829.35,3050000,829.35 1964-08-07,823.40,831.60,822.75,829.16,3190000,829.16 1964-08-06,833.05,835.90,822.56,823.40,3940000,823.40 1964-08-05,832.77,835.44,820.78,833.05,6160000,833.05 1964-08-04,840.35,840.91,829.96,832.77,4780000,832.77 1964-08-03,841.10,844.28,837.45,840.35,3780000,840.35 1964-07-31,839.37,844.80,836.89,841.10,4220000,841.10 1964-07-30,838.67,843.86,836.14,839.37,4530000,839.37 1964-07-29,837.35,842.93,834.31,838.67,4050000,838.67 1964-07-28,841.05,842.27,833.47,837.35,3860000,837.35 1964-07-27,845.64,849.53,838.95,841.05,4090000,841.05 1964-07-24,846.48,850.32,842.88,845.64,4210000,845.64 1964-07-23,847.65,851.54,842.93,846.48,4560000,846.48 1964-07-22,846.95,851.45,841.71,847.65,4570000,847.65 1964-07-21,849.39,851.49,841.85,846.95,4570000,846.95 1964-07-20,851.35,855.19,847.00,849.39,4390000,849.39 1964-07-17,847.47,854.11,845.45,851.35,4640000,851.35 1964-07-16,844.80,850.04,842.04,847.47,4640000,847.47 1964-07-15,843.63,849.06,839.27,844.80,4610000,844.80 1964-07-14,845.55,849.53,839.60,843.63,4760000,843.63 1964-07-13,847.51,851.54,841.90,845.55,4800000,845.55 1964-07-10,845.13,852.52,841.99,847.51,5420000,847.51 1964-07-09,845.45,849.85,841.01,845.13,5040000,845.13 1964-07-08,844.94,849.34,839.98,845.45,4760000,845.45 1964-07-07,844.24,849.15,841.66,844.94,5240000,844.94 1964-07-06,841.47,848.64,838.01,844.24,5080000,844.24 1964-07-02,838.06,844.75,836.04,841.47,5230000,841.47 1964-07-01,831.50,839.88,829.30,838.06,5320000,838.06 1964-06-30,830.94,834.64,826.31,831.50,4360000,831.50 1964-06-29,830.99,835.90,827.01,830.94,4380000,830.94 1964-06-26,827.48,833.05,825.09,830.99,4440000,830.99 1964-06-25,827.01,832.39,823.40,827.48,5010000,827.48 1964-06-24,822.70,831.17,819.80,827.01,4840000,827.01 1964-06-23,826.38,830.05,820.55,822.70,4060000,822.70 1964-06-22,825.25,831.97,823.30,826.38,4540000,826.38 1964-06-19,823.98,828.11,819.94,825.25,4050000,825.25 1964-06-18,823.35,829.42,820.62,823.98,4730000,823.98 1964-06-17,818.16,826.45,817.63,823.35,5340000,823.35 1964-06-16,813.56,821.22,812.63,818.16,4590000,818.16 1964-06-15,809.39,816.79,808.11,813.56,4110000,813.56 1964-06-12,811.25,814.22,806.52,809.39,3840000,809.39 1964-06-11,807.53,815.15,806.65,811.25,3620000,811.25 1964-06-10,805.54,812.10,803.99,807.53,4170000,807.53 1964-06-09,800.31,808.15,796.90,805.54,4470000,805.54 1964-06-08,806.03,808.95,798.50,800.31,4010000,800.31 1964-06-05,802.48,810.37,801.78,806.03,4240000,806.03 1964-06-04,811.79,813.38,800.75,802.48,4880000,802.48 1964-06-03,813.78,818.16,809.84,811.79,3990000,811.79 1964-06-02,818.56,820.34,811.87,813.78,4180000,813.78 1964-06-01,820.56,824.94,815.82,818.56,4300000,818.56 1964-05-28,817.94,824.14,814.80,820.56,4560000,820.56 1964-05-27,818.92,822.20,812.63,817.94,4450000,817.94 1964-05-26,820.25,824.01,816.88,818.92,4290000,818.92 1964-05-25,820.87,825.47,816.39,820.25,3990000,820.25 1964-05-22,819.80,824.81,816.57,820.87,4640000,820.87 1964-05-21,820.11,826.45,816.08,819.80,5350000,819.80 1964-05-20,817.28,824.10,815.15,820.11,4790000,820.11 1964-05-19,821.31,823.26,813.96,817.28,4360000,817.28 1964-05-18,826.23,829.33,819.18,821.31,4590000,821.31 1964-05-15,824.45,829.15,821.22,826.23,5070000,826.23 1964-05-14,825.78,828.62,819.63,824.45,4720000,824.45 1964-05-13,827.38,832.78,822.24,825.78,5890000,825.78 1964-05-12,827.07,832.25,824.41,827.38,5200000,827.38 1964-05-11,828.57,832.21,824.10,827.07,4490000,827.07 1964-05-08,830.17,834.55,825.12,828.57,4910000,828.57 1964-05-07,828.18,836.06,824.19,830.17,5600000,830.17 1964-05-06,826.63,832.34,822.55,828.18,5560000,828.18 1964-05-05,823.83,829.28,817.23,826.63,5340000,826.63 1964-05-04,817.10,828.09,816.39,823.83,5360000,823.83 1964-05-01,810.77,819.40,808.55,817.10,5990000,817.10 1964-04-30,812.81,816.88,807.18,810.77,5690000,810.77 1964-04-29,816.70,821.97,810.32,812.81,6200000,812.81 1964-04-28,811.87,820.11,809.04,816.70,4790000,816.70 1964-04-27,814.89,817.68,807.71,811.87,5070000,811.87 1964-04-24,821.66,823.79,813.51,814.89,5610000,814.89 1964-04-23,823.57,830.04,819.32,821.66,6690000,821.66 1964-04-22,826.45,828.09,820.25,823.57,5390000,823.57 1964-04-21,824.54,830.70,820.47,826.45,5750000,826.45 1964-04-20,827.33,829.81,821.49,824.54,5560000,824.54 1964-04-17,825.65,831.63,822.51,827.33,6030000,827.33 1964-04-16,825.43,830.12,821.22,825.65,5240000,825.65 1964-04-15,822.95,828.62,820.38,825.43,5270000,825.43 1964-04-14,821.31,826.63,818.12,822.95,5120000,822.95 1964-04-13,821.75,826.71,818.21,821.31,5330000,821.31 1964-04-10,821.35,825.34,819.14,821.75,4990000,821.75 1964-04-09,824.19,827.51,819.54,821.35,5300000,821.35 1964-04-08,822.77,828.09,818.87,824.19,5380000,824.19 1964-04-07,824.76,829.42,819.18,822.77,5900000,822.77 1964-04-06,822.99,828.88,820.51,824.76,5840000,824.76 1964-04-03,820.87,828.49,818.43,822.99,5990000,822.99 1964-04-02,816.08,825.12,815.20,820.87,6840000,820.87 1964-04-01,813.29,819.23,809.79,816.08,5510000,816.08 1964-03-31,815.29,817.94,810.37,813.29,5270000,813.29 1964-03-30,815.91,819.98,812.18,815.29,6060000,815.29 1964-03-26,813.16,819.45,811.39,815.91,5760000,815.91 1964-03-25,811.43,816.35,808.60,813.16,5420000,813.16 1964-03-24,813.60,816.92,808.95,811.43,5210000,811.43 1964-03-23,814.93,817.72,809.93,813.60,4940000,813.60 1964-03-20,819.36,820.47,810.81,814.93,5020000,814.93 1964-03-19,820.25,824.72,815.91,819.36,5670000,819.36 1964-03-18,818.16,824.23,814.67,820.25,5890000,820.25 1964-03-17,816.48,821.80,812.58,818.16,5480000,818.16 1964-03-16,816.22,821.35,813.25,816.48,5140000,816.48 1964-03-13,814.22,819.85,810.94,816.22,5660000,816.22 1964-03-12,813.87,818.08,809.22,814.22,5290000,814.22 1964-03-11,809.39,817.01,807.84,813.87,6180000,813.87 1964-03-10,807.18,811.96,803.37,809.39,5500000,809.39 1964-03-09,806.03,812.14,803.41,807.18,5510000,807.18 1964-03-06,803.77,809.13,801.16,806.03,4790000,806.03 1964-03-05,804.70,807.93,799.78,803.77,4680000,803.77 1964-03-04,805.72,810.19,800.80,804.70,5250000,804.70 1964-03-03,802.75,809.75,801.33,805.72,5350000,805.72 1964-03-02,800.14,807.22,798.05,802.75,5690000,802.75 1964-02-28,797.04,802.35,793.80,800.14,4980000,800.14 1964-02-27,799.38,803.46,795.44,797.04,5420000,797.04 1964-02-26,796.59,802.00,793.80,799.38,5350000,799.38 1964-02-25,797.12,800.54,792.96,796.59,5010000,796.59 1964-02-24,796.99,802.93,793.32,797.12,5630000,797.12 1964-02-20,794.91,800.93,791.41,796.99,4690000,796.99 1964-02-19,795.40,800.27,791.32,794.91,4280000,794.91 1964-02-18,796.19,800.67,791.32,795.40,4660000,795.40 1964-02-17,794.56,800.40,791.45,796.19,4780000,796.19 1964-02-14,794.42,798.85,789.95,794.56,4360000,794.56 1964-02-13,794.82,799.07,789.99,794.42,4820000,794.42 1964-02-12,792.16,798.14,790.39,794.82,4650000,794.82 1964-02-11,788.71,795.04,786.58,792.16,4040000,792.16 1964-02-10,791.59,797.17,786.76,788.71,4150000,788.71 1964-02-07,786.41,795.40,784.37,791.59,4710000,791.59 1964-02-06,783.04,789.42,780.69,786.41,4110000,786.41 1964-02-05,783.30,789.82,777.99,783.04,4010000,783.04 1964-02-04,784.72,787.78,778.96,783.30,4320000,783.30 1964-02-03,785.34,789.90,780.25,784.72,4140000,784.72 1964-01-31,783.44,789.77,780.34,785.34,4000000,785.34 1964-01-30,782.60,788.35,778.88,783.44,4230000,783.44 1964-01-29,787.78,790.04,779.72,782.60,4450000,782.60 1964-01-28,785.34,791.63,781.75,787.78,4720000,787.78 1964-01-27,783.04,790.39,780.43,785.34,5240000,785.34 1964-01-24,782.86,787.65,778.12,783.04,5080000,783.04 1964-01-23,781.31,787.60,777.95,782.86,5380000,782.86 1964-01-22,776.44,786.89,774.45,781.31,5430000,781.31 1964-01-21,773.03,779.54,767.80,776.44,4800000,776.44 1964-01-20,775.69,780.47,769.82,773.03,5570000,773.03 1964-01-17,776.13,779.41,770.59,775.69,5600000,775.69 1964-01-16,774.71,781.71,770.95,776.13,6200000,776.13 1964-01-15,774.49,779.63,768.55,774.71,6750000,774.71 1964-01-14,773.12,778.88,770.46,774.49,6500000,774.49 1964-01-13,774.33,777.86,768.38,773.12,5440000,773.12 1964-01-10,776.55,778.81,770.38,774.33,5260000,774.33 1964-01-09,774.46,780.59,770.29,776.55,5180000,776.55 1964-01-08,771.73,777.25,768.94,774.46,5380000,774.46 1964-01-07,769.51,776.12,766.12,771.73,5700000,771.73 1964-01-06,767.68,773.77,764.77,769.51,5480000,769.51 1964-01-03,766.08,771.73,763.77,767.68,5550000,767.68 1964-01-02,762.95,770.73,760.34,766.08,4680000,766.08 1963-12-31,759.90,767.55,758.64,762.95,6730000,762.95 1963-12-30,762.95,764.12,756.64,759.90,4930000,759.90 1963-12-27,760.21,767.77,758.51,762.95,4360000,762.95 1963-12-26,756.86,764.47,754.91,760.21,3700000,760.21 1963-12-24,758.30,763.16,752.82,756.86,3970000,756.86 1963-12-23,762.08,765.03,754.43,758.30,4540000,758.30 1963-12-20,763.86,767.29,757.91,762.08,4600000,762.08 1963-12-19,767.21,769.81,760.73,763.86,4410000,763.86 1963-12-18,766.38,773.07,762.43,767.21,6000000,767.21 1963-12-17,761.64,769.51,758.99,766.38,5140000,766.38 1963-12-16,760.17,765.03,756.12,761.64,4280000,761.64 1963-12-13,757.43,762.82,756.25,760.17,4290000,760.17 1963-12-12,757.21,761.60,754.30,757.43,4220000,757.43 1963-12-11,759.25,762.03,754.04,757.21,4400000,757.21 1963-12-10,759.08,764.25,754.95,759.25,4560000,759.25 1963-12-09,760.25,764.29,755.43,759.08,4430000,759.08 1963-12-06,763.86,767.21,757.12,760.25,4830000,760.25 1963-12-05,755.51,766.21,753.69,763.86,5190000,763.86 1963-12-04,751.82,758.51,747.17,755.51,4790000,755.51 1963-12-03,751.91,756.82,747.26,751.82,4520000,751.82 1963-12-02,750.52,757.95,746.78,751.91,4770000,751.91 1963-11-29,741.00,752.39,738.56,750.52,4810000,750.52 1963-11-27,743.52,746.91,735.87,741.00,5210000,741.00 1963-11-26,732.96,746.60,732.96,743.52,9320000,743.52 1963-11-22,732.65,739.00,710.83,711.49,6630000,711.49 1963-11-21,742.06,744.69,730.57,732.65,5670000,732.65 1963-11-20,736.65,747.51,733.44,742.06,5330000,742.06 1963-11-19,734.85,743.13,731.64,736.65,4430000,736.65 1963-11-18,740.00,743.09,731.08,734.85,4730000,734.85 1963-11-15,747.04,749.22,737.90,740.00,4790000,740.00 1963-11-14,751.11,753.30,744.42,747.04,4610000,747.04 1963-11-13,750.21,753.77,745.88,751.11,4710000,751.11 1963-11-12,753.77,756.82,748.36,750.21,4610000,750.21 1963-11-11,750.81,756.56,749.95,753.77,3970000,753.77 1963-11-08,745.66,753.47,744.63,750.81,4570000,750.81 1963-11-07,744.03,750.21,740.99,745.66,4320000,745.66 1963-11-06,749.22,749.82,738.41,744.03,5600000,744.03 1963-11-04,753.73,757.12,746.09,749.22,5440000,749.22 1963-11-01,755.23,759.56,750.30,753.73,5240000,753.73 1963-10-31,755.19,759.00,749.78,755.23,5030000,755.23 1963-10-30,760.50,762.78,751.80,755.19,5170000,755.19 1963-10-29,759.39,767.24,755.49,760.50,6100000,760.50 1963-10-28,755.61,765.70,752.95,759.39,7150000,759.39 1963-10-25,751.80,759.90,748.62,755.61,6390000,755.61 1963-10-24,746.48,755.91,744.12,751.80,6280000,751.80 1963-10-23,747.21,752.10,742.40,746.48,5830000,746.48 1963-10-22,752.31,753.60,742.70,747.21,6420000,747.21 1963-10-21,750.60,757.12,747.25,752.31,5450000,752.31 1963-10-18,750.77,755.36,747.94,750.60,5830000,750.60 1963-10-17,748.45,755.83,746.99,750.77,6790000,750.77 1963-10-16,742.19,750.42,740.04,748.45,5570000,748.45 1963-10-15,741.84,746.43,738.97,742.19,4550000,742.19 1963-10-14,741.76,744.98,738.03,741.84,4270000,741.84 1963-10-11,740.56,746.05,737.60,741.76,4740000,741.76 1963-10-10,739.83,743.52,733.69,740.56,4470000,740.56 1963-10-09,743.90,747.16,737.90,739.83,5520000,739.83 1963-10-08,743.86,748.97,739.23,743.90,4920000,743.90 1963-10-07,745.06,749.91,740.90,743.86,4050000,743.86 1963-10-04,744.25,750.98,741.72,745.06,5120000,745.06 1963-10-03,737.94,746.86,736.61,744.25,4510000,744.25 1963-10-02,738.33,742.06,734.64,737.94,3780000,737.94 1963-10-01,732.79,742.57,732.49,738.33,4420000,738.33 1963-09-30,737.98,739.83,728.63,732.79,3730000,732.79 1963-09-27,736.95,741.33,731.55,737.98,4350000,737.98 1963-09-26,743.69,745.28,734.68,736.95,5100000,736.95 1963-09-25,745.96,753.04,739.31,743.69,6340000,743.69 1963-09-24,740.43,748.54,737.13,745.96,5520000,745.96 1963-09-23,743.60,746.99,736.65,740.43,5140000,740.43 1963-09-20,743.22,749.27,740.64,743.60,5310000,743.60 1963-09-19,737.86,744.63,736.48,743.22,4080000,743.22 1963-09-18,740.13,744.08,734.98,737.86,5070000,737.86 1963-09-17,738.46,744.50,736.83,740.13,4950000,740.13 1963-09-16,740.13,744.68,736.61,738.46,4740000,738.46 1963-09-13,740.26,744.42,737.25,740.13,5230000,740.13 1963-09-12,740.34,744.46,735.45,740.26,5560000,740.26 1963-09-11,737.43,746.18,736.87,740.34,6670000,740.34 1963-09-10,732.92,740.34,730.56,737.43,5310000,737.43 1963-09-09,735.37,739.27,730.09,732.92,5020000,732.92 1963-09-06,737.98,742.66,733.22,735.37,7160000,735.37 1963-09-05,732.92,739.87,729.49,737.98,5700000,737.98 1963-09-04,732.02,737.60,728.89,732.92,6070000,732.92 1963-09-03,729.32,735.50,728.76,732.02,5570000,732.02 1963-08-30,726.40,731.68,724.00,729.32,4560000,729.32 1963-08-29,725.07,730.09,723.27,726.40,5110000,726.40 1963-08-28,719.88,728.72,719.67,725.07,5120000,725.07 1963-08-27,724.17,725.16,717.95,719.88,4080000,719.88 1963-08-26,723.14,728.38,721.00,724.17,4700000,724.17 1963-08-23,718.47,726.02,718.21,723.14,4880000,723.14 1963-08-22,715.72,720.57,712.59,718.47,4540000,718.47 1963-08-21,717.27,719.80,713.49,715.72,3820000,715.72 1963-08-20,718.81,721.34,714.39,717.27,3660000,717.27 1963-08-19,719.32,723.57,715.72,718.81,3650000,718.81 1963-08-16,718.55,722.93,714.91,719.32,4130000,719.32 1963-08-15,714.95,721.51,712.63,718.55,4980000,718.55 1963-08-14,711.13,717.39,707.61,714.95,4420000,714.95 1963-08-13,710.27,714.31,707.83,711.13,4450000,711.13 1963-08-12,708.39,714.09,705.51,710.27,4770000,710.27 1963-08-09,704.18,709.84,701.48,708.39,4050000,708.39 1963-08-08,703.92,707.36,699.85,704.18,3460000,704.18 1963-08-07,707.06,710.62,701.78,703.92,3790000,703.92 1963-08-06,702.55,709.42,700.92,707.06,3760000,707.06 1963-08-05,697.83,704.83,697.10,702.55,3370000,702.55 1963-08-02,694.87,700.54,693.11,697.83,2940000,697.83 1963-08-01,695.43,699.21,691.23,694.87,3410000,694.87 1963-07-31,696.42,701.99,693.03,695.43,3960000,695.43 1963-07-30,690.71,698.30,690.07,696.42,3550000,696.42 1963-07-29,689.38,693.20,686.68,690.71,2840000,690.71 1963-07-26,687.71,691.44,684.58,689.38,2510000,689.38 1963-07-25,690.88,696.20,685.95,687.71,3710000,687.71 1963-07-24,687.84,693.41,685.99,690.88,2810000,690.88 1963-07-23,688.74,694.83,685.74,687.84,3500000,687.84 1963-07-22,693.89,696.16,684.41,688.74,3700000,688.74 1963-07-19,695.90,697.53,689.21,693.89,3340000,693.89 1963-07-18,699.72,703.20,693.93,695.90,3710000,695.90 1963-07-17,702.12,705.73,697.10,699.72,3940000,699.72 1963-07-16,703.28,706.28,699.76,702.12,3000000,702.12 1963-07-15,707.70,708.86,700.58,703.28,3290000,703.28 1963-07-12,709.76,712.55,705.21,707.70,3660000,707.70 1963-07-11,712.12,715.98,707.87,709.76,4100000,709.76 1963-07-10,714.09,716.28,708.90,712.12,3730000,712.12 1963-07-09,710.66,717.22,709.07,714.09,3830000,714.09 1963-07-08,716.45,717.22,708.64,710.66,3290000,710.66 1963-07-05,713.36,719.02,712.20,716.45,2910000,716.45 1963-07-03,708.94,716.19,708.51,713.36,4030000,713.36 1963-07-02,701.35,711.35,699.93,708.94,3540000,708.94 1963-07-01,706.88,709.11,698.86,701.35,3360000,701.35 1963-06-28,706.03,710.79,702.89,706.88,3020000,706.88 1963-06-27,708.99,712.93,702.42,706.03,4540000,706.03 1963-06-26,716.32,716.49,706.46,708.99,4500000,708.99 1963-06-25,718.42,722.07,713.83,716.32,4120000,716.32 1963-06-24,720.78,723.87,715.64,718.42,3700000,718.42 1963-06-21,718.85,724.81,716.06,720.78,4190000,720.78 1963-06-20,719.84,722.76,711.95,718.85,4970000,718.85 1963-06-19,718.90,723.27,715.98,719.84,3970000,719.84 1963-06-18,718.21,722.50,714.99,718.90,3910000,718.90 1963-06-17,722.03,723.61,716.11,718.21,3510000,718.21 1963-06-14,721.43,725.72,718.34,722.03,3840000,722.03 1963-06-13,723.36,727.09,719.92,721.43,4690000,721.43 1963-06-12,718.38,727.60,717.05,723.36,5210000,723.36 1963-06-11,716.49,721.94,714.00,718.38,4390000,718.38 1963-06-10,722.41,722.88,711.65,716.49,4690000,716.49 1963-06-07,726.87,730.31,719.41,722.41,5110000,722.41 1963-06-06,725.93,730.78,721.81,726.87,4990000,726.87 1963-06-05,726.49,732.97,722.46,725.93,5860000,725.93 1963-06-04,726.27,730.01,721.30,726.49,5970000,726.49 1963-06-03,726.96,731.59,723.57,726.27,5400000,726.27 1963-05-31,722.50,730.43,721.85,726.96,4680000,726.96 1963-05-29,717.95,725.59,717.22,722.50,4320000,722.50 1963-05-28,718.25,722.16,714.26,717.95,3860000,717.95 1963-05-27,720.53,721.98,714.52,718.25,3760000,718.25 1963-05-24,721.38,724.56,716.66,720.53,4320000,720.53 1963-05-23,722.84,725.67,718.12,721.38,4400000,721.38 1963-05-22,724.04,728.76,720.18,722.84,5560000,722.84 1963-05-21,720.18,727.26,717.48,724.04,5570000,724.04 1963-05-20,724.81,726.19,716.45,720.18,4710000,720.18 1963-05-17,722.84,727.13,720.01,724.81,4410000,724.81 1963-05-16,724.34,728.63,719.75,722.84,5640000,722.84 1963-05-15,719.84,727.69,717.22,724.34,5650000,724.34 1963-05-14,723.01,724.94,717.95,719.84,4740000,719.84 1963-05-13,723.30,727.13,720.53,723.01,4920000,723.01 1963-05-10,721.97,726.02,718.62,723.30,5260000,723.30 1963-05-09,718.54,726.48,717.20,721.97,5600000,721.97 1963-05-08,712.55,720.96,710.96,718.54,5140000,718.54 1963-05-07,713.77,716.78,708.16,712.55,4140000,712.55 1963-05-06,718.08,721.84,711.68,713.77,4090000,713.77 1963-05-03,721.09,723.77,716.53,718.08,4760000,718.08 1963-05-02,719.67,724.69,717.32,721.09,4480000,721.09 1963-05-01,717.70,723.89,716.40,719.67,5060000,719.67 1963-04-30,715.11,721.46,711.93,717.70,4680000,717.70 1963-04-29,717.16,719.54,712.05,715.11,3980000,715.11 1963-04-26,718.33,721.59,714.77,717.16,4490000,717.16 1963-04-25,717.74,721.92,712.68,718.33,5070000,718.33 1963-04-24,714.98,720.96,713.89,717.74,5910000,717.74 1963-04-23,711.01,717.53,707.83,714.98,5220000,714.98 1963-04-22,711.68,717.07,708.70,711.01,5180000,711.01 1963-04-19,708.16,713.72,705.32,711.68,4660000,711.68 1963-04-18,710.25,713.31,705.44,708.16,4770000,708.16 1963-04-17,710.92,715.23,705.19,710.25,5220000,710.25 1963-04-16,711.38,716.74,706.32,710.92,5570000,710.92 1963-04-15,708.45,716.61,705.94,711.38,5930000,711.38 1963-04-11,704.35,711.34,700.09,708.45,5250000,708.45 1963-04-10,706.03,710.50,698.25,704.35,5880000,704.35 1963-04-09,706.03,709.83,701.05,706.03,5090000,706.03 1963-04-08,702.43,709.46,700.84,706.03,5940000,706.03 1963-04-05,697.12,703.77,693.18,702.43,5240000,702.43 1963-04-04,690.76,699.12,690.76,697.12,5300000,697.12 1963-04-03,684.27,692.81,683.23,690.51,4660000,690.51 1963-04-02,685.86,691.18,682.14,684.27,4360000,684.27 1963-04-01,682.52,689.34,680.55,685.86,3890000,685.86 1963-03-29,682.47,685.74,678.92,682.52,3390000,682.52 1963-03-28,684.73,687.37,680.80,682.47,3890000,682.47 1963-03-27,680.38,686.95,679.17,684.73,4270000,684.73 1963-03-26,678.17,684.23,676.79,680.38,4100000,680.38 1963-03-25,677.83,682.31,674.94,678.17,3700000,678.17 1963-03-22,675.57,680.68,673.19,677.83,3820000,677.83 1963-03-21,677.12,680.01,673.15,675.57,3220000,675.57 1963-03-20,672.06,679.34,671.10,677.12,3690000,677.12 1963-03-19,673.56,675.95,669.17,672.06,3180000,672.06 1963-03-18,676.33,678.92,671.14,673.56,3250000,673.56 1963-03-15,673.73,679.30,671.72,676.33,3400000,676.33 1963-03-14,677.66,680.09,671.97,673.73,3540000,673.73 1963-03-13,675.20,680.93,673.56,677.66,4120000,677.66 1963-03-12,674.02,678.17,671.31,675.20,3350000,675.20 1963-03-11,672.43,677.83,670.01,674.02,3180000,674.02 1963-03-08,671.43,675.57,668.38,672.43,3360000,672.43 1963-03-07,668.08,675.03,667.12,671.43,3350000,671.43 1963-03-06,667.16,669.80,662.02,668.08,3100000,668.08 1963-03-05,667.04,672.85,663.02,667.16,3280000,667.16 1963-03-04,659.72,670.30,659.22,667.04,3650000,667.04 1963-03-01,662.94,667.63,656.66,659.72,3920000,659.72 1963-02-28,670.80,672.64,661.27,662.94,4090000,662.94 1963-02-27,675.28,678.12,669.05,670.80,3680000,670.80 1963-02-26,674.61,679.21,671.31,675.28,3670000,675.28 1963-02-25,681.64,682.06,673.15,674.61,3680000,674.61 1963-02-21,682.06,684.94,675.87,681.64,3980000,681.64 1963-02-20,686.07,686.07,678.79,682.06,4120000,682.06 1963-02-19,688.96,690.51,683.73,686.83,4130000,686.83 1963-02-18,686.07,694.27,685.15,688.96,4700000,688.96 1963-02-15,684.86,689.67,680.89,686.07,4410000,686.07 1963-02-14,681.72,688.71,679.59,684.86,5640000,684.86 1963-02-13,676.62,684.82,674.48,681.72,4960000,681.72 1963-02-12,674.74,678.08,669.84,676.62,3710000,676.62 1963-02-11,679.92,681.85,672.98,674.74,3880000,674.74 1963-02-08,679.09,682.31,673.90,679.92,3890000,679.92 1963-02-07,682.52,686.32,676.49,679.09,4240000,679.09 1963-02-06,681.30,686.74,678.25,682.52,4340000,682.52 1963-02-05,682.01,683.65,672.60,681.30,4050000,681.30 1963-02-04,683.19,687.20,678.92,682.01,3670000,682.01 1963-02-01,682.85,686.57,679.21,683.19,4280000,683.19 1963-01-31,678.58,685.24,675.28,682.85,4270000,682.85 1963-01-30,683.73,684.48,675.82,678.58,3740000,678.58 1963-01-29,682.89,686.91,678.29,683.73,4360000,683.73 1963-01-28,679.71,686.53,678.50,682.89,4720000,682.89 1963-01-25,676.99,683.81,673.31,679.71,4770000,679.71 1963-01-24,677.58,681.09,673.94,676.99,4810000,676.99 1963-01-23,675.53,680.38,674.02,677.58,4820000,677.58 1963-01-22,675.24,679.13,671.81,675.53,4810000,675.53 1963-01-21,672.52,676.74,668.42,675.24,4090000,675.24 1963-01-18,672.98,679.09,669.55,672.52,4760000,672.52 1963-01-17,669.00,676.07,665.07,672.98,5230000,672.98 1963-01-16,675.36,676.03,666.70,669.00,4260000,669.00 1963-01-15,675.74,680.80,672.14,675.36,5930000,675.36 1963-01-14,671.60,678.46,669.38,675.74,5000000,675.74 1963-01-11,669.51,673.61,665.91,671.60,4410000,671.60 1963-01-10,668.00,673.73,666.54,669.51,4520000,669.51 1963-01-09,669.88,674.23,665.95,668.00,5110000,668.00 1963-01-08,662.14,672.18,661.10,669.88,5410000,669.88 1963-01-07,662.23,666.70,657.83,662.14,4440000,662.14 1963-01-04,657.42,665.66,656.25,662.23,5400000,662.23 1963-01-03,646.79,659.17,646.62,657.42,4570000,657.42 1963-01-02,652.10,654.53,643.57,646.79,2540000,646.79 1962-12-31,651.43,654.53,648.05,652.10,5420000,652.10 1962-12-28,650.56,653.90,647.21,651.43,4140000,651.43 1962-12-27,651.64,655.07,648.42,650.56,3670000,650.56 1962-12-26,647.71,654.40,647.33,651.64,3370000,651.64 1962-12-24,646.41,651.48,643.65,647.71,3180000,647.71 1962-12-21,648.55,651.81,643.40,646.41,3470000,646.41 1962-12-20,647.00,652.69,645.20,648.55,4220000,648.55 1962-12-19,640.14,648.63,636.88,647.00,4000000,647.00 1962-12-18,645.49,646.75,638.09,640.14,3620000,640.14 1962-12-17,648.09,650.97,643.03,645.49,3590000,645.49 1962-12-14,645.20,650.35,641.02,648.09,3280000,648.09 1962-12-13,647.33,651.94,641.73,645.20,3380000,645.20 1962-12-12,645.16,653.02,643.49,647.33,3760000,647.33 1962-12-11,645.08,648.46,638.88,645.16,3700000,645.16 1962-12-10,652.10,655.07,642.73,645.08,4270000,645.08 1962-12-07,651.73,655.83,646.87,652.10,3900000,652.10 1962-12-06,653.99,657.00,646.92,651.73,4600000,651.73 1962-12-05,651.48,658.78,649.68,653.99,6280000,653.99 1962-12-04,646.41,654.61,644.07,651.48,5210000,651.48 1962-12-03,649.30,651.22,642.06,646.41,3810000,646.41 1962-11-30,652.61,655.03,645.70,649.30,4570000,649.30 1962-11-29,651.85,655.95,646.54,652.61,5810000,652.61 1962-11-28,648.05,654.86,646.50,651.85,5980000,651.85 1962-11-27,642.06,650.39,639.72,648.05,5500000,648.05 1962-11-26,644.87,651.27,638.34,642.06,5650000,642.06 1962-11-23,637.25,649.26,635.50,644.87,5660000,644.87 1962-11-21,632.94,640.36,630.18,637.25,5100000,637.25 1962-11-20,626.21,634.91,624.20,632.94,4290000,632.94 1962-11-19,630.98,633.36,624.03,626.21,3410000,626.21 1962-11-16,629.14,633.82,623.87,630.98,4000000,630.98 1962-11-15,630.48,636.25,626.67,629.14,5050000,629.14 1962-11-14,623.11,632.99,619.77,630.48,5090000,630.48 1962-11-13,624.41,628.47,618.85,623.11,4550000,623.11 1962-11-12,617.38,627.25,617.38,624.41,5090000,624.41 1962-11-09,609.14,617.51,605.33,616.13,4340000,616.13 1962-11-08,615.75,619.68,606.84,609.14,4160000,609.14 1962-11-07,610.48,620.06,605.58,615.75,4580000,615.75 1962-11-05,604.58,614.45,602.66,610.48,4320000,610.48 1962-11-02,597.13,609.43,592.87,604.58,5470000,604.58 1962-11-01,589.77,599.60,582.66,597.13,3400000,597.13 1962-10-31,588.98,594.79,586.13,589.77,3090000,589.77 1962-10-30,579.35,591.49,578.94,588.98,3830000,588.98 1962-10-29,576.13,586.59,576.13,579.35,4280000,579.35 1962-10-26,570.86,573.54,564.88,569.02,2580000,569.02 1962-10-25,575.84,575.84,562.79,570.86,3950000,570.86 1962-10-24,558.06,578.52,549.65,576.68,6720000,576.68 1962-10-23,568.60,573.96,556.18,558.06,6110000,558.06 1962-10-22,573.29,573.41,563.16,568.60,5690000,568.60 1962-10-19,581.15,582.78,570.44,573.29,4650000,573.29 1962-10-18,587.68,588.01,579.44,581.15,3280000,581.15 1962-10-17,589.35,590.52,581.91,587.68,3240000,587.68 1962-10-16,589.69,594.12,587.01,589.35,2860000,589.35 1962-10-15,586.47,592.28,584.08,589.69,2640000,589.69 1962-10-12,586.47,589.02,582.83,586.47,2020000,586.47 1962-10-11,588.14,590.69,584.08,586.47,2460000,586.47 1962-10-10,587.18,594.00,586.26,588.14,3040000,588.14 1962-10-09,586.09,589.35,582.70,587.18,2340000,587.18 1962-10-08,586.59,589.90,583.24,586.09,1950000,586.09 1962-10-05,582.41,588.47,581.11,586.59,2730000,586.59 1962-10-04,578.52,583.37,575.38,582.41,2530000,582.41 1962-10-03,578.73,584.37,576.09,578.52,2610000,578.52 1962-10-02,571.95,582.66,570.78,578.73,3000000,578.73 1962-10-01,578.98,579.90,569.23,571.95,3090000,571.95 1962-09-28,574.12,582.70,573.20,578.98,2850000,578.98 1962-09-27,578.48,581.99,572.16,574.12,3540000,574.12 1962-09-26,588.22,591.28,576.30,578.48,3550000,578.48 1962-09-25,582.91,590.52,578.73,588.22,3620000,588.22 1962-09-24,589.06,589.06,578.73,582.91,5000000,582.91 1962-09-21,601.32,601.32,589.52,591.78,4280000,591.78 1962-09-20,607.09,608.93,600.31,601.65,3350000,601.65 1962-09-19,607.09,609.89,603.66,607.09,2950000,607.09 1962-09-18,607.63,612.44,604.83,607.09,3690000,607.09 1962-09-17,605.84,611.57,603.45,607.63,3330000,607.63 1962-09-14,603.99,607.93,601.28,605.84,2880000,605.84 1962-09-13,606.34,609.77,601.94,603.99,3100000,603.99 1962-09-12,603.99,608.97,601.28,606.34,3100000,606.34 1962-09-11,602.03,607.88,599.23,603.99,3040000,603.99 1962-09-10,600.86,603.66,595.38,602.03,2520000,602.03 1962-09-07,600.81,606.34,598.26,600.86,2890000,600.86 1962-09-06,599.14,603.32,594.08,600.81,3180000,600.81 1962-09-05,602.45,604.96,596.42,599.14,3050000,599.14 1962-09-04,609.18,612.44,601.28,602.45,2970000,602.45 1962-08-31,602.32,610.19,600.27,609.18,2830000,609.18 1962-08-30,603.24,606.80,599.85,602.32,2260000,602.32 1962-08-29,605.25,606.42,598.81,603.24,2900000,603.24 1962-08-28,612.57,613.11,602.95,605.25,3180000,605.25 1962-08-27,613.74,616.84,609.27,612.57,3140000,612.57 1962-08-24,616.00,617.67,608.81,613.74,2890000,613.74 1962-08-23,615.54,622.02,612.32,616.00,4770000,616.00 1962-08-22,608.64,617.63,606.42,615.54,4520000,615.54 1962-08-21,612.86,614.66,605.79,608.64,3730000,608.64 1962-08-20,610.02,615.67,608.09,612.86,4580000,612.86 1962-08-17,606.71,611.98,604.62,610.02,3430000,610.02 1962-08-16,606.76,611.15,602.74,606.71,4180000,606.71 1962-08-15,601.94,610.90,601.94,606.76,4880000,606.76 1962-08-14,595.29,603.91,593.03,601.90,3640000,601.90 1962-08-13,592.32,597.51,590.65,595.29,2670000,595.29 1962-08-10,591.19,595.67,587.55,592.32,2470000,592.32 1962-08-09,590.94,595.17,588.43,591.19,2670000,591.19 1962-08-08,588.35,592.11,582.12,590.94,3080000,590.94 1962-08-07,593.24,593.66,585.71,588.35,2970000,588.35 1962-08-06,596.38,598.56,590.27,593.24,3110000,593.24 1962-08-03,593.83,598.35,591.11,596.38,5990000,596.38 1962-08-02,591.36,596.55,588.26,593.83,3410000,593.83 1962-08-01,597.64,597.64,588.68,591.36,3100000,591.36 1962-07-31,591.78,601.15,591.78,597.93,4190000,597.93 1962-07-30,585.00,593.03,583.87,591.44,3200000,591.44 1962-07-27,579.61,586.80,577.14,585.00,2890000,585.00 1962-07-26,574.67,582.87,574.08,579.61,2790000,579.61 1962-07-25,574.12,576.55,568.10,574.67,2910000,574.67 1962-07-24,577.47,579.31,572.03,574.12,2560000,574.12 1962-07-23,577.18,582.24,574.50,577.47,2770000,577.47 1962-07-20,573.16,579.86,570.78,577.18,2610000,577.18 1962-07-19,571.24,578.68,568.98,573.16,3090000,573.16 1962-07-18,577.39,577.39,568.02,571.24,3620000,571.24 1962-07-17,588.10,588.77,576.59,577.85,3500000,577.85 1962-07-16,590.19,591.23,582.41,588.10,3130000,588.10 1962-07-13,590.27,592.99,583.87,590.19,3380000,590.19 1962-07-12,589.06,596.59,586.68,590.27,5370000,590.27 1962-07-11,586.01,590.94,580.36,589.06,4250000,589.06 1962-07-10,583.50,599.02,583.50,586.01,7120000,586.01 1962-07-09,576.17,582.28,569.65,580.82,2950000,580.82 1962-07-06,582.58,582.58,571.28,576.17,3110000,576.17 1962-07-05,579.48,586.30,577.39,583.87,3350000,583.87 1962-07-03,573.75,582.99,570.53,579.48,3920000,579.48 1962-07-02,561.28,576.63,557.31,573.75,3450000,573.75 1962-06-29,557.35,569.06,555.22,561.28,4720000,561.28 1962-06-28,541.49,559.32,541.49,557.35,5440000,557.35 1962-06-27,535.76,539.28,528.73,536.98,3890000,536.98 1962-06-26,536.77,548.61,533.46,535.76,4630000,535.76 1962-06-25,539.19,541.24,524.55,536.77,7090000,536.77 1962-06-22,550.49,551.99,537.56,539.19,5640000,539.19 1962-06-21,561.87,561.87,549.15,550.49,4560000,550.49 1962-06-20,571.61,574.59,561.28,563.08,3360000,563.08 1962-06-19,574.21,575.21,566.59,571.61,2680000,571.61 1962-06-18,578.18,583.08,567.05,574.21,4580000,574.21 1962-06-15,563.00,579.90,556.09,578.18,7130000,578.18 1962-06-14,574.04,579.14,560.28,563.00,6240000,563.00 1962-06-13,580.94,586.42,572.20,574.04,5850000,574.04 1962-06-12,593.83,593.83,580.11,580.94,4690000,580.94 1962-06-11,601.61,603.20,592.66,595.17,2870000,595.17 1962-06-08,602.20,607.30,598.64,601.61,2560000,601.61 1962-06-07,603.91,608.14,599.27,602.20,2760000,602.20 1962-06-06,595.50,611.82,595.50,603.91,4190000,603.91 1962-06-05,593.68,603.37,584.12,594.96,6140000,594.96 1962-06-04,608.82,608.82,591.37,593.68,5380000,593.68 1962-06-01,613.36,616.54,603.58,611.05,5760000,611.05 1962-05-31,605.73,625.00,605.73,613.36,10710000,613.36 1962-05-29,576.93,613.11,553.75,603.96,14750000,603.96 1962-05-28,609.77,609.77,573.55,576.93,9350000,576.93 1962-05-25,622.56,625.94,606.18,611.88,6380000,611.88 1962-05-24,626.52,634.24,619.84,622.56,5250000,622.56 1962-05-23,636.34,637.83,622.56,626.52,5450000,626.52 1962-05-22,648.59,649.33,634.57,636.34,3640000,636.34 1962-05-21,650.70,652.14,645.09,648.59,2260000,648.59 1962-05-18,649.79,653.63,644.55,650.70,2490000,650.70 1962-05-17,654.04,654.33,644.01,649.79,2950000,649.79 1962-05-16,655.36,660.23,650.12,654.04,3360000,654.04 1962-05-15,647.56,660.64,647.56,655.36,4780000,655.36 1962-05-14,640.63,649.13,626.85,646.20,5990000,646.20 1962-05-11,647.23,653.79,637.45,640.63,4510000,640.63 1962-05-10,654.70,656.06,641.50,647.23,4730000,647.23 1962-05-09,663.90,664.15,652.31,654.70,3670000,654.70 1962-05-08,670.99,672.68,661.38,663.90,3020000,663.90 1962-05-07,671.20,675.74,666.41,670.99,2530000,670.99 1962-05-04,675.49,676.77,666.79,671.20,3010000,671.20 1962-05-03,669.96,679.20,668.93,675.49,3320000,675.49 1962-05-02,671.24,677.35,666.08,669.96,3780000,669.96 1962-05-01,665.33,673.59,655.44,671.24,5100000,671.24 1962-04-30,672.20,678.64,661.48,665.33,4150000,665.33 1962-04-27,678.68,685.84,668.40,672.20,4140000,672.20 1962-04-26,683.69,687.82,676.29,678.68,3650000,678.68 1962-04-25,693.00,694.13,681.55,683.69,3340000,683.69 1962-04-24,694.61,698.42,690.29,693.00,3040000,693.00 1962-04-23,694.25,700.04,690.37,694.61,3240000,694.61 1962-04-19,691.01,696.80,688.63,694.25,3100000,694.25 1962-04-18,688.43,696.03,686.77,691.01,3350000,691.01 1962-04-17,684.06,690.93,681.31,688.43,2940000,688.43 1962-04-16,687.90,690.65,681.18,684.06,3070000,684.06 1962-04-13,685.67,691.86,679.08,687.90,3470000,687.90 1962-04-12,694.37,694.37,683.49,685.67,3320000,685.67 1962-04-11,695.46,703.68,693.40,694.90,3240000,694.90 1962-04-10,692.96,697.85,688.75,695.46,2880000,695.46 1962-04-09,699.63,701.17,691.30,692.96,3020000,692.96 1962-04-06,700.88,705.42,697.73,699.63,2730000,699.63 1962-04-05,696.88,703.35,693.32,700.88,3130000,700.88 1962-04-04,700.60,705.05,695.26,696.88,3290000,696.88 1962-04-03,705.42,707.52,697.61,700.60,3350000,700.60 1962-04-02,706.95,709.74,702.91,705.42,2790000,705.42 1962-03-30,711.28,712.17,703.27,706.95,2950000,706.95 1962-03-29,712.25,716.94,709.58,711.28,2870000,711.28 1962-03-28,707.28,714.64,705.70,712.25,2940000,712.25 1962-03-27,710.67,713.34,704.12,707.28,3090000,707.28 1962-03-26,716.46,717.88,708.69,710.67,3040000,710.67 1962-03-23,716.70,720.18,713.02,716.46,3050000,716.46 1962-03-22,716.62,720.18,713.38,716.70,3130000,716.70 1962-03-21,719.66,722.49,713.59,716.62,3360000,716.62 1962-03-20,720.38,723.17,716.78,719.66,3060000,719.66 1962-03-19,722.27,725.28,717.39,720.38,3220000,720.38 1962-03-16,723.54,727.14,719.29,722.27,3060000,722.27 1962-03-15,720.95,726.65,718.08,723.54,3250000,723.54 1962-03-14,716.58,724.67,715.89,720.95,3670000,720.95 1962-03-13,714.68,719.66,711.56,716.58,3200000,716.58 1962-03-12,714.44,718.44,709.62,714.68,3280000,714.68 1962-03-09,713.75,718.28,710.96,714.44,3340000,714.44 1962-03-08,706.63,715.25,704.57,713.75,3210000,713.75 1962-03-07,708.17,710.63,703.88,706.63,2890000,706.63 1962-03-06,709.99,712.94,704.81,708.17,2870000,708.17 1962-03-05,711.00,714.15,706.02,709.99,3020000,709.99 1962-03-02,711.81,715.61,707.64,711.00,2980000,711.00 1962-03-01,708.05,715.89,706.67,711.81,2960000,711.81 1962-02-28,709.22,713.75,704.93,708.05,3030000,708.05 1962-02-27,706.22,712.98,704.16,709.22,3110000,709.22 1962-02-26,709.54,711.77,702.91,706.22,2910000,706.22 1962-02-23,713.02,713.71,705.09,709.54,3230000,709.54 1962-02-21,717.55,720.14,710.43,713.02,3310000,713.02 1962-02-20,714.36,719.61,711.24,717.55,3300000,717.55 1962-02-19,716.46,720.14,711.28,714.36,3350000,714.36 1962-02-16,717.27,721.39,713.22,716.46,3700000,716.46 1962-02-15,713.67,720.79,711.77,717.27,3470000,717.27 1962-02-14,714.32,716.90,709.14,713.67,3630000,713.67 1962-02-13,714.92,718.72,711.32,714.32,3400000,714.32 1962-02-12,714.27,718.52,711.81,714.92,2620000,714.92 1962-02-09,716.82,718.40,709.34,714.27,3370000,714.27 1962-02-08,715.73,719.86,712.45,716.82,3810000,716.82 1962-02-07,710.39,717.35,708.94,715.73,4140000,715.73 1962-02-06,706.14,713.22,702.06,710.39,3650000,710.39 1962-02-05,706.55,710.84,702.34,706.14,3890000,706.14 1962-02-02,702.54,709.34,699.11,706.55,3950000,706.55 1962-02-01,700.00,707.36,696.07,702.54,4260000,702.54 1962-01-31,694.09,702.75,692.27,700.00,3840000,700.00 1962-01-30,689.92,698.38,687.82,694.09,3520000,694.09 1962-01-29,692.19,695.71,686.89,689.92,3050000,689.92 1962-01-26,696.52,698.13,689.11,692.19,3330000,692.19 1962-01-25,698.17,703.80,694.17,696.52,3560000,696.52 1962-01-24,698.54,701.69,689.52,698.17,3760000,698.17 1962-01-23,701.98,704.40,694.57,698.54,3350000,698.54 1962-01-22,700.72,706.27,698.05,701.98,3810000,701.98 1962-01-19,696.03,703.35,694.29,700.72,3800000,700.72 1962-01-18,697.41,700.12,690.21,696.03,3460000,696.03 1962-01-17,705.29,707.03,694.86,697.41,3780000,697.41 1962-01-16,709.50,711.20,700.97,705.29,3650000,705.29 1962-01-15,711.73,715.00,706.18,709.50,3450000,709.50 1962-01-12,710.67,717.71,707.92,711.73,3730000,711.73 1962-01-11,706.02,713.10,702.26,710.67,3390000,710.67 1962-01-10,707.64,711.81,701.69,706.02,3300000,706.02 1962-01-09,708.98,714.96,703.88,707.64,3600000,707.64 1962-01-08,714.84,715.69,698.42,708.98,4620000,708.98 1962-01-05,722.53,725.60,709.74,714.84,4630000,714.84 1962-01-04,726.01,733.77,718.00,722.53,4450000,722.53 1962-01-03,724.71,729.61,720.22,726.01,3590000,726.01 1962-01-02,731.14,734.38,721.39,724.71,3120000,724.71 1961-12-29,731.51,734.99,726.41,731.14,5370000,731.14 1961-12-28,731.43,738.79,728.88,731.51,4530000,731.51 1961-12-27,723.34,734.54,723.34,731.43,4170000,731.43 1961-12-26,720.87,727.42,717.92,723.09,3180000,723.09 1961-12-22,720.10,725.08,714.60,720.87,3390000,720.87 1961-12-21,722.57,725.64,717.27,720.10,3440000,720.10 1961-12-20,722.41,728.19,718.32,722.57,3640000,722.57 1961-12-19,727.66,727.66,719.25,722.41,3440000,722.41 1961-12-18,729.40,734.91,724.43,727.71,3810000,727.71 1961-12-15,730.94,734.58,725.72,729.40,3710000,729.40 1961-12-14,734.91,737.82,727.34,730.94,4350000,730.94 1961-12-13,734.02,739.88,730.58,734.91,4890000,734.91 1961-12-12,732.56,739.36,728.23,734.02,4680000,734.02 1961-12-11,728.23,736.40,725.84,732.56,4360000,732.56 1961-12-08,726.45,732.92,722.24,728.23,4010000,728.23 1961-12-07,730.09,732.96,723.42,726.45,3900000,726.45 1961-12-06,731.31,735.80,725.04,730.09,4200000,730.09 1961-12-05,731.22,734.91,726.82,731.31,4330000,731.31 1961-12-04,728.80,737.62,725.16,731.22,4560000,731.22 1961-12-01,721.60,731.99,719.74,728.80,4420000,728.80 1961-11-30,727.18,727.50,717.88,721.60,4210000,721.60 1961-11-29,728.07,732.11,722.57,727.18,4550000,727.18 1961-11-28,731.99,735.19,724.23,728.07,4360000,728.07 1961-11-27,732.60,738.38,726.94,731.99,4700000,731.99 1961-11-24,730.42,735.92,726.61,732.60,4020000,732.60 1961-11-22,729.32,734.18,724.95,730.42,4500000,730.42 1961-11-21,730.09,737.58,724.06,729.32,4890000,729.32 1961-11-20,729.53,735.59,725.32,730.09,4190000,730.09 1961-11-17,733.33,736.00,724.83,729.53,3960000,729.53 1961-11-16,734.34,737.54,725.84,733.33,3980000,733.33 1961-11-15,732.56,741.30,730.86,734.34,4660000,734.34 1961-11-14,728.43,736.65,724.35,732.56,4750000,732.56 1961-11-13,724.83,732.48,721.27,728.43,4540000,728.43 1961-11-10,722.28,727.46,718.28,724.83,4180000,724.83 1961-11-09,723.74,726.82,717.75,722.28,4680000,722.28 1961-11-08,714.68,726.73,714.68,723.74,6090000,723.74 1961-11-06,709.26,717.47,707.11,714.60,4340000,714.60 1961-11-03,706.83,712.62,702.62,709.26,4070000,709.26 1961-11-02,703.84,710.35,701.29,706.83,3890000,706.83 1961-11-01,703.92,708.09,700.12,703.84,3210000,703.84 1961-10-31,701.09,706.67,699.35,703.92,3350000,703.92 1961-10-30,698.74,704.57,696.35,701.09,3430000,701.09 1961-10-27,700.68,703.60,694.94,698.74,3200000,698.74 1961-10-26,700.72,704.81,694.82,700.68,3330000,700.68 1961-10-25,697.24,706.47,695.55,700.72,3590000,700.72 1961-10-24,698.98,704.00,693.77,697.24,3430000,697.24 1961-10-23,705.62,708.73,695.55,698.98,3440000,698.98 1961-10-20,704.85,708.77,700.52,705.62,3470000,705.62 1961-10-19,704.20,710.43,700.48,704.85,3850000,704.85 1961-10-18,701.98,708.37,699.63,704.20,3520000,704.20 1961-10-17,703.15,706.18,698.62,701.98,3110000,701.98 1961-10-16,703.31,707.84,699.23,703.15,2840000,703.15 1961-10-13,705.50,708.77,700.12,703.31,3090000,703.31 1961-10-12,705.62,709.54,701.13,705.50,3060000,705.50 1961-10-11,706.67,710.59,701.69,705.62,3670000,705.62 1961-10-10,705.42,709.62,700.56,706.67,3430000,706.67 1961-10-09,708.25,712.05,700.93,705.42,2920000,705.42 1961-10-06,708.49,714.07,704.40,708.25,3470000,708.25 1961-10-05,703.31,711.89,701.13,708.49,3920000,708.49 1961-10-04,698.66,706.75,698.13,703.31,3380000,703.31 1961-10-03,699.83,703.03,693.12,698.66,2680000,698.66 1961-10-02,701.21,704.97,695.63,699.83,2800000,699.83 1961-09-29,700.28,704.93,694.25,701.21,3060000,701.21 1961-09-28,701.13,706.18,696.80,700.28,3000000,700.28 1961-09-27,693.20,703.47,691.58,701.13,3440000,701.13 1961-09-26,691.86,700.44,689.07,693.20,3320000,693.20 1961-09-25,701.57,702.99,688.87,691.86,3700000,691.86 1961-09-22,706.31,707.48,698.70,701.57,3070000,701.57 1961-09-21,707.32,713.22,703.43,706.31,3340000,706.31 1961-09-20,702.54,709.22,699.87,707.32,2700000,707.32 1961-09-19,711.24,712.98,700.52,702.54,3260000,702.54 1961-09-18,716.30,716.94,705.62,711.24,3550000,711.24 1961-09-15,715.00,720.06,710.23,716.30,3130000,716.30 1961-09-14,722.20,723.22,713.14,715.00,2920000,715.00 1961-09-13,722.61,727.66,718.81,722.20,3110000,722.20 1961-09-12,714.36,725.88,713.99,722.61,2950000,722.61 1961-09-11,720.91,721.39,710.47,714.36,2790000,714.36 1961-09-08,726.53,727.30,717.71,720.91,3430000,720.91 1961-09-07,726.01,733.53,723.05,726.53,3900000,726.53 1961-09-06,719.61,728.76,719.61,726.01,3440000,726.01 1961-09-05,721.19,726.01,716.26,718.72,3000000,718.72 1961-09-01,719.94,724.95,716.30,721.19,2710000,721.19 1961-08-31,716.90,723.13,713.75,719.94,2920000,719.94 1961-08-30,714.15,720.06,711.08,716.90,3220000,716.90 1961-08-29,716.01,718.52,709.78,714.15,3160000,714.15 1961-08-28,716.70,722.24,712.33,716.01,3150000,716.01 1961-08-25,714.03,720.10,712.01,716.70,3050000,716.70 1961-08-24,719.41,719.41,709.54,714.03,3090000,714.03 1961-08-23,725.76,726.86,717.27,720.46,3550000,720.46 1961-08-22,724.75,730.17,720.34,725.76,3640000,725.76 1961-08-21,723.54,728.35,719.49,724.75,3880000,724.75 1961-08-18,721.84,727.54,719.25,723.54,4030000,723.54 1961-08-17,718.20,725.80,715.53,721.84,4130000,721.84 1961-08-16,716.18,720.67,712.62,718.20,3430000,718.20 1961-08-15,718.93,722.85,714.15,716.18,3320000,716.18 1961-08-14,722.61,724.02,714.48,718.93,3120000,718.93 1961-08-11,720.49,726.57,717.31,722.61,3260000,722.61 1961-08-10,717.57,722.98,713.46,720.49,3570000,720.49 1961-08-09,720.22,722.31,713.86,717.57,3710000,717.57 1961-08-08,719.58,725.78,715.91,720.22,4050000,720.22 1961-08-07,720.69,723.14,712.95,719.58,3560000,719.58 1961-08-04,715.71,723.57,715.08,720.69,3710000,720.69 1961-08-03,710.46,720.41,709.04,715.71,3650000,715.71 1961-08-02,713.94,719.86,707.93,710.46,4300000,710.46 1961-08-01,705.37,716.03,702.21,713.94,3990000,713.94 1961-07-31,705.13,709.75,700.51,705.37,3170000,705.37 1961-07-28,702.80,709.75,700.00,705.13,3610000,705.13 1961-07-27,694.19,705.96,693.40,702.80,4170000,702.80 1961-07-26,687.63,697.98,687.63,694.19,4070000,694.19 1961-07-25,682.14,688.89,679.66,686.37,3010000,686.37 1961-07-24,682.81,687.16,678.27,682.14,2490000,682.14 1961-07-21,682.97,686.84,679.10,682.81,2360000,682.81 1961-07-20,682.74,686.21,678.31,682.97,2530000,682.97 1961-07-19,679.30,685.22,674.80,682.74,2940000,682.74 1961-07-18,684.59,687.95,677.44,679.30,3010000,679.30 1961-07-17,690.95,693.48,683.49,684.59,2690000,684.59 1961-07-14,685.90,693.68,682.58,690.95,2760000,690.95 1961-07-13,690.79,690.83,683.17,685.90,2670000,685.90 1961-07-12,694.47,696.28,687.28,690.79,3070000,690.79 1961-07-11,693.16,698.18,690.95,694.47,3160000,694.47 1961-07-10,692.73,698.49,689.33,693.16,3180000,693.16 1961-07-07,694.27,697.90,689.61,692.73,3030000,692.73 1961-07-06,692.77,698.97,690.36,694.27,3470000,694.27 1961-07-05,689.81,696.76,688.34,692.77,3270000,692.77 1961-07-03,683.96,691.50,681.91,689.81,2180000,689.81 1961-06-30,681.95,687.16,678.23,683.96,2380000,683.96 1961-06-29,684.59,687.28,679.62,681.95,2560000,681.95 1961-06-28,683.88,690.04,681.20,684.59,2830000,684.59 1961-06-27,681.16,687.16,676.30,683.88,3090000,683.88 1961-06-26,688.66,689.96,679.22,681.16,2690000,681.16 1961-06-23,685.62,692.81,683.64,688.66,2720000,688.66 1961-06-22,686.09,689.96,680.13,685.62,2880000,685.62 1961-06-21,687.87,691.42,683.13,686.09,3210000,686.09 1961-06-20,680.68,691.03,679.50,687.87,3280000,687.87 1961-06-19,685.50,686.53,673.49,680.68,3980000,680.68 1961-06-16,691.27,691.98,681.16,685.50,3380000,685.50 1961-06-15,695.81,699.72,689.69,691.27,3220000,691.27 1961-06-14,694.15,700.47,691.42,695.81,3430000,695.81 1961-06-13,696.76,698.93,690.24,694.15,3030000,694.15 1961-06-12,700.90,702.29,693.20,696.76,3260000,696.76 1961-06-09,701.69,705.88,697.39,700.90,3520000,700.90 1961-06-08,700.86,704.46,695.26,701.69,3810000,701.69 1961-06-07,703.79,708.05,697.70,700.86,3980000,700.86 1961-06-06,703.43,708.88,699.21,703.79,4250000,703.79 1961-06-05,697.70,708.57,695.33,703.43,4150000,703.43 1961-06-02,695.37,700.82,691.54,697.70,3670000,697.70 1961-06-01,696.72,700.86,691.39,695.37,3770000,695.37 1961-05-31,696.28,703.04,692.65,696.72,4320000,696.72 1961-05-26,690.16,699.48,686.13,696.28,3780000,696.28 1961-05-25,696.52,699.13,687.55,690.16,3760000,690.16 1961-05-24,700.59,702.56,689.77,696.52,3970000,696.52 1961-05-23,702.44,707.34,697.31,700.59,3660000,700.59 1961-05-22,705.96,714.69,697.74,702.44,4070000,702.44 1961-05-19,701.14,708.88,698.38,705.96,4200000,705.96 1961-05-18,705.52,709.00,697.70,701.14,4610000,701.14 1961-05-17,697.74,708.49,695.18,705.52,5520000,705.52 1961-05-16,692.37,700.35,690.52,697.74,5110000,697.74 1961-05-15,687.91,696.44,684.47,692.37,4840000,692.37 1961-05-12,686.49,691.54,683.41,687.91,4840000,687.91 1961-05-11,686.61,689.81,682.06,686.49,5170000,686.49 1961-05-10,686.92,690.67,682.93,686.61,5450000,686.61 1961-05-09,689.06,692.73,683.49,686.92,5380000,686.92 1961-05-08,690.67,695.41,685.15,689.06,5170000,689.06 1961-05-05,692.25,697.31,687.59,690.67,4980000,690.67 1961-05-04,688.90,696.16,686.49,692.25,5350000,692.25 1961-05-03,682.34,691.54,682.10,688.90,4940000,688.90 1961-05-02,677.05,685.34,674.68,682.34,4110000,682.34 1961-05-01,678.71,683.17,672.98,677.05,3710000,677.05 1961-04-28,679.54,683.96,673.34,678.71,3710000,678.71 1961-04-27,682.18,686.76,675.78,679.54,4450000,679.54 1961-04-26,683.09,688.98,678.94,682.18,4980000,682.18 1961-04-25,672.66,686.72,672.59,683.09,4670000,683.09 1961-04-24,684.16,684.16,671.64,672.66,4590000,672.66 1961-04-21,684.24,689.41,678.79,685.26,4340000,685.26 1961-04-20,686.21,689.02,679.77,684.24,4810000,684.24 1961-04-19,690.60,690.67,679.58,686.21,4870000,686.21 1961-04-18,696.72,697.43,687.63,690.60,4830000,690.60 1961-04-17,693.72,700.75,689.29,696.72,5860000,696.72 1961-04-14,692.02,698.45,688.07,693.72,5240000,693.72 1961-04-13,690.16,696.12,685.54,692.02,4770000,692.02 1961-04-12,694.11,697.39,686.96,690.16,4870000,690.16 1961-04-11,692.06,700.31,687.87,694.11,5230000,694.11 1961-04-10,684.16,695.77,684.16,692.06,5550000,692.06 1961-04-07,679.34,687.76,676.52,683.68,5100000,683.68 1961-04-06,677.32,682.58,673.39,679.34,4910000,679.34 1961-04-05,678.73,680.98,671.79,677.32,5430000,677.32 1961-04-04,677.59,683.11,673.81,678.73,7080000,678.73 1961-04-03,676.63,684.29,672.98,677.59,6470000,677.59 1961-03-30,676.41,682.01,673.13,676.63,5610000,676.63 1961-03-29,669.58,679.19,667.64,676.41,5330000,676.41 1961-03-28,671.03,674.96,665.73,669.58,4630000,669.58 1961-03-27,672.48,675.30,667.22,671.03,4190000,671.03 1961-03-24,675.45,677.28,668.59,672.48,4390000,672.48 1961-03-23,679.38,680.48,671.79,675.45,2170000,675.45 1961-03-22,678.73,685.32,675.19,679.38,5840000,679.38 1961-03-21,678.84,682.92,672.44,678.73,5800000,678.73 1961-03-20,676.48,684.07,674.12,678.84,5780000,678.84 1961-03-17,670.80,681.63,670.80,676.48,5960000,676.48 1961-03-16,662.88,674.19,660.36,670.38,5610000,670.38 1961-03-15,661.08,665.96,656.36,662.88,4900000,662.88 1961-03-14,664.44,667.11,657.85,661.08,4900000,661.08 1961-03-13,663.56,668.52,659.48,664.44,5080000,664.44 1961-03-10,663.33,667.91,658.57,663.56,5950000,663.56 1961-03-09,666.15,670.08,660.40,663.33,6010000,663.33 1961-03-08,667.14,670.54,660.70,666.15,5910000,666.15 1961-03-07,674.46,674.84,662.76,667.14,5540000,667.14 1961-03-06,671.57,678.23,668.78,674.46,5650000,674.46 1961-03-03,669.39,676.14,666.61,671.57,5530000,671.57 1961-03-02,663.03,671.53,661.01,669.39,5300000,669.39 1961-03-01,662.08,666.27,657.12,663.03,4970000,663.03 1961-02-28,660.44,667.03,657.16,662.08,5830000,662.08 1961-02-27,655.60,662.11,652.59,660.44,5470000,660.44 1961-02-24,654.42,659.56,650.99,655.60,5330000,655.60 1961-02-23,652.40,657.46,648.55,654.42,5620000,654.42 1961-02-21,653.65,657.54,649.00,652.40,5070000,652.40 1961-02-20,651.67,657.27,649.04,653.65,4680000,653.65 1961-02-17,651.86,656.21,647.90,651.67,4640000,651.67 1961-02-16,648.89,656.36,646.64,651.86,5070000,651.86 1961-02-15,642.91,651.94,641.46,648.89,5200000,648.89 1961-02-14,637.04,646.37,635.44,642.91,4490000,642.91 1961-02-13,639.67,640.89,632.81,637.04,3560000,637.04 1961-02-10,645.12,645.99,635.55,639.67,4840000,639.67 1961-02-09,648.85,651.56,641.57,645.12,5590000,645.12 1961-02-08,643.94,651.41,641.04,648.85,4940000,648.85 1961-02-07,645.65,647.48,637.99,643.94,4020000,643.94 1961-02-06,652.97,653.65,642.07,645.65,3890000,645.65 1961-02-03,653.62,657.20,647.37,652.97,5210000,652.97 1961-02-02,649.39,655.56,646.41,653.62,4900000,653.62 1961-02-01,648.20,652.82,643.67,649.39,4380000,649.39 1961-01-31,650.64,655.18,644.24,648.20,4690000,648.20 1961-01-30,643.59,653.96,642.33,650.64,5190000,650.64 1961-01-27,638.87,648.17,636.47,643.59,4510000,643.59 1961-01-26,637.72,641.84,632.66,638.87,4110000,638.87 1961-01-25,638.79,643.10,633.76,637.72,4470000,637.72 1961-01-24,639.82,642.22,634.87,638.79,4280000,638.79 1961-01-23,634.37,642.68,634.07,639.82,4450000,639.82 1961-01-20,632.39,637.76,630.41,634.37,3270000,634.37 1961-01-19,634.10,637.15,628.77,632.39,4740000,632.39 1961-01-18,628.96,636.39,626.48,634.10,4390000,634.10 1961-01-17,633.19,634.26,625.49,628.96,3830000,628.96 1961-01-16,633.65,639.17,629.91,633.19,4510000,633.19 1961-01-13,628.50,636.20,626.48,633.65,4520000,633.65 1961-01-12,627.21,633.23,623.28,628.50,4270000,628.50 1961-01-11,625.72,631.85,622.37,627.21,4370000,627.21 1961-01-10,624.42,630.90,620.96,625.72,4840000,625.72 1961-01-09,621.64,627.93,620.12,624.42,4210000,624.42 1961-01-06,622.67,624.58,615.66,621.64,3620000,621.64 1961-01-05,621.49,628.35,619.24,622.67,4130000,622.67 1961-01-04,610.25,623.36,608.46,621.49,3840000,621.49 1961-01-03,615.89,616.65,606.09,610.25,2770000,610.25 1960-12-30,616.19,620.80,612.76,615.89,5300000,615.89 1960-12-29,615.75,620.37,612.42,616.19,4340000,616.19 1960-12-28,613.38,618.60,610.76,615.75,3620000,615.75 1960-12-27,613.23,617.78,609.65,613.38,3270000,613.38 1960-12-23,613.31,617.52,608.28,613.23,3580000,613.23 1960-12-22,615.42,619.67,610.65,613.31,3820000,613.31 1960-12-21,614.82,621.33,610.42,615.42,4060000,615.42 1960-12-20,615.56,619.82,610.94,614.82,3340000,614.82 1960-12-19,617.78,621.07,612.50,615.56,3630000,615.56 1960-12-16,610.76,620.37,609.06,617.78,3770000,617.78 1960-12-15,612.68,616.42,607.21,610.76,3660000,610.76 1960-12-14,611.72,618.34,608.24,612.68,3880000,612.68 1960-12-13,611.94,615.49,606.80,611.72,3500000,611.72 1960-12-12,610.90,615.31,606.87,611.94,3020000,611.94 1960-12-09,605.17,612.86,601.88,610.90,4460000,610.90 1960-12-08,604.62,608.61,601.14,605.17,3540000,605.17 1960-12-07,597.11,606.84,595.93,604.62,3660000,604.62 1960-12-06,593.49,599.44,591.34,597.11,3360000,597.11 1960-12-05,596.00,597.52,590.79,593.49,3290000,593.49 1960-12-02,594.56,600.14,592.12,596.00,3140000,596.00 1960-12-01,597.22,598.18,589.82,594.56,3090000,594.56 1960-11-30,602.40,602.99,595.15,597.22,3080000,597.22 1960-11-29,605.43,606.98,599.03,602.40,3630000,602.40 1960-11-28,606.47,611.31,602.25,605.43,3860000,605.43 1960-11-25,602.47,608.57,600.18,606.47,3190000,606.47 1960-11-23,601.10,605.10,597.00,602.47,3000000,602.47 1960-11-22,604.54,608.02,598.55,601.10,3430000,601.10 1960-11-21,603.62,607.87,600.44,604.54,3090000,604.54 1960-11-18,602.18,608.02,599.96,603.62,2760000,603.62 1960-11-17,604.77,606.58,598.89,602.18,2450000,602.18 1960-11-16,606.87,611.61,602.29,604.77,3110000,604.77 1960-11-15,604.80,608.83,598.66,606.87,2990000,606.87 1960-11-14,608.61,610.09,600.66,604.80,2660000,604.80 1960-11-11,611.79,611.79,604.43,608.61,2730000,608.61 1960-11-10,602.25,614.09,600.48,612.01,4030000,612.01 1960-11-09,597.63,603.62,587.35,602.25,3450000,602.25 1960-11-07,596.07,604.32,593.52,597.63,3540000,597.63 1960-11-04,590.82,599.40,589.71,596.07,3050000,596.07 1960-11-03,588.23,592.60,584.65,590.82,2580000,590.82 1960-11-02,585.24,592.97,584.35,588.23,2780000,588.23 1960-11-01,580.36,589.42,579.43,585.24,2600000,585.24 1960-10-31,577.92,582.28,573.11,580.36,2460000,580.36 1960-10-28,580.95,582.10,575.18,577.92,2490000,577.92 1960-10-27,575.18,583.80,575.07,580.95,2900000,580.95 1960-10-26,566.60,577.66,566.60,575.18,3020000,575.18 1960-10-25,571.93,575.73,564.23,566.05,3030000,566.05 1960-10-24,577.55,578.18,566.64,571.93,4420000,571.93 1960-10-21,582.69,584.61,575.40,577.55,3090000,577.55 1960-10-20,587.01,589.71,580.62,582.69,2910000,582.69 1960-10-19,588.75,590.64,582.32,587.01,2410000,587.01 1960-10-18,593.34,594.71,585.61,588.75,2220000,588.75 1960-10-17,596.48,600.03,590.45,593.34,2280000,593.34 1960-10-14,591.49,599.51,590.45,596.48,2470000,596.48 1960-10-13,585.83,594.30,584.87,591.49,2220000,591.49 1960-10-12,588.75,590.90,583.72,585.83,1890000,585.83 1960-10-11,587.31,593.01,585.02,588.75,2350000,588.75 1960-10-10,586.42,591.49,583.17,587.31,2030000,587.31 1960-10-07,583.69,590.82,581.28,586.42,2530000,586.42 1960-10-06,579.54,589.20,579.54,583.69,2510000,583.69 1960-10-05,573.15,580.36,569.04,578.88,2650000,578.88 1960-10-04,577.81,578.73,569.74,573.15,2270000,573.15 1960-10-03,580.14,582.98,574.26,577.81,2220000,577.81 1960-09-30,572.22,582.47,572.22,580.14,3370000,580.14 1960-09-29,569.08,575.77,565.49,570.59,2850000,570.59 1960-09-28,574.81,579.40,565.53,569.08,3520000,569.08 1960-09-27,577.14,581.80,570.85,574.81,3170000,574.81 1960-09-26,584.76,584.76,573.29,577.14,3930000,577.14 1960-09-23,592.15,592.64,583.61,585.20,2580000,585.20 1960-09-22,594.26,596.70,588.57,592.15,1970000,592.15 1960-09-21,589.31,597.85,589.31,594.26,2930000,594.26 1960-09-20,586.76,592.08,581.91,588.20,3660000,588.20 1960-09-19,601.18,601.18,585.72,586.76,3790000,586.76 1960-09-16,602.69,606.61,598.22,602.18,2340000,602.18 1960-09-15,605.69,607.24,598.26,602.69,2870000,602.69 1960-09-14,611.79,614.01,604.43,605.69,2530000,605.69 1960-09-13,609.35,614.34,606.47,611.79,2180000,611.79 1960-09-12,614.12,615.05,606.73,609.35,2160000,609.35 1960-09-09,611.42,617.41,609.87,614.12,2750000,614.12 1960-09-08,612.27,615.79,607.39,611.42,2670000,611.42 1960-09-07,620.85,621.85,611.35,612.27,2850000,612.27 1960-09-06,625.22,627.58,618.97,620.85,2580000,620.85 1960-09-02,626.10,629.17,622.07,625.22,2680000,625.22 1960-09-01,625.99,630.95,622.78,626.10,3460000,626.10 1960-08-31,626.40,628.73,620.93,625.99,3130000,625.99 1960-08-30,634.46,636.35,624.44,626.40,2890000,626.40 1960-08-29,636.13,639.31,631.76,634.46,2780000,634.46 1960-08-26,637.16,640.34,632.13,636.13,2780000,636.13 1960-08-25,641.56,643.71,634.24,637.16,2680000,637.16 1960-08-24,638.29,645.30,636.68,641.56,3500000,641.56 1960-08-23,631.25,640.40,631.25,638.29,3560000,638.29 1960-08-22,629.27,634.55,625.53,630.71,2760000,630.71 1960-08-19,625.82,631.42,624.64,629.27,2570000,629.27 1960-08-18,626.54,629.31,622.70,625.82,2890000,625.82 1960-08-17,625.43,628.98,621.62,626.54,3090000,626.54 1960-08-16,624.17,628.98,622.19,625.43,2710000,625.43 1960-08-15,626.18,628.41,621.15,624.17,2450000,624.17 1960-08-12,622.88,630.53,622.34,626.18,3160000,626.18 1960-08-11,617.52,624.85,616.27,622.88,3070000,622.88 1960-08-10,615.69,620.54,612.67,617.52,2810000,617.52 1960-08-09,614.79,619.10,610.95,615.69,2700000,615.69 1960-08-08,614.29,618.57,609.15,614.79,2960000,614.79 1960-08-05,609.23,617.88,606.60,614.29,3000000,614.29 1960-08-04,608.69,611.24,600.28,609.23,2840000,609.23 1960-08-03,613.68,614.94,605.67,608.69,2470000,608.69 1960-08-02,617.85,622.34,610.09,613.68,2090000,613.68 1960-08-01,616.73,622.37,613.36,617.85,2440000,617.85 1960-07-29,605.67,618.35,605.24,616.73,2730000,616.73 1960-07-28,601.76,610.88,599.53,605.67,3020000,605.67 1960-07-27,606.75,611.96,600.35,601.76,2560000,601.76 1960-07-26,601.68,612.78,600.50,606.75,2720000,606.75 1960-07-25,609.87,612.17,597.30,601.68,2840000,601.68 1960-07-22,616.63,617.60,605.45,609.87,2850000,609.87 1960-07-21,624.13,626.76,614.22,616.63,2510000,616.63 1960-07-20,624.78,629.23,620.72,624.13,2370000,624.13 1960-07-19,626.00,629.52,621.22,624.78,2490000,624.78 1960-07-18,630.24,632.93,623.85,626.00,2350000,626.00 1960-07-15,631.32,635.16,627.47,630.24,2140000,630.24 1960-07-14,632.11,637.14,629.34,631.32,2480000,631.32 1960-07-13,634.12,638.18,629.16,632.11,2590000,632.11 1960-07-12,640.44,642.24,631.64,634.12,2860000,634.12 1960-07-11,646.91,649.17,637.60,640.44,2920000,640.44 1960-07-08,644.89,650.10,641.52,646.91,3010000,646.91 1960-07-07,640.37,646.83,638.21,644.89,3050000,644.89 1960-07-06,640.91,645.22,636.45,640.37,2970000,640.37 1960-07-05,641.30,645.11,636.27,640.91,2780000,640.91 1960-07-01,640.62,644.71,636.60,641.30,2620000,641.30 1960-06-30,638.39,644.39,634.91,640.62,2940000,640.62 1960-06-29,637.46,642.78,634.37,638.39,3160000,638.39 1960-06-28,642.49,644.75,634.66,637.46,3120000,637.46 1960-06-27,647.01,650.75,640.33,642.49,2960000,642.49 1960-06-24,647.41,650.32,641.91,647.01,3220000,647.01 1960-06-23,645.36,651.97,642.52,647.41,3620000,647.41 1960-06-22,644.93,649.06,639.22,645.36,3600000,645.36 1960-06-21,647.52,650.89,640.55,644.93,3860000,644.93 1960-06-20,650.89,654.09,642.02,647.52,3970000,647.52 1960-06-17,648.27,655.24,644.79,650.89,3920000,650.89 1960-06-16,649.42,653.95,644.00,648.27,3540000,648.27 1960-06-15,654.88,657.32,647.37,649.42,3630000,649.42 1960-06-14,655.85,660.05,650.71,654.88,3430000,654.88 1960-06-13,654.88,661.45,650.61,655.85,3180000,655.85 1960-06-10,656.42,659.55,650.68,654.88,2940000,654.88 1960-06-09,650.35,663.64,647.41,656.42,3820000,656.42 1960-06-08,645.58,652.83,642.49,650.35,3800000,650.35 1960-06-07,637.85,647.98,637.85,645.58,3710000,645.58 1960-06-06,628.98,639.43,627.19,636.92,3220000,636.92 1960-06-03,627.87,634.33,624.92,628.98,3340000,628.98 1960-06-02,624.89,631.28,620.76,627.87,3730000,627.87 1960-06-01,625.50,629.88,620.61,624.89,3770000,624.89 1960-05-31,624.78,631.28,622.66,625.50,3750000,625.50 1960-05-27,622.79,628.81,620.86,624.78,3040000,624.78 1960-05-26,621.28,626.78,618.20,622.79,3720000,622.79 1960-05-25,621.39,625.66,616.87,621.28,3440000,621.28 1960-05-24,623.66,626.89,617.71,621.39,3240000,621.39 1960-05-23,625.24,629.34,620.34,623.66,2530000,623.66 1960-05-20,624.68,631.44,621.60,625.24,3170000,625.24 1960-05-19,623.00,628.08,618.62,624.68,3700000,624.68 1960-05-18,621.63,630.21,617.71,623.00,5240000,623.00 1960-05-17,617.39,624.92,613.30,621.63,4080000,621.63 1960-05-16,616.03,621.84,611.76,617.39,3530000,617.39 1960-05-13,607.87,618.20,607.31,616.03,3750000,616.03 1960-05-12,606.54,612.70,603.81,607.87,3220000,607.87 1960-05-11,604.82,608.88,601.70,606.54,2900000,606.54 1960-05-10,607.48,610.14,602.86,604.82,2870000,604.82 1960-05-09,607.62,612.74,604.19,607.48,2670000,607.48 1960-05-06,608.32,611.41,603.56,607.62,2560000,607.62 1960-05-05,610.99,613.79,605.80,608.32,2670000,608.32 1960-05-04,607.73,613.47,606.05,610.99,2870000,610.99 1960-05-03,599.61,610.14,598.73,607.73,2910000,607.73 1960-05-02,601.70,605.52,596.61,599.61,2930000,599.61 1960-04-29,604.33,607.85,598.35,601.70,2850000,601.70 1960-04-28,609.96,612.22,599.27,604.33,3190000,604.33 1960-04-27,610.92,616.76,606.89,609.96,3020000,609.96 1960-04-26,611.13,613.76,606.65,610.92,2940000,610.92 1960-04-25,616.32,617.00,607.71,611.13,2980000,611.13 1960-04-22,619.15,622.64,614.47,616.32,2850000,616.32 1960-04-21,618.71,621.92,615.19,619.15,2700000,619.15 1960-04-20,625.68,625.68,615.94,618.71,3150000,618.71 1960-04-19,630.77,634.97,624.86,626.40,3080000,626.40 1960-04-18,630.12,637.16,626.33,630.77,3200000,630.77 1960-04-14,626.50,632.54,624.62,630.12,2730000,630.12 1960-04-13,626.50,630.15,623.39,626.50,2730000,626.50 1960-04-12,624.89,628.62,621.41,626.50,2470000,626.50 1960-04-11,628.10,631.28,622.40,624.89,2520000,624.89 1960-04-08,629.03,632.07,624.14,628.10,2820000,628.10 1960-04-07,628.31,634.08,626.91,629.03,3070000,629.03 1960-04-06,622.64,631.35,622.64,628.31,3450000,628.31 1960-04-05,618.54,624.69,616.56,622.19,2840000,622.19 1960-04-04,615.98,621.68,613.35,618.54,2450000,618.54 1960-04-01,616.59,619.94,612.94,615.98,2260000,615.98 1960-03-31,619.94,622.74,614.75,616.59,2690000,616.59 1960-03-30,620.35,623.56,616.28,619.94,2450000,619.94 1960-03-29,621.78,623.97,617.68,620.35,2320000,620.35 1960-03-28,622.47,625.68,618.81,621.78,2500000,621.78 1960-03-25,624.00,625.95,619.29,622.47,2640000,622.47 1960-03-24,622.06,627.86,621.51,624.00,2940000,624.00 1960-03-23,618.09,624.35,616.25,622.06,3020000,622.06 1960-03-22,617.00,622.09,614.20,618.09,2490000,618.09 1960-03-21,616.42,620.52,613.99,617.00,2500000,617.00 1960-03-18,615.09,620.69,613.52,616.42,2620000,616.42 1960-03-17,616.73,618.61,610.85,615.09,2140000,615.09 1960-03-16,612.18,620.90,611.74,616.73,2960000,616.73 1960-03-15,606.79,614.68,605.52,612.18,2690000,612.18 1960-03-14,605.83,610.17,602.96,606.79,2530000,606.79 1960-03-11,602.31,608.70,599.61,605.83,2770000,605.83 1960-03-10,607.16,609.72,600.40,602.31,3350000,602.31 1960-03-09,599.10,608.29,596.20,607.16,3580000,607.16 1960-03-08,604.02,607.88,596.64,599.10,3370000,599.10 1960-03-07,609.79,614.61,601.59,604.02,2900000,604.02 1960-03-04,612.05,613.55,600.36,609.79,4060000,609.79 1960-03-03,621.37,622.64,610.03,612.05,3160000,612.05 1960-03-02,626.87,628.34,618.95,621.37,3110000,621.37 1960-03-01,630.12,632.65,623.77,626.87,2920000,626.87 1960-02-29,632.00,635.31,627.56,630.12,2990000,630.12 1960-02-26,628.51,636.30,627.76,632.00,3380000,632.00 1960-02-25,623.73,630.22,621.65,628.51,3600000,628.51 1960-02-24,626.19,627.90,620.79,623.73,2740000,623.73 1960-02-23,628.45,633.50,624.04,626.19,2960000,626.19 1960-02-19,622.19,630.26,621.31,628.45,3230000,628.45 1960-02-18,615.29,625.95,615.29,622.19,3800000,622.19 1960-02-17,611.33,615.33,603.34,613.55,4210000,613.55 1960-02-16,617.58,619.29,608.94,611.33,3270000,611.33 1960-02-15,622.23,624.31,615.94,617.58,2780000,617.58 1960-02-12,618.57,623.70,617.38,622.23,2230000,622.23 1960-02-11,623.36,626.09,617.34,618.57,2610000,618.57 1960-02-10,628.45,631.79,620.25,623.36,2440000,623.36 1960-02-09,619.43,630.53,617.72,628.45,2860000,628.45 1960-02-08,626.50,626.50,610.17,619.43,3350000,619.43 1960-02-05,631.14,632.10,623.63,626.77,2530000,626.77 1960-02-04,630.97,634.53,627.11,631.14,2600000,631.14 1960-02-03,636.92,640.30,629.03,630.97,3020000,630.97 1960-02-02,628.04,638.86,628.04,636.92,3080000,636.92 1960-02-01,622.62,630.75,619.68,626.20,2820000,626.20 1960-01-29,629.84,631.51,619.51,622.62,3060000,622.62 1960-01-28,637.67,638.27,627.77,629.84,2630000,629.84 1960-01-27,639.84,643.05,633.89,637.67,2460000,637.67 1960-01-26,639.07,642.81,633.49,639.84,3060000,639.84 1960-01-25,645.85,648.83,637.20,639.07,2790000,639.07 1960-01-22,645.43,650.92,643.33,645.85,2690000,645.85 1960-01-21,643.69,649.51,641.40,645.43,2700000,645.43 1960-01-20,645.07,650.13,641.57,643.69,2720000,643.69 1960-01-19,653.86,654.97,642.58,645.07,3100000,645.07 1960-01-18,659.68,662.06,651.70,653.86,3020000,653.86 1960-01-15,660.53,666.38,656.25,659.68,3400000,659.68 1960-01-14,656.44,663.14,655.13,660.53,3560000,660.53 1960-01-13,660.43,664.68,653.11,656.44,3470000,656.44 1960-01-12,667.16,667.39,655.49,660.43,3760000,660.43 1960-01-11,675.73,676.74,663.67,667.16,3470000,667.16 1960-01-08,677.66,680.86,671.38,675.73,3290000,675.73 1960-01-07,682.62,683.05,674.98,677.66,3310000,677.66 1960-01-06,685.47,687.36,678.34,682.62,3730000,682.62 1960-01-05,679.06,687.14,677.43,685.47,3710000,685.47 1960-01-04,679.36,688.21,677.39,679.06,3990000,679.06 1959-12-31,676.97,682.72,676.32,679.36,3810000,679.36 1959-12-30,672.23,679.49,672.00,676.97,3680000,676.97 1959-12-29,669.77,675.01,667.13,672.23,3020000,672.23 1959-12-28,670.69,674.41,665.96,669.77,2830000,669.77 1959-12-24,670.18,674.69,666.93,670.69,2320000,670.69 1959-12-23,671.82,675.73,667.75,670.18,2890000,670.18 1959-12-22,675.92,677.34,669.90,671.82,2930000,671.82 1959-12-21,676.65,681.54,673.05,675.92,3290000,675.92 1959-12-18,673.90,679.52,671.35,676.65,3230000,676.65 1959-12-17,675.20,678.54,671.54,673.90,3040000,673.90 1959-12-16,673.78,679.20,671.41,675.20,3270000,675.20 1959-12-15,675.07,681.00,671.63,673.78,3450000,673.78 1959-12-14,670.50,678.26,669.02,675.07,3100000,675.07 1959-12-11,672.74,674.72,667.28,670.50,2910000,670.50 1959-12-10,671.26,676.96,668.80,672.74,3170000,672.74 1959-12-09,675.39,678.03,668.76,671.26,3430000,671.26 1959-12-08,665.67,679.36,665.07,675.39,3870000,675.39 1959-12-07,664.00,670.44,661.83,665.67,3620000,665.67 1959-12-04,662.96,668.04,659.87,664.00,3590000,664.00 1959-12-03,661.29,666.05,658.48,662.96,3280000,662.96 1959-12-02,664.38,668.48,658.64,661.29,3490000,661.29 1959-12-01,659.97,668.70,659.97,664.38,3990000,664.38 1959-11-30,652.52,661.23,651.55,659.18,3670000,659.18 1959-11-27,651.10,655.58,647.92,652.52,3030000,652.52 1959-11-25,649.69,655.55,646.69,651.10,3550000,651.10 1959-11-24,646.75,653.66,644.86,649.69,3650000,649.69 1959-11-23,645.46,651.51,641.77,646.75,3400000,646.75 1959-11-20,643.32,649.97,641.42,645.46,2960000,645.46 1959-11-19,641.99,648.36,639.63,643.32,3230000,643.32 1959-11-18,635.62,644.96,634.77,641.99,3660000,641.99 1959-11-17,634.46,639.60,630.99,635.62,3570000,635.62 1959-11-16,641.71,644.48,632.72,634.46,3710000,634.46 1959-11-13,644.26,647.38,640.01,641.71,3050000,641.71 1959-11-12,647.32,650.88,642.78,644.26,3600000,644.26 1959-11-11,648.14,649.94,644.96,647.32,2820000,647.32 1959-11-10,650.92,652.08,644.86,648.14,3020000,648.14 1959-11-09,650.92,658.86,648.27,650.92,3700000,650.92 1959-11-06,647.57,654.38,644.92,650.92,3450000,650.92 1959-11-05,645.74,651.61,641.99,647.57,3170000,647.57 1959-11-04,645.46,651.96,641.01,645.74,3940000,645.74 1959-11-02,646.60,650.85,642.24,645.46,3320000,645.46 1959-10-30,645.11,649.40,641.68,646.60,3560000,646.60 1959-10-29,643.60,648.65,638.78,645.11,3890000,645.11 1959-10-28,642.18,647.04,637.55,643.60,3920000,643.60 1959-10-27,637.61,645.87,636.19,642.18,4160000,642.18 1959-10-26,633.07,641.14,633.07,637.61,3580000,637.61 1959-10-23,625.59,634.42,624.84,633.07,2880000,633.07 1959-10-22,632.69,635.75,624.55,625.59,3060000,625.59 1959-10-21,635.37,637.99,630.64,632.69,2730000,632.69 1959-10-20,639.66,642.18,634.01,635.37,2740000,635.37 1959-10-19,643.22,643.88,634.93,639.66,2470000,639.66 1959-10-16,637.67,646.53,637.67,643.22,2760000,643.22 1959-10-15,634.27,640.51,632.91,637.48,2190000,637.48 1959-10-14,637.83,639.50,632.28,634.27,2320000,634.27 1959-10-13,638.55,642.34,635.46,637.83,2530000,637.83 1959-10-12,636.98,641.27,635.56,638.55,1750000,638.55 1959-10-09,633.04,638.74,631.62,636.98,2540000,636.98 1959-10-08,635.37,637.42,631.24,633.04,2510000,633.04 1959-10-07,636.06,638.46,631.90,635.37,2380000,635.37 1959-10-06,637.01,639.06,631.87,636.06,2330000,636.06 1959-10-05,636.57,640.76,634.58,637.01,2100000,637.01 1959-10-02,633.60,639.22,630.77,636.57,2270000,636.57 1959-10-01,631.68,635.72,625.40,633.60,2660000,633.60 1959-09-30,640.10,640.16,628.94,631.68,2850000,631.68 1959-09-29,636.47,643.60,635.72,640.10,3220000,640.10 1959-09-28,632.59,638.93,629.35,636.47,2640000,636.47 1959-09-25,632.85,637.73,628.18,632.59,3280000,632.59 1959-09-24,625.06,635.28,625.06,632.85,3480000,632.85 1959-09-23,617.21,626.73,617.21,624.02,3010000,624.02 1959-09-22,618.15,624.24,613.30,616.45,3000000,616.45 1959-09-21,625.59,625.59,613.71,618.15,3240000,618.15 1959-09-18,629.00,629.60,621.97,625.78,2530000,625.78 1959-09-17,632.41,636.19,627.64,629.00,2090000,629.00 1959-09-16,630.80,636.16,629.47,632.41,2180000,632.41 1959-09-15,633.79,634.83,624.49,630.80,2830000,630.80 1959-09-14,637.36,641.08,632.31,633.79,2590000,633.79 1959-09-11,633.38,641.33,632.34,637.36,2640000,637.36 1959-09-10,637.67,641.30,630.45,633.38,2520000,633.38 1959-09-09,642.69,644.74,631.43,637.67,3030000,637.67 1959-09-08,651.74,651.74,636.98,642.69,2940000,642.69 1959-09-04,645.90,653.91,645.37,652.18,2300000,652.18 1959-09-03,655.80,655.93,644.51,645.90,2330000,645.90 1959-09-02,655.90,659.87,652.15,655.80,2370000,655.80 1959-09-01,664.41,664.95,652.74,655.90,2430000,655.90 1959-08-31,663.06,667.72,660.25,664.41,2140000,664.41 1959-08-28,663.34,666.71,659.75,663.06,1930000,663.06 1959-08-27,657.60,665.61,657.60,663.34,2550000,663.34 1959-08-26,655.96,660.34,652.84,657.57,2210000,657.57 1959-08-25,653.22,659.56,651.07,655.96,1960000,655.96 1959-08-24,655.39,657.82,650.57,653.22,1860000,653.22 1959-08-21,655.02,658.86,651.89,655.39,2000000,655.39 1959-08-20,647.01,657.29,647.01,655.02,2450000,655.02 1959-08-19,649.31,649.31,639.34,646.53,3050000,646.53 1959-08-18,658.42,659.15,649.50,650.79,2280000,650.79 1959-08-17,658.74,663.02,656.18,658.42,1980000,658.42 1959-08-14,655.43,661.42,654.61,658.74,1990000,658.74 1959-08-13,655.14,658.48,651.99,655.43,2020000,655.43 1959-08-12,658.07,662.46,653.41,655.14,2700000,655.14 1959-08-11,653.79,661.76,651.14,658.07,2980000,658.07 1959-08-10,665.14,665.14,647.93,653.79,4190000,653.79 1959-08-07,671.98,676.11,666.71,668.57,2580000,668.57 1959-08-06,672.33,676.11,669.36,671.98,2610000,671.98 1959-08-05,675.76,675.76,667.82,672.33,2630000,672.33 1959-08-04,678.10,679.71,673.40,676.30,2530000,676.30 1959-08-03,674.88,683.90,673.90,678.10,2410000,678.10 1959-07-31,673.37,677.28,670.50,674.88,2270000,674.88 1959-07-30,673.18,678.67,668.29,673.37,3240000,673.37 1959-07-29,672.04,677.12,668.48,673.18,3460000,673.18 1959-07-28,669.08,675.76,666.62,672.04,3190000,672.04 1959-07-27,663.72,671.92,663.34,669.08,2910000,669.08 1959-07-24,664.63,668.10,661.29,663.72,2720000,663.72 1959-07-23,664.38,668.23,661.26,664.63,3310000,664.63 1959-07-22,661.48,667.69,660.06,664.38,3310000,664.38 1959-07-21,654.54,663.78,652.62,661.48,2950000,661.48 1959-07-20,657.13,659.87,651.83,654.54,2500000,654.54 1959-07-17,658.29,659.81,653.69,657.13,2510000,657.13 1959-07-16,660.57,662.93,655.46,658.29,3170000,658.29 1959-07-15,657.70,663.91,655.90,660.57,3280000,660.57 1959-07-14,657.35,661.13,653.53,657.70,3230000,657.70 1959-07-13,663.56,665.45,655.05,657.35,3360000,657.35 1959-07-10,663.09,667.34,659.24,663.56,3600000,663.56 1959-07-09,663.81,667.12,658.89,663.09,3560000,663.09 1959-07-08,663.21,668.26,658.23,663.81,4010000,663.81 1959-07-07,660.09,665.89,656.31,663.21,3840000,663.21 1959-07-06,654.76,663.31,653.79,660.09,3720000,660.09 1959-07-02,650.88,659.18,650.22,654.76,3610000,654.76 1959-07-01,643.60,654.01,643.60,650.88,3150000,650.88 1959-06-30,643.06,647.29,639.69,643.60,3200000,643.60 1959-06-29,639.25,646.41,638.33,643.06,3000000,643.06 1959-06-26,637.23,643.51,635.02,639.25,3100000,639.25 1959-06-25,634.27,640.23,631.87,637.23,3250000,637.23 1959-06-24,630.73,636.91,628.53,634.27,3180000,634.27 1959-06-23,631.71,635.62,627.61,630.73,2600000,630.73 1959-06-22,629.76,637.42,627.86,631.71,2630000,631.71 1959-06-19,629.41,633.73,625.12,629.76,2260000,629.76 1959-06-18,628.05,633.87,627.20,629.41,3150000,629.41 1959-06-17,621.40,629.82,619.00,628.05,2850000,628.05 1959-06-16,624.59,626.60,618.28,621.40,2440000,621.40 1959-06-15,627.42,629.73,621.02,624.59,2410000,624.59 1959-06-12,627.49,632.03,623.54,627.42,2580000,627.42 1959-06-11,627.17,634.14,624.62,627.49,3120000,627.49 1959-06-10,620.45,630.67,620.45,627.17,3310000,627.17 1959-06-09,621.62,624.81,613.11,617.62,3490000,617.62 1959-06-08,629.98,632.03,620.33,621.62,2970000,621.62 1959-06-05,630.54,633.95,625.97,629.98,2800000,629.98 1959-06-04,637.39,639.78,628.94,630.54,3210000,630.54 1959-06-03,637.45,642.46,634.36,637.39,2910000,637.39 1959-06-02,643.10,643.10,632.85,637.45,3120000,637.45 1959-06-01,643.79,648.65,640.32,643.51,2730000,643.51 1959-05-29,639.58,647.24,637.25,643.79,2790000,643.79 1959-05-28,636.68,643.09,635.59,639.58,2970000,639.58 1959-05-27,632.38,639.22,631.99,636.68,2940000,636.68 1959-05-26,632.35,636.28,629.41,632.38,2910000,632.38 1959-05-25,634.74,639.64,630.59,632.35,3260000,632.35 1959-05-22,631.65,637.37,630.38,634.74,3030000,634.74 1959-05-21,631.87,634.38,626.84,631.65,3230000,631.65 1959-05-20,635.44,636.77,628.99,631.87,3550000,631.87 1959-05-19,633.53,639.19,631.20,635.44,3170000,635.44 1959-05-18,634.53,636.62,629.90,633.53,2970000,633.53 1959-05-15,637.04,640.92,632.44,634.53,3510000,634.53 1959-05-14,633.05,640.10,631.02,637.04,3660000,637.04 1959-05-13,627.66,635.38,625.51,633.05,3540000,633.05 1959-05-12,625.03,630.90,622.97,627.66,3550000,627.66 1959-05-11,621.36,627.66,620.58,625.03,3860000,625.03 1959-05-08,615.73,625.81,615.73,621.36,3930000,621.36 1959-05-07,624.39,624.45,611.68,615.64,4530000,615.64 1959-05-06,625.90,630.84,621.73,624.39,4110000,624.39 1959-05-05,625.06,629.05,622.88,625.90,3360000,625.90 1959-05-04,625.06,629.87,622.18,625.06,3060000,625.06 1959-05-01,623.75,628.11,620.49,625.06,3020000,625.06 1959-04-30,625.87,628.69,620.91,623.75,3510000,623.75 1959-04-29,628.87,629.75,622.60,625.87,3470000,625.87 1959-04-28,629.87,633.95,625.24,628.87,3920000,628.87 1959-04-27,627.39,635.29,624.60,629.87,3850000,629.87 1959-04-24,623.27,630.26,622.60,627.39,3790000,627.39 1959-04-23,625.15,627.39,618.94,623.27,3310000,623.27 1959-04-22,629.23,631.62,622.69,625.15,3430000,625.15 1959-04-21,627.08,632.17,623.42,629.23,3650000,629.23 1959-04-20,624.06,630.69,622.54,627.08,3610000,627.08 1959-04-17,618.34,626.66,618.34,624.06,3870000,624.06 1959-04-16,612.50,620.58,611.41,617.58,3790000,617.58 1959-04-15,609.53,615.07,608.65,612.50,3680000,612.50 1959-04-14,607.76,611.98,605.44,609.53,3320000,609.53 1959-04-13,605.97,610.81,603.47,607.76,3140000,607.76 1959-04-10,605.50,609.52,603.56,605.97,3000000,605.97 1959-04-09,606.44,609.11,602.12,605.50,2830000,605.50 1959-04-08,610.34,612.28,605.15,606.44,3260000,606.44 1959-04-07,611.16,612.66,606.08,610.34,3020000,610.34 1959-04-06,611.93,616.42,608.26,611.16,3510000,611.16 1959-04-03,607.52,614.39,607.52,611.93,3680000,611.93 1959-04-02,602.97,610.25,602.97,607.52,3220000,607.52 1959-04-01,601.71,606.97,599.65,602.94,2980000,602.94 1959-03-31,602.65,605.73,599.21,601.71,2820000,601.71 1959-03-30,606.58,608.64,600.98,602.65,2940000,602.65 1959-03-26,606.47,608.61,601.50,606.58,2900000,606.58 1959-03-25,606.73,611.16,603.82,606.47,3280000,606.47 1959-03-24,605.56,608.67,602.36,606.73,3000000,606.73 1959-03-23,610.37,612.78,603.38,605.56,3700000,605.56 1959-03-20,610.02,614.16,605.85,610.37,3770000,610.37 1959-03-19,610.87,613.92,605.64,610.02,4150000,610.02 1959-03-18,612.69,617.15,609.14,610.87,4530000,610.87 1959-03-17,607.93,615.83,607.93,612.69,4730000,612.69 1959-03-16,614.69,615.80,605.82,607.88,4420000,607.88 1959-03-13,613.75,618.80,610.78,614.69,4880000,614.69 1959-03-12,611.49,616.10,608.55,613.75,4690000,613.75 1959-03-11,611.14,614.34,608.08,611.49,4160000,611.49 1959-03-10,609.96,614.39,605.44,611.14,3920000,611.14 1959-03-09,609.52,615.01,606.14,609.96,3530000,609.96 1959-03-06,611.87,613.16,605.20,609.52,3930000,609.52 1959-03-05,611.84,614.57,608.32,611.87,3930000,611.87 1959-03-04,610.78,615.77,606.20,611.84,4150000,611.84 1959-03-03,605.03,613.04,604.29,610.78,4790000,610.78 1959-03-02,603.50,608.82,600.89,605.03,4210000,605.03 1959-02-27,602.00,606.53,599.77,603.50,4300000,603.50 1959-02-26,601.18,604.44,596.84,602.00,3930000,602.00 1959-02-25,602.91,605.35,597.86,601.18,3780000,601.18 1959-02-24,602.21,607.61,599.57,602.91,4340000,602.91 1959-02-20,595.04,603.91,594.93,602.21,4190000,602.21 1959-02-19,589.58,597.48,589.58,595.04,4160000,595.04 1959-02-18,586.71,590.76,583.62,588.82,3480000,588.82 1959-02-17,587.91,591.58,583.83,586.71,3190000,586.71 1959-02-16,587.97,593.14,584.39,587.91,3480000,587.91 1959-02-13,581.89,589.91,580.01,587.97,3070000,587.97 1959-02-12,584.03,586.73,579.98,581.89,2630000,581.89 1959-02-11,582.65,588.17,581.63,584.03,3000000,584.03 1959-02-10,574.72,583.89,574.72,582.65,2960000,582.65 1959-02-09,581.36,581.36,571.73,574.46,3130000,574.46 1959-02-06,586.12,587.56,580.54,582.33,3010000,582.33 1959-02-05,589.38,591.14,584.06,586.12,3140000,586.12 1959-02-04,592.34,594.16,587.50,589.38,3170000,589.38 1959-02-03,592.23,595.60,589.35,592.34,3220000,592.34 1959-02-02,593.96,597.66,589.14,592.23,3610000,592.23 1959-01-30,590.40,597.60,590.35,593.96,3600000,593.96 1959-01-29,588.53,594.19,585.88,590.40,3470000,590.40 1959-01-28,594.66,596.37,584.71,588.53,4190000,588.53 1959-01-27,592.37,597.31,589.47,594.66,3480000,594.66 1959-01-26,596.07,599.77,591.11,592.37,3980000,592.37 1959-01-23,595.69,598.36,591.40,596.07,3600000,596.07 1959-01-22,597.66,601.74,593.96,595.69,4250000,595.69 1959-01-21,595.69,600.51,593.28,597.66,3940000,597.66 1959-01-20,594.40,598.45,590.99,595.69,3680000,595.69 1959-01-19,595.75,598.57,590.32,594.40,3840000,594.40 1959-01-16,594.81,599.89,590.38,595.75,4300000,595.75 1959-01-15,591.64,598.10,589.88,594.81,4500000,594.81 1959-01-14,590.70,593.28,586.85,591.64,4090000,591.64 1959-01-13,592.64,595.04,587.47,590.70,3790000,590.70 1959-01-12,592.72,597.31,589.61,592.64,4320000,592.64 1959-01-09,588.14,594.84,588.00,592.72,4760000,592.72 1959-01-08,583.15,590.11,580.54,588.14,4030000,588.14 1959-01-07,591.37,592.58,581.07,583.15,4140000,583.15 1959-01-06,590.17,593.52,584.88,591.37,3690000,591.37 1959-01-05,587.59,594.31,585.24,590.17,4210000,590.17 1959-01-02,583.65,590.38,580.80,587.59,3380000,587.59 1958-12-31,581.80,587.44,579.25,583.65,3970000,583.65 1958-12-30,577.31,584.59,577.13,581.80,3900000,581.80 1958-12-29,572.73,580.63,572.73,577.31,3790000,577.31 1958-12-24,566.39,573.64,564.27,572.73,3050000,572.73 1958-12-23,571.23,573.26,564.15,566.39,2870000,566.39 1958-12-22,573.17,574.84,568.12,571.23,3030000,571.23 1958-12-19,572.38,577.90,570.38,573.17,3540000,573.17 1958-12-18,569.38,575.52,568.24,572.38,3900000,572.38 1958-12-17,565.18,573.29,561.51,569.38,3900000,569.38 1958-12-16,563.98,568.29,562.25,565.18,3970000,565.18 1958-12-15,562.27,566.24,559.96,563.98,3340000,563.98 1958-12-12,563.07,565.01,559.43,562.27,3140000,562.27 1958-12-11,564.98,569.50,561.57,563.07,4250000,563.07 1958-12-10,558.13,566.50,558.05,564.98,4340000,564.98 1958-12-09,556.08,561.51,555.58,558.13,3790000,558.13 1958-12-08,556.75,559.54,553.70,556.08,3590000,556.08 1958-12-05,559.10,560.37,554.85,556.75,3360000,556.75 1958-12-04,558.81,563.19,556.40,559.10,3630000,559.10 1958-12-03,558.57,560.95,554.55,558.81,3460000,558.81 1958-12-02,560.07,562.98,555.37,558.57,3320000,558.57 1958-12-01,557.46,563.30,555.52,560.07,3800000,560.07 1958-11-28,550.41,559.04,550.41,557.46,4120000,557.46 1958-11-26,542.40,551.50,542.40,549.15,4090000,549.15 1958-11-25,544.89,546.95,538.43,540.52,3940000,540.52 1958-11-24,554.88,554.88,543.07,544.89,4770000,544.89 1958-11-21,566.24,566.50,558.16,559.57,3950000,559.57 1958-11-20,565.97,569.70,563.21,566.24,4320000,566.24 1958-11-19,564.89,568.68,562.10,565.97,4090000,565.97 1958-11-18,567.44,569.06,560.48,564.89,3820000,564.89 1958-11-17,564.68,572.05,561.54,567.44,4540000,567.44 1958-11-14,560.75,566.91,559.07,564.68,4390000,564.68 1958-11-13,562.39,564.42,557.43,560.75,4200000,560.75 1958-11-12,561.13,565.53,558.19,562.39,4440000,562.39 1958-11-11,557.72,563.60,556.93,561.13,4040000,561.13 1958-11-10,554.26,560.10,552.64,557.72,3730000,557.72 1958-11-07,554.85,558.22,550.85,554.26,3700000,554.26 1958-11-06,550.76,559.43,550.76,554.85,4890000,554.85 1958-11-05,545.16,552.35,543.34,550.68,4080000,550.68 1958-11-03,543.22,547.15,540.28,545.16,3240000,545.16 1958-10-31,543.31,546.27,539.31,543.22,3920000,543.22 1958-10-30,542.72,546.83,540.69,543.31,4360000,543.31 1958-10-29,536.88,544.92,536.85,542.72,4790000,542.72 1958-10-28,535.00,538.26,530.94,536.88,3670000,536.88 1958-10-27,539.52,541.22,533.09,535.00,3980000,535.00 1958-10-24,540.72,543.16,536.43,539.52,3770000,539.52 1958-10-23,542.31,544.07,537.76,540.72,3610000,540.72 1958-10-22,543.72,545.04,538.96,542.31,3500000,542.31 1958-10-21,544.19,546.54,540.28,543.72,4010000,543.72 1958-10-20,546.36,548.36,541.57,544.19,4560000,544.19 1958-10-17,541.07,548.97,541.07,546.36,5360000,546.36 1958-10-16,536.14,541.37,529.42,540.11,4560000,540.11 1958-10-15,541.72,545.68,533.65,536.14,4810000,536.14 1958-10-14,545.95,549.71,540.22,541.72,5110000,541.72 1958-10-13,543.36,549.30,542.95,545.95,4550000,545.95 1958-10-10,539.61,545.74,538.17,543.36,4610000,543.36 1958-10-09,539.31,541.46,535.85,539.61,3670000,539.61 1958-10-08,539.40,542.28,535.97,539.31,3680000,539.31 1958-10-07,536.29,540.96,534.12,539.40,3570000,539.40 1958-10-06,533.73,539.37,533.21,536.29,3570000,536.29 1958-10-03,532.09,536.82,531.53,533.73,3830000,533.73 1958-10-02,530.94,535.14,528.83,532.09,3750000,532.09 1958-10-01,532.09,533.91,527.68,530.94,3780000,530.94 1958-09-30,529.04,535.00,528.68,532.09,4160000,532.09 1958-09-29,526.83,531.74,525.54,529.04,3680000,529.04 1958-09-26,525.83,529.59,523.31,526.83,3420000,526.83 1958-09-25,528.15,530.68,523.34,525.83,4490000,525.83 1958-09-24,525.89,530.18,523.57,528.15,3120000,528.15 1958-09-23,524.01,528.62,520.87,525.89,3950000,525.89 1958-09-22,526.48,528.39,522.19,524.01,3490000,524.01 1958-09-19,522.34,527.74,521.05,526.48,3880000,526.48 1958-09-18,525.89,526.77,520.52,522.34,3460000,522.34 1958-09-17,526.57,529.45,522.43,525.89,3790000,525.89 1958-09-16,523.40,529.09,522.72,526.57,3940000,526.57 1958-09-15,519.43,524.57,517.47,523.40,3040000,523.40 1958-09-12,520.43,523.57,517.06,519.43,3100000,519.43 1958-09-11,516.20,521.72,515.32,520.43,3300000,520.43 1958-09-10,518.64,520.43,514.59,516.20,2820000,516.20 1958-09-09,515.23,521.31,515.23,518.64,3480000,518.64 1958-09-08,512.77,517.47,511.77,515.23,3030000,515.23 1958-09-05,513.44,515.35,510.45,512.77,2520000,512.77 1958-09-04,513.71,516.59,511.07,513.44,3100000,513.44 1958-09-03,511.77,516.03,510.15,513.71,3240000,513.71 1958-09-02,508.63,513.09,508.39,511.77,2930000,511.77 1958-08-29,507.72,510.18,505.60,508.63,2260000,508.63 1958-08-28,510.24,510.24,505.49,507.72,2540000,507.72 1958-08-27,509.63,513.33,508.19,510.39,3250000,510.39 1958-08-26,508.28,510.95,506.72,509.63,2910000,509.63 1958-08-25,508.28,510.62,505.37,508.28,2610000,508.28 1958-08-22,507.10,509.66,505.54,508.28,2660000,508.28 1958-08-21,503.96,508.89,503.05,507.10,2500000,507.10 1958-08-20,503.64,506.51,501.73,503.96,2460000,503.96 1958-08-19,502.67,506.13,501.26,503.64,2250000,503.64 1958-08-18,505.75,505.75,499.82,502.67,2390000,502.67 1958-08-15,510.30,510.36,504.66,506.13,2960000,506.13 1958-08-14,509.22,512.00,507.48,510.30,3370000,510.30 1958-08-13,508.19,511.24,505.31,509.22,2790000,509.22 1958-08-12,512.42,512.47,506.78,508.19,2600000,508.19 1958-08-11,510.13,514.44,508.69,512.42,2870000,512.42 1958-08-08,506.60,513.47,506.60,510.13,3650000,510.13 1958-08-07,503.11,507.84,501.46,506.10,3200000,506.10 1958-08-06,506.95,508.51,500.32,503.11,3440000,503.11 1958-08-05,510.33,511.01,503.49,506.95,4210000,506.95 1958-08-04,505.43,512.27,505.22,510.33,4000000,510.33 1958-08-01,502.99,507.39,501.64,505.43,3380000,505.43 1958-07-31,504.37,508.39,501.49,502.99,4440000,502.99 1958-07-30,501.38,505.10,497.38,504.37,3680000,504.37 1958-07-29,502.81,504.52,498.73,501.38,3310000,501.38 1958-07-28,501.76,504.43,498.67,502.81,3940000,502.81 1958-07-25,497.12,502.64,496.71,501.76,4430000,501.76 1958-07-24,494.06,498.50,492.42,497.12,3740000,497.12 1958-07-23,494.89,497.21,490.86,494.06,3550000,494.06 1958-07-22,493.36,496.74,490.01,494.89,3420000,494.89 1958-07-21,487.10,494.36,487.10,493.36,3440000,493.36 1958-07-18,485.70,489.89,483.43,486.55,3350000,486.55 1958-07-17,481.00,487.60,478.00,485.70,3180000,485.70 1958-07-16,478.88,485.87,478.88,481.00,3240000,481.00 1958-07-15,476.89,479.82,472.07,478.82,3090000,478.82 1958-07-14,482.55,482.55,475.74,476.89,2540000,476.89 1958-07-11,478.97,484.23,478.94,482.85,2400000,482.85 1958-07-10,477.59,480.35,475.01,478.97,2510000,478.97 1958-07-09,480.00,482.79,476.92,477.59,2630000,477.59 1958-07-08,481.85,482.99,478.12,480.00,2430000,480.00 1958-07-07,480.17,483.76,478.88,481.85,2510000,481.85 1958-07-03,480.15,482.26,478.03,480.17,2630000,480.17 1958-07-02,478.82,481.61,476.86,480.15,2370000,480.15 1958-07-01,478.18,480.64,476.83,478.82,2600000,478.82 1958-06-30,475.42,479.59,474.33,478.18,2820000,478.18 1958-06-27,474.01,478.09,473.22,475.42,2800000,475.42 1958-06-26,471.54,475.68,470.07,474.01,2910000,474.01 1958-06-25,470.43,473.39,468.05,471.54,2720000,471.54 1958-06-24,471.66,472.42,467.37,470.43,2560000,470.43 1958-06-23,473.60,474.60,469.46,471.66,2340000,471.66 1958-06-20,471.57,475.36,470.81,473.60,2590000,473.60 1958-06-19,476.65,478.03,470.93,471.57,2690000,471.57 1958-06-18,478.97,479.70,474.21,476.65,2640000,476.65 1958-06-17,476.56,482.11,475.36,478.97,2950000,478.97 1958-06-16,474.77,477.97,473.16,476.56,2870000,476.56 1958-06-13,471.57,476.56,471.57,474.77,3100000,474.77 1958-06-12,467.93,472.98,467.23,471.42,2760000,471.42 1958-06-11,468.19,470.04,465.58,467.93,2570000,467.93 1958-06-10,469.46,470.37,466.32,468.19,2390000,468.19 1958-06-09,469.60,471.60,467.17,469.46,2380000,469.46 1958-06-06,468.55,471.75,467.49,469.60,2680000,469.60 1958-06-05,468.58,469.84,465.38,468.55,2600000,468.55 1958-06-04,468.14,470.69,466.37,468.58,2690000,468.58 1958-06-03,466.11,469.66,465.35,468.14,2780000,468.14 1958-06-02,462.70,468.61,462.70,466.11,2770000,466.11 1958-05-29,460.44,463.85,459.91,462.70,2350000,462.70 1958-05-28,460.68,462.85,458.97,460.44,2260000,460.44 1958-05-27,461.06,462.53,457.92,460.68,2180000,460.68 1958-05-26,461.03,463.47,459.39,461.06,2500000,461.06 1958-05-23,460.24,463.32,459.21,461.03,2570000,461.03 1958-05-22,458.50,463.20,457.10,460.24,2950000,460.24 1958-05-21,459.83,461.62,456.83,458.50,2580000,458.50 1958-05-20,455.98,460.74,455.30,459.83,2500000,459.83 1958-05-19,457.10,458.06,454.16,455.98,1910000,455.98 1958-05-16,457.86,459.47,455.57,457.10,2030000,457.10 1958-05-15,455.45,459.00,453.84,457.86,2470000,457.86 1958-05-14,459.86,460.79,454.57,455.45,3060000,455.45 1958-05-13,460.74,462.03,457.62,459.86,2940000,459.86 1958-05-12,462.56,463.70,459.41,460.74,2780000,460.74 1958-05-09,462.50,465.14,460.44,462.56,2760000,462.56 1958-05-08,462.88,464.35,460.38,462.50,2790000,462.50 1958-05-07,463.67,465.17,461.12,462.88,2770000,462.88 1958-05-06,461.12,465.32,460.09,463.67,3110000,463.67 1958-05-05,459.56,462.38,457.39,461.12,2670000,461.12 1958-05-02,457.01,460.56,455.66,459.56,2290000,459.56 1958-05-01,455.86,460.21,454.95,457.01,2630000,457.01 1958-04-30,451.78,457.39,451.19,455.86,2900000,455.86 1958-04-29,454.42,454.42,449.78,451.78,2190000,451.78 1958-04-28,454.92,458.33,452.66,454.51,2400000,454.51 1958-04-25,453.42,457.10,451.13,454.92,3020000,454.92 1958-04-24,450.11,455.51,449.34,453.42,2870000,453.42 1958-04-23,449.55,451.87,445.88,450.11,2720000,450.11 1958-04-22,450.72,452.34,448.37,449.55,2440000,449.55 1958-04-21,449.31,452.49,448.34,450.72,2550000,450.72 1958-04-18,445.09,450.75,445.03,449.31,2700000,449.31 1958-04-17,444.35,446.14,442.35,445.09,2500000,445.09 1958-04-16,447.58,447.85,442.53,444.35,2240000,444.35 1958-04-15,443.76,448.96,442.62,447.58,2590000,447.58 1958-04-14,441.24,444.88,440.06,443.76,2180000,443.76 1958-04-11,441.06,442.65,438.83,441.24,2060000,441.24 1958-04-10,441.88,442.94,438.77,441.06,2000000,441.06 1958-04-09,442.59,445.20,440.97,441.88,2040000,441.88 1958-04-08,440.09,444.79,439.59,442.59,2190000,442.59 1958-04-07,440.50,441.97,437.25,440.09,2090000,440.09 1958-04-03,441.21,442.97,437.98,440.50,2130000,440.50 1958-04-02,445.47,447.29,440.06,441.21,2390000,441.21 1958-04-01,446.76,447.82,443.29,445.47,2070000,445.47 1958-03-31,448.61,450.05,445.56,446.76,2050000,446.76 1958-03-28,448.64,450.17,446.64,448.61,1930000,448.61 1958-03-27,449.70,451.81,447.02,448.64,2140000,448.64 1958-03-26,450.96,451.46,447.61,449.70,1990000,449.70 1958-03-25,453.75,454.31,449.52,450.96,2210000,450.96 1958-03-24,452.49,455.36,450.25,453.75,2580000,453.75 1958-03-21,449.46,453.57,447.79,452.49,2430000,452.49 1958-03-20,449.96,452.57,447.76,449.46,2280000,449.46 1958-03-19,447.38,452.37,446.91,449.96,2410000,449.96 1958-03-18,448.23,448.84,443.41,447.38,2070000,447.38 1958-03-17,453.04,453.25,447.08,448.23,2130000,448.23 1958-03-14,454.10,454.75,450.52,453.04,2150000,453.04 1958-03-13,454.60,456.80,451.87,454.10,2830000,454.10 1958-03-12,455.92,456.83,452.72,454.60,2420000,454.60 1958-03-11,451.90,457.27,450.58,455.92,2640000,455.92 1958-03-10,451.49,453.45,448.96,451.90,1980000,451.90 1958-03-07,450.96,452.81,448.58,451.49,2130000,451.49 1958-03-06,446.58,452.43,446.05,450.96,2470000,450.96 1958-03-05,445.06,448.14,442.56,446.58,2020000,446.58 1958-03-04,443.38,447.11,443.03,445.06,2010000,445.06 1958-03-03,439.92,444.56,439.24,443.38,1810000,443.38 1958-02-28,437.80,441.15,436.66,439.92,1580000,439.92 1958-02-27,440.42,441.18,436.13,437.80,1670000,437.80 1958-02-26,437.42,442.24,437.42,440.42,1880000,440.42 1958-02-25,437.19,438.13,434.04,436.89,1920000,436.89 1958-02-24,439.62,440.80,436.16,437.19,1570000,437.19 1958-02-21,439.74,441.42,437.33,439.62,1700000,439.62 1958-02-20,443.06,445.53,438.89,439.74,2060000,439.74 1958-02-19,442.71,446.47,441.44,443.06,2070000,443.06 1958-02-18,442.27,444.32,439.89,442.71,1680000,442.71 1958-02-17,444.44,445.47,441.03,442.27,1700000,442.27 1958-02-14,440.24,445.76,439.54,444.44,2070000,444.44 1958-02-13,441.21,445.29,439.27,440.24,1880000,440.24 1958-02-12,442.35,443.29,438.13,441.21,2030000,441.21 1958-02-11,445.94,447.61,440.97,442.35,2110000,442.35 1958-02-10,448.52,448.52,443.68,445.94,1900000,445.94 1958-02-07,453.07,453.07,446.73,448.76,2220000,448.76 1958-02-06,454.89,455.30,450.14,453.13,2210000,453.13 1958-02-05,458.39,458.39,453.34,454.89,2480000,454.89 1958-02-04,453.98,459.77,451.63,458.65,2970000,458.65 1958-02-03,450.02,455.10,449.52,453.98,2490000,453.98 1958-01-31,449.72,451.46,446.70,450.02,2030000,450.02 1958-01-30,451.16,453.63,448.46,449.72,2150000,449.72 1958-01-29,448.67,452.51,447.02,451.16,2220000,451.16 1958-01-28,448.46,450.22,445.41,448.67,2030000,448.67 1958-01-27,450.66,451.40,446.61,448.46,2320000,448.46 1958-01-24,447.93,452.28,446.32,450.66,2830000,450.66 1958-01-23,445.70,449.02,444.18,447.93,1910000,447.93 1958-01-22,446.64,449.58,444.67,445.70,2390000,445.70 1958-01-21,447.29,449.17,445.11,446.64,2160000,446.64 1958-01-20,444.12,449.43,444.00,447.29,2310000,447.29 1958-01-17,445.23,445.67,440.62,444.12,2200000,444.12 1958-01-16,445.20,453.25,444.18,445.23,3950000,445.23 1958-01-15,441.80,446.26,440.36,445.20,2080000,445.20 1958-01-14,439.71,444.88,439.36,441.80,2010000,441.80 1958-01-13,438.68,441.27,434.37,439.71,1860000,439.71 1958-01-10,443.09,443.09,436.83,438.68,2010000,438.68 1958-01-09,446.61,448.40,442.27,443.24,2180000,443.24 1958-01-08,447.79,449.61,443.97,446.61,2230000,446.61 1958-01-07,442.56,448.23,440.18,447.79,2220000,447.79 1958-01-06,444.56,447.87,441.62,442.56,2500000,442.56 1958-01-03,439.27,446.08,438.60,444.56,2440000,444.56 1958-01-02,435.69,441.39,435.45,439.27,1800000,439.27 1957-12-31,432.43,438.54,432.43,435.69,5070000,435.69 1957-12-30,432.90,434.04,428.35,431.78,3750000,431.78 1957-12-27,434.16,437.39,431.52,432.90,2620000,432.90 1957-12-26,429.58,436.13,429.58,434.16,2280000,434.16 1957-12-24,428.08,432.22,426.67,429.11,2220000,429.11 1957-12-23,427.20,430.40,423.86,428.08,2790000,428.08 1957-12-20,431.26,434.13,425.94,427.20,2500000,427.20 1957-12-19,426.18,432.90,424.62,431.26,2740000,431.26 1957-12-18,425.65,431.93,424.15,426.18,2750000,426.18 1957-12-17,433.22,433.22,424.85,425.65,2820000,425.65 1957-12-16,440.30,440.30,432.05,433.40,2350000,433.40 1957-12-13,438.48,443.00,437.27,440.48,2310000,440.48 1957-12-12,439.36,441.94,436.31,438.48,2330000,438.48 1957-12-11,439.24,441.53,435.87,439.36,2240000,439.36 1957-12-10,443.03,443.03,436.34,439.24,2360000,439.24 1957-12-09,447.20,447.49,441.21,443.76,2230000,443.76 1957-12-06,449.55,450.69,445.29,447.20,2350000,447.20 1957-12-05,448.87,451.81,446.52,449.55,2020000,449.55 1957-12-04,446.55,451.55,446.03,448.87,2220000,448.87 1957-12-03,446.91,448.99,444.00,446.55,2060000,446.55 1957-12-02,449.87,452.16,444.18,446.91,2430000,446.91 1957-11-29,446.03,452.49,445.76,449.87,2740000,449.87 1957-11-27,439.51,448.02,439.51,446.03,3330000,446.03 1957-11-26,444.38,448.49,434.43,435.34,3650000,435.34 1957-11-25,442.68,446.11,439.01,444.38,2600000,444.38 1957-11-22,439.80,445.64,437.74,442.68,2850000,442.68 1957-11-21,434.51,443.21,434.51,439.80,2900000,439.80 1957-11-20,431.73,435.22,427.32,433.37,2400000,433.37 1957-11-19,434.96,435.84,427.23,431.73,2240000,431.73 1957-11-18,439.35,439.48,432.49,434.96,2110000,434.96 1957-11-15,434.50,442.82,434.50,439.35,3510000,439.35 1957-11-14,430.07,434.94,426.89,427.94,2450000,427.94 1957-11-13,429.75,432.61,426.51,430.07,2120000,430.07 1957-11-12,434.94,435.96,428.70,429.75,2050000,429.75 1957-11-11,434.12,437.16,432.37,434.94,1540000,434.94 1957-11-08,438.65,438.65,432.11,434.12,2140000,434.12 1957-11-07,435.82,440.49,432.14,438.91,2580000,438.91 1957-11-06,434.04,440.60,434.01,435.82,2550000,435.82 1957-11-04,434.71,435.20,425.46,434.04,2380000,434.04 1957-11-01,438.76,438.76,431.50,434.71,2060000,434.71 1957-10-31,440.28,446.06,439.03,441.04,2170000,441.04 1957-10-30,435.76,441.74,435.38,440.28,2060000,440.28 1957-10-29,432.14,439.61,431.59,435.76,1860000,435.76 1957-10-28,435.00,435.00,429.37,432.14,1800000,432.14 1957-10-25,436.40,436.95,428.90,435.15,2400000,435.15 1957-10-24,437.13,443.38,434.15,436.40,4030000,436.40 1957-10-23,425.46,438.59,425.46,437.13,4600000,437.13 1957-10-22,423.06,426.48,416.15,419.79,5090000,419.79 1957-10-21,433.83,434.36,421.20,423.06,4670000,423.06 1957-10-18,436.87,439.70,432.69,433.83,2670000,433.83 1957-10-17,442.24,442.24,433.86,436.87,3060000,436.87 1957-10-16,447.90,450.09,442.97,443.93,2050000,443.93 1957-10-15,443.78,450.93,443.61,447.90,2620000,447.90 1957-10-14,441.16,446.06,437.74,443.78,2770000,443.78 1957-10-11,441.71,443.58,434.15,441.16,4460000,441.16 1957-10-10,451.31,451.31,439.44,441.71,3300000,441.71 1957-10-09,450.56,456.51,450.12,451.40,2120000,451.40 1957-10-08,452.42,454.87,446.82,450.56,3190000,450.56 1957-10-07,461.70,462.14,451.81,452.42,2490000,452.42 1957-10-04,465.82,466.02,460.71,461.70,1520000,461.70 1957-10-03,465.03,466.84,461.67,465.82,1590000,465.82 1957-10-02,461.79,468.15,461.79,465.03,1760000,465.03 1957-10-01,456.30,461.91,455.55,460.80,1680000,460.80 1957-09-30,456.89,459.28,454.70,456.30,1520000,456.30 1957-09-27,457.01,462.40,454.20,456.89,1750000,456.89 1957-09-26,456.95,460.60,452.98,457.01,2130000,457.01 1957-09-25,462.87,463.57,452.19,456.95,2770000,456.95 1957-09-24,458.96,466.02,457.30,462.87,2840000,462.87 1957-09-23,465.97,465.97,457.15,458.96,3160000,458.96 1957-09-20,474.55,474.55,466.75,468.42,2340000,468.42 1957-09-19,478.60,479.59,475.01,476.12,1520000,476.12 1957-09-18,478.28,481.08,476.88,478.60,1540000,478.60 1957-09-17,478.08,480.44,474.17,478.28,1490000,478.28 1957-09-16,481.02,481.05,476.30,478.08,1290000,478.08 1957-09-13,480.56,483.68,478.72,481.02,1620000,481.02 1957-09-12,474.40,482.31,473.99,480.56,2010000,480.56 1957-09-11,470.23,475.30,467.10,474.40,2130000,474.40 1957-09-10,474.28,476.53,468.77,470.23,1870000,470.23 1957-09-09,478.49,478.49,472.77,474.28,1420000,474.28 1957-09-06,479.51,481.87,477.06,478.63,1320000,478.63 1957-09-05,481.64,481.64,476.76,479.51,1420000,479.51 1957-09-04,486.13,486.19,481.35,482.60,1260000,482.60 1957-09-03,484.35,487.59,481.32,486.13,1490000,486.13 1957-08-30,476.71,484.82,476.71,484.35,1600000,484.35 1957-08-29,477.79,478.19,471.98,476.06,1630000,476.06 1957-08-28,477.55,482.75,476.38,477.79,1840000,477.79 1957-08-27,470.84,479.36,470.84,477.55,2250000,477.55 1957-08-26,475.74,476.44,469.03,470.14,2680000,470.14 1957-08-23,481.46,481.73,474.81,475.74,1960000,475.74 1957-08-22,485.14,485.29,479.77,481.46,1500000,481.46 1957-08-21,483.86,488.23,482.10,485.14,1720000,485.14 1957-08-20,478.95,485.49,474.52,483.86,2700000,483.86 1957-08-19,487.68,487.68,478.16,478.95,2040000,478.95 1957-08-16,487.30,490.42,485.37,488.20,1470000,488.20 1957-08-15,485.93,490.28,482.10,487.30,2040000,487.30 1957-08-14,492.14,493.55,484.32,485.93,2040000,485.93 1957-08-13,492.32,495.68,489.96,492.14,1580000,492.14 1957-08-12,496.23,496.23,489.90,492.32,1650000,492.32 1957-08-09,496.87,498.59,493.46,496.78,1570000,496.78 1957-08-08,498.48,500.90,495.03,496.87,1690000,496.87 1957-08-07,494.13,499.59,490.25,498.48,2460000,498.48 1957-08-06,500.72,500.72,493.55,494.13,1910000,494.13 1957-08-05,505.10,505.57,498.62,500.78,1790000,500.78 1957-08-02,506.21,508.66,501.98,505.10,1610000,505.10 1957-08-01,508.52,509.98,504.69,506.21,1660000,506.21 1957-07-31,508.93,512.69,507.26,508.52,1830000,508.52 1957-07-30,508.25,510.44,504.90,508.93,1780000,508.93 1957-07-29,514.59,515.64,507.00,508.25,1990000,508.25 1957-07-26,516.69,517.97,512.89,514.59,1710000,514.59 1957-07-25,515.78,518.26,514.15,516.69,1800000,516.69 1957-07-24,515.61,518.24,512.46,515.78,1730000,515.78 1957-07-23,515.32,517.65,513.33,515.61,1840000,515.61 1957-07-22,515.73,518.24,513.62,515.32,1950000,515.32 1957-07-19,515.64,517.94,512.08,515.73,1930000,515.73 1957-07-18,515.11,519.34,513.33,515.64,2130000,515.64 1957-07-17,517.42,518.64,513.13,515.11,2060000,515.11 1957-07-16,520.16,523.11,516.28,517.42,2510000,517.42 1957-07-15,520.77,522.91,516.60,520.16,2480000,520.16 1957-07-12,517.97,521.94,514.88,520.77,2240000,520.77 1957-07-11,519.81,522.20,515.00,517.97,2830000,517.97 1957-07-10,516.37,521.68,515.11,519.81,2880000,519.81 1957-07-09,518.41,520.22,513.51,516.37,2450000,516.37 1957-07-08,516.89,521.71,515.32,518.41,2840000,518.41 1957-07-05,513.42,518.70,513.42,516.89,2240000,516.89 1957-07-03,507.99,515.23,507.99,513.25,2720000,513.25 1957-07-02,503.29,509.36,502.86,507.55,2450000,507.55 1957-07-01,503.29,506.18,500.75,503.29,1840000,503.29 1957-06-28,503.03,506.53,500.81,503.29,1770000,503.29 1957-06-27,500.78,504.29,498.36,503.03,1800000,503.03 1957-06-26,501.98,504.75,498.24,500.78,1870000,500.78 1957-06-25,497.69,504.69,497.69,501.98,2000000,501.98 1957-06-24,500.00,500.99,492.87,497.08,2040000,497.08 1957-06-21,503.56,504.52,497.81,500.00,1970000,500.00 1957-06-20,505.92,507.87,501.31,503.56,2050000,503.56 1957-06-19,511.32,512.69,505.28,505.92,2220000,505.92 1957-06-18,513.19,515.20,506.68,511.32,2440000,511.32 1957-06-17,511.79,516.81,510.38,513.19,2220000,513.19 1957-06-14,511.58,514.38,508.93,511.79,2090000,511.79 1957-06-13,509.66,514.27,508.23,511.58,2630000,511.58 1957-06-12,509.48,513.36,507.76,509.66,2600000,509.66 1957-06-11,504.93,511.70,504.93,509.48,2850000,509.48 1957-06-10,505.63,507.87,497.78,503.76,2050000,503.76 1957-06-07,504.55,508.14,502.88,505.63,2380000,505.63 1957-06-06,502.07,506.39,501.13,504.55,2300000,504.55 1957-06-05,502.97,504.37,499.38,502.07,1940000,502.07 1957-06-04,503.76,506.68,499.53,502.97,2200000,502.97 1957-06-03,504.93,507.96,500.46,503.76,2050000,503.76 1957-05-31,502.18,507.76,501.57,504.93,2050000,504.93 1957-05-29,498.10,503.56,498.10,502.18,2270000,502.18 1957-05-28,499.21,499.62,494.42,497.72,2070000,497.72 1957-05-27,504.02,505.07,497.89,499.21,2290000,499.21 1957-05-24,504.02,506.94,502.15,504.02,2340000,504.02 1957-05-23,504.43,506.04,500.90,504.02,2110000,504.02 1957-05-22,506.04,506.82,502.36,504.43,2060000,504.43 1957-05-21,505.98,507.90,503.32,506.04,2370000,506.04 1957-05-20,505.60,508.75,503.82,505.98,2300000,505.98 1957-05-17,504.84,508.87,503.58,505.60,2510000,505.60 1957-05-16,501.98,507.06,501.16,504.84,2690000,504.84 1957-05-15,500.46,504.87,498.27,501.98,2590000,501.98 1957-05-14,502.21,504.43,498.16,500.46,2580000,500.46 1957-05-13,498.51,503.93,498.51,502.21,2720000,502.21 1957-05-10,496.76,500.46,495.15,498.30,2430000,498.30 1957-05-09,496.73,499.21,494.80,496.76,2520000,496.76 1957-05-08,494.68,498.13,493.34,496.73,2590000,496.73 1957-05-07,496.32,497.78,492.29,494.68,2300000,494.68 1957-05-06,497.54,500.29,494.86,496.32,2210000,496.32 1957-05-03,498.56,501.34,495.00,497.54,2390000,497.54 1957-05-02,495.76,501.13,494.98,498.56,2860000,498.56 1957-05-01,494.36,498.04,493.05,495.76,2310000,495.76 1957-04-30,493.95,496.49,491.62,494.36,2200000,494.36 1957-04-29,491.50,496.67,490.77,493.95,2290000,493.95 1957-04-26,492.29,493.92,488.41,491.50,2380000,491.50 1957-04-25,493.66,496.03,490.19,492.29,2640000,492.29 1957-04-24,491.88,497.22,489.87,493.66,2990000,493.66 1957-04-23,488.79,494.10,487.12,491.88,2840000,491.88 1957-04-22,488.03,491.59,485.90,488.79,2560000,488.79 1957-04-18,485.02,489.43,483.51,488.03,2480000,488.03 1957-04-17,484.32,487.47,482.22,485.02,2290000,485.02 1957-04-16,485.84,487.15,482.69,484.32,1890000,484.32 1957-04-15,486.72,488.53,483.62,485.84,2010000,485.84 1957-04-12,484.70,488.76,482.78,486.72,2370000,486.72 1957-04-11,485.17,486.86,481.75,484.70,2350000,484.70 1957-04-10,482.66,486.98,482.02,485.17,2920000,485.17 1957-04-09,479.04,484.32,478.40,482.66,2400000,482.66 1957-04-08,477.61,480.44,475.86,479.04,1950000,479.04 1957-04-05,477.43,479.45,475.68,477.61,1830000,477.61 1957-04-04,478.31,479.62,475.45,477.43,1820000,477.43 1957-04-03,477.55,481.08,476.73,478.31,2160000,478.31 1957-04-02,474.98,478.40,473.70,477.55,2300000,477.55 1957-04-01,474.81,476.82,473.09,474.98,1620000,474.98 1957-03-29,475.01,477.32,473.14,474.81,1650000,474.81 1957-03-28,473.12,476.88,472.27,475.01,1930000,475.01 1957-03-27,472.24,475.10,471.01,473.12,1710000,473.12 1957-03-26,471.51,474.02,468.91,472.24,1660000,472.24 1957-03-25,472.94,474.05,469.70,471.51,1590000,471.51 1957-03-22,474.02,475.57,471.04,472.94,1610000,472.94 1957-03-21,473.93,475.98,471.36,474.02,1630000,474.02 1957-03-20,473.93,476.38,472.85,473.93,1830000,473.93 1957-03-19,472.30,475.22,471.01,473.93,1540000,473.93 1957-03-18,474.28,474.66,470.17,472.30,1450000,472.30 1957-03-15,473.93,476.12,471.19,474.28,1600000,474.28 1957-03-14,472.53,475.45,471.39,473.93,1580000,473.93 1957-03-13,470.31,474.37,469.88,472.53,1840000,472.53 1957-03-12,469.50,471.45,466.69,470.31,1600000,470.31 1957-03-11,471.63,472.06,467.37,469.50,1650000,469.50 1957-03-08,474.17,474.40,470.40,471.63,1630000,471.63 1957-03-07,474.87,477.58,472.62,474.17,1830000,474.17 1957-03-06,472.88,475.77,470.43,474.87,1840000,474.87 1957-03-05,471.48,475.13,471.04,472.88,1860000,472.88 1957-03-04,468.91,472.97,468.15,471.48,1890000,471.48 1957-03-01,464.62,469.76,463.66,468.91,1700000,468.91 1957-02-28,466.26,468.50,463.51,464.62,1620000,464.62 1957-02-27,467.72,469.00,463.95,466.26,1620000,466.26 1957-02-26,466.90,470.34,465.76,467.72,1580000,467.72 1957-02-25,466.93,469.00,463.40,466.90,1710000,466.90 1957-02-21,469.00,470.08,465.15,466.93,1680000,466.93 1957-02-20,466.84,471.10,465.99,469.00,1790000,469.00 1957-02-19,467.40,469.23,463.63,466.84,1670000,466.84 1957-02-18,468.07,471.22,465.21,467.40,1800000,467.40 1957-02-15,461.56,468.83,459.37,468.07,2060000,468.07 1957-02-14,462.14,467.04,459.69,461.56,2220000,461.56 1957-02-13,454.87,462.70,454.87,462.14,2380000,462.14 1957-02-12,457.44,459.87,453.07,454.82,2550000,454.82 1957-02-11,465.50,465.50,456.01,457.44,2740000,457.44 1957-02-08,468.71,468.88,463.19,466.29,2120000,466.29 1957-02-07,470.81,473.93,467.42,468.71,1840000,468.71 1957-02-06,469.96,472.02,466.40,470.81,2110000,470.81 1957-02-05,476.18,476.18,466.65,469.96,2610000,469.96 1957-02-04,477.22,479.54,475.49,477.19,1750000,477.19 1957-02-01,479.16,479.76,474.43,477.22,1680000,477.22 1957-01-31,480.53,483.02,478.04,479.16,1920000,479.16 1957-01-30,476.92,481.95,476.42,480.53,1950000,480.53 1957-01-29,474.59,478.31,473.19,476.92,1800000,476.92 1957-01-28,478.34,478.48,473.14,474.59,1700000,474.59 1957-01-25,481.30,481.76,475.19,478.34,2010000,478.34 1957-01-24,479.93,483.57,478.34,481.30,1910000,481.30 1957-01-23,477.49,481.24,475.25,479.93,1920000,479.93 1957-01-22,475.90,480.45,475.71,477.49,1920000,477.49 1957-01-21,477.46,478.29,471.06,475.90,2740000,475.90 1957-01-18,484.01,486.42,476.23,477.46,2400000,477.46 1957-01-17,485.05,486.91,481.08,484.01,2140000,484.01 1957-01-16,484.75,488.28,483.19,485.05,2210000,485.05 1957-01-15,489.29,490.41,482.94,484.75,2370000,484.75 1957-01-14,493.81,494.49,487.65,489.29,2350000,489.29 1957-01-11,495.51,497.78,492.11,493.81,2340000,493.81 1957-01-10,493.21,498.00,492.74,495.51,2470000,495.51 1957-01-09,493.86,496.90,490.77,493.21,2330000,493.21 1957-01-08,495.20,497.20,491.29,493.86,2230000,493.86 1957-01-07,498.22,500.90,491.78,495.20,2500000,495.20 1957-01-04,499.20,502.57,496.24,498.22,2710000,498.22 1957-01-03,496.03,501.56,494.16,499.20,2260000,499.20 1957-01-02,499.47,501.01,492.06,496.03,1960000,496.03 1956-12-31,496.41,501.56,494.96,499.47,3680000,499.47 1956-12-28,496.38,498.68,493.89,496.41,2790000,496.41 1956-12-27,496.74,499.39,494.79,496.38,2420000,496.38 1956-12-26,494.38,500.32,494.00,496.74,2440000,496.74 1956-12-21,490.44,496.22,489.10,494.38,2380000,494.38 1956-12-20,493.81,494.57,489.29,490.44,2060000,490.44 1956-12-19,495.09,497.15,492.19,493.81,1900000,493.81 1956-12-18,493.75,497.70,491.97,495.09,2370000,495.09 1956-12-17,492.08,497.26,490.82,493.75,2500000,493.75 1956-12-14,490.47,493.75,488.69,492.08,2450000,492.08 1956-12-13,487.51,492.77,486.03,490.47,2370000,490.47 1956-12-12,490.36,491.75,486.06,487.51,2180000,487.51 1956-12-11,493.18,494.44,487.68,490.36,2210000,490.36 1956-12-10,494.79,498.95,492.14,493.18,2600000,493.18 1956-12-07,492.74,497.09,491.21,494.79,2400000,494.79 1956-12-06,488.55,495.18,487.32,492.74,2470000,492.74 1956-12-05,481.38,490.71,480.61,488.55,2360000,488.55 1956-12-04,480.61,483.82,479.08,481.38,2180000,481.38 1956-12-03,474.26,482.80,474.26,480.61,2570000,480.61 1956-11-30,467.09,474.59,467.09,472.78,2300000,472.78 1956-11-29,466.10,468.10,460.41,466.62,2440000,466.62 1956-11-28,470.18,472.81,465.20,466.10,2190000,466.10 1956-11-27,470.29,472.18,467.25,470.18,2130000,470.18 1956-11-26,472.56,476.89,468.73,470.29,2230000,470.29 1956-11-23,467.91,473.69,466.73,472.56,1880000,472.56 1956-11-21,470.07,472.37,466.16,467.91,2310000,467.91 1956-11-20,474.56,475.66,468.16,470.07,2240000,470.07 1956-11-19,480.67,481.05,472.54,474.56,2560000,474.56 1956-11-16,480.20,483.51,478.15,480.67,1820000,480.67 1956-11-15,482.36,486.28,479.11,480.20,2210000,480.20 1956-11-14,486.69,487.27,479.96,482.36,2290000,482.36 1956-11-13,487.05,490.71,484.99,486.69,2140000,486.69 1956-11-12,485.35,488.91,483.60,487.05,1600000,487.05 1956-11-09,488.72,490.50,483.57,485.35,1690000,485.35 1956-11-08,491.15,491.32,483.21,488.72,1970000,488.72 1956-11-07,495.37,500.52,490.22,491.15,2650000,491.15 1956-11-05,490.47,497.70,488.72,495.37,2830000,495.37 1956-11-02,487.62,493.40,486.42,490.47,2180000,490.47 1956-11-01,480.23,488.72,480.23,487.62,1890000,487.62 1956-10-31,486.47,486.47,476.83,479.85,2280000,479.85 1956-10-30,486.94,488.58,483.40,486.47,1830000,486.47 1956-10-29,486.06,494.11,484.83,486.94,2420000,486.94 1956-10-26,481.08,487.07,480.91,486.06,1800000,486.06 1956-10-25,482.67,483.30,478.29,481.08,1580000,481.08 1956-10-24,485.05,485.90,481.05,482.67,1640000,482.67 1956-10-23,485.27,487.05,483.19,485.05,1390000,485.05 1956-10-22,486.12,487.98,483.95,485.27,1430000,485.27 1956-10-19,486.31,489.46,484.45,486.12,1720000,486.12 1956-10-18,484.66,487.35,481.57,486.31,1640000,486.31 1956-10-17,487.57,488.83,482.47,484.66,1640000,484.66 1956-10-16,489.40,490.58,485.10,487.57,1580000,487.57 1956-10-15,490.19,493.01,487.76,489.40,1610000,489.40 1956-10-12,488.06,490.93,486.36,490.19,1330000,490.19 1956-10-11,487.32,491.18,485.90,488.06,1760000,488.06 1956-10-10,481.32,487.54,480.37,487.32,1620000,487.32 1956-10-09,483.38,483.68,479.22,481.32,1220000,481.32 1956-10-08,482.39,486.47,481.54,483.38,1450000,483.38 1956-10-05,481.24,483.62,478.86,482.39,1580000,482.39 1956-10-04,482.04,483.98,478.37,481.24,1600000,481.24 1956-10-03,476.97,484.06,476.97,482.04,2180000,482.04 1956-10-02,468.73,477.11,468.73,475.41,2400000,475.41 1956-10-01,475.25,475.38,463.83,468.70,2600000,468.70 1956-09-28,479.76,481.13,474.23,475.25,1720000,475.25 1956-09-27,481.60,485.21,477.85,479.76,1770000,479.76 1956-09-26,481.08,483.46,474.86,481.60,2370000,481.60 1956-09-25,487.70,488.31,479.57,481.08,2100000,481.08 1956-09-24,490.33,492.08,486.53,487.70,1840000,487.70 1956-09-21,488.00,493.86,488.00,490.33,2110000,490.33 1956-09-20,488.72,489.40,482.86,487.13,2150000,487.13 1956-09-19,493.45,494.66,487.68,488.72,2040000,488.72 1956-09-18,498.76,499.12,490.50,493.45,2200000,493.45 1956-09-17,500.32,502.40,496.49,498.76,1940000,498.76 1956-09-14,499.69,503.39,497.94,500.32,2110000,500.32 1956-09-13,499.97,502.92,497.56,499.69,2000000,499.69 1956-09-12,502.16,504.43,498.43,499.97,1930000,499.97 1956-09-11,505.56,505.63,499.26,502.16,1920000,502.16 1956-09-10,506.76,509.79,503.92,505.56,1860000,505.56 1956-09-07,509.49,509.55,504.39,506.76,1690000,506.76 1956-09-06,509.82,512.25,507.74,509.49,1550000,509.49 1956-09-05,507.66,512.68,507.34,509.82,2130000,509.82 1956-09-04,502.04,508.32,501.17,507.66,1790000,507.66 1956-08-31,495.96,503.51,495.96,502.04,1620000,502.04 1956-08-30,499.91,499.91,492.19,495.96,2050000,495.96 1956-08-29,503.05,504.25,498.85,500.90,1530000,500.90 1956-08-28,505.70,507.74,502.26,503.05,1400000,503.05 1956-08-27,507.91,510.28,504.58,505.70,1420000,505.70 1956-08-24,507.06,510.77,505.83,507.91,1530000,507.91 1956-08-23,502.34,508.95,502.23,507.06,1590000,507.06 1956-08-22,505.43,507.72,501.60,502.34,1570000,502.34 1956-08-21,509.25,509.25,500.84,505.43,2440000,505.43 1956-08-20,515.79,516.26,510.12,511.24,1770000,511.24 1956-08-17,517.19,518.52,514.13,515.79,1720000,515.79 1956-08-16,517.70,519.42,514.73,517.19,1790000,517.19 1956-08-15,517.27,521.11,516.01,517.70,2000000,517.70 1956-08-14,514.40,519.23,512.77,517.27,1790000,517.27 1956-08-13,517.38,518.25,512.08,514.40,1730000,514.40 1956-08-10,519.04,519.67,513.15,517.38,2040000,517.38 1956-08-09,518.74,523.24,517.13,519.04,2550000,519.04 1956-08-08,515.88,521.41,515.33,518.74,2480000,518.74 1956-08-07,513.88,518.30,511.67,515.88,2180000,515.88 1956-08-06,518.85,518.85,509.65,513.88,2280000,513.88 1956-08-03,520.95,522.72,517.51,520.27,2210000,520.27 1956-08-02,518.69,523.33,516.83,520.95,2530000,520.95 1956-08-01,517.81,521.50,515.55,518.69,2230000,518.69 1956-07-31,513.42,519.97,512.85,517.81,2520000,517.81 1956-07-30,512.30,515.22,508.59,513.42,2100000,513.42 1956-07-27,515.85,516.50,507.85,512.30,2240000,512.30 1956-07-26,514.13,517.62,513.20,515.85,2060000,515.85 1956-07-25,513.17,516.80,511.67,514.13,2220000,514.13 1956-07-24,513.61,516.78,511.26,513.17,2040000,513.17 1956-07-23,514.57,516.01,511.65,513.61,1970000,513.61 1956-07-20,513.86,516.37,512.36,514.57,2020000,514.57 1956-07-19,513.39,515.96,511.65,513.86,1950000,513.86 1956-07-18,514.43,517.59,511.73,513.39,2530000,513.39 1956-07-17,512.98,516.78,510.91,514.43,2520000,514.43 1956-07-16,511.10,515.19,510.17,512.98,2260000,512.98 1956-07-13,507.44,512.36,506.46,511.10,2020000,511.10 1956-07-12,509.65,511.05,505.26,507.44,2180000,507.44 1956-07-11,508.34,511.92,507.20,509.65,2520000,509.65 1956-07-10,506.52,510.72,504.85,508.34,2450000,508.34 1956-07-09,504.14,509.08,503.24,506.52,2180000,506.52 1956-07-06,500.54,506.24,500.08,504.14,2180000,504.14 1956-07-05,495.74,502.51,494.67,500.54,2240000,500.54 1956-07-03,491.92,496.97,491.02,495.74,1840000,495.74 1956-07-02,492.78,493.65,488.36,491.92,1610000,491.92 1956-06-29,492.50,494.82,489.85,492.78,1780000,492.78 1956-06-28,492.04,494.34,488.91,492.50,1900000,492.50 1956-06-27,489.37,494.16,488.85,492.04,2090000,492.04 1956-06-26,486.43,490.77,484.83,489.37,1730000,489.37 1956-06-25,487.95,489.74,485.34,486.43,1500000,486.43 1956-06-22,488.26,491.46,486.12,487.95,1630000,487.95 1956-06-21,485.06,490.45,485.06,488.26,1820000,488.26 1956-06-20,484.52,487.05,482.70,485.00,1670000,485.00 1956-06-19,483.91,486.38,481.18,484.52,1430000,484.52 1956-06-18,485.91,487.30,482.90,483.91,1440000,483.91 1956-06-15,485.52,487.58,484.15,485.91,1550000,485.91 1956-06-14,487.08,488.02,483.99,485.52,1670000,485.52 1956-06-13,485.49,489.69,484.45,487.08,1760000,487.08 1956-06-12,479.41,486.10,478.70,485.49,1900000,485.49 1956-06-11,476.15,481.22,476.15,479.41,2000000,479.41 1956-06-08,480.83,480.83,467.99,475.29,3630000,475.29 1956-06-07,480.54,485.20,480.14,482.99,1630000,482.99 1956-06-06,483.19,483.36,477.95,480.54,1460000,480.54 1956-06-05,483.22,485.82,481.73,483.19,1650000,483.19 1956-06-04,480.63,484.60,479.48,483.22,1500000,483.22 1956-06-01,478.05,481.52,475.54,480.63,1440000,480.63 1956-05-31,477.68,483.53,476.43,478.05,2020000,478.05 1956-05-29,470.43,478.99,470.43,477.68,2430000,477.68 1956-05-28,472.49,474.16,463.85,468.81,2780000,468.81 1956-05-25,473.51,477.79,469.31,472.49,2570000,472.49 1956-05-24,480.16,481.81,471.71,473.51,2600000,473.51 1956-05-23,484.13,487.60,479.38,480.16,2140000,480.16 1956-05-22,490.08,490.08,481.86,484.13,2290000,484.13 1956-05-21,496.13,496.13,488.93,491.62,1940000,491.62 1956-05-18,496.63,500.65,494.75,496.39,2020000,496.39 1956-05-17,492.69,497.93,492.22,496.63,1970000,496.63 1956-05-16,494.83,497.67,490.97,492.69,2080000,492.69 1956-05-15,497.28,497.86,490.73,494.83,2650000,494.83 1956-05-14,501.25,505.79,496.00,497.28,2440000,497.28 1956-05-11,501.56,503.18,497.57,501.25,2450000,501.25 1956-05-10,508.16,509.44,499.86,501.56,2850000,501.56 1956-05-09,509.13,511.74,506.05,508.16,2550000,508.16 1956-05-08,512.89,513.62,508.03,509.13,2440000,509.13 1956-05-07,516.44,518.50,511.74,512.89,2550000,512.89 1956-05-04,514.03,519.80,513.49,516.44,2860000,516.44 1956-05-03,512.78,516.57,511.43,514.03,2640000,514.03 1956-05-02,513.96,515.08,510.93,512.78,2440000,512.78 1956-05-01,516.12,516.88,511.24,513.96,2500000,513.96 1956-04-30,512.03,518.42,511.19,516.12,2730000,516.12 1956-04-27,507.95,513.98,507.95,512.03,2760000,512.03 1956-04-26,503.86,508.92,503.86,507.12,2630000,507.12 1956-04-25,503.36,505.48,500.99,503.02,2270000,503.02 1956-04-24,507.28,508.95,501.77,503.36,2500000,503.36 1956-04-23,507.20,510.77,506.00,507.28,2440000,507.28 1956-04-20,504.33,508.66,503.91,507.20,2320000,507.20 1956-04-19,506.55,507.95,502.45,504.33,2210000,504.33 1956-04-18,507.95,511.03,504.61,506.55,2470000,506.55 1956-04-17,509.15,511.11,505.81,507.95,2330000,507.95 1956-04-16,509.99,512.83,506.99,509.15,2310000,509.15 1956-04-13,509.15,511.61,506.73,509.99,2450000,509.99 1956-04-12,512.70,515.03,508.27,509.15,2700000,509.15 1956-04-11,510.04,514.61,508.89,512.70,2440000,512.70 1956-04-10,516.72,516.72,508.24,510.04,2590000,510.04 1956-04-09,521.05,524.37,517.40,518.52,2760000,518.52 1956-04-06,516.57,522.12,516.28,521.05,2600000,521.05 1956-04-05,518.65,522.86,514.90,516.57,2950000,516.57 1956-04-04,515.91,519.88,513.54,518.65,2760000,518.65 1956-04-03,515.10,519.85,513.33,515.91,2760000,515.91 1956-04-02,511.79,519.10,510.67,515.10,3120000,515.10 1956-03-29,510.25,515.86,509.36,511.79,3480000,511.79 1956-03-28,508.68,511.61,506.21,510.25,2610000,510.25 1956-03-27,512.39,512.39,507.77,508.68,2540000,508.68 1956-03-26,513.03,515.05,509.81,512.42,2720000,512.42 1956-03-23,510.94,515.15,510.25,513.03,2980000,513.03 1956-03-22,507.92,512.21,505.85,510.94,2650000,510.94 1956-03-21,512.62,513.70,506.33,507.92,2930000,507.92 1956-03-20,509.76,514.69,507.13,512.62,2960000,512.62 1956-03-19,507.60,511.57,506.36,509.76,2570000,509.76 1956-03-16,507.50,509.61,504.67,507.60,3120000,507.60 1956-03-15,504.01,509.19,504.01,507.50,3270000,507.50 1956-03-14,499.36,505.60,499.36,503.88,3140000,503.88 1956-03-13,500.24,502.32,496.45,499.33,2790000,499.33 1956-03-12,497.84,502.03,496.59,500.24,3110000,500.24 1956-03-09,492.63,499.09,492.63,497.84,3430000,497.84 1956-03-08,491.26,493.95,489.82,492.36,2500000,492.36 1956-03-07,491.41,492.75,489.21,491.26,2380000,491.26 1956-03-06,491.68,494.42,489.77,491.41,2770000,491.41 1956-03-05,488.84,493.29,488.57,491.68,3090000,491.68 1956-03-02,486.69,491.07,485.88,488.84,2860000,488.84 1956-03-01,483.65,487.98,483.63,486.69,2410000,486.69 1956-02-29,485.71,491.09,482.77,483.65,3900000,483.65 1956-02-28,485.00,487.03,482.48,485.71,2540000,485.71 1956-02-27,485.66,488.06,482.50,485.00,2440000,485.00 1956-02-24,481.55,487.30,481.55,485.66,2890000,485.66 1956-02-23,477.03,482.75,477.03,481.50,2900000,481.50 1956-02-21,476.46,478.20,474.19,476.93,2240000,476.93 1956-02-20,477.05,480.21,473.28,476.46,2530000,476.46 1956-02-17,469.61,477.91,469.12,477.05,2840000,477.05 1956-02-16,470.64,471.79,467.80,469.61,1750000,469.61 1956-02-15,469.25,476.02,469.25,470.64,3000000,470.64 1956-02-14,467.17,468.24,463.89,465.72,1590000,465.72 1956-02-13,467.66,469.56,465.60,467.17,1420000,467.17 1956-02-10,467.22,470.25,465.14,467.66,1770000,467.66 1956-02-09,471.23,471.45,465.50,467.22,2080000,467.22 1956-02-08,476.56,478.52,470.08,471.23,2170000,471.23 1956-02-07,478.57,479.18,474.16,476.56,2060000,476.56 1956-02-06,477.44,481.80,475.88,478.57,2230000,478.57 1956-02-03,473.43,479.10,472.26,477.44,2110000,477.44 1956-02-02,473.28,475.51,470.54,473.43,1900000,473.43 1956-02-01,470.74,475.41,470.69,473.28,2010000,473.28 1956-01-31,467.56,471.99,466.78,470.74,1900000,470.74 1956-01-30,466.56,470.10,465.23,467.56,1830000,467.56 1956-01-27,466.82,468.73,463.33,466.56,1950000,466.56 1956-01-26,470.71,471.62,465.38,466.82,1840000,466.82 1956-01-25,467.93,472.57,467.93,470.71,1950000,470.71 1956-01-24,463.91,469.78,463.91,467.88,2160000,467.88 1956-01-23,464.40,465.85,458.21,462.35,2720000,462.35 1956-01-20,468.49,472.11,463.16,464.40,2430000,464.40 1956-01-19,472.89,473.43,466.60,468.49,2500000,468.49 1956-01-18,477.73,479.69,472.40,472.89,2110000,472.89 1956-01-17,476.24,480.55,475.12,477.73,2050000,477.73 1956-01-16,481.50,481.50,475.19,476.24,2260000,476.24 1956-01-13,481.80,484.41,479.96,481.80,2120000,481.80 1956-01-12,478.71,483.70,478.71,481.80,2330000,481.80 1956-01-11,476.12,481.33,475.85,478.42,2310000,478.42 1956-01-10,479.69,479.69,472.65,476.12,2640000,476.12 1956-01-09,485.68,487.91,478.62,479.74,2700000,479.74 1956-01-06,484.02,488.18,483.78,485.68,2570000,485.68 1956-01-05,484.00,487.25,482.31,484.02,2110000,484.02 1956-01-04,485.78,487.79,481.01,484.00,2290000,484.00 1956-01-03,488.40,490.92,484.27,485.78,2390000,485.78 1955-12-30,484.71,490.33,484.71,488.40,2820000,488.40 1955-12-29,484.22,487.18,482.77,484.56,2190000,484.56 1955-12-28,485.81,487.13,483.41,484.22,1990000,484.22 1955-12-27,486.59,488.91,484.00,485.81,2010000,485.81 1955-12-23,486.08,489.31,484.83,486.59,2090000,486.59 1955-12-22,485.49,489.45,484.56,486.08,2650000,486.08 1955-12-21,481.87,487.37,481.87,485.49,2540000,485.49 1955-12-20,481.80,483.87,478.59,481.84,2280000,481.84 1955-12-19,482.08,485.22,480.69,481.80,2380000,481.80 1955-12-16,480.72,484.17,479.87,482.08,2310000,482.08 1955-12-15,480.84,483.38,478.32,480.72,2260000,480.72 1955-12-14,484.29,485.48,479.70,480.84,2670000,480.84 1955-12-13,483.72,487.04,482.41,484.29,2430000,484.29 1955-12-12,487.42,487.42,482.41,483.72,2510000,483.72 1955-12-09,487.80,490.37,486.14,487.64,2660000,487.64 1955-12-08,486.35,489.68,484.83,487.80,2970000,487.80 1955-12-07,486.73,489.11,484.81,486.35,2480000,486.35 1955-12-06,487.16,490.56,485.55,486.73,2540000,486.73 1955-12-05,483.15,488.71,483.15,487.16,2440000,487.16 1955-12-02,481.39,483.88,478.87,482.72,2400000,482.72 1955-12-01,483.26,485.48,480.01,481.39,2370000,481.39 1955-11-30,482.60,485.95,481.53,483.26,2900000,483.26 1955-11-29,480.96,484.03,479.13,482.60,2370000,482.60 1955-11-28,482.88,485.81,479.87,480.96,2460000,480.96 1955-11-25,482.62,485.40,480.98,482.88,2190000,482.88 1955-11-23,481.91,485.97,480.72,482.62,2550000,482.62 1955-11-22,477.30,483.38,477.25,481.91,2270000,481.91 1955-11-21,482.91,483.48,476.42,477.30,1960000,477.30 1955-11-18,485.26,485.74,478.89,482.91,2320000,482.91 1955-11-17,487.38,488.99,484.17,485.26,2310000,485.26 1955-11-16,487.07,490.75,483.60,487.38,2460000,487.38 1955-11-15,484.88,490.35,480.84,487.07,2560000,487.07 1955-11-14,479.20,486.69,479.20,484.88,2760000,484.88 1955-11-11,472.52,478.01,472.21,476.54,2000000,476.54 1955-11-10,473.90,478.70,471.48,472.52,2550000,472.52 1955-11-09,470.58,476.68,469.29,473.90,2580000,473.90 1955-11-07,467.35,472.45,466.73,470.58,2230000,470.58 1955-11-04,461.97,468.70,461.03,467.35,2430000,467.35 1955-11-03,455.99,463.09,455.99,461.97,2260000,461.97 1955-11-02,454.89,456.18,452.12,454.92,1610000,454.92 1955-11-01,454.87,456.84,452.86,454.89,1590000,454.89 1955-10-31,454.85,458.31,453.84,454.87,1800000,454.87 1955-10-28,453.77,456.59,451.21,454.85,1720000,454.85 1955-10-27,455.72,458.97,452.31,453.77,1830000,453.77 1955-10-26,458.40,459.34,454.51,455.72,1660000,455.72 1955-10-25,460.82,463.02,457.48,458.40,1950000,458.40 1955-10-24,458.47,462.38,458.35,460.82,1820000,460.82 1955-10-21,457.66,460.39,455.49,458.47,1710000,458.47 1955-10-20,454.39,460.66,454.39,457.66,2160000,457.66 1955-10-19,448.76,454.44,448.76,453.09,1760000,453.09 1955-10-18,446.13,450.43,444.82,448.58,1550000,448.58 1955-10-17,444.68,450.75,443.68,446.13,1480000,446.13 1955-10-14,444.91,446.90,440.59,444.68,1640000,444.68 1955-10-13,445.58,451.30,443.68,444.91,1980000,444.91 1955-10-12,442.46,448.16,442.46,445.58,1900000,445.58 1955-10-11,441.14,444.04,433.19,438.59,3590000,438.59 1955-10-10,452.97,452.97,440.17,441.14,3100000,441.14 1955-10-07,458.19,458.74,451.05,454.41,2150000,454.41 1955-10-06,461.14,461.79,456.61,458.19,1690000,458.19 1955-10-05,458.85,464.60,458.79,461.14,1920000,461.14 1955-10-04,455.70,461.79,455.35,458.85,2020000,458.85 1955-10-03,465.40,465.40,454.85,455.70,2720000,455.70 1955-09-30,468.68,469.68,464.14,466.62,2140000,466.62 1955-09-29,472.61,474.08,467.37,468.68,2560000,468.68 1955-09-28,467.49,475.13,467.49,472.61,3780000,472.61 1955-09-27,456.22,467.42,456.22,465.93,5500000,465.93 1955-09-26,463.59,463.59,446.74,455.56,7720000,455.56 1955-09-23,485.96,489.94,483.44,487.45,2540000,487.45 1955-09-22,485.98,489.51,484.12,485.96,2550000,485.96 1955-09-21,483.67,488.02,482.97,485.98,2460000,485.98 1955-09-20,483.80,485.05,480.84,483.67,2090000,483.67 1955-09-19,483.67,487.79,482.42,483.80,2390000,483.80 1955-09-16,481.56,485.32,480.02,483.67,2540000,483.67 1955-09-15,482.90,485.73,480.04,481.56,2890000,481.56 1955-09-14,480.93,485.71,479.61,482.90,2570000,482.90 1955-09-13,476.51,482.76,475.54,480.93,2580000,480.93 1955-09-12,474.59,478.44,473.02,476.51,2520000,476.51 1955-09-09,475.06,476.87,472.48,474.59,2480000,474.59 1955-09-08,475.20,476.97,472.96,475.06,2470000,475.06 1955-09-07,476.24,478.50,473.00,475.20,2380000,475.20 1955-09-06,472.84,478.80,472.84,476.24,2360000,476.24 1955-09-02,469.63,473.52,469.47,472.53,1700000,472.53 1955-09-01,468.18,471.51,467.36,469.63,1860000,469.63 1955-08-31,464.80,469.51,464.80,468.18,1850000,468.18 1955-08-30,464.37,466.41,462.70,464.67,1740000,464.67 1955-08-29,463.70,466.62,463.08,464.37,1910000,464.37 1955-08-26,461.27,465.30,460.68,463.70,2200000,463.70 1955-08-25,459.39,462.93,458.40,461.27,2120000,461.27 1955-08-24,457.38,462.68,457.38,459.39,2140000,459.39 1955-08-23,452.55,457.88,451.17,457.35,1890000,457.35 1955-08-22,453.57,454.41,450.76,452.55,1430000,452.55 1955-08-19,452.53,454.66,450.67,453.57,1400000,453.57 1955-08-18,452.85,454.86,451.13,452.53,1560000,452.53 1955-08-17,453.26,454.66,449.88,452.85,1570000,452.85 1955-08-16,456.09,457.17,451.92,453.26,1520000,453.26 1955-08-15,457.01,458.87,453.84,456.09,1230000,456.09 1955-08-12,455.18,458.99,454.93,457.01,1530000,457.01 1955-08-11,450.29,456.22,449.75,455.18,1620000,455.18 1955-08-10,448.84,452.44,447.39,450.29,1580000,450.29 1955-08-09,453.30,453.30,445.67,448.84,2240000,448.84 1955-08-08,456.40,459.28,453.05,454.05,1730000,454.05 1955-08-05,454.18,458.44,453.62,456.40,1690000,456.40 1955-08-04,458.92,458.92,452.37,454.18,2210000,454.18 1955-08-03,460.82,463.60,459.17,460.98,2190000,460.98 1955-08-02,460.25,463.02,457.11,460.82,2260000,460.82 1955-08-01,465.85,467.48,458.71,460.25,2190000,460.25 1955-07-29,466.46,468.75,463.45,465.85,2070000,465.85 1955-07-28,468.45,470.74,463.26,466.46,2090000,466.46 1955-07-27,468.41,471.35,465.71,468.45,2170000,468.45 1955-07-26,468.02,471.73,465.14,468.41,2340000,468.41 1955-07-25,464.69,470.53,464.35,468.02,2500000,468.02 1955-07-22,461.07,466.28,460.73,464.69,2500000,464.69 1955-07-21,458.10,462.86,457.65,461.07,2530000,461.07 1955-07-20,456.72,459.89,454.34,458.10,2080000,458.10 1955-07-19,460.07,460.86,453.39,456.72,2300000,456.72 1955-07-18,460.23,463.45,458.08,460.07,2160000,460.07 1955-07-15,458.49,463.15,457.94,460.23,2230000,460.23 1955-07-14,457.40,460.71,454.82,458.49,1980000,458.49 1955-07-13,462.97,464.96,456.06,457.40,2360000,457.40 1955-07-12,464.24,468.99,460.62,462.97,2630000,462.97 1955-07-11,461.18,466.10,460.32,464.24,2420000,464.24 1955-07-08,460.23,465.08,456.68,461.18,2450000,461.18 1955-07-07,467.41,469.06,458.28,460.23,3300000,460.23 1955-07-06,460.89,471.15,460.89,467.41,3140000,467.41 1955-07-05,454.80,462.22,454.80,459.42,2680000,459.42 1955-07-01,451.38,456.11,449.50,453.82,2540000,453.82 1955-06-30,449.70,453.48,448.70,451.38,2370000,451.38 1955-06-29,449.02,451.13,444.74,449.70,2180000,449.70 1955-06-28,449.86,452.28,446.12,449.02,2180000,449.02 1955-06-27,448.93,451.53,446.64,449.86,2250000,449.86 1955-06-24,448.82,450.99,445.15,448.93,2410000,448.93 1955-06-23,447.37,451.49,445.67,448.82,2900000,448.82 1955-06-22,446.80,450.45,445.24,447.37,3010000,447.37 1955-06-21,444.38,448.52,443.27,446.80,2720000,446.80 1955-06-20,444.08,446.53,443.13,444.38,2490000,444.38 1955-06-17,442.48,445.04,440.96,444.08,2340000,444.08 1955-06-16,441.93,445.42,440.51,442.48,2760000,442.48 1955-06-15,438.45,443.47,438.45,441.93,2650000,441.93 1955-06-14,440.17,443.45,437.45,438.20,2860000,438.20 1955-06-13,437.72,442.23,437.65,440.17,2770000,440.17 1955-06-10,435.07,438.94,433.60,437.72,2470000,437.72 1955-06-09,436.95,439.80,434.32,435.07,2960000,435.07 1955-06-08,434.55,439.26,433.26,436.95,3300000,436.95 1955-06-07,431.49,437.99,430.93,434.55,3230000,434.55 1955-06-06,428.53,433.19,427.37,431.49,2560000,431.49 1955-06-03,425.88,430.25,425.88,428.53,2590000,428.53 1955-06-02,424.88,428.13,422.86,425.80,2610000,425.80 1955-06-01,424.86,426.75,422.48,424.88,2510000,424.88 1955-05-31,425.66,427.06,422.84,424.86,1990000,424.86 1955-05-27,424.95,427.71,423.68,425.66,2220000,425.66 1955-05-26,421.77,426.08,421.48,424.95,2260000,424.95 1955-05-25,420.39,423.44,419.63,421.77,2100000,421.77 1955-05-24,420.32,422.39,417.16,420.39,1650000,420.39 1955-05-23,422.89,425.22,419.75,420.32,1900000,420.32 1955-05-20,419.72,423.84,418.64,422.89,2240000,422.89 1955-05-19,417.83,421.57,417.16,419.72,2380000,419.72 1955-05-18,414.12,418.83,413.95,417.83,2010000,417.83 1955-05-17,415.01,417.05,412.60,414.12,1900000,414.12 1955-05-16,419.57,421.02,412.73,415.01,2160000,415.01 1955-05-13,418.20,421.98,417.03,419.57,1860000,419.57 1955-05-12,420.29,421.24,414.43,418.20,2830000,418.20 1955-05-11,423.80,425.02,419.03,420.29,2120000,420.29 1955-05-10,424.32,425.69,419.85,423.80,2150000,423.80 1955-05-09,423.84,426.32,422.43,424.32,2090000,424.32 1955-05-06,423.39,425.71,421.57,423.84,2250000,423.84 1955-05-05,422.54,425.56,421.22,423.39,2270000,423.39 1955-05-04,422.78,424.65,419.18,422.54,2220000,422.54 1955-05-03,426.30,426.84,420.81,422.78,2630000,422.78 1955-05-02,425.65,428.27,424.00,426.30,2220000,426.30 1955-04-29,423.19,426.99,422.82,425.65,2230000,425.65 1955-04-28,428.10,428.92,421.96,423.19,2550000,423.19 1955-04-27,430.64,432.07,425.41,428.10,2660000,428.10 1955-04-26,427.62,432.76,427.62,430.64,2720000,430.64 1955-04-25,425.52,428.34,420.52,426.86,2720000,426.86 1955-04-22,428.45,430.27,423.24,425.52,2800000,425.52 1955-04-21,428.62,431.18,425.54,428.45,2810000,428.45 1955-04-20,427.88,430.85,426.04,428.62,3090000,428.62 1955-04-19,428.42,429.25,425.30,427.88,2700000,427.88 1955-04-18,425.45,430.03,425.02,428.42,3080000,428.42 1955-04-15,422.46,427.23,421.71,425.45,3180000,425.45 1955-04-14,421.57,424.52,419.05,422.46,2890000,422.46 1955-04-13,420.94,424.19,419.87,421.57,2820000,421.57 1955-04-12,418.77,422.85,418.12,420.94,2770000,420.94 1955-04-11,418.20,420.89,416.27,418.77,2680000,418.77 1955-04-07,416.42,419.79,415.84,418.20,2330000,418.20 1955-04-06,415.90,419.33,414.75,416.42,2500000,416.42 1955-04-05,412.97,416.84,411.84,415.90,2100000,415.90 1955-04-04,413.84,416.71,412.10,412.97,2500000,412.97 1955-04-01,409.78,414.80,409.78,413.84,2660000,413.84 1955-03-31,410.13,413.21,407.96,409.70,2680000,409.70 1955-03-30,413.73,414.82,409.07,410.13,3410000,410.13 1955-03-29,412.91,415.10,411.39,413.73,2770000,413.73 1955-03-28,414.77,416.16,411.87,412.91,2540000,412.91 1955-03-25,414.49,416.64,412.06,414.77,2540000,414.77 1955-03-24,411.39,416.42,411.39,414.49,3170000,414.49 1955-03-23,405.68,411.32,405.68,410.87,2730000,410.87 1955-03-22,402.40,405.68,401.28,404.47,1910000,404.47 1955-03-21,404.75,406.16,401.64,402.40,2020000,402.40 1955-03-18,405.23,408.09,402.88,404.75,2050000,404.75 1955-03-17,403.14,406.14,401.41,405.23,2200000,405.23 1955-03-16,399.60,405.94,399.60,403.14,2900000,403.14 1955-03-15,391.36,399.67,391.18,399.28,3160000,399.28 1955-03-14,400.06,400.06,387.50,391.36,4220000,391.36 1955-03-11,406.66,406.66,398.56,401.08,3040000,401.08 1955-03-10,404.90,410.06,404.90,406.83,2760000,406.83 1955-03-09,409.13,409.22,400.84,404.90,3590000,404.90 1955-03-08,416.27,416.27,408.52,409.13,3160000,409.13 1955-03-07,419.68,420.85,415.69,416.84,2630000,416.84 1955-03-04,418.33,421.83,416.53,419.68,2770000,419.68 1955-03-03,417.18,420.61,416.25,418.33,3330000,418.33 1955-03-02,413.95,418.33,413.95,417.18,3370000,417.18 1955-03-01,411.87,416.10,411.24,413.71,2830000,413.71 1955-02-28,409.50,413.62,408.18,411.87,2620000,411.87 1955-02-25,410.30,411.17,406.35,409.50,2540000,409.50 1955-02-24,411.48,412.65,407.63,410.30,2920000,410.30 1955-02-23,411.28,413.04,409.72,411.48,3030000,411.48 1955-02-21,411.63,414.21,409.57,411.28,3010000,411.28 1955-02-18,410.41,414.62,409.07,411.63,3660000,411.63 1955-02-17,409.98,412.78,408.55,410.41,3030000,410.41 1955-02-16,411.95,414.27,408.91,409.98,3660000,409.98 1955-02-15,411.39,414.69,409.54,411.95,3510000,411.95 1955-02-14,413.99,415.42,410.26,411.39,2950000,411.39 1955-02-11,412.89,416.55,410.74,413.99,3260000,413.99 1955-02-10,410.41,414.90,410.41,412.89,3460000,412.89 1955-02-09,405.70,411.34,405.20,410.32,3360000,410.32 1955-02-08,409.59,410.78,403.73,405.70,3400000,405.70 1955-02-07,409.76,412.93,407.20,409.59,3610000,409.59 1955-02-04,405.85,411.17,405.09,409.76,3370000,409.76 1955-02-03,407.11,408.20,403.51,405.85,2890000,405.85 1955-02-02,409.70,410.17,405.42,407.11,3210000,407.11 1955-02-01,408.83,411.63,406.72,409.70,3320000,409.70 1955-01-31,404.68,410.61,404.27,408.83,3500000,408.83 1955-01-28,402.60,406.38,400.67,404.68,3290000,404.68 1955-01-27,401.97,406.98,400.21,402.60,3500000,402.60 1955-01-26,398.19,405.31,398.19,401.97,3860000,401.97 1955-01-25,396.00,397.85,392.14,397.00,3230000,397.00 1955-01-24,395.90,397.89,394.09,396.00,2910000,396.00 1955-01-21,393.03,397.64,392.50,395.90,2690000,395.90 1955-01-20,392.31,394.35,390.66,393.03,2210000,393.03 1955-01-19,391.19,395.81,391.19,392.31,2760000,392.31 1955-01-18,388.20,391.76,385.65,390.98,3020000,390.98 1955-01-17,396.54,396.75,387.54,388.20,3360000,388.20 1955-01-14,398.34,399.70,394.88,396.54,2630000,396.54 1955-01-13,399.78,402.12,396.28,398.34,3350000,398.34 1955-01-12,400.25,401.86,396.60,399.78,3400000,399.78 1955-01-11,400.89,402.88,398.02,400.25,3680000,400.25 1955-01-10,397.72,402.24,397.72,400.89,4300000,400.89 1955-01-07,391.91,396.88,391.91,395.60,4030000,395.60 1955-01-06,397.19,397.19,387.09,391.89,5300000,391.89 1955-01-05,405.39,405.39,396.41,397.24,4640000,397.24 1955-01-04,408.89,409.21,401.84,406.17,4420000,406.17 1955-01-03,404.39,412.47,403.58,408.89,4570000,408.89 1954-12-31,401.97,407.17,401.48,404.39,3840000,404.39 1954-12-30,401.97,404.41,399.42,401.97,3590000,401.97 1954-12-29,398.51,404.21,398.17,401.97,4430000,401.97 1954-12-28,393.88,399.61,392.06,398.51,3660000,398.51 1954-12-27,397.15,397.77,392.33,393.88,2970000,393.88 1954-12-23,397.07,398.85,395.28,397.15,3310000,397.15 1954-12-22,398.11,399.97,395.73,397.07,3460000,397.07 1954-12-21,397.32,400.04,395.35,398.11,3630000,398.11 1954-12-20,394.94,399.59,393.31,397.32,3770000,397.32 1954-12-17,393.14,396.34,391.85,394.94,3730000,394.94 1954-12-16,389.28,394.54,389.28,393.14,3390000,393.14 1954-12-15,387.03,389.96,385.03,388.92,2740000,388.92 1954-12-14,389.79,390.98,386.56,387.03,2650000,387.03 1954-12-13,390.08,391.87,387.73,389.79,2750000,389.79 1954-12-10,391.53,393.54,388.41,390.08,3250000,390.08 1954-12-09,393.08,393.56,388.75,391.53,3300000,391.53 1954-12-08,393.88,395.43,390.85,393.08,4150000,393.08 1954-12-07,392.48,395.67,390.32,393.88,3820000,393.88 1954-12-06,389.77,394.01,389.77,392.48,3960000,392.48 1954-12-03,385.63,391.74,385.58,389.60,3790000,389.60 1954-12-02,384.04,388.05,382.80,385.63,3190000,385.63 1954-12-01,386.77,388.68,381.81,384.04,3100000,384.04 1954-11-30,388.51,391.21,385.67,386.77,3440000,386.77 1954-11-29,387.79,390.72,385.90,388.51,3300000,388.51 1954-11-26,384.63,388.96,384.06,387.79,3010000,387.79 1954-11-24,382.74,387.05,382.47,384.63,3990000,384.63 1954-11-23,379.47,384.29,378.65,382.74,3690000,382.74 1954-11-22,378.01,381.21,376.82,379.47,3000000,379.47 1954-11-19,377.44,380.34,375.29,378.01,3130000,378.01 1954-11-18,379.69,381.32,376.29,377.44,3530000,377.44 1954-11-17,379.39,382.06,377.46,379.69,3830000,379.69 1954-11-16,376.74,380.77,373.85,379.39,3260000,379.39 1954-11-15,377.10,379.20,374.04,376.74,3080000,376.74 1954-11-12,374.91,379.73,374.19,377.10,3720000,377.10 1954-11-11,371.88,375.89,371.47,374.91,2960000,374.91 1954-11-10,371.07,374.00,368.88,371.88,2070000,371.88 1954-11-09,369.46,372.83,368.52,371.07,3240000,371.07 1954-11-08,366.00,371.60,365.28,369.46,3180000,369.46 1954-11-05,366.95,368.31,363.90,366.00,2950000,366.00 1954-11-04,361.99,368.50,361.99,366.95,3140000,366.95 1954-11-03,355.45,363.30,355.45,361.50,2700000,361.50 1954-11-01,352.14,354.90,350.16,353.96,1790000,353.96 1954-10-29,354.56,354.71,350.72,352.14,1900000,352.14 1954-10-28,355.73,357.55,353.60,354.56,2190000,354.56 1954-10-27,356.32,358.00,354.01,355.73,2030000,355.73 1954-10-26,356.34,358.29,354.73,356.32,2010000,356.32 1954-10-25,358.61,360.82,355.15,356.34,2340000,356.34 1954-10-22,358.08,360.31,356.81,358.61,2080000,358.61 1954-10-21,357.42,360.46,356.60,358.08,2320000,358.08 1954-10-20,354.75,358.72,354.69,357.42,2380000,357.42 1954-10-19,354.35,356.34,353.11,354.75,1900000,354.75 1954-10-18,353.20,355.64,351.84,354.35,1790000,354.35 1954-10-15,354.69,355.64,351.54,353.20,2250000,353.20 1954-10-14,358.91,359.74,353.94,354.69,2540000,354.69 1954-10-13,359.57,360.71,357.51,358.91,2070000,358.91 1954-10-12,361.12,361.12,357.95,359.57,1620000,359.57 1954-10-11,363.77,364.98,360.37,361.43,2100000,361.43 1954-10-08,363.79,366.04,362.35,363.77,2120000,363.77 1954-10-07,364.43,366.40,362.30,363.79,1810000,363.79 1954-10-06,363.37,366.02,362.43,364.43,2570000,364.43 1954-10-05,362.73,365.11,361.75,363.37,2300000,363.37 1954-10-04,359.88,364.21,359.35,362.73,2000000,362.73 1954-10-01,360.46,361.62,358.59,359.88,1850000,359.88 1954-09-30,361.73,362.98,358.95,360.46,1840000,360.46 1954-09-29,363.32,364.21,360.39,361.73,1810000,361.73 1954-09-28,362.26,366.02,361.12,363.32,1800000,363.32 1954-09-27,361.67,364.00,361.03,362.26,2190000,362.26 1954-09-24,359.63,362.90,357.97,361.67,2340000,361.67 1954-09-23,358.36,361.71,357.08,359.63,2340000,359.63 1954-09-22,356.40,359.46,356.13,358.36,2260000,358.36 1954-09-21,353.48,356.91,352.24,356.40,1770000,356.40 1954-09-20,355.32,357.32,353.11,353.48,2060000,353.48 1954-09-17,352.37,356.40,352.22,355.32,2250000,355.32 1954-09-16,350.63,353.43,349.06,352.37,1880000,352.37 1954-09-15,351.78,353.45,348.59,350.63,2110000,350.63 1954-09-14,351.10,353.92,350.06,351.78,2120000,351.78 1954-09-13,347.83,352.29,347.83,351.10,2030000,351.10 1954-09-10,346.73,349.38,345.50,347.83,1870000,347.83 1954-09-09,346.07,347.47,344.22,346.73,1700000,346.73 1954-09-08,345.37,348.25,344.92,346.07,1970000,346.07 1954-09-07,343.10,346.34,341.82,345.37,1860000,345.37 1954-09-03,341.15,343.80,340.23,343.10,1630000,343.10 1954-09-02,338.41,342.76,338.41,341.15,1600000,341.15 1954-09-01,335.80,340.42,334.97,338.13,1790000,338.13 1954-08-31,340.44,340.44,333.21,335.80,2640000,335.80 1954-08-30,344.48,345.07,339.53,341.25,1950000,341.25 1954-08-27,343.35,346.37,342.29,344.48,1740000,344.48 1954-08-26,344.60,345.35,341.10,343.35,2060000,343.35 1954-08-25,346.32,347.49,341.97,344.60,2280000,344.60 1954-08-24,347.64,348.19,344.26,346.32,2000000,346.32 1954-08-23,350.38,351.08,347.00,347.64,2020000,347.64 1954-08-20,349.89,352.20,348.59,350.38,2110000,350.38 1954-08-19,348.51,352.27,347.05,349.89,2320000,349.89 1954-08-18,348.38,350.00,344.77,348.51,2390000,348.51 1954-08-17,349.61,351.86,347.05,348.38,2900000,348.38 1954-08-16,346.64,350.91,346.11,349.61,2760000,349.61 1954-08-13,345.84,347.98,343.52,346.64,2500000,346.64 1954-08-12,346.41,348.53,344.31,345.84,2680000,345.84 1954-08-11,343.56,347.92,343.12,346.41,3440000,346.41 1954-08-10,340.87,345.60,340.61,343.56,2890000,343.56 1954-08-09,343.06,343.71,338.70,340.87,2280000,340.87 1954-08-06,347.02,347.02,339.64,343.06,3350000,343.06 1954-08-05,349.74,350.42,345.90,347.79,3150000,347.79 1954-08-04,349.61,351.50,345.75,349.74,3620000,349.74 1954-08-03,349.57,351.44,347.05,349.61,2970000,349.61 1954-08-02,347.92,351.29,346.22,349.57,2850000,349.57 1954-07-30,346.15,349.21,345.22,347.92,2800000,347.92 1954-07-29,345.11,347.77,344.01,346.15,2710000,346.15 1954-07-28,344.69,346.83,342.72,345.11,2740000,345.11 1954-07-27,343.39,346.41,342.06,344.69,2690000,344.69 1954-07-26,343.48,345.05,341.42,343.39,2110000,343.39 1954-07-23,342.97,344.92,341.51,343.48,2520000,343.48 1954-07-22,339.98,344.54,338.94,342.97,2890000,342.97 1954-07-21,337.62,341.06,336.46,339.98,2510000,339.98 1954-07-20,338.64,339.09,334.80,337.62,2580000,337.62 1954-07-19,339.96,340.87,336.96,338.64,2370000,338.64 1954-07-16,341.06,342.12,338.03,339.96,2540000,339.96 1954-07-15,340.44,343.01,339.17,341.06,3000000,341.06 1954-07-14,340.04,341.25,337.37,340.44,2520000,340.44 1954-07-13,340.91,342.82,338.53,340.04,2430000,340.04 1954-07-12,341.25,343.59,339.43,340.91,2330000,340.91 1954-07-09,339.81,342.91,337.62,341.25,2240000,341.25 1954-07-08,340.34,341.63,337.64,339.81,2080000,339.81 1954-07-07,341.12,343.20,338.15,340.34,2380000,340.34 1954-07-06,337.71,342.10,337.71,341.12,2560000,341.12 1954-07-02,334.12,338.64,333.00,337.66,1980000,337.66 1954-07-01,333.53,335.44,330.68,334.12,1860000,334.12 1954-06-30,336.90,337.96,332.20,333.53,1950000,333.53 1954-06-29,336.12,338.78,330.95,336.90,2580000,336.90 1954-06-28,332.53,337.30,331.29,336.12,1890000,336.12 1954-06-25,332.20,335.09,330.19,332.53,2060000,332.53 1954-06-24,330.72,333.84,329.58,332.20,2260000,332.20 1954-06-23,329.51,331.54,326.81,330.72,2090000,330.72 1954-06-22,328.56,330.99,327.40,329.51,2100000,329.51 1954-06-21,327.91,330.19,326.33,328.56,1820000,328.56 1954-06-18,327.21,329.07,325.73,327.91,1580000,327.91 1954-06-17,327.28,329.49,325.88,327.21,1810000,327.21 1954-06-16,325.21,328.67,325.00,327.28,2070000,327.28 1954-06-15,322.65,325.80,322.04,325.21,1630000,325.21 1954-06-14,322.09,323.54,320.67,322.65,1420000,322.65 1954-06-11,320.12,323.29,319.19,322.09,1630000,322.09 1954-06-10,319.27,321.85,318.20,320.12,1610000,320.12 1954-06-09,321.00,321.26,315.66,319.27,2360000,319.27 1954-06-08,327.92,327.92,319.94,321.00,2540000,321.00 1954-06-07,327.63,329.54,326.50,327.96,1520000,327.96 1954-06-04,328.63,329.84,326.54,327.63,1720000,327.63 1954-06-03,328.36,330.35,327.35,328.63,1810000,328.63 1954-06-02,328.67,330.45,327.06,328.36,1930000,328.36 1954-06-01,327.49,329.48,326.21,328.67,1850000,328.67 1954-05-28,326.37,328.57,324.83,327.49,1940000,327.49 1954-05-27,327.11,328.93,325.50,326.37,2230000,326.37 1954-05-26,325.02,328.26,324.26,327.11,2180000,327.11 1954-05-25,326.09,326.80,323.27,325.02,2050000,325.02 1954-05-24,326.09,327.94,324.59,326.09,2330000,326.09 1954-05-21,323.88,327.43,323.82,326.09,2620000,326.09 1954-05-20,323.21,325.42,322.40,323.88,2070000,323.88 1954-05-19,324.14,325.64,321.79,323.21,2170000,323.21 1954-05-18,323.33,326.29,322.90,324.14,2250000,324.14 1954-05-17,322.50,324.69,321.10,323.33,2040000,323.33 1954-05-14,320.39,323.39,319.39,322.50,1970000,322.50 1954-05-13,321.61,323.80,319.21,320.39,2340000,320.39 1954-05-12,319.74,322.70,318.68,321.61,2210000,321.61 1954-05-11,321.32,322.03,318.22,319.74,1770000,319.74 1954-05-10,321.30,322.66,320.02,321.32,1800000,321.32 1954-05-07,320.41,323.13,319.54,321.30,2070000,321.30 1954-05-06,317.93,321.53,316.80,320.41,1980000,320.41 1954-05-05,319.82,320.94,316.45,317.93,2020000,317.93 1954-05-04,319.35,321.65,316.84,319.82,1990000,319.82 1954-05-03,319.33,320.94,317.37,319.35,1870000,319.35 1954-04-30,318.22,321.71,317.24,319.33,2450000,319.33 1954-04-29,314.02,319.76,314.02,318.22,2150000,318.22 1954-04-28,313.49,315.01,309.37,313.75,2120000,313.75 1954-04-27,314.54,315.74,310.91,313.49,1970000,313.49 1954-04-26,313.37,316.23,312.35,314.54,2150000,314.54 1954-04-23,311.48,314.34,311.16,313.37,1990000,313.37 1954-04-22,310.91,312.54,309.05,311.48,1750000,311.48 1954-04-21,311.89,313.94,309.92,310.91,1870000,310.91 1954-04-20,311.78,314.38,310.02,311.89,1860000,311.89 1954-04-19,313.77,316.15,310.65,311.78,2430000,311.78 1954-04-15,311.76,314.44,310.71,313.77,2200000,313.77 1954-04-14,308.98,312.96,308.38,311.76,2330000,311.76 1954-04-13,309.19,310.93,307.58,308.98,2020000,308.98 1954-04-12,309.39,310.77,307.44,309.19,1790000,309.19 1954-04-09,307.79,310.32,307.20,309.39,2360000,309.39 1954-04-08,305.82,309.57,305.82,307.79,2300000,307.79 1954-04-07,304.26,306.45,301.70,305.41,1830000,305.41 1954-04-06,307.04,308.50,302.78,304.26,2120000,304.26 1954-04-05,306.67,308.46,305.27,307.04,1710000,307.04 1954-04-02,306.27,307.95,304.52,306.67,1830000,306.67 1954-04-01,303.51,307.30,303.18,306.27,2270000,306.27 1954-03-31,301.01,305.15,301.01,303.51,2690000,303.51 1954-03-30,300.06,302.57,299.47,300.89,2130000,300.89 1954-03-29,299.08,301.46,297.66,300.06,1870000,300.06 1954-03-26,296.67,300.10,296.67,299.08,1550000,299.08 1954-03-25,296.89,297.68,294.76,296.40,1720000,296.40 1954-03-24,299.02,299.73,295.59,296.89,1900000,296.89 1954-03-23,301.60,302.49,298.39,299.02,2180000,299.02 1954-03-22,301.44,302.80,299.49,301.60,1800000,301.60 1954-03-19,300.12,302.65,300.12,301.44,1930000,301.44 1954-03-18,298.31,301.44,297.99,300.10,2020000,300.10 1954-03-17,298.09,299.22,296.14,298.31,1740000,298.31 1954-03-16,298.88,300.50,297.34,298.09,1540000,298.09 1954-03-15,299.71,300.12,296.81,298.88,1680000,298.88 1954-03-12,300.83,302.17,298.76,299.71,1980000,299.71 1954-03-11,299.59,301.68,298.76,300.83,2050000,300.83 1954-03-10,299.45,300.95,298.01,299.59,1870000,299.59 1954-03-09,298.64,300.50,297.09,299.45,1630000,299.45 1954-03-08,299.45,300.66,297.44,298.64,1650000,298.64 1954-03-05,297.48,300.68,296.50,299.45,2030000,299.45 1954-03-04,297.03,298.80,295.37,297.48,1830000,297.48 1954-03-03,297.48,299.65,295.79,297.03,2240000,297.03 1954-03-02,296.55,298.66,294.78,297.48,1980000,297.48 1954-03-01,294.54,297.80,294.23,296.55,2040000,296.55 1954-02-26,291.41,295.19,290.78,294.54,1910000,294.54 1954-02-25,289.54,292.04,289.06,291.41,1470000,291.41 1954-02-24,290.03,290.42,287.84,289.54,1350000,289.54 1954-02-23,291.07,291.98,289.00,290.03,1470000,290.03 1954-02-19,291.51,292.59,289.87,291.07,1510000,291.07 1954-02-18,290.11,292.67,289.08,291.51,1500000,291.51 1954-02-17,289.61,290.92,287.37,290.11,1740000,290.11 1954-02-16,292.55,292.95,288.61,289.61,1870000,289.61 1954-02-15,293.99,294.92,291.93,292.55,2080000,292.55 1954-02-12,292.45,294.78,291.90,293.99,1730000,293.99 1954-02-11,292.95,294.84,291.27,292.45,1860000,292.45 1954-02-10,293.79,294.50,292.00,292.95,1790000,292.95 1954-02-09,293.58,294.84,291.94,293.79,1880000,293.79 1954-02-08,293.97,294.92,292.20,293.58,2180000,293.58 1954-02-05,294.03,295.43,292.53,293.97,2030000,293.97 1954-02-04,292.32,295.10,292.06,294.03,2040000,294.03 1954-02-03,291.17,292.99,289.20,292.32,1690000,292.32 1954-02-02,291.84,291.88,289.75,291.17,1420000,291.17 1954-02-01,292.39,293.50,290.46,291.84,1740000,291.84 1954-01-29,291.51,293.81,290.86,292.39,1950000,292.39 1954-01-28,292.22,293.12,289.81,291.51,1730000,291.51 1954-01-27,292.85,294.56,291.53,292.22,2020000,292.22 1954-01-26,290.40,293.44,290.11,292.85,2120000,292.85 1954-01-25,289.65,291.29,288.27,290.40,1860000,290.40 1954-01-22,289.48,290.86,288.18,289.65,1890000,289.65 1954-01-21,289.14,290.66,287.68,289.48,1780000,289.48 1954-01-20,288.27,290.15,287.58,289.14,1960000,289.14 1954-01-19,286.03,289.14,285.24,288.27,1840000,288.27 1954-01-18,286.72,287.78,284.69,286.03,1580000,286.03 1954-01-15,284.49,288.10,284.49,286.72,2180000,286.72 1954-01-14,283.03,285.67,282.83,284.49,1530000,284.49 1954-01-13,281.51,284.37,281.12,283.03,1420000,283.03 1954-01-12,279.87,282.54,279.46,281.51,1250000,281.51 1954-01-11,281.51,281.67,278.91,279.87,1220000,279.87 1954-01-08,282.60,283.01,280.39,281.51,1260000,281.51 1954-01-07,283.96,284.86,281.83,282.60,1540000,282.60 1954-01-06,284.19,285.67,283.05,283.96,1460000,283.96 1954-01-05,282.89,285.34,282.65,284.19,1520000,284.19 1954-01-04,280.90,283.48,279.72,282.89,1310000,282.89 1953-12-31,280.43,282.18,279.52,280.90,2490000,280.90 1953-12-30,278.30,281.08,277.92,280.43,2050000,280.43 1953-12-29,279.74,279.74,275.91,278.30,2140000,278.30 1953-12-28,280.92,281.25,278.81,279.91,1570000,279.91 1953-12-24,279.84,281.69,279.15,280.92,1270000,280.92 1953-12-23,279.99,281.11,278.14,279.84,1570000,279.84 1953-12-22,282.85,282.85,278.95,279.99,1720000,279.99 1953-12-21,283.54,284.69,282.38,282.99,1690000,282.99 1953-12-18,282.67,284.35,281.71,283.54,1550000,283.54 1953-12-17,282.87,284.55,281.57,282.67,1600000,282.67 1953-12-16,279.52,283.88,279.36,282.87,1880000,282.87 1953-12-15,279.26,280.27,277.49,279.52,1450000,279.52 1953-12-14,279.91,280.98,278.18,279.26,1540000,279.26 1953-12-11,279.89,280.76,278.38,279.91,1440000,279.91 1953-12-10,281.12,281.53,278.93,279.89,1420000,279.89 1953-12-09,281.45,282.54,279.78,281.12,1410000,281.12 1953-12-08,282.00,282.48,279.34,281.45,1390000,281.45 1953-12-07,282.71,283.50,281.20,282.00,1410000,282.00 1953-12-04,283.25,284.11,281.55,282.71,1390000,282.71 1953-12-03,282.81,285.20,282.10,283.25,1740000,283.25 1953-12-02,281.10,283.74,280.33,282.81,1850000,282.81 1953-12-01,281.37,282.08,279.46,281.10,1580000,281.10 1953-11-30,280.23,282.46,279.80,281.37,1960000,281.37 1953-11-27,277.78,280.78,277.19,280.23,1600000,280.23 1953-11-25,277.13,278.87,276.40,277.78,1540000,277.78 1953-11-24,275.42,277.61,274.26,277.13,1470000,277.13 1953-11-23,276.05,276.58,273.80,275.42,1410000,275.42 1953-11-20,276.09,277.47,274.79,276.05,1300000,276.05 1953-11-19,274.51,277.02,274.16,276.09,1420000,276.09 1953-11-18,273.88,275.52,273.05,274.51,1250000,274.51 1953-11-17,275.58,275.58,273.01,273.88,1250000,273.88 1953-11-16,277.53,278.57,275.48,275.93,1490000,275.93 1953-11-13,276.23,278.08,275.16,277.53,1540000,277.53 1953-11-12,275.89,277.65,274.63,276.23,1390000,276.23 1953-11-10,277.86,277.86,275.06,275.89,1340000,275.89 1953-11-09,278.83,279.93,276.80,278.26,1440000,278.26 1953-11-06,279.09,280.25,277.57,278.83,1700000,278.83 1953-11-05,276.82,280.33,276.80,279.09,1720000,279.09 1953-11-04,276.72,277.72,274.83,276.82,1480000,276.82 1953-11-02,275.81,278.18,274.83,276.72,1340000,276.72 1953-10-30,276.31,277.04,274.87,275.81,1400000,275.81 1953-10-29,274.14,276.94,273.92,276.31,1610000,276.31 1953-10-28,273.35,274.69,272.13,274.14,1260000,274.14 1953-10-27,274.33,274.33,271.85,273.35,1170000,273.35 1953-10-26,275.34,276.56,273.88,274.43,1340000,274.43 1953-10-23,274.89,276.52,274.12,275.34,1330000,275.34 1953-10-22,273.74,275.77,272.97,274.89,1330000,274.89 1953-10-21,273.90,274.61,272.30,273.74,1320000,273.74 1953-10-20,273.31,275.16,272.36,273.90,1280000,273.90 1953-10-19,272.80,273.78,271.14,273.31,1190000,273.31 1953-10-16,271.22,273.94,271.02,272.80,1620000,272.80 1953-10-15,267.51,271.81,267.41,271.22,1710000,271.22 1953-10-14,266.09,268.22,265.64,267.51,1290000,267.51 1953-10-13,267.04,267.69,265.36,266.09,1130000,266.09 1953-10-09,266.72,267.69,265.52,267.04,900000,267.04 1953-10-08,266.53,267.97,265.96,266.72,960000,266.72 1953-10-07,264.26,266.86,263.57,266.53,1010000,266.53 1953-10-06,265.25,265.25,262.45,264.26,1100000,264.26 1953-10-05,266.70,267.59,264.52,265.48,930000,265.48 1953-10-02,265.68,267.59,265.32,266.70,890000,266.70 1953-10-01,264.04,266.47,263.79,265.68,940000,265.68 1953-09-30,264.77,265.38,262.94,264.04,940000,264.04 1953-09-29,264.79,266.61,263.90,264.77,1170000,264.77 1953-09-28,263.31,265.86,263.16,264.79,1150000,264.79 1953-09-25,262.45,264.06,261.78,263.31,910000,263.31 1953-09-24,262.35,263.83,261.36,262.45,1020000,262.45 1953-09-23,261.40,263.83,261.40,262.35,1240000,262.35 1953-09-22,258.01,261.89,257.85,261.28,1300000,261.28 1953-09-21,258.78,260.00,257.36,258.01,1070000,258.01 1953-09-18,259.84,259.84,257.65,258.78,1190000,258.78 1953-09-17,259.07,261.30,258.42,259.88,1290000,259.88 1953-09-16,257.97,260.95,257.97,259.07,1570000,259.07 1953-09-15,255.49,258.68,254.36,257.67,2850000,257.67 1953-09-14,259.71,260.20,255.29,255.49,2550000,255.49 1953-09-11,262.37,262.37,258.94,259.71,1930000,259.71 1953-09-10,265.38,265.38,262.58,262.88,1010000,262.88 1953-09-09,265.42,266.23,264.56,265.48,860000,265.48 1953-09-08,264.34,266.23,264.04,265.42,740000,265.42 1953-09-04,263.61,265.09,262.70,264.34,770000,264.34 1953-09-03,263.96,264.65,262.50,263.61,900000,263.61 1953-09-02,262.54,265.21,262.43,263.96,1110000,263.96 1953-09-01,261.22,263.23,260.32,262.54,1580000,262.54 1953-08-31,265.70,265.70,260.83,261.22,2190000,261.22 1953-08-28,265.68,266.86,264.89,265.74,1060000,265.74 1953-08-27,266.51,267.45,264.65,265.68,1290000,265.68 1953-08-26,267.45,269.17,266.27,266.51,1060000,266.51 1953-08-25,268.70,268.83,265.92,267.45,1470000,267.45 1953-08-24,271.93,271.99,268.06,268.70,1320000,268.70 1953-08-21,271.73,272.80,270.81,271.93,850000,271.93 1953-08-20,271.50,272.66,270.41,271.73,860000,271.73 1953-08-19,273.01,273.01,270.06,271.50,1400000,271.50 1953-08-18,275.04,275.91,273.13,273.29,1030000,273.29 1953-08-17,275.71,276.13,273.92,275.04,910000,275.04 1953-08-14,276.74,277.33,274.89,275.71,1000000,275.71 1953-08-13,276.42,278.30,275.75,276.74,1040000,276.74 1953-08-12,275.30,276.92,273.98,276.42,990000,276.42 1953-08-11,275.32,276.05,273.72,275.30,940000,275.30 1953-08-10,275.54,276.48,274.39,275.32,1090000,275.32 1953-08-07,275.77,276.46,274.20,275.54,950000,275.54 1953-08-06,275.08,276.54,274.63,275.77,1200000,275.77 1953-08-05,275.40,275.40,273.19,275.08,1080000,275.08 1953-08-04,276.13,276.46,274.28,275.68,1000000,275.68 1953-08-03,275.38,277.19,274.71,276.13,1160000,276.13 1953-07-31,272.82,275.85,272.58,275.38,1320000,275.38 1953-07-30,270.49,273.41,270.49,272.82,1200000,272.82 1953-07-29,269.13,271.16,268.83,270.43,1000000,270.43 1953-07-28,268.46,269.52,267.30,269.13,1080000,269.13 1953-07-27,269.76,270.71,267.83,268.46,1210000,268.46 1953-07-24,269.94,270.92,268.79,269.76,890000,269.76 1953-07-23,269.39,271.14,268.89,269.94,1000000,269.94 1953-07-22,268.99,270.23,267.95,269.39,900000,269.39 1953-07-21,269.74,269.90,268.28,268.99,850000,268.99 1953-07-20,270.94,270.94,268.85,269.74,830000,269.74 1953-07-17,269.41,271.59,268.95,270.96,840000,270.96 1953-07-16,268.75,270.17,268.48,269.41,790000,269.41 1953-07-15,268.06,270.02,267.73,268.75,840000,268.75 1953-07-14,268.52,269.19,266.96,268.06,1030000,268.06 1953-07-13,271.06,271.16,268.08,268.52,1120000,268.52 1953-07-10,271.32,271.93,270.31,271.06,860000,271.06 1953-07-09,272.19,272.38,270.98,271.32,910000,271.32 1953-07-08,272.13,272.82,271.06,272.19,950000,272.19 1953-07-07,270.88,272.60,270.17,272.13,1030000,272.13 1953-07-06,270.53,271.71,269.94,270.88,820000,270.88 1953-07-03,270.23,271.44,269.41,270.53,830000,270.53 1953-07-02,269.39,271.20,269.15,270.23,1030000,270.23 1953-07-01,268.26,269.90,267.51,269.39,910000,269.39 1953-06-30,268.20,269.03,267.06,268.26,820000,268.26 1953-06-29,269.05,269.70,267.26,268.20,800000,268.20 1953-06-26,268.93,269.70,268.01,269.05,830000,269.05 1953-06-25,267.85,270.31,267.85,268.93,1160000,268.93 1953-06-24,268.48,269.11,266.80,267.79,1030000,267.79 1953-06-23,267.26,269.15,266.68,268.48,1050000,268.48 1953-06-22,265.88,268.70,265.88,267.26,1030000,267.26 1953-06-19,265.86,266.53,264.24,265.80,890000,265.80 1953-06-18,265.74,267.20,264.93,265.86,1010000,265.86 1953-06-17,262.88,266.57,262.88,265.74,1150000,265.74 1953-06-16,263.87,264.10,260.75,262.88,1370000,262.88 1953-06-15,265.78,266.41,263.12,263.87,1090000,263.87 1953-06-12,264.99,266.86,264.59,265.78,920000,265.78 1953-06-11,263.39,266.68,263.39,264.99,1220000,264.99 1953-06-10,263.39,264.18,260.89,263.35,1960000,263.35 1953-06-09,267.91,268.14,262.90,263.39,2200000,263.39 1953-06-08,268.32,269.46,267.28,267.91,1000000,267.91 1953-06-05,267.63,269.33,266.57,268.32,1160000,268.32 1953-06-04,269.60,269.98,266.17,267.63,1400000,267.63 1953-06-03,269.84,271.77,268.99,269.60,1050000,269.60 1953-06-02,268.40,270.41,267.00,269.84,1450000,269.84 1953-06-01,272.28,272.64,267.91,268.40,1490000,268.40 1953-05-29,271.48,273.07,270.88,272.28,920000,272.28 1953-05-28,273.76,273.76,270.65,271.48,1240000,271.48 1953-05-27,276.37,276.58,273.53,273.96,1330000,273.96 1953-05-26,277.43,277.43,275.12,276.37,1160000,276.37 1953-05-25,278.16,278.73,276.82,277.47,1180000,277.47 1953-05-22,278.51,279.28,276.72,278.16,1350000,278.16 1953-05-21,278.04,279.84,277.63,278.51,1590000,278.51 1953-05-20,275.91,278.53,275.12,278.04,1690000,278.04 1953-05-19,276.92,277.04,274.81,275.91,1120000,275.91 1953-05-18,277.90,278.14,275.75,276.92,1080000,276.92 1953-05-15,277.96,279.18,276.70,277.90,1200000,277.90 1953-05-14,276.80,278.71,276.25,277.96,1210000,277.96 1953-05-13,277.09,277.59,275.10,276.80,1120000,276.80 1953-05-12,278.79,278.91,276.29,277.09,1080000,277.09 1953-05-11,278.22,279.78,277.59,278.79,1010000,278.79 1953-05-08,277.43,278.55,275.85,278.22,1220000,278.22 1953-05-07,278.14,278.65,276.54,277.43,1110000,277.43 1953-05-06,278.22,279.32,276.94,278.14,1110000,278.14 1953-05-05,278.34,280.01,277.31,278.22,1290000,278.22 1953-05-04,275.66,279.46,275.52,278.34,1520000,278.34 1953-05-01,274.75,276.33,273.59,275.66,1200000,275.66 1953-04-30,275.38,276.07,273.31,274.75,1140000,274.75 1953-04-29,273.96,276.37,273.31,275.38,1310000,275.38 1953-04-28,272.70,274.59,271.32,273.96,1330000,273.96 1953-04-27,271.30,274.14,271.30,272.70,1400000,272.70 1953-04-24,270.73,272.30,269.25,271.26,1780000,271.26 1953-04-23,273.51,273.51,270.15,270.73,1920000,270.73 1953-04-22,275.48,275.93,272.86,273.55,1390000,273.55 1953-04-21,275.99,277.88,274.67,275.48,1250000,275.48 1953-04-20,274.41,276.62,272.80,275.99,1520000,275.99 1953-04-17,276.74,276.86,273.70,274.41,1430000,274.41 1953-04-16,277.35,278.51,275.97,276.74,1310000,276.74 1953-04-15,276.01,278.79,276.01,277.35,1580000,277.35 1953-04-14,274.73,276.76,273.86,275.85,1480000,275.85 1953-04-13,275.50,276.46,274.10,274.73,1280000,274.73 1953-04-10,276.23,276.52,274.28,275.50,1360000,275.50 1953-04-09,276.84,278.08,274.83,276.23,1520000,276.23 1953-04-08,275.38,278.85,275.38,276.84,1860000,276.84 1953-04-07,274.10,276.33,271.99,275.16,2500000,275.16 1953-04-06,279.42,279.42,272.40,274.10,3050000,274.10 1953-04-02,280.09,281.81,279.26,280.03,1720000,280.03 1953-04-01,279.87,281.47,277.53,280.09,2240000,280.09 1953-03-31,282.73,282.73,277.57,279.87,3120000,279.87 1953-03-30,286.16,286.16,281.59,283.07,2740000,283.07 1953-03-27,286.60,288.63,285.97,287.33,1640000,287.33 1953-03-26,287.98,289.08,285.63,286.60,2000000,286.60 1953-03-25,288.83,290.78,287.47,287.98,2320000,287.98 1953-03-24,287.39,289.18,286.72,288.83,1970000,288.83 1953-03-23,289.69,289.69,286.74,287.39,1750000,287.39 1953-03-20,289.97,291.01,288.63,289.69,1730000,289.69 1953-03-19,290.32,291.09,288.49,289.97,1840000,289.97 1953-03-18,290.64,291.96,289.26,290.32,2110000,290.32 1953-03-17,289.52,291.78,289.10,290.64,2110000,290.64 1953-03-16,289.04,290.52,288.08,289.52,1770000,289.52 1953-03-13,288.00,289.59,286.76,289.04,1760000,289.04 1953-03-12,288.02,289.14,286.70,288.00,1780000,288.00 1953-03-11,285.22,288.75,284.94,288.02,1890000,288.02 1953-03-10,284.90,285.97,283.17,285.22,1530000,285.22 1953-03-09,284.82,285.99,283.88,284.90,1600000,284.90 1953-03-06,283.86,285.28,283.01,284.82,1690000,284.82 1953-03-05,283.70,284.84,282.46,283.86,1540000,283.86 1953-03-04,284.71,286.09,282.52,283.70,2010000,283.70 1953-03-03,284.71,286.40,283.84,285.99,1850000,285.99 1953-03-02,284.27,285.67,283.09,284.71,1760000,284.71 1953-02-27,284.35,285.40,282.32,284.27,1990000,284.27 1953-02-26,284.45,285.95,283.27,284.35,2290000,284.35 1953-02-25,282.99,285.22,281.93,284.45,2360000,284.45 1953-02-24,282.04,285.04,282.04,282.99,2300000,282.99 1953-02-20,281.55,283.29,280.74,281.89,1400000,281.89 1953-02-19,281.14,283.01,280.51,281.55,1390000,281.55 1953-02-18,281.51,281.96,279.72,281.14,1220000,281.14 1953-02-17,282.18,282.73,280.23,281.51,1290000,281.51 1953-02-16,283.11,284.25,281.71,282.18,1330000,282.18 1953-02-13,281.57,283.86,281.10,283.11,1350000,283.11 1953-02-11,281.67,283.05,280.53,281.57,1240000,281.57 1953-02-10,281.96,283.50,280.84,281.67,1350000,281.67 1953-02-09,282.85,283.31,279.93,281.96,1780000,281.96 1953-02-06,286.20,286.42,282.14,282.85,1870000,282.85 1953-02-05,289.04,289.04,285.16,286.20,1900000,286.20 1953-02-04,290.19,290.84,288.33,289.08,1660000,289.08 1953-02-03,290.03,290.88,287.90,290.19,1560000,290.19 1953-02-02,289.77,290.94,288.00,290.03,1890000,290.03 1953-01-30,287.96,290.46,287.54,289.77,1760000,289.77 1953-01-29,287.39,289.12,286.44,287.96,1830000,287.96 1953-01-28,286.81,288.21,285.87,287.39,1640000,287.39 1953-01-27,286.54,287.96,285.75,286.81,1550000,286.81 1953-01-26,286.89,287.68,285.12,286.54,1420000,286.54 1953-01-23,287.84,288.59,285.77,286.89,1340000,286.89 1953-01-22,287.60,288.87,286.70,287.84,1380000,287.84 1953-01-21,288.00,288.63,286.56,287.60,1300000,287.60 1953-01-20,286.97,289.16,286.28,288.00,1490000,288.00 1953-01-19,287.17,287.98,285.30,286.97,1360000,286.97 1953-01-16,288.18,288.90,285.12,287.17,1710000,287.17 1953-01-15,287.37,288.85,286.40,288.18,1450000,288.18 1953-01-14,286.85,288.47,285.63,287.37,1370000,287.37 1953-01-13,285.24,288.63,285.10,286.85,1680000,286.85 1953-01-12,287.52,287.88,284.29,285.24,1500000,285.24 1953-01-09,290.36,290.46,286.09,287.52,2080000,287.52 1953-01-08,290.76,292.02,289.48,290.36,1780000,290.36 1953-01-07,291.96,291.96,288.69,290.76,1760000,290.76 1953-01-06,293.79,294.25,290.25,292.18,2080000,292.18 1953-01-05,292.14,295.06,291.51,293.79,2130000,293.79 1953-01-02,291.90,293.56,290.30,292.14,1450000,292.14 1952-12-31,292.00,293.50,290.86,291.90,2050000,291.90 1952-12-30,289.65,292.83,289.46,292.00,2070000,292.00 1952-12-29,288.23,290.74,287.66,289.65,1820000,289.65 1952-12-26,287.37,289.10,285.67,288.23,1290000,288.23 1952-12-24,286.99,288.59,285.69,287.37,1510000,287.37 1952-12-23,288.02,289.18,286.22,286.99,2100000,286.99 1952-12-22,286.52,288.85,285.55,288.02,2100000,288.02 1952-12-19,285.36,287.15,284.55,286.52,2050000,286.52 1952-12-18,285.67,286.68,284.41,285.36,1860000,285.36 1952-12-17,286.16,286.95,284.55,285.67,1700000,285.67 1952-12-16,285.99,287.58,285.00,286.16,1980000,286.16 1952-12-15,285.20,287.13,284.45,285.99,1940000,285.99 1952-12-12,284.57,286.18,283.76,285.20,2030000,285.20 1952-12-11,284.55,285.69,283.48,284.57,1790000,284.57 1952-12-10,285.12,286.03,282.77,284.55,1880000,284.55 1952-12-09,283.62,286.32,283.25,285.12,2120000,285.12 1952-12-08,282.06,284.37,281.45,283.62,1790000,283.62 1952-12-05,281.63,283.03,280.68,282.06,1510000,282.06 1952-12-04,282.89,283.82,281.10,281.63,1570000,281.63 1952-12-03,283.78,284.69,282.00,282.89,1610000,282.89 1952-12-02,283.70,284.65,281.89,283.78,1610000,283.78 1952-12-01,283.66,285.20,282.58,283.70,2100000,283.70 1952-11-28,282.44,284.65,281.59,283.66,2160000,283.66 1952-11-26,280.90,283.13,279.93,282.44,1920000,282.44 1952-11-25,281.08,282.32,279.99,280.90,1930000,280.90 1952-11-24,279.32,281.59,278.67,281.08,2100000,281.08 1952-11-21,279.50,280.31,278.16,279.32,1760000,279.32 1952-11-20,280.05,280.80,278.02,279.50,1740000,279.50 1952-11-19,278.04,281.16,277.78,280.05,2350000,280.05 1952-11-18,274.45,279.03,274.20,278.04,2250000,278.04 1952-11-17,273.27,275.16,272.44,274.45,1490000,274.45 1952-11-14,272.54,274.10,271.22,273.27,1700000,273.27 1952-11-13,271.97,273.68,271.16,272.54,1330000,272.54 1952-11-12,273.47,274.28,271.30,271.97,1490000,271.97 1952-11-10,273.47,274.39,272.11,273.47,1360000,273.47 1952-11-07,272.58,274.77,271.89,273.47,1540000,273.47 1952-11-06,271.30,273.82,268.89,272.58,1390000,272.58 1952-11-05,270.65,274.77,270.65,271.30,2030000,271.30 1952-11-03,269.23,271.53,268.79,270.23,1670000,270.23 1952-10-31,265.78,269.96,265.78,269.23,1760000,269.23 1952-10-30,265.46,266.57,264.54,265.72,1090000,265.72 1952-10-29,265.72,266.72,264.65,265.46,1020000,265.46 1952-10-28,265.90,266.80,264.87,265.72,1080000,265.72 1952-10-27,265.46,266.65,264.73,265.90,1000000,265.90 1952-10-24,263.87,266.31,263.39,265.46,1060000,265.46 1952-10-23,263.06,264.93,262.01,263.87,1260000,263.87 1952-10-22,265.64,265.64,262.60,263.06,1160000,263.06 1952-10-21,266.63,267.18,265.09,265.84,990000,265.84 1952-10-20,267.30,268.06,265.37,266.63,1050000,266.63 1952-10-17,265.21,268.36,265.21,267.30,1360000,267.30 1952-10-16,267.12,267.45,263.33,264.87,1730000,264.87 1952-10-15,270.43,270.59,266.09,267.12,1730000,267.12 1952-10-14,270.61,271.59,269.48,270.43,1130000,270.43 1952-10-10,270.98,271.81,269.94,270.61,1070000,270.61 1952-10-09,271.40,272.36,270.15,270.98,1090000,270.98 1952-10-08,269.88,271.91,269.52,271.40,1260000,271.40 1952-10-07,270.00,271.04,268.97,269.88,950000,269.88 1952-10-06,270.55,271.20,269.29,270.00,1070000,270.00 1952-10-03,270.75,271.85,269.52,270.55,980000,270.55 1952-10-02,270.17,271.55,269.72,270.75,1040000,270.75 1952-10-01,270.61,271.08,269.21,270.17,1060000,270.17 1952-09-30,271.73,272.21,269.98,270.61,1120000,270.61 1952-09-29,271.95,272.76,270.51,271.73,970000,271.73 1952-09-26,272.42,273.33,271.14,271.95,1180000,271.95 1952-09-25,272.26,273.74,271.44,272.42,1210000,272.42 1952-09-24,271.65,273.19,271.06,272.26,1390000,272.26 1952-09-23,270.77,272.24,269.48,271.65,1240000,271.65 1952-09-22,270.55,272.09,269.56,270.77,1160000,270.77 1952-09-19,269.72,271.26,268.62,270.55,1150000,270.55 1952-09-18,270.43,271.12,268.81,269.72,1030000,269.72 1952-09-17,269.03,271.18,268.89,270.43,1000000,270.43 1952-09-16,268.38,269.74,267.08,269.03,1140000,269.03 1952-09-15,271.02,271.14,267.91,268.38,1100000,268.38 1952-09-12,272.11,272.46,269.66,271.02,1040000,271.02 1952-09-11,271.65,273.25,270.90,272.11,970000,272.11 1952-09-10,273.01,273.01,269.56,271.65,1590000,271.65 1952-09-09,275.87,276.29,273.13,273.53,1310000,273.53 1952-09-08,276.50,277.55,275.38,275.87,1170000,275.87 1952-09-05,276.76,277.43,275.18,276.50,1040000,276.50 1952-09-04,277.15,278.10,276.03,276.76,1120000,276.76 1952-09-03,276.40,278.16,275.95,277.15,1200000,277.15 1952-09-02,275.10,277.25,275.10,276.40,970000,276.40 1952-08-29,274.41,275.93,274.12,275.04,890000,275.04 1952-08-28,273.84,275.28,273.35,274.41,980000,274.41 1952-08-27,273.17,274.33,272.72,273.84,930000,273.84 1952-08-26,273.57,274.37,272.28,273.17,890000,273.17 1952-08-25,274.43,274.61,272.86,273.57,840000,273.57 1952-08-22,274.45,275.24,273.74,274.43,910000,274.43 1952-08-21,274.35,275.24,273.43,274.45,800000,274.45 1952-08-20,274.14,275.62,273.41,274.35,960000,274.35 1952-08-19,274.31,275.06,272.95,274.14,980000,274.14 1952-08-18,277.19,277.19,273.90,274.31,1090000,274.31 1952-08-15,277.75,278.61,276.54,277.37,890000,277.37 1952-08-14,277.88,278.75,277.04,277.75,930000,277.75 1952-08-13,278.14,278.81,276.62,277.88,990000,277.88 1952-08-12,280.03,280.03,277.39,278.14,1110000,278.14 1952-08-11,279.84,281.47,279.15,280.29,1160000,280.29 1952-08-08,279.38,280.92,278.49,279.84,1170000,279.84 1952-08-07,279.07,280.09,278.34,279.38,1180000,279.38 1952-08-06,279.50,280.21,277.88,279.07,1140000,279.07 1952-08-05,279.87,280.03,277.96,279.50,1050000,279.50 1952-08-04,279.80,280.51,278.59,279.87,950000,279.87 1952-08-01,279.56,280.49,278.81,279.80,1050000,279.80 1952-07-31,279.24,280.25,278.34,279.56,1230000,279.56 1952-07-30,278.57,280.07,277.78,279.24,1240000,279.24 1952-07-29,277.94,279.40,277.17,278.57,1010000,278.57 1952-07-28,277.71,278.95,276.92,277.94,1030000,277.94 1952-07-25,279.26,279.76,277.02,277.71,1130000,277.71 1952-07-24,277.63,280.05,277.63,279.26,1270000,279.26 1952-07-23,275.95,278.28,275.64,277.63,1020000,277.63 1952-07-22,274.91,276.54,274.14,275.95,910000,275.95 1952-07-21,273.90,275.54,273.17,274.91,780000,274.91 1952-07-18,275.62,275.85,273.11,273.90,1020000,273.90 1952-07-17,276.76,277.17,274.95,275.62,1010000,275.62 1952-07-16,276.76,278.02,275.83,276.76,1120000,276.76 1952-07-15,275.08,277.35,274.81,276.76,1220000,276.76 1952-07-14,274.22,276.07,274.06,275.08,1090000,275.08 1952-07-11,272.58,274.89,272.28,274.22,1040000,274.22 1952-07-10,273.25,273.80,271.73,272.58,1010000,272.58 1952-07-09,274.43,275.62,272.93,273.25,1120000,273.25 1952-07-08,274.20,275.10,273.51,274.43,850000,274.43 1952-07-07,274.95,275.58,273.27,274.20,1080000,274.20 1952-07-03,274.87,275.64,273.59,274.95,1150000,274.95 1952-07-02,275.46,276.09,273.86,274.87,1320000,274.87 1952-07-01,274.26,276.25,273.94,275.46,1450000,275.46 1952-06-30,272.44,274.97,272.30,274.26,1380000,274.26 1952-06-27,271.24,272.95,270.77,272.44,1210000,272.44 1952-06-26,270.45,271.85,269.88,271.24,1190000,271.24 1952-06-25,269.92,270.98,268.75,270.45,1230000,270.45 1952-06-24,269.50,270.47,268.81,269.92,1200000,269.92 1952-06-23,270.19,271.28,268.64,269.50,1200000,269.50 1952-06-20,269.54,270.84,268.89,270.19,1190000,270.19 1952-06-19,269.09,270.59,268.40,269.54,1320000,269.54 1952-06-18,268.03,269.48,267.63,269.09,1270000,269.09 1952-06-17,267.83,268.68,266.80,268.03,920000,268.03 1952-06-16,268.56,269.15,267.28,267.83,980000,267.83 1952-06-13,267.91,269.17,267.28,268.56,1130000,268.56 1952-06-12,267.93,269.03,266.90,267.91,1370000,267.91 1952-06-11,267.67,268.52,266.33,267.93,1190000,267.93 1952-06-10,269.15,269.15,266.76,267.67,1220000,267.67 1952-06-09,268.03,269.92,267.67,269.15,1270000,269.15 1952-06-06,266.29,268.95,266.21,268.03,1520000,268.03 1952-06-05,263.77,266.80,263.77,266.29,1410000,266.29 1952-06-04,262.09,264.20,261.91,263.67,1200000,263.67 1952-06-03,262.31,262.82,260.83,262.09,940000,262.09 1952-06-02,262.94,264.61,261.48,262.31,1190000,262.31 1952-05-29,262.78,263.51,261.62,262.94,1100000,262.94 1952-05-28,263.92,264.54,262.25,262.78,1130000,262.78 1952-05-27,264.22,265.17,262.82,263.92,1040000,263.92 1952-05-26,263.23,264.87,262.62,264.22,940000,264.22 1952-05-23,263.33,264.59,262.54,263.27,1150000,263.27 1952-05-22,261.78,264.02,261.54,263.33,1360000,263.33 1952-05-21,261.26,262.78,260.89,261.78,1210000,261.78 1952-05-20,260.06,262.66,259.51,261.26,1150000,261.26 1952-05-19,259.88,261.12,259.15,260.06,780000,260.06 1952-05-16,260.10,260.93,259.00,259.82,910000,259.82 1952-05-15,260.99,261.07,258.66,260.10,1050000,260.10 1952-05-14,261.99,262.62,260.26,260.99,950000,260.99 1952-05-13,261.72,263.04,260.87,261.99,890000,261.99 1952-05-12,262.50,262.86,260.83,261.72,800000,261.72 1952-05-09,262.39,263.47,260.99,262.74,960000,262.74 1952-05-08,261.99,263.77,261.58,262.39,1230000,262.39 1952-05-07,261.01,262.70,259.96,261.99,1120000,261.99 1952-05-06,261.54,262.29,260.02,261.01,1120000,261.01 1952-05-05,260.55,261.95,259.35,261.54,860000,261.54 1952-05-02,257.67,260.93,257.67,260.00,1300000,260.00 1952-05-01,257.44,257.44,254.70,256.35,1400000,256.35 1952-04-30,259.34,259.39,257.06,257.63,1000000,257.63 1952-04-29,259.95,260.52,258.19,259.34,1170000,259.34 1952-04-28,260.27,260.96,258.65,259.95,980000,259.95 1952-04-25,258.86,260.08,257.88,259.80,1240000,259.80 1952-04-24,259.97,260.26,257.04,258.86,1580000,258.86 1952-04-23,261.10,261.56,259.41,259.97,1090000,259.97 1952-04-22,261.63,263.03,260.64,261.10,1240000,261.10 1952-04-21,260.14,262.50,259.60,261.63,1110000,261.63 1952-04-18,259.85,261.88,258.84,260.52,1240000,260.52 1952-04-17,261.48,261.60,258.40,259.85,1620000,259.85 1952-04-16,261.29,263.05,260.03,261.48,1400000,261.48 1952-04-15,264.10,264.29,260.33,261.29,1720000,261.29 1952-04-14,266.29,267.11,263.24,264.10,1790000,264.10 1952-04-10,265.04,266.96,264.31,265.75,1130000,265.75 1952-04-09,265.29,266.09,263.89,265.04,980000,265.04 1952-04-08,263.38,265.88,262.57,265.29,1090000,265.29 1952-04-07,265.31,265.31,262.44,263.38,1230000,263.38 1952-04-04,266.80,266.86,264.45,265.62,1190000,265.62 1952-04-03,267.03,267.87,265.81,266.80,1280000,266.80 1952-04-02,267.22,268.08,265.79,267.03,1260000,267.03 1952-04-01,269.46,269.81,266.42,267.22,1720000,267.22 1952-03-31,269.00,270.40,267.17,269.46,1680000,269.46 1952-03-28,265.21,267.76,265.06,266.96,1560000,266.96 1952-03-27,263.87,265.88,263.20,265.21,1370000,265.21 1952-03-26,264.28,265.06,262.82,263.87,1030000,263.87 1952-03-25,265.52,265.52,263.72,264.28,1060000,264.28 1952-03-24,265.69,266.48,264.58,265.60,1040000,265.60 1952-03-21,265.33,267.05,264.93,265.62,1290000,265.62 1952-03-20,264.37,266.23,263.70,265.33,1240000,265.33 1952-03-19,264.10,265.16,262.80,264.37,1090000,264.37 1952-03-18,264.08,265.08,262.59,264.10,1170000,264.10 1952-03-17,264.43,265.48,263.26,264.08,1150000,264.08 1952-03-14,264.24,265.46,262.50,264.05,1350000,264.05 1952-03-13,263.78,265.62,262.65,264.24,1270000,264.24 1952-03-12,262.76,264.56,261.75,263.78,1310000,263.78 1952-03-11,262.76,263.80,261.25,262.76,1210000,262.76 1952-03-10,264.14,264.64,262.32,262.76,1170000,262.76 1952-03-07,264.03,264.89,262.53,263.87,1410000,263.87 1952-03-06,264.66,265.04,262.72,264.03,1210000,264.03 1952-03-05,263.95,266.17,263.61,264.66,1380000,264.66 1952-03-04,260.35,264.24,260.35,263.95,1570000,263.95 1952-03-03,260.27,261.17,258.95,260.08,1020000,260.08 1952-02-29,260.49,261.46,259.36,260.08,1000000,260.08 1952-02-28,259.68,261.79,259.28,260.49,1150000,260.49 1952-02-27,259.30,260.49,257.44,259.68,1260000,259.68 1952-02-26,260.58,260.98,258.69,259.30,1080000,259.30 1952-02-25,261.40,262.86,259.89,260.58,1200000,260.58 1952-02-21,258.49,261.63,258.49,259.60,1360000,259.60 1952-02-20,261.37,261.46,257.46,258.49,1970000,258.49 1952-02-19,265.14,265.14,260.93,261.37,1630000,261.37 1952-02-18,266.30,266.94,264.35,265.35,1140000,265.35 1952-02-15,265.88,267.57,265.29,266.27,1200000,266.27 1952-02-14,266.21,266.67,264.54,265.88,1340000,265.88 1952-02-13,268.37,268.37,265.52,266.21,1300000,266.21 1952-02-11,269.83,269.88,267.57,268.45,1140000,268.45 1952-02-08,268.35,270.48,267.61,269.85,1350000,269.85 1952-02-07,268.77,269.50,267.05,268.35,1170000,268.35 1952-02-06,269.04,270.27,267.38,268.77,1310000,268.77 1952-02-05,269.79,270.17,266.80,269.04,1590000,269.04 1952-02-04,272.51,272.64,268.79,269.79,1640000,269.79 1952-02-01,270.69,272.91,269.94,271.68,1350000,271.68 1952-01-31,270.71,272.18,268.05,270.69,1810000,270.69 1952-01-30,274.00,275.38,270.53,270.71,1880000,270.71 1952-01-29,274.17,275.44,272.91,274.00,1730000,274.00 1952-01-28,273.69,275.13,273.00,274.17,1590000,274.17 1952-01-25,273.90,275.07,272.43,273.41,1650000,273.41 1952-01-24,274.27,275.19,272.68,273.90,1570000,273.90 1952-01-23,275.40,275.95,273.44,274.27,1680000,274.27 1952-01-22,274.10,276.26,273.58,275.40,1920000,275.40 1952-01-21,272.93,274.55,272.32,274.10,1730000,274.10 1952-01-18,271.91,272.69,271.03,272.10,1740000,272.10 1952-01-17,271.13,273.00,270.53,271.91,1590000,271.91 1952-01-16,270.46,272.28,269.27,271.13,1430000,271.13 1952-01-15,271.59,272.09,269.44,270.46,1340000,270.46 1952-01-14,270.73,272.70,270.36,271.59,1510000,271.59 1952-01-11,269.46,271.20,268.74,270.31,1760000,270.31 1952-01-10,268.14,271.09,268.14,269.46,1520000,269.46 1952-01-09,268.66,270.04,266.34,268.08,1370000,268.08 1952-01-08,270.34,270.98,268.14,268.66,1390000,268.66 1952-01-07,271.26,272.60,269.94,270.34,1540000,270.34 1952-01-04,270.38,271.95,269.73,271.03,1480000,271.03 1952-01-03,269.86,271.28,268.58,270.38,1220000,270.38 1952-01-02,269.23,271.01,268.24,269.86,1070000,269.86 1951-12-31,268.52,270.00,267.84,269.23,1440000,269.23 1951-12-28,266.74,269.02,266.30,268.18,1470000,268.18 1951-12-27,264.06,267.26,263.85,266.74,1460000,266.74 1951-12-26,265.79,266.67,263.26,264.06,1520000,264.06 1951-12-24,265.94,266.69,264.91,265.79,680000,265.79 1951-12-21,267.45,267.78,265.37,266.34,1250000,266.34 1951-12-20,267.61,268.91,266.42,267.45,1340000,267.45 1951-12-19,266.61,268.93,265.92,267.61,1510000,267.61 1951-12-18,265.79,267.45,265.14,266.61,1290000,266.61 1951-12-17,265.48,266.71,264.50,265.79,1220000,265.79 1951-12-14,265.81,267.07,264.60,265.71,1360000,265.71 1951-12-13,266.09,266.92,264.56,265.81,1380000,265.81 1951-12-12,265.77,267.24,264.33,266.09,1280000,266.09 1951-12-11,267.38,267.68,265.14,265.77,1360000,265.77 1951-12-10,266.90,268.45,266.07,267.38,1340000,267.38 1951-12-07,266.23,268.95,265.96,266.99,1990000,266.99 1951-12-06,263.85,266.90,263.85,266.23,1840000,266.23 1951-12-05,264.29,265.21,262.71,263.72,1330000,263.72 1951-12-04,263.24,265.19,262.48,264.29,1280000,264.29 1951-12-03,262.29,264.77,261.73,263.24,1220000,263.24 1951-11-30,258.96,261.92,258.68,261.27,1530000,261.27 1951-11-29,258.64,259.93,257.29,258.96,1070000,258.96 1951-11-28,259.46,260.43,258.12,258.64,1150000,258.64 1951-11-27,257.47,260.36,257.47,259.46,1310000,259.46 1951-11-26,255.95,258.38,255.20,257.44,1180000,257.44 1951-11-23,258.72,258.89,255.93,256.95,1210000,256.95 1951-11-21,259.30,260.15,258.16,258.72,1090000,258.72 1951-11-20,259.70,260.04,257.57,259.30,1130000,259.30 1951-11-19,260.82,261.36,258.77,259.70,1030000,259.70 1951-11-16,260.91,261.06,259.00,260.39,1140000,260.39 1951-11-15,261.27,262.29,260.32,260.91,1200000,260.91 1951-11-14,260.41,262.01,259.85,261.27,1220000,261.27 1951-11-13,261.29,262.18,259.39,260.41,1160000,260.41 1951-11-09,257.81,261.21,257.81,259.91,1470000,259.91 1951-11-08,257.14,258.35,254.91,257.14,1410000,257.14 1951-11-07,259.76,260.37,256.58,257.14,1490000,257.14 1951-11-05,259.57,262.10,258.59,259.76,1130000,259.76 1951-11-02,264.06,264.37,261.01,261.94,1230000,261.94 1951-11-01,262.35,265.82,261.86,264.06,1430000,264.06 1951-10-31,260.52,263.15,258.44,262.35,1490000,262.35 1951-10-30,260.43,263.46,259.69,260.52,1530000,260.52 1951-10-29,258.53,261.12,256.39,260.43,1780000,260.43 1951-10-26,264.17,264.26,260.08,262.27,1710000,262.27 1951-10-25,264.95,266.20,262.66,264.17,1360000,264.17 1951-10-24,263.50,266.79,263.22,264.95,1670000,264.95 1951-10-23,262.29,264.80,259.76,263.50,2110000,263.50 1951-10-22,265.90,265.90,259.46,262.29,2690000,262.29 1951-10-19,273.51,274.10,269.23,269.68,1990000,269.68 1951-10-18,273.53,274.75,272.22,273.51,1450000,273.51 1951-10-17,274.40,275.24,272.86,273.53,1460000,273.53 1951-10-16,275.74,276.02,272.50,274.40,1730000,274.40 1951-10-15,275.13,276.93,274.25,275.74,1720000,275.74 1951-10-11,272.76,274.68,271.80,274.10,1760000,274.10 1951-10-10,273.38,274.47,271.46,272.76,1320000,272.76 1951-10-09,275.14,275.26,272.65,273.38,1750000,273.38 1951-10-08,275.53,276.59,274.23,275.14,1860000,275.14 1951-10-05,275.35,277.02,274.21,275.63,2080000,275.63 1951-10-04,275.87,276.93,274.49,275.35,1810000,275.35 1951-10-03,274.34,277.30,274.29,275.87,2780000,275.87 1951-10-02,272.56,275.42,272.13,274.34,1870000,274.34 1951-10-01,271.16,273.25,270.23,272.56,1330000,272.56 1951-09-28,271.31,272.24,269.66,271.16,1390000,271.16 1951-09-27,272.24,272.93,269.77,271.31,1540000,271.31 1951-09-26,272.24,273.67,270.88,272.24,1520000,272.24 1951-09-25,270.77,273.51,270.05,272.24,1740000,272.24 1951-09-24,271.83,271.83,269.08,270.77,1630000,270.77 1951-09-21,274.10,274.10,270.21,272.11,2180000,272.11 1951-09-20,274.27,276.02,272.58,274.10,2100000,274.10 1951-09-19,274.38,276.09,273.08,274.27,2070000,274.27 1951-09-18,275.09,275.63,271.91,274.38,2030000,274.38 1951-09-17,276.06,277.12,274.12,275.09,1800000,275.09 1951-09-14,276.37,277.51,274.01,276.06,2170000,276.06 1951-09-13,275.31,277.15,274.34,276.37,2350000,276.37 1951-09-12,273.88,276.56,273.32,275.31,2180000,275.31 1951-09-11,275.25,276.24,272.63,273.88,2040000,273.88 1951-09-10,273.89,276.63,273.62,275.25,2190000,275.25 1951-09-07,272.28,274.54,271.53,273.89,1970000,273.89 1951-09-06,272.48,273.98,271.23,272.28,2150000,272.28 1951-09-05,270.63,273.11,270.25,272.48,1850000,272.48 1951-09-04,270.25,272.28,269.49,270.63,1520000,270.63 1951-08-31,269.94,271.64,268.58,270.25,1530000,270.25 1951-08-30,268.18,270.68,267.98,269.94,1950000,269.94 1951-08-29,265.56,268.62,265.39,268.18,1520000,268.18 1951-08-28,265.59,266.81,264.42,265.56,1280000,265.56 1951-08-27,266.30,267.06,264.54,265.59,1080000,265.59 1951-08-24,265.65,267.53,264.94,266.30,1210000,266.30 1951-08-23,264.07,266.26,262.89,265.65,1230000,265.65 1951-08-22,265.30,265.56,262.80,264.07,1130000,264.07 1951-08-21,266.19,267.66,264.47,265.30,1400000,265.30 1951-08-20,266.17,267.42,264.98,266.19,1130000,266.19 1951-08-17,265.48,267.44,264.54,266.17,1620000,266.17 1951-08-16,264.27,266.63,263.78,265.48,1750000,265.48 1951-08-15,262.88,264.72,262.04,264.27,1340000,264.27 1951-08-14,263.06,263.98,261.57,262.88,1180000,262.88 1951-08-13,261.92,264.76,261.26,263.06,1320000,263.06 1951-08-10,262.69,263.26,260.67,261.92,1260000,261.92 1951-08-09,263.73,264.43,261.77,262.69,1500000,262.69 1951-08-08,264.94,265.52,262.24,263.73,1410000,263.73 1951-08-07,265.21,266.52,263.85,264.94,1810000,264.94 1951-08-06,262.98,265.72,261.88,265.21,1600000,265.21 1951-08-03,262.89,264.78,261.25,262.98,1570000,262.98 1951-08-02,260.05,264.05,260.05,262.89,2130000,262.89 1951-08-01,257.86,260.72,256.81,259.89,1680000,259.89 1951-07-31,260.30,260.30,256.64,257.86,1550000,257.86 1951-07-30,259.23,262.21,258.89,260.70,1600000,260.70 1951-07-27,259.09,259.87,256.92,259.23,1450000,259.23 1951-07-26,258.11,259.94,256.46,259.09,1480000,259.09 1951-07-25,258.94,260.79,257.33,258.11,1870000,258.11 1951-07-24,255.68,259.40,255.36,258.94,1740000,258.94 1951-07-23,253.73,256.35,253.44,255.68,1320000,255.68 1951-07-20,253.75,255.48,252.78,253.73,1390000,253.73 1951-07-19,253.67,254.56,252.24,253.75,1120000,253.75 1951-07-18,253.89,255.18,252.84,253.67,1370000,253.67 1951-07-17,252.31,254.18,251.05,253.89,1280000,253.89 1951-07-16,254.32,254.98,251.72,252.31,1200000,252.31 1951-07-13,252.59,255.36,252.39,254.32,1320000,254.32 1951-07-12,250.97,253.11,250.70,252.59,1050000,252.59 1951-07-11,250.00,251.75,249.31,250.97,970000,250.97 1951-07-10,250.65,251.41,249.09,250.00,990000,250.00 1951-07-09,250.01,252.10,249.20,250.65,1110000,250.65 1951-07-06,250.27,251.30,248.78,250.01,1170000,250.01 1951-07-05,247.01,250.90,247.01,250.27,1410000,250.27 1951-07-03,244.07,246.77,244.07,245.92,1250000,245.92 1951-07-02,242.64,244.92,241.06,243.98,1350000,243.98 1951-06-29,244.00,244.36,240.72,242.64,1730000,242.64 1951-06-28,246.84,247.22,242.57,244.00,1940000,244.00 1951-06-27,246.28,247.98,244.90,246.84,1360000,246.84 1951-06-26,245.30,247.44,244.29,246.28,1260000,246.28 1951-06-25,245.99,245.99,241.84,245.30,2440000,245.30 1951-06-22,250.21,250.21,246.66,247.86,1340000,247.86 1951-06-21,251.57,251.57,249.20,250.43,1100000,250.43 1951-06-20,253.53,254.47,251.41,251.86,1120000,251.86 1951-06-19,253.80,254.54,251.70,253.53,1100000,253.53 1951-06-18,254.03,255.23,252.55,253.80,1050000,253.80 1951-06-15,252.46,255.05,252.26,254.03,1370000,254.03 1951-06-14,250.21,253.11,250.21,252.46,1300000,252.46 1951-06-13,250.57,251.35,248.73,250.03,1060000,250.03 1951-06-12,251.56,252.64,249.75,250.57,1200000,250.57 1951-06-11,250.39,252.77,249.76,251.56,1220000,251.56 1951-06-08,250.81,251.27,249.19,250.39,1000000,250.39 1951-06-07,249.64,251.73,249.24,250.81,1340000,250.81 1951-06-06,247.66,250.73,247.66,249.64,1200000,249.64 1951-06-05,246.79,248.15,244.91,247.59,1180000,247.59 1951-06-04,249.14,249.14,246.11,246.79,1100000,246.79 1951-06-01,249.65,250.15,247.85,249.33,9810000,249.33 1951-05-31,248.44,251.68,248.32,249.65,1220000,249.65 1951-05-29,247.03,249.52,246.43,248.44,1190000,248.44 1951-05-28,245.83,248.20,245.60,247.03,1240000,247.03 1951-05-25,245.78,247.57,244.84,245.27,1210000,245.27 1951-05-24,246.94,246.94,241.89,245.78,2580000,245.78 1951-05-23,249.30,249.79,246.58,247.03,1540000,247.03 1951-05-22,249.98,251.72,248.49,249.30,1440000,249.30 1951-05-21,250.63,251.36,248.03,249.98,1580000,249.98 1951-05-18,254.57,255.03,250.01,250.10,1660000,250.10 1951-05-17,252.40,255.49,252.40,254.57,1370000,254.57 1951-05-16,252.08,254.19,250.81,252.14,1660000,252.14 1951-05-15,256.08,256.24,251.02,252.08,2020000,252.08 1951-05-14,257.26,258.10,255.45,256.08,1250000,256.08 1951-05-11,260.07,260.93,257.91,258.56,1640000,258.56 1951-05-10,261.49,261.83,259.14,260.07,1660000,260.07 1951-05-09,261.10,262.82,260.24,261.49,1960000,261.49 1951-05-08,261.23,262.05,259.31,261.10,1600000,261.10 1951-05-07,261.76,262.48,259.60,261.23,1580000,261.23 1951-05-04,263.13,264.44,261.74,262.77,2050000,262.77 1951-05-03,261.27,263.69,260.31,263.13,2060000,263.13 1951-05-02,260.71,262.41,259.70,261.27,1900000,261.27 1951-05-01,259.13,261.80,258.23,260.71,1760000,260.71 1951-04-30,259.08,260.83,257.99,259.13,1790000,259.13 1951-04-27,257.33,259.95,257.33,258.96,2120000,258.96 1951-04-26,254.92,257.98,254.92,257.13,1800000,257.13 1951-04-25,254.19,255.38,252.53,254.75,1520000,254.75 1951-04-24,255.12,255.67,253.48,254.19,1420000,254.19 1951-04-23,255.02,255.92,253.94,255.12,1160000,255.12 1951-04-20,254.92,255.51,253.44,254.82,940000,254.82 1951-04-19,256.01,256.53,253.99,254.92,1520000,254.92 1951-04-18,255.34,257.30,254.53,256.01,1780000,256.01 1951-04-17,254.85,256.06,253.88,255.34,1470000,255.34 1951-04-16,256.18,257.03,254.05,254.85,1730000,254.85 1951-04-13,251.76,255.31,251.76,254.75,2120000,254.75 1951-04-12,249.76,252.10,249.37,251.66,1530000,251.66 1951-04-11,250.42,250.57,247.70,249.76,1420000,249.76 1951-04-10,250.57,251.88,249.52,250.42,1280000,250.42 1951-04-09,250.28,251.46,249.62,250.57,1110000,250.57 1951-04-06,250.32,252.22,250.06,250.83,1450000,250.83 1951-04-05,247.75,250.91,247.75,250.32,1790000,250.32 1951-04-04,246.02,247.80,244.98,247.31,1300000,247.31 1951-04-03,246.63,247.69,245.46,246.02,1220000,246.02 1951-04-02,247.82,247.82,245.02,246.63,1280000,246.63 1951-03-30,246.95,249.25,246.95,248.53,1150000,248.53 1951-03-29,246.19,248.28,245.95,246.90,1300000,246.90 1951-03-28,248.74,249.15,245.34,246.19,1770000,246.19 1951-03-27,249.13,250.81,248.18,248.74,1250000,248.74 1951-03-26,248.14,249.79,247.04,249.13,1230000,249.13 1951-03-22,249.37,251.57,249.04,250.52,1290000,250.52 1951-03-21,247.87,250.39,247.45,249.37,1310000,249.37 1951-03-20,248.08,248.64,246.53,247.87,1020000,247.87 1951-03-19,249.03,249.45,246.45,248.08,1120000,248.08 1951-03-16,245.88,249.33,245.88,248.62,1660000,248.62 1951-03-15,243.95,245.80,242.06,244.85,2070000,244.85 1951-03-14,245.88,246.56,243.13,243.95,2110000,243.95 1951-03-13,249.35,249.35,244.61,245.88,2330000,245.88 1951-03-12,252.00,252.00,248.70,249.89,1640000,249.89 1951-03-09,252.81,254.27,251.98,252.75,1610000,252.75 1951-03-08,252.45,253.68,251.55,252.81,1440000,252.81 1951-03-07,251.55,253.41,250.71,252.45,1770000,252.45 1951-03-06,251.82,252.37,250.43,251.55,1490000,251.55 1951-03-05,253.43,253.43,250.23,251.82,1690000,251.82 1951-03-02,252.80,254.42,252.22,253.61,1570000,253.61 1951-03-01,252.05,253.73,250.96,252.80,1610000,252.80 1951-02-28,251.34,253.14,249.70,252.05,1640000,252.05 1951-02-27,253.18,253.73,250.81,251.34,1680000,251.34 1951-02-26,252.93,254.47,251.57,253.18,1650000,253.18 1951-02-23,252.28,253.59,251.21,252.18,1540000,252.18 1951-02-21,251.12,253.53,250.77,252.28,1670000,252.28 1951-02-20,251.67,252.15,248.78,251.12,2010000,251.12 1951-02-19,254.70,255.08,251.29,251.67,1910000,251.67 1951-02-16,253.61,255.58,253.10,254.90,1860000,254.90 1951-02-15,255.10,255.30,253.13,253.61,1700000,253.61 1951-02-14,255.71,256.34,253.34,255.10,2050000,255.10 1951-02-13,254.80,257.06,254.34,255.71,2400000,255.71 1951-02-09,253.34,255.58,253.08,254.24,2550000,254.24 1951-02-08,252.70,254.27,250.92,253.34,2120000,253.34 1951-02-07,254.47,254.47,251.69,252.70,2020000,252.70 1951-02-06,255.17,255.30,252.25,254.62,2370000,254.62 1951-02-05,253.92,256.06,252.91,255.17,2680000,255.17 1951-02-02,250.76,253.71,250.66,252.78,3030000,252.78 1951-02-01,248.83,251.17,247.04,250.76,2380000,250.76 1951-01-31,249.58,250.56,247.43,248.83,2340000,248.83 1951-01-30,248.64,250.46,247.49,249.58,2480000,249.58 1951-01-29,247.36,249.48,246.50,248.64,2630000,248.64 1951-01-26,242.22,245.42,241.86,244.51,2230000,244.51 1951-01-25,244.08,244.08,240.11,242.22,2520000,242.22 1951-01-24,245.30,246.28,243.45,244.36,1990000,244.36 1951-01-23,244.33,245.87,242.40,245.30,2080000,245.30 1951-01-22,246.91,247.48,242.70,244.33,2570000,244.33 1951-01-19,247.39,248.62,245.45,246.76,3170000,246.76 1951-01-18,248.01,249.07,245.60,247.39,3490000,247.39 1951-01-17,246.65,248.95,243.76,248.01,3880000,248.01 1951-01-16,245.02,247.64,244.76,246.65,3740000,246.65 1951-01-15,243.61,245.75,241.47,245.02,2830000,245.02 1951-01-12,244.72,245.87,242.77,243.81,2950000,243.81 1951-01-11,240.40,245.20,240.35,244.72,3490000,244.72 1951-01-10,243.50,244.61,239.12,240.40,3270000,240.40 1951-01-09,242.29,245.10,242.02,243.50,3800000,243.50 1951-01-08,240.68,243.05,239.62,242.29,2780000,242.29 1951-01-05,240.86,242.60,240.15,240.96,3390000,240.96 1951-01-04,238.99,241.71,237.64,240.86,3390000,240.86 1951-01-03,239.92,241.29,237.43,238.99,3370000,238.99 1951-01-02,235.41,240.46,234.93,239.92,3030000,239.92 1950-12-29,235.34,236.52,233.63,235.42,3440000,235.42 1950-12-28,234.21,236.25,233.02,235.34,3560000,235.34 1950-12-27,230.30,234.64,230.30,234.21,2940000,234.21 1950-12-26,231.54,232.41,228.44,229.65,2660000,229.65 1950-12-22,230.43,232.94,229.65,231.54,2720000,231.54 1950-12-21,231.20,232.02,229.06,230.43,3990000,230.43 1950-12-20,231.54,232.27,229.10,231.20,3510000,231.20 1950-12-19,231.01,233.05,229.77,231.54,3650000,231.54 1950-12-18,228.58,233.27,228.58,231.01,4500000,231.01 1950-12-15,225.89,226.57,223.19,224.70,2420000,224.70 1950-12-14,228.82,229.47,224.98,225.89,2660000,225.89 1950-12-13,229.27,230.35,227.42,228.82,2030000,228.82 1950-12-12,229.19,231.13,228.13,229.27,2140000,229.27 1950-12-11,227.30,230.78,227.05,229.19,2600000,229.19 1950-12-08,225.94,227.33,224.23,226.74,2310000,226.74 1950-12-07,226.16,227.35,224.41,225.94,1810000,225.94 1950-12-06,225.44,228.10,224.78,226.16,2010000,226.16 1950-12-05,222.33,225.97,222.01,225.44,1940000,225.44 1950-12-04,225.23,225.23,221.31,222.33,2510000,222.33 1950-12-01,227.60,230.42,227.05,228.89,1870000,228.89 1950-11-30,226.42,229.70,225.97,227.60,2080000,227.60 1950-11-29,227.78,227.78,223.82,226.42,2770000,226.42 1950-11-28,231.34,231.34,227.40,228.61,2970000,228.61 1950-11-27,235.06,236.09,232.75,234.96,1740000,234.96 1950-11-24,233.81,236.63,233.81,235.47,2620000,235.47 1950-11-22,231.16,234.26,230.37,233.81,2730000,233.81 1950-11-21,231.53,232.64,229.99,231.16,2010000,231.16 1950-11-20,231.64,233.55,230.48,231.53,2250000,231.53 1950-11-17,228.94,231.01,228.29,230.27,2130000,230.27 1950-11-16,229.52,229.75,226.70,228.94,1760000,228.94 1950-11-15,229.54,230.45,227.98,229.52,1620000,229.52 1950-11-14,229.44,230.17,227.58,229.54,1780000,229.54 1950-11-13,229.29,230.83,227.98,229.44,1630000,229.44 1950-11-10,227.17,229.90,226.74,229.29,1640000,229.29 1950-11-09,224.25,228.01,224.23,227.17,1760000,227.17 1950-11-08,222.86,226.55,222.86,224.25,1850000,224.25 1950-11-06,224.91,224.91,220.59,222.52,2580000,222.52 1950-11-03,227.25,229.44,226.21,228.10,1560000,228.10 1950-11-02,225.69,228.74,225.64,227.25,1580000,227.25 1950-11-01,225.01,226.75,223.07,225.69,1780000,225.69 1950-10-31,226.42,227.32,223.59,225.01,2010000,225.01 1950-10-30,228.56,230.05,225.87,226.42,1790000,226.42 1950-10-27,226.65,228.92,225.77,228.28,1800000,228.28 1950-10-26,230.85,230.85,225.44,226.65,3000000,226.65 1950-10-25,231.39,232.75,230.40,231.49,1930000,231.49 1950-10-24,230.62,231.87,229.55,231.39,1790000,231.39 1950-10-23,230.88,232.02,229.95,230.62,1850000,230.62 1950-10-20,230.83,231.41,229.07,230.33,1840000,230.33 1950-10-19,230.60,232.01,229.62,230.83,2250000,230.83 1950-10-18,229.26,231.54,229.26,230.60,2410000,230.60 1950-10-17,227.50,229.74,227.42,229.22,2010000,229.22 1950-10-16,227.63,228.38,225.39,227.50,1630000,227.50 1950-10-13,228.97,229.97,227.53,228.54,2030000,228.54 1950-10-11,227.60,229.92,226.59,228.97,2200000,228.97 1950-10-10,230.02,230.33,226.79,227.60,1870000,227.60 1950-10-09,231.81,232.47,228.49,230.02,2330000,230.02 1950-10-06,229.85,232.50,229.57,231.74,2360000,231.74 1950-10-05,231.15,232.17,229.16,229.85,2490000,229.85 1950-10-04,228.89,232.31,228.21,231.15,2920000,231.15 1950-10-03,228.94,230.86,228.01,228.89,2480000,228.89 1950-10-02,226.46,229.57,226.46,228.94,2200000,228.94 1950-09-29,225.93,227.40,225.32,226.36,1800000,226.36 1950-09-28,225.74,227.69,225.09,225.93,2200000,225.93 1950-09-27,222.84,226.06,222.00,225.74,2360000,225.74 1950-09-26,226.06,226.49,222.26,222.84,2280000,222.84 1950-09-25,226.64,227.51,224.77,226.06,2020000,226.06 1950-09-22,226.01,228.17,225.61,226.64,2510000,226.64 1950-09-21,224.33,226.49,223.58,226.01,1650000,226.01 1950-09-20,225.78,226.49,223.00,224.33,2100000,224.33 1950-09-19,226.78,226.85,224.40,225.78,1590000,225.78 1950-09-18,225.85,227.89,225.45,226.78,2040000,226.78 1950-09-15,224.48,226.32,223.13,225.85,2410000,225.85 1950-09-14,223.42,225.19,222.71,224.48,2350000,224.48 1950-09-13,221.18,224.22,221.18,223.42,2600000,223.42 1950-09-12,218.17,221.19,218.17,220.81,1680000,220.81 1950-09-11,220.03,220.77,217.44,218.10,1860000,218.10 1950-09-08,218.52,220.94,218.52,220.03,1960000,220.03 1950-09-07,218.20,219.20,217.15,218.33,1340000,218.33 1950-09-06,219.52,219.52,217.38,218.20,1300000,218.20 1950-09-05,218.42,220.71,217.88,220.02,1250000,220.02 1950-09-01,217.12,219.00,217.12,218.42,1290000,218.42 1950-08-31,217.05,217.79,216.21,216.87,1140000,216.87 1950-08-30,218.29,218.83,216.43,217.05,1490000,217.05 1950-08-29,218.55,220.20,217.86,218.29,1890000,218.29 1950-08-28,218.10,219.29,216.81,218.55,1300000,218.55 1950-08-25,219.45,219.45,217.10,218.10,1610000,218.10 1950-08-24,221.51,222.17,220.34,221.13,1620000,221.13 1950-08-23,219.79,221.93,219.48,221.51,1580000,221.51 1950-08-22,220.21,220.81,218.91,219.79,1550000,219.79 1950-08-21,219.23,222.12,218.87,220.21,1840000,220.21 1950-08-18,217.76,219.88,217.26,219.23,1780000,219.23 1950-08-17,215.82,218.94,215.82,217.76,2170000,217.76 1950-08-16,215.31,216.58,214.65,215.78,1770000,215.78 1950-08-15,215.31,216.24,214.11,215.31,1330000,215.31 1950-08-14,215.03,216.10,213.98,215.31,1280000,215.31 1950-08-11,216.64,216.89,214.41,215.03,1680000,215.03 1950-08-10,216.97,217.48,215.56,216.64,1870000,216.64 1950-08-09,215.44,217.45,214.16,216.97,1760000,216.97 1950-08-08,215.82,217.53,214.76,215.44,2180000,215.44 1950-08-07,212.66,216.21,212.48,215.82,1850000,215.82 1950-08-04,211.26,213.14,210.41,212.66,1600000,212.66 1950-08-03,211.26,212.28,209.55,211.26,1660000,211.26 1950-08-02,211.87,213.71,209.78,211.26,1980000,211.26 1950-08-01,209.56,212.98,209.56,211.87,1970000,211.87 1950-07-31,208.21,209.85,207.19,209.40,1590000,209.40 1950-07-28,206.76,209.56,206.76,208.21,2050000,208.21 1950-07-27,204.39,207.77,204.39,206.37,2300000,206.37 1950-07-26,203.83,205.68,201.91,204.39,2460000,204.39 1950-07-25,206.57,206.57,202.99,203.83,2770000,203.83 1950-07-24,207.65,208.66,205.64,206.95,2300000,206.95 1950-07-21,207.73,208.89,205.51,207.65,2810000,207.65 1950-07-20,205.13,208.62,204.41,207.73,3160000,207.73 1950-07-19,202.08,205.61,202.08,205.13,2430000,205.13 1950-07-18,198.00,202.42,198.00,201.88,1820000,201.88 1950-07-17,199.47,199.47,196.44,197.63,1520000,197.63 1950-07-14,197.89,201.48,197.89,199.83,1900000,199.83 1950-07-13,199.09,199.69,195.40,197.46,2660000,197.46 1950-07-12,203.83,203.83,197.44,199.09,3200000,199.09 1950-07-11,208.09,209.39,203.83,204.60,3250000,204.60 1950-07-10,208.59,209.42,206.31,208.09,1960000,208.09 1950-07-07,210.85,211.75,207.78,208.59,1870000,208.59 1950-07-06,210.03,212.19,209.46,210.85,1570000,210.85 1950-07-05,208.35,210.92,207.27,210.03,1400000,210.03 1950-07-03,209.11,209.48,205.92,208.35,1550000,208.35 1950-06-30,206.72,211.96,206.03,209.11,2660000,209.11 1950-06-29,214.68,214.74,206.33,206.72,3040000,206.72 1950-06-28,212.39,216.99,212.39,214.68,2600000,214.68 1950-06-27,213.91,217.13,206.33,212.22,4860000,212.22 1950-06-26,220.75,220.75,213.03,213.91,3950000,213.91 1950-06-23,224.51,225.55,223.57,224.35,1700000,224.35 1950-06-22,222.53,225.00,222.47,224.51,1830000,224.51 1950-06-21,220.81,223.07,220.81,222.53,1750000,222.53 1950-06-20,222.09,222.19,218.99,220.72,1470000,220.72 1950-06-19,222.71,223.94,221.49,222.09,1290000,222.09 1950-06-16,222.46,223.67,221.89,222.71,1180000,222.71 1950-06-15,223.32,224.97,221.79,222.46,1530000,222.46 1950-06-14,226.02,226.02,222.76,223.32,1650000,223.32 1950-06-13,228.09,228.09,225.58,226.44,1790000,226.44 1950-06-12,226.86,229.20,226.40,228.38,1790000,228.38 1950-06-09,225.52,227.82,225.16,226.86,2130000,226.86 1950-06-08,223.68,226.21,223.57,225.52,1780000,225.52 1950-06-07,223.46,225.17,221.82,223.68,1750000,223.68 1950-06-06,221.76,224.32,218.66,223.46,2250000,223.46 1950-06-05,223.71,224.12,221.13,221.76,1630000,221.76 1950-06-02,223.23,224.34,222.53,223.71,1450000,223.71 1950-06-01,223.42,224.06,222.40,223.23,1580000,223.23 1950-05-31,222.47,224.18,222.40,223.42,1530000,223.42 1950-05-29,221.71,223.11,221.16,222.47,1110000,222.47 1950-05-26,222.44,223.11,221.22,221.93,1330000,221.93 1950-05-25,222.57,223.10,220.74,222.44,1480000,222.44 1950-05-24,222.47,224.24,221.83,222.57,1850000,222.57 1950-05-23,221.55,222.82,220.85,222.47,1460000,222.47 1950-05-22,222.41,223.14,220.95,221.55,1620000,221.55 1950-05-19,220.63,222.79,220.23,222.11,2110000,222.11 1950-05-18,220.60,221.52,219.70,220.63,5240000,220.63 1950-05-17,219.70,221.48,219.39,220.60,2020000,220.60 1950-05-16,218.04,220.05,217.86,219.70,1730000,219.70 1950-05-15,217.78,218.90,216.87,218.04,1220000,218.04 1950-05-12,218.72,218.96,216.37,217.61,1790000,217.61 1950-05-11,218.64,219.32,217.26,218.72,1750000,218.72 1950-05-10,217.41,219.64,217.41,218.64,1880000,218.64 1950-05-09,216.71,217.86,216.00,217.40,1720000,217.40 1950-05-08,217.03,217.76,215.72,216.71,1680000,216.71 1950-05-05,214.87,216.30,214.14,215.72,1800000,215.72 1950-05-04,216.26,216.71,214.25,214.87,2150000,214.87 1950-05-03,214.87,217.13,214.30,216.26,2120000,216.26 1950-05-02,215.81,216.46,214.30,214.87,2250000,214.87 1950-05-01,214.33,216.35,214.06,215.81,2390000,215.81 1950-04-28,212.44,214.29,212.16,213.56,2190000,213.56 1950-04-27,211.72,213.21,210.76,212.44,2070000,212.44 1950-04-26,212.55,213.05,210.51,211.72,1880000,211.72 1950-04-25,212.58,213.65,211.50,212.55,1830000,212.55 1950-04-24,213.90,214.17,211.02,212.58,2310000,212.58 1950-04-21,213.72,215.09,212.85,214.14,2710000,214.14 1950-04-20,215.21,215.65,212.67,213.72,2590000,213.72 1950-04-19,215.05,216.32,214.22,215.21,2950000,215.21 1950-04-18,214.41,216.04,213.59,215.05,3320000,215.05 1950-04-17,214.48,215.27,213.18,214.41,2520000,214.41 1950-04-14,214.14,216.17,214.14,215.31,2750000,215.31 1950-04-13,213.94,215.40,213.36,214.13,2410000,214.13 1950-04-12,211.47,214.35,211.18,213.94,2010000,213.94 1950-04-11,212.29,213.40,210.15,211.47,2010000,211.47 1950-04-10,212.55,213.60,211.47,212.29,2070000,212.29 1950-04-06,210.34,212.68,210.26,212.10,2000000,212.10 1950-04-05,209.05,210.76,208.70,210.34,1430000,210.34 1950-04-04,208.44,210.19,208.15,209.05,2010000,209.05 1950-04-03,206.37,208.82,205.93,208.44,1570000,208.44 1950-03-31,206.43,207.54,205.28,206.05,1880000,206.05 1950-03-30,208.40,208.42,205.53,206.43,2370000,206.43 1950-03-29,209.50,209.58,207.77,208.40,2090000,208.40 1950-03-28,209.10,210.08,207.74,209.50,1780000,209.50 1950-03-27,210.62,211.22,208.74,209.10,1930000,209.10 1950-03-24,209.62,210.22,208.01,209.78,1570000,209.78 1950-03-23,209.31,210.67,208.61,209.62,2020000,209.62 1950-03-22,208.27,209.72,207.76,209.31,2010000,209.31 1950-03-21,207.78,209.16,207.38,208.27,1400000,208.27 1950-03-20,208.09,209.11,207.40,207.78,1430000,207.78 1950-03-17,207.89,208.92,206.81,207.57,1600000,207.57 1950-03-16,207.46,209.43,206.92,207.89,2060000,207.89 1950-03-15,204.85,207.80,204.85,207.46,1830000,207.46 1950-03-14,203.09,205.10,202.71,204.70,1140000,204.70 1950-03-13,202.96,203.91,202.09,203.09,1060000,203.09 1950-03-10,202.33,202.88,201.14,202.44,1260000,202.44 1950-03-09,203.71,203.95,201.97,202.33,1330000,202.33 1950-03-08,203.69,204.42,202.69,203.71,1360000,203.71 1950-03-07,204.88,204.89,202.33,203.69,1590000,203.69 1950-03-06,204.71,205.65,204.20,204.88,1470000,204.88 1950-03-03,203.54,204.85,203.09,204.48,1520000,204.48 1950-03-02,203.62,204.52,202.96,203.54,1340000,203.54 1950-03-01,203.44,204.02,202.15,203.62,1410000,203.62 1950-02-28,204.33,204.53,202.82,203.44,1310000,203.44 1950-02-27,204.15,204.85,203.46,204.33,1410000,204.33 1950-02-24,203.32,204.87,203.22,204.15,1710000,204.15 1950-02-23,203.35,203.95,202.31,203.32,1310000,203.32 1950-02-21,203.47,203.93,202.55,203.35,1260000,203.35 1950-02-20,203.97,204.26,202.53,203.47,1420000,203.47 1950-02-17,201.69,203.49,201.13,203.17,1940000,203.17 1950-02-16,201.93,202.49,200.59,201.69,1920000,201.69 1950-02-15,202.02,202.60,200.63,201.93,1730000,201.93 1950-02-14,203.32,203.32,200.75,202.02,2210000,202.02 1950-02-10,203.80,204.87,202.69,203.49,1790000,203.49 1950-02-09,202.71,204.67,202.24,203.80,1810000,203.80 1950-02-08,203.43,203.43,201.60,202.71,1470000,202.71 1950-02-07,204.40,204.40,202.59,203.53,1360000,203.53 1950-02-06,205.03,205.39,203.58,204.59,1490000,204.59 1950-02-03,204.11,205.39,203.55,204.53,2210000,204.53 1950-02-02,201.89,204.55,201.35,204.11,2040000,204.11 1950-02-01,201.79,202.96,201.36,201.89,1810000,201.89 1950-01-31,201.39,202.51,200.82,201.79,1690000,201.79 1950-01-30,200.08,201.93,199.66,201.39,1640000,201.39 1950-01-27,198.53,199.58,197.69,199.08,1250000,199.08 1950-01-26,198.39,199.22,197.54,198.53,1150000,198.53 1950-01-25,199.32,199.32,196.64,198.39,1700000,198.39 1950-01-24,200.42,200.42,198.78,199.62,1250000,199.62 1950-01-23,200.97,201.39,199.43,200.42,1340000,200.42 1950-01-20,199.80,200.86,199.14,200.13,1440000,200.13 1950-01-19,199.50,200.49,198.49,199.80,1170000,199.80 1950-01-18,198.78,200.74,198.35,199.50,1570000,199.50 1950-01-17,197.17,200.08,197.14,198.78,1790000,198.78 1950-01-16,196.92,197.75,195.57,197.17,1460000,197.17 1950-01-13,197.93,197.93,193.94,196.81,3330000,196.81 1950-01-12,201.61,202.08,197.53,197.93,2970000,197.93 1950-01-11,201.17,202.42,200.52,201.61,2630000,201.61 1950-01-10,201.98,202.04,200.13,201.17,2160000,201.17 1950-01-09,201.94,202.92,200.86,201.98,2520000,201.98 1950-01-06,200.57,201.62,199.84,200.96,2010000,200.96 1950-01-05,200.20,201.76,199.69,200.57,2550000,200.57 1950-01-04,198.89,200.55,198.26,200.20,1890000,200.20 1950-01-03,200.13,200.20,197.73,198.89,1260000,198.89 1949-12-30,199.39,200.91,198.97,200.52,2090000,200.52 1949-12-29,199.59,200.26,198.75,199.39,1820000,199.39 1949-12-28,198.28,200.19,198.10,199.59,1560000,199.59 1949-12-27,198.88,199.58,197.73,198.28,1560000,198.28 1949-12-23,198.52,199.73,197.88,198.88,1470000,198.88 1949-12-22,196.49,198.91,196.49,198.52,1630000,198.52 1949-12-21,197.22,197.64,195.79,196.45,1270000,196.45 1949-12-20,198.17,198.28,196.55,197.22,1330000,197.22 1949-12-19,197.98,199.15,197.46,198.17,1420000,198.17 1949-12-16,198.05,199.13,196.90,197.88,1960000,197.88 1949-12-15,197.51,198.59,196.34,198.05,2070000,198.05 1949-12-14,196.81,198.10,196.12,197.51,2210000,197.51 1949-12-13,196.17,197.22,195.22,196.81,2080000,196.81 1949-12-12,194.68,196.57,194.39,196.17,1780000,196.17 1949-12-09,194.45,194.99,193.54,194.35,1500000,194.35 1949-12-08,194.21,195.32,193.61,194.45,1720000,194.45 1949-12-07,194.59,194.59,192.59,194.21,1630000,194.21 1949-12-06,194.74,195.25,193.91,194.64,1430000,194.64 1949-12-05,194.43,195.54,193.69,194.74,1830000,194.74 1949-12-02,192.71,194.16,191.94,193.63,2020000,193.63 1949-12-01,191.55,193.11,191.12,192.71,1470000,192.71 1949-11-30,191.62,192.35,190.68,191.55,1320000,191.55 1949-11-29,192.24,192.54,190.54,191.62,1310000,191.62 1949-11-28,193.23,193.43,191.72,192.24,1080000,192.24 1949-11-25,193.52,194.34,192.27,192.78,1270000,192.78 1949-11-23,193.23,194.35,192.48,193.52,1460000,193.52 1949-11-22,192.35,193.65,191.69,193.23,1400000,193.23 1949-11-21,193.62,193.84,191.72,192.35,1180000,192.35 1949-11-18,191.77,193.97,191.77,193.41,1690000,193.41 1949-11-17,189.37,191.82,189.27,191.34,1410000,191.34 1949-11-16,187.98,190.13,187.83,189.37,1210000,189.37 1949-11-15,189.25,189.25,186.98,187.98,1250000,187.98 1949-11-14,190.46,191.08,188.71,189.27,1270000,189.27 1949-11-10,190.60,191.07,189.65,190.42,1170000,190.42 1949-11-09,190.89,192.43,190.06,190.60,1510000,190.60 1949-11-07,191.37,192.05,190.41,190.89,1170000,190.89 1949-11-04,192.19,192.31,190.57,191.29,1390000,191.29 1949-11-03,192.96,193.19,191.34,192.19,1370000,192.19 1949-11-02,191.23,193.63,191.15,192.96,1570000,192.96 1949-11-01,189.54,191.74,189.25,191.23,1300000,191.23 1949-10-31,190.36,191.08,189.15,189.54,1140000,189.54 1949-10-28,190.36,191.44,189.47,190.16,1480000,190.16 1949-10-27,189.08,191.12,188.96,190.36,1760000,190.36 1949-10-26,187.70,189.41,187.30,189.08,1620000,189.08 1949-10-25,186.54,188.01,186.38,187.70,1190000,187.70 1949-10-24,186.20,187.01,185.58,186.54,1240000,186.54 1949-10-21,186.64,186.97,185.62,186.20,1290000,186.20 1949-10-20,187.04,187.65,185.87,186.64,1270000,186.64 1949-10-19,186.12,187.34,185.81,187.04,1370000,187.04 1949-10-18,184.72,186.56,184.72,186.12,1220000,186.12 1949-10-17,186.06,186.06,183.94,184.72,1130000,184.72 1949-10-14,186.78,187.26,185.47,186.43,1190000,186.43 1949-10-13,186.74,187.91,186.29,186.78,1790000,186.78 1949-10-11,185.15,187.04,184.75,186.74,1660000,186.74 1949-10-10,185.36,185.55,184.49,185.15,1120000,185.15 1949-10-07,185.37,185.62,183.92,185.27,1280000,185.27 1949-10-06,184.80,185.99,184.53,185.37,1520000,185.37 1949-10-05,184.13,185.38,184.00,184.80,1470000,184.80 1949-10-04,182.68,184.39,182.68,184.13,1310000,184.13 1949-10-03,181.98,183.05,180.90,182.67,770000,182.67 1949-09-30,182.43,183.16,181.71,182.51,1100000,182.51 1949-09-29,181.31,183.42,181.00,182.43,1380000,182.43 1949-09-28,180.06,182.09,180.06,181.31,1300000,181.31 1949-09-27,180.54,180.54,178.61,179.63,1070000,179.63 1949-09-26,181.30,182.25,180.36,180.86,1020000,180.86 1949-09-23,180.83,181.74,180.17,181.30,1290000,181.30 1949-09-22,180.02,181.37,179.87,180.83,1280000,180.83 1949-09-21,178.04,180.42,177.71,180.02,1150000,180.02 1949-09-20,180.91,180.91,177.63,178.04,1350000,178.04 1949-09-19,182.13,182.13,180.18,181.42,1300000,181.42 1949-09-16,182.16,182.76,181.34,182.32,1160000,182.32 1949-09-15,182.71,182.71,181.00,182.16,1160000,182.16 1949-09-14,183.29,184.09,182.61,183.14,1700000,183.14 1949-09-13,181.27,183.51,181.27,183.29,1720000,183.29 1949-09-12,180.24,181.60,180.17,181.15,1080000,181.15 1949-09-09,180.53,180.86,179.89,180.24,770000,180.24 1949-09-08,180.21,181.30,179.99,180.53,940000,180.53 1949-09-07,179.20,180.54,178.91,180.21,850000,180.21 1949-09-06,179.38,179.85,178.65,179.20,640000,179.20 1949-09-02,179.52,179.91,178.97,179.38,750000,179.38 1949-09-01,178.66,180.20,178.54,179.52,840000,179.52 1949-08-31,178.69,179.05,177.77,178.66,720000,178.66 1949-08-30,177.75,179.04,177.60,178.69,590000,178.69 1949-08-29,179.08,179.08,177.45,177.75,640000,177.75 1949-08-26,179.01,179.69,178.42,179.24,660000,179.24 1949-08-25,178.78,179.52,178.44,179.01,730000,179.01 1949-08-24,178.51,179.15,177.86,178.78,720000,178.78 1949-08-23,180.18,180.18,178.28,178.51,840000,178.51 1949-08-22,181.16,181.33,180.06,180.53,710000,180.53 1949-08-19,182.02,182.38,180.83,181.16,850000,181.16 1949-08-18,181.59,182.67,181.44,182.02,1180000,182.02 1949-08-17,180.00,181.78,179.80,181.59,1440000,181.59 1949-08-16,178.97,180.40,178.71,180.00,840000,180.00 1949-08-15,179.29,179.71,178.55,178.97,720000,178.97 1949-08-12,180.02,180.10,178.62,179.29,770000,179.29 1949-08-11,180.60,180.98,179.48,180.02,1030000,180.02 1949-08-10,179.52,180.96,179.23,180.60,1280000,180.60 1949-08-09,180.35,180.35,178.86,179.52,1140000,179.52 1949-08-08,179.18,181.15,179.18,180.54,1660000,180.54 1949-08-05,177.06,179.38,176.77,179.07,1440000,179.07 1949-08-04,177.19,177.44,176.51,177.06,910000,177.06 1949-08-03,177.56,177.91,176.72,177.19,1270000,177.19 1949-08-02,176.84,177.78,176.35,177.56,800000,177.56 1949-08-01,175.92,177.17,175.68,176.84,860000,176.84 1949-07-29,176.26,176.54,175.30,175.92,640000,175.92 1949-07-28,176.46,176.84,175.57,176.26,790000,176.26 1949-07-27,176.37,177.04,175.93,176.46,1030000,176.46 1949-07-26,175.12,176.65,174.86,176.37,1310000,176.37 1949-07-25,174.53,175.99,174.28,175.12,860000,175.12 1949-07-22,174.59,174.95,173.85,174.53,730000,174.53 1949-07-21,175.49,175.49,174.15,174.59,780000,174.59 1949-07-20,175.31,176.14,174.86,175.60,1380000,175.60 1949-07-19,174.04,175.60,173.93,175.31,1590000,175.31 1949-07-18,173.48,174.40,173.20,174.04,810000,174.04 1949-07-15,173.59,173.89,172.64,173.48,800000,173.48 1949-07-14,173.24,174.02,172.47,173.59,1170000,173.59 1949-07-13,171.78,173.34,171.59,173.24,1050000,173.24 1949-07-12,170.81,171.96,170.52,171.78,870000,171.78 1949-07-11,170.92,171.41,170.10,170.81,680000,170.81 1949-07-08,171.01,171.53,170.41,170.92,640000,170.92 1949-07-07,170.68,171.27,169.95,171.01,890000,171.01 1949-07-06,169.02,170.83,168.77,170.68,1410000,170.68 1949-07-05,168.08,169.48,167.90,169.02,630000,169.02 1949-07-01,167.42,168.61,166.94,168.08,720000,168.08 1949-06-30,166.76,167.96,166.72,167.42,580000,167.42 1949-06-29,165.75,167.05,165.24,166.76,590000,166.76 1949-06-28,166.52,166.52,164.65,165.75,790000,165.75 1949-06-27,166.99,167.82,166.29,166.79,670000,166.79 1949-06-24,166.77,167.49,166.39,166.99,570000,166.99 1949-06-23,165.74,167.30,165.74,166.77,730000,166.77 1949-06-22,165.71,165.97,165.09,165.64,550000,165.64 1949-06-21,165.49,166.65,165.09,165.71,770000,165.71 1949-06-20,163.78,165.68,163.46,165.49,780000,165.49 1949-06-17,163.94,164.30,162.95,163.78,540000,163.78 1949-06-16,164.58,165.03,163.50,163.94,680000,163.94 1949-06-15,162.10,164.81,162.10,164.58,950000,164.58 1949-06-14,161.60,162.51,160.62,161.86,1120000,161.86 1949-06-13,163.90,163.90,160.95,161.60,1350000,161.60 1949-06-10,165.53,165.53,164.14,164.61,800000,164.61 1949-06-09,165.76,166.28,165.19,165.73,600000,165.73 1949-06-08,165.29,166.39,164.96,165.76,790000,165.76 1949-06-07,165.15,165.96,164.32,165.29,1040000,165.29 1949-06-06,167.24,167.35,164.27,165.15,1380000,165.15 1949-06-03,168.15,168.31,166.77,167.24,700000,167.24 1949-06-02,167.98,168.98,167.59,168.15,670000,168.15 1949-06-01,168.36,168.54,166.53,167.98,1140000,167.98 1949-05-31,171.53,171.60,168.14,168.36,1240000,168.36 1949-05-27,171.95,172.07,170.95,171.53,690000,171.53 1949-05-26,171.84,172.79,171.51,171.95,700000,171.95 1949-05-25,171.49,172.07,170.57,171.84,880000,171.84 1949-05-24,172.32,172.43,171.06,171.49,840000,171.49 1949-05-23,173.43,173.43,172.10,172.32,720000,172.32 1949-05-20,174.14,174.18,172.86,173.49,740000,173.49 1949-05-19,174.92,175.12,173.76,174.14,840000,174.14 1949-05-18,175.32,175.61,174.39,174.92,750000,174.92 1949-05-17,175.76,176.07,174.86,175.32,780000,175.32 1949-05-16,175.20,176.26,175.17,175.76,1030000,175.76 1949-05-13,174.70,175.40,174.26,174.82,780000,174.82 1949-05-12,174.40,175.17,174.14,174.70,790000,174.70 1949-05-11,174.37,175.11,173.75,174.40,790000,174.40 1949-05-10,175.17,175.32,173.97,174.37,730000,174.37 1949-05-09,175.39,175.75,174.70,175.17,610000,175.17 1949-05-06,176.24,176.24,174.74,175.50,780000,175.50 1949-05-05,176.63,177.18,175.91,176.33,920000,176.33 1949-05-04,175.09,177.04,175.09,176.63,1180000,176.63 1949-05-03,174.53,175.32,173.86,175.00,830000,175.00 1949-05-02,174.16,175.06,173.93,174.53,740000,174.53 1949-04-29,173.89,174.48,173.52,174.06,810000,174.06 1949-04-28,174.56,174.65,173.58,173.89,770000,173.89 1949-04-27,174.21,175.24,173.92,174.56,830000,174.56 1949-04-26,173.64,174.60,173.02,174.21,870000,174.21 1949-04-25,173.76,174.38,173.05,173.64,740000,173.64 1949-04-22,173.24,173.77,172.64,173.42,890000,173.42 1949-04-21,175.49,175.49,173.08,173.24,1310000,173.24 1949-04-20,176.73,176.79,175.38,175.69,970000,175.69 1949-04-19,177.16,177.50,176.39,176.73,830000,176.73 1949-04-18,177.07,177.71,176.47,177.16,890000,177.16 1949-04-14,176.81,177.30,176.18,176.62,800000,176.62 1949-04-13,176.99,177.69,172.26,176.81,920000,176.81 1949-04-12,176.54,177.46,175.61,176.99,860000,176.99 1949-04-11,176.75,177.14,175.94,176.54,720000,176.54 1949-04-08,176.04,176.62,175.34,176.44,850000,176.44 1949-04-07,176.71,176.94,175.43,176.04,850000,176.04 1949-04-06,177.04,177.53,176.19,176.71,930000,176.71 1949-04-05,176.59,177.67,175.94,177.04,900000,177.04 1949-04-04,176.88,177.78,176.15,176.59,920000,176.59 1949-04-01,177.04,177.04,175.86,176.28,850000,176.28 1949-03-31,178.43,178.43,176.87,177.10,980000,177.10 1949-03-30,178.39,179.19,177.69,178.45,1850000,178.45 1949-03-29,176.81,178.90,176.81,178.39,1800000,178.39 1949-03-28,175.82,176.72,175.35,175.99,700000,175.99 1949-03-25,176.41,176.49,175.29,175.83,630000,175.83 1949-03-24,176.20,177.37,176.13,176.41,920000,176.41 1949-03-23,174.83,176.54,174.32,176.20,960000,176.20 1949-03-22,175.61,175.85,174.34,174.83,840000,174.83 1949-03-21,176.07,176.47,175.27,175.61,620000,175.61 1949-03-18,176.33,176.78,175.73,176.29,670000,176.29 1949-03-17,175.53,176.67,175.17,176.33,760000,176.33 1949-03-16,176.02,176.08,174.87,175.53,670000,175.53 1949-03-15,176.82,176.82,175.43,176.02,740000,176.02 1949-03-14,176.96,177.66,176.35,176.98,800000,176.98 1949-03-11,175.64,176.87,175.41,176.52,1070000,176.52 1949-03-10,175.76,176.20,175.03,175.64,630000,175.64 1949-03-09,176.09,176.29,175.22,175.76,640000,175.76 1949-03-08,175.55,176.62,175.30,176.09,940000,176.09 1949-03-07,174.93,175.82,174.30,175.55,840000,175.55 1949-03-04,173.76,174.06,172.59,173.66,730000,173.66 1949-03-03,173.82,174.44,173.17,173.76,600000,173.76 1949-03-02,174.18,174.34,173.15,173.82,690000,173.82 1949-03-01,173.36,174.61,173.36,174.18,720000,174.18 1949-02-28,171.63,173.38,171.63,173.06,740000,173.06 1949-02-25,171.48,172.11,170.56,171.10,830000,171.10 1949-02-24,172.76,172.76,171.19,171.48,880000,171.48 1949-02-23,174.19,174.60,173.07,173.23,770000,173.23 1949-02-21,174.53,174.67,173.63,174.19,560000,174.19 1949-02-18,174.82,175.62,174.11,174.71,700000,174.71 1949-02-17,173.45,175.55,173.45,174.82,960000,174.82 1949-02-16,172.48,173.92,172.27,173.28,700000,173.28 1949-02-15,172.16,172.86,171.27,172.48,610000,172.48 1949-02-14,171.93,173.17,171.57,172.16,700000,172.16 1949-02-11,172.41,172.69,171.03,171.93,880000,171.93 1949-02-10,174.61,174.80,172.23,172.41,980000,172.41 1949-02-09,173.71,175.20,173.57,174.61,880000,174.61 1949-02-08,174.37,174.37,172.91,173.71,960000,173.71 1949-02-07,175.60,176.76,173.79,174.43,1330000,174.43 1949-02-04,180.09,180.32,177.69,177.92,1060000,177.92 1949-02-03,180.27,180.88,179.57,180.09,770000,180.09 1949-02-02,180.39,180.94,179.63,180.27,720000,180.27 1949-02-01,179.12,180.55,178.92,180.39,730000,180.39 1949-01-31,179.35,179.73,178.46,179.12,610000,179.12 1949-01-28,179.48,179.48,177.88,178.82,840000,178.82 1949-01-27,179.88,180.30,178.58,179.52,840000,179.52 1949-01-26,179.65,181.31,178.96,179.88,1040000,179.88 1949-01-25,180.83,181.05,179.06,179.65,820000,179.65 1949-01-24,181.54,182.28,180.51,180.83,850000,180.83 1949-01-21,181.43,181.89,180.69,181.00,770000,181.00 1949-01-20,181.12,181.85,180.44,181.43,820000,181.43 1949-01-19,180.55,181.47,179.75,181.12,760000,181.12 1949-01-18,180.14,181.55,180.09,180.55,770000,180.55 1949-01-17,179.15,180.48,177.99,180.14,720000,180.14 1949-01-14,180.17,180.20,178.19,178.80,930000,178.80 1949-01-13,180.69,181.06,179.79,180.17,700000,180.17 1949-01-12,180.76,181.66,180.21,180.69,710000,180.69 1949-01-11,180.57,181.00,179.73,180.76,710000,180.76 1949-01-10,181.41,181.47,179.78,180.57,770000,180.57 1949-01-07,180.47,182.50,180.47,181.31,1400000,181.31 1949-01-06,177.16,180.59,177.16,180.22,1150000,180.22 1949-01-05,175.50,177.76,175.50,177.08,800000,177.08 1949-01-04,175.03,175.97,174.56,175.49,640000,175.49 1949-01-03,176.99,176.99,174.37,175.03,980000,175.03 1948-12-31,177.92,178.50,176.81,177.30,1550000,177.30 1948-12-30,177.58,179.15,177.10,177.92,1390000,177.92 1948-12-29,175.98,177.85,175.88,177.58,1380000,177.58 1948-12-28,177.37,177.37,175.19,175.98,1650000,175.98 1948-12-27,177.42,178.35,176.63,177.40,1060000,177.40 1948-12-24,176.49,177.82,176.04,177.42,970000,177.42 1948-12-23,176.39,177.29,175.70,176.49,1080000,176.49 1948-12-22,176.35,176.93,175.62,176.39,1000000,176.39 1948-12-21,176.84,177.49,175.92,176.35,1000000,176.35 1948-12-20,175.69,177.60,175.14,176.84,980000,176.84 1948-12-17,175.83,176.78,175.17,175.92,1010000,175.92 1948-12-16,176.20,176.59,175.20,175.83,1010000,175.83 1948-12-15,176.59,177.26,175.78,176.20,920000,176.20 1948-12-14,177.34,177.73,175.86,176.59,1000000,176.59 1948-12-13,177.49,178.43,176.67,177.34,1180000,177.34 1948-12-10,175.75,176.79,175.17,176.41,1040000,176.41 1948-12-09,176.29,176.86,175.44,175.75,1220000,175.75 1948-12-08,176.67,176.98,175.25,176.29,1140000,176.29 1948-12-07,176.26,177.25,175.39,176.67,1160000,176.67 1948-12-06,176.22,177.12,175.59,176.26,1180000,176.26 1948-12-03,173.61,175.34,173.20,175.00,1100000,175.00 1948-12-02,173.22,174.88,173.15,173.61,1210000,173.61 1948-12-01,171.67,174.02,171.67,173.22,1320000,173.22 1948-11-30,171.99,172.49,170.35,171.20,1200000,171.20 1948-11-29,172.90,173.55,171.72,171.99,1010000,171.99 1948-11-26,173.40,174.14,172.38,173.16,1040000,173.16 1948-11-24,175.88,175.88,172.96,173.40,1290000,173.40 1948-11-23,176.33,176.72,175.17,176.17,1010000,176.17 1948-11-22,177.42,177.80,175.75,176.33,890000,176.33 1948-11-19,176.07,177.96,175.99,176.98,990000,176.98 1948-11-18,176.07,176.89,175.52,176.07,780000,176.07 1948-11-17,176.20,176.92,175.14,176.07,980000,176.07 1948-11-16,176.01,177.42,175.44,176.20,1060000,176.20 1948-11-15,174.37,176.56,174.37,176.01,1030000,176.01 1948-11-12,173.48,175.47,173.37,173.93,1110000,173.93 1948-11-10,173.94,174.93,172.13,173.48,2100000,173.48 1948-11-09,178.19,178.60,173.54,173.94,2260000,173.94 1948-11-08,178.94,179.85,177.46,178.19,1130000,178.19 1948-11-05,184.42,184.42,178.11,178.38,2530000,178.38 1948-11-04,182.92,185.79,182.92,184.54,1530000,184.54 1948-11-03,183.08,183.08,179.65,182.46,3240000,182.46 1948-11-01,188.62,190.45,188.39,189.76,1220000,189.76 1948-10-29,187.73,188.60,186.99,188.28,860000,188.28 1948-10-28,189.28,189.59,187.29,187.73,970000,187.73 1948-10-27,189.76,190.06,188.11,189.28,970000,189.28 1948-10-26,189.52,190.88,188.97,189.76,1140000,189.76 1948-10-25,190.19,190.53,188.54,189.52,1100000,189.52 1948-10-22,186.70,190.08,186.70,189.76,1800000,189.76 1948-10-21,186.51,187.16,185.49,186.44,1200000,186.44 1948-10-20,186.18,187.00,185.62,186.51,1180000,186.51 1948-10-19,185.33,186.58,185.15,186.18,1030000,186.18 1948-10-18,184.93,186.22,184.63,185.33,1030000,185.33 1948-10-15,184.52,185.26,183.76,184.62,910000,184.62 1948-10-14,183.84,185.46,183.65,184.52,980000,184.52 1948-10-13,182.41,184.25,182.30,183.84,830000,183.84 1948-10-11,182.09,182.76,181.71,182.41,510000,182.41 1948-10-08,182.52,182.99,181.55,182.02,630000,182.02 1948-10-07,181.72,183.10,181.58,182.52,800000,182.52 1948-10-06,181.23,182.34,180.91,181.72,710000,181.72 1948-10-05,181.70,181.89,180.52,181.23,550000,181.23 1948-10-04,180.78,182.19,180.74,181.70,610000,181.70 1948-10-01,178.30,180.08,178.22,179.87,680000,179.87 1948-09-30,179.04,179.71,177.95,178.30,700000,178.30 1948-09-29,177.59,179.53,177.59,179.04,810000,179.04 1948-09-28,175.99,177.91,175.84,177.54,910000,177.54 1948-09-27,178.32,178.32,175.84,175.99,1210000,175.99 1948-09-24,178.77,179.71,178.49,179.28,650000,179.28 1948-09-23,179.16,179.22,178.22,178.77,550000,178.77 1948-09-22,178.61,179.70,178.11,179.16,850000,179.16 1948-09-21,177.37,178.89,176.94,178.61,920000,178.61 1948-09-20,179.93,179.93,176.96,177.37,1260000,177.37 1948-09-17,180.69,180.78,179.60,180.06,680000,180.06 1948-09-16,180.62,181.10,179.84,180.69,580000,180.69 1948-09-15,180.63,181.47,179.85,180.62,710000,180.62 1948-09-14,179.41,181.12,179.41,180.63,710000,180.63 1948-09-13,180.51,180.51,178.87,179.38,680000,179.38 1948-09-10,180.33,181.26,179.04,180.61,970000,180.61 1948-09-09,182.83,182.83,179.74,180.33,2000000,180.33 1948-09-08,184.88,184.88,182.63,182.90,880000,182.90 1948-09-07,184.35,185.64,183.68,185.36,910000,185.36 1948-09-03,184.39,184.90,183.49,184.35,660000,184.35 1948-09-02,183.60,184.88,183.16,184.39,900000,184.39 1948-09-01,181.71,183.99,181.44,183.60,920000,183.60 1948-08-31,182.09,182.47,181.18,181.71,610000,181.71 1948-08-30,183.21,183.95,181.89,182.09,690000,182.09 1948-08-27,182.52,183.55,182.38,183.21,540000,183.21 1948-08-26,182.41,182.99,181.93,182.52,540000,182.52 1948-08-25,182.58,183.01,181.89,182.41,520000,182.41 1948-08-24,181.75,182.99,181.67,182.58,620000,182.58 1948-08-23,183.52,183.52,181.45,181.75,630000,181.75 1948-08-20,182.57,183.92,182.50,183.60,710000,183.60 1948-08-19,182.12,182.78,181.45,182.57,580000,182.57 1948-08-18,182.15,183.21,181.76,182.12,640000,182.12 1948-08-17,180.75,182.63,180.75,182.15,680000,182.15 1948-08-16,179.63,180.62,179.46,180.30,470000,180.30 1948-08-13,179.63,180.44,178.99,179.63,510000,179.63 1948-08-12,179.27,180.17,178.51,179.63,630000,179.63 1948-08-11,179.88,179.88,177.40,179.27,1310000,179.27 1948-08-10,182.26,182.26,179.91,180.02,840000,180.02 1948-08-09,183.01,183.75,182.02,182.26,670000,182.26 1948-08-06,182.92,183.44,181.89,183.01,680000,183.01 1948-08-05,183.06,184.54,182.45,182.92,880000,182.92 1948-08-04,180.98,183.29,180.82,183.06,870000,183.06 1948-08-03,181.13,181.72,180.24,180.98,720000,180.98 1948-08-02,181.33,182.61,180.61,181.13,710000,181.13 1948-07-30,182.56,182.56,180.04,181.33,1310000,181.33 1948-07-29,185.15,185.28,183.19,183.57,750000,183.57 1948-07-28,186.09,187.00,184.92,185.15,840000,185.15 1948-07-27,184.17,186.37,183.81,186.09,870000,186.09 1948-07-26,185.31,185.83,183.81,184.17,720000,184.17 1948-07-23,185.29,186.36,184.74,185.31,820000,185.31 1948-07-22,184.44,185.90,184.19,185.29,850000,185.29 1948-07-21,183.57,185.96,183.39,184.44,1200000,184.44 1948-07-20,181.27,184.41,181.27,183.57,1470000,183.57 1948-07-19,184.76,184.76,179.58,181.20,2570000,181.20 1948-07-16,187.70,188.34,184.98,185.90,1760000,185.90 1948-07-15,190.66,190.69,187.62,187.70,1620000,187.70 1948-07-14,190.36,191.13,189.41,190.66,1340000,190.66 1948-07-13,191.47,191.65,190.08,190.36,1200000,190.36 1948-07-12,191.62,192.38,190.95,191.47,1300000,191.47 1948-07-09,190.58,192.09,189.98,191.62,1370000,191.62 1948-07-08,190.06,191.16,189.73,190.58,1000000,190.58 1948-07-07,190.55,191.04,189.31,190.06,920000,190.06 1948-07-06,190.06,191.37,189.54,190.55,950000,190.55 1948-07-02,189.03,190.45,188.46,190.06,920000,190.06 1948-07-01,189.46,189.85,188.43,189.03,820000,189.03 1948-06-30,188.49,190.03,187.73,189.46,990000,189.46 1948-06-29,187.90,189.19,187.50,188.49,820000,188.49 1948-06-28,189.66,189.66,186.56,187.90,1210000,187.90 1948-06-25,190.87,191.06,188.95,190.00,1150000,190.00 1948-06-24,190.73,191.87,190.00,190.87,1560000,190.87 1948-06-23,189.66,191.32,189.42,190.73,1760000,190.73 1948-06-22,189.71,190.31,188.41,189.66,1410000,189.66 1948-06-21,191.65,192.08,189.52,189.71,1750000,189.71 1948-06-18,192.15,192.19,190.56,191.65,1250000,191.65 1948-06-17,192.34,192.99,191.12,192.15,1520000,192.15 1948-06-16,193.16,193.19,191.06,192.34,1580000,192.34 1948-06-15,192.86,193.93,191.62,193.16,1630000,193.16 1948-06-14,192.96,194.49,192.14,192.86,1750000,192.86 1948-06-11,192.50,193.65,191.48,192.96,1520000,192.96 1948-06-10,192.56,193.28,191.06,192.50,1700000,192.50 1948-06-09,192.16,193.54,191.86,192.56,1880000,192.56 1948-06-08,190.31,192.59,190.31,192.16,1520000,192.16 1948-06-07,190.18,190.74,188.87,190.13,930000,190.13 1948-06-04,191.05,191.31,189.56,190.18,1100000,190.18 1948-06-03,191.32,192.09,190.40,191.05,1300000,191.05 1948-06-02,191.18,192.18,190.30,191.32,1300000,191.32 1948-06-01,190.74,192.00,190.29,191.18,1310000,191.18 1948-05-28,190.97,191.48,189.49,190.74,1240000,190.74 1948-05-27,191.06,192.31,190.39,190.97,1830000,190.97 1948-05-26,189.71,191.57,189.06,191.06,1840000,191.06 1948-05-25,189.82,190.77,188.55,189.71,1810000,189.71 1948-05-24,190.00,190.67,188.98,189.82,1560000,189.82 1948-05-21,189.26,191.44,189.17,189.78,2680000,189.78 1948-05-20,188.28,190.00,187.52,189.26,2480000,189.26 1948-05-19,188.56,189.44,187.46,188.28,1850000,188.28 1948-05-18,190.44,191.01,188.16,188.56,2480000,188.56 1948-05-17,190.25,191.00,188.37,190.44,3050000,190.44 1948-05-14,185.01,188.93,185.01,188.60,3840000,188.60 1948-05-13,183.95,185.40,183.32,184.82,2030000,184.82 1948-05-12,183.75,184.39,182.49,183.95,1530000,183.95 1948-05-11,182.94,184.25,182.21,183.75,1750000,183.75 1948-05-10,182.50,183.46,181.68,182.94,1440000,182.94 1948-05-07,181.68,183.20,181.68,182.29,1670000,182.29 1948-05-06,180.94,182.14,180.16,181.65,1310000,181.65 1948-05-05,181.44,182.05,180.56,180.94,1240000,180.94 1948-05-04,181.09,182.43,180.70,181.44,1460000,181.44 1948-05-03,180.28,181.71,179.47,181.09,1150000,181.09 1948-04-30,180.65,181.64,179.69,180.51,1450000,180.51 1948-04-29,181.01,181.47,179.33,180.65,1480000,180.65 1948-04-28,180.97,182.01,180.16,181.01,1400000,181.01 1948-04-27,181.32,181.87,180.07,180.97,1420000,180.97 1948-04-26,183.09,183.09,180.93,181.32,1410000,181.32 1948-04-23,182.98,184.48,182.67,183.78,2470000,183.78 1948-04-22,181.37,183.40,180.88,182.98,2330000,182.98 1948-04-21,180.72,181.79,179.49,181.37,1670000,181.37 1948-04-20,181.05,181.87,179.98,180.72,1700000,180.72 1948-04-19,180.38,181.42,179.90,181.05,1530000,181.05 1948-04-16,180.27,181.69,179.90,180.63,2140000,180.63 1948-04-15,179.13,180.94,178.60,180.27,1650000,180.27 1948-04-14,179.45,179.91,178.59,179.13,1030000,179.13 1948-04-13,179.05,179.89,178.58,179.45,950000,179.45 1948-04-12,179.48,180.19,178.26,179.05,1030000,179.05 1948-04-09,178.80,179.91,178.40,179.16,1380000,179.16 1948-04-08,178.33,179.38,177.91,178.80,1060000,178.80 1948-04-07,178.77,179.14,177.70,178.33,1160000,178.33 1948-04-06,177.92,179.31,177.62,178.77,1310000,178.77 1948-04-05,177.45,178.27,177.06,177.92,1040000,177.92 1948-04-02,177.61,178.10,175.96,177.32,1080000,177.32 1948-04-01,177.20,178.14,176.08,177.61,1490000,177.61 1948-03-31,175.63,177.61,175.63,177.20,1780000,177.20 1948-03-30,173.65,175.38,173.30,175.23,1060000,175.23 1948-03-29,173.95,174.33,172.55,173.65,760000,173.65 1948-03-25,173.62,174.63,173.08,174.05,1040000,174.05 1948-03-24,173.50,174.45,172.84,173.62,1040000,173.62 1948-03-23,173.66,174.26,172.71,173.50,1160000,173.50 1948-03-22,173.12,175.23,173.05,173.66,2040000,173.66 1948-03-19,166.92,169.75,166.50,169.67,1160000,169.67 1948-03-18,166.24,168.02,166.07,166.92,890000,166.92 1948-03-17,165.39,166.60,165.03,166.24,930000,166.24 1948-03-16,167.21,167.21,165.03,165.39,940000,165.39 1948-03-15,167.62,168.16,167.09,167.62,690000,167.62 1948-03-12,167.21,167.54,166.51,166.99,690000,166.99 1948-03-11,167.48,168.27,166.67,167.21,820000,167.21 1948-03-10,166.76,167.74,166.04,167.48,730000,167.48 1948-03-09,167.54,167.54,166.04,166.76,660000,166.76 1948-03-08,168.94,169.13,167.46,167.71,740000,167.71 1948-03-05,168.13,168.64,167.20,168.35,630000,168.35 1948-03-04,168.61,168.92,167.69,168.13,580000,168.13 1948-03-03,168.75,169.28,168.04,168.61,760000,168.61 1948-03-02,168.14,169.18,167.78,168.75,780000,168.75 1948-03-01,167.46,168.62,167.46,168.14,770000,168.14 1948-02-27,167.56,167.56,165.92,166.80,770000,166.80 1948-02-26,168.39,168.71,167.17,167.56,620000,167.56 1948-02-25,167.80,169.01,167.72,168.39,710000,168.39 1948-02-24,167.60,168.24,166.81,167.80,640000,167.80 1948-02-20,167.86,167.96,166.38,167.44,700000,167.44 1948-02-19,168.04,168.98,167.34,167.86,680000,167.86 1948-02-18,167.89,168.36,167.23,168.04,610000,168.04 1948-02-17,168.30,169.23,167.47,167.89,720000,167.89 1948-02-16,166.86,168.96,166.86,168.30,830000,168.30 1948-02-13,165.69,167.23,164.99,166.33,930000,166.33 1948-02-11,165.65,166.41,164.07,165.69,1490000,165.69 1948-02-10,168.92,168.92,165.26,165.65,1460000,165.65 1948-02-09,169.79,170.83,169.33,169.82,660000,169.82 1948-02-06,169.18,169.67,167.95,168.81,920000,168.81 1948-02-05,170.43,170.43,168.13,169.18,1200000,169.18 1948-02-04,173.39,173.39,170.41,170.95,1200000,170.95 1948-02-03,174.92,175.03,173.54,173.95,700000,173.95 1948-02-02,175.05,176.05,174.57,174.92,770000,174.92 1948-01-30,174.47,175.58,173.61,174.76,890000,174.76 1948-01-29,173.39,175.06,173.39,174.47,1060000,174.47 1948-01-28,171.68,173.49,171.68,172.97,860000,172.97 1948-01-27,171.18,172.66,170.83,171.42,830000,171.42 1948-01-26,171.67,172.41,170.70,171.18,650000,171.18 1948-01-23,172.15,173.04,171.36,171.97,790000,171.97 1948-01-22,173.33,173.33,171.37,172.15,1110000,172.15 1948-01-21,175.27,176.10,173.32,173.53,1190000,173.53 1948-01-20,175.95,176.13,174.62,175.27,710000,175.27 1948-01-19,177.24,177.34,175.21,175.95,1050000,175.95 1948-01-16,177.03,177.71,176.28,177.15,760000,177.15 1948-01-15,177.49,178.06,176.54,177.03,780000,177.03 1948-01-14,177.49,178.16,176.50,177.37,820000,177.37 1948-01-13,179.29,179.29,177.25,177.49,950000,177.49 1948-01-12,180.20,180.62,178.70,179.33,1000000,179.33 1948-01-09,180.60,181.40,179.74,180.09,980000,180.09 1948-01-08,179.83,181.04,179.35,180.60,900000,180.60 1948-01-07,179.12,180.40,178.48,179.83,820000,179.83 1948-01-06,179.53,179.88,177.78,179.12,1030000,179.12 1948-01-05,181.04,181.69,179.19,179.53,1090000,179.53 1948-01-02,181.16,181.53,180.01,181.04,710000,181.04 1947-12-31,180.56,181.82,180.29,181.16,1540000,181.16 1947-12-30,178.58,180.93,178.43,180.56,1380000,180.56 1947-12-29,179.23,179.81,177.93,178.58,1190000,178.58 1947-12-26,180.71,180.71,178.90,179.28,930000,179.28 1947-12-24,180.71,181.49,179.72,180.84,1190000,180.84 1947-12-23,180.21,181.25,179.61,180.71,1360000,180.71 1947-12-22,181.06,181.78,179.98,180.21,1450000,180.21 1947-12-19,179.44,180.64,178.62,180.09,1250000,180.09 1947-12-18,179.81,180.53,179.03,179.44,1130000,179.44 1947-12-17,179.44,180.23,178.69,179.81,1250000,179.81 1947-12-16,179.69,180.25,178.91,179.44,1090000,179.44 1947-12-15,179.34,180.56,178.74,179.69,1430000,179.69 1947-12-12,177.37,178.92,176.82,178.37,1220000,178.37 1947-12-11,177.58,177.98,176.44,177.37,940000,177.37 1947-12-10,177.47,178.22,176.89,177.58,1120000,177.58 1947-12-09,176.71,177.98,176.00,177.47,1100000,177.47 1947-12-08,175.74,177.35,175.19,176.71,960000,176.71 1947-12-05,178.57,178.57,175.46,176.10,1290000,176.10 1947-12-04,179.63,179.83,178.16,178.79,970000,178.79 1947-12-03,180.57,180.57,179.18,179.63,930000,179.63 1947-12-02,180.61,181.77,180.20,180.76,890000,180.76 1947-12-01,179.40,181.09,179.09,180.61,800000,180.61 1947-11-28,180.94,181.15,179.30,179.51,930000,179.51 1947-11-26,181.35,182.39,180.67,180.94,910000,180.94 1947-11-25,181.98,182.43,180.92,181.35,870000,181.35 1947-11-24,182.33,183.06,181.23,181.98,790000,181.98 1947-11-21,183.17,183.97,182.23,182.61,980000,182.61 1947-11-20,182.71,183.68,181.15,183.17,960000,183.17 1947-11-19,181.71,182.98,181.69,182.71,1050000,182.71 1947-11-18,180.40,181.99,180.12,181.71,930000,181.71 1947-11-17,180.26,180.95,179.57,180.40,750000,180.40 1947-11-14,180.00,181.00,179.67,180.05,780000,180.05 1947-11-13,180.96,180.96,179.60,180.00,790000,180.00 1947-11-12,182.21,182.56,180.56,181.04,900000,181.04 1947-11-10,181.49,182.70,181.17,182.21,720000,182.21 1947-11-07,182.00,182.45,181.12,181.54,760000,181.54 1947-11-06,181.89,182.33,180.61,182.00,850000,182.00 1947-11-05,182.65,183.57,181.61,181.89,1050000,181.89 1947-11-03,182.48,183.51,181.85,182.65,760000,182.65 1947-10-31,181.32,182.40,180.43,181.81,800000,181.81 1947-10-30,183.04,183.04,179.93,181.32,1390000,181.32 1947-10-29,183.21,184.70,182.47,183.06,1140000,183.06 1947-10-28,183.27,184.24,182.72,183.21,930000,183.21 1947-10-27,182.73,183.70,181.76,183.27,850000,183.27 1947-10-24,184.47,184.47,181.55,182.53,1650000,182.53 1947-10-23,184.36,185.30,183.71,184.50,1190000,184.50 1947-10-22,185.09,185.89,184.00,184.36,1260000,184.36 1947-10-21,185.29,186.01,184.05,185.09,1400000,185.09 1947-10-20,184.27,186.24,184.27,185.29,1770000,185.29 1947-10-17,183.54,184.39,182.40,183.52,1270000,183.52 1947-10-16,183.28,184.56,182.71,183.54,1400000,183.54 1947-10-15,182.73,184.27,182.25,183.28,1930000,183.28 1947-10-14,180.49,183.14,180.47,182.73,1810000,182.73 1947-10-10,180.11,181.34,179.63,180.44,1120000,180.44 1947-10-09,178.78,180.37,178.56,180.11,790000,180.11 1947-10-08,180.01,180.56,178.26,178.78,1130000,178.78 1947-10-07,180.08,180.49,179.33,180.01,880000,180.01 1947-10-06,179.44,180.61,179.03,180.08,930000,180.08 1947-10-03,178.47,180.31,178.26,179.53,1270000,179.53 1947-10-02,178.10,178.96,177.50,178.47,850000,178.47 1947-10-01,177.49,178.87,176.89,178.10,1160000,178.10 1947-09-30,175.85,177.70,175.36,177.49,960000,177.49 1947-09-29,174.86,176.44,174.74,175.85,740000,175.85 1947-09-26,175.29,175.57,174.42,174.86,640000,174.86 1947-09-25,176.39,176.84,174.92,175.29,770000,175.29 1947-09-24,176.04,176.79,175.27,176.39,570000,176.39 1947-09-23,178.02,178.25,175.54,176.04,880000,176.04 1947-09-22,178.12,178.76,177.36,178.02,670000,178.02 1947-09-19,178.31,178.61,177.03,178.12,750000,178.12 1947-09-18,178.73,179.27,177.68,178.31,940000,178.31 1947-09-17,176.86,179.37,176.86,178.73,1260000,178.73 1947-09-16,175.30,177.01,174.73,176.70,740000,176.70 1947-09-15,175.92,175.99,174.82,175.30,500000,175.30 1947-09-12,176.16,176.67,174.98,175.92,600000,175.92 1947-09-11,176.24,177.11,175.31,176.16,800000,176.16 1947-09-10,175.32,176.61,174.70,176.24,740000,176.24 1947-09-09,175.14,176.07,174.02,175.32,750000,175.32 1947-09-08,176.97,176.97,174.79,175.14,830000,175.14 1947-09-05,177.27,177.51,175.69,177.13,720000,177.13 1947-09-04,178.84,178.84,176.57,177.27,870000,177.27 1947-09-03,179.81,180.08,178.55,179.09,650000,179.09 1947-09-02,178.85,180.56,178.75,179.81,650000,179.81 1947-08-29,177.70,179.15,177.63,178.85,580000,178.85 1947-08-28,177.88,178.64,177.22,177.70,580000,177.70 1947-08-27,177.73,178.24,177.14,177.88,480000,177.88 1947-08-26,177.57,178.05,176.54,177.73,620000,177.73 1947-08-25,179.74,179.78,177.33,177.57,810000,177.57 1947-08-22,179.42,180.29,179.34,179.74,580000,179.74 1947-08-21,179.01,179.77,178.50,179.42,580000,179.42 1947-08-20,179.75,180.10,178.76,179.01,600000,179.01 1947-08-19,180.44,180.64,179.23,179.75,600000,179.75 1947-08-18,181.04,181.43,179.98,180.44,710000,180.44 1947-08-15,179.87,181.58,179.44,181.04,880000,181.04 1947-08-14,179.80,180.43,178.64,179.87,690000,179.87 1947-08-13,179.94,180.71,179.17,179.80,690000,179.80 1947-08-12,179.14,180.76,179.14,179.94,690000,179.94 1947-08-11,180.13,180.16,178.22,178.98,720000,178.98 1947-08-08,181.99,181.99,179.77,180.13,790000,180.13 1947-08-07,182.35,182.57,181.03,182.11,660000,182.11 1947-08-06,183.08,183.48,181.85,182.35,660000,182.35 1947-08-05,182.51,184.05,182.27,183.08,750000,183.08 1947-08-04,183.69,183.69,182.01,182.51,730000,182.51 1947-08-01,183.18,184.38,182.35,183.81,770000,183.81 1947-07-31,181.17,183.56,181.17,183.18,840000,183.18 1947-07-30,182.05,182.33,179.77,180.91,1170000,180.91 1947-07-29,184.95,185.28,181.47,182.05,1390000,182.05 1947-07-28,186.38,187.51,184.76,184.95,1090000,184.95 1947-07-25,186.85,187.66,185.59,186.38,1150000,186.38 1947-07-24,185.08,187.36,185.08,186.85,1570000,186.85 1947-07-23,183.86,185.59,183.86,184.95,1070000,184.95 1947-07-22,183.52,184.41,182.73,183.78,780000,183.78 1947-07-21,184.60,184.86,183.02,183.52,850000,183.52 1947-07-18,183.83,184.87,182.51,184.60,920000,184.60 1947-07-17,185.46,185.92,183.43,183.83,1110000,183.83 1947-07-16,185.38,186.23,184.05,185.46,1070000,185.46 1947-07-15,185.60,186.24,184.23,185.38,1180000,185.38 1947-07-14,184.77,187.15,184.64,185.60,1660000,185.60 1947-07-11,183.14,185.52,183.14,184.77,1590000,184.77 1947-07-10,181.72,183.24,180.82,182.80,1020000,182.80 1947-07-09,182.66,183.29,181.12,181.72,1040000,181.72 1947-07-08,182.04,183.74,181.53,182.66,1390000,182.66 1947-07-07,181.73,182.61,180.62,182.04,1050000,182.04 1947-07-03,179.88,182.07,179.18,181.73,1250000,181.73 1947-07-02,180.33,181.17,179.33,179.88,1180000,179.88 1947-07-01,177.48,180.42,177.48,180.33,1090000,180.33 1947-06-30,176.56,177.66,175.82,177.30,670000,177.30 1947-06-27,176.58,177.20,175.77,176.56,650000,176.56 1947-06-26,175.73,177.15,175.44,176.58,820000,176.58 1947-06-25,174.54,176.19,173.93,175.73,830000,175.73 1947-06-24,177.44,177.55,174.13,174.54,1090000,174.54 1947-06-23,176.44,178.08,176.22,177.44,950000,177.44 1947-06-20,176.14,177.52,175.54,176.44,1130000,176.44 1947-06-19,174.94,176.81,174.43,176.14,1010000,176.14 1947-06-18,174.98,176.42,174.25,174.94,910000,174.94 1947-06-17,175.81,176.11,174.28,174.98,670000,174.98 1947-06-16,175.49,177.23,174.26,175.81,990000,175.81 1947-06-13,173.78,176.02,173.26,175.49,960000,175.49 1947-06-12,174.68,175.30,173.24,173.78,1040000,173.78 1947-06-11,171.50,174.78,171.50,174.68,1350000,174.68 1947-06-10,169.88,171.36,169.10,171.10,650000,171.10 1947-06-09,170.28,170.71,169.26,169.88,550000,169.88 1947-06-06,169.41,170.62,168.54,170.28,660000,170.28 1947-06-05,169.56,170.16,168.90,169.41,520000,169.41 1947-06-04,170.35,171.35,169.16,169.56,820000,169.56 1947-06-03,168.00,170.69,168.00,170.35,690000,170.35 1947-06-02,168.66,168.66,167.43,168.00,520000,168.00 1947-05-29,168.06,169.78,168.06,169.25,900000,169.25 1947-05-28,166.64,168.90,166.64,168.06,890000,168.06 1947-05-27,166.29,166.53,164.59,166.17,660000,166.17 1947-05-26,166.97,167.22,165.86,166.29,540000,166.29 1947-05-23,166.73,167.68,166.06,166.95,680000,166.95 1947-05-22,165.81,167.56,165.81,166.73,950000,166.73 1947-05-21,163.59,166.03,162.98,165.77,1030000,165.77 1947-05-20,163.55,164.81,162.99,163.59,920000,163.59 1947-05-19,163.21,164.43,161.38,163.55,1860000,163.55 1947-05-16,167.88,168.02,164.46,164.96,1430000,164.96 1947-05-15,166.96,168.42,166.96,167.88,770000,167.88 1947-05-14,167.34,167.34,165.93,166.68,1050000,166.68 1947-05-13,169.17,169.17,167.07,167.34,1210000,167.34 1947-05-12,171.67,171.69,169.76,169.80,700000,169.80 1947-05-09,171.56,172.21,170.52,171.54,720000,171.54 1947-05-08,172.49,172.87,171.20,171.56,670000,171.56 1947-05-07,172.77,173.14,172.08,172.49,600000,172.49 1947-05-06,173.83,173.83,171.89,172.77,750000,172.77 1947-05-05,174.00,175.08,173.58,174.21,740000,174.21 1947-05-02,171.91,174.45,171.85,173.45,950000,173.45 1947-05-01,170.64,172.65,170.62,171.91,920000,171.91 1947-04-30,168.76,170.85,168.76,170.64,780000,170.64 1947-04-29,168.85,169.47,167.42,168.70,840000,168.70 1947-04-28,169.13,170.00,168.56,168.85,590000,168.85 1947-04-25,170.19,170.38,168.60,168.93,780000,168.93 1947-04-24,170.87,171.20,169.81,170.19,620000,170.19 1947-04-23,170.94,171.71,169.92,170.87,700000,170.87 1947-04-22,169.50,171.25,169.24,170.94,890000,170.94 1947-04-21,169.32,171.47,169.32,169.50,1160000,169.50 1947-04-18,168.01,168.68,166.49,166.77,970000,166.77 1947-04-17,168.22,168.91,167.49,168.01,760000,168.01 1947-04-16,166.82,168.85,166.60,168.22,900000,168.22 1947-04-15,166.69,168.09,165.39,166.82,1450000,166.82 1947-04-14,170.42,170.42,166.17,166.69,2200000,166.69 1947-04-11,173.98,174.76,172.90,173.43,860000,173.43 1947-04-10,173.44,174.78,173.44,173.98,680000,173.98 1947-04-09,173.29,173.72,172.39,173.40,850000,173.40 1947-04-08,175.39,175.52,172.68,173.29,1020000,173.29 1947-04-07,176.16,176.16,174.82,175.39,630000,175.39 1947-04-03,177.21,177.21,175.60,176.52,680000,176.52 1947-04-02,177.45,178.28,176.87,177.32,680000,177.32 1947-04-01,177.20,177.74,176.18,177.45,770000,177.45 1947-03-31,178.36,178.58,177.01,177.20,720000,177.20 1947-03-28,179.19,179.68,178.18,178.63,830000,178.63 1947-03-27,177.67,179.51,177.67,179.19,1140000,179.19 1947-03-26,175.29,177.31,174.61,177.10,940000,177.10 1947-03-25,176.40,176.66,175.05,175.29,660000,175.29 1947-03-24,177.27,177.61,176.18,176.40,580000,176.40 1947-03-21,175.37,177.12,175.37,176.90,710000,176.90 1947-03-20,175.78,176.06,174.76,175.37,630000,175.37 1947-03-19,175.11,176.90,175.11,175.78,760000,175.78 1947-03-18,173.35,175.22,173.26,174.95,660000,174.95 1947-03-17,172.53,174.15,172.53,173.35,640000,173.35 1947-03-14,173.95,173.95,171.94,172.58,820000,172.58 1947-03-13,174.68,175.32,173.95,174.35,650000,174.35 1947-03-12,173.83,175.62,173.43,174.68,950000,174.68 1947-03-11,175.18,175.20,173.09,173.83,1190000,173.83 1947-03-10,175.84,176.36,174.73,175.18,830000,175.18 1947-03-07,181.19,181.19,176.87,177.05,1210000,177.05 1947-03-06,181.16,182.48,180.60,181.88,1020000,181.88 1947-03-05,179.77,181.56,179.32,181.16,990000,181.16 1947-03-04,179.43,180.31,178.46,179.77,680000,179.77 1947-03-03,179.29,179.84,178.29,179.43,690000,179.43 1947-02-28,178.91,179.73,177.75,178.90,690000,178.90 1947-02-27,177.67,179.35,177.67,178.91,960000,178.91 1947-02-26,179.02,179.02,176.34,177.22,1350000,177.22 1947-02-25,181.40,181.92,178.86,179.31,1280000,179.31 1947-02-24,182.26,182.47,181.03,181.40,810000,181.40 1947-02-21,181.22,182.76,181.22,182.26,860000,182.26 1947-02-20,180.78,181.40,179.12,180.74,1000000,180.74 1947-02-19,181.85,181.85,179.85,180.78,980000,180.78 1947-02-18,182.20,182.91,181.26,181.93,870000,181.93 1947-02-17,181.36,182.74,180.89,182.20,870000,182.20 1947-02-14,182.18,182.23,180.62,181.64,940000,181.64 1947-02-13,184.06,184.25,181.64,182.18,1340000,182.18 1947-02-11,183.57,184.43,181.81,184.06,1300000,184.06 1947-02-10,184.49,184.96,183.12,183.57,1300000,183.57 1947-02-07,181.57,184.18,181.33,183.74,1980000,183.74 1947-02-06,182.27,182.27,180.72,181.57,1120000,181.57 1947-02-05,182.28,183.12,181.45,182.52,1180000,182.52 1947-02-04,181.92,183.15,181.44,182.28,1350000,182.28 1947-02-03,180.88,182.37,180.57,181.92,1360000,181.92 1947-01-31,179.74,181.34,179.49,180.44,1310000,180.44 1947-01-30,180.17,181.18,179.33,179.74,1340000,179.74 1947-01-29,178.68,180.47,178.03,180.17,1530000,180.17 1947-01-28,177.28,178.98,177.17,178.68,1080000,178.68 1947-01-27,175.35,177.55,175.05,177.28,900000,177.28 1947-01-24,175.13,175.94,174.49,175.49,950000,175.49 1947-01-23,173.77,175.35,173.59,175.13,880000,175.13 1947-01-22,173.51,174.19,172.81,173.77,670000,173.77 1947-01-21,173.95,173.95,172.64,173.51,700000,173.51 1947-01-20,175.46,175.46,173.69,174.06,810000,174.06 1947-01-17,172.58,174.87,172.58,174.76,850000,174.76 1947-01-16,172.10,172.39,170.13,171.95,1050000,171.95 1947-01-15,172.63,173.46,171.70,172.10,770000,172.10 1947-01-14,172.49,173.24,171.31,172.63,850000,172.63 1947-01-13,174.50,174.50,170.99,172.49,1590000,172.49 1947-01-10,178.43,178.84,176.59,177.43,1060000,177.43 1947-01-09,178.06,179.06,177.94,178.43,720000,178.43 1947-01-08,177.49,178.43,176.54,178.06,790000,178.06 1947-01-07,178.43,179.25,177.12,177.49,980000,177.49 1947-01-06,176.99,179.04,176.99,178.43,980000,178.43 1947-01-03,176.39,177.16,174.71,176.76,880000,176.76 1947-01-02,177.20,177.83,176.04,176.39,750000,176.39 1946-12-31,176.23,177.89,175.79,177.20,1820000,177.20 1946-12-30,175.77,177.00,175.15,176.23,1400000,176.23 1946-12-27,175.21,176.17,173.88,175.66,1290000,175.66 1946-12-26,176.79,176.79,174.54,175.21,1150000,175.21 1946-12-24,177.36,177.51,176.25,176.95,950000,176.95 1946-12-23,178.32,178.54,176.85,177.36,1170000,177.36 1946-12-20,177.29,178.58,176.82,177.85,1470000,177.85 1946-12-19,175.28,178.06,175.28,177.29,1740000,177.29 1946-12-18,174.47,175.62,173.62,174.84,1110000,174.84 1946-12-17,174.85,175.42,173.93,174.47,1020000,174.47 1946-12-16,174.73,175.93,174.18,174.85,1010000,174.85 1946-12-13,173.91,174.41,172.57,173.90,1000000,173.90 1946-12-12,175.75,175.75,173.17,173.91,1060000,173.91 1946-12-11,175.92,176.67,174.80,176.07,1230000,176.07 1946-12-10,175.76,177.21,174.84,175.92,1730000,175.92 1946-12-09,173.43,176.48,173.43,175.76,2840000,175.76 1946-12-06,169.95,170.92,169.20,170.39,1060000,170.39 1946-12-05,170.33,170.66,168.63,169.95,1000000,169.95 1946-12-04,168.05,170.98,167.48,170.33,1440000,170.33 1946-12-03,167.50,168.60,166.20,168.05,960000,168.05 1946-12-02,169.07,169.07,166.74,167.50,810000,167.50 1946-11-29,168.34,170.25,168.23,169.78,1010000,169.78 1946-11-27,167.35,169.18,167.35,168.34,1070000,168.34 1946-11-26,165.31,167.81,165.31,166.94,1080000,166.94 1946-11-25,165.10,165.99,164.37,165.23,790000,165.23 1946-11-22,164.12,164.49,162.29,163.55,1200000,163.55 1946-11-21,165.98,165.98,163.50,164.12,1380000,164.12 1946-11-20,167.88,167.99,166.20,166.91,970000,166.91 1946-11-19,167.91,168.35,166.28,167.88,910000,167.88 1946-11-18,169.03,169.11,167.23,167.91,770000,167.91 1946-11-15,170.88,171.48,168.89,169.67,930000,169.67 1946-11-14,169.84,171.45,169.16,170.88,960000,170.88 1946-11-13,170.52,170.52,168.02,169.84,1050000,169.84 1946-11-12,171.80,173.48,170.41,170.87,1370000,170.87 1946-11-08,169.75,171.78,169.75,170.79,1000000,170.79 1946-11-07,168.88,170.59,167.50,169.60,1140000,169.60 1946-11-06,174.40,175.00,168.38,168.88,2000000,168.88 1946-11-04,173.03,175.78,173.03,174.40,1830000,174.40 1946-11-01,169.50,172.33,169.50,171.76,1670000,171.76 1946-10-31,164.71,169.68,164.71,169.15,1700000,169.15 1946-10-30,164.21,165.32,160.49,164.20,1940000,164.20 1946-10-29,166.04,166.62,163.98,164.21,1240000,164.21 1946-10-28,168.44,168.48,165.98,166.04,1000000,166.04 1946-10-25,169.96,169.96,167.84,168.76,920000,168.76 1946-10-24,170.67,170.90,169.14,169.98,860000,169.98 1946-10-23,171.25,171.27,168.68,170.67,950000,170.67 1946-10-22,171.93,172.34,170.16,171.25,870000,171.25 1946-10-21,171.34,172.59,170.63,171.93,840000,171.93 1946-10-18,171.76,172.51,170.32,171.65,920000,171.65 1946-10-17,173.61,173.61,170.47,171.76,1310000,171.76 1946-10-16,175.94,177.05,174.03,174.35,1650000,174.35 1946-10-15,173.05,176.52,173.05,175.94,2370000,175.94 1946-10-14,167.97,170.60,167.53,169.86,1290000,169.86 1946-10-11,165.60,168.59,165.60,167.97,1470000,167.97 1946-10-10,163.12,165.53,161.61,164.94,2220000,164.94 1946-10-09,166.53,166.53,162.80,163.12,2020000,163.12 1946-10-08,168.87,170.00,167.00,167.34,1260000,167.34 1946-10-07,169.00,169.83,167.82,168.87,950000,168.87 1946-10-04,171.13,171.13,169.06,169.80,920000,169.80 1946-10-03,172.72,173.10,171.13,171.64,920000,171.64 1946-10-02,171.47,173.39,171.40,172.72,960000,172.72 1946-10-01,172.42,172.42,170.43,171.47,890000,171.47 1946-09-30,173.56,173.56,170.44,172.42,1060000,172.42 1946-09-27,174.96,175.18,173.35,174.09,980000,174.09 1946-09-26,172.95,175.45,171.59,174.96,1300000,174.96 1946-09-25,169.43,173.44,169.43,172.95,1800000,172.95 1946-09-24,166.56,169.75,164.21,168.89,2230000,168.89 1946-09-23,169.06,172.15,166.09,166.56,2120000,166.56 1946-09-20,165.17,170.13,164.77,169.06,2820000,169.06 1946-09-19,169.05,169.05,164.09,165.17,2890000,165.17 1946-09-18,173.43,173.43,168.40,169.07,2100000,169.07 1946-09-17,174.45,174.82,172.13,173.66,1390000,173.66 1946-09-16,173.39,176.26,173.02,174.45,1490000,174.45 1946-09-13,171.70,173.99,170.87,173.39,1720000,173.39 1946-09-12,172.13,173.34,170.00,171.70,2010000,171.70 1946-09-11,167.80,173.20,167.80,172.13,2870000,172.13 1946-09-10,172.03,172.89,166.56,167.30,3300000,167.30 1946-09-09,177.52,177.52,170.53,172.03,2840000,172.03 1946-09-06,181.18,181.67,178.43,179.96,1670000,179.96 1946-09-05,177.96,181.81,177.96,181.18,2360000,181.18 1946-09-04,178.68,178.93,173.64,176.72,3620000,176.72 1946-09-03,188.21,188.21,177.49,178.68,2910000,178.68 1946-08-30,190.47,190.49,187.44,189.19,1170000,189.19 1946-08-29,190.03,192.50,189.71,190.47,1060000,190.47 1946-08-28,191.04,191.28,187.26,190.03,2100000,190.03 1946-08-27,195.46,195.46,190.03,191.04,1790000,191.04 1946-08-26,197.75,198.97,196.47,196.99,760000,196.99 1946-08-23,196.91,198.69,196.91,197.75,770000,197.75 1946-08-22,198.05,198.05,194.53,196.66,1540000,196.66 1946-08-21,201.27,202.49,199.70,200.00,840000,200.00 1946-08-20,200.22,201.54,200.22,201.27,700000,201.27 1946-08-19,200.69,201.18,198.90,200.19,660000,200.19 1946-08-16,201.38,201.38,200.14,200.69,690000,200.69 1946-08-15,203.81,203.81,202.30,202.49,620000,202.49 1946-08-14,204.52,205.01,203.59,203.99,800000,203.99 1946-08-13,203.36,204.94,203.28,204.52,930000,204.52 1946-08-12,203.57,203.90,202.81,203.36,700000,203.36 1946-08-09,204.10,204.46,203.16,203.57,910000,203.57 1946-08-08,203.13,204.56,203.13,204.10,890000,204.10 1946-08-07,201.35,203.08,201.18,202.96,880000,202.96 1946-08-06,201.93,202.05,200.47,201.35,730000,201.35 1946-08-05,202.82,203.03,201.39,201.93,700000,201.93 1946-08-02,202.26,203.38,201.70,202.82,750000,202.82 1946-08-01,201.56,202.97,201.32,202.26,830000,202.26 1946-07-31,199.40,202.15,198.78,201.56,1020000,201.56 1946-07-30,198.22,200.13,198.15,199.40,810000,199.40 1946-07-29,197.63,199.11,197.46,198.22,720000,198.22 1946-07-26,196.47,198.53,196.47,197.63,980000,197.63 1946-07-25,195.37,197.58,195.28,196.25,900000,196.25 1946-07-24,195.22,196.30,194.33,195.37,1170000,195.37 1946-07-23,199.83,199.83,194.79,195.22,1690000,195.22 1946-07-22,201.13,201.60,199.89,200.54,690000,200.54 1946-07-19,201.86,202.06,200.68,201.13,650000,201.13 1946-07-18,202.25,203.46,201.47,201.86,730000,201.86 1946-07-17,200.85,202.79,200.85,202.25,920000,202.25 1946-07-16,200.86,201.44,199.48,200.71,1180000,200.71 1946-07-15,204.20,204.42,200.31,200.86,1170000,200.86 1946-07-12,205.49,205.49,202.85,204.20,1130000,204.20 1946-07-11,207.52,207.52,205.96,206.30,990000,206.30 1946-07-10,207.43,208.17,206.84,207.56,910000,207.56 1946-07-09,206.62,207.91,206.16,207.43,790000,207.43 1946-07-08,206.72,207.40,206.37,206.62,680000,206.62 1946-07-05,207.06,207.40,206.47,206.72,490000,206.72 1946-07-03,206.64,207.50,206.36,207.06,650000,207.06 1946-07-02,206.47,207.43,205.56,206.64,760000,206.64 1946-07-01,205.79,208.59,205.79,206.47,1570000,206.47 1946-06-28,205.03,206.41,204.32,205.62,1010000,205.62 1946-06-27,202.83,205.68,202.83,205.03,1200000,205.03 1946-06-26,202.54,202.98,200.52,202.10,1020000,202.10 1946-06-25,203.56,203.83,202.11,202.54,880000,202.54 1946-06-24,203.09,204.65,202.68,203.56,990000,203.56 1946-06-21,200.52,203.42,198.98,203.09,1340000,203.09 1946-06-20,205.65,205.65,200.31,200.52,1310000,200.52 1946-06-19,206.93,206.93,205.24,205.74,1010000,205.74 1946-06-18,210.01,210.01,207.42,207.71,1150000,207.71 1946-06-17,210.36,211.16,209.65,210.13,1020000,210.13 1946-06-14,210.56,211.46,209.73,210.36,980000,210.36 1946-06-13,209.26,211.25,209.26,210.56,1150000,210.56 1946-06-12,209.05,209.34,207.52,208.96,970000,208.96 1946-06-11,210.68,211.14,208.92,209.05,1090000,209.05 1946-06-10,209.96,211.44,209.73,210.68,1010000,210.68 1946-06-07,209.50,210.40,208.63,209.96,1020000,209.96 1946-06-06,209.78,210.03,208.33,209.50,980000,209.50 1946-06-05,210.03,211.42,209.30,209.78,1170000,209.78 1946-06-04,211.37,211.37,209.03,210.03,1210000,210.03 1946-06-03,212.28,212.48,210.90,211.47,1210000,211.47 1946-05-31,212.50,213.29,210.97,212.28,1310000,212.28 1946-05-29,211.70,213.36,210.34,212.50,2000000,212.50 1946-05-28,209.42,212.18,209.29,211.70,2220000,211.70 1946-05-27,208.41,210.34,208.41,209.42,1730000,209.42 1946-05-24,208.05,208.26,205.96,207.69,1210000,207.69 1946-05-23,208.00,209.15,207.21,208.05,1350000,208.05 1946-05-22,207.25,208.38,207.00,208.00,1610000,208.00 1946-05-21,207.13,208.53,206.58,207.25,1250000,207.25 1946-05-20,205.80,207.42,205.05,207.13,910000,207.13 1946-05-17,206.17,207.04,205.50,206.56,980000,206.56 1946-05-16,205.07,206.53,204.38,206.17,1140000,206.17 1946-05-15,206.69,206.82,204.23,205.07,1020000,205.07 1946-05-14,207.34,207.47,205.88,206.69,1140000,206.69 1946-05-13,208.06,208.40,206.63,207.34,1250000,207.34 1946-05-10,204.09,207.84,204.09,207.10,1820000,207.10 1946-05-09,204.17,204.68,202.37,204.07,1070000,204.07 1946-05-08,203.51,205.05,202.35,204.17,1210000,204.17 1946-05-07,200.65,203.97,199.94,203.51,1350000,203.51 1946-05-06,202.00,202.00,199.26,200.65,1080000,200.65 1946-05-03,204.31,204.31,202.53,203.25,1020000,203.25 1946-05-02,205.67,206.25,204.47,204.98,1000000,204.98 1946-05-01,206.59,206.59,205.07,205.67,980000,205.67 1946-04-30,206.09,207.23,205.53,206.77,1000000,206.77 1946-04-29,206.13,206.97,205.07,206.09,1000000,206.09 1946-04-26,204.60,205.21,203.13,204.59,1210000,204.59 1946-04-25,205.60,205.60,203.09,204.60,1390000,204.60 1946-04-24,207.31,208.12,205.81,206.13,1500000,206.13 1946-04-23,207.99,208.19,206.01,207.31,1380000,207.31 1946-04-22,208.06,208.94,207.33,207.99,1210000,207.99 1946-04-18,207.93,209.10,207.27,208.31,1500000,208.31 1946-04-17,207.97,209.36,206.97,207.93,1530000,207.93 1946-04-16,206.28,208.51,206.28,207.97,1460000,207.97 1946-04-15,206.02,206.36,204.57,206.01,1170000,206.01 1946-04-12,206.93,207.89,206.16,206.96,1240000,206.96 1946-04-11,207.90,207.90,206.05,206.93,1210000,206.93 1946-04-10,208.03,208.93,207.20,208.02,1580000,208.02 1946-04-09,205.43,208.54,205.43,208.03,1710000,208.03 1946-04-08,204.98,206.00,204.31,205.43,1250000,205.43 1946-04-05,204.77,205.18,202.88,204.04,1660000,204.04 1946-04-04,203.25,205.79,203.25,204.77,2130000,204.77 1946-04-03,199.83,203.29,199.81,203.12,1560000,203.12 1946-04-02,199.19,200.16,198.98,199.83,1040000,199.83 1946-04-01,199.75,199.97,198.47,199.19,1050000,199.19 1946-03-29,198.23,200.36,198.23,199.56,1230000,199.56 1946-03-28,198.73,198.92,197.27,198.18,900000,198.18 1946-03-27,199.81,199.81,197.83,198.73,1000000,198.73 1946-03-26,200.55,201.85,200.04,200.56,1440000,200.56 1946-03-25,198.27,200.80,198.27,200.55,1640000,200.55 1946-03-22,196.70,198.20,195.92,197.19,1180000,197.19 1946-03-21,195.53,197.00,195.10,196.70,1210000,196.70 1946-03-20,194.09,195.90,193.63,195.53,1280000,195.53 1946-03-19,195.33,196.23,193.77,194.09,1050000,194.09 1946-03-18,194.05,195.86,194.05,195.33,1070000,195.33 1946-03-15,189.98,192.14,189.71,191.66,1060000,191.66 1946-03-14,190.36,191.76,189.32,189.98,1090000,189.98 1946-03-13,192.87,192.87,188.86,190.36,1620000,190.36 1946-03-12,192.89,193.86,192.39,193.52,720000,193.52 1946-03-11,194.45,194.73,192.34,192.89,880000,192.89 1946-03-08,192.08,194.40,192.08,193.70,970000,193.70 1946-03-07,190.30,191.93,190.30,191.50,900000,191.50 1946-03-06,190.49,190.77,189.39,190.28,890000,190.28 1946-03-05,188.91,191.16,188.91,190.49,1050000,190.49 1946-03-04,188.73,188.87,186.77,188.46,970000,188.46 1946-03-01,190.09,190.58,188.90,189.42,820000,189.42 1946-02-28,189.06,190.64,188.19,190.09,1180000,190.09 1946-02-27,187.34,189.56,187.34,189.06,1400000,189.06 1946-02-26,187.23,188.09,184.05,186.02,2650000,186.02 1946-02-25,194.04,194.04,186.87,187.23,2400000,187.23 1946-02-21,193.05,196.30,193.05,195.62,1590000,195.62 1946-02-20,195.06,195.06,191.17,192.38,2150000,192.38 1946-02-19,200.42,200.42,194.90,196.13,2300000,196.13 1946-02-18,204.13,204.13,200.84,201.63,1560000,201.63 1946-02-15,201.06,203.56,201.06,203.09,1780000,203.09 1946-02-14,198.74,201.18,198.45,199.75,1290000,199.75 1946-02-13,200.81,200.81,197.65,198.74,1690000,198.74 1946-02-11,202.30,202.72,200.59,201.14,1440000,201.14 1946-02-08,205.09,205.60,203.62,204.38,1260000,204.38 1946-02-07,205.48,205.73,203.63,205.09,1490000,205.09 1946-02-06,206.61,207.24,205.15,205.48,1750000,205.48 1946-02-05,205.84,206.84,204.55,206.61,1670000,206.61 1946-02-04,206.97,207.49,205.05,205.84,1660000,205.84 1946-02-01,204.67,206.06,203.69,205.79,1560000,205.79 1946-01-31,204.84,205.22,203.07,204.67,1690000,204.67 1946-01-30,205.35,206.59,203.37,204.84,2280000,204.84 1946-01-29,204.62,206.49,203.75,205.35,2910000,205.35 1946-01-28,202.10,205.50,202.10,204.62,3490000,204.62 1946-01-25,200.04,200.13,198.17,199.50,1760000,199.50 1946-01-24,198.84,200.86,198.68,200.04,2210000,200.04 1946-01-23,197.35,199.48,197.20,198.84,2050000,198.84 1946-01-22,196.63,198.00,195.63,197.35,1530000,197.35 1946-01-21,198.03,198.03,195.52,196.63,1680000,196.63 1946-01-18,203.49,203.70,200.78,202.18,3230000,202.18 1946-01-17,203.81,205.03,202.95,203.49,2200000,203.49 1946-01-16,202.97,204.50,201.93,203.81,2100000,203.81 1946-01-15,201.93,204.02,201.73,202.97,2720000,202.97 1946-01-14,200.25,202.59,200.25,201.93,2740000,201.93 1946-01-11,199.16,200.86,198.67,200.04,2250000,200.04 1946-01-10,197.33,199.78,197.17,199.16,2470000,199.16 1946-01-09,195.16,198.40,195.16,197.33,2930000,197.33 1946-01-08,192.04,194.98,192.04,194.65,2160000,194.65 1946-01-07,191.47,192.40,190.87,191.77,1230000,191.77 1946-01-04,191.25,191.81,190.27,190.90,1080000,190.90 1946-01-03,191.66,191.97,189.77,191.25,1390000,191.25 1946-01-02,192.65,192.65,191.15,191.66,1050000,191.66 1945-12-31,192.84,193.47,192.07,192.91,1010000,192.91 1945-12-28,192.31,192.95,191.58,192.43,1090000,192.43 1945-12-27,192.76,193.89,191.76,192.31,1330000,192.31 1945-12-26,191.59,193.45,191.59,192.76,1420000,192.76 1945-12-21,189.36,189.67,187.51,189.07,940000,189.07 1945-12-20,190.22,190.22,188.84,189.36,800000,189.36 1945-12-19,190.98,191.43,190.11,190.62,980000,190.62 1945-12-18,190.37,191.78,189.33,190.98,1360000,190.98 1945-12-17,192.87,192.87,189.80,190.37,1990000,190.37 1945-12-14,193.52,193.86,192.50,193.34,1130000,193.34 1945-12-13,193.96,194.62,192.84,193.52,1220000,193.52 1945-12-12,195.39,195.39,193.18,193.96,1680000,193.96 1945-12-11,195.64,196.47,194.34,195.82,1690000,195.82 1945-12-10,195.18,196.59,194.76,195.64,2140000,195.64 1945-12-07,193.84,194.78,193.18,194.08,2050000,194.08 1945-12-06,193.08,194.62,192.70,193.84,2290000,193.84 1945-12-05,193.06,193.67,191.75,193.08,1860000,193.08 1945-12-04,193.65,194.12,192.17,193.06,2310000,193.06 1945-12-03,192.40,194.36,192.16,193.65,2610000,193.65 1945-11-30,189.77,191.84,189.77,191.46,1820000,191.46 1945-11-29,189.99,190.22,188.80,189.58,1750000,189.58 1945-11-28,190.45,191.47,189.58,189.99,1800000,189.99 1945-11-27,188.79,190.90,188.79,190.45,1830000,190.45 1945-11-26,186.44,188.55,186.44,188.16,1530000,188.16 1945-11-23,189.26,189.26,187.34,187.82,1350000,187.82 1945-11-21,191.47,191.47,188.97,189.54,1910000,189.54 1945-11-20,191.51,192.83,190.69,192.12,2180000,192.12 1945-11-19,192.27,192.50,190.20,191.51,2020000,191.51 1945-11-16,191.13,192.95,191.02,192.13,2340000,192.13 1945-11-15,189.77,191.60,189.58,191.13,2010000,191.13 1945-11-14,190.56,190.61,188.82,189.77,1610000,189.77 1945-11-13,191.37,192.46,190.23,190.56,2500000,190.56 1945-11-09,191.72,192.46,190.68,191.46,1830000,191.46 1945-11-08,192.04,192.78,190.79,191.72,1960000,191.72 1945-11-07,190.13,192.76,190.13,192.04,2380000,192.04 1945-11-05,188.58,189.69,188.02,189.50,1650000,189.50 1945-11-02,188.84,189.40,187.54,188.62,1840000,188.62 1945-11-01,187.02,189.33,187.02,188.84,2210000,188.84 1945-10-31,184.16,186.74,183.59,186.60,2060000,186.60 1945-10-30,184.24,184.61,182.88,184.16,1170000,184.16 1945-10-29,185.39,186.02,183.90,184.24,1260000,184.24 1945-10-26,184.54,186.01,184.09,185.39,1300000,185.39 1945-10-25,183.72,185.13,183.43,184.54,1230000,184.54 1945-10-24,185.52,185.52,183.02,183.72,1370000,183.72 1945-10-23,187.06,187.52,184.76,186.15,1370000,186.15 1945-10-22,185.60,187.26,185.29,187.06,1140000,187.06 1945-10-19,186.78,187.50,185.12,185.34,1730000,185.34 1945-10-18,186.10,187.55,185.68,186.78,1670000,186.78 1945-10-17,185.49,186.72,184.92,186.10,1800000,186.10 1945-10-16,185.51,185.98,184.42,185.49,1630000,185.49 1945-10-15,185.72,186.32,185.00,185.51,1630000,185.51 1945-10-11,186.05,186.10,184.83,185.72,1560000,185.72 1945-10-10,185.43,186.52,185.07,186.05,1700000,186.05 1945-10-09,185.46,185.95,184.26,185.43,1640000,185.43 1945-10-08,184.77,186.02,184.66,185.46,1780000,185.46 1945-10-05,183.06,184.48,182.80,183.94,1420000,183.94 1945-10-04,183.33,183.75,182.32,183.06,1340000,183.06 1945-10-03,183.85,184.20,182.32,183.33,1430000,183.33 1945-10-02,183.37,185.05,183.13,183.85,1800000,183.85 1945-10-01,181.93,183.92,181.93,183.37,1990000,183.37 1945-09-28,178.83,180.31,178.83,180.11,1380000,180.11 1945-09-27,178.95,179.36,177.82,178.57,1060000,178.57 1945-09-26,179.41,179.41,178.12,178.95,1040000,178.95 1945-09-25,179.51,180.42,178.89,179.42,1190000,179.42 1945-09-24,179.49,179.79,178.71,179.51,890000,179.51 1945-09-21,180.22,180.80,178.87,179.69,1340000,179.69 1945-09-20,179.51,180.72,178.92,180.22,1500000,180.22 1945-09-19,178.32,180.06,178.32,179.51,1690000,179.51 1945-09-18,175.19,177.76,175.19,177.58,1160000,177.58 1945-09-17,175.11,175.11,173.30,174.75,900000,174.75 1945-09-14,178.59,178.59,177.20,177.74,1020000,177.74 1945-09-13,178.99,179.33,178.21,178.59,1170000,178.59 1945-09-12,177.77,179.33,177.55,178.99,1340000,178.99 1945-09-11,177.03,178.04,176.33,177.77,1130000,177.77 1945-09-10,176.99,177.78,176.62,177.03,1110000,177.03 1945-09-07,175.96,177.57,175.76,176.61,1330000,176.61 1945-09-06,174.24,176.14,174.17,175.96,1380000,175.96 1945-09-05,173.90,174.49,173.23,174.24,980000,174.24 1945-09-04,174.29,174.86,173.52,173.90,1070000,173.90 1945-08-31,172.73,174.54,172.73,174.29,1110000,174.29 1945-08-30,172.09,172.58,171.46,172.37,890000,172.37 1945-08-29,172.32,172.51,171.34,172.09,920000,172.09 1945-08-28,171.96,172.93,171.43,172.32,1260000,172.32 1945-08-27,170.11,172.26,170.11,171.96,1570000,171.96 1945-08-24,167.64,170.04,167.54,169.89,1320000,169.89 1945-08-23,164.99,167.94,164.99,167.64,1190000,167.64 1945-08-22,163.38,164.94,163.35,164.54,760000,164.54 1945-08-21,163.11,163.88,162.28,163.38,1160000,163.38 1945-08-20,164.38,164.82,162.68,163.11,1320000,163.11 1945-08-17,164.79,166.08,164.01,164.38,1210000,164.38 1945-08-14,164.11,165.22,164.04,164.79,910000,164.79 1945-08-13,165.02,165.02,162.81,164.11,970000,164.11 1945-08-10,164.55,166.54,164.07,165.14,1690000,165.14 1945-08-09,161.83,164.82,161.14,164.55,1460000,164.55 1945-08-08,161.55,162.74,161.17,161.83,700000,161.83 1945-08-07,162.87,162.87,161.16,161.55,980000,161.55 1945-08-06,163.06,163.69,162.66,163.19,490000,163.19 1945-08-03,162.49,163.23,162.27,163.06,510000,163.06 1945-08-02,162.72,163.08,161.82,162.49,600000,162.49 1945-08-01,162.88,163.21,162.27,162.72,650000,162.72 1945-07-31,162.21,163.36,162.21,162.88,870000,162.88 1945-07-30,160.92,162.51,160.88,162.09,910000,162.09 1945-07-27,160.91,161.34,159.95,160.92,920000,160.92 1945-07-26,162.44,162.91,160.71,160.91,1440000,160.91 1945-07-25,162.10,162.63,162.10,162.44,620000,162.44 1945-07-24,161.65,162.00,160.98,161.73,640000,161.73 1945-07-23,162.50,162.82,161.49,161.65,760000,161.65 1945-07-20,162.81,163.22,162.04,162.50,650000,162.50 1945-07-19,161.95,163.39,161.95,162.81,780000,162.81 1945-07-18,162.43,162.87,160.62,161.69,1450000,161.69 1945-07-17,165.22,165.22,162.29,162.43,1560000,162.43 1945-07-16,166.67,167.08,165.46,165.82,790000,165.82 1945-07-13,166.85,167.41,166.29,166.67,960000,166.67 1945-07-12,166.59,167.24,165.51,166.85,970000,166.85 1945-07-11,167.09,167.49,166.16,166.59,810000,166.59 1945-07-10,166.60,167.79,166.60,167.09,940000,167.09 1945-07-09,164.67,166.76,164.64,166.55,800000,166.55 1945-07-06,164.26,165.07,163.47,164.67,960000,164.67 1945-07-05,165.36,165.36,164.02,164.26,920000,164.26 1945-07-03,165.91,166.39,165.35,165.73,860000,165.73 1945-07-02,165.39,166.75,165.39,165.91,1380000,165.91 1945-06-29,166.22,166.24,163.78,164.57,2020000,164.57 1945-06-28,168.78,168.80,165.70,166.22,2940000,166.22 1945-06-27,168.92,169.27,168.36,168.78,1690000,168.78 1945-06-26,168.59,169.55,168.40,168.92,2140000,168.92 1945-06-25,168.24,168.90,167.97,168.59,1890000,168.59 1945-06-22,168.14,168.69,167.48,167.90,1850000,167.90 1945-06-21,167.74,168.84,167.53,168.14,2100000,168.14 1945-06-20,167.23,168.01,166.81,167.74,1680000,167.74 1945-06-19,166.94,167.80,166.50,167.23,1560000,167.23 1945-06-18,167.54,167.72,166.67,166.94,1930000,166.94 1945-06-15,167.08,168.10,166.92,167.64,1900000,167.64 1945-06-14,166.75,167.47,166.51,167.08,1850000,167.08 1945-06-13,166.39,167.10,166.07,166.75,1590000,166.75 1945-06-12,166.25,166.78,165.89,166.39,1320000,166.39 1945-06-11,166.85,167.20,166.09,166.25,1490000,166.25 1945-06-08,167.16,167.67,166.54,166.83,1710000,166.83 1945-06-07,167.29,167.63,166.58,167.16,1300000,167.16 1945-06-06,168.13,168.16,166.58,167.29,1520000,167.29 1945-06-05,168.08,168.80,167.54,168.13,1510000,168.13 1945-06-04,167.96,168.49,167.36,168.08,1540000,168.08 1945-06-01,168.30,168.51,167.47,168.08,1430000,168.08 1945-05-31,169.08,169.41,167.90,168.30,1220000,168.30 1945-05-29,168.21,169.53,168.06,169.08,1600000,169.08 1945-05-28,166.45,168.75,166.45,168.21,1570000,168.21 1945-05-25,164.41,165.55,164.38,165.12,1300000,165.12 1945-05-24,164.50,164.81,163.73,164.41,960000,164.41 1945-05-23,165.72,165.72,163.80,164.50,1310000,164.50 1945-05-22,165.99,166.61,165.47,165.91,1050000,165.91 1945-05-21,166.44,166.93,165.61,165.99,1080000,165.99 1945-05-18,165.20,166.47,165.19,166.17,1430000,166.17 1945-05-17,164.50,165.85,164.23,165.20,1370000,165.20 1945-05-16,164.00,165.07,163.76,164.50,1210000,164.50 1945-05-15,163.45,164.31,163.07,164.00,1010000,164.00 1945-05-14,163.96,164.41,163.16,163.45,980000,163.45 1945-05-11,163.09,163.82,162.60,163.21,1070000,163.21 1945-05-10,164.56,164.56,162.68,163.09,1510000,163.09 1945-05-09,166.42,166.72,165.06,165.24,1490000,165.24 1945-05-08,166.53,167.25,165.62,166.42,1580000,166.42 1945-05-07,166.71,167.25,165.76,166.53,1950000,166.53 1945-05-04,165.84,166.61,165.40,166.27,1410000,166.27 1945-05-03,165.03,166.02,164.77,165.84,1340000,165.84 1945-05-02,165.09,165.38,164.45,165.03,1140000,165.03 1945-05-01,165.44,166.18,164.49,165.09,1530000,165.09 1945-04-30,164.71,165.71,164.10,165.44,1500000,165.44 1945-04-27,163.22,164.27,163.12,163.94,1360000,163.94 1945-04-26,163.78,163.78,162.06,163.22,1370000,163.22 1945-04-25,164.31,164.46,163.17,163.91,1420000,163.91 1945-04-24,163.58,164.62,163.33,164.31,1830000,164.31 1945-04-23,163.20,164.08,162.88,163.58,1380000,163.58 1945-04-20,163.18,163.67,162.46,162.83,1130000,162.83 1945-04-19,163.83,164.45,162.72,163.18,1680000,163.18 1945-04-18,162.60,164.17,162.35,163.83,1710000,163.83 1945-04-17,162.43,163.72,162.15,162.60,2070000,162.60 1945-04-16,160.57,162.76,160.57,162.43,2500000,162.43 1945-04-13,158.48,159.95,157.73,159.75,1800000,159.75 1945-04-12,158.06,159.14,157.89,158.48,1060000,158.48 1945-04-11,156.59,158.17,156.59,158.06,1060000,158.06 1945-04-10,156.10,156.82,155.54,156.51,760000,156.51 1945-04-09,156.33,156.71,155.91,156.10,570000,156.10 1945-04-06,154.99,155.95,154.70,155.85,610000,155.85 1945-04-05,155.96,156.26,154.77,154.99,680000,154.99 1945-04-04,156.20,156.41,155.69,155.96,520000,155.96 1945-04-03,155.94,156.97,155.94,156.20,730000,156.20 1945-04-02,154.51,156.05,154.51,155.86,650000,155.86 1945-03-29,153.79,154.46,153.48,154.06,660000,154.06 1945-03-28,152.89,154.08,152.89,153.79,720000,153.79 1945-03-27,152.27,153.51,151.77,152.78,1110000,152.78 1945-03-26,153.62,153.62,151.74,152.27,1390000,152.27 1945-03-23,155.07,156.00,154.68,155.45,680000,155.45 1945-03-22,155.30,155.73,154.82,155.07,830000,155.07 1945-03-21,156.10,156.10,154.08,155.30,1340000,155.30 1945-03-20,157.71,157.71,156.14,156.37,920000,156.37 1945-03-19,158.75,158.88,157.36,157.89,1070000,157.89 1945-03-16,158.53,159.42,158.41,158.92,880000,158.92 1945-03-15,157.84,158.95,157.84,158.53,800000,158.53 1945-03-14,157.59,158.16,157.29,157.83,750000,157.83 1945-03-13,157.88,158.24,156.94,157.59,760000,157.59 1945-03-12,157.33,158.38,157.33,157.88,920000,157.88 1945-03-09,158.29,158.29,155.96,156.34,2060000,156.34 1945-03-08,161.34,161.34,158.31,158.86,1860000,158.86 1945-03-07,161.50,162.21,160.96,161.52,1400000,161.52 1945-03-06,160.68,162.22,160.56,161.50,1690000,161.50 1945-03-05,159.71,160.85,159.56,160.68,1190000,160.68 1945-03-02,160.72,160.76,159.63,159.95,1770000,159.95 1945-03-01,160.40,161.15,160.11,160.72,2090000,160.72 1945-02-28,159.51,160.85,159.51,160.40,1960000,160.40 1945-02-27,158.41,159.47,158.23,159.30,1190000,159.30 1945-02-26,158.69,158.91,157.45,158.41,1260000,158.41 1945-02-23,159.66,159.80,158.55,158.99,1320000,158.99 1945-02-21,159.57,160.17,158.04,159.66,1720000,159.66 1945-02-20,159.01,159.86,158.69,159.57,1770000,159.57 1945-02-19,158.23,159.54,157.92,159.01,1880000,159.01 1945-02-16,158.24,158.82,157.57,158.23,1900000,158.23 1945-02-15,157.08,158.37,157.06,158.24,1870000,158.24 1945-02-14,156.37,157.81,156.37,157.08,2060000,157.08 1945-02-13,154.90,156.49,154.90,156.34,1800000,156.34 1945-02-09,155.48,155.48,154.25,154.75,1180000,154.75 1945-02-08,155.71,156.17,155.19,155.54,1530000,155.54 1945-02-07,155.50,156.07,155.13,155.71,1500000,155.71 1945-02-06,155.35,156.00,155.18,155.50,1600000,155.50 1945-02-05,154.76,155.76,154.56,155.35,1800000,155.35 1945-02-02,153.79,154.71,153.30,154.45,1870000,154.45 1945-02-01,153.67,154.15,153.22,153.79,1560000,153.79 1945-01-31,153.45,153.93,152.62,153.67,1180000,153.67 1945-01-30,154.06,154.58,153.31,153.45,1530000,153.45 1945-01-29,154.13,154.25,153.30,154.06,1370000,154.06 1945-01-26,152.53,153.57,152.53,153.30,1260000,153.30 1945-01-25,151.39,152.62,151.39,152.26,970000,152.26 1945-01-24,151.36,152.15,150.53,151.35,1010000,151.35 1945-01-23,152.03,152.97,151.23,151.36,1370000,151.36 1945-01-22,152.41,152.41,151.10,152.03,1470000,152.03 1945-01-19,154.58,154.58,153.37,153.84,1610000,153.84 1945-01-18,155.33,155.50,153.95,154.61,1630000,154.61 1945-01-17,154.60,155.99,154.53,155.33,1830000,155.33 1945-01-16,154.76,155.04,153.96,154.60,1210000,154.60 1945-01-15,155.58,155.72,154.30,154.76,2010000,154.76 1945-01-12,155.85,156.22,154.99,155.42,1750000,155.42 1945-01-11,155.67,156.68,155.32,155.85,2210000,155.85 1945-01-10,155.01,155.95,154.38,155.67,2110000,155.67 1945-01-09,154.85,155.72,154.52,155.01,2260000,155.01 1945-01-08,153.69,155.04,153.69,154.85,2000000,154.85 1945-01-05,154.42,155.02,153.79,154.00,1800000,154.00 1945-01-04,154.31,155.00,153.72,154.42,1830000,154.42 1945-01-03,153.02,154.61,153.02,154.31,2160000,154.31 1945-01-02,152.32,152.83,151.63,152.58,1340000,152.58 1944-12-29,150.48,152.53,150.48,151.93,1890000,151.93 1944-12-28,148.71,150.73,148.62,150.47,1230000,150.47 1944-12-27,149.46,149.46,147.93,148.71,1580000,148.71 1944-12-26,150.36,150.36,149.41,149.66,1000000,149.66 1944-12-22,150.28,151.04,150.04,150.43,1280000,150.43 1944-12-21,150.59,150.73,149.88,150.28,950000,150.28 1944-12-20,151.39,151.39,150.15,150.59,1480000,150.59 1944-12-19,151.53,151.90,150.84,151.62,1280000,151.62 1944-12-18,152.28,152.28,151.15,151.53,980000,151.53 1944-12-15,150.93,152.75,150.93,152.28,2140000,152.28 1944-12-14,150.64,151.02,150.11,150.80,1390000,150.80 1944-12-13,151.20,151.34,150.43,150.64,1130000,150.64 1944-12-12,151.62,151.75,150.69,151.20,1160000,151.20 1944-12-11,151.31,152.14,150.65,151.62,1500000,151.62 1944-12-08,149.23,150.68,149.07,150.48,1680000,150.48 1944-12-07,148.77,149.64,148.55,149.23,1350000,149.23 1944-12-06,148.58,149.14,148.03,148.77,1090000,148.77 1944-12-05,148.22,149.23,148.05,148.58,1480000,148.58 1944-12-04,147.50,148.41,147.21,148.22,1430000,148.22 1944-12-01,147.33,147.65,146.76,147.30,930000,147.30 1944-11-30,147.81,148.06,147.06,147.33,990000,147.33 1944-11-29,147.14,148.07,146.89,147.81,1160000,147.81 1944-11-28,146.92,147.40,146.62,147.14,870000,147.14 1944-11-27,146.63,147.21,146.30,146.92,740000,146.92 1944-11-24,146.92,147.04,145.99,146.40,680000,146.40 1944-11-22,147.03,147.59,146.74,146.92,770000,146.92 1944-11-21,146.34,147.27,146.34,147.03,860000,147.03 1944-11-20,146.02,146.56,145.71,146.33,690000,146.33 1944-11-17,145.67,146.15,145.33,145.77,710000,145.77 1944-11-16,145.64,146.15,145.17,145.67,850000,145.67 1944-11-15,145.60,146.04,145.22,145.64,820000,145.64 1944-11-14,146.90,146.90,145.36,145.60,1100000,145.60 1944-11-13,148.08,148.22,146.78,146.97,920000,146.97 1944-11-10,147.75,148.39,147.64,148.08,1120000,148.08 1944-11-09,147.52,148.02,146.98,147.75,850000,147.75 1944-11-08,147.92,147.92,146.82,147.52,730000,147.52 1944-11-06,147.37,148.19,147.06,147.92,870000,147.92 1944-11-03,147.53,147.59,146.87,147.16,730000,147.16 1944-11-02,146.73,147.69,146.71,147.53,790000,147.53 1944-11-01,146.53,147.05,146.38,146.73,710000,146.73 1944-10-31,146.28,147.01,146.12,146.53,680000,146.53 1944-10-30,146.50,146.68,145.80,146.28,610000,146.28 1944-10-27,145.83,146.46,145.33,146.29,830000,146.29 1944-10-26,146.37,146.65,145.47,145.83,930000,145.83 1944-10-25,146.58,146.82,146.03,146.37,640000,146.37 1944-10-24,146.58,146.86,145.98,146.58,850000,146.58 1944-10-23,148.35,148.46,146.37,146.58,1030000,146.58 1944-10-20,148.55,148.70,147.96,148.21,750000,148.21 1944-10-19,148.87,149.18,148.21,148.55,830000,148.55 1944-10-18,148.47,149.18,148.47,148.87,910000,148.87 1944-10-17,148.09,148.58,147.85,148.39,670000,148.39 1944-10-16,148.59,148.67,147.97,148.09,580000,148.09 1944-10-13,148.79,149.03,148.48,148.70,680000,148.70 1944-10-11,148.25,149.00,148.19,148.79,720000,148.79 1944-10-10,148.06,148.56,147.68,148.25,600000,148.25 1944-10-09,148.86,148.86,147.98,148.06,610000,148.06 1944-10-06,148.62,149.20,148.51,148.84,800000,148.84 1944-10-05,147.89,148.95,147.78,148.62,1000000,148.62 1944-10-04,146.93,148.06,146.93,147.89,820000,147.89 1944-10-03,146.92,147.29,146.49,146.91,750000,146.91 1944-10-02,146.73,147.30,146.41,146.92,820000,146.92 1944-09-29,146.11,146.47,145.70,146.31,750000,146.31 1944-09-28,146.28,146.49,145.67,146.11,640000,146.11 1944-09-27,146.52,146.76,146.03,146.28,480000,146.28 1944-09-26,146.77,147.08,146.32,146.52,600000,146.52 1944-09-25,146.07,147.06,146.07,146.77,790000,146.77 1944-09-22,145.43,145.95,145.33,145.60,550000,145.60 1944-09-21,145.85,146.09,145.31,145.43,560000,145.43 1944-09-20,145.62,146.29,145.42,145.85,700000,145.85 1944-09-19,144.75,145.97,144.75,145.62,720000,145.62 1944-09-18,144.36,144.85,144.24,144.75,350000,144.75 1944-09-15,143.36,144.24,143.36,144.08,640000,144.08 1944-09-14,143.44,143.44,142.65,142.96,730000,142.96 1944-09-13,144.88,145.05,143.87,143.94,880000,143.94 1944-09-12,144.40,145.09,144.40,144.88,550000,144.88 1944-09-11,143.64,144.56,143.64,144.30,570000,144.30 1944-09-08,143.58,144.28,143.10,143.51,630000,143.51 1944-09-07,144.08,144.08,142.53,143.58,1480000,143.58 1944-09-06,146.44,146.44,144.26,144.42,1470000,144.42 1944-09-05,147.16,147.45,146.44,146.56,870000,146.56 1944-09-01,146.99,147.47,146.73,147.16,640000,147.16 1944-08-31,147.28,147.42,146.77,146.99,610000,146.99 1944-08-30,147.12,147.69,146.93,147.28,900000,147.28 1944-08-29,146.87,147.40,146.70,147.12,620000,147.12 1944-08-28,147.02,147.17,146.53,146.87,550000,146.87 1944-08-25,147.11,147.38,146.42,147.02,600000,147.02 1944-08-24,147.87,147.88,146.81,147.11,770000,147.11 1944-08-23,147.81,148.18,147.49,147.87,790000,147.87 1944-08-22,148.44,148.44,147.59,147.81,800000,147.81 1944-08-21,148.96,149.27,148.31,148.52,830000,148.52 1944-08-18,148.46,149.28,148.42,148.96,1150000,148.96 1944-08-17,147.62,148.58,147.62,148.46,1240000,148.46 1944-08-16,146.45,147.42,146.40,147.30,860000,147.30 1944-08-15,146.77,146.83,146.06,146.45,780000,146.45 1944-08-14,146.56,146.99,146.45,146.77,690000,146.77 1944-08-11,145.82,146.63,145.82,146.27,940000,146.27 1944-08-10,144.95,145.81,144.95,145.65,1020000,145.65 1944-08-09,144.97,145.25,144.48,144.90,960000,144.90 1944-08-08,145.32,145.69,144.70,144.97,1270000,144.97 1944-08-07,145.07,145.75,144.75,145.32,1070000,145.32 1944-08-04,146.29,146.49,145.20,145.30,1120000,145.30 1944-08-03,146.77,146.99,146.08,146.29,800000,146.29 1944-08-02,146.50,147.07,146.50,146.77,800000,146.77 1944-08-01,146.11,146.92,145.92,146.39,750000,146.39 1944-07-31,146.14,146.59,145.86,146.11,580000,146.11 1944-07-28,146.68,146.68,145.88,146.14,560000,146.14 1944-07-27,146.64,147.08,146.07,146.74,690000,146.74 1944-07-26,146.74,147.59,146.40,146.64,830000,146.64 1944-07-25,145.91,146.87,145.91,146.74,830000,146.74 1944-07-24,145.58,146.37,145.26,145.77,1010000,145.77 1944-07-21,148.01,148.01,146.42,146.77,1430000,146.77 1944-07-20,149.01,149.57,148.11,148.27,1340000,148.27 1944-07-19,148.10,149.46,147.98,149.01,1140000,149.01 1944-07-18,149.01,149.01,147.74,148.10,1280000,148.10 1944-07-17,150.49,150.49,148.71,149.28,1480000,149.28 1944-07-14,150.08,150.86,149.86,150.34,1090000,150.34 1944-07-13,150.42,150.80,149.66,150.08,1380000,150.08 1944-07-12,150.18,150.84,149.69,150.42,1420000,150.42 1944-07-11,150.50,150.80,149.53,150.18,1350000,150.18 1944-07-10,150.03,150.88,149.97,150.50,1840000,150.50 1944-07-07,149.07,149.61,148.61,149.36,1280000,149.36 1944-07-06,149.66,150.12,148.69,149.07,1710000,149.07 1944-07-05,149.32,150.42,149.17,149.66,2440000,149.66 1944-07-03,148.46,149.50,148.42,149.32,1560000,149.32 1944-06-30,148.07,148.82,147.93,148.38,1750000,148.38 1944-06-29,147.93,148.54,147.25,148.07,1890000,148.07 1944-06-28,148.48,148.52,147.40,147.93,1750000,147.93 1944-06-27,148.12,149.00,147.76,148.48,2180000,148.48 1944-06-26,147.48,148.46,147.40,148.12,1720000,148.12 1944-06-23,147.65,147.94,147.15,147.50,1300000,147.50 1944-06-22,147.90,148.32,147.18,147.65,1280000,147.65 1944-06-21,148.63,148.95,147.30,147.90,1520000,147.90 1944-06-20,148.42,149.15,147.79,148.63,1620000,148.63 1944-06-19,147.46,149.07,147.46,148.42,2370000,148.42 1944-06-16,145.94,147.57,145.94,146.96,2520000,146.96 1944-06-15,145.03,146.38,144.73,145.86,1850000,145.86 1944-06-14,145.05,145.60,144.26,145.03,1440000,145.03 1944-06-13,144.13,145.55,144.13,145.05,2330000,145.05 1944-06-12,142.90,144.31,142.90,144.08,2240000,144.08 1944-06-09,141.93,142.37,141.58,142.06,850000,142.06 1944-06-08,142.12,142.60,141.61,141.93,860000,141.93 1944-06-07,142.21,142.77,141.76,142.12,860000,142.12 1944-06-06,141.62,143.01,140.90,142.21,1790000,142.21 1944-06-05,142.34,142.53,141.49,141.62,860000,141.62 1944-06-02,142.14,142.52,141.80,142.07,820000,142.07 1944-06-01,142.24,142.92,141.95,142.14,1190000,142.14 1944-05-31,141.53,142.44,141.28,142.24,1180000,142.24 1944-05-29,141.24,141.68,140.97,141.53,810000,141.53 1944-05-26,140.38,141.20,140.31,141.03,840000,141.03 1944-05-25,140.48,140.69,139.89,140.38,800000,140.38 1944-05-24,139.91,140.98,139.91,140.48,1090000,140.48 1944-05-23,139.43,140.24,139.19,139.87,770000,139.87 1944-05-22,139.37,139.74,139.13,139.43,700000,139.43 1944-05-19,139.20,139.69,139.00,139.34,790000,139.34 1944-05-18,138.99,139.54,138.63,139.20,940000,139.20 1944-05-17,138.41,139.09,138.38,138.99,820000,138.99 1944-05-16,138.60,138.89,138.23,138.41,540000,138.41 1944-05-15,138.60,138.84,138.41,138.60,340000,138.60 1944-05-12,138.93,139.38,138.22,138.51,750000,138.51 1944-05-11,138.76,139.14,138.50,138.93,620000,138.93 1944-05-10,138.65,139.03,138.41,138.76,650000,138.76 1944-05-09,138.65,139.07,138.29,138.65,620000,138.65 1944-05-08,138.87,139.08,138.38,138.65,590000,138.65 1944-05-05,137.85,139.00,137.83,138.75,790000,138.75 1944-05-04,137.83,138.14,137.52,137.85,510000,137.85 1944-05-03,137.15,138.12,137.14,137.83,630000,137.83 1944-05-02,137.06,137.53,136.77,137.15,570000,137.15 1944-05-01,136.23,137.12,136.18,137.06,570000,137.06 1944-04-28,136.07,136.50,135.77,136.21,560000,136.21 1944-04-27,135.69,136.53,135.69,136.07,520000,136.07 1944-04-26,135.18,135.93,135.18,135.67,490000,135.67 1944-04-25,135.00,135.41,134.75,135.11,550000,135.11 1944-04-24,136.19,136.22,134.91,135.00,690000,135.00 1944-04-21,136.20,136.61,135.96,136.17,480000,136.17 1944-04-20,135.74,136.59,135.74,136.20,530000,136.20 1944-04-19,136.07,136.15,135.09,135.48,900000,135.48 1944-04-18,137.76,137.76,135.99,136.07,1190000,136.07 1944-04-17,138.06,138.25,137.64,137.77,540000,137.77 1944-04-14,137.65,137.95,137.52,137.69,480000,137.69 1944-04-13,137.93,137.93,137.33,137.65,660000,137.65 1944-04-12,138.74,138.86,137.88,137.98,720000,137.98 1944-04-11,139.11,139.16,138.50,138.74,730000,138.74 1944-04-10,139.10,139.45,138.80,139.11,570000,139.11 1944-04-06,138.31,139.24,138.31,138.91,760000,138.91 1944-04-05,138.06,138.63,137.95,138.17,770000,138.17 1944-04-04,138.01,138.39,137.67,138.06,570000,138.06 1944-04-03,138.66,138.66,137.70,138.01,690000,138.01 1944-03-31,138.60,139.29,138.50,138.84,800000,138.84 1944-03-30,137.74,138.89,137.74,138.60,680000,138.60 1944-03-29,137.88,138.17,136.98,137.45,1110000,137.45 1944-03-28,139.12,139.36,137.64,137.88,1340000,137.88 1944-03-27,139.19,139.67,138.99,139.12,690000,139.12 1944-03-24,138.95,139.60,138.81,139.28,840000,139.28 1944-03-23,139.98,140.33,138.81,138.95,1170000,138.95 1944-03-22,140.20,140.77,139.83,139.98,1570000,139.98 1944-03-21,139.89,140.47,139.63,140.20,1460000,140.20 1944-03-20,140.28,140.28,139.53,139.89,1200000,139.89 1944-03-17,140.91,141.10,140.24,140.80,1350000,140.80 1944-03-16,140.71,141.43,140.44,140.91,1590000,140.91 1944-03-15,140.37,141.00,140.08,140.71,1190000,140.71 1944-03-14,140.75,140.75,139.90,140.37,1010000,140.37 1944-03-13,140.44,141.37,140.33,141.00,1620000,141.00 1944-03-10,139.33,140.34,139.14,140.01,1150000,140.01 1944-03-09,139.50,139.70,138.89,139.33,930000,139.33 1944-03-08,138.34,139.89,138.34,139.50,1690000,139.50 1944-03-07,137.48,138.67,137.48,138.33,1210000,138.33 1944-03-06,136.79,137.46,136.75,137.21,750000,137.21 1944-03-03,136.69,136.97,136.38,136.59,760000,136.59 1944-03-02,136.44,136.91,136.33,136.69,710000,136.69 1944-03-01,136.30,136.75,135.86,136.44,630000,136.44 1944-02-29,136.79,136.91,136.02,136.30,870000,136.30 1944-02-28,136.58,137.01,136.36,136.79,750000,136.79 1944-02-25,136.58,136.74,136.11,136.56,900000,136.56 1944-02-24,136.51,137.00,136.25,136.58,1160000,136.58 1944-02-23,135.71,136.63,135.52,136.51,940000,136.51 1944-02-21,135.91,136.11,135.52,135.71,590000,135.71 1944-02-18,136.58,136.74,135.81,136.08,670000,136.08 1944-02-17,136.04,136.77,135.74,136.58,900000,136.58 1944-02-16,136.19,136.73,135.64,136.04,870000,136.04 1944-02-15,135.39,136.36,135.28,136.19,1030000,136.19 1944-02-14,135.41,135.71,135.00,135.39,550000,135.39 1944-02-11,135.55,136.08,135.19,135.41,850000,135.41 1944-02-10,135.03,135.74,134.80,135.55,860000,135.55 1944-02-09,135.06,135.44,134.55,135.03,630000,135.03 1944-02-08,134.36,135.24,134.36,135.06,610000,135.06 1944-02-07,135.11,135.11,134.10,134.22,570000,134.22 1944-02-04,135.89,135.89,134.75,135.04,600000,135.04 1944-02-03,137.08,137.16,135.76,136.24,960000,136.24 1944-02-02,137.45,137.45,136.79,137.08,900000,137.08 1944-02-01,137.40,137.69,137.09,137.45,860000,137.45 1944-01-31,137.15,137.85,136.83,137.40,630000,137.40 1944-01-28,136.65,137.37,136.65,137.19,620000,137.19 1944-01-27,136.71,137.10,136.38,136.59,600000,136.59 1944-01-26,137.77,137.77,136.59,136.71,780000,136.71 1944-01-25,137.97,138.21,137.59,137.97,690000,137.97 1944-01-24,138.24,138.50,137.70,137.97,690000,137.97 1944-01-21,138.16,138.32,137.69,138.07,810000,138.07 1944-01-20,137.83,138.49,137.79,138.16,730000,138.16 1944-01-19,137.87,138.28,137.20,137.83,640000,137.83 1944-01-18,138.10,138.22,137.51,137.87,820000,137.87 1944-01-17,138.40,138.60,137.78,138.10,820000,138.10 1944-01-14,137.36,138.31,137.21,138.15,940000,138.15 1944-01-13,137.86,137.86,136.99,137.36,680000,137.36 1944-01-12,138.47,138.71,137.74,137.94,710000,137.94 1944-01-11,137.97,138.89,137.97,138.47,1000000,138.47 1944-01-10,138.09,138.21,137.40,137.80,720000,137.80 1944-01-07,138.34,138.65,137.67,138.08,830000,138.08 1944-01-06,138.65,138.80,137.98,138.34,840000,138.34 1944-01-05,137.78,138.88,137.78,138.65,1160000,138.65 1944-01-04,135.92,137.23,135.91,137.15,730000,137.15 1944-01-03,135.89,136.47,135.51,135.92,520000,135.92 1943-12-31,136.20,136.73,135.54,135.89,990000,135.89 1943-12-30,135.00,136.37,135.00,136.20,1140000,136.20 1943-12-29,135.04,135.51,134.08,134.61,1000000,134.61 1943-12-28,136.08,136.08,134.81,135.04,920000,135.04 1943-12-27,136.24,136.60,135.67,136.14,650000,136.14 1943-12-24,136.07,136.47,135.75,136.24,510000,136.24 1943-12-23,136.15,136.57,135.77,136.07,580000,136.07 1943-12-22,135.86,136.53,135.65,136.15,560000,136.15 1943-12-21,136.10,136.49,135.51,135.86,660000,135.86 1943-12-20,135.89,136.56,135.47,136.10,860000,136.10 1943-12-17,135.19,135.90,134.92,135.44,880000,135.44 1943-12-16,134.18,135.47,133.96,135.19,760000,135.19 1943-12-15,134.19,134.53,133.69,134.18,660000,134.18 1943-12-14,134.74,134.74,133.72,134.19,680000,134.19 1943-12-13,135.28,135.81,134.37,134.80,730000,134.80 1943-12-10,134.05,135.47,133.96,135.04,870000,135.04 1943-12-09,134.42,134.89,133.57,134.05,860000,134.05 1943-12-08,133.64,134.85,133.64,134.42,1320000,134.42 1943-12-07,132.45,133.46,132.21,133.37,880000,133.37 1943-12-06,131.87,133.07,131.71,132.45,820000,132.45 1943-12-03,131.67,132.32,131.42,131.91,560000,131.91 1943-12-02,130.75,132.06,130.75,131.67,720000,131.67 1943-12-01,129.76,131.16,129.76,130.68,710000,130.68 1943-11-30,129.95,130.34,128.94,129.57,710000,129.57 1943-11-29,131.25,131.34,129.86,129.95,700000,129.95 1943-11-26,132.10,132.25,131.06,131.33,600000,131.33 1943-11-24,132.45,132.86,131.76,132.10,710000,132.10 1943-11-23,132.65,132.93,132.26,132.45,540000,132.45 1943-11-22,132.94,133.10,132.30,132.65,610000,132.65 1943-11-19,130.96,132.51,130.96,132.30,910000,132.30 1943-11-18,130.24,131.29,130.08,130.79,620000,130.79 1943-11-17,131.18,131.19,129.86,130.24,830000,130.24 1943-11-16,131.56,131.91,130.86,131.18,620000,131.18 1943-11-15,131.76,132.13,131.08,131.56,680000,131.56 1943-11-12,132.68,133.07,131.63,132.15,800000,132.15 1943-11-10,132.11,133.37,132.11,132.68,930000,132.68 1943-11-09,131.68,132.48,130.84,131.85,1510000,131.85 1943-11-08,135.03,135.03,131.42,131.68,2340000,131.68 1943-11-05,136.30,136.49,135.23,135.47,800000,135.47 1943-11-04,137.35,137.94,136.10,136.30,1150000,136.30 1943-11-03,138.50,138.96,137.19,137.35,1070000,137.35 1943-11-01,138.27,138.79,137.85,138.50,650000,138.50 1943-10-29,138.97,139.06,138.02,138.29,670000,138.29 1943-10-28,139.35,139.74,138.84,138.97,720000,138.97 1943-10-27,138.69,139.65,138.55,139.35,880000,139.35 1943-10-26,138.22,139.08,138.19,138.69,900000,138.69 1943-10-25,138.29,138.66,137.88,138.22,650000,138.22 1943-10-22,138.00,138.53,137.84,138.25,560000,138.25 1943-10-21,138.72,138.72,137.74,138.00,590000,138.00 1943-10-20,138.71,139.21,138.43,138.88,610000,138.88 1943-10-19,138.40,138.93,138.11,138.71,610000,138.71 1943-10-18,138.40,138.87,138.06,138.40,540000,138.40 1943-10-15,137.30,138.30,137.30,137.90,560000,137.90 1943-10-14,136.48,137.30,136.34,137.01,470000,137.01 1943-10-13,136.61,136.91,135.92,136.48,590000,136.48 1943-10-11,137.10,137.21,136.38,136.61,480000,136.61 1943-10-08,136.39,137.17,136.21,136.74,560000,136.74 1943-10-07,137.43,137.43,136.01,136.39,850000,136.39 1943-10-06,139.15,139.15,137.71,137.84,720000,137.84 1943-10-05,139.63,139.76,138.87,139.27,490000,139.27 1943-10-04,140.17,140.17,139.36,139.63,500000,139.63 1943-10-01,140.12,140.63,139.87,140.33,560000,140.33 1943-09-30,139.75,140.48,139.55,140.12,570000,140.12 1943-09-29,139.27,139.95,139.06,139.75,470000,139.75 1943-09-28,139.41,139.98,139.04,139.27,610000,139.27 1943-09-27,140.14,140.14,139.11,139.41,620000,139.41 1943-09-24,140.30,140.61,139.79,140.21,640000,140.21 1943-09-23,141.09,141.18,139.75,140.30,670000,140.30 1943-09-22,141.49,141.68,140.61,141.09,660000,141.09 1943-09-21,141.75,141.87,141.03,141.49,740000,141.49 1943-09-20,141.42,142.50,141.42,141.75,1100000,141.75 1943-09-17,138.42,139.71,138.42,139.60,890000,139.60 1943-09-16,137.62,138.43,137.50,138.36,530000,138.36 1943-09-15,137.53,138.21,137.31,137.62,600000,137.62 1943-09-14,137.82,137.98,137.24,137.53,440000,137.53 1943-09-13,138.04,138.17,137.52,137.82,510000,137.82 1943-09-10,137.75,138.26,137.50,137.96,760000,137.96 1943-09-09,136.92,138.12,136.92,137.75,1090000,137.75 1943-09-08,137.59,137.72,136.37,136.91,800000,136.91 1943-09-07,137.33,137.75,137.17,137.59,350000,137.59 1943-09-03,137.11,137.43,136.53,137.18,400000,137.18 1943-09-02,137.12,137.48,136.67,137.11,490000,137.11 1943-09-01,136.65,137.48,136.65,137.12,570000,137.12 1943-08-31,135.95,137.01,135.95,136.62,610000,136.62 1943-08-30,135.79,136.10,135.30,135.73,340000,135.73 1943-08-27,136.25,136.26,135.64,135.83,420000,135.83 1943-08-26,135.90,136.40,135.81,136.25,440000,136.25 1943-08-25,135.60,136.25,135.54,135.90,460000,135.90 1943-08-24,135.05,136.04,135.00,135.60,480000,135.60 1943-08-23,135.81,135.81,134.40,135.05,720000,135.05 1943-08-20,138.31,138.31,136.80,136.93,540000,136.93 1943-08-19,138.45,138.83,137.93,138.34,470000,138.34 1943-08-18,137.59,138.83,137.59,138.45,560000,138.45 1943-08-17,137.08,137.93,136.96,137.54,550000,137.54 1943-08-16,137.23,137.74,136.78,137.08,490000,137.08 1943-08-13,136.44,137.49,136.40,137.39,440000,137.39 1943-08-12,136.79,137.05,136.15,136.44,450000,136.44 1943-08-11,136.23,137.15,136.21,136.79,620000,136.79 1943-08-10,135.33,136.44,135.33,136.23,650000,136.23 1943-08-09,135.38,136.08,134.75,135.18,560000,135.18 1943-08-06,136.76,136.80,135.36,135.58,620000,135.58 1943-08-05,136.87,137.17,136.33,136.76,540000,136.76 1943-08-04,136.20,137.54,136.20,136.87,730000,136.87 1943-08-03,134.04,136.00,134.04,135.64,1200000,135.64 1943-08-02,135.95,136.50,133.87,134.00,1350000,134.00 1943-07-30,139.41,139.66,136.87,137.25,1230000,137.25 1943-07-29,138.28,139.90,138.28,139.41,1020000,139.41 1943-07-28,138.75,139.08,136.72,137.64,1850000,137.64 1943-07-27,142.07,142.07,138.65,138.75,1790000,138.75 1943-07-26,143.65,143.65,141.73,142.07,1460000,142.07 1943-07-23,143.77,144.18,143.31,143.80,910000,143.80 1943-07-22,143.94,144.13,143.38,143.77,820000,143.77 1943-07-21,143.93,144.22,143.33,143.94,770000,143.94 1943-07-20,144.74,144.79,143.50,143.93,990000,143.93 1943-07-19,144.72,145.31,144.39,144.74,830000,144.74 1943-07-16,144.87,145.67,144.41,144.75,1040000,144.75 1943-07-15,145.82,146.41,144.76,144.87,1220000,144.87 1943-07-14,145.30,146.26,145.08,145.82,1680000,145.82 1943-07-13,144.62,145.54,144.37,145.30,1390000,145.30 1943-07-12,144.23,144.79,143.94,144.62,1050000,144.62 1943-07-09,143.64,144.32,143.34,144.18,1050000,144.18 1943-07-08,143.41,144.09,143.15,143.64,960000,143.64 1943-07-07,143.76,143.82,142.83,143.41,710000,143.41 1943-07-06,143.70,144.41,143.46,143.76,960000,143.76 1943-07-02,143.58,144.05,143.17,143.68,890000,143.68 1943-07-01,143.38,144.02,142.98,143.58,1150000,143.58 1943-06-30,142.62,143.57,142.50,143.38,940000,143.38 1943-06-29,143.00,143.30,142.06,142.62,810000,142.62 1943-06-28,142.88,143.70,142.44,143.00,1030000,143.00 1943-06-25,140.96,142.49,140.96,142.27,1150000,142.27 1943-06-24,140.04,141.06,139.70,140.86,730000,140.86 1943-06-23,139.30,140.43,139.30,140.04,830000,140.04 1943-06-22,138.79,139.30,138.07,139.03,700000,139.03 1943-06-21,139.52,139.52,138.34,138.79,720000,138.79 1943-06-18,139.85,140.22,139.27,139.68,680000,139.68 1943-06-17,139.78,140.41,139.49,139.85,740000,139.85 1943-06-16,139.39,140.55,139.26,139.78,880000,139.78 1943-06-15,139.09,139.58,138.21,139.39,1010000,139.39 1943-06-14,140.91,140.91,138.86,139.09,1350000,139.09 1943-06-11,141.68,142.01,141.11,141.44,840000,141.44 1943-06-10,141.49,142.42,141.21,141.68,910000,141.68 1943-06-09,141.44,141.71,140.45,141.49,810000,141.49 1943-06-08,141.82,141.91,140.56,141.44,1080000,141.44 1943-06-07,142.99,142.99,141.50,141.82,1210000,141.82 1943-06-04,142.75,143.04,141.82,142.28,1260000,142.28 1943-06-03,142.39,143.05,141.51,142.75,1180000,142.75 1943-06-02,142.43,143.17,141.85,142.39,1310000,142.39 1943-06-01,142.06,142.90,141.72,142.43,1260000,142.43 1943-05-28,140.82,141.58,140.30,141.18,1050000,141.18 1943-05-27,140.38,141.54,140.13,140.82,1470000,140.82 1943-05-26,139.24,140.82,139.24,140.38,1320000,140.38 1943-05-25,138.84,139.37,138.06,139.17,890000,139.17 1943-05-24,138.78,139.14,138.41,138.84,790000,138.84 1943-05-21,138.84,139.25,138.10,138.90,920000,138.90 1943-05-20,139.15,140.09,138.60,138.84,1300000,138.84 1943-05-19,138.05,139.39,138.04,139.15,1490000,139.15 1943-05-18,136.98,138.39,136.92,138.05,930000,138.05 1943-05-17,137.31,137.43,136.30,136.98,850000,136.98 1943-05-14,137.85,137.85,136.13,136.82,1630000,136.82 1943-05-13,138.24,138.55,137.56,137.88,1030000,137.88 1943-05-12,138.36,138.96,137.83,138.24,1430000,138.24 1943-05-11,138.64,138.75,137.54,138.36,1790000,138.36 1943-05-10,138.36,139.30,138.12,138.64,2520000,138.64 1943-05-07,138.85,139.04,136.99,137.27,2150000,137.27 1943-05-06,138.34,139.13,137.97,138.85,2080000,138.85 1943-05-05,138.18,138.79,137.54,138.34,2470000,138.34 1943-05-04,137.43,138.71,137.27,138.18,2810000,138.18 1943-05-03,136.20,137.69,136.08,137.43,2350000,137.43 1943-04-30,135.24,136.17,135.01,135.48,1360000,135.48 1943-04-29,134.14,135.49,134.11,135.24,1340000,135.24 1943-04-28,134.39,134.61,133.46,134.14,880000,134.14 1943-04-27,134.34,134.75,133.75,134.39,830000,134.39 1943-04-26,134.34,134.89,133.91,134.34,1130000,134.34 1943-04-22,134.00,134.75,133.70,134.20,1250000,134.20 1943-04-21,133.17,134.31,133.17,134.00,1140000,134.00 1943-04-20,133.46,133.64,132.68,133.09,730000,133.09 1943-04-19,133.59,134.23,133.20,133.46,910000,133.46 1943-04-16,133.49,133.71,132.40,133.07,900000,133.07 1943-04-15,132.85,134.19,132.85,133.49,1390000,133.49 1943-04-14,131.38,132.90,131.38,132.49,1090000,132.49 1943-04-13,131.27,131.49,129.79,131.18,1410000,131.18 1943-04-12,131.63,132.86,131.11,131.27,1270000,131.27 1943-04-09,134.02,134.02,131.01,131.22,2520000,131.22 1943-04-08,136.00,136.68,135.18,135.52,1810000,135.52 1943-04-07,136.73,136.73,135.14,136.00,2100000,136.00 1943-04-06,136.44,137.45,136.09,136.93,2460000,136.93 1943-04-05,135.62,137.10,135.62,136.44,2650000,136.44 1943-04-02,136.44,136.44,135.08,135.67,1660000,135.67 1943-04-01,136.57,137.04,135.94,136.56,1740000,136.56 1943-03-31,136.82,137.07,135.86,136.57,1540000,136.57 1943-03-30,136.10,137.20,135.52,136.82,1940000,136.82 1943-03-29,134.75,136.41,134.75,136.10,2000000,136.10 1943-03-26,133.22,134.51,133.21,133.96,2140000,133.96 1943-03-25,130.91,133.30,130.91,133.22,2120000,133.22 1943-03-24,129.98,131.00,129.74,130.62,1160000,130.62 1943-03-23,129.46,130.47,129.46,129.98,1190000,129.98 1943-03-22,129.13,129.65,128.67,129.44,850000,129.44 1943-03-19,129.66,129.95,129.09,129.25,1070000,129.25 1943-03-18,129.49,130.00,129.07,129.66,870000,129.66 1943-03-17,130.22,130.22,128.86,129.49,1270000,129.49 1943-03-16,130.64,130.83,129.91,130.33,1030000,130.33 1943-03-15,130.73,130.95,130.03,130.64,1370000,130.64 1943-03-12,130.48,131.39,130.07,130.73,1970000,130.73 1943-03-11,129.16,130.74,128.95,130.48,1430000,130.48 1943-03-10,129.76,129.76,128.49,129.16,930000,129.16 1943-03-09,130.52,130.52,128.68,129.80,1420000,129.80 1943-03-08,130.74,131.23,130.14,130.56,1250000,130.56 1943-03-05,130.38,130.93,129.75,130.61,1210000,130.61 1943-03-04,130.03,131.20,129.97,130.38,2010000,130.38 1943-03-03,128.60,130.20,128.51,130.03,2020000,130.03 1943-03-02,129.18,129.18,127.91,128.60,1330000,128.60 1943-03-01,130.11,130.61,129.13,129.44,2000000,129.44 1943-02-26,130.04,130.25,129.20,129.71,1440000,129.71 1943-02-25,129.58,130.43,129.28,130.04,1780000,130.04 1943-02-24,128.78,130.00,128.39,129.58,1690000,129.58 1943-02-23,127.80,129.06,127.34,128.78,1430000,128.78 1943-02-19,127.06,127.25,125.82,126.67,900000,126.67 1943-02-18,128.29,128.29,126.82,127.06,1040000,127.06 1943-02-17,128.31,128.88,127.86,128.41,1100000,128.41 1943-02-16,128.60,128.88,127.70,128.31,1180000,128.31 1943-02-15,128.00,129.15,128.00,128.60,1840000,128.60 1943-02-11,127.01,127.54,126.53,127.09,1410000,127.09 1943-02-10,126.30,127.55,126.21,127.01,1500000,127.01 1943-02-09,125.57,126.59,125.38,126.30,920000,126.30 1943-02-08,125.81,126.10,125.19,125.57,730000,125.57 1943-02-05,125.07,126.00,124.71,125.75,880000,125.75 1943-02-04,125.56,125.57,124.69,125.07,800000,125.07 1943-02-03,125.88,126.10,124.84,125.56,830000,125.56 1943-02-02,125.86,126.38,125.51,125.88,1050000,125.88 1943-02-01,125.58,126.15,124.87,125.86,1090000,125.86 1943-01-29,124.38,125.71,124.23,125.41,1230000,125.41 1943-01-28,124.08,124.57,123.41,124.38,990000,124.38 1943-01-27,124.31,124.47,122.92,124.08,1030000,124.08 1943-01-26,123.74,124.59,123.63,124.31,1040000,124.31 1943-01-25,122.38,123.94,122.30,123.74,800000,123.74 1943-01-22,121.79,122.92,121.50,121.99,880000,121.99 1943-01-21,120.63,122.00,120.63,121.79,810000,121.79 1943-01-20,120.48,120.86,119.71,120.55,520000,120.55 1943-01-19,121.56,121.59,120.25,120.48,770000,120.48 1943-01-18,121.60,121.93,121.24,121.56,710000,121.56 1943-01-15,120.86,121.84,120.86,121.58,1000000,121.58 1943-01-14,120.25,121.03,120.09,120.79,680000,120.79 1943-01-13,119.98,120.59,119.80,120.25,630000,120.25 1943-01-12,119.95,120.52,119.54,119.98,630000,119.98 1943-01-11,119.51,120.41,119.51,119.95,810000,119.95 1943-01-08,119.37,119.71,118.92,119.26,800000,119.26 1943-01-07,119.66,119.95,118.84,119.37,710000,119.37 1943-01-06,119.70,120.31,119.40,119.66,550000,119.66 1943-01-05,120.25,120.62,119.50,119.70,670000,119.70 1943-01-04,119.93,120.82,119.75,120.25,620000,120.25 1942-12-31,119.56,120.19,119.08,119.40,1050000,119.40 1942-12-30,118.40,119.83,118.28,119.56,1120000,119.56 1942-12-29,118.50,118.95,117.30,118.40,1440000,118.40 1942-12-28,119.71,119.96,118.22,118.50,1200000,118.50 1942-12-24,119.07,119.67,118.56,119.27,800000,119.27 1942-12-23,118.49,119.58,118.24,119.07,920000,119.07 1942-12-22,118.66,119.23,118.09,118.49,780000,118.49 1942-12-21,118.75,119.35,118.12,118.66,770000,118.66 1942-12-18,118.68,119.76,118.41,118.97,1090000,118.97 1942-12-17,117.06,119.07,116.60,118.68,1310000,118.68 1942-12-16,116.31,117.38,116.08,117.06,910000,117.06 1942-12-15,115.83,116.64,115.65,116.31,700000,116.31 1942-12-14,115.82,116.13,115.21,115.83,690000,115.83 1942-12-11,116.00,116.55,115.16,115.70,640000,115.70 1942-12-10,115.93,116.38,115.41,116.00,570000,116.00 1942-12-09,115.76,116.38,115.35,115.93,620000,115.93 1942-12-08,115.03,116.05,115.03,115.76,620000,115.76 1942-12-07,115.24,115.57,114.71,115.00,500000,115.00 1942-12-04,115.19,115.42,114.41,115.02,530000,115.02 1942-12-03,115.16,115.87,114.76,115.19,630000,115.19 1942-12-02,114.61,115.61,114.43,115.16,540000,115.16 1942-12-01,114.50,115.12,114.08,114.61,560000,114.61 1942-11-30,114.95,115.03,114.01,114.50,470000,114.50 1942-11-27,114.13,115.12,114.08,114.86,530000,114.86 1942-11-25,114.10,114.66,113.55,114.13,570000,114.13 1942-11-24,114.46,114.61,113.46,114.10,640000,114.10 1942-11-23,115.27,115.27,114.03,114.46,630000,114.46 1942-11-20,114.55,115.67,114.37,115.27,660000,115.27 1942-11-19,114.64,114.95,114.24,114.55,500000,114.55 1942-11-18,114.53,115.08,114.12,114.64,510000,114.64 1942-11-17,115.70,116.24,114.42,114.53,670000,114.53 1942-11-16,116.24,116.32,115.41,115.70,520000,115.70 1942-11-13,116.46,116.76,115.80,116.26,670000,116.26 1942-11-12,116.30,117.14,115.63,116.46,710000,116.46 1942-11-10,117.14,117.14,115.96,116.30,770000,116.30 1942-11-09,116.92,118.18,116.61,117.30,1210000,117.30 1942-11-06,114.87,116.25,114.80,116.12,860000,116.12 1942-11-05,114.56,115.29,114.23,114.87,600000,114.87 1942-11-04,114.68,115.55,114.29,114.56,770000,114.56 1942-11-02,114.07,115.09,114.02,114.68,760000,114.68 1942-10-30,113.13,113.64,112.80,113.50,520000,113.50 1942-10-29,113.11,113.47,112.59,113.13,460000,113.13 1942-10-28,113.86,113.87,112.57,113.11,500000,113.11 1942-10-27,115.06,115.06,113.50,113.86,630000,113.86 1942-10-26,115.01,115.61,114.74,115.29,630000,115.29 1942-10-23,114.94,115.52,114.45,114.88,730000,114.88 1942-10-22,115.09,115.22,114.15,114.94,550000,114.94 1942-10-21,115.22,116.01,114.61,115.09,700000,115.09 1942-10-20,114.17,115.47,114.17,115.22,670000,115.22 1942-10-19,113.40,113.89,113.13,113.64,400000,113.64 1942-10-16,113.27,113.79,112.71,113.55,510000,113.55 1942-10-15,114.56,114.56,113.11,113.27,600000,113.27 1942-10-14,115.01,115.24,113.99,114.69,660000,114.69 1942-10-13,114.93,115.80,114.68,115.01,860000,115.01 1942-10-09,113.60,114.67,113.45,113.93,1050000,113.93 1942-10-08,112.01,113.86,112.01,113.60,1090000,113.60 1942-10-07,111.53,112.11,111.25,111.86,660000,111.86 1942-10-06,111.93,112.09,111.16,111.53,700000,111.53 1942-10-05,111.34,112.29,111.03,111.93,790000,111.93 1942-10-02,109.68,111.02,109.68,110.83,900000,110.83 1942-10-01,109.16,109.90,109.16,109.65,560000,109.65 1942-09-30,109.24,109.37,108.70,109.11,340000,109.11 1942-09-29,109.56,109.75,109.01,109.24,430000,109.24 1942-09-28,109.32,109.98,109.29,109.56,400000,109.56 1942-09-25,109.11,109.72,108.89,109.37,680000,109.37 1942-09-24,108.27,109.42,108.18,109.11,850000,109.11 1942-09-23,107.59,108.40,107.45,108.27,700000,108.27 1942-09-22,107.27,107.83,107.16,107.59,520000,107.59 1942-09-21,107.22,107.45,107.01,107.27,320000,107.27 1942-09-18,106.72,107.59,106.72,107.47,420000,107.47 1942-09-17,106.66,106.96,106.44,106.66,410000,106.66 1942-09-16,106.49,106.93,106.19,106.66,380000,106.66 1942-09-15,106.17,106.82,106.17,106.49,390000,106.49 1942-09-14,106.20,106.32,105.92,106.15,310000,106.15 1942-09-11,106.36,106.36,105.58,106.03,380000,106.03 1942-09-10,107.17,107.17,106.30,106.38,350000,106.38 1942-09-09,107.62,107.80,106.97,107.26,360000,107.26 1942-09-08,106.76,107.88,106.76,107.62,400000,107.62 1942-09-04,106.34,106.70,106.09,106.39,310000,106.39 1942-09-03,106.49,106.50,106.07,106.34,280000,106.34 1942-09-02,106.28,106.68,106.05,106.49,270000,106.49 1942-09-01,106.33,106.52,105.76,106.28,290000,106.28 1942-08-31,106.41,106.81,106.08,106.33,250000,106.33 1942-08-28,106.03,106.64,105.92,106.23,300000,106.23 1942-08-27,105.55,106.20,105.49,106.03,270000,106.03 1942-08-26,106.28,106.28,105.37,105.55,360000,105.55 1942-08-25,107.11,107.11,106.23,106.51,350000,106.51 1942-08-24,107.30,107.73,106.94,107.25,380000,107.25 1942-08-21,106.83,107.25,106.53,107.07,370000,107.07 1942-08-20,107.21,107.21,106.46,106.83,310000,106.83 1942-08-19,107.55,107.88,106.67,107.28,500000,107.28 1942-08-18,106.96,107.72,106.96,107.55,560000,107.55 1942-08-17,106.38,107.04,106.21,106.68,290000,106.68 1942-08-14,105.75,106.65,105.75,106.15,380000,106.15 1942-08-13,105.47,105.86,105.08,105.70,320000,105.70 1942-08-12,105.42,105.81,105.00,105.47,280000,105.47 1942-08-11,105.01,105.67,105.01,105.42,260000,105.42 1942-08-10,104.90,105.18,104.55,104.91,210000,104.91 1942-08-07,104.80,105.33,104.50,105.05,210000,105.05 1942-08-06,104.85,105.07,104.55,104.80,250000,104.80 1942-08-05,105.30,105.30,104.50,104.85,290000,104.85 1942-08-04,106.08,106.21,105.43,105.55,320000,105.55 1942-08-03,105.90,106.44,105.67,106.08,280000,106.08 1942-07-31,105.37,106.27,105.37,105.72,310000,105.72 1942-07-30,105.44,105.73,104.79,105.24,250000,105.24 1942-07-29,106.34,106.34,105.30,105.44,290000,105.44 1942-07-28,106.66,106.82,106.22,106.48,270000,106.48 1942-07-27,106.53,106.97,106.32,106.66,260000,106.66 1942-07-24,106.65,106.65,105.84,106.37,260000,106.37 1942-07-23,107.88,107.88,106.45,106.65,330000,106.65 1942-07-22,108.36,108.94,107.69,108.03,440000,108.03 1942-07-21,107.98,108.68,107.94,108.36,280000,108.36 1942-07-20,107.69,108.26,107.50,107.98,210000,107.98 1942-07-17,108.91,108.92,107.68,107.82,280000,107.82 1942-07-16,108.89,109.21,108.17,108.91,270000,108.91 1942-07-15,108.72,109.49,108.50,108.89,390000,108.89 1942-07-14,108.22,108.89,107.40,108.72,370000,108.72 1942-07-13,108.69,108.69,107.79,108.22,280000,108.22 1942-07-10,108.75,108.99,107.68,108.66,440000,108.66 1942-07-09,107.94,109.26,107.60,108.75,840000,108.75 1942-07-08,105.76,108.01,105.48,107.94,580000,107.94 1942-07-07,106.10,106.34,105.32,105.76,330000,105.76 1942-07-06,104.92,106.30,104.92,106.10,420000,106.10 1942-07-03,103.84,104.77,103.84,104.49,360000,104.49 1942-07-02,102.69,103.89,102.27,103.73,340000,103.73 1942-07-01,103.29,103.29,102.28,102.69,210000,102.69 1942-06-30,103.17,103.61,102.93,103.34,250000,103.34 1942-06-29,102.68,103.57,102.68,103.17,260000,103.17 1942-06-26,102.71,103.09,102.27,102.54,290000,102.54 1942-06-25,102.67,103.06,101.94,102.71,250000,102.71 1942-06-24,103.03,103.24,102.43,102.67,240000,102.67 1942-06-23,102.77,103.44,102.73,103.03,210000,103.03 1942-06-22,103.52,103.52,102.11,102.77,310000,102.77 1942-06-19,105.52,105.52,104.55,104.77,290000,104.77 1942-06-18,106.29,106.63,105.49,105.70,350000,105.70 1942-06-17,104.68,106.38,104.68,106.29,390000,106.29 1942-06-16,104.41,104.94,104.18,104.51,280000,104.51 1942-06-15,104.08,104.54,103.99,104.41,260000,104.41 1942-06-12,104.32,104.32,103.27,103.77,220000,103.77 1942-06-11,104.19,104.69,103.94,104.49,280000,104.49 1942-06-10,104.90,104.90,103.70,104.19,310000,104.19 1942-06-09,105.55,106.34,104.83,105.09,400000,105.09 1942-06-08,104.71,105.86,104.71,105.55,360000,105.55 1942-06-05,103.61,105.26,103.60,104.41,490000,104.41 1942-06-04,102.25,103.87,102.25,103.61,520000,103.61 1942-06-03,101.30,102.33,101.01,102.15,330000,102.15 1942-06-02,101.37,101.67,100.93,101.30,320000,101.30 1942-06-01,100.96,101.84,100.96,101.37,310000,101.37 1942-05-29,100.99,101.29,100.43,100.88,250000,100.88 1942-05-28,101.09,101.50,100.52,100.99,350000,100.99 1942-05-27,99.41,101.21,99.31,101.09,430000,101.09 1942-05-26,99.18,100.00,99.02,99.41,290000,99.41 1942-05-25,99.25,99.47,98.68,99.18,230000,99.18 1942-05-22,99.72,99.95,98.83,99.18,330000,99.18 1942-05-21,98.13,100.21,98.11,99.72,560000,99.72 1942-05-20,97.96,98.50,97.22,98.13,410000,98.13 1942-05-19,98.65,98.84,97.70,97.96,380000,97.96 1942-05-18,98.63,99.16,98.24,98.65,220000,98.65 1942-05-15,97.38,98.27,97.38,97.98,260000,97.98 1942-05-14,97.21,97.37,96.39,97.13,280000,97.13 1942-05-13,98.19,98.19,96.92,97.21,340000,97.21 1942-05-12,99.20,99.35,98.31,98.56,250000,98.56 1942-05-11,98.70,99.49,98.45,99.20,290000,99.20 1942-05-08,97.77,98.57,97.65,97.91,310000,97.91 1942-05-07,96.93,98.11,96.93,97.77,340000,97.77 1942-05-06,97.29,97.35,96.19,96.71,270000,96.71 1942-05-05,96.70,97.74,96.54,97.29,270000,97.29 1942-05-04,96.44,97.15,96.34,96.70,260000,96.70 1942-05-01,95.35,96.29,95.08,95.83,300000,95.83 1942-04-30,94.71,95.76,94.71,95.35,280000,95.35 1942-04-29,92.92,95.18,92.74,94.65,410000,94.65 1942-04-28,93.69,93.69,92.69,92.92,310000,92.92 1942-04-27,94.31,94.89,93.66,93.89,280000,93.89 1942-04-24,94.80,94.80,93.59,94.13,390000,94.13 1942-04-23,96.91,96.91,94.84,94.98,430000,94.98 1942-04-22,97.51,97.64,96.79,97.20,260000,97.20 1942-04-21,97.25,98.02,97.16,97.51,270000,97.51 1942-04-20,96.92,97.47,96.63,97.25,240000,97.25 1942-04-17,97.61,97.61,95.80,96.05,420000,96.05 1942-04-16,98.06,98.46,97.61,97.87,260000,97.87 1942-04-15,97.89,98.75,97.50,98.06,350000,98.06 1942-04-14,99.21,99.21,97.77,97.89,560000,97.89 1942-04-13,99.45,99.78,99.25,99.44,240000,99.44 1942-04-10,99.69,100.15,99.31,99.74,300000,99.74 1942-04-09,100.56,100.56,99.51,99.69,350000,99.69 1942-04-08,101.89,101.97,100.89,101.23,300000,101.23 1942-04-07,102.50,102.75,101.73,101.89,310000,101.89 1942-04-06,101.11,102.69,101.05,102.50,340000,102.50 1942-04-02,100.19,101.31,100.19,100.89,370000,100.89 1942-04-01,99.53,100.19,99.32,99.95,280000,99.95 1942-03-31,100.04,100.21,99.25,99.53,280000,99.53 1942-03-30,100.00,100.54,99.76,100.04,230000,100.04 1942-03-27,101.00,101.00,99.78,100.00,310000,100.00 1942-03-26,101.48,101.54,100.73,101.05,300000,101.05 1942-03-25,102.09,102.16,101.39,101.48,320000,101.48 1942-03-24,101.20,102.43,101.02,102.09,360000,102.09 1942-03-23,100.82,101.58,100.81,101.20,280000,101.20 1942-03-20,101.25,101.31,100.43,100.75,280000,100.75 1942-03-19,101.64,101.82,100.91,101.25,280000,101.25 1942-03-18,102.54,102.73,101.26,101.64,340000,101.64 1942-03-17,100.82,102.70,100.82,102.54,470000,102.54 1942-03-16,99.64,100.81,99.50,100.68,320000,100.68 1942-03-13,99.23,100.39,98.93,99.73,340000,99.73 1942-03-12,99.21,100.25,98.32,99.23,460000,99.23 1942-03-11,101.14,101.14,99.11,99.21,410000,99.21 1942-03-10,102.09,102.32,101.17,101.49,340000,101.49 1942-03-09,102.31,102.88,101.56,102.09,310000,102.09 1942-03-06,104.08,104.08,102.03,102.10,640000,102.10 1942-03-05,105.59,105.59,104.37,104.55,450000,104.55 1942-03-04,106.97,107.04,105.69,105.99,380000,105.99 1942-03-03,105.75,107.16,105.48,106.97,410000,106.97 1942-03-02,106.49,106.49,105.33,105.75,330000,105.75 1942-02-27,106.16,106.93,106.16,106.58,360000,106.58 1942-02-26,105.64,106.24,105.40,105.88,350000,105.88 1942-02-25,106.00,106.30,105.41,105.64,340000,105.64 1942-02-24,105.54,106.73,105.54,106.00,390000,106.00 1942-02-20,105.57,105.57,104.78,105.10,330000,105.10 1942-02-19,105.35,106.03,104.93,105.57,350000,105.57 1942-02-18,105.40,105.70,104.67,105.35,340000,105.35 1942-02-17,107.05,107.05,105.20,105.40,390000,105.40 1942-02-16,107.30,107.96,106.70,107.31,380000,107.31 1942-02-13,106.51,107.01,106.12,106.73,320000,106.73 1942-02-11,106.75,107.19,106.00,106.51,420000,106.51 1942-02-10,107.81,107.81,106.21,106.75,640000,106.75 1942-02-09,108.92,108.92,107.92,108.12,400000,108.12 1942-02-06,110.44,110.53,109.30,109.47,430000,109.47 1942-02-05,110.80,110.97,110.09,110.44,440000,110.44 1942-02-04,109.99,111.04,109.91,110.80,500000,110.80 1942-02-03,109.47,110.29,109.38,109.99,370000,109.99 1942-02-02,109.11,109.68,109.08,109.47,330000,109.47 1942-01-30,109.90,110.20,109.23,109.41,380000,109.41 1942-01-29,110.15,110.39,109.50,109.90,420000,109.90 1942-01-28,110.68,110.99,109.69,110.15,460000,110.15 1942-01-27,110.67,111.20,110.31,110.68,550000,110.68 1942-01-26,109.52,111.00,109.52,110.67,600000,110.67 1942-01-23,108.94,109.44,108.39,109.12,430000,109.12 1942-01-22,109.06,109.16,108.30,108.94,430000,108.94 1942-01-21,110.12,110.12,108.71,109.06,580000,109.06 1942-01-20,110.81,111.21,110.24,110.45,490000,110.45 1942-01-19,110.68,111.34,109.98,110.81,430000,110.81 1942-01-16,112.59,112.64,111.01,111.25,480000,111.25 1942-01-15,112.59,113.02,112.17,112.59,450000,112.59 1942-01-14,112.44,113.29,112.05,112.59,610000,112.59 1942-01-13,110.90,112.61,110.90,112.44,730000,112.44 1942-01-12,110.54,111.48,110.10,110.65,490000,110.65 1942-01-09,111.55,111.97,110.45,111.02,660000,111.02 1942-01-08,112.91,112.91,111.20,111.55,530000,111.55 1942-01-07,113.93,113.93,112.62,113.10,630000,113.10 1942-01-06,114.22,114.96,113.22,113.99,800000,113.99 1942-01-05,113.75,114.76,113.20,114.22,720000,114.22 1942-01-02,110.96,113.00,110.07,112.77,580000,112.77 1941-12-31,111.32,111.99,109.75,110.96,1750000,110.96 1941-12-30,107.56,112.04,107.45,111.32,2560000,111.32 1941-12-29,107.54,108.94,106.36,107.56,2930000,107.56 1941-12-26,106.67,107.86,105.92,106.95,1410000,106.95 1941-12-24,106.34,107.56,105.52,106.67,1380000,106.67 1941-12-23,106.59,107.21,105.57,106.34,1420000,106.34 1941-12-22,107.81,108.83,106.23,106.59,1460000,106.59 1941-12-19,108.21,109.52,107.61,108.28,1270000,108.28 1941-12-18,109.36,109.67,107.18,108.21,1310000,108.21 1941-12-17,110.86,111.05,108.68,109.36,1220000,109.36 1941-12-16,111.15,112.30,110.29,110.86,1230000,110.86 1941-12-15,110.73,112.01,109.97,111.15,1110000,111.15 1941-12-12,110.91,111.76,109.53,110.58,1120000,110.58 1941-12-11,109.01,111.73,108.38,110.91,1400000,110.91 1941-12-10,109.27,109.76,106.87,109.01,2090000,109.01 1941-12-09,112.52,112.73,107.56,109.27,2560000,109.27 1941-12-08,115.46,115.46,111.53,112.52,2030000,112.52 1941-12-05,116.44,116.44,115.09,115.90,980000,115.90 1941-12-04,116.65,117.54,115.96,116.60,1130000,116.60 1941-12-03,115.57,117.00,115.24,116.65,1090000,116.65 1941-12-02,113.60,115.79,113.60,115.57,1180000,115.57 1941-12-01,114.23,114.89,113.06,113.59,840000,113.59 1941-11-28,115.64,115.68,114.10,114.66,870000,114.66 1941-11-27,115.93,116.49,115.00,115.64,810000,115.64 1941-11-26,116.96,116.98,115.61,115.93,850000,115.93 1941-11-25,117.30,117.60,116.38,116.96,840000,116.96 1941-11-24,117.04,118.19,116.89,117.30,820000,117.30 1941-11-21,116.68,117.50,116.07,117.05,850000,117.05 1941-11-19,115.87,116.90,115.47,116.68,800000,116.68 1941-11-18,116.20,116.62,115.20,115.87,680000,115.87 1941-11-17,116.72,117.03,115.69,116.20,630000,116.20 1941-11-14,115.75,117.30,115.75,116.81,840000,116.81 1941-11-13,115.44,116.34,114.91,115.67,830000,115.67 1941-11-12,117.16,117.16,115.03,115.44,1020000,115.44 1941-11-10,118.26,118.41,117.24,117.45,630000,117.45 1941-11-07,118.84,118.84,117.64,118.33,700000,118.33 1941-11-06,119.80,119.80,118.41,118.84,670000,118.84 1941-11-05,118.87,120.34,118.84,119.85,920000,119.85 1941-11-03,118.11,119.32,118.11,118.87,600000,118.87 1941-10-31,119.17,119.17,117.40,117.82,640000,117.82 1941-10-30,119.37,119.65,118.57,119.18,550000,119.18 1941-10-29,119.60,119.96,118.99,119.37,550000,119.37 1941-10-28,119.43,120.07,119.03,119.60,560000,119.60 1941-10-27,120.04,120.04,119.11,119.43,480000,119.43 1941-10-24,120.62,121.69,120.62,121.18,710000,121.18 1941-10-23,120.56,120.84,119.77,120.47,540000,120.47 1941-10-22,121.07,121.19,120.25,120.56,490000,120.56 1941-10-21,120.13,121.27,119.83,121.07,580000,121.07 1941-10-20,120.10,120.75,119.64,120.13,620000,120.13 1941-10-17,118.52,119.56,117.88,119.15,670000,119.15 1941-10-16,120.04,120.04,118.43,118.52,840000,118.52 1941-10-15,121.36,121.36,120.19,120.52,480000,120.52 1941-10-14,122.63,122.80,121.62,121.82,440000,121.82 1941-10-10,122.53,123.06,122.01,122.46,490000,122.46 1941-10-09,124.08,124.08,122.18,122.53,720000,122.53 1941-10-08,124.42,124.52,123.61,124.13,440000,124.13 1941-10-07,125.83,125.86,124.08,124.42,600000,124.42 1941-10-06,126.10,126.20,125.50,125.83,490000,125.83 1941-10-03,126.15,126.44,125.57,126.06,420000,126.06 1941-10-02,126.85,127.06,126.08,126.15,470000,126.15 1941-10-01,126.82,127.20,126.39,126.85,370000,126.85 1941-09-30,126.49,127.31,126.49,126.82,460000,126.82 1941-09-29,126.03,126.54,125.76,126.05,400000,126.05 1941-09-26,126.38,126.68,125.55,125.81,490000,125.81 1941-09-25,127.54,127.71,125.33,126.38,1170000,126.38 1941-09-24,128.03,128.79,127.35,127.54,550000,127.54 1941-09-23,127.64,128.50,127.59,128.03,490000,128.03 1941-09-22,127.54,128.08,127.25,127.64,420000,127.64 1941-09-19,128.77,128.81,127.74,127.95,580000,127.95 1941-09-18,129.32,130.00,128.54,128.77,790000,128.77 1941-09-17,127.43,129.48,127.27,129.32,890000,129.32 1941-09-16,127.20,127.71,126.85,127.43,610000,127.43 1941-09-15,127.28,127.54,126.95,127.20,460000,127.20 1941-09-12,127.15,127.72,126.87,127.18,520000,127.18 1941-09-11,126.53,127.45,126.31,127.15,690000,127.15 1941-09-10,127.43,127.48,126.37,126.53,520000,126.53 1941-09-09,127.51,128.38,127.16,127.43,870000,127.43 1941-09-08,127.26,127.94,126.99,127.51,620000,127.51 1941-09-05,127.51,127.54,126.88,127.17,580000,127.17 1941-09-04,127.88,127.88,127.21,127.51,540000,127.51 1941-09-03,128.31,128.56,127.54,127.91,460000,127.91 1941-09-02,127.77,128.62,127.77,128.31,520000,128.31 1941-08-29,127.77,127.88,127.05,127.43,350000,127.43 1941-08-28,127.25,127.95,127.25,127.77,400000,127.77 1941-08-27,126.60,127.37,126.60,127.08,430000,127.08 1941-08-26,126.08,126.82,126.08,126.56,460000,126.56 1941-08-25,125.91,126.25,125.50,125.86,330000,125.86 1941-08-22,125.99,126.12,125.39,125.84,360000,125.84 1941-08-21,126.01,126.42,125.72,125.99,390000,125.99 1941-08-20,125.57,126.42,125.48,126.01,490000,126.01 1941-08-19,125.62,125.91,125.20,125.57,390000,125.57 1941-08-18,125.20,125.96,125.20,125.62,400000,125.62 1941-08-15,125.96,125.97,124.66,124.90,390000,124.90 1941-08-14,125.65,126.43,125.57,125.96,420000,125.96 1941-08-13,125.81,126.10,125.42,125.65,420000,125.65 1941-08-12,126.01,126.24,125.30,125.81,430000,125.81 1941-08-11,126.40,126.61,125.63,126.01,450000,126.01 1941-08-08,128.09,128.27,127.31,127.48,550000,127.48 1941-08-07,128.10,128.57,127.68,128.09,500000,128.09 1941-08-06,128.14,128.55,127.62,128.10,580000,128.10 1941-08-05,128.17,128.54,127.61,128.14,640000,128.14 1941-08-04,128.21,128.56,127.61,128.17,630000,128.17 1941-08-01,128.68,128.68,127.64,128.22,680000,128.22 1941-07-31,128.95,129.45,128.43,128.79,850000,128.79 1941-07-30,129.19,129.34,128.07,128.95,750000,128.95 1941-07-29,130.06,130.33,128.93,129.19,960000,129.19 1941-07-28,128.70,130.37,128.65,130.06,940000,130.06 1941-07-25,128.59,128.92,127.74,128.06,810000,128.06 1941-07-24,129.16,129.40,128.39,128.59,620000,128.59 1941-07-23,129.58,129.71,128.79,129.16,630000,129.16 1941-07-22,129.51,131.10,129.22,129.58,1350000,129.58 1941-07-21,127.98,129.57,127.78,129.51,910000,129.51 1941-07-18,127.14,127.89,126.92,127.69,420000,127.69 1941-07-17,127.83,127.88,126.75,127.14,460000,127.14 1941-07-16,128.19,128.70,127.62,127.83,640000,127.83 1941-07-15,127.89,128.68,127.53,128.19,700000,128.19 1941-07-14,127.80,128.17,127.44,127.89,560000,127.89 1941-07-11,127.78,128.70,127.35,127.90,810000,127.90 1941-07-10,127.63,128.36,127.05,127.78,840000,127.78 1941-07-09,127.64,128.77,127.12,127.63,1100000,127.63 1941-07-08,126.16,128.00,126.07,127.64,1380000,127.64 1941-07-07,124.54,126.24,124.54,126.16,900000,126.16 1941-07-03,123.58,124.43,123.43,124.04,470000,124.04 1941-07-02,122.85,123.90,122.78,123.58,390000,123.58 1941-07-01,123.14,123.37,122.54,122.85,350000,122.85 1941-06-30,123.40,123.58,122.87,123.14,270000,123.14 1941-06-27,123.96,124.03,123.22,123.46,410000,123.46 1941-06-26,123.52,124.13,123.42,123.96,530000,123.96 1941-06-25,123.24,123.83,122.85,123.52,430000,123.52 1941-06-24,123.97,124.03,122.88,123.24,440000,123.24 1941-06-23,123.41,125.14,123.41,123.97,760000,123.97 1941-06-20,123.30,123.30,121.85,122.19,360000,122.19 1941-06-19,123.50,123.73,122.61,123.48,460000,123.48 1941-06-18,123.17,124.31,123.17,123.50,580000,123.50 1941-06-17,122.14,123.21,122.14,123.12,400000,123.12 1941-06-16,122.04,122.58,121.72,121.95,340000,121.95 1941-06-13,122.88,122.88,121.75,122.31,440000,122.31 1941-06-12,122.18,123.48,122.17,122.98,560000,122.98 1941-06-11,121.89,122.64,121.46,122.18,540000,122.18 1941-06-10,120.61,122.48,120.61,121.89,830000,121.89 1941-06-09,118.99,120.27,118.99,120.16,440000,120.16 1941-06-06,118.13,118.26,117.78,118.00,330000,118.00 1941-06-05,117.68,118.60,117.59,118.13,610000,118.13 1941-06-04,117.38,117.98,116.89,117.68,430000,117.68 1941-06-03,116.32,117.85,116.32,117.38,420000,117.38 1941-06-02,115.76,116.45,115.52,116.18,260000,116.18 1941-05-29,116.16,116.80,115.84,116.23,350000,116.23 1941-05-28,115.95,116.50,115.56,116.16,340000,116.16 1941-05-27,115.73,116.31,115.33,115.95,390000,115.95 1941-05-26,116.28,116.28,115.51,115.73,300000,115.73 1941-05-23,116.81,117.16,116.22,116.73,260000,116.73 1941-05-22,117.82,117.98,116.56,116.81,400000,116.81 1941-05-21,117.65,118.45,117.34,117.82,540000,117.82 1941-05-20,116.31,117.78,116.31,117.65,470000,117.65 1941-05-19,116.11,116.36,115.92,116.15,220000,116.15 1941-05-16,115.73,116.38,115.36,115.86,290000,115.86 1941-05-15,117.01,117.37,115.54,115.73,500000,115.73 1941-05-14,117.21,117.36,116.67,117.01,320000,117.01 1941-05-13,117.14,117.93,116.88,117.21,430000,117.21 1941-05-12,117.54,117.83,116.85,117.14,440000,117.14 1941-05-09,116.34,116.90,116.18,116.46,400000,116.46 1941-05-08,116.76,116.76,115.97,116.34,440000,116.34 1941-05-07,117.10,117.49,116.53,116.87,560000,116.87 1941-05-06,116.02,117.63,116.02,117.10,910000,117.10 1941-05-05,115.55,116.20,114.97,115.84,420000,115.84 1941-05-02,115.41,116.34,115.41,115.72,400000,115.72 1941-05-01,115.54,115.64,114.78,115.30,310000,115.30 1941-04-30,116.73,116.78,115.36,115.54,410000,115.54 1941-04-29,116.63,117.48,116.45,116.73,510000,116.73 1941-04-28,116.43,116.86,115.96,116.63,310000,116.63 1941-04-25,117.35,117.56,116.45,116.58,430000,116.58 1941-04-24,116.61,117.85,116.61,117.35,490000,117.35 1941-04-23,115.78,116.82,115.33,116.59,480000,116.59 1941-04-22,116.06,117.03,115.36,115.78,440000,115.78 1941-04-21,116.15,116.39,115.49,116.06,430000,116.06 1941-04-18,117.74,117.74,116.11,116.28,490000,116.28 1941-04-17,118.60,118.75,117.78,118.16,400000,118.16 1941-04-16,118.59,119.01,117.65,118.60,440000,118.60 1941-04-15,118.89,119.61,118.24,118.59,450000,118.59 1941-04-14,118.60,119.05,118.01,118.89,460000,118.89 1941-04-10,119.85,120.45,119.40,119.66,360000,119.66 1941-04-09,120.72,120.72,119.56,119.85,590000,119.85 1941-04-08,122.94,122.94,121.00,121.21,740000,121.21 1941-04-07,124.11,124.11,123.36,123.64,360000,123.64 1941-04-04,124.65,125.28,124.23,124.64,700000,124.64 1941-04-03,123.74,124.93,123.74,124.65,940000,124.65 1941-04-02,123.26,123.55,122.66,123.43,450000,123.43 1941-04-01,122.87,123.57,122.87,123.26,430000,123.26 1941-03-31,122.37,123.02,122.32,122.72,440000,122.72 1941-03-28,123.33,123.70,122.47,122.68,440000,122.68 1941-03-27,122.84,123.75,122.84,123.33,550000,123.33 1941-03-26,122.78,123.21,122.55,122.70,530000,122.70 1941-03-25,122.39,123.00,121.98,122.78,450000,122.78 1941-03-24,121.92,122.62,121.82,122.39,370000,122.39 1941-03-21,123.39,123.39,122.26,122.47,470000,122.47 1941-03-20,123.55,123.92,123.26,123.60,490000,123.60 1941-03-19,123.92,124.35,123.24,123.55,540000,123.55 1941-03-18,123.46,124.10,123.02,123.92,410000,123.92 1941-03-17,123.40,124.12,123.18,123.46,380000,123.46 1941-03-14,122.56,123.12,122.42,122.75,320000,122.75 1941-03-13,123.19,123.27,122.35,122.56,340000,122.56 1941-03-12,123.27,124.04,122.73,123.19,460000,123.19 1941-03-11,123.64,124.20,123.03,123.27,510000,123.27 1941-03-10,121.48,123.71,121.48,123.64,620000,123.64 1941-03-07,121.63,122.25,121.34,121.59,350000,121.59 1941-03-06,120.30,121.68,120.10,121.63,480000,121.63 1941-03-05,121.15,121.15,119.98,120.30,290000,120.30 1941-03-04,120.88,121.44,120.62,121.16,310000,121.16 1941-03-03,121.65,121.65,120.58,120.88,330000,120.88 1941-02-28,121.87,122.69,121.60,121.97,410000,121.97 1941-02-27,122.22,122.22,121.22,121.87,310000,121.87 1941-02-26,122.40,122.90,121.99,122.39,380000,122.39 1941-02-25,121.60,122.77,121.60,122.40,360000,122.40 1941-02-24,120.44,121.74,120.44,121.49,350000,121.49 1941-02-21,119.99,120.64,119.60,120.24,300000,120.24 1941-02-20,118.84,120.52,118.84,119.99,450000,119.99 1941-02-19,118.71,118.71,117.43,117.94,470000,117.94 1941-02-18,119.18,119.65,118.53,118.98,320000,118.98 1941-02-17,118.70,119.73,118.70,119.18,360000,119.18 1941-02-14,120.74,120.74,117.57,117.66,930000,117.66 1941-02-13,122.06,122.06,120.57,121.10,640000,121.10 1941-02-11,123.80,123.80,122.51,122.61,420000,122.61 1941-02-10,124.71,125.13,123.93,124.19,300000,124.19 1941-02-07,124.58,124.58,123.57,124.30,340000,124.30 1941-02-06,124.25,125.27,124.25,124.76,420000,124.76 1941-02-05,122.77,124.55,122.77,124.14,490000,124.14 1941-02-04,122.67,123.29,122.29,122.63,350000,122.63 1941-02-03,123.28,123.75,122.40,122.67,490000,122.67 1941-01-31,124.05,124.85,123.86,124.13,470000,124.13 1941-01-30,125.96,125.96,123.94,124.05,780000,124.05 1941-01-29,127.93,127.93,125.76,126.00,600000,126.00 1941-01-28,129.03,129.27,128.42,128.60,470000,128.60 1941-01-27,128.96,129.47,128.54,129.03,360000,129.03 1941-01-24,128.34,128.92,127.68,128.52,410000,128.52 1941-01-23,128.65,129.15,127.74,128.34,470000,128.34 1941-01-22,128.20,129.03,127.98,128.65,520000,128.65 1941-01-21,129.24,129.51,127.83,128.20,580000,128.20 1941-01-20,129.75,129.99,128.78,129.24,380000,129.24 1941-01-17,129.93,130.20,128.73,129.54,580000,129.54 1941-01-16,131.36,131.36,129.82,129.93,610000,129.93 1941-01-15,132.43,132.43,131.20,131.51,400000,131.51 1941-01-14,133.11,133.11,132.17,132.44,470000,132.44 1941-01-13,133.49,133.85,132.92,133.25,480000,133.25 1941-01-10,133.39,134.27,133.14,133.59,750000,133.59 1941-01-09,133.02,133.94,132.79,133.39,860000,133.39 1941-01-08,133.02,133.74,132.35,133.02,640000,133.02 1941-01-07,132.83,133.50,132.19,133.02,530000,133.02 1941-01-06,132.42,133.68,132.42,132.83,720000,132.83 1941-01-03,130.57,132.19,130.24,132.01,510000,132.01 1941-01-02,131.13,131.88,130.39,130.57,530000,130.57 1940-12-31,131.04,131.86,130.40,131.13,1070000,131.13 1940-12-30,130.18,131.85,130.18,131.04,1180000,131.04 1940-12-27,129.02,130.11,128.55,129.51,1270000,129.51 1940-12-26,128.89,129.70,128.42,129.02,840000,129.02 1940-12-24,128.41,129.47,128.08,128.89,830000,128.89 1940-12-23,128.89,129.29,127.83,128.41,820000,128.41 1940-12-20,128.84,129.66,128.19,128.87,830000,128.87 1940-12-19,129.42,129.43,128.19,128.84,790000,128.84 1940-12-18,130.53,130.92,129.31,129.42,780000,129.42 1940-12-17,131.07,131.34,130.09,130.53,700000,130.53 1940-12-16,132.28,132.28,130.74,131.07,660000,131.07 1940-12-13,132.14,133.00,131.56,132.35,960000,132.35 1940-12-12,131.76,132.53,131.24,132.14,780000,132.14 1940-12-11,131.37,132.50,131.24,131.76,810000,131.76 1940-12-10,131.46,131.97,130.69,131.37,610000,131.37 1940-12-09,131.29,132.22,130.91,131.46,630000,131.46 1940-12-06,129.96,130.86,129.62,130.33,540000,130.33 1940-12-05,130.75,130.81,129.54,129.96,600000,129.96 1940-12-04,130.78,131.21,129.99,130.75,650000,130.75 1940-12-03,130.93,131.23,130.25,130.78,450000,130.78 1940-12-02,131.00,131.96,130.54,130.93,480000,130.93 1940-11-29,130.14,130.78,129.49,130.03,530000,130.03 1940-11-28,129.78,130.57,129.13,130.14,470000,130.14 1940-11-27,131.45,131.45,129.29,129.78,850000,129.78 1940-11-26,131.96,132.72,131.28,131.94,590000,131.94 1940-11-25,131.47,132.76,131.35,131.96,520000,131.96 1940-11-22,132.22,133.36,131.29,131.74,710000,131.74 1940-11-20,134.08,134.08,131.72,132.22,810000,132.22 1940-11-19,134.74,134.99,133.51,134.48,700000,134.48 1940-11-18,134.73,135.47,134.36,134.74,570000,134.74 1940-11-15,136.97,137.44,135.24,135.59,1050000,135.59 1940-11-14,136.61,137.78,136.20,136.97,1380000,136.97 1940-11-13,137.41,137.61,135.77,136.61,1070000,136.61 1940-11-12,138.12,138.50,136.56,137.41,1450000,137.41 1940-11-08,137.75,138.77,135.76,136.64,1750000,136.64 1940-11-07,131.98,138.13,131.93,137.75,2080000,137.75 1940-11-06,135.00,135.00,131.47,131.98,1210000,131.98 1940-11-04,134.85,135.83,133.81,135.21,1240000,135.21 1940-11-01,134.61,135.84,134.13,134.41,1260000,134.41 1940-10-31,133.06,135.10,133.06,134.61,1340000,134.61 1940-10-30,132.19,133.43,131.90,132.98,670000,132.98 1940-10-29,131.77,132.80,131.66,132.19,590000,132.19 1940-10-28,132.26,132.27,130.96,131.77,470000,131.77 1940-10-25,131.36,131.71,130.38,131.16,520000,131.16 1940-10-24,132.29,132.29,131.06,131.36,540000,131.36 1940-10-23,131.98,132.79,131.26,132.40,810000,132.40 1940-10-22,131.37,132.44,131.04,131.98,540000,131.98 1940-10-21,132.18,132.28,131.13,131.37,370000,131.37 1940-10-18,132.49,132.90,131.92,132.45,590000,132.45 1940-10-17,131.97,132.84,131.20,132.49,650000,132.49 1940-10-16,131.48,132.69,131.05,131.97,660000,131.97 1940-10-15,130.73,131.63,129.47,131.48,550000,131.48 1940-10-14,131.04,131.49,130.32,130.73,400000,130.73 1940-10-11,130.39,131.62,130.20,131.04,400000,131.04 1940-10-10,130.54,131.22,130.09,130.39,380000,130.39 1940-10-09,131.31,131.31,130.11,130.54,450000,130.54 1940-10-08,133.31,133.31,131.20,131.31,500000,131.31 1940-10-07,133.90,134.54,133.31,133.51,390000,133.51 1940-10-04,134.48,134.48,133.37,133.79,470000,133.79 1940-10-03,134.97,135.86,134.59,135.09,780000,135.09 1940-10-02,134.33,135.41,133.70,134.97,770000,134.97 1940-10-01,133.04,135.04,133.04,134.33,810000,134.33 1940-09-30,132.32,133.07,132.10,132.64,400000,132.64 1940-09-27,132.98,132.98,131.38,131.76,560000,131.76 1940-09-26,134.00,134.00,133.00,133.50,440000,133.50 1940-09-25,134.44,134.58,133.40,134.15,600000,134.15 1940-09-24,135.10,135.48,133.89,134.44,710000,134.44 1940-09-23,132.98,135.31,132.98,135.10,980000,135.10 1940-09-20,131.34,131.95,130.77,131.61,380000,131.61 1940-09-19,131.28,132.03,130.83,131.34,470000,131.34 1940-09-18,130.43,131.67,129.91,131.28,480000,131.28 1940-09-17,129.44,130.89,129.44,130.43,400000,130.43 1940-09-16,128.89,130.04,128.89,129.44,290000,129.44 1940-09-13,127.87,128.37,127.22,127.74,270000,127.74 1940-09-12,129.32,129.32,127.46,127.87,400000,127.87 1940-09-11,129.61,131.21,129.07,129.36,450000,129.36 1940-09-10,129.73,130.53,129.18,129.61,360000,129.61 1940-09-09,130.98,130.98,129.36,129.73,590000,129.73 1940-09-06,134.10,134.19,132.71,133.12,710000,133.12 1940-09-05,132.31,134.54,132.31,134.10,1250000,134.10 1940-09-04,129.74,132.25,128.89,132.16,780000,132.16 1940-09-03,129.42,130.59,129.12,129.74,550000,129.74 1940-08-30,126.98,129.18,126.98,128.88,560000,128.88 1940-08-29,126.87,127.37,126.49,126.87,270000,126.87 1940-08-28,125.81,127.37,125.81,126.87,380000,126.87 1940-08-27,125.71,125.76,124.95,125.33,220000,125.33 1940-08-26,125.48,126.04,125.34,125.71,160000,125.71 1940-08-23,126.34,126.34,124.81,125.34,290000,125.34 1940-08-22,125.62,126.97,125.62,126.46,440000,126.46 1940-08-21,123.95,125.38,123.95,125.07,360000,125.07 1940-08-20,122.52,123.41,122.52,123.17,240000,123.17 1940-08-19,121.98,122.37,121.70,122.06,130000,122.06 1940-08-16,122.50,122.50,120.90,121.28,310000,121.28 1940-08-15,122.73,123.46,122.73,123.04,220000,123.04 1940-08-14,122.98,123.17,122.00,122.25,270000,122.25 1940-08-13,126.42,126.42,122.64,122.98,640000,122.98 1940-08-12,126.99,127.55,126.50,127.26,290000,127.26 1940-08-09,125.44,126.81,125.44,126.40,310000,126.40 1940-08-08,125.12,125.48,124.87,125.13,210000,125.13 1940-08-07,125.27,125.47,124.61,125.12,240000,125.12 1940-08-06,126.28,126.28,125.11,125.27,290000,125.27 1940-08-05,126.36,126.73,125.57,126.44,280000,126.44 1940-08-02,126.13,126.97,125.77,126.37,300000,126.37 1940-08-01,126.14,126.86,125.57,126.13,330000,126.13 1940-07-31,125.97,127.18,125.47,126.14,560000,126.14 1940-07-30,123.58,126.18,123.58,125.97,670000,125.97 1940-07-29,122.45,123.38,122.16,123.15,260000,123.15 1940-07-26,121.93,122.75,121.70,122.05,270000,122.05 1940-07-25,121.64,122.09,121.19,121.93,250000,121.93 1940-07-24,122.23,122.30,121.49,121.64,200000,121.64 1940-07-23,122.06,122.53,121.49,122.23,250000,122.23 1940-07-22,121.87,122.47,121.62,122.06,230000,122.06 1940-07-19,122.93,122.93,122.01,122.18,260000,122.18 1940-07-18,122.82,123.22,122.52,123.00,220000,123.00 1940-07-17,123.12,123.91,122.34,122.82,380000,122.82 1940-07-16,121.82,123.73,121.82,123.12,440000,123.12 1940-07-15,121.48,122.13,121.29,121.72,230000,121.72 1940-07-12,121.58,121.95,121.32,121.63,260000,121.63 1940-07-11,121.49,122.30,121.28,121.58,330000,121.58 1940-07-10,121.60,121.82,120.83,121.49,280000,121.49 1940-07-09,121.63,122.33,121.36,121.60,300000,121.60 1940-07-08,121.59,122.04,121.39,121.63,230000,121.63 1940-07-05,121.02,121.96,121.02,121.51,280000,121.51 1940-07-03,120.96,121.68,120.14,120.96,380000,120.96 1940-07-02,121.12,122.01,120.71,120.96,320000,120.96 1940-07-01,121.77,121.77,120.79,121.12,270000,121.12 1940-06-28,121.82,124.42,121.82,122.06,1170000,122.06 1940-06-27,120.01,121.23,120.01,120.69,440000,120.69 1940-06-26,120.74,120.74,118.67,119.73,640000,119.73 1940-06-25,123.76,123.99,120.78,121.05,700000,121.05 1940-06-24,122.83,124.05,122.60,123.76,470000,123.76 1940-06-21,122.35,123.07,121.89,122.61,330000,122.61 1940-06-20,123.79,123.79,121.96,122.35,590000,122.35 1940-06-19,123.21,124.51,122.50,123.86,560000,123.86 1940-06-18,122.80,125.31,122.35,123.21,720000,123.21 1940-06-17,123.36,123.73,119.18,122.80,1210000,122.80 1940-06-14,119.91,122.95,118.96,122.27,950000,122.27 1940-06-13,121.46,122.10,119.37,119.91,880000,119.91 1940-06-12,117.41,122.38,117.41,121.46,1360000,121.46 1940-06-11,113.24,116.38,113.24,115.97,760000,115.97 1940-06-10,114.42,114.42,110.41,111.84,970000,111.84 1940-06-07,114.60,116.58,114.60,115.67,470000,115.67 1940-06-06,113.25,114.80,113.05,114.48,430000,114.48 1940-06-05,115.12,115.12,112.30,113.25,670000,113.25 1940-06-04,114.75,116.25,114.75,115.79,410000,115.79 1940-06-03,115.67,116.44,114.35,114.73,450000,114.73 1940-05-31,115.24,117.15,115.13,116.22,530000,116.22 1940-05-29,114.26,116.66,114.08,115.24,660000,115.24 1940-05-28,114.76,114.76,110.51,114.26,1260000,114.26 1940-05-27,115.07,117.71,115.07,116.35,790000,116.35 1940-05-24,114.71,116.15,113.37,113.94,870000,113.94 1940-05-23,114.75,117.84,112.78,114.71,1640000,114.71 1940-05-22,114.13,116.50,112.43,114.75,2130000,114.75 1940-05-21,120.52,120.52,110.61,114.13,3940000,114.13 1940-05-20,122.43,124.98,121.68,122.43,1240000,122.43 1940-05-17,130.43,131.21,122.93,124.20,3080000,124.20 1940-05-16,129.08,131.35,127.54,130.43,2350000,130.43 1940-05-15,128.27,131.17,125.76,129.08,3770000,129.08 1940-05-14,136.85,136.85,128.11,128.27,3680000,128.27 1940-05-13,144.42,144.42,137.25,137.63,2560000,137.63 1940-05-10,148.17,148.48,144.51,144.77,2090000,144.77 1940-05-09,147.96,148.60,147.65,148.17,850000,148.17 1940-05-08,147.74,148.70,147.49,147.96,690000,147.96 1940-05-07,147.33,148.22,146.89,147.74,580000,147.74 1940-05-06,147.55,148.12,147.05,147.33,530000,147.33 1940-05-03,147.76,148.70,146.42,147.65,1070000,147.65 1940-05-02,147.15,148.18,147.15,147.76,650000,147.76 1940-05-01,148.14,148.14,146.84,147.13,810000,147.13 1940-04-30,148.41,149.06,147.98,148.43,590000,148.43 1940-04-29,148.12,148.88,148.02,148.41,570000,148.41 1940-04-26,148.56,148.72,147.37,147.73,850000,147.73 1940-04-25,148.45,149.22,148.07,148.56,820000,148.56 1940-04-24,148.93,149.45,148.20,148.45,850000,148.45 1940-04-23,148.01,149.18,147.77,148.93,880000,148.93 1940-04-22,147.67,148.68,147.45,148.01,870000,148.01 1940-04-19,147.15,147.31,145.86,146.80,1160000,146.80 1940-04-18,148.35,148.64,146.76,147.15,1210000,147.15 1940-04-17,148.18,149.12,147.77,148.35,900000,148.35 1940-04-16,149.72,150.24,147.43,148.18,1510000,148.18 1940-04-15,149.66,150.67,149.20,149.72,1260000,149.72 1940-04-12,149.98,150.09,148.68,149.20,830000,149.20 1940-04-11,149.59,150.96,149.49,149.98,890000,149.98 1940-04-10,150.31,150.55,149.12,149.59,1290000,149.59 1940-04-09,151.29,152.01,149.16,150.31,2140000,150.31 1940-04-08,151.10,152.09,150.63,151.29,1260000,151.29 1940-04-05,150.41,151.32,149.94,150.36,1260000,150.36 1940-04-04,149.65,151.15,149.56,150.41,2000000,150.41 1940-04-03,147.92,149.74,147.56,149.65,1730000,149.65 1940-04-02,147.72,148.37,147.48,147.92,840000,147.92 1940-04-01,147.95,148.44,147.49,147.72,750000,147.72 1940-03-29,147.25,148.04,146.82,147.54,840000,147.54 1940-03-28,147.47,148.16,146.92,147.25,1020000,147.25 1940-03-27,146.24,147.83,146.24,147.47,1190000,147.47 1940-03-26,146.25,146.56,145.49,145.86,620000,145.86 1940-03-25,146.73,146.96,146.15,146.25,600000,146.25 1940-03-21,146.91,147.20,146.38,146.73,580000,146.73 1940-03-20,146.43,147.30,146.34,146.91,650000,146.91 1940-03-19,145.61,146.85,145.61,146.43,650000,146.43 1940-03-18,145.76,146.18,145.08,145.59,510000,145.59 1940-03-15,148.11,148.21,146.34,146.53,880000,146.53 1940-03-14,148.32,148.57,147.63,148.11,660000,148.11 1940-03-13,148.37,148.92,147.69,148.32,630000,148.32 1940-03-12,148.15,149.45,147.93,148.37,880000,148.37 1940-03-11,148.14,148.65,147.57,148.15,590000,148.15 1940-03-08,148.32,148.80,147.76,148.07,750000,148.07 1940-03-07,147.97,148.67,147.83,148.32,690000,148.32 1940-03-06,147.08,148.37,147.08,147.97,860000,147.97 1940-03-05,146.43,147.15,146.41,146.89,570000,146.89 1940-03-04,146.33,146.77,146.00,146.43,460000,146.43 1940-03-01,146.54,146.79,145.85,146.23,600000,146.23 1940-02-29,146.56,147.21,146.10,146.54,620000,146.54 1940-02-28,146.17,147.16,146.07,146.56,570000,146.56 1940-02-27,146.44,147.10,145.90,146.17,510000,146.17 1940-02-26,146.72,146.82,145.81,146.44,440000,146.44 1940-02-23,148.17,148.17,146.93,147.35,650000,147.35 1940-02-21,148.65,149.04,148.02,148.34,780000,148.34 1940-02-20,148.46,148.95,147.51,148.65,810000,148.65 1940-02-19,148.72,148.95,147.93,148.46,630000,148.46 1940-02-16,148.46,148.60,147.38,148.20,680000,148.20 1940-02-15,148.33,149.19,148.20,148.46,750000,148.46 1940-02-14,148.78,148.83,147.65,148.33,650000,148.33 1940-02-13,148.84,149.64,148.38,148.78,580000,148.78 1940-02-09,148.54,150.04,148.54,148.94,1100000,148.94 1940-02-08,146.63,148.50,146.32,148.40,870000,148.40 1940-02-07,145.93,146.97,145.84,146.63,490000,146.63 1940-02-06,145.00,146.00,144.79,145.93,540000,145.93 1940-02-05,145.58,145.58,144.69,145.00,410000,145.00 1940-02-02,145.23,145.97,144.83,145.33,520000,145.33 1940-02-01,145.33,145.57,144.73,145.23,460000,145.23 1940-01-31,145.63,145.91,145.12,145.33,610000,145.33 1940-01-30,146.26,146.37,145.19,145.63,550000,145.63 1940-01-29,146.51,147.05,145.86,146.26,490000,146.26 1940-01-26,146.29,147.16,146.00,146.61,600000,146.61 1940-01-25,147.00,147.29,146.04,146.29,540000,146.29 1940-01-24,145.71,147.11,145.71,147.00,710000,147.00 1940-01-23,145.13,145.97,144.85,145.49,510000,145.49 1940-01-22,145.64,145.66,144.57,145.13,440000,145.13 1940-01-19,145.61,146.75,145.52,145.86,640000,145.86 1940-01-18,145.81,146.44,144.77,145.61,610000,145.61 1940-01-17,145.67,146.71,145.30,145.81,470000,145.81 1940-01-16,144.65,146.11,144.37,145.67,530000,145.67 1940-01-15,145.19,145.95,143.06,144.65,860000,144.65 1940-01-12,148.23,148.35,145.76,145.96,1110000,145.96 1940-01-11,150.15,150.64,148.22,148.23,850000,148.23 1940-01-10,149.84,150.47,149.35,150.15,600000,150.15 1940-01-09,151.21,151.21,149.45,149.84,670000,149.84 1940-01-08,151.19,152.11,151.08,151.34,630000,151.34 1940-01-05,152.43,152.89,151.27,151.54,760000,151.54 1940-01-04,152.80,153.26,152.06,152.43,860000,152.43 1940-01-03,151.86,153.29,151.86,152.80,1020000,152.80 1940-01-02,150.45,151.79,150.45,151.43,580000,151.43 1939-12-29,149.48,150.70,149.06,149.99,1140000,149.99 1939-12-28,148.52,150.10,148.24,149.48,1080000,149.48 1939-12-27,149.27,149.51,147.66,148.52,1150000,148.52 1939-12-26,149.85,150.11,148.83,149.27,720000,149.27 1939-12-22,149.10,149.97,148.87,149.59,720000,149.59 1939-12-21,149.13,149.90,148.59,149.10,740000,149.10 1939-12-20,148.93,149.86,148.41,149.13,910000,149.13 1939-12-19,149.22,149.59,148.35,148.93,750000,148.93 1939-12-18,149.36,149.80,148.69,149.22,730000,149.22 1939-12-15,148.93,150.11,148.74,149.64,700000,149.64 1939-12-14,148.94,150.09,148.25,148.93,890000,148.93 1939-12-13,147.12,149.29,147.12,148.94,1060000,148.94 1939-12-12,147.05,147.59,146.43,146.93,610000,146.93 1939-12-11,147.93,148.27,146.67,147.05,570000,147.05 1939-12-08,148.57,148.57,147.50,147.86,580000,147.86 1939-12-07,148.78,149.59,147.98,148.70,1010000,148.70 1939-12-06,146.83,148.99,146.83,148.78,990000,148.78 1939-12-05,146.34,147.29,146.05,146.49,590000,146.49 1939-12-04,146.62,146.79,145.74,146.34,430000,146.34 1939-12-01,145.69,146.94,145.51,146.54,610000,146.54 1939-11-30,146.73,146.73,144.85,145.69,880000,145.69 1939-11-29,148.31,149.18,146.82,146.89,780000,146.89 1939-11-28,148.59,149.65,148.02,148.31,620000,148.31 1939-11-27,148.64,149.05,147.98,148.59,520000,148.59 1939-11-24,150.34,150.46,148.12,148.47,820000,148.47 1939-11-22,150.67,150.67,149.62,150.34,570000,150.34 1939-11-21,151.69,152.11,150.72,150.98,560000,150.98 1939-11-20,151.53,152.58,151.02,151.69,750000,151.69 1939-11-17,151.15,152.21,150.55,151.00,770000,151.00 1939-11-16,149.53,151.42,149.27,151.15,830000,151.15 1939-11-15,149.77,150.48,149.23,149.53,640000,149.53 1939-11-14,149.07,150.53,148.98,149.77,780000,149.77 1939-11-13,149.09,149.70,148.55,149.07,650000,149.07 1939-11-10,148.75,149.33,147.74,149.09,1090000,149.09 1939-11-09,150.35,150.87,148.53,148.75,1200000,148.75 1939-11-08,151.43,151.43,149.81,150.35,1070000,150.35 1939-11-06,152.35,152.35,150.76,151.46,1270000,151.46 1939-11-03,151.56,153.18,150.04,152.64,1820000,152.64 1939-11-02,151.60,152.20,150.47,151.56,850000,151.56 1939-11-01,151.88,152.26,150.64,151.60,790000,151.60 1939-10-31,153.21,153.24,151.32,151.88,1010000,151.88 1939-10-30,153.12,153.60,152.39,153.21,640000,153.21 1939-10-27,154.05,154.34,152.30,153.46,1060000,153.46 1939-10-26,155.48,155.95,153.84,154.05,1680000,154.05 1939-10-25,154.07,155.95,153.98,155.48,1690000,155.48 1939-10-24,153.71,154.76,153.04,154.07,1160000,154.07 1939-10-23,153.86,154.56,153.19,153.71,970000,153.71 1939-10-20,153.36,153.87,152.55,153.00,790000,153.00 1939-10-19,153.54,154.42,152.78,153.36,1160000,153.36 1939-10-18,154.56,155.28,153.23,153.54,1400000,153.54 1939-10-17,151.29,154.81,151.29,154.56,1840000,154.56 1939-10-16,150.38,151.34,149.95,150.84,490000,150.84 1939-10-13,151.34,152.40,150.43,150.85,740000,150.85 1939-10-11,150.66,151.89,150.49,151.34,630000,151.34 1939-10-10,149.92,152.02,149.92,150.66,950000,150.66 1939-10-09,149.60,150.51,148.91,149.89,620000,149.89 1939-10-06,150.48,153.06,149.61,150.61,1330000,150.61 1939-10-05,150.25,151.68,149.72,150.48,910000,150.48 1939-10-04,150.23,151.20,148.73,150.25,980000,150.25 1939-10-03,151.41,152.20,149.29,150.23,1000000,150.23 1939-10-02,152.36,152.36,150.68,151.41,840000,151.41 1939-09-29,150.89,150.89,148.92,150.16,1130000,150.16 1939-09-28,153.08,153.13,150.41,151.12,1570000,151.12 1939-09-27,153.54,154.92,152.01,153.08,2340000,153.08 1939-09-26,152.64,154.27,151.77,153.54,1710000,153.54 1939-09-25,152.99,153.91,152.05,152.64,1230000,152.64 1939-09-22,153.48,154.76,151.97,152.57,1660000,152.57 1939-09-21,152.25,154.56,151.49,153.48,1730000,153.48 1939-09-20,152.14,154.96,151.57,152.25,2140000,152.25 1939-09-19,148.46,152.83,148.46,152.14,1830000,152.14 1939-09-18,150.57,150.57,147.35,147.78,1730000,147.78 1939-09-15,153.71,155.57,152.56,154.03,1590000,154.03 1939-09-14,154.10,155.54,151.96,153.71,2010000,153.71 1939-09-13,155.92,157.77,152.74,154.10,3760000,154.10 1939-09-12,155.12,157.30,151.78,155.92,4170000,155.92 1939-09-11,150.91,156.34,150.85,155.12,4680000,155.12 1939-09-08,148.32,152.58,148.08,150.04,3510000,150.04 1939-09-07,148.04,150.52,146.74,148.32,2600000,148.32 1939-09-06,148.12,150.76,146.08,148.04,3940000,148.04 1939-09-05,142.38,150.07,142.38,148.12,5930000,148.12 1939-09-01,134.41,136.03,127.51,135.25,1970000,135.25 1939-08-31,135.76,135.76,133.38,134.41,460000,134.41 1939-08-30,137.39,138.07,135.76,136.16,500000,136.16 1939-08-29,135.91,137.84,135.91,137.39,480000,137.39 1939-08-28,136.39,136.40,132.68,134.66,670000,134.66 1939-08-25,131.33,134.53,130.58,133.73,690000,133.73 1939-08-24,131.82,132.42,128.60,131.33,1290000,131.33 1939-08-23,134.80,134.80,131.49,131.82,790000,131.82 1939-08-22,133.07,135.84,133.07,135.07,860000,135.07 1939-08-21,133.93,133.93,132.11,132.81,850000,132.81 1939-08-18,138.15,138.15,134.83,135.54,840000,135.54 1939-08-17,138.47,138.89,137.38,138.33,440000,138.33 1939-08-16,139.86,139.86,137.61,138.47,640000,138.47 1939-08-15,140.76,142.35,140.76,141.29,660000,141.29 1939-08-14,138.75,140.54,138.75,140.18,550000,140.18 1939-08-11,137.25,138.93,136.38,137.29,700000,137.29 1939-08-10,138.83,138.83,136.62,137.25,700000,137.25 1939-08-09,140.58,140.58,139.14,139.75,470000,139.75 1939-08-08,140.76,141.93,140.55,141.10,450000,141.10 1939-08-07,142.11,142.38,140.31,140.76,520000,140.76 1939-08-04,144.06,144.06,141.26,141.73,900000,141.73 1939-08-03,144.26,145.75,143.79,144.24,1010000,144.24 1939-08-02,143.36,144.90,142.69,144.26,1030000,144.26 1939-08-01,143.26,143.89,142.45,143.36,580000,143.36 1939-07-31,144.00,144.04,142.77,143.26,520000,143.26 1939-07-28,144.51,145.04,143.48,144.11,810000,144.11 1939-07-27,143.82,144.92,143.08,144.51,820000,144.51 1939-07-26,143.10,144.39,142.41,143.82,890000,143.82 1939-07-25,144.18,145.72,142.83,143.10,1230000,143.10 1939-07-24,144.71,144.89,143.38,144.18,1070000,144.18 1939-07-21,141.88,144.28,141.88,143.46,1270000,143.46 1939-07-20,142.64,143.24,141.05,141.24,810000,141.24 1939-07-19,143.60,143.60,141.70,142.64,1020000,142.64 1939-07-18,142.96,144.74,142.96,143.76,1890000,143.76 1939-07-17,138.48,143.20,138.48,142.58,1750000,142.58 1939-07-14,138.02,138.41,137.24,137.57,540000,137.57 1939-07-13,137.34,139.05,137.34,138.02,950000,138.02 1939-07-12,134.67,137.24,134.67,136.98,910000,136.98 1939-07-11,133.84,135.06,133.84,134.56,430000,134.56 1939-07-10,133.24,134.21,133.22,133.79,280000,133.79 1939-07-07,133.58,133.84,132.93,133.22,330000,133.22 1939-07-06,133.68,134.31,132.98,133.58,410000,133.58 1939-07-05,132.74,133.94,132.74,133.68,350000,133.68 1939-07-03,131.73,132.22,131.18,131.93,240000,131.93 1939-06-30,130.05,131.16,128.97,130.63,600000,130.63 1939-06-29,131.81,131.81,129.71,130.05,820000,130.05 1939-06-28,135.06,135.06,132.76,132.83,540000,132.83 1939-06-27,135.09,135.77,134.01,135.42,480000,135.42 1939-06-26,136.77,136.77,134.83,135.09,500000,135.09 1939-06-23,136.88,137.95,136.48,137.42,480000,137.42 1939-06-22,137.61,137.63,136.34,136.88,450000,136.88 1939-06-21,137.57,138.04,136.87,137.61,470000,137.61 1939-06-20,136.72,137.97,136.72,137.57,490000,137.57 1939-06-19,135.72,137.02,135.72,136.40,350000,136.40 1939-06-16,134.41,135.36,133.79,134.67,400000,134.67 1939-06-15,137.03,137.03,134.26,134.41,580000,134.41 1939-06-14,138.02,138.02,136.44,137.50,400000,137.50 1939-06-13,139.09,139.09,137.38,138.20,530000,138.20 1939-06-12,139.95,139.95,138.34,139.13,420000,139.13 1939-06-09,138.61,140.75,138.61,140.09,790000,140.09 1939-06-08,138.71,138.84,137.53,138.49,410000,138.49 1939-06-07,138.36,139.79,138.17,138.71,530000,138.71 1939-06-06,137.06,138.84,136.98,138.36,600000,138.36 1939-06-05,137.12,137.36,136.34,137.06,350000,137.06 1939-06-02,136.29,137.42,136.29,136.74,400000,136.74 1939-06-01,137.36,137.36,135.52,136.20,600000,136.20 1939-05-31,137.80,139.23,137.52,138.18,670000,138.18 1939-05-29,136.80,137.91,136.42,137.80,600000,137.80 1939-05-26,135.53,136.76,135.22,136.09,620000,136.09 1939-05-25,135.11,137.16,135.11,135.53,1010000,135.53 1939-05-24,132.09,135.14,132.09,135.04,1010000,135.04 1939-05-23,132.45,132.88,131.49,131.77,420000,131.77 1939-05-22,131.22,132.54,130.50,132.45,420000,132.45 1939-05-19,129.43,130.90,129.22,130.38,400000,130.38 1939-05-18,129.09,130.28,128.79,129.43,420000,129.43 1939-05-17,129.77,129.77,128.35,129.09,530000,129.09 1939-05-16,132.56,132.56,129.79,129.86,620000,129.86 1939-05-15,132.40,133.68,132.35,132.65,340000,132.65 1939-05-12,132.79,132.79,131.74,132.16,340000,132.16 1939-05-11,132.82,133.26,131.85,132.92,400000,132.92 1939-05-10,133.67,134.66,132.63,132.82,690000,132.82 1939-05-09,132.11,134.02,132.11,133.67,710000,133.67 1939-05-08,131.74,132.22,130.70,131.67,350000,131.67 1939-05-05,131.86,132.12,130.76,131.47,330000,131.47 1939-05-04,132.30,133.24,131.30,131.86,660000,131.86 1939-05-03,129.60,132.64,129.60,132.30,740000,132.30 1939-05-02,128.31,130.13,128.31,129.32,450000,129.32 1939-05-01,128.45,128.55,127.53,127.83,280000,127.83 1939-04-28,129.78,131.42,127.58,128.38,730000,128.38 1939-04-27,128.56,130.21,128.49,129.78,540000,129.78 1939-04-26,127.36,129.06,126.44,128.56,580000,128.56 1939-04-25,127.34,128.53,127.02,127.36,420000,127.36 1939-04-24,128.55,129.03,127.20,127.34,410000,127.34 1939-04-21,128.41,129.62,127.97,128.71,390000,128.71 1939-04-20,127.87,129.59,127.87,128.41,520000,128.41 1939-04-19,125.63,127.61,125.63,127.01,440000,127.01 1939-04-18,127.02,127.02,124.81,125.38,440000,125.38 1939-04-17,128.01,128.01,126.15,127.34,520000,127.34 1939-04-14,126.58,126.58,124.52,126.20,620000,126.20 1939-04-13,126.29,129.32,126.29,127.51,860000,127.51 1939-04-12,125.15,127.51,125.15,126.15,1070000,126.15 1939-04-11,124.03,124.42,120.04,123.75,1660000,123.75 1939-04-10,121.44,124.27,120.82,124.03,1650000,124.03 1939-04-06,129.23,129.23,125.49,126.32,1310000,126.32 1939-04-05,129.80,131.38,128.64,130.34,880000,130.34 1939-04-04,131.09,131.09,127.16,129.80,1530000,129.80 1939-04-03,132.83,135.57,130.49,132.25,1470000,132.25 1939-03-31,136.69,138.01,131.35,131.84,2890000,131.84 1939-03-30,139.75,140.55,136.41,136.69,990000,136.69 1939-03-29,139.33,140.55,138.84,139.75,470000,139.75 1939-03-28,139.98,139.98,138.46,139.33,680000,139.33 1939-03-27,141.55,143.14,140.91,141.14,570000,141.14 1939-03-24,141.13,142.80,141.13,141.82,650000,141.82 1939-03-23,139.51,142.17,139.42,140.33,830000,140.33 1939-03-22,141.97,141.97,138.42,139.51,1440000,139.51 1939-03-21,142.67,144.31,142.67,143.41,690000,143.41 1939-03-20,141.68,143.14,140.57,141.28,950000,141.28 1939-03-17,146.13,146.13,142.99,143.89,1470000,143.89 1939-03-16,147.66,148.17,146.45,147.54,670000,147.54 1939-03-15,150.28,150.28,147.16,147.66,1110000,147.66 1939-03-14,150.79,151.39,149.91,151.10,690000,151.10 1939-03-13,151.58,151.58,150.20,150.79,650000,150.79 1939-03-10,151.33,152.71,151.22,152.28,1210000,152.28 1939-03-09,151.42,152.42,150.52,151.33,1360000,151.33 1939-03-08,149.37,151.56,149.17,151.42,1050000,151.42 1939-03-07,148.86,149.88,148.86,149.37,570000,149.37 1939-03-06,149.49,150.23,148.37,148.84,840000,148.84 1939-03-03,147.31,148.89,147.31,148.76,1020000,148.76 1939-03-02,147.15,147.19,146.34,146.96,600000,146.96 1939-03-01,147.30,147.88,146.62,147.15,640000,147.15 1939-02-28,146.76,148.16,146.76,147.30,1060000,147.30 1939-02-27,146.82,147.32,146.10,146.62,750000,146.62 1939-02-24,143.46,145.69,143.46,145.44,970000,145.44 1939-02-23,142.64,143.63,142.45,142.93,460000,142.93 1939-02-21,142.74,143.13,142.05,142.64,470000,142.64 1939-02-20,144.54,144.54,142.48,142.74,690000,142.74 1939-02-17,145.39,146.03,144.69,144.95,680000,144.95 1939-02-16,144.60,146.12,144.50,145.39,850000,145.39 1939-02-15,144.13,144.95,143.83,144.60,500000,144.60 1939-02-14,144.48,144.48,143.49,144.13,420000,144.13 1939-02-10,143.99,144.01,142.70,143.68,450000,143.68 1939-02-09,145.43,145.56,143.83,143.99,550000,143.99 1939-02-08,144.45,145.55,144.45,145.43,610000,145.43 1939-02-07,145.03,145.11,143.50,144.10,570000,144.10 1939-02-06,145.07,146.43,144.78,145.03,1040000,145.03 1939-02-03,144.34,144.50,143.02,143.55,540000,143.55 1939-02-02,142.59,144.49,142.59,144.34,700000,144.34 1939-02-01,143.47,143.47,141.92,142.43,580000,142.43 1939-01-31,142.52,144.74,142.52,143.76,1120000,143.76 1939-01-30,139.32,141.80,139.32,141.56,790000,141.56 1939-01-27,136.84,139.27,136.84,138.90,1060000,138.90 1939-01-26,139.58,139.58,136.10,136.42,1540000,136.42 1939-01-25,141.35,141.68,139.37,140.72,900000,140.72 1939-01-24,141.32,142.83,139.62,141.35,1700000,141.35 1939-01-23,144.13,144.13,141.00,141.32,1870000,141.32 1939-01-20,149.47,149.75,148.77,149.11,740000,149.11 1939-01-19,148.99,149.88,148.35,149.47,890000,149.47 1939-01-18,148.93,149.56,148.55,148.99,630000,148.99 1939-01-17,148.26,149.23,147.49,148.93,820000,148.93 1939-01-16,148.26,148.71,147.70,148.26,670000,148.26 1939-01-13,147.33,147.84,146.03,146.52,850000,146.52 1939-01-12,148.65,149.43,146.17,147.33,1360000,147.33 1939-01-11,150.18,150.18,148.45,148.65,920000,148.65 1939-01-10,150.19,151.32,150.04,150.48,710000,150.48 1939-01-09,151.07,151.07,149.23,150.19,1100000,150.19 1939-01-06,153.18,153.55,152.42,152.87,950000,152.87 1939-01-05,154.85,155.47,153.02,153.18,1570000,153.18 1939-01-04,153.64,155.19,153.14,154.85,1500000,154.85 1939-01-03,154.76,154.99,153.16,153.64,1150000,153.64 1938-12-30,153.62,154.94,153.52,154.36,1400000,154.36 1938-12-29,152.06,153.85,152.06,153.62,1880000,153.62 1938-12-28,150.43,151.95,149.56,151.45,2160000,151.45 1938-12-27,151.38,151.74,150.04,150.43,1340000,150.43 1938-12-23,150.53,152.02,150.52,151.39,1220000,151.39 1938-12-22,149.58,150.79,149.28,150.53,1040000,150.53 1938-12-21,150.46,150.72,149.06,149.58,1060000,149.58 1938-12-20,150.38,151.29,149.69,150.46,940000,150.46 1938-12-19,150.36,151.77,149.72,150.38,1100000,150.38 1938-12-16,151.82,152.21,150.52,150.89,1150000,150.89 1938-12-15,151.83,153.16,151.44,151.82,1800000,151.82 1938-12-14,149.59,152.08,149.38,151.83,1970000,151.83 1938-12-13,148.65,150.16,148.60,149.59,1090000,149.59 1938-12-12,148.31,149.71,148.23,148.65,900000,148.65 1938-12-09,147.63,148.31,146.92,147.39,700000,147.39 1938-12-08,148.73,148.79,147.35,147.63,740000,147.63 1938-12-07,148.33,149.98,148.30,148.73,1110000,148.73 1938-12-06,147.47,149.27,147.01,148.33,990000,148.33 1938-12-05,147.50,147.88,146.44,147.47,680000,147.47 1938-12-02,148.50,148.50,146.68,147.57,820000,147.57 1938-12-01,149.82,150.20,148.17,148.63,860000,148.63 1938-11-30,147.68,149.97,147.68,149.82,980000,149.82 1938-11-29,146.38,147.85,146.38,147.07,820000,147.07 1938-11-28,147.93,147.93,145.21,146.14,1240000,146.14 1938-11-25,149.88,151.13,149.79,150.10,810000,150.10 1938-11-23,149.56,151.00,149.27,149.88,1000000,149.88 1938-11-22,150.14,150.14,148.90,149.56,880000,149.56 1938-11-21,150.38,151.03,149.15,150.26,940000,150.26 1938-11-18,152.78,153.19,149.16,149.93,1420000,149.93 1938-11-17,151.54,153.18,151.34,152.78,1000000,152.78 1938-11-16,154.66,155.60,151.41,151.54,1800000,151.54 1938-11-15,155.33,155.33,153.17,154.66,1470000,154.66 1938-11-14,157.57,157.57,155.16,155.61,1650000,155.61 1938-11-10,158.08,158.90,156.79,157.47,2180000,157.47 1938-11-09,155.62,158.39,155.62,158.08,3100000,158.08 1938-11-07,152.12,155.56,152.02,154.91,1760000,154.91 1938-11-04,152.31,153.08,151.44,152.10,1200000,152.10 1938-11-03,152.21,153.13,151.72,152.31,1070000,152.31 1938-11-02,151.39,152.64,150.68,152.21,780000,152.21 1938-11-01,151.73,152.83,150.93,151.39,1280000,151.39 1938-10-31,151.07,152.17,150.28,151.73,1090000,151.73 1938-10-28,152.69,152.82,150.75,151.07,1560000,151.07 1938-10-27,152.40,153.93,151.46,152.69,2000000,152.69 1938-10-26,153.52,153.52,151.28,152.40,1700000,152.40 1938-10-25,154.12,155.22,153.46,154.17,1500000,154.17 1938-10-24,154.11,155.38,153.52,154.12,1680000,154.12 1938-10-21,151.52,153.01,151.36,152.15,1720000,152.15 1938-10-20,150.02,151.90,149.46,151.52,1620000,151.52 1938-10-19,152.10,153.02,149.41,150.02,2430000,150.02 1938-10-18,150.81,152.37,148.68,152.10,2410000,152.10 1938-10-17,151.96,153.15,150.46,150.81,2520000,150.81 1938-10-14,152.46,153.19,150.96,151.45,1950000,151.45 1938-10-13,150.05,152.93,150.05,152.46,2360000,152.46 1938-10-11,149.55,150.40,148.21,149.41,1530000,149.41 1938-10-10,149.75,150.57,148.70,149.55,1660000,149.55 1938-10-07,148.10,149.43,147.29,148.41,1470000,148.41 1938-10-06,148.32,150.48,147.70,148.10,2450000,148.10 1938-10-05,144.59,148.51,144.59,148.32,2240000,148.32 1938-10-04,144.29,144.83,143.26,144.23,950000,144.23 1938-10-03,143.13,145.21,142.64,144.29,1460000,144.29 1938-09-30,139.61,142.05,139.61,141.45,1900000,141.45 1938-09-29,134.58,137.45,134.58,137.16,1230000,137.16 1938-09-28,130.19,134.54,127.85,133.68,1570000,133.68 1938-09-27,129.91,132.49,129.62,130.19,770000,130.19 1938-09-26,131.82,131.82,127.88,129.91,1230000,129.91 1938-09-23,136.07,136.07,133.71,134.08,720000,134.08 1938-09-22,138.17,138.17,136.81,137.35,470000,137.35 1938-09-21,138.41,140.20,137.57,139.29,1030000,139.29 1938-09-20,137.11,139.49,137.11,138.41,1200000,138.41 1938-09-19,133.49,135.24,133.49,134.10,830000,134.10 1938-09-16,135.53,135.53,134.06,134.85,670000,134.85 1938-09-15,134.47,136.97,134.47,136.22,1140000,136.22 1938-09-14,134.19,136.91,130.38,132.93,2820000,132.93 1938-09-13,140.19,141.95,133.90,134.19,1700000,134.19 1938-09-12,138.29,140.67,138.16,140.19,600000,140.19 1938-09-09,141.39,141.39,139.20,139.90,700000,139.90 1938-09-08,142.90,142.90,141.63,142.19,570000,142.19 1938-09-07,141.47,143.42,141.05,143.08,890000,143.08 1938-09-06,142.48,142.80,141.10,141.47,420000,141.47 1938-09-02,138.52,141.48,138.52,141.38,550000,141.38 1938-09-01,139.27,139.45,137.65,138.36,510000,138.36 1938-08-31,138.30,139.80,138.30,139.27,460000,139.27 1938-08-30,137.08,138.89,137.08,138.26,630000,138.26 1938-08-29,139.56,139.56,136.64,137.06,1250000,137.06 1938-08-26,144.07,144.61,142.69,142.94,820000,142.94 1938-08-25,143.53,144.36,142.57,144.07,830000,144.07 1938-08-24,143.70,145.30,143.28,143.53,1240000,143.53 1938-08-23,141.03,144.00,141.03,143.70,1100000,143.70 1938-08-22,141.20,141.61,140.12,140.92,400000,140.92 1938-08-19,139.33,142.05,139.18,141.13,830000,141.13 1938-08-18,139.03,139.76,138.38,139.33,450000,139.33 1938-08-17,138.58,140.89,138.58,139.03,600000,139.03 1938-08-16,137.09,139.38,137.09,138.44,610000,138.44 1938-08-15,136.45,138.20,136.45,136.98,590000,136.98 1938-08-12,138.31,138.31,135.38,136.51,1480000,136.51 1938-08-11,142.40,142.75,139.13,139.32,1100000,139.32 1938-08-10,143.21,143.67,141.80,142.40,810000,142.40 1938-08-09,143.87,143.87,141.59,143.21,830000,143.21 1938-08-08,145.67,145.89,143.75,144.33,910000,144.33 1938-08-05,142.34,144.76,142.34,144.47,1170000,144.47 1938-08-04,141.73,142.76,141.04,142.13,610000,142.13 1938-08-03,141.97,143.40,141.10,141.73,820000,141.73 1938-08-02,140.37,142.50,139.90,141.97,820000,141.97 1938-08-01,140.96,140.96,139.83,140.37,590000,140.37 1938-07-29,142.20,143.57,140.93,141.20,1200000,141.20 1938-07-28,140.24,142.50,139.51,142.20,1070000,142.20 1938-07-27,143.33,144.03,139.39,140.24,1970000,140.24 1938-07-26,144.18,144.18,142.48,143.33,1250000,143.33 1938-07-25,144.24,146.31,144.23,144.91,2110000,144.91 1938-07-22,141.92,143.13,141.20,142.25,1220000,142.25 1938-07-21,141.84,143.51,140.52,141.92,1810000,141.92 1938-07-20,143.67,143.97,141.09,141.84,2510000,141.84 1938-07-19,141.27,144.12,141.27,143.67,2940000,143.67 1938-07-18,138.53,140.78,137.70,140.39,1560000,140.39 1938-07-15,135.81,138.17,135.52,137.30,930000,137.30 1938-07-14,136.90,137.85,135.22,135.81,1160000,135.81 1938-07-13,137.49,140.52,136.46,136.90,2620000,136.90 1938-07-12,134.56,138.22,133.84,137.49,1620000,137.49 1938-07-11,136.20,136.72,133.97,134.56,1090000,134.56 1938-07-08,136.76,136.76,134.51,135.66,1570000,135.66 1938-07-07,137.78,140.05,136.44,137.45,2770000,137.45 1938-07-06,136.52,138.20,134.57,137.78,1820000,137.78 1938-07-05,138.50,138.50,135.19,136.52,1700000,136.52 1938-07-01,133.88,137.13,133.53,136.53,2030000,136.53 1938-06-30,135.87,138.19,133.24,133.88,2580000,133.88 1938-06-29,130.38,136.13,130.36,135.87,2660000,135.87 1938-06-28,130.48,132.28,129.44,130.38,1290000,130.38 1938-06-27,131.94,132.98,129.79,130.48,2110000,130.48 1938-06-24,127.54,130.71,127.54,129.06,2290000,129.06 1938-06-23,123.99,128.49,123.71,127.40,2400000,127.40 1938-06-22,121.34,124.74,120.65,123.99,1710000,123.99 1938-06-21,119.01,122.36,119.01,121.34,1460000,121.34 1938-06-20,115.31,118.92,115.31,118.61,1090000,118.61 1938-06-17,113.97,114.51,112.82,113.06,330000,113.06 1938-06-16,113.24,114.30,112.93,113.97,340000,113.97 1938-06-15,112.88,114.18,112.88,113.24,340000,113.24 1938-06-14,111.87,113.06,111.54,112.78,350000,112.78 1938-06-13,113.37,113.37,111.75,111.87,330000,111.87 1938-06-10,115.74,116.08,114.29,114.47,410000,114.47 1938-06-09,113.79,116.03,113.79,115.74,590000,115.74 1938-06-08,113.12,114.04,112.78,113.75,280000,113.75 1938-06-07,113.19,114.51,112.84,113.12,370000,113.12 1938-06-06,112.01,114.27,112.01,113.19,470000,113.19 1938-06-03,110.52,110.52,109.35,109.71,290000,109.71 1938-06-02,110.61,111.94,110.35,110.68,480000,110.68 1938-06-01,107.74,110.87,107.58,110.61,540000,110.61 1938-05-31,108.50,108.50,106.94,107.74,400000,107.74 1938-05-27,108.28,108.55,106.44,107.98,760000,107.98 1938-05-26,110.60,111.30,108.14,108.28,780000,108.28 1938-05-25,112.24,112.24,110.43,110.60,560000,110.60 1938-05-24,113.97,114.66,112.26,112.35,420000,112.35 1938-05-23,113.25,114.27,112.88,113.97,330000,113.97 1938-05-20,115.28,115.48,114.08,114.99,440000,114.99 1938-05-19,117.02,117.08,114.92,115.28,490000,115.28 1938-05-18,116.36,117.49,116.31,117.02,400000,117.02 1938-05-17,115.38,116.65,114.75,116.36,410000,116.36 1938-05-16,117.11,117.11,115.19,115.38,400000,115.38 1938-05-13,118.41,118.41,116.30,116.87,610000,116.87 1938-05-12,118.52,119.67,117.88,118.55,600000,118.55 1938-05-11,117.93,119.70,117.78,118.52,980000,118.52 1938-05-10,119.43,120.28,117.75,117.93,1040000,117.93 1938-05-09,117.21,119.81,116.36,119.43,1020000,119.43 1938-05-06,113.46,117.44,113.27,117.16,1020000,117.16 1938-05-05,113.88,115.42,112.86,113.46,690000,113.46 1938-05-04,112.71,114.32,111.69,113.88,550000,113.88 1938-05-03,110.75,113.12,110.75,112.71,470000,112.71 1938-05-02,110.55,110.55,109.40,110.09,350000,110.09 1938-04-29,111.88,111.88,109.83,111.66,540000,111.66 1938-04-28,114.83,114.83,111.73,111.98,540000,111.98 1938-04-27,114.75,116.22,114.75,115.25,430000,115.25 1938-04-26,115.70,115.70,113.51,113.94,450000,113.94 1938-04-25,116.86,116.86,115.40,116.23,400000,116.23 1938-04-22,115.74,119.67,115.74,118.52,1120000,118.52 1938-04-21,114.90,115.98,114.05,115.40,600000,115.40 1938-04-20,115.50,115.50,112.47,114.90,780000,114.90 1938-04-19,118.46,118.46,116.00,116.34,570000,116.34 1938-04-18,121.00,121.54,118.45,118.99,860000,118.99 1938-04-14,114.85,117.57,113.56,116.82,1010000,116.82 1938-04-13,113.88,116.29,113.79,114.85,640000,114.85 1938-04-12,112.93,114.15,111.60,113.88,600000,113.88 1938-04-11,115.32,115.71,112.43,112.93,1100000,112.93 1938-04-08,106.89,110.23,106.89,109.57,830000,109.57 1938-04-07,106.29,106.93,104.78,105.43,330000,105.43 1938-04-06,107.90,107.90,105.72,106.29,480000,106.29 1938-04-05,105.58,108.67,104.85,108.36,690000,108.36 1938-04-04,106.11,106.79,104.47,105.58,690000,105.58 1938-04-01,100.95,103.37,100.95,103.02,860000,103.02 1938-03-31,100.97,102.86,97.46,98.95,1270000,98.95 1938-03-30,101.92,103.89,100.17,100.97,1670000,100.97 1938-03-29,105.87,105.87,101.56,101.92,1720000,101.92 1938-03-28,106.63,108.55,105.68,107.25,1250000,107.25 1938-03-25,114.37,114.37,108.12,108.57,1680000,108.57 1938-03-24,114.38,115.97,112.73,114.64,890000,114.64 1938-03-23,115.95,115.95,112.78,114.38,1470000,114.38 1938-03-22,120.29,120.35,116.47,117.11,690000,117.11 1938-03-21,120.43,122.48,120.09,120.29,540000,120.29 1938-03-18,121.92,121.92,117.20,118.41,1580000,118.41 1938-03-17,122.87,124.60,121.90,122.03,640000,122.03 1938-03-16,126.10,126.10,121.84,122.87,1020000,122.87 1938-03-15,124.52,127.44,124.52,127.24,760000,127.24 1938-03-14,122.85,124.53,122.85,123.68,430000,123.68 1938-03-11,123.89,123.89,121.93,122.44,770000,122.44 1938-03-10,125.67,126.35,124.52,124.71,460000,124.71 1938-03-09,125.33,126.83,124.57,125.67,560000,125.67 1938-03-08,125.38,125.85,122.81,125.33,740000,125.33 1938-03-07,127.63,127.63,125.19,125.38,620000,125.38 1938-03-04,128.22,129.57,127.37,127.78,490000,127.78 1938-03-03,128.75,128.75,127.39,128.22,470000,128.22 1938-03-02,130.47,130.68,129.19,129.38,410000,129.38 1938-03-01,129.64,131.03,128.84,130.47,530000,130.47 1938-02-28,130.89,130.89,128.63,129.64,560000,129.64 1938-02-25,130.85,132.66,129.85,131.58,900000,131.58 1938-02-24,132.05,132.05,130.47,130.85,720000,130.85 1938-02-23,130.69,132.86,130.69,132.41,1300000,132.41 1938-02-21,127.60,130.19,127.60,129.49,760000,129.49 1938-02-18,127.59,128.74,125.61,126.29,770000,126.29 1938-02-17,125.03,128.35,125.03,127.59,860000,127.59 1938-02-16,124.93,125.35,123.39,124.90,470000,124.90 1938-02-15,125.97,127.00,124.60,124.93,520000,124.93 1938-02-14,125.04,126.44,125.04,125.97,400000,125.97 1938-02-11,125.54,126.53,124.45,124.94,390000,124.94 1938-02-10,125.00,127.23,124.56,125.54,630000,125.54 1938-02-09,125.52,126.98,124.65,125.00,750000,125.00 1938-02-08,122.39,125.76,122.39,125.52,770000,125.52 1938-02-07,122.88,123.15,120.88,121.39,510000,121.39 1938-02-04,118.49,120.91,117.13,120.52,810000,120.52 1938-02-03,121.49,121.49,117.78,118.49,1090000,118.49 1938-02-02,123.97,125.00,122.74,123.06,580000,123.06 1938-02-01,122.69,124.71,122.69,123.97,690000,123.97 1938-01-31,120.14,122.28,120.09,121.87,760000,121.87 1938-01-28,121.57,122.32,118.94,120.66,1190000,120.66 1938-01-27,123.23,124.54,120.44,121.57,1210000,121.57 1938-01-26,126.10,126.10,121.86,123.23,1620000,123.23 1938-01-25,129.47,129.47,127.97,128.33,540000,128.33 1938-01-24,130.00,130.52,129.13,129.89,540000,129.89 1938-01-21,132.33,133.23,130.56,130.69,790000,130.69 1938-01-20,130.39,132.54,130.39,132.33,820000,132.33 1938-01-19,131.53,131.68,128.68,130.09,1000000,130.09 1938-01-18,132.49,132.98,130.88,131.53,780000,131.53 1938-01-17,134.31,134.70,132.19,132.49,930000,132.49 1938-01-14,131.60,132.37,130.29,131.84,850000,131.84 1938-01-13,133.22,133.40,131.27,131.60,970000,131.60 1938-01-12,134.35,134.95,132.94,133.22,1210000,133.22 1938-01-11,133.55,134.63,132.36,134.35,1510000,134.35 1938-01-10,130.84,134.27,130.81,133.55,1830000,133.55 1938-01-07,128.97,129.51,127.30,128.21,1050000,128.21 1938-01-06,125.38,129.23,125.38,128.97,1210000,128.97 1938-01-05,124.61,127.27,124.17,124.66,1150000,124.66 1938-01-04,121.69,124.96,121.69,124.61,940000,124.61 1938-01-03,120.85,123.07,119.60,120.57,920000,120.57 1937-12-31,121.49,121.49,119.65,120.85,780000,120.85 1937-12-30,120.71,122.63,120.71,121.56,910000,121.56 1937-12-29,118.93,121.00,117.71,120.15,2460000,120.15 1937-12-28,122.51,122.51,118.31,118.93,2380000,118.93 1937-12-27,126.59,126.59,122.96,123.45,1360000,123.45 1937-12-24,127.63,128.60,126.85,127.36,840000,127.36 1937-12-23,128.53,128.53,126.50,127.63,1060000,127.63 1937-12-22,129.98,130.52,127.99,128.55,1160000,128.55 1937-12-21,129.08,130.76,128.60,129.98,1280000,129.98 1937-12-20,127.29,130.38,127.29,129.08,1400000,129.08 1937-12-17,125.75,126.29,124.44,124.98,790000,124.98 1937-12-16,124.52,126.92,124.52,125.75,1030000,125.75 1937-12-15,123.50,125.63,123.33,124.19,930000,124.19 1937-12-14,122.83,124.25,121.85,123.50,900000,123.50 1937-12-13,125.86,125.86,122.18,122.83,1020000,122.83 1937-12-10,128.15,128.27,125.49,126.72,1080000,126.72 1937-12-09,129.80,130.37,127.62,128.15,1080000,128.15 1937-12-08,128.31,131.15,128.22,129.80,1520000,129.80 1937-12-07,126.21,128.56,124.85,128.31,970000,128.31 1937-12-06,127.79,128.36,125.76,126.21,840000,126.21 1937-12-03,125.52,129.40,125.52,127.55,1560000,127.55 1937-12-02,122.11,125.38,120.21,125.14,940000,125.14 1937-12-01,123.48,124.09,121.41,122.11,700000,122.11 1937-11-30,121.58,125.43,121.49,123.48,1150000,123.48 1937-11-29,123.39,123.39,120.37,121.58,1150000,121.58 1937-11-26,114.37,118.78,114.37,118.26,1180000,118.26 1937-11-24,115.78,116.17,112.72,113.64,990000,113.64 1937-11-23,114.19,117.08,112.54,115.78,1640000,115.78 1937-11-22,119.28,119.28,113.77,114.19,1520000,114.19 1937-11-19,124.32,124.32,117.98,118.13,1890000,118.13 1937-11-18,127.54,127.73,124.35,125.48,900000,125.48 1937-11-17,127.98,129.94,127.02,127.54,760000,127.54 1937-11-16,129.22,129.63,125.34,127.98,1270000,127.98 1937-11-15,133.05,134.36,129.08,129.22,1450000,129.22 1937-11-12,132.16,135.70,131.64,133.09,1880000,133.09 1937-11-10,128.63,132.59,128.63,132.16,1920000,132.16 1937-11-09,124.07,127.11,124.07,126.16,1050000,126.16 1937-11-08,124.93,124.93,121.61,123.98,1380000,123.98 1937-11-05,128.84,132.31,128.35,128.92,1250000,128.92 1937-11-04,130.14,130.17,126.67,128.84,1470000,128.84 1937-11-03,135.49,135.49,129.56,130.14,1740000,130.14 1937-11-01,137.01,137.01,135.00,135.94,1030000,135.94 1937-10-29,136.46,141.22,136.46,138.48,2800000,138.48 1937-10-28,132.58,138.31,132.58,135.22,2460000,135.22 1937-10-27,132.78,134.01,130.48,132.26,1060000,132.26 1937-10-26,134.43,136.79,131.77,132.78,1820000,132.78 1937-10-25,127.15,135.12,124.56,134.43,2340000,134.43 1937-10-22,135.48,135.66,131.74,132.26,2110000,132.26 1937-10-21,134.56,137.82,132.09,135.48,3640000,135.48 1937-10-20,126.87,135.48,126.87,134.56,4340000,134.56 1937-10-19,125.73,127.61,115.84,126.85,7290000,126.85 1937-10-18,136.30,137.00,125.14,125.73,3230000,125.73 1937-10-15,136.54,138.14,133.95,135.48,2540000,135.48 1937-10-14,138.20,140.88,136.09,136.54,1680000,136.54 1937-10-13,138.79,140.94,134.79,138.20,2570000,138.20 1937-10-11,143.66,143.66,137.71,138.79,1750000,138.79 1937-10-08,146.59,148.26,143.00,144.03,1150000,144.03 1937-10-07,147.18,150.47,145.76,146.59,1190000,146.59 1937-10-06,144.08,147.62,141.63,147.18,1790000,147.18 1937-10-05,149.97,149.97,143.01,144.08,1680000,144.08 1937-10-04,154.08,154.63,151.84,152.19,630000,152.19 1937-10-01,154.57,155.11,152.50,153.89,680000,153.89 1937-09-30,154.70,157.12,153.97,154.57,1050000,154.57 1937-09-29,153.16,155.24,150.09,154.70,1350000,154.70 1937-09-28,152.03,155.22,151.41,153.16,1310000,153.16 1937-09-27,147.47,152.49,146.25,152.03,2210000,152.03 1937-09-24,151.58,151.58,146.22,147.38,2480000,147.38 1937-09-23,157.45,157.51,153.79,153.98,890000,153.98 1937-09-22,156.56,158.89,156.55,157.45,740000,157.45 1937-09-21,155.96,159.26,155.96,156.56,980000,156.56 1937-09-20,156.92,156.92,152.36,155.56,1550000,155.56 1937-09-17,164.19,164.19,161.59,162.15,810000,162.15 1937-09-16,162.85,165.37,162.29,164.75,890000,164.75 1937-09-15,162.90,165.16,161.26,162.85,1140000,162.85 1937-09-14,160.00,164.37,160.00,162.90,1510000,162.90 1937-09-13,159.96,163.12,154.94,158.00,2560000,158.00 1937-09-10,166.36,166.87,157.35,157.98,2320000,157.98 1937-09-09,164.88,168.06,164.88,166.36,1410000,166.36 1937-09-08,164.39,166.06,162.17,163.37,2260000,163.37 1937-09-07,170.29,170.29,163.18,164.39,1870000,164.39 1937-09-03,171.82,174.03,171.82,172.17,690000,172.17 1937-09-02,172.71,172.71,169.75,170.84,1200000,170.84 1937-09-01,176.11,176.11,172.89,173.08,820000,173.08 1937-08-31,177.88,179.10,177.29,177.41,500000,177.41 1937-08-30,176.10,178.26,176.10,177.88,460000,177.88 1937-08-27,178.52,179.23,175.09,175.91,890000,175.91 1937-08-26,180.71,180.71,177.57,178.52,970000,178.52 1937-08-25,182.39,183.00,181.39,181.70,500000,181.70 1937-08-24,181.88,182.87,180.80,182.39,560000,182.39 1937-08-23,183.74,184.20,181.31,181.88,580000,181.88 1937-08-20,185.28,185.28,182.30,182.95,800000,182.95 1937-08-19,186.97,186.97,184.75,185.28,760000,185.28 1937-08-18,188.68,189.10,187.30,187.39,700000,187.39 1937-08-17,189.18,189.18,188.12,188.68,660000,188.68 1937-08-16,189.94,189.94,188.60,189.34,620000,189.34 1937-08-13,187.62,189.76,187.35,189.29,1040000,189.29 1937-08-12,186.72,188.30,186.39,187.62,790000,187.62 1937-08-11,186.98,187.31,185.93,186.72,570000,186.72 1937-08-10,186.75,187.67,186.23,186.98,690000,186.98 1937-08-09,186.41,187.33,186.06,186.75,750000,186.75 1937-08-06,186.09,186.56,185.16,185.43,680000,185.43 1937-08-05,186.80,187.22,185.76,186.09,800000,186.09 1937-08-04,185.91,187.31,185.38,186.80,900000,186.80 1937-08-03,186.91,187.14,185.43,185.91,900000,185.91 1937-08-02,185.61,187.28,185.35,186.91,790000,186.91 1937-07-30,183.01,184.66,182.92,184.01,620000,184.01 1937-07-29,182.57,183.61,182.07,183.01,610000,183.01 1937-07-28,184.24,184.54,182.17,182.57,870000,182.57 1937-07-27,184.42,184.73,183.43,184.24,740000,184.24 1937-07-26,184.85,185.15,183.72,184.42,900000,184.42 1937-07-23,182.96,184.75,182.70,183.78,910000,183.78 1937-07-22,182.35,184.13,182.14,182.96,960000,182.96 1937-07-21,183.32,183.94,181.87,182.35,980000,182.35 1937-07-20,182.06,183.90,182.01,183.32,1200000,183.32 1937-07-19,180.11,182.39,180.11,182.06,950000,182.06 1937-07-16,179.71,180.45,178.65,179.53,700000,179.53 1937-07-15,178.57,180.09,177.78,179.71,740000,179.71 1937-07-14,178.24,180.17,177.83,178.57,1040000,178.57 1937-07-13,178.70,179.53,177.50,178.24,850000,178.24 1937-07-12,176.72,179.16,176.69,178.70,1020000,178.70 1937-07-09,177.70,178.36,176.65,177.40,850000,177.40 1937-07-08,177.74,178.38,176.49,177.70,1030000,177.70 1937-07-07,176.80,178.88,176.25,177.74,1410000,177.74 1937-07-06,173.18,177.08,173.18,176.80,1410000,176.80 1937-07-02,170.13,172.49,169.58,172.22,840000,172.22 1937-07-01,169.32,171.28,169.13,170.13,670000,170.13 1937-06-30,167.11,170.02,167.00,169.32,690000,169.32 1937-06-29,166.71,168.10,166.11,167.11,550000,167.11 1937-06-28,168.45,168.99,166.26,166.71,730000,166.71 1937-06-25,170.08,170.98,169.10,169.59,580000,169.59 1937-06-24,169.01,170.46,168.79,170.08,550000,170.08 1937-06-23,168.22,169.72,168.22,169.01,550000,169.01 1937-06-22,167.98,169.42,167.59,168.20,530000,168.20 1937-06-21,168.60,168.79,167.28,167.98,420000,167.98 1937-06-18,167.74,169.37,167.05,168.79,690000,168.79 1937-06-17,165.86,167.95,163.31,167.74,1260000,167.74 1937-06-16,167.40,167.59,165.49,165.86,700000,165.86 1937-06-15,165.51,168.10,164.69,167.40,930000,167.40 1937-06-14,168.45,168.45,163.73,165.51,1310000,165.51 1937-06-11,172.59,172.59,170.33,170.77,720000,170.77 1937-06-10,173.47,174.06,172.53,172.82,570000,172.82 1937-06-09,174.33,175.00,173.12,173.47,620000,173.47 1937-06-08,173.88,174.91,173.25,174.33,600000,174.33 1937-06-07,175.00,175.40,173.56,173.88,590000,173.88 1937-06-04,172.82,175.39,172.19,175.14,780000,175.14 1937-06-03,172.63,173.09,171.56,172.82,550000,172.82 1937-06-02,172.25,173.73,172.25,172.63,540000,172.63 1937-06-01,172.69,172.69,170.72,171.59,750000,171.59 1937-05-28,174.19,175.22,174.04,174.71,560000,174.71 1937-05-27,173.70,175.03,173.22,174.19,600000,174.19 1937-05-26,173.79,174.47,172.62,173.70,580000,173.70 1937-05-25,175.59,176.11,173.30,173.79,840000,173.79 1937-05-24,175.00,176.25,174.51,175.59,680000,175.59 1937-05-21,173.59,174.59,173.12,173.83,770000,173.83 1937-05-20,170.00,173.99,170.00,173.59,1230000,173.59 1937-05-19,169.97,170.85,169.11,169.75,790000,169.75 1937-05-18,167.84,170.55,166.20,169.97,1200000,169.97 1937-05-17,169.60,169.98,167.64,167.84,600000,167.84 1937-05-14,167.46,169.89,166.58,169.15,1230000,169.15 1937-05-13,171.43,171.43,166.88,167.46,1770000,167.46 1937-05-12,172.55,173.72,171.81,172.24,670000,172.24 1937-05-11,173.04,173.28,171.59,172.55,750000,172.55 1937-05-10,175.19,175.19,172.78,173.04,780000,173.04 1937-05-07,175.81,176.91,175.50,175.89,820000,175.89 1937-05-06,174.67,176.05,174.06,175.81,760000,175.81 1937-05-05,176.30,176.81,174.41,174.67,770000,174.67 1937-05-04,175.20,176.69,175.20,176.30,870000,176.30 1937-05-03,174.42,175.58,174.16,174.59,640000,174.59 1937-04-30,171.51,174.66,171.51,174.27,1450000,174.27 1937-04-29,170.13,172.88,169.37,170.52,2030000,170.52 1937-04-28,173.78,173.78,168.77,170.13,2530000,170.13 1937-04-27,172.36,175.18,172.36,174.52,1410000,174.52 1937-04-26,175.33,175.33,171.20,171.97,2020000,171.97 1937-04-23,181.70,182.60,178.00,178.54,1200000,178.54 1937-04-22,183.60,184.33,181.28,181.70,1180000,181.70 1937-04-21,181.44,184.05,181.39,183.60,1250000,183.60 1937-04-20,180.82,182.63,180.28,181.44,1130000,181.44 1937-04-19,180.51,181.38,179.76,180.82,820000,180.82 1937-04-16,181.19,182.27,179.70,180.75,1070000,180.75 1937-04-15,181.93,182.25,180.69,181.19,940000,181.19 1937-04-14,182.10,183.34,181.27,181.93,1480000,181.93 1937-04-13,180.59,183.43,180.59,182.10,1590000,182.10 1937-04-12,178.26,180.12,176.39,179.74,1130000,179.74 1937-04-09,178.18,180.38,175.86,178.94,1730000,178.94 1937-04-08,178.07,179.94,176.72,178.18,1920000,178.18 1937-04-07,182.13,182.13,177.68,178.07,2290000,178.07 1937-04-06,184.19,184.66,182.06,182.98,1250000,182.98 1937-04-05,183.54,185.09,183.39,184.19,980000,184.19 1937-04-02,184.43,184.43,180.89,182.75,1640000,182.75 1937-04-01,186.41,186.63,184.37,185.19,1210000,185.19 1937-03-31,186.77,187.99,185.77,186.41,1660000,186.41 1937-03-30,184.09,186.93,183.64,186.77,1230000,186.77 1937-03-29,184.95,185.75,183.79,184.09,870000,184.09 1937-03-25,184.32,186.11,183.73,184.08,1250000,184.08 1937-03-24,181.87,184.97,181.63,184.32,1430000,184.32 1937-03-23,179.93,182.96,179.93,181.87,1600000,181.87 1937-03-22,182.83,182.83,179.28,179.82,2030000,179.82 1937-03-19,184.73,185.94,183.72,184.56,1740000,184.56 1937-03-18,187.52,187.52,183.72,184.73,2280000,184.73 1937-03-17,189.95,190.52,188.15,188.50,2120000,188.50 1937-03-16,189.41,191.29,188.88,189.95,1750000,189.95 1937-03-15,190.58,190.81,188.59,189.41,1770000,189.41 1937-03-12,192.22,192.27,189.39,191.24,2290000,191.24 1937-03-11,194.40,195.33,191.77,192.22,2740000,192.22 1937-03-10,193.29,195.59,192.77,194.40,2820000,194.40 1937-03-09,192.69,194.89,191.65,193.29,2390000,193.29 1937-03-08,194.15,195.20,191.53,192.69,3180000,192.69 1937-03-05,192.17,195.17,192.17,194.14,2830000,194.14 1937-03-04,192.91,193.95,190.98,191.63,2730000,191.63 1937-03-03,190.29,193.86,190.29,192.91,3570000,192.91 1937-03-02,187.68,190.41,187.64,189.91,2300000,189.91 1937-03-01,187.30,188.48,186.00,187.68,1660000,187.68 1937-02-26,186.68,187.93,185.82,187.17,1780000,187.17 1937-02-25,187.35,188.31,186.19,186.68,2230000,186.68 1937-02-24,186.50,187.88,185.15,187.35,2080000,187.35 1937-02-23,189.36,189.36,185.96,186.50,2870000,186.50 1937-02-19,188.07,190.42,187.71,189.37,2730000,189.37 1937-02-18,187.98,188.90,186.82,188.07,2130000,188.07 1937-02-17,188.18,189.39,187.30,187.98,2580000,187.98 1937-02-16,188.39,189.20,187.05,188.18,2220000,188.18 1937-02-15,189.58,189.58,187.45,188.39,1960000,188.39 1937-02-11,189.35,191.39,188.69,190.29,2920000,190.29 1937-02-10,187.68,189.83,187.58,189.35,2920000,189.35 1937-02-09,187.82,188.79,186.74,187.68,2590000,187.68 1937-02-08,187.11,188.66,186.68,187.82,2990000,187.82 1937-02-05,188.39,189.07,184.85,186.01,3320000,186.01 1937-02-04,188.69,189.64,187.65,188.39,2390000,188.39 1937-02-03,188.20,189.94,187.48,188.69,2450000,188.69 1937-02-02,186.61,189.13,186.13,188.20,2430000,188.20 1937-02-01,185.74,187.77,185.10,186.61,2360000,186.61 1937-01-29,183.41,185.41,182.34,184.74,1970000,184.74 1937-01-28,183.97,185.59,182.77,183.41,2340000,183.41 1937-01-27,183.19,184.32,182.15,183.97,1940000,183.97 1937-01-26,185.62,185.68,182.64,183.19,2180000,183.19 1937-01-25,186.69,186.97,185.02,185.62,2220000,185.62 1937-01-22,186.90,187.80,185.01,186.53,2680000,186.53 1937-01-21,185.96,187.49,184.98,186.90,2990000,186.90 1937-01-20,184.10,186.88,184.10,185.96,3270000,185.96 1937-01-19,184.95,185.16,183.22,184.02,2620000,184.02 1937-01-18,185.73,185.93,183.74,184.95,2860000,184.95 1937-01-15,183.71,185.40,183.55,184.53,2900000,184.53 1937-01-14,183.01,184.66,182.53,183.71,3260000,183.71 1937-01-13,183.30,184.01,182.25,183.01,3680000,183.01 1937-01-12,183.26,184.49,182.08,183.30,3570000,183.30 1937-01-11,182.75,183.82,181.77,183.26,3080000,183.26 1937-01-08,181.77,183.58,181.36,182.95,3220000,182.95 1937-01-07,179.32,182.11,179.32,181.77,3060000,181.77 1937-01-06,179.07,179.90,178.17,178.92,1920000,178.92 1937-01-05,177.72,179.66,177.64,179.07,1870000,179.07 1937-01-04,178.39,178.39,176.96,177.72,1510000,177.72 1936-12-31,180.57,181.77,179.34,179.90,1760000,179.90 1936-12-30,178.02,181.13,178.02,180.57,2310000,180.57 1936-12-29,177.12,178.48,176.26,177.60,2280000,177.60 1936-12-28,178.60,179.52,176.71,177.12,1790000,177.12 1936-12-24,178.36,179.54,177.75,178.60,1610000,178.60 1936-12-23,177.30,179.00,176.70,178.36,1870000,178.36 1936-12-22,176.14,178.20,176.14,177.30,1670000,177.30 1936-12-21,177.61,178.28,175.31,175.85,1760000,175.85 1936-12-18,180.78,181.10,179.03,179.42,1910000,179.42 1936-12-17,181.58,182.18,180.35,180.78,1950000,180.78 1936-12-16,181.97,182.57,180.87,181.58,1950000,181.58 1936-12-15,181.87,183.30,181.30,181.97,2480000,181.97 1936-12-14,180.92,182.67,180.74,181.87,2880000,181.87 1936-12-11,182.18,182.33,180.76,181.10,2610000,181.10 1936-12-10,181.16,182.77,180.67,182.18,2440000,182.18 1936-12-09,180.57,181.77,179.93,181.16,1860000,181.16 1936-12-08,180.13,181.29,179.95,180.57,1620000,180.57 1936-12-07,181.05,181.49,179.74,180.13,1680000,180.13 1936-12-04,181.29,182.34,180.36,180.97,2160000,180.97 1936-12-03,180.25,181.92,179.90,181.29,2040000,181.29 1936-12-02,181.89,181.89,179.66,180.25,2320000,180.25 1936-12-01,183.22,183.59,181.56,182.05,2230000,182.05 1936-11-30,183.32,184.03,181.87,183.22,2140000,183.22 1936-11-27,180.91,183.27,180.91,182.81,2280000,182.81 1936-11-25,181.11,181.49,179.68,180.78,1860000,180.78 1936-11-24,178.84,181.51,178.84,181.11,1920000,181.11 1936-11-23,181.78,181.78,177.91,178.62,2160000,178.62 1936-11-20,182.21,182.35,180.24,180.74,1820000,180.74 1936-11-19,184.26,184.26,181.63,182.21,2440000,182.21 1936-11-18,184.90,186.39,183.73,184.44,2920000,184.44 1936-11-17,182.94,185.55,182.94,184.90,3270000,184.90 1936-11-16,181.45,183.39,181.00,182.65,2380000,182.65 1936-11-13,183.15,184.28,181.39,182.24,2480000,182.24 1936-11-12,184.01,185.52,182.26,183.15,2580000,183.15 1936-11-10,183.65,185.24,182.82,184.01,2700000,184.01 1936-11-09,183.38,184.77,182.39,183.65,3140000,183.65 1936-11-06,182.25,183.53,180.68,181.60,2720000,181.60 1936-11-05,180.66,183.31,180.57,182.25,3620000,182.25 1936-11-04,177.00,181.15,177.00,180.66,3290000,180.66 1936-11-02,177.19,177.74,175.35,176.67,1600000,176.67 1936-10-30,176.31,178.09,176.20,177.15,1680000,177.15 1936-10-29,174.84,176.60,174.61,176.31,1710000,176.31 1936-10-28,174.47,176.61,174.47,174.84,1630000,174.84 1936-10-27,172.89,174.90,172.89,174.36,1310000,174.36 1936-10-26,174.74,174.74,172.16,172.30,1480000,172.30 1936-10-23,174.90,176.23,174.64,175.60,1510000,175.60 1936-10-22,176.70,177.01,174.50,174.90,1980000,174.90 1936-10-21,176.78,177.63,175.81,176.70,1630000,176.70 1936-10-20,177.42,177.82,175.98,176.78,1670000,176.78 1936-10-19,177.63,178.44,176.66,177.42,1890000,177.42 1936-10-16,175.33,177.31,175.33,176.66,2050000,176.66 1936-10-15,175.57,176.32,174.13,175.22,1790000,175.22 1936-10-14,176.29,176.79,175.03,175.57,1640000,175.57 1936-10-13,176.05,177.68,175.87,176.29,2070000,176.29 1936-10-09,174.93,176.45,174.23,175.19,2240000,175.19 1936-10-08,174.59,175.49,173.41,174.93,2230000,174.93 1936-10-07,174.42,175.92,173.94,174.59,3030000,174.59 1936-10-06,172.93,174.82,172.93,174.42,2260000,174.42 1936-10-05,172.44,174.04,172.04,172.81,2080000,172.81 1936-10-02,168.63,170.94,168.63,170.76,1930000,170.76 1936-10-01,167.82,168.83,167.54,168.26,1100000,168.26 1936-09-30,168.48,169.55,167.47,167.82,1350000,167.82 1936-09-29,168.79,169.85,168.01,168.48,1380000,168.48 1936-09-28,168.07,169.62,167.72,168.79,1450000,168.79 1936-09-25,169.03,169.03,165.91,166.36,1160000,166.36 1936-09-24,169.01,169.79,168.10,169.14,1190000,169.14 1936-09-23,169.47,170.72,168.68,169.01,1480000,169.01 1936-09-22,168.90,170.47,168.37,169.47,1550000,169.47 1936-09-21,168.93,170.25,168.34,168.90,1770000,168.90 1936-09-18,166.25,168.36,166.10,167.76,1270000,167.76 1936-09-17,165.16,166.51,164.82,166.25,770000,166.25 1936-09-16,166.44,166.75,164.97,165.16,1040000,165.16 1936-09-15,166.86,167.14,165.41,166.44,1130000,166.44 1936-09-14,167.98,167.98,166.11,166.86,1010000,166.86 1936-09-11,169.00,169.38,167.59,168.59,1400000,168.59 1936-09-10,168.50,169.59,168.05,169.00,1550000,169.00 1936-09-09,169.55,169.73,168.07,168.50,1570000,168.50 1936-09-08,168.32,170.02,168.32,169.55,1720000,169.55 1936-09-04,166.24,167.62,166.01,167.04,1180000,167.04 1936-09-03,166.65,167.25,165.64,166.24,1050000,166.24 1936-09-02,166.35,167.89,166.15,166.65,1350000,166.65 1936-09-01,166.29,167.21,165.24,166.35,1140000,166.35 1936-08-31,166.91,167.25,165.84,166.29,1150000,166.29 1936-08-28,166.77,168.02,166.31,166.78,1380000,166.78 1936-08-27,163.32,166.94,163.21,166.77,1340000,166.77 1936-08-26,164.34,165.00,162.98,163.32,910000,163.32 1936-08-25,163.78,165.23,163.60,164.34,800000,164.34 1936-08-24,162.90,164.64,162.90,163.78,800000,163.78 1936-08-21,164.69,164.69,160.52,160.80,1480000,160.80 1936-08-20,166.04,166.70,165.23,165.59,960000,165.59 1936-08-19,165.42,166.87,165.26,166.04,1010000,166.04 1936-08-18,165.38,166.37,164.89,165.42,790000,165.42 1936-08-17,165.86,167.01,164.66,165.38,830000,165.38 1936-08-14,167.64,167.80,165.40,165.75,1070000,165.75 1936-08-13,169.05,169.59,167.30,167.64,1400000,167.64 1936-08-12,167.86,169.63,167.50,169.05,1260000,169.05 1936-08-11,168.80,169.08,167.20,167.86,1100000,167.86 1936-08-10,169.10,170.15,168.15,168.80,1340000,168.80 1936-08-07,165.71,168.63,165.34,168.01,1670000,168.01 1936-08-06,165.07,166.24,164.17,165.71,1170000,165.71 1936-08-05,165.41,166.57,163.91,165.07,1280000,165.07 1936-08-04,165.32,165.91,164.41,165.41,1050000,165.41 1936-08-03,165.42,166.53,164.63,165.32,1010000,165.32 1936-07-31,165.98,167.27,164.32,164.86,1160000,164.86 1936-07-30,165.67,167.05,165.13,165.98,1510000,165.98 1936-07-29,167.01,167.83,165.13,165.67,1950000,165.67 1936-07-28,166.92,168.23,165.98,167.01,1900000,167.01 1936-07-27,165.56,167.63,165.51,166.92,1830000,166.92 1936-07-24,164.61,165.31,164.00,164.37,1320000,164.37 1936-07-23,164.49,165.06,163.46,164.61,1340000,164.61 1936-07-22,165.23,166.06,163.99,164.49,1450000,164.49 1936-07-21,164.43,165.81,163.75,165.23,1590000,165.23 1936-07-20,164.42,165.48,163.84,164.43,1420000,164.43 1936-07-17,163.64,165.07,163.02,163.55,1560000,163.55 1936-07-16,163.24,164.38,162.27,163.64,1480000,163.64 1936-07-15,162.80,164.42,162.28,163.24,1980000,163.24 1936-07-14,161.35,163.22,160.99,162.80,1660000,162.80 1936-07-13,160.72,162.14,160.33,161.35,1440000,161.35 1936-07-10,158.07,160.67,158.07,160.07,1690000,160.07 1936-07-09,156.20,158.17,156.05,157.71,1290000,157.71 1936-07-08,155.60,156.76,154.85,156.20,870000,156.20 1936-07-07,156.87,156.87,154.86,155.60,970000,155.60 1936-07-06,158.11,158.86,156.73,157.11,850000,157.11 1936-07-03,157.51,159.13,156.93,158.11,1020000,158.11 1936-07-02,158.38,159.55,156.87,157.51,1070000,157.51 1936-07-01,157.69,159.16,156.82,158.38,970000,158.38 1936-06-30,158.01,158.76,157.11,157.69,820000,157.69 1936-06-29,158.46,159.66,157.58,158.01,770000,158.01 1936-06-26,158.64,159.54,157.86,158.21,890000,158.21 1936-06-25,160.66,161.00,158.27,158.64,1340000,158.64 1936-06-24,158.94,161.15,158.56,160.66,1240000,160.66 1936-06-23,159.13,160.25,158.24,158.94,970000,158.94 1936-06-22,157.40,159.66,157.40,159.13,990000,159.13 1936-06-19,157.38,157.46,156.11,156.53,830000,156.53 1936-06-18,156.97,158.05,156.54,157.38,940000,157.38 1936-06-17,156.70,157.96,156.47,156.97,1220000,156.97 1936-06-16,155.09,156.94,154.88,156.70,1120000,156.70 1936-06-15,154.64,155.81,154.57,155.09,720000,155.09 1936-06-12,155.16,155.91,153.55,153.71,1000000,153.71 1936-06-11,153.02,155.38,152.65,155.16,1090000,155.16 1936-06-10,152.90,153.87,152.44,153.02,1040000,153.02 1936-06-09,151.44,153.12,151.44,152.90,880000,152.90 1936-06-08,150.40,152.01,150.40,151.39,690000,151.39 1936-06-05,149.39,149.95,148.52,149.26,640000,149.26 1936-06-04,151.10,151.10,149.22,149.39,770000,149.39 1936-06-03,151.97,152.38,151.20,151.53,640000,151.53 1936-06-02,152.84,153.22,151.30,151.97,760000,151.97 1936-06-01,152.64,154.02,152.62,152.84,790000,152.84 1936-05-29,151.77,152.92,151.53,152.64,740000,152.64 1936-05-28,152.57,152.91,151.49,151.77,760000,151.77 1936-05-27,152.26,153.57,151.44,152.57,1220000,152.57 1936-05-26,150.83,152.48,150.48,152.26,1140000,152.26 1936-05-25,150.65,151.62,150.31,150.83,690000,150.83 1936-05-22,148.80,150.26,148.61,149.58,680000,149.58 1936-05-21,148.94,150.21,148.46,148.80,670000,148.80 1936-05-20,147.49,149.28,147.30,148.94,690000,148.94 1936-05-19,149.56,149.56,147.21,147.49,910000,147.49 1936-05-18,151.42,152.44,150.09,150.35,990000,150.35 1936-05-15,151.49,152.43,150.56,151.60,990000,151.60 1936-05-14,149.00,151.97,149.00,151.49,1390000,151.49 1936-05-13,146.92,148.38,146.92,147.90,590000,147.90 1936-05-12,146.85,148.16,146.10,146.70,600000,146.70 1936-05-11,147.85,148.85,146.44,146.85,680000,146.85 1936-05-08,147.14,147.33,145.68,146.87,780000,146.87 1936-05-07,149.73,149.96,146.29,147.14,1010000,147.14 1936-05-06,148.56,150.52,148.52,149.73,1130000,149.73 1936-05-05,147.69,149.97,147.69,148.56,1180000,148.56 1936-05-04,146.41,147.63,144.18,146.96,1070000,146.96 1936-05-01,145.67,147.86,145.67,147.07,1160000,147.07 1936-04-30,143.65,146.20,141.53,145.67,2310000,145.67 1936-04-29,146.75,147.23,143.20,143.65,1790000,143.65 1936-04-28,147.05,147.79,144.80,146.75,2230000,146.75 1936-04-27,151.81,151.81,145.96,147.05,2300000,147.05 1936-04-24,151.08,152.51,149.63,151.54,1660000,151.54 1936-04-23,154.92,155.16,149.63,151.08,2060000,151.08 1936-04-22,153.60,155.59,153.60,154.92,1200000,154.92 1936-04-21,152.40,154.09,151.29,153.36,1880000,153.36 1936-04-20,156.07,156.73,152.10,152.40,1660000,152.40 1936-04-17,158.49,159.22,157.32,157.78,1160000,157.78 1936-04-16,159.61,160.33,157.93,158.49,1150000,158.49 1936-04-15,158.41,160.27,157.92,159.61,1320000,159.61 1936-04-14,160.76,161.10,157.51,158.41,1940000,158.41 1936-04-13,160.48,161.26,159.71,160.76,1450000,160.76 1936-04-09,160.97,161.44,159.46,160.26,1650000,160.26 1936-04-08,160.94,162.54,160.34,160.97,1650000,160.97 1936-04-07,161.99,162.33,160.42,160.94,1580000,160.94 1936-04-06,161.50,163.07,161.18,161.99,2030000,161.99 1936-04-03,160.43,161.84,159.80,160.09,1560000,160.09 1936-04-02,159.12,161.55,159.12,160.43,2190000,160.43 1936-04-01,157.04,159.42,157.04,158.96,1690000,158.96 1936-03-31,155.37,157.01,155.06,156.34,1040000,156.34 1936-03-30,155.54,156.73,155.16,155.37,950000,155.37 1936-03-27,157.73,157.82,155.01,155.52,1550000,155.52 1936-03-26,157.88,159.53,157.08,157.73,1870000,157.73 1936-03-25,156.56,158.74,155.81,157.88,1910000,157.88 1936-03-24,157.62,158.76,156.23,156.56,1900000,156.56 1936-03-23,156.42,158.22,156.20,157.62,1680000,157.62 1936-03-20,157.40,158.81,156.69,157.42,1890000,157.42 1936-03-19,155.85,158.22,155.85,157.40,2020000,157.40 1936-03-18,156.34,157.30,155.04,155.82,1750000,155.82 1936-03-17,153.85,156.73,153.85,156.34,2240000,156.34 1936-03-16,154.07,154.73,152.14,153.25,1840000,153.25 1936-03-13,153.13,153.46,149.65,150.42,2660000,150.42 1936-03-12,156.63,156.63,152.35,153.13,2920000,153.13 1936-03-11,155.37,157.95,155.37,156.85,2190000,156.85 1936-03-10,153.50,155.87,153.35,155.37,2320000,155.37 1936-03-09,156.86,156.86,153.33,153.50,2750000,153.50 1936-03-06,157.52,159.87,157.27,158.75,2890000,158.75 1936-03-05,156.70,158.56,156.03,157.52,2590000,157.52 1936-03-04,156.19,158.24,155.45,156.70,2980000,156.70 1936-03-03,154.08,156.61,154.08,156.19,2700000,156.19 1936-03-02,152.15,154.54,151.65,154.08,1980000,154.08 1936-02-28,152.64,153.94,151.61,152.53,2460000,152.53 1936-02-27,150.59,153.02,150.59,152.64,2320000,152.64 1936-02-26,150.78,151.10,149.08,149.81,2040000,149.81 1936-02-25,152.74,153.25,149.99,150.78,2390000,150.78 1936-02-24,153.74,153.75,152.00,152.74,2200000,152.74 1936-02-21,154.43,155.14,153.05,153.74,3010000,153.74 1936-02-20,153.09,155.14,152.30,154.43,3460000,154.43 1936-02-19,153.36,155.69,152.54,153.09,4580000,153.09 1936-02-18,151.40,153.76,151.23,153.36,3530000,153.36 1936-02-17,152.40,153.57,150.43,151.40,4720000,151.40 1936-02-14,152.53,153.01,150.86,151.97,2600000,151.97 1936-02-13,152.25,153.67,151.48,152.53,2920000,152.53 1936-02-11,151.15,153.16,150.80,152.25,3350000,152.25 1936-02-10,150.40,151.88,149.72,151.15,2460000,151.15 1936-02-07,150.86,151.67,149.50,150.17,2570000,150.17 1936-02-06,150.60,151.97,150.14,150.86,2750000,150.86 1936-02-05,150.94,151.94,150.07,150.60,2920000,150.60 1936-02-04,150.62,151.77,149.92,150.94,3010000,150.94 1936-02-03,149.58,151.05,148.32,150.62,2320000,150.62 1936-01-31,146.98,150.00,146.79,149.49,3230000,149.49 1936-01-30,147.71,148.50,146.25,146.98,3010000,146.98 1936-01-29,146.94,148.50,146.33,147.71,2680000,147.71 1936-01-28,147.30,147.84,146.38,146.94,2290000,146.94 1936-01-27,147.01,148.30,146.73,147.30,3100000,147.30 1936-01-24,146.97,147.91,146.15,146.59,2540000,146.59 1936-01-23,145.99,147.70,145.80,146.97,2940000,146.97 1936-01-22,143.79,146.36,143.79,145.99,2150000,145.99 1936-01-21,144.06,144.29,142.77,143.50,1330000,143.50 1936-01-20,144.93,145.23,143.60,144.06,1810000,144.06 1936-01-17,145.92,146.58,144.64,145.81,2350000,145.81 1936-01-16,145.66,146.73,145.03,145.92,3110000,145.92 1936-01-15,146.32,147.13,145.02,145.66,4630000,145.66 1936-01-14,146.52,146.89,145.41,146.32,2790000,146.32 1936-01-13,146.73,147.45,145.67,146.52,2600000,146.52 1936-01-10,145.68,148.02,145.68,147.08,3270000,147.08 1936-01-09,146.16,146.83,144.63,145.66,3000000,145.66 1936-01-08,144.92,147.29,144.62,146.16,2530000,146.16 1936-01-07,143.11,145.22,142.05,144.92,3080000,144.92 1936-01-06,144.08,145.35,141.55,143.11,3730000,143.11 1936-01-03,144.13,145.28,143.49,144.69,2830000,144.69 1936-01-02,144.13,145.22,143.34,144.13,2240000,144.13 1935-12-31,143.00,145.02,142.93,144.13,2440000,144.13 1935-12-30,141.35,143.37,141.35,143.00,1630000,143.00 1935-12-27,141.54,142.83,140.67,141.58,2130000,141.58 1935-12-26,141.53,142.64,140.87,141.54,2340000,141.54 1935-12-24,140.58,141.88,140.04,141.53,1710000,141.53 1935-12-23,140.19,141.42,139.75,140.58,1920000,140.58 1935-12-20,138.94,140.00,138.46,139.50,1410000,139.50 1935-12-19,139.95,139.95,138.33,138.94,1260000,138.94 1935-12-18,140.60,141.73,139.71,140.10,1690000,140.10 1935-12-17,139.11,140.89,138.90,140.60,1390000,140.60 1935-12-16,140.38,141.28,138.91,139.11,1400000,139.11 1935-12-13,141.34,142.12,139.56,140.16,1890000,140.16 1935-12-12,142.84,143.46,140.86,141.34,2140000,141.34 1935-12-11,142.31,143.90,141.63,142.84,2130000,142.84 1935-12-10,144.10,144.32,141.61,142.31,2340000,142.31 1935-12-09,144.47,145.07,143.06,144.10,2510000,144.10 1935-12-06,143.72,144.78,143.09,143.48,2370000,143.48 1935-12-05,144.04,145.09,143.12,143.72,2260000,143.72 1935-12-04,143.58,145.13,143.29,144.04,2960000,144.04 1935-12-03,140.72,143.71,140.60,143.58,1930000,143.58 1935-12-02,142.35,142.95,140.38,140.72,1520000,140.72 1935-11-29,143.38,144.27,141.15,142.34,2170000,142.34 1935-11-27,142.59,144.30,141.94,143.38,1860000,143.38 1935-11-26,144.72,145.25,141.80,142.59,2330000,142.59 1935-11-25,146.12,147.50,143.89,144.72,3370000,144.72 1935-11-22,147.37,148.10,143.48,144.61,3920000,144.61 1935-11-21,146.65,147.83,145.96,147.37,3280000,147.37 1935-11-20,148.44,149.42,145.98,146.65,3820000,146.65 1935-11-19,147.06,148.81,146.93,148.44,2880000,148.44 1935-11-18,147.31,148.94,146.35,147.06,3200000,147.06 1935-11-15,145.59,147.40,145.10,146.32,2940000,146.32 1935-11-14,143.85,146.39,143.85,145.59,3950000,145.59 1935-11-13,142.56,143.97,141.60,143.59,2050000,143.59 1935-11-12,144.36,144.60,142.20,142.56,2140000,142.56 1935-11-08,143.40,145.40,142.80,144.25,3350000,144.25 1935-11-07,142.90,145.28,142.15,143.40,2790000,143.40 1935-11-06,141.19,143.48,141.19,142.90,3080000,142.90 1935-11-04,141.20,141.78,139.99,141.07,1750000,141.07 1935-11-01,140.07,141.77,140.07,141.31,2040000,141.31 1935-10-31,139.35,140.52,138.85,139.74,1810000,139.74 1935-10-30,140.49,140.56,138.40,139.35,2150000,139.35 1935-10-29,140.78,141.23,139.49,140.49,1710000,140.49 1935-10-28,141.47,142.08,139.86,140.78,2110000,140.78 1935-10-25,139.58,141.89,139.58,140.68,2470000,140.68 1935-10-24,139.58,140.84,138.41,139.42,2160000,139.42 1935-10-23,138.77,140.46,138.32,139.58,2760000,139.58 1935-10-22,138.96,140.08,137.84,138.77,2840000,138.77 1935-10-21,137.11,139.50,137.11,138.96,2870000,138.96 1935-10-18,135.57,136.11,134.54,135.13,1450000,135.13 1935-10-17,135.68,136.76,135.10,135.57,1610000,135.57 1935-10-16,136.26,137.15,134.83,135.68,2240000,135.68 1935-10-15,135.03,137.11,134.85,136.26,2570000,136.26 1935-10-14,133.56,135.25,133.17,135.03,1590000,135.03 1935-10-11,133.03,134.56,133.03,133.56,2060000,133.56 1935-10-10,131.14,133.09,131.14,132.99,1860000,132.99 1935-10-09,130.06,130.81,129.51,130.59,880000,130.59 1935-10-08,130.77,131.30,129.85,130.06,1180000,130.06 1935-10-07,130.35,131.35,130.24,130.77,950000,130.77 1935-10-04,129.05,130.59,128.82,129.76,1420000,129.76 1935-10-03,128.06,129.41,126.95,129.05,1490000,129.05 1935-10-02,130.43,130.43,127.56,128.06,2190000,128.06 1935-10-01,131.92,133.19,131.18,131.51,1420000,131.51 1935-09-30,131.75,132.60,131.17,131.92,1260000,131.92 1935-09-27,131.06,132.12,130.74,131.48,1120000,131.48 1935-09-26,131.52,131.67,129.95,131.06,1090000,131.06 1935-09-25,131.02,132.45,130.99,131.52,1080000,131.52 1935-09-24,130.26,131.68,130.26,131.02,1010000,131.02 1935-09-23,128.97,130.66,128.97,129.55,1010000,129.55 1935-09-20,130.38,130.38,127.98,128.42,2220000,128.42 1935-09-19,134.11,134.49,131.10,131.44,1920000,131.44 1935-09-18,133.11,135.34,133.07,134.11,1940000,134.11 1935-09-17,132.91,133.78,131.86,133.11,1330000,133.11 1935-09-16,133.40,133.86,131.91,132.91,1490000,132.91 1935-09-13,133.33,134.67,132.88,133.52,1730000,133.52 1935-09-12,134.01,134.72,132.63,133.33,1880000,133.33 1935-09-11,133.22,135.05,132.90,134.01,2590000,134.01 1935-09-10,132.48,134.06,131.50,133.22,1980000,133.22 1935-09-09,131.86,133.36,131.13,132.48,2000000,132.48 1935-09-06,129.34,131.34,128.94,130.75,2150000,130.75 1935-09-05,128.46,130.34,128.29,129.34,1890000,129.34 1935-09-04,127.27,128.70,126.43,128.46,1000000,128.46 1935-09-03,127.89,128.47,126.81,127.27,900000,127.27 1935-08-30,126.95,127.70,126.76,127.35,830000,127.35 1935-08-29,126.61,127.62,126.57,126.95,900000,126.95 1935-08-28,126.81,127.32,125.65,126.61,1390000,126.61 1935-08-27,128.99,129.97,126.27,126.81,2160000,126.81 1935-08-26,127.93,129.53,127.61,128.99,1430000,128.99 1935-08-23,128.52,129.59,127.82,128.93,1890000,128.93 1935-08-22,127.66,129.49,127.33,128.52,1670000,128.52 1935-08-21,126.59,128.25,126.59,127.66,1750000,127.66 1935-08-20,126.33,126.68,124.97,126.31,1980000,126.31 1935-08-19,127.96,128.39,126.07,126.33,2070000,126.33 1935-08-16,127.47,128.03,126.51,127.63,1710000,127.63 1935-08-15,128.27,128.72,127.07,127.47,1580000,127.47 1935-08-14,128.09,128.94,127.35,128.27,1950000,128.27 1935-08-13,128.00,128.85,127.15,128.09,2630000,128.09 1935-08-12,127.94,128.84,127.44,128.00,2430000,128.00 1935-08-09,125.98,127.80,125.72,127.27,2190000,127.27 1935-08-08,125.61,126.59,125.16,125.98,1430000,125.98 1935-08-07,125.64,126.54,125.18,125.61,1390000,125.61 1935-08-06,126.07,126.96,125.00,125.64,1770000,125.64 1935-08-05,125.90,126.62,125.31,126.07,1740000,126.07 1935-08-02,125.85,126.13,124.28,124.93,1520000,124.93 1935-08-01,126.23,126.88,125.19,125.85,1890000,125.85 1935-07-31,125.57,127.04,125.00,126.23,1910000,126.23 1935-07-30,126.56,126.78,125.03,125.57,1680000,125.57 1935-07-29,125.27,126.89,125.10,126.56,1750000,126.56 1935-07-26,123.80,124.45,123.37,124.02,990000,124.02 1935-07-25,124.60,125.27,123.19,123.80,1330000,123.80 1935-07-24,124.14,124.93,123.83,124.60,1310000,124.60 1935-07-23,124.10,125.36,123.58,124.14,1730000,124.14 1935-07-22,122.69,124.34,122.67,124.10,1370000,124.10 1935-07-19,123.41,123.88,121.94,122.33,1150000,122.33 1935-07-18,122.91,124.24,122.60,123.41,1500000,123.41 1935-07-17,122.34,123.49,122.25,122.91,1360000,122.91 1935-07-16,121.72,122.56,121.00,122.34,900000,122.34 1935-07-15,121.88,122.74,121.40,121.72,950000,121.72 1935-07-12,121.93,122.63,121.29,122.20,1100000,122.20 1935-07-11,122.69,122.90,121.14,121.93,1000000,121.93 1935-07-10,122.15,123.14,121.97,122.69,1150000,122.69 1935-07-09,122.55,123.34,121.74,122.15,1350000,122.15 1935-07-08,121.02,122.68,120.69,122.55,1310000,122.55 1935-07-05,119.02,120.75,119.02,120.61,880000,120.61 1935-07-03,118.69,118.98,117.80,118.81,720000,118.81 1935-07-02,118.82,119.70,118.32,118.69,1200000,118.69 1935-07-01,118.21,119.30,117.97,118.82,680000,118.82 1935-06-28,117.69,118.83,117.69,118.36,760000,118.36 1935-06-27,117.64,118.50,116.91,117.56,740000,117.56 1935-06-26,118.73,119.49,117.51,117.64,960000,117.64 1935-06-25,119.64,119.64,117.85,118.73,1140000,118.73 1935-06-24,120.75,121.30,119.67,120.04,1120000,120.04 1935-06-21,117.80,120.04,117.80,119.48,1520000,119.48 1935-06-20,117.47,117.47,115.85,117.24,1000000,117.24 1935-06-19,119.32,119.76,117.47,118.12,1630000,118.12 1935-06-18,118.67,119.71,118.67,119.32,890000,119.32 1935-06-17,119.17,119.33,118.21,118.67,910000,118.67 1935-06-14,117.89,119.67,117.64,119.00,1280000,119.00 1935-06-13,117.14,118.40,116.49,117.89,860000,117.89 1935-06-12,117.08,118.53,116.27,117.14,1290000,117.14 1935-06-11,115.89,117.53,115.77,117.08,1150000,117.08 1935-06-10,114.72,116.12,114.04,115.89,630000,115.89 1935-06-07,113.54,114.27,113.15,114.01,590000,114.01 1935-06-06,113.92,114.89,113.27,113.54,680000,113.54 1935-06-05,113.58,115.08,112.96,113.92,1100000,113.92 1935-06-04,111.95,113.73,111.95,113.58,870000,113.58 1935-06-03,110.15,111.84,110.15,111.45,600000,111.45 1935-05-31,111.85,112.62,110.40,110.64,1120000,110.64 1935-05-29,113.67,113.67,111.37,111.85,1500000,111.85 1935-05-28,116.74,117.62,112.99,113.76,2310000,113.76 1935-05-27,115.90,117.12,115.32,116.74,820000,116.74 1935-05-24,116.81,117.43,115.91,116.17,1180000,116.17 1935-05-23,116.24,117.51,116.20,116.81,1290000,116.81 1935-05-22,115.56,116.69,115.07,116.24,1150000,116.24 1935-05-21,114.67,116.31,114.57,115.56,1140000,115.56 1935-05-20,114.58,115.27,114.18,114.67,970000,114.67 1935-05-17,116.58,117.09,115.28,115.81,1820000,115.81 1935-05-16,114.67,117.30,114.67,116.58,2420000,116.58 1935-05-15,114.18,115.11,113.87,114.61,1050000,114.61 1935-05-14,114.23,115.03,113.71,114.18,1210000,114.18 1935-05-13,114.08,114.93,113.36,114.23,1130000,114.23 1935-05-10,113.10,114.50,112.84,113.67,1580000,113.67 1935-05-09,112.63,113.92,112.11,113.10,1660000,113.10 1935-05-08,110.18,112.74,110.18,112.63,1400000,112.63 1935-05-07,110.53,110.68,109.23,109.79,810000,109.79 1935-05-06,110.83,111.60,110.22,110.53,1030000,110.53 1935-05-03,109.04,110.93,109.04,110.49,950000,110.49 1935-05-02,108.71,109.47,107.82,108.84,880000,108.84 1935-05-01,109.45,110.26,108.52,108.71,820000,108.71 1935-04-30,109.91,110.61,108.75,109.45,860000,109.45 1935-04-29,109.68,110.40,108.65,109.91,890000,109.91 1935-04-26,110.47,111.33,109.23,110.37,1520000,110.37 1935-04-25,109.45,111.52,108.94,110.47,1640000,110.47 1935-04-24,110.06,110.75,109.08,109.45,1280000,109.45 1935-04-23,110.27,110.70,109.56,110.06,1230000,110.06 1935-04-22,109.76,110.91,109.42,110.27,1380000,110.27 1935-04-18,105.68,108.33,105.68,107.97,820000,107.97 1935-04-17,106.33,106.67,105.24,105.43,850000,105.43 1935-04-16,105.93,106.67,105.44,106.33,740000,106.33 1935-04-15,105.42,106.44,105.05,105.93,1110000,105.93 1935-04-12,104.03,104.69,103.59,104.45,840000,104.45 1935-04-11,104.06,104.59,103.57,104.03,730000,104.03 1935-04-10,104.32,105.06,103.74,104.06,990000,104.06 1935-04-09,102.65,104.46,102.58,104.32,860000,104.32 1935-04-08,103.04,103.40,102.31,102.65,710000,102.65 1935-04-05,101.35,102.87,101.35,102.53,1210000,102.53 1935-04-04,100.39,101.38,100.34,101.13,560000,101.13 1935-04-03,101.00,101.11,99.75,100.39,530000,100.39 1935-04-02,101.23,101.86,100.84,101.00,530000,101.00 1935-04-01,100.81,101.59,100.67,101.23,440000,101.23 1935-03-29,100.59,101.33,100.26,100.78,460000,100.78 1935-03-28,100.35,101.54,99.69,100.59,610000,100.59 1935-03-27,98.97,100.72,98.87,100.35,460000,100.35 1935-03-26,99.50,99.80,98.61,98.97,440000,98.97 1935-03-25,99.84,99.99,98.63,99.50,460000,99.50 1935-03-22,99.72,100.88,99.10,100.68,780000,100.68 1935-03-21,98.29,100.55,98.03,99.72,890000,99.72 1935-03-20,98.31,99.11,97.75,98.29,490000,98.29 1935-03-19,97.05,98.61,97.05,98.31,510000,98.31 1935-03-18,97.62,97.62,95.95,97.01,590000,97.01 1935-03-15,96.71,98.51,96.47,98.21,770000,98.21 1935-03-14,98.02,98.50,96.49,96.71,810000,96.71 1935-03-13,97.66,98.52,96.81,98.02,1080000,98.02 1935-03-12,99.39,99.62,97.60,97.66,1050000,97.66 1935-03-11,101.18,101.60,99.24,99.39,800000,99.39 1935-03-08,101.27,102.37,101.27,101.58,440000,101.58 1935-03-07,100.22,101.37,100.13,101.17,540000,101.17 1935-03-06,100.09,101.94,98.77,100.22,1290000,100.22 1935-03-05,102.29,102.29,100.00,100.09,900000,100.09 1935-03-04,103.16,103.16,102.24,102.58,420000,102.58 1935-03-01,102.38,103.59,102.03,103.27,640000,103.27 1935-02-28,102.55,103.27,102.14,102.38,570000,102.38 1935-02-27,102.24,102.86,101.27,102.55,930000,102.55 1935-02-26,103.14,103.60,102.08,102.24,950000,102.24 1935-02-25,103.25,103.57,102.33,103.14,740000,103.14 1935-02-21,104.97,105.47,104.28,104.86,700000,104.86 1935-02-20,105.89,106.25,104.60,104.97,970000,104.97 1935-02-19,107.17,107.36,105.59,105.89,1100000,105.89 1935-02-18,104.54,108.29,103.64,107.17,1910000,107.17 1935-02-15,103.43,105.07,103.43,104.67,730000,104.67 1935-02-14,102.69,103.31,102.56,103.05,410000,103.05 1935-02-13,102.42,103.06,102.18,102.69,390000,102.69 1935-02-11,102.66,102.67,101.65,102.42,360000,102.42 1935-02-08,101.16,102.58,101.16,102.35,590000,102.35 1935-02-07,100.23,101.34,100.02,101.00,520000,101.00 1935-02-06,100.74,100.90,99.95,100.23,560000,100.23 1935-02-05,101.40,101.40,100.46,100.74,560000,100.74 1935-02-04,102.17,102.17,101.35,101.56,350000,101.56 1935-02-01,101.69,102.05,101.10,101.53,490000,101.53 1935-01-31,101.05,102.12,101.05,101.69,530000,101.69 1935-01-30,100.69,101.39,100.38,101.00,430000,101.00 1935-01-29,101.51,101.58,100.24,100.69,580000,100.69 1935-01-28,102.31,102.31,101.03,101.51,690000,101.51 1935-01-25,102.44,103.12,102.32,102.86,520000,102.86 1935-01-24,102.85,102.85,102.01,102.44,440000,102.44 1935-01-23,102.77,103.43,102.43,102.88,620000,102.88 1935-01-22,103.35,103.64,102.60,102.77,590000,102.77 1935-01-21,102.96,103.93,102.57,103.35,690000,103.35 1935-01-18,101.92,102.63,101.58,102.36,690000,102.36 1935-01-17,101.54,102.30,101.04,101.92,740000,101.92 1935-01-16,100.49,101.95,100.40,101.54,670000,101.54 1935-01-15,102.76,103.20,99.54,100.49,1370000,100.49 1935-01-14,102.39,103.37,102.39,102.76,550000,102.76 1935-01-11,104.87,105.49,102.50,103.35,1380000,103.35 1935-01-10,105.05,105.65,104.41,104.87,780000,104.87 1935-01-09,105.03,105.68,104.28,105.05,900000,105.05 1935-01-08,105.88,106.22,104.56,105.03,1190000,105.03 1935-01-07,105.56,106.71,105.24,105.88,1290000,105.88 1935-01-04,105.14,105.43,104.18,104.69,970000,104.69 1935-01-03,104.51,105.63,104.12,105.14,1070000,105.14 1935-01-02,104.04,104.93,103.05,104.51,880000,104.51 1934-12-31,103.90,104.46,103.36,104.04,1020000,104.04 1934-12-28,100.57,103.29,100.57,103.15,1280000,103.15 1934-12-27,100.35,101.41,99.58,100.26,1630000,100.26 1934-12-26,100.69,101.32,100.05,100.35,1050000,100.35 1934-12-24,99.73,101.00,99.52,100.69,810000,100.69 1934-12-21,99.59,100.38,98.99,99.90,920000,99.90 1934-12-20,99.78,100.01,98.93,99.59,880000,99.59 1934-12-19,100.88,100.88,99.40,99.78,980000,99.78 1934-12-18,100.92,101.37,100.41,101.00,820000,101.00 1934-12-17,100.84,101.66,100.45,100.92,900000,100.92 1934-12-14,100.68,101.30,100.34,100.69,940000,100.69 1934-12-13,100.97,102.02,100.39,100.68,1000000,100.68 1934-12-12,100.81,101.62,100.52,100.97,790000,100.97 1934-12-11,102.76,103.58,100.50,100.81,1280000,100.81 1934-12-10,102.83,103.52,102.18,102.76,850000,102.76 1934-12-07,103.47,103.90,102.75,103.16,1020000,103.16 1934-12-06,103.42,104.23,102.91,103.47,1420000,103.47 1934-12-05,102.69,104.08,102.69,103.42,1640000,103.42 1934-12-04,101.92,102.93,101.59,102.57,950000,102.57 1934-12-03,102.77,102.77,101.74,101.92,750000,101.92 1934-11-30,102.75,103.36,101.94,102.94,800000,102.94 1934-11-28,102.38,103.47,101.90,102.75,1160000,102.75 1934-11-27,103.08,103.12,101.80,102.38,1010000,102.38 1934-11-26,102.40,103.51,102.16,103.08,1410000,103.08 1934-11-23,100.17,101.99,100.17,101.62,1130000,101.62 1934-11-22,99.47,100.48,98.93,99.93,770000,99.93 1934-11-21,99.90,100.43,98.95,99.47,810000,99.47 1934-11-20,99.89,100.14,98.96,99.90,870000,99.90 1934-11-19,99.45,100.68,99.24,99.89,980000,99.89 1934-11-16,99.72,100.08,98.70,99.39,1030000,99.39 1934-11-15,99.42,100.80,99.34,99.72,1540000,99.72 1934-11-14,99.19,99.81,98.49,99.42,960000,99.42 1934-11-13,99.21,99.98,98.49,99.19,1130000,99.19 1934-11-09,97.26,99.34,96.97,99.02,1230000,99.02 1934-11-08,97.55,98.31,96.83,97.26,840000,97.26 1934-11-07,96.06,97.81,95.31,97.55,1110000,97.55 1934-11-05,94.95,96.17,94.83,96.06,760000,96.06 1934-11-02,93.83,94.94,93.83,94.42,650000,94.42 1934-11-01,93.36,93.98,92.79,93.46,540000,93.46 1934-10-31,93.05,94.04,92.90,93.36,420000,93.36 1934-10-30,92.59,93.46,92.59,93.05,430000,93.05 1934-10-29,92.86,93.60,92.37,92.53,430000,92.53 1934-10-26,93.86,93.86,92.20,93.01,870000,93.01 1934-10-25,95.60,96.15,94.05,94.19,1030000,94.19 1934-10-24,94.65,95.71,93.96,95.60,770000,95.60 1934-10-23,94.78,95.17,94.19,94.65,540000,94.65 1934-10-22,95.02,95.79,94.65,94.78,570000,94.78 1934-10-19,95.33,95.33,94.39,94.90,530000,94.90 1934-10-18,95.29,96.03,94.63,95.34,660000,95.34 1934-10-17,95.25,96.36,94.83,95.29,660000,95.29 1934-10-16,94.16,95.81,94.15,95.25,680000,95.25 1934-10-15,94.90,95.37,94.01,94.16,510000,94.16 1934-10-11,93.94,96.04,93.94,95.50,1400000,95.50 1934-10-10,91.78,94.01,91.78,93.75,990000,93.75 1934-10-09,92.50,93.03,91.31,91.71,770000,91.71 1934-10-08,92.85,93.21,92.16,92.50,450000,92.50 1934-10-05,91.74,93.58,91.74,92.96,870000,92.96 1934-10-04,91.04,91.44,89.84,91.01,610000,91.01 1934-10-03,90.88,91.82,90.66,91.04,410000,91.04 1934-10-02,90.41,91.44,90.30,90.88,370000,90.88 1934-10-01,92.17,92.17,90.14,90.41,620000,90.41 1934-09-28,93.40,93.52,92.24,92.49,510000,92.49 1934-09-27,92.44,94.02,92.23,93.40,800000,93.40 1934-09-26,92.72,93.12,92.05,92.44,800000,92.44 1934-09-25,90.45,92.79,89.91,92.72,840000,92.72 1934-09-24,91.08,91.50,89.97,90.45,510000,90.45 1934-09-21,89.65,91.34,89.65,91.10,710000,91.10 1934-09-20,89.34,89.93,88.76,89.35,490000,89.35 1934-09-19,87.91,89.58,87.91,89.34,560000,89.34 1934-09-18,86.69,87.84,86.57,87.37,530000,87.37 1934-09-17,87.34,87.41,85.72,86.69,650000,86.69 1934-09-14,89.25,89.25,86.64,86.83,840000,86.83 1934-09-13,89.62,90.45,89.12,89.44,420000,89.44 1934-09-12,89.25,90.14,89.14,89.62,400000,89.62 1934-09-11,89.27,89.75,88.42,89.25,630000,89.25 1934-09-10,90.83,91.32,89.04,89.27,700000,89.27 1934-09-07,91.82,92.52,90.50,90.99,690000,90.99 1934-09-06,93.65,94.05,91.67,91.82,600000,91.82 1934-09-05,92.64,93.96,92.64,93.65,480000,93.65 1934-09-04,92.59,92.59,91.74,92.30,310000,92.30 1934-08-31,92.76,93.05,92.05,92.86,400000,92.86 1934-08-30,93.59,93.59,92.01,92.76,650000,92.76 1934-08-29,94.19,95.59,93.64,93.69,760000,93.69 1934-08-28,94.46,94.48,93.55,94.19,400000,94.19 1934-08-27,95.31,95.31,94.25,94.46,530000,94.46 1934-08-24,94.05,95.55,93.85,95.48,750000,95.48 1934-08-23,94.32,94.97,93.64,94.05,750000,94.05 1934-08-22,92.68,94.95,92.68,94.32,1300000,94.32 1934-08-21,90.87,92.70,90.87,92.57,580000,92.57 1934-08-20,90.86,91.02,90.08,90.44,280000,90.44 1934-08-17,91.69,92.25,91.00,91.12,480000,91.12 1934-08-16,91.08,92.51,91.08,91.69,610000,91.69 1934-08-15,91.12,91.70,90.66,91.00,580000,91.00 1934-08-14,91.80,91.98,90.82,91.12,530000,91.12 1934-08-13,89.79,92.56,89.69,91.80,810000,91.80 1934-08-10,91.34,91.47,89.39,89.66,770000,89.66 1934-08-09,88.97,91.80,87.19,91.34,1420000,91.34 1934-08-08,87.47,89.28,87.16,88.97,690000,88.97 1934-08-07,88.11,89.10,86.70,87.47,610000,87.47 1934-08-06,88.43,88.51,86.32,88.11,780000,88.11 1934-08-03,90.87,91.08,89.82,90.14,470000,90.14 1934-08-02,90.57,91.12,89.75,90.87,560000,90.87 1934-08-01,88.55,90.96,88.55,90.57,780000,90.57 1934-07-31,88.17,88.51,87.17,88.05,360000,88.05 1934-07-30,88.72,88.96,86.90,88.17,810000,88.17 1934-07-27,86.64,88.86,86.64,87.84,2210000,87.84 1934-07-26,91.32,91.32,84.58,85.51,3340000,85.51 1934-07-25,91.01,92.08,90.09,91.57,1350000,91.57 1934-07-24,91.98,92.91,90.63,91.01,1600000,91.01 1934-07-23,94.62,95.17,91.96,91.98,1880000,91.98 1934-07-20,97.20,97.20,94.43,94.74,1240000,94.74 1934-07-19,98.26,98.70,96.92,97.24,610000,97.24 1934-07-18,97.18,98.45,97.18,98.26,490000,98.26 1934-07-17,97.04,97.75,96.50,96.79,620000,96.79 1934-07-16,98.51,98.51,96.91,97.04,590000,97.04 1934-07-13,98.32,99.08,97.66,98.82,530000,98.82 1934-07-12,98.67,99.01,97.93,98.32,470000,98.32 1934-07-11,98.12,99.35,98.12,98.67,650000,98.67 1934-07-10,97.04,98.63,97.02,98.07,650000,98.07 1934-07-09,97.15,97.54,96.54,97.04,320000,97.04 1934-07-06,96.56,97.80,96.56,97.32,460000,97.32 1934-07-05,95.19,96.92,95.19,96.44,440000,96.44 1934-07-03,94.80,95.13,94.25,94.77,400000,94.77 1934-07-02,95.71,95.71,94.59,94.80,410000,94.80 1934-06-29,96.84,96.84,95.59,95.75,440000,95.75 1934-06-28,96.94,97.88,96.18,97.14,640000,97.14 1934-06-27,97.33,98.31,96.74,96.94,630000,96.94 1934-06-26,95.79,97.53,95.46,97.33,620000,97.33 1934-06-25,96.59,97.03,95.49,95.79,490000,95.79 1934-06-22,97.50,97.87,95.48,95.93,930000,95.93 1934-06-21,98.25,98.62,97.39,97.50,530000,97.50 1934-06-20,99.02,99.10,97.94,98.25,550000,98.25 1934-06-19,100.42,101.11,98.75,99.02,850000,99.02 1934-06-18,99.85,100.70,99.32,100.42,610000,100.42 1934-06-15,97.23,99.29,97.23,98.70,730000,98.70 1934-06-14,98.49,98.49,96.92,97.15,630000,97.15 1934-06-13,98.78,99.92,98.32,98.75,880000,98.75 1934-06-12,97.82,99.60,97.77,98.78,950000,98.78 1934-06-11,98.70,98.70,97.40,97.82,750000,97.82 1934-06-08,94.82,98.55,94.82,98.44,1610000,98.44 1934-06-07,94.77,95.24,94.04,94.72,470000,94.72 1934-06-06,94.66,95.68,94.27,94.77,670000,94.77 1934-06-05,92.74,94.94,92.74,94.66,740000,94.66 1934-06-04,91.84,93.04,91.84,92.73,360000,92.73 1934-06-01,93.71,93.71,91.70,91.79,630000,91.79 1934-05-31,94.78,94.78,93.71,94.00,440000,94.00 1934-05-29,95.56,95.85,94.85,95.32,380000,95.32 1934-05-28,95.32,96.33,95.32,95.56,620000,95.56 1934-05-25,93.37,94.96,93.25,94.50,540000,94.50 1934-05-24,92.86,94.08,92.57,93.37,500000,93.37 1934-05-23,93.61,93.74,92.23,92.86,660000,92.86 1934-05-22,95.76,96.29,93.26,93.61,830000,93.61 1934-05-21,95.13,95.96,94.81,95.76,380000,95.76 1934-05-18,95.98,96.57,94.62,95.17,910000,95.17 1934-05-17,92.73,96.17,92.63,95.98,1290000,95.98 1934-05-16,92.84,93.83,91.95,92.73,720000,92.73 1934-05-15,91.81,93.65,91.47,92.84,890000,92.84 1934-05-14,92.22,92.95,89.10,91.81,1680000,91.81 1934-05-11,93.91,95.19,92.99,93.18,1000000,93.18 1934-05-10,95.59,95.59,92.16,93.91,2130000,93.91 1934-05-09,97.16,97.81,95.38,95.71,1030000,95.71 1934-05-08,95.51,97.73,94.47,97.16,1860000,97.16 1934-05-07,98.20,98.71,94.30,95.51,2360000,95.51 1934-05-04,99.02,100.66,99.02,99.29,840000,99.29 1934-05-03,98.82,100.06,98.36,98.94,1110000,98.94 1934-05-02,100.62,101.36,98.47,98.82,1340000,98.82 1934-05-01,100.49,101.20,99.55,100.62,1340000,100.62 1934-04-30,102.72,102.72,100.31,100.49,1490000,100.49 1934-04-27,103.56,104.30,103.12,103.65,840000,103.65 1934-04-26,105.05,105.51,102.95,103.56,1640000,103.56 1934-04-25,105.31,105.55,104.53,105.05,960000,105.05 1934-04-24,105.92,106.19,104.70,105.31,1270000,105.31 1934-04-23,106.34,106.73,105.31,105.92,1110000,105.92 1934-04-20,105.52,107.00,105.08,106.55,1890000,106.55 1934-04-19,105.45,106.18,104.34,105.52,1330000,105.52 1934-04-18,104.58,106.26,104.58,105.45,1540000,105.45 1934-04-17,103.57,104.82,103.34,104.46,940000,104.46 1934-04-16,105.04,105.31,102.88,103.57,1290000,103.57 1934-04-13,104.80,105.72,104.42,104.98,1180000,104.98 1934-04-12,105.16,105.82,104.30,104.80,1330000,104.80 1934-04-11,105.05,106.23,104.64,105.16,1550000,105.16 1934-04-10,103.95,105.51,103.95,105.05,1410000,105.05 1934-04-09,103.60,104.07,102.92,103.54,850000,103.54 1934-04-06,103.37,104.30,102.86,103.97,1010000,103.97 1934-04-05,103.19,104.09,102.47,103.37,1420000,103.37 1934-04-04,102.74,103.91,102.21,103.19,1560000,103.19 1934-04-03,101.96,103.00,101.18,102.74,1340000,102.74 1934-04-02,101.85,102.82,101.41,101.96,1370000,101.96 1934-03-29,99.18,100.83,99.18,100.31,1020000,100.31 1934-03-28,98.76,99.84,98.60,99.02,840000,99.02 1934-03-27,99.32,99.32,97.41,98.76,1590000,98.76 1934-03-26,100.92,102.67,100.70,100.95,1280000,100.95 1934-03-23,100.53,100.53,99.47,99.80,760000,99.80 1934-03-22,99.33,100.94,98.87,100.54,1030000,100.54 1934-03-21,100.73,100.73,98.45,99.33,1070000,99.33 1934-03-20,99.68,101.82,98.75,101.01,1540000,101.01 1934-03-19,101.04,101.04,98.99,99.68,1510000,99.68 1934-03-16,102.21,103.26,101.46,102.72,1170000,102.72 1934-03-15,103.54,103.73,101.43,102.21,1340000,102.21 1934-03-14,104.00,104.69,103.31,103.54,1360000,103.54 1934-03-13,104.23,104.89,103.54,104.00,1280000,104.00 1934-03-12,102.77,104.61,102.66,104.23,1260000,104.23 1934-03-09,103.21,103.91,102.13,102.44,1370000,102.44 1934-03-08,101.59,103.44,100.78,103.21,1700000,103.21 1934-03-07,103.84,104.59,101.12,101.59,1730000,101.59 1934-03-06,104.94,104.94,103.40,103.84,810000,103.84 1934-03-05,105.56,105.89,103.79,105.02,950000,105.02 1934-03-02,103.74,105.99,103.74,105.79,1480000,105.79 1934-03-01,103.46,103.81,101.93,103.18,1240000,103.18 1934-02-28,103.67,105.37,103.09,103.46,1320000,103.46 1934-02-27,103.12,104.55,102.63,103.67,1270000,103.67 1934-02-26,104.49,104.49,102.21,103.12,2190000,103.12 1934-02-23,108.50,109.36,105.72,106.14,2290000,106.14 1934-02-21,108.14,109.39,107.55,108.50,1900000,108.50 1934-02-20,107.53,108.68,107.24,108.14,1220000,108.14 1934-02-19,109.07,109.57,107.04,107.53,2350000,107.53 1934-02-16,108.30,109.96,107.60,108.61,2770000,108.61 1934-02-15,106.92,109.04,106.92,108.30,2980000,108.30 1934-02-14,106.10,107.21,104.64,106.78,1940000,106.78 1934-02-13,105.47,107.35,104.78,106.10,2060000,106.10 1934-02-09,108.45,108.76,104.29,106.09,3340000,106.09 1934-02-08,107.95,109.09,106.05,108.45,3200000,108.45 1934-02-07,110.20,110.20,106.53,107.95,4500000,107.95 1934-02-06,110.74,111.25,108.88,110.24,4330000,110.24 1934-02-05,109.50,111.93,109.50,110.74,4940000,110.74 1934-02-02,108.95,109.69,107.67,108.31,2870000,108.31 1934-02-01,107.94,110.35,107.94,108.95,4710000,108.95 1934-01-31,108.99,109.17,106.81,107.22,3110000,107.22 1934-01-30,107.91,110.06,107.91,108.99,4240000,108.99 1934-01-29,106.11,108.42,106.11,107.90,2780000,107.90 1934-01-26,106.85,107.93,105.85,106.38,2510000,106.38 1934-01-25,107.02,107.52,105.44,106.85,2270000,106.85 1934-01-24,106.62,108.20,106.26,107.02,3370000,107.02 1934-01-23,105.09,107.00,104.47,106.62,2380000,106.62 1934-01-22,105.52,106.92,104.34,105.09,2660000,105.09 1934-01-19,103.46,106.19,103.46,105.60,3540000,105.60 1934-01-18,103.50,104.48,102.50,103.30,2130000,103.30 1934-01-17,103.40,104.87,102.38,103.50,2850000,103.50 1934-01-16,103.19,104.60,102.66,103.40,3440000,103.40 1934-01-15,99.50,103.48,99.50,103.19,3740000,103.19 1934-01-12,99.38,99.98,98.24,98.73,1600000,98.73 1934-01-11,99.77,100.49,98.77,99.38,1700000,99.38 1934-01-10,97.78,99.99,97.78,99.77,1420000,99.77 1934-01-09,97.09,98.53,97.09,97.57,870000,97.57 1934-01-08,96.94,97.93,96.26,96.73,720000,96.73 1934-01-05,98.78,99.39,96.97,97.23,1060000,97.23 1934-01-04,99.09,99.13,96.48,98.78,1190000,98.78 1934-01-03,100.36,100.83,97.75,99.09,1380000,99.09 1934-01-02,99.90,101.94,99.61,100.36,1270000,100.36 1933-12-29,99.29,99.73,97.85,98.67,1130000,98.67 1933-12-28,97.16,100.04,97.16,99.29,1480000,99.29 1933-12-27,96.30,98.21,95.16,96.80,3080000,96.80 1933-12-26,97.76,97.76,95.56,96.30,1300000,96.30 1933-12-22,95.93,99.90,95.93,98.87,2420000,98.87 1933-12-21,95.28,96.16,94.78,95.50,1020000,95.50 1933-12-20,97.25,97.96,93.70,95.28,2160000,95.28 1933-12-19,97.20,97.99,96.30,97.25,1030000,97.25 1933-12-18,98.06,98.42,95.77,97.20,1340000,97.20 1933-12-15,101.44,101.67,99.44,99.95,1170000,99.95 1933-12-14,100.69,102.92,100.58,101.44,1560000,101.44 1933-12-13,101.64,101.98,99.94,100.69,1330000,100.69 1933-12-12,101.94,103.03,101.06,101.64,1650000,101.64 1933-12-11,102.92,103.97,101.63,101.94,2450000,101.94 1933-12-08,102.04,102.47,100.30,101.04,1330000,101.04 1933-12-07,101.28,103.01,101.07,102.04,1680000,102.04 1933-12-06,101.99,102.72,100.70,101.28,1440000,101.28 1933-12-05,99.20,102.44,99.20,101.99,2020000,101.99 1933-12-04,99.07,99.41,98.19,98.89,670000,98.89 1933-12-01,98.41,100.08,98.41,98.89,810000,98.89 1933-11-29,96.57,98.51,96.57,98.14,750000,98.14 1933-11-28,95.77,97.79,95.59,96.23,1010000,96.23 1933-11-27,99.28,99.45,95.32,95.77,1560000,95.77 1933-11-24,98.59,100.81,98.09,99.52,1420000,99.52 1933-11-23,100.07,100.29,97.20,98.59,1370000,98.59 1933-11-22,100.29,101.61,98.80,100.07,1570000,100.07 1933-11-21,101.28,101.94,99.67,100.29,1800000,100.29 1933-11-20,98.67,101.83,98.52,101.28,1900000,101.28 1933-11-17,99.01,100.59,97.54,98.09,2320000,98.09 1933-11-16,94.36,99.34,94.12,99.01,2580000,99.01 1933-11-15,95.50,96.35,93.27,94.36,1350000,94.36 1933-11-14,95.98,98.26,94.73,95.50,2170000,95.50 1933-11-13,96.10,97.15,95.25,95.98,1090000,95.98 1933-11-10,96.40,97.21,94.60,95.06,1370000,95.06 1933-11-09,95.54,98.34,95.46,96.40,2900000,96.40 1933-11-08,92.50,96.05,91.82,95.54,1800000,95.54 1933-11-06,93.09,93.14,91.67,92.50,690000,92.50 1933-11-03,90.54,93.92,89.96,93.60,1500000,93.60 1933-11-02,89.62,91.38,89.17,90.54,1120000,90.54 1933-11-01,88.16,89.92,86.83,89.62,1140000,89.62 1933-10-31,88.43,89.44,86.50,88.16,1130000,88.16 1933-10-30,92.01,93.99,88.05,88.43,1470000,88.43 1933-10-27,92.02,94.11,90.83,93.22,1110000,93.22 1933-10-26,93.54,93.95,91.29,92.02,1220000,92.02 1933-10-25,91.35,95.23,91.23,93.54,2880000,93.54 1933-10-24,88.13,91.67,87.10,91.35,2110000,91.35 1933-10-23,86.25,90.54,86.25,88.13,2130000,88.13 1933-10-20,84.38,88.41,83.57,86.63,2700000,86.63 1933-10-19,88.62,88.62,84.26,84.38,2900000,84.38 1933-10-18,92.67,92.72,88.47,88.95,1730000,88.95 1933-10-17,90.49,93.47,88.69,92.67,2480000,92.67 1933-10-16,94.93,94.93,89.35,90.49,2670000,90.49 1933-10-13,98.77,98.77,95.14,95.36,1270000,95.36 1933-10-11,98.77,99.94,97.54,98.85,1030000,98.85 1933-10-10,99.72,100.20,98.05,98.77,1140000,98.77 1933-10-09,98.20,100.58,98.14,99.72,1250000,99.72 1933-10-06,98.05,99.18,95.92,97.54,1460000,97.54 1933-10-05,98.60,99.34,96.95,98.05,1660000,98.05 1933-10-04,94.89,99.21,94.89,98.60,2140000,98.60 1933-10-03,92.99,94.43,91.93,93.55,930000,93.55 1933-10-02,94.82,95.32,92.69,92.99,960000,92.99 1933-09-29,94.66,97.21,93.94,94.24,1640000,94.24 1933-09-28,93.18,95.30,92.89,94.66,1440000,94.66 1933-09-27,96.91,96.91,92.44,93.18,2320000,93.18 1933-09-26,98.03,100.23,96.84,97.41,1430000,97.41 1933-09-25,99.34,99.34,96.46,98.03,1310000,98.03 1933-09-22,97.56,99.93,95.73,99.06,3320000,99.06 1933-09-21,102.29,102.29,97.15,97.56,3650000,97.56 1933-09-20,105.71,105.71,102.26,103.99,2420000,103.99 1933-09-19,105.30,106.25,102.44,105.74,2820000,105.74 1933-09-18,105.32,107.68,104.36,105.30,2720000,105.30 1933-09-15,104.66,106.14,102.14,102.63,2450000,102.63 1933-09-14,103.81,106.53,103.81,104.66,2900000,104.66 1933-09-13,102.90,104.13,102.90,103.65,740000,103.65 1933-09-12,103.59,104.90,102.33,102.84,2240000,102.84 1933-09-11,99.42,103.74,99.26,103.59,1920000,103.59 1933-09-08,99.20,100.41,98.16,99.58,1300000,99.58 1933-09-07,100.33,101.12,98.82,99.20,1070000,99.20 1933-09-06,100.22,100.90,97.74,100.33,1890000,100.33 1933-09-05,103.34,103.34,99.80,100.22,1250000,100.22 1933-09-01,102.41,103.89,101.86,103.66,1220000,103.66 1933-08-31,102.35,103.31,101.48,102.41,1140000,102.41 1933-08-30,103.59,104.13,100.38,102.35,2170000,102.35 1933-08-29,104.72,105.39,100.23,103.59,3120000,103.59 1933-08-28,105.07,105.53,103.02,104.72,2120000,104.72 1933-08-25,102.84,105.60,102.84,105.07,3330000,105.07 1933-08-24,100.38,102.77,99.52,101.41,1730000,101.41 1933-08-23,101.34,102.75,99.58,100.38,2580000,100.38 1933-08-22,100.17,101.71,99.02,101.34,1960000,101.34 1933-08-21,98.57,100.86,98.57,100.17,1560000,100.17 1933-08-18,99.30,100.77,96.81,98.32,2090000,98.32 1933-08-17,94.44,99.49,93.59,99.30,2470000,99.30 1933-08-16,96.28,96.28,92.95,94.44,1800000,94.44 1933-08-15,96.53,97.53,95.72,96.63,910000,96.63 1933-08-14,97.40,97.40,94.63,96.53,1220000,96.53 1933-08-11,97.58,98.68,96.28,97.47,1340000,97.47 1933-08-10,99.06,100.14,96.48,97.58,2820000,97.58 1933-08-09,96.17,99.39,96.17,99.06,2560000,99.06 1933-08-08,93.16,96.10,93.16,95.84,1230000,95.84 1933-08-07,92.62,93.32,91.63,92.55,770000,92.55 1933-08-04,93.45,93.45,92.03,92.62,500000,92.62 1933-08-03,94.84,95.86,93.21,94.10,1510000,94.10 1933-08-02,92.70,95.27,91.97,94.84,1730000,94.84 1933-08-01,90.77,93.23,89.61,92.70,1780000,92.70 1933-07-31,94.18,94.18,87.75,90.77,3090000,90.77 1933-07-28,95.90,95.90,93.75,94.54,1390000,94.54 1933-07-27,95.05,97.28,94.12,96.03,2460000,96.03 1933-07-26,92.83,95.50,92.23,95.05,2040000,95.05 1933-07-25,94.28,96.27,91.77,92.83,3540000,92.83 1933-07-24,90.63,94.75,90.63,94.28,3420000,94.28 1933-07-21,96.26,98.69,84.45,88.71,9570000,88.71 1933-07-20,103.58,105.65,94.76,96.26,8120000,96.26 1933-07-19,108.67,109.23,102.32,103.58,7450000,103.58 1933-07-18,108.27,110.53,106.98,108.67,6590000,108.67 1933-07-17,106.22,110.30,106.22,108.27,6380000,108.27 1933-07-14,105.51,107.63,103.94,105.04,5230000,105.04 1933-07-13,104.55,107.77,104.54,105.51,7450000,105.51 1933-07-12,103.08,105.46,101.87,104.55,5190000,104.55 1933-07-11,104.08,105.10,101.97,103.08,5240000,103.08 1933-07-10,105.15,105.87,103.12,104.08,4840000,104.08 1933-07-07,104.98,107.51,103.23,105.35,6970000,105.35 1933-07-06,102.73,105.56,101.21,104.98,6540000,104.98 1933-07-05,103.77,104.70,101.02,102.73,5800000,102.73 1933-07-03,101.64,104.99,101.64,103.77,6720000,103.77 1933-06-30,96.99,98.69,95.50,98.14,3630000,98.14 1933-06-29,97.74,99.23,95.90,96.99,4590000,96.99 1933-06-28,98.74,100.48,96.69,97.74,5510000,97.74 1933-06-27,98.49,100.27,98.01,98.74,5640000,98.74 1933-06-26,95.93,99.10,95.93,98.49,4530000,98.49 1933-06-23,92.93,95.59,91.93,95.53,3310000,95.53 1933-06-22,95.91,97.79,91.69,92.93,4370000,92.93 1933-06-21,95.23,97.34,94.41,95.91,3890000,95.91 1933-06-20,95.99,98.34,94.57,95.23,5540000,95.23 1933-06-19,92.86,96.36,92.86,95.99,5480000,95.99 1933-06-16,88.87,91.11,86.48,89.22,5710000,89.22 1933-06-15,93.94,93.94,88.17,88.87,4890000,88.87 1933-06-14,94.79,96.29,91.41,94.06,5550000,94.06 1933-06-13,96.75,97.92,94.21,94.79,6300000,94.79 1933-06-12,94.42,97.10,94.16,96.75,5810000,96.75 1933-06-09,93.52,95.03,91.64,94.29,5310000,94.29 1933-06-08,92.98,95.15,91.59,93.52,6360000,93.52 1933-06-07,91.90,94.38,91.04,92.98,6640000,92.98 1933-06-06,91.89,93.83,90.66,91.90,6220000,91.90 1933-06-05,90.02,92.37,89.14,91.89,5010000,91.89 1933-06-02,89.10,92.66,88.97,92.21,6880000,92.21 1933-06-01,88.11,90.57,87.37,89.10,4750000,89.10 1933-05-31,90.02,91.05,87.72,88.11,6080000,88.11 1933-05-29,89.61,91.33,87.87,90.02,6950000,90.02 1933-05-26,83.73,86.98,83.47,86.42,4350000,86.42 1933-05-25,84.29,85.48,82.70,83.73,4010000,83.73 1933-05-24,83.53,85.47,83.53,84.29,4710000,84.29 1933-05-23,80.56,83.49,80.56,83.06,3140000,83.06 1933-05-22,80.21,81.08,78.61,79.94,2220000,79.94 1933-05-19,82.57,83.20,80.65,81.75,3280000,81.75 1933-05-18,82.64,84.13,80.70,82.57,4110000,82.57 1933-05-17,81.33,84.18,81.33,82.64,4790000,82.64 1933-05-16,79.70,82.25,79.54,81.29,3290000,81.29 1933-05-15,80.85,81.23,79.06,79.70,3150000,79.70 1933-05-12,82.48,82.75,80.13,82.14,4560000,82.14 1933-05-11,81.18,83.61,81.18,82.48,6160000,82.48 1933-05-10,77.95,81.01,77.95,80.78,3820000,80.78 1933-05-09,76.63,78.03,75.61,77.23,2230000,77.23 1933-05-08,77.61,79.67,76.01,76.63,3200000,76.63 1933-05-05,79.16,81.27,78.81,79.78,5000000,79.78 1933-05-04,77.37,79.80,76.19,79.16,4590000,79.16 1933-05-03,77.29,78.92,75.69,77.37,4640000,77.37 1933-05-02,77.79,78.37,75.66,77.29,3900000,77.29 1933-05-01,77.66,79.98,76.91,77.79,6050000,77.79 1933-04-28,71.71,73.34,69.78,73.10,2160000,73.10 1933-04-27,72.64,73.06,70.72,71.71,1880000,71.71 1933-04-26,72.45,73.58,70.86,72.64,2920000,72.64 1933-04-25,73.69,73.70,70.77,72.45,3500000,72.45 1933-04-24,72.32,74.84,72.32,73.69,4810000,73.69 1933-04-21,72.27,72.80,68.64,69.78,5220000,69.78 1933-04-20,69.78,75.20,69.78,72.27,7130000,72.27 1933-04-19,63.56,68.70,63.56,68.31,5090000,68.31 1933-04-18,61.59,63.31,60.74,62.65,1440000,62.65 1933-04-17,62.75,62.75,60.93,61.59,1010000,61.59 1933-04-13,60.35,63.31,60.35,62.69,1660000,62.69 1933-04-12,61.08,61.08,59.68,60.26,750000,60.26 1933-04-11,62.11,62.65,60.71,61.15,1440000,61.15 1933-04-10,59.98,62.23,59.98,62.11,1760000,62.11 1933-04-07,58.80,60.06,58.18,58.78,950000,58.78 1933-04-06,57.73,59.56,57.73,58.80,1230000,58.80 1933-04-05,56.53,58.69,56.53,57.50,1150000,57.50 1933-04-04,55.69,56.61,55.00,56.09,720000,56.09 1933-04-03,55.66,56.62,55.26,55.69,600000,55.69 1933-03-31,56.49,57.05,54.94,55.40,880000,55.40 1933-03-30,56.81,57.17,56.10,56.49,620000,56.49 1933-03-29,57.90,57.90,56.57,56.81,640000,56.81 1933-03-28,56.53,58.02,56.12,57.92,600000,57.92 1933-03-27,57.62,57.62,56.31,56.53,500000,56.53 1933-03-24,58.06,58.41,56.90,57.93,640000,57.93 1933-03-23,57.69,59.91,57.69,58.06,980000,58.06 1933-03-22,57.58,58.48,56.35,56.86,990000,56.86 1933-03-21,59.85,59.85,57.23,57.58,1210000,57.58 1933-03-20,60.56,61.33,59.72,59.90,780000,59.90 1933-03-17,62.54,62.54,60.32,60.73,1730000,60.73 1933-03-16,62.10,64.56,62.05,62.95,3300000,62.95 1933-03-15,57.11,62.55,57.11,62.10,3070000,62.10 1933-03-03,52.54,55.44,51.54,53.84,1410000,53.84 1933-03-02,52.54,53.01,50.25,52.54,1000000,52.54 1933-03-01,51.39,52.97,50.81,52.54,790000,52.54 1933-02-28,50.16,52.12,49.83,51.39,910000,51.39 1933-02-27,50.93,52.13,49.68,50.16,1250000,50.16 1933-02-24,51.94,54.13,51.13,53.84,1070000,53.84 1933-02-23,53.60,53.60,51.65,51.94,1330000,51.94 1933-02-21,54.26,54.84,53.52,53.99,690000,53.99 1933-02-20,55.61,55.61,53.82,54.26,860000,54.26 1933-02-17,55.49,56.78,55.15,56.37,650000,56.37 1933-02-16,56.63,56.63,54.69,55.49,1080000,55.49 1933-02-15,56.57,57.59,56.08,56.71,750000,56.71 1933-02-14,58.19,58.19,56.04,56.57,1540000,56.57 1933-02-10,60.06,60.06,58.81,59.11,720000,59.11 1933-02-09,59.17,60.85,59.17,60.09,1080000,60.09 1933-02-08,58.38,59.21,57.85,58.87,720000,58.87 1933-02-07,58.07,58.67,57.50,58.38,590000,58.38 1933-02-06,57.55,58.27,56.65,58.07,670000,58.07 1933-02-03,58.03,58.46,57.11,58.11,910000,58.11 1933-02-02,58.90,58.90,57.45,58.03,1250000,58.03 1933-02-01,60.89,60.89,58.63,59.08,1190000,59.08 1933-01-31,60.77,61.34,60.48,60.90,660000,60.90 1933-01-30,60.71,61.05,60.09,60.77,480000,60.77 1933-01-27,61.73,61.98,60.03,61.43,970000,61.43 1933-01-26,62.33,62.79,61.61,61.73,810000,61.73 1933-01-25,61.30,62.66,60.84,62.33,750000,62.33 1933-01-24,61.46,61.99,60.67,61.30,490000,61.30 1933-01-23,61.79,62.10,60.55,61.46,660000,61.46 1933-01-20,61.02,62.68,60.90,61.63,710000,61.63 1933-01-19,60.37,61.99,60.37,61.02,620000,61.02 1933-01-18,61.75,62.03,60.07,60.36,690000,60.36 1933-01-17,61.62,62.09,60.93,61.75,660000,61.75 1933-01-16,63.09,63.87,61.38,61.62,870000,61.62 1933-01-13,63.09,63.94,62.12,63.18,830000,63.18 1933-01-12,63.81,64.80,62.84,63.09,920000,63.09 1933-01-11,64.35,65.28,63.62,63.81,1620000,63.81 1933-01-10,62.31,64.57,61.83,64.35,1150000,64.35 1933-01-09,62.96,63.64,61.90,62.31,930000,62.31 1933-01-06,62.25,63.85,62.07,62.96,1140000,62.96 1933-01-05,62.35,63.39,61.58,62.25,1140000,62.25 1933-01-04,59.54,62.62,59.54,62.35,1090000,62.35 1933-01-03,59.93,60.17,58.87,59.29,490000,59.29 1932-12-30,59.12,60.84,58.82,60.26,1050000,60.26 1932-12-29,57.85,59.38,57.39,59.12,1610000,59.12 1932-12-28,57.60,59.49,57.10,57.85,1600000,57.85 1932-12-27,57.98,58.57,56.95,57.60,800000,57.60 1932-12-23,56.55,57.30,56.07,56.80,930000,56.80 1932-12-22,58.97,59.29,56.24,56.55,1300000,56.55 1932-12-21,58.78,59.65,58.27,58.97,730000,58.97 1932-12-20,60.08,60.19,58.28,58.78,1000000,58.78 1932-12-19,60.11,61.51,59.82,60.08,920000,60.08 1932-12-16,61.16,61.40,59.75,60.52,920000,60.52 1932-12-15,61.93,62.89,60.98,61.16,1180000,61.16 1932-12-14,60.35,62.11,59.19,61.93,1020000,61.93 1932-12-13,61.38,61.38,59.98,60.35,730000,60.35 1932-12-12,61.25,62.50,60.68,61.48,920000,61.48 1932-12-09,60.05,61.90,59.28,61.58,1180000,61.58 1932-12-08,59.77,60.51,58.97,60.05,710000,60.05 1932-12-07,59.58,60.97,59.12,59.77,1190000,59.77 1932-12-06,56.95,59.96,56.95,59.58,1110000,59.58 1932-12-05,55.83,58.06,55.58,56.53,730000,56.53 1932-12-02,57.50,57.50,55.70,55.91,690000,55.91 1932-12-01,56.35,58.40,56.02,58.02,760000,58.02 1932-11-30,58.77,59.33,55.94,56.35,1090000,56.35 1932-11-29,59.17,59.74,58.31,58.77,530000,58.77 1932-11-28,58.89,59.71,58.10,59.17,540000,59.17 1932-11-25,59.47,59.54,57.47,58.78,1000000,58.78 1932-11-23,62.60,62.60,59.10,59.47,1200000,59.47 1932-11-22,63.65,64.40,62.86,63.16,540000,63.16 1932-11-21,64.14,64.68,63.23,63.65,610000,63.65 1932-11-18,62.99,64.73,62.66,62.96,730000,62.96 1932-11-17,63.24,63.83,62.18,62.99,710000,62.99 1932-11-16,65.26,65.26,62.56,63.24,950000,63.24 1932-11-15,65.57,66.17,63.54,65.26,1050000,65.26 1932-11-14,67.88,67.88,64.87,65.57,1310000,65.57 1932-11-11,65.54,68.27,65.41,68.03,2630000,68.03 1932-11-10,61.67,65.61,61.44,65.54,1570000,65.54 1932-11-09,64.58,65.42,61.02,61.67,1270000,61.67 1932-11-07,63.22,65.43,63.22,64.58,1610000,64.58 1932-11-04,58.99,62.07,58.99,61.53,970000,61.53 1932-11-03,58.53,59.06,57.21,58.28,1020000,58.28 1932-11-02,60.22,61.13,57.96,58.53,1100000,58.53 1932-11-01,61.57,61.57,59.86,60.22,520000,60.22 1932-10-31,62.09,62.19,61.06,61.90,390000,61.90 1932-10-28,61.86,63.51,61.81,63.09,690000,63.09 1932-10-27,61.36,62.83,60.77,61.86,720000,61.86 1932-10-26,60.32,61.73,59.03,61.36,860000,61.36 1932-10-25,61.03,61.69,59.70,60.32,600000,60.32 1932-10-24,60.85,61.58,60.07,61.03,550000,61.03 1932-10-21,63.64,63.64,60.71,61.01,1240000,61.01 1932-10-20,65.74,66.13,63.92,64.40,1060000,64.40 1932-10-19,63.49,66.06,63.35,65.74,1300000,65.74 1932-10-18,62.69,64.82,62.06,63.49,1020000,63.49 1932-10-17,64.07,64.07,62.21,62.69,770000,62.69 1932-10-14,60.27,65.45,60.27,63.84,2030000,63.84 1932-10-13,61.66,62.54,58.84,59.76,1230000,59.76 1932-10-11,59.07,62.52,59.07,61.66,1750000,61.66 1932-10-10,61.17,62.57,57.67,58.47,2280000,58.47 1932-10-07,66.28,66.89,62.16,62.67,2300000,62.67 1932-10-06,66.07,67.08,64.67,66.28,1940000,66.28 1932-10-05,70.92,70.92,65.90,66.07,2950000,66.07 1932-10-04,71.21,72.63,70.45,71.16,1240000,71.16 1932-10-03,72.09,72.32,69.88,71.21,1000000,71.21 1932-09-30,71.53,72.00,69.75,71.56,1160000,71.56 1932-09-29,73.52,74.36,71.24,71.53,1340000,71.53 1932-09-28,71.49,74.07,71.40,73.52,1380000,73.52 1932-09-27,71.06,73.42,69.98,71.49,1400000,71.49 1932-09-26,74.83,75.67,70.41,71.06,2080000,71.06 1932-09-23,72.71,75.16,72.42,73.92,2200000,73.92 1932-09-22,75.16,76.01,72.14,72.71,3690000,72.71 1932-09-21,69.46,75.53,69.46,75.16,4350000,75.16 1932-09-20,65.06,67.65,64.68,67.49,1250000,67.49 1932-09-19,66.44,67.40,64.82,65.06,1260000,65.06 1932-09-16,67.94,69.84,66.11,67.10,1910000,67.10 1932-09-15,65.88,68.58,64.27,67.94,3140000,67.94 1932-09-14,69.85,72.41,65.54,65.88,3250000,65.88 1932-09-13,71.54,71.54,66.38,69.85,5100000,69.85 1932-09-12,76.54,76.68,70.81,72.33,4050000,72.33 1932-09-09,77.49,79.91,75.69,76.19,4040000,76.19 1932-09-08,79.93,81.39,76.92,77.49,5370000,77.49 1932-09-07,77.28,80.28,76.78,79.93,4150000,79.93 1932-09-06,78.33,80.36,76.66,77.28,4360000,77.28 1932-09-02,74.00,77.12,74.00,76.77,3490000,76.77 1932-09-01,73.16,74.55,71.92,73.67,2420000,73.67 1932-08-31,74.30,74.44,71.11,73.16,3000000,73.16 1932-08-30,75.22,76.73,73.59,74.30,3300000,74.30 1932-08-29,75.61,77.01,74.25,75.22,3930000,75.22 1932-08-26,73.31,75.41,71.28,74.43,3120000,74.43 1932-08-25,73.55,75.88,72.54,73.31,4170000,73.31 1932-08-24,72.13,74.01,70.82,73.55,3690000,73.55 1932-08-23,70.87,73.80,70.69,72.13,4570000,72.13 1932-08-22,67.50,71.11,67.50,70.87,3180000,70.87 1932-08-19,67.93,69.47,66.10,66.84,2170000,66.84 1932-08-18,67.50,68.52,65.65,67.93,1790000,67.93 1932-08-17,68.91,70.56,65.62,67.50,2870000,67.50 1932-08-16,67.16,70.54,67.16,68.91,3610000,68.91 1932-08-15,63.19,66.72,62.93,66.51,1910000,66.51 1932-08-12,68.90,69.06,62.45,63.11,3710000,63.11 1932-08-11,69.39,71.62,66.95,68.90,4400000,68.90 1932-08-10,67.08,71.34,65.65,69.39,4430000,69.39 1932-08-09,67.71,69.53,65.36,67.08,3840000,67.08 1932-08-08,66.56,71.49,64.86,67.71,5460000,67.71 1932-08-05,59.73,63.42,59.73,62.60,2680000,62.60 1932-08-04,58.22,62.13,57.90,59.63,3520000,59.63 1932-08-03,53.16,58.69,52.99,58.22,2400000,58.22 1932-08-02,54.94,55.57,52.40,53.16,1440000,53.16 1932-08-01,54.26,56.92,53.59,54.94,2110000,54.94 1932-07-29,52.61,54.68,51.52,53.89,2100000,53.89 1932-07-28,51.34,53.84,50.67,52.61,2730000,52.61 1932-07-27,49.04,51.71,48.18,51.34,1700000,51.34 1932-07-26,49.78,50.41,48.49,49.04,1500000,49.04 1932-07-25,48.01,50.23,48.01,49.78,1550000,49.78 1932-07-22,46.50,48.31,46.35,47.69,1450000,47.69 1932-07-21,45.43,46.86,45.12,46.50,930000,46.50 1932-07-20,44.22,45.56,44.22,45.43,630000,45.43 1932-07-19,44.07,44.51,43.53,43.79,470000,43.79 1932-07-18,45.29,45.61,43.83,44.07,610000,44.07 1932-07-15,44.34,45.82,43.74,45.47,810000,45.47 1932-07-14,44.88,45.85,43.91,44.34,1000000,44.34 1932-07-13,42.68,45.05,42.35,44.88,980000,44.88 1932-07-12,42.99,43.65,42.36,42.68,700000,42.68 1932-07-11,41.63,43.03,40.92,42.99,600000,42.99 1932-07-08,41.81,42.61,40.56,41.22,720000,41.22 1932-07-07,44.08,44.26,41.63,41.81,780000,41.81 1932-07-06,43.47,44.50,42.31,44.08,730000,44.08 1932-07-05,44.39,44.43,42.53,43.47,610000,43.47 1932-07-01,42.84,44.63,42.64,44.39,610000,44.39 1932-06-30,43.66,44.44,42.41,42.84,630000,42.84 1932-06-29,43.18,44.21,42.54,43.66,630000,43.66 1932-06-28,42.93,44.30,42.31,43.18,830000,43.18 1932-06-27,44.76,44.82,42.52,42.93,770000,42.93 1932-06-24,46.83,47.57,44.55,44.84,770000,44.84 1932-06-23,46.27,47.47,46.15,46.83,470000,46.83 1932-06-22,46.58,46.66,45.43,46.27,610000,46.27 1932-06-21,47.80,48.42,46.47,46.58,500000,46.58 1932-06-20,47.55,48.66,47.41,47.80,390000,47.80 1932-06-17,50.34,50.36,47.44,47.56,790000,47.56 1932-06-16,50.62,51.43,49.73,50.34,850000,50.34 1932-06-15,49.37,51.43,49.37,50.62,1160000,50.62 1932-06-14,48.11,49.70,47.59,49.00,760000,49.00 1932-06-13,48.26,48.97,47.12,48.11,570000,48.11 1932-06-10,45.32,49.52,45.22,48.94,1270000,48.94 1932-06-09,45.20,47.55,44.45,45.32,1190000,45.32 1932-06-08,47.21,47.21,45.01,45.20,990000,45.20 1932-06-07,49.32,49.74,47.18,47.47,830000,47.47 1932-06-06,50.63,50.63,48.54,49.32,960000,49.32 1932-06-03,47.35,50.29,47.35,48.40,1890000,48.40 1932-06-02,44.93,47.74,43.49,47.25,1870000,47.25 1932-06-01,44.74,48.60,44.13,44.93,1840000,44.93 1932-05-31,46.93,46.93,44.27,44.74,1480000,44.74 1932-05-27,49.94,49.94,47.17,47.47,900000,47.47 1932-05-26,49.10,50.72,47.02,49.99,1850000,49.99 1932-05-25,50.79,50.79,48.65,49.10,1300000,49.10 1932-05-24,52.98,53.20,50.59,50.85,980000,50.85 1932-05-23,53.04,54.34,52.61,52.98,560000,52.98 1932-05-20,53.14,55.50,52.97,53.31,770000,53.31 1932-05-19,52.81,53.88,51.79,53.14,680000,53.14 1932-05-18,54.04,54.68,52.28,52.81,680000,52.81 1932-05-17,53.96,55.65,52.16,54.04,930000,54.04 1932-05-16,52.48,54.63,50.21,53.96,1310000,53.96 1932-05-13,55.33,55.33,53.23,53.46,870000,53.46 1932-05-12,57.71,57.71,54.91,55.62,920000,55.62 1932-05-11,57.68,59.52,57.47,57.83,690000,57.83 1932-05-10,57.04,58.94,56.68,57.68,740000,57.68 1932-05-09,58.04,58.74,56.29,57.04,640000,57.04 1932-05-06,54.58,59.76,54.58,59.01,1630000,59.01 1932-05-05,54.88,55.74,52.73,54.10,1000000,54.10 1932-05-04,54.15,55.21,52.33,54.88,1320000,54.88 1932-05-03,55.37,56.12,53.80,54.15,900000,54.15 1932-05-02,56.11,56.28,54.20,55.37,780000,55.37 1932-04-29,57.81,57.81,55.37,55.93,1160000,55.93 1932-04-28,61.28,61.36,57.98,58.24,930000,58.24 1932-04-27,59.71,62.15,59.40,61.28,1120000,61.28 1932-04-26,58.92,61.01,58.51,59.71,790000,59.71 1932-04-25,59.22,60.51,58.06,58.92,640000,58.92 1932-04-22,61.39,61.39,58.26,58.88,920000,58.88 1932-04-21,59.46,62.71,59.04,62.01,1110000,62.01 1932-04-20,59.75,60.81,58.70,59.46,990000,59.46 1932-04-19,60.85,61.45,58.91,59.75,1030000,59.75 1932-04-18,63.21,63.21,60.57,60.85,850000,60.85 1932-04-15,63.27,66.51,62.21,64.49,1540000,64.49 1932-04-14,61.18,63.84,59.10,63.27,1720000,63.27 1932-04-13,62.33,63.74,60.66,61.18,1100000,61.18 1932-04-12,62.04,64.13,60.62,62.33,1550000,62.33 1932-04-11,64.48,64.54,60.76,62.04,1700000,62.04 1932-04-08,65.73,65.73,61.98,62.90,2130000,62.90 1932-04-07,66.46,68.29,65.23,66.20,1800000,66.20 1932-04-06,68.07,68.93,65.85,66.46,3000000,66.46 1932-04-05,70.67,70.67,67.29,68.07,1480000,68.07 1932-04-04,71.30,71.84,68.32,71.19,1610000,71.19 1932-04-01,73.28,74.38,71.21,72.18,1530000,72.18 1932-03-31,77.15,77.80,72.49,73.28,1480000,73.28 1932-03-30,75.50,77.64,75.08,77.15,1010000,77.15 1932-03-29,75.09,76.98,74.68,75.50,1110000,75.50 1932-03-28,75.69,75.79,73.55,75.09,1350000,75.09 1932-03-24,78.64,79.58,77.49,77.99,840000,77.99 1932-03-23,79.55,80.08,78.23,78.64,840000,78.64 1932-03-22,79.90,80.59,77.79,79.55,1080000,79.55 1932-03-21,78.09,80.19,77.32,79.90,890000,79.90 1932-03-18,80.59,80.59,77.63,78.82,1410000,78.82 1932-03-17,79.11,81.55,77.57,80.87,1770000,80.87 1932-03-16,81.02,81.84,78.76,79.11,1460000,79.11 1932-03-15,81.12,82.44,79.66,81.02,1470000,81.02 1932-03-14,84.30,84.30,80.44,81.12,2030000,81.12 1932-03-11,85.70,85.70,83.26,83.61,1260000,83.61 1932-03-10,86.94,87.47,85.44,86.25,1050000,86.25 1932-03-09,88.78,89.87,86.57,86.94,1330000,86.94 1932-03-08,87.16,89.87,86.90,88.78,1640000,88.78 1932-03-07,88.49,89.78,86.80,87.16,1580000,87.16 1932-03-04,86.13,87.87,84.54,86.11,1510000,86.11 1932-03-03,86.28,88.35,85.47,86.13,1720000,86.13 1932-03-02,82.10,86.50,82.10,86.28,1760000,86.28 1932-03-01,81.44,82.20,80.50,81.87,730000,81.87 1932-02-29,82.02,83.89,80.84,81.44,880000,81.44 1932-02-26,82.05,83.53,81.37,82.09,890000,82.09 1932-02-25,82.73,82.92,80.13,82.05,1040000,82.05 1932-02-24,80.26,83.32,79.57,82.73,1080000,82.73 1932-02-23,83.59,84.03,79.85,80.26,1280000,80.26 1932-02-19,85.26,89.84,85.26,85.98,2430000,85.98 1932-02-18,82.24,85.74,81.45,85.13,1680000,85.13 1932-02-17,85.75,87.18,81.67,82.24,2190000,82.24 1932-02-16,82.18,86.51,80.49,85.75,2500000,85.75 1932-02-15,85.36,85.36,80.88,82.18,1980000,82.18 1932-02-11,74.27,79.62,74.27,78.60,2560000,78.60 1932-02-10,72.38,73.98,70.64,71.80,1300000,71.80 1932-02-09,73.45,74.50,71.85,72.38,1160000,72.38 1932-02-08,74.45,75.15,72.31,73.45,1150000,73.45 1932-02-05,77.50,77.50,74.71,75.00,1080000,75.00 1932-02-04,78.26,78.88,77.15,77.66,680000,77.66 1932-02-03,77.82,78.96,76.77,78.26,810000,78.26 1932-02-02,79.63,80.74,77.61,77.82,1120000,77.82 1932-02-01,76.32,80.62,76.32,79.63,1520000,79.63 1932-01-29,76.73,77.14,74.19,76.55,1530000,76.55 1932-01-28,77.82,78.03,75.85,76.73,1120000,76.73 1932-01-27,79.20,79.20,76.66,77.82,1280000,77.82 1932-01-26,78.92,80.79,78.92,79.76,760000,79.76 1932-01-25,77.98,79.94,77.42,78.66,830000,78.66 1932-01-22,83.42,83.51,78.56,78.81,1560000,78.81 1932-01-21,83.57,85.03,82.70,83.42,1240000,83.42 1932-01-20,81.10,83.79,80.45,83.57,1210000,83.57 1932-01-19,81.45,82.79,80.35,81.10,1090000,81.10 1932-01-18,84.44,85.32,81.29,81.45,1380000,81.45 1932-01-15,85.35,86.59,83.24,85.88,1640000,85.88 1932-01-14,84.70,87.78,84.70,85.35,2650000,85.35 1932-01-13,79.96,84.67,79.96,84.36,2070000,84.36 1932-01-12,80.44,82.08,78.84,79.39,1360000,79.39 1932-01-11,79.98,82.20,77.23,80.44,1800000,80.44 1932-01-08,78.03,82.11,76.74,81.80,1970000,81.80 1932-01-07,76.31,80.17,76.11,78.03,2180000,78.03 1932-01-06,73.02,77.16,73.02,76.31,1840000,76.31 1932-01-05,71.59,72.78,69.85,71.24,1420000,71.24 1932-01-04,74.06,74.06,70.91,71.59,1510000,71.59 1931-12-31,77.14,79.92,76.92,77.90,1510000,77.90 1931-12-30,75.84,77.86,75.12,77.14,2110000,77.14 1931-12-29,73.84,77.81,73.54,75.84,2440000,75.84 1931-12-28,75.84,76.27,72.41,73.84,2000000,73.84 1931-12-24,76.02,77.45,75.14,75.84,1110000,75.84 1931-12-23,79.55,80.17,75.42,76.02,1560000,76.02 1931-12-22,78.08,80.32,77.26,79.55,1400000,79.55 1931-12-21,80.75,81.76,77.55,78.08,1930000,78.08 1931-12-18,73.79,81.10,72.62,80.69,3620000,80.69 1931-12-17,76.33,76.33,71.79,73.79,2940000,73.79 1931-12-16,78.60,80.00,75.93,76.49,1960000,76.49 1931-12-15,77.22,79.44,75.50,78.60,2630000,78.60 1931-12-14,78.93,82.57,76.15,77.22,2890000,77.22 1931-12-11,82.46,83.11,79.26,79.63,2350000,79.63 1931-12-10,84.14,84.15,80.75,82.46,2660000,82.46 1931-12-09,86.50,86.52,82.49,84.14,2260000,84.14 1931-12-08,90.11,90.98,85.81,86.50,1600000,86.50 1931-12-07,90.14,92.60,89.16,90.11,1460000,90.11 1931-12-04,89.70,91.97,85.75,86.76,1920000,86.76 1931-12-03,87.90,90.59,86.28,89.70,1800000,89.70 1931-12-02,91.17,93.06,87.39,87.90,1890000,87.90 1931-12-01,93.38,93.38,87.78,91.17,2030000,91.17 1931-11-30,90.02,95.77,89.35,93.87,2000000,93.87 1931-11-27,93.79,93.79,90.65,91.55,1820000,91.55 1931-11-25,98.54,98.54,93.67,94.15,1500000,94.15 1931-11-24,96.60,100.10,96.53,98.61,1250000,98.61 1931-11-23,97.42,98.83,95.00,96.60,1350000,96.60 1931-11-20,100.99,100.99,96.45,97.96,2040000,97.96 1931-11-19,101.69,103.81,99.83,101.25,1510000,101.25 1931-11-18,105.86,105.86,101.21,101.69,1670000,101.69 1931-11-17,104.76,108.02,103.76,106.16,1460000,106.16 1931-11-16,106.35,107.86,103.43,104.76,1510000,104.76 1931-11-13,111.95,112.39,106.71,107.33,1790000,107.33 1931-11-12,112.01,114.61,110.86,111.95,1440000,111.95 1931-11-11,113.98,114.63,110.38,112.01,1490000,112.01 1931-11-10,116.58,116.58,112.24,113.98,1750000,113.98 1931-11-09,115.60,119.15,114.47,116.79,3050000,116.79 1931-11-06,108.58,113.28,106.43,112.72,2280000,112.72 1931-11-05,108.33,110.68,107.45,108.58,1520000,108.58 1931-11-04,104.50,108.85,103.19,108.33,1480000,108.33 1931-11-02,105.43,107.76,103.94,104.50,1460000,104.50 1931-10-30,100.75,104.51,100.75,103.97,1560000,103.97 1931-10-29,100.52,101.66,98.19,100.66,1340000,100.66 1931-10-28,104.25,104.50,99.20,100.52,1770000,100.52 1931-10-27,106.31,106.31,102.43,104.25,1390000,104.25 1931-10-26,109.36,109.36,105.69,106.37,1190000,106.37 1931-10-23,105.02,109.17,104.58,108.88,1330000,108.88 1931-10-22,108.39,108.58,104.09,105.02,1390000,105.02 1931-10-21,108.65,109.69,103.86,108.39,2240000,108.39 1931-10-20,104.69,109.59,104.69,108.65,2510000,108.65 1931-10-19,102.28,104.19,100.87,103.45,860000,103.45 1931-10-16,98.71,103.19,98.24,102.49,1420000,102.49 1931-10-15,97.45,102.67,97.45,98.71,1380000,98.71 1931-10-14,100.24,102.50,96.01,97.27,1640000,97.27 1931-10-13,105.37,105.37,99.74,100.24,1250000,100.24 1931-10-09,105.79,108.98,102.79,104.46,3220000,104.46 1931-10-08,98.19,106.43,98.19,105.79,2870000,105.79 1931-10-07,99.34,103.84,96.79,97.32,2820000,97.32 1931-10-06,87.51,100.49,87.51,99.34,4310000,99.34 1931-10-05,92.14,92.14,85.51,86.48,3190000,86.48 1931-10-02,95.66,100.03,93.64,96.88,2530000,96.88 1931-10-01,96.61,98.51,92.88,95.66,3640000,95.66 1931-09-30,99.80,102.39,95.76,96.61,3210000,96.61 1931-09-29,104.39,104.64,99.02,99.80,2900000,99.80 1931-09-28,107.36,108.80,103.58,104.39,1490000,104.39 1931-09-25,107.79,112.64,104.73,109.86,2850000,109.86 1931-09-24,115.99,116.95,106.64,107.79,3050000,107.79 1931-09-23,110.77,117.75,110.77,115.99,2930000,115.99 1931-09-22,110.83,112.65,108.12,109.40,2050000,109.40 1931-09-21,111.74,114.59,104.79,110.83,4400000,110.83 1931-09-18,121.27,121.27,114.47,115.08,2900000,115.08 1931-09-17,119.26,123.28,117.29,121.76,2420000,121.76 1931-09-16,120.59,123.10,118.41,119.26,1980000,119.26 1931-09-15,121.30,123.03,118.70,120.59,2170000,120.59 1931-09-14,123.00,123.00,119.12,121.30,2450000,121.30 1931-09-11,127.30,129.47,124.50,128.23,1970000,128.23 1931-09-10,128.43,130.45,126.04,127.30,1510000,127.30 1931-09-09,129.19,130.67,126.36,128.43,2020000,128.43 1931-09-08,131.57,131.57,127.91,129.19,2040000,129.19 1931-09-04,133.14,133.82,131.44,132.62,1200000,132.62 1931-09-03,136.54,136.54,132.21,133.14,2130000,133.14 1931-09-02,140.08,140.08,136.62,137.31,960000,137.31 1931-09-01,139.41,140.54,138.51,140.13,540000,140.13 1931-08-31,142.07,142.07,138.96,139.41,740000,139.41 1931-08-28,138.82,142.11,138.82,140.78,930000,140.78 1931-08-27,139.93,140.75,137.88,138.66,830000,138.66 1931-08-26,136.65,140.32,135.98,139.93,840000,139.93 1931-08-25,137.62,139.22,135.69,136.65,860000,136.65 1931-08-24,137.76,137.99,135.62,137.62,820000,137.62 1931-08-21,141.93,142.49,137.35,138.60,1310000,138.60 1931-08-20,141.72,143.93,140.96,141.93,1070000,141.93 1931-08-19,141.26,142.61,140.04,141.72,1080000,141.72 1931-08-18,140.98,144.43,139.36,141.26,1710000,141.26 1931-08-17,144.27,144.27,140.34,140.98,1320000,140.98 1931-08-14,140.82,145.05,140.82,144.15,1830000,144.15 1931-08-13,137.63,141.30,137.03,140.22,1380000,140.22 1931-08-12,140.16,140.99,137.03,137.63,1260000,137.63 1931-08-11,134.26,141.42,133.80,140.16,1600000,140.16 1931-08-10,134.85,134.85,132.79,134.26,710000,134.26 1931-08-07,133.77,135.72,132.96,135.13,740000,135.13 1931-08-06,134.10,134.93,132.55,133.77,910000,133.77 1931-08-05,135.83,135.83,133.59,134.10,820000,134.10 1931-08-04,137.50,137.77,134.85,136.50,720000,136.50 1931-08-03,136.65,139.35,136.36,137.50,880000,137.50 1931-07-31,136.93,138.12,133.70,135.39,1220000,135.39 1931-07-30,136.19,138.05,134.33,136.93,1350000,136.93 1931-07-29,139.26,139.26,134.99,136.19,1580000,136.19 1931-07-28,139.65,142.12,139.65,141.53,650000,141.53 1931-07-27,138.24,140.16,137.72,139.64,570000,139.64 1931-07-24,142.41,142.41,138.20,139.01,1070000,139.01 1931-07-23,142.52,143.77,140.81,142.63,790000,142.63 1931-07-22,145.88,145.88,142.11,142.52,990000,142.52 1931-07-21,144.48,147.69,144.21,146.70,1150000,146.70 1931-07-20,142.75,145.00,142.75,144.48,710000,144.48 1931-07-17,141.99,144.84,141.44,142.61,1270000,142.61 1931-07-16,137.86,142.30,137.30,141.99,1520000,141.99 1931-07-15,140.13,140.13,134.39,137.86,2610000,137.86 1931-07-14,142.43,143.19,139.67,140.85,1110000,140.85 1931-07-13,143.44,143.44,139.59,142.43,1280000,142.43 1931-07-10,144.91,148.50,143.46,146.97,1290000,146.97 1931-07-09,143.83,145.97,142.24,144.91,1510000,144.91 1931-07-08,145.92,147.24,141.36,143.83,2360000,143.83 1931-07-07,152.80,155.39,144.65,145.92,3010000,145.92 1931-07-06,154.08,154.08,151.20,152.80,1050000,152.80 1931-07-03,152.21,156.74,152.21,155.26,2050000,155.26 1931-07-02,152.66,154.84,150.72,151.48,1330000,151.48 1931-07-01,150.18,153.86,147.44,152.66,1710000,152.66 1931-06-30,152.67,153.67,148.63,150.18,1950000,150.18 1931-06-29,156.59,156.59,151.30,152.67,2140000,152.67 1931-06-26,150.75,155.50,150.75,154.04,3120000,154.04 1931-06-25,151.60,156.33,148.79,150.36,4320000,150.36 1931-06-24,144.56,153.42,144.56,151.60,5070000,151.60 1931-06-23,145.82,146.71,141.41,143.89,2600000,143.89 1931-06-22,140.08,147.97,140.08,145.82,4590000,145.82 1931-06-19,130.56,131.86,128.64,130.31,1150000,130.31 1931-06-18,133.58,133.58,130.09,130.56,1150000,130.56 1931-06-17,135.47,135.93,132.91,133.68,920000,133.68 1931-06-16,135.26,136.39,132.93,135.47,1120000,135.47 1931-06-15,137.03,138.58,134.82,135.26,1270000,135.26 1931-06-12,136.57,138.16,133.98,136.98,1590000,136.98 1931-06-11,136.82,138.47,134.24,136.57,1750000,136.57 1931-06-10,132.97,137.30,132.04,136.82,1800000,136.82 1931-06-09,135.92,138.88,132.08,132.97,1890000,132.97 1931-06-08,129.91,136.40,127.96,135.92,1710000,135.92 1931-06-05,134.73,138.89,131.69,133.33,2850000,133.33 1931-06-04,130.37,136.10,128.35,134.73,3170000,134.73 1931-06-03,121.70,130.64,120.79,130.37,3310000,130.37 1931-06-02,122.77,126.20,119.89,121.70,3320000,121.70 1931-06-01,128.40,128.40,121.76,122.77,3100000,122.77 1931-05-29,131.81,134.60,127.30,128.46,2050000,128.46 1931-05-28,130.76,134.16,128.72,131.81,2090000,131.81 1931-05-27,133.11,134.59,127.95,130.76,2510000,130.76 1931-05-26,132.87,136.38,130.75,133.11,2410000,133.11 1931-05-25,137.43,137.43,132.15,132.87,1880000,132.87 1931-05-22,139.54,141.37,136.93,139.49,1560000,139.49 1931-05-21,137.74,141.07,135.19,139.54,2350000,139.54 1931-05-20,138.86,142.49,136.18,137.74,2320000,137.74 1931-05-19,139.52,141.93,136.05,138.86,2780000,138.86 1931-05-18,142.95,143.09,138.54,139.52,2540000,139.52 1931-05-15,146.64,147.23,141.84,144.49,2380000,144.49 1931-05-14,149.63,150.61,146.02,146.64,1770000,146.64 1931-05-13,150.24,152.63,147.55,149.63,1670000,149.63 1931-05-12,151.56,152.11,148.28,150.24,1310000,150.24 1931-05-11,151.31,153.37,148.19,151.56,1650000,151.56 1931-05-08,148.88,155.65,147.43,154.41,2660000,154.41 1931-05-07,149.73,152.57,146.77,148.88,1690000,148.88 1931-05-06,148.99,150.54,145.65,149.73,1500000,149.73 1931-05-05,150.50,153.19,147.88,148.99,1580000,148.99 1931-05-04,147.49,151.26,146.80,150.50,1360000,150.50 1931-05-01,151.19,153.82,144.91,145.58,2870000,145.58 1931-04-30,143.61,152.50,142.12,151.19,3340000,151.19 1931-04-29,147.59,147.59,141.78,143.61,3180000,143.61 1931-04-28,149.78,151.09,144.52,147.95,2860000,147.95 1931-04-27,151.98,152.98,146.31,149.78,3650000,149.78 1931-04-24,157.43,159.45,153.55,155.76,2600000,155.76 1931-04-23,156.37,159.53,153.13,157.43,3820000,157.43 1931-04-22,158.83,160.06,155.61,156.37,2670000,156.37 1931-04-21,163.41,164.06,157.82,158.83,1990000,158.83 1931-04-20,162.37,164.42,159.45,163.41,1560000,163.41 1931-04-17,162.59,164.34,158.50,160.23,2550000,160.23 1931-04-16,164.66,165.64,161.63,162.59,2330000,162.59 1931-04-15,167.64,167.64,163.89,164.66,2050000,164.66 1931-04-14,171.07,173.24,167.11,168.43,1940000,168.43 1931-04-13,168.03,171.61,167.11,171.07,1630000,171.07 1931-04-10,168.77,171.06,166.96,168.72,1570000,168.72 1931-04-09,169.54,171.43,167.66,168.77,1940000,168.77 1931-04-08,167.03,171.49,166.34,169.54,2050000,169.54 1931-04-07,169.72,170.44,166.10,167.03,2190000,167.03 1931-04-06,172.43,174.69,169.44,169.72,1460000,169.72 1931-04-02,170.82,173.15,168.30,169.89,2510000,169.89 1931-04-01,172.36,173.72,169.18,170.82,2270000,170.82 1931-03-31,172.56,176.50,171.09,172.36,2410000,172.36 1931-03-30,174.06,175.42,170.64,172.56,3190000,172.56 1931-03-27,181.70,182.02,176.63,177.30,2950000,177.30 1931-03-26,184.20,185.56,180.85,181.70,2550000,181.70 1931-03-25,186.00,187.18,183.20,184.20,2100000,184.20 1931-03-24,184.32,186.86,181.65,186.00,1880000,186.00 1931-03-23,185.24,186.38,182.05,184.32,1990000,184.32 1931-03-20,186.56,189.31,185.15,187.72,2740000,187.72 1931-03-19,184.06,188.42,184.06,186.56,3530000,186.56 1931-03-18,180.61,184.53,179.62,183.95,2100000,183.95 1931-03-17,183.61,185.82,179.94,180.61,2800000,180.61 1931-03-16,180.76,184.11,180.32,183.61,2130000,183.61 1931-03-13,180.14,180.47,175.89,178.91,2380000,178.91 1931-03-12,181.91,182.96,178.57,180.14,2490000,180.14 1931-03-11,183.63,185.01,180.51,181.91,2290000,181.91 1931-03-10,185.38,188.10,182.49,183.63,3240000,183.63 1931-03-09,183.85,186.51,181.80,185.38,2850000,185.38 1931-03-06,184.69,186.62,178.49,179.73,3860000,179.73 1931-03-05,180.96,185.18,179.41,184.69,2730000,184.69 1931-03-04,183.76,184.65,179.39,180.96,3090000,180.96 1931-03-03,184.38,186.56,182.00,183.76,2940000,183.76 1931-03-02,189.66,191.93,183.53,184.38,3320000,184.38 1931-02-27,192.23,194.53,188.92,190.34,3730000,190.34 1931-02-26,190.72,195.95,189.81,192.23,4620000,192.23 1931-02-25,194.36,195.17,188.75,190.72,4390000,190.72 1931-02-24,191.32,196.96,190.19,194.36,5350000,194.36 1931-02-20,184.86,189.55,184.86,188.22,3830000,188.22 1931-02-19,181.51,185.35,181.51,184.46,2480000,184.46 1931-02-18,179.55,184.69,179.22,181.10,2840000,181.10 1931-02-17,182.88,186.29,178.70,179.55,3990000,179.55 1931-02-16,180.68,184.77,180.68,182.88,3170000,182.88 1931-02-13,181.88,184.50,179.14,180.99,2750000,180.99 1931-02-11,181.20,185.89,179.14,181.88,4700000,181.88 1931-02-10,177.72,184.12,176.83,181.20,4760000,181.20 1931-02-09,173.24,178.58,173.24,177.72,4130000,177.72 1931-02-06,169.38,171.84,168.55,169.88,1660000,169.88 1931-02-05,171.13,171.31,167.16,169.38,1490000,169.38 1931-02-04,169.71,172.13,168.89,171.13,1520000,171.13 1931-02-03,168.71,170.77,167.59,169.71,1190000,169.71 1931-02-02,167.55,169.32,165.60,168.71,1160000,168.71 1931-01-30,168.87,172.42,168.15,169.34,2210000,169.34 1931-01-29,166.84,169.82,164.81,168.87,1650000,168.87 1931-01-28,170.44,170.44,165.69,166.84,1620000,166.84 1931-01-27,171.19,172.33,168.91,170.82,1600000,170.82 1931-01-26,169.80,172.12,168.03,171.19,1540000,171.19 1931-01-23,168.87,172.97,168.87,171.84,2870000,171.84 1931-01-22,164.76,168.78,164.25,168.46,1860000,168.46 1931-01-21,165.82,167.18,163.29,164.76,1410000,164.76 1931-01-20,162.06,166.42,162.06,165.82,1330000,165.82 1931-01-19,162.89,163.18,160.09,161.45,1120000,161.45 1931-01-16,162.82,165.55,161.52,164.94,1320000,164.94 1931-01-15,167.46,167.72,161.95,162.82,1930000,162.82 1931-01-14,165.95,168.20,164.90,167.46,1280000,167.46 1931-01-13,167.99,168.42,164.76,165.95,1710000,165.95 1931-01-12,171.71,172.12,167.23,167.99,1500000,167.99 1931-01-09,173.04,175.66,169.68,170.18,2800000,170.18 1931-01-08,171.86,173.62,170.02,173.04,1710000,173.04 1931-01-07,172.66,175.32,171.07,171.86,2140000,171.86 1931-01-06,170.71,173.48,168.43,172.66,1910000,172.66 1931-01-05,172.12,173.30,167.77,170.71,2090000,170.71 1931-01-02,164.58,170.09,161.46,169.84,2030000,169.84 1930-12-31,163.09,167.99,162.48,164.58,1940000,164.58 1930-12-30,160.16,165.34,159.68,163.09,3430000,163.09 1930-12-29,160.30,162.60,158.41,160.16,2790000,160.16 1930-12-26,165.20,166.96,160.63,161.18,1800000,161.18 1930-12-24,163.57,168.00,163.57,165.20,1580000,165.20 1930-12-23,162.42,165.93,160.22,162.93,2450000,162.93 1930-12-22,169.42,170.02,162.15,162.42,2100000,162.42 1930-12-19,166.71,170.43,164.96,168.99,2270000,168.99 1930-12-18,165.60,171.64,164.99,166.71,3290000,166.71 1930-12-17,157.51,166.83,154.45,165.60,5010000,165.60 1930-12-16,163.34,166.04,156.44,157.51,4160000,157.51 1930-12-15,163.34,166.13,160.66,163.34,3440000,163.34 1930-12-12,170.31,173.08,167.99,168.68,2030000,168.68 1930-12-11,173.02,173.02,166.97,170.31,2890000,170.31 1930-12-10,176.50,178.10,170.21,173.98,3150000,173.98 1930-12-09,176.09,178.21,173.88,176.50,2120000,176.50 1930-12-08,177.98,177.98,174.27,176.09,1980000,176.09 1930-12-05,180.99,181.70,177.26,181.11,1590000,181.11 1930-12-04,184.06,184.06,179.75,180.99,1590000,180.99 1930-12-03,186.82,187.07,183.30,184.11,1220000,184.11 1930-12-02,185.48,187.96,183.36,186.82,1580000,186.82 1930-12-01,183.39,186.33,182.32,185.48,1100000,185.48 1930-11-28,183.06,183.06,178.88,180.91,1740000,180.91 1930-11-26,185.47,186.98,181.93,183.11,1950000,183.11 1930-11-25,188.27,191.28,185.17,185.47,2150000,185.47 1930-11-24,188.04,189.08,184.32,188.27,1630000,188.27 1930-11-21,187.09,191.28,185.32,190.30,2250000,190.30 1930-11-20,187.57,191.04,185.76,187.09,2630000,187.09 1930-11-19,183.42,188.61,182.21,187.57,2480000,187.57 1930-11-18,180.50,184.16,177.63,183.42,2020000,183.42 1930-11-17,185.13,185.13,178.86,180.50,2140000,180.50 1930-11-14,180.38,184.64,177.69,184.03,2640000,184.03 1930-11-13,177.33,183.08,174.96,180.38,3450000,180.38 1930-11-12,173.30,177.50,168.58,177.33,3420000,177.33 1930-11-11,171.60,176.93,169.38,173.30,3330000,173.30 1930-11-10,173.14,177.07,168.32,171.60,4420000,171.60 1930-11-07,180.72,181.00,172.85,174.38,2370000,174.38 1930-11-06,179.81,182.40,177.80,180.72,2450000,180.72 1930-11-05,185.39,185.47,178.78,179.81,2150000,179.81 1930-11-03,184.89,187.23,183.12,185.39,1260000,185.39 1930-10-31,188.00,188.00,181.64,183.35,2250000,183.35 1930-10-30,190.55,190.55,185.99,188.07,1910000,188.07 1930-10-29,194.35,194.35,189.33,190.73,1670000,190.73 1930-10-28,195.09,198.59,193.28,194.95,2020000,194.95 1930-10-27,193.34,195.73,189.17,195.09,1810000,195.09 1930-10-24,189.07,195.79,189.07,195.09,2760000,195.09 1930-10-23,184.98,190.95,184.75,188.10,2670000,188.10 1930-10-22,186.40,188.34,181.53,184.98,2740000,184.98 1930-10-21,193.32,193.95,184.22,186.40,2430000,186.40 1930-10-20,187.07,194.44,187.07,193.32,2140000,193.32 1930-10-17,195.30,195.30,186.74,187.37,2660000,187.37 1930-10-16,200.13,200.13,194.78,196.62,1860000,196.62 1930-10-15,196.70,201.64,193.40,200.26,2380000,200.26 1930-10-14,193.05,198.01,186.99,196.70,3390000,196.70 1930-10-10,192.00,198.86,186.70,198.50,6300000,198.50 1930-10-09,199.96,199.96,190.17,192.00,5050000,192.00 1930-10-08,203.62,205.21,198.61,200.56,2070000,200.56 1930-10-07,202.76,207.04,198.51,203.62,3570000,203.62 1930-10-06,209.01,209.01,201.90,202.76,2370000,202.76 1930-10-03,211.13,216.89,211.13,214.18,2050000,214.18 1930-10-02,214.14,214.76,206.25,211.04,2320000,211.04 1930-10-01,205.92,215.32,205.92,214.14,3160000,214.14 1930-09-30,208.14,209.95,201.95,204.90,4500000,204.90 1930-09-29,212.52,216.96,207.30,208.14,3760000,208.14 1930-09-26,217.75,220.21,211.31,213.27,3710000,213.27 1930-09-25,222.10,223.89,215.31,217.75,3070000,217.75 1930-09-24,226.75,228.87,218.08,222.10,3440000,222.10 1930-09-23,222.78,227.30,221.82,226.75,1920000,226.75 1930-09-22,229.29,229.29,222.00,222.78,2330000,222.78 1930-09-19,233.52,233.52,226.39,229.02,2950000,229.02 1930-09-18,237.45,237.45,233.47,234.18,1380000,234.18 1930-09-17,237.22,239.31,236.51,237.74,1190000,237.74 1930-09-16,236.62,237.75,232.85,237.22,1770000,237.22 1930-09-15,239.96,239.96,234.95,236.62,1560000,236.62 1930-09-12,242.88,244.42,239.49,241.17,1910000,241.17 1930-09-11,245.09,245.23,241.67,242.88,1740000,242.88 1930-09-10,244.29,247.21,243.30,245.09,2480000,245.09 1930-09-09,242.84,245.48,241.55,244.29,1940000,244.29 1930-09-08,243.64,246.08,241.78,242.84,2240000,242.84 1930-09-05,236.09,240.93,236.09,240.37,1650000,240.37 1930-09-04,237.45,239.65,234.35,236.04,1520000,236.04 1930-09-03,240.42,242.40,236.71,237.45,1750000,237.45 1930-09-02,240.42,242.77,238.51,240.42,1770000,240.42 1930-08-29,237.79,241.35,236.75,240.42,1860000,240.42 1930-08-28,237.93,239.33,235.19,237.79,1440000,237.79 1930-08-27,235.47,239.54,235.38,237.93,2200000,237.93 1930-08-26,231.52,236.30,230.73,235.47,1750000,235.47 1930-08-25,234.42,236.46,230.68,231.52,1600000,231.52 1930-08-22,231.27,234.04,229.64,232.63,1340000,232.63 1930-08-21,232.98,234.91,229.21,231.27,1710000,231.27 1930-08-20,230.68,235.26,229.84,232.98,1820000,232.98 1930-08-19,227.79,233.46,227.43,230.68,1860000,230.68 1930-08-18,228.02,229.08,224.42,227.79,1410000,227.79 1930-08-15,221.08,229.44,219.82,228.55,2110000,228.55 1930-08-14,220.35,224.38,219.24,221.08,1530000,221.08 1930-08-13,217.24,221.35,214.49,220.35,2290000,220.35 1930-08-12,223.74,223.74,216.48,217.24,2090000,217.24 1930-08-11,222.59,226.05,220.09,224.13,1750000,224.13 1930-08-08,231.73,231.73,222.24,222.82,3310000,222.82 1930-08-07,234.38,234.39,230.50,232.69,1450000,232.69 1930-08-06,237.99,237.99,233.67,234.38,1320000,234.38 1930-08-05,238.16,240.95,237.22,238.47,1220000,238.47 1930-08-04,234.50,238.57,233.26,238.16,1200000,238.16 1930-08-01,233.99,235.78,231.86,233.57,1090000,233.57 1930-07-31,231.08,235.19,229.09,233.99,2160000,233.99 1930-07-30,238.40,239.93,230.53,231.08,2510000,231.08 1930-07-29,240.81,241.53,237.00,238.40,1860000,238.40 1930-07-28,240.31,243.65,239.04,240.81,2430000,240.81 1930-07-25,235.51,238.58,233.77,237.48,1360000,237.48 1930-07-24,239.33,239.51,234.65,235.51,1480000,235.51 1930-07-23,234.94,241.76,234.94,239.33,2530000,239.33 1930-07-22,229.29,235.29,228.81,234.30,2080000,234.30 1930-07-21,236.58,236.58,228.72,229.29,1950000,229.29 1930-07-18,239.07,242.01,236.47,240.57,2750000,240.57 1930-07-17,235.63,240.27,234.03,239.07,2500000,239.07 1930-07-16,233.79,236.47,230.59,235.63,2590000,235.63 1930-07-15,234.21,237.19,231.73,233.79,3090000,233.79 1930-07-14,229.23,234.54,229.02,234.21,2740000,234.21 1930-07-11,227.39,228.65,223.97,224.86,1530000,224.86 1930-07-10,222.04,228.35,219.18,227.39,2170000,227.39 1930-07-09,219.11,223.52,219.11,222.04,1360000,222.04 1930-07-08,218.33,220.22,214.64,219.08,1560000,219.08 1930-07-07,221.31,221.31,216.33,218.33,1480000,218.33 1930-07-03,225.25,226.26,219.94,222.46,1380000,222.46 1930-07-02,223.03,227.24,222.14,225.25,1230000,225.25 1930-07-01,226.34,229.53,221.92,223.03,2280000,223.03 1930-06-30,219.12,226.85,219.06,226.34,1840000,226.34 1930-06-27,220.58,222.84,213.97,218.78,2080000,218.78 1930-06-26,215.58,222.11,214.25,220.58,2270000,220.58 1930-06-25,211.84,216.80,207.74,215.58,3400000,215.58 1930-06-24,219.58,222.89,211.07,211.84,2870000,211.84 1930-06-23,215.30,221.26,209.16,219.58,3840000,219.58 1930-06-20,228.97,232.69,219.70,221.92,5660000,221.92 1930-06-19,219.83,229.83,219.83,228.97,3760000,228.97 1930-06-18,226.29,226.29,212.27,218.84,6430000,218.84 1930-06-17,230.05,234.94,224.37,228.57,5020000,228.57 1930-06-16,242.18,242.18,228.94,230.05,5660000,230.05 1930-06-13,247.18,251.63,243.27,249.69,2230000,249.69 1930-06-12,249.08,250.90,241.00,247.18,3900000,247.18 1930-06-11,257.24,257.24,243.90,249.08,4480000,249.08 1930-06-10,250.78,257.72,247.62,257.29,4770000,257.29 1930-06-09,257.82,259.60,249.51,250.78,4650000,250.78 1930-06-06,268.59,268.69,263.29,263.93,2160000,263.93 1930-06-05,272.44,272.65,266.90,268.59,2390000,268.59 1930-06-04,271.18,274.03,269.54,272.44,1690000,272.44 1930-06-03,274.45,274.69,270.01,271.18,1750000,271.18 1930-06-02,275.07,276.86,273.03,274.45,1710000,274.45 1930-05-29,273.84,276.66,273.05,275.07,2200000,275.07 1930-05-28,272.43,275.17,270.64,273.84,2410000,273.84 1930-05-27,272.14,274.72,270.54,272.43,2260000,272.43 1930-05-26,271.33,276.18,270.64,272.14,2250000,272.14 1930-05-23,267.01,272.14,267.01,270.01,2160000,270.01 1930-05-22,265.52,268.65,262.53,266.89,1860000,266.89 1930-05-21,267.10,268.94,262.67,265.52,2080000,265.52 1930-05-20,265.87,268.40,260.76,267.10,3530000,267.10 1930-05-19,272.36,272.36,264.63,265.87,2410000,265.87 1930-05-16,269.91,272.96,268.54,271.52,2090000,271.52 1930-05-15,274.35,274.35,268.45,269.91,2680000,269.91 1930-05-14,274.17,277.22,272.22,274.40,3180000,274.40 1930-05-13,270.16,276.09,269.51,274.17,2700000,274.17 1930-05-12,272.01,275.11,268.56,270.16,3030000,270.16 1930-05-09,263.93,269.08,261.64,267.29,3010000,267.29 1930-05-08,263.69,266.11,257.74,263.93,3760000,263.93 1930-05-07,268.81,272.15,262.20,263.69,4300000,263.69 1930-05-06,259.68,270.34,259.04,268.81,4760000,268.81 1930-05-05,258.31,262.84,249.82,259.68,8280000,259.68 1930-05-02,274.59,276.81,264.93,266.56,5990000,266.56 1930-05-01,279.23,281.95,273.00,274.59,4640000,274.59 1930-04-30,278.43,283.51,275.97,279.23,4550000,279.23 1930-04-29,276.94,280.71,272.24,278.43,5410000,278.43 1930-04-28,285.46,286.12,275.71,276.94,4850000,276.94 1930-04-25,286.18,289.28,283.68,285.76,4730000,285.76 1930-04-24,288.78,290.58,283.74,286.18,5230000,286.18 1930-04-23,290.01,293.27,286.25,288.78,5570000,288.78 1930-04-22,288.23,291.39,284.28,290.01,4590000,290.01 1930-04-21,294.07,295.88,287.24,288.23,4490000,288.23 1930-04-17,292.20,296.05,290.38,294.07,3940000,294.07 1930-04-16,293.26,297.25,290.27,292.20,4400000,292.20 1930-04-15,293.18,295.69,289.34,293.26,4220000,293.26 1930-04-14,293.43,295.81,289.72,293.18,4150000,293.18 1930-04-11,292.19,296.35,288.42,292.65,5630000,292.65 1930-04-10,291.15,295.98,289.18,292.19,5680000,292.19 1930-04-09,288.36,293.36,287.37,291.15,5190000,291.15 1930-04-08,290.19,291.89,285.96,288.36,4690000,288.36 1930-04-07,289.96,293.43,287.03,290.19,5490000,290.19 1930-04-04,285.77,291.44,284.92,288.35,5930000,288.35 1930-04-03,285.27,288.17,282.29,285.77,4630000,285.77 1930-04-02,287.11,290.15,283.59,285.27,5300000,285.27 1930-04-01,286.10,290.15,283.64,287.11,5400000,287.11 1930-03-31,283.85,289.13,282.85,286.10,5160000,286.10 1930-03-28,281.63,287.06,280.83,283.85,5070000,283.85 1930-03-27,283.22,286.10,279.98,281.63,4710000,281.63 1930-03-26,280.50,285.38,279.31,283.22,5030000,283.22 1930-03-25,279.11,284.01,278.21,280.50,4530000,280.50 1930-03-24,276.43,280.92,275.03,279.11,4130000,279.11 1930-03-21,279.41,284.08,276.78,280.55,4630000,280.55 1930-03-20,277.88,282.23,275.25,279.41,3260000,279.41 1930-03-19,277.27,281.05,274.91,277.88,4340000,277.88 1930-03-18,274.26,279.79,273.90,277.27,4250000,277.27 1930-03-17,270.25,274.84,268.94,274.26,3640000,274.26 1930-03-14,273.47,275.82,270.38,271.34,3950000,271.34 1930-03-13,272.13,274.77,269.60,273.47,3850000,273.47 1930-03-12,276.25,277.06,270.01,272.13,4470000,272.13 1930-03-11,276.85,278.76,274.25,276.25,2640000,276.25 1930-03-10,275.46,279.40,273.71,276.85,3990000,276.85 1930-03-07,274.51,278.48,272.84,275.57,3640000,275.57 1930-03-06,270.59,276.05,269.01,274.51,3360000,274.51 1930-03-05,273.51,276.10,269.03,270.59,3720000,270.59 1930-03-04,271.11,275.19,269.16,273.51,3460000,273.51 1930-03-03,273.24,274.47,269.59,271.11,3630000,271.11 1930-02-28,269.39,272.55,267.41,271.11,3210000,271.11 1930-02-27,269.06,272.13,267.43,269.39,3310000,269.39 1930-02-26,262.94,269.92,262.94,269.06,3020000,269.06 1930-02-25,262.47,264.64,259.78,262.80,2630000,262.80 1930-02-24,265.81,266.33,261.40,262.47,2320000,262.47 1930-02-21,263.41,266.68,262.01,265.81,2570000,265.81 1930-02-20,268.46,269.72,262.43,263.41,3660000,263.41 1930-02-19,270.73,273.35,267.09,268.46,3490000,268.46 1930-02-18,270.54,274.41,268.98,270.73,3800000,270.73 1930-02-17,269.25,271.62,265.29,270.54,3290000,270.54 1930-02-14,272.27,274.20,268.60,271.52,3510000,271.52 1930-02-13,271.05,275.00,268.97,272.27,3670000,272.27 1930-02-11,268.56,272.67,267.00,271.05,3320000,271.05 1930-02-10,269.78,271.78,266.37,268.56,3170000,268.56 1930-02-07,268.56,271.62,265.36,267.82,3390000,267.82 1930-02-06,272.06,273.59,266.96,268.56,3710000,268.56 1930-02-05,268.85,274.01,268.85,272.06,4360000,272.06 1930-02-04,266.54,270.00,264.41,268.48,3230000,268.48 1930-02-03,268.41,271.54,264.84,266.54,3800000,266.54 1930-01-31,263.28,268.71,262.81,267.14,3740000,267.14 1930-01-30,262.18,266.30,260.48,263.28,3640000,263.28 1930-01-29,257.90,263.73,257.38,262.18,3250000,262.18 1930-01-28,260.93,261.89,256.70,257.90,2910000,257.90 1930-01-27,259.06,262.57,257.24,260.93,3460000,260.93 1930-01-24,253.52,258.98,252.98,256.31,3480000,256.31 1930-01-23,250.19,254.39,249.62,253.52,3230000,253.52 1930-01-22,249.58,251.85,248.60,250.19,2310000,250.19 1930-01-21,247.31,250.62,246.25,249.58,2230000,249.58 1930-01-20,246.84,248.92,244.86,247.31,1700000,247.31 1930-01-17,248.99,249.88,244.50,246.33,2680000,246.33 1930-01-16,251.54,253.49,248.16,248.99,3040000,248.99 1930-01-15,250.44,252.57,247.76,251.54,2630000,251.54 1930-01-14,249.62,252.39,248.98,250.44,1880000,250.44 1930-01-13,248.71,250.83,246.82,249.62,1460000,249.62 1930-01-10,249.68,252.91,248.80,250.03,2400000,250.03 1930-01-09,245.70,250.52,245.67,249.68,2400000,249.68 1930-01-08,246.50,248.04,243.80,245.70,1640000,245.70 1930-01-07,248.10,249.40,243.80,246.50,2030000,246.50 1930-01-06,248.85,250.37,245.49,248.10,2170000,248.10 1930-01-03,244.20,248.71,243.00,247.19,2070000,247.19 1930-01-02,248.48,252.29,241.78,244.20,2930000,244.20 1929-12-31,241.90,249.24,241.90,248.48,2680000,248.48 1929-12-30,238.43,242.95,235.95,241.06,4160000,241.06 1929-12-27,240.96,246.35,239.13,240.66,3350000,240.66 1929-12-26,234.07,242.62,233.89,240.96,2580000,240.96 1929-12-24,232.65,237.94,231.96,234.07,1000000,234.07 1929-12-23,235.42,236.37,226.39,232.65,3490000,232.65 1929-12-20,240.42,241.92,227.20,230.89,5550000,230.89 1929-12-19,246.84,247.14,238.80,240.42,3410000,240.42 1929-12-18,249.58,250.51,245.03,246.84,2290000,246.84 1929-12-17,245.88,250.23,243.63,249.58,2440000,249.58 1929-12-16,253.02,253.17,244.34,245.88,2590000,245.88 1929-12-13,243.14,251.79,239.58,249.60,4390000,249.60 1929-12-12,258.44,258.56,241.40,243.14,4510000,243.14 1929-12-11,262.20,264.23,256.45,258.44,3900000,258.44 1929-12-10,259.18,263.98,255.52,262.20,3650000,262.20 1929-12-09,263.46,267.56,257.41,259.18,5020000,259.18 1929-12-06,251.51,261.52,250.27,260.12,4720000,260.12 1929-12-05,254.64,256.45,249.55,251.51,4380000,251.51 1929-12-04,249.61,256.63,248.05,254.64,4440000,254.64 1929-12-03,241.70,251.13,241.38,249.61,3810000,249.61 1929-12-02,238.95,243.39,236.13,241.70,2510000,241.70 1929-11-27,235.35,240.66,233.59,238.95,2430000,238.95 1929-11-26,243.44,243.97,234.51,235.35,3630000,235.35 1929-11-25,245.74,246.06,237.41,243.44,3020000,243.44 1929-11-22,248.49,250.75,243.36,245.74,2930000,245.74 1929-11-21,241.23,249.57,238.61,248.49,3140000,248.49 1929-11-20,235.18,243.48,235.18,241.23,2830000,241.23 1929-11-19,227.56,234.56,222.93,234.02,2720000,234.02 1929-11-18,228.73,233.36,224.71,227.56,2750000,227.56 1929-11-15,222.50,232.77,222.50,228.73,4340000,228.73 1929-11-14,205.61,219.49,205.61,217.28,5570000,217.28 1929-11-13,209.74,211.92,195.35,198.69,7760000,198.69 1929-11-12,220.39,222.56,208.09,209.74,6450000,209.74 1929-11-11,235.13,235.13,219.34,220.39,3640000,220.39 1929-11-08,238.19,245.28,234.63,236.53,3220000,236.53 1929-11-07,232.13,242.10,217.84,238.19,7180000,238.19 1929-11-06,252.20,252.20,228.35,232.13,5920000,232.13 1929-11-04,269.75,269.75,255.43,257.68,6200000,257.68 1929-10-31,264.97,281.54,264.97,273.51,7150000,273.51 1929-10-30,230.98,260.93,230.98,258.47,10730000,258.47 1929-10-29,252.38,252.38,212.33,230.07,16410000,230.07 1929-10-28,295.18,295.18,256.75,260.64,9210000,260.64 1929-10-25,299.47,306.02,295.59,301.22,5920000,301.22 1929-10-24,305.85,312.76,272.32,299.47,12900000,299.47 1929-10-23,326.51,329.94,303.84,305.85,6370000,305.85 1929-10-22,322.03,333.01,322.03,326.51,4130000,326.51 1929-10-21,323.87,328.28,314.55,320.91,6090000,320.91 1929-10-18,341.86,343.12,332.16,333.29,3510000,333.29 1929-10-17,336.13,343.24,332.11,341.86,3860000,341.86 1929-10-16,346.99,346.99,335.12,336.13,4090000,336.13 1929-10-15,350.97,354.09,345.58,347.24,3110000,347.24 1929-10-14,352.69,358.20,348.94,350.97,2760000,350.97 1929-10-11,352.86,358.77,349.64,352.69,3960000,352.69 1929-10-10,346.66,355.63,345.63,352.86,4000000,352.86 1929-10-09,345.00,349.03,338.86,346.66,3160000,346.66 1929-10-08,345.72,349.67,340.86,345.00,3760000,345.00 1929-10-07,341.36,348.54,338.86,345.72,4260000,345.72 1929-10-04,329.95,333.28,320.45,325.17,5620000,325.17 1929-10-03,344.50,345.30,327.71,329.95,4750000,329.95 1929-10-02,342.57,350.19,339.45,344.50,3370000,344.50 1929-10-01,343.45,345.67,335.99,342.57,4530000,342.57 1929-09-30,347.17,349.37,341.06,343.45,3210000,343.45 1929-09-27,354.63,354.63,343.26,344.87,4590000,344.87 1929-09-26,352.57,358.16,349.73,355.95,4000000,355.95 1929-09-25,352.61,355.42,344.85,352.57,4960000,352.57 1929-09-24,359.00,363.67,350.84,352.61,4410000,352.61 1929-09-23,361.16,365.03,355.63,359.00,4390000,359.00 1929-09-20,369.97,371.10,360.44,362.05,4880000,362.05 1929-09-19,370.90,375.20,367.70,369.97,4130000,369.97 1929-09-18,368.52,374.03,365.65,370.90,4040000,370.90 1929-09-17,372.39,374.96,366.89,368.52,4290000,368.52 1929-09-16,367.01,373.63,364.80,372.39,4180000,372.39 1929-09-13,366.35,369.67,359.70,366.85,5070000,366.85 1929-09-12,370.91,375.52,363.11,366.35,5020000,366.35 1929-09-11,367.29,375.05,366.22,370.91,4790000,370.91 1929-09-10,374.93,379.16,364.46,367.29,4520000,367.29 1929-09-09,377.56,380.57,373.49,374.93,4860000,374.93 1929-09-06,369.77,378.71,369.46,376.29,5120000,376.29 1929-09-05,379.61,382.01,367.35,369.77,5560000,369.77 1929-09-04,380.12,380.12,376.33,379.61,4690000,379.61 1929-09-03,380.33,386.10,378.23,381.17,4440000,381.17 1929-08-30,376.18,383.96,376.16,380.33,4570000,380.33 1929-08-29,372.06,378.76,370.79,376.18,3480000,376.18 1929-08-28,373.79,377.56,370.34,372.06,3960000,372.06 1929-08-27,374.46,378.16,371.76,373.79,3900000,373.79 1929-08-26,375.44,380.18,372.09,374.46,4430000,374.46 1929-08-23,369.96,378.66,369.96,374.61,4800000,374.61 1929-08-22,365.55,373.45,365.39,369.95,3440000,369.95 1929-08-21,367.67,374.67,363.41,365.55,4720000,365.55 1929-08-20,366.31,372.59,366.31,367.67,4640000,367.67 1929-08-19,360.70,367.78,355.68,365.20,3980000,365.20 1929-08-16,356.84,364.39,356.84,361.49,4800000,361.49 1929-08-15,354.86,358.65,351.68,354.42,3410000,354.42 1929-08-14,354.03,361.55,352.05,354.86,4200000,354.86 1929-08-13,351.13,357.88,348.24,354.03,4100000,354.03 1929-08-12,345.71,354.36,345.71,351.13,3610000,351.13 1929-08-09,344.89,344.89,336.13,337.99,5020000,337.99 1929-08-08,348.44,354.17,347.79,352.10,2830000,352.10 1929-08-07,351.39,353.27,344.67,348.44,3160000,348.44 1929-08-06,352.50,354.52,346.98,351.39,3800000,351.39 1929-08-05,355.62,358.66,350.68,352.50,3860000,352.50 1929-08-02,350.56,358.31,349.77,353.08,4030000,353.08 1929-08-01,347.70,353.30,346.39,350.56,3320000,350.56 1929-07-31,343.12,349.79,342.65,347.70,3410000,347.70 1929-07-30,339.21,346.04,339.10,343.12,2690000,343.12 1929-07-29,343.73,344.93,336.36,339.21,2760000,339.21 1929-07-26,344.67,349.45,341.95,345.47,3550000,345.47 1929-07-25,343.04,348.15,340.09,344.67,3480000,344.67 1929-07-24,345.48,349.30,341.95,343.04,3780000,343.04 1929-07-23,341.37,347.85,339.65,345.48,3780000,345.48 1929-07-22,345.87,347.85,339.32,341.37,3680000,341.37 1929-07-19,344.59,349.19,343.15,345.20,4200000,345.20 1929-07-18,345.63,347.69,343.40,344.59,3730000,344.59 1929-07-17,344.24,349.79,341.41,345.63,4360000,345.63 1929-07-16,341.93,347.98,339.98,344.24,4500000,344.24 1929-07-15,345.94,347.04,340.05,341.93,4290000,341.93 1929-07-12,343.34,350.26,343.34,346.37,4760000,346.37 1929-07-11,343.30,347.12,340.12,343.04,4210000,343.04 1929-07-10,345.57,347.64,340.19,343.30,4210000,343.30 1929-07-09,346.55,349.29,342.00,345.57,4250000,345.57 1929-07-08,344.66,350.09,342.45,346.55,3520000,346.55 1929-07-05,341.99,348.67,340.84,344.27,3750000,344.27 1929-07-03,340.28,345.84,337.50,341.99,4690000,341.99 1929-07-02,335.22,343.07,333.48,340.28,4590000,340.28 1929-07-01,333.79,339.09,332.23,335.22,4090000,335.22 1929-06-28,328.91,335.25,328.15,331.65,3950000,331.65 1929-06-27,328.60,333.66,325.68,328.91,3910000,328.91 1929-06-26,326.16,332.75,325.13,328.60,4030000,328.60 1929-06-25,321.15,327.95,319.27,326.16,2930000,326.16 1929-06-24,322.23,325.73,319.91,321.15,3030000,321.15 1929-06-21,317.73,323.46,316.32,320.68,3190000,320.68 1929-06-20,316.41,320.43,314.32,317.73,2760000,317.73 1929-06-19,319.67,321.26,314.62,316.41,3060000,316.41 1929-06-18,319.33,323.30,317.64,319.67,3340000,319.67 1929-06-17,314.26,322.43,313.58,319.33,3210000,319.33 1929-06-14,313.05,317.39,311.76,313.68,3240000,313.68 1929-06-13,306.80,314.21,306.80,313.05,3160000,313.05 1929-06-12,306.64,309.29,304.25,306.68,2130000,306.68 1929-06-11,303.27,307.60,301.22,306.64,2140000,306.64 1929-06-10,305.12,307.60,301.86,303.27,2200000,303.27 1929-06-07,307.72,312.00,304.75,307.46,3080000,307.46 1929-06-06,307.68,310.50,305.33,307.72,2930000,307.72 1929-06-05,310.57,311.97,305.42,307.68,3340000,307.68 1929-06-04,304.33,311.44,304.33,310.57,3410000,310.57 1929-06-03,299.12,307.07,298.53,304.20,3020000,304.20 1929-05-31,296.76,300.27,290.02,297.41,3300000,297.41 1929-05-29,298.87,302.32,295.18,296.76,2980000,296.76 1929-05-28,293.42,300.08,291.80,298.87,3940000,298.87 1929-05-27,304.19,304.19,291.82,293.42,4350000,293.42 1929-05-24,308.09,313.30,304.32,305.64,3270000,305.64 1929-05-23,300.83,309.51,300.42,308.09,3810000,308.09 1929-05-22,311.89,311.89,300.54,300.83,4840000,300.83 1929-05-21,312.70,316.41,308.32,314.09,4410000,314.09 1929-05-20,321.48,322.65,312.18,312.70,3810000,312.70 1929-05-17,320.09,325.64,318.81,321.38,3330000,321.38 1929-05-16,319.35,321.89,314.51,320.09,3440000,320.09 1929-05-15,320.79,324.38,317.93,319.35,3350000,319.35 1929-05-14,316.49,322.33,315.17,320.79,3630000,320.79 1929-05-13,324.49,324.49,313.56,316.49,4630000,316.49 1929-05-10,321.49,328.00,321.49,325.70,3920000,325.70 1929-05-09,323.51,324.87,317.09,321.17,3660000,321.17 1929-05-08,321.91,325.90,318.87,323.51,3470000,323.51 1929-05-07,326.16,327.55,320.60,321.91,3490000,321.91 1929-05-06,327.08,331.01,323.01,326.16,3810000,326.16 1929-05-03,322.33,329.11,322.33,325.56,4530000,325.56 1929-05-02,320.13,324.60,317.73,321.52,4180000,321.52 1929-05-01,319.29,323.99,317.90,320.13,4690000,320.13 1929-04-30,314.02,321.09,314.02,319.29,4320000,319.29 1929-04-29,315.55,315.55,309.62,313.84,3270000,313.84 1929-04-26,314.28,319.31,311.00,314.15,4010000,314.15 1929-04-25,315.66,317.23,312.37,314.28,3340000,314.28 1929-04-24,316.62,320.00,313.56,315.66,4070000,315.66 1929-04-23,315.33,320.10,314.20,316.62,4130000,316.62 1929-04-22,311.98,318.26,311.98,315.33,3570000,315.33 1929-04-19,311.87,313.81,308.67,310.58,3060000,310.58 1929-04-18,309.91,315.22,309.63,311.87,3770000,311.87 1929-04-17,304.19,311.19,304.16,309.91,3500000,309.91 1929-04-16,302.43,305.51,299.30,304.19,2370000,304.19 1929-04-15,304.41,305.55,301.54,302.43,2640000,302.43 1929-04-12,304.09,309.79,303.61,305.43,3410000,305.43 1929-04-11,300.67,307.90,299.40,304.09,3100000,304.09 1929-04-10,299.13,304.60,295.71,300.67,3280000,300.67 1929-04-09,301.49,306.29,297.17,299.13,3630000,299.13 1929-04-08,302.81,305.70,299.52,301.49,2720000,301.49 1929-04-05,305.37,307.97,301.39,303.04,3410000,303.04 1929-04-04,300.35,306.99,295.79,305.37,3330000,305.37 1929-04-03,303.49,307.70,298.58,300.35,3700000,300.35 1929-04-02,300.40,305.20,298.07,303.49,3780000,303.49 1929-04-01,304.21,304.21,294.11,300.40,4160000,300.40 1929-03-28,303.22,311.13,302.93,308.85,5100000,308.85 1929-03-27,296.51,305.87,293.70,303.22,5620000,303.22 1929-03-26,297.50,300.60,281.51,296.51,8250000,296.51 1929-03-25,306.21,311.55,294.34,297.50,5860000,297.50 1929-03-22,314.63,316.94,307.50,310.26,4830000,310.26 1929-03-21,316.44,319.31,313.12,314.63,4460000,314.63 1929-03-20,317.53,320.06,313.27,316.44,5190000,316.44 1929-03-19,317.59,321.28,315.23,317.53,4450000,317.53 1929-03-18,320.00,321.70,315.49,317.59,5020000,317.59 1929-03-15,316.26,322.75,315.92,319.70,5890000,319.70 1929-03-14,311.02,317.98,311.02,316.26,4630000,316.26 1929-03-13,306.79,312.30,306.79,310.29,3330000,310.29 1929-03-12,305.75,312.76,305.20,306.14,3060000,306.14 1929-03-11,311.61,312.96,305.20,305.75,3630000,305.75 1929-03-08,308.99,313.25,304.78,311.59,3950000,311.59 1929-03-07,305.20,310.39,303.05,308.99,3630000,308.99 1929-03-06,310.20,313.45,302.93,305.20,4490000,305.20 1929-03-05,313.86,316.17,308.22,310.20,4430000,310.20 1929-03-04,319.12,320.22,312.85,313.86,4560000,313.86 1929-03-01,317.79,324.40,317.79,321.18,6020000,321.18 1929-02-28,314.53,319.69,312.91,317.41,4970000,317.41 1929-02-27,311.25,317.03,309.71,314.53,4370000,314.53 1929-02-26,311.24,313.49,307.04,311.25,3740000,311.25 1929-02-25,310.06,315.08,308.10,311.24,3510000,311.24 1929-02-21,305.99,311.31,303.45,310.06,3400000,310.06 1929-02-20,301.10,307.55,301.05,305.99,2910000,305.99 1929-02-19,300.74,305.58,299.00,301.10,3210000,301.10 1929-02-18,295.85,301.68,293.40,300.74,3480000,300.74 1929-02-15,306.49,309.79,299.38,300.41,3900000,300.41 1929-02-14,308.07,308.29,300.60,306.49,3730000,306.49 1929-02-13,310.35,316.06,307.15,308.07,4530000,308.07 1929-02-11,301.53,311.24,299.58,310.35,3890000,310.35 1929-02-08,305.75,309.26,298.03,301.53,4550000,301.53 1929-02-07,312.56,312.56,302.40,305.75,5210000,305.75 1929-02-06,322.06,323.31,313.32,317.18,4680000,317.18 1929-02-05,319.05,324.51,317.28,322.06,4070000,322.06 1929-02-04,319.76,323.74,316.91,319.05,4050000,319.05 1929-02-01,317.51,324.16,315.64,319.68,4970000,319.68 1929-01-31,312.60,319.96,311.34,317.51,4680000,317.51 1929-01-30,312.60,316.15,308.47,312.60,4130000,312.60 1929-01-29,314.04,316.33,309.23,312.60,4290000,312.60 1929-01-28,314.56,319.58,310.44,314.04,4980000,314.04 1929-01-25,311.28,319.36,311.28,315.13,5510000,315.13 1929-01-24,310.33,314.01,305.94,309.39,4500000,309.39 1929-01-23,307.06,315.49,306.46,310.33,4920000,310.33 1929-01-22,304.64,310.08,303.05,307.06,5120000,307.06 1929-01-21,305.96,308.22,302.36,304.64,4900000,304.64 1929-01-18,303.95,308.05,301.05,304.14,4940000,304.14 1929-01-17,302.66,306.96,301.09,303.95,4260000,303.95 1929-01-16,297.66,304.36,297.00,302.66,3670000,302.66 1929-01-15,304.06,306.09,295.78,297.66,4180000,297.66 1929-01-14,301.25,306.26,299.06,304.06,3920000,304.06 1929-01-11,301.58,305.85,298.68,301.66,4240000,301.66 1929-01-10,300.83,305.20,299.60,301.58,4020000,301.58 1929-01-09,296.98,303.04,296.37,300.83,4050000,300.83 1929-01-08,297.70,300.87,292.89,296.98,3850000,296.98 1929-01-07,301.85,301.85,293.35,297.70,4800000,297.70 1929-01-04,305.72,307.51,299.92,304.75,5530000,304.75 1929-01-03,307.01,311.46,302.90,305.72,5100000,305.72 1929-01-02,300.31,308.66,300.31,307.01,5410000,307.01 1928-12-31,297.28,301.61,291.99,300.00,4890000,300.00 1928-12-28,290.95,297.79,283.68,296.52,4800000,296.52 1928-12-27,286.13,291.39,281.55,290.95,3570000,290.95 1928-12-26,287.89,292.53,283.94,286.13,3620000,286.13 1928-12-24,285.94,291.17,284.30,287.89,3700000,287.89 1928-12-21,282.01,288.39,281.31,286.53,3460000,286.53 1928-12-20,280.50,285.60,277.91,282.01,3810000,282.01 1928-12-19,275.41,282.84,275.09,280.50,3400000,280.50 1928-12-18,270.23,276.41,269.61,275.41,2270000,275.41 1928-12-17,270.72,272.27,266.21,270.23,2230000,270.23 1928-12-14,266.99,275.45,266.99,272.26,3010000,272.26 1928-12-13,266.82,269.53,260.51,266.88,3270000,266.88 1928-12-12,269.34,272.74,264.95,266.82,3990000,266.82 1928-12-11,263.95,270.41,263.71,269.34,3920000,269.34 1928-12-10,257.33,266.22,254.36,263.95,5220000,263.95 1928-12-07,279.79,284.44,269.58,271.05,6190000,271.05 1928-12-06,290.68,292.56,278.66,279.79,5410000,279.79 1928-12-05,291.30,294.20,289.91,290.68,4380000,290.68 1928-12-04,289.23,295.61,287.61,291.30,4920000,291.30 1928-12-03,290.80,291.05,283.89,289.23,4490000,289.23 1928-11-30,295.62,299.07,288.82,293.38,6410000,293.38 1928-11-28,292.39,299.35,290.68,295.62,6370000,295.62 1928-11-27,291.16,294.75,289.28,292.39,5280000,292.39 1928-11-26,288.22,296.10,288.14,291.16,5320000,291.16 1928-11-23,290.34,295.17,284.40,288.22,6940000,288.22 1928-11-22,281.28,292.12,281.28,290.34,5840000,290.34 1928-11-21,283.90,288.71,276.91,280.53,6170000,280.53 1928-11-20,278.78,288.13,278.05,283.90,6440000,283.90 1928-11-19,277.48,280.68,272.98,278.78,5100000,278.78 1928-11-16,271.48,278.65,271.48,276.66,6730000,276.66 1928-11-15,268.60,271.63,266.48,269.42,4730000,269.42 1928-11-14,269.89,272.94,266.80,268.60,5410000,268.60 1928-11-13,269.67,272.03,264.88,269.89,5240000,269.89 1928-11-12,265.08,272.47,264.42,269.67,5680000,269.67 1928-11-09,261.11,266.24,259.37,263.05,4950000,263.05 1928-11-08,260.68,265.26,258.96,261.11,4850000,261.11 1928-11-07,258.48,263.97,258.48,260.68,4830000,260.68 1928-11-05,254.16,259.68,253.89,257.58,3740000,257.58 1928-11-02,255.23,256.99,252.46,254.38,3490000,254.38 1928-11-01,252.16,256.59,251.56,255.23,3450000,255.23 1928-10-31,253.70,256.16,248.76,252.16,3490000,252.16 1928-10-30,257.13,259.00,252.39,253.70,3450000,253.70 1928-10-29,255.51,259.76,255.03,257.13,3690000,257.13 1928-10-26,256.48,259.16,250.00,251.44,4520000,251.44 1928-10-25,257.03,260.03,254.93,256.48,4210000,256.48 1928-10-24,256.04,260.39,249.09,257.03,4550000,257.03 1928-10-23,253.60,258.60,252.73,256.04,4160000,256.04 1928-10-22,253.75,257.32,250.08,253.60,3860000,253.60 1928-10-19,251.88,259.19,250.35,256.59,4560000,256.59 1928-10-18,250.87,255.24,249.65,251.88,4400000,251.88 1928-10-17,249.43,253.60,247.74,250.87,3960000,250.87 1928-10-16,249.85,255.27,247.92,249.43,4530000,249.43 1928-10-15,249.13,252.70,247.33,249.85,4040000,249.85 1928-10-11,246.53,250.14,245.57,247.69,3970000,247.69 1928-10-10,241.73,249.06,241.57,246.53,4170000,246.53 1928-10-09,239.55,242.86,236.79,241.73,3820000,241.73 1928-10-08,240.17,243.33,237.72,239.55,3940000,239.55 1928-10-05,240.00,243.08,238.22,240.44,4360000,240.44 1928-10-04,237.75,242.53,237.72,240.00,4330000,240.00 1928-10-03,238.14,239.14,233.60,237.75,4060000,237.75 1928-10-02,240.01,241.54,235.42,238.14,3850000,238.14 1928-10-01,239.43,242.46,238.24,240.01,3500000,240.01 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_cusum.py000066400000000000000000000062141224417117700261750ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Apr 02 11:41:25 2010 Author: josef-pktd """ import numpy as np from scipy import stats from numpy.testing import assert_almost_equal import statsmodels.api as sm from statsmodels.sandbox.regression.onewaygls import OneWayLS from statsmodels.stats.diagnostic import recursive_olsresiduals from statsmodels.sandbox.stats.diagnostic import _recursive_olsresiduals2 as recursive_olsresiduals2 #examples from ex_onewaygls.py #choose example #-------------- example = ['null', 'smalldiff', 'mediumdiff', 'largediff'][1] example_size = [20, 100][1] example_groups = ['2', '2-2'][1] #'2-2': 4 groups, # groups 0 and 1 and groups 2 and 3 have identical parameters in DGP #generate example #---------------- #np.random.seed(87654589) nobs = example_size x1 = 0.1+np.random.randn(nobs) y1 = 10 + 15*x1 + 2*np.random.randn(nobs) x1 = sm.add_constant(x1, prepend=False) #assert_almost_equal(x1, np.vander(x1[:,0],2), 16) #res1 = sm.OLS(y1, x1).fit() #print res1.params #print np.polyfit(x1[:,0], y1, 1) #assert_almost_equal(res1.params, np.polyfit(x1[:,0], y1, 1), 14) #print res1.summary(xname=['x1','const1']) #regression 2 x2 = 0.1+np.random.randn(nobs) if example == 'null': y2 = 10 + 15*x2 + 2*np.random.randn(nobs) # if H0 is true elif example == 'smalldiff': y2 = 11 + 16*x2 + 2*np.random.randn(nobs) elif example == 'mediumdiff': y2 = 12 + 16*x2 + 2*np.random.randn(nobs) else: y2 = 19 + 17*x2 + 2*np.random.randn(nobs) x2 = sm.add_constant(x2, prepend=False) # stack x = np.concatenate((x1,x2),0) y = np.concatenate((y1,y2)) if example_groups == '2': groupind = (np.arange(2*nobs)>nobs-1).astype(int) else: groupind = np.mod(np.arange(2*nobs),4) groupind.sort() #x = np.column_stack((x,x*groupind[:,None])) res1 = sm.OLS(y, x).fit() skip = 8 rresid, rparams, rypred, rresid_standardized, rresid_scaled, rcusum, rcusumci = \ recursive_olsresiduals(res1, skip) print rcusum print rresid_scaled[skip-1:] assert_almost_equal(rparams[-1], res1.params) import matplotlib.pyplot as plt plt.plot(rcusum) plt.plot(rcusumci[0]) plt.plot(rcusumci[1]) plt.figure() plt.plot(rresid) plt.plot(np.abs(rresid)) print 'cusum test reject:' print ((rcusum[1:]>rcusumci[1])|(rcusum[1:]>> dir(form) ['_Formula__namespace', '__add__', '__call__', '__class__', '__delattr__', '__dict__', '__doc__', '__getattribute__', '__getitem__', '__hash__', '__init__', '__module__', '__mul__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', '__sub__', '__weakref__', '_del_namespace', '_get_namespace', '_names', '_set_namespace', '_termnames', '_terms_changed', 'design', 'hasterm', 'names', 'namespace', 'termcolumns', 'termnames', 'terms'] >>> form.design().shape (40, 10) >>> form.termnames() ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] >>> form.namespace.keys() ['A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'J'] >>> form.names() ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'] >>> form.termcolumns(formula.Term('C')) [2] >>> form.termcolumns('C') Traceback (most recent call last): File "", line 1, in form.termcolumns('C') File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental\scikits\statsmodels\sandbox\formula.py", line 494, in termcolumns raise ValueError, 'term not in formula' ValueError: term not in formula ''' print form.hasterm('C') print form.termcolumns(formula.Term('C')) #doesn't work with string argument #Example: use two columns and get contrast f2 = (form['A']+form['B']) print f2 print repr(f2) f2.namespace.keys() #namespace is still empty f2.namespace = namespace #associate data f2.namespace.keys() f2.design().shape contrast.Contrast(formula.Term('A'), f2).matrix ''' >>> f2 = (form['A']+form['B']) >>> print f2 >>> print repr(f2) >>> f2.namespace.keys() #namespace is still empty [] >>> f2.namespace = namespace #associate data >>> f2.namespace.keys() ['A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'J'] >>> f2.design().shape (40, 2) >>> contrast.Contrast(formula.Term('A'), f2).matrix array([ 1., 0.]) ''' #Example: product of terms #------------------------- f3 = (form['A']*form['B']) f3.namespace f3.namespace = namespace f3.design().shape np.min(np.abs(f3.design() - f2.design().prod(1))) ''' >>> f3 = (form['A']*form['B']) >>> f3.namespace {} >>> f3.namespace = namespace >>> f3.design().shape (40,) >>> np.min(np.abs(f3.design() - f2.design().prod(1))) 0.0 ''' #Example: Interactions of two terms #---------------------------------- #I don't get contrast of product term f4 = formula.interactions([form['A'],form['B']]) f4.namespace f4.namespace = namespace print f4 f4.names() f4.design().shape contrast.Contrast(formula.Term('A'), f4).matrix #contrast.Contrast(formula.Term('A*B'), f4).matrix ''' >>> formula.interactions([form['A'],form['B']]) >>> f4 = formula.interactions([form['A'],form['B']]) >>> f4.namespace {} >>> f4.namespace = namespace >>> print f4 >>> f4.names() ['A*B', 'A', 'B'] >>> f4.design().shape (40, 3) >>> contrast.Contrast(formula.Term('A'), f4).matrix array([ 0.00000000e+00, 1.00000000e+00, 7.63278329e-17]) >>> contrast.Contrast(formula.Term('A*B'), f4).matrix Traceback (most recent call last): File "c:\...\scikits\statsmodels\sandbox\contrast_old.py", line 112, in _get_matrix self.compute_matrix() File "c:\...\scikits\statsmodels\sandbox\contrast_old.py", line 91, in compute_matrix T = np.transpose(np.array(t(*args, **kw))) File "c:\...\scikits\statsmodels\sandbox\formula.py", line 150, in __call__ If the term has no 'func' attribute, it returns KeyError: 'A*B' ''' #Other #----- '''Exception if there is no data or key: >>> contrast.Contrast(formula.Term('a'), f2).matrix Traceback (most recent call last): File "c:\..\scikits\statsmodels\sandbox\contrast_old.py", line 112, in _get_matrix self.compute_matrix() File "c:\...\scikits\statsmodels\sandbox\contrast_old.py", line 91, in compute_matrix T = np.transpose(np.array(t(*args, **kw))) File "c:\...\scikits\statsmodels\sandbox\formula.py", line 150, in __call__ If the term has no 'func' attribute, it returns KeyError: 'a' ''' f = ['a']*3 + ['b']*3 + ['c']*2 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} #Example: formula with factor # I don't manage to combine factors with formulas, e.g. a joint # designmatrix # also I don't manage to get contrast matrices with factors # it looks like I might have to add namespace for dummies myself ? # even then combining still doesn't work f5 = formula.Term('A') + fac namespace['A'] = form.namespace['A'] formula.Formula(fac).design() ''' >>> formula.Formula(fac).design() array([[ 1., 0., 0.], [ 1., 0., 0.], [ 1., 0., 0.], [ 0., 1., 0.], [ 0., 1., 0.], [ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 1.]]) >>> contrast.Contrast(formula.Term('(ff==a)'), fac).matrix Traceback (most recent call last): File "c:\...\scikits\statsmodels\sandbox\contrast_old.py", line 112, in _get_matrix self.compute_matrix() File "c:\...\scikits\statsmodels\sandbox\contrast_old.py", line 91, in compute_matrix T = np.transpose(np.array(t(*args, **kw))) File "c:\...\scikits\statsmodels\sandbox\formula.py", line 150, in __call__ If the term has no 'func' attribute, it returns KeyError: '(ff==a)' ''' #convert factor to formula f7 = formula.Formula(fac) # explicit updating of namespace with f7.namespace.update(dict(zip(fac.names(),fac()))) # contrast matrix with 2 of 3 terms contrast.Contrast(formula.Term('(ff==b)')+formula.Term('(ff==a)'), f7).matrix #array([[ 1., 0., 0.], # [ 0., 1., 0.]]) # contrast matrix for all terms contrast.Contrast(f7, f7).matrix #array([[ 1., 0., 0.], # [ 0., 1., 0.], # [ 0., 0., 1.]]) # contrast matrix for difference groups 1,2 versus group 0 contrast.Contrast(formula.Term('(ff==b)')+formula.Term('(ff==c)'), f7).matrix - contrast.Contrast(formula.Term('(ff==a)'), f7).matrix #array([[-1., 1., 0.], # [-1., 0., 1.]]) # all pairwise contrasts cont = [] for i,j in zip(*np.triu_indices(len(f7.names()),1)): ci = contrast.Contrast(formula.Term(f7.names()[i]), f7).matrix ci -= contrast.Contrast(formula.Term(f7.names()[j]), f7).matrix cont.append(ci) cont = np.array(cont) cont #array([[ 1., -1., 0.], # [ 1., 0., -1.], # [ 0., 1., -1.]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_formula_factor.py000066400000000000000000000016461224417117700300500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat May 15 19:59:42 2010 Author: josef-pktd """ import numpy as np from statsmodels.sandbox import formula import statsmodels.sandbox.contrast_old as contrast #define a categorical variable - factor f0 = ['a','b','c']*4 f = ['a']*4 + ['b']*3 + ['c']*4 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} fac.values() [f for f in dir(fac) if f[0] != '_'] #create dummy variable fac.get_columns().shape fac.get_columns().T #this is a way of encoding effects from a categorical variable #different from using dummy variables #I never seen a reference for this. fac.main_effect(reference=1) #dir(fac.main_effect(reference=1)) fac.main_effect(reference=1)() #fac.main_effect(reference=1).func fac.main_effect(reference=1).names() fac.main_effect(reference=2).names() fac.main_effect(reference=2)().shape #columns for the design matrix fac.main_effect(reference=2)().T fac.names() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_gam_results.py000066400000000000000000000031741224417117700273700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example results for GAM from tests Created on Mon Nov 07 13:13:15 2011 Author: Josef Perktold The example is loaded from a test module. The test still fails but the results look relatively good. I don't know yet why there is the small difference and why GAM doesn't converge in this case """ from statsmodels.sandbox.tests.test_gam import _estGAMGaussianLogLink tt = _estGAMGaussianLogLink() comp, const = tt.res_gam.smoothed_demeaned(tt.mod_gam.exog) comp_glm_ = tt.res2.model.exog * tt.res2.params comp1 = comp_glm_[:,1:4].sum(1) mean1 = comp1.mean() comp1 -= mean1 comp2 = comp_glm_[:,4:].sum(1) mean2 = comp2.mean() comp2 -= mean2 comp1_true = tt.res2.model.exog[:,1:4].sum(1) mean1 = comp1_true.mean() comp1_true -= mean1 comp2_true = tt.res2.model.exog[:,4:].sum(1) mean2 = comp2_true.mean() comp2_true -= mean2 noise = tt.res2.model.endog - tt.mu_true noise_eta = tt.family.link(tt.res2.model.endog) - tt.y_true import matplotlib.pyplot as plt plt.figure() plt.plot(noise, 'k.') plt.figure() plt.plot(comp, 'r-') plt.plot(comp1, 'b-') plt.plot(comp2, 'b-') plt.plot(comp1_true, 'k--', lw=2) plt.plot(comp2_true, 'k--', lw=2) #the next doesn't make sense - non-linear #c1 = tt.family.link(tt.family.link.inverse(comp1_true) + noise) #c2 = tt.family.link(tt.family.link.inverse(comp2_true) + noise) #not nice in example/plot: noise variance is constant not proportional plt.plot(comp1_true + noise_eta, 'g.', alpha=0.95) plt.plot(comp2_true + noise_eta, 'r.', alpha=0.95) #plt.plot(c1, 'g.', alpha=0.95) #plt.plot(c2, 'r.', alpha=0.95) plt.title('Gaussian loglink, GAM (red), GLM (blue), true (black)') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_kaplan_meier.py000066400000000000000000000052321224417117700274670ustar00rootroot00000000000000#An example for the Kaplan-Meier estimator import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np from statsmodels.sandbox.survival2 import KaplanMeier #Getting the strike data as an array dta = sm.datasets.strikes.load() print 'basic data' print '\n' dta = dta.values()[-1] print dta[range(5),:] print '\n' #Create the KaplanMeier object and fit the model km = KaplanMeier(dta,0) km.fit() #show the results km.plot() print 'basic model' print '\n' km.summary() print '\n' #Mutiple survival curves km2 = KaplanMeier(dta,0,exog=1) km2.fit() print 'more than one curve' print '\n' km2.summary() print '\n' km2.plot() #with censoring censoring = np.ones_like(dta[:,0]) censoring[dta[:,0] > 80] = 0 dta = np.c_[dta,censoring] print 'with censoring' print '\n' print dta[range(5),:] print '\n' km3 = KaplanMeier(dta,0,exog=1,censoring=2) km3.fit() km3.summary() print '\n' km3.plot() #Test for difference of survival curves log_rank = km3.test_diff([0.0645,-0.03957]) print 'log rank test' print '\n' print log_rank print '\n' #The zeroth element of log_rank is the chi-square test statistic #for the difference between the survival curves for exog = 0.0645 #and exog = -0.03957, the index one element is the degrees of freedom for #the test, and the index two element is the p-value for the test wilcoxon = km3.test_diff([0.0645,-0.03957], rho=1) print 'Wilcoxon' print '\n' print wilcoxon print '\n' #Same info as log_rank, but for Peto and Peto modification to the #Gehan-Wilcoxon test #User specified functions for tests #A wider range of rates can be accessed by using the 'weight' parameter #for the test_diff method #For example, if the desire weights are S(t)*(1-S(t)), where S(t) is a pooled #estimate for the survival function, this could be computed by doing def weights(t): #must accept one arguement, even though it is not used here s = KaplanMeier(dta,0,censoring=2) s.fit() s = s.results[0][0] s = s * (1 - s) return s #KaplanMeier provides an array of times to the weighting function #internally, so the weighting function must accept one arguement test = km3.test_diff([0.0645,-0.03957], weight=weights) print 'user specified weights' print '\n' print test print '\n' #Groups with nan names #These can be handled by passing the data to KaplanMeier as an array of strings groups = np.ones_like(dta[:,1]) groups = groups.astype('S4') groups[dta[:,1] > 0] = 'high' groups[dta[:,1] <= 0] = 'low' dta = dta.astype('S4') dta[:,1] = groups print 'with nan group names' print '\n' print dta[range(5),:] print '\n' km4 = KaplanMeier(dta,0,exog=1,censoring=2) km4.fit() km4.summary() print '\n' km4.plot() #show all the plots plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_mixed_lls_0.py000066400000000000000000000120411224417117700272330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example using OneWayMixed Created on Sat Dec 03 10:15:55 2011 Author: Josef Perktold This example constructs a linear model with individual specific random effects and random coefficients, and uses OneWayMixed to estimate it. """ import numpy as np from statsmodels.sandbox.panel.mixed import OneWayMixed, Unit examples = ['ex1'] if 'ex1' in examples: #np.random.seed(54321) np.random.seed(978326) nsubj = 2000 units = [] nobs_i = 4 #number of observations per unit, changed below nx = 4 #number fixed effects nz = 2 ##number random effects beta = np.ones(nx) gamma = 0.5 * np.ones(nz) #mean of random effect gamma[0] = 0 gamma_re_true = [] for i in range(nsubj): #create data for one unit #random effect/coefficient gamma_re = gamma + 0.2 * np.random.standard_normal(nz) #store true parameter for checking gamma_re_true.append(gamma_re) #for testing unbalanced case, let's change nobs per unit if i > nsubj//4: nobs_i = 6 #generate exogenous variables X = np.random.standard_normal((nobs_i, nx)) Z = np.random.standard_normal((nobs_i, nz-1)) Z = np.column_stack((np.ones(nobs_i), Z)) noise = 0.1 * np.random.randn(nobs_i) #sig_e = 0.1 #generate endogenous variable Y = np.dot(X, beta) + np.dot(Z, gamma_re) + noise #add random effect design matrix also to fixed effects to #capture the mean #this seems to be necessary to force mean of RE to zero !? #(It's not required for estimation but interpretation of random #effects covariance matrix changes - still need to check details. X = np.hstack((X,Z)) #create units and append to list unit = Unit(Y, X, Z) units.append(unit) m = OneWayMixed(units) import time t0 = time.time() m.initialize() res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6) t1 = time.time() print 'time for initialize and fit', t1-t0 print 'number of iterations', m.iterations #print dir(m) #print vars(m) print '\nestimates for fixed effects' print m.a print m.params bfixed_cov = m.cov_fixed() print 'beta fixed standard errors' print np.sqrt(np.diag(bfixed_cov)) print m.bse b_re = m.params_random_units print 'RE mean:', b_re.mean(0) print 'RE columns std', b_re.std(0) print 'np.cov(b_re, rowvar=0), sample statistic' print np.cov(b_re, rowvar=0) print 'std of above' print np.sqrt(np.diag(np.cov(b_re, rowvar=0))) print 'm.cov_random()' print m.cov_random() print 'std of above' print res.std_random() print np.sqrt(np.diag(m.cov_random())) print '\n(non)convergence of llf' print m.history['llf'][-4:] print 'convergence of parameters' #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0) print np.diff(np.vstack(m.history['params'][-4:]),axis=0) print 'convergence of D' print np.diff(np.array(m.history['D'][-4:]), axis=0) #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units]) zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units]) '''if Z is not included in X: >>> np.dot(b_re.T, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) >>> m.cov_random() array([[ 0.0348722 , -0.00909159], [-0.00909159, 0.26846254]]) >>> #note cov_random doesn't subtract mean! ''' print '\nchecking the random effects distribution and prediction' gamma_re_true = np.array(gamma_re_true) print 'mean of random effect true', gamma_re_true.mean(0) print 'mean from fixed effects ', m.params[-2:] print 'mean of estimated RE ', b_re.mean(0) print absmean_true = np.abs(gamma_re_true).mean(0) mape = ((m.params[-2:] + b_re) / gamma_re_true - 1).mean(0)*100 mean_abs_perc = np.abs((m.params[-2:] + b_re) - gamma_re_true).mean(0) \ / absmean_true*100 median_abs_perc = np.median(np.abs((m.params[-2:] + b_re) - gamma_re_true), 0) \ / absmean_true*100 rmse_perc = ((m.params[-2:] + b_re) - gamma_re_true).std(0) \ / absmean_true*100 print 'mape ', mape print 'mean_abs_perc ', mean_abs_perc print 'median_abs_perc', median_abs_perc print 'rmse_perc (std)', rmse_perc from numpy.testing import assert_almost_equal #assert is for n_units=100 in original example #I changed random number generation, so this won't work anymore #assert_almost_equal(rmse_perc, [ 34.14783884, 11.6031684 ], decimal=8) #now returns res print res.llf #based on MLE, does not include constant print res.tvalues print res.pvalues print res.t_test([1,-1,0,0,0,0]) print 'test mean of both random effects variables is zero' print res.f_test([[0,0,0,0,1,0], [0,0,0,0,0,1]]) plots = res.plot_random_univariate(bins=50) fig = res.plot_scatter_pairs(0, 1) import matplotlib.pyplot as plt plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_mixed_lls_re.py000066400000000000000000000123501224417117700275050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example using OneWayMixed Created on Sat Dec 03 10:15:55 2011 Author: Josef Perktold This example constructs a linear model with individual specific random effects, and uses OneWayMixed to estimate it. This is a variation on ex_mixed_lls_0.py. Here we only have a single individual specific constant, that is just a random effect without exogenous regressors. """ import numpy as np from statsmodels.sandbox.panel.mixed import OneWayMixed, Unit examples = ['ex1'] if 'ex1' in examples: #np.random.seed(54321) np.random.seed(978326) nsubj = 2000 units = [] nobs_i = 4 #number of observations per unit, changed below nx = 0 #number fixed effects nz = 1 ##number random effects beta = np.ones(nx) gamma = 0.5 * np.ones(nz) #mean of random effect gamma[0] = 0 gamma_re_true = [] for i in range(nsubj): #create data for one unit #random effect/coefficient gamma_re = gamma + 0.2 * np.random.standard_normal(nz) #store true parameter for checking gamma_re_true.append(gamma_re) #for testing unbalanced case, let's change nobs per unit if i > nsubj//4: nobs_i = 6 #generate exogenous variables X = np.random.standard_normal((nobs_i, nx)) Z = np.random.standard_normal((nobs_i, nz-1)) Z = np.column_stack((np.ones(nobs_i), Z)) noise = 0.1 * np.random.randn(nobs_i) #sig_e = 0.1 #generate endogenous variable Y = np.dot(X, beta) + np.dot(Z, gamma_re) + noise #add random effect design matrix also to fixed effects to #capture the mean #this seems to be necessary to force mean of RE to zero !? #(It's not required for estimation but interpretation of random #effects covariance matrix changes - still need to check details. X = np.hstack((X,Z)) #create units and append to list unit = Unit(Y, X, Z) units.append(unit) m = OneWayMixed(units) import time t0 = time.time() m.initialize() res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6) t1 = time.time() print 'time for initialize and fit', t1-t0 print 'number of iterations', m.iterations #print dir(m) #print vars(m) print '\nestimates for fixed effects' print m.a print m.params bfixed_cov = m.cov_fixed() print 'beta fixed standard errors' print np.sqrt(np.diag(bfixed_cov)) print m.bse b_re = m.params_random_units print 'RE mean:', b_re.mean(0) print 'RE columns std', b_re.std(0) print 'np.cov(b_re, rowvar=0), sample statistic' print np.cov(b_re, rowvar=0) print 'std of above' #need atleast_1d or diag raises exception print np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0)))) print 'm.cov_random()' print m.cov_random() print 'std of above' print res.std_random() print np.sqrt(np.diag(m.cov_random())) print '\n(non)convergence of llf' print m.history['llf'][-4:] print 'convergence of parameters' #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0) print np.diff(np.vstack(m.history['params'][-4:]),axis=0) print 'convergence of D' print np.diff(np.array(m.history['D'][-4:]), axis=0) #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units]) zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units]) '''if Z is not included in X: >>> np.dot(b_re.T, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) >>> m.cov_random() array([[ 0.0348722 , -0.00909159], [-0.00909159, 0.26846254]]) >>> #note cov_random doesn't subtract mean! ''' print '\nchecking the random effects distribution and prediction' gamma_re_true = np.array(gamma_re_true) print 'mean of random effect true', gamma_re_true.mean(0) print 'mean from fixed effects ', m.params[-2:] print 'mean of estimated RE ', b_re.mean(0) print absmean_true = np.abs(gamma_re_true).mean(0) mape = ((m.params[-2:] + b_re) / gamma_re_true - 1).mean(0)*100 mean_abs_perc = np.abs((m.params[-2:] + b_re) - gamma_re_true).mean(0) \ / absmean_true*100 median_abs_perc = np.median(np.abs((m.params[-2:] + b_re) - gamma_re_true), 0) \ / absmean_true*100 rmse_perc = ((m.params[-2:] + b_re) - gamma_re_true).std(0) \ / absmean_true*100 print 'mape ', mape print 'mean_abs_perc ', mean_abs_perc print 'median_abs_perc', median_abs_perc print 'rmse_perc (std)', rmse_perc from numpy.testing import assert_almost_equal #assert is for n_units=100 in original example #I changed random number generation, so this won't work anymore #assert_almost_equal(rmse_perc, [ 34.14783884, 11.6031684 ], decimal=8) #now returns res print 'llf', res.llf #based on MLE, does not include constant print 'tvalues', res.tvalues print 'pvalues', res.pvalues print res.t_test([1]) print 'test mean of both random effects variables is zero' print res.f_test([[1]]) plots = res.plot_random_univariate(bins=50) #fig = res.plot_scatter_pairs(0, 1) #no pairs import matplotlib.pyplot as plt plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py000066400000000000000000000171421224417117700307270ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example using OneWayMixed with within group intertemporal correlation Created on Sat Dec 03 10:15:55 2011 Author: Josef Perktold This example constructs a linear model with individual specific random effects, and uses OneWayMixed to estimate it. This is a variation on ex_mixed_lls_0.py. Here we use time dummies as random effects (all except 1st time period). I think, this should allow for (almost) arbitrary intertemporal correlation. The assumption is that each unit can have different constants, however the intertemporal covariance matrix is the same for all units. One caveat, to avoid singular matrices, we have to treat one time period differently. Estimation requires that the number of units is larger than the number of time periods. Also, it requires that we have the same number of periods for each unit. I needed to remove the first observation from the time dummies to avoid a singular matrix. So, interpretation of time effects should be relative to first observation. (I didn't check the math.) TODO: Note, I don't already have constant in X. Constant for first time observation is missing. Do I need all dummies in exog_fe, Z, but not in exog_re, Z? Tried this and it works. In the error decomposition we also have the noise variable, I guess this works like constant, so we get full rank (square) with only T-1 time dummies. But we don't get correlation with the noise, or do we? conditional? -> sample correlation of estimated random effects looks a bit high, upward bias? or still some problems with initial condition? correlation from estimated cov_random looks good. Since we include the time dummies also in the fixed effect, we can have arbitrary trends, different constants in each period. Intertemporal correlation in data generating process, DGP, to see if the results correctly estimate it. used AR(1) as example, but only starting at second period. (?) Note: we don't impose AR structure in the estimation """ import numpy as np from statsmodels.sandbox.panel.mixed import OneWayMixed, Unit examples = ['ex1'] if 'ex1' in examples: #np.random.seed(54321) #np.random.seed(978326) nsubj = 200 units = [] nobs_i = 8 #number of observations per unit, changed below nx = 1 #number fixed effects nz = nobs_i - 1 ##number random effects beta = np.ones(nx) gamma = 0.5 * np.ones(nz) #mean of random effect #gamma[0] = 0 gamma_re_true = [] for i in range(nsubj): #create data for one unit #random effect/coefficient use_correlated = True if not use_correlated: gamma_re = gamma + 0.2 * np.random.standard_normal(nz) else: #coefficients are AR(1) for all but first time periods from scipy import linalg as splinalg rho = 0.6 corr_re = splinalg.toeplitz(rho**np.arange(nz)) rvs = np.random.multivariate_normal(np.zeros(nz), corr_re) gamma_re = gamma + 0.2 * rvs #store true parameter for checking gamma_re_true.append(gamma_re) #generate exogenous variables X = np.random.standard_normal((nobs_i, nx)) #try Z should be time dummies time_dummies = (np.arange(nobs_i)[:, None] == np.arange(nobs_i)[None, :]).astype(float) Z = time_dummies[:,1:] # Z = np.random.standard_normal((nobs_i, nz-1)) # Z = np.column_stack((np.ones(nobs_i), Z)) noise = 0.1 * np.random.randn(nobs_i) #sig_e = 0.1 #generate endogenous variable Y = np.dot(X, beta) + np.dot(Z, gamma_re) + noise #add random effect design matrix also to fixed effects to #capture the mean #this seems to be necessary to force mean of RE to zero !? #(It's not required for estimation but interpretation of random #effects covariance matrix changes - still need to check details. #X = np.hstack((X,Z)) X = np.hstack((X, time_dummies)) #create units and append to list unit = Unit(Y, X, Z) units.append(unit) m = OneWayMixed(units) import time t0 = time.time() m.initialize() res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6) t1 = time.time() print 'time for initialize and fit', t1-t0 print 'number of iterations', m.iterations #print dir(m) #print vars(m) print '\nestimates for fixed effects' print m.a print m.params bfixed_cov = m.cov_fixed() print 'beta fixed standard errors' print np.sqrt(np.diag(bfixed_cov)) print m.bse b_re = m.params_random_units print 'RE mean:', b_re.mean(0) print 'RE columns std', b_re.std(0) print 'np.cov(b_re, rowvar=0), sample statistic' print np.cov(b_re, rowvar=0) print 'sample correlation of estimated random effects' print np.corrcoef(b_re, rowvar=0) print 'std of above' #need atleast_1d or diag raises exception print np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0)))) print 'm.cov_random()' print m.cov_random() print 'correlation from above' print res.cov_random()/ res.std_random()[:,None] /res.std_random() print 'std of above' print res.std_random() print np.sqrt(np.diag(m.cov_random())) print '\n(non)convergence of llf' print m.history['llf'][-4:] print 'convergence of parameters' #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0) print np.diff(np.vstack(m.history['params'][-4:]),axis=0) print 'convergence of D' print np.diff(np.array(m.history['D'][-4:]), axis=0) #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units]) zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units]) '''if Z is not included in X: >>> np.dot(b_re.T, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) >>> m.cov_random() array([[ 0.0348722 , -0.00909159], [-0.00909159, 0.26846254]]) >>> #note cov_random doesn't subtract mean! ''' print '\nchecking the random effects distribution and prediction' gamma_re_true = np.array(gamma_re_true) print 'mean of random effect true', gamma_re_true.mean(0) print 'mean from fixed effects ', m.params[-2:] print 'mean of estimated RE ', b_re.mean(0) print absmean_true = np.abs(gamma_re_true).mean(0) mape = ((m.params[-nz:] + b_re) / gamma_re_true - 1).mean(0)*100 mean_abs_perc = np.abs((m.params[-nz:] + b_re) - gamma_re_true).mean(0) \ / absmean_true*100 median_abs_perc = np.median(np.abs((m.params[-nz:] + b_re) - gamma_re_true), 0) \ / absmean_true*100 rmse_perc = ((m.params[-nz:] + b_re) - gamma_re_true).std(0) \ / absmean_true*100 print 'mape ', mape print 'mean_abs_perc ', mean_abs_perc print 'median_abs_perc', median_abs_perc print 'rmse_perc (std)', rmse_perc from numpy.testing import assert_almost_equal #assert is for n_units=100 in original example #I changed random number generation, so this won't work anymore #assert_almost_equal(rmse_perc, [ 34.14783884, 11.6031684 ], decimal=8) #now returns res print 'llf', res.llf #based on MLE, does not include constant print 'tvalues', res.tvalues print 'pvalues', res.pvalues rmat = np.zeros(len(res.params)) rmat[-nz:] = 1 print 't_test mean of random effects variables are zero' print res.t_test(rmat) print 'f_test mean of both random effects variables is zero (joint hypothesis)' print res.f_test(rmat) plots = res.plot_random_univariate() #(bins=50) fig = res.plot_scatter_all_pairs() import matplotlib.pyplot as plt plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_onewaygls.py000066400000000000000000000153351224417117700270550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example: Test for equality of coefficients across groups/regressions Created on Sat Mar 27 22:36:51 2010 Author: josef-pktd """ import numpy as np from scipy import stats #from numpy.testing import assert_almost_equal import statsmodels.api as sm from statsmodels.sandbox.regression.onewaygls import OneWayLS #choose example #-------------- example = ['null', 'diff'][1] #null: identical coefficients across groups example_size = [10, 100][0] example_size = [(10,2), (100,2)][0] example_groups = ['2', '2-2'][1] #'2-2': 4 groups, # groups 0 and 1 and groups 2 and 3 have identical parameters in DGP #generate example #---------------- np.random.seed(87654589) nobs, nvars = example_size x1 = np.random.normal(size=(nobs, nvars)) y1 = 10 + np.dot(x1,[15.]*nvars) + 2*np.random.normal(size=nobs) x1 = sm.add_constant(x1, prepend=False) #assert_almost_equal(x1, np.vander(x1[:,0],2), 16) #res1 = sm.OLS(y1, x1).fit() #print res1.params #print np.polyfit(x1[:,0], y1, 1) #assert_almost_equal(res1.params, np.polyfit(x1[:,0], y1, 1), 14) #print res1.summary(xname=['x1','const1']) #regression 2 x2 = np.random.normal(size=(nobs,nvars)) if example == 'null': y2 = 10 + np.dot(x2,[15.]*nvars) + 2*np.random.normal(size=nobs) # if H0 is true else: y2 = 19 + np.dot(x2,[17.]*nvars) + 2*np.random.normal(size=nobs) x2 = sm.add_constant(x2, prepend=False) # stack x = np.concatenate((x1,x2),0) y = np.concatenate((y1,y2)) if example_groups == '2': groupind = (np.arange(2*nobs)>nobs-1).astype(int) else: groupind = np.mod(np.arange(2*nobs),4) groupind.sort() #x = np.column_stack((x,x*groupind[:,None])) def print_results(res): groupind = res.groups #res.fitjoint() #not really necessary, because called by ftest_summary ft = res.ftest_summary() #print ft[0] #skip because table is nicer print '\nTable of F-tests for overall or pairwise equality of coefficients' ## print 'hypothesis F-statistic p-value df_denom df_num reject' ## for row in ft[1]: ## print row, ## if row[1][1]<0.05: ## print '*' ## else: ## print '' from statsmodels.iolib import SimpleTable print SimpleTable([(['%r'%(row[0],)] + list(row[1]) + ['*']*(row[1][1]>0.5).item() ) for row in ft[1]], headers=['pair', 'F-statistic','p-value','df_denom', 'df_num']) print 'Notes: p-values are not corrected for many tests' print ' (no Bonferroni correction)' print ' * : reject at 5% uncorrected confidence level' print 'Null hypothesis: all or pairwise coefficient are the same' print 'Alternative hypothesis: all coefficients are different' print '\nComparison with stats.f_oneway' print stats.f_oneway(*[y[groupind==gr] for gr in res.unique]) print '\nLikelihood Ratio Test' print 'likelihood ratio p-value df' print res.lr_test() print 'Null model: pooled all coefficients are the same across groups,' print 'Alternative model: all coefficients are allowed to be different' print 'not verified but looks close to f-test result' print '\nOls parameters by group from individual, separate ols regressions' for group in sorted(res.olsbygroup): r = res.olsbygroup[group] print group, r.params print '\nCheck for heteroscedasticity, ' print 'variance and standard deviation for individual regressions' print ' '*12, ' '.join('group %-10s' %(gr) for gr in res.unique) print 'variance ', res.sigmabygroup print 'standard dev', np.sqrt(res.sigmabygroup) #now added to class def print_results2(res): groupind = res.groups #res.fitjoint() #not really necessary, because called by ftest_summary ft = res.ftest_summary() txt = '' #print ft[0] #skip because table is nicer templ = \ '''Table of F-tests for overall or pairwise equality of coefficients' %(tab)s Notes: p-values are not corrected for many tests (no Bonferroni correction) * : reject at 5%% uncorrected confidence level Null hypothesis: all or pairwise coefficient are the same' Alternative hypothesis: all coefficients are different' Comparison with stats.f_oneway %(statsfow)s Likelihood Ratio Test %(lrtest)s Null model: pooled all coefficients are the same across groups,' Alternative model: all coefficients are allowed to be different' not verified but looks close to f-test result' Ols parameters by group from individual, separate ols regressions' %(olsbg)s for group in sorted(res.olsbygroup): r = res.olsbygroup[group] print group, r.params Check for heteroscedasticity, ' variance and standard deviation for individual regressions' %(grh)s variance ', res.sigmabygroup standard dev', np.sqrt(res.sigmabygroup) ''' from statsmodels.iolib import SimpleTable resvals = {} resvals['tab'] = str(SimpleTable([(['%r'%(row[0],)] + list(row[1]) + ['*']*(row[1][1]>0.5).item() ) for row in ft[1]], headers=['pair', 'F-statistic','p-value','df_denom', 'df_num'])) resvals['statsfow'] = str(stats.f_oneway(*[y[groupind==gr] for gr in res.unique])) #resvals['lrtest'] = str(res.lr_test()) resvals['lrtest'] = str(SimpleTable([res.lr_test()], headers=['likelihood ratio', 'p-value', 'df'] )) resvals['olsbg'] = str(SimpleTable([[group] + res.olsbygroup[group].params.tolist() for group in sorted(res.olsbygroup)])) resvals['grh'] = str(SimpleTable(np.vstack([res.sigmabygroup, np.sqrt(res.sigmabygroup)]), headers=res.unique.tolist())) return templ % resvals #get results for example #----------------------- print '\nTest for equality of coefficients for all exogenous variables' print '-------------------------------------------------------------' res = OneWayLS(y,x, groups=groupind.astype(int)) print_results(res) print '\n\nOne way ANOVA, constant is the only regressor' print '---------------------------------------------' print 'this is the same as scipy.stats.f_oneway' res = OneWayLS(y,np.ones(len(y)), groups=groupind) print_results(res) print '\n\nOne way ANOVA, constant is the only regressor with het is true' print '--------------------------------------------------------------' print 'this is the similar to scipy.stats.f_oneway,' print 'but variance is not assumed to be the same across groups' res = OneWayLS(y,np.ones(len(y)), groups=groupind.astype(str), het=True) print_results(res) print res.print_summary() #(res) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/ex_random_panel.py000066400000000000000000000135531224417117700275040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri May 18 13:05:47 2012 Author: Josef Perktold moved example from main of random_panel """ import numpy as np from statsmodels.sandbox.panel.panel_short import ShortPanelGLS, ShortPanelGLS2 from statsmodels.sandbox.panel.random_panel import PanelSample import statsmodels.sandbox.panel.correlation_structures as cs import statsmodels.stats.sandwich_covariance as sw #from statsmodels.stats.sandwich_covariance import ( # S_hac_groupsum, weights_bartlett, _HCCM2) from statsmodels.stats.moment_helpers import cov2corr, se_cov cov_nw_panel2 = sw.cov_nw_groupsum examples = ['ex1'] if 'ex1' in examples: nobs = 100 nobs_i = 5 n_groups = nobs // nobs_i k_vars = 3 # dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_equi, # corr_args=(0.6,)) # dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_ar, # corr_args=([1, -0.95],)) dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma, corr_args=([1], [1., -0.9],), seed=377769) print 'seed', dgp.seed y = dgp.generate_panel() noise = y - dgp.y_true print np.corrcoef(y.reshape(-1,n_groups, order='F')) print np.corrcoef(noise.reshape(-1,n_groups, order='F')) mod = ShortPanelGLS2(y, dgp.exog, dgp.groups) res = mod.fit() print res.params print res.bse #Now what? #res.resid is of transformed model #np.corrcoef(res.resid.reshape(-1,n_groups, order='F')) y_pred = np.dot(mod.exog, res.params) resid = y - y_pred print np.corrcoef(resid.reshape(-1,n_groups, order='F')) print resid.std() err = y_pred - dgp.y_true print err.std() #OLS standard errors are too small mod.res_pooled.params mod.res_pooled.bse #heteroscedasticity robust doesn't help mod.res_pooled.HC1_se #compare with cluster robust se print sw.se_cov(sw.cov_cluster(mod.res_pooled, dgp.groups.astype(int))) #not bad, pretty close to panel estimator #and with Newey-West Hac print sw.se_cov(sw.cov_nw_panel(mod.res_pooled, 4, mod.group.groupidx)) #too small, assuming no bugs, #see Peterson assuming it refers to same kind of model print dgp.cov mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups) res2 = mod2.fit_iterative(2) print res2.params print res2.bse #both implementations produce the same results: from numpy.testing import assert_almost_equal assert_almost_equal(res.params, res2.params, decimal=12) assert_almost_equal(res.bse, res2.bse, decimal=13) mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups) res5 = mod5.fit_iterative(5) print res5.params print res5.bse #fitting once is the same as OLS #note: I need to create new instance, otherwise it continuous fitting mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups) res1 = mod1.fit_iterative(1) res_ols = mod1._fit_ols() assert_almost_equal(res1.params, res_ols.params, decimal=12) assert_almost_equal(res1.bse, res_ols.bse, decimal=13) #cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced #panel with full length kernel #I fixe default correction to be equal mod2._fit_ols() cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int)) clubse = se_cov(cov_clu) cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform, use_correction='cluster') assert_almost_equal(cov_uni, cov_clu, decimal=7) #without correction cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int), use_correction=False) cov_uni2 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform, use_correction=False) assert_almost_equal(cov_uni2, cov_clu2, decimal=8) cov_white = sw.cov_white_simple(mod2.res_pooled) cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx, use_correction='hac') assert_almost_equal(cov_pnw0, cov_white, decimal=13) time = np.tile(np.arange(nobs_i), n_groups) #time = mod2.group.group_int cov_pnw1 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx) cov_pnw2 = cov_nw_panel2(mod2.res_pooled, 4, time) #s = sw.group_sums(x, time) c2, ct, cg = sw.cov_cluster_2groups(mod2.res_pooled, time, dgp.groups.astype(int), use_correction=False) ct_nw0 = cov_nw_panel2(mod2.res_pooled, 0, time, weights_func=sw.weights_uniform, use_correction=False) cg_nw0 = cov_nw_panel2(mod2.res_pooled, 0, dgp.groups.astype(int), weights_func=sw.weights_uniform, use_correction=False) assert_almost_equal(ct_nw0, ct, decimal=13) assert_almost_equal(cg_nw0, cg, decimal=13) #pnw2 0 lags assert_almost_equal(cov_clu2, cg, decimal=13) assert_almost_equal(cov_uni2, cg, decimal=8) #pnw all lags import pandas as pa #pandas.DataFrame doesn't do inplace append se = pa.DataFrame(res_ols.bse[None,:], index=['OLS']) se = se.append(pa.DataFrame(res5.bse[None,:], index=['PGLSit5'])) clbse = sw.se_cov(sw.cov_cluster(mod.res_pooled, dgp.groups.astype(int))) se = se.append(pa.DataFrame(clbse[None,:], index=['OLSclu'])) pnwse = sw.se_cov(sw.cov_nw_panel(mod.res_pooled, 4, mod.group.groupidx)) se = se.append(pa.DataFrame(pnwse[None,:], index=['OLSpnw'])) print se #list(se.index) from statsmodels.iolib.table import SimpleTable headers = [str(i) for i in se.columns] stubs=list(se.index) # print SimpleTable(np.round(np.asarray(se), 4), # headers=headers, # stubs=stubs) print SimpleTable(np.asarray(se), headers=headers, stubs=stubs, txt_fmt=dict(data_fmts=['%10.4f']), title='Standard Errors') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_crossval.py000066400000000000000000000042471224417117700277200ustar00rootroot00000000000000 import numpy as np from statsmodels.sandbox.tools import cross_val if __name__ == '__main__': #A: josef-pktd import statsmodels.api as sm from statsmodels.api import OLS #from statsmodels.datasets.longley import load from statsmodels.datasets.stackloss import load from statsmodels.iolib.table import (SimpleTable, default_txt_fmt, default_latex_fmt, default_html_fmt) import numpy as np data = load() data.exog = sm.tools.add_constant(data.exog, prepend=False) resols = sm.OLS(data.endog, data.exog).fit() print '\n OLS leave 1 out' for inidx, outidx in cross_val.LeaveOneOut(len(data.endog)): res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit() print data.endog[outidx], res.model.predict(res.params, data.exog[outidx,:]), print data.endog[outidx] - res.model.predict(res.params, data.exog[outidx,:]) print '\n OLS leave 2 out' resparams = [] for inidx, outidx in cross_val.LeavePOut(len(data.endog), 2): res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit() #print data.endog[outidx], res.model.predict(data.exog[outidx,:]), #print ((data.endog[outidx] - res.model.predict(data.exog[outidx,:]))**2).sum() resparams.append(res.params) resparams = np.array(resparams) print resparams doplots = 1 if doplots: import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties plt.figure() figtitle = 'Leave2out parameter estimates' t = plt.gcf().text(0.5, 0.95, figtitle, horizontalalignment='center', fontproperties=FontProperties(size=16)) for i in range(resparams.shape[1]): plt.subplot(4, 2, i+1) plt.hist(resparams[:,i], bins = 10) #plt.title("Leave2out parameter estimates") plt.show() for inidx, outidx in cross_val.KStepAhead(20,2): #note the following were broken because KStepAhead returns now a slice by default print inidx print np.ones(20)[inidx].sum(), np.arange(20)[inidx][-4:] print outidx print np.nonzero(np.ones(20)[outidx])[0][()] statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_gam.py000066400000000000000000000044411224417117700266240ustar00rootroot00000000000000'''original example for checking how far GAM works Note: uncomment plt.show() to display graphs ''' example = 2 # 1,2 or 3 import numpy as np import numpy.random as R import matplotlib.pyplot as plt from statsmodels.sandbox.gam import AdditiveModel from statsmodels.sandbox.gam import Model as GAM #? from statsmodels.genmod.families import family from statsmodels.genmod.generalized_linear_model import GLM standardize = lambda x: (x - x.mean()) / x.std() demean = lambda x: (x - x.mean()) nobs = 150 x1 = R.standard_normal(nobs) x1.sort() x2 = R.standard_normal(nobs) x2.sort() y = R.standard_normal((nobs,)) f1 = lambda x1: (x1 + x1**2 - 3 - 1 * x1**3 + 0.1 * np.exp(-x1/4.)) f2 = lambda x2: (x2 + x2**2 - 0.1 * np.exp(x2/4.)) z = standardize(f1(x1)) + standardize(f2(x2)) z = standardize(z) * 2 # 0.1 y += z d = np.array([x1,x2]).T if example == 1: print "normal" m = AdditiveModel(d) m.fit(y) x = np.linspace(-2,2,50) print m y_pred = m.results.predict(d) plt.figure() plt.plot(y, '.') plt.plot(z, 'b-', label='true') plt.plot(y_pred, 'r-', label='AdditiveModel') plt.legend() plt.title('gam.AdditiveModel') import scipy.stats, time if example == 2: print "binomial" f = family.Binomial() b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)]) b.shape = y.shape m = GAM(b, d, family=f) toc = time.time() m.fit(b) tic = time.time() print tic-toc if example == 3: print "Poisson" f = family.Poisson() y = y/y.max() * 3 yp = f.link.inverse(y) p = np.asarray([scipy.stats.poisson.rvs(p) for p in f.link.inverse(y)], float) p.shape = y.shape m = GAM(p, d, family=f) toc = time.time() m.fit(p) tic = time.time() print tic-toc plt.figure() plt.plot(x1, standardize(m.smoothers[0](x1)), 'r') plt.plot(x1, standardize(f1(x1)), linewidth=2) plt.figure() plt.plot(x2, standardize(m.smoothers[1](x2)), 'r') plt.plot(x2, standardize(f2(x2)), linewidth=2) plt.show() ## pylab.figure(num=1) ## pylab.plot(x1, standardize(m.smoothers[0](x1)), 'b') ## pylab.plot(x1, standardize(f1(x1)), linewidth=2) ## pylab.figure(num=2) ## pylab.plot(x2, standardize(m.smoothers[1](x2)), 'b') ## pylab.plot(x2, standardize(f2(x2)), linewidth=2) ## pylab.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_gam_0.py000066400000000000000000000106531224417117700270450ustar00rootroot00000000000000'''first examples for gam and PolynomialSmoother used for debugging This example was written as a test case. The data generating process is chosen so the parameters are well identified and estimated. Note: uncomment plt.show() to display graphs ''' example = 2 #3 # 1,2 or 3 import numpy as np import numpy.random as R import matplotlib.pyplot as plt from statsmodels.sandbox.gam import AdditiveModel from statsmodels.sandbox.gam import Model as GAM #? from statsmodels.genmod import families from statsmodels.genmod.generalized_linear_model import GLM #np.random.seed(987654) standardize = lambda x: (x - x.mean()) / x.std() demean = lambda x: (x - x.mean()) nobs = 500 lb, ub = -1., 1. #for Poisson #lb, ub = -0.75, 2 #0.75 #for Binomial x1 = R.uniform(lb, ub, nobs) #R.standard_normal(nobs) x1 = np.linspace(lb, ub, nobs) x1.sort() x2 = R.uniform(lb, ub, nobs) # #x2 = R.standard_normal(nobs) x2.sort() #x2 = np.cos(x2) x2 = x2 + np.exp(x2/2.) #x2 = np.log(x2-x2.min()+0.1) y = 0.5 * R.uniform(lb, ub, nobs) #R.standard_normal((nobs,)) f1 = lambda x1: (2*x1 - 0.5 * x1**2 - 0.75 * x1**3) # + 0.1 * np.exp(-x1/4.)) f2 = lambda x2: (x2 - 1* x2**2) # - 0.75 * np.exp(x2)) z = standardize(f1(x1)) + standardize(f2(x2)) z = standardize(z) + 1 # 0.1 #try this z = f1(x1) + f2(x2) #z = demean(z) z -= np.median(z) print'z.std()', z.std() #z = standardize(z) + 0.2 # with standardize I get better values, but I don't know what the true params are print z.mean(), z.min(), z.max() #y += z #noise y = z d = np.array([x1,x2]).T if example == 1: print "normal" m = AdditiveModel(d) m.fit(y) x = np.linspace(-2,2,50) print m import scipy.stats, time if example == 2: print "binomial" mod_name = 'Binomial' f = families.Binomial() #b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)]) b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(z)]) b.shape = y.shape m = GAM(b, d, family=f) toc = time.time() m.fit(b) tic = time.time() print tic-toc #for plotting yp = f.link.inverse(y) p = b if example == 3: print "Poisson" f = families.Poisson() #y = y/y.max() * 3 yp = f.link.inverse(z) #p = np.asarray([scipy.stats.poisson.rvs(p) for p in f.link.inverse(y)], float) p = np.asarray([scipy.stats.poisson.rvs(p) for p in f.link.inverse(z)], float) p.shape = y.shape m = GAM(p, d, family=f) toc = time.time() m.fit(p) tic = time.time() print tic-toc if example > 1: y_pred = m.results.mu# + m.results.alpha#m.results.predict(d) plt.figure() plt.subplot(2,2,1) plt.plot(p, '.') plt.plot(yp, 'b-', label='true') plt.plot(y_pred, 'r-', label='GAM') plt.legend(loc='upper left') plt.title('gam.GAM ' + mod_name) counter = 2 for ii, xx in zip(['z', 'x1', 'x2'], [z, x1, x2]): sortidx = np.argsort(xx) #plt.figure() plt.subplot(2, 2, counter) plt.plot(xx[sortidx], p[sortidx], '.') plt.plot(xx[sortidx], yp[sortidx], 'b.', label='true') plt.plot(xx[sortidx], y_pred[sortidx], 'r.', label='GAM') plt.legend(loc='upper left') plt.title('gam.GAM ' + mod_name + ' ' + ii) counter += 1 # counter = 2 # for ii, xx in zip(['z', 'x1', 'x2'], [z, x1, x2]): # #plt.figure() # plt.subplot(2, 2, counter) # plt.plot(xx, p, '.') # plt.plot(xx, yp, 'b-', label='true') # plt.plot(xx, y_pred, 'r-', label='GAM') # plt.legend(loc='upper left') # plt.title('gam.GAM Poisson ' + ii) # counter += 1 plt.figure() plt.plot(z, 'b-', label='true' ) plt.plot(np.log(m.results.mu), 'r-', label='GAM') plt.title('GAM Poisson, raw') plt.figure() plt.plot(x1, standardize(m.smoothers[0](x1)), 'r') plt.plot(x1, standardize(f1(x1)), linewidth=2) plt.figure() plt.plot(x2, standardize(m.smoothers[1](x2)), 'r') plt.plot(x2, standardize(f2(x2)), linewidth=2) ##y_pred = m.results.predict(d) ##plt.figure() ##plt.plot(z, p, '.') ##plt.plot(z, yp, 'b-', label='true') ##plt.plot(z, y_pred, 'r-', label='AdditiveModel') ##plt.legend() ##plt.title('gam.AdditiveModel') #plt.show() ## pylab.figure(num=1) ## pylab.plot(x1, standardize(m.smoothers[0](x1)), 'b') ## pylab.plot(x1, standardize(f1(x1)), linewidth=2) ## pylab.figure(num=2) ## pylab.plot(x2, standardize(m.smoothers[1](x2)), 'b') ## pylab.plot(x2, standardize(f2(x2)), linewidth=2) ## pylab.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_garch.py000066400000000000000000000043601224417117700271440ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt #import scikits.timeseries as ts #import scikits.timeseries.lib.plotlib as tpl import statsmodels.api as sm #from statsmodels.sandbox import tsa from statsmodels.sandbox.tsa.garch import * # local import #dta2 = ts.tsfromtxt(r'gspc_table.csv', # datecols=0, skiprows=0, delimiter=',',names=True, freq='D') #print dta2 aa=np.genfromtxt(r'gspc_table.csv', skip_header=0, delimiter=',', names=True) cl = aa['Close'] ret = np.diff(np.log(cl))[-2000:]*1000. ggmod = Garch(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod.nar = 1 ggmod.nma = 1 ggmod._start_params = np.array([-0.1, 0.1, 0.1, 0.1]) ggres = ggmod.fit(start_params=np.array([-0.1, 0.1, 0.1, 0.0]), maxiter=1000,method='bfgs') print 'ggres.params', ggres.params garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated') use_rpy = False if use_rpy: from rpy import r r.library('fGarch') f = r.formula('~garch(1, 1)') fit = r.garchFit(f, data = ret - ret.mean(), include_mean=False) f = r.formula('~arma(1,1) + ~garch(1, 1)') fit = r.garchFit(f, data = ret) ggmod0 = Garch0(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 1 ggmod.nma = 1 start_params = np.array([-0.1, 0.1, ret.var()]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000) print 'ggres0.params', ggres0.params g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, ret - ret.mean())[0], [0.01, 0.1, 0.1]) print g11res llf = loglike_GARCH11(g11res, ret - ret.mean()) print llf[0] ggmod0 = Garch0(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 2 ggmod.nma = 2 start_params = np.array([-0.1,-0.1, 0.1, 0.1, ret.var()]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)#, method='ncg') print 'ggres0.params', ggres0.params ggmod = Garch(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod.nar = 2 ggmod.nma = 2 start_params = np.array([-0.1,-0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) ggmod._start_params = start_params ggres = ggmod.fit(start_params=start_params, maxiter=1000)#,method='bfgs') print 'ggres.params', ggres.params statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_maxent.py000066400000000000000000000023711224417117700273540ustar00rootroot00000000000000""" This is an example of using scipy.maxentropy to solve Jaynes' dice problem See Golan, Judge, and Miller Section 2.3 """ from scipy import maxentropy import numpy as np samplespace = [1., 2., 3., 4., 5., 6.] def sump(x): return x in samplespace def meanp(x): return np.mean(x) # Set the constraints # 1) We have a proper probability # 2) The mean is equal to... F = [sump, meanp] model = maxentropy.model(F, samplespace) # set the desired feature expectations K = np.ones((5,2)) K[:,1] = [2.,3.,3.5,4.,5.] model.verbose = False for i in range(K.shape[0]): model.fit(K[i]) # Output the distribution print "\nFitted model parameters are:\n" + str(model.params) print "\nFitted distribution is:" p = model.probdist() for j in range(len(model.samplespace)): x = model.samplespace[j] print "y = %-15s\tx = %-15s" %(str(K[i,1])+":",str(x) + ":") + \ " p(x) = "+str(p[j]) # Now show how well the constraints are satisfied: print print "Desired constraints:" print "\tsum_{i}p_{i}= 1" print "\tE[X] = %-15s" % str(K[i,1]) print print "Actual expectations under the fitted model:" print "\tsum_{i}p_{i} =", np.sum(p) print "\tE[X] = " + str(np.sum(p*np.arange(1,7))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_mle.py000066400000000000000000000040631224417117700266350ustar00rootroot00000000000000'''Examples to compare MLE with OLS TODO: compare standard error of parameter estimates ''' from scipy import optimize import numpy as np import statsmodels.base.model as models print '\nExample 1: Artificial Data' print '--------------------------\n' import statsmodels.api as sm np.random.seed(54321) X = np.random.rand(40,2) X = sm.add_constant(X, prepend=False) beta = np.array((3.5, 5.7, 150)) Y = np.dot(X,beta) + np.random.standard_normal(40) mod2 = sm.OLS(Y,X) res2 = mod2.fit() f2 = lambda params: -1*mod2.loglike(params) resfmin = optimize.fmin(f2, np.ones(3), ftol=1e-10) print 'OLS' print res2.params print 'MLE' print resfmin print '\nExample 2: Longley Data, high multicollinearity' print '-----------------------------------------------\n' from statsmodels.datasets.longley import load data = load() data.exog = sm.add_constant(data.exog, prepend=False) mod = sm.OLS(data.endog, data.exog) f = lambda params: -1*mod.loglike(params) score = lambda params: -1*mod.score(params) #now you're set up to try and minimize or root find, but I couldn't get this one to work #note that if you want to get the results, it's also a property of mod, so you can do res = mod.fit() #print mod.results.params print 'OLS' print res.params print 'MLE' #resfmin2 = optimize.fmin(f, mod.results.params*0.9, maxfun=5000, maxiter=5000, xtol=1e-10, ftol= 1e-10) resfmin2 = optimize.fmin(f, np.ones(7), maxfun=5000, maxiter=5000, xtol=1e-10, ftol= 1e-10) print resfmin2 # there isn't a unique solution? Is this due to the multicollinearity? Improved with use of analytically # defined score function? #check X'X matrix xtxi = np.linalg.inv(np.dot(data.exog.T,data.exog)) eval, evec = np.linalg.eig(xtxi) print 'Eigenvalues' print eval # look at correlation print 'correlation matrix' print np.corrcoef(data.exog[:,:-1], rowvar=0) #exclude constant # --> conclusion high multicollinearity # compare print 'with matrix formula' print np.dot(xtxi,np.dot(data.exog.T, data.endog[:,np.newaxis])).ravel() print 'with pinv' print np.dot(np.linalg.pinv(data.exog), data.endog[:,np.newaxis]).ravel() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_nbin.py000066400000000000000000000315011224417117700270030ustar00rootroot00000000000000# -*- coding: utf-8 -*- ''' Author: Vincent Arel-Bundock Date: 2012-08-25 This example file implements 5 variations of the negative binomial regression model for count data: NB-P, NB-1, NB-2, geometric and left-truncated. The NBin class inherits from the GenericMaximumLikelihood statsmodels class which provides automatic numerical differentiation for the score and hessian. NB-1, NB-2 and geometric are implemented as special cases of the NB-P model described in Greene (2008) Functional forms for the negative binomial model for count data. Economics Letters, v99n3. Tests are included to check how NB-1, NB-2 and geometric coefficient estimates compare to equivalent models in R. Results usually agree up to the 4th digit. The NB-P and left-truncated model results have not been compared to other implementations. Note that NB-P appears to only have been implemented in the LIMDEP software. ''' import numpy as np from scipy.special import gammaln from scipy.stats import nbinom from statsmodels.base.model import GenericLikelihoodModel from statsmodels.base.model import GenericLikelihoodModelResults import statsmodels.api as sm #### Negative Binomial Log-likelihoods #### def _ll_nbp(y, X, beta, alph, Q): ''' Negative Binomial Log-likelihood -- type P References: Greene, W. 2008. "Functional forms for the negtive binomial model for count data". Economics Letters. Volume 99, Number 3, pp.585-590. Hilbe, J.M. 2011. "Negative binomial regression". Cambridge University Press. Following notation in Greene (2008), with negative binomial heterogeneity parameter :math:`\alpha`: .. math:: \lambda_i = exp(X\beta)\\ \theta = 1 / \alpha \\ g_i = \theta \lambda_i^Q \\ w_i = g_i/(g_i + \lambda_i) \\ r_i = \theta / (\theta+\lambda_i) \\ ln \mathcal{L}_i = ln \Gamma(y_i+g_i) - ln \Gamma(1+y_i) + g_iln (r_i) + y_i ln(1-r_i) ''' mu = np.exp(np.dot(X, beta)) size = 1/alph*mu**Q prob = size/(size+mu) ll = nbinom.logpmf(y, size, prob) return ll def _ll_nb1(y, X, beta, alph): '''Negative Binomial regression (type 1 likelihood)''' ll = _ll_nbp(y, X, beta, alph, Q=1) return ll def _ll_nb2(y, X, beta, alph): '''Negative Binomial regression (type 2 likelihood)''' ll = _ll_nbp(y, X, beta, alph, Q=0) return ll def _ll_geom(y, X, beta): '''Geometric regression''' ll = _ll_nbp(y, X, beta, alph=1, Q=0) return ll def _ll_nbt(y, X, beta, alph, C=0): ''' Negative Binomial (truncated) Truncated densities for count models (Cameron & Trivedi, 2005, 680): .. math:: f(y|\beta, y \geq C+1) = \frac{f(y|\beta)}{1-F(C|\beta)} ''' Q = 0 mu = np.exp(np.dot(X, beta)) size = 1/alph*mu**Q prob = size/(size+mu) ll = nbinom.logpmf(y, size, prob) - np.log(1 - nbinom.cdf(C, size, prob)) return ll #### Model Classes #### class NBin(GenericLikelihoodModel): ''' Negative Binomial regression Parameters ---------- endog : array-like 1-d array of the response variable. exog : array-like `exog` is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data. ll_type: string log-likelihood type `nb2`: Negative Binomial type-2 (most common) `nb1`: Negative Binomial type-1 `nbp`: Negative Binomial type-P (Greene, 2008) `nbt`: Left-truncated Negative Binomial (type-2) `geom`: Geometric regression model C: integer Cut-point for `nbt` model ''' def __init__(self, endog, exog, ll_type='nb2', C=0, **kwds): self.exog = np.array(exog) self.endog = np.array(endog) self.C = C super(NBin, self).__init__(endog, exog, **kwds) # Check user input if ll_type not in ['nb2', 'nb1', 'nbp', 'nbt', 'geom']: raise NameError('Valid ll_type are: nb2, nb1, nbp, nbt, geom') self.ll_type = ll_type # Starting values (assumes first column of exog is constant) if ll_type == 'geom': self.start_params_default = np.zeros(self.exog.shape[1]) elif ll_type == 'nbp': # Greene recommends starting NB-P at NB-2 start_mod = NBin(endog, exog, 'nb2') start_res = start_mod.fit(disp=False) self.start_params_default = np.append(start_res.params, 0) else: self.start_params_default = np.append(np.zeros(self.exog.shape[1]), .5) self.start_params_default[0] = np.log(self.endog.mean()) # Define loglik based on ll_type argument if ll_type == 'nb1': self.ll_func = _ll_nb1 elif ll_type == 'nb2': self.ll_func = _ll_nb2 elif ll_type == 'geom': self.ll_func = _ll_geom elif ll_type == 'nbp': self.ll_func = _ll_nbp elif ll_type == 'nbt': self.ll_func = _ll_nbt def nloglikeobs(self, params): alph = params[-1] beta = params[:self.exog.shape[1]] if self.ll_type == 'geom': return -self.ll_func(self.endog, self.exog, beta) elif self.ll_type == 'nbt': return -self.ll_func(self.endog, self.exog, beta, alph, self.C) elif self.ll_type == 'nbp': Q = params[-2] return -self.ll_func(self.endog, self.exog, beta, alph, Q) else: return -self.ll_func(self.endog, self.exog, beta, alph) def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds): if start_params==None: countfit = super(NBin, self).fit(start_params=self.start_params_default, maxiter=maxiter, maxfun=maxfun, **kwds) else: countfit = super(NBin, self).fit(start_params=start_params, maxiter=maxiter, maxfun=maxfun, **kwds) countfit = CountResults(self, countfit) return countfit class CountResults(GenericLikelihoodModelResults): def __init__(self, model, mlefit): self.model = model self.__dict__.update(mlefit.__dict__) def summary(self, yname=None, xname=None, title=None, alpha=.05, yname_list=None): top_left = [('Dep. Variable:', None), ('Model:', [self.model.__class__.__name__]), ('Method:', ['MLE']), ('Date:', None), ('Time:', None), ('Converged:', ["%s" % self.mle_retvals['converged']]) ] top_right = [('No. Observations:', None), ('Log-Likelihood:', None), ] if title is None: title = self.model.__class__.__name__ + ' ' + "Regression Results" #boiler plate from statsmodels.iolib.summary import Summary smry = Summary() # for top of table smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[], yname=yname, xname=xname, title=title) # for parameters, etc smry.add_table_params(self, yname=yname_list, xname=xname, alpha=alpha, use_t=True) return smry #### Score function for NB-P #### from scipy.special import digamma def _score_nbp(y, X, beta, thet, Q): ''' Negative Binomial Score -- type P likelihood from Greene (2007) .. math:: \lambda_i = exp(X\beta)\\ g_i = \theta \lambda_i^Q \\ w_i = g_i/(g_i + \lambda_i) \\ r_i = \theta / (\theta+\lambda_i) \\ A_i = \left [ \Psi(y_i+g_i) - \Psi(g_i) + ln w_i \right ] \\ B_i = \left [ g_i (1-w_i) - y_iw_i \right ] \\ \partial ln \mathcal{L}_i / \partial \begin{pmatrix} \lambda_i \\ \theta \\ Q \end{pmatrix}= [A_i+B_i] \begin{pmatrix} Q/\lambda_i \\ 1/\theta \\ ln(\lambda_i) \end{pmatrix} -B_i \begin{pmatrix} 1/\lambda_i\\ 0 \\ 0 \end{pmatrix} \\ \frac{\partial \lambda}{\partial \beta} = \lambda_i \mathbf{x}_i \\ \frac{\partial \mathcal{L}_i}{\partial \beta} = \left (\frac{\partial\mathcal{L}_i}{\partial \lambda_i} \right ) \frac{\partial \lambda_i}{\partial \beta} ''' lamb = np.exp(np.dot(X, beta)) g = thet * lamb**Q w = g / (g + lamb) r = thet / (thet+lamb) A = digamma(y+g) - digamma(g) + np.log(w) B = g*(1-w) - y*w dl = (A+B) * Q/lamb - B * 1/lamb dt = (A+B) * 1/thet dq = (A+B) * np.log(lamb) db = X * (dl * lamb)[:,np.newaxis] sc = np.array([dt.sum(), dq.sum()]) sc = np.concatenate([db.sum(axis=0), sc]) return sc #### Tests #### from numpy.testing import assert_almost_equal import pandas import patsy from urllib2 import urlopen medpar = pandas.read_csv(urlopen('http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv')) mdvis = pandas.read_csv(urlopen('http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/mdvis.csv')) # NB-2 ''' # R v2.15.1 library(MASS) library(COUNT) data(medpar) f <- los~factor(type)+hmo+white mod <- glm.nb(f, medpar) summary(mod) Call: glm.nb(formula = f, data = medpar, init.theta = 2.243376203, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.4671 -0.9090 -0.2693 0.4320 3.8668 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.31028 0.06745 34.253 < 2e-16 *** factor(type)2 0.22125 0.05046 4.385 1.16e-05 *** factor(type)3 0.70616 0.07600 9.292 < 2e-16 *** hmo -0.06796 0.05321 -1.277 0.202 white -0.12907 0.06836 -1.888 0.059 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(2.2434) family taken to be 1) Null deviance: 1691.1 on 1494 degrees of freedom Residual deviance: 1568.1 on 1490 degrees of freedom AIC: 9607 Number of Fisher Scoring iterations: 1 Theta: 2.2434 Std. Err.: 0.0997 2 x log-likelihood: -9594.9530 ''' def test_nb2(): y, X = patsy.dmatrices('los ~ C(type) + hmo + white', medpar) y = np.array(y)[:,0] nb2 = NBin(y,X,'nb2').fit(maxiter=10000, maxfun=5000) assert_almost_equal(nb2.params, [2.31027893349935, 0.221248978197356, 0.706158824346228, -0.067955221930748, -0.129065442248951, 0.4457567], decimal=2) # NB-1 ''' # R v2.15.1 # COUNT v1.2.3 library(COUNT) data(medpar) f <- los~factor(type)+hmo+white ml.nb1(f, medpar) Estimate SE Z LCL UCL (Intercept) 2.34918407 0.06023641 38.9994023 2.23112070 2.46724744 factor(type)2 0.16175471 0.04585569 3.5274735 0.07187757 0.25163186 factor(type)3 0.41879257 0.06553258 6.3906006 0.29034871 0.54723643 hmo -0.04533566 0.05004714 -0.9058592 -0.14342805 0.05275673 white -0.12951295 0.06071130 -2.1332593 -0.24850710 -0.01051880 alpha 4.57898241 0.22015968 20.7984603 4.14746943 5.01049539 ''' #def test_nb1(): #y, X = patsy.dmatrices('los ~ C(type) + hmo + white', medpar) #y = np.array(y)[:,0] ## TODO: Test fails with some of the other optimization methods #nb1 = NBin(y,X,'nb1').fit(method='ncg', maxiter=10000, maxfun=5000) #assert_almost_equal(nb1.params, #[2.34918407014186, 0.161754714412848, 0.418792569970658, #-0.0453356614650342, -0.129512952033423, 4.57898241219275], #decimal=2) # NB-Geometric ''' MASS v7.3-20 R v2.15.1 library(MASS) data(medpar) f <- los~factor(type)+hmo+white mod <- glm(f, family=negative.binomial(1), data=medpar) summary(mod) Call: glm(formula = f, family = negative.binomial(1), data = medpar) Deviance Residuals: Min 1Q Median 3Q Max -1.7942 -0.6545 -0.1896 0.3044 2.6844 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.30849 0.07071 32.649 < 2e-16 *** factor(type)2 0.22121 0.05283 4.187 2.99e-05 *** factor(type)3 0.70599 0.08092 8.724 < 2e-16 *** hmo -0.06779 0.05521 -1.228 0.2197 white -0.12709 0.07169 -1.773 0.0765 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(1) family taken to be 0.5409721) Null deviance: 872.29 on 1494 degrees of freedom Residual deviance: 811.95 on 1490 degrees of freedom AIC: 9927.3 Number of Fisher Scoring iterations: 5 ''' #def test_geom(): #y, X = patsy.dmatrices('los ~ C(type) + hmo + white', medpar) #y = np.array(y)[:,0] ## TODO: remove alph from geom params #geom = NBin(y,X,'geom').fit(maxiter=10000, maxfun=5000) #assert_almost_equal(geom.params, #[2.3084850946241, 0.221206159108742, 0.705986369841159, #-0.0677871843613577, -0.127088772164963], #decimal=4) test_nb2() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_pca.py000066400000000000000000000010021224417117700266110ustar00rootroot00000000000000#!/usr/bin/env python import numpy as np from statsmodels.sandbox.pca import Pca x=np.random.randn(1000) y=x*2.3+5+np.random.randn(1000) z=x*3.1+2.1*y+np.random.randn(1000)/2 #create the Pca object - requires a p x N array as the input p=Pca((x,y,z)) print 'energies:',p.getEnergies() print 'vecs:',p.getEigenvectors() print 'projected data',p.project(vals=np.ones((3,10))) #p.plot2d() #requires matplotlib #from matplotlib import pyplot as plt #plt.show() #necessary for script #p.plot3d() #requires mayavi statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_pca_regression.py000066400000000000000000000072001224417117700310570ustar00rootroot00000000000000'''Example: Principal Component Regression * simulate model with 2 factors and 4 explanatory variables * use pca to extract factors from data, * run OLS on factors, * use information criteria to choose "best" model Warning: pca sorts factors by explaining variance in explanatory variables, which are not necessarily the most important factors for explaining the endogenous variable. # try out partial correlation for dropping (or adding) factors # get algorithm for partial least squares as an alternative to PCR ''' import numpy as np from numpy.testing import assert_array_almost_equal import statsmodels.api as sm from statsmodels.sandbox.tools import pca from statsmodels.sandbox.tools.cross_val import LeaveOneOut # Example: principal component regression nobs = 1000 f0 = np.c_[np.random.normal(size=(nobs,2)), np.ones((nobs,1))] f2xcoef = np.c_[np.repeat(np.eye(2),2,0),np.arange(4)[::-1]].T f2xcoef = np.array([[ 1., 1., 0., 0.], [ 0., 0., 1., 1.], [ 3., 2., 1., 0.]]) f2xcoef = np.array([[ 0.1, 3., 1., 0.], [ 0., 0., 1.5, 0.1], [ 3., 2., 1., 0.]]) x0 = np.dot(f0, f2xcoef) x0 += 0.1*np.random.normal(size=x0.shape) ytrue = np.dot(f0,[1., 1., 1.]) y0 = ytrue + 0.1*np.random.normal(size=ytrue.shape) xred, fact, eva, eve = pca(x0, keepdim=0) print eve print fact[:5] print f0[:5] import statsmodels.api as sm res = sm.OLS(y0, sm.add_constant(x0, prepend=False)).fit() print 'OLS on original data' print res.params print res.aic print res.rsquared #print 'OLS on Factors' #for k in range(x0.shape[1]): # xred, fact, eva, eve = pca(x0, keepdim=k, normalize=1) # fact_wconst = sm.add_constant(fact) # res = sm.OLS(y0, fact_wconst).fit() # print 'k =', k # print res.params # print 'aic: ', res.aic # print 'bic: ', res.bic # print 'llf: ', res.llf # print 'R2 ', res.rsquared # print 'R2 adj', res.rsquared_adj print 'OLS on Factors' results = [] xred, fact, eva, eve = pca(x0, keepdim=0, normalize=1) for k in range(0, x0.shape[1]+1): #xred, fact, eva, eve = pca(x0, keepdim=k, normalize=1) # this is faster and same result fact_wconst = sm.add_constant(fact[:,:k], prepend=False) res = sm.OLS(y0, fact_wconst).fit() ## print 'k =', k ## print res.params ## print 'aic: ', res.aic ## print 'bic: ', res.bic ## print 'llf: ', res.llf ## print 'R2 ', res.rsquared ## print 'R2 adj', res.rsquared_adj prederr2 = 0. for inidx, outidx in LeaveOneOut(len(y0)): resl1o = sm.OLS(y0[inidx], fact_wconst[inidx,:]).fit() #print data.endog[outidx], res.model.predict(data.exog[outidx,:]), prederr2 += (y0[outidx] - resl1o.predict(fact_wconst[outidx,:]))**2. results.append([k, res.aic, res.bic, res.rsquared_adj, prederr2]) results = np.array(results) print results print 'best result for k, by AIC, BIC, R2_adj, L1O' print np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0), np.argmin(results[:,-1],0))] from statsmodels.iolib.table import (SimpleTable, default_txt_fmt, default_latex_fmt, default_html_fmt) headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ') numformat = ['%6d'] + ['%10.3f']*4 #'%10.4f' txt_fmt1 = dict(data_fmts = numformat) tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1) print "PCA regression on simulated data," print "DGP: 2 factors and 4 explanatory variables" print tabl print "Notes: k is number of components of PCA," print " constant is added additionally" print " k=0 means regression on constant only" print " L1O: sum of squared prediction errors for leave-one-out" statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/example_sysreg.py000066400000000000000000000174561224417117700274060ustar00rootroot00000000000000"""Example: statsmodels.sandbox.sysreg """ #TODO: this is going to change significantly once we have a panel data structure import numpy as np import statsmodels.api as sm from statsmodels.sandbox.sysreg import * #for Python 3 compatibility from statsmodels.compatnp.py3k import asbytes # Seemingly Unrelated Regressions (SUR) Model # This example uses the subset of the Grunfeld data in Greene's Econometric # Analysis Chapter 14 (5th Edition) grun_data = sm.datasets.grunfeld.load() firms = ['General Motors', 'Chrysler', 'General Electric', 'Westinghouse', 'US Steel'] #for Python 3 compatibility firms = map(asbytes, firms) grun_exog = grun_data.exog grun_endog = grun_data.endog # Right now takes SUR takes a list of arrays # The array alternates between the LHS of an equation and RHS side of an # equation # This is very likely to change grun_sys = [] for i in firms: index = grun_exog['firm'] == i grun_sys.append(grun_endog[index]) exog = grun_exog[index][['value','capital']].view(float).reshape(-1,2) exog = sm.add_constant(exog, prepend=True) grun_sys.append(exog) # Note that the results in Greene (5th edition) uses a slightly different # version of the Grunfeld data. To reproduce Table 14.1 the following changes # are necessary. grun_sys[-2][5] = 261.6 grun_sys[-2][-3] = 645.2 grun_sys[-1][11,2] = 232.6 grun_mod = SUR(grun_sys) grun_res = grun_mod.fit() print "Results for the 2-step GLS" print "Compare to Greene Table 14.1, 5th edition" print grun_res.params # or you can do an iterative fit # you have to define a new model though this will be fixed # TODO: note the above print "Results for iterative GLS (equivalent to MLE)" print "Compare to Greene Table 14.3" #TODO: these are slightly off, could be a convergence issue # or might use a different default DOF correction? grun_imod = SUR(grun_sys) grun_ires = grun_imod.fit(igls=True) print grun_ires.params # Two-Stage Least Squares for Simultaneous Equations #TODO: we are going to need *some kind* of formula framework # This follows the simple macroeconomic model given in # Greene Example 15.1 (5th Edition) # The data however is from statsmodels and is not the same as # Greene's # The model is # consumption: c_{t} = \alpha_{0} + \alpha_{1}y_{t} + \alpha_{2}c_{t-1} + \epsilon_{t1} # investment: i_{t} = \beta_{0} + \beta_{1}r_{t} + \beta_{2}\left(y_{t}-y_{t-1}\right) + \epsilon_{t2} # demand: y_{t} = c_{t} + I_{t} + g_{t} # See Greene's Econometric Analysis for more information # Load the data macrodata = sm.datasets.macrodata.load().data # Not needed, but make sure the data is sorted macrodata = np.sort(macrodata, order=['year','quarter']) # Impose the demand restriction y = macrodata['realcons'] + macrodata['realinv'] + macrodata['realgovt'] # Build the system macro_sys = [] # First equation LHS macro_sys.append(macrodata['realcons'][1:]) # leave off first date # First equation RHS exog1 = np.column_stack((y[1:],macrodata['realcons'][:-1])) #TODO: it might be nice to have "lag" and "lead" functions exog1 = sm.add_constant(exog1, prepend=True) macro_sys.append(exog1) # Second equation LHS macro_sys.append(macrodata['realinv'][1:]) # Second equation RHS exog2 = np.column_stack((macrodata['tbilrate'][1:], np.diff(y))) exog2 = sm.add_constant(exog2, prepend=True) macro_sys.append(exog2) # We need to say that y_{t} in the RHS of equation 1 is an endogenous regressor # We will call these independent endogenous variables # Right now, we use a dictionary to declare these indep_endog = {0 : [1]} # We also need to create a design of our instruments # This will be done automatically in the future instruments = np.column_stack((macrodata[['realgovt', 'tbilrate']][1:].view(float).reshape(-1,2),macrodata['realcons'][:-1], y[:-1])) instruments = sm.add_constant(instruments, prepend=True) macro_mod = Sem2SLS(macro_sys, indep_endog=indep_endog, instruments=instruments) # Right now this only returns parameters macro_params = macro_mod.fit() print "The parameters for the first equation are correct." print "The parameters for the second equation are not." print macro_params #TODO: Note that the above is incorrect, because we have no way of telling the # model that *part* of the y_{t} - y_{t-1} is an independent endogenous variable # To correct for this we would have to do the following y_instrumented = macro_mod.wexog[0][:,1] whitened_ydiff = y_instrumented - y[:-1] wexog = np.column_stack((macrodata['tbilrate'][1:],whitened_ydiff)) wexog = sm.add_constant(wexog, prepend=True) correct_params = sm.GLS(macrodata['realinv'][1:], wexog).fit().params print "If we correctly instrument everything, then these are the parameters" print "for the second equation" print correct_params print "Compare to output of R script statsmodels/sandbox/tests/macrodata.s" print '\nUsing IV2SLS' from statsmodels.sandbox.regression.gmm import IV2SLS miv = IV2SLS(macro_sys[0], macro_sys[1], instruments) resiv = miv.fit() print "equation 1" print resiv.params miv2 = IV2SLS(macro_sys[2], macro_sys[3], instruments) resiv2 = miv2.fit() print "equation 2" print resiv2.params ### Below is the same example using Greene's data ### run_greene = 0 if run_greene: try: data3 = np.genfromtxt('/home/skipper/school/MetricsII/Greene \ TableF5-1.txt', names=True) except: raise ValueError, "Based on Greene TableF5-1. You should download it \ from his web site and edit this script accordingly." # Example 15.1 in Greene 5th Edition # c_t = constant + y_t + c_t-1 # i_t = constant + r_t + (y_t - y_t-1) # y_t = c_t + i_t + g_t sys3 = [] sys3.append(data3['realcons'][1:]) # have to leave off a beg. date # impose 3rd equation on y y = data3['realcons'] + data3['realinvs'] + data3['realgovt'] exog1 = np.column_stack((y[1:],data3['realcons'][:-1])) exog1 = sm.add_constant(exog1, prepend=False) sys3.append(exog1) sys3.append(data3['realinvs'][1:]) exog2 = np.column_stack((data3['tbilrate'][1:], np.diff(y))) # realint is missing 1st observation exog2 = sm.add_constant(exog2, prepend=False) sys3.append(exog2) indep_endog = {0 : [0]} # need to be able to say that y_1 is an instrument.. instruments = np.column_stack((data3[['realgovt', 'tbilrate']][1:].view(float).reshape(-1,2),data3['realcons'][:-1], y[:-1])) instruments = sm.add_constant(instruments, prepend=False) sem_mod = Sem2SLS(sys3, indep_endog = indep_endog, instruments=instruments) sem_params = sem_mod.fit() # first equation is right, but not second? # should y_t in the diff be instrumented? # how would R know this in the script? # well, let's check... y_instr = sem_mod.wexog[0][:,0] wyd = y_instr - y[:-1] wexog = np.column_stack((data3['tbilrate'][1:],wyd)) wexog = sm.add_constant(wexog, prepend=False) params = sm.GLS(data3['realinvs'][1:], wexog).fit().params print "These are the simultaneous equation estimates for Greene's \ example 13-1 (Also application 13-1 in 6th edition." print sem_params print "The first set of parameters is correct. The second set is not." print "Compare to the solution manual at \ http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm" print "The reason is the restriction on (y_t - y_1)" print "Compare to R script GreeneEx15_1.s" print "Somehow R carries y.1 in yd to know that it needs to be \ instrumented" print "If we replace our estimate with the instrumented one" print params print "We get the right estimate" print "Without a formula framework we have to be able to do restrictions." # yep!, but how in the world does R know this when we just fed it yd?? # must be implicit in the formula framework... # we are going to need to keep the two equations separate and use # a restrictions matrix. Ugh, is a formula framework really, necessary to get # around this? statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/gspc_table.csv000066400000000000000000030742421224417117700266240ustar00rootroot00000000000000Date,Open,High,Low,Close,Volume,Adj Close 2010-02-12,1075.95,1077.81,1062.97,1075.51,4160680000,1075.51 2010-02-11,1067.10,1080.04,1060.59,1078.47,4400870000,1078.47 2010-02-10,1069.68,1073.67,1059.34,1068.13,4251450000,1068.13 2010-02-09,1060.06,1079.28,1060.06,1070.52,5114260000,1070.52 2010-02-08,1065.51,1071.20,1056.51,1056.74,4089820000,1056.74 2010-02-05,1064.12,1067.13,1044.50,1066.19,6438900000,1066.19 2010-02-04,1097.25,1097.25,1062.78,1063.11,5859690000,1063.11 2010-02-03,1100.67,1102.72,1093.97,1097.28,4285450000,1097.28 2010-02-02,1090.05,1104.73,1087.96,1103.32,4749540000,1103.32 2010-02-01,1073.89,1089.38,1073.89,1089.19,4077610000,1089.19 2010-01-29,1087.61,1096.45,1071.59,1073.87,5412850000,1073.87 2010-01-28,1096.93,1100.22,1078.46,1084.53,5452400000,1084.53 2010-01-27,1091.94,1099.51,1083.11,1097.50,5319120000,1097.50 2010-01-26,1095.80,1103.69,1089.86,1092.17,4731910000,1092.17 2010-01-25,1092.40,1102.97,1092.40,1096.78,4481390000,1096.78 2010-01-22,1115.49,1115.49,1090.18,1091.76,6208650000,1091.76 2010-01-21,1138.68,1141.58,1114.84,1116.48,6874289600,1116.48 2010-01-20,1147.95,1147.95,1129.25,1138.04,4810560000,1138.04 2010-01-19,1136.03,1150.45,1135.77,1150.23,4724830000,1150.23 2010-01-15,1147.72,1147.77,1131.39,1136.03,4758730000,1136.03 2010-01-14,1145.68,1150.41,1143.80,1148.46,3915200000,1148.46 2010-01-13,1137.31,1148.40,1133.18,1145.68,4170360000,1145.68 2010-01-12,1143.81,1143.81,1131.77,1136.22,4716160000,1136.22 2010-01-11,1145.96,1149.74,1142.02,1146.98,4255780000,1146.98 2010-01-08,1140.52,1145.39,1136.22,1144.98,4389590000,1144.98 2010-01-07,1136.27,1142.46,1131.32,1141.69,5270680000,1141.69 2010-01-06,1135.71,1139.19,1133.95,1137.14,4972660000,1137.14 2010-01-05,1132.66,1136.63,1129.66,1136.52,2491020000,1136.52 2010-01-04,1116.56,1133.87,1116.56,1132.99,3991400000,1132.99 2009-12-31,1126.60,1127.64,1114.81,1115.10,2076990000,1115.10 2009-12-30,1125.53,1126.42,1121.94,1126.42,2277300000,1126.42 2009-12-29,1128.55,1130.38,1126.08,1126.20,2491020000,1126.20 2009-12-28,1127.53,1130.38,1123.51,1127.78,2716400000,1127.78 2009-12-24,1121.08,1126.48,1121.08,1126.48,1267710000,1126.48 2009-12-23,1118.84,1121.58,1116.00,1120.59,3166870000,1120.59 2009-12-22,1114.51,1120.27,1114.51,1118.02,3641130000,1118.02 2009-12-21,1105.31,1117.68,1105.31,1114.05,3977340000,1114.05 2009-12-18,1097.86,1103.74,1093.88,1102.47,6325890000,1102.47 2009-12-17,1106.36,1106.36,1095.88,1096.08,7615070400,1096.08 2009-12-16,1108.61,1116.21,1107.96,1109.18,4829820000,1109.18 2009-12-15,1114.11,1114.11,1105.35,1107.93,5045100000,1107.93 2009-12-14,1107.84,1114.76,1107.84,1114.11,4548490000,1114.11 2009-12-11,1103.96,1108.50,1101.34,1106.41,3791090000,1106.41 2009-12-10,1098.69,1106.25,1098.69,1102.35,3996490000,1102.35 2009-12-09,1091.07,1097.04,1085.89,1095.95,4115410000,1095.95 2009-12-08,1103.04,1103.04,1088.61,1091.94,4748030000,1091.94 2009-12-07,1105.52,1110.72,1100.83,1103.25,4103360000,1103.25 2009-12-04,1100.43,1119.13,1096.52,1105.98,5781140000,1105.98 2009-12-03,1110.59,1117.28,1098.74,1099.92,4810030000,1099.92 2009-12-02,1109.03,1115.58,1105.29,1109.24,3941340000,1109.24 2009-12-01,1098.89,1112.28,1098.89,1108.86,4249310000,1108.86 2009-11-30,1091.07,1097.24,1086.25,1095.63,3895520000,1095.63 2009-11-27,1105.47,1105.47,1083.74,1091.49,2362910000,1091.49 2009-11-25,1106.49,1111.18,1104.75,1110.63,3036350000,1110.63 2009-11-24,1105.83,1107.56,1097.63,1105.65,3700820000,1105.65 2009-11-23,1094.86,1112.38,1094.86,1106.24,3827920000,1106.24 2009-11-20,1094.66,1094.66,1086.81,1091.38,3751230000,1091.38 2009-11-19,1106.44,1106.44,1088.40,1094.90,4178030000,1094.90 2009-11-18,1109.44,1111.10,1102.70,1109.80,4293340000,1109.80 2009-11-17,1109.22,1110.52,1102.19,1110.32,3824070000,1110.32 2009-11-16,1094.13,1113.69,1094.13,1109.30,4565850000,1109.30 2009-11-13,1087.59,1097.79,1085.33,1093.48,3792610000,1093.48 2009-11-12,1098.31,1101.97,1084.90,1087.24,4160250000,1087.24 2009-11-11,1096.04,1105.37,1093.81,1098.51,4286700000,1098.51 2009-11-10,1091.86,1096.42,1087.40,1093.01,4394770000,1093.01 2009-11-09,1072.31,1093.19,1072.31,1093.08,4460030000,1093.08 2009-11-06,1064.95,1071.48,1059.32,1069.30,4277130000,1069.30 2009-11-05,1047.30,1066.65,1047.30,1066.63,4848350000,1066.63 2009-11-04,1047.14,1061.00,1045.15,1046.50,5635510000,1046.50 2009-11-03,1040.92,1046.36,1033.94,1045.41,5487500000,1045.41 2009-11-02,1036.18,1052.18,1029.38,1042.88,6202640000,1042.88 2009-10-30,1065.41,1065.41,1033.38,1036.19,6512420000,1036.19 2009-10-29,1043.69,1066.83,1043.69,1066.11,5595040000,1066.11 2009-10-28,1061.51,1063.26,1042.19,1042.63,6600350000,1042.63 2009-10-27,1067.54,1072.48,1060.62,1063.41,5337380000,1063.41 2009-10-26,1080.36,1091.75,1065.23,1066.95,6363380000,1066.95 2009-10-23,1095.62,1095.83,1075.49,1079.60,4767460000,1079.60 2009-10-22,1080.96,1095.21,1074.31,1092.91,5192410000,1092.91 2009-10-21,1090.36,1101.36,1080.77,1081.40,5616290000,1081.40 2009-10-20,1098.64,1098.64,1086.16,1091.06,5396930000,1091.06 2009-10-19,1088.22,1100.17,1086.48,1097.91,4619240000,1097.91 2009-10-16,1094.67,1094.67,1081.53,1087.68,4894740000,1087.68 2009-10-15,1090.36,1096.56,1086.41,1096.56,5369780000,1096.56 2009-10-14,1078.68,1093.17,1078.68,1092.02,5406420000,1092.02 2009-10-13,1074.96,1075.30,1066.71,1073.19,4320480000,1073.19 2009-10-12,1071.63,1079.46,1071.63,1076.19,3710430000,1076.19 2009-10-09,1065.28,1071.51,1063.00,1071.49,3763780000,1071.49 2009-10-08,1060.03,1070.67,1060.03,1065.48,4988400000,1065.48 2009-10-07,1053.65,1058.02,1050.10,1057.58,4238220000,1057.58 2009-10-06,1042.02,1060.55,1042.02,1054.72,5029840000,1054.72 2009-10-05,1026.87,1042.58,1025.92,1040.46,4313310000,1040.46 2009-10-02,1029.71,1030.60,1019.95,1025.21,5583240000,1025.21 2009-10-01,1054.91,1054.91,1029.45,1029.85,5791450000,1029.85 2009-09-30,1061.02,1063.40,1046.47,1057.08,5998860000,1057.08 2009-09-29,1063.69,1069.62,1057.83,1060.61,4949900000,1060.61 2009-09-28,1045.38,1065.13,1045.38,1062.98,3726950000,1062.98 2009-09-25,1049.48,1053.47,1041.17,1044.38,4507090000,1044.38 2009-09-24,1062.56,1066.29,1045.85,1050.78,5505610000,1050.78 2009-09-23,1072.69,1080.15,1060.39,1060.87,5531930000,1060.87 2009-09-22,1066.35,1073.81,1066.35,1071.66,5246600000,1071.66 2009-09-21,1067.14,1067.28,1057.46,1064.66,4615280000,1064.66 2009-09-18,1066.60,1071.52,1064.27,1068.30,5607970000,1068.30 2009-09-17,1067.87,1074.77,1061.20,1065.49,6668110000,1065.49 2009-09-16,1053.99,1068.76,1052.87,1068.76,6793529600,1068.76 2009-09-15,1049.03,1056.04,1043.42,1052.63,6185620000,1052.63 2009-09-14,1040.15,1049.74,1035.00,1049.34,4979610000,1049.34 2009-09-11,1043.92,1048.18,1038.40,1042.73,4922600000,1042.73 2009-09-10,1032.99,1044.14,1028.04,1044.14,5191380000,1044.14 2009-09-09,1025.36,1036.34,1023.97,1033.37,5202550000,1033.37 2009-09-08,1018.67,1026.07,1018.67,1025.39,5235160000,1025.39 2009-09-04,1003.84,1016.48,1001.65,1016.40,4097370000,1016.40 2009-09-03,996.12,1003.43,992.25,1003.24,4624280000,1003.24 2009-09-02,996.07,1000.34,991.97,994.75,5842730000,994.75 2009-09-01,1019.52,1028.45,996.28,998.04,6862360000,998.04 2009-08-31,1025.21,1025.21,1014.62,1020.62,5004560000,1020.62 2009-08-28,1031.62,1039.47,1023.13,1028.93,5785780000,1028.93 2009-08-27,1027.81,1033.33,1016.20,1030.98,5785880000,1030.98 2009-08-26,1027.35,1032.47,1021.57,1028.12,5080060000,1028.12 2009-08-25,1026.63,1037.75,1026.21,1028.00,5768740000,1028.00 2009-08-24,1026.59,1035.82,1022.48,1025.57,6302450000,1025.57 2009-08-21,1009.06,1027.59,1009.06,1026.13,5885550000,1026.13 2009-08-20,996.41,1008.92,996.39,1007.37,4893160000,1007.37 2009-08-19,986.88,999.61,980.62,996.46,4257000000,996.46 2009-08-18,980.62,991.20,980.62,989.67,4198970000,989.67 2009-08-17,998.18,998.18,978.51,979.73,4088570000,979.73 2009-08-14,1012.23,1012.60,994.60,1004.09,4940750000,1004.09 2009-08-13,1005.86,1013.14,1000.82,1012.73,5250660000,1012.73 2009-08-12,994.00,1012.78,993.36,1005.81,5498170000,1005.81 2009-08-11,1005.77,1005.77,992.40,994.35,5773160000,994.35 2009-08-10,1008.89,1010.12,1000.99,1007.10,5406080000,1007.10 2009-08-07,999.83,1018.00,999.83,1010.48,6827089600,1010.48 2009-08-06,1004.06,1008.00,992.49,997.08,6753380000,997.08 2009-08-05,1005.41,1006.64,994.31,1002.72,7242120000,1002.72 2009-08-04,1001.41,1007.12,996.68,1005.65,5713700000,1005.65 2009-08-03,990.22,1003.61,990.22,1002.63,5603440000,1002.63 2009-07-31,986.80,993.18,982.85,987.48,5139070000,987.48 2009-07-30,976.01,996.68,976.01,986.75,6035180000,986.75 2009-07-29,977.66,977.76,968.65,975.15,5178770000,975.15 2009-07-28,981.48,982.35,969.35,979.62,5490350000,979.62 2009-07-27,978.63,982.49,972.29,982.18,4631290000,982.18 2009-07-24,972.16,979.79,965.95,979.26,4458300000,979.26 2009-07-23,954.07,979.42,953.27,976.29,5761650000,976.29 2009-07-22,953.40,959.83,947.75,954.07,4634100000,954.07 2009-07-21,951.97,956.53,943.22,954.58,5309300000,954.58 2009-07-20,942.07,951.62,940.99,951.13,4853150000,951.13 2009-07-17,940.56,941.89,934.65,940.38,5141380000,940.38 2009-07-16,930.17,943.96,927.45,940.74,4898640000,940.74 2009-07-15,910.15,933.95,910.15,932.68,5238830000,932.68 2009-07-14,900.77,905.84,896.50,905.84,4149030000,905.84 2009-07-13,879.57,901.05,875.32,901.05,4499440000,901.05 2009-07-10,880.03,883.57,872.81,879.13,3912080000,879.13 2009-07-09,881.28,887.86,878.45,882.68,4347170000,882.68 2009-07-08,881.90,886.80,869.32,879.56,5721780000,879.56 2009-07-07,898.60,898.60,879.93,881.03,4673300000,881.03 2009-07-06,894.27,898.72,886.36,898.72,4712580000,898.72 2009-07-02,921.24,921.24,896.42,896.42,3931000000,896.42 2009-07-01,920.82,931.92,920.82,923.33,3919400000,923.33 2009-06-30,927.15,930.01,912.86,919.32,4627570000,919.32 2009-06-29,919.86,927.99,916.18,927.23,4211760000,927.23 2009-06-26,918.84,922.00,913.03,918.90,6076660000,918.90 2009-06-25,899.45,921.42,896.27,920.26,4911240000,920.26 2009-06-24,896.31,910.85,896.31,900.94,4636720000,900.94 2009-06-23,893.46,898.69,888.86,895.10,5071020000,895.10 2009-06-22,918.13,918.13,893.04,893.04,4903940000,893.04 2009-06-19,919.96,927.09,915.80,921.23,5713390000,921.23 2009-06-18,910.86,921.93,907.94,918.37,4684010000,918.37 2009-06-17,911.89,918.44,903.78,910.71,5523650000,910.71 2009-06-16,925.60,928.00,911.60,911.97,4951200000,911.97 2009-06-15,942.45,942.45,919.65,923.72,4697880000,923.72 2009-06-12,943.44,946.30,935.66,946.21,4528120000,946.21 2009-06-11,939.04,956.23,939.04,944.89,5500840000,944.89 2009-06-10,942.73,949.77,927.97,939.15,5379420000,939.15 2009-06-09,940.35,946.92,936.15,942.43,4439950000,942.43 2009-06-08,938.12,946.33,926.44,939.14,4483430000,939.14 2009-06-05,945.67,951.69,934.13,940.09,5277910000,940.09 2009-06-04,932.49,942.47,929.32,942.46,5352890000,942.46 2009-06-03,942.51,942.51,923.85,931.76,5323770000,931.76 2009-06-02,942.87,949.38,938.46,944.74,5987340000,944.74 2009-06-01,923.26,947.77,923.26,942.87,6370440000,942.87 2009-05-29,907.02,920.02,903.56,919.14,6050420000,919.14 2009-05-28,892.96,909.45,887.60,906.83,5738980000,906.83 2009-05-27,909.95,913.84,891.87,893.06,5698800000,893.06 2009-05-26,887.00,911.76,881.46,910.33,5667050000,910.33 2009-05-22,888.68,896.65,883.75,887.00,5155320000,887.00 2009-05-21,900.42,900.42,879.61,888.33,6019840000,888.33 2009-05-20,908.62,924.60,901.37,903.47,8205060000,903.47 2009-05-19,909.67,916.39,905.22,908.13,6616270000,908.13 2009-05-18,886.07,910.00,886.07,909.71,5702150000,909.71 2009-05-15,892.76,896.97,878.94,882.88,5439720000,882.88 2009-05-14,884.24,898.36,882.52,893.07,6134870000,893.07 2009-05-13,905.40,905.40,882.80,883.92,7091820000,883.92 2009-05-12,910.52,915.57,896.46,908.35,6871750400,908.35 2009-05-11,922.99,922.99,908.68,909.24,6150600000,909.24 2009-05-08,909.03,930.17,909.03,929.23,8163280000,929.23 2009-05-07,919.58,929.58,901.36,907.39,9120100000,907.39 2009-05-06,903.95,920.28,903.95,919.53,8555040000,919.53 2009-05-05,906.10,907.70,897.34,903.80,6882860000,903.80 2009-05-04,879.21,907.85,879.21,907.24,7038840000,907.24 2009-05-01,872.74,880.48,866.10,877.52,5312170000,877.52 2009-04-30,876.59,888.70,868.51,872.81,6862540000,872.81 2009-04-29,856.85,882.06,856.85,873.64,6101620000,873.64 2009-04-28,854.48,864.48,847.12,855.16,6328000000,855.16 2009-04-27,862.82,868.83,854.65,857.51,5613460000,857.51 2009-04-24,853.91,871.80,853.91,866.23,7114440000,866.23 2009-04-23,844.62,852.87,835.45,851.92,6563100000,851.92 2009-04-22,847.26,861.78,840.57,843.55,7327860000,843.55 2009-04-21,831.25,850.09,826.83,850.08,7436489600,850.08 2009-04-20,868.27,868.27,832.39,832.39,6973960000,832.39 2009-04-17,865.18,875.63,860.87,869.60,7352009600,869.60 2009-04-16,854.54,870.35,847.04,865.30,6598670000,865.30 2009-04-15,839.44,852.93,835.58,852.06,6241100000,852.06 2009-04-14,856.88,856.88,840.25,841.50,7569840000,841.50 2009-04-13,855.33,864.31,845.35,858.73,6434890000,858.73 2009-04-09,829.29,856.91,829.29,856.56,7600710400,856.56 2009-04-08,816.76,828.42,814.84,825.16,5938460000,825.16 2009-04-07,834.12,834.12,814.53,815.55,5155580000,815.55 2009-04-06,839.75,839.75,822.79,835.48,6210000000,835.48 2009-04-03,835.13,842.50,826.70,842.50,5855640000,842.50 2009-04-02,814.53,845.61,814.53,834.38,7542809600,834.38 2009-04-01,793.59,813.62,783.32,811.08,6034140000,811.08 2009-03-31,790.88,810.48,790.88,797.87,6089100000,797.87 2009-03-30,809.07,809.07,779.81,787.53,5912660000,787.53 2009-03-27,828.68,828.68,813.43,815.94,5600210000,815.94 2009-03-26,814.06,832.98,814.06,832.86,6992960000,832.86 2009-03-25,806.81,826.78,791.37,813.88,7687180000,813.88 2009-03-24,820.60,823.65,805.48,806.12,6767980000,806.12 2009-03-23,772.31,823.37,772.31,822.92,7715769600,822.92 2009-03-20,784.58,788.91,766.20,768.54,7643720000,768.54 2009-03-19,797.92,803.24,781.82,784.04,9033870400,784.04 2009-03-18,776.01,803.04,765.64,794.35,9098449600,794.35 2009-03-17,753.88,778.12,749.93,778.12,6156800000,778.12 2009-03-16,758.84,774.53,753.37,753.89,7883540000,753.89 2009-03-13,751.97,758.29,742.46,756.55,6787089600,756.55 2009-03-12,720.89,752.63,714.76,750.74,7326630400,750.74 2009-03-11,719.59,731.92,713.85,721.36,7287809600,721.36 2009-03-10,679.28,719.60,679.28,719.60,8618329600,719.60 2009-03-09,680.76,695.27,672.88,676.53,7277320000,676.53 2009-03-06,684.04,699.09,666.79,683.38,7331830400,683.38 2009-03-05,708.27,708.27,677.93,682.55,7507249600,682.55 2009-03-04,698.60,724.12,698.60,712.87,7673620000,712.87 2009-03-03,704.44,711.67,692.30,696.33,7583230400,696.33 2009-03-02,729.57,729.57,699.70,700.82,7868289600,700.82 2009-02-27,749.93,751.27,734.52,735.09,8926480000,735.09 2009-02-26,765.76,779.42,751.75,752.83,7599969600,752.83 2009-02-25,770.64,780.12,752.89,764.90,7483640000,764.90 2009-02-24,744.69,775.49,744.69,773.14,7234489600,773.14 2009-02-23,773.25,777.85,742.37,743.33,6509300000,743.33 2009-02-20,775.87,778.69,754.25,770.05,8210590400,770.05 2009-02-19,787.91,797.58,777.03,778.94,5746940000,778.94 2009-02-18,791.06,796.17,780.43,788.42,5740710000,788.42 2009-02-17,818.61,818.61,789.17,789.17,5907820000,789.17 2009-02-13,833.95,839.43,825.21,826.84,5296650000,826.84 2009-02-12,829.91,835.48,808.06,835.19,6476460000,835.19 2009-02-11,827.41,838.22,822.30,833.74,5926460000,833.74 2009-02-10,866.87,868.05,822.99,827.16,6770169600,827.16 2009-02-09,868.24,875.01,861.65,869.89,5574370000,869.89 2009-02-06,846.09,870.75,845.42,868.60,6484100000,868.60 2009-02-05,831.75,850.55,819.91,845.85,6624030000,845.85 2009-02-04,837.77,851.85,829.18,832.23,6420450000,832.23 2009-02-03,825.69,842.60,821.98,838.51,5886310000,838.51 2009-02-02,823.09,830.78,812.87,825.44,5673270000,825.44 2009-01-30,845.69,851.66,821.67,825.88,5350580000,825.88 2009-01-29,868.89,868.89,844.15,845.14,5067060000,845.14 2009-01-28,845.73,877.86,845.73,874.09,6199180000,874.09 2009-01-27,837.30,850.45,835.40,845.71,5353260000,845.71 2009-01-26,832.50,852.53,827.69,836.57,6039940000,836.57 2009-01-23,822.16,838.61,806.07,831.95,5832160000,831.95 2009-01-22,839.74,839.74,811.29,827.50,5843830000,827.50 2009-01-21,806.77,841.72,804.30,840.24,6467830000,840.24 2009-01-20,849.64,849.64,804.47,805.22,6375230000,805.22 2009-01-16,844.45,858.13,830.66,850.12,6786040000,850.12 2009-01-15,841.99,851.59,817.04,843.74,7807350400,843.74 2009-01-14,867.28,867.28,836.93,842.62,5407880000,842.62 2009-01-13,869.79,877.02,862.02,871.79,5017470000,871.79 2009-01-12,890.40,890.40,864.32,870.26,4725050000,870.26 2009-01-09,909.91,911.93,888.31,890.35,4716500000,890.35 2009-01-08,905.73,910.00,896.81,909.73,4991550000,909.73 2009-01-07,927.45,927.45,902.37,906.65,4704940000,906.65 2009-01-06,931.17,943.85,927.28,934.70,5392620000,934.70 2009-01-05,929.17,936.63,919.53,927.45,5413910000,927.45 2009-01-02,902.99,934.73,899.35,931.80,4048270000,931.80 2008-12-31,890.59,910.32,889.67,903.25,4172940000,903.25 2008-12-30,870.58,891.12,870.58,890.64,3627800000,890.64 2008-12-29,872.37,873.70,857.07,869.42,3323430000,869.42 2008-12-26,869.51,873.74,866.52,872.80,1880050000,872.80 2008-12-24,863.87,869.79,861.44,868.15,1546550000,868.15 2008-12-23,874.31,880.44,860.10,863.16,4051970000,863.16 2008-12-22,887.20,887.37,857.09,871.63,4869850000,871.63 2008-12-19,886.96,905.47,883.02,887.88,6705310000,887.88 2008-12-18,905.98,911.02,877.44,885.28,5675000000,885.28 2008-12-17,908.16,918.85,895.94,904.42,5907380000,904.42 2008-12-16,871.53,914.66,871.53,913.18,6009780000,913.18 2008-12-15,881.07,884.63,857.72,868.57,4982390000,868.57 2008-12-12,871.79,883.24,851.35,879.73,5959590000,879.73 2008-12-11,898.35,904.63,868.73,873.59,5513840000,873.59 2008-12-10,892.17,908.27,885.45,899.24,5942130000,899.24 2008-12-09,906.48,916.26,885.38,888.67,5693110000,888.67 2008-12-08,882.71,918.57,882.71,909.70,6553600000,909.70 2008-12-05,844.43,879.42,818.41,876.07,6165370000,876.07 2008-12-04,869.75,875.60,833.60,845.22,5860390000,845.22 2008-12-03,843.60,873.12,827.60,870.74,6221880000,870.74 2008-12-02,817.94,850.54,817.94,848.81,6170100000,848.81 2008-12-01,888.61,888.61,815.69,816.21,6052010000,816.21 2008-11-28,886.89,896.25,881.21,896.24,2740860000,896.24 2008-11-26,852.90,887.68,841.37,887.68,5793260000,887.68 2008-11-25,853.40,868.94,834.99,857.39,6952700000,857.39 2008-11-24,801.20,865.60,801.20,851.81,7879440000,851.81 2008-11-21,755.84,801.20,741.02,800.03,9495900000,800.03 2008-11-20,805.87,820.52,747.78,752.44,9093740000,752.44 2008-11-19,859.03,864.57,806.18,806.58,6548600000,806.58 2008-11-18,852.34,865.90,826.84,859.12,6679470000,859.12 2008-11-17,873.23,882.29,848.98,850.75,4927490000,850.75 2008-11-14,904.36,916.88,869.88,873.29,5881030000,873.29 2008-11-13,853.13,913.01,818.69,911.29,7849120000,911.29 2008-11-12,893.39,893.39,850.48,852.30,5764180000,852.30 2008-11-11,917.15,917.15,884.90,898.95,4998340000,898.95 2008-11-10,936.75,951.95,907.47,919.21,4572000000,919.21 2008-11-07,907.44,931.46,906.90,930.99,4931640000,930.99 2008-11-06,952.40,952.40,899.73,904.88,6102230000,904.88 2008-11-05,1001.84,1001.84,949.86,952.77,5426640000,952.77 2008-11-04,971.31,1007.51,971.31,1005.75,5531290000,1005.75 2008-11-03,968.67,975.57,958.82,966.30,4492280000,966.30 2008-10-31,953.11,984.38,944.59,968.75,6394350000,968.75 2008-10-30,939.38,963.23,928.50,954.09,6175830000,954.09 2008-10-29,939.51,969.97,922.26,930.09,7077800000,930.09 2008-10-28,848.92,940.51,845.27,940.51,7096950400,940.51 2008-10-27,874.28,893.78,846.75,848.92,5558050000,848.92 2008-10-24,895.22,896.30,852.85,876.77,6550050000,876.77 2008-10-23,899.08,922.83,858.44,908.11,7184180000,908.11 2008-10-22,951.67,951.67,875.81,896.78,6147980000,896.78 2008-10-21,980.40,985.44,952.47,955.05,5121830000,955.05 2008-10-20,943.51,985.40,943.51,985.40,5175640000,985.40 2008-10-17,942.29,984.64,918.74,940.55,6581780000,940.55 2008-10-16,909.53,947.71,865.83,946.43,7984500000,946.43 2008-10-15,994.60,994.60,903.99,907.84,6542330000,907.84 2008-10-14,1009.97,1044.31,972.07,998.01,8161990400,998.01 2008-10-13,912.75,1006.93,912.75,1003.35,7263369600,1003.35 2008-10-10,902.31,936.36,839.80,899.22,11456230400,899.22 2008-10-09,988.42,1005.25,909.19,909.92,6819000000,909.92 2008-10-08,988.91,1021.06,970.97,984.94,8716329600,984.94 2008-10-07,1057.60,1072.91,996.23,996.23,7069209600,996.23 2008-10-06,1097.56,1097.56,1007.97,1056.89,7956020000,1056.89 2008-10-03,1115.16,1153.82,1098.14,1099.23,6716120000,1099.23 2008-10-02,1160.64,1160.64,1111.43,1114.28,6285640000,1114.28 2008-10-01,1164.17,1167.03,1140.77,1161.06,5782130000,1161.06 2008-09-30,1113.78,1168.03,1113.78,1166.36,4937680000,1166.36 2008-09-29,1209.07,1209.07,1106.42,1106.42,7305060000,1106.42 2008-09-26,1204.47,1215.77,1187.54,1213.27,5383610000,1213.27 2008-09-25,1187.87,1220.03,1187.87,1209.18,5877640000,1209.18 2008-09-24,1188.79,1197.41,1179.79,1185.87,4820360000,1185.87 2008-09-23,1207.61,1221.15,1187.06,1188.22,5185730000,1188.22 2008-09-22,1255.37,1255.37,1205.61,1207.09,5332130000,1207.09 2008-09-19,1213.11,1265.12,1213.11,1255.08,9387169600,1255.08 2008-09-18,1157.08,1211.14,1133.50,1206.51,10082689600,1206.51 2008-09-17,1210.34,1210.34,1155.88,1156.39,9431870400,1156.39 2008-09-16,1188.31,1214.84,1169.28,1213.60,9459830400,1213.60 2008-09-15,1250.92,1250.92,1192.70,1192.70,8279510400,1192.70 2008-09-12,1245.88,1255.09,1233.81,1251.70,6273260000,1251.70 2008-09-11,1229.04,1249.98,1211.54,1249.05,6869249600,1249.05 2008-09-10,1227.50,1243.90,1221.60,1232.04,6543440000,1232.04 2008-09-09,1267.98,1268.66,1224.51,1224.51,7380630400,1224.51 2008-09-08,1249.50,1274.42,1247.12,1267.79,7351340000,1267.79 2008-09-05,1233.21,1244.94,1217.23,1242.31,5017080000,1242.31 2008-09-04,1271.80,1271.80,1232.83,1236.83,5212500000,1236.83 2008-09-03,1276.61,1280.60,1265.59,1274.98,5056980000,1274.98 2008-09-02,1287.83,1303.04,1272.20,1277.58,4783560000,1277.58 2008-08-29,1296.49,1297.59,1282.74,1282.83,3288120000,1282.83 2008-08-28,1283.79,1300.68,1283.79,1300.68,3854280000,1300.68 2008-08-27,1271.29,1285.05,1270.03,1281.66,3499610000,1281.66 2008-08-26,1267.03,1275.65,1263.21,1271.51,3587570000,1271.51 2008-08-25,1290.47,1290.47,1264.87,1266.84,3420600000,1266.84 2008-08-22,1277.59,1293.09,1277.59,1292.20,3741070000,1292.20 2008-08-21,1271.07,1281.40,1265.22,1277.72,4032590000,1277.72 2008-08-20,1267.34,1276.01,1261.16,1274.54,4555030000,1274.54 2008-08-19,1276.65,1276.65,1263.11,1266.69,4159760000,1266.69 2008-08-18,1298.14,1300.22,1274.51,1278.60,3829290000,1278.60 2008-08-15,1293.85,1302.05,1290.74,1298.20,4041820000,1298.20 2008-08-14,1282.11,1300.11,1276.84,1292.93,4064000000,1292.93 2008-08-13,1288.64,1294.03,1274.86,1285.83,4787600000,1285.83 2008-08-12,1304.79,1304.79,1285.64,1289.59,4711290000,1289.59 2008-08-11,1294.42,1313.15,1291.41,1305.32,5067310000,1305.32 2008-08-08,1266.29,1297.85,1262.11,1296.32,4966810000,1296.32 2008-08-07,1286.51,1286.51,1264.29,1266.07,5319380000,1266.07 2008-08-06,1283.99,1291.67,1276.00,1289.19,4873420000,1289.19 2008-08-05,1254.87,1284.88,1254.67,1284.88,1219310000,1284.88 2008-08-04,1253.27,1260.49,1247.45,1249.01,4562280000,1249.01 2008-08-01,1269.42,1270.52,1254.54,1260.31,4684870000,1260.31 2008-07-31,1281.37,1284.93,1265.97,1267.38,5346050000,1267.38 2008-07-30,1264.52,1284.33,1264.52,1284.26,5631330000,1284.26 2008-07-29,1236.38,1263.20,1236.38,1263.20,5414240000,1263.20 2008-07-28,1257.76,1260.09,1234.37,1234.37,4282960000,1234.37 2008-07-25,1253.51,1263.23,1251.75,1257.76,4672560000,1257.76 2008-07-24,1283.22,1283.22,1251.48,1252.54,6127980000,1252.54 2008-07-23,1278.87,1291.17,1276.06,1282.19,6705830000,1282.19 2008-07-22,1257.08,1277.42,1248.83,1277.00,6180230000,1277.00 2008-07-21,1261.82,1267.74,1255.70,1260.00,4630640000,1260.00 2008-07-18,1258.22,1262.23,1251.81,1260.68,5653280000,1260.68 2008-07-17,1246.31,1262.31,1241.49,1260.32,7365209600,1260.32 2008-07-16,1214.65,1245.52,1211.39,1245.36,6738630400,1245.36 2008-07-15,1226.83,1234.35,1200.44,1214.91,7363640000,1214.91 2008-07-14,1241.61,1253.50,1225.01,1228.30,5434860000,1228.30 2008-07-11,1248.66,1257.27,1225.35,1239.49,6742200000,1239.49 2008-07-10,1245.25,1257.65,1236.76,1253.39,5840430000,1253.39 2008-07-09,1273.38,1277.36,1244.57,1244.69,5181000000,1244.69 2008-07-08,1251.84,1274.17,1242.84,1273.70,6034110000,1273.70 2008-07-07,1262.90,1273.95,1240.68,1252.31,5265420000,1252.31 2008-07-03,1262.96,1271.48,1252.01,1262.90,3247590000,1262.90 2008-07-02,1285.82,1292.17,1261.51,1261.52,5276090000,1261.52 2008-07-01,1276.69,1285.31,1260.68,1284.91,5846290000,1284.91 2008-06-30,1278.06,1290.31,1274.86,1280.00,5032330000,1280.00 2008-06-27,1283.60,1289.45,1272.00,1278.38,6208260000,1278.38 2008-06-26,1316.29,1316.29,1283.15,1283.15,5231280000,1283.15 2008-06-25,1314.54,1335.63,1314.54,1321.97,4825640000,1321.97 2008-06-24,1317.23,1326.02,1304.42,1314.29,4705050000,1314.29 2008-06-23,1319.77,1323.78,1315.31,1318.00,4186370000,1318.00 2008-06-20,1341.02,1341.02,1314.46,1317.93,5324900000,1317.93 2008-06-19,1336.89,1347.66,1330.50,1342.83,4811670000,1342.83 2008-06-18,1349.59,1349.59,1333.40,1337.81,4573570000,1337.81 2008-06-17,1360.71,1366.59,1350.54,1350.93,3801960000,1350.93 2008-06-16,1358.85,1364.70,1352.07,1360.14,3706940000,1360.14 2008-06-13,1341.81,1360.03,1341.71,1360.03,4080420000,1360.03 2008-06-12,1335.78,1353.03,1331.29,1339.87,4734240000,1339.87 2008-06-11,1357.09,1357.09,1335.47,1335.49,4779980000,1335.49 2008-06-10,1358.98,1366.84,1351.56,1358.44,4635070000,1358.44 2008-06-09,1360.83,1370.63,1350.62,1361.76,4404570000,1361.76 2008-06-06,1400.06,1400.06,1359.90,1360.68,4771660000,1360.68 2008-06-05,1377.48,1404.05,1377.48,1404.05,4350790000,1404.05 2008-06-04,1376.26,1388.18,1371.74,1377.20,4338640000,1377.20 2008-06-03,1386.42,1393.12,1370.12,1377.65,4396380000,1377.65 2008-06-02,1399.62,1399.62,1377.79,1385.67,3714320000,1385.67 2008-05-30,1398.36,1404.46,1398.08,1400.38,3845630000,1400.38 2008-05-29,1390.50,1406.32,1388.59,1398.26,3894440000,1398.26 2008-05-28,1386.54,1391.25,1378.16,1390.84,3927240000,1390.84 2008-05-27,1375.97,1387.40,1373.07,1385.35,3588860000,1385.35 2008-05-23,1392.20,1392.20,1373.72,1375.93,3516380000,1375.93 2008-05-22,1390.83,1399.07,1390.23,1394.35,3955960000,1394.35 2008-05-21,1414.06,1419.12,1388.81,1390.71,4517990000,1390.71 2008-05-20,1424.49,1424.49,1409.09,1413.40,3854320000,1413.40 2008-05-19,1425.28,1440.24,1421.63,1426.63,3683970000,1426.63 2008-05-16,1423.89,1425.82,1414.35,1425.35,3842590000,1425.35 2008-05-15,1408.36,1424.40,1406.87,1423.57,3836480000,1423.57 2008-05-14,1405.65,1420.19,1405.65,1408.66,3979370000,1408.66 2008-05-13,1404.40,1406.30,1396.26,1403.04,4018590000,1403.04 2008-05-12,1389.40,1404.06,1386.20,1403.58,3370630000,1403.58 2008-05-09,1394.90,1394.90,1384.11,1388.28,3518620000,1388.28 2008-05-08,1394.29,1402.35,1389.39,1397.68,3827550000,1397.68 2008-05-07,1417.49,1419.54,1391.16,1392.57,4075860000,1392.57 2008-05-06,1405.60,1421.57,1397.10,1418.26,3924100000,1418.26 2008-05-05,1415.34,1415.34,1404.37,1407.49,3410090000,1407.49 2008-05-02,1409.16,1422.72,1406.25,1413.90,3953030000,1413.90 2008-05-01,1385.97,1410.07,1383.07,1409.34,4448780000,1409.34 2008-04-30,1391.22,1404.57,1384.25,1385.59,4508890000,1385.59 2008-04-29,1395.61,1397.00,1386.70,1390.94,3815320000,1390.94 2008-04-28,1397.96,1402.90,1394.40,1396.37,3607000000,1396.37 2008-04-25,1387.88,1399.11,1379.98,1397.84,3891150000,1397.84 2008-04-24,1380.52,1397.72,1371.09,1388.82,4461660000,1388.82 2008-04-23,1378.40,1387.87,1372.24,1379.93,4103610000,1379.93 2008-04-22,1386.43,1386.43,1369.84,1375.94,3821900000,1375.94 2008-04-21,1387.72,1390.23,1379.25,1388.17,3420570000,1388.17 2008-04-18,1369.00,1395.90,1369.00,1390.33,4222380000,1390.33 2008-04-17,1363.37,1368.60,1357.25,1365.56,3713880000,1365.56 2008-04-16,1337.02,1365.49,1337.02,1364.71,4260370000,1364.71 2008-04-15,1331.72,1337.72,1324.35,1334.43,3581230000,1334.43 2008-04-14,1332.20,1335.64,1326.16,1328.32,3565020000,1328.32 2008-04-11,1357.98,1357.98,1331.21,1332.83,3723790000,1332.83 2008-04-10,1355.37,1367.24,1350.11,1360.55,3686150000,1360.55 2008-04-09,1365.50,1368.39,1349.97,1354.49,3556670000,1354.49 2008-04-08,1370.16,1370.16,1360.62,1365.54,3602500000,1365.54 2008-04-07,1373.69,1386.74,1369.02,1372.54,3747780000,1372.54 2008-04-04,1369.85,1380.91,1362.83,1370.40,3703100000,1370.40 2008-04-03,1365.69,1375.66,1358.68,1369.31,3920100000,1369.31 2008-04-02,1369.96,1377.95,1361.55,1367.53,4320440000,1367.53 2008-04-01,1326.41,1370.18,1326.41,1370.18,4745120000,1370.18 2008-03-31,1315.92,1328.52,1312.81,1322.70,4188990000,1322.70 2008-03-28,1327.02,1334.87,1312.95,1315.22,3686980000,1315.22 2008-03-27,1340.34,1345.62,1325.66,1325.76,4037930000,1325.76 2008-03-26,1352.45,1352.45,1336.41,1341.13,4055670000,1341.13 2008-03-25,1349.07,1357.47,1341.21,1352.99,4145120000,1352.99 2008-03-24,1330.29,1359.68,1330.29,1349.88,4499000000,1349.88 2008-03-20,1299.67,1330.67,1295.22,1329.51,6145220000,1329.51 2008-03-19,1330.97,1341.51,1298.42,1298.42,1203830000,1298.42 2008-03-18,1277.16,1330.74,1277.16,1330.74,5335630000,1330.74 2008-03-17,1283.21,1287.50,1256.98,1276.60,5683010000,1276.60 2008-03-14,1316.05,1321.47,1274.86,1288.14,5153780000,1288.14 2008-03-13,1305.26,1321.68,1282.11,1315.48,5073360000,1315.48 2008-03-12,1321.13,1333.26,1307.86,1308.77,4414280000,1308.77 2008-03-11,1274.40,1320.65,1274.40,1320.65,5109080000,1320.65 2008-03-10,1293.16,1295.01,1272.66,1273.37,4261240000,1273.37 2008-03-07,1301.53,1313.24,1282.43,1293.37,4565410000,1293.37 2008-03-06,1332.20,1332.20,1303.42,1304.34,4323460000,1304.34 2008-03-05,1327.69,1344.19,1320.22,1333.70,4277710000,1333.70 2008-03-04,1329.58,1331.03,1307.39,1326.75,4757180000,1326.75 2008-03-03,1330.45,1335.13,1320.04,1331.34,4117570000,1331.34 2008-02-29,1364.07,1364.07,1325.42,1330.63,4426730000,1330.63 2008-02-28,1378.16,1378.16,1363.16,1367.68,3938580000,1367.68 2008-02-27,1378.95,1388.34,1372.00,1380.02,3904700000,1380.02 2008-02-26,1371.76,1387.34,1363.29,1381.29,4096060000,1381.29 2008-02-25,1352.75,1374.36,1346.03,1371.80,3866350000,1371.80 2008-02-22,1344.22,1354.30,1327.04,1353.11,3572660000,1353.11 2008-02-21,1362.21,1367.94,1339.34,1342.53,3696660000,1342.53 2008-02-20,1348.39,1363.71,1336.55,1360.03,3870520000,1360.03 2008-02-19,1355.86,1367.28,1345.05,1348.78,3613550000,1348.78 2008-02-15,1347.52,1350.00,1338.13,1349.99,3583300000,1349.99 2008-02-14,1367.33,1368.16,1347.31,1348.86,3644760000,1348.86 2008-02-13,1353.12,1369.23,1350.78,1367.21,3856420000,1367.21 2008-02-12,1340.55,1362.10,1339.36,1348.86,4044640000,1348.86 2008-02-11,1331.92,1341.40,1320.32,1339.13,3593140000,1339.13 2008-02-08,1336.88,1341.22,1321.06,1331.29,3768490000,1331.29 2008-02-07,1324.01,1347.16,1316.75,1336.91,4589160000,1336.91 2008-02-06,1339.48,1351.96,1324.34,1326.45,4008120000,1326.45 2008-02-05,1380.28,1380.28,1336.64,1336.64,4315740000,1336.64 2008-02-04,1395.38,1395.38,1379.69,1380.82,3495780000,1380.82 2008-02-01,1378.60,1396.02,1375.93,1395.42,4650770000,1395.42 2008-01-31,1351.98,1385.62,1334.08,1378.55,4970290000,1378.55 2008-01-30,1362.22,1385.86,1352.95,1355.81,4742760000,1355.81 2008-01-29,1355.94,1364.93,1350.19,1362.30,4232960000,1362.30 2008-01-28,1330.70,1353.97,1322.26,1353.96,4100930000,1353.96 2008-01-25,1357.32,1368.56,1327.50,1330.61,4882250000,1330.61 2008-01-24,1340.13,1355.15,1334.31,1352.07,5735300000,1352.07 2008-01-23,1310.41,1339.09,1270.05,1338.60,3241680000,1338.60 2008-01-22,1312.94,1322.09,1274.29,1310.50,6544690000,1310.50 2008-01-18,1333.90,1350.28,1312.51,1325.19,6004840000,1325.19 2008-01-17,1374.79,1377.72,1330.67,1333.25,5303130000,1333.25 2008-01-16,1377.41,1391.99,1364.27,1373.20,5440620000,1373.20 2008-01-15,1411.88,1411.88,1380.60,1380.95,4601640000,1380.95 2008-01-14,1402.91,1417.89,1402.91,1416.25,3682090000,1416.25 2008-01-11,1419.91,1419.91,1394.83,1401.02,4495840000,1401.02 2008-01-10,1406.78,1429.09,1395.31,1420.33,5170490000,1420.33 2008-01-09,1390.25,1409.19,1378.70,1409.13,5351030000,1409.13 2008-01-08,1415.71,1430.28,1388.30,1390.19,4705390000,1390.19 2008-01-07,1414.07,1423.87,1403.45,1416.18,4221260000,1416.18 2008-01-04,1444.01,1444.01,1411.19,1411.63,4166000000,1411.63 2008-01-03,1447.55,1456.80,1443.73,1447.16,3429500000,1447.16 2008-01-02,1467.97,1471.77,1442.07,1447.16,3452650000,1447.16 2007-12-31,1475.25,1475.83,1465.13,1468.36,2440880000,1468.36 2007-12-28,1479.83,1488.01,1471.70,1478.49,2420510000,1478.49 2007-12-27,1495.05,1495.05,1475.86,1476.27,2365770000,1476.27 2007-12-26,1495.12,1498.85,1488.20,1497.66,2010500000,1497.66 2007-12-24,1484.55,1497.63,1484.55,1496.45,1267420000,1496.45 2007-12-21,1463.19,1485.40,1463.19,1484.46,4508590000,1484.46 2007-12-20,1456.42,1461.53,1447.22,1460.12,3526890000,1460.12 2007-12-19,1454.70,1464.42,1445.31,1453.00,3401300000,1453.00 2007-12-18,1445.92,1460.16,1435.65,1454.98,3723690000,1454.98 2007-12-17,1465.05,1465.05,1445.43,1445.90,3569030000,1445.90 2007-12-14,1486.19,1486.67,1467.78,1467.95,3401050000,1467.95 2007-12-13,1483.27,1489.40,1469.21,1488.41,3635170000,1488.41 2007-12-12,1487.58,1511.96,1468.23,1486.59,4482120000,1486.59 2007-12-11,1516.68,1523.57,1475.99,1477.65,4080180000,1477.65 2007-12-10,1505.11,1518.27,1504.96,1515.96,2911760000,1515.96 2007-12-07,1508.60,1510.63,1502.66,1504.66,3177710000,1504.66 2007-12-06,1484.59,1508.02,1482.19,1507.34,3568570000,1507.34 2007-12-05,1465.22,1486.09,1465.22,1485.01,3663660000,1485.01 2007-12-04,1471.34,1471.34,1460.66,1462.79,3343620000,1462.79 2007-12-03,1479.63,1481.16,1470.08,1472.42,3323250000,1472.42 2007-11-30,1471.83,1488.94,1470.89,1481.14,4422200000,1481.14 2007-11-29,1467.41,1473.81,1458.36,1469.72,3524730000,1469.72 2007-11-28,1432.95,1471.62,1432.95,1469.02,4508020000,1469.02 2007-11-27,1409.59,1429.49,1407.43,1428.23,4320720000,1428.23 2007-11-26,1440.74,1446.09,1406.10,1407.22,3706470000,1407.22 2007-11-23,1417.62,1440.86,1417.62,1440.70,1612720000,1440.70 2007-11-21,1434.71,1436.40,1415.64,1416.77,4076230000,1416.77 2007-11-20,1434.51,1452.64,1419.28,1439.70,4875150000,1439.70 2007-11-19,1456.70,1456.70,1430.42,1433.27,4119650000,1433.27 2007-11-16,1453.09,1462.18,1443.99,1458.74,4168870000,1458.74 2007-11-15,1468.04,1472.67,1443.49,1451.15,3941010000,1451.15 2007-11-14,1483.40,1492.14,1466.47,1470.58,4031470000,1470.58 2007-11-13,1441.35,1481.37,1441.35,1481.05,4141310000,1481.05 2007-11-12,1453.66,1464.94,1438.53,1439.18,4192520000,1439.18 2007-11-09,1467.59,1474.09,1448.51,1453.70,4587050000,1453.70 2007-11-08,1475.27,1482.50,1450.31,1474.77,5439720000,1474.77 2007-11-07,1515.46,1515.46,1475.04,1475.62,4353160000,1475.62 2007-11-06,1505.33,1520.77,1499.07,1520.27,3879160000,1520.27 2007-11-05,1505.61,1510.84,1489.95,1502.17,3819330000,1502.17 2007-11-02,1511.07,1513.15,1492.53,1509.65,4285990000,1509.65 2007-11-01,1545.79,1545.79,1506.66,1508.44,4241470000,1508.44 2007-10-31,1532.15,1552.76,1529.40,1549.38,3953070000,1549.38 2007-10-30,1539.42,1539.42,1529.55,1531.02,3212520000,1531.02 2007-10-29,1536.92,1544.67,1536.43,1540.98,3124480000,1540.98 2007-10-26,1522.17,1535.53,1520.18,1535.28,3612120000,1535.28 2007-10-25,1516.15,1523.24,1500.46,1514.40,4183960000,1514.40 2007-10-24,1516.61,1517.23,1489.56,1515.88,4003300000,1515.88 2007-10-23,1509.30,1520.01,1503.61,1519.59,3309120000,1519.59 2007-10-22,1497.79,1508.06,1490.40,1506.33,3471830000,1506.33 2007-10-19,1540.00,1540.00,1500.26,1500.63,4160970000,1500.63 2007-10-18,1539.29,1542.79,1531.76,1540.08,3203210000,1540.08 2007-10-17,1544.44,1550.66,1526.01,1541.24,3638070000,1541.24 2007-10-16,1547.81,1547.81,1536.29,1538.53,3234560000,1538.53 2007-10-15,1562.25,1564.74,1540.81,1548.71,3139290000,1548.71 2007-10-12,1555.41,1563.03,1554.09,1561.80,2788690000,1561.80 2007-10-11,1564.72,1576.09,1546.72,1554.41,3911260000,1554.41 2007-10-10,1564.98,1565.42,1555.46,1562.47,3044760000,1562.47 2007-10-09,1553.18,1565.26,1551.82,1565.15,2932040000,1565.15 2007-10-08,1556.51,1556.51,1549.00,1552.58,2040650000,1552.58 2007-10-05,1543.84,1561.91,1543.84,1557.59,2919030000,1557.59 2007-10-04,1539.91,1544.02,1537.63,1542.84,2690430000,1542.84 2007-10-03,1545.80,1545.84,1536.34,1539.59,3065320000,1539.59 2007-10-02,1546.96,1548.01,1540.37,1546.63,3101910000,1546.63 2007-10-01,1527.29,1549.02,1527.25,1547.04,3281990000,1547.04 2007-09-28,1531.24,1533.74,1521.99,1526.75,2925350000,1526.75 2007-09-27,1527.32,1532.46,1525.81,1531.38,2872180000,1531.38 2007-09-26,1518.62,1529.39,1518.62,1525.42,3237390000,1525.42 2007-09-25,1516.34,1518.27,1507.13,1517.21,3187770000,1517.21 2007-09-24,1525.75,1530.18,1516.15,1517.73,3131310000,1517.73 2007-09-21,1518.75,1530.89,1518.75,1525.75,3679460000,1525.75 2007-09-20,1528.69,1529.14,1516.42,1518.75,2957700000,1518.75 2007-09-19,1519.75,1538.74,1519.75,1529.03,3846750000,1529.03 2007-09-18,1476.63,1519.89,1476.63,1519.78,3708940000,1519.78 2007-09-17,1484.24,1484.24,1471.82,1476.65,2598390000,1476.65 2007-09-14,1483.95,1485.99,1473.18,1484.25,2641740000,1484.25 2007-09-13,1471.47,1489.58,1471.47,1483.95,2877080000,1483.95 2007-09-12,1471.10,1479.50,1465.75,1471.56,2885720000,1471.56 2007-09-11,1451.69,1472.48,1451.69,1471.49,3015330000,1471.49 2007-09-10,1453.50,1462.25,1439.29,1451.70,2835720000,1451.70 2007-09-07,1478.55,1478.55,1449.07,1453.55,3191080000,1453.55 2007-09-06,1472.03,1481.49,1467.41,1478.55,2459590000,1478.55 2007-09-05,1488.76,1488.76,1466.34,1472.29,2991600000,1472.29 2007-09-04,1473.96,1496.40,1472.15,1489.42,2766600000,1489.42 2007-08-31,1457.61,1481.47,1457.61,1473.99,2731610000,1473.99 2007-08-30,1463.67,1468.43,1451.25,1457.64,2582960000,1457.64 2007-08-29,1432.01,1463.76,1432.01,1463.76,2824070000,1463.76 2007-08-28,1466.72,1466.72,1432.01,1432.36,3078090000,1432.36 2007-08-27,1479.36,1479.36,1465.98,1466.79,2406180000,1466.79 2007-08-24,1462.34,1479.40,1460.54,1479.37,2541400000,1479.37 2007-08-23,1464.05,1472.06,1453.88,1462.50,3084390000,1462.50 2007-08-22,1447.03,1464.86,1447.03,1464.07,3309120000,1464.07 2007-08-21,1445.55,1455.32,1439.76,1447.12,3012150000,1447.12 2007-08-20,1445.94,1451.75,1430.54,1445.55,3321340000,1445.55 2007-08-17,1411.26,1450.33,1411.26,1445.94,3570040000,1445.94 2007-08-16,1406.64,1415.97,1370.60,1411.27,6509300000,1411.27 2007-08-15,1426.15,1440.78,1404.36,1406.70,4290930000,1406.70 2007-08-14,1452.87,1456.74,1426.20,1426.54,3814630000,1426.54 2007-08-13,1453.42,1466.29,1451.54,1452.92,3696280000,1452.92 2007-08-10,1453.09,1462.02,1429.74,1453.64,5345780000,1453.64 2007-08-09,1497.21,1497.21,1453.09,1453.09,5889600000,1453.09 2007-08-08,1476.22,1503.89,1476.22,1497.49,5499560000,1497.49 2007-08-07,1467.62,1488.30,1455.80,1476.71,4909390000,1476.71 2007-08-06,1433.04,1467.67,1427.39,1467.67,5067200000,1467.67 2007-08-03,1472.18,1473.23,1432.80,1433.06,4272110000,1433.06 2007-08-02,1465.46,1476.43,1460.58,1472.20,4368850000,1472.20 2007-08-01,1455.18,1468.38,1439.59,1465.81,5256780000,1465.81 2007-07-31,1473.90,1488.30,1454.25,1455.27,4524520000,1455.27 2007-07-30,1458.93,1477.88,1454.32,1473.91,4128780000,1473.91 2007-07-27,1482.44,1488.53,1458.95,1458.95,4784650000,1458.95 2007-07-26,1518.09,1518.09,1465.30,1482.66,4472550000,1482.66 2007-07-25,1511.03,1524.31,1503.73,1518.09,4283200000,1518.09 2007-07-24,1541.57,1541.57,1508.62,1511.04,4115830000,1511.04 2007-07-23,1534.06,1547.23,1534.06,1541.57,3102700000,1541.57 2007-07-20,1553.19,1553.19,1529.20,1534.10,3745780000,1534.10 2007-07-19,1546.13,1555.20,1546.13,1553.08,3251450000,1553.08 2007-07-18,1549.20,1549.20,1533.67,1546.17,3609220000,1546.17 2007-07-17,1549.52,1555.32,1547.74,1549.37,3007140000,1549.37 2007-07-16,1552.50,1555.90,1546.69,1549.52,2704110000,1549.52 2007-07-13,1547.68,1555.10,1544.85,1552.50,2801120000,1552.50 2007-07-12,1518.74,1547.92,1518.74,1547.70,3489600000,1547.70 2007-07-11,1509.93,1519.34,1506.10,1518.76,3082920000,1518.76 2007-07-10,1531.85,1531.85,1510.01,1510.12,3244280000,1510.12 2007-07-09,1530.43,1534.26,1527.45,1531.85,2715330000,1531.85 2007-07-06,1524.96,1532.40,1520.47,1530.44,2441520000,1530.44 2007-07-05,1524.86,1526.57,1517.72,1525.40,2622950000,1525.40 2007-07-03,1519.12,1526.01,1519.12,1524.87,1560790000,1524.87 2007-07-02,1504.66,1519.45,1504.66,1519.43,2644990000,1519.43 2007-06-29,1505.70,1517.53,1493.61,1503.35,3165410000,1503.35 2007-06-28,1506.32,1514.84,1503.41,1505.71,3006710000,1505.71 2007-06-27,1492.62,1506.80,1484.18,1506.34,3398150000,1506.34 2007-06-26,1497.68,1506.12,1490.54,1492.89,3398530000,1492.89 2007-06-25,1502.56,1514.29,1492.68,1497.74,3287250000,1497.74 2007-06-22,1522.19,1522.19,1500.74,1502.56,4284320000,1502.56 2007-06-21,1512.50,1522.90,1504.75,1522.19,3161110000,1522.19 2007-06-20,1533.68,1537.32,1512.36,1512.84,3286900000,1512.84 2007-06-19,1531.02,1535.85,1525.67,1533.70,2873590000,1533.70 2007-06-18,1532.90,1535.44,1529.31,1531.05,2480240000,1531.05 2007-06-15,1522.97,1538.71,1522.97,1532.91,3406030000,1532.91 2007-06-14,1515.58,1526.45,1515.58,1522.97,2813630000,1522.97 2007-06-13,1492.65,1515.70,1492.65,1515.67,3077930000,1515.67 2007-06-12,1509.12,1511.33,1492.97,1493.00,3056200000,1493.00 2007-06-11,1507.64,1515.53,1503.35,1509.12,2525280000,1509.12 2007-06-08,1490.71,1507.76,1487.41,1507.67,2993460000,1507.67 2007-06-07,1517.36,1517.36,1490.37,1490.72,3538470000,1490.72 2007-06-06,1530.57,1530.57,1514.13,1517.38,2964190000,1517.38 2007-06-05,1539.12,1539.12,1525.62,1530.95,2939450000,1530.95 2007-06-04,1536.28,1540.53,1532.31,1539.18,2738930000,1539.18 2007-06-01,1530.62,1540.56,1530.62,1536.34,2927020000,1536.34 2007-05-31,1530.19,1535.56,1528.26,1530.62,3335530000,1530.62 2007-05-30,1517.60,1530.23,1510.06,1530.23,2980210000,1530.23 2007-05-29,1515.55,1521.80,1512.02,1518.11,2571790000,1518.11 2007-05-25,1507.50,1517.41,1507.50,1515.73,2316250000,1515.73 2007-05-24,1522.10,1529.31,1505.18,1507.51,3365530000,1507.51 2007-05-23,1524.09,1532.43,1521.90,1522.28,3084260000,1522.28 2007-05-22,1525.10,1529.24,1522.05,1524.12,2860500000,1524.12 2007-05-21,1522.75,1529.87,1522.71,1525.10,3465360000,1525.10 2007-05-18,1512.74,1522.75,1512.74,1522.75,2959050000,1522.75 2007-05-17,1514.01,1517.14,1509.29,1512.75,2868640000,1512.75 2007-05-16,1500.75,1514.15,1500.75,1514.14,2915350000,1514.14 2007-05-15,1503.11,1514.83,1500.43,1501.19,3071020000,1501.19 2007-05-14,1505.76,1510.90,1498.34,1503.15,2776130000,1503.15 2007-05-11,1491.47,1506.24,1491.47,1505.85,2720780000,1505.85 2007-05-10,1512.33,1512.33,1491.42,1491.47,3031240000,1491.47 2007-05-09,1507.32,1513.80,1503.77,1512.58,2935550000,1512.58 2007-05-08,1509.36,1509.36,1500.66,1507.72,2795720000,1507.72 2007-05-07,1505.57,1511.00,1505.54,1509.48,2545090000,1509.48 2007-05-04,1502.35,1510.34,1501.80,1505.62,2761930000,1505.62 2007-05-03,1495.56,1503.34,1495.56,1502.39,3007970000,1502.39 2007-05-02,1486.13,1499.10,1486.13,1495.92,3189800000,1495.92 2007-05-01,1482.37,1487.27,1476.70,1486.30,3400350000,1486.30 2007-04-30,1494.07,1497.16,1482.29,1482.37,3093420000,1482.37 2007-04-27,1494.21,1497.32,1488.67,1494.07,2732810000,1494.07 2007-04-26,1495.27,1498.02,1491.17,1494.25,3211800000,1494.25 2007-04-25,1480.28,1496.59,1480.28,1495.42,3252590000,1495.42 2007-04-24,1480.93,1483.82,1473.74,1480.41,3119750000,1480.41 2007-04-23,1484.33,1487.32,1480.19,1480.93,2575020000,1480.93 2007-04-20,1470.69,1484.74,1470.69,1484.35,3329940000,1484.35 2007-04-19,1472.48,1474.23,1464.47,1470.73,2913610000,1470.73 2007-04-18,1471.47,1476.57,1466.41,1472.50,2971330000,1472.50 2007-04-17,1468.47,1474.35,1467.15,1471.48,2920570000,1471.48 2007-04-16,1452.84,1468.62,1452.84,1468.33,2870140000,1468.33 2007-04-13,1447.80,1453.11,1444.15,1452.85,2690020000,1452.85 2007-04-12,1438.87,1448.02,1433.91,1447.80,2770570000,1447.80 2007-04-11,1448.23,1448.39,1436.15,1438.87,2950190000,1438.87 2007-04-10,1444.58,1448.73,1443.99,1448.39,2510110000,1448.39 2007-04-09,1443.77,1448.10,1443.28,1444.61,2349410000,1444.61 2007-04-05,1438.94,1444.88,1436.67,1443.76,2357230000,1443.76 2007-04-04,1437.75,1440.16,1435.08,1439.37,2616320000,1439.37 2007-04-03,1424.27,1440.57,1424.27,1437.77,2921760000,1437.77 2007-04-02,1420.83,1425.49,1416.37,1424.55,2875880000,1424.55 2007-03-30,1422.52,1429.22,1408.90,1420.86,2903960000,1420.86 2007-03-29,1417.17,1426.24,1413.27,1422.53,2854710000,1422.53 2007-03-28,1428.35,1428.35,1414.07,1417.23,3000440000,1417.23 2007-03-27,1437.49,1437.49,1425.54,1428.61,2673040000,1428.61 2007-03-26,1436.11,1437.65,1423.28,1437.50,2754660000,1437.50 2007-03-23,1434.54,1438.89,1433.21,1436.11,2619020000,1436.11 2007-03-22,1435.04,1437.66,1429.88,1434.54,3129970000,1434.54 2007-03-21,1410.92,1437.77,1409.75,1435.04,3184770000,1435.04 2007-03-20,1402.04,1411.53,1400.70,1410.94,2795940000,1410.94 2007-03-19,1386.95,1403.20,1386.95,1402.06,2777180000,1402.06 2007-03-16,1392.28,1397.51,1383.63,1386.95,3393640000,1386.95 2007-03-15,1387.11,1395.73,1385.16,1392.28,2821900000,1392.28 2007-03-14,1377.86,1388.09,1363.98,1387.17,3758350000,1387.17 2007-03-13,1406.23,1406.23,1377.71,1377.95,3485570000,1377.95 2007-03-12,1402.80,1409.34,1398.40,1406.60,2664000000,1406.60 2007-03-09,1401.89,1410.15,1397.30,1402.84,2623050000,1402.84 2007-03-08,1391.88,1407.93,1391.88,1401.89,3014850000,1401.89 2007-03-07,1395.02,1401.16,1390.64,1391.97,3141350000,1391.97 2007-03-06,1374.06,1397.90,1374.06,1395.41,3358160000,1395.41 2007-03-05,1387.11,1391.86,1373.97,1374.12,3480520000,1374.12 2007-03-02,1403.16,1403.40,1386.87,1387.17,3312260000,1387.17 2007-03-01,1406.80,1409.46,1380.87,1403.17,3874910000,1403.17 2007-02-28,1398.64,1415.89,1396.65,1406.82,3925250000,1406.82 2007-02-27,1449.25,1449.25,1389.42,1399.04,4065230000,1399.04 2007-02-26,1451.04,1456.95,1445.48,1449.37,2822170000,1449.37 2007-02-23,1456.22,1456.22,1448.36,1451.19,2579950000,1451.19 2007-02-22,1457.29,1461.57,1450.51,1456.38,1950770000,1456.38 2007-02-21,1459.60,1459.60,1452.02,1457.63,2606980000,1457.63 2007-02-20,1455.53,1460.53,1449.20,1459.68,2337860000,1459.68 2007-02-16,1456.77,1456.77,1451.57,1455.54,2399450000,1455.54 2007-02-15,1455.15,1457.97,1453.19,1456.81,2490920000,1456.81 2007-02-14,1443.91,1457.65,1443.91,1455.30,2699290000,1455.30 2007-02-13,1433.22,1444.41,1433.22,1444.26,2652150000,1444.26 2007-02-12,1438.00,1439.11,1431.44,1433.37,2395680000,1433.37 2007-02-09,1448.25,1452.45,1433.44,1438.06,2951810000,1438.06 2007-02-08,1449.99,1450.45,1442.81,1448.31,2816180000,1448.31 2007-02-07,1447.41,1452.99,1446.44,1450.02,2618820000,1450.02 2007-02-06,1446.98,1450.19,1443.40,1448.00,2608710000,1448.00 2007-02-05,1448.33,1449.38,1443.85,1446.99,2439430000,1446.99 2007-02-02,1445.94,1449.33,1444.49,1448.39,2569450000,1448.39 2007-02-01,1437.90,1446.64,1437.90,1445.94,2914890000,1445.94 2007-01-31,1428.65,1441.61,1424.78,1438.24,2976690000,1438.24 2007-01-30,1420.61,1428.82,1420.61,1428.82,2706250000,1428.82 2007-01-29,1422.03,1426.94,1418.46,1420.62,2730480000,1420.62 2007-01-26,1423.90,1427.27,1416.96,1422.18,2626620000,1422.18 2007-01-25,1440.12,1440.69,1422.34,1423.90,2994330000,1423.90 2007-01-24,1427.96,1440.14,1427.96,1440.13,2783180000,1440.13 2007-01-23,1422.95,1431.33,1421.66,1427.99,2975070000,1427.99 2007-01-22,1430.47,1431.39,1420.40,1422.95,2540120000,1422.95 2007-01-19,1426.35,1431.57,1425.19,1430.50,2777480000,1430.50 2007-01-18,1430.59,1432.96,1424.21,1426.37,2822430000,1426.37 2007-01-17,1431.77,1435.27,1428.57,1430.62,2690270000,1430.62 2007-01-16,1430.73,1433.93,1428.62,1431.90,2599530000,1431.90 2007-01-12,1423.82,1431.23,1422.58,1430.73,2686480000,1430.73 2007-01-11,1414.84,1427.12,1414.84,1423.82,2857870000,1423.82 2007-01-10,1408.70,1415.99,1405.32,1414.85,2764660000,1414.85 2007-01-09,1412.84,1415.61,1405.42,1412.11,3038380000,1412.11 2007-01-08,1409.26,1414.98,1403.97,1412.84,2763340000,1412.84 2007-01-05,1418.34,1418.34,1405.75,1409.71,2919400000,1409.71 2007-01-04,1416.60,1421.84,1408.43,1418.34,3004460000,1418.34 2007-01-03,1418.03,1429.42,1407.86,1416.60,3429160000,1416.60 2006-12-29,1424.71,1427.00,1416.84,1418.30,1678200000,1418.30 2006-12-28,1426.77,1427.26,1422.05,1424.73,1508570000,1424.73 2006-12-27,1416.63,1427.72,1416.63,1426.84,1667370000,1426.84 2006-12-26,1410.75,1417.91,1410.45,1416.90,1310310000,1416.90 2006-12-22,1418.10,1418.82,1410.28,1410.76,1647590000,1410.76 2006-12-21,1423.20,1426.40,1415.90,1418.30,2322410000,1418.30 2006-12-20,1425.51,1429.05,1423.51,1423.53,2387630000,1423.53 2006-12-19,1422.42,1428.30,1414.88,1425.55,2717060000,1425.55 2006-12-18,1427.08,1431.81,1420.65,1422.48,2568140000,1422.48 2006-12-15,1425.48,1431.63,1425.48,1427.09,3229580000,1427.09 2006-12-14,1413.16,1427.23,1413.16,1425.49,2729700000,1425.49 2006-12-13,1411.32,1416.64,1411.05,1413.21,2552260000,1413.21 2006-12-12,1413.00,1413.78,1404.75,1411.56,2738170000,1411.56 2006-12-11,1409.81,1415.60,1408.56,1413.04,2289900000,1413.04 2006-12-08,1407.27,1414.09,1403.67,1409.84,2440460000,1409.84 2006-12-07,1412.86,1418.27,1406.80,1407.29,2743150000,1407.29 2006-12-06,1414.40,1415.93,1411.05,1412.90,2725280000,1412.90 2006-12-05,1409.10,1415.27,1408.78,1414.76,2755700000,1414.76 2006-12-04,1396.67,1411.23,1396.67,1409.12,2766320000,1409.12 2006-12-01,1400.63,1402.46,1385.93,1396.71,2800980000,1396.71 2006-11-30,1399.47,1406.30,1393.83,1400.63,4006230000,1400.63 2006-11-29,1386.11,1401.14,1386.11,1399.48,2790970000,1399.48 2006-11-28,1381.61,1387.91,1377.83,1386.72,2639750000,1386.72 2006-11-27,1400.95,1400.95,1381.44,1381.96,2711210000,1381.96 2006-11-24,1405.94,1405.94,1399.25,1400.95,832550000,1400.95 2006-11-22,1402.69,1407.89,1402.26,1406.09,2237710000,1406.09 2006-11-21,1400.43,1403.49,1399.99,1402.81,2597940000,1402.81 2006-11-20,1401.17,1404.37,1397.85,1400.50,2546710000,1400.50 2006-11-17,1399.76,1401.21,1394.55,1401.20,2726100000,1401.20 2006-11-16,1396.53,1403.76,1396.53,1399.76,2835730000,1399.76 2006-11-15,1392.91,1401.35,1392.13,1396.57,2831130000,1396.57 2006-11-14,1384.36,1394.49,1379.07,1393.22,3027480000,1393.22 2006-11-13,1380.58,1387.61,1378.80,1384.42,2386340000,1384.42 2006-11-10,1378.33,1381.04,1375.60,1380.90,2290200000,1380.90 2006-11-09,1385.43,1388.92,1377.31,1378.33,3012050000,1378.33 2006-11-08,1382.50,1388.61,1379.33,1385.72,2814820000,1385.72 2006-11-07,1379.75,1388.19,1379.19,1382.84,2636390000,1382.84 2006-11-06,1364.27,1381.40,1364.27,1379.78,2533550000,1379.78 2006-11-03,1367.31,1371.68,1360.98,1364.30,2419730000,1364.30 2006-11-02,1367.44,1368.39,1362.21,1367.34,2646180000,1367.34 2006-11-01,1377.76,1381.95,1366.26,1367.81,2821160000,1367.81 2006-10-31,1377.93,1381.21,1372.19,1377.94,2803030000,1377.94 2006-10-30,1377.30,1381.22,1373.46,1377.93,2770440000,1377.93 2006-10-27,1388.89,1388.89,1375.85,1377.34,2458450000,1377.34 2006-10-26,1382.21,1389.45,1379.47,1389.08,2793350000,1389.08 2006-10-25,1377.36,1383.61,1376.00,1382.22,2953540000,1382.22 2006-10-24,1377.02,1377.78,1372.42,1377.38,2876890000,1377.38 2006-10-23,1368.58,1377.40,1363.94,1377.02,2480430000,1377.02 2006-10-20,1366.94,1368.66,1362.10,1368.60,2526410000,1368.60 2006-10-19,1365.95,1368.09,1362.06,1366.96,2619830000,1366.96 2006-10-18,1363.93,1372.87,1360.95,1365.80,2658840000,1365.80 2006-10-17,1369.05,1369.05,1356.87,1364.05,2519620000,1364.05 2006-10-16,1365.61,1370.20,1364.48,1369.06,2305920000,1369.06 2006-10-13,1362.82,1366.63,1360.50,1365.62,2482920000,1365.62 2006-10-12,1349.94,1363.76,1349.94,1362.83,2514350000,1362.83 2006-10-11,1353.28,1353.97,1343.57,1349.95,2521000000,1349.95 2006-10-10,1350.62,1354.23,1348.60,1353.42,2376140000,1353.42 2006-10-09,1349.58,1352.69,1346.55,1350.66,1935170000,1350.66 2006-10-06,1353.22,1353.22,1344.21,1349.59,2523000000,1349.59 2006-10-05,1349.84,1353.79,1347.75,1353.22,2817240000,1353.22 2006-10-04,1333.81,1350.20,1331.48,1350.20,3019880000,1350.20 2006-10-03,1331.32,1338.31,1327.10,1334.11,2682690000,1334.11 2006-10-02,1335.82,1338.54,1330.28,1331.32,2154480000,1331.32 2006-09-29,1339.15,1339.88,1335.64,1335.85,2273430000,1335.85 2006-09-28,1336.56,1340.28,1333.75,1338.88,2397820000,1338.88 2006-09-27,1336.12,1340.08,1333.54,1336.59,2749190000,1336.59 2006-09-26,1326.35,1336.60,1325.30,1336.35,2673350000,1336.35 2006-09-25,1314.78,1329.35,1311.58,1326.37,2710240000,1326.37 2006-09-22,1318.03,1318.03,1310.94,1314.78,2162880000,1314.78 2006-09-21,1324.89,1328.19,1315.45,1318.03,2627440000,1318.03 2006-09-20,1318.28,1328.53,1318.28,1325.18,2543070000,1325.18 2006-09-19,1321.17,1322.04,1312.17,1317.64,2390850000,1317.64 2006-09-18,1319.85,1324.87,1318.16,1321.18,2325080000,1321.18 2006-09-15,1316.28,1324.65,1316.28,1319.66,3198030000,1319.66 2006-09-14,1318.00,1318.00,1313.25,1316.28,2351220000,1316.28 2006-09-13,1312.74,1319.92,1311.12,1318.07,2597220000,1318.07 2006-09-12,1299.53,1314.28,1299.53,1313.00,2791580000,1313.00 2006-09-11,1298.86,1302.36,1290.93,1299.54,2506430000,1299.54 2006-09-08,1294.02,1300.14,1294.02,1298.92,2132890000,1298.92 2006-09-07,1300.21,1301.25,1292.13,1294.02,2325850000,1294.02 2006-09-06,1313.04,1313.04,1299.28,1300.26,2329870000,1300.26 2006-09-05,1310.94,1314.67,1308.82,1313.25,2114480000,1313.25 2006-09-01,1303.80,1312.03,1303.80,1311.01,1800520000,1311.01 2006-08-31,1304.25,1306.11,1302.45,1303.82,1974540000,1303.82 2006-08-30,1303.70,1306.74,1302.15,1305.37,2060690000,1305.37 2006-08-29,1301.57,1305.02,1295.29,1304.28,2093720000,1304.28 2006-08-28,1295.09,1305.02,1293.97,1301.78,1834920000,1301.78 2006-08-25,1295.92,1298.88,1292.39,1295.09,1667580000,1295.09 2006-08-24,1292.97,1297.23,1291.40,1296.06,1930320000,1296.06 2006-08-23,1298.73,1301.50,1289.82,1292.99,1893670000,1292.99 2006-08-22,1297.52,1302.49,1294.44,1298.82,1908740000,1298.82 2006-08-21,1302.30,1302.30,1295.51,1297.52,1759240000,1297.52 2006-08-18,1297.48,1302.30,1293.57,1302.30,2033910000,1302.30 2006-08-17,1295.37,1300.78,1292.71,1297.48,2458340000,1297.48 2006-08-16,1285.27,1296.21,1285.27,1295.43,2554570000,1295.43 2006-08-15,1268.19,1286.23,1268.19,1285.58,2334100000,1285.58 2006-08-14,1266.67,1278.90,1266.67,1268.21,2118020000,1268.21 2006-08-11,1271.64,1271.64,1262.08,1266.74,2004540000,1266.74 2006-08-10,1265.72,1272.55,1261.30,1271.81,2402190000,1271.81 2006-08-09,1271.13,1283.74,1264.73,1265.95,2555180000,1265.95 2006-08-08,1275.67,1282.75,1268.37,1271.48,2457840000,1271.48 2006-08-07,1279.31,1279.31,1273.00,1275.77,2045660000,1275.77 2006-08-04,1280.26,1292.92,1273.82,1279.36,2530970000,1279.36 2006-08-03,1278.22,1283.96,1271.25,1280.27,2728440000,1280.27 2006-08-02,1270.73,1283.42,1270.73,1277.41,2610750000,1277.41 2006-08-01,1278.53,1278.66,1265.71,1270.92,2527690000,1270.92 2006-07-31,1278.53,1278.66,1274.31,1276.66,2461300000,1276.66 2006-07-28,1263.15,1280.42,1263.15,1278.55,2480420000,1278.55 2006-07-27,1268.20,1275.85,1261.92,1263.20,2776710000,1263.20 2006-07-26,1268.87,1273.89,1261.94,1268.40,2667710000,1268.40 2006-07-25,1260.91,1272.39,1257.19,1268.88,2563930000,1268.88 2006-07-24,1240.25,1262.50,1240.25,1260.91,2312720000,1260.91 2006-07-21,1249.12,1250.96,1238.72,1240.29,2704090000,1240.29 2006-07-20,1259.81,1262.56,1249.13,1249.13,2345580000,1249.13 2006-07-19,1236.74,1261.81,1236.74,1259.81,2701980000,1259.81 2006-07-18,1234.48,1239.86,1224.54,1236.86,2481750000,1236.86 2006-07-17,1236.20,1240.07,1231.49,1234.49,2146410000,1234.49 2006-07-14,1242.29,1242.70,1228.45,1236.20,2467120000,1236.20 2006-07-13,1258.58,1258.58,1241.43,1242.28,2545760000,1242.28 2006-07-12,1272.39,1273.31,1257.29,1258.60,2250450000,1258.60 2006-07-11,1267.26,1273.64,1259.65,1272.43,2310850000,1272.43 2006-07-10,1265.46,1274.06,1264.46,1267.34,1854590000,1267.34 2006-07-07,1274.08,1275.38,1263.13,1265.48,1988150000,1265.48 2006-07-06,1270.58,1278.32,1270.58,1274.08,2009160000,1274.08 2006-07-05,1280.05,1280.05,1265.91,1270.91,2165070000,1270.91 2006-07-03,1270.06,1280.38,1270.06,1280.19,1114470000,1280.19 2006-06-30,1272.86,1276.30,1270.20,1270.20,3049560000,1270.20 2006-06-29,1245.94,1272.88,1245.94,1272.87,2621250000,1272.87 2006-06-28,1238.99,1247.06,1237.59,1246.00,2085490000,1246.00 2006-06-27,1250.55,1253.37,1238.94,1239.20,2203130000,1239.20 2006-06-26,1244.50,1250.92,1243.68,1250.56,1878580000,1250.56 2006-06-23,1245.59,1253.13,1241.43,1244.50,2017270000,1244.50 2006-06-22,1251.92,1251.92,1241.53,1245.60,2148180000,1245.60 2006-06-21,1240.09,1257.96,1240.09,1252.20,2361230000,1252.20 2006-06-20,1240.12,1249.01,1238.87,1240.12,2232950000,1240.12 2006-06-19,1251.54,1255.93,1237.17,1240.13,2517200000,1240.13 2006-06-16,1256.16,1256.27,1246.33,1251.54,2783390000,1251.54 2006-06-15,1230.01,1258.64,1230.01,1256.16,2775480000,1256.16 2006-06-14,1223.66,1231.46,1219.29,1230.04,2667990000,1230.04 2006-06-13,1236.08,1243.37,1222.52,1223.69,3215770000,1223.69 2006-06-12,1252.27,1255.22,1236.43,1237.44,2247010000,1237.44 2006-06-09,1257.93,1262.58,1250.03,1252.30,2214000000,1252.30 2006-06-08,1256.08,1259.85,1235.18,1257.93,3543790000,1257.93 2006-06-07,1263.61,1272.47,1255.77,1256.15,2644170000,1256.15 2006-06-06,1265.23,1269.88,1254.46,1263.85,2697650000,1263.85 2006-06-05,1288.16,1288.16,1264.66,1265.29,2313470000,1265.29 2006-06-02,1285.71,1290.68,1280.22,1288.22,2295540000,1288.22 2006-06-01,1270.05,1285.71,1269.19,1285.71,2360160000,1285.71 2006-05-31,1259.38,1270.09,1259.38,1270.09,2692160000,1270.09 2006-05-30,1280.04,1280.04,1259.87,1259.87,2176190000,1259.87 2006-05-26,1272.71,1280.54,1272.50,1280.16,1814020000,1280.16 2006-05-25,1258.41,1273.26,1258.41,1272.88,2372730000,1272.88 2006-05-24,1256.56,1264.53,1245.34,1258.57,2999030000,1258.57 2006-05-23,1262.06,1273.67,1256.15,1256.58,2605250000,1256.58 2006-05-22,1267.03,1268.77,1252.98,1262.07,2773010000,1262.07 2006-05-19,1261.81,1272.15,1256.28,1267.03,2982300000,1267.03 2006-05-18,1270.25,1274.89,1261.75,1261.81,2537490000,1261.81 2006-05-17,1291.73,1291.73,1267.31,1270.32,2830200000,1270.32 2006-05-16,1294.50,1297.88,1288.51,1292.08,2386210000,1292.08 2006-05-15,1291.19,1294.81,1284.51,1294.50,2505660000,1294.50 2006-05-12,1305.88,1305.88,1290.38,1291.24,2567970000,1291.24 2006-05-11,1322.63,1322.63,1303.45,1305.92,2531520000,1305.92 2006-05-10,1324.57,1325.51,1317.44,1322.85,2268550000,1322.85 2006-05-09,1324.66,1326.60,1322.48,1325.14,2157290000,1325.14 2006-05-08,1325.76,1326.70,1322.87,1324.66,2151300000,1324.66 2006-05-05,1312.25,1326.53,1312.25,1325.76,2294760000,1325.76 2006-05-04,1307.85,1315.14,1307.85,1312.25,2431450000,1312.25 2006-05-03,1313.21,1313.47,1303.92,1308.12,2395230000,1308.12 2006-05-02,1305.19,1313.66,1305.19,1313.21,2403470000,1313.21 2006-05-01,1310.61,1317.21,1303.46,1305.19,2437040000,1305.19 2006-04-28,1309.72,1316.04,1306.16,1310.61,2419920000,1310.61 2006-04-27,1305.41,1315.00,1295.57,1309.72,2772010000,1309.72 2006-04-26,1301.74,1310.97,1301.74,1305.41,2502690000,1305.41 2006-04-25,1308.11,1310.79,1299.17,1301.74,2366380000,1301.74 2006-04-24,1311.28,1311.28,1303.79,1308.11,2117330000,1308.11 2006-04-21,1311.46,1317.67,1306.59,1311.28,2392630000,1311.28 2006-04-20,1309.93,1318.16,1306.38,1311.46,2512920000,1311.46 2006-04-19,1307.65,1310.39,1302.79,1309.93,2447310000,1309.93 2006-04-18,1285.33,1309.02,1285.33,1307.28,2595440000,1307.28 2006-04-17,1289.12,1292.45,1280.74,1285.33,1794650000,1285.33 2006-04-13,1288.12,1292.09,1283.37,1289.12,1891940000,1289.12 2006-04-12,1286.57,1290.93,1286.45,1288.12,1938100000,1288.12 2006-04-11,1296.60,1300.71,1282.96,1286.57,2232880000,1286.57 2006-04-10,1295.51,1300.74,1293.17,1296.62,1898320000,1296.62 2006-04-07,1309.04,1314.07,1294.18,1295.50,2082470000,1295.50 2006-04-06,1311.56,1311.99,1302.44,1309.04,2281680000,1309.04 2006-04-05,1305.93,1312.81,1304.82,1311.56,2420020000,1311.56 2006-04-04,1297.81,1307.55,1294.71,1305.93,2147660000,1305.93 2006-04-03,1302.88,1309.19,1296.65,1297.81,2494080000,1297.81 2006-03-31,1300.25,1303.00,1294.87,1294.87,2236710000,1294.87 2006-03-30,1302.89,1310.15,1296.72,1300.25,2294560000,1300.25 2006-03-29,1293.23,1305.60,1293.23,1302.89,2143540000,1302.89 2006-03-28,1301.61,1306.24,1291.84,1293.23,2148580000,1293.23 2006-03-27,1302.95,1303.74,1299.09,1301.61,2029700000,1301.61 2006-03-24,1301.67,1306.53,1298.89,1302.95,2326070000,1302.95 2006-03-23,1305.04,1305.04,1298.11,1301.67,1980940000,1301.67 2006-03-22,1297.23,1305.97,1295.81,1305.04,2039810000,1305.04 2006-03-21,1305.08,1310.88,1295.82,1297.23,2147370000,1297.23 2006-03-20,1307.25,1310.00,1303.59,1305.08,1976830000,1305.08 2006-03-17,1305.33,1309.79,1305.32,1307.25,2549620000,1307.25 2006-03-16,1303.02,1310.45,1303.02,1305.33,2292180000,1305.33 2006-03-15,1297.48,1304.40,1294.97,1303.02,2293000000,1303.02 2006-03-14,1284.13,1298.14,1282.67,1297.48,2165270000,1297.48 2006-03-13,1281.58,1287.37,1281.58,1284.13,2070330000,1284.13 2006-03-10,1272.23,1284.37,1271.11,1281.42,2123450000,1281.42 2006-03-09,1278.47,1282.74,1272.23,1272.23,2140110000,1272.23 2006-03-08,1275.88,1280.33,1268.42,1278.47,2442870000,1278.47 2006-03-07,1278.26,1278.26,1271.11,1275.88,2268050000,1275.88 2006-03-06,1287.23,1288.23,1275.67,1278.26,2280190000,1278.26 2006-03-03,1289.14,1297.33,1284.20,1287.23,2152950000,1287.23 2006-03-02,1291.24,1291.24,1283.21,1289.14,2494590000,1289.14 2006-03-01,1280.66,1291.80,1280.66,1291.24,2308320000,1291.24 2006-02-28,1294.12,1294.12,1278.66,1280.66,2370860000,1280.66 2006-02-27,1289.43,1297.57,1289.43,1294.12,1975320000,1294.12 2006-02-24,1287.79,1292.11,1285.62,1289.43,1933010000,1289.43 2006-02-23,1292.67,1293.84,1285.14,1287.79,2144210000,1287.79 2006-02-22,1283.03,1294.17,1283.03,1292.67,2222380000,1292.67 2006-02-21,1287.24,1291.92,1281.33,1283.03,2104320000,1283.03 2006-02-17,1289.38,1289.47,1284.07,1287.24,2128260000,1287.24 2006-02-16,1280.00,1289.39,1280.00,1289.38,2251490000,1289.38 2006-02-15,1275.53,1281.00,1271.06,1280.00,2317590000,1280.00 2006-02-14,1262.86,1278.21,1260.80,1275.53,2437940000,1275.53 2006-02-13,1266.99,1266.99,1258.34,1262.86,1850080000,1262.86 2006-02-10,1263.82,1269.89,1254.98,1266.99,2290050000,1266.99 2006-02-09,1265.65,1274.56,1262.80,1263.78,2441920000,1263.78 2006-02-08,1254.78,1266.47,1254.78,1265.65,2456860000,1265.65 2006-02-07,1265.02,1265.78,1253.61,1254.78,2366370000,1254.78 2006-02-06,1264.03,1267.04,1261.62,1265.02,2132360000,1265.02 2006-02-03,1270.84,1270.87,1261.02,1264.03,2282210000,1264.03 2006-02-02,1282.46,1282.46,1267.72,1270.84,2565300000,1270.84 2006-02-01,1280.08,1283.33,1277.57,1282.46,2589410000,1282.46 2006-01-31,1285.20,1285.20,1276.85,1280.08,2708310000,1280.08 2006-01-30,1283.72,1287.94,1283.51,1285.19,2282730000,1285.19 2006-01-27,1273.83,1286.38,1273.83,1283.72,2623620000,1283.72 2006-01-26,1264.68,1276.44,1264.68,1273.83,2856780000,1273.83 2006-01-25,1266.86,1271.87,1259.42,1264.68,2617060000,1264.68 2006-01-24,1263.82,1271.47,1263.82,1266.86,2608720000,1266.86 2006-01-23,1261.49,1268.19,1261.49,1263.82,2256070000,1263.82 2006-01-20,1285.04,1285.04,1260.92,1261.49,2845810000,1261.49 2006-01-19,1277.93,1287.79,1277.93,1285.04,2444020000,1285.04 2006-01-18,1282.93,1282.93,1272.08,1277.93,2233200000,1277.93 2006-01-17,1287.61,1287.61,1278.61,1283.03,2179970000,1283.03 2006-01-13,1286.06,1288.96,1282.78,1287.61,2206510000,1287.61 2006-01-12,1294.18,1294.18,1285.04,1286.06,2318350000,1286.06 2006-01-11,1289.72,1294.90,1288.12,1294.18,2406130000,1294.18 2006-01-10,1290.15,1290.15,1283.76,1289.69,2373080000,1289.69 2006-01-09,1285.45,1290.78,1284.82,1290.15,2301490000,1290.15 2006-01-06,1273.48,1286.09,1273.48,1285.45,2446560000,1285.45 2006-01-05,1273.46,1276.91,1270.30,1273.48,2433340000,1273.48 2006-01-04,1268.80,1275.37,1267.74,1273.46,2515330000,1273.46 2006-01-03,1248.29,1270.22,1245.74,1268.80,2554570000,1268.80 2005-12-30,1254.42,1254.42,1246.59,1248.29,1443500000,1248.29 2005-12-29,1258.17,1260.61,1254.18,1254.42,1382540000,1254.42 2005-12-28,1256.54,1261.10,1256.54,1258.17,1422360000,1258.17 2005-12-27,1268.66,1271.83,1256.54,1256.54,1540470000,1256.54 2005-12-23,1268.12,1269.76,1265.92,1268.66,1285810000,1268.66 2005-12-22,1262.79,1268.19,1262.50,1268.12,1888500000,1268.12 2005-12-21,1259.62,1269.37,1259.62,1262.79,2065170000,1262.79 2005-12-20,1259.92,1263.86,1257.21,1259.62,1996690000,1259.62 2005-12-19,1267.32,1270.51,1259.28,1259.92,2208810000,1259.92 2005-12-16,1270.94,1275.24,1267.32,1267.32,2584190000,1267.32 2005-12-15,1272.74,1275.17,1267.74,1270.94,2180590000,1270.94 2005-12-14,1267.43,1275.80,1267.07,1272.74,2145520000,1272.74 2005-12-13,1260.43,1272.11,1258.56,1267.43,2390020000,1267.43 2005-12-12,1259.37,1263.86,1255.52,1260.43,1876550000,1260.43 2005-12-09,1255.84,1263.08,1254.24,1259.37,1896290000,1259.37 2005-12-08,1257.37,1263.36,1250.91,1255.84,2178300000,1255.84 2005-12-07,1263.70,1264.85,1253.02,1257.37,2093830000,1257.37 2005-12-06,1262.09,1272.89,1262.09,1263.70,2110740000,1263.70 2005-12-05,1265.08,1265.08,1258.12,1262.09,2325840000,1262.09 2005-12-02,1264.67,1266.85,1261.42,1265.08,2125580000,1265.08 2005-12-01,1249.48,1266.17,1249.48,1264.67,2614830000,1264.67 2005-11-30,1257.48,1260.93,1249.39,1249.48,2374690000,1249.48 2005-11-29,1257.46,1266.18,1257.46,1257.48,2268340000,1257.48 2005-11-28,1268.25,1268.44,1257.17,1257.46,2016900000,1257.46 2005-11-25,1265.61,1268.78,1265.54,1268.25,724940000,1268.25 2005-11-23,1261.23,1270.64,1259.51,1265.61,1985400000,1265.61 2005-11-22,1254.85,1261.90,1251.40,1261.23,2291420000,1261.23 2005-11-21,1248.27,1255.89,1246.90,1254.85,2117350000,1254.85 2005-11-18,1242.80,1249.58,1240.71,1248.27,2453290000,1248.27 2005-11-17,1231.21,1242.96,1231.21,1242.80,2298040000,1242.80 2005-11-16,1229.01,1232.24,1227.18,1231.21,2121580000,1231.21 2005-11-15,1233.76,1237.94,1226.41,1229.01,2359370000,1229.01 2005-11-14,1234.72,1237.20,1231.78,1233.76,1899780000,1233.76 2005-11-11,1230.96,1235.70,1230.72,1234.72,1773140000,1234.72 2005-11-10,1220.65,1232.41,1215.05,1230.96,2378460000,1230.96 2005-11-09,1218.59,1226.59,1216.53,1220.65,2214460000,1220.65 2005-11-08,1222.81,1222.81,1216.08,1218.59,1965050000,1218.59 2005-11-07,1220.14,1224.18,1217.29,1222.81,1987580000,1222.81 2005-11-04,1219.94,1222.52,1214.45,1220.14,2050510000,1220.14 2005-11-03,1214.76,1224.70,1214.76,1219.94,2716630000,1219.94 2005-11-02,1202.76,1215.17,1201.07,1214.76,2648090000,1214.76 2005-11-01,1207.01,1207.34,1201.66,1202.76,2457850000,1202.76 2005-10-31,1198.41,1211.43,1198.41,1207.01,2567470000,1207.01 2005-10-28,1178.90,1198.41,1178.90,1198.41,2379400000,1198.41 2005-10-27,1191.38,1192.65,1178.89,1178.90,2395370000,1178.90 2005-10-26,1196.54,1204.01,1191.38,1191.38,2467750000,1191.38 2005-10-25,1199.38,1201.30,1189.29,1196.54,2312470000,1196.54 2005-10-24,1179.59,1199.39,1179.59,1199.38,2197790000,1199.38 2005-10-21,1177.80,1186.46,1174.92,1179.59,2470920000,1179.59 2005-10-20,1195.76,1197.30,1173.30,1177.80,2617250000,1177.80 2005-10-19,1178.14,1195.76,1170.55,1195.76,2703590000,1195.76 2005-10-18,1190.10,1190.10,1178.13,1178.14,2197010000,1178.14 2005-10-17,1186.57,1191.21,1184.48,1190.10,2054570000,1190.10 2005-10-14,1176.84,1187.13,1175.44,1186.57,2188940000,1186.57 2005-10-13,1177.68,1179.56,1168.20,1176.84,2351150000,1176.84 2005-10-12,1184.87,1190.02,1173.65,1177.68,2491280000,1177.68 2005-10-11,1187.33,1193.10,1183.16,1184.87,2299040000,1184.87 2005-10-10,1195.90,1196.52,1186.12,1187.33,2195990000,1187.33 2005-10-07,1191.49,1199.71,1191.46,1195.90,2126080000,1195.90 2005-10-06,1196.39,1202.14,1181.92,1191.49,2792030000,1191.49 2005-10-05,1214.47,1214.47,1196.25,1196.39,2546780000,1196.39 2005-10-04,1226.70,1229.88,1214.02,1214.47,2341420000,1214.47 2005-10-03,1228.81,1233.34,1225.15,1226.70,2097490000,1226.70 2005-09-30,1227.68,1229.57,1225.22,1228.81,2097520000,1228.81 2005-09-29,1216.89,1228.70,1211.54,1227.68,2176120000,1227.68 2005-09-28,1215.66,1220.98,1212.72,1216.89,2106980000,1216.89 2005-09-27,1215.63,1220.17,1211.11,1215.66,1976270000,1215.66 2005-09-26,1215.29,1222.56,1211.84,1215.63,2022220000,1215.63 2005-09-23,1214.62,1218.83,1209.80,1215.29,1973020000,1215.29 2005-09-22,1210.20,1216.64,1205.35,1214.62,2424720000,1214.62 2005-09-21,1221.34,1221.52,1209.89,1210.20,2548150000,1210.20 2005-09-20,1231.02,1236.49,1220.07,1221.34,2319250000,1221.34 2005-09-19,1237.91,1237.91,1227.65,1231.02,2076540000,1231.02 2005-09-16,1228.42,1237.95,1228.42,1237.91,3152470000,1237.91 2005-09-15,1227.16,1231.88,1224.85,1227.73,2079340000,1227.73 2005-09-14,1231.20,1234.74,1226.16,1227.16,1986750000,1227.16 2005-09-13,1240.57,1240.57,1231.20,1231.20,2082360000,1231.20 2005-09-12,1241.48,1242.60,1239.15,1240.56,1938050000,1240.56 2005-09-09,1231.67,1243.13,1231.67,1241.48,1992560000,1241.48 2005-09-08,1236.36,1236.36,1229.51,1231.67,1955380000,1231.67 2005-09-07,1233.39,1237.06,1230.93,1236.36,2067700000,1236.36 2005-09-06,1218.02,1233.61,1218.02,1233.39,1932090000,1233.39 2005-09-02,1221.59,1224.45,1217.75,1218.02,1640160000,1218.02 2005-09-01,1220.33,1227.29,1216.18,1221.59,2229860000,1221.59 2005-08-31,1208.41,1220.36,1204.40,1220.33,2365510000,1220.33 2005-08-30,1212.28,1212.28,1201.07,1208.41,1916470000,1208.41 2005-08-29,1205.10,1214.28,1201.53,1212.28,1599450000,1212.28 2005-08-26,1212.40,1212.40,1204.23,1205.10,1541090000,1205.10 2005-08-25,1209.59,1213.73,1209.57,1212.37,1571110000,1212.37 2005-08-24,1217.57,1224.15,1209.37,1209.59,1930800000,1209.59 2005-08-23,1221.73,1223.04,1214.44,1217.59,1678620000,1217.59 2005-08-22,1219.71,1228.96,1216.47,1221.73,1621330000,1221.73 2005-08-19,1219.02,1225.08,1219.02,1219.71,1558790000,1219.71 2005-08-18,1220.24,1222.64,1215.93,1219.02,1808170000,1219.02 2005-08-17,1219.34,1225.63,1218.07,1220.24,1859150000,1220.24 2005-08-16,1233.87,1233.87,1219.05,1219.34,1820410000,1219.34 2005-08-15,1230.40,1236.24,1226.20,1233.87,1562880000,1233.87 2005-08-12,1237.81,1237.81,1225.87,1230.39,1709300000,1230.39 2005-08-11,1229.13,1237.81,1228.33,1237.81,1941560000,1237.81 2005-08-10,1231.38,1242.69,1226.58,1229.13,2172320000,1229.13 2005-08-09,1223.13,1234.11,1223.13,1231.38,1897520000,1231.38 2005-08-08,1226.42,1232.28,1222.67,1223.13,1804140000,1223.13 2005-08-05,1235.86,1235.86,1225.62,1226.42,1930280000,1226.42 2005-08-04,1245.04,1245.04,1235.15,1235.86,1981220000,1235.86 2005-08-03,1244.12,1245.86,1240.57,1245.04,1999980000,1245.04 2005-08-02,1235.35,1244.69,1235.35,1244.12,2043120000,1244.12 2005-08-01,1234.18,1239.10,1233.80,1235.35,1716870000,1235.35 2005-07-29,1243.72,1245.04,1234.18,1234.18,1789600000,1234.18 2005-07-28,1236.79,1245.15,1235.81,1243.72,2001680000,1243.72 2005-07-27,1231.16,1237.64,1230.15,1236.79,1945800000,1236.79 2005-07-26,1229.03,1234.42,1229.03,1231.16,1934180000,1231.16 2005-07-25,1233.68,1238.36,1228.15,1229.03,1717580000,1229.03 2005-07-22,1227.04,1234.19,1226.15,1233.68,1766990000,1233.68 2005-07-21,1235.20,1235.83,1224.70,1227.04,2129840000,1227.04 2005-07-20,1229.35,1236.56,1222.91,1235.20,2063340000,1235.20 2005-07-19,1221.13,1230.34,1221.13,1229.35,2041280000,1229.35 2005-07-18,1227.92,1227.92,1221.13,1221.13,1582100000,1221.13 2005-07-15,1226.50,1229.53,1223.50,1227.92,1716400000,1227.92 2005-07-14,1223.29,1233.16,1223.29,1226.50,2048710000,1226.50 2005-07-13,1222.21,1224.46,1219.64,1223.29,1812500000,1223.29 2005-07-12,1219.44,1225.54,1216.60,1222.21,1932010000,1222.21 2005-07-11,1211.86,1220.03,1211.86,1219.44,1846300000,1219.44 2005-07-08,1197.87,1212.73,1197.20,1211.86,1900810000,1211.86 2005-07-07,1194.94,1198.46,1183.55,1197.87,1952440000,1197.87 2005-07-06,1204.99,1206.11,1194.78,1194.94,1883470000,1194.94 2005-07-05,1194.44,1206.34,1192.49,1204.99,1805820000,1204.99 2005-07-01,1191.33,1197.89,1191.33,1194.44,1593820000,1194.44 2005-06-30,1199.85,1203.27,1190.51,1191.33,2109490000,1191.33 2005-06-29,1201.57,1204.07,1198.70,1199.85,1769280000,1199.85 2005-06-28,1190.69,1202.54,1190.69,1201.57,1772410000,1201.57 2005-06-27,1191.57,1194.33,1188.30,1190.69,1738620000,1190.69 2005-06-24,1200.73,1200.90,1191.45,1191.57,2418800000,1191.57 2005-06-23,1213.88,1216.45,1200.72,1200.73,2029920000,1200.73 2005-06-22,1213.61,1219.59,1211.69,1213.88,1823250000,1213.88 2005-06-21,1216.10,1217.13,1211.86,1213.61,1720700000,1213.61 2005-06-20,1216.96,1219.10,1210.65,1216.10,1714530000,1216.10 2005-06-17,1210.93,1219.55,1210.93,1216.96,2407370000,1216.96 2005-06-16,1206.55,1212.10,1205.47,1210.96,1776040000,1210.96 2005-06-15,1203.91,1208.08,1198.66,1206.58,1840440000,1206.58 2005-06-14,1200.82,1207.53,1200.18,1203.91,1698150000,1203.91 2005-06-13,1198.11,1206.03,1194.51,1200.82,1661350000,1200.82 2005-06-10,1200.93,1202.79,1192.64,1198.11,1664180000,1198.11 2005-06-09,1194.67,1201.86,1191.09,1200.93,1824120000,1200.93 2005-06-08,1197.26,1201.97,1193.33,1194.67,1715490000,1194.67 2005-06-07,1197.51,1208.85,1197.26,1197.26,1851370000,1197.26 2005-06-06,1196.02,1198.78,1192.75,1197.51,1547120000,1197.51 2005-06-03,1204.29,1205.09,1194.55,1196.02,1627520000,1196.02 2005-06-02,1202.27,1204.67,1198.42,1204.29,1813790000,1204.29 2005-06-01,1191.50,1205.64,1191.03,1202.22,1810100000,1202.22 2005-05-31,1198.78,1198.78,1191.50,1191.50,1840680000,1191.50 2005-05-27,1197.62,1199.56,1195.28,1198.78,1381430000,1198.78 2005-05-26,1190.01,1198.95,1190.01,1197.62,1654110000,1197.62 2005-05-25,1194.07,1194.07,1185.96,1190.01,1742180000,1190.01 2005-05-24,1193.86,1195.29,1189.87,1194.07,1681000000,1194.07 2005-05-23,1189.28,1197.44,1188.76,1193.86,1681170000,1193.86 2005-05-20,1191.08,1191.22,1185.19,1189.28,1631750000,1189.28 2005-05-19,1185.56,1191.09,1184.49,1191.08,1775860000,1191.08 2005-05-18,1173.80,1187.90,1173.80,1185.56,2266320000,1185.56 2005-05-17,1165.69,1174.35,1159.86,1173.80,1887260000,1173.80 2005-05-16,1154.05,1165.75,1153.64,1165.69,1856860000,1165.69 2005-05-13,1159.36,1163.75,1146.18,1154.05,2188590000,1154.05 2005-05-12,1171.11,1173.37,1157.76,1159.36,1995290000,1159.36 2005-05-11,1166.22,1171.77,1157.71,1171.11,1834970000,1171.11 2005-05-10,1178.84,1178.84,1162.98,1166.22,1889660000,1166.22 2005-05-09,1171.35,1178.87,1169.38,1178.84,1857020000,1178.84 2005-05-06,1172.63,1177.75,1170.50,1171.35,1707200000,1171.35 2005-05-05,1175.65,1178.62,1166.77,1172.63,1997100000,1172.63 2005-05-04,1161.17,1176.01,1161.17,1175.65,2306480000,1175.65 2005-05-03,1162.16,1166.89,1156.71,1161.17,2167020000,1161.17 2005-05-02,1156.85,1162.87,1154.71,1162.16,1980040000,1162.16 2005-04-29,1143.22,1156.97,1139.19,1156.85,2362360000,1156.85 2005-04-28,1156.38,1156.38,1143.22,1143.22,2182270000,1143.22 2005-04-27,1151.74,1159.87,1144.42,1156.38,2151520000,1156.38 2005-04-26,1162.10,1164.80,1151.83,1151.83,1959740000,1151.83 2005-04-25,1152.12,1164.05,1152.12,1162.10,1795030000,1162.10 2005-04-22,1159.95,1159.95,1142.95,1152.12,2045880000,1152.12 2005-04-21,1137.50,1159.95,1137.50,1159.95,2308560000,1159.95 2005-04-20,1152.78,1155.50,1136.15,1137.50,2217050000,1137.50 2005-04-19,1145.98,1154.67,1145.98,1152.78,2142700000,1152.78 2005-04-18,1142.62,1148.92,1139.80,1145.98,2180670000,1145.98 2005-04-15,1162.05,1162.05,1141.92,1142.62,2689960000,1142.62 2005-04-14,1173.79,1174.67,1161.70,1162.05,2355040000,1162.05 2005-04-13,1187.76,1187.76,1171.40,1173.79,2049740000,1173.79 2005-04-12,1181.21,1190.17,1170.85,1187.76,1979830000,1187.76 2005-04-11,1181.20,1184.07,1178.69,1181.21,1525310000,1181.21 2005-04-08,1191.14,1191.75,1181.13,1181.20,1661330000,1181.20 2005-04-07,1184.07,1191.88,1183.81,1191.14,1900620000,1191.14 2005-04-06,1181.39,1189.34,1181.39,1184.07,1797400000,1184.07 2005-04-05,1176.12,1183.56,1176.12,1181.39,1870800000,1181.39 2005-04-04,1172.79,1178.61,1167.72,1176.12,2079770000,1176.12 2005-04-01,1180.59,1189.80,1169.91,1172.92,2168690000,1172.92 2005-03-31,1181.41,1184.53,1179.49,1180.59,2214230000,1180.59 2005-03-30,1165.36,1181.54,1165.36,1181.41,2097110000,1181.41 2005-03-29,1174.28,1179.39,1163.69,1165.36,2223250000,1165.36 2005-03-28,1171.42,1179.91,1171.42,1174.28,1746220000,1174.28 2005-03-24,1172.53,1180.11,1171.42,1171.42,1721720000,1171.42 2005-03-23,1171.71,1176.26,1168.70,1172.53,2246870000,1172.53 2005-03-22,1183.78,1189.59,1171.63,1171.71,2114470000,1171.71 2005-03-21,1189.65,1189.65,1178.82,1183.78,1819440000,1183.78 2005-03-18,1190.21,1191.98,1182.78,1189.65,2344370000,1189.65 2005-03-17,1188.07,1193.28,1186.34,1190.21,1581930000,1190.21 2005-03-16,1197.75,1197.75,1185.61,1188.07,1653190000,1188.07 2005-03-15,1206.83,1210.54,1197.75,1197.75,1513530000,1197.75 2005-03-14,1200.08,1206.83,1199.51,1206.83,1437430000,1206.83 2005-03-11,1209.25,1213.04,1198.15,1200.08,1449820000,1200.08 2005-03-10,1207.01,1211.23,1201.41,1209.25,1604020000,1209.25 2005-03-09,1219.43,1219.43,1206.66,1207.01,1704970000,1207.01 2005-03-08,1225.31,1225.69,1218.57,1219.43,1523090000,1219.43 2005-03-07,1222.12,1229.11,1222.12,1225.31,1488830000,1225.31 2005-03-04,1210.47,1224.76,1210.47,1222.12,1636820000,1222.12 2005-03-03,1210.08,1215.72,1204.45,1210.47,1616240000,1210.47 2005-03-02,1210.41,1215.79,1204.22,1210.08,1568540000,1210.08 2005-03-01,1203.60,1212.25,1203.60,1210.41,1708060000,1210.41 2005-02-28,1211.37,1211.37,1198.13,1203.60,1795480000,1203.60 2005-02-25,1200.20,1212.15,1199.61,1211.37,1523680000,1211.37 2005-02-24,1190.80,1200.42,1187.80,1200.20,1518750000,1200.20 2005-02-23,1184.16,1193.52,1184.16,1190.80,1501090000,1190.80 2005-02-22,1201.59,1202.48,1184.16,1184.16,1744940000,1184.16 2005-02-18,1200.75,1202.92,1197.35,1201.59,1551200000,1201.59 2005-02-17,1210.34,1211.33,1200.74,1200.75,1580120000,1200.75 2005-02-16,1210.12,1212.44,1205.06,1210.34,1490100000,1210.34 2005-02-15,1206.14,1212.44,1205.52,1210.12,1527080000,1210.12 2005-02-14,1205.30,1206.93,1203.59,1206.14,1290180000,1206.14 2005-02-11,1197.01,1208.38,1193.28,1205.30,1562300000,1205.30 2005-02-10,1191.99,1198.75,1191.54,1197.01,1491670000,1197.01 2005-02-09,1202.30,1203.83,1191.54,1191.99,1511040000,1191.99 2005-02-08,1201.72,1205.11,1200.16,1202.30,1416170000,1202.30 2005-02-07,1203.03,1204.15,1199.27,1201.72,1347270000,1201.72 2005-02-04,1189.89,1203.47,1189.67,1203.03,1648160000,1203.03 2005-02-03,1193.19,1193.19,1185.64,1189.89,1554460000,1189.89 2005-02-02,1189.41,1195.25,1188.92,1193.19,1561740000,1193.19 2005-02-01,1181.27,1190.39,1180.95,1189.41,1681980000,1189.41 2005-01-31,1171.36,1182.07,1171.36,1181.27,1679800000,1181.27 2005-01-28,1174.55,1175.61,1166.25,1171.36,1641800000,1171.36 2005-01-27,1174.07,1177.50,1170.15,1174.55,1600600000,1174.55 2005-01-26,1168.41,1175.96,1168.41,1174.07,1635900000,1174.07 2005-01-25,1163.75,1174.30,1163.75,1168.41,1610400000,1168.41 2005-01-24,1167.87,1173.03,1163.75,1163.75,1494600000,1163.75 2005-01-21,1175.41,1179.45,1167.82,1167.87,1643500000,1167.87 2005-01-20,1184.63,1184.63,1173.42,1175.41,1692000000,1175.41 2005-01-19,1195.98,1195.98,1184.41,1184.63,1498700000,1184.63 2005-01-18,1184.52,1195.98,1180.10,1195.98,1596800000,1195.98 2005-01-14,1177.45,1185.21,1177.45,1184.52,1335400000,1184.52 2005-01-13,1187.70,1187.70,1175.81,1177.45,1510300000,1177.45 2005-01-12,1182.99,1187.92,1175.64,1187.70,1562100000,1187.70 2005-01-11,1190.25,1190.25,1180.43,1182.99,1488800000,1182.99 2005-01-10,1186.19,1194.78,1184.80,1190.25,1490400000,1190.25 2005-01-07,1187.89,1192.20,1182.16,1186.19,1477900000,1186.19 2005-01-06,1183.74,1191.63,1183.27,1187.89,1569100000,1187.89 2005-01-05,1188.05,1192.73,1183.72,1183.74,1738900000,1183.74 2005-01-04,1202.08,1205.84,1185.39,1188.05,1721000000,1188.05 2005-01-03,1211.92,1217.80,1200.32,1202.08,1510800000,1202.08 2004-12-31,1213.55,1217.33,1211.65,1211.92,786900000,1211.92 2004-12-30,1213.45,1216.47,1213.41,1213.55,829800000,1213.55 2004-12-29,1213.54,1213.85,1210.95,1213.45,925900000,1213.45 2004-12-28,1204.92,1213.54,1204.92,1213.54,983000000,1213.54 2004-12-27,1210.13,1214.13,1204.92,1204.92,922000000,1204.92 2004-12-23,1209.57,1213.66,1208.71,1210.13,956100000,1210.13 2004-12-22,1205.45,1211.42,1203.85,1209.57,1390800000,1209.57 2004-12-21,1194.65,1205.93,1194.65,1205.45,1483700000,1205.45 2004-12-20,1194.20,1203.43,1193.36,1194.65,1422800000,1194.65 2004-12-17,1203.21,1203.21,1193.49,1194.20,2335000000,1194.20 2004-12-16,1205.72,1207.97,1198.41,1203.21,1793900000,1203.21 2004-12-15,1203.38,1206.61,1199.44,1205.72,1695800000,1205.72 2004-12-14,1198.68,1205.29,1197.84,1203.38,1544400000,1203.38 2004-12-13,1188.00,1198.74,1188.00,1198.68,1436100000,1198.68 2004-12-10,1189.24,1191.45,1185.24,1188.00,1443700000,1188.00 2004-12-09,1182.81,1190.51,1173.79,1189.24,1624700000,1189.24 2004-12-08,1177.07,1184.05,1177.07,1182.81,1525200000,1182.81 2004-12-07,1190.25,1192.17,1177.07,1177.07,1533900000,1177.07 2004-12-06,1191.17,1192.41,1185.18,1190.25,1354400000,1190.25 2004-12-03,1190.33,1197.46,1187.71,1191.17,1566700000,1191.17 2004-12-02,1191.37,1194.80,1186.72,1190.33,1774900000,1190.33 2004-12-01,1173.78,1191.37,1173.78,1191.37,1772800000,1191.37 2004-11-30,1178.57,1178.66,1173.81,1173.82,1553500000,1173.82 2004-11-29,1182.65,1186.94,1172.37,1178.57,1378500000,1178.57 2004-11-26,1181.76,1186.62,1181.08,1182.65,504580000,1182.65 2004-11-24,1176.94,1182.46,1176.94,1181.76,1149600000,1181.76 2004-11-23,1177.24,1179.52,1171.41,1176.94,1428300000,1176.94 2004-11-22,1170.34,1178.18,1167.89,1177.24,1392700000,1177.24 2004-11-19,1183.55,1184.00,1169.19,1170.34,1526600000,1170.34 2004-11-18,1181.94,1184.90,1180.15,1183.55,1456700000,1183.55 2004-11-17,1175.43,1188.46,1175.43,1181.94,1684200000,1181.94 2004-11-16,1183.81,1183.81,1175.32,1175.43,1364400000,1175.43 2004-11-15,1184.17,1184.48,1179.85,1183.81,1453300000,1183.81 2004-11-12,1173.48,1184.17,1171.43,1184.17,1531600000,1184.17 2004-11-11,1162.91,1174.80,1162.91,1173.48,1393000000,1173.48 2004-11-10,1164.08,1169.25,1162.51,1162.91,1504300000,1162.91 2004-11-09,1164.89,1168.96,1162.48,1164.08,1450800000,1164.08 2004-11-08,1166.17,1166.77,1162.32,1164.89,1358700000,1164.89 2004-11-05,1161.67,1170.87,1160.66,1166.17,1724400000,1166.17 2004-11-04,1143.20,1161.67,1142.34,1161.67,1782700000,1161.67 2004-11-03,1130.54,1147.57,1130.54,1143.20,1767500000,1143.20 2004-11-02,1130.51,1140.48,1128.12,1130.56,1659000000,1130.56 2004-11-01,1130.20,1133.41,1127.60,1130.51,1395900000,1130.51 2004-10-29,1127.44,1131.40,1124.62,1130.20,1500800000,1130.20 2004-10-28,1125.34,1130.67,1120.60,1127.44,1628200000,1127.44 2004-10-27,1111.09,1126.29,1107.43,1125.40,1741900000,1125.40 2004-10-26,1094.81,1111.10,1094.81,1111.09,1685400000,1111.09 2004-10-25,1095.74,1096.81,1090.29,1094.80,1380500000,1094.80 2004-10-22,1106.49,1108.14,1095.47,1095.74,1469600000,1095.74 2004-10-21,1103.66,1108.87,1098.47,1106.49,1673000000,1106.49 2004-10-20,1103.23,1104.09,1094.25,1103.66,1685700000,1103.66 2004-10-19,1114.02,1117.96,1103.15,1103.23,1737500000,1103.23 2004-10-18,1108.20,1114.46,1103.33,1114.02,1373300000,1114.02 2004-10-15,1103.29,1113.17,1102.14,1108.20,1645100000,1108.20 2004-10-14,1113.65,1114.96,1102.06,1103.29,1489500000,1103.29 2004-10-13,1121.84,1127.01,1109.63,1113.65,1546200000,1113.65 2004-10-12,1124.39,1124.39,1115.77,1121.84,1320100000,1121.84 2004-10-11,1122.14,1126.20,1122.14,1124.39,943800000,1124.39 2004-10-08,1130.65,1132.92,1120.19,1122.14,1291600000,1122.14 2004-10-07,1142.05,1142.05,1130.50,1130.65,1447500000,1130.65 2004-10-06,1134.48,1142.05,1132.94,1142.05,1416700000,1142.05 2004-10-05,1135.17,1137.87,1132.03,1134.48,1418400000,1134.48 2004-10-04,1131.50,1140.13,1131.50,1135.17,1534000000,1135.17 2004-10-01,1114.58,1131.64,1114.58,1131.50,1582200000,1131.50 2004-09-30,1114.80,1116.31,1109.68,1114.58,1748000000,1114.58 2004-09-29,1110.06,1114.80,1107.42,1114.80,1402900000,1114.80 2004-09-28,1103.52,1111.77,1101.29,1110.06,1396600000,1110.06 2004-09-27,1110.11,1110.11,1103.24,1103.52,1263500000,1103.52 2004-09-24,1108.36,1113.81,1108.36,1110.11,1255400000,1110.11 2004-09-23,1113.56,1113.61,1108.05,1108.36,1286300000,1108.36 2004-09-22,1129.30,1129.30,1112.67,1113.56,1379900000,1113.56 2004-09-21,1122.20,1131.54,1122.20,1129.30,1325000000,1129.30 2004-09-20,1128.55,1128.55,1120.34,1122.20,1197600000,1122.20 2004-09-17,1123.50,1130.14,1123.50,1128.55,1422600000,1128.55 2004-09-16,1120.37,1126.06,1120.37,1123.50,1113900000,1123.50 2004-09-15,1128.33,1128.33,1119.82,1120.37,1256000000,1120.37 2004-09-14,1125.82,1129.46,1124.72,1128.33,1204500000,1128.33 2004-09-13,1123.92,1129.78,1123.35,1125.82,1299800000,1125.82 2004-09-10,1118.38,1125.26,1114.39,1123.92,1261200000,1123.92 2004-09-09,1116.27,1121.30,1113.62,1118.38,1371300000,1118.38 2004-09-08,1121.30,1123.05,1116.27,1116.27,1246300000,1116.27 2004-09-07,1113.63,1124.08,1113.63,1121.30,1214400000,1121.30 2004-09-03,1118.31,1120.80,1113.57,1113.63,924170000,1113.63 2004-09-02,1105.91,1119.11,1105.60,1118.31,1118400000,1118.31 2004-09-01,1104.24,1109.24,1099.18,1105.91,1142100000,1105.91 2004-08-31,1099.15,1104.24,1094.72,1104.24,1138200000,1104.24 2004-08-30,1107.77,1107.77,1099.15,1099.15,843100000,1099.15 2004-08-27,1105.09,1109.68,1104.62,1107.77,845400000,1107.77 2004-08-26,1104.96,1106.78,1102.46,1105.09,1023600000,1105.09 2004-08-25,1096.19,1106.29,1093.24,1104.96,1192200000,1104.96 2004-08-24,1095.68,1100.94,1092.82,1096.19,1092500000,1096.19 2004-08-23,1098.35,1101.40,1094.73,1095.68,1021900000,1095.68 2004-08-20,1091.23,1100.26,1089.57,1098.35,1199900000,1098.35 2004-08-19,1095.17,1095.17,1086.28,1091.23,1249400000,1091.23 2004-08-18,1081.71,1095.17,1078.93,1095.17,1282500000,1095.17 2004-08-17,1079.34,1086.78,1079.34,1081.71,1267800000,1081.71 2004-08-16,1064.80,1080.66,1064.80,1079.34,1206200000,1079.34 2004-08-13,1063.23,1067.58,1060.72,1064.80,1175100000,1064.80 2004-08-12,1075.79,1075.79,1062.82,1063.23,1405100000,1063.23 2004-08-11,1079.04,1079.04,1065.92,1075.79,1410400000,1075.79 2004-08-10,1065.22,1079.04,1065.22,1079.04,1245600000,1079.04 2004-08-09,1063.97,1069.46,1063.97,1065.22,1086000000,1065.22 2004-08-06,1080.70,1080.70,1062.23,1063.97,1521000000,1063.97 2004-08-05,1098.63,1098.79,1079.98,1080.70,1397400000,1080.70 2004-08-04,1099.69,1102.45,1092.40,1098.63,1369200000,1098.63 2004-08-03,1106.62,1106.62,1099.26,1099.69,1338300000,1099.69 2004-08-02,1101.72,1108.60,1097.34,1106.62,1276000000,1106.62 2004-07-30,1100.43,1103.73,1096.96,1101.72,1298200000,1101.72 2004-07-29,1095.42,1103.51,1095.42,1100.43,1530100000,1100.43 2004-07-28,1094.83,1098.84,1082.17,1095.42,1554300000,1095.42 2004-07-27,1084.07,1096.65,1084.07,1094.83,1610800000,1094.83 2004-07-26,1086.20,1089.82,1078.78,1084.07,1413400000,1084.07 2004-07-23,1096.84,1096.84,1083.56,1086.20,1337500000,1086.20 2004-07-22,1093.88,1099.66,1084.16,1096.84,1680800000,1096.84 2004-07-21,1108.67,1116.27,1093.88,1093.88,1679500000,1093.88 2004-07-20,1100.90,1108.88,1099.10,1108.67,1445800000,1108.67 2004-07-19,1101.39,1105.52,1096.55,1100.90,1319900000,1100.90 2004-07-16,1106.69,1112.17,1101.07,1101.39,1450300000,1101.39 2004-07-15,1111.47,1114.63,1106.67,1106.69,1408700000,1106.69 2004-07-14,1115.14,1119.60,1107.83,1111.47,1462000000,1111.47 2004-07-13,1114.35,1116.30,1112.99,1115.14,1199700000,1115.14 2004-07-12,1112.81,1116.11,1106.71,1114.35,1114600000,1114.35 2004-07-09,1109.11,1115.57,1109.11,1112.81,1186300000,1112.81 2004-07-08,1118.33,1119.12,1108.72,1109.11,1401100000,1109.11 2004-07-07,1116.21,1122.37,1114.92,1118.33,1328600000,1118.33 2004-07-06,1125.38,1125.38,1113.21,1116.21,1283300000,1116.21 2004-07-02,1128.94,1129.15,1123.26,1125.38,1085000000,1125.38 2004-07-01,1140.84,1140.84,1123.06,1128.94,1495700000,1128.94 2004-06-30,1136.20,1144.20,1133.62,1140.84,1473800000,1140.84 2004-06-29,1133.35,1138.26,1131.81,1136.20,1375000000,1136.20 2004-06-28,1134.43,1142.60,1131.72,1133.35,1354600000,1133.35 2004-06-25,1140.65,1145.97,1134.24,1134.43,1812900000,1134.43 2004-06-24,1144.06,1146.34,1139.94,1140.65,1394900000,1140.65 2004-06-23,1134.41,1145.15,1131.73,1144.06,1444200000,1144.06 2004-06-22,1130.30,1135.05,1124.37,1134.41,1382300000,1134.41 2004-06-21,1135.02,1138.05,1129.64,1130.30,1123900000,1130.30 2004-06-18,1132.05,1138.96,1129.83,1135.02,1500600000,1135.02 2004-06-17,1133.56,1133.56,1126.89,1132.05,1296700000,1132.05 2004-06-16,1132.01,1135.28,1130.55,1133.56,1168400000,1133.56 2004-06-15,1125.29,1137.36,1125.29,1132.01,1345900000,1132.01 2004-06-14,1136.47,1136.47,1122.16,1125.29,1179400000,1125.29 2004-06-10,1131.33,1136.47,1131.33,1136.47,1160600000,1136.47 2004-06-09,1142.18,1142.18,1131.17,1131.33,1276800000,1131.33 2004-06-08,1140.42,1142.18,1135.45,1142.18,1190300000,1142.18 2004-06-07,1122.50,1140.54,1122.50,1140.42,1211800000,1140.42 2004-06-04,1116.64,1129.17,1116.64,1122.50,1115300000,1122.50 2004-06-03,1124.99,1125.31,1116.57,1116.64,1232400000,1116.64 2004-06-02,1121.20,1128.10,1118.64,1124.99,1251700000,1124.99 2004-06-01,1120.68,1122.70,1113.32,1121.20,1238000000,1121.20 2004-05-28,1121.28,1122.69,1118.10,1120.68,1172600000,1120.68 2004-05-27,1114.94,1123.95,1114.86,1121.28,1447500000,1121.28 2004-05-26,1113.05,1116.71,1109.91,1114.94,1369400000,1114.94 2004-05-25,1095.41,1113.80,1090.74,1113.05,1545700000,1113.05 2004-05-24,1093.56,1101.28,1091.77,1095.41,1227500000,1095.41 2004-05-21,1089.19,1099.64,1089.19,1093.56,1258600000,1093.56 2004-05-20,1088.68,1092.62,1085.43,1089.19,1211000000,1089.19 2004-05-19,1091.49,1105.93,1088.49,1088.68,1548600000,1088.68 2004-05-18,1084.10,1094.10,1084.10,1091.49,1353000000,1091.49 2004-05-17,1095.70,1095.70,1079.36,1084.10,1430100000,1084.10 2004-05-14,1096.44,1102.10,1088.24,1095.70,1335900000,1095.70 2004-05-13,1097.28,1102.77,1091.76,1096.44,1411100000,1096.44 2004-05-12,1095.45,1097.55,1076.32,1097.28,1697600000,1097.28 2004-05-11,1087.12,1095.69,1087.12,1095.45,1533800000,1095.45 2004-05-10,1098.70,1098.70,1079.63,1087.12,1918400000,1087.12 2004-05-07,1113.99,1117.30,1098.63,1098.70,1653600000,1098.70 2004-05-06,1121.53,1121.53,1106.30,1113.99,1509300000,1113.99 2004-05-05,1119.55,1125.07,1117.90,1121.53,1469000000,1121.53 2004-05-04,1117.49,1127.74,1112.89,1119.55,1662100000,1119.55 2004-05-03,1107.30,1118.72,1107.30,1117.49,1571600000,1117.49 2004-04-30,1113.89,1119.26,1107.23,1107.30,1634700000,1107.30 2004-04-29,1122.41,1128.80,1108.04,1113.89,1859000000,1113.89 2004-04-28,1138.11,1138.11,1121.70,1122.41,1855600000,1122.41 2004-04-27,1135.53,1146.56,1135.53,1138.11,1518000000,1138.11 2004-04-26,1140.60,1145.08,1132.91,1135.53,1290600000,1135.53 2004-04-23,1139.93,1141.92,1134.81,1140.60,1396100000,1140.60 2004-04-22,1124.09,1142.77,1121.95,1139.93,1826700000,1139.93 2004-04-21,1118.15,1125.72,1116.03,1124.09,1738100000,1124.09 2004-04-20,1135.82,1139.26,1118.09,1118.15,1508500000,1118.15 2004-04-19,1134.56,1136.18,1129.84,1135.82,1194900000,1135.82 2004-04-16,1128.84,1136.80,1126.90,1134.61,1487800000,1134.61 2004-04-15,1128.17,1134.08,1120.75,1128.84,1568700000,1128.84 2004-04-14,1129.44,1132.52,1122.15,1128.17,1547700000,1128.17 2004-04-13,1145.20,1147.78,1127.70,1129.44,1423200000,1129.44 2004-04-12,1139.32,1147.29,1139.32,1145.20,1102400000,1145.20 2004-04-08,1140.53,1148.97,1134.52,1139.32,1199800000,1139.32 2004-04-07,1148.16,1148.16,1138.41,1140.53,1458800000,1140.53 2004-04-06,1150.57,1150.57,1143.30,1148.16,1397700000,1148.16 2004-04-05,1141.81,1150.57,1141.64,1150.57,1413700000,1150.57 2004-04-02,1132.17,1144.81,1132.17,1141.81,1629200000,1141.81 2004-04-01,1126.21,1135.67,1126.20,1132.17,1560700000,1132.17 2004-03-31,1127.00,1130.83,1121.46,1126.21,1560700000,1126.21 2004-03-30,1122.47,1127.60,1119.66,1127.00,1332400000,1127.00 2004-03-29,1108.06,1124.37,1108.06,1122.47,1405500000,1122.47 2004-03-26,1109.19,1115.27,1106.13,1108.06,1319100000,1108.06 2004-03-25,1091.33,1110.38,1091.33,1109.19,1471700000,1109.19 2004-03-24,1093.95,1098.32,1087.16,1091.33,1527800000,1091.33 2004-03-23,1095.40,1101.52,1091.57,1093.95,1458200000,1093.95 2004-03-22,1109.78,1109.78,1089.54,1095.40,1452300000,1095.40 2004-03-19,1122.32,1122.72,1109.69,1109.78,1457400000,1109.78 2004-03-18,1123.75,1125.50,1113.25,1122.32,1369200000,1122.32 2004-03-17,1110.70,1125.76,1110.70,1123.75,1490100000,1123.75 2004-03-16,1104.49,1113.76,1102.61,1110.70,1500700000,1110.70 2004-03-15,1120.57,1120.57,1103.36,1104.49,1600600000,1104.49 2004-03-12,1106.78,1120.63,1106.78,1120.57,1388500000,1120.57 2004-03-11,1123.89,1125.96,1105.87,1106.78,1889900000,1106.78 2004-03-10,1140.58,1141.45,1122.53,1123.89,1648400000,1123.89 2004-03-09,1147.20,1147.32,1136.84,1140.58,1499400000,1140.58 2004-03-08,1156.86,1159.94,1146.97,1147.20,1254400000,1147.20 2004-03-05,1154.87,1163.23,1148.77,1156.86,1398200000,1156.86 2004-03-04,1151.03,1154.97,1149.81,1154.87,1265800000,1154.87 2004-03-03,1149.10,1152.44,1143.78,1151.03,1334500000,1151.03 2004-03-02,1155.97,1156.54,1147.31,1149.10,1476000000,1149.10 2004-03-01,1144.94,1157.45,1144.94,1155.97,1497100000,1155.97 2004-02-27,1145.80,1151.68,1141.80,1144.94,1540400000,1144.94 2004-02-26,1143.67,1147.23,1138.62,1144.91,1383900000,1144.91 2004-02-25,1139.09,1145.24,1138.96,1143.67,1360700000,1143.67 2004-02-24,1140.99,1144.54,1134.43,1139.09,1543600000,1139.09 2004-02-23,1144.11,1146.69,1136.98,1140.99,1380400000,1140.99 2004-02-20,1147.06,1149.81,1139.00,1144.11,1479600000,1144.11 2004-02-19,1151.82,1158.57,1146.85,1147.06,1562800000,1147.06 2004-02-18,1156.99,1157.40,1149.54,1151.82,1382400000,1151.82 2004-02-17,1145.81,1158.98,1145.81,1156.99,1396500000,1156.99 2004-02-13,1152.11,1156.88,1143.24,1145.81,1329200000,1145.81 2004-02-12,1157.76,1157.76,1151.44,1152.11,1464300000,1152.11 2004-02-11,1145.54,1158.89,1142.33,1157.76,1699300000,1157.76 2004-02-10,1139.81,1147.02,1138.70,1145.54,1403900000,1145.54 2004-02-09,1142.76,1144.46,1139.21,1139.81,1303500000,1139.81 2004-02-06,1128.59,1142.79,1128.39,1142.76,1477600000,1142.76 2004-02-05,1126.52,1131.17,1124.44,1128.59,1566600000,1128.59 2004-02-04,1136.03,1136.03,1124.74,1126.52,1634800000,1126.52 2004-02-03,1135.26,1137.44,1131.33,1136.03,1476900000,1136.03 2004-02-02,1131.13,1142.45,1127.87,1135.26,1599200000,1135.26 2004-01-30,1134.11,1134.17,1127.73,1131.13,1635000000,1131.13 2004-01-29,1128.48,1134.39,1122.38,1134.11,1921900000,1134.11 2004-01-28,1144.05,1149.14,1126.50,1128.48,1842000000,1128.48 2004-01-27,1155.37,1155.37,1144.05,1144.05,1673100000,1144.05 2004-01-26,1141.55,1155.38,1141.00,1155.37,1480600000,1155.37 2004-01-23,1143.94,1150.31,1136.85,1141.55,1561200000,1141.55 2004-01-22,1147.62,1150.51,1143.01,1143.94,1693700000,1143.94 2004-01-21,1138.77,1149.21,1134.62,1147.62,1757600000,1147.62 2004-01-20,1139.83,1142.93,1135.40,1138.77,1698200000,1138.77 2004-01-16,1132.05,1139.83,1132.05,1139.83,1721100000,1139.83 2004-01-15,1130.52,1137.11,1124.54,1132.05,1695000000,1132.05 2004-01-14,1121.22,1130.75,1121.22,1130.52,1514600000,1130.52 2004-01-13,1127.23,1129.07,1115.19,1121.22,1595900000,1121.22 2004-01-12,1121.86,1127.85,1120.90,1127.23,1510200000,1127.23 2004-01-09,1131.92,1131.92,1120.90,1121.86,1720700000,1121.86 2004-01-08,1126.33,1131.92,1124.91,1131.92,1868400000,1131.92 2004-01-07,1123.67,1126.33,1116.45,1126.33,1704900000,1126.33 2004-01-06,1122.22,1124.46,1118.44,1123.67,1494500000,1123.67 2004-01-05,1108.48,1122.22,1108.48,1122.22,1578200000,1122.22 2004-01-02,1111.92,1118.85,1105.08,1108.48,1153200000,1108.48 2003-12-31,1109.64,1112.56,1106.21,1111.92,1027500000,1111.92 2003-12-30,1109.48,1109.75,1106.41,1109.64,1012600000,1109.64 2003-12-29,1095.89,1109.48,1095.89,1109.48,1058800000,1109.48 2003-12-26,1094.04,1098.47,1094.04,1095.89,356070000,1095.89 2003-12-24,1096.02,1096.40,1092.73,1094.04,518060000,1094.04 2003-12-23,1092.94,1096.95,1091.73,1096.02,1145300000,1096.02 2003-12-22,1088.66,1092.94,1086.14,1092.94,1251700000,1092.94 2003-12-19,1089.18,1091.06,1084.19,1088.66,1657300000,1088.66 2003-12-18,1076.48,1089.50,1076.48,1089.18,1579900000,1089.18 2003-12-17,1075.13,1076.54,1071.14,1076.48,1441700000,1076.48 2003-12-16,1068.04,1075.94,1068.04,1075.13,1547900000,1075.13 2003-12-15,1074.14,1082.79,1068.00,1068.04,1520800000,1068.04 2003-12-12,1071.21,1074.76,1067.64,1074.14,1223100000,1074.14 2003-12-11,1059.05,1073.63,1059.05,1071.21,1441100000,1071.21 2003-12-10,1060.18,1063.02,1053.41,1059.05,1444000000,1059.05 2003-12-09,1069.30,1071.94,1059.16,1060.18,1465500000,1060.18 2003-12-08,1061.50,1069.59,1060.93,1069.30,1218900000,1069.30 2003-12-05,1069.72,1069.72,1060.09,1061.50,1265900000,1061.50 2003-12-04,1064.73,1070.37,1063.15,1069.72,1463100000,1069.72 2003-12-03,1066.62,1074.30,1064.63,1064.73,1441700000,1064.73 2003-12-02,1070.12,1071.22,1065.22,1066.62,1383200000,1066.62 2003-12-01,1058.20,1070.47,1058.20,1070.12,1375000000,1070.12 2003-11-28,1058.45,1060.63,1056.77,1058.20,487220000,1058.20 2003-11-26,1053.89,1058.45,1048.28,1058.45,1097700000,1058.45 2003-11-25,1052.08,1058.05,1049.31,1053.89,1333700000,1053.89 2003-11-24,1035.28,1052.08,1035.28,1052.08,1302800000,1052.08 2003-11-21,1033.65,1037.57,1031.20,1035.28,1273800000,1035.28 2003-11-20,1042.44,1046.48,1033.42,1033.65,1326700000,1033.65 2003-11-19,1034.15,1043.95,1034.15,1042.44,1326200000,1042.44 2003-11-18,1043.63,1048.77,1034.00,1034.15,1354300000,1034.15 2003-11-17,1050.35,1050.35,1035.28,1043.63,1374300000,1043.63 2003-11-14,1058.41,1063.65,1048.11,1050.35,1356100000,1050.35 2003-11-13,1058.56,1059.62,1052.96,1058.41,1383000000,1058.41 2003-11-12,1046.57,1059.10,1046.57,1058.53,1349300000,1058.53 2003-11-11,1047.11,1048.23,1043.46,1046.57,1162500000,1046.57 2003-11-10,1053.21,1053.65,1045.58,1047.11,1243600000,1047.11 2003-11-07,1058.05,1062.39,1052.17,1053.21,1440500000,1053.21 2003-11-06,1051.81,1058.94,1046.93,1058.05,1453900000,1058.05 2003-11-05,1053.25,1054.54,1044.88,1051.81,1401800000,1051.81 2003-11-04,1059.02,1059.02,1051.70,1053.25,1417600000,1053.25 2003-11-03,1050.71,1061.44,1050.71,1059.02,1378200000,1059.02 2003-10-31,1046.94,1053.09,1046.94,1050.71,1498900000,1050.71 2003-10-30,1048.11,1052.81,1043.82,1046.94,1629700000,1046.94 2003-10-29,1046.79,1049.83,1043.35,1048.11,1562600000,1048.11 2003-10-28,1031.13,1046.79,1031.13,1046.79,1629200000,1046.79 2003-10-27,1028.91,1037.75,1028.91,1031.13,1371800000,1031.13 2003-10-24,1033.77,1033.77,1018.32,1028.91,1420300000,1028.91 2003-10-23,1030.36,1035.44,1025.89,1033.77,1604300000,1033.77 2003-10-22,1046.03,1046.03,1028.39,1030.36,1647200000,1030.36 2003-10-21,1044.68,1048.57,1042.59,1046.03,1498000000,1046.03 2003-10-20,1039.32,1044.69,1036.13,1044.68,1172600000,1044.68 2003-10-17,1050.07,1051.89,1036.57,1039.32,1352000000,1039.32 2003-10-16,1046.76,1052.94,1044.04,1050.07,1417700000,1050.07 2003-10-15,1049.48,1053.79,1043.15,1046.76,1521100000,1046.76 2003-10-14,1045.35,1049.49,1040.84,1049.48,1271900000,1049.48 2003-10-13,1038.06,1048.90,1038.06,1045.35,1040500000,1045.35 2003-10-10,1038.73,1040.84,1035.74,1038.06,1108100000,1038.06 2003-10-09,1033.78,1048.28,1033.78,1038.73,1578700000,1038.73 2003-10-08,1039.25,1040.06,1030.96,1033.78,1262500000,1033.78 2003-10-07,1034.35,1039.25,1026.27,1039.25,1279500000,1039.25 2003-10-06,1029.85,1036.48,1029.15,1034.35,1025800000,1034.35 2003-10-03,1020.24,1039.31,1020.24,1029.85,1570500000,1029.85 2003-10-02,1018.22,1021.87,1013.38,1020.24,1269300000,1020.24 2003-10-01,995.97,1018.22,995.97,1018.22,1566300000,1018.22 2003-09-30,1006.58,1006.58,990.36,995.97,1590500000,995.97 2003-09-29,996.85,1006.89,995.31,1006.58,1366500000,1006.58 2003-09-26,1003.27,1003.45,996.08,996.85,1472500000,996.85 2003-09-25,1009.38,1015.97,1003.26,1003.27,1530000000,1003.27 2003-09-24,1029.03,1029.83,1008.93,1009.38,1556000000,1009.38 2003-09-23,1022.82,1030.12,1021.54,1029.03,1301700000,1029.03 2003-09-22,1036.30,1036.30,1018.30,1022.82,1278800000,1022.82 2003-09-19,1039.58,1040.29,1031.89,1036.30,1518600000,1036.30 2003-09-18,1025.97,1040.16,1025.75,1039.58,1498800000,1039.58 2003-09-17,1029.32,1031.34,1024.53,1025.97,1338210000,1025.97 2003-09-16,1014.81,1029.66,1014.81,1029.32,1403200000,1029.32 2003-09-15,1018.63,1019.79,1013.59,1014.81,1151300000,1014.81 2003-09-12,1016.42,1019.65,1007.71,1018.63,1236700000,1018.63 2003-09-11,1010.92,1020.88,1010.92,1016.42,1335900000,1016.42 2003-09-10,1023.17,1023.17,1009.74,1010.92,1582100000,1010.92 2003-09-09,1031.64,1031.64,1021.14,1023.17,1414800000,1023.17 2003-09-08,1021.39,1032.41,1021.39,1031.64,1299300000,1031.64 2003-09-05,1027.97,1029.21,1018.19,1021.39,1465200000,1021.39 2003-09-04,1026.27,1029.17,1022.19,1027.97,1453900000,1027.97 2003-09-03,1021.99,1029.34,1021.99,1026.27,1675600000,1026.27 2003-09-02,1008.01,1022.59,1005.73,1021.99,1470500000,1021.99 2003-08-29,1002.84,1008.85,999.52,1008.01,945100000,1008.01 2003-08-28,996.79,1004.12,991.42,1002.84,1165200000,1002.84 2003-08-27,996.73,998.05,993.33,996.79,1051400000,996.79 2003-08-26,993.71,997.93,983.57,996.73,1178700000,996.73 2003-08-25,993.06,993.71,987.91,993.71,971700000,993.71 2003-08-22,1003.27,1011.01,992.62,993.06,1308900000,993.06 2003-08-21,1000.30,1009.53,999.33,1003.27,1407100000,1003.27 2003-08-20,1002.35,1003.54,996.62,1000.30,1210800000,1000.30 2003-08-19,999.74,1003.30,995.30,1002.35,1300600000,1002.35 2003-08-18,990.67,1000.35,990.67,999.74,1127600000,999.74 2003-08-15,990.51,992.39,987.10,990.67,636370000,990.67 2003-08-14,984.03,991.91,980.36,990.51,1186800000,990.51 2003-08-13,990.35,992.50,980.85,984.03,1208800000,984.03 2003-08-12,980.59,990.41,979.90,990.35,1132300000,990.35 2003-08-11,977.59,985.46,974.21,980.59,1022200000,980.59 2003-08-08,974.12,980.57,973.83,977.59,1086600000,977.59 2003-08-07,967.08,974.89,963.82,974.12,1389300000,974.12 2003-08-06,965.46,975.74,960.84,967.08,1491000000,967.08 2003-08-05,982.82,982.82,964.97,965.46,1351700000,965.46 2003-08-04,980.15,985.75,966.79,982.82,1318700000,982.82 2003-08-01,990.31,990.31,978.86,980.15,1390600000,980.15 2003-07-31,987.49,1004.59,987.49,990.31,1608000000,990.31 2003-07-30,989.28,992.62,985.96,987.49,1391900000,987.49 2003-07-29,996.52,998.64,984.15,989.28,1508900000,989.28 2003-07-28,998.68,1000.68,993.59,996.52,1328600000,996.52 2003-07-25,981.60,998.71,977.49,998.68,1397500000,998.68 2003-07-24,988.61,998.89,981.07,981.60,1559000000,981.60 2003-07-23,988.11,989.86,979.79,988.61,1362700000,988.61 2003-07-22,978.80,990.29,976.08,988.11,1439700000,988.11 2003-07-21,993.32,993.32,975.63,978.80,1254200000,978.80 2003-07-18,981.73,994.25,981.71,993.32,1365200000,993.32 2003-07-17,994.00,994.00,978.60,981.73,1661400000,981.73 2003-07-16,1000.42,1003.47,989.30,994.09,1662000000,994.09 2003-07-15,1003.86,1009.61,996.67,1000.42,1518600000,1000.42 2003-07-14,998.14,1015.41,998.14,1003.86,1448900000,1003.86 2003-07-11,988.70,1000.86,988.70,998.14,1212700000,998.14 2003-07-10,1002.21,1002.21,983.63,988.70,1465700000,988.70 2003-07-09,1007.84,1010.43,998.17,1002.21,1618000000,1002.21 2003-07-08,1004.42,1008.92,998.73,1007.84,1565700000,1007.84 2003-07-07,985.70,1005.56,985.70,1004.42,1429100000,1004.42 2003-07-03,993.75,995.00,983.34,985.70,775900000,985.70 2003-07-02,982.32,993.78,982.32,993.75,1519300000,993.75 2003-07-01,974.50,983.26,962.10,982.32,1460200000,982.32 2003-06-30,976.22,983.61,973.60,974.50,1587200000,974.50 2003-06-27,985.82,988.88,974.29,976.22,1267800000,976.22 2003-06-26,975.32,986.53,973.80,985.82,1387400000,985.82 2003-06-25,983.45,991.64,974.86,975.32,1459200000,975.32 2003-06-24,981.64,987.84,979.08,983.45,1388300000,983.45 2003-06-23,995.69,995.69,977.40,981.64,1398100000,981.64 2003-06-20,994.70,1002.09,993.36,995.69,1698000000,995.69 2003-06-19,1010.09,1011.22,993.08,994.70,1530100000,994.70 2003-06-18,1011.66,1015.12,1004.61,1010.09,1488900000,1010.09 2003-06-17,1010.74,1015.33,1007.04,1011.66,1479700000,1011.66 2003-06-16,988.61,1010.86,988.61,1010.74,1345900000,1010.74 2003-06-13,998.51,1000.92,984.27,988.61,1271600000,988.61 2003-06-12,997.48,1002.74,991.27,998.51,1553100000,998.51 2003-06-11,984.84,997.48,981.61,997.48,1520000000,997.48 2003-06-10,975.93,984.84,975.93,984.84,1275400000,984.84 2003-06-09,987.76,987.76,972.59,975.93,1307000000,975.93 2003-06-06,990.14,1007.69,986.01,987.76,1837200000,987.76 2003-06-05,986.24,990.14,978.13,990.14,1693100000,990.14 2003-06-04,971.56,987.85,970.72,986.24,1618700000,986.24 2003-06-03,967.00,973.02,964.47,971.56,1450200000,971.56 2003-06-02,963.59,979.11,963.59,967.00,1662500000,967.00 2003-05-30,949.64,965.38,949.64,963.59,1688800000,963.59 2003-05-29,953.22,962.08,946.23,949.64,1685800000,949.64 2003-05-28,951.48,959.39,950.12,953.22,1559000000,953.22 2003-05-27,933.22,952.76,927.33,951.48,1532000000,951.48 2003-05-23,931.87,935.20,927.42,933.22,1201000000,933.22 2003-05-22,923.42,935.30,922.54,931.87,1448500000,931.87 2003-05-21,919.73,923.85,914.91,923.42,1457800000,923.42 2003-05-20,920.77,925.34,912.05,919.73,1505300000,919.73 2003-05-19,944.30,944.30,920.23,920.77,1375700000,920.77 2003-05-16,946.67,948.65,938.60,944.30,1505500000,944.30 2003-05-15,939.28,948.23,938.79,946.67,1508700000,946.67 2003-05-14,942.30,947.29,935.24,939.28,1401800000,939.28 2003-05-13,945.11,947.51,938.91,942.30,1418100000,942.30 2003-05-12,933.41,946.84,929.30,945.11,1378800000,945.11 2003-05-09,920.27,933.77,920.27,933.41,1326100000,933.41 2003-05-08,929.62,929.62,919.72,920.27,1379600000,920.27 2003-05-07,934.39,937.22,926.41,929.62,1531900000,929.62 2003-05-06,926.55,939.61,926.38,934.39,1649600000,934.39 2003-05-05,930.08,933.88,924.55,926.55,1446300000,926.55 2003-05-02,916.30,930.56,912.35,930.08,1554300000,930.08 2003-05-01,916.92,919.68,902.83,916.30,1397500000,916.30 2003-04-30,917.84,922.01,911.70,916.92,1788510000,916.92 2003-04-29,914.84,924.24,911.10,917.84,1525600000,917.84 2003-04-28,898.81,918.15,898.81,914.84,1273000000,914.84 2003-04-25,911.43,911.43,897.52,898.81,1335800000,898.81 2003-04-24,919.02,919.02,906.69,911.43,1648100000,911.43 2003-04-23,911.37,919.74,909.89,919.02,1667200000,919.02 2003-04-22,892.01,911.74,886.70,911.37,1631200000,911.37 2003-04-21,893.58,898.01,888.17,892.01,1118700000,892.01 2003-04-17,879.91,893.83,879.20,893.58,1430600000,893.58 2003-04-16,890.81,896.77,877.93,879.91,1587600000,879.91 2003-04-15,885.23,891.27,881.85,890.81,1460200000,890.81 2003-04-14,868.30,885.26,868.30,885.23,1131000000,885.23 2003-04-11,871.58,883.34,865.92,868.30,1141600000,868.30 2003-04-10,865.99,871.78,862.76,871.58,1275300000,871.58 2003-04-09,878.29,887.35,865.72,865.99,1293700000,865.99 2003-04-08,879.93,883.11,874.68,878.29,1235400000,878.29 2003-04-07,878.85,904.89,878.85,879.93,1494000000,879.93 2003-04-04,876.45,882.73,874.23,878.85,1241200000,878.85 2003-04-03,880.90,885.89,876.12,876.45,1339500000,876.45 2003-04-02,858.48,884.57,858.48,880.90,1589800000,880.90 2003-04-01,848.18,861.28,847.85,858.48,1461600000,858.48 2003-03-31,863.50,863.50,843.68,848.18,1495500000,848.18 2003-03-28,868.52,869.88,860.83,863.50,1227000000,863.50 2003-03-27,869.95,874.15,858.09,868.52,1232900000,868.52 2003-03-26,874.74,875.80,866.47,869.95,1319700000,869.95 2003-03-25,864.23,879.87,862.59,874.74,1333400000,874.74 2003-03-24,895.79,895.79,862.02,864.23,1293000000,864.23 2003-03-21,875.84,895.90,875.84,895.79,1883710000,895.79 2003-03-20,874.02,879.60,859.01,875.67,1439100000,875.67 2003-03-19,866.45,874.99,861.21,874.02,1473400000,874.02 2003-03-18,862.79,866.94,857.36,866.45,1555100000,866.45 2003-03-17,833.27,862.79,827.17,862.79,1700420000,862.79 2003-03-14,831.89,841.39,828.26,833.27,1541900000,833.27 2003-03-13,804.19,832.02,804.19,831.90,1816300000,831.90 2003-03-12,800.73,804.19,788.90,804.19,1620000000,804.19 2003-03-11,807.48,814.25,800.30,800.73,1427700000,800.73 2003-03-10,828.89,828.89,806.57,807.48,1255000000,807.48 2003-03-07,822.10,829.55,811.23,828.89,1368500000,828.89 2003-03-06,829.85,829.85,819.85,822.10,1299200000,822.10 2003-03-05,821.99,829.87,819.00,829.85,1332700000,829.85 2003-03-04,834.81,835.43,821.96,821.99,1256600000,821.99 2003-03-03,841.15,852.34,832.74,834.81,1208900000,834.81 2003-02-28,837.28,847.00,837.28,841.15,1373300000,841.15 2003-02-27,827.55,842.19,827.55,837.28,1287800000,837.28 2003-02-26,838.57,840.10,826.68,827.55,1374400000,827.55 2003-02-25,832.58,839.55,818.54,838.57,1483700000,838.57 2003-02-24,848.17,848.17,832.16,832.58,1229200000,832.58 2003-02-21,837.10,852.28,831.48,848.17,1398200000,848.17 2003-02-20,845.13,849.37,836.56,837.10,1194100000,837.10 2003-02-19,851.17,851.17,838.79,845.13,1075600000,845.13 2003-02-18,834.89,852.87,834.89,851.17,1250800000,851.17 2003-02-14,817.37,834.89,815.03,834.89,1404600000,834.89 2003-02-13,818.68,821.25,806.29,817.37,1489300000,817.37 2003-02-12,829.20,832.12,818.49,818.68,1260500000,818.68 2003-02-11,835.97,843.02,825.09,829.20,1307000000,829.20 2003-02-10,829.69,837.16,823.53,835.97,1238200000,835.97 2003-02-07,838.15,845.73,826.70,829.69,1276800000,829.69 2003-02-06,843.59,844.23,833.25,838.15,1430900000,838.15 2003-02-05,848.20,861.63,842.11,843.59,1450800000,843.59 2003-02-04,860.32,860.32,840.19,848.20,1451600000,848.20 2003-02-03,855.70,864.64,855.70,860.32,1258500000,860.32 2003-01-31,844.61,858.33,840.34,855.70,1578530000,855.70 2003-01-30,864.36,865.48,843.74,844.61,1510300000,844.61 2003-01-29,858.54,868.72,845.86,864.36,1595400000,864.36 2003-01-28,847.48,860.76,847.48,858.54,1459100000,858.54 2003-01-27,861.40,863.95,844.25,847.48,1435900000,847.48 2003-01-24,887.34,887.34,859.71,861.40,1574800000,861.40 2003-01-23,878.36,890.25,876.89,887.34,1744550000,887.34 2003-01-22,887.62,889.74,877.64,878.36,1560800000,878.36 2003-01-21,901.78,906.00,887.62,887.62,1335200000,887.62 2003-01-17,914.60,914.60,899.02,901.78,1358200000,901.78 2003-01-16,918.22,926.03,911.98,914.60,1534600000,914.60 2003-01-15,931.66,932.59,916.70,918.22,1432100000,918.22 2003-01-14,926.26,931.66,921.72,931.66,1379400000,931.66 2003-01-13,927.57,935.05,922.05,926.26,1396300000,926.26 2003-01-10,927.58,932.89,917.66,927.57,1485400000,927.57 2003-01-09,909.93,928.31,909.93,927.57,1560300000,927.57 2003-01-08,922.93,922.93,908.32,909.93,1467600000,909.93 2003-01-07,929.01,930.81,919.93,922.93,1545200000,922.93 2003-01-06,908.59,931.77,908.59,929.01,1435900000,929.01 2003-01-03,909.03,911.25,903.07,908.59,1130800000,908.59 2003-01-02,879.82,909.03,879.82,909.03,1229200000,909.03 2002-12-31,879.39,881.93,869.45,879.82,1088500000,879.82 2002-12-30,875.40,882.10,870.23,879.39,1057800000,879.39 2002-12-27,889.66,890.46,873.62,875.40,758400000,875.40 2002-12-26,892.47,903.89,887.48,889.66,721100000,889.66 2002-12-24,897.38,897.38,892.29,892.47,458310000,892.47 2002-12-23,895.74,902.43,892.26,897.38,1112100000,897.38 2002-12-20,884.25,897.79,884.25,895.76,1782730000,895.76 2002-12-19,890.02,899.19,880.32,884.25,1385900000,884.25 2002-12-18,902.99,902.99,887.82,891.12,1446200000,891.12 2002-12-17,910.40,911.22,901.74,902.99,1251800000,902.99 2002-12-16,889.48,910.42,889.48,910.40,1271600000,910.40 2002-12-13,901.58,901.58,888.48,889.48,1330800000,889.48 2002-12-12,904.96,908.37,897.00,901.58,1255300000,901.58 2002-12-11,904.45,909.94,896.48,904.96,1285100000,904.96 2002-12-10,892.00,904.95,892.00,904.45,1286600000,904.45 2002-12-09,912.23,912.23,891.97,892.00,1320800000,892.00 2002-12-06,906.55,915.48,895.96,912.23,1241100000,912.23 2002-12-05,917.58,921.49,905.90,906.55,1250200000,906.55 2002-12-04,920.75,925.25,909.51,917.58,1588900000,917.58 2002-12-03,934.53,934.53,918.73,920.75,1488400000,920.75 2002-12-02,936.31,954.28,927.72,934.53,1612000000,934.53 2002-11-29,938.87,941.82,935.58,936.31,643460000,936.31 2002-11-27,913.31,940.41,913.31,938.87,1350300000,938.87 2002-11-26,932.87,932.87,912.10,913.31,1543600000,913.31 2002-11-25,930.55,937.15,923.31,932.87,1574000000,932.87 2002-11-22,933.76,937.28,928.41,930.55,1626800000,930.55 2002-11-21,914.15,935.13,914.15,933.76,2415100000,933.76 2002-11-20,896.74,915.01,894.93,914.15,1517300000,914.15 2002-11-19,900.36,905.45,893.09,896.74,1337400000,896.74 2002-11-18,909.83,915.91,899.48,900.36,1282600000,900.36 2002-11-15,904.27,910.21,895.35,909.83,1400100000,909.83 2002-11-14,882.53,904.27,882.53,904.27,1519000000,904.27 2002-11-13,882.95,892.51,872.05,882.53,1463400000,882.53 2002-11-12,876.19,894.30,876.19,882.95,1377100000,882.95 2002-11-11,894.74,894.74,874.63,876.19,1113000000,876.19 2002-11-08,902.65,910.11,891.62,894.74,1446500000,894.74 2002-11-07,923.76,923.76,898.68,902.65,1466900000,902.65 2002-11-06,915.39,925.66,905.00,923.76,1674000000,923.76 2002-11-05,908.35,915.83,904.91,915.39,1354100000,915.39 2002-11-04,900.96,924.58,900.96,908.35,1645900000,908.35 2002-11-01,885.76,903.42,877.71,900.96,1450400000,900.96 2002-10-31,890.71,898.83,879.75,885.76,1641300000,885.76 2002-10-30,882.15,895.28,879.19,890.71,1422300000,890.71 2002-10-29,890.23,890.64,867.91,882.15,1529700000,882.15 2002-10-28,897.65,907.44,886.15,890.23,1382600000,890.23 2002-10-25,882.50,897.71,877.03,897.65,1340400000,897.65 2002-10-24,896.14,902.94,879.00,882.50,1700570000,882.50 2002-10-23,890.16,896.14,873.82,896.14,1593900000,896.14 2002-10-22,899.72,899.72,882.40,890.16,1549200000,890.16 2002-10-21,884.39,900.69,873.06,899.72,1447000000,899.72 2002-10-18,879.20,886.68,866.58,884.39,1423100000,884.39 2002-10-17,860.02,885.35,860.02,879.20,1780390000,879.20 2002-10-16,881.27,881.27,856.28,860.02,1585000000,860.02 2002-10-15,841.44,881.27,841.44,881.27,1956000000,881.27 2002-10-14,835.32,844.39,828.37,841.44,1200300000,841.44 2002-10-11,803.92,843.27,803.92,835.32,1854130000,835.32 2002-10-10,776.76,806.51,768.63,803.92,2090230000,803.92 2002-10-09,798.55,798.55,775.80,776.76,1885030000,776.76 2002-10-08,785.28,808.86,779.50,798.55,1938430000,798.55 2002-10-07,800.58,808.21,782.96,785.28,1576500000,785.28 2002-10-04,818.95,825.90,794.10,800.58,1835930000,800.58 2002-10-03,827.91,840.02,817.25,818.95,1674500000,818.95 2002-10-02,843.77,851.93,826.50,827.91,1668900000,827.91 2002-10-01,815.28,847.93,812.82,847.91,1780900000,847.91 2002-09-30,827.37,827.37,800.20,815.28,1721870000,815.28 2002-09-27,854.95,854.95,826.84,827.37,1507300000,827.37 2002-09-26,839.66,856.60,839.66,854.95,1650000000,854.95 2002-09-25,819.27,844.22,818.46,839.66,1651500000,839.66 2002-09-24,833.70,833.70,817.38,819.29,1670240000,819.29 2002-09-23,845.39,845.39,825.76,833.70,1381100000,833.70 2002-09-20,843.32,849.32,839.09,845.39,1792800000,845.39 2002-09-19,869.46,869.46,843.09,843.32,1524000000,843.32 2002-09-18,873.52,878.45,857.39,869.46,1501000000,869.46 2002-09-17,891.10,902.68,872.38,873.52,1448600000,873.52 2002-09-16,889.81,891.84,878.91,891.10,1001400000,891.10 2002-09-13,886.91,892.75,877.05,889.81,1271000000,889.81 2002-09-12,909.45,909.45,884.84,886.91,1191600000,886.91 2002-09-11,910.63,924.02,908.47,909.45,846600000,909.45 2002-09-10,902.96,909.89,900.50,909.58,1186400000,909.58 2002-09-09,893.92,907.34,882.92,902.96,1130600000,902.96 2002-09-06,879.15,899.07,879.15,893.92,1184500000,893.92 2002-09-05,893.40,893.40,870.50,879.15,1401300000,879.15 2002-09-04,878.02,896.10,875.73,893.40,1372100000,893.40 2002-09-03,916.07,916.07,877.51,878.02,1289800000,878.02 2002-08-30,917.80,928.15,910.17,916.07,929900000,916.07 2002-08-29,917.87,924.59,903.33,917.80,1271100000,917.80 2002-08-28,934.82,934.82,913.21,917.87,1146600000,917.87 2002-08-27,947.95,955.82,930.36,934.82,1307700000,934.82 2002-08-26,940.86,950.80,930.42,947.95,1016900000,947.95 2002-08-23,962.70,962.70,937.17,940.86,1071500000,940.86 2002-08-22,949.36,965.00,946.43,962.70,1373000000,962.70 2002-08-21,937.43,951.59,931.32,949.36,1353100000,949.36 2002-08-20,950.70,950.70,931.86,937.43,1308500000,937.43 2002-08-19,928.77,951.17,927.21,950.70,1299800000,950.70 2002-08-16,930.25,935.38,916.21,928.77,1265300000,928.77 2002-08-15,919.62,933.29,918.17,930.25,1505100000,930.25 2002-08-14,884.21,920.21,876.20,919.62,1533800000,919.62 2002-08-13,903.80,911.71,883.62,884.21,1297700000,884.21 2002-08-12,908.64,908.64,892.38,903.80,1036500000,903.80 2002-08-09,898.73,913.95,890.77,908.64,1294900000,908.64 2002-08-08,876.77,905.84,875.17,905.46,1646700000,905.46 2002-08-07,859.57,878.74,854.15,876.77,1490400000,876.77 2002-08-06,834.60,874.44,834.60,859.57,1514100000,859.57 2002-08-05,864.24,864.24,833.44,834.60,1425500000,834.60 2002-08-02,884.40,884.72,853.95,864.24,1538100000,864.24 2002-08-01,911.62,911.62,882.48,884.66,1672200000,884.66 2002-07-31,902.78,911.64,889.88,911.62,2049360000,911.62 2002-07-30,898.96,909.81,884.70,902.78,1826090000,902.78 2002-07-29,852.84,898.96,852.84,898.96,1778650000,898.96 2002-07-26,838.68,852.85,835.92,852.84,1796100000,852.84 2002-07-25,843.42,853.83,816.11,838.68,2424700000,838.68 2002-07-24,797.71,844.32,775.68,843.43,2775560000,843.43 2002-07-23,819.85,827.69,796.13,797.70,2441020000,797.70 2002-07-22,847.76,854.13,813.26,819.85,2248060000,819.85 2002-07-19,881.56,881.56,842.07,847.75,2654100000,847.75 2002-07-18,905.45,907.80,880.60,881.56,1736300000,881.56 2002-07-17,901.05,926.52,895.03,906.04,2566500000,906.04 2002-07-16,917.93,918.65,897.13,900.94,1843700000,900.94 2002-07-15,921.39,921.39,876.46,917.93,2574800000,917.93 2002-07-12,927.37,934.31,913.71,921.39,1607400000,921.39 2002-07-11,920.47,929.16,900.94,927.37,2080480000,927.37 2002-07-10,952.83,956.34,920.29,920.47,1816900000,920.47 2002-07-09,976.98,979.63,951.71,952.83,1348900000,952.83 2002-07-08,989.03,993.56,972.91,976.98,1184400000,976.98 2002-07-05,953.99,989.07,953.99,989.03,699400000,989.03 2002-07-03,948.09,954.30,934.87,953.99,1527800000,953.99 2002-07-02,968.65,968.65,945.54,948.09,1823000000,948.09 2002-07-01,989.82,994.46,967.43,968.65,1425500000,968.65 2002-06-28,990.64,1001.79,988.31,989.82,2117000000,989.82 2002-06-27,973.53,990.67,963.74,990.64,1908600000,990.64 2002-06-26,976.14,977.43,952.92,973.53,2014290000,973.53 2002-06-25,992.72,1005.88,974.21,976.14,1513700000,976.14 2002-06-24,989.14,1002.11,970.85,992.72,1552600000,992.72 2002-06-21,1006.29,1006.29,985.65,989.14,1497200000,989.14 2002-06-20,1019.99,1023.33,1004.59,1006.29,1389700000,1006.29 2002-06-19,1037.14,1037.61,1017.88,1019.99,1336100000,1019.99 2002-06-18,1036.17,1040.83,1030.92,1037.14,1193100000,1037.14 2002-06-17,1007.27,1036.17,1007.27,1036.17,1236600000,1036.17 2002-06-14,1009.56,1009.56,981.63,1007.27,1549000000,1007.27 2002-06-13,1020.26,1023.47,1008.12,1009.56,1405500000,1009.56 2002-06-12,1013.26,1021.85,1002.58,1020.26,1795720000,1020.26 2002-06-11,1030.74,1039.04,1012.94,1013.60,1212400000,1013.60 2002-06-10,1027.53,1038.18,1025.45,1030.74,1226200000,1030.74 2002-06-07,1029.15,1033.02,1012.49,1027.53,1341300000,1027.53 2002-06-06,1049.90,1049.90,1026.91,1029.15,1601500000,1029.15 2002-06-05,1040.69,1050.11,1038.84,1049.90,1300100000,1049.90 2002-06-04,1040.68,1046.06,1030.52,1040.69,1466600000,1040.69 2002-06-03,1067.14,1070.74,1039.90,1040.68,1324300000,1040.68 2002-05-31,1064.66,1079.93,1064.66,1067.14,1277300000,1067.14 2002-05-30,1067.66,1069.50,1054.26,1064.66,1286600000,1064.66 2002-05-29,1074.55,1074.83,1067.66,1067.66,1081800000,1067.66 2002-05-28,1083.82,1085.98,1070.31,1074.55,996500000,1074.55 2002-05-24,1097.08,1097.08,1082.19,1083.82,885400000,1083.82 2002-05-23,1086.02,1097.10,1080.55,1097.08,1192900000,1097.08 2002-05-22,1079.88,1086.02,1075.64,1086.02,1136300000,1086.02 2002-05-21,1091.88,1099.55,1079.08,1079.88,1200500000,1079.88 2002-05-20,1106.59,1106.59,1090.61,1091.88,989800000,1091.88 2002-05-17,1098.23,1106.59,1096.77,1106.59,1274400000,1106.59 2002-05-16,1091.07,1099.29,1089.17,1098.23,1256600000,1098.23 2002-05-15,1097.28,1104.23,1088.94,1091.07,1420200000,1091.07 2002-05-14,1074.56,1097.71,1074.56,1097.28,1414500000,1097.28 2002-05-13,1054.99,1074.84,1053.90,1074.56,1088600000,1074.56 2002-05-10,1073.01,1075.43,1053.93,1054.99,1171900000,1054.99 2002-05-09,1088.85,1088.85,1072.23,1073.01,1153000000,1073.01 2002-05-08,1049.49,1088.92,1049.49,1088.85,1502000000,1088.85 2002-05-07,1052.67,1058.67,1048.96,1049.49,1354700000,1049.49 2002-05-06,1073.43,1075.96,1052.65,1052.67,1122600000,1052.67 2002-05-03,1084.56,1084.56,1068.89,1073.43,1284500000,1073.43 2002-05-02,1086.46,1091.42,1079.46,1084.56,1364000000,1084.56 2002-05-01,1076.92,1088.32,1065.29,1086.46,1451400000,1086.46 2002-04-30,1065.45,1082.62,1063.46,1076.92,1628600000,1076.92 2002-04-29,1076.32,1078.95,1063.62,1065.45,1314700000,1065.45 2002-04-26,1091.48,1096.77,1076.31,1076.32,1374200000,1076.32 2002-04-25,1093.14,1094.36,1084.81,1091.48,1517400000,1091.48 2002-04-24,1100.96,1108.46,1092.51,1093.14,1373200000,1093.14 2002-04-23,1107.83,1111.17,1098.94,1100.96,1388500000,1100.96 2002-04-22,1125.17,1125.17,1105.62,1107.83,1181800000,1107.83 2002-04-19,1124.47,1128.82,1122.59,1125.17,1185000000,1125.17 2002-04-18,1126.07,1130.49,1109.29,1124.47,1359300000,1124.47 2002-04-17,1128.37,1133.00,1123.37,1126.07,1376900000,1126.07 2002-04-16,1102.55,1129.40,1102.55,1128.37,1341300000,1128.37 2002-04-15,1111.01,1114.86,1099.41,1102.55,1120400000,1102.55 2002-04-12,1103.69,1112.77,1102.74,1111.01,1282100000,1111.01 2002-04-11,1130.47,1130.47,1102.42,1103.69,1505600000,1103.69 2002-04-10,1117.80,1131.76,1117.80,1130.47,1447900000,1130.47 2002-04-09,1125.29,1128.29,1116.73,1117.80,1235400000,1117.80 2002-04-08,1122.73,1125.41,1111.79,1125.29,1095300000,1125.29 2002-04-05,1126.34,1133.31,1119.49,1122.73,1110200000,1122.73 2002-04-04,1125.40,1130.45,1120.06,1126.34,1283800000,1126.34 2002-04-03,1136.76,1138.85,1119.68,1125.40,1219700000,1125.40 2002-04-02,1146.54,1146.54,1135.71,1136.76,1176700000,1136.76 2002-04-01,1147.39,1147.84,1132.87,1146.54,1050900000,1146.54 2002-03-28,1144.58,1154.45,1144.58,1147.39,1147600000,1147.39 2002-03-27,1138.49,1146.95,1135.33,1144.58,1180100000,1144.58 2002-03-26,1131.87,1147.00,1131.61,1138.49,1223600000,1138.49 2002-03-25,1148.70,1151.04,1131.87,1131.87,1057900000,1131.87 2002-03-22,1153.59,1156.49,1144.60,1148.70,1243300000,1148.70 2002-03-21,1151.85,1155.10,1139.48,1153.59,1339200000,1153.59 2002-03-20,1170.29,1170.29,1151.61,1151.85,1304900000,1151.85 2002-03-19,1165.55,1173.94,1165.55,1170.29,1255000000,1170.29 2002-03-18,1166.16,1172.73,1159.14,1165.55,1169500000,1165.55 2002-03-15,1153.04,1166.48,1153.04,1166.16,1493900000,1166.16 2002-03-14,1154.09,1157.83,1151.08,1153.04,1208800000,1153.04 2002-03-13,1165.58,1165.58,1151.01,1154.09,1354000000,1154.09 2002-03-12,1168.26,1168.26,1154.34,1165.58,1304400000,1165.58 2002-03-11,1164.31,1173.03,1159.58,1168.26,1210200000,1168.26 2002-03-08,1157.54,1172.76,1157.54,1164.31,1412000000,1164.31 2002-03-07,1162.77,1167.94,1150.69,1157.54,1517400000,1157.54 2002-03-06,1146.14,1165.29,1145.11,1162.77,1541300000,1162.77 2002-03-05,1153.84,1157.74,1144.78,1146.14,1549300000,1146.14 2002-03-04,1131.78,1153.84,1130.93,1153.84,1594300000,1153.84 2002-03-01,1106.73,1131.79,1106.73,1131.78,1456500000,1131.78 2002-02-28,1109.89,1121.57,1106.73,1106.73,1392200000,1106.73 2002-02-27,1109.38,1123.06,1102.26,1109.89,1393800000,1109.89 2002-02-26,1109.43,1115.05,1101.72,1109.38,1309200000,1109.38 2002-02-25,1089.84,1112.71,1089.84,1109.43,1367400000,1109.43 2002-02-22,1080.95,1093.93,1074.39,1089.84,1411000000,1089.84 2002-02-21,1097.98,1101.50,1080.24,1080.95,1381600000,1080.95 2002-02-20,1083.34,1098.32,1074.36,1097.98,1438900000,1097.98 2002-02-19,1104.18,1104.18,1082.24,1083.34,1189900000,1083.34 2002-02-15,1116.48,1117.09,1103.23,1104.18,1359200000,1104.18 2002-02-14,1118.51,1124.72,1112.30,1116.48,1272500000,1116.48 2002-02-13,1107.50,1120.56,1107.50,1118.51,1215900000,1118.51 2002-02-12,1111.94,1112.68,1102.98,1107.50,1094200000,1107.50 2002-02-11,1096.22,1112.01,1094.68,1111.94,1159400000,1111.94 2002-02-08,1080.17,1096.30,1079.91,1096.22,1371900000,1096.22 2002-02-07,1083.51,1094.03,1078.44,1080.17,1441600000,1080.17 2002-02-06,1090.02,1093.58,1077.78,1083.51,1665800000,1083.51 2002-02-05,1094.44,1100.96,1082.58,1090.02,1778300000,1090.02 2002-02-04,1122.20,1122.20,1092.25,1094.44,1437600000,1094.44 2002-02-01,1130.20,1130.20,1118.51,1122.20,1367200000,1122.20 2002-01-31,1113.57,1130.21,1113.30,1130.20,1557000000,1130.20 2002-01-30,1100.64,1113.79,1081.66,1113.57,2019600000,1113.57 2002-01-29,1133.06,1137.47,1098.74,1100.64,1812000000,1100.64 2002-01-28,1133.28,1138.63,1126.66,1133.06,1186800000,1133.06 2002-01-25,1132.15,1138.31,1127.82,1133.28,1345100000,1133.28 2002-01-24,1128.18,1139.50,1128.18,1132.15,1552800000,1132.15 2002-01-23,1119.31,1131.94,1117.43,1128.18,1479200000,1128.18 2002-01-22,1127.58,1135.26,1117.91,1119.31,1311600000,1119.31 2002-01-18,1138.88,1138.88,1124.45,1127.58,1333300000,1127.58 2002-01-17,1127.57,1139.27,1127.57,1138.88,1380100000,1138.88 2002-01-16,1146.19,1146.19,1127.49,1127.57,1482500000,1127.57 2002-01-15,1138.41,1148.81,1136.88,1146.19,1386900000,1146.19 2002-01-14,1145.60,1145.60,1138.15,1138.41,1286400000,1138.41 2002-01-11,1156.55,1159.41,1145.45,1145.60,1211900000,1145.60 2002-01-10,1155.14,1159.93,1150.85,1156.55,1299000000,1156.55 2002-01-09,1160.71,1174.26,1151.89,1155.14,1452000000,1155.14 2002-01-08,1164.89,1167.60,1157.46,1160.71,1258800000,1160.71 2002-01-07,1172.51,1176.97,1163.55,1164.89,1308300000,1164.89 2002-01-04,1165.27,1176.55,1163.42,1172.51,1513000000,1172.51 2002-01-03,1154.67,1165.27,1154.01,1165.27,1398900000,1165.27 2002-01-02,1148.08,1154.67,1136.23,1154.67,1171000000,1154.67 2001-12-31,1161.02,1161.16,1148.04,1148.08,943600000,1148.08 2001-12-28,1157.13,1164.64,1157.13,1161.02,917400000,1161.02 2001-12-27,1149.37,1157.13,1149.37,1157.13,876300000,1157.13 2001-12-26,1144.65,1159.18,1144.65,1149.37,791100000,1149.37 2001-12-24,1144.89,1147.83,1144.62,1144.65,439670000,1144.65 2001-12-21,1139.93,1147.46,1139.93,1144.89,1694000000,1144.89 2001-12-20,1149.56,1151.42,1139.93,1139.93,1490500000,1139.93 2001-12-19,1142.92,1152.44,1134.75,1149.56,1484900000,1149.56 2001-12-18,1134.36,1145.10,1134.36,1142.92,1354000000,1142.92 2001-12-17,1123.09,1137.30,1122.66,1134.36,1260400000,1134.36 2001-12-14,1119.38,1128.28,1114.53,1123.09,1306800000,1123.09 2001-12-13,1137.07,1137.07,1117.85,1119.38,1511500000,1119.38 2001-12-12,1136.76,1141.58,1126.01,1137.07,1449700000,1137.07 2001-12-11,1139.93,1150.89,1134.32,1136.76,1367200000,1136.76 2001-12-10,1158.31,1158.31,1139.66,1139.93,1218700000,1139.93 2001-12-07,1167.10,1167.10,1152.66,1158.31,1248200000,1158.31 2001-12-06,1170.35,1173.35,1164.43,1167.10,1487900000,1167.10 2001-12-05,1143.77,1173.62,1143.77,1170.35,1765300000,1170.35 2001-12-04,1129.90,1144.80,1128.86,1144.80,1318500000,1144.80 2001-12-03,1139.45,1139.45,1125.78,1129.90,1202900000,1129.90 2001-11-30,1140.20,1143.57,1135.89,1139.45,1343600000,1139.45 2001-11-29,1128.52,1140.40,1125.51,1140.20,1375700000,1140.20 2001-11-28,1149.50,1149.50,1128.29,1128.52,1423700000,1128.52 2001-11-27,1157.42,1163.38,1140.81,1149.50,1288000000,1149.50 2001-11-26,1150.34,1157.88,1146.17,1157.42,1129800000,1157.42 2001-11-23,1137.03,1151.05,1135.90,1150.34,410300000,1150.34 2001-11-21,1142.66,1142.66,1129.78,1137.03,1029300000,1137.03 2001-11-20,1151.06,1152.45,1142.17,1142.66,1330200000,1142.66 2001-11-19,1138.65,1151.06,1138.65,1151.06,1316800000,1151.06 2001-11-16,1142.24,1143.52,1129.92,1138.65,1337400000,1138.65 2001-11-15,1141.21,1146.46,1135.06,1142.24,1454500000,1142.24 2001-11-14,1139.09,1148.28,1132.87,1141.21,1443400000,1141.21 2001-11-13,1118.33,1139.14,1118.33,1139.09,1370100000,1139.09 2001-11-12,1120.31,1121.71,1098.32,1118.33,991600000,1118.33 2001-11-09,1118.54,1123.02,1111.13,1120.31,1093800000,1120.31 2001-11-08,1115.80,1135.75,1115.42,1118.54,1517500000,1118.54 2001-11-07,1118.86,1126.62,1112.98,1115.80,1411300000,1115.80 2001-11-06,1102.84,1119.73,1095.36,1118.86,1356000000,1118.86 2001-11-05,1087.20,1106.72,1087.20,1102.84,1267700000,1102.84 2001-11-02,1084.10,1089.63,1075.58,1087.20,1121900000,1087.20 2001-11-01,1059.78,1085.61,1054.31,1084.10,1317400000,1084.10 2001-10-31,1059.79,1074.79,1057.55,1059.78,1352500000,1059.78 2001-10-30,1078.30,1078.30,1053.61,1059.79,1297400000,1059.79 2001-10-29,1104.61,1104.61,1078.30,1078.30,1106100000,1078.30 2001-10-26,1100.09,1110.61,1094.24,1104.61,1244500000,1104.61 2001-10-25,1085.20,1100.09,1065.64,1100.09,1364400000,1100.09 2001-10-24,1084.78,1090.26,1079.98,1085.20,1336200000,1085.20 2001-10-23,1089.90,1098.99,1081.53,1084.78,1317300000,1084.78 2001-10-22,1073.48,1090.57,1070.79,1089.90,1105700000,1089.90 2001-10-19,1068.61,1075.52,1057.24,1073.48,1294900000,1073.48 2001-10-18,1077.09,1077.94,1064.54,1068.61,1262900000,1068.61 2001-10-17,1097.54,1107.12,1076.57,1077.09,1452200000,1077.09 2001-10-16,1089.98,1101.66,1087.13,1097.54,1210500000,1097.54 2001-10-15,1091.65,1091.65,1078.19,1089.98,1024700000,1089.98 2001-10-12,1097.43,1097.43,1072.15,1091.65,1331400000,1091.65 2001-10-11,1080.99,1099.16,1080.99,1097.43,1704580000,1097.43 2001-10-10,1056.75,1081.62,1052.76,1080.99,1312400000,1080.99 2001-10-09,1062.44,1063.37,1053.83,1056.75,1227800000,1056.75 2001-10-08,1071.37,1071.37,1056.88,1062.44,979000000,1062.44 2001-10-05,1069.62,1072.35,1053.50,1071.38,1301700000,1071.38 2001-10-04,1072.28,1084.12,1067.82,1069.63,1609100000,1069.63 2001-10-03,1051.33,1075.38,1041.48,1072.28,1650600000,1072.28 2001-10-02,1038.55,1051.33,1034.47,1051.33,1289800000,1051.33 2001-10-01,1040.94,1040.94,1026.76,1038.55,1175600000,1038.55 2001-09-28,1018.61,1040.94,1018.61,1040.94,1631500000,1040.94 2001-09-27,1007.04,1018.92,998.24,1018.61,1467000000,1018.61 2001-09-26,1012.27,1020.29,1002.62,1007.04,1519100000,1007.04 2001-09-25,1003.45,1017.14,998.33,1012.27,1613800000,1012.27 2001-09-24,965.80,1008.44,965.80,1003.45,1746600000,1003.45 2001-09-21,984.54,984.54,944.75,965.80,2317300000,965.80 2001-09-20,1016.10,1016.10,984.49,984.54,2004800000,984.54 2001-09-19,1032.74,1038.91,984.62,1016.10,2120550000,1016.10 2001-09-18,1038.77,1046.42,1029.25,1032.74,1650410000,1032.74 2001-09-17,1092.54,1092.54,1037.46,1038.77,2330830000,1038.77 2001-09-10,1085.78,1096.94,1073.15,1092.54,1276600000,1092.54 2001-09-07,1106.40,1106.40,1082.12,1085.78,1424300000,1085.78 2001-09-06,1131.74,1131.74,1105.83,1106.40,1359700000,1106.40 2001-09-05,1132.94,1135.52,1114.86,1131.74,1384500000,1131.74 2001-09-04,1133.58,1155.40,1129.06,1132.94,1178300000,1132.94 2001-08-31,1129.03,1141.83,1126.38,1133.58,920100000,1133.58 2001-08-30,1148.60,1151.75,1124.87,1129.03,1157000000,1129.03 2001-08-29,1161.51,1166.97,1147.38,1148.56,963700000,1148.56 2001-08-28,1179.21,1179.66,1161.17,1161.51,987100000,1161.51 2001-08-27,1184.93,1186.85,1178.07,1179.21,842600000,1179.21 2001-08-24,1162.09,1185.15,1162.09,1184.93,1043600000,1184.93 2001-08-23,1165.31,1169.86,1160.96,1162.09,986200000,1162.09 2001-08-22,1157.26,1168.56,1153.34,1165.31,1110800000,1165.31 2001-08-21,1171.41,1179.85,1156.56,1157.26,1041600000,1157.26 2001-08-20,1161.97,1171.41,1160.94,1171.41,897100000,1171.41 2001-08-17,1181.66,1181.66,1156.07,1161.97,974300000,1161.97 2001-08-16,1178.02,1181.80,1166.08,1181.66,1055400000,1181.66 2001-08-15,1186.73,1191.21,1177.61,1178.02,1065600000,1178.02 2001-08-14,1191.29,1198.79,1184.26,1186.73,964600000,1186.73 2001-08-13,1190.16,1193.82,1185.12,1191.29,837600000,1191.29 2001-08-10,1183.43,1193.33,1169.55,1190.16,960900000,1190.16 2001-08-09,1183.53,1184.71,1174.68,1183.43,1104200000,1183.43 2001-08-08,1204.40,1206.79,1181.27,1183.53,1124600000,1183.53 2001-08-07,1200.47,1207.56,1195.64,1204.40,1012000000,1204.40 2001-08-06,1214.35,1214.35,1197.35,1200.48,811700000,1200.48 2001-08-03,1220.75,1220.75,1205.31,1214.35,939900000,1214.35 2001-08-02,1215.93,1226.27,1215.31,1220.75,1218300000,1220.75 2001-08-01,1211.23,1223.04,1211.23,1215.93,1340300000,1215.93 2001-07-31,1204.52,1222.74,1204.52,1211.23,1129200000,1211.23 2001-07-30,1205.82,1209.05,1200.41,1204.52,909100000,1204.52 2001-07-27,1202.93,1209.26,1195.99,1205.82,1015300000,1205.82 2001-07-26,1190.49,1204.18,1182.65,1202.93,1213900000,1202.93 2001-07-25,1171.65,1190.52,1171.28,1190.49,1280700000,1190.49 2001-07-24,1191.03,1191.03,1165.54,1171.65,1198700000,1171.65 2001-07-23,1210.85,1215.22,1190.50,1191.03,986900000,1191.03 2001-07-20,1215.02,1215.69,1207.04,1210.85,1170900000,1210.85 2001-07-19,1207.71,1225.04,1205.80,1215.02,1343500000,1215.02 2001-07-18,1214.44,1214.44,1198.33,1207.71,1316300000,1207.71 2001-07-17,1202.45,1215.36,1196.14,1214.44,1238100000,1214.44 2001-07-16,1215.68,1219.63,1200.05,1202.45,1039800000,1202.45 2001-07-13,1208.14,1218.54,1203.61,1215.68,1121700000,1215.68 2001-07-12,1180.18,1210.25,1180.18,1208.14,1394000000,1208.14 2001-07-11,1181.52,1184.93,1168.46,1180.18,1384100000,1180.18 2001-07-10,1198.78,1203.43,1179.93,1181.52,1263800000,1181.52 2001-07-09,1190.59,1201.76,1189.75,1198.78,1045700000,1198.78 2001-07-06,1219.24,1219.24,1188.74,1190.59,1056700000,1190.59 2001-07-05,1234.45,1234.45,1219.15,1219.24,934900000,1219.24 2001-07-03,1236.71,1236.71,1229.43,1234.45,622110000,1234.45 2001-07-02,1224.42,1239.78,1224.03,1236.72,1128300000,1236.72 2001-06-29,1226.20,1237.29,1221.14,1224.38,1832360000,1224.38 2001-06-28,1211.07,1234.44,1211.07,1226.20,1327300000,1226.20 2001-06-27,1216.76,1219.92,1207.29,1211.07,1162100000,1211.07 2001-06-26,1218.60,1220.70,1204.64,1216.76,1198900000,1216.76 2001-06-25,1225.35,1231.50,1213.60,1218.60,1050100000,1218.60 2001-06-22,1237.04,1237.73,1221.41,1225.35,1189200000,1225.35 2001-06-21,1223.14,1240.24,1220.25,1237.04,1546820000,1237.04 2001-06-20,1212.58,1225.61,1210.07,1223.14,1350100000,1223.14 2001-06-19,1208.43,1226.11,1207.71,1212.58,1184900000,1212.58 2001-06-18,1214.36,1221.23,1208.33,1208.43,1111600000,1208.43 2001-06-15,1219.87,1221.50,1203.03,1214.36,1635550000,1214.36 2001-06-14,1241.60,1241.60,1218.90,1219.87,1242900000,1219.87 2001-06-13,1255.85,1259.75,1241.59,1241.60,1063600000,1241.60 2001-06-12,1254.39,1261.00,1235.75,1255.85,1136500000,1255.85 2001-06-11,1264.96,1264.96,1249.23,1254.39,870100000,1254.39 2001-06-08,1276.96,1277.11,1259.99,1264.96,726200000,1264.96 2001-06-07,1270.03,1277.08,1265.08,1276.96,1089600000,1276.96 2001-06-06,1283.57,1283.85,1269.01,1270.03,1061900000,1270.03 2001-06-05,1267.11,1286.62,1267.11,1283.57,1116800000,1283.57 2001-06-04,1260.67,1267.17,1256.36,1267.11,836500000,1267.11 2001-06-01,1255.82,1265.34,1246.88,1260.67,1015000000,1260.67 2001-05-31,1248.08,1261.91,1248.07,1255.82,1226600000,1255.82 2001-05-30,1267.93,1267.93,1245.96,1248.08,1158600000,1248.08 2001-05-29,1277.89,1278.42,1265.41,1267.93,1026000000,1267.93 2001-05-25,1293.17,1293.17,1276.42,1277.89,828100000,1277.89 2001-05-24,1289.05,1295.04,1281.22,1293.17,1100700000,1293.17 2001-05-23,1309.38,1309.38,1288.70,1289.05,1134800000,1289.05 2001-05-22,1312.83,1315.93,1306.89,1309.38,1260400000,1309.38 2001-05-21,1291.96,1312.95,1287.87,1312.83,1174900000,1312.83 2001-05-18,1288.49,1292.06,1281.15,1291.96,1130800000,1291.96 2001-05-17,1284.99,1296.48,1282.65,1288.49,1355600000,1288.49 2001-05-16,1249.44,1286.39,1243.02,1284.99,1405300000,1284.99 2001-05-15,1248.92,1257.45,1245.36,1249.44,1071800000,1249.44 2001-05-14,1245.67,1249.68,1241.02,1248.92,858200000,1248.92 2001-05-11,1255.18,1259.84,1240.79,1245.67,906200000,1245.67 2001-05-10,1255.54,1268.14,1254.56,1255.18,1056700000,1255.18 2001-05-09,1261.20,1261.65,1247.83,1255.54,1132400000,1255.54 2001-05-08,1266.71,1267.01,1253.00,1261.20,1006300000,1261.20 2001-05-07,1266.61,1270.00,1259.19,1263.51,949000000,1263.51 2001-05-04,1248.58,1267.51,1232.00,1266.61,1082100000,1266.61 2001-05-03,1267.43,1267.43,1239.88,1248.58,1137900000,1248.58 2001-05-02,1266.44,1272.93,1257.70,1267.43,1342200000,1267.43 2001-05-01,1249.46,1266.47,1243.55,1266.44,1181300000,1266.44 2001-04-30,1253.05,1269.30,1243.99,1249.46,1266800000,1249.46 2001-04-27,1234.52,1253.07,1234.52,1253.05,1091300000,1253.05 2001-04-26,1228.75,1248.30,1228.75,1234.52,1345200000,1234.52 2001-04-25,1209.47,1232.36,1207.38,1228.75,1203600000,1228.75 2001-04-24,1224.36,1233.54,1208.89,1209.47,1216500000,1209.47 2001-04-23,1242.98,1242.98,1217.47,1224.36,1012600000,1224.36 2001-04-20,1253.70,1253.70,1234.41,1242.98,1338700000,1242.98 2001-04-19,1238.16,1253.71,1233.39,1253.69,1486800000,1253.69 2001-04-18,1191.81,1248.42,1191.81,1238.16,1918900000,1238.16 2001-04-17,1179.68,1192.25,1168.90,1191.81,1109600000,1191.81 2001-04-16,1183.50,1184.64,1167.38,1179.68,913900000,1179.68 2001-04-12,1165.89,1183.51,1157.73,1183.50,1102000000,1183.50 2001-04-11,1168.38,1182.24,1160.26,1165.89,1290300000,1165.89 2001-04-10,1137.59,1173.92,1137.59,1168.38,1349600000,1168.38 2001-04-09,1128.43,1146.13,1126.38,1137.59,1062800000,1137.59 2001-04-06,1151.44,1151.44,1119.29,1128.43,1266800000,1128.43 2001-04-05,1103.25,1151.47,1103.25,1151.44,1368000000,1151.44 2001-04-04,1106.46,1117.50,1091.99,1103.25,1425590000,1103.25 2001-04-03,1145.87,1145.87,1100.19,1106.46,1386100000,1106.46 2001-04-02,1160.33,1169.51,1137.51,1145.87,1254900000,1145.87 2001-03-30,1147.95,1162.80,1143.83,1160.33,1280800000,1160.33 2001-03-29,1153.29,1161.69,1136.26,1147.95,1234500000,1147.95 2001-03-28,1182.17,1182.17,1147.83,1153.29,1333400000,1153.29 2001-03-27,1152.69,1183.35,1150.96,1182.17,1314200000,1182.17 2001-03-26,1139.83,1160.02,1139.83,1152.69,1114000000,1152.69 2001-03-23,1117.58,1141.83,1117.58,1139.83,1364900000,1139.83 2001-03-22,1122.14,1124.27,1081.19,1117.58,1723950000,1117.58 2001-03-21,1142.62,1149.39,1118.74,1122.14,1346300000,1122.14 2001-03-20,1170.81,1180.56,1142.19,1142.62,1235900000,1142.62 2001-03-19,1150.53,1173.50,1147.18,1170.81,1126200000,1170.81 2001-03-16,1173.56,1173.56,1148.64,1150.53,1543560000,1150.53 2001-03-15,1166.71,1182.04,1166.71,1173.56,1259500000,1173.56 2001-03-14,1197.66,1197.66,1155.35,1166.71,1397400000,1166.71 2001-03-13,1180.16,1197.83,1171.50,1197.66,1360900000,1197.66 2001-03-12,1233.42,1233.42,1176.78,1180.16,1229000000,1180.16 2001-03-09,1264.74,1264.74,1228.42,1233.42,1085900000,1233.42 2001-03-08,1261.89,1266.50,1257.60,1264.74,1114100000,1264.74 2001-03-07,1253.80,1263.86,1253.80,1261.89,1132200000,1261.89 2001-03-06,1241.41,1267.42,1241.41,1253.80,1091800000,1253.80 2001-03-05,1234.18,1242.55,1234.04,1241.41,929200000,1241.41 2001-03-02,1241.23,1251.01,1219.74,1234.18,1294000000,1234.18 2001-03-01,1239.94,1241.36,1214.50,1241.23,1294900000,1241.23 2001-02-28,1257.94,1263.47,1229.65,1239.94,1225300000,1239.94 2001-02-27,1267.65,1272.76,1252.26,1257.94,1114100000,1257.94 2001-02-26,1245.86,1267.69,1241.71,1267.65,1130800000,1267.65 2001-02-23,1252.82,1252.82,1215.44,1245.86,1231300000,1245.86 2001-02-22,1255.27,1259.94,1228.33,1252.82,1365900000,1252.82 2001-02-21,1278.94,1282.97,1253.16,1255.27,1208500000,1255.27 2001-02-20,1301.53,1307.16,1278.44,1278.94,1112200000,1278.94 2001-02-16,1326.61,1326.61,1293.18,1301.53,1257200000,1301.53 2001-02-15,1315.92,1331.29,1315.92,1326.61,1153700000,1326.61 2001-02-14,1318.80,1320.73,1304.72,1315.92,1150300000,1315.92 2001-02-13,1330.31,1336.62,1317.51,1318.80,1075200000,1318.80 2001-02-12,1314.76,1330.96,1313.64,1330.31,1039100000,1330.31 2001-02-09,1332.53,1332.53,1309.98,1314.76,1075500000,1314.76 2001-02-08,1341.10,1350.32,1332.42,1332.53,1107200000,1332.53 2001-02-07,1352.26,1352.26,1334.26,1340.89,1158300000,1340.89 2001-02-06,1354.31,1363.55,1350.04,1352.26,1059600000,1352.26 2001-02-05,1349.47,1354.56,1344.48,1354.31,1013000000,1354.31 2001-02-02,1373.47,1376.38,1348.72,1349.47,1048400000,1349.47 2001-02-01,1366.01,1373.50,1359.34,1373.47,1118800000,1373.47 2001-01-31,1373.73,1383.37,1364.66,1366.01,1295300000,1366.01 2001-01-30,1364.17,1375.68,1356.20,1373.73,1149800000,1373.73 2001-01-29,1354.92,1365.54,1350.36,1364.17,1053100000,1364.17 2001-01-26,1357.51,1357.51,1342.75,1354.95,1098000000,1354.95 2001-01-25,1364.30,1367.35,1354.63,1357.51,1258000000,1357.51 2001-01-24,1360.40,1369.75,1357.28,1364.30,1309000000,1364.30 2001-01-23,1342.90,1362.90,1339.63,1360.40,1232600000,1360.40 2001-01-22,1342.54,1353.62,1333.84,1342.90,1164000000,1342.90 2001-01-19,1347.97,1354.55,1336.74,1342.54,1407800000,1342.54 2001-01-18,1329.89,1352.71,1327.41,1347.97,1445000000,1347.97 2001-01-17,1326.65,1346.92,1325.41,1329.47,1349100000,1329.47 2001-01-16,1318.32,1327.81,1313.33,1326.65,1205700000,1326.65 2001-01-12,1326.82,1333.21,1311.59,1318.55,1276000000,1318.55 2001-01-11,1313.27,1332.19,1309.72,1326.82,1411200000,1326.82 2001-01-10,1300.80,1313.76,1287.28,1313.27,1296500000,1313.27 2001-01-09,1295.86,1311.72,1295.14,1300.80,1191300000,1300.80 2001-01-08,1298.35,1298.35,1276.29,1295.86,1115500000,1295.86 2001-01-05,1333.34,1334.77,1294.95,1298.35,1430800000,1298.35 2001-01-04,1347.56,1350.24,1329.14,1333.34,2131000000,1333.34 2001-01-03,1283.27,1347.76,1274.62,1347.56,1880700000,1347.56 2001-01-02,1320.28,1320.28,1276.05,1283.27,1129400000,1283.27 2000-12-29,1334.22,1340.10,1317.51,1320.28,1035500000,1320.28 2000-12-28,1328.92,1335.93,1325.78,1334.22,1015300000,1334.22 2000-12-27,1315.19,1332.03,1310.96,1328.92,1092700000,1328.92 2000-12-26,1305.97,1315.94,1301.64,1315.19,806500000,1315.19 2000-12-22,1274.86,1305.97,1274.86,1305.95,1087100000,1305.95 2000-12-21,1264.74,1285.31,1254.07,1274.86,1449900000,1274.86 2000-12-20,1305.60,1305.60,1261.16,1264.74,1421600000,1264.74 2000-12-19,1322.96,1346.44,1305.20,1305.60,1324900000,1305.60 2000-12-18,1312.15,1332.32,1312.15,1322.74,1189900000,1322.74 2000-12-15,1340.93,1340.93,1305.38,1312.15,1561100000,1312.15 2000-12-14,1359.99,1359.99,1340.48,1340.93,1061300000,1340.93 2000-12-13,1371.18,1385.82,1358.48,1359.99,1195100000,1359.99 2000-12-12,1380.20,1380.27,1370.27,1371.18,1083400000,1371.18 2000-12-11,1369.89,1389.05,1364.14,1380.20,1202400000,1380.20 2000-12-08,1343.55,1380.33,1343.55,1369.89,1358300000,1369.89 2000-12-07,1351.46,1353.50,1339.26,1343.55,1128000000,1343.55 2000-12-06,1376.54,1376.54,1346.15,1351.46,1399300000,1351.46 2000-12-05,1324.97,1376.56,1324.97,1376.54,900300000,1376.54 2000-12-04,1315.18,1332.06,1310.23,1324.97,1103000000,1324.97 2000-12-01,1314.95,1334.67,1307.02,1315.23,1195200000,1315.23 2000-11-30,1341.91,1341.91,1294.90,1314.95,1186530000,1314.95 2000-11-29,1336.09,1352.38,1329.28,1341.93,402100000,1341.93 2000-11-28,1348.97,1358.81,1334.97,1336.09,1028200000,1336.09 2000-11-27,1341.77,1362.50,1341.77,1348.97,946100000,1348.97 2000-11-24,1322.36,1343.83,1322.36,1341.77,404870000,1341.77 2000-11-22,1347.35,1347.35,1321.89,1322.36,963200000,1322.36 2000-11-21,1342.62,1355.87,1333.62,1347.35,1137100000,1347.35 2000-11-20,1367.72,1367.72,1341.67,1342.62,955800000,1342.62 2000-11-17,1372.32,1384.85,1355.55,1367.72,1070400000,1367.72 2000-11-16,1389.81,1394.76,1370.39,1372.32,956300000,1372.32 2000-11-15,1382.95,1395.96,1374.75,1389.81,1066800000,1389.81 2000-11-14,1351.26,1390.06,1351.26,1382.95,1118800000,1382.95 2000-11-13,1365.98,1365.98,1328.62,1351.26,1129300000,1351.26 2000-11-10,1400.14,1400.14,1365.97,1365.98,962500000,1365.98 2000-11-09,1409.28,1409.28,1369.68,1400.14,1111000000,1400.14 2000-11-08,1431.87,1437.28,1408.78,1409.28,909300000,1409.28 2000-11-07,1432.19,1436.22,1423.26,1431.87,880900000,1431.87 2000-11-06,1428.76,1438.46,1427.72,1432.19,930900000,1432.19 2000-11-03,1428.32,1433.21,1420.92,1426.69,997700000,1426.69 2000-11-02,1421.22,1433.40,1421.22,1428.32,1167700000,1428.32 2000-11-01,1429.40,1429.60,1410.45,1421.22,1206800000,1421.22 2000-10-31,1398.66,1432.22,1398.66,1429.40,1366400000,1429.40 2000-10-30,1379.58,1406.36,1376.86,1398.66,1186500000,1398.66 2000-10-27,1364.44,1384.57,1364.13,1379.58,1086300000,1379.58 2000-10-26,1364.90,1372.72,1337.81,1364.44,1303800000,1364.44 2000-10-25,1398.13,1398.13,1362.21,1364.90,1315600000,1364.90 2000-10-24,1395.78,1415.64,1388.13,1398.13,1158600000,1398.13 2000-10-23,1396.93,1406.96,1387.75,1395.78,1046800000,1395.78 2000-10-20,1388.76,1408.47,1382.19,1396.93,1177400000,1396.93 2000-10-19,1342.13,1389.93,1342.13,1388.76,1297900000,1388.76 2000-10-18,1349.97,1356.65,1305.79,1342.13,1441700000,1342.13 2000-10-17,1374.62,1380.99,1342.34,1349.97,1161500000,1349.97 2000-10-16,1374.17,1379.48,1365.06,1374.62,1005400000,1374.62 2000-10-13,1329.78,1374.17,1327.08,1374.17,1223900000,1374.17 2000-10-12,1364.59,1374.93,1328.06,1329.78,1388600000,1329.78 2000-10-11,1387.02,1387.02,1349.67,1364.59,1387500000,1364.59 2000-10-10,1402.03,1408.83,1383.85,1387.02,1044000000,1387.02 2000-10-09,1408.99,1409.69,1392.48,1402.03,716600000,1402.03 2000-10-06,1436.28,1443.30,1397.06,1408.99,1150100000,1408.99 2000-10-05,1434.32,1444.17,1431.80,1436.28,1176100000,1436.28 2000-10-04,1426.46,1439.99,1416.31,1434.32,1167400000,1434.32 2000-10-03,1436.23,1454.82,1425.28,1426.46,1098100000,1426.46 2000-10-02,1436.52,1445.60,1429.83,1436.23,1051200000,1436.23 2000-09-29,1458.29,1458.29,1436.29,1436.51,1197100000,1436.51 2000-09-28,1426.57,1461.69,1425.78,1458.29,1206200000,1458.29 2000-09-27,1427.21,1437.22,1419.44,1426.57,1174700000,1426.57 2000-09-26,1439.03,1448.04,1425.25,1427.21,1106600000,1427.21 2000-09-25,1448.72,1457.42,1435.93,1439.03,982400000,1439.03 2000-09-22,1449.05,1449.05,1421.88,1448.72,1185500000,1448.72 2000-09-21,1451.34,1452.77,1436.30,1449.05,1105400000,1449.05 2000-09-20,1459.90,1460.49,1430.95,1451.34,1104000000,1451.34 2000-09-19,1444.51,1461.16,1444.51,1459.90,1024900000,1459.90 2000-09-18,1465.81,1467.77,1441.92,1444.51,962500000,1444.51 2000-09-15,1480.87,1480.96,1460.22,1465.81,1268400000,1465.81 2000-09-14,1484.91,1494.16,1476.73,1480.87,1014000000,1480.87 2000-09-13,1481.99,1487.45,1473.61,1484.91,1068300000,1484.91 2000-09-12,1489.26,1496.93,1479.67,1481.99,991200000,1481.99 2000-09-11,1494.50,1506.76,1483.01,1489.26,899300000,1489.26 2000-09-08,1502.51,1502.51,1489.88,1494.50,961000000,1494.50 2000-09-07,1492.25,1505.34,1492.25,1502.51,985500000,1502.51 2000-09-06,1507.08,1512.61,1492.12,1492.25,995100000,1492.25 2000-09-05,1520.77,1520.77,1504.21,1507.08,838500000,1507.08 2000-09-01,1517.68,1530.09,1515.53,1520.77,767700000,1520.77 2000-08-31,1502.59,1525.21,1502.59,1517.68,1056600000,1517.68 2000-08-30,1509.84,1510.49,1500.09,1502.59,818400000,1502.59 2000-08-29,1514.09,1514.81,1505.46,1509.84,795600000,1509.84 2000-08-28,1506.45,1523.95,1506.45,1514.09,733600000,1514.09 2000-08-25,1508.31,1513.47,1505.09,1506.45,685600000,1506.45 2000-08-24,1505.97,1511.16,1501.25,1508.31,837100000,1508.31 2000-08-23,1498.13,1507.20,1489.52,1505.97,871000000,1505.97 2000-08-22,1499.48,1508.45,1497.42,1498.13,818800000,1498.13 2000-08-21,1491.72,1502.84,1491.13,1499.48,731600000,1499.48 2000-08-18,1496.07,1499.47,1488.99,1491.72,821400000,1491.72 2000-08-17,1479.85,1499.32,1479.85,1496.07,922400000,1496.07 2000-08-16,1484.43,1496.09,1475.74,1479.85,929800000,1479.85 2000-08-15,1491.56,1493.12,1482.74,1484.43,895900000,1484.43 2000-08-14,1471.84,1491.64,1468.56,1491.56,783800000,1491.56 2000-08-11,1460.25,1475.72,1453.06,1471.84,835500000,1471.84 2000-08-10,1472.87,1475.15,1459.89,1460.25,940800000,1460.25 2000-08-09,1482.80,1490.33,1471.16,1472.87,1054000000,1472.87 2000-08-08,1479.32,1484.52,1472.61,1482.80,992200000,1482.80 2000-08-07,1462.93,1480.80,1460.72,1479.32,854800000,1479.32 2000-08-04,1452.56,1462.93,1451.31,1462.93,956000000,1462.93 2000-08-03,1438.70,1454.19,1425.43,1452.56,1095600000,1452.56 2000-08-02,1438.10,1451.59,1433.49,1438.70,994500000,1438.70 2000-08-01,1430.83,1443.54,1428.96,1438.10,938700000,1438.10 2000-07-31,1419.89,1437.65,1418.71,1430.83,952600000,1430.83 2000-07-28,1449.62,1456.68,1413.89,1419.89,980000000,1419.89 2000-07-27,1452.42,1464.91,1445.33,1449.62,1156400000,1449.62 2000-07-26,1474.47,1474.47,1452.42,1452.42,1235800000,1452.42 2000-07-25,1464.29,1476.23,1464.29,1474.47,969400000,1474.47 2000-07-24,1480.19,1485.88,1463.80,1464.29,880300000,1464.29 2000-07-21,1495.57,1495.57,1477.91,1480.19,968300000,1480.19 2000-07-20,1481.96,1501.92,1481.96,1495.57,1064600000,1495.57 2000-07-19,1493.74,1495.63,1479.92,1481.96,909400000,1481.96 2000-07-18,1510.49,1510.49,1491.35,1493.74,908300000,1493.74 2000-07-17,1509.98,1517.32,1505.26,1510.49,906000000,1510.49 2000-07-14,1495.84,1509.99,1494.56,1509.98,960600000,1509.98 2000-07-13,1492.92,1501.39,1489.65,1495.84,1026800000,1495.84 2000-07-12,1480.88,1497.69,1480.88,1492.92,1001200000,1492.92 2000-07-11,1475.62,1488.77,1470.48,1480.88,980500000,1480.88 2000-07-10,1478.90,1486.56,1474.76,1475.62,838700000,1475.62 2000-07-07,1456.67,1484.12,1456.67,1478.90,931700000,1478.90 2000-07-06,1446.23,1461.65,1439.56,1456.67,947300000,1456.67 2000-07-05,1469.54,1469.54,1442.45,1446.23,1019300000,1446.23 2000-07-03,1454.60,1469.58,1450.85,1469.54,451900000,1469.54 2000-06-30,1442.39,1454.68,1438.71,1454.60,1459700000,1454.60 2000-06-29,1454.82,1455.14,1434.63,1442.39,1110900000,1442.39 2000-06-28,1450.55,1467.63,1450.55,1454.82,1095100000,1454.82 2000-06-27,1455.31,1463.35,1450.55,1450.55,1042500000,1450.55 2000-06-26,1441.48,1459.66,1441.48,1455.31,889000000,1455.31 2000-06-23,1452.18,1459.94,1438.31,1441.48,847600000,1441.48 2000-06-22,1479.13,1479.13,1448.03,1452.18,1022700000,1452.18 2000-06-21,1475.95,1482.19,1468.00,1479.13,1009600000,1479.13 2000-06-20,1486.00,1487.32,1470.18,1475.95,1031500000,1475.95 2000-06-19,1464.46,1488.93,1459.05,1486.00,921700000,1486.00 2000-06-16,1478.73,1480.77,1460.42,1464.46,1250800000,1464.46 2000-06-15,1470.54,1482.04,1464.62,1478.73,1011400000,1478.73 2000-06-14,1469.44,1483.62,1467.71,1470.54,929700000,1470.54 2000-06-13,1446.00,1470.42,1442.38,1469.44,935900000,1469.44 2000-06-12,1456.95,1462.93,1445.99,1446.00,774100000,1446.00 2000-06-09,1461.67,1472.67,1454.96,1456.95,786000000,1456.95 2000-06-08,1471.36,1475.65,1456.49,1461.67,854300000,1461.67 2000-06-07,1457.84,1474.64,1455.06,1471.36,854600000,1471.36 2000-06-06,1467.63,1471.36,1454.74,1457.84,950100000,1457.84 2000-06-05,1477.26,1477.28,1464.68,1467.63,838600000,1467.63 2000-06-02,1448.81,1483.23,1448.81,1477.26,1162400000,1477.26 2000-06-01,1420.60,1448.81,1420.60,1448.81,960100000,1448.81 2000-05-31,1422.44,1434.49,1415.50,1420.60,960500000,1420.60 2000-05-30,1378.02,1422.45,1378.02,1422.45,844200000,1422.45 2000-05-26,1381.52,1391.42,1369.75,1378.02,722600000,1378.02 2000-05-25,1399.05,1411.65,1373.93,1381.52,984500000,1381.52 2000-05-24,1373.86,1401.75,1361.09,1399.05,1152300000,1399.05 2000-05-23,1400.72,1403.77,1373.43,1373.86,869900000,1373.86 2000-05-22,1406.95,1410.55,1368.73,1400.72,869000000,1400.72 2000-05-19,1437.21,1437.21,1401.74,1406.95,853700000,1406.95 2000-05-18,1447.80,1458.04,1436.59,1437.21,807900000,1437.21 2000-05-17,1466.04,1466.04,1441.67,1447.80,820500000,1447.80 2000-05-16,1452.36,1470.40,1450.76,1466.04,955500000,1466.04 2000-05-15,1420.96,1452.39,1416.54,1452.36,854600000,1452.36 2000-05-12,1407.81,1430.13,1407.81,1420.96,858200000,1420.96 2000-05-11,1383.05,1410.26,1383.05,1407.81,953600000,1407.81 2000-05-10,1412.14,1412.14,1375.14,1383.05,1006400000,1383.05 2000-05-09,1424.17,1430.28,1401.85,1412.14,896600000,1412.14 2000-05-08,1432.63,1432.63,1417.05,1424.17,787600000,1424.17 2000-05-05,1409.57,1436.03,1405.08,1432.63,805500000,1432.63 2000-05-04,1415.10,1420.99,1404.94,1409.57,925800000,1409.57 2000-05-03,1446.29,1446.29,1398.36,1415.10,991600000,1415.10 2000-05-02,1468.25,1468.25,1445.22,1446.29,1011500000,1446.29 2000-05-01,1452.43,1481.51,1452.43,1468.25,966300000,1468.25 2000-04-28,1464.92,1473.62,1448.15,1452.43,984600000,1452.43 2000-04-27,1460.99,1469.21,1434.81,1464.92,1111000000,1464.92 2000-04-26,1477.44,1482.94,1456.98,1460.99,999600000,1460.99 2000-04-25,1429.86,1477.67,1429.86,1477.44,1071100000,1477.44 2000-04-24,1434.54,1434.54,1407.13,1429.86,868700000,1429.86 2000-04-20,1427.47,1435.49,1422.08,1434.54,896200000,1434.54 2000-04-19,1441.61,1447.69,1424.26,1427.47,1001400000,1427.47 2000-04-18,1401.44,1441.61,1397.81,1441.61,1109400000,1441.61 2000-04-17,1356.56,1401.53,1346.50,1401.44,1204700000,1401.44 2000-04-14,1440.51,1440.51,1339.40,1356.56,1279700000,1356.56 2000-04-13,1467.17,1477.52,1439.34,1440.51,1032000000,1440.51 2000-04-12,1500.59,1509.08,1466.15,1467.17,1175900000,1467.17 2000-04-11,1504.46,1512.80,1486.78,1500.59,971400000,1500.59 2000-04-10,1516.35,1527.19,1503.35,1504.46,853700000,1504.46 2000-04-07,1501.34,1518.68,1501.34,1516.35,891600000,1516.35 2000-04-06,1487.37,1511.76,1487.37,1501.34,1008000000,1501.34 2000-04-05,1494.73,1506.55,1478.05,1487.37,1110300000,1487.37 2000-04-04,1505.98,1526.45,1416.41,1494.73,1515460000,1494.73 2000-04-03,1498.58,1507.19,1486.96,1505.97,1021700000,1505.97 2000-03-31,1487.92,1519.81,1484.38,1498.58,1227400000,1498.58 2000-03-30,1508.52,1517.38,1474.63,1487.92,1193400000,1487.92 2000-03-29,1507.73,1521.45,1497.45,1508.52,1061900000,1508.52 2000-03-28,1523.86,1527.36,1507.09,1507.73,959100000,1507.73 2000-03-27,1527.46,1534.63,1518.46,1523.86,901000000,1523.86 2000-03-24,1527.35,1552.87,1516.83,1527.46,1052200000,1527.46 2000-03-23,1500.64,1532.50,1492.39,1527.35,1078300000,1527.35 2000-03-22,1493.87,1505.08,1487.33,1500.64,1075000000,1500.64 2000-03-21,1456.63,1493.92,1446.06,1493.87,1065900000,1493.87 2000-03-20,1464.47,1470.30,1448.49,1456.63,920800000,1456.63 2000-03-17,1458.47,1477.33,1453.32,1464.47,1295100000,1464.47 2000-03-16,1392.15,1458.47,1392.15,1458.47,1482300000,1458.47 2000-03-15,1359.15,1397.99,1356.99,1392.14,1302800000,1392.14 2000-03-14,1383.62,1395.15,1359.15,1359.15,1094000000,1359.15 2000-03-13,1395.07,1398.39,1364.84,1383.62,1016100000,1383.62 2000-03-10,1401.69,1413.46,1392.07,1395.07,1138800000,1395.07 2000-03-09,1366.70,1401.82,1357.88,1401.69,1123000000,1401.69 2000-03-08,1355.62,1373.79,1346.62,1366.70,1203000000,1366.70 2000-03-07,1391.28,1399.21,1349.99,1355.62,1314100000,1355.62 2000-03-06,1409.17,1409.74,1384.75,1391.28,1029000000,1391.28 2000-03-03,1381.76,1410.88,1381.76,1409.17,1150300000,1409.17 2000-03-02,1379.19,1386.56,1370.35,1381.76,1198600000,1381.76 2000-03-01,1366.42,1383.46,1366.42,1379.19,1274100000,1379.19 2000-02-29,1348.05,1369.63,1348.05,1366.42,1204300000,1366.42 2000-02-28,1333.36,1360.82,1325.07,1348.05,1026500000,1348.05 2000-02-25,1353.43,1362.14,1329.15,1333.36,1065200000,1333.36 2000-02-24,1360.69,1364.80,1329.88,1353.43,1215000000,1353.43 2000-02-23,1352.17,1370.11,1342.44,1360.69,993700000,1360.69 2000-02-22,1346.09,1358.11,1331.88,1352.17,980000000,1352.17 2000-02-18,1388.26,1388.59,1345.32,1346.09,1042300000,1346.09 2000-02-17,1387.67,1399.88,1380.07,1388.26,1034800000,1388.26 2000-02-16,1402.05,1404.55,1385.58,1387.67,1018800000,1387.67 2000-02-15,1389.94,1407.72,1376.25,1402.05,1092100000,1402.05 2000-02-14,1387.12,1394.93,1380.53,1389.94,927300000,1389.94 2000-02-11,1416.83,1416.83,1378.89,1387.12,1025700000,1387.12 2000-02-10,1411.70,1422.10,1406.43,1416.83,1058800000,1416.83 2000-02-09,1441.72,1444.55,1411.65,1411.71,1050500000,1411.71 2000-02-08,1424.24,1441.83,1424.24,1441.72,1047700000,1441.72 2000-02-07,1424.37,1427.15,1413.33,1424.24,918100000,1424.24 2000-02-04,1424.97,1435.91,1420.63,1424.37,1045100000,1424.37 2000-02-03,1409.12,1425.78,1398.52,1424.97,1146500000,1424.97 2000-02-02,1409.28,1420.61,1403.49,1409.12,1038600000,1409.12 2000-02-01,1394.46,1412.49,1384.79,1409.28,981000000,1409.28 2000-01-31,1360.16,1394.48,1350.14,1394.46,993800000,1394.46 2000-01-28,1398.56,1398.56,1356.20,1360.16,1095800000,1360.16 2000-01-27,1404.09,1418.86,1370.99,1398.56,1129500000,1398.56 2000-01-26,1410.03,1412.73,1400.16,1404.09,1117300000,1404.09 2000-01-25,1401.53,1414.26,1388.49,1410.03,1073700000,1410.03 2000-01-24,1441.36,1454.09,1395.42,1401.53,1115800000,1401.53 2000-01-21,1445.57,1453.18,1439.60,1441.36,1209800000,1441.36 2000-01-20,1455.90,1465.71,1438.54,1445.57,1100700000,1445.57 2000-01-19,1455.14,1461.39,1448.68,1455.90,1087800000,1455.90 2000-01-18,1465.15,1465.15,1451.30,1455.14,1056700000,1455.14 2000-01-14,1449.68,1473.00,1449.68,1465.15,1085900000,1465.15 2000-01-13,1432.25,1454.20,1432.25,1449.68,1030400000,1449.68 2000-01-12,1438.56,1442.60,1427.08,1432.25,974600000,1432.25 2000-01-11,1457.60,1458.66,1434.42,1438.56,1014000000,1438.56 2000-01-10,1441.47,1464.36,1441.47,1457.60,1064800000,1457.60 2000-01-07,1403.45,1441.47,1400.73,1441.47,1225200000,1441.47 2000-01-06,1402.11,1411.90,1392.10,1403.45,1092300000,1403.45 2000-01-05,1399.42,1413.27,1377.68,1402.11,1085500000,1402.11 2000-01-04,1455.22,1455.22,1397.43,1399.42,1009000000,1399.42 2000-01-03,1469.25,1478.00,1438.36,1455.22,931800000,1455.22 1999-12-31,1464.47,1472.42,1458.19,1469.25,374050000,1469.25 1999-12-30,1463.46,1473.10,1462.60,1464.47,554680000,1464.47 1999-12-29,1457.66,1467.47,1457.66,1463.46,567860000,1463.46 1999-12-28,1457.09,1462.68,1452.78,1457.66,655400000,1457.66 1999-12-27,1458.34,1463.19,1450.83,1457.10,722600000,1457.10 1999-12-23,1436.13,1461.44,1436.13,1458.34,728600000,1458.34 1999-12-22,1433.43,1440.02,1429.13,1436.13,850000000,1436.13 1999-12-21,1418.09,1436.47,1414.80,1433.43,963500000,1433.43 1999-12-20,1421.03,1429.16,1411.10,1418.09,904600000,1418.09 1999-12-17,1418.78,1431.77,1418.78,1421.03,1349800000,1421.03 1999-12-16,1413.32,1423.11,1408.35,1418.78,1070300000,1418.78 1999-12-15,1403.17,1417.40,1396.20,1413.33,1033900000,1413.33 1999-12-14,1415.22,1418.30,1401.59,1403.17,1027800000,1403.17 1999-12-13,1417.04,1421.58,1410.10,1415.22,977600000,1415.22 1999-12-10,1408.11,1421.58,1405.65,1417.04,987200000,1417.04 1999-12-09,1403.88,1418.43,1391.47,1408.11,1122100000,1408.11 1999-12-08,1409.17,1415.66,1403.88,1403.88,957000000,1403.88 1999-12-07,1423.33,1426.81,1409.17,1409.17,1085800000,1409.17 1999-12-06,1433.30,1434.15,1418.25,1423.33,916800000,1423.33 1999-12-03,1409.04,1447.42,1409.04,1433.30,1006400000,1433.30 1999-12-02,1397.72,1409.04,1397.72,1409.04,900700000,1409.04 1999-12-01,1388.91,1400.12,1387.38,1397.72,884000000,1397.72 1999-11-30,1407.83,1410.59,1386.95,1388.91,951500000,1388.91 1999-11-29,1416.62,1416.62,1404.15,1407.83,866100000,1407.83 1999-11-26,1417.08,1425.24,1416.14,1416.62,312120000,1416.62 1999-11-24,1404.64,1419.71,1399.17,1417.08,734800000,1417.08 1999-11-23,1420.94,1423.91,1402.20,1404.64,926100000,1404.64 1999-11-22,1422.00,1425.00,1412.40,1420.94,873500000,1420.94 1999-11-19,1424.94,1424.94,1417.54,1422.00,893800000,1422.00 1999-11-18,1410.71,1425.31,1410.71,1424.94,1022800000,1424.94 1999-11-17,1420.07,1423.44,1410.69,1410.71,960000000,1410.71 1999-11-16,1394.39,1420.36,1394.39,1420.07,942200000,1420.07 1999-11-15,1396.06,1398.58,1392.28,1394.39,795700000,1394.39 1999-11-12,1381.46,1396.12,1368.54,1396.06,900200000,1396.06 1999-11-11,1373.46,1382.12,1372.19,1381.46,891300000,1381.46 1999-11-10,1365.28,1379.18,1359.98,1373.46,984700000,1373.46 1999-11-09,1377.01,1383.81,1361.45,1365.28,854300000,1365.28 1999-11-08,1370.23,1380.78,1365.87,1377.01,806800000,1377.01 1999-11-05,1362.64,1387.48,1362.64,1370.23,1007300000,1370.23 1999-11-04,1354.93,1369.41,1354.93,1362.64,981700000,1362.64 1999-11-03,1347.74,1360.33,1347.74,1354.93,914400000,1354.93 1999-11-02,1354.12,1369.32,1346.41,1347.74,904500000,1347.74 1999-11-01,1362.93,1367.30,1354.05,1354.12,861000000,1354.12 1999-10-29,1342.44,1373.17,1342.44,1362.93,1120500000,1362.93 1999-10-28,1296.71,1342.47,1296.71,1342.44,1135100000,1342.44 1999-10-27,1281.91,1299.39,1280.48,1296.71,950100000,1296.71 1999-10-26,1293.63,1303.46,1281.86,1281.91,878300000,1281.91 1999-10-25,1301.65,1301.68,1286.07,1293.63,777000000,1293.63 1999-10-22,1283.61,1308.81,1283.61,1301.65,959200000,1301.65 1999-10-21,1289.43,1289.43,1265.61,1283.61,1012500000,1283.61 1999-10-20,1261.32,1289.44,1261.32,1289.43,928800000,1289.43 1999-10-19,1254.13,1279.32,1254.13,1261.32,905700000,1261.32 1999-10-18,1247.41,1254.13,1233.70,1254.13,818700000,1254.13 1999-10-15,1283.42,1283.42,1245.39,1247.41,912600000,1247.41 1999-10-14,1285.55,1289.63,1267.62,1283.42,892300000,1283.42 1999-10-13,1313.04,1313.04,1282.80,1285.55,821500000,1285.55 1999-10-12,1335.21,1335.21,1311.80,1313.04,778300000,1313.04 1999-10-11,1336.02,1339.23,1332.96,1335.21,655900000,1335.21 1999-10-08,1317.64,1336.61,1311.88,1336.02,897300000,1336.02 1999-10-07,1325.40,1328.05,1314.13,1317.64,827800000,1317.64 1999-10-06,1301.35,1325.46,1301.35,1325.40,895200000,1325.40 1999-10-05,1304.60,1316.41,1286.44,1301.35,965700000,1301.35 1999-10-04,1282.81,1304.60,1282.81,1304.60,803300000,1304.60 1999-10-01,1282.71,1283.17,1265.78,1282.81,896200000,1282.81 1999-09-30,1268.37,1291.31,1268.37,1282.71,1017600000,1282.71 1999-09-29,1282.20,1288.83,1268.16,1268.37,856000000,1268.37 1999-09-28,1283.31,1285.55,1256.26,1282.20,885400000,1282.20 1999-09-27,1277.36,1295.03,1277.36,1283.31,780600000,1283.31 1999-09-24,1280.41,1281.17,1263.84,1277.36,872800000,1277.36 1999-09-23,1310.51,1315.25,1277.30,1280.41,890800000,1280.41 1999-09-22,1307.58,1316.18,1297.81,1310.51,822200000,1310.51 1999-09-21,1335.52,1335.53,1301.97,1307.58,817300000,1307.58 1999-09-20,1335.42,1338.38,1330.61,1335.53,568000000,1335.53 1999-09-17,1318.48,1337.59,1318.48,1335.42,861900000,1335.42 1999-09-16,1317.97,1322.51,1299.97,1318.48,739000000,1318.48 1999-09-15,1336.29,1347.21,1317.97,1317.97,787300000,1317.97 1999-09-14,1344.13,1344.18,1330.61,1336.29,734500000,1336.29 1999-09-13,1351.66,1351.66,1341.70,1344.13,657900000,1344.13 1999-09-10,1347.66,1357.62,1346.20,1351.66,808500000,1351.66 1999-09-09,1344.15,1347.66,1333.91,1347.66,773900000,1347.66 1999-09-08,1350.45,1355.18,1337.36,1344.15,791200000,1344.15 1999-09-07,1357.24,1361.39,1349.59,1350.45,715300000,1350.45 1999-09-03,1319.11,1357.74,1319.11,1357.24,663200000,1357.24 1999-09-02,1331.07,1331.07,1304.88,1319.11,687100000,1319.11 1999-09-01,1320.41,1331.18,1320.39,1331.07,708200000,1331.07 1999-08-31,1324.02,1333.27,1306.96,1320.41,861700000,1320.41 1999-08-30,1348.27,1350.70,1322.80,1324.02,597900000,1324.02 1999-08-27,1362.01,1365.63,1347.35,1348.27,570050000,1348.27 1999-08-26,1381.79,1381.79,1361.53,1362.01,719000000,1362.01 1999-08-25,1363.50,1382.84,1359.20,1381.79,864600000,1381.79 1999-08-24,1360.22,1373.32,1353.63,1363.50,732700000,1363.50 1999-08-23,1336.61,1360.24,1336.61,1360.22,682600000,1360.22 1999-08-20,1323.59,1336.61,1323.59,1336.61,661200000,1336.61 1999-08-19,1332.84,1332.84,1315.35,1323.59,684200000,1323.59 1999-08-18,1344.16,1344.16,1332.13,1332.84,682800000,1332.84 1999-08-17,1330.77,1344.16,1328.76,1344.16,691500000,1344.16 1999-08-16,1327.68,1331.05,1320.51,1330.77,583550000,1330.77 1999-08-13,1298.16,1327.72,1298.16,1327.68,691700000,1327.68 1999-08-12,1301.93,1313.61,1298.06,1298.16,745600000,1298.16 1999-08-11,1281.43,1301.93,1281.43,1301.93,792300000,1301.93 1999-08-10,1297.80,1298.62,1267.73,1281.43,836200000,1281.43 1999-08-09,1300.29,1306.68,1295.99,1297.80,684300000,1297.80 1999-08-06,1313.71,1316.74,1293.19,1300.29,698900000,1300.29 1999-08-05,1305.33,1313.71,1287.23,1313.71,859300000,1313.71 1999-08-04,1322.18,1330.16,1304.50,1305.33,789300000,1305.33 1999-08-03,1328.05,1336.13,1314.91,1322.18,739600000,1322.18 1999-08-02,1328.72,1344.69,1325.21,1328.05,649550000,1328.05 1999-07-30,1341.03,1350.92,1328.49,1328.72,736800000,1328.72 1999-07-29,1365.40,1365.40,1332.82,1341.03,770100000,1341.03 1999-07-28,1362.84,1370.53,1355.54,1365.40,690900000,1365.40 1999-07-27,1347.75,1368.70,1347.75,1362.84,723800000,1362.84 1999-07-26,1356.94,1358.61,1346.20,1347.76,613450000,1347.76 1999-07-23,1360.97,1367.41,1349.91,1356.94,630580000,1356.94 1999-07-22,1379.29,1379.29,1353.98,1360.97,771700000,1360.97 1999-07-21,1377.10,1386.66,1372.63,1379.29,785500000,1379.29 1999-07-20,1407.65,1407.65,1375.15,1377.10,754800000,1377.10 1999-07-19,1418.78,1420.33,1404.56,1407.65,642330000,1407.65 1999-07-16,1409.62,1418.78,1407.07,1418.78,714100000,1418.78 1999-07-15,1398.17,1409.84,1398.17,1409.62,818800000,1409.62 1999-07-14,1393.56,1400.05,1386.51,1398.17,756100000,1398.17 1999-07-13,1399.10,1399.10,1386.84,1393.56,736000000,1393.56 1999-07-12,1403.28,1406.82,1394.70,1399.10,685300000,1399.10 1999-07-09,1394.42,1403.28,1394.42,1403.28,701000000,1403.28 1999-07-08,1395.86,1403.25,1386.69,1394.42,830600000,1394.42 1999-07-07,1388.12,1395.88,1384.95,1395.86,791200000,1395.86 1999-07-06,1391.22,1405.29,1387.08,1388.12,722900000,1388.12 1999-07-02,1380.96,1391.22,1379.57,1391.22,613570000,1391.22 1999-07-01,1372.71,1382.80,1360.80,1380.96,843400000,1380.96 1999-06-30,1351.45,1372.93,1338.78,1372.71,1117000000,1372.71 1999-06-29,1331.35,1351.51,1328.40,1351.45,820100000,1351.45 1999-06-28,1315.31,1333.68,1315.31,1331.35,652910000,1331.35 1999-06-25,1315.78,1329.13,1312.64,1315.31,623460000,1315.31 1999-06-24,1333.06,1333.06,1308.47,1315.78,690400000,1315.78 1999-06-23,1335.87,1335.88,1322.55,1333.06,731800000,1333.06 1999-06-22,1349.00,1351.12,1335.52,1335.88,716500000,1335.88 1999-06-21,1342.84,1349.06,1337.63,1349.00,686600000,1349.00 1999-06-18,1339.90,1344.48,1333.52,1342.84,914500000,1342.84 1999-06-17,1330.41,1343.54,1322.75,1339.90,700300000,1339.90 1999-06-16,1301.16,1332.83,1301.16,1330.41,806800000,1330.41 1999-06-15,1294.00,1310.76,1294.00,1301.16,696600000,1301.16 1999-06-14,1293.64,1301.99,1292.20,1294.00,669400000,1294.00 1999-06-11,1302.82,1311.97,1287.88,1293.64,698200000,1293.64 1999-06-10,1318.64,1318.64,1293.28,1302.82,716500000,1302.82 1999-06-09,1317.33,1326.01,1314.73,1318.64,662000000,1318.64 1999-06-08,1334.52,1334.52,1312.83,1317.33,685900000,1317.33 1999-06-07,1327.75,1336.42,1325.89,1334.52,664300000,1334.52 1999-06-04,1299.54,1327.75,1299.54,1327.75,694500000,1327.75 1999-06-03,1294.81,1304.15,1294.20,1299.54,719600000,1299.54 1999-06-02,1294.26,1297.10,1277.47,1294.81,728000000,1294.81 1999-06-01,1301.84,1301.84,1281.44,1294.26,683800000,1294.26 1999-05-28,1281.41,1304.00,1281.41,1301.84,649960000,1301.84 1999-05-27,1304.76,1304.76,1277.31,1281.41,811400000,1281.41 1999-05-26,1284.40,1304.85,1278.43,1304.76,870800000,1304.76 1999-05-25,1306.65,1317.52,1284.38,1284.40,826700000,1284.40 1999-05-24,1330.29,1333.02,1303.53,1306.65,754700000,1306.65 1999-05-21,1338.83,1340.88,1326.19,1330.29,686600000,1330.29 1999-05-20,1344.23,1350.49,1338.83,1338.83,752200000,1338.83 1999-05-19,1333.32,1344.23,1327.05,1344.23,801100000,1344.23 1999-05-18,1339.49,1345.44,1323.46,1333.32,753400000,1333.32 1999-05-17,1337.80,1339.95,1321.19,1339.49,665500000,1339.49 1999-05-14,1367.56,1367.56,1332.63,1337.80,727800000,1337.80 1999-05-13,1364.00,1375.98,1364.00,1367.56,796900000,1367.56 1999-05-12,1355.61,1367.36,1333.10,1364.00,825500000,1364.00 1999-05-11,1340.30,1360.00,1340.30,1355.61,836100000,1355.61 1999-05-10,1345.00,1352.01,1334.00,1340.30,773300000,1340.30 1999-05-07,1332.05,1345.99,1332.05,1345.00,814900000,1345.00 1999-05-06,1347.31,1348.36,1322.56,1332.05,875400000,1332.05 1999-05-05,1332.00,1347.32,1317.44,1347.31,913500000,1347.31 1999-05-04,1354.63,1354.64,1330.64,1332.00,933100000,1332.00 1999-05-03,1335.18,1354.63,1329.01,1354.63,811400000,1354.63 1999-04-30,1342.83,1351.83,1314.58,1335.18,936500000,1335.18 1999-04-29,1350.91,1356.75,1336.81,1342.83,1003600000,1342.83 1999-04-28,1362.80,1368.62,1348.29,1350.91,951700000,1350.91 1999-04-27,1360.04,1371.56,1356.55,1362.80,891700000,1362.80 1999-04-26,1356.85,1363.56,1353.72,1360.04,712000000,1360.04 1999-04-23,1358.83,1363.65,1348.45,1356.85,744900000,1356.85 1999-04-22,1336.12,1358.84,1336.12,1358.82,927900000,1358.82 1999-04-21,1306.17,1336.12,1301.84,1336.12,920000000,1336.12 1999-04-20,1289.48,1306.30,1284.21,1306.17,985400000,1306.17 1999-04-19,1319.00,1340.10,1284.48,1289.48,1214400000,1289.48 1999-04-16,1322.86,1325.03,1311.40,1319.00,1002300000,1319.00 1999-04-15,1328.44,1332.41,1308.38,1322.85,1089800000,1322.85 1999-04-14,1349.82,1357.24,1326.41,1328.44,952000000,1328.44 1999-04-13,1358.64,1362.38,1344.03,1349.82,810900000,1349.82 1999-04-12,1348.35,1358.69,1333.48,1358.63,810800000,1358.63 1999-04-09,1343.98,1351.22,1335.24,1348.35,716100000,1348.35 1999-04-08,1326.89,1344.08,1321.60,1343.98,850500000,1343.98 1999-04-07,1317.89,1329.58,1312.59,1326.89,816400000,1326.89 1999-04-06,1321.12,1326.76,1311.07,1317.89,787500000,1317.89 1999-04-05,1293.72,1321.12,1293.72,1321.12,695800000,1321.12 1999-04-01,1286.37,1294.54,1282.56,1293.72,703000000,1293.72 1999-03-31,1300.75,1313.60,1285.87,1286.37,924300000,1286.37 1999-03-30,1310.17,1310.17,1295.47,1300.75,729000000,1300.75 1999-03-29,1282.80,1311.76,1282.80,1310.17,747900000,1310.17 1999-03-26,1289.99,1289.99,1277.25,1282.80,707200000,1282.80 1999-03-25,1268.59,1289.99,1268.59,1289.99,784200000,1289.99 1999-03-24,1262.14,1269.02,1256.43,1268.59,761900000,1268.59 1999-03-23,1297.01,1297.01,1257.46,1262.14,811300000,1262.14 1999-03-22,1299.29,1303.84,1294.26,1297.01,658200000,1297.01 1999-03-19,1316.55,1323.82,1298.92,1299.29,914700000,1299.29 1999-03-18,1297.82,1317.62,1294.75,1316.55,831000000,1316.55 1999-03-17,1306.38,1306.55,1292.63,1297.82,752300000,1297.82 1999-03-16,1307.26,1311.11,1302.29,1306.38,751900000,1306.38 1999-03-15,1294.59,1307.47,1291.03,1307.26,727200000,1307.26 1999-03-12,1297.68,1304.42,1289.17,1294.59,825800000,1294.59 1999-03-11,1286.84,1306.43,1286.84,1297.68,904800000,1297.68 1999-03-10,1279.84,1287.02,1275.16,1286.84,841900000,1286.84 1999-03-09,1282.73,1293.74,1275.11,1279.84,803700000,1279.84 1999-03-08,1275.47,1282.74,1271.58,1282.73,714600000,1282.73 1999-03-05,1246.64,1275.73,1246.64,1275.47,834900000,1275.47 1999-03-04,1227.70,1247.74,1227.70,1246.64,770900000,1246.64 1999-03-03,1225.50,1231.63,1216.03,1227.70,751700000,1227.70 1999-03-02,1236.16,1248.31,1221.87,1225.50,753600000,1225.50 1999-03-01,1238.33,1238.70,1221.88,1236.16,699500000,1236.16 1999-02-26,1245.02,1246.73,1226.24,1238.33,784600000,1238.33 1999-02-25,1253.41,1253.41,1225.01,1245.02,740500000,1245.02 1999-02-24,1271.18,1283.84,1251.94,1253.41,782000000,1253.41 1999-02-23,1272.14,1280.38,1263.36,1271.18,781100000,1271.18 1999-02-22,1239.22,1272.22,1239.22,1272.14,718500000,1272.14 1999-02-19,1237.28,1247.91,1232.03,1239.22,700000000,1239.22 1999-02-18,1224.03,1239.13,1220.70,1237.28,742400000,1237.28 1999-02-17,1241.87,1249.31,1220.92,1224.03,735100000,1224.03 1999-02-16,1230.13,1252.17,1230.13,1241.87,653760000,1241.87 1999-02-12,1254.04,1254.04,1225.53,1230.13,691500000,1230.13 1999-02-11,1223.55,1254.05,1223.19,1254.04,815800000,1254.04 1999-02-10,1216.14,1226.78,1211.89,1223.55,721400000,1223.55 1999-02-09,1243.77,1243.97,1215.63,1216.14,736000000,1216.14 1999-02-08,1239.40,1246.93,1231.98,1243.77,705400000,1243.77 1999-02-05,1248.49,1251.86,1232.28,1239.40,872000000,1239.40 1999-02-04,1272.07,1272.23,1248.36,1248.49,854400000,1248.49 1999-02-03,1261.99,1276.04,1255.27,1272.07,876500000,1272.07 1999-02-02,1273.00,1273.49,1247.56,1261.99,845500000,1261.99 1999-02-01,1279.64,1283.75,1271.31,1273.00,799400000,1273.00 1999-01-29,1265.37,1280.37,1255.18,1279.64,917000000,1279.64 1999-01-28,1243.17,1266.40,1243.17,1265.37,848800000,1265.37 1999-01-27,1252.31,1262.61,1242.82,1243.17,893800000,1243.17 1999-01-26,1233.98,1253.25,1233.98,1252.31,896400000,1252.31 1999-01-25,1225.19,1233.98,1219.46,1233.98,723900000,1233.98 1999-01-22,1235.16,1236.41,1217.97,1225.19,785900000,1225.19 1999-01-21,1256.62,1256.94,1232.19,1235.16,871800000,1235.16 1999-01-20,1252.00,1274.07,1251.54,1256.62,905700000,1256.62 1999-01-19,1243.26,1253.27,1234.91,1252.00,785500000,1252.00 1999-01-15,1212.19,1243.26,1212.19,1243.26,798100000,1243.26 1999-01-14,1234.40,1236.81,1209.54,1212.19,797200000,1212.19 1999-01-13,1239.51,1247.75,1205.46,1234.40,931500000,1234.40 1999-01-12,1263.88,1264.45,1238.29,1239.51,800200000,1239.51 1999-01-11,1275.09,1276.22,1253.34,1263.88,818000000,1263.88 1999-01-08,1269.73,1278.24,1261.82,1275.09,937800000,1275.09 1999-01-07,1272.34,1272.34,1257.68,1269.73,863000000,1269.73 1999-01-06,1244.78,1272.50,1244.78,1272.34,986900000,1272.34 1999-01-05,1228.10,1246.11,1228.10,1244.78,775000000,1244.78 1999-01-04,1229.23,1248.81,1219.10,1228.10,877000000,1228.10 1998-12-31,1231.93,1237.18,1224.96,1229.23,719200000,1229.23 1998-12-30,1241.81,1244.93,1231.20,1231.93,594220000,1231.93 1998-12-29,1225.49,1241.86,1220.78,1241.81,586490000,1241.81 1998-12-28,1226.27,1231.52,1221.17,1225.49,531560000,1225.49 1998-12-24,1228.54,1229.72,1224.85,1226.27,246980000,1226.27 1998-12-23,1203.57,1229.89,1203.57,1228.54,697500000,1228.54 1998-12-22,1202.84,1209.22,1192.72,1203.57,680500000,1203.57 1998-12-21,1188.03,1210.88,1188.03,1202.84,744800000,1202.84 1998-12-18,1179.98,1188.89,1178.27,1188.03,839600000,1188.03 1998-12-17,1161.94,1180.03,1161.94,1179.98,739400000,1179.98 1998-12-16,1162.83,1166.29,1154.69,1161.94,725500000,1161.94 1998-12-15,1141.20,1162.83,1141.20,1162.83,777900000,1162.83 1998-12-14,1166.46,1166.46,1136.89,1141.20,741800000,1141.20 1998-12-11,1165.02,1167.89,1153.19,1166.46,688900000,1166.46 1998-12-10,1183.49,1183.77,1163.75,1165.02,748600000,1165.02 1998-12-09,1181.38,1185.22,1175.89,1183.49,694200000,1183.49 1998-12-08,1187.70,1193.53,1172.78,1181.38,727700000,1181.38 1998-12-07,1176.74,1188.96,1176.71,1187.70,671200000,1187.70 1998-12-04,1150.14,1176.74,1150.14,1176.74,709700000,1176.74 1998-12-03,1171.25,1176.99,1149.61,1150.14,799100000,1150.14 1998-12-02,1175.28,1175.28,1157.76,1171.25,727400000,1171.25 1998-12-01,1163.63,1175.89,1150.31,1175.28,789200000,1175.28 1998-11-30,1192.33,1192.72,1163.63,1163.63,687900000,1163.63 1998-11-27,1186.87,1192.97,1186.83,1192.33,256950000,1192.33 1998-11-25,1182.99,1187.16,1179.37,1186.87,583580000,1186.87 1998-11-24,1188.21,1191.30,1181.81,1182.99,766200000,1182.99 1998-11-23,1163.55,1188.21,1163.55,1188.21,774100000,1188.21 1998-11-20,1152.61,1163.55,1152.61,1163.55,721200000,1163.55 1998-11-19,1144.48,1155.10,1144.42,1152.61,671000000,1152.61 1998-11-18,1139.32,1144.52,1133.07,1144.48,652510000,1144.48 1998-11-17,1135.87,1151.71,1129.67,1139.32,705200000,1139.32 1998-11-16,1125.72,1138.72,1125.72,1135.87,615580000,1135.87 1998-11-13,1117.69,1126.34,1116.76,1125.72,602270000,1125.72 1998-11-12,1120.97,1126.57,1115.55,1117.69,662300000,1117.69 1998-11-11,1128.26,1136.25,1117.40,1120.97,715700000,1120.97 1998-11-10,1130.20,1135.37,1122.80,1128.26,671300000,1128.26 1998-11-09,1141.01,1141.01,1123.17,1130.20,592990000,1130.20 1998-11-06,1133.85,1141.30,1131.18,1141.01,683100000,1141.01 1998-11-05,1118.67,1133.88,1109.55,1133.85,770200000,1133.85 1998-11-04,1110.84,1127.18,1110.59,1118.67,861100000,1118.67 1998-11-03,1111.60,1115.02,1106.42,1110.84,704300000,1110.84 1998-11-02,1098.67,1114.44,1098.67,1111.60,753800000,1111.60 1998-10-30,1085.93,1103.78,1085.93,1098.67,785000000,1098.67 1998-10-29,1068.09,1086.11,1065.95,1085.93,699400000,1085.93 1998-10-28,1065.34,1072.79,1059.65,1068.09,677500000,1068.09 1998-10-27,1072.32,1087.08,1063.06,1065.34,764500000,1065.34 1998-10-26,1070.67,1081.23,1068.17,1072.32,609910000,1072.32 1998-10-23,1078.48,1078.48,1067.43,1070.67,637640000,1070.67 1998-10-22,1069.92,1080.43,1061.47,1078.48,754900000,1078.48 1998-10-21,1063.93,1073.61,1058.08,1069.92,745100000,1069.92 1998-10-20,1062.39,1084.06,1060.61,1063.93,958200000,1063.93 1998-10-19,1056.42,1065.21,1054.23,1062.39,738600000,1062.39 1998-10-16,1047.49,1062.65,1047.49,1056.42,1042200000,1056.42 1998-10-15,1005.53,1053.09,1000.12,1047.49,937600000,1047.49 1998-10-14,994.80,1014.42,987.80,1005.53,791200000,1005.53 1998-10-13,997.71,1000.78,987.55,994.80,733300000,994.80 1998-10-12,984.39,1010.71,984.39,997.71,691100000,997.71 1998-10-09,959.44,984.42,953.04,984.39,878100000,984.39 1998-10-08,970.68,970.68,923.32,959.44,1114600000,959.44 1998-10-07,984.59,995.66,957.15,970.68,977000000,970.68 1998-10-06,988.56,1008.77,974.81,984.59,845700000,984.59 1998-10-05,1002.60,1002.60,964.72,988.56,817500000,988.56 1998-10-02,986.39,1005.45,971.69,1002.60,902900000,1002.60 1998-10-01,1017.01,1017.01,981.29,986.39,899700000,986.39 1998-09-30,1049.02,1049.02,1015.73,1017.01,800100000,1017.01 1998-09-29,1048.69,1056.31,1039.88,1049.02,760100000,1049.02 1998-09-28,1044.75,1061.46,1042.23,1048.69,690500000,1048.69 1998-09-25,1042.72,1051.89,1028.49,1044.75,736800000,1044.75 1998-09-24,1066.09,1066.11,1033.04,1042.72,805900000,1042.72 1998-09-23,1029.63,1066.09,1029.63,1066.09,899700000,1066.09 1998-09-22,1023.89,1033.89,1021.96,1029.63,694900000,1029.63 1998-09-21,1020.09,1026.02,993.82,1023.89,609880000,1023.89 1998-09-18,1018.87,1022.01,1011.86,1020.09,794700000,1020.09 1998-09-17,1045.48,1045.48,1016.05,1018.87,694500000,1018.87 1998-09-16,1037.68,1046.07,1029.31,1045.48,797500000,1045.48 1998-09-15,1029.72,1037.90,1021.42,1037.68,724600000,1037.68 1998-09-14,1009.06,1038.38,1009.06,1029.72,714400000,1029.72 1998-09-11,980.19,1009.06,969.71,1009.06,819100000,1009.06 1998-09-10,1006.20,1006.20,968.64,980.19,880300000,980.19 1998-09-09,1023.46,1027.72,1004.56,1006.20,704300000,1006.20 1998-09-08,973.89,1023.46,973.89,1023.46,814800000,1023.46 1998-09-04,982.26,991.41,956.51,973.89,780300000,973.89 1998-09-03,990.47,990.47,969.32,982.26,880500000,982.26 1998-09-02,994.26,1013.19,988.40,990.48,894600000,990.48 1998-09-01,957.28,1000.71,939.98,994.26,1216600000,994.26 1998-08-31,1027.14,1033.47,957.28,957.28,917500000,957.28 1998-08-28,1042.59,1051.80,1021.04,1027.14,840300000,1027.14 1998-08-27,1084.19,1084.19,1037.61,1042.59,938600000,1042.59 1998-08-26,1092.85,1092.85,1075.91,1084.19,674100000,1084.19 1998-08-25,1088.14,1106.64,1085.53,1092.85,664900000,1092.85 1998-08-24,1081.24,1093.82,1081.24,1088.14,558100000,1088.14 1998-08-21,1091.60,1091.60,1054.92,1081.24,725700000,1081.24 1998-08-20,1098.06,1098.79,1089.55,1091.60,621630000,1091.60 1998-08-19,1101.20,1106.32,1094.93,1098.06,633630000,1098.06 1998-08-18,1083.67,1101.72,1083.67,1101.20,690600000,1101.20 1998-08-17,1062.75,1083.67,1055.08,1083.67,584380000,1083.67 1998-08-14,1074.91,1083.92,1057.22,1062.75,644030000,1062.75 1998-08-13,1084.22,1091.50,1074.91,1074.91,660700000,1074.91 1998-08-12,1068.98,1084.70,1068.98,1084.22,711700000,1084.22 1998-08-11,1083.14,1083.14,1054.00,1068.98,774400000,1068.98 1998-08-10,1089.45,1092.82,1081.76,1083.14,579180000,1083.14 1998-08-07,1089.63,1102.54,1084.72,1089.45,759100000,1089.45 1998-08-06,1081.43,1090.95,1074.94,1089.63,768400000,1089.63 1998-08-05,1072.12,1084.80,1057.35,1081.43,851600000,1081.43 1998-08-04,1112.44,1119.73,1071.82,1072.12,852600000,1072.12 1998-08-03,1120.67,1121.79,1110.39,1112.44,620400000,1112.44 1998-07-31,1142.95,1142.97,1114.30,1120.67,645910000,1120.67 1998-07-30,1125.21,1143.07,1125.21,1142.95,687400000,1142.95 1998-07-29,1130.24,1138.56,1121.98,1125.21,644350000,1125.21 1998-07-28,1147.27,1147.27,1119.44,1130.24,703600000,1130.24 1998-07-27,1140.80,1147.27,1128.19,1147.27,619990000,1147.27 1998-07-24,1139.75,1150.14,1129.11,1140.80,698600000,1140.80 1998-07-23,1164.08,1164.35,1139.75,1139.75,741600000,1139.75 1998-07-22,1165.07,1167.67,1155.20,1164.08,739800000,1164.08 1998-07-21,1184.10,1187.37,1163.05,1165.07,659700000,1165.07 1998-07-20,1186.75,1190.58,1179.19,1184.10,560580000,1184.10 1998-07-17,1183.99,1188.10,1182.42,1186.75,618030000,1186.75 1998-07-16,1174.81,1184.02,1170.40,1183.99,677800000,1183.99 1998-07-15,1177.58,1181.48,1174.73,1174.81,723900000,1174.81 1998-07-14,1165.19,1179.76,1165.19,1177.58,700300000,1177.58 1998-07-13,1164.33,1166.98,1160.21,1165.19,574880000,1165.19 1998-07-10,1158.57,1166.93,1150.88,1164.33,576080000,1164.33 1998-07-09,1166.38,1166.38,1156.03,1158.56,663600000,1158.56 1998-07-08,1154.66,1166.89,1154.66,1166.38,607230000,1166.38 1998-07-07,1157.33,1159.81,1152.85,1154.66,624890000,1154.66 1998-07-06,1146.42,1157.33,1145.03,1157.33,514750000,1157.33 1998-07-02,1148.56,1148.56,1142.99,1146.42,510210000,1146.42 1998-07-01,1133.84,1148.56,1133.84,1148.56,701600000,1148.56 1998-06-30,1138.49,1140.80,1131.98,1133.84,757200000,1133.84 1998-06-29,1133.20,1145.15,1133.20,1138.49,564350000,1138.49 1998-06-26,1129.28,1136.83,1129.28,1133.20,520050000,1133.20 1998-06-25,1132.88,1142.04,1127.60,1129.28,669900000,1129.28 1998-06-24,1119.49,1134.40,1115.10,1132.88,714900000,1132.88 1998-06-23,1103.21,1119.49,1103.21,1119.49,657100000,1119.49 1998-06-22,1100.65,1109.01,1099.42,1103.21,531550000,1103.21 1998-06-19,1106.37,1111.25,1097.10,1100.65,715500000,1100.65 1998-06-18,1107.11,1109.36,1103.71,1106.37,590440000,1106.37 1998-06-17,1087.59,1112.87,1087.58,1107.11,744400000,1107.11 1998-06-16,1077.01,1087.59,1074.67,1087.59,664600000,1087.59 1998-06-15,1098.84,1098.84,1077.01,1077.01,595820000,1077.01 1998-06-12,1094.58,1098.84,1080.83,1098.84,633300000,1098.84 1998-06-11,1112.28,1114.20,1094.28,1094.58,627470000,1094.58 1998-06-10,1118.41,1126.00,1110.27,1112.28,609410000,1112.28 1998-06-09,1115.72,1119.92,1111.31,1118.41,563610000,1118.41 1998-06-08,1113.86,1119.70,1113.31,1115.72,543390000,1115.72 1998-06-05,1095.10,1113.88,1094.83,1113.86,558440000,1113.86 1998-06-04,1082.73,1095.93,1078.10,1094.83,577470000,1094.83 1998-06-03,1093.22,1097.43,1081.09,1082.73,584480000,1082.73 1998-06-02,1090.98,1098.71,1089.67,1093.22,590930000,1093.22 1998-06-01,1090.82,1097.85,1084.22,1090.98,537660000,1090.98 1998-05-29,1097.60,1104.16,1090.82,1090.82,556780000,1090.82 1998-05-28,1092.23,1099.73,1089.06,1097.60,588900000,1097.60 1998-05-27,1094.02,1094.44,1074.39,1092.23,682040000,1092.23 1998-05-26,1110.47,1116.79,1094.01,1094.02,541410000,1094.02 1998-05-22,1114.64,1116.89,1107.99,1110.47,444070000,1110.47 1998-05-21,1119.06,1124.45,1111.94,1114.64,551970000,1114.64 1998-05-20,1109.52,1119.08,1107.51,1119.06,587240000,1119.06 1998-05-19,1105.82,1113.50,1105.82,1109.52,566020000,1109.52 1998-05-18,1108.73,1112.44,1097.99,1105.82,519100000,1105.82 1998-05-15,1117.37,1118.66,1107.11,1108.73,621990000,1108.73 1998-05-14,1118.86,1124.03,1112.43,1117.37,578380000,1117.37 1998-05-13,1115.79,1122.22,1114.93,1118.86,600010000,1118.86 1998-05-12,1106.64,1115.96,1102.78,1115.79,604420000,1115.79 1998-05-11,1108.14,1119.13,1103.72,1106.64,560840000,1106.64 1998-05-08,1095.14,1111.42,1094.53,1108.14,567890000,1108.14 1998-05-07,1104.92,1105.58,1094.59,1095.14,582240000,1095.14 1998-05-06,1115.50,1118.39,1104.64,1104.92,606540000,1104.92 1998-05-05,1122.07,1122.07,1111.16,1115.50,583630000,1115.50 1998-05-04,1121.00,1130.52,1121.00,1122.07,551700000,1122.07 1998-05-01,1111.75,1121.02,1111.75,1121.00,581970000,1121.00 1998-04-30,1094.63,1116.97,1094.63,1111.75,695600000,1111.75 1998-04-29,1085.11,1098.24,1084.65,1094.62,638790000,1094.62 1998-04-28,1086.54,1095.94,1081.49,1085.11,678600000,1085.11 1998-04-27,1107.90,1107.90,1076.70,1086.54,685960000,1086.54 1998-04-24,1119.58,1122.81,1104.77,1107.90,633890000,1107.90 1998-04-23,1130.54,1130.54,1117.49,1119.58,653190000,1119.58 1998-04-22,1126.67,1132.98,1126.29,1130.54,696740000,1130.54 1998-04-21,1123.65,1129.65,1119.54,1126.67,675640000,1126.67 1998-04-20,1122.72,1124.88,1118.43,1123.65,595190000,1123.65 1998-04-17,1108.17,1122.72,1104.95,1122.72,672290000,1122.72 1998-04-16,1119.32,1119.32,1105.27,1108.17,699570000,1108.17 1998-04-15,1115.75,1119.90,1112.24,1119.32,685020000,1119.32 1998-04-14,1109.69,1115.95,1109.48,1115.75,613730000,1115.75 1998-04-13,1110.67,1110.75,1100.60,1109.69,564480000,1109.69 1998-04-09,1101.65,1111.45,1101.65,1110.67,548940000,1110.67 1998-04-08,1109.55,1111.60,1098.21,1101.65,616330000,1101.65 1998-04-07,1121.38,1121.38,1102.44,1109.55,670760000,1109.55 1998-04-06,1122.70,1131.99,1121.37,1121.38,625810000,1121.38 1998-04-03,1120.01,1126.36,1118.12,1122.70,653880000,1122.70 1998-04-02,1108.15,1121.01,1107.89,1120.01,674340000,1120.01 1998-04-01,1101.75,1109.19,1095.29,1108.15,677310000,1108.15 1998-03-31,1093.55,1110.13,1093.55,1101.75,674930000,1101.75 1998-03-30,1095.44,1099.10,1090.02,1093.60,497400000,1093.60 1998-03-27,1100.80,1107.18,1091.14,1095.44,582190000,1095.44 1998-03-26,1101.93,1106.28,1097.00,1100.80,606770000,1100.80 1998-03-25,1105.65,1113.07,1092.84,1101.93,676550000,1101.93 1998-03-24,1095.55,1106.75,1095.55,1105.65,605720000,1105.65 1998-03-23,1099.16,1101.16,1094.25,1095.55,631350000,1095.55 1998-03-20,1089.74,1101.04,1089.39,1099.16,717310000,1099.16 1998-03-19,1085.52,1089.74,1084.30,1089.74,598240000,1089.74 1998-03-18,1080.45,1085.52,1077.77,1085.52,632690000,1085.52 1998-03-17,1079.27,1080.52,1073.29,1080.45,680960000,1080.45 1998-03-16,1068.61,1079.46,1068.61,1079.27,548980000,1079.27 1998-03-13,1069.92,1075.86,1066.57,1068.61,597800000,1068.61 1998-03-12,1068.47,1071.87,1063.54,1069.92,594940000,1069.92 1998-03-11,1064.25,1069.18,1064.22,1068.47,655260000,1068.47 1998-03-10,1052.31,1064.59,1052.31,1064.25,631920000,1064.25 1998-03-09,1055.69,1058.55,1050.02,1052.31,624700000,1052.31 1998-03-06,1035.05,1055.69,1035.05,1055.69,665500000,1055.69 1998-03-05,1047.33,1047.33,1030.87,1035.05,648270000,1035.05 1998-03-04,1052.02,1052.02,1042.74,1047.33,644280000,1047.33 1998-03-03,1047.70,1052.02,1043.41,1052.02,612360000,1052.02 1998-03-02,1049.34,1053.98,1044.70,1047.70,591470000,1047.70 1998-02-27,1048.67,1051.66,1044.40,1049.34,574480000,1049.34 1998-02-26,1042.90,1048.68,1039.85,1048.67,646280000,1048.67 1998-02-25,1030.56,1045.79,1030.56,1042.90,611350000,1042.90 1998-02-24,1038.14,1038.73,1028.89,1030.56,589880000,1030.56 1998-02-23,1034.21,1038.68,1031.76,1038.14,550730000,1038.14 1998-02-20,1028.28,1034.21,1022.69,1034.21,594300000,1034.21 1998-02-19,1032.08,1032.93,1026.62,1028.28,581820000,1028.28 1998-02-18,1022.76,1032.08,1021.70,1032.08,606000000,1032.08 1998-02-17,1020.09,1028.02,1020.09,1022.76,605890000,1022.76 1998-02-13,1024.14,1024.14,1017.71,1020.09,531940000,1020.09 1998-02-12,1020.01,1026.30,1008.55,1024.14,611480000,1024.14 1998-02-11,1019.01,1020.71,1016.38,1020.01,599300000,1020.01 1998-02-10,1010.74,1022.15,1010.71,1019.01,642800000,1019.01 1998-02-09,1012.46,1015.33,1006.28,1010.74,524810000,1010.74 1998-02-06,1003.54,1013.07,1003.36,1012.46,569650000,1012.46 1998-02-05,1006.90,1013.51,1000.27,1003.54,703980000,1003.54 1998-02-04,1006.00,1009.52,999.43,1006.90,695420000,1006.90 1998-02-03,1001.27,1006.13,996.90,1006.00,692120000,1006.00 1998-02-02,980.28,1002.48,980.28,1001.27,724320000,1001.27 1998-01-30,985.49,987.41,979.63,980.28,613380000,980.28 1998-01-29,977.46,992.65,975.21,985.49,750760000,985.49 1998-01-28,969.02,978.63,969.02,977.46,708470000,977.46 1998-01-27,956.95,973.23,956.26,969.02,679140000,969.02 1998-01-26,957.59,963.04,954.24,956.95,555080000,956.95 1998-01-23,963.04,966.44,950.86,957.59,635770000,957.59 1998-01-22,970.81,970.81,959.49,963.04,646570000,963.04 1998-01-21,978.60,978.60,963.29,970.81,626160000,970.81 1998-01-20,961.51,978.60,961.48,978.60,644790000,978.60 1998-01-16,950.73,965.12,950.73,961.51,670080000,961.51 1998-01-15,957.94,957.94,950.27,950.73,569050000,950.73 1998-01-14,952.12,958.12,948.00,957.94,603280000,957.94 1998-01-13,939.21,952.14,939.21,952.12,646740000,952.12 1998-01-12,927.69,939.25,912.83,939.21,705450000,939.21 1998-01-09,956.05,956.05,921.72,927.69,746420000,927.69 1998-01-08,964.00,964.00,955.04,956.05,652140000,956.05 1998-01-07,966.58,966.58,952.67,964.00,667390000,964.00 1998-01-06,977.07,977.07,962.68,966.58,618360000,966.58 1998-01-05,975.04,982.63,969.00,977.07,628070000,977.07 1998-01-02,970.43,975.04,965.73,975.04,366730000,975.04 1997-12-31,970.84,975.02,967.41,970.43,467280000,970.43 1997-12-30,953.35,970.84,953.35,970.84,499500000,970.84 1997-12-29,936.46,953.95,936.46,953.35,443160000,953.35 1997-12-26,932.70,939.99,932.70,936.46,154900000,936.46 1997-12-24,939.13,942.88,932.70,932.70,265980000,932.70 1997-12-23,953.70,954.51,938.91,939.13,515070000,939.13 1997-12-22,946.78,956.73,946.25,953.70,530670000,953.70 1997-12-19,955.30,955.30,924.92,946.78,793200000,946.78 1997-12-18,965.54,965.54,950.55,955.30,618870000,955.30 1997-12-17,968.04,974.30,964.25,965.54,618900000,965.54 1997-12-16,963.39,973.00,963.39,968.04,623320000,968.04 1997-12-15,953.39,965.96,953.39,963.39,597150000,963.39 1997-12-12,954.94,961.32,947.00,953.39,579280000,953.39 1997-12-11,969.79,969.79,951.89,954.94,631770000,954.94 1997-12-10,975.78,975.78,962.68,969.79,602290000,969.79 1997-12-09,982.37,982.37,973.81,975.78,539130000,975.78 1997-12-08,983.79,985.67,979.57,982.37,490320000,982.37 1997-12-05,973.10,986.25,969.10,983.79,563590000,983.79 1997-12-04,976.77,983.36,971.37,973.10,633470000,973.10 1997-12-03,971.68,980.81,966.16,976.77,624610000,976.77 1997-12-02,974.78,976.20,969.83,971.68,576120000,971.68 1997-12-01,955.40,974.77,955.40,974.77,590300000,974.77 1997-11-28,951.64,959.13,951.64,955.40,189070000,955.40 1997-11-26,950.82,956.47,950.82,951.64,487750000,951.64 1997-11-25,946.67,954.47,944.71,950.82,587890000,950.82 1997-11-24,963.09,963.09,945.22,946.67,514920000,946.67 1997-11-21,958.98,964.55,954.60,963.09,611000000,963.09 1997-11-20,944.59,961.83,944.59,958.98,602610000,958.98 1997-11-19,938.23,947.28,934.83,944.59,542720000,944.59 1997-11-18,946.20,947.65,937.43,938.23,521380000,938.23 1997-11-17,928.35,949.66,928.35,946.20,576540000,946.20 1997-11-14,916.66,930.44,915.34,928.35,635760000,928.35 1997-11-13,905.96,917.79,900.61,916.66,653960000,916.66 1997-11-12,923.78,923.88,905.34,905.96,585340000,905.96 1997-11-11,921.13,928.29,919.63,923.78,435660000,923.78 1997-11-10,927.51,935.90,920.26,921.13,464140000,921.13 1997-11-07,938.03,938.03,915.39,927.51,569980000,927.51 1997-11-06,942.76,942.85,934.16,938.03,522890000,938.03 1997-11-05,940.76,949.62,938.16,942.76,565680000,942.76 1997-11-04,938.99,941.40,932.66,940.76,541590000,940.76 1997-11-03,914.62,939.02,914.62,938.99,564740000,938.99 1997-10-31,903.68,919.93,903.68,914.62,638070000,914.62 1997-10-30,919.16,923.28,903.68,903.68,712230000,903.68 1997-10-29,921.85,935.24,913.88,919.16,777660000,919.16 1997-10-28,876.99,923.09,855.27,921.85,1202550000,921.85 1997-10-27,941.64,941.64,876.73,876.99,693730000,876.99 1997-10-24,950.69,960.04,937.55,941.64,677630000,941.64 1997-10-23,968.49,968.49,944.16,950.69,673270000,950.69 1997-10-22,972.28,972.61,965.66,968.49,613490000,968.49 1997-10-21,955.61,972.56,955.61,972.28,582310000,972.28 1997-10-20,944.16,955.72,941.43,955.61,483880000,955.61 1997-10-17,955.23,955.23,931.58,944.16,624980000,944.16 1997-10-16,965.72,973.38,950.77,955.25,597010000,955.25 1997-10-15,970.28,970.28,962.75,965.72,505310000,965.72 1997-10-14,968.10,972.86,961.87,970.28,510330000,970.28 1997-10-13,966.98,973.46,966.95,968.10,354800000,968.10 1997-10-10,970.62,970.62,963.42,966.98,500680000,966.98 1997-10-09,973.84,974.72,963.34,970.62,551840000,970.62 1997-10-08,983.12,983.12,968.65,973.84,573110000,973.84 1997-10-07,972.69,983.12,971.95,983.12,551970000,983.12 1997-10-06,965.03,974.16,965.03,972.69,495620000,972.69 1997-10-03,960.46,975.47,955.13,965.03,623370000,965.03 1997-10-02,955.41,960.46,952.94,960.46,474760000,960.46 1997-10-01,947.28,956.71,947.28,955.41,598660000,955.41 1997-09-30,953.34,955.17,947.28,947.28,587500000,947.28 1997-09-29,945.22,953.96,941.94,953.34,477100000,953.34 1997-09-26,937.91,946.44,937.91,945.22,505340000,945.22 1997-09-25,944.48,947.00,937.38,937.91,524880000,937.91 1997-09-24,951.93,959.78,944.07,944.48,639460000,944.48 1997-09-23,955.43,955.78,948.07,951.93,522930000,951.93 1997-09-22,950.51,960.59,950.51,955.43,490900000,955.43 1997-09-19,947.29,952.35,943.90,950.51,631040000,950.51 1997-09-18,943.00,958.19,943.00,947.29,566830000,947.29 1997-09-17,945.64,950.29,941.99,943.00,590550000,943.00 1997-09-16,919.77,947.66,919.77,945.64,636380000,945.64 1997-09-15,923.91,928.90,919.41,919.77,468030000,919.77 1997-09-12,912.59,925.05,906.70,923.91,544150000,923.91 1997-09-11,919.03,919.03,902.56,912.59,575020000,912.59 1997-09-10,933.62,933.62,918.76,919.03,517620000,919.03 1997-09-09,931.20,938.90,927.28,933.62,502200000,933.62 1997-09-08,929.05,936.50,929.05,931.20,466430000,931.20 1997-09-05,930.87,940.37,924.05,929.05,536400000,929.05 1997-09-04,927.86,933.36,925.59,930.87,559310000,930.87 1997-09-03,927.58,935.90,926.87,927.86,549060000,927.86 1997-09-02,899.47,927.58,899.47,927.58,491870000,927.58 1997-08-29,903.67,907.28,896.82,899.47,413910000,899.47 1997-08-28,913.70,915.90,898.65,903.67,486300000,903.67 1997-08-27,913.02,916.23,903.83,913.70,492150000,913.70 1997-08-26,920.16,922.47,911.72,913.02,449110000,913.02 1997-08-25,923.55,930.93,917.29,920.16,388990000,920.16 1997-08-22,925.05,925.05,905.42,923.54,460160000,923.54 1997-08-21,939.35,939.47,921.35,925.05,499000000,925.05 1997-08-20,926.01,939.35,924.58,939.35,521270000,939.35 1997-08-19,912.49,926.01,912.49,926.01,545630000,926.01 1997-08-18,900.81,912.57,893.34,912.49,514330000,912.49 1997-08-15,924.77,924.77,900.81,900.81,537820000,900.81 1997-08-14,922.02,930.07,916.92,924.77,530460000,924.77 1997-08-13,926.53,935.77,916.54,922.02,587210000,922.02 1997-08-12,937.00,942.99,925.66,926.53,499310000,926.53 1997-08-11,933.54,938.50,925.39,937.00,480340000,937.00 1997-08-08,951.19,951.19,925.74,933.54,563420000,933.54 1997-08-07,960.32,964.17,950.87,951.19,576030000,951.19 1997-08-06,952.37,962.43,949.45,960.32,565200000,960.32 1997-08-05,950.30,954.21,948.92,952.37,525710000,952.37 1997-08-04,947.14,953.18,943.60,950.30,456000000,950.30 1997-08-01,954.29,955.35,939.04,947.14,513750000,947.14 1997-07-31,952.29,957.73,948.89,954.31,547830000,954.31 1997-07-30,942.29,953.98,941.98,952.29,568470000,952.29 1997-07-29,936.45,942.96,932.56,942.29,544540000,942.29 1997-07-28,938.79,942.97,935.19,936.45,466920000,936.45 1997-07-25,940.30,945.65,936.09,938.79,521510000,938.79 1997-07-24,936.56,941.51,926.91,940.30,571020000,940.30 1997-07-23,933.98,941.80,933.98,936.56,616930000,936.56 1997-07-22,912.94,934.38,912.94,933.98,579590000,933.98 1997-07-21,915.30,915.38,907.12,912.94,459500000,912.94 1997-07-18,931.61,931.61,912.90,915.30,589710000,915.30 1997-07-17,936.59,936.96,927.90,931.61,629250000,931.61 1997-07-16,925.76,939.32,925.76,936.59,647390000,936.59 1997-07-15,918.38,926.15,914.52,925.76,598370000,925.76 1997-07-14,916.68,921.78,912.02,918.38,485960000,918.38 1997-07-11,913.78,919.74,913.11,916.68,500050000,916.68 1997-07-10,907.54,916.54,904.31,913.78,551340000,913.78 1997-07-09,918.75,922.03,902.48,907.54,589110000,907.54 1997-07-08,912.20,918.76,911.56,918.75,526010000,918.75 1997-07-07,916.92,923.26,909.69,912.20,518780000,912.20 1997-07-03,904.03,917.82,904.03,916.92,374680000,916.92 1997-07-02,891.03,904.05,891.03,904.03,526970000,904.03 1997-07-01,885.14,893.88,884.54,891.03,544190000,891.03 1997-06-30,887.30,892.62,879.82,885.14,561540000,885.14 1997-06-27,883.68,894.70,883.68,887.30,472540000,887.30 1997-06-26,888.99,893.21,879.32,883.68,499780000,883.68 1997-06-25,896.34,902.09,882.24,888.99,603040000,888.99 1997-06-24,878.62,896.75,878.62,896.34,542650000,896.34 1997-06-23,898.70,898.70,878.43,878.62,492940000,878.62 1997-06-20,897.99,901.77,897.77,898.70,653110000,898.70 1997-06-19,889.06,900.09,888.99,897.99,536940000,897.99 1997-06-18,894.42,894.42,887.03,889.06,491740000,889.06 1997-06-17,893.90,897.60,886.19,894.42,543010000,894.42 1997-06-16,893.27,895.17,891.21,893.90,414280000,893.90 1997-06-13,883.48,894.69,883.48,893.27,575810000,893.27 1997-06-12,869.57,884.34,869.01,883.46,592730000,883.46 1997-06-11,865.27,870.66,865.15,869.57,513740000,869.57 1997-06-10,862.91,870.05,862.18,865.27,526980000,865.27 1997-06-09,858.01,865.14,858.01,862.91,465810000,862.91 1997-06-06,843.43,859.24,843.36,858.01,488940000,858.01 1997-06-05,840.11,848.89,840.11,843.43,452610000,843.43 1997-06-04,845.48,845.55,838.82,840.11,466690000,840.11 1997-06-03,846.36,850.56,841.51,845.48,527120000,845.48 1997-06-02,848.28,851.34,844.61,846.36,435950000,846.36 1997-05-30,844.08,851.87,831.87,848.28,537200000,848.28 1997-05-29,847.21,848.96,842.61,844.08,462600000,844.08 1997-05-28,849.71,850.95,843.21,847.21,487340000,847.21 1997-05-27,847.03,851.53,840.96,849.71,436150000,849.71 1997-05-23,835.66,848.49,835.66,847.03,417030000,847.03 1997-05-22,839.35,841.91,833.86,835.66,426940000,835.66 1997-05-21,841.66,846.87,835.22,839.35,540730000,839.35 1997-05-20,833.27,841.96,826.41,841.66,450850000,841.66 1997-05-19,829.75,835.92,828.87,833.27,345140000,833.27 1997-05-16,841.88,841.88,829.15,829.75,486780000,829.75 1997-05-15,836.04,842.45,833.34,841.88,458170000,841.88 1997-05-14,833.13,841.29,833.13,836.04,504960000,836.04 1997-05-13,837.66,838.49,829.12,833.13,489760000,833.13 1997-05-12,824.78,838.56,824.78,837.66,459370000,837.66 1997-05-09,820.26,827.69,815.78,824.78,455690000,824.78 1997-05-08,815.62,829.09,811.84,820.26,534120000,820.26 1997-05-07,827.76,827.76,814.70,815.62,500580000,815.62 1997-05-06,830.24,832.29,824.70,827.76,603680000,827.76 1997-05-05,812.97,830.29,811.80,830.29,549410000,830.29 1997-05-02,798.53,812.99,798.53,812.97,499770000,812.97 1997-05-01,801.34,802.95,793.21,798.53,460380000,798.53 1997-04-30,794.05,804.13,791.21,801.34,556070000,801.34 1997-04-29,772.96,794.44,772.96,794.05,547690000,794.05 1997-04-28,765.37,773.89,763.30,772.96,404470000,772.96 1997-04-25,771.18,771.18,764.63,765.37,414350000,765.37 1997-04-24,773.64,779.89,769.72,771.18,493640000,771.18 1997-04-23,774.61,778.19,771.90,773.64,489350000,773.64 1997-04-22,760.37,774.64,759.90,774.61,507500000,774.61 1997-04-21,766.34,767.39,756.38,760.37,397300000,760.37 1997-04-18,761.77,767.93,761.77,766.34,468940000,766.34 1997-04-17,763.53,768.55,760.49,761.77,503760000,761.77 1997-04-16,754.72,763.53,751.99,763.53,498820000,763.53 1997-04-15,743.73,754.72,743.73,754.72,507370000,754.72 1997-04-14,737.65,743.73,733.54,743.73,406800000,743.73 1997-04-11,758.34,758.34,737.64,737.65,444380000,737.65 1997-04-10,760.60,763.73,757.65,758.34,421790000,758.34 1997-04-09,766.12,769.53,759.15,760.60,451500000,760.60 1997-04-08,762.13,766.25,758.36,766.12,450790000,766.12 1997-04-07,757.90,764.82,757.90,762.13,453790000,762.13 1997-04-04,750.32,757.90,744.04,757.90,544580000,757.90 1997-04-03,750.11,751.04,744.40,750.32,498010000,750.32 1997-04-02,759.64,759.65,747.59,750.11,478210000,750.11 1997-04-01,757.12,761.49,751.26,759.64,515770000,759.64 1997-03-31,773.88,773.88,756.13,757.12,555880000,757.12 1997-03-27,790.50,792.58,767.32,773.88,476790000,773.88 1997-03-26,789.07,794.89,786.77,790.50,506670000,790.50 1997-03-25,790.89,798.11,788.39,789.07,487520000,789.07 1997-03-24,784.10,791.01,780.79,790.89,451970000,790.89 1997-03-21,782.65,786.44,782.65,784.10,638760000,784.10 1997-03-20,785.77,786.29,778.04,782.65,497480000,782.65 1997-03-19,789.66,791.59,780.03,785.77,535580000,785.77 1997-03-18,795.71,797.18,785.47,789.66,467330000,789.66 1997-03-17,793.17,796.28,782.98,795.71,495260000,795.71 1997-03-14,789.56,796.88,789.56,793.17,491540000,793.17 1997-03-13,804.26,804.26,789.44,789.56,507560000,789.56 1997-03-12,811.34,811.34,801.07,804.26,490200000,804.26 1997-03-11,813.65,814.90,810.77,811.34,493250000,811.34 1997-03-10,804.97,813.66,803.66,813.65,468780000,813.65 1997-03-07,798.56,808.19,798.56,804.97,508270000,804.97 1997-03-06,801.99,804.11,797.50,798.56,540310000,798.56 1997-03-05,790.95,801.99,790.95,801.99,532500000,801.99 1997-03-04,795.31,798.93,789.98,790.95,537890000,790.95 1997-03-03,790.82,795.31,785.66,795.31,437220000,795.31 1997-02-28,795.07,795.70,788.50,790.82,508280000,790.82 1997-02-27,805.68,805.68,795.06,795.07,464660000,795.07 1997-02-26,812.10,812.70,798.13,805.68,573920000,805.68 1997-02-25,810.28,812.85,807.65,812.03,527450000,812.03 1997-02-24,801.77,810.64,798.42,810.28,462450000,810.28 1997-02-21,802.80,804.94,799.99,801.77,478450000,801.77 1997-02-20,812.49,812.49,800.35,802.80,492220000,802.80 1997-02-19,816.29,817.68,811.20,812.49,519350000,812.49 1997-02-18,808.48,816.29,806.34,816.29,474110000,816.29 1997-02-14,811.82,812.20,808.15,808.48,491540000,808.48 1997-02-13,802.77,812.93,802.77,811.82,593710000,811.82 1997-02-12,789.59,802.77,789.59,802.77,563890000,802.77 1997-02-11,785.43,789.60,780.95,789.59,483090000,789.59 1997-02-10,789.56,793.46,784.69,785.43,471590000,785.43 1997-02-07,780.15,789.72,778.19,789.56,540910000,789.56 1997-02-06,778.28,780.35,774.45,780.15,519660000,780.15 1997-02-05,789.26,792.71,773.43,778.28,580520000,778.28 1997-02-04,786.73,789.28,783.68,789.26,506530000,789.26 1997-02-03,786.16,787.14,783.12,786.73,463600000,786.73 1997-01-31,784.17,791.86,784.17,786.16,578550000,786.16 1997-01-30,772.50,784.17,772.50,784.17,524160000,784.17 1997-01-29,765.02,772.70,765.02,772.50,498390000,772.50 1997-01-28,765.02,776.32,761.75,765.02,541580000,765.02 1997-01-27,770.52,771.43,764.18,765.02,445760000,765.02 1997-01-24,777.56,778.21,768.17,770.52,542920000,770.52 1997-01-23,786.23,794.67,776.64,777.56,685070000,777.56 1997-01-22,782.72,786.23,779.56,786.23,589230000,786.23 1997-01-21,776.70,783.72,772.00,782.72,571280000,782.72 1997-01-20,776.17,780.08,774.19,776.70,440470000,776.70 1997-01-17,769.75,776.37,769.72,776.17,534640000,776.17 1997-01-16,767.20,772.05,765.25,769.75,537290000,769.75 1997-01-15,768.86,770.95,763.72,767.20,524990000,767.20 1997-01-14,759.51,772.04,759.51,768.86,531600000,768.86 1997-01-13,759.50,762.85,756.69,759.51,445400000,759.51 1997-01-10,754.85,759.65,746.92,759.50,545850000,759.50 1997-01-09,748.41,757.68,748.41,754.85,555370000,754.85 1997-01-08,753.23,755.72,747.71,748.41,557510000,748.41 1997-01-07,747.65,753.26,742.18,753.23,538220000,753.23 1997-01-06,748.03,753.31,743.82,747.65,531350000,747.65 1997-01-03,737.01,748.24,737.01,748.03,452970000,748.03 1997-01-02,740.74,742.81,729.55,737.01,463230000,737.01 1996-12-31,753.85,753.95,740.74,740.74,399760000,740.74 1996-12-30,756.79,759.20,752.73,753.85,339060000,753.85 1996-12-27,755.82,758.75,754.82,756.79,253810000,756.79 1996-12-26,751.03,757.07,751.02,755.82,254630000,755.82 1996-12-24,746.92,751.03,746.92,751.03,165140000,751.03 1996-12-23,748.87,750.40,743.28,746.92,343280000,746.92 1996-12-20,745.76,755.41,745.76,748.87,654340000,748.87 1996-12-19,731.54,746.06,731.54,745.76,526410000,745.76 1996-12-18,726.04,732.76,726.04,731.54,500490000,731.54 1996-12-17,720.98,727.67,716.69,726.04,519840000,726.04 1996-12-16,728.64,732.68,719.40,720.98,447560000,720.98 1996-12-13,729.33,731.40,721.97,728.64,458540000,728.64 1996-12-12,740.73,744.86,729.30,729.30,492920000,729.30 1996-12-11,747.54,747.54,732.75,740.73,494210000,740.73 1996-12-10,749.76,753.43,747.02,747.54,446120000,747.54 1996-12-09,739.60,749.76,739.60,749.76,381570000,749.76 1996-12-06,744.38,744.38,726.89,739.60,500860000,739.60 1996-12-05,745.10,747.65,742.61,744.38,483710000,744.38 1996-12-04,748.28,748.40,738.46,745.10,498240000,745.10 1996-12-03,756.56,761.75,747.58,748.28,516160000,748.28 1996-12-02,757.02,757.03,751.49,756.56,412520000,756.56 1996-11-29,755.00,758.27,755.00,757.02,14990000,757.02 1996-11-27,755.96,757.30,753.18,755.00,377780000,755.00 1996-11-26,757.03,762.12,752.83,755.96,527380000,755.96 1996-11-25,748.73,757.05,747.99,757.03,475260000,757.03 1996-11-22,742.75,748.73,742.75,748.73,525210000,748.73 1996-11-21,743.95,745.20,741.08,742.75,464430000,742.75 1996-11-20,742.16,746.99,740.40,743.95,497900000,743.95 1996-11-19,737.02,742.18,736.87,742.16,461980000,742.16 1996-11-18,737.62,739.24,734.39,737.02,388520000,737.02 1996-11-15,735.88,741.92,735.15,737.62,529100000,737.62 1996-11-14,731.13,735.99,729.20,735.88,480350000,735.88 1996-11-13,729.56,732.11,728.03,731.13,429840000,731.13 1996-11-12,731.87,733.04,728.20,729.56,471740000,729.56 1996-11-11,730.82,732.60,729.94,731.87,353960000,731.87 1996-11-08,727.65,730.82,725.22,730.82,402320000,730.82 1996-11-07,724.59,729.49,722.23,727.65,502530000,727.65 1996-11-06,714.14,724.60,712.83,724.59,509600000,724.59 1996-11-05,706.73,714.56,706.73,714.14,486660000,714.14 1996-11-04,703.77,707.02,702.84,706.73,398790000,706.73 1996-11-01,705.27,708.60,701.30,703.77,465510000,703.77 1996-10-31,700.90,706.61,700.35,705.27,482840000,705.27 1996-10-30,701.50,703.44,700.05,700.90,437770000,700.90 1996-10-29,697.26,703.25,696.22,701.50,443890000,701.50 1996-10-28,700.92,705.40,697.25,697.26,383620000,697.26 1996-10-25,702.29,704.11,700.53,700.92,367640000,700.92 1996-10-24,707.27,708.25,702.11,702.29,418970000,702.29 1996-10-23,706.57,707.31,700.98,707.27,442170000,707.27 1996-10-22,709.85,709.85,704.55,706.57,410790000,706.57 1996-10-21,710.82,714.10,707.71,709.85,414630000,709.85 1996-10-18,706.99,711.04,706.11,710.82,473020000,710.82 1996-10-17,705.00,708.52,704.76,706.99,478550000,706.99 1996-10-16,702.57,704.42,699.15,704.41,441410000,704.41 1996-10-15,703.54,708.07,699.07,702.57,458980000,702.57 1996-10-14,700.66,705.16,700.66,703.54,322000000,703.54 1996-10-11,694.61,700.67,694.61,700.66,396050000,700.66 1996-10-10,696.74,696.82,693.34,694.61,394950000,694.61 1996-10-09,700.64,702.36,694.42,696.74,408450000,696.74 1996-10-08,703.34,705.76,699.88,700.64,435070000,700.64 1996-10-07,701.46,704.17,701.39,703.34,380750000,703.34 1996-10-04,692.78,701.74,692.78,701.46,463940000,701.46 1996-10-03,694.01,694.81,691.78,692.78,386500000,692.78 1996-10-02,689.08,694.82,689.08,694.01,440130000,694.01 1996-10-01,687.31,689.54,684.44,689.08,421550000,689.08 1996-09-30,686.19,690.11,686.03,687.33,388570000,687.33 1996-09-27,685.86,687.11,683.73,686.19,414760000,686.19 1996-09-26,685.83,690.15,683.77,685.86,500870000,685.86 1996-09-25,685.61,688.26,684.92,685.83,451710000,685.83 1996-09-24,686.48,690.88,683.54,685.61,460150000,685.61 1996-09-23,687.03,687.03,681.01,686.48,297760000,686.48 1996-09-20,683.00,687.07,683.00,687.03,519420000,687.03 1996-09-19,681.47,684.07,679.06,683.00,398580000,683.00 1996-09-18,682.94,683.77,679.75,681.47,396600000,681.47 1996-09-17,683.98,685.80,679.96,682.94,449850000,682.94 1996-09-16,680.54,686.48,680.53,683.98,430080000,683.98 1996-09-13,671.15,681.39,671.15,680.54,488360000,680.54 1996-09-12,667.28,673.07,667.28,671.15,398820000,671.15 1996-09-11,663.81,667.73,661.79,667.28,376880000,667.28 1996-09-10,663.76,665.57,661.55,663.81,372960000,663.81 1996-09-09,655.68,663.77,655.68,663.76,311530000,663.76 1996-09-06,649.44,658.21,649.44,655.68,348710000,655.68 1996-09-05,655.61,655.61,648.89,649.44,361430000,649.44 1996-09-04,654.72,655.82,652.93,655.61,351290000,655.61 1996-09-03,651.99,655.13,643.97,654.72,345740000,654.72 1996-08-30,657.40,657.71,650.52,651.99,258380000,651.99 1996-08-29,664.81,664.81,655.35,657.40,321120000,657.40 1996-08-28,666.40,667.41,664.39,664.81,296440000,664.81 1996-08-27,663.88,666.40,663.50,666.40,310520000,666.40 1996-08-26,667.03,667.03,662.36,663.88,281430000,663.88 1996-08-23,670.68,670.68,664.93,667.03,308010000,667.03 1996-08-22,665.07,670.68,664.88,670.68,354950000,670.68 1996-08-21,665.69,665.69,662.16,665.07,348820000,665.07 1996-08-20,666.58,666.99,665.15,665.69,334960000,665.69 1996-08-19,665.21,667.12,665.00,666.58,294080000,666.58 1996-08-16,662.28,666.34,662.26,665.21,337650000,665.21 1996-08-15,662.05,664.18,660.64,662.28,323950000,662.28 1996-08-14,660.20,662.42,658.47,662.05,343460000,662.05 1996-08-13,665.77,665.77,659.13,660.20,362470000,660.20 1996-08-12,662.10,665.77,658.95,665.77,312170000,665.77 1996-08-09,662.59,665.37,660.31,662.10,327280000,662.10 1996-08-08,664.16,664.17,661.28,662.59,334570000,662.59 1996-08-07,662.38,664.61,660.00,664.16,394340000,664.16 1996-08-06,660.23,662.75,656.83,662.38,347290000,662.38 1996-08-05,662.49,663.64,659.03,660.23,307240000,660.23 1996-08-02,650.02,662.49,650.02,662.49,442080000,662.49 1996-08-01,639.95,650.66,639.49,650.02,439110000,650.02 1996-07-31,635.26,640.54,633.74,639.95,403560000,639.95 1996-07-30,630.91,635.26,629.22,635.26,341090000,635.26 1996-07-29,635.90,635.90,630.90,630.91,281560000,630.91 1996-07-26,631.17,636.23,631.17,635.90,349900000,635.90 1996-07-25,626.65,633.57,626.65,631.17,405390000,631.17 1996-07-24,626.19,629.10,616.43,626.65,463030000,626.65 1996-07-23,633.79,637.70,625.65,626.87,421900000,626.87 1996-07-22,638.73,638.73,630.38,633.77,327300000,633.77 1996-07-19,643.51,643.51,635.50,638.73,408070000,638.73 1996-07-18,634.07,644.44,633.29,643.56,474460000,643.56 1996-07-17,628.37,636.61,628.37,634.07,513830000,634.07 1996-07-16,629.80,631.99,605.88,628.37,682980000,628.37 1996-07-15,646.19,646.19,629.69,629.80,419020000,629.80 1996-07-12,645.67,647.64,640.21,646.19,396740000,646.19 1996-07-11,656.06,656.06,639.52,645.67,520470000,645.67 1996-07-10,654.75,656.27,648.39,656.06,421350000,656.06 1996-07-09,652.54,656.60,652.54,654.75,400170000,654.75 1996-07-08,657.44,657.65,651.13,652.54,367560000,652.54 1996-07-05,672.40,672.40,657.41,657.44,181470000,657.44 1996-07-03,673.61,673.64,670.21,672.40,336260000,672.40 1996-07-02,675.88,675.88,672.55,673.61,388000000,673.61 1996-07-01,670.63,675.88,670.63,675.88,345750000,675.88 1996-06-28,668.55,672.68,668.55,670.63,470460000,670.63 1996-06-27,664.39,668.90,661.56,668.55,405580000,668.55 1996-06-26,668.48,668.49,663.67,664.39,386520000,664.39 1996-06-25,668.85,670.65,667.29,668.48,391900000,668.48 1996-06-24,666.84,671.07,666.84,668.85,333840000,668.85 1996-06-21,662.10,666.84,662.10,666.84,520340000,666.84 1996-06-20,661.96,664.96,658.75,662.10,441060000,662.10 1996-06-19,662.06,665.62,661.21,661.96,383610000,661.96 1996-06-18,665.16,666.36,661.34,662.06,373290000,662.06 1996-06-17,665.85,668.27,664.09,665.16,298410000,665.16 1996-06-14,667.92,668.40,664.35,665.85,390630000,665.85 1996-06-13,669.04,670.54,665.49,667.92,397620000,667.92 1996-06-12,670.97,673.67,668.77,669.04,397190000,669.04 1996-06-11,672.16,676.72,669.94,670.97,405390000,670.97 1996-06-10,673.31,673.61,670.15,672.16,337480000,672.16 1996-06-07,673.03,673.31,662.48,673.31,445710000,673.31 1996-06-06,678.44,680.32,673.02,673.03,466940000,673.03 1996-06-05,672.56,678.45,672.09,678.44,380360000,678.44 1996-06-04,667.68,672.60,667.68,672.56,386040000,672.56 1996-06-03,669.12,669.12,665.19,667.68,318470000,667.68 1996-05-31,671.70,673.46,667.00,669.12,351750000,669.12 1996-05-30,667.93,673.51,664.56,671.70,381960000,671.70 1996-05-29,672.23,673.73,666.09,667.93,346730000,667.93 1996-05-28,678.51,679.98,671.52,672.23,341480000,672.23 1996-05-24,676.00,679.72,676.00,678.51,329150000,678.51 1996-05-23,678.42,681.10,673.45,676.00,431850000,676.00 1996-05-22,672.76,678.42,671.23,678.42,423670000,678.42 1996-05-21,673.15,675.56,672.26,672.76,409610000,672.76 1996-05-20,668.91,673.66,667.64,673.15,385000000,673.15 1996-05-17,664.85,669.84,664.85,668.91,429140000,668.91 1996-05-16,665.42,667.11,662.79,664.85,392070000,664.85 1996-05-15,665.60,669.82,664.46,665.42,447790000,665.42 1996-05-14,661.51,666.96,661.51,665.60,460440000,665.60 1996-05-13,652.09,662.16,652.09,661.51,394180000,661.51 1996-05-10,645.44,653.00,645.44,652.09,428370000,652.09 1996-05-09,644.77,647.95,643.18,645.44,404310000,645.44 1996-05-08,638.26,644.79,630.07,644.77,495460000,644.77 1996-05-07,640.81,641.40,636.96,638.26,410770000,638.26 1996-05-06,641.63,644.64,636.19,640.81,375820000,640.81 1996-05-03,643.38,648.45,640.23,641.63,434010000,641.63 1996-05-02,654.58,654.58,642.13,643.38,442960000,643.38 1996-05-01,654.17,656.44,652.26,654.58,404620000,654.58 1996-04-30,654.16,654.59,651.05,654.17,393390000,654.17 1996-04-29,653.46,654.71,651.60,654.16,344030000,654.16 1996-04-26,652.87,656.43,651.96,653.46,402530000,653.46 1996-04-25,650.17,654.18,647.06,652.87,462120000,652.87 1996-04-24,651.58,653.37,648.25,650.17,494220000,650.17 1996-04-23,647.89,651.59,647.70,651.58,452690000,651.58 1996-04-22,645.07,650.91,645.07,647.89,395370000,647.89 1996-04-19,643.61,647.32,643.61,645.07,435690000,645.07 1996-04-18,641.61,644.66,640.76,643.61,415150000,643.61 1996-04-17,645.00,645.00,638.71,641.61,465200000,641.61 1996-04-16,642.49,645.57,642.15,645.00,453310000,645.00 1996-04-15,636.71,642.49,636.71,642.49,346370000,642.49 1996-04-12,631.18,637.14,631.18,636.71,413270000,636.71 1996-04-11,633.50,635.26,624.14,631.18,519710000,631.18 1996-04-10,642.19,642.78,631.76,633.50,475150000,633.50 1996-04-09,644.24,646.33,640.84,642.19,426790000,642.19 1996-04-08,655.86,655.86,638.04,644.24,411810000,644.24 1996-04-04,655.88,656.68,654.89,655.86,383400000,655.86 1996-04-03,655.26,655.89,651.81,655.88,386620000,655.88 1996-04-02,653.73,655.27,652.81,655.26,406640000,655.26 1996-04-01,645.50,653.87,645.50,653.73,392120000,653.73 1996-03-29,648.94,650.96,644.89,645.50,413510000,645.50 1996-03-28,648.91,649.58,646.36,648.94,370750000,648.94 1996-03-27,652.97,653.94,647.60,648.91,406280000,648.91 1996-03-26,650.04,654.31,648.15,652.97,400090000,652.97 1996-03-25,650.62,655.50,648.82,650.04,336700000,650.04 1996-03-22,649.19,652.08,649.19,650.62,329390000,650.62 1996-03-21,649.98,651.54,648.10,649.19,367180000,649.19 1996-03-20,651.69,653.13,645.57,649.98,409780000,649.98 1996-03-19,652.65,656.18,649.80,651.69,438300000,651.69 1996-03-18,641.43,652.65,641.43,652.65,437100000,652.65 1996-03-15,640.87,642.87,638.35,641.43,529970000,641.43 1996-03-14,638.55,644.17,638.55,640.87,492630000,640.87 1996-03-13,637.09,640.52,635.19,638.55,413030000,638.55 1996-03-12,640.02,640.02,628.82,637.09,454980000,637.09 1996-03-11,633.50,640.41,629.95,640.02,449500000,640.02 1996-03-08,653.65,653.65,627.63,633.50,546550000,633.50 1996-03-07,652.00,653.65,649.54,653.65,425790000,653.65 1996-03-06,655.79,656.97,651.61,652.00,428220000,652.00 1996-03-05,650.81,655.80,648.77,655.79,445700000,655.79 1996-03-04,644.37,653.54,644.37,650.81,417270000,650.81 1996-03-01,640.43,644.38,635.00,644.37,471480000,644.37 1996-02-29,644.75,646.95,639.01,640.43,453170000,640.43 1996-02-28,647.24,654.39,643.99,644.75,447790000,644.75 1996-02-27,650.46,650.62,643.87,647.24,431340000,647.24 1996-02-26,659.08,659.08,650.16,650.46,399330000,650.46 1996-02-23,658.86,663.00,652.25,659.08,443130000,659.08 1996-02-22,648.10,659.75,648.10,658.86,485470000,658.86 1996-02-21,640.65,648.11,640.65,648.10,431220000,648.10 1996-02-20,647.98,647.98,638.79,640.65,395910000,640.65 1996-02-16,651.32,651.42,646.99,647.98,445570000,647.98 1996-02-15,655.58,656.84,651.15,651.32,415320000,651.32 1996-02-14,660.51,661.53,654.36,655.58,421790000,655.58 1996-02-13,661.45,664.23,657.92,660.51,441540000,660.51 1996-02-12,656.37,662.95,656.34,661.45,397890000,661.45 1996-02-09,656.07,661.08,653.64,656.37,477640000,656.37 1996-02-08,649.93,656.54,647.93,656.07,474970000,656.07 1996-02-07,646.33,649.93,645.59,649.93,462730000,649.93 1996-02-06,641.43,646.67,639.68,646.33,465940000,646.33 1996-02-05,635.84,641.43,633.71,641.43,377760000,641.43 1996-02-02,638.46,639.26,634.29,635.84,420020000,635.84 1996-02-01,636.02,638.46,634.54,638.46,461430000,638.46 1996-01-31,630.15,636.18,629.48,636.02,472210000,636.02 1996-01-30,624.22,630.29,624.22,630.15,464350000,630.15 1996-01-29,621.62,624.22,621.42,624.22,363330000,624.22 1996-01-26,617.03,621.70,615.26,621.62,385700000,621.62 1996-01-25,619.96,620.15,616.62,617.03,453270000,617.03 1996-01-24,612.79,619.96,612.79,619.96,476380000,619.96 1996-01-23,613.40,613.40,610.65,612.79,416910000,612.79 1996-01-22,611.83,613.45,610.95,613.40,398040000,613.40 1996-01-19,608.24,612.92,606.76,611.83,497720000,611.83 1996-01-18,606.37,608.27,604.12,608.24,450410000,608.24 1996-01-17,608.44,609.93,604.70,606.37,458720000,606.37 1996-01-16,599.82,608.44,599.05,608.44,425220000,608.44 1996-01-15,601.81,603.43,598.47,599.82,306180000,599.82 1996-01-12,602.69,604.80,597.46,601.81,383400000,601.81 1996-01-11,598.48,602.71,597.54,602.69,408800000,602.69 1996-01-10,609.45,609.45,597.29,598.48,496830000,598.48 1996-01-09,618.46,619.15,608.21,609.45,417400000,609.45 1996-01-08,616.71,618.46,616.49,618.46,130360000,618.46 1996-01-05,617.70,617.70,612.02,616.71,437110000,616.71 1996-01-04,621.32,624.49,613.96,617.70,512580000,617.70 1996-01-03,620.73,623.25,619.56,621.32,468950000,621.32 1996-01-02,615.93,620.74,613.17,620.73,364180000,620.73 1995-12-29,614.12,615.93,612.36,615.93,321250000,615.93 1995-12-28,614.53,615.50,612.40,614.12,288660000,614.12 1995-12-27,614.30,615.73,613.75,614.53,252300000,614.53 1995-12-26,611.96,614.50,611.96,614.30,217280000,614.30 1995-12-22,610.49,613.50,610.45,611.95,289600000,611.95 1995-12-21,605.94,610.52,605.94,610.49,415810000,610.49 1995-12-20,611.93,614.27,605.93,605.94,437680000,605.94 1995-12-19,606.81,611.94,605.05,611.93,478280000,611.93 1995-12-18,616.34,616.34,606.13,606.81,426270000,606.81 1995-12-15,616.92,617.72,614.46,616.34,636800000,616.34 1995-12-14,621.69,622.88,616.13,616.92,465300000,616.92 1995-12-13,618.78,622.02,618.27,621.69,415290000,621.69 1995-12-12,619.52,619.55,617.68,618.78,349860000,618.78 1995-12-11,617.48,620.90,617.14,619.52,342070000,619.52 1995-12-08,616.17,617.82,614.32,617.48,327900000,617.48 1995-12-07,620.18,620.19,615.21,616.17,379260000,616.17 1995-12-06,617.68,621.11,616.69,620.18,417780000,620.18 1995-12-05,613.68,618.48,613.14,617.68,437360000,617.68 1995-12-04,606.98,613.83,606.84,613.68,405480000,613.68 1995-12-01,605.37,608.11,605.37,606.98,393310000,606.98 1995-11-30,607.64,608.69,605.37,605.37,440050000,605.37 1995-11-29,606.45,607.66,605.47,607.64,398280000,607.64 1995-11-28,601.32,606.45,599.02,606.45,408860000,606.45 1995-11-27,599.97,603.35,599.97,601.32,359130000,601.32 1995-11-24,598.40,600.24,598.40,599.97,125870000,599.97 1995-11-22,600.24,600.71,598.40,598.40,404980000,598.40 1995-11-21,596.85,600.28,595.42,600.24,408320000,600.24 1995-11-20,600.07,600.40,596.17,596.85,333150000,596.85 1995-11-17,597.34,600.14,597.30,600.07,437200000,600.07 1995-11-16,593.96,597.91,593.52,597.34,423280000,597.34 1995-11-15,589.29,593.97,588.36,593.96,376100000,593.96 1995-11-14,592.30,592.30,588.98,589.29,354420000,589.29 1995-11-13,592.72,593.72,590.58,592.30,295840000,592.30 1995-11-10,593.26,593.26,590.39,592.72,298690000,592.72 1995-11-09,591.71,593.90,590.89,593.26,380760000,593.26 1995-11-08,586.32,591.71,586.32,591.71,359780000,591.71 1995-11-07,588.46,588.46,584.24,586.32,364680000,586.32 1995-11-06,590.57,590.64,588.31,588.46,309100000,588.46 1995-11-03,589.72,590.57,588.65,590.57,348500000,590.57 1995-11-02,584.22,589.72,584.22,589.72,397070000,589.72 1995-11-01,581.50,584.24,581.04,584.22,378090000,584.22 1995-10-31,583.25,586.71,581.50,581.50,377390000,581.50 1995-10-30,579.70,583.79,579.70,583.25,319160000,583.25 1995-10-27,576.72,579.71,573.21,579.70,379230000,579.70 1995-10-26,582.47,582.63,572.53,576.72,464270000,576.72 1995-10-25,586.54,587.19,581.41,582.47,433620000,582.47 1995-10-24,585.06,587.31,584.75,586.54,415540000,586.54 1995-10-23,587.46,587.46,583.73,585.06,330750000,585.06 1995-10-20,590.65,590.66,586.78,587.46,389360000,587.46 1995-10-19,587.44,590.66,586.34,590.65,406620000,590.65 1995-10-18,586.78,589.77,586.27,587.44,411270000,587.44 1995-10-17,583.03,586.78,581.90,586.78,356380000,586.78 1995-10-16,584.50,584.86,582.63,583.03,300750000,583.03 1995-10-13,583.10,587.39,583.10,584.50,374680000,584.50 1995-10-12,579.46,583.12,579.46,583.10,344060000,583.10 1995-10-11,577.52,579.52,577.08,579.46,340740000,579.46 1995-10-10,578.37,578.37,571.55,577.52,412710000,577.52 1995-10-09,582.49,582.49,576.35,578.37,275320000,578.37 1995-10-06,582.63,584.54,582.10,582.49,313680000,582.49 1995-10-05,581.47,582.63,579.58,582.63,367480000,582.63 1995-10-04,582.34,582.34,579.91,581.47,339380000,581.47 1995-10-03,581.72,582.34,578.48,582.34,385940000,582.34 1995-10-02,584.41,585.05,580.54,581.72,304990000,581.72 1995-09-29,585.87,587.61,584.00,584.41,335250000,584.41 1995-09-28,581.04,585.88,580.69,585.87,367720000,585.87 1995-09-27,581.41,581.42,574.68,581.04,411300000,581.04 1995-09-26,581.81,584.66,580.65,581.41,363630000,581.41 1995-09-25,581.73,582.14,579.50,581.81,273120000,581.81 1995-09-22,583.00,583.00,578.25,581.73,370790000,581.73 1995-09-21,586.77,586.79,580.91,583.00,367100000,583.00 1995-09-20,584.20,586.77,584.18,586.77,400050000,586.77 1995-09-19,582.78,584.24,580.75,584.20,371170000,584.20 1995-09-18,583.35,583.37,579.36,582.77,326090000,582.77 1995-09-15,583.61,585.07,581.79,583.35,459370000,583.35 1995-09-14,578.77,583.99,578.77,583.61,382880000,583.61 1995-09-13,576.51,579.72,575.47,578.77,384380000,578.77 1995-09-12,573.91,576.51,573.11,576.51,344540000,576.51 1995-09-11,572.68,575.15,572.68,573.91,296840000,573.91 1995-09-08,570.29,572.68,569.27,572.68,317940000,572.68 1995-09-07,570.17,571.11,569.23,570.29,321720000,570.29 1995-09-06,569.17,570.53,569.00,570.17,369540000,570.17 1995-09-05,563.86,569.20,563.84,569.17,332670000,569.17 1995-09-01,561.88,564.62,561.01,563.84,256730000,563.84 1995-08-31,561.09,562.36,560.49,561.88,300920000,561.88 1995-08-30,560.00,561.52,559.49,560.92,329840000,560.92 1995-08-29,559.05,560.01,555.71,560.00,311290000,560.00 1995-08-28,560.10,562.22,557.99,559.05,267860000,559.05 1995-08-25,557.46,561.31,557.46,560.10,255990000,560.10 1995-08-24,557.14,558.63,555.20,557.46,299200000,557.46 1995-08-23,559.52,560.00,557.08,557.14,291890000,557.14 1995-08-22,558.11,559.52,555.87,559.52,290890000,559.52 1995-08-21,559.21,563.34,557.89,558.11,303200000,558.11 1995-08-18,559.04,561.24,558.34,559.21,320490000,559.21 1995-08-17,559.97,559.97,557.42,559.04,354460000,559.04 1995-08-16,558.57,559.98,557.37,559.97,390170000,559.97 1995-08-15,559.74,559.98,555.22,558.57,330070000,558.57 1995-08-14,555.11,559.74,554.76,559.74,264920000,559.74 1995-08-11,557.45,558.50,553.04,555.11,267850000,555.11 1995-08-10,559.71,560.63,556.05,557.45,306660000,557.45 1995-08-09,560.39,561.59,559.29,559.71,303390000,559.71 1995-08-08,560.03,561.53,558.32,560.39,306090000,560.39 1995-08-07,558.94,561.24,558.94,560.03,277050000,560.03 1995-08-04,558.75,559.57,557.91,558.94,314740000,558.94 1995-08-03,558.80,558.80,554.10,558.75,353110000,558.75 1995-08-02,559.64,565.62,557.87,558.80,374330000,558.80 1995-08-01,562.06,562.11,556.67,559.64,332210000,559.64 1995-07-31,562.93,563.49,560.06,562.06,291950000,562.06 1995-07-28,565.22,565.40,562.04,562.93,311590000,562.93 1995-07-27,561.61,565.33,561.61,565.22,356570000,565.22 1995-07-26,561.10,563.78,560.85,561.61,393470000,561.61 1995-07-25,556.63,561.75,556.34,561.10,373200000,561.10 1995-07-24,553.62,557.21,553.62,556.63,315300000,556.63 1995-07-21,553.34,554.73,550.91,553.62,431830000,553.62 1995-07-20,550.98,554.43,549.10,553.54,383380000,553.54 1995-07-19,556.58,558.46,542.51,550.98,489850000,550.98 1995-07-18,562.55,562.72,556.86,558.46,372230000,558.46 1995-07-17,560.34,562.94,559.45,562.72,322540000,562.72 1995-07-14,561.00,561.00,556.41,559.89,312930000,559.89 1995-07-13,560.89,562.00,559.07,561.00,387500000,561.00 1995-07-12,555.27,561.56,554.27,560.89,416360000,560.89 1995-07-11,556.78,557.19,553.80,554.78,376770000,554.78 1995-07-10,556.37,558.48,555.77,557.19,409700000,557.19 1995-07-07,553.90,556.57,553.05,556.37,466540000,556.37 1995-07-06,547.26,553.99,546.59,553.99,420500000,553.99 1995-07-05,547.09,549.98,546.28,547.26,357850000,547.26 1995-07-03,544.75,547.10,544.43,547.09,117900000,547.09 1995-06-30,543.87,546.82,543.51,544.75,311650000,544.75 1995-06-29,544.73,546.25,540.79,543.87,313080000,543.87 1995-06-28,542.43,546.33,540.72,544.73,368060000,544.73 1995-06-27,544.11,547.07,542.19,542.43,346950000,542.43 1995-06-26,549.71,549.79,544.06,544.13,296720000,544.13 1995-06-23,551.07,551.07,548.23,549.71,321660000,549.71 1995-06-22,543.98,551.07,543.98,551.07,421000000,551.07 1995-06-21,544.98,545.93,543.90,543.98,398210000,543.98 1995-06-20,545.22,545.44,543.43,544.98,382370000,544.98 1995-06-19,539.83,545.22,539.83,545.22,322990000,545.22 1995-06-16,537.51,539.98,537.12,539.83,442740000,539.83 1995-06-15,536.48,539.07,535.56,537.12,334700000,537.12 1995-06-14,536.05,536.48,533.83,536.47,330770000,536.47 1995-06-13,530.88,536.23,530.88,536.05,339660000,536.05 1995-06-12,527.94,532.54,527.94,530.88,289920000,530.88 1995-06-09,532.35,532.35,526.00,527.94,327570000,527.94 1995-06-08,533.13,533.56,531.65,532.35,289880000,532.35 1995-06-07,535.55,535.55,531.66,533.13,327790000,533.13 1995-06-06,535.60,537.09,535.14,535.55,340490000,535.55 1995-06-05,532.51,537.73,532.47,535.60,337520000,535.60 1995-06-02,533.49,536.91,529.55,532.51,366000000,532.51 1995-06-01,533.40,534.21,530.05,533.49,345920000,533.49 1995-05-31,523.70,533.41,522.17,533.40,358180000,533.40 1995-05-30,523.65,525.58,521.38,523.58,283020000,523.58 1995-05-26,528.59,528.59,522.51,523.65,291220000,523.65 1995-05-25,528.37,529.04,524.89,528.59,341820000,528.59 1995-05-24,528.59,531.91,525.57,528.61,391770000,528.61 1995-05-23,523.65,528.59,523.65,528.59,362690000,528.59 1995-05-22,519.19,524.34,519.19,523.65,285600000,523.65 1995-05-19,519.58,519.58,517.07,519.19,354010000,519.19 1995-05-18,526.88,526.88,519.58,519.58,351900000,519.58 1995-05-17,528.19,528.42,525.38,527.07,347930000,527.07 1995-05-16,527.74,529.08,526.45,528.19,366180000,528.19 1995-05-15,525.55,527.74,525.00,527.74,316240000,527.74 1995-05-12,524.37,527.05,523.30,525.55,361000000,525.55 1995-05-11,524.33,524.89,522.70,524.37,339900000,524.37 1995-05-10,523.74,524.40,521.53,524.36,381990000,524.36 1995-05-09,523.96,525.99,521.79,523.56,361300000,523.56 1995-05-08,520.09,525.15,519.14,523.96,291810000,523.96 1995-05-05,520.75,522.35,518.28,520.12,342380000,520.12 1995-05-04,520.48,525.40,519.44,520.54,434990000,520.54 1995-05-03,514.93,520.54,514.86,520.48,392370000,520.48 1995-05-02,514.23,515.18,513.03,514.86,302560000,514.86 1995-05-01,514.76,515.60,513.42,514.26,296830000,514.26 1995-04-28,513.64,515.29,510.90,514.71,320440000,514.71 1995-04-27,512.70,513.62,511.63,513.55,350850000,513.55 1995-04-26,511.99,513.04,510.47,512.66,350810000,512.66 1995-04-25,512.80,513.54,511.32,512.10,351790000,512.10 1995-04-24,508.49,513.02,507.44,512.89,326280000,512.89 1995-04-21,505.63,508.49,505.63,508.49,403250000,508.49 1995-04-20,504.92,506.50,503.44,505.29,368450000,505.29 1995-04-19,505.37,505.89,501.19,504.92,378050000,504.92 1995-04-18,506.43,507.65,504.12,505.37,344680000,505.37 1995-04-17,509.23,512.03,505.43,506.13,333930000,506.13 1995-04-13,507.19,509.83,507.17,509.23,301580000,509.23 1995-04-12,505.59,507.17,505.07,507.17,327880000,507.17 1995-04-11,507.24,508.85,505.29,505.53,310660000,505.53 1995-04-10,506.30,507.01,504.61,507.01,260980000,507.01 1995-04-07,506.13,507.19,503.59,506.42,314760000,506.42 1995-04-06,505.63,507.10,505.00,506.08,320460000,506.08 1995-04-05,505.27,505.57,503.17,505.57,315170000,505.57 1995-04-04,501.85,505.26,501.85,505.24,330580000,505.24 1995-04-03,500.70,501.91,500.20,501.85,296430000,501.85 1995-03-31,501.94,502.22,495.70,500.71,353060000,500.71 1995-03-30,503.17,504.66,501.00,502.22,362940000,502.22 1995-03-29,503.92,508.15,500.96,503.12,385940000,503.12 1995-03-28,503.19,503.91,501.83,503.90,320360000,503.90 1995-03-27,500.97,503.20,500.93,503.20,296270000,503.20 1995-03-24,496.07,500.97,496.07,500.97,358370000,500.97 1995-03-23,495.67,496.77,494.19,495.95,318530000,495.95 1995-03-22,495.07,495.67,493.67,495.67,313120000,495.67 1995-03-21,496.15,499.19,494.04,495.07,367110000,495.07 1995-03-20,495.52,496.61,495.27,496.14,301740000,496.14 1995-03-17,495.43,496.67,494.95,495.52,417380000,495.52 1995-03-16,491.87,495.74,491.78,495.41,336670000,495.41 1995-03-15,492.89,492.89,490.83,491.88,309540000,491.88 1995-03-14,490.05,493.69,490.05,492.89,346160000,492.89 1995-03-13,489.57,491.28,489.35,490.05,275280000,490.05 1995-03-10,483.16,490.37,483.16,489.57,382940000,489.57 1995-03-09,483.14,483.74,482.05,483.16,319320000,483.16 1995-03-08,482.12,484.08,481.57,483.14,349780000,483.14 1995-03-07,485.63,485.63,479.70,482.12,355550000,482.12 1995-03-06,485.42,485.70,481.52,485.63,298870000,485.63 1995-03-03,485.13,485.42,483.07,485.42,330840000,485.42 1995-03-02,485.65,485.71,483.19,485.13,330030000,485.13 1995-03-01,487.39,487.83,484.92,485.65,362600000,485.65 1995-02-28,483.81,487.44,483.77,487.39,317220000,487.39 1995-02-27,488.26,488.26,483.18,483.81,285790000,483.81 1995-02-24,486.82,488.28,485.70,488.11,302930000,488.11 1995-02-23,485.07,489.19,485.07,486.91,394280000,486.91 1995-02-22,482.74,486.15,482.45,485.07,339460000,485.07 1995-02-21,481.95,483.26,481.94,482.72,308090000,482.72 1995-02-17,485.15,485.22,481.97,481.97,347970000,481.97 1995-02-16,484.56,485.22,483.05,485.22,360990000,485.22 1995-02-15,482.55,485.54,481.77,484.54,378040000,484.54 1995-02-14,481.65,482.94,480.89,482.55,300720000,482.55 1995-02-13,481.46,482.86,481.07,481.65,256270000,481.65 1995-02-10,480.19,481.96,479.53,481.46,295600000,481.46 1995-02-09,481.19,482.00,479.91,480.19,325570000,480.19 1995-02-08,480.81,482.60,480.40,481.19,318430000,481.19 1995-02-07,481.14,481.32,479.69,480.81,314660000,480.81 1995-02-06,478.64,481.95,478.36,481.14,325660000,481.14 1995-02-03,472.78,479.91,472.78,478.65,441000000,478.65 1995-02-02,470.40,472.79,469.95,472.79,322110000,472.79 1995-02-01,470.42,472.75,469.29,470.40,395310000,470.40 1995-01-31,468.51,471.03,468.18,470.42,411590000,470.42 1995-01-30,470.39,470.52,467.49,468.51,318550000,468.51 1995-01-27,468.32,471.36,468.32,470.39,339510000,470.39 1995-01-26,467.44,468.62,466.90,468.32,304730000,468.32 1995-01-25,465.86,469.51,464.40,467.44,342610000,467.44 1995-01-24,465.81,466.88,465.47,465.86,315430000,465.86 1995-01-23,464.78,466.23,461.14,465.82,325830000,465.82 1995-01-20,466.95,466.99,463.99,464.78,378190000,464.78 1995-01-19,469.72,469.72,466.40,466.95,297220000,466.95 1995-01-18,470.05,470.43,468.03,469.71,344660000,469.71 1995-01-17,469.38,470.15,468.19,470.05,331520000,470.05 1995-01-16,465.97,470.39,465.97,469.38,315810000,469.38 1995-01-13,461.64,466.43,461.64,465.97,336740000,465.97 1995-01-12,461.64,461.93,460.63,461.64,313040000,461.64 1995-01-11,461.68,463.61,458.65,461.66,346310000,461.66 1995-01-10,460.90,464.59,460.90,461.68,352450000,461.68 1995-01-09,460.67,461.77,459.74,460.83,278790000,460.83 1995-01-06,460.38,462.49,459.47,460.68,308070000,460.68 1995-01-05,460.73,461.30,459.75,460.34,309050000,460.34 1995-01-04,459.13,460.72,457.56,460.71,319510000,460.71 1995-01-03,459.21,459.27,457.20,459.11,262450000,459.11 1994-12-30,461.17,462.12,459.24,459.27,256260000,459.27 1994-12-29,460.92,461.81,460.36,461.17,250650000,461.17 1994-12-28,462.47,462.49,459.00,460.86,246260000,460.86 1994-12-27,459.85,462.73,459.85,462.47,211180000,462.47 1994-12-23,459.70,461.32,459.39,459.83,196540000,459.83 1994-12-22,459.62,461.21,459.33,459.68,340330000,459.68 1994-12-21,457.24,461.70,457.17,459.61,379130000,459.61 1994-12-20,458.08,458.45,456.37,457.10,326530000,457.10 1994-12-19,458.78,458.78,456.64,457.91,271850000,457.91 1994-12-16,455.35,458.80,455.35,458.80,481860000,458.80 1994-12-15,454.97,456.84,454.50,455.34,332790000,455.34 1994-12-14,450.05,456.16,450.05,454.97,355000000,454.97 1994-12-13,449.52,451.69,449.43,450.15,307110000,450.15 1994-12-12,446.95,449.48,445.62,449.47,285730000,449.47 1994-12-09,445.45,446.98,442.88,446.96,336440000,446.96 1994-12-08,451.23,452.06,444.59,445.45,362290000,445.45 1994-12-07,453.11,453.11,450.01,451.23,283490000,451.23 1994-12-06,453.29,453.93,450.35,453.11,298930000,453.11 1994-12-05,453.30,455.04,452.06,453.32,258490000,453.32 1994-12-02,448.92,453.31,448.00,453.30,284750000,453.30 1994-12-01,453.55,453.91,447.97,448.92,285920000,448.92 1994-11-30,455.17,457.13,453.27,453.69,298650000,453.69 1994-11-29,454.23,455.17,452.14,455.17,286620000,455.17 1994-11-28,452.26,454.19,451.04,454.16,265480000,454.16 1994-11-25,449.94,452.87,449.94,452.29,118290000,452.29 1994-11-23,450.01,450.61,444.18,449.93,430760000,449.93 1994-11-22,457.95,458.03,450.08,450.09,387270000,450.09 1994-11-21,461.69,463.41,457.55,458.30,293030000,458.30 1994-11-18,463.60,463.84,460.25,461.47,356730000,461.47 1994-11-17,465.71,465.83,461.47,463.57,323190000,463.57 1994-11-16,465.06,466.25,464.28,465.62,296980000,465.62 1994-11-15,466.04,468.51,462.95,465.03,336450000,465.03 1994-11-14,462.44,466.29,462.35,466.04,260380000,466.04 1994-11-11,464.17,464.17,461.45,462.35,220800000,462.35 1994-11-10,465.40,467.79,463.73,464.37,280910000,464.37 1994-11-09,465.65,469.95,463.46,465.40,337780000,465.40 1994-11-08,463.08,467.54,463.07,465.65,290860000,465.65 1994-11-07,462.31,463.56,461.25,463.07,255030000,463.07 1994-11-04,467.96,469.28,462.28,462.28,280560000,462.28 1994-11-03,466.50,468.64,466.40,467.91,285170000,467.91 1994-11-02,468.41,470.92,466.36,466.51,331360000,466.51 1994-11-01,472.26,472.26,467.64,468.42,314940000,468.42 1994-10-31,473.76,474.74,472.33,472.35,302820000,472.35 1994-10-28,465.84,473.78,465.80,473.77,381450000,473.77 1994-10-27,462.68,465.85,462.62,465.85,327790000,465.85 1994-10-26,461.55,463.77,461.22,462.62,322570000,462.62 1994-10-25,460.83,461.95,458.26,461.53,326110000,461.53 1994-10-24,464.89,466.37,460.80,460.83,282800000,460.83 1994-10-21,466.69,466.69,463.83,464.89,315310000,464.89 1994-10-20,470.37,470.37,465.39,466.85,331460000,466.85 1994-10-19,467.69,471.43,465.96,470.28,317030000,470.28 1994-10-18,469.02,469.19,466.54,467.66,259730000,467.66 1994-10-17,469.11,469.88,468.16,468.96,238490000,468.96 1994-10-14,467.78,469.53,466.11,469.10,251770000,469.10 1994-10-13,465.56,471.30,465.56,467.79,337900000,467.79 1994-10-12,465.78,466.70,464.79,465.47,269550000,465.47 1994-10-11,459.04,466.34,459.04,465.79,355540000,465.79 1994-10-10,455.12,459.29,455.12,459.04,213110000,459.04 1994-10-07,452.37,455.67,452.13,455.10,284230000,455.10 1994-10-06,453.52,454.49,452.13,452.36,272620000,452.36 1994-10-05,454.59,454.59,449.27,453.52,359670000,453.52 1994-10-04,461.77,462.46,454.03,454.59,325620000,454.59 1994-10-03,462.69,463.31,460.33,461.74,269130000,461.74 1994-09-30,462.27,465.30,461.91,462.71,291900000,462.71 1994-09-29,464.84,464.84,461.51,462.24,302280000,462.24 1994-09-28,462.10,465.55,462.10,464.84,330020000,464.84 1994-09-27,460.82,462.75,459.83,462.05,290330000,462.05 1994-09-26,459.65,460.87,459.31,460.82,272530000,460.82 1994-09-23,461.27,462.14,459.01,459.67,300060000,459.67 1994-09-22,461.45,463.22,460.96,461.27,305210000,461.27 1994-09-21,463.42,464.01,458.47,461.46,351830000,461.46 1994-09-20,470.83,470.83,463.36,463.36,326050000,463.36 1994-09-19,471.21,473.15,470.68,470.85,277110000,470.85 1994-09-16,474.81,474.81,470.06,471.19,410750000,471.19 1994-09-15,468.80,474.81,468.79,474.81,281920000,474.81 1994-09-14,467.55,468.86,466.82,468.80,297480000,468.80 1994-09-13,466.27,468.76,466.27,467.51,293370000,467.51 1994-09-12,468.18,468.42,466.15,466.21,244680000,466.21 1994-09-09,473.13,473.13,466.55,468.18,293360000,468.18 1994-09-08,470.96,473.40,470.86,473.14,295010000,473.14 1994-09-07,471.86,472.41,470.20,470.99,290330000,470.99 1994-09-06,471.00,471.92,469.64,471.86,199670000,471.86 1994-09-02,473.20,474.89,470.67,470.99,216150000,470.99 1994-09-01,475.49,475.49,471.74,473.17,282830000,473.17 1994-08-31,476.07,477.59,474.43,475.49,354650000,475.49 1994-08-30,474.59,476.61,473.56,476.07,294520000,476.07 1994-08-29,473.89,477.14,473.89,474.59,266080000,474.59 1994-08-26,468.08,474.65,468.08,473.80,305120000,473.80 1994-08-25,469.07,470.12,467.64,468.08,284230000,468.08 1994-08-24,464.51,469.05,464.51,469.03,310510000,469.03 1994-08-23,462.39,466.58,462.39,464.51,307240000,464.51 1994-08-22,463.61,463.61,461.46,462.32,235870000,462.32 1994-08-19,463.25,464.37,461.81,463.68,276630000,463.68 1994-08-18,465.10,465.10,462.30,463.17,287330000,463.17 1994-08-17,465.11,465.91,464.57,465.17,309250000,465.17 1994-08-16,461.22,465.20,459.89,465.01,306640000,465.01 1994-08-15,461.97,463.34,461.21,461.23,223210000,461.23 1994-08-12,458.88,462.27,458.88,461.94,249280000,461.94 1994-08-11,460.31,461.41,456.88,458.88,275690000,458.88 1994-08-10,457.98,460.48,457.98,460.30,279500000,460.30 1994-08-09,457.89,458.16,456.66,457.92,259140000,457.92 1994-08-08,457.08,458.30,457.01,457.89,217680000,457.89 1994-08-05,458.34,458.34,456.08,457.09,230270000,457.09 1994-08-04,461.45,461.49,458.40,458.40,289150000,458.40 1994-08-03,460.65,461.46,459.51,461.45,283840000,461.45 1994-08-02,461.01,462.77,459.70,460.56,294740000,460.56 1994-08-01,458.28,461.01,458.08,461.01,258180000,461.01 1994-07-29,454.25,459.33,454.25,458.26,269560000,458.26 1994-07-28,452.57,454.93,452.30,454.24,245990000,454.24 1994-07-27,453.36,453.38,451.36,452.57,251680000,452.57 1994-07-26,454.25,454.25,452.78,453.36,232670000,453.36 1994-07-25,453.10,454.32,452.76,454.25,213470000,454.25 1994-07-22,452.61,454.03,452.33,453.11,261600000,453.11 1994-07-21,451.60,453.22,451.00,452.61,292120000,452.61 1994-07-20,453.89,454.16,450.69,451.60,267840000,451.60 1994-07-19,455.22,455.30,453.86,453.86,251530000,453.86 1994-07-18,454.41,455.71,453.26,455.22,227460000,455.22 1994-07-15,453.28,454.33,452.80,454.16,275860000,454.16 1994-07-14,448.73,454.33,448.73,453.41,322330000,453.41 1994-07-13,448.03,450.06,447.97,448.73,265840000,448.73 1994-07-12,448.02,448.16,444.65,447.95,252250000,447.95 1994-07-11,449.56,450.24,445.27,448.06,222970000,448.06 1994-07-08,448.38,449.75,446.53,449.55,236520000,449.55 1994-07-07,446.15,448.64,446.15,448.38,259740000,448.38 1994-07-06,446.29,447.28,444.18,446.13,236230000,446.13 1994-07-05,446.20,447.62,445.14,446.37,195410000,446.37 1994-07-01,444.27,446.45,443.58,446.20,199030000,446.20 1994-06-30,447.63,448.61,443.66,444.27,293410000,444.27 1994-06-29,446.05,449.83,446.04,447.63,264430000,447.63 1994-06-28,447.36,448.47,443.08,446.07,267740000,446.07 1994-06-27,442.78,447.76,439.83,447.31,250080000,447.31 1994-06-24,449.63,449.63,442.51,442.80,261260000,442.80 1994-06-23,453.09,454.16,449.43,449.63,256480000,449.63 1994-06-22,451.40,453.91,451.40,453.09,251110000,453.09 1994-06-21,455.48,455.48,449.45,451.34,298730000,451.34 1994-06-20,458.45,458.45,454.46,455.48,229520000,455.48 1994-06-17,461.93,462.16,458.44,458.45,373450000,458.45 1994-06-16,460.61,461.93,459.80,461.93,256390000,461.93 1994-06-15,462.38,463.23,459.95,460.61,269740000,460.61 1994-06-14,459.10,462.52,459.10,462.37,288550000,462.37 1994-06-13,458.67,459.36,457.18,459.10,243640000,459.10 1994-06-10,457.86,459.48,457.36,458.67,222480000,458.67 1994-06-09,457.06,457.87,455.86,457.86,252870000,457.86 1994-06-08,458.21,459.74,455.43,457.06,256000000,457.06 1994-06-07,458.88,459.46,457.65,458.21,234680000,458.21 1994-06-06,460.13,461.87,458.85,458.88,259080000,458.88 1994-06-03,457.65,460.86,456.27,460.13,271490000,460.13 1994-06-02,457.62,458.50,457.26,457.65,271630000,457.65 1994-06-01,456.50,458.29,453.99,457.63,279910000,457.63 1994-05-31,457.32,457.61,455.16,456.50,216700000,456.50 1994-05-27,457.03,457.33,454.67,457.33,186430000,457.33 1994-05-26,456.33,457.77,455.79,457.06,255740000,457.06 1994-05-25,454.84,456.34,452.20,456.34,254420000,456.34 1994-05-24,453.21,456.77,453.21,454.81,280040000,454.81 1994-05-23,454.92,454.92,451.79,453.20,249420000,453.20 1994-05-20,456.48,456.48,454.22,454.92,295180000,454.92 1994-05-19,453.69,456.88,453.00,456.48,303680000,456.48 1994-05-18,449.39,454.45,448.87,453.69,337670000,453.69 1994-05-17,444.49,449.37,443.70,449.37,311280000,449.37 1994-05-16,444.15,445.82,443.62,444.49,234700000,444.49 1994-05-13,443.62,444.72,441.21,444.14,252070000,444.14 1994-05-12,441.50,444.80,441.50,443.75,272770000,443.75 1994-05-11,446.03,446.03,440.78,441.49,277400000,441.49 1994-05-10,442.37,446.84,442.37,446.01,297660000,446.01 1994-05-09,447.82,447.82,441.84,442.32,250870000,442.32 1994-05-06,451.37,451.37,445.64,447.82,291910000,447.82 1994-05-05,451.72,452.82,450.72,451.38,255690000,451.38 1994-05-04,453.04,453.11,449.87,451.72,267940000,451.72 1994-05-03,453.06,453.98,450.51,453.03,288270000,453.03 1994-05-02,450.91,453.57,449.05,453.02,296130000,453.02 1994-04-29,449.07,451.35,447.91,450.91,293970000,450.91 1994-04-28,451.84,452.23,447.97,449.10,325200000,449.10 1994-04-26,452.71,452.79,450.66,451.87,288120000,451.87 1994-04-25,447.64,452.71,447.58,452.71,262320000,452.71 1994-04-22,448.73,449.96,447.16,447.63,295710000,447.63 1994-04-21,441.96,449.14,441.96,448.73,378770000,448.73 1994-04-20,442.54,445.01,439.40,441.96,366540000,441.96 1994-04-19,442.54,444.82,438.83,442.54,323280000,442.54 1994-04-18,446.27,447.87,441.48,442.46,271470000,442.46 1994-04-15,446.38,447.85,445.81,446.18,309550000,446.18 1994-04-14,446.26,447.55,443.57,446.38,275130000,446.38 1994-04-13,447.63,448.57,442.62,446.26,278030000,446.26 1994-04-12,449.83,450.80,447.33,447.57,257990000,447.57 1994-04-11,447.12,450.34,447.10,449.87,243180000,449.87 1994-04-08,450.89,450.89,445.51,447.10,264090000,447.10 1994-04-07,448.11,451.10,446.38,450.88,289280000,450.88 1994-04-06,448.29,449.63,444.98,448.05,302000000,448.05 1994-04-05,439.14,448.29,439.14,448.29,365990000,448.29 1994-04-04,445.66,445.66,435.86,438.92,344390000,438.92 1994-03-31,445.55,447.16,436.16,445.77,403580000,445.77 1994-03-30,452.48,452.49,445.55,445.55,390520000,445.55 1994-03-29,460.00,460.32,452.43,452.48,305360000,452.48 1994-03-28,460.58,461.12,456.10,460.00,287350000,460.00 1994-03-25,464.35,465.29,460.58,460.58,249640000,460.58 1994-03-24,468.57,468.57,462.41,464.35,303740000,464.35 1994-03-23,468.89,470.38,468.52,468.54,281500000,468.54 1994-03-22,468.40,470.47,467.88,468.80,282240000,468.80 1994-03-21,471.06,471.06,467.23,468.54,247380000,468.54 1994-03-18,470.89,471.09,467.83,471.06,462240000,471.06 1994-03-17,469.42,471.05,468.62,470.90,303930000,470.90 1994-03-16,467.04,469.85,465.48,469.42,307640000,469.42 1994-03-15,467.39,468.99,466.04,467.01,303750000,467.01 1994-03-14,466.44,467.60,466.08,467.39,260150000,467.39 1994-03-11,463.86,466.61,462.54,466.44,303890000,466.44 1994-03-10,467.08,467.29,462.46,463.90,369370000,463.90 1994-03-09,465.94,467.42,463.40,467.06,309810000,467.06 1994-03-08,466.92,467.79,465.02,465.88,298110000,465.88 1994-03-07,464.74,468.07,464.74,466.91,285590000,466.91 1994-03-04,463.03,466.16,462.41,464.74,311850000,464.74 1994-03-03,464.81,464.83,462.50,463.01,291790000,463.01 1994-03-02,464.40,464.87,457.49,464.81,361130000,464.81 1994-03-01,467.19,467.43,462.02,464.44,304450000,464.44 1994-02-28,466.07,469.16,466.07,467.14,268690000,467.14 1994-02-25,464.33,466.48,464.33,466.07,273680000,466.07 1994-02-24,470.65,470.65,464.26,464.26,342940000,464.26 1994-02-23,471.48,472.41,469.47,470.69,309910000,470.69 1994-02-22,467.69,471.65,467.58,471.46,270900000,471.46 1994-02-18,470.29,471.09,466.07,467.69,293210000,467.69 1994-02-17,472.79,475.12,468.44,470.34,340030000,470.34 1994-02-16,472.53,474.16,471.94,472.79,295450000,472.79 1994-02-15,470.23,473.41,470.23,472.52,306790000,472.52 1994-02-14,470.18,471.99,469.05,470.23,263190000,470.23 1994-02-11,468.93,471.13,466.89,470.18,213740000,470.18 1994-02-10,472.81,473.13,468.91,468.93,327250000,468.93 1994-02-09,471.05,473.41,471.05,472.77,332670000,472.77 1994-02-08,471.76,472.33,469.50,471.05,318180000,471.05 1994-02-07,469.81,472.09,467.57,471.76,348270000,471.76 1994-02-04,480.68,481.02,469.28,469.81,378380000,469.81 1994-02-03,481.96,481.96,478.71,480.71,318350000,480.71 1994-02-02,479.62,482.23,479.57,482.00,328960000,482.00 1994-02-01,481.60,481.64,479.18,479.62,322510000,479.62 1994-01-31,478.70,482.85,478.70,481.61,322870000,481.61 1994-01-28,477.05,479.75,477.05,478.70,313140000,478.70 1994-01-27,473.20,477.52,473.20,477.05,346500000,477.05 1994-01-26,470.92,473.44,470.72,473.20,304660000,473.20 1994-01-25,471.97,472.56,470.27,470.92,326120000,470.92 1994-01-24,474.72,475.20,471.49,471.97,296900000,471.97 1994-01-21,474.98,475.56,473.72,474.72,346350000,474.72 1994-01-20,474.30,475.00,473.42,474.98,310450000,474.98 1994-01-19,474.25,474.70,472.21,474.30,311370000,474.30 1994-01-18,473.30,475.19,473.29,474.25,308840000,474.25 1994-01-17,474.91,474.91,472.84,473.30,233980000,473.30 1994-01-14,472.50,475.32,472.50,474.91,304920000,474.91 1994-01-13,474.17,474.17,471.80,472.47,277970000,472.47 1994-01-12,474.13,475.06,472.14,474.17,310690000,474.17 1994-01-11,475.27,475.28,473.27,474.13,305490000,474.13 1994-01-10,469.90,475.27,469.55,475.27,319490000,475.27 1994-01-07,467.09,470.26,467.03,469.90,324920000,469.90 1994-01-06,467.55,469.00,467.02,467.12,365960000,467.12 1994-01-05,466.89,467.82,465.92,467.55,400030000,467.55 1994-01-04,465.44,466.89,464.44,466.89,326600000,466.89 1994-01-03,466.51,466.94,464.36,465.44,270140000,465.44 1993-12-31,468.66,470.75,466.45,466.45,168590000,466.45 1993-12-30,470.58,470.58,468.09,468.64,195860000,468.64 1993-12-29,470.88,471.29,469.87,470.58,269570000,470.58 1993-12-28,470.61,471.05,469.43,470.94,200960000,470.94 1993-12-27,467.40,470.55,467.35,470.54,171200000,470.54 1993-12-23,467.30,468.97,467.30,467.38,227240000,467.38 1993-12-22,465.08,467.38,465.08,467.32,272440000,467.32 1993-12-21,465.84,465.92,464.03,465.30,273370000,465.30 1993-12-20,466.38,466.90,465.53,465.85,255900000,465.85 1993-12-17,463.34,466.38,463.34,466.38,363750000,466.38 1993-12-16,461.86,463.98,461.86,463.34,284620000,463.34 1993-12-15,463.06,463.69,461.84,461.84,331770000,461.84 1993-12-14,465.73,466.12,462.46,463.06,275050000,463.06 1993-12-13,463.93,465.71,462.71,465.70,256580000,465.70 1993-12-10,464.18,464.87,462.66,463.93,245620000,463.93 1993-12-09,466.29,466.54,463.87,464.18,287570000,464.18 1993-12-08,465.88,466.73,465.42,466.29,314460000,466.29 1993-12-07,466.43,466.77,465.44,466.76,285690000,466.76 1993-12-06,464.89,466.89,464.40,466.43,292370000,466.43 1993-12-03,463.13,464.89,462.67,464.89,268360000,464.89 1993-12-02,461.89,463.22,461.45,463.11,256370000,463.11 1993-12-01,461.93,464.47,461.63,461.89,293870000,461.89 1993-11-30,461.90,463.62,460.45,461.79,286660000,461.79 1993-11-29,463.06,464.83,461.83,461.90,272710000,461.90 1993-11-26,462.36,463.63,462.36,463.06,90220000,463.06 1993-11-24,461.03,462.90,461.03,462.36,230630000,462.36 1993-11-23,459.13,461.77,458.47,461.03,260400000,461.03 1993-11-22,462.60,462.60,457.08,459.13,280130000,459.13 1993-11-19,463.59,463.60,460.03,462.60,302970000,462.60 1993-11-18,464.83,464.88,461.73,463.62,313490000,463.62 1993-11-17,466.74,467.24,462.73,464.81,316940000,464.81 1993-11-16,463.75,466.74,462.97,466.74,303980000,466.74 1993-11-15,465.39,466.13,463.01,463.75,251030000,463.75 1993-11-12,462.64,465.84,462.64,465.39,326240000,465.39 1993-11-11,463.72,464.96,462.49,462.64,283820000,462.64 1993-11-10,460.40,463.72,459.57,463.72,283450000,463.72 1993-11-09,460.21,463.42,460.21,460.33,276360000,460.33 1993-11-08,459.57,461.54,458.78,460.21,234340000,460.21 1993-11-05,457.49,459.63,454.36,459.57,336890000,459.57 1993-11-04,463.02,463.16,457.26,457.49,323430000,457.49 1993-11-03,468.44,468.61,460.95,463.02,342110000,463.02 1993-11-02,469.10,469.10,466.20,468.44,304780000,468.44 1993-11-01,467.83,469.11,467.33,469.10,256030000,469.10 1993-10-29,467.72,468.20,467.37,467.83,270570000,467.83 1993-10-28,464.52,468.76,464.52,467.73,301220000,467.73 1993-10-27,464.30,464.61,463.36,464.61,279830000,464.61 1993-10-26,464.20,464.32,462.65,464.30,284530000,464.30 1993-10-25,463.27,464.49,462.05,464.20,260310000,464.20 1993-10-22,465.36,467.82,463.27,463.27,301440000,463.27 1993-10-21,466.06,466.64,464.38,465.36,289600000,465.36 1993-10-20,466.21,466.87,464.54,466.07,305670000,466.07 1993-10-19,468.41,468.64,464.80,466.21,304400000,466.21 1993-10-18,469.50,470.04,468.02,468.45,329580000,468.45 1993-10-15,466.83,471.10,466.83,469.50,366110000,469.50 1993-10-14,461.55,466.83,461.55,466.83,352530000,466.83 1993-10-13,461.12,461.98,460.76,461.49,290930000,461.49 1993-10-12,461.04,462.47,460.73,461.12,263970000,461.12 1993-10-11,460.31,461.87,460.31,460.88,183060000,460.88 1993-10-08,459.18,460.99,456.40,460.31,243600000,460.31 1993-10-07,460.71,461.13,459.08,459.18,255210000,459.18 1993-10-06,461.24,462.60,460.26,460.74,277070000,460.74 1993-10-05,461.34,463.15,459.45,461.20,294570000,461.20 1993-10-04,461.28,461.80,460.02,461.34,229380000,461.34 1993-10-01,458.93,461.48,458.35,461.28,256880000,461.28 1993-09-30,460.11,460.56,458.28,458.93,280980000,458.93 1993-09-29,461.60,462.17,459.51,460.11,277690000,460.11 1993-09-28,461.84,462.08,460.91,461.53,243320000,461.53 1993-09-27,457.63,461.81,457.63,461.80,244920000,461.80 1993-09-24,457.74,458.56,456.92,457.63,248270000,457.63 1993-09-23,456.25,458.69,456.25,457.74,275350000,457.74 1993-09-22,452.94,456.92,452.94,456.20,298960000,456.20 1993-09-21,455.05,455.80,449.64,452.95,300310000,452.95 1993-09-20,458.84,459.91,455.00,455.05,231130000,455.05 1993-09-17,459.43,459.43,457.09,458.83,381370000,458.83 1993-09-16,461.54,461.54,459.00,459.43,229700000,459.43 1993-09-15,459.90,461.96,456.31,461.60,294410000,461.60 1993-09-14,461.93,461.93,458.15,459.90,258650000,459.90 1993-09-13,461.70,463.38,461.41,462.06,244970000,462.06 1993-09-10,457.49,461.86,457.49,461.72,269950000,461.72 1993-09-09,456.65,458.11,455.17,457.50,258070000,457.50 1993-09-08,458.52,458.53,453.75,456.65,283100000,456.65 1993-09-07,461.34,462.07,457.95,458.52,229500000,458.52 1993-09-03,461.30,462.05,459.91,461.34,197160000,461.34 1993-09-02,463.13,463.54,461.07,461.30,259870000,461.30 1993-09-01,463.55,463.80,461.77,463.15,245040000,463.15 1993-08-31,461.90,463.56,461.29,463.56,252830000,463.56 1993-08-30,460.54,462.58,460.28,461.90,194180000,461.90 1993-08-27,461.05,461.05,459.19,460.54,196140000,460.54 1993-08-26,460.04,462.87,458.82,461.04,254070000,461.04 1993-08-25,459.75,462.04,459.30,460.13,301650000,460.13 1993-08-24,455.23,459.77,455.04,459.77,270700000,459.77 1993-08-23,456.12,456.12,454.29,455.23,212500000,455.23 1993-08-20,456.51,456.68,454.60,456.16,276800000,456.16 1993-08-19,456.01,456.76,455.20,456.43,293330000,456.43 1993-08-18,453.21,456.99,453.21,456.04,312940000,456.04 1993-08-17,452.38,453.70,451.96,453.13,261320000,453.13 1993-08-16,450.25,453.41,450.25,452.38,233640000,452.38 1993-08-13,448.97,450.25,448.97,450.14,214370000,450.14 1993-08-12,450.47,451.63,447.53,448.96,278530000,448.96 1993-08-11,449.60,451.00,449.60,450.46,268330000,450.46 1993-08-10,450.71,450.71,449.10,449.45,255520000,449.45 1993-08-09,448.68,451.51,448.31,450.72,232750000,450.72 1993-08-06,448.13,449.26,447.87,448.68,221170000,448.68 1993-08-05,448.55,449.61,446.94,448.13,261900000,448.13 1993-08-04,449.27,449.72,447.93,448.54,230040000,448.54 1993-08-03,450.15,450.43,447.59,449.27,253110000,449.27 1993-08-02,448.13,450.15,448.03,450.15,230380000,450.15 1993-07-30,450.19,450.22,446.98,448.13,254420000,448.13 1993-07-29,447.19,450.77,447.19,450.24,261240000,450.24 1993-07-28,448.25,448.61,446.59,447.19,273100000,447.19 1993-07-27,449.00,449.44,446.76,448.24,256750000,448.24 1993-07-26,447.06,449.50,447.04,449.09,222580000,449.09 1993-07-23,444.54,447.10,444.54,447.10,222170000,447.10 1993-07-22,447.18,447.23,443.72,444.51,249630000,444.51 1993-07-21,447.28,447.50,445.84,447.18,278590000,447.18 1993-07-20,446.03,447.63,443.71,447.31,277420000,447.31 1993-07-19,445.75,446.78,444.83,446.03,216370000,446.03 1993-07-16,449.07,449.08,445.66,445.75,263100000,445.75 1993-07-15,450.09,450.12,447.26,449.22,277810000,449.22 1993-07-14,448.08,451.12,448.08,450.08,297430000,450.08 1993-07-13,449.00,450.70,448.07,448.09,236720000,448.09 1993-07-12,448.13,449.11,447.71,448.98,202310000,448.98 1993-07-09,448.64,448.94,446.74,448.11,235210000,448.11 1993-07-08,442.84,448.64,442.84,448.64,282910000,448.64 1993-07-07,441.40,443.63,441.40,442.83,253170000,442.83 1993-07-06,445.86,446.87,441.42,441.43,234810000,441.43 1993-07-02,449.02,449.02,445.20,445.84,220750000,445.84 1993-07-01,450.54,451.15,448.71,449.02,292040000,449.02 1993-06-30,450.69,451.47,450.15,450.53,281120000,450.53 1993-06-29,451.89,451.90,449.67,450.69,276310000,450.69 1993-06-28,447.60,451.90,447.60,451.85,242090000,451.85 1993-06-25,446.62,448.64,446.62,447.60,210430000,447.60 1993-06-24,443.04,447.21,442.50,446.62,267450000,446.62 1993-06-23,445.96,445.96,443.19,443.19,278260000,443.19 1993-06-22,446.25,446.29,444.94,445.93,259530000,445.93 1993-06-21,443.68,446.22,443.68,446.22,223650000,446.22 1993-06-18,448.54,448.59,443.68,443.68,300500000,443.68 1993-06-17,447.43,448.98,446.91,448.54,239810000,448.54 1993-06-16,446.27,447.43,443.61,447.43,267500000,447.43 1993-06-15,447.73,448.28,446.18,446.27,234110000,446.27 1993-06-14,447.26,448.64,447.23,447.71,210440000,447.71 1993-06-11,445.38,448.19,445.38,447.26,256750000,447.26 1993-06-10,445.78,446.22,444.09,445.38,232600000,445.38 1993-06-09,444.71,447.39,444.66,445.78,249030000,445.78 1993-06-08,447.65,447.65,444.31,444.71,240640000,444.71 1993-06-07,450.07,450.75,447.32,447.69,236920000,447.69 1993-06-04,452.43,452.43,448.92,450.06,226440000,450.06 1993-06-03,453.84,453.85,451.12,452.49,285570000,452.49 1993-06-02,453.83,454.53,452.68,453.85,295560000,453.85 1993-06-01,450.23,455.63,450.23,453.83,229690000,453.83 1993-05-28,452.41,452.41,447.67,450.19,207820000,450.19 1993-05-27,453.44,454.55,451.14,452.41,300810000,452.41 1993-05-26,448.85,453.51,448.82,453.44,274230000,453.44 1993-05-25,448.00,449.04,447.70,448.85,222090000,448.85 1993-05-24,445.84,448.44,445.26,448.00,197990000,448.00 1993-05-21,450.59,450.59,444.89,445.84,279120000,445.84 1993-05-20,447.57,450.59,447.36,450.59,289160000,450.59 1993-05-19,440.32,447.86,436.86,447.57,342420000,447.57 1993-05-18,440.39,441.26,437.95,440.32,264300000,440.32 1993-05-17,439.56,440.38,437.83,440.37,227580000,440.37 1993-05-14,439.22,439.82,438.10,439.56,252910000,439.56 1993-05-13,444.75,444.75,439.23,439.23,293920000,439.23 1993-05-12,444.32,445.16,442.87,444.80,255680000,444.80 1993-05-11,442.80,444.57,441.52,444.36,218480000,444.36 1993-05-10,442.34,445.42,442.05,442.80,235580000,442.80 1993-05-07,443.28,443.70,441.69,442.31,223570000,442.31 1993-05-06,444.60,444.81,442.90,443.26,255460000,443.26 1993-05-05,443.98,446.09,443.76,444.52,274240000,444.52 1993-05-04,442.58,445.19,442.45,444.05,268310000,444.05 1993-05-03,440.19,442.59,438.25,442.46,224970000,442.46 1993-04-30,438.89,442.29,438.89,440.19,247460000,440.19 1993-04-29,438.02,438.96,435.59,438.89,249760000,438.89 1993-04-28,438.01,438.80,436.68,438.02,267980000,438.02 1993-04-27,433.52,438.02,433.14,438.01,284140000,438.01 1993-04-26,437.03,438.35,432.30,433.54,283260000,433.54 1993-04-23,439.49,439.49,436.82,437.03,259810000,437.03 1993-04-22,443.55,445.73,439.46,439.46,310390000,439.46 1993-04-21,445.09,445.77,443.08,443.63,287300000,443.63 1993-04-20,447.46,447.46,441.81,445.10,317990000,445.10 1993-04-19,448.94,449.14,445.85,447.46,244710000,447.46 1993-04-16,448.41,449.39,447.67,448.94,305160000,448.94 1993-04-15,448.60,449.11,446.39,448.40,259500000,448.40 1993-04-14,449.22,450.00,448.02,448.66,257340000,448.66 1993-04-13,448.41,450.40,447.66,449.22,286690000,449.22 1993-04-12,441.84,448.37,441.84,448.37,259690000,448.37 1993-04-08,442.71,443.77,440.02,441.84,284370000,441.84 1993-04-07,441.16,442.73,440.50,442.73,300000000,442.73 1993-04-06,442.29,443.38,439.48,441.16,293680000,441.16 1993-04-05,441.42,442.43,440.53,442.29,296080000,442.29 1993-04-02,450.28,450.28,440.71,441.39,323330000,441.39 1993-04-01,451.67,452.63,449.60,450.30,234530000,450.30 1993-03-31,451.97,454.88,451.67,451.67,279190000,451.67 1993-03-30,450.79,452.06,449.63,451.97,231190000,451.97 1993-03-29,447.76,452.81,447.75,450.77,199970000,450.77 1993-03-26,450.91,452.09,447.69,447.78,226650000,447.78 1993-03-25,448.09,451.75,447.93,450.88,251530000,450.88 1993-03-24,448.71,450.90,446.10,448.07,274300000,448.07 1993-03-23,448.88,449.80,448.30,448.76,232730000,448.76 1993-03-22,450.17,450.17,446.08,448.88,233190000,448.88 1993-03-19,451.90,453.32,449.91,450.18,339660000,450.18 1993-03-18,448.36,452.39,448.36,451.89,241180000,451.89 1993-03-17,451.36,451.36,447.99,448.31,241270000,448.31 1993-03-16,451.43,452.36,451.01,451.37,218820000,451.37 1993-03-15,449.83,451.43,449.40,451.43,195930000,451.43 1993-03-12,453.70,453.70,447.04,449.83,255420000,449.83 1993-03-11,456.35,456.76,453.48,453.72,257060000,453.72 1993-03-10,454.40,456.34,452.70,456.33,255610000,456.33 1993-03-09,454.67,455.52,453.68,454.40,290670000,454.40 1993-03-08,446.12,454.71,446.12,454.71,275290000,454.71 1993-03-05,447.34,449.59,445.56,446.11,253480000,446.11 1993-03-04,449.26,449.52,446.72,447.34,234220000,447.34 1993-03-03,447.90,450.00,447.73,449.26,277380000,449.26 1993-03-02,442.00,447.91,441.07,447.90,269750000,447.90 1993-03-01,443.38,444.18,441.34,442.01,232460000,442.01 1993-02-26,442.34,443.77,440.98,443.38,234160000,443.38 1993-02-25,440.70,442.34,439.67,442.34,252860000,442.34 1993-02-24,434.76,440.87,434.68,440.87,316750000,440.87 1993-02-23,435.34,436.84,432.41,434.80,329060000,434.80 1993-02-22,434.21,436.49,433.53,435.24,311570000,435.24 1993-02-19,431.93,434.26,431.68,434.22,310700000,434.22 1993-02-18,433.30,437.79,428.25,431.90,311180000,431.90 1993-02-17,433.93,433.97,430.92,433.30,302210000,433.30 1993-02-16,444.53,444.53,433.47,433.91,332850000,433.91 1993-02-12,447.66,447.70,444.58,444.58,216810000,444.58 1993-02-11,446.21,449.36,446.21,447.66,257190000,447.66 1993-02-10,445.33,446.37,444.24,446.23,251910000,446.23 1993-02-09,448.04,448.04,444.52,445.33,240410000,445.33 1993-02-08,448.94,450.04,447.70,447.85,243400000,447.85 1993-02-05,449.56,449.56,446.95,448.93,324710000,448.93 1993-02-04,447.20,449.86,447.20,449.56,351140000,449.56 1993-02-03,442.56,447.35,442.56,447.20,345410000,447.20 1993-02-02,442.52,442.87,440.76,442.55,271560000,442.55 1993-02-01,438.78,442.52,438.78,442.52,238570000,442.52 1993-01-29,438.67,438.93,436.91,438.78,247200000,438.78 1993-01-28,438.13,439.14,437.30,438.66,256980000,438.66 1993-01-27,439.95,440.04,436.82,438.11,277020000,438.11 1993-01-26,440.05,442.66,439.54,439.95,314110000,439.95 1993-01-25,436.11,440.53,436.11,440.01,288740000,440.01 1993-01-22,435.49,437.81,435.49,436.11,293320000,436.11 1993-01-21,433.37,435.75,432.48,435.49,257620000,435.49 1993-01-20,435.14,436.23,433.37,433.37,268790000,433.37 1993-01-19,436.84,437.70,434.59,435.13,283240000,435.13 1993-01-18,437.13,437.13,435.92,436.84,196030000,436.84 1993-01-15,435.87,439.49,435.84,437.15,309720000,437.15 1993-01-14,433.08,435.96,433.08,435.94,281040000,435.94 1993-01-13,431.03,433.44,429.99,433.03,245360000,433.03 1993-01-12,430.95,431.39,428.19,431.04,239410000,431.04 1993-01-11,429.04,431.04,429.01,430.95,217150000,430.95 1993-01-08,430.73,430.73,426.88,429.05,263470000,429.05 1993-01-07,434.52,435.46,429.76,430.73,304850000,430.73 1993-01-06,434.34,435.17,432.52,434.52,295240000,434.52 1993-01-05,435.38,435.40,433.55,434.34,240350000,434.34 1993-01-04,435.70,437.32,434.48,435.38,201210000,435.38 1992-12-31,438.82,439.59,435.71,435.71,165910000,435.71 1992-12-30,437.98,439.37,437.12,438.82,183930000,438.82 1992-12-29,439.15,442.65,437.60,437.98,213660000,437.98 1992-12-28,439.77,439.77,437.26,439.15,143970000,439.15 1992-12-24,439.03,439.81,439.03,439.77,95240000,439.77 1992-12-23,440.29,441.11,439.03,439.03,234140000,439.03 1992-12-22,440.70,441.64,438.25,440.31,250430000,440.31 1992-12-21,441.26,441.26,439.65,440.70,224680000,440.70 1992-12-18,435.46,441.29,435.46,441.28,389300000,441.28 1992-12-17,431.52,435.44,431.46,435.43,251640000,435.43 1992-12-16,432.58,434.22,430.88,431.52,242130000,431.52 1992-12-15,432.82,433.66,431.92,432.57,227770000,432.57 1992-12-14,433.73,435.26,432.83,432.84,187040000,432.84 1992-12-11,434.64,434.64,433.34,433.73,164510000,433.73 1992-12-10,435.66,435.66,432.65,434.64,240640000,434.64 1992-12-09,436.99,436.99,433.98,435.65,230060000,435.65 1992-12-08,435.31,436.99,434.68,436.99,234330000,436.99 1992-12-07,432.06,435.31,432.06,435.31,217700000,435.31 1992-12-04,429.93,432.89,429.74,432.06,234960000,432.06 1992-12-03,429.98,430.99,428.80,429.91,238050000,429.91 1992-12-02,430.78,430.87,428.61,429.89,247010000,429.89 1992-12-01,431.35,431.47,429.20,430.78,259050000,430.78 1992-11-30,430.19,431.53,429.36,431.35,230150000,431.35 1992-11-27,429.19,431.93,429.17,430.16,106020000,430.16 1992-11-25,427.59,429.41,427.58,429.19,207700000,429.19 1992-11-24,425.14,429.31,424.83,427.59,241540000,427.59 1992-11-23,426.65,426.65,424.95,425.12,192530000,425.12 1992-11-20,423.61,426.98,423.61,426.65,257460000,426.65 1992-11-19,422.86,423.61,422.50,423.61,218720000,423.61 1992-11-18,419.27,423.49,419.24,422.85,219080000,422.85 1992-11-17,420.63,420.97,418.31,419.27,187660000,419.27 1992-11-16,422.44,422.44,420.35,420.68,173600000,420.68 1992-11-13,422.89,422.91,421.04,422.43,192950000,422.43 1992-11-12,422.20,423.10,421.70,422.87,226010000,422.87 1992-11-11,418.62,422.33,418.40,422.20,243750000,422.20 1992-11-10,418.59,419.71,417.98,418.62,223180000,418.62 1992-11-09,417.58,420.13,416.79,418.59,197560000,418.59 1992-11-06,418.35,418.35,417.01,417.58,205310000,417.58 1992-11-05,417.08,418.40,415.58,418.34,219730000,418.34 1992-11-04,419.91,421.07,416.61,417.11,194400000,417.11 1992-11-03,422.75,422.81,418.59,419.92,208140000,419.92 1992-11-02,418.66,422.75,418.12,422.75,203280000,422.75 1992-10-30,420.86,421.13,418.54,418.68,201930000,418.68 1992-10-29,420.15,421.16,419.83,420.86,206550000,420.86 1992-10-28,418.49,420.13,417.56,420.13,203910000,420.13 1992-10-27,418.18,419.20,416.97,418.49,201730000,418.49 1992-10-26,414.09,418.17,413.71,418.16,188060000,418.16 1992-10-23,414.90,416.23,413.68,414.10,199060000,414.10 1992-10-22,415.67,416.81,413.10,414.90,216400000,414.90 1992-10-21,415.53,416.15,414.54,415.67,219100000,415.67 1992-10-20,414.98,417.98,414.49,415.48,258210000,415.48 1992-10-19,411.73,414.98,410.66,414.98,222150000,414.98 1992-10-16,409.60,411.73,407.43,411.73,235920000,411.73 1992-10-15,409.34,411.03,407.92,409.60,213590000,409.60 1992-10-14,409.30,411.52,407.86,409.37,175900000,409.37 1992-10-13,407.44,410.64,406.83,409.30,186650000,409.30 1992-10-12,402.66,407.44,402.66,407.44,126670000,407.44 1992-10-09,407.75,407.75,402.42,402.66,178940000,402.66 1992-10-08,404.29,408.04,404.29,407.75,205000000,407.75 1992-10-07,407.17,408.60,403.91,404.25,184380000,404.25 1992-10-06,407.57,408.56,404.84,407.18,203500000,407.18 1992-10-05,410.47,410.47,396.80,407.57,286550000,407.57 1992-10-02,416.29,416.35,410.45,410.47,188030000,410.47 1992-10-01,417.80,418.67,415.46,416.29,204780000,416.29 1992-09-30,416.79,418.58,416.67,417.80,184470000,417.80 1992-09-29,416.62,417.38,415.34,416.80,170750000,416.80 1992-09-28,414.35,416.62,413.00,416.62,158760000,416.62 1992-09-25,418.47,418.63,412.71,414.35,213670000,414.35 1992-09-24,417.46,419.01,417.46,418.47,187960000,418.47 1992-09-23,417.14,417.88,416.00,417.44,205700000,417.44 1992-09-22,422.14,422.14,417.13,417.14,188810000,417.14 1992-09-21,422.90,422.90,421.18,422.14,153940000,422.14 1992-09-18,419.92,422.93,419.92,422.93,237440000,422.93 1992-09-17,419.92,421.43,419.62,419.93,188270000,419.93 1992-09-16,419.71,422.44,417.77,419.92,231450000,419.92 1992-09-15,425.22,425.22,419.54,419.77,211860000,419.77 1992-09-14,419.65,425.27,419.65,425.27,250940000,425.27 1992-09-11,419.95,420.58,419.13,419.58,180560000,419.58 1992-09-10,416.34,420.52,416.34,419.95,221990000,419.95 1992-09-09,414.44,416.44,414.44,416.36,178800000,416.36 1992-09-08,417.08,417.18,414.30,414.44,161440000,414.44 1992-09-04,417.98,418.62,416.76,417.08,124380000,417.08 1992-09-03,417.98,420.31,417.49,417.98,212500000,417.98 1992-09-02,416.07,418.28,415.31,417.98,187480000,417.98 1992-09-01,414.03,416.07,413.35,416.07,172680000,416.07 1992-08-31,414.87,415.29,413.76,414.03,161480000,414.03 1992-08-28,413.54,414.95,413.38,414.84,152260000,414.84 1992-08-27,413.51,415.83,413.51,413.53,178600000,413.53 1992-08-26,411.65,413.61,410.53,413.51,171860000,413.51 1992-08-25,410.73,411.64,408.30,411.61,202760000,411.61 1992-08-24,414.80,414.80,410.07,410.72,165690000,410.72 1992-08-21,418.27,420.35,413.58,414.85,204800000,414.85 1992-08-20,418.19,418.85,416.93,418.26,183420000,418.26 1992-08-19,421.34,421.62,418.19,418.19,187070000,418.19 1992-08-18,420.74,421.40,419.78,421.34,171750000,421.34 1992-08-17,419.89,421.89,419.44,420.74,152830000,420.74 1992-08-14,417.74,420.40,417.74,419.91,166820000,419.91 1992-08-13,417.78,419.88,416.40,417.73,185750000,417.73 1992-08-12,418.89,419.75,416.43,417.78,176560000,417.78 1992-08-11,419.45,419.72,416.53,418.90,173940000,418.90 1992-08-10,418.87,419.42,417.04,419.42,142480000,419.42 1992-08-07,420.59,423.45,418.51,418.88,190640000,418.88 1992-08-06,422.19,422.36,420.26,420.59,181440000,420.59 1992-08-05,424.35,424.35,421.92,422.19,172450000,422.19 1992-08-04,425.09,425.14,423.10,424.36,166760000,424.36 1992-08-03,424.19,425.09,422.84,425.09,164460000,425.09 1992-07-31,423.92,424.80,422.46,424.21,172920000,424.21 1992-07-30,422.20,423.94,421.57,423.92,193410000,423.92 1992-07-29,417.52,423.02,417.52,422.23,275850000,422.23 1992-07-28,411.55,417.55,411.55,417.52,218060000,417.52 1992-07-27,411.60,412.67,411.27,411.54,164700000,411.54 1992-07-24,412.07,412.07,409.93,411.60,163890000,411.60 1992-07-23,410.93,412.08,409.81,412.08,175490000,412.08 1992-07-22,413.74,413.74,409.95,410.93,190160000,410.93 1992-07-21,413.75,414.92,413.10,413.76,173760000,413.76 1992-07-20,415.62,415.62,410.72,413.75,165760000,413.75 1992-07-17,417.54,417.54,412.96,415.62,192120000,415.62 1992-07-16,417.04,417.93,414.79,417.54,206900000,417.54 1992-07-15,417.68,417.81,416.29,417.10,206560000,417.10 1992-07-14,414.86,417.69,414.33,417.68,195570000,417.68 1992-07-13,414.62,415.86,413.93,414.87,148870000,414.87 1992-07-10,414.23,415.88,413.34,414.62,164770000,414.62 1992-07-09,410.28,414.69,410.26,414.23,207980000,414.23 1992-07-08,409.15,410.28,407.20,410.28,201030000,410.28 1992-07-07,413.83,415.33,408.58,409.16,226050000,409.16 1992-07-06,411.77,413.84,410.46,413.84,186920000,413.84 1992-07-02,412.88,415.71,410.07,411.77,220200000,411.77 1992-07-01,408.20,412.88,408.20,412.88,214250000,412.88 1992-06-30,408.94,409.63,407.85,408.14,195530000,408.14 1992-06-29,403.47,408.96,403.47,408.94,176750000,408.94 1992-06-26,403.12,403.51,401.94,403.45,154430000,403.45 1992-06-25,403.83,405.53,402.01,403.12,182960000,403.12 1992-06-24,404.05,404.76,403.26,403.84,193870000,403.84 1992-06-23,403.40,405.41,403.40,404.04,189190000,404.04 1992-06-22,403.64,403.64,399.92,403.40,169370000,403.40 1992-06-19,400.96,404.23,400.96,403.67,233460000,403.67 1992-06-18,402.26,402.68,400.51,400.96,225600000,400.96 1992-06-17,408.33,408.33,401.98,402.26,227760000,402.26 1992-06-16,410.29,411.40,408.32,408.32,194400000,408.32 1992-06-15,409.76,411.68,408.13,410.29,164080000,410.29 1992-06-12,409.08,411.86,409.08,409.76,181860000,409.76 1992-06-11,407.25,409.05,406.11,409.05,204780000,409.05 1992-06-10,410.06,410.10,406.81,407.25,210750000,407.25 1992-06-09,413.40,413.56,409.30,410.06,191170000,410.06 1992-06-08,413.48,413.95,412.03,413.36,161240000,413.36 1992-06-05,413.26,413.85,410.97,413.48,199050000,413.48 1992-06-04,414.60,414.98,412.97,413.26,204450000,413.26 1992-06-03,413.50,416.54,413.04,414.59,215770000,414.59 1992-06-02,417.30,417.30,413.50,413.50,202560000,413.50 1992-06-01,415.35,417.30,412.44,417.30,180800000,417.30 1992-05-29,416.74,418.36,415.35,415.35,204010000,415.35 1992-05-28,412.17,416.77,411.81,416.74,195300000,416.74 1992-05-27,411.41,412.68,411.06,412.17,182240000,412.17 1992-05-26,414.02,414.02,410.23,411.41,197700000,411.41 1992-05-22,412.61,414.82,412.60,414.02,146710000,414.02 1992-05-21,415.40,415.41,411.57,412.60,184860000,412.60 1992-05-20,416.37,416.83,415.37,415.39,198180000,415.39 1992-05-19,412.82,416.51,412.26,416.37,187130000,416.37 1992-05-18,410.13,413.34,410.13,412.81,151380000,412.81 1992-05-15,413.14,413.14,409.85,410.09,192740000,410.09 1992-05-14,416.45,416.52,411.82,413.14,189150000,413.14 1992-05-13,416.29,417.04,415.86,416.45,175850000,416.45 1992-05-12,418.49,418.68,414.69,416.29,192870000,416.29 1992-05-11,416.05,418.75,416.05,418.49,155730000,418.49 1992-05-08,415.87,416.85,414.41,416.05,168720000,416.05 1992-05-07,416.79,416.84,415.38,415.85,168980000,415.85 1992-05-06,416.84,418.48,416.40,416.79,199950000,416.79 1992-05-05,416.91,418.53,415.77,416.84,200550000,416.84 1992-05-04,412.54,417.84,412.54,416.91,174540000,416.91 1992-05-01,414.95,415.21,409.87,412.53,177390000,412.53 1992-04-30,412.02,414.95,412.02,414.95,223590000,414.95 1992-04-29,409.11,412.31,409.11,412.02,206780000,412.02 1992-04-28,408.45,409.69,406.33,409.11,189220000,409.11 1992-04-27,409.03,409.60,407.64,408.45,172900000,408.45 1992-04-24,411.60,412.48,408.74,409.02,199310000,409.02 1992-04-23,409.81,411.60,406.86,411.60,235860000,411.60 1992-04-22,410.26,411.30,409.23,409.81,218850000,409.81 1992-04-21,410.16,411.09,408.20,410.26,214460000,410.26 1992-04-20,416.05,416.05,407.93,410.18,191980000,410.18 1992-04-16,416.28,416.28,413.40,416.04,233230000,416.04 1992-04-15,412.39,416.28,412.39,416.28,229710000,416.28 1992-04-14,406.08,413.86,406.08,412.39,231130000,412.39 1992-04-13,404.28,406.08,403.90,406.08,143140000,406.08 1992-04-10,400.59,405.12,400.59,404.29,199530000,404.29 1992-04-09,394.50,401.04,394.50,400.64,231430000,400.64 1992-04-08,398.05,398.05,392.41,394.50,249280000,394.50 1992-04-07,405.59,405.75,397.97,398.06,205210000,398.06 1992-04-06,401.54,405.93,401.52,405.59,179910000,405.59 1992-04-03,400.50,401.59,398.21,401.55,188580000,401.55 1992-04-02,404.17,404.63,399.28,400.50,185210000,400.50 1992-04-01,403.67,404.50,400.75,404.23,186530000,404.23 1992-03-31,403.00,405.21,402.22,403.69,182360000,403.69 1992-03-30,403.50,404.30,402.97,403.00,133990000,403.00 1992-03-27,407.86,407.86,402.87,403.50,166140000,403.50 1992-03-26,407.52,409.44,406.75,407.86,176720000,407.86 1992-03-25,408.88,409.87,407.52,407.52,192650000,407.52 1992-03-24,409.91,411.43,407.99,408.88,191610000,408.88 1992-03-23,411.29,411.29,408.87,409.91,157050000,409.91 1992-03-20,409.80,411.30,408.53,411.30,246210000,411.30 1992-03-19,409.15,410.57,409.12,409.80,197310000,409.80 1992-03-18,409.58,410.84,408.23,409.15,191720000,409.15 1992-03-17,406.39,409.72,406.39,409.58,187250000,409.58 1992-03-16,405.85,406.40,403.55,406.39,155950000,406.39 1992-03-13,403.92,406.69,403.92,405.84,177900000,405.84 1992-03-12,404.03,404.72,401.94,403.89,180310000,403.89 1992-03-11,406.88,407.02,402.64,404.03,186330000,404.03 1992-03-10,405.21,409.16,405.21,406.89,203000000,406.89 1992-03-09,404.45,405.64,404.25,405.21,160650000,405.21 1992-03-06,406.51,407.51,403.65,404.44,185190000,404.44 1992-03-05,409.33,409.33,405.42,406.51,205770000,406.51 1992-03-04,412.86,413.27,409.33,409.33,206860000,409.33 1992-03-03,412.45,413.78,411.88,412.85,200890000,412.85 1992-03-02,412.68,413.74,411.52,412.45,180380000,412.45 1992-02-28,413.86,416.07,411.80,412.70,202320000,412.70 1992-02-27,415.35,415.99,413.47,413.86,215110000,413.86 1992-02-26,410.48,415.35,410.48,415.35,241500000,415.35 1992-02-25,412.27,412.27,408.02,410.45,210350000,410.45 1992-02-24,411.46,412.94,410.34,412.27,177540000,412.27 1992-02-21,413.90,414.26,409.72,411.43,261650000,411.43 1992-02-20,408.26,413.90,408.26,413.90,270650000,413.90 1992-02-19,407.38,408.70,406.54,408.26,232970000,408.26 1992-02-18,412.48,413.27,406.34,407.38,234300000,407.38 1992-02-14,413.69,413.84,411.20,412.48,215110000,412.48 1992-02-13,417.13,417.77,412.07,413.69,229360000,413.69 1992-02-12,413.77,418.08,413.36,417.13,237630000,417.13 1992-02-11,413.77,414.38,412.24,413.76,200130000,413.76 1992-02-10,411.07,413.77,411.07,413.77,184410000,413.77 1992-02-07,413.82,415.29,408.04,411.09,231120000,411.09 1992-02-06,413.87,414.55,411.93,413.82,242050000,413.82 1992-02-05,413.88,416.17,413.18,413.84,262440000,413.84 1992-02-04,409.60,413.85,409.28,413.85,233680000,413.85 1992-02-03,408.79,409.95,407.45,409.53,185290000,409.53 1992-01-31,411.65,412.63,408.64,408.78,197620000,408.78 1992-01-30,410.34,412.17,409.26,411.62,194680000,411.62 1992-01-29,414.96,417.83,409.17,410.34,248940000,410.34 1992-01-28,414.98,416.41,414.54,414.96,218400000,414.96 1992-01-27,415.44,416.84,414.48,414.99,190970000,414.99 1992-01-24,414.96,417.27,414.29,415.48,213630000,415.48 1992-01-23,418.13,419.78,414.36,414.96,234580000,414.96 1992-01-22,412.65,418.13,412.49,418.13,228140000,418.13 1992-01-21,416.36,416.39,411.32,412.64,218750000,412.64 1992-01-20,418.86,418.86,415.80,416.36,180900000,416.36 1992-01-17,418.20,419.45,416.00,418.86,287370000,418.86 1992-01-16,420.77,420.85,415.37,418.21,336240000,418.21 1992-01-15,420.45,421.18,418.79,420.77,314830000,420.77 1992-01-14,414.34,420.44,414.32,420.44,265900000,420.44 1992-01-13,415.05,415.36,413.54,414.34,200270000,414.34 1992-01-10,417.62,417.62,413.31,415.10,236130000,415.10 1992-01-09,418.09,420.50,415.85,417.61,292350000,417.61 1992-01-08,417.36,420.23,415.02,418.10,290750000,418.10 1992-01-07,417.96,417.96,415.20,417.40,252780000,417.40 1992-01-06,419.31,419.44,416.92,417.96,251210000,417.96 1992-01-03,417.27,419.79,416.16,419.34,224270000,419.34 1992-01-02,417.03,417.27,411.04,417.26,207570000,417.26 1991-12-31,415.14,418.32,412.73,417.09,247080000,417.09 1991-12-30,406.49,415.14,406.49,415.14,245600000,415.14 1991-12-27,404.84,406.58,404.59,406.46,157950000,406.46 1991-12-26,399.33,404.92,399.31,404.84,149230000,404.84 1991-12-24,396.82,401.79,396.82,399.33,162640000,399.33 1991-12-23,387.05,397.44,386.96,396.82,228900000,396.82 1991-12-20,382.52,388.24,382.52,387.04,316140000,387.04 1991-12-19,383.46,383.46,380.64,382.52,199330000,382.52 1991-12-18,382.74,383.51,380.88,383.48,192410000,383.48 1991-12-17,384.46,385.05,382.60,382.74,191310000,382.74 1991-12-16,384.48,385.84,384.37,384.46,173080000,384.46 1991-12-13,381.55,385.04,381.55,384.47,194470000,384.47 1991-12-12,377.70,381.62,377.70,381.55,192950000,381.55 1991-12-11,377.90,379.42,374.78,377.70,207430000,377.70 1991-12-10,378.26,379.57,376.64,377.90,192920000,377.90 1991-12-09,379.09,381.42,377.67,378.26,174760000,378.26 1991-12-06,377.39,382.39,375.41,379.10,199160000,379.10 1991-12-05,380.07,380.07,376.58,377.39,166350000,377.39 1991-12-04,380.96,381.51,378.07,380.07,187960000,380.07 1991-12-03,381.40,381.48,379.92,380.96,187230000,380.96 1991-12-02,375.11,381.40,371.36,381.40,188410000,381.40 1991-11-29,376.55,376.55,374.65,375.22,76830000,375.22 1991-11-27,377.96,378.11,375.98,376.55,167720000,376.55 1991-11-26,375.34,378.29,371.63,377.96,213810000,377.96 1991-11-25,376.14,377.07,374.00,375.34,175870000,375.34 1991-11-22,380.05,380.05,374.52,376.14,188240000,376.14 1991-11-21,378.53,381.12,377.41,380.06,195130000,380.06 1991-11-20,379.42,381.51,377.84,378.53,192760000,378.53 1991-11-19,385.24,385.24,374.90,379.42,243880000,379.42 1991-11-18,382.62,385.40,379.70,385.24,241940000,385.24 1991-11-15,397.15,397.16,382.62,382.62,239690000,382.62 1991-11-14,397.41,398.22,395.85,397.15,200030000,397.15 1991-11-13,396.74,397.42,394.01,397.41,184480000,397.41 1991-11-12,393.12,397.13,393.12,396.74,198610000,396.74 1991-11-11,392.90,393.57,392.32,393.12,128920000,393.12 1991-11-08,393.72,396.43,392.42,392.89,183260000,392.89 1991-11-07,389.97,393.72,389.97,393.72,205480000,393.72 1991-11-06,388.71,389.97,387.58,389.97,167440000,389.97 1991-11-05,390.28,392.17,388.19,388.71,172090000,388.71 1991-11-04,391.29,391.29,388.09,390.28,155660000,390.28 1991-11-01,392.46,395.10,389.67,391.32,205780000,391.32 1991-10-31,392.96,392.96,391.58,392.45,179680000,392.45 1991-10-30,391.48,393.11,390.78,392.96,195400000,392.96 1991-10-29,389.52,391.70,386.88,391.48,192810000,391.48 1991-10-28,384.20,389.52,384.20,389.52,161630000,389.52 1991-10-25,385.07,386.13,382.97,384.20,167310000,384.20 1991-10-24,387.94,388.32,383.45,385.07,179040000,385.07 1991-10-23,387.83,389.08,386.52,387.94,185390000,387.94 1991-10-22,390.02,391.20,387.40,387.83,194160000,387.83 1991-10-21,392.49,392.49,388.96,390.02,154140000,390.02 1991-10-18,391.92,392.80,391.77,392.50,204090000,392.50 1991-10-17,392.79,393.81,390.32,391.92,206030000,391.92 1991-10-16,391.01,393.29,390.14,392.80,225380000,392.80 1991-10-15,386.47,391.50,385.95,391.01,213540000,391.01 1991-10-14,381.45,386.47,381.45,386.47,130120000,386.47 1991-10-11,380.55,381.46,379.90,381.45,148850000,381.45 1991-10-10,376.80,380.55,376.11,380.55,164240000,380.55 1991-10-09,380.57,380.57,376.35,376.80,186710000,376.80 1991-10-08,379.50,381.23,379.18,380.67,177120000,380.67 1991-10-07,381.22,381.27,379.07,379.50,148430000,379.50 1991-10-04,384.47,385.19,381.24,381.25,164000000,381.25 1991-10-03,388.23,388.23,384.47,384.47,174360000,384.47 1991-10-02,389.20,390.03,387.62,388.26,166380000,388.26 1991-10-01,387.86,389.56,387.86,389.20,163570000,389.20 1991-09-30,385.91,388.29,384.32,387.86,146780000,387.86 1991-09-27,386.49,389.09,384.87,385.90,160660000,385.90 1991-09-26,386.87,388.39,385.30,386.49,158980000,386.49 1991-09-25,387.72,388.25,385.99,386.88,153910000,386.88 1991-09-24,385.92,388.13,384.46,387.71,170350000,387.71 1991-09-23,387.90,388.55,385.76,385.92,145940000,385.92 1991-09-20,387.56,388.82,386.49,387.92,254520000,387.92 1991-09-19,386.94,389.42,386.27,387.56,211010000,387.56 1991-09-18,385.49,386.94,384.28,386.94,141340000,386.94 1991-09-17,385.78,387.13,384.97,385.50,168340000,385.50 1991-09-16,383.59,385.79,382.77,385.78,172560000,385.78 1991-09-13,387.16,387.95,382.85,383.59,169630000,383.59 1991-09-12,385.09,387.34,385.09,387.34,160420000,387.34 1991-09-11,384.56,385.60,383.59,385.09,148000000,385.09 1991-09-10,388.57,388.63,383.78,384.56,143390000,384.56 1991-09-09,389.11,389.34,387.88,388.57,115100000,388.57 1991-09-06,389.14,390.71,387.36,389.10,166560000,389.10 1991-09-05,389.97,390.97,388.49,389.14,162380000,389.14 1991-09-04,392.15,392.62,388.68,389.97,157520000,389.97 1991-09-03,395.43,397.62,392.10,392.15,153600000,392.15 1991-08-30,396.47,396.47,393.60,395.43,143440000,395.43 1991-08-29,396.65,396.82,395.14,396.47,154150000,396.47 1991-08-28,393.06,396.64,393.05,396.64,169890000,396.64 1991-08-27,393.85,393.87,391.77,393.06,144670000,393.06 1991-08-26,394.17,394.39,392.75,393.85,130570000,393.85 1991-08-23,391.33,395.34,390.69,394.17,188870000,394.17 1991-08-22,390.59,391.98,390.21,391.33,173090000,391.33 1991-08-21,379.55,390.59,379.55,390.59,232690000,390.59 1991-08-20,376.47,380.35,376.47,379.43,184260000,379.43 1991-08-19,385.58,385.58,374.09,376.47,230350000,376.47 1991-08-16,389.33,390.41,383.16,385.58,189480000,385.58 1991-08-15,389.91,391.92,389.29,389.33,174690000,389.33 1991-08-14,389.62,391.85,389.13,389.90,124230000,389.90 1991-08-13,388.02,392.12,388.02,389.62,212760000,389.62 1991-08-12,387.11,388.17,385.90,388.02,145440000,388.02 1991-08-09,389.32,389.89,387.04,387.12,143740000,387.12 1991-08-08,390.56,391.80,388.15,389.32,163890000,389.32 1991-08-07,390.62,391.59,389.86,390.56,172220000,390.56 1991-08-06,385.06,390.80,384.29,390.62,174460000,390.62 1991-08-05,387.17,387.17,384.48,385.06,128050000,385.06 1991-08-02,387.12,389.56,386.05,387.18,162270000,387.18 1991-08-01,387.81,387.95,386.48,387.12,170610000,387.12 1991-07-31,386.69,387.81,386.19,387.81,166830000,387.81 1991-07-30,383.15,386.92,383.15,386.69,169010000,386.69 1991-07-29,380.93,383.15,380.45,383.15,136000000,383.15 1991-07-26,380.96,381.76,379.81,380.93,127760000,380.93 1991-07-25,378.64,381.13,378.15,380.96,145800000,380.96 1991-07-24,379.42,380.46,378.29,378.64,158700000,378.64 1991-07-23,382.88,384.86,379.39,379.42,160190000,379.42 1991-07-22,384.21,384.55,381.84,382.88,149050000,382.88 1991-07-19,385.38,385.83,383.65,384.22,190700000,384.22 1991-07-18,381.18,385.37,381.18,385.37,200930000,385.37 1991-07-17,381.50,382.86,381.13,381.18,195460000,381.18 1991-07-16,382.39,382.94,380.80,381.54,182990000,381.54 1991-07-15,380.28,383.00,380.24,382.39,161750000,382.39 1991-07-12,376.97,381.41,375.79,380.25,174770000,380.25 1991-07-11,375.73,377.68,375.51,376.97,157930000,376.97 1991-07-10,376.11,380.35,375.20,375.74,178290000,375.74 1991-07-09,377.94,378.58,375.37,376.11,151820000,376.11 1991-07-08,374.09,377.94,370.92,377.94,138330000,377.94 1991-07-05,373.34,375.51,372.17,374.08,69910000,374.08 1991-07-03,377.47,377.47,372.08,373.33,140580000,373.33 1991-07-02,377.92,377.93,376.62,377.47,157290000,377.47 1991-07-01,371.18,377.92,371.18,377.92,167480000,377.92 1991-06-28,374.40,374.40,367.98,371.16,163770000,371.16 1991-06-27,371.59,374.40,371.59,374.40,163080000,374.40 1991-06-26,370.65,372.73,368.34,371.59,187170000,371.59 1991-06-25,370.94,372.62,369.56,370.65,155710000,370.65 1991-06-24,377.74,377.74,370.73,370.94,137940000,370.94 1991-06-21,375.42,377.75,375.33,377.75,193310000,377.75 1991-06-20,375.09,376.29,373.87,375.42,163980000,375.42 1991-06-19,378.57,378.57,374.36,375.09,156440000,375.09 1991-06-18,380.13,381.83,377.99,378.59,155200000,378.59 1991-06-17,382.30,382.31,380.13,380.13,134230000,380.13 1991-06-14,377.63,382.30,377.63,382.29,167950000,382.29 1991-06-13,376.65,377.90,376.08,377.63,145650000,377.63 1991-06-12,381.05,381.05,374.46,376.65,166140000,376.65 1991-06-11,378.57,381.63,378.57,381.05,161610000,381.05 1991-06-10,379.43,379.75,377.95,378.57,127720000,378.57 1991-06-07,383.63,383.63,378.76,379.43,169570000,379.43 1991-06-06,385.10,385.85,383.13,383.63,168260000,383.63 1991-06-05,387.74,388.23,384.45,385.09,186560000,385.09 1991-06-04,388.06,388.06,385.14,387.74,180450000,387.74 1991-06-03,389.81,389.81,386.97,388.06,173990000,388.06 1991-05-31,386.96,389.85,385.01,389.83,232040000,389.83 1991-05-30,382.79,388.17,382.50,386.96,234440000,386.96 1991-05-29,381.94,383.66,381.37,382.79,188450000,382.79 1991-05-28,377.49,382.10,377.12,381.94,162350000,381.94 1991-05-24,374.97,378.08,374.97,377.49,124640000,377.49 1991-05-23,376.19,378.07,373.55,374.96,173080000,374.96 1991-05-22,375.35,376.50,374.40,376.19,159310000,376.19 1991-05-21,372.28,376.66,372.28,375.35,176620000,375.35 1991-05-20,372.39,373.65,371.26,372.28,109510000,372.28 1991-05-17,372.19,373.01,369.44,372.39,174210000,372.39 1991-05-16,368.57,372.51,368.57,372.19,154460000,372.19 1991-05-15,371.55,372.47,365.83,368.57,193110000,368.57 1991-05-14,375.51,375.53,370.82,371.62,207890000,371.62 1991-05-13,375.74,377.02,374.62,376.76,129620000,376.76 1991-05-10,383.26,383.91,375.61,375.74,172730000,375.74 1991-05-09,378.51,383.56,378.51,383.25,180460000,383.25 1991-05-08,377.33,379.26,376.21,378.51,157240000,378.51 1991-05-07,380.08,380.91,377.31,377.32,153290000,377.32 1991-05-06,380.78,380.78,377.86,380.08,129110000,380.08 1991-05-03,380.52,381.00,378.82,380.80,158150000,380.80 1991-05-02,380.29,382.14,379.82,380.52,187090000,380.52 1991-05-01,375.35,380.46,375.27,380.29,181900000,380.29 1991-04-30,373.66,377.86,373.01,375.34,206230000,375.34 1991-04-29,379.01,380.96,373.66,373.66,149860000,373.66 1991-04-26,379.25,380.11,376.77,379.02,154550000,379.02 1991-04-25,382.89,382.89,378.43,379.25,166940000,379.25 1991-04-24,381.76,383.02,379.99,382.76,166800000,382.76 1991-04-23,380.95,383.55,379.67,381.76,167840000,381.76 1991-04-22,384.19,384.19,380.16,380.95,164410000,380.95 1991-04-19,388.46,388.46,383.90,384.20,195520000,384.20 1991-04-18,390.45,390.97,388.13,388.46,217410000,388.46 1991-04-17,387.62,391.26,387.30,390.45,246930000,390.45 1991-04-16,381.19,387.62,379.64,387.62,214480000,387.62 1991-04-15,380.40,382.32,378.78,381.19,161800000,381.19 1991-04-12,377.65,381.07,376.89,380.40,198610000,380.40 1991-04-11,373.15,379.53,373.15,377.63,196570000,377.63 1991-04-10,373.57,374.83,371.21,373.15,167940000,373.15 1991-04-09,378.65,379.02,373.11,373.56,169940000,373.56 1991-04-08,375.35,378.76,374.69,378.66,138580000,378.66 1991-04-05,379.78,381.12,374.15,375.36,187410000,375.36 1991-04-04,378.94,381.88,377.05,379.77,198120000,379.77 1991-04-03,379.50,381.56,378.49,378.94,213720000,378.94 1991-04-02,371.30,379.50,371.30,379.50,189530000,379.50 1991-04-01,375.22,375.22,370.27,371.30,144010000,371.30 1991-03-28,375.35,376.60,374.40,375.22,150750000,375.22 1991-03-27,376.28,378.48,374.73,375.35,201830000,375.35 1991-03-26,369.83,376.30,369.37,376.30,198720000,376.30 1991-03-25,367.48,371.31,367.46,369.83,153920000,369.83 1991-03-22,366.58,368.22,365.58,367.48,160890000,367.48 1991-03-21,367.94,371.01,366.51,366.58,199830000,366.58 1991-03-20,366.59,368.85,365.80,367.92,196810000,367.92 1991-03-19,372.11,372.11,366.54,366.59,177070000,366.59 1991-03-18,373.59,374.09,369.46,372.11,163100000,372.11 1991-03-15,373.50,374.58,370.21,373.59,237650000,373.59 1991-03-14,374.59,378.28,371.76,373.50,232070000,373.50 1991-03-13,370.03,374.65,370.03,374.57,176000000,374.57 1991-03-12,372.96,374.35,369.55,370.03,176440000,370.03 1991-03-11,374.94,375.10,372.52,372.96,161600000,372.96 1991-03-08,375.91,378.69,374.43,374.95,206850000,374.95 1991-03-07,376.16,377.49,375.58,375.91,197060000,375.91 1991-03-06,376.72,379.66,375.02,376.17,262290000,376.17 1991-03-05,369.33,377.89,369.33,376.72,253700000,376.72 1991-03-04,370.47,371.99,369.07,369.33,199830000,369.33 1991-03-01,367.07,370.47,363.73,370.47,221510000,370.47 1991-02-28,367.73,369.91,365.95,367.07,223010000,367.07 1991-02-27,362.81,368.38,362.81,367.74,211410000,367.74 1991-02-26,367.26,367.26,362.19,362.81,164170000,362.81 1991-02-25,365.65,370.19,365.16,367.26,193820000,367.26 1991-02-22,364.97,370.96,364.23,365.65,218760000,365.65 1991-02-21,365.14,366.79,364.50,364.97,180770000,364.97 1991-02-20,369.37,369.37,364.38,365.14,185680000,365.14 1991-02-19,369.06,370.11,367.05,369.39,189900000,369.39 1991-02-15,364.23,369.49,364.23,369.06,228480000,369.06 1991-02-14,369.02,370.26,362.77,364.22,230750000,364.22 1991-02-13,365.50,369.49,364.64,369.02,209960000,369.02 1991-02-12,368.58,370.54,365.50,365.50,256160000,365.50 1991-02-11,359.36,368.58,359.32,368.58,265350000,368.58 1991-02-08,356.52,359.35,356.02,359.35,187830000,359.35 1991-02-07,358.07,363.43,355.53,356.52,292190000,356.52 1991-02-06,351.26,358.07,349.58,358.07,276940000,358.07 1991-02-05,348.34,351.84,347.21,351.26,290570000,351.26 1991-02-04,343.05,348.71,342.96,348.34,250750000,348.34 1991-02-01,343.91,344.90,340.37,343.05,246670000,343.05 1991-01-31,340.92,343.93,340.47,343.93,204520000,343.93 1991-01-30,335.80,340.91,335.71,340.91,226790000,340.91 1991-01-29,336.03,336.03,334.26,335.84,155740000,335.84 1991-01-28,336.06,337.41,335.81,336.03,141270000,336.03 1991-01-25,334.78,336.92,334.20,336.07,194350000,336.07 1991-01-24,330.21,335.83,330.19,334.78,223150000,334.78 1991-01-23,328.30,331.04,327.93,330.21,168620000,330.21 1991-01-22,331.06,331.26,327.83,328.31,177060000,328.31 1991-01-21,332.23,332.23,328.87,331.06,136290000,331.06 1991-01-18,327.93,332.23,327.08,332.23,226770000,332.23 1991-01-17,316.25,327.97,316.25,327.97,319080000,327.97 1991-01-16,313.73,316.94,312.94,316.17,134560000,316.17 1991-01-15,312.49,313.73,311.84,313.73,110000000,313.73 1991-01-14,315.23,315.23,309.35,312.49,120830000,312.49 1991-01-11,314.53,315.24,313.59,315.23,123050000,315.23 1991-01-10,311.51,314.77,311.51,314.53,124510000,314.53 1991-01-09,314.90,320.73,310.93,311.49,191100000,311.49 1991-01-08,315.44,316.97,313.79,314.90,143390000,314.90 1991-01-07,320.97,320.97,315.44,315.44,130610000,315.44 1991-01-04,321.91,322.35,318.87,321.00,140820000,321.00 1991-01-03,326.46,326.53,321.90,321.91,141450000,321.91 1991-01-02,330.20,330.75,326.45,326.45,126280000,326.45 1990-12-31,328.71,330.23,327.50,330.22,114130000,330.22 1990-12-28,328.29,328.72,327.24,328.72,111030000,328.72 1990-12-27,330.85,331.04,328.23,328.29,102900000,328.29 1990-12-26,329.89,331.69,329.89,330.85,78730000,330.85 1990-12-24,331.74,331.74,329.16,329.90,57200000,329.90 1990-12-21,330.12,332.47,330.12,331.75,233400000,331.75 1990-12-20,330.20,330.74,326.94,330.12,174700000,330.12 1990-12-19,330.04,330.80,329.39,330.20,180380000,330.20 1990-12-18,326.02,330.43,325.75,330.05,176460000,330.05 1990-12-17,326.82,326.82,324.46,326.02,118560000,326.02 1990-12-14,329.34,329.34,325.16,326.82,151010000,326.82 1990-12-13,330.14,330.58,328.77,329.34,162110000,329.34 1990-12-12,326.44,330.36,326.44,330.19,182270000,330.19 1990-12-11,328.88,328.88,325.65,326.44,145330000,326.44 1990-12-10,327.75,328.97,326.15,328.89,138650000,328.89 1990-12-07,329.09,329.39,326.39,327.75,164950000,327.75 1990-12-06,329.94,333.98,328.37,329.07,256380000,329.07 1990-12-05,326.36,329.92,325.66,329.92,205820000,329.92 1990-12-04,324.11,326.77,321.97,326.35,185820000,326.35 1990-12-03,322.23,324.90,322.23,324.10,177000000,324.10 1990-11-30,316.42,323.02,315.42,322.22,192350000,322.22 1990-11-29,317.95,317.95,315.03,316.42,140920000,316.42 1990-11-28,318.11,319.96,317.62,317.95,145490000,317.95 1990-11-27,316.51,318.69,315.80,318.10,147590000,318.10 1990-11-26,315.08,316.51,311.48,316.51,131540000,316.51 1990-11-23,316.03,317.30,315.06,315.10,63350000,315.10 1990-11-21,315.31,316.15,312.42,316.03,140660000,316.03 1990-11-20,319.34,319.34,315.31,315.31,161170000,315.31 1990-11-19,317.15,319.39,317.15,319.34,140950000,319.34 1990-11-16,317.02,318.80,314.99,317.12,165440000,317.12 1990-11-15,320.40,320.40,316.13,317.02,151370000,317.02 1990-11-14,317.66,321.70,317.23,320.40,179310000,320.40 1990-11-13,319.48,319.48,317.26,317.67,160240000,317.67 1990-11-12,313.74,319.77,313.73,319.48,161390000,319.48 1990-11-09,307.61,313.78,307.61,313.74,145160000,313.74 1990-11-08,306.01,309.77,305.03,307.61,155570000,307.61 1990-11-07,311.62,311.62,305.79,306.01,149130000,306.01 1990-11-06,314.59,314.76,311.43,311.62,142660000,311.62 1990-11-05,311.85,314.61,311.41,314.59,147510000,314.59 1990-11-02,307.02,311.94,306.88,311.85,168700000,311.85 1990-11-01,303.99,307.27,301.61,307.02,159270000,307.02 1990-10-31,304.06,305.70,302.33,304.00,156060000,304.00 1990-10-30,301.88,304.36,299.44,304.06,153450000,304.06 1990-10-29,304.74,307.41,300.69,301.88,133980000,301.88 1990-10-26,310.17,310.17,304.71,304.71,130190000,304.71 1990-10-25,312.60,313.71,309.70,310.17,141460000,310.17 1990-10-24,312.36,313.51,310.74,312.60,149290000,312.60 1990-10-23,314.76,315.06,312.06,312.36,146300000,312.36 1990-10-22,312.48,315.83,310.47,314.76,152650000,314.76 1990-10-19,305.74,312.48,305.74,312.48,221480000,312.48 1990-10-18,298.75,305.74,298.75,305.74,204110000,305.74 1990-10-17,298.92,301.50,297.79,298.76,161260000,298.76 1990-10-16,303.23,304.34,298.12,298.92,149570000,298.92 1990-10-15,300.03,304.79,296.41,303.23,164980000,303.23 1990-10-12,295.45,301.68,295.22,300.03,187940000,300.03 1990-10-11,300.39,301.45,294.51,295.46,180060000,295.46 1990-10-10,305.09,306.43,299.21,300.39,169190000,300.39 1990-10-09,313.46,313.46,305.09,305.10,145610000,305.10 1990-10-08,311.50,315.03,311.50,313.48,99470000,313.48 1990-10-05,312.69,314.79,305.76,311.50,153380000,311.50 1990-10-04,311.40,313.40,308.59,312.69,145410000,312.69 1990-10-03,315.21,316.26,310.70,311.40,135490000,311.40 1990-10-02,314.94,319.69,314.94,315.21,188360000,315.21 1990-10-01,306.10,314.94,306.10,314.94,202210000,314.94 1990-09-28,300.97,306.05,295.98,306.05,201010000,306.05 1990-09-27,305.06,307.47,299.10,300.97,182690000,300.97 1990-09-26,308.26,308.28,303.05,305.06,155570000,305.06 1990-09-25,305.46,308.27,304.23,308.26,155940000,308.26 1990-09-24,311.30,311.30,303.58,304.59,164070000,304.59 1990-09-21,311.53,312.17,307.98,311.32,201050000,311.32 1990-09-20,316.60,316.60,310.55,311.48,145100000,311.48 1990-09-19,318.60,319.35,316.25,316.60,147530000,316.60 1990-09-18,317.77,318.85,314.27,318.60,141130000,318.60 1990-09-17,316.83,318.05,315.21,317.77,110600000,317.77 1990-09-14,318.65,318.65,314.76,316.83,133390000,316.83 1990-09-13,322.51,322.51,318.02,318.65,123390000,318.65 1990-09-12,321.04,322.55,319.60,322.54,129890000,322.54 1990-09-11,321.63,322.18,319.60,321.04,113220000,321.04 1990-09-10,323.42,326.53,320.31,321.63,119730000,321.63 1990-09-07,320.46,324.18,319.71,323.40,123800000,323.40 1990-09-06,324.39,324.39,319.37,320.46,125620000,320.46 1990-09-05,323.09,324.52,320.99,324.39,120610000,324.39 1990-09-04,322.56,323.09,319.11,323.09,92940000,323.09 1990-08-31,318.71,322.57,316.59,322.56,96480000,322.56 1990-08-30,324.19,324.57,317.82,318.71,120890000,318.71 1990-08-29,321.34,325.83,320.87,324.19,134240000,324.19 1990-08-28,321.44,322.20,320.25,321.34,127660000,321.34 1990-08-27,311.55,323.11,311.55,321.44,160150000,321.44 1990-08-24,307.06,311.65,306.18,311.51,199040000,311.51 1990-08-23,316.55,316.55,306.56,307.06,250440000,307.06 1990-08-22,321.86,324.15,316.55,316.55,175550000,316.55 1990-08-21,328.51,328.51,318.78,321.86,194630000,321.86 1990-08-20,327.83,329.90,327.07,328.51,129630000,328.51 1990-08-17,332.36,332.36,324.63,327.83,212560000,327.83 1990-08-16,340.06,340.06,332.39,332.39,138850000,332.39 1990-08-15,339.39,341.92,339.38,340.06,136710000,340.06 1990-08-14,338.84,340.96,337.19,339.39,130320000,339.39 1990-08-13,335.39,338.88,332.02,338.84,122820000,338.84 1990-08-10,339.90,339.90,334.22,335.52,145340000,335.52 1990-08-09,338.35,340.56,337.56,339.94,155810000,339.94 1990-08-08,334.83,339.21,334.83,338.35,190400000,338.35 1990-08-07,334.43,338.63,332.22,334.83,231580000,334.83 1990-08-06,344.86,344.86,333.27,334.43,240400000,334.43 1990-08-03,351.48,351.48,338.20,344.86,295880000,344.86 1990-08-02,355.52,355.52,349.73,351.48,253090000,351.48 1990-08-01,356.15,357.35,353.82,355.52,178260000,355.52 1990-07-31,355.55,357.54,353.91,356.15,175380000,356.15 1990-07-30,353.44,355.55,351.15,355.55,146470000,355.55 1990-07-27,355.90,355.94,352.14,353.44,149070000,353.44 1990-07-26,357.09,357.47,353.95,355.91,155040000,355.91 1990-07-25,355.79,357.52,354.80,357.09,163530000,357.09 1990-07-24,355.31,356.09,351.46,355.79,181920000,355.79 1990-07-23,361.61,361.61,350.09,355.31,209030000,355.31 1990-07-20,365.32,366.64,361.58,361.61,177810000,361.61 1990-07-19,364.22,365.32,361.29,365.32,161990000,365.32 1990-07-18,367.52,367.52,362.95,364.22,168760000,364.22 1990-07-17,368.95,369.40,364.99,367.52,176790000,367.52 1990-07-16,367.31,369.78,367.31,368.95,149430000,368.95 1990-07-13,365.45,369.68,365.45,367.31,215600000,367.31 1990-07-12,361.23,365.46,360.57,365.44,213180000,365.44 1990-07-11,356.49,361.23,356.49,361.23,162220000,361.23 1990-07-10,359.52,359.74,356.41,356.49,147630000,356.49 1990-07-09,358.42,360.05,358.11,359.52,119390000,359.52 1990-07-06,355.69,359.02,354.64,358.42,111730000,358.42 1990-07-05,360.16,360.16,354.86,355.68,128320000,355.68 1990-07-03,359.54,360.73,359.44,360.16,130050000,360.16 1990-07-02,358.02,359.58,357.54,359.54,130200000,359.54 1990-06-29,357.64,359.09,357.30,358.02,145510000,358.02 1990-06-28,355.16,357.63,355.16,357.63,136120000,357.63 1990-06-27,352.06,355.89,351.23,355.14,146620000,355.14 1990-06-26,352.32,356.09,351.85,352.06,141420000,352.06 1990-06-25,355.42,356.41,351.91,352.31,133100000,352.31 1990-06-22,360.52,363.20,355.31,355.43,172570000,355.43 1990-06-21,359.10,360.88,357.63,360.47,138570000,360.47 1990-06-20,358.47,359.91,357.00,359.10,137420000,359.10 1990-06-19,356.88,358.90,356.18,358.47,134930000,358.47 1990-06-18,362.91,362.91,356.88,356.88,133470000,356.88 1990-06-15,362.89,363.14,360.71,362.91,205130000,362.91 1990-06-14,364.90,364.90,361.64,362.90,135770000,362.90 1990-06-13,366.25,367.09,364.51,364.90,158910000,364.90 1990-06-12,361.63,367.27,361.15,366.25,157100000,366.25 1990-06-11,358.71,361.63,357.70,361.63,119550000,361.63 1990-06-08,363.15,363.49,357.68,358.71,142600000,358.71 1990-06-07,365.92,365.92,361.60,363.15,160360000,363.15 1990-06-06,366.64,366.64,364.42,364.96,164030000,364.96 1990-06-05,367.40,368.78,365.49,366.64,199720000,366.64 1990-06-04,363.16,367.85,362.43,367.40,175520000,367.40 1990-06-01,361.26,363.52,361.21,363.16,187860000,363.16 1990-05-31,360.86,361.84,360.23,361.23,165690000,361.23 1990-05-30,360.65,362.26,360.00,360.86,199540000,360.86 1990-05-29,354.58,360.65,354.55,360.65,137410000,360.65 1990-05-25,358.41,358.41,354.32,354.58,120250000,354.58 1990-05-24,359.29,359.56,357.87,358.41,155140000,358.41 1990-05-23,358.43,359.29,356.99,359.29,172330000,359.29 1990-05-22,358.00,360.50,356.09,358.43,203350000,358.43 1990-05-21,354.64,359.07,353.78,358.00,166280000,358.00 1990-05-18,354.47,354.64,352.52,354.64,162520000,354.64 1990-05-17,354.00,356.92,354.00,354.47,164770000,354.47 1990-05-16,354.27,354.68,351.95,354.00,159810000,354.00 1990-05-15,354.75,355.09,352.84,354.28,165730000,354.28 1990-05-14,352.00,358.41,351.95,354.75,225410000,354.75 1990-05-11,343.82,352.31,343.82,352.00,234040000,352.00 1990-05-10,342.87,344.98,342.77,343.82,158460000,343.82 1990-05-09,342.01,343.08,340.90,342.86,152220000,342.86 1990-05-08,340.53,342.03,340.17,342.01,144230000,342.01 1990-05-07,338.39,341.07,338.11,340.53,132760000,340.53 1990-05-04,335.58,338.46,335.17,338.39,140550000,338.39 1990-05-03,334.48,337.02,334.47,335.57,145560000,335.57 1990-05-02,332.25,334.48,332.15,334.48,141610000,334.48 1990-05-01,330.80,332.83,330.80,332.25,149020000,332.25 1990-04-30,329.11,331.31,327.76,330.80,122750000,330.80 1990-04-27,332.92,333.57,328.71,329.11,130630000,329.11 1990-04-26,332.03,333.76,330.67,332.92,141330000,332.92 1990-04-25,330.36,332.74,330.36,332.03,133480000,332.03 1990-04-24,331.05,332.97,329.71,330.36,137360000,330.36 1990-04-23,335.12,335.12,330.09,331.05,136150000,331.05 1990-04-20,338.09,338.52,333.41,335.12,174260000,335.12 1990-04-19,340.72,340.72,337.59,338.09,152930000,338.09 1990-04-18,344.68,345.33,340.11,340.72,147130000,340.72 1990-04-17,344.74,345.19,342.06,344.68,127990000,344.68 1990-04-16,344.34,347.30,344.10,344.74,142810000,344.74 1990-04-12,341.92,344.79,341.91,344.34,142470000,344.34 1990-04-11,342.07,343.00,341.26,341.92,141080000,341.92 1990-04-10,341.37,342.41,340.62,342.07,136020000,342.07 1990-04-09,340.08,341.83,339.88,341.37,114970000,341.37 1990-04-06,340.73,341.73,338.94,340.08,137490000,340.08 1990-04-05,341.09,342.85,340.63,340.73,144170000,340.73 1990-04-04,343.64,344.12,340.40,341.09,159530000,341.09 1990-04-03,338.70,343.76,338.70,343.64,154310000,343.64 1990-04-02,339.94,339.94,336.33,338.70,124360000,338.70 1990-03-30,340.79,341.41,338.21,339.94,139340000,339.94 1990-03-29,342.00,342.07,339.77,340.79,132190000,340.79 1990-03-28,341.50,342.58,340.60,342.00,142300000,342.00 1990-03-27,337.63,341.50,337.03,341.50,131610000,341.50 1990-03-26,337.22,339.74,337.22,337.63,116110000,337.63 1990-03-23,335.69,337.58,335.69,337.22,132070000,337.22 1990-03-22,339.74,339.77,333.62,335.69,175930000,335.69 1990-03-21,341.57,342.34,339.56,339.74,130990000,339.74 1990-03-20,343.53,344.49,340.87,341.57,177320000,341.57 1990-03-19,341.91,343.76,339.12,343.53,142300000,343.53 1990-03-16,338.07,341.91,338.07,341.91,222520000,341.91 1990-03-15,336.87,338.91,336.87,338.07,144410000,338.07 1990-03-14,336.00,337.63,334.93,336.87,145060000,336.87 1990-03-13,338.67,338.67,335.36,336.00,145440000,336.00 1990-03-12,337.93,339.08,336.14,338.67,114790000,338.67 1990-03-09,340.12,340.27,336.84,337.93,150410000,337.93 1990-03-08,336.95,340.66,336.95,340.27,170900000,340.27 1990-03-07,337.93,338.84,336.33,336.95,163580000,336.95 1990-03-06,333.74,337.93,333.57,337.93,143640000,337.93 1990-03-05,335.54,336.38,333.49,333.74,140110000,333.74 1990-03-02,332.74,335.54,332.72,335.54,164330000,335.54 1990-03-01,331.89,334.40,331.08,332.74,157930000,332.74 1990-02-28,330.26,333.48,330.16,331.89,184400000,331.89 1990-02-27,328.68,331.94,328.47,330.26,152590000,330.26 1990-02-26,324.16,328.67,323.98,328.67,148900000,328.67 1990-02-23,325.70,326.15,322.10,324.15,148490000,324.15 1990-02-22,327.67,330.98,325.70,325.70,184320000,325.70 1990-02-21,327.91,328.17,324.47,327.67,159240000,327.67 1990-02-20,332.72,332.72,326.26,327.99,147300000,327.99 1990-02-16,334.89,335.64,332.42,332.72,166840000,332.72 1990-02-15,332.01,335.21,331.61,334.89,174620000,334.89 1990-02-14,331.02,333.20,330.64,332.01,138530000,332.01 1990-02-13,330.08,331.61,327.92,331.02,144490000,331.02 1990-02-12,333.62,333.62,329.97,330.08,118390000,330.08 1990-02-09,333.02,334.60,332.41,333.62,146910000,333.62 1990-02-08,333.75,336.09,332.00,332.96,176240000,332.96 1990-02-07,329.66,333.76,326.55,333.75,186710000,333.75 1990-02-06,331.85,331.86,328.20,329.66,134070000,329.66 1990-02-05,330.92,332.16,330.45,331.85,130950000,331.85 1990-02-02,328.79,332.10,328.09,330.92,164400000,330.92 1990-02-01,329.08,329.86,327.76,328.79,154580000,328.79 1990-01-31,322.98,329.08,322.98,329.08,189660000,329.08 1990-01-30,325.20,325.73,319.83,322.98,186030000,322.98 1990-01-29,325.80,327.31,321.79,325.20,150770000,325.20 1990-01-26,326.09,328.58,321.44,325.80,198190000,325.80 1990-01-25,330.26,332.33,325.33,326.08,172270000,326.08 1990-01-24,331.61,331.71,324.17,330.26,207830000,330.26 1990-01-23,330.38,332.76,328.67,331.61,179300000,331.61 1990-01-22,339.14,339.96,330.28,330.38,148380000,330.38 1990-01-19,338.19,340.48,338.19,339.15,185590000,339.15 1990-01-18,337.40,338.38,333.98,338.19,178590000,338.19 1990-01-17,340.77,342.01,336.26,337.40,170470000,337.40 1990-01-16,337.00,340.75,333.37,340.75,186070000,340.75 1990-01-15,339.93,339.94,336.57,337.00,140590000,337.00 1990-01-12,348.53,348.53,339.49,339.93,183880000,339.93 1990-01-11,347.31,350.14,347.31,348.53,154390000,348.53 1990-01-10,349.62,349.62,344.32,347.31,175990000,347.31 1990-01-09,353.83,354.17,349.61,349.62,155210000,349.62 1990-01-08,352.20,354.24,350.54,353.79,140110000,353.79 1990-01-05,355.67,355.67,351.35,352.20,158530000,352.20 1990-01-04,358.76,358.76,352.89,355.67,177000000,355.67 1990-01-03,359.69,360.59,357.89,358.76,192330000,358.76 1990-01-02,353.40,359.69,351.98,359.69,162070000,359.69 1989-12-29,350.68,353.41,350.67,353.40,145940000,353.40 1989-12-28,348.80,350.68,348.76,350.67,128030000,350.67 1989-12-27,346.84,349.12,346.81,348.81,133740000,348.81 1989-12-26,347.42,347.87,346.53,346.81,77610000,346.81 1989-12-22,344.78,347.53,344.76,347.42,120980000,347.42 1989-12-21,342.84,345.03,342.84,344.78,175150000,344.78 1989-12-20,342.50,343.70,341.79,342.84,176520000,342.84 1989-12-19,343.69,343.74,339.63,342.46,186060000,342.46 1989-12-18,350.14,350.88,342.19,343.69,184750000,343.69 1989-12-15,350.97,351.86,346.08,350.14,240390000,350.14 1989-12-14,352.74,352.75,350.08,350.93,178700000,350.93 1989-12-13,351.70,354.10,351.65,352.75,184660000,352.75 1989-12-12,348.56,352.21,348.41,351.73,176820000,351.73 1989-12-11,348.68,348.74,346.39,348.56,147130000,348.56 1989-12-08,347.60,349.60,347.59,348.69,144910000,348.69 1989-12-07,348.55,349.84,346.00,347.59,161980000,347.59 1989-12-06,349.58,349.94,347.91,348.55,145850000,348.55 1989-12-05,351.41,352.24,349.58,349.58,154640000,349.58 1989-12-04,350.63,351.51,350.32,351.41,150360000,351.41 1989-12-01,346.01,351.88,345.99,350.63,199200000,350.63 1989-11-30,343.60,346.50,343.57,345.99,153200000,345.99 1989-11-29,345.77,345.77,343.36,343.60,147270000,343.60 1989-11-28,345.61,346.33,344.41,345.77,153770000,345.77 1989-11-27,343.98,346.24,343.97,345.61,149390000,345.61 1989-11-24,341.92,344.24,341.91,343.97,86290000,343.97 1989-11-22,339.59,341.92,339.59,341.91,145730000,341.91 1989-11-21,339.35,340.21,337.53,339.59,147900000,339.59 1989-11-20,341.61,341.90,338.29,339.35,128170000,339.35 1989-11-17,340.58,342.24,339.85,341.61,151020000,341.61 1989-11-16,340.54,341.02,338.93,340.58,148370000,340.58 1989-11-15,338.00,340.54,337.14,340.54,155130000,340.54 1989-11-14,339.55,340.41,337.06,337.99,143170000,337.99 1989-11-13,339.08,340.51,337.93,339.55,140750000,339.55 1989-11-10,336.57,339.10,336.57,339.10,131800000,339.10 1989-11-09,338.15,338.73,336.21,336.57,143390000,336.57 1989-11-08,334.81,339.41,334.81,338.15,170150000,338.15 1989-11-07,332.61,334.82,330.91,334.81,163000000,334.81 1989-11-06,337.61,337.62,332.33,332.61,135480000,332.61 1989-11-03,338.48,339.67,337.37,337.62,131500000,337.62 1989-11-02,341.20,341.20,336.61,338.48,152440000,338.48 1989-11-01,340.36,341.74,339.79,341.20,154240000,341.20 1989-10-31,335.08,340.86,335.07,340.36,176100000,340.36 1989-10-30,335.06,337.04,334.48,335.07,126630000,335.07 1989-10-27,337.93,337.97,333.26,335.06,170330000,335.06 1989-10-26,342.50,342.50,337.20,337.93,175240000,337.93 1989-10-25,343.70,344.51,341.96,342.50,155650000,342.50 1989-10-24,344.83,344.83,335.13,343.70,237960000,343.70 1989-10-23,347.11,348.19,344.22,344.83,135860000,344.83 1989-10-20,347.04,347.57,344.47,347.16,164830000,347.16 1989-10-19,341.76,348.82,341.76,347.13,198120000,347.13 1989-10-18,341.16,343.39,339.03,341.76,166900000,341.76 1989-10-17,342.84,342.85,335.69,341.16,224070000,341.16 1989-10-16,333.65,342.87,327.12,342.85,416290000,342.85 1989-10-13,355.39,355.53,332.81,333.65,251170000,333.65 1989-10-12,356.99,356.99,354.91,355.39,160120000,355.39 1989-10-11,359.13,359.13,356.08,356.99,164070000,356.99 1989-10-10,359.80,360.44,358.11,359.13,147560000,359.13 1989-10-09,358.76,359.86,358.06,359.80,86810000,359.80 1989-10-06,356.97,359.05,356.97,358.78,172520000,358.78 1989-10-05,356.94,357.63,356.28,356.97,177890000,356.97 1989-10-04,354.71,357.49,354.71,356.94,194590000,356.94 1989-10-03,350.87,354.73,350.85,354.71,182550000,354.71 1989-10-02,349.15,350.99,348.35,350.87,127410000,350.87 1989-09-29,348.60,350.31,348.12,349.15,155300000,349.15 1989-09-28,345.10,348.61,345.10,348.60,164240000,348.60 1989-09-27,344.33,345.47,342.85,345.10,158400000,345.10 1989-09-26,344.23,347.02,344.13,344.33,158350000,344.33 1989-09-25,347.05,347.05,343.70,344.23,121130000,344.23 1989-09-22,345.70,347.57,345.69,347.05,133350000,347.05 1989-09-21,346.47,348.46,344.96,345.70,146930000,345.70 1989-09-20,346.55,347.27,346.18,346.47,136640000,346.47 1989-09-19,346.73,348.17,346.44,346.55,141610000,346.55 1989-09-18,345.06,346.84,344.60,346.73,136940000,346.73 1989-09-15,343.16,345.06,341.37,345.06,234860000,345.06 1989-09-14,345.46,345.61,342.55,343.16,149250000,343.16 1989-09-13,348.70,350.10,345.46,345.46,175330000,345.46 1989-09-12,347.66,349.46,347.50,348.70,142140000,348.70 1989-09-11,348.76,348.76,345.91,347.66,126020000,347.66 1989-09-08,348.35,349.18,345.74,348.76,154090000,348.76 1989-09-07,349.24,350.31,348.15,348.35,160160000,348.35 1989-09-06,352.56,352.56,347.98,349.24,161800000,349.24 1989-09-05,353.73,354.13,351.82,352.56,145180000,352.56 1989-09-01,351.45,353.90,350.88,353.73,133300000,353.73 1989-08-31,350.65,351.45,350.21,351.45,144820000,351.45 1989-08-30,349.84,352.27,348.66,350.65,174350000,350.65 1989-08-29,352.09,352.12,348.86,349.84,175210000,349.84 1989-08-28,350.52,352.09,349.08,352.09,131180000,352.09 1989-08-25,351.52,352.73,350.09,350.52,165930000,350.52 1989-08-24,344.70,351.52,344.70,351.52,225520000,351.52 1989-08-23,341.19,344.80,341.19,344.70,159640000,344.70 1989-08-22,340.67,341.25,339.00,341.19,141930000,341.19 1989-08-21,346.03,346.25,340.55,340.67,136800000,340.67 1989-08-18,344.45,346.03,343.89,346.03,145810000,346.03 1989-08-17,345.66,346.39,342.97,344.45,157560000,344.45 1989-08-16,344.71,346.37,344.71,345.66,150060000,345.66 1989-08-15,343.06,345.03,343.05,344.71,148770000,344.71 1989-08-14,344.71,345.44,341.96,343.06,142010000,343.06 1989-08-11,348.28,351.18,344.01,344.74,197550000,344.74 1989-08-10,346.94,349.78,345.31,348.25,198660000,348.25 1989-08-09,349.30,351.00,346.86,346.94,209900000,346.94 1989-08-08,349.41,349.84,348.28,349.35,200340000,349.35 1989-08-07,343.92,349.42,343.91,349.41,197580000,349.41 1989-08-04,344.74,345.42,342.60,343.92,169750000,343.92 1989-08-03,344.34,345.22,343.81,344.74,168690000,344.74 1989-08-02,343.75,344.34,342.47,344.34,181760000,344.34 1989-08-01,346.08,347.99,342.93,343.75,225280000,343.75 1989-07-31,342.13,346.08,342.02,346.08,166650000,346.08 1989-07-28,341.94,342.96,341.30,342.15,180610000,342.15 1989-07-27,338.05,342.00,338.05,341.99,213680000,341.99 1989-07-26,333.88,338.05,333.19,338.05,188270000,338.05 1989-07-25,333.67,336.29,332.60,333.88,179270000,333.88 1989-07-24,335.90,335.90,333.44,333.67,136260000,333.67 1989-07-21,333.50,335.91,332.46,335.90,174880000,335.90 1989-07-20,335.74,337.40,333.22,333.51,204590000,333.51 1989-07-19,331.37,335.73,331.35,335.73,215740000,335.73 1989-07-18,332.42,332.44,330.75,331.35,152350000,331.35 1989-07-17,331.78,333.02,331.02,332.44,131960000,332.44 1989-07-14,329.96,331.89,327.13,331.84,183480000,331.84 1989-07-13,329.81,330.37,329.08,329.95,153820000,329.95 1989-07-12,328.78,330.39,327.92,329.81,160550000,329.81 1989-07-11,327.07,330.42,327.07,328.78,171590000,328.78 1989-07-10,324.93,327.07,324.91,327.07,131870000,327.07 1989-07-07,321.55,325.87,321.08,324.91,166430000,324.91 1989-07-06,320.64,321.55,320.45,321.55,140450000,321.55 1989-07-05,319.23,321.22,317.26,320.64,127710000,320.64 1989-07-03,317.98,319.27,317.27,319.23,68870000,319.23 1989-06-30,319.67,319.97,314.38,317.98,170490000,317.98 1989-06-29,325.81,325.81,319.54,319.68,167100000,319.68 1989-06-28,328.44,328.44,324.30,325.81,158470000,325.81 1989-06-27,326.60,329.19,326.59,328.44,171090000,328.44 1989-06-26,328.00,328.15,326.31,326.60,143600000,326.60 1989-06-23,322.32,328.00,322.32,328.00,198720000,328.00 1989-06-22,320.48,322.34,320.20,322.32,176510000,322.32 1989-06-21,321.25,321.87,319.25,320.48,168830000,320.48 1989-06-20,321.89,322.78,321.03,321.25,167650000,321.25 1989-06-19,321.35,321.89,320.40,321.89,130720000,321.89 1989-06-16,319.96,321.36,318.69,321.35,244510000,321.35 1989-06-15,323.83,323.83,319.21,320.08,179480000,320.08 1989-06-14,323.91,324.89,322.80,323.83,170540000,323.83 1989-06-13,326.24,326.24,322.96,323.91,164870000,323.91 1989-06-12,326.69,326.69,323.73,326.24,151460000,326.24 1989-06-09,326.75,327.32,325.16,326.69,173240000,326.69 1989-06-08,326.95,327.37,325.92,326.75,212310000,326.75 1989-06-07,324.24,327.39,324.24,326.95,213710000,326.95 1989-06-06,322.03,324.48,321.27,324.24,187570000,324.24 1989-06-05,325.52,325.93,322.02,322.03,163420000,322.03 1989-06-02,321.97,325.63,321.97,325.52,229140000,325.52 1989-06-01,320.51,322.57,320.01,321.97,223160000,321.97 1989-05-31,319.05,321.30,318.68,320.52,162530000,320.52 1989-05-30,321.59,322.53,317.83,319.05,151780000,319.05 1989-05-26,319.17,321.59,319.14,321.59,143120000,321.59 1989-05-25,319.14,319.60,318.42,319.17,154470000,319.17 1989-05-24,318.32,319.14,317.58,319.14,178600000,319.14 1989-05-23,321.98,321.98,318.20,318.32,187690000,318.32 1989-05-22,321.24,323.06,320.45,321.98,185010000,321.98 1989-05-19,317.97,321.38,317.97,321.24,242410000,321.24 1989-05-18,317.48,318.52,316.54,317.97,177480000,317.97 1989-05-17,315.28,317.94,315.11,317.48,191210000,317.48 1989-05-16,316.16,316.16,314.99,315.28,173100000,315.28 1989-05-15,313.84,316.16,313.84,316.16,179350000,316.16 1989-05-12,306.95,313.84,306.95,313.84,221490000,313.84 1989-05-11,305.80,307.34,305.80,306.95,151620000,306.95 1989-05-10,305.19,306.25,304.85,305.80,146000000,305.80 1989-05-09,306.00,306.99,304.06,305.19,150090000,305.19 1989-05-08,307.61,307.61,304.74,306.00,135130000,306.00 1989-05-05,307.77,310.69,306.98,307.61,180810000,307.61 1989-05-04,308.16,308.40,307.32,307.77,153130000,307.77 1989-05-03,308.12,308.52,307.11,308.16,171690000,308.16 1989-05-02,309.13,310.45,308.12,308.12,172560000,308.12 1989-05-01,309.64,309.64,307.40,309.12,138050000,309.12 1989-04-28,309.58,309.65,308.48,309.64,158390000,309.64 1989-04-27,306.93,310.45,306.93,309.58,191170000,309.58 1989-04-26,306.78,307.30,306.07,306.93,146090000,306.93 1989-04-25,308.69,309.65,306.74,306.75,165430000,306.75 1989-04-24,309.61,309.61,307.83,308.69,142100000,308.69 1989-04-21,306.19,309.61,306.19,309.61,187310000,309.61 1989-04-20,307.15,307.96,304.53,306.19,175970000,306.19 1989-04-19,306.02,307.68,305.36,307.15,191510000,307.15 1989-04-18,301.72,306.25,301.72,306.02,208650000,306.02 1989-04-17,301.36,302.01,300.71,301.72,128540000,301.72 1989-04-14,296.40,301.38,296.40,301.36,169780000,301.36 1989-04-13,298.99,299.00,296.27,296.40,141590000,296.40 1989-04-12,298.49,299.81,298.49,298.99,165200000,298.99 1989-04-11,297.11,298.87,297.11,298.49,146830000,298.49 1989-04-10,297.16,297.94,296.85,297.11,123990000,297.11 1989-04-07,295.29,297.62,294.35,297.16,156950000,297.16 1989-04-06,296.22,296.24,294.52,295.29,146530000,295.29 1989-04-05,295.31,296.43,295.28,296.24,165880000,296.24 1989-04-04,296.40,296.40,294.72,295.31,160680000,295.31 1989-04-03,294.87,297.04,294.62,296.39,164660000,296.39 1989-03-31,292.52,294.96,292.52,294.87,170960000,294.87 1989-03-30,292.35,293.80,291.50,292.52,159950000,292.52 1989-03-29,291.59,292.75,291.42,292.35,144240000,292.35 1989-03-28,290.57,292.32,290.57,291.59,146420000,291.59 1989-03-27,288.98,290.57,288.07,290.57,112960000,290.57 1989-03-23,290.49,291.51,288.56,288.98,153750000,288.98 1989-03-22,291.33,291.46,289.90,290.49,146570000,290.49 1989-03-21,289.92,292.38,289.92,291.33,142010000,291.33 1989-03-20,292.69,292.69,288.56,289.92,151260000,289.92 1989-03-17,299.44,299.44,291.08,292.69,242900000,292.69 1989-03-16,296.67,299.99,296.66,299.44,196040000,299.44 1989-03-15,295.14,296.78,295.14,296.67,167070000,296.67 1989-03-14,295.32,296.29,294.63,295.14,139970000,295.14 1989-03-13,292.88,296.18,292.88,295.32,140460000,295.32 1989-03-10,293.93,293.93,291.60,292.88,146830000,292.88 1989-03-09,294.08,294.69,293.85,293.93,143160000,293.93 1989-03-08,293.87,295.62,293.51,294.08,167620000,294.08 1989-03-07,294.81,295.16,293.50,293.87,172500000,293.87 1989-03-06,291.20,294.81,291.18,294.81,168880000,294.81 1989-03-03,289.94,291.18,289.44,291.18,151790000,291.18 1989-03-02,287.11,290.32,287.11,289.95,161980000,289.95 1989-03-01,288.86,290.28,286.46,287.11,177210000,287.11 1989-02-28,287.82,289.42,287.63,288.86,147430000,288.86 1989-02-27,287.13,288.12,286.26,287.82,139900000,287.82 1989-02-24,292.05,292.05,287.13,287.13,160680000,287.13 1989-02-23,290.91,292.05,289.83,292.05,150370000,292.05 1989-02-22,295.98,295.98,290.76,290.91,163140000,290.91 1989-02-21,296.76,297.04,295.16,295.98,141950000,295.98 1989-02-17,294.81,297.12,294.69,296.76,159520000,296.76 1989-02-16,294.24,295.15,294.22,294.81,177450000,294.81 1989-02-15,291.81,294.42,291.49,294.24,154220000,294.24 1989-02-14,292.54,294.37,291.41,291.81,150610000,291.81 1989-02-13,292.02,293.07,290.88,292.54,143520000,292.54 1989-02-10,296.06,296.06,291.96,292.02,173560000,292.02 1989-02-09,298.65,298.79,295.16,296.06,224220000,296.06 1989-02-08,299.62,300.57,298.41,298.65,189420000,298.65 1989-02-07,296.04,300.34,295.78,299.63,217260000,299.63 1989-02-06,296.97,296.99,294.96,296.04,150980000,296.04 1989-02-03,296.84,297.66,296.15,296.97,172980000,296.97 1989-02-02,297.09,297.92,295.81,296.84,183430000,296.84 1989-02-01,297.47,298.33,296.22,297.09,215640000,297.09 1989-01-31,294.99,297.51,293.57,297.47,194050000,297.47 1989-01-30,293.82,295.13,293.54,294.99,167830000,294.99 1989-01-27,291.69,296.08,291.69,293.82,254870000,293.82 1989-01-26,289.14,292.62,288.13,291.69,212250000,291.69 1989-01-25,288.49,289.15,287.97,289.14,183610000,289.14 1989-01-24,284.50,288.49,284.50,288.49,189620000,288.49 1989-01-23,287.85,287.98,284.50,284.50,141640000,284.50 1989-01-20,286.90,287.04,285.75,286.63,166120000,286.63 1989-01-19,286.53,287.90,286.14,286.91,192030000,286.91 1989-01-18,283.55,286.87,282.65,286.53,187450000,286.53 1989-01-17,284.14,284.14,283.06,283.55,143930000,283.55 1989-01-16,283.87,284.88,283.63,284.14,117380000,284.14 1989-01-13,283.17,284.12,282.71,283.87,132320000,283.87 1989-01-12,282.01,284.63,282.01,283.17,183000000,283.17 1989-01-11,280.38,282.01,280.21,282.01,148950000,282.01 1989-01-10,280.98,281.58,279.44,280.38,140420000,280.38 1989-01-09,280.67,281.89,280.32,280.98,163180000,280.98 1989-01-06,280.01,282.06,280.01,280.67,161330000,280.67 1989-01-05,279.43,281.51,279.43,280.01,174040000,280.01 1989-01-04,275.31,279.75,275.31,279.43,149700000,279.43 1989-01-03,277.72,277.72,273.81,275.31,128500000,275.31 1988-12-30,279.39,279.78,277.72,277.72,127210000,277.72 1988-12-29,277.08,279.42,277.08,279.40,131290000,279.40 1988-12-28,276.83,277.55,276.17,277.08,110630000,277.08 1988-12-27,277.87,278.09,276.74,276.83,87490000,276.83 1988-12-23,276.87,277.99,276.87,277.87,81760000,277.87 1988-12-22,277.38,277.89,276.86,276.87,150510000,276.87 1988-12-21,277.47,277.83,276.30,277.38,147250000,277.38 1988-12-20,278.91,280.45,277.47,277.47,161090000,277.47 1988-12-19,276.29,279.31,275.61,278.91,162250000,278.91 1988-12-16,274.28,276.29,274.28,276.29,196480000,276.29 1988-12-15,275.32,275.62,274.01,274.28,136820000,274.28 1988-12-14,276.31,276.31,274.58,275.31,132350000,275.31 1988-12-13,276.52,276.52,274.58,276.31,132340000,276.31 1988-12-12,277.03,278.82,276.52,276.52,124160000,276.52 1988-12-09,276.57,277.82,276.34,277.03,133770000,277.03 1988-12-08,278.13,278.13,276.55,276.59,124150000,276.59 1988-12-07,277.59,279.01,277.34,278.13,148360000,278.13 1988-12-06,274.93,277.89,274.62,277.59,158340000,277.59 1988-12-05,274.93,275.62,271.81,274.93,144660000,274.93 1988-12-02,272.49,272.49,270.47,271.81,124610000,271.81 1988-12-01,273.68,273.70,272.27,272.49,129380000,272.49 1988-11-30,270.91,274.36,270.90,273.70,157810000,273.70 1988-11-29,268.60,271.31,268.13,270.91,127420000,270.91 1988-11-28,267.22,268.98,266.97,268.64,123480000,268.64 1988-11-25,268.99,269.00,266.47,267.23,72090000,267.23 1988-11-23,267.22,269.56,267.21,269.00,112010000,269.00 1988-11-22,266.19,267.85,265.42,267.21,127000000,267.21 1988-11-21,266.35,266.47,263.41,266.22,120430000,266.22 1988-11-18,264.60,266.62,264.60,266.47,119320000,266.47 1988-11-17,264.61,265.63,263.45,264.60,141280000,264.60 1988-11-16,268.41,268.41,262.85,263.82,161710000,263.82 1988-11-15,267.73,268.75,267.72,268.34,115170000,268.34 1988-11-14,267.93,269.25,266.79,267.72,142900000,267.72 1988-11-11,273.65,273.69,267.92,267.92,135500000,267.92 1988-11-10,273.32,274.37,272.98,273.69,128920000,273.69 1988-11-09,275.14,275.15,272.15,273.33,153140000,273.33 1988-11-08,273.95,275.80,273.93,275.15,141660000,275.15 1988-11-07,276.30,276.31,273.62,273.93,133870000,273.93 1988-11-04,279.11,279.20,276.31,276.31,143580000,276.31 1988-11-03,279.04,280.37,279.04,279.20,152980000,279.20 1988-11-02,279.07,279.45,277.08,279.06,161300000,279.06 1988-11-01,278.97,279.57,278.01,279.06,151250000,279.06 1988-10-31,278.54,279.39,277.14,278.97,143460000,278.97 1988-10-28,277.29,279.48,277.28,278.53,146300000,278.53 1988-10-27,281.35,281.38,276.00,277.28,196540000,277.28 1988-10-26,282.37,282.52,280.54,281.38,181550000,281.38 1988-10-25,282.28,282.84,281.87,282.38,155190000,282.38 1988-10-24,283.63,283.95,282.28,282.28,170590000,282.28 1988-10-21,282.88,283.66,281.16,283.66,195410000,283.66 1988-10-20,276.97,282.88,276.93,282.88,189580000,282.88 1988-10-19,279.40,280.53,274.41,276.97,186350000,276.97 1988-10-18,276.43,279.39,276.41,279.38,162500000,279.38 1988-10-17,275.48,276.65,275.01,276.41,119290000,276.41 1988-10-14,275.27,277.01,274.08,275.50,160240000,275.50 1988-10-13,273.95,275.83,273.39,275.22,154530000,275.22 1988-10-12,277.91,277.93,273.05,273.98,154840000,273.98 1988-10-11,278.15,278.24,276.33,277.93,140900000,277.93 1988-10-10,278.06,278.69,277.10,278.24,124660000,278.24 1988-10-07,272.38,278.07,272.37,278.07,216390000,278.07 1988-10-06,271.87,272.39,271.30,272.39,153570000,272.39 1988-10-05,270.63,272.45,270.08,271.86,175130000,271.86 1988-10-04,271.37,271.79,270.34,270.62,157760000,270.62 1988-10-03,271.89,271.91,268.84,271.38,130380000,271.38 1988-09-30,272.55,274.87,271.66,271.91,175750000,271.91 1988-09-29,269.09,273.02,269.08,272.59,155790000,272.59 1988-09-28,268.22,269.08,267.77,269.08,113720000,269.08 1988-09-27,268.89,269.36,268.01,268.26,113010000,268.26 1988-09-26,269.77,269.80,268.61,268.88,116420000,268.88 1988-09-23,269.16,270.31,268.28,269.76,145100000,269.76 1988-09-22,270.19,270.58,268.26,269.18,150670000,269.18 1988-09-21,269.76,270.64,269.48,270.16,127400000,270.16 1988-09-20,268.83,270.07,268.50,269.73,142220000,269.73 1988-09-19,270.64,270.65,267.41,268.82,135770000,268.82 1988-09-16,268.13,270.81,267.33,270.65,211110000,270.65 1988-09-15,269.30,269.78,268.03,268.13,161210000,268.13 1988-09-14,267.50,269.47,267.41,269.31,177220000,269.31 1988-09-13,266.45,267.43,265.22,267.43,162490000,267.43 1988-09-12,266.85,267.64,266.22,266.47,114880000,266.47 1988-09-09,265.88,268.26,263.66,266.84,141540000,266.84 1988-09-08,265.87,266.54,264.88,265.88,149380000,265.88 1988-09-07,265.62,266.98,264.93,265.87,139590000,265.87 1988-09-06,264.42,265.94,264.40,265.59,122250000,265.59 1988-09-02,258.35,264.90,258.35,264.48,159840000,264.48 1988-09-01,261.52,261.52,256.98,258.35,144090000,258.35 1988-08-31,262.51,263.80,261.21,261.52,130480000,261.52 1988-08-30,262.33,263.18,261.53,262.51,108720000,262.51 1988-08-29,259.68,262.56,259.68,262.33,99280000,262.33 1988-08-26,259.18,260.15,258.87,259.68,89240000,259.68 1988-08-25,261.10,261.13,257.56,259.18,127640000,259.18 1988-08-24,257.16,261.13,257.09,261.13,127800000,261.13 1988-08-23,256.99,257.86,256.53,257.09,119540000,257.09 1988-08-22,260.24,260.71,256.94,256.98,122250000,256.98 1988-08-19,261.05,262.27,260.23,260.24,122370000,260.24 1988-08-18,260.76,262.76,260.75,261.03,139820000,261.03 1988-08-17,260.57,261.84,259.33,260.77,169500000,260.77 1988-08-16,258.68,262.61,257.50,260.56,162790000,260.56 1988-08-15,262.49,262.55,258.68,258.69,128560000,258.69 1988-08-12,262.70,262.94,261.37,262.55,176960000,262.55 1988-08-11,261.92,262.77,260.34,262.75,173000000,262.75 1988-08-10,266.43,266.49,261.03,261.90,200950000,261.90 1988-08-09,270.00,270.20,265.06,266.49,200710000,266.49 1988-08-08,271.13,272.47,269.93,269.98,148800000,269.98 1988-08-05,271.70,271.93,270.08,271.15,113400000,271.15 1988-08-04,273.00,274.20,271.77,271.93,157240000,271.93 1988-08-03,272.03,273.42,271.15,272.98,203590000,272.98 1988-08-02,272.19,273.68,270.37,272.06,166660000,272.06 1988-08-01,272.03,272.80,271.21,272.21,138170000,272.21 1988-07-29,266.04,272.02,266.02,272.02,192340000,272.02 1988-07-28,262.52,266.55,262.50,266.02,154570000,266.02 1988-07-27,265.18,265.83,262.48,262.50,135890000,262.50 1988-07-26,264.70,266.09,264.32,265.19,121960000,265.19 1988-07-25,263.49,265.17,263.03,264.68,215140000,264.68 1988-07-22,266.65,266.66,263.29,263.50,148880000,263.50 1988-07-21,269.99,270.00,266.66,266.66,149460000,266.66 1988-07-20,268.52,270.24,268.47,270.00,151990000,270.00 1988-07-19,270.49,271.21,267.01,268.47,144110000,268.47 1988-07-18,271.99,272.05,268.66,270.51,156210000,270.51 1988-07-15,270.23,272.06,269.53,272.05,199710000,272.05 1988-07-14,269.33,270.69,268.58,270.26,172410000,270.26 1988-07-13,267.87,269.46,266.12,269.32,218930000,269.32 1988-07-12,270.54,270.70,266.96,267.85,161650000,267.85 1988-07-11,270.03,271.64,270.02,270.55,123300000,270.55 1988-07-08,271.76,272.31,269.86,270.02,136070000,270.02 1988-07-07,272.00,272.05,269.31,271.78,156100000,271.78 1988-07-06,275.80,276.36,269.92,272.02,189630000,272.02 1988-07-05,271.78,275.81,270.51,275.81,171790000,275.81 1988-07-01,273.50,273.80,270.78,271.78,238330000,271.78 1988-06-30,271.00,273.51,270.97,273.50,227410000,273.50 1988-06-29,272.32,273.01,269.49,270.98,159590000,270.98 1988-06-28,269.07,272.80,269.06,272.31,152370000,272.31 1988-06-27,273.78,273.79,268.85,269.06,264410000,269.06 1988-06-24,274.81,275.19,273.53,273.78,179880000,273.78 1988-06-23,275.62,275.89,274.26,274.82,185770000,274.82 1988-06-22,271.69,276.88,271.67,275.66,217510000,275.66 1988-06-21,268.95,271.67,267.52,271.67,155060000,271.67 1988-06-20,270.67,270.68,268.59,268.94,116750000,268.94 1988-06-17,269.79,270.77,268.09,270.68,343920000,270.68 1988-06-16,274.44,274.45,268.76,269.77,161550000,269.77 1988-06-15,274.29,274.45,272.75,274.45,150260000,274.45 1988-06-14,271.58,276.14,271.44,274.30,227150000,274.30 1988-06-13,271.28,271.94,270.53,271.43,125310000,271.43 1988-06-10,270.22,273.21,270.20,271.26,155710000,271.26 1988-06-09,271.50,272.29,270.19,270.20,235160000,270.20 1988-06-08,265.32,272.01,265.17,271.52,310030000,271.52 1988-06-07,267.02,267.28,264.50,265.17,168710000,265.17 1988-06-06,266.46,267.05,264.97,267.05,152460000,267.05 1988-06-03,265.34,267.11,264.42,266.45,189600000,266.45 1988-06-02,266.65,266.71,264.12,265.33,193540000,265.33 1988-06-01,262.16,267.43,262.10,266.69,234560000,266.69 1988-05-31,253.44,262.16,253.42,262.16,247610000,262.16 1988-05-27,254.62,254.63,252.74,253.42,133590000,253.42 1988-05-26,253.75,254.98,253.52,254.63,164260000,254.63 1988-05-25,253.52,255.34,253.51,253.76,138310000,253.76 1988-05-24,250.84,253.51,250.83,253.51,139930000,253.51 1988-05-23,253.00,253.02,249.82,250.83,102640000,250.83 1988-05-20,252.61,253.70,251.79,253.02,120600000,253.02 1988-05-19,251.36,252.57,248.85,252.57,165160000,252.57 1988-05-18,255.40,255.67,250.73,251.35,209420000,251.35 1988-05-17,258.72,260.20,255.35,255.39,133850000,255.39 1988-05-16,256.75,258.71,256.28,258.71,155010000,258.71 1988-05-13,253.88,256.83,253.85,256.78,147240000,256.78 1988-05-12,253.32,254.87,253.31,253.85,143880000,253.85 1988-05-11,257.60,257.62,252.32,253.31,176720000,253.31 1988-05-10,256.53,258.30,255.93,257.62,131200000,257.62 1988-05-09,257.47,258.22,255.45,256.54,166320000,256.54 1988-05-06,258.80,260.31,257.03,257.48,129080000,257.48 1988-05-05,260.30,260.32,258.13,258.79,171840000,258.79 1988-05-04,263.05,263.23,260.31,260.32,141320000,260.32 1988-05-03,261.55,263.70,261.55,263.00,176920000,263.00 1988-05-02,261.36,261.56,259.99,261.56,136470000,261.56 1988-04-29,262.59,262.61,259.97,261.33,135620000,261.33 1988-04-28,263.79,263.80,262.22,262.61,128680000,262.61 1988-04-27,263.94,265.09,263.45,263.80,133810000,263.80 1988-04-26,262.45,265.06,262.18,263.93,152300000,263.93 1988-04-25,260.15,263.29,260.14,262.51,156950000,262.51 1988-04-22,256.45,261.16,256.42,260.14,152520000,260.14 1988-04-21,256.15,260.44,254.71,256.42,168440000,256.42 1988-04-20,257.91,258.54,256.12,256.13,147590000,256.13 1988-04-19,259.24,262.38,257.91,257.92,161910000,257.92 1988-04-18,259.75,259.81,258.03,259.21,144650000,259.21 1988-04-15,259.74,260.39,255.97,259.77,234160000,259.77 1988-04-14,271.55,271.57,259.37,259.75,211810000,259.75 1988-04-13,271.33,271.70,269.23,271.58,185120000,271.58 1988-04-12,269.88,272.05,269.66,271.37,146400000,271.37 1988-04-11,269.43,270.41,268.61,270.16,146370000,270.16 1988-04-08,266.15,270.22,266.11,269.43,169300000,269.43 1988-04-07,265.51,267.32,265.22,266.16,177840000,266.16 1988-04-06,258.52,265.50,258.22,265.49,189760000,265.49 1988-04-05,256.10,258.52,256.03,258.51,135290000,258.51 1988-04-04,258.89,259.06,255.68,256.09,182240000,256.09 1988-03-31,258.03,259.03,256.16,258.89,139870000,258.89 1988-03-30,260.06,261.59,257.92,258.07,151810000,258.07 1988-03-29,258.11,260.86,258.06,260.07,152690000,260.07 1988-03-28,258.50,258.51,256.07,258.06,142820000,258.06 1988-03-25,263.34,263.44,258.12,258.51,163170000,258.51 1988-03-24,268.91,268.91,262.48,263.35,184910000,263.35 1988-03-23,268.81,269.79,268.01,268.91,167370000,268.91 1988-03-22,268.73,269.61,267.90,268.84,142000000,268.84 1988-03-21,271.10,271.12,267.42,268.74,128830000,268.74 1988-03-18,271.22,272.64,269.76,271.12,245750000,271.12 1988-03-17,268.66,271.22,268.65,271.22,211920000,271.22 1988-03-16,266.11,268.68,264.81,268.65,153590000,268.65 1988-03-15,266.34,266.41,264.92,266.13,133170000,266.13 1988-03-14,264.93,266.55,264.52,266.37,131890000,266.37 1988-03-11,263.85,264.94,261.27,264.94,200020000,264.94 1988-03-10,269.07,269.35,263.80,263.84,197260000,263.84 1988-03-09,269.46,270.76,268.65,269.06,210900000,269.06 1988-03-08,267.38,270.06,267.38,269.43,237680000,269.43 1988-03-07,267.28,267.69,265.94,267.38,152980000,267.38 1988-03-04,267.87,268.40,264.72,267.30,201410000,267.30 1988-03-03,267.98,268.40,266.82,267.88,203310000,267.88 1988-03-02,267.23,268.75,267.00,267.98,199630000,267.98 1988-03-01,267.82,267.95,265.39,267.22,199990000,267.22 1988-02-29,262.46,267.82,262.46,267.82,236050000,267.82 1988-02-26,261.56,263.00,261.38,262.46,158060000,262.46 1988-02-25,264.39,267.75,261.05,261.58,213490000,261.58 1988-02-24,265.01,266.25,263.87,264.43,212730000,264.43 1988-02-23,265.62,266.12,263.11,265.02,192260000,265.02 1988-02-22,261.60,266.06,260.88,265.64,178930000,265.64 1988-02-19,257.90,261.61,257.62,261.61,180300000,261.61 1988-02-18,258.82,259.60,256.90,257.91,151430000,257.91 1988-02-17,259.94,261.47,257.83,259.21,176830000,259.21 1988-02-16,257.61,259.84,256.57,259.83,135380000,259.83 1988-02-12,255.95,258.86,255.85,257.63,177190000,257.63 1988-02-11,256.63,257.77,255.12,255.95,200760000,255.95 1988-02-10,251.74,256.92,251.72,256.66,187980000,256.66 1988-02-09,249.11,251.72,248.66,251.72,162350000,251.72 1988-02-08,250.95,250.96,247.82,249.10,168850000,249.10 1988-02-05,252.22,253.85,250.90,250.96,161310000,250.96 1988-02-04,252.20,253.03,250.34,252.21,186490000,252.21 1988-02-03,255.56,256.98,250.56,252.21,237270000,252.21 1988-02-02,255.05,256.08,252.80,255.57,164920000,255.57 1988-02-01,257.05,258.27,254.93,255.04,210660000,255.04 1988-01-29,253.31,257.07,252.70,257.07,211880000,257.07 1988-01-28,249.39,253.66,249.38,253.29,166430000,253.29 1988-01-27,249.58,253.02,248.50,249.38,176360000,249.38 1988-01-26,252.13,252.17,249.10,249.57,138380000,249.57 1988-01-25,246.53,252.87,246.50,252.17,275250000,252.17 1988-01-22,243.14,246.50,243.14,246.50,147050000,246.50 1988-01-21,242.65,244.25,240.17,243.14,158080000,243.14 1988-01-20,249.31,249.32,241.14,242.63,181660000,242.63 1988-01-19,251.84,253.33,248.75,249.32,153550000,249.32 1988-01-18,252.05,252.86,249.98,251.88,135100000,251.88 1988-01-15,246.02,253.65,245.88,252.05,197940000,252.05 1988-01-14,245.83,247.00,243.97,245.88,140570000,245.88 1988-01-13,245.41,249.25,241.41,245.81,154020000,245.81 1988-01-12,247.44,247.49,240.46,245.42,165730000,245.42 1988-01-11,243.38,247.51,241.07,247.49,158980000,247.49 1988-01-08,261.05,261.07,242.95,243.40,197300000,243.40 1988-01-07,258.87,261.32,256.18,261.07,175360000,261.07 1988-01-06,258.64,259.79,257.18,258.89,169730000,258.89 1988-01-05,255.95,261.78,255.95,258.63,209520000,258.63 1988-01-04,247.10,256.44,247.08,255.94,181810000,255.94 1987-12-31,247.84,247.86,245.22,247.08,170140000,247.08 1987-12-30,244.63,248.06,244.59,247.86,149230000,247.86 1987-12-29,245.58,245.88,244.28,244.59,111580000,244.59 1987-12-28,252.01,252.02,244.19,245.57,131220000,245.57 1987-12-24,253.13,253.16,251.68,252.03,108800000,252.03 1987-12-23,249.96,253.35,249.95,253.16,203110000,253.16 1987-12-22,249.56,249.97,247.01,249.95,192650000,249.95 1987-12-21,249.14,250.25,248.30,249.54,161790000,249.54 1987-12-18,243.01,249.18,243.01,249.16,276220000,249.16 1987-12-17,248.08,248.60,242.96,242.98,191780000,242.98 1987-12-16,242.81,248.11,242.80,248.08,193820000,248.08 1987-12-15,242.19,245.59,241.31,242.81,214970000,242.81 1987-12-14,235.30,242.34,235.04,242.19,187680000,242.19 1987-12-11,233.60,235.48,233.35,235.32,151680000,235.32 1987-12-10,238.89,240.05,233.40,233.57,188960000,233.57 1987-12-09,234.91,240.09,233.83,238.89,231430000,238.89 1987-12-08,228.77,234.92,228.69,234.91,227310000,234.91 1987-12-07,223.98,228.77,223.92,228.76,146660000,228.76 1987-12-04,225.20,225.77,221.24,223.92,184800000,223.92 1987-12-03,233.46,233.90,225.21,225.21,204160000,225.21 1987-12-02,232.01,234.56,230.31,233.45,148890000,233.45 1987-12-01,230.32,234.02,230.30,232.00,149870000,232.00 1987-11-30,240.27,240.34,225.75,230.30,268910000,230.30 1987-11-27,244.11,244.12,240.34,240.34,86360000,240.34 1987-11-25,246.42,246.54,244.08,244.10,139780000,244.10 1987-11-24,242.98,247.90,242.98,246.39,199520000,246.39 1987-11-23,242.00,242.99,240.50,242.99,143160000,242.99 1987-11-20,240.04,242.01,235.89,242.00,189170000,242.00 1987-11-19,245.54,245.55,239.70,240.05,157140000,240.05 1987-11-18,243.09,245.55,240.67,245.55,158270000,245.55 1987-11-17,246.73,246.76,240.81,243.04,148240000,243.04 1987-11-16,245.69,249.54,244.98,246.76,164340000,246.76 1987-11-13,248.54,249.42,245.64,245.64,174920000,245.64 1987-11-12,241.93,249.90,241.90,248.52,206280000,248.52 1987-11-11,239.01,243.86,239.00,241.90,147850000,241.90 1987-11-10,243.14,243.17,237.64,239.00,184310000,239.00 1987-11-09,250.41,250.41,243.01,243.17,160690000,243.17 1987-11-06,254.49,257.21,249.68,250.41,228290000,250.41 1987-11-05,248.93,256.09,247.72,254.48,226000000,254.48 1987-11-04,250.81,251.00,246.34,248.96,202500000,248.96 1987-11-03,255.75,255.75,242.78,250.82,227800000,250.82 1987-11-02,251.73,255.75,249.15,255.75,176000000,255.75 1987-10-30,244.77,254.04,244.77,251.79,303400000,251.79 1987-10-29,233.31,246.69,233.28,244.77,258100000,244.77 1987-10-28,233.19,238.58,226.26,233.28,279400000,233.28 1987-10-27,227.67,237.81,227.67,233.19,260200000,233.19 1987-10-26,248.20,248.22,227.26,227.67,308800000,227.67 1987-10-23,248.29,250.70,242.76,248.22,245600000,248.22 1987-10-22,258.24,258.38,242.99,248.25,392200000,248.25 1987-10-21,236.83,259.27,236.83,258.38,449600000,258.38 1987-10-20,225.06,245.62,216.46,236.83,608100000,236.83 1987-10-19,282.70,282.70,224.83,224.84,604300000,224.84 1987-10-16,298.08,298.92,281.52,282.70,338500000,282.70 1987-10-15,305.21,305.23,298.07,298.08,263200000,298.08 1987-10-14,314.52,314.52,304.78,305.23,207400000,305.23 1987-10-13,309.39,314.53,309.39,314.52,172900000,314.52 1987-10-12,311.07,311.07,306.76,309.39,141900000,309.39 1987-10-09,314.16,315.04,310.97,311.07,158300000,311.07 1987-10-08,318.54,319.34,312.02,314.16,198700000,314.16 1987-10-07,319.22,319.39,315.78,318.54,186300000,318.54 1987-10-06,328.08,328.08,319.17,319.22,175600000,319.22 1987-10-05,328.07,328.57,326.09,328.08,159700000,328.08 1987-10-02,327.33,328.94,327.22,328.07,189100000,328.07 1987-10-01,321.83,327.34,321.83,327.33,193200000,327.33 1987-09-30,321.69,322.53,320.16,321.83,183100000,321.83 1987-09-29,323.20,324.63,320.27,321.69,173500000,321.69 1987-09-28,320.16,325.33,320.16,323.20,188100000,323.20 1987-09-25,319.72,320.55,318.10,320.16,138000000,320.16 1987-09-24,321.09,322.01,319.12,319.72,162200000,319.72 1987-09-23,319.49,321.83,319.12,321.19,220300000,321.19 1987-09-22,310.54,319.51,308.69,319.50,209500000,319.50 1987-09-21,314.92,317.66,310.12,310.54,170100000,310.54 1987-09-18,314.98,316.99,314.86,314.86,188100000,314.86 1987-09-17,314.94,316.08,313.45,314.93,150700000,314.93 1987-09-16,317.75,319.50,314.61,314.86,195700000,314.86 1987-09-15,323.07,323.08,317.63,317.74,136200000,317.74 1987-09-14,322.02,323.81,320.40,323.08,154400000,323.08 1987-09-11,317.14,322.45,317.13,321.98,178000000,321.98 1987-09-10,313.92,317.59,313.92,317.13,179800000,317.13 1987-09-09,313.60,315.41,312.29,313.92,164900000,313.92 1987-09-08,316.68,316.70,308.56,313.56,242900000,313.56 1987-09-04,320.21,322.03,316.53,316.70,129100000,316.70 1987-09-03,321.47,324.29,317.39,320.21,165200000,320.21 1987-09-02,323.40,324.53,318.76,321.68,199900000,321.68 1987-09-01,329.81,332.18,322.83,323.40,193500000,323.40 1987-08-31,327.03,330.09,326.99,329.80,165800000,329.80 1987-08-28,331.37,331.38,327.03,327.04,156300000,327.04 1987-08-27,334.56,334.57,331.10,331.38,163600000,331.38 1987-08-26,336.77,337.39,334.46,334.57,196200000,334.57 1987-08-25,333.37,337.89,333.33,336.77,213500000,336.77 1987-08-24,335.89,335.90,331.92,333.33,149400000,333.33 1987-08-21,334.85,336.37,334.30,335.90,189600000,335.90 1987-08-20,331.49,335.19,329.83,334.84,196600000,334.84 1987-08-19,329.26,329.89,326.54,329.83,180900000,329.83 1987-08-18,334.10,334.11,326.43,329.25,198400000,329.25 1987-08-17,333.98,335.43,332.88,334.11,166100000,334.11 1987-08-14,334.63,336.08,332.63,333.99,196100000,333.99 1987-08-13,332.38,335.52,332.38,334.65,217100000,334.65 1987-08-12,333.32,334.57,331.06,332.39,235800000,332.39 1987-08-11,328.02,333.40,328.00,333.33,278100000,333.33 1987-08-10,322.98,328.00,322.95,328.00,187200000,328.00 1987-08-07,322.10,324.15,321.82,323.00,212700000,323.00 1987-08-06,318.49,322.09,317.50,322.09,192000000,322.09 1987-08-05,316.25,319.74,316.23,318.45,192700000,318.45 1987-08-04,317.59,318.25,314.51,316.23,166500000,316.23 1987-08-03,318.62,320.26,316.52,317.57,207800000,317.57 1987-07-31,318.05,318.85,317.56,318.66,181900000,318.66 1987-07-30,315.69,318.53,315.65,318.05,208000000,318.05 1987-07-29,312.34,315.65,311.73,315.65,196200000,315.65 1987-07-28,310.65,312.33,310.28,312.33,172600000,312.33 1987-07-27,309.30,310.70,308.61,310.65,152000000,310.65 1987-07-24,307.82,309.28,307.78,309.27,158400000,309.27 1987-07-23,308.50,309.63,306.10,307.81,163700000,307.81 1987-07-22,308.56,309.12,307.22,308.47,174700000,308.47 1987-07-21,311.36,312.41,307.51,308.55,186600000,308.55 1987-07-20,314.56,314.59,311.24,311.39,168100000,311.39 1987-07-17,312.71,314.59,312.38,314.59,210000000,314.59 1987-07-16,311.00,312.83,310.42,312.70,210900000,312.70 1987-07-15,310.67,312.08,309.07,310.42,202300000,310.42 1987-07-14,307.67,310.69,307.46,310.68,185900000,310.68 1987-07-13,308.41,308.41,305.49,307.63,152500000,307.63 1987-07-10,307.55,308.40,306.96,308.37,172100000,308.37 1987-07-09,308.30,309.56,307.42,307.52,195400000,307.52 1987-07-08,307.41,308.48,306.01,308.29,207500000,308.29 1987-07-07,304.91,308.63,304.73,307.40,200700000,307.40 1987-07-06,305.64,306.75,304.23,304.92,155000000,304.92 1987-07-02,302.96,306.34,302.94,305.63,154900000,305.63 1987-07-01,303.99,304.00,302.53,302.94,157000000,302.94 1987-06-30,307.89,308.00,303.01,304.00,165500000,304.00 1987-06-29,307.15,308.15,306.75,307.90,142500000,307.90 1987-06-26,308.94,308.96,306.36,307.16,150500000,307.16 1987-06-25,306.87,309.44,306.86,308.96,173500000,308.96 1987-06-24,308.44,308.91,306.32,306.86,153800000,306.86 1987-06-23,309.66,310.27,307.48,308.43,194200000,308.43 1987-06-22,306.98,310.20,306.97,309.65,178200000,309.65 1987-06-19,305.71,306.97,305.55,306.97,220500000,306.97 1987-06-18,304.78,306.13,303.38,305.69,168600000,305.69 1987-06-17,304.77,305.74,304.03,304.81,184700000,304.81 1987-06-16,303.12,304.86,302.60,304.76,157800000,304.76 1987-06-15,301.62,304.11,301.62,303.14,156900000,303.14 1987-06-12,298.77,302.26,298.73,301.62,175100000,301.62 1987-06-11,297.50,298.94,297.47,298.73,138900000,298.73 1987-06-10,297.28,300.81,295.66,297.47,197400000,297.47 1987-06-09,296.72,297.59,295.90,297.28,164200000,297.28 1987-06-08,293.46,297.03,291.55,296.72,136400000,296.72 1987-06-05,295.11,295.11,292.80,293.45,129100000,293.45 1987-06-04,293.46,295.09,292.76,295.09,140300000,295.09 1987-06-03,288.56,293.47,288.56,293.47,164200000,293.47 1987-06-02,289.82,290.94,286.93,288.46,153400000,288.46 1987-06-01,290.12,291.96,289.23,289.83,149300000,289.83 1987-05-29,290.77,292.87,289.70,290.10,153500000,290.10 1987-05-28,288.73,291.50,286.33,290.76,153800000,290.76 1987-05-27,289.07,290.78,288.19,288.73,171400000,288.73 1987-05-26,282.16,289.11,282.16,289.11,152500000,289.11 1987-05-22,280.17,283.33,280.17,282.16,135800000,282.16 1987-05-21,278.23,282.31,278.21,280.17,164800000,280.17 1987-05-20,279.62,280.89,277.01,278.21,206800000,278.21 1987-05-19,286.66,287.39,278.83,279.62,175400000,279.62 1987-05-18,287.43,287.43,282.57,286.65,174200000,286.65 1987-05-15,294.23,294.24,287.11,287.43,180800000,287.43 1987-05-14,293.98,295.10,292.95,294.24,152000000,294.24 1987-05-13,293.31,294.54,290.74,293.98,171000000,293.98 1987-05-12,291.57,293.30,290.18,293.30,155300000,293.30 1987-05-11,293.37,298.69,291.55,291.57,203700000,291.57 1987-05-08,294.73,296.18,291.73,293.37,161900000,293.37 1987-05-07,295.45,296.80,294.07,294.71,215200000,294.71 1987-05-06,295.35,296.19,293.60,295.47,196600000,295.47 1987-05-05,289.36,295.40,289.34,295.34,192300000,295.34 1987-05-04,288.02,289.99,286.39,289.36,140600000,289.36 1987-05-01,286.99,289.71,286.52,288.03,160100000,288.03 1987-04-30,284.58,290.08,284.57,288.36,183100000,288.36 1987-04-29,282.58,286.42,282.58,284.57,173600000,284.57 1987-04-28,281.83,285.95,281.83,282.51,180100000,282.51 1987-04-27,281.52,284.45,276.22,281.83,222700000,281.83 1987-04-24,286.81,286.82,281.18,281.52,178000000,281.52 1987-04-23,287.19,289.12,284.28,286.82,173900000,286.82 1987-04-22,293.05,293.46,286.98,287.19,185900000,287.19 1987-04-21,285.88,293.07,282.89,293.07,191300000,293.07 1987-04-20,286.91,288.36,284.55,286.09,139100000,286.09 1987-04-16,284.45,289.57,284.44,286.91,189600000,286.91 1987-04-15,279.17,285.14,279.16,284.44,198200000,284.44 1987-04-14,285.61,285.62,275.67,279.16,266500000,279.16 1987-04-13,292.48,293.36,285.62,285.62,181000000,285.62 1987-04-10,292.82,293.74,290.94,292.49,169500000,292.49 1987-04-09,297.25,297.71,291.50,292.86,180300000,292.86 1987-04-08,296.72,299.20,295.18,297.26,179800000,297.26 1987-04-07,301.94,303.65,296.67,296.69,186400000,296.69 1987-04-06,300.46,302.21,300.41,301.95,173700000,301.95 1987-04-03,293.64,301.30,292.30,300.41,213400000,300.41 1987-04-02,292.41,294.47,292.02,293.63,183000000,293.63 1987-04-01,291.59,292.38,288.34,292.38,182600000,292.38 1987-03-31,289.21,291.87,289.07,291.70,171800000,291.70 1987-03-30,296.10,296.13,286.69,289.20,208400000,289.20 1987-03-27,300.96,301.41,296.06,296.13,184400000,296.13 1987-03-26,300.39,302.72,300.38,300.93,196000000,300.93 1987-03-25,301.52,301.85,299.36,300.38,171300000,300.38 1987-03-24,301.17,301.92,300.14,301.64,189900000,301.64 1987-03-23,298.16,301.17,297.50,301.16,189100000,301.16 1987-03-20,294.08,298.17,294.08,298.17,234000000,298.17 1987-03-19,292.73,294.46,292.26,294.08,166100000,294.08 1987-03-18,292.49,294.58,290.87,292.78,198100000,292.78 1987-03-17,288.09,292.47,287.96,292.47,177300000,292.47 1987-03-16,289.88,289.89,286.64,288.23,134900000,288.23 1987-03-13,291.22,291.79,289.88,289.89,150900000,289.89 1987-03-12,290.33,291.91,289.66,291.22,174500000,291.22 1987-03-11,290.87,292.51,289.33,290.31,186900000,290.31 1987-03-10,288.30,290.87,287.89,290.86,174800000,290.86 1987-03-09,290.66,290.66,287.12,288.30,165400000,288.30 1987-03-06,290.52,290.67,288.77,290.66,181600000,290.66 1987-03-05,288.62,291.24,288.60,290.52,205400000,290.52 1987-03-04,284.12,288.62,284.12,288.62,198400000,288.62 1987-03-03,283.00,284.19,282.92,284.12,149200000,284.12 1987-03-02,284.17,284.83,282.30,283.00,156700000,283.00 1987-02-27,282.96,284.55,282.77,284.20,142800000,284.20 1987-02-26,284.00,284.40,280.73,282.96,165800000,282.96 1987-02-25,282.88,285.35,282.14,284.00,184100000,284.00 1987-02-24,282.38,283.33,281.45,282.88,151300000,282.88 1987-02-23,285.48,285.50,279.37,282.38,170500000,282.38 1987-02-20,285.57,285.98,284.31,285.48,175800000,285.48 1987-02-19,285.42,286.24,283.84,285.57,181500000,285.57 1987-02-18,285.49,287.55,282.97,285.42,218200000,285.42 1987-02-17,279.70,285.49,279.70,285.49,187800000,285.49 1987-02-13,275.62,280.91,275.01,279.70,184400000,279.70 1987-02-12,277.54,278.04,273.89,275.62,200400000,275.62 1987-02-11,275.07,277.71,274.71,277.54,172400000,277.54 1987-02-10,278.16,278.16,273.49,275.07,168300000,275.07 1987-02-09,280.04,280.04,277.24,278.16,143300000,278.16 1987-02-06,281.16,281.79,279.87,280.04,184100000,280.04 1987-02-05,279.64,282.26,278.66,281.16,256700000,281.16 1987-02-04,275.99,279.65,275.35,279.64,222400000,279.64 1987-02-03,276.45,277.83,275.84,275.99,198100000,275.99 1987-02-02,274.08,277.35,273.16,276.45,177400000,276.45 1987-01-30,274.24,274.24,271.38,274.08,163400000,274.08 1987-01-29,275.40,276.85,272.54,274.24,205300000,274.24 1987-01-28,273.75,275.71,273.03,275.40,195800000,275.40 1987-01-27,269.61,274.31,269.61,273.75,192300000,273.75 1987-01-26,270.10,270.40,267.73,269.61,138900000,269.61 1987-01-23,273.91,280.96,268.41,270.10,302400000,270.10 1987-01-22,267.84,274.05,267.32,273.91,188700000,273.91 1987-01-21,269.04,270.87,267.35,267.84,184200000,267.84 1987-01-20,269.34,271.03,267.65,269.04,224800000,269.04 1987-01-19,266.26,269.34,264.00,269.34,162800000,269.34 1987-01-16,265.46,267.24,264.31,266.28,218400000,266.28 1987-01-15,262.65,266.68,262.64,265.49,253100000,265.49 1987-01-14,259.95,262.72,259.62,262.64,214200000,262.64 1987-01-13,260.30,260.45,259.21,259.95,170900000,259.95 1987-01-12,258.72,261.36,257.92,260.30,184200000,260.30 1987-01-09,257.26,259.20,256.11,258.73,193000000,258.73 1987-01-08,255.36,257.28,254.97,257.28,194500000,257.28 1987-01-07,252.78,255.72,252.65,255.33,190900000,255.33 1987-01-06,252.20,253.99,252.14,252.78,189300000,252.78 1987-01-05,246.45,252.57,246.45,252.19,181900000,252.19 1987-01-02,242.17,246.45,242.17,246.45,91880000,246.45 1986-12-31,243.37,244.03,241.28,242.17,139200000,242.17 1986-12-30,244.66,244.67,243.04,243.37,126200000,243.37 1986-12-29,246.90,246.92,244.31,244.67,99800000,244.67 1986-12-26,246.75,247.09,246.73,246.92,48860000,246.92 1986-12-24,246.34,247.22,246.02,246.75,95410000,246.75 1986-12-23,248.75,248.75,245.85,246.34,188700000,246.34 1986-12-22,249.73,249.73,247.45,248.75,157600000,248.75 1986-12-19,246.79,249.96,245.89,249.73,244700000,249.73 1986-12-18,247.56,247.81,246.45,246.78,155400000,246.78 1986-12-17,250.01,250.04,247.19,247.56,148800000,247.56 1986-12-16,248.21,250.04,247.40,250.04,157000000,250.04 1986-12-15,247.31,248.23,244.92,248.21,148200000,248.21 1986-12-12,248.17,248.31,247.02,247.35,126600000,247.35 1986-12-11,250.97,250.98,247.15,248.17,136000000,248.17 1986-12-10,249.28,251.53,248.94,250.96,139700000,250.96 1986-12-09,251.16,251.27,249.25,249.28,128700000,249.28 1986-12-08,251.16,252.36,248.82,251.16,159000000,251.16 1986-12-05,253.05,253.89,250.71,251.17,139800000,251.17 1986-12-04,253.85,254.42,252.88,253.04,156900000,253.04 1986-12-03,254.00,254.87,253.24,253.85,200100000,253.85 1986-12-02,249.06,254.00,249.05,254.00,230400000,254.00 1986-12-01,249.22,249.22,245.72,249.05,133800000,249.05 1986-11-28,248.82,249.22,248.07,249.22,93530000,249.22 1986-11-26,248.14,248.90,247.73,248.77,152000000,248.77 1986-11-25,247.44,248.18,246.30,248.17,154600000,248.17 1986-11-24,245.86,248.00,245.21,247.45,150800000,247.45 1986-11-21,242.03,246.38,241.97,245.86,200700000,245.86 1986-11-20,237.66,242.05,237.66,242.05,158100000,242.05 1986-11-19,236.77,237.94,235.51,237.66,183300000,237.66 1986-11-18,243.20,243.23,236.65,236.78,185300000,236.78 1986-11-17,244.50,244.80,242.29,243.21,133300000,243.21 1986-11-14,243.01,244.51,241.96,244.50,172100000,244.50 1986-11-13,246.63,246.66,242.98,243.02,164000000,243.02 1986-11-12,247.06,247.67,245.68,246.64,162200000,246.64 1986-11-11,246.15,247.10,246.12,247.08,118500000,247.08 1986-11-10,245.75,246.22,244.68,246.13,120200000,246.13 1986-11-07,245.85,246.13,244.92,245.77,142300000,245.77 1986-11-06,246.54,246.90,244.30,245.87,165300000,245.87 1986-11-05,246.09,247.05,245.21,246.58,183200000,246.58 1986-11-04,245.80,246.43,244.42,246.20,163200000,246.20 1986-11-03,243.97,245.80,243.93,245.80,138200000,245.80 1986-10-31,243.70,244.51,242.95,243.98,147200000,243.98 1986-10-30,240.97,244.08,240.94,243.71,194200000,243.71 1986-10-29,239.23,241.00,238.98,240.94,164400000,240.94 1986-10-28,238.81,240.58,238.77,239.26,145900000,239.26 1986-10-27,238.22,238.77,236.72,238.77,133200000,238.77 1986-10-24,239.30,239.65,238.25,238.26,137500000,238.26 1986-10-23,236.28,239.76,236.26,239.28,150900000,239.28 1986-10-22,235.89,236.64,235.82,236.26,114000000,236.26 1986-10-21,236.03,236.49,234.95,235.88,110000000,235.88 1986-10-20,238.84,238.84,234.78,235.97,109000000,235.97 1986-10-17,239.50,239.53,237.71,238.84,124100000,238.84 1986-10-16,238.83,240.18,238.80,239.53,156900000,239.53 1986-10-15,235.36,239.03,235.27,238.80,144300000,238.80 1986-10-14,235.90,236.37,234.37,235.37,116800000,235.37 1986-10-13,235.52,235.91,235.02,235.91,54990000,235.91 1986-10-10,235.84,236.27,235.31,235.48,105100000,235.48 1986-10-09,236.67,238.20,235.72,235.85,153400000,235.85 1986-10-08,234.41,236.84,233.68,236.68,141700000,236.68 1986-10-07,234.74,235.18,233.46,234.41,125100000,234.41 1986-10-06,233.71,235.34,233.17,234.78,88250000,234.78 1986-10-03,233.92,236.16,232.79,233.71,128100000,233.71 1986-10-02,233.60,234.33,232.77,233.92,128100000,233.92 1986-10-01,231.32,234.62,231.32,233.60,143600000,233.60 1986-09-30,229.91,233.01,229.91,231.32,124900000,231.32 1986-09-29,232.23,232.23,228.08,229.91,115600000,229.91 1986-09-26,231.83,233.68,230.64,232.23,115300000,232.23 1986-09-25,231.83,236.28,230.67,231.83,134300000,231.83 1986-09-24,235.66,237.06,235.53,236.28,134600000,236.28 1986-09-23,234.96,235.88,234.50,235.67,132600000,235.67 1986-09-22,232.20,234.93,232.20,234.93,126100000,234.93 1986-09-19,232.30,232.31,230.69,232.21,153900000,232.21 1986-09-18,231.67,232.87,230.57,232.31,132200000,232.31 1986-09-17,231.73,233.81,231.38,231.68,141000000,231.68 1986-09-16,231.93,231.94,228.32,231.72,131200000,231.72 1986-09-15,230.67,232.82,229.44,231.94,155600000,231.94 1986-09-12,235.18,235.45,228.74,230.67,240500000,230.67 1986-09-11,247.06,247.06,234.67,235.18,237600000,235.18 1986-09-10,247.67,247.76,246.11,247.06,140300000,247.06 1986-09-09,248.14,250.21,246.94,247.67,137500000,247.67 1986-09-08,250.47,250.47,247.02,248.14,153300000,248.14 1986-09-05,253.83,254.13,250.33,250.47,180600000,250.47 1986-09-04,250.08,254.01,250.03,253.83,189400000,253.83 1986-09-03,248.52,250.08,247.59,250.08,154300000,250.08 1986-09-02,252.93,253.30,248.14,248.52,135500000,248.52 1986-08-29,252.84,254.07,251.73,252.93,125300000,252.93 1986-08-28,253.30,253.67,251.91,252.84,125100000,252.84 1986-08-27,252.84,254.24,252.66,253.30,143300000,253.30 1986-08-26,247.81,252.91,247.81,252.84,156600000,252.84 1986-08-25,250.19,250.26,247.76,247.81,104400000,247.81 1986-08-22,249.67,250.61,249.27,250.19,118100000,250.19 1986-08-21,249.77,250.45,249.11,249.67,135200000,249.67 1986-08-20,246.53,249.77,246.51,249.77,156600000,249.77 1986-08-19,247.38,247.42,245.82,246.51,109300000,246.51 1986-08-18,247.15,247.83,245.48,247.38,112800000,247.38 1986-08-15,246.25,247.15,245.70,247.15,123500000,247.15 1986-08-14,245.67,246.79,245.53,246.25,123800000,246.25 1986-08-13,243.34,246.51,243.06,245.67,156400000,245.67 1986-08-12,240.68,243.37,240.35,243.34,131700000,243.34 1986-08-11,236.88,241.20,236.87,240.68,125600000,240.68 1986-08-08,237.04,238.06,236.37,236.88,106300000,236.88 1986-08-07,236.84,238.02,236.31,237.04,122400000,237.04 1986-08-06,237.03,237.35,235.48,236.84,127500000,236.84 1986-08-05,235.99,238.31,235.97,237.03,153100000,237.03 1986-08-04,234.91,236.86,231.92,235.99,130000000,235.99 1986-08-01,236.12,236.89,234.59,234.91,114900000,234.91 1986-07-31,236.59,236.92,235.89,236.12,112700000,236.12 1986-07-30,234.57,237.38,233.07,236.59,146700000,236.59 1986-07-29,235.72,236.01,234.40,234.55,115700000,234.55 1986-07-28,240.20,240.25,235.23,236.01,128000000,236.01 1986-07-25,237.99,240.36,237.95,240.22,132000000,240.22 1986-07-24,238.69,239.05,237.32,237.95,134700000,237.95 1986-07-23,238.19,239.25,238.17,238.67,133300000,238.67 1986-07-22,236.24,238.42,235.92,238.18,138500000,238.18 1986-07-21,236.36,236.45,235.53,236.24,106300000,236.24 1986-07-18,236.07,238.22,233.94,236.36,149700000,236.36 1986-07-17,235.01,236.65,235.01,236.07,132400000,236.07 1986-07-16,233.66,236.19,233.66,235.01,160800000,235.01 1986-07-15,238.09,238.12,233.60,233.66,184000000,233.66 1986-07-14,242.22,242.22,238.04,238.11,123200000,238.11 1986-07-11,243.01,243.48,241.68,242.22,124500000,242.22 1986-07-10,242.82,243.44,239.66,243.01,146200000,243.01 1986-07-09,241.59,243.07,241.46,242.82,142900000,242.82 1986-07-08,244.05,244.06,239.07,241.59,174100000,241.59 1986-07-07,251.79,251.81,243.63,244.05,138200000,244.05 1986-07-03,252.70,252.94,251.23,251.79,108300000,251.79 1986-07-02,252.04,253.20,251.79,252.70,150000000,252.70 1986-07-01,250.67,252.04,250.53,252.04,147700000,252.04 1986-06-30,249.60,251.81,249.60,250.84,135100000,250.84 1986-06-27,248.74,249.74,248.74,249.60,123800000,249.60 1986-06-26,248.93,249.43,247.72,248.74,134100000,248.74 1986-06-25,247.03,250.13,247.03,248.93,161800000,248.93 1986-06-24,245.26,248.26,244.53,247.03,140600000,247.03 1986-06-23,247.58,247.58,244.45,245.26,123800000,245.26 1986-06-20,244.06,247.60,243.98,247.58,149100000,247.58 1986-06-19,244.99,245.80,244.05,244.06,129000000,244.06 1986-06-18,244.35,245.25,242.57,244.99,117000000,244.99 1986-06-17,246.13,246.26,243.60,244.35,123100000,244.35 1986-06-16,245.73,246.50,245.17,246.13,112100000,246.13 1986-06-13,241.71,245.91,241.71,245.73,141200000,245.73 1986-06-12,241.24,241.64,240.70,241.49,109100000,241.49 1986-06-11,239.58,241.13,239.21,241.13,127400000,241.13 1986-06-10,239.96,240.08,238.23,239.58,125000000,239.58 1986-06-09,245.67,245.67,239.68,239.96,123300000,239.96 1986-06-06,245.65,246.07,244.43,245.67,110900000,245.67 1986-06-05,243.94,245.66,243.41,245.65,110900000,245.65 1986-06-04,245.51,246.30,242.59,243.94,117000000,243.94 1986-06-03,245.04,245.51,243.67,245.51,114700000,245.51 1986-06-02,246.04,247.74,243.83,245.04,120600000,245.04 1986-05-30,247.98,249.19,246.43,247.35,151200000,247.35 1986-05-29,246.63,248.32,245.29,247.98,135700000,247.98 1986-05-28,244.75,247.40,244.75,246.63,159600000,246.63 1986-05-27,241.35,244.76,241.35,244.75,121200000,244.75 1986-05-23,240.12,242.16,240.12,241.35,130200000,241.35 1986-05-22,235.45,240.25,235.45,240.12,144900000,240.12 1986-05-21,236.11,236.83,235.45,235.45,117100000,235.45 1986-05-20,233.20,236.12,232.58,236.11,113000000,236.11 1986-05-19,232.76,233.54,232.41,233.20,85840000,233.20 1986-05-16,234.43,234.43,232.26,232.76,113500000,232.76 1986-05-15,237.54,237.54,233.93,234.43,131600000,234.43 1986-05-14,236.41,237.54,235.85,237.54,132100000,237.54 1986-05-13,237.58,237.87,236.02,236.41,119200000,236.41 1986-05-12,237.85,238.53,237.02,237.58,125400000,237.58 1986-05-09,237.13,238.01,235.85,237.85,137400000,237.85 1986-05-08,236.08,237.96,236.08,237.13,136000000,237.13 1986-05-07,236.56,237.24,233.98,236.08,129900000,236.08 1986-05-06,237.73,238.28,236.26,237.24,121200000,237.24 1986-05-05,234.79,237.73,234.79,237.73,102400000,237.73 1986-05-02,235.16,236.52,234.15,234.79,126300000,234.79 1986-05-01,235.52,236.01,234.21,235.16,146500000,235.16 1986-04-30,240.52,240.52,235.26,235.52,147500000,235.52 1986-04-29,243.08,243.57,239.23,240.51,148800000,240.51 1986-04-28,242.29,243.08,241.23,243.08,123900000,243.08 1986-04-25,242.02,242.80,240.91,242.29,142300000,242.29 1986-04-24,241.75,243.13,241.65,242.02,146600000,242.02 1986-04-23,242.42,242.42,240.08,241.75,149700000,241.75 1986-04-22,244.74,245.47,241.30,242.42,161500000,242.42 1986-04-21,242.38,244.78,241.88,244.74,136100000,244.74 1986-04-18,243.03,243.47,241.74,242.38,153600000,242.38 1986-04-17,242.22,243.36,241.89,243.03,161400000,243.03 1986-04-16,237.73,242.57,237.73,242.22,173800000,242.22 1986-04-15,237.28,238.09,236.64,237.73,123700000,237.73 1986-04-14,235.97,237.48,235.43,237.28,106700000,237.28 1986-04-11,236.44,237.85,235.13,235.97,139400000,235.97 1986-04-10,233.75,236.54,233.75,236.44,184800000,236.44 1986-04-09,233.52,235.57,232.13,233.75,156300000,233.75 1986-04-08,228.63,233.70,228.63,233.52,146300000,233.52 1986-04-07,228.69,228.83,226.30,228.63,129800000,228.63 1986-04-04,232.47,232.56,228.32,228.69,147300000,228.69 1986-04-03,235.71,236.42,232.07,232.47,148200000,232.47 1986-04-02,235.14,235.71,233.40,235.71,145300000,235.71 1986-04-01,238.90,239.10,234.57,235.14,167400000,235.14 1986-03-31,238.97,239.86,238.08,238.90,134400000,238.90 1986-03-27,237.30,240.11,237.30,238.97,178100000,238.97 1986-03-26,234.72,237.79,234.71,237.30,161500000,237.30 1986-03-25,235.33,235.33,233.62,234.72,139300000,234.72 1986-03-24,233.34,235.33,232.92,235.33,143800000,235.33 1986-03-21,236.54,237.35,233.29,233.34,199100000,233.34 1986-03-20,235.60,237.09,235.60,236.54,148000000,236.54 1986-03-19,235.78,236.52,235.13,235.60,150000000,235.60 1986-03-18,234.67,236.52,234.14,235.78,148000000,235.78 1986-03-17,236.55,236.55,233.69,234.67,137500000,234.67 1986-03-14,233.19,236.55,232.58,236.55,181900000,236.55 1986-03-13,232.54,233.89,231.27,233.19,171500000,233.19 1986-03-12,231.69,234.70,231.68,232.54,210300000,232.54 1986-03-11,226.58,231.81,226.58,231.69,187300000,231.69 1986-03-10,225.57,226.98,225.36,226.58,129900000,226.58 1986-03-07,225.13,226.33,224.44,225.57,163200000,225.57 1986-03-06,224.39,225.50,224.13,225.13,159000000,225.13 1986-03-05,224.14,224.37,222.18,224.34,154600000,224.34 1986-03-04,225.42,227.33,223.94,224.38,174500000,224.38 1986-03-03,226.92,226.92,224.41,225.42,142700000,225.42 1986-02-28,226.77,227.92,225.42,226.92,191700000,226.92 1986-02-27,224.04,226.88,223.41,226.77,181700000,226.77 1986-02-26,223.72,224.59,223.15,224.04,158000000,224.04 1986-02-25,224.34,224.40,222.63,223.79,148000000,223.79 1986-02-24,224.58,225.29,223.31,224.34,144700000,224.34 1986-02-21,222.22,224.62,222.22,224.62,177600000,224.62 1986-02-20,219.76,222.22,219.22,222.22,139700000,222.22 1986-02-19,222.45,222.96,219.73,219.76,152000000,219.76 1986-02-18,219.76,222.45,219.26,222.45,160200000,222.45 1986-02-14,217.40,219.76,217.22,219.76,155600000,219.76 1986-02-13,215.97,217.41,215.38,217.40,136500000,217.40 1986-02-12,215.92,216.28,215.13,215.97,136400000,215.97 1986-02-11,216.24,216.67,215.54,215.92,141300000,215.92 1986-02-10,214.56,216.24,214.47,216.24,129900000,216.24 1986-02-07,213.47,215.27,211.13,214.56,144400000,214.56 1986-02-06,212.96,214.51,212.60,213.47,146100000,213.47 1986-02-05,212.84,213.03,211.21,212.96,134300000,212.96 1986-02-04,213.96,214.57,210.82,212.79,175700000,212.79 1986-02-03,211.78,214.18,211.60,213.96,145300000,213.96 1986-01-31,209.33,212.42,209.19,211.78,143500000,211.78 1986-01-30,210.29,211.54,209.15,209.33,125300000,209.33 1986-01-29,209.81,212.36,209.81,210.29,193800000,210.29 1986-01-28,207.42,209.82,207.40,209.81,145700000,209.81 1986-01-27,206.43,207.69,206.43,207.39,122900000,207.39 1986-01-24,204.25,206.43,204.25,206.43,128900000,206.43 1986-01-23,203.49,204.43,202.60,204.25,130300000,204.25 1986-01-22,205.79,206.03,203.41,203.49,131200000,203.49 1986-01-21,207.53,207.78,205.05,205.79,128300000,205.79 1986-01-20,208.43,208.43,206.62,207.53,85340000,207.53 1986-01-17,209.17,209.40,207.59,208.43,132100000,208.43 1986-01-16,208.26,209.18,207.61,209.17,130500000,209.17 1986-01-15,206.64,208.27,206.64,208.26,122400000,208.26 1986-01-14,206.72,207.37,206.06,206.64,113900000,206.64 1986-01-13,205.96,206.83,205.52,206.72,108700000,206.72 1986-01-10,206.11,207.33,205.52,205.96,122800000,205.96 1986-01-09,207.97,207.97,204.51,206.11,176500000,206.11 1986-01-08,213.80,214.57,207.49,207.97,180300000,207.97 1986-01-07,210.65,213.80,210.65,213.80,153000000,213.80 1986-01-06,210.88,210.98,209.93,210.65,99610000,210.65 1986-01-03,209.59,210.88,209.51,210.88,105000000,210.88 1986-01-02,211.28,211.28,208.93,209.59,98960000,209.59 1985-12-31,210.68,211.61,210.68,211.28,112700000,211.28 1985-12-30,209.61,210.70,209.17,210.68,91970000,210.68 1985-12-27,207.65,209.62,207.65,209.61,81560000,209.61 1985-12-26,207.14,207.76,207.05,207.65,62050000,207.65 1985-12-24,208.57,208.57,206.44,207.14,78300000,207.14 1985-12-23,210.57,210.94,208.44,208.57,107900000,208.57 1985-12-20,210.02,211.77,210.02,210.94,170300000,210.94 1985-12-19,209.81,210.13,209.25,210.02,130200000,210.02 1985-12-18,210.65,211.23,209.24,209.81,137900000,209.81 1985-12-17,212.02,212.45,210.58,210.65,155200000,210.65 1985-12-16,209.94,213.08,209.91,212.02,176000000,212.02 1985-12-13,206.73,210.31,206.73,209.94,177900000,209.94 1985-12-12,206.31,207.65,205.83,206.73,170500000,206.73 1985-12-11,204.39,206.68,204.17,206.31,178500000,206.31 1985-12-10,204.25,205.16,203.68,204.39,156500000,204.39 1985-12-09,202.99,204.65,202.98,204.25,144000000,204.25 1985-12-06,203.88,203.88,202.45,202.99,125500000,202.99 1985-12-05,204.23,205.86,203.79,203.88,181000000,203.88 1985-12-04,200.86,204.23,200.86,204.23,153200000,204.23 1985-12-03,200.46,200.98,200.10,200.86,109700000,200.86 1985-12-02,202.17,202.19,200.20,200.46,103500000,200.46 1985-11-29,202.54,203.40,201.92,202.17,84060000,202.17 1985-11-27,200.67,202.65,200.67,202.54,143700000,202.54 1985-11-26,200.35,201.16,200.11,200.67,123100000,200.67 1985-11-25,201.52,201.52,200.08,200.35,91710000,200.35 1985-11-22,201.41,202.01,201.05,201.52,133800000,201.52 1985-11-21,198.99,201.43,198.99,201.41,150300000,201.41 1985-11-20,198.67,199.20,198.52,198.99,105100000,198.99 1985-11-19,198.71,199.52,198.01,198.67,126100000,198.67 1985-11-18,198.11,198.71,197.51,198.71,108400000,198.71 1985-11-15,199.06,199.58,197.90,198.11,130200000,198.11 1985-11-14,197.10,199.19,196.88,199.06,124900000,199.06 1985-11-13,198.08,198.11,196.91,197.10,109700000,197.10 1985-11-12,197.28,198.66,196.97,198.08,170800000,198.08 1985-11-11,193.72,197.29,193.70,197.28,126500000,197.28 1985-11-08,192.62,193.97,192.53,193.72,115000000,193.72 1985-11-07,192.78,192.96,192.16,192.62,119000000,192.62 1985-11-06,192.37,193.01,191.83,192.76,129500000,192.76 1985-11-05,191.25,192.43,190.99,192.37,119200000,192.37 1985-11-04,191.45,191.96,190.66,191.25,104900000,191.25 1985-11-01,189.82,191.53,189.37,191.53,129400000,191.53 1985-10-31,190.07,190.15,189.35,189.82,121500000,189.82 1985-10-30,189.23,190.09,189.14,190.07,120400000,190.07 1985-10-29,187.76,189.78,187.76,189.23,110600000,189.23 1985-10-28,187.52,187.76,186.93,187.76,97880000,187.76 1985-10-25,188.50,188.51,187.32,187.52,101800000,187.52 1985-10-24,189.09,189.45,188.41,188.50,123100000,188.50 1985-10-23,188.04,189.09,188.04,189.09,121700000,189.09 1985-10-22,186.96,188.56,186.96,188.04,111300000,188.04 1985-10-21,187.04,187.30,186.79,186.96,95680000,186.96 1985-10-18,187.66,188.11,186.89,187.04,107100000,187.04 1985-10-17,187.98,188.52,187.42,187.66,140500000,187.66 1985-10-16,186.08,187.98,186.08,187.98,117400000,187.98 1985-10-15,186.37,187.16,185.66,186.08,110400000,186.08 1985-10-14,184.31,186.37,184.28,186.37,78540000,186.37 1985-10-11,182.78,184.28,182.61,184.28,96370000,184.28 1985-10-10,182.52,182.79,182.05,182.78,90910000,182.78 1985-10-09,181.87,183.27,181.87,182.52,99140000,182.52 1985-10-08,181.87,182.30,181.16,181.87,97170000,181.87 1985-10-07,183.22,183.22,181.30,181.87,95550000,181.87 1985-10-04,184.36,184.36,182.65,183.22,101200000,183.22 1985-10-03,184.06,185.17,183.59,184.36,127500000,184.36 1985-10-02,185.07,185.94,184.06,184.06,147300000,184.06 1985-10-01,182.06,185.08,182.02,185.07,130200000,185.07 1985-09-30,181.30,182.08,181.22,182.08,103600000,182.08 1985-09-26,180.66,181.29,179.45,181.29,106100000,181.29 1985-09-25,182.62,182.62,180.62,180.66,92120000,180.66 1985-09-24,184.30,184.30,182.42,182.62,97870000,182.62 1985-09-23,182.05,184.65,182.05,184.30,104800000,184.30 1985-09-20,183.39,183.99,182.04,182.05,101400000,182.05 1985-09-19,181.71,183.40,181.71,183.39,100300000,183.39 1985-09-18,181.36,181.83,180.81,181.71,105700000,181.71 1985-09-17,182.88,182.88,180.78,181.36,111900000,181.36 1985-09-16,182.91,182.91,182.45,182.88,66700000,182.88 1985-09-13,183.69,184.19,182.05,182.91,111400000,182.91 1985-09-12,185.03,185.21,183.49,183.69,107100000,183.69 1985-09-11,186.90,186.90,184.79,185.03,100400000,185.03 1985-09-10,188.25,188.26,186.50,186.90,104700000,186.90 1985-09-09,188.24,188.80,187.90,188.25,89850000,188.25 1985-09-06,187.27,188.43,187.27,188.24,95040000,188.24 1985-09-05,187.37,187.52,186.89,187.27,94480000,187.27 1985-09-04,187.91,187.92,186.97,187.37,85510000,187.37 1985-09-03,188.63,188.63,187.38,187.91,81190000,187.91 1985-08-30,188.93,189.13,188.00,188.63,81620000,188.63 1985-08-29,188.73,188.94,188.38,188.93,85660000,188.93 1985-08-28,188.10,188.83,187.90,188.83,88530000,188.83 1985-08-27,187.31,188.10,187.31,188.10,82140000,188.10 1985-08-26,187.17,187.44,186.46,187.31,70290000,187.31 1985-08-23,187.22,187.35,186.59,187.17,75270000,187.17 1985-08-22,189.11,189.23,187.20,187.36,90600000,187.36 1985-08-21,188.08,189.16,188.08,189.16,94880000,189.16 1985-08-20,186.38,188.27,186.38,188.08,91230000,188.08 1985-08-19,186.10,186.82,186.10,186.38,67930000,186.38 1985-08-16,187.26,187.26,186.10,186.10,87910000,186.10 1985-08-15,187.41,187.74,186.62,187.26,86100000,187.26 1985-08-14,187.30,187.87,187.30,187.41,85780000,187.41 1985-08-13,187.63,188.15,186.51,187.30,80300000,187.30 1985-08-12,188.32,188.32,187.43,187.63,77340000,187.63 1985-08-09,188.95,189.05,188.11,188.32,81750000,188.32 1985-08-08,187.68,188.96,187.68,188.95,102900000,188.95 1985-08-07,187.93,187.93,187.39,187.68,100000000,187.68 1985-08-06,190.62,190.72,187.87,187.93,104000000,187.93 1985-08-05,191.48,191.48,189.95,190.62,79610000,190.62 1985-08-02,192.11,192.11,191.27,191.48,87860000,191.48 1985-08-01,190.92,192.17,190.91,192.11,121500000,192.11 1985-07-31,189.93,191.33,189.93,190.92,124200000,190.92 1985-07-30,189.62,190.05,189.30,189.93,102300000,189.93 1985-07-29,192.40,192.42,189.53,189.60,95960000,189.60 1985-07-26,192.06,192.78,191.58,192.40,107000000,192.40 1985-07-25,191.58,192.23,191.17,192.06,123300000,192.06 1985-07-24,192.55,192.55,190.66,191.58,128600000,191.58 1985-07-23,194.35,194.98,192.28,192.55,143600000,192.55 1985-07-22,195.13,195.13,193.58,194.35,93540000,194.35 1985-07-19,194.38,195.13,194.28,195.13,114800000,195.13 1985-07-18,195.65,195.65,194.34,194.38,131400000,194.38 1985-07-17,194.86,196.07,194.72,195.65,159900000,195.65 1985-07-16,192.72,194.72,192.72,194.72,132500000,194.72 1985-07-15,193.29,193.84,192.55,192.72,103900000,192.72 1985-07-12,192.94,193.32,192.64,193.29,120300000,193.29 1985-07-11,192.37,192.95,192.28,192.94,122800000,192.94 1985-07-10,191.05,192.37,190.99,192.37,108200000,192.37 1985-07-09,191.93,191.93,190.81,191.05,99060000,191.05 1985-07-08,192.47,192.52,191.26,191.93,83670000,191.93 1985-07-05,191.45,192.67,191.45,192.52,62450000,192.52 1985-07-03,192.01,192.08,191.37,191.45,98410000,191.45 1985-07-02,192.43,192.63,191.84,192.01,111100000,192.01 1985-07-01,191.85,192.43,191.17,192.43,96080000,192.43 1985-06-28,191.23,191.85,191.04,191.85,105200000,191.85 1985-06-27,190.06,191.36,190.06,191.23,106700000,191.23 1985-06-26,189.74,190.26,189.44,190.06,94130000,190.06 1985-06-25,189.15,190.96,189.15,189.74,115700000,189.74 1985-06-24,188.77,189.61,187.84,189.15,96040000,189.15 1985-06-21,186.73,189.66,186.43,189.61,125400000,189.61 1985-06-20,186.63,186.74,185.97,186.73,87500000,186.73 1985-06-19,187.34,187.98,186.63,186.63,108300000,186.63 1985-06-18,186.53,187.65,186.51,187.34,106900000,187.34 1985-06-17,187.10,187.10,185.98,186.53,82170000,186.53 1985-06-14,185.33,187.10,185.33,187.10,93090000,187.10 1985-06-13,187.61,187.61,185.03,185.33,107000000,185.33 1985-06-12,189.04,189.04,187.59,187.61,97700000,187.61 1985-06-11,189.51,189.61,188.78,189.04,102100000,189.04 1985-06-10,189.68,189.68,188.82,189.51,87940000,189.51 1985-06-07,191.06,191.29,189.55,189.68,99630000,189.68 1985-06-06,189.75,191.06,189.13,191.06,117200000,191.06 1985-06-05,190.04,191.02,190.04,190.16,143900000,190.16 1985-06-04,189.32,190.27,188.88,190.04,115400000,190.04 1985-06-03,189.55,190.36,188.93,189.32,125000000,189.32 1985-05-31,187.75,189.59,187.45,189.55,134100000,189.55 1985-05-30,187.68,188.04,187.09,187.75,108300000,187.75 1985-05-29,187.86,187.86,187.11,187.68,96540000,187.68 1985-05-28,188.29,188.94,187.38,187.86,90600000,187.86 1985-05-24,187.60,188.29,187.29,188.29,85970000,188.29 1985-05-23,188.56,188.56,187.45,187.60,101000000,187.60 1985-05-22,189.64,189.64,187.71,188.56,101400000,188.56 1985-05-21,189.72,189.81,188.78,189.64,130200000,189.64 1985-05-20,187.42,189.98,187.42,189.72,146300000,189.72 1985-05-17,185.66,187.94,185.47,187.42,124600000,187.42 1985-05-16,184.54,185.74,184.54,185.66,99420000,185.66 1985-05-15,183.87,185.43,183.86,184.54,106100000,184.54 1985-05-14,184.61,185.17,183.65,183.87,97360000,183.87 1985-05-13,184.28,184.61,184.19,184.61,85830000,184.61 1985-05-10,181.92,184.74,181.92,184.28,140300000,184.28 1985-05-09,180.62,181.97,180.62,181.92,111000000,181.92 1985-05-08,180.76,180.76,179.96,180.62,101300000,180.62 1985-05-07,179.99,181.09,179.87,180.76,100200000,180.76 1985-05-06,180.08,180.56,179.82,179.99,85650000,179.99 1985-05-03,179.01,180.30,179.01,180.08,94870000,180.08 1985-05-02,178.37,179.01,178.37,179.01,107700000,179.01 1985-05-01,179.83,180.04,178.35,178.37,101600000,178.37 1985-04-30,180.63,180.63,178.86,179.83,111800000,179.83 1985-04-29,182.18,182.34,180.62,180.63,88860000,180.63 1985-04-26,183.43,183.61,182.11,182.18,86570000,182.18 1985-04-25,182.26,183.43,182.12,183.43,108600000,183.43 1985-04-24,181.88,182.27,181.74,182.26,99600000,182.26 1985-04-23,180.70,181.97,180.34,181.88,108900000,181.88 1985-04-22,181.11,181.23,180.25,180.70,79930000,180.70 1985-04-19,180.84,181.25,180.42,181.11,81110000,181.11 1985-04-18,181.68,182.56,180.75,180.84,100600000,180.84 1985-04-17,181.20,181.91,181.14,181.68,96020000,181.68 1985-04-16,180.92,181.78,180.19,181.20,98480000,181.20 1985-04-15,180.54,181.15,180.45,180.92,80660000,180.92 1985-04-12,180.19,180.55,180.06,180.54,86220000,180.54 1985-04-11,179.42,180.91,179.42,180.19,108400000,180.19 1985-04-10,178.21,179.90,178.21,179.42,108200000,179.42 1985-04-09,178.03,178.67,177.97,178.21,83980000,178.21 1985-04-08,179.03,179.46,177.86,178.03,79960000,178.03 1985-04-04,179.11,179.13,178.29,179.03,86910000,179.03 1985-04-03,180.53,180.53,178.64,179.11,95480000,179.11 1985-04-02,181.27,181.86,180.28,180.53,101700000,180.53 1985-04-01,180.66,181.27,180.43,181.27,89900000,181.27 1985-03-29,179.54,180.66,179.54,180.66,101400000,180.66 1985-03-28,179.54,180.60,179.43,179.54,99780000,179.54 1985-03-27,178.43,179.80,178.43,179.54,101000000,179.54 1985-03-26,177.97,178.86,177.88,178.43,89930000,178.43 1985-03-25,179.04,179.04,177.85,177.97,74040000,177.97 1985-03-22,179.35,179.92,178.86,179.04,99250000,179.04 1985-03-21,179.08,180.22,178.89,179.35,95930000,179.35 1985-03-20,179.54,179.78,178.79,179.08,107500000,179.08 1985-03-19,176.88,179.56,176.87,179.54,119200000,179.54 1985-03-18,176.53,177.66,176.53,176.88,94020000,176.88 1985-03-15,177.84,178.41,176.53,176.53,105200000,176.53 1985-03-14,178.19,178.53,177.61,177.84,103400000,177.84 1985-03-13,179.66,179.96,178.02,178.19,101700000,178.19 1985-03-12,178.79,180.14,178.70,179.66,92840000,179.66 1985-03-11,179.10,179.46,178.15,178.79,84110000,178.79 1985-03-08,179.51,179.97,179.07,179.10,96390000,179.10 1985-03-07,180.65,180.65,179.44,179.51,112100000,179.51 1985-03-06,182.23,182.25,180.59,180.65,116900000,180.65 1985-03-05,182.06,182.65,181.42,182.23,116400000,182.23 1985-03-04,183.23,183.41,181.40,182.06,102100000,182.06 1985-03-01,181.18,183.89,181.16,183.23,139900000,183.23 1985-02-28,180.71,181.21,180.33,181.18,100700000,181.18 1985-02-27,181.17,181.87,180.50,180.71,107700000,180.71 1985-02-26,179.23,181.58,179.16,181.17,114200000,181.17 1985-02-25,179.36,179.36,178.13,179.23,89740000,179.23 1985-02-22,180.19,180.41,179.23,179.36,93680000,179.36 1985-02-21,181.18,181.18,180.02,180.19,104000000,180.19 1985-02-20,181.33,182.10,180.64,181.18,118200000,181.18 1985-02-19,181.60,181.61,180.95,181.33,90400000,181.33 1985-02-15,182.41,182.65,181.23,181.60,106500000,181.60 1985-02-14,183.35,183.95,182.39,182.41,139700000,182.41 1985-02-13,180.56,183.86,180.50,183.35,142500000,183.35 1985-02-12,180.51,180.75,179.45,180.56,111100000,180.56 1985-02-11,182.19,182.19,180.11,180.51,104000000,180.51 1985-02-08,181.82,182.39,181.67,182.19,116500000,182.19 1985-02-07,180.43,181.96,180.43,181.82,151700000,181.82 1985-02-06,180.61,181.50,180.32,180.43,141000000,180.43 1985-02-05,180.35,181.53,180.07,180.61,143900000,180.61 1985-02-04,178.63,180.35,177.75,180.35,113700000,180.35 1985-02-01,179.63,179.63,178.44,178.63,105400000,178.63 1985-01-31,179.39,179.83,178.56,179.63,132500000,179.63 1985-01-30,179.18,180.27,179.05,179.39,170000000,179.39 1985-01-29,177.40,179.19,176.58,179.18,115700000,179.18 1985-01-28,177.35,178.19,176.56,177.40,128400000,177.40 1985-01-25,176.71,177.75,176.54,177.35,122400000,177.35 1985-01-24,177.30,178.16,176.56,176.71,160700000,176.71 1985-01-23,175.48,177.30,175.15,177.30,144400000,177.30 1985-01-22,175.23,176.63,175.14,175.48,174800000,175.48 1985-01-21,171.32,175.45,171.31,175.23,146800000,175.23 1985-01-18,170.73,171.42,170.66,171.32,104700000,171.32 1985-01-17,171.19,171.34,170.22,170.73,113600000,170.73 1985-01-16,170.81,171.94,170.41,171.19,135500000,171.19 1985-01-15,170.51,171.82,170.40,170.81,155300000,170.81 1985-01-14,167.91,170.55,167.58,170.51,124900000,170.51 1985-01-11,168.31,168.72,167.58,167.91,107600000,167.91 1985-01-10,165.18,168.31,164.99,168.31,124700000,168.31 1985-01-09,163.99,165.57,163.99,165.18,99230000,165.18 1985-01-08,164.24,164.59,163.91,163.99,92110000,163.99 1985-01-07,163.68,164.71,163.68,164.24,86190000,164.24 1985-01-04,164.55,164.55,163.36,163.68,77480000,163.68 1985-01-03,165.37,166.11,164.38,164.57,88880000,164.57 1985-01-02,167.20,167.20,165.19,165.37,67820000,165.37 1984-12-31,166.26,167.34,166.06,167.24,80260000,167.24 1984-12-28,165.75,166.32,165.67,166.26,77070000,166.26 1984-12-27,166.47,166.50,165.62,165.75,70100000,165.75 1984-12-26,166.76,166.76,166.29,166.47,46700000,166.47 1984-12-24,165.51,166.93,165.50,166.76,55550000,166.76 1984-12-21,166.34,166.38,164.62,165.51,101200000,165.51 1984-12-20,167.16,167.58,166.29,166.38,93220000,166.38 1984-12-19,168.11,169.03,166.84,167.16,139600000,167.16 1984-12-18,163.61,168.11,163.61,168.11,169000000,168.11 1984-12-17,162.69,163.63,162.44,163.61,89490000,163.61 1984-12-14,161.81,163.53,161.63,162.69,95060000,162.69 1984-12-13,162.63,162.92,161.54,161.81,80850000,161.81 1984-12-12,163.07,163.18,162.55,162.63,78710000,162.63 1984-12-11,162.83,163.18,162.56,163.07,80240000,163.07 1984-12-10,162.26,163.32,161.54,162.83,81140000,162.83 1984-12-07,162.76,163.31,162.26,162.26,81000000,162.26 1984-12-06,162.10,163.11,161.76,162.76,96560000,162.76 1984-12-05,163.38,163.40,161.93,162.10,88700000,162.10 1984-12-04,162.82,163.91,162.82,163.38,81250000,163.38 1984-12-03,163.58,163.58,162.29,162.82,95300000,162.82 1984-11-30,163.91,163.91,162.99,163.58,77580000,163.58 1984-11-29,165.02,165.02,163.78,163.91,75860000,163.91 1984-11-28,166.29,166.90,164.97,165.02,86300000,165.02 1984-11-27,165.55,166.85,165.07,166.29,95470000,166.29 1984-11-26,166.92,166.92,165.37,165.55,76520000,165.55 1984-11-23,164.52,166.92,164.52,166.92,73910000,166.92 1984-11-21,164.18,164.68,163.29,164.51,81620000,164.51 1984-11-20,163.10,164.47,163.10,164.18,83240000,164.18 1984-11-19,164.10,164.34,163.03,163.09,69730000,163.09 1984-11-16,165.89,166.24,164.09,164.10,83140000,164.10 1984-11-15,165.99,166.49,165.61,165.89,81530000,165.89 1984-11-14,165.97,166.43,165.39,165.99,73940000,165.99 1984-11-13,167.36,167.38,165.79,165.97,69790000,165.97 1984-11-12,167.65,167.65,166.67,167.36,55610000,167.36 1984-11-09,168.68,169.46,167.44,167.60,83620000,167.60 1984-11-08,169.19,169.27,168.27,168.68,88580000,168.68 1984-11-07,170.41,170.41,168.44,169.17,110800000,169.17 1984-11-06,168.58,170.41,168.58,170.41,101200000,170.41 1984-11-05,167.42,168.65,167.33,168.58,84730000,168.58 1984-11-02,167.49,167.95,167.24,167.42,96810000,167.42 1984-11-01,166.09,167.83,166.09,167.49,107300000,167.49 1984-10-31,166.74,166.95,165.99,166.09,91890000,166.09 1984-10-30,164.78,167.33,164.78,166.84,95200000,166.84 1984-10-29,165.29,165.29,164.67,164.78,63200000,164.78 1984-10-26,166.31,166.31,164.93,165.29,83900000,165.29 1984-10-25,167.20,167.62,166.17,166.31,92760000,166.31 1984-10-24,167.09,167.54,166.82,167.20,91620000,167.20 1984-10-23,167.36,168.27,166.83,167.09,92260000,167.09 1984-10-22,167.96,168.36,167.26,167.36,81020000,167.36 1984-10-19,168.08,169.62,167.31,167.96,186900000,167.96 1984-10-18,164.14,168.10,163.80,168.10,149500000,168.10 1984-10-17,164.78,165.04,163.71,164.14,99740000,164.14 1984-10-16,165.78,165.78,164.66,164.78,82930000,164.78 1984-10-15,164.18,166.15,164.09,165.77,87590000,165.77 1984-10-12,162.78,164.47,162.78,164.18,92190000,164.18 1984-10-11,162.11,162.87,162.00,162.78,87020000,162.78 1984-10-10,161.67,162.12,160.02,162.11,94270000,162.11 1984-10-09,162.13,162.84,161.62,161.67,76840000,161.67 1984-10-08,162.68,162.68,161.80,162.13,46360000,162.13 1984-10-05,162.92,163.32,162.51,162.68,82950000,162.68 1984-10-04,162.44,163.22,162.44,162.92,76700000,162.92 1984-10-03,163.59,163.59,162.20,162.44,92400000,162.44 1984-10-02,164.62,165.24,163.55,163.59,89360000,163.59 1984-10-01,166.10,166.10,164.48,164.62,73630000,164.62 1984-09-28,166.96,166.96,165.77,166.10,78950000,166.10 1984-09-27,166.75,167.18,166.33,166.96,88880000,166.96 1984-09-26,165.62,167.20,165.61,166.28,100200000,166.28 1984-09-25,165.28,165.97,164.45,165.62,86250000,165.62 1984-09-24,165.67,166.12,164.98,165.28,76380000,165.28 1984-09-21,167.47,168.67,165.66,165.67,120600000,165.67 1984-09-20,166.94,167.47,166.70,167.47,92030000,167.47 1984-09-19,167.65,168.76,166.89,166.94,119900000,166.94 1984-09-18,168.87,168.87,167.64,167.65,107700000,167.65 1984-09-17,168.78,169.37,167.99,168.87,88790000,168.87 1984-09-14,167.94,169.65,167.94,168.78,137400000,168.78 1984-09-13,164.68,167.94,164.68,167.94,110500000,167.94 1984-09-12,164.45,164.81,164.14,164.68,77980000,164.68 1984-09-11,165.22,166.17,164.28,164.45,101300000,164.45 1984-09-10,164.37,165.05,163.06,164.26,74410000,164.26 1984-09-07,165.65,166.31,164.22,164.37,84110000,164.37 1984-09-06,164.29,165.95,164.29,165.65,91920000,165.65 1984-09-05,164.88,164.88,163.84,164.29,69250000,164.29 1984-09-04,166.68,166.68,164.73,164.88,62110000,164.88 1984-08-31,166.60,166.68,165.78,166.68,57460000,166.68 1984-08-30,167.10,167.19,166.55,166.60,70840000,166.60 1984-08-29,167.40,168.21,167.03,167.09,90660000,167.09 1984-08-28,166.44,167.43,166.21,167.40,70560000,167.40 1984-08-27,167.51,167.51,165.81,166.44,57660000,166.44 1984-08-24,167.12,167.52,167.12,167.51,69640000,167.51 1984-08-23,167.06,167.78,166.61,167.12,83130000,167.12 1984-08-22,167.83,168.80,166.92,167.06,116000000,167.06 1984-08-21,164.94,168.22,164.93,167.83,128100000,167.83 1984-08-20,164.14,164.94,163.76,164.94,75450000,164.94 1984-08-17,164.30,164.61,163.78,164.14,71500000,164.14 1984-08-16,162.80,164.42,162.75,163.77,93610000,163.77 1984-08-15,164.42,164.42,162.75,162.80,91880000,162.80 1984-08-14,165.43,166.09,164.28,164.42,81470000,164.42 1984-08-13,164.84,165.49,163.98,165.43,77960000,165.43 1984-08-10,165.54,168.59,165.24,165.42,171000000,165.42 1984-08-09,161.75,165.88,161.47,165.54,131100000,165.54 1984-08-08,162.71,163.87,161.75,161.75,121200000,161.75 1984-08-07,162.60,163.58,160.81,162.72,127900000,162.72 1984-08-06,162.35,165.27,162.09,162.60,203000000,162.60 1984-08-03,160.28,162.56,158.00,162.35,236500000,162.35 1984-08-02,154.08,157.99,154.08,157.99,172800000,157.99 1984-08-01,150.66,154.08,150.66,154.08,127500000,154.08 1984-07-31,150.19,150.77,149.65,150.66,86910000,150.66 1984-07-30,151.19,151.19,150.14,150.19,72330000,150.19 1984-07-27,150.08,151.38,149.99,151.19,101350000,151.19 1984-07-26,148.83,150.16,148.83,150.08,90410000,150.08 1984-07-25,147.82,149.30,147.26,148.83,90520000,148.83 1984-07-24,148.95,149.28,147.78,147.82,74370000,147.82 1984-07-23,149.55,149.55,147.85,148.95,77990000,148.95 1984-07-20,150.37,150.58,149.07,149.55,79090000,149.55 1984-07-19,151.40,151.40,150.27,150.37,85230000,150.37 1984-07-18,152.38,152.38,151.11,151.40,76640000,151.40 1984-07-17,151.60,152.60,151.26,152.38,82890000,152.38 1984-07-16,150.88,151.60,150.01,151.60,73420000,151.60 1984-07-13,150.03,151.16,150.03,150.88,75480000,150.88 1984-07-12,150.56,151.06,149.63,150.03,86050000,150.03 1984-07-11,152.89,152.89,150.55,150.56,89540000,150.56 1984-07-10,153.36,153.53,152.57,152.89,74010000,152.89 1984-07-09,152.24,153.53,151.44,153.36,74830000,153.36 1984-07-06,152.76,152.76,151.63,152.24,65850000,152.24 1984-07-05,153.70,153.87,152.71,152.76,66100000,152.76 1984-07-03,153.20,153.86,153.10,153.70,69960000,153.70 1984-07-02,153.16,153.22,152.44,153.20,69230000,153.20 1984-06-29,152.84,154.08,152.82,153.18,90770000,153.18 1984-06-28,151.64,153.07,151.62,152.84,77660000,152.84 1984-06-27,152.71,152.88,151.30,151.64,78400000,151.64 1984-06-26,153.97,153.97,152.47,152.71,82600000,152.71 1984-06-25,154.46,154.67,153.86,153.97,72850000,153.97 1984-06-22,154.51,154.92,153.89,154.46,98400000,154.46 1984-06-21,154.84,155.64,154.05,154.51,123380000,154.51 1984-06-20,151.89,154.84,150.96,154.84,99090000,154.84 1984-06-19,151.73,153.00,151.73,152.61,98000000,152.61 1984-06-18,149.03,151.92,148.53,151.73,94900000,151.73 1984-06-15,150.49,150.71,149.02,149.03,85460000,149.03 1984-06-14,152.12,152.14,150.31,150.39,79120000,150.39 1984-06-13,152.19,152.85,151.86,152.13,67510000,152.13 1984-06-12,153.06,153.07,151.61,152.19,84660000,152.19 1984-06-11,155.17,155.17,153.00,153.06,69050000,153.06 1984-06-08,154.92,155.40,154.57,155.17,67840000,155.17 1984-06-07,155.01,155.11,154.36,154.92,82120000,154.92 1984-06-06,153.65,155.03,153.38,155.01,83440000,155.01 1984-06-05,154.34,154.34,153.28,153.65,84840000,153.65 1984-06-04,153.24,155.10,153.24,154.34,96740000,154.34 1984-06-01,150.55,153.24,150.55,153.24,96040000,153.24 1984-05-31,150.35,150.69,149.76,150.55,81890000,150.55 1984-05-30,150.29,151.43,148.68,150.35,105660000,150.35 1984-05-29,151.62,151.86,149.95,150.29,69060000,150.29 1984-05-25,151.23,152.02,150.85,151.62,78190000,151.62 1984-05-24,153.15,153.15,150.80,151.23,99040000,151.23 1984-05-23,153.88,154.02,153.10,153.15,82690000,153.15 1984-05-22,154.73,154.73,152.99,153.88,88030000,153.88 1984-05-21,155.78,156.11,154.63,154.73,73380000,154.73 1984-05-18,156.57,156.77,155.24,155.78,81270000,155.78 1984-05-17,157.99,157.99,156.15,156.57,90310000,156.57 1984-05-16,158.00,158.41,157.83,157.99,89210000,157.99 1984-05-15,157.50,158.27,157.29,158.00,88250000,158.00 1984-05-14,158.49,158.49,157.20,157.50,64900000,157.50 1984-05-11,160.00,160.00,157.42,158.49,82780000,158.49 1984-05-10,160.11,160.45,159.61,160.00,101810000,160.00 1984-05-09,160.52,161.31,159.39,160.11,100590000,160.11 1984-05-08,159.47,160.52,159.14,160.52,81610000,160.52 1984-05-07,159.11,159.48,158.63,159.47,72760000,159.47 1984-05-04,161.20,161.20,158.93,159.11,98580000,159.11 1984-05-03,161.90,161.90,160.95,161.20,91910000,161.20 1984-05-02,161.68,162.11,161.41,161.90,107080000,161.90 1984-05-01,160.05,161.69,160.05,161.68,110550000,161.68 1984-04-30,159.89,160.43,159.30,160.05,72740000,160.05 1984-04-27,160.30,160.69,159.77,159.89,88530000,159.89 1984-04-26,158.65,160.50,158.65,160.30,98000000,160.30 1984-04-25,158.07,158.77,157.80,158.65,83520000,158.65 1984-04-24,156.80,158.38,156.61,158.07,87060000,158.07 1984-04-23,158.02,158.05,156.79,156.80,73080000,156.80 1984-04-19,157.90,158.02,157.10,158.02,75860000,158.02 1984-04-18,158.97,158.97,157.64,157.90,85040000,157.90 1984-04-17,158.32,159.59,158.32,158.97,98150000,158.97 1984-04-16,157.31,158.35,156.49,158.32,73870000,158.32 1984-04-13,157.73,158.87,157.13,157.31,99620000,157.31 1984-04-12,155.00,157.74,154.17,157.73,96330000,157.73 1984-04-11,155.93,156.31,154.90,155.00,80280000,155.00 1984-04-10,155.45,156.57,155.45,155.87,78990000,155.87 1984-04-09,155.48,155.86,154.71,155.45,71570000,155.45 1984-04-06,155.04,155.48,154.12,155.48,86620000,155.48 1984-04-05,157.54,158.10,154.96,155.04,101750000,155.04 1984-04-04,157.66,158.11,157.29,157.54,92860000,157.54 1984-04-03,157.99,158.27,157.17,157.66,87980000,157.66 1984-04-02,159.18,159.87,157.63,157.98,85680000,157.98 1984-03-30,159.52,159.52,158.92,159.18,71590000,159.18 1984-03-29,159.88,160.46,159.52,159.52,81470000,159.52 1984-03-28,157.30,159.90,157.30,159.88,104870000,159.88 1984-03-27,156.67,157.30,156.61,157.30,73670000,157.30 1984-03-26,156.86,157.18,156.31,156.67,69070000,156.67 1984-03-23,156.69,156.92,156.02,156.86,79760000,156.86 1984-03-22,158.66,158.67,156.61,156.69,87340000,156.69 1984-03-21,158.86,159.26,158.59,158.66,87170000,158.66 1984-03-20,157.78,159.17,157.78,158.86,86460000,158.86 1984-03-19,159.27,159.27,157.28,157.78,64060000,157.78 1984-03-16,157.41,160.45,157.41,159.27,118000000,159.27 1984-03-15,156.78,158.05,156.73,157.41,79520000,157.41 1984-03-14,156.78,157.17,156.22,156.77,77250000,156.77 1984-03-13,156.34,157.93,156.34,156.78,102600000,156.78 1984-03-12,154.35,156.35,154.35,156.34,84470000,156.34 1984-03-09,155.12,155.19,153.77,154.35,73170000,154.35 1984-03-08,154.57,155.80,154.35,155.19,80630000,155.19 1984-03-07,156.25,156.25,153.81,154.57,90080000,154.57 1984-03-06,157.89,158.37,156.21,156.25,83590000,156.25 1984-03-05,159.24,159.24,157.59,157.89,69870000,157.89 1984-03-02,158.19,159.90,158.19,159.24,108270000,159.24 1984-03-01,157.06,158.19,156.77,158.19,82010000,158.19 1984-02-29,156.82,158.27,156.41,157.06,92810000,157.06 1984-02-28,159.30,159.30,156.59,156.82,91010000,156.82 1984-02-27,157.51,159.58,157.08,159.30,99140000,159.30 1984-02-24,154.31,157.51,154.29,157.51,102620000,157.51 1984-02-23,154.02,154.45,152.13,154.29,100220000,154.29 1984-02-22,154.52,155.10,153.94,154.31,90080000,154.31 1984-02-21,155.71,155.74,154.47,154.64,71890000,154.64 1984-02-17,156.13,156.80,155.51,155.74,76600000,155.74 1984-02-16,155.94,156.44,155.44,156.13,81750000,156.13 1984-02-15,156.61,157.48,156.10,156.25,94870000,156.25 1984-02-14,154.95,156.61,154.95,156.61,91800000,156.61 1984-02-13,156.30,156.32,154.13,154.95,78460000,154.95 1984-02-10,155.42,156.52,155.42,156.30,92220000,156.30 1984-02-09,155.85,156.17,154.30,155.42,128190000,155.42 1984-02-08,158.74,159.07,155.67,155.85,96890000,155.85 1984-02-07,157.91,158.81,157.01,158.74,107640000,158.74 1984-02-06,160.91,160.91,158.02,158.08,109090000,158.08 1984-02-03,163.44,163.98,160.82,160.91,109100000,160.91 1984-02-02,162.74,163.36,162.24,163.36,111330000,163.36 1984-02-01,163.41,164.00,162.27,162.74,107100000,162.74 1984-01-31,162.87,163.60,162.03,163.41,113510000,163.41 1984-01-30,164.40,164.67,162.40,162.87,103120000,162.87 1984-01-27,164.24,164.33,163.07,163.94,103720000,163.94 1984-01-26,164.84,165.55,164.12,164.24,111100000,164.24 1984-01-25,165.94,167.12,164.74,164.84,113470000,164.84 1984-01-24,164.87,166.35,164.84,165.94,103050000,165.94 1984-01-23,166.21,166.21,164.83,164.87,82010000,164.87 1984-01-20,167.04,167.06,165.87,166.21,93360000,166.21 1984-01-19,167.55,167.65,166.67,167.04,98340000,167.04 1984-01-18,167.83,168.34,167.02,167.55,109010000,167.55 1984-01-17,167.18,167.84,167.01,167.83,92750000,167.83 1984-01-16,167.02,167.55,166.77,167.18,93790000,167.18 1984-01-13,167.75,168.59,166.64,167.02,101790000,167.02 1984-01-12,167.79,168.40,167.68,167.75,99410000,167.75 1984-01-11,167.95,168.07,167.27,167.80,98660000,167.80 1984-01-10,168.90,169.54,167.87,167.95,109570000,167.95 1984-01-09,169.18,169.46,168.48,168.90,107100000,168.90 1984-01-06,168.81,169.31,168.49,169.28,137590000,169.28 1984-01-05,166.78,169.10,166.78,168.81,159990000,168.81 1984-01-04,164.09,166.78,164.04,166.78,112980000,166.78 1984-01-03,164.93,164.93,163.98,164.04,71340000,164.04 1983-12-30,164.86,165.05,164.58,164.93,71840000,164.93 1983-12-29,165.33,165.84,164.83,164.86,86560000,164.86 1983-12-28,164.69,165.34,164.30,165.34,85660000,165.34 1983-12-27,163.22,164.76,163.22,164.76,63800000,164.76 1983-12-23,163.27,163.31,162.90,163.22,62710000,163.22 1983-12-22,163.56,164.18,163.17,163.53,106260000,163.53 1983-12-21,162.00,163.57,161.99,163.56,108080000,163.56 1983-12-20,162.33,162.80,161.64,162.00,83740000,162.00 1983-12-19,162.34,162.88,162.27,162.32,75180000,162.32 1983-12-16,161.69,162.39,161.58,162.39,81030000,162.39 1983-12-15,163.33,163.33,161.66,161.66,88300000,161.66 1983-12-14,164.93,164.93,163.25,163.33,85430000,163.33 1983-12-13,165.62,165.63,164.85,164.93,93500000,164.93 1983-12-12,165.13,165.62,164.99,165.62,77340000,165.62 1983-12-09,165.20,165.29,164.50,165.08,98280000,165.08 1983-12-08,165.91,166.01,164.86,165.20,96530000,165.20 1983-12-07,165.47,166.34,165.35,165.91,105670000,165.91 1983-12-06,165.77,165.93,165.34,165.47,89690000,165.47 1983-12-05,165.44,165.79,164.71,165.76,88330000,165.76 1983-12-02,166.49,166.70,165.25,165.44,93960000,165.44 1983-12-01,166.37,166.77,166.08,166.49,106970000,166.49 1983-11-30,167.91,168.07,166.33,166.40,120130000,166.40 1983-11-29,166.54,167.92,166.17,167.91,100460000,167.91 1983-11-28,167.20,167.22,166.21,166.54,78210000,166.54 1983-11-25,167.02,167.20,166.73,167.18,57820000,167.18 1983-11-23,166.88,167.21,166.26,166.96,108080000,166.96 1983-11-22,166.05,167.26,166.05,166.84,117550000,166.84 1983-11-21,165.04,166.05,165.00,166.05,97740000,166.05 1983-11-18,166.08,166.13,164.50,165.09,88280000,165.09 1983-11-17,166.08,166.49,165.51,166.13,80740000,166.13 1983-11-16,165.36,166.41,165.34,166.08,83380000,166.08 1983-11-15,166.58,166.59,165.28,165.36,77840000,165.36 1983-11-14,166.29,167.58,166.27,166.58,86880000,166.58 1983-11-11,164.41,166.30,164.34,166.29,74270000,166.29 1983-11-10,163.99,164.71,163.97,164.41,88730000,164.41 1983-11-09,161.74,163.97,161.74,163.97,83100000,163.97 1983-11-08,161.91,162.15,161.63,161.76,64900000,161.76 1983-11-07,162.42,162.56,161.84,161.91,69400000,161.91 1983-11-04,162.68,163.45,162.22,162.44,72080000,162.44 1983-11-03,164.84,164.85,163.42,163.45,85350000,163.45 1983-11-02,165.21,165.21,163.55,164.84,95210000,164.84 1983-11-01,163.55,163.66,162.37,163.66,84460000,163.66 1983-10-31,163.37,164.58,162.86,163.55,79460000,163.55 1983-10-28,164.89,165.19,163.23,163.37,81180000,163.37 1983-10-27,165.31,165.38,164.41,164.84,79570000,164.84 1983-10-26,166.49,166.65,165.36,165.38,79570000,165.38 1983-10-25,166.00,167.15,166.00,166.47,82530000,166.47 1983-10-24,165.85,165.99,163.85,165.99,85420000,165.99 1983-10-21,166.97,167.23,164.98,165.95,91640000,165.95 1983-10-20,166.77,167.35,166.44,166.98,86000000,166.98 1983-10-19,167.81,167.81,165.67,166.73,107790000,166.73 1983-10-18,170.41,170.41,167.67,167.81,91080000,167.81 1983-10-17,169.85,171.18,169.63,170.43,77730000,170.43 1983-10-14,169.88,169.99,169.18,169.86,71600000,169.86 1983-10-13,169.63,170.12,169.13,169.87,67750000,169.87 1983-10-12,170.34,170.84,169.34,169.62,75630000,169.62 1983-10-11,172.59,172.59,170.34,170.34,79510000,170.34 1983-10-10,170.77,172.65,170.05,172.65,67050000,172.65 1983-10-07,170.32,171.10,170.31,170.80,103630000,170.80 1983-10-06,167.76,170.28,167.76,170.28,118270000,170.28 1983-10-05,166.29,167.74,165.92,167.74,101710000,167.74 1983-10-04,165.81,166.80,165.81,166.27,90270000,166.27 1983-10-03,165.99,166.07,164.93,165.81,77230000,165.81 1983-09-30,167.23,167.23,165.63,166.07,70860000,166.07 1983-09-29,168.02,168.35,167.23,167.23,73730000,167.23 1983-09-28,168.42,168.53,167.52,168.00,75820000,168.00 1983-09-27,170.02,170.02,167.95,168.43,81100000,168.43 1983-09-26,169.53,170.41,169.16,170.07,86400000,170.07 1983-09-23,169.76,170.17,168.88,169.51,93180000,169.51 1983-09-22,168.40,169.78,168.22,169.76,97050000,169.76 1983-09-21,169.27,169.30,168.21,168.41,91280000,168.41 1983-09-20,167.64,169.38,167.64,169.24,103050000,169.24 1983-09-19,166.27,168.09,166.26,167.62,85630000,167.62 1983-09-16,164.42,166.57,164.39,166.25,75530000,166.25 1983-09-15,165.39,165.58,164.38,164.38,70420000,164.38 1983-09-14,164.80,165.42,164.63,165.35,73370000,165.35 1983-09-13,165.48,165.48,164.17,164.80,73970000,164.80 1983-09-12,166.95,169.20,165.27,165.48,114020000,165.48 1983-09-09,167.77,167.77,166.91,166.92,77990000,166.92 1983-09-08,167.96,168.14,167.12,167.77,79250000,167.77 1983-09-07,167.90,168.48,167.46,167.96,94240000,167.96 1983-09-06,165.20,167.90,165.03,167.89,87500000,167.89 1983-09-02,164.25,165.07,164.21,165.00,59300000,165.00 1983-09-01,164.40,164.66,163.95,164.23,76120000,164.23 1983-08-31,162.55,164.40,162.32,164.40,80800000,164.40 1983-08-30,162.25,163.13,162.11,162.58,62370000,162.58 1983-08-29,162.14,162.32,160.97,162.25,53030000,162.25 1983-08-26,160.85,162.16,160.25,162.14,61650000,162.14 1983-08-25,161.27,161.28,159.96,160.84,70140000,160.84 1983-08-24,162.77,162.77,161.20,161.25,72200000,161.25 1983-08-23,164.33,164.33,162.54,162.77,66800000,162.77 1983-08-22,164.18,165.64,163.77,164.34,76420000,164.34 1983-08-19,163.58,164.27,163.22,163.98,58950000,163.98 1983-08-18,165.29,165.91,163.55,163.55,82280000,163.55 1983-08-17,163.58,165.40,163.43,165.29,87800000,165.29 1983-08-16,163.74,163.84,162.72,163.41,71780000,163.41 1983-08-15,162.22,164.76,162.22,163.70,83200000,163.70 1983-08-12,161.55,162.60,161.55,162.16,71840000,162.16 1983-08-11,161.55,162.14,161.41,161.54,70630000,161.54 1983-08-10,160.11,161.77,159.47,161.54,82900000,161.54 1983-08-09,159.20,160.14,158.50,160.13,81420000,160.13 1983-08-08,161.73,161.73,159.18,159.18,71460000,159.18 1983-08-05,161.33,161.88,160.89,161.74,67850000,161.74 1983-08-04,163.28,163.42,159.63,161.33,100870000,161.33 1983-08-03,162.01,163.44,161.52,163.44,80370000,163.44 1983-08-02,162.06,163.04,161.97,162.01,74460000,162.01 1983-08-01,162.34,162.78,161.55,162.04,77210000,162.04 1983-07-29,165.03,165.03,161.50,162.56,95240000,162.56 1983-07-28,167.32,167.79,164.99,165.04,78410000,165.04 1983-07-27,170.68,170.72,167.49,167.59,99290000,167.59 1983-07-26,169.62,170.63,169.26,170.53,91280000,170.53 1983-07-25,167.67,169.74,167.63,169.53,73680000,169.53 1983-07-22,168.51,169.08,168.40,168.89,68850000,168.89 1983-07-21,169.29,169.80,168.33,169.06,101830000,169.06 1983-07-20,164.89,169.29,164.89,169.29,109310000,169.29 1983-07-19,163.95,165.18,163.95,164.82,74030000,164.82 1983-07-18,164.28,164.29,163.30,163.95,69110000,163.95 1983-07-15,166.01,166.04,164.03,164.29,63160000,164.29 1983-07-14,165.61,166.96,165.61,166.01,83500000,166.01 1983-07-13,165.00,165.68,164.77,165.46,68900000,165.46 1983-07-12,168.05,168.05,165.51,165.53,70220000,165.53 1983-07-11,167.09,168.11,167.09,168.11,61610000,168.11 1983-07-08,167.56,167.98,166.95,167.08,66520000,167.08 1983-07-07,168.48,169.15,167.08,167.56,97130000,167.56 1983-07-06,166.71,168.88,166.49,168.48,85670000,168.48 1983-07-05,166.55,168.80,165.80,166.60,67320000,166.60 1983-07-01,168.11,168.64,167.77,168.64,65110000,168.64 1983-06-30,167.64,167.64,167.64,167.64,76310000,167.64 1983-06-29,165.78,166.64,165.43,166.64,81580000,166.64 1983-06-28,168.45,168.81,165.67,165.68,82730000,165.68 1983-06-27,170.40,170.46,168.32,168.46,69360000,168.46 1983-06-24,170.57,170.69,170.03,170.41,80810000,170.41 1983-06-23,170.99,171.00,170.13,170.57,89590000,170.57 1983-06-22,170.53,171.60,170.42,170.99,110270000,170.99 1983-06-21,169.03,170.60,168.25,170.53,102880000,170.53 1983-06-20,169.13,170.10,168.59,169.02,84270000,169.02 1983-06-17,169.11,169.64,168.60,169.13,93630000,169.13 1983-06-16,167.11,169.38,167.11,169.14,124560000,169.14 1983-06-15,165.52,167.12,165.07,167.12,93410000,167.12 1983-06-14,164.87,165.93,164.87,165.53,97710000,165.53 1983-06-13,162.70,164.84,162.70,164.84,90700000,164.84 1983-06-10,161.86,162.76,161.86,162.68,78470000,162.68 1983-06-09,161.37,161.92,160.80,161.83,87440000,161.83 1983-06-08,162.78,162.78,161.35,161.36,96600000,161.36 1983-06-07,164.84,164.93,162.77,162.77,88550000,162.77 1983-06-06,164.43,165.09,163.75,164.83,87670000,164.83 1983-06-03,163.96,164.79,163.96,164.42,83110000,164.42 1983-06-02,162.56,164.00,162.56,163.98,89750000,163.98 1983-06-01,162.38,162.64,161.33,162.55,84460000,162.55 1983-05-31,164.44,164.44,162.12,162.39,73910000,162.39 1983-05-27,165.49,165.49,164.33,164.46,76290000,164.46 1983-05-26,166.22,166.39,165.27,165.48,94980000,165.48 1983-05-25,165.54,166.21,164.79,166.21,121050000,166.21 1983-05-24,163.45,165.59,163.45,165.54,109850000,165.54 1983-05-23,162.06,163.50,160.29,163.43,84960000,163.43 1983-05-20,161.97,162.14,161.25,162.14,73150000,162.14 1983-05-19,163.27,163.61,161.98,161.99,83260000,161.99 1983-05-18,163.73,165.18,163.16,163.27,99780000,163.27 1983-05-17,163.40,163.71,162.55,163.71,79510000,163.71 1983-05-16,164.90,164.90,162.33,163.40,76250000,163.40 1983-05-13,164.26,165.23,164.26,164.91,83110000,164.91 1983-05-12,164.98,165.35,163.82,164.25,84060000,164.25 1983-05-11,165.95,166.30,164.53,164.96,99820000,164.96 1983-05-10,165.82,166.40,165.74,165.95,104010000,165.95 1983-05-09,166.10,166.46,164.90,165.81,93670000,165.81 1983-05-06,164.30,166.99,164.30,166.10,128200000,166.10 1983-05-05,163.35,164.30,163.35,164.28,107860000,164.28 1983-05-04,162.38,163.64,162.38,163.31,101690000,163.31 1983-05-03,162.10,162.35,160.80,162.34,89550000,162.34 1983-05-02,164.41,164.42,161.99,162.11,88170000,162.11 1983-04-29,162.97,164.43,162.72,164.43,105750000,164.43 1983-04-28,161.44,162.96,161.44,162.95,94410000,162.95 1983-04-27,161.85,162.77,160.76,161.44,118140000,161.44 1983-04-26,158.81,161.81,158.07,161.81,91210000,161.81 1983-04-25,160.43,160.83,158.72,158.81,90150000,158.81 1983-04-22,160.04,160.76,160.02,160.42,92270000,160.42 1983-04-21,160.73,161.08,159.96,160.05,106170000,160.05 1983-04-20,158.71,160.83,158.71,160.71,110240000,160.71 1983-04-19,159.74,159.74,158.54,158.71,91210000,158.71 1983-04-18,158.75,159.75,158.41,159.74,88560000,159.74 1983-04-15,158.11,158.75,158.11,158.75,89590000,158.75 1983-04-14,156.80,158.12,156.55,158.12,90160000,158.12 1983-04-13,155.82,157.22,155.82,156.77,100520000,156.77 1983-04-12,155.15,155.82,154.78,155.82,79900000,155.82 1983-04-11,152.87,155.14,152.87,155.14,81440000,155.14 1983-04-08,151.77,152.85,151.39,152.85,67710000,152.85 1983-04-07,151.04,151.76,150.81,151.76,69480000,151.76 1983-04-06,151.90,151.90,150.17,151.04,77140000,151.04 1983-04-05,153.04,153.92,151.81,151.90,76810000,151.90 1983-04-04,152.92,153.02,152.23,153.02,66010000,153.02 1983-03-31,153.41,155.02,152.86,152.96,100570000,152.96 1983-03-30,151.60,153.39,151.60,153.39,75800000,153.39 1983-03-29,151.85,152.46,151.42,151.59,65300000,151.59 1983-03-28,152.67,152.67,151.56,151.85,58510000,151.85 1983-03-25,153.37,153.71,152.30,152.67,77330000,152.67 1983-03-24,152.82,153.78,152.82,153.37,92340000,153.37 1983-03-23,150.65,152.98,150.65,152.81,94980000,152.81 1983-03-22,151.21,151.59,150.60,150.66,79610000,150.66 1983-03-21,149.82,151.20,149.32,151.19,72160000,151.19 1983-03-18,149.59,150.29,149.56,149.90,75110000,149.90 1983-03-17,149.80,149.80,149.12,149.59,70290000,149.59 1983-03-16,151.36,151.62,149.78,149.81,83570000,149.81 1983-03-15,150.83,151.37,150.40,151.37,62410000,151.37 1983-03-14,151.28,151.30,150.24,150.83,61890000,150.83 1983-03-11,151.75,151.75,150.65,151.24,67240000,151.24 1983-03-10,152.87,154.01,151.75,151.80,95410000,151.80 1983-03-09,151.25,152.87,150.84,152.87,84250000,152.87 1983-03-08,153.63,153.63,151.26,151.26,79410000,151.26 1983-03-07,153.67,154.00,152.65,153.67,84020000,153.67 1983-03-04,153.47,153.67,152.53,153.67,90930000,153.67 1983-03-03,152.31,154.16,152.31,153.48,114440000,153.48 1983-03-02,150.91,152.63,150.91,152.30,112600000,152.30 1983-03-01,148.07,150.88,148.07,150.88,103750000,150.88 1983-02-28,149.74,149.74,147.81,148.06,83750000,148.06 1983-02-25,149.60,150.88,149.60,149.74,100970000,149.74 1983-02-24,146.80,149.67,146.80,149.60,113220000,149.60 1983-02-23,145.47,146.79,145.40,146.79,84100000,146.79 1983-02-22,148.01,148.11,145.42,145.48,84080000,145.48 1983-02-18,147.44,148.29,147.21,148.00,77420000,148.00 1983-02-17,147.43,147.57,143.84,147.44,74930000,147.44 1983-02-16,148.31,148.66,147.41,147.43,82100000,147.43 1983-02-15,148.94,149.41,148.13,148.30,89040000,148.30 1983-02-14,147.71,149.14,147.40,148.93,72640000,148.93 1983-02-11,147.51,148.81,147.18,147.65,86700000,147.65 1983-02-10,145.04,147.75,145.04,147.50,93510000,147.50 1983-02-09,145.70,145.83,144.09,145.00,84520000,145.00 1983-02-08,146.93,147.21,145.52,145.70,76580000,145.70 1983-02-07,146.14,147.42,146.14,146.93,86030000,146.93 1983-02-04,144.26,146.14,144.14,146.14,87000000,146.14 1983-02-03,143.25,144.43,143.25,144.26,78890000,144.26 1983-02-02,142.95,143.52,141.90,143.23,77220000,143.23 1983-02-01,145.29,145.29,142.96,142.96,82750000,142.96 1983-01-31,144.51,145.30,143.93,145.30,67140000,145.30 1983-01-28,144.31,145.47,144.25,144.51,89490000,144.51 1983-01-27,141.54,144.30,141.54,144.27,88120000,144.27 1983-01-26,141.77,142.16,141.16,141.54,73720000,141.54 1983-01-25,139.98,141.75,139.98,141.75,79740000,141.75 1983-01-24,143.84,143.84,139.10,139.97,90800000,139.97 1983-01-21,146.30,146.30,143.25,143.85,77110000,143.85 1983-01-20,145.29,146.62,145.29,146.29,82790000,146.29 1983-01-19,146.40,146.45,144.51,145.27,80900000,145.27 1983-01-18,146.71,146.74,145.52,146.40,78380000,146.40 1983-01-17,146.65,147.90,146.64,146.72,89210000,146.72 1983-01-14,145.72,147.12,145.72,146.65,86480000,146.65 1983-01-13,146.67,146.94,145.67,145.73,77030000,145.73 1983-01-12,145.76,148.36,145.76,146.69,109850000,146.69 1983-01-11,146.79,146.83,145.38,145.78,98250000,145.78 1983-01-10,145.19,147.25,144.58,146.78,101890000,146.78 1983-01-07,145.27,146.46,145.15,145.18,127290000,145.18 1983-01-06,142.01,145.77,142.01,145.27,129410000,145.27 1983-01-05,141.35,142.60,141.15,141.96,95390000,141.96 1983-01-04,138.33,141.36,138.08,141.36,75530000,141.36 1983-01-03,140.65,141.33,138.20,138.34,59080000,138.34 1982-12-31,140.34,140.78,140.27,140.64,42110000,140.64 1982-12-30,141.24,141.68,140.22,140.33,56380000,140.33 1982-12-29,140.77,141.73,140.68,141.24,54810000,141.24 1982-12-28,142.18,142.34,140.75,140.77,58610000,140.77 1982-12-27,139.73,142.32,139.72,142.17,64690000,142.17 1982-12-23,138.84,139.94,138.84,139.72,62880000,139.72 1982-12-22,138.63,139.69,138.60,138.83,83470000,138.83 1982-12-21,136.24,139.27,136.07,138.61,78010000,138.61 1982-12-20,137.49,137.84,136.19,136.25,62210000,136.25 1982-12-17,135.35,137.71,135.35,137.49,76010000,137.49 1982-12-16,135.22,135.78,134.79,135.30,73680000,135.30 1982-12-15,137.40,137.40,135.12,135.24,81030000,135.24 1982-12-14,139.99,142.50,137.34,137.40,98380000,137.40 1982-12-13,139.57,140.12,139.50,139.95,63140000,139.95 1982-12-10,139.99,141.15,139.35,139.57,86430000,139.57 1982-12-09,141.80,141.80,139.92,140.00,90320000,140.00 1982-12-08,142.71,143.58,141.82,141.82,97430000,141.82 1982-12-07,141.79,143.68,141.79,142.72,111620000,142.72 1982-12-06,138.70,141.77,138.01,141.77,83880000,141.77 1982-12-03,138.87,139.59,138.59,138.69,71540000,138.69 1982-12-02,138.72,139.63,138.66,138.82,77600000,138.82 1982-12-01,138.56,140.37,138.35,138.72,107850000,138.72 1982-11-30,134.20,138.53,134.19,138.53,93470000,138.53 1982-11-29,134.89,135.29,133.69,134.20,61080000,134.20 1982-11-26,133.89,134.88,133.89,134.88,38810000,134.88 1982-11-24,132.92,133.88,132.92,133.88,67220000,133.88 1982-11-23,134.21,134.28,132.89,132.93,72920000,132.93 1982-11-22,137.03,137.10,134.21,134.22,74960000,134.22 1982-11-19,138.35,138.93,137.00,137.02,70310000,137.02 1982-11-18,137.93,138.78,137.47,138.34,77620000,138.34 1982-11-17,135.47,137.93,135.47,137.93,84440000,137.93 1982-11-16,136.97,136.97,134.05,135.42,102910000,135.42 1982-11-15,139.54,139.54,137.00,137.03,78900000,137.03 1982-11-12,141.75,141.85,139.53,139.53,95080000,139.53 1982-11-11,141.15,141.75,139.88,141.75,78410000,141.75 1982-11-10,143.04,144.36,140.80,141.16,113240000,141.16 1982-11-09,140.48,143.16,140.46,143.02,111220000,143.02 1982-11-08,142.12,142.12,139.98,140.44,75240000,140.44 1982-11-05,141.85,142.43,141.32,142.16,96550000,142.16 1982-11-04,142.85,143.99,141.65,141.85,149350000,141.85 1982-11-03,137.53,142.88,137.53,142.87,137010000,142.87 1982-11-02,135.48,138.51,135.48,137.49,104770000,137.49 1982-11-01,133.72,136.03,133.22,135.47,73530000,135.47 1982-10-29,133.54,134.02,132.64,133.72,74830000,133.72 1982-10-28,135.28,135.42,133.59,133.59,73590000,133.59 1982-10-27,134.48,135.92,134.48,135.29,81670000,135.29 1982-10-26,133.29,134.48,131.50,134.48,102080000,134.48 1982-10-25,138.81,138.81,133.32,133.32,83720000,133.32 1982-10-22,139.06,140.40,138.75,138.83,101120000,138.83 1982-10-21,139.23,140.27,137.63,139.06,122460000,139.06 1982-10-20,136.58,139.23,136.37,139.23,98680000,139.23 1982-10-19,136.73,137.96,135.72,136.58,100850000,136.58 1982-10-18,133.59,136.73,133.59,136.73,83790000,136.73 1982-10-15,134.55,134.61,133.28,133.57,80290000,133.57 1982-10-14,136.71,136.89,134.55,134.57,107530000,134.57 1982-10-13,134.42,137.97,134.14,136.71,139800000,136.71 1982-10-12,134.48,135.85,133.59,134.44,126310000,134.44 1982-10-11,131.06,135.53,131.06,134.47,138530000,134.47 1982-10-08,128.79,131.11,128.79,131.05,122250000,131.05 1982-10-07,125.99,128.96,125.99,128.80,147070000,128.80 1982-10-06,122.00,125.97,122.00,125.97,93570000,125.97 1982-10-05,121.60,122.73,121.60,121.98,69770000,121.98 1982-10-04,121.97,121.97,120.56,121.51,55650000,121.51 1982-10-01,120.40,121.97,120.15,121.97,65000000,121.97 1982-09-30,121.62,121.62,120.14,120.42,62610000,120.42 1982-09-29,123.24,123.24,121.28,121.63,62550000,121.63 1982-09-28,123.62,124.16,123.21,123.24,65900000,123.24 1982-09-27,123.32,123.62,122.75,123.62,44840000,123.62 1982-09-24,123.79,123.80,123.11,123.32,54600000,123.32 1982-09-23,123.99,124.19,122.96,123.81,68260000,123.81 1982-09-22,124.90,126.43,123.99,123.99,113150000,123.99 1982-09-21,122.51,124.91,122.51,124.88,82920000,124.88 1982-09-20,122.54,122.54,121.48,122.51,58520000,122.51 1982-09-17,123.76,123.76,122.34,122.55,63950000,122.55 1982-09-16,124.28,124.88,123.65,123.77,78900000,123.77 1982-09-15,123.09,124.81,122.72,124.29,69680000,124.29 1982-09-14,122.27,123.69,122.27,123.10,83070000,123.10 1982-09-13,120.94,122.24,120.25,122.24,59520000,122.24 1982-09-10,121.97,121.98,120.27,120.97,71080000,120.97 1982-09-09,122.19,123.22,121.90,121.97,73090000,121.97 1982-09-08,121.33,123.11,121.19,122.20,77960000,122.20 1982-09-07,122.68,122.68,121.19,121.37,68960000,121.37 1982-09-03,120.31,123.64,120.31,122.68,130910000,122.68 1982-09-02,118.24,120.32,117.84,120.29,74740000,120.29 1982-09-01,119.52,120.05,117.98,118.25,82830000,118.25 1982-08-31,117.65,119.60,117.65,119.51,86360000,119.51 1982-08-30,117.05,117.66,115.79,117.66,59560000,117.66 1982-08-27,117.38,118.56,116.63,117.11,74410000,117.11 1982-08-26,117.57,120.26,117.57,118.55,137330000,118.55 1982-08-25,115.35,118.12,115.11,117.58,106200000,117.58 1982-08-24,116.11,116.39,115.08,115.35,121650000,115.35 1982-08-23,113.02,116.11,112.65,116.11,110310000,116.11 1982-08-20,109.19,113.02,109.19,113.02,95890000,113.02 1982-08-19,108.53,109.86,108.34,109.16,78270000,109.16 1982-08-18,109.04,111.58,108.46,108.54,132690000,108.54 1982-08-17,105.40,109.04,104.09,109.04,92860000,109.04 1982-08-16,103.86,105.52,103.86,104.09,55420000,104.09 1982-08-13,102.42,103.85,102.40,103.85,44720000,103.85 1982-08-12,102.60,103.22,102.39,102.42,50080000,102.42 1982-08-11,102.83,103.01,102.48,102.60,49040000,102.60 1982-08-10,103.11,103.84,102.82,102.84,52680000,102.84 1982-08-09,103.69,103.69,102.20,103.08,54560000,103.08 1982-08-06,105.16,105.16,103.67,103.71,48660000,103.71 1982-08-05,106.10,106.10,104.76,105.16,54700000,105.16 1982-08-04,107.83,107.83,106.11,106.14,53440000,106.14 1982-08-03,108.98,109.43,107.81,107.83,60480000,107.83 1982-08-02,107.71,109.09,107.11,108.98,53460000,108.98 1982-07-30,107.35,107.95,107.01,107.09,39270000,107.09 1982-07-29,107.42,107.92,106.62,107.72,55680000,107.72 1982-07-28,109.42,109.42,107.53,107.74,53830000,107.74 1982-07-27,110.26,110.35,109.36,109.43,45740000,109.43 1982-07-26,110.66,111.16,110.29,110.36,37740000,110.36 1982-07-23,111.46,111.58,111.05,111.17,47280000,111.17 1982-07-22,110.95,112.02,110.94,111.48,53870000,111.48 1982-07-21,112.15,112.39,111.38,111.42,66770000,111.42 1982-07-20,111.11,111.56,110.35,111.54,61060000,111.54 1982-07-19,111.75,111.78,110.66,110.73,53030000,110.73 1982-07-16,110.16,111.48,110.16,111.07,58740000,111.07 1982-07-15,110.83,110.95,110.27,110.47,61090000,110.47 1982-07-14,109.68,110.44,109.08,110.44,58160000,110.44 1982-07-13,109.19,110.07,109.19,109.45,66170000,109.45 1982-07-12,109.48,109.62,108.89,109.57,74690000,109.57 1982-07-09,108.23,108.97,107.56,108.83,65870000,108.83 1982-07-08,106.85,107.53,105.57,107.53,63270000,107.53 1982-07-07,107.08,107.61,106.99,107.22,46920000,107.22 1982-07-06,107.27,107.67,106.74,107.29,44350000,107.29 1982-07-02,108.10,108.71,107.60,107.65,43760000,107.65 1982-07-01,109.52,109.63,108.62,108.71,47900000,108.71 1982-06-30,110.95,111.00,109.50,109.61,65280000,109.61 1982-06-29,110.26,110.57,109.68,110.21,46990000,110.21 1982-06-28,109.30,110.45,109.17,110.26,40700000,110.26 1982-06-25,109.56,109.83,109.09,109.14,38740000,109.14 1982-06-24,110.25,110.92,109.79,109.83,55860000,109.83 1982-06-23,108.59,110.14,108.09,110.14,62710000,110.14 1982-06-22,107.25,108.30,107.17,108.30,55290000,108.30 1982-06-21,107.28,107.88,107.01,107.20,50370000,107.20 1982-06-18,107.60,107.60,107.07,107.28,53800000,107.28 1982-06-17,108.01,108.85,107.48,107.60,49230000,107.60 1982-06-16,110.10,110.13,108.82,108.87,56280000,108.87 1982-06-15,109.63,109.96,108.98,109.69,44970000,109.69 1982-06-14,110.50,111.22,109.90,109.96,40100000,109.96 1982-06-11,111.11,111.48,109.65,111.24,68610000,111.24 1982-06-10,109.35,109.70,108.96,109.61,50950000,109.61 1982-06-09,109.46,109.63,108.53,108.99,55770000,108.99 1982-06-08,110.33,110.33,109.60,109.63,46820000,109.63 1982-06-07,109.59,110.59,109.42,110.12,44630000,110.12 1982-06-04,111.66,111.85,110.02,110.09,44110000,110.09 1982-06-03,112.04,112.48,111.45,111.86,48450000,111.86 1982-06-02,111.74,112.19,111.55,112.04,49220000,112.04 1982-06-01,111.97,112.07,111.66,111.68,41650000,111.68 1982-05-28,112.79,112.80,111.66,111.88,43900000,111.88 1982-05-27,113.11,113.12,112.58,112.66,44730000,112.66 1982-05-26,113.68,114.40,112.88,113.11,51250000,113.11 1982-05-25,115.50,115.51,114.40,114.40,44010000,114.40 1982-05-24,114.46,114.86,114.24,114.79,38510000,114.79 1982-05-21,115.03,115.13,114.60,114.89,45260000,114.89 1982-05-20,114.85,115.07,114.37,114.59,48330000,114.59 1982-05-19,115.61,115.96,114.82,114.89,48840000,114.89 1982-05-18,116.35,116.70,115.71,115.84,48970000,115.84 1982-05-17,117.62,118.02,116.66,116.71,45600000,116.71 1982-05-14,118.20,118.40,118.01,118.01,49900000,118.01 1982-05-13,119.08,119.20,118.13,118.22,58230000,118.22 1982-05-12,119.89,119.92,118.76,119.17,59210000,119.17 1982-05-11,118.54,119.59,118.32,119.42,54680000,119.42 1982-05-10,119.08,119.49,118.37,118.38,46300000,118.38 1982-05-07,119.08,119.89,118.71,119.47,67130000,119.47 1982-05-06,118.82,118.83,117.68,118.68,67540000,118.68 1982-05-05,117.85,118.05,117.31,117.67,58860000,117.67 1982-05-04,117.41,117.64,116.85,117.46,58720000,117.46 1982-05-03,115.96,116.82,115.91,116.82,46490000,116.82 1982-04-30,116.21,116.78,116.07,116.44,48200000,116.44 1982-04-29,116.40,117.24,116.11,116.14,51330000,116.14 1982-04-28,117.83,118.05,116.94,117.26,50530000,117.26 1982-04-27,119.07,119.26,117.73,118.00,56480000,118.00 1982-04-26,118.94,119.33,118.25,119.26,60500000,119.26 1982-04-23,118.02,118.64,117.19,118.64,71840000,118.64 1982-04-22,115.72,117.25,115.72,117.19,64470000,117.19 1982-04-21,115.48,115.87,115.30,115.72,57820000,115.72 1982-04-20,115.80,117.14,114.83,115.44,54610000,115.44 1982-04-19,116.81,118.16,115.83,116.70,58470000,116.70 1982-04-16,116.35,117.70,115.68,116.81,55890000,116.81 1982-04-15,115.83,116.86,115.02,116.35,45700000,116.35 1982-04-14,115.99,116.69,114.80,115.83,45150000,115.83 1982-04-13,116.00,117.12,115.16,115.99,48660000,115.99 1982-04-12,116.22,117.02,115.16,116.00,46520000,116.00 1982-04-08,115.46,116.94,114.94,116.22,60190000,116.22 1982-04-07,115.36,116.45,114.58,115.46,53130000,115.46 1982-04-06,114.73,115.92,113.70,115.36,43200000,115.36 1982-04-05,115.12,115.90,113.94,114.73,46900000,114.73 1982-04-02,113.79,115.79,113.65,115.12,59800000,115.12 1982-04-01,111.96,114.22,111.48,113.79,57100000,113.79 1982-03-31,112.27,113.17,111.32,111.96,43300000,111.96 1982-03-30,112.30,113.09,111.30,112.27,43900000,112.27 1982-03-29,111.94,112.82,110.90,112.30,37100000,112.30 1982-03-26,113.21,113.43,111.26,111.94,42400000,111.94 1982-03-25,112.97,114.26,112.02,113.21,51970000,113.21 1982-03-24,113.55,114.31,112.23,112.97,49380000,112.97 1982-03-23,112.77,114.51,112.29,113.55,67130000,113.55 1982-03-22,110.71,113.35,110.71,112.77,57610000,112.77 1982-03-19,110.30,111.59,109.64,110.61,46250000,110.61 1982-03-18,109.08,111.02,108.85,110.30,54270000,110.30 1982-03-17,109.28,110.10,108.11,109.08,48900000,109.08 1982-03-16,109.45,110.92,108.57,109.28,48900000,109.28 1982-03-15,108.61,109.99,107.47,109.45,43370000,109.45 1982-03-12,109.36,109.72,104.46,108.61,49600000,108.61 1982-03-11,109.41,110.87,108.38,109.36,52960000,109.36 1982-03-10,108.83,110.98,108.09,109.41,59440000,109.41 1982-03-09,107.34,109.88,106.17,108.83,76060000,108.83 1982-03-08,109.34,111.06,107.03,107.34,67330000,107.34 1982-03-05,109.88,110.90,108.31,109.34,67440000,109.34 1982-03-04,110.92,111.78,108.77,109.88,74340000,109.88 1982-03-03,112.51,112.51,109.98,110.92,70230000,110.92 1982-03-02,113.31,114.80,112.03,112.68,63800000,112.68 1982-03-01,113.11,114.32,111.86,113.31,53010000,113.31 1982-02-26,113.21,114.01,112.04,113.11,43840000,113.11 1982-02-25,113.47,114.86,112.44,113.21,54160000,113.21 1982-02-24,111.51,113.88,110.71,113.47,64800000,113.47 1982-02-23,111.59,112.46,110.03,111.51,60100000,111.51 1982-02-22,113.22,114.90,111.20,111.59,58310000,111.59 1982-02-19,113.82,114.58,112.33,113.22,51340000,113.22 1982-02-18,113.69,115.04,112.97,113.82,60810000,113.82 1982-02-17,114.06,115.09,112.97,113.69,47660000,113.69 1982-02-16,114.38,114.63,112.06,114.06,48880000,114.06 1982-02-12,114.43,115.39,113.70,114.38,37070000,114.38 1982-02-11,114.66,115.59,113.41,114.43,46730000,114.43 1982-02-10,113.68,115.62,113.45,114.66,46620000,114.66 1982-02-09,114.63,115.15,112.82,113.68,54420000,113.68 1982-02-08,117.04,117.04,114.20,114.63,48500000,114.63 1982-02-05,116.42,118.26,115.74,117.26,53350000,117.26 1982-02-04,116.48,117.49,114.88,116.42,53300000,116.42 1982-02-03,118.01,118.67,116.04,116.48,49560000,116.48 1982-02-02,117.78,119.15,116.91,118.01,45020000,118.01 1982-02-01,119.81,119.81,117.14,117.78,47720000,117.78 1982-01-29,118.92,121.38,118.64,120.40,73400000,120.40 1982-01-28,116.10,119.35,116.10,118.92,66690000,118.92 1982-01-27,115.19,116.60,114.38,115.74,50060000,115.74 1982-01-26,115.41,116.60,114.49,115.19,44870000,115.19 1982-01-25,115.38,115.93,113.63,115.41,43170000,115.41 1982-01-22,115.75,116.53,114.58,115.38,44370000,115.38 1982-01-21,115.27,116.92,114.60,115.75,48610000,115.75 1982-01-20,115.97,116.64,114.29,115.27,48860000,115.27 1982-01-19,117.22,118.15,115.52,115.97,45070000,115.97 1982-01-18,116.33,117.69,114.85,117.22,44920000,117.22 1982-01-15,115.54,117.14,115.10,116.33,43310000,116.33 1982-01-14,114.88,116.30,114.07,115.54,42940000,115.54 1982-01-13,116.30,117.46,114.24,114.88,49130000,114.88 1982-01-12,116.78,117.49,115.18,116.30,49800000,116.30 1982-01-11,119.55,120.34,116.47,116.78,51900000,116.78 1982-01-08,118.93,120.59,118.55,119.55,42050000,119.55 1982-01-07,119.18,119.88,117.70,118.93,43410000,118.93 1982-01-06,120.05,120.45,117.99,119.18,51510000,119.18 1982-01-05,122.61,122.61,119.57,120.05,47510000,120.05 1982-01-04,122.55,123.72,121.48,122.74,36760000,122.74 1981-12-31,122.30,123.42,121.57,122.55,40780000,122.55 1981-12-30,121.67,123.11,121.04,122.30,42960000,122.30 1981-12-29,122.27,122.90,121.12,121.67,35300000,121.67 1981-12-28,122.54,123.36,121.73,122.27,28320000,122.27 1981-12-24,122.31,123.06,121.57,122.54,23940000,122.54 1981-12-23,122.88,123.59,121.58,122.31,42910000,122.31 1981-12-22,123.34,124.17,122.19,122.88,48320000,122.88 1981-12-21,124.00,124.71,122.67,123.34,41290000,123.34 1981-12-18,123.12,124.87,122.56,124.00,50940000,124.00 1981-12-17,122.42,123.79,121.82,123.12,47230000,123.12 1981-12-16,122.99,123.66,121.73,122.42,42770000,122.42 1981-12-15,122.78,123.78,121.83,122.99,44130000,122.99 1981-12-14,124.37,124.37,122.17,122.78,44740000,122.78 1981-12-11,125.71,126.26,124.32,124.93,45850000,124.93 1981-12-10,125.48,126.54,124.60,125.71,47020000,125.71 1981-12-09,124.82,126.08,124.09,125.48,44810000,125.48 1981-12-08,125.19,125.75,123.52,124.82,45140000,124.82 1981-12-07,126.26,126.91,124.67,125.19,45720000,125.19 1981-12-04,125.12,127.32,125.12,126.26,55040000,126.26 1981-12-03,124.69,125.84,123.63,125.12,43770000,125.12 1981-12-02,126.10,126.45,124.18,124.69,44510000,124.69 1981-12-01,126.35,127.30,124.84,126.10,53980000,126.10 1981-11-30,125.09,126.97,124.18,126.35,47580000,126.35 1981-11-27,124.05,125.71,123.63,125.09,32770000,125.09 1981-11-25,123.51,125.29,123.07,124.05,58570000,124.05 1981-11-24,121.60,124.04,121.22,123.51,53200000,123.51 1981-11-23,121.71,123.09,120.76,121.60,45250000,121.60 1981-11-20,120.71,122.59,120.13,121.71,52010000,121.71 1981-11-19,120.26,121.67,119.42,120.71,48890000,120.71 1981-11-18,121.15,121.66,119.61,120.26,49980000,120.26 1981-11-17,120.24,121.78,119.50,121.15,43190000,121.15 1981-11-16,121.64,121.64,119.13,120.24,43740000,120.24 1981-11-13,123.19,123.61,121.06,121.67,45550000,121.67 1981-11-12,122.92,124.71,122.19,123.19,55720000,123.19 1981-11-11,122.70,123.82,121.51,122.92,41920000,122.92 1981-11-10,123.29,124.69,122.01,122.70,53940000,122.70 1981-11-09,122.67,124.13,121.59,123.29,48310000,123.29 1981-11-06,123.54,124.03,121.85,122.67,43270000,122.67 1981-11-05,124.74,125.80,122.98,123.54,50860000,123.54 1981-11-04,124.80,126.00,123.64,124.74,53450000,124.74 1981-11-03,124.20,125.52,123.14,124.80,54620000,124.80 1981-11-02,122.35,125.14,122.35,124.20,65100000,124.20 1981-10-30,119.06,122.53,118.43,121.89,59570000,121.89 1981-10-29,119.45,120.37,118.14,119.06,40070000,119.06 1981-10-28,119.29,120.96,118.39,119.45,48100000,119.45 1981-10-27,118.16,120.43,117.80,119.29,53030000,119.29 1981-10-26,118.60,119.00,116.81,118.16,38210000,118.16 1981-10-23,119.64,119.92,117.78,118.60,41990000,118.60 1981-10-22,120.10,120.78,118.48,119.64,40630000,119.64 1981-10-21,120.28,121.94,119.35,120.10,48490000,120.10 1981-10-20,118.98,121.29,118.78,120.28,51530000,120.28 1981-10-19,119.19,119.85,117.58,118.98,41590000,118.98 1981-10-16,119.71,120.46,118.38,119.19,37800000,119.19 1981-10-15,118.80,120.58,118.01,119.71,42830000,119.71 1981-10-14,120.78,120.97,118.38,118.80,40260000,118.80 1981-10-13,121.21,122.37,119.96,120.78,43360000,120.78 1981-10-12,121.45,122.37,120.17,121.21,30030000,121.21 1981-10-09,122.31,123.28,120.63,121.45,50060000,121.45 1981-10-08,121.31,123.08,120.23,122.31,47090000,122.31 1981-10-07,119.39,121.87,119.09,121.31,50030000,121.31 1981-10-06,119.51,121.39,118.08,119.39,45460000,119.39 1981-10-05,119.36,121.54,118.61,119.51,51290000,119.51 1981-10-02,117.08,120.16,117.07,119.36,54540000,119.36 1981-10-01,116.18,117.66,115.00,117.08,41600000,117.08 1981-09-30,115.94,117.05,114.60,116.18,40700000,116.18 1981-09-29,115.53,117.75,114.75,115.94,49800000,115.94 1981-09-28,112.77,115.83,110.19,115.53,61320000,115.53 1981-09-25,114.69,114.69,111.64,112.77,54390000,112.77 1981-09-24,115.65,117.47,114.32,115.01,48880000,115.01 1981-09-23,116.68,116.68,113.60,115.65,52700000,115.65 1981-09-22,117.24,118.19,115.93,116.68,46830000,116.68 1981-09-21,116.26,118.07,115.04,117.24,44570000,117.24 1981-09-18,117.15,117.69,115.18,116.26,47350000,116.26 1981-09-17,118.87,119.87,116.63,117.15,48300000,117.15 1981-09-16,119.77,120.00,117.89,118.87,43660000,118.87 1981-09-15,120.66,121.77,119.27,119.77,38580000,119.77 1981-09-14,121.61,122.00,119.67,120.66,34040000,120.66 1981-09-11,120.14,122.13,119.29,121.61,42170000,121.61 1981-09-10,118.40,122.18,118.33,120.14,47430000,120.14 1981-09-09,117.98,119.49,116.87,118.40,43910000,118.40 1981-09-08,120.07,120.12,116.85,117.98,47340000,117.98 1981-09-04,121.24,121.54,119.24,120.07,42760000,120.07 1981-09-03,123.49,124.16,120.82,121.24,41730000,121.24 1981-09-02,123.02,124.58,122.54,123.49,37570000,123.49 1981-09-01,122.79,123.92,121.59,123.02,45110000,123.02 1981-08-31,124.08,125.58,122.29,122.79,40360000,122.79 1981-08-28,123.51,125.09,122.85,124.08,38020000,124.08 1981-08-27,124.96,125.31,122.90,123.51,43900000,123.51 1981-08-26,125.13,126.17,123.99,124.96,39980000,124.96 1981-08-25,125.50,125.77,123.00,125.13,54600000,125.13 1981-08-24,128.59,128.59,125.02,125.50,46750000,125.50 1981-08-21,130.69,131.06,128.70,129.23,37670000,129.23 1981-08-20,130.49,131.74,129.84,130.69,38270000,130.69 1981-08-19,130.11,131.20,128.99,130.49,39390000,130.49 1981-08-18,131.22,131.73,129.10,130.11,47270000,130.11 1981-08-17,132.49,133.02,130.75,131.22,40840000,131.22 1981-08-14,133.51,134.33,131.91,132.49,42580000,132.49 1981-08-13,133.40,134.58,132.53,133.51,42460000,133.51 1981-08-12,133.85,135.18,132.73,133.40,53650000,133.40 1981-08-11,132.54,134.63,132.09,133.85,52600000,133.85 1981-08-10,131.75,133.32,130.83,132.54,38370000,132.54 1981-08-07,132.64,133.04,130.96,131.75,38370000,131.75 1981-08-06,132.67,134.04,131.74,132.64,52070000,132.64 1981-08-05,131.18,133.39,130.76,132.67,54290000,132.67 1981-08-04,130.48,131.66,129.43,131.18,39460000,131.18 1981-08-03,130.92,131.74,129.42,130.48,39650000,130.48 1981-07-31,130.01,131.78,129.60,130.92,43480000,130.92 1981-07-30,129.16,130.68,128.56,130.01,41560000,130.01 1981-07-29,129.14,130.09,128.37,129.16,37610000,129.16 1981-07-28,129.90,130.44,128.28,129.14,38160000,129.14 1981-07-27,128.46,130.61,128.43,129.90,39610000,129.90 1981-07-24,127.40,129.31,127.11,128.46,38880000,128.46 1981-07-23,127.13,128.26,125.96,127.40,41790000,127.40 1981-07-22,128.34,129.72,126.70,127.13,47500000,127.13 1981-07-21,128.72,129.60,127.08,128.34,47280000,128.34 1981-07-20,130.60,130.60,127.98,128.72,40240000,128.72 1981-07-17,130.34,131.60,129.49,130.76,42780000,130.76 1981-07-16,130.23,131.41,129.30,130.34,39010000,130.34 1981-07-15,129.65,131.59,128.89,130.23,48950000,130.23 1981-07-14,129.64,130.78,128.14,129.65,45230000,129.65 1981-07-13,129.37,130.82,128.79,129.64,38100000,129.64 1981-07-10,129.30,130.43,128.38,129.37,39950000,129.37 1981-07-09,128.32,130.08,127.57,129.30,45510000,129.30 1981-07-08,128.24,129.57,126.95,128.32,46000000,128.32 1981-07-07,127.37,129.60,126.39,128.24,53560000,128.24 1981-07-06,128.64,128.99,126.44,127.37,44590000,127.37 1981-07-02,129.77,130.48,127.84,128.64,45100000,128.64 1981-07-01,131.21,131.69,129.04,129.77,49080000,129.77 1981-06-30,131.89,132.67,130.31,131.21,41550000,131.21 1981-06-29,132.56,133.50,131.20,131.89,37930000,131.89 1981-06-26,132.81,133.75,131.71,132.56,39240000,132.56 1981-06-25,132.66,134.30,131.78,132.81,43920000,132.81 1981-06-24,133.35,133.90,131.65,132.66,46650000,132.66 1981-06-23,131.95,133.98,131.16,133.35,51840000,133.35 1981-06-22,132.27,133.54,131.10,131.95,41790000,131.95 1981-06-19,131.64,133.27,130.49,132.27,46430000,132.27 1981-06-18,133.32,133.98,130.94,131.64,48400000,131.64 1981-06-17,132.15,133.98,130.81,133.32,55470000,133.32 1981-06-16,133.61,134.00,131.29,132.15,57780000,132.15 1981-06-15,133.49,135.67,132.78,133.61,63350000,133.61 1981-06-12,133.75,135.09,132.40,133.49,60790000,133.49 1981-06-11,132.32,134.31,131.58,133.75,59530000,133.75 1981-06-10,131.97,133.49,131.04,132.32,53200000,132.32 1981-06-09,132.24,133.30,130.94,131.97,44600000,131.97 1981-06-08,132.22,133.68,131.29,132.24,41580000,132.24 1981-06-05,130.96,132.98,130.17,132.22,47180000,132.22 1981-06-04,130.71,132.21,129.72,130.96,48940000,130.96 1981-06-03,130.62,131.37,128.77,130.71,54700000,130.71 1981-06-02,132.41,132.96,129.84,130.62,53930000,130.62 1981-06-01,132.59,134.62,131.49,132.41,62170000,132.41 1981-05-29,133.45,134.36,131.52,132.59,51580000,132.59 1981-05-28,133.77,134.92,132.00,133.45,59500000,133.45 1981-05-27,132.77,134.65,131.85,133.77,58730000,133.77 1981-05-26,131.33,133.30,130.64,132.77,42760000,132.77 1981-05-22,131.75,132.65,130.42,131.33,40710000,131.33 1981-05-21,132.00,133.03,130.70,131.75,46820000,131.75 1981-05-20,132.09,133.03,130.59,132.00,42370000,132.00 1981-05-19,132.54,133.22,130.78,132.09,42220000,132.09 1981-05-18,132.17,133.65,131.49,132.54,42510000,132.54 1981-05-15,131.28,133.21,130.75,132.17,45460000,132.17 1981-05-14,130.55,132.15,129.91,131.28,42750000,131.28 1981-05-13,130.72,131.96,129.53,130.55,42600000,130.55 1981-05-12,129.71,131.17,128.78,130.72,40440000,130.72 1981-05-11,131.66,132.23,129.11,129.71,37640000,129.71 1981-05-08,131.67,132.69,130.84,131.66,41860000,131.66 1981-05-07,130.78,132.41,130.21,131.67,42590000,131.67 1981-05-06,130.32,132.38,130.09,130.78,47100000,130.78 1981-05-05,130.67,131.33,128.93,130.32,49000000,130.32 1981-05-04,131.78,131.78,129.61,130.67,40430000,130.67 1981-05-01,132.81,134.17,131.43,132.72,48360000,132.72 1981-04-30,133.05,134.44,131.85,132.81,47970000,132.81 1981-04-29,134.33,134.69,131.82,133.05,53340000,133.05 1981-04-28,135.48,136.09,133.10,134.33,58210000,134.33 1981-04-27,135.14,136.56,134.13,135.48,51080000,135.48 1981-04-24,133.94,136.00,132.88,135.14,60000000,135.14 1981-04-23,134.14,135.90,132.90,133.94,64200000,133.94 1981-04-22,134.23,135.54,132.72,134.14,60660000,134.14 1981-04-21,135.45,136.38,133.49,134.23,60280000,134.23 1981-04-20,134.70,136.25,133.19,135.45,51020000,135.45 1981-04-16,134.17,135.82,133.43,134.70,52950000,134.70 1981-04-15,132.68,134.79,132.20,134.17,56040000,134.17 1981-04-14,133.15,134.03,131.58,132.68,48350000,132.68 1981-04-13,134.51,134.91,132.24,133.15,49860000,133.15 1981-04-10,134.67,136.23,133.18,134.51,58130000,134.51 1981-04-09,134.31,135.80,132.59,134.67,59520000,134.67 1981-04-08,133.91,135.34,133.26,134.31,48000000,134.31 1981-04-07,133.93,135.27,132.96,133.91,44540000,133.91 1981-04-06,135.49,135.61,132.91,133.93,43190000,133.93 1981-04-03,136.32,137.04,134.67,135.49,48680000,135.49 1981-04-02,136.57,137.72,135.16,136.32,52570000,136.32 1981-04-01,136.00,137.56,135.04,136.57,54880000,136.57 1981-03-31,134.68,137.15,134.68,136.00,50980000,136.00 1981-03-30,134.65,135.87,133.51,134.28,33500000,134.28 1981-03-27,136.27,136.89,133.91,134.65,46930000,134.65 1981-03-26,137.11,138.38,135.29,136.27,60370000,136.27 1981-03-25,134.67,137.32,133.92,137.11,56320000,137.11 1981-03-24,135.69,137.40,134.10,134.67,66400000,134.67 1981-03-23,134.08,136.50,133.41,135.69,57880000,135.69 1981-03-20,133.46,135.29,132.50,134.08,61980000,134.08 1981-03-19,134.22,135.37,132.37,133.46,62440000,133.46 1981-03-18,133.92,135.66,132.80,134.22,55740000,134.22 1981-03-17,134.68,136.09,132.80,133.92,65920000,133.92 1981-03-16,133.11,135.35,132.10,134.68,49940000,134.68 1981-03-13,133.19,135.53,132.39,133.11,68290000,133.11 1981-03-12,129.95,133.56,129.76,133.19,54640000,133.19 1981-03-11,130.46,131.20,128.72,129.95,47390000,129.95 1981-03-10,131.12,132.64,129.72,130.46,56610000,130.46 1981-03-09,129.85,131.94,129.39,131.12,46180000,131.12 1981-03-06,129.93,131.18,128.56,129.85,43940000,129.85 1981-03-05,130.86,131.82,129.25,129.93,45380000,129.93 1981-03-04,130.56,132.07,129.57,130.86,47260000,130.86 1981-03-03,132.01,132.72,129.66,130.56,48730000,130.56 1981-03-02,131.27,132.96,130.15,132.01,47710000,132.01 1981-02-27,130.10,132.02,129.35,131.27,53210000,131.27 1981-02-26,128.52,130.93,128.02,130.10,60300000,130.10 1981-02-25,127.39,129.21,125.77,128.52,45710000,128.52 1981-02-24,127.35,128.76,126.49,127.39,43960000,127.39 1981-02-23,126.58,128.28,125.69,127.35,39590000,127.35 1981-02-20,126.61,127.65,124.66,126.58,41900000,126.58 1981-02-19,128.48,129.07,125.98,126.61,41630000,126.61 1981-02-18,127.81,129.25,127.09,128.48,40410000,128.48 1981-02-17,126.98,128.75,126.43,127.81,37940000,127.81 1981-02-13,127.48,128.34,126.04,126.98,33360000,126.98 1981-02-12,128.24,128.95,126.78,127.48,34700000,127.48 1981-02-11,129.24,129.92,127.60,128.24,37770000,128.24 1981-02-10,129.27,130.19,128.05,129.24,40820000,129.24 1981-02-09,130.60,131.39,128.61,129.27,38330000,129.27 1981-02-06,129.63,131.81,129.03,130.60,45820000,130.60 1981-02-05,128.59,130.49,127.99,129.63,45320000,129.63 1981-02-04,128.46,129.71,127.29,128.59,45520000,128.59 1981-02-03,126.91,128.92,125.89,128.46,45950000,128.46 1981-02-02,129.48,129.48,125.82,126.91,44070000,126.91 1981-01-30,130.24,131.65,128.61,129.55,41160000,129.55 1981-01-29,130.34,131.78,128.97,130.24,38170000,130.24 1981-01-28,131.12,132.41,129.82,130.34,36690000,130.34 1981-01-27,129.84,131.95,129.32,131.12,42260000,131.12 1981-01-26,130.23,131.18,128.57,129.84,35380000,129.84 1981-01-23,130.26,131.34,129.00,130.23,37220000,130.23 1981-01-22,131.36,132.08,129.23,130.26,39880000,130.26 1981-01-21,131.65,132.48,129.93,131.36,39190000,131.36 1981-01-20,134.37,135.30,131.26,131.65,41750000,131.65 1981-01-19,134.77,135.86,133.51,134.37,36470000,134.37 1981-01-16,134.22,135.91,133.35,134.77,43260000,134.77 1981-01-15,133.47,135.15,132.44,134.22,39640000,134.22 1981-01-14,133.29,135.25,132.65,133.47,41390000,133.47 1981-01-13,133.52,134.27,131.69,133.29,40890000,133.29 1981-01-12,133.48,135.88,132.79,133.52,48760000,133.52 1981-01-09,133.06,134.76,131.71,133.48,50190000,133.48 1981-01-08,135.08,136.10,131.96,133.06,55350000,133.06 1981-01-07,136.02,136.02,132.30,135.08,92890000,135.08 1981-01-06,137.97,140.32,135.78,138.12,67400000,138.12 1981-01-05,136.34,139.24,135.86,137.97,58710000,137.97 1981-01-02,135.76,137.10,134.61,136.34,28870000,136.34 1980-12-31,135.33,136.76,134.29,135.76,41210000,135.76 1980-12-30,135.03,136.51,134.04,135.33,39750000,135.33 1980-12-29,136.57,137.51,134.36,135.03,36060000,135.03 1980-12-26,135.88,137.02,135.20,136.57,16130000,136.57 1980-12-24,135.30,136.55,134.15,135.88,29490000,135.88 1980-12-23,135.78,137.48,134.01,135.30,55260000,135.30 1980-12-22,133.70,136.68,132.88,135.78,51950000,135.78 1980-12-19,133.00,134.00,131.80,133.70,50770000,133.70 1980-12-18,132.89,135.90,131.89,133.00,69570000,133.00 1980-12-17,130.60,133.59,130.22,132.89,50800000,132.89 1980-12-16,129.45,131.22,128.33,130.60,41630000,130.60 1980-12-15,129.23,131.33,128.64,129.45,39700000,129.45 1980-12-12,127.36,129.98,127.15,129.23,39530000,129.23 1980-12-11,128.26,128.73,125.32,127.36,60220000,127.36 1980-12-10,130.48,131.99,127.94,128.26,49860000,128.26 1980-12-09,130.61,131.92,128.77,130.48,53220000,130.48 1980-12-08,133.19,133.19,129.71,130.61,53390000,130.61 1980-12-05,136.37,136.37,132.91,134.03,51990000,134.03 1980-12-04,136.71,138.40,135.09,136.48,51170000,136.48 1980-12-03,136.97,138.09,135.43,136.71,43430000,136.71 1980-12-02,137.21,138.11,134.37,136.97,52340000,136.97 1980-12-01,140.52,140.66,136.75,137.21,48180000,137.21 1980-11-28,140.17,141.54,139.00,140.52,34240000,140.52 1980-11-26,139.33,141.96,138.60,140.17,55340000,140.17 1980-11-25,138.31,140.83,137.42,139.33,55840000,139.33 1980-11-24,139.11,139.36,136.36,138.31,51120000,138.31 1980-11-21,140.40,141.24,138.10,139.11,55950000,139.11 1980-11-20,139.06,141.24,137.79,140.40,60180000,140.40 1980-11-19,139.70,141.76,138.06,139.06,69230000,139.06 1980-11-18,137.91,140.92,137.91,139.70,70380000,139.70 1980-11-17,137.15,138.46,134.90,137.75,50260000,137.75 1980-11-14,136.49,138.96,135.12,137.15,71630000,137.15 1980-11-13,134.59,137.21,134.12,136.49,69340000,136.49 1980-11-12,131.33,135.12,131.33,134.59,58500000,134.59 1980-11-11,129.48,132.30,129.48,131.26,41520000,131.26 1980-11-10,129.18,130.51,128.19,129.48,35720000,129.48 1980-11-07,128.91,130.08,127.74,129.18,40070000,129.18 1980-11-06,131.30,131.30,128.23,128.91,48890000,128.91 1980-11-05,130.77,135.65,130.77,131.33,84080000,131.33 1980-11-03,127.47,129.85,127.23,129.04,35820000,129.04 1980-10-31,126.29,128.24,125.29,127.47,40110000,127.47 1980-10-30,127.91,128.71,125.78,126.29,39060000,126.29 1980-10-29,128.05,129.91,127.07,127.91,37200000,127.91 1980-10-28,127.88,128.86,126.36,128.05,40300000,128.05 1980-10-27,129.85,129.94,127.34,127.88,34430000,127.88 1980-10-24,129.53,130.55,128.04,129.85,41050000,129.85 1980-10-23,131.92,132.54,128.87,129.53,49200000,129.53 1980-10-22,131.84,132.97,130.62,131.92,43060000,131.92 1980-10-21,132.61,134.01,130.78,131.84,51220000,131.84 1980-10-20,131.52,133.21,130.04,132.61,40910000,132.61 1980-10-17,132.22,133.07,130.22,131.52,43920000,131.52 1980-10-16,133.70,135.88,131.64,132.22,65450000,132.22 1980-10-15,132.02,134.35,131.59,133.70,48260000,133.70 1980-10-14,132.03,133.57,131.16,132.02,48830000,132.02 1980-10-13,130.29,132.46,129.37,132.03,31360000,132.03 1980-10-10,131.04,132.15,129.58,130.29,44040000,130.29 1980-10-09,131.65,132.65,130.25,131.04,43980000,131.04 1980-10-08,131.00,132.78,130.28,131.65,46580000,131.65 1980-10-07,131.73,132.88,130.10,131.00,50310000,131.00 1980-10-06,129.35,132.38,129.35,131.73,50130000,131.73 1980-10-03,128.09,130.44,127.65,129.33,47510000,129.33 1980-10-02,127.13,128.82,126.04,128.09,46160000,128.09 1980-10-01,125.46,127.88,124.66,127.13,48720000,127.13 1980-09-30,123.54,126.09,123.54,125.46,40290000,125.46 1980-09-29,125.41,125.41,122.87,123.54,46410000,123.54 1980-09-26,128.17,128.17,125.29,126.35,49460000,126.35 1980-09-25,130.37,131.53,128.13,128.72,49510000,128.72 1980-09-24,129.43,131.34,128.45,130.37,56860000,130.37 1980-09-23,130.40,132.17,128.55,129.43,64390000,129.43 1980-09-22,129.25,130.99,127.89,130.40,53140000,130.40 1980-09-19,128.40,130.33,127.57,129.25,53780000,129.25 1980-09-18,128.87,130.38,127.63,128.40,63390000,128.40 1980-09-17,126.74,129.68,126.37,128.87,63990000,128.87 1980-09-16,125.67,127.78,125.15,126.74,57290000,126.74 1980-09-15,125.54,126.35,124.09,125.67,44630000,125.67 1980-09-12,125.66,126.75,124.72,125.54,47180000,125.54 1980-09-11,124.81,126.48,124.19,125.66,44770000,125.66 1980-09-10,124.07,125.95,123.60,124.81,51430000,124.81 1980-09-09,123.31,124.52,121.94,124.07,44460000,124.07 1980-09-08,124.88,125.67,122.78,123.31,42050000,123.31 1980-09-05,125.42,126.12,124.08,124.88,37990000,124.88 1980-09-04,126.12,127.70,124.42,125.42,59030000,125.42 1980-09-03,123.87,126.43,123.87,126.12,52370000,126.12 1980-09-02,122.38,124.36,121.79,123.74,35290000,123.74 1980-08-29,122.08,123.01,121.06,122.38,33510000,122.38 1980-08-28,123.52,123.91,121.61,122.08,39890000,122.08 1980-08-27,124.84,124.98,122.93,123.52,44000000,123.52 1980-08-26,125.16,126.29,124.01,124.84,41700000,124.84 1980-08-25,126.02,126.28,124.65,125.16,35400000,125.16 1980-08-22,125.46,127.78,125.18,126.02,58210000,126.02 1980-08-21,123.77,125.99,123.61,125.46,50770000,125.46 1980-08-20,122.60,124.27,121.91,123.77,42560000,123.77 1980-08-19,123.39,124.00,121.97,122.60,41930000,122.60 1980-08-18,125.28,125.28,122.82,123.39,41890000,123.39 1980-08-15,125.25,126.61,124.57,125.72,47780000,125.72 1980-08-14,123.28,125.62,122.68,125.25,47700000,125.25 1980-08-13,123.79,124.67,122.49,123.28,44350000,123.28 1980-08-12,124.78,125.78,123.29,123.79,52050000,123.79 1980-08-11,123.61,125.31,122.85,124.78,44690000,124.78 1980-08-08,123.30,125.23,122.82,123.61,58860000,123.61 1980-08-07,121.66,123.84,121.66,123.30,61820000,123.30 1980-08-06,120.74,122.01,119.94,121.55,45050000,121.55 1980-08-05,120.98,122.09,119.96,120.74,45510000,120.74 1980-08-04,121.21,121.63,119.42,120.98,41550000,120.98 1980-08-01,121.67,122.38,120.08,121.21,46440000,121.21 1980-07-31,122.23,122.34,119.40,121.67,54610000,121.67 1980-07-30,122.40,123.93,121.16,122.23,58060000,122.23 1980-07-29,121.43,122.99,120.76,122.40,44840000,122.40 1980-07-28,120.78,122.02,119.78,121.43,35330000,121.43 1980-07-25,121.79,121.96,119.94,120.78,36250000,120.78 1980-07-24,121.93,122.98,120.83,121.79,42420000,121.79 1980-07-23,122.19,123.26,120.93,121.93,45890000,121.93 1980-07-22,122.51,123.90,121.38,122.19,52230000,122.19 1980-07-21,122.04,123.15,120.85,122.51,42750000,122.51 1980-07-18,121.44,123.19,120.88,122.04,58040000,122.04 1980-07-17,119.63,121.84,119.43,121.44,48850000,121.44 1980-07-16,119.30,120.87,118.54,119.63,49140000,119.63 1980-07-15,120.01,121.56,118.85,119.30,60920000,119.30 1980-07-14,117.84,120.37,117.45,120.01,45500000,120.01 1980-07-11,116.95,118.38,116.29,117.84,38310000,117.84 1980-07-10,117.98,118.57,116.38,116.95,43730000,116.95 1980-07-09,117.84,119.52,117.10,117.98,52010000,117.98 1980-07-08,118.29,119.11,117.07,117.84,45830000,117.84 1980-07-07,117.46,118.85,116.96,118.29,42540000,118.29 1980-07-03,115.68,117.80,115.49,117.46,47230000,117.46 1980-07-02,114.93,116.44,114.36,115.68,42950000,115.68 1980-07-01,114.24,115.45,113.54,114.93,34340000,114.93 1980-06-30,116.00,116.04,113.55,114.24,29910000,114.24 1980-06-27,116.19,116.93,115.06,116.00,33110000,116.00 1980-06-26,116.72,117.98,115.58,116.19,45110000,116.19 1980-06-25,115.14,117.37,115.07,116.72,46500000,116.72 1980-06-24,114.51,115.75,113.76,115.14,37730000,115.14 1980-06-23,114.06,115.28,113.35,114.51,34180000,114.51 1980-06-20,114.66,114.90,113.12,114.06,36530000,114.06 1980-06-19,116.26,116.81,114.36,114.66,38280000,114.66 1980-06-18,116.03,116.84,114.77,116.26,41960000,116.26 1980-06-17,116.09,117.16,115.13,116.03,41990000,116.03 1980-06-16,115.81,116.80,114.78,116.09,36190000,116.09 1980-06-13,115.52,116.94,114.67,115.81,41880000,115.81 1980-06-12,116.02,117.01,114.28,115.52,47300000,115.52 1980-06-11,114.66,116.64,114.22,116.02,43800000,116.02 1980-06-10,113.71,115.50,113.17,114.66,42030000,114.66 1980-06-09,113.20,114.51,112.68,113.71,36820000,113.71 1980-06-06,112.78,114.01,112.11,113.20,37230000,113.20 1980-06-05,112.61,114.38,111.89,112.78,49070000,112.78 1980-06-04,110.51,113.45,110.22,112.61,44180000,112.61 1980-06-03,110.76,111.63,109.77,110.51,33150000,110.51 1980-06-02,111.24,112.15,110.06,110.76,32710000,110.76 1980-05-30,110.27,111.55,108.87,111.24,34820000,111.24 1980-05-29,112.06,112.64,109.86,110.27,42000000,110.27 1980-05-28,111.40,112.72,110.42,112.06,38580000,112.06 1980-05-27,110.62,112.30,110.35,111.40,40810000,111.40 1980-05-23,109.01,111.37,109.01,110.62,45790000,110.62 1980-05-22,107.72,109.73,107.34,109.01,41040000,109.01 1980-05-21,107.62,108.31,106.54,107.72,34830000,107.72 1980-05-20,107.67,108.39,106.75,107.62,31800000,107.62 1980-05-19,107.35,108.43,106.51,107.67,30970000,107.67 1980-05-16,106.99,107.89,106.25,107.35,31710000,107.35 1980-05-15,106.85,107.99,106.07,106.99,41120000,106.99 1980-05-14,106.30,107.89,106.00,106.85,40840000,106.85 1980-05-13,104.78,106.76,104.44,106.30,35460000,106.30 1980-05-12,104.72,105.48,103.50,104.78,28220000,104.78 1980-05-09,106.13,106.20,104.18,104.72,30280000,104.72 1980-05-08,107.18,108.02,105.50,106.13,39280000,106.13 1980-05-07,106.25,108.12,105.83,107.18,42600000,107.18 1980-05-06,106.38,107.83,105.36,106.25,40160000,106.25 1980-05-05,105.58,106.83,104.64,106.38,34090000,106.38 1980-05-02,105.46,106.25,104.61,105.58,28040000,105.58 1980-05-01,106.29,106.86,104.72,105.46,32480000,105.46 1980-04-30,105.86,106.72,104.50,106.29,30850000,106.29 1980-04-29,105.64,106.70,104.86,105.86,27940000,105.86 1980-04-28,105.16,106.79,104.64,105.64,30600000,105.64 1980-04-25,104.40,105.57,103.02,105.16,28590000,105.16 1980-04-24,103.73,105.43,102.93,104.40,35790000,104.40 1980-04-23,103.43,105.11,102.81,103.73,42620000,103.73 1980-04-22,100.81,104.02,100.81,103.43,47920000,103.43 1980-04-21,100.55,101.26,98.95,99.80,27560000,99.80 1980-04-18,101.05,102.07,99.97,100.55,26880000,100.55 1980-04-17,101.54,102.21,100.12,101.05,32770000,101.05 1980-04-16,102.63,104.42,101.13,101.54,39730000,101.54 1980-04-15,102.84,103.94,101.85,102.63,26670000,102.63 1980-04-14,103.79,103.92,102.08,102.84,23060000,102.84 1980-04-11,104.08,105.15,103.20,103.79,29960000,103.79 1980-04-10,103.11,105.00,102.81,104.08,33940000,104.08 1980-04-09,101.20,103.60,101.01,103.11,33020000,103.11 1980-04-08,100.19,101.88,99.23,101.20,31700000,101.20 1980-04-07,102.15,102.27,99.73,100.19,29130000,100.19 1980-04-03,102.68,103.34,101.31,102.15,27970000,102.15 1980-04-02,102.18,103.87,101.45,102.68,35210000,102.68 1980-04-01,102.09,103.28,100.85,102.18,32230000,102.18 1980-03-31,100.68,102.65,100.02,102.09,35840000,102.09 1980-03-28,98.22,101.43,97.72,100.68,46720000,100.68 1980-03-27,98.68,99.58,94.23,98.22,63680000,98.22 1980-03-26,99.19,101.22,98.10,98.68,37370000,98.68 1980-03-25,99.28,100.58,97.89,99.19,43790000,99.19 1980-03-24,102.18,102.18,98.88,99.28,39230000,99.28 1980-03-21,103.12,103.73,101.55,102.31,32220000,102.31 1980-03-20,104.31,105.17,102.52,103.12,32580000,103.12 1980-03-19,104.10,105.72,103.35,104.31,36520000,104.31 1980-03-18,102.26,104.71,101.14,104.10,47340000,104.10 1980-03-17,105.23,105.23,101.82,102.26,37020000,102.26 1980-03-14,105.62,106.49,104.01,105.43,35180000,105.43 1980-03-13,106.87,107.55,105.10,105.62,33070000,105.62 1980-03-12,107.78,108.40,105.42,106.87,37990000,106.87 1980-03-11,106.51,108.54,106.18,107.78,41350000,107.78 1980-03-10,106.90,107.86,104.92,106.51,43750000,106.51 1980-03-07,108.65,108.96,105.99,106.90,50950000,106.90 1980-03-06,111.13,111.29,107.85,108.65,49610000,108.65 1980-03-05,112.78,113.94,110.58,111.13,49240000,111.13 1980-03-04,112.50,113.41,110.83,112.78,44310000,112.78 1980-03-03,113.66,114.34,112.01,112.50,38690000,112.50 1980-02-29,112.35,114.12,111.77,113.66,38810000,113.66 1980-02-28,112.38,113.70,111.33,112.35,40330000,112.35 1980-02-27,113.98,115.12,111.91,112.38,46430000,112.38 1980-02-26,113.33,114.76,112.30,113.98,40000000,113.98 1980-02-25,114.93,114.93,112.62,113.33,39140000,113.33 1980-02-22,115.28,116.46,113.43,115.04,48210000,115.04 1980-02-21,116.47,117.90,114.44,115.28,51530000,115.28 1980-02-20,114.60,117.18,114.06,116.47,44340000,116.47 1980-02-19,115.41,115.67,113.35,114.60,39480000,114.60 1980-02-15,116.70,116.70,114.12,115.41,46680000,115.41 1980-02-14,118.44,119.30,116.04,116.72,50540000,116.72 1980-02-13,117.90,120.22,117.57,118.44,65230000,118.44 1980-02-12,117.12,118.41,115.75,117.90,48090000,117.90 1980-02-11,117.95,119.05,116.31,117.12,58660000,117.12 1980-02-08,116.28,118.66,115.72,117.95,57860000,117.95 1980-02-07,115.72,117.87,115.22,116.28,57690000,116.28 1980-02-06,114.66,116.57,113.65,115.72,51950000,115.72 1980-02-05,114.37,115.25,112.15,114.66,41880000,114.66 1980-02-04,115.12,116.01,113.83,114.37,43070000,114.37 1980-02-01,114.16,115.54,113.13,115.12,46610000,115.12 1980-01-31,115.20,117.17,113.78,114.16,65900000,114.16 1980-01-30,114.07,115.85,113.37,115.20,51170000,115.20 1980-01-29,114.85,115.77,113.03,114.07,55480000,114.07 1980-01-28,113.61,115.65,112.93,114.85,53620000,114.85 1980-01-25,113.70,114.45,112.36,113.61,47100000,113.61 1980-01-24,113.44,115.27,112.95,113.70,59070000,113.70 1980-01-23,111.51,113.93,110.93,113.44,50730000,113.44 1980-01-22,112.10,113.10,110.92,111.51,50620000,111.51 1980-01-21,111.07,112.90,110.66,112.10,48040000,112.10 1980-01-18,110.70,111.74,109.88,111.07,47150000,111.07 1980-01-17,111.05,112.01,109.81,110.70,54170000,110.70 1980-01-16,111.14,112.90,110.38,111.05,67700000,111.05 1980-01-15,110.38,111.93,109.45,111.14,52320000,111.14 1980-01-14,109.92,111.44,109.34,110.38,52930000,110.38 1980-01-11,109.89,111.16,108.89,109.92,52890000,109.92 1980-01-10,109.05,110.86,108.47,109.89,55980000,109.89 1980-01-09,108.95,111.09,108.41,109.05,65260000,109.05 1980-01-08,106.81,109.29,106.29,108.95,53390000,108.95 1980-01-07,106.52,107.80,105.80,106.81,44500000,106.81 1980-01-04,105.22,107.08,105.09,106.52,39130000,106.52 1980-01-03,105.76,106.08,103.26,105.22,50480000,105.22 1980-01-02,107.94,108.43,105.29,105.76,40610000,105.76 1979-12-31,107.84,108.53,107.26,107.94,31530000,107.94 1979-12-28,107.96,108.61,107.16,107.84,34430000,107.84 1979-12-27,107.78,108.50,107.14,107.96,31410000,107.96 1979-12-26,107.66,108.37,107.06,107.78,24960000,107.78 1979-12-24,107.59,108.08,106.80,107.66,19150000,107.66 1979-12-21,108.26,108.76,106.99,107.59,36160000,107.59 1979-12-20,108.20,109.24,107.40,108.26,40380000,108.26 1979-12-19,108.30,108.79,107.02,108.20,41780000,108.20 1979-12-18,109.33,109.83,107.83,108.30,43310000,108.30 1979-12-17,108.92,110.33,108.36,109.33,43830000,109.33 1979-12-14,107.67,109.49,107.37,108.92,41800000,108.92 1979-12-13,107.52,108.29,106.68,107.67,36690000,107.67 1979-12-12,107.49,108.32,106.78,107.52,34630000,107.52 1979-12-11,107.67,108.58,106.79,107.49,36160000,107.49 1979-12-10,107.52,108.27,106.65,107.67,32270000,107.67 1979-12-07,108.00,109.24,106.55,107.52,42370000,107.52 1979-12-06,107.25,108.47,106.71,108.00,37510000,108.00 1979-12-05,106.79,108.36,106.60,107.25,39300000,107.25 1979-12-04,105.83,107.25,105.66,106.79,33510000,106.79 1979-12-03,106.16,106.65,105.07,105.83,29030000,105.83 1979-11-30,106.81,107.16,105.56,106.16,30480000,106.16 1979-11-29,106.77,107.84,106.17,106.81,33550000,106.81 1979-11-28,106.38,107.55,105.29,106.77,39690000,106.77 1979-11-27,106.80,107.89,105.64,106.38,45140000,106.38 1979-11-26,104.83,107.44,104.83,106.80,47940000,106.80 1979-11-23,103.89,105.13,103.56,104.67,23300000,104.67 1979-11-21,103.69,104.23,102.04,103.89,37020000,103.89 1979-11-20,104.23,105.11,103.14,103.69,35010000,103.69 1979-11-19,103.79,105.08,103.17,104.23,33090000,104.23 1979-11-16,104.13,104.72,103.07,103.79,30060000,103.79 1979-11-15,103.39,104.94,103.10,104.13,32380000,104.13 1979-11-14,102.94,104.13,101.91,103.39,30970000,103.39 1979-11-13,103.51,104.21,102.42,102.94,29240000,102.94 1979-11-12,101.51,103.72,101.27,103.51,26640000,103.51 1979-11-09,100.58,102.18,100.58,101.51,30060000,101.51 1979-11-08,99.87,101.00,99.49,100.30,26270000,100.30 1979-11-07,100.97,100.97,99.42,99.87,30830000,99.87 1979-11-06,101.82,102.01,100.77,101.20,21960000,101.20 1979-11-05,102.51,102.66,101.24,101.82,20470000,101.82 1979-11-02,102.57,103.21,101.92,102.51,23670000,102.51 1979-11-01,101.82,103.07,101.10,102.57,25880000,102.57 1979-10-31,102.67,103.16,101.38,101.82,27780000,101.82 1979-10-30,100.71,102.83,100.41,102.67,28890000,102.67 1979-10-29,100.57,101.56,100.13,100.71,22720000,100.71 1979-10-26,100.00,101.31,99.59,100.57,29660000,100.57 1979-10-25,100.44,101.39,99.56,100.00,28440000,100.00 1979-10-24,100.28,101.45,99.66,100.44,31480000,100.44 1979-10-23,100.71,101.44,99.61,100.28,32910000,100.28 1979-10-22,101.38,101.38,99.06,100.71,45240000,100.71 1979-10-19,103.58,103.58,101.24,101.60,42430000,101.60 1979-10-18,103.39,104.62,102.92,103.61,29590000,103.61 1979-10-17,103.19,104.54,102.74,103.39,29650000,103.39 1979-10-16,103.36,104.37,102.52,103.19,33770000,103.19 1979-10-15,104.49,104.74,102.69,103.36,34850000,103.36 1979-10-12,105.05,106.20,104.01,104.49,36390000,104.49 1979-10-11,105.30,106.33,103.70,105.05,47530000,105.05 1979-10-10,106.23,106.23,102.31,105.30,81620000,105.30 1979-10-09,109.43,109.43,106.04,106.63,55560000,106.63 1979-10-08,111.27,111.83,109.65,109.88,32610000,109.88 1979-10-05,110.17,112.16,110.16,111.27,48250000,111.27 1979-10-04,109.59,110.81,109.14,110.17,38800000,110.17 1979-10-03,109.59,110.43,108.88,109.59,36470000,109.59 1979-10-02,108.56,110.08,108.03,109.59,38310000,109.59 1979-10-01,109.19,109.19,107.70,108.56,24980000,108.56 1979-09-28,110.21,110.67,108.70,109.32,35950000,109.32 1979-09-27,109.96,110.75,109.19,110.21,33110000,110.21 1979-09-26,109.68,111.25,109.37,109.96,37700000,109.96 1979-09-25,109.61,110.19,108.27,109.68,32410000,109.68 1979-09-24,110.47,110.90,109.16,109.61,33790000,109.61 1979-09-21,110.51,111.58,109.46,110.47,52380000,110.47 1979-09-20,108.28,110.69,107.59,110.51,45100000,110.51 1979-09-19,108.00,109.02,107.52,108.28,35370000,108.28 1979-09-18,108.84,109.00,107.32,108.00,38750000,108.00 1979-09-17,108.76,110.06,108.40,108.84,37610000,108.84 1979-09-14,107.85,109.48,107.42,108.76,41980000,108.76 1979-09-13,107.82,108.53,107.06,107.85,35240000,107.85 1979-09-12,107.51,108.41,106.72,107.82,39350000,107.82 1979-09-11,108.17,108.83,106.80,107.51,42530000,107.51 1979-09-10,107.66,108.71,107.21,108.17,32980000,108.17 1979-09-07,106.85,108.09,106.30,107.66,34360000,107.66 1979-09-06,106.40,107.61,105.97,106.85,30330000,106.85 1979-09-05,107.19,107.19,105.38,106.40,41650000,106.40 1979-09-04,109.32,109.41,107.22,107.44,33350000,107.44 1979-08-31,109.02,109.80,108.58,109.32,26370000,109.32 1979-08-30,109.02,109.59,108.40,109.02,29300000,109.02 1979-08-29,109.02,109.59,108.36,109.02,30810000,109.02 1979-08-28,109.14,109.65,108.47,109.02,29430000,109.02 1979-08-27,108.60,109.84,108.12,109.14,32050000,109.14 1979-08-24,108.63,109.11,107.65,108.60,32730000,108.60 1979-08-23,108.99,109.59,108.12,108.63,35710000,108.63 1979-08-22,108.91,109.56,108.09,108.99,38450000,108.99 1979-08-21,108.83,109.68,108.17,108.91,38860000,108.91 1979-08-20,108.30,109.32,107.69,108.83,32300000,108.83 1979-08-17,108.09,108.94,107.25,108.30,31630000,108.30 1979-08-16,108.25,109.18,107.38,108.09,47000000,108.09 1979-08-15,107.52,108.64,106.75,108.25,46130000,108.25 1979-08-14,107.42,108.03,106.60,107.52,40910000,107.52 1979-08-13,106.40,107.90,106.28,107.42,41980000,107.42 1979-08-10,105.49,106.79,104.81,106.40,36740000,106.40 1979-08-09,105.98,106.25,104.89,105.49,34630000,105.49 1979-08-08,105.65,106.84,105.20,105.98,44970000,105.98 1979-08-07,104.30,106.23,104.12,105.65,45410000,105.65 1979-08-06,104.04,104.66,103.27,104.30,27190000,104.30 1979-08-03,104.10,104.56,103.36,104.04,28160000,104.04 1979-08-02,104.17,105.02,103.59,104.10,37720000,104.10 1979-08-01,103.81,104.57,103.14,104.17,36570000,104.17 1979-07-31,103.15,104.26,102.89,103.81,34360000,103.81 1979-07-30,103.10,103.63,102.42,103.15,28640000,103.15 1979-07-27,103.10,103.50,102.29,103.10,27760000,103.10 1979-07-26,103.08,103.63,102.34,103.10,32270000,103.10 1979-07-25,101.97,103.44,101.85,103.08,34890000,103.08 1979-07-24,101.59,102.50,101.14,101.97,29690000,101.97 1979-07-23,101.82,102.13,100.84,101.59,26860000,101.59 1979-07-20,101.61,102.32,101.06,101.82,26360000,101.82 1979-07-19,101.69,102.42,101.04,101.61,26780000,101.61 1979-07-18,101.83,102.06,100.35,101.69,35950000,101.69 1979-07-17,102.74,103.06,101.27,101.83,34270000,101.83 1979-07-16,102.32,103.20,101.81,102.74,26620000,102.74 1979-07-13,102.69,102.99,101.49,102.32,33080000,102.32 1979-07-12,103.64,103.72,102.22,102.69,31780000,102.69 1979-07-11,104.20,104.34,102.87,103.64,36650000,103.64 1979-07-10,104.47,105.17,103.52,104.20,39730000,104.20 1979-07-09,103.62,105.07,103.36,104.47,42460000,104.47 1979-07-06,102.43,103.91,102.12,103.62,38570000,103.62 1979-07-05,102.09,102.88,101.59,102.43,30290000,102.43 1979-07-03,101.99,102.57,101.31,102.09,31670000,102.09 1979-07-02,102.91,103.00,101.45,101.99,32060000,101.99 1979-06-29,102.80,103.67,102.04,102.91,34690000,102.91 1979-06-28,102.27,103.46,101.91,102.80,38470000,102.80 1979-06-27,101.66,102.95,101.29,102.27,36720000,102.27 1979-06-26,102.09,102.09,101.22,101.66,34680000,101.66 1979-06-25,102.64,102.91,101.45,102.09,31330000,102.09 1979-06-22,102.09,103.16,101.91,102.64,36410000,102.64 1979-06-21,101.63,102.74,101.20,102.09,36490000,102.09 1979-06-20,101.58,102.19,100.93,101.63,33790000,101.63 1979-06-19,101.56,102.28,100.91,101.58,30780000,101.58 1979-06-18,102.09,102.48,101.05,101.56,30970000,101.56 1979-06-15,102.20,102.78,101.38,102.09,40740000,102.09 1979-06-14,102.31,102.63,101.04,102.20,37850000,102.20 1979-06-13,102.85,103.58,101.83,102.31,40740000,102.31 1979-06-12,101.91,103.64,101.81,102.85,45450000,102.85 1979-06-11,101.49,102.24,100.91,101.91,28270000,101.91 1979-06-08,101.79,102.23,100.91,101.49,31470000,101.49 1979-06-07,101.30,102.54,101.15,101.79,43380000,101.79 1979-06-06,100.62,101.96,100.38,101.30,39830000,101.30 1979-06-05,99.32,101.07,99.17,100.62,35050000,100.62 1979-06-04,99.17,99.76,98.61,99.32,24040000,99.32 1979-06-01,99.08,99.70,98.57,99.17,24560000,99.17 1979-05-31,99.11,99.61,98.29,99.08,30300000,99.08 1979-05-30,100.05,100.25,98.79,99.11,29250000,99.11 1979-05-29,100.22,100.76,99.56,100.05,27040000,100.05 1979-05-25,99.93,100.68,99.52,100.22,27810000,100.22 1979-05-24,99.89,100.44,99.14,99.93,25710000,99.93 1979-05-23,100.51,101.31,99.63,99.89,30390000,99.89 1979-05-22,100.14,100.93,99.45,100.51,30400000,100.51 1979-05-21,99.93,100.75,99.37,100.14,25550000,100.14 1979-05-18,99.94,100.73,99.33,99.93,26590000,99.93 1979-05-17,98.42,100.22,98.29,99.94,30550000,99.94 1979-05-16,98.14,98.80,97.49,98.42,28350000,98.42 1979-05-15,98.06,98.90,97.60,98.14,26190000,98.14 1979-05-14,98.52,98.95,97.71,98.06,22450000,98.06 1979-05-11,98.52,99.03,97.92,98.52,24010000,98.52 1979-05-10,99.46,99.63,98.22,98.52,25230000,98.52 1979-05-09,99.17,100.01,98.50,99.46,27670000,99.46 1979-05-08,99.02,99.56,97.98,99.17,32720000,99.17 1979-05-07,100.37,100.37,98.78,99.02,30480000,99.02 1979-05-04,101.81,102.08,100.42,100.69,30630000,100.69 1979-05-03,101.72,102.57,101.25,101.81,30870000,101.81 1979-05-02,101.68,102.28,101.00,101.72,30510000,101.72 1979-05-01,101.76,102.50,101.22,101.68,31040000,101.68 1979-04-30,101.80,102.24,100.91,101.76,26440000,101.76 1979-04-27,102.01,102.32,101.04,101.80,29610000,101.80 1979-04-26,102.50,102.91,101.58,102.01,32400000,102.01 1979-04-25,102.20,103.07,101.79,102.50,31750000,102.50 1979-04-24,101.57,103.02,101.39,102.20,35540000,102.20 1979-04-23,101.23,102.00,100.68,101.57,25610000,101.57 1979-04-20,101.28,101.81,100.46,101.23,28830000,101.23 1979-04-19,101.70,102.40,100.88,101.28,31150000,101.28 1979-04-18,101.24,102.23,100.96,101.70,29510000,101.70 1979-04-17,101.12,101.94,100.65,101.24,29260000,101.24 1979-04-16,102.00,102.02,100.67,101.12,28050000,101.12 1979-04-12,102.31,102.77,101.51,102.00,26780000,102.00 1979-04-11,103.34,103.77,101.92,102.31,32900000,102.31 1979-04-10,102.87,103.83,102.42,103.34,31900000,103.34 1979-04-09,103.18,103.56,102.28,102.87,27230000,102.87 1979-04-06,103.26,103.95,102.58,103.18,34710000,103.18 1979-04-05,102.65,103.60,102.16,103.26,34520000,103.26 1979-04-04,102.40,103.73,102.16,102.65,41940000,102.65 1979-04-03,100.90,102.67,100.81,102.40,33530000,102.40 1979-04-02,101.56,101.56,100.14,100.90,28990000,100.90 1979-03-30,102.03,102.51,101.03,101.59,29970000,101.59 1979-03-29,102.12,102.78,101.43,102.03,28510000,102.03 1979-03-28,102.48,103.31,101.74,102.12,39920000,102.12 1979-03-27,101.04,102.71,100.81,102.48,32940000,102.48 1979-03-26,101.60,101.77,100.60,101.04,23430000,101.04 1979-03-23,101.67,102.37,101.02,101.60,33570000,101.60 1979-03-22,101.25,102.41,101.04,101.67,34380000,101.67 1979-03-21,100.50,101.48,99.87,101.25,31120000,101.25 1979-03-20,101.06,101.34,100.01,100.50,27180000,100.50 1979-03-19,100.69,101.94,100.35,101.06,34620000,101.06 1979-03-16,99.86,101.16,99.53,100.69,31770000,100.69 1979-03-15,99.71,100.57,99.11,99.86,29370000,99.86 1979-03-14,99.84,100.43,99.23,99.71,24630000,99.71 1979-03-13,99.67,100.66,99.13,99.84,31170000,99.84 1979-03-12,99.54,100.04,98.56,99.67,25740000,99.67 1979-03-09,99.58,100.58,99.12,99.54,33410000,99.54 1979-03-08,98.44,99.82,98.10,99.58,32000000,99.58 1979-03-07,97.87,99.23,97.67,98.44,28930000,98.44 1979-03-06,98.06,98.53,97.36,97.87,24490000,97.87 1979-03-05,97.03,98.64,97.03,98.06,25690000,98.06 1979-03-02,96.90,97.55,96.44,96.97,23130000,96.97 1979-03-01,96.28,97.28,95.98,96.90,23830000,96.90 1979-02-28,96.13,96.69,95.38,96.28,25090000,96.28 1979-02-27,97.65,97.65,95.69,96.13,31470000,96.13 1979-02-26,97.78,98.28,97.20,97.67,22620000,97.67 1979-02-23,98.33,98.50,97.29,97.78,22750000,97.78 1979-02-22,99.07,99.21,97.88,98.33,26290000,98.33 1979-02-21,99.42,100.07,98.69,99.07,26050000,99.07 1979-02-20,98.67,99.67,98.26,99.42,22010000,99.42 1979-02-16,98.73,99.23,98.11,98.67,21110000,98.67 1979-02-15,98.87,99.13,97.96,98.73,22550000,98.73 1979-02-14,98.93,99.64,98.21,98.87,27220000,98.87 1979-02-13,98.25,99.58,98.25,98.93,28470000,98.93 1979-02-12,97.87,98.55,97.05,98.20,20610000,98.20 1979-02-09,97.65,98.50,97.28,97.87,24320000,97.87 1979-02-08,97.16,98.11,96.82,97.65,23360000,97.65 1979-02-07,98.05,98.07,96.51,97.16,28450000,97.16 1979-02-06,98.09,98.74,97.48,98.05,23570000,98.05 1979-02-05,99.07,99.07,97.57,98.09,26490000,98.09 1979-02-02,99.96,100.52,99.10,99.50,25350000,99.50 1979-02-01,99.93,100.38,99.01,99.96,27930000,99.96 1979-01-31,101.05,101.41,99.47,99.93,30330000,99.93 1979-01-30,101.55,102.07,100.68,101.05,26910000,101.05 1979-01-29,101.86,102.33,100.99,101.55,24170000,101.55 1979-01-26,101.19,102.59,101.03,101.86,34230000,101.86 1979-01-25,100.16,101.66,99.99,101.19,31440000,101.19 1979-01-24,100.60,101.31,99.67,100.16,31730000,100.16 1979-01-23,99.90,101.05,99.35,100.60,30130000,100.60 1979-01-22,99.75,100.35,98.90,99.90,24390000,99.90 1979-01-19,99.72,100.57,99.22,99.75,26800000,99.75 1979-01-18,99.48,100.35,98.91,99.72,27260000,99.72 1979-01-17,99.46,100.00,98.33,99.48,25310000,99.48 1979-01-16,100.69,100.88,99.11,99.46,30340000,99.46 1979-01-15,99.93,101.13,99.58,100.69,27520000,100.69 1979-01-12,99.32,100.91,99.32,99.93,37120000,99.93 1979-01-11,98.77,99.41,97.95,99.10,24580000,99.10 1979-01-10,99.33,99.75,98.28,98.77,24990000,98.77 1979-01-09,98.80,99.96,98.62,99.33,27340000,99.33 1979-01-08,99.13,99.30,97.83,98.80,21440000,98.80 1979-01-05,98.58,99.79,98.25,99.13,28890000,99.13 1979-01-04,97.80,99.42,97.52,98.58,33290000,98.58 1979-01-03,96.81,98.54,96.81,97.80,29180000,97.80 1979-01-02,96.11,96.96,95.22,96.73,18340000,96.73 1978-12-29,96.28,97.03,95.48,96.11,30030000,96.11 1978-12-28,96.66,97.19,95.82,96.28,25440000,96.28 1978-12-27,97.51,97.51,96.15,96.66,23580000,96.66 1978-12-26,96.31,97.89,95.99,97.52,21470000,97.52 1978-12-22,94.77,96.62,94.77,96.31,23790000,96.31 1978-12-21,94.68,95.66,94.11,94.71,28670000,94.71 1978-12-20,94.24,95.20,93.70,94.68,26520000,94.68 1978-12-19,93.44,94.85,93.05,94.24,25960000,94.24 1978-12-18,94.33,94.33,92.64,93.44,32900000,93.44 1978-12-15,96.04,96.28,94.88,95.33,23620000,95.33 1978-12-14,96.06,96.44,95.20,96.04,20840000,96.04 1978-12-13,96.59,97.07,95.59,96.06,22480000,96.06 1978-12-12,97.11,97.58,96.27,96.59,22210000,96.59 1978-12-11,96.63,97.56,96.07,97.11,21000000,97.11 1978-12-08,97.08,97.48,96.14,96.63,18560000,96.63 1978-12-07,97.49,98.10,96.58,97.08,21170000,97.08 1978-12-06,97.44,98.58,96.83,97.49,29680000,97.49 1978-12-05,96.15,97.70,95.88,97.44,25670000,97.44 1978-12-04,96.28,96.96,95.37,96.15,22020000,96.15 1978-12-01,95.01,96.69,95.01,96.28,26830000,96.28 1978-11-30,93.75,94.94,93.29,94.70,19900000,94.70 1978-11-29,94.92,94.92,93.48,93.75,21160000,93.75 1978-11-28,95.99,96.51,94.88,95.15,22740000,95.15 1978-11-27,95.79,96.52,95.17,95.99,19790000,95.99 1978-11-24,95.48,96.17,94.98,95.79,14590000,95.79 1978-11-22,95.01,95.91,94.54,95.48,20010000,95.48 1978-11-21,95.25,95.83,94.49,95.01,20750000,95.01 1978-11-20,94.42,95.86,94.29,95.25,24440000,95.25 1978-11-17,93.71,95.03,93.59,94.42,25170000,94.42 1978-11-16,92.71,94.08,92.59,93.71,21340000,93.71 1978-11-15,92.49,94.00,92.29,92.71,26280000,92.71 1978-11-14,93.13,93.53,91.77,92.49,30610000,92.49 1978-11-13,94.77,94.90,92.96,93.13,20960000,93.13 1978-11-10,94.42,95.39,93.94,94.77,16750000,94.77 1978-11-09,94.45,95.50,93.81,94.42,23320000,94.42 1978-11-08,93.85,94.74,92.89,94.45,23560000,94.45 1978-11-07,94.75,94.75,93.14,93.85,25320000,93.85 1978-11-06,96.18,96.49,94.84,95.19,20450000,95.19 1978-11-03,95.61,96.98,94.78,96.18,25990000,96.18 1978-11-02,96.85,97.31,94.84,95.61,41030000,95.61 1978-11-01,94.13,97.41,94.13,96.85,50450000,96.85 1978-10-31,95.06,95.80,92.72,93.15,42720000,93.15 1978-10-30,94.59,95.49,91.65,95.06,59480000,95.06 1978-10-27,96.03,96.62,94.30,94.59,40360000,94.59 1978-10-26,97.31,97.71,95.59,96.03,31990000,96.03 1978-10-25,97.49,98.56,96.33,97.31,31380000,97.31 1978-10-24,98.18,98.95,97.13,97.49,28880000,97.49 1978-10-23,97.95,98.84,96.63,98.18,36090000,98.18 1978-10-20,99.26,99.26,97.12,97.95,43670000,97.95 1978-10-19,100.49,101.03,99.04,99.33,31810000,99.33 1978-10-18,101.26,101.76,99.89,100.49,32940000,100.49 1978-10-17,102.35,102.35,100.47,101.26,37870000,101.26 1978-10-16,104.63,104.63,102.43,102.61,24600000,102.61 1978-10-13,104.88,105.34,104.07,104.66,21920000,104.66 1978-10-12,105.39,106.23,104.42,104.88,30170000,104.88 1978-10-11,104.46,105.64,103.80,105.39,21740000,105.39 1978-10-10,104.59,105.36,103.90,104.46,25470000,104.46 1978-10-09,103.52,104.89,103.31,104.59,19720000,104.59 1978-10-06,103.27,104.23,102.82,103.52,27380000,103.52 1978-10-05,103.06,104.10,102.54,103.27,27820000,103.27 1978-10-04,102.60,103.36,101.76,103.06,25090000,103.06 1978-10-03,102.96,103.56,102.18,102.60,22540000,102.60 1978-10-02,102.54,103.42,102.13,102.96,18700000,102.96 1978-09-29,101.96,103.08,101.65,102.54,23610000,102.54 1978-09-28,101.66,102.38,100.94,101.96,24390000,101.96 1978-09-27,102.62,103.44,101.33,101.66,28370000,101.66 1978-09-26,101.86,103.15,101.58,102.62,26330000,102.62 1978-09-25,101.84,102.36,101.05,101.86,20970000,101.86 1978-09-22,101.90,102.69,101.13,101.84,27960000,101.84 1978-09-21,101.73,102.54,100.66,101.90,33640000,101.90 1978-09-20,102.53,103.29,101.28,101.73,35080000,101.73 1978-09-19,103.21,103.82,102.12,102.53,31660000,102.53 1978-09-18,104.12,105.03,102.75,103.21,35860000,103.21 1978-09-15,105.10,105.12,103.56,104.12,37290000,104.12 1978-09-14,106.34,106.62,104.77,105.10,37400000,105.10 1978-09-13,106.99,107.85,105.87,106.34,43340000,106.34 1978-09-12,106.98,107.48,106.02,106.99,34400000,106.99 1978-09-11,106.79,108.05,106.42,106.98,39670000,106.98 1978-09-08,105.50,107.19,105.50,106.79,42170000,106.79 1978-09-07,105.38,106.49,104.76,105.42,40310000,105.42 1978-09-06,104.51,106.19,104.51,105.38,42600000,105.38 1978-09-05,103.68,104.83,103.31,104.49,32170000,104.49 1978-09-01,103.29,104.27,102.73,103.68,35070000,103.68 1978-08-31,103.50,104.05,102.63,103.29,33850000,103.29 1978-08-30,103.39,104.26,102.70,103.50,37750000,103.50 1978-08-29,103.96,104.34,102.92,103.39,33780000,103.39 1978-08-28,104.90,105.14,103.61,103.96,31760000,103.96 1978-08-25,105.08,105.68,104.24,104.90,36190000,104.90 1978-08-24,104.91,105.86,104.29,105.08,38500000,105.08 1978-08-23,104.31,105.68,104.12,104.91,39630000,104.91 1978-08-22,103.89,104.79,103.14,104.31,29620000,104.31 1978-08-21,104.73,105.20,103.44,103.89,29440000,103.89 1978-08-18,105.08,105.98,104.23,104.73,34650000,104.73 1978-08-17,104.65,106.27,104.34,105.08,45270000,105.08 1978-08-16,103.85,105.15,103.41,104.65,36120000,104.65 1978-08-15,103.97,104.38,102.86,103.85,29760000,103.85 1978-08-14,103.96,104.98,103.40,103.97,32320000,103.97 1978-08-11,103.66,104.67,102.85,103.96,33550000,103.96 1978-08-10,104.50,105.11,103.10,103.66,39760000,103.66 1978-08-09,104.01,105.72,103.70,104.50,48800000,104.50 1978-08-08,103.55,104.35,102.60,104.01,34290000,104.01 1978-08-07,103.92,104.84,103.03,103.55,33350000,103.55 1978-08-04,103.51,104.67,102.75,103.92,37910000,103.92 1978-08-03,102.92,105.41,102.82,103.51,66370000,103.51 1978-08-02,100.66,103.21,100.18,102.92,47470000,102.92 1978-08-01,100.68,101.46,99.95,100.66,34810000,100.66 1978-07-31,100.00,101.18,99.37,100.68,33990000,100.68 1978-07-28,99.54,100.51,98.90,100.00,33390000,100.00 1978-07-27,99.08,100.17,98.60,99.54,33970000,99.54 1978-07-26,99.08,99.08,99.08,99.08,36830000,99.08 1978-07-25,97.72,98.73,97.20,98.44,25400000,98.44 1978-07-24,97.75,98.13,96.72,97.72,23280000,97.72 1978-07-21,98.03,98.57,97.02,97.75,26060000,97.75 1978-07-20,98.12,99.18,97.49,98.03,33350000,98.03 1978-07-19,96.87,98.41,96.71,98.12,30850000,98.12 1978-07-18,97.78,97.98,96.52,96.87,22860000,96.87 1978-07-17,97.58,98.84,97.24,97.78,29180000,97.78 1978-07-14,96.25,97.88,95.89,97.58,28370000,97.58 1978-07-13,96.24,96.66,95.42,96.25,23620000,96.25 1978-07-12,95.93,96.83,95.50,96.24,26640000,96.24 1978-07-11,95.27,96.49,94.92,95.93,27470000,95.93 1978-07-10,94.89,95.67,94.28,95.27,22470000,95.27 1978-07-07,94.32,95.32,94.02,94.89,23480000,94.89 1978-07-06,94.27,94.83,93.59,94.32,24990000,94.32 1978-07-05,95.09,95.20,93.78,94.27,23730000,94.27 1978-07-03,95.53,95.65,94.62,95.09,11560000,95.09 1978-06-30,95.57,95.96,94.87,95.53,18100000,95.53 1978-06-29,95.40,96.26,95.00,95.57,21660000,95.57 1978-06-28,94.98,95.79,94.44,95.40,23260000,95.40 1978-06-27,94.60,95.48,93.99,94.98,29280000,94.98 1978-06-26,95.85,96.06,94.31,94.60,29250000,94.60 1978-06-23,96.24,96.98,95.49,95.85,28530000,95.85 1978-06-22,96.01,96.76,95.52,96.24,27160000,96.24 1978-06-21,96.51,96.74,95.42,96.01,29100000,96.01 1978-06-20,97.49,97.78,96.15,96.51,27920000,96.51 1978-06-19,97.42,97.94,96.53,97.49,25500000,97.49 1978-06-16,98.34,98.59,97.10,97.42,27690000,97.42 1978-06-15,99.48,99.54,97.97,98.34,29280000,98.34 1978-06-14,99.57,100.68,98.89,99.48,37290000,99.48 1978-06-13,99.55,99.98,98.43,99.57,30760000,99.57 1978-06-12,99.93,100.60,99.16,99.55,24440000,99.55 1978-06-09,100.21,100.71,99.30,99.93,32470000,99.93 1978-06-08,100.12,101.21,99.55,100.21,39380000,100.21 1978-06-07,100.32,100.81,99.36,100.12,33060000,100.12 1978-06-06,99.95,101.84,99.90,100.32,51970000,100.32 1978-06-05,98.14,100.27,97.97,99.95,39580000,99.95 1978-06-02,97.35,98.52,97.01,98.14,31860000,98.14 1978-06-01,97.24,97.95,96.63,97.35,28750000,97.35 1978-05-31,96.86,97.97,96.50,97.24,29070000,97.24 1978-05-30,96.58,97.23,95.95,96.86,21040000,96.86 1978-05-26,96.80,97.14,96.01,96.58,21410000,96.58 1978-05-25,97.08,97.80,96.30,96.80,28410000,96.80 1978-05-24,97.74,97.74,96.27,97.08,31450000,97.08 1978-05-23,99.09,99.17,97.53,98.05,33230000,98.05 1978-05-22,98.12,99.43,97.65,99.09,28680000,99.09 1978-05-19,98.62,99.06,97.42,98.12,34360000,98.12 1978-05-18,99.60,100.04,98.19,98.62,42270000,98.62 1978-05-17,99.35,100.32,98.63,99.60,45490000,99.60 1978-05-16,98.76,100.16,98.61,99.35,48170000,99.35 1978-05-15,98.07,99.11,97.40,98.76,33890000,98.76 1978-05-12,97.20,98.89,97.14,98.07,46600000,98.07 1978-05-11,95.92,97.47,95.60,97.20,36630000,97.20 1978-05-10,95.90,96.69,95.35,95.92,33330000,95.92 1978-05-09,96.19,96.68,95.33,95.90,30860000,95.90 1978-05-08,96.53,97.50,95.82,96.19,34680000,96.19 1978-05-05,95.93,97.44,95.56,96.53,42680000,96.53 1978-05-04,96.26,96.43,94.57,95.93,37520000,95.93 1978-05-03,97.25,97.61,95.84,96.26,37560000,96.26 1978-05-02,97.67,98.11,96.44,97.25,41400000,97.25 1978-05-01,96.83,98.30,96.41,97.67,37020000,97.67 1978-04-28,95.86,97.10,95.24,96.83,32850000,96.83 1978-04-27,96.82,96.93,95.30,95.86,35470000,95.86 1978-04-26,96.64,97.75,95.96,96.82,44430000,96.82 1978-04-25,96.05,97.91,96.05,96.64,55800000,96.64 1978-04-24,94.34,96.00,94.08,95.77,34510000,95.77 1978-04-21,94.54,95.09,93.71,94.34,31540000,94.34 1978-04-20,93.97,95.71,93.97,94.54,43230000,94.54 1978-04-19,93.43,94.48,92.75,93.86,35060000,93.86 1978-04-18,94.45,94.72,92.87,93.43,38950000,93.43 1978-04-17,93.60,95.89,93.60,94.45,63510000,94.45 1978-04-14,91.40,93.31,91.40,92.92,52280000,92.92 1978-04-13,90.11,91.27,89.82,90.98,31580000,90.98 1978-04-12,90.25,90.78,89.65,90.11,26210000,90.11 1978-04-11,90.49,90.79,89.77,90.25,24300000,90.25 1978-04-10,90.17,90.88,89.73,90.49,25740000,90.49 1978-04-07,89.79,90.59,89.39,90.17,25160000,90.17 1978-04-06,89.64,90.46,89.31,89.79,27360000,89.79 1978-04-05,88.86,89.91,88.62,89.64,27260000,89.64 1978-04-04,88.46,89.18,88.16,88.86,20130000,88.86 1978-04-03,89.20,89.20,88.07,88.46,20230000,88.46 1978-03-31,89.41,89.64,88.68,89.21,20130000,89.21 1978-03-30,89.64,89.89,88.97,89.41,20460000,89.41 1978-03-29,89.50,90.17,89.14,89.64,25450000,89.64 1978-03-28,88.87,89.76,88.47,89.50,21600000,89.50 1978-03-27,89.36,89.50,88.51,88.87,18870000,88.87 1978-03-23,89.47,89.90,88.83,89.36,21290000,89.36 1978-03-22,89.79,90.07,88.99,89.47,21950000,89.47 1978-03-21,90.82,91.06,89.50,89.79,24410000,89.79 1978-03-20,90.20,91.35,90.10,90.82,28360000,90.82 1978-03-17,89.51,90.52,89.17,90.20,28470000,90.20 1978-03-16,89.12,89.77,88.58,89.51,25400000,89.51 1978-03-15,89.35,89.73,88.52,89.12,23340000,89.12 1978-03-14,88.95,89.62,88.21,89.35,24300000,89.35 1978-03-13,88.88,89.77,88.48,88.95,24070000,88.95 1978-03-10,87.89,89.25,87.82,88.88,27090000,88.88 1978-03-09,87.84,88.49,87.34,87.89,21820000,87.89 1978-03-08,87.36,88.08,86.97,87.84,22030000,87.84 1978-03-07,86.90,87.63,86.55,87.36,19900000,87.36 1978-03-06,87.45,87.52,86.48,86.90,17230000,86.90 1978-03-03,87.32,87.98,86.83,87.45,20120000,87.45 1978-03-02,87.19,87.81,86.69,87.32,20280000,87.32 1978-03-01,87.04,87.63,86.45,87.19,21010000,87.19 1978-02-28,87.72,87.76,86.58,87.04,19750000,87.04 1978-02-27,88.49,88.97,87.49,87.72,19990000,87.72 1978-02-24,87.66,88.87,87.66,88.49,22510000,88.49 1978-02-23,87.56,87.92,86.83,87.64,18720000,87.64 1978-02-22,87.59,88.15,87.19,87.56,18450000,87.56 1978-02-21,87.96,88.19,87.09,87.59,21890000,87.59 1978-02-17,88.08,88.70,87.55,87.96,18500000,87.96 1978-02-16,88.77,88.77,87.64,88.08,21570000,88.08 1978-02-15,89.04,89.40,88.30,88.83,20170000,88.83 1978-02-14,89.86,89.89,88.70,89.04,20470000,89.04 1978-02-13,90.08,90.30,89.38,89.86,16810000,89.86 1978-02-10,90.30,90.69,89.56,90.08,19480000,90.08 1978-02-09,90.83,90.96,89.84,90.30,17940000,90.30 1978-02-08,90.33,91.32,90.09,90.83,21300000,90.83 1978-02-07,89.50,90.53,89.38,90.33,14730000,90.33 1978-02-06,89.62,89.85,88.95,89.50,11630000,89.50 1978-02-03,90.13,90.32,89.19,89.62,19400000,89.62 1978-02-02,89.93,90.91,89.54,90.13,23050000,90.13 1978-02-01,89.25,90.24,88.82,89.93,22240000,89.93 1978-01-31,89.34,89.92,88.61,89.25,19870000,89.25 1978-01-30,88.58,89.67,88.26,89.34,17400000,89.34 1978-01-27,88.58,89.10,88.02,88.58,17600000,88.58 1978-01-26,89.39,89.79,88.31,88.58,19600000,88.58 1978-01-25,89.25,89.94,88.83,89.39,18690000,89.39 1978-01-24,89.24,89.80,88.67,89.25,18690000,89.25 1978-01-23,89.89,90.08,88.81,89.24,19380000,89.24 1978-01-20,90.09,90.27,89.41,89.89,7580000,89.89 1978-01-19,90.56,91.04,89.74,90.09,21500000,90.09 1978-01-18,89.88,90.86,89.59,90.56,21390000,90.56 1978-01-17,89.43,90.31,89.05,89.88,19360000,89.88 1978-01-16,89.69,90.11,88.88,89.43,18760000,89.43 1978-01-13,89.82,90.47,89.26,89.69,18010000,89.69 1978-01-12,89.74,90.60,89.25,89.82,22730000,89.82 1978-01-11,90.17,90.70,89.23,89.74,22880000,89.74 1978-01-10,90.64,91.29,89.72,90.17,25180000,90.17 1978-01-09,91.48,91.48,89.97,90.64,27990000,90.64 1978-01-06,92.66,92.66,91.05,91.62,26150000,91.62 1978-01-05,93.52,94.53,92.51,92.74,23570000,92.74 1978-01-04,93.82,94.10,92.57,93.52,24090000,93.52 1978-01-03,95.10,95.15,93.49,93.82,17720000,93.82 1977-12-30,94.94,95.67,94.44,95.10,23560000,95.10 1977-12-29,94.75,95.43,94.10,94.94,23610000,94.94 1977-12-28,94.69,95.20,93.99,94.75,19630000,94.75 1977-12-27,94.69,95.21,94.09,94.69,16750000,94.69 1977-12-23,93.80,94.99,93.75,94.69,20080000,94.69 1977-12-22,93.05,94.37,93.05,93.80,28100000,93.80 1977-12-21,92.50,93.58,92.20,93.05,24510000,93.05 1977-12-20,92.69,93.00,91.76,92.50,23250000,92.50 1977-12-19,93.40,93.71,92.42,92.69,21150000,92.69 1977-12-16,93.55,94.04,92.93,93.40,20270000,93.40 1977-12-15,94.03,94.42,93.23,93.55,21610000,93.55 1977-12-14,93.56,94.26,92.94,94.03,22110000,94.03 1977-12-13,93.63,94.04,92.90,93.56,19190000,93.56 1977-12-12,93.65,94.29,93.18,93.63,18180000,93.63 1977-12-09,92.96,94.11,92.77,93.65,19210000,93.65 1977-12-08,92.78,93.76,92.51,92.96,20400000,92.96 1977-12-07,92.83,93.39,92.15,92.78,21050000,92.78 1977-12-06,94.09,94.09,92.44,92.83,23770000,92.83 1977-12-05,94.67,95.01,93.91,94.27,19160000,94.27 1977-12-02,94.69,95.25,94.08,94.67,21160000,94.67 1977-12-01,94.83,95.45,94.23,94.69,24220000,94.69 1977-11-30,94.55,95.17,93.78,94.83,22670000,94.83 1977-11-29,96.04,96.09,94.28,94.55,22950000,94.55 1977-11-28,96.69,96.98,95.67,96.04,21570000,96.04 1977-11-25,96.49,97.11,95.86,96.69,17910000,96.69 1977-11-23,96.09,96.94,95.60,96.49,29150000,96.49 1977-11-22,95.25,96.52,95.05,96.09,28600000,96.09 1977-11-21,95.33,95.77,94.59,95.25,20110000,95.25 1977-11-18,95.16,95.88,94.70,95.33,23930000,95.33 1977-11-17,95.45,95.88,94.59,95.16,25110000,95.16 1977-11-16,95.93,96.47,95.06,95.45,24950000,95.45 1977-11-15,95.32,96.47,94.73,95.93,27740000,95.93 1977-11-14,95.98,96.38,94.91,95.32,23220000,95.32 1977-11-11,95.10,96.49,95.10,95.98,35260000,95.98 1977-11-10,92.98,95.10,92.69,94.71,31980000,94.71 1977-11-09,92.46,93.27,92.01,92.98,21330000,92.98 1977-11-08,92.29,92.97,91.82,92.46,19210000,92.46 1977-11-07,91.58,92.70,91.32,92.29,21270000,92.29 1977-11-04,90.76,91.97,90.72,91.58,21700000,91.58 1977-11-03,90.71,91.18,90.01,90.76,18090000,90.76 1977-11-02,91.35,91.59,90.29,90.71,20760000,90.71 1977-11-01,92.19,92.19,91.00,91.35,17170000,91.35 1977-10-31,92.61,93.03,91.85,92.34,17070000,92.34 1977-10-28,92.34,93.13,91.88,92.61,18050000,92.61 1977-10-27,92.10,93.15,91.54,92.34,21920000,92.34 1977-10-26,91.00,92.46,90.44,92.10,24860000,92.10 1977-10-25,91.63,91.71,90.20,91.00,23590000,91.00 1977-10-24,92.32,92.62,91.36,91.63,19210000,91.63 1977-10-21,92.67,92.99,91.80,92.32,20230000,92.32 1977-10-20,92.38,93.12,91.60,92.67,20520000,92.67 1977-10-19,93.46,93.71,92.07,92.38,22030000,92.38 1977-10-18,93.47,94.19,93.01,93.46,20130000,93.46 1977-10-17,93.56,94.03,92.87,93.47,17340000,93.47 1977-10-14,93.46,94.19,92.88,93.56,20410000,93.56 1977-10-13,94.04,94.32,92.89,93.46,23870000,93.46 1977-10-12,94.82,94.82,93.40,94.04,22440000,94.04 1977-10-11,95.75,95.97,94.73,94.93,17870000,94.93 1977-10-10,95.97,96.15,95.32,95.75,10580000,95.75 1977-10-07,96.05,96.51,95.48,95.97,16250000,95.97 1977-10-06,95.68,96.45,95.30,96.05,18490000,96.05 1977-10-05,96.03,96.36,95.20,95.68,18300000,95.68 1977-10-04,96.74,97.27,95.73,96.03,20850000,96.03 1977-10-03,96.53,97.11,95.86,96.74,19460000,96.74 1977-09-30,95.85,96.85,95.66,96.53,21170000,96.53 1977-09-29,95.31,96.28,95.09,95.85,21160000,95.85 1977-09-28,95.24,95.91,94.73,95.31,17960000,95.31 1977-09-27,95.38,96.01,94.76,95.24,19080000,95.24 1977-09-26,95.04,95.68,94.44,95.38,18230000,95.38 1977-09-23,95.09,95.69,94.60,95.04,18760000,95.04 1977-09-22,95.10,95.61,94.51,95.09,16660000,95.09 1977-09-21,95.89,96.52,94.83,95.10,22200000,95.10 1977-09-20,95.85,96.29,95.23,95.89,19030000,95.89 1977-09-19,96.48,96.59,95.46,95.85,16890000,95.85 1977-09-16,96.80,97.30,96.05,96.48,18340000,96.48 1977-09-15,96.55,97.31,96.15,96.80,18230000,96.80 1977-09-14,96.09,96.88,95.66,96.55,17330000,96.55 1977-09-13,96.03,96.56,95.48,96.09,14900000,96.09 1977-09-12,96.37,96.64,95.37,96.03,18700000,96.03 1977-09-09,97.10,97.10,95.97,96.37,18100000,96.37 1977-09-08,98.01,98.43,97.01,97.28,18290000,97.28 1977-09-07,97.71,98.38,97.33,98.01,18070000,98.01 1977-09-06,97.45,98.13,96.93,97.71,16130000,97.71 1977-09-02,96.83,97.76,96.51,97.45,15620000,97.45 1977-09-01,96.77,97.54,96.35,96.83,18820000,96.83 1977-08-31,96.38,97.00,95.59,96.77,19080000,96.77 1977-08-30,96.92,97.55,96.04,96.38,18220000,96.38 1977-08-29,96.06,97.25,95.93,96.92,15280000,96.92 1977-08-26,96.15,96.42,95.04,96.06,18480000,96.06 1977-08-25,97.18,97.18,95.81,96.15,19400000,96.15 1977-08-24,97.62,97.99,96.77,97.23,18170000,97.23 1977-08-23,97.79,98.52,97.18,97.62,20290000,97.62 1977-08-22,97.51,98.29,96.84,97.79,17870000,97.79 1977-08-19,97.68,98.29,96.78,97.51,20800000,97.51 1977-08-18,97.74,98.69,97.21,97.68,21040000,97.68 1977-08-17,97.73,98.40,97.12,97.74,20920000,97.74 1977-08-16,98.18,98.60,97.35,97.73,19340000,97.73 1977-08-15,97.88,98.56,97.14,98.18,15750000,98.18 1977-08-12,98.16,98.51,97.31,97.88,16870000,97.88 1977-08-11,98.92,99.45,97.90,98.16,21740000,98.16 1977-08-10,98.05,99.06,97.67,98.92,18280000,98.92 1977-08-09,97.99,98.63,97.48,98.05,19900000,98.05 1977-08-08,98.76,98.86,97.68,97.99,15870000,97.99 1977-08-05,98.74,99.44,98.31,98.76,19940000,98.76 1977-08-04,98.37,99.19,97.79,98.74,18870000,98.74 1977-08-03,98.50,98.86,97.53,98.37,21710000,98.37 1977-08-02,99.12,99.27,98.14,98.50,17910000,98.50 1977-08-01,98.85,99.84,98.46,99.12,17920000,99.12 1977-07-29,98.79,99.21,97.71,98.85,20350000,98.85 1977-07-28,98.64,99.36,97.78,98.79,26340000,98.79 1977-07-27,100.27,100.29,98.31,98.64,26440000,98.64 1977-07-26,100.85,100.92,99.72,100.27,21390000,100.27 1977-07-25,101.67,101.85,100.46,100.85,20430000,100.85 1977-07-22,101.59,102.28,101.02,101.67,23110000,101.67 1977-07-21,101.73,102.19,100.85,101.59,26880000,101.59 1977-07-20,101.79,102.57,101.14,101.73,29380000,101.73 1977-07-19,100.95,102.17,100.68,101.79,31930000,101.79 1977-07-18,100.18,101.40,99.94,100.95,29890000,100.95 1977-07-15,99.59,100.68,99.28,100.18,29120000,100.18 1977-07-13,99.45,99.99,98.83,99.59,23160000,99.59 1977-07-12,99.55,100.01,98.81,99.45,22470000,99.45 1977-07-11,99.79,100.16,98.90,99.55,19790000,99.55 1977-07-08,99.93,100.62,99.37,99.79,23820000,99.79 1977-07-07,99.58,100.30,99.12,99.93,21740000,99.93 1977-07-06,100.09,100.41,99.20,99.58,21230000,99.58 1977-07-05,100.10,100.72,99.62,100.09,16850000,100.09 1977-07-01,100.48,100.76,99.63,100.10,18160000,100.10 1977-06-30,100.11,100.88,99.68,100.48,19410000,100.48 1977-06-29,100.14,100.49,99.30,100.11,19000000,100.11 1977-06-28,100.98,101.36,99.87,100.14,22670000,100.14 1977-06-27,101.19,101.63,100.47,100.98,19870000,100.98 1977-06-24,100.62,101.65,100.41,101.19,27490000,101.19 1977-06-23,100.46,101.10,99.88,100.62,24330000,100.62 1977-06-22,100.74,101.07,99.90,100.46,25070000,100.46 1977-06-21,100.42,101.41,100.16,100.74,29730000,100.74 1977-06-20,99.97,100.76,99.56,100.42,22950000,100.42 1977-06-17,99.85,100.47,99.34,99.97,21960000,99.97 1977-06-16,99.61,100.33,98.91,99.85,24310000,99.85 1977-06-15,99.86,100.31,99.12,99.61,22640000,99.61 1977-06-14,98.76,100.12,98.76,99.86,25390000,99.86 1977-06-13,98.46,99.21,98.06,98.74,20250000,98.74 1977-06-10,98.14,98.86,97.68,98.46,20630000,98.46 1977-06-09,98.20,98.62,97.51,98.14,19940000,98.14 1977-06-08,97.73,98.75,97.49,98.20,22200000,98.20 1977-06-07,97.23,98.01,96.60,97.73,21110000,97.73 1977-06-06,97.69,98.26,96.89,97.23,18930000,97.23 1977-06-03,96.74,98.12,96.55,97.69,20330000,97.69 1977-06-02,96.93,97.53,96.23,96.74,18620000,96.74 1977-06-01,96.12,97.27,95.89,96.93,18320000,96.93 1977-05-31,96.27,96.75,95.52,96.12,17800000,96.12 1977-05-27,97.01,97.26,95.92,96.27,15730000,96.27 1977-05-26,96.77,97.47,96.20,97.01,18620000,97.01 1977-05-25,97.67,98.14,96.50,96.77,20710000,96.77 1977-05-24,98.15,98.25,97.00,97.67,20050000,97.67 1977-05-23,99.35,99.35,97.88,98.15,18290000,98.15 1977-05-20,99.88,100.12,98.91,99.45,18950000,99.45 1977-05-19,100.30,100.74,99.49,99.88,21280000,99.88 1977-05-18,99.77,100.93,99.58,100.30,27800000,100.30 1977-05-17,99.47,100.11,98.76,99.77,22290000,99.77 1977-05-16,99.03,99.98,98.79,99.47,21170000,99.47 1977-05-13,98.73,99.52,98.37,99.03,19780000,99.03 1977-05-12,98.78,99.25,97.91,98.73,21980000,98.73 1977-05-11,99.47,99.77,98.40,98.78,18980000,98.78 1977-05-10,99.18,100.09,98.82,99.47,21090000,99.47 1977-05-09,99.49,99.78,98.66,99.18,15230000,99.18 1977-05-06,100.11,100.20,98.95,99.49,19370000,99.49 1977-05-05,99.96,100.79,99.28,100.11,23450000,100.11 1977-05-04,99.43,100.56,98.90,99.96,23330000,99.96 1977-05-03,98.93,99.96,98.72,99.43,21950000,99.43 1977-05-02,98.44,99.26,97.97,98.93,17970000,98.93 1977-04-29,98.20,98.87,97.58,98.44,18330000,98.44 1977-04-28,97.96,98.77,97.47,98.20,18370000,98.20 1977-04-27,97.11,98.47,96.90,97.96,20590000,97.96 1977-04-26,97.15,97.94,96.53,97.11,20040000,97.11 1977-04-25,98.32,98.32,96.54,97.15,20440000,97.15 1977-04-22,99.67,99.67,98.08,98.44,20700000,98.44 1977-04-21,100.40,101.20,99.35,99.75,22740000,99.75 1977-04-20,100.07,100.98,99.49,100.40,25090000,100.40 1977-04-19,100.54,100.81,99.58,100.07,19510000,100.07 1977-04-18,101.04,101.36,100.09,100.54,17830000,100.54 1977-04-15,101.00,101.63,100.35,101.04,20230000,101.04 1977-04-14,100.42,102.07,100.42,101.00,30490000,101.00 1977-04-13,100.15,100.72,99.02,100.16,21800000,100.16 1977-04-12,98.97,100.58,98.97,100.15,23760000,100.15 1977-04-11,98.35,99.37,98.08,98.88,17650000,98.88 1977-04-07,97.91,98.65,97.48,98.35,17260000,98.35 1977-04-06,98.01,98.61,97.45,97.91,16600000,97.91 1977-04-05,98.23,98.60,97.43,98.01,18330000,98.01 1977-04-04,99.21,99.50,97.98,98.23,16250000,98.23 1977-04-01,98.42,99.57,98.38,99.21,17050000,99.21 1977-03-31,98.54,99.14,97.80,98.42,16510000,98.42 1977-03-30,99.69,99.99,98.18,98.54,18810000,98.54 1977-03-29,99.00,100.12,98.95,99.69,17030000,99.69 1977-03-28,99.06,99.54,98.35,99.00,16710000,99.00 1977-03-25,99.70,100.05,98.71,99.06,16550000,99.06 1977-03-24,100.20,100.60,99.26,99.70,19650000,99.70 1977-03-23,101.00,101.42,99.88,100.20,19360000,100.20 1977-03-22,101.31,101.58,100.35,101.00,18660000,101.00 1977-03-21,101.86,102.13,100.92,101.31,18040000,101.31 1977-03-18,102.08,102.61,101.39,101.86,19840000,101.86 1977-03-17,102.17,102.58,101.28,102.08,20700000,102.08 1977-03-16,101.98,102.70,101.52,102.17,22140000,102.17 1977-03-15,101.42,102.61,101.34,101.98,23940000,101.98 1977-03-14,100.65,101.75,100.24,101.42,19290000,101.42 1977-03-11,100.67,101.37,100.14,100.65,18230000,100.65 1977-03-10,100.10,100.96,99.49,100.67,18620000,100.67 1977-03-09,100.87,100.89,99.63,100.10,19680000,100.10 1977-03-08,101.25,101.85,100.48,100.87,19520000,100.87 1977-03-07,101.20,101.77,100.64,101.25,17410000,101.25 1977-03-04,100.88,101.67,100.52,101.20,18950000,101.20 1977-03-03,100.39,101.28,100.01,100.88,17560000,100.88 1977-03-02,100.66,101.24,99.97,100.39,18010000,100.39 1977-03-01,99.82,101.03,99.65,100.66,19480000,100.66 1977-02-28,99.48,100.06,98.91,99.82,16220000,99.82 1977-02-25,99.60,100.02,98.82,99.48,17610000,99.48 1977-02-24,100.19,100.42,99.18,99.60,19730000,99.60 1977-02-23,100.49,100.95,99.78,100.19,18240000,100.19 1977-02-22,100.49,101.22,99.94,100.49,17730000,100.49 1977-02-18,100.92,101.13,99.95,100.49,18040000,100.49 1977-02-17,101.50,101.76,100.43,100.92,19040000,100.92 1977-02-16,101.04,102.22,100.68,101.50,23430000,101.50 1977-02-15,100.74,101.67,100.35,101.04,21620000,101.04 1977-02-14,100.22,101.06,99.51,100.74,19230000,100.74 1977-02-11,100.82,101.18,99.74,100.22,20510000,100.22 1977-02-10,100.73,101.51,100.16,100.82,22340000,100.82 1977-02-09,101.60,101.88,100.12,100.73,23640000,100.73 1977-02-08,101.89,102.65,101.16,101.60,24040000,101.60 1977-02-07,101.88,102.43,101.25,101.89,20700000,101.89 1977-02-04,101.85,102.71,101.30,101.88,23130000,101.88 1977-02-03,102.36,102.57,101.28,101.85,23790000,101.85 1977-02-02,102.54,103.32,101.89,102.36,25700000,102.36 1977-02-01,102.03,103.06,101.57,102.54,23700000,102.54 1977-01-31,101.93,102.44,100.91,102.03,22920000,102.03 1977-01-28,101.79,102.61,101.08,101.93,22700000,101.93 1977-01-27,102.34,102.81,101.27,101.79,24360000,101.79 1977-01-26,103.13,103.48,101.84,102.34,27840000,102.34 1977-01-25,103.25,104.08,102.42,103.13,26340000,103.13 1977-01-24,103.32,104.06,102.50,103.25,22890000,103.25 1977-01-21,102.97,103.91,102.35,103.32,23930000,103.32 1977-01-20,103.85,104.45,102.50,102.97,26520000,102.97 1977-01-19,103.32,104.38,102.83,103.85,27120000,103.85 1977-01-18,103.73,104.29,102.71,103.32,24380000,103.32 1977-01-17,104.01,104.37,103.04,103.73,21060000,103.73 1977-01-14,104.20,104.71,103.37,104.01,24480000,104.01 1977-01-13,103.40,104.60,103.21,104.20,24780000,104.20 1977-01-12,104.12,104.18,102.75,103.40,22670000,103.40 1977-01-11,105.20,105.60,103.76,104.12,24100000,104.12 1977-01-10,105.01,105.75,104.46,105.20,20860000,105.20 1977-01-07,105.02,105.59,104.30,105.01,21720000,105.01 1977-01-06,104.76,105.86,104.40,105.02,23920000,105.02 1977-01-05,105.70,106.07,104.33,104.76,25010000,104.76 1977-01-04,107.00,107.31,105.40,105.70,22740000,105.70 1977-01-03,107.46,107.97,106.42,107.00,21280000,107.00 1976-12-31,106.88,107.82,106.55,107.46,19170000,107.46 1976-12-30,106.34,107.41,105.97,106.88,23700000,106.88 1976-12-29,106.77,107.17,105.83,106.34,21910000,106.34 1976-12-28,106.06,107.36,105.90,106.77,25790000,106.77 1976-12-27,104.84,106.31,104.58,106.06,20130000,106.06 1976-12-23,104.71,105.49,104.09,104.84,24560000,104.84 1976-12-22,104.22,105.59,104.03,104.71,26970000,104.71 1976-12-21,103.65,104.66,102.99,104.22,24390000,104.22 1976-12-20,104.26,104.63,103.21,103.65,20690000,103.65 1976-12-17,104.80,105.60,103.89,104.26,23870000,104.26 1976-12-16,105.14,105.53,104.07,104.80,23920000,104.80 1976-12-15,105.07,105.89,104.33,105.14,28300000,105.14 1976-12-14,104.63,105.44,103.80,105.07,25130000,105.07 1976-12-13,104.70,105.33,103.94,104.63,24830000,104.63 1976-12-10,104.51,105.36,103.90,104.70,25960000,104.70 1976-12-09,104.08,105.27,103.71,104.51,31800000,104.51 1976-12-08,103.49,104.40,102.94,104.08,24560000,104.08 1976-12-07,103.56,104.40,102.96,103.49,26140000,103.49 1976-12-06,102.76,104.15,102.53,103.56,24830000,103.56 1976-12-03,102.12,103.31,101.75,102.76,22640000,102.76 1976-12-02,102.49,103.30,101.70,102.12,23300000,102.12 1976-12-01,102.10,103.03,101.62,102.49,21960000,102.49 1976-11-30,102.44,102.72,101.46,102.10,17030000,102.10 1976-11-29,103.15,103.46,102.07,102.44,18750000,102.44 1976-11-26,102.41,103.51,102.13,103.15,15000000,103.15 1976-11-24,101.96,102.85,101.41,102.41,20420000,102.41 1976-11-23,102.59,102.90,101.50,101.96,19090000,101.96 1976-11-22,101.92,103.15,101.63,102.59,20930000,102.59 1976-11-19,101.89,102.77,101.17,101.92,24550000,101.92 1976-11-18,100.61,102.22,100.49,101.89,24000000,101.89 1976-11-17,100.04,101.32,99.64,100.61,19900000,100.61 1976-11-16,99.90,101.12,99.44,100.04,21020000,100.04 1976-11-15,99.24,100.16,98.53,99.90,16710000,99.90 1976-11-12,99.64,99.95,98.51,99.24,15550000,99.24 1976-11-11,98.81,99.89,98.35,99.64,13230000,99.64 1976-11-10,99.32,99.98,98.18,98.81,18890000,98.81 1976-11-09,99.60,100.21,98.38,99.32,19210000,99.32 1976-11-08,100.62,100.62,99.10,99.60,16520000,99.60 1976-11-05,102.41,102.70,100.48,100.82,20780000,100.82 1976-11-04,101.92,103.16,101.40,102.41,21700000,102.41 1976-11-03,102.49,102.49,100.73,101.92,19350000,101.92 1976-11-01,102.90,103.78,102.19,103.10,18390000,103.10 1976-10-29,101.61,103.10,101.15,102.90,17030000,102.90 1976-10-28,101.76,102.50,101.12,101.61,16920000,101.61 1976-10-27,101.06,102.12,100.61,101.76,15790000,101.76 1976-10-26,100.07,101.50,99.91,101.06,15490000,101.06 1976-10-25,99.96,100.60,99.21,100.07,13310000,100.07 1976-10-22,100.77,100.93,99.24,99.96,17870000,99.96 1976-10-21,101.74,102.32,100.49,100.77,17980000,100.77 1976-10-20,101.45,102.23,100.81,101.74,15860000,101.74 1976-10-19,101.47,102.04,100.42,101.45,16200000,101.45 1976-10-18,100.88,101.99,100.62,101.47,15710000,101.47 1976-10-15,100.85,101.50,100.02,100.88,16210000,100.88 1976-10-14,102.12,102.14,100.28,100.85,18610000,100.85 1976-10-13,100.81,102.44,100.54,102.12,21690000,102.12 1976-10-12,101.64,102.19,100.38,100.81,18210000,100.81 1976-10-11,102.48,102.48,100.98,101.64,14620000,101.64 1976-10-08,103.54,104.00,102.24,102.56,16740000,102.56 1976-10-07,102.97,103.90,102.16,103.54,19830000,103.54 1976-10-06,103.23,103.72,102.05,102.97,20870000,102.97 1976-10-05,104.03,104.25,102.51,103.23,19200000,103.23 1976-10-04,104.17,104.62,103.42,104.03,12630000,104.03 1976-10-01,105.24,105.75,103.60,104.17,20620000,104.17 1976-09-30,105.37,105.84,104.57,105.24,14700000,105.24 1976-09-29,105.92,106.45,104.83,105.37,18090000,105.37 1976-09-28,107.27,107.54,105.61,105.92,20440000,105.92 1976-09-27,106.80,107.70,106.35,107.27,17430000,107.27 1976-09-24,106.92,107.36,106.03,106.80,17400000,106.80 1976-09-23,107.46,107.96,106.40,106.92,24210000,106.92 1976-09-22,107.83,108.72,106.92,107.46,32970000,107.46 1976-09-21,106.32,108.13,106.09,107.83,30300000,107.83 1976-09-20,106.27,107.20,105.74,106.32,21730000,106.32 1976-09-17,105.34,106.81,105.14,106.27,28270000,106.27 1976-09-16,104.25,105.59,103.84,105.34,19620000,105.34 1976-09-15,103.94,104.70,103.28,104.25,17570000,104.25 1976-09-14,104.29,104.50,103.31,103.94,15550000,103.94 1976-09-13,104.65,105.29,103.88,104.29,16100000,104.29 1976-09-10,104.40,105.03,103.79,104.65,16930000,104.65 1976-09-09,104.94,105.12,103.91,104.40,16540000,104.40 1976-09-08,105.03,105.73,104.34,104.94,19750000,104.94 1976-09-07,104.30,105.31,103.93,105.03,16310000,105.03 1976-09-03,103.92,104.63,103.36,104.30,13280000,104.30 1976-09-02,104.06,104.84,103.47,103.92,18920000,103.92 1976-09-01,102.91,104.30,102.60,104.06,18640000,104.06 1976-08-31,102.07,103.38,101.94,102.91,15480000,102.91 1976-08-30,101.48,102.51,101.22,102.07,11140000,102.07 1976-08-27,101.32,101.90,100.55,101.48,12120000,101.48 1976-08-26,102.03,102.59,101.01,101.32,15270000,101.32 1976-08-25,101.27,102.41,100.43,102.03,17400000,102.03 1976-08-24,101.96,102.65,100.98,101.27,16740000,101.27 1976-08-23,102.37,102.49,101.04,101.96,15450000,101.96 1976-08-20,103.31,103.31,101.96,102.37,14920000,102.37 1976-08-19,104.56,104.74,103.01,103.39,17230000,103.39 1976-08-18,104.80,105.41,104.12,104.56,17150000,104.56 1976-08-17,104.43,105.25,103.98,104.80,18500000,104.80 1976-08-16,104.25,104.99,103.74,104.43,16210000,104.43 1976-08-13,104.22,104.79,103.61,104.25,13930000,104.25 1976-08-12,104.06,104.64,103.38,104.22,15560000,104.22 1976-08-11,104.41,105.24,103.73,104.06,18710000,104.06 1976-08-10,103.49,104.71,103.21,104.41,16690000,104.41 1976-08-09,103.79,104.02,103.01,103.49,11700000,103.49 1976-08-06,103.85,104.25,103.10,103.79,13930000,103.79 1976-08-05,104.43,104.76,103.48,103.85,15530000,103.85 1976-08-04,104.14,105.18,103.72,104.43,20650000,104.43 1976-08-03,103.19,104.49,102.79,104.14,18500000,104.14 1976-08-02,103.44,103.98,102.64,103.19,13870000,103.19 1976-07-30,102.93,103.88,102.47,103.44,14830000,103.44 1976-07-29,103.05,103.59,102.36,102.93,13330000,102.93 1976-07-28,103.48,103.58,102.31,103.05,16000000,103.05 1976-07-27,104.07,104.51,103.13,103.48,15580000,103.48 1976-07-26,104.06,104.69,103.46,104.07,13530000,104.07 1976-07-23,103.93,104.71,103.49,104.06,15870000,104.06 1976-07-22,103.82,104.42,103.15,103.93,15600000,103.93 1976-07-21,103.72,104.56,103.21,103.82,18350000,103.82 1976-07-20,104.29,104.57,103.05,103.72,18810000,103.72 1976-07-19,104.68,105.32,103.84,104.29,18200000,104.29 1976-07-16,105.20,105.27,103.87,104.68,20450000,104.68 1976-07-15,105.95,106.25,104.76,105.20,20400000,105.20 1976-07-14,105.67,106.61,105.05,105.95,23840000,105.95 1976-07-13,105.90,106.78,105.15,105.67,27550000,105.67 1976-07-12,104.98,106.30,104.74,105.90,23750000,105.90 1976-07-09,103.98,105.41,103.80,104.98,23500000,104.98 1976-07-08,103.83,104.75,103.44,103.98,21710000,103.98 1976-07-07,103.54,104.23,102.80,103.83,18470000,103.83 1976-07-06,104.11,104.67,103.19,103.54,16130000,103.54 1976-07-02,103.59,104.53,103.13,104.11,16730000,104.11 1976-07-01,104.28,104.98,103.14,103.59,21130000,103.59 1976-06-30,103.86,105.07,103.52,104.28,23830000,104.28 1976-06-29,103.43,104.33,102.95,103.86,19620000,103.86 1976-06-28,103.72,104.35,102.97,103.43,17490000,103.43 1976-06-25,103.79,104.54,103.17,103.72,17830000,103.72 1976-06-24,103.25,104.37,102.90,103.79,19850000,103.79 1976-06-23,103.47,103.90,102.40,103.25,17530000,103.25 1976-06-22,104.28,104.82,103.16,103.47,21150000,103.47 1976-06-21,103.76,104.73,103.18,104.28,18930000,104.28 1976-06-18,103.61,104.80,103.06,103.76,25720000,103.76 1976-06-17,102.01,104.12,101.97,103.61,27810000,103.61 1976-06-16,101.46,102.65,100.96,102.01,21620000,102.01 1976-06-15,101.95,102.39,100.84,101.46,18440000,101.46 1976-06-14,101.00,102.51,101.00,101.95,21250000,101.95 1976-06-11,99.56,101.22,99.38,100.92,19470000,100.92 1976-06-10,98.74,99.98,98.55,99.56,16100000,99.56 1976-06-09,98.80,99.49,98.23,98.74,14560000,98.74 1976-06-08,98.63,99.71,98.32,98.80,16660000,98.80 1976-06-07,99.15,99.39,97.97,98.63,14510000,98.63 1976-06-04,100.13,100.27,98.79,99.15,15960000,99.15 1976-06-03,100.22,101.10,99.68,100.13,18900000,100.13 1976-06-02,99.85,100.69,99.26,100.22,16120000,100.22 1976-06-01,100.18,100.74,99.36,99.85,13880000,99.85 1976-05-28,99.38,100.64,99.00,100.18,16860000,100.18 1976-05-27,99.34,99.77,98.26,99.38,15310000,99.38 1976-05-26,99.49,100.14,98.65,99.34,16750000,99.34 1976-05-25,99.44,100.02,98.48,99.49,18770000,99.49 1976-05-24,101.07,101.07,99.11,99.44,16560000,99.44 1976-05-21,102.00,102.34,100.81,101.26,18730000,101.26 1976-05-20,101.18,102.53,100.69,102.00,22560000,102.00 1976-05-19,101.26,102.01,100.55,101.18,18450000,101.18 1976-05-18,101.09,102.00,100.72,101.26,17410000,101.26 1976-05-17,101.34,101.71,100.41,101.09,14720000,101.09 1976-05-14,102.16,102.23,100.82,101.34,16800000,101.34 1976-05-13,102.77,103.03,101.73,102.16,16730000,102.16 1976-05-12,102.95,103.55,102.14,102.77,18510000,102.77 1976-05-11,103.10,103.99,102.39,102.95,23590000,102.95 1976-05-10,101.88,103.51,101.76,103.10,22760000,103.10 1976-05-07,101.16,102.27,100.77,101.88,17810000,101.88 1976-05-06,100.88,101.70,100.31,101.16,16200000,101.16 1976-05-05,101.46,101.92,100.45,100.88,14970000,100.88 1976-05-04,100.92,101.93,100.29,101.46,17240000,101.46 1976-05-03,101.64,101.73,100.14,100.92,15180000,100.92 1976-04-30,102.13,102.65,101.16,101.64,14530000,101.64 1976-04-29,102.13,102.97,101.45,102.13,17740000,102.13 1976-04-28,101.86,102.46,100.91,102.13,15790000,102.13 1976-04-27,102.43,103.18,101.51,101.86,17760000,101.86 1976-04-26,102.29,102.80,101.36,102.43,15520000,102.43 1976-04-23,102.98,103.21,101.70,102.29,17000000,102.29 1976-04-22,103.32,104.04,102.52,102.98,20220000,102.98 1976-04-21,102.87,104.03,102.30,103.32,26600000,103.32 1976-04-20,101.44,103.32,101.42,102.87,23500000,102.87 1976-04-19,100.67,101.83,100.32,101.44,16500000,101.44 1976-04-15,100.31,101.18,99.73,100.67,15100000,100.67 1976-04-14,101.05,101.77,99.98,100.31,18440000,100.31 1976-04-13,100.20,101.39,99.64,101.05,15990000,101.05 1976-04-12,100.35,101.30,99.57,100.20,16030000,100.20 1976-04-09,101.28,101.74,99.87,100.35,19050000,100.35 1976-04-08,102.21,102.38,100.53,101.28,20860000,101.28 1976-04-07,103.36,103.85,101.92,102.21,20190000,102.21 1976-04-06,103.51,104.63,102.93,103.36,24170000,103.36 1976-04-05,102.32,104.13,102.32,103.51,21940000,103.51 1976-04-02,102.24,102.76,101.23,102.25,17420000,102.25 1976-04-01,102.77,103.24,101.50,102.24,17910000,102.24 1976-03-31,102.01,103.08,101.60,102.77,17520000,102.77 1976-03-30,102.41,103.36,101.25,102.01,17930000,102.01 1976-03-29,102.85,103.36,101.99,102.41,16100000,102.41 1976-03-26,102.85,103.65,102.20,102.85,18510000,102.85 1976-03-25,103.42,104.00,102.19,102.85,22510000,102.85 1976-03-24,102.51,104.39,102.51,103.42,32610000,103.42 1976-03-23,100.71,102.54,100.32,102.24,22450000,102.24 1976-03-22,100.58,101.53,100.14,100.71,19410000,100.71 1976-03-19,100.45,101.23,99.70,100.58,18090000,100.58 1976-03-18,100.86,101.37,99.73,100.45,20330000,100.45 1976-03-17,100.92,102.01,100.28,100.86,26190000,100.86 1976-03-16,99.80,101.25,99.38,100.92,22780000,100.92 1976-03-15,100.86,100.90,99.24,99.80,19570000,99.80 1976-03-12,101.89,102.46,100.49,100.86,26020000,100.86 1976-03-11,100.94,102.41,100.62,101.89,27300000,101.89 1976-03-10,100.58,101.80,99.98,100.94,24900000,100.94 1976-03-09,100.19,101.90,99.95,100.58,31770000,100.58 1976-03-08,99.11,100.71,98.93,100.19,25060000,100.19 1976-03-05,98.92,99.88,98.23,99.11,23030000,99.11 1976-03-04,99.98,100.40,98.49,98.92,24410000,98.92 1976-03-03,100.58,100.97,99.23,99.98,25450000,99.98 1976-03-02,100.02,101.26,99.61,100.58,25590000,100.58 1976-03-01,99.71,100.64,98.67,100.02,22070000,100.02 1976-02-27,100.11,100.53,98.60,99.71,26940000,99.71 1976-02-26,101.69,102.36,99.74,100.11,34320000,100.11 1976-02-25,102.03,102.71,100.69,101.69,34680000,101.69 1976-02-24,101.61,102.92,101.03,102.03,34380000,102.03 1976-02-23,102.10,102.54,100.69,101.61,31460000,101.61 1976-02-20,101.41,103.07,101.18,102.10,44510000,102.10 1976-02-19,99.94,101.92,99.94,101.41,39210000,101.41 1976-02-18,99.05,100.43,98.50,99.85,29900000,99.85 1976-02-17,99.67,100.25,98.56,99.05,25460000,99.05 1976-02-13,100.25,100.66,99.01,99.67,23870000,99.67 1976-02-12,100.77,101.55,99.82,100.25,28610000,100.25 1976-02-11,100.47,101.80,100.10,100.77,32300000,100.77 1976-02-10,99.62,100.96,99.11,100.47,27660000,100.47 1976-02-09,99.46,100.66,98.77,99.62,25340000,99.62 1976-02-06,100.39,100.53,98.64,99.46,27360000,99.46 1976-02-05,101.91,102.30,100.06,100.39,33780000,100.39 1976-02-04,101.18,102.57,100.70,101.91,38270000,101.91 1976-02-03,100.87,101.97,99.58,101.18,34080000,101.18 1976-02-02,100.86,101.39,99.74,100.87,24000000,100.87 1976-01-30,100.11,101.99,99.94,100.86,38510000,100.86 1976-01-29,98.53,100.54,98.32,100.11,29800000,100.11 1976-01-28,99.07,99.64,97.66,98.53,27370000,98.53 1976-01-27,99.68,100.52,98.28,99.07,32070000,99.07 1976-01-26,99.21,100.75,98.92,99.68,34470000,99.68 1976-01-23,98.04,99.88,97.68,99.21,33640000,99.21 1976-01-22,98.24,98.79,97.07,98.04,27420000,98.04 1976-01-21,98.86,99.24,97.12,98.24,34470000,98.24 1976-01-20,98.32,99.44,97.43,98.86,36690000,98.86 1976-01-19,97.00,98.84,96.36,98.32,29450000,98.32 1976-01-16,96.61,97.73,95.84,97.00,25940000,97.00 1976-01-15,97.13,98.34,96.15,96.61,38450000,96.61 1976-01-14,95.57,97.47,94.91,97.13,30340000,97.13 1976-01-13,96.33,97.39,95.11,95.57,34530000,95.57 1976-01-12,94.95,96.76,94.38,96.33,30440000,96.33 1976-01-09,94.58,95.71,94.05,94.95,26510000,94.95 1976-01-08,93.95,95.47,93.41,94.58,29030000,94.58 1976-01-07,93.53,95.15,92.91,93.95,33170000,93.95 1976-01-06,92.58,94.18,92.37,93.53,31270000,93.53 1976-01-05,90.90,92.84,90.85,92.58,21960000,92.58 1976-01-02,90.19,91.18,89.81,90.90,10300000,90.90 1975-12-31,89.77,90.75,89.17,90.19,16970000,90.19 1975-12-30,90.13,90.55,89.20,89.77,16040000,89.77 1975-12-29,90.25,91.09,89.63,90.13,17070000,90.13 1975-12-26,89.46,90.45,89.25,90.25,10020000,90.25 1975-12-24,88.73,89.84,88.73,89.46,11150000,89.46 1975-12-23,88.14,89.23,87.64,88.73,17750000,88.73 1975-12-22,88.80,89.13,87.74,88.14,15340000,88.14 1975-12-19,89.43,89.81,88.39,88.80,17720000,88.80 1975-12-18,89.15,90.09,88.62,89.43,18040000,89.43 1975-12-17,88.93,89.80,88.46,89.15,16560000,89.15 1975-12-16,88.09,89.49,87.78,88.93,18350000,88.93 1975-12-15,87.83,88.64,87.32,88.09,13960000,88.09 1975-12-12,87.80,88.22,87.05,87.83,13100000,87.83 1975-12-11,88.08,88.79,87.41,87.80,15300000,87.80 1975-12-10,87.30,88.39,86.91,88.08,15680000,88.08 1975-12-09,87.07,87.80,86.16,87.30,16040000,87.30 1975-12-08,86.82,87.75,86.15,87.07,14150000,87.07 1975-12-05,87.84,88.38,86.54,86.82,14050000,86.82 1975-12-04,87.60,88.39,86.68,87.84,16380000,87.84 1975-12-03,88.83,88.83,87.08,87.60,21320000,87.60 1975-12-02,90.67,90.81,89.08,89.33,17930000,89.33 1975-12-01,91.24,91.90,90.33,90.67,16050000,90.67 1975-11-28,90.94,91.74,90.44,91.24,12870000,91.24 1975-11-26,90.71,91.58,90.17,90.94,18780000,90.94 1975-11-25,89.70,91.10,89.66,90.71,17490000,90.71 1975-11-24,89.53,90.17,88.65,89.70,13930000,89.70 1975-11-21,89.64,90.23,88.79,89.53,14110000,89.53 1975-11-20,89.98,90.68,89.09,89.64,16460000,89.64 1975-11-19,91.00,91.28,89.47,89.98,16820000,89.98 1975-11-18,91.46,92.30,90.60,91.00,20760000,91.00 1975-11-17,90.97,91.99,90.50,91.46,17660000,91.46 1975-11-14,91.04,91.59,90.19,90.97,16460000,90.97 1975-11-13,91.19,92.33,90.56,91.04,25070000,91.04 1975-11-12,89.87,91.63,89.80,91.19,23960000,91.19 1975-11-11,89.34,90.47,89.04,89.87,14640000,89.87 1975-11-10,89.33,89.98,88.35,89.34,14910000,89.34 1975-11-07,89.55,90.18,88.67,89.33,15930000,89.33 1975-11-06,89.15,90.15,88.16,89.55,18600000,89.55 1975-11-05,88.51,90.08,88.32,89.15,17390000,89.15 1975-11-04,88.09,89.03,87.63,88.51,11570000,88.51 1975-11-03,89.04,89.21,87.78,88.09,11400000,88.09 1975-10-31,89.31,89.80,88.35,89.04,12910000,89.04 1975-10-30,89.39,90.20,88.70,89.31,15080000,89.31 1975-10-29,90.51,90.61,88.89,89.39,16110000,89.39 1975-10-28,89.73,91.01,89.40,90.51,17060000,90.51 1975-10-27,89.83,90.40,88.85,89.73,13100000,89.73 1975-10-24,91.24,91.52,89.46,89.83,18120000,89.83 1975-10-23,90.71,91.75,90.09,91.24,17900000,91.24 1975-10-22,90.56,91.38,89.77,90.71,16060000,90.71 1975-10-21,89.82,91.43,89.79,90.56,20800000,90.56 1975-10-20,88.86,90.14,88.43,89.82,13250000,89.82 1975-10-17,89.37,89.87,88.08,88.86,15650000,88.86 1975-10-16,89.23,90.73,88.90,89.37,18910000,89.37 1975-10-15,89.28,90.07,88.50,89.23,14440000,89.23 1975-10-14,89.46,90.80,88.81,89.28,19960000,89.28 1975-10-13,88.21,89.67,87.73,89.46,12020000,89.46 1975-10-10,88.37,89.17,87.44,88.21,14880000,88.21 1975-10-09,87.94,89.42,87.60,88.37,17770000,88.37 1975-10-08,86.77,88.46,86.34,87.94,17800000,87.94 1975-10-07,86.88,87.32,85.56,86.77,13530000,86.77 1975-10-06,85.98,87.64,85.98,86.88,15470000,86.88 1975-10-03,83.88,86.21,83.88,85.95,16360000,85.95 1975-10-02,82.93,84.33,82.82,83.82,14290000,83.82 1975-10-01,83.87,85.45,82.57,82.93,14070000,82.93 1975-09-30,85.01,85.01,83.44,83.87,12520000,83.87 1975-09-29,86.19,86.38,84.74,85.03,10580000,85.03 1975-09-26,85.64,86.86,85.13,86.19,12570000,86.19 1975-09-25,85.74,86.41,84.79,85.64,12890000,85.64 1975-09-24,85.03,86.70,85.03,85.74,16060000,85.74 1975-09-23,85.07,85.51,83.80,84.94,12800000,84.94 1975-09-22,85.88,86.70,84.70,85.07,14750000,85.07 1975-09-19,84.26,86.39,84.26,85.88,20830000,85.88 1975-09-18,82.37,84.34,82.23,84.06,14560000,84.06 1975-09-17,82.09,82.93,81.57,82.37,12190000,82.37 1975-09-16,82.88,83.43,81.79,82.09,13090000,82.09 1975-09-15,83.30,83.49,82.29,82.88,8670000,82.88 1975-09-12,83.45,84.47,82.84,83.30,12230000,83.30 1975-09-11,83.79,84.30,82.88,83.45,11100000,83.45 1975-09-10,84.59,84.59,83.00,83.79,14780000,83.79 1975-09-09,85.89,86.73,84.37,84.60,15790000,84.60 1975-09-08,85.62,86.31,84.89,85.89,11500000,85.89 1975-09-05,86.20,86.49,85.19,85.62,11680000,85.62 1975-09-04,86.03,86.91,85.29,86.20,12810000,86.20 1975-09-03,85.48,86.38,84.62,86.03,12260000,86.03 1975-09-02,86.88,87.42,85.21,85.48,11460000,85.48 1975-08-29,86.40,87.73,86.10,86.88,15480000,86.88 1975-08-28,84.68,86.64,84.68,86.40,14530000,86.40 1975-08-27,83.96,84.79,83.35,84.43,11100000,84.43 1975-08-26,85.06,85.40,83.65,83.96,11350000,83.96 1975-08-25,84.28,85.58,84.06,85.06,11250000,85.06 1975-08-22,83.07,84.61,82.79,84.28,13050000,84.28 1975-08-21,83.22,84.15,82.21,83.07,16610000,83.07 1975-08-20,84.78,84.78,82.76,83.22,18630000,83.22 1975-08-19,86.20,86.47,84.66,84.95,14990000,84.95 1975-08-18,86.36,87.21,85.76,86.20,10810000,86.20 1975-08-15,85.60,86.76,85.33,86.36,10610000,86.36 1975-08-14,85.97,86.34,85.02,85.60,12460000,85.60 1975-08-13,87.12,87.41,85.61,85.97,12000000,85.97 1975-08-12,86.55,88.17,86.49,87.12,14510000,87.12 1975-08-11,86.02,86.89,85.34,86.55,12350000,86.55 1975-08-08,86.30,87.00,85.52,86.02,11660000,86.02 1975-08-07,86.25,87.24,85.69,86.30,12390000,86.30 1975-08-06,86.23,87.04,85.34,86.25,16280000,86.25 1975-08-05,87.15,87.81,85.89,86.23,15470000,86.23 1975-08-04,87.99,88.17,86.68,87.15,12620000,87.15 1975-08-01,88.75,89.04,87.46,87.99,13320000,87.99 1975-07-31,88.83,90.07,88.31,88.75,14540000,88.75 1975-07-30,88.19,89.49,87.68,88.83,16150000,88.83 1975-07-29,88.69,89.91,87.71,88.19,19000000,88.19 1975-07-28,89.29,89.68,88.02,88.69,14850000,88.69 1975-07-25,90.07,90.72,88.72,89.29,15110000,89.29 1975-07-24,90.18,90.95,88.90,90.07,20550000,90.07 1975-07-23,91.45,92.15,89.83,90.18,20150000,90.18 1975-07-22,92.44,92.49,90.63,91.45,20660000,91.45 1975-07-21,93.20,93.93,92.03,92.44,16690000,92.44 1975-07-18,93.63,93.96,92.39,93.20,16870000,93.20 1975-07-17,94.61,95.03,92.99,93.63,21420000,93.63 1975-07-16,95.61,96.37,94.20,94.61,25250000,94.61 1975-07-15,95.19,96.58,94.71,95.61,28340000,95.61 1975-07-14,94.66,95.76,94.04,95.19,21900000,95.19 1975-07-11,94.81,95.69,93.83,94.66,22210000,94.66 1975-07-10,94.80,96.19,94.25,94.81,28880000,94.81 1975-07-09,93.39,95.22,93.38,94.80,26350000,94.80 1975-07-08,93.54,94.03,92.51,93.39,18990000,93.39 1975-07-07,94.36,94.82,93.16,93.54,15850000,93.54 1975-07-03,94.18,95.04,93.49,94.36,19000000,94.36 1975-07-02,94.85,94.91,93.37,94.18,18530000,94.18 1975-07-01,95.19,95.73,94.13,94.85,20390000,94.85 1975-06-30,94.81,95.85,94.30,95.19,19430000,95.19 1975-06-27,94.81,95.66,94.10,94.81,18820000,94.81 1975-06-26,94.62,95.72,93.88,94.81,24560000,94.81 1975-06-25,94.19,95.29,93.53,94.62,21610000,94.62 1975-06-24,93.62,95.23,93.31,94.19,26620000,94.19 1975-06-23,92.61,93.98,91.81,93.62,20720000,93.62 1975-06-20,92.02,93.75,91.83,92.61,26260000,92.61 1975-06-19,90.39,92.37,90.12,92.02,21450000,92.02 1975-06-18,90.58,91.07,89.60,90.39,15590000,90.39 1975-06-17,91.46,92.22,90.17,90.58,19440000,90.58 1975-06-16,90.52,91.85,90.12,91.46,16660000,91.46 1975-06-13,90.08,91.06,89.30,90.52,16300000,90.52 1975-06-12,90.55,91.36,89.64,90.08,15970000,90.08 1975-06-11,90.44,91.67,90.00,90.55,18230000,90.55 1975-06-10,91.21,91.21,89.46,90.44,21130000,90.44 1975-06-09,92.48,92.87,90.91,91.21,20670000,91.21 1975-06-06,92.69,93.60,91.75,92.48,22230000,92.48 1975-06-05,92.60,93.16,91.41,92.69,21610000,92.69 1975-06-04,92.89,93.61,91.82,92.60,24900000,92.60 1975-06-03,92.58,93.76,91.88,92.89,26560000,92.89 1975-06-02,91.32,93.41,91.32,92.58,28240000,92.58 1975-05-30,89.87,91.62,89.87,91.15,22670000,91.15 1975-05-29,89.71,90.59,88.83,89.68,18570000,89.68 1975-05-28,90.34,91.14,89.07,89.71,21850000,89.71 1975-05-27,90.58,91.29,89.60,90.34,17050000,90.34 1975-05-23,89.39,91.02,89.30,90.58,17870000,90.58 1975-05-22,89.06,90.30,88.35,89.39,17610000,89.39 1975-05-21,90.07,90.25,88.47,89.06,17640000,89.06 1975-05-20,90.53,91.45,89.58,90.07,18310000,90.07 1975-05-19,90.43,91.07,88.98,90.53,17870000,90.53 1975-05-16,91.41,91.59,89.74,90.43,16630000,90.43 1975-05-15,92.27,93.51,90.94,91.41,27690000,91.41 1975-05-14,91.58,93.23,91.17,92.27,29050000,92.27 1975-05-13,90.61,92.26,89.99,91.58,24950000,91.58 1975-05-12,90.53,91.67,89.91,90.61,22410000,90.61 1975-05-09,89.56,91.24,89.33,90.53,28440000,90.53 1975-05-08,89.08,90.13,88.23,89.56,22980000,89.56 1975-05-07,88.64,89.75,87.60,89.08,22250000,89.08 1975-05-06,90.08,90.86,88.15,88.64,25410000,88.64 1975-05-05,89.22,90.82,88.26,90.08,22370000,90.08 1975-05-02,88.10,89.98,87.91,89.22,25210000,89.22 1975-05-01,87.30,89.10,86.94,88.10,20660000,88.10 1975-04-30,85.64,87.61,85.00,87.30,18060000,87.30 1975-04-29,86.23,86.79,85.04,85.64,17740000,85.64 1975-04-28,86.62,87.33,85.54,86.23,17850000,86.23 1975-04-25,86.04,87.50,85.62,86.62,20260000,86.62 1975-04-24,86.12,86.92,85.00,86.04,19050000,86.04 1975-04-23,87.09,87.42,85.65,86.12,20040000,86.12 1975-04-22,87.23,88.64,86.58,87.09,26120000,87.09 1975-04-21,86.30,87.99,85.92,87.23,23960000,87.23 1975-04-18,87.25,87.59,85.53,86.30,26610000,86.30 1975-04-17,86.60,88.79,86.43,87.25,32650000,87.25 1975-04-16,86.30,87.10,84.93,86.60,22970000,86.60 1975-04-15,85.60,87.24,85.03,86.30,29620000,86.30 1975-04-14,84.18,86.12,83.98,85.60,26800000,85.60 1975-04-11,83.77,84.68,82.93,84.18,20160000,84.18 1975-04-10,82.84,84.70,82.68,83.77,24990000,83.77 1975-04-09,80.99,83.22,80.91,82.84,18120000,82.84 1975-04-08,80.35,81.65,80.13,80.99,14320000,80.99 1975-04-07,80.88,81.11,79.66,80.35,13860000,80.35 1975-04-04,81.51,81.90,80.29,80.88,14170000,80.88 1975-04-03,82.43,82.84,80.88,81.51,13920000,81.51 1975-04-02,82.64,83.57,81.80,82.43,15600000,82.43 1975-04-01,83.36,83.59,81.98,82.64,14480000,82.64 1975-03-31,83.85,84.62,82.84,83.36,16270000,83.36 1975-03-27,83.59,84.88,83.04,83.85,18300000,83.85 1975-03-26,82.16,84.24,82.16,83.59,18580000,83.59 1975-03-25,81.42,82.67,80.08,82.06,18500000,82.06 1975-03-24,82.39,82.39,80.60,81.42,17810000,81.42 1975-03-21,83.61,84.11,82.52,83.39,15940000,83.39 1975-03-20,84.34,85.30,83.02,83.61,20960000,83.61 1975-03-19,85.13,85.17,83.43,84.34,19030000,84.34 1975-03-18,86.01,87.08,84.75,85.13,29180000,85.13 1975-03-17,84.76,86.52,84.39,86.01,26780000,86.01 1975-03-14,83.74,85.43,83.50,84.76,24840000,84.76 1975-03-13,83.59,84.26,82.52,83.74,18620000,83.74 1975-03-12,84.36,84.73,82.87,83.59,21560000,83.59 1975-03-11,84.95,85.89,83.80,84.36,31280000,84.36 1975-03-10,84.30,85.47,83.43,84.95,25890000,84.95 1975-03-07,83.69,85.14,83.25,84.30,25930000,84.30 1975-03-06,83.90,84.17,81.94,83.69,21780000,83.69 1975-03-05,83.56,84.71,82.16,83.90,24120000,83.90 1975-03-04,83.03,85.43,82.85,83.56,34140000,83.56 1975-03-03,81.59,83.46,81.32,83.03,24100000,83.03 1975-02-28,80.77,82.02,80.07,81.59,17560000,81.59 1975-02-27,80.37,81.64,80.06,80.77,16430000,80.77 1975-02-26,79.53,80.89,78.91,80.37,18790000,80.37 1975-02-25,81.09,81.09,79.05,79.53,20910000,79.53 1975-02-24,82.62,82.71,80.87,81.44,19150000,81.44 1975-02-21,82.21,83.56,81.72,82.62,24440000,82.62 1975-02-20,81.44,82.78,80.82,82.21,22260000,82.21 1975-02-19,80.93,81.94,79.83,81.44,21930000,81.44 1975-02-18,81.50,82.45,80.16,80.93,23990000,80.93 1975-02-14,81.01,82.33,80.13,81.50,23290000,81.50 1975-02-13,79.98,82.53,79.98,81.01,35160000,81.01 1975-02-12,78.58,80.21,77.94,79.92,19790000,79.92 1975-02-11,78.36,79.07,77.38,78.58,16470000,78.58 1975-02-10,78.63,79.40,77.77,78.36,16120000,78.36 1975-02-07,78.56,79.12,77.00,78.63,19060000,78.63 1975-02-06,78.95,80.72,78.09,78.56,32020000,78.56 1975-02-05,77.61,79.40,76.81,78.95,25830000,78.95 1975-02-04,77.82,78.37,76.00,77.61,25040000,77.61 1975-02-03,76.98,78.55,76.36,77.82,25400000,77.82 1975-01-31,76.21,77.72,75.41,76.98,24640000,76.98 1975-01-30,77.26,78.69,75.82,76.21,29740000,76.21 1975-01-29,76.03,78.03,75.23,77.26,27410000,77.26 1975-01-28,75.37,77.59,75.36,76.03,31760000,76.03 1975-01-27,73.76,76.03,73.76,75.37,32130000,75.37 1975-01-24,72.07,73.57,71.55,72.98,20670000,72.98 1975-01-23,71.74,73.11,71.09,72.07,17960000,72.07 1975-01-22,70.70,71.97,69.86,71.74,15330000,71.74 1975-01-21,71.08,72.04,70.25,70.70,14780000,70.70 1975-01-20,70.96,71.46,69.80,71.08,13450000,71.08 1975-01-17,72.05,72.36,70.56,70.96,14260000,70.96 1975-01-16,72.14,72.93,71.26,72.05,17110000,72.05 1975-01-15,71.68,72.77,70.45,72.14,16580000,72.14 1975-01-14,72.31,72.70,71.02,71.68,16610000,71.68 1975-01-13,72.61,73.81,71.83,72.31,19780000,72.31 1975-01-10,71.60,73.75,71.60,72.61,25890000,72.61 1975-01-09,70.04,71.42,69.04,71.17,16340000,71.17 1975-01-08,71.02,71.53,69.65,70.04,15600000,70.04 1975-01-07,71.07,71.75,69.92,71.02,14890000,71.02 1975-01-06,70.71,72.24,70.33,71.07,17550000,71.07 1975-01-03,70.23,71.64,69.29,70.71,15270000,70.71 1975-01-02,68.65,70.92,68.65,70.23,14800000,70.23 1974-12-31,67.16,69.04,67.15,68.56,20970000,68.56 1974-12-30,67.14,67.65,66.23,67.16,18520000,67.16 1974-12-27,67.44,67.99,66.49,67.14,13060000,67.14 1974-12-26,66.88,68.19,66.62,67.44,11810000,67.44 1974-12-24,65.96,67.25,65.86,66.88,9540000,66.88 1974-12-23,66.91,67.18,65.34,65.96,18040000,65.96 1974-12-20,67.65,67.93,66.36,66.91,15840000,66.91 1974-12-19,67.90,68.62,66.93,67.65,15900000,67.65 1974-12-18,67.58,69.01,67.30,67.90,18050000,67.90 1974-12-17,66.46,67.92,65.86,67.58,16880000,67.58 1974-12-16,67.07,67.74,66.02,66.46,15370000,66.46 1974-12-13,67.45,68.15,66.32,67.07,14000000,67.07 1974-12-12,67.67,68.61,66.56,67.45,15390000,67.45 1974-12-11,67.28,69.03,66.83,67.67,15700000,67.67 1974-12-10,65.88,68.17,65.88,67.28,15690000,67.28 1974-12-09,65.01,66.29,64.13,65.60,14660000,65.60 1974-12-06,66.13,66.20,64.40,65.01,15500000,65.01 1974-12-05,67.41,68.00,65.90,66.13,12890000,66.13 1974-12-04,67.17,68.32,66.61,67.41,12580000,67.41 1974-12-03,68.11,68.13,66.62,67.17,13620000,67.17 1974-12-02,69.80,69.80,67.81,68.11,11140000,68.11 1974-11-29,69.94,70.49,69.18,69.97,7400000,69.97 1974-11-27,69.47,71.31,69.17,69.94,14810000,69.94 1974-11-26,68.83,70.36,68.19,69.47,13600000,69.47 1974-11-25,68.90,69.68,67.79,68.83,11300000,68.83 1974-11-22,68.24,70.00,68.24,68.90,13020000,68.90 1974-11-21,67.90,68.94,66.85,68.18,13820000,68.18 1974-11-20,68.20,69.25,67.36,67.90,12430000,67.90 1974-11-19,69.27,69.71,67.66,68.20,15720000,68.20 1974-11-18,71.10,71.10,68.95,69.27,15230000,69.27 1974-11-15,73.06,73.27,71.41,71.91,12480000,71.91 1974-11-14,73.35,74.54,72.53,73.06,13540000,73.06 1974-11-13,73.67,74.25,72.32,73.35,16040000,73.35 1974-11-12,75.15,75.59,73.34,73.67,15040000,73.67 1974-11-11,74.91,75.70,74.04,75.15,13220000,75.15 1974-11-08,75.21,76.00,74.01,74.91,15890000,74.91 1974-11-07,74.75,76.30,73.85,75.21,17150000,75.21 1974-11-06,75.11,77.41,74.23,74.75,23930000,74.75 1974-11-05,73.08,75.36,72.49,75.11,15960000,75.11 1974-11-04,73.80,73.80,71.93,73.08,12740000,73.08 1974-11-01,73.90,74.85,72.68,73.88,13470000,73.88 1974-10-31,74.31,75.90,73.15,73.90,18840000,73.90 1974-10-30,72.83,75.45,72.40,74.31,20130000,74.31 1974-10-29,70.49,73.19,70.49,72.83,15610000,72.83 1974-10-28,70.12,70.67,68.89,70.09,10540000,70.09 1974-10-25,70.22,71.59,69.46,70.12,12650000,70.12 1974-10-24,70.98,70.98,68.80,70.22,14910000,70.22 1974-10-23,72.81,72.81,70.40,71.03,14200000,71.03 1974-10-22,73.50,75.09,72.55,73.13,18930000,73.13 1974-10-21,72.28,73.92,71.24,73.50,14500000,73.50 1974-10-18,71.20,73.34,71.20,72.28,16460000,72.28 1974-10-17,70.33,72.00,69.41,71.17,14470000,71.17 1974-10-16,71.44,71.98,69.54,70.33,14790000,70.33 1974-10-15,72.74,73.35,70.61,71.44,17390000,71.44 1974-10-14,71.17,74.43,71.17,72.74,19770000,72.74 1974-10-11,69.79,71.99,68.80,71.14,20090000,71.14 1974-10-10,68.30,71.48,68.30,69.79,26360000,69.79 1974-10-09,64.84,68.15,63.74,67.82,18820000,67.82 1974-10-08,64.95,66.07,63.95,64.84,15460000,64.84 1974-10-07,62.78,65.40,62.78,64.95,15000000,64.95 1974-10-04,62.28,63.23,60.96,62.34,15910000,62.34 1974-10-03,63.38,63.48,61.66,62.28,13150000,62.28 1974-10-02,63.39,64.62,62.74,63.38,12230000,63.38 1974-10-01,63.54,64.37,61.75,63.39,16890000,63.39 1974-09-30,64.85,64.85,62.52,63.54,15000000,63.54 1974-09-27,66.46,67.09,64.58,64.94,12320000,64.94 1974-09-26,67.40,67.40,65.79,66.46,9060000,66.46 1974-09-25,68.02,69.77,66.86,67.57,17620000,67.57 1974-09-24,69.03,69.03,67.42,68.02,9840000,68.02 1974-09-23,70.14,71.02,68.79,69.42,12130000,69.42 1974-09-20,70.09,71.12,68.62,70.14,16250000,70.14 1974-09-19,68.36,70.76,68.36,70.09,17000000,70.09 1974-09-18,67.38,68.14,65.92,67.72,11760000,67.72 1974-09-17,66.45,68.84,66.45,67.38,13730000,67.38 1974-09-16,65.20,66.92,64.15,66.26,18370000,66.26 1974-09-13,66.71,66.91,64.74,65.20,16070000,65.20 1974-09-12,68.54,68.54,66.22,66.71,16920000,66.71 1974-09-11,69.24,70.00,68.22,68.55,11820000,68.55 1974-09-10,69.72,70.47,68.55,69.24,11980000,69.24 1974-09-09,71.35,71.35,69.38,69.72,11160000,69.72 1974-09-06,70.87,72.42,70.08,71.42,15130000,71.42 1974-09-05,68.69,71.30,68.65,70.87,14210000,70.87 1974-09-04,69.85,69.85,67.64,68.69,16930000,68.69 1974-09-03,72.15,73.01,70.28,70.52,12750000,70.52 1974-08-30,70.22,72.68,70.22,72.15,16230000,72.15 1974-08-29,70.76,71.22,69.37,69.99,13690000,69.99 1974-08-28,70.94,72.17,70.13,70.76,16670000,70.76 1974-08-27,72.16,72.50,70.50,70.94,12970000,70.94 1974-08-26,71.55,73.17,70.42,72.16,14630000,72.16 1974-08-23,72.80,73.71,70.75,71.55,13590000,71.55 1974-08-22,73.51,74.05,71.61,72.80,15690000,72.80 1974-08-21,74.95,75.50,73.16,73.51,11650000,73.51 1974-08-20,74.57,76.11,73.82,74.95,13820000,74.95 1974-08-19,75.65,75.65,73.78,74.57,11670000,74.57 1974-08-16,76.30,77.02,75.29,75.67,10510000,75.67 1974-08-15,76.73,77.52,75.19,76.30,11130000,76.30 1974-08-14,76.73,76.73,76.73,76.73,11750000,76.73 1974-08-13,79.75,79.95,77.83,78.49,10140000,78.49 1974-08-12,80.86,81.26,79.30,79.75,7780000,79.75 1974-08-09,81.57,81.88,80.11,80.86,10160000,80.86 1974-08-08,82.65,83.53,80.86,81.57,16060000,81.57 1974-08-07,80.52,82.93,80.13,82.65,13380000,82.65 1974-08-06,79.78,82.65,79.78,80.52,15770000,80.52 1974-08-05,78.59,80.31,78.03,79.29,11230000,79.29 1974-08-02,78.75,79.39,77.84,78.59,10110000,78.59 1974-08-01,79.31,80.02,77.97,78.75,11470000,78.75 1974-07-31,80.50,80.82,78.96,79.31,10960000,79.31 1974-07-30,80.94,81.52,79.58,80.50,11360000,80.50 1974-07-29,82.02,82.02,80.22,80.94,11560000,80.94 1974-07-26,83.98,84.17,82.00,82.40,10420000,82.40 1974-07-25,84.99,85.67,83.13,83.98,13310000,83.98 1974-07-24,84.65,85.64,83.61,84.99,12870000,84.99 1974-07-23,83.81,85.63,83.67,84.65,12910000,84.65 1974-07-22,83.54,84.44,82.59,83.81,9290000,83.81 1974-07-19,83.78,84.67,82.87,83.54,11080000,83.54 1974-07-18,83.70,85.39,83.13,83.78,13980000,83.78 1974-07-17,82.81,84.13,81.70,83.70,11320000,83.70 1974-07-16,83.78,83.85,82.14,82.81,9920000,82.81 1974-07-15,83.15,84.89,82.65,83.78,13560000,83.78 1974-07-12,80.97,83.65,80.97,83.15,17770000,83.15 1974-07-11,79.99,81.08,79.08,79.89,14640000,79.89 1974-07-10,81.48,82.22,79.74,79.99,13490000,79.99 1974-07-09,81.09,82.50,80.35,81.48,15580000,81.48 1974-07-08,83.13,83.13,80.48,81.09,15510000,81.09 1974-07-05,84.25,84.45,83.17,83.66,7400000,83.66 1974-07-03,84.30,85.15,83.46,84.25,13430000,84.25 1974-07-02,86.02,86.26,83.98,84.30,13460000,84.30 1974-07-01,86.00,86.89,85.32,86.02,10270000,86.02 1974-06-28,86.31,86.78,85.13,86.00,12010000,86.00 1974-06-27,87.61,87.61,85.88,86.31,12650000,86.31 1974-06-26,88.98,89.12,87.30,87.61,11410000,87.61 1974-06-25,87.69,89.48,87.67,88.98,11920000,88.98 1974-06-24,87.46,88.38,86.70,87.69,9960000,87.69 1974-06-21,88.21,88.31,86.77,87.46,11830000,87.46 1974-06-20,88.84,89.35,87.80,88.21,11990000,88.21 1974-06-19,89.45,89.80,88.39,88.84,10550000,88.84 1974-06-18,90.04,90.53,88.92,89.45,10110000,89.45 1974-06-17,91.30,91.34,89.63,90.04,9680000,90.04 1974-06-14,92.23,92.23,90.73,91.30,10030000,91.30 1974-06-13,92.06,93.33,91.48,92.34,11540000,92.34 1974-06-12,92.28,92.61,90.89,92.06,11150000,92.06 1974-06-11,93.10,93.57,91.76,92.28,12380000,92.28 1974-06-10,92.55,93.64,91.53,93.10,13540000,93.10 1974-06-07,91.96,93.76,91.74,92.55,19020000,92.55 1974-06-06,90.31,92.31,89.71,91.96,13360000,91.96 1974-06-05,90.14,91.42,89.04,90.31,13680000,90.31 1974-06-04,89.10,91.13,89.09,90.14,16040000,90.14 1974-06-03,87.28,89.40,86.78,89.10,12490000,89.10 1974-05-31,87.43,88.02,86.19,87.28,10810000,87.28 1974-05-30,86.89,88.09,85.87,87.43,13580000,87.43 1974-05-29,88.37,88.84,86.52,86.89,12300000,86.89 1974-05-28,88.58,89.37,87.69,88.37,10580000,88.37 1974-05-24,87.29,89.27,87.20,88.58,13740000,88.58 1974-05-23,87.09,87.98,86.12,87.29,14770000,87.29 1974-05-22,87.91,88.79,86.72,87.09,15450000,87.09 1974-05-21,87.86,88.98,87.19,87.91,12190000,87.91 1974-05-20,88.21,89.09,87.19,87.86,10550000,87.86 1974-05-17,89.53,89.53,87.67,88.21,13870000,88.21 1974-05-16,90.45,91.31,89.36,89.72,12090000,89.72 1974-05-15,90.69,91.22,89.65,90.45,11240000,90.45 1974-05-14,90.66,91.68,90.05,90.69,10880000,90.69 1974-05-13,91.47,91.72,89.91,90.66,11290000,90.66 1974-05-10,92.96,93.57,91.03,91.47,15270000,91.47 1974-05-09,91.64,93.49,91.27,92.96,14710000,92.96 1974-05-08,91.46,92.34,90.71,91.64,11850000,91.64 1974-05-07,91.12,92.36,90.69,91.46,10710000,91.46 1974-05-06,91.29,91.60,90.13,91.12,9450000,91.12 1974-05-03,92.09,92.27,90.59,91.29,11080000,91.29 1974-05-02,92.22,93.59,91.46,92.09,13620000,92.09 1974-05-01,90.31,93.03,89.82,92.22,15120000,92.22 1974-04-30,90.00,91.09,89.38,90.31,10980000,90.31 1974-04-29,90.18,90.78,89.02,90.00,10170000,90.00 1974-04-26,89.57,91.10,89.06,90.18,13250000,90.18 1974-04-25,90.30,90.53,88.62,89.57,15870000,89.57 1974-04-24,91.81,91.82,89.91,90.30,16010000,90.30 1974-04-23,93.38,93.51,91.53,91.81,14110000,91.81 1974-04-22,93.75,94.12,92.71,93.38,10520000,93.38 1974-04-19,94.77,94.77,93.20,93.75,10710000,93.75 1974-04-18,94.36,95.42,93.75,94.78,12470000,94.78 1974-04-17,93.66,95.04,93.12,94.36,14020000,94.36 1974-04-16,92.05,94.06,92.05,93.66,14530000,93.66 1974-04-15,92.12,92.94,91.49,92.05,10130000,92.05 1974-04-11,92.40,92.92,91.55,92.12,9970000,92.12 1974-04-10,92.61,93.52,91.89,92.40,11160000,92.40 1974-04-09,92.03,93.28,91.61,92.61,11330000,92.61 1974-04-08,93.00,93.00,91.50,92.03,10740000,92.03 1974-04-05,94.24,94.24,92.55,93.01,11670000,93.01 1974-04-04,94.33,95.14,93.55,94.33,11650000,94.33 1974-04-03,93.35,94.70,92.94,94.33,11500000,94.33 1974-04-02,93.25,94.15,92.59,93.35,12010000,93.35 1974-04-01,93.98,94.68,92.82,93.25,11470000,93.25 1974-03-29,94.82,95.12,93.44,93.98,12150000,93.98 1974-03-28,96.20,96.20,94.36,94.82,14940000,94.82 1974-03-27,97.95,98.26,96.32,96.59,11690000,96.59 1974-03-26,97.64,98.66,97.11,97.95,11840000,97.95 1974-03-25,97.27,98.02,95.69,97.64,10540000,97.64 1974-03-22,97.34,98.04,96.35,97.27,11930000,97.27 1974-03-21,97.57,98.59,96.82,97.34,12950000,97.34 1974-03-20,97.23,98.22,96.67,97.57,12960000,97.57 1974-03-19,98.05,98.20,96.63,97.23,12800000,97.23 1974-03-18,99.28,99.71,97.62,98.05,14010000,98.05 1974-03-15,99.65,99.99,98.22,99.28,14500000,99.28 1974-03-14,99.74,101.05,98.80,99.65,19770000,99.65 1974-03-13,99.15,100.73,98.72,99.74,16820000,99.74 1974-03-12,98.88,100.02,97.97,99.15,17250000,99.15 1974-03-11,97.78,99.40,96.38,98.88,18470000,98.88 1974-03-08,96.94,98.28,95.77,97.78,16210000,97.78 1974-03-07,97.98,98.20,96.37,96.94,14500000,96.94 1974-03-06,97.32,98.57,96.54,97.98,19140000,97.98 1974-03-05,95.98,98.17,95.98,97.32,21980000,97.32 1974-03-04,95.53,95.95,94.19,95.53,12270000,95.53 1974-03-01,96.22,96.40,94.81,95.53,12880000,95.53 1974-02-28,96.40,96.98,95.20,96.22,13680000,96.22 1974-02-27,96.00,97.43,95.49,96.40,18730000,96.40 1974-02-26,95.03,96.38,94.20,96.00,15860000,96.00 1974-02-25,95.39,95.96,94.24,95.03,12900000,95.03 1974-02-22,94.71,96.19,94.08,95.39,16360000,95.39 1974-02-21,93.44,95.19,93.20,94.71,13930000,94.71 1974-02-20,92.12,93.92,91.34,93.44,11670000,93.44 1974-02-19,92.27,94.44,91.68,92.12,15940000,92.12 1974-02-15,90.95,92.98,90.62,92.27,12640000,92.27 1974-02-14,90.98,91.89,90.17,90.95,12230000,90.95 1974-02-13,90.94,92.13,90.37,90.98,10990000,90.98 1974-02-12,90.66,91.60,89.53,90.94,12920000,90.94 1974-02-11,92.33,92.54,90.26,90.66,12930000,90.66 1974-02-08,93.30,93.79,91.87,92.33,12990000,92.33 1974-02-07,93.26,94.09,92.43,93.30,11750000,93.30 1974-02-06,93.00,94.09,92.37,93.26,11610000,93.26 1974-02-05,93.29,94.17,92.26,93.00,12820000,93.00 1974-02-04,94.89,94.89,92.74,93.29,14380000,93.29 1974-02-01,96.57,96.63,94.66,95.32,12480000,95.32 1974-01-31,97.06,98.06,96.11,96.57,14020000,96.57 1974-01-30,96.02,97.90,96.02,97.06,16790000,97.06 1974-01-29,96.09,96.81,94.97,96.01,12850000,96.01 1974-01-28,96.63,97.32,95.37,96.09,13410000,96.09 1974-01-25,96.82,97.64,95.68,96.63,14860000,96.63 1974-01-24,97.07,97.75,95.49,96.82,15980000,96.82 1974-01-23,96.55,98.11,95.88,97.07,16890000,97.07 1974-01-22,95.40,97.41,94.92,96.55,17330000,96.55 1974-01-21,95.56,95.96,93.23,95.40,15630000,95.40 1974-01-18,97.30,97.63,95.00,95.56,16470000,95.56 1974-01-17,95.67,98.35,95.67,97.30,21040000,97.30 1974-01-16,94.23,96.20,93.78,95.67,14930000,95.67 1974-01-15,93.42,95.26,92.84,94.23,13250000,94.23 1974-01-14,93.66,95.24,92.35,93.42,14610000,93.42 1974-01-11,92.39,94.57,91.75,93.66,15140000,93.66 1974-01-10,93.42,94.63,91.62,92.39,16120000,92.39 1974-01-09,95.40,95.40,92.63,93.42,18070000,93.42 1974-01-08,98.07,98.26,95.58,96.12,18080000,96.12 1974-01-07,98.90,99.31,96.86,98.07,19070000,98.07 1974-01-04,99.80,100.70,97.70,98.90,21700000,98.90 1974-01-03,98.02,100.94,98.02,99.80,24850000,99.80 1974-01-02,97.55,98.38,96.25,97.68,12060000,97.68 1973-12-31,97.54,98.30,95.95,97.55,23470000,97.55 1973-12-28,97.74,98.76,96.41,97.54,21310000,97.54 1973-12-27,96.00,98.53,96.00,97.74,22720000,97.74 1973-12-26,93.87,96.52,93.87,95.74,18620000,95.74 1973-12-24,93.54,93.77,91.68,92.90,11540000,92.90 1973-12-21,94.55,95.11,92.70,93.54,18680000,93.54 1973-12-20,94.82,96.26,93.51,94.55,17340000,94.55 1973-12-19,94.74,96.83,93.81,94.82,20670000,94.82 1973-12-18,92.75,95.41,92.18,94.74,19490000,94.74 1973-12-17,93.29,94.00,91.87,92.75,12930000,92.75 1973-12-14,92.38,94.53,91.05,93.29,20000000,93.29 1973-12-13,93.57,94.68,91.64,92.38,18130000,92.38 1973-12-12,95.52,95.52,92.90,93.57,18190000,93.57 1973-12-11,97.95,99.09,95.62,96.04,20100000,96.04 1973-12-10,96.51,98.58,95.44,97.95,18590000,97.95 1973-12-07,94.49,97.58,94.49,96.51,23230000,96.51 1973-12-06,92.16,94.89,91.68,94.42,23260000,94.42 1973-12-05,93.59,93.93,91.55,92.16,19180000,92.16 1973-12-04,93.90,95.23,92.60,93.59,19030000,93.59 1973-12-03,95.83,95.83,92.92,93.90,17900000,93.90 1973-11-30,97.31,97.55,95.40,95.96,15380000,95.96 1973-11-29,97.65,98.72,96.01,97.31,18870000,97.31 1973-11-28,95.70,98.40,95.22,97.65,19990000,97.65 1973-11-27,96.58,97.70,94.88,95.70,19750000,95.70 1973-11-26,98.64,98.64,95.79,96.58,19830000,96.58 1973-11-23,99.76,100.49,98.59,99.44,11470000,99.44 1973-11-21,98.66,101.33,97.87,99.76,24260000,99.76 1973-11-20,100.65,100.65,97.64,98.66,23960000,98.66 1973-11-19,103.65,103.65,100.37,100.71,16700000,100.71 1973-11-16,102.43,105.41,101.77,103.88,22510000,103.88 1973-11-15,102.45,103.85,100.69,102.43,24530000,102.43 1973-11-14,104.36,105.25,101.87,102.45,22710000,102.45 1973-11-13,104.44,105.42,102.91,104.36,20310000,104.36 1973-11-12,105.30,105.75,103.12,104.44,19250000,104.44 1973-11-09,107.02,107.27,104.77,105.30,17320000,105.30 1973-11-08,106.10,108.45,106.10,107.02,19650000,107.02 1973-11-07,104.96,106.72,104.53,105.80,16570000,105.80 1973-11-06,105.52,107.00,104.52,104.96,16430000,104.96 1973-11-05,106.97,106.97,104.87,105.52,17150000,105.52 1973-11-02,107.69,108.35,106.33,107.07,16340000,107.07 1973-11-01,108.29,109.20,106.88,107.69,16920000,107.69 1973-10-31,109.33,109.82,107.64,108.29,17890000,108.29 1973-10-30,111.15,111.30,108.95,109.33,17580000,109.33 1973-10-29,111.38,112.56,110.52,111.15,17960000,111.15 1973-10-26,110.50,112.31,110.08,111.38,17800000,111.38 1973-10-25,110.27,111.33,108.85,110.50,15580000,110.50 1973-10-24,109.75,110.98,109.03,110.27,15840000,110.27 1973-10-23,109.16,110.91,107.40,109.75,17230000,109.75 1973-10-22,110.22,110.56,108.18,109.16,14290000,109.16 1973-10-19,110.01,111.56,109.30,110.22,17880000,110.22 1973-10-18,109.97,111.43,108.97,110.01,19210000,110.01 1973-10-17,110.19,111.41,109.19,109.97,18600000,109.97 1973-10-16,110.05,110.80,108.50,110.19,18780000,110.19 1973-10-15,111.32,111.32,109.29,110.05,16160000,110.05 1973-10-12,111.09,112.82,110.52,111.44,22730000,111.44 1973-10-11,109.22,111.77,108.96,111.09,20740000,111.09 1973-10-10,110.13,111.31,108.51,109.22,19010000,109.22 1973-10-09,110.23,111.19,109.05,110.13,19440000,110.13 1973-10-08,109.85,110.93,108.02,110.23,18990000,110.23 1973-10-05,108.41,110.46,107.76,109.85,18820000,109.85 1973-10-04,108.78,109.53,107.30,108.41,19730000,108.41 1973-10-03,108.79,109.95,107.74,108.78,22040000,108.78 1973-10-02,108.21,109.46,107.48,108.79,20770000,108.79 1973-10-01,108.43,108.98,107.08,108.21,15830000,108.21 1973-09-28,109.08,109.42,107.48,108.43,16300000,108.43 1973-09-27,108.83,110.45,108.02,109.08,23660000,109.08 1973-09-26,108.05,109.61,107.43,108.83,21130000,108.83 1973-09-25,107.36,108.79,106.50,108.05,21530000,108.05 1973-09-24,107.20,108.36,106.21,107.36,19490000,107.36 1973-09-21,106.76,108.02,105.43,107.20,23760000,107.20 1973-09-20,105.88,107.55,105.32,106.76,25960000,106.76 1973-09-19,103.80,106.43,103.80,105.88,24570000,105.88 1973-09-18,104.15,104.62,102.41,103.77,16400000,103.77 1973-09-17,104.44,105.41,103.21,104.15,15100000,104.15 1973-09-14,103.36,104.75,102.66,104.44,13760000,104.44 1973-09-13,103.06,104.09,102.37,103.36,11670000,103.36 1973-09-12,103.22,103.98,102.15,103.06,12040000,103.06 1973-09-11,103.85,104.09,102.13,103.22,12690000,103.22 1973-09-10,104.76,105.12,103.33,103.85,11620000,103.85 1973-09-07,105.15,105.87,104.04,104.76,14930000,104.76 1973-09-06,104.64,105.95,104.05,105.15,15670000,105.15 1973-09-05,104.51,105.33,103.60,104.64,14580000,104.64 1973-09-04,104.25,105.35,103.60,104.51,14210000,104.51 1973-08-31,103.88,104.72,103.15,104.25,10530000,104.25 1973-08-30,104.03,104.84,103.29,103.88,12100000,103.88 1973-08-29,103.02,104.92,102.69,104.03,15690000,104.03 1973-08-28,102.42,103.66,102.06,103.02,11810000,103.02 1973-08-27,101.62,102.82,101.09,102.42,9740000,102.42 1973-08-24,101.91,102.65,100.88,101.62,11200000,101.62 1973-08-23,100.62,102.50,100.62,101.91,11390000,101.91 1973-08-22,100.89,101.39,99.74,100.53,10770000,100.53 1973-08-21,101.61,102.10,100.51,100.89,11480000,100.89 1973-08-20,102.31,102.54,101.11,101.61,8970000,101.61 1973-08-17,102.29,102.98,101.38,102.31,11110000,102.31 1973-08-16,103.01,103.97,101.85,102.29,12990000,102.29 1973-08-15,102.71,103.79,101.92,103.01,12040000,103.01 1973-08-14,103.71,104.29,102.34,102.71,11740000,102.71 1973-08-13,104.77,104.83,103.13,103.71,11330000,103.71 1973-08-10,105.61,106.03,104.21,104.77,10870000,104.77 1973-08-09,105.55,106.65,104.89,105.61,12880000,105.61 1973-08-08,106.55,106.73,105.04,105.55,12440000,105.55 1973-08-07,106.73,107.57,105.87,106.55,13510000,106.55 1973-08-06,106.49,107.54,105.45,106.73,12320000,106.73 1973-08-03,106.67,107.17,105.68,106.49,9940000,106.49 1973-08-02,106.83,107.38,105.51,106.67,16080000,106.67 1973-08-01,108.17,108.17,106.29,106.83,13530000,106.83 1973-07-31,109.25,110.09,107.89,108.22,13530000,108.22 1973-07-30,109.59,110.12,108.24,109.25,11170000,109.25 1973-07-27,109.85,110.49,108.70,109.59,12910000,109.59 1973-07-26,109.64,111.04,108.51,109.85,18410000,109.85 1973-07-25,108.14,110.76,107.92,109.64,22220000,109.64 1973-07-24,107.52,108.63,106.31,108.14,16280000,108.14 1973-07-23,107.14,108.42,106.54,107.52,15580000,107.52 1973-07-20,106.55,108.02,105.95,107.14,16300000,107.14 1973-07-19,106.35,107.58,105.06,106.55,18650000,106.55 1973-07-18,105.72,107.05,104.73,106.35,17020000,106.35 1973-07-17,105.67,107.28,104.99,105.72,18750000,105.72 1973-07-16,104.09,106.01,103.42,105.67,12920000,105.67 1973-07-13,105.50,105.80,103.66,104.09,11390000,104.09 1973-07-12,105.80,106.62,104.38,105.50,16400000,105.50 1973-07-11,103.64,106.21,103.64,105.80,18730000,105.80 1973-07-10,102.26,104.20,102.26,103.52,15090000,103.52 1973-07-09,101.28,102.45,100.44,102.14,11560000,102.14 1973-07-06,101.78,102.22,100.67,101.28,9980000,101.28 1973-07-05,101.87,102.48,100.80,101.78,10500000,101.78 1973-07-03,102.90,103.02,101.14,101.87,10560000,101.87 1973-07-02,104.10,104.10,102.44,102.90,9830000,102.90 1973-06-29,104.69,105.30,103.68,104.26,10770000,104.26 1973-06-28,103.62,105.17,103.18,104.69,12760000,104.69 1973-06-27,103.30,104.23,102.29,103.62,12660000,103.62 1973-06-26,102.25,103.78,101.45,103.30,14040000,103.30 1973-06-25,103.64,103.64,101.71,102.25,11670000,102.25 1973-06-22,103.21,105.66,103.07,103.70,18470000,103.70 1973-06-21,104.44,104.77,102.84,103.21,11630000,103.21 1973-06-20,103.99,105.13,103.51,104.44,10600000,104.44 1973-06-19,103.60,104.96,102.46,103.99,12970000,103.99 1973-06-18,104.96,104.96,103.08,103.60,11460000,103.60 1973-06-15,106.21,106.21,104.37,105.10,11970000,105.10 1973-06-14,107.60,108.27,105.83,106.40,13210000,106.40 1973-06-13,108.29,109.52,107.08,107.60,15700000,107.60 1973-06-12,106.70,108.78,106.40,108.29,13840000,108.29 1973-06-11,107.03,107.79,106.11,106.70,9940000,106.70 1973-06-08,105.84,107.75,105.60,107.03,14050000,107.03 1973-06-07,104.31,106.39,104.19,105.84,14160000,105.84 1973-06-06,104.62,105.78,103.60,104.31,13080000,104.31 1973-06-05,102.97,105.27,102.61,104.62,14080000,104.62 1973-06-04,103.93,103.98,102.33,102.97,11230000,102.97 1973-06-01,104.95,105.04,103.31,103.93,10410000,103.93 1973-05-31,105.91,106.30,104.35,104.95,12190000,104.95 1973-05-30,107.51,107.64,105.48,105.91,11730000,105.91 1973-05-29,107.94,108.58,106.77,107.51,11300000,107.51 1973-05-25,107.14,108.86,106.08,107.94,19270000,107.94 1973-05-24,104.07,107.44,103.59,107.14,17310000,107.14 1973-05-23,103.58,105.10,102.82,104.07,14950000,104.07 1973-05-22,102.73,105.04,102.58,103.58,18020000,103.58 1973-05-21,103.77,103.77,101.36,102.73,20690000,102.73 1973-05-18,105.41,105.41,103.18,103.86,17080000,103.86 1973-05-17,106.43,106.82,105.15,105.56,13060000,105.56 1973-05-16,106.57,107.61,105.49,106.43,13800000,106.43 1973-05-15,105.90,107.16,104.12,106.57,18530000,106.57 1973-05-14,107.74,107.74,105.52,105.90,13520000,105.90 1973-05-11,109.49,109.49,107.70,108.17,12980000,108.17 1973-05-10,110.44,110.86,108.86,109.54,13520000,109.54 1973-05-09,111.25,112.25,109.97,110.44,16050000,110.44 1973-05-08,110.53,111.72,109.46,111.25,13730000,111.25 1973-05-07,111.00,111.38,109.68,110.53,12500000,110.53 1973-05-04,110.22,111.99,109.89,111.00,19510000,111.00 1973-05-03,108.43,110.64,106.81,110.22,17760000,110.22 1973-05-02,107.10,109.06,106.95,108.43,14380000,108.43 1973-05-01,106.97,108.00,105.34,107.10,15380000,107.10 1973-04-30,107.23,107.90,105.44,106.97,14820000,106.97 1973-04-27,108.89,109.28,106.76,107.23,13730000,107.23 1973-04-26,108.34,109.66,107.14,108.89,16210000,108.89 1973-04-25,109.82,109.82,107.79,108.34,15960000,108.34 1973-04-24,111.57,111.89,109.64,109.99,13830000,109.99 1973-04-23,112.17,112.66,110.91,111.57,12580000,111.57 1973-04-19,111.54,112.93,111.06,112.17,14560000,112.17 1973-04-18,110.94,112.03,109.99,111.54,13890000,111.54 1973-04-17,111.44,111.81,110.19,110.94,12830000,110.94 1973-04-16,112.08,112.61,110.91,111.44,11350000,111.44 1973-04-13,112.58,112.91,111.23,112.08,14390000,112.08 1973-04-12,112.68,113.65,111.83,112.58,16360000,112.58 1973-04-11,112.21,113.27,111.21,112.68,14890000,112.68 1973-04-10,110.92,112.85,110.92,112.21,16770000,112.21 1973-04-09,109.28,111.24,108.74,110.86,13740000,110.86 1973-04-06,108.52,110.04,108.22,109.28,13890000,109.28 1973-04-05,108.77,109.15,107.44,108.52,12750000,108.52 1973-04-04,109.24,109.96,108.10,108.77,11890000,108.77 1973-04-03,110.18,110.35,108.47,109.24,12910000,109.24 1973-04-02,111.52,111.70,109.68,110.18,10640000,110.18 1973-03-30,112.71,112.87,110.89,111.52,13740000,111.52 1973-03-29,111.62,113.22,111.07,112.71,16050000,112.71 1973-03-28,111.56,112.47,110.54,111.62,15850000,111.62 1973-03-27,109.95,112.07,109.95,111.56,17500000,111.56 1973-03-26,108.88,110.40,108.29,109.84,14980000,109.84 1973-03-23,108.84,109.97,107.41,108.88,18470000,108.88 1973-03-22,110.39,110.39,108.19,108.84,17130000,108.84 1973-03-21,111.95,112.81,110.17,110.49,16080000,110.49 1973-03-20,112.17,112.68,111.02,111.95,13250000,111.95 1973-03-19,113.50,113.50,111.65,112.17,12460000,112.17 1973-03-16,114.12,114.62,112.84,113.54,15130000,113.54 1973-03-15,114.98,115.47,113.77,114.12,14450000,114.12 1973-03-14,114.48,115.61,113.97,114.98,14460000,114.98 1973-03-13,113.86,115.05,113.32,114.48,14210000,114.48 1973-03-12,113.79,114.80,113.25,113.86,13810000,113.86 1973-03-09,114.23,114.55,112.93,113.79,14070000,113.79 1973-03-08,114.45,115.23,113.57,114.23,15100000,114.23 1973-03-07,114.10,115.12,112.83,114.45,19310000,114.45 1973-03-06,112.68,114.71,112.57,114.10,17710000,114.10 1973-03-05,112.28,113.43,111.33,112.68,13720000,112.68 1973-03-02,111.05,112.62,109.45,112.28,17710000,112.28 1973-03-01,111.68,112.98,110.68,111.05,18210000,111.05 1973-02-28,110.90,112.21,109.80,111.68,17950000,111.68 1973-02-27,112.19,112.90,110.50,110.90,16130000,110.90 1973-02-26,113.16,113.26,111.15,112.19,15860000,112.19 1973-02-23,114.44,114.67,112.77,113.16,15450000,113.16 1973-02-22,114.69,115.20,113.44,114.44,14570000,114.44 1973-02-21,115.40,116.01,114.13,114.69,14880000,114.69 1973-02-20,114.98,116.26,114.57,115.40,14020000,115.40 1973-02-16,114.45,115.47,113.73,114.98,13320000,114.98 1973-02-15,115.10,115.68,113.70,114.45,13940000,114.45 1973-02-14,116.78,116.92,114.52,115.10,16520000,115.10 1973-02-13,116.09,118.98,116.09,116.78,25320000,116.78 1973-02-12,114.69,116.66,114.69,116.06,16130000,116.06 1973-02-09,113.16,115.20,113.08,114.68,19260000,114.68 1973-02-08,113.66,114.05,111.85,113.16,18440000,113.16 1973-02-07,114.45,115.48,113.24,113.66,17960000,113.66 1973-02-06,114.23,115.33,113.45,114.45,15720000,114.45 1973-02-05,114.35,115.15,113.62,114.23,14580000,114.23 1973-02-02,114.76,115.40,113.45,114.35,17470000,114.35 1973-02-01,116.03,117.01,114.26,114.76,20670000,114.76 1973-01-31,115.83,116.84,115.05,116.03,14870000,116.03 1973-01-30,116.01,117.11,115.26,115.83,15270000,115.83 1973-01-29,116.45,117.18,115.13,116.01,14680000,116.01 1973-01-26,116.73,117.29,114.97,116.45,21130000,116.45 1973-01-24,118.22,119.04,116.09,116.73,20870000,116.73 1973-01-23,118.21,119.00,116.84,118.22,19060000,118.22 1973-01-22,118.78,119.63,117.72,118.21,15570000,118.21 1973-01-19,118.85,119.45,117.46,118.78,17020000,118.78 1973-01-18,118.68,119.93,118.15,118.85,17810000,118.85 1973-01-17,118.14,119.35,117.61,118.68,17680000,118.68 1973-01-16,118.44,119.17,117.04,118.14,19170000,118.14 1973-01-15,119.30,120.82,118.04,118.44,21520000,118.44 1973-01-12,120.24,121.27,118.69,119.30,22230000,119.30 1973-01-11,119.43,121.74,119.01,120.24,25050000,120.24 1973-01-10,119.73,120.44,118.78,119.43,20880000,119.43 1973-01-09,119.85,120.40,118.89,119.73,16830000,119.73 1973-01-08,119.87,120.55,119.04,119.85,16840000,119.85 1973-01-05,119.40,120.71,118.88,119.87,19330000,119.87 1973-01-04,119.57,120.17,118.12,119.40,20230000,119.40 1973-01-03,119.10,120.45,118.69,119.57,20620000,119.57 1973-01-02,118.06,119.90,118.06,119.10,17090000,119.10 1972-12-29,116.93,118.77,116.70,118.05,27550000,118.05 1972-12-27,116.30,117.55,115.89,116.93,19100000,116.93 1972-12-26,115.83,116.87,115.54,116.30,11120000,116.30 1972-12-22,115.11,116.40,114.78,115.83,12540000,115.83 1972-12-21,115.95,116.60,114.63,115.11,18290000,115.11 1972-12-20,116.34,117.13,115.38,115.95,18490000,115.95 1972-12-19,116.90,117.37,115.69,116.34,17000000,116.34 1972-12-18,117.88,117.88,115.89,116.90,17540000,116.90 1972-12-15,118.24,119.25,117.37,118.26,18300000,118.26 1972-12-14,118.56,119.19,117.63,118.24,17930000,118.24 1972-12-13,118.66,119.23,117.77,118.56,16540000,118.56 1972-12-12,119.12,119.79,118.09,118.66,17040000,118.66 1972-12-11,118.86,119.78,118.24,119.12,17230000,119.12 1972-12-08,118.60,119.54,117.92,118.86,18030000,118.86 1972-12-07,118.01,119.17,117.57,118.60,19320000,118.60 1972-12-06,117.58,118.56,116.90,118.01,18610000,118.01 1972-12-05,117.77,118.42,116.89,117.58,17800000,117.58 1972-12-04,117.38,118.54,116.99,117.77,19730000,117.77 1972-12-01,116.67,118.18,116.29,117.38,22570000,117.38 1972-11-30,116.52,117.39,115.74,116.67,19340000,116.67 1972-11-29,116.47,117.14,115.56,116.52,17380000,116.52 1972-11-28,116.72,117.48,115.78,116.47,19210000,116.47 1972-11-27,117.27,117.55,115.66,116.72,18190000,116.72 1972-11-24,116.90,117.91,116.19,117.27,15760000,117.27 1972-11-22,116.21,117.61,115.67,116.90,24510000,116.90 1972-11-21,115.53,116.84,115.04,116.21,22110000,116.21 1972-11-20,115.49,116.25,114.57,115.53,16680000,115.53 1972-11-17,115.13,116.23,114.44,115.49,20220000,115.49 1972-11-16,114.50,115.57,113.73,115.13,19580000,115.13 1972-11-15,114.95,116.07,113.87,114.50,23270000,114.50 1972-11-14,113.90,115.41,113.36,114.95,20200000,114.95 1972-11-13,113.73,114.75,112.91,113.90,17210000,113.90 1972-11-10,113.50,115.15,112.85,113.73,24360000,113.73 1972-11-09,113.35,114.11,112.08,113.50,17040000,113.50 1972-11-08,113.98,115.23,112.77,113.35,24620000,113.35 1972-11-06,114.22,115.17,112.91,113.98,21330000,113.98 1972-11-03,113.23,114.81,112.71,114.22,22510000,114.22 1972-11-02,112.67,113.81,111.96,113.23,20690000,113.23 1972-11-01,111.58,113.31,111.32,112.67,21360000,112.67 1972-10-31,110.59,112.05,110.40,111.58,15450000,111.58 1972-10-30,110.62,111.19,109.66,110.59,11820000,110.59 1972-10-27,110.99,111.62,109.99,110.62,15470000,110.62 1972-10-26,110.72,112.26,110.26,110.99,20790000,110.99 1972-10-25,110.81,111.56,109.96,110.72,17430000,110.72 1972-10-24,110.35,111.34,109.38,110.81,15240000,110.81 1972-10-23,109.51,111.10,109.51,110.35,14190000,110.35 1972-10-20,108.05,109.79,107.59,109.24,15740000,109.24 1972-10-19,108.19,108.81,107.40,108.05,13850000,108.05 1972-10-18,107.50,109.11,107.36,108.19,17290000,108.19 1972-10-17,106.77,108.04,106.27,107.50,13410000,107.50 1972-10-16,107.92,108.40,106.38,106.77,10940000,106.77 1972-10-13,108.60,108.88,107.17,107.92,12870000,107.92 1972-10-12,109.50,109.69,108.03,108.60,13130000,108.60 1972-10-11,109.99,110.51,108.77,109.50,11900000,109.50 1972-10-10,109.90,111.11,109.32,109.99,13310000,109.99 1972-10-09,109.62,110.44,109.28,109.90,7940000,109.90 1972-10-06,108.89,110.49,107.78,109.62,16630000,109.62 1972-10-05,110.09,110.52,108.49,108.89,17730000,108.89 1972-10-04,110.30,111.35,109.58,110.09,16640000,110.09 1972-10-03,110.16,110.90,109.47,110.30,13090000,110.30 1972-10-02,110.55,110.98,109.49,110.16,12440000,110.16 1972-09-29,110.35,110.55,108.05,110.55,16250000,110.55 1972-09-28,109.66,110.75,108.75,110.35,14710000,110.35 1972-09-27,108.12,109.92,107.79,109.66,14620000,109.66 1972-09-26,108.05,108.97,107.35,108.12,13150000,108.12 1972-09-25,108.52,109.09,107.67,108.05,10920000,108.05 1972-09-22,108.43,109.20,107.72,108.52,12570000,108.52 1972-09-21,108.60,109.13,107.75,108.43,11940000,108.43 1972-09-20,108.55,109.12,107.84,108.60,11980000,108.60 1972-09-19,108.61,109.57,108.08,108.55,13330000,108.55 1972-09-18,108.81,109.22,107.86,108.61,8880000,108.61 1972-09-15,108.93,109.49,108.10,108.81,11690000,108.81 1972-09-14,108.90,109.64,108.21,108.93,12500000,108.93 1972-09-13,108.47,109.36,107.84,108.90,13070000,108.90 1972-09-12,109.51,109.84,107.81,108.47,13560000,108.47 1972-09-11,110.15,110.57,109.01,109.51,10710000,109.51 1972-09-08,110.29,110.90,109.67,110.15,10980000,110.15 1972-09-07,110.55,111.06,109.71,110.29,11090000,110.29 1972-09-06,111.23,111.38,110.04,110.55,12010000,110.55 1972-09-05,111.51,112.08,110.75,111.23,10630000,111.23 1972-09-01,111.09,112.12,110.70,111.51,11600000,111.51 1972-08-31,110.57,111.52,110.08,111.09,12340000,111.09 1972-08-30,110.41,111.33,109.90,110.57,12470000,110.57 1972-08-29,110.23,111.02,109.26,110.41,12300000,110.41 1972-08-28,110.67,111.24,109.71,110.23,10720000,110.23 1972-08-25,111.02,111.53,109.78,110.67,13840000,110.67 1972-08-24,112.26,112.81,110.62,111.02,18280000,111.02 1972-08-23,112.41,113.27,111.30,112.26,18670000,112.26 1972-08-22,111.72,113.16,111.28,112.41,18560000,112.41 1972-08-21,111.76,112.74,110.75,111.72,14290000,111.72 1972-08-18,111.34,112.53,110.81,111.76,16150000,111.76 1972-08-17,111.66,112.41,110.72,111.34,14360000,111.34 1972-08-16,112.06,112.80,110.87,111.66,14950000,111.66 1972-08-15,112.55,113.04,111.27,112.06,16670000,112.06 1972-08-14,111.95,113.45,111.66,112.55,18870000,112.55 1972-08-11,111.05,112.40,110.52,111.95,16570000,111.95 1972-08-10,110.86,111.68,110.09,111.05,15260000,111.05 1972-08-09,110.69,111.57,109.98,110.86,15730000,110.86 1972-08-08,110.61,111.32,109.67,110.69,14550000,110.69 1972-08-07,110.43,111.38,109.69,110.61,13220000,110.61 1972-08-04,110.14,111.12,109.37,110.43,15700000,110.43 1972-08-03,109.29,110.88,108.90,110.14,19970000,110.14 1972-08-02,108.40,109.85,108.12,109.29,17920000,109.29 1972-08-01,107.39,108.85,107.06,108.40,15540000,108.40 1972-07-31,107.38,108.06,106.60,107.39,11120000,107.39 1972-07-28,107.28,108.03,106.52,107.38,13050000,107.38 1972-07-27,107.53,108.31,106.61,107.28,13870000,107.28 1972-07-26,107.60,108.42,106.79,107.53,14130000,107.53 1972-07-25,107.92,108.88,107.06,107.60,17180000,107.60 1972-07-24,106.66,108.67,106.63,107.92,18020000,107.92 1972-07-21,105.81,107.05,104.99,106.66,14010000,106.66 1972-07-20,106.14,106.68,105.12,105.81,15050000,105.81 1972-07-19,105.83,107.36,105.47,106.14,17880000,106.14 1972-07-18,105.88,106.40,104.43,105.83,16820000,105.83 1972-07-17,106.80,107.37,105.55,105.88,13170000,105.88 1972-07-14,106.28,107.58,105.77,106.80,13910000,106.80 1972-07-13,106.89,107.30,105.62,106.28,14740000,106.28 1972-07-12,107.32,108.15,106.42,106.89,16150000,106.89 1972-07-11,108.11,108.35,106.87,107.32,12830000,107.32 1972-07-10,108.69,109.16,107.62,108.11,11700000,108.11 1972-07-07,109.04,109.66,108.16,108.69,12900000,108.69 1972-07-06,108.28,110.27,108.28,109.04,19520000,109.04 1972-07-05,107.49,108.80,107.14,108.10,14710000,108.10 1972-07-03,107.14,107.95,106.72,107.49,8140000,107.49 1972-06-30,106.82,107.91,106.40,107.14,12860000,107.14 1972-06-29,107.02,107.47,105.94,106.82,14610000,106.82 1972-06-28,107.37,107.87,106.49,107.02,12140000,107.02 1972-06-27,107.48,108.29,106.70,107.37,13750000,107.37 1972-06-26,108.23,108.23,106.68,107.48,12720000,107.48 1972-06-23,108.68,109.33,107.69,108.27,13940000,108.27 1972-06-22,108.79,109.26,107.62,108.68,13410000,108.68 1972-06-21,108.56,109.66,107.98,108.79,15510000,108.79 1972-06-20,108.11,109.12,107.64,108.56,14970000,108.56 1972-06-19,108.36,108.78,107.37,108.11,11660000,108.11 1972-06-16,108.44,108.94,107.54,108.36,13010000,108.36 1972-06-15,108.39,109.52,107.78,108.44,16940000,108.44 1972-06-14,107.55,109.15,107.38,108.39,18320000,108.39 1972-06-13,107.01,108.03,106.38,107.55,15710000,107.55 1972-06-12,106.86,107.92,106.29,107.01,13390000,107.01 1972-06-09,107.28,107.68,106.30,106.86,12790000,106.86 1972-06-08,107.65,108.52,106.90,107.28,13820000,107.28 1972-06-07,108.21,108.52,106.91,107.65,15220000,107.65 1972-06-06,108.82,109.32,107.71,108.21,15980000,108.21 1972-06-05,109.73,109.92,108.28,108.82,13450000,108.82 1972-06-02,109.69,110.51,108.93,109.73,15400000,109.73 1972-06-01,109.53,110.35,108.97,109.69,14910000,109.69 1972-05-31,110.35,110.52,108.92,109.53,15230000,109.53 1972-05-30,110.66,111.48,109.78,110.35,15810000,110.35 1972-05-26,110.46,111.31,109.84,110.66,15730000,110.66 1972-05-25,110.31,111.20,109.67,110.46,16480000,110.46 1972-05-24,109.78,111.07,109.39,110.31,17870000,110.31 1972-05-23,109.69,110.46,108.91,109.78,16410000,109.78 1972-05-22,108.98,110.37,108.79,109.69,16030000,109.69 1972-05-19,107.94,109.59,107.74,108.98,19580000,108.98 1972-05-18,106.89,108.39,106.72,107.94,17370000,107.94 1972-05-17,106.66,107.38,106.02,106.89,13600000,106.89 1972-05-16,106.86,107.55,106.13,106.66,14070000,106.66 1972-05-15,106.38,107.45,106.06,106.86,13600000,106.86 1972-05-12,105.77,107.02,105.49,106.38,13990000,106.38 1972-05-11,105.42,106.45,104.90,105.77,12900000,105.77 1972-05-10,104.74,106.10,104.43,105.42,13870000,105.42 1972-05-09,106.06,106.06,103.83,104.74,19910000,104.74 1972-05-08,106.63,106.81,105.36,106.14,11250000,106.14 1972-05-05,106.25,107.33,105.70,106.63,13210000,106.63 1972-05-04,105.99,106.81,105.14,106.25,14790000,106.25 1972-05-03,106.08,107.24,105.44,105.99,15900000,105.99 1972-05-02,106.69,107.37,105.55,106.08,15370000,106.08 1972-05-01,107.67,108.00,106.30,106.69,12880000,106.69 1972-04-28,107.05,108.28,106.70,107.67,14160000,107.67 1972-04-27,106.89,107.89,106.42,107.05,15740000,107.05 1972-04-26,107.12,107.89,106.18,106.89,17710000,106.89 1972-04-25,108.19,108.29,106.70,107.12,17030000,107.12 1972-04-24,108.89,109.19,107.62,108.19,14650000,108.19 1972-04-21,109.04,109.92,108.30,108.89,18200000,108.89 1972-04-20,109.20,109.69,108.08,109.04,18190000,109.04 1972-04-19,109.77,110.35,108.71,109.20,19180000,109.20 1972-04-18,109.51,110.64,109.02,109.77,19410000,109.77 1972-04-17,109.84,110.22,108.77,109.51,15390000,109.51 1972-04-14,109.91,110.56,109.07,109.84,17460000,109.84 1972-04-13,110.18,110.79,109.37,109.91,17990000,109.91 1972-04-12,109.76,111.11,109.36,110.18,24690000,110.18 1972-04-11,109.45,110.38,108.76,109.76,19930000,109.76 1972-04-10,109.62,110.54,108.89,109.45,19470000,109.45 1972-04-07,109.53,110.15,108.53,109.62,19900000,109.62 1972-04-06,109.00,110.29,108.53,109.53,22830000,109.53 1972-04-05,108.12,109.64,107.96,109.00,22960000,109.00 1972-04-04,107.48,108.62,106.77,108.12,18110000,108.12 1972-04-03,107.20,108.26,106.75,107.48,14990000,107.48 1972-03-30,106.49,107.67,106.07,107.20,14360000,107.20 1972-03-29,107.17,107.41,105.98,106.49,13860000,106.49 1972-03-28,107.30,108.08,106.22,107.17,15380000,107.17 1972-03-27,107.52,108.00,106.53,107.30,12180000,107.30 1972-03-24,107.75,108.36,106.95,107.52,15390000,107.52 1972-03-23,106.84,108.33,106.67,107.75,18380000,107.75 1972-03-22,106.69,107.52,106.00,106.84,15400000,106.84 1972-03-21,107.59,107.68,105.86,106.69,18610000,106.69 1972-03-20,107.92,108.81,107.18,107.59,16420000,107.59 1972-03-17,107.50,108.61,106.89,107.92,16040000,107.92 1972-03-16,107.75,108.22,106.55,107.50,16700000,107.50 1972-03-15,107.61,108.55,107.09,107.75,19460000,107.75 1972-03-14,107.33,108.20,106.71,107.61,22370000,107.61 1972-03-13,108.38,108.52,106.71,107.33,16730000,107.33 1972-03-10,108.94,109.37,107.77,108.38,19690000,108.38 1972-03-09,108.96,109.75,108.19,108.94,21460000,108.94 1972-03-08,108.87,109.68,108.04,108.96,21290000,108.96 1972-03-07,108.77,109.72,108.02,108.87,22640000,108.87 1972-03-06,107.94,109.40,107.64,108.77,21000000,108.77 1972-03-03,107.32,108.51,106.78,107.94,20420000,107.94 1972-03-02,107.35,108.39,106.63,107.32,22200000,107.32 1972-03-01,106.57,108.13,106.21,107.35,23670000,107.35 1972-02-29,106.19,107.16,105.45,106.57,20320000,106.57 1972-02-28,106.18,107.04,105.37,106.19,18200000,106.19 1972-02-25,105.45,106.73,105.04,106.18,18180000,106.18 1972-02-24,105.38,106.24,104.76,105.45,16000000,105.45 1972-02-23,105.29,106.18,104.72,105.38,16770000,105.38 1972-02-22,105.28,106.18,104.65,105.29,16670000,105.29 1972-02-18,105.59,106.01,104.47,105.28,16590000,105.28 1972-02-17,105.62,106.65,104.96,105.59,22330000,105.59 1972-02-16,105.03,106.25,104.65,105.62,20670000,105.62 1972-02-15,104.59,105.59,104.10,105.03,17770000,105.03 1972-02-14,105.08,105.53,104.03,104.59,15840000,104.59 1972-02-11,105.59,105.91,104.45,105.08,17850000,105.08 1972-02-10,105.55,106.69,104.97,105.59,23460000,105.59 1972-02-09,104.74,106.03,104.36,105.55,19850000,105.55 1972-02-08,104.54,105.22,103.90,104.74,17390000,104.74 1972-02-07,104.86,105.46,103.97,104.54,16930000,104.54 1972-02-04,104.64,105.48,104.05,104.86,17890000,104.86 1972-02-03,104.68,105.43,103.85,104.64,19880000,104.64 1972-02-02,104.01,105.41,103.50,104.68,24070000,104.68 1972-02-01,103.94,104.57,103.10,104.01,19600000,104.01 1972-01-31,104.16,104.88,103.30,103.94,18250000,103.94 1972-01-28,103.50,104.98,103.22,104.16,25000000,104.16 1972-01-27,102.50,103.93,102.20,103.50,20360000,103.50 1972-01-26,102.70,103.31,101.81,102.50,14940000,102.50 1972-01-25,102.57,103.59,101.63,102.70,17570000,102.70 1972-01-24,103.65,104.03,102.20,102.57,15640000,102.57 1972-01-21,103.88,104.40,102.75,103.65,18810000,103.65 1972-01-20,103.88,105.00,103.32,103.88,20210000,103.88 1972-01-19,104.05,104.61,102.83,103.88,18800000,103.88 1972-01-18,103.70,104.85,103.35,104.05,21070000,104.05 1972-01-17,103.39,104.24,102.80,103.70,15860000,103.70 1972-01-14,102.99,103.89,102.41,103.39,14960000,103.39 1972-01-13,103.59,103.80,102.29,102.99,16410000,102.99 1972-01-12,103.65,104.66,103.05,103.59,20970000,103.59 1972-01-11,103.32,104.30,102.85,103.65,17970000,103.65 1972-01-10,103.47,103.97,102.44,103.32,15320000,103.32 1972-01-07,103.51,104.29,102.38,103.47,17140000,103.47 1972-01-06,103.06,104.20,102.66,103.51,21100000,103.51 1972-01-05,102.09,103.69,101.90,103.06,21350000,103.06 1972-01-04,101.67,102.59,100.87,102.09,15190000,102.09 1972-01-03,102.09,102.85,101.19,101.67,12570000,101.67 1971-12-31,102.09,102.09,102.09,102.09,14040000,102.09 1971-12-30,101.78,101.78,101.78,101.78,13810000,101.78 1971-12-29,102.21,102.21,102.21,102.21,17150000,102.21 1971-12-28,101.95,101.95,101.95,101.95,15090000,101.95 1971-12-27,100.95,100.95,100.95,100.95,11890000,100.95 1971-12-23,100.74,100.74,100.74,100.74,16000000,100.74 1971-12-22,101.18,101.18,101.18,101.18,18930000,101.18 1971-12-21,101.80,101.80,101.80,101.80,20460000,101.80 1971-12-20,101.55,101.55,101.55,101.55,23810000,101.55 1971-12-17,100.26,100.26,100.26,100.26,18270000,100.26 1971-12-16,99.74,99.74,99.74,99.74,21070000,99.74 1971-12-15,98.54,98.54,98.54,98.54,16890000,98.54 1971-12-14,97.67,97.67,97.67,97.67,16070000,97.67 1971-12-13,97.97,97.97,97.97,97.97,17020000,97.97 1971-12-10,97.69,97.69,97.69,97.69,17510000,97.69 1971-12-09,96.96,96.96,96.96,96.96,14710000,96.96 1971-12-08,96.87,97.65,96.08,96.92,16650000,96.92 1971-12-07,96.51,97.35,95.40,96.87,15250000,96.87 1971-12-06,97.06,98.17,96.07,96.51,17480000,96.51 1971-12-03,95.84,97.57,95.36,97.06,16760000,97.06 1971-12-02,95.44,96.59,94.73,95.84,17780000,95.84 1971-12-01,93.99,96.12,93.95,95.44,21040000,95.44 1971-11-30,93.41,94.43,92.51,93.99,18320000,93.99 1971-11-29,92.04,94.90,92.04,93.41,18910000,93.41 1971-11-26,90.33,92.19,90.27,91.94,10870000,91.94 1971-11-24,90.16,91.14,89.73,90.33,11870000,90.33 1971-11-23,90.79,91.10,89.34,90.16,16840000,90.16 1971-11-22,91.61,92.12,90.51,90.79,11390000,90.79 1971-11-19,92.13,92.38,90.95,91.61,12420000,91.61 1971-11-18,92.85,93.62,91.88,92.13,13010000,92.13 1971-11-17,92.71,93.35,91.80,92.85,12840000,92.85 1971-11-16,91.81,93.15,91.21,92.71,13300000,92.71 1971-11-15,92.12,92.69,91.38,91.81,9370000,91.81 1971-11-12,92.12,92.90,90.93,92.12,14540000,92.12 1971-11-11,93.41,93.54,91.64,92.12,13310000,92.12 1971-11-10,94.46,94.84,93.10,93.41,13410000,93.41 1971-11-09,94.39,95.31,93.94,94.46,12080000,94.46 1971-11-08,94.46,94.97,93.78,94.39,8520000,94.39 1971-11-05,94.79,95.01,93.64,94.46,10780000,94.46 1971-11-04,94.91,96.08,94.37,94.79,15750000,94.79 1971-11-03,93.27,95.31,93.27,94.91,14590000,94.91 1971-11-02,92.80,93.73,91.84,93.18,13330000,93.18 1971-11-01,94.23,94.43,92.48,92.80,10960000,92.80 1971-10-29,93.96,94.71,93.28,94.23,11710000,94.23 1971-10-28,93.79,94.75,92.96,93.96,15530000,93.96 1971-10-27,94.74,94.99,93.39,93.79,13480000,93.79 1971-10-26,95.02,95.02,94.38,94.74,13390000,94.74 1971-10-25,95.57,95.76,94.57,95.10,7340000,95.10 1971-10-22,95.60,96.83,94.97,95.57,14560000,95.57 1971-10-21,95.65,96.33,94.59,95.60,14990000,95.60 1971-10-20,97.00,97.45,95.23,95.65,16340000,95.65 1971-10-19,97.35,97.66,96.05,97.00,13040000,97.00 1971-10-18,97.79,98.33,96.98,97.35,10420000,97.35 1971-10-15,98.13,98.45,97.03,97.79,13120000,97.79 1971-10-14,99.03,99.25,97.74,98.13,12870000,98.13 1971-10-13,99.57,100.08,98.61,99.03,13540000,99.03 1971-10-12,99.21,100.20,98.62,99.57,14340000,99.57 1971-10-11,99.36,99.62,98.58,99.21,7800000,99.21 1971-10-08,100.02,100.30,98.87,99.36,13870000,99.36 1971-10-07,99.82,100.96,99.42,100.02,17780000,100.02 1971-10-06,99.11,100.13,98.49,99.82,15630000,99.82 1971-10-05,99.21,99.78,98.34,99.11,12360000,99.11 1971-10-04,98.93,100.04,98.62,99.21,14570000,99.21 1971-10-01,98.34,99.49,97.96,98.93,13400000,98.93 1971-09-30,97.90,98.97,97.48,98.34,13490000,98.34 1971-09-29,97.88,98.51,97.29,97.90,8580000,97.90 1971-09-28,97.62,98.55,97.12,97.88,11250000,97.88 1971-09-27,98.15,98.41,96.97,97.62,10220000,97.62 1971-09-24,98.38,99.35,97.78,98.15,13460000,98.15 1971-09-23,98.47,99.12,97.61,98.38,13250000,98.38 1971-09-22,99.34,99.72,98.15,98.47,14250000,98.47 1971-09-21,99.68,100.08,98.71,99.34,10640000,99.34 1971-09-20,99.96,100.40,99.14,99.68,9540000,99.68 1971-09-17,99.66,100.52,99.26,99.96,11020000,99.96 1971-09-16,99.77,100.35,99.07,99.66,10550000,99.66 1971-09-15,99.34,100.24,98.79,99.77,11080000,99.77 1971-09-14,100.07,100.35,98.99,99.34,11410000,99.34 1971-09-13,100.42,100.84,99.49,100.07,10000000,100.07 1971-09-10,100.80,101.01,99.69,100.42,11380000,100.42 1971-09-09,101.34,101.88,100.38,100.80,15790000,100.80 1971-09-08,101.15,101.94,100.52,101.34,14230000,101.34 1971-09-07,100.69,102.25,100.43,101.15,17080000,101.15 1971-09-03,99.29,100.93,99.10,100.69,14040000,100.69 1971-09-02,99.07,99.80,98.52,99.29,10690000,99.29 1971-09-01,99.03,99.84,98.50,99.07,10770000,99.07 1971-08-31,99.52,99.76,98.32,99.03,10430000,99.03 1971-08-30,100.48,100.89,99.17,99.52,11140000,99.52 1971-08-27,100.24,101.22,99.76,100.48,12490000,100.48 1971-08-26,100.41,101.12,99.40,100.24,13990000,100.24 1971-08-25,100.40,101.51,99.77,100.41,18280000,100.41 1971-08-24,99.25,101.02,99.15,100.40,18700000,100.40 1971-08-23,98.33,99.96,98.09,99.25,13040000,99.25 1971-08-20,98.16,98.94,97.52,98.33,11890000,98.33 1971-08-19,98.60,99.07,97.35,98.16,14190000,98.16 1971-08-18,99.99,100.19,98.06,98.60,20680000,98.60 1971-08-17,98.76,101.00,98.49,99.99,26790000,99.99 1971-08-16,97.90,100.96,97.90,98.76,31730000,98.76 1971-08-13,96.00,96.53,95.19,95.69,9960000,95.69 1971-08-12,94.81,96.50,94.81,96.00,15910000,96.00 1971-08-11,93.54,95.06,93.35,94.66,11370000,94.66 1971-08-10,93.53,94.13,92.81,93.54,9460000,93.54 1971-08-09,94.25,94.55,93.17,93.53,8110000,93.53 1971-08-06,94.09,94.91,93.63,94.25,9490000,94.25 1971-08-05,93.89,94.89,93.33,94.09,12100000,94.09 1971-08-04,94.51,95.34,93.35,93.89,15410000,93.89 1971-08-03,95.96,96.11,94.06,94.51,13490000,94.51 1971-08-02,95.58,96.76,95.22,95.96,11870000,95.96 1971-07-30,96.02,96.78,95.08,95.58,12970000,95.58 1971-07-29,97.07,97.22,95.37,96.02,14570000,96.02 1971-07-28,97.78,98.15,96.51,97.07,13940000,97.07 1971-07-27,98.14,98.99,97.42,97.78,11560000,97.78 1971-07-26,98.94,99.47,96.67,98.14,9930000,98.14 1971-07-23,99.11,99.60,98.26,98.94,12370000,98.94 1971-07-22,99.28,99.82,98.50,99.11,12570000,99.11 1971-07-21,99.32,100.00,98.74,99.28,11920000,99.28 1971-07-20,98.93,100.01,98.60,99.32,12540000,99.32 1971-07-19,99.11,99.57,98.11,98.93,11430000,98.93 1971-07-16,99.28,100.35,98.64,99.11,13870000,99.11 1971-07-15,99.22,100.48,98.76,99.28,13080000,99.28 1971-07-14,99.50,99.83,98.23,99.22,14360000,99.22 1971-07-13,100.82,101.06,99.07,99.50,13540000,99.50 1971-07-12,100.69,101.52,100.19,100.82,12020000,100.82 1971-07-09,100.34,101.33,99.86,100.69,12640000,100.69 1971-07-08,100.04,101.03,99.59,100.34,13920000,100.34 1971-07-07,99.76,100.83,99.25,100.04,14520000,100.04 1971-07-06,99.78,100.35,99.10,99.76,10440000,99.76 1971-07-02,99.78,100.31,99.09,99.78,9960000,99.78 1971-07-01,99.16,100.65,99.16,99.78,13090000,99.78 1971-06-30,98.82,100.29,98.68,98.70,15410000,98.70 1971-06-29,97.74,99.39,97.61,98.82,14460000,98.82 1971-06-28,97.99,98.48,97.02,97.74,9810000,97.74 1971-06-25,98.13,98.66,97.33,97.99,10580000,97.99 1971-06-24,98.41,99.00,97.59,98.13,11360000,98.13 1971-06-23,97.59,98.95,97.36,98.41,12640000,98.41 1971-06-22,97.87,98.66,96.92,97.59,15200000,97.59 1971-06-21,98.97,99.18,97.22,97.87,16490000,97.87 1971-06-18,100.50,100.63,98.65,98.97,15040000,98.97 1971-06-17,100.52,101.37,99.87,100.50,13980000,100.50 1971-06-16,100.32,101.29,99.68,100.52,14300000,100.52 1971-06-15,100.22,101.10,99.45,100.32,13550000,100.32 1971-06-14,101.07,101.28,99.78,100.22,11530000,100.22 1971-06-11,100.64,101.71,100.18,101.07,12270000,101.07 1971-06-10,100.29,101.23,99.78,100.64,12450000,100.64 1971-06-09,100.32,100.97,99.28,100.29,14250000,100.29 1971-06-08,101.09,101.50,99.91,100.32,13610000,100.32 1971-06-07,101.30,102.02,100.55,101.09,13800000,101.09 1971-06-04,101.01,101.88,100.43,101.30,14400000,101.30 1971-06-03,100.96,102.07,100.30,101.01,18790000,101.01 1971-06-02,100.20,101.53,99.89,100.96,17740000,100.96 1971-06-01,99.63,100.76,99.22,100.20,11930000,100.20 1971-05-28,99.40,100.17,98.68,99.63,11760000,99.63 1971-05-27,99.59,100.14,98.78,99.40,12610000,99.40 1971-05-26,99.47,100.49,98.93,99.59,13550000,99.59 1971-05-25,100.13,100.39,98.73,99.47,16050000,99.47 1971-05-24,100.99,101.24,99.72,100.13,12060000,100.13 1971-05-21,101.31,101.84,100.41,100.99,12090000,100.99 1971-05-20,101.07,102.17,100.61,101.31,11740000,101.31 1971-05-19,100.83,101.75,100.30,101.07,17640000,101.07 1971-05-18,100.69,101.62,99.68,100.83,17640000,100.83 1971-05-17,102.08,102.08,100.25,100.69,15980000,100.69 1971-05-14,102.69,103.17,101.65,102.21,16430000,102.21 1971-05-13,102.90,103.57,101.98,102.69,17640000,102.69 1971-05-12,102.62,103.57,102.12,102.90,15140000,102.90 1971-05-11,102.36,103.37,101.50,102.62,17730000,102.62 1971-05-10,102.87,103.15,101.71,102.36,12810000,102.36 1971-05-07,103.23,103.50,101.86,102.87,16490000,102.87 1971-05-06,103.78,104.42,102.80,103.23,19300000,103.23 1971-05-05,103.79,104.28,102.68,103.78,17270000,103.78 1971-05-04,103.29,104.36,102.71,103.79,17310000,103.79 1971-05-03,103.95,104.11,102.37,103.29,16120000,103.29 1971-04-30,104.63,104.96,103.25,103.95,17490000,103.95 1971-04-29,104.77,105.58,103.90,104.63,20340000,104.63 1971-04-28,104.59,105.60,103.85,104.77,24820000,104.77 1971-04-27,103.94,105.07,103.23,104.59,21250000,104.59 1971-04-26,104.05,104.83,103.19,103.94,18860000,103.94 1971-04-23,103.56,104.63,102.79,104.05,20150000,104.05 1971-04-22,103.36,104.27,102.58,103.56,19270000,103.56 1971-04-21,103.61,104.16,102.55,103.36,17040000,103.36 1971-04-20,104.01,104.58,103.06,103.61,17880000,103.61 1971-04-19,103.49,104.63,103.09,104.01,17730000,104.01 1971-04-16,103.52,104.18,102.68,103.49,18280000,103.49 1971-04-15,103.37,104.40,102.76,103.52,22540000,103.52 1971-04-14,102.98,104.01,102.28,103.37,19440000,103.37 1971-04-13,102.88,103.96,102.25,102.98,23200000,102.98 1971-04-12,102.10,103.54,101.75,102.88,19410000,102.88 1971-04-08,101.98,102.86,101.30,102.10,17590000,102.10 1971-04-07,101.51,102.87,101.13,101.98,22270000,101.98 1971-04-06,100.79,102.11,100.30,101.51,19990000,101.51 1971-04-05,100.56,101.41,99.88,100.79,16040000,100.79 1971-04-02,100.39,101.23,99.86,100.56,14520000,100.56 1971-04-01,100.31,100.99,99.63,100.39,13470000,100.39 1971-03-31,100.26,101.05,99.69,100.31,17610000,100.31 1971-03-30,100.03,100.86,99.41,100.26,15430000,100.26 1971-03-29,99.95,100.74,99.36,100.03,13650000,100.03 1971-03-26,99.61,100.65,99.18,99.95,15560000,99.95 1971-03-25,99.62,100.03,98.36,99.61,15870000,99.61 1971-03-24,100.28,100.63,99.15,99.62,15770000,99.62 1971-03-23,100.62,101.06,99.62,100.28,16470000,100.28 1971-03-22,101.01,101.46,100.08,100.62,14290000,100.62 1971-03-19,101.19,101.74,100.35,101.01,15150000,101.01 1971-03-18,101.12,102.03,100.43,101.19,17910000,101.19 1971-03-17,101.21,101.66,99.98,101.12,17070000,101.12 1971-03-16,100.71,101.94,100.36,101.21,22270000,101.21 1971-03-15,99.57,101.15,99.12,100.71,18920000,100.71 1971-03-12,99.39,100.09,98.64,99.57,14680000,99.57 1971-03-11,99.30,100.29,98.57,99.39,19830000,99.39 1971-03-10,99.46,100.10,98.63,99.30,17220000,99.30 1971-03-09,99.38,100.31,98.72,99.46,20490000,99.46 1971-03-08,98.96,99.44,98.42,99.38,19340000,99.38 1971-03-05,97.92,99.49,97.82,98.96,22430000,98.96 1971-03-04,96.95,98.38,96.90,97.92,17350000,97.92 1971-03-03,96.98,97.54,96.30,96.95,14680000,96.95 1971-03-02,97.00,97.60,96.32,96.98,14870000,96.98 1971-03-01,96.75,97.48,96.11,97.00,13020000,97.00 1971-02-26,96.96,97.54,95.84,96.75,17250000,96.75 1971-02-25,96.73,97.71,96.08,96.96,16200000,96.96 1971-02-24,96.09,97.34,95.86,96.73,15930000,96.73 1971-02-23,95.72,96.67,94.92,96.09,15080000,96.09 1971-02-22,96.65,96.65,94.97,95.72,15840000,95.72 1971-02-19,97.56,97.79,96.25,96.74,17860000,96.74 1971-02-18,98.20,98.60,96.96,97.56,16650000,97.56 1971-02-17,98.66,99.32,97.32,98.20,18720000,98.20 1971-02-16,98.43,99.59,97.85,98.66,21350000,98.66 1971-02-12,97.91,98.96,97.56,98.43,18470000,98.43 1971-02-11,97.39,98.49,96.99,97.91,19260000,97.91 1971-02-10,97.51,97.97,96.23,97.39,19040000,97.39 1971-02-09,97.45,98.50,96.90,97.51,28250000,97.51 1971-02-08,96.93,98.04,96.13,97.45,25590000,97.45 1971-02-05,96.62,97.58,95.84,96.93,20480000,96.93 1971-02-04,96.63,97.26,95.69,96.62,20860000,96.62 1971-02-03,96.43,97.19,95.58,96.63,21680000,96.63 1971-02-02,96.42,97.19,95.60,96.43,22030000,96.43 1971-02-01,95.88,97.05,95.38,96.42,20650000,96.42 1971-01-29,95.21,96.49,94.79,95.88,20960000,95.88 1971-01-28,94.89,95.78,94.12,95.21,18840000,95.21 1971-01-27,95.59,95.78,93.96,94.89,20640000,94.89 1971-01-26,95.28,96.36,94.69,95.59,21380000,95.59 1971-01-25,94.88,95.93,94.16,95.28,19050000,95.28 1971-01-22,94.19,95.53,93.96,94.88,21680000,94.88 1971-01-21,93.78,94.69,93.15,94.19,19060000,94.19 1971-01-20,93.76,94.53,93.07,93.78,18330000,93.78 1971-01-19,93.41,94.28,92.85,93.76,15800000,93.76 1971-01-18,93.03,94.11,92.63,93.41,15400000,93.41 1971-01-15,92.80,93.94,92.25,93.03,18010000,93.03 1971-01-14,92.56,93.36,91.67,92.80,17600000,92.80 1971-01-13,92.72,93.66,91.88,92.56,19070000,92.56 1971-01-12,91.98,93.28,91.63,92.72,17820000,92.72 1971-01-11,92.19,92.67,90.99,91.98,14720000,91.98 1971-01-08,92.38,93.02,91.60,92.19,14100000,92.19 1971-01-07,92.35,93.26,91.75,92.38,16460000,92.38 1971-01-06,91.80,93.00,91.50,92.35,16960000,92.35 1971-01-05,91.15,92.28,90.69,91.80,12600000,91.80 1971-01-04,92.15,92.19,90.64,91.15,10010000,91.15 1970-12-31,92.27,92.79,91.36,92.15,13390000,92.15 1970-12-30,92.08,92.99,91.60,92.27,19140000,92.27 1970-12-29,91.09,92.38,90.73,92.08,17750000,92.08 1970-12-28,90.61,91.49,90.28,91.09,12290000,91.09 1970-12-24,90.10,91.08,89.81,90.61,12140000,90.61 1970-12-23,90.04,90.86,89.35,90.10,15400000,90.10 1970-12-22,89.94,90.84,89.35,90.04,14510000,90.04 1970-12-21,90.22,90.77,89.36,89.94,12690000,89.94 1970-12-18,90.04,90.77,89.42,90.22,14360000,90.22 1970-12-17,89.72,90.61,89.31,90.04,13660000,90.04 1970-12-16,89.66,90.22,88.77,89.72,14240000,89.72 1970-12-15,89.80,90.32,88.93,89.66,13420000,89.66 1970-12-14,90.26,90.81,89.28,89.80,13810000,89.80 1970-12-11,89.92,90.93,89.44,90.26,15790000,90.26 1970-12-10,89.54,90.87,89.01,89.92,14610000,89.92 1970-12-09,89.47,90.03,88.48,89.54,13550000,89.54 1970-12-08,89.94,90.47,88.87,89.47,14370000,89.47 1970-12-07,89.46,90.39,88.76,89.94,15530000,89.94 1970-12-04,88.90,89.89,88.12,89.46,15980000,89.46 1970-12-03,88.48,89.87,88.11,88.90,20480000,88.90 1970-12-02,87.47,88.83,86.72,88.48,17960000,88.48 1970-12-01,87.20,88.61,86.11,87.47,20170000,87.47 1970-11-30,85.93,87.60,85.79,87.20,17700000,87.20 1970-11-27,85.09,86.21,84.67,85.93,10130000,85.93 1970-11-25,84.78,85.70,84.35,85.09,13490000,85.09 1970-11-24,84.24,85.18,83.59,84.78,12560000,84.78 1970-11-23,83.72,84.92,83.47,84.24,12720000,84.24 1970-11-20,82.91,84.06,82.49,83.72,10920000,83.72 1970-11-19,82.79,83.48,82.23,82.91,9280000,82.91 1970-11-18,83.47,83.53,82.41,82.79,9850000,82.79 1970-11-17,83.24,84.17,82.81,83.47,9450000,83.47 1970-11-16,83.37,83.75,82.34,83.24,9160000,83.24 1970-11-13,84.15,84.33,82.92,83.37,11890000,83.37 1970-11-12,85.03,85.54,83.81,84.15,12520000,84.15 1970-11-11,84.79,86.24,84.69,85.03,13520000,85.03 1970-11-10,84.67,85.69,84.18,84.79,12030000,84.79 1970-11-09,84.22,85.27,83.82,84.67,10890000,84.67 1970-11-06,84.10,84.73,83.55,84.22,9970000,84.22 1970-11-05,84.39,84.79,83.53,84.10,10800000,84.10 1970-11-04,84.22,85.26,83.82,84.39,12180000,84.39 1970-11-03,83.51,84.77,83.21,84.22,11760000,84.22 1970-11-02,83.25,83.99,82.66,83.51,9470000,83.51 1970-10-30,83.36,83.80,82.52,83.25,10520000,83.25 1970-10-29,83.43,84.10,82.82,83.36,10440000,83.36 1970-10-28,83.12,83.81,82.29,83.43,10660000,83.43 1970-10-27,83.31,83.73,82.52,83.12,9680000,83.12 1970-10-26,83.77,84.26,82.89,83.31,9200000,83.31 1970-10-23,83.38,84.30,82.91,83.77,10270000,83.77 1970-10-22,83.66,84.04,82.77,83.38,9000000,83.38 1970-10-21,83.64,84.72,83.21,83.66,11330000,83.66 1970-10-20,83.15,84.19,82.62,83.64,10630000,83.64 1970-10-19,84.28,84.29,82.81,83.15,9890000,83.15 1970-10-16,84.65,85.21,83.83,84.28,11300000,84.28 1970-10-15,84.19,85.28,83.82,84.65,11250000,84.65 1970-10-14,84.06,84.83,83.42,84.19,9920000,84.19 1970-10-13,84.17,84.70,83.24,84.06,9500000,84.06 1970-10-12,85.05,85.05,83.58,84.17,8570000,84.17 1970-10-09,85.95,86.25,84.54,85.08,13980000,85.08 1970-10-08,86.89,87.37,85.55,85.95,14500000,85.95 1970-10-07,86.85,87.47,85.55,86.89,15610000,86.89 1970-10-06,86.47,87.75,86.04,86.85,20240000,86.85 1970-10-05,85.16,86.99,85.01,86.47,19760000,86.47 1970-10-02,84.32,85.56,84.06,85.16,15420000,85.16 1970-10-01,84.30,84.70,83.46,84.32,9700000,84.32 1970-09-30,83.86,84.99,82.78,84.30,14830000,84.30 1970-09-29,83.91,84.57,83.11,83.86,17880000,83.86 1970-09-28,82.83,84.56,82.61,83.91,14390000,83.91 1970-09-25,81.66,83.60,81.41,82.83,20470000,82.83 1970-09-24,81.91,82.24,80.82,81.66,21340000,81.66 1970-09-23,81.86,83.15,81.52,81.91,16940000,81.91 1970-09-22,81.91,82.24,80.82,81.86,12110000,81.86 1970-09-21,82.62,83.15,81.52,81.91,12540000,81.91 1970-09-18,82.29,83.50,81.77,82.62,15900000,82.62 1970-09-17,81.79,83.09,81.51,82.29,15530000,82.29 1970-09-16,81.36,82.57,80.61,81.79,12090000,81.79 1970-09-15,82.07,82.11,80.75,81.36,9830000,81.36 1970-09-14,82.52,83.13,81.43,82.07,11900000,82.07 1970-09-11,82.30,83.19,81.81,82.52,12140000,82.52 1970-09-10,82.79,82.98,81.62,82.30,11900000,82.30 1970-09-09,83.04,83.78,81.90,82.79,16250000,82.79 1970-09-08,82.83,83.69,81.48,83.04,17110000,83.04 1970-09-04,82.09,83.42,81.79,82.83,15360000,82.83 1970-09-03,80.96,82.63,80.88,82.09,14110000,82.09 1970-09-02,80.95,81.35,79.95,80.96,9710000,80.96 1970-09-01,81.52,81.80,80.43,80.95,10960000,80.95 1970-08-31,81.86,82.33,80.95,81.52,10740000,81.52 1970-08-28,81.08,82.47,80.69,81.86,13820000,81.86 1970-08-27,81.21,81.91,80.13,81.08,12440000,81.08 1970-08-26,81.12,82.26,80.60,81.21,15970000,81.21 1970-08-25,80.99,81.81,79.69,81.12,17520000,81.12 1970-08-24,79.41,81.62,79.41,80.99,18910000,80.99 1970-08-21,77.84,79.60,77.46,79.24,13420000,79.24 1970-08-20,76.96,77.99,76.30,77.84,10170000,77.84 1970-08-19,76.20,77.58,76.01,76.96,9870000,76.96 1970-08-18,75.33,76.79,75.30,76.20,9500000,76.20 1970-08-17,75.18,75.79,74.52,75.33,6940000,75.33 1970-08-14,74.76,75.74,74.39,75.18,7850000,75.18 1970-08-13,75.42,75.69,74.13,74.76,8640000,74.76 1970-08-12,75.82,76.24,75.04,75.42,7440000,75.42 1970-08-11,76.20,76.33,75.16,75.82,7330000,75.82 1970-08-10,77.28,77.40,75.72,76.20,7580000,76.20 1970-08-07,77.08,78.09,76.46,77.28,9370000,77.28 1970-08-06,77.18,77.68,76.39,77.08,7560000,77.08 1970-08-05,77.19,77.86,76.59,77.18,7660000,77.18 1970-08-04,77.02,77.56,76.12,77.19,8310000,77.19 1970-08-03,78.05,78.24,76.56,77.02,7650000,77.02 1970-07-31,78.07,79.03,77.44,78.05,11640000,78.05 1970-07-30,78.04,78.66,77.36,78.07,10430000,78.07 1970-07-29,77.77,78.81,77.28,78.04,12580000,78.04 1970-07-28,77.65,78.35,76.96,77.77,9040000,77.77 1970-07-27,77.82,78.27,77.07,77.65,7460000,77.65 1970-07-24,78.00,78.48,76.96,77.82,9520000,77.82 1970-07-23,77.03,78.51,76.46,78.00,12460000,78.00 1970-07-22,76.98,78.20,76.22,77.03,12460000,77.03 1970-07-21,77.79,77.94,76.39,76.98,9940000,76.98 1970-07-20,77.69,78.72,77.04,77.79,11660000,77.79 1970-07-17,76.37,78.23,76.37,77.69,13870000,77.69 1970-07-16,75.23,77.09,75.12,76.34,12200000,76.34 1970-07-15,74.42,75.68,74.06,75.23,8860000,75.23 1970-07-14,74.55,75.04,73.78,74.42,7360000,74.42 1970-07-13,74.45,75.37,73.83,74.55,7450000,74.55 1970-07-10,74.06,75.21,73.49,74.45,10160000,74.45 1970-07-09,73.00,74.77,72.88,74.06,12820000,74.06 1970-07-08,71.23,73.30,70.99,73.00,10970000,73.00 1970-07-07,71.78,72.32,70.69,71.23,10470000,71.23 1970-07-06,72.92,73.12,71.38,71.78,9340000,71.78 1970-07-02,72.94,73.92,72.43,72.92,8440000,72.92 1970-07-01,72.72,73.66,72.11,72.94,8610000,72.94 1970-06-30,72.89,73.89,72.25,72.72,9280000,72.72 1970-06-29,73.47,73.86,72.34,72.89,8770000,72.89 1970-06-26,74.02,74.68,73.09,73.47,9160000,73.47 1970-06-25,73.97,74.93,73.30,74.02,8200000,74.02 1970-06-24,74.76,75.42,73.40,73.97,12630000,73.97 1970-06-23,76.64,76.83,74.52,74.76,10790000,74.76 1970-06-22,77.05,77.43,75.61,76.64,8700000,76.64 1970-06-19,76.51,78.05,76.31,77.05,10980000,77.05 1970-06-18,76.00,77.17,74.99,76.51,8870000,76.51 1970-06-17,76.15,78.04,75.63,76.00,9870000,76.00 1970-06-16,74.58,76.76,74.21,76.15,11330000,76.15 1970-06-15,73.88,75.27,73.67,74.58,6920000,74.58 1970-06-12,74.45,74.84,73.25,73.88,8890000,73.88 1970-06-11,75.48,75.52,73.96,74.45,7770000,74.45 1970-06-10,76.25,76.62,74.92,75.48,7240000,75.48 1970-06-09,76.29,79.96,75.58,76.25,7050000,76.25 1970-06-08,76.17,77.37,75.30,76.29,8040000,76.29 1970-06-05,77.36,77.48,75.25,76.17,12450000,76.17 1970-06-04,78.52,79.42,76.99,77.36,14380000,77.36 1970-06-03,77.84,79.22,76.97,78.52,16600000,78.52 1970-06-02,77.84,78.73,76.51,77.84,13480000,77.84 1970-06-01,76.55,78.40,75.84,77.84,15020000,77.84 1970-05-29,74.61,76.92,73.53,76.55,14630000,76.55 1970-05-28,72.77,75.44,72.59,74.61,18910000,74.61 1970-05-27,69.37,73.22,69.37,72.77,17460000,72.77 1970-05-26,70.25,71.17,68.61,69.29,17030000,69.29 1970-05-25,72.16,72.16,69.92,70.25,12660000,70.25 1970-05-22,72.16,73.42,71.42,72.25,12170000,72.25 1970-05-21,73.51,73.51,70.94,72.16,16710000,72.16 1970-05-20,75.35,75.35,73.25,73.52,13020000,73.52 1970-05-19,76.96,77.20,75.21,75.46,9480000,75.46 1970-05-18,76.90,77.68,76.07,76.96,8280000,76.96 1970-05-15,75.44,77.42,74.59,76.90,14570000,76.90 1970-05-14,76.53,76.64,74.03,75.44,13920000,75.44 1970-05-13,77.75,77.75,75.92,76.53,10720000,76.53 1970-05-12,78.60,79.15,77.06,77.85,10850000,77.85 1970-05-11,79.44,79.72,78.29,78.60,6650000,78.60 1970-05-08,79.83,80.15,78.71,79.44,6930000,79.44 1970-05-07,79.47,80.60,78.89,79.83,9530000,79.83 1970-05-06,78.60,80.91,78.23,79.47,14380000,79.47 1970-05-05,79.37,79.83,78.02,78.60,10580000,78.60 1970-05-04,81.28,81.28,78.85,79.37,11450000,79.37 1970-05-01,81.52,82.32,80.27,81.44,8290000,81.44 1970-04-30,81.81,82.57,80.76,81.52,9880000,81.52 1970-04-29,80.27,83.23,79.31,81.81,15800000,81.81 1970-04-28,81.46,82.16,79.86,80.27,12620000,80.27 1970-04-27,82.77,83.08,81.08,81.46,10240000,81.46 1970-04-24,83.04,83.62,81.96,82.77,10410000,82.77 1970-04-23,84.27,84.30,82.61,83.04,11050000,83.04 1970-04-22,85.38,85.51,83.84,84.27,10780000,84.27 1970-04-21,85.83,86.54,84.99,85.38,8490000,85.38 1970-04-20,85.67,86.36,84.99,85.83,8280000,85.83 1970-04-17,85.88,86.36,84.75,85.67,10990000,85.67 1970-04-16,86.73,87.13,85.51,85.88,10250000,85.88 1970-04-15,86.89,87.71,86.53,86.73,9410000,86.73 1970-04-14,87.64,87.73,86.01,86.89,10840000,86.89 1970-04-13,88.24,88.67,87.15,87.64,8810000,87.64 1970-04-10,88.53,89.14,87.82,88.24,10020000,88.24 1970-04-09,88.49,89.32,87.96,88.53,9060000,88.53 1970-04-08,88.52,89.09,87.83,88.49,9070000,88.49 1970-04-07,88.76,89.31,87.94,88.52,8490000,88.52 1970-04-06,89.39,89.61,88.15,88.76,8380000,88.76 1970-04-03,89.79,90.16,88.81,89.39,9920000,89.39 1970-04-02,90.07,90.70,89.28,89.79,10520000,89.79 1970-04-01,89.63,90.62,89.30,90.07,9810000,90.07 1970-03-31,89.63,90.17,88.85,89.63,8370000,89.63 1970-03-30,89.92,90.41,88.91,89.63,9600000,89.63 1970-03-26,89.77,90.65,89.18,89.92,11350000,89.92 1970-03-25,88.11,91.07,88.11,89.77,17500000,89.77 1970-03-24,86.99,88.43,86.90,87.98,8840000,87.98 1970-03-23,87.06,87.64,86.19,86.99,7330000,86.99 1970-03-20,87.42,87.77,86.43,87.06,7910000,87.06 1970-03-19,87.54,88.20,86.88,87.42,8930000,87.42 1970-03-18,87.29,88.28,86.93,87.54,9790000,87.54 1970-03-17,86.91,87.86,86.36,87.29,9090000,87.29 1970-03-16,87.86,87.97,86.39,86.91,8910000,86.91 1970-03-13,88.33,89.43,87.29,87.86,9560000,87.86 1970-03-12,88.69,89.09,87.68,88.33,9140000,88.33 1970-03-11,88.75,89.58,88.11,88.69,9180000,88.69 1970-03-10,88.51,89.41,87.89,88.75,9450000,88.75 1970-03-09,89.43,89.43,87.94,88.51,9760000,88.51 1970-03-06,90.00,90.36,88.84,89.44,10980000,89.44 1970-03-05,90.04,90.99,89.38,90.00,11370000,90.00 1970-03-04,90.23,91.05,89.32,90.04,11850000,90.04 1970-03-03,89.71,90.67,88.96,90.23,11700000,90.23 1970-03-02,89.50,90.80,88.92,89.71,12270000,89.71 1970-02-27,88.90,90.33,88.42,89.50,12890000,89.50 1970-02-26,89.35,89.63,87.63,88.90,11540000,88.90 1970-02-25,87.99,89.80,87.11,89.35,13210000,89.35 1970-02-24,88.03,88.91,87.28,87.99,10810000,87.99 1970-02-20,87.76,88.74,86.87,88.03,10790000,88.03 1970-02-19,87.44,88.70,86.94,87.76,12890000,87.76 1970-02-18,86.37,88.07,86.19,87.44,11950000,87.44 1970-02-17,86.47,87.08,85.57,86.37,10140000,86.37 1970-02-16,86.54,87.30,85.80,86.47,19780000,86.47 1970-02-13,86.73,87.30,85.71,86.54,11060000,86.54 1970-02-12,86.94,87.54,85.93,86.73,10010000,86.73 1970-02-11,86.10,87.38,85.30,86.94,12260000,86.94 1970-02-10,87.01,87.40,85.58,86.10,10110000,86.10 1970-02-09,86.33,87.85,86.16,87.01,10830000,87.01 1970-02-06,85.90,86.88,85.23,86.33,10150000,86.33 1970-02-05,86.24,86.62,84.95,85.90,9430000,85.90 1970-02-04,86.77,87.66,85.59,86.24,11040000,86.24 1970-02-03,85.75,87.54,84.64,86.77,16050000,86.77 1970-02-02,85.02,86.76,84.76,85.75,13440000,85.75 1970-01-30,85.69,86.33,84.42,85.02,12320000,85.02 1970-01-29,86.79,87.09,85.02,85.69,12210000,85.69 1970-01-28,87.62,88.24,86.44,86.79,10510000,86.79 1970-01-27,88.17,88.54,86.92,87.62,9630000,87.62 1970-01-26,89.23,89.23,87.49,88.17,10670000,88.17 1970-01-23,90.04,90.45,88.74,89.37,11000000,89.37 1970-01-22,89.95,90.80,89.20,90.04,11050000,90.04 1970-01-21,89.83,90.61,89.20,89.95,9880000,89.95 1970-01-20,89.65,90.45,88.64,89.83,11050000,89.83 1970-01-19,90.72,90.72,89.14,89.65,9500000,89.65 1970-01-16,91.68,92.49,90.36,90.92,11940000,90.92 1970-01-15,91.65,92.35,90.73,91.68,11120000,91.68 1970-01-14,91.92,92.40,90.88,91.65,10380000,91.65 1970-01-13,91.70,92.61,90.99,91.92,9870000,91.92 1970-01-12,92.40,92.67,91.20,91.70,8900000,91.70 1970-01-09,92.68,93.25,91.82,92.40,9380000,92.40 1970-01-08,92.63,93.47,91.99,92.68,10670000,92.68 1970-01-07,92.82,93.38,91.93,92.63,10010000,92.63 1970-01-06,93.46,93.81,92.13,92.82,11460000,92.82 1970-01-05,93.00,94.25,92.53,93.46,11490000,93.46 1970-01-02,92.06,93.54,91.79,93.00,8050000,93.00 1969-12-31,91.60,92.94,91.15,92.06,19380000,92.06 1969-12-30,91.25,92.20,90.47,91.60,15790000,91.60 1969-12-29,91.89,92.49,90.66,91.25,12500000,91.25 1969-12-26,91.18,92.30,90.94,91.89,6750000,91.89 1969-12-24,90.23,91.89,89.93,91.18,11670000,91.18 1969-12-23,90.58,91.13,89.40,90.23,13890000,90.23 1969-12-22,91.38,92.03,90.10,90.58,12680000,90.58 1969-12-19,90.61,92.34,90.33,91.38,15420000,91.38 1969-12-18,89.20,91.15,88.62,90.61,15950000,90.61 1969-12-17,89.72,90.32,88.94,89.20,12840000,89.20 1969-12-16,90.54,91.05,89.23,89.72,11880000,89.72 1969-12-15,90.81,91.42,89.96,90.54,11100000,90.54 1969-12-12,90.52,91.67,90.05,90.81,11630000,90.81 1969-12-11,90.48,91.37,89.74,90.52,10430000,90.52 1969-12-10,90.55,91.22,89.33,90.48,12590000,90.48 1969-12-09,90.84,91.79,89.93,90.55,12290000,90.55 1969-12-08,91.73,92.05,90.29,90.84,9990000,90.84 1969-12-05,91.95,92.91,91.14,91.73,11150000,91.73 1969-12-04,91.65,92.45,90.36,91.95,13230000,91.95 1969-12-03,92.65,93.05,91.25,91.65,11300000,91.65 1969-12-02,93.22,93.54,91.95,92.65,9940000,92.65 1969-12-01,93.81,94.47,92.78,93.22,9950000,93.22 1969-11-28,93.27,94.41,92.88,93.81,8550000,93.81 1969-11-26,92.94,93.85,92.24,93.27,10630000,93.27 1969-11-25,93.24,94.17,92.38,92.94,11560000,92.94 1969-11-24,94.32,94.43,92.63,93.24,10940000,93.24 1969-11-21,94.91,95.34,93.87,94.32,9840000,94.32 1969-11-20,95.90,95.94,94.12,94.91,12010000,94.91 1969-11-19,96.39,96.95,95.36,95.90,11240000,95.90 1969-11-18,96.41,97.00,95.57,96.39,11010000,96.39 1969-11-17,97.07,97.36,95.82,96.41,10120000,96.41 1969-11-14,97.42,97.44,96.36,97.07,10580000,97.07 1969-11-13,97.89,98.34,96.54,97.42,12090000,97.42 1969-11-12,98.07,98.72,97.28,97.89,12480000,97.89 1969-11-11,98.33,98.79,97.45,98.07,10080000,98.07 1969-11-10,98.26,99.23,97.65,98.33,12490000,98.33 1969-11-07,97.67,99.01,97.18,98.26,13280000,98.26 1969-11-06,97.64,98.31,96.80,97.67,11110000,97.67 1969-11-05,97.21,98.39,96.75,97.64,12110000,97.64 1969-11-04,97.15,97.82,95.84,97.21,12340000,97.21 1969-11-03,97.12,97.82,96.19,97.15,11140000,97.15 1969-10-31,96.93,98.03,96.33,97.12,13100000,97.12 1969-10-30,96.81,97.47,95.61,96.93,12820000,96.93 1969-10-29,97.66,97.92,96.26,96.81,12380000,96.81 1969-10-28,97.97,98.55,97.02,97.66,12410000,97.66 1969-10-27,98.12,98.78,97.49,97.97,12160000,97.97 1969-10-24,97.46,98.83,96.97,98.12,15430000,98.12 1969-10-23,97.83,98.39,96.46,97.46,14780000,97.46 1969-10-22,97.20,98.61,96.56,97.83,19320000,97.83 1969-10-21,96.46,97.84,95.86,97.20,16460000,97.20 1969-10-20,96.26,97.17,95.29,96.46,13540000,96.46 1969-10-17,96.37,97.24,95.38,96.26,13740000,96.26 1969-10-16,95.72,97.54,95.05,96.37,19500000,96.37 1969-10-15,95.70,96.56,94.65,95.72,15740000,95.72 1969-10-14,94.55,96.53,94.32,95.70,19950000,95.70 1969-10-13,93.56,94.86,93.20,94.55,13620000,94.55 1969-10-10,93.03,94.19,92.60,93.56,12210000,93.56 1969-10-09,92.67,93.55,91.75,93.03,10420000,93.03 1969-10-08,93.09,93.56,92.04,92.67,10370000,92.67 1969-10-07,93.38,94.03,92.59,93.09,10050000,93.09 1969-10-06,93.19,93.99,92.50,93.38,9180000,93.38 1969-10-03,93.24,94.39,92.65,93.19,12410000,93.19 1969-10-02,92.52,93.63,91.66,93.24,11430000,93.24 1969-10-01,93.12,93.51,92.12,92.52,9090000,92.52 1969-09-30,93.41,94.05,92.55,93.12,9180000,93.12 1969-09-29,94.16,94.45,92.62,93.41,10170000,93.41 1969-09-26,94.77,95.23,93.53,94.16,9680000,94.16 1969-09-25,95.50,95.92,94.28,94.77,10690000,94.77 1969-09-24,95.63,96.20,94.75,95.50,11320000,95.50 1969-09-23,95.63,96.62,94.86,95.63,13030000,95.63 1969-09-22,95.19,96.13,94.58,95.63,9280000,95.63 1969-09-19,94.90,95.92,94.35,95.19,12270000,95.19 1969-09-18,94.76,95.53,94.05,94.90,11170000,94.90 1969-09-17,94.95,95.70,94.04,94.76,10980000,94.76 1969-09-16,94.87,95.73,94.06,94.95,11160000,94.95 1969-09-15,94.13,95.61,93.73,94.87,10680000,94.87 1969-09-12,94.22,95.04,93.26,94.13,10800000,94.13 1969-09-11,94.95,95.77,93.72,94.22,12370000,94.22 1969-09-10,93.38,95.35,93.23,94.95,11490000,94.95 1969-09-09,92.70,93.94,91.77,93.38,10980000,93.38 1969-09-08,93.64,93.76,92.35,92.70,8310000,92.70 1969-09-05,94.20,94.51,93.09,93.64,8890000,93.64 1969-09-04,94.98,95.20,93.66,94.20,9380000,94.20 1969-09-03,95.54,96.11,94.38,94.98,8760000,94.98 1969-09-02,95.51,96.31,94.85,95.54,8560000,95.54 1969-08-29,94.89,95.51,94.46,95.51,8850000,95.51 1969-08-28,94.49,95.38,94.04,94.89,7730000,94.89 1969-08-27,94.30,95.16,93.76,94.49,9100000,94.49 1969-08-26,94.93,95.04,93.65,94.30,8910000,94.30 1969-08-25,95.92,96.13,94.52,94.93,8410000,94.93 1969-08-22,95.35,96.43,94.91,95.92,10140000,95.92 1969-08-21,95.07,95.87,94.56,95.35,8420000,95.35 1969-08-20,95.07,95.64,94.25,95.07,9680000,95.07 1969-08-19,94.57,95.18,93.95,95.07,12640000,95.07 1969-08-18,94.00,95.00,93.51,94.57,9420000,94.57 1969-08-15,93.34,94.50,92.92,94.00,10210000,94.00 1969-08-14,92.70,93.87,92.32,93.34,9690000,93.34 1969-08-13,92.63,93.26,91.48,92.70,9910000,92.70 1969-08-12,93.36,93.66,92.19,92.63,7870000,92.63 1969-08-11,93.94,94.24,92.77,93.36,6680000,93.36 1969-08-08,93.99,94.63,93.29,93.94,8760000,93.94 1969-08-07,93.92,94.77,93.17,93.99,9450000,93.99 1969-08-06,93.41,94.76,93.02,93.92,11100000,93.92 1969-08-05,92.99,94.02,92.13,93.41,8940000,93.41 1969-08-04,93.47,94.42,92.29,92.99,10700000,92.99 1969-08-01,91.92,94.19,91.92,93.47,15070000,93.47 1969-07-31,89.96,92.40,89.96,91.83,14160000,91.83 1969-07-30,89.48,90.82,88.04,89.93,15580000,89.93 1969-07-29,90.21,91.56,89.06,89.48,13630000,89.48 1969-07-28,91.91,91.91,89.83,90.21,11800000,90.21 1969-07-25,92.80,93.28,91.54,92.06,9800000,92.06 1969-07-24,93.12,93.87,92.29,92.80,9750000,92.80 1969-07-23,93.52,93.99,92.07,93.12,11680000,93.12 1969-07-22,94.95,95.45,93.15,93.52,9780000,93.52 1969-07-18,95.76,95.84,94.18,94.95,8590000,94.95 1969-07-17,95.18,96.71,95.07,95.76,10450000,95.76 1969-07-16,94.24,95.83,94.22,95.18,10470000,95.18 1969-07-15,94.55,95.00,93.11,94.24,11110000,94.24 1969-07-14,95.77,96.17,94.20,94.55,8310000,94.55 1969-07-11,95.38,96.65,94.81,95.77,11730000,95.77 1969-07-10,96.88,97.04,95.03,95.38,11450000,95.38 1969-07-09,97.63,97.85,96.33,96.88,9320000,96.88 1969-07-08,98.98,98.98,97.15,97.63,9320000,97.63 1969-07-07,99.61,100.33,98.45,99.03,9970000,99.03 1969-07-03,98.94,100.25,98.62,99.61,10110000,99.61 1969-07-02,98.08,99.50,97.81,98.94,11350000,98.94 1969-07-01,97.71,98.66,97.13,98.08,9890000,98.08 1969-06-30,97.33,98.64,96.82,97.71,8640000,97.71 1969-06-27,97.25,98.15,96.65,97.33,9020000,97.33 1969-06-26,97.01,97.91,95.97,97.25,10310000,97.25 1969-06-25,97.32,98.30,96.56,97.01,10490000,97.01 1969-06-24,96.29,98.04,96.29,97.32,11460000,97.32 1969-06-23,96.67,97.17,95.21,96.23,12900000,96.23 1969-06-20,97.24,98.22,96.29,96.67,11360000,96.67 1969-06-19,97.81,98.38,96.61,97.24,11160000,97.24 1969-06-18,97.95,99.20,97.45,97.81,11290000,97.81 1969-06-17,98.32,98.71,96.88,97.95,12210000,97.95 1969-06-16,98.65,99.64,97.91,98.32,10400000,98.32 1969-06-13,98.26,99.51,97.59,98.65,13070000,98.65 1969-06-12,99.05,99.78,97.96,98.26,11790000,98.26 1969-06-11,100.42,100.71,99.02,99.05,13640000,99.05 1969-06-10,101.20,101.76,100.02,100.42,10660000,100.42 1969-06-09,102.12,102.16,100.54,101.20,10650000,101.20 1969-06-06,102.76,103.41,101.68,102.12,12520000,102.12 1969-06-05,102.59,103.45,102.05,102.76,12350000,102.76 1969-06-04,102.63,103.45,102.07,102.59,10840000,102.59 1969-06-03,102.94,103.60,102.09,102.63,11190000,102.63 1969-06-02,103.46,103.75,102.40,102.94,9180000,102.94 1969-05-29,103.26,104.27,102.76,103.46,11770000,103.46 1969-05-28,103.57,103.91,102.29,103.26,11330000,103.26 1969-05-27,104.36,104.68,103.12,103.57,10580000,103.57 1969-05-26,104.59,105.14,103.80,104.36,9030000,104.36 1969-05-23,104.60,105.32,103.78,104.59,10900000,104.59 1969-05-22,104.47,105.66,103.92,104.60,13710000,104.60 1969-05-21,104.04,105.03,103.37,104.47,12100000,104.47 1969-05-20,104.97,105.16,103.56,104.04,10280000,104.04 1969-05-19,105.94,106.15,104.52,104.97,9790000,104.97 1969-05-16,105.85,106.59,105.18,105.94,12280000,105.94 1969-05-15,106.16,106.69,105.08,105.85,11930000,105.85 1969-05-14,105.34,106.74,105.07,106.16,14360000,106.16 1969-05-13,104.89,105.91,104.31,105.34,12910000,105.34 1969-05-12,105.05,105.65,104.12,104.89,10550000,104.89 1969-05-09,105.10,106.01,104.35,105.05,12530000,105.05 1969-05-08,104.67,105.74,104.10,105.10,13050000,105.10 1969-05-07,104.86,105.59,103.83,104.67,14030000,104.67 1969-05-06,104.37,105.50,103.84,104.86,14700000,104.86 1969-05-05,104.00,105.08,103.48,104.37,13300000,104.37 1969-05-02,103.51,104.63,102.98,104.00,13070000,104.00 1969-05-01,103.69,104.59,102.74,103.51,14380000,103.51 1969-04-30,102.79,104.56,102.50,103.69,19350000,103.69 1969-04-29,102.03,103.31,101.51,102.79,14730000,102.79 1969-04-28,101.72,102.65,100.97,102.03,11120000,102.03 1969-04-25,101.27,102.29,100.81,101.72,12480000,101.72 1969-04-24,100.80,101.80,100.21,101.27,11340000,101.27 1969-04-23,100.78,101.77,100.15,100.80,12220000,100.80 1969-04-22,100.56,101.29,99.52,100.78,10250000,100.78 1969-04-21,101.24,101.68,100.11,100.56,10010000,100.56 1969-04-18,100.78,102.09,100.30,101.24,10850000,101.24 1969-04-17,100.63,101.41,99.99,100.78,9360000,100.78 1969-04-16,101.53,101.78,100.16,100.63,9680000,100.63 1969-04-15,101.57,102.15,100.76,101.53,9610000,101.53 1969-04-14,101.65,102.40,101.02,101.57,8990000,101.57 1969-04-11,101.55,102.28,100.97,101.65,10650000,101.65 1969-04-10,101.02,102.22,100.73,101.55,12200000,101.55 1969-04-09,100.14,101.44,99.88,101.02,12530000,101.02 1969-04-08,99.89,101.27,99.35,100.14,9360000,100.14 1969-04-07,100.63,100.63,99.08,99.89,9430000,99.89 1969-04-03,100.78,101.30,99.87,100.68,10300000,100.68 1969-04-02,101.42,101.65,100.61,100.78,10110000,100.78 1969-04-01,101.51,102.45,100.84,101.42,12360000,101.42 1969-03-28,101.10,102.35,100.73,101.51,12430000,101.51 1969-03-27,100.39,101.81,100.03,101.10,11900000,101.10 1969-03-26,99.66,100.86,99.24,100.39,11030000,100.39 1969-03-25,99.50,100.30,98.88,99.66,9820000,99.66 1969-03-24,99.63,100.16,98.85,99.50,8110000,99.50 1969-03-21,99.84,100.37,98.88,99.63,9830000,99.63 1969-03-20,99.21,100.39,98.90,99.84,10260000,99.84 1969-03-19,98.49,99.70,98.03,99.21,9740000,99.21 1969-03-18,98.25,99.41,97.83,98.49,11210000,98.49 1969-03-17,98.00,98.71,97.06,98.25,9150000,98.25 1969-03-14,98.39,98.70,97.40,98.00,8640000,98.00 1969-03-13,99.05,99.35,97.82,98.39,10030000,98.39 1969-03-12,99.32,99.87,98.35,99.05,8720000,99.05 1969-03-11,98.99,100.14,98.58,99.32,9870000,99.32 1969-03-10,98.65,99.47,97.87,98.99,8920000,98.99 1969-03-07,98.70,99.13,97.32,98.65,10830000,98.65 1969-03-06,99.71,99.93,98.11,98.70,9670000,98.70 1969-03-05,99.32,100.48,98.95,99.71,11370000,99.71 1969-03-04,98.38,99.76,98.17,99.32,9320000,99.32 1969-03-03,98.13,99.08,97.61,98.38,8260000,98.38 1969-02-28,98.14,99.02,97.53,98.13,8990000,98.13 1969-02-27,98.45,99.00,97.50,98.14,9670000,98.14 1969-02-26,97.98,99.10,97.36,98.45,9540000,98.45 1969-02-25,98.60,99.65,97.50,97.98,9540000,97.98 1969-02-24,99.79,100.07,98.09,98.60,12730000,98.60 1969-02-20,100.65,101.03,99.29,99.79,10990000,99.79 1969-02-19,101.40,102.07,100.30,100.65,10390000,100.65 1969-02-18,102.27,102.27,100.58,101.40,12490000,101.40 1969-02-17,103.61,104.03,102.04,102.44,11670000,102.44 1969-02-14,103.71,104.37,102.88,103.61,11460000,103.61 1969-02-13,103.63,104.36,102.86,103.71,12010000,103.71 1969-02-12,103.65,104.34,102.98,103.63,11530000,103.63 1969-02-11,103.53,104.61,102.96,103.65,12320000,103.65 1969-02-07,103.54,104.22,102.50,103.53,12780000,103.53 1969-02-06,103.20,104.30,102.55,103.54,12570000,103.54 1969-02-05,102.92,103.84,102.26,103.20,13750000,103.20 1969-02-04,102.89,103.59,102.15,102.92,12550000,102.92 1969-02-03,103.01,103.75,102.04,102.89,12510000,102.89 1969-01-31,102.55,103.64,102.08,103.01,12020000,103.01 1969-01-30,102.51,103.33,101.73,102.55,13010000,102.55 1969-01-29,102.41,103.31,101.69,102.51,11470000,102.51 1969-01-28,102.40,103.30,101.56,102.41,12070000,102.41 1969-01-27,102.38,103.15,101.64,102.40,11020000,102.40 1969-01-24,102.43,103.23,101.71,102.38,12520000,102.38 1969-01-23,101.98,103.21,101.57,102.43,13140000,102.43 1969-01-22,101.63,102.55,101.06,101.98,11480000,101.98 1969-01-21,101.69,102.40,100.88,101.63,10910000,101.63 1969-01-20,102.03,102.60,101.00,101.69,10950000,101.69 1969-01-17,102.18,103.06,101.32,102.03,11590000,102.03 1969-01-16,101.62,103.25,101.27,102.18,13120000,102.18 1969-01-15,101.13,102.48,100.78,101.62,11810000,101.62 1969-01-14,100.44,101.63,99.04,101.13,10700000,101.13 1969-01-13,100.93,101.35,96.63,100.44,11160000,100.44 1969-01-10,101.22,102.14,100.32,100.93,12680000,100.93 1969-01-09,100.80,102.09,100.35,101.22,12100000,101.22 1969-01-08,101.22,102.12,100.14,100.80,13840000,100.80 1969-01-07,102.47,102.68,100.15,101.22,15740000,101.22 1969-01-06,103.99,104.36,101.94,102.47,12720000,102.47 1969-01-03,103.93,104.87,103.17,103.99,12750000,103.99 1969-01-02,103.86,104.85,103.21,103.93,9800000,103.93 1968-12-31,103.80,104.61,102.98,103.86,13130000,103.86 1968-12-30,104.74,104.99,103.09,103.80,12080000,103.80 1968-12-27,105.15,105.87,104.20,104.74,11200000,104.74 1968-12-26,105.04,106.03,104.29,105.15,9670000,105.15 1968-12-24,105.21,105.95,104.37,105.04,11540000,105.04 1968-12-23,106.34,106.68,104.61,105.21,12970000,105.21 1968-12-20,106.97,107.98,105.73,106.34,15910000,106.34 1968-12-19,106.66,107.67,105.10,106.97,19630000,106.97 1968-12-17,107.10,107.65,105.86,106.66,14700000,106.66 1968-12-16,107.58,108.40,106.40,107.10,15950000,107.10 1968-12-13,107.32,108.50,106.56,107.58,16740000,107.58 1968-12-12,107.39,108.43,106.33,107.32,18160000,107.32 1968-12-10,107.66,108.33,106.68,107.39,14500000,107.39 1968-12-09,107.93,108.77,106.89,107.66,15800000,107.66 1968-12-06,107.67,108.91,106.85,107.93,15320000,107.93 1968-12-05,108.02,108.90,106.71,107.67,19330000,107.67 1968-12-03,108.12,108.74,107.02,108.02,15460000,108.02 1968-12-02,108.37,109.37,107.15,108.12,15390000,108.12 1968-11-29,107.76,109.09,107.32,108.37,14390000,108.37 1968-11-27,107.26,108.55,106.59,107.76,16550000,107.76 1968-11-26,106.48,107.93,106.11,107.26,16360000,107.26 1968-11-25,106.30,107.29,105.47,106.48,14490000,106.48 1968-11-22,105.97,106.89,105.21,106.30,15420000,106.30 1968-11-21,106.14,106.77,104.85,105.97,18320000,105.97 1968-11-19,105.92,106.84,105.06,106.14,15120000,106.14 1968-11-18,105.78,106.74,105.05,105.92,14390000,105.92 1968-11-15,105.20,106.44,104.61,105.78,15040000,105.78 1968-11-14,105.13,106.01,104.34,105.20,14900000,105.20 1968-11-13,104.62,105.76,104.08,105.13,15660000,105.13 1968-11-12,103.95,105.28,103.51,104.62,17250000,104.62 1968-11-08,103.50,104.59,102.96,103.95,14250000,103.95 1968-11-07,103.27,104.47,102.31,103.50,11660000,103.50 1968-11-06,103.10,104.41,102.45,103.27,12640000,103.27 1968-11-04,103.06,103.69,101.85,103.10,10930000,103.10 1968-11-01,103.41,104.30,102.36,103.06,14480000,103.06 1968-10-31,103.30,104.57,102.43,103.41,17650000,103.41 1968-10-29,103.90,104.50,102.65,103.30,12340000,103.30 1968-10-28,104.20,104.89,103.16,103.90,11740000,103.90 1968-10-25,103.84,104.81,103.14,104.20,14150000,104.20 1968-10-24,104.57,105.15,103.15,103.84,18300000,103.84 1968-10-22,104.99,105.48,103.84,104.57,13970000,104.57 1968-10-21,104.82,105.78,104.09,104.99,14380000,104.99 1968-10-18,104.01,105.34,103.54,104.82,15130000,104.82 1968-10-17,103.81,105.01,103.81,104.01,21060000,104.01 1968-10-15,103.32,104.25,102.66,103.53,13410000,103.53 1968-10-14,103.18,104.03,102.48,103.32,11980000,103.32 1968-10-11,103.29,103.90,102.39,103.18,12650000,103.18 1968-10-10,103.74,104.30,102.61,103.29,17000000,103.29 1968-10-08,103.70,104.45,102.84,103.74,14000000,103.74 1968-10-07,103.71,104.40,102.93,103.70,12420000,103.70 1968-10-04,103.22,104.35,102.65,103.71,15350000,103.71 1968-10-03,102.86,104.13,102.34,103.22,21110000,103.22 1968-10-01,102.67,103.58,101.80,102.86,15560000,102.86 1968-09-30,102.31,103.29,101.71,102.67,13610000,102.67 1968-09-27,102.36,103.07,101.36,102.31,13860000,102.31 1968-09-26,102.59,103.63,101.59,102.36,18950000,102.36 1968-09-24,102.24,103.21,101.59,102.59,15210000,102.59 1968-09-23,101.66,102.82,101.20,102.24,11550000,102.24 1968-09-20,101.59,102.37,100.81,101.66,14190000,101.66 1968-09-19,101.50,102.53,100.84,101.59,17910000,101.59 1968-09-17,101.24,102.18,100.64,101.50,13920000,101.50 1968-09-16,100.86,102.01,100.33,101.24,13260000,101.24 1968-09-13,100.52,101.53,99.89,100.86,13070000,100.86 1968-09-12,100.73,101.40,99.70,100.52,14630000,100.52 1968-09-10,101.23,101.81,100.12,100.73,11430000,100.73 1968-09-09,101.20,102.09,100.47,101.23,11890000,101.23 1968-09-06,100.74,101.88,100.23,101.20,13180000,101.20 1968-09-05,100.02,101.34,99.63,100.74,12980000,100.74 1968-09-04,99.32,100.49,98.95,100.02,10040000,100.02 1968-09-03,98.86,99.89,98.31,99.32,8620000,99.32 1968-08-30,98.74,99.52,98.20,98.86,8190000,98.86 1968-08-29,98.81,99.49,97.90,98.74,10940000,98.74 1968-08-27,98.94,99.61,98.16,98.81,9710000,98.81 1968-08-26,98.69,99.67,98.29,98.94,9740000,98.94 1968-08-23,98.70,99.57,97.71,98.69,9890000,98.69 1968-08-22,98.96,99.58,97.71,98.70,15140000,98.70 1968-08-20,99.00,99.65,98.08,98.96,10640000,98.96 1968-08-19,98.68,99.64,98.16,99.00,9900000,99.00 1968-08-16,98.07,99.21,97.62,98.68,9940000,98.68 1968-08-15,98.53,99.36,97.48,98.07,12710000,98.07 1968-08-13,98.01,99.20,97.68,98.53,12730000,98.53 1968-08-12,97.01,98.49,96.72,98.01,10420000,98.01 1968-08-09,97.04,97.56,96.11,97.01,8390000,97.01 1968-08-08,97.25,98.32,96.58,97.04,12920000,97.04 1968-08-06,96.85,97.82,96.42,97.25,9620000,97.25 1968-08-05,96.63,97.51,95.95,96.85,8850000,96.85 1968-08-02,97.28,97.47,95.79,96.63,9860000,96.63 1968-08-01,97.74,98.82,96.78,97.28,14380000,97.28 1968-07-30,97.65,98.62,96.84,97.74,10250000,97.74 1968-07-29,98.34,98.78,96.89,97.65,10940000,97.65 1968-07-26,97.94,99.14,97.22,98.34,11690000,98.34 1968-07-25,99.21,100.07,97.43,97.94,16140000,97.94 1968-07-23,99.33,99.93,97.89,99.21,13570000,99.21 1968-07-22,100.46,100.88,98.51,99.33,13530000,99.33 1968-07-19,101.44,101.82,99.80,100.46,14620000,100.46 1968-07-18,101.70,102.65,100.49,101.44,17420000,101.44 1968-07-16,102.26,102.72,100.97,101.70,13380000,101.70 1968-07-15,102.34,103.15,101.44,102.26,13390000,102.26 1968-07-12,102.39,103.24,101.39,102.34,14810000,102.34 1968-07-11,102.23,103.67,101.41,102.39,20290000,102.39 1968-07-09,101.94,102.93,101.19,102.23,16540000,102.23 1968-07-08,100.91,102.76,100.72,101.94,16860000,101.94 1968-07-03,99.74,101.36,99.60,100.91,14390000,100.91 1968-07-02,99.40,100.60,98.60,99.74,13350000,99.74 1968-07-01,99.58,100.33,98.77,99.40,11280000,99.40 1968-06-28,99.98,100.63,98.91,99.58,12040000,99.58 1968-06-27,100.08,101.01,99.11,99.98,15370000,99.98 1968-06-25,100.39,101.10,99.28,100.08,13200000,100.08 1968-06-24,100.66,101.48,99.66,100.39,12320000,100.39 1968-06-21,101.51,101.59,99.80,100.66,13450000,100.66 1968-06-20,99.99,101.60,99.52,101.51,16290000,101.51 1968-06-18,100.13,101.09,99.43,99.99,13630000,99.99 1968-06-17,101.13,101.71,99.43,100.13,12570000,100.13 1968-06-14,101.25,101.82,99.98,101.13,14690000,101.13 1968-06-13,101.66,102.84,100.55,101.25,21350000,101.25 1968-06-11,101.41,102.40,100.74,101.66,15700000,101.66 1968-06-10,101.27,102.25,100.42,101.41,14640000,101.41 1968-06-07,100.65,101.89,100.24,101.27,17320000,101.27 1968-06-06,99.89,101.59,99.50,100.65,16130000,100.65 1968-06-05,100.38,101.13,99.26,99.89,15590000,99.89 1968-06-04,99.99,101.26,99.32,100.38,18030000,100.38 1968-06-03,98.72,100.62,98.72,99.99,14970000,99.99 1968-05-31,97.92,99.40,97.66,98.68,13090000,98.68 1968-05-29,97.62,98.74,97.01,97.92,14100000,97.92 1968-05-28,96.99,98.20,96.41,97.62,13850000,97.62 1968-05-27,97.15,97.81,96.29,96.99,12720000,96.99 1968-05-24,96.97,97.73,96.21,97.15,13300000,97.15 1968-05-23,97.18,97.79,96.38,96.97,12840000,96.97 1968-05-22,96.93,98.17,96.47,97.18,14200000,97.18 1968-05-21,96.45,97.52,95.92,96.93,13160000,96.93 1968-05-20,96.90,97.41,95.80,96.45,11180000,96.45 1968-05-17,97.60,97.81,96.11,96.90,11830000,96.90 1968-05-16,98.07,98.69,97.05,97.60,13030000,97.60 1968-05-15,98.12,98.79,97.32,98.07,13180000,98.07 1968-05-14,98.19,98.85,97.33,98.12,13160000,98.12 1968-05-13,98.50,99.10,97.52,98.19,11860000,98.19 1968-05-10,98.39,99.30,97.76,98.50,11700000,98.50 1968-05-09,98.91,99.47,97.68,98.39,12890000,98.39 1968-05-08,98.90,99.74,98.25,98.91,13120000,98.91 1968-05-07,98.35,99.59,97.86,98.90,13920000,98.90 1968-05-06,98.66,99.11,97.27,98.35,12160000,98.35 1968-05-03,98.59,100.19,97.98,98.66,17990000,98.66 1968-05-02,97.97,99.18,97.53,98.59,14260000,98.59 1968-05-01,97.46,98.61,96.84,97.97,14440000,97.97 1968-04-30,97.97,98.17,96.58,97.46,14380000,97.46 1968-04-29,97.21,98.61,96.81,97.97,12030000,97.97 1968-04-26,96.62,97.83,96.22,97.21,13500000,97.21 1968-04-25,96.92,97.48,95.68,96.62,14430000,96.62 1968-04-24,96.62,97.81,95.98,96.92,14810000,96.92 1968-04-23,95.68,97.48,95.68,96.62,14010000,96.62 1968-04-22,95.85,96.07,94.22,95.32,11720000,95.32 1968-04-19,97.08,97.08,95.15,95.85,14560000,95.85 1968-04-18,96.81,97.89,96.12,97.08,15890000,97.08 1968-04-17,96.62,97.40,95.76,96.81,14090000,96.81 1968-04-16,96.59,97.54,95.72,96.62,15680000,96.62 1968-04-15,96.53,97.36,95.33,96.59,14220000,96.59 1968-04-11,95.67,96.93,94.81,96.53,14230000,96.53 1968-04-10,94.95,97.11,94.74,95.67,20410000,95.67 1968-04-08,93.29,95.45,93.11,94.95,13010000,94.95 1968-04-05,93.84,94.51,92.67,93.29,12570000,93.29 1968-04-04,93.47,94.59,92.63,93.84,14340000,93.84 1968-04-03,92.64,95.13,92.24,93.47,19290000,93.47 1968-04-02,92.48,93.44,91.39,92.64,14520000,92.64 1968-04-01,91.11,93.55,91.11,92.48,17730000,92.48 1968-03-29,89.57,90.92,89.21,90.20,9000000,90.20 1968-03-28,89.66,90.40,89.05,89.57,8000000,89.57 1968-03-27,88.93,90.20,88.88,89.66,8970000,89.66 1968-03-26,88.33,89.50,88.10,88.93,8670000,88.93 1968-03-25,88.42,88.88,87.65,88.33,6700000,88.33 1968-03-22,88.33,89.14,87.50,88.42,9900000,88.42 1968-03-21,88.98,89.48,88.05,88.33,8580000,88.33 1968-03-20,88.99,89.65,88.48,88.98,7390000,88.98 1968-03-19,89.59,90.05,88.61,88.99,7410000,88.99 1968-03-18,89.11,91.09,89.11,89.59,10800000,89.59 1968-03-15,88.32,89.75,87.61,89.10,11210000,89.10 1968-03-14,89.75,89.75,87.81,88.32,11640000,88.32 1968-03-13,90.23,90.71,89.40,90.03,8990000,90.03 1968-03-12,90.13,90.78,89.39,90.23,9250000,90.23 1968-03-11,89.03,90.56,88.81,90.13,9520000,90.13 1968-03-08,89.10,89.57,88.23,89.03,7410000,89.03 1968-03-07,89.26,89.98,88.44,89.10,8630000,89.10 1968-03-06,87.72,89.76,87.64,89.26,9900000,89.26 1968-03-05,87.92,88.72,86.99,87.72,11440000,87.72 1968-03-04,89.11,89.33,87.52,87.92,10590000,87.92 1968-03-01,89.36,89.82,88.58,89.11,8610000,89.11 1968-02-29,90.08,90.24,88.93,89.36,7700000,89.36 1968-02-28,90.53,91.19,89.71,90.08,8020000,90.08 1968-02-27,90.18,90.91,89.56,90.53,7600000,90.53 1968-02-26,90.89,91.08,89.67,90.18,7810000,90.18 1968-02-23,91.24,91.80,90.28,90.89,8810000,90.89 1968-02-21,91.24,91.87,90.54,91.24,9170000,91.24 1968-02-20,90.31,91.34,89.95,91.24,8800000,91.24 1968-02-19,89.96,90.87,89.42,90.31,7270000,90.31 1968-02-16,90.30,90.62,89.28,89.96,9070000,89.96 1968-02-15,90.30,90.30,90.30,90.30,9770000,90.30 1968-02-14,89.07,90.60,88.66,90.14,11390000,90.14 1968-02-13,89.86,90.46,86.73,89.07,10830000,89.07 1968-02-09,90.90,91.00,89.23,89.86,11850000,89.86 1968-02-08,92.06,92.40,90.60,90.90,9660000,90.90 1968-02-07,91.90,92.74,91.48,92.06,8380000,92.06 1968-02-06,91.87,92.52,91.15,91.90,8560000,91.90 1968-02-05,92.27,92.72,91.24,91.87,8980000,91.87 1968-02-02,92.56,93.44,91.69,92.27,10120000,92.27 1968-02-01,92.24,93.14,91.57,92.56,10590000,92.56 1968-01-31,92.89,93.26,91.27,92.24,9410000,92.24 1968-01-30,93.35,93.71,92.18,92.89,10110000,92.89 1968-01-29,93.45,94.38,92.71,93.35,9950000,93.35 1968-01-26,93.30,94.34,92.77,93.45,9980000,93.45 1968-01-25,93.17,94.11,91.96,93.30,12410000,93.30 1968-01-24,93.66,94.12,92.45,93.17,10570000,93.17 1968-01-23,94.03,94.66,92.88,93.66,11030000,93.66 1968-01-22,95.24,95.40,93.55,94.03,10630000,94.03 1968-01-19,95.56,96.22,94.60,95.24,11950000,95.24 1968-01-18,95.64,96.66,95.01,95.56,13840000,95.56 1968-01-17,95.82,96.41,94.78,95.64,12910000,95.64 1968-01-16,96.42,96.91,95.32,95.82,12340000,95.82 1968-01-15,96.72,97.46,95.85,96.42,12640000,96.42 1968-01-12,96.62,97.44,95.87,96.72,13080000,96.72 1968-01-11,96.52,97.82,95.88,96.62,13220000,96.62 1968-01-10,96.50,97.26,95.66,96.52,11670000,96.52 1968-01-09,96.62,97.84,95.89,96.50,13720000,96.50 1968-01-08,95.94,97.40,95.54,96.62,14260000,96.62 1968-01-05,95.36,96.66,94.97,95.94,11880000,95.94 1968-01-04,95.67,96.23,94.31,95.36,13440000,95.36 1968-01-03,96.11,96.95,95.04,95.67,12650000,95.67 1968-01-02,96.47,97.33,95.31,96.11,11080000,96.11 1967-12-29,95.89,96.90,95.85,96.47,14950000,96.47 1967-12-28,95.91,96.65,94.91,95.89,12530000,95.89 1967-12-27,95.26,96.42,94.82,95.91,12690000,95.91 1967-12-26,95.20,96.02,94.61,95.26,9150000,95.26 1967-12-22,95.38,96.11,94.61,95.20,9570000,95.20 1967-12-21,95.15,96.25,94.69,95.38,11010000,95.38 1967-12-20,94.63,95.75,94.17,95.15,11390000,95.15 1967-12-19,94.77,95.41,94.00,94.63,10610000,94.63 1967-12-18,95.03,95.88,94.17,94.77,10320000,94.77 1967-12-15,95.47,96.20,94.51,95.03,11530000,95.03 1967-12-14,95.34,96.35,94.85,95.47,12310000,95.47 1967-12-13,95.01,96.00,94.58,95.34,12480000,95.34 1967-12-12,95.12,95.78,94.34,95.01,10860000,95.01 1967-12-11,95.42,95.99,94.50,95.12,10500000,95.12 1967-12-08,95.53,96.25,94.78,95.42,10710000,95.42 1967-12-07,95.64,96.67,95.04,95.53,12490000,95.53 1967-12-06,95.23,96.16,94.10,95.64,11940000,95.64 1967-12-05,95.10,96.27,94.52,95.23,12940000,95.23 1967-12-04,94.50,95.68,94.09,95.10,11740000,95.10 1967-12-01,94.00,94.95,93.41,94.50,9740000,94.50 1967-11-30,94.47,94.94,93.49,94.00,8860000,94.00 1967-11-29,94.49,95.51,93.85,94.47,11400000,94.47 1967-11-28,94.17,95.08,93.57,94.49,11040000,94.49 1967-11-27,93.90,94.80,93.32,94.17,10040000,94.17 1967-11-24,93.65,94.46,92.74,93.90,9470000,93.90 1967-11-22,93.10,94.41,92.70,93.65,12180000,93.65 1967-11-21,91.65,93.71,91.64,93.10,12300000,93.10 1967-11-20,92.38,92.38,90.09,91.65,12750000,91.65 1967-11-17,92.60,93.62,92.02,92.82,10050000,92.82 1967-11-16,91.76,93.28,91.50,92.60,10570000,92.60 1967-11-15,91.39,92.25,90.44,91.76,10000000,91.76 1967-11-14,91.97,92.49,90.81,91.39,10350000,91.39 1967-11-13,92.21,93.23,91.46,91.97,10130000,91.97 1967-11-10,91.59,92.84,91.29,92.21,9960000,92.21 1967-11-09,91.14,92.25,90.61,91.59,8890000,91.59 1967-11-08,91.48,93.07,90.80,91.14,12630000,91.14 1967-11-06,91.78,92.23,90.39,91.48,10320000,91.48 1967-11-03,92.34,92.90,91.33,91.78,8800000,91.78 1967-11-02,92.71,93.69,91.85,92.34,10760000,92.34 1967-11-01,93.30,94.21,92.45,92.71,10930000,92.71 1967-10-31,94.79,95.25,93.29,93.30,12020000,93.30 1967-10-30,94.96,95.67,94.14,94.79,10250000,94.79 1967-10-27,94.94,95.79,94.31,94.96,9880000,94.96 1967-10-26,94.52,95.56,93.99,94.94,9920000,94.94 1967-10-25,94.42,95.18,93.47,94.52,10300000,94.52 1967-10-24,94.96,95.98,94.05,94.42,11110000,94.42 1967-10-23,95.38,95.69,93.92,94.96,9680000,94.96 1967-10-20,95.43,96.12,94.62,95.38,9510000,95.38 1967-10-19,95.25,96.46,94.86,95.43,11620000,95.43 1967-10-18,95.00,95.82,94.34,95.25,10500000,95.25 1967-10-17,95.25,95.92,94.19,95.00,10290000,95.00 1967-10-16,96.00,96.55,94.85,95.25,9080000,95.25 1967-10-13,95.75,96.69,95.16,96.00,9040000,96.00 1967-10-12,96.37,96.70,95.32,95.75,7770000,95.75 1967-10-11,96.84,97.34,95.70,96.37,11230000,96.37 1967-10-10,97.51,98.15,96.38,96.84,12000000,96.84 1967-10-09,97.26,98.25,96.70,97.51,11180000,97.51 1967-10-06,96.67,97.83,96.34,97.26,9830000,97.26 1967-10-05,96.43,97.25,95.89,96.67,8490000,96.67 1967-10-04,96.65,97.47,95.94,96.43,11520000,96.43 1967-10-03,96.32,97.23,95.75,96.65,10320000,96.65 1967-10-02,96.71,97.25,95.82,96.32,9240000,96.32 1967-09-29,96.79,97.37,96.06,96.71,9710000,96.71 1967-09-28,96.79,97.59,96.19,96.79,10470000,96.79 1967-09-27,96.76,97.54,96.00,96.79,8810000,96.79 1967-09-26,97.59,98.20,96.40,96.76,10940000,96.76 1967-09-25,97.00,98.31,96.74,97.59,10910000,97.59 1967-09-22,96.75,97.61,96.11,97.00,11160000,97.00 1967-09-21,96.13,97.50,95.67,96.75,11290000,96.75 1967-09-20,96.17,96.84,95.39,96.13,10980000,96.13 1967-09-19,96.53,97.35,95.84,96.17,11540000,96.17 1967-09-18,96.27,97.31,95.73,96.53,11620000,96.53 1967-09-15,96.20,96.94,95.47,96.27,10270000,96.27 1967-09-14,95.99,97.40,95.59,96.20,12220000,96.20 1967-09-13,94.99,96.62,94.80,95.99,12400000,95.99 1967-09-12,94.54,95.48,94.01,94.99,9930000,94.99 1967-09-11,94.36,95.26,93.88,94.54,9170000,94.54 1967-09-08,94.33,95.04,93.70,94.36,9300000,94.36 1967-09-07,94.39,94.95,93.70,94.33,8910000,94.33 1967-09-06,94.21,95.06,93.72,94.39,9550000,94.39 1967-09-05,93.68,94.70,93.36,94.21,8320000,94.21 1967-09-01,93.64,94.21,93.00,93.68,7460000,93.68 1967-08-31,93.07,94.19,92.84,93.64,8840000,93.64 1967-08-30,92.88,93.67,92.43,93.07,7200000,93.07 1967-08-29,92.64,93.58,92.17,92.88,6350000,92.88 1967-08-28,92.70,93.31,92.01,92.64,6270000,92.64 1967-08-25,93.09,93.38,92.04,92.70,7250000,92.70 1967-08-24,93.61,94.28,92.77,93.09,7740000,93.09 1967-08-23,93.74,94.15,92.77,93.61,8760000,93.61 1967-08-22,94.25,94.72,93.35,93.74,7940000,93.74 1967-08-21,94.78,95.22,93.79,94.25,8600000,94.25 1967-08-18,94.63,95.40,94.16,94.78,8250000,94.78 1967-08-17,94.55,95.33,94.11,94.63,8790000,94.63 1967-08-16,94.77,95.15,93.93,94.55,8220000,94.55 1967-08-15,94.64,95.54,94.18,94.77,8710000,94.77 1967-08-14,95.15,95.40,94.02,94.64,7990000,94.64 1967-08-11,95.53,95.98,94.62,95.15,8250000,95.15 1967-08-10,95.78,96.67,95.05,95.53,9040000,95.53 1967-08-09,95.69,96.47,95.11,95.78,10100000,95.78 1967-08-08,95.58,96.28,95.04,95.69,8970000,95.69 1967-08-07,95.83,96.43,95.02,95.58,10160000,95.58 1967-08-04,95.66,96.54,95.15,95.83,11130000,95.83 1967-08-03,95.78,96.36,94.42,95.66,13440000,95.66 1967-08-02,95.37,96.64,95.03,95.78,13510000,95.78 1967-08-01,94.75,95.84,94.20,95.37,12290000,95.37 1967-07-31,94.49,95.51,94.01,94.75,10330000,94.75 1967-07-28,94.35,95.23,93.77,94.49,10900000,94.49 1967-07-27,94.06,95.19,93.51,94.35,12400000,94.35 1967-07-26,93.24,94.71,93.12,94.06,11160000,94.06 1967-07-25,93.73,94.56,93.03,93.24,9890000,93.24 1967-07-24,94.04,94.68,92.91,93.73,9580000,93.73 1967-07-21,93.85,94.92,93.24,94.04,11710000,94.04 1967-07-20,93.65,94.49,93.01,93.85,11160000,93.85 1967-07-19,93.50,94.40,92.83,93.65,12850000,93.65 1967-07-18,92.75,94.05,92.30,93.50,12060000,93.50 1967-07-17,92.74,93.53,92.10,92.75,10390000,92.75 1967-07-14,92.42,93.35,91.87,92.74,10880000,92.74 1967-07-13,92.40,93.17,91.82,92.42,10730000,92.42 1967-07-12,92.48,93.10,91.62,92.40,11240000,92.40 1967-07-11,92.05,93.16,91.58,92.48,12400000,92.48 1967-07-10,91.69,92.80,91.11,92.05,12130000,92.05 1967-07-07,91.32,92.28,90.76,91.69,11540000,91.69 1967-07-06,91.36,92.03,90.64,91.32,10170000,91.32 1967-07-05,90.91,91.91,90.56,91.36,9170000,91.36 1967-07-03,90.64,91.32,90.12,90.91,6040000,90.91 1967-06-30,90.64,90.64,90.64,90.64,7850000,90.64 1967-06-29,90.85,90.85,90.85,90.85,9940000,90.85 1967-06-28,91.31,91.31,91.31,91.31,9310000,91.31 1967-06-27,91.30,91.30,91.30,91.30,8780000,91.30 1967-06-26,91.64,91.64,91.64,91.64,9040000,91.64 1967-06-23,92.00,92.00,92.00,92.00,9130000,92.00 1967-06-22,91.97,91.97,91.97,91.97,9550000,91.97 1967-06-21,92.20,92.20,92.20,92.20,9760000,92.20 1967-06-20,92.48,92.48,92.48,92.48,10350000,92.48 1967-06-19,92.51,92.51,92.51,92.51,8570000,92.51 1967-06-16,92.49,93.28,91.98,92.54,10740000,92.54 1967-06-15,92.40,93.26,91.76,92.49,11240000,92.49 1967-06-14,92.62,93.21,91.81,92.40,10960000,92.40 1967-06-13,92.04,93.27,91.65,92.62,11570000,92.62 1967-06-12,91.56,92.66,91.12,92.04,10230000,92.04 1967-06-09,91.40,92.26,90.77,91.56,9650000,91.56 1967-06-08,90.91,91.78,90.24,91.40,8300000,91.40 1967-06-07,90.23,91.75,89.92,90.91,10170000,90.91 1967-06-06,88.48,90.59,88.48,90.23,9230000,90.23 1967-06-05,89.56,89.56,87.19,88.43,11110000,88.43 1967-06-02,90.23,90.90,89.27,89.79,8070000,89.79 1967-06-01,89.08,90.76,88.81,90.23,9040000,90.23 1967-05-31,90.39,90.39,88.71,89.08,8870000,89.08 1967-05-29,90.98,91.22,89.92,90.49,6590000,90.49 1967-05-26,91.19,91.70,90.34,90.98,7810000,90.98 1967-05-25,90.18,91.84,90.04,91.19,8960000,91.19 1967-05-24,91.23,91.36,89.68,90.18,10290000,90.18 1967-05-23,91.67,92.07,90.58,91.23,9810000,91.23 1967-05-22,92.07,92.40,90.83,91.67,9600000,91.67 1967-05-19,92.53,92.86,91.40,92.07,10560000,92.07 1967-05-18,92.78,93.30,91.98,92.53,10290000,92.53 1967-05-17,93.14,93.75,92.34,92.78,9560000,92.78 1967-05-16,92.71,93.85,92.19,93.14,10700000,93.14 1967-05-15,93.48,93.75,92.27,92.71,8320000,92.71 1967-05-12,93.75,94.45,92.94,93.48,10470000,93.48 1967-05-11,93.35,94.37,92.90,93.75,10320000,93.75 1967-05-10,93.60,94.04,92.51,93.35,10410000,93.35 1967-05-09,94.58,95.25,93.28,93.60,10830000,93.60 1967-05-08,94.44,95.22,93.71,94.58,10330000,94.58 1967-05-05,94.32,95.14,93.64,94.44,10630000,94.44 1967-05-04,93.91,94.92,93.41,94.32,12850000,94.32 1967-05-03,93.67,94.48,92.94,93.91,11550000,93.91 1967-05-02,93.84,94.42,93.06,93.67,10260000,93.67 1967-05-01,94.01,94.60,93.08,93.84,9410000,93.84 1967-04-28,93.81,94.77,93.33,94.01,11200000,94.01 1967-04-27,93.02,94.25,92.41,93.81,10250000,93.81 1967-04-26,93.11,93.99,92.44,93.02,10560000,93.02 1967-04-25,92.62,93.57,92.01,93.11,10420000,93.11 1967-04-24,92.30,93.45,91.78,92.62,10250000,92.62 1967-04-21,92.11,92.90,91.48,92.30,10210000,92.30 1967-04-20,91.94,92.61,91.21,92.11,9690000,92.11 1967-04-19,91.86,92.73,91.25,91.94,10860000,91.94 1967-04-18,91.07,92.31,90.70,91.86,10500000,91.86 1967-04-17,90.43,91.78,90.18,91.07,9070000,91.07 1967-04-14,89.46,91.08,89.26,90.43,8810000,90.43 1967-04-13,88.78,89.86,88.49,89.46,7610000,89.46 1967-04-12,88.88,89.54,88.36,88.78,7750000,88.78 1967-04-11,88.24,89.34,87.92,88.88,7710000,88.88 1967-04-10,89.32,89.32,87.86,88.24,8110000,88.24 1967-04-07,89.94,90.60,88.96,89.36,9090000,89.36 1967-04-06,89.79,90.74,89.44,89.94,9470000,89.94 1967-04-05,89.22,90.31,88.92,89.79,8810000,89.79 1967-04-04,89.24,89.93,88.45,89.22,8750000,89.22 1967-04-03,90.20,90.37,88.76,89.24,8530000,89.24 1967-03-31,90.70,91.15,89.75,90.20,8130000,90.20 1967-03-30,90.73,91.32,90.06,90.70,8340000,90.70 1967-03-29,90.91,91.45,90.17,90.73,8430000,90.73 1967-03-28,90.87,91.62,90.23,90.91,8940000,90.91 1967-03-27,90.94,91.72,90.19,90.87,9260000,90.87 1967-03-23,90.25,91.51,90.04,90.94,9500000,90.94 1967-03-22,90.00,90.70,89.17,90.25,8820000,90.25 1967-03-21,90.20,91.05,89.52,90.00,9820000,90.00 1967-03-20,90.25,90.87,89.35,90.20,9040000,90.20 1967-03-17,90.09,90.84,89.39,90.25,10020000,90.25 1967-03-16,89.19,90.66,89.09,90.09,12170000,90.09 1967-03-15,88.35,89.60,88.00,89.19,10830000,89.19 1967-03-14,88.43,89.07,87.58,88.35,10260000,88.35 1967-03-13,88.89,89.41,87.93,88.43,9910000,88.43 1967-03-10,88.53,90.37,88.46,88.89,14900000,88.89 1967-03-09,88.27,89.04,87.70,88.53,10480000,88.53 1967-03-08,88.16,89.10,87.69,88.27,11070000,88.27 1967-03-07,88.10,88.74,87.34,88.16,9810000,88.16 1967-03-06,88.29,89.08,87.46,88.10,10400000,88.10 1967-03-03,88.16,89.00,87.51,88.29,11100000,88.29 1967-03-02,87.68,88.85,87.39,88.16,11900000,88.16 1967-03-01,86.78,88.36,86.67,87.68,11510000,87.68 1967-02-28,86.46,87.26,85.61,86.78,9970000,86.78 1967-02-27,87.41,87.61,85.68,86.46,10210000,86.46 1967-02-24,87.45,88.16,86.76,87.41,9830000,87.41 1967-02-23,87.34,88.00,86.64,87.45,10010000,87.45 1967-02-21,87.40,88.01,86.80,87.34,9030000,87.34 1967-02-20,87.89,88.13,86.65,87.40,8640000,87.40 1967-02-17,87.86,88.40,87.25,87.89,8530000,87.89 1967-02-16,88.27,88.80,87.43,87.86,8490000,87.86 1967-02-15,88.17,89.00,87.62,88.27,10480000,88.27 1967-02-14,87.58,88.74,87.15,88.17,9760000,88.17 1967-02-13,87.63,88.19,86.95,87.58,7570000,87.58 1967-02-10,87.36,88.19,86.79,87.63,8850000,87.63 1967-02-09,87.72,88.57,86.99,87.36,10970000,87.36 1967-02-08,86.95,88.25,86.64,87.72,11220000,87.72 1967-02-07,87.18,87.52,86.48,86.95,6400000,86.95 1967-02-06,87.36,87.98,86.61,87.18,10680000,87.18 1967-02-03,86.73,87.97,86.51,87.36,12010000,87.36 1967-02-02,86.43,87.31,85.87,86.73,10720000,86.73 1967-02-01,86.61,87.04,85.68,86.43,9580000,86.43 1967-01-31,86.66,87.46,86.06,86.61,11540000,86.61 1967-01-30,86.16,87.35,85.84,86.66,10250000,86.66 1967-01-27,85.81,86.76,85.34,86.16,9690000,86.16 1967-01-26,85.85,86.66,84.87,85.81,10630000,85.81 1967-01-25,86.51,87.02,85.47,85.85,10260000,85.85 1967-01-24,86.39,87.00,85.29,86.51,10430000,86.51 1967-01-23,86.07,88.17,85.64,86.39,10830000,86.39 1967-01-20,85.82,86.47,85.07,86.07,9530000,86.07 1967-01-19,85.79,86.61,85.17,85.82,10230000,85.82 1967-01-18,85.24,86.36,84.90,85.79,11390000,85.79 1967-01-17,84.31,85.81,84.03,85.24,11590000,85.24 1967-01-16,84.53,85.28,83.73,84.31,10280000,84.31 1967-01-13,83.91,84.90,83.10,84.53,10000000,84.53 1967-01-12,83.47,84.80,83.11,83.91,12830000,83.91 1967-01-11,82.81,83.92,81.37,83.47,13230000,83.47 1967-01-10,82.81,83.54,82.22,82.81,8120000,82.81 1967-01-09,82.18,83.31,81.78,82.81,9180000,82.81 1967-01-06,81.60,82.79,81.32,82.18,7830000,82.18 1967-01-05,80.55,81.93,80.50,81.60,7320000,81.60 1967-01-04,80.38,81.01,79.43,80.55,6150000,80.55 1967-01-03,80.33,81.61,79.59,80.38,6100000,80.38 1966-12-30,80.37,81.14,79.66,80.33,11330000,80.33 1966-12-29,80.61,81.08,79.84,80.37,7900000,80.37 1966-12-28,81.00,81.67,80.29,80.61,7160000,80.61 1966-12-27,81.47,81.84,80.55,81.00,6280000,81.00 1966-12-23,81.69,82.22,80.97,81.47,7350000,81.47 1966-12-22,81.38,82.34,81.00,81.69,8560000,81.69 1966-12-21,80.96,81.91,80.42,81.38,7690000,81.38 1966-12-20,81.27,81.69,80.31,80.96,6830000,80.96 1966-12-19,81.58,82.06,80.56,81.27,7340000,81.27 1966-12-16,81.64,82.21,80.94,81.58,6980000,81.58 1966-12-15,82.64,82.89,81.20,81.64,7150000,81.64 1966-12-14,82.73,83.35,81.97,82.64,7470000,82.64 1966-12-13,83.00,83.88,82.28,82.73,9650000,82.73 1966-12-12,82.14,83.54,81.94,83.00,9530000,83.00 1966-12-09,82.05,82.68,81.33,82.14,7650000,82.14 1966-12-08,81.72,82.72,81.34,82.05,8370000,82.05 1966-12-07,80.84,82.19,80.59,81.72,8980000,81.72 1966-12-06,80.24,81.29,79.95,80.84,7670000,80.84 1966-12-05,80.13,80.81,79.60,80.24,6470000,80.24 1966-12-02,80.08,81.29,79.49,80.13,6230000,80.13 1966-12-01,80.45,81.04,79.66,80.08,8480000,80.08 1966-11-30,80.42,80.90,79.62,80.45,7230000,80.45 1966-11-29,80.71,81.16,79.94,80.42,7320000,80.42 1966-11-28,80.85,81.38,79.96,80.71,7630000,80.71 1966-11-25,80.21,81.37,79.83,80.85,6810000,80.85 1966-11-23,79.67,80.85,79.39,80.21,7350000,80.21 1966-11-22,80.09,80.32,78.89,79.67,6430000,79.67 1966-11-21,81.09,81.09,79.51,80.09,7450000,80.09 1966-11-18,81.80,82.05,80.79,81.26,6900000,81.26 1966-11-17,82.37,82.80,81.24,81.80,8900000,81.80 1966-11-16,81.69,83.01,81.55,82.37,10350000,82.37 1966-11-15,81.37,82.07,80.82,81.69,7190000,81.69 1966-11-14,81.94,82.18,80.81,81.37,6540000,81.37 1966-11-11,81.89,82.36,81.27,81.94,6690000,81.94 1966-11-10,81.38,82.43,81.00,81.89,8870000,81.89 1966-11-09,80.73,81.90,80.46,81.38,8390000,81.38 1966-11-07,80.81,81.48,80.16,80.73,6120000,80.73 1966-11-04,80.56,81.21,79.64,80.81,6530000,80.81 1966-11-03,80.88,81.35,79.98,80.56,5860000,80.56 1966-11-02,80.81,81.68,80.30,80.88,6740000,80.88 1966-11-01,80.20,81.18,79.79,80.81,6480000,80.81 1966-10-31,80.24,80.82,79.34,80.20,5860000,80.20 1966-10-28,80.23,80.91,79.49,80.24,6420000,80.24 1966-10-27,79.58,80.72,79.28,80.23,6670000,80.23 1966-10-26,78.90,80.29,78.70,79.58,6760000,79.58 1966-10-25,78.42,79.22,77.56,78.90,6190000,78.90 1966-10-24,78.19,79.20,77.73,78.42,5780000,78.42 1966-10-21,77.84,78.62,77.16,78.19,5690000,78.19 1966-10-20,78.05,78.96,77.26,77.84,6840000,77.84 1966-10-19,78.68,79.34,77.54,78.05,6460000,78.05 1966-10-18,77.47,79.08,77.35,78.68,7180000,78.68 1966-10-17,76.60,78.41,76.48,77.47,5570000,77.47 1966-10-14,76.89,77.80,76.01,76.60,5610000,76.60 1966-10-13,77.04,78.45,76.22,76.89,8680000,76.89 1966-10-12,74.91,77.26,74.37,77.04,6910000,77.04 1966-10-11,74.53,76.20,74.22,74.91,8430000,74.91 1966-10-10,73.20,74.97,72.28,74.53,9630000,74.53 1966-10-07,74.05,74.67,72.77,73.20,8140000,73.20 1966-10-06,74.69,75.09,73.47,74.05,8110000,74.05 1966-10-05,75.10,76.10,74.31,74.69,5880000,74.69 1966-10-04,74.90,75.76,73.91,75.10,8910000,75.10 1966-10-03,76.56,76.98,74.71,74.90,6490000,74.90 1966-09-30,76.31,77.09,75.45,76.56,6170000,76.56 1966-09-29,77.11,77.28,75.85,76.31,6110000,76.31 1966-09-28,78.10,78.36,76.70,77.11,5990000,77.11 1966-09-27,77.86,79.10,77.56,78.10,6300000,78.10 1966-09-26,77.67,78.34,76.88,77.86,4960000,77.86 1966-09-23,77.94,78.43,77.15,77.67,4560000,77.67 1966-09-22,77.71,78.41,76.81,77.94,5760000,77.94 1966-09-21,79.04,79.15,77.52,77.71,5360000,77.71 1966-09-20,79.59,79.90,78.57,79.04,4560000,79.04 1966-09-19,79.99,80.50,79.02,79.59,4920000,79.59 1966-09-16,80.08,80.81,79.33,79.99,5150000,79.99 1966-09-15,79.13,80.60,78.87,80.08,6140000,80.08 1966-09-14,78.32,79.43,77.73,79.13,6250000,79.13 1966-09-13,77.91,79.16,77.66,78.32,6870000,78.32 1966-09-12,76.47,78.34,76.47,77.91,6780000,77.91 1966-09-09,76.05,76.94,75.43,76.29,5280000,76.29 1966-09-08,76.37,76.95,75.03,76.05,6660000,76.05 1966-09-07,76.96,77.26,75.77,76.37,5530000,76.37 1966-09-06,77.42,78.16,76.55,76.96,4350000,76.96 1966-09-02,77.70,78.20,76.27,77.42,6080000,77.42 1966-09-01,77.10,78.50,76.66,77.70,6250000,77.70 1966-08-31,75.98,78.06,75.98,77.10,8690000,77.10 1966-08-30,74.53,76.46,73.91,75.86,11230000,75.86 1966-08-29,76.24,76.24,74.18,74.53,10900000,74.53 1966-08-26,77.85,77.85,76.10,76.41,8190000,76.41 1966-08-25,79.07,79.79,77.80,78.06,6760000,78.06 1966-08-24,78.11,79.63,77.92,79.07,7050000,79.07 1966-08-23,78.24,79.24,77.05,78.11,9830000,78.11 1966-08-22,79.62,79.88,77.58,78.24,8690000,78.24 1966-08-19,80.16,80.78,79.24,79.62,7070000,79.62 1966-08-18,81.18,81.38,79.60,80.16,7000000,80.16 1966-08-17,81.63,81.90,80.53,81.18,6630000,81.18 1966-08-16,82.71,82.71,81.26,81.63,6130000,81.63 1966-08-15,83.17,83.69,82.39,82.74,5680000,82.74 1966-08-12,83.02,83.88,82.57,83.17,6230000,83.17 1966-08-11,83.11,83.53,82.34,83.02,5700000,83.02 1966-08-10,83.49,83.83,82.69,83.11,5290000,83.11 1966-08-09,83.75,84.36,83.04,83.49,6270000,83.49 1966-08-08,84.00,84.31,82.97,83.75,4900000,83.75 1966-08-05,83.93,84.70,83.43,84.00,5500000,84.00 1966-08-04,83.15,84.54,83.07,83.93,6880000,83.93 1966-08-03,82.33,83.71,82.30,83.15,6220000,83.15 1966-08-02,82.31,83.04,81.77,82.33,5710000,82.33 1966-08-01,83.50,83.50,81.98,82.31,5880000,82.31 1966-07-29,83.77,84.30,83.10,83.60,5150000,83.60 1966-07-28,84.10,84.76,83.44,83.77,5680000,83.77 1966-07-27,83.70,84.83,83.50,84.10,6070000,84.10 1966-07-26,83.83,84.67,83.05,83.70,7610000,83.70 1966-07-25,85.41,85.57,83.56,83.83,7050000,83.83 1966-07-22,85.52,86.11,84.93,85.41,6540000,85.41 1966-07-21,85.51,86.24,84.77,85.52,6200000,85.52 1966-07-20,86.33,86.64,85.26,85.51,5470000,85.51 1966-07-19,86.99,87.17,85.75,86.33,5960000,86.33 1966-07-18,87.08,87.59,86.42,86.99,5110000,86.99 1966-07-15,86.82,87.68,86.44,87.08,6090000,87.08 1966-07-14,86.30,87.34,85.85,86.82,5950000,86.82 1966-07-13,86.88,87.06,85.83,86.30,5580000,86.30 1966-07-12,87.45,87.78,86.45,86.88,5180000,86.88 1966-07-11,87.61,88.19,86.97,87.45,6200000,87.45 1966-07-08,87.38,88.04,86.85,87.61,6100000,87.61 1966-07-07,87.06,88.02,86.67,87.38,7200000,87.38 1966-07-06,85.82,87.38,85.57,87.06,6860000,87.06 1966-07-05,85.61,86.41,85.09,85.82,4610000,85.82 1966-07-01,84.74,86.08,84.74,85.61,5200000,85.61 1966-06-30,84.86,85.37,83.75,84.74,7250000,84.74 1966-06-29,85.67,85.98,84.52,84.86,6020000,84.86 1966-06-28,86.08,86.43,85.00,85.67,6280000,85.67 1966-06-27,86.58,87.31,85.77,86.08,5330000,86.08 1966-06-24,86.50,87.31,85.68,86.58,7140000,86.58 1966-06-23,86.85,87.73,86.11,86.50,7930000,86.50 1966-06-22,86.71,87.38,86.15,86.85,7800000,86.85 1966-06-21,86.48,87.28,86.07,86.71,6860000,86.71 1966-06-20,86.51,87.03,85.84,86.48,5940000,86.48 1966-06-17,86.47,87.11,85.89,86.51,6580000,86.51 1966-06-16,86.73,87.18,85.88,86.47,6870000,86.47 1966-06-15,87.07,87.74,86.33,86.73,8520000,86.73 1966-06-14,86.83,87.57,86.02,87.07,7600000,87.07 1966-06-13,86.44,87.59,86.20,86.83,7600000,86.83 1966-06-10,85.50,86.97,85.32,86.44,8240000,86.44 1966-06-09,84.93,85.98,84.56,85.50,5810000,85.50 1966-06-08,84.83,85.43,84.31,84.93,4580000,84.93 1966-06-07,85.42,85.54,84.25,84.83,5040000,84.83 1966-06-06,86.06,86.28,85.03,85.42,4260000,85.42 1966-06-03,85.96,86.55,85.43,86.06,4430000,86.06 1966-06-02,86.10,86.85,85.55,85.96,5080000,85.96 1966-06-01,86.13,86.65,85.28,86.10,5290000,86.10 1966-05-31,87.33,87.65,85.80,86.13,5770000,86.13 1966-05-27,87.07,87.42,86.43,87.33,4790000,87.33 1966-05-26,87.07,87.88,86.54,87.07,6080000,87.07 1966-05-25,86.77,87.48,86.05,87.07,5820000,87.07 1966-05-24,86.20,87.70,86.19,86.77,7210000,86.77 1966-05-23,85.43,86.91,85.29,86.20,7080000,86.20 1966-05-20,85.02,85.79,84.21,85.43,6430000,85.43 1966-05-19,85.12,86.33,84.54,85.02,8640000,85.02 1966-05-18,83.72,85.64,83.72,85.12,9310000,85.12 1966-05-17,84.41,85.03,83.18,83.63,9870000,83.63 1966-05-16,85.47,86.04,83.90,84.41,9260000,84.41 1966-05-13,86.23,86.31,84.77,85.47,8970000,85.47 1966-05-12,87.23,87.49,85.72,86.23,8210000,86.23 1966-05-11,87.08,88.38,86.84,87.23,7470000,87.23 1966-05-10,86.32,87.88,86.12,87.08,9050000,87.08 1966-05-09,87.84,87.96,85.92,86.32,9290000,86.32 1966-05-06,87.93,88.52,86.24,87.84,13110000,87.84 1966-05-05,89.39,89.77,87.60,87.93,10100000,87.93 1966-05-04,89.85,90.11,88.54,89.39,9740000,89.39 1966-05-03,90.90,91.10,89.46,89.85,8020000,89.85 1966-05-02,91.06,91.75,90.43,90.90,7070000,90.90 1966-04-29,91.13,91.86,90.57,91.06,7220000,91.06 1966-04-28,91.76,91.92,90.24,91.13,8310000,91.13 1966-04-27,91.99,92.49,91.10,91.76,7950000,91.76 1966-04-26,92.08,92.77,91.47,91.99,7540000,91.99 1966-04-25,92.27,92.86,91.41,92.08,7270000,92.08 1966-04-22,92.42,92.87,91.60,92.27,8650000,92.27 1966-04-21,92.08,93.02,91.78,92.42,9560000,92.42 1966-04-20,91.57,92.75,91.34,92.08,10530000,92.08 1966-04-19,91.58,92.31,90.89,91.57,8820000,91.57 1966-04-18,91.99,92.59,91.09,91.58,9150000,91.58 1966-04-15,91.87,92.75,91.28,91.99,10270000,91.99 1966-04-14,91.54,92.80,91.12,91.87,12980000,91.87 1966-04-13,91.45,92.81,90.73,91.54,10440000,91.54 1966-04-12,91.79,92.51,90.92,91.45,10500000,91.45 1966-04-11,91.76,92.60,91.08,91.79,9310000,91.79 1966-04-07,91.56,92.42,90.99,91.76,9650000,91.76 1966-04-06,91.31,92.10,90.77,91.56,9040000,91.56 1966-04-05,90.76,92.04,90.47,91.31,10560000,91.31 1966-04-04,89.94,91.33,89.92,90.76,9360000,90.76 1966-04-01,89.23,90.37,88.96,89.94,9050000,89.94 1966-03-31,88.78,89.70,88.47,89.23,6690000,89.23 1966-03-30,89.27,89.57,88.31,88.78,7980000,88.78 1966-03-29,89.62,90.04,88.63,89.27,8300000,89.27 1966-03-28,89.54,90.41,89.15,89.62,8640000,89.62 1966-03-25,89.29,90.14,88.96,89.54,7750000,89.54 1966-03-24,89.13,89.80,88.68,89.29,7880000,89.29 1966-03-23,89.46,89.80,88.69,89.13,6720000,89.13 1966-03-22,89.20,90.28,89.01,89.46,8910000,89.46 1966-03-21,88.53,89.73,88.40,89.20,7230000,89.20 1966-03-18,88.17,89.23,87.82,88.53,6450000,88.53 1966-03-17,87.86,88.60,87.45,88.17,5460000,88.17 1966-03-16,87.35,88.55,87.09,87.86,7330000,87.86 1966-03-15,87.85,88.20,86.69,87.35,9440000,87.35 1966-03-14,88.85,88.92,87.56,87.85,7400000,87.85 1966-03-11,88.96,89.63,88.30,88.85,7000000,88.85 1966-03-10,88.96,90.14,88.36,88.96,10310000,88.96 1966-03-09,88.18,89.21,87.96,88.96,7980000,88.96 1966-03-08,88.04,89.00,87.17,88.18,10120000,88.18 1966-03-07,89.24,89.39,87.67,88.04,9370000,88.04 1966-03-04,89.47,90.25,88.72,89.24,9000000,89.24 1966-03-03,89.15,90.03,88.26,89.47,9900000,89.47 1966-03-02,90.06,90.65,88.70,89.15,10470000,89.15 1966-03-01,91.22,91.65,89.76,90.06,11030000,90.06 1966-02-28,91.14,91.95,90.65,91.22,9910000,91.22 1966-02-25,90.89,91.88,90.43,91.14,8140000,91.14 1966-02-24,91.48,91.81,90.45,90.89,7860000,90.89 1966-02-23,91.87,92.21,90.99,91.48,8080000,91.48 1966-02-21,92.41,92.83,91.35,91.87,8510000,91.87 1966-02-18,92.66,93.14,91.80,92.41,8470000,92.41 1966-02-17,93.16,93.58,92.11,92.66,9330000,92.66 1966-02-16,93.17,93.74,92.63,93.16,9180000,93.16 1966-02-15,93.53,94.04,92.67,93.17,8750000,93.17 1966-02-14,93.81,94.40,93.15,93.53,8360000,93.53 1966-02-11,93.83,94.52,93.25,93.81,8150000,93.81 1966-02-10,94.06,94.70,93.32,93.83,9790000,93.83 1966-02-09,93.55,94.72,93.29,94.06,9760000,94.06 1966-02-08,93.59,94.29,92.58,93.55,10560000,93.55 1966-02-07,93.26,94.22,92.85,93.59,8000000,93.59 1966-02-04,92.65,93.70,92.33,93.26,7560000,93.26 1966-02-03,92.53,93.67,92.11,92.65,8160000,92.65 1966-02-02,92.16,92.91,91.32,92.53,8130000,92.53 1966-02-01,92.88,93.36,91.61,92.16,9090000,92.16 1966-01-31,93.31,93.77,92.46,92.88,7800000,92.88 1966-01-28,93.67,94.15,92.84,93.31,9000000,93.31 1966-01-27,93.70,94.34,93.09,93.67,8970000,93.67 1966-01-26,93.85,94.53,93.18,93.70,9910000,93.70 1966-01-25,93.71,94.56,93.24,93.85,9300000,93.85 1966-01-24,93.47,94.41,93.07,93.71,8780000,93.71 1966-01-21,93.36,93.97,92.60,93.47,9180000,93.47 1966-01-20,93.69,94.33,92.87,93.36,8670000,93.36 1966-01-19,93.95,94.62,93.16,93.69,10230000,93.69 1966-01-18,93.77,94.64,93.23,93.95,9790000,93.95 1966-01-17,93.50,94.46,93.10,93.77,9430000,93.77 1966-01-14,93.36,94.14,92.98,93.50,9210000,93.50 1966-01-13,93.19,94.00,92.68,93.36,8860000,93.36 1966-01-12,93.41,93.98,92.80,93.19,8530000,93.19 1966-01-11,93.33,94.05,92.85,93.41,8910000,93.41 1966-01-10,93.14,93.94,92.75,93.33,7720000,93.33 1966-01-07,93.06,93.64,92.47,93.14,7600000,93.14 1966-01-06,92.85,93.65,92.51,93.06,7880000,93.06 1966-01-05,92.26,93.33,91.99,92.85,9650000,92.85 1966-01-04,92.18,93.04,91.68,92.26,7540000,92.26 1966-01-03,92.43,92.87,91.63,92.18,5950000,92.18 1965-12-31,92.20,93.05,91.82,92.43,7240000,92.43 1965-12-30,91.81,92.68,91.52,92.20,7060000,92.20 1965-12-29,91.53,92.39,91.14,91.81,7610000,91.81 1965-12-28,91.52,92.13,90.63,91.53,7280000,91.53 1965-12-27,92.19,92.71,91.28,91.52,5950000,91.52 1965-12-23,92.29,92.89,91.58,92.19,6870000,92.19 1965-12-22,92.01,93.07,91.53,92.29,9720000,92.29 1965-12-21,91.65,92.59,91.24,92.01,8230000,92.01 1965-12-20,92.08,92.35,91.09,91.65,7350000,91.65 1965-12-17,92.12,92.76,91.51,92.08,9490000,92.08 1965-12-16,92.02,92.95,91.53,92.12,9950000,92.12 1965-12-15,91.88,92.67,91.30,92.02,9560000,92.02 1965-12-14,91.83,92.59,91.35,91.88,9920000,91.88 1965-12-13,91.80,92.45,91.27,91.83,8660000,91.83 1965-12-10,91.56,92.28,91.14,91.80,8740000,91.80 1965-12-09,91.28,92.06,90.87,91.56,9150000,91.56 1965-12-08,91.39,92.24,90.84,91.28,10120000,91.28 1965-12-07,90.59,92.00,90.45,91.39,9340000,91.39 1965-12-06,91.20,91.20,89.20,90.59,11440000,90.59 1965-12-03,91.21,91.80,90.53,91.27,8160000,91.27 1965-12-02,91.50,91.95,90.69,91.21,9070000,91.21 1965-12-01,91.61,92.26,91.02,91.50,10140000,91.50 1965-11-30,91.80,92.14,90.81,91.61,8990000,91.61 1965-11-29,92.03,92.60,91.37,91.80,8760000,91.80 1965-11-26,91.94,92.65,91.39,92.03,6970000,92.03 1965-11-24,91.78,92.50,91.14,91.94,7870000,91.94 1965-11-23,91.64,92.24,91.15,91.78,7150000,91.78 1965-11-22,92.24,92.48,91.16,91.64,6370000,91.64 1965-11-19,92.22,92.88,91.73,92.24,6850000,92.24 1965-11-18,92.60,92.94,91.72,92.22,7040000,92.22 1965-11-17,92.41,93.28,91.85,92.60,9120000,92.60 1965-11-16,92.63,93.13,91.90,92.41,8380000,92.41 1965-11-15,92.55,93.30,92.04,92.63,8310000,92.63 1965-11-12,92.11,93.07,91.83,92.55,7780000,92.55 1965-11-11,91.83,92.37,91.31,92.11,5430000,92.11 1965-11-10,91.93,92.40,91.35,91.83,4860000,91.83 1965-11-09,92.23,92.65,91.47,91.93,6680000,91.93 1965-11-08,92.37,92.97,91.63,92.23,7000000,92.23 1965-11-05,92.46,92.92,91.78,92.37,7310000,92.37 1965-11-04,92.31,93.07,91.90,92.46,8380000,92.46 1965-11-03,92.23,92.79,91.62,92.31,7520000,92.31 1965-11-01,92.42,92.92,91.73,92.23,6340000,92.23 1965-10-29,92.21,92.94,91.83,92.42,7240000,92.42 1965-10-28,92.51,92.95,91.60,92.21,7230000,92.21 1965-10-27,92.20,93.19,91.95,92.51,7670000,92.51 1965-10-26,91.67,92.63,91.36,92.20,6750000,92.20 1965-10-25,91.98,92.72,91.34,91.67,7090000,91.67 1965-10-22,91.94,92.74,91.54,91.98,8960000,91.98 1965-10-21,91.78,92.51,91.42,91.94,9170000,91.94 1965-10-20,91.80,92.26,91.12,91.78,8200000,91.78 1965-10-19,91.68,92.45,91.35,91.80,8620000,91.80 1965-10-18,91.38,92.28,91.06,91.68,8180000,91.68 1965-10-15,91.19,92.09,90.76,91.38,7470000,91.38 1965-10-14,91.34,91.90,90.71,91.19,8580000,91.19 1965-10-13,91.35,91.81,90.73,91.34,9470000,91.34 1965-10-12,91.37,91.94,90.83,91.35,9470000,91.35 1965-10-11,90.85,91.84,90.73,91.37,9600000,91.37 1965-10-08,90.47,91.31,90.30,90.85,7670000,90.85 1965-10-07,90.54,91.09,90.09,90.47,6670000,90.47 1965-10-06,90.63,90.94,89.74,90.54,6010000,90.54 1965-10-05,90.08,91.02,89.92,90.63,6980000,90.63 1965-10-04,89.90,90.56,89.47,90.08,5590000,90.08 1965-10-01,89.96,90.48,89.30,89.90,7470000,89.90 1965-09-30,90.02,90.71,89.51,89.96,8670000,89.96 1965-09-29,90.43,91.11,89.56,90.02,10600000,90.02 1965-09-28,90.65,91.13,89.83,90.43,8750000,90.43 1965-09-27,90.65,90.65,90.65,90.65,6820000,90.65 1965-09-24,89.86,90.47,89.13,90.02,7810000,90.02 1965-09-23,90.22,90.78,89.43,89.86,9990000,89.86 1965-09-22,89.81,90.67,89.45,90.22,8290000,90.22 1965-09-21,90.08,90.66,89.43,89.81,7750000,89.81 1965-09-20,90.05,90.67,89.51,90.08,7040000,90.08 1965-09-17,90.02,90.47,89.32,90.05,6610000,90.05 1965-09-16,90.02,90.02,90.02,90.02,7410000,90.02 1965-09-15,89.03,89.96,88.71,89.52,6220000,89.52 1965-09-14,89.38,90.01,88.69,89.03,7830000,89.03 1965-09-13,89.12,89.91,88.77,89.38,7020000,89.38 1965-09-10,88.89,89.85,88.41,89.12,6650000,89.12 1965-09-09,88.66,89.46,88.35,88.89,7360000,88.89 1965-09-08,88.36,89.08,87.93,88.66,6240000,88.66 1965-09-07,88.06,88.77,87.76,88.36,5750000,88.36 1965-09-03,87.65,88.41,87.52,88.06,6010000,88.06 1965-09-02,87.17,87.96,86.98,87.65,6470000,87.65 1965-09-01,87.17,87.63,86.69,87.17,5890000,87.17 1965-08-31,87.21,87.79,86.78,87.17,5170000,87.17 1965-08-30,87.20,87.64,86.76,87.21,4400000,87.21 1965-08-27,87.14,87.74,86.81,87.20,5570000,87.20 1965-08-26,86.81,87.52,86.40,87.14,6010000,87.14 1965-08-25,86.71,87.27,86.33,86.81,6240000,86.81 1965-08-24,86.56,87.19,86.22,86.71,4740000,86.71 1965-08-23,86.69,87.10,86.22,86.56,4470000,86.56 1965-08-20,86.79,87.14,86.21,86.69,4170000,86.69 1965-08-19,86.99,87.48,86.49,86.79,5000000,86.79 1965-08-18,87.04,87.57,86.63,86.99,5850000,86.99 1965-08-17,86.87,87.42,86.48,87.04,4520000,87.04 1965-08-16,86.77,87.43,86.46,86.87,5270000,86.87 1965-08-13,86.38,87.14,86.09,86.77,5430000,86.77 1965-08-12,86.13,86.75,85.85,86.38,5160000,86.38 1965-08-11,85.87,86.48,85.64,86.13,5030000,86.13 1965-08-10,85.86,86.31,85.45,85.87,4690000,85.87 1965-08-09,86.07,86.54,85.52,85.86,4540000,85.86 1965-08-06,85.79,86.40,85.42,86.07,4200000,86.07 1965-08-05,85.79,86.28,85.43,85.79,4920000,85.79 1965-08-04,85.46,86.12,85.22,85.79,4830000,85.79 1965-08-03,85.42,85.81,84.80,85.46,4640000,85.46 1965-08-02,85.25,85.87,84.87,85.42,4220000,85.42 1965-07-30,84.68,85.64,84.64,85.25,5200000,85.25 1965-07-29,84.03,85.00,83.79,84.68,4690000,84.68 1965-07-28,83.87,84.52,83.30,84.03,4760000,84.03 1965-07-27,84.05,84.59,83.58,83.87,4190000,83.87 1965-07-26,84.07,84.47,83.49,84.05,3790000,84.05 1965-07-23,83.85,84.52,83.57,84.07,3600000,84.07 1965-07-22,84.07,84.45,83.53,83.85,3310000,83.85 1965-07-21,84.55,84.84,83.76,84.07,4350000,84.07 1965-07-20,85.63,85.85,84.39,84.55,4670000,84.55 1965-07-19,85.69,86.04,85.21,85.63,3220000,85.63 1965-07-16,85.72,86.14,85.26,85.69,3520000,85.69 1965-07-15,85.87,86.47,85.44,85.72,4420000,85.72 1965-07-14,85.59,86.23,85.18,85.87,4100000,85.87 1965-07-13,85.69,86.01,85.12,85.59,3260000,85.59 1965-07-12,85.71,86.08,85.24,85.69,3690000,85.69 1965-07-09,85.39,86.11,85.11,85.71,4800000,85.71 1965-07-08,84.67,85.60,84.29,85.39,4380000,85.39 1965-07-07,84.99,85.14,84.28,84.67,3020000,84.67 1965-07-06,85.16,85.63,84.57,84.99,3400000,84.99 1965-07-02,84.48,85.40,84.13,85.16,4260000,85.16 1965-07-01,84.12,84.64,83.57,84.48,4520000,84.48 1965-06-30,82.97,84.63,82.97,84.12,6930000,84.12 1965-06-29,81.60,83.04,80.73,82.41,10450000,82.41 1965-06-28,83.06,83.34,81.36,81.60,7650000,81.60 1965-06-25,83.56,83.83,82.60,83.06,5790000,83.06 1965-06-24,84.67,84.73,83.30,83.56,5840000,83.56 1965-06-23,85.21,85.59,84.52,84.67,3580000,84.67 1965-06-22,85.05,85.70,84.76,85.21,3330000,85.21 1965-06-21,85.34,85.64,84.53,85.05,3280000,85.05 1965-06-18,85.74,86.10,84.90,85.34,4330000,85.34 1965-06-17,85.20,86.22,84.98,85.74,5220000,85.74 1965-06-16,84.58,85.79,84.58,85.20,6290000,85.20 1965-06-15,84.01,84.86,83.01,84.49,8450000,84.49 1965-06-14,85.12,85.68,83.64,84.01,5920000,84.01 1965-06-11,84.73,85.68,84.50,85.12,5350000,85.12 1965-06-10,85.04,85.82,84.10,84.73,7470000,84.73 1965-06-09,85.93,86.37,84.75,85.04,7070000,85.04 1965-06-08,86.88,87.10,85.74,85.93,4660000,85.93 1965-06-07,87.11,87.45,86.04,86.88,4680000,86.88 1965-06-04,86.90,87.46,86.36,87.11,4530000,87.11 1965-06-03,87.09,88.05,86.58,86.90,5720000,86.90 1965-06-02,87.87,87.87,86.25,87.09,6790000,87.09 1965-06-01,88.42,88.80,87.88,88.72,4830000,88.72 1965-05-28,87.84,88.68,87.58,88.42,4270000,88.42 1965-05-27,88.30,88.36,87.24,87.84,5520000,87.84 1965-05-26,88.60,89.22,88.04,88.30,5330000,88.30 1965-05-25,88.09,88.96,87.82,88.60,4950000,88.60 1965-05-24,88.75,88.89,87.75,88.09,4790000,88.09 1965-05-21,89.18,89.41,88.40,88.75,4660000,88.75 1965-05-20,89.67,89.86,88.74,89.18,5750000,89.18 1965-05-19,89.46,90.15,89.17,89.67,5860000,89.67 1965-05-18,89.54,89.84,88.87,89.46,5130000,89.46 1965-05-17,90.10,90.44,89.24,89.54,4980000,89.54 1965-05-14,90.27,90.66,89.63,90.10,5860000,90.10 1965-05-13,89.94,90.68,89.68,90.27,6460000,90.27 1965-05-12,89.55,90.31,89.30,89.94,6310000,89.94 1965-05-11,89.66,89.98,89.05,89.55,5150000,89.55 1965-05-10,89.85,90.22,89.22,89.66,5600000,89.66 1965-05-07,89.92,90.30,89.33,89.85,5820000,89.85 1965-05-06,89.71,90.57,89.39,89.92,6340000,89.92 1965-05-05,89.51,90.40,89.14,89.71,6350000,89.71 1965-05-04,89.23,89.89,88.82,89.51,5720000,89.51 1965-05-03,89.11,89.68,88.62,89.23,5340000,89.23 1965-04-30,88.93,89.44,88.50,89.11,5190000,89.11 1965-04-29,89.00,89.43,88.47,88.93,5510000,88.93 1965-04-28,89.04,89.48,88.51,89.00,5680000,89.00 1965-04-27,88.89,89.64,88.71,89.04,6310000,89.04 1965-04-26,88.88,89.29,88.30,88.89,5410000,88.89 1965-04-23,88.78,89.41,88.48,88.88,5860000,88.88 1965-04-22,88.30,89.13,88.12,88.78,5990000,88.78 1965-04-21,88.46,88.82,87.70,88.30,5590000,88.30 1965-04-20,88.51,89.07,88.02,88.46,6480000,88.46 1965-04-19,88.15,88.90,87.90,88.51,5700000,88.51 1965-04-15,88.24,88.63,87.55,88.15,5830000,88.15 1965-04-14,88.04,88.65,87.71,88.24,6580000,88.24 1965-04-13,87.94,88.48,87.54,88.04,6690000,88.04 1965-04-12,87.56,88.36,87.31,87.94,6040000,87.94 1965-04-09,87.04,87.87,86.86,87.56,6580000,87.56 1965-04-08,86.55,87.35,86.34,87.04,5770000,87.04 1965-04-07,86.50,86.88,86.14,86.55,4430000,86.55 1965-04-06,86.53,86.91,86.08,86.50,4610000,86.50 1965-04-05,86.53,87.08,86.14,86.53,4920000,86.53 1965-04-02,86.32,86.89,86.08,86.53,5060000,86.53 1965-04-01,86.16,86.73,85.87,86.32,4890000,86.32 1965-03-31,86.20,86.64,85.83,86.16,4470000,86.16 1965-03-30,86.03,86.53,85.69,86.20,4270000,86.20 1965-03-29,86.20,86.66,85.65,86.03,4590000,86.03 1965-03-26,86.84,87.06,85.96,86.20,5020000,86.20 1965-03-25,87.09,87.50,86.55,86.84,5460000,86.84 1965-03-24,86.93,87.55,86.68,87.09,5420000,87.09 1965-03-23,86.83,87.34,86.45,86.93,4820000,86.93 1965-03-22,86.84,87.34,86.41,86.83,4920000,86.83 1965-03-19,86.81,87.37,86.43,86.84,5040000,86.84 1965-03-18,87.02,87.48,86.50,86.81,4990000,86.81 1965-03-17,87.13,87.51,86.63,87.02,5120000,87.02 1965-03-16,87.24,87.61,86.67,87.13,5480000,87.13 1965-03-15,87.21,87.92,86.82,87.24,6000000,87.24 1965-03-12,86.90,87.65,86.60,87.21,6370000,87.21 1965-03-11,86.54,87.29,86.17,86.90,5770000,86.90 1965-03-10,86.69,87.07,86.20,86.54,5100000,86.54 1965-03-09,86.83,87.27,86.33,86.69,5210000,86.69 1965-03-08,86.80,87.28,86.31,86.83,5250000,86.83 1965-03-05,86.98,87.26,86.00,86.80,6120000,86.80 1965-03-04,87.26,87.72,86.63,86.98,7300000,86.98 1965-03-03,87.40,87.83,86.88,87.26,6600000,87.26 1965-03-02,87.25,87.79,86.84,87.40,5730000,87.40 1965-03-01,87.43,87.93,86.92,87.25,5780000,87.25 1965-02-26,87.20,87.84,86.81,87.43,5800000,87.43 1965-02-25,87.17,87.70,86.70,87.20,6680000,87.20 1965-02-24,86.64,87.72,86.43,87.17,7160000,87.17 1965-02-23,86.21,87.01,86.03,86.64,5880000,86.64 1965-02-19,86.05,86.67,85.71,86.21,5560000,86.21 1965-02-18,85.77,86.48,85.47,86.05,6060000,86.05 1965-02-17,85.67,86.25,85.25,85.77,5510000,85.77 1965-02-16,86.07,86.31,85.33,85.67,5000000,85.67 1965-02-15,86.17,86.86,85.75,86.07,5760000,86.07 1965-02-12,85.54,86.48,85.54,86.17,4960000,86.17 1965-02-11,86.46,86.89,85.40,85.54,5800000,85.54 1965-02-10,87.24,87.70,86.20,86.46,7210000,86.46 1965-02-09,86.95,87.64,86.70,87.24,5690000,87.24 1965-02-08,87.00,87.00,85.95,86.95,6010000,86.95 1965-02-05,87.57,87.98,86.90,87.29,5690000,87.29 1965-02-04,87.63,88.06,87.06,87.57,6230000,87.57 1965-02-03,87.55,88.01,87.07,87.63,6130000,87.63 1965-02-02,87.58,87.94,87.03,87.55,5460000,87.55 1965-02-01,87.56,88.01,87.05,87.58,5690000,87.58 1965-01-29,87.48,88.19,87.18,87.56,6940000,87.56 1965-01-28,87.23,87.88,86.89,87.48,6730000,87.48 1965-01-27,86.94,87.67,86.70,87.23,6010000,87.23 1965-01-26,86.86,87.45,86.51,86.94,5760000,86.94 1965-01-25,86.74,87.27,86.39,86.86,5370000,86.86 1965-01-22,86.52,87.15,86.20,86.74,5430000,86.74 1965-01-21,86.60,86.90,86.02,86.52,4780000,86.52 1965-01-20,86.63,87.10,86.26,86.60,5550000,86.60 1965-01-19,86.49,87.09,86.15,86.63,5550000,86.63 1965-01-18,86.21,87.15,85.99,86.49,5550000,86.49 1965-01-15,85.84,86.52,85.60,86.21,5340000,86.21 1965-01-14,85.84,86.38,85.41,85.84,5810000,85.84 1965-01-13,85.61,86.27,85.35,85.84,6160000,85.84 1965-01-12,85.40,85.98,85.13,85.61,5400000,85.61 1965-01-11,85.37,85.81,84.90,85.40,5440000,85.40 1965-01-08,85.26,85.84,84.91,85.37,5340000,85.37 1965-01-07,84.89,85.62,84.66,85.26,5080000,85.26 1965-01-06,84.63,85.38,84.45,84.89,4850000,84.89 1965-01-05,84.23,85.02,84.02,84.63,4110000,84.63 1965-01-04,84.75,85.15,83.77,84.23,3930000,84.23 1964-12-31,84.30,85.18,84.18,84.75,6470000,84.75 1964-12-30,83.81,84.63,83.63,84.30,5610000,84.30 1964-12-29,84.07,84.35,83.38,83.81,4450000,83.81 1964-12-28,84.15,84.58,83.70,84.07,3990000,84.07 1964-12-24,84.15,84.59,83.74,84.15,3600000,84.15 1964-12-23,84.33,84.76,83.79,84.15,4470000,84.15 1964-12-22,84.38,84.88,83.94,84.33,4520000,84.33 1964-12-21,84.29,84.91,84.11,84.38,4470000,84.38 1964-12-18,83.90,84.65,83.73,84.29,4630000,84.29 1964-12-17,83.55,84.24,83.34,83.90,4850000,83.90 1964-12-16,83.22,83.94,83.00,83.55,4610000,83.55 1964-12-15,83.45,83.79,82.65,83.22,5340000,83.22 1964-12-14,83.66,84.17,83.10,83.45,4340000,83.45 1964-12-11,83.45,84.05,83.09,83.66,4530000,83.66 1964-12-10,83.46,83.96,82.98,83.45,4790000,83.45 1964-12-09,84.00,84.24,83.24,83.46,5120000,83.46 1964-12-08,84.33,84.71,83.69,84.00,4990000,84.00 1964-12-07,84.35,85.03,84.04,84.33,4770000,84.33 1964-12-04,84.35,84.35,84.35,84.35,4340000,84.35 1964-12-03,83.79,84.74,83.71,84.18,4250000,84.18 1964-12-02,83.55,84.23,83.12,83.79,4930000,83.79 1964-12-01,84.42,84.56,83.36,83.55,4940000,83.55 1964-11-30,85.16,85.41,84.10,84.42,4890000,84.42 1964-11-27,85.44,85.68,84.55,85.16,4070000,85.16 1964-11-25,85.73,86.18,85.10,85.44,4800000,85.44 1964-11-24,86.00,86.12,85.15,85.73,5070000,85.73 1964-11-23,86.28,86.59,85.48,86.00,4860000,86.00 1964-11-20,86.18,86.80,85.73,86.28,5210000,86.28 1964-11-19,86.22,86.57,85.60,86.18,5570000,86.18 1964-11-18,86.03,86.80,85.73,86.22,6560000,86.22 1964-11-17,85.65,86.55,85.48,86.03,5920000,86.03 1964-11-16,85.21,85.94,84.88,85.65,4870000,85.65 1964-11-13,85.19,85.68,84.76,85.21,4860000,85.21 1964-11-12,85.08,85.63,84.75,85.19,5250000,85.19 1964-11-11,84.84,85.30,84.49,85.08,3790000,85.08 1964-11-10,85.19,85.55,84.49,84.84,5020000,84.84 1964-11-09,85.23,85.72,84.93,85.19,4560000,85.19 1964-11-06,85.16,85.55,84.65,85.23,4810000,85.23 1964-11-05,85.14,85.62,84.72,85.16,4380000,85.16 1964-11-04,85.18,85.90,84.80,85.14,4720000,85.14 1964-11-02,84.86,85.54,84.51,85.18,4430000,85.18 1964-10-30,84.73,85.22,84.41,84.86,4120000,84.86 1964-10-29,84.69,85.15,84.36,84.73,4390000,84.73 1964-10-28,85.00,85.37,84.43,84.69,4890000,84.69 1964-10-27,85.00,85.40,84.61,85.00,4470000,85.00 1964-10-26,85.14,85.70,84.65,85.00,5230000,85.00 1964-10-23,84.94,85.42,84.57,85.14,3830000,85.14 1964-10-22,85.10,85.44,84.51,84.94,4670000,84.94 1964-10-21,85.18,85.64,84.77,85.10,5170000,85.10 1964-10-20,84.93,85.57,84.56,85.18,5140000,85.18 1964-10-19,84.83,85.36,84.47,84.93,5010000,84.93 1964-10-16,84.25,85.10,84.10,84.83,5140000,84.83 1964-10-15,84.79,84.99,83.65,84.25,6500000,84.25 1964-10-14,84.96,85.29,84.50,84.79,4530000,84.79 1964-10-13,85.24,85.57,84.63,84.96,5400000,84.96 1964-10-12,85.22,85.58,84.88,85.24,4110000,85.24 1964-10-09,85.04,85.60,84.72,85.22,5290000,85.22 1964-10-08,84.80,85.40,84.47,85.04,5060000,85.04 1964-10-07,84.79,85.25,84.42,84.80,5090000,84.80 1964-10-06,84.74,85.24,84.37,84.79,4820000,84.79 1964-10-05,84.36,85.25,84.20,84.74,4850000,84.74 1964-10-02,84.08,84.64,83.71,84.36,4370000,84.36 1964-10-01,84.18,84.53,83.74,84.08,4470000,84.08 1964-09-30,84.24,84.66,83.86,84.18,4720000,84.18 1964-09-29,84.28,84.80,83.84,84.24,5070000,84.24 1964-09-28,84.21,84.73,83.79,84.28,4810000,84.28 1964-09-25,84.00,84.62,83.56,84.21,6170000,84.21 1964-09-24,83.91,84.43,83.45,84.00,5840000,84.00 1964-09-23,83.89,84.37,83.45,83.91,5920000,83.91 1964-09-22,83.86,84.44,83.53,83.89,5250000,83.89 1964-09-21,83.48,84.32,83.41,83.86,5310000,83.86 1964-09-18,83.79,84.29,83.03,83.48,6160000,83.48 1964-09-17,83.24,84.18,83.17,83.79,6380000,83.79 1964-09-16,83.00,83.52,82.57,83.24,4230000,83.24 1964-09-15,83.22,83.68,82.69,83.00,5690000,83.00 1964-09-14,83.45,83.89,82.88,83.22,5370000,83.22 1964-09-11,83.10,83.84,82.79,83.45,5630000,83.45 1964-09-10,83.05,83.50,82.60,83.10,5470000,83.10 1964-09-09,82.87,83.51,82.54,83.05,5690000,83.05 1964-09-08,82.76,83.24,82.46,82.87,4090000,82.87 1964-09-04,82.56,83.03,82.31,82.76,4210000,82.76 1964-09-03,82.31,82.83,82.04,82.56,4310000,82.56 1964-09-02,82.18,82.76,81.95,82.31,4800000,82.31 1964-09-01,81.83,82.50,81.57,82.18,4650000,82.18 1964-08-31,81.99,82.48,81.46,81.83,3340000,81.83 1964-08-28,81.70,82.29,81.54,81.99,3760000,81.99 1964-08-27,81.32,81.94,81.07,81.70,3560000,81.70 1964-08-26,81.44,81.74,80.99,81.32,3300000,81.32 1964-08-25,81.91,82.13,81.20,81.44,3780000,81.44 1964-08-24,82.07,82.48,81.64,81.91,3790000,81.91 1964-08-21,81.94,82.43,81.64,82.07,3620000,82.07 1964-08-20,82.32,82.57,81.60,81.94,3840000,81.94 1964-08-19,82.40,82.80,81.99,82.32,4160000,82.32 1964-08-18,82.36,82.79,82.01,82.40,4180000,82.40 1964-08-17,82.35,82.85,82.02,82.36,3780000,82.36 1964-08-14,82.41,82.83,82.03,82.35,4080000,82.35 1964-08-13,82.17,82.87,81.98,82.41,4600000,82.41 1964-08-12,81.76,82.53,81.60,82.17,4140000,82.17 1964-08-11,81.78,82.25,81.45,81.76,3450000,81.76 1964-08-10,81.86,82.23,81.43,81.78,3050000,81.78 1964-08-07,81.34,82.20,81.19,81.86,3190000,81.86 1964-08-06,82.09,82.45,81.20,81.34,3940000,81.34 1964-08-05,81.96,82.41,80.80,82.09,6160000,82.09 1964-08-04,83.00,83.02,81.68,81.96,4780000,81.96 1964-08-03,83.18,83.49,82.65,83.00,3780000,83.00 1964-07-31,83.09,83.57,82.72,83.18,4220000,83.18 1964-07-30,82.92,83.50,82.63,83.09,4530000,83.09 1964-07-29,82.85,83.30,82.47,82.92,4050000,82.92 1964-07-28,83.08,83.30,82.40,82.85,3860000,82.85 1964-07-27,83.46,83.82,82.82,83.08,4090000,83.08 1964-07-24,83.48,83.92,83.07,83.46,4210000,83.46 1964-07-23,83.52,83.91,83.06,83.48,4560000,83.48 1964-07-22,83.54,83.95,82.96,83.52,4570000,83.52 1964-07-21,83.74,83.99,83.06,83.54,4570000,83.54 1964-07-20,84.01,84.33,83.44,83.74,4390000,83.74 1964-07-17,83.64,84.33,83.37,84.01,4640000,84.01 1964-07-16,83.34,83.98,83.06,83.64,4640000,83.64 1964-07-15,83.06,83.67,82.72,83.34,4610000,83.34 1964-07-14,83.31,83.71,82.72,83.06,4760000,83.06 1964-07-13,83.36,83.86,82.92,83.31,4800000,83.31 1964-07-10,83.22,83.99,82.87,83.36,5420000,83.36 1964-07-09,83.12,83.64,82.74,83.22,5040000,83.22 1964-07-08,83.12,83.56,82.58,83.12,4760000,83.12 1964-07-07,82.98,83.53,82.60,83.12,5240000,83.12 1964-07-06,82.60,83.38,82.37,82.98,5080000,82.98 1964-07-02,82.27,82.98,82.09,82.60,5230000,82.60 1964-07-01,81.69,82.51,81.46,82.27,5320000,82.27 1964-06-30,81.64,82.07,81.19,81.69,4360000,81.69 1964-06-29,81.46,82.10,81.10,81.64,4380000,81.64 1964-06-26,81.21,81.78,80.86,81.46,4440000,81.46 1964-06-25,81.06,81.73,80.75,81.21,5010000,81.21 1964-06-24,80.77,81.45,80.41,81.06,4840000,81.06 1964-06-23,81.11,81.43,80.50,80.77,4060000,80.77 1964-06-22,80.89,81.54,80.66,81.11,4540000,81.11 1964-06-19,80.79,81.23,80.39,80.89,4050000,80.89 1964-06-18,80.81,81.34,80.43,80.79,4730000,80.79 1964-06-17,80.40,81.13,80.22,80.81,5340000,80.81 1964-06-16,79.97,80.72,79.85,80.40,4590000,80.40 1964-06-15,79.60,80.33,79.39,79.97,4110000,79.97 1964-06-12,79.73,80.05,79.19,79.60,3840000,79.60 1964-06-11,79.44,80.13,79.24,79.73,3620000,79.73 1964-06-10,79.14,79.84,79.02,79.44,4170000,79.44 1964-06-09,78.64,79.39,78.15,79.14,4470000,79.14 1964-06-08,79.02,79.44,78.44,78.64,4010000,78.64 1964-06-05,78.67,79.45,78.50,79.02,4240000,79.02 1964-06-04,79.49,79.75,78.44,78.67,4880000,78.67 1964-06-03,79.70,80.12,79.27,79.49,3990000,79.49 1964-06-02,80.11,80.60,79.50,79.70,4180000,79.70 1964-06-01,80.37,80.83,79.83,80.11,4300000,80.11 1964-05-28,80.26,80.75,79.88,80.37,4560000,80.37 1964-05-27,80.39,80.72,79.78,80.26,4450000,80.26 1964-05-26,80.56,80.94,80.12,80.39,4290000,80.39 1964-05-25,80.73,81.16,80.21,80.56,3990000,80.56 1964-05-22,80.94,81.15,80.36,80.73,4640000,80.73 1964-05-21,80.66,81.49,80.36,80.94,5350000,80.94 1964-05-20,80.30,81.02,80.09,80.66,4790000,80.66 1964-05-19,80.72,81.04,79.96,80.30,4360000,80.30 1964-05-18,81.10,81.47,80.42,80.72,4590000,80.72 1964-05-15,80.86,81.45,80.49,81.10,5070000,81.10 1964-05-14,80.97,81.28,80.37,80.86,4720000,80.86 1964-05-13,81.16,81.65,80.66,80.97,5890000,80.97 1964-05-12,80.90,81.81,80.66,81.16,5200000,81.16 1964-05-11,81.00,81.51,80.58,80.90,4490000,80.90 1964-05-08,81.00,81.00,81.00,81.00,4910000,81.00 1964-05-07,81.06,81.72,80.67,81.15,5600000,81.15 1964-05-06,80.88,81.57,80.53,81.06,5560000,81.06 1964-05-05,80.47,81.20,79.99,80.88,5340000,80.88 1964-05-04,80.17,81.01,79.87,80.47,5360000,80.47 1964-05-01,79.46,80.47,79.46,80.17,5990000,80.17 1964-04-30,79.70,80.08,79.08,79.46,5690000,79.46 1964-04-29,79.90,80.60,79.29,79.70,6200000,79.70 1964-04-28,79.35,80.26,79.14,79.90,4790000,79.90 1964-04-27,79.75,80.01,78.90,79.35,5070000,79.35 1964-04-24,80.38,80.62,79.45,79.75,5610000,79.75 1964-04-23,80.49,81.20,80.09,80.38,6690000,80.38 1964-04-22,80.54,80.92,80.06,80.49,5390000,80.49 1964-04-21,80.50,80.98,80.05,80.54,5750000,80.54 1964-04-20,80.55,81.04,80.11,80.50,5560000,80.50 1964-04-17,80.20,80.98,79.99,80.55,6030000,80.55 1964-04-16,80.09,80.62,79.73,80.20,5240000,80.20 1964-04-15,79.99,80.50,79.63,80.09,5270000,80.09 1964-04-14,79.77,80.37,79.46,79.99,5120000,79.99 1964-04-13,79.85,80.30,79.42,79.77,5330000,79.77 1964-04-10,79.70,80.26,79.43,79.85,4990000,79.85 1964-04-09,79.75,80.23,79.36,79.70,5300000,79.70 1964-04-08,79.74,80.17,79.26,79.75,5380000,79.75 1964-04-07,80.02,80.44,79.41,79.74,5900000,79.74 1964-04-06,79.94,80.45,79.55,80.02,5840000,80.02 1964-04-03,79.70,80.37,79.45,79.94,5990000,79.94 1964-04-02,79.24,80.09,79.13,79.70,6840000,79.70 1964-04-01,78.98,79.58,78.67,79.24,5510000,79.24 1964-03-31,79.14,79.51,78.57,78.98,5270000,78.98 1964-03-30,79.19,79.67,78.75,79.14,6060000,79.14 1964-03-26,78.98,79.58,78.67,79.19,5760000,79.19 1964-03-25,78.79,79.33,78.17,78.98,5420000,78.98 1964-03-24,78.93,79.34,78.51,78.79,5210000,78.79 1964-03-23,78.92,79.33,78.45,78.93,4940000,78.93 1964-03-20,79.30,79.35,78.92,78.92,5020000,78.92 1964-03-19,79.38,79.85,78.94,79.30,5670000,79.30 1964-03-18,79.32,79.89,78.90,79.38,5890000,79.38 1964-03-17,79.14,79.65,78.77,79.32,5480000,79.32 1964-03-16,79.14,79.60,78.72,79.14,5140000,79.14 1964-03-13,79.08,79.59,78.74,79.14,5660000,79.14 1964-03-12,78.95,79.41,78.55,79.08,5290000,79.08 1964-03-11,78.59,79.42,78.45,78.95,6180000,78.95 1964-03-10,78.33,78.90,77.95,78.59,5500000,78.59 1964-03-09,78.31,78.88,77.95,78.33,5510000,78.33 1964-03-06,78.06,78.60,77.85,78.31,4790000,78.31 1964-03-05,78.07,78.44,77.58,78.06,4680000,78.06 1964-03-04,78.22,78.70,77.70,78.07,5250000,78.07 1964-03-03,77.97,78.66,77.69,78.22,5350000,78.22 1964-03-02,77.80,78.38,77.50,77.97,5690000,77.97 1964-02-28,77.62,78.06,77.20,77.80,4980000,77.80 1964-02-27,77.87,78.29,77.38,77.62,5420000,77.62 1964-02-26,77.68,78.13,77.33,77.87,5350000,77.87 1964-02-25,77.68,78.31,77.19,77.68,5010000,77.68 1964-02-24,77.62,78.16,77.27,77.68,5630000,77.68 1964-02-20,77.55,77.99,77.16,77.62,4690000,77.62 1964-02-19,77.47,77.98,77.13,77.55,4280000,77.55 1964-02-18,77.46,77.90,77.00,77.47,4660000,77.47 1964-02-17,77.48,77.93,77.04,77.46,4780000,77.46 1964-02-14,77.52,77.82,77.02,77.48,4360000,77.48 1964-02-13,77.57,77.93,77.10,77.52,4820000,77.52 1964-02-12,77.33,77.88,77.14,77.57,4650000,77.57 1964-02-11,77.05,77.65,76.81,77.33,4040000,77.33 1964-02-10,77.18,77.77,76.83,77.05,4150000,77.05 1964-02-07,76.93,77.51,76.66,77.18,4710000,77.18 1964-02-06,76.75,77.26,76.47,76.93,4110000,76.93 1964-02-05,76.88,77.28,76.36,76.75,4010000,76.75 1964-02-04,76.97,77.31,76.46,76.88,4320000,76.88 1964-02-03,77.04,77.55,76.53,76.97,4140000,76.97 1964-01-31,76.70,77.37,76.39,77.04,4000000,77.04 1964-01-30,76.63,77.20,76.26,76.70,4230000,76.70 1964-01-29,77.10,77.36,76.33,76.63,4450000,76.63 1964-01-28,77.08,77.56,76.63,77.10,4720000,77.10 1964-01-27,77.11,77.78,76.64,77.08,5240000,77.08 1964-01-24,77.09,77.56,76.58,77.11,5080000,77.11 1964-01-23,77.03,77.62,76.67,77.09,5380000,77.09 1964-01-22,76.62,77.62,76.45,77.03,5430000,77.03 1964-01-21,76.41,76.99,75.87,76.62,4800000,76.62 1964-01-20,76.56,77.19,76.02,76.41,5570000,76.41 1964-01-17,76.55,77.09,76.02,76.56,5600000,76.56 1964-01-16,76.64,77.21,76.05,76.55,6200000,76.55 1964-01-15,76.36,77.06,75.96,76.64,6750000,76.64 1964-01-14,76.22,76.85,75.88,76.36,6500000,76.36 1964-01-13,76.24,76.71,75.78,76.22,5440000,76.22 1964-01-10,76.28,76.67,75.74,76.24,5260000,76.24 1964-01-09,76.00,76.64,75.60,76.28,5180000,76.28 1964-01-08,75.69,76.35,75.39,76.00,5380000,76.00 1964-01-07,75.67,76.24,75.25,75.69,5700000,75.69 1964-01-06,75.50,76.12,75.18,75.67,5480000,75.67 1964-01-03,75.43,76.04,75.09,75.50,5550000,75.50 1964-01-02,75.02,75.79,74.82,75.43,4680000,75.43 1963-12-31,74.56,75.36,74.40,75.02,6730000,75.02 1963-12-30,74.44,74.94,74.13,74.56,4930000,74.56 1963-12-27,74.32,74.91,74.09,74.44,4360000,74.44 1963-12-26,73.97,74.63,73.74,74.32,3700000,74.32 1963-12-24,73.81,74.48,73.44,73.97,3970000,73.97 1963-12-23,74.28,74.45,73.49,73.81,4540000,73.81 1963-12-20,74.40,74.75,73.85,74.28,4600000,74.28 1963-12-19,74.63,74.92,74.08,74.40,4410000,74.40 1963-12-18,74.74,75.21,74.25,74.63,6000000,74.63 1963-12-17,74.30,75.08,74.07,74.74,5140000,74.74 1963-12-16,74.06,74.66,73.78,74.30,4280000,74.30 1963-12-13,73.91,74.39,73.68,74.06,4290000,74.06 1963-12-12,73.90,74.31,73.58,73.91,4220000,73.91 1963-12-11,73.99,74.37,73.58,73.90,4400000,73.90 1963-12-10,73.96,74.48,73.40,73.99,4560000,73.99 1963-12-09,74.00,74.41,73.56,73.96,4430000,73.96 1963-12-06,74.28,74.63,73.62,74.00,4830000,74.00 1963-12-05,73.80,74.57,73.45,74.28,5190000,74.28 1963-12-04,73.62,74.18,73.21,73.80,4790000,73.80 1963-12-03,73.66,74.01,73.14,73.62,4520000,73.62 1963-12-02,73.23,74.08,73.02,73.66,4770000,73.66 1963-11-29,72.25,73.47,72.05,73.23,4810000,73.23 1963-11-27,72.38,72.78,71.76,72.25,5210000,72.25 1963-11-26,71.40,72.74,71.40,72.38,9320000,72.38 1963-11-22,71.62,72.17,69.48,69.61,6630000,69.61 1963-11-21,72.56,72.86,71.40,71.62,5670000,71.62 1963-11-20,71.90,73.14,71.49,72.56,5330000,72.56 1963-11-19,71.83,72.61,71.42,71.90,4430000,71.90 1963-11-18,72.35,72.52,71.42,71.83,4730000,71.83 1963-11-15,72.95,73.20,72.09,72.35,4790000,72.35 1963-11-14,73.29,73.53,72.63,72.95,4610000,72.95 1963-11-13,73.23,73.67,72.89,73.29,4710000,73.29 1963-11-12,73.23,73.23,73.23,73.23,4610000,73.23 1963-11-11,73.52,73.52,73.52,73.52,3970000,73.52 1963-11-08,73.06,73.66,72.80,73.36,4570000,73.36 1963-11-07,72.81,73.48,72.58,73.06,4320000,73.06 1963-11-06,73.45,73.47,72.33,72.81,5600000,72.81 1963-11-04,73.83,74.27,73.09,73.45,5440000,73.45 1963-11-01,74.01,74.44,73.47,73.83,5240000,73.83 1963-10-31,73.80,74.35,73.25,74.01,5030000,74.01 1963-10-30,74.46,74.59,73.43,73.80,5170000,73.80 1963-10-29,74.48,75.18,73.97,74.46,6100000,74.46 1963-10-28,74.01,75.15,73.75,74.48,7150000,74.48 1963-10-25,73.28,74.41,73.06,74.01,6390000,74.01 1963-10-24,73.00,73.73,72.74,73.28,6280000,73.28 1963-10-23,72.96,73.55,72.59,73.00,5830000,73.00 1963-10-22,73.38,73.55,72.48,72.96,6420000,72.96 1963-10-21,73.32,73.87,73.03,73.38,5450000,73.38 1963-10-18,73.26,73.74,72.85,73.32,5830000,73.32 1963-10-17,72.97,73.77,72.84,73.26,6790000,73.26 1963-10-16,72.40,73.20,72.08,72.97,5570000,72.97 1963-10-15,72.30,72.79,71.99,72.40,4550000,72.40 1963-10-14,72.27,72.43,71.85,72.30,4270000,72.30 1963-10-11,72.20,72.71,71.87,72.27,4740000,72.27 1963-10-10,71.87,72.52,71.60,72.20,4470000,72.20 1963-10-09,71.98,71.98,71.60,71.87,5520000,71.87 1963-10-08,72.70,73.14,72.24,72.60,4920000,72.60 1963-10-07,72.85,73.27,72.39,72.70,4050000,72.70 1963-10-04,72.83,73.19,72.46,72.85,5120000,72.85 1963-10-03,72.30,73.10,72.10,72.83,4510000,72.83 1963-10-02,72.22,72.67,71.92,72.30,3780000,72.30 1963-10-01,71.70,72.65,71.57,72.22,4420000,72.22 1963-09-30,72.13,72.37,71.28,71.70,3730000,71.70 1963-09-27,72.27,72.60,71.60,72.13,4350000,72.13 1963-09-26,72.89,73.07,72.01,72.27,5100000,72.27 1963-09-25,73.30,73.87,72.58,72.89,6340000,72.89 1963-09-24,72.96,73.67,72.59,73.30,5520000,73.30 1963-09-23,73.30,73.53,72.62,72.96,5140000,72.96 1963-09-20,73.22,73.71,72.92,73.30,5310000,73.30 1963-09-19,72.80,73.47,72.61,73.22,4080000,73.22 1963-09-18,73.12,73.44,72.51,72.80,5070000,72.80 1963-09-17,73.07,73.64,72.79,73.12,4950000,73.12 1963-09-16,73.17,73.63,72.80,73.07,4740000,73.07 1963-09-13,73.15,73.59,72.82,73.17,5230000,73.17 1963-09-12,73.20,73.60,72.72,73.15,5560000,73.15 1963-09-11,72.99,73.79,72.83,73.20,6670000,73.20 1963-09-10,72.58,73.27,72.25,72.99,5310000,72.99 1963-09-09,72.84,73.23,72.26,72.58,5020000,72.58 1963-09-06,73.00,73.51,72.51,72.84,7160000,72.84 1963-09-05,72.64,73.19,72.15,73.00,5700000,73.00 1963-09-04,72.66,73.18,72.32,72.64,6070000,72.64 1963-09-03,72.50,73.09,72.30,72.66,5570000,72.66 1963-08-30,72.16,72.71,71.88,72.50,4560000,72.50 1963-08-29,72.04,72.56,71.83,72.16,5110000,72.16 1963-08-28,71.52,72.39,71.49,72.04,5120000,72.04 1963-08-27,71.91,72.04,71.27,71.52,4080000,71.52 1963-08-26,71.76,72.30,71.57,71.91,4700000,71.91 1963-08-23,71.54,72.14,71.33,71.76,4880000,71.76 1963-08-22,71.29,71.81,70.95,71.54,4540000,71.54 1963-08-21,71.38,71.73,71.00,71.29,3820000,71.29 1963-08-20,71.44,71.91,71.03,71.38,3660000,71.38 1963-08-19,71.49,71.92,71.15,71.44,3650000,71.44 1963-08-16,71.38,71.95,71.05,71.49,4130000,71.49 1963-08-15,71.07,71.71,70.81,71.38,4980000,71.38 1963-08-14,70.79,71.32,70.39,71.07,4420000,71.07 1963-08-13,70.59,71.09,70.32,70.79,4450000,70.79 1963-08-12,70.48,71.00,70.19,70.59,4770000,70.59 1963-08-09,70.02,70.65,69.83,70.48,4050000,70.48 1963-08-08,69.96,70.31,69.58,70.02,3460000,70.02 1963-08-07,70.17,70.53,69.69,69.96,3790000,69.96 1963-08-06,69.71,70.40,69.57,70.17,3760000,70.17 1963-08-05,69.30,69.97,69.20,69.71,3370000,69.71 1963-08-02,69.07,69.56,68.86,69.30,2940000,69.30 1963-08-01,69.13,69.47,68.64,69.07,3410000,69.07 1963-07-31,69.24,69.83,68.91,69.13,3960000,69.13 1963-07-30,68.67,69.45,68.58,69.24,3550000,69.24 1963-07-29,68.54,68.96,68.32,68.67,2840000,68.67 1963-07-26,68.26,68.76,68.03,68.54,2510000,68.54 1963-07-25,68.28,68.92,68.02,68.26,3710000,68.26 1963-07-24,67.91,68.54,67.76,68.28,2810000,68.28 1963-07-23,67.90,68.57,67.65,67.91,3500000,67.91 1963-07-22,68.35,68.60,67.54,67.90,3700000,67.90 1963-07-19,68.49,68.70,67.90,68.35,3340000,68.35 1963-07-18,68.93,69.27,68.34,68.49,3710000,68.49 1963-07-17,69.14,69.53,68.68,68.93,3940000,68.93 1963-07-16,69.20,69.51,68.85,69.14,3000000,69.14 1963-07-15,69.64,69.73,68.97,69.20,3290000,69.20 1963-07-12,69.76,70.13,69.36,69.64,3660000,69.64 1963-07-11,69.89,70.30,69.52,69.76,4100000,69.76 1963-07-10,70.04,70.31,69.56,69.89,3730000,69.89 1963-07-09,69.74,70.39,69.55,70.04,3830000,70.04 1963-07-08,70.22,70.35,69.47,69.74,3290000,69.74 1963-07-05,69.94,70.48,69.78,70.22,2910000,70.22 1963-07-03,69.46,70.28,69.42,69.94,4030000,69.94 1963-07-02,68.86,69.72,68.74,69.46,3540000,69.46 1963-07-01,69.37,69.53,68.58,68.86,3360000,68.86 1963-06-28,69.07,69.68,68.93,69.37,3020000,69.37 1963-06-27,69.41,69.81,68.78,69.07,4540000,69.07 1963-06-26,70.04,70.10,69.17,69.41,4500000,69.41 1963-06-25,70.20,70.51,69.75,70.04,4120000,70.04 1963-06-24,70.25,70.67,69.84,70.20,3700000,70.20 1963-06-21,70.01,70.57,69.79,70.25,4190000,70.25 1963-06-20,70.09,70.36,69.31,70.01,4970000,70.01 1963-06-19,70.02,70.47,69.75,70.09,3970000,70.09 1963-06-18,69.95,70.43,69.63,70.02,3910000,70.02 1963-06-17,69.95,69.95,69.95,69.95,3510000,69.95 1963-06-14,70.23,70.60,69.87,70.25,3840000,70.25 1963-06-13,70.41,70.85,69.98,70.23,4690000,70.23 1963-06-12,70.03,70.81,69.91,70.41,5210000,70.41 1963-06-11,69.94,70.41,69.58,70.03,4390000,70.03 1963-06-10,70.41,70.51,69.57,69.94,4690000,69.94 1963-06-07,70.58,70.98,70.10,70.41,5110000,70.41 1963-06-06,70.53,70.95,70.11,70.58,4990000,70.58 1963-06-05,70.70,71.17,70.17,70.53,5860000,70.53 1963-06-04,70.69,71.08,70.20,70.70,5970000,70.70 1963-06-03,70.80,71.24,70.39,70.69,5400000,70.69 1963-05-31,70.33,71.14,70.27,70.80,4680000,70.80 1963-05-29,70.01,70.65,69.86,70.33,4320000,70.33 1963-05-28,69.87,70.41,69.55,70.01,3860000,70.01 1963-05-27,70.02,70.27,69.48,69.87,3760000,69.87 1963-05-24,70.10,70.44,69.66,70.02,4320000,70.02 1963-05-23,70.14,70.53,69.79,70.10,4400000,70.10 1963-05-22,70.14,70.68,69.82,70.14,5560000,70.14 1963-05-21,69.96,70.51,69.62,70.14,5570000,70.14 1963-05-20,70.29,70.48,69.59,69.96,4710000,69.96 1963-05-17,70.25,70.63,69.83,70.29,4410000,70.29 1963-05-16,70.43,70.81,69.91,70.25,5640000,70.25 1963-05-15,70.21,70.77,69.87,70.43,5650000,70.43 1963-05-14,70.48,70.73,69.92,70.21,4740000,70.21 1963-05-13,70.52,70.89,70.11,70.48,4920000,70.48 1963-05-10,70.35,70.81,69.99,70.52,5260000,70.52 1963-05-09,70.01,70.74,69.86,70.35,5600000,70.35 1963-05-08,69.44,70.24,69.23,70.01,5140000,70.01 1963-05-07,69.53,69.92,69.03,69.44,4140000,69.44 1963-05-06,70.03,70.31,69.32,69.53,4090000,69.53 1963-05-03,70.17,70.51,69.78,70.03,4760000,70.03 1963-05-02,69.97,70.50,69.75,70.17,4480000,70.17 1963-05-01,69.80,70.43,69.61,69.97,5060000,69.97 1963-04-30,69.65,70.18,69.26,69.80,4680000,69.80 1963-04-29,69.70,70.04,69.26,69.65,3980000,69.65 1963-04-26,69.76,70.11,69.23,69.70,4490000,69.70 1963-04-25,69.72,70.08,69.25,69.76,5070000,69.76 1963-04-24,69.53,70.12,69.34,69.72,5910000,69.72 1963-04-23,69.30,69.83,68.95,69.53,5220000,69.53 1963-04-22,69.23,69.82,69.01,69.30,5180000,69.30 1963-04-19,68.89,69.46,68.60,69.23,4660000,69.23 1963-04-18,68.92,69.34,68.56,68.89,4770000,68.89 1963-04-17,69.14,69.37,68.47,68.92,5220000,68.92 1963-04-16,69.09,69.61,68.66,69.14,5570000,69.14 1963-04-15,68.77,69.56,68.58,69.09,5930000,69.09 1963-04-11,68.29,69.07,67.97,68.77,5250000,68.77 1963-04-10,68.45,68.89,67.66,68.29,5880000,68.29 1963-04-09,68.52,68.84,68.03,68.45,5090000,68.45 1963-04-08,68.28,68.91,68.05,68.52,5940000,68.52 1963-04-05,67.85,68.46,67.46,68.28,5240000,68.28 1963-04-04,67.36,68.12,67.28,67.85,5300000,67.85 1963-04-03,66.84,67.55,66.63,67.36,4660000,67.36 1963-04-02,66.85,67.36,66.51,66.84,4360000,66.84 1963-04-01,66.57,67.18,66.23,66.85,3890000,66.85 1963-03-29,66.58,66.90,66.23,66.57,3390000,66.57 1963-03-28,66.68,67.01,66.32,66.58,3890000,66.58 1963-03-27,66.40,66.93,66.21,66.68,4270000,66.68 1963-03-26,66.21,66.73,66.01,66.40,4100000,66.40 1963-03-25,66.19,66.60,65.92,66.21,3700000,66.21 1963-03-22,65.85,66.44,65.68,66.19,3820000,66.19 1963-03-21,65.95,66.25,65.60,65.85,3220000,65.85 1963-03-20,65.47,66.15,65.30,65.95,3690000,65.95 1963-03-19,65.61,65.85,65.19,65.47,3180000,65.47 1963-03-18,65.93,66.17,65.36,65.61,3250000,65.61 1963-03-15,65.60,66.22,65.39,65.93,3400000,65.93 1963-03-14,65.91,66.21,65.39,65.60,3540000,65.60 1963-03-13,65.67,66.27,65.54,65.91,4120000,65.91 1963-03-12,65.51,65.97,65.26,65.67,3350000,65.67 1963-03-11,65.33,65.86,65.11,65.51,3180000,65.51 1963-03-08,65.26,65.74,65.03,65.33,3360000,65.33 1963-03-07,64.85,65.60,64.81,65.26,3350000,65.26 1963-03-06,64.74,65.06,64.31,64.85,3100000,64.85 1963-03-05,64.72,65.27,64.41,64.74,3280000,64.74 1963-03-04,64.10,65.08,63.88,64.72,3650000,64.72 1963-03-01,64.29,64.75,63.80,64.10,3920000,64.10 1963-02-28,65.01,65.14,64.08,64.29,4090000,64.29 1963-02-27,65.47,65.74,64.86,65.01,3680000,65.01 1963-02-26,65.46,65.86,65.06,65.47,3670000,65.47 1963-02-25,65.92,66.09,65.24,65.46,3680000,65.46 1963-02-21,65.83,66.23,65.36,65.92,3980000,65.92 1963-02-20,66.20,66.28,65.44,65.83,4120000,65.83 1963-02-19,66.52,66.67,65.92,66.20,4130000,66.20 1963-02-18,66.41,66.96,66.10,66.52,4700000,66.52 1963-02-15,66.35,66.74,65.96,66.41,4410000,66.41 1963-02-14,66.15,66.75,65.93,66.35,5640000,66.35 1963-02-13,65.83,66.53,65.56,66.15,4960000,66.15 1963-02-12,65.76,66.01,65.16,65.83,3710000,65.83 1963-02-11,66.17,66.41,65.50,65.76,3880000,65.76 1963-02-08,66.17,66.45,65.65,66.17,3890000,66.17 1963-02-07,66.40,66.81,65.91,66.17,4240000,66.17 1963-02-06,66.11,66.76,65.88,66.40,4340000,66.40 1963-02-05,66.17,66.35,65.38,66.11,4050000,66.11 1963-02-04,66.31,66.66,65.89,66.17,3670000,66.17 1963-02-01,66.31,66.31,66.31,66.31,4280000,66.31 1963-01-31,65.85,66.45,65.51,66.20,4270000,66.20 1963-01-30,66.23,66.33,65.55,65.85,3740000,65.85 1963-01-29,66.24,66.58,65.83,66.23,4360000,66.23 1963-01-28,65.92,66.59,65.77,66.24,4720000,66.24 1963-01-25,65.75,66.23,65.38,65.92,4770000,65.92 1963-01-24,65.62,66.09,65.33,65.75,4810000,65.75 1963-01-23,65.44,65.91,65.23,65.62,4820000,65.62 1963-01-22,65.28,65.80,65.03,65.44,4810000,65.44 1963-01-21,65.18,65.52,64.64,65.28,4090000,65.28 1963-01-18,65.13,65.70,64.86,65.18,4760000,65.18 1963-01-17,64.67,65.40,64.35,65.13,5230000,65.13 1963-01-16,65.11,65.25,64.42,64.67,4260000,64.67 1963-01-15,65.20,65.62,64.82,65.11,5930000,65.11 1963-01-14,64.85,65.50,64.61,65.20,5000000,65.20 1963-01-11,64.71,65.10,64.31,64.85,4410000,64.85 1963-01-10,64.59,65.16,64.33,64.71,4520000,64.71 1963-01-09,64.74,65.22,64.32,64.59,5110000,64.59 1963-01-08,64.12,64.98,64.00,64.74,5410000,64.74 1963-01-07,64.13,64.59,63.67,64.12,4440000,64.12 1963-01-04,63.72,64.45,63.57,64.13,5400000,64.13 1963-01-03,62.69,63.89,62.67,63.72,4570000,63.72 1963-01-02,63.10,63.39,62.32,62.69,2540000,62.69 1962-12-31,62.96,63.43,62.68,63.10,5420000,63.10 1962-12-28,62.93,63.25,62.53,62.96,4140000,62.96 1962-12-27,63.02,63.41,62.67,62.93,3670000,62.93 1962-12-26,62.63,63.32,62.56,63.02,3370000,63.02 1962-12-24,62.64,63.03,62.19,62.63,3180000,62.63 1962-12-21,62.82,63.13,62.26,62.64,3470000,62.64 1962-12-20,62.58,63.28,62.44,62.82,4220000,62.82 1962-12-19,62.07,62.81,61.72,62.58,4000000,62.58 1962-12-18,62.37,62.66,61.78,62.07,3620000,62.07 1962-12-17,62.57,62.95,62.14,62.37,3590000,62.37 1962-12-14,62.42,62.83,61.96,62.57,3280000,62.57 1962-12-13,62.63,63.07,62.09,62.42,3380000,62.42 1962-12-12,62.32,63.16,62.13,62.63,3760000,62.63 1962-12-11,62.27,62.58,61.72,62.32,3700000,62.32 1962-12-10,63.06,63.35,61.96,62.27,4270000,62.27 1962-12-07,62.93,63.43,62.45,63.06,3900000,63.06 1962-12-06,62.39,63.36,62.28,62.93,4600000,62.93 1962-12-05,62.64,63.50,62.37,62.39,6280000,62.39 1962-12-04,61.94,62.93,61.77,62.64,5210000,62.64 1962-12-03,62.26,62.45,61.28,61.94,3810000,61.94 1962-11-30,62.41,62.78,61.78,62.26,4570000,62.26 1962-11-29,62.12,62.72,61.69,62.41,5810000,62.41 1962-11-28,61.73,62.48,61.51,62.12,5980000,62.12 1962-11-27,61.36,62.04,60.98,61.73,5500000,61.73 1962-11-26,61.54,62.13,60.95,61.36,5650000,61.36 1962-11-23,60.81,62.03,60.66,61.54,5660000,61.54 1962-11-21,60.45,61.18,60.19,60.81,5100000,60.81 1962-11-20,59.82,60.63,59.57,60.45,4290000,60.45 1962-11-19,60.16,60.42,59.46,59.82,3410000,59.82 1962-11-16,59.97,60.46,59.46,60.16,4000000,60.16 1962-11-15,60.16,60.67,59.74,59.97,5050000,59.97 1962-11-14,59.46,60.41,59.18,60.16,5090000,60.16 1962-11-13,59.59,60.06,59.06,59.46,4550000,59.46 1962-11-12,58.78,60.00,58.59,59.59,5090000,59.59 1962-11-09,58.32,58.99,57.90,58.78,4340000,58.78 1962-11-08,58.71,59.12,58.09,58.32,4160000,58.32 1962-11-07,58.35,59.11,57.76,58.71,4580000,58.71 1962-11-05,57.75,58.70,57.69,58.35,4320000,58.35 1962-11-02,57.12,58.19,56.78,57.75,5470000,57.75 1962-11-01,56.52,57.31,55.90,57.12,3400000,57.12 1962-10-31,56.54,57.00,56.19,56.52,3090000,56.52 1962-10-30,55.72,56.84,55.52,56.54,3830000,56.54 1962-10-29,55.34,56.38,55.34,55.72,4280000,55.72 1962-10-26,54.69,54.96,54.08,54.54,2580000,54.54 1962-10-25,55.17,55.17,53.82,54.69,3950000,54.69 1962-10-24,53.49,55.44,52.55,55.21,6720000,55.21 1962-10-23,54.96,55.19,53.24,53.49,6110000,53.49 1962-10-22,55.48,55.48,54.38,54.96,5690000,54.96 1962-10-19,56.34,56.54,55.34,55.59,4650000,55.59 1962-10-18,56.89,57.02,56.18,56.34,3280000,56.34 1962-10-17,57.08,57.23,56.37,56.89,3240000,56.89 1962-10-16,57.27,57.63,56.87,57.08,2860000,57.08 1962-10-15,56.95,57.50,56.66,57.27,2640000,57.27 1962-10-12,57.05,57.21,56.66,56.95,2020000,56.95 1962-10-11,57.24,57.46,56.78,57.05,2460000,57.05 1962-10-10,57.20,57.83,56.96,57.24,3040000,57.24 1962-10-09,57.07,57.40,56.71,57.20,2340000,57.20 1962-10-08,57.07,57.41,56.68,57.07,1950000,57.07 1962-10-05,56.70,57.30,56.55,57.07,2730000,57.07 1962-10-04,56.16,56.84,55.90,56.70,2530000,56.70 1962-10-03,56.10,56.71,55.84,56.16,2610000,56.16 1962-10-02,55.49,56.46,55.31,56.10,3000000,56.10 1962-10-01,56.27,56.31,55.26,55.49,3090000,55.49 1962-09-28,55.77,56.58,55.59,56.27,2850000,56.27 1962-09-27,56.15,56.55,55.53,55.77,3540000,55.77 1962-09-26,56.96,57.29,55.92,56.15,3550000,56.15 1962-09-25,56.63,57.22,56.12,56.96,3620000,56.96 1962-09-24,57.45,57.45,56.30,56.63,5000000,56.63 1962-09-21,58.54,58.64,57.43,57.69,4280000,57.69 1962-09-20,58.95,59.29,58.33,58.54,3350000,58.54 1962-09-19,59.03,59.26,58.59,58.95,2950000,58.95 1962-09-18,59.08,59.54,58.77,59.03,3690000,59.03 1962-09-17,58.89,59.42,58.65,59.08,3330000,59.08 1962-09-14,58.70,59.14,58.40,58.89,2880000,58.89 1962-09-13,58.84,59.18,58.46,58.70,3100000,58.70 1962-09-12,58.59,59.06,58.40,58.84,3100000,58.84 1962-09-11,58.45,58.93,58.17,58.59,3040000,58.59 1962-09-10,58.38,58.64,57.88,58.45,2520000,58.45 1962-09-07,58.36,58.90,58.09,58.38,2890000,58.38 1962-09-06,58.12,58.60,57.72,58.36,3180000,58.36 1962-09-05,58.56,58.77,57.95,58.12,3050000,58.12 1962-09-04,59.12,59.49,58.44,58.56,2970000,58.56 1962-08-31,58.68,59.25,58.45,59.12,2830000,59.12 1962-08-30,58.66,59.06,58.39,58.68,2260000,58.68 1962-08-29,58.79,58.96,58.17,58.66,2900000,58.66 1962-08-28,59.55,59.61,58.66,58.79,3180000,58.79 1962-08-27,59.58,59.94,59.24,59.55,3140000,59.55 1962-08-24,59.70,59.92,59.18,59.58,2890000,59.58 1962-08-23,59.78,60.33,59.47,59.70,4770000,59.70 1962-08-22,59.12,59.93,58.91,59.78,4520000,59.78 1962-08-21,59.37,59.66,58.90,59.12,3730000,59.12 1962-08-20,59.01,59.72,58.90,59.37,4580000,59.37 1962-08-17,58.64,59.24,58.43,59.01,3430000,59.01 1962-08-16,58.66,59.11,58.24,58.64,4180000,58.64 1962-08-15,58.25,59.11,58.22,58.66,4880000,58.66 1962-08-14,57.63,58.43,57.41,58.25,3640000,58.25 1962-08-13,57.55,57.90,57.22,57.63,2670000,57.63 1962-08-10,57.57,57.85,57.16,57.55,2470000,57.55 1962-08-09,57.51,57.88,57.19,57.57,2670000,57.57 1962-08-08,57.36,57.64,56.76,57.51,3080000,57.51 1962-08-07,57.75,57.81,57.07,57.36,2970000,57.36 1962-08-06,58.12,58.35,57.54,57.75,3110000,57.75 1962-08-03,57.98,58.32,57.63,58.12,5990000,58.12 1962-08-02,57.75,58.20,57.38,57.98,3410000,57.98 1962-08-01,58.23,58.30,57.51,57.75,3100000,57.75 1962-07-31,57.83,58.58,57.74,58.23,4190000,58.23 1962-07-30,57.20,57.98,57.08,57.83,3200000,57.83 1962-07-27,56.77,57.36,56.56,57.20,2890000,57.20 1962-07-26,56.46,57.18,56.16,56.77,2790000,56.77 1962-07-25,56.36,56.67,55.78,56.46,2910000,56.46 1962-07-24,56.80,56.93,56.14,56.36,2560000,56.36 1962-07-23,56.81,57.32,56.53,56.80,2770000,56.80 1962-07-20,56.42,57.09,56.27,56.81,2610000,56.81 1962-07-19,56.20,56.95,55.96,56.42,3090000,56.42 1962-07-18,56.78,56.81,55.86,56.20,3620000,56.20 1962-07-17,57.83,57.96,56.68,56.78,3500000,56.78 1962-07-16,57.83,58.10,57.18,57.83,3130000,57.83 1962-07-13,58.03,58.18,57.23,57.83,3380000,57.83 1962-07-12,57.73,58.67,57.59,58.03,5370000,58.03 1962-07-11,57.20,57.95,56.77,57.73,4250000,57.73 1962-07-10,56.99,58.36,56.99,57.20,7120000,57.20 1962-07-09,56.17,56.73,55.54,56.55,2950000,56.55 1962-07-06,56.73,56.73,55.64,56.17,3110000,56.17 1962-07-05,56.49,57.10,56.15,56.81,3350000,56.81 1962-07-03,55.86,56.74,55.57,56.49,3920000,56.49 1962-07-02,54.75,56.02,54.47,55.86,3450000,55.86 1962-06-29,54.41,55.47,54.20,54.75,4720000,54.75 1962-06-28,52.98,54.64,52.98,54.41,5440000,54.41 1962-06-27,52.32,52.83,51.77,52.60,3890000,52.60 1962-06-26,52.45,53.58,52.10,52.32,4630000,52.32 1962-06-25,52.68,52.96,51.35,52.45,7090000,52.45 1962-06-22,53.59,53.78,52.48,52.68,5640000,52.68 1962-06-21,54.78,54.78,53.50,53.59,4560000,53.59 1962-06-20,55.54,55.92,54.66,54.78,3360000,54.78 1962-06-19,55.74,55.88,54.98,55.54,2680000,55.54 1962-06-18,55.89,56.53,54.97,55.74,4580000,55.74 1962-06-15,54.33,55.96,53.66,55.89,7130000,55.89 1962-06-14,55.50,56.00,54.12,54.33,6240000,54.33 1962-06-13,56.34,56.80,55.24,55.50,5850000,55.50 1962-06-12,57.66,57.66,56.23,56.34,4690000,56.34 1962-06-11,58.45,58.58,57.51,57.82,2870000,57.82 1962-06-08,58.40,58.97,58.14,58.45,2560000,58.45 1962-06-07,58.39,58.90,58.00,58.40,2760000,58.40 1962-06-06,57.64,59.17,57.64,58.39,4190000,58.39 1962-06-05,57.27,58.42,56.33,57.57,6140000,57.57 1962-06-04,59.12,59.12,57.14,57.27,5380000,57.27 1962-06-01,59.63,59.96,58.52,59.38,5760000,59.38 1962-05-31,58.80,60.82,58.80,59.63,10710000,59.63 1962-05-29,55.50,58.29,53.13,58.08,14750000,58.08 1962-05-28,59.15,59.15,55.42,55.50,9350000,55.50 1962-05-25,60.62,60.98,59.00,59.47,6380000,59.47 1962-05-24,61.11,61.79,60.36,60.62,5250000,60.62 1962-05-23,62.34,62.42,60.90,61.11,5450000,61.11 1962-05-22,63.59,63.69,62.26,62.34,3640000,62.34 1962-05-21,63.82,64.00,63.21,63.59,2260000,63.59 1962-05-18,63.93,64.14,63.29,63.82,2490000,63.82 1962-05-17,64.27,64.41,63.38,63.93,2950000,63.93 1962-05-16,64.29,64.88,63.82,64.27,3360000,64.27 1962-05-15,63.41,64.87,63.41,64.29,4780000,64.29 1962-05-14,62.65,63.31,61.11,63.10,5990000,63.10 1962-05-11,63.57,64.10,62.44,62.65,4510000,62.65 1962-05-10,64.26,64.39,62.99,63.57,4730000,63.57 1962-05-09,65.17,65.17,64.02,64.26,3670000,64.26 1962-05-08,66.02,66.13,64.88,65.17,3020000,65.17 1962-05-07,66.24,66.56,65.66,66.02,2530000,66.02 1962-05-04,66.53,66.80,65.80,66.24,3010000,66.24 1962-05-03,65.99,66.93,65.81,66.53,3320000,66.53 1962-05-02,65.70,66.67,65.56,65.99,3780000,65.99 1962-05-01,65.24,65.94,63.76,65.70,5100000,65.70 1962-04-30,66.30,66.90,64.95,65.24,4150000,65.24 1962-04-27,67.05,67.61,65.99,66.30,4140000,66.30 1962-04-26,67.71,67.97,66.92,67.05,3650000,67.05 1962-04-25,68.46,68.58,67.53,67.71,3340000,67.71 1962-04-24,68.53,68.91,68.16,68.46,3040000,68.46 1962-04-23,68.59,69.01,68.17,68.53,3240000,68.53 1962-04-19,68.27,68.90,68.07,68.59,3100000,68.59 1962-04-18,67.90,68.72,67.83,68.27,3350000,68.27 1962-04-17,67.60,68.20,67.24,67.90,2940000,67.90 1962-04-16,67.90,68.19,67.21,67.60,3070000,67.60 1962-04-13,67.90,68.11,67.03,67.90,3470000,67.90 1962-04-12,68.41,68.43,67.47,67.90,3320000,67.90 1962-04-11,68.56,69.26,68.24,68.41,3240000,68.41 1962-04-10,68.31,68.80,67.94,68.56,2880000,68.56 1962-04-09,68.84,69.02,68.09,68.31,3020000,68.31 1962-04-06,68.91,69.42,68.58,68.84,2730000,68.84 1962-04-05,68.49,69.09,68.12,68.91,3130000,68.91 1962-04-04,68.81,69.22,68.33,68.49,3290000,68.49 1962-04-03,69.37,69.53,68.53,68.81,3350000,68.81 1962-04-02,69.55,69.82,69.13,69.37,2790000,69.37 1962-03-30,70.01,70.09,69.16,69.55,2950000,69.55 1962-03-29,70.04,70.50,69.81,70.01,2870000,70.01 1962-03-28,69.70,70.33,69.54,70.04,2940000,70.04 1962-03-27,69.89,70.20,69.41,69.70,3090000,69.70 1962-03-26,70.45,70.63,69.73,69.89,3040000,69.89 1962-03-23,70.40,70.78,70.12,70.45,3050000,70.45 1962-03-22,70.51,70.84,70.14,70.40,3130000,70.40 1962-03-21,70.66,70.93,70.16,70.51,3360000,70.51 1962-03-20,70.85,71.08,70.40,70.66,3060000,70.66 1962-03-19,70.94,71.31,70.53,70.85,3220000,70.85 1962-03-16,71.06,71.34,70.67,70.94,3060000,70.94 1962-03-15,70.91,71.44,70.59,71.06,3250000,71.06 1962-03-14,70.60,71.25,70.48,70.91,3670000,70.91 1962-03-13,70.40,70.86,70.06,70.60,3200000,70.60 1962-03-12,70.42,70.76,70.02,70.40,3280000,70.40 1962-03-09,70.19,70.71,70.00,70.42,3340000,70.42 1962-03-08,69.69,70.37,69.40,70.19,3210000,70.19 1962-03-07,69.78,70.07,69.37,69.69,2890000,69.69 1962-03-06,70.01,70.24,69.46,69.78,2870000,69.78 1962-03-05,70.16,70.48,69.65,70.01,3020000,70.01 1962-03-02,70.16,70.16,69.75,70.16,2980000,70.16 1962-03-01,69.96,70.60,69.76,70.20,2960000,70.20 1962-02-28,69.89,70.42,69.57,69.96,3030000,69.96 1962-02-27,69.76,70.32,69.48,69.89,3110000,69.89 1962-02-26,70.16,70.33,69.44,69.76,2910000,69.76 1962-02-23,70.32,70.57,69.73,70.16,3230000,70.16 1962-02-21,70.66,70.97,70.12,70.32,3310000,70.32 1962-02-20,70.41,70.91,70.13,70.66,3300000,70.66 1962-02-19,70.59,70.96,70.12,70.41,3350000,70.41 1962-02-16,70.74,71.13,70.27,70.59,3700000,70.59 1962-02-15,70.42,71.06,70.23,70.74,3470000,70.74 1962-02-14,70.45,70.79,70.03,70.42,3630000,70.42 1962-02-13,70.46,70.89,70.07,70.45,3400000,70.45 1962-02-12,70.48,70.81,70.14,70.46,2620000,70.46 1962-02-09,70.58,70.83,69.93,70.48,3370000,70.48 1962-02-08,70.42,70.95,70.16,70.58,3810000,70.58 1962-02-07,69.96,70.67,69.78,70.42,4140000,70.42 1962-02-06,69.88,70.32,69.41,69.96,3650000,69.96 1962-02-05,69.81,70.30,69.42,69.88,3890000,69.88 1962-02-02,69.26,70.02,69.02,69.81,3950000,69.81 1962-02-01,68.84,69.65,68.56,69.26,4260000,69.26 1962-01-31,68.17,69.09,68.12,68.84,3840000,68.84 1962-01-30,67.90,68.65,67.62,68.17,3520000,68.17 1962-01-29,68.13,68.50,67.55,67.90,3050000,67.90 1962-01-26,68.35,68.67,67.83,68.13,3330000,68.13 1962-01-25,68.40,69.05,68.10,68.35,3560000,68.35 1962-01-24,68.29,68.68,67.55,68.40,3760000,68.40 1962-01-23,68.81,68.96,68.00,68.29,3350000,68.29 1962-01-22,68.75,69.37,68.45,68.81,3810000,68.81 1962-01-19,68.39,70.08,68.14,68.75,3800000,68.75 1962-01-18,68.32,68.73,67.75,68.39,3460000,68.39 1962-01-17,69.07,69.31,68.13,68.32,3780000,68.32 1962-01-16,69.47,69.61,68.68,69.07,3650000,69.07 1962-01-15,69.61,69.96,69.06,69.47,3450000,69.47 1962-01-12,69.37,70.17,69.23,69.61,3730000,69.61 1962-01-11,68.96,69.54,68.57,69.37,3390000,69.37 1962-01-10,69.15,69.58,68.62,68.96,3300000,68.96 1962-01-09,69.12,69.93,68.83,69.15,3600000,69.15 1962-01-08,69.66,69.84,68.17,69.12,4620000,69.12 1962-01-05,70.64,70.84,69.35,69.66,4630000,69.66 1962-01-04,71.13,71.62,70.45,70.64,4450000,70.64 1962-01-03,70.96,71.48,70.38,71.13,3590000,71.13 1962-01-02,71.55,71.96,70.71,70.96,3120000,70.96 1961-12-29,71.55,71.55,71.55,71.55,5370000,71.55 1961-12-28,71.69,71.69,71.69,71.69,4530000,71.69 1961-12-27,71.65,71.65,71.65,71.65,4170000,71.65 1961-12-26,71.02,71.02,71.02,71.02,3180000,71.02 1961-12-22,70.91,70.91,70.91,70.91,3390000,70.91 1961-12-21,70.86,70.86,70.86,70.86,3440000,70.86 1961-12-20,71.12,71.12,71.12,71.12,3640000,71.12 1961-12-19,71.26,71.26,71.26,71.26,3440000,71.26 1961-12-18,71.76,71.76,71.76,71.76,3810000,71.76 1961-12-15,72.01,72.01,72.01,72.01,3710000,72.01 1961-12-14,71.98,71.98,71.98,71.98,4350000,71.98 1961-12-13,72.53,72.53,72.53,72.53,4890000,72.53 1961-12-12,72.64,72.64,72.64,72.64,4680000,72.64 1961-12-11,72.39,72.39,72.39,72.39,4360000,72.39 1961-12-08,72.04,72.04,72.04,72.04,4010000,72.04 1961-12-07,71.70,71.70,71.70,71.70,3900000,71.70 1961-12-06,71.99,71.99,71.99,71.99,4200000,71.99 1961-12-05,71.93,71.93,71.93,71.93,4330000,71.93 1961-12-04,72.01,72.01,72.01,72.01,4560000,72.01 1961-12-01,71.78,71.78,71.78,71.78,4420000,71.78 1961-11-30,71.32,71.32,71.32,71.32,4210000,71.32 1961-11-29,71.70,71.70,71.70,71.70,4550000,71.70 1961-11-28,71.75,71.75,71.75,71.75,4360000,71.75 1961-11-27,71.85,71.85,71.85,71.85,4700000,71.85 1961-11-24,71.84,71.84,71.84,71.84,4020000,71.84 1961-11-22,71.70,71.70,71.70,71.70,4500000,71.70 1961-11-21,71.78,71.78,71.78,71.78,4890000,71.78 1961-11-20,71.72,71.72,71.72,71.72,4190000,71.72 1961-11-17,71.62,71.62,71.62,71.62,3960000,71.62 1961-11-16,71.62,71.62,71.62,71.62,3980000,71.62 1961-11-15,71.67,71.67,71.67,71.67,4660000,71.67 1961-11-14,71.66,71.66,71.66,71.66,4750000,71.66 1961-11-13,71.27,71.27,71.27,71.27,4540000,71.27 1961-11-10,71.07,71.07,71.07,71.07,4180000,71.07 1961-11-09,70.77,70.77,70.77,70.77,4680000,70.77 1961-11-08,70.87,70.87,70.87,70.87,6090000,70.87 1961-11-06,70.01,70.01,70.01,70.01,4340000,70.01 1961-11-03,69.47,69.47,69.47,69.47,4070000,69.47 1961-11-02,69.11,69.11,69.11,69.11,3890000,69.11 1961-11-01,68.73,68.73,68.73,68.73,3210000,68.73 1961-10-31,68.62,68.62,68.62,68.62,3350000,68.62 1961-10-30,68.42,68.42,68.42,68.42,3430000,68.42 1961-10-27,68.34,68.34,68.34,68.34,3200000,68.34 1961-10-26,68.46,68.46,68.46,68.46,3330000,68.46 1961-10-25,68.34,68.34,68.34,68.34,3590000,68.34 1961-10-24,67.98,67.98,67.98,67.98,3430000,67.98 1961-10-23,68.06,68.06,68.06,68.06,3440000,68.06 1961-10-20,68.00,68.00,68.00,68.00,3470000,68.00 1961-10-19,68.45,68.45,68.45,68.45,3850000,68.45 1961-10-18,68.21,68.21,68.21,68.21,3520000,68.21 1961-10-17,67.87,67.87,67.87,67.87,3110000,67.87 1961-10-16,67.85,67.85,67.85,67.85,2840000,67.85 1961-10-13,68.04,68.04,68.04,68.04,3090000,68.04 1961-10-12,68.16,68.16,68.16,68.16,3060000,68.16 1961-10-11,68.17,68.17,68.17,68.17,3670000,68.17 1961-10-10,68.11,68.11,68.11,68.11,3430000,68.11 1961-10-09,67.94,67.94,67.94,67.94,2920000,67.94 1961-10-06,66.97,66.97,66.97,66.97,3470000,66.97 1961-10-05,67.77,67.77,67.77,67.77,3920000,67.77 1961-10-04,67.18,67.18,67.18,67.18,3380000,67.18 1961-10-03,66.73,66.73,66.73,66.73,2680000,66.73 1961-10-02,66.77,66.77,66.77,66.77,2800000,66.77 1961-09-29,66.73,66.73,66.73,66.73,3060000,66.73 1961-09-28,66.58,66.58,66.58,66.58,3000000,66.58 1961-09-27,66.47,66.47,66.47,66.47,3440000,66.47 1961-09-26,65.78,65.78,65.78,65.78,3320000,65.78 1961-09-25,65.77,65.77,65.77,65.77,3700000,65.77 1961-09-22,66.72,66.72,66.72,66.72,3070000,66.72 1961-09-21,66.99,66.99,66.99,66.99,3340000,66.99 1961-09-20,66.96,66.96,66.96,66.96,2700000,66.96 1961-09-19,66.08,66.08,66.08,66.08,3260000,66.08 1961-09-18,67.21,67.21,67.21,67.21,3550000,67.21 1961-09-15,67.65,67.65,67.65,67.65,3130000,67.65 1961-09-14,67.53,67.53,67.53,67.53,2920000,67.53 1961-09-13,68.01,68.01,68.01,68.01,3110000,68.01 1961-09-12,67.96,67.96,67.96,67.96,2950000,67.96 1961-09-11,67.28,67.28,67.28,67.28,2790000,67.28 1961-09-08,67.88,67.88,67.88,67.88,3430000,67.88 1961-09-07,68.35,68.35,68.35,68.35,3900000,68.35 1961-09-06,68.46,68.46,68.46,68.46,3440000,68.46 1961-09-05,67.96,67.96,67.96,67.96,3000000,67.96 1961-09-01,68.19,68.19,68.19,68.19,2710000,68.19 1961-08-31,68.07,68.07,68.07,68.07,2920000,68.07 1961-08-30,67.81,67.81,67.81,67.81,3220000,67.81 1961-08-29,67.55,67.55,67.55,67.55,3160000,67.55 1961-08-28,67.70,67.70,67.70,67.70,3150000,67.70 1961-08-25,67.67,67.67,67.67,67.67,3050000,67.67 1961-08-24,67.59,67.59,67.59,67.59,3090000,67.59 1961-08-23,67.98,67.98,67.98,67.98,3550000,67.98 1961-08-22,68.44,68.44,68.44,68.44,3640000,68.44 1961-08-21,68.43,68.43,68.43,68.43,3880000,68.43 1961-08-18,68.29,68.29,68.29,68.29,4030000,68.29 1961-08-17,68.11,68.11,68.11,68.11,4130000,68.11 1961-08-16,67.73,67.73,67.73,67.73,3430000,67.73 1961-08-15,67.55,67.55,67.55,67.55,3320000,67.55 1961-08-14,67.72,67.72,67.72,67.72,3120000,67.72 1961-08-11,68.06,68.06,68.06,68.06,3260000,68.06 1961-08-10,67.95,67.95,67.95,67.95,3570000,67.95 1961-08-09,67.74,67.74,67.74,67.74,3710000,67.74 1961-08-08,67.82,67.82,67.82,67.82,4050000,67.82 1961-08-07,67.67,67.67,67.67,67.67,3560000,67.67 1961-08-04,67.68,67.68,67.68,67.68,3710000,67.68 1961-08-03,67.29,67.29,67.29,67.29,3650000,67.29 1961-08-02,66.94,66.94,66.94,66.94,4300000,66.94 1961-08-01,67.37,67.37,67.37,67.37,3990000,67.37 1961-07-31,66.76,66.76,66.76,66.76,3170000,66.76 1961-07-28,66.71,66.71,66.71,66.71,3610000,66.71 1961-07-27,66.61,66.61,66.61,66.61,4170000,66.61 1961-07-26,65.84,65.84,65.84,65.84,4070000,65.84 1961-07-25,65.23,65.23,65.23,65.23,3010000,65.23 1961-07-24,64.87,64.87,64.87,64.87,2490000,64.87 1961-07-21,64.86,64.86,64.86,64.86,2360000,64.86 1961-07-20,64.71,64.71,64.71,64.71,2530000,64.71 1961-07-19,64.70,64.70,64.70,64.70,2940000,64.70 1961-07-18,64.41,64.41,64.41,64.41,3010000,64.41 1961-07-17,64.79,64.79,64.79,64.79,2690000,64.79 1961-07-14,65.28,65.28,65.28,65.28,2760000,65.28 1961-07-13,64.86,64.86,64.86,64.86,2670000,64.86 1961-07-12,65.32,65.32,65.32,65.32,3070000,65.32 1961-07-11,65.69,65.69,65.69,65.69,3160000,65.69 1961-07-10,65.71,65.71,65.71,65.71,3180000,65.71 1961-07-07,65.77,65.77,65.77,65.77,3030000,65.77 1961-07-06,65.81,65.81,65.81,65.81,3470000,65.81 1961-07-05,65.63,65.63,65.63,65.63,3270000,65.63 1961-07-03,65.21,65.21,65.21,65.21,2180000,65.21 1961-06-30,64.64,64.64,64.64,64.64,2380000,64.64 1961-06-29,64.52,64.52,64.52,64.52,2560000,64.52 1961-06-28,64.59,64.59,64.59,64.59,2830000,64.59 1961-06-27,64.47,64.47,64.47,64.47,3090000,64.47 1961-06-26,64.47,64.47,64.47,64.47,2690000,64.47 1961-06-23,65.16,65.16,65.16,65.16,2720000,65.16 1961-06-22,64.90,64.90,64.90,64.90,2880000,64.90 1961-06-21,65.14,65.14,65.14,65.14,3210000,65.14 1961-06-20,65.15,65.15,65.15,65.15,3280000,65.15 1961-06-19,64.58,64.58,64.58,64.58,3980000,64.58 1961-06-16,65.18,65.18,65.18,65.18,3380000,65.18 1961-06-15,65.69,65.69,65.69,65.69,3220000,65.69 1961-06-14,65.98,65.98,65.98,65.98,3430000,65.98 1961-06-13,65.80,65.80,65.80,65.80,3030000,65.80 1961-06-12,66.15,66.15,66.15,66.15,3260000,66.15 1961-06-09,66.66,66.66,66.66,66.66,3520000,66.66 1961-06-08,66.67,66.67,66.67,66.67,3810000,66.67 1961-06-07,65.64,65.64,65.64,65.64,3980000,65.64 1961-06-06,66.89,66.89,66.89,66.89,4250000,66.89 1961-06-05,67.08,67.08,67.08,67.08,4150000,67.08 1961-06-02,66.73,66.73,66.73,66.73,3670000,66.73 1961-06-01,66.56,66.56,66.56,66.56,3770000,66.56 1961-05-31,66.56,66.56,66.56,66.56,4320000,66.56 1961-05-26,66.43,66.43,66.43,66.43,3780000,66.43 1961-05-25,66.01,66.01,66.01,66.01,3760000,66.01 1961-05-24,66.26,66.26,66.26,66.26,3970000,66.26 1961-05-23,66.68,66.68,66.68,66.68,3660000,66.68 1961-05-22,66.85,66.85,66.85,66.85,4070000,66.85 1961-05-19,67.27,67.27,67.27,67.27,4200000,67.27 1961-05-18,66.99,66.99,66.99,66.99,4610000,66.99 1961-05-17,67.39,67.39,67.39,67.39,5520000,67.39 1961-05-16,67.08,67.08,67.08,67.08,5110000,67.08 1961-05-15,66.83,66.83,66.83,66.83,4840000,66.83 1961-05-12,66.50,66.50,66.50,66.50,4840000,66.50 1961-05-11,66.39,66.39,66.39,66.39,5170000,66.39 1961-05-10,66.41,66.41,66.41,66.41,5450000,66.41 1961-05-09,66.47,66.47,66.47,66.47,5380000,66.47 1961-05-08,66.41,66.41,66.41,66.41,5170000,66.41 1961-05-05,66.52,66.52,66.52,66.52,4980000,66.52 1961-05-04,66.44,66.44,66.44,66.44,5350000,66.44 1961-05-03,66.18,66.18,66.18,66.18,4940000,66.18 1961-05-02,65.64,65.64,65.64,65.64,4110000,65.64 1961-05-01,65.17,65.17,65.17,65.17,3710000,65.17 1961-04-28,65.31,65.31,65.31,65.31,3710000,65.31 1961-04-27,65.46,65.46,65.46,65.46,4450000,65.46 1961-04-26,65.55,65.55,65.55,65.55,4980000,65.55 1961-04-25,65.30,65.30,65.30,65.30,4670000,65.30 1961-04-24,64.40,64.40,64.40,64.40,4590000,64.40 1961-04-21,65.77,65.77,65.77,65.77,4340000,65.77 1961-04-20,65.82,65.82,65.82,65.82,4810000,65.82 1961-04-19,65.81,65.81,65.81,65.81,4870000,65.81 1961-04-18,66.20,66.20,66.20,66.20,4830000,66.20 1961-04-17,68.68,68.68,68.68,68.68,5860000,68.68 1961-04-14,66.37,66.37,66.37,66.37,5240000,66.37 1961-04-13,66.26,66.26,66.26,66.26,4770000,66.26 1961-04-12,66.31,66.31,66.31,66.31,4870000,66.31 1961-04-11,66.62,66.62,66.62,66.62,5230000,66.62 1961-04-10,66.53,66.53,66.53,66.53,5550000,66.53 1961-04-07,65.96,65.96,65.96,65.96,5100000,65.96 1961-04-06,65.61,65.61,65.61,65.61,4910000,65.61 1961-04-05,65.46,65.46,65.46,65.46,5430000,65.46 1961-04-04,65.66,65.66,65.66,65.66,7080000,65.66 1961-04-03,65.60,65.60,65.60,65.60,6470000,65.60 1961-03-30,65.06,65.06,65.06,65.06,5610000,65.06 1961-03-29,64.93,64.93,64.93,64.93,5330000,64.93 1961-03-28,64.38,64.38,64.38,64.38,4630000,64.38 1961-03-27,64.35,64.35,64.35,64.35,4190000,64.35 1961-03-24,64.42,64.42,64.42,64.42,4390000,64.42 1961-03-23,64.53,64.53,64.53,64.53,2170000,64.53 1961-03-22,64.70,64.70,64.70,64.70,5840000,64.70 1961-03-21,64.74,64.74,64.74,64.74,5800000,64.74 1961-03-20,64.86,64.86,64.86,64.86,5780000,64.86 1961-03-17,64.00,64.00,64.00,64.00,5960000,64.00 1961-03-16,64.21,64.21,64.21,64.21,5610000,64.21 1961-03-15,63.57,63.57,63.57,63.57,4900000,63.57 1961-03-14,63.38,63.38,63.38,63.38,4900000,63.38 1961-03-13,63.66,63.66,63.66,63.66,5080000,63.66 1961-03-10,63.48,63.48,63.48,63.48,5950000,63.48 1961-03-09,63.50,63.50,63.50,63.50,6010000,63.50 1961-03-08,63.44,63.44,63.44,63.44,5910000,63.44 1961-03-07,63.47,63.47,63.47,63.47,5540000,63.47 1961-03-06,64.05,64.05,64.05,64.05,5650000,64.05 1961-03-03,63.95,63.95,63.95,63.95,5530000,63.95 1961-03-02,63.85,63.85,63.85,63.85,5300000,63.85 1961-03-01,63.43,63.43,63.43,63.43,4970000,63.43 1961-02-28,63.44,63.44,63.44,63.44,5830000,63.44 1961-02-27,63.30,63.30,63.30,63.30,5470000,63.30 1961-02-24,62.84,62.84,62.84,62.84,5330000,62.84 1961-02-23,62.59,62.59,62.59,62.59,5620000,62.59 1961-02-21,62.36,62.36,62.36,62.36,5070000,62.36 1961-02-20,62.32,62.32,62.32,62.32,4680000,62.32 1961-02-17,62.10,62.10,62.10,62.10,4640000,62.10 1961-02-16,62.30,62.30,62.30,62.30,5070000,62.30 1961-02-15,61.92,61.92,61.92,61.92,5200000,61.92 1961-02-14,61.41,61.41,61.41,61.41,4490000,61.41 1961-02-13,61.14,61.14,61.14,61.14,3560000,61.14 1961-02-10,61.50,61.50,61.50,61.50,4840000,61.50 1961-02-09,62.02,62.02,62.02,62.02,5590000,62.02 1961-02-08,62.21,62.21,62.21,62.21,4940000,62.21 1961-02-07,61.65,61.65,61.65,61.65,4020000,61.65 1961-02-06,61.76,61.76,61.76,61.76,3890000,61.76 1961-02-03,62.22,62.22,62.22,62.22,5210000,62.22 1961-02-02,62.30,62.30,62.30,62.30,4900000,62.30 1961-02-01,61.90,61.90,61.90,61.90,4380000,61.90 1961-01-31,61.78,61.78,61.78,61.78,4690000,61.78 1961-01-30,61.97,61.97,61.97,61.97,5190000,61.97 1961-01-27,61.24,61.24,61.24,61.24,4510000,61.24 1961-01-26,60.62,60.62,60.62,60.62,4110000,60.62 1961-01-25,60.53,60.53,60.53,60.53,4470000,60.53 1961-01-24,60.45,60.45,60.45,60.45,4280000,60.45 1961-01-23,60.29,60.29,60.29,60.29,4450000,60.29 1961-01-20,59.96,59.96,59.96,59.96,3270000,59.96 1961-01-19,59.77,59.77,59.77,59.77,4740000,59.77 1961-01-18,59.68,59.68,59.68,59.68,4390000,59.68 1961-01-17,59.64,59.64,59.64,59.64,3830000,59.64 1961-01-16,59.58,59.58,59.58,59.58,4510000,59.58 1961-01-13,59.60,59.60,59.60,59.60,4520000,59.60 1961-01-12,59.32,59.32,59.32,59.32,4270000,59.32 1961-01-11,59.14,59.14,59.14,59.14,4370000,59.14 1961-01-10,58.97,58.97,58.97,58.97,4840000,58.97 1961-01-09,58.81,58.81,58.81,58.81,4210000,58.81 1961-01-06,58.40,58.40,58.40,58.40,3620000,58.40 1961-01-05,58.57,58.57,58.57,58.57,4130000,58.57 1961-01-04,58.36,58.36,58.36,58.36,3840000,58.36 1961-01-03,57.57,57.57,57.57,57.57,2770000,57.57 1960-12-30,58.11,58.11,58.11,58.11,5300000,58.11 1960-12-29,58.05,58.05,58.05,58.05,4340000,58.05 1960-12-28,57.78,57.78,57.78,57.78,3620000,57.78 1960-12-27,57.52,57.52,57.52,57.52,3270000,57.52 1960-12-23,57.44,57.44,57.44,57.44,3580000,57.44 1960-12-22,57.39,57.39,57.39,57.39,3820000,57.39 1960-12-21,57.55,57.55,57.55,57.55,4060000,57.55 1960-12-20,57.09,57.09,57.09,57.09,3340000,57.09 1960-12-19,57.13,57.13,57.13,57.13,3630000,57.13 1960-12-16,57.20,57.20,57.20,57.20,3770000,57.20 1960-12-15,56.68,56.68,56.68,56.68,3660000,56.68 1960-12-14,56.84,56.84,56.84,56.84,3880000,56.84 1960-12-13,56.88,56.88,56.88,56.88,3500000,56.88 1960-12-12,56.85,56.85,56.85,56.85,3020000,56.85 1960-12-09,56.65,56.65,56.65,56.65,4460000,56.65 1960-12-08,56.15,56.15,56.15,56.15,3540000,56.15 1960-12-07,56.02,56.02,56.02,56.02,3660000,56.02 1960-12-06,55.47,55.47,55.47,55.47,3360000,55.47 1960-12-05,55.31,55.31,55.31,55.31,3290000,55.31 1960-12-02,55.39,55.39,55.39,55.39,3140000,55.39 1960-12-01,55.30,55.30,55.30,55.30,3090000,55.30 1960-11-30,55.54,55.54,55.54,55.54,3080000,55.54 1960-11-29,55.83,55.83,55.83,55.83,3630000,55.83 1960-11-28,56.03,56.03,56.03,56.03,3860000,56.03 1960-11-25,56.13,56.13,56.13,56.13,3190000,56.13 1960-11-23,55.80,55.80,55.80,55.80,3000000,55.80 1960-11-22,55.72,55.72,55.72,55.72,3430000,55.72 1960-11-21,55.93,55.93,55.93,55.93,3090000,55.93 1960-11-18,55.82,55.82,55.82,55.82,2760000,55.82 1960-11-17,55.55,55.55,55.55,55.55,2450000,55.55 1960-11-16,55.70,55.70,55.70,55.70,3110000,55.70 1960-11-15,55.81,55.81,55.81,55.81,2990000,55.81 1960-11-14,55.59,55.59,55.59,55.59,2660000,55.59 1960-11-11,55.87,55.87,55.87,55.87,2730000,55.87 1960-11-10,56.43,56.43,56.43,56.43,4030000,56.43 1960-11-09,55.35,55.35,55.35,55.35,3450000,55.35 1960-11-07,55.11,55.11,55.11,55.11,3540000,55.11 1960-11-04,54.90,54.90,54.90,54.90,3050000,54.90 1960-11-03,54.43,54.43,54.43,54.43,2580000,54.43 1960-11-02,54.22,54.22,54.22,54.22,2780000,54.22 1960-11-01,53.94,53.94,53.94,53.94,2600000,53.94 1960-10-31,53.39,53.39,53.39,53.39,2460000,53.39 1960-10-28,53.41,53.41,53.41,53.41,2490000,53.41 1960-10-27,53.62,53.62,53.62,53.62,2900000,53.62 1960-10-26,53.05,53.05,53.05,53.05,3020000,53.05 1960-10-25,52.20,52.20,52.20,52.20,3030000,52.20 1960-10-24,52.70,52.70,52.70,52.70,4420000,52.70 1960-10-21,53.72,53.72,53.72,53.72,3090000,53.72 1960-10-20,53.86,53.86,53.86,53.86,2910000,53.86 1960-10-19,54.25,54.25,54.25,54.25,2410000,54.25 1960-10-18,54.35,54.35,54.35,54.35,2220000,54.35 1960-10-17,54.63,54.63,54.63,54.63,2280000,54.63 1960-10-14,54.86,54.86,54.86,54.86,2470000,54.86 1960-10-13,54.57,54.57,54.57,54.57,2220000,54.57 1960-10-12,54.15,54.15,54.15,54.15,1890000,54.15 1960-10-11,54.22,54.22,54.22,54.22,2350000,54.22 1960-10-10,54.14,54.14,54.14,54.14,2030000,54.14 1960-10-07,54.03,54.03,54.03,54.03,2530000,54.03 1960-10-06,53.72,53.72,53.72,53.72,2510000,53.72 1960-10-05,53.39,53.39,53.39,53.39,2650000,53.39 1960-10-04,52.99,52.99,52.99,52.99,2270000,52.99 1960-10-03,53.36,53.36,53.36,53.36,2220000,53.36 1960-09-30,53.52,53.52,53.52,53.52,3370000,53.52 1960-09-29,52.62,52.62,52.62,52.62,2850000,52.62 1960-09-28,52.48,52.48,52.48,52.48,3520000,52.48 1960-09-27,52.94,52.94,52.94,52.94,3170000,52.94 1960-09-26,53.06,53.06,53.06,53.06,3930000,53.06 1960-09-23,53.90,53.90,53.90,53.90,2580000,53.90 1960-09-22,54.36,54.36,54.36,54.36,1970000,54.36 1960-09-21,54.57,54.57,54.57,54.57,2930000,54.57 1960-09-20,54.01,54.01,54.01,54.01,3660000,54.01 1960-09-19,53.86,53.86,53.86,53.86,3790000,53.86 1960-09-16,55.11,55.11,55.11,55.11,2340000,55.11 1960-09-15,55.22,55.22,55.22,55.22,2870000,55.22 1960-09-14,55.44,55.44,55.44,55.44,2530000,55.44 1960-09-13,55.83,55.83,55.83,55.83,2180000,55.83 1960-09-12,55.72,55.72,55.72,55.72,2160000,55.72 1960-09-09,56.11,56.11,56.11,56.11,2750000,56.11 1960-09-08,55.74,55.74,55.74,55.74,2670000,55.74 1960-09-07,55.79,55.79,55.79,55.79,2850000,55.79 1960-09-06,56.49,56.49,56.49,56.49,2580000,56.49 1960-09-02,57.00,57.00,57.00,57.00,2680000,57.00 1960-09-01,57.09,57.09,57.09,57.09,3460000,57.09 1960-08-31,56.96,56.96,56.96,56.96,3130000,56.96 1960-08-30,56.84,56.84,56.84,56.84,2890000,56.84 1960-08-29,57.44,57.44,57.44,57.44,2780000,57.44 1960-08-26,57.60,57.60,57.60,57.60,2780000,57.60 1960-08-25,57.79,57.79,57.79,57.79,2680000,57.79 1960-08-24,58.07,58.07,58.07,58.07,3500000,58.07 1960-08-23,57.75,57.75,57.75,57.75,3560000,57.75 1960-08-22,57.19,57.19,57.19,57.19,2760000,57.19 1960-08-19,57.01,57.01,57.01,57.01,2570000,57.01 1960-08-18,56.81,56.81,56.81,56.81,2890000,56.81 1960-08-17,56.84,56.84,56.84,56.84,3090000,56.84 1960-08-16,56.72,56.72,56.72,56.72,2710000,56.72 1960-08-15,56.61,56.61,56.61,56.61,2450000,56.61 1960-08-12,56.66,56.66,56.66,56.66,3160000,56.66 1960-08-11,56.28,56.28,56.28,56.28,3070000,56.28 1960-08-10,56.07,56.07,56.07,56.07,2810000,56.07 1960-08-09,55.84,55.84,55.84,55.84,2700000,55.84 1960-08-08,55.52,55.52,55.52,55.52,2960000,55.52 1960-08-05,55.44,55.44,55.44,55.44,3000000,55.44 1960-08-04,54.89,54.89,54.89,54.89,2840000,54.89 1960-08-03,54.72,54.72,54.72,54.72,2470000,54.72 1960-08-02,55.04,55.04,55.04,55.04,2090000,55.04 1960-08-01,55.53,55.53,55.53,55.53,2440000,55.53 1960-07-29,55.51,55.51,55.51,55.51,2730000,55.51 1960-07-28,54.57,54.57,54.57,54.57,3020000,54.57 1960-07-27,54.17,54.17,54.17,54.17,2560000,54.17 1960-07-26,54.51,54.51,54.51,54.51,2720000,54.51 1960-07-25,54.18,54.18,54.18,54.18,2840000,54.18 1960-07-22,54.72,54.72,54.72,54.72,2850000,54.72 1960-07-21,55.10,55.10,55.10,55.10,2510000,55.10 1960-07-20,55.61,55.61,55.61,55.61,2370000,55.61 1960-07-19,55.70,55.70,55.70,55.70,2490000,55.70 1960-07-18,55.70,55.70,55.70,55.70,2350000,55.70 1960-07-15,56.05,56.05,56.05,56.05,2140000,56.05 1960-07-14,56.12,56.12,56.12,56.12,2480000,56.12 1960-07-13,56.10,56.10,56.10,56.10,2590000,56.10 1960-07-12,56.25,56.25,56.25,56.25,2860000,56.25 1960-07-11,56.87,56.87,56.87,56.87,2920000,56.87 1960-07-08,57.38,57.38,57.38,57.38,3010000,57.38 1960-07-07,57.24,57.24,57.24,57.24,3050000,57.24 1960-07-06,56.94,56.94,56.94,56.94,2970000,56.94 1960-07-05,57.02,57.02,57.02,57.02,2780000,57.02 1960-07-01,57.06,57.06,57.06,57.06,2620000,57.06 1960-06-30,56.92,56.92,56.92,56.92,2940000,56.92 1960-06-29,56.94,56.94,56.94,56.94,3160000,56.94 1960-06-28,56.94,56.94,56.94,56.94,3120000,56.94 1960-06-27,57.33,57.33,57.33,57.33,2960000,57.33 1960-06-24,57.68,57.68,57.68,57.68,3220000,57.68 1960-06-23,57.59,57.59,57.59,57.59,3620000,57.59 1960-06-22,57.28,57.28,57.28,57.28,3600000,57.28 1960-06-21,57.11,57.11,57.11,57.11,3860000,57.11 1960-06-20,57.16,57.16,57.16,57.16,3970000,57.16 1960-06-17,57.44,57.44,57.44,57.44,3920000,57.44 1960-06-16,57.50,57.50,57.50,57.50,3540000,57.50 1960-06-15,57.57,57.57,57.57,57.57,3630000,57.57 1960-06-14,57.91,57.91,57.91,57.91,3430000,57.91 1960-06-13,57.99,57.99,57.99,57.99,3180000,57.99 1960-06-10,57.97,57.97,57.97,57.97,2940000,57.97 1960-06-09,58.00,58.00,58.00,58.00,3820000,58.00 1960-06-08,57.89,57.89,57.89,57.89,3800000,57.89 1960-06-07,57.43,57.43,57.43,57.43,3710000,57.43 1960-06-06,56.89,56.89,56.89,56.89,3220000,56.89 1960-06-03,56.23,56.23,56.23,56.23,3340000,56.23 1960-06-02,56.13,56.13,56.13,56.13,3730000,56.13 1960-06-01,55.89,55.89,55.89,55.89,3770000,55.89 1960-05-31,55.83,55.83,55.83,55.83,3750000,55.83 1960-05-27,55.74,55.74,55.74,55.74,3040000,55.74 1960-05-26,55.71,55.71,55.71,55.71,3720000,55.71 1960-05-25,55.67,55.67,55.67,55.67,3440000,55.67 1960-05-24,55.70,55.70,55.70,55.70,3240000,55.70 1960-05-23,55.76,55.76,55.76,55.76,2530000,55.76 1960-05-20,55.73,55.73,55.73,55.73,3170000,55.73 1960-05-19,55.68,55.68,55.68,55.68,3700000,55.68 1960-05-18,55.44,55.44,55.44,55.44,5240000,55.44 1960-05-17,55.46,55.46,55.46,55.46,4080000,55.46 1960-05-16,55.25,55.25,55.25,55.25,3530000,55.25 1960-05-13,55.30,55.30,55.30,55.30,3750000,55.30 1960-05-12,54.85,54.85,54.85,54.85,3220000,54.85 1960-05-11,54.57,54.57,54.57,54.57,2900000,54.57 1960-05-10,54.42,54.42,54.42,54.42,2870000,54.42 1960-05-09,54.80,54.80,54.80,54.80,2670000,54.80 1960-05-06,54.75,54.75,54.75,54.75,2560000,54.75 1960-05-05,54.86,54.86,54.86,54.86,2670000,54.86 1960-05-04,55.04,55.04,55.04,55.04,2870000,55.04 1960-05-03,54.83,54.83,54.83,54.83,2910000,54.83 1960-05-02,54.13,54.13,54.13,54.13,2930000,54.13 1960-04-29,54.37,54.37,54.37,54.37,2850000,54.37 1960-04-28,54.56,54.56,54.56,54.56,3190000,54.56 1960-04-27,55.04,55.04,55.04,55.04,3020000,55.04 1960-04-26,55.04,55.04,55.04,55.04,2940000,55.04 1960-04-25,54.86,54.86,54.86,54.86,2980000,54.86 1960-04-22,55.42,55.42,55.42,55.42,2850000,55.42 1960-04-21,55.59,55.59,55.59,55.59,2700000,55.59 1960-04-20,55.44,55.44,55.44,55.44,3150000,55.44 1960-04-19,56.13,56.13,56.13,56.13,3080000,56.13 1960-04-18,56.59,56.59,56.59,56.59,3200000,56.59 1960-04-14,56.43,56.43,56.43,56.43,2730000,56.43 1960-04-13,56.30,56.30,56.30,56.30,2730000,56.30 1960-04-12,56.30,56.30,56.30,56.30,2470000,56.30 1960-04-11,56.17,56.17,56.17,56.17,2520000,56.17 1960-04-08,56.39,56.39,56.39,56.39,2820000,56.39 1960-04-07,56.52,56.52,56.52,56.52,3070000,56.52 1960-04-06,56.51,56.51,56.51,56.51,3450000,56.51 1960-04-05,55.37,55.37,55.37,55.37,2840000,55.37 1960-04-04,55.54,55.54,55.54,55.54,2450000,55.54 1960-04-01,55.43,55.43,55.43,55.43,2260000,55.43 1960-03-31,55.34,55.34,55.34,55.34,2690000,55.34 1960-03-30,55.66,55.66,55.66,55.66,2450000,55.66 1960-03-29,55.78,55.78,55.78,55.78,2320000,55.78 1960-03-28,55.86,55.86,55.86,55.86,2500000,55.86 1960-03-25,55.98,55.98,55.98,55.98,2640000,55.98 1960-03-24,55.98,55.98,55.98,55.98,2940000,55.98 1960-03-23,55.74,55.74,55.74,55.74,3020000,55.74 1960-03-22,55.29,55.29,55.29,55.29,2490000,55.29 1960-03-21,55.07,55.07,55.07,55.07,2500000,55.07 1960-03-18,55.01,55.01,55.01,55.01,2620000,55.01 1960-03-17,54.96,54.96,54.96,54.96,2140000,54.96 1960-03-16,55.04,55.04,55.04,55.04,2960000,55.04 1960-03-15,54.74,54.74,54.74,54.74,2690000,54.74 1960-03-14,54.32,54.32,54.32,54.32,2530000,54.32 1960-03-11,54.24,54.24,54.24,54.24,2770000,54.24 1960-03-10,53.83,53.83,53.83,53.83,3350000,53.83 1960-03-09,54.04,54.04,54.04,54.04,3580000,54.04 1960-03-08,53.47,53.47,53.47,53.47,3370000,53.47 1960-03-07,54.02,54.02,54.02,54.02,2900000,54.02 1960-03-04,54.57,54.57,54.57,54.57,4060000,54.57 1960-03-03,54.78,54.78,54.78,54.78,3160000,54.78 1960-03-02,55.62,55.62,55.62,55.62,3110000,55.62 1960-03-01,56.01,56.01,56.01,56.01,2920000,56.01 1960-02-29,56.12,56.12,56.12,56.12,2990000,56.12 1960-02-26,56.16,56.16,56.16,56.16,3380000,56.16 1960-02-25,55.93,55.93,55.93,55.93,3600000,55.93 1960-02-24,55.74,55.74,55.74,55.74,2740000,55.74 1960-02-23,55.94,55.94,55.94,55.94,2960000,55.94 1960-02-19,56.24,56.24,56.24,56.24,3230000,56.24 1960-02-18,55.80,55.80,55.80,55.80,3800000,55.80 1960-02-17,55.03,55.03,55.03,55.03,4210000,55.03 1960-02-16,54.73,54.73,54.73,54.73,3270000,54.73 1960-02-15,55.17,55.17,55.17,55.17,2780000,55.17 1960-02-12,55.46,55.46,55.46,55.46,2230000,55.46 1960-02-11,55.18,55.18,55.18,55.18,2610000,55.18 1960-02-10,55.49,55.49,55.49,55.49,2440000,55.49 1960-02-09,55.84,55.84,55.84,55.84,2860000,55.84 1960-02-08,55.32,55.32,55.32,55.32,3350000,55.32 1960-02-05,55.98,55.98,55.98,55.98,2530000,55.98 1960-02-04,56.27,56.27,56.27,56.27,2600000,56.27 1960-02-03,56.32,56.32,56.32,56.32,3020000,56.32 1960-02-02,56.82,56.82,56.82,56.82,3080000,56.82 1960-02-01,55.96,55.96,55.96,55.96,2820000,55.96 1960-01-29,55.61,55.61,55.61,55.61,3060000,55.61 1960-01-28,56.13,56.13,56.13,56.13,2630000,56.13 1960-01-27,56.72,56.72,56.72,56.72,2460000,56.72 1960-01-26,56.86,56.86,56.86,56.86,3060000,56.86 1960-01-25,56.78,56.78,56.78,56.78,2790000,56.78 1960-01-22,57.38,57.38,57.38,57.38,2690000,57.38 1960-01-21,57.21,57.21,57.21,57.21,2700000,57.21 1960-01-20,57.07,57.07,57.07,57.07,2720000,57.07 1960-01-19,57.27,57.27,57.27,57.27,3100000,57.27 1960-01-18,57.89,57.89,57.89,57.89,3020000,57.89 1960-01-15,58.38,58.38,58.38,58.38,3400000,58.38 1960-01-14,58.40,58.40,58.40,58.40,3560000,58.40 1960-01-13,58.08,58.08,58.08,58.08,3470000,58.08 1960-01-12,58.41,58.41,58.41,58.41,3760000,58.41 1960-01-11,58.77,58.77,58.77,58.77,3470000,58.77 1960-01-08,59.50,59.50,59.50,59.50,3290000,59.50 1960-01-07,59.69,59.69,59.69,59.69,3310000,59.69 1960-01-06,60.13,60.13,60.13,60.13,3730000,60.13 1960-01-05,60.39,60.39,60.39,60.39,3710000,60.39 1960-01-04,59.91,59.91,59.91,59.91,3990000,59.91 1959-12-31,59.89,59.89,59.89,59.89,3810000,59.89 1959-12-30,59.77,59.77,59.77,59.77,3680000,59.77 1959-12-29,59.30,59.30,59.30,59.30,3020000,59.30 1959-12-28,58.98,58.98,58.98,58.98,2830000,58.98 1959-12-24,59.00,59.00,59.00,59.00,2320000,59.00 1959-12-23,58.96,58.96,58.96,58.96,2890000,58.96 1959-12-22,59.14,59.14,59.14,59.14,2930000,59.14 1959-12-21,59.21,59.21,59.21,59.21,3290000,59.21 1959-12-18,59.14,59.14,59.14,59.14,3230000,59.14 1959-12-17,58.86,58.86,58.86,58.86,3040000,58.86 1959-12-16,58.97,58.97,58.97,58.97,3270000,58.97 1959-12-15,58.90,58.90,58.90,58.90,3450000,58.90 1959-12-14,59.04,59.04,59.04,59.04,3100000,59.04 1959-12-11,58.88,58.88,58.88,58.88,2910000,58.88 1959-12-10,59.02,59.02,59.02,59.02,3170000,59.02 1959-12-09,58.97,58.97,58.97,58.97,3430000,58.97 1959-12-08,59.34,59.34,59.34,59.34,3870000,59.34 1959-12-07,58.96,58.96,58.96,58.96,3620000,58.96 1959-12-04,58.85,58.85,58.85,58.85,3590000,58.85 1959-12-03,58.73,58.73,58.73,58.73,3280000,58.73 1959-12-02,58.60,58.60,58.60,58.60,3490000,58.60 1959-12-01,58.70,58.70,58.70,58.70,3990000,58.70 1959-11-30,58.28,58.28,58.28,58.28,3670000,58.28 1959-11-27,57.70,57.70,57.70,57.70,3030000,57.70 1959-11-25,57.44,57.44,57.44,57.44,3550000,57.44 1959-11-24,57.35,57.35,57.35,57.35,3650000,57.35 1959-11-23,57.08,57.08,57.08,57.08,3400000,57.08 1959-11-20,56.97,56.97,56.97,56.97,2960000,56.97 1959-11-19,56.94,56.94,56.94,56.94,3230000,56.94 1959-11-18,56.99,56.99,56.99,56.99,3660000,56.99 1959-11-17,56.38,56.38,56.38,56.38,3570000,56.38 1959-11-16,56.22,56.22,56.22,56.22,3710000,56.22 1959-11-13,56.85,56.85,56.85,56.85,3050000,56.85 1959-11-12,57.17,57.17,57.17,57.17,3600000,57.17 1959-11-11,57.49,57.49,57.49,57.49,2820000,57.49 1959-11-10,57.48,57.48,57.48,57.48,3020000,57.48 1959-11-09,57.50,57.50,57.50,57.50,3700000,57.50 1959-11-06,57.60,57.60,57.60,57.60,3450000,57.60 1959-11-05,57.32,57.32,57.32,57.32,3170000,57.32 1959-11-04,57.26,57.26,57.26,57.26,3940000,57.26 1959-11-02,57.41,57.41,57.41,57.41,3320000,57.41 1959-10-30,57.52,57.52,57.52,57.52,3560000,57.52 1959-10-29,57.41,57.41,57.41,57.41,3890000,57.41 1959-10-28,57.46,57.46,57.46,57.46,3920000,57.46 1959-10-27,57.42,57.42,57.42,57.42,4160000,57.42 1959-10-26,56.94,56.94,56.94,56.94,3580000,56.94 1959-10-23,56.56,56.56,56.56,56.56,2880000,56.56 1959-10-22,56.00,56.00,56.00,56.00,3060000,56.00 1959-10-21,56.55,56.55,56.55,56.55,2730000,56.55 1959-10-20,56.66,56.66,56.66,56.66,2740000,56.66 1959-10-19,57.01,57.01,57.01,57.01,2470000,57.01 1959-10-16,57.33,57.33,57.33,57.33,2760000,57.33 1959-10-15,56.87,56.87,56.87,56.87,2190000,56.87 1959-10-14,56.71,56.71,56.71,56.71,2320000,56.71 1959-10-13,57.16,57.16,57.16,57.16,2530000,57.16 1959-10-12,57.32,57.32,57.32,57.32,1750000,57.32 1959-10-09,57.00,57.00,57.00,57.00,2540000,57.00 1959-10-08,56.81,56.81,56.81,56.81,2510000,56.81 1959-10-07,56.94,56.94,56.94,56.94,2380000,56.94 1959-10-06,57.09,57.09,57.09,57.09,2330000,57.09 1959-10-05,57.14,57.14,57.14,57.14,2100000,57.14 1959-10-02,57.20,57.20,57.20,57.20,2270000,57.20 1959-10-01,56.94,56.94,56.94,56.94,2660000,56.94 1959-09-30,56.88,56.88,56.88,56.88,2850000,56.88 1959-09-29,57.51,57.51,57.51,57.51,3220000,57.51 1959-09-28,57.15,57.15,57.15,57.15,2640000,57.15 1959-09-25,56.73,56.73,56.73,56.73,3280000,56.73 1959-09-24,56.78,56.78,56.78,56.78,3480000,56.78 1959-09-23,55.82,55.82,55.82,55.82,3010000,55.82 1959-09-22,55.14,55.14,55.14,55.14,3000000,55.14 1959-09-21,55.27,55.27,55.27,55.27,3240000,55.27 1959-09-18,56.19,56.19,56.19,56.19,2530000,56.19 1959-09-17,56.41,56.41,56.41,56.41,2090000,56.41 1959-09-16,56.72,56.72,56.72,56.72,2180000,56.72 1959-09-15,56.68,56.68,56.68,56.68,2830000,56.68 1959-09-14,56.99,56.99,56.99,56.99,2590000,56.99 1959-09-11,57.41,57.41,57.41,57.41,2640000,57.41 1959-09-10,56.99,56.99,56.99,56.99,2520000,56.99 1959-09-09,57.29,57.29,57.29,57.29,3030000,57.29 1959-09-08,57.70,57.70,57.70,57.70,2940000,57.70 1959-09-04,58.54,58.54,58.54,58.54,2300000,58.54 1959-09-03,58.26,58.26,58.26,58.26,2330000,58.26 1959-09-02,58.92,58.92,58.92,58.92,2370000,58.92 1959-09-01,58.87,58.87,58.87,58.87,2430000,58.87 1959-08-31,59.60,59.60,59.60,59.60,2140000,59.60 1959-08-28,59.60,59.60,59.60,59.60,1930000,59.60 1959-08-27,59.58,59.58,59.58,59.58,2550000,59.58 1959-08-26,59.07,59.07,59.07,59.07,2210000,59.07 1959-08-25,58.99,58.99,58.99,58.99,1960000,58.99 1959-08-24,58.87,58.87,58.87,58.87,1860000,58.87 1959-08-21,59.08,59.08,59.08,59.08,2000000,59.08 1959-08-20,59.14,59.14,59.14,59.14,2450000,59.14 1959-08-19,58.27,58.27,58.27,58.27,3050000,58.27 1959-08-18,58.62,58.62,58.62,58.62,2280000,58.62 1959-08-17,59.17,59.17,59.17,59.17,1980000,59.17 1959-08-14,59.29,59.29,59.29,59.29,1990000,59.29 1959-08-13,59.15,59.15,59.15,59.15,2020000,59.15 1959-08-12,59.25,59.25,59.25,59.25,2700000,59.25 1959-08-11,59.39,59.39,59.39,59.39,2980000,59.39 1959-08-10,58.62,58.62,58.62,58.62,4190000,58.62 1959-08-07,59.87,59.87,59.87,59.87,2580000,59.87 1959-08-06,60.24,60.24,60.24,60.24,2610000,60.24 1959-08-05,60.30,60.30,60.30,60.30,2630000,60.30 1959-08-04,60.61,60.61,60.61,60.61,2530000,60.61 1959-08-03,60.71,60.71,60.71,60.71,2410000,60.71 1959-07-31,60.51,60.51,60.51,60.51,2270000,60.51 1959-07-30,60.50,60.50,60.50,60.50,3240000,60.50 1959-07-29,60.62,60.62,60.62,60.62,3460000,60.62 1959-07-28,60.32,60.32,60.32,60.32,3190000,60.32 1959-07-27,60.02,60.02,60.02,60.02,2910000,60.02 1959-07-24,59.65,59.65,59.65,59.65,2720000,59.65 1959-07-23,59.67,59.67,59.67,59.67,3310000,59.67 1959-07-22,59.61,59.61,59.61,59.61,3310000,59.61 1959-07-21,59.41,59.41,59.41,59.41,2950000,59.41 1959-07-20,58.91,58.91,58.91,58.91,2500000,58.91 1959-07-17,59.19,59.19,59.19,59.19,2510000,59.19 1959-07-16,59.41,59.41,59.41,59.41,3170000,59.41 1959-07-15,59.59,59.59,59.59,59.59,3280000,59.59 1959-07-14,59.55,59.55,59.55,59.55,3230000,59.55 1959-07-13,59.41,59.41,59.41,59.41,3360000,59.41 1959-07-10,59.91,59.91,59.91,59.91,3600000,59.91 1959-07-09,59.97,59.97,59.97,59.97,3560000,59.97 1959-07-08,60.03,60.03,60.03,60.03,4010000,60.03 1959-07-07,60.01,60.01,60.01,60.01,3840000,60.01 1959-07-06,59.65,59.65,59.65,59.65,3720000,59.65 1959-07-02,59.28,59.28,59.28,59.28,3610000,59.28 1959-07-01,58.97,58.97,58.97,58.97,3150000,58.97 1959-06-30,58.47,58.47,58.47,58.47,3200000,58.47 1959-06-29,58.37,58.37,58.37,58.37,3000000,58.37 1959-06-26,57.98,57.98,57.98,57.98,3100000,57.98 1959-06-25,57.73,57.73,57.73,57.73,3250000,57.73 1959-06-24,57.41,57.41,57.41,57.41,3180000,57.41 1959-06-23,57.12,57.12,57.12,57.12,2600000,57.12 1959-06-22,57.13,57.13,57.13,57.13,2630000,57.13 1959-06-19,57.13,57.13,57.13,57.13,2260000,57.13 1959-06-18,57.05,57.05,57.05,57.05,3150000,57.05 1959-06-17,57.09,57.09,57.09,57.09,2850000,57.09 1959-06-16,56.56,56.56,56.56,56.56,2440000,56.56 1959-06-15,56.99,56.99,56.99,56.99,2410000,56.99 1959-06-12,57.29,57.29,57.29,57.29,2580000,57.29 1959-06-11,57.25,57.25,57.25,57.25,3120000,57.25 1959-06-10,57.19,57.19,57.19,57.19,3310000,57.19 1959-06-09,56.36,56.36,56.36,56.36,3490000,56.36 1959-06-08,56.76,56.76,56.76,56.76,2970000,56.76 1959-06-05,57.51,57.51,57.51,57.51,2800000,57.51 1959-06-04,57.63,57.63,57.63,57.63,3210000,57.63 1959-06-03,58.25,58.25,58.25,58.25,2910000,58.25 1959-06-02,58.23,58.23,58.23,58.23,3120000,58.23 1959-06-01,58.63,58.63,58.63,58.63,2730000,58.63 1959-05-29,58.68,58.68,58.68,58.68,2790000,58.68 1959-05-28,58.39,58.39,58.39,58.39,2970000,58.39 1959-05-27,58.19,58.19,58.19,58.19,2940000,58.19 1959-05-26,58.09,58.09,58.09,58.09,2910000,58.09 1959-05-25,58.18,58.18,58.18,58.18,3260000,58.18 1959-05-22,58.33,58.33,58.33,58.33,3030000,58.33 1959-05-21,58.14,58.14,58.14,58.14,3230000,58.14 1959-05-20,58.09,58.09,58.09,58.09,3550000,58.09 1959-05-19,58.32,58.32,58.32,58.32,3170000,58.32 1959-05-18,58.15,58.15,58.15,58.15,2970000,58.15 1959-05-15,58.16,58.16,58.16,58.16,3510000,58.16 1959-05-14,58.37,58.37,58.37,58.37,3660000,58.37 1959-05-13,57.97,57.97,57.97,57.97,3540000,57.97 1959-05-12,57.96,57.96,57.96,57.96,3550000,57.96 1959-05-11,57.96,57.96,57.96,57.96,3860000,57.96 1959-05-08,57.32,57.32,57.32,57.32,3930000,57.32 1959-05-07,56.88,56.88,56.88,56.88,4530000,56.88 1959-05-06,57.61,57.61,57.61,57.61,4110000,57.61 1959-05-05,57.75,57.75,57.75,57.75,3360000,57.75 1959-05-04,57.65,57.65,57.65,57.65,3060000,57.65 1959-05-01,57.65,57.65,57.65,57.65,3020000,57.65 1959-04-30,57.59,57.59,57.59,57.59,3510000,57.59 1959-04-29,57.69,57.69,57.69,57.69,3470000,57.69 1959-04-28,57.92,57.92,57.92,57.92,3920000,57.92 1959-04-27,58.14,58.14,58.14,58.14,3850000,58.14 1959-04-24,57.96,57.96,57.96,57.96,3790000,57.96 1959-04-23,57.60,57.60,57.60,57.60,3310000,57.60 1959-04-22,57.73,57.73,57.73,57.73,3430000,57.73 1959-04-21,58.11,58.11,58.11,58.11,3650000,58.11 1959-04-20,58.17,58.17,58.17,58.17,3610000,58.17 1959-04-17,57.92,57.92,57.92,57.92,3870000,57.92 1959-04-16,57.43,57.43,57.43,57.43,3790000,57.43 1959-04-15,56.96,56.96,56.96,56.96,3680000,56.96 1959-04-14,56.71,56.71,56.71,56.71,3320000,56.71 1959-04-13,56.43,56.43,56.43,56.43,3140000,56.43 1959-04-10,56.22,56.22,56.22,56.22,3000000,56.22 1959-04-09,56.17,56.17,56.17,56.17,2830000,56.17 1959-04-08,56.21,56.21,56.21,56.21,3260000,56.21 1959-04-07,56.48,56.48,56.48,56.48,3020000,56.48 1959-04-06,56.60,56.60,56.60,56.60,3510000,56.60 1959-04-03,56.44,56.44,56.44,56.44,3680000,56.44 1959-04-02,56.00,56.00,56.00,56.00,3220000,56.00 1959-04-01,55.69,55.69,55.69,55.69,2980000,55.69 1959-03-31,55.44,55.44,55.44,55.44,2820000,55.44 1959-03-30,55.45,55.45,55.45,55.45,2940000,55.45 1959-03-26,55.76,55.76,55.76,55.76,2900000,55.76 1959-03-25,55.88,55.88,55.88,55.88,3280000,55.88 1959-03-24,55.96,55.96,55.96,55.96,3000000,55.96 1959-03-23,55.87,55.87,55.87,55.87,3700000,55.87 1959-03-20,56.39,56.39,56.39,56.39,3770000,56.39 1959-03-19,56.34,56.34,56.34,56.34,4150000,56.34 1959-03-18,56.39,56.39,56.39,56.39,4530000,56.39 1959-03-17,56.52,56.52,56.52,56.52,4730000,56.52 1959-03-16,56.06,56.06,56.06,56.06,4420000,56.06 1959-03-13,56.67,56.67,56.67,56.67,4880000,56.67 1959-03-12,56.60,56.60,56.60,56.60,4690000,56.60 1959-03-11,56.35,56.35,56.35,56.35,4160000,56.35 1959-03-10,56.31,56.31,56.31,56.31,3920000,56.31 1959-03-09,56.15,56.15,56.15,56.15,3530000,56.15 1959-03-06,56.21,56.21,56.21,56.21,3930000,56.21 1959-03-05,56.43,56.43,56.43,56.43,3930000,56.43 1959-03-04,56.35,56.35,56.35,56.35,4150000,56.35 1959-03-03,56.25,56.25,56.25,56.25,4790000,56.25 1959-03-02,55.73,55.73,55.73,55.73,4210000,55.73 1959-02-27,55.41,55.41,55.41,55.41,4300000,55.41 1959-02-26,55.34,55.34,55.34,55.34,3930000,55.34 1959-02-25,55.24,55.24,55.24,55.24,3780000,55.24 1959-02-24,55.48,55.48,55.48,55.48,4340000,55.48 1959-02-20,55.52,55.52,55.52,55.52,4190000,55.52 1959-02-19,55.50,55.50,55.50,55.50,4160000,55.50 1959-02-18,54.30,54.30,54.30,54.30,3480000,54.30 1959-02-17,54.29,54.29,54.29,54.29,3190000,54.29 1959-02-16,54.50,54.50,54.50,54.50,3480000,54.50 1959-02-13,54.42,54.42,54.42,54.42,3070000,54.42 1959-02-12,54.00,54.00,54.00,54.00,2630000,54.00 1959-02-11,54.35,54.35,54.35,54.35,3000000,54.35 1959-02-10,54.32,54.32,54.32,54.32,2960000,54.32 1959-02-09,53.58,53.58,53.58,53.58,3130000,53.58 1959-02-06,54.37,54.37,54.37,54.37,3010000,54.37 1959-02-05,54.81,54.81,54.81,54.81,3140000,54.81 1959-02-04,55.06,55.06,55.06,55.06,3170000,55.06 1959-02-03,55.28,55.28,55.28,55.28,3220000,55.28 1959-02-02,55.21,55.21,55.21,55.21,3610000,55.21 1959-01-30,55.45,55.45,55.45,55.45,3600000,55.45 1959-01-29,55.20,55.20,55.20,55.20,3470000,55.20 1959-01-28,55.16,55.16,55.16,55.16,4190000,55.16 1959-01-27,55.78,55.78,55.78,55.78,3480000,55.78 1959-01-26,55.77,55.77,55.77,55.77,3980000,55.77 1959-01-23,56.00,56.00,56.00,56.00,3600000,56.00 1959-01-22,55.97,55.97,55.97,55.97,4250000,55.97 1959-01-21,56.04,56.04,56.04,56.04,3940000,56.04 1959-01-20,55.72,55.72,55.72,55.72,3680000,55.72 1959-01-19,55.68,55.68,55.68,55.68,3840000,55.68 1959-01-16,55.81,55.81,55.81,55.81,4300000,55.81 1959-01-15,55.83,55.83,55.83,55.83,4500000,55.83 1959-01-14,55.62,55.62,55.62,55.62,4090000,55.62 1959-01-13,55.47,55.47,55.47,55.47,3790000,55.47 1959-01-12,55.78,55.78,55.78,55.78,4320000,55.78 1959-01-09,55.77,55.77,55.77,55.77,4760000,55.77 1959-01-08,55.40,55.40,55.40,55.40,4030000,55.40 1959-01-07,54.89,54.89,54.89,54.89,4140000,54.89 1959-01-06,55.59,55.59,55.59,55.59,3690000,55.59 1959-01-05,55.66,55.66,55.66,55.66,4210000,55.66 1959-01-02,55.44,55.44,55.44,55.44,3380000,55.44 1958-12-31,55.21,55.21,55.21,55.21,3970000,55.21 1958-12-30,54.93,54.93,54.93,54.93,3900000,54.93 1958-12-29,54.74,54.74,54.74,54.74,3790000,54.74 1958-12-24,54.11,54.11,54.11,54.11,3050000,54.11 1958-12-23,53.42,53.42,53.42,53.42,2870000,53.42 1958-12-22,53.71,53.71,53.71,53.71,3030000,53.71 1958-12-19,54.07,54.07,54.07,54.07,3540000,54.07 1958-12-18,54.15,54.15,54.15,54.15,3900000,54.15 1958-12-17,53.92,53.92,53.92,53.92,3900000,53.92 1958-12-16,53.57,53.57,53.57,53.57,3970000,53.57 1958-12-15,53.37,53.37,53.37,53.37,3340000,53.37 1958-12-12,53.22,53.22,53.22,53.22,3140000,53.22 1958-12-11,53.35,53.35,53.35,53.35,4250000,53.35 1958-12-10,53.46,53.46,53.46,53.46,4340000,53.46 1958-12-09,52.82,52.82,52.82,52.82,3790000,52.82 1958-12-08,52.46,52.46,52.46,52.46,3590000,52.46 1958-12-05,52.46,52.46,52.46,52.46,3360000,52.46 1958-12-04,52.55,52.55,52.55,52.55,3630000,52.55 1958-12-03,52.53,52.53,52.53,52.53,3460000,52.53 1958-12-02,52.46,52.46,52.46,52.46,3320000,52.46 1958-12-01,52.69,52.69,52.69,52.69,3800000,52.69 1958-11-28,52.48,52.48,52.48,52.48,4120000,52.48 1958-11-26,51.90,51.90,51.90,51.90,4090000,51.90 1958-11-25,51.02,51.02,51.02,51.02,3940000,51.02 1958-11-24,52.03,52.03,52.03,52.03,4770000,52.03 1958-11-21,52.70,52.70,52.70,52.70,3950000,52.70 1958-11-20,53.21,53.21,53.21,53.21,4320000,53.21 1958-11-19,53.20,53.20,53.20,53.20,4090000,53.20 1958-11-18,53.13,53.13,53.13,53.13,3820000,53.13 1958-11-17,53.24,53.24,53.24,53.24,4540000,53.24 1958-11-14,53.09,53.09,53.09,53.09,4390000,53.09 1958-11-13,52.83,52.83,52.83,52.83,4200000,52.83 1958-11-12,53.05,53.05,53.05,53.05,4440000,53.05 1958-11-11,52.98,52.98,52.98,52.98,4040000,52.98 1958-11-10,52.57,52.57,52.57,52.57,3730000,52.57 1958-11-07,52.26,52.26,52.26,52.26,3700000,52.26 1958-11-06,52.45,52.45,52.45,52.45,4890000,52.45 1958-11-05,52.03,52.03,52.03,52.03,4080000,52.03 1958-11-03,51.56,51.56,51.56,51.56,3240000,51.56 1958-10-31,51.33,51.33,51.33,51.33,3920000,51.33 1958-10-30,51.27,51.27,51.27,51.27,4360000,51.27 1958-10-29,51.07,51.07,51.07,51.07,4790000,51.07 1958-10-28,50.58,50.58,50.58,50.58,3670000,50.58 1958-10-27,50.42,50.42,50.42,50.42,3980000,50.42 1958-10-24,50.81,50.81,50.81,50.81,3770000,50.81 1958-10-23,50.97,50.97,50.97,50.97,3610000,50.97 1958-10-22,51.07,51.07,51.07,51.07,3500000,51.07 1958-10-21,51.27,51.27,51.27,51.27,4010000,51.27 1958-10-20,51.27,51.27,51.27,51.27,4560000,51.27 1958-10-17,51.46,51.46,51.46,51.46,5360000,51.46 1958-10-16,50.94,50.94,50.94,50.94,4560000,50.94 1958-10-15,50.58,50.58,50.58,50.58,4810000,50.58 1958-10-14,51.26,51.26,51.26,51.26,5110000,51.26 1958-10-13,51.62,51.62,51.62,51.62,4550000,51.62 1958-10-10,51.39,51.39,51.39,51.39,4610000,51.39 1958-10-09,51.05,51.05,51.05,51.05,3670000,51.05 1958-10-08,51.06,51.06,51.06,51.06,3680000,51.06 1958-10-07,51.07,51.07,51.07,51.07,3570000,51.07 1958-10-06,51.07,51.07,51.07,51.07,3570000,51.07 1958-10-03,50.37,50.37,50.37,50.37,3830000,50.37 1958-10-02,50.17,50.17,50.17,50.17,3750000,50.17 1958-10-01,49.98,49.98,49.98,49.98,3780000,49.98 1958-09-30,50.06,50.06,50.06,50.06,4160000,50.06 1958-09-29,49.87,49.87,49.87,49.87,3680000,49.87 1958-09-26,49.66,49.66,49.66,49.66,3420000,49.66 1958-09-25,49.57,49.57,49.57,49.57,4490000,49.57 1958-09-24,49.78,49.78,49.78,49.78,3120000,49.78 1958-09-23,49.56,49.56,49.56,49.56,3950000,49.56 1958-09-22,49.20,49.20,49.20,49.20,3490000,49.20 1958-09-19,49.40,49.40,49.40,49.40,3880000,49.40 1958-09-18,49.38,49.38,49.38,49.38,3460000,49.38 1958-09-17,49.35,49.35,49.35,49.35,3790000,49.35 1958-09-16,49.35,49.35,49.35,49.35,3940000,49.35 1958-09-15,48.96,48.96,48.96,48.96,3040000,48.96 1958-09-12,48.53,48.53,48.53,48.53,3100000,48.53 1958-09-11,48.64,48.64,48.64,48.64,3300000,48.64 1958-09-10,48.31,48.31,48.31,48.31,2820000,48.31 1958-09-09,48.46,48.46,48.46,48.46,3480000,48.46 1958-09-08,48.13,48.13,48.13,48.13,3030000,48.13 1958-09-05,47.97,47.97,47.97,47.97,2520000,47.97 1958-09-04,48.10,48.10,48.10,48.10,3100000,48.10 1958-09-03,48.18,48.18,48.18,48.18,3240000,48.18 1958-09-02,48.00,48.00,48.00,48.00,2930000,48.00 1958-08-29,47.75,47.75,47.75,47.75,2260000,47.75 1958-08-28,47.66,47.66,47.66,47.66,2540000,47.66 1958-08-27,47.91,47.91,47.91,47.91,3250000,47.91 1958-08-26,47.90,47.90,47.90,47.90,2910000,47.90 1958-08-25,47.74,47.74,47.74,47.74,2610000,47.74 1958-08-22,47.73,47.73,47.73,47.73,2660000,47.73 1958-08-21,47.63,47.63,47.63,47.63,2500000,47.63 1958-08-20,47.32,47.32,47.32,47.32,2460000,47.32 1958-08-19,47.30,47.30,47.30,47.30,2250000,47.30 1958-08-18,47.22,47.22,47.22,47.22,2390000,47.22 1958-08-15,47.50,47.50,47.50,47.50,2960000,47.50 1958-08-14,47.91,47.91,47.91,47.91,3370000,47.91 1958-08-13,47.81,47.81,47.81,47.81,2790000,47.81 1958-08-12,47.73,47.73,47.73,47.73,2600000,47.73 1958-08-11,48.16,48.16,48.16,48.16,2870000,48.16 1958-08-08,48.05,48.05,48.05,48.05,3650000,48.05 1958-08-07,47.77,47.77,47.77,47.77,3200000,47.77 1958-08-06,47.46,47.46,47.46,47.46,3440000,47.46 1958-08-05,47.75,47.75,47.75,47.75,4210000,47.75 1958-08-04,47.94,47.94,47.94,47.94,4000000,47.94 1958-08-01,47.49,47.49,47.49,47.49,3380000,47.49 1958-07-31,47.19,47.19,47.19,47.19,4440000,47.19 1958-07-30,47.09,47.09,47.09,47.09,3680000,47.09 1958-07-29,46.96,46.96,46.96,46.96,3310000,46.96 1958-07-28,47.15,47.15,47.15,47.15,3940000,47.15 1958-07-25,46.97,46.97,46.97,46.97,4430000,46.97 1958-07-24,46.65,46.65,46.65,46.65,3740000,46.65 1958-07-23,46.40,46.40,46.40,46.40,3550000,46.40 1958-07-22,46.41,46.41,46.41,46.41,3420000,46.41 1958-07-21,46.33,46.33,46.33,46.33,3440000,46.33 1958-07-18,45.77,45.77,45.77,45.77,3350000,45.77 1958-07-17,45.55,45.55,45.55,45.55,3180000,45.55 1958-07-16,45.25,45.25,45.25,45.25,3240000,45.25 1958-07-15,45.11,45.11,45.11,45.11,3090000,45.11 1958-07-14,45.14,45.14,45.14,45.14,2540000,45.14 1958-07-11,45.72,45.72,45.72,45.72,2400000,45.72 1958-07-10,45.42,45.42,45.42,45.42,2510000,45.42 1958-07-09,45.25,45.25,45.25,45.25,2630000,45.25 1958-07-08,45.40,45.40,45.40,45.40,2430000,45.40 1958-07-07,45.62,45.62,45.62,45.62,2510000,45.62 1958-07-03,45.47,45.47,45.47,45.47,2630000,45.47 1958-07-02,45.32,45.32,45.32,45.32,2370000,45.32 1958-07-01,45.28,45.28,45.28,45.28,2600000,45.28 1958-06-30,45.24,45.24,45.24,45.24,2820000,45.24 1958-06-27,44.90,44.90,44.90,44.90,2800000,44.90 1958-06-26,44.84,44.84,44.84,44.84,2910000,44.84 1958-06-25,44.63,44.63,44.63,44.63,2720000,44.63 1958-06-24,44.52,44.52,44.52,44.52,2560000,44.52 1958-06-23,44.69,44.69,44.69,44.69,2340000,44.69 1958-06-20,44.85,44.85,44.85,44.85,2590000,44.85 1958-06-19,44.61,44.61,44.61,44.61,2690000,44.61 1958-06-18,45.34,45.34,45.34,45.34,2640000,45.34 1958-06-17,44.94,44.94,44.94,44.94,2950000,44.94 1958-06-16,45.18,45.18,45.18,45.18,2870000,45.18 1958-06-13,45.02,45.02,45.02,45.02,3100000,45.02 1958-06-12,44.75,44.75,44.75,44.75,2760000,44.75 1958-06-11,44.49,44.49,44.49,44.49,2570000,44.49 1958-06-10,44.48,44.48,44.48,44.48,2390000,44.48 1958-06-09,44.57,44.57,44.57,44.57,2380000,44.57 1958-06-06,44.64,44.64,44.64,44.64,2680000,44.64 1958-06-05,44.55,44.55,44.55,44.55,2600000,44.55 1958-06-04,44.50,44.50,44.50,44.50,2690000,44.50 1958-06-03,44.46,44.46,44.46,44.46,2780000,44.46 1958-06-02,44.31,44.31,44.31,44.31,2770000,44.31 1958-05-29,44.09,44.09,44.09,44.09,2350000,44.09 1958-05-28,43.85,43.85,43.85,43.85,2260000,43.85 1958-05-27,43.79,43.79,43.79,43.79,2180000,43.79 1958-05-26,43.85,43.85,43.85,43.85,2500000,43.85 1958-05-23,43.87,43.87,43.87,43.87,2570000,43.87 1958-05-22,43.78,43.78,43.78,43.78,2950000,43.78 1958-05-21,43.55,43.55,43.55,43.55,2580000,43.55 1958-05-20,43.61,43.61,43.61,43.61,2500000,43.61 1958-05-19,43.24,43.24,43.24,43.24,1910000,43.24 1958-05-16,43.36,43.36,43.36,43.36,2030000,43.36 1958-05-15,43.34,43.34,43.34,43.34,2470000,43.34 1958-05-14,43.12,43.12,43.12,43.12,3060000,43.12 1958-05-13,43.62,43.62,43.62,43.62,2940000,43.62 1958-05-12,43.75,43.75,43.75,43.75,2780000,43.75 1958-05-09,44.09,44.09,44.09,44.09,2760000,44.09 1958-05-08,43.99,43.99,43.99,43.99,2790000,43.99 1958-05-07,43.93,43.93,43.93,43.93,2770000,43.93 1958-05-06,44.01,44.01,44.01,44.01,3110000,44.01 1958-05-05,43.79,43.79,43.79,43.79,2670000,43.79 1958-05-02,43.69,43.69,43.69,43.69,2290000,43.69 1958-05-01,43.54,43.54,43.54,43.54,2630000,43.54 1958-04-30,43.44,43.44,43.44,43.44,2900000,43.44 1958-04-29,43.00,43.00,43.00,43.00,2190000,43.00 1958-04-28,43.22,43.22,43.22,43.22,2400000,43.22 1958-04-25,43.36,43.36,43.36,43.36,3020000,43.36 1958-04-24,43.14,43.14,43.14,43.14,2870000,43.14 1958-04-23,42.80,42.80,42.80,42.80,2720000,42.80 1958-04-22,42.80,42.80,42.80,42.80,2440000,42.80 1958-04-21,42.93,42.93,42.93,42.93,2550000,42.93 1958-04-18,42.71,42.71,42.71,42.71,2700000,42.71 1958-04-17,42.25,42.25,42.25,42.25,2500000,42.25 1958-04-16,42.10,42.10,42.10,42.10,2240000,42.10 1958-04-15,42.43,42.43,42.43,42.43,2590000,42.43 1958-04-14,42.00,42.00,42.00,42.00,2180000,42.00 1958-04-11,41.74,41.74,41.74,41.74,2060000,41.74 1958-04-10,41.70,41.70,41.70,41.70,2000000,41.70 1958-04-09,41.65,41.65,41.65,41.65,2040000,41.65 1958-04-08,41.43,41.43,41.43,41.43,2190000,41.43 1958-04-07,41.33,41.33,41.33,41.33,2090000,41.33 1958-04-03,41.48,41.48,41.48,41.48,2130000,41.48 1958-04-02,41.60,41.60,41.60,41.60,2390000,41.60 1958-04-01,41.93,41.93,41.93,41.93,2070000,41.93 1958-03-31,42.10,42.10,42.10,42.10,2050000,42.10 1958-03-28,42.20,42.20,42.20,42.20,1930000,42.20 1958-03-27,42.17,42.17,42.17,42.17,2140000,42.17 1958-03-26,42.30,42.30,42.30,42.30,1990000,42.30 1958-03-25,42.44,42.44,42.44,42.44,2210000,42.44 1958-03-24,42.58,42.58,42.58,42.58,2580000,42.58 1958-03-21,42.42,42.42,42.42,42.42,2430000,42.42 1958-03-20,42.11,42.11,42.11,42.11,2280000,42.11 1958-03-19,42.09,42.09,42.09,42.09,2410000,42.09 1958-03-18,41.89,41.89,41.89,41.89,2070000,41.89 1958-03-17,42.04,42.04,42.04,42.04,2130000,42.04 1958-03-14,42.33,42.33,42.33,42.33,2150000,42.33 1958-03-13,42.46,42.46,42.46,42.46,2830000,42.46 1958-03-12,42.41,42.41,42.41,42.41,2420000,42.41 1958-03-11,42.51,42.51,42.51,42.51,2640000,42.51 1958-03-10,42.21,42.21,42.21,42.21,1980000,42.21 1958-03-07,42.07,42.07,42.07,42.07,2130000,42.07 1958-03-06,42.00,42.00,42.00,42.00,2470000,42.00 1958-03-05,41.47,41.47,41.47,41.47,2020000,41.47 1958-03-04,41.35,41.35,41.35,41.35,2010000,41.35 1958-03-03,41.13,41.13,41.13,41.13,1810000,41.13 1958-02-28,40.84,40.84,40.84,40.84,1580000,40.84 1958-02-27,40.68,40.68,40.68,40.68,1670000,40.68 1958-02-26,40.92,40.92,40.92,40.92,1880000,40.92 1958-02-25,40.61,40.61,40.61,40.61,1920000,40.61 1958-02-24,40.65,40.65,40.65,40.65,1570000,40.65 1958-02-21,40.88,40.88,40.88,40.88,1700000,40.88 1958-02-20,40.91,40.91,40.91,40.91,2060000,40.91 1958-02-19,41.15,41.15,41.15,41.15,2070000,41.15 1958-02-18,41.17,41.17,41.17,41.17,1680000,41.17 1958-02-17,41.11,41.11,41.11,41.11,1700000,41.11 1958-02-14,41.33,41.33,41.33,41.33,2070000,41.33 1958-02-13,40.94,40.94,40.94,40.94,1880000,40.94 1958-02-12,40.93,40.93,40.93,40.93,2030000,40.93 1958-02-11,41.11,41.11,41.11,41.11,2110000,41.11 1958-02-10,41.48,41.48,41.48,41.48,1900000,41.48 1958-02-07,41.73,41.73,41.73,41.73,2220000,41.73 1958-02-06,42.10,42.10,42.10,42.10,2210000,42.10 1958-02-05,42.19,42.19,42.19,42.19,2480000,42.19 1958-02-04,42.46,42.46,42.46,42.46,2970000,42.46 1958-02-03,42.04,42.04,42.04,42.04,2490000,42.04 1958-01-31,41.70,41.70,41.70,41.70,2030000,41.70 1958-01-30,41.68,41.68,41.68,41.68,2150000,41.68 1958-01-29,41.88,41.88,41.88,41.88,2220000,41.88 1958-01-28,41.63,41.63,41.63,41.63,2030000,41.63 1958-01-27,41.59,41.59,41.59,41.59,2320000,41.59 1958-01-24,41.71,41.71,41.71,41.71,2830000,41.71 1958-01-23,41.36,41.36,41.36,41.36,1910000,41.36 1958-01-22,41.20,41.20,41.20,41.20,2390000,41.20 1958-01-21,41.30,41.30,41.30,41.30,2160000,41.30 1958-01-20,41.35,41.35,41.35,41.35,2310000,41.35 1958-01-17,41.10,41.10,41.10,41.10,2200000,41.10 1958-01-16,41.06,41.06,41.06,41.06,3950000,41.06 1958-01-15,40.99,40.99,40.99,40.99,2080000,40.99 1958-01-14,40.67,40.67,40.67,40.67,2010000,40.67 1958-01-13,40.49,40.49,40.49,40.49,1860000,40.49 1958-01-10,40.37,40.37,40.37,40.37,2010000,40.37 1958-01-09,40.75,40.75,40.75,40.75,2180000,40.75 1958-01-08,40.99,40.99,40.99,40.99,2230000,40.99 1958-01-07,41.00,41.00,41.00,41.00,2220000,41.00 1958-01-06,40.68,40.68,40.68,40.68,2500000,40.68 1958-01-03,40.87,40.87,40.87,40.87,2440000,40.87 1958-01-02,40.33,40.33,40.33,40.33,1800000,40.33 1957-12-31,39.99,39.99,39.99,39.99,5070000,39.99 1957-12-30,39.58,39.58,39.58,39.58,3750000,39.58 1957-12-27,39.78,39.78,39.78,39.78,2620000,39.78 1957-12-26,39.92,39.92,39.92,39.92,2280000,39.92 1957-12-24,39.52,39.52,39.52,39.52,2220000,39.52 1957-12-23,39.48,39.48,39.48,39.48,2790000,39.48 1957-12-20,39.48,39.48,39.48,39.48,2500000,39.48 1957-12-19,39.80,39.80,39.80,39.80,2740000,39.80 1957-12-18,39.38,39.38,39.38,39.38,2750000,39.38 1957-12-17,39.42,39.42,39.42,39.42,2820000,39.42 1957-12-16,40.12,40.12,40.12,40.12,2350000,40.12 1957-12-13,40.73,40.73,40.73,40.73,2310000,40.73 1957-12-12,40.55,40.55,40.55,40.55,2330000,40.55 1957-12-11,40.51,40.51,40.51,40.51,2240000,40.51 1957-12-10,40.56,40.56,40.56,40.56,2360000,40.56 1957-12-09,40.92,40.92,40.92,40.92,2230000,40.92 1957-12-06,41.31,41.31,41.31,41.31,2350000,41.31 1957-12-05,41.52,41.52,41.52,41.52,2020000,41.52 1957-12-04,41.54,41.54,41.54,41.54,2220000,41.54 1957-12-03,41.37,41.37,41.37,41.37,2060000,41.37 1957-12-02,41.36,41.36,41.36,41.36,2430000,41.36 1957-11-29,41.72,41.72,41.72,41.72,2740000,41.72 1957-11-27,41.25,41.25,41.25,41.25,3330000,41.25 1957-11-26,40.09,40.09,40.09,40.09,3650000,40.09 1957-11-25,41.18,41.18,41.18,41.18,2600000,41.18 1957-11-22,40.87,40.87,40.87,40.87,2850000,40.87 1957-11-21,40.48,40.48,40.48,40.48,2900000,40.48 1957-11-20,39.92,39.92,39.92,39.92,2400000,39.92 1957-11-19,39.81,39.81,39.81,39.81,2240000,39.81 1957-11-18,40.04,40.04,40.04,40.04,2110000,40.04 1957-11-15,40.37,40.37,40.37,40.37,3510000,40.37 1957-11-14,39.44,39.44,39.44,39.44,2450000,39.44 1957-11-13,39.55,39.55,39.55,39.55,2120000,39.55 1957-11-12,39.60,39.60,39.60,39.60,2050000,39.60 1957-11-11,40.18,40.18,40.18,40.18,1540000,40.18 1957-11-08,40.19,40.19,40.19,40.19,2140000,40.19 1957-11-07,40.67,40.67,40.67,40.67,2580000,40.67 1957-11-06,40.43,40.43,40.43,40.43,2550000,40.43 1957-11-04,40.37,40.37,40.37,40.37,2380000,40.37 1957-11-01,40.44,40.44,40.44,40.44,2060000,40.44 1957-10-31,41.06,41.06,41.06,41.06,2170000,41.06 1957-10-30,41.02,41.02,41.02,41.02,2060000,41.02 1957-10-29,40.69,40.69,40.69,40.69,1860000,40.69 1957-10-28,40.42,40.42,40.42,40.42,1800000,40.42 1957-10-25,40.59,40.59,40.59,40.59,2400000,40.59 1957-10-24,40.71,40.71,40.71,40.71,4030000,40.71 1957-10-23,40.73,40.73,40.73,40.73,4600000,40.73 1957-10-22,38.98,38.98,38.98,38.98,5090000,38.98 1957-10-21,39.15,39.15,39.15,39.15,4670000,39.15 1957-10-18,40.33,40.33,40.33,40.33,2670000,40.33 1957-10-17,40.65,40.65,40.65,40.65,3060000,40.65 1957-10-16,41.33,41.33,41.33,41.33,2050000,41.33 1957-10-15,41.67,41.67,41.67,41.67,2620000,41.67 1957-10-14,41.24,41.24,41.24,41.24,2770000,41.24 1957-10-11,40.94,40.94,40.94,40.94,4460000,40.94 1957-10-10,40.96,40.96,40.96,40.96,3300000,40.96 1957-10-09,41.99,41.99,41.99,41.99,2120000,41.99 1957-10-08,41.95,41.95,41.95,41.95,3190000,41.95 1957-10-07,42.22,42.22,42.22,42.22,2490000,42.22 1957-10-04,42.79,42.79,42.79,42.79,1520000,42.79 1957-10-03,43.14,43.14,43.14,43.14,1590000,43.14 1957-10-02,43.10,43.10,43.10,43.10,1760000,43.10 1957-10-01,42.76,42.76,42.76,42.76,1680000,42.76 1957-09-30,42.42,42.42,42.42,42.42,1520000,42.42 1957-09-27,42.55,42.55,42.55,42.55,1750000,42.55 1957-09-26,42.57,42.57,42.57,42.57,2130000,42.57 1957-09-25,42.98,42.98,42.98,42.98,2770000,42.98 1957-09-24,42.98,42.98,42.98,42.98,2840000,42.98 1957-09-23,42.69,42.69,42.69,42.69,3160000,42.69 1957-09-20,43.69,43.69,43.69,43.69,2340000,43.69 1957-09-19,44.40,44.40,44.40,44.40,1520000,44.40 1957-09-18,44.69,44.69,44.69,44.69,1540000,44.69 1957-09-17,44.64,44.64,44.64,44.64,1490000,44.64 1957-09-16,44.58,44.58,44.58,44.58,1290000,44.58 1957-09-13,44.80,44.80,44.80,44.80,1620000,44.80 1957-09-12,44.82,44.82,44.82,44.82,2010000,44.82 1957-09-11,44.26,44.26,44.26,44.26,2130000,44.26 1957-09-10,43.87,43.87,43.87,43.87,1870000,43.87 1957-09-09,44.28,44.28,44.28,44.28,1420000,44.28 1957-09-06,44.68,44.68,44.68,44.68,1320000,44.68 1957-09-05,44.82,44.82,44.82,44.82,1420000,44.82 1957-09-04,45.05,45.05,45.05,45.05,1260000,45.05 1957-09-03,45.44,45.44,45.44,45.44,1490000,45.44 1957-08-30,45.22,45.22,45.22,45.22,1600000,45.22 1957-08-29,44.46,44.46,44.46,44.46,1630000,44.46 1957-08-28,44.64,44.64,44.64,44.64,1840000,44.64 1957-08-27,44.61,44.61,44.61,44.61,2250000,44.61 1957-08-26,43.89,43.89,43.89,43.89,2680000,43.89 1957-08-23,44.51,44.51,44.51,44.51,1960000,44.51 1957-08-22,45.16,45.16,45.16,45.16,1500000,45.16 1957-08-21,45.49,45.49,45.49,45.49,1720000,45.49 1957-08-20,45.29,45.29,45.29,45.29,2700000,45.29 1957-08-19,44.91,44.91,44.91,44.91,2040000,44.91 1957-08-16,45.83,45.83,45.83,45.83,1470000,45.83 1957-08-15,45.75,45.75,45.75,45.75,2040000,45.75 1957-08-14,45.73,45.73,45.73,45.73,2040000,45.73 1957-08-13,46.30,46.30,46.30,46.30,1580000,46.30 1957-08-12,46.33,46.33,46.33,46.33,1650000,46.33 1957-08-09,46.92,46.92,46.92,46.92,1570000,46.92 1957-08-08,46.90,46.90,46.90,46.90,1690000,46.90 1957-08-07,47.03,47.03,47.03,47.03,2460000,47.03 1957-08-06,46.67,46.67,46.67,46.67,1910000,46.67 1957-08-05,47.26,47.26,47.26,47.26,1790000,47.26 1957-08-02,47.68,47.68,47.68,47.68,1610000,47.68 1957-08-01,47.79,47.79,47.79,47.79,1660000,47.79 1957-07-31,47.91,47.91,47.91,47.91,1830000,47.91 1957-07-30,47.92,47.92,47.92,47.92,1780000,47.92 1957-07-29,47.92,47.92,47.92,47.92,1990000,47.92 1957-07-26,48.45,48.45,48.45,48.45,1710000,48.45 1957-07-25,48.61,48.61,48.61,48.61,1800000,48.61 1957-07-24,48.61,48.61,48.61,48.61,1730000,48.61 1957-07-23,48.56,48.56,48.56,48.56,1840000,48.56 1957-07-22,48.47,48.47,48.47,48.47,1950000,48.47 1957-07-19,48.58,48.58,48.58,48.58,1930000,48.58 1957-07-18,48.53,48.53,48.53,48.53,2130000,48.53 1957-07-17,48.58,48.58,48.58,48.58,2060000,48.58 1957-07-16,48.88,48.88,48.88,48.88,2510000,48.88 1957-07-15,49.13,49.13,49.13,49.13,2480000,49.13 1957-07-12,49.08,49.08,49.08,49.08,2240000,49.08 1957-07-11,48.86,48.86,48.86,48.86,2830000,48.86 1957-07-10,49.00,49.00,49.00,49.00,2880000,49.00 1957-07-09,48.90,48.90,48.90,48.90,2450000,48.90 1957-07-08,48.90,48.90,48.90,48.90,2840000,48.90 1957-07-05,48.69,48.69,48.69,48.69,2240000,48.69 1957-07-03,48.46,48.46,48.46,48.46,2720000,48.46 1957-07-02,47.90,47.90,47.90,47.90,2450000,47.90 1957-07-01,47.43,47.43,47.43,47.43,1840000,47.43 1957-06-28,47.37,47.37,47.37,47.37,1770000,47.37 1957-06-27,47.26,47.26,47.26,47.26,1800000,47.26 1957-06-26,47.09,47.09,47.09,47.09,1870000,47.09 1957-06-25,47.15,47.15,47.15,47.15,2000000,47.15 1957-06-24,46.78,46.78,46.78,46.78,2040000,46.78 1957-06-21,47.15,47.15,47.15,47.15,1970000,47.15 1957-06-20,47.43,47.43,47.43,47.43,2050000,47.43 1957-06-19,47.72,47.72,47.72,47.72,2220000,47.72 1957-06-18,48.04,48.04,48.04,48.04,2440000,48.04 1957-06-17,48.24,48.24,48.24,48.24,2220000,48.24 1957-06-14,48.15,48.15,48.15,48.15,2090000,48.15 1957-06-13,48.14,48.14,48.14,48.14,2630000,48.14 1957-06-12,48.05,48.05,48.05,48.05,2600000,48.05 1957-06-11,47.94,47.94,47.94,47.94,2850000,47.94 1957-06-10,47.90,47.90,47.90,47.90,2050000,47.90 1957-06-07,47.85,47.85,47.85,47.85,2380000,47.85 1957-06-06,47.80,47.80,47.80,47.80,2300000,47.80 1957-06-05,47.27,47.27,47.27,47.27,1940000,47.27 1957-06-04,47.28,47.28,47.28,47.28,2200000,47.28 1957-06-03,47.37,47.37,47.37,47.37,2050000,47.37 1957-05-31,47.43,47.43,47.43,47.43,2050000,47.43 1957-05-29,47.11,47.11,47.11,47.11,2270000,47.11 1957-05-28,46.69,46.69,46.69,46.69,2070000,46.69 1957-05-27,46.78,46.78,46.78,46.78,2290000,46.78 1957-05-24,47.21,47.21,47.21,47.21,2340000,47.21 1957-05-23,47.15,47.15,47.15,47.15,2110000,47.15 1957-05-22,47.14,47.14,47.14,47.14,2060000,47.14 1957-05-21,47.33,47.33,47.33,47.33,2370000,47.33 1957-05-20,47.35,47.35,47.35,47.35,2300000,47.35 1957-05-17,47.15,47.15,47.15,47.15,2510000,47.15 1957-05-16,47.02,47.02,47.02,47.02,2690000,47.02 1957-05-15,46.83,46.83,46.83,46.83,2590000,46.83 1957-05-14,46.67,46.67,46.67,46.67,2580000,46.67 1957-05-13,46.88,46.88,46.88,46.88,2720000,46.88 1957-05-10,46.59,46.59,46.59,46.59,2430000,46.59 1957-05-09,46.36,46.36,46.36,46.36,2520000,46.36 1957-05-08,46.31,46.31,46.31,46.31,2590000,46.31 1957-05-07,46.13,46.13,46.13,46.13,2300000,46.13 1957-05-06,46.27,46.27,46.27,46.27,2210000,46.27 1957-05-03,46.34,46.34,46.34,46.34,2390000,46.34 1957-05-02,46.39,46.39,46.39,46.39,2860000,46.39 1957-05-01,46.02,46.02,46.02,46.02,2310000,46.02 1957-04-30,45.74,45.74,45.74,45.74,2200000,45.74 1957-04-29,45.73,45.73,45.73,45.73,2290000,45.73 1957-04-26,45.50,45.50,45.50,45.50,2380000,45.50 1957-04-25,45.56,45.56,45.56,45.56,2640000,45.56 1957-04-24,45.72,45.72,45.72,45.72,2990000,45.72 1957-04-23,45.65,45.65,45.65,45.65,2840000,45.65 1957-04-22,45.48,45.48,45.48,45.48,2560000,45.48 1957-04-18,45.41,45.41,45.41,45.41,2480000,45.41 1957-04-17,45.08,45.08,45.08,45.08,2290000,45.08 1957-04-16,45.02,45.02,45.02,45.02,1890000,45.02 1957-04-15,44.95,44.95,44.95,44.95,2010000,44.95 1957-04-12,44.98,44.98,44.98,44.98,2370000,44.98 1957-04-11,44.98,44.98,44.98,44.98,2350000,44.98 1957-04-10,44.98,44.98,44.98,44.98,2920000,44.98 1957-04-09,44.79,44.79,44.79,44.79,2400000,44.79 1957-04-08,44.39,44.39,44.39,44.39,1950000,44.39 1957-04-05,44.49,44.49,44.49,44.49,1830000,44.49 1957-04-04,44.44,44.44,44.44,44.44,1820000,44.44 1957-04-03,44.54,44.54,44.54,44.54,2160000,44.54 1957-04-02,44.42,44.42,44.42,44.42,2300000,44.42 1957-04-01,44.14,44.14,44.14,44.14,1620000,44.14 1957-03-29,44.11,44.11,44.11,44.11,1650000,44.11 1957-03-28,44.18,44.18,44.18,44.18,1930000,44.18 1957-03-27,44.09,44.09,44.09,44.09,1710000,44.09 1957-03-26,43.91,43.91,43.91,43.91,1660000,43.91 1957-03-25,43.88,43.88,43.88,43.88,1590000,43.88 1957-03-22,44.06,44.06,44.06,44.06,1610000,44.06 1957-03-21,44.11,44.11,44.11,44.11,1630000,44.11 1957-03-20,44.10,44.10,44.10,44.10,1830000,44.10 1957-03-19,44.04,44.04,44.04,44.04,1540000,44.04 1957-03-18,43.85,43.85,43.85,43.85,1450000,43.85 1957-03-15,44.05,44.05,44.05,44.05,1600000,44.05 1957-03-14,44.07,44.07,44.07,44.07,1580000,44.07 1957-03-13,44.04,44.04,44.04,44.04,1840000,44.04 1957-03-12,43.75,43.75,43.75,43.75,1600000,43.75 1957-03-11,43.78,43.78,43.78,43.78,1650000,43.78 1957-03-08,44.07,44.07,44.07,44.07,1630000,44.07 1957-03-07,44.21,44.21,44.21,44.21,1830000,44.21 1957-03-06,44.23,44.23,44.23,44.23,1840000,44.23 1957-03-05,44.22,44.22,44.22,44.22,1860000,44.22 1957-03-04,44.06,44.06,44.06,44.06,1890000,44.06 1957-03-01,43.74,43.74,43.74,43.74,1700000,43.74 1957-02-28,43.26,43.26,43.26,43.26,1620000,43.26 1957-02-27,43.41,43.41,43.41,43.41,1620000,43.41 1957-02-26,43.45,43.45,43.45,43.45,1580000,43.45 1957-02-25,43.38,43.38,43.38,43.38,1710000,43.38 1957-02-21,43.48,43.48,43.48,43.48,1680000,43.48 1957-02-20,43.63,43.63,43.63,43.63,1790000,43.63 1957-02-19,43.49,43.49,43.49,43.49,1670000,43.49 1957-02-18,43.46,43.46,43.46,43.46,1800000,43.46 1957-02-15,43.51,43.51,43.51,43.51,2060000,43.51 1957-02-14,42.99,42.99,42.99,42.99,2220000,42.99 1957-02-13,43.04,43.04,43.04,43.04,2380000,43.04 1957-02-12,42.39,42.39,42.39,42.39,2550000,42.39 1957-02-11,42.57,42.57,42.57,42.57,2740000,42.57 1957-02-08,43.32,43.32,43.32,43.32,2120000,43.32 1957-02-07,43.62,43.62,43.62,43.62,1840000,43.62 1957-02-06,43.82,43.82,43.82,43.82,2110000,43.82 1957-02-05,43.89,43.89,43.89,43.89,2610000,43.89 1957-02-04,44.53,44.53,44.53,44.53,1750000,44.53 1957-02-01,44.62,44.62,44.62,44.62,1680000,44.62 1957-01-31,44.72,44.72,44.72,44.72,1920000,44.72 1957-01-30,44.91,44.91,44.91,44.91,1950000,44.91 1957-01-29,44.71,44.71,44.71,44.71,1800000,44.71 1957-01-28,44.49,44.49,44.49,44.49,1700000,44.49 1957-01-25,44.82,44.82,44.82,44.82,2010000,44.82 1957-01-24,45.03,45.03,45.03,45.03,1910000,45.03 1957-01-23,44.87,44.87,44.87,44.87,1920000,44.87 1957-01-22,44.53,44.53,44.53,44.53,1920000,44.53 1957-01-21,44.40,44.40,44.40,44.40,2740000,44.40 1957-01-18,44.64,44.64,44.64,44.64,2400000,44.64 1957-01-17,45.22,45.22,45.22,45.22,2140000,45.22 1957-01-16,45.23,45.23,45.23,45.23,2210000,45.23 1957-01-15,45.18,45.18,45.18,45.18,2370000,45.18 1957-01-14,45.86,45.86,45.86,45.86,2350000,45.86 1957-01-11,46.18,46.18,46.18,46.18,2340000,46.18 1957-01-10,46.27,46.27,46.27,46.27,2470000,46.27 1957-01-09,46.16,46.16,46.16,46.16,2330000,46.16 1957-01-08,46.25,46.25,46.25,46.25,2230000,46.25 1957-01-07,46.42,46.42,46.42,46.42,2500000,46.42 1957-01-04,46.66,46.66,46.66,46.66,2710000,46.66 1957-01-03,46.60,46.60,46.60,46.60,2260000,46.60 1957-01-02,46.20,46.20,46.20,46.20,1960000,46.20 1956-12-31,46.67,46.67,46.67,46.67,3680000,46.67 1956-12-28,46.56,46.56,46.56,46.56,2790000,46.56 1956-12-27,46.35,46.35,46.35,46.35,2420000,46.35 1956-12-26,46.39,46.39,46.39,46.39,2440000,46.39 1956-12-21,46.37,46.37,46.37,46.37,2380000,46.37 1956-12-20,46.07,46.07,46.07,46.07,2060000,46.07 1956-12-19,46.43,46.43,46.43,46.43,1900000,46.43 1956-12-18,46.54,46.54,46.54,46.54,2370000,46.54 1956-12-17,46.54,46.54,46.54,46.54,2500000,46.54 1956-12-14,46.54,46.54,46.54,46.54,2450000,46.54 1956-12-13,46.50,46.50,46.50,46.50,2370000,46.50 1956-12-12,46.13,46.13,46.13,46.13,2180000,46.13 1956-12-11,46.48,46.48,46.48,46.48,2210000,46.48 1956-12-10,46.80,46.80,46.80,46.80,2600000,46.80 1956-12-07,47.04,47.04,47.04,47.04,2400000,47.04 1956-12-06,46.81,46.81,46.81,46.81,2470000,46.81 1956-12-05,46.39,46.39,46.39,46.39,2360000,46.39 1956-12-04,45.84,45.84,45.84,45.84,2180000,45.84 1956-12-03,45.98,45.98,45.98,45.98,2570000,45.98 1956-11-30,45.08,45.08,45.08,45.08,2300000,45.08 1956-11-29,44.38,44.38,44.38,44.38,2440000,44.38 1956-11-28,44.43,44.43,44.43,44.43,2190000,44.43 1956-11-27,44.91,44.91,44.91,44.91,2130000,44.91 1956-11-26,44.87,44.87,44.87,44.87,2230000,44.87 1956-11-23,45.14,45.14,45.14,45.14,1880000,45.14 1956-11-21,44.67,44.67,44.67,44.67,2310000,44.67 1956-11-20,44.89,44.89,44.89,44.89,2240000,44.89 1956-11-19,45.29,45.29,45.29,45.29,2560000,45.29 1956-11-16,45.74,45.74,45.74,45.74,1820000,45.74 1956-11-15,45.72,45.72,45.72,45.72,2210000,45.72 1956-11-14,46.01,46.01,46.01,46.01,2290000,46.01 1956-11-13,46.27,46.27,46.27,46.27,2140000,46.27 1956-11-12,46.49,46.49,46.49,46.49,1600000,46.49 1956-11-09,46.34,46.34,46.34,46.34,1690000,46.34 1956-11-08,46.73,46.73,46.73,46.73,1970000,46.73 1956-11-07,47.11,47.11,47.11,47.11,2650000,47.11 1956-11-05,47.60,47.60,47.60,47.60,2830000,47.60 1956-11-02,46.98,46.98,46.98,46.98,2180000,46.98 1956-11-01,46.52,46.52,46.52,46.52,1890000,46.52 1956-10-31,45.58,45.58,45.58,45.58,2280000,45.58 1956-10-30,46.37,46.37,46.37,46.37,1830000,46.37 1956-10-29,46.40,46.40,46.40,46.40,2420000,46.40 1956-10-26,46.27,46.27,46.27,46.27,1800000,46.27 1956-10-25,45.85,45.85,45.85,45.85,1580000,45.85 1956-10-24,45.93,45.93,45.93,45.93,1640000,45.93 1956-10-23,46.12,46.12,46.12,46.12,1390000,46.12 1956-10-22,46.23,46.23,46.23,46.23,1430000,46.23 1956-10-19,46.24,46.24,46.24,46.24,1720000,46.24 1956-10-18,46.34,46.34,46.34,46.34,1640000,46.34 1956-10-17,46.26,46.26,46.26,46.26,1640000,46.26 1956-10-16,46.62,46.62,46.62,46.62,1580000,46.62 1956-10-15,46.86,46.86,46.86,46.86,1610000,46.86 1956-10-12,47.00,47.00,47.00,47.00,1330000,47.00 1956-10-11,46.81,46.81,46.81,46.81,1760000,46.81 1956-10-10,46.84,46.84,46.84,46.84,1620000,46.84 1956-10-09,46.20,46.20,46.20,46.20,1220000,46.20 1956-10-08,46.43,46.43,46.43,46.43,1450000,46.43 1956-10-05,46.45,46.45,46.45,46.45,1580000,46.45 1956-10-04,46.29,46.29,46.29,46.29,1600000,46.29 1956-10-03,46.28,46.28,46.28,46.28,2180000,46.28 1956-10-02,45.52,45.52,45.52,45.52,2400000,45.52 1956-10-01,44.70,44.70,44.70,44.70,2600000,44.70 1956-09-28,45.35,45.35,45.35,45.35,1720000,45.35 1956-09-27,45.60,45.60,45.60,45.60,1770000,45.60 1956-09-26,45.82,45.82,45.82,45.82,2370000,45.82 1956-09-25,45.75,45.75,45.75,45.75,2100000,45.75 1956-09-24,46.40,46.40,46.40,46.40,1840000,46.40 1956-09-21,46.58,46.58,46.58,46.58,2110000,46.58 1956-09-20,46.21,46.21,46.21,46.21,2150000,46.21 1956-09-19,46.24,46.24,46.24,46.24,2040000,46.24 1956-09-18,46.79,46.79,46.79,46.79,2200000,46.79 1956-09-17,47.10,47.10,47.10,47.10,1940000,47.10 1956-09-14,47.21,47.21,47.21,47.21,2110000,47.21 1956-09-13,46.09,46.09,46.09,46.09,2000000,46.09 1956-09-12,47.05,47.05,47.05,47.05,1930000,47.05 1956-09-11,47.38,47.38,47.38,47.38,1920000,47.38 1956-09-10,47.56,47.56,47.56,47.56,1860000,47.56 1956-09-07,47.81,47.81,47.81,47.81,1690000,47.81 1956-09-06,48.10,48.10,48.10,48.10,1550000,48.10 1956-09-05,48.02,48.02,48.02,48.02,2130000,48.02 1956-09-04,47.89,47.89,47.89,47.89,1790000,47.89 1956-08-31,47.51,47.51,47.51,47.51,1620000,47.51 1956-08-30,46.94,46.94,46.94,46.94,2050000,46.94 1956-08-29,47.36,47.36,47.36,47.36,1530000,47.36 1956-08-28,47.57,47.57,47.57,47.57,1400000,47.57 1956-08-27,47.66,47.66,47.66,47.66,1420000,47.66 1956-08-24,47.95,47.95,47.95,47.95,1530000,47.95 1956-08-23,48.00,48.00,48.00,48.00,1590000,48.00 1956-08-22,47.42,47.42,47.42,47.42,1570000,47.42 1956-08-21,47.89,47.89,47.89,47.89,2440000,47.89 1956-08-20,48.25,48.25,48.25,48.25,1770000,48.25 1956-08-17,48.82,48.82,48.82,48.82,1720000,48.82 1956-08-16,48.88,48.88,48.88,48.88,1790000,48.88 1956-08-15,48.99,48.99,48.99,48.99,2000000,48.99 1956-08-14,48.00,48.00,48.00,48.00,1790000,48.00 1956-08-13,48.58,48.58,48.58,48.58,1730000,48.58 1956-08-10,49.09,49.09,49.09,49.09,2040000,49.09 1956-08-09,49.32,49.32,49.32,49.32,2550000,49.32 1956-08-08,49.36,49.36,49.36,49.36,2480000,49.36 1956-08-07,49.16,49.16,49.16,49.16,2180000,49.16 1956-08-06,48.96,48.96,48.96,48.96,2280000,48.96 1956-08-03,49.64,49.64,49.64,49.64,2210000,49.64 1956-08-02,49.64,49.64,49.64,49.64,2530000,49.64 1956-08-01,49.62,49.62,49.62,49.62,2230000,49.62 1956-07-31,49.39,49.39,49.39,49.39,2520000,49.39 1956-07-30,49.00,49.00,49.00,49.00,2100000,49.00 1956-07-27,49.08,49.08,49.08,49.08,2240000,49.08 1956-07-26,49.48,49.48,49.48,49.48,2060000,49.48 1956-07-25,49.44,49.44,49.44,49.44,2220000,49.44 1956-07-24,49.33,49.33,49.33,49.33,2040000,49.33 1956-07-23,49.33,49.33,49.33,49.33,1970000,49.33 1956-07-20,49.35,49.35,49.35,49.35,2020000,49.35 1956-07-19,49.32,49.32,49.32,49.32,1950000,49.32 1956-07-18,49.30,49.30,49.30,49.30,2530000,49.30 1956-07-17,49.31,49.31,49.31,49.31,2520000,49.31 1956-07-16,49.14,49.14,49.14,49.14,2260000,49.14 1956-07-13,48.72,48.72,48.72,48.72,2020000,48.72 1956-07-12,48.58,48.58,48.58,48.58,2180000,48.58 1956-07-11,48.69,48.69,48.69,48.69,2520000,48.69 1956-07-10,48.54,48.54,48.54,48.54,2450000,48.54 1956-07-09,48.25,48.25,48.25,48.25,2180000,48.25 1956-07-06,48.04,48.04,48.04,48.04,2180000,48.04 1956-07-05,47.80,47.80,47.80,47.80,2240000,47.80 1956-07-03,47.32,47.32,47.32,47.32,1840000,47.32 1956-07-02,46.93,46.93,46.93,46.93,1610000,46.93 1956-06-29,46.97,46.97,46.97,46.97,1780000,46.97 1956-06-28,47.13,47.13,47.13,47.13,1900000,47.13 1956-06-27,47.07,47.07,47.07,47.07,2090000,47.07 1956-06-26,46.72,46.72,46.72,46.72,1730000,46.72 1956-06-25,46.41,46.41,46.41,46.41,1500000,46.41 1956-06-22,46.59,46.59,46.59,46.59,1630000,46.59 1956-06-21,46.73,46.73,46.73,46.73,1820000,46.73 1956-06-20,46.41,46.41,46.41,46.41,1670000,46.41 1956-06-19,46.22,46.22,46.22,46.22,1430000,46.22 1956-06-18,46.17,46.17,46.17,46.17,1440000,46.17 1956-06-15,46.37,46.37,46.37,46.37,1550000,46.37 1956-06-14,46.31,46.31,46.31,46.31,1670000,46.31 1956-06-13,46.42,46.42,46.42,46.42,1760000,46.42 1956-06-12,46.36,46.36,46.36,46.36,1900000,46.36 1956-06-11,45.71,45.71,45.71,45.71,2000000,45.71 1956-06-08,45.14,45.14,45.14,45.14,3630000,45.14 1956-06-07,45.99,45.99,45.99,45.99,1630000,45.99 1956-06-06,45.63,45.63,45.63,45.63,1460000,45.63 1956-06-05,45.86,45.86,45.86,45.86,1650000,45.86 1956-06-04,45.85,45.85,45.85,45.85,1500000,45.85 1956-06-01,45.58,45.58,45.58,45.58,1440000,45.58 1956-05-31,45.20,45.20,45.20,45.20,2020000,45.20 1956-05-29,45.11,45.11,45.11,45.11,2430000,45.11 1956-05-28,44.10,44.10,44.10,44.10,2780000,44.10 1956-05-25,44.62,44.62,44.62,44.62,2570000,44.62 1956-05-24,44.60,44.60,44.60,44.60,2600000,44.60 1956-05-23,45.02,45.02,45.02,45.02,2140000,45.02 1956-05-22,45.26,45.26,45.26,45.26,2290000,45.26 1956-05-21,45.99,45.99,45.99,45.99,1940000,45.99 1956-05-18,46.39,46.39,46.39,46.39,2020000,46.39 1956-05-17,46.61,46.61,46.61,46.61,1970000,46.61 1956-05-16,46.05,46.05,46.05,46.05,2080000,46.05 1956-05-15,46.37,46.37,46.37,46.37,2650000,46.37 1956-05-14,46.86,46.86,46.86,46.86,2440000,46.86 1956-05-11,47.12,47.12,47.12,47.12,2450000,47.12 1956-05-10,47.16,47.16,47.16,47.16,2850000,47.16 1956-05-09,47.94,47.94,47.94,47.94,2550000,47.94 1956-05-08,48.02,48.02,48.02,48.02,2440000,48.02 1956-05-07,48.22,48.22,48.22,48.22,2550000,48.22 1956-05-04,48.51,48.51,48.51,48.51,2860000,48.51 1956-05-03,48.34,48.34,48.34,48.34,2640000,48.34 1956-05-02,48.17,48.17,48.17,48.17,2440000,48.17 1956-05-01,48.16,48.16,48.16,48.16,2500000,48.16 1956-04-30,48.38,48.38,48.38,48.38,2730000,48.38 1956-04-27,47.99,47.99,47.99,47.99,2760000,47.99 1956-04-26,47.49,47.49,47.49,47.49,2630000,47.49 1956-04-25,47.09,47.09,47.09,47.09,2270000,47.09 1956-04-24,47.26,47.26,47.26,47.26,2500000,47.26 1956-04-23,47.65,47.65,47.65,47.65,2440000,47.65 1956-04-20,47.76,47.76,47.76,47.76,2320000,47.76 1956-04-19,47.57,47.57,47.57,47.57,2210000,47.57 1956-04-18,47.74,47.74,47.74,47.74,2470000,47.74 1956-04-17,47.93,47.93,47.93,47.93,2330000,47.93 1956-04-16,47.96,47.96,47.96,47.96,2310000,47.96 1956-04-13,47.95,47.95,47.95,47.95,2450000,47.95 1956-04-12,48.02,48.02,48.02,48.02,2700000,48.02 1956-04-11,48.31,48.31,48.31,48.31,2440000,48.31 1956-04-10,47.93,47.93,47.93,47.93,2590000,47.93 1956-04-09,48.61,48.61,48.61,48.61,2760000,48.61 1956-04-06,48.85,48.85,48.85,48.85,2600000,48.85 1956-04-05,48.57,48.57,48.57,48.57,2950000,48.57 1956-04-04,48.80,48.80,48.80,48.80,2760000,48.80 1956-04-03,48.53,48.53,48.53,48.53,2760000,48.53 1956-04-02,48.70,48.70,48.70,48.70,3120000,48.70 1956-03-29,48.48,48.48,48.48,48.48,3480000,48.48 1956-03-28,48.51,48.51,48.51,48.51,2610000,48.51 1956-03-27,48.25,48.25,48.25,48.25,2540000,48.25 1956-03-26,48.62,48.62,48.62,48.62,2720000,48.62 1956-03-23,48.83,48.83,48.83,48.83,2980000,48.83 1956-03-22,48.72,48.72,48.72,48.72,2650000,48.72 1956-03-21,48.23,48.23,48.23,48.23,2930000,48.23 1956-03-20,48.87,48.87,48.87,48.87,2960000,48.87 1956-03-19,48.59,48.59,48.59,48.59,2570000,48.59 1956-03-16,48.14,48.14,48.14,48.14,3120000,48.14 1956-03-15,47.99,47.99,47.99,47.99,3270000,47.99 1956-03-14,47.53,47.53,47.53,47.53,3140000,47.53 1956-03-13,47.06,47.06,47.06,47.06,2790000,47.06 1956-03-12,47.13,47.13,47.13,47.13,3110000,47.13 1956-03-09,46.70,46.70,46.70,46.70,3430000,46.70 1956-03-08,46.12,46.12,46.12,46.12,2500000,46.12 1956-03-07,46.01,46.01,46.01,46.01,2380000,46.01 1956-03-06,46.04,46.04,46.04,46.04,2770000,46.04 1956-03-05,46.06,46.06,46.06,46.06,3090000,46.06 1956-03-02,45.81,45.81,45.81,45.81,2860000,45.81 1956-03-01,45.54,45.54,45.54,45.54,2410000,45.54 1956-02-29,45.34,45.34,45.34,45.34,3900000,45.34 1956-02-28,45.43,45.43,45.43,45.43,2540000,45.43 1956-02-27,45.27,45.27,45.27,45.27,2440000,45.27 1956-02-24,45.32,45.32,45.32,45.32,2890000,45.32 1956-02-23,44.95,44.95,44.95,44.95,2900000,44.95 1956-02-21,44.56,44.56,44.56,44.56,2240000,44.56 1956-02-20,44.45,44.45,44.45,44.45,2530000,44.45 1956-02-17,44.52,44.52,44.52,44.52,2840000,44.52 1956-02-16,43.82,43.82,43.82,43.82,1750000,43.82 1956-02-15,44.04,44.04,44.04,44.04,3000000,44.04 1956-02-14,43.42,43.42,43.42,43.42,1590000,43.42 1956-02-13,43.58,43.58,43.58,43.58,1420000,43.58 1956-02-10,43.64,43.64,43.64,43.64,1770000,43.64 1956-02-09,43.66,43.66,43.66,43.66,2080000,43.66 1956-02-08,44.16,44.16,44.16,44.16,2170000,44.16 1956-02-07,44.60,44.60,44.60,44.60,2060000,44.60 1956-02-06,44.81,44.81,44.81,44.81,2230000,44.81 1956-02-03,44.78,44.78,44.78,44.78,2110000,44.78 1956-02-02,44.22,44.22,44.22,44.22,1900000,44.22 1956-02-01,44.03,44.03,44.03,44.03,2010000,44.03 1956-01-31,43.82,43.82,43.82,43.82,1900000,43.82 1956-01-30,43.50,43.50,43.50,43.50,1830000,43.50 1956-01-27,43.35,43.35,43.35,43.35,1950000,43.35 1956-01-26,43.46,43.46,43.46,43.46,1840000,43.46 1956-01-25,43.72,43.72,43.72,43.72,1950000,43.72 1956-01-24,43.65,43.65,43.65,43.65,2160000,43.65 1956-01-23,43.11,43.11,43.11,43.11,2720000,43.11 1956-01-20,43.22,43.22,43.22,43.22,2430000,43.22 1956-01-19,43.72,43.72,43.72,43.72,2500000,43.72 1956-01-18,44.17,44.17,44.17,44.17,2110000,44.17 1956-01-17,44.47,44.47,44.47,44.47,2050000,44.47 1956-01-16,44.14,44.14,44.14,44.14,2260000,44.14 1956-01-13,44.67,44.67,44.67,44.67,2120000,44.67 1956-01-12,44.75,44.75,44.75,44.75,2330000,44.75 1956-01-11,44.38,44.38,44.38,44.38,2310000,44.38 1956-01-10,44.16,44.16,44.16,44.16,2640000,44.16 1956-01-09,44.51,44.51,44.51,44.51,2700000,44.51 1956-01-06,45.14,45.14,45.14,45.14,2570000,45.14 1956-01-05,44.95,44.95,44.95,44.95,2110000,44.95 1956-01-04,45.00,45.00,45.00,45.00,2290000,45.00 1956-01-03,45.16,45.16,45.16,45.16,2390000,45.16 1955-12-30,45.48,45.48,45.48,45.48,2820000,45.48 1955-12-29,45.15,45.15,45.15,45.15,2190000,45.15 1955-12-28,45.05,45.05,45.05,45.05,1990000,45.05 1955-12-27,45.22,45.22,45.22,45.22,2010000,45.22 1955-12-23,45.50,45.50,45.50,45.50,2090000,45.50 1955-12-22,45.41,45.41,45.41,45.41,2650000,45.41 1955-12-21,45.84,45.84,45.84,45.84,2540000,45.84 1955-12-20,44.95,44.95,44.95,44.95,2280000,44.95 1955-12-19,45.02,45.02,45.02,45.02,2380000,45.02 1955-12-16,45.13,45.13,45.13,45.13,2310000,45.13 1955-12-15,45.06,45.06,45.06,45.06,2260000,45.06 1955-12-14,45.07,45.07,45.07,45.07,2670000,45.07 1955-12-13,45.45,45.45,45.45,45.45,2430000,45.45 1955-12-12,45.42,45.42,45.42,45.42,2510000,45.42 1955-12-09,45.89,45.89,45.89,45.89,2660000,45.89 1955-12-08,45.82,45.82,45.82,45.82,2970000,45.82 1955-12-07,45.55,45.55,45.55,45.55,2480000,45.55 1955-12-06,45.70,45.70,45.70,45.70,2540000,45.70 1955-12-05,45.70,45.70,45.70,45.70,2440000,45.70 1955-12-02,45.44,45.44,45.44,45.44,2400000,45.44 1955-12-01,45.35,45.35,45.35,45.35,2370000,45.35 1955-11-30,45.51,45.51,45.51,45.51,2900000,45.51 1955-11-29,45.56,45.56,45.56,45.56,2370000,45.56 1955-11-28,45.38,45.38,45.38,45.38,2460000,45.38 1955-11-25,45.68,45.68,45.68,45.68,2190000,45.68 1955-11-23,45.72,45.72,45.72,45.72,2550000,45.72 1955-11-22,45.66,45.66,45.66,45.66,2270000,45.66 1955-11-21,45.22,45.22,45.22,45.22,1960000,45.22 1955-11-18,45.54,45.54,45.54,45.54,2320000,45.54 1955-11-17,45.59,45.59,45.59,45.59,2310000,45.59 1955-11-16,45.91,45.91,45.91,45.91,2460000,45.91 1955-11-15,46.21,46.21,46.21,46.21,2560000,46.21 1955-11-14,46.41,46.41,46.41,46.41,2760000,46.41 1955-11-11,45.24,45.24,45.24,45.24,2000000,45.24 1955-11-10,44.72,44.72,44.72,44.72,2550000,44.72 1955-11-09,44.61,44.61,44.61,44.61,2580000,44.61 1955-11-07,44.15,44.15,44.15,44.15,2230000,44.15 1955-11-04,43.96,43.96,43.96,43.96,2430000,43.96 1955-11-03,43.24,43.24,43.24,43.24,2260000,43.24 1955-11-02,42.35,42.35,42.35,42.35,1610000,42.35 1955-11-01,42.28,42.28,42.28,42.28,1590000,42.28 1955-10-31,42.34,42.34,42.34,42.34,1800000,42.34 1955-10-28,42.37,42.37,42.37,42.37,1720000,42.37 1955-10-27,42.34,42.34,42.34,42.34,1830000,42.34 1955-10-26,42.29,42.29,42.29,42.29,1660000,42.29 1955-10-25,42.63,42.63,42.63,42.63,1950000,42.63 1955-10-24,42.91,42.91,42.91,42.91,1820000,42.91 1955-10-21,42.59,42.59,42.59,42.59,1710000,42.59 1955-10-20,42.59,42.59,42.59,42.59,2160000,42.59 1955-10-19,42.07,42.07,42.07,42.07,1760000,42.07 1955-10-18,41.65,41.65,41.65,41.65,1550000,41.65 1955-10-17,41.35,41.35,41.35,41.35,1480000,41.35 1955-10-14,41.22,41.22,41.22,41.22,1640000,41.22 1955-10-13,41.39,41.39,41.39,41.39,1980000,41.39 1955-10-12,41.52,41.52,41.52,41.52,1900000,41.52 1955-10-11,40.80,40.80,40.80,40.80,3590000,40.80 1955-10-10,41.15,41.15,41.15,41.15,3100000,41.15 1955-10-07,42.38,42.38,42.38,42.38,2150000,42.38 1955-10-06,42.70,42.70,42.70,42.70,1690000,42.70 1955-10-05,42.99,42.99,42.99,42.99,1920000,42.99 1955-10-04,42.82,42.82,42.82,42.82,2020000,42.82 1955-10-03,42.49,42.49,42.49,42.49,2720000,42.49 1955-09-30,43.67,43.67,43.67,43.67,2140000,43.67 1955-09-29,44.03,44.03,44.03,44.03,2560000,44.03 1955-09-28,44.31,44.31,44.31,44.31,3780000,44.31 1955-09-27,43.58,43.58,43.58,43.58,5500000,43.58 1955-09-26,42.61,42.61,42.61,42.61,7720000,42.61 1955-09-23,45.63,45.63,45.63,45.63,2540000,45.63 1955-09-22,45.39,45.39,45.39,45.39,2550000,45.39 1955-09-21,45.39,45.39,45.39,45.39,2460000,45.39 1955-09-20,45.13,45.13,45.13,45.13,2090000,45.13 1955-09-19,45.16,45.16,45.16,45.16,2390000,45.16 1955-09-16,45.09,45.09,45.09,45.09,2540000,45.09 1955-09-15,44.75,44.75,44.75,44.75,2890000,44.75 1955-09-14,44.99,44.99,44.99,44.99,2570000,44.99 1955-09-13,44.80,44.80,44.80,44.80,2580000,44.80 1955-09-12,44.19,44.19,44.19,44.19,2520000,44.19 1955-09-09,43.89,43.89,43.89,43.89,2480000,43.89 1955-09-08,43.88,43.88,43.88,43.88,2470000,43.88 1955-09-07,43.85,43.85,43.85,43.85,2380000,43.85 1955-09-06,43.86,43.86,43.86,43.86,2360000,43.86 1955-09-02,43.60,43.60,43.60,43.60,1700000,43.60 1955-09-01,43.37,43.37,43.37,43.37,1860000,43.37 1955-08-31,43.18,43.18,43.18,43.18,1850000,43.18 1955-08-30,42.92,42.92,42.92,42.92,1740000,42.92 1955-08-29,42.96,42.96,42.96,42.96,1910000,42.96 1955-08-26,42.99,42.99,42.99,42.99,2200000,42.99 1955-08-25,42.80,42.80,42.80,42.80,2120000,42.80 1955-08-24,42.61,42.61,42.61,42.61,2140000,42.61 1955-08-23,42.55,42.55,42.55,42.55,1890000,42.55 1955-08-22,41.98,41.98,41.98,41.98,1430000,41.98 1955-08-19,42.02,42.02,42.02,42.02,1400000,42.02 1955-08-18,41.84,41.84,41.84,41.84,1560000,41.84 1955-08-17,41.90,41.90,41.90,41.90,1570000,41.90 1955-08-16,41.86,41.86,41.86,41.86,1520000,41.86 1955-08-15,42.17,42.17,42.17,42.17,1230000,42.17 1955-08-12,42.21,42.21,42.21,42.21,1530000,42.21 1955-08-11,42.13,42.13,42.13,42.13,1620000,42.13 1955-08-10,41.74,41.74,41.74,41.74,1580000,41.74 1955-08-09,41.75,41.75,41.75,41.75,2240000,41.75 1955-08-08,42.31,42.31,42.31,42.31,1730000,42.31 1955-08-05,42.56,42.56,42.56,42.56,1690000,42.56 1955-08-04,42.36,42.36,42.36,42.36,2210000,42.36 1955-08-03,43.09,43.09,43.09,43.09,2190000,43.09 1955-08-02,43.03,43.03,43.03,43.03,2260000,43.03 1955-08-01,42.93,42.93,42.93,42.93,2190000,42.93 1955-07-29,43.52,43.52,43.52,43.52,2070000,43.52 1955-07-28,43.50,43.50,43.50,43.50,2090000,43.50 1955-07-27,43.76,43.76,43.76,43.76,2170000,43.76 1955-07-26,43.58,43.58,43.58,43.58,2340000,43.58 1955-07-25,43.48,43.48,43.48,43.48,2500000,43.48 1955-07-22,43.00,43.00,43.00,43.00,2500000,43.00 1955-07-21,42.64,42.64,42.64,42.64,2530000,42.64 1955-07-20,42.23,42.23,42.23,42.23,2080000,42.23 1955-07-19,42.10,42.10,42.10,42.10,2300000,42.10 1955-07-18,42.36,42.36,42.36,42.36,2160000,42.36 1955-07-15,42.40,42.40,42.40,42.40,2230000,42.40 1955-07-14,42.25,42.25,42.25,42.25,1980000,42.25 1955-07-13,42.24,42.24,42.24,42.24,2360000,42.24 1955-07-12,42.75,42.75,42.75,42.75,2630000,42.75 1955-07-11,42.75,42.75,42.75,42.75,2420000,42.75 1955-07-08,42.64,42.64,42.64,42.64,2450000,42.64 1955-07-07,42.58,42.58,42.58,42.58,3300000,42.58 1955-07-06,43.18,43.18,43.18,43.18,3140000,43.18 1955-07-05,41.69,41.69,41.69,41.69,2680000,41.69 1955-07-01,41.19,41.19,41.19,41.19,2540000,41.19 1955-06-30,41.03,41.03,41.03,41.03,2370000,41.03 1955-06-29,40.79,40.79,40.79,40.79,2180000,40.79 1955-06-28,40.77,40.77,40.77,40.77,2180000,40.77 1955-06-27,40.99,40.99,40.99,40.99,2250000,40.99 1955-06-24,40.96,40.96,40.96,40.96,2410000,40.96 1955-06-23,40.75,40.75,40.75,40.75,2900000,40.75 1955-06-22,40.60,40.60,40.60,40.60,3010000,40.60 1955-06-21,40.51,40.51,40.51,40.51,2720000,40.51 1955-06-20,40.14,40.14,40.14,40.14,2490000,40.14 1955-06-17,40.10,40.10,40.10,40.10,2340000,40.10 1955-06-16,39.96,39.96,39.96,39.96,2760000,39.96 1955-06-15,39.89,39.89,39.89,39.89,2650000,39.89 1955-06-14,39.67,39.67,39.67,39.67,2860000,39.67 1955-06-13,39.62,39.62,39.62,39.62,2770000,39.62 1955-06-10,39.25,39.25,39.25,39.25,2470000,39.25 1955-06-09,39.01,39.01,39.01,39.01,2960000,39.01 1955-06-08,39.22,39.22,39.22,39.22,3300000,39.22 1955-06-07,39.96,39.96,39.96,39.96,3230000,39.96 1955-06-06,39.69,39.69,39.69,39.69,2560000,39.69 1955-06-03,38.37,38.37,38.37,38.37,2590000,38.37 1955-06-02,38.01,38.01,38.01,38.01,2610000,38.01 1955-06-01,37.96,37.96,37.96,37.96,2510000,37.96 1955-05-31,37.91,37.91,37.91,37.91,1990000,37.91 1955-05-27,37.93,37.93,37.93,37.93,2220000,37.93 1955-05-26,37.85,37.85,37.85,37.85,2260000,37.85 1955-05-25,37.60,37.60,37.60,37.60,2100000,37.60 1955-05-24,37.46,37.46,37.46,37.46,1650000,37.46 1955-05-23,37.48,37.48,37.48,37.48,1900000,37.48 1955-05-20,37.74,37.74,37.74,37.74,2240000,37.74 1955-05-19,37.49,37.49,37.49,37.49,2380000,37.49 1955-05-18,37.28,37.28,37.28,37.28,2010000,37.28 1955-05-17,36.97,36.97,36.97,36.97,1900000,36.97 1955-05-16,37.02,37.02,37.02,37.02,2160000,37.02 1955-05-13,37.44,37.44,37.44,37.44,1860000,37.44 1955-05-12,37.20,37.20,37.20,37.20,2830000,37.20 1955-05-11,37.42,37.42,37.42,37.42,2120000,37.42 1955-05-10,37.85,37.85,37.85,37.85,2150000,37.85 1955-05-09,37.93,37.93,37.93,37.93,2090000,37.93 1955-05-06,37.89,37.89,37.89,37.89,2250000,37.89 1955-05-05,37.82,37.82,37.82,37.82,2270000,37.82 1955-05-04,37.64,37.64,37.64,37.64,2220000,37.64 1955-05-03,37.70,37.70,37.70,37.70,2630000,37.70 1955-05-02,38.04,38.04,38.04,38.04,2220000,38.04 1955-04-29,37.96,37.96,37.96,37.96,2230000,37.96 1955-04-28,37.68,37.68,37.68,37.68,2550000,37.68 1955-04-27,38.11,38.11,38.11,38.11,2660000,38.11 1955-04-26,38.31,38.31,38.31,38.31,2720000,38.31 1955-04-25,38.11,38.11,38.11,38.11,2720000,38.11 1955-04-22,38.01,38.01,38.01,38.01,2800000,38.01 1955-04-21,38.32,38.32,38.32,38.32,2810000,38.32 1955-04-20,38.28,38.28,38.28,38.28,3090000,38.28 1955-04-19,38.22,38.22,38.22,38.22,2700000,38.22 1955-04-18,38.27,38.27,38.27,38.27,3080000,38.27 1955-04-15,37.96,37.96,37.96,37.96,3180000,37.96 1955-04-14,37.79,37.79,37.79,37.79,2890000,37.79 1955-04-13,37.71,37.71,37.71,37.71,2820000,37.71 1955-04-12,37.66,37.66,37.66,37.66,2770000,37.66 1955-04-11,37.44,37.44,37.44,37.44,2680000,37.44 1955-04-07,37.34,37.34,37.34,37.34,2330000,37.34 1955-04-06,37.17,37.17,37.17,37.17,2500000,37.17 1955-04-05,36.98,36.98,36.98,36.98,2100000,36.98 1955-04-04,36.83,36.83,36.83,36.83,2500000,36.83 1955-04-01,36.95,36.95,36.95,36.95,2660000,36.95 1955-03-31,36.58,36.58,36.58,36.58,2680000,36.58 1955-03-30,36.52,36.52,36.52,36.52,3410000,36.52 1955-03-29,36.85,36.85,36.85,36.85,2770000,36.85 1955-03-28,36.83,36.83,36.83,36.83,2540000,36.83 1955-03-25,36.96,36.96,36.96,36.96,2540000,36.96 1955-03-24,36.93,36.93,36.93,36.93,3170000,36.93 1955-03-23,36.64,36.64,36.64,36.64,2730000,36.64 1955-03-22,36.17,36.17,36.17,36.17,1910000,36.17 1955-03-21,35.95,35.95,35.95,35.95,2020000,35.95 1955-03-18,36.18,36.18,36.18,36.18,2050000,36.18 1955-03-17,36.12,36.12,36.12,36.12,2200000,36.12 1955-03-16,35.98,35.98,35.98,35.98,2900000,35.98 1955-03-15,35.71,35.71,35.71,35.71,3160000,35.71 1955-03-14,34.96,34.96,34.96,34.96,4220000,34.96 1955-03-11,35.82,35.82,35.82,35.82,3040000,35.82 1955-03-10,36.45,36.45,36.45,36.45,2760000,36.45 1955-03-09,36.22,36.22,36.22,36.22,3590000,36.22 1955-03-08,36.58,36.58,36.58,36.58,3160000,36.58 1955-03-07,37.28,37.28,37.28,37.28,2630000,37.28 1955-03-04,37.52,37.52,37.52,37.52,2770000,37.52 1955-03-03,37.29,37.29,37.29,37.29,3330000,37.29 1955-03-02,37.15,37.15,37.15,37.15,3370000,37.15 1955-03-01,36.83,36.83,36.83,36.83,2830000,36.83 1955-02-28,36.76,36.76,36.76,36.76,2620000,36.76 1955-02-25,36.57,36.57,36.57,36.57,2540000,36.57 1955-02-24,36.62,36.62,36.62,36.62,2920000,36.62 1955-02-23,36.82,36.82,36.82,36.82,3030000,36.82 1955-02-21,36.85,36.85,36.85,36.85,3010000,36.85 1955-02-18,36.89,36.89,36.89,36.89,3660000,36.89 1955-02-17,36.84,36.84,36.84,36.84,3030000,36.84 1955-02-16,36.77,36.77,36.77,36.77,3660000,36.77 1955-02-15,36.89,36.89,36.89,36.89,3510000,36.89 1955-02-14,36.89,36.89,36.89,36.89,2950000,36.89 1955-02-11,37.15,37.15,37.15,37.15,3260000,37.15 1955-02-10,37.08,37.08,37.08,37.08,3460000,37.08 1955-02-09,36.75,36.75,36.75,36.75,3360000,36.75 1955-02-08,36.46,36.46,36.46,36.46,3400000,36.46 1955-02-07,36.96,36.96,36.96,36.96,3610000,36.96 1955-02-04,36.96,36.96,36.96,36.96,3370000,36.96 1955-02-03,36.44,36.44,36.44,36.44,2890000,36.44 1955-02-02,36.61,36.61,36.61,36.61,3210000,36.61 1955-02-01,36.72,36.72,36.72,36.72,3320000,36.72 1955-01-31,36.63,36.63,36.63,36.63,3500000,36.63 1955-01-28,36.19,36.19,36.19,36.19,3290000,36.19 1955-01-27,35.99,35.99,35.99,35.99,3500000,35.99 1955-01-26,35.95,35.95,35.95,35.95,3860000,35.95 1955-01-25,35.51,35.51,35.51,35.51,3230000,35.51 1955-01-24,35.52,35.52,35.52,35.52,2910000,35.52 1955-01-21,35.44,35.44,35.44,35.44,2690000,35.44 1955-01-20,35.13,35.13,35.13,35.13,2210000,35.13 1955-01-19,34.96,34.96,34.96,34.96,2760000,34.96 1955-01-18,34.80,34.80,34.80,34.80,3020000,34.80 1955-01-17,34.58,34.58,34.58,34.58,3360000,34.58 1955-01-14,35.28,35.28,35.28,35.28,2630000,35.28 1955-01-13,35.43,35.43,35.43,35.43,3350000,35.43 1955-01-12,35.58,35.58,35.58,35.58,3400000,35.58 1955-01-11,35.68,35.68,35.68,35.68,3680000,35.68 1955-01-10,35.79,35.79,35.79,35.79,4300000,35.79 1955-01-07,35.33,35.33,35.33,35.33,4030000,35.33 1955-01-06,35.04,35.04,35.04,35.04,5300000,35.04 1955-01-05,35.52,35.52,35.52,35.52,4640000,35.52 1955-01-04,36.42,36.42,36.42,36.42,4420000,36.42 1955-01-03,36.75,36.75,36.75,36.75,4570000,36.75 1954-12-31,35.98,35.98,35.98,35.98,3840000,35.98 1954-12-30,35.74,35.74,35.74,35.74,3590000,35.74 1954-12-29,35.74,35.74,35.74,35.74,4430000,35.74 1954-12-28,35.43,35.43,35.43,35.43,3660000,35.43 1954-12-27,35.07,35.07,35.07,35.07,2970000,35.07 1954-12-23,35.37,35.37,35.37,35.37,3310000,35.37 1954-12-22,35.34,35.34,35.34,35.34,3460000,35.34 1954-12-21,35.38,35.38,35.38,35.38,3630000,35.38 1954-12-20,35.33,35.33,35.33,35.33,3770000,35.33 1954-12-17,35.92,35.92,35.92,35.92,3730000,35.92 1954-12-16,34.93,34.93,34.93,34.93,3390000,34.93 1954-12-15,34.56,34.56,34.56,34.56,2740000,34.56 1954-12-14,34.35,34.35,34.35,34.35,2650000,34.35 1954-12-13,34.59,34.59,34.59,34.59,2750000,34.59 1954-12-10,34.56,34.56,34.56,34.56,3250000,34.56 1954-12-09,34.69,34.69,34.69,34.69,3300000,34.69 1954-12-08,34.86,34.86,34.86,34.86,4150000,34.86 1954-12-07,34.92,34.92,34.92,34.92,3820000,34.92 1954-12-06,34.76,34.76,34.76,34.76,3960000,34.76 1954-12-03,34.49,34.49,34.49,34.49,3790000,34.49 1954-12-02,34.18,34.18,34.18,34.18,3190000,34.18 1954-12-01,33.99,33.99,33.99,33.99,3100000,33.99 1954-11-30,34.24,34.24,34.24,34.24,3440000,34.24 1954-11-29,34.54,34.54,34.54,34.54,3300000,34.54 1954-11-26,34.55,34.55,34.55,34.55,3010000,34.55 1954-11-24,34.22,34.22,34.22,34.22,3990000,34.22 1954-11-23,34.03,34.03,34.03,34.03,3690000,34.03 1954-11-22,33.58,33.58,33.58,33.58,3000000,33.58 1954-11-19,33.45,33.45,33.45,33.45,3130000,33.45 1954-11-18,33.44,33.44,33.44,33.44,3530000,33.44 1954-11-17,33.63,33.63,33.63,33.63,3830000,33.63 1954-11-16,33.57,33.57,33.57,33.57,3260000,33.57 1954-11-15,33.47,33.47,33.47,33.47,3080000,33.47 1954-11-12,33.54,33.54,33.54,33.54,3720000,33.54 1954-11-11,33.47,33.47,33.47,33.47,2960000,33.47 1954-11-10,33.18,33.18,33.18,33.18,2070000,33.18 1954-11-09,33.15,33.15,33.15,33.15,3240000,33.15 1954-11-08,33.02,33.02,33.02,33.02,3180000,33.02 1954-11-05,32.71,32.71,32.71,32.71,2950000,32.71 1954-11-04,32.82,32.82,32.82,32.82,3140000,32.82 1954-11-03,32.44,32.44,32.44,32.44,2700000,32.44 1954-11-01,31.79,31.79,31.79,31.79,1790000,31.79 1954-10-29,31.68,31.68,31.68,31.68,1900000,31.68 1954-10-28,31.88,31.88,31.88,31.88,2190000,31.88 1954-10-27,32.02,32.02,32.02,32.02,2030000,32.02 1954-10-26,31.94,31.94,31.94,31.94,2010000,31.94 1954-10-25,31.96,31.96,31.96,31.96,2340000,31.96 1954-10-22,32.13,32.13,32.13,32.13,2080000,32.13 1954-10-21,32.13,32.13,32.13,32.13,2320000,32.13 1954-10-20,32.17,32.17,32.17,32.17,2380000,32.17 1954-10-19,31.91,31.91,31.91,31.91,1900000,31.91 1954-10-18,31.83,31.83,31.83,31.83,1790000,31.83 1954-10-15,31.71,31.71,31.71,31.71,2250000,31.71 1954-10-14,31.88,31.88,31.88,31.88,2540000,31.88 1954-10-13,32.27,32.27,32.27,32.27,2070000,32.27 1954-10-12,32.28,32.28,32.28,32.28,1620000,32.28 1954-10-11,32.41,32.41,32.41,32.41,2100000,32.41 1954-10-08,32.67,32.67,32.67,32.67,2120000,32.67 1954-10-07,32.69,32.69,32.69,32.69,1810000,32.69 1954-10-06,32.76,32.76,32.76,32.76,2570000,32.76 1954-10-05,32.63,32.63,32.63,32.63,2300000,32.63 1954-10-04,32.47,32.47,32.47,32.47,2000000,32.47 1954-10-01,32.29,32.29,32.29,32.29,1850000,32.29 1954-09-30,32.31,32.31,32.31,32.31,1840000,32.31 1954-09-29,32.50,32.50,32.50,32.50,1810000,32.50 1954-09-28,32.69,32.69,32.69,32.69,1800000,32.69 1954-09-27,32.53,32.53,32.53,32.53,2190000,32.53 1954-09-24,32.40,32.40,32.40,32.40,2340000,32.40 1954-09-23,32.18,32.18,32.18,32.18,2340000,32.18 1954-09-22,32.00,32.00,32.00,32.00,2260000,32.00 1954-09-21,31.79,31.79,31.79,31.79,1770000,31.79 1954-09-20,31.57,31.57,31.57,31.57,2060000,31.57 1954-09-17,31.71,31.71,31.71,31.71,2250000,31.71 1954-09-16,31.46,31.46,31.46,31.46,1880000,31.46 1954-09-15,31.29,31.29,31.29,31.29,2110000,31.29 1954-09-14,31.28,31.28,31.28,31.28,2120000,31.28 1954-09-13,31.12,31.12,31.12,31.12,2030000,31.12 1954-09-10,30.84,30.84,30.84,30.84,1870000,30.84 1954-09-09,30.73,30.73,30.73,30.73,1700000,30.73 1954-09-08,30.68,30.68,30.68,30.68,1970000,30.68 1954-09-07,30.66,30.66,30.66,30.66,1860000,30.66 1954-09-03,30.50,30.50,30.50,30.50,1630000,30.50 1954-09-02,30.27,30.27,30.27,30.27,1600000,30.27 1954-09-01,30.04,30.04,30.04,30.04,1790000,30.04 1954-08-31,29.83,29.83,29.83,29.83,2640000,29.83 1954-08-30,30.35,30.35,30.35,30.35,1950000,30.35 1954-08-27,30.66,30.66,30.66,30.66,1740000,30.66 1954-08-26,30.57,30.57,30.57,30.57,2060000,30.57 1954-08-25,30.65,30.65,30.65,30.65,2280000,30.65 1954-08-24,30.87,30.87,30.87,30.87,2000000,30.87 1954-08-23,31.00,31.00,31.00,31.00,2020000,31.00 1954-08-20,31.21,31.21,31.21,31.21,2110000,31.21 1954-08-19,31.16,31.16,31.16,31.16,2320000,31.16 1954-08-18,31.09,31.09,31.09,31.09,2390000,31.09 1954-08-17,31.12,31.12,31.12,31.12,2900000,31.12 1954-08-16,31.05,31.05,31.05,31.05,2760000,31.05 1954-08-13,30.72,30.72,30.72,30.72,2500000,30.72 1954-08-12,30.59,30.59,30.59,30.59,2680000,30.59 1954-08-11,30.72,30.72,30.72,30.72,3440000,30.72 1954-08-10,30.37,30.37,30.37,30.37,2890000,30.37 1954-08-09,30.12,30.12,30.12,30.12,2280000,30.12 1954-08-06,30.38,30.38,30.38,30.38,3350000,30.38 1954-08-05,30.77,30.77,30.77,30.77,3150000,30.77 1954-08-04,30.90,30.90,30.90,30.90,3620000,30.90 1954-08-03,30.93,30.93,30.93,30.93,2970000,30.93 1954-08-02,30.99,30.99,30.99,30.99,2850000,30.99 1954-07-30,30.88,30.88,30.88,30.88,2800000,30.88 1954-07-29,30.69,30.69,30.69,30.69,2710000,30.69 1954-07-28,30.58,30.58,30.58,30.58,2740000,30.58 1954-07-27,30.52,30.52,30.52,30.52,2690000,30.52 1954-07-26,30.34,30.34,30.34,30.34,2110000,30.34 1954-07-23,30.31,30.31,30.31,30.31,2520000,30.31 1954-07-22,30.27,30.27,30.27,30.27,2890000,30.27 1954-07-21,30.03,30.03,30.03,30.03,2510000,30.03 1954-07-20,29.84,29.84,29.84,29.84,2580000,29.84 1954-07-19,29.98,29.98,29.98,29.98,2370000,29.98 1954-07-16,30.06,30.06,30.06,30.06,2540000,30.06 1954-07-15,30.19,30.19,30.19,30.19,3000000,30.19 1954-07-14,30.09,30.09,30.09,30.09,2520000,30.09 1954-07-13,30.02,30.02,30.02,30.02,2430000,30.02 1954-07-12,30.12,30.12,30.12,30.12,2330000,30.12 1954-07-09,30.14,30.14,30.14,30.14,2240000,30.14 1954-07-08,29.94,29.94,29.94,29.94,2080000,29.94 1954-07-07,29.94,29.94,29.94,29.94,2380000,29.94 1954-07-06,29.92,29.92,29.92,29.92,2560000,29.92 1954-07-02,29.59,29.59,29.59,29.59,1980000,29.59 1954-07-01,29.21,29.21,29.21,29.21,1860000,29.21 1954-06-30,29.21,29.21,29.21,29.21,1950000,29.21 1954-06-29,29.43,29.43,29.43,29.43,2580000,29.43 1954-06-28,29.28,29.28,29.28,29.28,1890000,29.28 1954-06-25,29.20,29.20,29.20,29.20,2060000,29.20 1954-06-24,29.26,29.26,29.26,29.26,2260000,29.26 1954-06-23,29.13,29.13,29.13,29.13,2090000,29.13 1954-06-22,29.08,29.08,29.08,29.08,2100000,29.08 1954-06-21,29.06,29.06,29.06,29.06,1820000,29.06 1954-06-18,29.04,29.04,29.04,29.04,1580000,29.04 1954-06-17,28.96,28.96,28.96,28.96,1810000,28.96 1954-06-16,29.04,29.04,29.04,29.04,2070000,29.04 1954-06-15,28.83,28.83,28.83,28.83,1630000,28.83 1954-06-14,28.62,28.62,28.62,28.62,1420000,28.62 1954-06-11,28.58,28.58,28.58,28.58,1630000,28.58 1954-06-10,28.34,28.34,28.34,28.34,1610000,28.34 1954-06-09,28.15,28.15,28.15,28.15,2360000,28.15 1954-06-08,28.34,28.34,28.34,28.34,2540000,28.34 1954-06-07,28.99,28.99,28.99,28.99,1520000,28.99 1954-06-04,29.10,29.10,29.10,29.10,1720000,29.10 1954-06-03,29.15,29.15,29.15,29.15,1810000,29.15 1954-06-02,29.16,29.16,29.16,29.16,1930000,29.16 1954-06-01,29.19,29.19,29.19,29.19,1850000,29.19 1954-05-28,29.19,29.19,29.19,29.19,1940000,29.19 1954-05-27,29.05,29.05,29.05,29.05,2230000,29.05 1954-05-26,29.17,29.17,29.17,29.17,2180000,29.17 1954-05-25,28.93,28.93,28.93,28.93,2050000,28.93 1954-05-24,29.00,29.00,29.00,29.00,2330000,29.00 1954-05-21,28.99,28.99,28.99,28.99,2620000,28.99 1954-05-20,28.82,28.82,28.82,28.82,2070000,28.82 1954-05-19,28.72,28.72,28.72,28.72,2170000,28.72 1954-05-18,28.85,28.85,28.85,28.85,2250000,28.85 1954-05-17,28.84,28.84,28.84,28.84,2040000,28.84 1954-05-14,28.80,28.80,28.80,28.80,1970000,28.80 1954-05-13,28.56,28.56,28.56,28.56,2340000,28.56 1954-05-12,28.72,28.72,28.72,28.72,2210000,28.72 1954-05-11,28.49,28.49,28.49,28.49,1770000,28.49 1954-05-10,28.62,28.62,28.62,28.62,1800000,28.62 1954-05-07,28.65,28.65,28.65,28.65,2070000,28.65 1954-05-06,28.51,28.51,28.51,28.51,1980000,28.51 1954-05-05,28.29,28.29,28.29,28.29,2020000,28.29 1954-05-04,28.28,28.28,28.28,28.28,1990000,28.28 1954-05-03,28.21,28.21,28.21,28.21,1870000,28.21 1954-04-30,28.26,28.26,28.26,28.26,2450000,28.26 1954-04-29,28.18,28.18,28.18,28.18,2150000,28.18 1954-04-28,27.76,27.76,27.76,27.76,2120000,27.76 1954-04-27,27.71,27.71,27.71,27.71,1970000,27.71 1954-04-26,27.88,27.88,27.88,27.88,2150000,27.88 1954-04-23,27.78,27.78,27.78,27.78,1990000,27.78 1954-04-22,27.68,27.68,27.68,27.68,1750000,27.68 1954-04-21,27.64,27.64,27.64,27.64,1870000,27.64 1954-04-20,27.75,27.75,27.75,27.75,1860000,27.75 1954-04-19,27.76,27.76,27.76,27.76,2430000,27.76 1954-04-15,27.94,27.94,27.94,27.94,2200000,27.94 1954-04-14,27.85,27.85,27.85,27.85,2330000,27.85 1954-04-13,27.64,27.64,27.64,27.64,2020000,27.64 1954-04-12,27.57,27.57,27.57,27.57,1790000,27.57 1954-04-09,27.38,27.38,27.38,27.38,2360000,27.38 1954-04-08,27.38,27.38,27.38,27.38,2300000,27.38 1954-04-07,27.11,27.11,27.11,27.11,1830000,27.11 1954-04-06,27.01,27.01,27.01,27.01,2120000,27.01 1954-04-05,27.26,27.26,27.26,27.26,1710000,27.26 1954-04-02,27.21,27.21,27.21,27.21,1830000,27.21 1954-04-01,27.17,27.17,27.17,27.17,2270000,27.17 1954-03-31,26.94,26.94,26.94,26.94,2690000,26.94 1954-03-30,26.69,26.69,26.69,26.69,2130000,26.69 1954-03-29,26.66,26.66,26.66,26.66,1870000,26.66 1954-03-26,26.56,26.56,26.56,26.56,1550000,26.56 1954-03-25,26.42,26.42,26.42,26.42,1720000,26.42 1954-03-24,26.47,26.47,26.47,26.47,1900000,26.47 1954-03-23,26.60,26.60,26.60,26.60,2180000,26.60 1954-03-22,26.79,26.79,26.79,26.79,1800000,26.79 1954-03-19,26.81,26.81,26.81,26.81,1930000,26.81 1954-03-18,26.73,26.73,26.73,26.73,2020000,26.73 1954-03-17,26.62,26.62,26.62,26.62,1740000,26.62 1954-03-16,26.56,26.56,26.56,26.56,1540000,26.56 1954-03-15,26.57,26.57,26.57,26.57,1680000,26.57 1954-03-12,26.69,26.69,26.69,26.69,1980000,26.69 1954-03-11,26.69,26.69,26.69,26.69,2050000,26.69 1954-03-10,26.57,26.57,26.57,26.57,1870000,26.57 1954-03-09,26.51,26.51,26.51,26.51,1630000,26.51 1954-03-08,26.45,26.45,26.45,26.45,1650000,26.45 1954-03-05,26.52,26.52,26.52,26.52,2030000,26.52 1954-03-04,26.41,26.41,26.41,26.41,1830000,26.41 1954-03-03,26.32,26.32,26.32,26.32,2240000,26.32 1954-03-02,26.32,26.32,26.32,26.32,1980000,26.32 1954-03-01,26.25,26.25,26.25,26.25,2040000,26.25 1954-02-26,26.15,26.15,26.15,26.15,1910000,26.15 1954-02-25,25.91,25.91,25.91,25.91,1470000,25.91 1954-02-24,25.83,25.83,25.83,25.83,1350000,25.83 1954-02-23,25.83,25.83,25.83,25.83,1470000,25.83 1954-02-19,25.92,25.92,25.92,25.92,1510000,25.92 1954-02-18,25.86,25.86,25.86,25.86,1500000,25.86 1954-02-17,25.86,25.86,25.86,25.86,1740000,25.86 1954-02-16,25.81,25.81,25.81,25.81,1870000,25.81 1954-02-15,26.04,26.04,26.04,26.04,2080000,26.04 1954-02-12,26.12,26.12,26.12,26.12,1730000,26.12 1954-02-11,26.06,26.06,26.06,26.06,1860000,26.06 1954-02-10,26.14,26.14,26.14,26.14,1790000,26.14 1954-02-09,26.17,26.17,26.17,26.17,1880000,26.17 1954-02-08,26.23,26.23,26.23,26.23,2180000,26.23 1954-02-05,26.30,26.30,26.30,26.30,2030000,26.30 1954-02-04,26.20,26.20,26.20,26.20,2040000,26.20 1954-02-03,26.01,26.01,26.01,26.01,1690000,26.01 1954-02-02,25.92,25.92,25.92,25.92,1420000,25.92 1954-02-01,25.99,25.99,25.99,25.99,1740000,25.99 1954-01-29,26.08,26.08,26.08,26.08,1950000,26.08 1954-01-28,26.02,26.02,26.02,26.02,1730000,26.02 1954-01-27,26.01,26.01,26.01,26.01,2020000,26.01 1954-01-26,26.09,26.09,26.09,26.09,2120000,26.09 1954-01-25,25.93,25.93,25.93,25.93,1860000,25.93 1954-01-22,25.85,25.85,25.85,25.85,1890000,25.85 1954-01-21,25.79,25.79,25.79,25.79,1780000,25.79 1954-01-20,25.75,25.75,25.75,25.75,1960000,25.75 1954-01-19,25.68,25.68,25.68,25.68,1840000,25.68 1954-01-18,25.43,25.43,25.43,25.43,1580000,25.43 1954-01-15,25.43,25.43,25.43,25.43,2180000,25.43 1954-01-14,25.19,25.19,25.19,25.19,1530000,25.19 1954-01-13,25.07,25.07,25.07,25.07,1420000,25.07 1954-01-12,24.93,24.93,24.93,24.93,1250000,24.93 1954-01-11,24.80,24.80,24.80,24.80,1220000,24.80 1954-01-08,24.93,24.93,24.93,24.93,1260000,24.93 1954-01-07,25.06,25.06,25.06,25.06,1540000,25.06 1954-01-06,25.14,25.14,25.14,25.14,1460000,25.14 1954-01-05,25.10,25.10,25.10,25.10,1520000,25.10 1954-01-04,24.95,24.95,24.95,24.95,1310000,24.95 1953-12-31,24.81,24.81,24.81,24.81,2490000,24.81 1953-12-30,24.76,24.76,24.76,24.76,2050000,24.76 1953-12-29,24.55,24.55,24.55,24.55,2140000,24.55 1953-12-28,24.71,24.71,24.71,24.71,1570000,24.71 1953-12-24,24.80,24.80,24.80,24.80,1270000,24.80 1953-12-23,24.69,24.69,24.69,24.69,1570000,24.69 1953-12-22,24.76,24.76,24.76,24.76,1720000,24.76 1953-12-21,24.95,24.95,24.95,24.95,1690000,24.95 1953-12-18,24.99,24.99,24.99,24.99,1550000,24.99 1953-12-17,24.94,24.94,24.94,24.94,1600000,24.94 1953-12-16,24.96,24.96,24.96,24.96,1880000,24.96 1953-12-15,24.71,24.71,24.71,24.71,1450000,24.71 1953-12-14,24.69,24.69,24.69,24.69,1540000,24.69 1953-12-11,24.76,24.76,24.76,24.76,1440000,24.76 1953-12-10,24.78,24.78,24.78,24.78,1420000,24.78 1953-12-09,24.84,24.84,24.84,24.84,1410000,24.84 1953-12-08,24.87,24.87,24.87,24.87,1390000,24.87 1953-12-07,24.95,24.95,24.95,24.95,1410000,24.95 1953-12-04,24.98,24.98,24.98,24.98,1390000,24.98 1953-12-03,24.97,24.97,24.97,24.97,1740000,24.97 1953-12-02,24.95,24.95,24.95,24.95,1850000,24.95 1953-12-01,24.78,24.78,24.78,24.78,1580000,24.78 1953-11-30,24.76,24.76,24.76,24.76,1960000,24.76 1953-11-27,24.66,24.66,24.66,24.66,1600000,24.66 1953-11-25,24.52,24.52,24.52,24.52,1540000,24.52 1953-11-24,24.50,24.50,24.50,24.50,1470000,24.50 1953-11-23,24.36,24.36,24.36,24.36,1410000,24.36 1953-11-20,24.44,24.44,24.44,24.44,1300000,24.44 1953-11-19,24.40,24.40,24.40,24.40,1420000,24.40 1953-11-18,24.29,24.29,24.29,24.29,1250000,24.29 1953-11-17,24.25,24.25,24.25,24.25,1250000,24.25 1953-11-16,24.38,24.38,24.38,24.38,1490000,24.38 1953-11-13,24.54,24.54,24.54,24.54,1540000,24.54 1953-11-12,24.46,24.46,24.46,24.46,1390000,24.46 1953-11-10,24.37,24.37,24.37,24.37,1340000,24.37 1953-11-09,24.66,24.66,24.66,24.66,1440000,24.66 1953-11-06,24.61,24.61,24.61,24.61,1700000,24.61 1953-11-05,24.64,24.64,24.64,24.64,1720000,24.64 1953-11-04,24.51,24.51,24.51,24.51,1480000,24.51 1953-11-02,24.66,24.66,24.66,24.66,1340000,24.66 1953-10-30,24.54,24.54,24.54,24.54,1400000,24.54 1953-10-29,24.58,24.58,24.58,24.58,1610000,24.58 1953-10-28,24.29,24.29,24.29,24.29,1260000,24.29 1953-10-27,24.26,24.26,24.26,24.26,1170000,24.26 1953-10-26,24.31,24.31,24.31,24.31,1340000,24.31 1953-10-23,24.35,24.35,24.35,24.35,1330000,24.35 1953-10-22,24.30,24.30,24.30,24.30,1330000,24.30 1953-10-21,24.19,24.19,24.19,24.19,1320000,24.19 1953-10-20,24.17,24.17,24.17,24.17,1280000,24.17 1953-10-19,24.16,24.16,24.16,24.16,1190000,24.16 1953-10-16,24.14,24.14,24.14,24.14,1620000,24.14 1953-10-15,23.95,23.95,23.95,23.95,1710000,23.95 1953-10-14,23.68,23.68,23.68,23.68,1290000,23.68 1953-10-13,23.57,23.57,23.57,23.57,1130000,23.57 1953-10-09,23.66,23.66,23.66,23.66,900000,23.66 1953-10-08,23.62,23.62,23.62,23.62,960000,23.62 1953-10-07,23.58,23.58,23.58,23.58,1010000,23.58 1953-10-06,23.39,23.39,23.39,23.39,1100000,23.39 1953-10-05,23.48,23.48,23.48,23.48,930000,23.48 1953-10-02,23.59,23.59,23.59,23.59,890000,23.59 1953-10-01,23.49,23.49,23.49,23.49,940000,23.49 1953-09-30,23.35,23.35,23.35,23.35,940000,23.35 1953-09-29,23.49,23.49,23.49,23.49,1170000,23.49 1953-09-28,23.45,23.45,23.45,23.45,1150000,23.45 1953-09-25,23.30,23.30,23.30,23.30,910000,23.30 1953-09-24,23.24,23.24,23.24,23.24,1020000,23.24 1953-09-23,23.23,23.23,23.23,23.23,1240000,23.23 1953-09-22,23.20,23.20,23.20,23.20,1300000,23.20 1953-09-21,22.88,22.88,22.88,22.88,1070000,22.88 1953-09-18,22.95,22.95,22.95,22.95,1190000,22.95 1953-09-17,23.07,23.07,23.07,23.07,1290000,23.07 1953-09-16,23.01,23.01,23.01,23.01,1570000,23.01 1953-09-15,22.90,22.90,22.90,22.90,2850000,22.90 1953-09-14,22.71,22.71,22.71,22.71,2550000,22.71 1953-09-11,23.14,23.14,23.14,23.14,1930000,23.14 1953-09-10,23.41,23.41,23.41,23.41,1010000,23.41 1953-09-09,23.65,23.65,23.65,23.65,860000,23.65 1953-09-08,23.61,23.61,23.61,23.61,740000,23.61 1953-09-04,23.57,23.57,23.57,23.57,770000,23.57 1953-09-03,23.51,23.51,23.51,23.51,900000,23.51 1953-09-02,23.56,23.56,23.56,23.56,1110000,23.56 1953-09-01,23.42,23.42,23.42,23.42,1580000,23.42 1953-08-31,23.32,23.32,23.32,23.32,2190000,23.32 1953-08-28,23.74,23.74,23.74,23.74,1060000,23.74 1953-08-27,23.79,23.79,23.79,23.79,1290000,23.79 1953-08-26,23.86,23.86,23.86,23.86,1060000,23.86 1953-08-25,23.93,23.93,23.93,23.93,1470000,23.93 1953-08-24,24.09,24.09,24.09,24.09,1320000,24.09 1953-08-21,24.35,24.35,24.35,24.35,850000,24.35 1953-08-20,24.29,24.29,24.29,24.29,860000,24.29 1953-08-19,24.31,24.31,24.31,24.31,1400000,24.31 1953-08-18,24.46,24.46,24.46,24.46,1030000,24.46 1953-08-17,24.56,24.56,24.56,24.56,910000,24.56 1953-08-14,24.62,24.62,24.62,24.62,1000000,24.62 1953-08-13,24.73,24.73,24.73,24.73,1040000,24.73 1953-08-12,24.78,24.78,24.78,24.78,990000,24.78 1953-08-11,24.72,24.72,24.72,24.72,940000,24.72 1953-08-10,24.75,24.75,24.75,24.75,1090000,24.75 1953-08-07,24.78,24.78,24.78,24.78,950000,24.78 1953-08-06,24.80,24.80,24.80,24.80,1200000,24.80 1953-08-05,24.68,24.68,24.68,24.68,1080000,24.68 1953-08-04,24.78,24.78,24.78,24.78,1000000,24.78 1953-08-03,24.84,24.84,24.84,24.84,1160000,24.84 1953-07-31,24.75,24.75,24.75,24.75,1320000,24.75 1953-07-30,24.49,24.49,24.49,24.49,1200000,24.49 1953-07-29,24.26,24.26,24.26,24.26,1000000,24.26 1953-07-28,24.11,24.11,24.11,24.11,1080000,24.11 1953-07-27,24.07,24.07,24.07,24.07,1210000,24.07 1953-07-24,24.23,24.23,24.23,24.23,890000,24.23 1953-07-23,24.23,24.23,24.23,24.23,1000000,24.23 1953-07-22,24.19,24.19,24.19,24.19,900000,24.19 1953-07-21,24.16,24.16,24.16,24.16,850000,24.16 1953-07-20,24.22,24.22,24.22,24.22,830000,24.22 1953-07-17,24.35,24.35,24.35,24.35,840000,24.35 1953-07-16,24.18,24.18,24.18,24.18,790000,24.18 1953-07-15,24.15,24.15,24.15,24.15,840000,24.15 1953-07-14,24.08,24.08,24.08,24.08,1030000,24.08 1953-07-13,24.17,24.17,24.17,24.17,1120000,24.17 1953-07-10,24.41,24.41,24.41,24.41,860000,24.41 1953-07-09,24.43,24.43,24.43,24.43,910000,24.43 1953-07-08,24.50,24.50,24.50,24.50,950000,24.50 1953-07-07,24.51,24.51,24.51,24.51,1030000,24.51 1953-07-06,24.38,24.38,24.38,24.38,820000,24.38 1953-07-03,24.36,24.36,24.36,24.36,830000,24.36 1953-07-02,24.31,24.31,24.31,24.31,1030000,24.31 1953-07-01,24.24,24.24,24.24,24.24,910000,24.24 1953-06-30,24.14,24.14,24.14,24.14,820000,24.14 1953-06-29,24.14,24.14,24.14,24.14,800000,24.14 1953-06-26,24.21,24.21,24.21,24.21,830000,24.21 1953-06-25,24.19,24.19,24.19,24.19,1160000,24.19 1953-06-24,24.09,24.09,24.09,24.09,1030000,24.09 1953-06-23,24.12,24.12,24.12,24.12,1050000,24.12 1953-06-22,23.96,23.96,23.96,23.96,1030000,23.96 1953-06-19,23.84,23.84,23.84,23.84,890000,23.84 1953-06-18,23.84,23.84,23.84,23.84,1010000,23.84 1953-06-17,23.85,23.85,23.85,23.85,1150000,23.85 1953-06-16,23.55,23.55,23.55,23.55,1370000,23.55 1953-06-15,23.62,23.62,23.62,23.62,1090000,23.62 1953-06-12,23.82,23.82,23.82,23.82,920000,23.82 1953-06-11,23.75,23.75,23.75,23.75,1220000,23.75 1953-06-10,23.54,23.54,23.54,23.54,1960000,23.54 1953-06-09,23.60,23.60,23.60,23.60,2200000,23.60 1953-06-08,24.01,24.01,24.01,24.01,1000000,24.01 1953-06-05,24.09,24.09,24.09,24.09,1160000,24.09 1953-06-04,24.03,24.03,24.03,24.03,1400000,24.03 1953-06-03,24.18,24.18,24.18,24.18,1050000,24.18 1953-06-02,24.22,24.22,24.22,24.22,1450000,24.22 1953-06-01,24.15,24.15,24.15,24.15,1490000,24.15 1953-05-29,24.54,24.54,24.54,24.54,920000,24.54 1953-05-28,24.46,24.46,24.46,24.46,1240000,24.46 1953-05-27,24.64,24.64,24.64,24.64,1330000,24.64 1953-05-26,24.87,24.87,24.87,24.87,1160000,24.87 1953-05-25,24.99,24.99,24.99,24.99,1180000,24.99 1953-05-22,25.03,25.03,25.03,25.03,1350000,25.03 1953-05-21,25.06,25.06,25.06,25.06,1590000,25.06 1953-05-20,24.93,24.93,24.93,24.93,1690000,24.93 1953-05-19,24.70,24.70,24.70,24.70,1120000,24.70 1953-05-18,24.75,24.75,24.75,24.75,1080000,24.75 1953-05-15,24.84,24.84,24.84,24.84,1200000,24.84 1953-05-14,24.85,24.85,24.85,24.85,1210000,24.85 1953-05-13,24.71,24.71,24.71,24.71,1120000,24.71 1953-05-12,24.74,24.74,24.74,24.74,1080000,24.74 1953-05-11,24.91,24.91,24.91,24.91,1010000,24.91 1953-05-08,24.90,24.90,24.90,24.90,1220000,24.90 1953-05-07,24.90,24.90,24.90,24.90,1110000,24.90 1953-05-06,25.00,25.00,25.00,25.00,1110000,25.00 1953-05-05,25.03,25.03,25.03,25.03,1290000,25.03 1953-05-04,25.00,25.00,25.00,25.00,1520000,25.00 1953-05-01,24.73,24.73,24.73,24.73,1200000,24.73 1953-04-30,24.62,24.62,24.62,24.62,1140000,24.62 1953-04-29,24.68,24.68,24.68,24.68,1310000,24.68 1953-04-28,24.52,24.52,24.52,24.52,1330000,24.52 1953-04-27,24.34,24.34,24.34,24.34,1400000,24.34 1953-04-24,24.20,24.20,24.20,24.20,1780000,24.20 1953-04-23,24.19,24.19,24.19,24.19,1920000,24.19 1953-04-22,24.46,24.46,24.46,24.46,1390000,24.46 1953-04-21,24.67,24.67,24.67,24.67,1250000,24.67 1953-04-20,24.73,24.73,24.73,24.73,1520000,24.73 1953-04-17,24.62,24.62,24.62,24.62,1430000,24.62 1953-04-16,24.91,24.91,24.91,24.91,1310000,24.91 1953-04-15,24.96,24.96,24.96,24.96,1580000,24.96 1953-04-14,24.86,24.86,24.86,24.86,1480000,24.86 1953-04-13,24.77,24.77,24.77,24.77,1280000,24.77 1953-04-10,24.82,24.82,24.82,24.82,1360000,24.82 1953-04-09,24.88,24.88,24.88,24.88,1520000,24.88 1953-04-08,24.93,24.93,24.93,24.93,1860000,24.93 1953-04-07,24.71,24.71,24.71,24.71,2500000,24.71 1953-04-06,24.61,24.61,24.61,24.61,3050000,24.61 1953-04-02,25.23,25.23,25.23,25.23,1720000,25.23 1953-04-01,25.25,25.25,25.25,25.25,2240000,25.25 1953-03-31,25.29,25.29,25.29,25.29,3120000,25.29 1953-03-30,25.61,25.61,25.61,25.61,2740000,25.61 1953-03-27,25.99,25.99,25.99,25.99,1640000,25.99 1953-03-26,25.95,25.95,25.95,25.95,2000000,25.95 1953-03-25,26.10,26.10,26.10,26.10,2320000,26.10 1953-03-24,26.17,26.17,26.17,26.17,1970000,26.17 1953-03-23,26.02,26.02,26.02,26.02,1750000,26.02 1953-03-20,26.18,26.18,26.18,26.18,1730000,26.18 1953-03-19,26.22,26.22,26.22,26.22,1840000,26.22 1953-03-18,26.24,26.24,26.24,26.24,2110000,26.24 1953-03-17,26.33,26.33,26.33,26.33,2110000,26.33 1953-03-16,26.22,26.22,26.22,26.22,1770000,26.22 1953-03-13,26.18,26.18,26.18,26.18,1760000,26.18 1953-03-12,26.13,26.13,26.13,26.13,1780000,26.13 1953-03-11,26.12,26.12,26.12,26.12,1890000,26.12 1953-03-10,25.91,25.91,25.91,25.91,1530000,25.91 1953-03-09,25.83,25.83,25.83,25.83,1600000,25.83 1953-03-06,25.84,25.84,25.84,25.84,1690000,25.84 1953-03-05,25.79,25.79,25.79,25.79,1540000,25.79 1953-03-04,25.78,25.78,25.78,25.78,2010000,25.78 1953-03-03,26.00,26.00,26.00,26.00,1850000,26.00 1953-03-02,25.93,25.93,25.93,25.93,1760000,25.93 1953-02-27,25.90,25.90,25.90,25.90,1990000,25.90 1953-02-26,25.95,25.95,25.95,25.95,2290000,25.95 1953-02-25,25.91,25.91,25.91,25.91,2360000,25.91 1953-02-24,25.75,25.75,25.75,25.75,2300000,25.75 1953-02-20,25.63,25.63,25.63,25.63,1400000,25.63 1953-02-19,25.57,25.57,25.57,25.57,1390000,25.57 1953-02-18,25.48,25.48,25.48,25.48,1220000,25.48 1953-02-17,25.50,25.50,25.50,25.50,1290000,25.50 1953-02-16,25.65,25.65,25.65,25.65,1330000,25.65 1953-02-13,25.74,25.74,25.74,25.74,1350000,25.74 1953-02-11,25.64,25.64,25.64,25.64,1240000,25.64 1953-02-10,25.62,25.62,25.62,25.62,1350000,25.62 1953-02-09,25.69,25.69,25.69,25.69,1780000,25.69 1953-02-06,26.51,26.51,26.51,26.51,1870000,26.51 1953-02-05,26.15,26.15,26.15,26.15,1900000,26.15 1953-02-04,26.42,26.42,26.42,26.42,1660000,26.42 1953-02-03,26.54,26.54,26.54,26.54,1560000,26.54 1953-02-02,26.51,26.51,26.51,26.51,1890000,26.51 1953-01-30,26.38,26.38,26.38,26.38,1760000,26.38 1953-01-29,26.20,26.20,26.20,26.20,1830000,26.20 1953-01-28,26.13,26.13,26.13,26.13,1640000,26.13 1953-01-27,26.05,26.05,26.05,26.05,1550000,26.05 1953-01-26,26.02,26.02,26.02,26.02,1420000,26.02 1953-01-23,26.07,26.07,26.07,26.07,1340000,26.07 1953-01-22,26.12,26.12,26.12,26.12,1380000,26.12 1953-01-21,26.09,26.09,26.09,26.09,1300000,26.09 1953-01-20,26.14,26.14,26.14,26.14,1490000,26.14 1953-01-19,26.01,26.01,26.01,26.01,1360000,26.01 1953-01-16,26.02,26.02,26.02,26.02,1710000,26.02 1953-01-15,26.13,26.13,26.13,26.13,1450000,26.13 1953-01-14,26.08,26.08,26.08,26.08,1370000,26.08 1953-01-13,26.02,26.02,26.02,26.02,1680000,26.02 1953-01-12,25.86,25.86,25.86,25.86,1500000,25.86 1953-01-09,26.08,26.08,26.08,26.08,2080000,26.08 1953-01-08,26.33,26.33,26.33,26.33,1780000,26.33 1953-01-07,26.37,26.37,26.37,26.37,1760000,26.37 1953-01-06,26.48,26.48,26.48,26.48,2080000,26.48 1953-01-05,26.66,26.66,26.66,26.66,2130000,26.66 1953-01-02,26.54,26.54,26.54,26.54,1450000,26.54 1952-12-31,26.57,26.57,26.57,26.57,2050000,26.57 1952-12-30,26.59,26.59,26.59,26.59,2070000,26.59 1952-12-29,26.40,26.40,26.40,26.40,1820000,26.40 1952-12-26,26.25,26.25,26.25,26.25,1290000,26.25 1952-12-24,26.21,26.21,26.21,26.21,1510000,26.21 1952-12-23,26.19,26.19,26.19,26.19,2100000,26.19 1952-12-22,26.30,26.30,26.30,26.30,2100000,26.30 1952-12-19,26.15,26.15,26.15,26.15,2050000,26.15 1952-12-18,26.03,26.03,26.03,26.03,1860000,26.03 1952-12-17,26.04,26.04,26.04,26.04,1700000,26.04 1952-12-16,26.07,26.07,26.07,26.07,1980000,26.07 1952-12-15,26.04,26.04,26.04,26.04,1940000,26.04 1952-12-12,26.04,26.04,26.04,26.04,2030000,26.04 1952-12-11,25.96,25.96,25.96,25.96,1790000,25.96 1952-12-10,25.98,25.98,25.98,25.98,1880000,25.98 1952-12-09,25.93,25.93,25.93,25.93,2120000,25.93 1952-12-08,25.76,25.76,25.76,25.76,1790000,25.76 1952-12-05,25.62,25.62,25.62,25.62,1510000,25.62 1952-12-04,25.61,25.61,25.61,25.61,1570000,25.61 1952-12-03,25.71,25.71,25.71,25.71,1610000,25.71 1952-12-02,25.74,25.74,25.74,25.74,1610000,25.74 1952-12-01,25.68,25.68,25.68,25.68,2100000,25.68 1952-11-28,25.66,25.66,25.66,25.66,2160000,25.66 1952-11-26,25.52,25.52,25.52,25.52,1920000,25.52 1952-11-25,25.36,25.36,25.36,25.36,1930000,25.36 1952-11-24,25.42,25.42,25.42,25.42,2100000,25.42 1952-11-21,25.27,25.27,25.27,25.27,1760000,25.27 1952-11-20,25.28,25.28,25.28,25.28,1740000,25.28 1952-11-19,25.33,25.33,25.33,25.33,2350000,25.33 1952-11-18,25.16,25.16,25.16,25.16,2250000,25.16 1952-11-17,24.80,24.80,24.80,24.80,1490000,24.80 1952-11-14,24.75,24.75,24.75,24.75,1700000,24.75 1952-11-13,24.71,24.71,24.71,24.71,1330000,24.71 1952-11-12,24.65,24.65,24.65,24.65,1490000,24.65 1952-11-10,24.77,24.77,24.77,24.77,1360000,24.77 1952-11-07,24.78,24.78,24.78,24.78,1540000,24.78 1952-11-06,24.77,24.77,24.77,24.77,1390000,24.77 1952-11-05,24.67,24.67,24.67,24.67,2030000,24.67 1952-11-03,24.60,24.60,24.60,24.60,1670000,24.60 1952-10-31,24.52,24.52,24.52,24.52,1760000,24.52 1952-10-30,24.15,24.15,24.15,24.15,1090000,24.15 1952-10-29,24.15,24.15,24.15,24.15,1020000,24.15 1952-10-28,24.13,24.13,24.13,24.13,1080000,24.13 1952-10-27,24.09,24.09,24.09,24.09,1000000,24.09 1952-10-24,24.03,24.03,24.03,24.03,1060000,24.03 1952-10-23,23.87,23.87,23.87,23.87,1260000,23.87 1952-10-22,23.80,23.80,23.80,23.80,1160000,23.80 1952-10-21,24.07,24.07,24.07,24.07,990000,24.07 1952-10-20,24.13,24.13,24.13,24.13,1050000,24.13 1952-10-17,24.20,24.20,24.20,24.20,1360000,24.20 1952-10-16,23.91,23.91,23.91,23.91,1730000,23.91 1952-10-15,24.06,24.06,24.06,24.06,1730000,24.06 1952-10-14,24.48,24.48,24.48,24.48,1130000,24.48 1952-10-10,24.55,24.55,24.55,24.55,1070000,24.55 1952-10-09,24.57,24.57,24.57,24.57,1090000,24.57 1952-10-08,24.58,24.58,24.58,24.58,1260000,24.58 1952-10-07,24.40,24.40,24.40,24.40,950000,24.40 1952-10-06,24.44,24.44,24.44,24.44,1070000,24.44 1952-10-03,24.50,24.50,24.50,24.50,980000,24.50 1952-10-02,24.52,24.52,24.52,24.52,1040000,24.52 1952-10-01,24.48,24.48,24.48,24.48,1060000,24.48 1952-09-30,24.54,24.54,24.54,24.54,1120000,24.54 1952-09-29,24.68,24.68,24.68,24.68,970000,24.68 1952-09-26,24.73,24.73,24.73,24.73,1180000,24.73 1952-09-25,24.81,24.81,24.81,24.81,1210000,24.81 1952-09-24,24.79,24.79,24.79,24.79,1390000,24.79 1952-09-23,24.70,24.70,24.70,24.70,1240000,24.70 1952-09-22,24.59,24.59,24.59,24.59,1160000,24.59 1952-09-19,24.57,24.57,24.57,24.57,1150000,24.57 1952-09-18,24.51,24.51,24.51,24.51,1030000,24.51 1952-09-17,24.58,24.58,24.58,24.58,1000000,24.58 1952-09-16,24.53,24.53,24.53,24.53,1140000,24.53 1952-09-15,24.45,24.45,24.45,24.45,1100000,24.45 1952-09-12,24.71,24.71,24.71,24.71,1040000,24.71 1952-09-11,24.72,24.72,24.72,24.72,970000,24.72 1952-09-10,24.69,24.69,24.69,24.69,1590000,24.69 1952-09-09,24.86,24.86,24.86,24.86,1310000,24.86 1952-09-08,25.11,25.11,25.11,25.11,1170000,25.11 1952-09-05,25.21,25.21,25.21,25.21,1040000,25.21 1952-09-04,25.24,25.24,25.24,25.24,1120000,25.24 1952-09-03,25.25,25.25,25.25,25.25,1200000,25.25 1952-09-02,25.15,25.15,25.15,25.15,970000,25.15 1952-08-29,25.03,25.03,25.03,25.03,890000,25.03 1952-08-28,24.97,24.97,24.97,24.97,980000,24.97 1952-08-27,24.94,24.94,24.94,24.94,930000,24.94 1952-08-26,24.83,24.83,24.83,24.83,890000,24.83 1952-08-25,24.87,24.87,24.87,24.87,840000,24.87 1952-08-22,24.99,24.99,24.99,24.99,910000,24.99 1952-08-21,24.98,24.98,24.98,24.98,800000,24.98 1952-08-20,24.95,24.95,24.95,24.95,960000,24.95 1952-08-19,24.89,24.89,24.89,24.89,980000,24.89 1952-08-18,24.94,24.94,24.94,24.94,1090000,24.94 1952-08-15,25.20,25.20,25.20,25.20,890000,25.20 1952-08-14,25.28,25.28,25.28,25.28,930000,25.28 1952-08-13,25.28,25.28,25.28,25.28,990000,25.28 1952-08-12,25.31,25.31,25.31,25.31,1110000,25.31 1952-08-11,25.52,25.52,25.52,25.52,1160000,25.52 1952-08-08,25.55,25.55,25.55,25.55,1170000,25.55 1952-08-07,25.52,25.52,25.52,25.52,1180000,25.52 1952-08-06,25.44,25.44,25.44,25.44,1140000,25.44 1952-08-05,25.46,25.46,25.46,25.46,1050000,25.46 1952-08-04,25.43,25.43,25.43,25.43,950000,25.43 1952-08-01,25.45,25.45,25.45,25.45,1050000,25.45 1952-07-31,25.40,25.40,25.40,25.40,1230000,25.40 1952-07-30,25.37,25.37,25.37,25.37,1240000,25.37 1952-07-29,25.26,25.26,25.26,25.26,1010000,25.26 1952-07-28,25.20,25.20,25.20,25.20,1030000,25.20 1952-07-25,25.16,25.16,25.16,25.16,1130000,25.16 1952-07-24,25.24,25.24,25.24,25.24,1270000,25.24 1952-07-23,25.11,25.11,25.11,25.11,1020000,25.11 1952-07-22,25.00,25.00,25.00,25.00,910000,25.00 1952-07-21,24.95,24.95,24.95,24.95,780000,24.95 1952-07-18,24.85,24.85,24.85,24.85,1020000,24.85 1952-07-17,25.05,25.05,25.05,25.05,1010000,25.05 1952-07-16,25.16,25.16,25.16,25.16,1120000,25.16 1952-07-15,25.16,25.16,25.16,25.16,1220000,25.16 1952-07-14,25.03,25.03,25.03,25.03,1090000,25.03 1952-07-11,24.98,24.98,24.98,24.98,1040000,24.98 1952-07-10,24.81,24.81,24.81,24.81,1010000,24.81 1952-07-09,24.86,24.86,24.86,24.86,1120000,24.86 1952-07-08,24.96,24.96,24.96,24.96,850000,24.96 1952-07-07,24.97,24.97,24.97,24.97,1080000,24.97 1952-07-03,25.05,25.05,25.05,25.05,1150000,25.05 1952-07-02,25.06,25.06,25.06,25.06,1320000,25.06 1952-07-01,25.12,25.12,25.12,25.12,1450000,25.12 1952-06-30,24.96,24.96,24.96,24.96,1380000,24.96 1952-06-27,24.83,24.83,24.83,24.83,1210000,24.83 1952-06-26,24.75,24.75,24.75,24.75,1190000,24.75 1952-06-25,24.66,24.66,24.66,24.66,1230000,24.66 1952-06-24,24.60,24.60,24.60,24.60,1200000,24.60 1952-06-23,24.56,24.56,24.56,24.56,1200000,24.56 1952-06-20,24.59,24.59,24.59,24.59,1190000,24.59 1952-06-19,24.51,24.51,24.51,24.51,1320000,24.51 1952-06-18,24.43,24.43,24.43,24.43,1270000,24.43 1952-06-17,24.33,24.33,24.33,24.33,920000,24.33 1952-06-16,24.30,24.30,24.30,24.30,980000,24.30 1952-06-13,24.37,24.37,24.37,24.37,1130000,24.37 1952-06-12,24.31,24.31,24.31,24.31,1370000,24.31 1952-06-11,24.31,24.31,24.31,24.31,1190000,24.31 1952-06-10,24.23,24.23,24.23,24.23,1220000,24.23 1952-06-09,24.37,24.37,24.37,24.37,1270000,24.37 1952-06-06,24.26,24.26,24.26,24.26,1520000,24.26 1952-06-05,24.10,24.10,24.10,24.10,1410000,24.10 1952-06-04,23.95,23.95,23.95,23.95,1200000,23.95 1952-06-03,23.78,23.78,23.78,23.78,940000,23.78 1952-06-02,23.80,23.80,23.80,23.80,1190000,23.80 1952-05-29,23.86,23.86,23.86,23.86,1100000,23.86 1952-05-28,23.84,23.84,23.84,23.84,1130000,23.84 1952-05-27,23.88,23.88,23.88,23.88,1040000,23.88 1952-05-26,23.94,23.94,23.94,23.94,940000,23.94 1952-05-23,23.89,23.89,23.89,23.89,1150000,23.89 1952-05-22,23.91,23.91,23.91,23.91,1360000,23.91 1952-05-21,23.78,23.78,23.78,23.78,1210000,23.78 1952-05-20,23.74,23.74,23.74,23.74,1150000,23.74 1952-05-19,23.61,23.61,23.61,23.61,780000,23.61 1952-05-16,23.56,23.56,23.56,23.56,910000,23.56 1952-05-15,23.60,23.60,23.60,23.60,1050000,23.60 1952-05-14,23.68,23.68,23.68,23.68,950000,23.68 1952-05-13,23.78,23.78,23.78,23.78,890000,23.78 1952-05-12,23.75,23.75,23.75,23.75,800000,23.75 1952-05-09,23.84,23.84,23.84,23.84,960000,23.84 1952-05-08,23.86,23.86,23.86,23.86,1230000,23.86 1952-05-07,23.81,23.81,23.81,23.81,1120000,23.81 1952-05-06,23.67,23.67,23.67,23.67,1120000,23.67 1952-05-05,23.66,23.66,23.66,23.66,860000,23.66 1952-05-02,23.56,23.56,23.56,23.56,1300000,23.56 1952-05-01,23.17,23.17,23.17,23.17,1400000,23.17 1952-04-30,23.32,23.32,23.32,23.32,1000000,23.32 1952-04-29,23.49,23.49,23.49,23.49,1170000,23.49 1952-04-28,23.55,23.55,23.55,23.55,980000,23.55 1952-04-25,23.54,23.54,23.54,23.54,1240000,23.54 1952-04-24,23.43,23.43,23.43,23.43,1580000,23.43 1952-04-23,23.48,23.48,23.48,23.48,1090000,23.48 1952-04-22,23.58,23.58,23.58,23.58,1240000,23.58 1952-04-21,23.69,23.69,23.69,23.69,1110000,23.69 1952-04-18,23.50,23.50,23.50,23.50,1240000,23.50 1952-04-17,23.41,23.41,23.41,23.41,1620000,23.41 1952-04-16,23.58,23.58,23.58,23.58,1400000,23.58 1952-04-15,23.65,23.65,23.65,23.65,1720000,23.65 1952-04-14,23.95,23.95,23.95,23.95,1790000,23.95 1952-04-10,24.11,24.11,24.11,24.11,1130000,24.11 1952-04-09,23.94,23.94,23.94,23.94,980000,23.94 1952-04-08,23.91,23.91,23.91,23.91,1090000,23.91 1952-04-07,23.80,23.80,23.80,23.80,1230000,23.80 1952-04-04,24.02,24.02,24.02,24.02,1190000,24.02 1952-04-03,24.12,24.12,24.12,24.12,1280000,24.12 1952-04-02,24.12,24.12,24.12,24.12,1260000,24.12 1952-04-01,24.18,24.18,24.18,24.18,1720000,24.18 1952-03-31,24.37,24.37,24.37,24.37,1680000,24.37 1952-03-28,24.18,24.18,24.18,24.18,1560000,24.18 1952-03-27,23.99,23.99,23.99,23.99,1370000,23.99 1952-03-26,23.78,23.78,23.78,23.78,1030000,23.78 1952-03-25,23.79,23.79,23.79,23.79,1060000,23.79 1952-03-24,23.93,23.93,23.93,23.93,1040000,23.93 1952-03-21,23.93,23.93,23.93,23.93,1290000,23.93 1952-03-20,23.89,23.89,23.89,23.89,1240000,23.89 1952-03-19,23.82,23.82,23.82,23.82,1090000,23.82 1952-03-18,23.87,23.87,23.87,23.87,1170000,23.87 1952-03-17,23.92,23.92,23.92,23.92,1150000,23.92 1952-03-14,23.75,23.75,23.75,23.75,1350000,23.75 1952-03-13,23.75,23.75,23.75,23.75,1270000,23.75 1952-03-12,23.73,23.73,23.73,23.73,1310000,23.73 1952-03-11,23.62,23.62,23.62,23.62,1210000,23.62 1952-03-10,23.60,23.60,23.60,23.60,1170000,23.60 1952-03-07,23.72,23.72,23.72,23.72,1410000,23.72 1952-03-06,23.69,23.69,23.69,23.69,1210000,23.69 1952-03-05,23.71,23.71,23.71,23.71,1380000,23.71 1952-03-04,23.68,23.68,23.68,23.68,1570000,23.68 1952-03-03,23.29,23.29,23.29,23.29,1020000,23.29 1952-02-29,23.26,23.26,23.26,23.26,1000000,23.26 1952-02-28,23.29,23.29,23.29,23.29,1150000,23.29 1952-02-27,23.18,23.18,23.18,23.18,1260000,23.18 1952-02-26,23.15,23.15,23.15,23.15,1080000,23.15 1952-02-25,23.23,23.23,23.23,23.23,1200000,23.23 1952-02-21,23.16,23.16,23.16,23.16,1360000,23.16 1952-02-20,23.09,23.09,23.09,23.09,1970000,23.09 1952-02-19,23.36,23.36,23.36,23.36,1630000,23.36 1952-02-18,23.74,23.74,23.74,23.74,1140000,23.74 1952-02-15,23.86,23.86,23.86,23.86,1200000,23.86 1952-02-14,23.87,23.87,23.87,23.87,1340000,23.87 1952-02-13,23.92,23.92,23.92,23.92,1300000,23.92 1952-02-11,24.11,24.11,24.11,24.11,1140000,24.11 1952-02-08,24.24,24.24,24.24,24.24,1350000,24.24 1952-02-07,24.11,24.11,24.11,24.11,1170000,24.11 1952-02-06,24.18,24.18,24.18,24.18,1310000,24.18 1952-02-05,24.11,24.11,24.11,24.11,1590000,24.11 1952-02-04,24.12,24.12,24.12,24.12,1640000,24.12 1952-02-01,24.30,24.30,24.30,24.30,1350000,24.30 1952-01-31,24.14,24.14,24.14,24.14,1810000,24.14 1952-01-30,24.23,24.23,24.23,24.23,1880000,24.23 1952-01-29,24.57,24.57,24.57,24.57,1730000,24.57 1952-01-28,24.61,24.61,24.61,24.61,1590000,24.61 1952-01-25,24.55,24.55,24.55,24.55,1650000,24.55 1952-01-24,24.56,24.56,24.56,24.56,1570000,24.56 1952-01-23,24.54,24.54,24.54,24.54,1680000,24.54 1952-01-22,24.66,24.66,24.66,24.66,1920000,24.66 1952-01-21,24.46,24.46,24.46,24.46,1730000,24.46 1952-01-18,24.25,24.25,24.25,24.25,1740000,24.25 1952-01-17,24.20,24.20,24.20,24.20,1590000,24.20 1952-01-16,24.09,24.09,24.09,24.09,1430000,24.09 1952-01-15,24.06,24.06,24.06,24.06,1340000,24.06 1952-01-14,24.16,24.16,24.16,24.16,1510000,24.16 1952-01-11,23.98,23.98,23.98,23.98,1760000,23.98 1952-01-10,23.86,23.86,23.86,23.86,1520000,23.86 1952-01-09,23.74,23.74,23.74,23.74,1370000,23.74 1952-01-08,23.82,23.82,23.82,23.82,1390000,23.82 1952-01-07,23.91,23.91,23.91,23.91,1540000,23.91 1952-01-04,23.92,23.92,23.92,23.92,1480000,23.92 1952-01-03,23.88,23.88,23.88,23.88,1220000,23.88 1952-01-02,23.80,23.80,23.80,23.80,1070000,23.80 1951-12-31,23.77,23.77,23.77,23.77,1440000,23.77 1951-12-28,23.69,23.69,23.69,23.69,1470000,23.69 1951-12-27,23.65,23.65,23.65,23.65,1460000,23.65 1951-12-26,23.44,23.44,23.44,23.44,1520000,23.44 1951-12-24,23.54,23.54,23.54,23.54,680000,23.54 1951-12-21,23.51,23.51,23.51,23.51,1250000,23.51 1951-12-20,23.57,23.57,23.57,23.57,1340000,23.57 1951-12-19,23.57,23.57,23.57,23.57,1510000,23.57 1951-12-18,23.49,23.49,23.49,23.49,1290000,23.49 1951-12-17,23.41,23.41,23.41,23.41,1220000,23.41 1951-12-14,23.37,23.37,23.37,23.37,1360000,23.37 1951-12-13,23.39,23.39,23.39,23.39,1380000,23.39 1951-12-12,23.37,23.37,23.37,23.37,1280000,23.37 1951-12-11,23.30,23.30,23.30,23.30,1360000,23.30 1951-12-10,23.42,23.42,23.42,23.42,1340000,23.42 1951-12-07,23.38,23.38,23.38,23.38,1990000,23.38 1951-12-06,23.34,23.34,23.34,23.34,1840000,23.34 1951-12-05,23.07,23.07,23.07,23.07,1330000,23.07 1951-12-04,23.14,23.14,23.14,23.14,1280000,23.14 1951-12-03,23.01,23.01,23.01,23.01,1220000,23.01 1951-11-30,22.88,22.88,22.88,22.88,1530000,22.88 1951-11-29,22.67,22.67,22.67,22.67,1070000,22.67 1951-11-28,22.61,22.61,22.61,22.61,1150000,22.61 1951-11-27,22.66,22.66,22.66,22.66,1310000,22.66 1951-11-26,22.43,22.43,22.43,22.43,1180000,22.43 1951-11-23,22.40,22.40,22.40,22.40,1210000,22.40 1951-11-21,22.64,22.64,22.64,22.64,1090000,22.64 1951-11-20,22.68,22.68,22.68,22.68,1130000,22.68 1951-11-19,22.73,22.73,22.73,22.73,1030000,22.73 1951-11-16,22.82,22.82,22.82,22.82,1140000,22.82 1951-11-15,22.84,22.84,22.84,22.84,1200000,22.84 1951-11-14,22.85,22.85,22.85,22.85,1220000,22.85 1951-11-13,22.79,22.79,22.79,22.79,1160000,22.79 1951-11-09,22.75,22.75,22.75,22.75,1470000,22.75 1951-11-08,22.47,22.47,22.47,22.47,1410000,22.47 1951-11-07,22.49,22.49,22.49,22.49,1490000,22.49 1951-11-05,22.82,22.82,22.82,22.82,1130000,22.82 1951-11-02,22.93,22.93,22.93,22.93,1230000,22.93 1951-11-01,23.10,23.10,23.10,23.10,1430000,23.10 1951-10-31,22.94,22.94,22.94,22.94,1490000,22.94 1951-10-30,22.66,22.66,22.66,22.66,1530000,22.66 1951-10-29,22.69,22.69,22.69,22.69,1780000,22.69 1951-10-26,22.81,22.81,22.81,22.81,1710000,22.81 1951-10-25,22.96,22.96,22.96,22.96,1360000,22.96 1951-10-24,23.03,23.03,23.03,23.03,1670000,23.03 1951-10-23,22.84,22.84,22.84,22.84,2110000,22.84 1951-10-22,22.75,22.75,22.75,22.75,2690000,22.75 1951-10-19,23.32,23.32,23.32,23.32,1990000,23.32 1951-10-18,23.67,23.67,23.67,23.67,1450000,23.67 1951-10-17,23.69,23.69,23.69,23.69,1460000,23.69 1951-10-16,23.77,23.77,23.77,23.77,1730000,23.77 1951-10-15,23.85,23.85,23.85,23.85,1720000,23.85 1951-10-11,23.70,23.70,23.70,23.70,1760000,23.70 1951-10-10,23.61,23.61,23.61,23.61,1320000,23.61 1951-10-09,23.65,23.65,23.65,23.65,1750000,23.65 1951-10-08,23.75,23.75,23.75,23.75,1860000,23.75 1951-10-05,23.78,23.78,23.78,23.78,2080000,23.78 1951-10-04,23.72,23.72,23.72,23.72,1810000,23.72 1951-10-03,23.79,23.79,23.79,23.79,2780000,23.79 1951-10-02,23.64,23.64,23.64,23.64,1870000,23.64 1951-10-01,23.47,23.47,23.47,23.47,1330000,23.47 1951-09-28,23.26,23.26,23.26,23.26,1390000,23.26 1951-09-27,23.27,23.27,23.27,23.27,1540000,23.27 1951-09-26,23.40,23.40,23.40,23.40,1520000,23.40 1951-09-25,23.38,23.38,23.38,23.38,1740000,23.38 1951-09-24,23.30,23.30,23.30,23.30,1630000,23.30 1951-09-21,23.40,23.40,23.40,23.40,2180000,23.40 1951-09-20,23.57,23.57,23.57,23.57,2100000,23.57 1951-09-19,23.59,23.59,23.59,23.59,2070000,23.59 1951-09-18,23.59,23.59,23.59,23.59,2030000,23.59 1951-09-17,23.62,23.62,23.62,23.62,1800000,23.62 1951-09-14,23.69,23.69,23.69,23.69,2170000,23.69 1951-09-13,23.71,23.71,23.71,23.71,2350000,23.71 1951-09-12,23.60,23.60,23.60,23.60,2180000,23.60 1951-09-11,23.50,23.50,23.50,23.50,2040000,23.50 1951-09-10,23.62,23.62,23.62,23.62,2190000,23.62 1951-09-07,23.53,23.53,23.53,23.53,1970000,23.53 1951-09-06,23.47,23.47,23.47,23.47,2150000,23.47 1951-09-05,23.42,23.42,23.42,23.42,1850000,23.42 1951-09-04,23.28,23.28,23.28,23.28,1520000,23.28 1951-08-31,23.28,23.28,23.28,23.28,1530000,23.28 1951-08-30,23.24,23.24,23.24,23.24,1950000,23.24 1951-08-29,23.08,23.08,23.08,23.08,1520000,23.08 1951-08-28,22.90,22.90,22.90,22.90,1280000,22.90 1951-08-27,22.85,22.85,22.85,22.85,1080000,22.85 1951-08-24,22.88,22.88,22.88,22.88,1210000,22.88 1951-08-23,22.90,22.90,22.90,22.90,1230000,22.90 1951-08-22,22.75,22.75,22.75,22.75,1130000,22.75 1951-08-21,22.83,22.83,22.83,22.83,1400000,22.83 1951-08-20,22.93,22.93,22.93,22.93,1130000,22.93 1951-08-17,22.94,22.94,22.94,22.94,1620000,22.94 1951-08-16,22.87,22.87,22.87,22.87,1750000,22.87 1951-08-15,22.79,22.79,22.79,22.79,1340000,22.79 1951-08-14,22.70,22.70,22.70,22.70,1180000,22.70 1951-08-13,22.80,22.80,22.80,22.80,1320000,22.80 1951-08-10,22.79,22.79,22.79,22.79,1260000,22.79 1951-08-09,22.84,22.84,22.84,22.84,1500000,22.84 1951-08-08,22.93,22.93,22.93,22.93,1410000,22.93 1951-08-07,23.03,23.03,23.03,23.03,1810000,23.03 1951-08-06,23.01,23.01,23.01,23.01,1600000,23.01 1951-08-03,22.85,22.85,22.85,22.85,1570000,22.85 1951-08-02,22.82,22.82,22.82,22.82,2130000,22.82 1951-08-01,22.51,22.51,22.51,22.51,1680000,22.51 1951-07-31,22.40,22.40,22.40,22.40,1550000,22.40 1951-07-30,22.63,22.63,22.63,22.63,1600000,22.63 1951-07-27,22.53,22.53,22.53,22.53,1450000,22.53 1951-07-26,22.47,22.47,22.47,22.47,1480000,22.47 1951-07-25,22.32,22.32,22.32,22.32,1870000,22.32 1951-07-24,22.44,22.44,22.44,22.44,1740000,22.44 1951-07-23,22.10,22.10,22.10,22.10,1320000,22.10 1951-07-20,21.88,21.88,21.88,21.88,1390000,21.88 1951-07-19,21.84,21.84,21.84,21.84,1120000,21.84 1951-07-18,21.88,21.88,21.88,21.88,1370000,21.88 1951-07-17,21.92,21.92,21.92,21.92,1280000,21.92 1951-07-16,21.73,21.73,21.73,21.73,1200000,21.73 1951-07-13,21.98,21.98,21.98,21.98,1320000,21.98 1951-07-12,21.80,21.80,21.80,21.80,1050000,21.80 1951-07-11,21.68,21.68,21.68,21.68,970000,21.68 1951-07-10,21.63,21.63,21.63,21.63,990000,21.63 1951-07-09,21.73,21.73,21.73,21.73,1110000,21.73 1951-07-06,21.64,21.64,21.64,21.64,1170000,21.64 1951-07-05,21.64,21.64,21.64,21.64,1410000,21.64 1951-07-03,21.23,21.23,21.23,21.23,1250000,21.23 1951-07-02,21.10,21.10,21.10,21.10,1350000,21.10 1951-06-29,20.96,20.96,20.96,20.96,1730000,20.96 1951-06-28,21.10,21.10,21.10,21.10,1940000,21.10 1951-06-27,21.37,21.37,21.37,21.37,1360000,21.37 1951-06-26,21.30,21.30,21.30,21.30,1260000,21.30 1951-06-25,21.29,21.29,21.29,21.29,2440000,21.29 1951-06-22,21.55,21.55,21.55,21.55,1340000,21.55 1951-06-21,21.78,21.78,21.78,21.78,1100000,21.78 1951-06-20,21.91,21.91,21.91,21.91,1120000,21.91 1951-06-19,22.02,22.02,22.02,22.02,1100000,22.02 1951-06-18,22.05,22.05,22.05,22.05,1050000,22.05 1951-06-15,22.04,22.04,22.04,22.04,1370000,22.04 1951-06-14,21.84,21.84,21.84,21.84,1300000,21.84 1951-06-13,21.55,21.55,21.55,21.55,1060000,21.55 1951-06-12,21.52,21.52,21.52,21.52,1200000,21.52 1951-06-11,21.61,21.61,21.61,21.61,1220000,21.61 1951-06-08,21.49,21.49,21.49,21.49,1000000,21.49 1951-06-07,21.56,21.56,21.56,21.56,1340000,21.56 1951-06-06,21.48,21.48,21.48,21.48,1200000,21.48 1951-06-05,21.33,21.33,21.33,21.33,1180000,21.33 1951-06-04,21.24,21.24,21.24,21.24,1100000,21.24 1951-06-01,21.48,21.48,21.48,21.48,9810000,21.48 1951-05-31,21.52,21.52,21.52,21.52,1220000,21.52 1951-05-29,21.35,21.35,21.35,21.35,1190000,21.35 1951-05-28,21.21,21.21,21.21,21.21,1240000,21.21 1951-05-25,21.03,21.03,21.03,21.03,1210000,21.03 1951-05-24,21.05,21.05,21.05,21.05,2580000,21.05 1951-05-23,21.16,21.16,21.16,21.16,1540000,21.16 1951-05-22,21.36,21.36,21.36,21.36,1440000,21.36 1951-05-21,21.46,21.46,21.46,21.46,1580000,21.46 1951-05-18,21.51,21.51,21.51,21.51,1660000,21.51 1951-05-17,21.91,21.91,21.91,21.91,1370000,21.91 1951-05-16,21.69,21.69,21.69,21.69,1660000,21.69 1951-05-15,21.76,21.76,21.76,21.76,2020000,21.76 1951-05-14,22.18,22.18,22.18,22.18,1250000,22.18 1951-05-11,22.33,22.33,22.33,22.33,1640000,22.33 1951-05-10,22.51,22.51,22.51,22.51,1660000,22.51 1951-05-09,22.64,22.64,22.64,22.64,1960000,22.64 1951-05-08,22.61,22.61,22.61,22.61,1600000,22.61 1951-05-07,22.63,22.63,22.63,22.63,1580000,22.63 1951-05-04,22.77,22.77,22.77,22.77,2050000,22.77 1951-05-03,22.81,22.81,22.81,22.81,2060000,22.81 1951-05-02,22.62,22.62,22.62,22.62,1900000,22.62 1951-05-01,22.53,22.53,22.53,22.53,1760000,22.53 1951-04-30,22.43,22.43,22.43,22.43,1790000,22.43 1951-04-27,22.39,22.39,22.39,22.39,2120000,22.39 1951-04-26,22.16,22.16,22.16,22.16,1800000,22.16 1951-04-25,21.97,21.97,21.97,21.97,1520000,21.97 1951-04-24,21.96,21.96,21.96,21.96,1420000,21.96 1951-04-23,22.05,22.05,22.05,22.05,1160000,22.05 1951-04-20,22.04,22.04,22.04,22.04,940000,22.04 1951-04-19,22.04,22.04,22.04,22.04,1520000,22.04 1951-04-18,22.13,22.13,22.13,22.13,1780000,22.13 1951-04-17,22.09,22.09,22.09,22.09,1470000,22.09 1951-04-16,22.04,22.04,22.04,22.04,1730000,22.04 1951-04-13,22.09,22.09,22.09,22.09,2120000,22.09 1951-04-12,21.83,21.83,21.83,21.83,1530000,21.83 1951-04-11,21.64,21.64,21.64,21.64,1420000,21.64 1951-04-10,21.65,21.65,21.65,21.65,1280000,21.65 1951-04-09,21.68,21.68,21.68,21.68,1110000,21.68 1951-04-06,21.72,21.72,21.72,21.72,1450000,21.72 1951-04-05,21.69,21.69,21.69,21.69,1790000,21.69 1951-04-04,21.40,21.40,21.40,21.40,1300000,21.40 1951-04-03,21.26,21.26,21.26,21.26,1220000,21.26 1951-04-02,21.32,21.32,21.32,21.32,1280000,21.32 1951-03-30,21.48,21.48,21.48,21.48,1150000,21.48 1951-03-29,21.33,21.33,21.33,21.33,1300000,21.33 1951-03-28,21.26,21.26,21.26,21.26,1770000,21.26 1951-03-27,21.51,21.51,21.51,21.51,1250000,21.51 1951-03-26,21.53,21.53,21.53,21.53,1230000,21.53 1951-03-22,21.73,21.73,21.73,21.73,1290000,21.73 1951-03-21,21.64,21.64,21.64,21.64,1310000,21.64 1951-03-20,21.52,21.52,21.52,21.52,1020000,21.52 1951-03-19,21.56,21.56,21.56,21.56,1120000,21.56 1951-03-16,21.64,21.64,21.64,21.64,1660000,21.64 1951-03-15,21.29,21.29,21.29,21.29,2070000,21.29 1951-03-14,21.25,21.25,21.25,21.25,2110000,21.25 1951-03-13,21.41,21.41,21.41,21.41,2330000,21.41 1951-03-12,21.70,21.70,21.70,21.70,1640000,21.70 1951-03-09,21.95,21.95,21.95,21.95,1610000,21.95 1951-03-08,21.95,21.95,21.95,21.95,1440000,21.95 1951-03-07,21.86,21.86,21.86,21.86,1770000,21.86 1951-03-06,21.79,21.79,21.79,21.79,1490000,21.79 1951-03-05,21.79,21.79,21.79,21.79,1690000,21.79 1951-03-02,21.93,21.93,21.93,21.93,1570000,21.93 1951-03-01,21.85,21.85,21.85,21.85,1610000,21.85 1951-02-28,21.80,21.80,21.80,21.80,1640000,21.80 1951-02-27,21.76,21.76,21.76,21.76,1680000,21.76 1951-02-26,21.93,21.93,21.93,21.93,1650000,21.93 1951-02-23,21.92,21.92,21.92,21.92,1540000,21.92 1951-02-21,21.86,21.86,21.86,21.86,1670000,21.86 1951-02-20,21.79,21.79,21.79,21.79,2010000,21.79 1951-02-19,21.83,21.83,21.83,21.83,1910000,21.83 1951-02-16,22.13,22.13,22.13,22.13,1860000,22.13 1951-02-15,22.00,22.00,22.00,22.00,1700000,22.00 1951-02-14,22.12,22.12,22.12,22.12,2050000,22.12 1951-02-13,22.18,22.18,22.18,22.18,2400000,22.18 1951-02-09,22.17,22.17,22.17,22.17,2550000,22.17 1951-02-08,22.09,22.09,22.09,22.09,2120000,22.09 1951-02-07,21.99,21.99,21.99,21.99,2020000,21.99 1951-02-06,22.12,22.12,22.12,22.12,2370000,22.12 1951-02-05,22.20,22.20,22.20,22.20,2680000,22.20 1951-02-02,21.96,21.96,21.96,21.96,3030000,21.96 1951-02-01,21.77,21.77,21.77,21.77,2380000,21.77 1951-01-31,21.66,21.66,21.66,21.66,2340000,21.66 1951-01-30,21.74,21.74,21.74,21.74,2480000,21.74 1951-01-29,21.67,21.67,21.67,21.67,2630000,21.67 1951-01-26,21.26,21.26,21.26,21.26,2230000,21.26 1951-01-25,21.03,21.03,21.03,21.03,2520000,21.03 1951-01-24,21.16,21.16,21.16,21.16,1990000,21.16 1951-01-23,21.26,21.26,21.26,21.26,2080000,21.26 1951-01-22,21.18,21.18,21.18,21.18,2570000,21.18 1951-01-19,21.36,21.36,21.36,21.36,3170000,21.36 1951-01-18,21.40,21.40,21.40,21.40,3490000,21.40 1951-01-17,21.55,21.55,21.55,21.55,3880000,21.55 1951-01-16,21.46,21.46,21.46,21.46,3740000,21.46 1951-01-15,21.30,21.30,21.30,21.30,2830000,21.30 1951-01-12,21.11,21.11,21.11,21.11,2950000,21.11 1951-01-11,21.19,21.19,21.19,21.19,3490000,21.19 1951-01-10,20.85,20.85,20.85,20.85,3270000,20.85 1951-01-09,21.12,21.12,21.12,21.12,3800000,21.12 1951-01-08,21.00,21.00,21.00,21.00,2780000,21.00 1951-01-05,20.87,20.87,20.87,20.87,3390000,20.87 1951-01-04,20.87,20.87,20.87,20.87,3390000,20.87 1951-01-03,20.69,20.69,20.69,20.69,3370000,20.69 1951-01-02,20.77,20.77,20.77,20.77,3030000,20.77 1950-12-29,20.43,20.43,20.43,20.43,3440000,20.43 1950-12-28,20.38,20.38,20.38,20.38,3560000,20.38 1950-12-27,20.30,20.30,20.30,20.30,2940000,20.30 1950-12-26,19.92,19.92,19.92,19.92,2660000,19.92 1950-12-22,20.07,20.07,20.07,20.07,2720000,20.07 1950-12-21,19.98,19.98,19.98,19.98,3990000,19.98 1950-12-20,19.97,19.97,19.97,19.97,3510000,19.97 1950-12-19,19.96,19.96,19.96,19.96,3650000,19.96 1950-12-18,19.85,19.85,19.85,19.85,4500000,19.85 1950-12-15,19.33,19.33,19.33,19.33,2420000,19.33 1950-12-14,19.43,19.43,19.43,19.43,2660000,19.43 1950-12-13,19.67,19.67,19.67,19.67,2030000,19.67 1950-12-12,19.68,19.68,19.68,19.68,2140000,19.68 1950-12-11,19.72,19.72,19.72,19.72,2600000,19.72 1950-12-08,19.40,19.40,19.40,19.40,2310000,19.40 1950-12-07,19.40,19.40,19.40,19.40,1810000,19.40 1950-12-06,19.45,19.45,19.45,19.45,2010000,19.45 1950-12-05,19.31,19.31,19.31,19.31,1940000,19.31 1950-12-04,19.00,19.00,19.00,19.00,2510000,19.00 1950-12-01,19.66,19.66,19.66,19.66,1870000,19.66 1950-11-30,19.51,19.51,19.51,19.51,2080000,19.51 1950-11-29,19.37,19.37,19.37,19.37,2770000,19.37 1950-11-28,19.56,19.56,19.56,19.56,2970000,19.56 1950-11-27,20.18,20.18,20.18,20.18,1740000,20.18 1950-11-24,20.32,20.32,20.32,20.32,2620000,20.32 1950-11-22,20.16,20.16,20.16,20.16,2730000,20.16 1950-11-21,19.88,19.88,19.88,19.88,2010000,19.88 1950-11-20,19.93,19.93,19.93,19.93,2250000,19.93 1950-11-17,19.86,19.86,19.86,19.86,2130000,19.86 1950-11-16,19.72,19.72,19.72,19.72,1760000,19.72 1950-11-15,19.82,19.82,19.82,19.82,1620000,19.82 1950-11-14,19.86,19.86,19.86,19.86,1780000,19.86 1950-11-13,20.01,20.01,20.01,20.01,1630000,20.01 1950-11-10,19.94,19.94,19.94,19.94,1640000,19.94 1950-11-09,19.79,19.79,19.79,19.79,1760000,19.79 1950-11-08,19.56,19.56,19.56,19.56,1850000,19.56 1950-11-06,19.36,19.36,19.36,19.36,2580000,19.36 1950-11-03,19.85,19.85,19.85,19.85,1560000,19.85 1950-11-02,19.73,19.73,19.73,19.73,1580000,19.73 1950-11-01,19.56,19.56,19.56,19.56,1780000,19.56 1950-10-31,19.53,19.53,19.53,19.53,2010000,19.53 1950-10-30,19.61,19.61,19.61,19.61,1790000,19.61 1950-10-27,19.77,19.77,19.77,19.77,1800000,19.77 1950-10-26,19.61,19.61,19.61,19.61,3000000,19.61 1950-10-25,20.05,20.05,20.05,20.05,1930000,20.05 1950-10-24,20.08,20.08,20.08,20.08,1790000,20.08 1950-10-23,19.96,19.96,19.96,19.96,1850000,19.96 1950-10-20,19.96,19.96,19.96,19.96,1840000,19.96 1950-10-19,20.02,20.02,20.02,20.02,2250000,20.02 1950-10-18,20.01,20.01,20.01,20.01,2410000,20.01 1950-10-17,19.89,19.89,19.89,19.89,2010000,19.89 1950-10-16,19.71,19.71,19.71,19.71,1630000,19.71 1950-10-13,19.85,19.85,19.85,19.85,2030000,19.85 1950-10-11,19.86,19.86,19.86,19.86,2200000,19.86 1950-10-10,19.78,19.78,19.78,19.78,1870000,19.78 1950-10-09,20.00,20.00,20.00,20.00,2330000,20.00 1950-10-06,20.12,20.12,20.12,20.12,2360000,20.12 1950-10-05,19.89,19.89,19.89,19.89,2490000,19.89 1950-10-04,20.00,20.00,20.00,20.00,2920000,20.00 1950-10-03,19.66,19.66,19.66,19.66,2480000,19.66 1950-10-02,19.69,19.69,19.69,19.69,2200000,19.69 1950-09-29,19.45,19.45,19.45,19.45,1800000,19.45 1950-09-28,19.42,19.42,19.42,19.42,2200000,19.42 1950-09-27,19.41,19.41,19.41,19.41,2360000,19.41 1950-09-26,19.14,19.14,19.14,19.14,2280000,19.14 1950-09-25,19.42,19.42,19.42,19.42,2020000,19.42 1950-09-22,19.44,19.44,19.44,19.44,2510000,19.44 1950-09-21,19.37,19.37,19.37,19.37,1650000,19.37 1950-09-20,19.21,19.21,19.21,19.21,2100000,19.21 1950-09-19,19.31,19.31,19.31,19.31,1590000,19.31 1950-09-18,19.37,19.37,19.37,19.37,2040000,19.37 1950-09-15,19.29,19.29,19.29,19.29,2410000,19.29 1950-09-14,19.18,19.18,19.18,19.18,2350000,19.18 1950-09-13,19.09,19.09,19.09,19.09,2600000,19.09 1950-09-12,18.87,18.87,18.87,18.87,1680000,18.87 1950-09-11,18.61,18.61,18.61,18.61,1860000,18.61 1950-09-08,18.75,18.75,18.75,18.75,1960000,18.75 1950-09-07,18.59,18.59,18.59,18.59,1340000,18.59 1950-09-06,18.54,18.54,18.54,18.54,1300000,18.54 1950-09-05,18.68,18.68,18.68,18.68,1250000,18.68 1950-09-01,18.55,18.55,18.55,18.55,1290000,18.55 1950-08-31,18.42,18.42,18.42,18.42,1140000,18.42 1950-08-30,18.43,18.43,18.43,18.43,1490000,18.43 1950-08-29,18.54,18.54,18.54,18.54,1890000,18.54 1950-08-28,18.53,18.53,18.53,18.53,1300000,18.53 1950-08-25,18.54,18.54,18.54,18.54,1610000,18.54 1950-08-24,18.79,18.79,18.79,18.79,1620000,18.79 1950-08-23,18.82,18.82,18.82,18.82,1580000,18.82 1950-08-22,18.68,18.68,18.68,18.68,1550000,18.68 1950-08-21,18.70,18.70,18.70,18.70,1840000,18.70 1950-08-18,18.68,18.68,18.68,18.68,1780000,18.68 1950-08-17,18.54,18.54,18.54,18.54,2170000,18.54 1950-08-16,18.34,18.34,18.34,18.34,1770000,18.34 1950-08-15,18.32,18.32,18.32,18.32,1330000,18.32 1950-08-14,18.29,18.29,18.29,18.29,1280000,18.29 1950-08-11,18.28,18.28,18.28,18.28,1680000,18.28 1950-08-10,18.48,18.48,18.48,18.48,1870000,18.48 1950-08-09,18.61,18.61,18.61,18.61,1760000,18.61 1950-08-08,18.46,18.46,18.46,18.46,2180000,18.46 1950-08-07,18.41,18.41,18.41,18.41,1850000,18.41 1950-08-04,18.14,18.14,18.14,18.14,1600000,18.14 1950-08-03,17.99,17.99,17.99,17.99,1660000,17.99 1950-08-02,17.95,17.95,17.95,17.95,1980000,17.95 1950-08-01,18.02,18.02,18.02,18.02,1970000,18.02 1950-07-31,17.84,17.84,17.84,17.84,1590000,17.84 1950-07-28,17.69,17.69,17.69,17.69,2050000,17.69 1950-07-27,17.50,17.50,17.50,17.50,2300000,17.50 1950-07-26,17.27,17.27,17.27,17.27,2460000,17.27 1950-07-25,17.23,17.23,17.23,17.23,2770000,17.23 1950-07-24,17.48,17.48,17.48,17.48,2300000,17.48 1950-07-21,17.59,17.59,17.59,17.59,2810000,17.59 1950-07-20,17.61,17.61,17.61,17.61,3160000,17.61 1950-07-19,17.36,17.36,17.36,17.36,2430000,17.36 1950-07-18,17.06,17.06,17.06,17.06,1820000,17.06 1950-07-17,16.68,16.68,16.68,16.68,1520000,16.68 1950-07-14,16.87,16.87,16.87,16.87,1900000,16.87 1950-07-13,16.69,16.69,16.69,16.69,2660000,16.69 1950-07-12,16.87,16.87,16.87,16.87,3200000,16.87 1950-07-11,17.32,17.32,17.32,17.32,3250000,17.32 1950-07-10,17.59,17.59,17.59,17.59,1960000,17.59 1950-07-07,17.67,17.67,17.67,17.67,1870000,17.67 1950-07-06,17.91,17.91,17.91,17.91,1570000,17.91 1950-07-05,17.81,17.81,17.81,17.81,1400000,17.81 1950-07-03,17.64,17.64,17.64,17.64,1550000,17.64 1950-06-30,17.69,17.69,17.69,17.69,2660000,17.69 1950-06-29,17.44,17.44,17.44,17.44,3040000,17.44 1950-06-28,18.11,18.11,18.11,18.11,2600000,18.11 1950-06-27,17.91,17.91,17.91,17.91,4860000,17.91 1950-06-26,18.11,18.11,18.11,18.11,3950000,18.11 1950-06-23,19.14,19.14,19.14,19.14,1700000,19.14 1950-06-22,19.16,19.16,19.16,19.16,1830000,19.16 1950-06-21,19.00,19.00,19.00,19.00,1750000,19.00 1950-06-20,18.83,18.83,18.83,18.83,1470000,18.83 1950-06-19,18.92,18.92,18.92,18.92,1290000,18.92 1950-06-16,18.97,18.97,18.97,18.97,1180000,18.97 1950-06-15,18.93,18.93,18.93,18.93,1530000,18.93 1950-06-14,18.98,18.98,18.98,18.98,1650000,18.98 1950-06-13,19.25,19.25,19.25,19.25,1790000,19.25 1950-06-12,19.40,19.40,19.40,19.40,1790000,19.40 1950-06-09,19.26,19.26,19.26,19.26,2130000,19.26 1950-06-08,19.14,19.14,19.14,19.14,1780000,19.14 1950-06-07,18.93,18.93,18.93,18.93,1750000,18.93 1950-06-06,18.88,18.88,18.88,18.88,2250000,18.88 1950-06-05,18.60,18.60,18.60,18.60,1630000,18.60 1950-06-02,18.79,18.79,18.79,18.79,1450000,18.79 1950-06-01,18.77,18.77,18.77,18.77,1580000,18.77 1950-05-31,18.78,18.78,18.78,18.78,1530000,18.78 1950-05-29,18.72,18.72,18.72,18.72,1110000,18.72 1950-05-26,18.67,18.67,18.67,18.67,1330000,18.67 1950-05-25,18.69,18.69,18.69,18.69,1480000,18.69 1950-05-24,18.69,18.69,18.69,18.69,1850000,18.69 1950-05-23,18.71,18.71,18.71,18.71,1460000,18.71 1950-05-22,18.60,18.60,18.60,18.60,1620000,18.60 1950-05-19,18.68,18.68,18.68,18.68,2110000,18.68 1950-05-18,18.56,18.56,18.56,18.56,5240000,18.56 1950-05-17,18.52,18.52,18.52,18.52,2020000,18.52 1950-05-16,18.44,18.44,18.44,18.44,1730000,18.44 1950-05-15,18.26,18.26,18.26,18.26,1220000,18.26 1950-05-12,18.18,18.18,18.18,18.18,1790000,18.18 1950-05-11,18.29,18.29,18.29,18.29,1750000,18.29 1950-05-10,18.29,18.29,18.29,18.29,1880000,18.29 1950-05-09,18.27,18.27,18.27,18.27,1720000,18.27 1950-05-08,18.27,18.27,18.27,18.27,1680000,18.27 1950-05-05,18.22,18.22,18.22,18.22,1800000,18.22 1950-05-04,18.12,18.12,18.12,18.12,2150000,18.12 1950-05-03,18.27,18.27,18.27,18.27,2120000,18.27 1950-05-02,18.11,18.11,18.11,18.11,2250000,18.11 1950-05-01,18.22,18.22,18.22,18.22,2390000,18.22 1950-04-28,17.96,17.96,17.96,17.96,2190000,17.96 1950-04-27,17.86,17.86,17.86,17.86,2070000,17.86 1950-04-26,17.76,17.76,17.76,17.76,1880000,17.76 1950-04-25,17.83,17.83,17.83,17.83,1830000,17.83 1950-04-24,17.83,17.83,17.83,17.83,2310000,17.83 1950-04-21,17.96,17.96,17.96,17.96,2710000,17.96 1950-04-20,17.93,17.93,17.93,17.93,2590000,17.93 1950-04-19,18.05,18.05,18.05,18.05,2950000,18.05 1950-04-18,18.03,18.03,18.03,18.03,3320000,18.03 1950-04-17,17.88,17.88,17.88,17.88,2520000,17.88 1950-04-14,17.96,17.96,17.96,17.96,2750000,17.96 1950-04-13,17.98,17.98,17.98,17.98,2410000,17.98 1950-04-12,17.94,17.94,17.94,17.94,2010000,17.94 1950-04-11,17.75,17.75,17.75,17.75,2010000,17.75 1950-04-10,17.85,17.85,17.85,17.85,2070000,17.85 1950-04-06,17.78,17.78,17.78,17.78,2000000,17.78 1950-04-05,17.63,17.63,17.63,17.63,1430000,17.63 1950-04-04,17.55,17.55,17.55,17.55,2010000,17.55 1950-04-03,17.53,17.53,17.53,17.53,1570000,17.53 1950-03-31,17.29,17.29,17.29,17.29,1880000,17.29 1950-03-30,17.30,17.30,17.30,17.30,2370000,17.30 1950-03-29,17.44,17.44,17.44,17.44,2090000,17.44 1950-03-28,17.53,17.53,17.53,17.53,1780000,17.53 1950-03-27,17.46,17.46,17.46,17.46,1930000,17.46 1950-03-24,17.56,17.56,17.56,17.56,1570000,17.56 1950-03-23,17.56,17.56,17.56,17.56,2020000,17.56 1950-03-22,17.55,17.55,17.55,17.55,2010000,17.55 1950-03-21,17.45,17.45,17.45,17.45,1400000,17.45 1950-03-20,17.44,17.44,17.44,17.44,1430000,17.44 1950-03-17,17.45,17.45,17.45,17.45,1600000,17.45 1950-03-16,17.49,17.49,17.49,17.49,2060000,17.49 1950-03-15,17.45,17.45,17.45,17.45,1830000,17.45 1950-03-14,17.25,17.25,17.25,17.25,1140000,17.25 1950-03-13,17.12,17.12,17.12,17.12,1060000,17.12 1950-03-10,17.09,17.09,17.09,17.09,1260000,17.09 1950-03-09,17.07,17.07,17.07,17.07,1330000,17.07 1950-03-08,17.19,17.19,17.19,17.19,1360000,17.19 1950-03-07,17.20,17.20,17.20,17.20,1590000,17.20 1950-03-06,17.32,17.32,17.32,17.32,1470000,17.32 1950-03-03,17.29,17.29,17.29,17.29,1520000,17.29 1950-03-02,17.23,17.23,17.23,17.23,1340000,17.23 1950-03-01,17.24,17.24,17.24,17.24,1410000,17.24 1950-02-28,17.22,17.22,17.22,17.22,1310000,17.22 1950-02-27,17.28,17.28,17.28,17.28,1410000,17.28 1950-02-24,17.28,17.28,17.28,17.28,1710000,17.28 1950-02-23,17.21,17.21,17.21,17.21,1310000,17.21 1950-02-21,17.17,17.17,17.17,17.17,1260000,17.17 1950-02-20,17.20,17.20,17.20,17.20,1420000,17.20 1950-02-17,17.15,17.15,17.15,17.15,1940000,17.15 1950-02-16,16.99,16.99,16.99,16.99,1920000,16.99 1950-02-15,17.06,17.06,17.06,17.06,1730000,17.06 1950-02-14,17.06,17.06,17.06,17.06,2210000,17.06 1950-02-10,17.24,17.24,17.24,17.24,1790000,17.24 1950-02-09,17.28,17.28,17.28,17.28,1810000,17.28 1950-02-08,17.21,17.21,17.21,17.21,1470000,17.21 1950-02-07,17.23,17.23,17.23,17.23,1360000,17.23 1950-02-06,17.32,17.32,17.32,17.32,1490000,17.32 1950-02-03,17.29,17.29,17.29,17.29,2210000,17.29 1950-02-02,17.23,17.23,17.23,17.23,2040000,17.23 1950-02-01,17.05,17.05,17.05,17.05,1810000,17.05 1950-01-31,17.05,17.05,17.05,17.05,1690000,17.05 1950-01-30,17.02,17.02,17.02,17.02,1640000,17.02 1950-01-27,16.82,16.82,16.82,16.82,1250000,16.82 1950-01-26,16.73,16.73,16.73,16.73,1150000,16.73 1950-01-25,16.74,16.74,16.74,16.74,1700000,16.74 1950-01-24,16.86,16.86,16.86,16.86,1250000,16.86 1950-01-23,16.92,16.92,16.92,16.92,1340000,16.92 1950-01-20,16.90,16.90,16.90,16.90,1440000,16.90 1950-01-19,16.87,16.87,16.87,16.87,1170000,16.87 1950-01-18,16.85,16.85,16.85,16.85,1570000,16.85 1950-01-17,16.86,16.86,16.86,16.86,1790000,16.86 1950-01-16,16.72,16.72,16.72,16.72,1460000,16.72 1950-01-13,16.67,16.67,16.67,16.67,3330000,16.67 1950-01-12,16.76,16.76,16.76,16.76,2970000,16.76 1950-01-11,17.09,17.09,17.09,17.09,2630000,17.09 1950-01-10,17.03,17.03,17.03,17.03,2160000,17.03 1950-01-09,17.08,17.08,17.08,17.08,2520000,17.08 1950-01-06,16.98,16.98,16.98,16.98,2010000,16.98 1950-01-05,16.93,16.93,16.93,16.93,2550000,16.93 1950-01-04,16.85,16.85,16.85,16.85,1890000,16.85 1950-01-03,16.66,16.66,16.66,16.66,1260000,16.66 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/run_all.py000066400000000000000000000022431224417117700257770ustar00rootroot00000000000000'''run all examples to make sure we don't get an exception Note: If an example contaings plt.show(), then all plot windows have to be closed manually, at least in my setup. uncomment plt.show() to show all plot windows ''' stop_on_error = True filelist = ['example_pca.py', 'example_sysreg.py', 'example_mle.py', # 'example_gam.py', # exclude, currently we are not working on it 'example_pca_regression.py'] cont = raw_input("""Are you sure you want to run all of the examples? This is done mainly to check that they are up to date. (y/n) >>> """) if 'y' in cont.lower(): for run_all_f in filelist: try: print "Executing example file", run_all_f print "-----------------------" + "-"*len(run_all_f) execfile(run_all_f) except: #f might be overwritten in the executed file print "*********************" print "ERROR in example file", run_all_f print "**********************" + "*"*len(run_all_f) if stop_on_error: raise #plt.show() #plt.close('all') #close doesn't work because I never get here without closing plots manually statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/thirdparty/000077500000000000000000000000001224417117700261625ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py000066400000000000000000000104011224417117700314170ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Playing with correlation of DJ-30 stock returns this uses pickled data that needs to be created with findow.py to see graphs, uncomment plt.show() Created on Sat Jan 30 16:30:18 2010 Author: josef-pktd """ import numpy as np import matplotlib.finance as fin import matplotlib.pyplot as plt import datetime as dt import pandas as pa import pickle import statsmodels.api as sm import statsmodels.sandbox as sb import statsmodels.sandbox.tools as sbtools from statsmodels.graphics.correlation import plot_corr, plot_corr_grid try: rrdm = pickle.load(file('dj30rr','rb')) except Exception: #blanket for any unpickling error print "Error with unpickling, a new pickle file can be created with findow_1" raise ticksym = rrdm.columns.tolist() rr = rrdm.values[1:400] rrcorr = np.corrcoef(rr, rowvar=0) plot_corr(rrcorr, xnames=ticksym) nvars = rrcorr.shape[0] plt.figure() plt.hist(rrcorr[np.triu_indices(nvars,1)]) plt.title('Correlation Coefficients') xreda, facta, evaa, evea = sbtools.pcasvd(rr) evallcs = (evaa).cumsum() print evallcs/evallcs[-1] xred, fact, eva, eve = sbtools.pcasvd(rr, keepdim=4) pcacorr = np.corrcoef(xred, rowvar=0) plot_corr(pcacorr, xnames=ticksym, title='Correlation PCA') resid = rr-xred residcorr = np.corrcoef(resid, rowvar=0) plot_corr(residcorr, xnames=ticksym, title='Correlation Residuals') plt.matshow(residcorr) plt.imshow(residcorr, cmap=plt.cm.jet, interpolation='nearest', extent=(0,30,0,30), vmin=-1.0, vmax=1.0) plt.colorbar() normcolor = (0,1) #False #True fig = plt.figure() ax = fig.add_subplot(2,2,1) plot_corr(rrcorr, xnames=ticksym, normcolor=normcolor, ax=ax) ax2 = fig.add_subplot(2,2,3) #pcacorr = np.corrcoef(xred, rowvar=0) plot_corr(pcacorr, xnames=ticksym, title='Correlation PCA', normcolor=normcolor, ax=ax2) ax3 = fig.add_subplot(2,2,4) plot_corr(residcorr, xnames=ticksym, title='Correlation Residuals', normcolor=normcolor, ax=ax3) import matplotlib as mpl images = [c for ax in fig.axes for c in ax.get_children() if isinstance(c, mpl.image.AxesImage)] print images print ax.get_children() #cax = fig.add_subplot(2,2,2) #[0.85, 0.1, 0.075, 0.8] fig. subplots_adjust(bottom=0.1, right=0.9, top=0.9) cax = fig.add_axes([0.9, 0.1, 0.025, 0.8]) fig.colorbar(images[0], cax=cax) fig.savefig('corrmatrixgrid.png', dpi=120) has_sklearn = True try: import sklearn except ImportError: has_sklearn = False print 'sklearn not available' def cov2corr(cov): std_ = np.sqrt(np.diag(cov)) corr = cov / np.outer(std_, std_) return corr if has_sklearn: from sklearn.covariance import LedoitWolf, OAS, MCD lw = LedoitWolf(store_precision=False) lw.fit(rr, assume_centered=False) cov_lw = lw.covariance_ corr_lw = cov2corr(cov_lw) oas = OAS(store_precision=False) oas.fit(rr, assume_centered=False) cov_oas = oas.covariance_ corr_oas = cov2corr(cov_oas) mcd = MCD()#.fit(rr, reweight=None) mcd.fit(rr, assume_centered=False) cov_mcd = mcd.covariance_ corr_mcd = cov2corr(cov_mcd) titles = ['raw correlation', 'lw', 'oas', 'mcd'] normcolor = None fig = plt.figure() for i, c in enumerate([rrcorr, corr_lw, corr_oas, corr_mcd]): #for i, c in enumerate([np.cov(rr, rowvar=0), cov_lw, cov_oas, cov_mcd]): ax = fig.add_subplot(2,2,i+1) plot_corr(c, xnames=None, title=titles[i], normcolor=normcolor, ax=ax) images = [c for ax in fig.axes for c in ax.get_children() if isinstance(c, mpl.image.AxesImage)] fig. subplots_adjust(bottom=0.1, right=0.9, top=0.9) cax = fig.add_axes([0.9, 0.1, 0.025, 0.8]) fig.colorbar(images[0], cax=cax) corrli = [rrcorr, corr_lw, corr_oas, corr_mcd, pcacorr] diffssq = np.array([[((ci-cj)**2).sum() for ci in corrli] for cj in corrli]) diffsabs = np.array([[np.max(np.abs(ci-cj)) for ci in corrli] for cj in corrli]) print diffssq print '\nmaxabs' print diffsabs fig.savefig('corrmatrix_sklearn.png', dpi=120) fig2 = plot_corr_grid(corrli+[residcorr], ncols=3, titles=titles+['pca', 'pca-residual'], xnames=[], ynames=[]) fig2.savefig('corrmatrix_sklearn_2.png', dpi=120) #plt.show() #plt.close('all') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/thirdparty/findow_0.py000066400000000000000000000040671224417117700302500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A quick look at volatility of stock returns for 2009 Just an exercise to find my way around the pandas methods. Shows the daily rate of return, the square of it (volatility) and a 5 day moving average of the volatility. No guarantee for correctness. Assumes no missing values. colors of lines in graphs are not great uses DataFrame and WidePanel to hold data downloaded from yahoo using matplotlib. I haven't figured out storage, so the download happens at each run of the script. getquotes is from pandas\examples\finance.py Created on Sat Jan 30 16:30:18 2010 Author: josef-pktd """ import numpy as np import matplotlib.finance as fin import matplotlib.pyplot as plt import datetime as dt import pandas as pa def getquotes(symbol, start, end): quotes = fin.quotes_historical_yahoo(symbol, start, end) dates, open, close, high, low, volume = zip(*quotes) data = { 'open' : open, 'close' : close, 'high' : high, 'low' : low, 'volume' : volume } dates = pa.Index([dt.datetime.fromordinal(int(d)) for d in dates]) return pa.DataFrame(data, index=dates) start_date = dt.datetime(2009, 1, 1) end_date = dt.datetime(2010, 1, 1) mysym = ['msft', 'ibm', 'goog'] indexsym = ['gspc', 'dji'] # download data dmall = {} for sy in mysym: dmall[sy] = getquotes(sy, start_date, end_date) # combine into WidePanel pawp = pa.WidePanel.fromDict(dmall) print pawp.values.shape # select closing prices paclose = pawp.getMinorXS('close') # take log and first difference over time paclose_ratereturn = paclose.apply(np.log).diff() plt.figure() paclose_ratereturn.plot() plt.title('daily rate of return') # square the returns paclose_ratereturn_vol = paclose_ratereturn.apply(lambda x:np.power(x,2)) plt.figure() plt.title('volatility (with 5 day moving average') paclose_ratereturn_vol.plot() # use convolution to get moving average paclose_ratereturn_vol_mov = paclose_ratereturn_vol.apply( lambda x:np.convolve(x,np.ones(5)/5.,'same')) paclose_ratereturn_vol_mov.plot() #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/thirdparty/findow_1.py000066400000000000000000000047101224417117700302440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A quick look at volatility of stock returns for 2009 Just an exercise to find my way around the pandas methods. Shows the daily rate of return, the square of it (volatility) and a 5 day moving average of the volatility. No guarantee for correctness. Assumes no missing values. colors of lines in graphs are not great uses DataFrame and WidePanel to hold data downloaded from yahoo using matplotlib. I haven't figured out storage, so the download happens at each run of the script. getquotes is from pandas\examples\finance.py Created on Sat Jan 30 16:30:18 2010 Author: josef-pktd """ import numpy as np import matplotlib.finance as fin import matplotlib.pyplot as plt import datetime as dt import pandas as pa def getquotes(symbol, start, end): quotes = fin.quotes_historical_yahoo(symbol, start, end) dates, open, close, high, low, volume = zip(*quotes) data = { 'open' : open, 'close' : close, 'high' : high, 'low' : low, 'volume' : volume } dates = pa.Index([dt.datetime.fromordinal(int(d)) for d in dates]) return pa.DataFrame(data, index=dates) start_date = dt.datetime(2007, 1, 1) end_date = dt.datetime(2009, 12, 31) dj30 = ['MMM', 'AA', 'AXP', 'T', 'BAC', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DD', 'XOM', 'GE', 'HPQ', 'HD', 'INTC', 'IBM', 'JNJ', 'JPM', 'KFT', 'MCD', 'MRK', 'MSFT', 'PFE', 'PG', 'TRV', 'UTX', 'VZ', 'WMT', 'DIS'] mysym = ['msft', 'ibm', 'goog'] indexsym = ['gspc', 'dji'] # download data dmall = {} for sy in dj30: dmall[sy] = getquotes(sy, start_date, end_date) # combine into WidePanel pawp = pa.WidePanel.fromDict(dmall) print pawp.values.shape # select closing prices paclose = pawp.getMinorXS('close') # take log and first difference over time paclose_ratereturn = paclose.apply(np.log).diff() import os if not os.path.exists('dj30rr'): #if pandas is updated, then sometimes unpickling fails, and need to save again paclose_ratereturn.save('dj30rr') plt.figure() paclose_ratereturn.plot() plt.title('daily rate of return') # square the returns paclose_ratereturn_vol = paclose_ratereturn.apply(lambda x:np.power(x,2)) plt.figure() plt.title('volatility (with 5 day moving average') paclose_ratereturn_vol.plot() # use convolution to get moving average paclose_ratereturn_vol_mov = paclose_ratereturn_vol.apply( lambda x:np.convolve(x,np.ones(5)/5.,'same')) paclose_ratereturn_vol_mov.plot() #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/thirdparty/try_interchange.py000066400000000000000000000050771224417117700317320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """groupmean, groupby in pandas, la and tabular from a scikits.timeseries after a question on the scipy-user mailing list I tried to do groupmeans, which in this case are duplicate dates, in the 3 packages. I'm using the versions that I had installed, which are all based on repository checkout, but are not fully up-to-date some brief comments * la.larry and pandas.DataFrame require unique labels/index so groups have to represented in a separate data structure * pandas is missing GroupBy in the docs, but the docstring is helpful * both la and pandas handle datetime objects as object arrays * tabular requires conversion to structured dtype, but easy helper functions or methods are available in scikits.timeseries and tabular * not too bad for a first try Created on Sat Jan 30 08:33:11 2010 Author: josef-pktd """ import numpy as np import scikits.timeseries as ts s = ts.time_series([1,2,3,4,5], dates=ts.date_array(["2001-01","2001-01", "2001-02","2001-03","2001-03"],freq="M")) print '\nUsing la' import la dta = la.larry(s.data, label=[range(len(s.data))]) dat = la.larry(s.dates.tolist(), label=[range(len(s.data))]) s2 = ts.time_series(dta.group_mean(dat).x,dates=ts.date_array(dat.x,freq="M")) s2u = ts.remove_duplicated_dates(s2) print repr(s) print dat print repr(s2) print repr(s2u) print '\nUsing pandas' import pandas pdta = pandas.DataFrame(s.data, np.arange(len(s.data)), [1]) pa = pdta.groupby(dict(zip(np.arange(len(s.data)), s.dates.tolist()))).aggregate(np.mean) s3 = ts.time_series(pa.values.ravel(), dates=ts.date_array(pa.index.tolist(),freq="M")) print pa print repr(s3) print '\nUsing tabular' import tabular as tb X = tb.tabarray(array=s.torecords(), dtype=s.torecords().dtype) tabx = X.aggregate(On=['_dates'], AggFuncDict={'_data':np.mean,'_mask':np.all}) s4 = ts.time_series(tabx['_data'],dates=ts.date_array(tabx['_dates'],freq="M")) print tabx print repr(s4) from finance import * #hack to make it run as standalone #after running pandas/examples/finance.py larmsft = la.larry(msft.values, [msft.index.tolist(), msft.columns.tolist()]) laribm = la.larry(ibm.values, [ibm.index.tolist(), ibm.columns.tolist()]) lar1 = la.larry(np.dstack((msft.values,ibm.values)), [ibm.index.tolist(), ibm.columns.tolist(), ['msft', 'ibm']]) print lar1.mean(0) y = la.larry([[1.0, 2.0], [3.0, 4.0]], [['a', 'b'], ['c', 'd']]) ysr = np.empty(y.x.shape[0],dtype=([('index','S1')]+[(i,np.float) for i in y.label[1]])) ysr['index'] = y.label[0] for i in ysr.dtype.names[1:]: ysr[i] = y[y.labelindex(i, axis=1)].x statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/try_multiols.py000066400000000000000000000023311224417117700271070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun May 26 13:23:40 2013 Author: Josef Perktold, based on Enrico Giampieri's multiOLS """ #import numpy as np import pandas as pd import statsmodels.api as sm from statsmodels.sandbox.multilinear import multiOLS, multigroup data = sm.datasets.longley.load_pandas() df = data.exog df['TOTEMP'] = data.endog #This will perform the specified linear model on all the #other columns of the dataframe res0 = multiOLS('GNP + 1', df) #This select only a certain subset of the columns res = multiOLS('GNP + 0', df, ['GNPDEFL', 'TOTEMP', 'POP']) print res.to_string() url = "http://vincentarelbundock.github.com/" url = url + "Rdatasets/csv/HistData/Guerry.csv" df = pd.read_csv(url, index_col=1) #'dept') #evaluate the relationship between the various parameters whith the Wealth pvals = multiOLS('Wealth', df)['adj_pvals', '_f_test'] #define the groups groups = {} groups['crime'] = ['Crime_prop', 'Infanticide', 'Crime_parents', 'Desertion', 'Crime_pers'] groups['religion'] = ['Donation_clergy', 'Clergy', 'Donations'] groups['wealth'] = ['Commerce', 'Lottery', 'Instruction', 'Literacy'] #do the analysis of the significance res3 = multigroup(pvals < 0.05, groups) print res3 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/try_quantile_regression.py000066400000000000000000000023531224417117700313250ustar00rootroot00000000000000'''Example to illustrate Quantile Regression Author: Josef Perktold ''' import numpy as np import statsmodels.api as sm from statsmodels.regression.quantile_regression import QuantReg sige = 5 nobs, k_vars = 500, 5 x = np.random.randn(nobs, k_vars) #x[:,0] = 1 y = x.sum(1) + sige * (np.random.randn(nobs)/2 + 1)**3 p = 0.5 exog = np.column_stack((np.ones(nobs), x)) res_qr = QuantReg(y, exog).fit(p) res_qr2 = QuantReg(y, exog).fit(0.25) res_qr3 = QuantReg(y, exog).fit(0.75) res_ols = sm.OLS(y, exog).fit() ##print 'ols ', res_ols.params ##print '0.25', res_qr2 ##print '0.5 ', res_qr ##print '0.75', res_qr3 params = [res_ols.params, res_qr2.params, res_qr.params, res_qr3.params] labels = ['ols', 'qr 0.25', 'qr 0.5', 'qr 0.75'] import matplotlib.pyplot as plt #sortidx = np.argsort(y) fitted_ols = np.dot(res_ols.model.exog, params[0]) sortidx = np.argsort(fitted_ols) x_sorted = res_ols.model.exog[sortidx] fitted_ols = np.dot(x_sorted, params[0]) plt.figure() plt.plot(y[sortidx], 'o', alpha=0.75) for lab, beta in zip(['ols', 'qr 0.25', 'qr 0.5', 'qr 0.75'], params): print '%-8s'%lab, np.round(beta, 4) fitted = np.dot(x_sorted, beta) lw = 2 if lab == 'ols' else 1 plt.plot(fitted, lw=lw, label=lab) plt.legend() plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/try_quantile_regression1.py000066400000000000000000000021721224417117700314050ustar00rootroot00000000000000'''Example to illustrate Quantile Regression Author: Josef Perktold polynomial regression with systematic deviations above ''' import numpy as np from scipy import stats import statsmodels.api as sm from statsmodels.regression.quantile_regression import QuantReg sige = 0.1 nobs, k_vars = 500, 3 x = np.random.uniform(-1, 1, size=nobs) x.sort() exog = np.vander(x, k_vars+1)[:,::-1] mix = 0.1 * stats.norm.pdf(x[:,None], loc=np.linspace(-0.5, 0.75, 4), scale=0.01).sum(1) y = exog.sum(1) + mix + sige * (np.random.randn(nobs)/2 + 1)**3 p = 0.5 res_qr = QuantReg(y, exog).fit(p) res_qr2 = QuantReg(y, exog).fit(0.1) res_qr3 = QuantReg(y, exog).fit(0.75) res_ols = sm.OLS(y, exog).fit() params = [res_ols.params, res_qr2.params, res_qr.params, res_qr3.params] labels = ['ols', 'qr 0.1', 'qr 0.5', 'qr 0.75'] import matplotlib.pyplot as plt plt.figure() plt.plot(x, y, '.', alpha=0.5) for lab, beta in zip(['ols', 'qr 0.1', 'qr 0.5', 'qr 0.75'], params): print('%-8s'%lab, np.round(beta, 4)) fitted = np.dot(exog, beta) lw = 2 plt.plot(x, fitted, lw=lw, label=lab) plt.legend() plt.title('Quantile Regression') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/examples/try_smoothers.py000066400000000000000000000051221224417117700272630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 01 15:17:52 2011 Author: Mike Author: Josef mainly script for checking Kernel Regression """ import numpy as np if __name__ == "__main__": #from statsmodels.sandbox.nonparametric import smoothers as s from statsmodels.sandbox.nonparametric import smoothers, kernels import matplotlib.pyplot as plt #from numpy import sin, array, random import time np.random.seed(500) nobs = 250 sig_fac = 0.5 #x = np.random.normal(size=nobs) x = np.random.uniform(-2, 2, size=nobs) #y = np.array([np.sin(i*5)/i + 2*i + (3+i)*np.random.normal() for i in x]) y = np.sin(x*5)/x + 2*x + sig_fac * (3+x)*np.random.normal(size=nobs) K = kernels.Biweight(0.25) K2 = kernels.CustomKernel(lambda x: (1 - x*x)**2, 0.25, domain = [-1.0, 1.0]) KS = smoothers.KernelSmoother(x, y, K) KS2 = smoothers.KernelSmoother(x, y, K2) KSx = np.arange(-3, 3, 0.1) start = time.time() KSy = KS.conf(KSx) KVar = KS.std(KSx) print time.time() - start # This should be significantly quicker... start = time.time() # KS2y = KS2.conf(KSx) # K2Var = KS2.std(KSx) # print time.time() - start # ...than this. KSConfIntx, KSConfInty = KS.conf(15) print "Norm const should be 0.9375" print K2.norm_const print "L2 Norms Should Match:" print K.L2Norm print K2.L2Norm print "Fit values should match:" #print zip(KSy, KS2y) print KSy[28] print KS2y[28] print "Var values should match:" #print zip(KVar, K2Var) print KVar[39] print K2Var[39] fig = plt.figure() ax = fig.add_subplot(221) ax.plot(x, y, "+") ax.plot(KSx, KSy, "-o") #ax.set_ylim(-20, 30) ax2 = fig.add_subplot(222) ax2.plot(KSx, KVar, "-o") ax3 = fig.add_subplot(223) ax3.plot(x, y, "+") ax3.plot(KSx, KS2y, "-o") #ax3.set_ylim(-20, 30) ax4 = fig.add_subplot(224) ax4.plot(KSx, K2Var, "-o") fig2 = plt.figure() ax5 = fig2.add_subplot(111) ax5.plot(x, y, "+") ax5.plot(KSConfIntx, KSConfInty, "-o") import statsmodels.nonparametric.smoothers_lowess as lo ys = lo.lowess(y, x) ax5.plot(ys[:,0], ys[:,1], 'b-') ys2 = lo.lowess(y, x, frac=0.25) ax5.plot(ys2[:,0], ys2[:,1], 'b--', lw=2) #need to sort for matplolib plot ? xind = np.argsort(x) pmod = smoothers.PolySmoother(5, x[xind]) pmod.fit(y[xind]) yp = pmod(x[xind]) ax5.plot(x[xind], yp, 'k-') ax5.set_title('Kernel regression, lowess - blue, polysmooth - black') #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/formula.py000066400000000000000000000545251224417117700242040ustar00rootroot00000000000000""" Provides the basic classes needed to specify statistical models. namespace : dictionary mapping from names to data, used to associate data to a formula or term """ import copy import types import numpy as np try: set except NameError: from sets import Set as set __docformat__ = 'restructuredtext' default_namespace = {} class Term(object): """ This class is very simple: it is just a named term in a model formula. It is also callable: by default it namespace[self.name], where namespace defaults to formula.default_namespace. When called in an instance of formula, the namespace used is that formula's namespace. Inheritance of the namespace under +,*,- operations: ---------------------------------------------------- By default, the namespace is empty, which means it must be specified before evaluating the design matrix. When it is unambiguous, the namespaces of objects are derived from the context. Rules: ------ i) "X * I", "X + I", "X**i": these inherit X's namespace ii) "F.main_effect()": this inherits the Factor F's namespace iii) "A-B": this inherits A's namespace iv) if A.namespace == B.namespace, then A+B inherits this namespace v) if A.namespace == B.namespace, then A*B inherits this namespace Equality of namespaces: ----------------------- This is done by comparing the namespaces directly, if an exception is raised in the check of equality, they are assumed not to be equal. """ def __pow__(self, power): """ Raise the quantitative term's values to an integer power, i.e. polynomial. """ try: power = float(power) except: raise ValueError, 'expecting a float' if power == int(power): name = '%s^%d' % (self.name, int(power)) else: name = '%s^%0.2f' % (self.name, power) value = Quantitative(name, func=self, transform=lambda x: np.power(x, power)) value.power = power value.namespace = self.namespace return value def __init__(self, name, func=None, termname=None): self.name = name self.__namespace = None if termname is None: self.termname = name else: self.termname = termname if type(self.termname) is not types.StringType: raise ValueError, 'expecting a string for termname' if func: self.func = func # Namespace in which self.name will be looked up in, if needed def _get_namespace(self): if isinstance(self.__namespace, np.ndarray): return self.__namespace else: return self.__namespace or default_namespace def _set_namespace(self, value): self.__namespace = value def _del_namespace(self): del self.__namespace namespace = property(_get_namespace, _set_namespace, _del_namespace) def __str__(self): """ '' % self.termname """ return '' % self.termname def __add__(self, other): """ Formula(self) + Formula(other) """ fother = Formula(other, namespace=other.namespace) f = fother + self if _namespace_equal(fother.namespace, self.namespace): f.namespace = self.namespace return f def __mul__(self, other): """ Formula(self) * Formula(other) """ if type(other) is Term and other.name is 'intercept': f = Formula(self, namespace=self.namespace) elif self.name is 'intercept': f = Formula(other, namespace=other.namespace) else: other = Formula(other, namespace=other.namespace) f = other * self if _namespace_equal(other.namespace, self.namespace): f.namespace = self.namespace return f def names(self): """ Return the names of the columns in design associated to the terms, i.e. len(self.names()) = self().shape[0]. """ if type(self.name) is types.StringType: return [self.name] else: return list(self.name) def __call__(self, *args, **kw): """ Return the columns associated to self in a design matrix. If the term has no 'func' attribute, it returns ``self.namespace[self.termname]`` else, it returns ``self.func(*args, **kw)`` """ if not hasattr(self, 'func'): val = self.namespace[self.termname] else: val = self.func if callable(val): if isinstance(val, (Term, Formula)): val = copy.copy(val) val.namespace = self.namespace val = val(*args, **kw) val = np.asarray(val) return np.squeeze(val) class Factor(Term): """A categorical factor.""" def __init__(self, termname, keys, ordinal=False): """ Factor is initialized with keys, representing all valid levels of the factor. If ordinal is False, keys can have repeats: set(keys) is what is used. If ordinal is True, the order is taken from the keys, and there should be no repeats. """ if not ordinal: self.keys = list(set(keys)) self.keys.sort() else: self.keys = keys if len(set(keys)) != len(list(keys)): raise ValueError, 'keys for ordinal Factor should be unique, in increasing order' self._name = termname self.termname = termname self.ordinal = ordinal if self.ordinal: name = self.termname else: name = ['(%s==%s)' % (self.termname, str(key)) for key in self.keys] Term.__init__(self, name, termname=self.termname, func=self.get_columns) def get_columns(self, *args, **kw): """ Calling function for factor instance. """ v = self.namespace[self._name] while True: if callable(v): if isinstance(v, (Term, Formula)): v = copy.copy(v) v.namespace = self.namespace v = v(*args, **kw) else: break n = len(v) if self.ordinal: col = [float(self.keys.index(v[i])) for i in range(n)] return np.array(col) else: value = [] for key in self.keys: col = [float((v[i] == key)) for i in range(n)] value.append(col) return np.array(value) def values(self, *args, **kw): """ Return the keys of the factor, rather than the columns of the design matrix. """ del(self.func) val = self(*args, **kw) self.func = self.get_columns return val def verify(self, values): """ Verify that all values correspond to valid keys in self. """ s = set(values) if not s.issubset(self.keys): raise ValueError, 'unknown keys in values' def __add__(self, other): """ Formula(self) + Formula(other) When adding \'intercept\' to a factor, this just returns Formula(self, namespace=self.namespace) """ if type(other) is Term and other.name is 'intercept': return Formula(self, namespace=self.namespace) else: return Term.__add__(self, other) def main_effect(self, reference=None): """ Return the 'main effect' columns of a factor, choosing an optional reference key. The reference key can be one of the keys of the Factor, or an integer, representing which column to remove. It defaults to 0. """ names = self.names() if reference is None: reference = 0 else: try: reference = self.keys.index(reference) except ValueError: reference = int(reference) def maineffect_func(value, reference=reference): rvalue = [] keep = range(value.shape[0]) keep.pop(reference) for i in range(len(keep)): rvalue.append(value[keep[i]] - value[reference]) return np.array(rvalue) keep = range(len(self.names())) keep.pop(reference) __names = self.names() _names = ['%s-%s' % (__names[keep[i]], __names[reference]) for i in range(len(keep))] value = Quantitative(_names, func=self, termname='%s:maineffect' % self.termname, transform=maineffect_func) value.namespace = self.namespace return value def __getitem__(self, key): """ Retrieve the column corresponding to key in a Formula. :Parameters: key : one of the Factor's keys :Returns: ndarray corresponding to key, when evaluated in current namespace """ if not self.ordinal: i = self.names().index('(%s==%s)' % (self.termname, str(key))) return self()[i] else: v = self.namespace[self._name] return np.array([(vv == key) for vv in v]).astype(np.float) class Quantitative(Term): """ A subclass of term that can be used to apply point transformations of another term, i.e. to take powers: >>> import numpy as np >>> from nipy.fixes.scipy.stats.models import formula >>> X = np.linspace(0,10,101) >>> x = formula.Term('X') >>> x.namespace={'X':X} >>> x2 = x**2 >>> print np.allclose(x()**2, x2()) True >>> x3 = formula.Quantitative('x2', func=x, transform=lambda x: x**2) >>> x3.namespace = x.namespace >>> print np.allclose(x()**2, x3()) True """ def __init__(self, name, func=None, termname=None, transform=lambda x: x): self.transform = transform Term.__init__(self, name, func=func, termname=termname) def __call__(self, *args, **kw): """ A quantitative is just like term, except there is an additional transformation: self.transform. """ return self.transform(Term.__call__(self, *args, **kw)) class Formula(object): """ A formula object for manipulating design matrices in regression models, essentially consisting of a list of term instances. The object supports addition and multiplication which correspond to concatenation and pairwise multiplication, respectively, of the columns of the two formulas. """ def _get_namespace(self): if isinstance(self.__namespace, np.ndarray): return self.__namespace else: return self.__namespace or default_namespace def _set_namespace(self, value): self.__namespace = value def _del_namespace(self): del self.__namespace namespace = property(_get_namespace, _set_namespace, _del_namespace) def _terms_changed(self): self._names = self.names() self._termnames = self.termnames() def __init__(self, termlist, namespace=default_namespace): """ Create a formula from either: i. a `formula` object ii. a sequence of `term` instances iii. one `term` """ self.__namespace = namespace if isinstance(termlist, Formula): self.terms = copy.copy(list(termlist.terms)) elif type(termlist) is types.ListType: self.terms = termlist elif isinstance(termlist, Term): self.terms = [termlist] else: raise ValueError self._terms_changed() def __str__(self): """ String representation of list of termnames of a formula. """ value = [] for term in self.terms: value += [term.termname] return '' % ' + '.join(value) def __call__(self, *args, **kw): """ Create (transpose) of the design matrix of the formula within namespace. Extra arguments are passed to each term instance. If the formula just contains an intercept, then the keyword argument 'nrow' indicates the number of rows (observations). """ if 'namespace' in kw: namespace = kw['namespace'] else: namespace = self.namespace allvals = [] intercept = False iindex = 0 for t in self.terms: t = copy.copy(t) t.namespace = namespace val = t(*args, **kw) isintercept = False if hasattr(t, "termname"): if t.termname == 'intercept': intercept = True isintercept = True interceptindex = iindex allvals.append(None) if val.ndim == 1 and not isintercept: val.shape = (1, val.shape[0]) allvals.append(val) elif not isintercept: allvals.append(val) iindex += 1 if not intercept: try: allvals = np.concatenate(allvals) except: pass else: nrow = kw.get('nrow', -1) if allvals != []: if interceptindex > 0: n = allvals[0].shape[1] else: n = allvals[1].shape[1] allvals[interceptindex] = np.ones((1,n), np.float64) allvals = np.concatenate(allvals) elif nrow <= 1: raise ValueError, 'with only intercept in formula, keyword \'nrow\' argument needed' else: allvals = I(nrow=nrow) allvals.shape = (1,) + allvals.shape return np.squeeze(allvals) def hasterm(self, query_term): """ Determine whether a given term is in a formula. """ if not isinstance(query_term, Formula): if type(query_term) == type("name"): try: query = self[query_term] return query.termname in self.termnames() except: return False elif isinstance(query_term, Term): return query_term.termname in self.termnames() elif len(query_term.terms) == 1: query_term = query_term.terms[0] return query_term.termname in self.termnames() else: raise ValueError, 'more than one term passed to hasterm' def __getitem__(self, name): t = self.termnames() if name in t: return self.terms[t.index(name)] else: raise KeyError, 'formula has no such term: %s' % repr(name) def termcolumns(self, query_term, dict=False): """ Return a list of the indices of all columns associated to a given term. """ if self.hasterm(query_term): names = query_term.names() value = {} for name in names: value[name] = self._names.index(name) else: raise ValueError, 'term not in formula' if dict: return value else: return value.values() def names(self): """ Return a list of the names in the formula. The order of the names corresponds to the order of the columns when self is evaluated. """ allnames = [] for term in self.terms: allnames += term.names() return allnames def termnames(self): """ Return a list of the term names in the formula. These are the names of each term instance in self. """ names = [] for term in self.terms: names += [term.termname] return names def design(self, *args, **kw): """ ``transpose(self(*args, **kw))`` """ return self(*args, **kw).T def __mul__(self, other, nested=False): """ This returns a formula whose columns are the pairwise product of the columns of self and other. TO DO: check for nesting relationship. Should not be too difficult. """ other = Formula(other) selftermnames = self.termnames() othertermnames = other.termnames() I = len(selftermnames) J = len(othertermnames) terms = [] termnames = [] for i in range(I): for j in range(J): termname = '%s*%s' % (str(selftermnames[i]), str(othertermnames[j])) pieces = termname.split('*') pieces.sort() termname = '*'.join(pieces) termnames.append(termname) selfnames = self.terms[i].names() othernames = other.terms[j].names() if self.terms[i].name is 'intercept': _term = other.terms[j] _term.namespace = other.namespace elif other.terms[j].name is 'intercept': _term = self.terms[i] _term.namespace = self.namespace else: names = [] d1 = len(selfnames) d2 = len(othernames) for r in range(d1): for s in range(d2): name = '%s*%s' % (str(selfnames[r]), str(othernames[s])) pieces = name.split('*') pieces.sort() name = '*'.join(pieces) names.append(name) def product_func(value, d1=d1, d2=d2): out = [] for r in range(d1): for s in range(d2): out.append(value[r] * value[d1+s]) return np.array(out) cself = copy.copy(self.terms[i]) cother = copy.copy(other.terms[j]) sumterms = cself + cother sumterms.terms = [cself, cother] # enforce the order we want _term = Quantitative(names, func=sumterms, termname=termname, transform=product_func) if _namespace_equal(self.namespace, other.namespace): _term.namespace = self.namespace terms.append(_term) return Formula(terms) def __add__(self, other): """ Return a formula whose columns are the concatenation of the columns of self and other. terms in the formula are sorted alphabetically. """ other = Formula(other) terms = self.terms + other.terms pieces = [(term.name, term) for term in terms] pieces.sort() terms = [piece[1] for piece in pieces] f = Formula(terms) if _namespace_equal(self.namespace, other.namespace): f.namespace = self.namespace return f def __sub__(self, other): """ Return a formula with all terms in other removed from self. If other contains term instances not in formula, this function does not raise an exception. """ other = Formula(other) terms = copy.copy(self.terms) for term in other.terms: for i in range(len(terms)): if terms[i].termname == term.termname: terms.pop(i) break f = Formula(terms) f.namespace = self.namespace return f def isnested(A, B, namespace=None): """ Is factor B nested within factor A or vice versa: a very crude test which depends on the namespace. If they are nested, returns (True, F) where F is the finest level of the relationship. Otherwise, returns (False, None) """ if namespace is not None: A = copy.copy(A); A.namespace = namespace B = copy.copy(B); B.namespace = namespace a = A(values=True)[0] b = B(values=True)[0] if len(a) != len(b): raise ValueError, 'A() and B() should be sequences of the same length' nA = len(set(a)) nB = len(set(b)) n = max(nA, nB) AB = [(a[i],b[i]) for i in range(len(a))] nAB = len(set(AB)) if nAB == n: if nA > nB: F = A else: F = B return (True, F) else: return (False, None) def _intercept_fn(nrow=1, **extra): return np.ones((1,nrow)) I = Term('intercept', func=_intercept_fn) I.__doc__ = """ Intercept term in a formula. If intercept is the only term in the formula, then a keyword argument \'nrow\' is needed. >>> from nipy.fixes.scipy.stats.models.formula import Formula, I >>> I() array(1.0) >>> I(nrow=5) array([ 1., 1., 1., 1., 1.]) >>> f=Formula(I) >>> f(nrow=5) array([1, 1, 1, 1, 1]) """ def interactions(terms, order=[1,2]): """ Output all pairwise interactions of given order of a sequence of terms. The argument order is a sequence specifying which order of interactions should be generated -- the default creates main effects and two-way interactions. If order is an integer, it is changed to range(1,order+1), so order=3 is equivalent to order=[1,2,3], generating all one, two and three-way interactions. If any entry of order is greater than len(terms), it is effectively treated as len(terms). >>> print interactions([Term(l) for l in ['a', 'b', 'c']]) >>> >>> print interactions([Term(l) for l in ['a', 'b', 'c']], order=range(5)) >>> """ l = len(terms) values = {} if np.asarray(order).shape == (): order = range(1, int(order)+1) # First order for o in order: I = np.indices((l,)*(o)) I.shape = (I.shape[0], np.product(I.shape[1:])) for m in range(I.shape[1]): # only keep combinations that have unique entries if (np.unique(I[:,m]).shape == I[:,m].shape and np.alltrue(np.equal(np.sort(I[:,m]), I[:,m]))): ll = [terms[j] for j in I[:,m]] v = ll[0] for ii in range(len(ll)-1): v *= ll[ii+1] values[tuple(I[:,m])] = v key = values.keys()[0] value = values[key]; del(values[key]) for v in values.values(): value += v return value def _namespace_equal(space1, space2): return space1 is space2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/gam.py000066400000000000000000000356231224417117700233010ustar00rootroot00000000000000""" Generalized additive models Requirements for smoothers -------------------------- smooth(y, weights=xxx) : ? no return ? alias for fit predict(x=None) : smoothed values, fittedvalues or for new exog df_fit() : degress of freedom of fit ? Notes ----- - using PolySmoother works for AdditiveModel, and GAM with Poisson and Binomial - testfailure with Gamma, no other families tested - there is still an indeterminacy in the split up of the constant across components (smoothers) and alpha, sum, i.e. constant, looks good. - role of offset, that I haven't tried to figure out yet Refactoring ----------- currently result is attached to model instead of other way around split up Result in class for AdditiveModel and for GAM, subclass GLMResults, needs verification that result statistics are appropriate how much inheritance, double inheritance? renamings and cleanup interface to other smoothers, scipy splines basic unittests as support for refactoring exist, but we should have a test case for gamma and the others. Advantage of PolySmoother is that we can benchmark against the parametric GLM results. """ # JP: # changes: use PolySmoother instead of crashing bspline # TODO: check/catalogue required interface of a smoother # TODO: replace default smoother by corresponding function to initialize # other smoothers # TODO: fix iteration, don't define class with iterator methods, use looping; # add maximum iteration and other optional stop criteria # fixed some of the dimension problems in PolySmoother, # now graph for example looks good # NOTE: example script is now in examples folder #update: I did some of the above, see module docstring import numpy as np from statsmodels.genmod import families from statsmodels.sandbox.nonparametric.smoothers import PolySmoother from statsmodels.genmod.generalized_linear_model import GLM DEBUG = False def default_smoother(x, s_arg=None): ''' ''' # _x = x.copy() # _x.sort() _x = np.sort(x) n = x.shape[0] # taken form smooth.spline in R #if n < 50: if n < 500: nknots = n else: a1 = np.log(50) / np.log(2) a2 = np.log(100) / np.log(2) a3 = np.log(140) / np.log(2) a4 = np.log(200) / np.log(2) if n < 200: nknots = 2**(a1 + (a2 - a1) * (n - 50)/150.) elif n < 800: nknots = 2**(a2 + (a3 - a2) * (n - 200)/600.) elif n < 3200: nknots = 2**(a3 + (a4 - a3) * (n - 800)/2400.) else: nknots = 200 + (n - 3200.)**0.2 knots = _x[np.linspace(0, n-1, nknots).astype(np.int32)] #s = SmoothingSpline(knots, x=x.copy()) #when I set order=2, I get nans in the GAM prediction if s_arg is None: order = 3 #what about knots? need smoother *args or **kwds else: order = s_arg s = PolySmoother(order, x=x.copy()) #TODO: change order, why copy? # s.gram(d=2) # s.target_df = 5 return s class Offset(object): def __init__(self, fn, offset): self.fn = fn self.offset = offset def __call__(self, *args, **kw): return self.fn(*args, **kw) + self.offset class Results(object): def __init__(self, Y, alpha, exog, smoothers, family, offset): self.nobs, self.k_vars = exog.shape #assumes exog is 2d #weird: If I put the previous line after the definition of self.mu, # then the attributed don't get added self.Y = Y self.alpha = alpha self.smoothers = smoothers self.offset = offset self.family = family self.exog = exog self.offset = offset self.mu = self.linkinversepredict(exog) #TODO: remove __call__ def __call__(self, exog): '''expected value ? check new GLM, same as mu for given exog maybe remove this ''' return self.linkinversepredict(exog) def linkinversepredict(self, exog): #TODO what's the name in GLM '''expected value ? check new GLM, same as mu for given exog ''' return self.family.link.inverse(self.predict(exog)) def predict(self, exog): '''predict response, sum of smoothed components TODO: What's this in the case of GLM, corresponds to X*beta ? ''' #note: sum is here over axis=0, #TODO: transpose in smoothed and sum over axis=1 #BUG: there is some inconsistent orientation somewhere #temporary hack, won't work for 1d #print dir(self) #print 'self.nobs, self.k_vars', self.nobs, self.k_vars exog_smoothed = self.smoothed(exog) #print 'exog_smoothed.shape', exog_smoothed.shape if exog_smoothed.shape[0] == self.k_vars: import warnings warnings.warn("old orientation, colvars, will go away", FutureWarning) return np.sum(self.smoothed(exog), axis=0) + self.alpha if exog_smoothed.shape[1] == self.k_vars: return np.sum(exog_smoothed, axis=1) + self.alpha else: raise ValueError('shape mismatch in predict') def smoothed(self, exog): '''get smoothed prediction for each component ''' #bug: with exog in predict I get a shape error #print 'smoothed', exog.shape, self.smoothers[0].predict(exog).shape #there was a mistake exog didn't have column index i return np.array([self.smoothers[i].predict(exog[:,i]) + self.offset[i] #shouldn't be a mistake because exog[:,i] is attached to smoother, but #it is for different exog #return np.array([self.smoothers[i].predict() + self.offset[i] for i in range(exog.shape[1])]).T def smoothed_demeaned(self, exog): components = self.smoothed(exog) means = components.mean(0) constant = means.sum() + self.alpha components_demeaned = components - means return components_demeaned, constant class AdditiveModel(object): '''additive model with non-parametric, smoothed components Parameters ---------- exog : ndarray smoothers : None or list of smoother instances smoother instances not yet checked weights : None or ndarray family : None or family instance I think only used because of shared results with GAM and subclassing. If None, then Gaussian is used. ''' def __init__(self, exog, smoothers=None, weights=None, family=None): self.exog = exog if not weights is None: self.weights = weights else: self.weights = np.ones(self.exog.shape[0]) self.smoothers = smoothers or [default_smoother(exog[:,i]) for i in range(exog.shape[1])] #TODO: why do we set here df, refactoring temporary? for i in range(exog.shape[1]): self.smoothers[i].df = 10 if family is None: self.family = families.Gaussian() else: self.family = family #self.family = families.Gaussian() def _iter__(self): '''initialize iteration ?, should be removed ''' self.iter = 0 self.dev = np.inf return self def next(self): '''internal calculation for one fit iteration BUG: I think this does not improve, what is supposed to improve offset doesn't seem to be used, neither an old alpha The smoothers keep coef/params from previous iteration ''' _results = self.results Y = self.results.Y mu = _results.predict(self.exog) #TODO offset is never used ? offset = np.zeros(self.exog.shape[1], np.float64) alpha = (Y * self.weights).sum() / self.weights.sum() for i in range(self.exog.shape[1]): tmp = self.smoothers[i].predict() #TODO: check what smooth needs to do #smooth (alias for fit, fit given x to new y and attach #print 'next shape', (Y - alpha - mu + tmp).shape bad = np.isnan(Y - alpha - mu + tmp).any() if bad: #temporary assert while debugging print Y, alpha, mu, tmp raise #self.smoothers[i].smooth(Y - alpha - mu + tmp, self.smoothers[i].smooth(Y - mu + tmp, weights=self.weights) tmp2 = self.smoothers[i].predict() #fittedvalues of previous smooth/fit self.results.offset[i] = -(tmp2*self.weights).sum() / self.weights.sum() #self.offset used in smoothed if DEBUG: print self.smoothers[i].params mu += tmp2 - tmp #change setting offset here: tests still pass, offset equal to constant #in component ??? what's the effect of offset offset = self.results.offset #print self.iter #self.iter += 1 #missing incrementing of iter counter NOT return Results(Y, alpha, self.exog, self.smoothers, self.family, offset) def cont(self): '''condition to continue iteration loop Parameters ---------- tol Returns ------- cont : bool If true, then iteration should be continued. ''' self.iter += 1 #moved here to always count, not necessary if DEBUG: print self.iter, self.results.Y.shape, print self.results.predict(self.exog).shape, self.weights.shape curdev = (((self.results.Y - self.results.predict(self.exog))**2) * self.weights).sum() if self.iter > self.maxiter: #kill it, no max iterationoption return False if np.fabs((self.dev - curdev) / curdev) < self.rtol: self.dev = curdev return False #self.iter += 1 self.dev = curdev return True def df_resid(self): '''degrees of freedom of residuals, ddof is sum of all smoothers df ''' return self.results.Y.shape[0] - np.array([self.smoothers[i].df_fit() for i in range(self.exog.shape[1])]).sum() def estimate_scale(self): '''estimate standard deviation of residuals ''' #TODO: remove use of self.results.__call__ return ((self.results.Y - self.results(self.exog))**2).sum() / self.df_resid() def fit(self, Y, rtol=1.0e-06, maxiter=30): '''fit the model to a given endogenous variable Y This needs to change for consistency with statsmodels ''' self.rtol = rtol self.maxiter = maxiter #iter(self) # what does this do? anything? self._iter__() mu = 0 alpha = (Y * self.weights).sum() / self.weights.sum() offset = np.zeros(self.exog.shape[1], np.float64) for i in range(self.exog.shape[1]): self.smoothers[i].smooth(Y - alpha - mu, weights=self.weights) tmp = self.smoothers[i].predict() offset[i] = (tmp * self.weights).sum() / self.weights.sum() tmp -= tmp.sum() mu += tmp self.results = Results(Y, alpha, self.exog, self.smoothers, self.family, offset) while self.cont(): self.results = self.next() if self.iter >= self.maxiter: import warnings warnings.warn('maximum number of iterations reached') return self.results class Model(GLM, AdditiveModel): #class Model(AdditiveModel): #TODO: what does GLM do? Is it actually used ? #only used in __init__, dropping it doesn't change results #but where gets family attached now? - weird, it's Gaussian in this case now #also where is the link defined? #AdditiveModel overwrites family and sets it to Gaussian - corrected #I think both GLM and AdditiveModel subclassing is only used in __init__ #niter = 2 # def __init__(self, exog, smoothers=None, family=family.Gaussian()): # GLM.__init__(self, exog, family=family) # AdditiveModel.__init__(self, exog, smoothers=smoothers) # self.family = family def __init__(self, endog, exog, smoothers=None, family=families.Gaussian()): #self.family = family #TODO: inconsistent super __init__ AdditiveModel.__init__(self, exog, smoothers=smoothers, family=family) GLM.__init__(self, endog, exog, family=family) assert self.family is family #make sure we got the right family def next(self): _results = self.results Y = _results.Y if np.isnan(self.weights).all(): print "nanweights1" _results.mu = self.family.link.inverse(_results.predict(self.exog)) #eta = _results.predict(self.exog) #_results.mu = self.family.fitted(eta) weights = self.family.weights(_results.mu) if np.isnan(weights).all(): self.weights = weights print "nanweights2" self.weights = weights if DEBUG: print 'deriv isnan', np.isnan(self.family.link.deriv(_results.mu)).any() #Z = _results.predict(self.exog) + \ Z = _results.predict(self.exog) + \ self.family.link.deriv(_results.mu) * (Y - _results.mu) #- _results.alpha #?added alpha m = AdditiveModel(self.exog, smoothers=self.smoothers, weights=self.weights, family=self.family) #TODO: I don't know what the next two lines do, Z, Y ? which is endog? #Y is original endog, Z is endog for the next step in the iterative solver _results = m.fit(Z) self.history.append([Z, _results.predict(self.exog)]) _results.Y = Y _results.mu = self.family.link.inverse(_results.predict(self.exog)) self.iter += 1 self.results = _results return _results def estimate_scale(self, Y=None): """ Return Pearson\'s X^2 estimate of scale. """ if Y is None: Y = self.Y resid = Y - self.results.mu return (np.power(resid, 2) / self.family.variance(self.results.mu)).sum() \ / self.df_resid #TODO check this #/ AdditiveModel.df_resid(self) #what is the class doing here? def fit(self, Y, rtol=1.0e-06, maxiter=30): self.rtol = rtol self.maxiter = maxiter self.Y = np.asarray(Y, np.float64) self.history = [] #iter(self) self._iter__() #TODO code duplication with next? alpha = self.Y.mean() mu0 = self.family.starting_mu(Y) #Z = self.family.link(alpha) + self.family.link.deriv(alpha) * (Y - alpha) Z = self.family.link(alpha) + self.family.link.deriv(alpha) * (Y - mu0) m = AdditiveModel(self.exog, smoothers=self.smoothers, family=self.family) self.results = m.fit(Z) self.results.mu = self.family.link.inverse(self.results.predict(self.exog)) self.results.Y = Y while self.cont(): self.results = self.next() self.scale = self.results.scale = self.estimate_scale() if self.iter >= self.maxiter: import warnings warnings.warn('maximum number of iterations reached') return self.results statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/infotheo.py000066400000000000000000000400311224417117700243350ustar00rootroot00000000000000""" Information Theoretic and Entropy Measures References ---------- Golan, As. 2008. "Information and Entropy Econometrics -- A Review and Synthesis." Foundations And Trends in Econometrics 2(1-2), 1-145. Golan, A., Judge, G., and Miller, D. 1996. Maximum Entropy Econometrics. Wiley & Sons, Chichester. """ #For MillerMadow correction #Miller, G. 1955. Note on the bias of information estimates. Info. Theory # Psychol. Prob. Methods II-B:95-100. #For ChaoShen method #Chao, A., and T.-J. Shen. 2003. Nonparametric estimation of Shannon's index of diversity when #there are unseen species in sample. Environ. Ecol. Stat. 10:429-443. #Good, I. J. 1953. The population frequencies of species and the estimation of population parameters. #Biometrika 40:237-264. #Horvitz, D.G., and D. J. Thompson. 1952. A generalization of sampling without replacement from a finute universe. J. Am. Stat. Assoc. 47:663-685. #For NSB method #Nemenman, I., F. Shafee, and W. Bialek. 2002. Entropy and inference, revisited. In: Dietterich, T., #S. Becker, Z. Gharamani, eds. Advances in Neural Information Processing Systems 14: 471-478. #Cambridge (Massachusetts): MIT Press. #For shrinkage method #Dougherty, J., Kohavi, R., and Sahami, M. (1995). Supervised and unsupervised discretization of #continuous features. In International Conference on Machine Learning. #Yang, Y. and Webb, G. I. (2003). Discretization for naive-bayes learning: managing discretization #bias and variance. Technical Report 2003/131 School of Computer Science and Software Engineer- #ing, Monash University. from scipy import stats import numpy as np from matplotlib import pyplot as plt #TODO: change these to use maxentutils so that over/underflow is handled #with the logsumexp. try: from scipy.maxentropy import logsumexp as sp_logsumexp except: from scipy.misc import logsumexp as sp_logsumexp def logsumexp(a, axis=None): """ Compute the log of the sum of exponentials log(e^{a_1}+...e^{a_n}) of a Avoids numerical overflow. Parameters ---------- a : array-like The vector to exponentiate and sum axis : int, optional The axis along which to apply the operation. Defaults is None. Returns ------- sum(log(exp(a))) Notes ----- This function was taken from the mailing list http://mail.scipy.org/pipermail/scipy-user/2009-October/022931.html This should be superceded by the ufunc when it is finished. """ if axis is None: # Use the scipy.maxentropy version. return sp_logsumexp(a) a = np.asarray(a) shp = list(a.shape) shp[axis] = 1 a_max = a.max(axis=axis) s = np.log(np.exp(a - a_max.reshape(shp)).sum(axis=axis)) lse = a_max + s return lse def _isproperdist(X): """ Checks to see if `X` is a proper probability distribution """ X = np.asarray(X) if not np.allclose(np.sum(X), 1) or not np.all(X>=0) or not np.all(X<=1): return False else: return True def discretize(X, method="ef", nbins=None): """ Discretize `X` Parameters ---------- bins : int, optional Number of bins. Default is floor(sqrt(N)) method : string "ef" is equal-frequency binning "ew" is equal-width binning Examples -------- """ nobs = len(X) if nbins == None: nbins = np.floor(np.sqrt(nobs)) if method == "ef": discrete = np.ceil(nbins * stats.rankdata(X)/nobs) if method == "ew": width = np.max(X) - np.min(X) width = np.floor(width/nbins) svec, ivec = stats.fastsort(X) discrete = np.zeros(nobs) binnum = 1 base = svec[0] discrete[ivec[0]] = binnum for i in xrange(1,nobs): if svec[i] < base + width: discrete[ivec[i]] = binnum else: base = svec[i] binnum += 1 discrete[ivec[i]] = binnum return discrete #TODO: looks okay but needs more robust tests for corner cases def logbasechange(a,b): """ There is a one-to-one transformation of the entropy value from a log base b to a log base a : H_{b}(X)=log_{b}(a)[H_{a}(X)] Returns ------- log_{b}(a) """ return np.log(b)/np.log(a) def natstobits(X): """ Converts from nats to bits """ return logbasechange(np.e, 2) * X def bitstonats(X): """ Converts from bits to nats """ return logbasechange(2, np.e) * X #TODO: make this entropy, and then have different measures as #a method def shannonentropy(px, logbase=2): """ This is Shannon's entropy Parameters ----------- logbase, int or np.e The base of the log px : 1d or 2d array_like Can be a discrete probability distribution, a 2d joint distribution, or a sequence of probabilities. Returns ----- For log base 2 (bits) given a discrete distribution H(p) = sum(px * log2(1/px) = -sum(pk*log2(px)) = E[log2(1/p(X))] For log base 2 (bits) given a joint distribution H(px,py) = -sum_{k,j}*w_{kj}log2(w_{kj}) Notes ----- shannonentropy(0) is defined as 0 """ #TODO: haven't defined the px,py case? px = np.asarray(px) if not np.all(px <= 1) or not np.all(px >= 0): raise ValueError, "px does not define proper distribution" entropy = -np.sum(np.nan_to_num(px*np.log2(px))) if logbase != 2: return logbasechange(2,logbase) * entropy else: return entropy # Shannon's information content def shannoninfo(px, logbase=2): """ Shannon's information Parameters ---------- px : float or array-like `px` is a discrete probability distribution Returns ------- For logbase = 2 np.log2(px) """ px = np.asarray(px) if not np.all(px <= 1) or not np.all(px >= 0): raise ValueError, "px does not define proper distribution" if logbase != 2: return - logbasechange(2,logbase) * np.log2(px) else: return - np.log2(px) def condentropy(px, py, pxpy=None, logbase=2): """ Return the conditional entropy of X given Y. Parameters ---------- px : array-like py : array-like pxpy : array-like, optional If pxpy is None, the distributions are assumed to be independent and conendtropy(px,py) = shannonentropy(px) logbase : int or np.e Returns ------- sum_{kj}log(q_{j}/w_{kj} where q_{j} = Y[j] and w_kj = X[k,j] """ if not _isproperdist(px) or not _isproperdist(py): raise ValueError, "px or py is not a proper probability distribution" if pxpy != None and not _isproperdist(pxpy): raise ValueError, "pxpy is not a proper joint distribtion" if pxpy == None: pxpy = np.outer(py,px) condent = np.sum(pxpy * np.nan_to_num(np.log2(py/pxpy))) if logbase == 2: return condent else: return logbasechange(2, logbase) * condent def mutualinfo(px,py,pxpy, logbase=2): """ Returns the mutual information between X and Y. Parameters ---------- px : array-like Discrete probability distribution of random variable X py : array-like Discrete probability distribution of random variable Y pxpy : 2d array-like The joint probability distribution of random variables X and Y. Note that if X and Y are independent then the mutual information is zero. logbase : int or np.e, optional Default is 2 (bits) Returns ------- shannonentropy(px) - condentropy(px,py,pxpy) """ if not _isproperdist(px) or not _isproperdist(py): raise ValueError, "px or py is not a proper probability distribution" if pxpy != None and not _isproperdist(pxpy): raise ValueError, "pxpy is not a proper joint distribtion" if pxpy == None: pxpy = np.outer(py,px) return shannonentropy(px, logbase=logbase) - condentropy(px,py,pxpy, logbase=logbase) def corrent(px,py,pxpy,logbase=2): """ An information theoretic correlation measure. Reflects linear and nonlinear correlation between two random variables X and Y, characterized by the discrete probability distributions px and py respectively. Parameters ---------- px : array-like Discrete probability distribution of random variable X py : array-like Discrete probability distribution of random variable Y pxpy : 2d array-like, optional Joint probability distribution of X and Y. If pxpy is None, X and Y are assumed to be independent. logbase : int or np.e, optional Default is 2 (bits) Returns ------- mutualinfo(px,py,pxpy,logbase=logbase)/shannonentropy(py,logbase=logbase) Notes ----- This is also equivalent to corrent(px,py,pxpy) = 1 - condent(px,py,pxpy)/shannonentropy(py) """ if not _isproperdist(px) or not _isproperdist(py): raise ValueError, "px or py is not a proper probability distribution" if pxpy != None and not _isproperdist(pxpy): raise ValueError, "pxpy is not a proper joint distribtion" if pxpy == None: pxpy = np.outer(py,px) return mutualinfo(px,py,pxpy,logbase=logbase)/shannonentropy(py, logbase=logbase) def covent(px,py,pxpy,logbase=2): """ An information theoretic covariance measure. Reflects linear and nonlinear correlation between two random variables X and Y, characterized by the discrete probability distributions px and py respectively. Parameters ---------- px : array-like Discrete probability distribution of random variable X py : array-like Discrete probability distribution of random variable Y pxpy : 2d array-like, optional Joint probability distribution of X and Y. If pxpy is None, X and Y are assumed to be independent. logbase : int or np.e, optional Default is 2 (bits) Returns ------- condent(px,py,pxpy,logbase=logbase) + condent(py,px,pxpy, logbase=logbase) Notes ----- This is also equivalent to covent(px,py,pxpy) = condent(px,py,pxpy) + condent(py,px,pxpy) """ if not _isproperdist(px) or not _isproperdist(py): raise ValueError, "px or py is not a proper probability distribution" if pxpy != None and not _isproperdist(pxpy): raise ValueError, "pxpy is not a proper joint distribtion" if pxpy == None: pxpy = np.outer(py,px) return condent(px,py,pxpy,logbase=logbase) + condent(py,px,pxpy, logbase=logbase) #### Generalized Entropies #### def renyientropy(px,alpha=1,logbase=2,measure='R'): """ Renyi's generalized entropy Parameters ---------- px : array-like Discrete probability distribution of random variable X. Note that px is assumed to be a proper probability distribution. logbase : int or np.e, optional Default is 2 (bits) alpha : float or inf The order of the entropy. The default is 1, which in the limit is just Shannon's entropy. 2 is Renyi (Collision) entropy. If the string "inf" or numpy.inf is specified the min-entropy is returned. measure : str, optional The type of entropy measure desired. 'R' returns Renyi entropy measure. 'T' returns the Tsallis entropy measure. Returns ------- 1/(1-alpha)*log(sum(px**alpha)) In the limit as alpha -> 1, Shannon's entropy is returned. In the limit as alpha -> inf, min-entropy is returned. """ #TODO:finish returns #TODO:add checks for measure if not _isproperdist(px): raise ValueError, "px is not a proper probability distribution" alpha = float(alpha) if alpha == 1: genent = shannonentropy(px) if logbase != 2: return logbasechange(2, logbase) * genent return genent elif 'inf' in string(alpha).lower() or alpha == np.inf: return -np.log(np.max(px)) # gets here if alpha != (1 or inf) px = px**alpha genent = np.log(px.sum()) if logbase == 2: return 1/(1-alpha) * genent else: return 1/(1-alpha) * logbasechange(2, logbase) * genent #TODO: before completing this, need to rethink the organization of # (relative) entropy measures, ie., all put into one function # and have kwdargs, etc.? def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'): """ Generalized cross-entropy measures. Parameters ---------- px : array-like Discrete probability distribution of random variable X py : array-like Discrete probability distribution of random variable Y pxpy : 2d array-like, optional Joint probability distribution of X and Y. If pxpy is None, X and Y are assumed to be independent. logbase : int or np.e, optional Default is 2 (bits) measure : str, optional The measure is the type of generalized cross-entropy desired. 'T' is the cross-entropy version of the Tsallis measure. 'CR' is Cressie-Read measure. """ if __name__ == "__main__": print "From Golan (2008) \"Information and Entropy Econometrics -- A Review \ and Synthesis" print "Table 3.1" # Examples from Golan (2008) X = [.2,.2,.2,.2,.2] Y = [.322,.072,.511,.091,.004] for i in X: print shannoninfo(i) for i in Y: print shannoninfo(i) print shannonentropy(X) print shannonentropy(Y) p = [1e-5,1e-4,.001,.01,.1,.15,.2,.25,.3,.35,.4,.45,.5] plt.subplot(111) plt.ylabel("Information") plt.xlabel("Probability") x = np.linspace(0,1,100001) plt.plot(x, shannoninfo(x)) # plt.show() plt.subplot(111) plt.ylabel("Entropy") plt.xlabel("Probability") x = np.linspace(0,1,101) plt.plot(x, map(shannonentropy, zip(x,1-x))) # plt.show() # define a joint probability distribution # from Golan (2008) table 3.3 w = np.array([[0,0,1./3],[1/9.,1/9.,1/9.],[1/18.,1/9.,1/6.]]) # table 3.4 px = w.sum(0) py = w.sum(1) H_X = shannonentropy(px) H_Y = shannonentropy(py) H_XY = shannonentropy(w) H_XgivenY = condentropy(px,py,w) H_YgivenX = condentropy(py,px,w) # note that cross-entropy is not a distance measure as the following shows D_YX = logbasechange(2,np.e)*stats.entropy(px, py) D_XY = logbasechange(2,np.e)*stats.entropy(py, px) I_XY = mutualinfo(px,py,w) print "Table 3.3" print H_X,H_Y, H_XY, H_XgivenY, H_YgivenX, D_YX, D_XY, I_XY print "discretize functions" X=np.array([21.2,44.5,31.0,19.5,40.6,38.7,11.1,15.8,31.9,25.8,20.2,14.2, 24.0,21.0,11.3,18.0,16.3,22.2,7.8,27.8,16.3,35.1,14.9,17.1,28.2,16.4, 16.5,46.0,9.5,18.8,32.1,26.1,16.1,7.3,21.4,20.0,29.3,14.9,8.3,22.5, 12.8,26.9,25.5,22.9,11.2,20.7,26.2,9.3,10.8,15.6]) discX = discretize(X) #CF: R's infotheo #TODO: compare to pyentropy quantize? print print "Example in section 3.6 of Golan, using table 3.3" print "Bounding errors using Fano's inequality" print "H(P_{e}) + P_{e}log(K-1) >= H(X|Y)" print "or, a weaker inequality" print "P_{e} >= [H(X|Y) - 1]/log(K)" print "P(x) = %s" % px print "X = 3 has the highest probability, so this is the estimate Xhat" pe = 1 - px[2] print "The probability of error Pe is 1 - p(X=3) = %0.4g" % pe H_pe = shannonentropy([pe,1-pe]) print "H(Pe) = %0.4g and K=3" % H_pe print "H(Pe) + Pe*log(K-1) = %0.4g >= H(X|Y) = %0.4g" % \ (H_pe+pe*np.log2(2), H_XgivenY) print "or using the weaker inequality" print "Pe = %0.4g >= [H(X) - 1]/log(K) = %0.4g" % (pe, (H_X - 1)/np.log2(3)) print "Consider now, table 3.5, where there is additional information" print "The conditional probabilities of P(X|Y=y) are " w2 = np.array([[0.,0.,1.],[1/3.,1/3.,1/3.],[1/6.,1/3.,1/2.]]) print w2 # not a proper distribution? print "The probability of error given this information is" print "Pe = [H(X|Y) -1]/log(K) = %0.4g" % ((np.mean([0,shannonentropy(w2[1]),shannonentropy(w2[2])])-1)/np.log2(3)) print "such that more information lowers the error" ### Stochastic processes markovchain = np.array([[.553,.284,.163],[.465,.312,.223],[.420,.322,.258]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/km_class.py000066400000000000000000000266701224417117700243330ustar00rootroot00000000000000#a class for the Kaplan-Meier estimator import numpy as np from math import sqrt import matplotlib.pyplot as plt class KAPLAN_MEIER(object): def __init__(self, data, timesIn, groupIn, censoringIn): raise RuntimeError('Newer version of Kaplan-Meier class available in survival2.py') #store the inputs self.data = data self.timesIn = timesIn self.groupIn = groupIn self.censoringIn = censoringIn def fit(self): #split the data into groups based on the predicting variable #get a set of all the groups groups = list(set(self.data[:,self.groupIn])) #create an empty list to store the data for different groups groupList = [] #create an empty list for each group and add it to groups for i in range(len(groups)): groupList.append([]) #iterate through all the groups in groups for i in range(len(groups)): #iterate though the rows of dataArray for j in range(len(self.data)): #test if this row has the correct group if self.data[j,self.groupIn] == groups[i]: #add the row to groupList groupList[i].append(self.data[j]) #create an empty list to store the times for each group timeList = [] #iterate through all the groups for i in range(len(groupList)): #create an empty list times = [] #iterate through all the rows of the group for j in range(len(groupList[i])): #get a list of all the times in the group times.append(groupList[i][j][self.timesIn]) #get a sorted set of the times and store it in timeList times = list(sorted(set(times))) timeList.append(times) #get a list of the number at risk and events at each time #create an empty list to store the results in timeCounts = [] #create an empty list to hold points for plotting points = [] #create a list for points where censoring occurs censoredPoints = [] #iterate trough each group for i in range(len(groupList)): #initialize a variable to estimate the survival function survival = 1 #initialize a variable to estimate the variance of #the survival function varSum = 0 #initialize a counter for the number at risk riskCounter = len(groupList[i]) #create a list for the counts for this group counts = [] ##create a list for points to plot x = [] y = [] #iterate through the list of times for j in range(len(timeList[i])): if j != 0: if j == 1: #add an indicator to tell if the time #starts a new group groupInd = 1 #add (0,1) to the list of points x.append(0) y.append(1) #add the point time to the right of that x.append(timeList[i][j-1]) y.append(1) #add the point below that at survival x.append(timeList[i][j-1]) y.append(survival) #add the survival to y y.append(survival) else: groupInd = 0 #add survival twice to y y.append(survival) y.append(survival) #add the time twice to x x.append(timeList[i][j-1]) x.append(timeList[i][j-1]) #add each censored time, number of censorings and #its survival to censoredPoints censoredPoints.append([timeList[i][j-1], censoringNum,survival,groupInd]) #add the count to the list counts.append([timeList[i][j-1],riskCounter, eventCounter,survival, sqrt(((survival)**2)*varSum)]) #increment the number at risk riskCounter += -1*(riskChange) #initialize a counter for the change in the number at risk riskChange = 0 #initialize a counter to zero eventCounter = 0 #intialize a counter to tell when censoring occurs censoringCounter = 0 censoringNum = 0 #iterate through the observations in each group for k in range(len(groupList[i])): #check of the observation has the given time if (groupList[i][k][self.timesIn]) == (timeList[i][j]): #increment the number at risk counter riskChange += 1 #check if this is an event or censoring if groupList[i][k][self.censoringIn] == 1: #add 1 to the counter eventCounter += 1 else: censoringNum += 1 #check if there are any events at this time if eventCounter != censoringCounter: censoringCounter = eventCounter #calculate the estimate of the survival function survival *= ((float(riskCounter) - eventCounter)/(riskCounter)) try: #calculate the estimate of the variance varSum += (eventCounter)/((riskCounter) *(float(riskCounter)- eventCounter)) except ZeroDivisionError: varSum = 0 #append the last row to counts counts.append([timeList[i][len(timeList[i])-1], riskCounter,eventCounter,survival, sqrt(((survival)**2)*varSum)]) #add the last time once to x x.append(timeList[i][len(timeList[i])-1]) x.append(timeList[i][len(timeList[i])-1]) #add the last survival twice to y y.append(survival) #y.append(survival) censoredPoints.append([timeList[i][len(timeList[i])-1], censoringNum,survival,1]) #add the list for the group to al ist for all the groups timeCounts.append(np.array(counts)) points.append([x,y]) #returns a list of arrays, where each array has as it columns: the time, #the number at risk, the number of events, the estimated value of the #survival function at that time, and the estimated standard error at #that time, in that order self.results = timeCounts self.points = points self.censoredPoints = censoredPoints def plot(self): x = [] #iterate through the groups for i in range(len(self.points)): #plot x and y plt.plot(np.array(self.points[i][0]),np.array(self.points[i][1])) #create lists of all the x and y values x += self.points[i][0] for j in range(len(self.censoredPoints)): #check if censoring is occuring if (self.censoredPoints[j][1] != 0): #if this is the first censored point if (self.censoredPoints[j][3] == 1) and (j == 0): #calculate a distance beyond 1 to place it #so all the points will fit dx = ((1./((self.censoredPoints[j][1])+1.)) *(float(self.censoredPoints[j][0]))) #iterate through all the censored points at this time for k in range(self.censoredPoints[j][1]): #plot a vertical line for censoring plt.vlines((1+((k+1)*dx)), self.censoredPoints[j][2]-0.03, self.censoredPoints[j][2]+0.03) #if this censored point starts a new group elif ((self.censoredPoints[j][3] == 1) and (self.censoredPoints[j-1][3] == 1)): #calculate a distance beyond 1 to place it #so all the points will fit dx = ((1./((self.censoredPoints[j][1])+1.)) *(float(self.censoredPoints[j][0]))) #iterate through all the censored points at this time for k in range(self.censoredPoints[j][1]): #plot a vertical line for censoring plt.vlines((1+((k+1)*dx)), self.censoredPoints[j][2]-0.03, self.censoredPoints[j][2]+0.03) #if this is the last censored point elif j == (len(self.censoredPoints) - 1): #calculate a distance beyond the previous time #so that all the points will fit dx = ((1./((self.censoredPoints[j][1])+1.)) *(float(self.censoredPoints[j][0]))) #iterate through all the points at this time for k in range(self.censoredPoints[j][1]): #plot a vertical line for censoring plt.vlines((self.censoredPoints[j-1][0]+((k+1)*dx)), self.censoredPoints[j][2]-0.03, self.censoredPoints[j][2]+0.03) #if this is a point in the middle of the group else: #calcuate a distance beyond the current time #to place the point, so they all fit dx = ((1./((self.censoredPoints[j][1])+1.)) *(float(self.censoredPoints[j+1][0]) - self.censoredPoints[j][0])) #iterate through all the points at this time for k in range(self.censoredPoints[j][1]): #plot a vetical line for censoring plt.vlines((self.censoredPoints[j][0]+((k+1)*dx)), self.censoredPoints[j][2]-0.03, self.censoredPoints[j][2]+0.03) #set the size of the plot so it extends to the max x and above 1 for y plt.xlim((0,np.max(x))) plt.ylim((0,1.05)) #label the axes plt.xlabel('time') plt.ylabel('survival') plt.show() def show_results(self): #start a string that will be a table of the results resultsString = '' #iterate through all the groups for i in range(len(self.results)): #label the group and header resultsString += ('Group {0}\n\n'.format(i) + 'Time At Risk Events Survival Std. Err\n') for j in self.results[i]: #add the results to the string resultsString += ( '{0:<9d}{1:<12d}{2:<11d}{3:<13.4f}{4:<6.4f}\n'.format( int(j[0]),int(j[1]),int(j[2]),j[3],j[4])) print(resultsString) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mcevaluate/000077500000000000000000000000001224417117700243005ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mcevaluate/__init__.py000066400000000000000000000143221224417117700264130ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mcevaluate/arma.py000066400000000000000000000106671224417117700256040ustar00rootroot00000000000000 import numpy as np from statsmodels.tsa.arima_process import arma_generate_sample from statsmodels.tsa.arma_mle import Arma #TODO: still refactoring problem with cov_x #copied from sandbox.tsa.arima.py def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5): '''run Monte Carlo for ARMA(2,2) DGP parameters currently hard coded also sample size `nsample` was not a self contained function, used instances from outer scope now corrected ''' #nsample = 1000 #ar = [1.0, 0, 0] if ar is None: ar = [1.0, -0.55, -0.1] #ma = [1.0, 0, 0] if ma is None: ma = [1.0, 0.3, 0.2] results = [] results_bse = [] for _ in range(niter): y2 = arma_generate_sample(ar,ma,nsample+1000, sig)[-nsample:] y2 -= y2.mean() arest2 = Arma(y2) rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,2)) results.append(rhohat2a) err2a = arest2.geterrors(rhohat2a) sige2a = np.sqrt(np.dot(err2a,err2a)/nsample) #print 'sige2a', sige2a, #print 'cov_x2a.shape', cov_x2a.shape #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) if not cov_x2a is None: results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) else: results_bse.append(np.nan + np.zeros_like(rhohat2a)) return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse) def mc_summary(res, rt=None): if rt is None: rt = np.zeros(res.shape[1]) nanrows = np.isnan(res).any(1) print 'fractions of iterations with nans', nanrows.mean() res = res[~nanrows] print 'RMSE' print np.sqrt(((res-rt)**2).mean(0)) print 'mean bias' print (res-rt).mean(0) print 'median bias' print np.median((res-rt),0) print 'median bias percent' print np.median((res-rt)/rt*100,0) print 'median absolute error' print np.median(np.abs(res-rt),0) print 'positive error fraction' print (res > rt).mean(0) if __name__ == '__main__': #short version # true, est, bse = mcarma22(niter=50) # print true # #print est # print est.mean(0) ''' niter 50, sample size=1000, 2 runs [-0.55 -0.1 0.3 0.2 ] [-0.542401 -0.09904305 0.30840599 0.2052473 ] [-0.55 -0.1 0.3 0.2 ] [-0.54681176 -0.09742921 0.2996297 0.20624258] niter=50, sample size=200, 3 runs [-0.55 -0.1 0.3 0.2 ] [-0.64669489 -0.01134491 0.19972259 0.20634019] [-0.55 -0.1 0.3 0.2 ] [-0.53141595 -0.10653234 0.32297968 0.20505973] [-0.55 -0.1 0.3 0.2 ] [-0.50244588 -0.125455 0.33867488 0.19498214] niter=50, sample size=100, 5 runs --> ar1 too low, ma1 too high [-0.55 -0.1 0.3 0.2 ] [-0.35715008 -0.23392766 0.48771794 0.21901059] [-0.55 -0.1 0.3 0.2 ] [-0.3554852 -0.21581914 0.51744748 0.24759245] [-0.55 -0.1 0.3 0.2 ] [-0.3737861 -0.24665911 0.48031939 0.17274438] [-0.55 -0.1 0.3 0.2 ] [-0.30015385 -0.27705506 0.56168199 0.21995759] [-0.55 -0.1 0.3 0.2 ] [-0.35879991 -0.22999604 0.4761953 0.19670835] new version, with burnin 1000 in DGP and demean [-0.55 -0.1 0.3 0.2 ] [-0.56770228 -0.00076025 0.25621825 0.24492449] [-0.55 -0.1 0.3 0.2 ] [-0.27598305 -0.2312364 0.57599134 0.23582417] [-0.55 -0.1 0.3 0.2 ] [-0.38059051 -0.17413628 0.45147109 0.20046776] [-0.55 -0.1 0.3 0.2 ] [-0.47789765 -0.08650743 0.3554441 0.24196087] ''' ar = [1.0, -0.55, -0.1] ma = [1.0, 0.3, 0.2] nsample = 200 run_mc = True#False if run_mc: for sig in [0.1, 0.5, 1.]: import time t0 = time.time() rt, res_rho, res_bse = mcarma22(niter=100, sig=sig) print '\nResults for Monte Carlo' print 'true' print rt print 'nsample =', nsample, 'sigma = ', sig print 'elapsed time for Monte Carlo', time.time()-t0 # 20 seconds for ARMA(2,2), 1000 iterations with 1000 observations #sige2a = np.sqrt(np.dot(err2a,err2a)/nsample) #print '\nbse of one sample' #print sige2a * np.sqrt(np.diag(cov_x2a)) print '\nMC of rho versus true' mc_summary(res_rho, rt) print '\nMC of bse versus zero' # this implies inf in percent mc_summary(res_bse) print '\nMC of bse versus std' mc_summary(res_bse, res_rho.std(0)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mcevaluate/mcresuts_arma1.txt000066400000000000000000000207551224417117700300000ustar00rootroot00000000000000MonteCarlo for Arma(2,2) fit with conditional least squares =========================================================== Comments: --------- scikits.statsmodels.tsa.arma_mle.Arma.fit((2,0,2)) niter=100 didn't use seed some strange inf in median bias percent and positive error fraction equal to 1 Sample Size 1000 ---------------- Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 1000 sigma = 0.1 elapsed time for Monte Carlo 3.67199993134 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.20193839 0.1609546 0.19646184 0.03923539] mean bias [-0.00186601 0.00542719 -0.00533277 0.00676964] median bias [-0.00810009 0.01230101 -0.01806484 0.0026727 ] median bias percent [ 1.47274338 -12.30101426 -6.02161246 1.33634779] median absolute error [ 0.12849263 0.0892165 0.11939251 0.02566005] positive error fraction [ 0.49 0.52 0.48 0.54] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.18967743 0.1526568 0.18747709 0.0399396 ] mean bias [ 0.18424688 0.14866981 0.18194085 0.03985427] median bias [ 0.18197743 0.14733603 0.17935651 0.03915853] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.18197743 0.14733603 0.17935651 0.03915853] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.04840765 0.03674317 0.04747574 0.00287514] mean bias [-0.01768289 -0.01219327 -0.0144486 0.0012073 ] median bias [-0.01995233 -0.01352704 -0.01703294 0.00051156] median bias percent [-9.88082703 -8.40904072 -8.67304431 1.3236821 ] median absolute error [ 0.03143609 0.02330123 0.03006728 0.00095056] positive error fraction [ 0.31 0.31 0.35 0.65] Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 1000 sigma = 0.5 elapsed time for Monte Carlo 3.53200006485 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.23357913 0.18306959 0.23336364 0.04347842] mean bias [-0.01220215 0.01449933 -0.00740801 0.00400656] median bias [ 0.00474757 -0.00285192 -0.00179209 0.00881695] median bias percent [-0.86319392 2.85192134 -0.59736288 4.40847694] median absolute error [ 0.14260379 0.10966381 0.1388728 0.02879121] positive error fraction [ 0.51 0.49 0.5 0.59] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.19115836 0.15300091 0.18899884 0.04041533] mean bias [ 0.18636096 0.14958803 0.18410379 0.04029974] median bias [ 0.17674161 0.14286746 0.17423869 0.03952101] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.17674161 0.14286746 0.17423869 0.03952101] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.0633297 0.04599496 0.06512538 0.004277 ] mean bias [-0.04689923 -0.03290647 -0.04914224 -0.00299369] median bias [-0.05651859 -0.03962704 -0.05900734 -0.00377242] median bias percent [-24.22984657 -21.71410167 -25.29832734 -8.71360165] median absolute error [ 0.05810372 0.04048892 0.06100925 0.00383568] positive error fraction [ 0.13 0.11 0.11 0.1 ] Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 1000 sigma = 1.0 elapsed time for Monte Carlo 3.78200006485 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.23501983 0.18658536 0.23630675 0.04845701] mean bias [-0.0638577 0.0559638 -0.06116445 -0.00201522] median bias [-0.02571522 0.01710684 -0.02476506 -0.00040966] median bias percent [ 4.6754946 -17.10684152 -8.25501939 -0.20482813] median absolute error [ 0.13186172 0.11635244 0.1391435 0.03325726] positive error fraction [ 0.45 0.52 0.46 0.5 ] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.2057234 0.16345869 0.20369599 0.04110316] mean bias [ 0.19816783 0.15783531 0.19595057 0.04093056] median bias [ 0.18616527 0.14688067 0.18394698 0.03953418] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.18616527 0.14688067 0.18394698 0.03953418] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.06193706 0.04704427 0.06433453 0.00837719] mean bias [-0.02801022 -0.02015949 -0.03230322 -0.00748453] median bias [-0.04001279 -0.03111413 -0.04430681 -0.00888091] median bias percent [-17.69083491 -17.48036125 -19.41120455 -18.3432625 ] median absolute error [ 0.05454876 0.03993081 0.05735269 0.00892246] positive error fraction [ 0.24 0.21 0.2 0.07] Sample Size 200 --------------- Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 200 sigma = 0.1 elapsed time for Monte Carlo 3.76600003242 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.23797307 0.18908967 0.24248704 0.04532965] mean bias [-0.02240914 0.02006369 -0.02856247 0.00419784] median bias [ 0.0132667 -0.01695 -0.00779267 0.00943245] median bias percent [ -2.41212746 16.94999813 -2.5975563 4.71622452] median absolute error [ 0.14314755 0.10134399 0.13262758 0.02681113] positive error fraction [ 0.53 0.49 0.48 0.57] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.18994719 0.1524025 0.18777651 0.0401027 ] mean bias [ 0.18437936 0.14841141 0.18216825 0.03997125] median bias [ 0.18044553 0.14526126 0.17764634 0.03921749] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.18044553 0.14526126 0.17764634 0.03921749] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.0696006 0.05262689 0.07424492 0.00609829] mean bias [-0.05253626 -0.0396108 -0.05863073 -0.00516361] median bias [-0.0564701 -0.04276095 -0.06315264 -0.00591737] median bias percent [-23.83553085 -22.74250075 -26.22629031 -13.11041727] median absolute error [ 0.05851016 0.04572261 0.0646474 0.00594597] positive error fraction [ 0.11 0.13 0.1 0.04] Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 200 sigma = 0.5 elapsed time for Monte Carlo 3.86000013351 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.21997161 0.17584013 0.22184246 0.04268358] mean bias [-0.04259758 0.03350341 -0.05393998 -0.01056256] median bias [-0.02365517 0.01051654 -0.04060612 -0.00710624] median bias percent [ 4.3009406 -10.51654169 -13.53537431 -3.55312073] median absolute error [ 0.13186161 0.1056892 0.13501117 0.02523291] positive error fraction [ 0.45 0.55 0.42 0.42] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.21373305 0.17013489 0.21176584 0.04080147] mean bias [ 0.20622114 0.16464733 0.20408995 0.04068523] median bias [ 0.19022777 0.15274254 0.1879751 0.03969161] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.19022777 0.15274254 0.1879751 0.03969161] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.05697861 0.0435968 0.05757744 0.00314984] mean bias [-0.00958654 -0.00797153 -0.01109498 -0.00067079] median bias [-0.02557991 -0.01987633 -0.02720983 -0.00166441] median bias percent [-11.85310581 -11.5145739 -12.64485773 -4.02458691] median absolute error [ 0.03635737 0.0292344 0.03793175 0.00238808] positive error fraction [ 0.31 0.29 0.3 0.28] Results for Monte Carlo true [-0.55 -0.1 0.3 0.2 ] nsample = 200 sigma = 1.0 elapsed time for Monte Carlo 3.59400010109 MC of rho versus true fractions of iterations with nans 0.0 RMSE [ 0.22232599 0.17545665 0.21586404 0.04731953] mean bias [-0.02145001 0.02789994 -0.01930862 0.00418517] median bias [ 0.00685442 0.01411879 0.01616525 0.01340016] median bias percent [ -1.24625802 -14.11879188 5.38841584 6.70007966] median absolute error [ 0.1010917 0.09510124 0.10884815 0.02735024] positive error fraction [ 0.51 0.54 0.51 0.64] MC of bse versus zero fractions of iterations with nans 0.0 RMSE [ 0.19090008 0.15218083 0.18870297 0.04077881] mean bias [ 0.1863764 0.14894541 0.18407595 0.04063975] median bias [ 0.17861243 0.14324048 0.17665358 0.03948681] median bias percent [ Inf Inf Inf Inf] median absolute error [ 0.17861243 0.14324048 0.17665358 0.03948681] positive error fraction [ 1. 1. 1. 1.] MC of bse versus std fractions of iterations with nans 0.0 RMSE [ 0.05408839 0.03954405 0.0517791 0.00731427] mean bias [-0.03491242 -0.02427881 -0.03092279 -0.00649433] median bias [-0.04267639 -0.02998373 -0.03834516 -0.00764727] median bias percent [-19.2853791 -17.30920408 -17.83506488 -16.22449948] median absolute error [ 0.04673299 0.03434164 0.0433639 0.00769409] positive error fraction [ 0.13 0.14 0.16 0.07] statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mle.py000066400000000000000000000031731224417117700233050ustar00rootroot00000000000000'''What's the origin of this file? It is not ours. Does not run because of missing mtx files, now included changes: JP corrections to imports so it runs, comment out print ''' import numpy as np from numpy import dot, outer, random, argsort from scipy import io, linalg, optimize from scipy.sparse import eye as speye import matplotlib.pyplot as plt def R(v): rq = dot(v.T,A*v)/dot(v.T,B*v) res = (A*v-rq*B*v)/linalg.norm(B*v) data.append(linalg.norm(res)) return rq def Rp(v): """ Gradient """ result = 2*(A*v-R(v)*B*v)/dot(v.T,B*v) #print "Rp: ", result return result def Rpp(v): """ Hessian """ result = 2*(A-R(v)*B-outer(B*v,Rp(v))-outer(Rp(v),B*v))/dot(v.T,B*v) #print "Rpp: ", result return result A = io.mmread('nos4.mtx') # clustered eigenvalues #B = io.mmread('bcsstm02.mtx.gz') #A = io.mmread('bcsstk06.mtx.gz') # clustered eigenvalues #B = io.mmread('bcsstm06.mtx.gz') n = A.shape[0] B = speye(n,n) random.seed(1) v_0=random.rand(n) print "try fmin_bfgs" full_output = 1 data=[] v,fopt, gopt, Hopt, func_calls, grad_calls, warnflag, allvecs = \ optimize.fmin_bfgs(R,v_0,fprime=Rp,full_output=full_output,retall=1) if warnflag == 0: plt.semilogy(np.arange(0,len(data)),data) print 'Rayleigh quotient BFGS',R(v) print "fmin_bfgs OK" print "try fmin_ncg" # # WARNING: the program may hangs if fmin_ncg is used # data=[] v,fopt, fcalls, gcalls, hcalls, warnflag, allvecs = \ optimize.fmin_ncg(R,v_0,fprime=Rp,fhess=Rpp,full_output=full_output,retall=1) if warnflag==0: plt.figure() plt.semilogy(np.arange(0,len(data)),data) print 'Rayleigh quotient NCG',R(v) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/mlogitmath.lyx000066400000000000000000000145471224417117700250700ustar00rootroot00000000000000#LyX 1.6.2 created this file. For more info see http://www.lyx.org/ \lyxformat 345 \begin_document \begin_header \textclass article \use_default_options true \language english \inputencoding auto \font_roman default \font_sans default \font_typewriter default \font_default_family default \font_sc false \font_osf false \font_sf_scale 100 \font_tt_scale 100 \graphics default \paperfontsize default \use_hyperref false \papersize default \use_geometry false \use_amsmath 1 \use_esint 1 \cite_engine basic \use_bibtopic false \paperorientation portrait \secnumdepth 3 \tocdepth 3 \paragraph_separation indent \defskip medskip \quotes_language english \papercolumns 1 \papersides 1 \paperpagestyle default \tracking_changes false \output_changes false \author "" \author "" \end_header \begin_body \begin_layout Standard Notes on mlogit. \end_layout \begin_layout Standard Assume that \begin_inset Formula $J=3$ \end_inset , so that there are \begin_inset Formula $2$ \end_inset vectors of parameters for \begin_inset Formula $J-1$ \end_inset . For now the parameters are passed around as \begin_inset Formula \[ \left[\beta_{1}^{\prime}\beta_{2}^{\prime}\right]\] \end_inset \end_layout \begin_layout Standard So if \begin_inset Formula $K=3$ \end_inset (including the constant), then the matrix of parameters is \begin_inset Formula \[ \left[\begin{array}{cc} b_{10} & b_{20}\\ b_{11} & b_{21}\\ b_{12} & b_{22}\end{array}\right]^{\prime}\] \end_inset \end_layout \begin_layout Standard (changed to rows and added prime above, so this all changes and the score is also just transposed and flattend along the zero axis.) This is flattened along the zero axis for the sake of the solvers. So that it is passed internally as \begin_inset Formula \[ \left[\begin{array}{cccccc} b_{10} & b_{20} & b_{11} & b_{21} & b_{12} & b_{22}\end{array}\right]\] \end_inset Now the matrix of score vectors is \begin_inset Formula \[ \left[\begin{array}{cc} \frac{\partial\ln L}{\partial b_{10}} & \frac{\partial\ln L}{\partial b_{20}}\\ \frac{\partial\ln L}{\partial b_{11}} & \frac{\partial\ln L}{\partial b_{21}}\\ \frac{\partial\ln L}{\partial b_{12}} & \frac{\partial\ln L}{\partial b_{22}}\end{array}\right]\] \end_inset \end_layout \begin_layout Standard In Dhrymes notation, this would be column vectors \begin_inset Formula $\left(\partial\ln L/\partial\beta_{j}\right)^{\prime}\text{ for }j=1,2$ \end_inset in our example. So, our Jacobian is actually transposed vis-a-vis the more traditional notation. So that the solvers can handle this, though, it gets flattened but the score gets flattened along the first axis to make things easier, which is going to make things tricky. Now, in traditional notation, the Hessian would be \begin_inset Formula \[ \frac{\partial^{2}\ln L}{\partial\beta_{j}\partial\beta_{j}}=\frac{\partial}{\partial\beta_{j}}\vec{\left[\left(\frac{\partial\ln L}{\partial\beta_{j}}\right)\right]}\] \end_inset \end_layout \begin_layout Standard where \begin_inset Formula $\vec{}$ \end_inset denotes a vectorized matrix, i.e., for a \begin_inset Formula $n\times m$ \end_inset matrix \begin_inset Formula $X$ \end_inset , \begin_inset Formula $\vec{X}=\left(x_{\cdot1}^{\prime},x_{\cdot2}^{\prime},...,x_{\cdot m}^{\prime}\right)^{\prime}$ \end_inset such that \begin_inset Formula $x_{\cdot1}$ \end_inset is the first \begin_inset Formula $n$ \end_inset elements of column 1 of \begin_inset Formula $X$ \end_inset . This matrix is \begin_inset Formula $mn\times n$ \end_inset . In our case \begin_inset Formula $\ln L$ \end_inset is a scalar so \begin_inset Formula $m=1$ \end_inset , so each second derivative is \begin_inset Formula $n\times n$ \end_inset and \begin_inset Formula $n=K=3$ \end_inset in our example. Given our score \begin_inset Quotes eld \end_inset matrix, \begin_inset Quotes erd \end_inset our Hessian will look like \begin_inset Formula \[ H=\left[\begin{array}{cc} \frac{\partial\ln L}{\partial\beta_{1}\partial\beta_{1}} & \frac{\partial\ln L}{\partial\beta_{1}\partial\beta_{2}}\\ \frac{\partial\ln L}{\partial\beta_{2}\partial\beta_{1}} & \frac{\partial\ln L}{\partial\beta_{2}\partial\beta_{2}}\end{array}\right]\] \end_inset \begin_inset Formula \[ H=\left[\begin{array}{cccccc} \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{10}} & \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{11}} & \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{12}} & \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{20}} & \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{21}} & \frac{\partial^{2}\ln L}{\partial b_{10}\partial b_{22}}\\ \frac{\partial\ln L}{\partial b_{11}\partial b_{10}} & \frac{\partial\ln L}{\partial b_{11}\partial b_{11}} & \frac{\partial\ln L}{\partial b_{11}\partial b_{12}} & \frac{\partial\ln L}{\partial b_{11}\partial b_{20}} & \frac{\partial\ln L}{\partial b_{11}\partial b_{21}} & \frac{\partial\ln L}{\partial b_{11}\partial b_{22}}\\ \frac{\partial\ln L}{\partial b_{12}\partial b_{10}} & \frac{\partial\ln L}{\partial b_{12}\partial b_{11}} & \frac{\partial\ln L}{\partial b_{12}\partial b_{12}} & \frac{\partial\ln L}{\partial b_{12}\partial b_{20}} & \frac{\partial\ln L}{\partial b_{12}\partial b_{21}} & \frac{\partial\ln L}{\partial b_{12}\partial b_{22}}\\ \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{10}} & \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{11}} & \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{12}} & \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{20}} & \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{21}} & \frac{\partial^{2}\ln L}{\partial b_{20}\partial b_{22}}\\ \frac{\partial\ln L}{\partial b_{21}\partial b_{10}} & \frac{\partial\ln L}{\partial b_{21}\partial b_{11}} & \frac{\partial\ln L}{\partial b_{21}\partial b_{12}} & \frac{\partial\ln L}{\partial b_{21}\partial b_{20}} & \frac{\partial\ln L}{\partial b_{21}\partial b_{21}} & \frac{\partial\ln L}{\partial b_{21}\partial b_{22}}\\ \frac{\partial\ln L}{\partial b_{22}\partial b_{10}} & \frac{\partial\ln L}{\partial b_{22}\partial b_{11}} & \frac{\partial\ln L}{\partial b_{22}\partial b_{12}} & \frac{\partial\ln L}{\partial b_{22}\partial b_{20}} & \frac{\partial\ln L}{\partial b_{22}\partial b_{21}} & \frac{\partial\ln L}{\partial b_{22}\partial b_{22}}\end{array}\right]\] \end_inset \end_layout \begin_layout Standard But since our Jacobian is a row vector that alternate \end_layout \end_body \end_document statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/multilinear.py000066400000000000000000000333531224417117700250600ustar00rootroot00000000000000"""Analyze a set of multiple variables with a linear models multiOLS: take a model and test it on a series of variables defined over a pandas dataset, returning a summary for each variable multigroup: take a boolean vector and the definition of several groups of variables and test if the group has a fraction of true values higher than the rest. It allows to test if the variables in the group are significantly more significant than outside the group. """ from patsy import dmatrix import pandas as pd from statsmodels.api import OLS from statsmodels.api import stats import numpy as np import logging def _model2dataframe(model_endog, model_exog, model_type=OLS, **kwargs): """return a series containing the summary of a linear model All the exceding parameters will be redirected to the linear model """ # create the linear model and perform the fit model_result = model_type(model_endog, model_exog, **kwargs).fit() # keeps track of some global statistics statistics = pd.Series({'r2': model_result.rsquared, 'adj_r2': model_result.rsquared_adj}) # put them togher with the result for each term result_df = pd.DataFrame({'params': model_result.params, 'pvals': model_result.pvalues, 'std': model_result.bse, 'statistics': statistics}) # add the complexive results for f-value and the total p-value fisher_df = pd.DataFrame({'params': {'_f_test': model_result.fvalue}, 'pvals': {'_f_test': model_result.f_pvalue}}) # merge them and unstack to obtain a hierarchically indexed series res_series = pd.concat([result_df, fisher_df]).unstack() return res_series.dropna() def multiOLS(model, dataframe, column_list=None, method='fdr_bh', alpha=0.05, subset=None, model_type=OLS, **kwargs): """apply a linear model to several endogenous variables on a dataframe Take a linear model definition via formula and a dataframe that will be the environment of the model, and apply the linear model to a subset (or all) of the columns of the dataframe. It will return a dataframe with part of the information from the linear model summary. Parameters ---------- model : string formula description of the model dataframe : pandas.dataframe dataframe where the model will be evaluated column_list : list of strings, optional Names of the columns to analyze with the model. If None (Default) it will perform the function on all the eligible columns (numerical type and not in the model definition) model_type : model class, optional The type of model to be used. The default is the linear model. Can be any linear model (OLS, WLS, GLS, etc..) method: string, optional the method used to perform the pvalue correction for multiple testing. default is the Benjamini/Hochberg, other available methods are: `bonferroni` : one-step correction `sidak` : on-step correction `holm-sidak` : `holm` : `simes-hochberg` : `hommel` : `fdr_bh` : Benjamini/Hochberg `fdr_by` : Benjamini/Yekutieli alpha: float, optional the significance level used for the pvalue correction (default 0.05) subset: boolean array the selected rows to be used in the regression all the other parameters will be directed to the model creation. Returns ------- summary : pandas.DataFrame a dataframe containing an extract from the summary of the model obtained for each columns. It will give the model complexive f test result and p-value, and the regression value and standard deviarion for each of the regressors. The Dataframe has a hierachical column structure, divided as: - params: contains the parameters resulting from the models. Has an additional column named _f_test containing the result of the F test. - pval: the pvalue results of the models. Has the _f_test column for the significativity of the whole test. - adj_pval: the corrected pvalues via the multitest function. - std: uncertainties of the model parameters - statistics: contains the r squared statistics and the adjusted r squared. Notes ----- The main application of this function is on system biology to perform a linear model testing of a lot of different parameters, like the different genetic expression of several genes. See Also -------- statsmodels.stats.multitest contains several functions to perform the multiple p-value correction Examples -------- Using the longley data as dataframe example >>> import statsmodels.api as sm >>> data = sm.datasets.longley.load_pandas() >>> df = data.exog >>> df['TOTEMP'] = data.endog This will perform the specified linear model on all the other columns of the dataframe >>> multiOLS('GNP + 1', df) This select only a certain subset of the columns >>> multiOLS('GNP + 0', df, ['GNPDEFL', 'TOTEMP', 'POP']) It is possible to specify a trasformation also on the target column, conforming to the patsy formula specification >>> multiOLS('GNP + 0', df, ['I(GNPDEFL**2)', 'center(TOTEMP)']) It is possible to specify the subset of the dataframe on which perform the analysis >> multiOLS('GNP + 1', df, subset=df.GNPDEFL > 90) Even a single column name can be given without enclosing it in a list >>> multiOLS('GNP + 0', df, 'GNPDEFL') """ # data normalization # if None take all the numerical columns that aren't present in the model # it's not waterproof but is a good enough criterion for everyday use if column_list is None: column_list = [name for name in dataframe.columns if dataframe[name].dtype != object and name not in model] # if it's a single string transform it in a single element list if isinstance(column_list, basestring): column_list = [column_list] if subset is not None: dataframe = dataframe.ix[subset] # perform each model and retrieve the statistics col_results = {} # as the model will use always the same endogenous variables # we can create them once and reuse model_exog = dmatrix(model, data=dataframe, return_type="dataframe") for col_name in column_list: # it will try to interpret the column name as a valid dataframe # index as it can be several times faster. If it fails it # interpret it as a patsy formula (for example for centering) try: model_endog = dataframe[col_name] except KeyError: model_endog = dmatrix(col_name + ' + 0', data=dataframe) # retrieve the result and store them res = _model2dataframe(model_endog, model_exog, model_type, **kwargs) col_results[col_name] = res # mangle them togheter and sort by complexive p-value summary = pd.DataFrame(col_results) # order by the p-value: the most useful model first! summary = summary.T.sort([('pvals', '_f_test')]) summary.index.name = 'endogenous vars' # implementing the pvalue correction method smt = stats.multipletests for (key1, key2) in summary: if key1 != 'pvals': continue p_values = summary[key1, key2] corrected = smt(p_values, method=method, alpha=alpha)[1] # extend the dataframe of results with the column # of the corrected p_values summary['adj_' + key1, key2] = corrected return summary def _test_group(pvalues, group_name, group, exact=True): """test if the objects in the group are different from the general set. The test is performed on the pvalues set (ad a pandas series) over the group specified via a fisher exact test. """ from scipy.stats import fisher_exact try: from scipy.stats import chi2_contingency except ImportError: def chi2_contingency(*args, **kwds): raise ValueError('exact=False is not available with old scipy') totals = 1.0 * len(pvalues) total_significant = 1.0 * np.sum(pvalues) cross_index = [c for c in group if c in pvalues.index] missing = [c for c in group if c not in pvalues.index] if missing: s = ('the test is not well defined if the group ' 'has elements not presents in the significativity ' 'array. group name: {}, missing elements: {}') logging.warning(s.format(group_name, missing)) # how many are significant and not in the group group_total = 1.0 * len(cross_index) group_sign = 1.0 * len([c for c in cross_index if pvalues[c]]) group_nonsign = 1.0 * (group_total - group_sign) # how many are significant and not outside the group extern_sign = 1.0 * (total_significant - group_sign) extern_nonsign = 1.0 * (totals - total_significant - group_nonsign) # make the fisher test or the chi squared test = fisher_exact if exact else chi2_contingency table = [[extern_nonsign, extern_sign], [group_nonsign, group_sign]] pvalue = test(np.array(table))[1] # is the group more represented or less? part = group_sign, group_nonsign, extern_sign, extern_nonsign #increase = (group_sign / group_total) > (total_significant / totals) increase = np.log((totals * group_sign) / (total_significant * group_total)) return pvalue, increase, part def multigroup(pvals, groups, exact=True, keep_all=True, alpha=0.05): """Test if the given groups are different from the total partition. Given a boolean array test if each group has a proportion of positives different than the complexive proportion. The test can be done as an exact Fisher test or approximated as a Chi squared test for more speed. Parameters ---------- pvals: pandas series of boolean the significativity of the variables under analysis groups: dict of list the name of each category of variables under exam. each one is a list of the variables included exact: boolean, optional If True (default) use the fisher exact test, otherwise use the chi squared test for contingencies tables. For high number of elements in the array the fisher test can be significantly slower than the chi squared. keep_all: boolean, optional if False it will drop those groups where the fraction of positive is below the expected result. If True (default) it will keep all the significant results. alpha: float, optional the significativity level for the pvalue correction on the whole set of groups (not inside the groups themselves). Returns ------- result_df: pandas dataframe for each group returns: pvals - the fisher p value of the test adj_pvals - the adjusted pvals increase - the log of the odd ratio between the internal significant ratio versus the external one _in_sign - significative elements inside the group _in_non - non significative elements inside the group _out_sign - significative elements outside the group _out_non - non significative elements outside the group Notes ----- This test allow to see if a category of variables is generally better suited to be described for the model. For example to see if a predictor gives more information on demographic or economical parameters, by creating two groups containing the endogenous variables of each category. This function is conceived for medical dataset with a lot of variables that can be easily grouped into functional groups. This is because The significativity of a group require a rather large number of composing elements. Examples -------- A toy example on a real dataset, the Guerry dataset from R >>> url = "http://vincentarelbundock.github.com/" >>> url = url + "Rdatasets/csv/HistData/Guerry.csv" >>> df = pd.read_csv(url, index_col='dept') evaluate the relationship between the variuos paramenters whith the Wealth >>> pvals = multiOLS('Wealth', df)['adj_pvals', '_f_test'] define the groups >>> groups = {} >>> groups['crime'] = ['Crime_prop', 'Infanticide', ... 'Crime_parents', 'Desertion', 'Crime_pers'] >>> groups['religion'] = ['Donation_clergy', 'Clergy', 'Donations'] >>> groups['wealth'] = ['Commerce', 'Lottery', 'Instruction', 'Literacy'] do the analysis of the significativity >>> multigroup(pvals < 0.05, groups) """ pvals = pd.Series(pvals) if not (set(pvals.unique()) <= set([False, True])): raise ValueError("the series should be binary") if hasattr(pvals.index, 'is_unique') and not pvals.index.is_unique: raise ValueError("series with duplicated index is not accepted") results = {'pvals': {}, 'increase': {}, '_in_sign': {}, '_in_non': {}, '_out_sign': {}, '_out_non': {}} for group_name, group_list in groups.iteritems(): res = _test_group(pvals, group_name, group_list, exact) results['pvals'][group_name] = res[0] results['increase'][group_name] = res[1] results['_in_sign'][group_name] = res[2][0] results['_in_non'][group_name] = res[2][1] results['_out_sign'][group_name] = res[2][2] results['_out_non'][group_name] = res[2][3] result_df = pd.DataFrame(results).sort('pvals') if not keep_all: result_df = result_df[result_df.increase] smt = stats.multipletests corrected = smt(result_df['pvals'], method='fdr_bh', alpha=alpha)[1] result_df['adj_pvals'] = corrected return result_df statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/000077500000000000000000000000001224417117700250145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/__init__.py000066400000000000000000000000311224417117700271170ustar00rootroot00000000000000# -*- coding: utf-8 -*- statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/densityorthopoly.py000066400000000000000000000426571224417117700310430ustar00rootroot00000000000000# -*- coding: cp1252 -*- # some cut and paste characters are not ASCII '''density estimation based on orthogonal polynomials Author: Josef Perktold Created: 2011-05017 License: BSD 2 versions work: based on Fourier, FPoly, and chebychev T, ChebyTPoly also hermite polynomials, HPoly, works other versions need normalization TODO: * check fourier case again: base is orthonormal, but needs offsetfact = 0 and doesn't integrate to 1, rescaled looks good * hermite: works but DensityOrthoPoly requires currently finite bounds I use it with offsettfactor 0.5 in example * not implemented methods: - add bonafide density correction - add transformation to domain of polynomial base - DONE possible problem: what is the behavior at the boundary, offsetfact requires more work, check different cases, add as option moved to polynomial class by default, as attribute * convert examples to test cases * need examples with large density on boundary, beta ? * organize poly classes in separate module, check new numpy.polynomials, polyvander * MISE measures, order selection, ... enhancements: * other polynomial bases: especially for open and half open support * wavelets * local or piecewise approximations ''' from scipy import stats, integrate import numpy as np sqr2 = np.sqrt(2.) class FPoly(object): '''Orthonormal (for weight=1) Fourier Polynomial on [0,1] orthonormal polynomial but density needs corfactor that I don't see what it is analytically parameterization on [0,1] from Sam Efromovich: Orthogonal series density estimation, 2010 John Wiley & Sons, Inc. WIREs Comp Stat 2010 2 467–476 ''' def __init__(self, order): self.order = order self.domain = (0, 1) self.intdomain = self.domain def __call__(self, x): if self.order == 0: return np.ones_like(x) else: return sqr2 * np.cos(np.pi * self.order * x) class F2Poly(object): '''Orthogonal (for weight=1) Fourier Polynomial on [0,pi] is orthogonal but first component doesn't square-integrate to 1 final result seems to need a correction factor of sqrt(pi) _corfactor = sqrt(pi) from integrating the density Parameterization on [0, pi] from Peter Hall, Cross-Validation and the Smoothing of Orthogonal Series Density Estimators, JOURNAL OF MULTIVARIATE ANALYSIS 21, 189-206 (1987) ''' def __init__(self, order): self.order = order self.domain = (0, np.pi) self.intdomain = self.domain self.offsetfactor = 0 def __call__(self, x): if self.order == 0: return np.ones_like(x) / np.sqrt(np.pi) else: return sqr2 * np.cos(self.order * x) / np.sqrt(np.pi) class ChebyTPoly(object): '''Orthonormal (for weight=1) Chebychev Polynomial on (-1,1) Notes ----- integration requires to stay away from boundary, offsetfactor > 0 maybe this implies that we cannot use it for densities that are > 0 at boundary ??? or maybe there is a mistake close to the boundary, sometimes integration works. ''' def __init__(self, order): self.order = order from scipy.special import chebyt self.poly = chebyt(order) self.domain = (-1, 1) self.intdomain = (-1+1e-6, 1-1e-6) #not sure if I need this, in integration nans are possible on the boundary self.offsetfactor = 0.01 #required for integration def __call__(self, x): if self.order == 0: return np.ones_like(x) / (1-x**2)**(1/4.) /np.sqrt(np.pi) else: return self.poly(x) / (1-x**2)**(1/4.) /np.sqrt(np.pi) *np.sqrt(2) from scipy.misc import factorial from scipy import special logpi2 = np.log(np.pi)/2 class HPoly(object): '''Orthonormal (for weight=1) Hermite Polynomial, uses finite bounds for current use with DensityOrthoPoly domain is defined as [-6,6] ''' def __init__(self, order): self.order = order from scipy.special import hermite self.poly = hermite(order) self.domain = (-6, +6) self.offsetfactor = 0.5 # note this is def __call__(self, x): k = self.order lnfact = -(1./2)*(k*np.log(2.) + special.gammaln(k+1) + logpi2) - x*x/2 fact = np.exp(lnfact) return self.poly(x) * fact def polyvander(x, polybase, order=5): polyarr = np.column_stack([polybase(i)(x) for i in range(order)]) return polyarr def inner_cont(polys, lower, upper, weight=None): '''inner product of continuous function (with weight=1) Parameters ---------- polys : list of callables polynomial instances lower : float lower integration limit upper : float upper integration limit weight : callable or None weighting function Returns ------- innp : ndarray symmetric 2d square array with innerproduct of all function pairs err : ndarray numerical error estimate from scipy.integrate.quad, same dimension as innp Examples -------- >>> from scipy.special import chebyt >>> polys = [chebyt(i) for i in range(4)] >>> r, e = inner_cont(polys, -1, 1) >>> r array([[ 2. , 0. , -0.66666667, 0. ], [ 0. , 0.66666667, 0. , -0.4 ], [-0.66666667, 0. , 0.93333333, 0. ], [ 0. , -0.4 , 0. , 0.97142857]]) ''' n_polys = len(polys) innerprod = np.empty((n_polys, n_polys)) innerprod.fill(np.nan) interr = np.zeros((n_polys, n_polys)) for i in range(n_polys): for j in range(i+1): p1 = polys[i] p2 = polys[j] if not weight is None: innp, err = integrate.quad(lambda x: p1(x)*p2(x)*weight(x), lower, upper) else: innp, err = integrate.quad(lambda x: p1(x)*p2(x), lower, upper) innerprod[i,j] = innp interr[i,j] = err if not i == j: innerprod[j,i] = innp interr[j,i] = err return innerprod, interr def is_orthonormal_cont(polys, lower, upper, rtol=0, atol=1e-08): '''check whether functions are orthonormal Parameters ---------- polys : list of polynomials or function Returns ------- is_orthonormal : bool is False if the innerproducts are not close to 0 or 1 Notes ----- this stops as soon as the first deviation from orthonormality is found. Examples -------- >>> from scipy.special import chebyt >>> polys = [chebyt(i) for i in range(4)] >>> r, e = inner_cont(polys, -1, 1) >>> r array([[ 2. , 0. , -0.66666667, 0. ], [ 0. , 0.66666667, 0. , -0.4 ], [-0.66666667, 0. , 0.93333333, 0. ], [ 0. , -0.4 , 0. , 0.97142857]]) >>> is_orthonormal_cont(polys, -1, 1, atol=1e-6) False >>> polys = [ChebyTPoly(i) for i in range(4)] >>> r, e = inner_cont(polys, -1, 1) >>> r array([[ 1.00000000e+00, 0.00000000e+00, -9.31270888e-14, 0.00000000e+00], [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00, -9.47850712e-15], [ -9.31270888e-14, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], [ 0.00000000e+00, -9.47850712e-15, 0.00000000e+00, 1.00000000e+00]]) >>> is_orthonormal_cont(polys, -1, 1, atol=1e-6) True ''' for i in range(len(polys)): for j in range(i+1): p1 = polys[i] p2 = polys[j] innerprod = integrate.quad(lambda x: p1(x)*p2(x), lower, upper)[0] #print i,j, innerprod if not np.allclose(innerprod, i==j, rtol=rtol, atol=atol): return False return True #new versions class DensityOrthoPoly(object): '''Univariate density estimation by orthonormal series expansion Uses an orthonormal polynomial basis to approximate a univariate density. currently all arguments can be given to fit, I might change it to requiring arguments in __init__ instead. ''' def __init__(self, polybase=None, order=5): if not polybase is None: self.polybase = polybase self.polys = polys = [polybase(i) for i in range(order)] #try: #self.offsetfac = 0.05 #self.offsetfac = polys[0].offsetfactor #polys maybe not defined yet self._corfactor = 1 self._corshift = 0 def fit(self, x, polybase=None, order=5, limits=None): '''estimate the orthogonal polynomial approximation to the density ''' if polybase is None: polys = self.polys[:order] else: self.polybase = polybase self.polys = polys = [polybase(i) for i in range(order)] #move to init ? if not hasattr(self, 'offsetfac'): self.offsetfac = polys[0].offsetfactor xmin, xmax = x.min(), x.max() if limits is None: self.offset = offset = (xmax - xmin) * self.offsetfac limits = self.limits = (xmin - offset, xmax + offset) interval_length = limits[1] - limits[0] xinterval = xmax - xmin # need to cover (half-)open intervalls self.shrink = 1. / interval_length #xinterval/interval_length offset = (interval_length - xinterval ) / 2. self.shift = xmin - offset self.x = x = self._transform(x) coeffs = [(p(x)).mean() for p in polys] self.coeffs = coeffs self.polys = polys self._verify() #verify that it is a proper density return self #coeffs, polys def evaluate(self, xeval, order=None): xeval = self._transform(xeval) if order is None: order = len(self.polys) res = sum(c*p(xeval) for c, p in zip(self.coeffs, self.polys)[:order]) res = self._correction(res) return res def __call__(self, xeval): '''alias for evaluate, except no order argument''' return self.evaluate(xeval) def _verify(self): '''check for bona fide density correction currently only checks that density integrates to 1 ` non-negativity - NotImplementedYet ''' #watch out for circular/recursive usage #evaluate uses domain of data, we stay offset away from bounds intdomain = self.limits #self.polys[0].intdomain self._corfactor = 1./integrate.quad(self.evaluate, *intdomain)[0] #self._corshift = 0 #self._corfactor return self._corfactor def _correction(self, x): '''bona fide density correction affine shift of density to make it into a proper density ''' if self._corfactor != 1: x *= self._corfactor if self._corshift != 0: x += self._corshift return x def _transform(self, x): # limits=None): '''transform observation to the domain of the density uses shrink and shift attribute which are set in fit to stay ''' #use domain from first instance #class doesn't have domain self.polybase.domain[0] AttributeError domain = self.polys[0].domain ilen = (domain[1] - domain[0]) shift = self.shift - domain[0]/self.shrink/ilen shrink = self.shrink * ilen return (x - shift) * shrink #old version as a simple function def density_orthopoly(x, polybase, order=5, xeval=None): from scipy.special import legendre, hermitenorm, chebyt, chebyu, hermite #polybase = legendre #chebyt #hermitenorm# #polybase = chebyt #polybase = FPoly #polybase = ChtPoly #polybase = hermite #polybase = HPoly if xeval is None: xeval = np.linspace(x.min(),x.max(),50) #polys = [legendre(i) for i in range(order)] polys = [polybase(i) for i in range(order)] #coeffs = [(p(x)*(1-x**2)**(-1/2.)).mean() for p in polys] #coeffs = [(p(x)*np.exp(-x*x)).mean() for p in polys] coeffs = [(p(x)).mean() for p in polys] res = sum(c*p(xeval) for c, p in zip(coeffs, polys)) #res *= (1-xeval**2)**(-1/2.) #res *= np.exp(-xeval**2./2) return res, xeval, coeffs, polys if __name__ == '__main__': examples = ['chebyt', 'fourier', 'hermite']#[2] nobs = 10000 import matplotlib.pyplot as plt from statsmodels.sandbox.distributions.mixture_rvs import ( mixture_rvs, MixtureDistribution) #np.random.seed(12345) ## obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm], ## kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.75))) mix_kwds = (dict(loc=-0.5,scale=.5),dict(loc=1,scale=.2)) obs_dist = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm], kwargs=mix_kwds) mix = MixtureDistribution() #obs_dist = np.random.randn(nobs)/4. #np.sqrt(2) if "chebyt_" in examples: # needed for Cheby example below #obs_dist = np.clip(obs_dist, -2, 2)/2.01 #chebyt [0,1] obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/2.0 #/4. + 2/4.0 #fourier [0,1] #obs_dist = obs_dist[(obs_dist>-2) & (obs_dist<2)]/4. + 2/4.0 f_hat, grid, coeffs, polys = density_orthopoly(obs_dist, ChebyTPoly, order=20, xeval=None) #f_hat /= f_hat.sum() * (grid.max() - grid.min())/len(grid) f_hat0 = f_hat from scipy import integrate fint = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid)) #f_hat -= fint/2. print 'f_hat.min()', f_hat.min() f_hat = (f_hat - f_hat.min()) #/ f_hat.max() - f_hat.min fint2 = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid)) print 'fint2', fint, fint2 f_hat /= fint2 # note that this uses a *huge* grid by default #f_hat, grid = kdensityfft(emp_dist, kernel="gauss", bw="scott") # check the plot doplot = 0 if doplot: plt.hist(obs_dist, bins=50, normed=True, color='red') plt.plot(grid, f_hat, lw=2, color='black') plt.plot(grid, f_hat0, lw=2, color='g') plt.show() for i,p in enumerate(polys[:5]): for j,p2 in enumerate(polys[:5]): print i,j,integrate.quad(lambda x: p(x)*p2(x), -1,1)[0] for p in polys: print integrate.quad(lambda x: p(x)**2, -1,1) #examples using the new class if "chebyt" in examples: dop = DensityOrthoPoly().fit(obs_dist, ChebyTPoly, order=20) grid = np.linspace(obs_dist.min(), obs_dist.max()) xf = dop(grid) #print 'np.max(np.abs(xf - f_hat0))', np.max(np.abs(xf - f_hat0)) dopint = integrate.quad(dop, *dop.limits)[0] print 'dop F integral', dopint mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=mix_kwds) doplot = 1 if doplot: plt.figure() plt.hist(obs_dist, bins=50, normed=True, color='red') plt.plot(grid, xf, lw=2, color='black') plt.plot(grid, mpdf, lw=2, color='green') plt.title('using Chebychev polynomials') #plt.show() if "fourier" in examples: dop = DensityOrthoPoly() dop.offsetfac = 0.5 dop = dop.fit(obs_dist, F2Poly, order=30) grid = np.linspace(obs_dist.min(), obs_dist.max()) xf = dop(grid) #print np.max(np.abs(xf - f_hat0)) dopint = integrate.quad(dop, *dop.limits)[0] print 'dop F integral', dopint mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=mix_kwds) doplot = 1 if doplot: plt.figure() plt.hist(obs_dist, bins=50, normed=True, color='red') plt.title('using Fourier polynomials') plt.plot(grid, xf, lw=2, color='black') plt.plot(grid, mpdf, lw=2, color='green') #plt.show() #check orthonormality: print np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5))) if "hermite" in examples: dop = DensityOrthoPoly() dop.offsetfac = 0 dop = dop.fit(obs_dist, HPoly, order=20) grid = np.linspace(obs_dist.min(), obs_dist.max()) xf = dop(grid) #print np.max(np.abs(xf - f_hat0)) dopint = integrate.quad(dop, *dop.limits)[0] print 'dop F integral', dopint mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm], kwargs=mix_kwds) doplot = 1 if doplot: plt.figure() plt.hist(obs_dist, bins=50, normed=True, color='red') plt.plot(grid, xf, lw=2, color='black') plt.plot(grid, mpdf, lw=2, color='green') plt.title('using Hermite polynomials') plt.show() #check orthonormality: print np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5))) #check orthonormality hpolys = [HPoly(i) for i in range(5)] inn = inner_cont(hpolys, -6, 6)[0] print np.max(np.abs(inn - np.eye(5))) print (inn*100000).astype(int) from scipy.special import hermite, chebyt htpolys = [hermite(i) for i in range(5)] innt = inner_cont(htpolys, -10, 10)[0] print (innt*100000).astype(int) polysc = [chebyt(i) for i in range(4)] r, e = inner_cont(polysc, -1, 1, weight=lambda x: (1-x*x)**(-1/2.)) print np.max(np.abs(r - np.diag(np.diag(r)))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/dgp_examples.py000066400000000000000000000135701224417117700300440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Examples of non-linear functions for non-parametric regression Created on Sat Jan 05 20:21:22 2013 Author: Josef Perktold """ import numpy as np ## Functions def fg1(x): '''Fan and Gijbels example function 1 ''' return x + 2 * np.exp(-16 * x**2) def fg1eu(x): '''Eubank similar to Fan and Gijbels example function 1 ''' return x + 0.5 * np.exp(-50 * (x - 0.5)**2) def fg2(x): '''Fan and Gijbels example function 2 ''' return np.sin(2 * x) + 2 * np.exp(-16 * x**2) def func1(x): '''made up example with sin, square ''' return np.sin(x * 5) / x + 2. * x - 1. * x**2 ## Classes with Data Generating Processes doc = {'description': '''Base Class for Univariate non-linear example Does not work on it's own. needs additional at least self.func ''', 'ref': ''} class _UnivariateFunction(object): #Base Class for Univariate non-linear example. #Does not work on it's own. needs additionally at least self.func __doc__ = '''%(description)s Parameters ---------- nobs : int number of observations to simulate x : None or 1d array If x is given then it is used for the exogenous variable instead of creating a random sample distr_x : None or distribution instance Only used if x is None. The rvs method is used to create a random sample of the exogenous (explanatory) variable. distr_noise : None or distribution instance The rvs method is used to create a random sample of the errors. Attributes ---------- x : ndarray, 1-D exogenous or explanatory variable. x is sorted. y : ndarray, 1-D endogenous or response variable y_true : ndarray, 1-D expected values of endogenous or response variable, i.e. values of y without noise func : callable underlying function (defined by subclass) %(ref)s ''' #% doc def __init__(self, nobs=200, x=None, distr_x=None, distr_noise=None): if x is None: if distr_x is None: x = np.random.normal(loc=0, scale=self.s_x, size=nobs) else: x = distr_x.rvs(size=nobs) x.sort() self.x = x if distr_noise is None: noise = np.random.normal(loc=0, scale=self.s_noise, size=nobs) else: noise = distr_noise.rvs(size=nobs) if hasattr(self, 'het_scale'): noise *= self.het_scale(self.x) #self.func = fg1 self.y_true = y_true = self.func(x) self.y = y_true + noise def plot(self, scatter=True, ax=None): '''plot the mean function and optionally the scatter of the sample Parameters ---------- scatter: bool If true, then add scatterpoints of sample to plot. ax : None or matplotlib axis instance If None, then a matplotlib.pyplot figure is created, otherwise the given axis, ax, is used. Returns ------- fig : matplotlib figure This is either the created figure instance or the one associated with ax if ax is given. ''' if ax is None: import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1, 1, 1) if scatter: ax.plot(self.x, self.y, 'o', alpha=0.5) xx = np.linspace(self.x.min(), self.x.max(), 100) ax.plot(xx, self.func(xx), lw=2, color='b', label='dgp mean') return ax.figure doc = {'description': '''Fan and Gijbels example function 1 linear trend plus a hump ''', 'ref': ''' References ---------- Fan, Jianqing, and Irene Gijbels. 1992. "Variable Bandwidth and Local Linear Regression Smoothers." The Annals of Statistics 20 (4) (December): 2008-2036. doi:10.2307/2242378. '''} class UnivariateFanGijbels1(_UnivariateFunction): __doc__ = _UnivariateFunction.__doc__ % doc def __init__(self, nobs=200, x=None, distr_x=None, distr_noise=None): self.s_x = 1. self.s_noise = 0.7 self.func = fg1 super(self.__class__, self).__init__(nobs=nobs, x=x, distr_x=distr_x, distr_noise=distr_noise) doc['description'] =\ '''Fan and Gijbels example function 2 sin plus a hump ''' class UnivariateFanGijbels2(_UnivariateFunction): __doc__ = _UnivariateFunction.__doc__ % doc def __init__(self, nobs=200, x=None, distr_x=None, distr_noise=None): self.s_x = 1. self.s_noise = 0.5 self.func = fg2 super(self.__class__, self).__init__(nobs=nobs, x=x, distr_x=distr_x, distr_noise=distr_noise) class UnivariateFanGijbels1EU(_UnivariateFunction): ''' Eubank p.179f ''' def __init__(self, nobs=50, x=None, distr_x=None, distr_noise=None): if distr_x is None: from scipy import stats distr_x = stats.uniform self.s_noise = 0.15 self.func = fg1eu super(self.__class__, self).__init__(nobs=nobs, x=x, distr_x=distr_x, distr_noise=distr_noise) class UnivariateFunc1(_UnivariateFunction): ''' made up, with sin and quadratic trend ''' def __init__(self, nobs=200, x=None, distr_x=None, distr_noise=None): if x is None and distr_x is None: from scipy import stats distr_x = stats.uniform(-2, 4) else: nobs = x.shape[0] self.s_noise = 2. self.func = func1 super(UnivariateFunc1, self).__init__(nobs=nobs, x=x, distr_x=distr_x, distr_noise=distr_noise) def het_scale(self, x): return np.sqrt(np.abs(3+x)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/kde2.py000066400000000000000000000057701224417117700262240ustar00rootroot00000000000000# -*- coding: utf-8 -*- import numpy as np import kernels #TODO: should this be a function? class KDE(object): """ Kernel Density Estimator Parameters ---------- x : array-like N-dimensional array from which the density is to be estimated kernel : Kernel Class Should be a class from * """ #TODO: amend docs for Nd case? def __init__(self, x, kernel=None): x = np.asarray(x) if x.ndim == 1: x = x[:,None] nobs, n_series = x.shape if kernel is None: kernel = kernels.Gaussian() # no meaningful bandwidth yet if n_series > 1: if isinstance( kernel, kernels.CustomKernel ): kernel = kernels.NdKernel(n_series, kernels = kernel) self.kernel = kernel self.n = n_series #TODO change attribute self.x = x def density(self, x): return self.kernel.density(self.x, x) def __call__(self, x, h="scott"): return np.array([self.density(xx) for xx in x]) def evaluate(self, x, h="silverman"): density = self.kernel.density return np.array([density(xx) for xx in x]) if __name__ == "__main__": from numpy import random import matplotlib.pyplot as plt import statsmodels.nonparametric.bandwidths as bw from statsmodels.sandbox.nonparametric.testdata import kdetest # 1-D case random.seed(142) x = random.standard_t(4.2, size = 50) h = bw.bw_silverman(x) #NOTE: try to do it with convolution support = np.linspace(-10,10,512) kern = kernels.Gaussian(h = h) kde = KDE( x, kern) print kde.density(1.015469) print 0.2034675 Xs = np.arange(-10,10,0.1) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(Xs, kde(Xs), "-") ax.set_ylim(-10, 10) ax.set_ylim(0,0.4) # 2-D case x = zip(kdetest.faithfulData["eruptions"], kdetest.faithfulData["waiting"]) x = np.array(x) x = (x - x.mean(0))/x.std(0) nobs = x.shape[0] H = kdetest.Hpi kern = kernels.NdKernel( 2 ) kde = KDE( x, kern ) print kde.density( np.matrix( [1,2 ])) #.T ) plt.figure() plt.plot(x[:,0], x[:,1], 'o') n_grid = 50 xsp = np.linspace(x.min(0)[0], x.max(0)[0], n_grid) ysp = np.linspace(x.min(0)[1], x.max(0)[1], n_grid) # xsorted = np.sort(x) # xlow = xsorted[nobs/4] # xupp = xsorted[3*nobs/4] # xsp = np.linspace(xlow[0], xupp[0], n_grid) # ysp = np.linspace(xlow[1], xupp[1], n_grid) xr, yr = np.meshgrid(xsp, ysp) kde_vals = np.array([kde.density( np.matrix( [xi, yi ]) ) for xi, yi in zip(xr.ravel(), yr.ravel())]) plt.contour(xsp, ysp, kde_vals.reshape(n_grid, n_grid)) plt.show() # 5 D case # random.seed(142) # mu = [1.0, 4.0, 3.5, -2.4, 0.0] # sigma = np.matrix( # [[ 0.6 - 0.1*abs(i-j) if i != j else 1.0 for j in xrange(5)] for i in xrange(5)]) # x = random.multivariate_normal(mu, sigma, size = 100) # kern = kernel.Gaussian() # kde = KernelEstimate( x, kern ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/kdecovclass.py000066400000000000000000000131211224417117700276650ustar00rootroot00000000000000'''subclassing kde Author: josef pktd ''' import numpy as np import scipy from scipy import stats import matplotlib.pylab as plt class gaussian_kde_set_covariance(stats.gaussian_kde): ''' from Anne Archibald in mailinglist: http://www.nabble.com/Width-of-the-gaussian-in-stats.kde.gaussian_kde---td19558924.html#a19558924 ''' def __init__(self, dataset, covariance): self.covariance = covariance scipy.stats.gaussian_kde.__init__(self, dataset) def _compute_covariance(self): self.inv_cov = np.linalg.inv(self.covariance) self._norm_factor = sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n class gaussian_kde_covfact(stats.gaussian_kde): def __init__(self, dataset, covfact = 'scotts'): self.covfact = covfact scipy.stats.gaussian_kde.__init__(self, dataset) def _compute_covariance_(self): '''not used''' self.inv_cov = np.linalg.inv(self.covariance) self._norm_factor = sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n def covariance_factor(self): if self.covfact in ['sc', 'scotts']: return self.scotts_factor() if self.covfact in ['si', 'silverman']: return self.silverman_factor() elif self.covfact: return float(self.covfact) else: raise ValueError, \ 'covariance factor has to be scotts, silverman or a number' def reset_covfact(self, covfact): self.covfact = covfact self.covariance_factor() self._compute_covariance() def plotkde(covfact): gkde.reset_covfact(covfact) kdepdf = gkde.evaluate(ind) plt.figure() # plot histgram of sample plt.hist(xn, bins=20, normed=1) # plot estimated density plt.plot(ind, kdepdf, label='kde', color="g") # plot data generating density plt.plot(ind, alpha * stats.norm.pdf(ind, loc=mlow) + (1-alpha) * stats.norm.pdf(ind, loc=mhigh), color="r", label='DGP: normal mix') plt.title('Kernel Density Estimation - ' + str(gkde.covfact)) plt.legend() from numpy.testing import assert_array_almost_equal, \ assert_almost_equal, assert_ def test_kde_1d(): np.random.seed(8765678) n_basesample = 500 xn = np.random.randn(n_basesample) xnmean = xn.mean() xnstd = xn.std(ddof=1) print xnmean, xnstd # get kde for original sample gkde = stats.gaussian_kde(xn) # evaluate the density funtion for the kde for some points xs = np.linspace(-7,7,501) kdepdf = gkde.evaluate(xs) normpdf = stats.norm.pdf(xs, loc=xnmean, scale=xnstd) print 'MSE', np.sum((kdepdf - normpdf)**2) print 'axabserror', np.max(np.abs(kdepdf - normpdf)) intervall = xs[1] - xs[0] assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01) #assert_array_almost_equal(kdepdf, normpdf, decimal=2) print gkde.integrate_gaussian(0.0, 1.0) print gkde.integrate_box_1d(-np.inf, 0.0) print gkde.integrate_box_1d(0.0, np.inf) print gkde.integrate_box_1d(-np.inf, xnmean) print gkde.integrate_box_1d(xnmean, np.inf) assert_almost_equal(gkde.integrate_box_1d(xnmean, np.inf), 0.5, decimal=1) assert_almost_equal(gkde.integrate_box_1d(-np.inf, xnmean), 0.5, decimal=1) assert_almost_equal(gkde.integrate_box(xnmean, np.inf), 0.5, decimal=1) assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), 0.5, decimal=1) assert_almost_equal(gkde.integrate_kde(gkde), (kdepdf**2).sum()*intervall, decimal=2) assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2), (kdepdf*normpdf).sum()*intervall, decimal=2) ## assert_almost_equal(gkde.integrate_gaussian(0.0, 1.0), ## (kdepdf*normpdf).sum()*intervall, decimal=2) if __name__ == '__main__': # generate a sample n_basesample = 1000 np.random.seed(8765678) alpha = 0.6 #weight for (prob of) lower distribution mlow, mhigh = (-3,3) #mean locations for gaussian mixture xn = np.concatenate([mlow + np.random.randn(alpha * n_basesample), mhigh + np.random.randn((1-alpha) * n_basesample)]) # get kde for original sample #gkde = stats.gaussian_kde(xn) gkde = gaussian_kde_covfact(xn, 0.1) # evaluate the density funtion for the kde for some points ind = np.linspace(-7,7,101) kdepdf = gkde.evaluate(ind) plt.figure() # plot histgram of sample plt.hist(xn, bins=20, normed=1) # plot estimated density plt.plot(ind, kdepdf, label='kde', color="g") # plot data generating density plt.plot(ind, alpha * stats.norm.pdf(ind, loc=mlow) + (1-alpha) * stats.norm.pdf(ind, loc=mhigh), color="r", label='DGP: normal mix') plt.title('Kernel Density Estimation') plt.legend() gkde = gaussian_kde_covfact(xn, 'scotts') kdepdf = gkde.evaluate(ind) plt.figure() # plot histgram of sample plt.hist(xn, bins=20, normed=1) # plot estimated density plt.plot(ind, kdepdf, label='kde', color="g") # plot data generating density plt.plot(ind, alpha * stats.norm.pdf(ind, loc=mlow) + (1-alpha) * stats.norm.pdf(ind, loc=mhigh), color="r", label='DGP: normal mix') plt.title('Kernel Density Estimation') plt.legend() #plt.show() for cv in ['scotts', 'silverman', 0.05, 0.1, 0.5]: plotkde(cv) test_kde_1d() np.random.seed(8765678) n_basesample = 1000 xn = np.random.randn(n_basesample) xnmean = xn.mean() xnstd = xn.std(ddof=1) # get kde for original sample gkde = stats.gaussian_kde(xn) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/kernel_extras.py000066400000000000000000000332751224417117700302460ustar00rootroot00000000000000""" Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types References ---------- [1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007) [2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) http://dx.doi.org/10.1561/0800000009 [3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions with Categorical and Continuous Data." Working Paper. (2000) [4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional Distributions Annals of Economics and Finance 5, 211-235 (2004) [5] Liu, R., Yang, L. "Kernel estimation of multivariate cumulative distribution function." Journal of Nonparametric Statistics (2008) [6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF with Categorical and Continuous Data." Working Paper [7] Li, Q., Racine, J. "Cross-validated local linear nonparametric regression" Statistica Sinica 14(2004), pp. 485-512 [8] Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics [9] Racine, J., Hart, J., Li, Q., "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models", 2006, Econometric Reviews 25, 523-544 """ # TODO: make default behavior efficient=True above a certain n_obs import numpy as np from scipy import optimize from scipy.stats.mstats import mquantiles from statsmodels.nonparametric.api import KDEMultivariate, KernelReg from statsmodels.nonparametric._kernel_base import \ gpke, LeaveOneOut, _get_type_pos, _adjust_shape __all__ = ['SingleIndexModel', 'SemiLinear', 'TestFForm'] class TestFForm(object): """ Nonparametric test for functional form. Parameters ---------- endog: list Dependent variable (training set) exog: list of array_like objects The independent (right-hand-side) variables bw: array_like, str Bandwidths for exog or specify method for bandwidth selection fform: function The functional form ``y = g(b, x)`` to be tested. Takes as inputs the RHS variables `exog` and the coefficients ``b`` (betas) and returns a fitted ``y_hat``. var_type: str The type of the independent `exog` variables: - c: continuous - o: ordered - u: unordered estimator: function Must return the estimated coefficients b (betas). Takes as inputs ``(endog, exog)``. E.g. least square estimator:: lambda (x,y): np.dot(np.pinv(np.dot(x.T, x)), np.dot(x.T, y)) References ---------- See Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business \& Economics Statistics. See chapter 12 in [1] pp. 355-357. """ def __init__(self, endog, exog, bw, var_type, fform, estimator, nboot=100): self.endog = endog self.exog = exog self.var_type = var_type self.fform = fform self.estimator = estimator self.nboot = nboot self.bw = KDEMultivariate(exog, bw=bw, var_type=var_type).bw self.sig = self._compute_sig() def _compute_sig(self): Y = self.endog X = self.exog b = self.estimator(Y, X) m = self.fform(X, b) n = np.shape(X)[0] resid = Y - m resid = resid - np.mean(resid) # center residuals self.test_stat = self._compute_test_stat(resid) sqrt5 = np.sqrt(5.) fct1 = (1 - sqrt5) / 2. fct2 = (1 + sqrt5) / 2. u1 = fct1 * resid u2 = fct2 * resid r = fct2 / sqrt5 I_dist = np.empty((self.nboot,1)) for j in xrange(self.nboot): u_boot = u2.copy() prob = np.random.uniform(0,1, size = (n,)) ind = prob < r u_boot[ind] = u1[ind] Y_boot = m + u_boot b_hat = self.estimator(Y_boot, X) m_hat = self.fform(X, b_hat) u_boot_hat = Y_boot - m_hat I_dist[j] = self._compute_test_stat(u_boot_hat) self.boots_results = I_dist sig = "Not Significant" if self.test_stat > mquantiles(I_dist, 0.9): sig = "*" if self.test_stat > mquantiles(I_dist, 0.95): sig = "**" if self.test_stat > mquantiles(I_dist, 0.99): sig = "***" return sig def _compute_test_stat(self, u): n = np.shape(u)[0] XLOO = LeaveOneOut(self.exog) uLOO = LeaveOneOut(u[:,None]).__iter__() I = 0 S2 = 0 for i, X_not_i in enumerate(XLOO): u_j = uLOO.next() u_j = np.squeeze(u_j) # See Bootstrapping procedure on p. 357 in [1] K = gpke(self.bw, data=-X_not_i, data_predict=-self.exog[i, :], var_type=self.var_type, tosum=False) f_i = (u[i] * u_j * K) assert u_j.shape == K.shape I += f_i.sum() # See eq. 12.7 on p. 355 in [1] S2 += (f_i**2).sum() # See Theorem 12.1 on p.356 in [1] assert np.size(I) == 1 assert np.size(S2) == 1 I *= 1. / (n * (n - 1)) ix_cont = _get_type_pos(self.var_type)[0] hp = self.bw[ix_cont].prod() S2 *= 2 * hp / (n * (n - 1)) T = n * I * np.sqrt(hp / S2) return T class SingleIndexModel(KernelReg): """ Single index semiparametric model ``y = g(X * b) + e``. Parameters ---------- endog: array_like The dependent variable exog: array_like The independent variable(s) var_type: str The type of variables in X: - c: continuous - o: ordered - u: unordered Attributes ---------- b: array_like The linear coefficients b (betas) bw: array_like Bandwidths Methods ------- fit(): Computes the fitted values ``E[Y|X] = g(X * b)`` and the marginal effects ``dY/dX``. References ---------- See chapter on semiparametric models in [1] Notes ----- This model resembles the binary choice models. The user knows that X and b interact linearly, but ``g(X * b)`` is unknown. In the parametric binary choice models the user usually assumes some distribution of g() such as normal or logistic. """ def __init__(self, endog, exog, var_type): self.var_type = var_type self.K = len(var_type) self.var_type = self.var_type[0] self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, self.K) self.nobs = np.shape(self.exog)[0] self.data_type = self.var_type self.func = self._est_loc_linear self.b, self.bw = self._est_b_bw() def _est_b_bw(self): params0 = np.random.uniform(size=(self.K + 1, )) b_bw = optimize.fmin(self.cv_loo, params0, disp=0) b = b_bw[0:self.K] bw = b_bw[self.K:] bw = self._set_bw_bounds(bw) return b, bw def cv_loo(self, params): # See p. 254 in Textbook params = np.asarray(params) b = params[0 : self.K] bw = params[self.K:] LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() L = 0 for i, X_not_i in enumerate(LOO_X): Y = LOO_Y.next() #print b.shape, np.dot(self.exog[i:i+1, :], b).shape, bw, G = self.func(bw, endog=Y, exog=-np.dot(X_not_i, b)[:,None], #data_predict=-b*self.exog[i, :])[0] data_predict=-np.dot(self.exog[i:i+1, :], b))[0] #print G.shape L += (self.endog[i] - G) ** 2 # Note: There might be a way to vectorize this. See p.72 in [1] return L / self.nobs def fit(self, data_predict=None): if data_predict is None: data_predict = self.exog else: data_predict = _adjust_shape(data_predict, self.K) N_data_predict = np.shape(data_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.K)) for i in xrange(N_data_predict): mean_mfx = self.func(self.bw, self.endog, np.dot(self.exog, self.b)[:,None], data_predict=np.dot(data_predict[i:i+1, :],self.b)) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx def __repr__(self): """Provide something sane to print.""" repr = "Single Index Model \n" repr += "Number of variables: K = " + str(self.K) + "\n" repr += "Number of samples: nobs = " + str(self.nobs) + "\n" repr += "Variable types: " + self.var_type + "\n" repr += "BW selection method: cv_ls" + "\n" repr += "Estimator type: local constant" + "\n" return repr class SemiLinear(KernelReg): """ Semiparametric partially linear model, ``Y = Xb + g(Z) + e``. Parameters ---------- endog: array_like The dependent variable exog: array_like The linear component in the regression exog_nonparametric: array_like The nonparametric component in the regression var_type: str The type of the variables in the nonparametric component; - c: continuous - o: ordered - u: unordered k_linear : int The number of variables that comprise the linear component. Attributes ---------- bw: array_like Bandwidths for the nonparametric component exog_nonparametric b: array_like Coefficients in the linear component nobs : int The number of observations. k_linear : int The number of variables that comprise the linear component. Methods ------- fit(): Returns the fitted mean and marginal effects dy/dz Notes ----- This model uses only the local constant regression estimator References ---------- See chapter on Semiparametric Models in [1] """ def __init__(self, endog, exog, exog_nonparametric, var_type, k_linear): self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, k_linear) self.K = len(var_type) self.exog_nonparametric = _adjust_shape(exog_nonparametric, self.K) self.k_linear = k_linear self.nobs = np.shape(self.exog)[0] self.var_type = var_type self.data_type = self.var_type self.func = self._est_loc_linear self.b, self.bw = self._est_b_bw() def _est_b_bw(self): """ Computes the (beta) coefficients and the bandwidths. Minimizes ``cv_loo`` with respect to ``b`` and ``bw``. """ params0 = np.random.uniform(size=(self.k_linear + self.K, )) b_bw = optimize.fmin(self.cv_loo, params0, disp=0) b = b_bw[0 : self.k_linear] bw = b_bw[self.k_linear:] #bw = self._set_bw_bounds(np.asarray(bw)) return b, bw def cv_loo(self, params): """ Similar to the cross validation leave-one-out estimator. Modified to reflect the linear components. Parameters ---------- params: array_like Vector consisting of the coefficients (b) and the bandwidths (bw). The first ``k_linear`` elements are the coefficients. Returns ------- L: float The value of the objective function References ---------- See p.254 in [1] """ params = np.asarray(params) b = params[0 : self.k_linear] bw = params[self.k_linear:] LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() LOO_Z = LeaveOneOut(self.exog_nonparametric).__iter__() Xb = np.dot(self.exog, b)[:,None] L = 0 for ii, X_not_i in enumerate(LOO_X): Y = LOO_Y.next() Z = LOO_Z.next() Xb_j = np.dot(X_not_i, b)[:,None] Yx = Y - Xb_j G = self.func(bw, endog=Yx, exog=-Z, data_predict=-self.exog_nonparametric[ii, :])[0] lt = Xb[ii, :] #.sum() # linear term L += (self.endog[ii] - lt - G) ** 2 return L def fit(self, exog_predict=None, exog_nonparametric_predict=None): """Computes fitted values and marginal effects""" if exog_predict is None: exog_predict = self.exog else: exog_predict = _adjust_shape(exog_predict, self.k_linear) if exog_nonparametric_predict is None: exog_nonparametric_predict = self.exog_nonparametric else: exog_nonparametric_predict = _adjust_shape(exog_nonparametric_predict, self.K) N_data_predict = np.shape(exog_nonparametric_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.K)) Y = self.endog - np.dot(exog_predict, self.b)[:,None] for i in xrange(N_data_predict): mean_mfx = self.func(self.bw, Y, self.exog_nonparametric, data_predict=exog_nonparametric_predict[i, :]) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx def __repr__(self): """Provide something sane to print.""" repr = "Semiparamatric Partially Linear Model \n" repr += "Number of variables: K = " + str(self.K) + "\n" repr += "Number of samples: N = " + str(self.nobs) + "\n" repr += "Variable types: " + self.var_type + "\n" repr += "BW selection method: cv_ls" + "\n" repr += "Estimator type: local constant" + "\n" return repr statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/kernels.py000066400000000000000000000330561224417117700270400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ This models contains the Kernels for Kernel smoothing. Hopefully in the future they may be reused/extended for other kernel based method References: ---------- Pointwise Kernel Confidence Bounds (smoothconf) http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/anr/anrhtmlframe62.html """ # pylint: disable-msg=C0103 # pylint: disable-msg=W0142 # pylint: disable-msg=E1101 # pylint: disable-msg=E0611 import numpy as np import scipy.integrate from numpy import exp, multiply, square, divide, subtract, inf class NdKernel(object): """Generic N-dimensial kernel Parameters ---------- n : int The number of series for kernel estimates kernels : list kernels Can be constructed from either a) a list of n kernels which will be treated as indepent marginals on a gaussian copula (specified by H) or b) a single univariate kernel which will be applied radially to the mahalanobis distance defined by H. In the case of the Gaussian these are both equivalent, and the second constructiong is prefered. """ def __init__(self, n, kernels = None, H = None): if kernels is None: kernels = Gaussian() self._kernels = kernels if H is None: H = np.matrix( np.identity(n)) self._H = H self._Hrootinv = np.linalg.cholesky( H.I ) def getH(self): """Getter for kernel bandwidth, H""" return self._H def setH(self, value): """Setter for kernel bandwidth, H""" self._H = value H = property(getH, setH, doc="Kernel bandwidth matrix") def density(self, xs, x): n = len(xs) #xs = self.inDomain( xs, xs, x )[0] if len(xs)>0: ## Need to do product of marginal distributions #w = np.sum([self(self._Hrootinv * (xx-x).T ) for xx in xs])/n #vectorized doesn't work: w = np.mean(self((xs-x) * self._Hrootinv )) #transposed #w = np.mean([self(xd) for xd in ((xs-x) * self._Hrootinv)] ) #transposed return w else: return np.nan def _kernweight(self, x ): """returns the kernel weight for the independent multivariate kernel""" if isinstance( self._kernels, CustomKernel ): ## Radial case #d = x.T * x #x is matrix, 2d, element wise sqrt looks wrong #d = np.sqrt( x.T * x ) x = np.asarray(x) #d = np.sqrt( (x * x).sum(-1) ) d = (x * x).sum(-1) return self._kernels( np.asarray(d) ) def __call__(self, x): """ This simply returns the value of the kernel function at x Does the same as weight if the function is normalised """ return self._kernweight(x) class CustomKernel(object): """ Generic 1D Kernel object. Can be constructed by selecting a standard named Kernel, or providing a lambda expression and domain. The domain allows some algorithms to run faster for finite domain kernels. """ # MC: Not sure how this will look in the end - or even still exist. # Main purpose of this is to allow custom kernels and to allow speed up # from finite support. def __init__(self, shape, h = 1.0, domain = None, norm = None): """ shape should be a lambda taking and returning numeric type. For sanity it should always return positive or zero but this isn't enforced incase you want to do weird things. Bear in mind that the statistical tests etc. may not be valid for non-positive kernels. The bandwidth of the kernel is supplied as h. You may specify a domain as a list of 2 values [min,max], in which case kernel will be treated as zero outside these values. This will speed up calculation. You may also specify the normalisation constant for the supplied Kernel. If you do this number will be stored and used as the normalisation without calculation. It is recommended you do this if you know the constant, to speed up calculation. In particular if the shape function provided is already normalised you should provide norm = 1.0 or norm = True """ if norm is True: norm = 1.0 self._normconst = norm self.domain = domain if callable(shape): self._shape = shape else: raise TypeError("shape must be a callable object/function") self._h = h self._L2Norm = None def geth(self): """Getter for kernel bandwidth, h""" return self._h def seth(self, value): """Setter for kernel bandwidth, h""" self._h = value h = property(geth, seth, doc="Kernel Bandwidth") def inDomain(self, xs, ys, x): """ Returns the filtered (xs, ys) based on the Kernel domain centred on x """ # Disable black-list functions: filter used for speed instead of # list-comprehension # pylint: disable-msg=W0141 def isInDomain(xy): """Used for filter to check if point is in the domain""" u = (xy[0]-x)/self.h return u >= self.domain[0] and u <= self.domain[1] if self.domain is None: return (xs, ys) else: filtered = filter(isInDomain, zip(xs, ys)) if len(filtered) > 0: xs, ys = zip(*filtered) return (xs, ys) else: return ([], []) def density(self, xs, x): """Returns the kernel density estimate for point x based on x-values xs """ xs = np.asarray(xs) n = len(xs) # before inDomain? xs = self.inDomain( xs, xs, x )[0] if xs.ndim == 1: xs = xs[:,None] if len(xs)>0: h = self.h w = 1/h * np.mean(self((xs-x)/h), axis=0) return w else: return np.nan def smooth(self, xs, ys, x): """Returns the kernel smoothing estimate for point x based on x-values xs and y-values ys. Not expected to be called by the user. """ xs, ys = self.inDomain(xs, ys, x) if len(xs)>0: w = np.sum(self((xs-x)/self.h)) #TODO: change the below to broadcasting when shape is sorted v = np.sum([yy*self((xx-x)/self.h) for xx, yy in zip(xs, ys)]) return v / w else: return np.nan def smoothvar(self, xs, ys, x): """Returns the kernel smoothing estimate of the variance at point x. """ xs, ys = self.inDomain(xs, ys, x) if len(xs) > 0: fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs]) sqresid = square( subtract(ys, fittedvals) ) w = np.sum(self((xs-x)/self.h)) v = np.sum([rr*self((xx-x)/self.h) for xx, rr in zip(xs, sqresid)]) return v / w else: return np.nan def smoothconf(self, xs, ys, x): """Returns the kernel smoothing estimate with confidence 1sigma bounds """ xs, ys = self.inDomain(xs, ys, x) if len(xs) > 0: fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs]) sqresid = square( subtract(ys, fittedvals) ) w = np.sum(self((xs-x)/self.h)) v = np.sum([rr*self((xx-x)/self.h) for xx, rr in zip(xs, sqresid)]) var = v / w sd = np.sqrt(var) K = self.L2Norm yhat = self.smooth(xs, ys, x) err = sd * K / np.sqrt(w * self.h * self.norm_const) return (yhat - err, yhat, yhat + err) else: return (np.nan, np.nan, np.nan) @property def L2Norm(self): """Returns the integral of the square of the kernal from -inf to inf""" if self._L2Norm is None: L2Func = lambda x: (self.norm_const*self._shape(x))**2 if self.domain is None: self._L2Norm = scipy.integrate.quad(L2Func, -inf, inf)[0] else: self._L2Norm = scipy.integrate.quad(L2Func, self.domain[0], self.domain[1])[0] return self._L2Norm @property def norm_const(self): """ Normalising constant for kernel (integral from -inf to inf) """ if self._normconst is None: if self.domain is None: quadres = scipy.integrate.quad(self._shape, -inf, inf) else: quadres = scipy.integrate.quad(self._shape, self.domain[0], self.domain[1]) self._normconst = 1.0/(quadres[0]) return self._normconst def weight(self, x): """This returns the normalised weight at distance x""" return self.norm_const*self._shape(x) def __call__(self, x): """ This simply returns the value of the kernel function at x Does the same as weight if the function is normalised """ return self._shape(x) class Uniform(CustomKernel): def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 0.5, h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = 0.5 class Triangular(CustomKernel): def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 1 - abs(x), h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = 2.0/3.0 class Epanechnikov(CustomKernel): def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 0.75*(1 - x*x), h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = 0.6 class Biweight(CustomKernel): def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 0.9375*(1 - x*x)**2, h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = 5.0/7.0 def smooth(self, xs, ys, x): """Returns the kernel smoothing estimate for point x based on x-values xs and y-values ys. Not expected to be called by the user. Special implementation optimised for Biweight. """ xs, ys = self.inDomain(xs, ys, x) if len(xs) > 0: w = np.sum(square(subtract(1, square(divide(subtract(xs, x), self.h))))) v = np.sum(multiply(ys, square(subtract(1, square(divide( subtract(xs, x), self.h)))))) return v / w else: return np.nan def smoothvar(self, xs, ys, x): """ Returns the kernel smoothing estimate of the variance at point x. """ xs, ys = self.inDomain(xs, ys, x) if len(xs) > 0: fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs]) rs = square(subtract(ys, fittedvals)) w = np.sum(square(subtract(1.0, square(divide(subtract(xs, x), self.h))))) v = np.sum(multiply(rs, square(subtract(1, square(divide( subtract(xs, x), self.h)))))) return v / w else: return np.nan def smoothconf(self, xs, ys, x): """Returns the kernel smoothing estimate with confidence 1sigma bounds """ xs, ys = self.inDomain(xs, ys, x) if len(xs) > 0: fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs]) rs = square(subtract(ys, fittedvals)) w = np.sum(square(subtract(1.0, square(divide(subtract(xs, x), self.h))))) v = np.sum(multiply(rs, square(subtract(1, square(divide( subtract(xs, x), self.h)))))) var = v / w sd = np.sqrt(var) K = self.L2Norm yhat = self.smooth(xs, ys, x) err = sd * K / np.sqrt(0.9375 * w * self.h) return (yhat - err, yhat, yhat + err) else: return (np.nan, np.nan, np.nan) class Triweight(CustomKernel): def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 1.09375*(1 - x*x)**3, h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = 350.0/429.0 class Gaussian(CustomKernel): """ Gaussian (Normal) Kernel K(u) = 1 / (sqrt(2*pi)) exp(-0.5 u**2) """ def __init__(self, h=1.0): CustomKernel.__init__(self, shape = lambda x: 0.3989422804014327 * np.exp(-x**2/2.0), h = h, domain = None, norm = 1.0) self._L2Norm = 1.0/(2.0*np.sqrt(np.pi)) def smooth(self, xs, ys, x): """Returns the kernel smoothing estimate for point x based on x-values xs and y-values ys. Not expected to be called by the user. Special implementation optimised for Gaussian. """ w = np.sum(exp(multiply(square(divide(subtract(xs, x), self.h)),-0.5))) v = np.sum(multiply(ys, exp(multiply(square(divide(subtract(xs, x), self.h)), -0.5)))) return v/w class Cosine(CustomKernel): """ Cosine Kernel K(u) = pi/4 cos(0.5 * pi * u) between -1.0 and 1.0 """ def __init__(self, h=1.0): CustomKernel.__init__(self, shape=lambda x: 0.78539816339744828 * np.cos(np.pi/2.0 * x), h=h, domain=[-1.0, 1.0], norm = 1.0) self._L2Norm = np.pi**2/16.0 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/smoothers.py000066400000000000000000000304201224417117700274100ustar00rootroot00000000000000""" This module contains scatterplot smoothers, that is classes who generate a smooth fit of a set of (x,y) pairs. """ # pylint: disable-msg=C0103 # pylint: disable-msg=W0142 # pylint: disable-msg=E0611 # pylint: disable-msg=E1101 import numpy as np import kernels #import numbers #from scipy.linalg import solveh_banded #from scipy.optimize import golden #from models import _hbspline # Need to alter setup to be able to import # extension from models or drop for scipy #from models.bspline import BSpline, _band2array class KernelSmoother(object): """ 1D Kernel Density Regression/Kernel Smoother Requires: x - array_like of x values y - array_like of y values Kernel - Kernel object, Default is Gaussian. """ def __init__(self, x, y, Kernel = None): if Kernel is None: Kernel = kernels.Gaussian() self.Kernel = Kernel self.x = np.array(x) self.y = np.array(y) def fit(self): pass def __call__(self, x): return np.array([self.predict(xx) for xx in x]) def predict(self, x): """ Returns the kernel smoothed prediction at x If x is a real number then a single value is returned. Otherwise an attempt is made to cast x to numpy.ndarray and an array of corresponding y-points is returned. """ if np.size(x) == 1: # if isinstance(x, numbers.Real): return self.Kernel.smooth(self.x, self.y, x) else: return np.array([self.Kernel.smooth(self.x, self.y, xx) for xx in np.array(x)]) def conf(self, x): """ Returns the fitted curve and 1-sigma upper and lower point-wise confidence. These bounds are based on variance only, and do not include the bias. If the bandwidth is much larger than the curvature of the underlying funtion then the bias could be large. x is the points on which you want to evaluate the fit and the errors. Alternatively if x is specified as a positive integer, then the fit and confidence bands points will be returned after every xth sample point - so they are closer together where the data is denser. """ if isinstance(x, int): sorted_x = np.array(self.x) sorted_x.sort() confx = sorted_x[::x] conffit = self.conf(confx) return (confx, conffit) else: return np.array([self.Kernel.smoothconf(self.x, self.y, xx) for xx in x]) def var(self, x): return np.array([self.Kernel.smoothvar(self.x, self.y, xx) for xx in x]) def std(self, x): return np.sqrt(self.var(x)) class PolySmoother(object): """ Polynomial smoother up to a given order. Fit based on weighted least squares. The x values can be specified at instantiation or when called. This is a 3 liner with OLS or WLS, see test. It's here as a test smoother for GAM """ #JP: heavily adjusted to work as plugin replacement for bspline # smoother in gam.py initalized by function default_smoother # Only fixed exceptions, I didn't check whether it is statistically # correctand I think it is not, there are still be some dimension # problems, and there were some dimension problems initially. # TODO: undo adjustments and fix dimensions correctly # comment: this is just like polyfit with initialization options # and additional results (OLS on polynomial of x (x is 1d?)) def __init__(self, order, x=None): #order = 4 # set this because we get knots instead of order self.order = order #print order, x.shape self.coef = np.zeros((order+1,), np.float64) if x is not None: if x.ndim > 1: print 'Warning: 2d x detected in PolySmoother init, shape:', x.shape x=x[0,:] #check orientation self.X = np.array([x**i for i in range(order+1)]).T def df_fit(self): '''alias of df_model for backwards compatibility ''' return self.df_model() def df_model(self): """ Degrees of freedom used in the fit. """ return self.order + 1 def gram(self, d=None): #fake for spline imitation pass def smooth(self,*args, **kwds): '''alias for fit, for backwards compatibility, do we need it with different behavior than fit? ''' return self.fit(*args, **kwds) def df_resid(self): """ Residual degrees of freedom from last fit. """ return self.N - self.order - 1 def __call__(self, x=None): return self.predict(x=x) def predict(self, x=None): if x is not None: #if x.ndim > 1: x=x[0,:] #why this this should select column not row if x.ndim > 1: print 'Warning: 2d x detected in PolySmoother predict, shape:', x.shape x=x[:,0] #TODO: check and clean this up X = np.array([(x**i) for i in range(self.order+1)]) else: X = self.X #return np.squeeze(np.dot(X.T, self.coef)) #need to check what dimension this is supposed to be if X.shape[1] == self.coef.shape[0]: return np.squeeze(np.dot(X, self.coef))#[0] else: return np.squeeze(np.dot(X.T, self.coef))#[0] def fit(self, y, x=None, weights=None): self.N = y.shape[0] if y.ndim == 1: y = y[:,None] if weights is None or np.isnan(weights).all(): weights = 1 _w = 1 else: _w = np.sqrt(weights)[:,None] if x is None: if not hasattr(self, "X"): raise ValueError("x needed to fit PolySmoother") else: if x.ndim > 1: print 'Warning: 2d x detected in PolySmoother predict, shape:', x.shape #x=x[0,:] #TODO: check orientation, row or col self.X = np.array([(x**i) for i in range(self.order+1)]).T #print _w.shape X = self.X * _w _y = y * _w#[:,None] #self.coef = np.dot(L.pinv(X).T, _y[:,None]) #self.coef = np.dot(L.pinv(X), _y) self.coef = np.linalg.lstsq(X, _y)[0] self.params = np.squeeze(self.coef) # comment out for now to remove dependency on _hbspline ##class SmoothingSpline(BSpline): ## ## penmax = 30. ## ## def fit(self, y, x=None, weights=None, pen=0.): ## banded = True ## ## if x is None: ## x = self.tau[(self.M-1):-(self.M-1)] # internal knots ## ## if pen == 0.: # can't use cholesky for singular matrices ## banded = False ## ## if x.shape != y.shape: ## raise ValueError('x and y shape do not agree, by default x are the Bspline\'s internal knots') ## ## bt = self.basis(x) ## if pen >= self.penmax: ## pen = self.penmax ## ## if weights is None: ## weights = np.array(1.) ## ## wmean = weights.mean() ## _w = np.sqrt(weights / wmean) ## bt *= _w ## ## # throw out rows with zeros (this happens at boundary points!) ## ## mask = np.flatnonzero(1 - np.alltrue(np.equal(bt, 0), axis=0)) ## ## bt = bt[:, mask] ## y = y[mask] ## ## self.df_total = y.shape[0] ## ## if bt.shape[1] != y.shape[0]: ## raise ValueError("some x values are outside range of B-spline knots") ## bty = np.dot(bt, _w * y) ## self.N = y.shape[0] ## if not banded: ## self.btb = np.dot(bt, bt.T) ## _g = _band2array(self.g, lower=1, symmetric=True) ## self.coef, _, self.rank = L.lstsq(self.btb + pen*_g, bty)[0:3] ## self.rank = min(self.rank, self.btb.shape[0]) ## else: ## self.btb = np.zeros(self.g.shape, np.float64) ## nband, nbasis = self.g.shape ## for i in range(nbasis): ## for k in range(min(nband, nbasis-i)): ## self.btb[k, i] = (bt[i] * bt[i+k]).sum() ## ## bty.shape = (1, bty.shape[0]) ## self.chol, self.coef = solveh_banded(self.btb + ## pen*self.g, ## bty, lower=1) ## ## self.coef = np.squeeze(self.coef) ## self.resid = np.sqrt(wmean) * (y * _w - np.dot(self.coef, bt)) ## self.pen = pen ## ## def gcv(self): ## """ ## Generalized cross-validation score of current fit. ## """ ## ## norm_resid = (self.resid**2).sum() ## return norm_resid / (self.df_total - self.trace()) ## ## def df_resid(self): ## """ ## self.N - self.trace() ## ## where self.N is the number of observations of last fit. ## """ ## ## return self.N - self.trace() ## ## def df_fit(self): ## """ ## = self.trace() ## ## How many degrees of freedom used in the fit? ## """ ## return self.trace() ## ## def trace(self): ## """ ## Trace of the smoothing matrix S(pen) ## """ ## ## if self.pen > 0: ## _invband = _hbspline.invband(self.chol.copy()) ## tr = _trace_symbanded(_invband, self.btb, lower=1) ## return tr ## else: ## return self.rank ## ##class SmoothingSplineFixedDF(SmoothingSpline): ## """ ## Fit smoothing spline with approximately df degrees of freedom ## used in the fit, i.e. so that self.trace() is approximately df. ## ## In general, df must be greater than the dimension of the null space ## of the Gram inner product. For cubic smoothing splines, this means ## that df > 2. ## """ ## ## target_df = 5 ## ## def __init__(self, knots, order=4, coef=None, M=None, target_df=None): ## if target_df is not None: ## self.target_df = target_df ## BSpline.__init__(self, knots, order=order, coef=coef, M=M) ## self.target_reached = False ## ## def fit(self, y, x=None, df=None, weights=None, tol=1.0e-03): ## ## df = df or self.target_df ## ## apen, bpen = 0, 1.0e-03 ## olddf = y.shape[0] - self.m ## ## if not self.target_reached: ## while True: ## curpen = 0.5 * (apen + bpen) ## SmoothingSpline.fit(self, y, x=x, weights=weights, pen=curpen) ## curdf = self.trace() ## if curdf > df: ## apen, bpen = curpen, 2 * curpen ## else: ## apen, bpen = apen, curpen ## if apen >= self.penmax: ## raise ValueError("penalty too large, try setting penmax higher or decreasing df") ## if np.fabs(curdf - df) / df < tol: ## self.target_reached = True ## break ## else: ## SmoothingSpline.fit(self, y, x=x, weights=weights, pen=self.pen) ## ##class SmoothingSplineGCV(SmoothingSpline): ## ## """ ## Fit smoothing spline trying to optimize GCV. ## ## Try to find a bracketing interval for scipy.optimize.golden ## based on bracket. ## ## It is probably best to use target_df instead, as it is ## sometimes difficult to find a bracketing interval. ## ## """ ## ## def fit(self, y, x=None, weights=None, tol=1.0e-03, ## bracket=(0,1.0e-03)): ## ## def _gcv(pen, y, x): ## SmoothingSpline.fit(y, x=x, pen=np.exp(pen), weights=weights) ## a = self.gcv() ## return a ## ## a = golden(_gcv, args=(y,x), brack=(-100,20), tol=tol) ## ##def _trace_symbanded(a,b, lower=0): ## """ ## Compute the trace(a*b) for two upper or lower banded real symmetric matrices. ## """ ## ## if lower: ## t = _zero_triband(a * b, lower=1) ## return t[0].sum() + 2 * t[1:].sum() ## else: ## t = _zero_triband(a * b, lower=0) ## return t[-1].sum() + 2 * t[:-1].sum() ## ## ## ##def _zero_triband(a, lower=0): ## """ ## Zero out unnecessary elements of a real symmetric banded matrix. ## """ ## ## nrow, ncol = a.shape ## if lower: ## for i in range(nrow): a[i,(ncol-i):] = 0. ## else: ## for i in range(nrow): a[i,0:i] = 0. ## return a statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/testdata.py000066400000000000000000000062371224417117700272070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 04 07:36:28 2011 @author: Mike """ import numpy as np class kdetest(object): Hpi = np.matrix([[ 0.05163034, 0.5098923 ], [0.50989228, 8.8822365 ]]) faithfulData = dict( eruptions = [3.6, 1.8, 3.333, 2.283, 4.533, 2.883, 4.7, 3.6, 1.95, 4.35, 1.833, 3.917, 4.2, 1.75, 4.7, 2.167, 1.75, 4.8, 1.6, 4.25, 1.8, 1.75, 3.45, 3.067, 4.533, 3.6, 1.967, 4.083, 3.85, 4.433, 4.3, 4.467, 3.367, 4.033, 3.833, 2.017, 1.867, 4.833, 1.833, 4.783, 4.35, 1.883, 4.567, 1.75, 4.533, 3.317, 3.833, 2.1, 4.633, 2, 4.8, 4.716, 1.833, 4.833, 1.733, 4.883, 3.717, 1.667, 4.567, 4.317, 2.233, 4.5, 1.75, 4.8, 1.817, 4.4, 4.167, 4.7, 2.067, 4.7, 4.033, 1.967, 4.5, 4, 1.983, 5.067, 2.017, 4.567, 3.883, 3.6, 4.133, 4.333, 4.1, 2.633, 4.067, 4.933, 3.95, 4.517, 2.167, 4, 2.2, 4.333, 1.867, 4.817, 1.833, 4.3, 4.667, 3.75, 1.867, 4.9, 2.483, 4.367, 2.1, 4.5, 4.05, 1.867, 4.7, 1.783, 4.85, 3.683, 4.733, 2.3, 4.9, 4.417, 1.7, 4.633, 2.317, 4.6, 1.817, 4.417, 2.617, 4.067, 4.25, 1.967, 4.6, 3.767, 1.917, 4.5, 2.267, 4.65, 1.867, 4.167, 2.8, 4.333, 1.833, 4.383, 1.883, 4.933, 2.033, 3.733, 4.233, 2.233, 4.533, 4.817, 4.333, 1.983, 4.633, 2.017, 5.1, 1.8, 5.033, 4, 2.4, 4.6, 3.567, 4, 4.5, 4.083, 1.8, 3.967, 2.2, 4.15, 2, 3.833, 3.5, 4.583, 2.367, 5, 1.933, 4.617, 1.917, 2.083, 4.583, 3.333, 4.167, 4.333, 4.5, 2.417, 4, 4.167, 1.883, 4.583, 4.25, 3.767, 2.033, 4.433, 4.083, 1.833, 4.417, 2.183, 4.8, 1.833, 4.8, 4.1, 3.966, 4.233, 3.5, 4.366, 2.25, 4.667, 2.1, 4.35, 4.133, 1.867, 4.6, 1.783, 4.367, 3.85, 1.933, 4.5, 2.383, 4.7, 1.867, 3.833, 3.417, 4.233, 2.4, 4.8, 2, 4.15, 1.867, 4.267, 1.75, 4.483, 4, 4.117, 4.083, 4.267, 3.917, 4.55, 4.083, 2.417, 4.183, 2.217, 4.45, 1.883, 1.85, 4.283, 3.95, 2.333, 4.15, 2.35, 4.933, 2.9, 4.583, 3.833, 2.083, 4.367, 2.133, 4.35, 2.2, 4.45, 3.567, 4.5, 4.15, 3.817, 3.917, 4.45, 2, 4.283, 4.767, 4.533, 1.85, 4.25, 1.983, 2.25, 4.75, 4.117, 2.15, 4.417, 1.817, 4.467], waiting = [79, 54, 74, 62, 85, 55, 88, 85, 51, 85, 54, 84, 78, 47, 83, 52, 62, 84, 52, 79, 51, 47, 78, 69, 74, 83, 55, 76, 78, 79, 73, 77, 66, 80, 74, 52, 48, 80, 59, 90, 80, 58, 84, 58, 73, 83, 64, 53, 82, 59, 75, 90, 54, 80, 54, 83, 71, 64, 77, 81, 59, 84, 48, 82, 60, 92, 78, 78, 65, 73, 82, 56, 79, 71, 62, 76, 60, 78, 76, 83, 75, 82, 70, 65, 73, 88, 76, 80, 48, 86, 60, 90, 50, 78, 63, 72, 84, 75, 51, 82, 62, 88, 49, 83, 81, 47, 84, 52, 86, 81, 75, 59, 89, 79, 59, 81, 50, 85, 59, 87, 53, 69, 77, 56, 88, 81, 45, 82, 55, 90, 45, 83, 56, 89, 46, 82, 51, 86, 53, 79, 81, 60, 82, 77, 76, 59, 80, 49, 96, 53, 77, 77, 65, 81, 71, 70, 81, 93, 53, 89, 45, 86, 58, 78, 66, 76, 63, 88, 52, 93, 49, 57, 77, 68, 81, 81, 73, 50, 85, 74, 55, 77, 83, 83, 51, 78, 84, 46, 83, 55, 81, 57, 76, 84, 77, 81, 87, 77, 51, 78, 60, 82, 91, 53, 78, 46, 77, 84, 49, 83, 71, 80, 49, 75, 64, 76, 53, 94, 55, 76, 50, 82, 54, 75, 78, 79, 78, 78, 70, 79, 70, 54, 86, 50, 90, 54, 54, 77, 79, 64, 75, 47, 86, 63, 85, 82, 57, 82, 67, 74, 54, 83, 73, 73, 88, 80, 71, 83, 56, 79, 78, 84, 58, 83, 43, 60, 75, 81, 46, 90, 46, 74] ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/000077500000000000000000000000001224417117700261565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/__init__.py000066400000000000000000000000001224417117700302550ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py000066400000000000000000000047241224417117700313250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example for gam.AdditiveModel and PolynomialSmoother This example was written as a test case. The data generating process is chosen so the parameters are well identified and estimated. Created on Fri Nov 04 13:45:43 2011 Author: Josef Perktold """ import time import numpy as np #import matplotlib.pyplot as plt from numpy.testing import assert_almost_equal from scipy import stats from statsmodels.sandbox.gam import AdditiveModel from statsmodels.sandbox.gam import Model as GAM #? from statsmodels.genmod import families from statsmodels.genmod.generalized_linear_model import GLM from statsmodels.regression.linear_model import OLS, WLS np.random.seed(8765993) #seed is chosen for nice result, not randomly #other seeds are pretty off in the prediction #DGP: simple polynomial order = 3 sigma_noise = 0.5 nobs = 1000 #1000 #with 1000, OLS and Additivemodel aggree in params at 2 decimals lb, ub = -3.5, 4#2.5 x1 = np.linspace(lb, ub, nobs) x2 = np.sin(2*x1) x = np.column_stack((x1/x1.max()*2, x2)) exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1) idx = range((order+1)*2) del idx[order+1] exog_reduced = exog[:,idx] #remove duplicate constant y_true = exog.sum(1) / 2. z = y_true #alias check d = x y = y_true + sigma_noise * np.random.randn(nobs) example = 1 if example == 1: m = AdditiveModel(d) m.fit(y) y_pred = m.results.predict(d) for ss in m.smoothers: print ss.params res_ols = OLS(y, exog_reduced).fit() print res_ols.params #assert_almost_equal(y_pred, res_ols.fittedvalues, 3) if example > 0: import matplotlib.pyplot as plt plt.figure() plt.plot(exog) y_pred = m.results.mu# + m.results.alpha #m.results.predict(d) plt.figure() plt.subplot(2,2,1) plt.plot(y, '.', alpha=0.25) plt.plot(y_true, 'k-', label='true') plt.plot(res_ols.fittedvalues, 'g-', label='OLS', lw=2, alpha=-.7) plt.plot(y_pred, 'r-', label='AM') plt.legend(loc='upper left') plt.title('gam.AdditiveModel') counter = 2 for ii, xx in zip(['z', 'x1', 'x2'], [z, x[:,0], x[:,1]]): sortidx = np.argsort(xx) #plt.figure() plt.subplot(2, 2, counter) plt.plot(xx[sortidx], y[sortidx], '.', alpha=0.25) plt.plot(xx[sortidx], y_true[sortidx], 'k.', label='true', lw=2) plt.plot(xx[sortidx], y_pred[sortidx], 'r.', label='AM') plt.legend(loc='upper left') plt.title('gam.AdditiveModel ' + ii) counter += 1 plt.show()statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py000066400000000000000000000072501224417117700306450ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example for GAM with Poisson Model and PolynomialSmoother This example was written as a test case. The data generating process is chosen so the parameters are well identified and estimated. Created on Fri Nov 04 13:45:43 2011 Author: Josef Perktold """ import time import numpy as np #import matplotlib.pyplot as plt np.seterr(all='raise') from scipy import stats from statsmodels.sandbox.gam import AdditiveModel from statsmodels.sandbox.gam import Model as GAM #? from statsmodels.genmod.families import family from statsmodels.genmod.generalized_linear_model import GLM np.random.seed(8765993) #seed is chosen for nice result, not randomly #other seeds are pretty off in the prediction or end in overflow #DGP: simple polynomial order = 3 sigma_noise = 0.1 nobs = 1000 #lb, ub = -0.75, 3#1.5#0.75 #2.5 lb, ub = -3.5, 3 x1 = np.linspace(lb, ub, nobs) x2 = np.sin(2*x1) x = np.column_stack((x1/x1.max()*1, 1.*x2)) exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1) idx = range((order+1)*2) del idx[order+1] exog_reduced = exog[:,idx] #remove duplicate constant y_true = exog.sum(1) #/ 4. z = y_true #alias check d = x y = y_true + sigma_noise * np.random.randn(nobs) example = 3 if example == 2: print "binomial" f = family.Binomial() mu_true = f.link.inverse(z) #b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)]) b = np.asarray([stats.bernoulli.rvs(p) for p in f.link.inverse(z)]) b.shape = y.shape m = GAM(b, d, family=f) toc = time.time() m.fit(b) tic = time.time() print tic-toc #for plotting yp = f.link.inverse(y) p = b if example == 3: print "Poisson" f = family.Poisson() #y = y/y.max() * 3 yp = f.link.inverse(z) #p = np.asarray([scipy.stats.poisson.rvs(p) for p in f.link.inverse(y)], float) p = np.asarray([stats.poisson.rvs(p) for p in f.link.inverse(z)], float) p.shape = y.shape m = GAM(p, d, family=f) toc = time.time() m.fit(p) tic = time.time() print tic-toc for ss in m.smoothers: print ss.params if example > 1: import matplotlib.pyplot as plt plt.figure() for i in np.array(m.history[2:15:3]): plt.plot(i.T) plt.figure() plt.plot(exog) #plt.plot(p, '.', lw=2) plt.plot(y_true, lw=2) y_pred = m.results.mu # + m.results.alpha #m.results.predict(d) plt.figure() plt.subplot(2,2,1) plt.plot(p, '.') plt.plot(yp, 'b-', label='true') plt.plot(y_pred, 'r-', label='GAM') plt.legend(loc='upper left') plt.title('gam.GAM Poisson') counter = 2 for ii, xx in zip(['z', 'x1', 'x2'], [z, x[:,0], x[:,1]]): sortidx = np.argsort(xx) #plt.figure() plt.subplot(2, 2, counter) plt.plot(xx[sortidx], p[sortidx], 'k.', alpha=0.5) plt.plot(xx[sortidx], yp[sortidx], 'b.', label='true') plt.plot(xx[sortidx], y_pred[sortidx], 'r.', label='GAM') plt.legend(loc='upper left') plt.title('gam.GAM Poisson ' + ii) counter += 1 res = GLM(p, exog_reduced, family=f).fit() #plot component, compared to true component x1 = x[:,0] x2 = x[:,1] f1 = exog[:,:order+1].sum(1) - 1 #take out constant f2 = exog[:,order+1:].sum(1) - 1 plt.figure() #Note: need to correct for constant which is indeterminatedly distributed #plt.plot(x1, m.smoothers[0](x1)-m.smoothers[0].params[0]+1, 'r') #better would be subtract f(0) m.smoothers[0](np.array([0])) plt.plot(x1, f1, linewidth=2) plt.plot(x1, m.smoothers[0](x1)-m.smoothers[0].params[0], 'r') plt.figure() plt.plot(x2, f2, linewidth=2) plt.plot(x2, m.smoothers[1](x2)-m.smoothers[1].params[0], 'r') plt.show()statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py000066400000000000000000000025341224417117700312530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Nov 04 10:51:39 2011 @author: josef """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.sandbox.nonparametric import smoothers, kernels from statsmodels.regression.linear_model import OLS, WLS #DGP: simple polynomial order = 3 sigma_noise = 0.5 nobs = 100 lb, ub = -1, 2 x = np.linspace(lb, ub, nobs) x = np.sin(x) exog = x[:,None]**np.arange(order+1) y_true = exog.sum(1) y = y_true + sigma_noise * np.random.randn(nobs) #xind = np.argsort(x) pmod = smoothers.PolySmoother(2, x) pmod.fit(y) #no return y_pred = pmod.predict(x) error = y - y_pred mse = (error*error).mean() print mse res_ols = OLS(y, exog[:,:3]).fit() print np.squeeze(pmod.coef) - res_ols.params weights = np.ones(nobs) weights[:nobs//3] = 0.1 weights[-nobs//5:] = 2 pmodw = smoothers.PolySmoother(2, x) pmodw.fit(y, weights=weights) #no return y_predw = pmodw.predict(x) error = y - y_predw mse = (error*error).mean() print mse res_wls = WLS(y, exog[:,:3], weights=weights).fit() print np.squeeze(pmodw.coef) - res_wls.params doplot = 1 if doplot: import matplotlib.pyplot as plt plt.plot(y, '.') plt.plot(y_true, 'b-', label='true') plt.plot(y_pred, '-', label='poly') plt.plot(y_predw, '-', label='poly -w') plt.legend(loc='upper left') plt.close() #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py000066400000000000000000000063501224417117700324410ustar00rootroot00000000000000import numpy as np import numpy.testing as npt from statsmodels.sandbox.nonparametric.kernel_extras import SemiLinear class MyTest(object): def setUp(self): nobs = 60 np.random.seed(123456) self.o = np.random.binomial(2, 0.7, size=(nobs, 1)) self.o2 = np.random.binomial(3, 0.7, size=(nobs, 1)) self.c1 = np.random.normal(size=(nobs, 1)) self.c2 = np.random.normal(10, 1, size=(nobs, 1)) self.c3 = np.random.normal(10, 2, size=(nobs, 1)) self.noise = np.random.normal(size=(nobs, 1)) b0 = 0.3 b1 = 1.2 b2 = 3.7 # regression coefficients self.y = b0 + b1 * self.c1 + b2 * self.c2 + self.noise self.y2 = b0 + b1 * self.c1 + b2 * self.c2 + self.o + self.noise # Italy data from R's np package (the first 50 obs) R>> data (Italy) self.Italy_gdp = \ [8.556, 12.262, 9.587, 8.119, 5.537, 6.796, 8.638, 6.483, 6.212, 5.111, 6.001, 7.027, 4.616, 3.922, 4.688, 3.957, 3.159, 3.763, 3.829, 5.242, 6.275, 8.518, 11.542, 9.348, 8.02, 5.527, 6.865, 8.666, 6.672, 6.289, 5.286, 6.271, 7.94, 4.72, 4.357, 4.672, 3.883, 3.065, 3.489, 3.635, 5.443, 6.302, 9.054, 12.485, 9.896, 8.33, 6.161, 7.055, 8.717, 6.95] self.Italy_year = \ [1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1953, 1953, 1953, 1953, 1953, 1953, 1953, 1953] # OECD panel data from NP R>> data(oecdpanel) self.growth = \ [-0.0017584, 0.00740688, 0.03424461, 0.03848719, 0.02932506, 0.03769199, 0.0466038, 0.00199456, 0.03679607, 0.01917304, -0.00221, 0.00787269, 0.03441118, -0.0109228, 0.02043064, -0.0307962, 0.02008947, 0.00580313, 0.00344502, 0.04706358, 0.03585851, 0.01464953, 0.04525762, 0.04109222, -0.0087903, 0.04087915, 0.04551403, 0.036916, 0.00369293, 0.0718669, 0.02577732, -0.0130759, -0.01656641, 0.00676429, 0.08833017, 0.05092105, 0.02005877, 0.00183858, 0.03903173, 0.05832116, 0.0494571, 0.02078484, 0.09213897, 0.0070534, 0.08677202, 0.06830603, -0.00041, 0.0002856, 0.03421225, -0.0036825] self.oecd = \ [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0] class TestSemiLinear(MyTest): def test_basic(self): nobs = 300 np.random.seed(1234) C1 = np.random.normal(0,2, size=(nobs, )) C2 = np.random.normal(2, 1, size=(nobs, )) e = np.random.normal(size=(nobs, )) b1 = 1.3 b2 = -0.7 Y = b1 * C1 + np.exp(b2 * C2) + e model = SemiLinear(endog=[Y], exog=[C1], exog_nonparametric=[C2], var_type='c', k_linear=1) b_hat = np.squeeze(model.b) # Only tests for the linear part of the regression # Currently doesn't work well with the nonparametric part # Needs some more work npt.assert_allclose(b1, b_hat, rtol=0.1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nonparametric/tests/test_smoothers.py000066400000000000000000000056771224417117700316310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Nov 04 10:51:39 2011 Author: Josef Perktold License: BSD-3 """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.sandbox.nonparametric import smoothers, kernels from statsmodels.regression.linear_model import OLS, WLS class CheckSmoother(object): def test_predict(self): assert_almost_equal(self.res_ps.predict(self.x), self.res2.fittedvalues, decimal=13) assert_almost_equal(self.res_ps.predict(self.x[:10]), self.res2.fittedvalues[:10], decimal=13) def test_coef(self): #TODO: check dim of coef assert_almost_equal(self.res_ps.coef.ravel(), self.res2.params, decimal=14) def test_df(self): #TODO: make into attributes assert_equal(self.res_ps.df_model(), self.res2.df_model+1) #with const assert_equal(self.res_ps.df_fit(), self.res2.df_model+1) #alias assert_equal(self.res_ps.df_resid(), self.res2.df_resid) class BasePolySmoother(object): def __init__(self): #DGP: simple polynomial order = 3 sigma_noise = 0.5 nobs = 100 lb, ub = -1, 2 self.x = x = np.linspace(lb, ub, nobs) self.exog = exog = x[:,None]**np.arange(order+1) y_true = exog.sum(1) np.random.seed(987567) self.y = y = y_true + sigma_noise * np.random.randn(nobs) class TestPolySmoother1(BasePolySmoother, CheckSmoother): def __init__(self): super(self.__class__, self).__init__() #initialize DGP y, x, exog = self.y, self.x, self.exog #use order = 2 in regression pmod = smoothers.PolySmoother(2, x) pmod.fit(y) #no return self.res_ps = pmod self.res2 = OLS(y, exog[:,:2+1]).fit() class TestPolySmoother2(BasePolySmoother, CheckSmoother): def __init__(self): super(self.__class__, self).__init__() #initialize DGP y, x, exog = self.y, self.x, self.exog #use order = 3 in regression pmod = smoothers.PolySmoother(3, x) #pmod.fit(y) #no return pmod.smooth(y) #no return, use alias for fit self.res_ps = pmod self.res2 = OLS(y, exog[:,:3+1]).fit() class TestPolySmoother3(BasePolySmoother, CheckSmoother): def __init__(self): super(self.__class__, self).__init__() #initialize DGP y, x, exog = self.y, self.x, self.exog nobs = y.shape[0] weights = np.ones(nobs) weights[:nobs//3] = 0.1 weights[-nobs//5:] = 2 #use order = 2 in regression pmod = smoothers.PolySmoother(2, x) pmod.fit(y, weights=weights) #no return self.res_ps = pmod self.res2 = WLS(y, exog[:,:2+1], weights=weights).fit() if __name__ == '__main__': t1 = TestPolySmoother1() t1.test_predict() t1.test_coef() t1.test_df t3 = TestPolySmoother3() t3.test_predict() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/nos4.mtx000066400000000000000000000222511224417117700235710ustar00rootroot00000000000000%%MatrixMarket matrix coordinate real symmetric 100 100 347 1 1 1.7155418000000e-01 2 1 3.5777088000000e-02 3 1 -1.0000000000000e-01 13 1 -7.1554176000000e-02 14 1 -3.5777088000000e-02 2 2 4.1788854000000e-01 12 2 -2.0000000000000e-01 13 2 -3.5777088000000e-02 14 2 -1.7888544000000e-02 3 3 3.4310835000000e-01 5 3 -1.0000000000000e-01 4 4 4.3577709000000e-01 14 4 -2.0000000000000e-01 5 5 3.4310835000000e-01 7 5 -1.0000000000000e-01 13 5 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statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/000077500000000000000000000000001224417117700232515ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/__init__.py000066400000000000000000000000001224417117700253500ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/correlation_structures.py000066400000000000000000000116641224417117700304570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Correlation and Covariance Structures Created on Sat Dec 17 20:46:05 2011 Author: Josef Perktold License: BSD-3 Reference --------- quick reading of some section on mixed effects models in S-plus and of outline for GEE. """ import numpy as np def corr_equi(k_vars, rho): '''create equicorrelated correlation matrix with rho on off diagonal Parameters ---------- k_vars : int number of variables, correlation matrix will be (k_vars, k_vars) rho : float correlation between any two random variables Returns ------- corr : ndarray (k_vars, k_vars) correlation matrix ''' corr = np.empty((k_vars, k_vars)) corr.fill(rho) corr[np.diag_indices_from(corr)] = 1 return corr def corr_ar(k_vars, ar): '''create autoregressive correlation matrix This might be MA, not AR, process if used for residual process - check Parameters ---------- ar : array_like, 1d AR lag-polynomial including 1 for lag 0 ''' from scipy.linalg import toeplitz if len(ar) < k_vars: ar_ = np.zeros(k_vars) ar_[:len(ar)] = ar ar = ar_ return toeplitz(ar) def corr_arma(k_vars, ar, ma): '''create arma correlation matrix converts arma to autoregressive lag-polynomial with k_var lags ar and arma might need to be switched for generating residual process Parameters ---------- ar : array_like, 1d AR lag-polynomial including 1 for lag 0 ma : array_like, 1d MA lag-polynomial ''' from scipy.linalg import toeplitz from statsmodels.tsa.arima_process import arma2ar ar = arma2ar(ar, ma, nobs=k_vars)[:k_vars] #bug in arma2ar return toeplitz(ar) def corr2cov(corr, std): '''convert correlation matrix to covariance matrix Parameters ---------- corr : ndarray, (k_vars, k_vars) correlation matrix std : ndarray, (k_vars,) or scalar standard deviation for the vector of random variables. If scalar, then it is assumed that all variables have the same scale given by std. ''' if np.size(std) == 1: std = std*np.ones(corr.shape[0]) cov = corr * std[:,None] * std[None, :] #same as outer product return cov def whiten_ar(x, ar_coefs): """ Whiten a series of columns according to an AR(p) covariance structure. This drops the initial conditions (Cochran-Orcut ?) Uses loop, so for short ar polynomials only, use lfilter otherwise This needs to improve, option on method, full additional to conditional Parameters ---------- x : array-like, (nobs,) or (nobs, k_vars) The data to be whitened along axis 0 ar_coefs : array coefficients of AR lag- polynomial, TODO: ar or ar_coefs? Returns ------- x_new : ndarray transformed array """ rho = ar_coefs x = np.array(x, np.float64) #make copy #_x = x.copy() #dimension handling is not DRY # I think previous code worked for 2d because of single index rows in np if x.ndim == 2: rho = rho[:, None] for i in range(self.order): _x[(i+1):] = _x[(i+1):] - rho[i] * x[0:-(i+1)] return _x[self.order:] def yule_walker_acov(acov, order=1, method="unbiased", df=None, inv=False): """ Estimate AR(p) parameters from acovf using Yule-Walker equation. Parameters ---------- acov : array-like, 1d auto-covariance order : integer, optional The order of the autoregressive process. Default is 1. inv : bool If inv is True the inverse of R is also returned. Default is False. Returns ------- rho : ndarray The estimated autoregressive coefficients sigma TODO Rinv : ndarray inverse of the Toepliz matrix """ R = toeplitz(r[:-1]) rho = np.linalg.solve(R, r[1:]) sigmasq = r[0] - (r[1:]*rho).sum() if inv == True: return rho, np.sqrt(sigmasq), np.linalg.inv(R) else: return rho, np.sqrt(sigmasq) class ARCovariance(object): ''' experimental class for Covariance of AR process classmethod? staticmethods? ''' def __init__(self, ar=None, ar_coefs=None, sigma=1.): if ar is not None: self.ar = ar self.ar_coefs = -ar[1:] self.k_lags = len(ar) elif ar_coefs is not None: self.arcoefs = ar_coefs self.ar = np.hstack(([1], -ar_coefs)) self.k_lags = len(self.ar) @classmethod def fit(cls, cov, order, **kwds): rho, sigma = yule_walker_acov(cov, order=order, **kwds) return cls(ar_coefs=rho) def whiten(self, x): return whiten_ar(x, self.ar_coefs) def corr(self, k_vars=None): if k_vars is None: k_vars = len(self.ar) #this could move into corr_arr return corr_ar(k_vars, self.ar) def cov(self, k_vars=None): return cov2corr(corr(self, k_vars=None), self.sigma) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/mixed.py000066400000000000000000000507611224417117700247420ustar00rootroot00000000000000""" Mixed effects models Author: Jonathan Taylor Author: Josef Perktold License: BSD-3 Notes ------ It's pretty slow if the model is misspecified, in my first example convergence in loglike is not reached within 2000 iterations. Added stop criteria based on convergence of parameters instead. With correctly specified model, convergence is fast, in 6 iterations in example. """ import numpy as np import numpy.linalg as L from statsmodels.base.model import LikelihoodModelResults from statsmodels.tools.decorators import cache_readonly class Unit(object): """ Individual experimental unit for EM implementation of (repeated measures) mixed effects model. \'Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm\' Nan Laird; Nicholas Lange; Daniel Stram Journal of the American Statistical Association, Vol. 82, No. 397. (Mar., 1987), pp. 97-105. Parameters ---------- endog : ndarray, (nobs,) response, endogenous variable exog_fe : ndarray, (nobs, k_vars_fe) explanatory variables as regressors or fixed effects, should include exog_re to correct mean of random coefficients, see Notes exog_re : ndarray, (nobs, k_vars_re) explanatory variables or random effects or coefficients Notes ----- If the exog_re variables are not included in exog_fe, then the mean of the random constants or coefficients are not centered. The covariance matrix of the random parameter estimates are not centered in this case. (That's how it looks to me. JP) """ def __init__(self, endog, exog_fe, exog_re): self.Y = endog self.X = exog_fe self.Z = exog_re self.n = endog.shape[0] def _compute_S(self, D, sigma): """covariance of observations (nobs_i, nobs_i) (JP check) Display (3.3) from Laird, Lange, Stram (see help(Unit)) """ self.S = (np.identity(self.n) * sigma**2 + np.dot(self.Z, np.dot(D, self.Z.T))) def _compute_W(self): """inverse covariance of observations (nobs_i, nobs_i) (JP check) Display (3.2) from Laird, Lange, Stram (see help(Unit)) """ self.W = L.inv(self.S) def compute_P(self, Sinv): """projection matrix (nobs_i, nobs_i) (M in regression ?) (JP check, guessing) Display (3.10) from Laird, Lange, Stram (see help(Unit)) W - W X Sinv X' W' """ t = np.dot(self.W, self.X) self.P = self.W - np.dot(np.dot(t, Sinv), t.T) def _compute_r(self, alpha): """residual after removing fixed effects Display (3.5) from Laird, Lange, Stram (see help(Unit)) """ self.r = self.Y - np.dot(self.X, alpha) def _compute_b(self, D): """coefficients for random effects/coefficients Display (3.4) from Laird, Lange, Stram (see help(Unit)) D Z' W r """ self.b = np.dot(D, np.dot(np.dot(self.Z.T, self.W), self.r)) def fit(self, a, D, sigma): """ Compute unit specific parameters in Laird, Lange, Stram (see help(Unit)). Displays (3.2)-(3.5). """ self._compute_S(D, sigma) #random effect plus error covariance self._compute_W() #inv(S) self._compute_r(a) #residual after removing fixed effects/exogs self._compute_b(D) #? coefficients on random exog, Z ? def compute_xtwy(self): """ Utility function to compute X^tWY (transposed ?) for Unit instance. """ return np.dot(np.dot(self.W, self.Y), self.X) #is this transposed ? def compute_xtwx(self): """ Utility function to compute X^tWX for Unit instance. """ return np.dot(np.dot(self.X.T, self.W), self.X) def cov_random(self, D, Sinv=None): """ Approximate covariance of estimates of random effects. Just after Display (3.10) in Laird, Lange, Stram (see help(Unit)). D - D' Z' P Z D Notes ----- In example where the mean of the random coefficient is not zero, this is not a covariance but a non-centered moment. (proof by example) """ if Sinv is not None: self.compute_P(Sinv) t = np.dot(self.Z, D) return D - np.dot(np.dot(t.T, self.P), t) def logL(self, a, ML=False): """ Individual contributions to the log-likelihood, tries to return REML contribution by default though this requires estimated fixed effect a to be passed as an argument. no constant with pi included a is not used if ML=true (should be a=None in signature) If ML is false, then the residuals are calculated for the given fixed effects parameters a. """ if ML: return (np.log(L.det(self.W)) - (self.r * np.dot(self.W, self.r)).sum()) / 2. else: if a is None: raise ValueError('need fixed effect a for REML contribution to log-likelihood') r = self.Y - np.dot(self.X, a) return (np.log(L.det(self.W)) - (r * np.dot(self.W, r)).sum()) / 2. def deviance(self, ML=False): '''deviance defined as 2 times the negative loglikelihood ''' return - 2 * self.logL(ML=ML) class OneWayMixed(object): """ Model for EM implementation of (repeated measures) mixed effects model. \'Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm\' Nan Laird; Nicholas Lange; Daniel Stram Journal of the American Statistical Association, Vol. 82, No. 397. (Mar., 1987), pp. 97-105. Parameters ---------- units : list of units the data for the individual units should be attached to the units response, fixed and random : formula expression, called as argument to Formula *available results and alias* (subject to renaming, and coversion to cached attributes) params() -> self.a : coefficient for fixed effects or exog cov_params() -> self.Sinv : covariance estimate of fixed effects/exog bse() : standard deviation of params cov_random -> self.D : estimate of random effects covariance params_random_units -> [self.units[...].b] : random coefficient for each unit *attributes* (others) self.m : number of units self.p : k_vars_fixed self.q : k_vars_random self.N : nobs (total) Notes ----- Fit returns a result instance, but not all results that use the inherited methods have been checked. Parameters need to change: drop formula and we require a naming convention for the units (currently Y,X,Z). - endog, exog_fe, endog_re ? logL does not include constant, e.g. sqrt(pi) llf is for MLE not for REML convergence criteria for iteration Currently convergence in the iterative solver is reached if either the loglikelihood *or* the fixed effects parameter don't change above tolerance. In some examples, the fixed effects parameters converged to 1e-5 within 150 iterations while the log likelihood did not converge within 2000 iterations. This might be the case if the fixed effects parameters are well estimated, but there are still changes in the random effects. If params_rtol and params_atol are set at a higher level, then the random effects might not be estimated to a very high precision. The above was with a misspecified model, without a constant. With a correctly specified model convergence is fast, within a few iterations (6 in example). """ def __init__(self, units): self.units = units self.m = len(self.units) self.n_units = self.m self.N = sum(unit.X.shape[0] for unit in self.units) self.nobs = self.N #alias for now # Determine size of fixed effects d = self.units[0].X self.p = d.shape[1] # d.shape = p self.k_exog_fe = self.p #alias for now self.a = np.zeros(self.p, np.float64) # Determine size of D, and sensible initial estimates # of sigma and D d = self.units[0].Z self.q = d.shape[1] # Z.shape = q self.k_exog_re = self.q #alias for now self.D = np.zeros((self.q,)*2, np.float64) self.sigma = 1. self.dev = np.inf #initialize for iterations, move it? def _compute_a(self): """fixed effects parameters Display (3.1) of Laird, Lange, Stram (see help(Mixed)). """ for unit in self.units: unit.fit(self.a, self.D, self.sigma) S = sum([unit.compute_xtwx() for unit in self.units]) Y = sum([unit.compute_xtwy() for unit in self.units]) self.Sinv = L.pinv(S) self.a = np.dot(self.Sinv, Y) def _compute_sigma(self, ML=False): """ Estimate sigma. If ML is True, return the ML estimate of sigma, else return the REML estimate. If ML, this is (3.6) in Laird, Lange, Stram (see help(Mixed)), otherwise it corresponds to (3.8). sigma is the standard deviation of the noise (residual) """ sigmasq = 0. for unit in self.units: if ML: W = unit.W else: unit.compute_P(self.Sinv) W = unit.P t = unit.r - np.dot(unit.Z, unit.b) sigmasq += np.power(t, 2).sum() sigmasq += self.sigma**2 * np.trace(np.identity(unit.n) - self.sigma**2 * W) self.sigma = np.sqrt(sigmasq / self.N) def _compute_D(self, ML=False): """ Estimate random effects covariance D. If ML is True, return the ML estimate of sigma, else return the REML estimate. If ML, this is (3.7) in Laird, Lange, Stram (see help(Mixed)), otherwise it corresponds to (3.9). """ D = 0. for unit in self.units: if ML: W = unit.W else: unit.compute_P(self.Sinv) W = unit.P D += np.multiply.outer(unit.b, unit.b) t = np.dot(unit.Z, self.D) D += self.D - np.dot(np.dot(t.T, W), t) self.D = D / self.m def cov_fixed(self): """ Approximate covariance of estimates of fixed effects. Just after Display (3.10) in Laird, Lange, Stram (see help(Mixed)). """ return self.Sinv #----------- alias (JP) move to results class ? def cov_random(self): """ Estimate random effects covariance D. If ML is True, return the ML estimate of sigma, else return the REML estimate. see _compute_D, alias for self.D """ return self.D @property def params(self): ''' estimated coefficients for exogeneous variables or fixed effects see _compute_a, alias for self.a ''' return self.a @property def params_random_units(self): '''random coefficients for each unit ''' return np.array([unit.b for unit in self.units]) def cov_params(self): ''' estimated covariance for coefficients for exogeneous variables or fixed effects see cov_fixed, and Sinv in _compute_a ''' return self.cov_fixed() @property def bse(self): ''' standard errors of estimated coefficients for exogeneous variables (fixed) ''' return np.sqrt(np.diag(self.cov_params())) #----------- end alias def deviance(self, ML=False): '''deviance defined as 2 times the negative loglikelihood ''' return -2 * self.logL(ML=ML) def logL(self, ML=False): """ Return log-likelihood, REML by default. """ #I don't know what the difference between REML and ML is here. logL = 0. for unit in self.units: logL += unit.logL(a=self.a, ML=ML) if not ML: logL += np.log(L.det(self.Sinv)) / 2 return logL def initialize(self): S = sum([np.dot(unit.X.T, unit.X) for unit in self.units]) Y = sum([np.dot(unit.X.T, unit.Y) for unit in self.units]) self.a = L.lstsq(S, Y)[0] D = 0 t = 0 sigmasq = 0 for unit in self.units: unit.r = unit.Y - np.dot(unit.X, self.a) if self.q > 1: unit.b = L.lstsq(unit.Z, unit.r)[0] else: Z = unit.Z.reshape((unit.Z.shape[0], 1)) unit.b = L.lstsq(Z, unit.r)[0] sigmasq += (np.power(unit.Y, 2).sum() - (self.a * np.dot(unit.X.T, unit.Y)).sum() - (unit.b * np.dot(unit.Z.T, unit.r)).sum()) D += np.multiply.outer(unit.b, unit.b) t += L.pinv(np.dot(unit.Z.T, unit.Z)) #TODO: JP added df_resid check self.df_resid = (self.N - (self.m - 1) * self.q - self.p) sigmasq /= (self.N - (self.m - 1) * self.q - self.p) self.sigma = np.sqrt(sigmasq) self.D = (D - sigmasq * t) / self.m def cont(self, ML=False, rtol=1.0e-05, params_rtol=1e-5, params_atol=1e-4): '''convergence check for iterative estimation ''' self.dev, old = self.deviance(ML=ML), self.dev #self.history.append(np.hstack((self.dev, self.a))) self.history['llf'].append(self.dev) self.history['params'].append(self.a.copy()) self.history['D'].append(self.D.copy()) if np.fabs((self.dev - old) / self.dev) < rtol: #why is there times `*`? #print np.fabs((self.dev - old)), self.dev, old self.termination = 'llf' return False #break if parameters converged #TODO: check termination conditions, OR or AND if np.all(np.abs(self.a - self._a_old) < (params_rtol * self.a + params_atol)): self.termination = 'params' return False self._a_old = self.a.copy() return True def fit(self, maxiter=100, ML=False, rtol=1.0e-05, params_rtol=1e-6, params_atol=1e-6): #initialize for convergence criteria self._a_old = np.inf * self.a self.history = {'llf':[], 'params':[], 'D':[]} for i in range(maxiter): self._compute_a() #a, Sinv : params, cov_params of fixed exog self._compute_sigma(ML=ML) #sigma MLE or REML of sigma ? self._compute_D(ML=ML) #D : covariance of random effects, MLE or REML if not self.cont(ML=ML, rtol=rtol, params_rtol=params_rtol, params_atol=params_atol): break else: #if end of loop is reached without break self.termination = 'maxiter' print 'Warning: maximum number of iterations reached' self.iterations = i results = OneWayMixedResults(self) #compatibility functions for fixed effects/exog results.scale = 1 results.normalized_cov_params = self.cov_params() return results class OneWayMixedResults(LikelihoodModelResults): '''Results class for OneWayMixed models ''' def __init__(self, model): #TODO: check, change initialization to more standard pattern self.model = model self.params = model.params #need to overwrite this because we don't have a standard #model.loglike yet #TODO: what todo about REML loglike, logL is not normalized @cache_readonly def llf(self): return self.model.logL(ML=True) @property def params_random_units(self): return self.model.params_random_units def cov_random(self): return self.model.cov_random() def mean_random(self, idx='lastexog'): if idx == 'lastexog': meanr = self.params[-self.model.k_exog_re:] elif type(idx) == list: if not len(idx) == self.model.k_exog_re: raise ValueError('length of idx different from k_exog_re') else: meanr = self.params[idx] else: meanr = np.zeros(self.model.k_exog_re) return meanr def std_random(self): return np.sqrt(np.diag(self.cov_random())) def plot_random_univariate(self, bins=None, use_loc=True): '''create plot of marginal distribution of random effects Parameters ---------- bins : int or bin edges option for bins in matplotlibs hist method. Current default is not very sophisticated. All distributions use the same setting for bins. use_loc : bool If True, then the distribution with mean given by the fixed effect is used. Returns ------- fig : matplotlib figure instance figure with subplots Notes ----- What can make this fancier? Bin edges will not make sense if loc or scale differ across random effect distributions. ''' #outsource this import matplotlib.pyplot as plt from scipy.stats import norm as normal fig = plt.figure() k = self.model.k_exog_re if k > 3: rows, cols = int(np.ceil(k * 0.5)), 2 else: rows, cols = k, 1 if bins is None: #bins = self.model.n_units // 20 #TODO: just roughly, check # bins = np.sqrt(self.model.n_units) bins = 5 + 2 * self.model.n_units**(1./3.) if use_loc: loc = self.mean_random() else: loc = [0]*k scale = self.std_random() for ii in range(k): ax = fig.add_subplot(rows, cols, ii) freq, bins_, _ = ax.hist(loc[ii] + self.params_random_units[:,ii], bins=bins, normed=True) points = np.linspace(bins_[0], bins_[-1], 200) #ax.plot(points, normal.pdf(points, loc=loc, scale=scale)) #loc of sample is approx. zero, with Z appended to X #alternative, add fixed to mean ax.set_title('Random Effect %d Marginal Distribution' % ii) ax.plot(points, normal.pdf(points, loc=loc[ii], scale=scale[ii]), 'r') return fig def plot_scatter_pairs(self, idx1, idx2, title=None, ax=None): '''create scatter plot of two random effects Parameters ---------- idx1, idx2 : int indices of the two random effects to display, corresponding to columns of exog_re title : None or string If None, then a default title is added ax : None or matplotlib axis instance If None, then a figure with one axis is created and returned. If ax is not None, then the scatter plot is created on it, and this axis instance is returned. Returns ------- ax_or_fig : axis or figure instance see ax parameter Notes ----- Still needs ellipse from estimated parameters ''' import matplotlib.pyplot as plt if ax is None: fig = plt.figure() ax = fig.add_subplot(1,1,1) ax_or_fig = fig re1 = self.params_random_units[:,idx1] re2 = self.params_random_units[:,idx2] ax.plot(re1, re2, 'o', alpha=0.75) if title is None: title = 'Random Effects %d and %d' % (idx1, idx2) ax.set_title(title) ax_or_fig = ax return ax_or_fig def plot_scatter_all_pairs(self, title=None): from statsmodels.graphics.plot_grids import scatter_ellipse if self.model.k_exog_re < 2: raise ValueError('less than two variables available') return scatter_ellipse(self.params_random_units, ell_kwds={'color':'r'}) #ell_kwds not implemented yet # #note I have written this already as helper function, get it # import matplotlib.pyplot as plt # #from scipy.stats import norm as normal # fig = plt.figure() # k = self.model.k_exog_re # n_plots = k * (k - 1) // 2 # if n_plots > 3: # rows, cols = int(np.ceil(n_plots * 0.5)), 2 # else: # rows, cols = n_plots, 1 # # count = 1 # for ii in range(k): # for jj in range(ii): # ax = fig.add_subplot(rows, cols, count) # self.plot_scatter_pairs(ii, jj, title=None, ax=ax) # count += 1 # # return fig if __name__ == '__main__': #see examples/ex_mixed_lls_1.py pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/panel_short.py000066400000000000000000000201071224417117700261410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Panel data analysis for short T and large N Created on Sat Dec 17 19:32:00 2011 Author: Josef Perktold License: BSD-3 starting from scratch before looking at references again just a stub to get the basic structure for group handling target outsource as much as possible for reuse Notes ----- this is the basic version using a loop over individuals which will be more widely applicable. Depending on the special cases, there will be faster implementations possible (sparse, kroneker, ...) the only two group specific methods or get_within_cov and whiten """ import numpy as np from statsmodels.regression.linear_model import OLS, GLS from statsmodels.tools.grouputils import Group, GroupSorted #not used class Unit(object): def __init__(endog, exog): self.endog = endog self.exog = exog def sum_outer_product_loop(x, group_iter): '''sum outerproduct dot(x_i, x_i.T) over individuals loop version ''' mom = 0 for g in group_iter(): x_g = x[g] #print 'x_g.shape', x_g.shape mom += np.outer(x_g, x_g) return mom def sum_outer_product_balanced(x, n_groups): '''sum outerproduct dot(x_i, x_i.T) over individuals where x_i is (nobs_i, 1), and result is (nobs_i, nobs_i) reshape-dot version, for x.ndim=1 only ''' xrs = x.reshape(-1, n_groups, order='F') return np.dot(xrs, xrs.T) #should be (nobs_i, nobs_i) #x.reshape(n_groups, nobs_i, k_vars) #, order='F') #... ? this is getting 3-dimensional dot, tensordot? #needs (n_groups, k_vars, k_vars) array with sum over groups #NOT #I only need this for x is 1d, i.e. residual def whiten_individuals_loop(x, transform, group_iter): '''apply linear transform for each individual loop version ''' #Note: figure out dimension of transformed variable #so we can pre-allocate x_new = [] for g in group_iter(): x_g = x[g] x_new.append(np.dot(transform, x_g)) return np.concatenate(x_new) #np.vstack(x_new) #or np.array(x_new) #check shape class ShortPanelGLS2(object): '''Short Panel with general intertemporal within correlation assumes data is stacked by individuals, panel is balanced and within correlation structure is identical across individuals. It looks like this can just inherit GLS and overwrite whiten ''' def __init__(self, endog, exog, group): self.endog = endog self.exog = exog self.group = GroupSorted(group) self.n_groups = self.group.n_groups #self.nobs_group = #list for unbalanced? def fit_ols(self): self.res_pooled = OLS(self.endog, self.exog).fit() return self.res_pooled #return or not def get_within_cov(self, resid): #central moment or not? mom = sum_outer_product_loop(resid, self.group.group_iter) return mom / self.n_groups #df correction ? def whiten_groups(self, x, cholsigmainv_i): #from scipy import sparse #use sparse wx = whiten_individuals_loop(x, cholsigmainv_i, self.group.group_iter) return wx def fit(self): res_pooled = self.fit_ols() #get starting estimate sigma_i = self.get_within_cov(res_pooled.resid) self.cholsigmainv_i = np.linalg.cholesky(np.linalg.pinv(sigma_i)).T wendog = self.whiten_groups(self.endog, self.cholsigmainv_i) wexog = self.whiten_groups(self.exog, self.cholsigmainv_i) #print wendog.shape, wexog.shape self.res1 = OLS(wendog, wexog).fit() return self.res1 class ShortPanelGLS(GLS): '''Short Panel with general intertemporal within correlation assumes data is stacked by individuals, panel is balanced and within correlation structure is identical across individuals. It looks like this can just inherit GLS and overwrite whiten ''' def __init__(self, endog, exog, group, sigma_i=None): self.group = GroupSorted(group) self.n_groups = self.group.n_groups #self.nobs_group = #list for unbalanced? nobs_i = len(endog) / self.n_groups #endog might later not be an ndarray #balanced only for now, #which is a requirement anyway in this case (full cov) #needs to change for parameterized sigma_i # if sigma_i is None: sigma_i = np.eye(nobs_i) self.cholsigmainv_i = np.linalg.cholesky(np.linalg.pinv(sigma_i)).T #super is taking care of endog, exog and sigma super(self.__class__, self).__init__(endog, exog, sigma=None) def get_within_cov(self, resid): #central moment or not? mom = sum_outer_product_loop(resid, self.group.group_iter) return mom / self.n_groups #df correction ? def whiten_groups(self, x, cholsigmainv_i): #from scipy import sparse #use sparse wx = whiten_individuals_loop(x, cholsigmainv_i, self.group.group_iter) return wx def _fit_ols(self): #used as starting estimate in old explicity version self.res_pooled = OLS(self.endog, self.exog).fit() return self.res_pooled #return or not def _fit_old(self): #old explicit version res_pooled = self._fit_ols() #get starting estimate sigma_i = self.get_within_cov(res_pooled.resid) self.cholsigmainv_i = np.linalg.cholesky(np.linalg.pinv(sigma_i)).T wendog = self.whiten_groups(self.endog, self.cholsigmainv_i) wexog = self.whiten_groups(self.exog, self.cholsigmainv_i) self.res1 = OLS(wendog, wexog).fit() return self.res1 def whiten(self, x): #whiten x by groups, will be applied to endog and exog wx = whiten_individuals_loop(x, self.cholsigmainv_i, self.group.group_iter) return wx #copied from GLSHet and adjusted (boiler plate?) def fit_iterative(self, maxiter=3): """ Perform an iterative two-step procedure to estimate the GLS model. Parameters ---------- maxiter : integer, optional the number of iterations Notes ----- maxiter=1: returns the estimated based on given weights maxiter=2: performs a second estimation with the updated weights, this is 2-step estimation maxiter>2: iteratively estimate and update the weights TODO: possible extension stop iteration if change in parameter estimates is smaller than x_tol Repeated calls to fit_iterative, will do one redundant pinv_wexog calculation. Calling fit_iterative(maxiter) once does not do any redundant recalculations (whitening or calculating pinv_wexog). """ #Note: in contrast to GLSHet, we don't have an auxilliary regression here # might be needed if there is more structure in cov_i #because we only have the loop we are not attaching the ols_pooled #initial estimate anymore compared to original version if maxiter < 1: raise ValueError('maxiter needs to be at least 1') import collections self.history = collections.defaultdict(list) #not really necessary for i in range(maxiter): #pinv_wexog is cached, delete it to force recalculation if hasattr(self, 'pinv_wexog'): del self.pinv_wexog #fit with current cov, GLS, i.e. OLS on whitened endog, exog results = self.fit() self.history['self_params'].append(results.params) if not i == maxiter-1: #skip for last iteration, could break instead #print 'ols', self.results_old = results #store previous results for debugging #get cov from residuals of previous regression sigma_i = self.get_within_cov(results.resid) self.cholsigmainv_i = np.linalg.cholesky(np.linalg.pinv(sigma_i)).T #calculate new whitened endog and exog self.initialize() #note results is the wrapper, results._results is the results instance #results._results.results_residual_regression = res_resid return results if __name__ == '__main__': pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/panelmod.py000066400000000000000000000345561224417117700254370ustar00rootroot00000000000000""" Sandbox Panel Estimators References ----------- Baltagi, Badi H. `Econometric Analysis of Panel Data.` 4th ed. Wiley, 2008. """ from statsmodels.tools.tools import categorical from statsmodels.regression.linear_model import GLS, WLS import numpy as np __all__ = ["PanelModel"] try: from pandas import LongPanel, __version__ __version__ >= .1 except: raise ImportError("While in the sandbox this code depends on the pandas \ package. http://code.google.com/p/pandas/") def group(X): """ Returns unique numeric values for groups without sorting. Examples -------- >>> X = np.array(['a','a','b','c','b','c']) >>> group(X) >>> g array([ 0., 0., 1., 2., 1., 2.]) """ uniq_dict = {} group = np.zeros(len(X)) for i in xrange(len(X)): if not X[i] in uniq_dict: uniq_dict.update({X[i] : len(uniq_dict)}) group[i] = uniq_dict[X[i]] return group def repanel_cov(groups, sigmas): '''calculate error covariance matrix for random effects model Parameters ---------- groups : array, (nobs, nre) or (nobs,) array of group/category observations sigma : array, (nre+1,) array of standard deviations of random effects, last element is the standard deviation of the idiosyncratic error Returns ------- omega : array, (nobs, nobs) covariance matrix of error omegainv : array, (nobs, nobs) inverse covariance matrix of error omegainvsqrt : array, (nobs, nobs) squareroot inverse covariance matrix of error such that omega = omegainvsqrt * omegainvsqrt.T Notes ----- This does not use sparse matrices and constructs nobs by nobs matrices. Also, omegainvsqrt is not sparse, i.e. elements are non-zero ''' if groups.ndim == 1: groups = groups[:,None] nobs, nre = groups.shape omega = sigmas[-1]*np.eye(nobs) for igr in range(nre): group = groups[:,igr:igr+1] groupuniq = np.unique(group) dummygr = sigmas[igr] * (group == groupuniq).astype(float) omega += np.dot(dummygr, dummygr.T) ev, evec = np.linalg.eigh(omega) #eig doesn't work omegainv = np.dot(evec, (1/ev * evec).T) omegainvhalf = evec/np.sqrt(ev) return omega, omegainv, omegainvhalf class PanelData(LongPanel): pass class PanelModel(object): """ An abstract statistical model class for panel (longitudinal) datasets. Parameters --------- endog : array-like or str If a pandas object is used then endog should be the name of the endogenous variable as a string. # exog # panel_arr # time_arr panel_data : pandas.LongPanel object Notes ----- If a pandas object is supplied it is assumed that the major_axis is time and that the minor_axis has the panel variable. """ def __init__(self, endog=None, exog=None, panel=None, time=None, xtnames=None, equation=None, panel_data=None): if panel_data == None: # if endog == None and exog == None and panel == None and \ # time == None: # raise ValueError("If pandel_data is False then endog, exog, \ #panel_arr, and time_arr cannot be None.") self.initialize(endog, exog, panel, time, xtnames, equation) # elif aspandas != False: # if not isinstance(endog, str): # raise ValueError("If a pandas object is supplied then endog \ #must be a string containing the name of the endogenous variable") # if not isinstance(aspandas, LongPanel): # raise ValueError("Only pandas.LongPanel objects are supported") # self.initialize_pandas(endog, aspandas, panel_name) def initialize(self, endog, exog, panel, time, xtnames, equation): """ Initialize plain array model. See PanelModel """ #TODO: for now, we are going assume a constant, and then make the first #panel the base, add a flag for this.... # get names names = equation.split(" ") self.endog_name = names[0] exog_names = names[1:] # this makes the order matter in the array self.panel_name = xtnames[0] self.time_name = xtnames[1] novar = exog.var(0) == 0 if True in novar: cons_index = np.where(novar == 1)[0][0] # constant col. num exog_names.insert(cons_index, 'cons') self._cons_index = novar # used again in fit_fixed self.exog_names = exog_names self.endog = np.squeeze(np.asarray(endog)) exog = np.asarray(exog) self.exog = exog self.panel = np.asarray(panel) self.time = np.asarray(time) self.paneluniq = np.unique(panel) self.timeuniq = np.unique(time) #TODO: this structure can possibly be extracted somewhat to deal with #names in general #TODO: add some dimension checks, etc. # def initialize_pandas(self, endog, aspandas): # """ # Initialize pandas objects. # # See PanelModel. # """ # self.aspandas = aspandas # endog = aspandas[endog].values # self.endog = np.squeeze(endog) # exog_name = aspandas.columns.tolist() # exog_name.remove(endog) # self.exog = aspandas.filterItems(exog_name).values #TODO: can the above be simplified to slice notation? # if panel_name != None: # self.panel_name = panel_name # self.exog_name = exog_name # self.endog_name = endog # self.time_arr = aspandas.major_axis #TODO: is time always handled correctly in fromRecords? # self.panel_arr = aspandas.minor_axis #TODO: all of this might need to be refactored to explicitly rely (internally) # on the pandas LongPanel structure for speed and convenience. # not sure this part is finished... #TODO: doesn't conform to new initialize def initialize_pandas(self, panel_data, endog_name, exog_name): self.panel_data = panel_data endog = panel_data[endog_name].values # does this create a copy? self.endog = np.squeeze(endog) if exog_name == None: exog_name = panel_data.columns.tolist() exog_name.remove(endog_name) self.exog = panel_data.filterItems(exog_name).values # copy? self._exog_name = exog_name self._endog_name = endog_name self._timeseries = panel_data.major_axis # might not need these self._panelseries = panel_data.minor_axis #TODO: this could be pulled out and just have a by kwd that takes # the panel or time array #TODO: this also needs to be expanded for 'twoway' def _group_mean(self, X, index='oneway', counts=False, dummies=False): """ Get group means of X by time or by panel. index default is panel """ if index == 'oneway': Y = self.panel uniq = self.paneluniq elif index == 'time': Y = self.time uniq = self.timeuniq else: raise ValueError("index %s not understood" % index) #TODO: use sparse matrices dummy = (Y == uniq[:,None]).astype(float) if X.ndim > 1: mean = np.dot(dummy,X)/dummy.sum(1)[:,None] else: mean = np.dot(dummy,X)/dummy.sum(1) if counts == False and dummies == False: return mean elif counts == True and dummies == False: return mean, dummy.sum(1) elif counts == True and dummies == True: return mean, dummy.sum(1), dummy elif counts == False and dummies == True: return mean, dummy #TODO: Use kwd arguments or have fit_method methods? def fit(self, model=None, method=None, effects='oneway'): """ method : LSDV, demeaned, MLE, GLS, BE, FE, optional model : between fixed random pooled [gmm] effects : oneway time twoway femethod : demeaned (only one implemented) WLS remethod : swar - amemiya nerlove walhus Notes ------ This is unfinished. None of the method arguments work yet. Only oneway effects should work. """ if method: # get rid of this with default method = method.lower() model = model.lower() if method and method not in ["lsdv", "demeaned", "mle", "gls", "be", "fe"]: # get rid of if method with default raise ValueError("%s not a valid method" % method) # if method == "lsdv": # self.fit_lsdv(model) if model == 'pooled': return GLS(self.endog, self.exog).fit() if model == 'between': return self._fit_btwn(method, effects) if model == 'fixed': return self._fit_fixed(method, effects) # def fit_lsdv(self, effects): # """ # Fit using least squares dummy variables. # # Notes # ----- # Should only be used for small `nobs`. # """ # pdummies = None # tdummies = None def _fit_btwn(self, method, effects): # group mean regression or WLS if effects != "twoway": endog = self._group_mean(self.endog, index=effects) exog = self._group_mean(self.exog, index=effects) else: raise ValueError("%s effects is not valid for the between \ estimator" % s) befit = GLS(endog, exog).fit() return befit def _fit_fixed(self, method, effects): endog = self.endog exog = self.exog demeantwice = False if effects in ["oneway","twoways"]: if effects == "twoways": demeantwice = True effects = "oneway" endog_mean, counts = self._group_mean(endog, index=effects, counts=True) exog_mean = self._group_mean(exog, index=effects) counts = counts.astype(int) endog = endog - np.repeat(endog_mean, counts) exog = exog - np.repeat(exog_mean, counts, axis=0) if demeantwice or effects == "time": endog_mean, dummies = self._group_mean(endog, index="time", dummies=True) exog_mean = self._group_mean(exog, index="time") # This allows unbalanced panels endog = endog - np.dot(endog_mean, dummies) exog = exog - np.dot(dummies.T, exog_mean) fefit = GLS(endog, exog[:,-self._cons_index]).fit() #TODO: might fail with one regressor return fefit class SURPanel(PanelModel): pass class SEMPanel(PanelModel): pass class DynamicPanel(PanelModel): pass if __name__ == "__main__": try: import pandas pandas.version >= .1 except: raise ImportError("pandas >= .10 not installed") from pandas import LongPanel import statsmodels.api as sm import numpy.lib.recfunctions as nprf data = sm.datasets.grunfeld.load() # Baltagi doesn't include American Steel endog = data.endog[:-20] fullexog = data.exog[:-20] # fullexog.sort(order=['firm','year']) panel_arr = nprf.append_fields(fullexog, 'investment', endog, float, usemask=False) panel_panda = LongPanel.fromRecords(panel_arr, major_field='year', minor_field='firm') # the most cumbersome way of doing it as far as preprocessing by hand exog = fullexog[['value','capital']].view(float).reshape(-1,2) exog = sm.add_constant(exog, prepend=False) panel = group(fullexog['firm']) year = fullexog['year'] panel_mod = PanelModel(endog, exog, panel, year, xtnames=['firm','year'], equation='invest value capital') # note that equation doesn't actually do anything but name the variables panel_ols = panel_mod.fit(model='pooled') panel_be = panel_mod.fit(model='between', effects='oneway') panel_fe = panel_mod.fit(model='fixed', effects='oneway') panel_bet = panel_mod.fit(model='between', effects='time') panel_fet = panel_mod.fit(model='fixed', effects='time') panel_fe2 = panel_mod.fit(model='fixed', effects='twoways') #see also Baltagi (3rd edt) 3.3 THE RANDOM EFFECTS MODEL p.35 #for explicit formulas for spectral decomposition #but this works also for unbalanced panel # #I also just saw: 9.4.2 The Random Effects Model p.176 which is #partially almost the same as I did # #this needs to use sparse matrices for larger datasets # #""" # #import numpy as np # groups = np.array([0,0,0,1,1,2,2,2]) nobs = groups.shape[0] groupuniq = np.unique(groups) periods = np.array([0,1,2,1,2,0,1,2]) perioduniq = np.unique(periods) dummygr = (groups[:,None] == groupuniq).astype(float) dummype = (periods[:,None] == perioduniq).astype(float) sigma = 1. sigmagr = np.sqrt(2.) sigmape = np.sqrt(3.) #dummyall = np.c_[sigma*np.ones((nobs,1)), sigmagr*dummygr, # sigmape*dummype] #exclude constant ? dummyall = np.c_[sigmagr*dummygr, sigmape*dummype] # omega is the error variance-covariance matrix for the stacked # observations omega = np.dot(dummyall, dummyall.T) + sigma* np.eye(nobs) print omega print np.linalg.cholesky(omega) ev, evec = np.linalg.eigh(omega) #eig doesn't work omegainv = np.dot(evec, (1/ev * evec).T) omegainv2 = np.linalg.inv(omega) omegacomp = np.dot(evec, (ev * evec).T) print np.max(np.abs(omegacomp - omega)) #check #print np.dot(omegainv,omega) print np.max(np.abs(np.dot(omegainv,omega) - np.eye(nobs))) omegainvhalf = evec/np.sqrt(ev) #not sure whether ev shouldn't be column print np.max(np.abs(np.dot(omegainvhalf,omegainvhalf.T) - omegainv)) # now we can use omegainvhalf in GLS (instead of the cholesky) sigmas2 = np.array([sigmagr, sigmape, sigma]) groups2 = np.column_stack((groups, periods)) omega_, omegainv_, omegainvhalf_ = repanel_cov(groups2, sigmas2) print np.max(np.abs(omega_ - omega)) print np.max(np.abs(omegainv_ - omegainv)) print np.max(np.abs(omegainvhalf_ - omegainvhalf)) # notation Baltagi (3rd) section 9.4.1 (Fixed Effects Model) Pgr = reduce(np.dot,[dummygr, np.linalg.inv(np.dot(dummygr.T, dummygr)),dummygr.T]) Qgr = np.eye(nobs) - Pgr # within group effect: np.dot(Qgr, groups) # but this is not memory efficient, compared to groupstats print np.max(np.abs(np.dot(Qgr, groups))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/random_panel.py000066400000000000000000000115611224417117700262660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Generate a random process with panel structure Created on Sat Dec 17 22:15:27 2011 Author: Josef Perktold Notes ----- * written with unbalanced panels in mind, but not flexible enough yet * need more shortcuts and options for balanced panel * need to add random intercept or coefficients * only one-way (repeated measures) so far """ import numpy as np import correlation_structures as cs class PanelSample(object): '''data generating process for panel with within correlation allows various within correlation structures, but no random intercept yet Parameters ---------- nobs : int total number of observations k_vars : int number of explanatory variables to create in exog, including constant n_groups int number of groups in balanced sample exog : None or ndarray default is None, in which case a exog is created within : bool If True (default), then the exog vary within a group. If False, then only variation across groups is used. TODO: this option needs more work corr_structure : ndarray or ?? Default is np.eye. corr_args : tuple arguments for the corr_structure scale : float scale of noise, standard deviation of normal distribution seed : None or int If seed is given, then this is used to create the random numbers for the sample. Notes ----- The behavior for panel robust covariance estimators seems to differ by a large amount by whether exog have mostly within group or across group variation. I do not understand why this should be the case from the theory, and this would warrant more investigation. This is just used in one example so far and needs more usage to see what will be useful to add. ''' def __init__(self, nobs, k_vars, n_groups, exog=None, within=True, corr_structure=np.eye, corr_args=(), scale=1, seed=None): nobs_i = nobs//n_groups nobs = nobs_i * n_groups #make balanced self.nobs = nobs self.nobs_i = nobs_i self.n_groups = n_groups self.k_vars = k_vars self.corr_structure = corr_structure self.groups = np.repeat(np.arange(n_groups), nobs_i) self.group_indices = np.arange(n_groups+1) * nobs_i #check +1 if exog is None: if within: #t = np.tile(np.linspace(-1,1,nobs_i), n_groups) t = np.tile(np.linspace(0, 2, nobs_i), n_groups) #rs2 = np.random.RandomState(9876) #t = 1 + 0.3 * rs2.randn(nobs_i * n_groups) #mix within and across variation #t += np.repeat(np.linspace(-1,1,nobs_i), n_groups) else: #no within group variation, t = np.repeat(np.linspace(-1,1,nobs_i), n_groups) exog = t[:,None]**np.arange(k_vars) self.exog = exog #self.y_true = exog.sum(1) #all coefficients equal 1, #moved to make random coefficients #initialize self.y_true = None self.beta = None if seed is None: seed = np.random.randint(0, 999999) self.seed = seed self.random_state = np.random.RandomState(seed) #this makes overwriting difficult, move to method? self.std = scale * np.ones(nobs_i) corr = self.corr_structure(nobs_i, *corr_args) self.cov = cs.corr2cov(corr, self.std) self.group_means = np.zeros(n_groups) def get_y_true(self): if self.beta is None: self.y_true = self.exog.sum(1) else: self.y_true = np.dot(self.exog, self.beta) def generate_panel(self): ''' generate endog for a random panel dataset with within correlation ''' random = self.random_state if self.y_true is None: self.get_y_true() nobs_i = self.nobs_i n_groups = self.n_groups use_balanced = True if use_balanced: #much faster for balanced case noise = self.random_state.multivariate_normal(np.zeros(nobs_i), self.cov, size=n_groups).ravel() #need to add self.group_means noise += np.repeat(self.group_means, nobs_i) else: noise = np.empty(self.nobs, np.float64) noise.fill(np.nan) for ii in range(self.n_groups): #print ii, idx, idxupp = self.group_indices[ii:ii+2] #print idx, idxupp mean_i = self.group_means[ii] noise[idx:idxupp] = self.random_state.multivariate_normal( mean_i * np.ones(self.nobs_i), self.cov) endog = self.y_true + noise return endog if __name__ == '__main__': pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/sandwich_covariance.py000066400000000000000000000006641224417117700276230ustar00rootroot00000000000000'''temporary compatibility module TODO: remove in 0.5.0 ''' from statsmodels.stats.sandwich_covariance import * #from statsmodels.stats.moment_helpers import se_cov #not in __all__ def cov_hac_simple(results, nlags=None, weights_func=weights_bartlett, use_correction=True): c = cov_hac(results, nlags=nlags, weights_func=weights_func, use_correction=use_correction) return c, se_cov(c) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/sandwich_covariance_generic.py000066400000000000000000000073201224417117700313130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """covariance with (nobs,nobs) loop and general kernel This is a general implementation that is not efficient for any special cases. kernel is currently only for one continuous variable and any number of categorical groups. No spatial example, continuous is interpreted as time Created on Wed Nov 30 08:20:44 2011 Author: Josef Perktold License: BSD-3 """ import numpy as np def kernel(d1, d2, r=None, weights=None): '''general product kernel hardcoded split for the example: cat1 is continuous (time), other categories are discrete weights is e.g. Bartlett for cat1 r is (0,1) indicator vector for boolean weights 1{d1_i == d2_i} returns boolean if no continuous weights are used ''' diff = d1 - d2 if (weights is None) or (r[0] == 0): #time is irrelevant or treated as categorical return np.all((r * diff) == 0) #return bool else: #time uses continuous kernel, all other categorical return weights[diff] * np.all((r[1:] * diff[1:]) == 0) def aggregate_cov(x, d, r=None, weights=None): '''sum of outer procuct over groups and time selected by r This is for a generic reference implementation, it uses a nobs-nobs double loop. Parameters ---------- x : ndarray, (nobs,) or (nobs, k_vars) data, for robust standard error calculation, this is array of x_i * u_i d : ndarray, (nobs, n_groups) integer group labels, each column contains group (or time) indices r : ndarray, (n_groups,) indicator for which groups to include. If r[i] is zero, then this group is ignored. If r[i] is not zero, then the cluster robust standard errors include this group. weights : ndarray weights if the first group dimension uses a HAC kernel Returns ------- cov : ndarray (k_vars, k_vars) or scalar covariance matrix aggregates over group kernels count : int number of terms added in sum, mainly returned for cross-checking Notes ----- This uses `kernel` to calculate the weighted distance between two observations. ''' nobs = x.shape[0] #either 1d or 2d with obs in rows #next is not needed yet # if x.ndim == 2: # kvars = x.shape[1] # else: # kvars = 1 count = 0 #count non-zero pairs for cross checking, not needed res = 0 * np.outer(x[0], x[0]) #get output shape for ii in xrange(nobs): for jj in xrange(nobs): w = kernel(d[ii], d[jj], r=r, weights=weights) if w: #true or non-zero res += w * np.outer(x[0], x[0]) count *= 1 return res, count def weights_bartlett(nlags): #with lag zero, nlags is the highest lag included return 1 - np.arange(nlags+1)/(nlags+1.) #------- examples, cases: hardcoded for d is time and two categorical groups def S_all_hac(x, d, nlags=1): '''HAC independent of categorical group membership ''' r = np.zeros(d.shape[1]) r[0] = 1 weights = weights_bartlett(nlags) return aggregate_cov(x, d, r=r, weights=weights) def S_within_hac(x, d, nlags=1, groupidx=1): '''HAC for observations within a categorical group ''' r = np.zeros(d.shape[1]) r[0] = 1 r[groupidx] = 1 weights = weights_bartlett(nlags) return aggregate_cov(x, d, r=r, weights=weights) def S_cluster(x, d, groupidx=[1]): r = np.zeros(d.shape[1]) r[groupidx] = 1 return aggregate_cov(x, d, r=r, weights=None) def S_white(x, d): '''simple white heteroscedasticity robust covariance note: calculating this way is very inefficient, just for cross-checking ''' r = np.ones(d.shape[1]) #only points on diagonal return aggregate_cov(x, d, r=r, weights=None) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/tests/000077500000000000000000000000001224417117700244135ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/tests/__init__.py000066400000000000000000000000001224417117700265120ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/panel/tests/test_random_panel.py000066400000000000000000000124751224417117700304740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for short_panel and panel sandwich Created on Fri May 18 13:05:47 2012 Author: Josef Perktold moved example from main of random_panel """ import numpy as np from numpy.testing import assert_almost_equal import numpy.testing as npt import statsmodels.tools.eval_measures as em from statsmodels.stats.moment_helpers import cov2corr, se_cov from statsmodels.regression.linear_model import OLS from statsmodels.sandbox.panel.panel_short import ShortPanelGLS, ShortPanelGLS2 from statsmodels.sandbox.panel.random_panel import PanelSample import statsmodels.sandbox.panel.correlation_structures as cs import statsmodels.stats.sandwich_covariance as sw def assert_maxabs(actual, expected, value): npt.assert_array_less(em.maxabs(actual, expected, None), value) def test_short_panel(): #this checks that some basic statistical properties are satisfied by the #results, not verified results against other packages #Note: the ranking of robust bse is different if within=True #I added within keyword to PanelSample to be able to use old example #if within is False, then there is no within group variation in exog. nobs = 100 nobs_i = 5 n_groups = nobs // nobs_i k_vars = 3 dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma, corr_args=([1], [1., -0.9],), seed=377769, within=False) #print 'seed', dgp.seed y = dgp.generate_panel() noise = y - dgp.y_true #test dgp dgp_cov_e = np.array( [[ 1. , 0.9 , 0.81 , 0.729 , 0.6561], [ 0.9 , 1. , 0.9 , 0.81 , 0.729 ], [ 0.81 , 0.9 , 1. , 0.9 , 0.81 ], [ 0.729 , 0.81 , 0.9 , 1. , 0.9 ], [ 0.6561, 0.729 , 0.81 , 0.9 , 1. ]]) npt.assert_almost_equal(dgp.cov, dgp_cov_e, 13) cov_noise = np.cov(noise.reshape(-1,n_groups, order='F')) corr_noise = cov2corr(cov_noise) npt.assert_almost_equal(corr_noise, dgp.cov, 1) #estimate panel model mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups) res2 = mod2.fit_iterative(2) #whitened residual should be uncorrelated corr_wresid = np.corrcoef(res2.wresid.reshape(-1,n_groups, order='F')) assert_maxabs(corr_wresid, np.eye(5), 0.1) #residual should have same correlation as dgp corr_resid = np.corrcoef(res2.resid.reshape(-1,n_groups, order='F')) assert_maxabs(corr_resid, dgp.cov, 0.1) assert_almost_equal(res2.resid.std(),1, decimal=0) y_pred = np.dot(mod2.exog, res2.params) assert_almost_equal(res2.fittedvalues, y_pred, 13) #compare with OLS res2_ols = mod2._fit_ols() npt.assert_(mod2.res_pooled is res2_ols) res2_ols = mod2.res_pooled #TODO: BUG: requires call to _fit_ols #fitting once is the same as OLS #note: I need to create new instance, otherwise it continuous fitting mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups) res1 = mod1.fit_iterative(1) assert_almost_equal(res1.params, res2_ols.params, decimal=13) assert_almost_equal(res1.bse, res2_ols.bse, decimal=13) res_ols = OLS(y, dgp.exog).fit() assert_almost_equal(res1.params, res_ols.params, decimal=13) assert_almost_equal(res1.bse, res_ols.bse, decimal=13) #compare with old version mod_old = ShortPanelGLS2(y, dgp.exog, dgp.groups) res_old = mod_old.fit() assert_almost_equal(res2.params, res_old.params, decimal=13) assert_almost_equal(res2.bse, res_old.bse, decimal=13) mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups) res5 = mod5.fit_iterative(5) #make sure it's different #npt.assert_array_less(0.009, em.maxabs(res5.bse, res2.bse)) cov_clu = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int)) clubse = se_cov(cov_clu) pnwbse = se_cov(sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx)) bser = np.vstack((res2.bse, res5.bse, clubse, pnwbse)) bser_mean = np.mean(bser, axis=0) #cov_cluster close to robust and PanelGLS #is up to 24% larger than mean of bser #npt.assert_array_less(0, clubse / bser_mean - 1) npt.assert_array_less(clubse / bser_mean - 1, 0.25) #cov_nw_panel close to robust and PanelGLS npt.assert_array_less(pnwbse / bser_mean - 1, 0.1) #OLS underestimates bse, robust at least 60% larger npt.assert_array_less(0.6, bser_mean / res_ols.bse - 1) #cov_hac_panel with uniform_kernel is the same as cov_cluster for balanced #panel with full length kernel #I fixe default correction to be equal cov_uni = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform, use_correction='c') assert_almost_equal(cov_uni, cov_clu, decimal=13) #without correction cov_clu2 = sw.cov_cluster(mod2.res_pooled, dgp.groups.astype(int), use_correction=False) cov_uni2 = sw.cov_nw_panel(mod2.res_pooled, 4, mod2.group.groupidx, weights_func=sw.weights_uniform, use_correction=False) assert_almost_equal(cov_uni2, cov_clu2, decimal=13) cov_white = sw.cov_white_simple(mod2.res_pooled) cov_pnw0 = sw.cov_nw_panel(mod2.res_pooled, 0, mod2.group.groupidx, use_correction='hac') assert_almost_equal(cov_pnw0, cov_white, decimal=13) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/pca.py000066400000000000000000000155741224417117700233030ustar00rootroot00000000000000#Copyright (c) 2008 Erik Tollerud (etolleru@uci.edu) import numpy as np from math import pi class Pca(object): """ A basic class for Principal Component Analysis (PCA). p is the number of dimensions, while N is the number of data points """ _colors=('r','g','b','c','y','m','k') #defaults def __calc(self): A = self.A M=A-np.mean(A,axis=0) N=M/np.std(M,axis=0) self.M = M self.N = N self._eig = None def __init__(self,data,names=None): """ p X N matrix input """ from warnings import warn A = np.array(data).T n,p = A.shape self.n,self.p = n,p if p > n: warn('p > n - intentional?') self.A = A self._origA=A.copy() self.__calc() self._colors= np.tile(self._colors,int((p-1)/len(self._colors))+1)[:p] if names is not None and len(names) != p: raise ValueError('names must match data dimension') self.names = None if names is None else tuple([str(n) for n in names]) def getCovarianceMatrix(self): """ returns the covariance matrix for the dataset """ return np.cov(self.N.T) def getEigensystem(self): """ returns a tuple of (eigenvalues,eigenvectors) for the data set. """ if self._eig is None: res = np.linalg.eig(self.getCovarianceMatrix()) sorti=np.argsort(res[0])[::-1] res=(res[0][sorti],res[1][:,sorti]) self._eig=res return self._eig def getEigenvalues(self): return self.getEigensystem()[0] def getEigenvectors(self): return self.getEigensystem()[1] def getEnergies(self): """ "energies" are just normalized eigenvectors """ v=self.getEigenvalues() return v/np.sum(v) def plot2d(self,ix=0,iy=1,clf=True): """ Generates a 2-dimensional plot of the data set and principle components using matplotlib. ix specifies which p-dimension to put on the x-axis of the plot and iy specifies which to put on the y-axis (0-indexed) """ import matplotlib.pyplot as plt x,y=self.N[:,ix],self.N[:,iy] if clf: plt.clf() plt.scatter(x,y) vals,evs=self.getEigensystem() #evx,evy=evs[:,ix],evs[:,iy] xl,xu=plt.xlim() yl,yu=plt.ylim() dx,dy=(xu-xl),(yu-yl) for val,vec,c in zip(vals,evs.T,self._colors): plt.arrow(0,0,val*vec[ix],val*vec[iy],head_width=0.05*(dx*dy/4)**0.5,fc=c,ec=c) #plt.arrow(0,0,vals[ix]*evs[ix,ix],vals[ix]*evs[iy,ix],head_width=0.05*(dx*dy/4)**0.5,fc='g',ec='g') #plt.arrow(0,0,vals[iy]*evs[ix,iy],vals[iy]*evs[iy,iy],head_width=0.05*(dx*dy/4)**0.5,fc='r',ec='r') if self.names is not None: plt.xlabel('$'+self.names[ix]+'/\\sigma$') plt.ylabel('$'+self.names[iy]+'/\\sigma$') def plot3d(self,ix=0,iy=1,iz=2,clf=True): """ Generates a 3-dimensional plot of the data set and principle components using mayavi. ix, iy, and iz specify which of the input p-dimensions to place on each of the x,y,z axes, respectively (0-indexed). """ import enthought.mayavi.mlab as M if clf: M.clf() z3=np.zeros(3) v=(self.getEigenvectors()*self.getEigenvalues()) M.quiver3d(z3,z3,z3,v[ix],v[iy],v[iz],scale_factor=5) M.points3d(self.N[:,ix],self.N[:,iy],self.N[:,iz],scale_factor=0.3) if self.names: M.axes(xlabel=self.names[ix]+'/sigma',ylabel=self.names[iy]+'/sigma',zlabel=self.names[iz]+'/sigma') else: M.axes() def sigclip(self,sigs): """ clips out all data points that are more than a certain number of standard deviations from the mean. sigs can be either a single value or a length-p sequence that specifies the number of standard deviations along each of the p dimensions. """ if np.isscalar(sigs): sigs=sigs*np.ones(self.N.shape[1]) sigs = sigs*np.std(self.N,axis=1) n = self.N.shape[0] m = np.all(np.abs(self.N) < sigs,axis=1) self.A=self.A[m] self.__calc() return n-sum(m) def reset(self): self.A = self._origA.copy() self.__calc() def project(self,vals=None,enthresh=None,nPCs=None,cumen=None): """ projects the normalized values onto the components enthresh, nPCs, and cumen determine how many PCs to use if vals is None, the normalized data vectors are the values to project. Otherwise, it should be convertable to a p x N array returns n,p(>threshold) dimension array """ nonnones = sum([e != None for e in (enthresh,nPCs,cumen)]) if nonnones == 0: m = slice(None) elif nonnones > 1: raise ValueError("can't specify more than one threshold") else: if enthresh is not None: m = self.energies() > enthresh elif nPCs is not None: m = slice(None,nPCs) elif cumen is not None: m = np.cumsum(self.energies()) < cumen else: raise RuntimeError('Should be unreachable') if vals is None: vals = self.N.T else: vals = np.array(vals,copy=False) if self.N.T.shape[0] != vals.shape[0]: raise ValueError("shape for vals doesn't match") proj = np.matrix(self.getEigenvectors()).T*vals return proj[m].T def deproject(self,A,normed=True): """ input is an n X q array, where q <= p output is p X n """ A=np.atleast_2d(A) n,q = A.shape p = self.A.shape[1] if q > p : raise ValueError("q > p") evinv=np.linalg.inv(np.matrix(self.getEigenvectors()).T) zs = np.zeros((n,p)) zs[:,:q]=A proj = evinv*zs.T if normed: return np.array(proj.T).T else: mns=np.mean(self.A,axis=0) sds=np.std(self.M,axis=0) return (np.array(proj.T)*sds+mns).T def subtractPC(self,pc,vals=None): """ pc can be a scalar or any sequence of pc indecies if vals is None, the source data is self.A, else whatever is in vals (which must be p x m) """ if vals is None: vals = self.A else: vals = vals.T if vals.shape[1]!= self.A.shape[1]: raise ValueError("vals don't have the correct number of components") pcs=self.project() zpcs=np.zeros_like(pcs) zpcs[:,pc]=pcs[:,pc] upc=self.deproject(zpcs,False) A = vals.T-upc B = A.T*np.std(self.M,axis=0) return B+np.mean(self.A,axis=0) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/000077500000000000000000000000001224417117700243325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/__init__.py000066400000000000000000000001441224417117700264420ustar00rootroot00000000000000 #from anova_nistcertified import anova_oneway, anova_ols #from predstd import wls_prediction_std statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/anova_nistcertified.py000066400000000000000000000065201224417117700307270ustar00rootroot00000000000000'''calculating anova and verifying with NIST test data compares my implementations, stats.f_oneway and anova using statsmodels.OLS ''' import os import numpy as np from scipy import stats filenameli = ['SiRstv.dat', 'SmLs01.dat', 'SmLs02.dat', 'SmLs03.dat', 'AtmWtAg.dat', 'SmLs04.dat', 'SmLs05.dat', 'SmLs06.dat', 'SmLs07.dat', 'SmLs08.dat', 'SmLs09.dat'] ##filename = 'SmLs03.dat' #'SiRstv.dat' #'SmLs09.dat'#, 'AtmWtAg.dat' #'SmLs07.dat' ##path = __file__ ##print locals().keys() ###print path def getnist(filename): fname = os.path.abspath(os.path.join('./data', filename)) content = file(fname,'r').read().split('\n') data = [line.split() for line in content[60:]] certified = [line.split() for line in content[40:48] if line] dataf = np.loadtxt(fname, skiprows=60) y,x = dataf.T y = y.astype(int) caty = np.unique(y) f = float(certified[0][-1]) R2 = float(certified[2][-1]) resstd = float(certified[4][-1]) dfbn = int(certified[0][-4]) dfwn = int(certified[1][-3]) # dfbn->dfwn is this correct prob = stats.f.sf(f,dfbn,dfwn) return y, x, np.array([f, prob, R2, resstd]), certified, caty from try_catdata import groupsstats_dummy, groupstatsbin def anova_oneway(y, x, seq=0): # new version to match NIST # no generalization or checking of arguments, tested only for 1d yrvs = y[:,np.newaxis] #- min(y) #subracting mean increases numerical accuracy for NIST test data sets xrvs = x[:,np.newaxis] - x.mean() #for 1d#- 1e12 trick for 'SmLs09.dat' meang, varg, xdevmeangr, countg = groupsstats_dummy(yrvs[:,:1], xrvs[:,:1])#, seq=0) #the following does not work as replacement #gcount, gmean , meanarr, withinvar, withinvararr = groupstatsbin(y, x)#, seq=0) sswn = np.dot(xdevmeangr.T,xdevmeangr) ssbn = np.dot((meang-xrvs.mean())**2, countg.T) nobs = yrvs.shape[0] ncat = meang.shape[1] dfbn = ncat - 1 dfwn = nobs - ncat msb = ssbn/float(dfbn) msw = sswn/float(dfwn) f = msb/msw prob = stats.f.sf(f,dfbn,dfwn) R2 = (ssbn/(sswn+ssbn)) #R-squared resstd = np.sqrt(msw) #residual standard deviation #print f, prob def _fix2scalar(z): # return number if np.shape(z) == (1, 1): return z[0,0] else: return z f, prob, R2, resstd = map(_fix2scalar, (f, prob, R2, resstd)) return f, prob, R2, resstd import statsmodels.api as sm from try_ols_anova import data2dummy def anova_ols(y, x): X = sm.add_constant(data2dummy(x), prepend=False) res = sm.OLS(y, X).fit() return res.fvalue, res.f_pvalue, res.rsquared, np.sqrt(res.mse_resid) if __name__ == '__main__': print '\n using new ANOVA anova_oneway' print 'f, prob, R2, resstd' for fn in filenameli: print fn y, x, cert, certified, caty = getnist(fn) res = anova_oneway(y, x) print np.array(res) - cert print '\n using stats ANOVA f_oneway' for fn in filenameli: print fn y, x, cert, certified, caty = getnist(fn) xlist = [x[y==ii] for ii in caty] res = stats.f_oneway(*xlist) print np.array(res) - cert[:2] print '\n using statsmodels.OLS' print 'f, prob, R2, resstd' for fn in filenameli[:]: print fn y, x, cert, certified, caty = getnist(fn) res = anova_ols(x, y) print np.array(res) - cert statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/ar_panel.py000066400000000000000000000067321224417117700264750ustar00rootroot00000000000000'''Paneldata model with fixed effect (constants) and AR(1) errors checking fast evaluation of groupar1filter quickly written to try out grouparfilter without python loops maybe the example has MA(1) not AR(1) errors, I'm not sure and changed this. results look good, I'm also differencing the dummy variable (constants) ??? e.g. nobs = 35 true 0.6, 10, 20, 30 (alpha, mean_0, mean_1, mean_2) estimate 0.369453125 [ 10.14646929 19.87135086 30.12706505] Currently minimizes ssr but could switch to minimize llf, i.e. conditional MLE. This should correspond to iterative FGLS, where data are AR(1) transformed similar to GLSAR ? Result statistic from GLS return by OLS on transformed data should be asymptotically correct (check) Could be extended to AR(p) errors, but then requires panel with larger T ''' import numpy as np from scipy import optimize from statsmodels.regression.linear_model import OLS class PanelAR1(object): def __init__(self, endog, exog=None, groups=None): #take this from a super class, no checking is done here nobs = endog.shape[0] self.endog = endog if not exog is None: self.exog = exog self.groups_start = (np.diff(groups)!=0) self.groups_valid = ~self.groups_start def ar1filter(self, xy, alpha): #print alpha, return (xy[1:] - alpha * xy[:-1])[self.groups_valid] def fit_conditional(self, alpha): y = self.ar1filter(self.endog, alpha) x = self.ar1filter(self.exog, alpha) res = OLS(y, x).fit() return res.ssr #res.llf def fit(self): alpha0 = 0.1 #startvalue func = self.fit_conditional fitres = optimize.fmin(func, alpha0) # fit_conditional only returns ssr for now alpha = fitres[0] y = self.ar1filter(self.endog, alpha) x = self.ar1filter(self.exog, alpha) reso = OLS(y, x).fit() return fitres, reso if __name__ == '__main__': #------------ developement code for groupar1filter and example groups = np.array([0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2, 2,2,2,2,2,2,2,2]) nobs = len(groups) data0 = np.arange(nobs) data = np.arange(1,nobs+1) - 0.5*np.arange(nobs) + 0.1*np.random.randn(nobs) y00 = 0.5*np.random.randn(nobs+1) # I don't think a trend is handled yet data = np.arange(nobs) + y00[1:] + 0.2*y00[:-1] + 0.1*np.random.randn(nobs) #Are these AR(1) or MA(1) errors ??? data = y00[1:] + 0.6*y00[:-1] #+ 0.1*np.random.randn(nobs) group_codes = np.unique(groups) group_dummy = (groups[:,None] == group_codes).astype(int) groups_start = (np.diff(groups)!=0) groups_valid = (np.diff(groups)==0) #this applies to y with length for AR(1) #could use np.nonzero for index instead y = data + np.dot(group_dummy, np.array([10, 20, 30])) y0 = data0 + np.dot(group_dummy, np.array([10, 20, 30])) print groups_valid print np.diff(y)[groups_valid] alpha = 1 #test with 1 print (y0[1:] - alpha*y0[:-1])[groups_valid] alpha = 0.2 #test with 1 print (y0[1:] - alpha*y0[:-1] + 0.001)[groups_valid] #this is now AR(1) for each group separately #------------ #fitting the example exog = np.ones(nobs) exog = group_dummy mod = PanelAR1(y, exog, groups=groups) #mod = PanelAR1(data, exog, groups=groups) #data doesn't contain different means #print mod.ar1filter(mod.endog, 1) resa, reso = mod.fit() print resa[0], reso.params statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/000077500000000000000000000000001224417117700252435ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/AtmWtAg.dat000066400000000000000000000057671224417117700272600ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: AtmWtAg (AtmWtAg.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 108) Procedure: Analysis of Variance Reference: Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982). "The Absolute Isotopic Abundance & Atomic Weight of a Reference Sample of Silver". NBS Journal of Research, 87, pp. 9-19. Data: 1 Factor 2 Treatments 24 Replicates/Cell 48 Observations 7 Constant Leading Digits Average Level of Difficulty Observed Data Model: 3 Parameters (mu, tau_1, tau_2) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Instrument 1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01 Within Instrument 46 1.04951729166667E-08 2.28155932971014E-10 Certified R-Squared 2.57426544538321E-01 Certified Residual Standard Deviation 1.51048314446410E-05 Data: Instrument AgWt 1 107.8681568 1 107.8681465 1 107.8681572 1 107.8681785 1 107.8681446 1 107.8681903 1 107.8681526 1 107.8681494 1 107.8681616 1 107.8681587 1 107.8681519 1 107.8681486 1 107.8681419 1 107.8681569 1 107.8681508 1 107.8681672 1 107.8681385 1 107.8681518 1 107.8681662 1 107.8681424 1 107.8681360 1 107.8681333 1 107.8681610 1 107.8681477 2 107.8681079 2 107.8681344 2 107.8681513 2 107.8681197 2 107.8681604 2 107.8681385 2 107.8681642 2 107.8681365 2 107.8681151 2 107.8681082 2 107.8681517 2 107.8681448 2 107.8681198 2 107.8681482 2 107.8681334 2 107.8681609 2 107.8681101 2 107.8681512 2 107.8681469 2 107.8681360 2 107.8681254 2 107.8681261 2 107.8681450 2 107.8681368 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/Longley.dat000066400000000000000000000055371224417117700273600ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: Longley (Longley.dat) File Format: ASCII Certified Values (lines 31 to 51) Data (lines 61 to 76) Procedure: Linear Least Squares Regression Reference: Longley, J. W. (1967). An Appraisal of Least Squares Programs for the Electronic Computer from the Viewpoint of the User. Journal of the American Statistical Association, 62, pp. 819-841. Data: 1 Response Variable (y) 6 Predictor Variable (x) 16 Observations Higher Level of Difficulty Observed Data Model: Polynomial Class 7 Parameters (B0,B1,...,B7) y = B0 + B1*x1 + B2*x2 + B3*x3 + B4*x4 + B5*x5 + B6*x6 + e Certified Regression Statistics Standard Deviation Parameter Estimate of Estimate B0 -3482258.63459582 890420.383607373 B1 15.0618722713733 84.9149257747669 B2 -0.358191792925910E-01 0.334910077722432E-01 B3 -2.02022980381683 0.488399681651699 B4 -1.03322686717359 0.214274163161675 B5 -0.511041056535807E-01 0.226073200069370 B6 1829.15146461355 455.478499142212 Residual Standard Deviation 304.854073561965 R-Squared 0.995479004577296 Certified Analysis of Variance Table Source of Degrees of Sums of Mean Variation Freedom Squares Squares F Statistic Regression 6 184172401.944494 30695400.3240823 330.285339234588 Residual 9 836424.055505915 92936.0061673238 Data: y x1 x2 x3 x4 x5 x6 60323 83.0 234289 2356 1590 107608 1947 61122 88.5 259426 2325 1456 108632 1948 60171 88.2 258054 3682 1616 109773 1949 61187 89.5 284599 3351 1650 110929 1950 63221 96.2 328975 2099 3099 112075 1951 63639 98.1 346999 1932 3594 113270 1952 64989 99.0 365385 1870 3547 115094 1953 63761 100.0 363112 3578 3350 116219 1954 66019 101.2 397469 2904 3048 117388 1955 67857 104.6 419180 2822 2857 118734 1956 68169 108.4 442769 2936 2798 120445 1957 66513 110.8 444546 4681 2637 121950 1958 68655 112.6 482704 3813 2552 123366 1959 69564 114.2 502601 3931 2514 125368 1960 69331 115.7 518173 4806 2572 127852 1961 70551 116.9 554894 4007 2827 130081 1962 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SiRstv.dat000066400000000000000000000036331224417117700271740ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SiRstv (SiRstv.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 85) Procedure: Analysis of Variance Reference: Ehrstein, James and Croarkin, M. Carroll. Unpublished NIST dataset. Data: 1 Factor 5 Treatments 5 Replicates/Cell 25 Observations 3 Constant Leading Digits Lower Level of Difficulty Observed Data Model: 6 Parameters (mu,tau_1, ... , tau_5) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Instrument 4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00 Within Instrument 20 2.16636560000000E-01 1.08318280000000E-02 Certified R-Squared 1.90999039051129E-01 Certified Residual Standard Deviation 1.04076068334656E-01 Data: Instrument Resistance 1 196.3052 1 196.1240 1 196.1890 1 196.2569 1 196.3403 2 196.3042 2 196.3825 2 196.1669 2 196.3257 2 196.0422 3 196.1303 3 196.2005 3 196.2889 3 196.0343 3 196.1811 4 196.2795 4 196.1748 4 196.1494 4 196.1485 4 195.9885 5 196.2119 5 196.1051 5 196.1850 5 196.0052 5 196.2090 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs01.dat000066400000000000000000000136471224417117700267670ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs01 (SmLs01.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 249) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 21 Replicates/Cell 189 Observations 1 Constant Leading Digit Lower Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 Certified R-Squared 4.82758620689655E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1.4 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 2 1.3 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 3 1.5 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 4 1.3 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 5 1.5 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 6 1.3 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 7 1.5 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 7 1.4 7 1.6 8 1.3 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 9 1.5 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs02.dat000066400000000000000000001327411224417117700267650ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs02 (SmLs02.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 1869) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 201 Replicates/Cell 1809 Observations 1 Constant Leading Digit Lower Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 Certified R-Squared 4.71830985915493E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1.4 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 2 1.3 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 2 1.2 2 1.4 3 1.5 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 3 1.4 3 1.6 4 1.3 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 4 1.2 4 1.4 5 1.5 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 5 1.4 5 1.6 6 1.3 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 1.4 6 1.2 6 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1.6 7 1.4 7 1.6 8 1.3 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 8 1.4 8 1.2 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1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs03.dat000066400000000000000000015617561224417117700270030ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs03 (SmLs03.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 18069) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 2001 Replicates/Cell 18009 Observations 1 Constant Leading Digit Lower Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03 Within Treatment 18000 1.80000000000000E+02 1.00000000000000E-02 Certified R-Squared 4.70712773465067E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1.4 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 1 1.5 1 1.3 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1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 9 1.4 9 1.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs04.dat000066400000000000000000000152371224417117700267670ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs04 (SmLs04.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 249) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 21 Replicates/Cell 189 Observations 7 Constant Leading Digits Average Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 Certified R-Squared 4.82758620689655E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000.4 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 2 1000000.3 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 2 1000000.2 2 1000000.4 3 1000000.5 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 3 1000000.4 3 1000000.6 4 1000000.3 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 4 1000000.2 4 1000000.4 5 1000000.5 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 5 1000000.4 5 1000000.6 6 1000000.3 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 6 1000000.2 6 1000000.4 7 1000000.5 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 7 1000000.4 7 1000000.6 8 1000000.3 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 8 1000000.2 8 1000000.4 9 1000000.5 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs05.dat000066400000000000000000001510471224417117700267700ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs05 (SmLs05.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 1869) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 201 Replicates/Cell 1809 Observations 7 Constant Leading Digits Average Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 Certified R-Squared 4.71830985915493E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000.4 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 2 1000000.3 2 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1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs06.dat000066400000000000000000017765251224417117700270100ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs06 (SmLs06.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 18069) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 2001 Replicates/Cell 18009 Observations 7 Constant Leading Digits Average Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03 Within Treatment 18000 1.80000000000000E+02 1.00000000000000E-02 Certified R-Squared 4.70712773465067E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000.4 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 1000000.3 1 1000000.5 1 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1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 9 1000000.4 9 1000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs07.dat000066400000000000000000000163251224417117700267710ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs07 (SmLs07.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 249) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 21 Replicates/Cell 189 Observations 13 Constant Leading Digits Higher Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01 Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02 Certified R-Squared 4.82758620689655E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000000000.4 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 2 1000000000000.3 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 2 1000000000000.2 2 1000000000000.4 3 1000000000000.5 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 3 1000000000000.4 3 1000000000000.6 4 1000000000000.3 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 4 1000000000000.2 4 1000000000000.4 5 1000000000000.5 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 5 1000000000000.4 5 1000000000000.6 6 1000000000000.3 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 6 1000000000000.2 6 1000000000000.4 7 1000000000000.5 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 7 1000000000000.4 7 1000000000000.6 8 1000000000000.3 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 8 1000000000000.2 8 1000000000000.4 9 1000000000000.5 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs08.dat000066400000000000000000001635341224417117700267770ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs08 (SmLs08.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 1869) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 201 Replicates/Cell 1809 Observations 13 Constant Leading Digits Higher Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02 Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02 Certified R-Squared 4.71830985915493E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000000000.4 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 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1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 9 1000000000000.4 9 1000000000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/data/SmLs09.dat000066400000000000000000021501411224417117700267700ustar00rootroot00000000000000NIST/ITL StRD Dataset Name: SmLs09 (SmLs09.dat) File Format: ASCII Certified Values (lines 41 to 47) Data (lines 61 to 18069) Procedure: Analysis of Variance Reference: Simon, Stephen D. and Lesage, James P. (1989). "Assessing the Accuracy of ANOVA Calculations in Statistical Software". Computational Statistics & Data Analysis, 8, pp. 325-332. Data: 1 Factor 9 Treatments 2001 Replicates/Cell 18009 Observations 13 Constant Leading Digits Higher Level of Difficulty Generated Data Model: 10 Parameters (mu,tau_1, ... , tau_9) y_{ij} = mu + tau_i + epsilon_{ij} Certified Values: Source of Sums of Mean Variation df Squares Squares F Statistic Between Treatment 8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03 Within Treatment 18000 1.80000000000000E+02 1.00000000000000E-02 Certified R-Squared 4.70712773465067E-01 Certified Residual Standard Deviation 1.00000000000000E-01 Data: Treatment Response 1 1000000000000.4 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 1000000000000.3 1 1000000000000.5 1 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1000000000000.6 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/example_kernridge.py000066400000000000000000000023171224417117700303740ustar00rootroot00000000000000 import numpy as np import matplotlib.pyplot as plt from kernridgeregress_class import GaussProcess, kernel_euclid m,k = 50,4 upper = 6 scale = 10 xs = np.linspace(1,upper,m)[:,np.newaxis] #xs1 = xs1a*np.ones((1,4)) + 1/(1.0+np.exp(np.random.randn(m,k))) #xs1 /= np.std(xs1[::k,:],0) # normalize scale, could use cov to normalize ##y1true = np.sum(np.sin(xs1)+np.sqrt(xs1),1)[:,np.newaxis] xs1 = np.sin(xs)#[:,np.newaxis] y1true = np.sum(xs1 + 0.01*np.sqrt(np.abs(xs1)),1)[:,np.newaxis] y1 = y1true + 0.10 * np.random.randn(m,1) stride = 3 #use only some points as trainig points e.g 2 means every 2nd xstrain = xs1[::stride,:] ystrain = y1[::stride,:] xstrain = np.r_[xs1[:m/2,:], xs1[m/2+10:,:]] ystrain = np.r_[y1[:m/2,:], y1[m/2+10:,:]] index = np.hstack((np.arange(m/2), np.arange(m/2+10,m))) gp1 = GaussProcess(xstrain, ystrain, kernel=kernel_euclid, ridgecoeff=5*1e-4) yhatr1 = gp1.predict(xs1) plt.figure() plt.plot(y1true, y1,'bo',y1true, yhatr1,'r.') plt.title('euclid kernel: true y versus noisy y and estimated y') plt.figure() plt.plot(index,ystrain.ravel(),'bo-',y1true,'go-',yhatr1,'r.-') plt.title('euclid kernel: true (green), noisy (blue) and estimated (red) '+ 'observations') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/gmm.py000066400000000000000000000732621224417117700254760ustar00rootroot00000000000000'''Generalized Method of Moments, GMM, and Two-Stage Least Squares for instrumental variables IV2SLS Issues ------ * number of parameters, nparams, and starting values for parameters Where to put them? start was initially taken from global scope (bug) * When optimal weighting matrix cannot be calculated numerically In DistQuantilesGMM, we only have one row of moment conditions, not a moment condition for each observation, calculation for cov of moments breaks down. iter=1 works (weights is identity matrix) -> need method to do one iteration with an identity matrix or an analytical weighting matrix given as parameter. -> add result statistics for this case, e.g. cov_params, I have it in the standalone function (and in calc_covparams which is a copy of it), but not tested yet. DONE `fitonce` in DistQuantilesGMM, params are the same as in direct call to fitgmm move it to GMM class (once it's clearer for which cases I need this.) * GMM doesn't know anything about the underlying model, e.g. y = X beta + u or panel data model. It would be good if we can reuse methods from regressions, e.g. predict, fitted values, calculating the error term, and some result statistics. What's the best way to do this, multiple inheritance, outsourcing the functions, mixins or delegation (a model creates a GMM instance just for estimation). Unclear ------- * dof in Hausman - based on rank - differs between IV2SLS method and function used with GMM or (IV2SLS) - with GMM, covariance matrix difference has negative eigenvalues in iv example, ??? * jtest/jval - I'm not sure about the normalization (multiply or divide by nobs) in jtest. need a test case. Scaling of jval is irrelevant for estimation. jval in jtest looks to large in example, but I have no idea about the size * bse for fitonce look too large (no time for checking now) formula for calc_cov_params for the case without optimal weighting matrix is wrong. I don't have an estimate for omega in that case. And I'm confusing between weights and omega, which are *not* the same in this case. Author: josef-pktd License: BSD (3-clause) ''' import numpy as np from scipy import optimize, stats from statsmodels.tools.numdiff import approx_fprime, approx_hess from statsmodels.base.model import LikelihoodModel, LikelihoodModelResults from statsmodels.regression.linear_model import RegressionResults, OLS import statsmodels.tools.tools as tools def maxabs(x): '''just a shortcut to np.abs(x).max() ''' return np.abs(x).max() class IV2SLS(LikelihoodModel): ''' class for instrumental variables estimation using Two-Stage Least-Squares Parameters ---------- endog: array 1d endogenous variable exog : array explanatory variables instruments : array instruments for explanatory variables, needs to contain those exog variables that are not instrumented out Notes ----- All variables in exog are instrumented in the calculations. If variables in exog are not supposed to be instrumented out, then these variables need also to be included in the instrument array. ''' def __init__(self, endog, exog, instrument=None): self.instrument = instrument super(IV2SLS, self).__init__(endog, exog) # where is this supposed to be handled #Note: Greene p.77/78 dof correction is not necessary (because only # asy results), but most packages do it anyway self.df_resid = exog.shape[0] - exog.shape[1] + 1 def initialize(self): self.wendog = self.endog self.wexog = self.exog def whiten(self, X): pass def fit(self): '''estimate model using 2SLS IV regression Returns ------- results : instance of RegressionResults regression result Notes ----- This returns a generic RegressioResults instance as defined for the linear models. Parameter estimates and covariance are correct, but other results haven't been tested yet, to seee whether they apply without changes. ''' #Greene 5th edt., p.78 section 5.4 #move this maybe y,x,z = self.endog, self.exog, self.instrument ztz = np.dot(z.T, z) ztx = np.dot(z.T, x) self.xhatparams = xhatparams = np.linalg.solve(ztz, ztx) #print 'x.T.shape, xhatparams.shape', x.shape, xhatparams.shape F = xhat = np.dot(z, xhatparams) FtF = np.dot(F.T, F) self.xhatprod = FtF #store for Housman specification test Ftx = np.dot(F.T, x) Fty = np.dot(F.T, y) params = np.linalg.solve(FtF, Fty) Ftxinv = np.linalg.inv(Ftx) self.normalized_cov_params = np.dot(Ftxinv.T, np.dot(FtF, Ftxinv)) lfit = RegressionResults(self, params, normalized_cov_params=self.normalized_cov_params) self._results = lfit return lfit #copied from GLS, because I subclass currently LikelihoodModel and not GLS def predict(self, params, exog=None): """ Return linear predicted values from a design matrix. Parameters ---------- exog : array-like Design / exogenous data params : array-like, optional after fit has been called Parameters of a linear model Returns ------- An array of fitted values Notes ----- If the model as not yet been fit, params is not optional. """ if exog is None: exog = self.exog return np.dot(exog, params) #JP: this doesn't look correct for GLMAR #SS: it needs its own predict method if self._results is None and params is None: raise ValueError, "If the model has not been fit, then you must specify the params argument." if self._results is not None: return np.dot(exog, self._results.params) else: return np.dot(exog, params) def spec_hausman(self, dof=None): '''Hausman's specification test See Also -------- spec_hausman : generic function for Hausman's specification test ''' #use normalized cov_params for OLS resols = OLS(endog, exog).fit() normalized_cov_params_ols = resols.model.normalized_cov_params se2 = resols.mse_resid params_diff = self._results.params - resols.params cov_diff = np.linalg.pinv(self.xhatprod) - normalized_cov_params_ols #TODO: the following is very inefficient, solves problem (svd) twice #use linalg.lstsq or svd directly #cov_diff will very often be in-definite (singular) if not dof: dof = tools.rank(cov_diff) cov_diffpinv = np.linalg.pinv(cov_diff) H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff))/se2 pval = stats.chi2.sf(H, dof) return H, pval, dof ############# classes for Generalized Method of Moments GMM class GMM(object): ''' Class for estimation by Generalized Method of Moments needs to be subclassed, where the subclass defined the moment conditions `momcond` Parameters ---------- endog : array endogenous variable, see notes exog : array array of exogenous variables, see notes instrument : array array of instruments, see notes nmoms : None or int number of moment conditions, if None then it is set equal to the number of columns of instruments. Mainly needed to determin the shape or size of start parameters and starting weighting matrix. kwds : anything this is mainly if additional variables need to be stored for the calculations of the moment conditions Returns ------- *Attributes* results : instance of GMMResults currently just a storage class for params and cov_params without it's own methods bse : property return bse Notes ----- The GMM class only uses the moment conditions and does not use any data directly. endog, exog, instrument and kwds in the creation of the class instance are only used to store them for access in the moment conditions. Which of this are required and how they are used depends on the moment conditions of the subclass. Warning: Options for various methods have not been fully implemented and are still missing in several methods. ''' def __init__(self, endog, exog, instrument, nmoms=None, **kwds): ''' maybe drop and use mixin instead GMM doesn't really care about the data, just the moment conditions ''' self.endog = endog self.exog = exog self.instrument = instrument self.nmoms = nmoms or instrument.shape[1] self.results = GMMResults() self.__dict__.update(kwds) self.epsilon_iter = 1e-6 def fit(self, start=None): ''' Estimate the parameters using default settings. For estimation with more options use fititer method. Parameters ---------- start : array (optional) starting value for parameters ub minimization. If None then fitstart method is called for the starting values Returns ------- results : instance of GMMResults this is also attached as attribute results Notes ----- This function attaches the estimated parameters, params, the weighting matrix of the final iteration, weights, and the value of the GMM objective function, jval to results. The results are attached to this instance and also returned. fititer is called with maxiter=10 ''' #bug: where does start come from ??? if start is None: start = self.fitstart() #TODO: temporary hack params, weights = self.fititer(start, maxiter=10, start_weights=None, weights_method='cov', wargs=()) self.results.params = params self.results.weights = weights self.results.jval = self.gmmobjective(params, weights) return self.results def fitgmm(self, start, weights=None): '''estimate parameters using GMM Parameters ---------- start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns ------- paramest : array estimated parameters Notes ----- todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin ''' ## if not fixed is None: #fixed not defined in this version ## raise NotImplementedError #tmp = momcond(start, *args) # forgott to delete this #nmoms = tmp.shape[-1] if weights is None: weights = np.eye(self.nmoms) #TODO: add other optimization options and results return optimize.fmin(self.gmmobjective, start, (weights,), disp=0) def gmmobjective(self, params, weights): ''' objective function for GMM minimization Parameters ---------- params : array parameter values at which objective is evaluated weights : array weighting matrix Returns ------- jval : float value of objective function ''' moms = self.momcond(params) return np.dot(np.dot(moms.mean(0),weights), moms.mean(0)) def fititer(self, start, maxiter=2, start_weights=None, weights_method='cov', wargs=()): '''iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters ---------- start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmoms, nmoms) initial weighting matrix; if None, then the identity matrix is used weights_method : {'cov', ...} method to use to estimate the optimal weighting matrix, see calc_weightmatrix for details Returns ------- params : array estimated parameters weights : array optimal weighting matrix calculated with final parameter estimates Notes ----- ''' momcond = self.momcond if start_weights is None: w = np.eye(self.nmoms) else: w = start_weights #call fitgmm function #args = (self.endog, self.exog, self.instrument) #args is not used in the method version for it in range(maxiter): winv = np.linalg.inv(w) #this is still calling function not method ## resgmm = fitgmm(momcond, (), start, weights=winv, fixed=None, ## weightsoptimal=False) resgmm = self.fitgmm(start, weights=winv) moms = momcond(resgmm) w = self.calc_weightmatrix(moms, method='momcov', wargs=()) if it > 2 and maxabs(resgmm - start) < self.epsilon_iter: #check rule for early stopping break start = resgmm return resgmm, w def calc_weightmatrix(self, moms, method='momcov', wargs=()): '''calculate omega or the weighting matrix Parameters ---------- moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value method : 'momcov', anything else If method='momcov' is cov then the matrix is calculated as simple covariance of the moment conditions. For anything else, a constant cutoff window of length 5 is used. wargs : tuple parameters that are required by some kernel methods to estimate the long-run covariance. Not used yet. Returns ------- w : array (nmoms, nmoms) estimate for the weighting matrix or covariance of the moment condition Notes ----- currently a constant cutoff window is used TODO: implement long-run cov estimators, kernel-based Newey-West Andrews Andrews-Moy???? References ---------- Greene Hansen, Bruce ''' nobs = moms.shape[0] if method == 'momcov': w = np.cov(moms, rowvar=0) elif method == 'fakekernel': #uniform cut-off window moms_centered = moms - moms.mean() maxlag = 5 h = np.ones(maxlag) w = np.dot(moms.T, moms)/nobs for i in range(1,maxlag+1): w += (h * np.dot(moms_centered[i:].T, moms_centered[:-i]) / (nobs-i)) else: w = np.dot(moms.T, moms)/nobs return w def momcond_mean(self, params): ''' mean of moment conditions, ''' #endog, exog = args return self.momcond(params).mean(0) def gradient_momcond(self, params, epsilon=1e-4, method='centered'): momcond = self.momcond_mean if method == 'centered': gradmoms = (approx_fprime(params, momcond, epsilon=epsilon) + approx_fprime(params, momcond, epsilon=-epsilon))/2 else: gradmoms = approx_fprime(params, momcond, epsilon=epsilon) return gradmoms def cov_params(self, **kwds): #TODO add options ??? if not hasattr(self.results, 'params'): raise ValueError('the model has to be fit first') if hasattr(self.results, '_cov_params'): #replace with decorator later return self.results._cov_params gradmoms = self.gradient_momcond(self.results.params) moms = self.momcond(self.results.params) covparams = self.calc_cov_params(moms, gradmoms, **kwds) self.results._cov_params = covparams return self.results._cov_params #still needs to be fully converted to method def calc_cov_params(self, moms, gradmoms, weights=None, has_optimal_weights=True, method='momcov', wargs=()): '''calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_optimal_weights is assumed to be true. (API Note: The latter assumption could be changed if we allow for has_optimal_weights=None.) ''' nobs = moms.shape[0] if weights is None: omegahat = self.calc_weightmatrix(moms, method=method, wargs=wargs) has_optimal_weights = True #add other options, Barzen, ... longrun var estimators else: omegahat = weights #2 different names used, #TODO: this is wrong, I need an estimate for omega if has_optimal_weights: #has_optimal_weights: cov = np.linalg.inv(np.dot(gradmoms.T, np.dot(np.linalg.inv(omegahat), gradmoms))) else: gw = np.dot(gradmoms.T, weights) gwginv = np.linalg.inv(np.dot(gw, gradmoms)) cov = np.dot(np.dot(gwginv, np.dot(np.dot(gw, omegahat), gw.T)), gwginv) cov = np.linalg.inv(cov) return cov/nobs @property def bse(self): '''standard error of the parameter estimates ''' return self.get_bse() def get_bse(self, method=None): ''' method option not defined yet ''' return np.sqrt(np.diag(self.cov_params())) def jtest(self): '''overidentification test I guess this is missing a division by nobs, what's the normalization in jval ? ''' jstat = self.results.jval nparams = self.results.params.size #self.nparams return jstat, stats.chi2.sf(jstat, self.nmoms - nparams) class GMMResults(object): '''just a storage class right now''' pass class IVGMM(GMM): ''' Class for linear instrumental variables estimation with homoscedastic errors currently mainly a test case, doesn't exploit linear structure ''' def fitstart(self): return np.zeros(self.exog.shape[1]) def momcond(self, params): endog, exog, instrum = self.endog, self.exog, self.instrument return instrum * (endog - np.dot(exog, params))[:,None] #not tried out yet class NonlinearIVGMM(GMM): ''' Class for linear instrumental variables estimation with homoscedastic errors currently mainly a test case, not checked yet ''' def fitstart(self): #might not make sense for more general functions return np.zeros(self.exog.shape[1]) def __init__(self, endog, exog, instrument, **kwds): self.func = func def momcond(self, params): endog, exog, instrum = self.endog, self.exog, self.instrument return instrum * (endog - self.func(params, exog))[:,None] def spec_hausman(params_e, params_i, cov_params_e, cov_params_i, dof=None): '''Hausmans specification test Parameters ---------- params_e : array efficient and consistent under Null hypothesis, inconsistent under alternative hypothesis params_i: array consistent under Null hypothesis, consistent under alternative hypothesis cov_params_e : array, 2d covariance matrix of parameter estimates for params_e cov_params_i : array, 2d covariance matrix of parameter estimates for params_i example instrumental variables OLS estimator is `e`, IV estimator is `i` Notes ----- Todos,Issues - check dof calculations and verify for linear case - check one-sided hypothesis References ---------- Greene section 5.5 p.82/83 ''' params_diff = (params_i - params_e) cov_diff = cov_params_i - cov_params_e #TODO: the following is very inefficient, solves problem (svd) twice #use linalg.lstsq or svd directly #cov_diff will very often be in-definite (singular) if not dof: dof = tools.rank(cov_diff) cov_diffpinv = np.linalg.pinv(cov_diff) H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff)) pval = stats.chi2.sf(H, dof) evals = np.linalg.eigvalsh(cov_diff) return H, pval, dof, evals ########### class DistQuantilesGMM(GMM): ''' Estimate distribution parameters by GMM based on matching quantiles Currently mainly to try out different requirements for GMM when we cannot calculate the optimal weighting matrix. ''' def __init__(self, endog, exog, instrument, **kwds): #TODO: something wrong with super #super(self.__class__).__init__(endog, exog, instrument) #, **kwds) #self.func = func self.epsilon_iter = 1e-5 self.distfn = kwds['distfn'] #done by super doesn't work yet #TypeError: super does not take keyword arguments self.endog = endog #make this optional for fit if not 'pquant' in kwds: self.pquant = pquant = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99]) else: self.pquant = pquant = kwds['pquant'] #TODO: vectorize this: use edf self.xquant = np.array([stats.scoreatpercentile(endog, p) for p in pquant*100]) self.nmoms = len(self.pquant) #TODOcopied from GMM, make super work self.endog = endog self.exog = exog self.instrument = instrument self.results = GMMResults() #self.__dict__.update(kwds) self.epsilon_iter = 1e-6 def fitstart(self): #todo: replace with or add call to distfn._fitstart # added but not used during testing, avoid Travis distfn = self.distfn if hasattr(distfn, '_fitstart'): start = distfn._fitstart(x) else: start = [1]*distfn.numargs + [0.,1.] return np.array([1]*self.distfn.numargs + [0,1]) def momcond(self, params): #drop distfn as argument #, mom2, quantile=None, shape=None '''moment conditions for estimating distribution parameters by matching quantiles, defines as many moment conditions as quantiles. Returns ------- difference : array difference between theoretical and empirical quantiles Notes ----- This can be used for method of moments or for generalized method of moments. ''' #this check looks redundant/unused know if len(params) == 2: loc, scale = params elif len(params) == 3: shape, loc, scale = params else: #raise NotImplementedError pass #see whether this might work, seems to work for beta with 2 shape args #mom2diff = np.array(distfn.stats(*params)) - mom2 #if not quantile is None: pq, xq = self.pquant, self.xquant #ppfdiff = distfn.ppf(pq, alpha) cdfdiff = self.distfn.cdf(xq, *params) - pq #return np.concatenate([mom2diff, cdfdiff[:1]]) return np.atleast_2d(cdfdiff) def fitonce(self, start=None, weights=None, has_optimal_weights=False): '''fit without estimating an optimal weighting matrix and return results This is a convenience function that calls fitgmm and covparams with a given weight matrix or the identity weight matrix. This is useful if the optimal weight matrix is know (or is analytically given) or if an optimal weight matrix cannot be calculated. (Developer Notes: this function could go into GMM, but is needed in this class, at least at the moment.) Parameters ---------- Returns ------- results : GMMResult instance result instance with params and _cov_params attached See Also -------- fitgmm cov_params ''' if weights is None: weights = np.eye(self.nmoms) params = self.fitgmm(start=start) self.results.params = params #required before call to self.cov_params _cov_params = self.cov_params(weights=weights, has_optimal_weights=has_optimal_weights) self.results.weights = weights self.results.jval = self.gmmobjective(params, weights) return self.results if __name__ == '__main__': import statsmodels.api as sm examples = ['ivols', 'distquant'][:] if 'ivols' in examples: exampledata = ['ols', 'iv', 'ivfake'][1] nobs = nsample = 500 sige = 3 corrfactor = 0.025 x = np.linspace(0,10, nobs) X = tools.add_constant(np.column_stack((x, x**2)), prepend=False) beta = np.array([1, 0.1, 10]) def sample_ols(exog): endog = np.dot(exog, beta) + sige*np.random.normal(size=nobs) return endog, exog, None def sample_iv(exog): print 'using iv example' X = exog.copy() e = sige * np.random.normal(size=nobs) endog = np.dot(X, beta) + e exog[:,0] = X[:,0] + corrfactor * e z0 = X[:,0] + np.random.normal(size=nobs) z1 = X.sum(1) + np.random.normal(size=nobs) z2 = X[:,1] z3 = (np.dot(X, np.array([2,1, 0])) + sige/2. * np.random.normal(size=nobs)) z4 = X[:,1] + np.random.normal(size=nobs) instrument = np.column_stack([z0, z1, z2, z3, z4, X[:,-1]]) return endog, exog, instrument def sample_ivfake(exog): X = exog e = sige * np.random.normal(size=nobs) endog = np.dot(X, beta) + e #X[:,0] += 0.01 * e #z1 = X.sum(1) + np.random.normal(size=nobs) #z2 = X[:,1] z3 = (np.dot(X, np.array([2,1, 0])) + sige/2. * np.random.normal(size=nobs)) z4 = X[:,1] + np.random.normal(size=nobs) instrument = np.column_stack([X[:,:2], z3, z4, X[:,-1]]) #last is constant return endog, exog, instrument if exampledata == 'ols': endog, exog, _ = sample_ols(X) instrument = exog elif exampledata == 'iv': endog, exog, instrument = sample_iv(X) elif exampledata == 'ivfake': endog, exog, instrument = sample_ivfake(X) #using GMM and IV2SLS classes #---------------------------- mod = IVGMM(endog, exog, instrument, nmoms=instrument.shape[1]) res = mod.fit() modgmmols = IVGMM(endog, exog, exog, nmoms=exog.shape[1]) resgmmols = modgmmols.fit() #the next is the same as IV2SLS, (Z'Z)^{-1} as weighting matrix modgmmiv = IVGMM(endog, exog, instrument, nmoms=instrument.shape[1]) #same as mod resgmmiv = modgmmiv.fitgmm(np.ones(exog.shape[1], float), weights=np.linalg.inv(np.dot(instrument.T, instrument))) modls = IV2SLS(endog, exog, instrument) resls = modls.fit() modols = OLS(endog, exog) resols = modols.fit() print '\nIV case' print 'params' print 'IV2SLS', resls.params print 'GMMIV ', resgmmiv # .params print 'GMM ', res.params print 'diff ', res.params - resls.params print 'OLS ', resols.params print 'GMMOLS', resgmmols.params print '\nbse' print 'IV2SLS', resls.bse print 'GMM ', mod.bse #bse currently only attached to model not results print 'diff ', mod.bse - resls.bse print '%-diff', resls.bse / mod.bse * 100 - 100 print 'OLS ', resols.bse print 'GMMOLS', modgmmols.bse #print 'GMMiv', modgmmiv.bse print "Hausman's specification test" print modls.spec_hausman() print spec_hausman(resols.params, res.params, resols.cov_params(), mod.cov_params()) print spec_hausman(resgmmols.params, res.params, modgmmols.cov_params(), mod.cov_params()) if 'distquant' in examples: #estimating distribution parameters from quantiles #------------------------------------------------- #example taken from distribution_estimators.py gparrvs = stats.genpareto.rvs(2, size=5000) x0p = [1., gparrvs.min()-5, 1] moddist = DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto) #produces non-sense because optimal weighting matrix calculations don't #apply to this case #resgp = moddist.fit() #now with 'cov': LinAlgError: Singular matrix pit1, wit1 = moddist.fititer([1.5,0,1.5], maxiter=1) print pit1 p1 = moddist.fitgmm([1.5,0,1.5]) print p1 moddist2 = DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto, pquant=np.linspace(0.01,0.99,10)) pit1a, wit1a = moddist2.fititer([1.5,0,1.5], maxiter=1) print pit1a p1a = moddist2.fitgmm([1.5,0,1.5]) print p1a #Note: pit1a and p1a are the same and almost the same (1e-5) as # fitquantilesgmm version (functions instead of class) res1b = moddist2.fitonce([1.5,0,1.5]) print res1b.params print moddist2.bse #they look much too large print np.sqrt(np.diag(res1b._cov_params)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/kernridgeregress_class.py000066400000000000000000000174051224417117700314450ustar00rootroot00000000000000'''Kernel Ridge Regression for local non-parametric regression''' import numpy as np from scipy import spatial as ssp from numpy.testing import assert_equal import matplotlib.pylab as plt def plt_closeall(n=10): '''close a number of open matplotlib windows''' for i in range(n): plt.close() def kernel_rbf(x,y,scale=1, **kwds): #scale = kwds.get('scale',1) dist = ssp.minkowski_distance_p(x[:,np.newaxis,:],y[np.newaxis,:,:],2) return np.exp(-0.5/scale*(dist)) def kernel_euclid(x,y,p=2, **kwds): return ssp.minkowski_distance(x[:,np.newaxis,:],y[np.newaxis,:,:],p) class GaussProcess(object): '''class to perform kernel ridge regression (gaussian process) Warning: this class is memory intensive, it creates nobs x nobs distance matrix and its inverse, where nobs is the number of rows (observations). See sparse version for larger number of observations Notes ----- Todo: * normalize multidimensional x array on demand, either by var or cov * add confidence band * automatic selection or proposal of smoothing parameters Note: this is different from kernel smoothing regression, see for example http://en.wikipedia.org/wiki/Kernel_smoother In this version of the kernel ridge regression, the training points are fitted exactly. Needs a fast version for leave-one-out regression, for fitting each observation on all the other points. This version could be numerically improved for the calculation for many different values of the ridge coefficient. see also short summary by Isabelle Guyon (ETHZ) in a manuscript KernelRidge.pdf Needs verification and possibly additional statistical results or summary statistics for interpretation, but this is a problem with non-parametric, non-linear methods. Reference --------- Rasmussen, C.E. and C.K.I. Williams, 2006, Gaussian Processes for Machine Learning, the MIT Press, www.GaussianProcess.org/gpal, chapter 2 a short summary of the kernel ridge regression is at http://www.ics.uci.edu/~welling/teaching/KernelsICS273B/Kernel-Ridge.pdf ''' def __init__(self, x, y=None, kernel=kernel_rbf, scale=0.5, ridgecoeff = 1e-10, **kwds ): ''' Parameters ---------- x : 2d array (N,K) data array of explanatory variables, columns represent variables rows represent observations y : 2d array (N,1) (optional) endogenous variable that should be fitted or predicted can alternatively be specified as parameter to fit method kernel : function, default: kernel_rbf kernel: (x1,x2)->kernel matrix is a function that takes as parameter two column arrays and return the kernel or distance matrix scale : float (optional) smoothing parameter for the rbf kernel ridgecoeff : float (optional) coefficient that is multiplied with the identity matrix in the ridge regression Notes ----- After initialization, kernel matrix is calculated and if y is given as parameter then also the linear regression parameter and the fitted or estimated y values, yest, are calculated. yest is available as an attribute in this case. Both scale and the ridge coefficient smooth the fitted curve. ''' self.x = x self.kernel = kernel self.scale = scale self.ridgecoeff = ridgecoeff self.distxsample = kernel(x,x,scale=scale) self.Kinv = np.linalg.inv(self.distxsample + np.eye(*self.distxsample.shape)*ridgecoeff) if not y is None: self.y = y self.yest = self.fit(y) def fit(self,y): '''fit the training explanatory variables to a sample ouput variable''' self.parest = np.dot(self.Kinv, y) #self.kernel(y,y,scale=self.scale)) yhat = np.dot(self.distxsample,self.parest) return yhat ## print ds33.shape ## ds33_2 = kernel(x,x[::k,:],scale=scale) ## dsinv = np.linalg.inv(ds33+np.eye(*distxsample.shape)*ridgecoeff) ## B = np.dot(dsinv,y[::k,:]) def predict(self,x): '''predict new y values for a given array of explanatory variables''' self.xpredict = x distxpredict = self.kernel(x, self.x, scale=self.scale) self.ypredict = np.dot(distxpredict, self.parest) return self.ypredict def plot(self, y, plt=plt ): '''some basic plots''' #todo return proper graph handles plt.figure(); plt.plot(self.x,self.y, 'bo-', self.x, self.yest, 'r.-') plt.title('sample (training) points') plt.figure() plt.plot(self.xpredict,y,'bo-',self.xpredict,self.ypredict,'r.-') plt.title('all points') def example1(): m,k = 500,4 upper = 6 scale=10 xs1a = np.linspace(1,upper,m)[:,np.newaxis] xs1 = xs1a*np.ones((1,4)) + 1/(1.0+np.exp(np.random.randn(m,k))) xs1 /= np.std(xs1[::k,:],0) # normalize scale, could use cov to normalize y1true = np.sum(np.sin(xs1)+np.sqrt(xs1),1)[:,np.newaxis] y1 = y1true + 0.250 * np.random.randn(m,1) stride = 2 #use only some points as trainig points e.g 2 means every 2nd gp1 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_euclid, ridgecoeff=1e-10) yhatr1 = gp1.predict(xs1) plt.figure() plt.plot(y1true, y1,'bo',y1true, yhatr1,'r.') plt.title('euclid kernel: true y versus noisy y and estimated y') plt.figure() plt.plot(y1,'bo-',y1true,'go-',yhatr1,'r.-') plt.title('euclid kernel: true (green), noisy (blue) and estimated (red) '+ 'observations') gp2 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_rbf, scale=scale, ridgecoeff=1e-1) yhatr2 = gp2.predict(xs1) plt.figure() plt.plot(y1true, y1,'bo',y1true, yhatr2,'r.') plt.title('rbf kernel: true versus noisy (blue) and estimated (red) observations') plt.figure() plt.plot(y1,'bo-',y1true,'go-',yhatr2,'r.-') plt.title('rbf kernel: true (green), noisy (blue) and estimated (red) '+ 'observations') #gp2.plot(y1) def example2(m=100, scale=0.01, stride=2): #m,k = 100,1 upper = 6 xs1 = np.linspace(1,upper,m)[:,np.newaxis] y1true = np.sum(np.sin(xs1**2),1)[:,np.newaxis]/xs1 y1 = y1true + 0.05*np.random.randn(m,1) ridgecoeff = 1e-10 #stride = 2 #use only some points as trainig points e.g 2 means every 2nd gp1 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_euclid, ridgecoeff=1e-10) yhatr1 = gp1.predict(xs1) plt.figure() plt.plot(y1true, y1,'bo',y1true, yhatr1,'r.') plt.title('euclid kernel: true versus noisy (blue) and estimated (red) observations') plt.figure() plt.plot(y1,'bo-',y1true,'go-',yhatr1,'r.-') plt.title('euclid kernel: true (green), noisy (blue) and estimated (red) '+ 'observations') gp2 = GaussProcess(xs1[::stride,:],y1[::stride,:], kernel=kernel_rbf, scale=scale, ridgecoeff=1e-2) yhatr2 = gp2.predict(xs1) plt.figure() plt.plot(y1true, y1,'bo',y1true, yhatr2,'r.') plt.title('rbf kernel: true versus noisy (blue) and estimated (red) observations') plt.figure() plt.plot(y1,'bo-',y1true,'go-',yhatr2,'r.-') plt.title('rbf kernel: true (green), noisy (blue) and estimated (red) '+ 'observations') #gp2.plot(y1) if __name__ == '__main__': example2() #example2(m=1000, scale=0.01) #example2(m=100, scale=0.5) # oversmoothing #example2(m=2000, scale=0.005) # this looks good for rbf, zoom in #example2(m=200, scale=0.01,stride=4) example1() #plt.show() #plt_closeall() # use this to close the open figure windows statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/notes_runmnl.txt000066400000000000000000000026211224417117700276170ustar00rootroot00000000000000 access to data and parameters by branch and leaves -------------------------------------------------- * this could be used in a similar way in other system estimators * dictionary access to data per equation, branch, leaf * alternative dictionary of individual variables used in equations * memory: temporary copies while using linalg, or permanent copies * outsource acces into call-back function or method * with a parser the dictionaries could be created from a formula * similar applies for accessing parameters from the params array in optimization (MLE, GMM) when there are identical parameters across equations or leaves * in latest version use dictionary mapping coefficient/parameter names to index into params global/outer-scope variables ---------------------------- * for nested logit I need to access params and store, change probabilites from the branch * need variables in a scope outside of the recursion * nested function or save params and probs in instance attribute constraint estimation as alternative and extension -------------------------------------------------- * cross-equation and within equation restrictions on parameters could also be directly imposed, instead of relying on the dictionary * no idea yet on how to encode this * might be easier to use constraint optimizer and keep constraints (partially) separate from the parameterization in the likelihood calculations statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/ols_anova_original.py000066400000000000000000000242051224417117700305540ustar00rootroot00000000000000''' convenience functions for ANOVA type analysis with OLS Note: statistical results of ANOVA are not checked, OLS is checked but not whether the reported results are the ones used in ANOVA ''' import numpy as np #from scipy import stats import statsmodels.api as sm dt_b = np.dtype([('breed', int), ('sex', int), ('litter', int), ('pen', int), ('pig', int), ('age', float), ('bage', float), ('y', float)]) ''' too much work using structured masked arrays dta = np.mafromtxt('dftest3.data', dtype=dt_b) dta_use = np.ma.column_stack[[dta[col] for col in 'y sex age'.split()]] ''' dta = np.genfromtxt('dftest3.data') print dta.shape mask = np.isnan(dta) print "rows with missing values", mask.any(1).sum() vars = dict((v[0], (idx, v[1])) for idx, v in enumerate(( ('breed', int), ('sex', int), ('litter', int), ('pen', int), ('pig', int), ('age', float), ('bage', float), ('y', float)))) datavarnames = 'y sex age'.split() #possible to avoid temporary array ? dta_use = dta[:, [vars[col][0] for col in datavarnames]] keeprows = ~np.isnan(dta_use).any(1) print 'number of complete observations', keeprows.sum() dta_used = dta_use[keeprows,:] varsused = dict((k, [dta_used[:,idx], idx, vars[k][1]]) for idx, k in enumerate(datavarnames)) # use function for dummy #sexgroups = np.unique(dta_used[:,1]) #sexdummy = (dta_used[:,1][:, None] == sexgroups).astype(int) def data2dummy(x, returnall=False): '''convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only''' x = x.ravel() groups = np.unique(x) if returnall: return (x[:, None] == groups).astype(int) else: return (x[:, None] == groups).astype(int)[:,:-1] def data2proddummy(x): '''creates product dummy variables from 2 columns of 2d array drops last dummy variable, but not from each category singular with simple dummy variable but not with constant quickly written, no safeguards ''' #brute force, assumes x is 2d #replace with encoding if possible groups = np.unique(map(tuple, x.tolist())) #includes singularity with additive factors return (x==groups[:,None,:]).all(-1).T.astype(int)[:,:-1] def data2groupcont(x1,x2): '''create dummy continuous variable Parameters ---------- x1 : 1d array label or group array x2 : 1d array (float) continuous variable Notes ----- useful for group specific slope coefficients in regression ''' if x2.ndim == 1: x2 = x2[:,None] dummy = data2dummy(x1, returnall=True) return dummy * x2 sexdummy = data2dummy(dta_used[:,1]) factors = ['sex'] for k in factors: varsused[k][0] = data2dummy(varsused[k][0]) products = [('sex', 'age')] for k in products: varsused[''.join(k)] = data2proddummy(np.c_[varsused[k[0]][0],varsused[k[1]][0]]) # make dictionary of variables with dummies as one variable #vars_to_use = {name: data or dummy variables} X_b0 = np.c_[sexdummy, dta_used[:,2], np.ones((dta_used.shape[0],1))] y_b0 = dta_used[:,0] res_b0 = sm.OLS(y_b0, X_b0).results print res_b0.params print res_b0.ssr anova_str0 = ''' ANOVA statistics (model sum of squares excludes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ess)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' anova_str = ''' ANOVA statistics (model sum of squares includes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ssmwithmean)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' #print anova_str % dict([('df_model', res.df_model)]) #anovares = ['df_model' , 'df_resid' def anovadict(res): '''update regression results dictionary with ANOVA specific statistics not checked for completeness ''' ad = {} ad.update(res.__dict__) anova_attr = ['df_model', 'df_resid', 'ess', 'ssr','uncentered_tss', 'mse_model', 'mse_resid', 'mse_total', 'fvalue', 'f_pvalue', 'rsquared'] for key in anova_attr: ad[key] = getattr(res, key) ad['nobs'] = res.model.nobs ad['ssmwithmean'] = res.uncentered_tss - res.ssr return ad print anova_str0 % anovadict(res_b0) #the following leaves the constant in, not with NIST regression #but something fishy with res.ess negative in examples print anova_str % anovadict(res_b0) print 'using sex only' X2 = np.c_[sexdummy, np.ones((dta_used.shape[0],1))] res2 = sm.OLS(y_b0, X2).results print res2.params print res2.ssr print anova_str % anovadict(res2) print 'using age only' X3 = np.c_[ dta_used[:,2], np.ones((dta_used.shape[0],1))] res3 = sm.OLS(y_b0, X3).results print res3.params print res3.ssr print anova_str % anovadict(res3) def form2design(ss, data): '''convert string formula to data dictionary ss : string * I : add constant * varname : for simple varnames data is used as is * F:varname : create dummy variables for factor varname * P:varname1*varname2 : create product dummy variables for varnames * G:varname1*varname2 : create product between factor and continuous variable data : dict or structured array data set, access of variables by name as in dictionaries Returns ------- vars : dictionary dictionary of variables with converted dummy variables names : list list of names, product (P:) and grouped continuous variables (G:) have name by joining individual names sorted according to input Examples -------- >>> xx, n = form2design('I a F:b P:c*d G:c*f', testdata) >>> xx.keys() ['a', 'b', 'const', 'cf', 'cd'] >>> n ['const', 'a', 'b', 'cd', 'cf'] Notes ----- with sorted dict, separate name list wouldn't be necessary ''' vars = {} names = [] for item in ss.split(): if item == 'I': vars['const'] = np.ones(data.shape[0]) names.append('const') elif not ':' in item: vars[item] = data[item] names.append(item) elif item[:2] == 'F:': v = item.split(':')[1] vars[v] = data2dummy(data[v]) names.append(v) elif item[:2] == 'P:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2proddummy(np.c_[data[v[0]],data[v[1]]]) names.append(''.join(v)) elif item[:2] == 'G:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2groupcont(data[v[0]], data[v[1]]) names.append(''.join(v)) else: raise ValueError, 'unknown expression in formula' return vars, names nobs = 1000 testdataint = np.random.randint(3, size=(nobs,4)).view([('a',int),('b',int),('c',int),('d',int)]) testdatacont = np.random.normal( size=(nobs,2)).view([('e',float), ('f',float)]) import numpy.lib.recfunctions dt2 = numpy.lib.recfunctions.zip_descr((testdataint, testdatacont),flatten=True) # concatenate structured arrays testdata = np.empty((nobs,1), dt2) for name in testdataint.dtype.names: testdata[name] = testdataint[name] for name in testdatacont.dtype.names: testdata[name] = testdatacont[name] #print form2design('a',testdata) if 0: xx, n = form2design('F:a',testdata) print xx print form2design('P:a*b',testdata) print data2proddummy((np.c_[testdata['a'],testdata['b']])) xx, names = form2design('a F:b P:c*d',testdata) #xx, names = form2design('I a F:b F:c F:d P:c*d',testdata) xx, names = form2design('I a F:b P:c*d', testdata) xx, names = form2design('I a F:b P:c*d G:a*e f', testdata) X = np.column_stack([xx[nn] for nn in names]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).results print rest1.params print anova_str % anovadict(rest1) def dropname(ss, li): '''drop names from a list of strings, names to drop are in space delimeted list does not change original list ''' newli = li[:] for item in ss.split(): newli.remove(item) return newli X = np.column_stack([xx[nn] for nn in dropname('ae f', names)]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).results print rest1.params print anova_str % anovadict(rest1) # Example: from Bruce # ------------------- # read data set and drop rows with missing data dta = np.genfromtxt('dftest3.data', dt_b,missing='.', usemask=True) print 'missing', [dta.mask[k].sum() for k in dta.dtype.names] m = dta.mask.view(bool) droprows = m.reshape(-1,len(dta.dtype.names)).any(1) # get complete data as plain structured array # maybe doesn't work with masked arrays dta_use_b1 = dta[~droprows,:].data print dta_use_b1.shape print dta_use_b1.dtype #Example b1: variables from Bruce's glm # prepare data and dummy variables xx_b1, names_b1 = form2design('I F:sex age', dta_use_b1) # create design matrix X_b1 = np.column_stack([xx_b1[nn] for nn in dropname('', names_b1)]) y_b1 = dta_use_b1['y'] # estimate using OLS rest_b1 = sm.OLS(y_b1, X_b1).results # print results print rest_b1.params print anova_str % anovadict(rest_b1) #compare with original version only in original version print anova_str % anovadict(res_b0) # Example: use all variables except pig identifier allexog = ' '.join(dta.dtype.names[:-1]) #'breed sex litter pen pig age bage' xx_b1a, names_b1a = form2design('I F:breed F:sex F:litter F:pen age bage', dta_use_b1) X_b1a = np.column_stack([xx_b1a[nn] for nn in dropname('', names_b1a)]) y_b1a = dta_use_b1['y'] rest_b1a = sm.OLS(y_b1a, X_b1a).results print rest_b1a.params print anova_str % anovadict(rest_b1a) for dropn in names_b1a: print '\nResults dropping', dropn X_b1a_ = np.column_stack([xx_b1a[nn] for nn in dropname(dropn, names_b1a)]) y_b1a_ = dta_use_b1['y'] rest_b1a_ = sm.OLS(y_b1a_, X_b1a_).results #print rest_b1a_.params print anova_str % anovadict(rest_b1a_) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/onewaygls.py000066400000000000000000000353611224417117700267240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ F test for null hypothesis that coefficients in several regressions are the same * implemented by creating groupdummies*exog and testing appropriate contrast matrices * similar to test for structural change in all variables at predefined break points * allows only one group variable * currently tests for change in all exog variables * allows for heterogscedasticity, error variance varies across groups * does not work if there is a group with only a single observation TODO ---- * generalize anova structure, - structural break in only some variables - compare structural breaks in several exog versus constant only - fast way to construct comparisons * print anova style results * add all pairwise comparison tests (DONE) with and without Bonferroni correction * add additional test, likelihood-ratio, lagrange-multiplier, wald ? * test for heteroscedasticity, equality of variances - how? - like lagrange-multiplier in stattools heteroscedasticity tests * permutation or bootstrap test statistic or pvalues References ---------- Greene: section 7.4 Modeling and Testing for a Structural Break is not the same because I use a different normalization, which looks easier for more than 2 groups/subperiods after looking at Greene: * my version assumes that all groups are large enough to estimate the coefficients * in sections 7.4.2 and 7.5.3, predictive tests can also be used when there are insufficient (nobs>> res.contrasts.keys() [(0, 1), 1, 'all', 3, (1, 2), 2, (1, 3), (2, 3), (0, 3), (0, 2)] The keys are based on the original names or labels of the groups. TODO: keys can be numpy scalars and then the keys cannot be sorted ''' if not hasattr(self, 'weights'): self.fitbygroups() groupdummy = (self.groupsint[:,None] == self.uniqueint).astype(int) #order of dummy variables by variable - not used #dummyexog = self.exog[:,:,None]*groupdummy[:,None,1:] #order of dummy variables by grous - used dummyexog = self.exog[:,None,:]*groupdummy[:,1:,None] exog = np.c_[self.exog, dummyexog.reshape(self.exog.shape[0],-1)] #self.nobs ?? #Notes: I changed to drop first group from dummy #instead I want one full set dummies if self.het: weights = self.weights res = WLS(self.endog, exog, weights=weights).fit() else: res = OLS(self.endog, exog).fit() self.lsjoint = res contrasts = {} nvars = self.exog.shape[1] nparams = exog.shape[1] ndummies = nparams - nvars contrasts['all'] = np.c_[np.zeros((ndummies, nvars)), np.eye(ndummies)] for groupind, group in enumerate(self.unique[1:]): #need enumerate if groups != groupsint groupind = groupind + 1 contr = np.zeros((nvars, nparams)) contr[:,nvars*groupind:nvars*(groupind+1)] = np.eye(nvars) contrasts[group] = contr #save also for pairs, see next contrasts[(self.unique[0], group)] = contr #Note: I'm keeping some duplication for testing pairs = np.triu_indices(len(self.unique),1) for ind1,ind2 in zip(*pairs): #replace with group1, group2 in sorted(keys) if ind1 == 0: continue # need comparison with benchmark/normalization group separate g1 = self.unique[ind1] g2 = self.unique[ind2] group = (g1, g2) contr = np.zeros((nvars, nparams)) contr[:,nvars*ind1:nvars*(ind1+1)] = np.eye(nvars) contr[:,nvars*ind2:nvars*(ind2+1)] = -np.eye(nvars) contrasts[group] = contr self.contrasts = contrasts def fitpooled(self): '''fit the pooled model, which assumes there are no differences across groups ''' if self.het: if not hasattr(self, 'weights'): self.fitbygroups() weights = self.weights res = WLS(self.endog, self.exog, weights=weights).fit() else: res = OLS(self.endog, self.exog).fit() self.lspooled = res def ftest_summary(self): '''run all ftests on the joint model Returns ------- fres : str a string that lists the results of all individual f-tests summarytable : list of tuples contains (pair, (fvalue, pvalue,df_denom, df_num)) for each f-test Note ---- This are the raw results and not formatted for nice printing. ''' if not hasattr(self, 'lsjoint'): self.fitjoint() txt = [] summarytable = [] txt.append('F-test for equality of coefficients across groups') fres = self.lsjoint.f_test(self.contrasts['all']) txt.append(fres.__str__()) summarytable.append(('all',(fres.fvalue, fres.pvalue, fres.df_denom, fres.df_num))) # for group in self.unique[1:]: #replace with group1, group2 in sorted(keys) # txt.append('F-test for equality of coefficients between group' # ' %s and group %s' % (group, '0')) # fres = self.lsjoint.f_test(self.contrasts[group]) # txt.append(fres.__str__()) # summarytable.append((group,(fres.fvalue, fres.pvalue, fres.df_denom, fres.df_num))) pairs = np.triu_indices(len(self.unique),1) for ind1,ind2 in zip(*pairs): #replace with group1, group2 in sorted(keys) g1 = self.unique[ind1] g2 = self.unique[ind2] txt.append('F-test for equality of coefficients between group' ' %s and group %s' % (g1, g2)) group = (g1, g2) fres = self.lsjoint.f_test(self.contrasts[group]) txt.append(fres.__str__()) summarytable.append((group,(fres.fvalue, fres.pvalue, fres.df_denom, fres.df_num))) self.summarytable = summarytable return '\n'.join(txt), summarytable def print_summary(res): '''printable string of summary ''' groupind = res.groups #res.fitjoint() #not really necessary, because called by ftest_summary if hasattr(res, 'self.summarytable'): summtable = self.summarytable else: _, summtable = res.ftest_summary() txt = '' #print ft[0] #skip because table is nicer templ = \ '''Table of F-tests for overall or pairwise equality of coefficients' %(tab)s Notes: p-values are not corrected for many tests (no Bonferroni correction) * : reject at 5%% uncorrected confidence level Null hypothesis: all or pairwise coefficient are the same' Alternative hypothesis: all coefficients are different' Comparison with stats.f_oneway %(statsfow)s Likelihood Ratio Test %(lrtest)s Null model: pooled all coefficients are the same across groups,' Alternative model: all coefficients are allowed to be different' not verified but looks close to f-test result' Ols parameters by group from individual, separate ols regressions' %(olsbg)s for group in sorted(res.olsbygroup): r = res.olsbygroup[group] print group, r.params Check for heteroscedasticity, ' variance and standard deviation for individual regressions' %(grh)s variance ', res.sigmabygroup standard dev', np.sqrt(res.sigmabygroup) ''' from statsmodels.iolib import SimpleTable resvals = {} resvals['tab'] = str(SimpleTable([(['%r'%(row[0],)] + list(row[1]) + ['*']*(row[1][1]>0.5).item() ) for row in summtable], headers=['pair', 'F-statistic','p-value','df_denom', 'df_num'])) resvals['statsfow'] = str(stats.f_oneway(*[res.endog[groupind==gr] for gr in res.unique])) #resvals['lrtest'] = str(res.lr_test()) resvals['lrtest'] = str(SimpleTable([res.lr_test()], headers=['likelihood ratio', 'p-value', 'df'] )) resvals['olsbg'] = str(SimpleTable([[group] + res.olsbygroup[group].params.tolist() for group in sorted(res.olsbygroup)])) resvals['grh'] = str(SimpleTable(np.vstack([res.sigmabygroup, np.sqrt(res.sigmabygroup)]), headers=res.unique.tolist())) return templ % resvals # a variation of this has been added to RegressionResults as compare_lr def lr_test(self): '''generic likelihood ration test between nested models \begin{align} D & = -2(\ln(\text{likelihood for null model}) - \ln(\text{likelihood for alternative model})) \\ & = -2\ln\left( \frac{\text{likelihood for null model}}{\text{likelihood for alternative model}} \right). \end{align} is distributed as chisquare with df equal to difference in number of parameters or equivalently difference in residual degrees of freedom (sign?) TODO: put into separate function ''' if not hasattr(self, 'lsjoint'): self.fitjoint() if not hasattr(self, 'lspooled'): self.fitpooled() loglikejoint = self.lsjoint.llf loglikepooled = self.lspooled.llf lrstat = -2*(loglikepooled - loglikejoint) #??? check sign lrdf = self.lspooled.df_resid - self.lsjoint.df_resid lrpval = stats.chi2.sf(lrstat, lrdf) return lrstat, lrpval, lrdf statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/penalized.py000066400000000000000000000274021224417117700266640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """linear model with Theil prior probabilistic restrictions, generalized Ridge Created on Tue Dec 20 00:10:10 2011 Author: Josef Perktold License: BSD-3 open issues * selection of smoothing factor, strength of prior, cross validation * GLS, does this really work this way * None of inherited results have been checked yet, I'm not sure if any need to be adjusted or if only interpretation changes One question is which results are based on likelihood (residuals) and which are based on "posterior" as for example bse and cov_params * helper functions to construct priors? * increasing penalization for ordered regressors, e.g. polynomials * compare with random/mixed effects/coefficient, like estimated priors there is something fishy with the result instance, some things, e.g. normalized_cov_params, don't look like they update correctly as we search over lambda -> some stale state again ? I added df_model to result class using the hatmatrix, but df_model is defined in model instance not in result instance. -> not clear where refactoring should occur. df_resid doesn't get updated correctly. problem with definition of df_model, it has 1 subtracted for constant """ import numpy as np import statsmodels.base.model as base from statsmodels.regression.linear_model import OLS, GLS, RegressionResults def atleast_2dcols(x): x = np.asarray(x) if x.ndim == 1: x = x[:,None] return x class TheilGLS(GLS): '''GLS with probabilistic restrictions essentially Bayes with informative prior note: I'm making up the GLS part, might work only for OLS ''' def __init__(self, endog, exog, r_matrix, q_matrix=None, sigma_prior=None, sigma=None): self.r_matrix = np.asarray(r_matrix) self.q_matrix = atleast_2dcols(q_matrix) if np.size(sigma_prior) == 1: sigma_prior = sigma_prior * np.eye(self.r_matrix.shape[0]) #no numerical shortcuts self.sigma_prior = sigma_prior self.sigma_prior_inv = np.linalg.pinv(sigma_prior) #or inv super(self.__class__, self).__init__(endog, exog, sigma=sigma) def fit(self, lambd=1.): #this does duplicate transformation, but I need resid not wresid res_gls = GLS(self.endog, self.exog, sigma=self.sigma).fit() self.res_gls = res_gls sigma2_e = res_gls.mse_resid r_matrix = self.r_matrix q_matrix = self.q_matrix sigma_prior_inv = self.sigma_prior_inv x = self.wexog y = self.wendog[:,None] #why are sigma2_e * lambd multiplied, not ratio? #larger lambd -> stronger prior (it's not the variance) #print 'lambd inside fit', lambd xpx = np.dot(x.T, x) + \ sigma2_e * lambd * np.dot(r_matrix.T, np.dot(sigma_prior_inv, r_matrix)) xpy = np.dot(x.T, y) + \ sigma2_e * lambd * np.dot(r_matrix.T, np.dot(sigma_prior_inv, q_matrix)) #xpy = xpy[:,None] xpxi = np.linalg.pinv(xpx) params = np.dot(xpxi, xpy) #or solve params = np.squeeze(params) self.normalized_cov_params = xpxi #why attach it to self, i.e. model? lfit = TheilRegressionResults(self, params, normalized_cov_params=xpxi) lfit.penalization_factor = lambd return lfit def fit_minic(self): #this doesn't make sense, since number of parameters stays unchanged #need leave-one-out, gcv; or some penalization for weak priors #added extra penalization for lambd def get_bic(lambd): #return self.fit(lambd).bic #+lambd #+ 1./lambd #added 1/lambd for checking #return self.fit(lambd).gcv() #return self.fit(lambd).cv() return self.fit(lambd).aicc() from scipy import optimize lambd = optimize.fmin(get_bic, 1.) return lambd #TODO: #I need the hatmatrix in the model if I want to do iterative fitting, e.g. GCV #move to model or use it from a results instance inside the model, # each call to fit returns results instance class TheilRegressionResults(RegressionResults): #cache def hatmatrix_diag(self): ''' diag(X' xpxi X) where xpxi = (X'X + lambd * sigma_prior)^{-1} Notes ----- uses wexog, so this includes weights or sigma - check this case not clear whether I need to multiply by sigmahalf, i.e. (W^{-0.5} X) (X' W X)^{-1} (W^{-0.5} X)' or (W X) (X' W X)^{-1} (W X)' projection y_hat = H y or in terms of transformed variables (W^{-0.5} y) might be wrong for WLS and GLS case ''' xpxi = self.model.normalized_cov_params #something fishy with self.normalized_cov_params in result, doesn't update #print self.model.wexog.shape, np.dot(xpxi, self.model.wexog.T).shape return (self.model.wexog * np.dot(xpxi, self.model.wexog.T).T).sum(1) def hatmatrix_trace(self): return self.hatmatrix_diag().sum() #this doesn't update df_resid @property #needs to be property or attribute (no call) def df_model(self): return self.hatmatrix_trace() #Note: mse_resid uses df_resid not nobs-k_vars, which might differ if df_model, tr(H), is used #in paper for gcv ess/nobs is used instead of mse_resid def gcv(self): return self.mse_resid / (1. - self.hatmatrix_trace() / self.nobs)**2 def cv(self): return ((self.resid / (1. - self.hatmatrix_diag()))**2).sum() / self.nobs def aicc(self): aic = np.log(self.mse_resid) + 1 aic += 2 * (1. + self.hatmatrix_trace()) / (self.nobs - self.hatmatrix_trace() -2) return aic #contrast/restriction matrices, temporary location def coef_restriction_meandiff(n_coeffs, n_vars=None, position=0): reduced = np.eye(n_coeffs) - 1./n_coeffs if n_vars is None: return reduced else: full = np.zeros((n_coeffs, n_vars)) full[:, position:position+n_coeffs] = reduced return full def coef_restriction_diffbase(n_coeffs, n_vars=None, position=0, base_idx=0): reduced = -np.eye(n_coeffs) #make all rows, drop one row later reduced[:, base_idx] = 1 keep = range(n_coeffs) del keep[base_idx] reduced = np.take(reduced, keep, axis=0) if n_vars is None: return reduced else: full = np.zeros((n_coeffs-1, n_vars)) full[:, position:position+n_coeffs] = reduced return full def next_odd(d): return d + (1 - d % 2) def coef_restriction_diffseq(n_coeffs, degree=1, n_vars=None, position=0, base_idx=0): #check boundaries, returns "valid" ? if degree == 1: diff_coeffs = [-1, 1] n_points = 2 elif degree > 1: from scipy import misc n_points = next_odd(degree + 1) #next odd integer after degree+1 diff_coeffs = misc.central_diff_weights(n_points, ndiv=degree) dff = np.concatenate((diff_coeffs, np.zeros(n_coeffs - len(diff_coeffs)))) from scipy import linalg reduced = linalg.toeplitz(dff, np.zeros(n_coeffs - len(diff_coeffs) + 1)).T #reduced = np.kron(np.eye(n_coeffs-n_points), diff_coeffs) if n_vars is None: return reduced else: full = np.zeros((n_coeffs-1, n_vars)) full[:, position:position+n_coeffs] = reduced return full ## ## R = np.c_[np.zeros((n_groups, k_vars-1)), np.eye(n_groups)] ## r = np.zeros(n_groups) ## R = np.c_[np.zeros((n_groups-1, k_vars)), ## np.eye(n_groups-1)-1./n_groups * np.ones((n_groups-1, n_groups-1))] if __name__ == '__main__': import numpy as np import statsmodels.api as sm examples = [2] np.random.seed(765367) np.random.seed(97653679) nsample = 100 x = np.linspace(0,10, nsample) X = sm.add_constant(np.column_stack((x, x**2, (x/5.)**3)), prepend=True) beta = np.array([10, 1, 0.1, 0.5]) y = np.dot(X, beta) + np.random.normal(size=nsample) res_ols = sm.OLS(y, X).fit() R = [[0, 0, 0 , 1]] r = [0] #, 0, 0 , 0] lambd = 1 #1e-4 mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd) res = mod.fit() print res_ols.params print res.params #example 2 #I need more flexible penalization in example, the penalization should #get stronger for higher order terms #np.random.seed(1) nobs = 200 k_vars = 10 k_true = 6 sig_e = 0.25 #0.5 x = np.linspace(-2,2, nobs) #X = sm.add_constant(np.column_stack((x, x**2, (x/5.)**3)), prepend=True) X = (x/x.max())[:,None]**np.arange(k_vars) beta = np.zeros(k_vars) beta[:k_true] = np.array([1, -2, 0.5, 1.5, -0.1, 0.1])[:k_true] y_true = np.dot(X, beta) y = y_true + sig_e * np.random.normal(size=nobs) res_ols = sm.OLS(y, X).fit() #R = np.c_[np.zeros((k_vars-4, 4)), np.eye(k_vars-4)] # has two large true coefficients penalized not_penalized = 4 R = np.c_[np.zeros((k_vars-not_penalized, not_penalized)), np.eye(k_vars-not_penalized)] #increasingly strong penalization R = np.c_[np.zeros((k_vars-not_penalized, not_penalized)), np.diag((1+2*np.arange(k_vars-not_penalized)))] r = np.zeros(k_vars-not_penalized) ## R = -coef_restriction_diffseq(6, 1, n_vars=10, position=4) #doesn't make sense for polynomial ## R = np.vstack((R, np.zeros(R.shape[1]))) ## R[-1,-1] = 1 r = np.zeros(R.shape[0]) lambd = 2 #1e-4 mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd) res = mod.fit() print res_ols.params print res.params res_bic = mod.fit_minic() #this will just return zero res = mod.fit(res_bic) print res_bic for lambd in np.linspace(0, 80, 21): res_l = mod.fit(lambd) #print lambd, res_l.params[-2:], res_l.bic, res_l.bic + 1./lambd, res.df_model print lambd, res_l.params[-2:], res_l.bic, res.df_model, np.trace(res.normalized_cov_params) import matplotlib.pyplot as plt plt.figure() plt.plot(beta, 'k-o', label='true') plt.plot(res_ols.params, '-o', label='ols') plt.plot(res.params, '-o', label='theil') plt.legend() plt.title('Polynomial fitting: estimated coefficients') plt.figure() plt.plot(y, 'o') plt.plot(y_true, 'k-', label='true') plt.plot(res_ols.fittedvalues, '-', label='ols') plt.plot(res.fittedvalues, '-', label='theil') plt.legend() plt.title('Polynomial fitting: fitted values') #plt.show() if 3 in examples: #example 3 nobs = 600 nobs_i = 20 n_groups = nobs // nobs_i k_vars = 3 from statsmodels.sandbox.panel.random_panel import PanelSample dgp = PanelSample(nobs, k_vars, n_groups) dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept print 'seed', dgp.seed y = dgp.generate_panel() X = np.column_stack((dgp.exog[:,1:], dgp.groups[:,None] == np.arange(n_groups))) res_ols = sm.OLS(y, X).fit() R = np.c_[np.zeros((n_groups, k_vars-1)), np.eye(n_groups)] r = np.zeros(n_groups) R = np.c_[np.zeros((n_groups-1, k_vars)), np.eye(n_groups-1)-1./n_groups * np.ones((n_groups-1, n_groups-1))] r = np.zeros(n_groups-1) R[:, k_vars-1] = -1 lambd = 1 #1e-4 mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd) res = mod.fit() print res.params params_l = [] for lambd in np.linspace(0, 20, 21): params_l.append(mod.fit(5.*lambd).params) params_l = np.array(params_l) plt.figure() plt.plot(params_l.T) plt.title('Panel Data with random intercept: shrinkage to being equal') plt.xlabel('parameter index') plt.figure() plt.plot(params_l[:,k_vars:]) plt.title('Panel Data with random intercept: shrinkage to being equal') plt.xlabel('strength of prior') #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/predstd.py000066400000000000000000000076461224417117700263660ustar00rootroot00000000000000'''Additional functions prediction standard errors and confidence intervals A: josef pktd ''' import numpy as np from scipy import stats def atleast_2dcol(x): ''' convert array_like to 2d from 1d or 0d not tested because not used ''' x = np.asarray(x) if (x.ndim == 1): x = x[:, None] elif (x.ndim == 0): x = np.atleast_2d(x) elif (x.ndim > 0): raise ValueError('too many dimensions') return x def wls_prediction_std(res, exog=None, weights=None, alpha=0.05): '''calculate standard deviation and confidence interval for prediction applies to WLS and OLS, not to general GLS, that is independently but not identically distributed observations Parameters ---------- res : regression result instance results of WLS or OLS regression required attributes see notes exog : array_like (optional) exogenous variables for points to predict weights : scalar or array_like (optional) weights as defined for WLS (inverse of variance of observation) alpha : float (default: alpha = 0.05) confidence level for two-sided hypothesis Returns ------- predstd : array_like, 1d standard error of prediction same length as rows of exog interval_l, interval_u : array_like lower und upper confidence bounds Notes ----- The result instance needs to have at least the following res.model.predict() : predicted values or res.fittedvalues : values used in estimation res.cov_params() : covariance matrix of parameter estimates If exog is 1d, then it is interpreted as one observation, i.e. a row vector. testing status: not compared with other packages References ---------- Greene p.111 for OLS, extended to WLS by analogy ''' # work around current bug: # fit doesn't attach results to model, predict broken #res.model.results covb = res.cov_params() if exog is None: exog = res.model.exog predicted = res.fittedvalues else: exog = np.atleast_2d(exog) if covb.shape[1] != exog.shape[1]: raise ValueError('wrong shape of exog') predicted = res.model.predict(res.params, exog) if weights is None: weights = res.model.weights # full covariance: #predvar = res3.mse_resid + np.diag(np.dot(X2,np.dot(covb,X2.T))) # predication variance only predvar = res.mse_resid/weights + (exog * np.dot(covb, exog.T).T).sum(1) predstd = np.sqrt(predvar) tppf = stats.t.isf(alpha/2., res.df_resid) interval_u = predicted + tppf * predstd interval_l = predicted - tppf * predstd return predstd, interval_l, interval_u if __name__ == '__main__': import statsmodels.api as sm # generate dataset nsample = 50 x1 = np.linspace(0, 20, nsample) X = np.c_[x1, (x1-5)**2, np.ones(nsample)] np.random.seed(0)#9876789) #9876543) beta = [0.5, -0.01, 5.] y_true2 = np.dot(X, beta) w = np.ones(nsample) w[nsample*6/10:] = 3 sig = 0.5 y2 = y_true2 + sig*w* np.random.normal(size=nsample) X2 = X[:,[0,2]] # estimate OLS, WLS, (OLS not used in these tests) res2 = sm.OLS(y2, X2).fit() res3 = sm.WLS(y2, X2, 1./w).fit() #direct calculation covb = res3.cov_params() predvar = res3.mse_resid*w + (X2 * np.dot(covb,X2.T).T).sum(1) predstd = np.sqrt(predvar) prstd, iv_l, iv_u = wls_prediction_std(res3) np.testing.assert_almost_equal(predstd, prstd, 15) # testing shapes of exog prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1:,:], weights=3.) np.testing.assert_equal( prstd[-1], prstd) prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1,:], weights=3.) np.testing.assert_equal( prstd[-1], prstd) #use wrong size for exog #prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1,0], weights=3.) np.testing.assert_raises(ValueError, wls_prediction_std, res3, X2[-1,0], weights=3.) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/runmnl.py000066400000000000000000000271641224417117700262310ustar00rootroot00000000000000'''conditional logit and nested conditional logit nested conditional logit is supposed to be the random utility version (RU2 and maybe RU1) References: ----------- currently based on: Greene, Econometric Analysis, 5th edition and draft (?) Hess, Florian, 2002, Structural Choice analysis with nested logit models, The Stats Journal 2(3) pp 227-252 not yet used: Silberhorn Nadja, Yasemin Boztug, Lutz Hildebrandt, 2008, Estimation with the nested logit model: specifications and software particularities, OR Spectrum Koppelman, Frank S., and Chandra Bhat with technical support from Vaneet Sethi, Sriram Subramanian, Vincent Bernardin and Jian Zhang, 2006, A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models Author: josef-pktd License: BSD (simplified) ''' import numpy as np import numpy.lib.recfunctions as recf class TryCLogit(object): ''' Conditional Logit, data handling test Parameters ---------- endog : array (nobs,nchoices) dummy encoding of realized choices exog_bychoices : list of arrays explanatory variables, one array of exog for each choice. Variables with common coefficients have to be first in each array ncommon : int number of explanatory variables with common coefficients Notes ----- Utility for choice j is given by $V_j = X_j * beta + Z * gamma_j$ where X_j contains generic variables (terminology Hess) that have the same coefficient across choices, and Z are variables, like individual-specific variables that have different coefficients across variables. If there are choice specific constants, then they should be contained in Z. For identification, the constant of one choice should be dropped. ''' def __init__(self, endog, exog_bychoices, ncommon): self.endog = endog self.exog_bychoices = exog_bychoices self.ncommon = ncommon self.nobs, self.nchoices = endog.shape self.nchoices = len(exog_bychoices) #TODO: rename beta to params and include inclusive values for nested CL betaind = [exog_bychoices[ii].shape[1]-ncommon for ii in range(4)] zi = np.r_[[ncommon], ncommon + np.array(betaind).cumsum()] beta_indices = [np.r_[np.array([0, 1]),z[zi[ii]:zi[ii+1]]] for ii in range(len(zi)-1)] self.beta_indices = beta_indices #for testing only beta = np.arange(7) betaidx_bychoices = [beta[idx] for idx in beta_indices] def xbetas(self, params): '''these are the V_i ''' res = np.empty((self.nobs, self.nchoices)) for choiceind in range(self.nchoices): res[:,choiceind] = np.dot(self.exog_bychoices[choiceind], params[self.beta_indices[choiceind]]) return res def loglike(self, params): #normalization ? xb = self.xbetas(params) expxb = np.exp(xb) sumexpxb = expxb.sum(1)#[:,None] probs = expxb/expxb.sum(1)[:,None] #we don't really need this for all loglike = (self.endog * np.log(probs)).sum(1) #is this the same: YES #self.logliketest = (self.endog * xb).sum(1) - np.log(sumexpxb) #if self.endog where index then xb[self.endog] return -loglike.sum() #return sum for now not for each observation def fit(self, start_params=None): if start_params is None: start_params = np.zeros(6) # need better np.zeros(6) return optimize.fmin(self.loglike, start_params, maxfun=10000) class TryNCLogit(object): ''' Nested Conditional Logit (RUNMNL), data handling test unfinished, doesn't do anything yet ''' def __init__(self, endog, exog_bychoices, ncommon): self.endog = endog self.exog_bychoices = exog_bychoices self.ncommon = ncommon self.nobs, self.nchoices = endog.shape self.nchoices = len(exog_bychoices) #TODO rename beta to params and include inclusive values for nested CL betaind = [exog_bychoices[ii].shape[1]-ncommon for ii in range(4)] zi = np.r_[[ncommon], ncommon + np.array(betaind).cumsum()] beta_indices = [np.r_[np.array([0, 1]),z[zi[ii]:zi[ii+1]]] for ii in range(len(zi)-1)] self.beta_indices = beta_indices #for testing only beta = np.arange(7) betaidx_bychoices = [beta[idx] for idx in beta_indices] def xbetas(self, params): '''these are the V_i ''' res = np.empty((self.nobs, self.nchoices)) for choiceind in range(self.nchoices): res[:,choiceind] = np.dot(self.exog_bychoices[choiceind], params[self.beta_indices[choiceind]]) return res def loglike_leafbranch(self, params, tau): #normalization ? #check/change naming for tau xb = self.xbetas(params) expxb = np.exp(xb/tau) sumexpxb = expxb.sum(1)#[:,None] logsumexpxb = np.log(sumexpxb) #loglike = (self.endog * xb).sum(1) - logsumexpxb probs = expxb/sumexpxb[:,None] return probs, logsumexpxp #if self.endog where index then xb[self.endog] #return -loglike.sum() #return sum for now not for each observation def loglike_branch(self, params, tau): #not yet sure how to keep track of branches during walking of tree ivs = [] for b in branches: probs, iv = loglike_leafbranch(self, params, tau) ivs.append(iv) #ivs = np.array(ivs) #note ivs is (nobs,nbranchchoices) ivs = np.column_stack(ivs) # this way ? exptiv = np.exp(tau*ivs) sumexptiv = exptiv.sum(1) logsumexpxb = np.log(sumexpxb) probs = exptiv/sumexptiv[:,None] ####### obsolete version to try out attaching data, ####### new in treewalkerclass.py, copy new version to replace this ####### problem with bzr I will disconnect history when copying testxb = 0 #global to class class RU2NMNL(object): '''Nested Multinomial Logit with Random Utility 2 parameterization ''' def __init__(self, endog, exog, tree, paramsind): self.endog = endog self.datadict = exog self.tree = tree self.paramsind = paramsind self.branchsum = '' self.probs = {} def calc_prob(self, tree, keys=None): '''walking a tree bottom-up based on dictionary ''' endog = self.endog datadict = self.datadict paramsind = self.paramsind branchsum = self.branchsum if type(tree) == tuple: #assumes leaves are int for choice index name, subtree = tree print name, datadict[name] print 'subtree', subtree keys = [] if testxb: branchsum = datadict[name] else: branchsum = name #0 for b in subtree: print b #branchsum += branch2(b) branchsum = branchsum + self.calc_prob(b, keys) print 'branchsum', branchsum, keys for k in keys: self.probs[k] = self.probs[k] + ['*' + name + '-prob'] else: keys.append(tree) self.probs[tree] = [tree + '-prob' + '(%s)' % ', '.join(self.paramsind[tree])] if testxb: leavessum = sum((datadict[bi] for bi in tree)) print 'final branch with', tree, ''.join(tree), leavessum #sum(tree) return leavessum #sum(xb[tree]) else: return ''.join(tree) #sum(tree) print 'working on branch', tree, branchsum return branchsum #Trying out ways to handle data #------------------------------ #travel data from Greene dta = np.genfromtxt('TableF23-2.txt', skip_header=1, names='Mode Ttme Invc Invt GC Hinc PSize'.split()) endog = dta['Mode'].reshape(-1,4).copy() #I don't want a view nobs, nchoices = endog.shape datafloat = dta.view(float).reshape(-1,7) exog = datafloat[:,1:].reshape(-1,6*nchoices).copy() #I don't want a view print endog.sum(0) varnames = dta.dtype.names print varnames[1:] modes = ['Air', 'Train', 'Bus', 'Car'] print exog.mean(0).reshape(nchoices, -1) # Greene Table 23.23 #try dummy encoding for individual-specific variables exog_choice_names = ['GC', 'Ttme'] exog_choice = np.column_stack([dta[name] for name in exog_choice_names]) exog_choice = exog_choice.reshape(-1,len(exog_choice_names)*nchoices) exog_choice = np.c_[endog, exog_choice] # add constant dummy exog_individual = dta['Hinc'][:,None] #exog2 = np.c_[exog_choice, exog_individual*endog] # we can also overwrite and select in original datafloat # e.g. Hinc*endog{choice) choice_index = np.arange(dta.shape[0]) % nchoices hinca = dta['Hinc']*(choice_index==0) dta2=recf.append_fields(dta, ['Hinca'],[hinca], usemask=False) #another version xi = [] for ii in range(4): xi.append(datafloat[choice_index==ii]) #one more dta1 = recf.append_fields(dta, ['Const'],[np.ones(dta.shape[0])], usemask=False) xivar = [['GC', 'Ttme', 'Const', 'Hinc'], ['GC', 'Ttme', 'Const'], ['GC', 'Ttme', 'Const'], ['GC', 'Ttme']] #need to drop one constant xi = [] for ii in range(4): xi.append(dta1[xivar[ii]][choice_index==ii]) #this doesn't change sequence of columns, bug report by Skipper I think ncommon = 2 betaind = [len(xi[ii].dtype.names)-ncommon for ii in range(4)] zi=np.r_[[ncommon], ncommon+np.array(betaind).cumsum()] z=np.arange(7) #what is n? betaindices = [np.r_[np.array([0, 1]),z[zi[ii]:zi[ii+1]]] for ii in range(len(zi)-1)] beta = np.arange(7) betai = [beta[idx] for idx in betaindices] #examples for TryCLogit #---------------------- #get exogs as float xifloat = [xx.view(float).reshape(nobs,-1) for xx in xi] clogit = TryCLogit(endog, xifloat, 2) from scipy import optimize debug = 0 if debug: res = optimize.fmin(clogit.loglike, np.ones(6)) #estimated parameters from Greene: tab2324 = [-0.15501, -0.09612, 0.01329, 5.2074, 3.8690, 3.1632] if debug: res2 = optimize.fmin(clogit.loglike, tab2324) res3 = optimize.fmin(clogit.loglike, np.zeros(6),maxfun=10000) #this has same numbers as Greene table 23.24, but different sequence #coefficient on GC is exactly 10% of Greene's #TODO: get better starting values ''' Optimization terminated successfully. Current function value: 199.128369 Iterations: 957 Function evaluations: 1456 array([-0.0961246 , -0.0155019 , 0.01328757, 5.20741244, 3.86905293, 3.16319074]) ''' res3corr = res3[[1, 0, 2, 3, 4, 5]] res3corr[0] *= 10 print res3corr - tab2324 # diff 1e-5 to 1e-6 #199.128369 - 199.1284 #llf same up to print precision of Greene print clogit.fit() tree0 = ('top', [('Fly',['Air']), ('Ground', ['Train', 'Car', 'Bus']) ] ) datadict = dict(zip(['Air', 'Train', 'Bus', 'Car'], [xifloat[i]for i in range(4)])) #for testing only (mock that returns it's own name datadict = dict(zip(['Air', 'Train', 'Bus', 'Car'], ['Airdata', 'Traindata', 'Busdata', 'Cardata'])) datadict.update({'top' : [], 'Fly' : [], 'Ground': []}) paramsind = {'top' : [], 'Fly' : [], 'Ground': [], 'Air' : ['GC', 'Ttme', 'ConstA', 'Hinc'], 'Train' : ['GC', 'Ttme', 'ConstT'], 'Bus' : ['GC', 'Ttme', 'ConstB'], 'Car' : ['GC', 'Ttme'] } modru = RU2NMNL(endog, datadict, tree0, paramsind) print modru.calc_prob(modru.tree) print '\nmodru.probs' print modru.probs statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/sympy_diff.py000066400000000000000000000032661224417117700270640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Mar 13 07:56:22 2010 Author: josef-pktd """ import sympy as sy def pdf(x, mu, sigma): """Return the probability density function as an expression in x""" #x = sy.sympify(x) return 1/(sigma*sy.sqrt(2*sy.pi)) * sy.exp(-(x-mu)**2 / (2*sigma**2)) def cdf(x, mu, sigma): """Return the cumulative density function as an expression in x""" #x = sy.sympify(x) return (1+sy.erf((x-mu)/(sigma*sy.sqrt(2))))/2 mu = sy.Symbol('mu') sigma = sy.Symbol('sigma') sigma2 = sy.Symbol('sigma2') x = sy.Symbol('x') y = sy.Symbol('y') df = sy.Symbol('df') s = sy.Symbol('s') dldxnorm = sy.log(pdf(x, mu,sigma)).diff(x) print sy.simplify(dldxnorm) print sy.diff(sy.log(sy.gamma((s+1)/2)),s) print sy.diff((df+1)/2. * sy.log(1+df/(df-2)), df) #standard t distribution, not verified tllf1 = sy.log(sy.gamma((df+1)/2.)) - sy.log(sy.gamma(df/2.)) - 0.5*sy.log((df)*sy.pi) tllf2 = (df+1.)/2. * sy.log(1. + (y-mu)**2/(df)/sigma2) + 0.5 * sy.log(sigma2) tllf2std = (df+1.)/2. * sy.log(1. + y**2/df) + 0.5 tllf = tllf1 - tllf2 print tllf1.diff(df) print tllf2.diff(y) dlddf = (tllf1-tllf2).diff(df) print dlddf print sy.cse(dlddf) print '\n derivative of loglike of t distribution wrt df' for k,v in sy.cse(dlddf)[0]: print k,'=',v print sy.cse(dlddf)[1][0] print '\nstandard t distribution, dll_df, dll_dy' tllfstd = tllf1 - tllf2std print tllfstd.diff(df) print tllfstd.diff(y) print '\n' print dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001)) print dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001)).evalf() # Note: derivatives of nested function doesn't work in sympy # at least not higher order derivatives (second or larger) # looks like print failure statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/tools.py000066400000000000000000000314151224417117700260500ustar00rootroot00000000000000'''gradient/Jacobian of normal and t loglikelihood use chain rule normal derivative wrt mu, sigma and beta new version: loc-scale distributions, derivative wrt loc, scale also includes "standardized" t distribution (for use in GARCH) TODO: * use sympy for derivative of loglike wrt shape parameters it works for df of t distribution dlog(gamma(a))da = polygamma(0,a) check polygamma is available in scipy.special * get loc-scale example to work with mean = X*b * write some full unit test examples A: josef-pktd ''' import numpy as np from scipy import special from scipy.special import gammaln def norm_lls(y, params): '''normal loglikelihood given observations and mean mu and variance sigma2 Parameters ---------- y : array, 1d normally distributed random variable params: array, (nobs, 2) array of mean, variance (mu, sigma2) with observations in rows Returns ------- lls : array contribution to loglikelihood for each observation ''' mu, sigma2 = params.T lls = -0.5*(np.log(2*np.pi) + np.log(sigma2) + (y-mu)**2/sigma2) return lls def norm_lls_grad(y, params): '''Jacobian of normal loglikelihood wrt mean mu and variance sigma2 Parameters ---------- y : array, 1d normally distributed random variable params: array, (nobs, 2) array of mean, variance (mu, sigma2) with observations in rows Returns ------- grad : array (nobs, 2) derivative of loglikelihood for each observation wrt mean in first column, and wrt variance in second column Notes ----- this is actually the derivative wrt sigma not sigma**2, but evaluated with parameter sigma2 = sigma**2 ''' mu, sigma2 = params.T dllsdmu = (y-mu)/sigma2 dllsdsigma2 = ((y-mu)**2/sigma2 - 1)/np.sqrt(sigma2) return np.column_stack((dllsdmu, dllsdsigma2)) def mean_grad(x, beta): '''gradient/Jacobian for d (x*beta)/ d beta ''' return x def normgrad(y, x, params): '''Jacobian of normal loglikelihood wrt mean mu and variance sigma2 Parameters ---------- y : array, 1d normally distributed random variable with mean x*beta, and variance sigma2 x : array, 2d explanatory variables, observation in rows, variables in columns params: array_like, (nvars + 1) array of coefficients and variance (beta, sigma2) Returns ------- grad : array (nobs, 2) derivative of loglikelihood for each observation wrt mean in first column, and wrt scale (sigma) in second column assume params = (beta, sigma2) Notes ----- TODO: for heteroscedasticity need sigma to be a 1d array ''' beta = params[:-1] sigma2 = params[-1]*np.ones((len(y),1)) dmudbeta = mean_grad(x, beta) mu = np.dot(x, beta) #print beta, sigma2 params2 = np.column_stack((mu,sigma2)) dllsdms = norm_lls_grad(y,params2) grad = np.column_stack((dllsdms[:,:1]*dmudbeta, dllsdms[:,:1])) return grad def tstd_lls(y, params, df): '''t loglikelihood given observations and mean mu and variance sigma2 = 1 Parameters ---------- y : array, 1d normally distributed random variable params: array, (nobs, 2) array of mean, variance (mu, sigma2) with observations in rows df : integer degrees of freedom of the t distribution Returns ------- lls : array contribution to loglikelihood for each observation Notes ----- parameterized for garch ''' mu, sigma2 = params.T df = df*1.0 #lls = gammaln((df+1)/2.) - gammaln(df/2.) - 0.5*np.log((df-2)*np.pi) #lls -= (df+1)/2. * np.log(1. + (y-mu)**2/(df-2.)/sigma2) + 0.5 * np.log(sigma2) lls = gammaln((df+1)/2.) - gammaln(df/2.) - 0.5*np.log((df-2)*np.pi) lls -= (df+1)/2. * np.log(1. + (y-mu)**2/(df-2)/sigma2) + 0.5 * np.log(sigma2) return lls def norm_dlldy(y): '''derivative of log pdf of standard normal with respect to y ''' return -y def ts_dlldy(y, df): '''derivative of log pdf of standardized (?) t with respect to y Notes ----- parameterized for garch, with mean 0 and variance 1 ''' #(df+1)/2. / (1 + y**2/(df-2.)) * 2.*y/(df-2.) #return -(df+1)/(df-2.) / (1 + y**2/(df-2.)) * y return -(df+1)/(df) / (1 + y**2/(df)) * y def tstd_pdf(x, df): '''pdf for standardized (not standard) t distribution, variance is one ''' r = np.array(df*1.0) Px = np.exp(special.gammaln((r+1)/2.)-special.gammaln(r/2.))/np.sqrt((r-2)*pi) Px /= (1+(x**2)/(r-2))**((r+1)/2.) return Px def ts_lls(y, params, df): '''t loglikelihood given observations and mean mu and variance sigma2 = 1 Parameters ---------- y : array, 1d normally distributed random variable params: array, (nobs, 2) array of mean, variance (mu, sigma2) with observations in rows df : integer degrees of freedom of the t distribution Returns ------- lls : array contribution to loglikelihood for each observation Notes ----- parameterized for garch normalized/rescaled so that sigma2 is the variance >>> df = 10; sigma = 1. >>> stats.t.stats(df, loc=0., scale=sigma.*np.sqrt((df-2.)/df)) (array(0.0), array(1.0)) >>> sigma = np.sqrt(2.) >>> stats.t.stats(df, loc=0., scale=sigma*np.sqrt((df-2.)/df)) (array(0.0), array(2.0)) ''' print y, params, df mu, sigma2 = params.T df = df*1.0 #lls = gammaln((df+1)/2.) - gammaln(df/2.) - 0.5*np.log((df-2)*np.pi) #lls -= (df+1)/2. * np.log(1. + (y-mu)**2/(df-2.)/sigma2) + 0.5 * np.log(sigma2) lls = gammaln((df+1)/2.) - gammaln(df/2.) - 0.5*np.log((df)*np.pi) lls -= (df+1.)/2. * np.log(1. + (y-mu)**2/(df)/sigma2) + 0.5 * np.log(sigma2) return lls def ts_dlldy(y, df): '''derivative of log pdf of standard t with respect to y Parameters ---------- y : array_like data points of random variable at which loglike is evaluated df : array_like degrees of freedom,shape parameters of log-likelihood function of t distribution Returns ------- dlldy : array derivative of loglikelihood wrt random variable y evaluated at the points given in y Notes ----- with mean 0 and scale 1, but variance is df/(df-2) ''' df = df*1. #(df+1)/2. / (1 + y**2/(df-2.)) * 2.*y/(df-2.) #return -(df+1)/(df-2.) / (1 + y**2/(df-2.)) * y return -(df+1)/(df) / (1 + y**2/(df)) * y def tstd_dlldy(y, df): '''derivative of log pdf of standardized t with respect to y Parameters ---------- y : array_like data points of random variable at which loglike is evaluated df : array_like degrees of freedom,shape parameters of log-likelihood function of t distribution Returns ------- dlldy : array derivative of loglikelihood wrt random variable y evaluated at the points given in y Notes ----- parameterized for garch, standardized to variance=1 ''' #(df+1)/2. / (1 + y**2/(df-2.)) * 2.*y/(df-2.) return -(df+1)/(df-2.) / (1 + y**2/(df-2.)) * y #return (df+1)/(df) / (1 + y**2/(df)) * y def locscale_grad(y, loc, scale, dlldy, *args): '''derivative of log-likelihood with respect to location and scale Parameters ---------- y : array_like data points of random variable at which loglike is evaluated loc : float location parameter of distribution scale : float scale parameter of distribution dlldy : function derivative of loglikelihood fuction wrt. random variable x args : array_like shape parameters of log-likelihood function Returns ------- dlldloc : array derivative of loglikelihood wrt location evaluated at the points given in y dlldscale : array derivative of loglikelihood wrt scale evaluated at the points given in y ''' yst = (y-loc)/scale #ystandardized dlldloc = -dlldy(yst, *args) / scale dlldscale = -1./scale - dlldy(yst, *args) * (y-loc)/scale**2 return dlldloc, dlldscale if __name__ == '__main__': verbose = 0 if verbose: sig = 0.1 beta = np.ones(2) rvs = np.random.randn(10,3) x = rvs[:,1:] y = np.dot(x,beta) + sig*rvs[:,0] params = [1,1,1] print normgrad(y, x, params) dllfdbeta = (y-np.dot(x, beta))[:,None]*x #for sigma = 1 print dllfdbeta print locscale_grad(y, np.dot(x, beta), 1, norm_dlldy) print (y-np.dot(x, beta)) from scipy import stats, misc def llt(y,loc,scale,df): return np.log(stats.t.pdf(y, df, loc=loc, scale=scale)) def lltloc(loc,y,scale,df): return np.log(stats.t.pdf(y, df, loc=loc, scale=scale)) def lltscale(scale,y,loc,df): return np.log(stats.t.pdf(y, df, loc=loc, scale=scale)) def llnorm(y,loc,scale): return np.log(stats.norm.pdf(y, loc=loc, scale=scale)) def llnormloc(loc,y,scale): return np.log(stats.norm.pdf(y, loc=loc, scale=scale)) def llnormscale(scale,y,loc): return np.log(stats.norm.pdf(y, loc=loc, scale=scale)) if verbose: print '\ngradient of t' print misc.derivative(llt, 1, dx=1e-6, n=1, args=(0,1,10), order=3) print 't ', locscale_grad(1, 0, 1, tstd_dlldy, 10) print 'ts', locscale_grad(1, 0, 1, ts_dlldy, 10) print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,1,20), order=3), print 'ts', locscale_grad(1.5, 0, 1, ts_dlldy, 20) print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,2,20), order=3), print 'ts', locscale_grad(1.5, 0, 2, ts_dlldy, 20) print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(1,2,20), order=3), print 'ts', locscale_grad(1.5, 1, 2, ts_dlldy, 20) print misc.derivative(lltloc, 1, dx=1e-10, n=1, args=(1.5,2,20), order=3), print misc.derivative(lltscale, 2, dx=1e-10, n=1, args=(1.5,1,20), order=3) y,loc,scale,df = 1.5, 1, 2, 20 print 'ts', locscale_grad(y,loc,scale, ts_dlldy, 20) print misc.derivative(lltloc, loc, dx=1e-10, n=1, args=(y,scale,df), order=3), print misc.derivative(lltscale, scale, dx=1e-10, n=1, args=(y,loc,df), order=3) print '\ngradient of norm' print misc.derivative(llnorm, 1, dx=1e-6, n=1, args=(0,1), order=3) print locscale_grad(1, 0, 1, norm_dlldy) y,loc,scale = 1.5, 1, 2 print 'ts', locscale_grad(y,loc,scale, norm_dlldy) print misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3), print misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3) y,loc,scale = 1.5, 0, 1 print 'ts', locscale_grad(y,loc,scale, norm_dlldy) print misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3), print misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3) #print 'still something wrong with handling of scale and variance' #looks ok now print '\nloglike of t' print tstd_lls(1, np.array([0,1]), 100), llt(1,0,1,100), 'differently standardized' print tstd_lls(1, np.array([0,1]), 10), llt(1,0,1,10), 'differently standardized' print ts_lls(1, np.array([0,1]), 10), llt(1,0,1,10) print tstd_lls(1, np.array([0,1.*10./8.]), 10), llt(1.,0,1.,10) print ts_lls(1, np.array([0,1]), 100), llt(1,0,1,100) print tstd_lls(1, np.array([0,1]), 10), llt(1,0,1.*np.sqrt(8/10.),10) from numpy.testing import assert_almost_equal params =[(0, 1), (1.,1.), (0.,2.), ( 1., 2.)] yt = np.linspace(-2.,2.,11) for loc,scale in params: dlldlo = misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(yt,scale), order=3) dlldsc = misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(yt,loc), order=3) gr = locscale_grad(yt, loc, scale, norm_dlldy) assert_almost_equal(dlldlo, gr[0], 5, err_msg='deriv loc') assert_almost_equal(dlldsc, gr[1], 5, err_msg='deriv scale') for df in [3, 10, 100]: for loc,scale in params: dlldlo = misc.derivative(lltloc, loc, dx=1e-10, n=1, args=(yt,scale,df), order=3) dlldsc = misc.derivative(lltscale, scale, dx=1e-10, n=1, args=(yt,loc,df), order=3) gr = locscale_grad(yt, loc, scale, ts_dlldy, df) assert_almost_equal(dlldlo, gr[0], 4, err_msg='deriv loc') assert_almost_equal(dlldsc, gr[1], 4, err_msg='deriv scale') assert_almost_equal(ts_lls(yt, np.array([loc, scale**2]), df), llt(yt,loc,scale,df), 5, err_msg='loglike') assert_almost_equal(tstd_lls(yt, np.array([loc, scale**2]), df), llt(yt,loc,scale*np.sqrt((df-2.)/df),df), 5, err_msg='loglike') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/treewalkerclass.py000066400000000000000000000514371224417117700301110ustar00rootroot00000000000000''' Formulas -------- This follows mostly Greene notation (in slides) partially ignoring factors tau or mu for now, ADDED (if all tau==1, then runmnl==clogit) leaf k probability : Prob(k|j) = exp(b_k * X_k / mu_j)/ sum_{i in L(j)} (exp(b_i * X_i / mu_j) branch j probabilities : Prob(j) = exp(b_j * X_j + mu*IV_j )/ sum_{i in NB(j)} (exp(b_i * X_i + mu_i*IV_i) inclusive value of branch j : IV_j = log( sum_{i in L(j)} (exp(b_i * X_i / mu_j) ) this is the log of the denominator of the leaf probabilities L(j) : leaves at branch j, where k is child of j NB(j) : set of j and it's siblings Design ------ * splitting calculation transmission between returns and changes to instance.probs - probability for each leaf is in instance.probs - inclusive values and contribution of exog on branch level need to be added separately. handed up the tree through returns * question: should params array be accessed directly through `self.recursionparams[self.parinddict[name]]` or should the dictionary return the values of the params, e.g. `self.params_node_dict[name]`. The second would be easier for fixing tau=1 for degenerate branches. The easiest might be to do the latter only for the taus and default to 1 if the key ('tau_'+branchname) is not found. I also need to exclude tau for degenerate branches from params, but then I cannot change them from the outside for testing and experimentation. (?) * SAS manual describes restrictions on tau (though their model is a bit different), e.g. equal tau across sibling branches, fixed tau. The also allow linear and non-linear (? not sure) restriction on params, the regression coefficients. Related to previous issue, callback without access to the underlying array, where params_node_dict returns the actual params value would provide more flexibility to impose different kinds of restrictions. bugs/problems ------------- * singleton branches return zero to `top`, not a value I'm not sure what they are supposed to return, given the split between returns and instance.probs DONE * Why does 'Air' (singleton branch) get probability exactly 0.5 ? DONE TODO ---- * add tau, normalization for nested logit, currently tau is 1 (clogit) taus also needs to become part of params MOSTLY DONE * add effect of branch level explanatory variables DONE * write a generic multinomial logit that takes arbitrary probabilities, this would be the same for MNL, clogit and runmnl, delegate calculation of probabilities * test on actual data, - tau=1 replicate clogit numbers, - transport example from Greene tests 1-level tree and degenerate sub-trees - test example for multi-level trees ??? * starting values: Greene mentiones that the starting values for the nested version come from the (non-nested) MNL version. SPSS uses constant equal (? check transformation) to sample frequencies and zeros for slope coefficient as starting values for (non-nested) MNL * associated test statistics - (I don't think I will fight with the gradient or hessian of the log-like.) - basic MLE statistics can be generic - tests specific to the model (?) * nice printouts since I'm currently collecting a lot of information in the tree recursion and everything has names The only parts that are really necessary to get a functional nested logit are adding the taus (DONE) and the MLE wrapper class. The rest are enhancements. I added fake tau, one fixed tau for all branches. (OBSOLETE) It's not clear where the tau for leaf should be added either at original assignment of self.probs, or as part of the one-step-down probability correction in the bottom branches. The second would be cleaner (would make treatment of leaves and branches more symmetric, but requires that initial assignment in the leaf only does initialization. e.g self.probs = 1. ??? DONE added taus still todo: - tau for degenerate branches are not identified, set to 1 for MLE - rename parinddict to paramsinddict Author: Josef Perktold License : BSD (3-clause) ''' import numpy as np from pprint import pprint def randintw(w, size=1): '''generate integer random variables given probabilties useful because it can be used as index into any array or sequence type Parameters ---------- w : 1d array_like sequence of weights, probabilites. The weights are normalized to add to one. size : int or tuple of ints shape of output array Returns ------- rvs : array of shape given by size random variables each distributed according to the same discrete distribution defined by (normalized) w. Examples -------- >>> np.random.seed(0) >>> randintw([0.4, 0.4, 0.2], size=(2,6)) array([[1, 1, 1, 1, 1, 1], [1, 2, 2, 0, 1, 1]]) >>> np.bincount(randintw([0.6, 0.4, 0.0], size=3000))/3000. array([ 0.59566667, 0.40433333]) ''' #from Charles Harris, numpy mailing list from numpy.random import random p = np.cumsum(w)/np.sum(w) rvs = p.searchsorted(random(np.prod(size))).reshape(size) return rvs def getbranches(tree): ''' walk tree to get list of branches Parameters ---------- tree : list of tuples tree as defined for RU2NMNL Returns ------- branch : list list of all branch names ''' if type(tree) == tuple: name, subtree = tree a = [name] for st in subtree: a.extend(getbranches(st)) return a return [] def getnodes(tree): ''' walk tree to get list of branches and list of leaves Parameters ---------- tree : list of tuples tree as defined for RU2NMNL Returns ------- branch : list list of all branch names leaves : list list of all leaves names ''' if type(tree) == tuple: name, subtree = tree ab = [name] al = [] #degenerate branches if len(subtree) == 1: adeg = [name] else: adeg = [] for st in subtree: b, l, d = getnodes(st) ab.extend(b) al.extend(l) adeg.extend(d) return ab, al, adeg return [], [tree], [] testxb = 2 #global to class to return strings instead of numbers class RU2NMNL(object): '''Nested Multinomial Logit with Random Utility 2 parameterization Parameters ---------- endog : array not used in this part exog : dict_like dictionary access to data where keys correspond to branch and leaf names. The values are the data arrays for the exog in that node. tree : nested tuples and lists each branch, tree or subtree, is defined by a tuple (branch_name, [subtree1, subtree2, ..., subtreek]) Bottom branches have as subtrees the list of leaf names. paramsind : dictionary dictionary that maps branch and leaf names to the names of parameters, the coefficients for exogs) Methods ------- get_probs Attributes ---------- branches leaves paramsnames parinddict Notes ----- endog needs to be encoded so it is consistent with self.leaves, which defines the columns for the probability array. The ordering in leaves is determined by the ordering of the tree. In the dummy encoding of endog, the columns of endog need to have the same order as self.leaves. In the integer encoding, the integer for a choice has to correspond to the index in self.leaves. (This could be made more robust, by handling the endog encoding internally by leaf names, if endog is defined as categorical variable with associated category level names.) ''' def __init__(self, endog, exog, tree, paramsind): self.endog = endog self.datadict = exog self.tree = tree self.paramsind = paramsind self.branchsum = '' self.probs = {} self.probstxt = {} self.branchleaves = {} self.branchvalues = {} #just to keep track of returns by branches self.branchsums = {} self.bprobs = {} self.branches, self.leaves, self.branches_degenerate = getnodes(tree) self.nbranches = len(self.branches) #copied over but not quite sure yet #unique, parameter array names, #sorted alphabetically, order is/should be only internal self.paramsnames = (sorted(set([i for j in paramsind.values() for i in j])) + ['tau_%s' % bname for bname in self.branches]) self.nparams = len(self.paramsnames) #mapping coefficient names to indices to unique/parameter array self.paramsidx = dict((name, idx) for (idx,name) in enumerate(self.paramsnames)) #mapping branch and leaf names to index in parameter array self.parinddict = dict((k, [self.paramsidx[j] for j in v]) for k,v in self.paramsind.items()) self.recursionparams = 1. + np.arange(len(self.paramsnames)) #for testing that individual parameters are used in the right place self.recursionparams = np.zeros(len(self.paramsnames)) #self.recursionparams[2] = 1 self.recursionparams[-self.nbranches:] = 1 #values for tau's #self.recursionparams[-2] = 2 def get_probs(self, params): ''' obtain the probability array given an array of parameters This is the function that can be called by loglike or other methods that need the probabilities as function of the params. Parameters ---------- params : 1d array, (nparams,) coefficients and tau that parameterize the model. The required length can be obtained by nparams. (and will depend on the number of degenerate leaves - not yet) Returns ------- probs : array, (nobs, nchoices) probabilites for all choices for each observation. The order is available by attribute leaves. See note in docstring of class ''' self.recursionparams = params self.calc_prob(self.tree) probs_array = np.array([self.probs[leaf] for leaf in self.leaves]) return probs_array #what's the ordering? Should be the same as sequence in tree. #TODO: need a check/assert that this sequence is the same as the # encoding in endog def calc_prob(self, tree, parent=None): '''walking a tree bottom-up based on dictionary ''' #0.5#2 #placeholder for now #should be tau=self.taus[name] but as part of params for optimization endog = self.endog datadict = self.datadict paramsind = self.paramsind branchsum = self.branchsum if type(tree) == tuple: #assumes leaves are int for choice index name, subtree = tree self.branchleaves[name] = [] #register branch in dictionary tau = self.recursionparams[self.paramsidx['tau_'+name]] if DEBUG: print '----------- starting next branch-----------' print name, datadict[name], 'tau=', tau print 'subtree', subtree branchvalue = [] if testxb == 2: branchsum = 0 elif testxb == 1: branchsum = datadict[name] else: branchsum = name for b in subtree: if DEBUG: print b bv = self.calc_prob(b, name) bv = np.exp(bv/tau) #this shouldn't be here, when adding branch data branchvalue.append(bv) branchsum = branchsum + bv self.branchvalues[name] = branchvalue #keep track what was returned if DEBUG: print '----------- returning to branch-----------', print name print 'branchsum in branch', name, branchsum if parent: if DEBUG: print 'parent', parent self.branchleaves[parent].extend(self.branchleaves[name]) if 0: #not name == 'top': # not used anymore !!! ??? #if not name == 'top': #TODO: do I need this only on the lowest branches ? tmpsum = 0 for k in self.branchleaves[name]: #similar to this is now also in return branch values #depends on what will be returned tmpsum += self.probs[k] iv = np.log(tmpsum) for k in self.branchleaves[name]: self.probstxt[k] = self.probstxt[k] + ['*' + name + '-prob' + '(%s)' % ', '.join(self.paramsind[name])] #TODO: does this use the denominator twice now self.probs[k] = self.probs[k] / tmpsum if np.size(self.datadict[name])>0: #not used yet, might have to move one indentation level #self.probs[k] = self.probs[k] / tmpsum ## np.exp(-self.datadict[name] * ## np.sum(self.recursionparams[self.parinddict[name]])) if DEBUG: print 'self.datadict[name], self.probs[k]', print self.datadict[name], self.probs[k] #if not name == 'top': # self.probs[k] = self.probs[k] * np.exp( iv) #walk one level down again to add branch probs to instance.probs self.bprobs[name] = [] for bidx, b in enumerate(subtree): if DEBUG: print 'repr(b)', repr(b), bidx #if len(b) == 1: #TODO: skip leaves, check this if not type(b) == tuple: # isinstance(b, str): #TODO: replace this with a check for branch (tuple) instead #this implies name is a bottom branch, #possible to add special things here self.bprobs[name].append(self.probs[b]) #TODO: need tau possibly here self.probs[b] = self.probs[b] / branchsum if DEBUG: print '*********** branchsum at bottom branch', branchsum #self.bprobs[name].append(self.probs[b]) else: bname = b[0] branchsum2 = sum(self.branchvalues[name]) assert np.abs(branchsum - branchsum2).sum() < 1e-8 bprob = branchvalue[bidx]/branchsum self.bprobs[name].append(bprob) for k in self.branchleaves[bname]: if DEBUG: print 'branchprob', bname, k, bprob, branchsum #temporary hack with maximum to avoid zeros self.probs[k] = self.probs[k] * np.maximum(bprob, 1e-4) if DEBUG: print 'working on branch', tree, branchsum if testxb<2: return branchsum else: #this is the relevant part self.branchsums[name] = branchsum if np.size(self.datadict[name])>0: branchxb = np.sum(self.datadict[name] * self.recursionparams[self.parinddict[name]]) else: branchxb = 0 if not name=='top': tau = self.recursionparams[self.paramsidx['tau_'+name]] else: tau = 1 iv = branchxb + tau * branchsum #which tau: name or parent??? return branchxb + tau * np.log(branchsum) #iv #branchsum is now IV, TODO: add effect of branch variables else: tau = self.recursionparams[self.paramsidx['tau_'+parent]] if DEBUG: print 'parent', parent self.branchleaves[parent].append(tree) # register leave with parent self.probstxt[tree] = [tree + '-prob' + '(%s)' % ', '.join(self.paramsind[tree])] #this is not yet a prob, not normalized to 1, it is exp(x*b) leafprob = np.exp(np.sum(self.datadict[tree] * self.recursionparams[self.parinddict[tree]]) / tau) # fake tau for now, wrong spot ??? #it seems I get the same answer with and without tau here self.probs[tree] = leafprob #= 1 #try initialization only #TODO: where should I add tau in the leaves if testxb == 2: return np.log(leafprob) elif testxb == 1: leavessum = np.array(datadict[tree]) # sum((datadict[bi] for bi in datadict[tree])) if DEBUG: print 'final branch with', tree, ''.join(tree), leavessum #sum(tree) return leavessum #sum(xb[tree]) elif testxb == 0: return ''.join(tree) #sum(tree) if __name__ == '__main__': DEBUG = 0 endog = 5 # dummy place holder ############## Example similar to Greene #get pickled data #endog3, xifloat3 = pickle.load(open('xifloat2.pickle','rb')) tree0 = ('top', [('Fly',['Air']), ('Ground', ['Train', 'Car', 'Bus']) ] ) ''' this is with real data from Greene's clogit example datadict = dict(zip(['Air', 'Train', 'Bus', 'Car'], [xifloat[i]for i in range(4)])) ''' #for testing only (mock that returns it's own name datadict = dict(zip(['Air', 'Train', 'Bus', 'Car'], ['Airdata', 'Traindata', 'Busdata', 'Cardata'])) if testxb: datadict = dict(zip(['Air', 'Train', 'Bus', 'Car'], np.arange(4))) datadict.update({'top' : [], 'Fly' : [], 'Ground': []}) paramsind = {'top' : [], 'Fly' : [], 'Ground': [], 'Air' : ['GC', 'Ttme', 'ConstA', 'Hinc'], 'Train' : ['GC', 'Ttme', 'ConstT'], 'Bus' : ['GC', 'Ttme', 'ConstB'], 'Car' : ['GC', 'Ttme'] } modru = RU2NMNL(endog, datadict, tree0, paramsind) modru.recursionparams[-1] = 2 modru.recursionparams[1] = 1 print 'Example 1' print '---------\n' print modru.calc_prob(modru.tree) print 'Tree' pprint(modru.tree) print '\nmodru.probs' pprint(modru.probs) ############## example with many layers tree2 = ('top', [('B1',['a','b']), ('B2', [('B21',['c', 'd']), ('B22',['e', 'f', 'g']) ] ), ('B3',['h']) ] ) #Note: dict looses ordering paramsind2 = { 'B1': [], 'a': ['consta', 'p'], 'b': ['constb', 'p'], 'B2': ['const2', 'x2'], 'B21': [], 'c': ['constc', 'p', 'time'], 'd': ['constd', 'p', 'time'], 'B22': ['x22'], 'e': ['conste', 'p', 'hince'], 'f': ['constf', 'p', 'hincf'], 'g': [ 'p', 'hincg'], 'B3': [], 'h': ['consth', 'p', 'h'], 'top': []} datadict2 = dict([i for i in zip('abcdefgh',range(8))]) datadict2.update({'top':1000, 'B1':100, 'B2':200, 'B21':21,'B22':22, 'B3':300}) ''' >>> pprint(datadict2) {'B1': 100, 'B2': 200, 'B21': 21, 'B22': 22, 'B3': 300, 'a': 0.5, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'top': 1000} ''' modru2 = RU2NMNL(endog, datadict2, tree2, paramsind2) modru2.recursionparams[-3] = 2 modru2.recursionparams[3] = 1 print '\n\nExample 2' print '---------\n' print modru2.calc_prob(modru2.tree) print 'Tree' pprint(modru2.tree) print '\nmodru.probs' pprint(modru2.probs) print 'sum of probs', sum(modru2.probs.values()) print 'branchvalues' print modru2.branchvalues print modru.branchvalues print 'branch probabilities' print modru.bprobs print 'degenerate branches' print modru.branches_degenerate ''' >>> modru.bprobs {'Fly': [], 'top': [0.0016714179077931082, 0.99832858209220687], 'Ground': []} >>> modru2.bprobs {'top': [0.25000000000000006, 0.62499999999999989, 0.12500000000000003], 'B22': [], 'B21': [], 'B1': [], 'B2': [0.40000000000000008, 0.59999999999999998], 'B3': []} ''' params1 = np.array([ 0., 1., 0., 0., 0., 0., 1., 1., 2.]) print modru.get_probs(params1) params2 = np.array([ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 2., 1., 1.]) print modru2.get_probs(params2) #raises IndexError statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/try_catdata.py000066400000000000000000000112121224417117700272000ustar00rootroot00000000000000 import numpy as np #from numpy import linalg as npla from scipy import stats, optimize ''' Working with categorical data ============================= use of dummy variables, group statistics, within and between statistics examples for efficient matrix algebra dummy versions require that the number of unique groups or categories is not too large group statistics with scipy.ndimage can handle large number of observations and groups scipy.ndimage stats is missing count new: np.bincount can also be used for calculating values per label ''' from scipy import ndimage #problem: ndimage does not allow axis argument, # calculates mean or var corresponding to axis=None in np.mean, np.var # useless for multivariate application def labelmeanfilter(y, x): # requires integer labels # from mailing list scipy-user 2009-02-11 labelsunique = np.arange(np.max(y)+1) labelmeans = np.array(ndimage.mean(x, labels=y, index=labelsunique)) # returns label means for each original observation return labelmeans[y] #groupcount: i.e. number of observation by group/label #np.array(ndimage.histogram(yrvs[:,0],0,10,1,labels=yrvs[:,0],index=np.unique(yrvs[:,0]))) def labelmeanfilter_nd(y, x): # requires integer labels # from mailing list scipy-user 2009-02-11 # adjusted for 2d x with column variables labelsunique = np.arange(np.max(y)+1) labmeansdata = [] labmeans = [] for xx in x.T: labelmeans = np.array(ndimage.mean(xx, labels=y, index=labelsunique)) labmeansdata.append(labelmeans[y]) labmeans.append(labelmeans) # group count: labelcount = np.array(ndimage.histogram(y, labelsunique[0], labelsunique[-1]+1, 1, labels=y, index=labelsunique)) # returns array of lable/group counts and of label/group means # and label/group means for each original observation return labelcount, np.array(labmeans), np.array(labmeansdata).T def labelmeanfilter_str(ys, x): # works also for string labels in ys, but requires 1D # from mailing list scipy-user 2009-02-11 unil, unilinv = np.unique1d(ys, return_index=False, return_inverse=True) labelmeans = np.array(ndimage.mean(x, labels=unilinv, index=np.arange(np.max(unil)+1))) arr3 = labelmeans[unilinv] return arr3 def groupstatsbin(factors, values): '''uses np.bincount, assumes factors/labels are integers ''' n = len(factors) ix,rind = np.unique1d(factors, return_inverse=1) gcount = np.bincount(rind) gmean = np.bincount(rind, weights=values)/ (1.0*gcount) meanarr = gmean[rind] withinvar = np.bincount(rind, weights=(values-meanarr)**2) / (1.0*gcount) withinvararr = withinvar[rind] return gcount, gmean , meanarr, withinvar, withinvararr def convertlabels(ys, indices=None): '''convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels ''' if indices == None: ylabel = ys else: idx = np.array(indices) if idx.size > 1 and ys.ndim == 2: ylabel = np.array(['@%s@'%ii[:2].tostring() for ii in ys])[:,np.newaxis] #alternative ## if ys[:,idx].dtype.kind == 'S': ## ylabel = nd.array([' '.join(ii[:2]) for ii in ys])[:,np.newaxis] else: # there might be a problem here ylabel = ys unil, unilinv = np.unique1d(ylabel, return_index=False, return_inverse=True) return unilinv, np.arange(len(unil)), unil def groupsstats_1d(y, x, labelsunique): '''use ndimage to get fast mean and variance''' labelmeans = np.array(ndimage.mean(x, labels=y, index=labelsunique)) labelvars = np.array(ndimage.var(x, labels=y, index=labelsunique)) return labelmeans, labelvars def cat2dummy(y, nonseq=0): if nonseq or (y.ndim == 2 and y.shape[1] > 1): ycat, uniques, unitransl = convertlabels(y, range(y.shape[1])) else: ycat = y.copy() ymin = y.min() uniques = np.arange(ymin,y.max()+1) if ycat.ndim == 1: ycat = ycat[:,np.newaxis] # this builds matrix nobs*ncat dummy = (ycat == uniques).astype(int) return dummy def groupsstats_dummy(y, x, nonseq=0): if x.ndim == 1: # use groupsstats_1d x = x[:,np.newaxis] dummy = cat2dummy(y, nonseq=nonseq) countgr = dummy.sum(0, dtype=float) meangr = np.dot(x.T,dummy)/countgr meandata = np.dot(dummy,meangr.T) # category/group means as array in shape of x xdevmeangr = x - meandata # deviation from category/group mean vargr = np.dot((xdevmeangr * xdevmeangr).T, dummy) / countgr return meangr, vargr, xdevmeangr, countgr if __name__ == '__main__': pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/try_ols_anova.py000066400000000000000000000216771224417117700276000ustar00rootroot00000000000000''' convenience functions for ANOVA type analysis with OLS Note: statistical results of ANOVA are not checked, OLS is checked but not whether the reported results are the ones used in ANOVA includes form2design for creating dummy variables TODO: * ... * ''' import numpy as np #from scipy import stats import statsmodels.api as sm def data2dummy(x, returnall=False): '''convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only''' x = x.ravel() groups = np.unique(x) if returnall: return (x[:, None] == groups).astype(int) else: return (x[:, None] == groups).astype(int)[:,:-1] def data2proddummy(x): '''creates product dummy variables from 2 columns of 2d array drops last dummy variable, but not from each category singular with simple dummy variable but not with constant quickly written, no safeguards ''' #brute force, assumes x is 2d #replace with encoding if possible groups = np.unique(map(tuple, x.tolist())) #includes singularity with additive factors return (x==groups[:,None,:]).all(-1).T.astype(int)[:,:-1] def data2groupcont(x1,x2): '''create dummy continuous variable Parameters ---------- x1 : 1d array label or group array x2 : 1d array (float) continuous variable Notes ----- useful for group specific slope coefficients in regression ''' if x2.ndim == 1: x2 = x2[:,None] dummy = data2dummy(x1, returnall=True) return dummy * x2 # Result strings #the second leaves the constant in, not with NIST regression #but something fishy with res.ess negative in examples ? #not checked if these are all the right ones anova_str0 = ''' ANOVA statistics (model sum of squares excludes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ess)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' anova_str = ''' ANOVA statistics (model sum of squares includes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ssmwithmean)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' def anovadict(res): '''update regression results dictionary with ANOVA specific statistics not checked for completeness ''' ad = {} ad.update(res.__dict__) #dict doesn't work with cached attributes anova_attr = ['df_model', 'df_resid', 'ess', 'ssr','uncentered_tss', 'mse_model', 'mse_resid', 'mse_total', 'fvalue', 'f_pvalue', 'rsquared'] for key in anova_attr: ad[key] = getattr(res, key) ad['nobs'] = res.model.nobs ad['ssmwithmean'] = res.uncentered_tss - res.ssr return ad def form2design(ss, data): '''convert string formula to data dictionary ss : string * I : add constant * varname : for simple varnames data is used as is * F:varname : create dummy variables for factor varname * P:varname1*varname2 : create product dummy variables for varnames * G:varname1*varname2 : create product between factor and continuous variable data : dict or structured array data set, access of variables by name as in dictionaries Returns ------- vars : dictionary dictionary of variables with converted dummy variables names : list list of names, product (P:) and grouped continuous variables (G:) have name by joining individual names sorted according to input Examples -------- >>> xx, n = form2design('I a F:b P:c*d G:c*f', testdata) >>> xx.keys() ['a', 'b', 'const', 'cf', 'cd'] >>> n ['const', 'a', 'b', 'cd', 'cf'] Notes ----- with sorted dict, separate name list wouldn't be necessary ''' vars = {} names = [] for item in ss.split(): if item == 'I': vars['const'] = np.ones(data.shape[0]) names.append('const') elif not ':' in item: vars[item] = data[item] names.append(item) elif item[:2] == 'F:': v = item.split(':')[1] vars[v] = data2dummy(data[v]) names.append(v) elif item[:2] == 'P:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2proddummy(np.c_[data[v[0]],data[v[1]]]) names.append(''.join(v)) elif item[:2] == 'G:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2groupcont(data[v[0]], data[v[1]]) names.append(''.join(v)) else: raise ValueError('unknown expression in formula') return vars, names def dropname(ss, li): '''drop names from a list of strings, names to drop are in space delimeted list does not change original list ''' newli = li[:] for item in ss.split(): newli.remove(item) return newli if __name__ == '__main__': # Test Example with created data # ------------------------------ nobs = 1000 testdataint = np.random.randint(3, size=(nobs,4)).view([('a',int),('b',int),('c',int),('d',int)]) testdatacont = np.random.normal( size=(nobs,2)).view([('e',float), ('f',float)]) import numpy.lib.recfunctions dt2 = numpy.lib.recfunctions.zip_descr((testdataint, testdatacont),flatten=True) # concatenate structured arrays testdata = np.empty((nobs,1), dt2) for name in testdataint.dtype.names: testdata[name] = testdataint[name] for name in testdatacont.dtype.names: testdata[name] = testdatacont[name] #print form2design('a',testdata) if 0: # print only when nobs is small, e.g. nobs=10 xx, n = form2design('F:a',testdata) print xx print form2design('P:a*b',testdata) print data2proddummy((np.c_[testdata['a'],testdata['b']])) xx, names = form2design('a F:b P:c*d',testdata) #xx, names = form2design('I a F:b F:c F:d P:c*d',testdata) xx, names = form2design('I a F:b P:c*d', testdata) xx, names = form2design('I a F:b P:c*d G:a*e f', testdata) X = np.column_stack([xx[nn] for nn in names]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).fit() #results print rest1.params print anova_str % anovadict(rest1) X = np.column_stack([xx[nn] for nn in dropname('ae f', names)]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).fit() print rest1.params print anova_str % anovadict(rest1) # Example: from Bruce # ------------------- #get data and clean it #^^^^^^^^^^^^^^^^^^^^^ # requires file 'dftest3.data' posted by Bruce # read data set and drop rows with missing data dt_b = np.dtype([('breed', int), ('sex', int), ('litter', int), ('pen', int), ('pig', int), ('age', float), ('bage', float), ('y', float)]) dta = np.genfromtxt('dftest3.data', dt_b,missing='.', usemask=True) print 'missing', [dta.mask[k].sum() for k in dta.dtype.names] m = dta.mask.view(bool) droprows = m.reshape(-1,len(dta.dtype.names)).any(1) # get complete data as plain structured array # maybe doesn't work with masked arrays dta_use_b1 = dta[~droprows,:].data print dta_use_b1.shape print dta_use_b1.dtype #Example b1: variables from Bruce's glm #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # prepare data and dummy variables xx_b1, names_b1 = form2design('I F:sex age', dta_use_b1) # create design matrix X_b1 = np.column_stack([xx_b1[nn] for nn in dropname('', names_b1)]) y_b1 = dta_use_b1['y'] # estimate using OLS rest_b1 = sm.OLS(y_b1, X_b1).fit() # print results print rest_b1.params print anova_str % anovadict(rest_b1) #compare with original version only in original version #print anova_str % anovadict(res_b0) # Example: use all variables except pig identifier #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ allexog = ' '.join(dta.dtype.names[:-1]) #'breed sex litter pen pig age bage' xx_b1a, names_b1a = form2design('I F:breed F:sex F:litter F:pen age bage', dta_use_b1) X_b1a = np.column_stack([xx_b1a[nn] for nn in dropname('', names_b1a)]) y_b1a = dta_use_b1['y'] rest_b1a = sm.OLS(y_b1a, X_b1a).fit() print rest_b1a.params print anova_str % anovadict(rest_b1a) for dropn in names_b1a: print '\nResults dropping', dropn X_b1a_ = np.column_stack([xx_b1a[nn] for nn in dropname(dropn, names_b1a)]) y_b1a_ = dta_use_b1['y'] rest_b1a_ = sm.OLS(y_b1a_, X_b1a_).fit() #print rest_b1a_.params print anova_str % anovadict(rest_b1a_) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/regression/try_treewalker.py000066400000000000000000000103431224417117700277500ustar00rootroot00000000000000'''Trying out tree structure for nested logit sum is standing for likelihood calculations should collect and aggregate likelihood contributions bottom up ''' import numpy as np tree = [[0,1],[[2,3],[4,5,6]],[7]] #singleton/degenerate branch needs to be list xb = 2*np.arange(8) testxb = 1 #0 def branch(tree): '''walking a tree bottom-up ''' if not type(tree[0]) == int: #assumes leaves are int for choice index branchsum = 0 for b in tree: branchsum += branch(b) else: print tree print 'final branch with', tree, sum(tree) if testxb: return sum(xb[tree]) else: return sum(tree) print 'working on branch', tree, branchsum return branchsum print branch(tree) #new version that also keeps track of branch name and allows V_j for a branch # as in Greene, V_j + lamda * IV doesn't look the same as including the # explanatory variables in leaf X_j, V_j is linear in X, IV is logsumexp of X, testxb = 0#1#0 def branch2(tree): '''walking a tree bottom-up based on dictionary ''' if type(tree) == tuple: #assumes leaves are int for choice index name, subtree = tree print name, data2[name] print 'subtree', subtree if testxb: branchsum = data2[name] else: branchsum = name #0 for b in subtree: #branchsum += branch2(b) branchsum = branchsum + branch2(b) else: leavessum = sum((data2[bi] for bi in tree)) print 'final branch with', tree, ''.join(tree), leavessum #sum(tree) if testxb: return leavessum #sum(xb[tree]) else: return ''.join(tree) #sum(tree) print 'working on branch', tree, branchsum return branchsum tree = [[0,1],[[2,3],[4,5,6]],[7]] tree2 = ('top', [('B1',['a','b']), ('B2', [('B21',['c', 'd']), ('B22',['e', 'f', 'g']) ] ), ('B3',['h']) ] ) data2 = dict([i for i in zip('abcdefgh',range(8))]) #data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':300,'B22':400, 'B3':400}) data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':21,'B22':22, 'B3':300}) #data2 #{'a': 0, 'c': 2, 'b': 1, 'e': 4, 'd': 3, 'g': 6, 'f': 5, 'h': 7, #'top': 1000, 'B22': 22, 'B21': 21, 'B1': 100, 'B2': 200, 'B3': 300} print '\n tree with dictionary data' print branch2(tree2) # results look correct for testxb=0 and 1 #parameters/coefficients map coefficient names to indices, list of indices into #a 1d params one for each leave and branch #Note: dict looses ordering paramsind = { 'B1': [], 'a': ['consta', 'p'], 'b': ['constb', 'p'], 'B2': ['const2', 'x2'], 'B21': [], 'c': ['consta', 'p', 'time'], 'd': ['consta', 'p', 'time'], 'B22': ['x22'], 'e': ['conste', 'p', 'hince'], 'f': ['constt', 'p', 'hincf'], 'g': [ 'p', 'hincg'], 'B3': [], 'h': ['consth', 'p', 'h'], 'top': []} #unique, parameter array names, #sorted alphabetically, order is/should be only internal paramsnames = sorted(set([i for j in paramsind.values() for i in j])) #mapping coefficient names to indices to unique/parameter array paramsidx = dict((name, idx) for (idx,name) in enumerate(paramsnames)) #mapping branch and leaf names to index in parameter array inddict = dict((k,[paramsidx[j] for j in v]) for k,v in paramsind.items()) ''' >>> paramsnames ['const2', 'consta', 'constb', 'conste', 'consth', 'constt', 'h', 'hince', 'hincf', 'hincg', 'p', 'time', 'x2', 'x22'] >>> parmasidx {'conste': 3, 'consta': 1, 'constb': 2, 'h': 6, 'time': 11, 'consth': 4, 'p': 10, 'constt': 5, 'const2': 0, 'x2': 12, 'x22': 13, 'hince': 7, 'hincg': 9, 'hincf': 8} >>> inddict {'a': [1, 10], 'c': [1, 10, 11], 'b': [2, 10], 'e': [3, 10, 7], 'd': [1, 10, 11], 'g': [10, 9], 'f': [5, 10, 8], 'h': [4, 10, 6], 'top': [], 'B22': [13], 'B21': [], 'B1': [], 'B2': [0, 12], 'B3': []} >>> paramsind {'a': ['consta', 'p'], 'c': ['consta', 'p', 'time'], 'b': ['constb', 'p'], 'e': ['conste', 'p', 'hince'], 'd': ['consta', 'p', 'time'], 'g': ['p', 'hincg'], 'f': ['constt', 'p', 'hincf'], 'h': ['consth', 'p', 'h'], 'top': [], 'B22': ['x22'], 'B21': [], 'B1': [], 'B2': ['const2', 'x2'], 'B3': []} ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/rls.py000066400000000000000000000120251224417117700233240ustar00rootroot00000000000000"""Restricted least squares from pandas License: Simplified BSD """ import numpy as np from statsmodels.regression.linear_model import WLS, GLS, RegressionResults class RLS(GLS): """ Restricted general least squares model that handles linear constraints Parameters ---------- endog: array-like n length array containing the dependent variable exog: array-like n-by-p array of independent variables constr: array-like k-by-p array of linear constraints param (0.): array-like or scalar p-by-1 array (or scalar) of constraint parameters sigma (None): scalar or array-like The weighting matrix of the covariance. No scaling by default (OLS). If sigma is a scalar, then it is converted into an n-by-n diagonal matrix with sigma as each diagonal element. If sigma is an n-length array, then it is assumed to be a diagonal matrix with the given sigma on the diagonal (WLS). Notes ----- endog = exog * beta + epsilon weights' * constr * beta = param See Greene and Seaks, "The Restricted Least Squares Estimator: A Pedagogical Note", The Review of Economics and Statistics, 1991. """ def __init__(self, endog, exog, constr, param=0., sigma=None): N, Q = exog.shape constr = np.asarray(constr) if constr.ndim == 1: K, P = 1, constr.shape[0] else: K, P = constr.shape if Q != P: raise Exception('Constraints and design do not align') self.ncoeffs = Q self.nconstraint = K self.constraint = constr if np.isscalar(param) and K > 1: param = np.ones((K,)) * param self.param = param if sigma is None: sigma = 1. if np.isscalar(sigma): sigma = np.ones(N) * sigma sigma = np.squeeze(sigma) if sigma.ndim == 1: self.sigma = np.diag(sigma) self.cholsigmainv = np.diag(np.sqrt(sigma)) else: self.sigma = sigma self.cholsigmainv = np.linalg.cholesky(np.linalg.pinv(self.sigma)).T super(GLS, self).__init__(endog, exog) _rwexog = None @property def rwexog(self): """Whitened exogenous variables augmented with restrictions""" if self._rwexog is None: P = self.ncoeffs K = self.nconstraint design = np.zeros((P + K, P + K)) design[:P, :P] = np.dot(self.wexog.T, self.wexog) #top left constr = np.reshape(self.constraint, (K, P)) design[:P, P:] = constr.T #top right partition design[P:, :P] = constr #bottom left partition design[P:, P:] = np.zeros((K, K)) #bottom right partition self._rwexog = design return self._rwexog _inv_rwexog = None @property def inv_rwexog(self): """Inverse of self.rwexog""" if self._inv_rwexog is None: self._inv_rwexog = np.linalg.inv(self.rwexog) return self._inv_rwexog _rwendog = None @property def rwendog(self): """Whitened endogenous variable augmented with restriction parameters""" if self._rwendog is None: P = self.ncoeffs K = self.nconstraint response = np.zeros((P + K,)) response[:P] = np.dot(self.wexog.T, self.wendog) response[P:] = self.param self._rwendog = response return self._rwendog _ncp = None @property def rnorm_cov_params(self): """Parameter covariance under restrictions""" if self._ncp is None: P = self.ncoeffs self._ncp = self.inv_rwexog[:P, :P] return self._ncp _wncp = None @property def wrnorm_cov_params(self): """ Heteroskedasticity-consistent parameter covariance Used to calculate White standard errors. """ if self._wncp is None: df = self.df_resid pred = np.dot(self.wexog, self.coeffs) eps = np.diag((self.wendog - pred) ** 2) sigmaSq = np.sum(eps) pinvX = np.dot(self.rnorm_cov_params, self.wexog.T) self._wncp = np.dot(np.dot(pinvX, eps), pinvX.T) * df / sigmaSq return self._wncp _coeffs = None @property def coeffs(self): """Estimated parameters""" if self._coeffs is None: betaLambda = np.dot(self.inv_rwexog, self.rwendog) self._coeffs = betaLambda[:self.ncoeffs] return self._coeffs def fit(self): rncp = self.wrnorm_cov_params lfit = RegressionResults(self, self.coeffs, normalized_cov_params=rncp) return lfit if __name__=="__main__": import statsmodels.api as sm dta = np.genfromtxt('./rlsdata.txt', names=True) design = np.column_stack((dta['Y'],dta['Y']**2,dta[['NE','NC','W','S']].view(float).reshape(dta.shape[0],-1))) design = sm.add_constant(design, prepend=True) rls_mod = RLS(dta['G'],design, constr=[0,0,0,1,1,1,1]) rls_fit = rls_mod.fit() print rls_fit.params statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/rlsdata.txt000066400000000000000000000016271224417117700243530ustar00rootroot00000000000000G Y NE NC S W 1.62 1550 1 0 0 0 2.09 3474 1 0 0 0 2.96 4424 1 0 0 0 3.5 5444 1 0 0 0 4.94 6404 1 0 0 0 6.14 7464 1 0 0 0 6.43 8919 1 0 0 0 7 10817 1 0 0 0 8.24 13287 1 0 0 0 9.16 17043 1 0 0 0 10.87 21862 1 0 0 0 12.17 33892 1 0 0 0 1.81 1644 0 1 0 0 2.96 3434 0 1 0 0 3.81 4474 0 1 0 0 4.75 5399 0 1 0 0 5.84 6440 0 1 0 0 6.38 7401 0 1 0 0 7.28 8897 0 1 0 0 8.51 10807 0 1 0 0 8.44 13213 0 1 0 0 10.68 17156 0 1 0 0 10.93 22058 0 1 0 0 12.76 33926 0 1 0 0 2.17 1621 0 0 1 0 3.89 3449 0 0 1 0 5.09 4436 0 0 1 0 5.08 5402 0 0 1 0 6.03 6403 0 0 1 0 6.73 7406 0 0 1 0 7.86 8887 0 0 1 0 9.32 10811 0 0 1 0 9.4 13238 0 0 1 0 10.48 16970 0 0 1 0 11.12 21909 0 0 1 0 11.81 37702 0 0 1 0 2.12 1596 0 0 0 1 3.93 3463 0 0 0 1 5.02 4478 0 0 0 1 6.49 5375 0 0 0 1 5.5 6408 0 0 0 1 6.67 7390 0 0 0 1 7.29 8917 0 0 0 1 8.92 10804 0 0 0 1 9.52 13268 0 0 0 1 10.4 17094 0 0 0 1 11.41 21914 0 0 0 1 11.61 36618 0 0 0 1 statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/000077500000000000000000000000001224417117700233105ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/__init__.py000066400000000000000000000012301224417117700254150ustar00rootroot00000000000000'''temporary location for enhancements to scipy.stats includes ^^^^^^^^ * Per Brodtkorb's estimation enhancements to scipy.stats.distributions - distributions_per.py is copy of scipy.stats.distributions.py with changes - distributions_profile.py partially extracted classes and functions to separate code into more managable pieces * josef's extra distribution and helper functions - moment helpers - goodness of fit test - fitting distributions with some fixed parameters - find best distribution that fits data: working script * example and test folders to keep all together status ^^^^^^ mixed status : from not-working to well-tested ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/contrast_tools.py000066400000000000000000000701011224417117700267360ustar00rootroot00000000000000'''functions to work with contrasts for multiple tests contrast matrices for comparing all pairs, all levels to reference level, ... extension to 2-way groups in progress TwoWay: class for bringing two-way analysis together and try out various helper functions Idea for second part - get all transformation matrices to move in between different full rank parameterizations - standardize to one parameterization to get all interesting effects. - multivariate normal distribution - exploit or expand what we have in LikelihoodResults, cov_params, f_test, t_test, example: resols_dropf_full.cov_params(C2) - connect to new multiple comparison for contrast matrices, based on multivariate normal or t distribution (Hothorn, Bretz, Westfall) ''' import numpy as np #next 3 functions copied from multicomp.py def contrast_allpairs(nm): '''contrast or restriction matrix for all pairs of nm variables Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm*(nm-1)/2, nm) contrast matrix for all pairwise comparisons ''' contr = [] for i in range(nm): for j in range(i+1, nm): contr_row = np.zeros(nm) contr_row[i] = 1 contr_row[j] = -1 contr.append(contr_row) return np.array(contr) def contrast_all_one(nm): '''contrast or restriction matrix for all against first comparison Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm-1, nm) contrast matrix for all against first comparisons ''' contr = np.column_stack((np.ones(nm-1), -np.eye(nm-1))) return contr def contrast_diff_mean(nm): '''contrast or restriction matrix for all against mean comparison Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm-1, nm) contrast matrix for all against mean comparisons ''' return np.eye(nm) - np.ones((nm,nm))/nm def signstr(x, noplus=False): if x in [-1,0,1]: if not noplus: return '+' if np.sign(x)>=0 else '-' else: return '' if np.sign(x)>=0 else '-' else: return str(x) def contrast_labels(contrasts, names, reverse=False): if reverse: sl = slice(None, None, -1) else: sl = slice(None) labels = [''.join(['%s%s' % (signstr(c, noplus=True),v) for c,v in zip(row, names)[sl] if c != 0]) for row in contrasts] return labels def contrast_product(names1, names2, intgroup1=None, intgroup2=None, pairs=False): '''build contrast matrices for products of two categorical variables this is an experimental script and should be converted to a class Parameters ---------- names1, names2 : lists of strings contains the list of level labels for each categorical variable intgroup1, intgroup2 : ndarrays TODO: this part not tested, finished yet categorical variable Notes ----- This creates a full rank matrix. It does not do all pairwise comparisons, parameterization is using contrast_all_one to get differences with first level. ? does contrast_all_pairs work as a plugin to get all pairs ? ''' n1 = len(names1) n2 = len(names2) names_prod = ['%s_%s' % (i,j) for i in names1 for j in names2] ee1 = np.zeros((1,n1)) ee1[0,0] = 1 if not pairs: dd = np.r_[ee1, -contrast_all_one(n1)] else: dd = np.r_[ee1, -contrast_allpairs(n1)] contrast_prod = np.kron(dd[1:], np.eye(n2)) names_contrast_prod0 = contrast_labels(contrast_prod, names_prod, reverse=True) names_contrast_prod = [''.join(['%s%s' % (signstr(c, noplus=True),v) for c,v in zip(row, names_prod)[::-1] if c != 0]) for row in contrast_prod] ee2 = np.zeros((1,n2)) ee2[0,0] = 1 #dd2 = np.r_[ee2, -contrast_all_one(n2)] if not pairs: dd2 = np.r_[ee2, -contrast_all_one(n2)] else: dd2 = np.r_[ee2, -contrast_allpairs(n2)] contrast_prod2 = np.kron(np.eye(n1), dd2[1:]) names_contrast_prod2 = [''.join(['%s%s' % (signstr(c, noplus=True),v) for c,v in zip(row, names_prod)[::-1] if c != 0]) for row in contrast_prod2] if (not intgroup1 is None) and (not intgroup1 is None): d1, _ = dummy_1d(intgroup1) d2, _ = dummy_1d(intgroup2) dummy = dummy_product(d1, d2) else: dummy = None return (names_prod, contrast_prod, names_contrast_prod, contrast_prod2, names_contrast_prod2, dummy) def dummy_1d(x, varname=None): '''dummy variable for id integer groups Paramters --------- x : ndarray, 1d categorical variable, requires integers if varname is None varname : string name of the variable used in labels for category levels Returns ------- dummy : ndarray, 2d array of dummy variables, one column for each level of the category (full set) labels : list of strings labels for the columns, i.e. levels of each category Notes ----- use tools.categorical instead for more more options See Also -------- statsmodels.tools.categorical Examples -------- >>> x = np.array(['F', 'F', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'M', 'M'], dtype='|S1') >>> dummy_1d(x, varname='gender') (array([[1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]]), ['gender_F', 'gender_M']) ''' if varname is None: #assumes integer labels = ['level_%d' % i for i in range(x.max() + 1)] return (x[:,None]==np.arange(x.max()+1)).astype(int), labels else: grouplabels = np.unique(x) labels = [varname + '_%s' % str(i) for i in grouplabels] return (x[:,None]==grouplabels).astype(int), labels def dummy_product(d1, d2, method='full'): '''dummy variable from product of two dummy variables Parameters ---------- d1, d2 : ndarray two dummy variables, assumes full set for methods 'drop-last' and 'drop-first' method : {'full', 'drop-last', 'drop-first'} 'full' returns the full product, encoding of intersection of categories. The drop methods provide a difference dummy encoding: (constant, main effects, interaction effects). The first or last columns of the dummy variable (i.e. levels) are dropped to get full rank dummy matrix. Returns ------- dummy : ndarray dummy variable for product, see method ''' if method == 'full': dd = (d1[:,:,None]*d2[:,None,:]).reshape(d1.shape[0],-1) elif method == 'drop-last': #same as SAS transreg d12rl = dummy_product(d1[:,:-1], d2[:,:-1]) dd = np.column_stack((np.ones(d1.shape[0], int), d1[:,:-1], d2[:,:-1],d12rl)) #Note: dtype int should preserve dtype of d1 and d2 elif method == 'drop-first': d12r = dummy_product(d1[:,1:], d2[:,1:]) dd = np.column_stack((np.ones(d1.shape[0], int), d1[:,1:], d2[:,1:],d12r)) else: raise ValueError('method not recognized') return dd def dummy_limits(d): '''start and endpoints of groups in a sorted dummy variable array helper function for nested categories Examples -------- >>> d1 = np.array([[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]) >>> dummy_limits(d1) (array([0, 4, 8]), array([ 4, 8, 12])) get group slices from an array >>> [np.arange(d1.shape[0])[b:e] for b,e in zip(*dummy_limits(d1))] [array([0, 1, 2, 3]), array([4, 5, 6, 7]), array([ 8, 9, 10, 11])] >>> [np.arange(d1.shape[0])[b:e] for b,e in zip(*dummy_limits(d1))] [array([0, 1, 2, 3]), array([4, 5, 6, 7]), array([ 8, 9, 10, 11])] ''' nobs, nvars = d.shape start1, col1 = np.nonzero(np.diff(d,axis=0)==1) end1, col1_ = np.nonzero(np.diff(d,axis=0)==-1) cc = np.arange(nvars) #print cc, np.r_[[0], col1], np.r_[col1_, [nvars-1]] if ((not (np.r_[[0], col1] == cc).all()) or (not (np.r_[col1_, [nvars-1]] == cc).all())): raise ValueError('dummy variable is not sorted') start = np.r_[[0], start1+1] end = np.r_[end1+1, [nobs]] return start, end def dummy_nested(d1, d2, method='full'): '''unfinished and incomplete mainly copy past dummy_product dummy variable from product of two dummy variables Parameters ---------- d1, d2 : ndarray two dummy variables, d2 is assumed to be nested in d1 Assumes full set for methods 'drop-last' and 'drop-first'. method : {'full', 'drop-last', 'drop-first'} 'full' returns the full product, which in this case is d2. The drop methods provide an effects encoding: (constant, main effects, subgroup effects). The first or last columns of the dummy variable (i.e. levels) are dropped to get full rank encoding. Returns ------- dummy : ndarray dummy variable for product, see method ''' if method == 'full': return d2 start1, end1 = dummy_limits(d1) start2, end2 = dummy_limits(d2) first = np.in1d(start2, start1) last = np.in1d(end2, end1) equal = (first == last) col_dropf = ~first*~equal col_dropl = ~last*~equal if method == 'drop-last': d12rl = dummy_product(d1[:,:-1], d2[:,:-1]) dd = np.column_stack((np.ones(d1.shape[0], int), d1[:,:-1], d2[:,col_dropl])) #Note: dtype int should preserve dtype of d1 and d2 elif method == 'drop-first': d12r = dummy_product(d1[:,1:], d2[:,1:]) dd = np.column_stack((np.ones(d1.shape[0], int), d1[:,1:], d2[:,col_dropf])) else: raise ValueError('method not recognized') return dd, col_dropf, col_dropl class DummyTransform(object): '''Conversion between full rank dummy encodings y = X b + u b = C a a = C^{-1} b y = X C a + u define Z = X C, then y = Z a + u contrasts: R_b b = r R_a a = R_b C a = r where R_a = R_b C Here C is the transform matrix, with dot_left and dot_right as the main methods, and the same for the inverse transform matrix, C^{-1} Note: - The class was mainly written to keep left and right straight. - No checking is done. - not sure yet if method names make sense ''' def __init__(self, d1, d2): '''C such that d1 C = d2, with d1 = X, d2 = Z should be (x, z) in arguments ? ''' self.transf_matrix = np.linalg.lstsq(d1, d2)[0] self.invtransf_matrix = np.linalg.lstsq(d2, d1)[0] def dot_left(self, a): ''' b = C a ''' return np.dot(self.transf_matrix, a) def dot_right(self, x): ''' z = x C ''' return np.dot(x, self.transf_matrix) def inv_dot_left(self, b): ''' a = C^{-1} b ''' return np.dot(self.invtransf_matrix, b) def inv_dot_right(self, z): ''' x = z C^{-1} ''' return np.dot(z, self.invtransf_matrix) def groupmean_d(x, d): '''groupmeans using dummy variables Parameters ---------- x : array_like, ndim data array, tested for 1,2 and 3 dimensions d : ndarray, 1d dummy variable, needs to have the same length as x in axis 0. Returns ------- groupmeans : ndarray, ndim-1 means for each group along axis 0, the levels of the groups are the last axis Notes ----- This will be memory intensive if there are many levels in the categorical variable, i.e. many columns in the dummy variable. In this case it is recommended to use a more efficient version. ''' x = np.asarray(x) ## if x.ndim == 1: ## nvars = 1 ## else: nvars = x.ndim + 1 sli = [slice(None)] + [None]*(nvars-2) + [slice(None)] return (x[...,None] * d[sli]).sum(0)*1./d.sum(0) class TwoWay(object): '''a wrapper class for two way anova type of analysis with OLS currently mainly to bring things together Notes ----- unclear: adding multiple test might assume block design or orthogonality This estimates the full dummy version with OLS. The drop first dummy representation can be recovered through the transform method. TODO: add more methods, tests, pairwise, multiple, marginal effects try out what can be added for userfriendly access. missing: ANOVA table ''' def __init__(self, endog, factor1, factor2, varnames=None): self.nobs = factor1.shape[0] if varnames is None: vname1 = 'a' vname2 = 'b' else: vname1, vname1 = varnames self.d1, self.d1_labels = d1, d1_labels = dummy_1d(factor1, vname1) self.d2, self.d2_labels = d2, d2_labels = dummy_1d(factor2, vname2) self.nlevel1 = nlevel1 = d1.shape[1] self.nlevel2 = nlevel2 = d2.shape[1] #get product dummies res = contrast_product(d1_labels, d2_labels) prodlab, C1, C1lab, C2, C2lab, _ = res self.prod_label, self.C1, self.C1_label, self.C2, self.C2_label, _ = res dp_full = dummy_product(d1, d2, method='full') dp_dropf = dummy_product(d1, d2, method='drop-first') self.transform = DummyTransform(dp_full, dp_dropf) #estimate the model self.nvars = dp_full.shape[1] self.exog = dp_full self.resols = sm.OLS(endog, dp_full).fit() self.params = self.resols.params #get transformed parameters, (constant, main, interaction effect) self.params_dropf = self.transform.inv_dot_left(self.params) self.start_interaction = 1 + (nlevel1 - 1) + (nlevel2 - 1) self.n_interaction = self.nvars - self.start_interaction #convert to cached property def r_nointer(self): '''contrast/restriction matrix for no interaction ''' nia = self.n_interaction R_nointer = np.hstack((np.zeros((nia, self.nvars-nia)), np.eye(nia))) #inter_direct = resols_full_dropf.tval[-nia:] R_nointer_transf = self.transform.inv_dot_right(R_nointer) self.R_nointer_transf = R_nointer_transf return R_nointer_transf def ttest_interaction(self): '''ttests for no-interaction terms are zero ''' #use self.r_nointer instead nia = self.n_interaction R_nointer = np.hstack((np.zeros((nia, self.nvars-nia)), np.eye(nia))) #inter_direct = resols_full_dropf.tval[-nia:] R_nointer_transf = self.transform.inv_dot_right(R_nointer) self.R_nointer_transf = R_nointer_transf t_res = self.resols.t_test(R_nointer_transf) return t_res def ftest_interaction(self): '''ttests for no-interaction terms are zero ''' R_nointer_transf = self.r_nointer() return self.resols.f_test(R_nointer_transf) def ttest_conditional_effect(self, factorind): if factorind == 1: return self.resols.t_test(self.C1), self.C1_label else: return self.resols.t_test(self.C2), self.C2_label def summary_coeff(self): from statsmodels.iolib import SimpleTable params_arr = self.params.reshape(self.nlevel1, self.nlevel2) stubs = self.d1_labels headers = self.d2_labels title = 'Estimated Coefficients by factors' table_fmt = dict( data_fmts = ["%#10.4g"]*self.nlevel2) return SimpleTable(params_arr, headers, stubs, title=title, txt_fmt=table_fmt) #--------------- tests from numpy.testing import assert_equal #TODO: several tests still missing, several are in the example with print class TestContrastTools(object): def __init__(self): self.v1name = ['a0', 'a1', 'a2'] self.v2name = ['b0', 'b1'] self.d1 = np.array([[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]) def test_dummy_1d(self): x = np.array(['F', 'F', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'F', 'M', 'M'], dtype='|S1') d, labels = (np.array([[1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]]), ['gender_F', 'gender_M']) res_d, res_labels = dummy_1d(x, varname='gender') assert_equal(res_d, d) assert_equal(res_labels, labels) def test_contrast_product(self): res_cp = contrast_product(self.v1name, self.v2name) res_t = [0]*6 res_t[0] = ['a0_b0', 'a0_b1', 'a1_b0', 'a1_b1', 'a2_b0', 'a2_b1'] res_t[1] = np.array([[-1., 0., 1., 0., 0., 0.], [ 0., -1., 0., 1., 0., 0.], [-1., 0., 0., 0., 1., 0.], [ 0., -1., 0., 0., 0., 1.]]) res_t[2] = ['a1_b0-a0_b0', 'a1_b1-a0_b1', 'a2_b0-a0_b0', 'a2_b1-a0_b1'] res_t[3] = np.array([[-1., 1., 0., 0., 0., 0.], [ 0., 0., -1., 1., 0., 0.], [ 0., 0., 0., 0., -1., 1.]]) res_t[4] = ['a0_b1-a0_b0', 'a1_b1-a1_b0', 'a2_b1-a2_b0'] for ii in range(5): np.testing.assert_equal(res_cp[ii], res_t[ii], err_msg=str(ii)) def test_dummy_limits(self): b,e = dummy_limits(self.d1) assert_equal(b, np.array([0, 4, 8])) assert_equal(e, np.array([ 4, 8, 12])) if __name__ == '__main__': tt = TestContrastTools() tt.test_contrast_product() tt.test_dummy_1d() tt.test_dummy_limits() import statsmodels.api as sm examples = ['small', 'large', None][1] v1name = ['a0', 'a1', 'a2'] v2name = ['b0', 'b1'] res_cp = contrast_product(v1name, v2name) print res_cp y = np.arange(12) x1 = np.arange(12)//4 x2 = np.arange(12)//2%2 if 'small' in examples: d1, d1_labels = dummy_1d(x1) d2, d2_labels = dummy_1d(x2) if 'large' in examples: x1 = np.repeat(x1, 5, axis=0) x2 = np.repeat(x2, 5, axis=0) nobs = x1.shape[0] d1, d1_labels = dummy_1d(x1) d2, d2_labels = dummy_1d(x2) dd_full = dummy_product(d1, d2, method='full') dd_dropl = dummy_product(d1, d2, method='drop-last') dd_dropf = dummy_product(d1, d2, method='drop-first') #Note: full parameterization of dummies is orthogonal #np.eye(6)*10 in "large" example print (np.dot(dd_full.T, dd_full) == np.diag(dd_full.sum(0))).all() #check that transforms work #generate 3 data sets with the 3 different parameterizations effect_size = [1., 0.01][1] noise_scale = [0.001, 0.1][0] noise = noise_scale * np.random.randn(nobs) beta = effect_size * np.arange(1,7) ydata_full = (dd_full * beta).sum(1) + noise ydata_dropl = (dd_dropl * beta).sum(1) + noise ydata_dropf = (dd_dropf * beta).sum(1) + noise resols_full_full = sm.OLS(ydata_full, dd_full).fit() resols_full_dropf = sm.OLS(ydata_full, dd_dropf).fit() params_f_f = resols_full_full.params params_f_df = resols_full_dropf.params resols_dropf_full = sm.OLS(ydata_dropf, dd_full).fit() resols_dropf_dropf = sm.OLS(ydata_dropf, dd_dropf).fit() params_df_f = resols_dropf_full.params params_df_df = resols_dropf_dropf.params tr_of = np.linalg.lstsq(dd_dropf, dd_full)[0] tr_fo = np.linalg.lstsq(dd_full, dd_dropf)[0] print np.dot(tr_fo, params_df_df) - params_df_f print np.dot(tr_of, params_f_f) - params_f_df transf_f_df = DummyTransform(dd_full, dd_dropf) print np.max(np.abs((dd_full - transf_f_df.inv_dot_right(dd_dropf)))) print np.max(np.abs((dd_dropf - transf_f_df.dot_right(dd_full)))) print np.max(np.abs((params_df_df - transf_f_df.inv_dot_left(params_df_f)))) np.max(np.abs((params_f_df - transf_f_df.inv_dot_left(params_f_f)))) prodlab, C1, C1lab, C2, C2lab,_ = contrast_product(v1name, v2name) print '\ntvalues for no effect of factor 1' print 'each test is conditional on a level of factor 2' print C1lab print resols_dropf_full.t_test(C1).tvalue print '\ntvalues for no effect of factor 2' print 'each test is conditional on a level of factor 1' print C2lab print resols_dropf_full.t_test(C2).tvalue #covariance matrix of restrictions C2, note: orthogonal resols_dropf_full.cov_params(C2) #testing for no interaction effect R_noint = np.hstack((np.zeros((2,4)), np.eye(2))) inter_direct = resols_full_dropf.tvalues[-2:] inter_transf = resols_full_full.t_test(transf_f_df.inv_dot_right(R_noint)).tvalue print np.max(np.abs((inter_direct - inter_transf))) #now with class version tw = TwoWay(ydata_dropf, x1, x2) print tw.ttest_interaction().tvalue print tw.ttest_interaction().pvalue print tw.ftest_interaction().fvalue print tw.ftest_interaction().pvalue print tw.ttest_conditional_effect(1)[0].tvalue print tw.ttest_conditional_effect(2)[0].tvalue print tw.summary_coeff() ''' documentation for early examples while developing - some have changed already >>> y = np.arange(12) >>> y array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) >>> x1 = np.arange(12)//4 >>> x1 array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]) >>> x2 = np.arange(12)//2%2 >>> x2 array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]) >>> d1 = dummy_1d(x1) >>> d1 array([[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1]]) >>> d2 = dummy_1d(x2) >>> d2 array([[1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]]) >>> d12 = dummy_product(d1, d2) >>> d12 array([[1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]]) >>> d12rl = dummy_product(d1[:,:-1], d2[:,:-1]) >>> np.column_stack((np.ones(d1.shape[0]), d1[:,:-1], d2[:,:-1],d12rl)) array([[ 1., 1., 0., 1., 1., 0.], [ 1., 1., 0., 1., 1., 0.], [ 1., 1., 0., 0., 0., 0.], [ 1., 1., 0., 0., 0., 0.], [ 1., 0., 1., 1., 0., 1.], [ 1., 0., 1., 1., 0., 1.], [ 1., 0., 1., 0., 0., 0.], [ 1., 0., 1., 0., 0., 0.], [ 1., 0., 0., 1., 0., 0.], [ 1., 0., 0., 1., 0., 0.], [ 1., 0., 0., 0., 0., 0.], [ 1., 0., 0., 0., 0., 0.]]) ''' #nprod = ['%s_%s' % (i,j) for i in ['a0', 'a1', 'a2'] for j in ['b0', 'b1']] #>>> [''.join(['%s%s' % (signstr(c),v) for c,v in zip(row, nprod) if c != 0]) # for row in np.kron(dd[1:], np.eye(2))] ''' >>> nprod = ['%s_%s' % (i,j) for i in ['a0', 'a1', 'a2'] for j in ['b0', 'b1']] >>> nprod ['a0_b0', 'a0_b1', 'a1_b0', 'a1_b1', 'a2_b0', 'a2_b1'] >>> [''.join(['%s%s' % (signstr(c),v) for c,v in zip(row, nprod) if c != 0]) for row in np.kron(dd[1:], np.eye(2))] ['-a0b0+a1b0', '-a0b1+a1b1', '-a0b0+a2b0', '-a0b1+a2b1'] >>> [''.join(['%s%s' % (signstr(c),v) for c,v in zip(row, nprod)[::-1] if c != 0]) for row in np.kron(dd[1:], np.eye(2))] ['+a1_b0-a0_b0', '+a1_b1-a0_b1', '+a2_b0-a0_b0', '+a2_b1-a0_b1'] >>> np.r_[[[1,0,0,0,0]],contrast_all_one(5)] array([[ 1., 0., 0., 0., 0.], [ 1., -1., 0., 0., 0.], [ 1., 0., -1., 0., 0.], [ 1., 0., 0., -1., 0.], [ 1., 0., 0., 0., -1.]]) >>> idxprod = [(i,j) for i in range(3) for j in range(2)] >>> idxprod [(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1)] >>> np.array(idxprod).reshape(2,3,2,order='F')[:,:,0] array([[0, 1, 2], [0, 1, 2]]) >>> np.array(idxprod).reshape(2,3,2,order='F')[:,:,1] array([[0, 0, 0], [1, 1, 1]]) >>> dd3_ = np.r_[[[0,0,0]],contrast_all_one(3)] pairwise contrasts and reparameterization dd = np.r_[[[1,0,0,0,0]],-contrast_all_one(5)] >>> dd array([[ 1., 0., 0., 0., 0.], [-1., 1., 0., 0., 0.], [-1., 0., 1., 0., 0.], [-1., 0., 0., 1., 0.], [-1., 0., 0., 0., 1.]]) >>> np.dot(dd.T, np.arange(5)) array([-10., 1., 2., 3., 4.]) >>> np.round(np.linalg.inv(dd.T)).astype(int) array([[1, 1, 1, 1, 1], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) >>> np.round(np.linalg.inv(dd)).astype(int) array([[1, 0, 0, 0, 0], [1, 1, 0, 0, 0], [1, 0, 1, 0, 0], [1, 0, 0, 1, 0], [1, 0, 0, 0, 1]]) >>> dd array([[ 1., 0., 0., 0., 0.], [-1., 1., 0., 0., 0.], [-1., 0., 1., 0., 0.], [-1., 0., 0., 1., 0.], [-1., 0., 0., 0., 1.]]) >>> ddinv=np.round(np.linalg.inv(dd.T)).astype(int) >>> np.dot(ddinv, np.arange(5)) array([10, 1, 2, 3, 4]) >>> np.dot(dd, np.arange(5)) array([ 0., 1., 2., 3., 4.]) >>> np.dot(dd, 5+np.arange(5)) array([ 5., 1., 2., 3., 4.]) >>> ddinv2 = np.round(np.linalg.inv(dd)).astype(int) >>> np.dot(ddinv2, np.arange(5)) array([0, 1, 2, 3, 4]) >>> np.dot(ddinv2, 5+np.arange(5)) array([ 5, 11, 12, 13, 14]) >>> np.dot(ddinv2, [5, 0, 0 , 1, 2]) array([5, 5, 5, 6, 7]) >>> np.dot(ddinv2, dd) array([[ 1., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 1., 0.], [ 0., 0., 0., 0., 1.]]) >>> dd3 = -np.r_[[[1,0,0]],contrast_all_one(3)] >>> dd2 = -np.r_[[[1,0]],contrast_all_one(2)] >>> np.kron(np.eye(3), dd2) array([[-1., 0., 0., 0., 0., 0.], [-1., 1., 0., 0., 0., 0.], [ 0., 0., -1., 0., 0., 0.], [ 0., 0., -1., 1., 0., 0.], [ 0., 0., 0., 0., -1., 0.], [ 0., 0., 0., 0., -1., 1.]]) >>> dd2 array([[-1., 0.], [-1., 1.]]) >>> np.kron(np.eye(3), dd2[1:]) array([[-1., 1., 0., 0., 0., 0.], [ 0., 0., -1., 1., 0., 0.], [ 0., 0., 0., 0., -1., 1.]]) >>> np.kron(dd[1:], np.eye(2)) array([[-1., 0., 1., 0., 0., 0.], [ 0., -1., 0., 1., 0., 0.], [-1., 0., 0., 0., 1., 0.], [ 0., -1., 0., 0., 0., 1.]]) d_ = np.r_[[[1,0,0,0,0]],contrast_all_one(5)] >>> d_ array([[ 1., 0., 0., 0., 0.], [ 1., -1., 0., 0., 0.], [ 1., 0., -1., 0., 0.], [ 1., 0., 0., -1., 0.], [ 1., 0., 0., 0., -1.]]) >>> np.round(np.linalg.pinv(d_)).astype(int) array([[ 1, 0, 0, 0, 0], [ 1, -1, 0, 0, 0], [ 1, 0, -1, 0, 0], [ 1, 0, 0, -1, 0], [ 1, 0, 0, 0, -1]]) >>> np.linalg.inv(d_).astype(int) array([[ 1, 0, 0, 0, 0], [ 1, -1, 0, 0, 0], [ 1, 0, -1, 0, 0], [ 1, 0, 0, -1, 0], [ 1, 0, 0, 0, -1]]) group means >>> sli = [slice(None)] + [None]*(3-2) + [slice(None)] >>> (np.column_stack((y, x1, x2))[...,None] * d1[sli]).sum(0)*1./d1.sum(0) array([[ 1.5, 5.5, 9.5], [ 0. , 1. , 2. ], [ 0.5, 0.5, 0.5]]) >>> [(z[:,None] * d1).sum(0)*1./d1.sum(0) for z in np.column_stack((y, x1, x2)).T] [array([ 1.5, 5.5, 9.5]), array([ 0., 1., 2.]), array([ 0.5, 0.5, 0.5])] >>> ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/diagnostic.py000066400000000000000000001470041224417117700260140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Various Statistical Tests Author: josef-pktd License: BSD-3 Notes ----- Almost fully verified against R or Gretl, not all options are the same. In many cases of Lagrange multiplier tests both the LM test and the F test is returned. In some but not all cases, R has the option to choose the test statistic. Some alternative test statistic results have not been verified. TODO * refactor to store intermediate results * how easy is it to attach a test that is a class to a result instance, for example CompareCox as a method compare_cox(self, other) ? * StatTestMC has been moved and should be deleted missing: * pvalues for breaks_hansen * additional options, compare with R, check where ddof is appropriate * new tests: - breaks_ap, more recent breaks tests - specification tests against nonparametric alternatives """ import numpy as np from scipy import stats from statsmodels.regression.linear_model import OLS from statsmodels.tools.tools import add_constant from statsmodels.tsa.stattools import acf, adfuller from statsmodels.tsa.tsatools import lagmat #get the old signature back so the examples work def unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False): return adfuller(x, maxlag=maxlag, regression=trendorder, autolag=autolag, store=store, regresults=False) #TODO: I like the bunch pattern for this too. class ResultsStore(object): def __str__(self): return self._str class CompareCox(object): '''Cox Test for non-nested models Parameters ---------- results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code produces correct results for Example 8.3, Greene ''' def run(self, results_x, results_z, attach=True): '''run Cox test for non-nested models Parameters ---------- results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns ------- tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue : float two-sided pvalue for the t statistic Notes ----- Tests of non-nested hypothesis might not provide unambiguous answers. The test should be performed in both directions and it is possible that both or neither test rejects. see ??? for more information. References ---------- ??? ''' if not np.allclose(results_x.model.endog, results_z.model.endog): raise ValueError('endogenous variables in models are not the same') nobs = results_x.model.endog.shape[0] x = results_x.model.exog z = results_z.model.exog sigma2_x = results_x.ssr/nobs sigma2_z = results_z.ssr/nobs yhat_x = results_x.fittedvalues yhat_z = results_z.fittedvalues res_dx = OLS(yhat_x, z).fit() err_zx = res_dx.resid res_xzx = OLS(err_zx, x).fit() err_xzx = res_xzx.resid sigma2_zx = sigma2_x + np.dot(err_zx.T, err_zx)/nobs c01 = nobs/2. * (np.log(sigma2_z) - np.log(sigma2_zx)) v01 = sigma2_x * np.dot(err_xzx.T, err_xzx) / sigma2_zx**2 q = c01 / np.sqrt(v01) pval = 2*stats.norm.sf(np.abs(q)) if attach: self.res_dx = res_dx self.res_xzx = res_xzx self.c01 = c01 self.v01 = v01 self.q = q self.pvalue = pval self.dist = stats.norm return q, pval def __call__(self, results_x, results_z): return self.run(results_x, results_z, attach=False) compare_cox = CompareCox() compare_cox.__doc__ = CompareCox.__doc__ class CompareJ(object): '''J-Test for comparing non-nested models Parameters ---------- results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 produces correct results for Example 8.3, Greene - not checked yet #currently an exception, but I don't have clean reload in python session check what results should be attached ''' def run(self, results_x, results_z, attach=True): '''run J-test for non-nested models Parameters ---------- results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns ------- tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue : float two-sided pvalue for the t statistic Notes ----- Tests of non-nested hypothesis might not provide unambiguous answers. The test should be performed in both directions and it is possible that both or neither test rejects. see ??? for more information. References ---------- ??? ''' if not np.allclose(results_x.model.endog, results_z.model.endog): raise ValueError('endogenous variables in models are not the same') nobs = results_x.model.endog.shape[0] y = results_x.model.endog x = results_x.model.exog z = results_z.model.exog #sigma2_x = results_x.ssr/nobs #sigma2_z = results_z.ssr/nobs yhat_x = results_x.fittedvalues #yhat_z = results_z.fittedvalues res_zx = OLS(y, np.column_stack((yhat_x, z))).fit() self.res_zx = res_zx #for testing tstat = res_zx.tvalues[0] pval = res_zx.pvalues[0] if attach: self.res_zx = res_zx self.dist = stats.t(res_zx.model.df_resid) self.teststat = tstat self.pvalue = pval return tstat, pval def __call__(self, results_x, results_z): return self.run(results_x, results_z, attach=False) compare_j = CompareJ() compare_j.__doc__ = CompareJ.__doc__ def acorr_ljungbox(x, lags=None, boxpierce=False): '''Ljung-Box test for no autocorrelation Parameters ---------- x : array_like, 1d data series, regression residuals when used as diagnostic test lags : None, int or array_like If lags is an integer then this is taken to be the largest lag that is included, the test result is reported for all smaller lag length. If lags is a list or array, then all lags are included up to the largest lag in the list, however only the tests for the lags in the list are reported. If lags is None, then the default maxlag is 12*(nobs/100)^{1/4} boxpierce : {False, True} If true, then additional to the results of the Ljung-Box test also the Box-Pierce test results are returned Returns ------- lbvalue : float or array test statistic pvalue : float or array p-value based on chi-square distribution bpvalue : (optionsal), float or array test statistic for Box-Pierce test bppvalue : (optional), float or array p-value based for Box-Pierce test on chi-square distribution Notes ----- Ljung-Box and Box-Pierce statistic differ in their scaling of the autocorrelation function. Ljung-Box test is reported to have better small sample properties. TODO: could be extended to work with more than one series 1d or nd ? axis ? ravel ? needs more testing ''Verification'' Looks correctly sized in Monte Carlo studies. not yet compared to verified values Examples -------- see example script References ---------- Greene Wikipedia ''' x = np.asarray(x) nobs = x.shape[0] if lags is None: lags = range(1,41) #TODO: check default; SS: changed to 40 elif isinstance(lags, int): lags = range(1,lags+1) maxlag = max(lags) lags = np.asarray(lags) acfx = acf(x, nlags=maxlag) # normalize by nobs not (nobs-nlags) # SS: unbiased=False is default now # acf2norm = acfx[1:maxlag+1]**2 / (nobs - np.arange(1,maxlag+1)) acf2norm = acfx[1:maxlag+1]**2 / (nobs - np.arange(1,maxlag+1)) qljungbox = nobs * (nobs+2) * np.cumsum(acf2norm)[lags-1] pval = stats.chi2.sf(qljungbox, lags) if not boxpierce: return qljungbox, pval else: qboxpierce = nobs * np.cumsum(acfx[1:maxlag+1]**2)[lags-1] pvalbp = stats.chi2.sf(qboxpierce, lags) return qljungbox, pval, qboxpierce, pvalbp def acorr_lm(x, maxlag=None, autolag='AIC', store=False, regresults=False): '''Lagrange Multiplier tests for autocorrelation This is a generic Lagrange Multiplier test for autocorrelation. I don't have a reference for it, but it returns Engle's ARCH test if x is the squared residual array. A variation on it with additional exogenous variables is the Breush-Godfrey autocorrelation test. Parameters ---------- resid : ndarray, (nobs,) residuals from an estimation, or time series maxlag : int highest lag to use autolag : None or string If None, then a fixed number of lags given by maxlag is used. store : bool If true then the intermediate results are also returned Returns ------- lm : float Lagrange multiplier test statistic lmpval : float p-value for Lagrange multiplier test fval : float fstatistic for F test, alternative version of the same test based on F test for the parameter restriction fpval : float pvalue for F test resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=True See Also -------- het_arch acorr_breush_godfrey acorr_ljung_box ''' if regresults: store = True x = np.asarray(x) nobs = x.shape[0] if maxlag is None: #for adf from Greene referencing Schwert 1989 maxlag = int(np.ceil(12. * np.power(nobs/100., 1/4.)))#nobs//4 #TODO: check default, or do AIC/BIC xdiff = np.diff(x) # xdall = lagmat(x[:,None], maxlag, trim='both') nobs = xdall.shape[0] xdall = np.c_[np.ones((nobs,1)), xdall] xshort = x[-nobs:] if store: resstore = ResultsStore() if autolag: #search for lag length with highest information criteria #Note: I use the same number of observations to have comparable IC results = {} for mlag in range(1, maxlag+1): results[mlag] = OLS(xshort, xdall[:,:mlag+1]).fit() if autolag.lower() == 'aic': bestic, icbestlag = min((v.aic,k) for k,v in results.iteritems()) elif autolag.lower() == 'bic': icbest, icbestlag = min((v.bic,k) for k,v in results.iteritems()) else: raise ValueError("autolag can only be None, 'AIC' or 'BIC'") #rerun ols with best ic xdall = lagmat(x[:,None], icbestlag, trim='both') nobs = xdall.shape[0] xdall = np.c_[np.ones((nobs,1)), xdall] xshort = x[-nobs:] usedlag = icbestlag if regresults: resstore.results = results else: usedlag = maxlag resols = OLS(xshort, xdall[:,:usedlag+1]).fit() fval = resols.fvalue fpval = resols.f_pvalue lm = nobs * resols.rsquared lmpval = stats.chi2.sf(lm, usedlag) # Note: degrees of freedom for LM test is nvars minus constant = usedlags #return fval, fpval, lm, lmpval if store: resstore.resols = resols resstore.usedlag = usedlag return lm, lmpval, fval, fpval, resstore else: return lm, lmpval, fval, fpval def het_arch(resid, maxlag=None, autolag=None, store=False, regresults=False, ddof=0): '''Enlge's Test for Autoregressive Conditional Heteroscedasticity (ARCH) Parameters ---------- resid : ndarray, (nobs,) residuals from an estimation, or time series maxlag : int highest lag to use autolag : None or string If None, then a fixed number of lags given by maxlag is used. store : bool If true then the intermediate results are also returned ddof : int Not Implemented Yet If the residuals are from a regression, or ARMA estimation, then there are recommendations to correct the degrees of freedom by the number of parameters that have been estimated, for example ddof=p+a for an ARMA(p,q) (need reference, based on discussion on R finance mailinglist) Returns ------- lm : float Lagrange multiplier test statistic lmpval : float p-value for Lagrange multiplier test fval : float fstatistic for F test, alternative version of the same test based on F test for the parameter restriction fpval : float pvalue for F test resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=True Notes ----- verified agains R:FinTS::ArchTest ''' return acorr_lm(resid**2, maxlag=maxlag, autolag=autolag, store=store, regresults=regresults) def acorr_breush_godfrey(results, nlags=None, store=False): '''Breush Godfrey Lagrange Multiplier tests for residual autocorrelation Parameters ---------- results : Result instance Estimation results for which the residuals are tested for serial correlation nlags : int Number of lags to include in the auxiliary regression. (nlags is highest lag) store : bool If store is true, then an additional class instance that contains intermediate results is returned. Returns ------- lm : float Lagrange multiplier test statistic lmpval : float p-value for Lagrange multiplier test fval : float fstatistic for F test, alternative version of the same test based on F test for the parameter restriction fpval : float pvalue for F test resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=True Notes ----- BG adds lags of residual to exog in the design matrix for the auxiliary regression with residuals as endog, see Greene 12.7.1. References ---------- Greene Econometrics, 5th edition ''' x = np.asarray(results.resid) exog_old = results.model.exog nobs = x.shape[0] if nlags is None: #for adf from Greene referencing Schwert 1989 nlags = np.trunc(12. * np.power(nobs/100., 1/4.))#nobs//4 #TODO: check default, or do AIC/BIC x = np.concatenate((np.zeros(nlags), x)) #xdiff = np.diff(x) # xdall = lagmat(x[:,None], nlags, trim='both') nobs = xdall.shape[0] xdall = np.c_[np.ones((nobs,1)), xdall] xshort = x[-nobs:] exog = np.column_stack((exog_old, xdall)) k_vars = exog.shape[1] if store: resstore = ResultsStore() resols = OLS(xshort, exog).fit() ft = resols.f_test(np.eye(nlags, k_vars, k_vars - nlags)) fval = ft.fvalue fpval = ft.pvalue fval = np.squeeze(fval)[()] #TODO: fix this in ContrastResults fpval = np.squeeze(fpval)[()] lm = nobs * resols.rsquared lmpval = stats.chi2.sf(lm, nlags) # Note: degrees of freedom for LM test is nvars minus constant = usedlags #return fval, fpval, lm, lmpval if store: resstore.resols = resols resstore.usedlag = nlags return lm, lmpval, fval, fpval, resstore else: return lm, lmpval, fval, fpval def het_breushpagan(resid, exog_het): '''Breush-Pagan Lagrange Multiplier test for heteroscedasticity The tests the hypothesis that the residual variance does not depend on the variables in x in the form :math: \sigma_i = \\sigma * f(\\alpha_0 + \\alpha z_i) Homoscedasticity implies that $\\alpha=0$ Parameters ---------- resid : arraylike, (nobs,) For the Breush-Pagan test, this should be the residual of a regression. If an array is given in exog, then the residuals are calculated by the an OLS regression or resid on exog. In this case resid should contain the dependent variable. Exog can be the same as x. TODO: I dropped the exog option, should I add it back? exog_het : array_like, (nobs, nvars) This contains variables that might create data dependent heteroscedasticity. Returns ------- lm : float lagrange multiplier statistic lm_pvalue :float p-value of lagrange multiplier test fvalue : float f-statistic of the hypothesis that the error variance does not depend on x f_pvalue : float p-value for the f-statistic Notes ----- Assumes x contains constant (for counting dof and calculation of R^2). In the general description of LM test, Greene mentions that this test exaggerates the significance of results in small or moderately large samples. In this case the F-statistic is preferrable. *Verification* Chisquare test statistic is exactly (<1e-13) the same result as bptest in R-stats with defaults (studentize=True). Implementation This is calculated using the generic formula for LM test using $R^2$ (Greene, section 17.6) and not with the explicit formula (Greene, section 11.4.3). The degrees of freedom for the p-value assume x is full rank. References ---------- http://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test Greene 5th edition Breush, Pagan article ''' x = np.asarray(exog_het) y = np.asarray(resid)**2 nobs, nvars = x.shape resols = OLS(y, x).fit() fval = resols.fvalue fpval = resols.f_pvalue lm = nobs * resols.rsquared # Note: degrees of freedom for LM test is nvars minus constant return lm, stats.chi2.sf(lm, nvars-1), fval, fpval def het_white(resid, exog, retres=False): '''White's Lagrange Multiplier Test for Heteroscedasticity Parameters ---------- resid : array_like residuals, square of it is used as endogenous variable exog : array_like possible explanatory variables for variance, squares and interaction terms are included in the auxilliary regression. resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=True Returns ------- lm : float lagrange multiplier statistic lm_pvalue :float p-value of lagrange multiplier test fvalue : float f-statistic of the hypothesis that the error variance does not depend on x. This is an alternative test variant not the original LM test. f_pvalue : float p-value for the f-statistic Notes ----- assumes x contains constant (for counting dof) question: does f-statistic make sense? constant ? References ---------- Greene section 11.4.1 5th edition p. 222 now test statistic reproduces Greene 5th, example 11.3 ''' x = np.asarray(exog) y = np.asarray(resid) if x.ndim == 1: raise ValueError('x should have constant and at least one more variable') nobs, nvars0 = x.shape i0,i1 = np.triu_indices(nvars0) exog = x[:,i0]*x[:,i1] nobs, nvars = exog.shape assert nvars == nvars0*(nvars0-1)/2. + nvars0 resols = OLS(y**2, exog).fit() fval = resols.fvalue fpval = resols.f_pvalue lm = nobs * resols.rsquared # Note: degrees of freedom for LM test is nvars minus constant #degrees of freedom take possible reduced rank in exog into account #df_model checks the rank to determine df from statsmodels.tools.tools import rank #extra calculation that can be removed: assert resols.df_model == rank(exog) - 1 lmpval = stats.chi2.sf(lm, resols.df_model) return lm, lmpval, fval, fpval def _het_goldfeldquandt2_old(y, x, idx, split=None, retres=False): '''test whether variance is the same in 2 subsamples Parameters ---------- y : array_like endogenous variable x : array_like exogenous variable, regressors idx : integer column index of variable according to which observations are sorted for the split split : None or integer or float in intervall (0,1) index at which sample is split. If 01: fpval = stats.f.sf(fval, resols1.df_resid, resols2.df_resid) ordering = 'larger' else: fval = 1./fval; fpval = stats.f.sf(fval, resols2.df_resid, resols1.df_resid) ordering = 'smaller' if retres: res = ResultsStore() res.__doc__ = 'Test Results for Goldfeld-Quandt test of heterogeneity' res.fval = fval res.fpval = fpval res.df_fval = (resols2.df_resid, resols1.df_resid) res.resols1 = resols1 res.resols2 = resols2 res.ordering = ordering res.split = split #res.__str__ res._str = '''The Goldfeld-Quandt test for null hypothesis that the variance in the second subsample is %s than in the first subsample: F-statistic =%8.4f and p-value =%8.4f''' % (ordering, fval, fpval) return res else: return fval, fpval class HetGoldfeldQuandt(object): '''test whether variance is the same in 2 subsamples Parameters ---------- y : array_like endogenous variable x : array_like exogenous variable, regressors idx : integer column index of variable according to which observations are sorted for the split split : None or integer or float in intervall (0,1) index at which sample is split. If 01: if alternative.lower() in ['i', 'inc', 'increasing']: fpval = stats.f.sf(fval, resols1.df_resid, resols2.df_resid) ordering = 'increasing' elif alternative.lower() in ['d', 'dec', 'decreasing']: fval = fval; fpval = stats.f.sf(1./fval, resols2.df_resid, resols1.df_resid) ordering = 'decreasing' elif alternative.lower() in ['2', '2-sided', 'two-sided']: fpval_sm = stats.f.cdf(fval, resols2.df_resid, resols1.df_resid) fpval_la = stats.f.sf(fval, resols2.df_resid, resols1.df_resid) fpval = 2*min(fpval_sm, fpval_la) ordering = 'two-sided' else: raise ValueError('invalid alternative') if attach: res = self res.__doc__ = 'Test Results for Goldfeld-Quandt test of heterogeneity' res.fval = fval res.fpval = fpval res.df_fval = (resols2.df_resid, resols1.df_resid) res.resols1 = resols1 res.resols2 = resols2 res.ordering = ordering res.split = split #res.__str__ #TODO: check if string works res._str = '''The Goldfeld-Quandt test for null hypothesis that the variance in the second subsample is %s than in the first subsample: F-statistic =%8.4f and p-value =%8.4f''' % (ordering, fval, fpval) return fval, fpval, ordering #return self def __str__(self): try: return self._str except AttributeError: return repr(self) #TODO: missing the alternative option in call def __call__(self, y, x, idx=None, split=None, drop=None, alternative='increasing'): return self.run(y, x, idx=idx, split=split, drop=drop, attach=False, alternative=alternative) het_goldfeldquandt = HetGoldfeldQuandt() het_goldfeldquandt.__doc__ = het_goldfeldquandt.run.__doc__ def linear_harvey_collier(res): '''Harvey Collier test for linearity The Null hypothesis is that the regression is correctly modeled as linear. Parameters ---------- res : Result instance Returns ------- tvalue : float test statistic, based on ttest_1sample pvalue : float pvalue of the test Notes ----- TODO: add sort_by option This test is a t-test that the mean of the recursive ols residuals is zero. Calculating the recursive residuals might take some time for large samples. ''' #I think this has different ddof than #B.H. Baltagi, Econometrics, 2011, chapter 8 #but it matches Gretl and R:lmtest, pvalue at decimal=13 rr = recursive_olsresiduals(res, skip=3, alpha=0.95) from scipy import stats return stats.ttest_1samp(rr[3][3:], 0) def linear_rainbow(res, frac = 0.5): '''Rainbow test for linearity The Null hypothesis is that the regression is correctly modelled as linear. The alternative for which the power might be large are convex, check Parameters ---------- res : Result instance Returns ------- fstat : float test statistic based of F test pvalue : float pvalue of the test ''' nobs = res.nobs endog = res.model.endog exog = res.model.exog lowidx = np.ceil(0.5 * (1 - frac) * nobs).astype(int) uppidx = np.floor(lowidx + frac * nobs).astype(int) mi_sl = slice(lowidx, uppidx) res_mi = OLS(endog[mi_sl], exog[mi_sl]).fit() nobs_mi = res_mi.model.endog.shape[0] ss_mi = res_mi.ssr ss = res.ssr fstat = (ss - ss_mi) / (nobs-nobs_mi) / ss_mi * res_mi.df_resid from scipy import stats pval = stats.f.sf(fstat, nobs - nobs_mi, res_mi.df_resid) return fstat, pval def linear_lm(resid, exog, func=None): '''Lagrange multiplier test for linearity against functional alternative limitations: Assumes currently that the first column is integer. Currently it doesn't check whether the transformed variables contain NaNs, for example log of negative number. Parameters ---------- resid : ndarray residuals of a regression exog : ndarray exogenous variables for which linearity is tested func : callable If func is None, then squares are used. func needs to take an array of exog and return an array of transformed variables. Returns ------- lm : float Lagrange multiplier test statistic lm_pval : float p-value of Lagrange multiplier tes ftest : ContrastResult instance the results from the F test variant of this test Notes ----- written to match Gretl's linearity test. The test runs an auxilliary regression of the residuals on the combined original and transformed regressors. The Null hypothesis is that the linear specification is correct. ''' from scipy import stats if func is None: func = lambda x: np.power(x, 2) exog_aux = np.column_stack((exog, func(exog[:,1:]))) nobs, k_vars = exog.shape ls = OLS(resid, exog_aux).fit() ftest = ls.f_test(np.eye(k_vars - 1, k_vars * 2 - 1, k_vars)) lm = nobs * ls.rsquared lm_pval = stats.chi2.sf(lm, k_vars - 1) return lm, lm_pval, ftest def _neweywestcov(resid, x): ''' Did not run yet from regstats2 :: if idx(29) % HAC (Newey West) L = round(4*(nobs/100)^(2/9)); % L = nobs^.25; % as an alternative hhat = repmat(residuals',p,1).*X'; xuux = hhat*hhat'; for l = 1:L; za = hhat(:,(l+1):nobs)*hhat(:,1:nobs-l)'; w = 1 - l/(L+1); xuux = xuux + w*(za+za'); end d = struct; d.covb = xtxi*xuux*xtxi; ''' nobs = resid.shape[0] #TODO: check this can only be 1d nlags = int(round(4*(nobs/100.)**(2/9.))) hhat = resid * x.T xuux = np.dot(hhat, hhat.T) for lag in range(nlags): za = np.dot(hhat[:,lag:nobs], hhat[:,:nobs-lag].T) w = 1 - lag/(nobs + 1.) xuux = xuux + np.dot(w, za+za.T) xtxi = np.linalg.inv(np.dot(x.T, x)) #QR instead? covbNW = np.dot(xtxi, np.dot(xuux, xtxi)) return covbNW def _recursive_olsresiduals2(olsresults, skip): '''this is my original version based on Greene and references keep for now for comparison and benchmarking ''' y = olsresults.model.endog x = olsresults.model.exog nobs, nvars = x.shape rparams = np.nan * np.zeros((nobs,nvars)) rresid = np.nan * np.zeros((nobs)) rypred = np.nan * np.zeros((nobs)) rvarraw = np.nan * np.zeros((nobs)) #XTX = np.zeros((nvars,nvars)) #XTY = np.zeros((nvars)) x0 = x[:skip] y0 = y[:skip] XTX = np.dot(x0.T, x0) XTY = np.dot(x0.T, y0) #xi * y #np.dot(xi, y) beta = np.linalg.solve(XTX, XTY) rparams[skip-1] = beta yipred = np.dot(x[skip-1], beta) rypred[skip-1] = yipred rresid[skip-1] = y[skip-1] - yipred rvarraw[skip-1] = 1+np.dot(x[skip-1],np.dot(np.linalg.inv(XTX),x[skip-1])) for i in range(skip,nobs): xi = x[i:i+1,:] yi = y[i] xxT = np.dot(xi.T, xi) #xi is 2d 1 row xy = (xi*yi).ravel() # XTY is 1d #np.dot(xi, yi) #np.dot(xi, y) print xy.shape, XTY.shape print XTX print XTY beta = np.linalg.solve(XTX, XTY) rparams[i-1] = beta #this is beta based on info up to t-1 yipred = np.dot(xi, beta) rypred[i] = yipred rresid[i] = yi - yipred rvarraw[i] = 1 + np.dot(xi,np.dot(np.linalg.inv(XTX),xi.T)) XTX += xxT XTY += xy i = nobs beta = np.linalg.solve(XTX, XTY) rparams[i-1] = beta rresid_scaled = rresid/np.sqrt(rvarraw) #this is N(0,sigma2) distributed nrr = nobs-skip sigma2 = rresid_scaled[skip-1:].var(ddof=1) rresid_standardized = rresid_scaled/np.sqrt(sigma2) #N(0,1) distributed rcusum = rresid_standardized[skip-1:].cumsum() #confidence interval points in Greene p136 looks strange? #this assumes sum of independent standard normal #rcusumci = np.sqrt(np.arange(skip,nobs+1))*np.array([[-1.],[+1.]])*stats.norm.sf(0.025) a = 1.143 #for alpha=0.99 =0.948 for alpha=0.95 #following taken from Ploberger, crit = a*np.sqrt(nrr) rcusumci = (a*np.sqrt(nrr) + a*np.arange(0,nobs-skip)/np.sqrt(nrr)) \ * np.array([[-1.],[+1.]]) return (rresid, rparams, rypred, rresid_standardized, rresid_scaled, rcusum, rcusumci) def recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95): '''calculate recursive ols with residuals and cusum test statistic Parameters ---------- olsresults : instance of RegressionResults uses only endog and exog skip : int or None number of observations to use for initial OLS, if None then skip is set equal to the number of regressors (columns in exog) lamda : float weight for Ridge correction to initial (X'X)^{-1} alpha : {0.95, 0.99} confidence level of test, currently only two values supported, used for confidence interval in cusum graph Returns ------- rresid : array recursive ols residuals rparams : array recursive ols parameter estimates rypred : array recursive prediction of endogenous variable rresid_standardized : array recursive residuals standardized so that N(0,sigma2) distributed, where sigma2 is the error variance rresid_scaled : array recursive residuals normalize so that N(0,1) distributed rcusum : array cumulative residuals for cusum test rcusumci : array confidence interval for cusum test, currently hard coded for alpha=0.95 Notes ----- It produces same recursive residuals as other version. This version updates the inverse of the X'X matrix and does not require matrix inversion during updating. looks efficient but no timing Confidence interval in Greene and Brown, Durbin and Evans is the same as in Ploberger after a little bit of algebra. References ---------- jplv to check formulas, follows Harvey BigJudge 5.5.2b for formula for inverse(X'X) updating Greene section 7.5.2 Brown, R. L., J. Durbin, and J. M. Evans. “Techniques for Testing the Constancy of Regression Relationships over Time.†Journal of the Royal Statistical Society. Series B (Methodological) 37, no. 2 (1975): 149-192. ''' y = olsresults.model.endog x = olsresults.model.exog nobs, nvars = x.shape if skip is None: skip = nvars rparams = np.nan * np.zeros((nobs,nvars)) rresid = np.nan * np.zeros((nobs)) rypred = np.nan * np.zeros((nobs)) rvarraw = np.nan * np.zeros((nobs)) #intialize with skip observations x0 = x[:skip] y0 = y[:skip] #add Ridge to start (not in jplv XTXi = np.linalg.inv(np.dot(x0.T, x0)+lamda*np.eye(nvars)) XTY = np.dot(x0.T, y0) #xi * y #np.dot(xi, y) #beta = np.linalg.solve(XTX, XTY) beta = np.dot(XTXi, XTY) #print 'beta', beta rparams[skip-1] = beta yipred = np.dot(x[skip-1], beta) rypred[skip-1] = yipred rresid[skip-1] = y[skip-1] - yipred rvarraw[skip-1] = 1 + np.dot(x[skip-1],np.dot(XTXi, x[skip-1])) for i in range(skip,nobs): xi = x[i:i+1,:] yi = y[i] #xxT = np.dot(xi.T, xi) #xi is 2d 1 row xy = (xi*yi).ravel() # XTY is 1d #np.dot(xi, yi) #np.dot(xi, y) #print xy.shape, XTY.shape #print XTX #print XTY # get prediction error with previous beta yipred = np.dot(xi, beta) rypred[i] = yipred residi = yi - yipred rresid[i] = residi #update beta and inverse(X'X) tmp = np.dot(XTXi, xi.T) ft = 1 + np.dot(xi, tmp) XTXi = XTXi - np.dot(tmp,tmp.T) / ft #BigJudge equ 5.5.15 #print 'beta', beta beta = beta + (tmp*residi / ft).ravel() #BigJudge equ 5.5.14 # #version for testing # XTY += xy # beta = np.dot(XTXi, XTY) # print (tmp*yipred / ft).shape # print 'tmp.shape, ft.shape, beta.shape', tmp.shape, ft.shape, beta.shape rparams[i] = beta rvarraw[i] = ft i = nobs #beta = np.linalg.solve(XTX, XTY) #rparams[i] = beta rresid_scaled = rresid/np.sqrt(rvarraw) #this is N(0,sigma2) distributed nrr = nobs-skip #sigma2 = rresid_scaled[skip-1:].var(ddof=1) #var or sum of squares ? #Greene has var, jplv and Ploberger have sum of squares (Ass.:mean=0) #Gretl uses: by reverse engineering matching their numbers sigma2 = rresid_scaled[skip:].var(ddof=1) rresid_standardized = rresid_scaled/np.sqrt(sigma2) #N(0,1) distributed rcusum = rresid_standardized[skip-1:].cumsum() #confidence interval points in Greene p136 looks strange. Cleared up #this assumes sum of independent standard normal, which does not take into #account that we make many tests at the same time #rcusumci = np.sqrt(np.arange(skip,nobs+1))*np.array([[-1.],[+1.]])*stats.norm.sf(0.025) if alpha == 0.95: a = 0.948 #for alpha=0.95 elif alpha == 0.99: a = 1.143 #for alpha=0.99 elif alpha == 0.90: a = 0.850 else: raise ValueError('alpha can only be 0.9, 0.95 or 0.99') #following taken from Ploberger, crit = a*np.sqrt(nrr) rcusumci = (a*np.sqrt(nrr) + 2*a*np.arange(0,nobs-skip)/np.sqrt(nrr)) \ * np.array([[-1.],[+1.]]) return (rresid, rparams, rypred, rresid_standardized, rresid_scaled, rcusum, rcusumci) def breaks_hansen(olsresults): '''test for model stability, breaks in parameters for ols, Hansen 1992 Parameters ---------- olsresults : instance of RegressionResults uses only endog and exog Returns ------- teststat : float Hansen's test statistic crit : structured array critical values at alpha=0.95 for different nvars pvalue Not yet ft, s : arrays temporary return for debugging, will be removed Notes ----- looks good in example, maybe not very powerful for small changes in parameters According to Greene, distribution of test statistics depends on nvar but not on nobs. Test statistic is verified against R:strucchange References ---------- Greene section 7.5.1, notation follows Greene ''' y = olsresults.model.endog x = olsresults.model.exog resid = olsresults.resid nobs, nvars = x.shape resid2 = resid**2 ft = np.c_[x*resid[:,None], (resid2 - resid2.mean())] s = ft.cumsum(0) assert (np.abs(s[-1]) < 1e10).all() #can be optimized away F = nobs*(ft[:,:,None]*ft[:,None,:]).sum(0) S = (s[:,:,None]*s[:,None,:]).sum(0) H = np.trace(np.dot(np.linalg.inv(F), S)) crit95 = np.array([(2,1.9),(6,3.75),(15,3.75),(19,4.52)], dtype = [('nobs',int), ('crit', float)]) #TODO: get critical values from Bruce Hansens' 1992 paper return H, crit95, ft, s def breaks_cusumolsresid(olsresidual, ddof=0): '''cusum test for parameter stability based on ols residuals Parameters ---------- olsresiduals : ndarray array of residuals from an OLS estimation ddof : int number of parameters in the OLS estimation, used as degrees of freedom correction for error variance. Returns ------- sup_b : float test statistic, maximum of absolute value of scaled cumulative OLS residuals pval : float Probability of observing the data under the null hypothesis of no structural change, based on asymptotic distribution which is a Brownian Bridge crit: list tabulated critical values, for alpha = 1%, 5% and 10% Notes ----- tested agains R:strucchange Not clear: Assumption 2 in Ploberger, Kramer assumes that exog x have asymptotically zero mean, x.mean(0) = [1, 0, 0, ..., 0] Is this really necessary? I don't see how it can affect the test statistic under the null. It does make a difference under the alternative. Also, the asymptotic distribution of test statistic depends on this. From examples it looks like there is little power for standard cusum if exog (other than constant) have mean zero. References ---------- Ploberger, Werner, and Walter Kramer. “The Cusum Test with Ols Residuals.†Econometrica 60, no. 2 (March 1992): 271-285. ''' resid = olsresidual.ravel() nobs = len(resid) nobssigma2 = (resid**2).sum() if ddof > 0: #print 'ddof', ddof, 1. / (nobs - ddof) * nobs nobssigma2 = nobssigma2 / (nobs - ddof) * nobs #B is asymptotically a Brownian Bridge B = resid.cumsum()/np.sqrt(nobssigma2) # use T*sigma directly sup_b = np.abs(B).max() #asymptotically distributed as standard Brownian Bridge crit = [(1,1.63), (5, 1.36), (10, 1.22)] #Note stats.kstwobign.isf(0.1) is distribution of sup.abs of Brownian Bridge #>>> stats.kstwobign.isf([0.01,0.05,0.1]) #array([ 1.62762361, 1.35809864, 1.22384787]) pval = stats.kstwobign.sf(sup_b) return sup_b, pval, crit #def breaks_cusum(recolsresid): # '''renormalized cusum test for parameter stability based on recursive residuals # # # still incorrect: in PK, the normalization for sigma is by T not T-K # also the test statistic is asymptotically a Wiener Process, Brownian motion # not Brownian Bridge # for testing: result reject should be identical as in standard cusum version # # References # ---------- # Ploberger, Werner, and Walter Kramer. “The Cusum Test with Ols Residuals.†# Econometrica 60, no. 2 (March 1992): 271-285. # # ''' # resid = recolsresid.ravel() # nobssigma2 = (resid**2).sum() # #B is asymptotically a Brownian Bridge # B = resid.cumsum()/np.sqrt(nobssigma2) # use T*sigma directly # nobs = len(resid) # denom = 1. + 2. * np.arange(nobs)/(nobs-1.) #not sure about limits # sup_b = np.abs(B/denom).max() # #asymptotically distributed as standard Brownian Bridge # crit = [(1,1.63), (5, 1.36), (10, 1.22)] # #Note stats.kstwobign.isf(0.1) is distribution of sup.abs of Brownian Bridge # #>>> stats.kstwobign.isf([0.01,0.05,0.1]) # #array([ 1.62762361, 1.35809864, 1.22384787]) # pval = stats.kstwobign.sf(sup_b) # return sup_b, pval, crit def breaks_AP(endog, exog, skip): '''supLM, expLM and aveLM by Andrews, and Andrews,Ploberger p-values by B Hansen just idea for computation of sequence of tests with given change point (Chow tests) run recursive ols both forward and backward, match the two so they form a split of the data, calculate sum of squares for residuals and get test statistic for each breakpoint between skip and nobs-skip need to put recursive ols (residuals) into separate function alternative: B Hansen loops over breakpoints only once and updates x'x and xe'xe update: Andrews is based on GMM estimation not OLS, LM test statistic is easy to compute because it only requires full sample GMM estimate (p.837) with GMM the test has much wider applicability than just OLS for testing loop over single breakpoint Chow test function ''' pass #delete when testing is finished class StatTestMC(object): """class to run Monte Carlo study on a statistical test''' TODO print summary, for quantiles and for histogram draft in trying out script log this has been copied to tools/mctools.py, with improvements """ def __init__(self, dgp, statistic): self.dgp = dgp #staticmethod(dgp) #no self self.statistic = statistic # staticmethod(statistic) #no self def run(self, nrepl, statindices=None, dgpargs=[], statsargs=[]): '''run the actual Monte Carlo and save results ''' self.nrepl = nrepl self.statindices = statindices self.dgpargs = dgpargs self.statsargs = statsargs dgp = self.dgp statfun = self.statistic # name ? #single return statistic if statindices is None: self.nreturn = nreturns = 1 mcres = np.zeros(nrepl) for ii in range(nrepl-1): x = dgp(*dgpargs) #(1e-4+np.random.randn(nobs)).cumsum() mcres[ii] = statfun(x, *statsargs) #unitroot_adf(x, 2,trendorder=0, autolag=None) #more than one return statistic else: self.nreturn = nreturns = len(statindices) self.mcres = mcres = np.zeros((nrepl, nreturns)) for ii in range(nrepl-1): x = dgp(*dgpargs) #(1e-4+np.random.randn(nobs)).cumsum() ret = statfun(x, *statsargs) mcres[ii] = [ret[i] for i in statindices] self.mcres = mcres def histogram(self, idx=None, critval=None): '''calculate histogram values does not do any plotting ''' if self.mcres.ndim == 2: if not idx is None: mcres = self.mcres[:,idx] else: raise ValueError('currently only 1 statistic at a time') else: mcres = self.mcres if critval is None: histo = np.histogram(mcres, bins=10) else: if not critval[0] == -np.inf: bins=np.r_[-np.inf, critval, np.inf] if not critval[0] == -np.inf: bins=np.r_[bins, np.inf] histo = np.histogram(mcres, bins=np.r_[-np.inf, critval, np.inf]) self.histo = histo self.cumhisto = np.cumsum(histo[0])*1./self.nrepl self.cumhistoreversed = np.cumsum(histo[0][::-1])[::-1]*1./self.nrepl return histo, self.cumhisto, self.cumhistoreversed def quantiles(self, idx=None, frac=[0.01, 0.025, 0.05, 0.1, 0.975]): '''calculate quantiles of Monte Carlo results ''' if self.mcres.ndim == 2: if not idx is None: mcres = self.mcres[:,idx] else: raise ValueError('currently only 1 statistic at a time') else: mcres = self.mcres self.frac = frac = np.asarray(frac) self.mcressort = mcressort = np.sort(self.mcres) return frac, mcressort[(self.nrepl*frac).astype(int)] if __name__ == '__main__': examples = ['adf'] if 'adf' in examples: x = np.random.randn(20) print acorr_ljungbox(x,4) print unitroot_adf(x) nrepl = 100 nobs = 100 mcres = np.zeros(nrepl) for ii in range(nrepl-1): x = (1e-4+np.random.randn(nobs)).cumsum() mcres[ii] = unitroot_adf(x, 2,trendorder=0, autolag=None)[0] print (mcres<-2.57).sum() print np.histogram(mcres) mcressort = np.sort(mcres) for ratio in [0.01, 0.025, 0.05, 0.1]: print ratio, mcressort[int(nrepl*ratio)] print 'critical values in Green table 20.5' print 'sample size = 100' print 'with constant' print '0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47' print '0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26' crvdg = dict([map(float,s.split(':')) for s in ('0.01: -19.8, 0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47'.split(','))]) crvd = dict([map(float,s.split(':')) for s in ('0.01: -3.50, 0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26'.split(','))]) ''' >>> crvd {0.050000000000000003: -13.699999999999999, 0.97499999999999998: 0.46999999999999997, 0.025000000000000001: -16.300000000000001, 0.01: -11.0} >>> sorted(crvd.values()) [-16.300000000000001, -13.699999999999999, -11.0, 0.46999999999999997] ''' #for trend = 0 crit_5lags0p05 =-4.41519 + (-14.0406)/nobs + (-12.575)/nobs**2 print crit_5lags0p05 adfstat, _,_,resstore = unitroot_adf(x, 2,trendorder=0, autolag=None, store=1) print (mcres>crit_5lags0p05).sum() print resstore.resols.model.exog[-5:] print x[-5:] print np.histogram(mcres, bins=[-np.inf, -3.5, -3.17, -2.9 , -2.58, 0.26, np.inf]) print mcressort[(nrepl*(np.array([0.01, 0.025, 0.05, 0.1, 0.975]))).astype(int)] def randwalksim(nobs=100, drift=0.0): return (drift+np.random.randn(nobs)).cumsum() def normalnoisesim(nobs=500, loc=0.0): return (loc+np.random.randn(nobs)) def adf20(x): return unitroot_adf(x, 2,trendorder=0, autolag=None)[:2] print '\nResults with MC class' mc1 = StatTestMC(randwalksim, adf20) mc1.run(1000, statindices=[0,1]) print mc1.histogram(0, critval=[-3.5, -3.17, -2.9 , -2.58, 0.26]) print mc1.quantiles(0) print '\nLjung Box' def lb4(x): s,p = acorr_ljungbox(x, lags=4) return s[-1], p[-1] def lb4(x): s,p = acorr_ljungbox(x, lags=1) return s[0], p[0] print 'Results with MC class' mc1 = StatTestMC(normalnoisesim, lb4) mc1.run(1000, statindices=[0,1]) print mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975]) print mc1.quantiles(1) print mc1.quantiles(0) print mc1.histogram(0) nobs = 100 x = np.ones((nobs,2)) x[:,1] = np.arange(nobs)/20. y = x.sum(1) + 1.01*(1+1.5*(x[:,1]>10))*np.random.rand(nobs) print het_goldfeldquandt(y,x, 1) y = x.sum(1) + 1.01*(1+0.5*(x[:,1]>10))*np.random.rand(nobs) print het_goldfeldquandt(y,x, 1) y = x.sum(1) + 1.01*(1-0.5*(x[:,1]>10))*np.random.rand(nobs) print het_goldfeldquandt(y,x, 1) print het_breushpagan(y,x) print het_white(y,x) f, fp, fo = het_goldfeldquandt(y,x, 1) print f, fp resgq = het_goldfeldquandt(y,x, 1, retres=True) print resgq #this is just a syntax check: print _neweywestcov(y, x) resols1 = OLS(y, x).fit() print _neweywestcov(resols1.resid, x) print resols1.cov_params() print resols1.HC0_se print resols1.cov_HC0 y = x.sum(1) + 10.*(1-0.5*(x[:,1]>10))*np.random.rand(nobs) print HetGoldfeldQuandt().run(y,x, 1, alternative='dec') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/ex_newtests.py000066400000000000000000000014051224417117700262320ustar00rootroot00000000000000 from diagnostic import unitroot_adf import statsmodels.datasets.macrodata.data as macro macrod = macro.load().data print macro.NOTE print macrod.dtype.names datatrendli = [ ('realgdp', 1), ('realcons', 1), ('realinv', 1), ('realgovt', 1), ('realdpi', 1), ('cpi', 1), ('m1', 1), ('tbilrate', 0), ('unemp',0), ('pop', 1), ('infl',0), ('realint', 0) ] print '%-10s %5s %-8s' % ('variable', 'trend', ' adf') for name, torder in datatrendli: adf_, pval = unitroot_adf(macrod[name], trendorder=torder)[:2] print '%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/multicomp.py000066400000000000000000002067321224417117700257050ustar00rootroot00000000000000''' from pystatsmodels mailinglist 20100524 Notes: - unfinished, unverified, but most parts seem to work in MonteCarlo - one example taken from lecture notes looks ok - needs cases with non-monotonic inequality for test to see difference between one-step, step-up and step-down procedures - FDR doesn't look really better then Bonferoni in the MC examples that I tried update: - now tested against R, stats and multtest, I have all of their methods for p-value correction - getting Hommel was impossible until I found reference for pvalue correction - now, since I have p-values correction, some of the original tests (rej/norej) implementation is not really needed anymore. I think I keep it for reference. Test procedure for Hommel in development session log - I haven't updated other functions and classes in here. - multtest has some good helper function according to docs - still need to update references, the real papers - fdr with estimated true hypothesis still missing - multiple comparison procedures incomplete or missing - I will get multiple comparison for now only for independent case, which might be conservative in correlated case (?). some References: Gibbons, Jean Dickinson and Chakraborti Subhabrata, 2003, Nonparametric Statistical Inference, Fourth Edition, Marcel Dekker p.363: 10.4 THE KRUSKAL-WALLIS ONE-WAY ANOVA TEST AND MULTIPLE COMPARISONS p.367: multiple comparison for kruskal formula used in multicomp.kruskal Sheskin, David J., 2004, Handbook of Parametric and Nonparametric Statistical Procedures, 3rd ed., Chapman&Hall/CRC Test 21: The Single-Factor Between-Subjects Analysis of Variance Test 22: The Kruskal-Wallis One-Way Analysis of Variance by Ranks Test Zwillinger, Daniel and Stephen Kokoska, 2000, CRC standard probability and statistics tables and formulae, Chapman&Hall/CRC 14.9 WILCOXON RANKSUM (MANN WHITNEY) TEST S. Paul Wright, Adjusted P-Values for Simultaneous Inference, Biometrics Vol. 48, No. 4 (Dec., 1992), pp. 1005-1013, International Biometric Society Stable URL: http://www.jstor.org/stable/2532694 (p-value correction for Hommel in appendix) for multicomparison new book "multiple comparison in R" Hsu is a good reference but I don't have it. Author: Josef Pktd and example from H Raja and rewrite from Vincent Davis TODO ---- * handle exception if empty, shows up only sometimes when running this - DONE I think Traceback (most recent call last): File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\multicomp.py", line 711, in print 'sh', multipletests(tpval, alpha=0.05, method='sh') File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\multicomp.py", line 241, in multipletests rejectmax = np.max(np.nonzero(reject)) File "C:\Programs\Python25\lib\site-packages\numpy\core\fromnumeric.py", line 1765, in amax return _wrapit(a, 'max', axis, out) File "C:\Programs\Python25\lib\site-packages\numpy\core\fromnumeric.py", line 37, in _wrapit result = getattr(asarray(obj),method)(*args, **kwds) ValueError: zero-size array to ufunc.reduce without identity * name of function multipletests, rename to something like pvalue_correction? ''' #import xlrd #import xlwt import scipy.stats import numpy import numpy as np import math import copy from scipy import stats from statsmodels.iolib.table import SimpleTable from numpy.testing import assert_almost_equal, assert_equal #temporary circular import from statsmodels.stats.multitest import multipletests, _ecdf as ecdf, fdrcorrection as fdrcorrection0, fdrcorrection_twostage from statsmodels.graphics import utils qcrit = ''' 2 3 4 5 6 7 8 9 10 5 3.64 5.70 4.60 6.98 5.22 7.80 5.67 8.42 6.03 8.91 6.33 9.32 6.58 9.67 6.80 9.97 6.99 10.24 6 3.46 5.24 4.34 6.33 4.90 7.03 5.30 7.56 5.63 7.97 5.90 8.32 6.12 8.61 6.32 8.87 6.49 9.10 7 3.34 4.95 4.16 5.92 4.68 6.54 5.06 7.01 5.36 7.37 5.61 7.68 5.82 7.94 6.00 8.17 6.16 8.37 8 3.26 4.75 4.04 5.64 4.53 6.20 4.89 6.62 5.17 6.96 5.40 7.24 5.60 7.47 5.77 7.68 5.92 7.86 9 3.20 4.60 3.95 5.43 4.41 5.96 4.76 6.35 5.02 6.66 5.24 6.91 5.43 7.13 5.59 7.33 5.74 7.49 10 3.15 4.48 3.88 5.27 4.33 5.77 4.65 6.14 4.91 6.43 5.12 6.67 5.30 6.87 5.46 7.05 5.60 7.21 11 3.11 4.39 3.82 5.15 4.26 5.62 4.57 5.97 4.82 6.25 5.03 6.48 5.20 6.67 5.35 6.84 5.49 6.99 12 3.08 4.32 3.77 5.05 4.20 5.50 4.51 5.84 4.75 6.10 4.95 6.32 5.12 6.51 5.27 6.67 5.39 6.81 13 3.06 4.26 3.73 4.96 4.15 5.40 4.45 5.73 4.69 5.98 4.88 6.19 5.05 6.37 5.19 6.53 5.32 6.67 14 3.03 4.21 3.70 4.89 4.11 5.32 4.41 5.63 4.64 5.88 4.83 6.08 4.99 6.26 5.13 6.41 5.25 6.54 15 3.01 4.17 3.67 4.84 4.08 5.25 4.37 5.56 4.59 5.80 4.78 5.99 4.94 6.16 5.08 6.31 5.20 6.44 16 3.00 4.13 3.65 4.79 4.05 5.19 4.33 5.49 4.56 5.72 4.74 5.92 4.90 6.08 5.03 6.22 5.15 6.35 17 2.98 4.10 3.63 4.74 4.02 5.14 4.30 5.43 4.52 5.66 4.70 5.85 4.86 6.01 4.99 6.15 5.11 6.27 18 2.97 4.07 3.61 4.70 4.00 5.09 4.28 5.38 4.49 5.60 4.67 5.79 4.82 5.94 4.96 6.08 5.07 6.20 19 2.96 4.05 3.59 4.67 3.98 5.05 4.25 5.33 4.47 5.55 4.65 5.73 4.79 5.89 4.92 6.02 5.04 6.14 20 2.95 4.02 3.58 4.64 3.96 5.02 4.23 5.29 4.45 5.51 4.62 5.69 4.77 5.84 4.90 5.97 5.01 6.09 24 2.92 3.96 3.53 4.55 3.90 4.91 4.17 5.17 4.37 5.37 4.54 5.54 4.68 5.69 4.81 5.81 4.92 5.92 30 2.89 3.89 3.49 4.45 3.85 4.80 4.10 5.05 4.30 5.24 4.46 5.40 4.60 5.54 4.72 5.65 4.82 5.76 40 2.86 3.82 3.44 4.37 3.79 4.70 4.04 4.93 4.23 5.11 4.39 5.26 4.52 5.39 4.63 5.50 4.73 5.60 60 2.83 3.76 3.40 4.28 3.74 4.59 3.98 4.82 4.16 4.99 4.31 5.13 4.44 5.25 4.55 5.36 4.65 5.45 120 2.80 3.70 3.36 4.20 3.68 4.50 3.92 4.71 4.10 4.87 4.24 5.01 4.36 5.12 4.47 5.21 4.56 5.30 infinity 2.77 3.64 3.31 4.12 3.63 4.40 3.86 4.60 4.03 4.76 4.17 4.88 4.29 4.99 4.39 5.08 4.47 5.16 ''' res = [line.split() for line in qcrit.replace('infinity','9999').split('\n')] c=np.array(res[2:-1]).astype(float) #c[c==9999] = np.inf ccols = np.arange(2,11) crows = c[:,0] cv005 = c[:, 1::2] cv001 = c[:, 2::2] from scipy import interpolate def get_tukeyQcrit(k, df, alpha=0.05): ''' return critical values for Tukey's HSD (Q) Parameters ---------- k : int in {2, ..., 10} number of tests df : int degrees of freedom of error term alpha : {0.05, 0.01} type 1 error, 1-confidence level not enough error checking for limitations ''' if alpha == 0.05: intp = interpolate.interp1d(crows, cv005[:,k-2]) elif alpha == 0.01: intp = interpolate.interp1d(crows, cv001[:,k-2]) else: raise ValueError('only implemented for alpha equal to 0.01 and 0.05') return intp(df) def get_tukeyQcrit2(k, df, alpha=0.05): ''' return critical values for Tukey's HSD (Q) Parameters ---------- k : int in {2, ..., 10} number of tests df : int degrees of freedom of error term alpha : {0.05, 0.01} type 1 error, 1-confidence level not enough error checking for limitations ''' from statsmodels.stats.libqsturng import qsturng return qsturng(1-alpha, k, df) def Tukeythreegene(first,second,third): #Performing the Tukey HSD post-hoc test for three genes ## qwb = xlrd.open_workbook('F:/Lab/bioinformatics/qcrittable.xls') ## #opening the workbook containing the q crit table ## qwb.sheet_names() ## qcrittable = qwb.sheet_by_name(u'Sheet1') firstmean = numpy.mean(first) #means of the three arrays secondmean = numpy.mean(second) thirdmean = numpy.mean(third) firststd = numpy.std(first) #standard deviations of the threearrays secondstd = numpy.std(second) thirdstd = numpy.std(third) firsts2 = math.pow(firststd,2) #standard deviation squared of the three arrays seconds2 = math.pow(secondstd,2) thirds2 = math.pow(thirdstd,2) mserrornum = firsts2*2+seconds2*2+thirds2*2 #numerator for mean square error mserrorden = (len(first)+len(second)+len(third))-3 #denominator for mean square error mserror = mserrornum/mserrorden #mean square error standarderror = math.sqrt(mserror/len(first)) #standard error, which is square root of mserror and the number of samples in a group dftotal = len(first)+len(second)+len(third)-1 #various degrees of freedom dfgroups = 2 dferror = dftotal-dfgroups qcrit = 0.5 # fix arbitrary#qcrittable.cell(dftotal, 3).value qcrit = get_tukeyQcrit(3, dftotal, alpha=0.05) #getting the q critical value, for degrees of freedom total and 3 groups qtest3to1 = (math.fabs(thirdmean-firstmean))/standarderror #calculating q test statistic values qtest3to2 = (math.fabs(thirdmean-secondmean))/standarderror qtest2to1 = (math.fabs(secondmean-firstmean))/standarderror conclusion = [] ## print qcrit print qtest3to1 print qtest3to2 print qtest2to1 if(qtest3to1>qcrit): #testing all q test statistic values to q critical values conclusion.append('3to1null') else: conclusion.append('3to1alt') if(qtest3to2>qcrit): conclusion.append('3to2null') else: conclusion.append('3to2alt') if(qtest2to1>qcrit): conclusion.append('2to1null') else: conclusion.append('2to1alt') return conclusion #rewrite by Vincent def Tukeythreegene2(genes): #Performing the Tukey HSD post-hoc test for three genes """gend is a list, ie [first, second, third]""" # qwb = xlrd.open_workbook('F:/Lab/bioinformatics/qcrittable.xls') #opening the workbook containing the q crit table # qwb.sheet_names() # qcrittable = qwb.sheet_by_name(u'Sheet1') means = [] stds = [] for gene in genes: means.append(numpy.mean(gene)) std.append(numpy.std(gene)) #firstmean = numpy.mean(first) #means of the three arrays #secondmean = numpy.mean(second) #thirdmean = numpy.mean(third) #firststd = numpy.std(first) #standard deviations of the three arrays #secondstd = numpy.std(second) #thirdstd = numpy.std(third) stds2 = [] for std in stds: stds2.append(math.pow(std,2)) #firsts2 = math.pow(firststd,2) #standard deviation squared of the three arrays #seconds2 = math.pow(secondstd,2) #thirds2 = math.pow(thirdstd,2) #mserrornum = firsts2*2+seconds2*2+thirds2*2 #numerator for mean square error mserrornum = sum(stds2)*2 mserrorden = (len(genes[0])+len(genes[1])+len(genes[2]))-3 #denominator for mean square error mserror = mserrornum/mserrorden #mean square error def catstack(args): x = np.hstack(args) labels = np.hstack([k*np.ones(len(arr)) for k,arr in enumerate(args)]) return x, labels def maxzero(x): '''find all up zero crossings and return the index of the highest Not used anymore >>> np.random.seed(12345) >>> x = np.random.randn(8) >>> x array([-0.20470766, 0.47894334, -0.51943872, -0.5557303 , 1.96578057, 1.39340583, 0.09290788, 0.28174615]) >>> maxzero(x) (4, array([1, 4])) no up-zero-crossing at end >>> np.random.seed(0) >>> x = np.random.randn(8) >>> x array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799, -0.97727788, 0.95008842, -0.15135721]) >>> maxzero(x) (None, array([6])) ''' x = np.asarray(x) cond1 = x[:-1] < 0 cond2 = x[1:] > 0 #allzeros = np.nonzero(np.sign(x[:-1])*np.sign(x[1:]) <= 0)[0] + 1 allzeros = np.nonzero((cond1 & cond2) | (x[1:]==0))[0] + 1 if x[-1] >=0: maxz = max(allzeros) else: maxz = None return maxz, allzeros def maxzerodown(x): '''find all up zero crossings and return the index of the highest Not used anymore >>> np.random.seed(12345) >>> x = np.random.randn(8) >>> x array([-0.20470766, 0.47894334, -0.51943872, -0.5557303 , 1.96578057, 1.39340583, 0.09290788, 0.28174615]) >>> maxzero(x) (4, array([1, 4])) no up-zero-crossing at end >>> np.random.seed(0) >>> x = np.random.randn(8) >>> x array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799, -0.97727788, 0.95008842, -0.15135721]) >>> maxzero(x) (None, array([6])) ''' x = np.asarray(x) cond1 = x[:-1] > 0 cond2 = x[1:] < 0 #allzeros = np.nonzero(np.sign(x[:-1])*np.sign(x[1:]) <= 0)[0] + 1 allzeros = np.nonzero((cond1 & cond2) | (x[1:]==0))[0] + 1 if x[-1] <=0: maxz = max(allzeros) else: maxz = None return maxz, allzeros def rejectionline(n, alpha=0.5): '''reference line for rejection in multiple tests Not used anymore from: section 3.2, page 60 ''' t = np.arange(n)/float(n) frej = t/( t * (1-alpha) + alpha) return frej #I don't remember what I changed or why 2 versions, #this follows german diss ??? with rline #this might be useful if the null hypothesis is not "all effects are zero" #rename to _bak and working again on fdrcorrection0 def fdrcorrection_bak(pvals, alpha=0.05, method='indep'): '''Reject False discovery rate correction for pvalues Old version, to be deleted missing: methods that estimate fraction of true hypotheses ''' pvals = np.asarray(pvals) pvals_sortind = np.argsort(pvals) pvals_sorted = pvals[pvals_sortind] pecdf = ecdf(pvals_sorted) if method in ['i', 'indep', 'p', 'poscorr']: rline = pvals_sorted / alpha elif method in ['n', 'negcorr']: cm = np.sum(1./np.arange(1, len(pvals))) rline = pvals_sorted / alpha * cm elif method in ['g', 'onegcorr']: #what's this ? german diss rline = pvals_sorted / (pvals_sorted*(1-alpha) + alpha) elif method in ['oth', 'o2negcorr']: # other invalid, cut-paste cm = np.sum(np.arange(len(pvals))) rline = pvals_sorted / alpha /cm else: raise ValueError('method not available') reject = pecdf >= rline if reject.any(): rejectmax = max(np.nonzero(reject)[0]) else: rejectmax = 0 reject[:rejectmax] = True return reject[pvals_sortind.argsort()] def mcfdr(nrepl=100, nobs=50, ntests=10, ntrue=6, mu=0.5, alpha=0.05, rho=0.): '''MonteCarlo to test fdrcorrection ''' nfalse = ntests - ntrue locs = np.array([0.]*ntrue + [mu]*(ntests - ntrue)) results = [] for i in xrange(nrepl): #rvs = locs + stats.norm.rvs(size=(nobs, ntests)) rvs = locs + randmvn(rho, size=(nobs, ntests)) tt, tpval = stats.ttest_1samp(rvs, 0) res = fdrcorrection_bak(np.abs(tpval), alpha=alpha, method='i') res0 = fdrcorrection0(np.abs(tpval), alpha=alpha) #res and res0 give the same results results.append([np.sum(res[:ntrue]), np.sum(res[ntrue:])] + [np.sum(res0[:ntrue]), np.sum(res0[ntrue:])] + res.tolist() + np.sort(tpval).tolist() + [np.sum(tpval[:ntrue] 1] ntot = float(len(xranks)); tiecorrection = 1 - (nties**3 - nties).sum()/(ntot**3 - ntot) return tiecorrection class GroupsStats(object): ''' statistics by groups (another version) groupstats as a class with lazy evaluation (not yet - decorators are still missing) written this time as equivalent of scipy.stats.rankdata gs = GroupsStats(X, useranks=True) assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15) TODO: incomplete doc strings ''' def __init__(self, x, useranks=False, uni=None, intlab=None): '''descriptive statistics by groups Parameters ---------- x : array, 2d first column data, second column group labels useranks : boolean if true, then use ranks as data corresponding to the scipy.stats.rankdata definition (start at 1, ties get mean) uni, intlab : arrays (optional) to avoid call to unique, these can be given as inputs ''' self.x = np.asarray(x) if intlab is None: uni, intlab = np.unique(x[:,1], return_inverse=True) elif uni is None: uni = np.unique(x[:,1]) self.useranks = useranks self.uni = uni self.intlab = intlab self.groupnobs = groupnobs = np.bincount(intlab) #temporary until separated and made all lazy self.runbasic(useranks=useranks) def runbasic_old(self, useranks=False): #check: refactoring screwed up case useranks=True #groupxsum = np.bincount(intlab, weights=X[:,0]) #groupxmean = groupxsum * 1.0 / groupnobs x = self.x if useranks: self.xx = x[:,1].argsort().argsort() + 1 #rankraw else: self.xx = x[:,0] self.groupsum = groupranksum = np.bincount(self.intlab, weights=self.xx) #print 'groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape # start at 1 for stats.rankdata : self.groupmean = grouprankmean = groupranksum * 1.0 / self.groupnobs # + 1 self.groupmeanfilter = grouprankmean[self.intlab] #return grouprankmean[intlab] def runbasic(self, useranks=False): #check: refactoring screwed up case useranks=True #groupxsum = np.bincount(intlab, weights=X[:,0]) #groupxmean = groupxsum * 1.0 / groupnobs x = self.x if useranks: xuni, xintlab = np.unique(x[:,0], return_inverse=True) ranksraw = x[:,0].argsort().argsort() + 1 #rankraw self.xx = GroupsStats(np.column_stack([ranksraw, xintlab]), useranks=False).groupmeanfilter else: self.xx = x[:,0] self.groupsum = groupranksum = np.bincount(self.intlab, weights=self.xx) #print 'groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape # start at 1 for stats.rankdata : self.groupmean = grouprankmean = groupranksum * 1.0 / self.groupnobs # + 1 self.groupmeanfilter = grouprankmean[self.intlab] #return grouprankmean[intlab] def groupdemean(self): return self.xx - self.groupmeanfilter def groupsswithin(self): xtmp = self.groupdemean() return np.bincount(self.intlab, weights=xtmp**2) def groupvarwithin(self): return self.groupsswithin()/(self.groupnobs-1) #.sum() class TukeyHSDResults(object): """Results from Tukey HSD test, with additional plot methods Can also compute and plot additional post-hoc evaluations using this results class. Attributes ---------- reject : array of boolean, True if we reject Null for group pair meandiffs : pairwise mean differences confint : confidence interval for pairwise mean differences std_pairs : standard deviation of pairwise mean differences q_crit : critical value of studentized range statistic at given alpha halfwidths : half widths of simultaneous confidence interval Notes ----- halfwidths is only available after call to `plot_simultaneous`. Other attributes contain information about the data from the MultiComparison instance: data, df_total, groups, groupsunique, variance. """ def __init__(self, mc_object, results_table, q_crit, reject=None, meandiffs=None, std_pairs=None, confint=None, df_total=None, reject2=None, variance=None): self._multicomp = mc_object self._results_table = results_table self.q_crit = q_crit self.reject = reject self.meandiffs = meandiffs self.std_pairs = std_pairs self.confint = confint self.df_total = df_total self.reject2 = reject2 self.variance = variance # Taken out of _multicomp for ease of access for unknowledgeable users self.data = self._multicomp.data self.groups =self._multicomp.groups self.groupsunique = self._multicomp.groupsunique def __str__(self): return str(self._results_table) def summary(self): '''Summary table that can be printed ''' return self._results_table def _simultaneous_ci(self): """Compute simultaneous confidence intervals for comparison of means. """ self.halfwidths = simultaneous_ci(self.q_crit, self.variance, self._multicomp.groupstats.groupnobs, self._multicomp.pairindices) def plot_simultaneous(self, comparison_name=None, ax=None, figsize=(10,6), xlabel=None, ylabel=None): """Plot a universal confidence interval of each group mean Visiualize significant differences in a plot with one confidence interval per group instead of all pairwise confidence intervals. Parameters ---------- comparison_name : string, optional if provided, plot_intervals will color code all groups that are significantly different from the comparison_name red, and will color code insignificant groups gray. Otherwise, all intervals will just be plotted in black. ax : matplotlib axis, optional An axis handle on which to attach the plot. figsize : tuple, optional tuple for the size of the figure generated xlabel : string, optional Name to be displayed on x axis ylabel : string, optional Name to be displayed on y axis Returns ------- fig : Matplotlib Figure object handle to figure object containing interval plots Notes ----- Multiple comparison tests are nice, but lack a good way to be visualized. If you have, say, 6 groups, showing a graph of the means between each group will require 15 confidence intervals. Instead, we can visualize inter-group differences with a single interval for each group mean. Hochberg et al. [1] first proposed this idea and used Tukey's Q critical value to compute the interval widths. Unlike plotting the differences in the means and their respective confidence intervals, any two pairs can be compared for significance by looking for overlap. References ---------- .. [1] Hochberg, Y., and A. C. Tamhane. Multiple Comparison Procedures. Hoboken, NJ: John Wiley & Sons, 1987. Examples -------- >>> from statsmodels.examples.try_tukey_hsd import cylinders, cyl_labels >>> from statsmodels.stats.multicomp import MultiComparison >>> cardata = MultiComparison(cylinders, cyl_labels) >>> results = cardata.tukeyhsd() >>> results.plot_simultaneous() This example shows an example plot comparing significant differences in group means. Significant differences at the alpha=0.05 level can be identified by intervals that do not overlap (i.e. USA vs Japan, USA vs Germany). >>> results.plot_simultaneous(comparison_name="USA") Optionally provide one of the group names to color code the plot to highlight group means different from comparison_name. """ fig, ax1 = utils.create_mpl_ax(ax) if figsize is not None: fig.set_size_inches(figsize) if getattr(self, 'halfwidths', None) is None: self._simultaneous_ci() means = self._multicomp.groupstats.groupmean sigidx = [] nsigidx = [] minrange = [means[i] - self.halfwidths[i] for i in range(len(means))] maxrange = [means[i] + self.halfwidths[i] for i in range(len(means))] if comparison_name is None: ax1.errorbar(means, range(len(means)), xerr=self.halfwidths, marker='o', linestyle='None', color='k', ecolor='k') else: if comparison_name not in self.groupsunique: raise ValueError, 'comparison_name not found in group names.' midx = np.where(self.groupsunique==comparison_name)[0] for i in range(len(means)): if self.groupsunique[i] == comparison_name: continue if (min(maxrange[i], maxrange[midx]) - max(minrange[i], minrange[midx]) < 0): sigidx.append(i) else: nsigidx.append(i) #Plot the master comparison ax1.errorbar(means[midx], midx, xerr=self.halfwidths[midx], marker='o', linestyle='None', color='b', ecolor='b') ax1.plot([minrange[midx]]*2, [-1, self._multicomp.ngroups], linestyle='--', color='0.7') ax1.plot([maxrange[midx]]*2, [-1, self._multicomp.ngroups], linestyle='--', color='0.7') #Plot those that are significantly different if len(sigidx) > 0: ax1.errorbar(means[sigidx], sigidx, xerr=self.halfwidths[sigidx], marker='o', linestyle='None', color='r', ecolor='r') #Plot those that are not significantly different if len(nsigidx) > 0: ax1.errorbar(means[nsigidx], nsigidx, xerr=self.halfwidths[nsigidx], marker='o', linestyle='None', color='0.5', ecolor='0.5') ax1.set_title('Multiple Comparisons Between All Pairs (Tukey)') r = np.max(maxrange) - np.min(minrange) ax1.set_ylim([-1, self._multicomp.ngroups]) ax1.set_xlim([np.min(minrange) - r / 10., np.max(maxrange) + r / 10.]) ax1.set_yticklabels(np.insert(self.groupsunique.astype(str), 0, '')) ax1.set_xlabel(xlabel if xlabel is not None else '') ax1.set_ylabel(ylabel if ylabel is not None else '') return fig class MultiComparison(object): '''Tests for multiple comparisons Parameters ---------- data : array independent data samples groups : array group labels corresponding to each data point group_order : list of strings, optional the desired order for the group mean results to be reported in. ''' def __init__(self, data, groups, group_order=None): self.data = np.asarray(data) self.groups = np.asarray(groups) # Allow for user-provided sorting of groups if group_order is None: self.groupsunique, self.groupintlab = np.unique(groups, return_inverse=True) else: #check if group_order has any names not in groups for grp in group_order: if grp not in groups: raise ValueError( "group_order value '%s' not found in groups"%grp) self.groupsunique = np.array(group_order) self.groupintlab = np.zeros(len(data)) for name in self.groupsunique: idx = np.where(self.groups == name)[0] self.groupintlab[idx] = np.where(self.groupsunique == name)[0] self.datali = [data[self.groups == k] for k in self.groupsunique] self.pairindices = np.triu_indices(len(self.groupsunique), 1) #tuple self.nobs = self.data.shape[0] self.ngroups = len(self.groupsunique) def getranks(self): '''convert data to rankdata and attach This creates rankdata as it is used for non-parametric tests, where in the case of ties the average rank is assigned. ''' #bug: the next should use self.groupintlab instead of self.groups #update: looks fixed #self.ranks = GroupsStats(np.column_stack([self.data, self.groups]), self.ranks = GroupsStats(np.column_stack([self.data, self.groupintlab]), useranks=True) self.rankdata = self.ranks.groupmeanfilter def kruskal(self, pairs=None, multimethod='T'): ''' pairwise comparison for kruskal-wallis test This is just a reimplementation of scipy.stats.kruskal and does not yet use a multiple comparison correction. ''' self.getranks() tot = self.nobs meanranks = self.ranks.groupmean groupnobs = self.ranks.groupnobs # simultaneous/separate treatment of multiple tests f=(tot * (tot + 1.) / 12.) / stats.tiecorrect(self.rankdata) #(xranks) print 'MultiComparison.kruskal' for i,j in zip(*self.pairindices): #pdiff = np.abs(mrs[i] - mrs[j]) pdiff = np.abs(meanranks[i] - meanranks[j]) se = np.sqrt(f * np.sum(1. / groupnobs[[i,j]] )) #np.array([8,8]))) #Fixme groupnobs[[i,j]] )) Q = pdiff / se # TODO : print statments, fix print i,j, pdiff, se, pdiff / se, pdiff / se > 2.6310, print stats.norm.sf(Q) * 2 return stats.norm.sf(Q) * 2 def allpairtest(self, testfunc, alpha=0.05, method='bonf', pvalidx=1): '''run a pairwise test on all pairs with multiple test correction The statistical test given in testfunc is calculated for all pairs and the p-values are adjusted by methods in multipletests. The p-value correction is generic and based only on the p-values, and does not take any special structure of the hypotheses into account. Parameters ---------- testfunc : function A test function for two (independent) samples. It is assumed that the return value on position pvalidx is the p-value. alpha : float familywise error rate method : string This specifies the method for the p-value correction. Any method of multipletests is possible. pvalidx : int (default: 1) position of the p-value in the return of testfunc Returns ------- sumtab : SimpleTable instance summary table for printing errors: TODO: check if this is still wrong, I think it's fixed. results from multipletests are in different order pval_corrected can be larger than 1 ??? ''' res = [] for i,j in zip(*self.pairindices): res.append(testfunc(self.datali[i], self.datali[j])) res = np.array(res) reject, pvals_corrected, alphacSidak, alphacBonf = \ multipletests(res[:, pvalidx], alpha=0.05, method=method) #print np.column_stack([res[:,0],res[:,1], reject, pvals_corrected]) i1, i2 = self.pairindices if pvals_corrected is None: resarr = np.array(zip(i1, i2, np.round(res[:,0],4), np.round(res[:,1],4), reject), dtype=[('group1', int), ('group2', int), ('stat',float), ('pval',float), ('reject', np.bool8)]) else: resarr = np.array(zip(i1, i2, np.round(res[:,0],4), np.round(res[:,1],4), np.round(pvals_corrected,4), reject), dtype=[('group1', int), ('group2', int), ('stat',float), ('pval',float), ('pval_corr',float), ('reject', np.bool8)]) from statsmodels.iolib.table import SimpleTable results_table = SimpleTable(resarr, headers=resarr.dtype.names) results_table.title = ( 'Test Multiple Comparison %s \n%s%4.2f method=%s' % (testfunc.__name__, 'FWER=', alpha, method) + '\nalphacSidak=%4.2f, alphacBonf=%5.3f' % (alphacSidak, alphacBonf)) return results_table, (res, reject, pvals_corrected, alphacSidak, alphacBonf), resarr def tukeyhsd(self, alpha=0.05): """Tukey's range test to compare means of all pairs of groups Parameters ---------- alpha : float, optional Value of FWER at which to calculate HSD. Returns ------- results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations """ self.groupstats = GroupsStats( np.column_stack([self.data, self.groupintlab]), useranks=False) gmeans = self.groupstats.groupmean gnobs = self.groupstats.groupnobs #var_ = self.groupstats.groupvarwithin() #possibly an error in varcorrection in this case var_ = np.var(self.groupstats.groupdemean(), ddof=len(gmeans)) #res contains: 0:(idx1, idx2), 1:reject, 2:meandiffs, 3: std_pairs, 4:confint, 5:q_crit, #6:df_total, 7:reject2 res = tukeyhsd(gmeans, gnobs, var_, df=None, alpha=alpha, q_crit=None) resarr = np.array(zip(res[0][0], res[0][1], np.round(res[2],4), np.round(res[4][:, 0],4), np.round(res[4][:, 1],4), res[1]), dtype=[('group1', int), ('group2', int), ('meandiff',float), ('lower',float), ('upper',float), ('reject', np.bool8)]) results_table = SimpleTable(resarr, headers=resarr.dtype.names) results_table.title = 'Multiple Comparison of Means - Tukey HSD,' + \ 'FWER=%4.2f' % alpha return TukeyHSDResults(self, results_table, res[5], res[1], res[2], res[3], res[4], res[6], res[7], var_) def rankdata(x): '''rankdata, equivalent to scipy.stats.rankdata just a different implementation, I have not yet compared speed ''' uni, intlab = np.unique(x[:,0], return_inverse=True) groupnobs = np.bincount(intlab) groupxsum = np.bincount(intlab, weights=X[:,0]) groupxmean = groupxsum * 1.0 / groupnobs rankraw = x[:,0].argsort().argsort() groupranksum = np.bincount(intlab, weights=rankraw) # start at 1 for stats.rankdata : grouprankmean = groupranksum * 1.0 / groupnobs + 1 return grouprankmean[intlab] #new def compare_ordered(vals, alpha): '''simple ordered sequential comparison of means vals : array_like means or rankmeans for independent groups incomplete, no return, not used yet ''' vals = np.asarray(vals) alphaf = alpha # Notation ? sortind = np.argsort(vals) pvals = vals[sortind] sortrevind = sortind.argsort() ntests = len(vals) #alphacSidak = 1 - np.power((1. - alphaf), 1./ntests) #alphacBonf = alphaf / float(ntests) v1, v2 = np.triu_indices(ntests, 1) #v1,v2 have wrong sequence for i in range(4): for j in range(4,i, -1): print i,j def varcorrection_unbalanced(nobs_all, srange=False): '''correction factor for variance with unequal sample sizes this is just a harmonic mean Parameters ---------- nobs_all : array_like The number of observations for each sample srange : bool if true, then the correction is divided by the number of samples for the variance of the studentized range statistic Returns ------- correction : float Correction factor for variance. Notes ----- variance correction factor is 1/k * sum_i 1/n_i where k is the number of samples and summation is over i=0,...,k-1. If all n_i are the same, then the correction factor is 1. This needs to be multiplied by the joint variance estimate, means square error, MSE. To obtain the correction factor for the standard deviation, square root needs to be taken. ''' nobs_all = np.asarray(nobs_all) if not srange: return (1./nobs_all).sum() else: return (1./nobs_all).sum()/len(nobs_all) def varcorrection_pairs_unbalanced(nobs_all, srange=False): '''correction factor for variance with unequal sample sizes for all pairs this is just a harmonic mean Parameters ---------- nobs_all : array_like The number of observations for each sample srange : bool if true, then the correction is divided by 2 for the variance of the studentized range statistic Returns ------- correction : array Correction factor for variance. Notes ----- variance correction factor is 1/k * sum_i 1/n_i where k is the number of samples and summation is over i=0,...,k-1. If all n_i are the same, then the correction factor is 1. This needs to be multiplies by the joint variance estimate, means square error, MSE. To obtain the correction factor for the standard deviation, square root needs to be taken. For the studentized range statistic, the resulting factor has to be divided by 2. ''' #TODO: test and replace with broadcasting n1, n2 = np.meshgrid(nobs_all, nobs_all) if not srange: return (1./n1 + 1./n2) else: return (1./n1 + 1./n2) / 2. def varcorrection_unequal(var_all, nobs_all, df_all): '''return joint variance from samples with unequal variances and unequal sample sizes something is wrong Parameters ---------- var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns ------- varjoint : float joint variance. dfjoint : float joint Satterthwait's degrees of freedom Notes ----- (copy, paste not correct) variance is 1/k * sum_i 1/n_i where k is the number of samples and summation is over i=0,...,k-1. If all n_i are the same, then the correction factor is 1/n. This needs to be multiplies by the joint variance estimate, means square error, MSE. To obtain the correction factor for the standard deviation, square root needs to be taken. This is for variance of mean difference not of studentized range. ''' var_all = np.asarray(var_all) var_over_n = var_all *1./ nobs_all #avoid integer division varjoint = var_over_n.sum() dfjoint = varjoint**2 / (var_over_n**2 * df_all).sum() return varjoint, dfjoint def varcorrection_pairs_unequal(var_all, nobs_all, df_all): '''return joint variance from samples with unequal variances and unequal sample sizes for all pairs something is wrong Parameters ---------- var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns ------- varjoint : array joint variance. dfjoint : array joint Satterthwait's degrees of freedom Notes ----- (copy, paste not correct) variance is 1/k * sum_i 1/n_i where k is the number of samples and summation is over i=0,...,k-1. If all n_i are the same, then the correction factor is 1. This needs to be multiplies by the joint variance estimate, means square error, MSE. To obtain the correction factor for the standard deviation, square root needs to be taken. TODO: something looks wrong with dfjoint, is formula from SPSS ''' #TODO: test and replace with broadcasting v1, v2 = np.meshgrid(var_all, var_all) n1, n2 = np.meshgrid(nobs_all, nobs_all) df1, df2 = np.meshgrid(df_all, df_all) varjoint = v1/n1 + v2/n2 dfjoint = varjoint**2 / (df1 * (v1/n1)**2 + df2 * (v2/n2)**2) return varjoint, dfjoint def tukeyhsd(mean_all, nobs_all, var_all, df=None, alpha=0.05, q_crit=None): '''simultaneous Tukey HSD check: instead of sorting, I use absolute value of pairwise differences in means. That's irrelevant for the test, but maybe reporting actual differences would be better. CHANGED: meandiffs are with sign, studentized range uses abs q_crit added for testing TODO: error in variance calculation when nobs_all is scalar, missing 1/n ''' mean_all = np.asarray(mean_all) #check if or when other ones need to be arrays n_means = len(mean_all) if df is None: df = nobs_all - 1 if np.size(df) == 1: # assumes balanced samples with df = n - 1, n_i = n df_total = n_means * df df = np.ones(n_means) * df else: df_total = np.sum(df) if (np.size(nobs_all) == 1) and (np.size(var_all) == 1): #balanced sample sizes and homogenous variance var_pairs = 1. * var_all / nobs_all * np.ones((n_means, n_means)) elif np.size(var_all) == 1: #unequal sample sizes and homogenous variance var_pairs = var_all * varcorrection_pairs_unbalanced(nobs_all, srange=True) elif np.size(var_all) > 1: var_pairs, df_sum = varcorrection_pairs_unequal(nobs_all, var_all, df) var_pairs /= 2. #check division by two for studentized range else: raise ValueError('not supposed to be here') #meandiffs_ = mean_all[:,None] - mean_all meandiffs_ = mean_all - mean_all[:,None] #reverse sign, check with R example std_pairs_ = np.sqrt(var_pairs) #select all pairs from upper triangle of matrix idx1, idx2 = np.triu_indices(n_means, 1) meandiffs = meandiffs_[idx1, idx2] std_pairs = std_pairs_[idx1, idx2] st_range = np.abs(meandiffs) / std_pairs #studentized range statistic df_total_ = max(df_total, 5) #TODO: smallest df in table if q_crit is None: q_crit = get_tukeyQcrit2(n_means, df_total, alpha=alpha) reject = st_range > q_crit crit_int = std_pairs * q_crit reject2 = np.abs(meandiffs) > crit_int confint = np.column_stack((meandiffs - crit_int, meandiffs + crit_int)) return (idx1, idx2), reject, meandiffs, std_pairs, confint, q_crit, \ df_total, reject2 def simultaneous_ci(q_crit, var, groupnobs, pairindices=None): """Compute simultaneous confidence intervals for comparison of means. q_crit value is generated from tukey hsd test. Variance is considered across all groups. Returned halfwidths can be thought of as uncertainty intervals around each group mean. They allow for simultaneous comparison of pairwise significance among any pairs (by checking for overlap) Parameters ---------- q_crit : float The Q critical value studentized range statistic from Tukey's HSD var : float The group variance groupnobs : array-like object Number of observations contained in each group. pairindices : tuple of lists, optional Indices corresponding to the upper triangle of matrix. Computed here if not supplied Returns ------- halfwidths : ndarray Half the width of each confidence interval for each group given in groupnobs See Also -------- MultiComparison : statistics class providing significance tests tukeyhsd : among other things, computes q_crit value References ---------- .. [1] Hochberg, Y., and A. C. Tamhane. Multiple Comparison Procedures. Hoboken, NJ: John Wiley & Sons, 1987.) """ # Set initial variables ng = len(groupnobs) if pairindices is None: pairindices = np.triu_indices(ng, 1) # Compute dij for all pairwise comparisons ala hochberg p. 95 gvar = var / groupnobs d12 = np.sqrt(gvar[pairindices[0]] + gvar[pairindices[1]]) # Create the full d matrix given all known dij vals d = np.zeros((ng, ng)) d[pairindices] = d12 d = d + d.conj().T # Compute the two global sums from hochberg eq 3.32 sum1 = np.sum(d12) sum2 = np.sum(d, axis=0) if (ng > 2): w = ((ng-1.) * sum2 - sum1) / ((ng - 1.) * (ng - 2.)) else: w = sum1 * ones(2, 1) / 2. return (q_crit / np.sqrt(2))*w def distance_st_range(mean_all, nobs_all, var_all, df=None, triu=False): '''pairwise distance matrix, outsourced from tukeyhsd CHANGED: meandiffs are with sign, studentized range uses abs q_crit added for testing TODO: error in variance calculation when nobs_all is scalar, missing 1/n ''' mean_all = np.asarray(mean_all) #check if or when other ones need to be arrays n_means = len(mean_all) if df is None: df = nobs_all - 1 if np.size(df) == 1: # assumes balanced samples with df = n - 1, n_i = n df_total = n_means * df else: df_total = np.sum(df) if (np.size(nobs_all) == 1) and (np.size(var_all) == 1): #balanced sample sizes and homogenous variance var_pairs = 1. * var_all / nobs_all * np.ones((n_means, n_means)) elif np.size(var_all) == 1: #unequal sample sizes and homogenous variance var_pairs = var_all * varcorrection_pairs_unbalanced(nobs_all, srange=True) elif np.size(var_all) > 1: var_pairs, df_sum = varcorrection_pairs_unequal(nobs_all, var_all, df) var_pairs /= 2. #check division by two for studentized range else: raise ValueError('not supposed to be here') #meandiffs_ = mean_all[:,None] - mean_all meandiffs = mean_all - mean_all[:,None] #reverse sign, check with R example std_pairs = np.sqrt(var_pairs) idx1, idx2 = np.triu_indices(n_means, 1) if triu: #select all pairs from upper triangle of matrix meandiffs = meandiffs_[idx1, idx2] std_pairs = std_pairs_[idx1, idx2] st_range = np.abs(meandiffs) / std_pairs #studentized range statistic return st_range, meandiffs, std_pairs, (idx1,idx2) #return square arrays def contrast_allpairs(nm): '''contrast or restriction matrix for all pairs of nm variables Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm*(nm-1)/2, nm) contrast matrix for all pairwise comparisons ''' contr = [] for i in range(nm): for j in range(i+1, nm): contr_row = np.zeros(nm) contr_row[i] = 1 contr_row[j] = -1 contr.append(contr_row) return np.array(contr) def contrast_all_one(nm): '''contrast or restriction matrix for all against first comparison Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm-1, nm) contrast matrix for all against first comparisons ''' contr = np.column_stack((np.ones(nm-1), -np.eye(nm-1))) return contr def contrast_diff_mean(nm): '''contrast or restriction matrix for all against mean comparison Parameters ---------- nm : int Returns ------- contr : ndarray, 2d, (nm-1, nm) contrast matrix for all against mean comparisons ''' return np.eye(nm) - np.ones((nm,nm))/nm def tukey_pvalues(std_range, nm, df): #corrected but very slow with warnings about integration from statsmodels.sandbox.distributions.multivariate import mvstdtprob #nm = len(std_range) contr = contrast_allpairs(nm) corr = np.dot(contr, contr.T)/2. tstat = std_range / np.sqrt(2) * np.ones(corr.shape[0]) #need len of all pairs return multicontrast_pvalues(tstat, corr, df=df) def test_tukey_pvalues(): #testcase with 3 is not good because all pairs has also 3*(3-1)/2=3 elements res = tukey_pvalues(3.649, 3, 16) #3.649*np.ones(3), 16) assert_almost_equal(0.05, res[0], 3) assert_almost_equal(0.05*np.ones(3), res[1], 3) def multicontrast_pvalues(tstat, tcorr, df=None, dist='t', alternative='two-sided'): '''pvalues for simultaneous tests ''' from statsmodels.sandbox.distributions.multivariate import mvstdtprob if (df is None) and (dist == 't'): raise ValueError('df has to be specified for the t-distribution') tstat = np.asarray(tstat) ntests = len(tstat) cc = np.abs(tstat) pval_global = 1 - mvstdtprob(-cc,cc, tcorr, df) pvals = [] for ti in cc: limits = ti*np.ones(ntests) pvals.append(1 - mvstdtprob(-cc,cc, tcorr, df)) return pval_global, np.asarray(pvals) class StepDown(object): '''a class for step down methods This is currently for simple tree subset descend, similar to homogeneous_subsets, but checks all leave-one-out subsets instead of assuming an ordered set. Comment in SAS manual: SAS only uses interval subsets of the sorted list, which is sufficient for range tests (maybe also equal variance and balanced sample sizes are required). For F-test based critical distances, the restriction to intervals is not sufficient. This version uses a single critical value of the studentized range distribution for all comparisons, and is therefore a step-down version of Tukey HSD. The class is written so it can be subclassed, where the get_distance_matrix and get_crit are overwritten to obtain other step-down procedures such as REGW. iter_subsets can be overwritten, to get a recursion as in the many to one comparison with a control such as in Dunnet's test. A one-sided right tail test is not covered because the direction of the inequality is hard coded in check_set. Also Peritz's check of partitions is not possible, but I have not seen it mentioned in any more recent references. I have only partially read the step-down procedure for closed tests by Westfall. One change to make it more flexible, is to separate out the decision on a subset, also because the F-based tests, FREGW in SPSS, take information from all elements of a set and not just pairwise comparisons. I haven't looked at the details of the F-based tests such as Sheffe yet. It looks like running an F-test on equality of means in each subset. This would also outsource how pairwise conditions are combined, any larger or max. This would also imply that the distance matrix cannot be calculated in advance for tests like the F-based ones. ''' def __init__(self, vals, nobs_all, var_all, df=None): self.vals = vals self.n_vals = len(vals) self.nobs_all = nobs_all self.var_all = var_all self.df = df # the following has been moved to run #self.cache_result = {} #self.crit = self.getcrit(0.5) #decide where to set alpha, moved to run #self.accepted = [] #store accepted sets, not unique def get_crit(self, alpha): #currently tukey Q, add others q_crit = get_tukeyQcrit(self.n_vals, self.df, alpha=alpha) return q_crit * np.ones(self.n_vals) def get_distance_matrix(self): '''studentized range statistic''' #make into property, decorate dres = distance_st_range(self.vals, self.nobs_all, self.var_all, df=self.df) self.distance_matrix = dres[0] def iter_subsets(self, indices): for ii in range(len(indices)): idxsub = copy.copy(indices) idxsub.pop(ii) yield idxsub def check_set(self, indices): '''check whether pairwise distances of indices satisfy condition ''' indtup = tuple(indices) if indtup in self.cache_result: return self.cache_result[indtup] else: set_distance_matrix = self.distance_matrix[np.asarray(indices)[:,None], indices] n_elements = len(indices) if np.any(set_distance_matrix > self.crit[n_elements-1]): res = True else: res = False self.cache_result[indtup] = res return res def stepdown(self, indices): print indices if self.check_set(indices): # larger than critical distance if (len(indices) > 2): # step down into subsets if more than 2 elements for subs in self.iter_subsets(indices): self.stepdown(subs) else: self.rejected.append(tuple(indices)) else: self.accepted.append(tuple(indices)) return indices def run(self, alpha): '''main function to run the test, could be done in __call__ instead this could have all the initialization code ''' self.cache_result = {} self.crit = self.get_crit(alpha) #decide where to set alpha, moved to run self.accepted = [] #store accepted sets, not unique self.rejected = [] self.get_distance_matrix() self.stepdown(range(self.n_vals)) return list(set(self.accepted)), list(set(sd.rejected)) def homogeneous_subsets(vals, dcrit): '''recursively check all pairs of vals for minimum distance step down method as in Newman-Keuls and Ryan procedures. This is not a closed procedure since not all partitions are checked. Parameters ---------- vals : array_like values that are pairwise compared dcrit : array_like or float critical distance for rejecting, either float, or 2-dimensional array with distances on the upper triangle. Returns ------- rejs : list of pairs list of pair-indices with (strictly) larger than critical difference nrejs : list of pairs list of pair-indices with smaller than critical difference lli : list of tuples list of subsets with smaller than critical difference res : tree result of all comparisons (for checking) this follows description in SPSS notes on Post-Hoc Tests Because of the recursive structure, some comparisons are made several times, but only unique pairs or sets are returned. Examples -------- >>> m = [0, 2, 2.5, 3, 6, 8, 9, 9.5,10 ] >>> rej, nrej, ssli, res = homogeneous_subsets(m, 2) >>> set_partition(ssli) ([(5, 6, 7, 8), (1, 2, 3), (4,)], [0]) >>> [np.array(m)[list(pp)] for pp in set_partition(ssli)[0]] [array([ 8. , 9. , 9.5, 10. ]), array([ 2. , 2.5, 3. ]), array([ 6.])] ''' nvals = len(vals) indices_ = range(nvals) rejected = [] subsetsli = [] if np.size(dcrit) == 1: dcrit = dcrit*np.ones((nvals, nvals)) #example numbers for experimenting def subsets(vals, indices_): '''recursive function for constructing homogeneous subset registers rejected and subsetli in outer scope ''' i, j = (indices_[0], indices_[-1]) if vals[-1] - vals[0] > dcrit[i,j]: rejected.append((indices_[0], indices_[-1])) return [subsets(vals[:-1], indices_[:-1]), subsets(vals[1:], indices_[1:]), (indices_[0], indices_[-1])] else: subsetsli.append(tuple(indices_)) return indices_ res = subsets(vals, indices_) all_pairs = [(i,j) for i in range(nvals) for j in range(nvals-1,i,-1)] rejs = set(rejected) not_rejected = list(set(all_pairs) - rejs) return list(rejs), not_rejected, list(set(subsetsli)), res def set_partition(ssli): '''extract a partition from a list of tuples this should be correctly called select largest disjoint sets. Begun and Gabriel 1981 don't seem to be bothered by sets of accepted hypothesis with joint elements, e.g. maximal_accepted_sets = { {1,2,3}, {2,3,4} } This creates a set partition from a list of sets given as tuples. It tries to find the partition with the largest sets. That is, sets are included after being sorted by length. If the list doesn't include the singletons, then it will be only a partial partition. Missing items are singletons (I think). Examples -------- >>> li [(5, 6, 7, 8), (1, 2, 3), (4, 5), (0, 1)] >>> set_partition(li) ([(5, 6, 7, 8), (1, 2, 3)], [0, 4]) ''' part = [] for s in sorted(list(set(ssli)), key=len)[::-1]: #print s, s_ = set(s).copy() if not any(set(s_).intersection(set(t)) for t in part): #print 'inside:', s part.append(s) #else: print part missing = list(set(i for ll in ssli for i in ll) - set(i for ll in part for i in ll)) return part, missing def set_remove_subs(ssli): '''remove sets that are subsets of another set from a list of tuples Parameters ---------- ssli : list of tuples each tuple is considered as a set Returns ------- part : list of tuples new list with subset tuples removed, it is sorted by set-length of tuples. The list contains original tuples, duplicate elements are not removed. Examples -------- >>> set_remove_subs([(0, 1), (1, 2), (1, 2, 3), (0,)]) [(1, 2, 3), (0, 1)] >>> set_remove_subs([(0, 1), (1, 2), (1,1, 1, 2, 3), (0,)]) [(1, 1, 1, 2, 3), (0, 1)] ''' #TODO: maybe convert all tuples to sets immediately, but I don't need the extra efficiency part = [] for s in sorted(list(set(ssli)), key=lambda x: len(set(x)))[::-1]: #print s, #s_ = set(s).copy() if not any(set(s).issubset(set(t)) for t in part): #print 'inside:', s part.append(s) #else: print part ## missing = list(set(i for ll in ssli for i in ll) ## - set(i for ll in part for i in ll)) return part if __name__ == '__main__': examples = ['tukey', 'tukeycrit', 'fdr', 'fdrmc', 'bonf', 'randmvn', 'multicompdev', 'None']#[-1] if 'tukey' in examples: #Example Tukey x = np.array([[0,0,1]]).T + np.random.randn(3, 20) print Tukeythreegene(*x) #Example FDR #------------ if ('fdr' in examples) or ('bonf' in examples): x1 = [1,1,1,0,-1,-1,-1,0,1,1,-1,1] print zip(np.arange(len(x1)), x1) print maxzero(x1) #[(0, 1), (1, 1), (2, 1), (3, 0), (4, -1), (5, -1), (6, -1), (7, 0), (8, 1), (9, 1), (10, -1), (11, 1)] #(11, array([ 3, 7, 11])) print maxzerodown(-np.array(x1)) locs = np.linspace(0,1,10) locs = np.array([0.]*6 + [0.75]*4) rvs = locs + stats.norm.rvs(size=(20,10)) tt, tpval = stats.ttest_1samp(rvs, 0) tpval_sortind = np.argsort(tpval) tpval_sorted = tpval[tpval_sortind] reject = tpval_sorted < ecdf(tpval_sorted)*0.05 reject2 = max(np.nonzero(reject)) print reject res = np.array(zip(np.round(rvs.mean(0),4),np.round(tpval,4), reject[tpval_sortind.argsort()]), dtype=[('mean',float), ('pval',float), ('reject', np.bool8)]) #from statsmodels.iolib import SimpleTable print SimpleTable(res, headers=res.dtype.names) print fdrcorrection_bak(tpval, alpha=0.05) print reject print '\nrandom example' print 'bonf', multipletests(tpval, alpha=0.05, method='bonf') print 'sidak', multipletests(tpval, alpha=0.05, method='sidak') print 'hs', multipletests(tpval, alpha=0.05, method='hs') print 'sh', multipletests(tpval, alpha=0.05, method='sh') pvals = np.array('0.0020 0.0045 0.0060 0.0080 0.0085 0.0090 0.0175 0.0250 ' '0.1055 0.5350'.split(), float) print '\nexample from lecturnotes' for meth in ['bonf', 'sidak', 'hs', 'sh']: print meth, multipletests(pvals, alpha=0.05, method=meth) if 'fdrmc' in examples: mcres = mcfdr(nobs=100, nrepl=1000, ntests=30, ntrue=30, mu=0.1, alpha=0.05, rho=0.3) mcmeans = np.array(mcres).mean(0) print mcmeans print mcmeans[0]/6., 1-mcmeans[1]/4. print mcmeans[:4], mcmeans[-4:] if 'randmvn' in examples: rvsmvn = randmvn(0.8, (5000,5)) print np.corrcoef(rvsmvn, rowvar=0) print rvsmvn.var(0) if 'tukeycrit' in examples: print get_tukeyQcrit(8, 8, alpha=0.05), 5.60 print get_tukeyQcrit(8, 8, alpha=0.01), 7.47 if 'multicompdev' in examples: #development of kruskal-wallis multiple-comparison #example from matlab file exchange X = np.array([[7.68, 1], [7.69, 1], [7.70, 1], [7.70, 1], [7.72, 1], [7.73, 1], [7.73, 1], [7.76, 1], [7.71, 2], [7.73, 2], [7.74, 2], [7.74, 2], [7.78, 2], [7.78, 2], [7.80, 2], [7.81, 2], [7.74, 3], [7.75, 3], [7.77, 3], [7.78, 3], [7.80, 3], [7.81, 3], [7.84, 3], [7.71, 4], [7.71, 4], [7.74, 4], [7.79, 4], [7.81, 4], [7.85, 4], [7.87, 4], [7.91, 4]]) xli = [X[X[:,1]==k,0] for k in range(1,5)] xranks = stats.rankdata(X[:,0]) xranksli = [xranks[X[:,1]==k] for k in range(1,5)] xnobs = np.array([len(x) for x in xli]) meanranks = [item.mean() for item in xranksli] sumranks = [item.sum() for item in xranksli] # equivalent function #from scipy import special #-np.sqrt(2.)*special.erfcinv(2-0.5) == stats.norm.isf(0.25) stats.norm.sf(0.67448975019608171) stats.norm.isf(0.25) mrs = np.sort(meanranks) v1, v2 = np.triu_indices(4,1) print '\nsorted rank differences' print mrs[v2] - mrs[v1] diffidx = np.argsort(mrs[v2] - mrs[v1])[::-1] mrs[v2[diffidx]] - mrs[v1[diffidx]] print '\nkruskal for all pairs' for i,j in zip(v2[diffidx], v1[diffidx]): print i,j, stats.kruskal(xli[i], xli[j]), mwu, mwupval = stats.mannwhitneyu(xli[i], xli[j], use_continuity=False) print mwu, mwupval*2, mwupval*2<0.05/6., mwupval*2<0.1/6. uni, intlab = np.unique(X[:,0], return_inverse=True) groupnobs = np.bincount(intlab) groupxsum = np.bincount(intlab, weights=X[:,0]) groupxmean = groupxsum * 1.0 / groupnobs rankraw = X[:,0].argsort().argsort() groupranksum = np.bincount(intlab, weights=rankraw) # start at 1 for stats.rankdata : grouprankmean = groupranksum * 1.0 / groupnobs + 1 assert_almost_equal(grouprankmean[intlab], stats.rankdata(X[:,0]), 15) gs = GroupsStats(X, useranks=True) print '\ngroupmeanfilter and grouprankmeans' print gs.groupmeanfilter print grouprankmean[intlab] #the following has changed #assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15) xuni, xintlab = np.unique(X[:,0], return_inverse=True) gs2 = GroupsStats(np.column_stack([X[:,0], xintlab]), useranks=True) #assert_almost_equal(gs2.groupmeanfilter, stats.rankdata(X[:,0]), 15) rankbincount = np.bincount(xranks.astype(int)) nties = rankbincount[rankbincount > 1] ntot = float(len(xranks)); tiecorrection = 1 - (nties**3 - nties).sum()/(ntot**3 - ntot) assert_almost_equal(tiecorrection, stats.tiecorrect(xranks),15) print '\ntiecorrection for data and ranks' print tiecorrection print tiecorrect(xranks) tot = X.shape[0] t=500 #168 f=(tot*(tot+1.)/12.)-(t/(6.*(tot-1.))) f=(tot*(tot+1.)/12.)/stats.tiecorrect(xranks) print '\npairs of mean rank differences' for i,j in zip(v2[diffidx], v1[diffidx]): #pdiff = np.abs(mrs[i] - mrs[j]) pdiff = np.abs(meanranks[i] - meanranks[j]) se = np.sqrt(f * np.sum(1./xnobs[[i,j]] )) #np.array([8,8]))) #Fixme groupnobs[[i,j]] )) print i,j, pdiff, se, pdiff/se, pdiff/se>2.6310 multicomp = MultiComparison(*X.T) multicomp.kruskal() gsr = GroupsStats(X, useranks=True) print '\nexamples for kruskal multicomparison' for i in range(10): x1, x2 = (np.random.randn(30,2) + np.array([0, 0.5])).T skw = stats.kruskal(x1, x2) mc2=MultiComparison(np.r_[x1, x2], np.r_[np.zeros(len(x1)), np.ones(len(x2))]) newskw = mc2.kruskal() print skw, np.sqrt(skw[0]), skw[1]-newskw, (newskw/skw[1]-1)*100 tablett, restt, arrtt = multicomp.allpairtest(stats.ttest_ind) tablemw, resmw, arrmw = multicomp.allpairtest(stats.mannwhitneyu) print print tablett print print tablemw tablemwhs, resmw, arrmw = multicomp.allpairtest(stats.mannwhitneyu, method='hs') print print tablemwhs if 'last' in examples: xli = (np.random.randn(60,4) + np.array([0, 0, 0.5, 0.5])).T #Xrvs = np.array(catstack(xli)) xrvs, xrvsgr = catstack(xli) multicompr = MultiComparison(xrvs, xrvsgr) tablett, restt, arrtt = multicompr.allpairtest(stats.ttest_ind) print tablett xli=[[8,10,9,10,9],[7,8,5,8,5],[4,8,7,5,7]] x,l = catstack(xli) gs4 = GroupsStats(np.column_stack([x,l])) print gs4.groupvarwithin() #test_tukeyhsd() #moved to test_multi.py gmeans = np.array([ 7.71375, 7.76125, 7.78428571, 7.79875]) gnobs = np.array([8, 8, 7, 8]) sd = StepDown(gmeans, gnobs, 0.001, [27]) #example from BKY pvals = [0.0001, 0.0004, 0.0019, 0.0095, 0.0201, 0.0278, 0.0298, 0.0344, 0.0459, 0.3240, 0.4262, 0.5719, 0.6528, 0.7590, 1.000 ] #same number of rejection as in BKY paper: #single step-up:4, two-stage:8, iterated two-step:9 #also alpha_star is the same as theirs for TST print fdrcorrection0(pvals, alpha=0.05, method='indep') print fdrcorrection_twostage(pvals, alpha=0.05, iter=False) res_tst = fdrcorrection_twostage(pvals, alpha=0.05, iter=False) assert_almost_equal([0.047619, 0.0649], res_tst[-1][:2],3) #alpha_star for stage 2 assert_equal(8, res_tst[0].sum()) print fdrcorrection_twostage(pvals, alpha=0.05, iter=True) print 'fdr_gbs', multipletests(pvals, alpha=0.05, method='fdr_gbs') #multicontrast_pvalues(tstat, tcorr, df) test_tukey_pvalues() tukey_pvalues(3.649, 3, 16) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/notes_fdr.txt000066400000000000000000000104271224417117700260400ustar00rootroot00000000000000 Multiple Tests and Multiple Comparisons ======================================= Introduction ------------ generic multiple testing procedures, p-value corrections and fdr don't use any additional information, only information contained in p-values. I don't know if there are any underlying assumption, except that the raw pvalues are uniformly on [0,1] distributed under the null hypothesis. fdr for microarray or fmri can use special structure A special case in general statistical literature is the comparison of means with specific methods to correct for multiple tests, e.g. Tukey (survey by Shaffer General Methods --------------- pvalue correction ~~~~~~~~~~~~~~~~~ implemented, basic fdr, BH, BY multiple testing procedures ~~~~~~~~~~~~~~~~~~~~~~~~~~~ pvalue correction is not available/implemented, return reject is True/False for a given confidence level. (pvalue correction were initially written this way) fdr bky 2-step procedure with estimation of fraction of false hypothesis. Multiple Comparisons -------------------- Tukey, Dunnet, ... see Hothorn, Bretz, Westfall, University of Munich 2008, working paper, published in ... The general theory in Bretz, Genz and Hothorn, Bretz, Westfall, multcomp, is based on the distribution of the maximum of the statistics when the statistics are distributed according to a joint t or normal distribution distribution. pvalues, whether to reject a hypothesis and joint confidence intervalls can be obtained directly from integration of the joint t or normal distribution. Scipy does not have the cdf of a multivariate t distribution, but according to Bretz and Gentz it can be obtained by a univariate integration from the cdf of the multivariate normal distribution. The distribution for Tukey's range statistic is half (?) of the distribution of the maximum of t-distributed random variables if the underlying means are assumed to be independently distributed. (my interpretation so far, but I have not verified the details yet, see ... ) Bretz and Genz also have a table with the summary of the contrast matrices for different tests, like Tukey and Dunnet. These contrast matrices are implemented in R, but for some of them they are not immediately obvious. As an aside, this seems to be related to getting simultaneous confidence bands for forecasting. But it is not clear to me yet how to define the confidence bands, I have the articles but have not read them yet. The older tests, Tukey, Dunnet and similar assume that the means are independently normally distributed, and critical values are tabulated for this case. Newer methods allow for arbitrary correlation (Bretz, Hothorn,...) or assume a specific structure for simplicity, for example factor structure for the covariance as in Hsu. The latter is the default approach in SAS. The tests based on independence are often categorized as post-hoc tests, a good overview of the formulas is in the SPSS description. It is also mentioned in the literature (?) that these multiple comparison tests loose power if they are only used conditional on the rejection of an overall F-test TODO: * finish Tukey, Dunnet because they will be familiar * add multivariate normal cdf to statsmodels * try Bretz/Gentz cdf of t distribution form the cdf of the multivariate normal special cases ------------- nipy.neurospin follows Schwartzman, A. et al., 2009. Empirical null and false discovery rate analysis in neuroimaging. NeuroImage, 44(1), pp.71-82. test defined for point-wise test on a 2 component mixture distribution. The probability of an observation to be in the null distribution can be estimated from the mixture distribution (not sure) fdr defined on observations not a sample mean alternative literature: use topological properties of the picture to correct fdr. TODO ==== * fdr_bky and check whether prior q can be used with current pvalue-correction * multi-comparison: Tukey, convenience classes/methods expand on current * Monte Carlo * maybe some resampling, bootstrap, permutation, later * for the rest I'm not really interested in doing the work: k-FWER, k-FDR, other estimators for fraction of true or false hypothesis multtest (R) check bootstrap references multcomp (R) contrMat has the contrast matrices for various standard test function parm parm(coef, vcov, df = 0) can work with only the estimates given statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/runs.py000066400000000000000000000470751224417117700246660ustar00rootroot00000000000000'''runstest formulas for mean and var of runs taken from SAS manual NPAR tests, also idea for runstest_1samp and runstest_2samp Description in NIST handbook and dataplot doesn't explain their expected values, or variance Note: There are (at least) two definitions of runs used in literature. The classical definition which is also used here, is that runs are sequences of identical observations separated by observations with different realizations. The second definition allows for overlapping runs, or runs where counting a run is also started after a run of a fixed length of the same kind. TODO * add one-sided tests where possible or where it makes sense ''' import numpy as np from scipy import stats class Runs(object): '''class for runs in a binary sequence Parameters ---------- x : array_like, 1d data array, Notes ----- This was written as a more general class for runs. This has some redundant calculations when only the runs_test is used. TODO: make it lazy The runs test could be generalized to more than 1d if there is a use case for it. This should be extended once I figure out what the distribution of runs of any length k is. The exact distribution for the runs test is also available but not yet verified. ''' def __init__(self, x): self.x = np.asarray(x) self.runstart = runstart = np.nonzero(np.diff(np.r_[[-np.inf], x, [np.inf]]))[0] self.runs = runs = np.diff(runstart) self.runs_sign = runs_sign = x[runstart[:-1]] self.runs_pos = runs[runs_sign==1] self.runs_neg = runs[runs_sign==0] self.runs_freqs = np.bincount(runs) self.n_runs = len(self.runs) self.n_pos = (x==1).sum() def runs_test(self, correction=True): '''basic version of runs test Parameters ---------- correction: bool Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. pvalue based on normal distribution, with integer correction ''' self.npo = npo = (self.runs_pos).sum() self.nne = nne = (self.runs_neg).sum() #n_r = self.n_runs n = npo + nne npn = npo * nne rmean = 2. * npn / n + 1 rvar = 2. * npn * (2.*npn - n) / n**2. / (n-1.) rstd = np.sqrt(rvar) rdemean = self.n_runs - rmean if n >= 50 or not correction: z = rdemean else: if rdemean > 0.5: z = rdemean - 0.5 elif rdemean < 0.5: z = rdemean + 0.5 else: z = 0. z /= rstd pval = 2 * stats.norm.sf(np.abs(z)) return z, pval def runstest_1samp(x, cutoff='mean', correction=True): '''use runs test on binary discretized data above/below cutoff Parameters ---------- x : array_like data, numeric cutoff : {'mean', 'median'} or number This specifies the cutoff to split the data into large and small values. correction: bool Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. Returns ------- z_stat : float test statistic, asymptotically normally distributed p-value : float p-value, reject the null hypothesis if it is below an type 1 error level, alpha . ''' if cutoff == 'mean': cutoff = np.mean(x) elif cutoff == 'median': cutoff = np.median(x) xindicator = (x >= cutoff).astype(int) return Runs(xindicator).runs_test(correction=correction) def runstest_2samp(x, y=None, groups=None, correction=True): '''Wald-Wolfowitz runstest for two samples This tests whether two samples come from the same distribution. Parameters ---------- x : array_like data, numeric, contains either one group, if y is also given, or both groups, if additionally a group indicator is provided y : array_like (optional) data, numeric groups : array_like group labels or indicator the data for both groups is given in a single 1-dimensional array, x. If group labels are not [0,1], then correction: bool Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. Returns ------- z_stat : float test statistic, asymptotically normally distributed p-value : float p-value, reject the null hypothesis if it is below an type 1 error level, alpha . Notes ----- Wald-Wolfowitz runs test. If there are ties, then then the test statistic and p-value that is reported, is based on the higher p-value between sorting all tied observations of the same group This test is intended for continuous distributions SAS has treatment for ties, but not clear, and sounds more complicated (minimum and maximum possible runs prevent use of argsort) (maybe it's not so difficult, idea: add small positive noise to first one, run test, then to the other, run test, take max(?) p-value - DONE This gives not the minimum and maximum of the number of runs, but should be close. Not true, this is close to minimum but far away from maximum. maximum number of runs would use alternating groups in the ties.) Maybe adding random noise would be the better approach. SAS has exact distribution for sample size <=30, doesn't look standard but should be easy to add. currently two-sided test only This has not been verified against a reference implementation. In a short Monte Carlo simulation where both samples are normally distribute, the test seems to be correctly sized for larger number of observations (30 or larger), but conservative (i.e. reject less often than nominal) with a sample size of 10 in each group. See Also -------- runs_test_1samp Runs RunsProb ''' x = np.asarray(x) if not y is None: y = np.asarray(y) groups = np.concatenate((np.zeros(len(x)), np.ones(len(y)))) # note reassigning x x = np.concatenate((x, y)) gruni = np.arange(2) elif not groups is None: gruni = np.unique(groups) if gruni.size != 2: # pylint: disable=E1103 raise ValueError('not exactly two groups specified') #require groups to be numeric ??? else: raise ValueError('either y or groups is necessary') xargsort = np.argsort(x) #check for ties x_sorted = x[xargsort] x_diff = np.diff(x_sorted) # used for detecting and handling ties if x_diff.min() == 0: print 'ties detected' #replace with warning x_mindiff = x_diff[x_diff > 0].min() eps = x_mindiff/2. xx = x.copy() #don't change original, just in case xx[groups==gruni[0]] += eps xargsort = np.argsort(xx) xindicator = groups[xargsort] z0, p0 = Runs(xindicator).runs_test(correction=correction) xx[groups==gruni[0]] -= eps #restore xx = x xx[groups==gruni[1]] += eps xargsort = np.argsort(xx) xindicator = groups[xargsort] z1, p1 = Runs(xindicator).runs_test(correction=correction) idx = np.argmax([p0,p1]) return [z0, z1][idx], [p0, p1][idx] else: xindicator = groups[xargsort] return Runs(xindicator).runs_test(correction=correction) try: from scipy import comb # pylint: disable=E0611 except ImportError: from scipy.misc import comb class TotalRunsProb(object): '''class for the probability distribution of total runs This is the exact probability distribution for the (Wald-Wolfowitz) runs test. The random variable is the total number of runs if the sample has (n0, n1) observations of groups 0 and 1. Notes ----- Written as a class so I can store temporary calculations, but I don't think it matters much. Formulas taken from SAS manual for one-sided significance level. Could be converted to a full univariate distribution, subclassing scipy.stats.distributions. *Status* Not verified yet except for mean. ''' def __init__(self, n0, n1): self.n0 = n0 self.n1 = n1 self.n = n = n0 + n1 self.comball = comb(n, n1) def runs_prob_even(self, r): n0, n1 = self.n0, self.n1 tmp0 = comb(n0-1, r//2-1) tmp1 = comb(n1-1, r//2-1) return tmp0 * tmp1 * 2. / self.comball def runs_prob_odd(self, r): n0, n1 = self.n0, self.n1 k = (r+1)//2 tmp0 = comb(n0-1, k-1) tmp1 = comb(n1-1, k-2) tmp3 = comb(n0-1, k-2) tmp4 = comb(n1-1, k-1) return (tmp0 * tmp1 + tmp3 * tmp4) / self.comball def pdf(self, r): r = np.asarray(r) r_isodd = np.mod(r, 2) > 0 r_odd = r[r_isodd] r_even = r[~r_isodd] runs_pdf = np.zeros(r.shape) runs_pdf[r_isodd] = self.runs_prob_odd(r_odd) runs_pdf[~r_isodd] = self.runs_prob_even(r_even) return runs_pdf def cdf(self, r): r_ = np.arange(2,r+1) cdfval = self.runs_prob_even(r_[::2]).sum() cdfval += self.runs_prob_odd(r_[1::2]).sum() return cdfval class RunsProb(object): '''distribution of success runs of length k or more (classical definition) The underlying process is assumed to be a sequence of Bernoulli trials of a given length n. not sure yet, how to interpret or use the distribution for runs of length k or more. Musseli also has longest success run, and waiting time distribution negative binomial of order k and geometric of order k need to compare with Godpole need a MonteCarlo function to do some quick tests before doing more ''' def pdf(self, x, k, n, p): '''distribution of success runs of length k or more Parameters ---------- x : float count of runs of length n k : int length of runs n : int total number of observations or trials p : float probability of success in each Bernoulli trial Returns ------- pdf : float probability that x runs of length of k are observed Notes ----- not yet vectorized References ---------- Muselli 1996, theorem 3 ''' q = 1-p m = np.arange(x, (n+1)//(k+1)+1)[:,None] terms = (-1)**(m-x) * comb(m, x) * p**(m*k) * q**(m-1) \ * (comb(n - m*k, m - 1) + q * comb(n - m*k, m)) return terms.sum(0) def pdf_nb(self, x, k, n, p): pass #y = np.arange(m-1, n-mk+1 ''' >>> [np.sum([RunsProb().pdf(xi, k, 16, 10/16.) for xi in range(0,16)]) for k in range(16)] [0.99999332193894064, 0.99999999999999367, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] >>> [(np.arange(0,16) * [RunsProb().pdf(xi, k, 16, 10/16.) for xi in range(0,16)]).sum() for k in range(16)] [6.9998931510341809, 4.1406249999999929, 2.4414062500000075, 1.4343261718749996, 0.83923339843749856, 0.48875808715820324, 0.28312206268310569, 0.1629814505577086, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] >>> np.array([(np.arange(0,16) * [RunsProb().pdf(xi, k, 16, 10/16.) for xi in range(0,16)]).sum() for k in range(16)])/11 array([ 0.63635392, 0.37642045, 0.22194602, 0.13039329, 0.07629395, 0.04443255, 0.02573837, 0.0148165 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]) >>> np.diff([(np.arange(0,16) * [RunsProb().pdf(xi, k, 16, 10/16.) for xi in range(0,16)]).sum() for k in range(16)][::-1]) array([ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.16298145, 0.12014061, 0.20563602, 0.35047531, 0.59509277, 1.00708008, 1.69921875, 2.85926815]) ''' def median_test_ksample(x, groups): '''chisquare test for equality of median/location This tests whether all groups have the same fraction of observations above the median. Parameters ---------- x : array_like data values stacked for all groups groups : array_like group labels or indicator Returns ------- stat : float test statistic pvalue : float pvalue from the chisquare distribution others ???? currently some test output, table and expected ''' x = np.asarray(x) gruni = np.unique(groups) xli = [x[groups==group] for group in gruni] xmedian = np.median(x) counts_larger = np.array([(xg > xmedian).sum() for xg in xli]) counts = np.array([len(xg) for xg in xli]) counts_smaller = counts - counts_larger nobs = counts.sum() n_larger = (x > xmedian).sum() n_smaller = nobs - n_larger table = np.vstack((counts_smaller, counts_larger)) #the following should be replaced by chisquare_contingency table expected = np.vstack((counts * 1. / nobs * n_smaller, counts * 1. / nobs * n_larger)) if (expected < 5).any(): print('Warning: There are cells with less than 5 expected' \ 'observations. The chisquare distribution might not be a good' \ 'approximation for the true distribution.') #check ddof return stats.chisquare(table.ravel(), expected.ravel(), ddof=1), table, expected def cochrans_q(x): '''Cochran's Q test for identical effect of k treatments Cochran's Q is a k-sample extension of the McNemar test. If there are only two treatments, then Cochran's Q test and McNemar test are equivalent. Test that the probability of success is the same for each treatment. The alternative is that at least two treatments have a different probability of success. Parameters ---------- x : array_like, 2d (N,k) data with N cases and k variables Returns ------- q_stat : float test statistic pvalue : float pvalue from the chisquare distribution Notes ----- In Wikipedia terminology, rows are blocks and columns are treatments. The number of rows N, should be large for the chisquare distribution to be a good approximation. The Null hypothesis of the test is that all treatments have the same effect. References ---------- http://en.wikipedia.org/wiki/Cochran_test SAS Manual for NPAR TESTS ''' x = np.asarray(x) gruni = np.unique(x) N, k = x.shape count_row_success = (x==gruni[-1]).sum(1, float) count_col_success = (x==gruni[-1]).sum(0, float) count_row_ss = count_row_success.sum() count_col_ss = count_col_success.sum() assert count_row_ss == count_col_ss #just a calculation check #this is SAS manual q_stat = (k-1) * (k * np.sum(count_col_success**2) - count_col_ss**2) \ / (k * count_row_ss - np.sum(count_row_success**2)) #Note: the denominator looks just like k times the variance of the #columns #Wikipedia uses a different, but equivalent expression ## q_stat = (k-1) * (k * np.sum(count_row_success**2) - count_row_ss**2) \ ## / (k * count_col_ss - np.sum(count_col_success**2)) return q_stat, stats.chi2.sf(q_stat, k-1) def mcnemar(x, y=None, exact=True, correction=True): '''McNemar test Parameters ---------- x, y : array_like two paired data samples. If y is None, then x can be a 2 by 2 contingency table. x and y can have more than one dimension, then the results are calculated under the assumption that axis zero contains the observation for the samples. exact : bool If exact is true, then the binomial distribution will be used. If exact is false, then the chisquare distribution will be used, which is the approximation to the distribution of the test statistic for large sample sizes. correction : bool If true, then a continuity correction is used for the chisquare distribution (if exact is false.) Returns ------- stat : float or int, array The test statistic is the chisquare statistic if exact is false. If the exact binomial distribution is used, then this contains the min(n1, n2), where n1, n2 are cases that are zero in one sample but one in the other sample. pvalue : float or array p-value of the null hypothesis of equal effects. Notes ----- This is a special case of Cochran's Q test. The results when the chisquare distribution is used are identical, except for continuity correction. ''' x = np.asarray(x) if y is None and x.shape[0] == x.shape[1]: if x.shape[0] != 2: raise ValueError('table needs to be 2 by 2') n1, n2 = x[1, 0], x[0, 1] else: # I'm not checking here whether x and y are binary, # isn't this also paired sign test n1 = np.sum(x < y, 0) n2 = np.sum(x > y, 0) if exact: stat = np.minimum(n1, n2) # binom is symmetric with p=0.5 pval = stats.binom.cdf(stat, n1 + n2, 0.5) * 2 pval = np.minimum(pval, 1) # limit to 1 if n1==n2 else: corr = int(correction) # convert bool to 0 or 1 stat = (np.abs(n1 - n2) - corr)**2 / (1. * (n1 + n2)) df = 1 pval = stats.chi2.sf(stat, df) return stat, pval def symmetry_bowker(table): '''Test for symmetry of a (k, k) square contingency table This is an extension of the McNemar test to test the Null hypothesis that the contingency table is symmetric around the main diagonal, that is n_{i, j} = n_{j, i} for all i, j Parameters ---------- table : array_like, 2d, (k, k) a square contingency table that contains the count for k categories in rows and columns. Returns ------- statistic : float chisquare test statistic p-value : float p-value of the test statistic based on chisquare distribution df : int degrees of freedom of the chisquare distribution Notes ----- Implementation is based on the SAS documentation, R includes it in `mcnemar.test` if the table is not 2 by 2. The pvalue is based on the chisquare distribution which requires that the sample size is not very small to be a good approximation of the true distribution. For 2x2 contingency tables exact distribution can be obtained with `mcnemar` See Also -------- mcnemar ''' table = np.asarray(table) k, k2 = table.shape if k != k2: raise ValueError('table needs to be square') #low_idx = np.tril_indices(k, -1) # this doesn't have Fortran order upp_idx = np.triu_indices(k, 1) tril = table.T[upp_idx] # lower triangle in column order triu = table[upp_idx] # upper triangle in row order stat = ((tril - triu)**2 / (tril + triu + 1e-20)).sum() df = k * (k-1) / 2. pval = stats.chi2.sf(stat, df) return stat, pval, df if __name__ == '__main__': x1 = np.array([1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1]) print Runs(x1).runs_test() print runstest_1samp(x1, cutoff='mean') print runstest_2samp(np.arange(16,0,-1), groups=x1) print TotalRunsProb(7,9).cdf(11) print median_test_ksample(np.random.randn(100), np.random.randint(0,2,100)) print cochrans_q(np.random.randint(0,2,(100,8))) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/stats_dhuard.py000066400000000000000000000236321224417117700263550ustar00rootroot00000000000000''' from David Huard's scipy sandbox, also attached to a ticket and in the matplotlib-user mailinglist (links ???) Notes ===== out of bounds interpolation raises exception and wouldn't be completely defined :: >>> scoreatpercentile(x, [0,25,50,100]) Traceback (most recent call last): ... raise ValueError("A value in x_new is below the interpolation " ValueError: A value in x_new is below the interpolation range. >>> percentileofscore(x, [-50, 50]) Traceback (most recent call last): ... raise ValueError("A value in x_new is below the interpolation " ValueError: A value in x_new is below the interpolation range. idea ==== histogram and empirical interpolated distribution ------------------------------------------------- dual constructor * empirical cdf : cdf on all observations through linear interpolation * binned cdf : based on histogram both should work essentially the same, although pdf of empirical has many spikes, fluctuates a lot - alternative: binning based on interpolated cdf : example in script * ppf: quantileatscore based on interpolated cdf * rvs : generic from ppf * stats, expectation ? how does integration wrt cdf work - theory? Problems * limits, lower and upper bound of support does not work or is undefined with empirical cdf and interpolation * extending bounds ? matlab has pareto tails for empirical distribution, breaks linearity empirical distribution with higher order interpolation ------------------------------------------------------ * should work easily enough with interpolating splines * not piecewise linear * can use pareto (or other) tails * ppf how do I get the inverse function of a higher order spline? Chuck: resample and fit spline to inverse function this will have an approximation error in the inverse function * -> doesn't work: higher order spline doesn't preserve monotonicity see mailing list for response to my question * pmf from derivative available in spline -> forget this and use kernel density estimator instead bootstrap/empirical distribution: --------------------------------- discrete distribution on real line given observations what's defined? * cdf : step function * pmf : points with equal weight 1/nobs * rvs : resampling * ppf : quantileatscore on sample? * moments : from data ? * expectation ? sum_{all observations x} [func(x) * pmf(x)] * similar for discrete distribution on real line * References : ? * what's the point? most of it is trivial, just for the record ? Created on Monday, May 03, 2010, 11:47:03 AM Author: josef-pktd, parts based on David Huard License: BSD ''' import scipy.interpolate as interpolate import numpy as np def scoreatpercentile(data, percentile): """Return the score at the given percentile of the data. Example: >>> data = randn(100) >>> scoreatpercentile(data, 50) will return the median of sample `data`. """ per = np.array(percentile) cdf = empiricalcdf(data) interpolator = interpolate.interp1d(np.sort(cdf), np.sort(data)) return interpolator(per/100.) def percentileofscore(data, score): """Return the percentile-position of score relative to data. score: Array of scores at which the percentile is computed. Return percentiles (0-100). Example r = randn(50) x = linspace(-2,2,100) percentileofscore(r,x) Raise an error if the score is outside the range of data. """ cdf = empiricalcdf(data) interpolator = interpolate.interp1d(np.sort(data), np.sort(cdf)) return interpolator(score)*100. def empiricalcdf(data, method='Hazen'): """Return the empirical cdf. Methods available: Hazen: (i-0.5)/N Weibull: i/(N+1) Chegodayev: (i-.3)/(N+.4) Cunnane: (i-.4)/(N+.2) Gringorten: (i-.44)/(N+.12) California: (i-1)/N Where i goes from 1 to N. """ i = np.argsort(np.argsort(data)) + 1. N = len(data) method = method.lower() if method == 'hazen': cdf = (i-0.5)/N elif method == 'weibull': cdf = i/(N+1.) elif method == 'california': cdf = (i-1.)/N elif method == 'chegodayev': cdf = (i-.3)/(N+.4) elif method == 'cunnane': cdf = (i-.4)/(N+.2) elif method == 'gringorten': cdf = (i-.44)/(N+.12) else: raise ValueError('Unknown method. Choose among Weibull, Hazen,' 'Chegodayev, Cunnane, Gringorten and California.') return cdf class HistDist(object): '''Distribution with piecewise linear cdf, pdf is step function can be created from empiricial distribution or from a histogram (not done yet) work in progress, not finished ''' def __init__(self, data): self.data = np.atleast_1d(data) self.binlimit = np.array([self.data.min(), self.data.max()]) sortind = np.argsort(data) self._datasorted = data[sortind] self.ranking = np.argsort(sortind) cdf = self.empiricalcdf() self._empcdfsorted = np.sort(cdf) self.cdfintp = interpolate.interp1d(self._datasorted, self._empcdfsorted) self.ppfintp = interpolate.interp1d(self._empcdfsorted, self._datasorted) def empiricalcdf(self, data=None, method='Hazen'): """Return the empirical cdf. Methods available: Hazen: (i-0.5)/N Weibull: i/(N+1) Chegodayev: (i-.3)/(N+.4) Cunnane: (i-.4)/(N+.2) Gringorten: (i-.44)/(N+.12) California: (i-1)/N Where i goes from 1 to N. """ if data is None: data = self.data i = self.ranking else: i = np.argsort(np.argsort(data)) + 1. N = len(data) method = method.lower() if method == 'hazen': cdf = (i-0.5)/N elif method == 'weibull': cdf = i/(N+1.) elif method == 'california': cdf = (i-1.)/N elif method == 'chegodayev': cdf = (i-.3)/(N+.4) elif method == 'cunnane': cdf = (i-.4)/(N+.2) elif method == 'gringorten': cdf = (i-.44)/(N+.12) else: raise ValueError('Unknown method. Choose among Weibull, Hazen,' 'Chegodayev, Cunnane, Gringorten and California.') return cdf def cdf_emp(self, score): ''' this is score in dh ''' return self.cdfintp(score) #return percentileofscore(self.data, score) def ppf_emp(self, quantile): ''' this is score in dh ''' return self.ppfintp(quantile) #return scoreatpercentile(self.data, quantile*100) #from DHuard http://old.nabble.com/matplotlib-f2903.html def optimize_binning(self, method='Freedman'): """Find the optimal number of bins and update the bin countaccordingly. Available methods : Freedman Scott """ nobs = len(self.data) if method=='Freedman': IQR = self.ppf_emp(0.75) - self.ppf_emp(0.25) # Interquantile range(75% -25%) width = 2* IQR* nobs**(-1./3) elif method=='Scott': width = 3.49 * np.std(self.data) * nobs**(-1./3) self.nbin = (self.binlimit.ptp()/width) return self.nbin #changes: josef-pktd if __name__ == '__main__': import matplotlib.pyplot as plt nobs = 100 x = np.random.randn(nobs) examples = [2] if 1 in examples: empiricalcdf(x) print percentileofscore(x, 0.5) print scoreatpercentile(x, 50) import matplotlib.pyplot as plt xsupp = np.linspace(x.min(), x.max()) pos = percentileofscore(x, xsupp) plt.plot(xsupp, pos) #perc = np.linspace(2.5, 97.5) #plt.plot(scoreatpercentile(x, perc), perc) plt.plot(scoreatpercentile(x, pos), pos+1) #emp = interpolate.PiecewisePolynomial(np.sort(empiricalcdf(x)), np.sort(x)) emp=interpolate.InterpolatedUnivariateSpline(np.sort(x),np.sort(empiricalcdf(x)),k=1) pdfemp = np.array([emp.derivatives(xi)[1] for xi in xsupp]) plt.figure() plt.plot(xsupp,pdfemp) cdf_ongrid = emp(xsupp) plt.figure() plt.plot(xsupp, cdf_ongrid) #get pdf from interpolated cdf on a regular grid plt.figure() plt.step(xsupp[:-1],np.diff(cdf_ongrid)/np.diff(xsupp)) #reduce number of bins/steps xsupp2 = np.linspace(x.min(), x.max(), 25) plt.figure() plt.step(xsupp2[:-1],np.diff(emp(xsupp2))/np.diff(xsupp2)) #pdf using 25 original observations, every (nobs/25)th xso = np.sort(x) xs = xso[::nobs/25] plt.figure() plt.step(xs[:-1],np.diff(emp(xs))/np.diff(xs)) #lower end looks strange histd = HistDist(x) print histd.optimize_binning() print histd.cdf_emp(histd.binlimit) print histd.ppf_emp([0.25, 0.5, 0.75]) print histd.cdf_emp([-0.5, -0.25, 0, 0.25, 0.5]) xsupp = np.linspace(x.min(), x.max(), 500) emp=interpolate.InterpolatedUnivariateSpline(np.sort(x),np.sort(empiricalcdf(x)),k=1) #pdfemp = np.array([emp.derivatives(xi)[1] for xi in xsupp]) #plt.figure() #plt.plot(xsupp,pdfemp) cdf_ongrid = emp(xsupp) plt.figure() plt.plot(xsupp, cdf_ongrid) ppfintp = interpolate.InterpolatedUnivariateSpline(cdf_ongrid,xsupp,k=3) ppfs = ppfintp(cdf_ongrid) plt.plot(ppfs, cdf_ongrid) #ppfemp=interpolate.InterpolatedUnivariateSpline(np.sort(empiricalcdf(x)),np.sort(x),k=3) #Don't use interpolating splines for function approximation #with s=0.03 the spline is monotonic at the evaluated values ppfemp=interpolate.UnivariateSpline(np.sort(empiricalcdf(x)),np.sort(x),k=3, s=0.03) ppfe = ppfemp(cdf_ongrid) plt.plot(ppfe, cdf_ongrid) print 'negative density' print '(np.diff(ppfs)).min()', (np.diff(ppfs)).min() print '(np.diff(cdf_ongrid)).min()', (np.diff(cdf_ongrid)).min() #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/stats_mstats_short.py000066400000000000000000000350141224417117700276350ustar00rootroot00000000000000'''get versions of mstats percentile functions that also work with non-masked arrays uses dispatch to mstats version for difficult cases: - data is masked array - data requires nan handling (masknan=True) - data should be trimmed (limit is non-empty) handle simple cases directly, which doesn't require apply_along_axis changes compared to mstats: plotting_positions for n-dim with axis argument addition: plotting_positions_w1d: with weights, 1d ndarray only TODO: consistency with scipy.stats versions not checked docstrings from mstats not updated yet code duplication, better solutions (?) convert examples to tests rename alphap, betap for consistency timing question: one additional argsort versus apply_along_axis weighted plotting_positions - I haven't figured out nd version of weighted plotting_positions - add weighted quantiles ''' import numpy as np from numpy import ma from scipy import stats #from numpy.ma import nomask #####-------------------------------------------------------------------------- #---- --- Percentiles --- #####-------------------------------------------------------------------------- def quantiles(a, prob=list([.25,.5,.75]), alphap=.4, betap=.4, axis=None, limit=(), masknan=False): """ Computes empirical quantiles for a data array. Samples quantile are defined by :math:`Q(p) = (1-g).x[i] +g.x[i+1]`, where :math:`x[j]` is the *j*th order statistic, and `i = (floor(n*p+m))`, `m=alpha+p*(1-alpha-beta)` and `g = n*p + m - i`. Typical values of (alpha,beta) are: - (0,1) : *p(k) = k/n* : linear interpolation of cdf (R, type 4) - (.5,.5) : *p(k) = (k+1/2.)/n* : piecewise linear function (R, type 5) - (0,0) : *p(k) = k/(n+1)* : (R type 6) - (1,1) : *p(k) = (k-1)/(n-1)*. In this case, p(k) = mode[F(x[k])]. That's R default (R type 7) - (1/3,1/3): *p(k) = (k-1/3)/(n+1/3)*. Then p(k) ~ median[F(x[k])]. The resulting quantile estimates are approximately median-unbiased regardless of the distribution of x. (R type 8) - (3/8,3/8): *p(k) = (k-3/8)/(n+1/4)*. Blom. The resulting quantile estimates are approximately unbiased if x is normally distributed (R type 9) - (.4,.4) : approximately quantile unbiased (Cunnane) - (.35,.35): APL, used with PWM ?? JP - (0.35, 0.65): PWM ?? JP p(k) = (k-0.35)/n Parameters ---------- a : array-like Input data, as a sequence or array of dimension at most 2. prob : array-like, optional List of quantiles to compute. alpha : float, optional Plotting positions parameter, default is 0.4. beta : float, optional Plotting positions parameter, default is 0.4. axis : int, optional Axis along which to perform the trimming. If None (default), the input array is first flattened. limit : tuple Tuple of (lower, upper) values. Values of `a` outside this closed interval are ignored. Returns ------- quants : MaskedArray An array containing the calculated quantiles. Examples -------- >>> from scipy.stats.mstats import mquantiles >>> a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36.]) >>> mquantiles(a) array([ 19.2, 40. , 42.8]) Using a 2D array, specifying axis and limit. >>> data = np.array([[ 6., 7., 1.], [ 47., 15., 2.], [ 49., 36., 3.], [ 15., 39., 4.], [ 42., 40., -999.], [ 41., 41., -999.], [ 7., -999., -999.], [ 39., -999., -999.], [ 43., -999., -999.], [ 40., -999., -999.], [ 36., -999., -999.]]) >>> mquantiles(data, axis=0, limit=(0, 50)) array([[ 19.2 , 14.6 , 1.45], [ 40. , 37.5 , 2.5 ], [ 42.8 , 40.05, 3.55]]) >>> data[:, 2] = -999. >>> mquantiles(data, axis=0, limit=(0, 50)) masked_array(data = [[19.2 14.6 --] [40.0 37.5 --] [42.8 40.05 --]], mask = [[False False True] [False False True] [False False True]], fill_value = 1e+20) """ if isinstance(a, np.ma.MaskedArray): return stats.mstats.mquantiles(a, prob=prob, alphap=alphap, betap=alphap, axis=axis, limit=limit) if limit: marr = stats.mstats.mquantiles(a, prob=prob, alphap=alphap, betap=alphap, axis=axis, limit=limit) return ma.filled(marr, fill_value=np.nan) if masknan: nanmask = np.isnan(a) if nanmask.any(): marr = ma.array(a, mask=nanmask) marr = stats.mstats.mquantiles(marr, prob=prob, alphap=alphap, betap=alphap, axis=axis, limit=limit) return ma.filled(marr, fill_value=np.nan) # Initialization & checks --------- data = np.asarray(a) p = np.array(prob, copy=False, ndmin=1) m = alphap + p*(1.-alphap-betap) isrolled = False #from _quantiles1d if (axis is None): data = data.ravel() #reshape(-1,1) axis = 0 else: axis = np.arange(data.ndim)[axis] data = np.rollaxis(data, axis) isrolled = True # keep track, maybe can be removed x = np.sort(data, axis=0) n = x.shape[0] returnshape = list(data.shape) returnshape[axis] = p #TODO: check these if n == 0: return np.empty(len(p), dtype=float) elif n == 1: return np.resize(x, p.shape) aleph = (n*p + m) k = np.floor(aleph.clip(1, n-1)).astype(int) ind = [None]*x.ndim ind[0] = slice(None) gamma = (aleph-k).clip(0,1)[ind] q = (1.-gamma)*x[k-1] + gamma*x[k] if isrolled: return np.rollaxis(q, 0, axis+1) else: return q def scoreatpercentile(data, per, limit=(), alphap=.4, betap=.4, axis=0, masknan=None): """Calculate the score at the given 'per' percentile of the sequence a. For example, the score at per=50 is the median. This function is a shortcut to mquantile """ per = np.asarray(per, float) if (per < 0).any() or (per > 100.).any(): raise ValueError("The percentile should be between 0. and 100. !"\ " (got %s)" % per) return quantiles(data, prob=[per/100.], alphap=alphap, betap=betap, limit=limit, axis=axis, masknan=masknan).squeeze() def plotting_positions(data, alpha=0.4, beta=0.4, axis=0, masknan=False): """Returns the plotting positions (or empirical percentile points) for the data. Plotting positions are defined as (i-alpha)/(n+1-alpha-beta), where: - i is the rank order statistics (starting at 1) - n is the number of unmasked values along the given axis - alpha and beta are two parameters. Typical values for alpha and beta are: - (0,1) : *p(k) = k/n* : linear interpolation of cdf (R, type 4) - (.5,.5) : *p(k) = (k-1/2.)/n* : piecewise linear function (R, type 5) (Bliss 1967: "Rankit") - (0,0) : *p(k) = k/(n+1)* : Weibull (R type 6), (Van der Waerden 1952) - (1,1) : *p(k) = (k-1)/(n-1)*. In this case, p(k) = mode[F(x[k])]. That's R default (R type 7) - (1/3,1/3): *p(k) = (k-1/3)/(n+1/3)*. Then p(k) ~ median[F(x[k])]. The resulting quantile estimates are approximately median-unbiased regardless of the distribution of x. (R type 8), (Tukey 1962) - (3/8,3/8): *p(k) = (k-3/8)/(n+1/4)*. The resulting quantile estimates are approximately unbiased if x is normally distributed (R type 9) (Blom 1958) - (.4,.4) : approximately quantile unbiased (Cunnane) - (.35,.35): APL, used with PWM Parameters ---------- x : sequence Input data, as a sequence or array of dimension at most 2. prob : sequence List of quantiles to compute. alpha : {0.4, float} optional Plotting positions parameter. beta : {0.4, float} optional Plotting positions parameter. Notes ----- I think the adjustments assume that there are no ties in order to be a reasonable approximation to a continuous density function. TODO: check this References ---------- unknown, dates to original papers from Beasley, Erickson, Allison 2009 Behav Genet """ if isinstance(data, np.ma.MaskedArray): if axis is None or data.ndim == 1: return stats.mstats.plotting_positions(data, alpha=alpha, beta=beta) else: return ma.apply_along_axis(stats.mstats.plotting_positions, axis, data, alpha=alpha, beta=beta) if masknan: nanmask = np.isnan(data) if nanmask.any(): marr = ma.array(data, mask=nanmask) #code duplication: if axis is None or data.ndim == 1: marr = stats.mstats.plotting_positions(marr, alpha=alpha, beta=beta) else: marr = ma.apply_along_axis(stats.mstats.plotting_positions, axis, marr, alpha=alpha, beta=beta) return ma.filled(marr, fill_value=np.nan) data = np.asarray(data) if data.size == 1: # use helper function instead data = np.atleast_1d(data) axis = 0 if axis is None: data = data.ravel() axis = 0 n = data.shape[axis] if data.ndim == 1: plpos = np.empty(data.shape, dtype=float) plpos[data.argsort()] = (np.arange(1,n+1) - alpha)/(n+1.-alpha-beta) else: #nd assignment instead of second argsort doesn't look easy plpos = (data.argsort(axis).argsort(axis) + 1. - alpha)/(n+1.-alpha-beta) return plpos meppf = plotting_positions def plotting_positions_w1d(data, weights=None, alpha=0.4, beta=0.4, method='notnormed'): '''Weighted plotting positions (or empirical percentile points) for the data. observations are weighted and the plotting positions are defined as (ws-alpha)/(n-alpha-beta), where: - ws is the weighted rank order statistics or cumulative weighted sum, normalized to n if method is "normed" - n is the number of values along the given axis if method is "normed" and total weight otherwise - alpha and beta are two parameters. wtd.quantile in R package Hmisc seems to use the "notnormed" version. notnormed coincides with unweighted segment in example, drop "normed" version ? See Also -------- plotting_positions : unweighted version that works also with more than one dimension and has other options ''' x = np.atleast_1d(data) if x.ndim > 1: raise ValueError('currently implemented only for 1d') if weights is None: weights = np.ones(x.shape) else: weights = np.array(weights, float, copy=False, ndmin=1) #atleast_1d(weights) if weights.shape != x.shape: raise ValueError('if weights is given, it needs to be the same' 'shape as data') n = len(x) xargsort = x.argsort() ws = weights[xargsort].cumsum() res = np.empty(x.shape) if method == 'normed': res[xargsort] = (1.*ws/ws[-1]*n-alpha)/(n+1.-alpha-beta) else: res[xargsort] = (1.*ws-alpha)/(ws[-1]+1.-alpha-beta) return res def edf_normal_inverse_transformed(x, alpha=3./8, beta=3./8, axis=0): '''rank based normal inverse transformed cdf ''' from scipy import stats ranks = plotting_positions(data, alpha=alpha, beta=alpha, axis=0, masknan=False) ranks_transf = stats.norm.ppf(ranks) return ranks_transf if __name__ == '__main__': x = np.arange(5) print plotting_positions(x) x = np.arange(10).reshape(-1,2) print plotting_positions(x) print quantiles(x, axis=0) print quantiles(x, axis=None) print quantiles(x, axis=1) xm = ma.array(x) x2 = x.astype(float) x2[1,0] = np.nan print plotting_positions(xm, axis=0) # test 0d, 1d for sl1 in [slice(None), 0]: print (plotting_positions(xm[sl1,0]) == plotting_positions(x[sl1,0])).all(), print (quantiles(xm[sl1,0]) == quantiles(x[sl1,0])).all(), print (stats.mstats.mquantiles(ma.fix_invalid(x2[sl1,0])) == quantiles(x2[sl1,0], masknan=1)).all(), #test 2d for ax in [0, 1, None, -1]: print (plotting_positions(xm, axis=ax) == plotting_positions(x, axis=ax)).all(), print (quantiles(xm, axis=ax) == quantiles(x, axis=ax)).all(), print (stats.mstats.mquantiles(ma.fix_invalid(x2), axis=ax) == quantiles(x2, axis=ax, masknan=1)).all(), #stats version doesn't have axis print (stats.mstats.plotting_positions(ma.fix_invalid(x2)) == plotting_positions(x2, axis=None, masknan=1)).all(), #test 3d x3 = np.dstack((x,x)).T for ax in [1,2]: print (plotting_positions(x3, axis=ax)[0] == plotting_positions(x.T, axis=ax-1)).all(), np.testing.assert_equal(plotting_positions(np.arange(10), alpha=0.35, beta=1-0.35), (1+np.arange(10)-0.35)/10) np.testing.assert_equal(plotting_positions(np.arange(10), alpha=0.4, beta=0.4), (1+np.arange(10)-0.4)/(10+0.2)) np.testing.assert_equal(plotting_positions(np.arange(10)), (1+np.arange(10)-0.4)/(10+0.2)) print print scoreatpercentile(x, [10,90]) print plotting_positions_w1d(x[:,0]) print (plotting_positions_w1d(x[:,0]) == plotting_positions(x[:,0])).all() #weights versus replicating multiple occurencies of same x value w1 = [1, 1, 2, 1, 1] plotexample = 1 if plotexample: import matplotlib.pyplot as plt plt.figure() plt.title('ppf, cdf values on horizontal axis') plt.step(plotting_positions_w1d(x[:,0], weights=w1, method='0'), x[:,0], where='post') plt.step(stats.mstats.plotting_positions(np.repeat(x[:,0],w1,axis=0)),np.repeat(x[:,0],w1,axis=0),where='post') plt.plot(plotting_positions_w1d(x[:,0], weights=w1, method='0'), x[:,0], '-o') plt.plot(stats.mstats.plotting_positions(np.repeat(x[:,0],w1,axis=0)),np.repeat(x[:,0],w1,axis=0), '-o') plt.figure() plt.title('cdf, cdf values on vertical axis') plt.step(x[:,0], plotting_positions_w1d(x[:,0], weights=w1, method='0'),where='post') plt.step(np.repeat(x[:,0],w1,axis=0), stats.mstats.plotting_positions(np.repeat(x[:,0],w1,axis=0)),where='post') plt.plot(x[:,0], plotting_positions_w1d(x[:,0], weights=w1, method='0'), '-o') plt.plot(np.repeat(x[:,0],w1,axis=0), stats.mstats.plotting_positions(np.repeat(x[:,0],w1,axis=0)), '-o') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/tests/000077500000000000000000000000001224417117700244525ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/stats/tests/__init__.py000066400000000000000000000143221224417117700265650ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/survival.py000066400000000000000000000006541224417117700244040ustar00rootroot00000000000000import numpy as np class SurvivalTime(object): def __init__(self, time, delta): self.time, self.delta = time, delta def atrisk(self, time): raise NotImplementedError class RightCensored(SurvivalTime): def atrisk(self, time): return np.less_equal.outer(time, self.time) class LeftCensored(SurvivalTime): def atrisk(self, time): return np.greater_equal.outer(time, self.time) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/survival2.py000066400000000000000000000430041224417117700244620ustar00rootroot00000000000000#Kaplan-Meier Estimator import numpy as np import numpy.linalg as la import matplotlib.pyplot as plt from scipy import stats from statsmodels.iolib.table import SimpleTable class KaplanMeier(object): """ KaplanMeier(...) KaplanMeier(data, endog, exog=None, censoring=None) Create an object of class KaplanMeier for estimating Kaplan-Meier survival curves. Parameters ---------- data: array_like An array, with observations in each row, and variables in the columns endog: index (starting at zero) of the column containing the endogenous variable (time) exog: index of the column containing the exogenous variable (must be catagorical). If exog = None, this is equivalent to a single survival curve censoring: index of the column containing an indicator of whether an observation is an event, or a censored observation, with 0 for censored, and 1 for an event Attributes ----------- censorings: List of censorings associated with each unique time, at each value of exog events: List of the number of events at each unique time for each value of exog results: List of arrays containing estimates of the value value of the survival function and its standard error at each unique time, for each value of exog ts: List of unique times for each value of exog Methods ------- fit: Calcuate the Kaplan-Meier estimates of the survival function and its standard error at each time, for each value of exog plot: Plot the survival curves using matplotlib.plyplot summary: Display the results of fit in a table. Gives results for all (including censored) times test_diff: Test for difference between survival curves Examples -------- >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from statsmodels.sandbox.survival2 import KaplanMeier >>> dta = sm.datasets.strikes.load() >>> dta = dta.values()[-1] >>> dta[range(5),:] array([[ 7.00000000e+00, 1.13800000e-02], [ 9.00000000e+00, 1.13800000e-02], [ 1.30000000e+01, 1.13800000e-02], [ 1.40000000e+01, 1.13800000e-02], [ 2.60000000e+01, 1.13800000e-02]]) >>> km = KaplanMeier(dta,0) >>> km.fit() >>> km.plot() Doing >>> km.summary() will display a table of the estimated survival and standard errors for each time. The first few lines are Kaplan-Meier Curve ===================================== Time Survival Std. Err ------------------------------------- 1.0 0.983870967742 0.0159984306572 2.0 0.91935483871 0.0345807888235 3.0 0.854838709677 0.0447374942184 4.0 0.838709677419 0.0467104592871 5.0 0.822580645161 0.0485169952543 Doing >>> plt.show() will plot the survival curve Mutliple survival curves: >>> km2 = KaplanMeier(dta,0,exog=1) >>> km2.fit() km2 will estimate a survival curve for each value of industrial production, the column of dta with index one (1). With censoring: >>> censoring = np.ones_like(dta[:,0]) >>> censoring[dta[:,0] > 80] = 0 >>> dta = np.c_[dta,censoring] >>> dta[range(5),:] array([[ 7.00000000e+00, 1.13800000e-02, 1.00000000e+00], [ 9.00000000e+00, 1.13800000e-02, 1.00000000e+00], [ 1.30000000e+01, 1.13800000e-02, 1.00000000e+00], [ 1.40000000e+01, 1.13800000e-02, 1.00000000e+00], [ 2.60000000e+01, 1.13800000e-02, 1.00000000e+00]]) >>> km3 = KaplanMeier(dta,0,exog=1,censoring=2) >>> km3.fit() Test for difference of survival curves >>> log_rank = km3.test_diff([0.0645,-0.03957]) The zeroth element of log_rank is the chi-square test statistic for the difference between the survival curves for exog = 0.0645 and exog = -0.03957, the index one element is the degrees of freedom for the test, and the index two element is the p-value for the test Groups with nan names >>> groups = np.ones_like(dta[:,1]) >>> groups = groups.astype('S4') >>> groups[dta[:,1] > 0] = 'high' >>> groups[dta[:,1] <= 0] = 'low' >>> dta = dta.astype('S4') >>> dta[:,1] = groups >>> dta[range(5),:] array([['7.0', 'high', '1.0'], ['9.0', 'high', '1.0'], ['13.0', 'high', '1.0'], ['14.0', 'high', '1.0'], ['26.0', 'high', '1.0']], dtype='|S4') >>> km4 = KaplanMeier(dta,0,exog=1,censoring=2) >>> km4.fit() """ def __init__(self, data, endog, exog=None, censoring=None): self.exog = exog self.censoring = censoring cols = [endog] self.endog = 0 if exog != None: cols.append(exog) self.exog = 1 if censoring != None: cols.append(censoring) if exog != None: self.censoring = 2 else: self.censoring = 1 data = data[:,cols] if data.dtype == float or data.dtype == int: self.data = data[~np.isnan(data).any(1)] else: t = (data[:,self.endog]).astype(float) if exog != None: evec = data[:,self.exog] evec = evec[~np.isnan(t)] if censoring != None: cvec = (data[:,self.censoring]).astype(float) cvec = cvec[~np.isnan(t)] t = t[~np.isnan(t)] if censoring != None: t = t[~np.isnan(cvec)] if exog != None: evec = evec[~np.isnan(cvec)] cvec = cvec[~np.isnan(cvec)] cols = [t] if exog != None: cols.append(evec) if censoring != None: cols.append(cvec) data = (np.array(cols)).transpose() self.data = data def fit(self): """ Calculate the Kaplan-Meier estimator of the survival function """ self.results = [] self.ts = [] self.censorings = [] self.event = [] if self.exog == None: self.fitting_proc(self.data) else: groups = np.unique(self.data[:,self.exog]) self.groups = groups for g in groups: group = self.data[self.data[:,self.exog] == g] self.fitting_proc(group) def plot(self): """ Plot the estimated survival curves. After using this method do plt.show() to display the plot """ plt.figure() if self.exog == None: self.plotting_proc(0) else: for g in range(len(self.groups)): self.plotting_proc(g) plt.ylim(ymax=1.05) plt.ylabel('Survival') plt.xlabel('Time') def summary(self): """ Print a set of tables containing the estimates of the survival function, and its standard errors """ if self.exog == None: self.summary_proc(0) else: for g in range(len(self.groups)): self.summary_proc(g) def fitting_proc(self, group): """ For internal use """ t = ((group[:,self.endog]).astype(float)).astype(int) if self.censoring == None: events = np.bincount(t) t = np.unique(t) events = events[:,list(t)] events = events.astype(float) eventsSum = np.cumsum(events) eventsSum = np.r_[0,eventsSum] n = len(group) - eventsSum[:-1] else: censoring = ((group[:,self.censoring]).astype(float)).astype(int) reverseCensoring = -1*(censoring - 1) events = np.bincount(t,censoring) censored = np.bincount(t,reverseCensoring) t = np.unique(t) censored = censored[:,list(t)] censored = censored.astype(float) censoredSum = np.cumsum(censored) censoredSum = np.r_[0,censoredSum] events = events[:,list(t)] events = events.astype(float) eventsSum = np.cumsum(events) eventsSum = np.r_[0,eventsSum] n = len(group) - eventsSum[:-1] - censoredSum[:-1] (self.censorings).append(censored) survival = np.cumprod(1-events/n) var = ((survival*survival) * np.cumsum(events/(n*(n-events)))) se = np.sqrt(var) (self.results).append(np.array([survival,se])) (self.ts).append(t) (self.event).append(events) def plotting_proc(self, g): """ For internal use """ survival = self.results[g][0] t = self.ts[g] e = (self.event)[g] if self.censoring != None: c = self.censorings[g] csurvival = survival[c != 0] ct = t[c != 0] if len(ct) != 0: plt.vlines(ct,csurvival+0.02,csurvival-0.02) x = np.repeat(t[e != 0], 2) y = np.repeat(survival[e != 0], 2) if self.ts[g][-1] in t[e != 0]: x = np.r_[0,x] y = np.r_[1,1,y[:-1]] else: x = np.r_[0,x,self.ts[g][-1]] y = np.r_[1,1,y] plt.plot(x,y) def summary_proc(self, g): """ For internal use """ if self.exog != None: myTitle = ('exog = ' + str(self.groups[g]) + '\n') else: myTitle = "Kaplan-Meier Curve" table = np.transpose(self.results[g]) table = np.c_[np.transpose(self.ts[g]),table] table = SimpleTable(table, headers=['Time','Survival','Std. Err'], title = myTitle) print(table) def test_diff(self, groups, rho=None, weight=None): """ test_diff(groups, rho=0) Test for difference between survival curves Parameters ---------- groups: A list of the values for exog to test for difference. tests the null hypothesis that the survival curves for all values of exog in groups are equal rho: compute the test statistic with weight S(t)^rho, where S(t) is the pooled estimate for the Kaplan-Meier survival function. If rho = 0, this is the logrank test, if rho = 0, this is the Peto and Peto modification to the Gehan-Wilcoxon test. weight: User specified function that accepts as its sole arguement an array of times, and returns an array of weights for each time to be used in the test Returns ------- An array whose zeroth element is the chi-square test statistic for the global null hypothesis, that all survival curves are equal, the index one element is degrees of freedom for the test, and the index two element is the p-value for the test. Examples -------- >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from statsmodels.sandbox.survival2 import KaplanMeier >>> dta = sm.datasets.strikes.load() >>> dta = dta.values()[-1] >>> censoring = np.ones_like(dta[:,0]) >>> censoring[dta[:,0] > 80] = 0 >>> dta = np.c_[dta,censoring] >>> km = KaplanMeier(dta,0,exog=1,censoring=2) >>> km.fit() Test for difference of survival curves >>> log_rank = km3.test_diff([0.0645,-0.03957]) The zeroth element of log_rank is the chi-square test statistic for the difference between the survival curves using the log rank test for exog = 0.0645 and exog = -0.03957, the index one element is the degrees of freedom for the test, and the index two element is the p-value for the test >>> wilcoxon = km.test_diff([0.0645,-0.03957], rho=1) wilcoxon is the equivalent information as log_rank, but for the Peto and Peto modification to the Gehan-Wilcoxon test. User specified weight functions >>> log_rank = km3.test_diff([0.0645,-0.03957], weight=np.ones_like) This is equivalent to the log rank test More than two groups >>> log_rank = km.test_diff([0.0645,-0.03957,0.01138]) The test can be performed with arbitrarily many groups, so long as they are all in the column exog """ groups = np.asarray(groups) if self.exog == None: raise ValueError("Need an exogenous variable for logrank test") elif (np.in1d(groups,self.groups)).all(): data = self.data[np.in1d(self.data[:,self.exog],groups)] t = ((data[:,self.endog]).astype(float)).astype(int) tind = np.unique(t) NK = [] N = [] D = [] Z = [] if rho != None and weight != None: raise ValueError("Must use either rho or weights, not both") elif rho != None: s = KaplanMeier(data,self.endog,censoring=self.censoring) s.fit() s = (s.results[0][0]) ** (rho) s = np.r_[1,s[:-1]] elif weight != None: s = weight(tind) else: s = np.ones_like(tind) if self.censoring == None: for g in groups: dk = np.bincount((t[data[:,self.exog] == g])) d = np.bincount(t) if np.max(tind) != len(dk): dif = np.max(tind) - len(dk) + 1 dk = np.r_[dk,[0]*dif] dk = dk[:,list(tind)] d = d[:,list(tind)] dk = dk.astype(float) d = d.astype(float) dkSum = np.cumsum(dk) dSum = np.cumsum(d) dkSum = np.r_[0,dkSum] dSum = np.r_[0,dSum] nk = len(data[data[:,self.exog] == g]) - dkSum[:-1] n = len(data) - dSum[:-1] d = d[n>1] dk = dk[n>1] nk = nk[n>1] n = n[n>1] s = s[n>1] ek = (nk * d)/(n) Z.append(np.sum(s * (dk - ek))) NK.append(nk) N.append(n) D.append(d) else: for g in groups: censoring = ((data[:,self.censoring]).astype(float)).astype(int) reverseCensoring = -1*(censoring - 1) censored = np.bincount(t,reverseCensoring) ck = np.bincount((t[data[:,self.exog] == g]), reverseCensoring[data[:,self.exog] == g]) dk = np.bincount((t[data[:,self.exog] == g]), censoring[data[:,self.exog] == g]) d = np.bincount(t,censoring) if np.max(tind) != len(dk): dif = np.max(tind) - len(dk) + 1 dk = np.r_[dk,[0]*dif] ck = np.r_[ck,[0]*dif] dk = dk[:,list(tind)] ck = ck[:,list(tind)] d = d[:,list(tind)] dk = dk.astype(float) d = d.astype(float) ck = ck.astype(float) dkSum = np.cumsum(dk) dSum = np.cumsum(d) ck = np.cumsum(ck) ck = np.r_[0,ck] dkSum = np.r_[0,dkSum] dSum = np.r_[0,dSum] censored = censored[:,list(tind)] censored = censored.astype(float) censoredSum = np.cumsum(censored) censoredSum = np.r_[0,censoredSum] nk = (len(data[data[:,self.exog] == g]) - dkSum[:-1] - ck[:-1]) n = len(data) - dSum[:-1] - censoredSum[:-1] d = d[n>1] dk = dk[n>1] nk = nk[n>1] n = n[n>1] s = s[n>1] ek = (nk * d)/(n) Z.append(np.sum(s * (dk - ek))) NK.append(nk) N.append(n) D.append(d) Z = np.array(Z) N = np.array(N) D = np.array(D) NK = np.array(NK) sigma = -1 * np.dot((NK/N) * ((N - D)/(N - 1)) * D * np.array([(s ** 2)]*len(D)) ,np.transpose(NK/N)) np.fill_diagonal(sigma, np.diagonal(np.dot((NK/N) * ((N - D)/(N - 1)) * D * np.array([(s ** 2)]*len(D)) ,np.transpose(1 - (NK/N))))) chisq = np.dot(np.transpose(Z),np.dot(la.pinv(sigma), Z)) df = len(groups) - 1 return np.array([chisq, df, stats.chi2.sf(chisq,df)]) else: raise ValueError("groups must be in column exog") statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/sysreg.py000066400000000000000000000343201224417117700240420ustar00rootroot00000000000000from statsmodels.regression.linear_model import GLS import numpy as np import statsmodels.tools.tools as tools from statsmodels.base.model import LikelihoodModelResults from scipy import sparse #http://www.irisa.fr/aladin/wg-statlin/WORKSHOPS/RENNES02/SLIDES/Foschi.pdf __all__ = ['SUR', 'Sem2SLS'] #probably should have a SystemModel superclass # TODO: does it make sense of SUR equations to have # independent endogenous regressors? If so, then # change docs to LHS = RHS #TODO: make a dictionary that holds equation specific information #rather than these cryptic lists? Slower to get a dict value? #TODO: refine sigma definition class SUR(object): """ Seemingly Unrelated Regression Parameters ---------- sys : list [endog1, exog1, endog2, exog2,...] It will be of length 2 x M, where M is the number of equations endog = exog. sigma : array-like M x M array where sigma[i,j] is the covariance between equation i and j dfk : None, 'dfk1', or 'dfk2' Default is None. Correction for the degrees of freedom should be specified for small samples. See the notes for more information. Attributes ---------- cholsigmainv : array The transpose of the Cholesky decomposition of `pinv_wexog` df_model : array Model degrees of freedom of each equation. p_{m} - 1 where p is the number of regressors for each equation m and one is subtracted for the constant. df_resid : array Residual degrees of freedom of each equation. Number of observations less the number of parameters. endog : array The LHS variables for each equation in the system. It is a M x nobs array where M is the number of equations. exog : array The RHS variable for each equation in the system. It is a nobs x sum(p_{m}) array. Which is just each RHS array stacked next to each other in columns. history : dict Contains the history of fitting the model. Probably not of interest if the model is fit with `igls` = False. iterations : int The number of iterations until convergence if the model is fit iteratively. nobs : float The number of observations of the equations. normalized_cov_params : array sum(p_{m}) x sum(p_{m}) array :math:`\\left[X^{T}\\left(\\Sigma^{-1}\\otimes\\boldsymbol{I}\\right)X\\right]^{-1}` pinv_wexog : array The pseudo-inverse of the `wexog` sigma : array M x M covariance matrix of the cross-equation disturbances. See notes. sp_exog : CSR sparse matrix Contains a block diagonal sparse matrix of the design so that exog1 ... exogM are on the diagonal. wendog : array M * nobs x 1 array of the endogenous variables whitened by `cholsigmainv` and stacked into a single column. wexog : array M*nobs x sum(p_{m}) array of the whitened exogenous variables. Notes ----- All individual equations are assumed to be well-behaved, homoeskedastic iid errors. This is basically an extension of GLS, using sparse matrices. .. math:: \\Sigma=\\left[\\begin{array}{cccc} \\sigma_{11} & \\sigma_{12} & \\cdots & \\sigma_{1M}\\\\ \\sigma_{21} & \\sigma_{22} & \\cdots & \\sigma_{2M}\\\\ \\vdots & \\vdots & \\ddots & \\vdots\\\\ \\sigma_{M1} & \\sigma_{M2} & \\cdots & \\sigma_{MM}\\end{array}\\right] References ---------- Zellner (1962), Greene (2003) """ #TODO: Does each equation need nobs to be the same? def __init__(self, sys, sigma=None, dfk=None): if len(sys) % 2 != 0: raise ValueError("sys must be a list of pairs of endogenous and \ exogenous variables. Got length %s" % len(sys)) if dfk: if not dfk.lower() in ['dfk1','dfk2']: raise ValueError("dfk option %s not understood" % (dfk)) self._dfk = dfk M = len(sys[1::2]) self._M = M # exog = np.zeros((M,M), dtype=object) # for i,eq in enumerate(sys[1::2]): # exog[i,i] = np.asarray(eq) # not sure this exog is needed # used to compute resids for now exog = np.column_stack(np.asarray(sys[1::2][i]) for i in range(M)) # exog = np.vstack(np.asarray(sys[1::2][i]) for i in range(M)) self.exog = exog # 2d ndarray exog is better # Endog, might just go ahead and reshape this? endog = np.asarray(sys[::2]) self.endog = endog self.nobs = float(self.endog[0].shape[0]) # assumes all the same length # Degrees of Freedom df_resid = [] df_model = [] [df_resid.append(self.nobs - tools.rank(_)) \ for _ in sys[1::2]] [df_model.append(tools.rank(_) - 1) for _ in sys[1::2]] self.df_resid = np.asarray(df_resid) self.df_model = np.asarray(df_model) # "Block-diagonal" sparse matrix of exog sp_exog = sparse.lil_matrix((int(self.nobs*M), int(np.sum(self.df_model+1)))) # linked lists to build self._cols = np.cumsum(np.hstack((0, self.df_model+1))) for i in range(M): sp_exog[i*self.nobs:(i+1)*self.nobs, self._cols[i]:self._cols[i+1]] = sys[1::2][i] self.sp_exog = sp_exog.tocsr() # cast to compressed for efficiency # Deal with sigma, check shape earlier if given if np.any(sigma): sigma = np.asarray(sigma) # check shape elif sigma == None: resids = [] for i in range(M): resids.append(GLS(endog[i],exog[:, self._cols[i]:self._cols[i+1]]).fit().resid) resids = np.asarray(resids).reshape(M,-1) sigma = self._compute_sigma(resids) self.sigma = sigma self.cholsigmainv = np.linalg.cholesky(np.linalg.pinv(\ self.sigma)).T self.initialize() def initialize(self): self.wendog = self.whiten(self.endog) self.wexog = self.whiten(self.sp_exog) self.pinv_wexog = np.linalg.pinv(self.wexog) self.normalized_cov_params = np.dot(self.pinv_wexog, np.transpose(self.pinv_wexog)) self.history = {'params' : [np.inf]} self.iterations = 0 def _update_history(self, params): self.history['params'].append(params) def _compute_sigma(self, resids): """ Computes the sigma matrix and update the cholesky decomposition. """ M = self._M nobs = self.nobs sig = np.dot(resids, resids.T) # faster way to do this? if not self._dfk: div = nobs elif self._dfk.lower() == 'dfk1': div = np.zeros(M**2) for i in range(M): for j in range(M): div[i+j] = ((self.df_model[i]+1) *\ (self.df_model[j]+1))**(1/2) div.reshape(M,M) else: # 'dfk2' error checking is done earlier div = np.zeros(M**2) for i in range(M): for j in range(M): div[i+j] = nobs - np.max(self.df_model[i]+1, self.df_model[j]+1) div.reshape(M,M) # doesn't handle (#,) self.cholsigmainv = np.linalg.cholesky(np.linalg.pinv(sig/div)).T return sig/div def whiten(self, X): """ SUR whiten method. Parameters ----------- X : list of arrays Data to be whitened. Returns ------- If X is the exogenous RHS of the system. ``np.dot(np.kron(cholsigmainv,np.eye(M)),np.diag(X))`` If X is the endogenous LHS of the system. """ nobs = self.nobs if X is self.endog: # definitely not a robust check return np.dot(np.kron(self.cholsigmainv,np.eye(nobs)), X.reshape(-1,1)) elif X is self.sp_exog: return (sparse.kron(self.cholsigmainv, sparse.eye(nobs,nobs))*X).toarray()#*=dot until cast to array def fit(self, igls=False, tol=1e-5, maxiter=100): """ igls : bool Iterate until estimates converge if sigma is None instead of two-step GLS, which is the default is sigma is None. tol : float maxiter : int Notes ----- This ia naive implementation that does not exploit the block diagonal structure. It should work for ill-conditioned `sigma` but this is untested. """ if not np.any(self.sigma): self.sigma = self._compute_sigma(self.endog, self.exog) M = self._M beta = np.dot(self.pinv_wexog, self.wendog) self._update_history(beta) self.iterations += 1 if not igls: sur_fit = SysResults(self, beta, self.normalized_cov_params) return sur_fit conv = self.history['params'] while igls and (np.any(np.abs(conv[-2] - conv[-1]) > tol)) and \ (self.iterations < maxiter): fittedvalues = (self.sp_exog*beta).reshape(M,-1) resids = self.endog - fittedvalues # don't attach results yet self.sigma = self._compute_sigma(resids) # need to attach for compute? self.wendog = self.whiten(self.endog) self.wexog = self.whiten(self.sp_exog) self.pinv_wexog = np.linalg.pinv(self.wexog) self.normalized_cov_params = np.dot(self.pinv_wexog, np.transpose(self.pinv_wexog)) beta = np.dot(self.pinv_wexog, self.wendog) self._update_history(beta) self.iterations += 1 sur_fit = SysResults(self, beta, self.normalized_cov_params) return sur_fit def predict(self, design): pass #TODO: Should just have a general 2SLS estimator to subclass # for IV, FGLS, etc. # Also should probably have SEM class and estimators as subclasses class Sem2SLS(object): """ Two-Stage Least Squares for Simultaneous equations Parameters ---------- sys : list [endog1, exog1, endog2, exog2,...] It will be of length 2 x M, where M is the number of equations endog = exog. indep_endog : dict A dictionary mapping the equation to the column numbers of the the independent endogenous regressors in each equation. It is assumed that the system is inputed as broken up into LHS and RHS. For now, the values of the dict have to be sequences. Note that the keys for the equations should be zero-indexed. instruments : array Array of the exogenous independent variables. Notes ----- This is unfinished, and the design should be refactored. Estimation is done by brute force and there is no exploitation of the structure of the system. """ def __init__(self, sys, indep_endog=None, instruments=None): if len(sys) % 2 != 0: raise ValueError("sys must be a list of pairs of endogenous and \ exogenous variables. Got length %s" % len(sys)) M = len(sys[1::2]) self._M = M # The lists are probably a bad idea self.endog = sys[::2] # these are just list containers self.exog = sys[1::2] self._K = [tools.rank(_) for _ in sys[1::2]] # fullexog = np.column_stack((_ for _ in self.exog)) self.instruments = instruments # Keep the Y_j's in a container to get IVs instr_endog = {} [instr_endog.setdefault(_,[]) for _ in indep_endog.keys()] for eq_key in indep_endog: for varcol in indep_endog[eq_key]: instr_endog[eq_key].append(self.exog[eq_key][:,varcol]) # ^ copy needed? # self._instr_endog = instr_endog self._indep_endog = indep_endog _col_map = np.cumsum(np.hstack((0,self._K))) # starting col no.s # move this check to whiten since we're not going to build a full exog? for eq_key in indep_endog: try: iter(indep_endog[eq_key]) except: # eq_key = [eq_key] raise TypeError("The values of the indep_exog dict must be\ iterable. Got type %s for converter %s" % (type(del_col))) # for del_col in indep_endog[eq_key]: # fullexog = np.delete(fullexog, _col_map[eq_key]+del_col, 1) # _col_map[eq_key+1:] -= 1 # Josef's example for deleting reoccuring "rows" # fullexog = np.unique(fullexog.T.view([('',fullexog.dtype)]*\ # fullexog.shape[0])).view(fullexog.dtype).reshape(\ # fullexog.shape[0],-1) # From http://article.gmane.org/gmane.comp.python.numeric.general/32276/ # Or Jouni' suggetsion of taking a hash: # http://www.mail-archive.com/numpy-discussion@scipy.org/msg04209.html # not clear to me how this would work though, only if they are the *same* # elements? # self.fullexog = fullexog self.wexog = self.whiten(instr_endog) def whiten(self, Y): """ Runs the first stage of the 2SLS. Returns the RHS variables that include the instruments. """ wexog = [] indep_endog = self._indep_endog # this has the col mapping # fullexog = self.fullexog instruments = self.instruments for eq in range(self._M): # need to go through all equations regardless instr_eq = Y.get(eq, None) # Y has the eq to ind endog array map newRHS = self.exog[eq].copy() if instr_eq: for i,LHS in enumerate(instr_eq): yhat = GLS(LHS, self.instruments).fit().fittedvalues newRHS[:,indep_endog[eq][i]] = yhat # this might fail if there is a one variable column (nobs,) # in exog wexog.append(newRHS) return wexog def fit(self): """ """ delta = [] wexog = self.wexog endog = self.endog for j in range(self._M): delta.append(GLS(endog[j], wexog[j]).fit().params) return delta class SysResults(LikelihoodModelResults): """ Not implemented yet. """ def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(SysResults, self).__init__(model, params, normalized_cov_params, scale) self._get_results() def _get_results(self): pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/000077500000000000000000000000001224417117700233145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/GreeneEx15_1.s000066400000000000000000000020771224417117700255760ustar00rootroot00000000000000dta <- read.table('/home/skipper/school/MetricsII/Greene\ TableF5-1.txt', header = TRUE) attach(dta) library(systemfit) demand <- realcons + realinvs + realgovt c.1 <- realcons[-204] y.1 <- demand[-204] yd <- demand[-1] - y.1 eqConsump <- realcons[-1] ~ demand[-1] + c.1 eqInvest <- realinvs[-1] ~ tbilrate[-1] + yd system <- list( Consumption = eqConsump, Investment = eqInvest) instruments <- ~ realgovt[-1] + tbilrate[-1] + c.1 + y.1 # 2SLS greene2sls <- systemfit( system, "2SLS", inst = instruments, methodResidCov = "noDfCor" ) print(summary(greene2sls)) greene3sls <- systemfit( system, "3SLS", inst = instruments, methodResidCov = "noDfCor" ) print(summary(greene3sls)) # Python code for finding the dynamics # # Could have done this in R # #gamma = np.array([[1,0,1],[0,1,1],[-.058438620413,-16.5359646223,1]]) #phi = np.array([[-.99200661799,0,0],[0,0,0],[0,-16.5359646223,0]]) #Delta = np.dot(-phi,np.linalg.inv(gamma)) #delta = np.zeros((2,2)) #delta[0,0]=Delta[0,0] #delta[0,1]=Delta[0,-1] #delta[1,0]=Delta[-1,0] #delta[1,1]=Delta[-1,-1] #np.eigvals(delta) #np.max(_) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/__init__.py000066400000000000000000000143221224417117700254270ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/datamlw.py000066400000000000000000000423341224417117700253250ustar00rootroot00000000000000import numpy as np from numpy import array class Holder(object): pass data = Holder() data.comment = 'generated data, divide by 1000' data.name = 'data' data.xo = array([[ -419, -731, -1306, -1294], [ 6, 529, -200, -437], [ -27, -833, -6, -564], [ -304, -273, -502, -739], [ 1377, -912, 927, 280], [ -375, -517, -514, 49], [ 247, -504, 123, -259], [ 712, 534, -773, 286], [ 195, -1080, 3256, -178], [ -854, 75, -706, -1084], [-1219, -612, -15, -203], [ 550, -628, -483, -2686], [ -365, 1376, -1266, 317], [ -489, 544, -195, 431], [ -656, 854, 840, -723], [ 16, -1385, -880, -460], [ 258, -2252, 96, 54], [ 2049, -750, -1115, 381], [ -65, 280, -777, 416], [ 755, 82, -806, 1027], [ -39, -170, -2134, 743], [ -859, 780, 746, -133], [ 762, 252, -450, -459], [ -941, -202, 49, -202], [ -54, 115, 455, 388], [-1348, 1246, 1430, -480], [ 229, -535, -1831, 1524], [ -651, -167, 2116, 483], [-1249, -1373, 888, -1092], [ -75, -2162, 486, -496], [ 2436, -1627, -1069, 162], [ -63, 560, -601, 587], [ -60, 1051, -277, 1323], [ 1329, -1294, 68, 5], [ 1532, -633, -923, 696], [ 669, 895, -1762, -375], [ 1129, -548, 2064, 609], [ 1320, 573, 2119, 270], [ -213, -412, -2517, 1685], [ 73, -979, 1312, -1220], [-1360, -2107, -237, 1522], [ -645, 205, -543, -169], [ -212, 1072, 543, -128], [ -352, -129, -605, -904], [ 511, 85, 167, -1914], [ 1515, 1862, 942, 1622], [ -465, 623, -495, -89], [-1396, -979, 1758, 128], [ -255, -47, 980, 501], [-1282, -58, -49, -610], [ -889, -1177, -492, 494], [ 1415, 1146, 696, -722], [ 1237, -224, -1609, -64], [ -528, -1625, 231, 883], [ -327, 1636, -476, -361], [ -781, 793, 1882, 234], [ -506, -561, 1988, -810], [-1233, 1467, -261, 2164], [ 53, 1069, 824, 2123], [-1200, -441, -321, 339], [ 1606, 298, -995, 1292], [-1740, -672, -1628, -129], [-1450, -354, 224, -657], [-2556, 1006, -706, -1453], [ -717, -463, 345, -1821], [ 1056, -38, -420, -455], [ -523, 565, 425, 1138], [-1030, -187, 683, 78], [ -214, -312, -1171, -528], [ 819, 736, -265, 423], [ 1339, 351, 1142, 579], [ -387, -126, -1573, 2346], [ 969, 2, 327, -134], [ 163, 227, 90, 2021], [ 1022, -1076, 174, 304], [ 1042, 1317, 311, 880], [ 2018, -840, 295, 2651], [ -277, 566, 1147, -189], [ 20, 467, 1262, 263], [ -663, 1061, -1552, -1159], [ 1830, 391, 2534, -199], [ -487, 752, -1061, 351], [-2138, -556, -367, -457], [ -868, -411, -559, 726], [ 1770, 819, -892, -363], [ 553, -736, -169, -490], [ 388, -503, 809, -821], [ -516, -1452, -192, 483], [ 493, 2904, 1318, 2591], [ 175, 584, -1001, 1675], [ 1316, -1596, -460, 1500], [ 1212, 214, -644, -696], [ -501, 338, 1197, -841], [ -587, -469, -1101, 24], [-1205, 1910, 659, 1232], [ -150, 398, 594, 394], [ 34, -663, 235, -334], [-1580, 647, 239, -351], [-2177, -345, 1215, -1494], [ 1923, 329, -152, 1128]]) princomp1 = Holder() princomp1.comment = 'mlab.princomp(x, nout=3)' princomp1.factors = array([[-0.83487832815382, -1.75681522344645, -0.50882660928949, -0.59661466511045], [-0.18695786699253, -0.10732909330422, 0.23971799542554, -0.75468286946853], [-0.57403949255604, -0.39667006607544, -0.7927838094217 , 0.02652621881328], [-0.60828125251513, -0.75979035898754, -0.20148864200404, -0.40278856050237], [ 0.55997928601548, 0.88869370546643, -1.55474410845786, 0.23033958281961], [-0.18023239851961, -0.72398923145328, -0.07056264751117, 0.29292391015376], [-0.189029743271 , -0.05888596186903, -0.63882208368513, -0.05682951829677], [ 0.94694345324739, -0.33448036234864, 0.16665867708366, -0.67190948646953], [-1.355171899399 , 2.58899695901774, -1.53157119606928, 0.93743278678908], [-1.06797676403358, -1.01894055566289, 0.29181722134698, -0.65261957826524], [-1.08919199915725, -0.5395876105009 , 0.18846579824378, 0.61935728909742], [-1.36598849770841, -1.00986627679465, -1.6090477073157 , -1.82708847399443], [ 0.561511276285 , -0.74919011595195, 1.49872898209738, -0.80588545345232], [ 0.04805787176428, -0.05522267212748, 0.82943784435024, 0.01537039050312], [-1.12006939155398, 0.73462770352006, 0.58868274831601, -0.67786987413505], [-0.26087838474316, -1.33362289066951, -1.02932517860259, 0.24865839951801], [-0.24666198784909, -0.58247196399204, -1.78971960966265, 1.18908143657302], [ 1.80675592845666, -0.73341258204636, -1.45012544705912, -0.44875329121288], [ 0.4794281391435 , -0.57169295903913, 0.48557628591056, -0.11638075289238], [ 1.39425263398653, -0.3665732682294 , 0.06937942447187, 0.06683559082703], [ 1.11015707065101, -1.87631329249852, 0.48914958604867, 0.11096926802212], [-0.85159530389901, 0.68543874135386, 0.86736021483251, -0.17641002537865], [ 0.34109015314112, -0.25431311542374, -0.36804227540019, -0.95824474920131], [-0.86253950274987, -0.28796613689709, 0.30820634958709, 0.27228599921917], [ 0.01266190412089, 0.48559962017667, 0.14020630700546, 0.18517398749337], [-1.56345869427724, 1.27917754070516, 1.25640847929385, -0.36055181722313], [ 1.62834293379132, -1.51923809467869, 0.27754976407182, 0.79362967384835], [-0.94400458067084, 1.77733054371289, 0.03595731772774, 0.96570688640992], [-2.11906234438329, -0.13226430948321, -0.78992396115366, 0.66362103473975], [-0.94372331181891, -0.37502966791165, -1.77907324401749, 0.97801542954941], [ 1.76575198740032, -0.92309597844861, -2.3872195277998 , -0.21817018301121], [ 0.57418226616373, -0.2925257318724 , 0.71180507312941, -0.13937750314467], [ 1.01654397566275, 0.28855305878842, 1.25119859389106, 0.11257524396004], [ 0.58979013567212, -0.06866577243092, -1.74447546690995, 0.13917953157575], [ 1.62072087150051, -0.5835145063711 , -0.99029357957459, -0.06334029436682], [ 0.893493925425 , -1.23995040005948, 0.40058503790479, -1.49029669097391], [ 0.26990527585623, 2.03399854143898, -1.2335089890881 , 0.54010061879979], [ 0.33504096277444, 2.42394994177782, -0.6643863358332 , -0.42471161848557], [ 1.69952476943058, -2.1707037237448 , 0.79694026483866, 0.88177267205969], [-1.41498253257895, 0.65248089992094, -1.40045976465378, -0.12045332880702], [-0.22640706265253, -0.94114558124915, -0.18868114063537, 2.67652245892778], [-0.37493712386529, -0.61985213642068, 0.5383582946365 , -0.17931524703276], [-0.30437796317839, 0.74252786648649, 0.73255373596822, -0.64993745548429], [-0.68788283675831, -0.84714762684627, -0.10721753874211, -0.59777382822281], [-1.00667616522842, -0.06670525233919, -0.92973707141688, -1.60742284256649], [ 1.95220512266515, 2.05751265066695, 0.79640648143073, -0.59608004229343], [-0.15504464969388, -0.3882079443045 , 0.75049869361395, -0.44163703260023], [-1.6686863460652 , 0.96325894557423, -0.16453379247258, 1.4560996746313 ], [-0.25573631707529, 0.88265554068571, 0.08984550855664, 0.53561910563178], [-1.29430028690793, -0.48042359291447, 0.49318558750269, 0.03689178852848], [-0.34391235307349, -0.95154811896716, -0.09714022474353, 1.19792361047367], [ 0.34367523316975, 1.16641214447854, -0.39528838072965, -1.72565643987406], [ 1.23887392116229, -1.27474554996132, -0.65859544264097, -0.81757560038832], [-0.17739006831099, -0.29057501559843, -0.62533324788504, 1.7092669546224 ], [-0.08610919021307, -0.06524996994257, 1.3018284944661 , -1.28219607271255], [-0.95717735853496, 1.79841555744597, 0.75799149339397, 0.23542916575208], [-1.70175078442029, 1.33831900642462, -0.73979048943944, 0.26157699746442], [ 0.84631686421106, 0.32029666775009, 2.51638540556813, 0.90367536744335], [ 1.22693220256582, 1.45665385966518, 1.27480662666555, 0.78786331120259], [-0.59251239046609, -0.660398245535 , 0.53258334042042, 0.81248748854679], [ 2.22723057510913, -0.22856960444805, -0.15586801032885, -0.26957090658609], [-0.83192612439183, -2.11983096548132, 0.75319973501664, 0.62196293266702], [-1.577627210601 , -0.3747136286972 , 0.31736538266249, 0.30187577548949], [-2.28230005998543, -1.17283119424281, 1.83780755209602, -0.75928026219594], [-1.90574204329052, -0.34197417196464, -0.59978910354131, -0.68240235236779], [ 0.48132729275936, -0.2524965456322 , -0.75271273075 , -0.89651237903089], [ 0.26961427953002, 0.62968227134995, 0.99324664633985, 0.59917742452108], [-0.95910506784013, 0.31907970712369, 0.35568397653203, 0.60155535679072], [-0.18528259973205, -1.31831013869974, -0.09749195643548, -0.39885348684496], [ 0.9608404103702 , 0.23727553971573, 0.20695289013955, -0.65281918968052], [ 0.85302395609555, 1.5303724004181 , -0.56440186223081, -0.27348033453255], [ 1.72786301913767, -1.14859994931789, 1.16222121440674, 1.39284961909257], [ 0.37711527308989, 0.47231886947072, -0.69423676772182, -0.53515102147655], [ 1.35642227654922, 0.53204130038923, 0.69844068787197, 1.04544871561741], [ 0.57797880484094, 0.08044525072063, -1.32634695941334, 0.35179408060132], [ 1.29437232500619, 1.07461562326311, 0.54545226737269, -0.6836610122092 ], [ 2.74736726573105, 0.90881277479338, -0.98342785084735, 1.38171127911719], [-0.67749479829901, 1.10093727650063, 0.28416704607992, -0.24984509303044], [-0.24513961858774, 1.32098977907584, 0.16904762754153, 0.00886790270539], [-0.5392290825383 , -1.43851802284774, 1.0064737206577 , -1.52649870396689], [ 0.19486366400459, 2.77236000318994, -1.32201258472682, -0.75922390642504], [ 0.33271229220962, -0.78464273816827, 1.09930224781861, -0.32184679755027], [-1.72814706427698, -1.09275114767838, 0.7451569579997 , 0.72871211772761], [-0.035506207751 , -0.72161367235521, 0.52828318684787, 0.87177739169758], [ 1.31224955134141, -0.22742530984642, -0.44682270809773, -1.72769462581607], [-0.07125058353119, -0.36850925227739, -1.01188688859296, -0.24962251325969], [-0.69840680770104, 0.4925285516285 , -1.0255829922787 , -0.36214090052941], [-0.2530614593082 , -0.68595709316063, -0.56882710610856, 1.25787365685572], [ 1.93782484285419, 2.67095706598253, 2.4023579082791 , -0.09112046819432], [ 1.57782156817208, -0.39819017512275, 1.01938038947667, 0.39718992194809], [ 1.6839282738726 , -0.37808442385434, -1.36566197748227, 1.22029200163339], [ 0.54652714502605, -0.38206797548206, -0.70554510441189, -1.31224358889695], [-1.30026063006148, 0.90642495630747, 0.02711437433058, -0.44482098905042], [-0.1239033493518 , -1.29112252171673, 0.18092802221218, 0.22673242779457], [ 0.01152882540055, 1.13242883415094, 2.34980443084773, 0.17712319903618], [-0.0505195424414 , 0.6807219067402 , 0.37771832345982, 0.0842510459176 ], [-0.44230076745505, -0.07002728477811, -0.6716520563439 , 0.09637247949641], [-1.31245480585229, -0.01674966464909, 1.21063252882651, -0.03927111631335], [-2.94268586886381, 0.20925236551048, 0.30321714445262, 0.22027672852006], [ 2.04121905977187, 0.58496246543101, -0.5192457175416 , -0.37212298770116]]) princomp1.values = array([[ 1.29489288337888], [ 1.12722515391348], [ 0.94682423958163], [ 0.65890241090379]]) princomp1.name = 'princomp1' princomp1.coef = array([[ 0.65989917631713, 0.22621848650964, -0.5882833472413 , -0.40899997165748], [ 0.15824945056105, 0.3189419948895 , 0.71689623797385, -0.5994104597619 ], [-0.3488766362785 , 0.90294049788532, -0.17151017930575, 0.1832151967827 ], [ 0.64635538301471, 0.17832458477678, 0.33251578268108, 0.66321815082225]]) princomp2 = Holder() princomp2.comment = 'mlab.princomp(x[:20,], nout=3)' princomp2.factors = array([[ 0.74592631465403, -0.92093638563647, 1.10020213969681, -0.20234362115983], [ 0.40379773814409, -0.23694214086306, -0.53526599590626, 0.48048423978257], [-0.43826559396565, -0.26267383420164, 0.35939862515391, -0.15176605914773], [ 0.29427656853499, -0.56363285386285, 0.19525662206552, -0.0384830001072 ], [-1.4327917748351 , 1.18414191887856, 0.05435949672922, 0.46861687286613], [ 0.23033214569426, -0.00452237842477, 0.00346120473054, -0.61483888402985], [-0.40976419499281, 0.10137131352284, 0.02570805136468, 0.06798926306103], [ 0.83201287149759, 0.82736894861103, -0.35298970920805, 0.49344802383821], [-3.36634598435507, -0.18324521714611, -1.12118215528184, 0.2057949493723 ], [ 0.70198992281665, -1.1856449495675 , 0.02465727900177, -0.08333428418838], [-0.13789069679894, -0.79430992968357, -0.33106496391047, -1.01808298459082], [-0.10779840884825, -1.41970796854378, 1.55590290358904, 1.34014813517248], [ 1.8229340670437 , 0.13065838030104, -1.06152350166072, 0.11456488463131], [ 0.51650051521229, 0.07999783864926, -1.08601194413786, -0.28255247881905], [-0.24654203558433, -1.02895891025197, -1.34475655787845, 0.52240852619949], [ 0.03542169335227, -0.01198903021187, 1.12649412049726, -0.60518306798831], [-1.23945075955452, 0.48778599927278, 1.11522465483282, -0.994827967694 ], [ 0.30661562766349, 1.91993049714024, 1.08834307939522, 0.61608892787963], [ 0.8241280516035 , 0.43533554216801, -0.48261931874702, -0.22391158066897], [ 0.6649139327178 , 1.44597315984982, -0.33359403032613, -0.094219894409 ]]) princomp2.values = array([[ 1.16965204468073], [ 0.77687367815155], [ 0.72297937656591], [ 0.32548581375971]]) princomp2.name = 'princomp2' princomp2.coef = array([[-0.13957162231397, 0.6561182967648 , 0.32256106777669, 0.66781951188167], [ 0.49534264552989, -0.08241251099014, -0.6919444767593 , 0.51870674049413], [-0.85614372781797, -0.11427402995055, -0.47665923729502, 0.16357058078438], [ 0.04661912785591, 0.74138950947638, -0.43584764555793, -0.50813884128056]]) princomp3 = Holder() princomp3.comment = 'mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)' princomp3.factors = array([[ 0.74592631465403, -0.92093638563647, 1.10020213969681, -0.20234362115983], [ 0.40379773814409, -0.23694214086306, -0.53526599590626, 0.48048423978257], [-0.43826559396565, -0.26267383420164, 0.35939862515391, -0.15176605914773], [ 0.29427656853499, -0.56363285386285, 0.19525662206552, -0.0384830001072 ], [-1.4327917748351 , 1.18414191887856, 0.05435949672922, 0.46861687286613], [ 0.23033214569426, -0.00452237842477, 0.00346120473054, -0.61483888402985], [-0.40976419499281, 0.10137131352284, 0.02570805136468, 0.06798926306103], [ 0.83201287149759, 0.82736894861103, -0.35298970920805, 0.49344802383821], [-3.36634598435507, -0.18324521714611, -1.12118215528184, 0.2057949493723 ], [ 0.70198992281665, -1.1856449495675 , 0.02465727900177, -0.08333428418838], [-0.13789069679894, -0.79430992968357, -0.33106496391047, -1.01808298459082], [-0.10779840884825, -1.41970796854378, 1.55590290358904, 1.34014813517248], [ 1.8229340670437 , 0.13065838030104, -1.06152350166072, 0.11456488463131], [ 0.51650051521229, 0.07999783864926, -1.08601194413786, -0.28255247881905], [-0.24654203558433, -1.02895891025197, -1.34475655787845, 0.52240852619949], [ 0.03542169335227, -0.01198903021187, 1.12649412049726, -0.60518306798831], [-1.23945075955452, 0.48778599927278, 1.11522465483282, -0.994827967694 ], [ 0.30661562766349, 1.91993049714024, 1.08834307939522, 0.61608892787963], [ 0.8241280516035 , 0.43533554216801, -0.48261931874702, -0.22391158066897], [ 0.6649139327178 , 1.44597315984982, -0.33359403032613, -0.094219894409 ]]) princomp3.values = array([[ 1.16965204468073], [ 0.77687367815155], [ 0.72297937656591], [ 0.32548581375971]]) princomp3.name = 'princomp3' princomp3.coef = array([[-0.13957162231397, 0.6561182967648 , 0.32256106777669, 0.66781951188167], [ 0.49534264552989, -0.08241251099014, -0.6919444767593 , 0.51870674049413], [-0.85614372781797, -0.11427402995055, -0.47665923729502, 0.16357058078438], [ 0.04661912785591, 0.74138950947638, -0.43584764555793, -0.50813884128056]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/macrodata.s000066400000000000000000000020551224417117700254350ustar00rootroot00000000000000dta <- read.csv('../../datasets/macrodata/macrodata.csv', header = TRUE) attach(dta) library(systemfit) demand <- realcons + realinv + realgovt c.1 <- realcons[-203] y.1 <- demand[-203] yd <- demand[-1] - y.1 eqConsump <- realcons[-1] ~ demand[-1] + c.1 eqInvest <- realinv[-1] ~ tbilrate[-1] + yd system <- list( Consumption = eqConsump, Investment = eqInvest) instruments <- ~ realgovt[-1] + tbilrate[-1] + c.1 + y.1 # 2SLS greene2sls <- systemfit( system, "2SLS", inst = instruments, methodResidCov = "noDfCor" ) print(summary(greene2sls)) greene3sls <- systemfit( system, "3SLS", inst = instruments, methodResidCov = "noDfCor" ) print(summary(greene3sls)) # Python code for finding the dynamics # # Could have done this in R # #gamma = np.array([[1,0,1],[0,1,1],[-.058438620413,-16.5359646223,1]]) #phi = np.array([[-.99200661799,0,0],[0,0,0],[0,-16.5359646223,0]]) #Delta = np.dot(-phi,np.linalg.inv(gamma)) #delta = np.zeros((2,2)) #delta[0,0]=Delta[0,0] #delta[0,1]=Delta[0,-1] #delta[1,0]=Delta[-1,0] #delta[1,1]=Delta[-1,-1] #np.eigvals(delta) #np.max(_) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/maketests_mlabwrap.py000066400000000000000000000214761224417117700275650ustar00rootroot00000000000000'''generate py modules with test cases and results from mlabwrap currently matlab: princomp, garchar, garchma ''' import numpy as np from numpy.testing import assert_array_almost_equal from numpy import array xo = array([[ -419, -731, -1306, -1294], [ 6, 529, -200, -437], [ -27, -833, -6, -564], [ -304, -273, -502, -739], [ 1377, -912, 927, 280], [ -375, -517, -514, 49], [ 247, -504, 123, -259], [ 712, 534, -773, 286], [ 195, -1080, 3256, -178], [ -854, 75, -706, -1084], [-1219, -612, -15, -203], [ 550, -628, -483, -2686], [ -365, 1376, -1266, 317], [ -489, 544, -195, 431], [ -656, 854, 840, -723], [ 16, -1385, -880, -460], [ 258, -2252, 96, 54], [ 2049, -750, -1115, 381], [ -65, 280, -777, 416], [ 755, 82, -806, 1027], [ -39, -170, -2134, 743], [ -859, 780, 746, -133], [ 762, 252, -450, -459], [ -941, -202, 49, -202], [ -54, 115, 455, 388], [-1348, 1246, 1430, -480], [ 229, -535, -1831, 1524], [ -651, -167, 2116, 483], [-1249, -1373, 888, -1092], [ -75, -2162, 486, -496], [ 2436, -1627, -1069, 162], [ -63, 560, -601, 587], [ -60, 1051, -277, 1323], [ 1329, -1294, 68, 5], [ 1532, -633, -923, 696], [ 669, 895, -1762, -375], [ 1129, -548, 2064, 609], [ 1320, 573, 2119, 270], [ -213, -412, -2517, 1685], [ 73, -979, 1312, -1220], [-1360, -2107, -237, 1522], [ -645, 205, -543, -169], [ -212, 1072, 543, -128], [ -352, -129, -605, -904], [ 511, 85, 167, -1914], [ 1515, 1862, 942, 1622], [ -465, 623, -495, -89], [-1396, -979, 1758, 128], [ -255, -47, 980, 501], [-1282, -58, -49, -610], [ -889, -1177, -492, 494], [ 1415, 1146, 696, -722], [ 1237, -224, -1609, -64], [ -528, -1625, 231, 883], [ -327, 1636, -476, -361], [ -781, 793, 1882, 234], [ -506, -561, 1988, -810], [-1233, 1467, -261, 2164], [ 53, 1069, 824, 2123], [-1200, -441, -321, 339], [ 1606, 298, -995, 1292], [-1740, -672, -1628, -129], [-1450, -354, 224, -657], [-2556, 1006, -706, -1453], [ -717, -463, 345, -1821], [ 1056, -38, -420, -455], [ -523, 565, 425, 1138], [-1030, -187, 683, 78], [ -214, -312, -1171, -528], [ 819, 736, -265, 423], [ 1339, 351, 1142, 579], [ -387, -126, -1573, 2346], [ 969, 2, 327, -134], [ 163, 227, 90, 2021], [ 1022, -1076, 174, 304], [ 1042, 1317, 311, 880], [ 2018, -840, 295, 2651], [ -277, 566, 1147, -189], [ 20, 467, 1262, 263], [ -663, 1061, -1552, -1159], [ 1830, 391, 2534, -199], [ -487, 752, -1061, 351], [-2138, -556, -367, -457], [ -868, -411, -559, 726], [ 1770, 819, -892, -363], [ 553, -736, -169, -490], [ 388, -503, 809, -821], [ -516, -1452, -192, 483], [ 493, 2904, 1318, 2591], [ 175, 584, -1001, 1675], [ 1316, -1596, -460, 1500], [ 1212, 214, -644, -696], [ -501, 338, 1197, -841], [ -587, -469, -1101, 24], [-1205, 1910, 659, 1232], [ -150, 398, 594, 394], [ 34, -663, 235, -334], [-1580, 647, 239, -351], [-2177, -345, 1215, -1494], [ 1923, 329, -152, 1128]]) x = xo/1000. class HoldIt(object): def __init__(self, name): self.name = name def save(self, what=None, filename=None, header=True, useinstant=True, comment=None): if what is None: what = (i for i in self.__dict__ if i[0] != '_') if header: txt = ['import numpy as np\nfrom numpy import array\n\n'] if useinstant: txt.append('class Holder(object):\n pass\n\n') else: txt = [] if useinstant: txt.append('%s = Holder()' % self.name) prefix = '%s.' % self.name else: prefix = '' if not comment is None: txt.append("%scomment = '%s'" % (prefix, comment)) for x in what: txt.append('%s%s = %s' % (prefix, x, repr(getattr(self,x)))) txt.extend(['','']) #add empty lines at end if not filename is None: file(filename, 'a+').write('\n'.join(txt)) return txt def generate_princomp(xo, filen='testsave.py'): # import mlabwrap only when run as script import mlabwrap from mlabwrap import mlab np.set_printoptions(precision=14, linewidth=100) data = HoldIt('data') data.xo = xo data.save(filename='testsave.py', comment='generated data, divide by 1000') res_princomp = HoldIt('princomp1') res_princomp.coef, res_princomp.factors, res_princomp.values = \ mlab.princomp(x, nout=3) res_princomp.save(filename=filen, header=False, comment='mlab.princomp(x, nout=3)') res_princomp = HoldIt('princomp2') res_princomp.coef, res_princomp.factors, res_princomp.values = \ mlab.princomp(x[:20,], nout=3) np.set_printoptions(precision=14, linewidth=100) res_princomp.save(filename=filen, header=False, comment='mlab.princomp(x[:20,], nout=3)') res_princomp = HoldIt('princomp3') res_princomp.coef, res_princomp.factors, res_princomp.values = \ mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3) np.set_printoptions(precision=14, linewidth=100) res_princomp.save(filename=filen, header=False, comment='mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)') def generate_armarep(filen='testsave.py'): # import mlabwrap only when run as script import mlabwrap from mlabwrap import mlab res_armarep = HoldIt('armarep') res_armarep.ar = np.array([1., -0.5, +0.8]) res_armarep.ma = np.array([1., -0.6, 0.08]) res_armarep.marep = mlab.garchma(-res_armarep.ar[1:], res_armarep.ma[1:], 20) res_armarep.arrep = mlab.garchar(-res_armarep.ar[1:], res_armarep.ma[1:], 20) res_armarep.save(filename=filen, header=False, comment=("''mlab.garchma(-res_armarep.ar[1:], res_armarep.ma[1:], 20)\n" + "mlab.garchar(-res_armarep.ar[1:], res_armarep.ma[1:], 20)''")) def exampletest(): from statsmodels.sandbox import tsa arrep = tsa.arma_impulse_response(res_armarep.ma, res_armarep.ar, nobs=21)[1:] marep = tsa.arma_impulse_response(res_armarep.ar, res_armarep.ma, nobs=21)[1:] assert_array_almost_equal(res_armarep.marep.ravel(), marep, 14) #difference in sign convention to matlab for AR term assert_array_almost_equal(-res_armarep.arrep.ravel(), arrep, 14) if __name__ == '__main__': import mlabwrap from mlabwrap import mlab import savedrvs xo = savedrvs.rvsdata.xar2 x100 = xo[-100:]/1000. x1000 = xo/1000. filen = 'testsavetls.py' res_pacf = HoldIt('mlpacf') res_pacf.comment = 'mlab.parcorr(x, [], 2, nout=3)' res_pacf.pacf100, res_pacf.lags100, res_pacf.bounds100 = \ mlab.parcorr(x100, [], 2, nout=3) res_pacf.pacf1000, res_pacf.lags1000, res_pacf.bounds1000 = \ mlab.parcorr(x1000, [], 2, nout=3) res_pacf.save(filename=filen, header=True) res_acf = HoldIt('mlacf') res_acf.comment = 'mlab.autocorr(x, [], 2, nout=3)' res_acf.acf100, res_acf.lags100, res_acf.bounds100 = \ mlab.autocorr(x100, [], 2, nout=3) res_acf.acf1000, res_acf.lags1000, res_acf.bounds1000 = \ mlab.autocorr(x1000, [], 2, nout=3) res_acf.save(filename=filen, header=False) res_ccf = HoldIt('mlccf') res_ccf.comment = 'mlab.crosscorr(x[4:], x[:-4], [], 2, nout=3)' res_ccf.ccf100, res_ccf.lags100, res_ccf.bounds100 = \ mlab.crosscorr(x100[4:], x100[:-4], [], 2, nout=3) res_ccf.ccf1000, res_ccf.lags1000, res_ccf.bounds1000 = \ mlab.crosscorr(x1000[4:], x1000[:-4], [], 2, nout=3) res_ccf.save(filename=filen, header=False) res_ywar = HoldIt('mlywar') res_ywar.comment = "mlab.ar(x100-x100.mean(), 10, 'yw').a.ravel()" mbaryw = mlab.ar(x100-x100.mean(), 10, 'yw') res_ywar.arcoef100 = np.array(mbaryw.a.ravel()) mbaryw = mlab.ar(x1000-x1000.mean(), 20, 'yw') res_ywar.arcoef1000 = np.array(mbaryw.a.ravel()) res_ywar.save(filename=filen, header=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/model_results.py000066400000000000000000000002031224417117700265420ustar00rootroot00000000000000""" This should be merged into statsmodels/tests/model_results.py when things move out of the sandbox. """ import numpy as np statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/savervs.py000066400000000000000000000022551224417117700253630ustar00rootroot00000000000000'''generates some ARMA random samples and saves to python module file ''' import numpy as np from statsmodels.sandbox import tsa from statsmodels.tsa.arima_process import arma_generate_sample from maketests_mlabwrap import HoldIt if __name__ == '__main__': filen = 'savedrvs_tmp.py' np.set_printoptions(precision=14, linewidth=100) # check arma to return same as random.normal np.random.seed(10000) xo = arma_generate_sample([1], [1], nsample=100) xo2 = np.round(xo*1000).astype(int) np.random.seed(10000) rvs = np.random.normal(size=100) rvs2 = np.round(xo*1000).astype(int) assert (xo2==rvs2).all() nsample = 1000 data = HoldIt('rvsdata') np.random.seed(10000) xo = arma_generate_sample([1, -0.8, 0.5], [1], nsample=nsample) data.xar2 = np.round(xo*1000).astype(int) np.random.seed(10000) xo = np.random.normal(size=nsample) data.xnormal = np.round(xo*1000).astype(int) np.random.seed(10000) xo = arma_generate_sample([1, -0.8, 0.5, -0.3], [1, 0.3, 0.2], nsample=nsample) data.xarma32 = np.round(xo*1000).astype(int) data.save(filename=filen, comment='generated data, divide by 1000, see savervs') statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/sysreg.s000066400000000000000000000032701224417117700250160ustar00rootroot00000000000000# from the systemfit docs and sem docs # depends systemfit and its dependencies # depends sem # depends on plm # depends on R >= 2.9.0 (working on 2.9.2 but not on 2.8.1 at least) library( systemfit ) data( "Kmenta" ) eqDemand <- consump ~ price + income eqSupply <- consump ~ price + farmPrice + trend system <- list( demand = eqDemand, supply = eqSupply ) ## performs OLS on each of the equations in the system fitols <- systemfit( system, data = Kmenta ) # all coefficients coef( fitols ) coef( summary ( fitols ) ) modReg <- matrix(0,7,6) colnames( modReg ) <- c( "demIntercept", "demPrice", "demIncome", "supIntercept", "supPrice2", "supTrend" ) # a lot of typing for a model modReg[ 1, "demIntercept" ] <- 1 modReg[ 2, "demPrice" ] <- 1 modReg[ 3, "demIncome" ] <- 1 modReg[ 4, "supIntercept" ] <- 1 modReg[ 5, "supPrice2" ] <- 1 modReg[ 6, "supPrice2" ] <- 1 modReg[ 7, "supTrend" ] <- 1 fitols3 <- systemfit( system, data = Kmenta, restrict.regMat = modReg ) print(coef( fitols3, modified.regMat = TRUE )) # it seems to me like regMat does the opposite of what it says it does # in python # coef1 = np.array([99.8954229, -0.3162988, 0.3346356, 51.9296460, 0.2361566, 0.2361566, 0.2409308]) # i = np.eye(7,6) # i[-1,-1] = 1 # i[-2,-1] = 0 # i[-2,-2] = 1 # np.dot(coef,i) # regMat = TRUE? print(coef( fitols3 )) ### SUR ### data("GrunfeldGreene") library(plm) GGPanel <- plm.data( GrunfeldGreene, c("firm","year") ) formulaGrunfeld <- invest ~ value + capital greeneSUR <- systemfit( formulaGrunfeld, "SUR", data = GGPanel, methodResidCov = "noDfCor" ) #usinvest <- as.matrix(invest[81:100]) #usvalue <- as.matrix(value col5tbl14_2 <- lm(invest[81:100] ~ value[81:100] + capital[81:100]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/test_bspline.py.txt000066400000000000000000000036101224417117700271770ustar00rootroot00000000000000import warnings import numpy as np from nipy.testing import * bsp = None def setup(): # Suppress warnings during tests to reduce noise warnings.simplefilter("ignore") # import bspline module after suppressing UserWarnings global bsp import nipy.fixes.scipy.stats.models.bspline as bsp def teardown(): # Clear list of warning filters warnings.resetwarnings() class TestBSpline(TestCase): def test1(self): b = bsp.BSpline(np.linspace(0,10,11), x=np.linspace(0,10,101)) old = b._basisx.shape b.x = np.linspace(0,10,51) new = b._basisx.shape self.assertEqual((old[0], 51), new) # FIXME: Have no idea what this test does. It's here to simply verify the # C extension is working (in a technical sense, not functional). def test_basis(self): b = bsp.BSpline(np.linspace(0,1,11)) x = np.array([0.4, 0.5]) v = b.basis(x, lower=0, upper=13) t = np.array([[ 0. , 0. ], [ 0. , 0. ], [ 0. , 0. ], [ 0. , 0. ], [ 0.16666667, 0. ], [ 0.66666667, 0.16666667], [ 0.16666667, 0.66666667], [ 0. , 0.16666667], [ 0. , 0. ], [ 0. , 0. ], [ 0. , 0. ], [ 0. , 0. ], [ 0. , 0. ]]) assert_array_almost_equal(v, t, decimal=6) # FIXME: Have no idea what this test does. It's here to simply verify the # C extension is working (in a technical sense, not functional). def test_gram(self): b = bsp.BSpline(np.linspace(0,1,11)) grm = b.gram() assert grm.shape == (4, 13) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/test_formula.py000066400000000000000000000241761224417117700264040ustar00rootroot00000000000000""" Test functions for models.formula """ import string import numpy as np import numpy.random as R import numpy.linalg as L from numpy.testing import * import sys, nose #automatic conversion with 2to3 makes mistakes in formula, changes #"if type(self.name) is types.StringType" to "if type(self.name) is bytes" try: from statsmodels.sandbox import formula #, contrast #, utils from statsmodels.sandbox import contrast_old as contrast except: if sys.version_info[0] >= 3: raise nose.SkipTest('No tests here') else: raise def setup(): if sys.version_info[0] >= 3: raise nose.SkipTest('No tests here') class TestTerm(TestCase): def test_init(self): t1 = formula.Term("trivial") sqr = lambda x: x*x t2 = formula.Term("not_so_trivial", sqr, "sqr") self.assertRaises(ValueError, formula.Term, "name", termname=0) def test_str(self): t = formula.Term("name") s = str(t) def test_add(self): t1 = formula.Term("t1") t2 = formula.Term("t2") f = t1 + t2 self.assert_(isinstance(f, formula.Formula)) self.assert_(f.hasterm(t1)) self.assert_(f.hasterm(t2)) def test_mul(self): t1 = formula.Term("t1") t2 = formula.Term("t2") f = t1 * t2 self.assert_(isinstance(f, formula.Formula)) intercept = formula.Term("intercept") f = t1 * intercept self.assertEqual(str(f), str(formula.Formula(t1))) f = intercept * t1 self.assertEqual(str(f), str(formula.Formula(t1))) class TestFormula(TestCase): def setUp(self): self.X = R.standard_normal((40,10)) self.namespace = {} self.terms = [] for i in range(10): name = '%s' % string.uppercase[i] self.namespace[name] = self.X[:,i] self.terms.append(formula.Term(name)) self.formula = self.terms[0] for i in range(1, 10): self.formula += self.terms[i] self.formula.namespace = self.namespace def test_namespace(self): space1 = {'X':np.arange(50), 'Y':np.arange(50)*2} space2 = {'X':np.arange(20), 'Y':np.arange(20)*2} space3 = {'X':np.arange(30), 'Y':np.arange(30)*2} X = formula.Term('X') Y = formula.Term('Y') X.namespace = space1 assert_almost_equal(X(), np.arange(50)) Y.namespace = space2 assert_almost_equal(Y(), np.arange(20)*2) f = X + Y f.namespace = space1 self.assertEqual(f().shape, (2,50)) assert_almost_equal(Y(), np.arange(20)*2) assert_almost_equal(X(), np.arange(50)) f.namespace = space2 self.assertEqual(f().shape, (2,20)) assert_almost_equal(Y(), np.arange(20)*2) assert_almost_equal(X(), np.arange(50)) f.namespace = space3 self.assertEqual(f().shape, (2,30)) assert_almost_equal(Y(), np.arange(20)*2) assert_almost_equal(X(), np.arange(50)) xx = X**2 self.assertEqual(xx().shape, (50,)) xx.namespace = space3 self.assertEqual(xx().shape, (30,)) xx = X * formula.I self.assertEqual(xx().shape, (50,)) xx.namespace = space3 self.assertEqual(xx().shape, (30,)) xx = X * X self.assertEqual(xx.namespace, X.namespace) xx = X + Y self.assertEqual(xx.namespace, {}) Y.namespace = {'X':np.arange(50), 'Y':np.arange(50)*2} xx = X + Y self.assertEqual(xx.namespace, {}) Y.namespace = X.namespace xx = X+Y self.assertEqual(xx.namespace, Y.namespace) def test_termcolumns(self): t1 = formula.Term("A") t2 = formula.Term("B") f = t1 + t2 + t1 * t2 def other(val): return np.array([3.2*val,4.342*val**2, 5.234*val**3]) q = formula.Quantitative(['other%d' % i for i in range(1,4)], termname='other', func=t1, transform=other) f += q q.namespace = f.namespace = self.formula.namespace assert_almost_equal(q(), f()[f.termcolumns(q)]) def test_str(self): s = str(self.formula) def test_call(self): x = self.formula() self.assertEquals(np.array(x).shape, (10, 40)) def test_design(self): x = self.formula.design() self.assertEquals(x.shape, (40, 10)) def test_product(self): prod = self.formula['A'] * self.formula['C'] f = self.formula + prod f.namespace = self.namespace x = f.design() p = f['A*C'] p.namespace = self.namespace col = f.termcolumns(prod, dict=False) assert_almost_equal(np.squeeze(x[:,col]), self.X[:,0] * self.X[:,2]) assert_almost_equal(np.squeeze(p()), self.X[:,0] * self.X[:,2]) def test_intercept1(self): prod = self.terms[0] * self.terms[2] f = self.formula + formula.I icol = f.names().index('intercept') f.namespace = self.namespace assert_almost_equal(f()[icol], np.ones((40,))) def test_intercept3(self): t = self.formula['A'] t.namespace = self.namespace prod = t * formula.I prod.namespace = self.formula.namespace assert_almost_equal(np.squeeze(prod()), t()) def test_contrast1(self): term = self.terms[0] + self.terms[2] c = contrast.Contrast(term, self.formula) col1 = self.formula.termcolumns(self.terms[0], dict=False) col2 = self.formula.termcolumns(self.terms[1], dict=False) test = [[1] + [0]*9, [0]*2 + [1] + [0]*7] assert_almost_equal(c.matrix, test) def test_contrast2(self): dummy = formula.Term('zero') self.namespace['zero'] = np.zeros((40,), np.float64) term = dummy + self.terms[2] c = contrast.Contrast(term, self.formula) test = [0]*2 + [1] + [0]*7 assert_almost_equal(c.matrix, test) def test_contrast3(self): X = self.formula.design() P = np.dot(X, L.pinv(X)) dummy = formula.Term('noise') resid = np.identity(40) - P self.namespace['noise'] = np.transpose(np.dot(resid, R.standard_normal((40,5)))) terms = dummy + self.terms[2] terms.namespace = self.formula.namespace c = contrast.Contrast(terms, self.formula) self.assertEquals(c.matrix.shape, (10,)) def test_power(self): t = self.terms[2] t2 = t**2 t.namespace = t2.namespace = self.formula.namespace assert_almost_equal(t()**2, t2()) def test_quantitative(self): t = self.terms[2] sint = formula.Quantitative('t', func=t, transform=np.sin) t.namespace = sint.namespace = self.formula.namespace assert_almost_equal(np.sin(t()), sint()) def test_factor1(self): f = ['a','b','c']*10 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} self.assertEquals(list(fac.values()), f) def test_factor2(self): f = ['a','b','c']*10 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} self.assertEquals(fac().shape, (3,30)) def test_factor3(self): f = ['a','b','c']*10 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} m = fac.main_effect(reference=1) m.namespace = fac.namespace self.assertEquals(m().shape, (2,30)) def test_factor4(self): f = ['a','b','c']*10 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} m = fac.main_effect(reference=2) m.namespace = fac.namespace r = np.array([np.identity(3)]*10) r.shape = (30,3) r = r.T _m = np.array([r[0]-r[2],r[1]-r[2]]) assert_almost_equal(_m, m()) def test_factor5(self): f = ['a','b','c']*3 fac = formula.Factor('ff', f) fac.namespace = {'ff':f} assert_equal(fac(), [[1,0,0]*3, [0,1,0]*3, [0,0,1]*3]) assert_equal(fac['a'], [1,0,0]*3) assert_equal(fac['b'], [0,1,0]*3) assert_equal(fac['c'], [0,0,1]*3) def test_ordinal_factor(self): f = ['a','b','c']*3 fac = formula.Factor('ff', ['a','b','c'], ordinal=True) fac.namespace = {'ff':f} assert_equal(fac(), [0,1,2]*3) assert_equal(fac['a'], [1,0,0]*3) assert_equal(fac['b'], [0,1,0]*3) assert_equal(fac['c'], [0,0,1]*3) def test_ordinal_factor2(self): f = ['b','c', 'a']*3 fac = formula.Factor('ff', ['a','b','c'], ordinal=True) fac.namespace = {'ff':f} assert_equal(fac(), [1,2,0]*3) assert_equal(fac['a'], [0,0,1]*3) assert_equal(fac['b'], [1,0,0]*3) assert_equal(fac['c'], [0,1,0]*3) def test_contrast4(self): f = self.formula + self.terms[5] + self.terms[5] f.namespace = self.namespace estimable = False c = contrast.Contrast(self.terms[5], f) self.assertEquals(estimable, False) def test_interactions(self): f = formula.interactions([formula.Term(l) for l in ['a', 'b', 'c']]) assert_equal(set(f.termnames()), set(['a', 'b', 'c', 'a*b', 'a*c', 'b*c'])) f = formula.interactions([formula.Term(l) for l in ['a', 'b', 'c', 'd']], order=3) assert_equal(set(f.termnames()), set(['a', 'b', 'c', 'd', 'a*b', 'a*c', 'a*d', 'b*c', 'b*d', 'c*d', 'a*b*c', 'a*c*d', 'a*b*d', 'b*c*d'])) f = formula.interactions([formula.Term(l) for l in ['a', 'b', 'c', 'd']], order=[1,2,3]) assert_equal(set(f.termnames()), set(['a', 'b', 'c', 'd', 'a*b', 'a*c', 'a*d', 'b*c', 'b*d', 'c*d', 'a*b*c', 'a*c*d', 'a*b*d', 'b*c*d'])) f = formula.interactions([formula.Term(l) for l in ['a', 'b', 'c', 'd']], order=[3]) assert_equal(set(f.termnames()), set(['a*b*c', 'a*c*d', 'a*b*d', 'b*c*d'])) def test_subtract(self): f = formula.interactions([formula.Term(l) for l in ['a', 'b', 'c']]) ff = f - f['a*b'] assert_equal(set(ff.termnames()), set(['a', 'b', 'c', 'a*c', 'b*c'])) ff = f - f['a*b'] - f['a*c'] assert_equal(set(ff.termnames()), set(['a', 'b', 'c', 'b*c'])) ff = f - (f['a*b'] + f['a*c']) assert_equal(set(ff.termnames()), set(['a', 'b', 'c', 'b*c'])) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/test_gam.py000066400000000000000000000230421224417117700254720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for gam.AdditiveModel and GAM with Polynomials compared to OLS and GLM Created on Sat Nov 05 14:16:07 2011 Author: Josef Perktold License: BSD Notes ----- TODO: TestGAMGamma: has test failure (GLM looks good), adding log-link didn't help resolved: gamma doesn't fail anymore after tightening the convergence criterium (rtol=1e-6) TODO: TestGAMNegativeBinomial: rvs generation doesn't work, nbinom needs 2 parameters TODO: TestGAMGaussianLogLink: test failure, but maybe precision issue, not completely off but something is wrong, either the testcase or with the link >>> tt3.__class__ >>> tt3.res2.mu_pred.mean() 3.5616368292650766 >>> tt3.res1.mu_pred.mean() 3.6144278964707679 >>> tt3.mu_true.mean() 34.821904835958122 >>> >>> tt3.y_true.mean() 2.685225067611543 >>> tt3.res1.y_pred.mean() 0.52991541684645616 >>> tt3.res2.y_pred.mean() 0.44626406889363229 one possible change ~~~~~~~~~~~~~~~~~~~ add average, integral based tests, instead of or additional to sup * for example mean squared error for mu and eta (predict, fittedvalues) or mean absolute error, what's the scale for this? required precision? * this will also work for real non-parametric tests example: Gamma looks good in average bias and average RMSE (RMISE) >>> tt3 = _estGAMGamma() >>> np.mean((tt3.res2.mu_pred - tt3.mu_true))/tt3.mu_true.mean() -0.0051829977497423706 >>> np.mean((tt3.res2.y_pred - tt3.y_true))/tt3.y_true.mean() 0.00015255264651864049 >>> np.mean((tt3.res1.y_pred - tt3.y_true))/tt3.y_true.mean() 0.00015255538823786711 >>> np.mean((tt3.res1.mu_pred - tt3.mu_true))/tt3.mu_true.mean() -0.0051937668989744494 >>> np.sqrt(np.mean((tt3.res1.mu_pred - tt3.mu_true)**2))/tt3.mu_true.mean() 0.022946118520401692 >>> np.sqrt(np.mean((tt3.res2.mu_pred - tt3.mu_true)**2))/tt3.mu_true.mean() 0.022953913332599746 >>> maxabs = lambda x: np.max(np.abs(x)) >>> maxabs((tt3.res1.mu_pred - tt3.mu_true))/tt3.mu_true.mean() 0.079540546242707733 >>> maxabs((tt3.res2.mu_pred - tt3.mu_true))/tt3.mu_true.mean() 0.079578857986784574 >>> maxabs((tt3.res2.y_pred - tt3.y_true))/tt3.y_true.mean() 0.016282852522951426 >>> maxabs((tt3.res1.y_pred - tt3.y_true))/tt3.y_true.mean() 0.016288391235613865 """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal from scipy import stats from statsmodels.sandbox.gam import AdditiveModel from statsmodels.sandbox.gam import Model as GAM #? from statsmodels.genmod.families import family, links from statsmodels.genmod.generalized_linear_model import GLM from statsmodels.regression.linear_model import OLS class Dummy(object): pass class CheckAM(object): def test_predict(self): assert_almost_equal(self.res1.y_pred, self.res2.y_pred, decimal=2) assert_almost_equal(self.res1.y_predshort, self.res2.y_pred[:10], decimal=2) def _est_fitted(self): #check definition of fitted in GLM: eta or mu assert_almost_equal(self.res1.y_pred, self.res2.fittedvalues, decimal=2) assert_almost_equal(self.res1.y_predshort, self.res2.fittedvalues[:10], decimal=2) def test_params(self): #note: only testing slope coefficients #constant is far off in example 4 versus 2 assert_almost_equal(self.res1.params[1:], self.res2.params[1:], decimal=2) #constant assert_almost_equal(self.res1.params[1], self.res2.params[1], decimal=2) def _est_df(self): #not used yet, copied from PolySmoother tests assert_equal(self.res_ps.df_model(), self.res2.df_model) assert_equal(self.res_ps.df_fit(), self.res2.df_model) #alias assert_equal(self.res_ps.df_resid(), self.res2.df_resid) class CheckGAM(CheckAM): def test_mu(self): #problem with scale for precision assert_almost_equal(self.res1.mu_pred, self.res2.mu_pred, decimal=0) # assert_almost_equal(self.res1.y_predshort, # self.res2.y_pred[:10], decimal=2) class BaseAM(object): def __init__(self): #DGP: simple polynomial order = 3 nobs = 200 lb, ub = -3.5, 3 x1 = np.linspace(lb, ub, nobs) x2 = np.sin(2*x1) x = np.column_stack((x1/x1.max()*1, 1.*x2)) exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1) idx = range((order+1)*2) del idx[order+1] exog_reduced = exog[:,idx] #remove duplicate constant y_true = exog.sum(1) #/ 4. #z = y_true #alias check #d = x self.nobs = nobs self.y_true, self.x, self.exog = y_true, x, exog_reduced class TestAdditiveModel(BaseAM, CheckAM): def __init__(self): super(self.__class__, self).__init__() #initialize DGP nobs = self.nobs y_true, x, exog = self.y_true, self.x, self.exog np.random.seed(8765993) sigma_noise = 0.1 y = y_true + sigma_noise * np.random.randn(nobs) m = AdditiveModel(x) m.fit(y) res_gam = m.results #TODO: currently attached to class res_ols = OLS(y, exog).fit() #Note: there still are some naming inconsistencies self.res1 = res1 = Dummy() #for gam model #res2 = Dummy() #for benchmark self.res2 = res2 = res_ols #reuse existing ols results, will add additional res1.y_pred = res_gam.predict(x) res2.y_pred = res_ols.model.predict(res_ols.params, exog) res1.y_predshort = res_gam.predict(x[:10]) slopes = [i for ss in m.smoothers for i in ss.params[1:]] const = res_gam.alpha + sum([ss.params[1] for ss in m.smoothers]) #print const, slopes res1.params = np.array([const] + slopes) class BaseGAM(BaseAM, CheckGAM): def init(self): nobs = self.nobs y_true, x, exog = self.y_true, self.x, self.exog if not hasattr(self, 'scale'): scale = 1 else: scale = self.scale f = self.family self.mu_true = mu_true = f.link.inverse(y_true) np.random.seed(8765993) #y_obs = np.asarray([stats.poisson.rvs(p) for p in mu], float) if issubclass(self.rvs.im_class, stats.rv_discrete): # Discrete distributions don't take `scale`. y_obs = self.rvs(mu_true, size=nobs) else: y_obs = self.rvs(mu_true, scale=scale, size=nobs) m = GAM(y_obs, x, family=f) #TODO: y_obs is twice __init__ and fit m.fit(y_obs, maxiter=100) res_gam = m.results self.res_gam = res_gam #attached for debugging self.mod_gam = m #attached for debugging res_glm = GLM(y_obs, exog, family=f).fit() #Note: there still are some naming inconsistencies self.res1 = res1 = Dummy() #for gam model #res2 = Dummy() #for benchmark self.res2 = res2 = res_glm #reuse existing glm results, will add additional #eta in GLM terminology res2.y_pred = res_glm.model.predict(res_glm.params, exog, linear=True) res1.y_pred = res_gam.predict(x) res1.y_predshort = res_gam.predict(x[:10]) #, linear=True) #mu res2.mu_pred = res_glm.model.predict(res_glm.params, exog, linear=False) res1.mu_pred = res_gam.mu #parameters slopes = [i for ss in m.smoothers for i in ss.params[1:]] const = res_gam.alpha + sum([ss.params[1] for ss in m.smoothers]) res1.params = np.array([const] + slopes) class TestGAMPoisson(BaseGAM): def __init__(self): super(self.__class__, self).__init__() #initialize DGP self.family = family.Poisson() self.rvs = stats.poisson.rvs self.init() class TestGAMBinomial(BaseGAM): def __init__(self): super(self.__class__, self).__init__() #initialize DGP self.family = family.Binomial() self.rvs = stats.bernoulli.rvs self.init() class _estGAMGaussianLogLink(BaseGAM): #test failure, but maybe precision issue, not far off #>>> np.mean(np.abs(tt.res2.mu_pred - tt.mu_true)) #0.80409736263199649 #>>> np.mean(np.abs(tt.res2.mu_pred - tt.mu_true))/tt.mu_true.mean() #0.023258245077813208 #>>> np.mean((tt.res2.mu_pred - tt.mu_true)**2)/tt.mu_true.mean() #0.022989403735692578 def __init__(self): super(self.__class__, self).__init__() #initialize DGP self.family = family.Gaussian(links.log) self.rvs = stats.norm.rvs self.scale = 5 self.init() class TestGAMGamma(BaseGAM): def __init__(self): super(self.__class__, self).__init__() #initialize DGP self.family = family.Gamma(links.log) self.rvs = stats.gamma.rvs self.init() class _estGAMNegativeBinomial(BaseGAM): #rvs generation doesn't work, nbinom needs 2 parameters def __init__(self): super(self.__class__, self).__init__() #initialize DGP self.family = family.NegativeBinomial() self.rvs = stats.nbinom.rvs self.init() if __name__ == '__main__': t1 = TestAdditiveModel() t1.test_predict() t1.test_params() for tt in [TestGAMPoisson, TestGAMBinomial, TestGAMGamma, _estGAMGaussianLogLink]: #, TestGAMNegativeBinomial]: tt = tt() tt.test_predict() tt.test_params() tt.test_mu statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tests/test_pca.py000066400000000000000000000047031224417117700254740ustar00rootroot00000000000000'''tests for pca and arma to ar and ma representation compared with matlab princomp, and garchar, garchma TODO: * convert to generators with yield to have individual tests * incomplete: test relationship of pca-evecs and pinv (adding constant) ''' import numpy as np from numpy.testing import assert_array_almost_equal from statsmodels.sandbox import tools from statsmodels.sandbox.tools import pca, pcasvd from statsmodels.tsa.arima_process import arma_impulse_response from datamlw import * def check_pca_princomp(pcares, princomp): factors, evals, evecs = pcares[1:] #res_princomp.coef, res_princomp.factors, res_princomp.values msign = (evecs/princomp.coef)[0] assert_array_almost_equal(msign*evecs, princomp.coef, 13) assert_array_almost_equal(msign*factors, princomp.factors, 13) assert_array_almost_equal(evals, princomp.values.ravel(), 13) def check_pca_svd(pcares, pcasvdres): xreduced, factors, evals, evecs = pcares xred_svd, factors_svd, evals_svd, evecs_svd = pcasvdres assert_array_almost_equal(evals_svd, evals, 14) msign = (evecs/evecs_svd)[0] assert_array_almost_equal(msign*evecs_svd, evecs, 14) assert_array_almost_equal(msign*factors_svd, factors, 13) assert_array_almost_equal(xred_svd, xreduced, 13) xf = data.xo/1000. def test_pca_princomp(): pcares = pca(xf) check_pca_princomp(pcares, princomp1) pcares = pca(xf[:20,:]) check_pca_princomp(pcares, princomp2) pcares = pca(xf[:20,:]-xf[:20,:].mean(0)) check_pca_princomp(pcares, princomp3) pcares = pca(xf[:20,:]-xf[:20,:].mean(0), demean=0) check_pca_princomp(pcares, princomp3) def test_pca_svd(): xreduced, factors, evals, evecs = pca(xf) factors_wconst = np.c_[factors, np.ones((factors.shape[0],1))] beta = np.dot(np.linalg.pinv(factors_wconst), xf) #np.dot(np.linalg.pinv(factors_wconst),x2/1000.).T[:,:4] - evecs assert_array_almost_equal(beta.T[:,:4], evecs, 14) xred_svd, factors_svd, evals_svd, evecs_svd = pcasvd(xf, keepdim=0) assert_array_almost_equal(evals_svd, evals, 14) msign = (evecs/evecs_svd)[0] assert_array_almost_equal(msign*evecs_svd, evecs, 13) assert_array_almost_equal(msign*factors_svd, factors, 12) assert_array_almost_equal(xred_svd, xreduced, 13) pcares = pca(xf, keepdim=2) pcasvdres = pcasvd(xf, keepdim=2) check_pca_svd(pcares, pcasvdres) #print np.dot(factors[:,:3], evecs.T[:3,:])[:5] if __name__ == '__main__': test_pca_svd() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/000077500000000000000000000000001224417117700233125ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/TODO.txt000066400000000000000000000051701224417117700246230ustar00rootroot00000000000000 * make groupstats into a class with additional functions for easy access use bincount but loop over 2d, or maybe two versions 1d,2d (or nd?) similar to groupbys class GroupStats init with label data attributes/properties on demand: mean, var, callback, devfrommean, meanarr, vararr does var need bias option ? yes? def groupmean(label, data) def groupvar(label, data) def groupnormalize(label, data, reweight = True) * ANOVA wrapper usage example discussed on mailing list and proposed by Skipper create design matrix create restriction matrices for F tests, t tests ? normalization redundant ? reports ANOVA style * maybe: quick helpers for structured arrays and masked arrays formula for selection of variables specification for which variable are factors Anova(y, use='x1;x2;x3', factors='x2', data = dataarray) or Anova(y, use='x1; x2:F; x3', data = dataarray) masked arrays need row compression * meta object for variable names, and .... ? * OLSR, OLS with linear restriction -> design changes ? need to overwrite params_cov calculation - move it back to models * regression: OLS, WLS - add prediction * Granger causality test: which test statistic, F test is easiest, LR, Wald ? -> easy in R: example in Wikipedia * get lag matrix helper function to use lagged dependent and lagged independent regressors * nonlinear hypothesis tests for the estimate parameters, delta method ? -> easy, but derivatives by hand w/o sympy * minimal PCA, PCR, or PLS (NIPALS): interesting also for finance but not urgent -> messy multiplicity of definitions ? * non-linear least squares: use/imitate scipy.interpolate curvefit + full set of results statistics * tests -> needed - GLSAR: check, R's gls - ARMA: check R for arma, e.g. dynamo, also look more closely at GARCH_UCSD (BSD) and offspring (license ?) * other models in draft stage -> requires cleaning - gaussian process -> might fit in - multinomial logit -> requires ML, result statistics don't fit into current classes ? * stochastic processes, time series -> first step is relatively easy more simulators, random process generators, for fun and Monte Carlo and testing estimators to follow first group GARCH, continuous time: not until I need them * other multivariate analysis discriminance, factor analysis, more anova: not my business * MLE, GMM: big open question -> needs to wait until we have more code that uses it difference of approaches - parametric assumptions, distributions fully specified - problem misspecification - efficient estimation with full information MLE -> example panel data, yogurt paper statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/__init__.py000066400000000000000000000002761224417117700254300ustar00rootroot00000000000000'''some helper function for principal component and time series analysis Status ------ pca : tested against matlab pcasvd : tested against matlab ''' from tools_pca import * #pca, pcasvd statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/cross_val.py000066400000000000000000000300621224417117700256600ustar00rootroot00000000000000""" Utilities for cross validation. taken from scikits.learn # Author: Alexandre Gramfort , # Gael Varoquaux # License: BSD Style. # $Id$ changes to code by josef-pktd: - docstring formatting: underlines of headers """ import numpy as np try: from itertools import combinations except: # Using Python < 2.6 def combinations(seq, r=None): """Generator returning combinations of items from sequence taken at a time. Order is not significant. If is not given, the entire sequence is returned. """ if r == None: r = len(seq) if r <= 0: yield [] else: for i in xrange(len(seq)): for cc in combinations(seq[i+1:], r-1): yield [seq[i]]+cc ################################################################################ class LeaveOneOut(object): """ Leave-One-Out cross validation iterator: Provides train/test indexes to split data in train test sets """ def __init__(self, n): """ Leave-One-Out cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4]] >>> y = [1, 2] >>> loo = cross_val.LeaveOneOut(2) >>> for train_index, test_index in loo: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) ... print X_train, X_test, y_train, y_test TRAIN: [False True] TEST: [ True False] [[3 4]] [[1 2]] [2] [1] TRAIN: [ True False] TEST: [False True] [[1 2]] [[3 4]] [1] [2] """ self.n = n def __iter__(self): n = self.n for i in xrange(n): test_index = np.zeros(n, dtype=np.bool) test_index[i] = True train_index = np.logical_not(test_index) yield train_index, test_index def __repr__(self): return '%s.%s(n=%i)' % (self.__class__.__module__, self.__class__.__name__, self.n, ) ################################################################################ class LeavePOut(object): """ Leave-P-Out cross validation iterator: Provides train/test indexes to split data in train test sets """ def __init__(self, n, p): """ Leave-P-Out cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements p: int Size test sets Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]] >>> y = [1, 2, 3, 4] >>> lpo = cross_val.LeavePOut(4, 2) >>> for train_index, test_index in lpo: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) TRAIN: [False False True True] TEST: [ True True False False] TRAIN: [False True False True] TEST: [ True False True False] TRAIN: [False True True False] TEST: [ True False False True] TRAIN: [ True False False True] TEST: [False True True False] TRAIN: [ True False True False] TEST: [False True False True] TRAIN: [ True True False False] TEST: [False False True True] """ self.n = n self.p = p def __iter__(self): n = self.n p = self.p comb = combinations(range(n), p) for idx in comb: test_index = np.zeros(n, dtype=np.bool) test_index[np.array(idx)] = True train_index = np.logical_not(test_index) yield train_index, test_index def __repr__(self): return '%s.%s(n=%i, p=%i)' % ( self.__class__.__module__, self.__class__.__name__, self.n, self.p, ) ################################################################################ class KFold(object): """ K-Folds cross validation iterator: Provides train/test indexes to split data in train test sets """ def __init__(self, n, k): """ K-Folds cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements k: int number of folds Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]] >>> y = [1, 2, 3, 4] >>> kf = cross_val.KFold(4, k=2) >>> for train_index, test_index in kf: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) TRAIN: [False False True True] TEST: [ True True False False] TRAIN: [ True True False False] TEST: [False False True True] Notes ----- All the folds have size trunc(n/k), the last one has the complementary """ assert k>0, ValueError('cannot have k below 1') assert k>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]] >>> y = [1, 2, 1, 2] >>> labels = [1, 1, 2, 2] >>> lol = cross_val.LeaveOneLabelOut(labels) >>> for train_index, test_index in lol: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, \ test_index, X, y) ... print X_train, X_test, y_train, y_test TRAIN: [False False True True] TEST: [ True True False False] [[5 6] [7 8]] [[1 2] [3 4]] [1 2] [1 2] TRAIN: [ True True False False] TEST: [False False True True] [[1 2] [3 4]] [[5 6] [7 8]] [1 2] [1 2] """ self.labels = labels def __iter__(self): # We make a copy here to avoid side-effects during iteration labels = np.array(self.labels, copy=True) for i in np.unique(labels): test_index = np.zeros(len(labels), dtype=np.bool) test_index[labels==i] = True train_index = np.logical_not(test_index) yield train_index, test_index def __repr__(self): return '%s.%s(labels=%s)' % ( self.__class__.__module__, self.__class__.__name__, self.labels, ) def split(train_indexes, test_indexes, *args): """ For each arg return a train and test subsets defined by indexes provided in train_indexes and test_indexes """ ret = [] for arg in args: arg = np.asanyarray(arg) arg_train = arg[train_indexes] arg_test = arg[test_indexes] ret.append(arg_train) ret.append(arg_test) return ret ''' >>> cv = cross_val.LeaveOneLabelOut(X, y) # y making y optional and possible to add other arrays of the same shape[0] too >>> for X_train, y_train, X_test, y_test in cv: ... print np.sqrt((model.fit(X_train, y_train).predict(X_test) - y_test) ** 2).mean()) ''' ################################################################################ #below: Author: josef-pktd class KStepAhead(object): """ KStepAhead cross validation iterator: Provides fit/test indexes to split data in sequential sets """ def __init__(self, n, k=1, start=None, kall=True, return_slice=True): """ KStepAhead cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements k : int number of steps ahead start : int initial size of data for fitting kall : boolean if true. all values for up to k-step ahead are included in the test index. If false, then only the k-th step ahead value is returnd Notes ----- I don't think this is really useful, because it can be done with a very simple loop instead. Useful as a plugin, but it could return slices instead for faster array access. Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4]] >>> y = [1, 2] >>> loo = cross_val.LeaveOneOut(2) >>> for train_index, test_index in loo: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) ... print X_train, X_test, y_train, y_test TRAIN: [False True] TEST: [ True False] [[3 4]] [[1 2]] [2] [1] TRAIN: [ True False] TEST: [False True] [[1 2]] [[3 4]] [1] [2] """ self.n = n self.k = k if start is None: start = int(np.trunc(n*0.25)) # pick something arbitrary self.start = start self.kall = kall self.return_slice = return_slice def __iter__(self): n = self.n k = self.k start = self.start if self.return_slice: for i in xrange(start, n-k): train_slice = slice(None, i, None) if self.kall: test_slice = slice(i, i+k) else: test_slice = slice(i+k-1, i+k) yield train_slice, test_slice else: #for compatibility with other iterators for i in xrange(start, n-k): train_index = np.zeros(n, dtype=np.bool) train_index[:i] = True test_index = np.zeros(n, dtype=np.bool) if self.kall: test_index[i:i+k] = True # np.logical_not(test_index) else: test_index[i+k-1:i+k] = True #or faster to return np.arange(i,i+k) ? #returning slice should be faster in this case yield train_index, test_index def __repr__(self): return '%s.%s(n=%i)' % (self.__class__.__module__, self.__class__.__name__, self.n, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/mctools.py000066400000000000000000000412541224417117700253520ustar00rootroot00000000000000'''Helper class for Monte Carlo Studies for (currently) statistical tests Most of it should also be usable for Bootstrap, and for MC for estimators. Takes the sample generator, dgb, and the statistical results, statistic, as functions in the argument. Author: Josef Perktold (josef-pktd) License: BSD-3 TODOs, Design ------------- If we only care about univariate analysis, i.e. marginal if statistics returns more than one value, the we only need to store the sorted mcres not the original res. Do we want to extend to multivariate analysis? Use distribution function to keep track of MC results, ECDF, non-paramatric? Large parts are similar to a 2d array of independent multivariate random variables. Joint distribution is not used (yet). I guess this is currently only for one sided test statistics, e.g. for two-sided tests basend on t or normal distribution use the absolute value. ''' import numpy as np from statsmodels.iolib.table import SimpleTable #copied from stattools class StatTestMC(object): """class to run Monte Carlo study on a statistical test''' TODO print summary, for quantiles and for histogram draft in trying out script log Parameters ---------- dgp : callable Function that generates the data to be used in Monte Carlo that should return a new sample with each call statistic : callable Function that calculates the test statistic, which can return either a single statistic or a 1d array_like (tuple, list, ndarray). see also statindices in description of run Attributes ---------- many methods store intermediate results self.mcres : ndarray (nrepl, nreturns) or (nrepl, len(statindices)) Monte Carlo results stored by run Notes ----- .. Warning:: This is (currently) designed for a single call to run. If run is called a second time with different arguments, then some attributes might not be updated, and, therefore, not correspond to the same run. .. Warning:: Under Construction, don't expect stability in Api or implementation Examples -------- Define a function that defines our test statistic: def lb(x): s,p = acorr_ljungbox(x, lags=4) return np.r_[s, p] Note lb returns eight values. Define a random sample generator, for example 500 independently, normal distributed observations in a sample: def normalnoisesim(nobs=500, loc=0.0): return (loc+np.random.randn(nobs)) Create instance and run Monte Carlo. Using statindices=range(4) means that only the first for values of the return of the statistic (lb) are stored in the Monte Carlo results. mc1 = StatTestMC(normalnoisesim, lb) mc1.run(5000, statindices=range(4)) Most of the other methods take an idx which indicates for which columns the results should be presented, e.g. print mc1.cdf(crit, [1,2,3])[1] """ def __init__(self, dgp, statistic): self.dgp = dgp #staticmethod(dgp) #no self self.statistic = statistic # staticmethod(statistic) #no self def run(self, nrepl, statindices=None, dgpargs=[], statsargs=[]): '''run the actual Monte Carlo and save results Parameters ---------- nrepl : int number of Monte Carlo repetitions statindices : None or list of integers determines which values of the return of the statistic functions are stored in the Monte Carlo. Default None means the entire return. If statindices is a list of integers, then it will be used as index into the return. dgpargs : tuple optional parameters for the DGP statsargs : tuple optional parameters for the statistics function Returns ------- None, all results are attached ''' self.nrepl = nrepl self.statindices = statindices self.dgpargs = dgpargs self.statsargs = statsargs dgp = self.dgp statfun = self.statistic # name ? #introspect len of return of statfun, #possible problems with ndim>1, check ValueError mcres0 = statfun(dgp(*dgpargs), *statsargs) self.nreturn = nreturns = len(np.ravel(mcres0)) #single return statistic if statindices is None: #self.nreturn = nreturns = 1 mcres = np.zeros(nrepl) mcres[0] = mcres0 for ii in range(1, repl-1, nreturns): x = dgp(*dgpargs) #(1e-4+np.random.randn(nobs)).cumsum() #should I ravel? mcres[ii] = statfun(x, *statsargs) #unitroot_adf(x, 2,trendorder=0, autolag=None) #more than one return statistic else: self.nreturn = nreturns = len(statindices) self.mcres = mcres = np.zeros((nrepl, nreturns)) mcres[0] = [mcres0[i] for i in statindices] for ii in range(1, nrepl-1): x = dgp(*dgpargs) #(1e-4+np.random.randn(nobs)).cumsum() ret = statfun(x, *statsargs) mcres[ii] = [ret[i] for i in statindices] self.mcres = mcres def histogram(self, idx=None, critval=None): '''calculate histogram values does not do any plotting I don't remember what I wanted here, looks similar to the new cdf method, but this also does a binned pdf (self.histo) ''' if self.mcres.ndim == 2: if not idx is None: mcres = self.mcres[:,idx] else: raise ValueError('currently only 1 statistic at a time') else: mcres = self.mcres if critval is None: histo = np.histogram(mcres, bins=10) else: if not critval[0] == -np.inf: bins=np.r_[-np.inf, critval, np.inf] if not critval[0] == -np.inf: bins=np.r_[bins, np.inf] histo = np.histogram(mcres, bins=np.r_[-np.inf, critval, np.inf]) self.histo = histo self.cumhisto = np.cumsum(histo[0])*1./self.nrepl self.cumhistoreversed = np.cumsum(histo[0][::-1])[::-1]*1./self.nrepl return histo, self.cumhisto, self.cumhistoreversed #use cache decorator instead def get_mc_sorted(self): if not hasattr(self, 'mcressort'): self.mcressort = np.sort(self.mcres, axis=0) return self.mcressort def quantiles(self, idx=None, frac=[0.01, 0.025, 0.05, 0.1, 0.975]): '''calculate quantiles of Monte Carlo results similar to ppf Parameters ---------- idx : None or list of integers List of indices into the Monte Carlo results (columns) that should be used in the calculation frac : array_like, float Defines which quantiles should be calculated. For example a frac of 0.1 finds the 10% quantile, x such that cdf(x)=0.1 Returns ------- frac : ndarray same values as input, TODO: I should drop this again ? quantiles : ndarray, (len(frac), len(idx)) the quantiles with frac in rows and idx variables in columns Notes ----- rename to ppf ? make frac required change sequence idx, frac ''' if self.mcres.ndim == 2: if not idx is None: mcres = self.mcres[:,idx] else: raise ValueError('currently only 1 statistic at a time') else: mcres = self.mcres self.frac = frac = np.asarray(frac) mc_sorted = self.get_mc_sorted()[:,idx] return frac, mc_sorted[(self.nrepl*frac).astype(int)] def cdf(self, x, idx=None): '''calculate cumulative probabilities of Monte Carlo results Parameters ---------- idx : None or list of integers List of indices into the Monte Carlo results (columns) that should be used in the calculation frac : array_like, float Defines which quantiles should be calculated. For example a frac of 0.1 finds the 10% quantile, x such that cdf(x)=0.1 Returns ------- x : ndarray same as input, TODO: I should drop this again ? probs : ndarray, (len(x), len(idx)) the quantiles with frac in rows and idx variables in columns ''' idx = np.atleast_1d(idx).tolist() #assure iterable, use list ? # if self.mcres.ndim == 2: # if not idx is None: # mcres = self.mcres[:,idx] # else: # raise ValueError('currently only 1 statistic at a time') # else: # mcres = self.mcres mc_sorted = self.get_mc_sorted() x = np.asarray(x) #TODO:autodetect or explicit option ? if x.ndim > 1 and x.shape[1]==len(idx): use_xi = True else: use_xi = False x_ = x #alias probs = [] for i,ix in enumerate(idx): if use_xi: x_ = x[:,i] probs.append(np.searchsorted(mc_sorted[:,ix], x_)/float(self.nrepl)) probs = np.asarray(probs).T return x, probs def plot_hist(self, idx, distpdf=None, bins=50, ax=None, kwds=None): '''plot the histogram against a reference distribution Parameters ---------- idx : None or list of integers List of indices into the Monte Carlo results (columns) that should be used in the calculation distpdf : callable probability density function of reference distribution bins : integer or array_like used unchanged for matplotlibs hist call ax : TODO: not implemented yet kwds : None or tuple of dicts extra keyword options to the calls to the matplotlib functions, first dictionary is for his, second dictionary for plot of the reference distribution Returns ------- None ''' if kwds is None: kwds = ({},{}) if self.mcres.ndim == 2: if not idx is None: mcres = self.mcres[:,idx] else: raise ValueError('currently only 1 statistic at a time') else: mcres = self.mcres lsp = np.linspace(mcres.min(), mcres.max(), 100) import matplotlib.pyplot as plt #I don't want to figure this out now # if ax=None: # fig = plt.figure() # ax = fig.addaxis() fig = plt.figure() plt.hist(mcres, bins=bins, normed=True, **kwds[0]) plt.plot(lsp, distpdf(lsp), 'r', **kwds[1]) def summary_quantiles(self, idx, distppf, frac=[0.01, 0.025, 0.05, 0.1, 0.975], varnames=None, title=None): '''summary table for quantiles (critical values) Parameters ---------- idx : None or list of integers List of indices into the Monte Carlo results (columns) that should be used in the calculation distppf : callable probability density function of reference distribution TODO: use `crit` values instead or additional, see summary_cdf frac : array_like, float probabilities for which varnames : None, or list of strings optional list of variable names, same length as idx Returns ------- table : instance of SimpleTable use `print table` to see results ''' idx = np.atleast_1d(idx) #assure iterable, use list ? quant, mcq = self.quantiles(idx, frac=frac) #not sure whether this will work with single quantile #crit = stats.chi2([2,4]).ppf(np.atleast_2d(quant).T) crit = distppf(np.atleast_2d(quant).T) mml=[] for i, ix in enumerate(idx): #TODO: hardcoded 2 ? mml.extend([mcq[:,i], crit[:,i]]) #mmlar = np.column_stack(mml) mmlar = np.column_stack([quant] + mml) #print mmlar.shape if title: title = title +' Quantiles (critical values)' else: title='Quantiles (critical values)' #TODO use stub instead if varnames is None: varnames = ['var%d' % i for i in range(mmlar.shape[1]//2)] headers = ['\nprob'] + ['%s\n%s' % (i, t) for i in varnames for t in ['mc', 'dist']] return SimpleTable(mmlar, txt_fmt={'data_fmts': ["%#6.3f"]+["%#10.4f"]*(mmlar.shape[1]-1)}, title=title, headers=headers) def summary_cdf(self, idx, frac, crit, varnames=None, title=None): '''summary table for cumulative density function Parameters ---------- idx : None or list of integers List of indices into the Monte Carlo results (columns) that should be used in the calculation frac : array_like, float probabilities for which crit : array_like values for which cdf is calculated varnames : None, or list of strings optional list of variable names, same length as idx Returns ------- table : instance of SimpleTable use `print table` to see results ''' idx = np.atleast_1d(idx) #assure iterable, use list ? mml=[] #TODO:need broadcasting in cdf for i in range(len(idx)): #print i, mc1.cdf(crit[:,i], [idx[i]])[1].ravel() mml.append(self.cdf(crit[:,i], [idx[i]])[1].ravel()) #mml = self.cdf(crit, idx)[1] #mmlar = np.column_stack(mml) #print mml[0].shape, np.shape(frac) mmlar = np.column_stack([frac] + mml) #print mmlar.shape if title: title = title +' Probabilites' else: title='Probabilities' #TODO use stub instead #headers = ['\nprob'] + ['var%d\n%s' % (i, t) for i in range(mmlar.shape[1]-1) for t in ['mc']] if varnames is None: varnames = ['var%d' % i for i in range(mmlar.shape[1]-1)] headers = ['prob'] + varnames return SimpleTable(mmlar, txt_fmt={'data_fmts': ["%#6.3f"]+["%#10.4f"]*(np.array(mml).shape[1]-1)}, title=title, headers=headers) if __name__ == '__main__': from scipy import stats from statsmodels.iolib.table import SimpleTable from statsmodels.sandbox.stats.diagnostic import ( acorr_ljungbox, unitroot_adf) def randwalksim(nobs=100, drift=0.0): return (drift+np.random.randn(nobs)).cumsum() def normalnoisesim(nobs=500, loc=0.0): return (loc+np.random.randn(nobs)) def adf20(x): return unitroot_adf(x, 2,trendorder=0, autolag=None) # print '\nResults with MC class' # mc1 = StatTestMC(randwalksim, adf20) # mc1.run(1000) # print mc1.histogram(critval=[-3.5, -3.17, -2.9 , -2.58, 0.26]) # print mc1.quantiles() print '\nLjung Box' from statsmodels.sandbox.stats.diagnostic import acorr_ljungbox def lb4(x): s,p = acorr_ljungbox(x, lags=4) return s[-1], p[-1] def lb1(x): s,p = acorr_ljungbox(x, lags=1) return s[0], p[0] def lb(x): s,p = acorr_ljungbox(x, lags=4) return np.r_[s, p] print 'Results with MC class' mc1 = StatTestMC(normalnoisesim, lb) mc1.run(10000, statindices=range(8)) print mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975]) print mc1.quantiles(1) print mc1.quantiles(0) print mc1.histogram(0) #print mc1.summary_quantiles([1], stats.chi2([2]).ppf, title='acorr_ljungbox') print mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf, varnames=['lag 1', 'lag 2', 'lag 3'], title='acorr_ljungbox') print mc1.cdf(0.1026, 1) print mc1.cdf(0.7278, 3) print mc1.cdf(0.7278, [1,2,3]) frac = [0.01, 0.025, 0.05, 0.1, 0.975] crit = stats.chi2([2,4]).ppf(np.atleast_2d(frac).T) print mc1.summary_cdf([1,3], frac, crit, title='acorr_ljungbox') crit = stats.chi2([2,3,4]).ppf(np.atleast_2d(frac).T) print mc1.summary_cdf([1,2,3], frac, crit, varnames=['lag 1', 'lag 2', 'lag 3'], title='acorr_ljungbox') print mc1.cdf(crit, [1,2,3])[1].shape #fixed broadcasting in cdf Done 2d only ''' >>> mc1.cdf(crit[:,0], [1])[1].shape (5, 1) >>> mc1.cdf(crit[:,0], [1,3])[1].shape (5, 2) >>> mc1.cdf(crit[:,:], [1,3])[1].shape (2, 5, 2) ''' doplot=0 if doplot: import matplotlib.pyplot as plt mc1.plot_hist(0,stats.chi2(2).pdf) #which pdf plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/tools_pca.py000066400000000000000000000100561224417117700256510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Principal Component Analysis Created on Tue Sep 29 20:11:23 2009 Author: josef-pktd TODO : add class for better reuse of results """ import numpy as np def pca(data, keepdim=0, normalize=0, demean=True): '''principal components with eigenvector decomposition similar to princomp in matlab Parameters ---------- data : ndarray, 2d data with observations by rows and variables in columns keepdim : integer number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included normalize : boolean if true, then eigenvectors are normalized by sqrt of eigenvalues demean : boolean if true, then the column mean is subtracted from the data Returns ------- xreduced : ndarray, 2d, (nobs, nvars) projection of the data x on the kept eigenvectors factors : ndarray, 2d, (nobs, nfactors) factor matrix, given by np.dot(x, evecs) evals : ndarray, 2d, (nobs, nfactors) eigenvalues evecs : ndarray, 2d, (nobs, nfactors) eigenvectors, normalized if normalize is true Notes ----- See Also -------- pcasvd : principal component analysis using svd ''' x = np.array(data) #make copy so original doesn't change, maybe not necessary anymore if demean: m = x.mean(0) else: m = np.zeros(x.shape[1]) x -= m # Covariance matrix xcov = np.cov(x, rowvar=0) # Compute eigenvalues and sort into descending order evals, evecs = np.linalg.eig(xcov) indices = np.argsort(evals) indices = indices[::-1] evecs = evecs[:,indices] evals = evals[indices] if keepdim > 0 and keepdim < x.shape[1]: evecs = evecs[:,:keepdim] evals = evals[:keepdim] if normalize: #for i in range(shape(evecs)[1]): # evecs[:,i] / linalg.norm(evecs[:,i]) * sqrt(evals[i]) evecs = evecs/np.sqrt(evals) #np.sqrt(np.dot(evecs.T, evecs) * evals) # get factor matrix #x = np.dot(evecs.T, x.T) factors = np.dot(x, evecs) # get original data from reduced number of components #xreduced = np.dot(evecs.T, factors) + m #print x.shape, factors.shape, evecs.shape, m.shape xreduced = np.dot(factors, evecs.T) + m return xreduced, factors, evals, evecs def pcasvd(data, keepdim=0, demean=True): '''principal components with svd Parameters ---------- data : ndarray, 2d data with observations by rows and variables in columns keepdim : integer number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included demean : boolean if true, then the column mean is subtracted from the data Returns ------- xreduced : ndarray, 2d, (nobs, nvars) projection of the data x on the kept eigenvectors factors : ndarray, 2d, (nobs, nfactors) factor matrix, given by np.dot(x, evecs) evals : ndarray, 2d, (nobs, nfactors) eigenvalues evecs : ndarray, 2d, (nobs, nfactors) eigenvectors, normalized if normalize is true See Also ------- pca : principal component analysis using eigenvector decomposition Notes ----- This doesn't have yet the normalize option of pca. ''' nobs, nvars = data.shape #print nobs, nvars, keepdim x = np.array(data) #make copy so original doesn't change if demean: m = x.mean(0) else: m = 0 ## if keepdim == 0: ## keepdim = nvars ## "print reassigning keepdim to max", keepdim x -= m U, s, v = np.linalg.svd(x.T, full_matrices=1) factors = np.dot(U.T, x.T).T #princomps if keepdim: xreduced = np.dot(factors[:,:keepdim], U[:,:keepdim].T) + m else: xreduced = data keepdim = nvars "print reassigning keepdim to max", keepdim # s = evals, U = evecs # no idea why denominator for s is with minus 1 evals = s**2/(x.shape[0]-1) #print keepdim return xreduced, factors[:,:keepdim], evals[:keepdim], U[:,:keepdim] #, v __all__ = ['pca', 'pcasvd'] statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tools/try_mctools.py000066400000000000000000000035421224417117700262460ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Sep 30 15:20:45 2011 @author: josef """ import numpy as np from scipy import stats from statsmodels.sandbox.tools.mctools import StatTestMC from statsmodels.sandbox.stats.diagnostic import ( acorr_ljungbox, unitroot_adf) def normalnoisesim(nobs=500, loc=0.0): return (loc+np.random.randn(nobs)) def lb(x): s,p = acorr_ljungbox(x, lags=4) return np.r_[s, p] mc1 = StatTestMC(normalnoisesim, lb) mc1.run(5000, statindices=range(4)) print mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf, varnames=['lag 1', 'lag 2', 'lag 3'], title='acorr_ljungbox') print '\n\n' frac = [0.01, 0.025, 0.05, 0.1, 0.975] crit = stats.chi2([2,3,4]).ppf(np.atleast_2d(frac).T) print mc1.summary_cdf([1,2,3], frac, crit, varnames=['lag 1', 'lag 2', 'lag 3'], title='acorr_ljungbox') print mc1.cdf(crit, [1,2,3])[1] #---------------------- def randwalksim(nobs=500, drift=0.0): return (drift+np.random.randn(nobs)).cumsum() def adf20(x): return unitroot_adf(x, 2, trendorder=0, autolag=None) print adf20(np.random.randn(100)) mc2 = StatTestMC(randwalksim, adf20) mc2.run(10000, statindices=[0,1]) frac = [0.01, 0.05, 0.1] #bug crit = np.array([-3.4996365338407074, -2.8918307730370025, -2.5829283377617176])[:,None] print mc2.summary_cdf([0], frac, crit, varnames=['adf'], title='adf') #bug #crit2 = np.column_stack((crit, frac)) #print mc2.summary_cdf([0, 1], frac, crit, # varnames=['adf'], # title='adf') print mc2.quantiles([0]) print mc2.cdf(crit, [0]) doplot=1 if doplot: import matplotlib.pyplot as plt mc1.plot_hist([3],stats.chi2([4]).pdf) plt.title('acorr_ljungbox - MC versus chi2') plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/000077500000000000000000000000001224417117700227415ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/__init__.py000066400000000000000000000014271224417117700250560ustar00rootroot00000000000000'''functions and classes time series analysis Status ------ work in progress arima.py ^^^^^^^^ ARIMA : initial class, uses conditional least squares, needs merging with new class arma2ar arma2ma arma_acf arma_acovf arma_generate_sample arma_impulse_response deconvolve index2lpol lpol2index mcarma22 movstat.py ^^^^^^^^^^ I had tested the next group against matlab, but where are the tests ? acf acovf ccf ccovf pacf_ols pacf_yw These hat incorrect array size, were my first implementation, slow compared to cumsum version in la and cython version in pandas. These need checking, and merging/comparing with new class MovStats check_movorder expandarr movmean : movmoment : corrected cutoff movorder movvar ''' #from arima import * from movstat import * #from stattools import * statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/diffusion.py000066400000000000000000000443711224417117700253120ustar00rootroot00000000000000'''getting started with diffusions, continuous time stochastic processes Author: josef-pktd License: BSD References ---------- An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations Author(s): Desmond J. Higham Source: SIAM Review, Vol. 43, No. 3 (Sep., 2001), pp. 525-546 Published by: Society for Industrial and Applied Mathematics Stable URL: http://www.jstor.org/stable/3649798 http://www.sitmo.com/ especially the formula collection Notes ----- OU process: use same trick for ARMA with constant (non-zero mean) and drift some of the processes have easy multivariate extensions *Open Issues* include xzero in returned sample or not? currently not *TODOS* * Milstein from Higham paper, for which processes does it apply * Maximum Likelihood estimation * more statistical properties (useful for tests) * helper functions for display and MonteCarlo summaries (also for testing/checking) * more processes for the menagerie (e.g. from empirical papers) * characteristic functions * transformations, non-linear e.g. log * special estimators, e.g. Ait Sahalia, empirical characteristic functions * fft examples * check naming of methods, "simulate", "sample", "simexact", ... ? stochastic volatility models: estimation unclear finance applications ? option pricing, interest rate models ''' import numpy as np from scipy import stats, signal import matplotlib.pyplot as plt #np.random.seed(987656789) class Diffusion(object): '''Wiener Process, Brownian Motion with mu=0 and sigma=1 ''' def __init__(self): pass def simulateW(self, nobs=100, T=1, dt=None, nrepl=1): '''generate sample of Wiener Process ''' dt = T*1.0/nobs t = np.linspace(dt, 1, nobs) dW = np.sqrt(dt)*np.random.normal(size=(nrepl, nobs)) W = np.cumsum(dW,1) self.dW = dW return W, t def expectedsim(self, func, nobs=100, T=1, dt=None, nrepl=1): '''get expectation of a function of a Wiener Process by simulation initially test example from ''' W, t = self.simulateW(nobs=nobs, T=T, dt=dt, nrepl=nrepl) U = func(t, W) Umean = U.mean(0) return U, Umean, t class AffineDiffusion(Diffusion): ''' differential equation: :math:: dx_t = f(t,x)dt + \sigma(t,x)dW_t integral: :math:: x_T = x_0 + \\int_{0}^{T}f(t,S)dt + \\int_0^T \\sigma(t,S)dW_t TODO: check definition, affine, what about jump diffusion? ''' def __init__(self): pass def sim(self, nobs=100, T=1, dt=None, nrepl=1): # this doesn't look correct if drift or sig depend on x # see arithmetic BM W, t = self.simulateW(nobs=nobs, T=T, dt=dt, nrepl=nrepl) dx = self._drift() + self._sig() * W x = np.cumsum(dx,1) xmean = x.mean(0) return x, xmean, t def simEM(self, xzero=None, nobs=100, T=1, dt=None, nrepl=1, Tratio=4): ''' from Higham 2001 TODO: reverse parameterization to start with final nobs and DT TODO: check if I can skip the loop using my way from exactprocess problem might be Winc (reshape into 3d and sum) TODO: (later) check memory efficiency for large simulations ''' #TODO: reverse parameterization to start with final nobs and DT nobs = nobs * Tratio # simple way to change parameter # maybe wrong parameterization, # drift too large, variance too small ? which dt/Dt # _drift, _sig independent of dt is wrong if xzero is None: xzero = self.xzero if dt is None: dt = T*1.0/nobs W, t = self.simulateW(nobs=nobs, T=T, dt=dt, nrepl=nrepl) dW = self.dW t = np.linspace(dt, 1, nobs) Dt = Tratio*dt; L = nobs/Tratio; # L EM steps of size Dt = R*dt Xem = np.zeros((nrepl,L)); # preallocate for efficiency Xtemp = xzero Xem[:,0] = xzero for j in np.arange(1,L): #Winc = np.sum(dW[:,Tratio*(j-1)+1:Tratio*j],1) Winc = np.sum(dW[:,np.arange(Tratio*(j-1)+1,Tratio*j)],1) #Xtemp = Xtemp + Dt*lamda*Xtemp + mu*Xtemp*Winc; Xtemp = Xtemp + self._drift(x=Xtemp) + self._sig(x=Xtemp) * Winc #Dt*lamda*Xtemp + mu*Xtemp*Winc; Xem[:,j] = Xtemp return Xem ''' R = 4; Dt = R*dt; L = N/R; % L EM steps of size Dt = R*dt Xem = zeros(1,L); % preallocate for efficiency Xtemp = Xzero; for j = 1:L Winc = sum(dW(R*(j-1)+1:R*j)); Xtemp = Xtemp + Dt*lambda*Xtemp + mu*Xtemp*Winc; Xem(j) = Xtemp; end ''' class ExactDiffusion(AffineDiffusion): '''Diffusion that has an exact integral representation this is currently mainly for geometric, log processes ''' def __init__(self): pass def exactprocess(self, xzero, nobs, ddt=1., nrepl=2): '''ddt : discrete delta t should be the same as an AR(1) not tested yet ''' t = np.linspace(ddt, nobs*ddt, nobs) #expnt = np.exp(-self.lambd * t) expddt = np.exp(-self.lambd * ddt) normrvs = np.random.normal(size=(nrepl,nobs)) #do I need lfilter here AR(1) ? if mean reverting lag-coeff<1 #lfilter doesn't handle 2d arrays, it does? inc = self._exactconst(expddt) + self._exactstd(expddt) * normrvs return signal.lfilter([1.], [1.,-expddt], inc) def exactdist(self, xzero, t): expnt = np.exp(-self.lambd * t) meant = xzero * expnt + self._exactconst(expnt) stdt = self._exactstd(expnt) return stats.norm(loc=meant, scale=stdt) class ArithmeticBrownian(AffineDiffusion): ''' :math:: dx_t &= \\mu dt + \\sigma dW_t ''' def __init__(self, xzero, mu, sigma): self.xzero = xzero self.mu = mu self.sigma = sigma def _drift(self, *args, **kwds): return self.mu def _sig(self, *args, **kwds): return self.sigma def exactprocess(self, nobs, xzero=None, ddt=1., nrepl=2): '''ddt : discrete delta t not tested yet ''' if xzero is None: xzero = self.xzero t = np.linspace(ddt, nobs*ddt, nobs) normrvs = np.random.normal(size=(nrepl,nobs)) inc = self._drift + self._sigma * np.sqrt(ddt) * normrvs #return signal.lfilter([1.], [1.,-1], inc) return xzero + np.cumsum(inc,1) def exactdist(self, xzero, t): expnt = np.exp(-self.lambd * t) meant = self._drift * t stdt = self._sigma * np.sqrt(t) return stats.norm(loc=meant, scale=stdt) class GeometricBrownian(AffineDiffusion): '''Geometric Brownian Motion :math:: dx_t &= \\mu x_t dt + \\sigma x_t dW_t $x_t $ stochastic process of Geometric Brownian motion, $\mu $ is the drift, $\sigma $ is the Volatility, $W$ is the Wiener process (Brownian motion). ''' def __init__(self, xzero, mu, sigma): self.xzero = xzero self.mu = mu self.sigma = sigma def _drift(self, *args, **kwds): x = kwds['x'] return self.mu * x def _sig(self, *args, **kwds): x = kwds['x'] return self.sigma * x class OUprocess(AffineDiffusion): '''Ornstein-Uhlenbeck :math:: dx_t&=\\lambda(\\mu - x_t)dt+\\sigma dW_t mean reverting process TODO: move exact higher up in class hierarchy ''' def __init__(self, xzero, mu, lambd, sigma): self.xzero = xzero self.lambd = lambd self.mu = mu self.sigma = sigma def _drift(self, *args, **kwds): x = kwds['x'] return self.lambd * (self.mu - x) def _sig(self, *args, **kwds): x = kwds['x'] return self.sigma * x def exact(self, xzero, t, normrvs): #TODO: aggregate over time for process with observations for all t # i.e. exact conditional distribution for discrete time increment # -> exactprocess #TODO: for single t, return stats.norm -> exactdist expnt = np.exp(-self.lambd * t) return (xzero * expnt + self.mu * (1-expnt) + self.sigma * np.sqrt((1-expnt*expnt)/2./self.lambd) * normrvs) def exactprocess(self, xzero, nobs, ddt=1., nrepl=2): '''ddt : discrete delta t should be the same as an AR(1) not tested yet # after writing this I saw the same use of lfilter in sitmo ''' t = np.linspace(ddt, nobs*ddt, nobs) expnt = np.exp(-self.lambd * t) expddt = np.exp(-self.lambd * ddt) normrvs = np.random.normal(size=(nrepl,nobs)) #do I need lfilter here AR(1) ? lfilter doesn't handle 2d arrays, it does? from scipy import signal #xzero * expnt inc = ( self.mu * (1-expddt) + self.sigma * np.sqrt((1-expddt*expddt)/2./self.lambd) * normrvs ) return signal.lfilter([1.], [1.,-expddt], inc) def exactdist(self, xzero, t): #TODO: aggregate over time for process with observations for all t #TODO: for single t, return stats.norm expnt = np.exp(-self.lambd * t) meant = xzero * expnt + self.mu * (1-expnt) stdt = self.sigma * np.sqrt((1-expnt*expnt)/2./self.lambd) from scipy import stats return stats.norm(loc=meant, scale=stdt) def fitls(self, data, dt): '''assumes data is 1d, univariate time series formula from sitmo ''' # brute force, no parameter estimation errors nobs = len(data)-1 exog = np.column_stack((np.ones(nobs), data[:-1])) parest, res, rank, sing = np.linalg.lstsq(exog, data[1:]) const, slope = parest errvar = res/(nobs-2.) lambd = -np.log(slope)/dt sigma = np.sqrt(-errvar * 2.*np.log(slope)/ (1-slope**2)/dt) mu = const / (1-slope) return mu, lambd, sigma class SchwartzOne(ExactDiffusion): '''the Schwartz type 1 stochastic process :math:: dx_t = \\kappa (\\mu - \\ln x_t) x_t dt + \\sigma x_tdW \\ The Schwartz type 1 process is a log of the Ornstein-Uhlenbeck stochastic process. ''' def __init__(self, xzero, mu, kappa, sigma): self.xzero = xzero self.mu = mu self.kappa = kappa self.lambd = kappa #alias until I fix exact self.sigma = sigma def _exactconst(self, expnt): return (1-expnt) * (self.mu - self.sigma**2 / 2. /self.kappa) def _exactstd(self, expnt): return self.sigma * np.sqrt((1-expnt*expnt)/2./self.kappa) def exactprocess(self, xzero, nobs, ddt=1., nrepl=2): '''uses exact solution for log of process ''' lnxzero = np.log(xzero) lnx = super(self.__class__, self).exactprocess(xzero, nobs, ddt=ddt, nrepl=nrepl) return np.exp(lnx) def exactdist(self, xzero, t): expnt = np.exp(-self.lambd * t) #TODO: check this is still wrong, just guessing meant = np.log(xzero) * expnt + self._exactconst(expnt) stdt = self._exactstd(expnt) return stats.lognorm(loc=meant, scale=stdt) def fitls(self, data, dt): '''assumes data is 1d, univariate time series formula from sitmo ''' # brute force, no parameter estimation errors nobs = len(data)-1 exog = np.column_stack((np.ones(nobs),np.log(data[:-1]))) parest, res, rank, sing = np.linalg.lstsq(exog, np.log(data[1:])) const, slope = parest errvar = res/(nobs-2.) #check denominator estimate, of sigma too low kappa = -np.log(slope)/dt sigma = np.sqrt(errvar * kappa / (1-np.exp(-2*kappa*dt))) mu = const / (1-np.exp(-kappa*dt)) + sigma**2/2./kappa if np.shape(mu)== (1,): mu = mu[0] # how to remove scalar array ? if np.shape(sigma)== (1,): sigma = sigma[0] #mu, kappa are good, sigma too small return mu, kappa, sigma class BrownianBridge(object): def __init__(self): pass def simulate(self, x0, x1, nobs, nrepl=1, ddt=1., sigma=1.): nobs=nobs+1 dt = ddt*1./nobs t = np.linspace(dt, ddt-dt, nobs) t = np.linspace(dt, ddt, nobs) wm = [t/ddt, 1-t/ddt] #wmi = wm[1] #wm1 = x1*wm[0] wmi = 1-dt/(ddt-t) wm1 = x1*(dt/(ddt-t)) su = sigma* np.sqrt(t*(1-t)/ddt) s = sigma* np.sqrt(dt*(ddt-t-dt)/(ddt-t)) x = np.zeros((nrepl, nobs)) x[:,0] = x0 rvs = s*np.random.normal(size=(nrepl,nobs)) for i in range(1,nobs): x[:,i] = x[:,i-1]*wmi[i] + wm1[i] + rvs[:,i] return x, t, su class CompoundPoisson(object): '''nobs iid compound poisson distributions, not a process in time ''' def __init__(self, lambd, randfn=np.random.normal): if len(lambd) != len(randfn): raise ValueError('lambd and randfn need to have the same number of elements') self.nobj = len(lambd) self.randfn = randfn self.lambd = np.asarray(lambd) def simulate(self, nobs, nrepl=1): nobj = self.nobj x = np.zeros((nrepl, nobs, nobj)) N = np.random.poisson(self.lambd[None,None,:], size=(nrepl,nobs,nobj)) for io in range(nobj): randfnc = self.randfn[io] nc = N[:,:,io] #print nrepl,nobs,nc #xio = randfnc(size=(nrepl,nobs,np.max(nc))).cumsum(-1)[np.arange(nrepl)[:,None],np.arange(nobs),nc-1] rvs = randfnc(size=(nrepl,nobs,np.max(nc))) print 'rvs.sum()', rvs.sum(), rvs.shape xio = rvs.cumsum(-1)[np.arange(nrepl)[:,None],np.arange(nobs),nc-1] #print xio.shape x[:,:,io] = xio x[N==0] = 0 return x, N ''' randn('state',100) % set the state of randn T = 1; N = 500; dt = T/N; t = [dt:dt:1]; M = 1000; % M paths simultaneously dW = sqrt(dt)*randn(M,N); % increments W = cumsum(dW,2); % cumulative sum U = exp(repmat(t,[M 1]) + 0.5*W); Umean = mean(U); plot([0,t],[1,Umean],'b-'), hold on % plot mean over M paths plot([0,t],[ones(5,1),U(1:5,:)],'r--'), hold off % plot 5 individual paths xlabel('t','FontSize',16) ylabel('U(t)','FontSize',16,'Rotation',0,'HorizontalAlignment','right') legend('mean of 1000 paths','5 individual paths',2) averr = norm((Umean - exp(9*t/8)),'inf') % sample error ''' if __name__ == '__main__': doplot = 1 nrepl = 1000 examples = []#['all'] if 'all' in examples: w = Diffusion() # Wiener Process # ^^^^^^^^^^^^^^ ws = w.simulateW(1000, nrepl=nrepl) if doplot: plt.figure() tmp = plt.plot(ws[0].T) tmp = plt.plot(ws[0].mean(0), linewidth=2) plt.title('Standard Brownian Motion (Wiener Process)') func = lambda t, W: np.exp(t + 0.5*W) us = w.expectedsim(func, nobs=500, nrepl=nrepl) if doplot: plt.figure() tmp = plt.plot(us[0].T) tmp = plt.plot(us[1], linewidth=2) plt.title('Brownian Motion - exp') #plt.show() averr = np.linalg.norm(us[1] - np.exp(9*us[2]/8.), np.inf) print averr #print us[1][:10] #print np.exp(9.*us[2][:10]/8.) # Geometric Brownian # ^^^^^^^^^^^^^^^^^^ gb = GeometricBrownian(xzero=1., mu=0.01, sigma=0.5) gbs = gb.simEM(nobs=100, nrepl=100) if doplot: plt.figure() tmp = plt.plot(gbs.T) tmp = plt.plot(gbs.mean(0), linewidth=2) plt.title('Geometric Brownian') plt.figure() tmp = plt.plot(np.log(gbs).T) tmp = plt.plot(np.log(gbs.mean(0)), linewidth=2) plt.title('Geometric Brownian - log-transformed') ab = ArithmeticBrownian(xzero=1, mu=0.05, sigma=1) abs = ab.simEM(nobs=100, nrepl=100) if doplot: plt.figure() tmp = plt.plot(abs.T) tmp = plt.plot(abs.mean(0), linewidth=2) plt.title('Arithmetic Brownian') # Ornstein-Uhlenbeck # ^^^^^^^^^^^^^^^^^^ ou = OUprocess(xzero=2, mu=1, lambd=0.5, sigma=0.1) ous = ou.simEM() oue = ou.exact(1, 1, np.random.normal(size=(5,10))) ou.exact(0, np.linspace(0,10,10/0.1), 0) ou.exactprocess(0,10) print ou.exactprocess(0,10, ddt=0.1,nrepl=10).mean(0) #the following looks good, approaches mu oues = ou.exactprocess(0,100, ddt=0.1,nrepl=100) if doplot: plt.figure() tmp = plt.plot(oues.T) tmp = plt.plot(oues.mean(0), linewidth=2) plt.title('Ornstein-Uhlenbeck') # SchwartsOne # ^^^^^^^^^^^ so = SchwartzOne(xzero=0, mu=1, kappa=0.5, sigma=0.1) sos = so.exactprocess(0,50, ddt=0.1,nrepl=100) print sos.mean(0) print np.log(sos.mean(0)) doplot = 1 if doplot: plt.figure() tmp = plt.plot(sos.T) tmp = plt.plot(sos.mean(0), linewidth=2) plt.title('Schwartz One') print so.fitls(sos[0,:],dt=0.1) sos2 = so.exactprocess(0,500, ddt=0.1,nrepl=5) print 'true: mu=1, kappa=0.5, sigma=0.1' for i in range(5): print so.fitls(sos2[i],dt=0.1) # Brownian Bridge # ^^^^^^^^^^^^^^^ bb = BrownianBridge() #bbs = bb.sample(x0, x1, nobs, nrepl=1, ddt=1., sigma=1.) bbs, t, wm = bb.simulate(0, 0.5, 99, nrepl=500, ddt=1., sigma=0.1) if doplot: plt.figure() tmp = plt.plot(bbs.T) tmp = plt.plot(bbs.mean(0), linewidth=2) plt.title('Brownian Bridge') plt.figure() plt.plot(wm,'r', label='theoretical') plt.plot(bbs.std(0), label='simulated') plt.title('Brownian Bridge - Variance') plt.legend() # Compound Poisson # ^^^^^^^^^^^^^^^^ cp = CompoundPoisson([1,1], [np.random.normal,np.random.normal]) cps = cp.simulate(nobs=20000,nrepl=3) print cps[0].sum(-1).sum(-1) print cps[0].sum() print cps[0].mean(-1).mean(-1) print cps[0].mean() print cps[1].size print cps[1].sum() #Note Y = sum^{N} X is compound poisson of iid x, then #E(Y) = E(N)*E(X) eg. eq. (6.37) page 385 in http://ee.stanford.edu/~gray/sp.html #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/diffusion2.py000066400000000000000000000320661224417117700253720ustar00rootroot00000000000000""" Diffusion 2: jump diffusion, stochastic volatility, stochastic time Created on Tue Dec 08 15:03:49 2009 Author: josef-pktd following Meucci License: BSD contains: CIRSubordinatedBrownian Heston IG JumpDiffusionKou JumpDiffusionMerton NIG VG References ---------- Attilio Meucci, Review of Discrete and Continuous Processes in Finance: Theory and Applications Bloomberg Portfolio Research Paper No. 2009-02-CLASSROOM July 1, 2009 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1373102 this is currently mostly a translation from matlab of http://www.mathworks.com/matlabcentral/fileexchange/23554-review-of-discrete-and-continuous-processes-in-finance license BSD: Copyright (c) 2008, Attilio Meucci All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. TODO: * vectorize where possible * which processes are exactly simulated by finite differences ? * include or exclude (now) the initial observation ? * convert to and merge with diffusion.py (part 1 of diffusions) * which processes can be easily estimated ? loglike or characteristic function ? * tests ? check for possible index errors (random indices), graphs look ok * adjust notation, variable names, more consistent, more pythonic * delete a few unused lines, cleanup * docstrings random bug (showed up only once, need fuzz-testing to replicate) File "...\diffusion2.py", line 375, in x = jd.simulate(mu,sigma,lambd,a,D,ts,nrepl) File "...\diffusion2.py", line 129, in simulate jumps_ts[n] = CumS[Events] IndexError: index out of bounds CumS is empty array, Events == -1 """ import numpy as np #from scipy import stats # currently only uses np.random import matplotlib.pyplot as plt class JumpDiffusionMerton(object): ''' Example ------- mu=.00 # deterministic drift sig=.20 # Gaussian component l=3.45 # Poisson process arrival rate a=0 # drift of log-jump D=.2 # st.dev of log-jump X = JumpDiffusionMerton().simulate(mu,sig,lambd,a,D,ts,nrepl) plt.figure() plt.plot(X.T) plt.title('Merton jump-diffusion') ''' def __init__(self): pass def simulate(self, m,s,lambd,a,D,ts,nrepl): T = ts[-1] # time points # simulate number of jumps n_jumps = np.random.poisson(lambd*T, size=(nrepl, 1)) jumps=[] nobs=len(ts) jumps=np.zeros((nrepl,nobs)) for j in range(nrepl): # simulate jump arrival time t = T*np.random.rand(n_jumps[j])#,1) #uniform t = np.sort(t,0) # simulate jump size S = a + D*np.random.randn(n_jumps[j],1) # put things together CumS = np.cumsum(S) jumps_ts = np.zeros(nobs) for n in range(nobs): Events = np.sum(t<=ts[n])-1 #print n, Events, CumS.shape, jumps_ts.shape jumps_ts[n]=0 if Events > 0: jumps_ts[n] = CumS[Events] #TODO: out of bounds see top #jumps = np.column_stack((jumps, jumps_ts)) #maybe wrong transl jumps[j,:] = jumps_ts D_Diff = np.zeros((nrepl,nobs)) for k in range(nobs): Dt=ts[k] if k>1: Dt=ts[k]-ts[k-1] D_Diff[:,k]=m*Dt + s*np.sqrt(Dt)*np.random.randn(nrepl) x = np.hstack((np.zeros((nrepl,1)),np.cumsum(D_Diff,1)+jumps)) return x class JumpDiffusionKou(object): def __init__(self): pass def simulate(self, m,s,lambd,p,e1,e2,ts,nrepl): T=ts[-1] # simulate number of jumps N = np.random.poisson(lambd*T,size =(nrepl,1)) jumps=[] nobs=len(ts) jumps=np.zeros((nrepl,nobs)) for j in range(nrepl): # simulate jump arrival time t=T*np.random.rand(N[j]) t=np.sort(t) # simulate jump size ww = np.random.binomial(1, p, size=(N[j])) S = ww * np.random.exponential(e1, size=(N[j])) - \ (1-ww) * np.random.exponential(e2, N[j]) # put things together CumS = np.cumsum(S) jumps_ts = np.zeros(nobs) for n in range(nobs): Events = sum(t<=ts[n])-1 jumps_ts[n]=0 if Events: jumps_ts[n]=CumS[Events] jumps[j,:] = jumps_ts D_Diff = np.zeros((nrepl,nobs)) for k in range(nobs): Dt=ts[k] if k>1: Dt=ts[k]-ts[k-1] D_Diff[:,k]=m*Dt + s*np.sqrt(Dt)*np.random.normal(size=nrepl) x = np.hstack((np.zeros((nrepl,1)),np.cumsum(D_Diff,1)+jumps)) return x class VG(object): '''variance gamma process ''' def __init__(self): pass def simulate(self, m,s,kappa,ts,nrepl): T=len(ts) dXs = np.zeros((nrepl,T)) for t in range(T): dt=ts[1]-0 if t>1: dt = ts[t]-ts[t-1] #print dt/kappa #TODO: check parameterization of gamrnd, checked looks same as np d_tau = kappa * np.random.gamma(dt/kappa,1.,size=(nrepl)) #print s*np.sqrt(d_tau) # this raises exception: #dX = stats.norm.rvs(m*d_tau,(s*np.sqrt(d_tau))) # np.random.normal requires scale >0 dX = np.random.normal(loc=m*d_tau, scale=1e-6+s*np.sqrt(d_tau)) dXs[:,t] = dX x = np.cumsum(dXs,1) return x class IG(object): '''inverse-Gaussian ??? used by NIG ''' def __init__(self): pass def simulate(self, l,m,nrepl): N = np.random.randn(nrepl,1) Y = N**2 X = m + (.5*m*m/l)*Y - (.5*m/l)*np.sqrt(4*m*l*Y+m*m*(Y**2)) U = np.random.rand(nrepl,1) ind = U>m/(X+m) X[ind] = m*m/X[ind] return X.ravel() class NIG(object): '''normal-inverse-Gaussian ''' def __init__(self): pass def simulate(self, th,k,s,ts,nrepl): T = len(ts) DXs = np.zeros((nrepl,T)) for t in range(T): Dt=ts[1]-0 if t>1: Dt=ts[t]-ts[t-1] l = 1/k*(Dt**2) m = Dt DS = IG().simulate(l,m,nrepl) N = np.random.randn(nrepl) DX = s*N*np.sqrt(DS) + th*DS #print DS.shape, DX.shape, DXs.shape DXs[:,t] = DX x = np.cumsum(DXs,1) return x class Heston(object): '''Heston Stochastic Volatility ''' def __init__(self): pass def simulate(self, m, kappa, eta,lambd,r, ts, nrepl,tratio=1.): T = ts[-1] nobs = len(ts) dt = np.zeros(nobs) #/tratio dt[0] = ts[0]-0 dt[1:] = np.diff(ts) DXs = np.zeros((nrepl,nobs)) dB_1 = np.sqrt(dt) * np.random.randn(nrepl,nobs) dB_2u = np.sqrt(dt) * np.random.randn(nrepl,nobs) dB_2 = r*dB_1 + np.sqrt(1-r**2)*dB_2u vt = eta*np.ones(nrepl) v=[] dXs = np.zeros((nrepl,nobs)) vts = np.zeros((nrepl,nobs)) for t in range(nobs): dv = kappa*(eta-vt)*dt[t]+ lambd*np.sqrt(vt)*dB_2[:,t] dX = m*dt[t] + np.sqrt(vt*dt[t]) * dB_1[:,t] vt = vt + dv vts[:,t] = vt dXs[:,t] = dX x = np.cumsum(dXs,1) return x, vts class CIRSubordinatedBrownian(object): '''CIR subordinated Brownian Motion ''' def __init__(self): pass def simulate(self, m, kappa, T_dot,lambd,sigma, ts, nrepl): T = ts[-1] nobs = len(ts) dtarr = np.zeros(nobs) #/tratio dtarr[0] = ts[0]-0 dtarr[1:] = np.diff(ts) DXs = np.zeros((nrepl,nobs)) dB = np.sqrt(dtarr) * np.random.randn(nrepl,nobs) yt = 1. dXs = np.zeros((nrepl,nobs)) dtaus = np.zeros((nrepl,nobs)) y = np.zeros((nrepl,nobs)) for t in range(nobs): dt = dtarr[t] dy = kappa*(T_dot-yt)*dt + lambd*np.sqrt(yt)*dB[:,t] yt = np.maximum(yt+dy,1e-10) # keep away from zero ? dtau = np.maximum(yt*dt, 1e-6) dX = np.random.normal(loc=m*dtau, scale=sigma*np.sqrt(dtau)) y[:,t] = yt dtaus[:,t] = dtau dXs[:,t] = dX tau = np.cumsum(dtaus,1) x = np.cumsum(dXs,1) return x, tau, y def schout2contank(a,b,d): th = d*b/np.sqrt(a**2-b**2) k = 1/(d*np.sqrt(a**2-b**2)) s = np.sqrt(d/np.sqrt(a**2-b**2)) return th,k,s if __name__ == '__main__': #Merton Jump Diffusion #^^^^^^^^^^^^^^^^^^^^^ # grid of time values at which the process is evaluated #("0" will be added, too) nobs = 252.#1000 #252. ts = np.linspace(1./nobs, 1., nobs) nrepl=5 # number of simulations mu=.010 # deterministic drift sigma = .020 # Gaussian component lambd = 3.45 *10 # Poisson process arrival rate a=0 # drift of log-jump D=.2 # st.dev of log-jump jd = JumpDiffusionMerton() x = jd.simulate(mu,sigma,lambd,a,D,ts,nrepl) plt.figure() plt.plot(x.T) #Todo plt.title('Merton jump-diffusion') sigma = 0.2 lambd = 3.45 x = jd.simulate(mu,sigma,lambd,a,D,ts,nrepl) plt.figure() plt.plot(x.T) #Todo plt.title('Merton jump-diffusion') #Kou jump diffusion #^^^^^^^^^^^^^^^^^^ mu=.0 # deterministic drift lambd=4.25 # Poisson process arrival rate p=.5 # prob. of up-jump e1=.2 # parameter of up-jump e2=.3 # parameter of down-jump sig=.2 # Gaussian component x = JumpDiffusionKou().simulate(mu,sig,lambd,p,e1,e2,ts,nrepl) plt.figure() plt.plot(x.T) #Todo plt.title('double exponential (Kou jump diffusion)') #variance-gamma #^^^^^^^^^^^^^^ mu = .1 # deterministic drift in subordinated Brownian motion kappa = 1. #10. #1 # inverse for gamma shape parameter sig = 0.5 #.2 # s.dev in subordinated Brownian motion x = VG().simulate(mu,sig,kappa,ts,nrepl) plt.figure() plt.plot(x.T) #Todo plt.title('variance gamma') #normal-inverse-Gaussian #^^^^^^^^^^^^^^^^^^^^^^^ # (Schoutens notation) al = 2.1 be = 0 de = 1 # convert parameters to Cont-Tankov notation th,k,s = schout2contank(al,be,de) x = NIG().simulate(th,k,s,ts,nrepl) plt.figure() plt.plot(x.T) #Todo x-axis plt.title('normal-inverse-Gaussian') #Heston Stochastic Volatility #^^^^^^^^^^^^^^^^^^^^^^^^^^^^ m=.0 kappa = .6 # 2*Kappa*Eta>Lambda^2 eta = .3**2 lambd =.25 r = -.7 T = 20. nobs = 252.*T#1000 #252. tsh = np.linspace(T/nobs, T, nobs) x, vts = Heston().simulate(m,kappa, eta,lambd,r, tsh, nrepl, tratio=20.) plt.figure() plt.plot(x.T) plt.title('Heston Stochastic Volatility') plt.figure() plt.plot(np.sqrt(vts).T) plt.title('Heston Stochastic Volatility - CIR Vol.') plt.figure() plt.subplot(2,1,1) plt.plot(x[0]) plt.title('Heston Stochastic Volatility process') plt.subplot(2,1,2) plt.plot(np.sqrt(vts[0])) plt.title('CIR Volatility') #CIR subordinated Brownian #^^^^^^^^^^^^^^^^^^^^^^^^^ m=.1 sigma=.4 kappa=.6 # 2*Kappa*T_dot>Lambda^2 T_dot=1 lambd=1 #T=252*10 #dt=1/252 #nrepl=2 T = 10. nobs = 252.*T#1000 #252. tsh = np.linspace(T/nobs, T, nobs) x, tau, y = CIRSubordinatedBrownian().simulate(m, kappa, T_dot,lambd,sigma, tsh, nrepl) plt.figure() plt.plot(tsh, x.T) plt.title('CIRSubordinatedBrownian process') plt.figure() plt.plot(tsh, y.T) plt.title('CIRSubordinatedBrownian - CIR') plt.figure() plt.plot(tsh, tau.T) plt.title('CIRSubordinatedBrownian - stochastic time ') plt.figure() plt.subplot(2,1,1) plt.plot(tsh, x[0]) plt.title('CIRSubordinatedBrownian process') plt.subplot(2,1,2) plt.plot(tsh, y[0], label='CIR') plt.plot(tsh, tau[0], label='stoch. time') plt.legend(loc='upper left') plt.title('CIRSubordinatedBrownian') #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/example_arma.py000066400000000000000000000263271224417117700257600ustar00rootroot00000000000000'''trying to verify theoretical acf of arma explicit functions for autocovariance functions of ARIMA(1,1), MA(1), MA(2) plus 3 functions from nitime.utils ''' import numpy as np from numpy.testing import assert_array_almost_equal import matplotlib.mlab as mlab from statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response from statsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA #from movstat import acf, acovf #from statsmodels.sandbox.tsa import acf, acovf, pacf from statsmodels.tsa.stattools import acf, acovf, pacf ar = [1., -0.6] #ar = [1., 0.] ma = [1., 0.4] #ma = [1., 0.4, 0.6] #ma = [1., 0.] mod = ''#'ma2' x = arma_generate_sample(ar, ma, 5000) x_acf = acf(x)[:10] x_ir = arma_impulse_response(ar, ma) #print x_acf[:10] #print x_ir[:10] #irc2 = np.correlate(x_ir,x_ir,'full')[len(x_ir)-1:] #print irc2[:10] #print irc2[:10]/irc2[0] #print irc2[:10-1] / irc2[1:10] #print x_acf[:10-1] / x_acf[1:10] # detrend helper from matplotlib.mlab def detrend(x, key=None): if key is None or key=='constant': return detrend_mean(x) elif key=='linear': return detrend_linear(x) def demean(x, axis=0): "Return x minus its mean along the specified axis" x = np.asarray(x) if axis: ind = [slice(None)] * axis ind.append(np.newaxis) return x - x.mean(axis)[ind] return x - x.mean(axis) def detrend_mean(x): "Return x minus the mean(x)" return x - x.mean() def detrend_none(x): "Return x: no detrending" return x def detrend_linear(y): "Return y minus best fit line; 'linear' detrending " # This is faster than an algorithm based on linalg.lstsq. x = np.arange(len(y), dtype=np.float_) C = np.cov(x, y, bias=1) b = C[0,1]/C[0,0] a = y.mean() - b*x.mean() return y - (b*x + a) def acovf_explicit(ar, ma, nobs): '''add correlation of MA representation explicitely ''' ir = arma_impulse_response(ar, ma) acovfexpl = [np.dot(ir[:nobs-t], ir[t:nobs]) for t in range(10)] return acovfexpl def acovf_arma11(ar, ma): # ARMA(1,1) # Florens et al page 278 # wrong result ? # new calculation bigJudge p 311, now the same a = -ar[1] b = ma[1] #rho = [1.] #rho.append((1-a*b)*(a-b)/(1.+a**2-2*a*b)) rho = [(1.+b**2+2*a*b)/(1.-a**2)] rho.append((1+a*b)*(a+b)/(1.-a**2)) for _ in range(8): last = rho[-1] rho.append(a*last) return np.array(rho) # print acf11[:10] # print acf11[:10] /acf11[0] def acovf_ma2(ma): # MA(2) # from Greene p616 (with typo), Florens p280 b1 = -ma[1] b2 = -ma[2] rho = np.zeros(10) rho[0] = (1 + b1**2 + b2**2) rho[1] = (-b1 + b1*b2) rho[2] = -b2 return rho # rho2 = rho/rho[0] # print rho2 # print irc2[:10]/irc2[0] def acovf_ma1(ma): # MA(1) # from Greene p616 (with typo), Florens p280 b = -ma[1] rho = np.zeros(10) rho[0] = (1 + b**2) rho[1] = -b return rho # rho2 = rho/rho[0] # print rho2 # print irc2[:10]/irc2[0] ar1 = [1., -0.8] ar0 = [1., 0.] ma1 = [1., 0.4] ma2 = [1., 0.4, 0.6] ma0 = [1., 0.] comparefn = dict( [('ma1', acovf_ma1), ('ma2', acovf_ma2), ('arma11', acovf_arma11), ('ar1', acovf_arma11)]) cases = [('ma1', (ar0, ma1)), ('ma2', (ar0, ma2)), ('arma11', (ar1, ma1)), ('ar1', (ar1, ma0))] for c, args in cases: ar, ma = args print print c, ar, ma myacovf = arma_acovf(ar, ma, nobs=10) myacf = arma_acf(ar, ma, nobs=10) if c[:2]=='ma': othacovf = comparefn[c](ma) else: othacovf = comparefn[c](ar, ma) print myacovf[:5] print othacovf[:5] #something broke again, #for high persistence case eg ar=0.99, nobs of IR has to be large #made changes to arma_acovf assert_array_almost_equal(myacovf, othacovf,10) assert_array_almost_equal(myacf, othacovf/othacovf[0],10) #from nitime.utils def ar_generator(N=512, sigma=1.): # this generates a signal u(n) = a1*u(n-1) + a2*u(n-2) + ... + v(n) # where v(n) is a stationary stochastic process with zero mean # and variance = sigma # this sequence is shown to be estimated well by an order 8 AR system taps = np.array([2.7607, -3.8106, 2.6535, -0.9238]) v = np.random.normal(size=N, scale=sigma**0.5) u = np.zeros(N) P = len(taps) for l in xrange(P): u[l] = v[l] + np.dot(u[:l][::-1], taps[:l]) for l in xrange(P,N): u[l] = v[l] + np.dot(u[l-P:l][::-1], taps) return u, v, taps #JP: small differences to using np.correlate, because assumes mean(s)=0 # denominator is N, not N-k, biased estimator # misnomer: (biased) autocovariance not autocorrelation #from nitime.utils def autocorr(s, axis=-1): """Returns the autocorrelation of signal s at all lags. Adheres to the definition r(k) = E{s(n)s*(n-k)} where E{} is the expectation operator. """ N = s.shape[axis] S = np.fft.fft(s, n=2*N-1, axis=axis) sxx = np.fft.ifft(S*S.conjugate(), axis=axis).real[:N] return sxx/N #JP: with valid this returns a single value, if x and y have same length # e.g. norm_corr(x, x) # using std subtracts mean, but correlate doesn't, requires means are exactly 0 # biased, no n-k correction for laglength #from nitime.utils def norm_corr(x,y,mode = 'valid'): """Returns the correlation between two ndarrays, by calling np.correlate in 'same' mode and normalizing the result by the std of the arrays and by their lengths. This results in a correlation = 1 for an auto-correlation""" return ( np.correlate(x,y,mode) / (np.std(x)*np.std(y)*(x.shape[-1])) ) # from matplotlib axes.py # note: self is axis def pltacorr(self, x, **kwargs): """ call signature:: acorr(x, normed=True, detrend=detrend_none, usevlines=True, maxlags=10, **kwargs) Plot the autocorrelation of *x*. If *normed* = *True*, normalize the data by the autocorrelation at 0-th lag. *x* is detrended by the *detrend* callable (default no normalization). Data are plotted as ``plot(lags, c, **kwargs)`` Return value is a tuple (*lags*, *c*, *line*) where: - *lags* are a length 2*maxlags+1 lag vector - *c* is the 2*maxlags+1 auto correlation vector - *line* is a :class:`~matplotlib.lines.Line2D` instance returned by :meth:`plot` The default *linestyle* is None and the default *marker* is ``'o'``, though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with *mode* = 2. If *usevlines* is *True*, :meth:`~matplotlib.axes.Axes.vlines` rather than :meth:`~matplotlib.axes.Axes.plot` is used to draw vertical lines from the origin to the acorr. Otherwise, the plot style is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. *maxlags* is a positive integer detailing the number of lags to show. The default value of *None* will return all :math:`2 \mathrm{len}(x) - 1` lags. The return value is a tuple (*lags*, *c*, *linecol*, *b*) where - *linecol* is the :class:`~matplotlib.collections.LineCollection` - *b* is the *x*-axis. .. seealso:: :meth:`~matplotlib.axes.Axes.plot` or :meth:`~matplotlib.axes.Axes.vlines` For documentation on valid kwargs. **Example:** :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. **Example:** .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ return self.xcorr(x, x, **kwargs) def pltxcorr(self, x, y, normed=True, detrend=detrend_none, usevlines=True, maxlags=10, **kwargs): """ call signature:: def xcorr(self, x, y, normed=True, detrend=detrend_none, usevlines=True, maxlags=10, **kwargs): Plot the cross correlation between *x* and *y*. If *normed* = *True*, normalize the data by the cross correlation at 0-th lag. *x* and y are detrended by the *detrend* callable (default no normalization). *x* and *y* must be equal length. Data are plotted as ``plot(lags, c, **kwargs)`` Return value is a tuple (*lags*, *c*, *line*) where: - *lags* are a length ``2*maxlags+1`` lag vector - *c* is the ``2*maxlags+1`` auto correlation vector - *line* is a :class:`~matplotlib.lines.Line2D` instance returned by :func:`~matplotlib.pyplot.plot`. The default *linestyle* is *None* and the default *marker* is 'o', though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with *mode* = 2. If *usevlines* is *True*: :func:`~matplotlib.pyplot.vlines` rather than :func:`~matplotlib.pyplot.plot` is used to draw vertical lines from the origin to the xcorr. Otherwise the plotstyle is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. The return value is a tuple (*lags*, *c*, *linecol*, *b*) where *linecol* is the :class:`matplotlib.collections.LineCollection` instance and *b* is the *x*-axis. *maxlags* is a positive integer detailing the number of lags to show. The default value of *None* will return all ``(2*len(x)-1)`` lags. **Example:** :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. **Example:** .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ Nx = len(x) if Nx!=len(y): raise ValueError('x and y must be equal length') x = detrend(np.asarray(x)) y = detrend(np.asarray(y)) c = np.correlate(x, y, mode=2) if normed: c/= np.sqrt(np.dot(x,x) * np.dot(y,y)) if maxlags is None: maxlags = Nx - 1 if maxlags >= Nx or maxlags < 1: raise ValueError('maglags must be None or strictly ' 'positive < %d'%Nx) lags = np.arange(-maxlags,maxlags+1) c = c[Nx-1-maxlags:Nx+maxlags] if usevlines: a = self.vlines(lags, [0], c, **kwargs) b = self.axhline(**kwargs) kwargs.setdefault('marker', 'o') kwargs.setdefault('linestyle', 'None') d = self.plot(lags, c, **kwargs) else: kwargs.setdefault('marker', 'o') kwargs.setdefault('linestyle', 'None') a, = self.plot(lags, c, **kwargs) b = None return lags, c, a, b arrvs = ar_generator() ##arma = ARIMA() ##res = arma.fit(arrvs[0], 4, 0) arma = ARIMA(arrvs[0]) res = arma.fit((4,0, 0)) print res[0] acf1 = acf(arrvs[0]) acovf1b = acovf(arrvs[0], unbiased=False) acf2 = autocorr(arrvs[0]) acf2m = autocorr(arrvs[0]-arrvs[0].mean()) print acf1[:10] print acovf1b[:10] print acf2[:10] print acf2m[:10] x = arma_generate_sample([1.0, -0.8], [1.0], 500) print acf(x)[:20] import statsmodels.api as sm print sm.regression.yule_walker(x, 10) import matplotlib.pyplot as plt #ax = plt.axes() plt.plot(x) #plt.show() plt.figure() pltxcorr(plt,x,x) plt.figure() pltxcorr(plt,x,x, usevlines=False) plt.figure() #FIXME: plotacf was moved to graphics/tsaplots.py, and interface changed plotacf(plt, acf1[:20], np.arange(len(acf1[:20])), usevlines=True) plt.figure() ax = plt.subplot(211) plotacf(ax, acf1[:20], usevlines=True) ax = plt.subplot(212) plotacf(ax, acf1[:20], np.arange(len(acf1[:20])), usevlines=False) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/examples/000077500000000000000000000000001224417117700245575ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/examples/ex_mle_arma.py000066400000000000000000000106121224417117700274020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ TODO: broken because of changes to arguments and import paths fixing this needs a closer look Created on Thu Feb 11 23:41:53 2010 Author: josef-pktd copyright: Simplified BSD see license.txt """ import numpy as np from numpy.testing import assert_almost_equal import matplotlib.pyplot as plt import numdifftools as ndt import statsmodels.api as sm from statsmodels.sandbox import tsa from statsmodels.tsa.arma_mle import Arma # local import from statsmodels.tsa.arima_process import arma_generate_sample examples = ['arma'] if 'arma' in examples: print "\nExample 1" print '----------' ar = [1.0, -0.8] ma = [1.0, 0.5] y1 = arma_generate_sample(ar,ma,1000,0.1) y1 -= y1.mean() #no mean correction/constant in estimation so far arma1 = Arma(y1) arma1.nar = 1 arma1.nma = 1 arma1res = arma1.fit_mle(order=(1,1), method='fmin') print arma1res.params #Warning need new instance otherwise results carry over arma2 = Arma(y1) arma2.nar = 1 arma2.nma = 1 res2 = arma2.fit(method='bfgs') print res2.params print res2.model.hessian(res2.params) print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params) arest = tsa.arima.ARIMA(y1) resls = arest.fit((1,0,1)) print resls[0] print resls[1] print '\nparameter estimate - comparing methods' print '---------------------------------------' print 'parameter of DGP ar(1), ma(1), sigma_error' print [-0.8, 0.5, 0.1] print 'mle with fmin' print arma1res.params print 'mle with bfgs' print res2.params print 'cond. least squares uses optim.leastsq ?' errls = arest.error_estimate print resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0]) err = arma1.geterrors(res2.params) print 'cond least squares parameter cov' #print np.dot(err,err)/err.shape[0] * resls[1] #errls = arest.error_estimate print np.dot(errls,errls)/errls.shape[0] * resls[1] # print 'fmin hessian' # print arma1res.model.optimresults['Hopt'][:2,:2] print 'bfgs hessian' print res2.model.optimresults['Hopt'][:2,:2] print 'numdifftools inverse hessian' print -np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2] print '\nFitting Arma(1,1) to squared data' arma3 = Arma(y1**2) res3 = arma3.fit(method='bfgs') print res3.params print '\nFitting Arma(3,3) to data from DGP Arma(1,1)' arma4 = Arma(y1) arma4.nar = 3 arma4.nma = 3 #res4 = arma4.fit(method='bfgs') res4 = arma4.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5]) print res4.params print 'numdifftools inverse hessian' pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params)) #print pcov print 'standard error of parameter estimate from Hessian' pstd = np.sqrt(np.diag(pcov)) print pstd print 't-values' print res4.params/pstd print 'eigenvalues of pcov:' print np.linalg.eigh(pcov)[0] print 'sometimes they are negative' print "\nExample 2 - DGP is Arma(3,3)" print '-----------------------------' ar = [1.0, -0.6, -0.2, -0.1] ma = [1.0, 0.5, 0.1, 0.1] y2 = arest.generate_sample(ar,ma,1000,0.1) y2 -= y2.mean() #no mean correction/constant in estimation so far print '\nFitting Arma(3,3) to data from DGP Arma(3,3)' arma4 = Arma(y2) arma4.nar = 3 arma4.nma = 3 #res4 = arma4.fit(method='bfgs') print '\ntrue parameters' print 'ar', ar[1:] print 'ma', ma[1:] res4 = arma4.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5]) print res4.params print 'numdifftools inverse hessian' pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params)) #print pcov print 'standard error of parameter estimate from Hessian' pstd = np.sqrt(np.diag(pcov)) print pstd print 't-values' print res4.params/pstd print 'eigenvalues of pcov:' print np.linalg.eigh(pcov)[0] print 'sometimes they are negative' arma6 = Arma(y2) arma6.nar = 3 arma6.nma = 3 res6 = arma6.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5], method='bfgs') print '\nmle with bfgs' print res6.params print 'pstd with bfgs hessian' hopt = res6.model.optimresults['Hopt'] print np.sqrt(np.diag(hopt)) #fmin estimates for coefficients in ARMA(3,3) look good #but not inverse Hessian, sometimes negative values for variance statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/examples/ex_mle_garch.py000066400000000000000000000246011224417117700275510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Feb 12 01:01:50 2010 Author: josef-pktd latest result ------------- all are very close garch0 has different parameterization of constant ordering of parameters is different seed 2780185 h.shape (2000,) Optimization terminated successfully. Current function value: 2093.813397 Iterations: 387 Function evaluations: 676 ggres.params [-0.6146253 0.1914537 0.01039355 0.78802188] Optimization terminated successfully. Current function value: 2093.972953 Iterations: 201 Function evaluations: 372 ggres0.params [-0.61537527 0.19635128 4.00706058] Warning: Desired error not necessarily achieveddue to precision loss Current function value: 2093.972953 Iterations: 51 Function evaluations: 551 Gradient evaluations: 110 ggres0.params [-0.61537855 0.19635265 4.00694669] Optimization terminated successfully. Current function value: 2093.751420 Iterations: 103 Function evaluations: 187 [ 0.78671519 0.19692222 0.61457171] -2093.75141963 Final Estimate: LLH: 2093.750 norm LLH: 2.093750 omega alpha1 beta1 0.7867438 0.1970437 0.6145467 long run variance comparison ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ R >>> 0.7867438/(1- 0.1970437- 0.6145467) 4.1757097302897526 Garch (gjr) asymetric, longrun var ? >>> 1/(1-0.6146253 - 0.1914537 - 0.01039355) * 0.78802188 4.2937548579245242 >>> 1/(1-0.6146253 - 0.1914537 + 0.01039355) * 0.78802188 3.8569053452140345 Garch0 >>> (1-0.61537855 - 0.19635265) * 4.00694669 0.7543830449902722 >>> errgjr4.var() #for different random seed 4.0924199964716106 todo: add code and verify, check for longer lagpolys """ import numpy as np from numpy.testing import assert_almost_equal import matplotlib.pyplot as plt import numdifftools as ndt import statsmodels.api as sm from statsmodels.sandbox import tsa from statsmodels.sandbox.tsa.garch import * # local import nobs = 1000 examples = ['garch', 'rpyfit'] if 'garch' in examples: err,h = generate_kindofgarch(nobs, [1.0, -0.95], [1.0, 0.1], mu=0.5) plt.figure() plt.subplot(211) plt.plot(err) plt.subplot(212) plt.plot(h) #plt.show() seed = 3842774 #91234 #8837708 seed = np.random.randint(9999999) print 'seed', seed np.random.seed(seed) ar1 = -0.9 err,h = generate_garch(nobs, [1.0, ar1], [1.0, 0.50], mu=0.0,scale=0.1) # plt.figure() # plt.subplot(211) # plt.plot(err) # plt.subplot(212) # plt.plot(h) # plt.figure() # plt.subplot(211) # plt.plot(err[-400:]) # plt.subplot(212) # plt.plot(h[-400:]) #plt.show() garchplot(err, h) garchplot(err[-400:], h[-400:]) np.random.seed(seed) errgjr,hgjr, etax = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.5,0]], mu=0.0,scale=0.1) garchplot(errgjr[:nobs], hgjr[:nobs], 'GJR-GARCH(1,1) Simulation - symmetric') garchplot(errgjr[-400:nobs], hgjr[-400:nobs], 'GJR-GARCH(1,1) Simulation - symmetric') np.random.seed(seed) errgjr2,hgjr2, etax = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr2[:nobs], hgjr2[:nobs], 'GJR-GARCH(1,1) Simulation') garchplot(errgjr2[-400:nobs], hgjr2[-400:nobs], 'GJR-GARCH(1,1) Simulation') np.random.seed(seed) errgjr3,hgjr3, etax3 = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.1,0.9],[0.1,0.9],[0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr3[:nobs], hgjr3[:nobs], 'GJR-GARCH(1,3) Simulation') garchplot(errgjr3[-400:nobs], hgjr3[-400:nobs], 'GJR-GARCH(1,3) Simulation') np.random.seed(seed) errgjr4,hgjr4, etax4 = generate_gjrgarch(nobs, [1.0, ar1], [[1., 1,0],[0, 0.1,0.9],[0, 0.1,0.9],[0, 0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr4[:nobs], hgjr4[:nobs], 'GJR-GARCH(1,3) Simulation') garchplot(errgjr4[-400:nobs], hgjr4[-400:nobs], 'GJR-GARCH(1,3) Simulation') varinno = np.zeros(100) varinno[0] = 1. errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, -0.], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) garchplot(errgjr5[:20], hgjr5[:20], 'GJR-GARCH(1,3) Simulation') #garchplot(errgjr4[-400:nobs], hgjr4[-400:nobs], 'GJR-GARCH(1,3) Simulation') #plt.show() seed = np.random.randint(9999999) # 9188410 print 'seed', seed x = np.arange(20).reshape(10,2) x3 = np.column_stack((np.ones((x.shape[0],1)),x)) y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[0.0,0.0,0]]),x3) nobs = 1000 warmup = 1000 np.random.seed(seed) ar = [1.0, -0.7]#7, -0.16, -0.1] #ma = [[1., 1, 0],[0, 0.6,0.1],[0, 0.1,0.1],[0, 0.1,0.1]] ma = [[1., 0, 0],[0, 0.8,0.0]] #,[0, 0.9,0.0]] # errgjr4,hgjr4, etax4 = generate_gjrgarch(warmup+nobs, [1.0, -0.99], # [[1., 1, 0],[0, 0.6,0.1],[0, 0.1,0.1],[0, 0.1,0.1]], # mu=0.2, scale=0.25) errgjr4,hgjr4, etax4 = generate_gjrgarch(warmup+nobs, ar, ma, mu=0.4, scale=1.01) errgjr4,hgjr4, etax4 = errgjr4[warmup:], hgjr4[warmup:], etax4[warmup:] garchplot(errgjr4[:nobs], hgjr4[:nobs], 'GJR-GARCH(1,3) Simulation - DGP') ggmod = Garch(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod.nar = 1 ggmod.nma = 1 ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0]) ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000) print 'ggres.params', ggres.params garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated') ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 1 ggmod.nma = 1 start_params = np.array([-0.6, 0.2, 0.1]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000) print 'ggres0.params', ggres0.params ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 1 ggmod.nma = 1 start_params = np.array([-0.6, 0.2, 0.1]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000) print 'ggres0.params', ggres0.params g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2]) print g11res llf = loglike_GARCH11(g11res, errgjr4-errgjr4.mean()) print llf[0] if 'rpyfit' in examples: from rpy import r r.library('fGarch') f = r.formula('~garch(1, 1)') fit = r.garchFit(f, data = errgjr4-errgjr4.mean(), include_mean=False) if 'rpysim' in examples: from rpy import r f = r.formula('~garch(1, 1)') #fit = r.garchFit(f, data = errgjr4) x = r.garchSim( n = 500) print 'R acf', tsa.acf(np.power(x,2))[:15] arma3 = Arma(np.power(x,2)) arma3res = arma3.fit(start_params=[-0.2,0.1,0.5],maxiter=5000) print arma3res.params arma3b = Arma(np.power(x,2)) arma3bres = arma3b.fit(start_params=[-0.2,0.1,0.5],maxiter=5000, method='bfgs') print arma3bres.params xr = r.garchSim( n = 100) x = np.asarray(xr) ggmod = Garch(x-x.mean()) ggmod.nar = 1 ggmod.nma = 1 ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0]) ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000) print 'ggres.params', ggres.params g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, x-x.mean())[0], [0.6, 0.6, 0.2]) print g11res llf = loglike_GARCH11(g11res, x-x.mean()) print llf[0] garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated') fit = r.garchFit(f, data = x-x.mean(), include_mean=False, trace=False) print r.summary(fit) '''based on R default simulation model = list(omega = 1e-06, alpha = 0.1, beta = 0.8) nobs = 1000 (with nobs=500, gjrgarch doesn't do well >>> ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000) Optimization terminated successfully. Current function value: -448.861335 Iterations: 385 Function evaluations: 690 >>> print 'ggres.params', ggres.params ggres.params [ -7.75090330e-01 1.57714749e-01 -9.60223930e-02 8.76021411e-07] rearranged 8.76021411e-07 1.57714749e-01(-9.60223930e-02) 7.75090330e-01 >>> print g11res [ 2.97459808e-06 7.83128600e-01 2.41110860e-01] >>> llf = loglike_GARCH11(g11res, x-x.mean()) >>> print llf[0] 442.603541936 Log Likelihood: -448.9376 normalized: -4.489376 omega alpha1 beta1 1.01632e-06 1.02802e-01 7.57537e-01 ''' ''' the following is for errgjr4-errgjr4.mean() ggres.params [-0.54510407 0.22723132 0.06482633 0.82325803] Final Estimate: LLH: 2065.56 norm LLH: 2.06556 mu omega alpha1 beta1 0.07229732 0.83069480 0.26313883 0.53986167 ggres.params [-0.50779163 0.2236606 0.00700036 1.154832 Final Estimate: LLH: 2116.084 norm LLH: 2.116084 mu omega alpha1 beta1 -4.759227e-17 1.145404e+00 2.288348e-01 5.085949e-01 run3 DGP 0.4/?? 0.8 0.7 gjrgarch: ggres.params [-0.45196579 0.2569641 0.02201904 1.11942636] rearranged const/omega ma1/alpha1 ar1/beta1 1.11942636 0.2569641(+0.02201904) 0.45196579 g11: [ 1.10262688 0.26680468 0.45724957] -2055.73912687 R: Final Estimate: LLH: 2055.738 norm LLH: 2.055738 mu omega alpha1 beta1 -1.665226e-17 1.102396e+00 2.668712e-01 4.573224e-01 fit = r.garchFit(f, data = errgjr4-errgjr4.mean()) rpy.RPy_RException: Error in solve.default(fit$hessian) : Lapack routine dgesv: system is exactly singular run4 DGP: mu=0.4, scale=1.01 ma = [[1., 0, 0],[0, 0.8,0.0]], ar = [1.0, -0.7] maybe something wrong with simulation gjrgarch ggres.params [-0.50554663 0.24449867 -0.00521004 1.00796791] rearranged 1.00796791 0.24449867(-0.00521004) 0.50554663 garch11: [ 1.01258264 0.24149155 0.50479994] -2056.3877404 R include_constant=False Final Estimate: LLH: 2056.397 norm LLH: 2.056397 omega alpha1 beta1 1.0123560 0.2409589 0.5049154 ''' erro,ho, etaxo = generate_gjrgarch(20, ar, ma, mu=0.04, scale=0.01, varinnovation = np.ones(20)) if 'sp500' in examples: import tabular as tb import scikits.timeseries as ts a = tb.loadSV(r'C:\Josef\work-oth\gspc_table.csv') s = ts.time_series(a[0]['Close'][::-1], dates=ts.date_array(a[0]['Date'][::-1],freq="D")) sp500 = a[0]['Close'][::-1] sp500r = np.diff(np.log(sp500)) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/examples/example_var.py000066400000000000000000000023021224417117700274310ustar00rootroot00000000000000""" Look at some macro plots, then do some VARs and IRFs. """ import numpy as np import statsmodels.api as sm import scikits.timeseries as ts import scikits.timeseries.lib.plotlib as tplt from matplotlib import pyplot as plt data = sm.datasets.macrodata.load() data = data.data ### Create Timeseries Representations of a few vars dates = ts.date_array(start_date=ts.Date('Q', year=1959, quarter=1), end_date=ts.Date('Q', year=2009, quarter=3)) ts_data = data[['realgdp','realcons','cpi']].view(float).reshape(-1,3) ts_data = np.column_stack((ts_data, (1 - data['unemp']/100) * data['pop'])) ts_series = ts.time_series(ts_data, dates) fig = tplt.tsfigure() fsp = fig.add_tsplot(221) fsp.tsplot(ts_series[:,0],'-') fsp.set_title("Real GDP") fsp = fig.add_tsplot(222) fsp.tsplot(ts_series[:,1],'r-') fsp.set_title("Real Consumption") fsp = fig.add_tsplot(223) fsp.tsplot(ts_series[:,2],'g-') fsp.set_title("CPI") fsp = fig.add_tsplot(224) fsp.tsplot(ts_series[:,3],'y-') fsp.set_title("Employment") # Plot real GDP #plt.subplot(221) #plt.plot(data['realgdp']) #plt.title("Real GDP") # Plot employment #plt.subplot(222) # Plot cpi #plt.subplot(223) # Plot real consumption #plt.subplot(224) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/examples/try_ld_nitime.py000066400000000000000000000024401224417117700277730ustar00rootroot00000000000000'''Levinson Durbin recursion adjusted from nitime ''' import numpy as np from statsmodels.tsa.stattools import acovf def levinson_durbin_nitime(s, order=10, isacov=False): '''Levinson-Durbin recursion for autoregressive processes ''' #from nitime ## if sxx is not None and type(sxx) == np.ndarray: ## sxx_m = sxx[:order+1] ## else: ## sxx_m = ut.autocov(s)[:order+1] if isacov: sxx_m = s else: sxx_m = acovf(s)[:order+1] #not tested phi = np.zeros((order+1, order+1), 'd') sig = np.zeros(order+1) # initial points for the recursion phi[1,1] = sxx_m[1]/sxx_m[0] sig[1] = sxx_m[0] - phi[1,1]*sxx_m[1] for k in xrange(2,order+1): phi[k,k] = (sxx_m[k]-np.dot(phi[1:k,k-1], sxx_m[1:k][::-1]))/sig[k-1] for j in xrange(1,k): phi[j,k] = phi[j,k-1] - phi[k,k]*phi[k-j,k-1] sig[k] = sig[k-1]*(1 - phi[k,k]**2) sigma_v = sig[-1]; arcoefs = phi[1:,-1] return sigma_v, arcoefs, pacf, phi #return everything import nitime.utils as ut sxx=None order = 10 npts = 2048*10 sigma = 1 drop_transients = 1024 coefs = np.array([0.9, -0.5]) # Generate AR(2) time series X, v, _ = ut.ar_generator(npts, sigma, coefs, drop_transients) s = X import statsmodels.api as sm sm.tsa.stattools.pacf(X) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/fftarma.py000066400000000000000000000403671224417117700247450ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Dec 14 19:53:25 2009 Author: josef-pktd generate arma sample using fft with all the lfilter it looks slow to get the ma representation first apply arma filter (in ar representation) to time series to get white noise but seems slow to be useful for fast estimation for nobs=10000 change/check: instead of using marep, use fft-transform of ar and ma separately, use ratio check theory is correct and example works DONE : feels much faster than lfilter -> use for estimation of ARMA -> use pade (scipy.misc) approximation to get starting polynomial from autocorrelation (is autocorrelation of AR(p) related to marep?) check if pade is fast, not for larger arrays ? maybe pade doesn't do the right thing for this, not tried yet scipy.pade([ 1. , 0.6, 0.25, 0.125, 0.0625, 0.1],2) raises LinAlgError: singular matrix also doesn't have roots inside unit circle ?? -> even without initialization, it might be fast for estimation -> how do I enforce stationarity and invertibility, need helper function get function drop imag if close to zero from numpy/scipy source, where? """ import numpy as np import numpy.fft as fft #import scipy.fftpack as fft from scipy import signal #from try_var_convolve import maxabs from statsmodels.sandbox.archive.linalg_decomp_1 import OneTimeProperty from statsmodels.tsa.arima_process import ArmaProcess #trying to convert old experiments to a class class ArmaFft(ArmaProcess): '''fft tools for arma processes This class contains several methods that are providing the same or similar returns to try out and test different implementations. Notes ----- TODO: check whether we don't want to fix maxlags, and create new instance if maxlag changes. usage for different lengths of timeseries ? or fix frequency and length for fft check default frequencies w, terminology norw n_or_w some ffts are currently done without padding with zeros returns for spectral density methods needs checking, is it always the power spectrum hw*hw.conj() normalization of the power spectrum, spectral density: not checked yet, for example no variance of underlying process is used ''' def __init__(self, ar, ma, n): #duplicates now that are subclassing ArmaProcess super(ArmaFft, self).__init__(ar, ma) self.ar = np.asarray(ar) self.ma = np.asarray(ma) self.nobs = n #could make the polynomials into cached attributes self.arpoly = np.polynomial.Polynomial(ar) self.mapoly = np.polynomial.Polynomial(ma) self.nar = len(ar) #1d only currently self.nma = len(ma) if self.nar > 1: self.arroots = self.arpoly.roots() else: self.arroots = np.array([]) if self.nma > 1: self.maroots = self.mapoly.roots() else: self.maroots = np.array([]) def padarr(self, arr, maxlag, atend=True): '''pad 1d array with zeros at end to have length maxlag function that is a method, no self used Parameters ---------- arr : array_like, 1d array that will be padded with zeros maxlag : int length of array after padding atend : boolean If True (default), then the zeros are added to the end, otherwise to the front of the array Returns ------- arrp : ndarray zero-padded array Notes ----- This is mainly written to extend coefficient arrays for the lag-polynomials. It returns a copy. ''' if atend: return np.r_[arr, np.zeros(maxlag-len(arr))] else: return np.r_[np.zeros(maxlag-len(arr)), arr] def pad(self, maxlag): '''construct AR and MA polynomials that are zero-padded to a common length Parameters ---------- maxlag : int new length of lag-polynomials Returns ------- ar : ndarray extended AR polynomial coefficients ma : ndarray extended AR polynomial coefficients ''' arpad = np.r_[self.ar, np.zeros(maxlag-self.nar)] mapad = np.r_[self.ma, np.zeros(maxlag-self.nma)] return arpad, mapad def fftar(self, n=None): '''Fourier transform of AR polynomial, zero-padded at end to n Parameters ---------- n : int length of array after zero-padding Returns ------- fftar : ndarray fft of zero-padded ar polynomial ''' if n is None: n = len(self.ar) return fft.fft(self.padarr(self.ar, n)) def fftma(self, n): '''Fourier transform of MA polynomial, zero-padded at end to n Parameters ---------- n : int length of array after zero-padding Returns ------- fftar : ndarray fft of zero-padded ar polynomial ''' if n is None: n = len(self.ar) return fft.fft(self.padarr(self.ma, n)) #@OneTimeProperty # not while still debugging things def fftarma(self, n=None): '''Fourier transform of ARMA polynomial, zero-padded at end to n The Fourier transform of the ARMA process is calculated as the ratio of the fft of the MA polynomial divided by the fft of the AR polynomial. Parameters ---------- n : int length of array after zero-padding Returns ------- fftarma : ndarray fft of zero-padded arma polynomial ''' if n is None: n = self.nobs return (self.fftma(n) / self.fftar(n)) def spd(self, npos): '''raw spectral density, returns Fourier transform n is number of points in positive spectrum, the actual number of points is twice as large. different from other spd methods with fft ''' n = npos w = fft.fftfreq(2*n) * 2 * np.pi hw = self.fftarma(2*n) #not sure, need to check normalization #return (hw*hw.conj()).real[n//2-1:] * 0.5 / np.pi #doesn't show in plot return (hw*hw.conj()).real * 0.5 / np.pi, w def spdshift(self, n): '''power spectral density using fftshift currently returns two-sided according to fft frequencies, use first half ''' #size = s1+s2-1 mapadded = self.padarr(self.ma, n) arpadded = self.padarr(self.ar, n) hw = fft.fft(fft.fftshift(mapadded)) / fft.fft(fft.fftshift(arpadded)) #return np.abs(spd)[n//2-1:] w = fft.fftfreq(n) * 2 * np.pi wslice = slice(n//2-1, None, None) #return (hw*hw.conj()).real[wslice], w[wslice] return (hw*hw.conj()).real, w def spddirect(self, n): '''power spectral density using padding to length n done by fft currently returns two-sided according to fft frequencies, use first half ''' #size = s1+s2-1 #abs looks wrong hw = fft.fft(self.ma, n) / fft.fft(self.ar, n) w = fft.fftfreq(n) * 2 * np.pi wslice = slice(None, n//2, None) #return (np.abs(hw)**2)[wslice], w[wslice] return (np.abs(hw)**2) * 0.5/np.pi, w def _spddirect2(self, n): '''this looks bad, maybe with an fftshift ''' #size = s1+s2-1 hw = (fft.fft(np.r_[self.ma[::-1],self.ma], n) / fft.fft(np.r_[self.ar[::-1],self.ar], n)) return (hw*hw.conj()) #.real[n//2-1:] def spdroots(self, w): '''spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies) ''' return self.spdroots_(self.arroots, self.maroots, w) def spdroots_(self, arroots, maroots, w): '''spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies) Parameters ---------- arroots : ndarray roots of ar (denominator) lag-polynomial maroots : ndarray roots of ma (numerator) lag-polynomial w : array_like frequencies for which spd is calculated Notes ----- this should go into a function ''' w = np.atleast_2d(w).T cosw = np.cos(w) #Greene 5th edt. p626, section 20.2.7.a. maroots = 1./maroots arroots = 1./arroots num = 1 + maroots**2 - 2* maroots * cosw den = 1 + arroots**2 - 2* arroots * cosw #print 'num.shape, den.shape', num.shape, den.shape hw = 0.5 / np.pi * num.prod(-1) / den.prod(-1) #or use expsumlog return np.squeeze(hw), w.squeeze() def spdpoly(self, w, nma=50): '''spectral density from MA polynomial representation for ARMA process References ---------- Cochrane, section 8.3.3 ''' mpoly = np.polynomial.Polynomial(self.arma2ma(nma)) hw = mpoly(np.exp(1j * w)) spd = np.real_if_close(hw * hw.conj() * 0.5/np.pi) return spd, w def filter(self, x): ''' filter a timeseries with the ARMA filter padding with zero is missing, in example I needed the padding to get initial conditions identical to direct filter Initial filtered observations differ from filter2 and signal.lfilter, but at end they are the same. See Also -------- tsa.filters.fftconvolve ''' n = x.shape[0] if n == self.fftarma: fftarma = self.fftarma else: fftarma = self.fftma(n) / self.fftar(n) tmpfft = fftarma * fft.fft(x) return fft.ifft(tmpfft) def filter2(self, x, pad=0): '''filter a time series using fftconvolve3 with ARMA filter padding of x currently works only if x is 1d in example it produces same observations at beginning as lfilter even without padding. TODO: this returns 1 additional observation at the end ''' from statsmodels.tsa.filters import fftconvolve3 if not pad: pass elif pad == 'auto': #just guessing how much padding x = self.padarr(x, x.shape[0] + 2*(self.nma+self.nar), atend=False) else: x = self.padarr(x, x.shape[0] + int(pad), atend=False) return fftconvolve3(x, self.ma, self.ar) def acf2spdfreq(self, acovf, nfreq=100, w=None): ''' not really a method just for comparison, not efficient for large n or long acf this is also similarly use in tsa.stattools.periodogram with window ''' if w is None: w = np.linspace(0, np.pi, nfreq)[:, None] nac = len(acovf) hw = 0.5 / np.pi * (acovf[0] + 2 * (acovf[1:] * np.cos(w*np.arange(1,nac))).sum(1)) return hw def invpowerspd(self, n): '''autocovariance from spectral density scaling is correct, but n needs to be large for numerical accuracy maybe padding with zero in fft would be faster without slicing it returns 2-sided autocovariance with fftshift >>> ArmaFft([1, -0.5], [1., 0.4], 40).invpowerspd(2**8)[:10] array([ 2.08 , 1.44 , 0.72 , 0.36 , 0.18 , 0.09 , 0.045 , 0.0225 , 0.01125 , 0.005625]) >>> ArmaFft([1, -0.5], [1., 0.4], 40).acovf(10) array([ 2.08 , 1.44 , 0.72 , 0.36 , 0.18 , 0.09 , 0.045 , 0.0225 , 0.01125 , 0.005625]) ''' hw = self.fftarma(n) return np.real_if_close(fft.ifft(hw*hw.conj()), tol=200)[:n] def spdmapoly(self, w, twosided=False): '''ma only, need division for ar, use LagPolynomial ''' if w is None: w = np.linspace(0, np.pi, nfreq) return 0.5 / np.pi * self.mapoly(np.exp(w*1j)) def plot4(self, fig=None, nobs=100, nacf=20, nfreq=100): rvs = self.generate_sample(size=100, burnin=500) acf = self.acf(nacf)[:nacf] #TODO: check return length pacf = self.pacf(nacf) w = np.linspace(0, np.pi, nfreq) spdr, wr = self.spdroots(w) if fig is None: import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(2,2,1) ax.plot(rvs) ax.set_title('Random Sample \nar=%s, ma=%s' % (self.ar, self.ma)) ax = fig.add_subplot(2,2,2) ax.plot(acf) ax.set_title('Autocorrelation \nar=%s, ma=%rs' % (self.ar, self.ma)) ax = fig.add_subplot(2,2,3) ax.plot(wr, spdr) ax.set_title('Power Spectrum \nar=%s, ma=%s' % (self.ar, self.ma)) ax = fig.add_subplot(2,2,4) ax.plot(pacf) ax.set_title('Partial Autocorrelation \nar=%s, ma=%s' % (self.ar, self.ma)) return fig def spdar1(ar, w): if np.ndim(ar) == 0: rho = ar else: rho = -ar[1] return 0.5 / np.pi /(1 + rho*rho - 2 * rho * np.cos(w)) if __name__ == '__main__': def maxabs(x,y): return np.max(np.abs(x-y)) nobs = 200 #10000 ar = [1, 0.0] ma = [1, 0.0] ar2 = np.zeros(nobs) ar2[:2] = [1, -0.9] uni = np.zeros(nobs) uni[0]=1. #arrep = signal.lfilter(ma, ar, ar2) #marep = signal.lfilter([1],arrep, uni) # same faster: arcomb = np.convolve(ar, ar2, mode='same') marep = signal.lfilter(ma,arcomb, uni) #[len(ma):] print marep[:10] mafr = fft.fft(marep) rvs = np.random.normal(size=nobs) datafr = fft.fft(rvs) y = fft.ifft(mafr*datafr) print np.corrcoef(np.c_[y[2:], y[1:-1], y[:-2]],rowvar=0) arrep = signal.lfilter([1],marep, uni) print arrep[:20] # roundtrip to ar arfr = fft.fft(arrep) yfr = fft.fft(y) x = fft.ifft(arfr*yfr).real #imag part is e-15 # the next two are equal, roundtrip works print x[:5] print rvs[:5] print np.corrcoef(np.c_[x[2:], x[1:-1], x[:-2]],rowvar=0) # ARMA filter using fft with ratio of fft of ma/ar lag polynomial # seems much faster than using lfilter #padding, note arcomb is already full length arcombp = np.zeros(nobs) arcombp[:len(arcomb)] = arcomb map_ = np.zeros(nobs) #rename: map was shadowing builtin map_[:len(ma)] = ma ar0fr = fft.fft(arcombp) ma0fr = fft.fft(map_) y2 = fft.ifft(ma0fr/ar0fr*datafr) #the next two are (almost) equal in real part, almost zero but different in imag print y2[:10] print y[:10] print maxabs(y, y2) # from chfdiscrete #1.1282071239631782e-014 ar = [1, -0.4] ma = [1, 0.2] arma1 = ArmaFft([1, -0.5,0,0,0,00, -0.7, 0.3], [1, 0.8], nobs) nfreq = nobs w = np.linspace(0, np.pi, nfreq) w2 = np.linspace(0, 2*np.pi, nfreq) import matplotlib.pyplot as plt plt.close('all') plt.figure() spd1, w1 = arma1.spd(2**10) print spd1.shape _ = plt.plot(spd1) plt.title('spd fft complex') plt.figure() spd2, w2 = arma1.spdshift(2**10) print spd2.shape _ = plt.plot(w2, spd2) plt.title('spd fft shift') plt.figure() spd3, w3 = arma1.spddirect(2**10) print spd3.shape _ = plt.plot(w3, spd3) plt.title('spd fft direct') plt.figure() spd3b = arma1._spddirect2(2**10) print spd3b.shape _ = plt.plot(spd3b) plt.title('spd fft direct mirrored') plt.figure() spdr, wr = arma1.spdroots(w) print spdr.shape plt.plot(w, spdr) plt.title('spd from roots') plt.figure() spdar1_ = spdar1(arma1.ar, w) print spdar1_.shape _ = plt.plot(w, spdar1_) plt.title('spd ar1') plt.figure() wper, spdper = arma1.periodogram(nfreq) print spdper.shape _ = plt.plot(w, spdper) plt.title('periodogram') startup = 1000 rvs = arma1.generate_sample(startup+10000)[startup:] import matplotlib.mlab as mlb plt.figure() sdm, wm = mlb.psd(x) print 'sdm.shape', sdm.shape sdm = sdm.ravel() plt.plot(wm, sdm) plt.title('matplotlib') from nitime.algorithms import LD_AR_est #yule_AR_est(s, order, Nfreqs) wnt, spdnt = LD_AR_est(rvs, 10, 512) plt.figure() print 'spdnt.shape', spdnt.shape _ = plt.plot(spdnt.ravel()) print spdnt[:10] plt.title('nitime') fig = plt.figure() arma1.plot4(fig) #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/garch.py000066400000000000000000001454621224417117700244130ustar00rootroot00000000000000'''general non-linear MLE for time series analysis idea for general version ------------------------ subclass defines geterrors(parameters) besides loglike,... and covariance matrix of parameter estimates (e.g. from hessian or outerproduct of jacobian) update: I don't really need geterrors directly, but get_h the conditional variance process new version Garch0 looks ok, time to clean up and test no constraints yet in some cases: "Warning: Maximum number of function evaluations has been exceeded." Notes ----- idea: cache intermediate design matrix for geterrors so it doesn't need to be build at each function call superclass or result class calculates result statistic based on errors, loglike, jacobian and cov/hessian -> aic, bic, ... -> test statistics, tvalue, fvalue, ... -> new to add: distribution (mean, cov) of non-linear transformation -> parameter restrictions or transformation with corrected covparams (?) -> sse, rss, rsquared ??? are they defined from this in general -> robust parameter cov ??? -> additional residual based tests, NW, ... likelihood ratio, lagrange multiplier tests ??? how much can be reused from linear model result classes where `errorsest = y - X*beta` ? for tsa: what's the division of labor between model, result instance and process examples: * arma: ls and mle look good * arimax: add exog, especially mean, trend, prefilter, e.g. (1-L) * arma_t: arma with t distributed errors (just a change in loglike) * garch: need loglike and (recursive) errorest * regime switching model without unobserved state, e.g. threshold roadmap for garch: * simple case * starting values: garch11 explicit formulas * arma-garch, assumed separable, blockdiagonal Hessian * empirical example: DJI, S&P500, MSFT, ??? * other standard garch: egarch, pgarch, * non-normal distributions * other methods: forecast, news impact curves (impulse response) * analytical gradient, Hessian for basic garch * cleaner simulation of garch * result statistics, AIC, ... * parameter constraints * try penalization for higher lags * other garch: regime-switching for pgarch (power garch) need transformation of etax given the parameters, but then misofilter should work general class aparch (see garch glossary) References ---------- see notes_references.txt Created on Feb 6, 2010 @author: "josef pktd" ''' import numpy as np from numpy.testing import assert_almost_equal #from scipy.stats import t, norm from scipy import optimize, signal from scipy.misc import derivative from scipy.stats import ss as sumofsq import matplotlib.pyplot as plt import numdifftools as ndt from statsmodels.base.model import Model, LikelihoodModelResults from statsmodels.sandbox import tsa def normloglike(x, mu=0, sigma2=1, returnlls=False, axis=0): x = np.asarray(x) x = np.atleast_1d(x) if axis is None: x = x.ravel() #T,K = x.shape if x.ndim > 1: nobs = x.shape[axis] else: nobs = len(x) x = x - mu # assume can be broadcasted if returnlls: #Compute the individual log likelihoods if needed lls = -0.5*(np.log(2*np.pi) + np.log(sigma2) + x**2/sigma2) # Use these to comput the LL LL = np.sum(lls,axis) return LL, lls else: #Compute the log likelihood #print np.sum(np.log(sigma2),axis) LL = -0.5 * (np.sum(np.log(sigma2),axis) + np.sum((x**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return LL # copied from model.py class LikelihoodModel(Model): """ Likelihood model is a subclass of Model. """ def __init__(self, endog, exog=None): super(LikelihoodModel, self).__init__(endog, exog) self.initialize() def initialize(self): """ Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed. """ pass #TODO: if the intent is to re-initialize the model with new data then # this method needs to take inputs... def loglike(self, params): """ Log-likelihood of model. """ raise NotImplementedError def score(self, params): """ Score vector of model. The gradient of logL with respect to each parameter. """ raise NotImplementedError def information(self, params): """ Fisher information matrix of model Returns -Hessian of loglike evaluated at params. """ raise NotImplementedError def hessian(self, params): """ The Hessian matrix of the model """ raise NotImplementedError def fit(self, start_params=None, method='newton', maxiter=35, tol=1e-08): """ Fit method for likelihood based models Parameters ---------- start_params : array-like, optional An optional method : str Method can be 'newton', 'bfgs', 'powell', 'cg', or 'ncg'. The default is newton. See scipy.optimze for more information. """ methods = ['newton', 'bfgs', 'powell', 'cg', 'ncg', 'fmin'] if start_params is None: start_params = [0]*self.exog.shape[1] # will fail for shape (K,) if not method in methods: raise ValueError("Unknown fit method %s" % method) f = lambda params: -self.loglike(params) score = lambda params: -self.score(params) # hess = lambda params: -self.hessian(params) hess = None #TODO: can we have a unified framework so that we can just do func = method # and write one call for each solver? if method.lower() == 'newton': iteration = 0 start = np.array(start_params) history = [np.inf, start] while (iteration < maxiter and np.all(np.abs(history[-1] - \ history[-2])>tol)): H = self.hessian(history[-1]) newparams = history[-1] - np.dot(np.linalg.inv(H), self.score(history[-1])) history.append(newparams) iteration += 1 mlefit = LikelihoodModelResults(self, newparams) mlefit.iteration = iteration elif method == 'bfgs': score=None xopt, fopt, gopt, Hopt, func_calls, grad_calls, warnflag = \ optimize.fmin_bfgs(f, start_params, score, full_output=1, maxiter=maxiter, gtol=tol) converge = not warnflag mlefit = LikelihoodModelResults(self, xopt) optres = 'xopt, fopt, gopt, Hopt, func_calls, grad_calls, warnflag' self.optimresults = dict(zip(optres.split(', '),[ xopt, fopt, gopt, Hopt, func_calls, grad_calls, warnflag])) elif method == 'ncg': xopt, fopt, fcalls, gcalls, hcalls, warnflag = \ optimize.fmin_ncg(f, start_params, score, fhess=hess, full_output=1, maxiter=maxiter, avextol=tol) mlefit = LikelihoodModelResults(self, xopt) converge = not warnflag elif method == 'fmin': #fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None) xopt, fopt, niter, funcalls, warnflag = \ optimize.fmin(f, start_params, full_output=1, maxiter=maxiter, xtol=tol) mlefit = LikelihoodModelResults(self, xopt) converge = not warnflag self._results = mlefit return mlefit #TODO: I take it this is only a stub and should be included in another # model class? class TSMLEModel(LikelihoodModel): """ univariate time series model for estimation with maximum likelihood Note: This is not working yet """ def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(TSMLEModel, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() def geterrors(self, params): raise NotImplementedError def loglike(self, params): """ Loglikelihood for timeseries model Notes ----- needs to be overwritten by subclass """ raise NotImplementedError def score(self, params): """ Score vector for Arma model """ #return None #print params jac = ndt.Jacobian(self.loglike, stepMax=1e-4) return jac(params)[-1] def hessian(self, params): """ Hessian of arma model. Currently uses numdifftools """ #return None Hfun = ndt.Jacobian(self.score, stepMax=1e-4) return Hfun(params)[-1] def fit(self, start_params=None, maxiter=5000, method='fmin', tol=1e-08): '''estimate model by minimizing negative loglikelihood does this need to be overwritten ? ''' if start_params is None and hasattr(self, '_start_params'): start_params = self._start_params #start_params = np.concatenate((0.05*np.ones(self.nar + self.nma), [1])) mlefit = super(TSMLEModel, self).fit(start_params=start_params, maxiter=maxiter, method=method, tol=tol) return mlefit class Garch0(TSMLEModel): '''Garch model, still experimentation stage: simplified structure, plain garch, no constraints still looking for the design of the base class serious bug: ar estimate looks ok, ma estimate awful -> check parameterization of lagpolys and constant looks ok after adding missing constant but still difference to garch11 function corrected initial condition -> only small differences left between the 3 versions ar estimate is close to true/DGP model note constant has different parameterization but design looks better ''' def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(Garch0, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() # put this in fit (?) or in initialize instead self._etax = endog**2 self._icetax = np.atleast_1d(self._etax.mean()) def initialize(self): pass def geth(self, params): ''' Parameters ---------- params : tuple, (ar, ma) try to keep the params conversion in loglike copied from generate_gjrgarch needs to be extracted to separate function ''' #mu, ar, ma = params ar, ma, mu = params #etax = self.endog #this would be enough for basic garch version etax = self._etax + mu icetax = self._icetax #read ic-eta-x, initial condition #TODO: where does my go with lfilter ????????????? # shouldn't matter except for interpretation nobs = etax.shape[0] #check arguments of lfilter zi = signal.lfiltic(ma,ar, icetax) #h = signal.lfilter(ar, ma, etax, zi=zi) #np.atleast_1d(etax[:,1].mean())) #just guessing: b/c ValueError: BUG: filter coefficient a[0] == 0 not supported yet h = signal.lfilter(ma, ar, etax, zi=zi)[0] return h def loglike(self, params): """ Loglikelihood for timeseries model Notes ----- needs to be overwritten by subclass make more generic with using function _convertparams which could also include parameter transformation _convertparams_in, _convertparams_out allow for different distributions t, ged,... """ p, q = self.nar, self.nma ar = np.concatenate(([1], params[:p])) # check where constant goes #ma = np.zeros((q+1,3)) #ma[0,0] = params[-1] #lag coefficients for ma innovation ma = np.concatenate(([0], params[p:p+q])) mu = params[-1] params = (ar, ma, mu) #(ar, ma) h = self.geth(params) #temporary safe for debugging: self.params_converted = params self.h = h #for testing sigma2 = np.maximum(h, 1e-6) axis = 0 nobs = len(h) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 axis=0 #no choice of axis # same as with y = self.endog, ht = sigma2 # np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum() llike = -0.5 * (np.sum(np.log(sigma2),axis) + np.sum(((self.endog)**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return llike class GarchX(TSMLEModel): '''Garch model, still experimentation stage: another version, this time with exog and miso_filter still looking for the design of the base class not done yet, just a design idea * use misofilter as in garch (gjr) * but take etax = exog this can include constant, asymetric effect (gjr) and other explanatory variables (e.g. high-low spread) todo: renames eta -> varprocess etax -> varprocessx icetax -> varprocessic (is actually ic of eta/sigma^2) ''' def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(Garch0, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() # put this in fit (?) or in initialize instead #nobs defined in super - verify #self.nobs = nobs = endog.shape[0] #add nexog to super #self.nexog = nexog = exog.shape[1] self._etax = np.column_stack(np.ones((nobs,1)), endog**2, exog) self._icetax = np.atleast_1d(self._etax.mean()) def initialize(self): pass def convert_mod2params(ar, ma, mu): pass def geth(self, params): ''' Parameters ---------- params : tuple, (ar, ma) try to keep the params conversion in loglike copied from generate_gjrgarch needs to be extracted to separate function ''' #mu, ar, ma = params ar, ma, mu = params #etax = self.endog #this would be enough for basic garch version etax = self._etax + mu icetax = self._icetax #read ic-eta-x, initial condition #TODO: where does my go with lfilter ????????????? # shouldn't matter except for interpretation nobs = self.nobs ## #check arguments of lfilter ## zi = signal.lfiltic(ma,ar, icetax) ## #h = signal.lfilter(ar, ma, etax, zi=zi) #np.atleast_1d(etax[:,1].mean())) ## #just guessing: b/c ValueError: BUG: filter coefficient a[0] == 0 not supported yet ## h = signal.lfilter(ma, ar, etax, zi=zi)[0] ## h = miso_lfilter(ar, ma, etax, useic=self._icetax)[0] #print 'h.shape', h.shape hneg = h<0 if hneg.any(): #h[hneg] = 1e-6 h = np.abs(h) #todo: raise warning, maybe not during optimization calls return h def loglike(self, params): """ Loglikelihood for timeseries model Notes ----- needs to be overwritten by subclass make more generic with using function _convertparams which could also include parameter transformation _convertparams_in, _convertparams_out allow for different distributions t, ged,... """ p, q = self.nar, self.nma ar = np.concatenate(([1], params[:p])) # check where constant goes #ma = np.zeros((q+1,3)) #ma[0,0] = params[-1] #lag coefficients for ma innovation ma = np.concatenate(([0], params[p:p+q])) mu = params[-1] params = (ar, ma, mu) #(ar, ma) h = self.geth(params) #temporary safe for debugging: self.params_converted = params self.h = h #for testing sigma2 = np.maximum(h, 1e-6) axis = 0 nobs = len(h) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 axis=0 #no choice of axis # same as with y = self.endog, ht = sigma2 # np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum() llike = -0.5 * (np.sum(np.log(sigma2),axis) + np.sum(((self.endog)**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return llike class Garch(TSMLEModel): '''Garch model gjrgarch (t-garch) still experimentation stage, try with ''' def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(Garch, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() def initialize(self): pass def geterrors(self, params): ''' Parameters ---------- params : tuple, (mu, ar, ma) try to keep the params conversion in loglike copied from generate_gjrgarch needs to be extracted to separate function ''' #mu, ar, ma = params ar, ma = params eta = self.endog nobs = eta.shape[0] etax = np.empty((nobs,3)) etax[:,0] = 1 etax[:,1:] = (eta**2)[:,None] etax[eta>0,2] = 0 #print 'etax.shape', etax.shape h = miso_lfilter(ar, ma, etax, useic=np.atleast_1d(etax[:,1].mean()))[0] #print 'h.shape', h.shape hneg = h<0 if hneg.any(): #h[hneg] = 1e-6 h = np.abs(h) #print 'Warning negative variance found' #check timing, starting time for h and eta, do they match #err = np.sqrt(h[:len(eta)])*eta #np.random.standard_t(8, size=len(h)) # let it break if there is a len/shape mismatch err = np.sqrt(h)*eta return err, h, etax def loglike(self, params): """ Loglikelihood for timeseries model Notes ----- needs to be overwritten by subclass """ p, q = self.nar, self.nma ar = np.concatenate(([1], params[:p])) #ar = np.concatenate(([1], -np.abs(params[:p]))) #??? #better safe than fast and sorry # ma = np.zeros((q+1,3)) ma[0,0] = params[-1] #lag coefficients for ma innovation ma[:,1] = np.concatenate(([0], params[p:p+q])) #delta lag coefficients for negative ma innovation ma[:,2] = np.concatenate(([0], params[p+q:p+2*q])) mu = params[-1] params = (ar, ma) #(mu, ar, ma) errorsest, h, etax = self.geterrors(params) #temporary safe for debugging self.params_converted = params self.errorsest, self.h, self.etax = errorsest, h, etax #h = h[:-1] #correct this in geterrors #print 'shapes errorsest, h, etax', errorsest.shape, h.shape, etax.shape sigma2 = np.maximum(h, 1e-6) axis = 0 nobs = len(errorsest) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 axis=0 #not used # muy = errorsest.mean() # # llike is verified, see below # # same as with y = errorsest, ht = sigma2 # # np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum() # llike = -0.5 * (np.sum(np.log(sigma2),axis) # + np.sum(((errorsest)**2)/sigma2, axis) # + nobs*np.log(2*np.pi)) # return llike muy = errorsest.mean() # llike is verified, see below # same as with y = errorsest, ht = sigma2 # np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum() llike = -0.5 * (np.sum(np.log(sigma2),axis) + np.sum(((self.endog)**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return llike def gjrconvertparams(self, params, nar, nma): """ flat to matrix Notes ----- needs to be overwritten by subclass """ p, q = nar, nma ar = np.concatenate(([1], params[:p])) #ar = np.concatenate(([1], -np.abs(params[:p]))) #??? #better safe than fast and sorry # ma = np.zeros((q+1,3)) ma[0,0] = params[-1] #lag coefficients for ma innovation ma[:,1] = np.concatenate(([0], params[p:p+q])) #delta lag coefficients for negative ma innovation ma[:,2] = np.concatenate(([0], params[p+q:p+2*q])) mu = params[-1] params2 = (ar, ma) #(mu, ar, ma) return paramsclass #TODO: this should be generalized to ARMA? #can possibly also leverage TSME above # also note that this is NOT yet general # it was written for my homework, assumes constant is zero # and that process is AR(1) # examples at the end of run as main below class AR(LikelihoodModel): """ Notes ----- This is not general, only written for the AR(1) case. Fit methods that use super and broyden do not yet work. """ def __init__(self, endog, exog=None, nlags=1): if exog is None: # extend to handle ADL(p,q) model? or subclass? exog = endog[:-nlags] endog = endog[nlags:] super(AR, self).__init__(endog, exog) self.nobs += nlags # add lags back to nobs for real T #TODO: need to fix underscore in Model class. #Done? def initialize(self): pass def loglike(self, params): """ The unconditional loglikelihood of an AR(p) process Notes ----- Contains constant term. """ nobs = self.nobs y = self.endog ylag = self.exog penalty = self.penalty if isinstance(params,tuple): # broyden (all optimize.nonlin return a tuple until rewrite commit) params = np.asarray(params) usepenalty=False if not np.all(np.abs(params)<1) and penalty: oldparams = params params = np.array([.9999]) # make it the edge usepenalty=True diffsumsq = sumofsq(y-np.dot(ylag,params)) # concentrating the likelihood means that sigma2 is given by sigma2 = 1/nobs*(diffsumsq-ylag[0]**2*(1-params**2)) loglike = -nobs/2 * np.log(2*np.pi) - nobs/2*np.log(sigma2) + \ .5 * np.log(1-params**2) - .5*diffsumsq/sigma2 -\ ylag[0]**2 * (1-params**2)/(2*sigma2) if usepenalty: # subtract a quadratic penalty since we min the negative of loglike loglike -= 1000 *(oldparams-.9999)**2 return loglike def score(self, params): """ Notes ----- Need to generalize for AR(p) and for a constant. Not correct yet. Returns numerical gradient. Depends on package numdifftools. """ y = self.endog ylag = self.exog nobs = self.nobs diffsumsq = sumofsq(y-np.dot(ylag,params)) dsdr = 1/nobs * -2 *np.sum(ylag*(y-np.dot(ylag,params))[:,None])+\ 2*params*ylag[0]**2 sigma2 = 1/nobs*(diffsumsq-ylag[0]**2*(1-params**2)) gradient = -nobs/(2*sigma2)*dsdr + params/(1-params**2) + \ 1/sigma2*np.sum(ylag*(y-np.dot(ylag, params))[:,None])+\ .5*sigma2**-2*diffsumsq*dsdr+\ ylag[0]**2*params/sigma2 +\ ylag[0]**2*(1-params**2)/(2*sigma2**2)*dsdr if self.penalty: pass j = Jacobian(self.loglike) return j(params) # return gradient def information(self, params): """ Not Implemented Yet """ return def hessian(self, params): """ Returns numerical hessian for now. Depends on numdifftools. """ h = Hessian(self.loglike) return h(params) def fit(self, start_params=None, method='bfgs', maxiter=35, tol=1e-08, penalty=False): """ Fit the unconditional maximum likelihood of an AR(p) process. Parameters ---------- start_params : array-like, optional A first guess on the parameters. Defaults is a vector of zeros. method : str, optional Unconstrained solvers: Default is 'bfgs', 'newton' (newton-raphson), 'ncg' (Note that previous 3 are not recommended at the moment.) and 'powell' Constrained solvers: 'bfgs-b', 'tnc' See notes. maxiter : int, optional The maximum number of function evaluations. Default is 35. tol = float The convergence tolerance. Default is 1e-08. penalty : bool Whether or not to use a penalty function. Default is False, though this is ignored at the moment and the penalty is always used if appropriate. See notes. Notes ----- The unconstrained solvers use a quadratic penalty (regardless if penalty kwd is True or False) in order to ensure that the solution stays within (-1,1). The constrained solvers default to using a bound of (-.999,.999). """ self.penalty = penalty method = method.lower() #TODO: allow user-specified penalty function # if penalty and method not in ['bfgs_b','tnc','cobyla','slsqp']: # minfunc = lambda params : -self.loglike(params) - \ # self.penfunc(params) # else: minfunc = lambda params: -self.loglike(params) if method in ['newton', 'bfgs', 'ncg']: super(AR, self).fit(start_params=start_params, method=method, maxiter=maxiter, tol=tol) else: bounds = [(-.999,.999)] # assume stationarity if start_params == None: start_params = np.array([0]) #TODO: assumes AR(1) if method == 'bfgs-b': retval = optimize.fmin_l_bfgs_b(minfunc, start_params, approx_grad=True, bounds=bounds) self.params, self.llf = retval[0:2] if method == 'tnc': retval = optimize.fmin_tnc(minfunc, start_params, approx_grad=True, bounds = bounds) self.params = retval[0] if method == 'powell': retval = optimize.fmin_powell(minfunc,start_params) self.params = retval[None] #TODO: write regression tests for Pauli's branch so that # new line_search and optimize.nonlin can get put in. #http://projects.scipy.org/scipy/ticket/791 # if method == 'broyden': # retval = optimize.broyden2(minfunc, [.5], verbose=True) # self.results = retval class Arma(LikelihoodModel): """ univariate Autoregressive Moving Average model Note: This is not working yet, or does it this can subclass TSMLEModel """ def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(Arma, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() def initialize(self): pass def geterrors(self, params): #copied from sandbox.tsa.arima.ARIMA p, q = self.nar, self.nma rhoy = np.concatenate(([1], params[:p])) rhoe = np.concatenate(([1], params[p:p+q])) errorsest = signal.lfilter(rhoy, rhoe, self.endog) return errorsest def loglike(self, params): """ Loglikelihood for arma model Notes ----- The ancillary parameter is assumed to be the last element of the params vector """ # #copied from sandbox.tsa.arima.ARIMA # p = self.nar # rhoy = np.concatenate(([1], params[:p])) # rhoe = np.concatenate(([1], params[p:-1])) # errorsest = signal.lfilter(rhoy, rhoe, self.endog) errorsest = self.geterrors(params) sigma2 = np.maximum(params[-1]**2, 1e-6) axis = 0 nobs = len(errorsest) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 # llike = -0.5 * (np.sum(np.log(sigma2),axis) # + np.sum((errorsest**2)/sigma2, axis) # + nobs*np.log(2*np.pi)) llike = -0.5 * (nobs*np.log(sigma2) + np.sum((errorsest**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return llike def score(self, params): """ Score vector for Arma model """ #return None #print params jac = ndt.Jacobian(self.loglike, stepMax=1e-4) return jac(params)[-1] def hessian(self, params): """ Hessian of arma model. Currently uses numdifftools """ #return None Hfun = ndt.Jacobian(self.score, stepMax=1e-4) return Hfun(params)[-1] def fit(self, start_params=None, maxiter=5000, method='fmin', tol=1e-08): if start_params is None: start_params = np.concatenate((0.05*np.ones(self.nar + self.nma), [1])) mlefit = super(Arma, self).fit(start_params=start_params, maxiter=maxiter, method=method, tol=tol) return mlefit def generate_kindofgarch(nobs, ar, ma, mu=1.): '''simulate garch like process but not squared errors in arma used for initial trial but produces nice graph ''' #garm1, gmam1 = [0.4], [0.2] #pqmax = 1 # res = np.zeros(nobs+pqmax) # rvs = np.random.randn(nobs+pqmax,2) # for t in range(pqmax,nobs+pqmax): # res[i] = #ar = [1.0, -0.99] #ma = [1.0, 0.5] #this has the wrong distribution, should be eps**2 #TODO: use new version tsa.arima.??? instead, has distr option #arest = tsa.arima.ARIMA() #arest = tsa.arima.ARIMA #try class method, ARIMA needs data in constructor from statsmodels.tsa.arima_process import arma_generate_sample h = arma_generate_sample(ar,ma,nobs,0.1) #h = np.abs(h) h = (mu+h)**2 h = np.exp(h) err = np.sqrt(h)*np.random.randn(nobs) return err, h def generate_garch(nobs, ar, ma, mu=1., scale=0.1): '''simulate standard garch scale : float scale/standard deviation of innovation process in GARCH process ''' eta = scale*np.random.randn(nobs) # copied from armageneratesample h = signal.lfilter(ma, ar, eta**2) # #h = (mu+h)**2 #h = np.abs(h) #h = np.exp(h) #err = np.sqrt(h)*np.random.randn(nobs) err = np.sqrt(h)*eta #np.random.standard_t(8, size=nobs) return err, h def generate_gjrgarch(nobs, ar, ma, mu=1., scale=0.1, varinnovation=None): '''simulate gjr garch process Parameters ---------- ar : array_like, 1d autoregressive term for variance ma : array_like, 2d moving average term for variance, with coefficients for negative shocks in second column mu : float constant in variance law of motion scale : float scale/standard deviation of innovation process in GARCH process Returns ------- err : array 1d, (nobs+?,) simulated gjr-garch process, h : array 1d, (nobs+?,) simulated variance etax : array 1d, (nobs+?,) data matrix for constant and ma terms in variance equation Notes ----- References ---------- ''' if varinnovation is None: # rename ? eta = scale*np.random.randn(nobs) else: eta = varinnovation # copied from armageneratesample etax = np.empty((nobs,3)) etax[:,0] = mu etax[:,1:] = (eta**2)[:,None] etax[eta>0,2] = 0 h = miso_lfilter(ar, ma, etax)[0] # #h = (mu+h)**2 #h = np.abs(h) #h = np.exp(h) #err = np.sqrt(h)*np.random.randn(nobs) print 'h.shape', h.shape err = np.sqrt(h[:len(eta)])*eta #np.random.standard_t(8, size=len(h)) return err, h, etax def loglike_GARCH11(params, y): # Computes the likelihood vector of a GARCH11 # assumes y is centered w = params[0] # constant (1); alpha = params[1] # coefficient of lagged squared error beta = params[2] # coefficient of lagged variance y2 = y**2; nobs = y2.shape[0] ht = np.zeros(nobs); ht[0] = y2.mean() #sum(y2)/T; for i in range(1,nobs): ht[i] = w + alpha*y2[i-1] + beta * ht[i-1] sqrtht = np.sqrt(ht) x = y/sqrtht llvalues = -0.5*np.log(2*np.pi) - np.log(sqrtht) - 0.5*(x**2); return llvalues.sum(), llvalues, ht from statsmodels.tsa.filters import miso_lfilter #copied to statsmodels.tsa.filters.filtertools def miso_lfilter_old(ar, ma, x, useic=False): #[0.1,0.1]): ''' use nd convolution to merge inputs, then use lfilter to produce output arguments for column variables return currently 1d Parameters ---------- ar : array_like, 1d, float autoregressive lag polynomial including lag zero, ar(L)y_t ma : array_like, same ndim as x, currently 2d moving average lag polynomial ma(L)x_t x : array_like, 2d input data series, time in rows, variables in columns Returns ------- y : array, 1d filtered output series inp : array, 1d combined input series Notes ----- currently for 2d inputs only, no choice of axis Use of signal.lfilter requires that ar lag polynomial contains floating point numbers does not cut off invalid starting and final values miso_lfilter find array y such that:: ar(L)y_t = ma(L)x_t with shapes y (nobs,), x (nobs,nvars), ar (narlags,), ma (narlags,nvars) ''' ma = np.asarray(ma) ar = np.asarray(ar) #inp = signal.convolve(x, ma, mode='valid') #inp = signal.convolve(x, ma)[:, (x.shape[1]+1)//2] #Note: convolve mixes up the variable left-right flip #I only want the flip in time direction #this might also be a mistake or problem in other code where I #switched from correlate to convolve # correct convolve version, for use with fftconvolve in other cases inp2 = signal.convolve(x, ma[:,::-1])[:, (x.shape[1]+1)//2] inp = signal.correlate(x, ma[::-1,:])[:, (x.shape[1]+1)//2] assert_almost_equal(inp2, inp) nobs = x.shape[0] # cut of extra values at end #todo initialize also x for correlate if useic: return signal.lfilter([1], ar, inp, #zi=signal.lfilter_ic(np.array([1.,0.]),ar, ic))[0][:nobs], inp[:nobs] zi=signal.lfiltic(np.array([1.,0.]),ar, useic))[0][:nobs], inp[:nobs] else: return signal.lfilter([1], ar, inp)[:nobs], inp[:nobs] #return signal.lfilter([1], ar, inp), inp def test_misofilter(): x = np.arange(20).reshape(10,2) y, inp = miso_lfilter([1., -1],[[1,1],[0,0]], x) assert_almost_equal(y[:-1], x.sum(1).cumsum(), decimal=15) inp2 = signal.convolve(np.arange(20),np.ones(2))[1::2] assert_almost_equal(inp[:-1], inp2, decimal=15) inp2 = signal.convolve(np.arange(20),np.ones(4))[1::2] y, inp = miso_lfilter([1., -1],[[1,1],[1,1]], x) assert_almost_equal(y, inp2.cumsum(), decimal=15) assert_almost_equal(inp, inp2, decimal=15) y, inp = miso_lfilter([1., 0],[[1,1],[1,1]], x) assert_almost_equal(y, inp2, decimal=15) assert_almost_equal(inp, inp2, decimal=15) x3 = np.column_stack((np.ones((x.shape[0],1)),x)) y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[0.0,0.0,0]]),x3) y3 = (x3*np.array([-2,3,1])).sum(1) assert_almost_equal(y[:-1], y3, decimal=15) assert_almost_equal(y, inp, decimal=15) y4 = y3.copy() y4[1:] += x3[:-1,1] y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[0.0,1.0,0]]),x3) assert_almost_equal(y[:-1], y4, decimal=15) assert_almost_equal(y, inp, decimal=15) y4 = y3.copy() y4[1:] += x3[:-1,0] y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[1.0,0.0,0]]),x3) assert_almost_equal(y[:-1], y4, decimal=15) assert_almost_equal(y, inp, decimal=15) y, inp = miso_lfilter([1., -1],np.array([[-2.0,3,1],[1.0,0.0,0]]),x3) assert_almost_equal(y[:-1], y4.cumsum(), decimal=15) y4 = y3.copy() y4[1:] += x3[:-1,2] y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[0.0,0.0,1.0]]),x3) assert_almost_equal(y[:-1], y4, decimal=15) assert_almost_equal(y, inp, decimal=15) y, inp = miso_lfilter([1., -1],np.array([[-2.0,3,1],[0.0,0.0,1.0]]),x3) assert_almost_equal(y[:-1], y4.cumsum(), decimal=15) y, inp = miso_lfilter([1., 0],[[1,0],[1,0],[1,0]], x) yt = np.convolve(x[:,0], [1,1,1]) assert_almost_equal(y, yt, decimal=15) assert_almost_equal(inp, yt, decimal=15) y, inp = miso_lfilter([1., 0],[[0,1],[0,1],[0,1]], x) yt = np.convolve(x[:,1], [1,1,1]) assert_almost_equal(y, yt, decimal=15) assert_almost_equal(inp, yt, decimal=15) y, inp = miso_lfilter([1., 0],[[0,1],[0,1],[1,1]], x) yt = np.convolve(x[:,1], [1,1,1]) yt[2:] += x[:,0] assert_almost_equal(y, yt, decimal=15) assert_almost_equal(inp, yt, decimal=15) def test_gjrgarch(): # test impulse response of gjr simulator varinno = np.zeros(100) varinno[0] = 1. errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, 0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) ht = np.array([ 1., 0.1, 0.05, 0.01, 0., 0. ]) assert_almost_equal(hgjr5[:6], ht, decimal=15) errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, -1.0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) assert_almost_equal(hgjr5[:6], ht.cumsum(), decimal=15) errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, 1.0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) ht1 = [0] for h in ht: ht1.append(h-ht1[-1]) assert_almost_equal(hgjr5[:6], ht1[1:], decimal=15) # negative shock varinno = np.zeros(100) varinno[0] = -1. errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, 0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) ht = np.array([ 1. , 0.9 , 0.75, 0.61, 0. , 0. ]) assert_almost_equal(hgjr5[:6], ht, decimal=15) errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, -1.0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) assert_almost_equal(hgjr5[:6], ht.cumsum(), decimal=15) errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, 1.0], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) ht1 = [0] for h in ht: ht1.append(h-ht1[-1]) assert_almost_equal(hgjr5[:6], ht1[1:], decimal=15) ''' >>> print signal.correlate(x3, np.array([[-2.0,3,1],[0.0,0.0,0]])[::-1,:],mode='full')[:-1, (x3.shape[1]+1)//2] [ -1. 7. 15. 23. 31. 39. 47. 55. 63. 71.] >>> (x3*np.array([-2,3,1])).sum(1) array([ -1., 7., 15., 23., 31., 39., 47., 55., 63., 71.]) ''' def garchplot(err, h, title='Garch simulation'): plt.figure() plt.subplot(311) plt.plot(err) plt.title(title) plt.ylabel('y') plt.subplot(312) plt.plot(err**2) plt.ylabel('$y^2$') plt.subplot(313) plt.plot(h) plt.ylabel('conditional variance') if __name__ == '__main__': #test_misofilter() #test_gjrgarch() examples = ['garch'] if 'arma' in examples: arest = tsa.arima.ARIMA() print "\nExample 1" ar = [1.0, -0.8] ma = [1.0, 0.5] y1 = arest.generate_sample(ar,ma,1000,0.1) y1 -= y1.mean() #no mean correction/constant in estimation so far arma1 = Arma(y1) arma1.nar = 1 arma1.nma = 1 arma1res = arma1.fit(method='fmin') print arma1res.params #Warning need new instance otherwise results carry over arma2 = Arma(y1) res2 = arma2.fit(method='bfgs') print res2.params print res2.model.hessian(res2.params) print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params) resls = arest.fit(y1,1,1) print resls[0] print resls[1] print '\nparameter estimate' print 'parameter of DGP ar(1), ma(1), sigma_error' print [-0.8, 0.5, 0.1] print 'mle with fmin' print arma1res.params print 'mle with bfgs' print res2.params print 'cond. least squares uses optim.leastsq ?' errls = arest.error_estimate print resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0]) err = arma1.geterrors(res2.params) print 'cond least squares parameter cov' #print np.dot(err,err)/err.shape[0] * resls[1] #errls = arest.error_estimate print np.dot(errls,errls)/errls.shape[0] * resls[1] # print 'fmin hessian' # print arma1res.model.optimresults['Hopt'][:2,:2] print 'bfgs hessian' print res2.model.optimresults['Hopt'][:2,:2] print 'numdifftools inverse hessian' print -np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2] arma3 = Arma(y1**2) res3 = arma3.fit(method='bfgs') print res3.params nobs = 1000 if 'garch' in examples: err,h = generate_kindofgarch(nobs, [1.0, -0.95], [1.0, 0.1], mu=0.5) import matplotlib.pyplot as plt plt.figure() plt.subplot(211) plt.plot(err) plt.subplot(212) plt.plot(h) #plt.show() seed = 3842774 #91234 #8837708 seed = np.random.randint(9999999) print 'seed', seed np.random.seed(seed) ar1 = -0.9 err,h = generate_garch(nobs, [1.0, ar1], [1.0, 0.50], mu=0.0,scale=0.1) # plt.figure() # plt.subplot(211) # plt.plot(err) # plt.subplot(212) # plt.plot(h) # plt.figure() # plt.subplot(211) # plt.plot(err[-400:]) # plt.subplot(212) # plt.plot(h[-400:]) #plt.show() garchplot(err, h) garchplot(err[-400:], h[-400:]) np.random.seed(seed) errgjr,hgjr, etax = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.5,0]], mu=0.0,scale=0.1) garchplot(errgjr[:nobs], hgjr[:nobs], 'GJR-GARCH(1,1) Simulation - symmetric') garchplot(errgjr[-400:nobs], hgjr[-400:nobs], 'GJR-GARCH(1,1) Simulation - symmetric') np.random.seed(seed) errgjr2,hgjr2, etax = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr2[:nobs], hgjr2[:nobs], 'GJR-GARCH(1,1) Simulation') garchplot(errgjr2[-400:nobs], hgjr2[-400:nobs], 'GJR-GARCH(1,1) Simulation') np.random.seed(seed) errgjr3,hgjr3, etax3 = generate_gjrgarch(nobs, [1.0, ar1], [[1,0],[0.1,0.9],[0.1,0.9],[0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr3[:nobs], hgjr3[:nobs], 'GJR-GARCH(1,3) Simulation') garchplot(errgjr3[-400:nobs], hgjr3[-400:nobs], 'GJR-GARCH(1,3) Simulation') np.random.seed(seed) errgjr4,hgjr4, etax4 = generate_gjrgarch(nobs, [1.0, ar1], [[1., 1,0],[0, 0.1,0.9],[0, 0.1,0.9],[0, 0.1,0.9]], mu=0.0,scale=0.1) garchplot(errgjr4[:nobs], hgjr4[:nobs], 'GJR-GARCH(1,3) Simulation') garchplot(errgjr4[-400:nobs], hgjr4[-400:nobs], 'GJR-GARCH(1,3) Simulation') varinno = np.zeros(100) varinno[0] = 1. errgjr5,hgjr5, etax5 = generate_gjrgarch(100, [1.0, -0.], [[1., 1,0],[0, 0.1,0.8],[0, 0.05,0.7],[0, 0.01,0.6]], mu=0.0,scale=0.1, varinnovation=varinno) garchplot(errgjr5[:20], hgjr5[:20], 'GJR-GARCH(1,3) Simulation') #garchplot(errgjr4[-400:nobs], hgjr4[-400:nobs], 'GJR-GARCH(1,3) Simulation') #plt.show() seed = np.random.randint(9999999) # 9188410 print 'seed', seed x = np.arange(20).reshape(10,2) x3 = np.column_stack((np.ones((x.shape[0],1)),x)) y, inp = miso_lfilter([1., 0],np.array([[-2.0,3,1],[0.0,0.0,0]]),x3) nobs = 1000 warmup = 1000 np.random.seed(seed) ar = [1.0, -0.7]#7, -0.16, -0.1] #ma = [[1., 1, 0],[0, 0.6,0.1],[0, 0.1,0.1],[0, 0.1,0.1]] ma = [[1., 0, 0],[0, 0.4,0.0]] #,[0, 0.9,0.0]] # errgjr4,hgjr4, etax4 = generate_gjrgarch(warmup+nobs, [1.0, -0.99], # [[1., 1, 0],[0, 0.6,0.1],[0, 0.1,0.1],[0, 0.1,0.1]], # mu=0.2, scale=0.25) errgjr4,hgjr4, etax4 = generate_gjrgarch(warmup+nobs, ar, ma, mu=0.4, scale=1.01) errgjr4,hgjr4, etax4 = errgjr4[warmup:], hgjr4[warmup:], etax4[warmup:] garchplot(errgjr4[:nobs], hgjr4[:nobs], 'GJR-GARCH(1,3) Simulation') ggmod = Garch(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod.nar = 1 ggmod.nma = 1 ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0]) ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000) print 'ggres.params', ggres.params garchplot(ggmod.errorsest, ggmod.h) #plt.show() print 'Garch11' print optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2]) ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 1 ggmod.nma = 1 start_params = np.array([-0.6, 0.2, 0.1]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000) print 'ggres0.params', ggres0.params ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4) ggmod0.nar = 1 ggmod.nma = 1 start_params = np.array([-0.6, 0.2, 0.1]) ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0]) ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000) print 'ggres0.params', ggres0.params if 'rpy' in examples: from rpy import r f = r.formula('~garch(1, 1)') #fit = r.garchFit(f, data = errgjr4) x = r.garchSim( n = 500) print 'R acf', tsa.acf(np.power(x,2))[:15] arma3 = Arma(np.power(x,2)) arma3res = arma3.fit(start_params=[-0.2,0.1,0.5],maxiter=5000) print arma3res.params arma3b = Arma(np.power(x,2)) arma3bres = arma3b.fit(start_params=[-0.2,0.1,0.5],maxiter=5000, method='bfgs') print arma3bres.params llf = loglike_GARCH11([0.93, 0.9, 0.2], errgjr4) print llf[0] erro,ho, etaxo = generate_gjrgarch(20, ar, ma, mu=0.04, scale=0.01, varinnovation = np.ones(20)) ''' this looks relatively good >>> Arma.initialize = lambda x: x >>> arma3 = Arma(errgjr4**2) >>> arma3res = arma3.fit() Warning: Maximum number of function evaluations has been exceeded. >>> arma3res.params array([-0.775, -0.583, -0.001]) >>> arma2.nar 1 >>> arma2.nma 1 unit root ? >>> arma3 = Arma(hgjr4) >>> arma3res = arma3.fit() Optimization terminated successfully. Current function value: -3641.529780 Iterations: 250 Function evaluations: 458 >>> arma3res.params array([ -1.000e+00, -3.096e-04, 6.343e-03]) or maybe not great >>> arma3res = arma3.fit(start_params=[-0.8,0.1,0.5],maxiter=5000) Warning: Maximum number of function evaluations has been exceeded. >>> arma3res.params array([-0.086, 0.186, -0.001]) >>> arma3res = arma3.fit(start_params=[-0.8,0.1,0.5],maxiter=5000,method='bfgs') Divide-by-zero encountered: rhok assumed large Optimization terminated successfully. Current function value: -5988.332952 Iterations: 16 Function evaluations: 245 Gradient evaluations: 49 >>> arma3res.params array([ -9.995e-01, -9.715e-01, 6.501e-04]) ''' ''' current problems persistence in errgjr looks too low, small tsa.acf(errgjr4**2)[:15] as a consequence the ML estimate has also very little persistence, estimated ar term is much too small -> need to compare with R or matlab help.search("garch") : ccgarch, garchSim(fGarch), garch(tseries) HestonNandiGarchFit(fOptions) > library('fGarch') > spec = garchSpec() > x = garchSim(model = spec@model, n = 500) > acf(x**2) # has low correlation but fit has high parameters: > fit = garchFit(~garch(1, 1), data = x) with rpy: from rpy import r r.library('fGarch') f = r.formula('~garch(1, 1)') fit = r.garchFit(f, data = errgjr4) Final Estimate: LLH: -3198.2 norm LLH: -3.1982 mu omega alpha1 beta1 1.870485e-04 9.437557e-05 3.457349e-02 1.000000e-08 second run with ar = [1.0, -0.8] ma = [[1., 0, 0],[0, 1.0,0.0]] Final Estimate: LLH: -3979.555 norm LLH: -3.979555 mu omega alpha1 beta1 1.465050e-05 1.641482e-05 1.092600e-01 9.654438e-02 mine: >>> ggres.params array([ -2.000e-06, 3.283e-03, 3.769e-01, -1.000e-06]) another rain, same ar, ma Final Estimate: LLH: -3956.197 norm LLH: -3.956197 mu omega alpha1 beta1 7.487278e-05 1.171238e-06 1.511080e-03 9.440843e-01 every step needs to be compared and tested something looks wrong with likelihood function, either a silly mistake or still some conceptional problems * found the silly mistake, I was normalizing the errors before plugging into espression for likelihood function * now gjr garch estimation works and produces results that are very close to the explicit garch11 estimation initial conditions for miso_filter need to be cleaned up lots of clean up to to after the bug hunting ''' y = np.random.randn(20) params = [0.93, 0.9, 0.2] lls, llt, ht = loglike_GARCH11(params, y) sigma2 = ht axis=0 nobs = len(ht) llike = -0.5 * (np.sum(np.log(sigma2),axis) + np.sum((y**2)/sigma2, axis) + nobs*np.log(2*np.pi)) print lls, llike #print np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum() ''' >>> optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4)[0], [0.93, 0.9, 0.2]) Optimization terminated successfully. Current function value: 7312.393886 Iterations: 95 Function evaluations: 175 array([ 3.691, 0.072, 0.932]) >>> ar [1.0, -0.93000000000000005] >>> ma [[1.0, 0, 0], [0, 0.90000000000000002, 0.0]] ''' np.random.seed(1) tseries = np.zeros(200) # set first observation for i in range(1,200): # get 99 more observations based on the given process error = np.random.randn() tseries[i] = .9 * tseries[i-1] + .01 * error tseries = tseries[100:] armodel = AR(tseries) #armodel.fit(method='bfgs-b') #armodel.fit(method='tnc') #powell should be the most robust, see Hamilton 5.7 armodel.fit(method='powell', penalty=True) # The below don't work yet #armodel.fit(method='newton', penalty=True) #armodel.fit(method='broyden', penalty=True) print "Unconditional MLE for AR(1) y_t = .9*y_t-1 +.01 * err" print armodel.params statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/movstat.py000066400000000000000000000347411224417117700250210ustar00rootroot00000000000000'''using scipy signal and numpy correlate to calculate some time series statistics original developer notes see also scikits.timeseries (movstat is partially inspired by it) added 2009-08-29 timeseries moving stats are in c, autocorrelation similar to here I thought I saw moving stats somewhere in python, maybe not) TODO moving statistics - filters don't handle boundary conditions nicely (correctly ?) e.g. minimum order filter uses 0 for out of bounds value -> append and prepend with last resp. first value - enhance for nd arrays, with axis = 0 Note: Equivalence for 1D signals >>> np.all(signal.correlate(x,[1,1,1],'valid')==np.correlate(x,[1,1,1])) True >>> np.all(ndimage.filters.correlate(x,[1,1,1], origin = -1)[:-3+1]==np.correlate(x,[1,1,1])) True # multidimensional, but, it looks like it uses common filter across time series, no VAR ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1) ndimage.filters.correlate(x,[1,1,1],origin = 1)) ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), \ origin = 1) >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[1,1,1],[0,0,0]]), origin = 1)[0]==\ ndimage.filters.correlate(x,[1,1,1],origin = 1)) True >>> np.all(ndimage.filters.correlate(np.vstack([x,x]),np.array([[0.5,0.5,0.5],[0.5,0.5,0.5]]), \ origin = 1)[0]==ndimage.filters.correlate(x,[1,1,1],origin = 1)) update 2009-09-06: cosmetic changes, rearrangements ''' import numpy as np from scipy import signal from numpy.testing import assert_array_equal, assert_array_almost_equal import statsmodels.api as sm def expandarr(x,k): #make it work for 2D or nD with axis kadd = k if np.ndim(x) == 2: kadd = (kadd, np.shape(x)[1]) return np.r_[np.ones(kadd)*x[0],x,np.ones(kadd)*x[-1]] def movorder(x, order = 'med', windsize=3, lag='lagged'): '''moving order statistics Parameters ---------- x : array time series data order : float or 'med', 'min', 'max' which order statistic to calculate windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- filtered array ''' #if windsize is even should it raise ValueError if lag == 'lagged': lead = windsize//2 elif lag == 'centered': lead = 0 elif lag == 'leading': lead = -windsize//2 +1 else: raise ValueError if np.isfinite(order) == True: #if np.isnumber(order): ord = order # note: ord is a builtin function elif order == 'med': ord = (windsize - 1)/2 elif order == 'min': ord = 0 elif order == 'max': ord = windsize - 1 else: raise ValueError #return signal.order_filter(x,np.ones(windsize),ord)[:-lead] xext = expandarr(x, windsize) #np.r_[np.ones(windsize)*x[0],x,np.ones(windsize)*x[-1]] return signal.order_filter(xext,np.ones(windsize),ord)[windsize-lead:-(windsize+lead)] def check_movorder(): '''graphical test for movorder''' import matplotlib.pylab as plt x = np.arange(1,10) xo = movorder(x, order='max') assert_array_equal(xo, x) x = np.arange(10,1,-1) xo = movorder(x, order='min') assert_array_equal(xo, x) assert_array_equal(movorder(x, order='min', lag='centered')[:-1], x[1:]) tt = np.linspace(0,2*np.pi,15) x = np.sin(tt) + 1 xo = movorder(x, order='max') plt.figure() plt.plot(tt,x,'.-',tt,xo,'.-') plt.title('moving max lagged') xo = movorder(x, order='max', lag='centered') plt.figure() plt.plot(tt,x,'.-',tt,xo,'.-') plt.title('moving max centered') xo = movorder(x, order='max', lag='leading') plt.figure() plt.plot(tt,x,'.-',tt,xo,'.-') plt.title('moving max leading') # identity filter ##>>> signal.order_filter(x,np.ones(1),0) ##array([ 1., 2., 3., 4., 5., 6., 7., 8., 9.]) # median filter ##signal.medfilt(np.sin(x), kernel_size=3) ##>>> plt.figure() ## ##>>> x=np.linspace(0,3,100);plt.plot(x,np.sin(x),x,signal.medfilt(np.sin(x), kernel_size=3)) # remove old version ##def movmeanvar(x, windowsize=3, valid='same'): ## ''' ## this should also work along axis or at least for columns ## ''' ## n = x.shape[0] ## x = expandarr(x, windowsize - 1) ## takeslice = slice(windowsize-1, n + windowsize-1) ## avgkern = (np.ones(windowsize)/float(windowsize)) ## m = np.correlate(x, avgkern, 'same')#[takeslice] ## print m.shape ## print x.shape ## xm = x - m ## v = np.correlate(x*x, avgkern, 'same') - m**2 ## v1 = np.correlate(xm*xm, avgkern, valid) #not correct for var of window ###>>> np.correlate(xm*xm,np.array([1,1,1])/3.0,'valid')-np.correlate(xm*xm,np.array([1,1,1])/3.0,'valid')**2 ## return m[takeslice], v[takeslice], v1 def movmean(x, windowsize=3, lag='lagged'): '''moving window mean Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array moving mean, with same shape as x Notes ----- for leading and lagging the data array x is extended by the closest value of the array ''' return movmoment(x, 1, windowsize=windowsize, lag=lag) def movvar(x, windowsize=3, lag='lagged'): '''moving window variance Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array moving variance, with same shape as x ''' m1 = movmoment(x, 1, windowsize=windowsize, lag=lag) m2 = movmoment(x, 2, windowsize=windowsize, lag=lag) return m2 - m1*m1 def movmoment(x, k, windowsize=3, lag='lagged'): '''non-central moment Parameters ---------- x : array time series data windsize : int window size lag : 'lagged', 'centered', or 'leading' location of window relative to current position Returns ------- mk : array k-th moving non-central moment, with same shape as x Notes ----- If data x is 2d, then moving moment is calculated for each column. ''' windsize = windowsize #if windsize is even should it raise ValueError if lag == 'lagged': #lead = -0 + windsize #windsize//2 lead = -0# + (windsize-1) + windsize//2 sl = slice((windsize-1) or None, -2*(windsize-1) or None) elif lag == 'centered': lead = -windsize//2 #0#-1 #+ #(windsize-1) sl = slice((windsize-1)+windsize//2 or None, -(windsize-1)-windsize//2 or None) elif lag == 'leading': #lead = -windsize +1#+1 #+ (windsize-1)#//2 +1 lead = -windsize +2 #-windsize//2 +1 sl = slice(2*(windsize-1)+1+lead or None, -(2*(windsize-1)+lead)+1 or None) else: raise ValueError avgkern = (np.ones(windowsize)/float(windowsize)) xext = expandarr(x, windsize-1) #Note: expandarr increases the array size by 2*(windsize-1) #sl = slice(2*(windsize-1)+1+lead or None, -(2*(windsize-1)+lead)+1 or None) print sl if xext.ndim == 1: return np.correlate(xext**k, avgkern, 'full')[sl] #return np.correlate(xext**k, avgkern, 'same')[windsize-lead:-(windsize+lead)] else: print xext.shape print avgkern[:,None].shape # try first with 2d along columns, possibly ndim with axis return signal.correlate(xext**k, avgkern[:,None], 'full')[sl,:] #x=0.5**np.arange(10);xm=x-x.mean();a=np.correlate(xm,[1],'full') #x=0.5**np.arange(3);np.correlate(x,x,'same') ##>>> x=0.5**np.arange(10);xm=x-x.mean();a=np.correlate(xm,xo,'full') ## ##>>> xo=np.ones(10);d=np.correlate(xo,xo,'full') ##>>> xo ##xo=np.ones(10);d=np.correlate(xo,xo,'full') ##>>> x=np.ones(10);xo=x-x.mean();a=np.correlate(xo,xo,'full') ##>>> xo=np.ones(10);d=np.correlate(xo,xo,'full') ##>>> d ##array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 9., ## 8., 7., 6., 5., 4., 3., 2., 1.]) ##def ccovf(): ## pass ## #x=0.5**np.arange(10);xm=x-x.mean();a=np.correlate(xm,xo,'full') __all__ = ['movorder', 'movmean', 'movvar', 'movmoment'] if __name__ == '__main__': print '\ncheckin moving mean and variance' nobs = 10 x = np.arange(nobs) ws = 3 ave = np.array([ 0., 1/3., 1., 2., 3., 4., 5., 6., 7., 8., 26/3., 9]) va = np.array([[ 0. , 0. ], [ 0.22222222, 0.88888889], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.66666667, 2.66666667], [ 0.22222222, 0.88888889], [ 0. , 0. ]]) ave2d = np.c_[ave, 2*ave] print movmean(x, windowsize=ws, lag='lagged') print movvar(x, windowsize=ws, lag='lagged') print [np.var(x[i-ws:i]) for i in range(ws, nobs)] m1 = movmoment(x, 1, windowsize=3, lag='lagged') m2 = movmoment(x, 2, windowsize=3, lag='lagged') print m1 print m2 print m2 - m1*m1 # this implicitly also tests moment assert_array_almost_equal(va[ws-1:,0], movvar(x, windowsize=3, lag='leading')) assert_array_almost_equal(va[ws//2:-ws//2+1,0], movvar(x, windowsize=3, lag='centered')) assert_array_almost_equal(va[:-ws+1,0], movvar(x, windowsize=ws, lag='lagged')) print '\nchecking moving moment for 2d (columns only)' x2d = np.c_[x, 2*x] print movmoment(x2d, 1, windowsize=3, lag='centered') print movmean(x2d, windowsize=ws, lag='lagged') print movvar(x2d, windowsize=ws, lag='lagged') assert_array_almost_equal(va[ws-1:,:], movvar(x2d, windowsize=3, lag='leading')) assert_array_almost_equal(va[ws//2:-ws//2+1,:], movvar(x2d, windowsize=3, lag='centered')) assert_array_almost_equal(va[:-ws+1,:], movvar(x2d, windowsize=ws, lag='lagged')) assert_array_almost_equal(ave2d[ws-1:], movmoment(x2d, 1, windowsize=3, lag='leading')) assert_array_almost_equal(ave2d[ws//2:-ws//2+1], movmoment(x2d, 1, windowsize=3, lag='centered')) assert_array_almost_equal(ave2d[:-ws+1], movmean(x2d, windowsize=ws, lag='lagged')) from scipy import ndimage print ndimage.filters.correlate1d(x2d, np.array([1,1,1])/3., axis=0) #regression test check xg = np.array([ 0. , 0.1, 0.3, 0.6, 1. , 1.5, 2.1, 2.8, 3.6, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5, 90.5, 91.5, 92.5, 93.5, 94.5]) assert_array_almost_equal(xg, movmean(np.arange(100), 10,'lagged')) xd = np.array([ 0.3, 0.6, 1. , 1.5, 2.1, 2.8, 3.6, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5, 90.5, 91.5, 92.5, 93.5, 94.5, 95.4, 96.2, 96.9, 97.5, 98. , 98.4, 98.7, 98.9, 99. ]) assert_array_almost_equal(xd, movmean(np.arange(100), 10,'leading')) xc = np.array([ 1.36363636, 1.90909091, 2.54545455, 3.27272727, 4.09090909, 5. , 6. , 7. , 8. , 9. , 10. , 11. , 12. , 13. , 14. , 15. , 16. , 17. , 18. , 19. , 20. , 21. , 22. , 23. , 24. , 25. , 26. , 27. , 28. , 29. , 30. , 31. , 32. , 33. , 34. , 35. , 36. , 37. , 38. , 39. , 40. , 41. , 42. , 43. , 44. , 45. , 46. , 47. , 48. , 49. , 50. , 51. , 52. , 53. , 54. , 55. , 56. , 57. , 58. , 59. , 60. , 61. , 62. , 63. , 64. , 65. , 66. , 67. , 68. , 69. , 70. , 71. , 72. , 73. , 74. , 75. , 76. , 77. , 78. , 79. , 80. , 81. , 82. , 83. , 84. , 85. , 86. , 87. , 88. , 89. , 90. , 91. , 92. , 93. , 94. , 94.90909091, 95.72727273, 96.45454545, 97.09090909, 97.63636364]) assert_array_almost_equal(xc, movmean(np.arange(100), 11,'centered')) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/notes_organize.txt000066400000000000000000000152571224417117700265420ustar00rootroot00000000000000 scikits.statsmodels.sandbox.tsa.kalmanf --------------------------------------- ARMA : ARMA model using the exact Kalman Filter StateSpaceModel : kalmanfilter : Returns the negative log-likelihood of y conditional on the information set kalmansmooth : updatematrices : TODO: change API, update names scikits.statsmodels.sandbox.tsa.arima ------------------------------------- runs ok, no refactoring bugs has examples and monte carlo that can be split up into example files ARIMA : currently ARMA only, no differencing used - no I arma2ar : get the AR representation of an ARMA process arma2ma : get the impulse response function (MA representation) for ARMA process arma_acf : theoretical autocovariance function of ARMA process arma_acovf : theoretical autocovariance function of ARMA process arma_generate_sample : generate an random sample of an ARMA process arma_impulse_response : get the impulse response function (MA representation) for ARMA process arma_pacf : partial autocorrelation function of an ARMA process deconvolve : Deconvolves divisor out of signal, division of polynomials for n terms index2lpol : expand coefficients to lag poly lpol2index : remove zeros from lagpolynomial, squeezed representation with index mcarma22 : run Monte Carlo for ARMA(2,2) scikits.statsmodels.sandbox.tsa.varma ------------------------------------- just filter experiments needed to fix import for acf example VAR : multivariate linear filter VARMA : multivariate linear filter scikits.statsmodels.sandbox.tsa.varma_tools ------------------------------------------- Helper and filter functions for VAR and VARMA, and basic VAR class needed import fix in top of module maybe rename to varma_process in "main" example for VarmaPoly, and some Var fit Var could be used for Granger Causality tests, otherwise it's pretty limited Var : simultaneous OLS estimation VarmaPoly : class to keep track of Varma polynomial format working with and transforming VARMA Lag-Polynomials (3d) ar2full : make reduced lagpolynomial into a right side lagpoly array ar2lhs : convert full (rhs) lagpolynomial into a reduced, left side lagpoly array padone : pad with zeros along one axis, currently only axis=0 trimone : trim number of array elements along one axis varfilter : apply an autoregressive filter to a series x vargenerate : generate an VAR process with errors u varinversefilter : creates inverse ar filter (MA representation) recursively scikits.statsmodels.sandbox.tsa.try_fi -------------------------------------- (not included by script that generates this list) various functions to build lag-polynomials for fractional and seasonal integration and function ar2arma minimizes distance in terms of impulse response function move these to a module or rename scikits.statsmodels.sandbox.tsa.try_var_convolve.py --------------------------------------------------- (not included by script that generates this list) two functions: arfilter : autoregressive filter for 1d, 2d and 3d fftconvolve : multidimensional filtering using fft many examples, but I'm not sure this (fft) is correct incompletely copied for interpreter session currently raises exception because a variable (imp) is not defined scikits.statsmodels.sandbox.tsa.try_var_convolve.py --------------------------------------------------- (not included by script that generates this list) includes functions for detrending, (theoretical) acovf and similar for special cases acf plot functions (partially copied from matplotlib.mlab) currently exception: FIXED uses arima.ARIMA class without data in constructor, and order now has 3 values and is keyword with tuple as value move plot function to new graphics directory ? scikits.statsmodels.sandbox.regression.mle ------------------------------------------ one refactoring bug fixed, because arima.ARIMA needs data, use class method instead runs without exception, but I didn't look at any results "main" has quite a lot AR : Notes Arma : univariate Autoregressive Moving Average model Garch : Garch model gjrgarch (t-garch) Garch0 : Garch model, GarchX : Garch model, LikelihoodModel : Likelihood model is a subclass of Model. TSMLEModel : univariate time series model for estimation with maximum likelihood garchplot : generate_garch : simulate standard garch generate_gjrgarch : simulate gjr garch process generate_kindofgarch : simulate garch like process but not squared errors in arma gjrconvertparams : flat to matrix loglike_GARCH11 : miso_lfilter : use nd convolution to merge inputs, normloglike : test_gjrgarch : test_misofilter : Other ----- diffusion: continuous time processes, produce nice graphs but parameterization is a bit inconsistent. script files ============ sandbox/tsa/try_arma_more.py ---------------------------- imports scikits.talkbox which is not compiled against my current numpy and doesn't run contains arma_periodogram : theoretical periodogram Proposed Structure (preliminary) ================================ arima_estimation ---------------- ARIMA class for estimation, wrapper or containing different estimators other wrappers: here or in separate ??? - support for choosing lag-length arma_process ------------ all theoretical properties for given parameters simulation method with options: initial conditions, errors, (?) not sure what else varma_process ------------- including VarmaPoly and impulse response functions filters ------- miso_filter (should be in cython eventually) ar_filter : fast VAR filter with convolution or fft convolution (not sure what's the relationship between the two) others ??? stattools --------- empirical properties acf, ... tsatools -------- helper functions lagmat detrend ??? others, unclear --------------- ??? open questions ============== support for exog ---------------- is incomplete or missing from some implementations not clear parameterization - ARMAX A(L)y_t = C(L)x_t + B(L)e_t - ARMAX-simple A(L)y_t = beta x_t + B(L)e_t Note: covers previous version by extending x_t - ARMA residuals y_t = beta x_t + u_t, and A(L)u_t = B(L)e_t - ARMAX 2-step A(L)(y_t - beta x_t) = B(L)e_t Note: looks the same as ARMA residuals, implies A(L)y_t = A(L)x_t + B(L)e_t - ARMAX A(L)(y_t - A^{-1}(L) C(L) x_t) = B(L)e_t this doesn't look useful, unless we cutoff A^{-1}(L) problem: signal.lfilter can only handle ARMAX residuals model (I think) deterministic trend have ARMAX-simple model, e.g. in unit root tests support for seasonal and "sparse" lag-polynomials ------------------------------------------------- - fit functions need support for different lag structures, e.g. zeros, multiplicative - support for pre-filters, e.g. (seasonal) differencing statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/try_arma_more.py000066400000000000000000000071711224417117700261610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Periodograms for ARMA and time series theoretical periodogram of ARMA process and different version of periodogram estimation uses scikits.talkbox and matplotlib Created on Wed Oct 14 23:02:19 2009 Author: josef-pktd """ import numpy as np from scipy import signal, ndimage import matplotlib.mlab as mlb import matplotlib.pyplot as plt from statsmodels.tsa.arima_process import arma_generate_sample, arma_periodogram from statsmodels.tsa.stattools import acovf hastalkbox = False try: import scikits.talkbox as stb import scikits.talkbox.spectral.basic as stbs except: hastalkbox = False ar = [1., -0.7]#[1,0,0,0,0,0,0,-0.7] ma = [1., 0.3] ar = np.convolve([1.]+[0]*50 +[-0.6], ar) ar = np.convolve([1., -0.5]+[0]*49 +[-0.3], ar) n_startup = 1000 nobs = 1000 # throwing away samples at beginning makes sample more "stationary" xo = arma_generate_sample(ar,ma,n_startup+nobs) x = xo[n_startup:] #moved to tsa.arima_process #def arma_periodogram(ar, ma, **kwds): # '''periodogram for ARMA process given by lag-polynomials ar and ma # # Parameters # ---------- # ar : array_like # autoregressive lag-polynomial with leading 1 and lhs sign # ma : array_like # moving average lag-polynomial with leading 1 # kwds : options # options for scipy.signal.freqz # default: worN=None, whole=0 # # Returns # ------- # w : array # frequencies # sd : array # periodogram, spectral density # # Notes # ----- # Normalization ? # # ''' # w, h = signal.freqz(ma, ar, **kwds) # sd = np.abs(h)**2/np.sqrt(2*np.pi) # if np.sum(np.isnan(h)) > 0: # # this happens with unit root or seasonal unit root' # print 'Warning: nan in frequency response h' # return w, sd plt.figure() plt.plot(x) rescale = 0 w, h = signal.freqz(ma, ar) sd = np.abs(h)**2/np.sqrt(2*np.pi) if np.sum(np.isnan(h)) > 0: # this happens with unit root or seasonal unit root' print 'Warning: nan in frequency response h' h[np.isnan(h)] = 1. rescale = 0 #replace with signal.order_filter ? pm = ndimage.filters.maximum_filter(sd, footprint=np.ones(5)) maxind = np.nonzero(pm == sd) print 'local maxima frequencies' wmax = w[maxind] sdmax = sd[maxind] plt.figure() plt.subplot(2,3,1) if rescale: plt.plot(w, sd/sd[0], '-', wmax, sdmax/sd[0], 'o') # plt.plot(w, sd/sd[0], '-') # plt.hold() # plt.plot(wmax, sdmax/sd[0], 'o') else: plt.plot(w, sd, '-', wmax, sdmax, 'o') # plt.hold() # plt.plot(wmax, sdmax, 'o') plt.title('DGP') sdm, wm = mlb.psd(x) sdm = sdm.ravel() pm = ndimage.filters.maximum_filter(sdm, footprint=np.ones(5)) maxind = np.nonzero(pm == sdm) plt.subplot(2,3,2) if rescale: plt.plot(wm,sdm/sdm[0], '-', wm[maxind], sdm[maxind]/sdm[0], 'o') else: plt.plot(wm, sdm, '-', wm[maxind], sdm[maxind], 'o') plt.title('matplotlib') if hastalkbox: sdp, wp = stbs.periodogram(x) plt.subplot(2,3,3) if rescale: plt.plot(wp,sdp/sdp[0]) else: plt.plot(wp, sdp) plt.title('stbs.periodogram') xacov = acovf(x, unbiased=False) plt.subplot(2,3,4) plt.plot(xacov) plt.title('autocovariance') nr = len(x)#*2/3 #xacovfft = np.fft.fft(xacov[:nr], 2*nr-1) xacovfft = np.fft.fft(np.correlate(x,x,'full')) #abs(xacovfft)**2 or equivalently xacovfft = xacovfft * xacovfft.conj() plt.subplot(2,3,5) if rescale: plt.plot(xacovfft[:nr]/xacovfft[0]) else: plt.plot(xacovfft[:nr]) plt.title('fft') if hastalkbox: sdpa, wpa = stbs.arspec(x, 50) plt.subplot(2,3,6) if rescale: plt.plot(wpa,sdpa/sdpa[0]) else: plt.plot(wpa, sdpa) plt.title('stbs.arspec') #plt.show() statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/try_fi.py000066400000000000000000000055001224417117700246070ustar00rootroot00000000000000 ''' using lfilter to get fractional integration polynomial (1-L)^d, d<1 `ri` is (1-L)^(-d), d<1 second part in here is ar2arma only examples left ''' import numpy as np #from numpy.testing import assert_array_almost_equal from scipy.special import gamma, gammaln from scipy import signal #from statsmodels.sandbox import tsa from statsmodels.tsa.arima_process import arma_impulse_response #-------------------- # functions have been moved to arima_process from statsmodels.tsa.arima_process import (lpol_fiar, lpol_fima, lpol_sdiff, ar2arma) #----------------------------------- if __name__ == '__main__': d = 0.4 n = 1000 j = np.arange(n*10) ri0 = gamma(d+j)/(gamma(j+1)*gamma(d)) #ri = np.exp(gammaln(d+j) - gammaln(j+1) - gammaln(d)) (d not -d) ri = lpol_fima(d, n=n) # get_ficoefs(d, n=n) old naming? riinv = signal.lfilter([1], ri, [1]+[0]*(n-1))#[[5,10,20,25]] ''' array([-0.029952 , -0.01100641, -0.00410998, -0.00299859]) >>> d=0.4; j=np.arange(1000);ri=gamma(d+j)/(gamma(j+1)*gamma(d)) >>> # (1-L)^d, d<1 is >>> lfilter([1], ri, [1]+[0]*30) array([ 1. , -0.4 , -0.12 , -0.064 , -0.0416 , -0.029952 , -0.0229632 , -0.01837056, -0.01515571, -0.01279816, -0.01100641, -0.0096056 , -0.00848495, -0.00757118, -0.00681406, -0.00617808, -0.0056375 , -0.00517324, -0.00477087, -0.00441934, -0.00410998, -0.00383598, -0.00359188, -0.00337324, -0.00317647, -0.00299859, -0.00283712, -0.00269001, -0.00255551, -0.00243214, -0.00231864]) >>> # verified for points [[5,10,20,25]] at 4 decimals with Bhardwaj, Swanson, Journal of Eonometrics 2006 ''' print lpol_fiar(0.4, n=20) print lpol_fima(-0.4, n=20) print np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2) #different signs print np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2) #corrected signs #test is now in statsmodels.tsa.tests.test_arima_process from statsmodels.tsa.tests.test_arima_process import test_fi test_fi() ar_true = [1, -0.4] ma_true = [1, 0.5] ar_desired = arma_impulse_response(ma_true, ar_true) ar_app, ma_app, res = ar2arma(ar_desired, 2,1, n=100, mse='ar', start=[0.1]) print ar_app, ma_app ar_app, ma_app, res = ar2arma(ar_desired, 2,2, n=100, mse='ar', start=[-0.1, 0.1]) print ar_app, ma_app ar_app, ma_app, res = ar2arma(ar_desired, 2,3, n=100, mse='ar')#, start = [-0.1, 0.1]) print ar_app, ma_app slow = 1 if slow: ar_desired = lpol_fiar(0.4, n=100) ar_app, ma_app, res = ar2arma(ar_desired, 3, 1, n=100, mse='ar')#, start = [-0.1, 0.1]) print ar_app, ma_app ar_app, ma_app, res = ar2arma(ar_desired, 10, 10, n=100, mse='ar')#, start = [-0.1, 0.1]) print ar_app, ma_app statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/try_var_convolve.py000066400000000000000000000247511224417117700267250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """trying out VAR filtering and multidimensional fft Note: second half is copy and paste and doesn't run as script incomplete definitions of variables, some I created in shell Created on Thu Jan 07 12:23:40 2010 Author: josef-pktd update 2010-10-22 2 arrays were not defined, copied from fft_filter.log.py but I didn't check what the results are. Runs now without raising exception """ import numpy as np from numpy.testing import assert_equal from scipy import signal from scipy.signal.signaltools import _centered as trim_centered _centered = trim_centered x = np.arange(40).reshape((2,20)).T x = np.arange(60).reshape((3,20)).T a3f = np.array([[[0.5, 1.], [1., 0.5]], [[0.5, 1.], [1., 0.5]]]) a3f = np.ones((2,3,3)) nlags = a3f.shape[0] ntrim = nlags//2 y0 = signal.convolve(x,a3f[:,:,0], mode='valid') y1 = signal.convolve(x,a3f[:,:,1], mode='valid') yf = signal.convolve(x[:,:,None],a3f) y = yf[:,1,:] # yvalid = yf[ntrim:-ntrim,yf.shape[1]//2,:] #same result with fftconvolve #signal.fftconvolve(x[:,:,None],a3f).shape #signal.fftconvolve(x[:,:,None],a3f)[:,1,:] print trim_centered(y, x.shape) # this raises an exception: #print trim_centered(yf, (x.shape).shape) assert_equal(yvalid[:,0], y0.ravel()) assert_equal(yvalid[:,1], y1.ravel()) from statsmodels.tsa.filters import arfilter #copied/moved to statsmodels.tsa.filters def arfilter_old(x, a): '''apply an autoregressive filter to a series x x can be 2d, a can be 1d, 2d, or 3d Parameters ---------- x : array_like data array, 1d or 2d, if 2d then observations in rows a : array_like autoregressive filter coefficients, ar lag polynomial see Notes Returns ------- y : ndarray, 2d filtered array, number of columns determined by x and a Notes ----- In general form this uses the linear filter :: y = a(L)x where x : nobs, nvars a : nlags, nvars, npoly Depending on the shape and dimension of a this uses different Lag polynomial arrays case 1 : a is 1d or (nlags,1) one lag polynomial is applied to all variables (columns of x) case 2 : a is 2d, (nlags, nvars) each series is independently filtered with its own lag polynomial, uses loop over nvar case 3 : a is 3d, (nlags, nvars, npoly) the ith column of the output array is given by the linear filter defined by the 2d array a[:,:,i], i.e. :: y[:,i] = a(.,.,i)(L) * x y[t,i] = sum_p sum_j a(p,j,i)*x(t-p,j) for p = 0,...nlags-1, j = 0,...nvars-1, for all t >= nlags Note: maybe convert to axis=1, Not TODO: initial conditions ''' x = np.asarray(x) a = np.asarray(a) if x.ndim == 1: x = x[:,None] if x.ndim > 2: raise ValueError('x array has to be 1d or 2d') nvar = x.shape[1] nlags = a.shape[0] ntrim = nlags//2 # for x is 2d with ncols >1 if a.ndim == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a[:,None], mode='valid') # alternative: #return signal.lfilter(a,[1],x.astype(float),axis=0) elif a.ndim == 2: if min(a.shape) == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a, mode='valid') # case: independent ar #(a bit like recserar in gauss, but no x yet) result = np.zeros((x.shape[0]-nlags+1, nvar)) for i in range(nvar): # could also use np.convolve, but easier for swiching to fft result[:,i] = signal.convolve(x[:,i], a[:,i], mode='valid') return result elif a.ndim == 3: # case: vector autoregressive with lag matrices # #not necessary: # if np.any(a.shape[1:] != nvar): # raise ValueError('if 3d shape of a has to be (nobs,nvar,nvar)') yf = signal.convolve(x[:,:,None], a) yvalid = yf[ntrim:-ntrim, yf.shape[1]//2,:] return yvalid a3f = np.ones((2,3,3)) y0ar = arfilter(x,a3f[:,:,0]) print y0ar, x[1:] + x[:-1] yres = arfilter(x,a3f[:,:,:2]) print np.all(yres == (x[1:,:].sum(1) + x[:-1].sum(1))[:,None]) # don't do these imports, here just for copied fftconvolve from scipy.fftpack import fft, ifft, ifftshift, fft2, ifft2, fftn, \ ifftn, fftfreq from numpy import product,array from statsmodels.tsa.filters.filtertools import fftconvolveinv as fftconvolve #copied/moved to statsmodels.tsa.filters def fftconvolve_old(in1, in2, in3=None, mode="full"): """Convolve two N-dimensional arrays using FFT. See convolve. copied from scipy.signal.signaltools, but here used to try out inverse filter doesn't work or I can't get it to work 2010-10-23: looks ok to me for 1d, from results below with padded data array (fftp) but it doesn't work for multidimensional inverse filter (fftn) original signal.fftconvolve also uses fftn """ s1 = array(in1.shape) s2 = array(in2.shape) complex_result = (np.issubdtype(in1.dtype, np.complex) or np.issubdtype(in2.dtype, np.complex)) size = s1+s2-1 # Always use 2**n-sized FFT fsize = 2**np.ceil(np.log2(size)) IN1 = fftn(in1,fsize) #IN1 *= fftn(in2,fsize) #JP: this looks like the only change I made IN1 /= fftn(in2,fsize) # use inverse filter # note the inverse is elementwise not matrix inverse # is this correct, NO doesn't seem to work for VARMA fslice = tuple([slice(0, int(sz)) for sz in size]) ret = ifftn(IN1)[fslice].copy() del IN1 if not complex_result: ret = ret.real if mode == "full": return ret elif mode == "same": if product(s1,axis=0) > product(s2,axis=0): osize = s1 else: osize = s2 return _centered(ret,osize) elif mode == "valid": return _centered(ret,abs(s2-s1)+1) yff = fftconvolve(x.astype(float)[:,:,None],a3f) rvs = np.random.randn(500) ar1fft = fftconvolve(rvs,np.array([1,-0.8])) #ar1fftp = fftconvolve(np.r_[np.zeros(100),rvs,np.zeros(100)],np.array([1,-0.8])) ar1fftp = fftconvolve(np.r_[np.zeros(100),rvs],np.array([1,-0.8])) ar1lf = signal.lfilter([1], [1,-0.8], rvs) ar1 = np.zeros(501) for i in range(1,501): ar1[i] = 0.8*ar1[i-1] + rvs[i-1] #the previous looks wrong, is for generating ar with delayed error, #or maybe for an ma(1) filter, (generating ar and applying ma filter are the same) #maybe not since it replicates lfilter and fftp #still strange explanation for convolution #ok. because this is my fftconvolve, which is an inverse filter (read the namespace!) #This is an AR filter errar1 = np.zeros(501) for i in range(1,500): errar1[i] = rvs[i] - 0.8*rvs[i-1] #print ar1[-10:] #print ar1fft[-11:-1] #print ar1lf[-10:] #print ar1[:10] #print ar1fft[1:11] #print ar1lf[:10] #print ar1[100:110] #print ar1fft[100:110] #print ar1lf[100:110] # #arloop - lfilter - fftp (padded) are the same print '\n compare: \nerrloop - arloop - fft - lfilter - fftp (padded)' #print np.column_stack((ar1[1:31],ar1fft[:30], ar1lf[:30])) print np.column_stack((errar1[1:31], ar1[1:31],ar1fft[:30], ar1lf[:30], ar1fftp[100:130])) def maxabs(x,y): return np.max(np.abs(x-y)) print maxabs(ar1[1:], ar1lf) #0 print maxabs(ar1[1:], ar1fftp[100:-1]) # around 1e-15 rvs3 = np.random.randn(500,3) a3n = np.array([[1,1,1],[-0.8,0.5,0.1]]) a3n = np.array([[1,1,1],[-0.8,0.0,0.0]]) a3n = np.array([[1,-1,-1],[-0.8,0.0,0.0]]) a3n = np.array([[1,0,0],[-0.8,0.0,0.0]]) a3ne = np.r_[np.ones((1,3)),-0.8*np.eye(3)] a3ne = np.r_[np.ones((1,3)),-0.8*np.eye(3)] ar13fft = fftconvolve(rvs3,a3n) ar13 = np.zeros((501,3)) for i in range(1,501): ar13[i] = np.sum(a3n[1,:]*ar13[i-1]) + rvs[i-1] #changes imp was not defined, not sure what it is supposed to be #copied from a .log file imp = np.zeros((10,3)) imp[0]=1 a3n = np.array([[1,0,0],[-0.8,0.0,0.0]]) fftconvolve(np.r_[np.zeros((100,3)),imp],a3n)[100:] a3n = np.array([[1,0,0],[-0.8,-0.50,0.0]]) fftconvolve(np.r_[np.zeros((100,3)),imp],a3n)[100:] a3n3 = np.array([[[ 1. , 0. , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[-0.8, 0. , 0. ], [ 0. , -0.8, 0. ], [ 0. , 0. , -0.8]]]) a3n3 = np.array([[[ 1. , 0.5 , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[-0.8, 0. , 0. ], [ 0. , -0.8, 0. ], [ 0. , 0. , -0.8]]]) ttt = fftconvolve(np.r_[np.zeros((100,3)),imp][:,:,None],a3n3.T)[100:] gftt = ttt/ttt[0,:,:] a3n3 = np.array([[[ 1. , 0 , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[-0.8, 0.2 , 0. ], [ 0 , 0.0, 0. ], [ 0. , 0. , 0.8]]]) ttt = fftconvolve(np.r_[np.zeros((100,3)),imp][:,:,None],a3n3)[100:] gftt = ttt/ttt[0,:,:] signal.fftconvolve(np.dstack((imp,imp,imp)),a3n3)[1,:,:] nobs = 10 imp = np.zeros((nobs,3)) imp[1] = 1. ar13 = np.zeros((nobs+1,3)) for i in range(1,nobs+1): ar13[i] = np.dot(a3n3[1,:,:],ar13[i-1]) + imp[i-1] a3n3inv = np.zeros((nobs+1,3,3)) a3n3inv[0,:,:] = a3n3[0] a3n3inv[1,:,:] = -a3n3[1] for i in range(2,nobs+1): a3n3inv[i,:,:] = np.dot(-a3n3[1],a3n3inv[i-1,:,:]) a3n3sy = np.array([[[ 1. , 0 , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[-0.8, 0.2 , 0. ], [ 0 , 0.0, 0. ], [ 0. , 0. , 0.8]]]) nobs = 10 a = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0.0 ], [ -0.1 , -0.8]]]) a2n3inv = np.zeros((nobs+1,2,2)) a2n3inv[0,:,:] = a[0] a2n3inv[1,:,:] = -a[1] for i in range(2,nobs+1): a2n3inv[i,:,:] = np.dot(-a[1],a2n3inv[i-1,:,:]) nobs = 10 imp = np.zeros((nobs,2)) imp[0,0] = 1. #a2 was missing, copied from .log file, not sure if correct a2 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0. ], [0.1, -0.8]]]) ar12 = np.zeros((nobs+1,2)) for i in range(1,nobs+1): ar12[i] = np.dot(-a2[1,:,:],ar12[i-1]) + imp[i-1] u = np.random.randn(10,2) ar12r = np.zeros((nobs+1,2)) for i in range(1,nobs+1): ar12r[i] = np.dot(-a2[1,:,:],ar12r[i-1]) + u[i-1] a2inv = np.zeros((nobs+1,2,2)) a2inv[0,:,:] = a2[0] a2inv[1,:,:] = -a2[1] for i in range(2,nobs+1): a2inv[i,:,:] = np.dot(-a2[1],a2inv[i-1,:,:]) import scipy.stats as stats import numpy as np nbins = 12 binProb = np.zeros(nbins) + 1.0/nbins binSumProb = np.add.accumulate(binProb) print binSumProb print stats.gamma.ppf(binSumProb,0.6379,loc=1.6,scale=39.555) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/tsa/varma.py000066400000000000000000000115671224417117700244330ustar00rootroot00000000000000'''VAR and VARMA process this doesn't actually do much, trying out a version for a time loop alternative representation: * textbook, different blocks in matrices * Kalman filter * VAR, VARX and ARX could be calculated with signal.lfilter only tried some examples, not implemented TODO: try minimizing sum of squares of (Y-Yhat) Note: filter has smallest lag at end of array and largest lag at beginning, be careful for asymmetric lags coefficients check this again if it is consistently used changes 2009-09-08 : separated from movstat.py Author : josefpkt License : BSD ''' import numpy as np from scipy import signal #import matplotlib.pylab as plt from numpy.testing import assert_array_equal, assert_array_almost_equal #NOTE: this just returns that predicted values given the #B matrix in polynomial form. #TODO: make sure VAR class returns B/params in this form. def VAR(x,B, const=0): ''' multivariate linear filter Parameters ---------- x: (TxK) array columns are variables, rows are observations for time period B: (PxKxK) array b_t-1 is bottom "row", b_t-P is top "row" when printing B(:,:,0) is lag polynomial matrix for variable 1 B(:,:,k) is lag polynomial matrix for variable k B(p,:,k) is pth lag for variable k B[p,:,:].T corresponds to A_p in Wikipedia const: float or array (not tested) constant added to autoregression Returns ------- xhat: (TxK) array filtered, predicted values of x array Notes ----- xhat(t,i) = sum{_p}sum{_k} { x(t-P:t,:) .* B(:,:,i) } for all i = 0,K-1, for all t=p..T xhat does not include the forecasting observation, xhat(T+1), xhat is 1 row shorter than signal.correlate References ---------- http://en.wikipedia.org/wiki/Vector_Autoregression http://en.wikipedia.org/wiki/General_matrix_notation_of_a_VAR(p) ''' p = B.shape[0] T = x.shape[0] xhat = np.zeros(x.shape) for t in range(p,T): #[p+2]:# ## print p,T ## print x[t-p:t,:,np.newaxis].shape ## print B.shape #print x[t-p:t,:,np.newaxis] xhat[t,:] = const + (x[t-p:t,:,np.newaxis]*B).sum(axis=1).sum(axis=0) return xhat def VARMA(x,B,C, const=0): ''' multivariate linear filter x (TxK) B (PxKxK) xhat(t,i) = sum{_p}sum{_k} { x(t-P:t,:) .* B(:,:,i) } + sum{_q}sum{_k} { e(t-Q:t,:) .* C(:,:,i) }for all i = 0,K-1 ''' P = B.shape[0] Q = C.shape[0] T = x.shape[0] xhat = np.zeros(x.shape) e = np.zeros(x.shape) start = max(P,Q) for t in range(start,T): #[p+2]:# ## print p,T ## print x[t-p:t,:,np.newaxis].shape ## print B.shape #print x[t-p:t,:,np.newaxis] xhat[t,:] = const + (x[t-P:t,:,np.newaxis]*B).sum(axis=1).sum(axis=0) + \ (e[t-Q:t,:,np.newaxis]*C).sum(axis=1).sum(axis=0) e[t,:] = x[t,:] - xhat[t,:] return xhat, e if __name__ == '__main__': T = 20 K = 2 P = 3 #x = np.arange(10).reshape(5,2) x = np.column_stack([np.arange(T)]*K) B = np.ones((P,K,K)) #B[:,:,1] = 2 B[:,:,1] = [[0,0],[0,0],[0,1]] xhat = VAR(x,B) print np.all(xhat[P:,0]==np.correlate(x[:-1,0],np.ones(P))*2) #print xhat T = 20 K = 2 Q = 2 P = 3 const = 1 #x = np.arange(10).reshape(5,2) x = np.column_stack([np.arange(T)]*K) B = np.ones((P,K,K)) #B[:,:,1] = 2 B[:,:,1] = [[0,0],[0,0],[0,1]] C = np.zeros((Q,K,K)) xhat1 = VAR(x,B, const=const) xhat2, err2 = VARMA(x,B,C, const=const) print np.all(xhat2 == xhat1) print np.all(xhat2[P:,0] == np.correlate(x[:-1,0],np.ones(P))*2+const) C[1,1,1] = 0.5 xhat3, err3 = VARMA(x,B,C) x = np.r_[np.zeros((P,K)),x] #prepend inital conditions xhat4, err4 = VARMA(x,B,C) C[1,1,1] = 1 B[:,:,1] = [[0,0],[0,0],[0,1]] xhat5, err5 = VARMA(x,B,C) #print err5 #in differences #VARMA(np.diff(x,axis=0),B,C) #Note: # * signal correlate applies same filter to all columns if kernel.shape[1] possible to run signal.correlate K times with different filters, # see the following example, which replicates VAR filter x0 = np.column_stack([np.arange(T), 2*np.arange(T)]) B[:,:,0] = np.ones((P,K)) B[:,:,1] = np.ones((P,K)) B[1,1,1] = 0 xhat0 = VAR(x0,B) xcorr00 = signal.correlate(x0,B[:,:,0])#[:,0] xcorr01 = signal.correlate(x0,B[:,:,1]) print np.all(signal.correlate(x0,B[:,:,0],'valid')[:-1,0]==xhat0[P:,0]) print np.all(signal.correlate(x0,B[:,:,1],'valid')[:-1,0]==xhat0[P:,1]) #import error #from movstat import acovf, acf from statsmodels.tsa.stattools import acovf, acf aav = acovf(x[:,0]) print aav[0] == np.var(x[:,0]) aac = acf(x[:,0]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/sandbox/utils_old.py000066400000000000000000000077741224417117700245410ustar00rootroot00000000000000import numpy as np import numpy.linalg as L import scipy.interpolate import scipy.linalg __docformat__ = 'restructuredtext' def recipr(X): """ Return the reciprocal of an array, setting all entries less than or equal to 0 to 0. Therefore, it presumes that X should be positive in general. """ x = np.maximum(np.asarray(X).astype(np.float64), 0) return np.greater(x, 0.) / (x + np.less_equal(x, 0.)) def mad(a, c=0.6745, axis=0): """ Median Absolute Deviation: median(abs(a - median(a))) / c """ _shape = a.shape a.shape = np.product(a.shape,axis=0) m = np.median(np.fabs(a - np.median(a))) / c a.shape = _shape return m def recipr0(X): """ Return the reciprocal of an array, setting all entries equal to 0 as 0. It does not assume that X should be positive in general. """ test = np.equal(np.asarray(X), 0) return np.where(test, 0, 1. / X) def clean0(matrix): """ Erase columns of zeros: can save some time in pseudoinverse. """ colsum = np.add.reduce(matrix**2, 0) val = [matrix[:,i] for i in np.flatnonzero(colsum)] return np.array(np.transpose(val)) def rank(X, cond=1.0e-12): """ Return the rank of a matrix X based on its generalized inverse, not the SVD. """ X = np.asarray(X) if len(X.shape) == 2: D = scipy.linalg.svdvals(X) return int(np.add.reduce(np.greater(D / D.max(), cond).astype(np.int32))) else: return int(not np.alltrue(np.equal(X, 0.))) def fullrank(X, r=None): """ Return a matrix whose column span is the same as X. If the rank of X is known it can be specified as r -- no check is made to ensure that this really is the rank of X. """ if r is None: r = rank(X) V, D, U = L.svd(X, full_matrices=0) order = np.argsort(D) order = order[::-1] value = [] for i in range(r): value.append(V[:,order[i]]) return np.asarray(np.transpose(value)).astype(np.float64) class StepFunction: """ A basic step function: values at the ends are handled in the simplest way possible: everything to the left of x[0] is set to ival; everything to the right of x[-1] is set to y[-1]. Examples -------- >>> from numpy import arange >>> from nipy.fixes.scipy.stats.models.utils import StepFunction >>> >>> x = arange(20) >>> y = arange(20) >>> f = StepFunction(x, y) >>> >>> print f(3.2) 3.0 >>> print f([[3.2,4.5],[24,-3.1]]) [[ 3. 4.] [ 19. 0.]] """ def __init__(self, x, y, ival=0., sorted=False): _x = np.asarray(x) _y = np.asarray(y) if _x.shape != _y.shape: raise ValueError, 'in StepFunction: x and y do not have the same shape' if len(_x.shape) != 1: raise ValueError, 'in StepFunction: x and y must be 1-dimensional' self.x = np.hstack([[-np.inf], _x]) self.y = np.hstack([[ival], _y]) if not sorted: asort = np.argsort(self.x) self.x = np.take(self.x, asort, 0) self.y = np.take(self.y, asort, 0) self.n = self.x.shape[0] def __call__(self, time): tind = np.searchsorted(self.x, time) - 1 _shape = tind.shape return self.y[tind] def ECDF(values): """ Return the ECDF of an array as a step function. """ x = np.array(values, copy=True) x.sort() x.shape = np.product(x.shape,axis=0) n = x.shape[0] y = (np.arange(n) + 1.) / n return StepFunction(x, y) def monotone_fn_inverter(fn, x, vectorized=True, **keywords): """ Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x. """ if vectorized: y = fn(x, **keywords) else: y = [] for _x in x: y.append(fn(_x, **keywords)) y = np.array(y) a = np.argsort(y) return scipy.interpolate.interp1d(y[a], x[a]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/src/000077500000000000000000000000001224417117700213035ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/src/bspline_ext.c000066400000000000000000000063511224417117700237700ustar00rootroot00000000000000#include "Python.h" #include "numpy/arrayobject.h" /* function prototypes */ double *bspline(double*, double*, int, double *, int, int, int, int, int); void bspline_gram(double*, double *, int, int, int, int); void invband_compute(double*, double *, int, int); static PyObject *BSpline_Invband(PyObject *self, PyObject *args) { double *data; double *L_data; npy_intp *dims_invband; npy_intp *dims_L; PyArrayObject *L = NULL; PyArrayObject *invband = NULL; if(!PyArg_ParseTuple(args, "O", &L)) goto exit; dims_L = PyArray_DIMS(L); L_data = (double *)PyArray_DATA(L); dims_invband = calloc(2, sizeof(npy_intp)); dims_invband[0] = dims_L[0]; dims_invband[1] = dims_L[1]; invband = (PyArrayObject*)PyArray_SimpleNew(2, dims_invband, PyArray_DOUBLE); data = (double *)PyArray_DATA(invband); free(dims_invband); invband_compute(data, L_data, (int)dims_L[0], (int)dims_L[1]); exit: return PyErr_Occurred() ? NULL : (PyObject*)Py_BuildValue("O", invband); } static PyObject *BSpline_Gram(PyObject *self, PyObject *args) { int m; int dl; int dr; double *knots; double *data; npy_intp *nknots; npy_intp *dims_gram; PyArrayObject *knots_array = NULL; PyArrayObject *gram_array = NULL; if(!PyArg_ParseTuple(args, "Oiii", &knots_array, &m, &dl, &dr)) goto exit; nknots = PyArray_DIMS(knots_array); knots = (double *)PyArray_DATA(knots_array); dims_gram = calloc(2, sizeof(npy_intp)); dims_gram[0] = (int)nknots[0] - m; dims_gram[1] = m; gram_array = (PyArrayObject*)PyArray_SimpleNew(2, dims_gram, PyArray_DOUBLE); data = (double *)PyArray_DATA(gram_array); free(dims_gram); bspline_gram(data, knots, (int)nknots[0], m, dl, dr); exit: return PyErr_Occurred() ? NULL : (PyObject*)Py_BuildValue("O", gram_array); } static PyObject *BSpline_Evaluate(PyObject *self, PyObject *args) { int i; int upper; int lower; int m; int d; double *knots; double *x; double *data; npy_intp *nknots; npy_intp *nx; npy_intp dims_basis[2]; PyArrayObject *knots_array = NULL; PyArrayObject *x_array = NULL; PyArrayObject *basis_array = NULL; if(!PyArg_ParseTuple(args, "OOiiii", &x_array, &knots_array, &m, &d, &lower, &upper)) goto exit; nknots = PyArray_DIMS(knots_array); nx = PyArray_DIMS(x_array); knots = (double *)PyArray_DATA(knots_array); x = (double *)PyArray_DATA(x_array); dims_basis[0] = upper-lower; dims_basis[1] = (int)nx[0]; basis_array = (PyArrayObject*)PyArray_SimpleNew(2, dims_basis, PyArray_DOUBLE); data = (double *)PyArray_DATA(basis_array); bspline(data, x, (int)nx[0], knots, (int)nknots[0], m, d, lower, upper); exit: return PyErr_Occurred() ? NULL : (PyObject*)Py_BuildValue("O", basis_array); } static PyMethodDef BSplineMethods[] = { { "evaluate", BSpline_Evaluate, METH_VARARGS, NULL }, { "gram", BSpline_Gram, METH_VARARGS, NULL }, { "invband", BSpline_Invband, METH_VARARGS, NULL }, { NULL, NULL, 0, NULL}, }; PyMODINIT_FUNC init_hbspline(void) { Py_InitModule("_hbspline", BSplineMethods); import_array(); } statsmodels-0.5.0+git13-g8e07d34/statsmodels/src/bspline_impl.c000066400000000000000000000203221224417117700241230ustar00rootroot00000000000000 #include /* function prototypes */ double *bspline(double *, double *, int, double *, int, int, int, int, int); double bspline_quad(double *, int, int, int, int, int, int); double *bspline_prod(double *, int, double *, int, int, int, int, int, int); void bspline_gram(double *, double *, int, int, int, int); void invband_compute(double *, double *, int, int); double *bspline(double *output, double *x, int nx, double *knots, int nknots, int m, int d, int lower, int upper){ int nbasis; int index, i, j, k; double *result, *b, *b0, *b1; double *f0, *f1; double denom; nbasis = upper - lower; result = output; f0 = (double *) malloc(sizeof(double) * nx); f1 = (double *) malloc(sizeof(double) * nx); if (m == 1) { for(i=0; i= knots[index]) * (x[k] < knots[index+1]); result++; } } else { for (k=0; k nknots - 1) { upper = nknots-1; } for (k=lower; k 0) { data[j*n+i] = 0;} } } for (i=n-1; i>=0; i--) { for (j=1; j <= (m= 0.00 and ad2a < 0.200): pval = 1 - np.exp(-13.436 + 101.14 * ad2a - 223.73 * ad2a**2) elif ad2a < 0.340: pval = 1 - np.exp(-8.318 + 42.796 * ad2a - 59.938 * ad2a**2) elif ad2a < 0.600: pval = np.exp(0.9177 - 4.279 * ad2a - 1.38 * ad2a**2) elif ad2a <= 13: pval = np.exp(1.2937 - 5.709 * ad2a + 0.0186 * ad2a**2) else: pval = 0.0 # is < 4.9542108058458799e-31 else: bounds = np.array([0.0, 0.200, 0.340, 0.600]) pval0 = lambda ad2a: np.nan*np.ones_like(ad2a) pval1 = lambda ad2a: 1 - np.exp(-13.436 + 101.14 * ad2a - 223.73 * ad2a**2) pval2 = lambda ad2a: 1 - np.exp(-8.318 + 42.796 * ad2a - 59.938 * ad2a**2) pval3 = lambda ad2a: np.exp(0.9177 - 4.279 * ad2a - 1.38 * ad2a**2) pval4 = lambda ad2a: np.exp(1.2937 - 5.709 * ad2a + 0.0186 * ad2a**2) pvalli = [pval0, pval1, pval2, pval3, pval4] idx = np.searchsorted(bounds, ad2a, side='right') pval = np.nan*np.ones_like(ad2a) for i in range(5): mask = (idx == i) pval[mask] = pvalli[i](ad2a[mask]) return ad2, pval if __name__ == '__main__': x = np.array([-0.1184, -1.3403, 0.0063, -0.612 , -0.3869, -0.2313, -2.8485, -0.2167, 0.4153, 1.8492, -0.3706, 0.9726, -0.1501, -0.0337, -1.4423, 1.2489, 0.9182, -0.2331, -0.6182, 0.183 ]) r_res = np.array([0.58672353588821502, 0.1115380760041617]) ad2, pval = normal_ad(x) print ad2, pval print r_res - [ad2, pval] print anderson_statistic((x-x.mean())/x.std(), dist=stats.norm, fit=0) print anderson_statistic(x, dist=stats.norm, fit=True) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/anova.py000066400000000000000000000321271224417117700233350ustar00rootroot00000000000000import numpy as np from scipy import stats from pandas import DataFrame, Index from statsmodels.formula.formulatools import (_remove_intercept_patsy, _has_intercept, _intercept_idx) def _get_covariance(model, robust): if robust is None: return model.cov_params() elif robust == "hc0": se = model.HC0_se return model.cov_HC0 elif robust == "hc1": se = model.HC1_se return model.cov_HC1 elif robust == "hc2": se = model.HC2_se return model.cov_HC2 elif robust == "hc3": se = model.HC3_se return model.cov_HC3 else: # pragma: no cover raise ValueError("robust options %s not understood" % robust) #NOTE: these need to take into account weights ! def anova_single(model, **kwargs): """ ANOVA table for one fitted linear model. Parameters ---------- model : fitted linear model results instance A fitted linear model typ : int or str {1,2,3} or {"I","II","III"} Type of sum of squares to use. **kwargs** scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {"F", "Chisq", "Cp"} or None Test statistics to provide. Default is "F". Notes ----- Use of this function is discouraged. Use anova_lm instead. """ test = kwargs.get("test", "F") scale = kwargs.get("scale", None) typ = kwargs.get("typ", 1) robust = kwargs.get("robust", None) if robust: robust = robust.lower() endog = model.model.endog exog = model.model.exog nobs = exog.shape[0] response_name = model.model.endog_names design_info = model.model.data.orig_exog.design_info exog_names = model.model.exog_names # +1 for resids n_rows = (len(design_info.terms) - _has_intercept(design_info) + 1) pr_test = "PR(>%s)" % test names = ['df', 'sum_sq', 'mean_sq', test, pr_test] table = DataFrame(np.zeros((n_rows, 5)), columns = names) if typ in [1,"I"]: return anova1_lm_single(model, endog, exog, nobs, design_info, table, n_rows, test, pr_test, robust) elif typ in [2, "II"]: return anova2_lm_single(model, design_info, n_rows, test, pr_test, robust) elif typ in [3, "III"]: return anova3_lm_single(model, design_info, n_rows, test, pr_test, robust) elif typ in [4, "IV"]: raise NotImplemented("Type IV not yet implemented") else: # pragma: no cover raise ValueError("Type %s not understood" % str(typ)) def anova1_lm_single(model, endog, exog, nobs, design_info, table, n_rows, test, pr_test, robust): """ ANOVA table for one fitted linear model. Parameters ---------- model : fitted linear model results instance A fitted linear model **kwargs** scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {"F", "Chisq", "Cp"} or None Test statistics to provide. Default is "F". Notes ----- Use of this function is discouraged. Use anova_lm instead. """ #maybe we should rethink using pinv > qr in OLS/linear models? effects = getattr(model, 'effects', None) if effects is None: q,r = np.linalg.qr(exog) effects = np.dot(q.T, endog) arr = np.zeros((len(design_info.terms), len(design_info.column_names))) slices = [design_info.slice(name) for name in design_info.term_names] for i,slice_ in enumerate(slices): arr[i, slice_] = 1 sum_sq = np.dot(arr, effects**2) #NOTE: assumes intercept is first column idx = _intercept_idx(design_info) sum_sq = sum_sq[~idx] term_names = np.array(design_info.term_names) # want boolean indexing term_names = term_names[~idx] index = term_names.tolist() table.index = Index(index + ['Residual']) table.ix[index, ['df', 'sum_sq']] = np.c_[arr[~idx].sum(1), sum_sq] if test == 'F': table[:n_rows][test] = ((table['sum_sq']/table['df'])/ (model.ssr/model.df_resid)) table[:n_rows][pr_test] = stats.f.sf(table["F"], table["df"], model.df_resid) # fill in residual table.ix[['Residual'], ['sum_sq','df', test, pr_test]] = (model.ssr, model.df_resid, np.nan, np.nan) table['mean_sq'] = table['sum_sq'] / table['df'] return table #NOTE: the below is not agnostic about formula... def anova2_lm_single(model, design_info, n_rows, test, pr_test, robust): """ ANOVA type II table for one fitted linear model. Parameters ---------- model : fitted linear model results instance A fitted linear model **kwargs** scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {"F", "Chisq", "Cp"} or None Test statistics to provide. Default is "F". Notes ----- Use of this function is discouraged. Use anova_lm instead. Type II Sum of Squares compares marginal contribution of terms. Thus, it is not particularly useful for models with significant interaction terms. """ terms_info = design_info.terms[:] # copy terms_info = _remove_intercept_patsy(terms_info) names = ['sum_sq', 'df', test, pr_test] table = DataFrame(np.zeros((n_rows, 4)), columns = names) cov = _get_covariance(model, None) robust_cov = _get_covariance(model, robust) col_order = [] index = [] for i, term in enumerate(terms_info): # grab all varaibles except interaction effects that contain term # need two hypotheses matrices L1 is most restrictive, ie., term==0 # L2 is everything except term==0 cols = design_info.slice(term) L1 = range(cols.start, cols.stop) L2 = [] term_set = set(term.factors) for t in terms_info: # for the term you have other_set = set(t.factors) if term_set.issubset(other_set) and not term_set == other_set: col = design_info.slice(t) # on a higher order term containing current `term` L1.extend(range(col.start, col.stop)) L2.extend(range(col.start, col.stop)) L1 = np.eye(model.model.exog.shape[1])[L1] L2 = np.eye(model.model.exog.shape[1])[L2] if L2.size: LVL = np.dot(np.dot(L1,robust_cov),L2.T) from scipy import linalg orth_compl,_ = linalg.qr(LVL) r = L1.shape[0] - L2.shape[0] # L1|2 # use the non-unique orthogonal completion since L12 is rank r L12 = np.dot(orth_compl[:,-r:].T, L1) else: L12 = L1 r = L1.shape[0] #from IPython.core.debugger import Pdb; Pdb().set_trace() if test == 'F': f = model.f_test(L12, cov_p=robust_cov) table.ix[i][test] = test_value = f.fvalue table.ix[i][pr_test] = f.pvalue # need to back out SSR from f_test table.ix[i]['df'] = r col_order.append(cols.start) index.append(term.name()) table.index = Index(index + ['Residual']) table = table.ix[np.argsort(col_order + [model.model.exog.shape[1]+1])] # back out sum of squares from f_test ssr = table[test] * table['df'] * model.ssr/model.df_resid table['sum_sq'] = ssr # fill in residual table.ix['Residual'][['sum_sq','df', test, pr_test]] = (model.ssr, model.df_resid, np.nan, np.nan) return table def anova3_lm_single(model, design_info, n_rows, test, pr_test, robust): n_rows += _has_intercept(design_info) terms_info = design_info.terms names = ['sum_sq', 'df', test, pr_test] table = DataFrame(np.zeros((n_rows, 4)), columns = names) cov = _get_covariance(model, robust) col_order = [] index = [] for i, term in enumerate(terms_info): # grab term, hypothesis is that term == 0 cols = design_info.slice(term) L1 = np.eye(model.model.exog.shape[1])[cols] L12 = L1 r = L1.shape[0] if test == 'F': f = model.f_test(L12, cov_p=cov) table.ix[i][test] = test_value = f.fvalue table.ix[i][pr_test] = f.pvalue # need to back out SSR from f_test table.ix[i]['df'] = r #col_order.append(cols.start) index.append(term.name()) table.index = Index(index + ['Residual']) #NOTE: Don't need to sort because terms are an ordered dict now #table = table.ix[np.argsort(col_order + [model.model.exog.shape[1]+1])] # back out sum of squares from f_test ssr = table[test] * table['df'] * model.ssr/model.df_resid table['sum_sq'] = ssr # fill in residual table.ix['Residual'][['sum_sq','df', test, pr_test]] = (model.ssr, model.df_resid, np.nan, np.nan) return table def anova_lm(*args, **kwargs): """ ANOVA table for one or more fitted linear models. Parameters ---------- args : fitted linear model results instance One or more fitted linear models scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {"F", "Chisq", "Cp"} or None Test statistics to provide. Default is "F". typ : str or int {"I","II","III"} or {1,2,3} The type of ANOVA test to perform. See notes. robust : {None, "hc0", "hc1", "hc2", "hc3"} Use heteroscedasticity-corrected coefficient covariance matrix. If robust covariance is desired, it is recommended to use `hc3`. Returns ------- anova : DataFrame A DataFrame containing. Notes ----- Model statistics are given in the order of args. Models must have been fit using the formula api. See Also -------- model_results.compare_f_test, model_results.compare_lm_test Examples -------- >>> import statsmodels.api as sm >>> from statsmodels.formula.api import ols >>> moore = sm.datasets.get_rdataset("Moore", "car", ... cache=True) # load data >>> data = moore.data >>> data = data.rename(columns={"partner.status" : ... "partner_status"}) # make name pythonic >>> moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)', ... data=data).fit() >>> table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame >>> print table """ typ = kwargs.get('typ', 1) ### Farm Out Single model ANOVA Type I, II, III, and IV ### if len(args) == 1: model = args[0] return anova_single(model, **kwargs) try: assert typ in [1,"I"] except: raise ValueError("Multiple models only supported for type I. " "Got type %s" % str(typ)) ### COMPUTE ANOVA TYPE I ### # if given a single model if len(args) == 1: return anova_single(*args, **kwargs) # received multiple fitted models test = kwargs.get("test", "F") scale = kwargs.get("scale", None) n_models = len(args) model_formula = [] pr_test = "Pr(>%s)" % test names = ['df_resid', 'ssr', 'df_diff', 'ss_diff', test, pr_test] table = DataFrame(np.zeros((n_models, 6)), columns = names) if not scale: # assume biggest model is last scale = args[-1].scale table["ssr"] = map(getattr, args, ["ssr"]*n_models) table["df_resid"] = map(getattr, args, ["df_resid"]*n_models) table.ix[1:]["df_diff"] = np.diff(map(getattr, args, ["df_model"]*n_models)) table["ss_diff"] = -table["ssr"].diff() if test == "F": table["F"] = table["ss_diff"] / table["df_diff"] / scale table[pr_test] = stats.f.sf(table["F"], table["df_diff"], table["df_resid"]) # for earlier scipy - stats.f.sf(np.nan, 10, 2) -> 0 not nan table[pr_test][table['F'].isnull()] = np.nan return table if __name__ == "__main__": import pandas from statsmodels.formula.api import ols # in R #library(car) #write.csv(Moore, "moore.csv", row.names=FALSE) moore = pandas.read_table('moore.csv', delimiter=",", skiprows=1, names=['partner_status','conformity', 'fcategory','fscore']) moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)', data=moore).fit() mooreB = ols('conformity ~ C(partner_status, Sum)', data=moore).fit() # for each term you just want to test vs the model without its # higher-order terms # using Monette-Fox slides and Marden class notes for linear algebra / # orthogonal complement # https://netfiles.uiuc.edu/jimarden/www/Classes/STAT324/ table = anova_lm(moore_lm, typ=2) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/api.py000066400000000000000000000040521224417117700227760ustar00rootroot00000000000000# pylint: disable=W0611 import diagnostic from .diagnostic import ( acorr_ljungbox, acorr_breush_godfrey, CompareCox, compare_cox, CompareJ, compare_j, HetGoldfeldQuandt, het_goldfeldquandt, het_breushpagan, het_white, het_arch, linear_harvey_collier, linear_rainbow, linear_lm, breaks_cusumolsresid, breaks_hansen, recursive_olsresiduals, unitroot_adf, normal_ad, lillifors ) import multicomp from .multitest import (multipletests, fdrcorrection, fdrcorrection_twostage) from .multicomp import tukeyhsd import gof from .gof import (powerdiscrepancy, gof_chisquare_discrete, chisquare_effectsize) import stattools from .stattools import durbin_watson, omni_normtest, jarque_bera import sandwich_covariance from .sandwich_covariance import ( cov_cluster, cov_cluster_2groups, cov_nw_panel, cov_hac, cov_white_simple, cov_hc0, cov_hc1, cov_hc2, cov_hc3, se_cov ) from .weightstats import (DescrStatsW, CompareMeans, ttest_ind, ttost_ind, ttost_paired, ztest, ztost, zconfint) from .proportion import (binom_test_reject_interval, binom_test, binom_tost, binom_tost_reject_interval, power_binom_tost, power_ztost_prop, proportion_confint, proportion_effectsize, proportions_chisquare, proportions_chisquare_allpairs, proportions_chisquare_pairscontrol, proportions_ztest, proportions_ztost) from .power import (TTestPower, TTestIndPower, GofChisquarePower, NormalIndPower, FTestAnovaPower, FTestPower, tt_solve_power, tt_ind_solve_power, zt_ind_solve_power) from .descriptivestats import Describe from .anova import anova_lm import moment_helpers from .correlation_tools import corr_nearest, corr_clipped, cov_nearest from statsmodels.sandbox.stats.runs import (mcnemar, cochrans_q, symmetry_bowker, Runs, runstest_1samp, runstest_2samp) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/base.py000066400000000000000000000070131224417117700231370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Base classes for statistical test results Created on Mon Apr 22 14:03:21 2013 Author: Josef Perktold """ import numpy as np class AllPairsResults(object): '''Results class for pairwise comparisons, based on p-values Parameters ---------- pvals_raw : array_like, 1-D p-values from a pairwise comparison test all_pairs : list of tuples list of indices, one pair for each comparison multitest_method : string method that is used by default for p-value correction. This is used as default by the methods like if the multiple-testing method is not specified as argument. levels : None or list of strings optional names of the levels or groups n_levels : None or int If None, then the number of levels or groups is inferred from the other arguments. It can be explicitly specified, if the inferred number is incorrect. Notes ----- This class can also be used for other pairwise comparisons, for example comparing several treatments to a control (as in Dunnet's test). ''' def __init__(self, pvals_raw, all_pairs, multitest_method='hs', levels=None, n_levels=None): self.pvals_raw = pvals_raw self.all_pairs = all_pairs if n_levels is None: # for all_pairs nobs*(nobs-1)/2 #self.n_levels = (1. + np.sqrt(1 + 8 * len(all_pairs))) * 0.5 self.n_levels = np.max(all_pairs) + 1 else: self.n_levels = n_levels self.multitest_method = multitest_method self.levels = levels if levels is None: self.all_pairs_names = ['%r' % (pairs,) for pairs in all_pairs] else: self.all_pairs_names = ['%s-%s' % (levels[pairs[0]], levels[pairs[1]]) for pairs in all_pairs] def pval_corrected(self, method=None): '''p-values corrected for multiple testing problem This uses the default p-value correction of the instance stored in ``self.multitest_method`` if method is None. ''' import statsmodels.stats.multitest as smt if method is None: method = self.multitest_method #TODO: breaks with method=None return smt.multipletests(self.pvals_raw, method=method)[1] def __str__(self): return self.summary() def pval_table(self): '''create a (n_levels, n_levels) array with corrected p_values this needs to improve, similar to R pairwise output ''' k = self.n_levels pvals_mat = np.zeros((k, k)) # if we don't assume we have all pairs pvals_mat[zip(*self.all_pairs)] = self.pval_corrected() #pvals_mat[np.triu_indices(k, 1)] = self.pval_corrected() return pvals_mat def summary(self): '''returns text summarizing the results uses the default pvalue correction of the instance stored in ``self.multitest_method`` ''' import statsmodels.stats.multitest as smt maxlevel = max((len(ss) for ss in self.all_pairs_names)) text = 'Corrected p-values using %s p-value correction\n\n' % \ smt.multitest_methods_names[self.multitest_method] text += 'Pairs' + (' ' * (maxlevel - 5 + 1)) + 'p-values\n' text += '\n'.join(('%s %6.4g' % (pairs, pv) for (pairs, pv) in zip(self.all_pairs_names, self.pval_corrected()))) return text statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/contrast.py000066400000000000000000000134271224417117700240700ustar00rootroot00000000000000import numpy as np from scipy.stats import f as fdist from scipy.stats import t as student_t from statsmodels.tools.tools import clean0, rank, fullrank #TODO: should this be public if it's just a container? class ContrastResults(object): """ Container class for looking at contrasts of coefficients in a model. The class does nothing, it is a container for the results from T and F. """ def __init__(self, t=None, F=None, sd=None, effect=None, df_denom=None, df_num=None): if F is not None: self.fvalue = F self.df_denom = df_denom self.df_num = df_num self.pvalue = fdist.sf(F, df_num, df_denom) else: self.tvalue = t self.sd = sd self.effect = effect self.df_denom = df_denom self.pvalue = student_t.sf(np.abs(t), df_denom) * 2 def __array__(self): if hasattr(self, "fvalue"): return self.fvalue else: return self.tvalue def __str__(self): if hasattr(self, 'fvalue'): return '' % \ (`self.fvalue`, self.pvalue, self.df_denom, self.df_num) else: return '' % \ (`self.effect`, `self.sd`, `self.tvalue`, `self.pvalue`, self.df_denom) def __repr__(self): return str(self.__class__) + '\n' + self.__str__() class Contrast(object): """ This class is used to construct contrast matrices in regression models. They are specified by a (term, design) pair. The term, T, is a linear combination of columns of the design matrix. The matrix attribute of Contrast is a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D. Further, the matrix Tnew = dot(C, D) is full rank. The rank attribute is the rank of dot(D, dot(pinv(D), T)) In a regression model, the contrast tests that E(dot(Tnew, Y)) = 0 for each column of Tnew. Parameters ---------- term ; array-like design : array-like Attributes ---------- contrast_matrix Examples --------- >>>import numpy.random as R >>>import statsmodels.api as sm >>>import numpy as np >>>R.seed(54321) >>>X = R.standard_normal((40,10)) Get a contrast >>>new_term = np.column_stack((X[:,0], X[:,2])) >>>c = sm.contrast.Contrast(new_term, X) >>>test = [[1] + [0]*9, [0]*2 + [1] + [0]*7] >>>np.allclose(c.contrast_matrix, test) True Get another contrast >>>P = np.dot(X, np.linalg.pinv(X)) >>>resid = np.identity(40) - P >>>noise = np.dot(resid,R.standard_normal((40,5))) >>>new_term2 = np.column_stack((noise,X[:,2])) >>>c2 = Contrast(new_term2, X) >>>print c2.contrast_matrix [ -1.26424750e-16 8.59467391e-17 1.56384718e-01 -2.60875560e-17 -7.77260726e-17 -8.41929574e-18 -7.36359622e-17 -1.39760860e-16 1.82976904e-16 -3.75277947e-18] Get another contrast >>>zero = np.zeros((40,)) >>>new_term3 = np.column_stack((zero,X[:,2])) >>>c3 = sm.contrast.Contrast(new_term3, X) >>>test2 = [0]*2 + [1] + [0]*7 >>>np.allclose(c3.contrast_matrix, test2) True """ def _get_matrix(self): """ Gets the contrast_matrix property """ if not hasattr(self, "_contrast_matrix"): self.compute_matrix() return self._contrast_matrix contrast_matrix = property(_get_matrix) def __init__(self, term, design): self.term = np.asarray(term) self.design = np.asarray(design) def compute_matrix(self): """ Construct a contrast matrix C so that colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T))) where pinv(D) is the generalized inverse of D=design. """ T = self.term if T.ndim == 1: T = T[:,None] self.T = clean0(T) self.D = self.design self._contrast_matrix = contrastfromcols(self.T, self.D) try: self.rank = self.matrix.shape[1] except: self.rank = 1 #TODO: fix docstring after usage is settled def contrastfromcols(L, D, pseudo=None): """ From an n x p design matrix D and a matrix L, tries to determine a p x q contrast matrix C which determines a contrast of full rank, i.e. the n x q matrix dot(transpose(C), pinv(D)) is full rank. L must satisfy either L.shape[0] == n or L.shape[1] == p. If L.shape[0] == n, then L is thought of as representing columns in the column space of D. If L.shape[1] == p, then L is thought of as what is known as a contrast matrix. In this case, this function returns an estimable contrast corresponding to the dot(D, L.T) Note that this always produces a meaningful contrast, not always with the intended properties because q is always non-zero unless L is identically 0. That is, it produces a contrast that spans the column space of L (after projection onto the column space of D). Parameters ---------- L : array-like D : array-like """ L = np.asarray(L) D = np.asarray(D) n, p = D.shape if L.shape[0] != n and L.shape[1] != p: raise ValueError("shape of L and D mismatched") if pseudo is None: pseudo = np.linalg.pinv(D) # D^+ \approx= ((dot(D.T,D))^(-1),D.T) if L.shape[0] == n: C = np.dot(pseudo, L).T else: C = L C = np.dot(pseudo, np.dot(D, C.T)).T Lp = np.dot(D, C.T) if len(Lp.shape) == 1: Lp.shape = (n, 1) if rank(Lp) != Lp.shape[1]: Lp = fullrank(Lp) C = np.dot(pseudo, Lp).T return np.squeeze(C) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/correlation_tools.py000066400000000000000000000147541224417117700260000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Aug 17 13:10:52 2012 Author: Josef Perktold License: BSD-3 """ import numpy as np def clip_evals(x, value=0): #threshold=0, value=0): evals, evecs = np.linalg.eigh(x) clipped = np.any(evals < 0) x_new = np.dot(evecs * np.maximum(evals, value), evecs.T) return x_new, clipped def corr_nearest(corr, threshold=1e-15, n_fact=100): ''' Find the nearest correlation matrix that is positive semi-definite. The function iteratively adjust the correlation matrix by clipping the eigenvalues of a difference matrix. The diagonal elements are set to one. Parameters ---------- corr : ndarray, (k, k) initial correlation matrix threshold : float clipping threshold for smallest eigenvalue, see Notes n_fact : int or float factor to determine the maximum number of iterations. The maximum number of iterations is the integer part of the number of columns in the correlation matrix times n_fact. Returns ------- corr_new : ndarray, (optional) corrected correlation matrix Notes ----- The smallest eigenvalue of the corrected correlation matrix is approximately equal to the ``threshold``. If the threshold=0, then the smallest eigenvalue of the correlation matrix might be negative, but zero within a numerical error, for example in the range of -1e-16. Assumes input correlation matrix is symmetric. Stops after the first step if correlation matrix is already positive semi-definite or positive definite, so that smallest eigenvalue is above threshold. In this case, the returned array is not the original, but is equal to it within numerical precision. See Also -------- corr_clipped cov_nearest ''' k_vars = corr.shape[0] if k_vars != corr.shape[1]: raise ValueError("matrix is not square") diff = np.zeros(corr.shape) x_new = corr.copy() diag_idx = np.arange(k_vars) for ii in range(int(len(corr) * n_fact)): x_adj = x_new - diff x_psd, clipped = clip_evals(x_adj, value=threshold) if not clipped: x_new = x_psd break diff = x_psd - x_adj x_new = x_psd.copy() x_new[diag_idx, diag_idx] = 1 else: import warnings warnings.warn('maximum iteration reached') return x_new def corr_clipped(corr, threshold=1e-15): ''' Find a near correlation matrix that is positive semi-definite This function clips the eigenvalues, replacing eigenvalues smaller than the threshold by the threshold. The new matrix is normalized, so that the diagonal elements are one. Compared to corr_nearest, the distance between the original correlation matrix and the positive definite correlation matrix is larger, however, it is much faster since it only computes eigenvalues only once. Parameters ---------- corr : ndarray, (k, k) initial correlation matrix threshold : float clipping threshold for smallest eigenvalue, see Notes Returns ------- corr_new : ndarray, (optional) corrected correlation matrix Notes ----- The smallest eigenvalue of the corrected correlation matrix is approximately equal to the ``threshold``. In examples, the smallest eigenvalue can be by a factor of 10 smaller than the threshold, e.g. threshold 1e-8 can result in smallest eigenvalue in the range between 1e-9 and 1e-8. If the threshold=0, then the smallest eigenvalue of the correlation matrix might be negative, but zero within a numerical error, for example in the range of -1e-16. Assumes input correlation matrix is symmetric. The diagonal elements of returned correlation matrix is set to ones. If the correlation matrix is already positive semi-definite given the threshold, then the original correlation matrix is returned. ``cov_clipped`` is 40 or more times faster than ``cov_nearest`` in simple example, but has a slightly larger approximation error. See Also -------- corr_nearest cov_nearest ''' x_new, clipped = clip_evals(corr, value=threshold) if not clipped: return corr #cov2corr x_std = np.sqrt(np.diag(x_new)) x_new = x_new / x_std / x_std[:,None] return x_new def cov_nearest(cov, method='clipped', threshold=1e-15, n_fact=100, return_all=False): ''' Find the nearest covariance matrix that is postive (semi-) definite This leaves the diagonal, i.e. the variance, unchanged Parameters ---------- cov : ndarray, (k,k) initial covariance matrix method : string if "clipped", then the faster but less accurate ``corr_clipped`` is used. if "nearest", then ``corr_nearest`` is used threshold : float clipping threshold for smallest eigen value, see Notes nfact : int or float factor to determine the maximum number of iterations in ``corr_nearest``. See its doc string return_all : bool if False (default), then only the covariance matrix is returned. If True, then correlation matrix and standard deviation are additionally returned. Returns ------- cov_ : ndarray corrected covariance matrix corr_ : ndarray, (optional) corrected correlation matrix std_ : ndarray, (optional) standard deviation Notes ----- This converts the covariance matrix to a correlation matrix. Then, finds the nearest correlation matrix that is positive semidefinite and converts it back to a covariance matrix using the initial standard deviation. The smallest eigenvalue of the intermediate correlation matrix is approximately equal to the ``threshold``. If the threshold=0, then the smallest eigenvalue of the correlation matrix might be negative, but zero within a numerical error, for example in the range of -1e-16. Assumes input covariance matrix is symmetric. See Also -------- corr_nearest corr_clipped ''' from statsmodels.stats.moment_helpers import cov2corr, corr2cov cov_, std_ = cov2corr(cov, return_std=True) if method == 'clipped': corr_ = corr_clipped(cov_, threshold=threshold) elif method == 'nearest': corr_ = corr_nearest(cov_, threshold=threshold, n_fact=n_fact) cov_ = corr2cov(corr_, std_) if return_all: return cov_, corr_, std_ else: return cov_ if __name__ == '__main__': pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/descriptivestats.py000066400000000000000000000324141224417117700256300ustar00rootroot00000000000000import sys import numpy as np from scipy import stats #from statsmodels.iolib.table import SimpleTable from statsmodels.iolib.table import SimpleTable from statsmodels.tools.decorators import nottest def _kurtosis(a): '''wrapper for scipy.stats.kurtosis that returns nan instead of raising Error missing options ''' try: res = stats.kurtosis(a) except ValueError: res = np.nan return res def _skew(a): '''wrapper for scipy.stats.skew that returns nan instead of raising Error missing options ''' try: res = stats.skew(a) except ValueError: res = np.nan return res _sign_test_doc = ''' Signs test. Parameters ---------- samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns --------- M, p-value Notes ----- The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above `mu0`, N(-) is the number of values below. Values equal to `mu0` are discarded. The p-value for M is calculated using the binomial distrubution and can be intrepreted the same as for a t-test. The test-statistic is distributed Binom(min(N(+), N(-)), n_trials, .5) where n_trials equals N(+) + N(-). See Also --------- scipy.stats.wilcoxon ''' @nottest def sign_test(samp, mu0=0): samp = np.asarray(samp) pos = np.sum(samp > mu0) neg = np.sum(samp < mu0) M = (pos-neg)/2. p = stats.binom_test(min(pos,neg), pos+neg, .5) return M, p sign_test.__doc__ = _sign_test_doc class Describe(object): ''' Calculates descriptive statistics for data. Defaults to a basic set of statistics, "all" can be specified, or a list can be given. Parameters ---------- dataset : array-like 2D dataset for descriptive statistics. ''' def __init__(self, dataset): self.dataset = dataset #better if this is initially a list to define order, or use an # ordered dict. First position is the function # Second position is the tuple/list of column names/numbers # third is are the results in order of the columns self.univariate = dict( obs = [len, None, None], mean = [np.mean, None, None], std = [np.std, None, None], min = [np.min, None, None], max = [np.max, None, None], ptp = [np.ptp, None, None], var = [np.var, None, None], mode_val = [self._mode_val, None, None], mode_bin = [self._mode_bin, None, None], median = [np.median, None, None], skew = [stats.skew, None, None], uss = [stats.ss, None, None], kurtosis = [stats.kurtosis, None, None], percentiles = [self._percentiles, None, None], #BUG: not single value #sign_test_M = [self.sign_test_m, None, None], #sign_test_P = [self.sign_test_p, None, None] ) #TODO: Basic stats for strings #self.strings = dict( #unique = [np.unique, None, None], #number_uniq = [len( #most = [ #least = [ #TODO: Multivariate #self.multivariate = dict( #corrcoef(x[, y, rowvar, bias]), #cov(m[, y, rowvar, bias]), #histogram2d(x, y[, bins, range, normed, weights]) #) self._arraytype = None self._columns_list = None def _percentiles(self,x): p = [stats.scoreatpercentile(x,per) for per in (1,5,10,25,50,75,90,95,99)] return p def _mode_val(self,x): return stats.mode(x)[0][0] def _mode_bin(self,x): return stats.mode(x)[1][0] def _array_typer(self): """if not a sctructured array""" if not(self.dataset.dtype.names): """homogeneous dtype array""" self._arraytype = 'homog' elif self.dataset.dtype.names: """structured or rec array""" self._arraytype = 'sctruct' else: assert self._arraytype == 'sctruct' or self._arraytype == 'homog' def _is_dtype_like(self, col): """ Check whether self.dataset.[col][0] behaves like a string, numbern unknown. `numpy.lib._iotools._is_string_like` """ def string_like(): #TODO: not sure what the result is if the first item is some type of # missing value try: self.dataset[col][0] + '' except (TypeError, ValueError): return False return True def number_like(): try: self.dataset[col][0] + 1.0 except (TypeError, ValueError): return False return True if number_like()==True and string_like()==False: return 'number' elif number_like()==False and string_like()==True: return 'string' else: assert (number_like()==True or string_like()==True), '\ Not sure of dtype'+str(self.dataset[col][0]) #@property def summary(self, stats='basic', columns='all', orientation='auto'): """ Return a summary of descriptive statistics. Parameters ----------- stats: list or str The desired statistics, Accepts 'basic' or 'all' or a list. 'basic' = ('obs', 'mean', 'std', 'min', 'max') 'all' = ('obs', 'mean', 'std', 'min', 'max', 'ptp', 'var', 'mode', 'meadian', 'skew', 'uss', 'kurtosis', 'percentiles') columns : list or str The columns/variables to report the statistics, default is 'all' If an object with named columns is given, you may specify the column names. For example """ #NOTE # standard array: Specifiy column numbers (NEED TO TEST) # percentiles currently broken # mode requires mode_val and mode_bin separately if self._arraytype == None: self._array_typer() if stats == 'basic': stats = ('obs', 'mean', 'std', 'min', 'max') elif stats == 'all': #stats = self.univariate.keys() #dict doesn't keep an order, use full list instead stats = ['obs', 'mean', 'std', 'min', 'max', 'ptp', 'var', 'mode_val', 'mode_bin', 'median', 'uss', 'skew', 'kurtosis', 'percentiles'] else: for astat in stats: pass #assert astat in self.univariate #hack around percentiles multiple output #bad naming import scipy.stats #BUG: the following has all per the same per=99 ##perdict = dict(('perc_%2d'%per, [lambda x: # scipy.stats.scoreatpercentile(x, per), None, None]) ## for per in (1,5,10,25,50,75,90,95,99)) def _fun(per): return lambda x: scipy.stats.scoreatpercentile(x, per) perdict = dict(('perc_%02d'%per, [_fun(per), None, None]) for per in (1,5,10,25,50,75,90,95,99)) if 'percentiles' in stats: self.univariate.update(perdict) idx = stats.index('percentiles') stats[idx:idx+1] = sorted(perdict.keys()) #JP: this doesn't allow a change in sequence, sequence in stats is #ignored #this is just an if condition if any([aitem[1] for aitem in self.univariate.items() if aitem[0] in stats]): if columns == 'all': self._columns_list = [] if self._arraytype == 'sctruct': self._columns_list = self.dataset.dtype.names #self._columns_list = [col for col in # self.dataset.dtype.names if #(self._is_dtype_like(col)=='number')] else: self._columns_list = range(self.dataset.shape[1]) else: self._columns_list = columns if self._arraytype == 'sctruct': for col in self._columns_list: assert (col in self.dataset.dtype.names) else: assert self._is_dtype_like(self.dataset) == 'number' columstypes = self.dataset.dtype #TODO: do we need to make sure they dtype is float64 ? for astat in stats: calc = self.univariate[astat] if self._arraytype == 'sctruct': calc[1] = self._columns_list calc[2] = [calc[0](self.dataset[col]) for col in self._columns_list if (self._is_dtype_like(col) == 'number')] #calc[2].append([len(np.unique(self.dataset[col])) for col #in self._columns_list if #self._is_dtype_like(col)=='string'] else: calc[1] = ['Col '+str(col) for col in self._columns_list] calc[2] = [calc[0](self.dataset[:,col]) for col in self._columns_list] return self.print_summary(stats, orientation=orientation) else: return self.print_summary(stats, orientation=orientation) def print_summary(self, stats, orientation='auto'): #TODO: need to specify a table formating for the numbers, using defualt title = 'Summary Statistics' header = stats stubs = self.univariate['obs'][1] data = [[self.univariate[astat][2][col] for astat in stats] for col in range(len(self.univariate['obs'][2]))] if (orientation == 'varcols') or \ (orientation == 'auto' and len(stubs) < len(header)): #swap rows and columns data = map(lambda *row: list(row), *data) header, stubs = stubs, header part_fmt = dict(data_fmts = ["%#8.4g"]*(len(header)-1)) table = SimpleTable(data, header, stubs, title=title, txt_fmt = part_fmt) return table def sign_test(self, samp, mu0=0): return sign_test(samp, mu0) sign_test.__doc__ = _sign_test_doc #TODO: There must be a better way but formating the stats of a fuction that # returns 2 values is a problem. #def sign_test_m(samp,mu0=0): #return self.sign_test(samp,mu0)[0] #def sign_test_p(samp,mu0=0): #return self.sign_test(samp,mu0)[1] if __name__ == "__main__": #unittest.main() t1 = Describe(data4) #print(t1.summary(stats='all')) noperc = ['obs', 'mean', 'std', 'min', 'max', 'ptp', #'mode', #'var', 'median', 'skew', 'uss', 'kurtosis'] #TODO: mode var raise exception, #TODO: percentile writes list in cell (?), huge wide format print(t1.summary(stats=noperc)) print(t1.summary()) print(t1.summary( orientation='varcols')) print(t1.summary(stats=['mean', 'median', 'min', 'max'], orientation=('varcols'))) print(t1.summary(stats='all')) import unittest data1 = np.array([(1,2,'a','aa'), (2,3,'b','bb'), (2,4,'b','cc')], dtype = [('alpha',float), ('beta', int), ('gamma', '|S1'), ('delta', '|S2')]) data2 = np.array([(1,2), (2,3), (2,4)], dtype = [('alpha',float), ('beta', float)]) data3 = np.array([[1,2,4,4], [2,3,3,3], [2,4,4,3]], dtype=float) data4 = np.array([[1,2,3,4,5,6], [6,5,4,3,2,1], [9,9,9,9,9,9]]) class TestSimpleTable(unittest.TestCase): #from statsmodels.iolib.table import SimpleTable, default_txt_fmt def test_basic_1(self): print('test_basic_1') t1 = Describe(data1) print(t1.summary()) def test_basic_2(self): print('test_basic_2') t2 = Describe(data2) print(t2.summary()) def test_basic_3(self): print('test_basic_3') t1 = Describe(data3) print(t1.summary()) def test_basic_4(self): print('test_basic_4') t1 = Describe(data4) print(t1.summary()) def test_basic_1a(self): print('test_basic_1a') t1 = Describe(data1) print(t1.summary(stats='basic', columns=['alpha'])) def test_basic_1b(self): print('test_basic_1b') t1 = Describe(data1) print(t1.summary(stats='basic', columns='all')) def test_basic_2a(self): print('test_basic_2a') t2 = Describe(data2) print(t2.summary(stats='all')) def test_basic_3(aself): t1 = Describe(data3) print(t1.summary(stats='all')) def test_basic_4a(self): t1 = Describe(data4) print(t1.summary(stats='all')) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/diagnostic.py000066400000000000000000000007741224417117700243600ustar00rootroot00000000000000#collect some imports of verified (at least one example) functions from statsmodels.sandbox.stats.diagnostic import ( acorr_ljungbox, breaks_cusumolsresid, breaks_hansen, CompareCox, CompareJ, compare_cox, compare_j, het_breushpagan, HetGoldfeldQuandt, het_goldfeldquandt, het_arch, het_white, recursive_olsresiduals, acorr_breush_godfrey, linear_harvey_collier, linear_rainbow, linear_lm, unitroot_adf) from .lilliefors import kstest_normal, lillifors from .adnorm import normal_ad statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/gof.py000066400000000000000000000405701224417117700230050ustar00rootroot00000000000000'''extra statistical function and helper functions contains: * goodness-of-fit tests - powerdiscrepancy - gof_chisquare_discrete - gof_binning_discrete Author: Josef Perktold License : BSD-3 changes ------- 2013-02-25 : add chisquare_power, effectsize and "value" ''' import numpy as np from scipy import stats # copied from regression/stats.utils def powerdiscrepancy(observed, expected, lambd=0.0, axis=0, ddof=0): """Calculates power discrepancy, a class of goodness-of-fit tests as a measure of discrepancy between observed and expected data. This contains several goodness-of-fit tests as special cases, see the describtion of lambd, the exponent of the power discrepancy. The pvalue is based on the asymptotic chi-square distribution of the test statistic. freeman_tukey: D(x|\theta) = \sum_j (\sqrt{x_j} - \sqrt{e_j})^2 Parameters ---------- o : Iterable Observed values e : Iterable Expected values lambd : float or string * float : exponent `a` for power discrepancy * 'loglikeratio': a = 0 * 'freeman_tukey': a = -0.5 * 'pearson': a = 1 (standard chisquare test statistic) * 'modified_loglikeratio': a = -1 * 'cressie_read': a = 2/3 * 'neyman' : a = -2 (Neyman-modified chisquare, reference from a book?) axis : int axis for observations of one series ddof : int degrees of freedom correction, Returns ------- D_obs : Discrepancy of observed values pvalue : pvalue References ---------- Cressie, Noel and Timothy R. C. Read, Multinomial Goodness-of-Fit Tests, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 46, No. 3 (1984), pp. 440-464 Campbell B. Read: Freeman-Tukey chi-squared goodness-of-fit statistics, Statistics & Probability Letters 18 (1993) 271-278 Nobuhiro Taneichi, Yuri Sekiya, Akio Suzukawa, Asymptotic Approximations for the Distributions of the Multinomial Goodness-of-Fit Statistics under Local Alternatives, Journal of Multivariate Analysis 81, 335?359 (2002) Steele, M. 1,2, C. Hurst 3 and J. Chaseling, Simulated Power of Discrete Goodness-of-Fit Tests for Likert Type Data Examples -------- >>> observed = np.array([ 2., 4., 2., 1., 1.]) >>> expected = np.array([ 0.2, 0.2, 0.2, 0.2, 0.2]) for checking correct dimension with multiple series >>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd='freeman_tukey',axis=1) (array([[ 2.745166, 2.745166]]), array([[ 0.6013346, 0.6013346]])) >>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected,axis=1) (array([[ 2.77258872, 2.77258872]]), array([[ 0.59657359, 0.59657359]])) >>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=0,axis=1) (array([[ 2.77258872, 2.77258872]]), array([[ 0.59657359, 0.59657359]])) >>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=1,axis=1) (array([[ 3., 3.]]), array([[ 0.5578254, 0.5578254]])) >>> powerdiscrepancy(np.column_stack((observed,observed)).T, 10*expected, lambd=2/3.0,axis=1) (array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]])) >>> powerdiscrepancy(np.column_stack((observed,observed)).T, expected, lambd=2/3.0,axis=1) (array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]])) >>> powerdiscrepancy(np.column_stack((observed,observed)), expected, lambd=2/3.0, axis=0) (array([[ 2.89714546, 2.89714546]]), array([[ 0.57518277, 0.57518277]])) each random variable can have different total count/sum >>> powerdiscrepancy(np.column_stack((observed,2*observed)), expected, lambd=2/3.0, axis=0) (array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]])) >>> powerdiscrepancy(np.column_stack((observed,2*observed)), expected, lambd=2/3.0, axis=0) (array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]])) >>> powerdiscrepancy(np.column_stack((2*observed,2*observed)), expected, lambd=2/3.0, axis=0) (array([[ 5.79429093, 5.79429093]]), array([[ 0.21504648, 0.21504648]])) >>> powerdiscrepancy(np.column_stack((2*observed,2*observed)), 20*expected, lambd=2/3.0, axis=0) (array([[ 5.79429093, 5.79429093]]), array([[ 0.21504648, 0.21504648]])) >>> powerdiscrepancy(np.column_stack((observed,2*observed)), np.column_stack((10*expected,20*expected)), lambd=2/3.0, axis=0) (array([[ 2.89714546, 5.79429093]]), array([[ 0.57518277, 0.21504648]])) >>> powerdiscrepancy(np.column_stack((observed,2*observed)), np.column_stack((10*expected,20*expected)), lambd=-1, axis=0) (array([[ 2.77258872, 5.54517744]]), array([[ 0.59657359, 0.2357868 ]])) """ o = np.array(observed) e = np.array(expected) if np.isfinite(lambd) == True: # check whether lambd is a number a = lambd else: if lambd == 'loglikeratio': a = 0 elif lambd == 'freeman_tukey': a = -0.5 elif lambd == 'pearson': a = 1 elif lambd == 'modified_loglikeratio': a = -1 elif lambd == 'cressie_read': a = 2/3.0 else: raise ValueError('lambd has to be a number or one of ' + \ 'loglikeratio, freeman_tukey, pearson, ' +\ 'modified_loglikeratio or cressie_read') n = np.sum(o, axis=axis) nt = n if n.size>1: n = np.atleast_2d(n) if axis == 1: nt = n.T # need both for 2d, n and nt for broadcasting if e.ndim == 1: e = np.atleast_2d(e) if axis == 0: e = e.T if np.all(np.sum(e, axis=axis) == n): p = e/(1.0*nt) elif np.all(np.sum(e, axis=axis) == 1): p = e e = nt * e else: raise ValueError('observed and expected need to have the same ' +\ 'number of observations, or e needs to add to 1') k = o.shape[axis] if e.shape[axis] != k: raise ValueError('observed and expected need to have the same ' +\ 'number of bins') # Note: taken from formulas, to simplify cancel n if a == 0: # log likelihood ratio D_obs = 2*n * np.sum(o/(1.0*nt) * np.log(o/e), axis=axis) elif a == -1: # modified log likelihood ratio D_obs = 2*n * np.sum(e/(1.0*nt) * np.log(e/o), axis=axis) else: D_obs = 2*n/a/(a+1) * np.sum(o/(1.0*nt) * ((o/e)**a - 1), axis=axis) return D_obs, stats.chi2.sf(D_obs,k-1-ddof) #todo: need also binning for continuous distribution # and separated binning function to be used for powerdiscrepancy def gof_chisquare_discrete(distfn, arg, rvs, alpha, msg): '''perform chisquare test for random sample of a discrete distribution Parameters ---------- distname : string name of distribution function arg : sequence parameters of distribution alpha : float significance level, threshold for p-value Returns ------- result : bool 0 if test passes, 1 if test fails Notes ----- originally written for scipy.stats test suite, still needs to be checked for standalone usage, insufficient input checking may not run yet (after copy/paste) refactor: maybe a class, check returns, or separate binning from test results ''' # define parameters for test ## n=2000 n = len(rvs) nsupp = 20 wsupp = 1.0/nsupp ## distfn = getattr(stats, distname) ## np.random.seed(9765456) ## rvs = distfn.rvs(size=n,*arg) # construct intervals with minimum mass 1/nsupp # intervalls are left-half-open as in a cdf difference distsupport = xrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1) last = 0 distsupp = [max(distfn.a, -1000)] distmass = [] for ii in distsupport: current = distfn.cdf(ii,*arg) if current - last >= wsupp-1e-14: distsupp.append(ii) distmass.append(current - last) last = current if current > (1-wsupp): break if distsupp[-1] < distfn.b: distsupp.append(distfn.b) distmass.append(1-last) distsupp = np.array(distsupp) distmass = np.array(distmass) # convert intervals to right-half-open as required by histogram histsupp = distsupp+1e-8 histsupp[0] = distfn.a # find sample frequencies and perform chisquare test #TODO: move to compatibility.py if np.__version__ < '1.5': freq,hsupp = np.histogram(rvs, histsupp, new=True) else: freq,hsupp = np.histogram(rvs,histsupp) cdfs = distfn.cdf(distsupp,*arg) (chis,pval) = stats.chisquare(np.array(freq),n*distmass) return chis, pval, (pval > alpha), 'chisquare - test for %s' \ 'at arg = %s with pval = %s' % (msg,str(arg),str(pval)) # copy/paste, remove code duplication when it works def gof_binning_discrete(rvs, distfn, arg, nsupp=20): '''get bins for chisquare type gof tests for a discrete distribution Parameters ---------- rvs : array sample data distname : string name of distribution function arg : sequence parameters of distribution nsupp : integer number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller. Returns ------- freq : array empirical frequencies for sample; not normalized, adds up to sample size expfreq : array theoretical frequencies according to distribution histsupp : array bin boundaries for histogram, (added 1e-8 for numerical robustness) Notes ----- The results can be used for a chisquare test :: (chis,pval) = stats.chisquare(freq, expfreq) originally written for scipy.stats test suite, still needs to be checked for standalone usage, insufficient input checking may not run yet (after copy/paste) refactor: maybe a class, check returns, or separate binning from test results todo : optimal number of bins ? (check easyfit), recommendation in literature at least 5 expected observations in each bin ''' # define parameters for test ## n=2000 n = len(rvs) wsupp = 1.0/nsupp ## distfn = getattr(stats, distname) ## np.random.seed(9765456) ## rvs = distfn.rvs(size=n,*arg) # construct intervals with minimum mass 1/nsupp # intervalls are left-half-open as in a cdf difference distsupport = xrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1) last = 0 distsupp = [max(distfn.a, -1000)] distmass = [] for ii in distsupport: current = distfn.cdf(ii,*arg) if current - last >= wsupp-1e-14: distsupp.append(ii) distmass.append(current - last) last = current if current > (1-wsupp): break if distsupp[-1] < distfn.b: distsupp.append(distfn.b) distmass.append(1-last) distsupp = np.array(distsupp) distmass = np.array(distmass) # convert intervals to right-half-open as required by histogram histsupp = distsupp+1e-8 histsupp[0] = distfn.a # find sample frequencies and perform chisquare test if np.__version__ < '1.5': freq,hsupp = np.histogram(rvs, histsupp, new=True) else: freq,hsupp = np.histogram(rvs,histsupp) #freq,hsupp = np.histogram(rvs,histsupp,new=True) cdfs = distfn.cdf(distsupp,*arg) return np.array(freq), n*distmass, histsupp # -*- coding: utf-8 -*- """Extension to chisquare goodness-of-fit test Created on Mon Feb 25 13:46:53 2013 Author: Josef Perktold License: BSD-3 """ def chisquare(f_obs, f_exp=None, value=0, ddof=0, return_basic=True): '''chisquare goodness-of-fit test The null hypothesis is that the distance between the expected distribution and the observed frequencies is ``value``. The alternative hypothesis is that the distance is larger than ``value``. ``value`` is normalized in terms of effect size. The standard chisquare test has the null hypothesis that ``value=0``, that is the distributions are the same. Notes ----- The case with value greater than zero is similar to an equivalence test, that the exact null hypothesis is replaced by an approximate hypothesis. However, TOST "reverses" null and alternative hypothesis, while here the alternative hypothesis is that the distance (divergence) is larger than a threshold. References ---------- McLaren, ... Drost,... See Also -------- powerdiscrepancy scipy.stats.chisquare ''' f_obs = np.asarray(f_obs) n_bins = len(f_obs) nobs = f_obs.sum(0) if f_exp is None: # uniform distribution f_exp = np.empty(n_bins, float) f_exp.fill(nobs / float(n_bins)) f_exp = np.asarray(f_exp, float) chisq = ((f_obs - f_exp)**2 / f_exp).sum(0) if value == 0: pvalue = stats.chi2.sf(chisq, n_bins - 1 - ddof) else: pvalue = stats.ncx2.sf(chisq, n_bins - 1 - ddof, value**2 * nobs) if return_basic: return chisq, pvalue else: return chisq, pvalue #TODO: replace with TestResults def chisquare_power(effect_size, nobs, n_bins, alpha=0.05, ddof=0): '''power of chisquare goodness of fit test effect size is sqrt of chisquare statistic divided by nobs Parameters ---------- effect_size : float This is the deviation from the Null of the normalized chi_square statistic. This follows Cohen's definition (sqrt). nobs : int or float number of observations n_bins : int (or float) number of bins, or points in the discrete distribution alpha : float in (0,1) significance level of the test, default alpha=0.05 Returns ------- power : float power of the test at given significance level at effect size Notes ----- This function also works vectorized if all arguments broadcast. This can also be used to calculate the power for power divergence test. However, for the range of more extreme values of the power divergence parameter, this power is not a very good approximation for samples of small to medium size (Drost et al. 1989) References ---------- Drost, ... See Also -------- chisquare_effectsize statsmodels.stats.GofChisquarePower ''' crit = stats.chi2.isf(alpha, n_bins - 1 - ddof) power = stats.ncx2.sf(crit, n_bins - 1 - ddof, effect_size**2 * nobs) return power def chisquare_effectsize(probs0, probs1, correction=None, cohen=True, axis=0): '''effect size for a chisquare goodness-of-fit test Parameters ---------- probs0 : array_like probabilities or cell frequencies under the Null hypothesis probs1 : array_like probabilities or cell frequencies under the Alternative hypothesis probs0 and probs1 need to have the same length in the ``axis`` dimension. and broadcast in the other dimensions Both probs0 and probs1 are normalized to add to one (in the ``axis`` dimension). correction : None or tuple (nobs, df) If None, then the effect size is the chisquare statistic divide by the number of observations. If the correction is a tuple (nobs, df), then the effectsize is corrected to have less bias and a smaller variance. However, the correction can make the effectsize negative. In that case, the effectsize is set to zero. Pederson and Johnson (1990) as referenced in McLaren et all. (1994) cohen : bool If True, then the square root is returned as in the definition of the effect size by Cohen (1977), If False, then the original effect size is returned. axis : int If the probability arrays broadcast to more than 1 dimension, then this is the axis over which the sums are taken. Returns ------- effectsize : float effect size of chisquare test ''' probs0 = np.asarray(probs0, float) probs1 = np.asarray(probs1, float) probs0 = probs0 / probs0.sum(axis) probs1 = probs1 / probs1.sum(axis) d2 = ((probs1 - probs0)**2 / probs0).sum(axis) if correction is not None: nobs, df = correction diff = ((probs1 - probs0) / probs0).sum(axis) d2 = np.maximum((d2 * nobs - diff - df) / (nobs - 1.), 0) if cohen: return np.sqrt(d2) else: return d2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/inter_rater.py000066400000000000000000000412131224417117700245430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Inter Rater Agreement contains -------- fleiss_kappa cohens_kappa aggregate_raters: helper function to get data into fleiss_kappa format to_table: helper function to create contingency table, can be used for cohens_kappa Created on Thu Dec 06 22:57:56 2012 Author: Josef Perktold License: BSD-3 References ---------- Wikipedia: kappa's initially based on these two pages http://en.wikipedia.org/wiki/Fleiss%27_kappa http://en.wikipedia.org/wiki/Cohen's_kappa SAS-Manual : formulas for cohens_kappa, especially variances see also R package irr TODO ---- standard errors and hypothesis tests for fleiss_kappa other statistics and tests, in R package irr, SAS has more inconsistent internal naming, changed variable names as I added more functionality convenience functions to create required data format from raw data DONE """ import numpy as np from scipy import stats #get rid of this? need only norm.sf class ResultsBunch(dict): template = '%r' def __init__(self, **kwds): dict.__init__(self, kwds) self.__dict__ = self self._initialize() def _initialize(self): pass def __str__(self): return self.template % self def _int_ifclose(x, dec=1, width=4): '''helper function for creating result string for int or float only dec=1 and width=4 is implemented Parameters ---------- x : int or float value to format dec : 1 number of decimals to print if x is not an integer width : 4 width of string Returns ------- xint : int or float x is converted to int if it is within 1e-14 of an integer x_string : str x formatted as string, either '%4d' or '%4.1f' ''' xint = int(round(x)) if np.max(np.abs(xint - x)) < 1e-14: return xint, '%4d' % xint else: return x, '%4.1f' % x def aggregate_raters(data, n_cat=None): '''convert raw data with shape (subject, rater) to (subject, cat_counts) brings data into correct format for fleiss_kappa bincount will raise exception if data cannot be converted to integer. Parameters ---------- data : array_like, 2-Dim data containing category assignment with subjects in rows and raters in columns. n_cat : None or int If None, then the data is converted to integer categories, 0,1,2,...,n_cat-1. Because of the relabeling only category levels with non-zero counts are included. If this is an integer, then the category levels in the data are already assumed to be in integers, 0,1,2,...,n_cat-1. In this case, the returned array may contain columns with zero count, if no subject has been categorized with this level. Returns ------- arr : nd_array, (n_rows, n_cat) Contains counts of raters that assigned a category level to individuals. Subjects are in rows, category levels in columns. ''' data = np.asarray(data) n_rows = data.shape[0] if n_cat is None: #I could add int conversion (reverse_index) to np.unique cat_uni, cat_int = np.unique(data.ravel(), return_inverse=True) n_cat = len(cat_uni) data_ = cat_int.reshape(data.shape) else: cat_uni = np.arange(n_cat) #for return only, assumed cat levels data_ = data tt = np.zeros((n_rows, n_cat), int) for idx, row in enumerate(data_): ro = np.bincount(row) tt[idx, :len(ro)] = ro return tt, cat_uni def to_table(data, bins=None): '''convert raw data with shape (subject, rater) to (rater1, rater2) brings data into correct format for cohens_kappa Parameters ---------- data : array_like, 2-Dim data containing category assignment with subjects in rows and raters in columns. bins : None, int or tuple of array_like If None, then the data is converted to integer categories, 0,1,2,...,n_cat-1. Because of the relabeling only category levels with non-zero counts are included. If this is an integer, then the category levels in the data are already assumed to be in integers, 0,1,2,...,n_cat-1. In this case, the returned array may contain columns with zero count, if no subject has been categorized with this level. If bins are a tuple of two array_like, then the bins are directly used by ``numpy.histogramdd``. This is useful if we want to merge categories. Returns ------- arr : nd_array, (n_cat, n_cat) Contingency table that contains counts of category level with rater1 in rows and rater2 in columns. Notes ----- no NaN handling, delete rows with missing values This works also for more than two raters. In that case the dimension of the resulting contingency table is the same as the number of raters instead of 2-dimensional. ''' data = np.asarray(data) n_rows, n_cols = data.shape if bins is None: #I could add int conversion (reverse_index) to np.unique cat_uni, cat_int = np.unique(data.ravel(), return_inverse=True) n_cat = len(cat_uni) data_ = cat_int.reshape(data.shape) bins_ = np.arange(n_cat+1) - 0.5 #alternative implementation with double loop #tt = np.asarray([[(x == [i,j]).all(1).sum() for j in cat_uni] # for i in cat_uni] ) #other altervative: unique rows and bincount elif np.isscalar(bins): bins_ = np.arange(bins+1) - 0.5 data_ = data else: bins_ = bins data_ = data tt = np.histogramdd(data_, (bins_,)*n_cols) return tt[0], bins_ def fleiss_kappa(table): '''Fleiss' kappa multi-rater agreement measure Parameters ---------- table : array_like, 2-D assumes subjects in rows, and categories in columns Returns ------- kappa : float Fleiss's kappa statistic for inter rater agreement Notes ----- coded from Wikipedia page http://en.wikipedia.org/wiki/Fleiss%27_kappa no variance or tests yet ''' table = 1.0 * np.asarray(table) #avoid integer division n_sub, n_cat = table.shape n_total = table.sum() n_rater = table.sum(1) n_rat = n_rater.max() #assume fully ranked assert n_total == n_sub * n_rat #marginal frequency of categories p_cat = table.sum(0) / n_total table2 = table * table p_rat = (table2.sum(1) - n_rat) / (n_rat * (n_rat - 1.)) p_mean = p_rat.mean() p_mean_exp = (p_cat*p_cat).sum() kappa = (p_mean - p_mean_exp) / (1- p_mean_exp) return kappa def cohens_kappa(table, weights=None, return_results=True, wt=None): '''Compute Cohen's kappa with variance and equal-zero test Parameters ---------- table : array_like, 2-Dim square array with results of two raters, one rater in rows, second rater in columns weights : array_like The interpretation of weights depends on the wt argument. If both are None, then the simple kappa is computed. see wt for the case when wt is not None If weights is two dimensional, then it is directly used as a weight matrix. For computing the variance of kappa, the maximum of the weights is assumed to be smaller or equal to one. TODO: fix conflicting definitions in the 2-Dim case for wt : None or string If wt and weights are None, then the simple kappa is computed. If wt is given, but weights is None, then the weights are set to be [0, 1, 2, ..., k]. If weights is a one-dimensional array, then it is used to construct the weight matrix given the following options. wt in ['linear', 'ca' or None] : use linear weights, Cicchetti-Allison actual weights are linear in the score "weights" difference wt in ['quadratic', 'fc'] : use linear weights, Fleiss-Cohen actual weights are squared in the score "weights" difference wt = 'toeplitz' : weight matrix is constructed as a toeplitz matrix from the one dimensional weights. return_results : bool If True (default), then an instance of KappaResults is returned. If False, then only kappa is computed and returned. Returns ------- results or kappa If return_results is True (default), then a results instance with all statistics is returned If return_results is False, then only kappa is calculated and returned. Notes ----- There are two conflicting definitions of the weight matrix, Wikipedia versus SAS manual. However, the computation are invariant to rescaling of the weights matrix, so there is no difference in the results. Weights for 'linear' and 'quadratic' are interpreted as scores for the categories, the weights in the computation are based on the pairwise difference between the scores. Weights for 'toeplitz' are a interpreted as weighted distance. The distance only depends on how many levels apart two entries in the table are but not on the levels themselves. example: weights = '0, 1, 2, 3' and wt is either linear or toeplitz means that the weighting only depends on the simple distance of levels. weights = '0, 0, 1, 1' and wt = 'linear' means that the first two levels are zero distance apart and the same for the last two levels. This is the sampe as forming two aggregated levels by merging the first two and the last two levels, respectively. weights = [0, 1, 2, 3] and wt = 'quadratic' is the same as squaring these weights and using wt = 'toeplitz'. References ---------- Wikipedia SAS Manual ''' table = np.asarray(table, float) #avoid integer division agree = np.diag(table).sum() nobs = table.sum() probs = table / nobs freqs = probs #TODO: rename to use freqs instead of probs for observed probs_diag = np.diag(probs) freq_row = table.sum(1) / nobs freq_col = table.sum(0) / nobs prob_exp = freq_col * freq_row[:, None] assert np.allclose(prob_exp.sum(), 1) #print prob_exp.sum() agree_exp = np.diag(prob_exp).sum() #need for kappa_max if weights is None and wt is None: kind = 'Simple' kappa = (agree / nobs - agree_exp) / (1 - agree_exp) if return_results: #variance term_a = probs_diag * (1 - (freq_row + freq_col) * (1 - kappa))**2 term_a = term_a.sum() term_b = probs * (freq_col[:, None] + freq_row)**2 d_idx = np.arange(table.shape[0]) term_b[d_idx, d_idx] = 0 #set diagonal to zero term_b = (1 - kappa)**2 * term_b.sum() term_c = (kappa - agree_exp * (1-kappa))**2 var_kappa = (term_a + term_b - term_c) / (1 - agree_exp)**2 / nobs #term_c = freq_col * freq_row[:, None] * (freq_col + freq_row[:,None]) term_c = freq_col * freq_row * (freq_col + freq_row) var_kappa0 = (agree_exp + agree_exp**2 - term_c.sum()) var_kappa0 /= (1 - agree_exp)**2 * nobs else: if weights is None: weights = np.arange(table.shape[0]) #weights follows the Wikipedia definition, not the SAS, which is 1 - kind = 'Weighted' weights = np.asarray(weights, float) if weights.ndim == 1: if wt in ['ca', 'linear', None]: weights = np.abs(weights[:, None] - weights) / \ (weights[-1] - weights[0]) elif wt in ['fc', 'quadratic']: weights = (weights[:, None] - weights)**2 / \ (weights[-1] - weights[0])**2 elif wt == 'toeplitz': #assume toeplitz structure from scipy.linalg import toeplitz #weights = toeplitz(np.arange(table.shape[0])) weights = toeplitz(weights) else: raise ValueError('wt option is not known') else: rows, cols = table.shape if (table.shape != weights.shape): raise ValueError('weights are not square') #this is formula from Wikipedia kappa = 1 - (weights * table).sum() / nobs / (weights * prob_exp).sum() #TODO: add var_kappa for weighted version if return_results: var_kappa = np.nan var_kappa0 = np.nan #switch to SAS manual weights, problem if user specifies weights #w is negative in some examples, #but weights is scale invariant in examples and rough check of source w = 1. - weights w_row = (freq_col * w).sum(1) w_col = (freq_row[:, None] * w).sum(0) agree_wexp = (w * freq_col * freq_row[:, None]).sum() term_a = freqs * (w - (w_col + w_row[:, None]) * (1 - kappa))**2 fac = 1. / ((1 - agree_wexp)**2 * nobs) var_kappa = term_a.sum() - (kappa - agree_wexp * (1 - kappa))**2 var_kappa *= fac freqse = freq_col * freq_row[:, None] var_kappa0 = (freqse * (w - (w_col + w_row[:, None]))**2).sum() var_kappa0 -= agree_wexp**2 var_kappa0 *= fac kappa_max = (np.minimum(freq_row, freq_col).sum() - agree_exp) / \ (1 - agree_exp) if return_results: res = KappaResults( kind=kind, kappa=kappa, kappa_max=kappa_max, weights=weights, var_kappa=var_kappa, var_kappa0=var_kappa0 ) return res else: return kappa _kappa_template = '''\ %(kind)s Kappa Coefficient -------------------------------- Kappa %(kappa)6.4f ASE %(std_kappa)6.4f %(alpha_ci)s%% Lower Conf Limit %(kappa_low)6.4f %(alpha_ci)s%% Upper Conf Limit %(kappa_upp)6.4f Test of H0: %(kind)s Kappa = 0 ASE under H0 %(std_kappa0)6.4f Z %(z_value)6.4f One-sided Pr > Z %(pvalue_one_sided)6.4f Two-sided Pr > |Z| %(pvalue_two_sided)6.4f ''' ''' Weighted Kappa Coefficient -------------------------------- Weighted Kappa 0.4701 ASE 0.1457 95% Lower Conf Limit 0.1845 95% Upper Conf Limit 0.7558 Test of H0: Weighted Kappa = 0 ASE under H0 0.1426 Z 3.2971 One-sided Pr > Z 0.0005 Two-sided Pr > |Z| 0.0010 ''' class KappaResults(ResultsBunch): '''Results for Cohen's kappa Attributes ---------- kappa : cohen's kappa var_kappa : variance of kappa std_kappa : standard deviation of kappa alpha : one-sided probability for confidence interval kappa_low : lower (1-alpha) confidence limit kappa_upp : upper (1-alpha) confidence limit var_kappa0 : variance of kappa under H0: kappa=0 std_kappa0 : standard deviation of kappa under H0: kappa=0 z_value : test statistic for H0: kappa=0, is standard normal distributed pvalue_one_sided : one sided p-value for H0: kappa=0 and H1: kappa>0 pvalue_two_sided : two sided p-value for H0: kappa=0 and H1: kappa!=0 distribution_kappa : asymptotic normal distribution of kappa distribution_zero_null : asymptotic normal distribution of kappa under H0: kappa=0 The confidence interval for kappa and the statistics for the test of H0: kappa=0 are based on the asymptotic normal distribution of kappa. ''' template = _kappa_template def _initialize(self): if not 'alpha' in self: self['alpha'] = 0.025 self['alpha_ci'] = _int_ifclose(100 - 0.025 * 200)[1] self['std_kappa'] = np.sqrt(self['var_kappa']) self['std_kappa0'] = np.sqrt(self['var_kappa0']) self['z_value'] = self['kappa'] / self['std_kappa0'] self['pvalue_one_sided'] = stats.norm.sf(self['z_value']) self['pvalue_two_sided'] = stats.norm.sf(np.abs(self['z_value'])) * 2 delta = stats.norm.isf(self['alpha']) * self['std_kappa'] self['kappa_low'] = self['kappa'] - delta self['kappa_upp'] = self['kappa'] + delta self['distribution_kappa'] = stats.norm(loc=self['kappa'], scale=self['std_kappa']) self['distribution_zero_null'] = stats.norm(loc=0, scale=self['std_kappa0']) def __str__(self): return self.template % self statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/000077500000000000000000000000001224417117700240445ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/CH.r000066400000000000000000000046061224417117700245270ustar00rootroot00000000000000% Copyright (c) 2011, Roger Lew BSD [see LICENSE.txt] % This software is funded in part by NIH Grant P20 RR016454. % This is a collection of scripts used to generate C-H comparisons % for qsturng. As you can probably guess, my R's skills aren't all that good. setwd('D:\\USERS\\roger\\programming\\python\\development\\qsturng') ps = seq(length=100, from=.5, to=.999) for (r in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21, 22,23,24,25,26,27,28,29,30,35,40,50,60,70,80,90,100,200)) { for (v in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20, 22,24,26,30,35,40,50,60,90,120,240,480,1e38)) { m = qtukey(ps, r, v) fname = sprintf('CH_r=%i,v=%.0f.dat',r,v) print(fname) write(rbind(ps, m), file=fname, ncolumns=2, append=FALSE, sep=',') } } rs = c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,30,40,60,80,100) for (v in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, 17,18,19,20,24,30,40,60,120,1e38)) { m = qtukey(0.30, rs, v) fname = sprintf('CH_p30.dat') print(fname) write(rbind(m), file=fname, ncolumns=26, append=TRUE, sep=' ') } for i in for (v in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, 17,18,19,20,24,30,40,60,120,1e38)) { m = qtukey(0.675, rs, v) fname = sprintf('CH_p675.dat',r,v) print(fname) write(rbind(m), file=fname, ncolumns=26, append=TRUE, sep=' ') } for (v in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, 17,18,19,20,24,30,40,60,120,1e38)) { m = qtukey(0.75, rs, v) fname = sprintf('CH_p75.dat',r,v) print(fname) write(rbind(m), file=fname, ncolumns=26, append=TRUE, sep=' ') } for (v in c(2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, 17,18,19,20,24,30,40,60,120,1e38)) { m = qtukey(0.975, rs, v) fname = sprintf('CH_p975.dat') print(fname) write(rbind(m), file=fname, ncolumns=26, append=TRUE, sep=' ') } i = 0; for (i in 0:9999) { p = runif(1, .5, .95); r = sample(2:100, 1); v = runif(1, 2, 1000); q = qtukey(p,r,v); if (!is.nan(q)) { write(c(p,r,v,q), file='bootleg.dat', ncolumns=4, append=TRUE, sep=','); i = i + 1; } }statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/LICENSE.txt000066400000000000000000000030361224417117700256710ustar00rootroot00000000000000Copyright (c) 2011, Roger Lew [see LICENSE.txt] All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the organizations affiliated with the contributors or the names of its contributors themselves may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/__init__.py000066400000000000000000000001561224417117700261570ustar00rootroot00000000000000 from qsturng_ import psturng, qsturng, p_keys, v_keys from numpy.testing import Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/make_tbls.py000066400000000000000000001744701224417117700263740ustar00rootroot00000000000000"""this script builds the T table and A table for the upper quantile stundentized range algorithm""" import math import scipy.stats from scipy.optimize import leastsq import numpy as np from statsmodels.compatnp.collections import OrderedDict from numpy.random import random # The values for p in [.5, .75, .9, .95, .975, .99, .995, .999] # were pulled from: # http://www.stata.com/stb/stb46/dm64/sturng.pdf # # Values for p in [.1, .675, .8, .85] were calculated using R's qtukey function # # the table was programmed by Gleason and extends Harter's (1960) table # using the Copenhaver & Holland (1988) algorithm (C&H). Gleason found # that the 4th significant digit of the C&H differed from Harter's # tables on about 20% of the values. Gleason states this was do to # consevative rounding by Harter. In those event the table reflects # Harter's orginal approximations. q0100 = """\ 2 0.2010022 0.6351172 0.9504689 1.179321 1.354691 1.495126 1.611354 1.709984 1.795325 1.87032 1.937057 1.997068 2.051505 2.101256 2.147016 2.189342 2.228683 2.265408 2.299823 2.558612 2.729718 2.95625 3.184742938 3.398609188 3 0.193179 0.6294481 0.9564746 1.19723 1.383028 1.532369 1.656225 1.761451 1.852559 1.93265 2.003933 2.068034 2.126178 2.179312 2.228177 2.273367 2.315364 2.354561 2.391287 2.667213 2.849389 3.009265469 3.237758406 3.451624656 4 0.1892648 0.6266441 0.9606115 1.2089 1.401557 1.55691 1.686009 1.795829 1.890994 1.974697 2.049222 2.116253 2.177065 2.232641 2.283754 2.331023 2.37495 2.415949 2.454361 2.742846 2.933173 3.062280938 3.290773875 3.504640125 5 0.1869239 0.6249713 0.963532 1.217021 1.414548 1.574255 1.707205 1.820437 1.91864 2.005066 2.082048 2.151312 2.214162 2.271609 2.324449 2.37332 2.418737 2.461128 2.500844 2.7991 2.9958 3.115296406 3.343789344 3.557655594 6 0.185369 0.6238602 0.9656833 1.22298 1.424151 1.587166 1.723076 1.838955 1.939532 2.028098 2.107021 2.178053 2.242524 2.301465 2.355686 2.40584 2.452454 2.495964 2.536731 2.842892 3.027993254 3.168311875 3.396804813 3.610671063 7 0.1842618 0.6230685 0.9673274 1.227534 1.431536 1.597154 1.735417 1.853415 1.955904 2.046203 2.126704 2.19918 2.264979 2.325144 2.380502 2.431713 2.479315 2.52375 2.565387 2.878126 3.060186557 3.221327344 3.449820281 3.663686531 8 0.1834338 0.6224757 0.9686225 1.231126 1.437392 1.605113 1.745294 1.86503 1.969097 2.060832 2.142645 2.216325 2.283234 2.344427 2.400739 2.45284 2.501275 2.546491 2.588864 2.861237 3.092379859 3.274342813 3.50283575 3.716702 9 0.1827912 0.6220153 0.969668 1.23403 1.442149 1.611608 1.753382 1.874572 1.979964 2.07291 2.155833 2.230535 2.298388 2.360458 2.417585 2.470448 2.519597 2.565484 2.608488 2.871492 3.1631665 3.362394 3.613696 3.7452106 10 0.1822783 0.6216474 0.9705293 1.236426 1.446091 1.617009 1.76013 1.882554 1.989077 2.083059 2.166935 2.242517 2.311185 2.374011 2.431845 2.485368 2.535137 2.581607 2.625162 2.898717 3.1760535 3.45339 3.6807265 3.7737192 11 0.1818593 0.6213467 0.9712507 1.238437 1.449411 1.621571 1.765847 1.889333 1.996831 2.091711 2.176415 2.252763 2.322141 2.38563 2.444082 2.498185 2.548497 2.59548 2.639519 2.923558 3.19971275 3.4758675 3.70202225 3.8022278 12 0.1815106 0.6210962 0.9718637 1.240149 1.452244 1.625478 1.770753 1.895163 2.003512 2.099178 2.184609 2.26163 2.331635 2.395707 2.454706 2.509321 2.560115 2.607554 2.652022 2.94649 3.2224175 3.498345 3.7242725 3.8307364 13 0.181216 0.6208845 0.9723908 1.241623 1.454692 1.62886 1.77501 1.900231 2.009331 2.10569 2.191763 2.269382 2.339943 2.404535 2.46402 2.519093 2.570318 2.618162 2.663015 2.967868 3.242456 3.517044 3.741632 3.859245 14 0.1809637 0.620703 0.972849 1.242906 1.456827 1.631817 1.778739 1.904678 2.014444 2.11142 2.198067 2.276219 2.347278 2.412335 2.472257 2.52774 2.579352 2.627563 2.672763 2.987963 3.245505708 3.503048417 3.748851667 3.878849 15 0.1807453 0.6205458 0.9732508 1.244033 1.458706 1.634424 1.782034 1.908613 2.018974 2.116503 2.203664 2.282296 2.353802 2.41928 2.479596 2.53545 2.587413 2.635954 2.681468 3.006982 3.275778125 3.489052833 3.758247778 3.896564 16 0.1805544 0.6204084 0.973606 1.24503 1.460373 1.636741 1.784965 1.912119 2.023015 2.121043 2.208668 2.287733 2.359646 2.425503 2.486177 2.542369 2.59465 2.643493 2.689292 3.025091 3.281966688 3.49479125 3.763291 3.9179256 17 0.1803861 0.6202871 0.9739223 1.245919 1.461861 1.638813 1.78759 1.915263 2.026643 2.125123 2.21317 2.292628 2.36491 2.431114 2.492114 2.548614 2.601186 2.650304 2.696365 3.032426 3.272395 3.50687125 3.77704 3.9392872 18 0.1802366 0.6201793 0.9742057 1.246717 1.463198 1.640677 1.789955 1.918099 2.029919 2.128809 2.21724 2.297059 2.369678 2.436199 2.497498 2.55428 2.607118 2.656491 2.702792 3.039093 3.282026 3.586406 3.789645 3.9606488 19 0.1801029 0.620083 0.9744612 1.247436 1.464405 1.642363 1.792096 1.920669 2.032891 2.132158 2.22094 2.301089 2.374017 2.44083 2.502404 2.559444 2.612529 2.662134 2.708658 3.045182 3.296680985 3.603883649 3.801248 3.974888443 20 0.1799827 0.6199962 0.9746925 1.248088 1.465502 1.643895 1.794045 1.923011 2.035601 2.135212 2.224318 2.30477 2.377983 2.445065 2.506892 2.564173 2.617485 2.667306 2.714034 3.050762 3.311335993 3.621361325 3.788047375 3.989130221 24 0.1796023 0.6197217 0.9754345 1.250184 1.469033 1.648844 1.800353 1.930604 2.044404 2.145153 2.235327 2.316784 2.390945 2.45892 2.521592 2.579673 2.633745 2.684289 2.731705 3.089271 3.325991 3.638839 3.77484675 4.003372 30 0.1792227 0.6194474 0.9761909 1.252326 1.47266 1.653946 1.806877 1.938484 2.053567 2.155527 2.246844 2.329381 2.404562 2.473504 2.537093 2.596046 2.650947 2.702281 2.750452 3.088623 3.365526818 3.632653344 3.810852875 4.0480485 40 0.1788439 0.6191733 0.976962 1.254517 1.476384 1.659208 1.813634 1.946672 2.063118 2.166372 2.258917 2.342618 2.418905 2.488898 2.553488 2.613394 2.669206 2.731569 2.770415 3.109261 3.405062637 3.629560516 3.846859 4.092725 60 0.1784658 0.6188994 0.9777482 1.256759 1.480212 1.664639 1.820636 1.955191 2.07309 2.177731 2.271599 2.356562 2.434053 2.505196 2.570884 2.631843 2.688663 2.745483 2.802303 3.159123 3.406221516 3.626467688 3.90118925 4.1497775 120 0.1780885 0.6186256 0.9785495 1.259052 1.484147 1.67025 1.827902 1.964066 2.083518 2.189653 2.284954 2.371292 2.450102 2.522511 2.589417 2.651546 2.71243375 2.763748 2.8156585 3.201588 3.459897 3.67055425 3.9555195 4.20683 1e38 0.177712 0.6183521 0.9793662 1.261398 1.488195 1.676051 1.835449 1.973327 2.094446 2.202195 2.299057 2.386902 2.467168 2.540983 2.609248 2.677513 2.745778 2.787396 2.829014 3.236691 3.487830797 3.721309063 4.01874075 4.279424""" q0300 = """\ 2 0.6289521 1.248281 1.638496 1.916298 2.129504 2.301246 2.444313 2.566465 2.672747 2.766604 2.850494 2.926224 2.995161 3.05836 3.116655 3.170712 3.221076 3.268192 3.312433 3.647666 3.871606 4.170521 4.372227 4.52341 3 0.5999117 1.209786 1.598235 1.875707 2.088948 2.260822 2.404037 2.52633 2.632732 2.726691 2.810665 2.886463 2.955453 3.018695 3.077022 3.131103 3.181483 3.228609 3.272854 3.607961 3.831649 4.130021 4.331231 4.48198 4 0.5857155 1.19124 1.579749 1.858059 2.072245 2.245015 2.389043 2.512062 2.619115 2.713659 2.798159 2.874434 2.943859 3.007497 3.066189 3.120607 3.171298 3.218713 3.263228 3.600301 3.825208 4.125075 4.327208 4.478602 5 0.5773226 1.18033 1.569213 1.84843 2.063579 2.237255 2.382108 2.505872 2.613597 2.708749 2.793803 2.870583 2.94047 3.004536 3.063622 3.118406 3.169439 3.217173 3.261987 3.601296 3.827646 4.129353 4.332665 4.48491 6 0.5717854 1.173145 1.562427 1.842442 2.058433 2.232905 2.378487 2.502915 2.611243 2.706943 2.792499 2.869739 2.940051 3.004509 3.06396 3.119085 3.170435 3.218468 3.263562 3.604991 3.832733 4.136241 4.340726 4.493824 7 0.5678608 1.168056 1.5577 1.838389 2.055092 2.230242 2.376451 2.501451 2.610302 2.706482 2.792477 2.870123 2.94081 3.005616 3.065391 3.120818 3.172464 3.220752 3.266098 3.609448 3.838467 4.143645 4.349225 4.503125 8 0.5649349 1.164263 1.55422 1.835477 2.052781 2.228508 2.375251 2.500743 2.610046 2.706641 2.793019 2.871019 2.942034 3.007146 3.067207 3.122901 3.174787 3.223323 3.268893 3.61396 3.844133 4.150832 4.357416 4.512054 9 0.56267 1.161326 1.551554 1.833289 2.051105 2.227323 2.374526 2.500442 2.610136 2.707092 2.793803 2.872112 2.943416 3.008796 3.069108 3.125039 3.177147 3.225893 3.27166 3.618262 3.849473 4.157551 4.365051 4.520364 10 0.5608651 1.158985 1.549445 1.831589 2.049842 2.226484 2.374084 2.500369 2.610403 2.707674 2.794677 2.873258 2.944815 3.010432 3.070966 3.127106 3.179411 3.228343 3.274287 3.62226 3.854408 4.16298725 4.372072 4.51335807 11 0.5593931 1.157076 1.547737 1.830233 2.048863 2.225872 2.373818 2.500424 2.610757 2.708305 2.795566 2.874386 2.946167 3.011994 3.072726 3.129052 3.181532 3.23063 3.276732 3.625936 3.8583805 4.1684235 4.378495 4.527370035 12 0.5581697 1.155489 1.546325 1.829125 2.048085 2.225415 2.373664 2.500554 2.61115 2.708943 2.796432 2.875466 2.947446 3.01346 3.074368 3.13086 3.183497 3.232744 3.278987 3.629302 3.862353 4.17385975 4.384365 4.541382 13 0.557137 1.154148 1.545138 1.828206 2.047454 2.225068 2.373584 2.500725 2.611555 2.709566 2.797258 2.876482 2.948641 3.014823 3.075889 3.13253 3.185309 3.23469 3.28106 3.63238 3.8663255 4.179296 4.3895105 4.547231 14 0.5562535 1.153001 1.544127 1.82743 2.046934 2.224798 2.373553 2.500918 2.611956 2.710162 2.798038 2.877433 2.949752 3.016085 3.077294 3.134069 3.186976 3.236478 3.282963 3.635197 3.870298 4.183625 4.394656 4.552594 15 0.5554892 1.152009 1.543255 1.826767 2.046499 2.224587 2.373555 2.50112 2.612347 2.710728 2.798768 2.878317 2.950781 3.017251 3.078589 3.135487 3.188509 3.238121 3.284712 3.637674 3.8733385 4.187597 4.399177 4.557525 16 0.5548214 1.151142 1.542495 1.826194 2.04613 2.224419 2.373579 2.501325 2.612721 2.711261 2.799449 2.879139 2.951735 3.018329 3.079784 3.136793 3.189921 3.239634 3.28632 3.640151 3.876379 4.191252 4.40334 4.56207 17 0.554233 1.150377 1.541828 1.825694 2.045813 2.224284 2.373618 2.501527 2.613077 2.711761 2.800085 2.879902 2.952618 3.019325 3.080888 3.137998 3.191223 3.241028 3.287802 3.642251 3.878959 4.1944965 4.407184 4.56627 18 0.5537107 1.149699 1.541236 1.825254 2.04554 2.224175 2.373667 2.501725 2.613414 2.71223 2.800678 2.880611 2.953437 3.020248 3.081909 3.139113 3.192426 3.242315 3.28917 3.644351 3.881539 4.197741 4.410743 4.570162 19 0.5532438 1.149092 1.540709 1.824864 2.045301 2.224086 2.373722 2.501915 2.613733 2.71267 2.801231 2.881271 2.954198 3.021104 3.082855 3.140145 3.19354 3.243507 3.290436 3.641213698 3.877674372 4.200632 4.414046 4.573776 20 0.552824 1.148546 1.540235 1.824516 2.045091 2.224013 2.37378 2.502099 2.614034 2.713082 2.801747 2.881885 2.954905 3.0219 3.083734 3.141103 3.194574 3.244613 3.291611 3.647488349 3.885403686 4.203319 4.4207865 4.577142 24 0.5514973 1.146821 1.538745 1.823433 2.044456 2.223827 2.374026 2.502754 2.615077 2.71449 2.8035 2.883965 2.957293 3.02458 3.086693 3.144327 3.198049 3.248329 3.295558 3.653763 3.893133 4.212396 4.427527 4.588561 30 0.5501747 1.1451 1.537267 1.822378 2.04387 2.223711 2.374365 2.503527 2.616262 2.716064 2.805443 2.886257 2.959917 3.027519 3.089932 3.147852 3.201849 3.252391 3.29987 3.646771403 3.901009 4.201241112 4.423813037 4.57425581 40 0.5488561 1.143384 1.535803 1.821351 2.043333 2.223667 2.3748 2.504422 2.617594 2.717812 2.807585 2.888774 2.962791 3.030734 3.093472 3.151703 3.205997 3.256824 3.304577 3.660754702 3.910106 4.223551056 4.431241019 4.602866405 60 0.5475416 1.141671 1.53435 1.820353 2.042846 2.223698 2.375337 2.505445 2.619082 2.719743 2.809939 2.891531 2.965932 3.034243 3.097332 3.1559 3.210518 3.261657 3.30971 3.674738 3.919203 4.245861 4.438669 4.631477 120 0.5462314 1.139963 1.532911 1.819385 2.04241 2.223806 2.375978 2.506602 2.620733 2.721868 2.812516 2.894541 2.969356 3.038064 3.101534 3.160468 3.215439 3.39400025 3.5725615 3.75112275 3.929684 4.256342 4.44915 4.641958 1e38 0.5449254 1.138259 1.531485 1.818447 2.042028 2.223993 2.376728 2.507898 2.622556 2.724195 2.815328 2.897817 2.973079 3.042215 3.106097 3.165428 3.220399 3.39896025 3.5775215 3.75608275 3.934644 4.261302 4.45411 4.646918""" q0500 = """\ 2 1.155 1.908 2.377 2.713 2.973 3.184 3.361 3.513 3.645 3.762 3.867 3.963 4.049 4.129 4.203 4.271 4.335 4.394 4.451 4.878 5.165 5.549 5.810 6.006 3 1.082 1.791 2.230 2.545 2.789 2.986 3.152 3.294 3.418 3.528 3.626 3.715 3.796 3.871 3.940 4.004 4.064 4.120 4.172 4.573 4.842 5.202 5.447 5.630 4 1.048 1.736 2.163 2.468 2.704 2.895 3.055 3.193 3.313 3.419 3.515 3.601 3.680 3.752 3.819 3.881 3.939 3.993 4.044 4.432 4.693 5.043 5.279 5.457 5 1.028 1.705 2.124 2.423 2.655 2.843 3.000 3.135 3.253 3.357 3.451 3.535 3.613 3.684 3.749 3.810 3.867 3.920 3.970 4.351 4.608 4.951 5.184 5.358 6 1.015 1.684 2.098 2.394 2.623 2.808 2.964 3.097 3.213 3.317 3.409 3.493 3.569 3.639 3.704 3.764 3.820 3.873 3.922 4.299 4.552 4.891 5.121 5.294 7 1.006 1.670 2.080 2.374 2.601 2.784 2.938 3.070 3.186 3.288 3.380 3.463 3.538 3.608 3.672 3.732 3.787 3.840 3.889 4.262 4.513 4.849 5.077 5.249 8 .9990 1.659 2.067 2.359 2.584 2.767 2.919 3.051 3.165 3.267 3.358 3.440 3.515 3.584 3.648 3.708 3.763 3.815 3.863 4.234 4.484 4.818 5.045 5.215 9 .9938 1.651 2.057 2.347 2.571 2.753 2.905 3.035 3.149 3.250 3.341 3.423 3.498 3.566 3.630 3.689 3.744 3.796 3.844 4.213 4.461 4.794 5.020 5.189 10 .9897 1.645 2.049 2.338 2.561 2.742 2.893 3.023 3.137 3.237 3.328 3.409 3.484 3.552 3.615 3.674 3.729 3.780 3.829 4.196 4.443 4.775 5.000 5.168 11 .9863 1.639 2.042 2.330 2.553 2.733 2.884 3.013 3.127 3.227 3.317 3.398 3.472 3.540 3.603 3.662 3.717 3.768 3.816 4.182 4.429 4.759 4.983 5.152 12 .9836 1.635 2.037 2.324 2.546 2.726 2.876 3.005 3.118 3.218 3.308 3.389 3.463 3.531 3.594 3.652 3.706 3.757 3.805 4.171 4.417 4.746 4.970 5.138 13 .9812 1.631 2.032 2.319 2.540 2.719 2.869 2.998 3.111 3.210 3.300 3.381 3.455 3.522 3.585 3.643 3.698 3.749 3.796 4.161 4.406 4.735 4.958 5.126 14 .9792 1.628 2.028 2.314 2.535 2.714 2.864 2.992 3.105 3.204 3.293 3.374 3.448 3.515 3.578 3.636 3.690 3.741 3.789 4.153 4.397 4.726 4.948 5.115 15 .9775 1.625 2.025 2.310 2.531 2.709 2.859 2.987 3.099 3.199 3.288 3.368 3.442 3.509 3.572 3.630 3.684 3.735 3.782 4.145 4.390 4.718 4.940 5.107 16 .9760 1.623 2.022 2.307 2.527 2.705 2.855 2.983 3.095 3.194 3.283 3.363 3.436 3.504 3.566 3.624 3.678 3.729 3.776 4.139 4.383 4.710 4.932 5.099 17 .9747 1.621 2.019 2.304 2.524 2.702 2.851 2.979 3.090 3.189 3.278 3.359 3.432 3.499 3.561 3.619 3.673 3.724 3.771 4.133 4.377 4.704 4.926 5.092 18 .9735 1.619 2.017 2.301 2.521 2.699 2.848 2.975 3.087 3.186 3.274 3.354 3.428 3.495 3.557 3.615 3.669 3.719 3.767 4.128 4.372 4.698 4.920 5.086 19 .9724 1.617 2.015 2.299 2.518 2.696 2.845 2.972 3.084 3.182 3.271 3.351 3.424 3.491 3.553 3.611 3.665 3.715 3.763 4.124 4.367 4.693 4.914 5.080 20 .9715 1.616 2.013 2.297 2.516 2.693 2.842 2.969 3.081 3.179 3.268 3.348 3.421 3.488 3.550 3.607 3.661 3.712 3.759 4.120 4.363 4.688 4.909 5.075 24 .9685 1.611 2.007 2.290 2.508 2.685 2.833 2.960 3.071 3.170 3.258 3.337 3.410 3.477 3.539 3.596 3.650 3.700 3.747 4.107 4.349 4.674 4.894 5.060 30 .9656 1.606 2.001 2.283 2.501 2.677 2.825 2.951 3.062 3.160 3.248 3.327 3.400 3.466 3.528 3.585 3.638 3.688 3.735 4.094 4.335 4.659 4.878 5.043 40 .9626 1.602 1.996 2.277 2.494 2.669 2.816 2.942 3.053 3.150 3.238 3.317 3.389 3.455 3.517 3.574 3.627 3.677 3.723 4.080 4.321 4.643 4.862 5.027 60 .9597 1.597 1.990 2.270 2.486 2.661 2.808 2.933 3.043 3.140 3.227 3.306 3.378 3.444 3.505 3.562 3.615 3.665 3.711 4.067 4.306 4.627 4.845 5.009 120 .9568 1.592 1.984 2.263 2.479 2.653 2.799 2.924 3.034 3.130 3.217 3.296 3.367 3.433 3.494 3.550 3.603 3.652 3.699 4.052 4.290 4.610 4.827 4.990 1e38 .9539 1.588 1.978 2.257 2.472 2.645 2.791 2.915 3.024 3.121 3.207 3.285 3.356 3.422 3.482 3.538 3.591 3.640 3.686 4.037 4.274 4.591 4.806 4.968""" q0675 = """\ 2 1.829602 2.751705 3.332700 3.754119 4.082579 4.350351 4.575528 4.769258 4.938876 5.089456 5.22465 5.347168 5.459072 5.56197 5.657136 5.745596 5.828188 5.905606 5.978428 6.534036 6.908522 7.411898 7.753537 8.010516 3 1.660743 2.469725 2.973973 3.338757 3.622958 3.854718 4.049715 4.217574 4.364624 4.495236 4.612559 4.718926 4.816117 4.905518 4.988228 5.065133 5.136955 5.204295 5.267653 5.751485 6.07799 6.517299 6.815682 7.040219 4 1.585479 2.344680 2.814410 3.153343 3.417165 3.632254 3.813232 3.96905 4.105579 4.226877 4.335857 4.434684 4.525004 4.608102 4.684995 4.756504 4.823298 4.885932 4.944872 5.395226 5.699385 6.108899 6.387203 6.596702 5 1.543029 2.27426 2.72431 3.048331 3.300303 3.505645 3.678397 3.827131 3.957462 4.073264 4.177319 4.27169 4.35795 4.437321 4.510774 4.579091 4.642911 4.702763 4.759089 5.189651 5.480611 5.872552 6.139026 6.339673 6 1.515809 2.229127 2.666435 2.980707 3.224876 3.423769 3.591058 3.735076 3.861273 3.973403 4.074164 4.165554 4.249094 4.325968 4.397115 4.463293 4.525119 4.583105 4.637678 5.054965 5.337079 5.717251 5.975813 6.170546 7 1.496881 2.197746 2.626119 2.933501 3.17212 3.366405 3.529778 3.670406 3.793624 3.903106 4.001488 4.090723 4.172295 4.247362 4.31684 4.381468 4.441849 4.498482 4.551786 4.959448 5.235148 5.606794 5.859629 6.050083 8 1.482962 2.174667 2.596423 2.898666 3.133126 3.323942 3.484357 3.622418 3.743376 3.850846 3.947419 4.035012 4.115085 4.188774 4.256978 4.320423 4.379701 4.435301 4.487633 4.887938 5.158734 5.523864 5.772325 5.959514 9 1.472298 2.156982 2.573637 2.871897 3.103116 3.29122 3.449316 3.585359 3.704538 3.810421 3.905564 3.991859 4.070746 4.143343 4.210539 4.273046 4.331448 4.386229 4.437792 4.832253 5.099153 5.459107 5.704096 5.888693 10 1.463868 2.142999 2.555601 2.85068 3.079300 3.265222 3.421445 3.555857 3.673594 3.778189 3.872171 3.957411 4.035332 4.107041 4.173413 4.235156 4.292845 4.346957 4.39789 4.787576 5.05129 5.407011 5.649161 5.831642 11 1.457037 2.131666 2.540969 2.833447 3.059936 3.244061 3.398740 3.531802 3.648345 3.751871 3.844888 3.929251 4.006369 4.077337 4.143024 4.204129 4.261223 4.314777 4.365186 4.750882 5.01193 5.36411 5.603886 5.784574 12 1.451389 2.122295 2.528860 2.819172 3.043878 3.226497 3.379879 3.511805 3.627341 3.729965 3.822167 3.905787 3.982224 4.052565 4.117671 4.178235 4.234823 4.287903 4.337867 4.720168 4.978946 5.32811 5.565864 5.745066 13 1.446642 2.114418 2.518673 2.807152 3.030346 3.211683 3.363958 3.494914 3.609588 3.71144 3.802942 3.885925 3.961777 4.031579 4.096185 4.156283 4.212436 4.265108 4.314686 4.694058 4.950875 5.297431 5.533435 5.711335 14 1.442597 2.107703 2.509984 2.796892 3.018785 3.199019 3.350338 3.480454 3.594382 3.695564 3.786459 3.868888 3.944232 4.013564 4.077734 4.137427 4.1932 4.245515 4.294759 4.671571 4.926672 5.270944 5.505418 5.682176 15 1.439108 2.101911 2.502485 2.788030 3.008793 3.188066 3.338551 3.467934 3.581209 3.681803 3.772166 3.854108 3.929005 3.997925 4.061712 4.121047 4.176486 4.228488 4.277436 4.651989 4.905573 5.247825 5.480945 5.656694 16 1.436068 2.096865 2.495948 2.7803 3.000071 3.178498 3.328250 3.456985 3.569684 3.669759 3.75965 3.841162 3.915663 3.984216 4.047663 4.106681 4.161824 4.213546 4.262231 4.634774 4.887015 5.227455 5.459365 5.634213 17 1.433397 2.092428 2.490198 2.773497 2.992391 3.170069 3.319169 3.447329 3.559514 3.659127 3.748598 3.829725 3.903873 3.972099 4.035242 4.093976 4.148852 4.200325 4.248775 4.619514 4.870529 5.209359 5.440181 5.614218 18 1.431030 2.088497 2.485101 2.767464 2.985576 3.162585 3.311102 3.438749 3.550474 3.649671 3.738765 3.819547 3.893377 3.961308 4.024178 4.082656 4.137293 4.188541 4.236778 4.605888 4.855804 5.193166 5.423003 5.596306 19 1.428918 2.08499 2.480552 2.762076 2.979488 3.155896 3.30389 3.431072 3.542383 3.641206 3.729960 3.810429 3.883971 3.951636 4.014258 4.072505 4.126925 4.177969 4.226014 4.593643 4.84256 5.178585 5.407524 5.580158 20 1.427023 2.081842 2.476467 2.757236 2.974016 3.149882 3.297401 3.424164 3.535099 3.633583 3.722027 3.802213 3.875493 3.942916 4.005312 4.063349 4.117571 4.168429 4.216298 4.582577 4.830579 5.16538 5.393498 5.565519 24 1.421053 2.071924 2.463589 2.741964 2.956732 3.130867 3.276871 3.402288 3.512015 3.609405 3.696852 3.776122 3.848556 3.915194 3.976858 4.03421 4.087789 4.138042 4.185340 4.547205 4.792208 5.122986 5.348394 5.518394 30 1.415131 2.062082 2.450796 2.726770 2.939512 3.111895 3.256356 3.380399 3.488888 3.585153 3.67157 3.749892 3.821449 3.887270 3.948172 4.00481 4.057717 4.107336 4.154034 4.511241 4.753052 5.079524 5.302021 5.469846 40 1.409257 2.052316 2.438086 2.711654 2.922351 3.092956 3.235846 3.358481 3.465697 3.5608 3.646150 3.723487 3.794128 3.859096 3.919198 3.975085 4.027284 4.076232 4.122296 4.474532 4.712898 5.034679 5.253982 5.419412 60 1.40343 2.042626 2.425459 2.696611 2.905244 3.074043 3.215327 3.336516 3.442417 3.536316 3.620555 3.696861 3.766541 3.830609 3.889866 3.944956 3.996401 4.044636 4.09002 4.436878 4.671454 4.987998 5.203693 5.366394 120 1.397651 2.033010 2.412913 2.681639 2.888185 3.055146 3.194785 3.314484 3.419022 3.511665 3.59474 3.66996 3.738623 3.801735 3.86009 3.914325 3.96496 4.012423 4.057072 4.398008 4.628308 4.938805 5.150236 5.309666 1e38 1.391918 2.023469 2.400447 2.666735 2.871167 3.036254 3.174203 3.292360 3.395479 3.486805 3.568651 3.642718 3.710296 3.772381 3.829761 3.883069 3.93282 3.979437 4.023276 4.357546 4.582861 4.886029 5.092081 5.247256""" q0750 = """\ 2 2.267583 3.308014 3.969236 4.451126 4.82785 5.13561 5.394819 5.618097 5.813776 5.987632 6.143829 6.285461 6.41489 6.533954 6.644113 6.746546 6.842214 6.931913 7.01631 7.660853 8.09584 8.68119 9.0788 9.377929 3 2.011896 2.883775 3.431223 3.829258 4.140443 4.394852 4.609323 4.794233 4.956425 5.10064 5.230299 5.347941 5.455509 5.554514 5.646158 5.73141 5.811064 5.885775 5.956093 6.493827 6.857365 7.3472 7.680302 7.931152 4 1.901267 2.701018 3.198596 3.559322 3.841087 4.071417 4.265624 4.433118 4.580085 4.710812 4.828384 4.935098 5.032703 5.122566 5.205771 5.283192 5.355547 5.423427 5.48733 5.976418 6.307462 6.753955 7.057827 7.286775 5 1.839820 2.599651 3.069171 3.40865 3.673526 3.889955 4.072422 4.229795 4.367901 4.490764 4.601285 4.701617 4.793402 4.877922 4.956192 5.029034 5.09712 5.161005 5.221154 5.681792 5.993844 6.415033 6.70187 6.918073 6 1.800788 2.535293 2.986795 3.312495 3.566338 3.773641 3.948369 4.099056 4.231292 4.348941 4.45478 4.550869 4.638783 4.719746 4.794731 4.864523 4.929764 4.990987 5.048635 5.490302 5.789693 6.194025 6.469523 6.677248 7 1.773818 2.490830 2.929770 3.245783 3.491823 3.692639 3.86185 4.007755 4.135786 4.249692 4.352165 4.445203 4.530329 4.608729 4.681345 4.748937 4.812126 4.871427 4.927269 5.35523 5.64547 6.037624 6.304935 6.506543 8 1.754075 2.458283 2.887956 3.196776 3.436989 3.632943 3.798002 3.940301 4.065154 4.176226 4.276148 4.366871 4.44988 4.526332 4.597147 4.663065 4.724692 4.782528 4.836995 5.254505 5.537762 5.920623 6.18169 6.378634 9 1.739001 2.433431 2.855984 3.159245 3.394934 3.587097 3.748912 3.888384 4.010742 4.119586 4.2175 4.306396 4.387735 4.462649 4.532041 4.596635 4.657026 4.713704 4.767081 5.176307 5.454025 5.829512 6.085625 6.27887 10 1.727116 2.413835 2.830746 3.129578 3.361648 3.550769 3.709972 3.847165 3.967506 4.074547 4.170832 4.258248 4.33823 4.411895 4.480128 4.543645 4.60303 4.658765 4.711254 5.113722 5.386914 5.756374 6.008439 6.198662 11 1.717506 2.397989 2.810315 3.105535 3.334641 3.521265 3.678317 3.813629 3.932304 4.037852 4.132788 4.218976 4.297831 4.370457 4.437728 4.500349 4.558895 4.613844 4.665594 5.062423 5.331834 5.696254 5.944934 6.13263 12 1.709576 2.384911 2.793439 3.085654 3.312288 3.496821 3.65207 3.785802 3.903075 4.007365 4.101163 4.186312 4.264215 4.335963 4.402419 4.46428 4.522117 4.5764 4.627523 5.019561 5.285754 5.645883 5.891679 6.077222 13 1.70292 2.373934 2.779263 3.068939 3.293478 3.476235 3.62995 3.762334 3.878409 3.981623 4.074447 4.158707 4.235793 4.306786 4.372542 4.433751 4.490977 4.544687 4.595269 4.983178 5.246593 5.603014 5.846316 6.029999 14 1.697255 2.364590 2.767188 3.05469 3.27743 3.458659 3.611050 3.742271 3.85731 3.959593 4.051572 4.13506 4.211437 4.281774 4.346922 4.407563 4.464258 4.517468 4.567581 4.951886 5.212872 5.566049 5.807169 5.989222 15 1.692374 2.356539 2.756778 3.042397 3.263576 3.443476 3.594714 3.724919 3.839053 3.940521 4.031759 4.114571 4.190326 4.260088 4.324701 4.384844 4.441071 4.493842 4.54354 4.92467 5.183511 5.533819 5.773008 5.95362 16 1.688126 2.349531 2.747712 3.031684 3.251494 3.430227 3.580451 3.709761 3.823097 3.923845 4.01443 4.096644 4.171848 4.2411 4.305239 4.364939 4.420752 4.473133 4.522464 4.900769 5.1577 5.505449 5.742915 5.92224 17 1.684395 2.343375 2.739744 3.022264 3.240865 3.418565 3.567889 3.696405 3.809031 3.909139 3.999142 4.080823 4.155535 4.224333 4.288048 4.347353 4.402795 4.454828 4.50383 4.879604 5.134819 5.48027 5.716186 5.894353 18 1.681092 2.337926 2.732687 3.013916 3.23144 3.408218 3.55674 3.684546 3.796536 3.896071 3.985552 4.066754 4.141026 4.209415 4.27275 4.331699 4.386808 4.438527 4.487232 4.860723 5.114389 5.457759 5.692272 5.86939 19 1.678147 2.333067 2.726393 3.006467 3.223026 3.398978 3.546777 3.673945 3.785363 3.884381 3.973391 4.054162 4.128035 4.196054 4.259045 4.317673 4.37248 4.423914 4.472351 4.843772 5.096029 5.437505 5.67074 5.846902 20 1.675506 2.328708 2.720745 2.999780 3.215469 3.390674 3.537821 3.664411 3.775312 3.873861 3.962444 4.042823 4.116334 4.184018 4.246696 4.305031 4.359563 4.410739 4.458932 4.828464 5.079434 5.419178 5.651242 5.82653 24 1.667194 2.314991 2.702957 2.978701 3.191627 3.364455 3.50952 3.63426 3.7435 3.840544 3.927753 4.006868 4.079211 4.14581 4.207477 4.264864 4.318505 4.368841 4.416241 4.779619 5.026378 5.360437 5.58865 5.761054 30 1.658964 2.301406 2.68532 2.957771 3.167919 3.338345 3.481298 3.604155 3.711699 3.8072 3.892996 3.970809 4.041947 4.107423 4.168039 4.224442 4.277156 4.326617 4.373187 4.730101 4.9724 5.300398 5.524486 5.693793 40 1.650814 2.28795 2.667830 2.936984 3.144337 3.312335 3.453142 3.574077 3.679882 3.773798 3.858136 3.934602 4.004488 4.068796 4.128318 4.183691 4.235434 4.283976 4.329675 4.679735 4.917252 5.238689 5.458277 5.62419 60 1.642744 2.274622 2.650486 2.916339 3.120874 3.286413 3.425034 3.544004 3.648023 3.740302 3.823131 3.898196 3.966776 4.029861 4.088234 4.142523 4.19324 4.24081 4.285584 4.628295 4.860604 5.174794 5.389348 5.551435 120 1.634753 2.261421 2.633285 2.895829 3.097525 3.260567 3.396959 3.513912 3.616089 3.706672 3.787929 3.861531 3.92874 3.990536 4.047692 4.10083 4.150455 4.196985 4.240768 4.575490 4.802013 5.107977 5.316696 5.474283 1e38 1.626840 2.248346 2.616224 2.875451 3.074279 3.234786 3.368898 3.483775 3.584045 3.672862 3.752475 3.824535 3.890294 3.950721 4.006580 4.058483 4.106932 4.152338 4.195044 4.520933 4.740866 5.037152 5.238766 5.390726""" q0800 = """\ 2 2.666345 3.820436 4.558532 5.098158 5.520848 5.866626 6.158145 6.409446 6.62982 6.825717 7.001791 7.161505 7.307502 7.441845 7.566171 7.681802 7.789818 7.891113 7.986436 8.714887 9.206808 9.868718 10.31830 10.65683 3 2.316120 3.245426 3.832597 4.261107 4.596942 4.871989 5.104169 5.304561 5.480484 5.637021 5.777843 5.905682 6.022626 6.130305 6.230013 6.322797 6.409513 6.49087 6.567462 7.153711 7.55053 8.085698 8.449862 8.724212 4 2.168283 3.003795 3.52645 3.90676 4.204595 4.44853 4.654519 4.832388 4.988615 5.127694 5.25287 5.366554 5.470593 5.566425 5.655195 5.737827 5.815079 5.887577 5.955847 6.47896 6.833568 7.31242 7.638648 7.884592 5 2.087215 2.871505 3.358337 3.711564 3.987876 4.214094 4.405111 4.57007 4.714986 4.844026 4.960193 5.065723 5.162321 5.25132 5.333779 5.410553 5.482342 5.549725 5.613191 6.099852 6.430105 6.876484 7.180827 7.410389 6 2.036122 2.788188 3.252203 3.588013 3.850385 4.06507 4.246305 4.402806 4.540296 4.662734 4.772969 4.873124 4.964814 5.049304 5.127595 5.200498 5.268677 5.33268 5.392969 5.855517 6.169658 6.594568 6.884463 7.103222 7 2.001005 2.730943 3.179141 3.502777 3.755348 3.961886 4.136188 4.286677 4.418877 4.536604 4.642603 4.738913 4.82709 4.908349 4.983652 5.05378 5.119368 5.180945 5.238953 5.684175 5.986732 6.39621 6.675724 6.886723 8 1.975395 2.689205 3.125785 3.440421 3.685706 3.886163 4.055272 4.201248 4.329470 4.443647 4.546448 4.639854 4.725375 4.804189 4.87723 4.945254 5.008879 5.068616 5.124894 5.556957 5.850708 6.248453 6.520077 6.725179 9 1.955898 2.657432 3.085114 3.392817 3.632464 3.828199 3.993263 4.135716 4.260824 4.37222 4.472512 4.563637 4.64707 4.723960 4.79522 4.861587 4.923664 4.981949 5.036862 5.458529 5.745314 6.133773 6.399153 6.59959 10 1.940561 2.632439 3.053086 3.355281 3.590431 3.782386 3.944205 4.083824 4.206424 4.315576 4.413841 4.503121 4.584862 4.660193 4.730009 4.795032 4.855852 4.912959 4.966762 5.37997 5.661078 6.041965 6.302252 6.498885 11 1.928182 2.612267 3.027211 3.324922 3.556399 3.745257 3.904411 4.041698 4.16223 4.269529 4.366119 4.453871 4.534212 4.608251 4.676869 4.740775 4.800552 4.85668 4.909561 5.315725 5.5921 5.966667 6.222702 6.41616 12 1.917981 2.595645 3.005872 3.299861 3.528278 3.714552 3.871475 4.006806 4.125603 4.231343 4.326522 4.412988 4.492148 4.565096 4.632701 4.695665 4.754559 4.809858 4.861959 5.262152 5.534506 5.9037 6.156119 6.346875 13 1.909431 2.581711 2.987972 3.278821 3.504650 3.688731 3.843759 3.977426 4.094742 4.199153 4.293127 4.378492 4.45664 4.528654 4.595391 4.657546 4.715682 4.770269 4.8217 5.216755 5.485643 5.850201 6.099497 6.287919 14 1.90216 2.569864 2.972742 3.260906 3.484516 3.666713 3.82011 3.952342 4.068380 4.171642 4.264573 4.348985 4.426256 4.497459 4.563444 4.624895 4.682373 4.736342 4.787189 5.177769 5.443632 5.804138 6.050703 6.237084 15 1.895903 2.559666 2.959626 3.245467 3.467154 3.647714 3.799691 3.930673 4.045596 4.147854 4.239872 4.32345 4.399954 4.470446 4.53577 4.596605 4.653505 4.706931 4.757265 5.143906 5.4071 5.764029 6.00818 6.192757 16 1.89046 2.550797 2.948212 3.232024 3.452027 3.631152 3.781882 3.911763 4.025705 4.127077 4.218291 4.301132 4.376957 4.446821 4.511561 4.571849 4.628237 4.681181 4.731062 5.114203 5.375025 5.728765 5.970765 6.153733 17 1.885683 2.543012 2.93819 3.220213 3.438729 3.616585 3.766210 3.895117 4.008187 4.108772 4.19927 4.281455 4.356676 4.42598 4.490198 4.549999 4.605930 4.658444 4.707919 5.08793 5.346623 5.697501 5.937567 6.119088 18 1.881457 2.536125 2.929319 3.209754 3.426947 3.603672 3.752313 3.880348 3.992639 4.09252 4.182377 4.263975 4.338653 4.407454 4.471204 4.530568 4.586089 4.638215 4.687324 5.064516 5.321288 5.66958 5.907896 6.088107 19 1.877691 2.529988 2.921412 3.200427 3.416435 3.592147 3.739903 3.867156 3.978745 4.077993 4.167272 4.24834 4.322529 4.390877 4.454204 4.513172 4.568321 4.620098 4.668877 5.043513 5.298541 5.644481 5.881205 6.060224 20 1.874315 2.524485 2.914320 3.192058 3.407000 3.581797 3.728755 3.855300 3.966255 4.064929 4.153685 4.234272 4.308018 4.375954 4.438897 4.497506 4.552317 4.603776 4.652254 5.024562 5.277998 5.621789 5.857057 6.034984 24 1.863701 2.507187 2.89201 3.165709 3.377268 3.549158 3.69357 3.817854 3.92678 4.023614 4.110690 4.189731 4.262048 4.328656 4.390359 4.447807 4.501527 4.551956 4.599461 4.964202 5.212441 5.549191 5.779675 5.954012 30 1.853207 2.490080 2.869925 3.139592 3.347756 3.516716 3.658554 3.780544 3.887403 3.982357 4.067712 4.145167 4.216014 4.281252 4.341675 4.397921 4.450509 4.49987 4.546362 4.903188 5.145946 5.47522 5.700597 5.871091 40 1.842829 2.473164 2.848060 3.113699 3.318456 3.484461 3.62369 3.743347 3.848096 3.941126 4.024712 4.100533 4.16986 4.233681 4.292775 4.34777 4.399178 4.447421 4.492854 4.841333 5.078248 5.399473 5.619306 5.785608 60 1.832568 2.456435 2.826413 3.088026 3.289358 3.452379 3.588962 3.70624 3.808829 3.899882 3.981645 4.055774 4.123524 4.185868 4.243575 4.297261 4.34743 4.394499 4.438814 4.778404 5.009002 5.321406 5.535087 5.696698 120 1.822478 2.439890 2.804980 3.062567 3.260456 3.420458 3.55435 3.669198 3.769570 3.858583 3.938458 4.010829 4.076934 4.137731 4.193979 4.246285 4.295145 4.340968 4.384094 4.714106 4.937761 5.24027 5.446912 5.603078 1e38 1.812388 2.423529 2.783758 3.037317 3.231739 3.388684 3.519834 3.632192 3.73028 3.817183 3.895093 3.965627 4.030005 4.089173 4.143877 4.194716 4.242179 4.286668 4.328517 4.648069 4.863937 5.155024 5.353283 5.50281""" q0850 = """\ 2 3.226562 4.548022 5.398759 6.022701 6.512387 6.913502 7.251997 7.54401 7.800236 8.028116 8.233021 8.418953 8.588968 8.74545 8.890294 9.02503 9.150913 9.268977 9.380094 10.22972 10.80450 11.58094 12.11086 12.51097 3 2.721399 3.731515 4.374509 4.845675 5.215912 5.5197 5.776502 5.998388 6.193356 6.366968 6.523249 6.665198 6.795111 6.914781 7.025634 7.128823 7.225292 7.315823 7.401073 8.054202 8.496827 9.094477 9.501702 9.808753 4 2.514747 3.399285 3.956491 4.363675 4.68348 4.945965 5.16798 5.359938 5.52872 5.679113 5.814574 5.937683 6.050411 6.154302 6.25058 6.34024 6.424092 6.502812 6.576963 7.145835 7.532079 8.054293 8.410406 8.679063 5 2.403262 3.220436 3.730867 4.102766 4.394545 4.633955 4.836465 5.011596 5.165628 5.302922 5.426626 5.539086 5.642096 5.737057 5.825086 5.907085 5.983793 6.055822 6.123687 6.644817 6.999123 7.478735 7.806159 8.05333 6 2.333697 3.108965 3.589945 3.939419 4.213263 4.437836 4.627757 4.791998 4.936465 5.06525 5.181307 5.286833 5.38351 5.47265 5.555297 5.632296 5.704339 5.771998 5.835756 6.325681 6.659107 7.110867 7.419518 7.652631 7 2.286206 3.032919 3.493643 3.827573 4.088914 4.303095 4.484169 4.640738 4.77845 4.901217 5.011858 5.112468 5.20465 5.289655 5.368478 5.441922 5.510646 5.575196 5.63603 6.103717 6.422253 6.85415 7.149428 7.372543 8 2.251741 2.977758 3.423692 3.746199 3.998303 4.204778 4.379271 4.530117 4.662781 4.781044 4.887624 4.984545 5.073351 5.155248 5.231193 5.30196 5.368185 5.430392 5.489022 5.939926 6.24721 6.664096 6.949269 7.16483 9 2.225598 2.935932 3.370588 3.684337 3.92933 4.12985 4.299243 4.445643 4.57438 4.68913 4.792542 4.88658 4.972745 5.052208 5.125899 5.194568 5.258832 5.319201 5.376101 5.81381 6.112237 6.517297 6.794508 7.004117 10 2.205093 2.903132 3.328904 3.635722 3.875064 4.070838 4.236155 4.378996 4.504581 4.61651 4.717372 4.809088 4.893124 4.970624 5.042494 5.109469 5.172148 5.231029 5.28653 5.713546 6.004781 6.400234 6.670974 6.875744 11 2.18858 2.876725 3.295316 3.596509 3.831250 4.023149 4.18513 4.325052 4.448048 4.557656 4.656418 4.74622 4.828499 4.904377 4.974743 5.040315 5.101683 5.159333 5.213674 5.631818 5.917077 6.304535 6.569888 6.770631 12 2.174999 2.855009 3.267675 3.564211 3.795132 3.983803 4.143002 4.280484 4.401312 4.508975 4.605974 4.694168 4.77497 4.849483 4.918582 4.982973 5.043235 5.099847 5.153209 5.563854 5.84405 6.22473 6.485512 6.682838 13 2.163633 2.836837 3.244531 3.537147 3.764842 3.950784 4.107624 4.243034 4.36202 4.468027 4.563524 4.650346 4.729887 4.803233 4.871249 4.93463 4.993946 5.049668 5.102191 5.5064 5.782244 6.157087 6.413931 6.608311 14 2.153982 2.821408 3.224869 3.514139 3.739075 3.922677 4.077491 4.21112 4.328519 4.433097 4.527298 4.612934 4.691385 4.763723 4.830801 4.893306 4.951801 5.006752 5.058548 5.457164 5.729217 6.09897 6.352377 6.544185 15 2.145684 2.808145 3.207959 3.494339 3.716887 3.898461 4.051515 4.183594 4.299611 4.402944 4.496014 4.580615 4.658112 4.729568 4.795825 4.857564 4.91534 4.969615 5.020773 5.414476 5.683193 6.048461 6.298836 6.488374 16 2.138475 2.796621 3.193261 3.47712 3.697581 3.877377 4.028889 4.159607 4.274409 4.376647 4.468721 4.55241 4.629066 4.699743 4.765275 4.826337 4.883478 4.937154 4.987748 5.377097 5.642851 6.004129 6.251805 6.439321 17 2.132153 2.786517 3.180367 3.462007 3.680628 3.858856 4.009003 4.138517 4.252242 4.353508 4.444698 4.527576 4.603485 4.673469 4.738356 4.798814 4.855389 4.908532 4.958622 5.344083 5.607184 5.964886 6.21014 6.395841 18 2.126564 2.777584 3.168965 3.448637 3.665623 3.842455 3.991387 4.119827 4.232591 4.332989 4.423388 4.505541 4.58078 4.650143 4.714452 4.774369 4.830436 4.883101 4.932739 5.314701 5.575413 5.929888 6.172953 6.357013 19 2.121587 2.769631 3.158810 3.436724 3.652248 3.82783 3.975673 4.103149 4.21505 4.314667 4.404354 4.485854 4.560491 4.629294 4.693081 4.752509 4.808118 4.860351 4.909581 5.288379 5.546924 5.898469 6.139544 6.322112 20 2.117128 2.762505 3.149708 3.426043 3.640251 3.814707 3.961568 4.088173 4.199295 4.298206 4.387249 4.468158 4.542248 4.610545 4.673858 4.732844 4.788036 4.839877 4.888736 5.264656 5.521227 5.870097 6.109355 6.290558 24 2.103128 2.740133 3.121118 3.392466 3.60251 3.773393 3.917129 4.040961 4.149593 4.246248 4.33323 4.412242 4.484578 4.551244 4.613036 4.670595 4.724445 4.775019 4.822681 5.189274 5.439419 5.779554 6.012859 6.189586 30 2.089309 2.718054 3.092876 3.359261 3.56514 3.732436 3.873024 3.994053 4.10016 4.19452 4.2794 4.356475 4.427015 4.492009 4.552236 4.608325 4.660792 4.710058 4.756481 5.113372 5.356776 5.687685 5.914664 6.08662 40 2.07567 2.696264 3.064978 3.326418 3.52813 3.691822 3.829233 3.947423 4.050965 4.142986 4.225718 4.300807 4.369502 4.432773 4.491385 4.545955 4.596987 4.644896 4.69003 5.036754 5.27302 5.594062 5.814221 5.981005 60 2.062208 2.674759 3.037417 3.293931 3.491470 3.651535 3.785736 3.901046 4.001976 4.091607 4.172136 4.245183 4.311975 4.373463 4.430401 4.483391 4.532928 4.579419 4.623205 4.95919 5.187807 5.498134 5.710792 5.871841 120 2.048920 2.653534 3.010189 3.261791 3.455148 3.611561 3.742514 3.854896 3.953159 4.040341 4.118605 4.189543 4.254363 4.314 4.369191 4.42053 4.4685 4.513501 4.555864 4.880396 5.100706 5.399172 5.60337 5.757859 1e38 2.035805 2.632586 2.983286 3.229990 3.419154 3.571884 3.699544 3.808945 3.904479 3.989143 4.065068 4.133821 4.19659 4.254292 4.307653 4.357255 4.403572 4.446994 4.487848 4.800043 5.011193 5.296241 5.4906 5.637297""" q0900 = """\ 1 8.929 13.44 16.36 18.49 20.15 21.51 22.64 23.62 24.48 25.24 25.92 26.54 27.10 27.62 28.10 28.54 28.96 29.35 29.71 32.50 34.38 36.91 38.62 39.91 2 4.129 5.733 6.773 7.538 8.139 8.633 9.049 9.409 9.725 10.01 10.26 10.49 10.70 10.89 11.07 11.24 11.39 11.54 11.68 12.73 13.44 14.40 15.04 15.54 3 3.328 4.467 5.199 5.738 6.162 6.511 6.806 7.062 7.287 7.487 7.667 7.831 7.982 8.120 8.248 8.368 8.479 8.584 8.683 9.440 9.954 10.65 11.12 11.48 4 3.015 3.976 4.586 5.035 5.388 5.679 5.926 6.139 6.327 6.494 6.645 6.783 6.909 7.025 7.132 7.233 7.326 7.414 7.497 8.135 8.569 9.156 9.558 9.861 5 2.850 3.717 4.264 4.664 4.979 5.238 5.458 5.648 5.816 5.965 6.100 6.223 6.336 6.439 6.536 6.626 6.710 6.788 6.863 7.435 7.824 8.353 8.714 8.987 6 2.748 3.558 4.065 4.435 4.726 4.966 5.168 5.344 5.499 5.637 5.762 5.875 5.979 6.075 6.164 6.247 6.325 6.398 6.466 6.996 7.358 7.848 8.184 8.438 7 2.679 3.451 3.931 4.280 4.555 4.780 4.971 5.137 5.283 5.413 5.530 5.637 5.735 5.826 5.910 5.988 6.061 6.130 6.195 6.695 7.036 7.500 7.818 8.059 8 2.630 3.374 3.834 4.169 4.431 4.646 4.829 4.987 5.126 5.250 5.362 5.464 5.558 5.644 5.724 5.799 5.869 5.935 5.997 6.475 6.801 7.245 7.550 7.780 9 2.592 3.316 3.761 4.084 4.337 4.545 4.721 4.873 5.007 5.126 5.234 5.333 5.423 5.506 5.583 5.655 5.722 5.786 5.845 6.306 6.621 7.049 7.343 7.566 10 2.563 3.270 3.704 4.018 4.264 4.465 4.636 4.783 4.913 5.029 5.134 5.229 5.316 5.397 5.472 5.542 5.607 5.668 5.726 6.173 6.478 6.894 7.180 7.396 11 2.540 3.234 3.658 3.965 4.205 4.401 4.567 4.711 4.838 4.951 5.053 5.145 5.231 5.309 5.382 5.450 5.514 5.573 5.630 6.065 6.363 6.768 7.046 7.257 12 2.521 3.204 3.621 3.921 4.156 4.349 4.511 4.652 4.776 4.886 4.986 5.076 5.160 5.236 5.308 5.374 5.436 5.495 5.550 5.975 6.267 6.663 6.936 7.142 13 2.504 3.179 3.589 3.885 4.116 4.304 4.464 4.602 4.724 4.832 4.930 5.019 5.100 5.175 5.245 5.310 5.371 5.429 5.483 5.900 6.186 6.575 6.842 7.045 14 2.491 3.158 3.563 3.854 4.081 4.267 4.424 4.560 4.679 4.786 4.882 4.969 5.050 5.124 5.192 5.256 5.316 5.372 5.426 5.836 6.116 6.499 6.762 6.961 15 2.479 3.140 3.540 3.828 4.052 4.235 4.390 4.524 4.641 4.746 4.841 4.927 5.006 5.079 5.146 5.209 5.268 5.324 5.376 5.780 6.056 6.433 6.692 6.888 16 2.469 3.124 3.520 3.804 4.026 4.207 4.360 4.492 4.608 4.712 4.805 4.890 4.968 5.040 5.106 5.169 5.227 5.282 5.333 5.731 6.004 6.376 6.631 6.825 17 2.460 3.110 3.503 3.784 4.003 4.182 4.334 4.464 4.579 4.681 4.774 4.857 4.934 5.005 5.071 5.133 5.190 5.244 5.295 5.688 5.958 6.325 6.577 6.769 18 2.452 3.098 3.487 3.766 3.984 4.161 4.310 4.440 4.553 4.654 4.746 4.829 4.905 4.975 5.040 5.101 5.158 5.211 5.262 5.650 5.917 6.280 6.529 6.718 19 2.445 3.087 3.474 3.751 3.966 4.142 4.290 4.418 4.530 4.630 4.721 4.803 4.878 4.948 5.012 5.072 5.129 5.182 5.232 5.616 5.880 6.239 6.486 6.673 20 2.439 3.077 3.462 3.736 3.950 4.124 4.271 4.398 4.510 4.609 4.699 4.780 4.855 4.923 4.987 5.047 5.103 5.155 5.205 5.586 5.847 6.202 6.447 6.633 24 2.420 3.047 3.423 3.692 3.900 4.070 4.213 4.336 4.445 4.541 4.628 4.707 4.780 4.847 4.909 4.966 5.020 5.071 5.119 5.489 5.741 6.086 6.323 6.503 30 2.400 3.017 3.386 3.648 3.851 4.016 4.155 4.275 4.381 4.474 4.559 4.635 4.706 4.770 4.830 4.886 4.939 4.988 5.034 5.391 5.636 5.969 6.198 6.372 40 2.381 2.988 3.348 3.605 3.802 3.963 4.099 4.215 4.317 4.408 4.490 4.564 4.632 4.694 4.752 4.806 4.857 4.904 4.949 5.294 5.529 5.850 6.071 6.238 60 2.363 2.959 3.312 3.562 3.755 3.911 4.042 4.155 4.254 4.342 4.421 4.493 4.558 4.619 4.675 4.727 4.775 4.821 4.864 5.196 5.422 5.730 5.941 6.101 120 2.344 2.930 3.276 3.520 3.707 3.859 3.986 4.096 4.191 4.276 4.353 4.422 4.485 4.543 4.597 4.647 4.694 4.738 4.779 5.097 5.313 5.606 5.808 5.960 1e38 2.326 2.902 3.240 3.478 3.661 3.808 3.931 4.037 4.129 4.211 4.285 4.351 4.412 4.468 4.519 4.568 4.612 4.654 4.694 4.997 5.202 5.480 5.669 5.812""" q0950 = """\ 1 17.97 26.98 32.82 37.08 40.41 43.12 45.40 47.36 49.07 50.59 51.96 53.20 54.33 55.36 56.32 57.22 58.04 58.83 59.56 65.15 68.92 73.97 77.40 79.98 2 6.085 8.331 9.799 10.88 11.73 12.43 13.03 13.54 13.99 14.40 14.76 15.09 15.39 15.65 15.92 16.14 16.38 16.57 16.78 18.27 19.28 20.66 21.59 22.29 3 4.501 5.910 6.825 7.502 8.037 8.478 8.852 9.177 9.462 9.717 9.946 10.15 10.35 10.52 10.69 10.84 10.98 11.11 11.24 12.21 12.86 13.76 14.36 14.82 4 3.926 5.040 5.757 6.287 6.706 7.053 7.347 7.602 7.826 8.027 8.208 8.373 8.524 8.664 8.793 8.914 9.027 9.133 9.233 10.00 10.53 11.24 11.73 12.10 5 3.635 4.602 5.218 5.673 6.033 6.330 6.582 6.801 6.995 7.167 7.323 7.466 7.596 7.716 7.828 7.932 8.030 8.122 8.208 8.875 9.330 9.949 10.37 10.69 6 3.460 4.339 4.896 5.305 5.628 5.895 6.122 6.319 6.493 6.649 6.789 6.917 7.034 7.143 7.244 7.338 7.426 7.508 7.586 8.189 8.601 9.162 9.547 9.839 7 3.344 4.165 4.681 5.060 5.359 5.606 5.815 5.997 6.158 6.302 6.431 6.550 6.658 6.759 6.852 6.939 7.020 7.097 7.169 7.727 8.110 8.631 8.989 9.260 8 3.261 4.041 4.529 4.886 5.167 5.399 5.596 5.767 5.918 6.053 6.175 6.287 6.389 6.483 6.571 6.653 6.729 6.801 6.869 7.395 7.756 8.248 8.586 8.843 9 3.199 3.948 4.415 4.755 5.024 5.244 5.432 5.595 5.738 5.867 5.983 6.089 6.186 6.276 6.359 6.437 6.510 6.579 6.643 7.144 7.488 7.958 8.281 8.526 10 3.151 3.877 4.327 4.654 4.912 5.124 5.304 5.460 5.598 5.722 5.833 5.935 6.028 6.114 6.194 6.269 6.339 6.405 6.467 6.948 7.278 7.730 8.041 8.276 11 3.113 3.820 4.256 4.574 4.823 5.028 5.202 5.353 5.486 5.605 5.713 5.811 5.901 5.984 6.062 6.134 6.202 6.265 6.325 6.790 7.109 7.546 7.847 8.075 12 3.081 3.773 4.199 4.508 4.750 4.950 5.119 5.265 5.395 5.510 5.615 5.710 5.797 5.878 5.953 6.023 6.089 6.151 6.209 6.660 6.970 7.394 7.687 7.908 13 3.055 3.734 4.151 4.453 4.690 4.884 5.049 5.192 5.318 5.431 5.533 5.625 5.711 5.789 5.862 5.931 5.995 6.055 6.112 6.551 6.853 7.267 7.552 7.769 14 3.033 3.701 4.111 4.407 4.639 4.829 4.990 5.130 5.253 5.364 5.463 5.554 5.637 5.714 5.785 5.852 5.915 5.973 6.029 6.459 6.754 7.159 7.437 7.649 15 3.014 3.673 4.076 4.367 4.595 4.782 4.940 5.077 5.198 5.306 5.403 5.492 5.574 5.649 5.719 5.785 5.846 5.904 5.958 6.379 6.669 7.065 7.338 7.546 16 2.998 3.649 4.046 4.333 4.557 4.741 4.896 5.031 5.150 5.256 5.352 5.439 5.519 5.593 5.662 5.726 5.786 5.843 5.896 6.310 6.594 6.983 7.252 7.456 17 2.984 3.628 4.020 4.303 4.524 4.705 4.858 4.991 5.108 5.212 5.306 5.392 5.471 5.544 5.612 5.675 5.734 5.790 5.842 6.249 6.529 6.912 7.176 7.377 18 2.971 3.609 3.997 4.276 4.494 4.673 4.824 4.955 5.071 5.173 5.266 5.351 5.429 5.501 5.567 5.629 5.688 5.743 5.794 6.195 6.471 6.848 7.108 7.307 19 2.960 3.593 3.977 4.253 4.468 4.645 4.794 4.924 5.037 5.139 5.231 5.314 5.391 5.462 5.528 5.589 5.647 5.701 5.752 6.147 6.419 6.791 7.048 7.244 20 2.950 3.578 3.958 4.232 4.445 4.620 4.768 4.895 5.008 5.108 5.199 5.282 5.357 5.427 5.492 5.553 5.610 5.663 5.714 6.104 6.372 6.740 6.994 7.187 24 2.919 3.532 3.901 4.166 4.373 4.541 4.684 4.807 4.915 5.012 5.099 5.179 5.251 5.319 5.381 5.439 5.494 5.545 5.594 5.968 6.226 6.578 6.822 7.007 30 2.888 3.486 3.845 4.102 4.301 4.464 4.601 4.720 4.824 4.917 5.001 5.077 5.147 5.211 5.271 5.327 5.379 5.429 5.475 5.833 6.080 6.417 6.650 6.827 40 2.858 3.442 3.791 4.039 4.232 4.388 4.521 4.634 4.735 4.824 4.904 4.977 5.044 5.106 5.163 5.216 5.266 5.313 5.358 5.700 5.934 6.255 6.477 6.645 60 2.829 3.399 3.737 3.977 4.163 4.314 4.441 4.550 4.646 4.732 4.808 4.878 4.942 5.001 5.056 5.107 5.154 5.199 5.241 5.566 5.789 6.093 6.302 6.462 120 2.800 3.356 3.685 3.917 4.096 4.241 4.363 4.468 4.560 4.641 4.714 4.781 4.842 4.898 4.950 4.998 5.043 5.086 5.126 5.434 5.644 5.929 6.126 6.275 1e38 2.772 3.314 3.633 3.858 4.030 4.170 4.286 4.387 4.474 4.552 4.622 4.685 4.743 4.796 4.845 4.891 4.934 4.974 5.012 5.301 5.498 5.764 5.947 6.085""" q0975 = """\ 1 35.99 54.00 65.69 74.22 80.87 86.29 90.85 94.77 98.20 101.3 104.0 106.5 108.8 110.8 112.7 114.5 116.2 117.7 119.2 130.4 137.9 148.1 154.9 160.0 2 8.776 11.94 14.02 15.54 16.75 17.74 18.58 19.31 19.95 20.52 21.03 21.49 21.91 22.30 22.67 23.01 23.32 23.62 23.89 26.03 27.47 29.42 30.74 31.74 3 5.907 7.661 8.808 9.659 10.33 10.89 11.36 11.77 12.14 12.46 12.75 13.01 13.25 13.47 13.68 13.87 14.05 14.22 14.38 15.62 16.46 17.58 18.37 18.95 4 4.943 6.244 7.088 7.715 8.213 8.625 8.975 9.279 9.548 9.788 10.00 10.20 10.38 10.55 10.71 10.85 10.99 11.11 11.23 12.16 12.78 13.65 14.23 14.68 5 4.474 5.558 6.257 6.775 7.186 7.526 7.816 8.068 8.291 8.490 8.670 8.834 8.984 9.124 9.253 9.373 9.486 9.592 9.693 10.47 11.00 11.72 12.21 12.59 6 4.198 5.158 5.772 6.226 6.586 6.884 7.138 7.359 7.554 7.729 7.887 8.031 8.163 8.285 8.399 8.505 8.605 8.698 8.787 9.469 9.937 10.57 11.01 11.34 7 4.018 4.897 5.455 5.867 6.194 6.464 6.694 6.894 7.071 7.230 7.373 7.504 7.624 7.735 7.838 7.935 8.025 8.110 8.191 8.812 9.239 9.822 10.22 10.53 8 3.891 4.714 5.233 5.616 5.919 6.169 6.382 6.567 6.731 6.878 7.011 7.132 7.244 7.347 7.442 7.532 7.616 7.694 7.769 8.346 8.743 9.286 9.660 9.944 9 3.797 4.578 5.069 5.430 5.715 5.950 6.151 6.325 6.479 6.617 6.742 6.856 6.961 7.057 7.148 7.232 7.311 7.385 7.455 7.999 8.373 8.885 9.238 9.506 10 3.725 4.474 4.943 5.286 5.558 5.782 5.972 6.138 6.284 6.415 6.534 6.642 6.742 6.834 6.920 7.000 7.075 7.145 7.212 7.729 8.085 8.574 8.910 9.166 11 3.667 4.391 4.843 5.173 5.433 5.648 5.830 5.989 6.130 6.255 6.369 6.473 6.568 6.656 6.738 6.815 6.887 6.955 7.018 7.514 7.856 8.324 8.648 8.894 12 3.620 4.324 4.761 5.080 5.332 5.539 5.715 5.868 6.004 6.125 6.235 6.335 6.426 6.511 6.591 6.664 6.734 6.799 6.860 7.338 7.668 8.120 8.433 8.670 13 3.582 4.269 4.694 5.004 5.248 5.449 5.620 5.768 5.899 6.017 6.123 6.220 6.309 6.391 6.468 6.539 6.606 6.670 6.729 7.192 7.511 7.950 8.253 8.484 14 3.549 4.222 4.638 4.940 5.178 5.373 5.540 5.684 5.812 5.926 6.029 6.123 6.210 6.290 6.364 6.434 6.499 6.560 6.618 7.068 7.379 7.806 8.100 8.325 15 3.521 4.182 4.589 4.885 5.117 5.309 5.471 5.612 5.736 5.848 5.948 6.040 6.125 6.205 6.275 6.343 6.407 6.467 6.523 6.962 7.265 7.682 7.969 8.189 16 3.497 4.148 4.548 4.838 5.065 5.253 5.412 5.550 5.671 5.780 5.879 5.969 6.051 6.128 6.199 6.265 6.327 6.386 6.441 6.870 7.167 7.574 7.856 8.070 17 3.476 4.118 4.511 4.797 5.020 5.204 5.360 5.495 5.615 5.722 5.818 5.906 5.987 6.062 6.132 6.197 6.258 6.315 6.369 6.790 7.080 7.479 7.756 7.966 18 3.458 4.091 4.479 4.760 4.980 5.161 5.315 5.448 5.565 5.670 5.765 5.851 5.931 6.004 6.073 6.137 6.196 6.253 6.306 6.719 7.004 7.396 7.667 7.874 19 3.441 4.068 4.451 4.728 4.945 5.123 5.274 5.405 5.521 5.624 5.717 5.803 5.881 5.953 6.020 6.083 6.142 6.197 6.250 6.656 6.936 7.322 7.589 7.792 20 3.427 4.047 4.426 4.699 4.914 5.089 5.238 5.367 5.481 5.583 5.675 5.759 5.836 5.907 5.974 6.035 6.093 6.148 6.199 6.599 6.876 7.255 7.518 7.718 24 3.381 3.982 4.347 4.610 4.816 4.984 5.126 5.250 5.358 5.455 5.543 5.623 5.696 5.764 5.827 5.886 5.941 5.993 6.042 6.422 6.685 7.046 7.295 7.486 30 3.337 3.919 4.271 4.523 4.720 4.881 5.017 5.134 5.238 5.330 5.414 5.490 5.560 5.624 5.684 5.740 5.792 5.841 5.888 6.248 6.497 6.838 7.075 7.255 40 3.294 3.858 4.196 4.439 4.627 4.780 4.910 5.022 5.120 5.208 5.287 5.360 5.426 5.487 5.543 5.596 5.646 5.692 5.736 6.077 6.311 6.633 6.855 7.025 60 3.251 3.798 4.124 4.356 4.536 4.682 4.806 4.912 5.006 5.089 5.164 5.232 5.295 5.352 5.406 5.456 5.502 5.546 5.588 5.908 6.127 6.428 6.636 6.795 120 3.210 3.739 4.053 4.275 4.447 4.587 4.704 4.805 4.894 4.972 5.043 5.107 5.166 5.221 5.271 5.318 5.362 5.403 5.442 5.741 5.946 6.225 6.418 6.564 1e38 3.170 3.682 3.984 4.197 4.361 4.494 4.605 4.700 4.784 4.858 4.925 4.985 5.041 5.092 5.139 5.183 5.224 5.262 5.299 5.577 5.766 6.023 6.199 6.333""" q0990 = """\ 1 90.02 135.0 164.3 185.6 202.2 215.8 227.2 237.0 245.6 253.2 260.0 266.2 271.8 277.0 281.8 286.3 290.4 294.3 298.0 326.0 344.8 370.1 387.3 400.1 2 14.04 19.02 22.29 24.72 26.63 28.20 29.53 30.68 31.69 32.59 33.40 34.13 34.81 35.43 36.00 36.53 37.03 37.50 37.95 41.32 43.61 46.70 48.80 50.38 3 8.260 10.62 12.17 13.32 14.24 15.00 15.65 16.21 16.69 17.13 17.53 17.89 18.22 18.52 18.81 19.07 19.32 19.55 19.77 21.44 22.59 24.13 25.19 25.99 4 6.511 8.120 9.173 9.958 10.58 11.10 11.54 11.92 12.26 12.57 12.84 13.09 13.32 13.53 13.72 13.91 14.08 14.24 14.39 15.57 16.38 17.46 18.20 18.77 5 5.702 6.976 7.804 8.421 8.913 9.321 9.669 9.971 10.24 10.48 10.70 10.89 11.08 11.24 11.40 11.55 11.68 11.81 11.93 12.87 13.51 14.39 14.99 15.45 6 5.243 6.331 7.033 7.556 7.972 8.318 8.612 8.869 9.097 9.300 9.485 9.653 9.808 9.951 10.08 10.21 10.32 10.43 10.54 11.34 11.89 12.65 13.17 13.55 7 4.949 5.919 6.542 7.005 7.373 7.678 7.939 8.166 8.367 8.548 8.711 8.860 8.997 9.124 9.242 9.353 9.456 9.553 9.645 10.36 10.85 11.52 11.98 12.34 8 4.745 5.635 6.204 6.625 6.959 7.237 7.474 7.680 7.863 8.027 8.176 8.311 8.436 8.552 8.659 8.760 8.854 8.943 9.027 9.677 10.13 10.74 11.17 11.49 9 4.596 5.428 5.957 6.347 6.657 6.915 7.134 7.325 7.494 7.646 7.784 7.910 8.025 8.132 8.232 8.325 8.412 8.495 8.573 9.177 9.594 10.17 10.56 10.86 10 4.482 5.270 5.769 6.136 6.428 6.669 6.875 7.054 7.213 7.356 7.485 7.603 7.712 7.812 7.906 7.993 8.075 8.153 8.226 8.794 9.186 9.726 10.10 10.38 11 4.392 5.146 5.621 5.970 6.247 6.476 6.671 6.841 6.992 7.127 7.250 7.362 7.464 7.560 7.648 7.731 7.809 7.883 7.952 8.491 8.864 9.377 9.732 10.00 12 4.320 5.046 5.502 5.836 6.101 6.320 6.507 6.670 6.814 6.943 7.060 7.166 7.265 7.356 7.441 7.520 7.594 7.664 7.730 8.246 8.602 9.093 9.433 9.693 13 4.260 4.964 5.404 5.726 5.981 6.192 6.372 6.528 6.666 6.791 6.903 7.006 7.100 7.188 7.269 7.345 7.417 7.484 7.548 8.043 8.386 8.859 9.186 9.436 14 4.210 4.895 5.322 5.634 5.881 6.085 6.258 6.409 6.543 6.663 6.772 6.871 6.962 7.047 7.125 7.199 7.268 7.333 7.394 7.873 8.204 8.661 8.978 9.219 15 4.167 4.836 5.252 5.556 5.796 5.994 6.162 6.309 6.438 6.555 6.660 6.756 6.845 6.927 7.003 7.074 7.141 7.204 7.264 7.727 8.049 8.492 8.800 9.034 16 4.131 4.786 5.192 5.489 5.722 5.915 6.079 6.222 6.348 6.461 6.564 6.658 6.744 6.823 6.897 6.967 7.032 7.093 7.151 7.602 7.915 8.346 8.646 8.874 17 4.099 4.742 5.140 5.430 5.659 5.847 6.007 6.147 6.270 6.380 6.480 6.572 6.656 6.733 6.806 6.873 6.937 6.997 7.053 7.493 7.798 8.219 8.511 8.734 18 4.071 4.703 5.094 5.379 5.603 5.787 5.944 6.081 6.201 6.309 6.407 6.496 6.579 6.655 6.725 6.791 6.854 6.912 6.967 7.397 7.696 8.107 8.393 8.611 19 4.046 4.669 5.054 5.334 5.553 5.735 5.889 6.022 6.141 6.246 6.342 6.430 6.510 6.585 6.654 6.719 6.780 6.837 6.891 7.312 7.605 8.008 8.288 8.501 20 4.024 4.639 5.018 5.293 5.510 5.688 5.839 5.970 6.086 6.190 6.285 6.370 6.449 6.523 6.591 6.654 6.714 6.770 6.823 7.237 7.523 7.919 8.194 8.404 24 3.955 4.546 4.907 5.168 5.373 5.542 5.685 5.809 5.919 6.017 6.105 6.186 6.261 6.330 6.394 6.453 6.510 6.562 6.612 7.001 7.270 7.641 7.900 8.097 30 3.889 4.455 4.799 5.048 5.242 5.401 5.536 5.653 5.756 5.848 5.932 6.008 6.078 6.142 6.202 6.258 6.311 6.361 6.407 6.771 7.023 7.370 7.611 7.796 40 3.825 4.367 4.695 4.931 5.114 5.265 5.392 5.502 5.599 5.685 5.764 5.835 5.900 5.961 6.017 6.069 6.118 6.165 6.208 6.547 6.781 7.104 7.328 7.499 60 3.762 4.282 4.594 4.818 4.991 5.133 5.253 5.356 5.447 5.528 5.601 5.667 5.728 5.784 5.837 5.886 5.931 5.974 6.015 6.329 6.546 6.843 7.049 7.207 120 3.702 4.200 4.497 4.708 4.872 5.005 5.118 5.214 5.299 5.375 5.443 5.505 5.561 5.614 5.662 5.708 5.750 5.790 5.827 6.117 6.316 6.588 6.776 6.919 1e38 3.643 4.120 4.403 4.603 4.757 4.882 4.987 5.078 5.157 5.227 5.290 5.348 5.400 5.448 5.493 5.535 5.574 5.611 5.645 5.911 6.092 6.338 6.507 6.636""" q0995 = """\ 1 180.1 270.1 328.5 371.2 404.4 431.6 454.4 474.0 491.1 506.3 520.0 532.4 543.6 554.0 563.6 572.5 580.9 588.7 596.0 652.0 689.6 740.2 774.5 800.3 2 19.92 26.97 31.60 35.02 37.73 39.95 41.83 43.46 44.89 46.16 47.31 48.35 49.30 50.17 50.99 51.74 52.45 53.12 53.74 58.52 61.76 66.13 69.10 71.35 3 10.54 13.51 15.45 16.91 18.06 19.01 19.83 20.53 21.15 21.70 22.20 22.66 23.08 23.46 23.82 24.15 24.46 24.76 25.03 27.15 28.60 30.55 31.88 32.90 4 7.916 9.813 11.06 11.99 12.74 13.35 13.88 14.33 14.74 15.10 15.42 15.72 15.99 16.24 16.47 16.70 16.90 17.09 17.28 18.68 19.63 20.93 21.83 22.50 5 6.751 8.195 9.140 9.846 10.41 10.88 11.28 11.62 11.93 12.21 12.46 12.69 12.90 13.09 13.27 13.44 13.60 13.74 13.89 14.96 15.71 16.72 17.41 17.94 6 6.105 7.306 8.087 8.670 9.135 9.522 9.852 10.14 10.39 10.62 10.83 11.02 11.19 11.35 11.50 11.64 11.78 11.90 12.02 12.92 13.54 14.40 14.98 15.43 7 5.698 6.750 7.429 7.935 8.339 8.674 8.961 9.211 9.433 9.632 9.812 9.976 10.13 10.27 10.40 10.52 10.64 10.74 10.84 11.64 12.18 12.93 13.45 13.85 8 5.420 6.370 6.981 7.435 7.796 8.097 8.354 8.578 8.777 8.955 9.117 9.265 9.401 9.527 9.644 9.754 9.856 9.953 10.04 10.76 11.25 11.92 12.39 12.75 9 5.218 6.096 6.657 7.073 7.405 7.680 7.915 8.120 8.302 8.466 8.614 8.749 8.874 8.989 9.097 9.197 9.292 9.381 9.465 10.12 10.57 11.19 11.62 11.95 10 5.065 5.888 6.412 6.800 7.109 7.365 7.584 7.775 7.944 8.096 8.233 8.359 8.475 8.583 8.683 8.777 8.864 8.947 9.025 9.635 10.06 10.64 11.04 11.35 11 4.945 5.726 6.221 6.587 6.878 7.119 7.325 7.504 7.664 7.807 7.936 8.055 8.164 8.265 8.359 8.447 8.530 8.608 8.681 9.255 9.653 10.20 10.58 10.87 12 4.849 5.596 6.068 6.416 6.693 6.922 7.117 7.288 7.439 7.574 7.697 7.810 7.913 8.009 8.099 8.182 8.261 8.335 8.405 8.950 9.328 9.850 10.21 10.49 13 4.769 5.489 5.943 6.276 6.541 6.760 6.947 7.110 7.254 7.384 7.502 7.609 7.708 7.800 7.885 7.965 8.040 8.111 8.178 8.699 9.061 9.560 9.907 10.17 14 4.703 5.401 5.838 6.160 6.414 6.625 6.805 6.962 7.101 7.225 7.338 7.442 7.537 7.625 7.707 7.784 7.856 7.924 7.988 8.489 8.837 9.317 9.651 9.906 15 4.647 5.325 5.750 6.061 6.307 6.511 6.685 6.836 6.970 7.091 7.200 7.300 7.391 7.476 7.556 7.630 7.699 7.765 7.827 8.310 8.647 9.111 9.434 9.680 16 4.599 5.261 5.674 5.976 6.216 6.413 6.582 6.729 6.859 6.975 7.081 7.178 7.267 7.349 7.426 7.498 7.565 7.629 7.689 8.157 8.483 8.933 9.246 9.486 17 4.557 5.205 5.608 5.903 6.136 6.329 6.493 6.636 6.762 6.876 6.978 7.072 7.159 7.239 7.313 7.383 7.449 7.510 7.569 8.024 8.341 8.779 9.083 9.316 18 4.521 5.156 5.550 5.839 6.067 6.255 6.415 6.554 6.677 6.788 6.888 6.980 7.064 7.142 7.215 7.283 7.347 7.407 7.464 7.908 8.216 8.643 8.940 9.167 19 4.488 5.112 5.500 5.782 6.005 6.189 6.346 6.482 6.603 6.711 6.809 6.898 6.981 7.057 7.128 7.194 7.257 7.316 7.371 7.805 8.106 8.523 8.813 9.035 20 4.460 5.074 5.455 5.732 5.951 6.131 6.285 6.418 6.536 6.642 6.738 6.826 6.906 6.981 7.051 7.116 7.177 7.234 7.289 7.713 8.008 8.416 8.700 8.917 24 4.371 4.955 5.315 5.577 5.783 5.952 6.096 6.221 6.331 6.430 6.520 6.602 6.677 6.747 6.812 6.872 6.929 6.983 7.034 7.429 7.703 8.083 8.348 8.551 30 4.285 4.841 5.181 5.428 5.621 5.779 5.914 6.031 6.134 6.226 6.310 6.386 6.456 6.521 6.581 6.638 6.691 6.740 6.787 7.154 7.409 7.760 8.005 8.193 40 4.202 4.731 5.052 5.284 5.465 5.614 5.739 5.848 5.944 6.030 6.108 6.178 6.243 6.303 6.359 6.411 6.460 6.507 6.550 6.888 7.123 7.447 7.672 7.844 60 4.122 4.625 4.928 5.146 5.316 5.454 5.571 5.673 5.762 5.841 5.913 5.979 6.039 6.094 6.146 6.194 6.239 6.281 6.321 6.632 6.846 7.142 7.347 7.504 120 4.044 4.523 4.809 5.013 5.172 5.301 5.410 5.504 5.586 5.660 5.726 5.786 5.842 5.893 5.940 5.984 6.025 6.064 6.101 6.384 6.579 6.846 7.031 7.172 1e38 3.970 4.424 4.694 4.886 5.033 5.154 5.255 5.341 5.418 5.485 5.546 5.602 5.652 5.699 5.742 5.783 5.820 5.856 5.889 6.146 6.322 6.561 6.725 6.850""" q0999 = """\ 1 900.3 1351. 1643. 1856. 2022. 2158. 2272. 2370. 2455. 2532. 2600. 2662. 2718. 2770. 2818. 2863. 2904. 2943. 2980. 3260. 3448. 3701. 3873. 4002. 2 44.69 60.42 70.77 78.43 84.49 89.46 93.67 97.30 100.5 103.3 105.9 108.2 110.4 112.3 114.2 115.9 117.4 118.9 120.3 131.0 138.3 148.0 154.7 159.7 3 18.28 23.32 26.65 29.13 31.11 32.74 34.12 35.33 36.39 37.34 38.20 38.98 39.69 40.35 40.97 41.54 42.07 42.58 43.05 46.68 49.16 52.51 54.81 56.53 4 12.18 14.98 16.84 18.23 19.34 20.26 21.04 21.73 22.33 22.87 23.36 23.81 24.21 24.59 24.94 25.27 25.58 25.87 26.14 28.24 29.68 31.65 32.98 34.00 5 9.714 11.67 12.96 13.93 14.71 15.35 15.91 16.39 16.82 17.18 17.53 17.85 18.13 18.41 18.66 18.89 19.10 19.31 19.51 21.01 22.03 23.45 24.41 25.15 6 8.427 9.960 10.97 11.72 12.32 12.82 13.25 13.63 13.96 14.26 14.53 14.78 15.00 15.21 15.41 15.59 15.78 15.94 16.09 17.28 18.10 19.22 20.00 20.58 7 7.648 8.930 9.768 10.40 10.90 11.32 11.67 11.99 12.27 12.52 12.74 12.95 13.14 13.32 13.48 13.64 13.78 13.92 14.05 15.06 15.74 16.69 17.35 17.85 8 7.129 8.250 8.977 9.522 9.958 10.32 10.63 10.90 11.15 11.36 11.56 11.74 11.91 12.06 12.20 12.34 12.46 12.58 12.69 13.57 14.17 15.01 15.59 16.02 9 6.761 7.768 8.419 8.906 9.295 9.619 9.896 10.14 10.35 10.55 10.72 10.89 11.03 11.17 11.30 11.42 11.53 11.64 11.74 12.52 13.07 13.82 14.34 14.74 10 6.487 7.411 8.006 8.449 8.804 9.099 9.352 9.573 9.769 9.946 10.11 10.25 10.39 10.51 10.63 10.74 10.84 10.94 11.03 11.75 12.25 12.94 13.42 13.79 11 6.275 7.135 7.687 8.098 8.426 8.699 8.933 9.137 9.319 9.482 9.630 9.766 9.891 10.01 10.12 10.22 10.31 10.40 10.49 11.15 11.61 12.25 12.70 13.03 12 6.106 6.917 7.435 7.820 8.127 8.382 8.601 8.792 8.962 9.115 9.253 9.380 9.497 9.606 9.707 9.802 9.891 9.975 10.05 10.68 11.11 11.71 12.12 12.44 13 5.969 6.740 7.231 7.595 7.885 8.126 8.332 8.513 8.673 8.817 8.948 9.068 9.178 9.280 9.376 9.465 9.549 9.629 9.704 10.29 10.70 11.27 11.66 11.96 14 5.855 6.593 7.062 7.409 7.685 7.914 8.110 8.282 8.434 8.571 8.695 8.809 8.914 9.011 9.102 9.187 9.267 9.342 9.414 9.972 10.36 10.90 11.28 11.57 15 5.760 6.470 6.920 7.252 7.517 7.736 7.924 8.088 8.234 8.364 8.483 8.592 8.692 8.785 8.872 8.953 9.030 9.102 9.170 9.703 10.08 10.59 10.95 11.23 16 5.678 6.365 6.799 7.119 7.374 7.585 7.765 7.923 8.063 8.189 8.303 8.407 8.504 8.593 8.676 8.754 8.828 8.897 8.962 9.475 9.832 10.33 10.68 10.94 17 5.608 6.274 6.695 7.004 7.250 7.454 7.629 7.781 7.916 8.037 8.147 8.248 8.341 8.427 8.507 8.583 8.653 8.720 8.783 9.277 9.623 10.10 10.44 10.69 18 5.546 6.195 6.604 6.905 7.143 7.341 7.510 7.657 7.788 7.905 8.012 8.109 8.199 8.283 8.361 8.433 8.502 8.566 8.627 9.106 9.440 9.904 10.23 10.48 19 5.492 6.126 6.524 6.817 7.049 7.241 7.405 7.549 7.676 7.790 7.893 7.988 8.075 8.156 8.232 8.302 8.369 8.431 8.491 8.955 9.279 9.729 10.04 10.29 20 5.444 6.065 6.454 6.740 6.966 7.153 7.313 7.453 7.576 7.687 7.788 7.880 7.965 8.044 8.118 8.186 8.251 8.312 8.370 8.821 9.136 9.575 9.881 10.12 24 5.297 5.877 6.238 6.502 6.711 6.884 7.031 7.159 7.272 7.374 7.467 7.551 7.629 7.701 7.768 7.831 7.890 7.946 7.999 8.411 8.699 9.100 9.380 9.595 30 5.156 5.698 6.033 6.277 6.469 6.628 6.763 6.880 6.984 7.077 7.161 7.239 7.310 7.375 7.437 7.494 7.548 7.598 7.646 8.021 8.283 8.646 8.901 9.096 40 5.022 5.527 5.838 6.063 6.240 6.385 6.509 6.616 6.710 6.795 6.872 6.942 7.007 7.066 7.122 7.174 7.223 7.268 7.312 7.651 7.887 8.214 8.442 8.618 60 4.893 5.365 5.653 5.860 6.022 6.155 6.268 6.365 6.451 6.528 6.598 6.661 6.720 6.773 6.824 6.870 6.914 6.956 6.995 7.299 7.510 7.802 8.005 8.161 120 4.771 5.211 5.476 5.667 5.815 5.937 6.039 6.128 6.206 6.275 6.338 6.395 6.448 6.496 6.541 6.583 6.623 6.660 6.695 6.966 7.153 7.410 7.589 7.726 1e38 4.654 5.063 5.309 5.484 5.619 5.730 5.823 5.903 5.973 6.036 6.092 6.144 6.191 6.234 6.274 6.312 6.347 6.380 6.411 6.651 6.816 7.041 7.196 7.314""" # Build the T+ 'matrix' # T is a dict of dicts of lists # Building them as OrderedDicts ensures that we can # iterate over them in order # [alpha keys] [v keys] # [table values as lists of floats] T = OrderedDict([(0.100, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0100.split('\n')])), (0.500, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0500.split('\n')])), (0.675, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0675.split('\n')])), (0.750, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0750.split('\n')])), (0.800, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0800.split('\n')])), (0.850, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0850.split('\n')])), (0.900, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0900.split('\n')])), (0.950, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0950.split('\n')])), (0.975, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0975.split('\n')])), (0.990, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0990.split('\n')])), (0.995, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0995.split('\n')])), (0.999, OrderedDict([(float(L.split()[0]), map(float, L.split()[1:])) for L in q0999.split('\n')]))]) # This dict maps r values to the correct list index R = OrderedDict(zip([2,3,4,5,6,7,8,9,10,11,12,13,14,15,16, 17,18,19,20,30,40,60,80,100], range(24))) inf = np.inf # we will need a tinv function _tinv = lambda p, df : scipy.stats.t.isf(p, df) _phi = lambda p : scipy.stats.norm.isf(p) # Now we can build the A 'matrix' # these are for the least squares fitting def qhat(a, p, r, v): # eq. 2.3 p_ = (1. + p) /2. f = a[0]*np.log(r-1.) + \ a[1]*np.log(r-1.)**2 + \ a[2]*np.log(r-1.)**3 + \ a[3]*np.log(r-1.)**4 # eq. 2.7 and 2.8 corrections for i, r_ in enumerate(r): if r_ == 3: f[i] += -0.002 / (1. + 12. * _phi(p)**2) if v <= 4.364: f[i] += 1./517. - 1./(312.*v) else: f[i] += 1./(191.*v) return math.sqrt(2) * (f - 1.) * _tinv(p_, v) errfunc = lambda a, p, r, v, q: qhat(a, p, r, v) - q A = {} # this is the error matrix for p in T: for v in T[p]: #eq. 2.4 a0 = random(4) a1, success = leastsq(errfunc, a0, args=(p, np.array(R.keys()), v, np.array(T[p][v]))) if v == 1e38: A[(p,inf)] = list(a1) else: A[(p,v)] = list(a1) raise Exception("we don't want to import this") # uncomment the lines below to repr-ize A ##import pprint ##pprint.pprint(A, width=160) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/qsturng_.py000066400000000000000000001475271224417117700263000ustar00rootroot00000000000000# Copyright (c) 2011, Roger Lew [see LICENSE.txt] # This software is funded in part by NIH Grant P20 RR016454. """ Implementation of Gleason's (1999) non-iterative upper quantile studentized range approximation. According to Gleason this method should be more accurate than the AS190 FORTRAN algorithm of Lund and Lund (1983) and works from .5 <= p <= .999 (The AS190 only works from .9 <= p <= .99). It is more efficient then the Copenhaver & Holland (1988) algorithm (used by the _qtukey_ R function) although it requires storing the A table in memory. (q distribution) approximations in Python. see: Gleason, J. R. (1999). An accurate, non-iterative approximation for studentized range quantiles. Computational Statistics & Data Analysis, (31), 147-158. Gleason, J. R. (1998). A table of quantile points of the Studentized range distribution. http://www.stata.com/stb/stb46/dm64/sturng.pdf """ import math import scipy.stats import numpy as np from scipy.optimize import fminbound inf = np.inf __version__ = '0.2.3' # changelog # 0.1 - initial release # 0.1.1 - vectorized # 0.2 - psturng added # 0.2.1 - T, R generation script relegated to make_tbls.py # 0.2.2 # - select_points refactored for performance to select_ps and # select_vs # - pysturng tester added. # 0.2.3 - uses np.inf and np.isinf # Gleason's table was derived using least square estimation on the tabled # r values for combinations of p and v. In total there are 206 # estimates over p-values of .5, .75, .9, .95, .975, .99, .995, # and .999, and over v (degrees of freedom) of (1) - 20, 24, 30, 40, # 60, 120, and inf. combinations with p < .95 don't have coefficients # for v = 1. Hence the parentheses. These coefficients allow us to # form f-hat. f-hat with the inverse t transform of tinv(p,v) yields # a fairly accurate estimate of the studentized range distribution # across a wide range of values. According to Gleason this method # should be more accurate than algorithm AS190 of Lund and Lund (1983) # and work across a wider range of values (The AS190 only works # from .9 <= p <= .99). R's qtukey algorithm was used to add tables # at .675, .8, and .85. These aid approximations when p < .9. # # The code that generated this table is called make_tbls.py and is # located in version control. A = {(0.1, 2.0): [-2.2485085243379075, -1.5641014278923464, 0.55942294426816752, -0.060006608853883377], (0.1, 3.0): [-2.2061105943901564, -1.8415406600571855, 0.61880788039834955, -0.062217093661209831], (0.1, 4.0): [-2.1686691786678178, -2.008196172372553, 0.65010084431947401, -0.06289005500114471], (0.1, 5.0): [-2.145077200277393, -2.112454843879346, 0.66701240582821342, -0.062993502233654797], (0.1, 6.0): [-2.0896098049743155, -2.2400004934286497, 0.70088523391700142, -0.065907568563272748], (0.1, 7.0): [-2.0689296655661584, -2.3078445479584873, 0.71577374609418909, -0.067081034249350552], (0.1, 8.0): [-2.0064956480711262, -2.437400413087452, 0.76297532367415266, -0.072805518121505458], (0.1, 9.0): [-2.3269477513436061, -2.0469494712773089, 0.60662518717720593, -0.054887108437009016], (0.1, 10.0): [-2.514024350177229, -1.8261187841127482, 0.51674358077906746, -0.044590425150963633], (0.1, 11.0): [-2.5130181309130828, -1.8371718595995694, 0.51336701694862252, -0.043761825829092445], (0.1, 12.0): [-2.5203508109278823, -1.8355687130611862, 0.5063486549107169, -0.042646205063108261], (0.1, 13.0): [-2.5142536438310477, -1.8496969402776282, 0.50616991367764153, -0.042378379905665363], (0.1, 14.0): [-2.3924634153781352, -2.013859173066078, 0.56421893251638688, -0.048716888109540266], (0.1, 15.0): [-2.3573552940582574, -2.0576676976224362, 0.57424068771143233, -0.049367487649225841], (0.1, 16.0): [-2.3046427483044871, -2.1295959138627993, 0.59778272657680553, -0.051864829216301617], (0.1, 17.0): [-2.2230551072316125, -2.2472837435427127, 0.64255758243215211, -0.057186665209197643], (0.1, 18.0): [-2.3912859179716897, -2.0350604070641269, 0.55924788749333332, -0.047729331835226464], (0.1, 19.0): [-2.4169773092220623, -2.0048217969339146, 0.54493039319748915, -0.045991241346224065], (0.1, 20.0): [-2.4264087194660751, -1.9916614057049267, 0.53583555139648154, -0.04463049934517662], (0.1, 24.0): [-2.3969903132061869, -2.0252941869225345, 0.53428382141200137, -0.043116495567779786], (0.1, 30.0): [-2.2509922780354623, -2.2309248956124894, 0.60748041324937263, -0.051427415888817322], (0.1, 40.0): [-2.1310090183854946, -2.3908466074610564, 0.65844375382323217, -0.05676653804036895], (0.1, 60.0): [-1.9240060179027036, -2.6685751031012233, 0.75678826647453024, -0.067938584352398995], (0.1, 120.0): [-1.9814895487030182, -2.5962051736978373, 0.71793969041292693, -0.063126863201511618], (0.1, inf): [-1.913410267066703, -2.6947367328724732, 0.74742335122750592, -0.06660897234304515], (0.5, 2.0): [-0.88295935738770648, -0.1083576698911433, 0.035214966839394388, -0.0028576288978276461], (0.5, 3.0): [-0.89085829205846834, -0.10255696422201063, 0.033613638666631696, -0.0027101699918520737], (0.5, 4.0): [-0.89627345339338116, -0.099072524607668286, 0.032657774808907684, -0.0026219007698204916], (0.5, 5.0): [-0.89959145511941052, -0.097272836582026817, 0.032236187675182958, -0.0025911555217019663], (0.5, 6.0): [-0.89959428735702474, -0.098176292411106647, 0.032590766960226995, -0.0026319890073613164], (0.5, 7.0): [-0.90131491102863937, -0.097135907620296544, 0.032304124993269533, -0.0026057965808244125], (0.5, 8.0): [-0.90292500599432901, -0.096047500971337962, 0.032030946615574568, -0.0025848748659053891], (0.5, 9.0): [-0.90385598607803697, -0.095390771554571888, 0.031832651111105899, -0.0025656060219315991], (0.5, 10.0): [-0.90562524936125388, -0.093954488089771915, 0.031414451048323286, -0.0025257834705432031], (0.5, 11.0): [-0.90420347371173826, -0.095851656370277288, 0.0321150356209743, -0.0026055056400093451], (0.5, 12.0): [-0.90585973471757664, -0.094449306296728028, 0.031705945923210958, -0.0025673330195780191], (0.5, 13.0): [-0.90555437067293054, -0.094792991050780248, 0.031826594964571089, -0.0025807109129488545], (0.5, 14.0): [-0.90652756604388762, -0.093792156994564738, 0.031468966328889042, -0.0025395175361083741], (0.5, 15.0): [-0.90642323700400085, -0.094173017520487984, 0.031657517378893905, -0.0025659271829033877], (0.5, 16.0): [-0.90716338636685234, -0.093785178083820434, 0.031630091949657997, -0.0025701459247416637], (0.5, 17.0): [-0.90790133816769714, -0.093001147638638884, 0.031376863944487084, -0.002545143621663892], (0.5, 18.0): [-0.9077432927051563, -0.093343516378180599, 0.031518139662395313, -0.0025613906133277178], (0.5, 19.0): [-0.90789499456490286, -0.09316964789456067, 0.031440782366342901, -0.0025498353345867453], (0.5, 20.0): [-0.90842707861030725, -0.092696016476608592, 0.031296040311388329, -0.0025346963982742186], (0.5, 24.0): [-0.9083281347135469, -0.092959308144970776, 0.031464063190077093, -0.0025611384271086285], (0.5, 30.0): [-0.90857624050016828, -0.093043139391980514, 0.031578791729341332, -0.0025766595412777147], (0.5, 40.0): [-0.91034085045438684, -0.091978035738914568, 0.031451631000052639, -0.0025791418103733297], (0.5, 60.0): [-0.91084356681030032, -0.091452675572423425, 0.031333147984820044, -0.0025669786958144843], (0.5, 120.0): [-0.90963649561463833, 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-0.00023913038468772782], (0.95, 24.0): [-0.29403146911167666, -0.015332330986025032, 0.0039292170319163728, -0.00024003445648641732], (0.95, 30.0): [-0.29080775563775879, -0.013844059210779323, 0.0039279165616059892, -0.00026085104496801666], (0.95, 40.0): [-0.28821583032805109, -0.011894686715666892, 0.0038202623278839982, -0.00026933325102031252], (0.95, 60.0): [-0.28525636737751447, -0.010235910558409797, 0.0038147029777580001, -0.00028598362144178959], (0.95, 120.0): [-0.28241065885026539, -0.0086103836327305026, 0.0038450612886908714, -0.00030206053671559411], (0.95, inf): [-0.27885570064169296, -0.0078122455524849222, 0.0041798538053623453, -0.0003469494881774609], (0.975, 1.0): [-0.65203598304297983, -0.12608944279227957, 0.035710038757117347, -0.0028116024425349053], (0.975, 2.0): [-0.46371891130382281, -0.096954458319996509, 0.023958312519912289, -0.0017124565391080503], (0.975, 3.0): [-0.38265282195259875, -0.076782539231612282, 0.017405078796142955, -0.0011610853687902553], (0.975, 4.0): [-0.34051193158878401, -0.063652342734671602, 0.013528310336964293, -0.00083644708934990761], (0.975, 5.0): [-0.31777655705536484, -0.051694686914334619, 0.010115807205265859, -0.00054517465344192009], (0.975, 6.0): [-0.30177149019958716, -0.044806697631189059, 0.008483551848413786, -0.00042827853925009264], (0.975, 7.0): [-0.29046972313293562, -0.039732822689098744, 0.007435356037378946, -0.00037562928283350671], (0.975, 8.0): [-0.28309484007368141, -0.034764904940713388, 0.0062932513694928518, -0.00029339243611357956], (0.975, 9.0): [-0.27711707948119785, -0.031210465194810709, 0.0055576244284178435, -0.00024663798208895803], (0.975, 10.0): [-0.27249203448553611, -0.028259756468251584, 0.00499112012528406, -0.00021535380417035389], (0.975, 11.0): [-0.26848515860011007, -0.026146703336893323, 0.0046557767110634073, -0.00020400628148271448], (0.975, 12.0): [-0.26499921540008192, -0.024522931106167097, 0.0044259624958665278, -0.00019855685376441687], (0.975, 13.0): [-0.2625023751891592, -0.022785875653297854, 0.004150277321193792, -0.00018801223218078264], (0.975, 14.0): [-0.26038552414321758, -0.021303509859738341, 0.0039195608280464681, -0.00017826200169385824], (0.975, 15.0): [-0.25801244886414665, -0.020505508012402567, 0.0038754868932712929, -0.00018588907991739744], (0.975, 16.0): [-0.25685316062360508, -0.018888418269740373, 0.0035453092842317293, -0.00016235770674204116], (0.975, 17.0): [-0.25501132271353549, -0.018362951972357794, 0.0035653933105288631, -0.00017470353354992729], (0.975, 18.0): [-0.25325045404452656, -0.017993537285026156, 0.0036035867405376691, -0.00018635492166426884], (0.975, 19.0): [-0.25236899494677928, -0.016948921372207198, 0.0034138931781330802, -0.00017462253414687881], (0.975, 20.0): [-0.25134498025027691, -0.016249564498874988, 0.0033197284005334333, -0.00017098091103245596], (0.975, 24.0): [-0.24768690797476625, -0.014668160763513996, 0.0032850791186852558, -0.00019013480716844995], (0.975, 30.0): [-0.24420834707522676, -0.012911171716272752, 0.0031977676700968051, -0.00020114907914487053], (0.975, 40.0): [-0.24105725356215926, -0.010836526056169627, 0.0030231303550754159, -0.00020128696343148667], (0.975, 60.0): [-0.23732082703955223, -0.0095442727157385391, 0.0031432904473555259, -0.00023062224109383941], (0.975, 120.0): [-0.23358581879594578, -0.0081281259918709343, 0.0031877298679120094, -0.00024496230446851501], (0.975, inf): [-0.23004105093119268, -0.0067112585174133573, 0.0032760251638919435, -0.00026244001319462992], (0.99, 1.0): [-0.65154119422706203, -0.1266603927572312, 0.03607480609672048, -0.0028668112687608113], (0.99, 2.0): [-0.45463403324378804, -0.098701236234527367, 0.024412715761684689, -0.0017613772919362193], (0.99, 3.0): [-0.36402060051035778, -0.079244959193729148, 0.017838124021360584, -0.00119080116484847], (0.99, 4.0): [-0.31903506063953818, -0.061060740682445241, 0.012093154962939612, -0.00067268347188443093], (0.99, 5.0): [-0.28917014580689182, -0.052940780099313689, 0.010231009146279354, -0.00057178339184615239], (0.99, 6.0): [-0.27283240161179012, -0.042505435573209085, 0.0072753401118264534, -0.00031314034710725922], (0.99, 7.0): [-0.25773968720546719, -0.039384214480463406, 0.0069120882597286867, -0.00032994068754356204], (0.99, 8.0): [-0.24913629282433833, -0.033831567178432859, 0.0055516244725724185, -0.00022570786249671376], (0.99, 9.0): [-0.24252380896373404, -0.029488280751457097, 0.0045215453527922998, -0.00014424552929022646], (0.99, 10.0): [-0.23654349556639986, -0.02705600214566789, 0.0041627255469343632, -0.00013804427029504753], (0.99, 11.0): [-0.23187404969432468, -0.024803662094970855, 0.0037885852786822475, -0.00012334999287725012], (0.99, 12.0): [-0.22749929386320905, -0.023655085290534145, 0.0037845051889055896, -0.00014785715789924055], (0.99, 13.0): [-0.22458989143485605, -0.021688394892771506, 0.0034075294601425251, -0.00012436961982044268], (0.99, 14.0): [-0.22197623872225777, -0.020188830700102918, 0.0031648685865587473, -0.00011320740119998819], (0.99, 15.0): [-0.2193924323730066, -0.019327469111698265, 0.0031295453754886576, -0.00012373072900083014], (0.99, 16.0): [-0.21739436875855705, -0.018215854969324128, 0.0029638341057222645, -0.00011714667871412003], (0.99, 17.0): [-0.21548926805467686, -0.017447822179412719, 0.0028994805120482812, -0.00012001887015183794], (0.99, 18.0): [-0.21365014687077843, -0.01688869353338961, 0.0028778031289216546, -0.00012591199104792711], (0.99, 19.0): [-0.21236653761262406, -0.016057151563612645, 0.0027571468998022017, -0.00012049196593780046], (0.99, 20.0): [-0.21092693178421842, -0.015641706950956638, 0.0027765989877361293, -0.00013084915163086915], (0.99, 24.0): [-0.20681960327410207, -0.013804298040271909, 0.0026308276736585674, -0.0001355061502101814], (0.99, 30.0): [-0.20271691131071576, -0.01206095288359876, 0.0025426138004198909, -0.00014589047959047533], (0.99, 40.0): [-0.19833098054449289, -0.010714533963740719, 0.0025985992420317597, -0.0001688279944262007], (0.99, 60.0): [-0.19406768821236584, -0.0093297106482013985, 0.0026521518387539584, -0.00018884874193665104], (0.99, 120.0): [-0.19010213174677365, -0.0075958207221300924, 0.0025660823297025633, -0.00018906475172834352], (0.99, inf): [-0.18602070255787137, -0.0062121155165363188, 0.0026328293420766593, -0.00020453366529867131], (0.995, 1.0): [-0.65135583544951825, -0.1266868999507193, 0.036067522182457165, -0.0028654516958844922], (0.995, 2.0): [-0.45229774013072793, -0.09869462954369547, 0.024381858599368908, -0.0017594734553033394], (0.995, 3.0): [-0.35935765236429706, -0.076650408326671915, 0.016823026893528978, -0.0010835134496404637], (0.995, 4.0): [-0.30704474720931169, -0.063093047731613019, 0.012771683306774929, -0.00075852491621809955], (0.995, 5.0): [-0.27582551740863454, -0.052533353137885791, 0.0097776009845174372, -0.00051338031756399129], (0.995, 6.0): [-0.25657971464398704, -0.043424914996692286, 0.0074324147435969991, -0.00034105188850494067], (0.995, 7.0): [-0.24090407819707738, -0.039591604712200287, 0.0068848429451020387, -0.00034737131709273414], (0.995, 8.0): [-0.23089540800827862, -0.034353305816361958, 0.0056009527629820111, -0.00024389336976992433], (0.995, 9.0): [-0.22322694848310584, -0.030294770709722547, 0.0046751239747245543, -0.00017437479314218922], (0.995, 10.0): [-0.21722684126671632, -0.026993563560163809, 0.0039811592710905491, -0.00013135281785826703], (0.995, 11.0): [-0.21171635822852911, -0.025156193618212551, 0.0037507759652964205, -0.00012959836685175671], (0.995, 12.0): [-0.20745332165849167, -0.023318819535607219, 0.0034935020002058903, -0.00012642826898405916], (0.995, 13.0): [-0.20426054591612508, -0.021189796175249527, 0.003031472176128759, -9.0497733877531618e-05], (0.995, 14.0): [-0.20113536905578902, -0.020011536696623061, 0.0029215880889956729, -9.571527213951222e-05], (0.995, 15.0): [-0.19855601561006403, -0.018808533734002542, 0.0027608859956002344, -9.2472995256929217e-05], (0.995, 16.0): [-0.19619157579534008, -0.017970461530551096, 0.0027113719105000371, -9.9864874982890861e-05], (0.995, 17.0): [-0.19428015140726104, -0.017009762497670704, 0.0025833389598201345, -9.6137545738061124e-05], (0.995, 18.0): [-0.19243180236773033, -0.01631617252107519, 0.0025227443561618621, -9.8067580523432881e-05], (0.995, 19.0): [-0.19061294393069844, -0.01586226613672222, 0.0025207005902641781, -0.00010466151274918466], (0.995, 20.0): [-0.18946302696580328, -0.014975796567260896, 0.0023700506576419867, -9.5507779057884629e-05], (0.995, 24.0): [-0.18444251428695257, -0.013770955893918012, 0.0024579445553339903, -0.00012688402863358003], (0.995, 30.0): [-0.18009742499570078, -0.011831341846559026, 0.0022801125189390046, -0.00012536249967254906], (0.995, 40.0): [-0.17562721880943261, -0.010157142650455463, 0.0022121943861923474, -0.000134542652873434], (0.995, 60.0): [-0.17084630673594547, -0.0090224965852754805, 0.0023435529965815565, -0.00016240306777440115], (0.995, 120.0): [-0.16648414081054147, -0.0074792163241677225, 0.0023284585524533607, -0.00017116464012147041], (0.995, inf): [-0.16213921875452461, -0.0058985998630496144, 0.0022605819363689093, -0.00016896211491119114], (0.999, 1.0): [-0.65233994072089363, -0.12579427445444219, 0.035830577995679271, -0.0028470555202945564], (0.999, 2.0): [-0.45050164311326341, -0.098294804380698292, 0.024134463919493736, -0.0017269603956852841], (0.999, 3.0): [-0.35161741499307819, -0.076801152272374273, 0.016695693063138672, -0.0010661121974071864], (0.999, 4.0): [-0.29398448788574133, -0.06277319725219685, 0.012454220010543127, -0.00072644165723402445], (0.999, 5.0): [-0.25725364564365477, -0.053463787584337355, 0.0099664236557431545, -0.00054866039388980659], (0.999, 6.0): [-0.23674225795168574, -0.040973155890031254, 0.0062599481191736696, -0.00021565734226586692], (0.999, 7.0): [-0.21840108878983297, -0.037037020271877719, 0.0055908063671900703, -0.00020238790479809623], (0.999, 8.0): [-0.2057964743918449, -0.032500885103194356, 0.0046441644585661756, -0.00014769592268680274], (0.999, 9.0): [-0.19604592954882674, -0.029166922919677936, 0.0040644333111949814, -0.00012854052861297006], (0.999, 10.0): [-0.18857328935948367, -0.026316705703161091, 0.0035897350868809275, -0.00011572282691335702], (0.999, 11.0): [-0.18207431428535406, -0.024201081944369412, 0.0031647372098056077, -8.1145935982296439e-05], (0.999, 12.0): [-0.17796358148991101, -0.021054306118620879, 0.0023968085939602055, -1.5907156771296993e-05], (0.999, 13.0): [-0.17371965962745489, -0.019577162950177709, 0.0022391783473999739, -2.0613023472812558e-05], (0.999, 14.0): [-0.16905298116759873, -0.01967115985443986, 0.0026495208325889269, -9.1074275220634073e-05], (0.999, 15.0): [-0.16635662558214312, -0.017903767183469876, 0.0022301322677100496, -5.1956773935885426e-05], (0.999, 16.0): [-0.16388776549525449, -0.016671918839902419, 0.0020365289602744382, -4.3592447599724942e-05], (0.999, 17.0): [-0.16131934177990759, -0.015998918405126326, 0.0019990454743285904, -4.8176277491327653e-05], (0.999, 18.0): [-0.15880633110376571, -0.015830715141055916, 0.0021688405343832091, -8.061825248932771e-05], (0.999, 19.0): [-0.15644841913314136, -0.015729364721105681, 0.0022981443610378136, -0.00010093672643417343], (0.999, 20.0): [-0.15516596606222705, -0.014725095968258637, 0.0021117117014292155, -8.8806880297328484e-05], (0.999, 24.0): [-0.14997437768645827, -0.012755323295476786, 0.0018871651510496939, -8.0896370662414938e-05], (0.999, 30.0): [-0.14459974882323703, -0.011247323832877647, 0.0018637400643826279, -9.6415323191606741e-05], (0.999, 40.0): [-0.13933285919392555, -0.0097151769692496587, 0.0018131251876208683, -0.00010452598991994023], (0.999, 60.0): [-0.13424555343804143, -0.0082163027951669444, 0.0017883427892173382, -0.00011415865110808405], (0.999, 120.0): [-0.12896119523040372, -0.0070426701112581112, 0.0018472364154226955, -0.00012862202979478294], (0.999, inf): [-0.12397213562666673, -0.0056901201604149998, 0.0018260689406957129, -0.00013263452567995485]} # p values that are defined in the A table p_keys = [.1,.5,.675,.75,.8,.85,.9,.95,.975,.99,.995,.999] # v values that are defined in the A table v_keys = range(2, 21) + [24, 30, 40, 60, 120, inf] def _isfloat(x): """ returns True if x is a float, returns False otherwise """ try: float(x) except: return False return True ##def _phi(p): ## """returns the pth quantile inverse norm""" ## return scipy.stats.norm.isf(p) def _phi( p ): # this function is faster than using scipy.stats.norm.isf(p) # but the permissity of the license isn't explicitly listed. # using scipy.stats.norm.isf(p) is an acceptable alternative """ Modified from the author's original perl code (original comments follow below) by dfield@yahoo-inc.com. May 3, 2004. Lower tail quantile for standard normal distribution function. This function returns an approximation of the inverse cumulative standard normal distribution function. I.e., given P, it returns an approximation to the X satisfying P = Pr{Z <= X} where Z is a random variable from the standard normal distribution. The algorithm uses a minimax approximation by rational functions and the result has a relative error whose absolute value is less than 1.15e-9. Author: Peter John Acklam Time-stamp: 2000-07-19 18:26:14 E-mail: pjacklam@online.no WWW URL: http://home.online.no/~pjacklam """ if p <= 0 or p >= 1: # The original perl code exits here, we'll throw an exception instead raise ValueError( "Argument to ltqnorm %f must be in open interval (0,1)" % p ) # Coefficients in rational approximations. a = (-3.969683028665376e+01, 2.209460984245205e+02, \ -2.759285104469687e+02, 1.383577518672690e+02, \ -3.066479806614716e+01, 2.506628277459239e+00) b = (-5.447609879822406e+01, 1.615858368580409e+02, \ -1.556989798598866e+02, 6.680131188771972e+01, \ -1.328068155288572e+01 ) c = (-7.784894002430293e-03, -3.223964580411365e-01, \ -2.400758277161838e+00, -2.549732539343734e+00, \ 4.374664141464968e+00, 2.938163982698783e+00) d = ( 7.784695709041462e-03, 3.224671290700398e-01, \ 2.445134137142996e+00, 3.754408661907416e+00) # Define break-points. plow = 0.02425 phigh = 1 - plow # Rational approximation for lower region: if p < plow: q = math.sqrt(-2*math.log(p)) return -(((((c[0]*q+c[1])*q+c[2])*q+c[3])*q+c[4])*q+c[5]) / \ ((((d[0]*q+d[1])*q+d[2])*q+d[3])*q+1) # Rational approximation for upper region: if phigh < p: q = math.sqrt(-2*math.log(1-p)) return (((((c[0]*q+c[1])*q+c[2])*q+c[3])*q+c[4])*q+c[5]) / \ ((((d[0]*q+d[1])*q+d[2])*q+d[3])*q+1) # Rational approximation for central region: q = p - 0.5 r = q*q return -(((((a[0]*r+a[1])*r+a[2])*r+a[3])*r+a[4])*r+a[5])*q / \ (((((b[0]*r+b[1])*r+b[2])*r+b[3])*r+b[4])*r+1) def _ptransform(p): """function for p-value abcissa transformation""" return -1. / (1. + 1.5 * _phi((1. + p)/2.)) def _func(a, p, r, v): """ calculates f-hat for the coefficients in a, probability p, sample mean difference r, and degrees of freedom v. """ # eq. 2.3 f = a[0]*math.log(r-1.) + \ a[1]*math.log(r-1.)**2 + \ a[2]*math.log(r-1.)**3 + \ a[3]*math.log(r-1.)**4 # eq. 2.7 and 2.8 corrections if r == 3: f += -0.002 / (1. + 12. * _phi(p)**2) if v <= 4.364: f += 1./517. - 1./(312.*(v,1e38)[np.isinf(v)]) else: f += 1./(191.*(v,1e38)[np.isinf(v)]) return -f def _select_ps(p): # There are more generic ways of doing this but profiling # revealed that selecting these points is one of the slow # things that is easy to change. This is about 11 times # faster than the generic algorithm it is replacing. # # it is possible that different break points could yield # better estimates, but the function this is refactoring # just used linear distance. """returns the points to use for interpolating p""" if p >= .99: return .990, .995, .999 elif p >= .975: return .975, .990, .995 elif p >= .95: return .950, .975, .990 elif p >= .9125: return .900, .950, .975 elif p >= .875: return .850, .900, .950 elif p >= .825: return .800, .850, .900 elif p >= .7625: return .750, .800, .850 elif p >= .675: return .675, .750, .800 elif p >= .500: return .500, .675, .750 else: return .100, .500, .675 def _interpolate_p(p, r, v): """ interpolates p based on the values in the A table for the scalar value of r and the scalar value of v """ # interpolate p (v should be in table) # if .5 < p < .75 use linear interpolation in q # if p > .75 use quadratic interpolation in log(y + r/v) # by -1. / (1. + 1.5 * _phi((1. + p)/2.)) # find the 3 closest v values p0, p1, p2 = _select_ps(p) try: y0 = _func(A[(p0, v)], p0, r, v) + 1. except: print p,r,v y1 = _func(A[(p1, v)], p1, r, v) + 1. y2 = _func(A[(p2, v)], p2, r, v) + 1. y_log0 = math.log(y0 + float(r)/float(v)) y_log1 = math.log(y1 + float(r)/float(v)) y_log2 = math.log(y2 + float(r)/float(v)) # If p < .85 apply only the ordinate transformation # if p > .85 apply the ordinate and the abcissa transformation # In both cases apply quadratic interpolation if p > .85: p_t = _ptransform(p) p0_t = _ptransform(p0) p1_t = _ptransform(p1) p2_t = _ptransform(p2) # calculate derivatives for quadratic interpolation d2 = 2*((y_log2-y_log1)/(p2_t-p1_t) - \ (y_log1-y_log0)/(p1_t-p0_t))/(p2_t-p0_t) if (p2+p0)>=(p1+p1): d1 = (y_log2-y_log1)/(p2_t-p1_t) - 0.5*d2*(p2_t-p1_t) else: d1 = (y_log1-y_log0)/(p1_t-p0_t) + 0.5*d2*(p1_t-p0_t) d0 = y_log1 # interpolate value y_log = (d2/2.) * (p_t-p1_t)**2. + d1 * (p_t-p1_t) + d0 # transform back to y y = math.exp(y_log) - float(r)/float(v) elif p > .5: # calculate derivatives for quadratic interpolation d2 = 2*((y_log2-y_log1)/(p2-p1) - \ (y_log1-y_log0)/(p1-p0))/(p2-p0) if (p2+p0)>=(p1+p1): d1 = (y_log2-y_log1)/(p2-p1) - 0.5*d2*(p2-p1) else: d1 = (y_log1-y_log0)/(p1-p0) + 0.5*d2*(p1-p0) d0 = y_log1 # interpolate values y_log = (d2/2.) * (p-p1)**2. + d1 * (p-p1) + d0 # transform back to y y = math.exp(y_log) - float(r)/float(v) else: # linear interpolation in q and p q0 = math.sqrt(2) * -y0 * \ scipy.stats.t.isf((1.+p0)/2., (v,1e38)[v>1e38]) q1 = math.sqrt(2) * -y1 * \ scipy.stats.t.isf((1.+p1)/2., (v,1e38)[v>1e38]) d1 = (q1-q0)/(p1-p0) d0 = q0 # interpolate values q = d1 * (p-p0) + d0 # transform back to y y = -q / (math.sqrt(2) * \ scipy.stats.t.isf((1.+p)/2., (v,1e38)[v>1e38])) return y def _select_vs(v, p): # This one is is about 30 times faster than # the generic algorithm it is replacing. """returns the points to use for interpolating v""" if v >= 120.: return 60, 120, inf elif v >= 60.: return 40, 60, 120 elif v >= 40.: return 30, 40, 60 elif v >= 30.: return 24, 30, 40 elif v >= 24.: return 20, 24, 30 elif v >= 19.5: return 19, 20, 24 if p >= .9: if v < 2.5: return 1, 2, 3 else: if v < 3.5: return 2, 3, 4 vi = int(round(v)) return vi - 1, vi, vi + 1 def _interpolate_v(p, r, v): """ interpolates v based on the values in the A table for the scalar value of r and th """ # interpolate v (p should be in table) # ordinate: y**2 # abcissa: 1./v # find the 3 closest v values # only p >= .9 have table values for 1 degree of freedom. # The boolean is used to index the tuple and append 1 when # p >= .9 v0, v1, v2 = _select_vs(v, p) # y = f - 1. y0_sq = (_func(A[(p,v0)], p, r, v0) + 1.)**2. y1_sq = (_func(A[(p,v1)], p, r, v1) + 1.)**2. y2_sq = (_func(A[(p,v2)], p, r, v2) + 1.)**2. # if v2 is inf set to a big number so interpolation # calculations will work if v2 > 1e38: v2 = 1e38 # transform v v_, v0_, v1_, v2_ = 1./v, 1./v0, 1./v1, 1./v2 # calculate derivatives for quadratic interpolation d2 = 2.*((y2_sq-y1_sq)/(v2_-v1_) - \ (y0_sq-y1_sq)/(v0_-v1_)) / (v2_-v0_) if (v2_ + v0_) >= (v1_ + v1_): d1 = (y2_sq-y1_sq) / (v2_-v1_) - 0.5*d2*(v2_-v1_) else: d1 = (y1_sq-y0_sq) / (v1_-v0_) + 0.5*d2*(v1_-v0_) d0 = y1_sq # calculate y y = math.sqrt((d2/2.)*(v_-v1_)**2. + d1*(v_-v1_)+ d0) return y def _qsturng(p, r, v): """scalar version of qsturng""" ## print 'q',p # r is interpolated through the q to y here we only need to # account for when p and/or v are not found in the table. global A, p_keys, v_keys if p < .1 or p > .999: raise ValueError('p must be between .1 and .999') if p < .9: if v < 2: raise ValueError('v must be > 2 when p < .9') else: if v < 1: raise ValueError('v must be > 1 when p >= .9') # The easy case. A tabled value is requested. #numpy 1.4.1: TypeError: unhashable type: 'numpy.ndarray' : p = float(p) if isinstance(v, np.ndarray): v = v.item() if A.has_key((p,v)): y = _func(A[(p,v)], p, r, v) + 1. elif p not in p_keys and v not in v_keys+([],[1])[p>=.90]: # apply bilinear (quadratic) interpolation # # p0,v2 + o + p1,v2 + p2,v2 # r2 # # 1 # - (p,v) # v x # # r1 # p0,v1 + o + p1,v1 + p2,v1 # # # p0,v0 + o r0 + p1,v0 + p2,v0 # # _ptransform(p) # # (p1 and v1 may be below or above (p,v). The algorithm # works in both cases. For diagramatic simplicity it is # shown as above) # # 1. at v0, v1, and v2 use quadratic interpolation # to find r0, r1, r2 # # 2. use r0, r1, r2 and quadratic interpolaiton # to find y and (p,v) # find the 3 closest v values v0, v1, v2 = _select_vs(v, p) # find the 3 closest p values p0, p1, p2 = _select_ps(p) # calculate r0, r1, and r2 r0_sq = _interpolate_p(p, r, v0)**2 r1_sq = _interpolate_p(p, r, v1)**2 r2_sq = _interpolate_p(p, r, v2)**2 # transform v v_, v0_, v1_, v2_ = 1./v, 1./v0, 1./v1, 1./v2 # calculate derivatives for quadratic interpolation d2 = 2.*((r2_sq-r1_sq)/(v2_-v1_) - \ (r0_sq-r1_sq)/(v0_-v1_)) / (v2_-v0_) if (v2_ + v0_) >= (v1_ + v1_): d1 = (r2_sq-r1_sq) / (v2_-v1_) - 0.5*d2*(v2_-v1_) else: d1 = (r1_sq-r0_sq) / (v1_-v0_) + 0.5*d2*(v1_-v0_) d0 = r1_sq # calculate y y = math.sqrt((d2/2.)*(v_-v1_)**2. + d1*(v_-v1_)+ d0) elif v not in v_keys+([],[1])[p>=.90]: y = _interpolate_v(p, r, v) elif p not in p_keys: y = _interpolate_p(p, r, v) return math.sqrt(2) * -y * \ scipy.stats.t.isf((1.+p)/2., (v,1e38)[v>1e38]) # make a qsturng functinon that will accept list-like objects _vqsturng = np.vectorize(_qsturng) _vqsturng.__doc__ = """vector version of qsturng""" def qsturng(p, r, v): """Approximates the quantile p for a studentized range distribution having v degrees of freedom and r samples for probability p. Parameters ---------- p : (scalar, array_like) The cumulative probability value p >= .1 and p <=.999 (values under .5 are not recommended) r : (scalar, array_like) The number of samples r >= 2 and r <= 200 (values over 200 are permitted but not recommended) v : (scalar, array_like) The sample degrees of freedom if p >= .9: v >=1 and v >= inf else: v >=2 and v >= inf Returns ------- q : (scalar, array_like) approximation of the Studentized Range """ if all(map(_isfloat, [p, r, v])): return _qsturng(p, r, v) return _vqsturng(p, r, v) ##def _qsturng0(p, r, v): #### print 'q0',p ## """ ## returns a first order approximation of q studentized range ## value. Based on Lund and Lund's 1983 based on the FORTRAN77 ## algorithm AS 190.2 Appl. Statist. (1983). ## """ ## vmax = 120. ## c = [0.8843, 0.2368, 1.214, 1.208, 1.4142] ## ## t = -_phi(.5+.5*p) ## if (v < vmax): ## t += (t**3. + t) / float(v) / 4. ## ## q = c[0] - c[1] * t ## if (v < vmax): ## q = q - c[2] / float(v) + c[3] * t / float(v) ## q = t * (q * math.log(r - 1.) + c[4]) ## ## # apply "bar napkin" correction for when p < .85 ## # this is good enough for our intended purpose ## if p < .85: ## q += math.log10(r) * 2.25 * (.85-p) ## return q def _psturng(q, r, v): """scalar version of psturng""" if q < 0.: raise ValueError('q should be >= 0') opt_func = lambda p, r, v : abs(_qsturng(p, r, v) - q) if v == 1: if q < _qsturng(.9, r, 1): return .1 elif q > _qsturng(.999, r, 1): return .001 return 1. - fminbound(opt_func, .9, .999, args=(r,v)) else: if q < _qsturng(.1, r, v): return .9 elif q > _qsturng(.999, r, v): return .001 return 1. - fminbound(opt_func, .1, .999, args=(r,v)) _vpsturng = np.vectorize(_psturng) _vpsturng.__doc__ = """vector version of psturng""" def psturng(q, r, v): """Evaluates the probability from 0 to q for a studentized range having v degrees of freedom and r samples. Parameters ---------- q : (scalar, array_like) quantile value of Studentized Range q >= 0. r : (scalar, array_like) The number of samples r >= 2 and r <= 200 (values over 200 are permitted but not recommended) v : (scalar, array_like) The sample degrees of freedom if p >= .9: v >=1 and v >= inf else: v >=2 and v >= inf Returns ------- p : (scalar, array_like) 1. - area from zero to q under the Studentized Range distribution. When v == 1, p is bound between .001 and .1, when v > 1, p is bound between .001 and .9. Values between .5 and .9 are 1st order appoximations. """ if all(map(_isfloat, [q, r, v])): return _psturng(q, r, v) return _vpsturng(q, r, v) ##p, r, v = .9, 10, 20 ##print ##print 'p and v interpolation' ##print '\t20\t22\t24' ##print '.75',qsturng(.75, r, 20),qsturng(.75, r, 22),qsturng(.75, r, 24) ##print '.85',qsturng(.85, r, 20),qsturng(.85, r, 22),qsturng(.85, r, 24) ##print '.90',qsturng(.90, r, 20),qsturng(.90, r, 22),qsturng(.90, r, 24) ##print ##print 'p and v interpolation' ##print '\t120\t500\tinf' ##print '.950',qsturng(.95, r, 120),qsturng(.95, r, 500),qsturng(.95, r, inf) ##print '.960',qsturng(.96, r, 120),qsturng(.96, r, 500),qsturng(.96, r, inf) ##print '.975',qsturng(.975, r, 120),qsturng(.975, r, 500),qsturng(.975, r, inf) ##print ##print 'p and v interpolation' ##print '\t40\t50\t60' ##print '.950',qsturng(.95, r, 40),qsturng(.95, r, 50),qsturng(.95, r, 60) ##print '.960',qsturng(.96, r, 40),qsturng(.96, r, 50),qsturng(.96, r, 60) ##print '.975',qsturng(.975, r, 40),qsturng(.975, r, 50),qsturng(.975, r, 60) ##print ##print 'p and v interpolation' ##print '\t20\t22\t24' ##print '.50',qsturng(.5, r, 20),qsturng(.5, r, 22),qsturng(.5, r, 24) ##print '.60',qsturng(.6, r, 20),qsturng(.6, r, 22),qsturng(.6, r, 24) ##print '.75',qsturng(.75, r, 20),qsturng(.75, r, 22),qsturng(.75, r, 24) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/tests/000077500000000000000000000000001224417117700252065ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/tests/__init__.py000066400000000000000000000000001224417117700273050ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/tests/bootleg.dat000066400000000000000000010505601224417117700273420ustar00rootroot000000000000000.950300,2,4.43400,3.787121 0.613586,75,149.1170,4.974391 0.8372366,53,501.2317,5.193843 0.9023396,58,546.2543,5.495572 0.9061918,96,424.4232,5.856011 0.7911643,8,200.5667,3.511362 0.5360133,7,806.1763,2.722873 0.6327574,56,56.17163,4.84627 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0.7115514,90,793.238,5.25328 0.7011985,35,200.5195,4.561698 0.5771735,40,98.96083,4.438054 0.8109071,76,518.7619,5.368861 0.8176633,37,18.97150,5.311783 0.7706448,80,46.71877,5.489751 0.5891102,78,840.1204,4.933083 0.7720898,79,189.6653,5.332601 0.7299095,48,298.656,4.85679 0.6956586,32,739.8781,4.458631 0.6287844,35,862.2396,4.395717 0.8391402,38,300.8604,4.973045 0.510979,99,235.7683,4.990011 0.508311,83,427.9368,4.852489 0.6374083,53,722.2017,4.733101 0.8431894,21,933.971,4.51148 0.6286747,89,648.9947,5.096027 0.9137417,88,684.3439,5.819049 0.7000148,65,253.8147,5.020509 0.9283806,36,485.4688,5.30663 0.5502186,97,901.5118,5.026643 0.6794158,92,491.8415,5.2134 0.5004906,9,369.889,2.919374 0.8813509,4,156.3115,3.159320 0.5316867,79,888.0104,4.849485 0.7776448,23,552.1755,4.391651 0.6669258,42,541.5799,4.614754 statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/libqsturng/tests/test_qsturng.py000066400000000000000000000214641224417117700303310ustar00rootroot00000000000000# Copyright (c) 2011 BSD, Roger Lew [see LICENSE.txt] # This software is funded in part by NIH Grant P20 RR016454. """The 'handful' tests are intended to aid refactoring. The tests with the @dec.slow are empirical (test within error limits) and intended to more extensively ensure the stability and accuracy of the functions""" from __future__ import with_statement from numpy.testing import TestCase, rand, assert_, assert_equal, \ assert_almost_equal, assert_array_almost_equal, assert_array_equal, \ assert_approx_equal, assert_raises, run_module_suite, dec import numpy as np from statsmodels.stats.libqsturng import qsturng, psturng,p_keys,v_keys def read_ch(fname): with open(fname) as f: lines = f.readlines() ps,rs,vs,qs = zip(*[L.split(',') for L in lines]) return map(float, ps), map(float, rs),map(float, vs), map(float, qs) class test_qsturng(TestCase): def test_scalar(self): "scalar input -> scalar output" assert_almost_equal(4.43645545899562, qsturng(.9,5,6), 5) def test_vector(self): "vector input -> vector output" assert_array_almost_equal(np.array([3.98832389, 4.56835318, 6.26400894]), qsturng([.8932, .9345,.9827], [4, 4, 4], [6, 6, 6]), 5) def test_invalid_parameters(self): # p < .1 assert_raises(ValueError, qsturng, -.1,5,6) # p > .999 assert_raises(ValueError, qsturng, .9991,5,6) # p < .9, v = 1 assert_raises(ValueError, qsturng, .89,5,1) # p >= .9, v = 0 assert_raises(ValueError, qsturng, .9,5,0) # r < 2 assert_raises((ValueError, OverflowError), qsturng, .9,1,2) def test_handful_to_tbl(self): cases = [(0.75, 30.0, 12.0, 5.01973488482), (0.975, 15.0, 18.0, 6.00428263999), (0.1, 8.0, 11.0, 1.76248712658), (0.995, 6.0, 17.0, 6.13684839819), (0.85, 15.0, 18.0, 4.65007986215), (0.75, 17.0, 18.0, 4.33179650607), (0.75, 60.0, 16.0, 5.50520795792), (0.99, 100.0, 2.0, 50.3860723433), (0.9, 2.0, 40.0, 2.38132493732), (0.8, 12.0, 20.0, 4.15361239056), (0.675, 8.0, 14.0, 3.35011529943), (0.75, 30.0, 24.0, 4.77976803574), (0.75, 2.0, 18.0, 1.68109190167), (0.99, 7.0, 120.0, 5.00525918406), (0.8, 19.0, 15.0, 4.70694373713), (0.8, 15.0, 8.0, 4.80392205906), (0.5, 12.0, 11.0, 3.31672775449), (0.85, 30.0, 2.0, 10.2308503607), (0.675, 20.0, 18.0, 4.23706426096), (0.1, 60.0, 60.0, 3.69215469278)] for p,r,v,q in cases: assert_almost_equal(q, qsturng(p,r,v), 5) #remove from testsuite, used only for table generation and fails on #Debian S390, no idea why @dec.slow def t_est_all_to_tbl(self): from statsmodels.stats.libqsturng.make_tbls import T,R ps, rs, vs, qs = [], [], [], [] for p in T: for v in T[p]: for r in R.keys(): ps.append(p) vs.append(v) rs.append(r) qs.append(T[p][v][R[r]]) qs = np.array(qs) errors = np.abs(qs-qsturng(ps,rs,vs))/qs assert_equal(np.array([]), np.where(errors > .03)[0]) def test_handful_to_ch(self): cases = [(0.8699908, 10.0, 465.4956, 3.997799075635331), (0.8559087, 43.0, 211.7474, 5.1348419692951675), (0.6019187, 11.0, 386.5556, 3.3383101487698821), (0.658888, 51.0, 74.652, 4.8108880483153733), (0.6183604, 77.0, 479.8493, 4.9864059321732874), (0.9238978, 77.0, 787.5278, 5.7871053003022936), (0.8408322, 7.0, 227.3483, 3.5555798311413578), (0.5930279, 60.0, 325.3461, 4.7658023123882396), (0.6236158, 61.0, 657.5285, 4.8207812755987867), (0.9344575, 72.0, 846.4138, 5.8014341329259107), (0.8761198, 56.0, 677.8171, 5.362460718311719), (0.7901517, 41.0, 131.525, 4.9222831341950544), (0.6396423, 44.0, 624.3828, 4.6015127250083152), (0.8085966, 14.0, 251.4224, 4.0793058424719746), (0.716179, 45.0, 136.7055, 4.8055498089340087), (0.8204, 6.0, 290.9876, 3.3158771384085597), (0.8705345, 83.0, 759.6216, 5.5969334564485376), (0.8249085, 18.0, 661.9321, 4.3283725986180395), (0.9503, 2.0, 4.434, 3.7871158594867262), (0.7276132, 95.0, 91.43983, 5.4100384868499889)] for p,r,v,q in cases: assert_almost_equal(q, qsturng(p,r,v), 5) @dec.slow def test_10000_to_ch(self): import os curdir = os.path.dirname(os.path.abspath(__file__)) #ps, rs, vs, qs = read_ch(curdir + '/bootleg.dat') # <- generated by qtukey in R # work around problem getting libqsturng.tests.bootleg.dat installed ps, rs, vs, qs = read_ch(os.path.split(os.path.split(curdir)[0])[0] + '/tests/results/bootleg.csv') qs = np.array(qs) errors = np.abs(qs-qsturng(ps,rs,vs))/qs assert_equal(np.array([]), np.where(errors > .03)[0]) class test_psturng(TestCase): def test_scalar(self): "scalar input -> scalar output" assert_almost_equal(.1, psturng(4.43645545899562,5,6), 5) def test_vector(self): "vector input -> vector output" assert_array_almost_equal(np.array([0.10679889, 0.06550009, 0.01730145]), psturng([3.98832389, 4.56835318, 6.26400894], [4, 4, 4], [6, 6, 6]), 5) def test_v_equal_one(self): assert_almost_equal(.1, psturng(.2,5,1), 5) def test_invalid_parameters(self): # q < .1 assert_raises(ValueError, psturng, -.1,5,6) # r < 2 assert_raises((ValueError, OverflowError), psturng, .9,1,2) def test_handful_to_known_values(self): cases = [(0.71499578726111435, 67, 956.70742488392386, 5.0517658443070692), (0.42974234855067672, 16, 723.50261736502318, 3.3303582093701354), (0.94936429359548424, 2, 916.1867328010926, 2.7677975546417244), (0.85357381770725038, 66, 65.67055060832368, 5.5647438108270109), (0.87372108021900929, 74, 626.42369474993632, 5.5355540570701107), (0.53891960564713726, 49, 862.63799438485785, 4.5108645923377146), (0.98818659555664567, 18, 36.269686711464274, 6.0906643750886156), (0.53031994896037626, 50, 265.29558652727917, 4.5179640079726795), (0.7318857887397332, 59, 701.41497552251201, 4.9980139875409915), (0.65332019368982697, 61, 591.01183664195912, 4.8706581766706893), (0.55403221657248558, 77, 907.34156725405194, 4.8786135917984632), (0.30783916857266003, 83, 82.446923487980882, 4.4396401242858294), (0.29321720242415661, 16, 709.64382575553009, 3.0304277540702729), (0.27146478168880306, 31, 590.00594683574172, 3.5870031664477215), (0.67348796958433776, 81, 608.02706111127657, 5.1096199974432936), (0.32774393945968938, 18, 17.706224399250839, 3.2119038163765432), (0.7081637474795982, 72, 443.10678914889695, 5.0990030889410649), (0.33354939276757861, 47, 544.0772192199048, 4.0613352964193279), (0.60412143947363051, 36, 895.83526933271548, 4.381717596850172), (0.88739052300665977, 77, 426.03665511558262, 5.6333929480341309)] for p,r,v,q in cases: assert_almost_equal(1.-p, psturng(q,r,v), 5) @dec.slow def test_100_random_values(self): n = 100 ps = np.random.random(n)*(.999 - .1) + .1 rs = np.random.random_integers(2, 100, n) vs = np.random.random(n)*998. + 2. qs = qsturng(ps, rs, vs) estimates = psturng(qs, rs, vs) actuals = 1. - ps errors = estimates - actuals assert_equal(np.array([]), np.where(errors > 1e-5)[0]) ## def test_more_exotic_stuff(self, level=3): ## something_obscure_and_expensive() if __name__ == '__main__': run_module_suite() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/lilliefors.py000066400000000000000000000301001224417117700243620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Oct 01 13:16:49 2011 Author: Josef Perktold License: BSD-3 pvalues for Lilliefors test are based on formula and table in An Analytic Approximation to the Distribution of Lilliefors's Test Statistic for Normality Author(s): Gerard E. Dallal and Leland WilkinsonSource: The American Statistician, Vol. 40, No. 4 (Nov., 1986), pp. 294-296Published by: American Statistical AssociationStable URL: http://www.jstor.org/stable/2684607 . On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown Hubert W. Lilliefors Journal of the American Statistical Association, Vol. 62, No. 318. (Jun., 1967), pp. 399-402. """ import numpy as np from scipy.interpolate import interp1d from scipy import stats def ksstat(x, cdf, alternative='two_sided', args=()): """ Calculate statistic for the Kolmogorov-Smirnov test for goodness of fit This calculates the test statistic for a test of the distribution G(x) of an observed variable against a given distribution F(x). Under the null hypothesis the two distributions are identical, G(x)=F(x). The alternative hypothesis can be either 'two_sided' (default), 'less' or 'greater'. The KS test is only valid for continuous distributions. Parameters ---------- x : array_like, 1d array of observations cdf : string or callable string: name of a distribution in scipy.stats callable: function to evaluate cdf alternative : 'two_sided' (default), 'less' or 'greater' defines the alternative hypothesis (see explanation) args : tuple, sequence distribution parameters for call to cdf Returns ------- D : float KS test statistic, either D, D+ or D- See Also -------- scipy.stats.kstest Notes ----- In the one-sided test, the alternative is that the empirical cumulative distribution function of the random variable is "less" or "greater" than the cumulative distribution function F(x) of the hypothesis, G(x)<=F(x), resp. G(x)>=F(x). In contrast to scipy.stats.kstest, this function only calculates the statistic which can be used either as distance measure or to implement case specific p-values. """ nobs = float(len(x)) if isinstance(cdf, basestring): cdf = getattr(stats.distributions, cdf).cdf elif hasattr(cdf, 'cdf'): cdf = getattr(cdf, 'cdf') x = np.sort(x) cdfvals = cdf(x, *args) if alternative in ['two_sided', 'greater']: Dplus = (np.arange(1.0, nobs+1)/nobs - cdfvals).max() if alternative == 'greater': return Dplus if alternative in ['two_sided', 'less']: Dmin = (cdfvals - np.arange(0.0, nobs)/nobs).max() if alternative == 'less': return Dmin D = np.max([Dplus,Dmin]) return D #new version with tabledist #-------------------------- def get_lilliefors_table(): #function just to keep things together from tabledist import TableDist #for this test alpha is sf probability, i.e. right tail probability alpha = np.array([ 0.2 , 0.15 , 0.1 , 0.05 , 0.01 , 0.001])[::-1] size = np.array([ 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 100, 400, 900], float) #critical values, rows are by sample size, columns are by alpha crit_lf = np.array( [[303, 321, 346, 376, 413, 433], [289, 303, 319, 343, 397, 439], [269, 281, 297, 323, 371, 424], [252, 264, 280, 304, 351, 402], [239, 250, 265, 288, 333, 384], [227, 238, 252, 274, 317, 365], [217, 228, 241, 262, 304, 352], [208, 218, 231, 251, 291, 338], [200, 210, 222, 242, 281, 325], [193, 202, 215, 234, 271, 314], [187, 196, 208, 226, 262, 305], [181, 190, 201, 219, 254, 296], [176, 184, 195, 213, 247, 287], [171, 179, 190, 207, 240, 279], [167, 175, 185, 202, 234, 273], [163, 170, 181, 197, 228, 266], [159, 166, 176, 192, 223, 260], [143, 150, 159, 173, 201, 236], [131, 138, 146, 159, 185, 217], [115, 120, 128, 139, 162, 189], [ 74, 77, 82, 89, 104, 122], [ 37, 39, 41, 45, 52, 61], [ 25, 26, 28, 30, 35, 42]])[:,::-1] / 1000. lf = TableDist(alpha, size, crit_lf) return lf lillifors_table = get_lilliefors_table() def pval_lf(Dmax, n): '''approximate pvalues for Lilliefors test of normality This is only valid for pvalues smaller than 0.1 which is not checked in this function. Parameters ---------- Dmax : array_like two-sided Kolmogorov-Smirnov test statistic n : int or float sample size Returns ------- p-value : float or ndarray pvalue according to approximation formula of Dallal and Wilkinson. Notes ----- This is mainly a helper function where the calling code should dispatch on bound violations. Therefore it doesn't check whether the pvalue is in the valid range. Precision for the pvalues is around 2 to 3 decimals. This approximation is also used by other statistical packages (e.g. R:fBasics) but might not be the most precise available. References ---------- DallalWilkinson1986 ''' #todo: check boundaries, valid range for n and Dmax if n>100: Dmax *= (n/100.)**0.49 n = 100 pval = np.exp(-7.01256*Dmax**2 *(n + 2.78019) + 2.99587 * Dmax * np.sqrt(n + 2.78019) - 0.122119 + 0.974598/np.sqrt(n) + 1.67997/n) return pval def kstest_normal(x, pvalmethod='approx'): '''Lillifors test for normality, Kolmogorov Smirnov test with estimated mean and variance Parameters ---------- x : array_like, 1d data series, sample pvalmethod : 'approx', 'table' 'approx' uses the approximation formula of Dalal and Wilkinson, valid for pvalues < 0.1. If the pvalue is larger than 0.1, then the result of `table` is returned 'table' uses the table from Dalal and Wilkinson, which is available for pvalues between 0.001 and 0.2, and the formula of Lilliefors for large n (n>900). Values in the table are linearly interpolated. Values outside the range will be returned as bounds, 0.2 for large and 0.001 for small pvalues. Returns ------- ksstat : float Kolmogorov-Smirnov test statistic with estimated mean and variance. pvalue : float If the pvalue is lower than some threshold, e.g. 0.05, then we can reject the Null hypothesis that the sample comes from a normal distribution Notes ----- Reported power to distinguish normal from some other distributions is lower than with the Anderson-Darling test. could be vectorized ''' x = np.asarray(x) z = (x-x.mean())/x.std(ddof=1) nobs = len(z) d_ks = ksstat(z, stats.norm.cdf, alternative='two_sided') if pvalmethod == 'approx': pval = pval_lf(d_ks, nobs) elif pvalmethod == 'table': #pval = pval_lftable(d_ks, nobs) pval = lillifors_table.prob(d_ks, nobs) return d_ks, pval lillifors = kstest_normal #old version: #------------ tble = '''\ 00 20 15 10 05 01 .1 4 .303 .321 .346 .376 .413 .433 5 .289 .303 .319 .343 .397 .439 6 .269 .281 .297 .323 .371 .424 7 .252 .264 .280 .304 .351 .402 8 .239 .250 .265 .288 .333 .384 9 .227 .238 .252 .274 .317 .365 10 .217 .228 .241 .262 .304 .352 11 .208 .218 .231 .251 .291 .338 12 .200 .210 .222 .242 .281 .325 13 .193 .202 .215 .234 .271 .314 14 .187 .196 .208 .226 .262 .305 15 .181 .190 .201 .219 .254 .296 16 .176 .184 .195 .213 .247 .287 17 .171 .179 .190 .207 .240 .279 18 .167 .175 .185 .202 .234 .273 19 .163 .170 .181 .197 .228 .266 20 .159 .166 .176 .192 .223 .260 25 .143 .150 .159 .173 .201 .236 30 .131 .138 .146 .159 .185 .217 40 .115 .120 .128 .139 .162 .189 100 .074 .077 .082 .089 .104 .122 400 .037 .039 .041 .045 .052 .061 900 .025 .026 .028 .030 .035 .042''' ''' parr = np.array([line.split() for line in tble.split('\n')],float) size = parr[1:,0] alpha = parr[0,1:] / 100. crit = parr[1:, 1:] alpha = np.array([ 0.2 , 0.15 , 0.1 , 0.05 , 0.01 , 0.001]) size = np.array([ 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 100, 400, 900], float) #critical values, rows are by sample size, columns are by alpha crit_lf = np.array( [[303, 321, 346, 376, 413, 433], [289, 303, 319, 343, 397, 439], [269, 281, 297, 323, 371, 424], [252, 264, 280, 304, 351, 402], [239, 250, 265, 288, 333, 384], [227, 238, 252, 274, 317, 365], [217, 228, 241, 262, 304, 352], [208, 218, 231, 251, 291, 338], [200, 210, 222, 242, 281, 325], [193, 202, 215, 234, 271, 314], [187, 196, 208, 226, 262, 305], [181, 190, 201, 219, 254, 296], [176, 184, 195, 213, 247, 287], [171, 179, 190, 207, 240, 279], [167, 175, 185, 202, 234, 273], [163, 170, 181, 197, 228, 266], [159, 166, 176, 192, 223, 260], [143, 150, 159, 173, 201, 236], [131, 138, 146, 159, 185, 217], [115, 120, 128, 139, 162, 189], [ 74, 77, 82, 89, 104, 122], [ 37, 39, 41, 45, 52, 61], [ 25, 26, 28, 30, 35, 42]]) / 1000. #original Lilliefors paper crit_greater30 = lambda n: np.array([0.736, 0.768, 0.805, 0.886, 1.031])/np.sqrt(n) alpha_greater30 = np.array([ 0.2 , 0.15 , 0.1 , 0.05 , 0.01 , 0.001]) n_alpha = 6 polyn = [interp1d(size, crit[:,i]) for i in range(n_alpha)] def critpolys(n): return np.array([p(n) for p in polyn]) def pval_lftable(x, n): #returns extrem probabilities, 0.001 and 0.2, for out of range critvals = critpolys(n) if x < critvals[0]: return alpha[0] elif x > critvals[-1]: return alpha[-1] else: return interp1d(critvals, alpha)(x) for n in [19, 19.5, 20, 21, 25]: print critpolys(n) print pval_lftable(0.166, 20) print pval_lftable(0.166, 21) print 'n=25:', '.103 .052 .010' print [pval_lf(x, 25) for x in [.159, .173, .201, .236]] print 'n=10', '.103 .050 .009' print [pval_lf(x, 10) for x in [.241, .262, .304, .352]] print 'n=400', '.104 .050 .011' print [pval_lf(x, 400) for x in crit[-2,2:-1]] print 'n=900', '.093 .054 .011' print [pval_lf(x, 900) for x in crit[-1,2:-1]] print [pval_lftable(x, 400) for x in crit[-2,:]] print [pval_lftable(x, 300) for x in crit[-3,:]] xx = np.random.randn(40) print kstest_normal(xx) xx2 = np.array([ 1.138, -0.325, -1.461, -0.441, -0.005, -0.957, -1.52 , 0.481, 0.713, 0.175, -1.764, -0.209, -0.681, 0.671, 0.204, 0.403, -0.165, 1.765, 0.127, -1.261, -0.101, 0.527, 1.114, -0.57 , -1.172, 0.697, 0.146, 0.704, 0.422, 0.63 , 0.661, 0.025, 0.177, 0.578, 0.945, 0.211, 0.153, 0.279, 0.35 , 0.396]) ( 1.138, -0.325, -1.461, -0.441, -0.005, -0.957, -1.52 , 0.481, 0.713, 0.175, -1.764, -0.209, -0.681, 0.671, 0.204, 0.403, -0.165, 1.765, 0.127, -1.261, -0.101, 0.527, 1.114, -0.57 , -1.172, 0.697, 0.146, 0.704, 0.422, 0.63 , 0.661, 0.025, 0.177, 0.578, 0.945, 0.211, 0.153, 0.279, 0.35 , 0.396) r_lillieTest = [0.15096827429598147, 0.02225473302348436] print kstest_normal(xx2), np.array(kstest_normal(xx2)) - r_lillieTest print kstest_normal(xx2, 'table') ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/moment_helpers.py000066400000000000000000000137011224417117700252470ustar00rootroot00000000000000'''helper functions conversion between moments contains: * conversion between central and non-central moments, skew, kurtosis and cummulants * cov2corr : convert covariance matrix to correlation matrix Author: Josef Perktold License: BSD-3 ''' import numpy as np from scipy.misc import comb ## start moment helpers def mc2mnc(mc): '''convert central to non-central moments, uses recursive formula optionally adjusts first moment to return mean ''' n = len(mc) mean = mc[0] mc = [1] + list(mc) # add zero moment = 1 mc[1] = 0 # define central mean as zero for formula mnc = [1, mean] # zero and first raw moments for nn,m in enumerate(mc[2:]): n=nn+2 mnc.append(0) for k in range(n+1): mnc[n] += comb(n,k,exact=1) * mc[k] * mean**(n-k) return mnc[1:] def mnc2mc(mnc, wmean = True): '''convert non-central to central moments, uses recursive formula optionally adjusts first moment to return mean ''' n = len(mnc) mean = mnc[0] mnc = [1] + list(mnc) # add zero moment = 1 mu = [] #np.zeros(n+1) for n,m in enumerate(mnc): mu.append(0) #[comb(n-1,k,exact=1) for k in range(n)] for k in range(n+1): mu[n] += (-1)**(n-k) * comb(n,k,exact=1) * mnc[k] * mean**(n-k) if wmean: mu[1] = mean return mu[1:] def cum2mc(kappa): '''convert non-central moments to cumulants recursive formula produces as many cumulants as moments References ---------- Kenneth Lange: Numerical Analysis for Statisticians, page 40 (http://books.google.ca/books?id=gm7kwttyRT0C&pg=PA40&lpg=PA40&dq=convert+cumulants+to+moments&source=web&ots=qyIaY6oaWH&sig=cShTDWl-YrWAzV7NlcMTRQV6y0A&hl=en&sa=X&oi=book_result&resnum=1&ct=result) ''' mc = [1,0.0] #_kappa[0]] #insert 0-moment and mean kappa0 = kappa[0] kappa = [1] + list(kappa) for nn,m in enumerate(kappa[2:]): n = nn+2 mc.append(0) for k in range(n-1): mc[n] += comb(n-1,k,exact=1) * kappa[n-k]*mc[k] mc[1] = kappa0 # insert mean as first moments by convention return mc[1:] def mnc2cum(mnc): '''convert non-central moments to cumulants recursive formula produces as many cumulants as moments http://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments ''' mnc = [1] + list(mnc) kappa = [1] for nn,m in enumerate(mnc[1:]): n = nn+1 kappa.append(m) for k in range(1,n): kappa[n] -= comb(n-1,k-1,exact=1) * kappa[k]*mnc[n-k] return kappa[1:] def mc2cum(mc): '''just chained because I have still the test case ''' return mnc2cum(mc2mnc(mc)) def mvsk2mc(args): '''convert mean, variance, skew, kurtosis to central moments''' mu,sig2,sk,kur = args cnt = [None]*4 cnt[0] = mu cnt[1] = sig2 cnt[2] = sk * sig2**1.5 cnt[3] = (kur+3.0) * sig2**2.0 return tuple(cnt) def mvsk2mnc(args): '''convert mean, variance, skew, kurtosis to non-central moments''' mc, mc2, skew, kurt = args mnc = mc mnc2 = mc2 + mc*mc mc3 = skew*(mc2**1.5) # 3rd central moment mnc3 = mc3+3*mc*mc2+mc**3 # 3rd non-central moment mc4 = (kurt+3.0)*(mc2**2.0) # 4th central moment mnc4 = mc4+4*mc*mc3+6*mc*mc*mc2+mc**4 return (mnc, mnc2, mnc3, mnc4) def mc2mvsk(args): '''convert central moments to mean, variance, skew, kurtosis ''' mc, mc2, mc3, mc4 = args skew = np.divide(mc3, mc2**1.5) kurt = np.divide(mc4, mc2**2.0) - 3.0 return (mc, mc2, skew, kurt) def mnc2mvsk(args): '''convert central moments to mean, variance, skew, kurtosis ''' #convert four non-central moments to central moments mnc, mnc2, mnc3, mnc4 = args mc = mnc mc2 = mnc2 - mnc*mnc mc3 = mnc3 - (3*mc*mc2+mc**3) # 3rd central moment mc4 = mnc4 - (4*mc*mc3+6*mc*mc*mc2+mc**4) return mc2mvsk((mc, mc2, mc3, mc4)) #def mnc2mc(args): # '''convert four non-central moments to central moments # ''' # mnc, mnc2, mnc3, mnc4 = args # mc = mnc # mc2 = mnc2 - mnc*mnc # mc3 = mnc3 - (3*mc*mc2+mc**3) # 3rd central moment # mc4 = mnc4 - (4*mc*mc3+6*mc*mc*mc2+mc**4) # return mc, mc2, mc #TODO: no return, did it get lost in cut-paste? def cov2corr(cov, return_std=False): '''convert covariance matrix to correlation matrix Parameters ---------- cov : array_like, 2d covariance matrix, see Notes Returns ------- corr : ndarray (subclass) correlation matrix return_std : bool If this is true then the standard deviation is also returned. By default only the correlation matrix is returned. Notes ----- This function does not convert subclasses of ndarrays. This requires that division is defined elementwise. np.ma.array and np.matrix are allowed. ''' cov = np.asanyarray(cov) std_ = np.sqrt(np.diag(cov)) corr = cov / np.outer(std_, std_) if return_std: return corr, std_ else: return corr def corr2cov(corr, std): '''convert correlation matrix to covariance matrix given standard deviation Parameters ---------- corr : array_like, 2d correlation matrix, see Notes std : array_like, 1d standard deviation Returns ------- cov : ndarray (subclass) covariance matrix Notes ----- This function does not convert subclasses of ndarrays. This requires that multiplication is defined elementwise. np.ma.array are allowed, but not matrices. ''' corr = np.asanyarray(corr) std_ = np.asanyarray(std) cov = corr * np.outer(std_, std_) return cov def se_cov(cov): '''get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters ---------- cov : array_like, square covariance matrix Returns ------- std : ndarray standard deviation from diagonal of cov ''' return np.sqrt(np.diag(cov)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/multicomp.py000066400000000000000000000016431224417117700242410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 30 18:27:25 2012 Author: Josef Perktold """ from statsmodels.sandbox.stats.multicomp import tukeyhsd, MultiComparison def pairwise_tukeyhsd(endog, groups, alpha=0.05): '''calculate all pairwise comparisons with TukeyHSD confidence intervals this is just a wrapper around tukeyhsd method of MultiComparison Parameters ---------- endog : ndarray, float, 1d response variable groups : ndarray, 1d array with groups, can be string or integers alpha : float significance level for the test Returns ------- results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations See Also -------- MultiComparison tukeyhsd statsmodels.sandbox.stats.multicomp.TukeyHSDResults ''' return MultiComparison(endog, groups).tukeyhsd(alpha=alpha) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/multitest.py000066400000000000000000000335221224417117700242630ustar00rootroot00000000000000'''Multiple Testing and P-Value Correction Author: Josef Perktold License: BSD-3 ''' from statsmodels.compatnp.collections import OrderedDict import numpy as np #============================================== # # Part 1: Multiple Tests and P-Value Correction # #============================================== def _ecdf(x): '''no frills empirical cdf used in fdrcorrection ''' nobs = len(x) return np.arange(1,nobs+1)/float(nobs) multitest_methods_names = {'b': 'Bonferroni', 's': 'Sidak', 'h': 'Holm', 'hs': 'Holm-Sidak', 'sh': 'Simes-Hochberg', 'ho': 'Hommel', 'fdr_bh': 'FDR Benjamini-Hochberg', 'fdr_by': 'FDR Benjamini-Yekutieli', 'fdr_tsbh': 'FDR 2-stage Benjamini-Hochberg', 'fdr_tsbky': 'FDR 2-stage Benjamini-Krieger-Yekutieli', 'fdr_gbs': 'FDR adaptive Gavrilov-Benjamini-Sarkar' } _alias_list = [['b', 'bonf', 'bonferroni'], ['s', 'sidak'], ['h', 'holm'], ['hs', 'holm-sidak'], ['sh', 'simes-hochberg'], ['ho', 'hommel'], ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp'], ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr'], ['fdr_tsbh', 'fdr_2sbh'], ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage'], ['fdr_gbs'] ] multitest_alias = OrderedDict() for m in _alias_list: multitest_alias[m[0]] = m[0] for a in m[1:]: multitest_alias[a] = m[0] def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False): '''test results and p-value correction for multiple tests Parameters ---------- pvals : array_like uncorrected p-values alpha : float FWER, family-wise error rate, e.g. 0.1 method : string Method used for testing and adjustment of pvalues. Can be either the full name or initial letters. Available methods are :: `bonferroni` : one-step correction `sidak` : one-step correction `holm-sidak` : step down method using Sidak adjustments `holm` : step-down method using Bonferroni adjustments `simes-hochberg` : step-up method (independent) `hommel` : closed method based on Simes tests (non-negative) `fdr_bh` : Benjamini/Hochberg (non-negative) `fdr_by` : Benjamini/Yekutieli (negative) `fdr_tsbh` : two stage fdr correction (non-negative) `fdr_tsbky` : two stage fdr correction (non-negative) returnsorted : bool not tested, return sorted p-values instead of original sequence Returns ------- reject : array, boolean true for hypothesis that can be rejected for given alpha pvals_corrected : array p-values corrected for multiple tests alphacSidak: float corrected alpha for Sidak method alphacBonf: float corrected alpha for Bonferroni method Notes ----- Except for 'fdr_twostage', the p-value correction is independent of the alpha specified as argument. In these cases the corrected p-values can also be compared with a different alpha. In the case of 'fdr_twostage', the corrected p-values are specific to the given alpha, see ``fdrcorrection_twostage``. all corrected pvalues now tested against R. insufficient "cosmetic" tests yet new procedure 'fdr_gbs' not verified yet, p-values derived from scratch not reference All procedures that are included, control FWER or FDR in the independent case, and most are robust in the positively correlated case. fdr_gbs: high power, fdr control for independent case and only small violation in positively correlated case there will be API changes. References ---------- ''' pvals = np.asarray(pvals) alphaf = alpha # Notation ? sortind = np.argsort(pvals) pvals = pvals[sortind] sortrevind = sortind.argsort() ntests = len(pvals) alphacSidak = 1 - np.power((1. - alphaf), 1./ntests) alphacBonf = alphaf / float(ntests) if method.lower() in ['b', 'bonf', 'bonferroni']: reject = pvals <= alphacBonf pvals_corrected = pvals * float(ntests) elif method.lower() in ['s', 'sidak']: reject = pvals <= alphacSidak pvals_corrected = 1 - np.power((1. - pvals), ntests) elif method.lower() in ['hs', 'holm-sidak']: alphacSidak_all = 1 - np.power((1. - alphaf), 1./np.arange(ntests, 0, -1)) notreject = pvals > alphacSidak_all nr_index = np.nonzero(notreject)[0] if nr_index.size == 0: # nonreject is empty, all rejected notrejectmin = len(pvals) else: notrejectmin = np.min(nr_index) notreject[notrejectmin:] = True reject = ~notreject pvals_corrected_raw = 1 - np.power((1. - pvals), np.arange(ntests, 0, -1)) pvals_corrected = np.maximum.accumulate(pvals_corrected_raw) elif method.lower() in ['h', 'holm']: notreject = pvals > alphaf / np.arange(ntests, 0, -1) nr_index = np.nonzero(notreject)[0] if nr_index.size == 0: # nonreject is empty, all rejected notrejectmin = len(pvals) else: notrejectmin = np.min(nr_index) notreject[notrejectmin:] = True reject = ~notreject pvals_corrected_raw = pvals * np.arange(ntests, 0, -1) pvals_corrected = np.maximum.accumulate(pvals_corrected_raw) elif method.lower() in ['sh', 'simes-hochberg']: alphash = alphaf / np.arange(ntests, 0, -1) reject = pvals <= alphash rejind = np.nonzero(reject) if rejind[0].size > 0: rejectmax = np.max(np.nonzero(reject)) reject[:rejectmax] = True pvals_corrected_raw = np.arange(ntests, 0, -1) * pvals pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1] elif method.lower() in ['ho', 'hommel']: a=pvals.copy() for m in range(ntests, 1, -1): cim = np.min(m * pvals[-m:] / np.arange(1,m+1.)) a[-m:] = np.maximum(a[-m:], cim) a[:-m] = np.maximum(a[:-m], np.minimum(m * pvals[:-m], cim)) pvals_corrected = a reject = a <= alphaf elif method.lower() in ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp']: # delegate, call with sorted pvals reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha, method='indep') elif method.lower() in ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr']: # delegate, call with sorted pvals reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha, method='n') elif method.lower() in ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage']: # delegate, call with sorted pvals reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha, method='bky')[:2] elif method.lower() in ['fdr_tsbh', 'fdr_2sbh']: # delegate, call with sorted pvals reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha, method='bh')[:2] elif method.lower() in ['fdr_gbs']: #adaptive stepdown in Gavrilov, Benjamini, Sarkar, Annals of Statistics 2009 ## notreject = pvals > alphaf / np.arange(ntests, 0, -1) #alphacSidak ## notrejectmin = np.min(np.nonzero(notreject)) ## notreject[notrejectmin:] = True ## reject = ~notreject ii = np.arange(1, ntests + 1) q = (ntests + 1. - ii)/ii * pvals / (1. - pvals) pvals_corrected_raw = np.maximum.accumulate(q) #up requirementd pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1] reject = pvals_corrected <= alpha else: raise ValueError('method not recognized') if not pvals_corrected is None: #not necessary anymore pvals_corrected[pvals_corrected>1] = 1 if returnsorted: return reject, pvals_corrected, alphacSidak, alphacBonf else: if pvals_corrected is None: return reject[sortrevind], pvals_corrected, alphacSidak, alphacBonf else: return reject[sortrevind], pvals_corrected[sortrevind], alphacSidak, alphacBonf #TODO: rename drop 0 at end def fdrcorrection(pvals, alpha=0.05, method='indep'): '''pvalue correction for false discovery rate This covers Benjamini/Hochberg for independent or positively correlated and Benjamini/Yekutieli for general or negatively correlated tests. Both are available in the function multipletests, as method=`fdr_bh`, resp. `fdr_by`. Parameters ---------- pvals : array_like set of p-values of the individual tests. alpha : float error rate method : {'indep', 'negcorr') Returns ------- rejected : array, bool True if a hypothesis is rejected, False if not pvalue-corrected : array pvalues adjusted for multiple hypothesis testing to limit FDR Notes ----- If there is prior information on the fraction of true hypothesis, then alpha should be set to alpha * m/m_0 where m is the number of tests, given by the p-values, and m_0 is an estimate of the true hypothesis. (see Benjamini, Krieger and Yekuteli) The two-step method of Benjamini, Krieger and Yekutiel that estimates the number of false hypotheses will be available (soon). Method names can be abbreviated to first letter, 'i' or 'p' for fdr_bh and 'n' for fdr_by. ''' pvals = np.asarray(pvals) pvals_sortind = np.argsort(pvals) pvals_sorted = pvals[pvals_sortind] sortrevind = pvals_sortind.argsort() if method in ['i', 'indep', 'p', 'poscorr']: ecdffactor = _ecdf(pvals_sorted) elif method in ['n', 'negcorr']: cm = np.sum(1./np.arange(1, len(pvals_sorted)+1)) #corrected this ecdffactor = _ecdf(pvals_sorted) / cm ## elif method in ['n', 'negcorr']: ## cm = np.sum(np.arange(len(pvals))) ## ecdffactor = ecdf(pvals_sorted)/cm else: raise ValueError('only indep and necorr implemented') reject = pvals_sorted <= ecdffactor*alpha if reject.any(): rejectmax = max(np.nonzero(reject)[0]) reject[:rejectmax] = True pvals_corrected_raw = pvals_sorted / ecdffactor pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1] pvals_corrected[pvals_corrected>1] = 1 return reject[sortrevind], pvals_corrected[sortrevind] #return reject[pvals_sortind.argsort()] def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False): '''(iterated) two stage linear step-up procedure with estimation of number of true hypotheses Benjamini, Krieger and Yekuteli, procedure in Definition 6 Parameters ---------- pvals : array_like set of p-values of the individual tests. alpha : float error rate method : {'bky', 'bh') see Notes for details 'bky' : implements the procedure in Definition 6 of Benjamini, Krieger and Yekuteli 2006 'bh' : implements the two stage method of Benjamini and Hochberg iter ; bool Returns ------- rejected : array, bool True if a hypothesis is rejected, False if not pvalue-corrected : array pvalues adjusted for multiple hypotheses testing to limit FDR m0 : int ntest - rej, estimated number of true hypotheses alpha_stages : list of floats A list of alphas that have been used at each stage Notes ----- The returned corrected p-values are specific to the given alpha, they cannot be used for a different alpha. The returned corrected p-values are from the last stage of the fdr_bh linear step-up procedure (fdrcorrection0 with method='indep') corrected for the estimated fraction of true hypotheses. This means that the rejection decision can be obtained with ``pval_corrected <= alpha``, where ``alpha`` is the origianal significance level. (Note: This has changed from earlier versions (<0.5.0) of statsmodels.) BKY described several other multi-stage methods, which would be easy to implement. However, in their simulation the simple two-stage method (with iter=False) was the most robust to the presence of positive correlation TODO: What should be returned? ''' ntests = len(pvals) if method == 'bky': fact = (1.+alpha) alpha_prime = alpha / fact elif method == 'bh': fact = 1. alpha_prime = alpha else: raise ValueError("only 'bky' and 'bh' are available as method") alpha_stages = [alpha_prime] rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_prime, method='indep') r1 = rej.sum() if (r1 == 0) or (r1 == ntests): return rej, pvalscorr * fact, ntests - r1, alpha_stages ri_old = r1 while 1: ntests0 = 1.0 * ntests - ri_old alpha_star = alpha_prime * ntests / ntests0 alpha_stages.append(alpha_star) #print ntests0, alpha_star rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_star, method='indep') ri = rej.sum() if (not iter) or ri == ri_old: break elif ri < ri_old: # prevent cycles and endless loops raise RuntimeError(" oops - shouldn't be here") ri_old = ri # make adjustment to pvalscorr to reflect estimated number of Non-Null cases # decision is then pvalscorr < alpha (or <=) pvalscorr *= ntests0 * 1.0 / ntests if method == 'bky': pvalscorr *= (1. + alpha) return rej, pvalscorr, ntests - ri, alpha_stages statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/outliers_influence.py000066400000000000000000000617101224417117700261270ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Influence and Outlier Measures Created on Sun Jan 29 11:16:09 2012 Author: Josef Perktold License: BSD-3 """ from collections import defaultdict import numpy as np from statsmodels.regression.linear_model import OLS from statsmodels.tools.decorators import cache_readonly from statsmodels.stats.multitest import multipletests from statsmodels.tools.tools import maybe_unwrap_results # outliers test convenience wrapper def outlier_test(model_results, method='bonf', alpha=.05, labels=None, order=False): """ Outlier Tests for RegressionResults instances. Parameters ---------- model_results : RegressionResults instance Linear model results method : str - `bonferroni` : one-step correction - `sidak` : one-step correction - `holm-sidak` : - `holm` : - `simes-hochberg` : - `hommel` : - `fdr_bh` : Benjamini/Hochberg - `fdr_by` : Benjamini/Yekutieli See `statsmodels.stats.multitest.multipletests` for details. alpha : float familywise error rate order : bool Whether or not to order the results by the absolute value of the studentized residuals. If labels are provided they will also be sorted. Returns ------- table : ndarray or DataFrame Returns either an ndarray or a DataFrame if labels is not None. Will attempt to get labels from model_results if available. The columns are the Studentized residuals, the unadjusted p-value, and the corrected p-value according to method. Notes ----- The unadjusted p-value is stats.t.sf(abs(resid), df) where df = df_resid - 1. """ from scipy import stats # lazy import infl = getattr(model_results, 'get_influence', None) if infl is None: results = maybe_unwrap_results(model_results) raise AttributeError("model_results object %s does not have a " "get_influence method." % results.__class__.__name__) resid = infl().resid_studentized_external if order: idx = np.abs(resid).argsort()[::-1] resid = resid[idx] if labels is not None: labels = np.array(labels)[idx].tolist() df = model_results.df_resid - 1 unadj_p = stats.t.sf(np.abs(resid), df) * 2 adj_p = multipletests(unadj_p, alpha=alpha, method=method) data = np.c_[resid, unadj_p, adj_p[1]] if labels is None: labels = getattr(model_results.model.data, 'row_labels', None) if labels is not None: from pandas import DataFrame return DataFrame(data, columns=['student_resid', 'unadj_p', method+"(p)"], index=labels) return data #influence measures def reset_ramsey(res, degree=5): '''Ramsey's RESET specification test for linear models This is a general specification test, for additional non-linear effects in a model. Notes ----- The test fits an auxiliary OLS regression where the design matrix, exog, is augmented by powers 2 to degree of the fitted values. Then it performs an F-test whether these additional terms are significant. If the p-value of the f-test is below a threshold, e.g. 0.1, then this indicates that there might be additional non-linear effects in the model and that the linear model is mis-specified. References ---------- http://en.wikipedia.org/wiki/Ramsey_RESET_test ''' order = degree + 1 k_vars = res.model.exog.shape[1] #vander without constant and x: y_fitted_vander = np.vander(res.fittedvalues, order)[:, :-2] #drop constant exog = np.column_stack((res.model.exog, y_fitted_vander)) res_aux = OLS(res.model.endog, exog).fit() #r_matrix = np.eye(degree, exog.shape[1], k_vars) r_matrix = np.eye(degree-1, exog.shape[1], k_vars) #df1 = degree - 1 #df2 = exog.shape[0] - degree - res.df_model (without constant) return res_aux.f_test(r_matrix) #, r_matrix, res_aux def variance_inflation_factor(exog, exog_idx): '''variance inflation factor, VIF, for one exogenous variable The variance inflation factor is a measure for the increase of the variance of the parameter estimates if an additional variable, given by exog_idx is added to the linear regression. It is a measure for multicollinearity of the design matrix, exog. One recommendation is that if VIF is greater than 5, then the explanatory variable given by exog_idx is highly collinear with the other explanatory variables, and the parameter estimates will have large standard errors because of this. Parameters ---------- exog : ndarray, (nobs, k_vars) design matrix with all explanatory variables, as for example used in regression exog_idx : int index of the exogenous variable in the columns of exog Returns ------- vif : float variance inflation factor Notes ----- This function does not save the auxiliary regression. See Also -------- xxx : class for regression diagnostics TODO: doesn't exist yet References ---------- http://en.wikipedia.org/wiki/Variance_inflation_factor ''' k_vars = exog.shape[1] x_i = exog[:, exog_idx] mask = np.arange(k_vars) != exog_idx x_noti = exog[:, mask] r_squared_i = OLS(x_i, x_noti).fit().rsquared vif = 1. / (1. - r_squared_i) return vif class OLSInfluence(object): '''class to calculate outlier and influence measures for OLS result Parameters ---------- results : Regression Results instance currently assumes the results are from an OLS regression Notes ----- One part of the results can be calculated without any auxiliary regression (some of which have the `_internal` postfix in the name. Other statistics require leave-one-observation-out (LOOO) auxiliary regression, and will be slower (mainly results with `_external` postfix in the name). The auxiliary LOOO regression only the required results are stored. Using the LOO measures is currently only recommended if the data set is not too large. One possible approach for LOOO measures would be to identify possible problem observations with the _internal measures, and then run the leave-one-observation-out only with observations that are possible outliers. (However, this is not yet available in an automized way.) This should be extended to general least squares. The leave-one-variable-out (LOVO) auxiliary regression are currently not used. ''' def __init__(self, results): #check which model is allowed self.results = maybe_unwrap_results(results) self.nobs, self.k_vars = results.model.exog.shape self.endog = results.model.endog self.exog = results.model.exog self.model_class = results.model.__class__ self.sigma_est = np.sqrt(results.mse_resid) self.aux_regression_exog = {} self.aux_regression_endog = {} @cache_readonly def hat_matrix_diag(self): '''(cached attribute) diagonal of the hat_matrix for OLS Notes ----- temporarily calculated here, this should go to model class ''' return (self.exog * self.results.model.pinv_wexog.T).sum(1) @cache_readonly def resid_press(self): '''(cached attribute) PRESS residuals ''' hii = self.hat_matrix_diag return self.results.resid / (1 - hii) @cache_readonly def influence(self): '''(cached attribute) influence measure matches the influence measure that gretl reports u * h / (1 - h) where u are the residuals and h is the diagonal of the hat_matrix ''' hii = self.hat_matrix_diag return self.results.resid * hii / (1 - hii) @cache_readonly def hat_diag_factor(self): '''(cached attribute) factor of diagonal of hat_matrix used in influence this might be useful for internal reuse h / (1 - h) ''' hii = self.hat_matrix_diag return hii / (1 - hii) @cache_readonly def ess_press(self): '''(cached attribute) error sum of squares of PRESS residuals ''' return np.dot(self.resid_press, self.resid_press) @cache_readonly def resid_studentized_internal(self): '''(cached attribute) studentized residuals using variance from OLS this uses sigma from original estimate does not require leave one out loop ''' return self.get_resid_studentized_external(sigma=None) #return self.results.resid / self.sigma_est @cache_readonly def resid_studentized_external(self): '''(cached attribute) studentized residuals using LOOO variance this uses sigma from leave-one-out estimates requires leave one out loop for observations ''' sigma_looo = np.sqrt(self.sigma2_not_obsi) return self.get_resid_studentized_external(sigma=sigma_looo) def get_resid_studentized_external(self, sigma=None): '''calculate studentized residuals Parameters ---------- sigma : None or float estimate of the standard deviation of the residuals. If None, then the estimate from the regression results is used. Returns ------- stzd_resid : ndarray studentized residuals Notes ----- studentized residuals are defined as :: resid / sigma / np.sqrt(1 - hii) where resid are the residuals from the regression, sigma is an estimate of the standard deviation of the residuals, and hii is the diagonal of the hat_matrix. ''' hii = self.hat_matrix_diag if sigma is None: sigma2_est = self.results.mse_resid #can be replace by different estimators of sigma sigma = np.sqrt(sigma2_est) return self.results.resid / sigma / np.sqrt(1 - hii) @cache_readonly def dffits_internal(self): '''(cached attribute) dffits measure for influence of an observation based on resid_studentized_internal uses original results, no nobs loop ''' #TODO: do I want to use different sigma estimate in # resid_studentized_external # -> move definition of sigma_error to the __init__ hii = self.hat_matrix_diag dffits_ = self.resid_studentized_internal * np.sqrt(hii / (1 - hii)) dffits_threshold = 2 * np.sqrt(self.k_vars * 1. / self.nobs) return dffits_, dffits_threshold @cache_readonly def dffits(self): '''(cached attribute) dffits measure for influence of an observation based on resid_studentized_external, uses results from leave-one-observation-out loop It is recommended that observations with dffits large than a threshold of 2 sqrt{k / n} where k is the number of parameters, should be investigated. Returns ------- dffits: float dffits_threshold : float References ---------- `Wikipedia `_ ''' #TODO: do I want to use different sigma estimate in # resid_studentized_external # -> move definition of sigma_error to the __init__ hii = self.hat_matrix_diag dffits_ = self.resid_studentized_external * np.sqrt(hii / (1 - hii)) dffits_threshold = 2 * np.sqrt(self.k_vars * 1. / self.nobs) return dffits_, dffits_threshold @cache_readonly def dfbetas(self): '''(cached attribute) dfbetas uses results from leave-one-observation-out loop ''' dfbetas = self.results.params - self.params_not_obsi#[None,:] dfbetas /= np.sqrt(self.sigma2_not_obsi[:,None]) dfbetas /= np.sqrt(np.diag(self.results.normalized_cov_params)) return dfbetas @cache_readonly def sigma2_not_obsi(self): '''(cached attribute) error variance for all LOOO regressions This is 'mse_resid' from each auxiliary regression. uses results from leave-one-observation-out loop ''' return np.asarray(self._res_looo['mse_resid']) @cache_readonly def params_not_obsi(self): '''(cached attribute) parameter estimates for all LOOO regressions uses results from leave-one-observation-out loop ''' return np.asarray(self._res_looo['params']) @cache_readonly def det_cov_params_not_obsi(self): '''(cached attribute) determinant of cov_params of all LOOO regressions uses results from leave-one-observation-out loop ''' return np.asarray(self._res_looo['det_cov_params']) @cache_readonly def cooks_distance(self): '''(cached attribute) Cooks distance uses original results, no nobs loop ''' hii = self.hat_matrix_diag #Eubank p.93, 94 cooks_d2 = self.resid_studentized_internal**2 / self.k_vars cooks_d2 *= hii / (1 - hii) from scipy import stats #alpha = 0.1 #print stats.f.isf(1-alpha, n_params, res.df_modelwc) pvals = stats.f.sf(cooks_d2, self.k_vars, self.results.df_resid) return cooks_d2, pvals @cache_readonly def cov_ratio(self): '''(cached attribute) covariance ratio between LOOO and original This uses determinant of the estimate of the parameter covariance from leave-one-out estimates. requires leave one out loop for observations ''' #don't use inplace division / because then we change original cov_ratio = (self.det_cov_params_not_obsi / np.linalg.det(self.results.cov_params())) return cov_ratio @cache_readonly def resid_var(self): '''(cached attribute) estimate of variance of the residuals :: sigma2 = sigma2_OLS * (1 - hii) where hii is the diagonal of the hat matrix ''' #TODO:check if correct outside of ols return self.results.mse_resid * (1 - self.hat_matrix_diag) @cache_readonly def resid_std(self): '''(cached attribute) estimate of standard deviation of the residuals See Also -------- resid_var ''' return np.sqrt(self.resid_var) def _ols_xnoti(self, drop_idx, endog_idx='endog', store=True): '''regression results from LOVO auxiliary regression with cache The result instances are stored, which could use a large amount of memory if the datasets are large. There are too many combinations to store them all, except for small problems. Parameters ---------- drop_idx : int index of exog that is dropped from the regression endog_idx : 'endog' or int If 'endog', then the endogenous variable of the result instance is regressed on the exogenous variables, excluding the one at drop_idx. If endog_idx is an integer, then the exog with that index is regressed with OLS on all other exogenous variables. (The latter is the auxiliary regression for the variance inflation factor.) this needs more thought, memory versus speed not yet used in any other parts, not sufficiently tested ''' #reverse the structure, access store, if fail calculate ? #this creates keys in store even if store = false ! bug if endog_idx == 'endog': stored = self.aux_regression_endog if hasattr(stored, drop_idx): return stored[drop_idx] x_i = self.results.model.endog else: #nested dictionary try: self.aux_regression_exog[endog_idx][drop_idx] except KeyError: pass stored = self.aux_regression_exog[endog_idx] stored = {} x_i = self.exog[:, endog_idx] k_vars = self.exog.shape[1] mask = np.arange(k_vars) != drop_idx x_noti = self.exog[:, mask] res = OLS(x_i, x_noti).fit() if store: stored[drop_idx] = res return res def _get_drop_vari(self, attributes): '''regress endog on exog without one of the variables This uses a k_vars loop, only attributes of the OLS instance are stored. Parameters ---------- attributes : list of strings These are the names of the attributes of the auxiliary OLS results instance that are stored and returned. not yet used ''' from statsmodels.sandbox.tools.cross_val import LeaveOneOut endog = self.results.model.endog exog = self.exog cv_iter = LeaveOneOut(self.k_vars) res_loo = defaultdict(list) for inidx, outidx in cv_iter: for att in attributes: res_i = self.model_class(endog, exog[:,inidx]).fit() res_loo[att].append(getattr(res_i, att)) return res_loo @cache_readonly def _res_looo(self): '''collect required results from the LOOO loop all results will be attached. currently only 'params', 'mse_resid', 'det_cov_params' are stored regresses endog on exog dropping one observation at a time this uses a nobs loop, only attributes of the OLS instance are stored. ''' from statsmodels.sandbox.tools.cross_val import LeaveOneOut get_det_cov_params = lambda res: np.linalg.det(res.cov_params()) endog = self.endog exog = self.exog params = np.zeros_like(exog) mse_resid = np.zeros_like(endog) det_cov_params = np.zeros_like(endog) cv_iter = LeaveOneOut(self.nobs) for inidx, outidx in cv_iter: res_i = self.model_class(endog[inidx], exog[inidx]).fit() params[outidx] = res_i.params mse_resid[outidx] = res_i.mse_resid det_cov_params[outidx] = get_det_cov_params(res_i) return dict(params=params, mse_resid=mse_resid, det_cov_params=det_cov_params) def summary_frame(self): """ Creates a DataFrame with all available influence results. Returns ------- frame : DataFrame A DataFrame with all results. Notes ----- The resultant DataFrame contains six variables in addition to the DFBETAS. These are: * cooks_d : Cook's Distance defined in `Influence.cooks_distance` * standard_resid : Standardized residuals defined in `Influence.resid_studentized_internal` * hat_diag : The diagonal of the projection, or hat, matrix defined in `Influence.hat_matrix_diag` * dffits_internal : DFFITS statistics using internally Studentized residuals defined in `Influence.dffits_internal` * dffits : DFFITS statistics using externally Studentized residuals defined in `Influence.dffits` * student_resid : Externally Studentized residuals defined in `Influence.resid_studentized_external` """ from pandas import DataFrame # row and column labels data = self.results.model.data row_labels = data.row_labels beta_labels = ['dfb_' + i for i in data.xnames] # grab the results summary_data = DataFrame(dict( cooks_d = self.cooks_distance[0], standard_resid = self.resid_studentized_internal, hat_diag = self.hat_matrix_diag, dffits_internal = self.dffits_internal[0], student_resid = self.resid_studentized_external, dffits = self.dffits[0], ), index = row_labels) #NOTE: if we don't give columns, order of above will be arbitrary dfbeta = DataFrame(self.dfbetas, columns=beta_labels, index=row_labels) return dfbeta.join(summary_data) def summary_table(self, float_fmt="%6.3f"): '''create a summary table with all influence and outlier measures This does currently not distinguish between statistics that can be calculated from the original regression results and for which a leave-one-observation-out loop is needed Returns ------- res : SimpleTable instance SimpleTable instance with the results, can be printed Notes ----- This also attaches table_data to the instance. ''' #print self.dfbetas # table_raw = [ np.arange(self.nobs), # self.endog, # self.fittedvalues, # self.cooks_distance(), # self.resid_studentized_internal, # self.hat_matrix_diag, # self.dffits_internal, # self.resid_studentized_external, # self.dffits, # self.dfbetas # ] table_raw = [ ('obs', np.arange(self.nobs)), ('endog', self.endog), ('fitted\nvalue', self.results.fittedvalues), ("Cook's\nd", self.cooks_distance[0]), ("student.\nresidual", self.resid_studentized_internal), ('hat diag', self.hat_matrix_diag), ('dffits \ninternal', self.dffits_internal[0]), ("ext.stud.\nresidual", self.resid_studentized_external), ('dffits', self.dffits[0]) ] colnames, data = zip(*table_raw) #unzip data = np.column_stack(data) self.table_data = data from statsmodels.iolib.table import SimpleTable, default_html_fmt from statsmodels.iolib.tableformatting import fmt_base from copy import deepcopy fmt = deepcopy(fmt_base) fmt_html = deepcopy(default_html_fmt) fmt['data_fmts'] = ["%4d"] + [float_fmt] * (data.shape[1] - 1) #fmt_html['data_fmts'] = fmt['data_fmts'] return SimpleTable(data, headers=colnames, txt_fmt=fmt, html_fmt=fmt_html) def summary_table(res, alpha=0.05): '''generate summary table of outlier and influence similar to SAS Parameters ---------- alpha : float significance level for confidence interval Returns ------- st : SimpleTable instance table with results that can be printed data : ndarray calculated measures and statistics for the table ss2 : list of strings column_names for table (Note: rows of table are observations) ''' from scipy import stats from statsmodels.sandbox.regression.predstd import wls_prediction_std infl = OLSInfluence(res) #standard error for predicted mean #Note: using hat_matrix only works for fitted values predict_mean_se = np.sqrt(infl.hat_matrix_diag*res.mse_resid) tppf = stats.t.isf(alpha/2., res.df_resid) predict_mean_ci = np.column_stack([ res.fittedvalues - tppf * predict_mean_se, res.fittedvalues + tppf * predict_mean_se]) #standard error for predicted observation predict_se, predict_ci_low, predict_ci_upp = wls_prediction_std(res) predict_ci = np.column_stack((predict_ci_low, predict_ci_upp)) #standard deviation of residual resid_se = np.sqrt(res.mse_resid * (1 - infl.hat_matrix_diag)) table_sm = np.column_stack([ np.arange(res.nobs) + 1, res.model.endog, res.fittedvalues, predict_mean_se, predict_mean_ci[:,0], predict_mean_ci[:,1], predict_ci[:,0], predict_ci[:,1], res.resid, resid_se, infl.resid_studentized_internal, infl.cooks_distance[0] ]) #colnames, data = zip(*table_raw) #unzip data = table_sm ss2 = ['Obs', 'Dep Var\nPopulation', 'Predicted\nValue', 'Std Error\nMean Predict', 'Mean ci\n95% low', 'Mean ci\n95% upp', 'Predict ci\n95% low', 'Predict ci\n95% upp', 'Residual', 'Std Error\nResidual', 'Student\nResidual', "Cook's\nD"] colnames = ss2 #self.table_data = data #data = np.column_stack(data) from statsmodels.iolib.table import SimpleTable, default_html_fmt from statsmodels.iolib.tableformatting import fmt_base from copy import deepcopy fmt = deepcopy(fmt_base) fmt_html = deepcopy(default_html_fmt) fmt['data_fmts'] = ["%4d"] + ["%6.3f"] * (data.shape[1] - 1) #fmt_html['data_fmts'] = fmt['data_fmts'] st = SimpleTable(data, headers=colnames, txt_fmt=fmt, html_fmt=fmt_html) return st, data, ss2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/power.py000066400000000000000000001344511224417117700233700ustar00rootroot00000000000000# -*- coding: utf-8 -*- #pylint: disable-msg=W0142 """Statistical power, solving for nobs, ... - trial version Created on Sat Jan 12 21:48:06 2013 Author: Josef Perktold Example roundtrip - root with respect to all variables calculated, desired nobs 33.367204205 33.367204205 effect 0.5 0.5 alpha 0.05 0.05 power 0.8 0.8 TODO: refactoring - rename beta -> power, beta (type 2 error is beta = 1-power) DONE - I think the current implementation can handle any kinds of extra keywords (except for maybe raising meaningful exceptions - streamline code, I think internally classes can be merged how to extend to k-sample tests? user interface for different tests that map to the same (internal) test class - sequence of arguments might be inconsistent, arg and/or kwds so python checks what's required and what can be None. - templating for docstrings ? """ import numpy as np from scipy import stats, optimize from statsmodels.tools.rootfinding import brentq_expanding def ttest_power(effect_size, nobs, alpha, df=None, alternative='two-sided'): '''Calculate power of a ttest ''' d = effect_size if df is None: df = nobs - 1 if alternative in ['two-sided', '2s']: alpha_ = alpha / 2. #no inplace changes, doesn't work elif alternative in ['smaller', 'larger']: alpha_ = alpha else: raise ValueError("alternative has to be 'two-sided', 'larger' " + "or 'smaller'") pow_ = 0 if alternative in ['two-sided', '2s', 'larger']: crit_upp = stats.t.isf(alpha_, df) #print crit_upp, df, d*np.sqrt(nobs) # use private methods, generic methods return nan with negative d if np.any(np.isnan(crit_upp)): # avoid endless loop, https://github.com/scipy/scipy/issues/2667 pow_ = np.nan else: pow_ = stats.nct._sf(crit_upp, df, d*np.sqrt(nobs)) if alternative in ['two-sided', '2s', 'smaller']: crit_low = stats.t.ppf(alpha_, df) #print crit_low, df, d*np.sqrt(nobs) if np.any(np.isnan(crit_low)): pow_ = np.nan else: pow_ += stats.nct._cdf(crit_low, df, d*np.sqrt(nobs)) return pow_ def normal_power(effect_size, nobs, alpha, alternative='two-sided', sigma=1.): '''Calculate power of a normal distributed test statistic ''' d = effect_size if alternative in ['two-sided', '2s']: alpha_ = alpha / 2. #no inplace changes, doesn't work elif alternative in ['smaller', 'larger']: alpha_ = alpha else: raise ValueError("alternative has to be 'two-sided', 'larger' " + "or 'smaller'") pow_ = 0 if alternative in ['two-sided', '2s', 'larger']: crit = stats.norm.isf(alpha_) pow_ = stats.norm.sf(crit - d*np.sqrt(nobs)/sigma) if alternative in ['two-sided', '2s', 'smaller']: crit = stats.norm.ppf(alpha_) pow_ += stats.norm.cdf(crit - d*np.sqrt(nobs)/sigma) return pow_ def ftest_anova_power(effect_size, nobs, alpha, k_groups=2, df=None): '''power for ftest for one way anova with k equal sized groups nobs total sample size, sum over all groups should be general nobs observations, k_groups restrictions ??? ''' df_num = nobs - k_groups df_denom = k_groups - 1 crit = stats.f.isf(alpha, df_denom, df_num) pow_ = stats.ncf.sf(crit, df_denom, df_num, effect_size**2 * nobs) return pow_#, crit def ftest_power(effect_size, df_num, df_denom, alpha, ncc=1): '''Calculate the power of a F-test. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. df_num : int or float numerator degrees of freedom. df_denom : int or float denominator degrees of freedom. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. ncc : int degrees of freedom correction for non-centrality parameter. see Notes Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. Notes ----- sample size is given implicitly by df_num set ncc=0 to match t-test, or f-test in LikelihoodModelResults. ncc=1 matches the non-centrality parameter in R::pwr::pwr.f2.test ftest_power with ncc=0 should also be correct for f_test in regression models, with df_num and d_denom as defined there. (not verified yet) ''' nc = effect_size**2 * (df_denom + df_num + ncc) crit = stats.f.isf(alpha, df_denom, df_num) pow_ = stats.ncf.sf(crit, df_denom, df_num, nc) return pow_ #, crit, nc #class based implementation #-------------------------- class Power(object): '''Statistical Power calculations, Base Class so far this could all be class methods ''' def __init__(self, **kwds): self.__dict__.update(kwds) # used only for instance level start values self.start_ttp = dict(effect_size=0.01, nobs=10., alpha=0.15, power=0.6, nobs1=10., ratio=1, df_num=10, df_denom=3 # for FTestPower ) # TODO: nobs1 and ratio are for ttest_ind, # need start_ttp for each test/class separately, # possible rootfinding problem for effect_size, starting small seems to # work from collections import defaultdict self.start_bqexp = defaultdict(dict) for key in ['nobs', 'nobs1', 'df_num', 'df_denom']: self.start_bqexp[key] = dict(low=2., start_upp=50.) for key in ['df_denom']: self.start_bqexp[key] = dict(low=1., start_upp=50.) for key in ['ratio']: self.start_bqexp[key] = dict(low=1e-8, start_upp=2) for key in ['alpha']: self.start_bqexp[key] = dict(low=1e-12, upp=1 - 1e-12) def power(self, *args, **kwds): raise NotImplementedError def _power_identity(self, *args, **kwds): power_ = kwds.pop('power') return self.power(*args, **kwds) - power_ def solve_power(self, **kwds): '''solve for any one of the parameters of a t-test for t-test the keywords are: effect_size, nobs, alpha, power exactly one needs to be ``None``, all others need numeric values *attaches* cache_fit_res : list Cache of the result of the root finding procedure for the latest call to ``solve_power``, mainly for debugging purposes. The first element is the success indicator, one if successful. The remaining elements contain the return information of the up to three solvers that have been tried. ''' #TODO: maybe use explicit kwds, # nicer but requires inspect? and not generic across tests # I'm duplicating this in the subclass to get informative docstring key = [k for k,v in kwds.iteritems() if v is None] #print kwds, key; if len(key) != 1: raise ValueError('need exactly one keyword that is None') key = key[0] if key == 'power': del kwds['power'] return self.power(**kwds) self._counter = 0 def func(x): kwds[key] = x fval = self._power_identity(**kwds) self._counter += 1 #print self._counter, if self._counter > 500: raise RuntimeError('possible endless loop (500 NaNs)') if np.isnan(fval): return np.inf else: return fval #TODO: I'm using the following so I get a warning when start_ttp is not defined try: start_value = self.start_ttp[key] except KeyError: start_value = 0.9 print 'Warning: using default start_value for', key fit_kwds = self.start_bqexp[key] fit_res = [] #print vars() try: val, res = brentq_expanding(func, full_output=True, **fit_kwds) failed = False fit_res.append(res) except ValueError: failed = True fit_res.append(None) success = None if (not failed) and res.converged: success = 1 else: # try backup #TODO: check more cases to make this robust val, infodict, ier, msg = optimize.fsolve(func, start_value, full_output=True) #scalar #val = optimize.newton(func, start_value) #scalar fval = infodict['fvec'] fit_res.append(infodict) if ier == 1 and np.abs(fval) < 1e-4 : success = 1 else: #print infodict if key in ['alpha', 'power', 'effect_size']: val, r = optimize.brentq(func, 1e-8, 1-1e-8, full_output=True) #scalar success = 1 if r.converged else 0 fit_res.append(r) else: success = 0 if not success == 1: import warnings from statsmodels.tools.sm_exceptions import ConvergenceWarning warnings.warn('finding solution failed', ConvergenceWarning) #attach fit_res, for reading only, should be needed only for debugging fit_res.insert(0, success) self.cache_fit_res = fit_res return val def plot_power(self, dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds): '''plot power with number of observations or effect size on x-axis Parameters ---------- dep_var : string in ['nobs', 'effect_size', 'alpha'] This specifies which variable is used for the horizontal axis. If dep_var='nobs' (default), then one curve is created for each value of ``effect_size``. If dep_var='effect_size' or alpha, then one curve is created for each value of ``nobs``. nobs : scalar or array_like specifies the values of the number of observations in the plot effect_size : scalar or array_like specifies the values of the effect_size in the plot alpha : float or array_like The significance level (type I error) used in the power calculation. Can only be more than a scalar, if ``dep_var='alpha'`` ax : None or axis instance If ax is None, than a matplotlib figure is created. If ax is a matplotlib axis instance, then it is reused, and the plot elements are created with it. title : string title for the axis. Use an empty string, ``''``, to avoid a title. plt_kwds : None or dict not used yet kwds : optional keywords for power function These remaining keyword arguments are used as arguments to the power function. Many power function support ``alternative`` as a keyword argument, two-sample test support ``ratio``. Returns ------- fig : matplotlib figure instance Notes ----- This works only for classes where the ``power`` method has ``effect_size``, ``nobs`` and ``alpha`` as the first three arguments. If the second argument is ``nobs1``, then the number of observations in the plot are those for the first sample. TODO: fix this for FTestPower and GofChisquarePower TODO: maybe add line variable, if we want more than nobs and effectsize ''' #if pwr_kwds is None: # pwr_kwds = {} from statsmodels.graphics import utils from statsmodels.graphics.plottools import rainbow fig, ax = utils.create_mpl_ax(ax) import matplotlib.pyplot as plt colormap = plt.cm.Dark2 #pylint: disable-msg=E1101 plt_alpha = 1 #0.75 lw = 2 if dep_var == 'nobs': colors = rainbow(len(effect_size)) colors = [colormap(i) for i in np.linspace(0, 0.9, len(effect_size))] for ii, es in enumerate(effect_size): power = self.power(es, nobs, alpha, **kwds) ax.plot(nobs, power, lw=lw, alpha=plt_alpha, color=colors[ii], label='es=%4.2F' % es) xlabel = 'Number of Observations' elif dep_var in ['effect size', 'effect_size', 'es']: colors = rainbow(len(nobs)) colors = [colormap(i) for i in np.linspace(0, 0.9, len(nobs))] for ii, n in enumerate(nobs): power = self.power(effect_size, n, alpha, **kwds) ax.plot(effect_size, power, lw=lw, alpha=plt_alpha, color=colors[ii], label='N=%4.2F' % n) xlabel = 'Effect Size' elif dep_var in ['alpha']: # experimental nobs as defining separate lines colors = rainbow(len(nobs)) for ii, n in enumerate(nobs): power = self.power(effect_size, n, alpha, **kwds) ax.plot(alpha, power, lw=lw, alpha=plt_alpha, color=colors[ii], label='N=%4.2F' % n) xlabel = 'alpha' else: raise ValueError('depvar not implemented') if title is None: title = 'Power of Test' ax.set_xlabel(xlabel) ax.set_title(title) ax.legend(loc='lower right') return fig class TTestPower(Power): '''Statistical Power calculations for one sample or paired sample t-test ''' def power(self, effect_size, nobs, alpha, df=None, alternative='two-sided'): '''Calculate the power of a t-test for one sample or paired samples. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. df : int or float degrees of freedom. By default this is None, and the df from the one sample or paired ttest is used, ``df = nobs1 - 1`` alternative : string, 'two-sided' (default), 'larger', 'smaller' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either 'larger', 'smaller'. . Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' # for debugging #print 'calling ttest power with', (effect_size, nobs, alpha, df, alternative) return ttest_power(effect_size, nobs, alpha, df=df, alternative=alternative) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs=None, alpha=None, power=None, alternative='two-sided'): '''solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be ``None``, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of pairs. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. alternative : string, 'two-sided' (default) or 'one-sided' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. 'one-sided' assumes we are in the relevant tail. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters. *attaches* cache_fit_res : list Cache of the result of the root finding procedure for the latest call to ``solve_power``, mainly for debugging purposes. The first element is the success indicator, one if successful. The remaining elements contain the return information of the up to three solvers that have been tried. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' # for debugging #print 'calling ttest solve with', (effect_size, nobs, alpha, power, alternative) return super(TTestPower, self).solve_power(effect_size=effect_size, nobs=nobs, alpha=alpha, power=power, alternative=alternative) class TTestIndPower(Power): '''Statistical Power calculations for t-test for two independent sample currently only uses pooled variance ''' def power(self, effect_size, nobs1, alpha, ratio=1, df=None, alternative='two-sided'): '''Calculate the power of a t-test for two independent sample Parameters ---------- effect_size : float standardized effect size, difference between the two means divided by the standard deviation. `effect_size` has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ratio given the other arguments, it has to be explicitly set to None. df : int or float degrees of freedom. By default this is None, and the df from the ttest with pooled variance is used, ``df = (nobs1 - 1 + nobs2 - 1)`` alternative : string, 'two-sided' (default), 'larger', 'smaller' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either 'larger', 'smaller'. Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' nobs2 = nobs1*ratio #pooled variance if df is None: df = (nobs1 - 1 + nobs2 - 1) nobs = 1./ (1. / nobs1 + 1. / nobs2) #print 'calling ttest power with', (effect_size, nobs, alpha, df, alternative) return ttest_power(effect_size, nobs, alpha, df=df, alternative=alternative) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs1=None, alpha=None, power=None, ratio=1., alternative='two-sided'): '''solve for any one parameter of the power of a two sample t-test for t-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be ``None``, all others need numeric values Parameters ---------- effect_size : float standardized effect size, difference between the two means divided by the standard deviation. `effect_size` has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ratio given the other arguments it has to be explicitly set to None. alternative : string, 'two-sided' (default), 'larger', 'smaller' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either 'larger', 'smaller'. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' return super(TTestIndPower, self).solve_power(effect_size=effect_size, nobs1=nobs1, alpha=alpha, power=power, ratio=ratio, alternative=alternative) class NormalIndPower(Power): '''Statistical Power calculations for z-test for two independent samples. currently only uses pooled variance ''' def __init__(self, ddof=0, **kwds): self.ddof = ddof super(NormalIndPower, self).__init__(**kwds) def power(self, effect_size, nobs1, alpha, ratio=1, alternative='two-sided'): '''Calculate the power of a t-test for two independent sample Parameters ---------- effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` ``ratio`` can be set to zero in order to get the power for a one sample test. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ratio given the other arguments it has to be explicitly set to None. alternative : string, 'two-sided' (default), 'larger', 'smaller' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either 'larger', 'smaller'. Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' ddof = self.ddof # for correlation, ddof=3 # get effective nobs, factor for std of test statistic if ratio > 0: nobs2 = nobs1*ratio #equivalent to nobs = n1*n2/(n1+n2)=n1*ratio/(1+ratio) nobs = 1./ (1. / (nobs1 - ddof) + 1. / (nobs2 - ddof)) else: nobs = nobs1 - ddof return normal_power(effect_size, nobs, alpha, alternative=alternative) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs1=None, alpha=None, power=None, ratio=1., alternative='two-sided'): '''solve for any one parameter of the power of a two sample z-test for z-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be ``None``, all others need numeric values Parameters ---------- effect_size : float standardized effect size, difference between the two means divided by the standard deviation. If ratio=0, then this is the standardized mean in the one sample test. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` ``ratio`` can be set to zero in order to get the power for a one sample test. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ration given the other arguments it has to be explicitly set to None. alternative : string, 'two-sided' (default), 'larger', 'smaller' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either 'larger', 'smaller'. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' return super(NormalIndPower, self).solve_power(effect_size=effect_size, nobs1=nobs1, alpha=alpha, power=power, ratio=ratio, alternative=alternative) class FTestPower(Power): '''Statistical Power calculations for generic F-test ''' def power(self, effect_size, df_num, df_denom, alpha, ncc=1): '''Calculate the power of a F-test. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. df_num : int or float numerator degrees of freedom. df_denom : int or float denominator degrees of freedom. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. ncc : int degrees of freedom correction for non-centrality parameter. see Notes Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. Notes ----- sample size is given implicitly by df_num set ncc=0 to match t-test, or f-test in LikelihoodModelResults. ncc=1 matches the non-centrality parameter in R::pwr::pwr.f2.test ftest_power with ncc=0 should also be correct for f_test in regression models, with df_num and d_denom as defined there. (not verified yet) ''' pow_ = ftest_power(effect_size, df_num, df_denom, alpha, ncc=ncc) #print effect_size, df_num, df_denom, alpha, pow_ return pow_ #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1): '''solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, df_num, df_denom, alpha, power Exactly one needs to be ``None``, all others need numeric values. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. alternative : string, 'two-sided' (default) or 'one-sided' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. 'one-sided' assumes we are in the relevant tail. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' return super(FTestPower, self).solve_power(effect_size=effect_size, df_num=df_num, df_denom=df_denom, alpha=alpha, power=power, ncc=ncc) class FTestAnovaPower(Power): '''Statistical Power calculations F-test for one factor balanced ANOVA ''' def power(self, effect_size, nobs, alpha, k_groups=2): '''Calculate the power of a F-test for one factor ANOVA. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. k_groups : int or float number of groups in the ANOVA or k-sample comparison. Default is 2. Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' return ftest_anova_power(effect_size, nobs, alpha, k_groups=k_groups) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs=None, alpha=None, power=None, k_groups=2): '''solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be ``None``, all others need numeric values. Parameters ---------- effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' # update start values for root finding if not k_groups is None: self.start_ttp['nobs'] = k_groups * 10 self.start_bqexp['nobs'] = dict(low=k_groups * 2, start_upp=k_groups * 10) # first attempt at special casing if effect_size is None: return self._solve_effect_size(effect_size=effect_size, nobs=nobs, alpha=alpha, k_groups=k_groups, power=power) return super(FTestAnovaPower, self).solve_power(effect_size=effect_size, nobs=nobs, alpha=alpha, k_groups=k_groups, power=power) def _solve_effect_size(self, effect_size=None, nobs=None, alpha=None, power=None, k_groups=2): '''experimental, test failure in solve_power for effect_size ''' def func(x): effect_size = x return self._power_identity(effect_size=effect_size, nobs=nobs, alpha=alpha, k_groups=k_groups, power=power) val, r = optimize.brentq(func, 1e-8, 1-1e-8, full_output=True) if not r.converged: print r return val class GofChisquarePower(Power): '''Statistical Power calculations for one sample chisquare test ''' def power(self, effect_size, nobs, alpha, n_bins, ddof=0): #alternative='two-sided'): '''Calculate the power of a chisquare test for one sample Only two-sided alternative is implemented Parameters ---------- effect_size : float standardized effect size, according to Cohen's definition. see :func:`statsmodels.stats.gof.chisquare_effectsize` nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. n_bins : int number of bins or cells in the distribution. Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' from statsmodels.stats.gof import chisquare_power return chisquare_power(effect_size, nobs, n_bins, alpha, ddof=0) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs=None, alpha=None, power=None, n_bins=2): '''solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be ``None``, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters ---------- effect_size : float standardized effect size, according to Cohen's definition. see :func:`statsmodels.stats.gof.chisquare_effectsize` nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. n_bins : int number of bins or cells in the distribution Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' return super(GofChisquarePower, self).solve_power(effect_size=effect_size, nobs=nobs, n_bins=n_bins, alpha=alpha, power=power) class _GofChisquareIndPower(Power): '''Statistical Power calculations for chisquare goodness-of-fit test TODO: this is not working yet for 2sample case need two nobs in function no one-sided chisquare test, is there one? use normal distribution? -> drop one-sided options? ''' def power(self, effect_size, nobs1, alpha, ratio=1, alternative='two-sided'): '''Calculate the power of a chisquare for two independent sample Parameters ---------- effect_size : float standardize effect size, difference between the two means divided by the standard deviation. effect size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ration given the other arguments it has to be explicitely set to None. alternative : string, 'two-sided' (default) or 'one-sided' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. 'one-sided' assumes we are in the relevant tail. Returns ------- power : float Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ''' from statsmodels.stats.gof import chisquare_power nobs2 = nobs1*ratio #equivalent to nobs = n1*n2/(n1+n2)=n1*ratio/(1+ratio) nobs = 1./ (1. / nobs1 + 1. / nobs2) return chisquare_power(effect_size, nobs, alpha) #method is only added to have explicit keywords and docstring def solve_power(self, effect_size=None, nobs1=None, alpha=None, power=None, ratio=1., alternative='two-sided'): '''solve for any one parameter of the power of a two sample z-test for z-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be ``None``, all others need numeric values Parameters ---------- effect_size : float standardize effect size, difference between the two means divided by the standard deviation. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. ``nobs2 = nobs1 * ratio`` alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true. power : float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true. ratio : float ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ration given the other arguments it has to be explicitely set to None. alternative : string, 'two-sided' (default) or 'one-sided' extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. 'one-sided' assumes we are in the relevant tail. Returns ------- value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters. Notes ----- The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses ``brentq`` with a prior search for bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve`` also fails, then, for ``alpha``, ``power`` and ``effect_size``, ``brentq`` with fixed bounds is used. However, there can still be cases where this fails. ''' return super(_GofChisquareIndPower, self).solve_power(effect_size=effect_size, nobs1=nobs1, alpha=alpha, power=power, ratio=ratio, alternative=alternative) #shortcut functions tt_solve_power = TTestPower().solve_power tt_ind_solve_power = TTestIndPower().solve_power zt_ind_solve_power = NormalIndPower().solve_power statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/proportion.py000066400000000000000000000656301224417117700244510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests and Confidence Intervals for Binomial Proportions Created on Fri Mar 01 00:23:07 2013 Author: Josef Perktold License: BSD-3 """ import numpy as np from scipy import stats, optimize from statsmodels.stats.base import AllPairsResults #import statsmodels.stats.multitest as smt def proportion_confint(count, nobs, alpha=0.05, method='normal'): '''confidence interval for a binomial proportion Parameters ---------- count : int or array number of successes nobs : int total number of trials alpha : float in (0, 1) significance level, default 0.05 method : string in ['normal'] method to use for confidence interval, currently available methods : - `normal` : asymptotic normal approximation - `agresti_coull` : Agresti-Coull interval - `beta` : Clopper-Pearson interval based on Beta distribution - `wilson` : Wilson Score interval - `jeffrey` : Jeffrey's Bayesian Interval - `binom_test` : experimental, inversion of binom_test Returns ------- ci_low, ci_upp : float lower and upper confidence level with coverage (approximately) 1-alpha. Note: Beta has coverage coverage is only 1-alpha on average for some other methods.) Notes ----- Beta, the Clopper-Pearson interval has coverage at least 1-alpha, but is in general conservative. Most of the other methods have average coverage equal to 1-alpha, but will have smaller coverage in some cases. Method "binom_test" directly inverts the binomial test in scipy.stats. which has discrete steps. TODO: binom_test intervals raise an exception in small samples if one interval bound is close to zero or one. References ---------- http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban (2001). "Interval Estimation for a Binomial Proportion", Statistical Science 16 (2): 101–133. doi:10.1214/ss/1009213286. TODO: Is this the correct one ? ''' q_ = count * 1. / nobs alpha_2 = 0.5 * alpha if method == 'normal': std_ = np.sqrt(q_ * (1 - q_) / nobs) dist = stats.norm.isf(alpha / 2.) * std_ ci_low = q_ - dist ci_upp = q_ + dist elif method == 'binom_test': # inverting the binomial test def func(qi): #return stats.binom_test(qi * nobs, nobs, p=q_) - alpha #/ 2. return stats.binom_test(q_ * nobs, nobs, p=qi) - alpha # Note: only approximate, step function at integer values of count # possible problems if bounds are too narrow # problem if we hit 0 or 1 # brentq fails ValueError: f(a) and f(b) must have different signs ci_low = optimize.brentq(func, q_ * 0.1, q_) #ci_low = stats.binom_test(qi_low * nobs, nobs, p=q_) #ci_low = np.floor(qi_low * nobs) / nobs ub = np.minimum(q_ + 2 * (q_ - ci_low), 1) ci_upp = optimize.brentq(func, q_, ub) #ci_upp = stats.binom_test(qi_upp * nobs, nobs, p=q_) #ci_upp = np.ceil(qi_upp * nobs) / nobs # TODO: check if we should round up or down, or interpolate elif method == 'beta': ci_low = stats.beta.ppf(alpha_2 , count, nobs - count + 1) ci_upp = stats.beta.isf(alpha_2, count + 1, nobs - count) elif method == 'agresti_coull': crit = stats.norm.isf(alpha / 2.) nobs_c = nobs + crit**2 q_c = (count + crit**2 / 2.) / nobs_c std_c = np.sqrt(q_c * (1. - q_c) / nobs_c) dist = crit * std_c ci_low = q_c - dist ci_upp = q_c + dist elif method == 'wilson': crit = stats.norm.isf(alpha / 2.) crit2 = crit**2 denom = 1 + crit2 / nobs center = (q_ + crit2 / (2 * nobs)) / denom dist = crit * np.sqrt(q_ * (1. - q_) / nobs + crit2 / (4. * nobs**2)) dist /= denom ci_low = center - dist ci_upp = center + dist elif method == 'jeffrey': ci_low, ci_upp = stats.beta.interval(1 - alpha, count + 0.5, nobs - count + 0.5) else: raise NotImplementedError('method "%s" is not available' % method) return ci_low, ci_upp def samplesize_confint_proportion(proportion, half_length, alpha=0.05, method='normal'): '''find sample size to get desired confidence interval length Parameters ---------- proportion : float in (0, 1) proportion or quantile half_length : float in (0, 1) desired half length of the confidence interval alpha : float in (0, 1) significance level, default 0.05, coverage of the two-sided interval is (approximately) ``1 - alpha`` method : string in ['normal'] method to use for confidence interval, currently only normal approximation Returns ------- n : float sample size to get the desired half length of the confidence interval Notes ----- this is mainly to store the formula. possible application: number of replications in bootstrap samples ''' q_ = proportion if method == 'normal': n = q_ * (1 - q_) / (half_length / stats.norm.isf(alpha / 2.))**2 else: raise NotImplementedError('only "normal" is available') return n def proportion_effectsize(prop1, prop2, method='normal'): '''effect size for a test comparing two proportions for use in power function Parameters ---------- prop1, prop2: float or array_like Returns ------- es : float or ndarray effect size for (transformed) prop1 - prop2 Notes ----- only method='normal' is implemented to match pwr.p2.test see http://www.statmethods.net/stats/power.html Effect size for `normal` is defined as :: 2 * (arcsin(sqrt(prop1)) - arcsin(sqrt(prop2))) I think other conversions to normality can be used, but I need to check. Examples -------- >>> smpr.proportion_effectsize(0.5, 0.4) 0.20135792079033088 >>> smpr.proportion_effectsize([0.3, 0.4, 0.5], 0.4) array([-0.21015893, 0. , 0.20135792]) ''' if method != 'normal': raise ValueError('only "normal" is implemented') es = 2 * (np.arcsin(np.sqrt(prop1)) - np.arcsin(np.sqrt(prop2))) return es def std_prop(prop, nobs): '''standard error for the estimate of a proportion This is just ``np.sqrt(p * (1. - p) / nobs)`` Parameters ---------- prop : array_like proportion nobs : int, array_like number of observations Returns ------- std : array_like standard error for a proportion of nobs independent observations ''' return np.sqrt(prop * (1. - prop) / nobs) def _power_ztost(mean_low, var_low, mean_upp, var_upp, mean_alt, var_alt, alpha=0.05, discrete=True, dist='norm', nobs=None, continuity=0, critval_continuity=0): '''Generic statistical power function for normal based equivalence test This includes options to adjust the normal approximation and can use the binomial to evaluate the probability of the rejection region see power_ztost_prob for a description of the options ''' # TODO: refactor structure, separate norm and binom better if not isinstance(continuity, tuple): continuity = (continuity, continuity) crit = stats.norm.isf(alpha) k_low = mean_low + np.sqrt(var_low) * crit k_upp = mean_upp - np.sqrt(var_upp) * crit if discrete or dist == 'binom': k_low = np.ceil(k_low * nobs + 0.5 * critval_continuity) k_upp = np.trunc(k_upp * nobs - 0.5 * critval_continuity) if dist == 'norm': #need proportion k_low = (k_low) * 1. / nobs #-1 to match PASS k_upp = k_upp * 1. / nobs # else: # if dist == 'binom': # #need counts # k_low *= nobs # k_upp *= nobs #print mean_low, np.sqrt(var_low), crit, var_low #print mean_upp, np.sqrt(var_upp), crit, var_upp if np.any(k_low > k_upp): #vectorize import warnings warnings.warn("no overlap, power is zero") std_alt = np.sqrt(var_alt) z_low = (k_low - mean_alt - continuity[0] * 0.5 / nobs) / std_alt z_upp = (k_upp - mean_alt + continuity[1] * 0.5 / nobs) / std_alt if dist == 'norm': power = stats.norm.cdf(z_upp) - stats.norm.cdf(z_low) elif dist == 'binom': power = (stats.binom.cdf(k_upp, nobs, mean_alt) - stats.binom.cdf(k_low-1, nobs, mean_alt)) return power, (k_low, k_upp, z_low, z_upp) def binom_tost(count, nobs, low, upp): '''exact TOST test for one proportion using binomial distribution Parameters ---------- count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns ------- pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-sided tests ''' # binom_test_stat only returns pval tt1 = binom_test(count, nobs, alternative='larger', prop=low) tt2 = binom_test(count, nobs, alternative='smaller', prop=upp) return np.maximum(tt1, tt2), tt1, tt2, def binom_tost_reject_interval(low, upp, nobs, alpha=0.05): '''rejection region for binomial TOST The interval includes the end points, `reject` if and only if `r_low <= x <= r_upp`. The interval might be empty with `r_upp < r_low`. Parameters ---------- low, upp : floats lower and upper limit of equivalence region nobs : integer the number of trials or observations. Returns ------- x_low, x_upp : float lower and upper bound of rejection region ''' x_low = stats.binom.isf(alpha, nobs, low) + 1 x_upp = stats.binom.ppf(alpha, nobs, upp) - 1 return x_low, x_upp def binom_test_reject_interval(value, nobs, alpha=0.05, alternative='two-sided'): '''rejection region for binomial test for one sample proportion The interval includes the end points of the rejection region. Parameters ---------- value : float proportion under the Null hypothesis nobs : integer the number of trials or observations. Returns ------- x_low, x_upp : float lower and upper bound of rejection region ''' if alternative in ['2s', 'two-sided']: alternative = '2s' # normalize alternative name alpha = alpha / 2 if alternative in ['2s', 'smaller']: x_low = stats.binom.ppf(alpha, nobs, value) - 1 else: x_low = 0 if alternative in ['2s', 'larger']: x_upp = stats.binom.isf(alpha, nobs, value) + 1 else : x_upp = nobs return x_low, x_upp def binom_test(count, nobs, prop=0.5, alternative='two-sided'): '''Perform a test that the probability of success is p. This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is `p`. Parameters ---------- count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The probability of success under the null hypothesis, `0 <= prop <= 1`. The default value is `prop = 0.5` alternative : string in ['two-sided', 'smaller', 'larger'] alternative hypothesis, which can be two-sided or either one of the one-sided tests. Returns ------- p-value : float The p-value of the hypothesis test Notes ----- This uses scipy.stats.binom_test for the two-sided alternative. ''' if np.any(prop > 1.0) or np.any(prop < 0.0): raise ValueError("p must be in range [0,1]") if alternative in ['2s', 'two-sided']: pval = stats.binom_test(count, n=nobs, p=prop) elif alternative in ['l', 'larger']: pval = stats.binom.sf(count-1, nobs, prop) elif alternative in ['s', 'smaller']: pval = stats.binom.cdf(count, nobs, prop) else: raise ValueError('alternative not recognized\n' 'should be two-sided, larger or smaller') return pval def power_binom_tost(low, upp, nobs, p_alt=None, alpha=0.05): if p_alt is None: p_alt = 0.5 * (low + upp) x_low, x_upp = binom_tost_reject_interval(low, upp, nobs, alpha=alpha) power = (stats.binom.cdf(x_upp, nobs, p_alt) - stats.binom.cdf(x_low-1, nobs, p_alt)) return power def power_ztost_prop(low, upp, nobs, p_alt, alpha=0.05, dist='norm', variance_prop=None, discrete=True, continuity=0, critval_continuity=0): '''Power of proportions equivalence test based on normal distribution Parameters ---------- low, upp : floats lower and upper limit of equivalence region nobs : int number of observations p_alt : float in (0,1) proportion under the alternative alpha : float in (0,1) significance level of the test dist : string in ['norm', 'binom'] This defines the distribution to evalute the power of the test. The critical values of the TOST test are always based on the normal approximation, but the distribution for the power can be either the normal (default) or the binomial (exact) distribution. variance_prop : None or float in (0,1) If this is None, then the variances for the two one sided tests are based on the proportions equal to the equivalence limits. If variance_prop is given, then it is used to calculate the variance for the TOST statistics. If this is based on an sample, then the estimated proportion can be used. discrete : bool If true, then the critical values of the rejection region are converted to integers. If dist is "binom", this is automatically assumed. If discrete is false, then the TOST critical values are used as floating point numbers, and the power is calculated based on the rejection region that is not discretized. continuity : bool or float adjust the rejection region for the normal power probability. This has and effect only if ``dist='norm'`` critval_continuity : bool or float If this is non-zero, then the critical values of the tost rejection region are adjusted before converting to integers. This affects both distributions, ``dist='norm'`` and ``dist='binom'``. Returns ------- power : float statistical power of the equivalence test. (k_low, k_upp, z_low, z_upp) : tuple of floats critical limits in intermediate steps temporary return, will be changed Notes ----- In small samples the power for the ``discrete`` version, has a sawtooth pattern as a function of the number of observations. As a consequence, small changes in the number of observations or in the normal approximation can have a large effect on the power. ``continuity`` and ``critval_continuity`` are added to match some results of PASS, and are mainly to investigate the sensitivity of the ztost power to small changes in the rejection region. From my interpretation of the equations in the SAS manual, both are zero in SAS. works vectorized **verification:** The ``dist='binom'`` results match PASS, The ``dist='norm'`` results look reasonable, but no benchmark is available. References ---------- SAS Manual: Chapter 68: The Power Procedure, Computational Resources PASS Chapter 110: Equivalence Tests for One Proportion. ''' mean_low = low var_low = std_prop(low, nobs)**2 mean_upp = upp var_upp = std_prop(upp, nobs)**2 mean_alt = p_alt var_alt = std_prop(p_alt, nobs)**2 if variance_prop is not None: var_low = var_upp = std_prop(variance_prop, nobs)**2 power = _power_ztost(mean_low, var_low, mean_upp, var_upp, mean_alt, var_alt, alpha=alpha, discrete=discrete, dist=dist, nobs=nobs, continuity=continuity, critval_continuity=critval_continuity) return np.maximum(power[0], 0), power[1:] def _table_proportion(count, nobs): '''create a k by 2 contingency table for proportion helper function for proportions_chisquare Parameters ---------- count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. Returns ------- table : ndarray (k, 2) contingency table Notes ----- recent scipy has more elaborate contingency table functions ''' table = np.column_stack((count, nobs - count)) expected = table.sum(0) * table.sum(1)[:,None] * 1. / table.sum() n_rows = table.shape[0] return table, expected, n_rows def proportions_ztest(count, nobs, value=None, alternative='two-sided', prop_var=False): '''test for proportions based on normal (z) test Parameters ---------- count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or array_like This is the value of the null hypothesis equal to the proportion in the case of a one sample test. In the case of a two-sample test, the null hypothesis is that prop[0] - prop[1] = value, where prop is the proportion in the two samples alternative : string in ['two-sided', 'smaller', 'larger'] The alternative hypothesis can be either two-sided or one of the one- sided tests, smaller means that the alternative hypothesis is ``prop < value` and larger means ``prop > value``, or the corresponding inequality for the two sample test. prop_var : False or float in (0, 1) If prop_var is false, then the variance of the proportion estimate is calculated based on the sample proportion. Alternatively, a proportion can be specified to calculate this variance. Common use case is to use the proportion under the Null hypothesis to specify the variance of the proportion estimate. TODO: change options similar to propotion_ztost ? Returns ------- zstat : float test statistic for the z-test p-value : float p-value for the z-test Notes ----- This uses a simple normal test for proportions. It should be the same as running the mean z-test on the data encoded 1 for event and 0 for no event, so that the sum corresponds to count. In the one and two sample cases with two-sided alternative, this test produces the same p-value as ``proportions_chisquare``, since the chisquare is the distribution of the square of a standard normal distribution. (TODO: verify that this really holds) TODO: add continuity correction or other improvements for small samples. ''' prop = count * 1. / nobs k_sample = np.size(prop) if k_sample == 1: diff = prop - value elif k_sample == 2: diff = prop[0] - prop[1] - value else: msg = 'more than two samples are not implemented yet' raise NotImplementedError(msg) p_pooled = np.sum(count) * 1. / np.sum(nobs) nobs_fact = np.sum(1. / nobs) if prop_var: p_pooled = prop_var var_ = p_pooled * (1 - p_pooled) * nobs_fact std_diff = np.sqrt(var_) from statsmodels.stats.weightstats import _zstat_generic2 return _zstat_generic2(diff, std_diff, alternative) def proportions_ztost(count, nobs, low, upp, prop_var='sample'): '''Equivalence test based on normal distribution Parameters ---------- count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. low, upp : float equivalence interval low < prop1 - prop2 < upp prop_var : string or float in (0, 1) prop_var determines which proportion is used for the calculation of the standard deviation of the proportion estimate The available options for string are 'sample' (default), 'null' and 'limits'. If prop_var is a float, then it is used directly. Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test Notes ----- checked only for 1 sample case ''' if prop_var == 'limits': prop_var_low = low prop_var_upp = upp elif prop_var == 'sample': prop_var_low = prop_var_upp = False #ztest uses sample elif prop_var == 'null': prop_var_low = prop_var_upp = 0.5 * (low + upp) elif np.isreal(prop_var): prop_var_low = prop_var_upp = prop_var tt1 = proportions_ztest(count, nobs, alternative='larger', prop_var=prop_var_low, value=low) tt2 = proportions_ztest(count, nobs, alternative='smaller', prop_var=prop_var_upp, value=upp) return np.maximum(tt1[1], tt2[1]), tt1, tt2, def proportions_chisquare(count, nobs, value=None): '''test for proportions based on chisquare test Parameters ---------- count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or array_like Returns ------- chi2stat : float test statistic for the chisquare test p-value : float p-value for the chisquare test (table, expected) table is a (k, 2) contingency table, ``expected`` is the corresponding table of counts that are expected under independence with given margins Notes ----- Recent version of scipy.stats have a chisquare test for independence in contingency tables. This function provides a similar interface to chisquare tests as ``prop.test`` in R, however without the option for Yates continuity correction. count can be the count for the number of events for a single proportion, or the counts for several independent proportions. If value is given, then all proportions are jointly tested against this value. If value is not given and count and nobs are not scalar, then the null hypothesis is that all samples have the same proportion. ''' nobs = np.atleast_1d(nobs) table, expected, n_rows = _table_proportion(count, nobs) if value is not None: expected = np.column_stack((nobs * value, nobs * (1 - value))) ddof = n_rows - 1 else: ddof = n_rows #print table, expected chi2stat, pval = stats.chisquare(table.ravel(), expected.ravel(), ddof=ddof) return chi2stat, pval, (table, expected) def proportions_chisquare_allpairs(count, nobs, multitest_method='hs'): '''chisquare test of proportions for all pairs of k samples Performs a chisquare test for proportions for all pairwise comparisons. The alternative is two-sided Parameters ---------- count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The probability of success under the null hypothesis, `0 <= prop <= 1`. The default value is `prop = 0.5` multitest_method : string This chooses the method for the multiple testing p-value correction, that is used as default in the results. It can be any method that is available in ``multipletesting``. The default is Holm-Sidak 'hs'. Returns ------- result : AllPairsResults instance The returned results instance has several statistics, such as p-values, attached, and additional methods for using a non-default ``multitest_method``. Notes ----- Yates continuity correction is not available. ''' #all_pairs = map(list, zip(*np.triu_indices(4, 1))) all_pairs = zip(*np.triu_indices(4, 1)) pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)])[1] for pair in all_pairs] return AllPairsResults(pvals, all_pairs, multitest_method=multitest_method) def proportions_chisquare_pairscontrol(count, nobs, value=None, multitest_method='hs', alternative='two-sided'): '''chisquare test of proportions for pairs of k samples compared to control Performs a chisquare test for proportions for pairwise comparisons with a control (Dunnet's test). The control is assumed to be the first element of ``count`` and ``nobs``. The alternative is two-sided, larger or smaller. Parameters ---------- count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The probability of success under the null hypothesis, `0 <= prop <= 1`. The default value is `prop = 0.5` multitest_method : string This chooses the method for the multiple testing p-value correction, that is used as default in the results. It can be any method that is available in ``multipletesting``. The default is Holm-Sidak 'hs'. alternative : string in ['two-sided', 'smaller', 'larger'] alternative hypothesis, which can be two-sided or either one of the one-sided tests. Returns ------- result : AllPairsResults instance The returned results instance has several statistics, such as p-values, attached, and additional methods for using a non-default ``multitest_method``. Notes ----- Yates continuity correction is not available. ``value`` and ``alternative`` options are not yet implemented. ''' if (value is not None) or (not alternative in ['two-sided', '2s']): raise NotImplementedError #all_pairs = map(list, zip(*np.triu_indices(4, 1))) all_pairs = [(0, k) for k in range(1, len(count))] pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)], #alternative=alternative)[1] )[1] for pair in all_pairs] return AllPairsResults(pvals, all_pairs, multitest_method=multitest_method) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/sandwich_covariance.py000066400000000000000000000635271224417117700262330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Sandwich covariance estimators Created on Sun Nov 27 14:10:57 2011 Author: Josef Perktold Author: Skipper Seabold for HCxxx in linear_model.RegressionResults License: BSD-3 Notes ----- for calculating it, we have two versions version 1: use pinv pinv(x) scale pinv(x) used currently in linear_model, with scale is 1d (or diagonal matrix) (x'x)^(-1) x' scale x (x'x)^(-1), scale in general is (nobs, nobs) so pretty large general formulas for scale in cluster case are in http://pubs.amstat.org/doi/abstract/10.1198/jbes.2010.07136 which also has the second version version 2: (x'x)^(-1) S (x'x)^(-1) with S = x' scale x, S is (kvar,kvars), (x'x)^(-1) is available as normalized_covparams. S = sum (x*u) dot (x*u)' = sum x*u*u'*x' where sum here can aggregate over observations or groups. u is regression residual. x is (nobs, k_var) u is (nobs, 1) x*u is (nobs, k_var) For cluster robust standard errors, we first sum (x*w) over other groups (including time) and then take the dot product (sum of outer products) S = sum_g(x*u)' dot sum_g(x*u) For HAC by clusters, we first sum over groups for each time period, and then use HAC on the group sums of (x*w). If we have several groups, we have to sum first over all relevant groups, and then take the outer product sum. This can be done by summing using indicator functions or matrices or with explicit loops. Alternatively we calculate separate covariance matrices for each group, sum them and subtract the duplicate counted intersection. Not checked in details yet: degrees of freedom or small sample correction factors, see (two) references (?) This is the general case for MLE and GMM also in MLE hessian H, outerproduct of jacobian S, cov_hjjh = HJJH, which reduces to the above in the linear case, but can be used generally, e.g. in discrete, and is misnomed in GenericLikelihoodModel in GMM it's similar but I would have to look up the details, (it comes out in sandwich form by default, it's in the sandbox), standard Newey West or similar are on the covariance matrix of the moment conditions quasi-MLE: MLE with mis-specified model where parameter estimates are fine (consistent ?) but cov_params needs to be adjusted similar or same as in sandwiches. (I didn't go through any details yet.) TODO ---- * small sample correction factors, Done for cluster, not yet for HAC * automatic lag-length selection for Newey-West HAC, -> added: nlag = floor[4(T/100)^(2/9)] Reference: xtscc paper, Newey-West note this will not be optimal in the panel context, see Peterson * HAC should maybe return the chosen nlags * get consistent notation, varies by paper, S, scale, sigma? * replace diag(hat_matrix) calculations in cov_hc2, cov_hc3 References ---------- John C. Driscoll and Aart C. Kraay, “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data,†Review of Economics and Statistics 80, no. 4 (1998): 549-560. Daniel Hoechle, "Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence", The Stata Journal Mitchell A. Petersen, “Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches,†Review of Financial Studies 22, no. 1 (January 1, 2009): 435 -480. A. Colin Cameron, Jonah B. Gelbach, and Douglas L. Miller, “Robust Inference With Multiway Clustering,†Journal of Business and Economic Statistics 29 (April 2011): 238-249. not used yet: A.C. Cameron, J.B. Gelbach, and D.L. Miller, “Bootstrap-based improvements for inference with clustered errors,†The Review of Economics and Statistics 90, no. 3 (2008): 414–427. """ import numpy as np from statsmodels.tools.grouputils import Group from statsmodels.stats.moment_helpers import se_cov __all__ = ['cov_cluster', 'cov_cluster_2groups', 'cov_hac', 'cov_nw_panel', 'cov_white_simple', 'cov_hc0', 'cov_hc1', 'cov_hc2', 'cov_hc3', 'se_cov', 'weights_bartlett', 'weights_uniform'] #----------- from linear_model.RegressionResults ''' HC0_se White's (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC0, which is the full heteroskedasticity consistent covariance matrix and also `het_scale`, which is in this case just resid**2. HCCM matrices are only appropriate for OLS. HC1_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC1, which is the full HCCM and also `het_scale`, which is in this case n/(n-p)*resid**2. HCCM matrices are only appropriate for OLS. HC2_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC2, which is the full HCCM and also `het_scale`, which is in this case is resid^(2)/(1-h_ii). HCCM matrices are only appropriate for OLS. HC3_se MacKinnon and White's (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC3, which is the full HCCM and also `het_scale`, which is in this case is resid^(2)/(1-h_ii)^(2). HCCM matrices are only appropriate for OLS. ''' def _HCCM(results, scale): ''' sandwich with pinv(x) * diag(scale) * pinv(x).T where pinv(x) = (X'X)^(-1) X and scale is (nobs,) ''' H = np.dot(results.model.pinv_wexog, scale[:,None]*results.model.pinv_wexog.T) return H def cov_hc0(results): """ See statsmodels.RegressionResults """ het_scale = results.resid**2 # or whitened residuals? only OLS? cov_hc0 = _HCCM(results, het_scale) return cov_hc0 def cov_hc1(results): """ See statsmodels.RegressionResults """ het_scale = results.nobs/(results.df_resid)*(results.resid**2) cov_hc1 = _HCCM(results, het_scale) return cov_hc1 def cov_hc2(results): """ See statsmodels.RegressionResults """ # probably could be optimized h = np.diag(np.dot(results.model.exog, np.dot(results.normalized_cov_params, results.model.exog.T))) het_scale = results.resid**2/(1-h) cov_hc2_ = _HCCM(results, het_scale) return cov_hc2_ def cov_hc3(results): """ See statsmodels.RegressionResults """ # above probably could be optimized to only calc the diag h = np.diag(np.dot(results.model.exog, np.dot(results.normalized_cov_params, results.model.exog.T))) het_scale=(results.resid/(1-h))**2 cov_hc3_ = _HCCM(results, het_scale) return cov_hc3_ #--------------------------------------- def _HCCM1(results, scale): ''' sandwich with pinv(x) * scale * pinv(x).T where pinv(x) = (X'X)^(-1) X and scale is (nobs, nobs), or (nobs,) with diagonal matrix diag(scale) Parameters ---------- results : result instance need to contain regression results, uses results.model.pinv_wexog scale : ndarray (nobs,) or (nobs, nobs) scale matrix, treated as diagonal matrix if scale is one-dimensional Returns ------- H : ndarray (k_vars, k_vars) robust covariance matrix for the parameter estimates ''' if scale.ndim == 1: H = np.dot(results.model.pinv_wexog, scale[:,None]*results.model.pinv_wexog.T) else: H = np.dot(results.model.pinv_wexog, np.dot(scale, results.model.pinv_wexog.T)) return H def _HCCM2(results, scale): ''' sandwich with (X'X)^(-1) * scale * (X'X)^(-1) scale is (kvars, kvars) this uses results.normalized_cov_params for (X'X)^(-1) Parameters ---------- results : result instance need to contain regression results, uses results.normalized_cov_params scale : ndarray (k_vars, k_vars) scale matrix Returns ------- H : ndarray (k_vars, k_vars) robust covariance matrix for the parameter estimates ''' if scale.ndim == 1: scale = scale[:,None] xxi = results.normalized_cov_params H = np.dot(np.dot(xxi, scale), xxi.T) return H #TODO: other kernels, move ? def weights_bartlett(nlags): '''Bartlett weights for HAC this will be moved to another module Parameters ---------- nlags : int highest lag in the kernel window, this does not include the zero lag Returns ------- kernel : ndarray, (nlags+1,) weights for Bartlett kernel ''' #with lag zero return 1 - np.arange(nlags+1)/(nlags+1.) def weights_uniform(nlags): '''uniform weights for HAC this will be moved to another module Parameters ---------- nlags : int highest lag in the kernel window, this does not include the zero lag Returns ------- kernel : ndarray, (nlags+1,) weights for uniform kernel ''' #with lag zero return np.ones(nlags+1.) def S_hac_simple(x, nlags=None, weights_func=weights_bartlett): '''inner covariance matrix for HAC (Newey, West) sandwich assumes we have a single time series with zero axis consecutive, equal spaced time periods Parameters ---------- x : ndarray (nobs,) or (nobs, k_var) data, for HAC this is array of x_i * u_i nlags : int or None highest lag to include in kernel window. If None, then nlags = floor(4(T/100)^(2/9)) is used. weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights Returns ------- S : ndarray, (k_vars, k_vars) inner covariance matrix for sandwich Notes ----- used by cov_hac_simple options might change when other kernels besides Bartlett are available. ''' if x.ndim == 1: x = x[:,None] n_periods = x.shape[0] if nlags is None: nlags = int(np.floor(4 * (n_periods / 100.)**(2./9.))) weights = weights_func(nlags) S = weights[0] * np.dot(x.T, x) #weights[0] just for completeness, is 1 for lag in range(1, nlags+1): s = np.dot(x[lag:].T, x[:-lag]) S += weights[lag] * (s + s.T) return S def S_white_simple(x): '''inner covariance matrix for White heteroscedastistity sandwich Parameters ---------- x : ndarray (nobs,) or (nobs, k_var) data, for HAC this is array of x_i * u_i Returns ------- S : ndarray, (k_vars, k_vars) inner covariance matrix for sandwich Notes ----- this is just dot(X.T, X) ''' if x.ndim == 1: x = x[:,None] return np.dot(x.T, x) def group_sums(x, group): '''sum x for each group, simple bincount version, again group : array, integer assumed to be consecutive integers no dtype checking because I want to raise in that case uses loop over columns of x #TODO: remove this, already copied to tools/grouputils ''' #TODO: transpose return in grou_sum, need test coverage first return np.array([np.bincount(group, weights=x[:,col]) for col in range(x.shape[1])]) def S_hac_groupsum(x, time, nlags=None, weights_func=weights_bartlett): '''inner covariance matrix for HAC over group sums sandwich This assumes we have complete equal spaced time periods. The number of time periods per group need not be the same, but we need at least one observation for each time period For a single categorical group only, or a everything else but time dimension. This first aggregates x over groups for each time period, then applies HAC on the sum per period. Parameters ---------- x : ndarray (nobs,) or (nobs, k_var) data, for HAC this is array of x_i * u_i time : ndarray, (nobs,) timeindes, assumed to be integers range(n_periods) nlags : int or None highest lag to include in kernel window. If None, then nlags = floor[4(T/100)^(2/9)] is used. weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights Returns ------- S : ndarray, (k_vars, k_vars) inner covariance matrix for sandwich References ---------- Daniel Hoechle, xtscc paper Driscoll and Kraay ''' #needs groupsums x_group_sums = group_sums(x, time).T #TODO: transpose return in grou_sum return S_hac_simple(x_group_sums, nlags=nlags, weights_func=weights_func) def S_crosssection(x, group): '''inner covariance matrix for White on group sums sandwich I guess for a single categorical group only, categorical group, can also be the product/intersection of groups This is used by cov_cluster and indirectly verified ''' x_group_sums = group_sums(x, group).T #TODO: why transposed return S_white_simple(x_group_sums) def cov_crosssection_0(results, group): '''this one is still wrong, use cov_cluster instead''' #TODO: currently used version of groupsums requires 2d resid scale = S_crosssection(results.resid[:,None], group) scale = np.squeeze(scale) cov = _HCCM1(results, scale) return cov def cov_cluster(results, group, use_correction=True): '''cluster robust covariance matrix Calculates sandwich covariance matrix for a single cluster, i.e. grouped variables. Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead use_correction : bool If true (default), then the small sample correction factor is used. Returns ------- cov : ndarray, (k_vars, k_vars) cluster robust covariance matrix for parameter estimates Notes ----- same result as Stata in UCLA example and same as Peterson ''' #TODO: currently used version of groupsums requires 2d resid xu = results.model.exog * results.resid[:, None] scale = S_crosssection(xu, group) nobs, k_vars = results.model.exog.shape n_groups = len(np.unique(group)) #replace with stored group attributes if available cov_c = _HCCM2(results, scale) if use_correction: cov_c *= n_groups / (n_groups - 1.) * ((nobs-1.) / float(nobs - k_vars)) return cov_c def cov_cluster_2groups(results, group, group2=None, use_correction=True): '''cluster robust covariance matrix for two groups/clusters Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead use_correction : bool If true (default), then the small sample correction factor is used. Returns ------- cov_both : ndarray, (k_vars, k_vars) cluster robust covariance matrix for parameter estimates, for both clusters cov_0 : ndarray, (k_vars, k_vars) cluster robust covariance matrix for parameter estimates for first cluster cov_1 : ndarray, (k_vars, k_vars) cluster robust covariance matrix for parameter estimates for second cluster Notes ----- verified against Peterson's table, (4 decimal print precision) ''' if group2 is None: if group.ndim !=2 or group.shape[1] != 2: raise ValueError('if group2 is not given, then groups needs to be ' + 'an array with two columns') group0 = group[:, 0] group1 = group[:, 1] else: group0 = group group1 = group2 group = (group0, group1) cov0 = cov_cluster(results, group0, use_correction=use_correction) #[0] because we get still also returns bse cov1 = cov_cluster(results, group1, use_correction=use_correction) group_intersection = Group(group) #cov of cluster formed by intersection of two groups cov01 = cov_cluster(results, group_intersection.group_int, use_correction=use_correction) #robust cov matrix for union of groups cov_both = cov0 + cov1 - cov01 #return all three (for now?) return cov_both, cov0, cov1 def cov_white_simple(results, use_correction=True): ''' heteroscedasticity robust covariance matrix (White) Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead Returns ------- cov : ndarray, (k_vars, k_vars) heteroscedasticity robust covariance matrix for parameter estimates Notes ----- This produces the same result as cov_hc0, and does not include any small sample correction. verified (against LinearRegressionResults and Peterson) See Also -------- cov_hc1, cov_hc2, cov_hc3 : heteroscedasticity robust covariance matrices with small sample corrections ''' xu = results.model.exog * results.resid[:, None] sigma = S_white_simple(xu) cov_w = _HCCM2(results, sigma) #add bread to sandwich if use_correction: nobs, k_vars = results.model.exog.shape cov_w *= nobs / float(nobs - k_vars) return cov_w def cov_hac_simple(results, nlags=None, weights_func=weights_bartlett, use_correction=True): ''' heteroscedasticity and autocorrelation robust covariance matrix (Newey-West) Assumes we have a single time series with zero axis consecutive, equal spaced time periods Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead nlags : int or None highest lag to include in kernel window. If None, then nlags = floor[4(T/100)^(2/9)] is used. weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights Returns ------- cov : ndarray, (k_vars, k_vars) HAC robust covariance matrix for parameter estimates Notes ----- verified only for nlags=0, which is just White just guessing on correction factor, need reference options might change when other kernels besides Bartlett are available. ''' xu = results.model.exog * results.resid[:, None] sigma = S_hac_simple(xu, nlags=nlags, weights_func=weights_func) cov_hac = _HCCM2(results, sigma) if use_correction: nobs, k_vars = results.model.exog.shape cov_hac *= nobs / float(nobs - k_vars) return cov_hac cov_hac = cov_hac_simple #alias for users #---------------------- use time lags corrected for groups #the following were copied from a different experimental script, #groupidx is tuple, observations assumed to be stacked by group member and #sorted by time, equal number of periods is not required, but equal spacing is. #I think this is pure within group HAC: apply HAC to each group member #separately def lagged_groups(x, lag, groupidx): ''' assumes sorted by time, groupidx is tuple of start and end values not optimized, just to get a working version, loop over groups ''' out0 = [] out_lagged = [] for l,u in groupidx: if l+lag < u: #group is longer than lag out0.append(x[l+lag:u]) out_lagged.append(x[l:u-lag]) if out0 == []: raise ValueError('all groups are empty taking lags') #return out0, out_lagged return np.vstack(out0), np.vstack(out_lagged) def S_nw_panel(xw, weights, groupidx): '''inner covariance matrix for HAC for panel data no denominator nobs used no reference for this, just accounting for time indices ''' nlags = len(weights)-1 S = weights[0] * np.dot(xw.T, xw) #weights just for completeness for lag in range(1, nlags+1): xw0, xwlag = lagged_groups(xw, lag, groupidx) s = np.dot(xw0.T, xwlag) S += weights[lag] * (s + s.T) return S def cov_nw_panel(results, nlags, groupidx, weights_func=weights_bartlett, use_correction='hac'): '''Panel HAC robust covariance matrix Assumes we have a panel of time series with consecutive, equal spaced time periods. Data is assumed to be in long format with time series of each individual stacked into one array. Panel can be unbalanced. Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead nlags : int or None Highest lag to include in kernel window. Currently, no default because the optimal length will depend on the number of observations per cross-sectional unit. groupidx : list of tuple each tuple should contain the start and end index for an individual. (groupidx might change in future). weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights use_correction : 'cluster' or 'hac' or False If False, then no small sample correction is used. If 'cluster' (default), then the same correction as in cov_cluster is used. If 'hac', then the same correction as in single time series, cov_hac is used. Returns ------- cov : ndarray, (k_vars, k_vars) HAC robust covariance matrix for parameter estimates Notes ----- For nlags=0, this is just White covariance, cov_white. If kernel is uniform, `weights_uniform`, with nlags equal to the number of observations per unit in a balance panel, then cov_cluster and cov_hac_panel are identical. Tested against STATA `newey` command with same defaults. Options might change when other kernels besides Bartlett and uniform are available. ''' if nlags == 0: #so we can reproduce HC0 White weights = [1, 0] #to avoid the scalar check in hac_nw else: weights = weights_func(nlags) xw = (results.model.exog * results.resid[:,None]) S_hac = S_nw_panel(xw, weights, groupidx) cov_hac = _HCCM2(results, S_hac) if use_correction: nobs, k_vars = results.model.exog.shape if use_correction == 'hac': cov_hac *= nobs / float(nobs - k_vars) elif use_correction in ['c', 'clu', 'cluster']: n_groups = len(groupidx) cov_hac *= n_groups / (n_groups - 1.) cov_hac *= ((nobs-1.) / float(nobs - k_vars)) return cov_hac def cov_nw_groupsum(results, nlags, time, weights_func=weights_bartlett, use_correction=0): '''Driscoll and Kraay Panel robust covariance matrix Robust covariance matrix for panel data of Driscoll and Kraay. Assumes we have a panel of time series where the time index is available. The time index is assumed to represent equal spaced periods. At least one observation per period is required. Parameters ---------- results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead nlags : int or None Highest lag to include in kernel window. Currently, no default because the optimal length will depend on the number of observations per cross-sectional unit. time : ndarray of int this should contain the coding for the time period of each observation. time periods should be integers in range(maxT) where maxT is obs of i weights_func : callable weights_func is called with nlags as argument to get the kernel weights. default are Bartlett weights use_correction : 'cluster' or 'hac' or False If False, then no small sample correction is used. If 'hac' (default), then the same correction as in single time series, cov_hac is used. If 'cluster', then the same correction as in cov_cluster is used. Returns ------- cov : ndarray, (k_vars, k_vars) HAC robust covariance matrix for parameter estimates Notes ----- Tested against STATA xtscc package, which uses no small sample correction This first averages relevant variables for each time period over all individuals/groups, and then applies the same kernel weighted averaging over time as in HAC. Warning: In the example with a short panel (few time periods and many individuals) with mainly across individual variation this estimator did not produce reasonable results. Options might change when other kernels besides Bartlett and uniform are available. References ---------- Daniel Hoechle, xtscc paper Driscoll and Kraay ''' xw = (results.model.exog * results.resid[:,None]) #S_hac = S_nw_panel(xw, weights, groupidx) S_hac = S_hac_groupsum(xw, time, nlags=nlags, weights_func=weights_func) cov_hac = _HCCM2(results, S_hac) if use_correction: nobs, k_vars = results.model.exog.shape if use_correction == 'hac': cov_hac *= nobs / float(nobs - k_vars) elif use_correction in ['c', 'cluster']: n_groups = len(np.unique(time)) cov_hac *= n_groups / (n_groups - 1.) cov_hac *= ((nobs-1.) / float(nobs - k_vars)) return cov_hac statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/stattools.py000066400000000000000000000042471224417117700242670ustar00rootroot00000000000000""" Statistical tests to be used in conjunction with the models Notes ----- These functions haven't been formally tested. """ from scipy import stats import numpy as np #TODO: these are pretty straightforward but they should be tested def durbin_watson(resids): """ Calculates the Durbin-Watson statistic Parameters ----------- resids : array-like Returns -------- Durbin Watson statistic. This is defined as sum_(t=2)^(T)((e_t - e_(t-1))^(2))/sum_(t=1)^(T)e_t^(2) """ resids=np.asarray(resids) diff_resids = np.diff(resids, 1) dw = np.dot(diff_resids, diff_resids) / np.dot(resids, resids) return dw def omni_normtest(resids, axis=0): """ Omnibus test for normality Parameters ----------- resid : array-like axis : int, optional Default is 0 Returns ------- Chi^2 score, two-tail probability """ #TODO: change to exception in summary branch and catch in summary() #behavior changed between scipy 0.9 and 0.10 resids = np.asarray(resids) n = resids.shape[axis] if n < 8: return np.nan, np.nan return_shape = list(resids.shape) del return_shape[axis] return np.nan * np.zeros(return_shape), np.nan * np.zeros(return_shape) raise ValueError( "skewtest is not valid with less than 8 observations; %i samples" " were given." % int(n)) return stats.normaltest(resids, axis=axis) def jarque_bera(resids): """ Calculate residual skewness, kurtosis, and do the JB test for normality Parameters ----------- resids : array-like Returns ------- JB, JBpv, skew, kurtosis JB = n/6*(S^2 + (K-3)^2/4) JBpv is the Chi^2 two-tail probability value skew is the measure of skewness kurtosis is the measure of kurtosis """ resids = np.asarray(resids) # Calculate residual skewness and kurtosis skew = stats.skew(resids) kurtosis = 3 + stats.kurtosis(resids) # Calculate the Jarque-Bera test for normality JB = (resids.shape[0] / 6.) * (skew**2 + (1 / 4.) * (kurtosis-3)**2) JBpv = stats.chi2.sf(JB,2) return JB, JBpv, skew, kurtosis statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tabledist.py000066400000000000000000000264031224417117700242040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Oct 01 20:20:16 2011 Author: Josef Perktold License: BSD-3 TODO: check orientation, size and alpha should be increasing for interp1d, but what is alpha? can be either sf or cdf probability change it to use one consistent notation check: instead of bound checking I could use the fill-value of the interpolators """ import numpy as np from scipy.interpolate import interp1d, interp2d, Rbf from statsmodels.tools.decorators import cache_readonly class TableDist(object): '''Distribution, critical values and p-values from tables currently only 1 extra parameter, e.g. sample size Parameters ---------- alpha : array_like, 1d probabiliy in the table, could be either sf (right tail) or cdf (left tail) size : array_like, 1d second paramater in the table crit_table : array_like, 2d array with critical values for sample size in rows and probability in columns Notes ----- size and alpha should be increasing ''' def __init__(self, alpha, size, crit_table): self.alpha = np.asarray(alpha) self.size = np.asarray(size) self.crit_table = np.asarray(crit_table) self.n_alpha = len(alpha) self.signcrit = np.sign(np.diff(self.crit_table, 1).mean()) if self.signcrit > 0: #increasing self.critv_bounds = self.crit_table[:,[0,1]] else: self.critv_bounds = self.crit_table[:,[1,0]] @cache_readonly def polyn(self): polyn = [interp1d(self.size, self.crit_table[:,i]) for i in range(self.n_alpha)] return polyn @cache_readonly def poly2d(self): #check for monotonicity ? #fix this, interp needs increasing poly2d = interp2d(self.size, self.alpha, self.crit_table) return poly2d @cache_readonly def polyrbf(self): xs, xa = np.meshgrid(self.size.astype(float), self.alpha) polyrbf = Rbf(xs.ravel(), xa.ravel(), self.crit_table.T.ravel(),function='linear') return polyrbf def _critvals(self, n): '''rows of the table, linearly interpolated for given sample size Parameters ---------- n : float sample size, second parameter of the table Returns ------- critv : ndarray, 1d critical values (ppf) corresponding to a row of the table Notes ----- This is used in two step interpolation, or if we want to know the critical values for all alphas for any sample size that we can obtain through interpolation ''' return np.array([p(n) for p in self.polyn]) def prob(self, x, n): '''find pvalues by interpolation, eiter cdf(x) or sf(x) returns extrem probabilities, 0.001 and 0.2, for out of range Parameters ---------- x : array_like observed value, assumed to follow the distribution in the table n : float sample size, second parameter of the table Returns ------- prob : arraylike This is the probability for each value of x, the p-value in underlying distribution is for a statistical test. ''' critv = self._critvals(n) alpha = self.alpha # if self.signcrit == 1: # if x < critv[0]: #generalize: ? np.sign(x - critvals[0]) == self.signcrit: # return alpha[0] # elif x > critv[-1]: # return alpha[-1] # elif self.signcrit == -1: # if x > critv[0]: # return alpha[0] # elif x < critv[-1]: # return alpha[-1] if self.signcrit < 1: #reverse if critv is decreasing critv, alpha = critv[::-1], alpha[::-1] #now critv is increasing if np.size(x) == 1: if x < critv[0]: return alpha[0] elif x > critv[-1]: return alpha[-1] return interp1d(critv, alpha)(x)[()] else: #vectorized cond_low = (x < critv[0]) cond_high = (x > critv[-1]) cond_interior = ~np.logical_or(cond_low, cond_high) probs = np.nan * np.ones(x.shape) #mistake if nan left probs[cond_low] = alpha[0] probs[cond_low] = alpha[-1] probs[cond_interior] = interp1d(critv, alpha)(x[cond_interior]) return probs def crit2(self, prob, n): '''returns interpolated quantiles, similar to ppf or isf this can be either cdf or sf depending on the table, twosided? this doesn't work, no more knots warning ''' return self.poly2d(n, prob) def crit(self, prob, n): '''returns interpolated quantiles, similar to ppf or isf use two sequential 1d interpolation, first by n then by prob Parameters ---------- prob : array_like probabilities corresponding to the definition of table columns n : int or float sample size, second parameter of the table Returns ------- ppf : array_like critical values with same shape as prob ''' prob = np.asarray(prob) alpha = self.alpha critv = self._critvals(n) #vectorized cond_ilow = (prob > alpha[0]) cond_ihigh = (prob < alpha[-1]) cond_interior = np.logical_or(cond_ilow, cond_ihigh) #scalar if prob.size == 1: if cond_interior: return interp1d(alpha, critv)(prob) else: return np.nan #vectorized quantile = np.nan * np.ones(prob.shape) #nans for outside quantile[cond_interior] = interp1d(alpha, critv)(prob[cond_interior]) return quantile def crit3(self, prob, n): '''returns interpolated quantiles, similar to ppf or isf uses Rbf to interpolate critical values as function of `prob` and `n` Parameters ---------- prob : array_like probabilities corresponding to the definition of table columns n : int or float sample size, second parameter of the table Returns ------- ppf : array_like critical values with same shape as prob, returns nan for arguments that are outside of the table bounds ''' prob = np.asarray(prob) alpha = self.alpha #vectorized cond_ilow = (prob > alpha[0]) cond_ihigh = (prob < alpha[-1]) cond_interior = np.logical_or(cond_ilow, cond_ihigh) #scalar if prob.size == 1: if cond_interior: return self.polyrbf(n, prob) else: return np.nan #vectorized quantile = np.nan * np.ones(prob.shape) #nans for outside quantile[cond_interior] = self.polyrbf(n, prob[cond_interior]) return quantile if __name__ == '__main__': ''' example Lilliefors test for normality An Analytic Approximation to the Distribution of Lilliefors's Test Statistic for Normality Author(s): Gerard E. Dallal and Leland WilkinsonSource: The American Statistician, Vol. 40, No. 4 (Nov., 1986), pp. 294-296Published by: American Statistical AssociationStable URL: http://www.jstor.org/stable/2684607 . ''' #for this test alpha is sf probability, i.e. right tail probability alpha = np.array([ 0.2 , 0.15 , 0.1 , 0.05 , 0.01 , 0.001])[::-1] size = np.array([ 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 100, 400, 900], float) #critical values, rows are by sample size, columns are by alpha crit_lf = np.array( [[303, 321, 346, 376, 413, 433], [289, 303, 319, 343, 397, 439], [269, 281, 297, 323, 371, 424], [252, 264, 280, 304, 351, 402], [239, 250, 265, 288, 333, 384], [227, 238, 252, 274, 317, 365], [217, 228, 241, 262, 304, 352], [208, 218, 231, 251, 291, 338], [200, 210, 222, 242, 281, 325], [193, 202, 215, 234, 271, 314], [187, 196, 208, 226, 262, 305], [181, 190, 201, 219, 254, 296], [176, 184, 195, 213, 247, 287], [171, 179, 190, 207, 240, 279], [167, 175, 185, 202, 234, 273], [163, 170, 181, 197, 228, 266], [159, 166, 176, 192, 223, 260], [143, 150, 159, 173, 201, 236], [131, 138, 146, 159, 185, 217], [115, 120, 128, 139, 162, 189], [ 74, 77, 82, 89, 104, 122], [ 37, 39, 41, 45, 52, 61], [ 25, 26, 28, 30, 35, 42]])[:,::-1] / 1000. lf = TableDist(alpha, size, crit_lf) print lf.prob(0.166, 20), 'should be:', 0.15 print print lf.crit2(0.15, 20), 'should be:', 0.166, 'interp2d bad' print lf.crit(0.15, 20), 'should be:', 0.166, 'two 1d' print lf.crit3(0.15, 20), 'should be:', 0.166, 'Rbf' print print lf.crit2(0.17, 20), 'should be in:', (.159, .166), 'interp2d bad' print lf.crit(0.17, 20), 'should be in:', (.159, .166), 'two 1d' print lf.crit3(0.17, 20), 'should be in:', (.159, .166), 'Rbf' print print lf.crit2(0.19, 20), 'should be in:', (.159, .166), 'interp2d bad' print lf.crit(0.19, 20), 'should be in:', (.159, .166), 'two 1d' print lf.crit3(0.19, 20), 'should be in:', (.159, .166), 'Rbf' print print lf.crit2(0.199, 20), 'should be in:', (.159, .166), 'interp2d bad' print lf.crit(0.199, 20), 'should be in:', (.159, .166), 'two 1d' print lf.crit3(0.199, 20), 'should be in:', (.159, .166), 'Rbf' #testing print np.max(np.abs(np.array([lf.prob(c, size[i]) for i in range(len(size)) for c in crit_lf[i]]).reshape(-1,lf.n_alpha) - lf.alpha)) #1.6653345369377348e-16 print np.max(np.abs(np.array([lf.crit(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf)) #6.9388939039072284e-18 print np.max(np.abs(np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf)) #4.0615705243496336e-12 print (np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) < crit_lf[:,:-1]).all() print (np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) > crit_lf[:,1:]).all() print (np.array([lf.prob(c*0.9, size[i]) for i in range(len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all() print (np.array([lf.prob(c*1.1, size[i]) for i in range(len(size)) for c in crit_lf[i,1:]]).reshape(-1,lf.n_alpha-1) < lf.alpha[1:]).all() #start at size_idx=2 because of non-monotonicity of lf_crit print (np.array([lf.prob(c, size[i]*0.9) for i in range(2,len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/000077500000000000000000000000001224417117700230145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/__init__.py000066400000000000000000000143221224417117700251270ustar00rootroot00000000000000''' Econometrics for a Datarich Environment ======================================= Introduction ------------ In many cases we are performing statistical analysis when many observed variables are available, when we are in a data rich environment. Machine learning has a wide variety of tools for dimension reduction and penalization when there are many varibles compared to the number of observation. Chemometrics has a long tradition of using Partial Least Squares, NIPALS and similar in these cases. In econometrics the same problem shows up when there are either many possible regressors, many (weak) instruments or when there are a large number of moment conditions in GMM. This section is intended to collect some models and tools in this area that are relevant for the statical analysis and econometrics. Covariance Matrices =================== Several methods are available to reduce the small sample noise in estimated covariance matrices with many variable. Some applications: weighting matrix with many moments, covariance matrix for portfolio choice Dimension Reduction =================== Principal Component and Partial Least Squares try to extract the important low dimensional factors from the data with many variables. Regression with many regressors =============================== Factor models, selection of regressors and shrinkage and penalization are used to improve the statistical properties, when the presence of too many regressors leads to over-fitting and too noisy small sample estimators and statistics. Regression with many moments or many instruments ================================================ The same tools apply and can be used in these two cases. e.g. Tychonov regularization of weighting matrix in GMM, similar to Ridge regression, the weighting matrix can be shrunk towards the identity matrix. Simplest case will be part of GMM. I don't know how much will be standalone functions. Intended Content ================ PLS --- what should be available in class? Factormodel and supporting helper functions ------------------------------------------- PCA based ~~~~~~~~~ First version based PCA on Stock/Watson and Bai/Ng, and recent papers on the selection of the number of factors. Not sure about Forni et al. in approach. Basic support of this needs additional results for PCA, error covariance matrix of data on reduced factors, required for criteria in Bai/Ng. Selection criteria based on eigenvalue cutoffs. Paper on PCA and structural breaks. Could add additional results during find_nfact to test for parameter stability. I haven't read the paper yet. Idea: for forecasting, use up to h-step ahead endogenous variables to directly get the forecasts. Asymptotic results and distribution: not too much idea yet. Standard OLS results are conditional on factors, paper by Haerdle (abstract seems to suggest that this is ok, Park 2009). Simulation: add function to simulate DGP of Bai/Ng and recent extension. Sensitivity of selection criteria to heteroscedasticity and autocorrelation. Bai, J. & Ng, S., 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), pp.191-221. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Alessi, L., Barigozzi, M. & Capasso, M., 2010. Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), pp.1806-1813. Breitung, J. & Eickmeier, S., Testing for structural breaks in dynamic factor models. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51G3W92-1/2/f45ce2332443374fd770e42e5a68ddb4 [Accessed November 15, 2010]. Croux, C., Renault, E. & Werker, B., 2004. Dynamic factor models. Journal of Econometrics, 119(2), pp.223-230. Forni, M. et al., 2009. Opening the Black Box: Structural Factor Models with Large Cross Sections. Econometric Theory, 25(05), pp.1319-1347. Forni, M. et al., 2000. The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, 82(4), pp.540-554. Forni, M. & Lippi, M., The general dynamic factor model: One-sided representation results. Journal of Econometrics, In Press, Accepted Manuscript. Available at: http://www.sciencedirect.com/science/article/B6VC0-51FNPJN-1/2/4fcdd0cfb66e3050ff5d19bf2752ed19 [Accessed November 15, 2010]. Kapetanios, G., 2010. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp.397-409. Onatski, A., 2010. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Review of Economics and Statistics, 92(4), pp.1004-1016. Park, B.U. et al., 2009. Time Series Modelling With Semiparametric Factor Dynamics. Journal of the American Statistical Association, 104(485), pp.284-298. other factor algorithm ~~~~~~~~~~~~~~~~~~~~~~ PLS should fit in reasonably well. Bai/Ng have a recent paper, where they compare LASSO, PCA, and similar, individual and in combination. Check how much we can use scikits.learn for this. miscellaneous ~~~~~~~~~~~~~ Time series modeling of factors for prediction, ARMA, VARMA. SUR and correlation structure What about sandwich estimation, robust covariance matrices? Similarity to Factor-Garch and Go-Garch Updating: incremental PCA, ...? TODO next ========= MVOLS : OLS with multivariate endogenous and identical exogenous variables. rewrite and expand current varma_process.VAR PCA : write a class after all, and/or adjust the current donated class and keep adding required statistics, e.g. residual variance, projection of X on k-factors, ... updating ? FactorModelUnivariate : started, does basic principal component regression, based on standard information criteria, not Bai/Ng adjusted FactorModelMultivariate : follow pattern for univariate version and use MVOLS ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/000077500000000000000000000000001224417117700245155ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/__init__.py000066400000000000000000000000001224417117700266140ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/anova.R000066400000000000000000000062501224417117700257470ustar00rootroot00000000000000dta <- read.table('data.dat', header=TRUE) dta$Duration <- factor(dta$Duration) dta$Weight <- factor(dta$Weight) dta$logDays <- log(dta$Days + 1) # Use log days to "stabilize" variance attach(dta) library(car) source('/home/skipper/statsmodels/statsmodels/tools/topy.R') sum.lm = lm(logDays ~ Duration * Weight, contrasts=list(Duration=contr.sum, Weight=contr.sum)) anova.lm.sum <- anova(sum.lm) for(name in names(anova.lm.sum)) { mkarray2(anova.lm.sum[[name]], name, TRUE) }; cat("\n") anova.lm.interaction <- anova(lm(logDays ~ Duration + Weight), sum.lm) for(name in names(anova.lm.interaction)) { mkarray2(anova.lm.interaction[[name]], name, TRUE) }; cat("\n") anova.lm.variable <- anova(lm(logDays ~ Duration), lm(logDays ~ Duration + Weight)) anova.lm.variable2 <- anova(lm(logDays ~ Weight), lm(logDays ~ Duration + Weight)) anova.i <- anova(sum.lm) anova.ii <- Anova(sum.lm, type='II') anova.iii <- Anova(sum.lm, type='III') nosum.lm = lm(logDays ~ Duration * Weight, contrasts=list(Duration=contr.treatment, Weight=contr.treatment)) anova.i.nosum <- anova(nosum.lm) anova.ii.nosum <- Anova(nosum.lm, type='II') anova.iii.nosum <- Anova(nosum.lm, type='III') dta.dropped <- dta[4:60, ] sum.lm.dropped <- lm(logDays ~ Duration * Weight, dta.dropped, contrasts=list(Duration=contr.sum, Weight=contr.sum)) anova.i.dropped <- anova(sum.lm.dropped) anova.ii.dropped <- Anova(sum.lm.dropped, type='II') anova.iii.dropped <- Anova(sum.lm.dropped, type='III') for(name in names(anova.ii.dropped)) { mkarray2(anova.ii.dropped[[name]], name, TRUE) }; cat("\n") for(name in names(anova.iii.dropped)) { mkarray2(anova.iii.dropped[[name]], name, TRUE) }; cat("\n") anova.iii.dropped <- Anova(sum.lm.dropped, white="hc0", type='III') for(name in names(anova.iii.dropped)) { mkarray2(anova.iii.dropped[[name]], name, TRUE) }; cat("\n") anova.iii.dropped <- Anova(sum.lm.dropped, white="hc1", type='III') for(name in names(anova.iii.dropped)) { mkarray2(anova.iii.dropped[[name]], name, TRUE) }; cat("\n") anova.iii.dropped <- Anova(sum.lm.dropped, white="hc2", type='III') for(name in names(anova.iii.dropped)) { mkarray2(anova.iii.dropped[[name]], name, TRUE) }; cat("\n") anova.iii.dropped <- Anova(sum.lm.dropped, white="hc3", type='III') for(name in names(anova.iii.dropped)) { mkarray2(anova.iii.dropped[[name]], name, TRUE) }; cat("\n") anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc0") for(name in names(anova.ii.dropped)) { mkarray2(anova.ii.dropped[[name]], name, TRUE) }; cat("\n") anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc1") for(name in names(anova.ii.dropped)) { mkarray2(anova.ii.dropped[[name]], name, TRUE) }; cat("\n") anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc2") for(name in names(anova.ii.dropped)) { mkarray2(anova.ii.dropped[[name]], name, TRUE) }; cat("\n") anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc3") for(name in names(anova.ii.dropped)) { mkarray2(anova.ii.dropped[[name]], name, TRUE) }; cat("\n") statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/bootleg.csv000066400000000000000000010505601224417117700266740ustar00rootroot000000000000000.950300,2,4.43400,3.787121 0.613586,75,149.1170,4.974391 0.8372366,53,501.2317,5.193843 0.9023396,58,546.2543,5.495572 0.9061918,96,424.4232,5.856011 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"196",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE "197",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE "198",FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE "199",FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE "200",FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE "201",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE "202",FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/results_panelrobust.py000066400000000000000000000063001224417117700312050ustar00rootroot00000000000000import numpy as np cov_clu_stata = np.array([ .00025262993207, -.00065043385106, .20961897960949, -.00065043385106, .00721940994738, -1.2171040967615, .20961897960949, -1.2171040967615, 417.18890043724]).reshape(3,3) cov_pnw0_stata = np.array([ .00004638910396, -.00006781406833, -.00501232990882, -.00006781406833, .00238784043122, -.49683062350622, -.00501232990882, -.49683062350622, 133.97367476797]).reshape(3,3) cov_pnw1_stata = np.array([ .00007381482253, -.00009936717692, -.00613513582975, -.00009936717692, .00341979122583, -.70768252183061, -.00613513582975, -.70768252183061, 197.31345000598]).reshape(3,3) cov_pnw4_stata = np.array([ .0001305958131, -.00022910455176, .00889686530849, -.00022910455176, .00468152667913, -.88403667445531, .00889686530849, -.88403667445531, 261.76140136858]).reshape(3,3) cov_dk0_stata = np.array([ .00005883478135, -.00011241470772, -.01670183921469, -.00011241470772, .00140649264687, -.29263014921586, -.01670183921469, -.29263014921586, 99.248049966902]).reshape(3,3) cov_dk1_stata = np.array([ .00009855800275, -.00018443722054, -.03257408922788, -.00018443722054, .00205106413403, -.3943459697384, -.03257408922788, -.3943459697384, 140.50692606398]).reshape(3,3) cov_dk4_stata = np.array([ .00018052657317, -.00035661054613, -.06728261073866, -.00035661054613, .0024312795189, -.32394785247278, -.06728261073866, -.32394785247278, 148.60456447156]).reshape(3,3) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(cov_clu_stata=cov_clu_stata, cov_pnw0_stata=cov_pnw0_stata, cov_pnw1_stata=cov_pnw1_stata, cov_pnw4_stata=cov_pnw4_stata, cov_dk0_stata=cov_dk0_stata, cov_dk1_stata=cov_dk1_stata, cov_dk4_stata=cov_dk4_stata, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/results_power.py000066400000000000000000000107201224417117700300040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Feb 28 13:23:09 2013 Author: Josef Perktold """ import collections class Holder(object): pass #def __repr__(self): # numbers from R package `pwr` pwr.chisq.test pwr_chisquare = collections.defaultdict(Holder) pwr_chisquare[0].w = 1e-04 pwr_chisquare[0].N = 5 pwr_chisquare[0].df = 4 pwr_chisquare[0].sig_level = 0.05 pwr_chisquare[0].power = 0.05000000244872708 pwr_chisquare[0].method = 'Chi squared power calculation' pwr_chisquare[0].note = 'N is the number of observations' pwr_chisquare[1].w = 0.005 pwr_chisquare[1].N = 5 pwr_chisquare[1].df = 4 pwr_chisquare[1].sig_level = 0.05 pwr_chisquare[1].power = 0.05000612192891004 pwr_chisquare[1].method = 'Chi squared power calculation' pwr_chisquare[1].note = 'N is the number of observations' pwr_chisquare[2].w = 0.1 pwr_chisquare[2].N = 5 pwr_chisquare[2].df = 4 pwr_chisquare[2].sig_level = 0.05 pwr_chisquare[2].power = 0.05246644635810126 pwr_chisquare[2].method = 'Chi squared power calculation' pwr_chisquare[2].note = 'N is the number of observations' pwr_chisquare[3].w = 1 pwr_chisquare[3].N = 5 pwr_chisquare[3].df = 4 pwr_chisquare[3].sig_level = 0.05 pwr_chisquare[3].power = 0.396188517504065 pwr_chisquare[3].method = 'Chi squared power calculation' pwr_chisquare[3].note = 'N is the number of observations' pwr_chisquare[4].w = 1e-04 pwr_chisquare[4].N = 100 pwr_chisquare[4].df = 4 pwr_chisquare[4].sig_level = 0.05 pwr_chisquare[4].power = 0.05000004897454883 pwr_chisquare[4].method = 'Chi squared power calculation' pwr_chisquare[4].note = 'N is the number of observations' pwr_chisquare[5].w = 0.005 pwr_chisquare[5].N = 100 pwr_chisquare[5].df = 4 pwr_chisquare[5].sig_level = 0.05 pwr_chisquare[5].power = 0.05012248082672883 pwr_chisquare[5].method = 'Chi squared power calculation' pwr_chisquare[5].note = 'N is the number of observations' pwr_chisquare[6].w = 0.1 pwr_chisquare[6].N = 100 pwr_chisquare[6].df = 4 pwr_chisquare[6].sig_level = 0.05 pwr_chisquare[6].power = 0.1054845044462312 pwr_chisquare[6].method = 'Chi squared power calculation' pwr_chisquare[6].note = 'N is the number of observations' pwr_chisquare[7].w = 1 pwr_chisquare[7].N = 100 pwr_chisquare[7].df = 4 pwr_chisquare[7].sig_level = 0.05 pwr_chisquare[7].power = 0.999999999999644 pwr_chisquare[7].method = 'Chi squared power calculation' pwr_chisquare[7].note = 'N is the number of observations' pwr_chisquare[8].w = 1e-04 pwr_chisquare[8].N = 1000 pwr_chisquare[8].df = 4 pwr_chisquare[8].sig_level = 0.05 pwr_chisquare[8].power = 0.0500004897461283 pwr_chisquare[8].method = 'Chi squared power calculation' pwr_chisquare[8].note = 'N is the number of observations' pwr_chisquare[9].w = 0.005 pwr_chisquare[9].N = 1000 pwr_chisquare[9].df = 4 pwr_chisquare[9].sig_level = 0.05 pwr_chisquare[9].power = 0.0512288025485101 pwr_chisquare[9].method = 'Chi squared power calculation' pwr_chisquare[9].note = 'N is the number of observations' pwr_chisquare[10].w = 0.1 pwr_chisquare[10].N = 1000 pwr_chisquare[10].df = 4 pwr_chisquare[10].sig_level = 0.05 pwr_chisquare[10].power = 0.715986350467412 pwr_chisquare[10].method = 'Chi squared power calculation' pwr_chisquare[10].note = 'N is the number of observations' pwr_chisquare[11].w = 1 pwr_chisquare[11].N = 1000 pwr_chisquare[11].df = 4 pwr_chisquare[11].sig_level = 0.05 pwr_chisquare[11].power = 1 pwr_chisquare[11].method = 'Chi squared power calculation' pwr_chisquare[11].note = 'N is the number of observations' pwr_chisquare[12].w = 1e-04 pwr_chisquare[12].N = 30000 pwr_chisquare[12].df = 4 pwr_chisquare[12].sig_level = 0.05 pwr_chisquare[12].power = 0.05001469300301765 pwr_chisquare[12].method = 'Chi squared power calculation' pwr_chisquare[12].note = 'N is the number of observations' pwr_chisquare[13].w = 0.005 pwr_chisquare[13].N = 30000 pwr_chisquare[13].df = 4 pwr_chisquare[13].sig_level = 0.05 pwr_chisquare[13].power = 0.0904799545200348 pwr_chisquare[13].method = 'Chi squared power calculation' pwr_chisquare[13].note = 'N is the number of observations' pwr_chisquare[14].w = 0.1 pwr_chisquare[14].N = 30000 pwr_chisquare[14].df = 4 pwr_chisquare[14].sig_level = 0.05 pwr_chisquare[14].power = 1 pwr_chisquare[14].method = 'Chi squared power calculation' pwr_chisquare[14].note = 'N is the number of observations' pwr_chisquare[15].w = 1 pwr_chisquare[15].N = 30000 pwr_chisquare[15].df = 4 pwr_chisquare[15].sig_level = 0.05 pwr_chisquare[15].power = 1 pwr_chisquare[15].method = 'Chi squared power calculation' pwr_chisquare[15].note = 'N is the number of observations' statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/results/results_proportion.py000066400000000000000000000067601224417117700310740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 01 14:48:59 2013 Author: Josef Perktold """ import collections import numpy as np class Holder(object): pass # numbers from R package `pwr` pwr.chisq.test res_binom = collections.defaultdict(Holder) res_binom_methods = ["agresti-coull", "asymptotic", "bayes", "cloglog", "exact", "logit", "probit", "profile", "lrt", "prop.test", "wilson"] #> bci = binom.confint(x = c(18), n = 20, tol = 1e-8) #> mkarray2(bci$lower, "res_binom[(18, 20)].ci_low") res_binom[(18, 20)].ci_low = np.array([ 0.6867561125596077, 0.768521618913513, 0.716146742695748, 0.656030707261567, 0.6830172859809176, 0.676197991611287, 0.7027685414174645, 0.722052946372325, 0.7220576251734515, 0.668722403162941, 0.6989663547715128 ]) #> mkarray2(bci$upper, "res_binom[(18, 20)].ci_upp") res_binom[(18, 20)].ci_upp = np.array([ 0.984343760998137, 1.031478381086487, 0.97862751197755, 0.974010174395775, 0.9876514728297052, 0.974866415649319, 0.978858461808406, 0.982318186566456, 0.982639913376776, 0.982487361226571, 0.972133518786232 ]) #> #> bci = binom.confint(x = c(4), n = 20, tol = 1e-8) #> mkarray2(bci$lower, "res_binom[(4, 20)].ci_low") res_binom[(4, 20)].ci_low = np.array([ 0.0749115102767071, 0.0246954918846837, 0.07152005247873425, 0.0623757232566298, 0.05733399705003284, 0.0771334546771001, 0.0710801045992076, 0.0668624655835687, 0.0668375191189685, 0.0661062308910436, 0.0806576625797981 ]) #> mkarray2(bci$upper, "res_binom[(4, 20)].ci_upp") res_binom[(4, 20)].ci_upp = np.array([ 0.4217635845549845, 0.3753045081153163, 0.4082257625169254, 0.393143902056907, 0.436614002996668, 0.427846901518118, 0.4147088121599544, 0.405367872119342, 0.405364309586823, 0.442686245059445, 0.4160174322518935 ]) #> #> bci = binom.confint(x = c(4), n = 200, tol = 1e-8) #> mkarray2(bci$lower, "res_binom[(4, 200)].ci_low") res_binom[(4, 200)].ci_low = np.array([ 0.005991954548218395, 0.000597346459104517, 0.00678759879519299, 0.006650668467968445, 0.005475565879556443, 0.00752663882411158, 0.00705442514086136, 0.00625387073493174, 0.00625223049303646, 0.00642601313670221, 0.00780442641634947 ]) #> mkarray2(bci$upper, "res_binom[(4, 200)].ci_upp") res_binom[(4, 200)].ci_upp = np.array([ 0.0520995587739575, 0.0394026535408955, 0.0468465669668423, 0.04722535678688564, 0.05041360908989634, 0.05206026227201098, 0.04916362085874019, 0.04585048214247203, 0.0458490848884339, 0.0537574613520185, 0.05028708690582643 ]) #> bci = binom.confint(x = c(190), n = 200, tol = 1e-8) #Warning message: #In binom.bayes(x, n, conf.level = conf.level, ...) : # 1 confidence interval failed to converge (marked by '*'). # Try changing 'tol' to a different value. #JP: I replace 0.02094150654714356 by np.nan in Bayes #> mkarray2(bci$lower, "res_binom[(190, 200)].ci_low") res_binom[(190, 200)].ci_low = np.array([ 0.909307307911624, 0.919794926420966, np.nan, 0.909066091776046, 0.9099724622986486, 0.9095820742314172, 0.9118101288857796, 0.913954651984184, 0.913956305842353, 0.9073089225133698, 0.910421851861224 ]) #> mkarray2(bci$upper, "res_binom[(190, 200)].ci_upp") res_binom[(190, 200)].ci_upp = np.array([ 0.973731898348837, 0.980205073579034, 1, 0.972780587302479, 0.975765834527891, 0.9728891271086528, 0.973671370402242, 0.974623779100809, 0.974626983311416, 0.974392083257476, 0.972617354399236 ]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_anova.py000066400000000000000000000344311224417117700255360ustar00rootroot00000000000000from StringIO import StringIO import numpy as np from statsmodels.stats.anova import anova_lm from statsmodels.formula.api import ols from pandas import read_table kidney_table = StringIO("""Days Duration Weight ID 0.0 1 1 1 2.0 1 1 2 1.0 1 1 3 3.0 1 1 4 0.0 1 1 5 2.0 1 1 6 0.0 1 1 7 5.0 1 1 8 6.0 1 1 9 8.0 1 1 10 2.0 1 2 1 4.0 1 2 2 7.0 1 2 3 12.0 1 2 4 15.0 1 2 5 4.0 1 2 6 3.0 1 2 7 1.0 1 2 8 5.0 1 2 9 20.0 1 2 10 15.0 1 3 1 10.0 1 3 2 8.0 1 3 3 5.0 1 3 4 25.0 1 3 5 16.0 1 3 6 7.0 1 3 7 30.0 1 3 8 3.0 1 3 9 27.0 1 3 10 0.0 2 1 1 1.0 2 1 2 1.0 2 1 3 0.0 2 1 4 4.0 2 1 5 2.0 2 1 6 7.0 2 1 7 4.0 2 1 8 0.0 2 1 9 3.0 2 1 10 5.0 2 2 1 3.0 2 2 2 2.0 2 2 3 0.0 2 2 4 1.0 2 2 5 1.0 2 2 6 3.0 2 2 7 6.0 2 2 8 7.0 2 2 9 9.0 2 2 10 10.0 2 3 1 8.0 2 3 2 12.0 2 3 3 3.0 2 3 4 7.0 2 3 5 15.0 2 3 6 4.0 2 3 7 9.0 2 3 8 6.0 2 3 9 1.0 2 3 10 """) class TestAnovaLM(object): @classmethod def setupClass(cls): # kidney data taken from JT's course # don't know the license kidney_table.seek(0) cls.data = read_table(kidney_table, sep="\s+") cls.kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=cls.data).fit() def test_results(self): Df = np.array([1, 2, 2, 54]) sum_sq = np.array([2.339693, 16.97129, 0.6356584, 28.9892]) mean_sq = np.array([2.339693, 8.485645, 0.3178292, 0.536837]) f_value = np.array([4.358293, 15.80674, 0.5920404, np.nan]) pr_f = np.array([0.0415617, 3.944502e-06, 0.5567479, np.nan]) results = anova_lm(self.kidney_lm) np.testing.assert_equal(results['df'].values, Df) np.testing.assert_almost_equal(results['sum_sq'].values, sum_sq, 4) np.testing.assert_almost_equal(results['F'].values, f_value, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, pr_f) class TestAnovaLMCompare(TestAnovaLM): def test_results(self): new_model = ols("np.log(Days+1) ~ C(Duration) + C(Weight)", self.data).fit() results = anova_lm(new_model, self.kidney_lm) Res_Df = np.array([ 56, 54 ]) RSS = np.array([ 29.62486, 28.9892 ]) Df = np.array([ 0, 2 ]) Sum_of_Sq = np.array([ np.nan, 0.6356584 ]) F = np.array([ np.nan, 0.5920404 ]) PrF = np.array([ np.nan, 0.5567479 ]) np.testing.assert_equal(results["df_resid"].values, Res_Df) np.testing.assert_almost_equal(results["ssr"].values, RSS, 4) np.testing.assert_almost_equal(results["df_diff"].values, Df) np.testing.assert_almost_equal(results["ss_diff"].values, Sum_of_Sq) np.testing.assert_almost_equal(results["F"].values, F) np.testing.assert_almost_equal(results["Pr(>F)"].values, PrF) class TestAnova2(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 3.067066, 13.27205, 0.1905093, 27.60181 ]) Df = np.array([ 1, 2, 2, 51 ]) F_value = np.array([ 5.667033, 12.26141, 0.1760025, np.nan ]) PrF = np.array([ 0.02106078, 4.487909e-05, 0.8391231, np.nan ]) results = anova_lm(anova_ii, typ="II") np.testing.assert_equal(results['df'].values, Df) np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F_value, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova2HC0(TestAnovaLM): #NOTE: R doesn't return SSq with robust covariance. Why? # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 2, 2, 51 ]) F = np.array([ 6.972744, 13.7804, 0.1709936, np.nan ]) PrF = np.array([ 0.01095599, 1.641682e-05, 0.8433081, np.nan ]) results = anova_lm(anova_ii, typ="II", robust="hc0") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova2HC1(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 2, 2, 51 ]) F = np.array([ 6.238771, 12.32983, 0.1529943, np.nan ]) PrF = np.array([ 0.01576555, 4.285456e-05, 0.858527, np.nan ]) results = anova_lm(anova_ii, typ="II", robust="hc1") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova2HC2(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 2, 2, 51 ]) F = np.array([ 6.267499, 12.25354, 0.1501224, np.nan ]) PrF = np.array([ 0.01554009, 4.511826e-05, 0.8609815, np.nan ]) results = anova_lm(anova_ii, typ="II", robust="hc2") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova2HC3(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 2, 2, 51 ]) F = np.array([ 5.633786, 10.89842, 0.1317223, np.nan ]) PrF = np.array([ 0.02142223, 0.0001145965, 0.8768817, np.nan ]) results = anova_lm(anova_ii, typ="II", robust="hc3") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova3(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_iii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 1, 2, 2, 51 ]) F_value = np.array([ 279.7545, 5.367071, 12.43245, 0.1760025, np.nan ]) PrF = np.array([ 2.379855e-22, 0.02457384, 3.999431e-05, 0.8391231, np.nan ]) results = anova_lm(anova_iii, typ="III") np.testing.assert_equal(results['df'].values, Df) np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F_value, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova3HC0(TestAnovaLM): #NOTE: R doesn't return SSq with robust covariance. Why? # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_iii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 1, 2, 2, 51 ]) F = np.array([ 298.3404, 5.723638, 13.76069, 0.1709936, np.nan ]) PrF = np.array([ 5.876255e-23, 0.02046031, 1.662826e-05, 0.8433081, np.nan ]) results = anova_lm(anova_iii, typ="III", robust="hc0") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova3HC1(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_iii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 1, 2, 2, 51 ]) F = np.array([ 266.9361, 5.12115, 12.3122, 0.1529943, np.nan ]) PrF = np.array([ 6.54355e-22, 0.02792296, 4.336712e-05, 0.858527, np.nan ]) results = anova_lm(anova_iii, typ="III", robust="hc1") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova3HC2(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_iii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 1, 2, 2, 51 ]) F = np.array([ 264.5137, 5.074677, 12.19158, 0.1501224, np.nan ]) PrF = np.array([ 7.958286e-22, 0.02860926, 4.704831e-05, 0.8609815, np.nan ]) results = anova_lm(anova_iii, typ="III", robust="hc2") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) class TestAnova3HC3(TestAnovaLM): # drop some observations to make an unbalanced, disproportionate panel # to make sure things are okay def test_results(self): data = self.data.drop([0,1,2]) anova_iii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() Sum_Sq = np.array([ 151.4065, 2.904723, 13.45718, 0.1905093, 27.60181 ]) Df = np.array([ 1, 1, 2, 2, 51 ]) F = np.array([ 234.4026, 4.496996, 10.79903, 0.1317223, np.nan ]) PrF = np.array([ 1.037224e-20, 0.03883841, 0.0001228716, 0.8768817, np.nan ]) results = anova_lm(anova_iii, typ="III", robust="hc3") np.testing.assert_equal(results['df'].values, Df) #np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4) np.testing.assert_almost_equal(results['F'].values, F, 4) np.testing.assert_almost_equal(results['PR(>F)'].values, PrF) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb-failure'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_contrast.py000066400000000000000000000025511224417117700262650ustar00rootroot00000000000000import numpy as np import numpy.random as R from numpy.testing import assert_almost_equal, assert_equal from statsmodels.stats.contrast import Contrast class TestContrast(object): @classmethod def setupClass(cls): R.seed(54321) cls.X = R.standard_normal((40,10)) def test_contrast1(self): term = np.column_stack((self.X[:,0], self.X[:,2])) c = Contrast(term, self.X) test_contrast = [[1] + [0]*9, [0]*2 + [1] + [0]*7] assert_almost_equal(test_contrast, c.contrast_matrix) def test_contrast2(self): zero = np.zeros((40,)) term = np.column_stack((zero, self.X[:,2])) c = Contrast(term, self.X) test_contrast = [0]*2 + [1] + [0]*7 assert_almost_equal(test_contrast, c.contrast_matrix) def test_contrast3(self): P = np.dot(self.X, np.linalg.pinv(self.X)) resid = np.identity(40) - P noise = np.dot(resid,R.standard_normal((40,5))) term = np.column_stack((noise, self.X[:,2])) c = Contrast(term, self.X) assert_equal(c.contrast_matrix.shape, (10,)) #TODO: this should actually test the value of the contrast, not only its dimension def test_estimable(self): X2 = np.column_stack((self.X, self.X[:,5])) c = Contrast(self.X[:,5],X2) #TODO: I don't think this should be estimable? isestimable correct? statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_corrpsd.py000066400000000000000000000170571224417117700261130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for findind a positive semi-definite correlation of covariance matrix Created on Mon May 27 12:07:02 2013 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_allclose from statsmodels.stats.correlation_tools import ( corr_nearest, corr_clipped, cov_nearest) def norm_f(x, y): '''Frobenious norm (squared sum) of difference between two arrays ''' d = ((x - y)**2).sum() return np.sqrt(d) class Holder(object): pass # R library Matrix results cov1_r = Holder() #> nc <- nearPD(pr, conv.tol = 1e-7, keepDiag = TRUE, doDykstra =FALSE, corr=TRUE) #> cat_items(nc, prefix="cov1_r.") cov1_r.mat = '''''' cov1_r.eigenvalues = np.array([ 4.197315628646795, 0.7540460243978023, 0.5077608149667492, 0.3801267599652769, 0.1607508970775889, 4.197315628646795e-08 ]) cov1_r.corr = '''TRUE''' cov1_r.normF = 0.0743805226512533 cov1_r.iterations = 11 cov1_r.rel_tol = 8.288594638441735e-08 cov1_r.converged = '''TRUE''' #> mkarray2(as.matrix(nc$mat), name="cov1_r.mat") cov1_r.mat = np.array([ 1, 0.487968018215892, 0.642651880010906, 0.4906386709070835, 0.6440990530811909, 0.8087111845493985, 0.487968018215892, 1, 0.5141147294352735, 0.2506688108312097, 0.672351311297074, 0.725832055882795, 0.642651880010906, 0.5141147294352735, 1, 0.596827778712154, 0.5821917790519067, 0.7449631633814129, 0.4906386709070835, 0.2506688108312097, 0.596827778712154, 1, 0.729882058012399, 0.772150225146826, 0.6440990530811909, 0.672351311297074, 0.5821917790519067, 0.729882058012399, 1, 0.813191720191944, 0.8087111845493985, 0.725832055882795, 0.7449631633814129, 0.772150225146826, 0.813191720191944, 1 ]).reshape(6,6, order='F') cov_r = Holder() #nc <- nearPD(pr+0.01*diag(6), conv.tol = 1e-7, keepDiag = TRUE, doDykstra =FALSE, corr=FALSE) #> cat_items(nc, prefix="cov_r.") #cov_r.mat = '''''' cov_r.eigenvalues = np.array([ 4.209897516692652, 0.7668341923072066, 0.518956980021938, 0.390838551407132, 0.1734728460460068, 4.209897516692652e-08 ]) cov_r.corr = '''FALSE''' cov_r.normF = 0.0623948693159157 cov_r.iterations = 11 cov_r.rel_tol = 5.83987595937896e-08 cov_r.converged = '''TRUE''' #> mkarray2(as.matrix(nc$mat), name="cov_r.mat") cov_r.mat = np.array([ 1.01, 0.486207476951913, 0.6428524769306785, 0.4886092840296514, 0.645175579158233, 0.811533860074678, 0.486207476951913, 1.01, 0.514394615153752, 0.2478398278204047, 0.673852495852274, 0.7297661648968664, 0.6428524769306785, 0.514394615153752, 1.01, 0.5971503271420517, 0.582018469844712, 0.7445177382760834, 0.4886092840296514, 0.2478398278204047, 0.5971503271420517, 1.01, 0.73161232298669, 0.7766852947049376, 0.645175579158233, 0.673852495852274, 0.582018469844712, 0.73161232298669, 1.01, 0.8107916469252828, 0.811533860074678, 0.7297661648968664, 0.7445177382760834, 0.7766852947049376, 0.8107916469252828, 1.01 ]).reshape(6,6, order='F') def test_corr_psd(): # test positive definite matrix is unchanged x = np.array([[1, -0.2, -0.9], [-0.2, 1, -0.2], [-0.9, -0.2, 1]]) y = corr_nearest(x, n_fact=100) #print np.max(np.abs(x - y)) assert_almost_equal(x, y, decimal=14) y = corr_clipped(x) assert_almost_equal(x, y, decimal=14) y = cov_nearest(x, n_fact=100) assert_almost_equal(x, y, decimal=14) x2 = x + 0.001 * np.eye(3) y = cov_nearest(x2, n_fact=100) assert_almost_equal(x2, y, decimal=14) class CheckCorrPSDMixin(object): def test_nearest(self): x = self.x res_r = self.res y = corr_nearest(x, threshold=1e-7, n_fact=100) #print np.max(np.abs(x - y)) assert_almost_equal(y, res_r.mat, decimal=3) d = norm_f(x, y) assert_allclose(d, res_r.normF, rtol=0.0015) evals = np.linalg.eigvalsh(y) #print 'evals', evals / res_r.eigenvalues[::-1] - 1 assert_allclose(evals, res_r.eigenvalues[::-1], rtol=0.003, atol=1e-7) #print evals[0] / 1e-7 - 1 assert_allclose(evals[0], 1e-7, rtol=1e-6) def test_clipped(self): x = self.x res_r = self.res y = corr_clipped(x, threshold=1e-7) #print np.max(np.abs(x - y)), np.max(np.abs((x - y) / y)) assert_almost_equal(y, res_r.mat, decimal=1) d = norm_f(x, y) assert_allclose(d, res_r.normF, rtol=0.15) evals = np.linalg.eigvalsh(y) assert_allclose(evals, res_r.eigenvalues[::-1], rtol=0.1, atol=1e-7) assert_allclose(evals[0], 1e-7, rtol=0.02) def test_cov_nearest(self): x = self.x res_r = self.res y = cov_nearest(x, method='nearest', threshold=1e-7) #print np.max(np.abs(x - y)) assert_almost_equal(y, res_r.mat, decimal=2) d = norm_f(x, y) assert_allclose(d, res_r.normF, rtol=0.0015) class TestCovPSD(object): @classmethod def setup_class(cls): x = np.array([ 1, 0.477, 0.644, 0.478, 0.651, 0.826, 0.477, 1, 0.516, 0.233, 0.682, 0.75, 0.644, 0.516, 1, 0.599, 0.581, 0.742, 0.478, 0.233, 0.599, 1, 0.741, 0.8, 0.651, 0.682, 0.581, 0.741, 1, 0.798, 0.826, 0.75, 0.742, 0.8, 0.798, 1]).reshape(6,6) cls.x = x + 0.01 * np.eye(6) cls.res = cov_r def test_cov_nearest(self): x = self.x res_r = self.res y = cov_nearest(x, method='nearest') #print np.max(np.abs(x - y)) assert_almost_equal(y, res_r.mat, decimal=3) d = norm_f(x, y) assert_allclose(d, res_r.normF, rtol=0.001) y = cov_nearest(x, method='clipped') #print np.max(np.abs(x - y)) assert_almost_equal(y, res_r.mat, decimal=2) d = norm_f(x, y) assert_allclose(d, res_r.normF, rtol=0.15) class TestCorrPSD1(CheckCorrPSDMixin): @classmethod def setup_class(cls): x = np.array([ 1, 0.477, 0.644, 0.478, 0.651, 0.826, 0.477, 1, 0.516, 0.233, 0.682, 0.75, 0.644, 0.516, 1, 0.599, 0.581, 0.742, 0.478, 0.233, 0.599, 1, 0.741, 0.8, 0.651, 0.682, 0.581, 0.741, 1, 0.798, 0.826, 0.75, 0.742, 0.8, 0.798, 1]).reshape(6,6) cls.x = x cls.res = cov1_r def test_corrpsd_threshold(): x = np.array([[1, -0.9, -0.9], [-0.9, 1, -0.9], [-0.9, -0.9, 1]]) #print np.linalg.eigvalsh(x) for threshold in [0, 1e-15, 1e-10, 1e-6]: y = corr_nearest(x, n_fact=100, threshold=threshold) evals = np.linalg.eigvalsh(y) #print 'evals', evals, threshold assert_allclose(evals[0], threshold, rtol=1e-6, atol=1e-15) y = corr_clipped(x, threshold=threshold) evals = np.linalg.eigvalsh(y) #print 'evals', evals, threshold assert_allclose(evals[0], threshold, rtol=0.25, atol=1e-15) y = cov_nearest(x, method='nearest', n_fact=100, threshold=threshold) evals = np.linalg.eigvalsh(y) #print 'evals', evals, threshold #print evals[0] / threshold - 1 assert_allclose(evals[0], threshold, rtol=1e-6, atol=1e-15) y = cov_nearest(x, n_fact=100, threshold=threshold) evals = np.linalg.eigvalsh(y) #print 'evals', evals, threshold #print evals[0] / threshold - 1 assert_allclose(evals[0], threshold, rtol=0.25, atol=1e-15) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_data.txt000066400000000000000000006437301224417117700255420ustar00rootroot00000000000000 1 1 -1.113973 2.251535 1 2 -.0808538 1.242346 1 3 -.2376072 -1.426376 1 4 -.1524857 -1.109394 1 5 -.0014262 .9146864 1 6 -1.212737 -1.424686 1 7 -.1272733 .7589449 1 8 -1.433539 .9296525 1 9 -.2421959 1.056465 1 10 .4609221 3.308434 2 1 -.5507909 -2.545477 2 2 -1.287685 -3.02192 2 3 -.220503 -1.003296 2 4 .8143178 -.118388 2 5 -.0463721 -1.27967 2 6 .6220436 -3.539696 2 7 -.6530094 -2.235361 2 8 .0294105 -2.552972 2 9 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3.720502 500 7 .2188469 .5591205 500 8 -.15553 -3.766785 500 9 -.0401722 .9033538 500 10 -.0011715 -.5297611 statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_descriptivestats.py000066400000000000000000000006111224417117700300230ustar00rootroot00000000000000from statsmodels.stats.descriptivestats import sign_test from numpy.testing import assert_almost_equal, assert_equal def test_sign_test(): x = [7.8, 6.6, 6.5, 7.4, 7.3, 7., 6.4, 7.1, 6.7, 7.6, 6.8] M, p = sign_test(x, mu0=6.5) # from R SIGN.test(x, md=6.5) # from R assert_almost_equal(p, 0.02148, 5) # not from R, we use a different convention assert_equal(M, 4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_diagnostic.py000066400000000000000000001147401224417117700265600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for Regression Diagnostics and Specification Tests Created on Thu Feb 09 13:19:47 2012 Author: Josef Perktold License: BSD-3 currently all tests are against R """ import os import numpy as np from numpy.testing import (assert_, assert_almost_equal, assert_equal, assert_approx_equal) from nose import SkipTest from statsmodels.regression.linear_model import OLS, GLSAR from statsmodels.tools.tools import add_constant from statsmodels.datasets import macrodata import statsmodels.stats.sandwich_covariance as sw import statsmodels.stats.diagnostic as smsdia #import statsmodels.sandbox.stats.diagnostic as smsdia import statsmodels.stats.outliers_influence as oi cur_dir = os.path.abspath(os.path.dirname(__file__)) def compare_t_est(sp, sp_dict, decimal=(14, 14)): assert_almost_equal(sp[0], sp_dict['statistic'], decimal=decimal[0]) assert_almost_equal(sp[1], sp_dict['pvalue'], decimal=decimal[1]) def notyet_atst(): d = macrodata.load().data realinv = d['realinv'] realgdp = d['realgdp'] realint = d['realint'] endog = realinv exog = add_constant(np.c_[realgdp, realint]) res_ols1 = OLS(endog, exog).fit() #growth rates gs_l_realinv = 400 * np.diff(np.log(d['realinv'])) gs_l_realgdp = 400 * np.diff(np.log(d['realgdp'])) lint = d['realint'][:-1] tbilrate = d['tbilrate'][:-1] endogg = gs_l_realinv exogg = add_constant(np.c_[gs_l_realgdp, lint]) exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate]) res_ols = OLS(endogg, exogg).fit() res_ols2 = OLS(endogg, exogg2).fit() #the following were done accidentally with res_ols1 in R, #with original Greene data params = np.array([-272.3986041341653, 0.1779455206941112, 0.2149432424658157]) cov_hac_4 = np.array([1321.569466333051, -0.2318836566017612, 37.01280466875694, -0.2318836566017614, 4.602339488102263e-05, -0.0104687835998635, 37.012804668757, -0.0104687835998635, 21.16037144168061]).reshape(3,3, order='F') cov_hac_10 = np.array([2027.356101193361, -0.3507514463299015, 54.81079621448568, -0.350751446329901, 6.953380432635583e-05, -0.01268990195095196, 54.81079621448564, -0.01268990195095195, 22.92512402151113]).reshape(3,3, order='F') #goldfeld-quandt het_gq_greater = dict(statistic=13.20512768685082, df1=99, df2=98, pvalue=1.246141976112324e-30, distr='f') het_gq_less = dict(statistic=13.20512768685082, df1=99, df2=98, pvalue=1.) het_gq_2sided = dict(statistic=13.20512768685082, df1=99, df2=98, pvalue=1.246141976112324e-30, distr='f') #goldfeld-quandt, fraction = 0.5 het_gq_greater_2 = dict(statistic=87.1328934692124, df1=48, df2=47, pvalue=2.154956842194898e-33, distr='f') gq = smsdia.het_goldfeldquandt(endog, exog, split=0.5) compare_t_est(gq, het_gq_greater, decimal=(13, 14)) assert_equal(gq[-1], 'increasing') harvey_collier = dict(stat=2.28042114041313, df=199, pvalue=0.02364236161988260, distr='t') #hc = harvtest(fm, order.by=ggdp , data = list()) harvey_collier_2 = dict(stat=0.7516918462158783, df=199, pvalue=0.4531244858006127, distr='t') ################################## class TestDiagnosticG(object): def __init__(self): d = macrodata.load().data #growth rates gs_l_realinv = 400 * np.diff(np.log(d['realinv'])) gs_l_realgdp = 400 * np.diff(np.log(d['realgdp'])) lint = d['realint'][:-1] tbilrate = d['tbilrate'][:-1] endogg = gs_l_realinv exogg = add_constant(np.c_[gs_l_realgdp, lint]) exogg2 = add_constant(np.c_[gs_l_realgdp, tbilrate]) exogg3 = add_constant(np.c_[gs_l_realgdp]) res_ols = OLS(endogg, exogg).fit() res_ols2 = OLS(endogg, exogg2).fit() res_ols3 = OLS(endogg, exogg3).fit() self.res = res_ols self.res2 = res_ols2 self.res3 = res_ols3 self.endog = self.res.model.endog self.exog = self.res.model.exog def test_basic(self): #mainly to check I got the right regression #> mkarray(fm$coefficients, "params") params = np.array([-9.48167277465485, 4.3742216647032, -0.613996969478989]) assert_almost_equal(self.res.params, params, decimal=12) def test_hac(self): res = self.res #> nw = NeweyWest(fm, lag = 4, prewhite = FALSE, verbose=TRUE) #> nw2 = NeweyWest(fm, lag=10, prewhite = FALSE, verbose=TRUE) #> mkarray(nw, "cov_hac_4") cov_hac_4 = np.array([1.385551290884014, -0.3133096102522685, -0.0597207976835705, -0.3133096102522685, 0.1081011690351306, 0.000389440793564336, -0.0597207976835705, 0.000389440793564339, 0.0862118527405036]).reshape(3,3, order='F') #> mkarray(nw2, "cov_hac_10") cov_hac_10 = np.array([1.257386180080192, -0.2871560199899846, -0.03958300024627573, -0.2871560199899845, 0.1049107028987101, 0.0003896205316866944, -0.03958300024627578, 0.0003896205316866961, 0.0985539340694839]).reshape(3,3, order='F') cov = sw.cov_hac_simple(res, nlags=4, use_correction=False) bse_hac = sw.se_cov(cov) assert_almost_equal(cov, cov_hac_4, decimal=14) assert_almost_equal(bse_hac, np.sqrt(np.diag(cov)), decimal=14) cov = sw.cov_hac_simple(res, nlags=10, use_correction=False) bse_hac = sw.se_cov(cov) assert_almost_equal(cov, cov_hac_10, decimal=14) assert_almost_equal(bse_hac, np.sqrt(np.diag(cov)), decimal=14) def test_het_goldfeldquandt(self): #TODO: test options missing #> gq = gqtest(fm, alternative='greater') #> mkhtest_f(gq, 'het_gq_greater', 'f') het_gq_greater = dict(statistic=0.5313259064778423, pvalue=0.9990217851193723, parameters=(98, 98), distr='f') #> gq = gqtest(fm, alternative='less') #> mkhtest_f(gq, 'het_gq_less', 'f') het_gq_less = dict(statistic=0.5313259064778423, pvalue=0.000978214880627621, parameters=(98, 98), distr='f') #> gq = gqtest(fm, alternative='two.sided') #> mkhtest_f(gq, 'het_gq_two_sided', 'f') het_gq_two_sided = dict(statistic=0.5313259064778423, pvalue=0.001956429761255241, parameters=(98, 98), distr='f') #> gq = gqtest(fm, fraction=0.1, alternative='two.sided') #> mkhtest_f(gq, 'het_gq_two_sided_01', 'f') het_gq_two_sided_01 = dict(statistic=0.5006976835928314, pvalue=0.001387126702579789, parameters=(88, 87), distr='f') #> gq = gqtest(fm, fraction=0.5, alternative='two.sided') #> mkhtest_f(gq, 'het_gq_two_sided_05', 'f') het_gq_two_sided_05 = dict(statistic=0.434815645134117, pvalue=0.004799321242905568, parameters=(48, 47), distr='f') endogg, exogg = self.endog, self.exog #tests gq = smsdia.het_goldfeldquandt(endogg, exogg, split=0.5) compare_t_est(gq, het_gq_greater, decimal=(14, 14)) assert_equal(gq[-1], 'increasing') gq = smsdia.het_goldfeldquandt(endogg, exogg, split=0.5, alternative='decreasing') compare_t_est(gq, het_gq_less, decimal=(14, 14)) assert_equal(gq[-1], 'decreasing') gq = smsdia.het_goldfeldquandt(endogg, exogg, split=0.5, alternative='two-sided') compare_t_est(gq, het_gq_two_sided, decimal=(14, 14)) assert_equal(gq[-1], 'two-sided') #TODO: forcing the same split as R 202-90-90-1=21 gq = smsdia.het_goldfeldquandt(endogg, exogg, split=90, drop=21, alternative='two-sided') compare_t_est(gq, het_gq_two_sided_01, decimal=(14, 14)) assert_equal(gq[-1], 'two-sided') #TODO other options ??? def test_het_breush_pagan(self): res = self.res bptest = dict(statistic=0.709924388395087, pvalue=0.701199952134347, parameters=(2,), distr='f') bp = smsdia.het_breushpagan(res.resid, res.model.exog) compare_t_est(bp, bptest, decimal=(12, 12)) def test_het_white(self): res = self.res #TODO: regressiontest, compare with Greene or Gretl or Stata hw = smsdia.het_white(res.resid, res.model.exog) hw_values = (33.503722896538441, 2.9887960597830259e-06, 7.7945101228430946, 1.0354575277704231e-06) assert_almost_equal(hw, hw_values) def test_het_arch(self): #test het_arch and indirectly het_lm against R #> library(FinTS) #> at = ArchTest(residuals(fm), lags=4) #> mkhtest(at, 'archtest_4', 'chi2') archtest_4 = dict(statistic=3.43473400836259, pvalue=0.487871315392619, parameters=(4,), distr='chi2') #> at = ArchTest(residuals(fm), lags=12) #> mkhtest(at, 'archtest_12', 'chi2') archtest_12 = dict(statistic=8.648320999014171, pvalue=0.732638635007718, parameters=(12,), distr='chi2') at4 = smsdia.het_arch(self.res.resid, maxlag=4) at12 = smsdia.het_arch(self.res.resid, maxlag=12) compare_t_est(at4[:2], archtest_4, decimal=(12, 13)) compare_t_est(at12[:2], archtest_12, decimal=(12, 13)) def test_het_arch2(self): #test autolag options, this also test het_lm #unfortunately optimal lag=1 for this data resid = self.res.resid res1 = smsdia.het_arch(resid, maxlag=1, autolag=None, store=True) rs1 = res1[-1] res2 = smsdia.het_arch(resid, maxlag=5, autolag='aic', store=True) rs2 = res2[-1] assert_almost_equal(rs2.resols.params, rs1.resols.params, decimal=13) assert_almost_equal(res2[:4], res1[:4], decimal=13) #test that smallest lag, maxlag=1 works res3 = smsdia.het_arch(resid, maxlag=1, autolag='aic') assert_almost_equal(res3[:4], res1[:4], decimal=13) def test_acorr_breush_godfrey(self): res = self.res #bgf = bgtest(fm, order = 4, type="F") breushgodfrey_f = dict(statistic=1.179280833676792, pvalue=0.321197487261203, parameters=(4,195,), distr='f') #> bgc = bgtest(fm, order = 4, type="Chisq") #> mkhtest(bgc, "breushpagan_c", "chi2") breushgodfrey_c = dict(statistic=4.771042651230007, pvalue=0.3116067133066697, parameters=(4,), distr='chi2') bg = smsdia.acorr_breush_godfrey(res, nlags=4) bg_r = [breushgodfrey_c['statistic'], breushgodfrey_c['pvalue'], breushgodfrey_f['statistic'], breushgodfrey_f['pvalue']] assert_almost_equal(bg, bg_r, decimal=13) # check that lag choice works bg2 = smsdia.acorr_breush_godfrey(res, nlags=None) bg3 = smsdia.acorr_breush_godfrey(res, nlags=14) assert_almost_equal(bg2, bg3, decimal=13) def test_acorr_ljung_box(self): res = self.res #> bt = Box.test(residuals(fm), lag=4, type = "Ljung-Box") #> mkhtest(bt, "ljung_box_4", "chi2") ljung_box_4 = dict(statistic=5.23587172795227, pvalue=0.263940335284713, parameters=(4,), distr='chi2') #> bt = Box.test(residuals(fm), lag=4, type = "Box-Pierce") #> mkhtest(bt, "ljung_box_bp_4", "chi2") ljung_box_bp_4 = dict(statistic=5.12462932741681, pvalue=0.2747471266820692, parameters=(4,), distr='chi2') #ddof correction for fitted parameters in ARMA(p,q) fitdf=p+q #> bt = Box.test(residuals(fm), lag=4, type = "Ljung-Box", fitdf=2) #> mkhtest(bt, "ljung_box_4df2", "chi2") ljung_box_4df2 = dict(statistic=5.23587172795227, pvalue=0.0729532930400377, parameters=(2,), distr='chi2') #> bt = Box.test(residuals(fm), lag=4, type = "Box-Pierce", fitdf=2) #> mkhtest(bt, "ljung_box_bp_4df2", "chi2") ljung_box_bp_4df2 = dict(statistic=5.12462932741681, pvalue=0.0771260128929921, parameters=(2,), distr='chi2') lb, lbpval, bp, bppval = smsdia.acorr_ljungbox(res.resid, 4, boxpierce=True) compare_t_est([lb[-1], lbpval[-1]], ljung_box_4, decimal=(13, 14)) compare_t_est([bp[-1], bppval[-1]], ljung_box_bp_4, decimal=(13, 14)) def test_harvey_collier(self): #> hc = harvtest(fm, order.by = NULL, data = list()) #> mkhtest_f(hc, 'harvey_collier', 't') harvey_collier = dict(statistic=0.494432160939874, pvalue=0.6215491310408242, parameters=(198), distr='t') #> hc2 = harvtest(fm, order.by=ggdp , data = list()) #> mkhtest_f(hc2, 'harvey_collier_2', 't') harvey_collier_2 = dict(statistic=1.42104628340473, pvalue=0.1568762892441689, parameters=(198), distr='t') hc = smsdia.linear_harvey_collier(self.res) compare_t_est(hc, harvey_collier, decimal=(12, 12)) def test_rainbow(self): #rainbow test #> rt = raintest(fm) #> mkhtest_f(rt, 'raintest', 'f') raintest = dict(statistic=0.6809600116739604, pvalue=0.971832843583418, parameters=(101, 98), distr='f') #> rt = raintest(fm, center=0.4) #> mkhtest_f(rt, 'raintest_center_04', 'f') raintest_center_04 = dict(statistic=0.682635074191527, pvalue=0.971040230422121, parameters=(101, 98), distr='f') #> rt = raintest(fm, fraction=0.4) #> mkhtest_f(rt, 'raintest_fraction_04', 'f') raintest_fraction_04 = dict(statistic=0.565551237772662, pvalue=0.997592305968473, parameters=(122, 77), distr='f') #> rt = raintest(fm, order.by=ggdp) #Warning message: #In if (order.by == "mahalanobis") { : # the condition has length > 1 and only the first element will be used #> mkhtest_f(rt, 'raintest_order_gdp', 'f') raintest_order_gdp = dict(statistic=1.749346160513353, pvalue=0.002896131042494884, parameters=(101, 98), distr='f') rb = smsdia.linear_rainbow(self.res) compare_t_est(rb, raintest, decimal=(13, 14)) rb = smsdia.linear_rainbow(self.res, frac=0.4) compare_t_est(rb, raintest_fraction_04, decimal=(13, 14)) def test_compare_lr(self): res = self.res res3 = self.res3 #nested within res #lrtest #lrt = lrtest(fm, fm2) #Model 1: ginv ~ ggdp + lint #Model 2: ginv ~ ggdp lrtest = dict(loglike1=-763.9752181602237, loglike2=-766.3091902020184, chi2value=4.66794408358942, pvalue=0.03073069384028677, df=(4,3,1)) lrt = res.compare_lr_test(res3) assert_almost_equal(lrt[0], lrtest['chi2value'], decimal=11) assert_almost_equal(lrt[1], lrtest['pvalue'], decimal=11) waldtest = dict(fvalue=4.65216373312492, pvalue=0.03221346195239025, df=(199,200,1)) wt = res.compare_f_test(res3) assert_almost_equal(wt[0], waldtest['fvalue'], decimal=11) assert_almost_equal(wt[1], waldtest['pvalue'], decimal=11) def test_compare_nonnested(self): res = self.res res2 = self.res2 #jt = jtest(fm, lm(ginv ~ ggdp + tbilrate)) #Estimate Std. Error t value Pr(>|t|) jtest = [('M1 + fitted(M2)', 1.591505670785873, 0.7384552861695823, 2.155182176352370, 0.032354572525314450, '*'), ('M2 + fitted(M1)', 1.305687653016899, 0.4808385176653064, 2.715438978051544, 0.007203854534057954, '**')] jt1 = smsdia.compare_j(res2, res) assert_almost_equal(jt1, jtest[0][3:5], decimal=13) jt2 = smsdia.compare_j(res, res2) assert_almost_equal(jt2, jtest[1][3:5], decimal=14) #Estimate Std. Error z value Pr(>|z|) coxtest = [('fitted(M1) ~ M2', -0.782030488930356, 0.599696502782265, -1.304043770977755, 1.922186587840554e-01, ' '), ('fitted(M2) ~ M1', -2.248817107408537, 0.392656854330139, -5.727181590258883, 1.021128495098556e-08, '***')] ct1 = smsdia.compare_cox(res, res2) assert_almost_equal(ct1, coxtest[0][3:5], decimal=13) ct2 = smsdia.compare_cox(res2, res) assert_almost_equal(ct2, coxtest[1][3:5], decimal=12) #TODO should be approx # Res.Df Df F Pr(>F) encomptest = [('M1 vs. ME', 198, -1, 4.644810213266983, 0.032354572525313666, '*'), ('M2 vs. ME', 198, -1, 7.373608843521585, 0.007203854534058054, '**')] # Estimate Std. Error t value petest = [('M1 + log(fit(M1))-fit(M2)', -229.281878354594596, 44.5087822087058598, -5.15139, 6.201281252449979e-07), ('M2 + fit(M1)-exp(fit(M2))', 0.000634664704814, 0.0000462387010349, 13.72583, 1.319536115230356e-30)] def test_cusum_ols(self): #R library(strucchange) #> sc = sctest(ginv ~ ggdp + lint, type="OLS-CUSUM") #> mkhtest(sc, 'cusum_ols', 'BB') cusum_ols = dict(statistic=1.055750610401214, pvalue=0.2149567397376543, parameters=(), distr='BB') #Brownian Bridge k_vars=3 cs_ols = smsdia.breaks_cusumolsresid(self.res.resid, ddof=k_vars) # compare_t_est(cs_ols, cusum_ols, decimal=(12, 12)) def test_breaks_hansen(self): #> sc = sctest(ginv ~ ggdp + lint, type="Nyblom-Hansen") #> mkhtest(sc, 'breaks_nyblom_hansen', 'BB') breaks_nyblom_hansen = dict(statistic=1.0300792740544484, pvalue=0.1136087530212015, parameters=(), distr='BB') bh = smsdia.breaks_hansen(self.res) assert_almost_equal(bh[0], breaks_nyblom_hansen['statistic'], decimal=13) #TODO: breaks_hansen doesn't return pvalues def test_recursive_residuals(self): reccumres_standardize = np.array([-2.151, -3.748, -3.114, -3.096, -1.865, -2.230, -1.194, -3.500, -3.638, -4.447, -4.602, -4.631, -3.999, -4.830, -5.429, -5.435, -6.554, -8.093, -8.567, -7.532, -7.079, -8.468, -9.320, -12.256, -11.932, -11.454, -11.690, -11.318, -12.665, -12.842, -11.693, -10.803, -12.113, -12.109, -13.002, -11.897, -10.787, -10.159, -9.038, -9.007, -8.634, -7.552, -7.153, -6.447, -5.183, -3.794, -3.511, -3.979, -3.236, -3.793, -3.699, -5.056, -5.724, -4.888, -4.309, -3.688, -3.918, -3.735, -3.452, -2.086, -6.520, -7.959, -6.760, -6.855, -6.032, -4.405, -4.123, -4.075, -3.235, -3.115, -3.131, -2.986, -1.813, -4.824, -4.424, -4.796, -4.000, -3.390, -4.485, -4.669, -4.560, -3.834, -5.507, -3.792, -2.427, -1.756, -0.354, 1.150, 0.586, 0.643, 1.773, -0.830, -0.388, 0.517, 0.819, 2.240, 3.791, 3.187, 3.409, 2.431, 0.668, 0.957, -0.928, 0.327, -0.285, -0.625, -2.316, -1.986, -0.744, -1.396, -1.728, -0.646, -2.602, -2.741, -2.289, -2.897, -1.934, -2.532, -3.175, -2.806, -3.099, -2.658, -2.487, -2.515, -2.224, -2.416, -1.141, 0.650, -0.947, 0.725, 0.439, 0.885, 2.419, 2.642, 2.745, 3.506, 4.491, 5.377, 4.624, 5.523, 6.488, 6.097, 5.390, 6.299, 6.656, 6.735, 8.151, 7.260, 7.846, 8.771, 8.400, 8.717, 9.916, 9.008, 8.910, 8.294, 8.982, 8.540, 8.395, 7.782, 7.794, 8.142, 8.362, 8.400, 7.850, 7.643, 8.228, 6.408, 7.218, 7.699, 7.895, 8.725, 8.938, 8.781, 8.350, 9.136, 9.056, 10.365, 10.495, 10.704, 10.784, 10.275, 10.389, 11.586, 11.033, 11.335, 11.661, 10.522, 10.392, 10.521, 10.126, 9.428, 9.734, 8.954, 9.949, 10.595, 8.016, 6.636, 6.975]) rr = smsdia.recursive_olsresiduals(self.res, skip=3, alpha=0.95) assert_equal(np.round(rr[5][1:], 3), reccumres_standardize) #extra zero in front #assert_equal(np.round(rr[3][4:], 3), np.diff(reccumres_standardize)) assert_almost_equal(rr[3][4:], np.diff(reccumres_standardize),3) assert_almost_equal(rr[4][3:].std(ddof=1), 10.7242, decimal=4) #regression number, visually checked with graph from gretl ub0 = np.array([ 13.37318571, 13.50758959, 13.64199346, 13.77639734, 13.91080121]) ub1 = np.array([ 39.44753774, 39.58194162, 39.7163455 , 39.85074937, 39.98515325]) lb, ub = rr[6] assert_almost_equal(ub[:5], ub0, decimal=7) assert_almost_equal(lb[:5], -ub0, decimal=7) assert_almost_equal(ub[-5:], ub1, decimal=7) assert_almost_equal(lb[-5:], -ub1, decimal=7) #test a few values with explicit OLS endog = self.res.model.endog exog = self.res.model.exog params = [] ypred = [] for i in range(3,10): resi = OLS(endog[:i], exog[:i]).fit() ypred.append(resi.model.predict(resi.params, exog[i])) params.append(resi.params) assert_almost_equal(rr[2][3:10], ypred, decimal=12) assert_almost_equal(rr[0][3:10], endog[3:10] - ypred, decimal=12) assert_almost_equal(rr[1][2:9], params, decimal=12) def test_normality(self): res = self.res #> library(nortest) #Lilliefors (Kolmogorov-Smirnov) normality test #> lt = lillie.test(residuals(fm)) #> mkhtest(lt, "lillifors", "-") lillifors1 = dict(statistic=0.0723390908786589, pvalue=0.01204113540102896, parameters=(), distr='-') #> lt = lillie.test(residuals(fm)**2) #> mkhtest(lt, "lillifors", "-") lillifors2 = dict(statistic=0.301311621898024, pvalue=1.004305736618051e-51, parameters=(), distr='-') #> lt = lillie.test(residuals(fm)[1:20]) #> mkhtest(lt, "lillifors", "-") lillifors3 = dict(statistic=0.1333956004203103, pvalue=0.4618672180799566, parameters=(), distr='-') lf1 = smsdia.lillifors(res.resid) lf2 = smsdia.lillifors(res.resid**2) lf3 = smsdia.lillifors(res.resid[:20]) compare_t_est(lf1, lillifors1, decimal=(14, 14)) compare_t_est(lf2, lillifors2, decimal=(14, 14)) #pvalue very small assert_approx_equal(lf2[1], lillifors2['pvalue'], significant=10) compare_t_est(lf3, lillifors3, decimal=(14, 1)) #R uses different approximation for pvalue in last case #> ad = ad.test(residuals(fm)) #> mkhtest(ad, "ad3", "-") adr1 = dict(statistic=1.602209621518313, pvalue=0.0003937979149362316, parameters=(), distr='-') #> ad = ad.test(residuals(fm)**2) #> mkhtest(ad, "ad3", "-") adr2 = dict(statistic=np.inf, pvalue=np.nan, parameters=(), distr='-') #> ad = ad.test(residuals(fm)[1:20]) #> mkhtest(ad, "ad3", "-") adr3 = dict(statistic=0.3017073732210775, pvalue=0.5443499281265933, parameters=(), distr='-') ad1 = smsdia.normal_ad(res.resid) compare_t_est(ad1, adr1, decimal=(11, 13)) ad2 = smsdia.normal_ad(res.resid**2) assert_(np.isinf(ad2[0])) ad3 = smsdia.normal_ad(res.resid[:20]) compare_t_est(ad3, adr3, decimal=(11, 12)) def test_influence(self): res = self.res #this test is slow infl = oi.OLSInfluence(res) try: import json except ImportError: raise SkipTest fp = open(os.path.join(cur_dir,"results/influence_lsdiag_R.json")) lsdiag = json.load(fp) #basic assert_almost_equal(np.array(lsdiag['cov.scaled']).reshape(3, 3), res.cov_params(), decimal=14) assert_almost_equal(np.array(lsdiag['cov.unscaled']).reshape(3, 3), res.normalized_cov_params, decimal=14) c0, c1 = infl.cooks_distance #TODO: what's c1 assert_almost_equal(c0, lsdiag['cooks'], decimal=14) assert_almost_equal(infl.hat_matrix_diag, lsdiag['hat'], decimal=14) assert_almost_equal(infl.resid_studentized_internal, lsdiag['std.res'], decimal=14) #slow: #infl._get_all_obs() #slow, nobs estimation loop, called implicitly dffits, dffth = infl.dffits assert_almost_equal(dffits, lsdiag['dfits'], decimal=14) assert_almost_equal(infl.resid_studentized_external, lsdiag['stud.res'], decimal=14) import pandas fn = os.path.join(cur_dir,"results/influence_measures_R.csv") infl_r = pandas.read_csv(fn, index_col=0) conv = lambda s: 1 if s=='TRUE' else 0 fn = os.path.join(cur_dir,"results/influence_measures_bool_R.csv") #not used yet: #infl_bool_r = pandas.read_csv(fn, index_col=0, # converters=dict(zip(range(7),[conv]*7))) infl_r2 = np.asarray(infl_r) assert_almost_equal(infl.dfbetas, infl_r2[:,:3], decimal=13) assert_almost_equal(infl.cov_ratio, infl_r2[:,4], decimal=14) #duplicates assert_almost_equal(dffits, infl_r2[:,3], decimal=14) assert_almost_equal(c0, infl_r2[:,5], decimal=14) assert_almost_equal(infl.hat_matrix_diag, infl_r2[:,6], decimal=14) #Note: for dffits, R uses a threshold around 0.36, mine: dffits[1]=0.24373 #TODO: finish and check thresholds and pvalues ''' R has >>> np.nonzero(np.asarray(infl_bool_r["dffit"]))[0] array([ 6, 26, 63, 76, 90, 199]) >>> np.nonzero(np.asarray(infl_bool_r["cov.r"]))[0] array([ 4, 26, 59, 61, 63, 72, 76, 84, 91, 92, 94, 95, 108, 197, 198]) >>> np.nonzero(np.asarray(infl_bool_r["hat"]))[0] array([ 62, 76, 84, 90, 91, 92, 95, 108, 197, 199]) ''' class TestDiagnosticGPandas(TestDiagnosticG): def __init__(self): d = macrodata.load_pandas().data #growth rates d['gs_l_realinv'] = 400 * np.log(d['realinv']).diff() d['gs_l_realgdp'] = 400 * np.log(d['realgdp']).diff() d['lint'] = d['realint'].shift(1) d['tbilrate'] = d['tbilrate'].shift(1) d = d.dropna() self.d = d endogg = d['gs_l_realinv'] exogg = add_constant(d[['gs_l_realgdp', 'lint']]) exogg2 = add_constant(d[['gs_l_realgdp', 'tbilrate']]) exogg3 = add_constant(d[['gs_l_realgdp']]) res_ols = OLS(endogg, exogg).fit() res_ols2 = OLS(endogg, exogg2).fit() res_ols3 = OLS(endogg, exogg3).fit() self.res = res_ols self.res2 = res_ols2 self.res3 = res_ols3 self.endog = self.res.model.endog self.exog = self.res.model.exog def grangertest(): #> gt = grangertest(ginv, ggdp, order=4) #> gt #Granger causality test # #Model 1: ggdp ~ Lags(ggdp, 1:4) + Lags(ginv, 1:4) #Model 2: ggdp ~ Lags(ggdp, 1:4) grangertest = dict(fvalue=1.589672703015157, pvalue=0.178717196987075, df=(198,193)) def test_outlier_influence_funcs(): #smoke test x = add_constant(np.random.randn(10, 2)) y = x.sum(1) + np.random.randn(10) res = OLS(y, x).fit() oi.summary_table(res, alpha=0.05) res2 = OLS(y, x[:,0]).fit() oi.summary_table(res2, alpha=0.05) infl = res2.get_influence() infl.summary_table() def test_influence_wrapped(): from pandas import DataFrame from pandas.util.testing import assert_series_equal d = macrodata.load_pandas().data #growth rates gs_l_realinv = 400 * np.log(d['realinv']).diff().dropna() gs_l_realgdp = 400 * np.log(d['realgdp']).diff().dropna() lint = d['realint'][:-1] # re-index these because they won't conform to lint gs_l_realgdp.index = lint.index gs_l_realinv.index = lint.index data = dict(const=np.ones_like(lint), lint=lint, lrealgdp=gs_l_realgdp) #order is important exog = DataFrame(data, columns=['const','lrealgdp','lint']) res = OLS(gs_l_realinv, exog).fit() #basic # already tested #assert_almost_equal(lsdiag['cov.scaled'], # res.cov_params().values.ravel(), decimal=14) #assert_almost_equal(lsdiag['cov.unscaled'], # res.normalized_cov_params.values.ravel(), decimal=14) infl = oi.OLSInfluence(res) # smoke test just to make sure it works, results separately tested df = infl.summary_frame() assert_(isinstance(df, DataFrame)) #this test is slow try: import json except ImportError: raise SkipTest fp = open(os.path.join(cur_dir,"results/influence_lsdiag_R.json")) lsdiag = json.load(fp) c0, c1 = infl.cooks_distance #TODO: what's c1, it's pvalues? -ss #NOTE: we get a hard-cored 5 decimals with pandas testing assert_almost_equal(c0, lsdiag['cooks'], 14) assert_almost_equal(infl.hat_matrix_diag, (lsdiag['hat']), 14) assert_almost_equal(infl.resid_studentized_internal, lsdiag['std.res'], 14) #slow: dffits, dffth = infl.dffits assert_almost_equal(dffits, lsdiag['dfits'], 14) assert_almost_equal(infl.resid_studentized_external, lsdiag['stud.res'], 14) import pandas fn = os.path.join(cur_dir,"results/influence_measures_R.csv") infl_r = pandas.read_csv(fn, index_col=0) conv = lambda s: 1 if s=='TRUE' else 0 fn = os.path.join(cur_dir,"results/influence_measures_bool_R.csv") #not used yet: #infl_bool_r = pandas.read_csv(fn, index_col=0, # converters=dict(zip(range(7),[conv]*7))) infl_r2 = np.asarray(infl_r) #TODO: finish wrapping this stuff assert_almost_equal(infl.dfbetas, infl_r2[:,:3], decimal=13) assert_almost_equal(infl.cov_ratio, infl_r2[:,4], decimal=14) def test_outlier_test(): # results from R with NA -> 1. Just testing interface here because # outlier_test is just a wrapper labels = ['accountant', 'pilot', 'architect', 'author', 'chemist', 'minister', 'professor', 'dentist', 'reporter', 'engineer', 'undertaker', 'lawyer', 'physician', 'welfare.worker', 'teacher', 'conductor', 'contractor', 'factory.owner', 'store.manager', 'banker', 'bookkeeper', 'mail.carrier', 'insurance.agent', 'store.clerk', 'carpenter', 'electrician', 'RR.engineer', 'machinist', 'auto.repairman', 'plumber', 'gas.stn.attendant', 'coal.miner', 'streetcar.motorman', 'taxi.driver', 'truck.driver', 'machine.operator', 'barber', 'bartender', 'shoe.shiner', 'cook', 'soda.clerk', 'watchman', 'janitor', 'policeman', 'waiter'] #Duncan's prestige data from car exog = [[1.0, 62.0, 86.0], [1.0, 72.0, 76.0], [1.0, 75.0, 92.0], [1.0, 55.0, 90.0], [1.0, 64.0, 86.0], [1.0, 21.0, 84.0], [1.0, 64.0, 93.0], [1.0, 80.0, 100.0], [1.0, 67.0, 87.0], [1.0, 72.0, 86.0], [1.0, 42.0, 74.0], [1.0, 76.0, 98.0], [1.0, 76.0, 97.0], [1.0, 41.0, 84.0], [1.0, 48.0, 91.0], [1.0, 76.0, 34.0], [1.0, 53.0, 45.0], [1.0, 60.0, 56.0], [1.0, 42.0, 44.0], [1.0, 78.0, 82.0], [1.0, 29.0, 72.0], [1.0, 48.0, 55.0], [1.0, 55.0, 71.0], [1.0, 29.0, 50.0], [1.0, 21.0, 23.0], [1.0, 47.0, 39.0], [1.0, 81.0, 28.0], [1.0, 36.0, 32.0], [1.0, 22.0, 22.0], [1.0, 44.0, 25.0], [1.0, 15.0, 29.0], [1.0, 7.0, 7.0], [1.0, 42.0, 26.0], [1.0, 9.0, 19.0], [1.0, 21.0, 15.0], [1.0, 21.0, 20.0], [1.0, 16.0, 26.0], [1.0, 16.0, 28.0], [1.0, 9.0, 17.0], [1.0, 14.0, 22.0], [1.0, 12.0, 30.0], [1.0, 17.0, 25.0], [1.0, 7.0, 20.0], [1.0, 34.0, 47.0], [1.0, 8.0, 32.0]] endog = [ 82., 83., 90., 76., 90., 87., 93., 90., 52., 88., 57., 89., 97., 59., 73., 38., 76., 81., 45., 92., 39., 34., 41., 16., 33., 53., 67., 57., 26., 29., 10., 15., 19., 10., 13., 24., 20., 7., 3., 16., 6., 11., 8., 41., 10.] ndarray_mod = OLS(endog, exog).fit() rstudent = [3.1345185839, -2.3970223990, 2.0438046359, -1.9309187757, 1.8870465798, -1.7604905300, -1.7040324156, 1.6024285876, -1.4332485037, -1.1044851583, 1.0688582315, 1.0185271840, -0.9024219332, -0.9023876471, -0.8830953936, 0.8265782334, 0.8089220547, 0.7682770197, 0.7319491074, -0.6665962829, 0.5227352794, -0.5135016547, 0.5083881518, 0.4999224372, -0.4980818221, -0.4759717075, -0.4293565820, -0.4114056499, -0.3779540862, 0.3556874030, 0.3409200462, 0.3062248646, 0.3038999429, -0.3030815773, -0.1873387893, 0.1738050251, 0.1424246593, -0.1292266025, 0.1272066463, -0.0798902878, 0.0788467222, 0.0722556991, 0.0505098280, 0.0233215136, 0.0007112055] unadj_p = [0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120, 0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623, 0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561, 0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039, 0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703, 0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263, 0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039, 0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197, 0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989] bonf_p = [0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354, 4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133, 16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245, 20.1026416, 21.0763395, 22.8943818, 27.1787396, 27.4660532, 27.6257642, 27.8911043, 27.9489466, 28.6479487, 30.1460253, 30.7313018, 31.8336506, 32.5754218, 33.0707100, 34.2442399, 34.3233506, 34.3512109, 38.3543568, 38.8293308, 39.9348989, 40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391, 43.1982627, 44.1678127, 44.9746195] bonf_p = np.array(bonf_p) bonf_p[bonf_p > 1] = 1 sorted_labels = ["minister", "reporter", "contractor", "insurance.agent", "machinist", "store.clerk", "conductor", "factory.owner", "mail.carrier", "streetcar.motorman", "carpenter", "coal.miner", "bartender", "bookkeeper", "soda.clerk", "chemist", "RR.engineer", "professor", "electrician", "gas.stn.attendant", "auto.repairman", "watchman", "banker", "machine.operator", "dentist", "waiter", "shoe.shiner", "welfare.worker", "plumber", "physician", "pilot", "engineer", "accountant", "lawyer", "undertaker", "barber", "store.manager", "truck.driver", "cook", "janitor", "policeman", "architect", "teacher", "taxi.driver", "author"] res2 = np.c_[rstudent, unadj_p, bonf_p] res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True) np.testing.assert_almost_equal(res.values, res2, 7) np.testing.assert_equal(res.index.tolist(), sorted_labels) # pylint: disable-msg=E1103 if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x'], exit=False) #t = TestDiagnosticG() #t.test_basic() #t.test_hac() #t.test_acorr_breush_godfrey() #t.test_acorr_ljung_box() #t.test_het_goldfeldquandt() #t.test_het_breush_pagan() #t.test_het_white() #t.test_compare_lr() #t.test_compare_nonnested() #t.test_influence() ################################################## ''' J test Model 1: ginv ~ ggdp + lint Model 2: ginv ~ ggdp + tbilrate Estimate Std. Error t value Pr(>|t|) M1 + fitted(M2) 1.591505670785873 0.7384552861695823 2.15518 0.0323546 * M2 + fitted(M1) 1.305687653016899 0.4808385176653064 2.71544 0.0072039 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 = lm(ginv ~ ggdp + tbilrate) > ct = coxtest(fm, fm3) > ct Cox test Model 1: ginv ~ ggdp + lint Model 2: ginv ~ ggdp + tbilrate Estimate Std. Error z value Pr(>|z|) fitted(M1) ~ M2 -0.782030488930356 0.599696502782265 -1.30404 0.19222 fitted(M2) ~ M1 -2.248817107408537 0.392656854330139 -5.72718 1.0211e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > et = encomptest(fm, fm3) > et Encompassing test Model 1: ginv ~ ggdp + lint Model 2: ginv ~ ggdp + tbilrate Model E: ginv ~ ggdp + lint + tbilrate Res.Df Df F Pr(>F) M1 vs. ME 198 -1 4.64481 0.0323546 * M2 vs. ME 198 -1 7.37361 0.0072039 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > fm4 = lm(realinv ~ realgdp + realint, data=d) > fm5 = lm(log(realinv) ~ realgdp + realint, data=d) > pet = petest(fm4, fm5) > pet PE test Model 1: realinv ~ realgdp + realint Model 2: log(realinv) ~ realgdp + realint Estimate Std. Error t value M1 + log(fit(M1))-fit(M2) -229.281878354594596 44.5087822087058598 -5.15139 M2 + fit(M1)-exp(fit(M2)) 0.000634664704814 0.0000462387010349 13.72583 Pr(>|t|) M1 + log(fit(M1))-fit(M2) 6.2013e-07 *** M2 + fit(M1)-exp(fit(M2)) < 2.22e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ''' statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_gof.py000066400000000000000000000065241224417117700252070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Thu Feb 28 13:24:59 2013 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.stats.gof import (chisquare, chisquare_power, chisquare_effectsize) class Holder(object): pass def test_chisquare_power(): from results.results_power import pwr_chisquare for case in pwr_chisquare.values(): power = chisquare_power(case.w, case.N, case.df + 1, alpha=case.sig_level) assert_almost_equal(power, case.power, decimal=6, err_msg=repr(vars(case))) def test_chisquare(): # TODO: no tests for ``value`` yet res1 = Holder() res2 = Holder() #> freq = c(1048, 660, 510, 420, 362) #> pr1 = c(1020, 690, 510, 420, 360) #> pr2 = c(1050, 660, 510, 420, 360) #> c = chisq.test(freq, p=pr1, rescale.p = TRUE) #> cat_items(c, "res1.") res1.statistic = 2.084086388178453 res1.parameter = 4 res1.p_value = 0.72029651761105 res1.method = 'Chi-squared test for given probabilities' res1.data_name = 'freq' res1.observed = np.array([ 1048, 660, 510, 420, 362 ]) res1.expected = np.array([ 1020, 690, 510, 420, 360 ]) res1.residuals = np.array([ 0.876714007519206, -1.142080481440321, -2.517068894406109e-15, -2.773674830645328e-15, 0.105409255338946 ]) #> c = chisq.test(freq, p=pr2, rescale.p = TRUE) #> cat_items(c, "res2.") res2.statistic = 0.01492063492063492 res2.parameter = 4 res2.p_value = 0.999972309849908 res2.method = 'Chi-squared test for given probabilities' res2.data_name = 'freq' res2.observed = np.array([ 1048, 660, 510, 420, 362 ]) res2.expected = np.array([ 1050, 660, 510, 420, 360 ]) res2.residuals = np.array([ -0.06172133998483677, 0, -2.517068894406109e-15, -2.773674830645328e-15, 0.105409255338946 ]) freq = np.array([1048, 660, 510, 420, 362]) pr1 = np.array([1020, 690, 510, 420, 360]) pr2 = np.array([1050, 660, 510, 420, 360]) for pr, res in zip([pr1, pr2], [res1, res2]): stat, pval = chisquare(freq, pr) assert_almost_equal(stat, res.statistic, decimal=12) assert_almost_equal(pval, res.p_value, decimal=13) def test_chisquare_effectsize(): pr1 = np.array([1020, 690, 510, 420, 360]) pr2 = np.array([1050, 660, 510, 420, 360]) #> library(pwr) #> ES.w1(pr1/3000, pr2/3000) es_r = 0.02699815282115563 es1 = chisquare_effectsize(pr1, pr2) es2 = chisquare_effectsize(pr1, pr2, cohen=False) assert_almost_equal(es1, es_r, decimal=14) assert_almost_equal(es2, es_r**2, decimal=14) # regression tests for correction res1 = chisquare_effectsize(pr1, pr2, cohen=False, correction=(3000, len(pr1)-1)) res0 = 0 #-0.00059994422693327625 assert_equal(res1, res0) pr3 = pr2 + [0,0,0,50,50] res1 = chisquare_effectsize(pr1, pr3, cohen=False, correction=(3000, len(pr1)-1)) res0 = 0.0023106468846296755 assert_almost_equal(res1, res0, decimal=14) # compare # res_nc = chisquare_effectsize(pr1, pr3, cohen=False) # 0.0036681143072077533 statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_groups_sw.py000066400000000000000000000052761224417117700264670ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for a helper function for PanelHAC robust covariance the functions should be rewritten to make it more efficient Created on Thu May 17 21:09:41 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_equal, assert_raises import statsmodels.stats.sandwich_covariance as sw from statsmodels.tools.grouputils import Group, GroupSorted class CheckPanelLagMixin(object): def calculate(self): self.g = g = GroupSorted(self.gind) # pylint: disable-msg=W0201 self.alla = [(lag, sw.lagged_groups(self.x, lag, g.groupidx)) # pylint: disable-msg=W0201 for lag in range(5)] def test_values(self): for lag, (y0, ylag) in self.alla: assert_equal(y0, self.alle[lag].T) assert_equal(y0, ylag + lag) def test_raises(self): mlag = self.mlag assert_raises(ValueError, sw.lagged_groups, self.x, mlag, self.g.groupidx) class TestBalanced(CheckPanelLagMixin): def __init__(self): self.gind = np.repeat([0,1,2], 5) self.mlag = 5 x = np.arange(15) x += 10**self.gind self.x = x[:,None] #expected result self.alle = { 0 : np.array([[ 1, 2, 3, 4, 5, 15, 16, 17, 18, 19, 110, 111, 112, 113, 114]]), 1 : np.array([[ 2, 3, 4, 5, 16, 17, 18, 19, 111, 112, 113, 114]]), 2 : np.array([[ 3, 4, 5, 17, 18, 19, 112, 113, 114]]), 3 : np.array([[ 4, 5, 18, 19, 113, 114]]), 4 : np.array([[ 5, 19, 114]]) } self.calculate() class TestUnBalanced(CheckPanelLagMixin): def __init__(self): self.gind = gind = np.repeat([0,1,2], [3, 5, 10]) self.mlag = 10 #maxlag x = np.arange(18) x += 10**gind self.x = x[:,None] #expected result self.alle = { 0 : np.array([[ 1, 2, 3, 13, 14, 15, 16, 17, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117]]), 1 : np.array([[ 2, 3, 14, 15, 16, 17, 109, 110, 111, 112, 113, 114, 115, 116, 117]]), 2 : np.array([[ 3, 15, 16, 17, 110, 111, 112, 113, 114, 115, 116, 117]]), 3 : np.array([[ 16, 17, 111, 112, 113, 114, 115, 116, 117]]), 4 : np.array([[ 17, 112, 113, 114, 115, 116, 117]]), 5 : np.array([[113, 114, 115, 116, 117]]), } self.calculate() if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb-failures'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_inter_rater.py000066400000000000000000000263711224417117700267540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Dec 10 09:18:14 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.stats.inter_rater import (fleiss_kappa, cohens_kappa, to_table, aggregate_raters) class Holder(object): pass table0 = np.asarray('''\ 1 0 0 0 0 14 1.000 2 0 2 6 4 2 0.253 3 0 0 3 5 6 0.308 4 0 3 9 2 0 0.440 5 2 2 8 1 1 0.330 6 7 7 0 0 0 0.462 7 3 2 6 3 0 0.242 8 2 5 3 2 2 0.176 9 6 5 2 1 0 0.286 10 0 2 2 3 7 0.286'''.split(), float).reshape(10,-1) table1 = table0[:, 1:-1] table10 = [[0, 4, 1], [0, 8, 0], [0, 1, 5]] #Fleiss 1971, Fleiss has only the transformed table diagnoses = np.array( [[4, 4, 4, 4, 4, 4], [2, 2, 2, 5, 5, 5], [2, 3, 3, 3, 3, 5], [5, 5, 5, 5, 5, 5], [2, 2, 2, 4, 4, 4], [1, 1, 3, 3, 3, 3], [3, 3, 3, 3, 5, 5], [1, 1, 3, 3, 3, 4], [1, 1, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [1, 4, 4, 4, 4, 4], [1, 2, 4, 4, 4, 4], [2, 2, 2, 3, 3, 3], [1, 4, 4, 4, 4, 4], [2, 2, 4, 4, 4, 5], [3, 3, 3, 3, 3, 5], [1, 1, 1, 4, 5, 5], [1, 1, 1, 1, 1, 2], [2, 2, 4, 4, 4, 4], [1, 3, 3, 5, 5, 5], [5, 5, 5, 5, 5, 5], [2, 4, 4, 4, 4, 4], [2, 2, 4, 5, 5, 5], [1, 1, 4, 4, 4, 4], [1, 4, 4, 4, 4, 5], [2, 2, 2, 2, 2, 4], [1, 1, 1, 1, 5, 5], [2, 2, 4, 4, 4, 4], [1, 3, 3, 3, 3, 3], [5, 5, 5, 5, 5, 5]]) diagnoses_rownames = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', ] diagnoses_colnames = ['rater1', 'rater2', 'rater3', 'rater4', 'rater5', 'rater6', ] def test_fleiss_kappa(): #currently only example from Wikipedia page kappa_wp = 0.210 assert_almost_equal(fleiss_kappa(table1), kappa_wp, decimal=3) class CheckCohens(object): def test_results(self): res = self.res res2 = self.res2 res_ = [res.kappa, res.std_kappa, res.kappa_low, res.kappa_upp, res.std_kappa0, res.z_value, res.pvalue_one_sided, res.pvalue_two_sided] assert_almost_equal(res_, res2, decimal=4) assert_equal(str(res), self.res_string) class UnweightedCohens(CheckCohens): #comparison to printout of a SAS example def __init__(self): #temporary: res instance is at last position self.res = cohens_kappa(table10) res10_sas = [0.4842, 0.1380, 0.2137, 0.7547] res10_sash0 = [0.1484, 3.2626, 0.0006, 0.0011] #for test H0:kappa=0 self.res2 = res10_sas + res10_sash0 #concatenate self.res_string = '''\ Simple Kappa Coefficient -------------------------------- Kappa 0.4842 ASE 0.1380 95% Lower Conf Limit 0.2137 95% Upper Conf Limit 0.7547 Test of H0: Simple Kappa = 0 ASE under H0 0.1484 Z 3.2626 One-sided Pr > Z 0.0006 Two-sided Pr > |Z| 0.0011''' + '\n' def test_option(self): kappa = cohens_kappa(table10, return_results=False) assert_almost_equal(kappa, self.res2[0], decimal=4) class TestWeightedCohens(CheckCohens): #comparison to printout of a SAS example def __init__(self): #temporary: res instance is at last position self.res = cohens_kappa(table10, weights=[0, 1, 2]) res10w_sas = [0.4701, 0.1457, 0.1845, 0.7558] res10w_sash0 = [0.1426, 3.2971, 0.0005, 0.0010] #for test H0:kappa=0 self.res2 = res10w_sas + res10w_sash0 #concatenate self.res_string = '''\ Weighted Kappa Coefficient -------------------------------- Kappa 0.4701 ASE 0.1457 95% Lower Conf Limit 0.1845 95% Upper Conf Limit 0.7558 Test of H0: Weighted Kappa = 0 ASE under H0 0.1426 Z 3.2971 One-sided Pr > Z 0.0005 Two-sided Pr > |Z| 0.0010''' + '\n' def test_option(self): kappa = cohens_kappa(table10, weights=[0, 1, 2], return_results=False) assert_almost_equal(kappa, self.res2[0], decimal=4) def test_cohenskappa_weights(): #some tests for equivalent results with different options np.random.seed(9743678) table = np.random.randint(0, 10, size=(5,5)) + 5*np.eye(5) #example aggregation, 2 groups of levels mat = np.array([[1,1,1, 0,0],[0,0,0,1,1]]) table_agg = np.dot(np.dot(mat, table), mat.T) res1 = cohens_kappa(table, weights=np.arange(5) > 2, wt='linear') res2 = cohens_kappa(table_agg, weights=np.arange(2), wt='linear') assert_almost_equal(res1.kappa, res2.kappa, decimal=14) assert_almost_equal(res1.var_kappa, res2.var_kappa, decimal=14) #equivalence toeplitz with linear for special cases res1 = cohens_kappa(table, weights=2*np.arange(5), wt='linear') res2 = cohens_kappa(table, weights=2*np.arange(5), wt='toeplitz') res3 = cohens_kappa(table, weights=res1.weights[0], wt='toeplitz') #2-Dim weights res4 = cohens_kappa(table, weights=res1.weights) assert_almost_equal(res1.kappa, res2.kappa, decimal=14) assert_almost_equal(res1.var_kappa, res2.var_kappa, decimal=14) assert_almost_equal(res1.kappa, res3.kappa, decimal=14) assert_almost_equal(res1.var_kappa, res3.var_kappa, decimal=14) assert_almost_equal(res1.kappa, res4.kappa, decimal=14) assert_almost_equal(res1.var_kappa, res4.var_kappa, decimal=14) #equivalence toeplitz with quadratic for special cases res1 = cohens_kappa(table, weights=5*np.arange(5)**2, wt='toeplitz') res2 = cohens_kappa(table, weights=5*np.arange(5), wt='quadratic') assert_almost_equal(res1.kappa, res2.kappa, decimal=14) assert_almost_equal(res1.var_kappa, res2.var_kappa, decimal=14) anxiety = np.array([ 3, 3, 3, 4, 5, 5, 2, 3, 5, 2, 2, 6, 1, 5, 2, 2, 1, 2, 4, 3, 3, 6, 4, 6, 2, 4, 2, 4, 3, 3, 2, 3, 3, 3, 2, 2, 1, 3, 3, 4, 2, 1, 4, 4, 3, 2, 1, 6, 1, 1, 1, 2, 3, 3, 1, 1, 3, 3, 2, 2 ]).reshape(20,3, order='F') anxiety_rownames = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', ] anxiety_colnames = ['rater1', 'rater2', 'rater3', ] def test_cohens_kappa_irr(): ck_w3 = Holder() ck_w4 = Holder() #>r = kappa2(anxiety[,1:2], c(0,0,0,1,1,1)) #> cat_items(r, pref="ck_w3.") ck_w3.method = "Cohen's Kappa for 2 Raters (Weights: 0,0,0,1,1,1)" ck_w3.irr_name = 'Kappa' ck_w3.value = 0.1891892 ck_w3.stat_name = 'z' ck_w3.statistic = 0.5079002 ck_w3.p_value = 0.6115233 #> r = kappa2(anxiety[,1:2], c(0,0,1,1,2,2)) #> cat_items(r, pref="ck_w4.") ck_w4.method = "Cohen's Kappa for 2 Raters (Weights: 0,0,1,1,2,2)" ck_w4.irr_name = 'Kappa' ck_w4.value = 0.2820513 ck_w4.stat_name = 'z' ck_w4.statistic = 1.257410 ck_w4.p_value = 0.2086053 ck_w1 = Holder() ck_w2 = Holder() ck_w3 = Holder() ck_w4 = Holder() #> r = kappa2(anxiety[,2:3]) #> cat_items(r, pref="ck_w1.") ck_w1.method = "Cohen's Kappa for 2 Raters (Weights: unweighted)" ck_w1.irr_name = 'Kappa' ck_w1.value = -0.006289308 ck_w1.stat_name = 'z' ck_w1.statistic = -0.0604067 ck_w1.p_value = 0.9518317 #> r = kappa2(anxiety[,2:3], "equal") #> cat_items(r, pref="ck_w2.") ck_w2.method = "Cohen's Kappa for 2 Raters (Weights: equal)" ck_w2.irr_name = 'Kappa' ck_w2.value = 0.1459075 ck_w2.stat_name = 'z' ck_w2.statistic = 1.282472 ck_w2.p_value = 0.1996772 #> r = kappa2(anxiety[,2:3], "squared") #> cat_items(r, pref="ck_w3.") ck_w3.method = "Cohen's Kappa for 2 Raters (Weights: squared)" ck_w3.irr_name = 'Kappa' ck_w3.value = 0.2520325 ck_w3.stat_name = 'z' ck_w3.statistic = 1.437451 ck_w3.p_value = 0.1505898 #> r = kappa2(anxiety[,2:3], c(0,0,1,1,2)) #> cat_items(r, pref="ck_w4.") ck_w4.method = "Cohen's Kappa for 2 Raters (Weights: 0,0,1,1,2)" ck_w4.irr_name = 'Kappa' ck_w4.value = 0.2391304 ck_w4.stat_name = 'z' ck_w4.statistic = 1.223734 ck_w4.p_value = 0.2210526 all_cases = [(ck_w1, None, None), (ck_w2, None, 'linear'), (ck_w2, np.arange(5), None), (ck_w2, np.arange(5), 'toeplitz'), (ck_w3, None, 'quadratic'), (ck_w3, np.arange(5)**2, 'toeplitz'), (ck_w3, 4*np.arange(5)**2, 'toeplitz'), (ck_w4, [0,0,1,1,2], 'toeplitz')] #Note R:irr drops the missing category level 4 and uses the reduced matrix r = np.histogramdd(anxiety[:,1:], ([1, 2, 3, 4, 6, 7], [1, 2, 3, 4, 6, 7])) for res2, w, wt in all_cases: msg = repr(w) + repr(wt) res1 = cohens_kappa(r[0], weights=w, wt=wt) assert_almost_equal(res1.kappa, res2.value, decimal=6, err_msg=msg) assert_almost_equal(res1.z_value, res2.statistic, decimal=5, err_msg=msg) assert_almost_equal(res1.pvalue_two_sided, res2.p_value, decimal=6, err_msg=msg) def test_fleiss_kappa_irr(): fleiss = Holder() #> r = kappam.fleiss(diagnoses) #> cat_items(r, pref="fleiss.") fleiss.method = "Fleiss' Kappa for m Raters" fleiss.irr_name = 'Kappa' fleiss.value = 0.4302445 fleiss.stat_name = 'z' fleiss.statistic = 17.65183 fleiss.p_value = 0 data_ = aggregate_raters(diagnoses)[0] res1_kappa = fleiss_kappa(data_) assert_almost_equal(res1_kappa, fleiss.value, decimal=7) def test_to_table(): data = diagnoses res1 = to_table(data[:,:2]-1, 5) res0 = np.asarray([[(data[:,:2]-1 == [i,j]).all(1).sum() for j in range(5)] for i in range(5)] ) assert_equal(res1[0], res0) res2 = to_table(data[:,:2]) assert_equal(res2[0], res0) bins = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5] res3 = to_table(data[:,:2], bins) assert_equal(res3[0], res0) #more than 2 columns res4 = to_table(data[:,:3]-1, bins=[-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]) res5 = to_table(data[:,:3]-1, bins=5) assert_equal(res4[0].sum(-1), res0) assert_equal(res5[0].sum(-1), res0) def test_aggregate_raters(): data = diagnoses resf = aggregate_raters(data) colsum = np.array([26, 26, 30, 55, 43]) assert_equal(resf[0].sum(0), colsum) if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x'#, '--pdb-failures' ], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_moment_helpers.py000066400000000000000000000103421224417117700274460ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sun Oct 16 17:33:56 2011 Author: Josef Perktold """ from statsmodels.stats import moment_helpers from statsmodels.stats.moment_helpers import (cov2corr, mvsk2mc, mc2mvsk, mnc2mc, mc2mnc, cum2mc, mc2cum, mnc2cum) import numpy as np from numpy.testing import assert_almost_equal, assert_, assert_equal def test_cov2corr(): cov_a = np.ones((3,3))+np.diag(np.arange(1,4)**2 - 1) corr_a = np.array([[1, 1/2., 1/3.],[1/2., 1, 1/2./3.],[1/3., 1/2./3., 1]]) corr = cov2corr(cov_a) assert_almost_equal(corr, corr_a, decimal=15) cov_mat = np.matrix(cov_a) corr_mat = cov2corr(cov_mat) assert_(isinstance(corr_mat, np.matrixlib.defmatrix.matrix)) assert_equal(corr_mat, corr) cov_ma = np.ma.array(cov_a) corr_ma = cov2corr(cov_ma) assert_equal(corr_mat, corr) assert_(isinstance(corr_ma, np.ma.core.MaskedArray)) cov_ma2 = np.ma.array(cov_a, mask = [[False, True, False], [True, False, False], [False, False, False]]) corr_ma2 = cov2corr(cov_ma2) assert_(np.ma.allclose(corr_ma, corr, atol=1e-15)) assert_equal(corr_ma2.mask, cov_ma2.mask) def test_moment_conversion(): #this was initially written for an old version of moment_helpers #I'm not sure whether there are not redundant cases after moving functions ms = [( [0.0, 1, 0, 3], [0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0] ), ( [1.0, 1, 0, 3], [1.0, 1.0, 0.0, 0.0], [1.0, 0.0, -1.0, 6.0] ), ( [0.0, 1, 1, 3], [0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0] ), ( [1.0, 1, 1, 3], [1.0, 1.0, 1.0, 0.0], [1.0, 0.0, 0.0, 2.0] ), ( [1.0, 1, 1, 4], [1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 3.0] ), ( [1.0, 2, 0, 3], [1.0, 2.0, 0.0, -9.0], [1.0, 1.0, -4.0, 9.0] ), ( [0.0, 2, 1, 3], [0.0, 2.0, 1.0, -9.0], [0.0, 2.0, 1.0, -9.0] ), ( [1.0, 0.5, 0, 3], [1.0, 0.5, 0.0, 2.25], [1.0, -0.5, 0.5, 2.25] ), #neg.variance if mnc2 cumulant assert_equal(mnc2cum(mc2mnc(mom[0])),mom[1]) assert_equal(mnc2cum(mom[0]),mom[2]) if len(mom) <= 4: assert_equal(mc2cum(mom[0]),mom[1]) for mom in ms: # test cumulant -> moment assert_equal(cum2mc(mom[1]),mom[0]) assert_equal(mc2mnc(cum2mc(mom[2])),mom[0]) if len(mom) <= 4: assert_equal(cum2mc(mom[1]),mom[0]) for mom in ms: #round trip: mnc -> cum -> mc == mnc -> mc, assert_equal(cum2mc(mnc2cum(mom[0])),mnc2mc(mom[0])) for mom in ms: #round trip: mc -> mnc -> mc == mc, assert_equal(mc2mnc(mnc2mc(mom[0])), mom[0]) for mom in (m for m in ms if len(m[0]) == 4): #print "testing", mom #round trip: mc -> mvsk -> mc == mc assert_equal(mvsk2mc(mc2mvsk(mom[0])), mom[0]) #round trip: mc -> mvsk -> mnc == mc -> mnc #TODO: mvsk2mnc not defined #assert_equal(mvsk2mnc(mc2mvsk(mom[0])), mc2mnc(mom[0])) def test_moment_conversion_types(): # written in 2009 #why did I use list as return type all_f = ['cum2mc', 'cum2mc', 'mc2cum', 'mc2mnc', 'mc2mvsk', 'mnc2cum', 'mnc2mc', 'mnc2mc', 'mvsk2mc', 'mvsk2mnc'] assert np.all([isinstance(getattr(moment_helpers,f)([1.0, 1, 0, 3]),list) or isinstance(getattr(moment_helpers,f)(np.array([1.0, 1, 0, 3])),tuple) for f in all_f]) assert np.all([isinstance(getattr(moment_helpers,f)(np.array([1.0, 1, 0, 3])),list) or isinstance(getattr(moment_helpers,f)(np.array([1.0, 1, 0, 3])),tuple) for f in all_f]) assert np.all([isinstance(getattr(moment_helpers,f)(tuple([1.0, 1, 0, 3])),list) or isinstance(getattr(moment_helpers,f)(np.array([1.0, 1, 0, 3])),tuple) for f in all_f]) if __name__ == '__main__': test_cov2corr() test_moment_conversion() test_moment_conversion_types() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_multi.py000066400000000000000000000424301224417117700255620ustar00rootroot00000000000000'''Tests for multipletests and fdr pvalue corrections Author : Josef Perktold ['b', 's', 'sh', 'hs', 'h', 'fdr_i', 'fdr_n', 'fdr_tsbh'] are tested against R:multtest 'hommel' is tested against R stats p_adjust (not available in multtest 'fdr_gbs', 'fdr_2sbky' I did not find them in R, currently tested for consistency only ''' import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_ from statsmodels.stats.multitest import (multipletests, fdrcorrection, fdrcorrection_twostage) from statsmodels.stats.multicomp import tukeyhsd pval0 = np.array([0.838541367553 , 0.642193923795 , 0.680845947633 , 0.967833824309 , 0.71626938238 , 0.177096952723 , 5.23656777208e-005 , 0.0202732688798 , 0.00028140506198 , 0.0149877310796]) res_multtest1 = np.array([[ 5.2365677720800003e-05, 5.2365677720800005e-04, 5.2365677720800005e-04, 5.2365677720800005e-04, 5.2353339704891422e-04, 5.2353339704891422e-04, 5.2365677720800005e-04, 1.5337740764175588e-03], [ 2.8140506198000000e-04, 2.8140506197999998e-03, 2.5326455578199999e-03, 2.5326455578199999e-03, 2.8104897961789277e-03, 2.5297966317768816e-03, 1.4070253098999999e-03, 4.1211324652269442e-03], [ 1.4987731079600001e-02, 1.4987731079600000e-01, 1.1990184863680001e-01, 1.1990184863680001e-01, 1.4016246580579017e-01, 1.1379719679449507e-01, 4.9959103598666670e-02, 1.4632862843720582e-01], [ 2.0273268879800001e-02, 2.0273268879799999e-01, 1.4191288215860001e-01, 1.4191288215860001e-01, 1.8520270949069695e-01, 1.3356756197485375e-01, 5.0683172199499998e-02, 1.4844940238274187e-01], [ 1.7709695272300000e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 8.5760763426056130e-01, 6.8947825122356643e-01, 3.5419390544599999e-01, 1.0000000000000000e+00], [ 6.4219392379499995e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 9.9996560644133570e-01, 9.9413539782557070e-01, 8.9533672797500008e-01, 1.0000000000000000e+00], [ 6.8084594763299999e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 9.9998903512635740e-01, 9.9413539782557070e-01, 8.9533672797500008e-01, 1.0000000000000000e+00], [ 7.1626938238000004e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 9.9999661886871472e-01, 9.9413539782557070e-01, 8.9533672797500008e-01, 1.0000000000000000e+00], [ 8.3854136755300002e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 9.9999998796038225e-01, 9.9413539782557070e-01, 9.3171263061444454e-01, 1.0000000000000000e+00], [ 9.6783382430900000e-01, 1.0000000000000000e+00, 1.0000000000000000e+00, 9.6783382430900000e-01, 9.9999999999999878e-01, 9.9413539782557070e-01, 9.6783382430900000e-01, 1.0000000000000000e+00]]) res_multtest2_columns = ['rawp', 'Bonferroni', 'Holm', 'Hochberg', 'SidakSS', 'SidakSD', 'BH', 'BY', 'ABH', 'TSBH_0.05'] rmethods = {'rawp':(0,'pval'), 'Bonferroni':(1,'b'), 'Holm':(2,'h'), 'Hochberg':(3,'sh'), 'SidakSS':(4,'s'), 'SidakSD':(5,'hs'), 'BH':(6,'fdr_i'), 'BY':(7,'fdr_n'), 'TSBH_0.05':(9, 'fdr_tsbh')} NA = np.nan # all rejections, except for Bonferroni and Sidak res_multtest2 = np.array([ 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.012, 0.024, 0.036, 0.048, 0.06, 0.072, 0.012, 0.02, 0.024, 0.024, 0.024, 0.024, 0.012, 0.012, 0.012, 0.012, 0.012, 0.012, 0.01194015976019192, 0.02376127616613988, 0.03546430060660932, 0.04705017875634587, 0.058519850599, 0.06987425045000606, 0.01194015976019192, 0.01984063872102404, 0.02378486270400004, 0.023808512, 0.023808512, 0.023808512, 0.012, 0.012, 0.012, 0.012, 0.012, 0.012, 0.0294, 0.0294, 0.0294, 0.0294, 0.0294, 0.0294, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, 0 ]).reshape(6,10, order='F') res_multtest3 = np.array([ 0.001, 0.002, 0.003, 0.004, 0.005, 0.05, 0.06, 0.07, 0.08, 0.09, 0.01, 0.02, 0.03, 0.04, 0.05, 0.5, 0.6, 0.7, 0.8, 0.9, 0.01, 0.018, 0.024, 0.028, 0.03, 0.25, 0.25, 0.25, 0.25, 0.25, 0.01, 0.018, 0.024, 0.028, 0.03, 0.09, 0.09, 0.09, 0.09, 0.09, 0.00995511979025177, 0.01982095664805061, 0.02959822305108317, 0.03928762649718986, 0.04888986953422814, 0.4012630607616213, 0.4613848859051006, 0.5160176928207072, 0.5656115457763677, 0.6105838818818925, 0.00995511979025177, 0.0178566699880266, 0.02374950634358763, 0.02766623106147537, 0.02962749064373438, 0.2262190625000001, 0.2262190625000001, 0.2262190625000001, 0.2262190625000001, 0.2262190625000001, 0.01, 0.01, 0.01, 0.01, 0.01, 0.08333333333333334, 0.0857142857142857, 0.0875, 0.0888888888888889, 0.09, 0.02928968253968254, 0.02928968253968254, 0.02928968253968254, 0.02928968253968254, 0.02928968253968254, 0.2440806878306878, 0.2510544217687075, 0.2562847222222222, 0.2603527336860670, 0.2636071428571428, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.005, 0.005, 0.005, 0.005, 0.005, 0.04166666666666667, 0.04285714285714286, 0.04375, 0.04444444444444445, 0.045 ]).reshape(10,10, order='F') res0_large = np.array([ 0.00031612, 0.0003965, 0.00048442, 0.00051932, 0.00101436, 0.00121506, 0.0014516, 0.00265684, 0.00430043, 0.01743686, 0.02080285, 0.02785414, 0.0327198, 0.03494679, 0.04206808, 0.08067095, 0.23882767, 0.28352304, 0.36140401, 0.43565145, 0.44866768, 0.45368782, 0.48282088, 0.49223781, 0.55451638, 0.6207473, 0.71847853, 0.72424145, 0.85950263, 0.89032747, 0.0094836, 0.011895, 0.0145326, 0.0155796, 0.0304308, 0.0364518, 0.043548, 0.0797052, 0.1290129, 0.5231058, 0.6240855, 0.8356242, 0.981594, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0094836, 0.0114985, 0.01356376, 0.01402164, 0.02637336, 0.0303765, 0.0348384, 0.06110732, 0.09460946, 0.36617406, 0.416057, 0.52922866, 0.5889564, 0.59409543, 0.67308928, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0094836, 0.0114985, 0.01356376, 0.01402164, 0.02637336, 0.0303765, 0.0348384, 0.06110732, 0.09460946, 0.36617406, 0.416057, 0.52922866, 0.5889564, 0.59409543, 0.67308928, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.89032747, 0.009440257627368331, 0.01182686507401931, 0.01443098172617119, 0.01546285007478554, 0.02998742566629453, 0.03581680249125385, 0.04264369065603335, 0.0767094173291795, 0.1212818694859857, 0.410051586220387, 0.4677640287633493, 0.5715077903157826, 0.631388450393325, 0.656016359012282, 0.724552174001554, 0.919808283456286, 0.999721715014484, 0.9999547032674126, 0.9999985652190126, 0.999999964809746, 0.999999982525548, 0.999999986719131, 0.999999997434160, 0.999999998521536, 0.999999999970829, 0.999999999999767, 1, 1, 1, 1, 0.009440257627368331, 0.01143489901147732, 0.0134754287611275, 0.01392738605848343, 0.0260416568490015, 0.02993768724817902, 0.0342629726119179, 0.0593542206208364, 0.09045742964699988, 0.308853956167216, 0.343245865702423, 0.4153483370083637, 0.4505333180190900, 0.453775200643535, 0.497247406680671, 0.71681858015803, 0.978083969553718, 0.986889206426321, 0.995400461639735, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.9981506396214986, 0.0038949, 0.0038949, 0.0038949, 0.0038949, 0.0060753, 0.0060753, 0.006221142857142857, 0.00996315, 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0.26777176, 0.323361482631579, 0.34866844875, 0.34866844875, 0.34866844875, 0.34866844875, 0.34866844875, 0.3770711384, 0.4058732346153846, 0.4397180232142857, 0.4397180232142857, 0.503846369310345, 0.504518899666667, 0.00272643, 0.00272643, 0.00272643, 0.00272643, 0.00425271, 0.00425271, 0.0043548, 0.006974205, 0.01003433666666667, 0.036617406, 0.03971453181818182, 0.048744745, 0.052420185, 0.052420185, 0.058895312, 0.105880621875, 0.295022415882353, 0.33077688, 0.399446537368421, 0.43070808375, 0.43070808375, 0.43070808375, 0.43070808375, 0.43070808375, 0.4657937592, 0.5013728192307692, 0.5431810875, 0.5431810875, 0.622398456206897, 0.623229229 ]).reshape(30,10, order='F') class CheckMultiTestsMixin(object): def test_multi_pvalcorrection(self): #test against R package multtest mt.rawp2adjp res_multtest = self.res2 pval0 = res_multtest[:,0] for k,v in rmethods.items(): if v[1] in self.methods: reject, pvalscorr = multipletests(pval0, alpha=self.alpha, method=v[1])[:2] assert_almost_equal(pvalscorr, res_multtest[:,v[0]], 15) assert_equal(reject, pvalscorr <= self.alpha) pvalscorr = np.sort(fdrcorrection(pval0, method='n')[1]) assert_almost_equal(pvalscorr, res_multtest[:,7], 15) pvalscorr = np.sort(fdrcorrection(pval0, method='i')[1]) assert_almost_equal(pvalscorr, res_multtest[:,6], 15) class TestMultiTests1(CheckMultiTestsMixin): def __init__(self): self.methods = ['b', 's', 'sh', 'hs', 'h', 'fdr_i', 'fdr_n'] self.alpha = 0.1 self.res2 = res_multtest1 class TestMultiTests2(CheckMultiTestsMixin): # case: all hypothesis rejected (except 'b' and 's' def __init__(self): self.methods = ['b', 's', 'sh', 'hs', 'h', 'fdr_i', 'fdr_n'] self.alpha = 0.05 self.res2 = res_multtest2 class TestMultiTests3(CheckMultiTestsMixin): def __init__(self): self.methods = ['b', 's', 'sh', 'hs', 'h', 'fdr_i', 'fdr_n', 'fdr_tsbh'] self.alpha = 0.05 self.res2 = res0_large class TestMultiTests4(CheckMultiTestsMixin): # in simulations, all two stage fdr, fdr_tsbky, fdr_tsbh, fdr_gbs, have in # some cases (cases with large Alternative) an FDR that looks too large # this is the first case #rejected = 12, DGP : has 10 false def __init__(self): self.methods = ['b', 's', 'sh', 'hs', 'h', 'fdr_i', 'fdr_n', 'fdr_tsbh'] self.alpha = 0.05 self.res2 = res_multtest3 def test_pvalcorrection_reject(): # consistency test for reject boolean and pvalscorr for alpha in [0.01, 0.05, 0.1]: for method in ['b', 's', 'sh', 'hs', 'h', 'hommel', 'fdr_i', 'fdr_n', 'fdr_tsbky', 'fdr_tsbh', 'fdr_gbs']: for ii in range(11): pval1 = np.hstack((np.linspace(0.0001, 0.0100, ii), np.linspace(0.05001, 0.11, 10 - ii))) # using .05001 instead of 0.05 to avoid edge case issue #768 reject, pvalscorr = multipletests(pval1, alpha=alpha, method=method)[:2] #print 'reject.sum', v[1], reject.sum() msg = 'case %s %3.2f rejected:%d\npval_raw=%r\npvalscorr=%r' % ( method, alpha, reject.sum(), pval1, pvalscorr) #assert_equal(reject, pvalscorr <= alpha, err_msg=msg) yield assert_equal, reject, pvalscorr <= alpha, msg def test_hommel(): #tested agains R stats p_adjust(pval0, method='hommel') pval0 = np.array( [ 0.00116, 0.00924, 0.01075, 0.01437, 0.01784, 0.01918, 0.02751, 0.02871, 0.03054, 0.03246, 0.04259, 0.06879, 0.0691 , 0.08081, 0.08593, 0.08993, 0.09386, 0.09412, 0.09718, 0.09758, 0.09781, 0.09788, 0.13282, 0.20191, 0.21757, 0.24031, 0.26061, 0.26762, 0.29474, 0.32901, 0.41386, 0.51479, 0.52461, 0.53389, 0.56276, 0.62967, 0.72178, 0.73403, 0.87182, 0.95384]) result_ho = np.array( [ 0.0464 , 0.25872 , 0.29025 , 0.3495714285714286, 0.41032 , 0.44114 , 0.57771 , 0.60291 , 0.618954 , 0.6492 , 0.7402725000000001, 0.86749 , 0.86749 , 0.8889100000000001, 0.8971477777777778, 0.8993 , 0.9175374999999999, 0.9175374999999999, 0.9175374999999999, 0.9175374999999999, 0.9175374999999999, 0.9175374999999999, 0.95384 , 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001, 0.9538400000000001]) rej, pvalscorr, _, _ = multipletests(pval0, alpha=0.1, method='ho') assert_almost_equal(pvalscorr, result_ho, 15) assert_equal(rej, result_ho < 0.1) #booleans def test_fdr_bky(): # test for fdrcorrection_twostage # example from BKY pvals = [0.0001, 0.0004, 0.0019, 0.0095, 0.0201, 0.0278, 0.0298, 0.0344, 0.0459, 0.3240, 0.4262, 0.5719, 0.6528, 0.7590, 1.000 ] #no test for corrected p-values, but they are inherited #same number of rejection as in BKY paper: #single step-up:4, two-stage:8, iterated two-step:9 #also alpha_star is the same as theirs for TST #print fdrcorrection0(pvals, alpha=0.05, method='indep') #print fdrcorrection_twostage(pvals, alpha=0.05, iter=False) res_tst = fdrcorrection_twostage(pvals, alpha=0.05, iter=False) assert_almost_equal([0.047619, 0.0649], res_tst[-1][:2],3) #alpha_star for stage 2 assert_equal(8, res_tst[0].sum()) #print fdrcorrection_twostage(pvals, alpha=0.05, iter=True) def test_tukeyhsd(): #example multicomp in R p 83 res = '''\ pair diff lwr upr p adj P-M 8.150000 -10.037586 26.3375861 0.670063958 S-M -3.258333 -21.445919 14.9292527 0.982419709 T-M 23.808333 5.620747 41.9959194 0.006783701 V-M 4.791667 -13.395919 22.9792527 0.931020848 S-P -11.408333 -29.595919 6.7792527 0.360680099 T-P 15.658333 -2.529253 33.8459194 0.113221634 V-P -3.358333 -21.545919 14.8292527 0.980350080 T-S 27.066667 8.879081 45.2542527 0.002027122 V-S 8.050000 -10.137586 26.2375861 0.679824487 V-T -19.016667 -37.204253 -0.8290806 0.037710044 ''' res = np.array([[ 8.150000, -10.037586, 26.3375861, 0.670063958], [-3.258333, -21.445919, 14.9292527, 0.982419709], [23.808333, 5.620747, 41.9959194, 0.006783701], [ 4.791667, -13.395919, 22.9792527, 0.931020848], [-11.408333, -29.595919, 6.7792527, 0.360680099], [15.658333, -2.529253, 33.8459194, 0.113221634], [-3.358333, -21.545919, 14.8292527, 0.980350080], [27.066667, 8.879081, 45.2542527, 0.002027122], [ 8.050000, -10.137586, 26.2375861, 0.679824487], [-19.016667, -37.204253, -0.8290806, 0.037710044]]) m_r = [94.39167, 102.54167, 91.13333, 118.20000, 99.18333] myres = tukeyhsd(m_r, 6, 110.8, alpha=0.05, df=4) pairs, reject, meandiffs, std_pairs, confint, q_crit = myres[:6] assert_almost_equal(meandiffs, res[:, 0], decimal=5) assert_almost_equal(confint, res[:, 1:3], decimal=2) assert_equal(reject, res[:, 3]<0.05) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_nonparametric.py000066400000000000000000000217051224417117700272740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Jul 05 14:05:24 2013 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_allclose, assert_almost_equal from statsmodels.sandbox.stats.runs import (mcnemar, cochrans_q, Runs, symmetry_bowker, runstest_1samp, runstest_2samp) def _expand_table(table): '''expand a 2 by 2 contingency table to observations ''' return np.repeat([[1, 1], [1, 0], [0, 1], [0, 0]], table.ravel(), axis=0) def test_mcnemar_exact(): f_obs1 = np.array([[101, 121], [59, 33]]) f_obs2 = np.array([[101, 70], [59, 33]]) f_obs3 = np.array([[101, 80], [59, 33]]) f_obs4 = np.array([[101, 30], [60, 33]]) f_obs5 = np.array([[101, 10], [30, 33]]) f_obs6 = np.array([[101, 10], [10, 33]]) #vassar college online computation res1 = 0.000004 res2 = 0.378688 res3 = 0.089452 res4 = 0.00206 res5 = 0.002221 res6 = 1. assert_almost_equal(mcnemar(f_obs1, exact=True), [59, res1], decimal=6) assert_almost_equal(mcnemar(f_obs2, exact=True), [59, res2], decimal=6) assert_almost_equal(mcnemar(f_obs3, exact=True), [59, res3], decimal=6) assert_almost_equal(mcnemar(f_obs4, exact=True), [30, res4], decimal=6) assert_almost_equal(mcnemar(f_obs5, exact=True), [10, res5], decimal=6) assert_almost_equal(mcnemar(f_obs6, exact=True), [10, res6], decimal=6) x, y = _expand_table(f_obs2).T # tuple unpack assert_allclose(mcnemar(f_obs2, exact=True), mcnemar(x, y, exact=True), rtol=1e-13) def test_mcnemar_chisquare(): f_obs1 = np.array([[101, 121], [59, 33]]) f_obs2 = np.array([[101, 70], [59, 33]]) f_obs3 = np.array([[101, 80], [59, 33]]) #> mcn = mcnemar.test(matrix(c(101, 121, 59, 33),nrow=2)) res1 = [2.067222e01, 5.450095e-06] res2 = [0.7751938, 0.3786151] res3 = [2.87769784, 0.08981434] assert_allclose(mcnemar(f_obs1, exact=False), res1, rtol=1e-6) assert_allclose(mcnemar(f_obs2, exact=False), res2, rtol=1e-6) assert_allclose(mcnemar(f_obs3, exact=False), res3, rtol=1e-6) # compare table versus observations x, y = _expand_table(f_obs2).T # tuple unpack assert_allclose(mcnemar(f_obs2, exact=False), mcnemar(x, y, exact=False), rtol=1e-13) # test correction = False res1 = [2.135556e01, 3.815136e-06] res2 = [0.9379845, 0.3327967] res3 = [3.17266187, 0.07488031] res = mcnemar(f_obs1, exact=False, correction=False) assert_allclose(res, res1, rtol=1e-6) res = mcnemar(f_obs2, exact=False, correction=False) assert_allclose(res, res2, rtol=1e-6) res = mcnemar(f_obs3, exact=False, correction=False) assert_allclose(res, res3, rtol=1e-6) def test_mcnemar_vectorized(): ttk = np.random.randint(5,15, size=(2,2,3)) mcnemar(ttk) res = mcnemar(ttk, exact=False) res1 = zip(*[mcnemar(ttk[:,:,i], exact=False) for i in range(3)]) assert_allclose(res, res1, rtol=1e-13) res = mcnemar(ttk, exact=False, correction=False) res1 = zip(*[mcnemar(ttk[:,:,i], exact=False, correction=False) for i in range(3)]) assert_allclose(res, res1, rtol=1e-13) res = mcnemar(ttk, exact=True) res1 = zip(*[mcnemar(ttk[:,:,i], exact=True) for i in range(3)]) assert_allclose(res, res1, rtol=1e-13) def test_symmetry_bowker(): table = np.array([0, 3, 4, 4, 2, 4, 1, 2, 4, 3, 5, 3, 0, 0, 2, 2, 3, 0, 0, 1, 5, 5, 5, 5, 5]).reshape(5, 5) res = symmetry_bowker(table) mcnemar5_1 = dict(statistic=7.001587, pvalue=0.7252951, parameters=(10,), distr='chi2') assert_allclose(res[:2], [mcnemar5_1['statistic'], mcnemar5_1['pvalue']], rtol=1e-7) res = symmetry_bowker(1 + table) mcnemar5_1b = dict(statistic=5.355988, pvalue=0.8661652, parameters=(10,), distr='chi2') assert_allclose(res[:2], [mcnemar5_1b['statistic'], mcnemar5_1b['pvalue']], rtol=1e-7) table = np.array([2, 2, 3, 6, 2, 3, 4, 3, 6, 6, 6, 7, 1, 9, 6, 7, 1, 1, 9, 8, 0, 1, 8, 9, 4]).reshape(5, 5) res = symmetry_bowker(table) mcnemar5_2 = dict(statistic=18.76432, pvalue=0.04336035, parameters=(10,), distr='chi2') assert_allclose(res[:2], [mcnemar5_2['statistic'], mcnemar5_2['pvalue']], rtol=1.5e-7) res = symmetry_bowker(1 + table) mcnemar5_2b = dict(statistic=14.55256, pvalue=0.1492461, parameters=(10,), distr='chi2') assert_allclose(res[:2], [mcnemar5_2b['statistic'], mcnemar5_2b['pvalue']], rtol=1e-7) def test_cochransq(): #example from dataplot docs, Conovover p. 253 #http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/cochran.htm x = np.array([[1, 1, 1], [1, 1, 1], [0, 1, 0], [1, 1, 0], [0, 0, 0], [1, 1, 1], [1, 1, 1], [1, 1, 0], [0, 0, 1], [0, 1, 0], [1, 1, 1], [1, 1, 1]]) res_qstat = 2.8 res_pvalue = 0.246597 assert_almost_equal(cochrans_q(x), [res_qstat, res_pvalue]) #equivalence of mcnemar and cochranq for 2 samples a,b = x[:,:2].T assert_almost_equal(mcnemar(a,b, exact=False, correction=False), cochrans_q(x[:,:2])) def test_cochransq2(): # from an example found on web, verifies 13.286 data = np.array(''' 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1'''.split(), int).reshape(-1, 4) res = cochrans_q(data) assert_allclose(res, [13.2857143, 0.00405776], rtol=1e-6) def test_cochransq3(): # another example compared to SAS # in frequency weight format dt = [('A', 'S1'), ('B', 'S1'), ('C', 'S1'), ('count', int)] dta = np.array([('F', 'F', 'F', 6), ('U', 'F', 'F', 2), ('F', 'F', 'U', 16), ('U', 'F', 'U', 4), ('F', 'U', 'F', 2), ('U', 'U', 'F', 6), ('F', 'U', 'U', 4), ('U', 'U', 'U', 6)], dt) cases = np.array([[0, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 1], [0, 1, 0], [1, 1, 0], [0, 1, 1], [1, 1, 1]]) count = np.array([ 6, 2, 16, 4, 2, 6, 4, 6]) data = np.repeat(cases, count, 0) res = cochrans_q(data) assert_allclose(res, [8.4706, 0.0145], atol=5e-5) def test_runstest(): #comparison numbers from R, tseries, runs.test #currently only 2-sided used x = np.array([1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1]) z_twosided = 1.386750 pvalue_twosided = 0.1655179 z_greater = 1.386750 pvalue_greater = 0.08275893 z_less = 1.386750 pvalue_less = 0.917241 #print Runs(x).runs_test(correction=False) assert_almost_equal(np.array(Runs(x).runs_test(correction=False)), [z_twosided, pvalue_twosided], decimal=6) # compare with runstest_1samp which should have same indicator assert_almost_equal(runstest_1samp(x, correction=False), [z_twosided, pvalue_twosided], decimal=6) x2 = x - 0.5 + np.random.uniform(-0.1, 0.1, size=len(x)) assert_almost_equal(runstest_1samp(x2, cutoff=0, correction=False), [z_twosided, pvalue_twosided], decimal=6) assert_almost_equal(runstest_1samp(x2, cutoff='mean', correction=False), [z_twosided, pvalue_twosided], decimal=6) assert_almost_equal(runstest_1samp(x2, cutoff=x2.mean(), correction=False), [z_twosided, pvalue_twosided], decimal=6) # check median assert_almost_equal(runstest_1samp(x2, cutoff='median', correction=False), runstest_1samp(x2, cutoff=np.median(x2), correction=False), decimal=6) def test_runstest_2sample(): # regression test, checked with MonteCarlo and looks reasonable x = [31.8, 32.8, 39.2, 36, 30, 34.5, 37.4] y = [35.5, 27.6, 21.3, 24.8, 36.7, 30] y[-1] += 1e-6 #avoid tie that creates warning groups = np.concatenate((np.zeros(len(x)), np.ones(len(y)))) res = runstest_2samp(x, y) res1 = (0.022428065200812752, 0.98210649318649212) assert_allclose(res, res1, rtol=1e-6) # check as stacked array res2 = runstest_2samp(x, y) assert_allclose(res2, res, rtol=1e-6) xy = np.concatenate((x, y)) res_1s = runstest_1samp(xy) assert_allclose(res_1s, res1, rtol=1e-6) # check cutoff res2_1s = runstest_1samp(xy, xy.mean()) assert_allclose(res2_1s, res_1s, rtol=1e-6) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_pairwise.py000066400000000000000000000210571224417117700262550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Mar 28 15:34:18 2012 Author: Josef Perktold """ from statsmodels.compatnp.py3k import BytesIO, asbytes import numpy as np from numpy.testing import assert_almost_equal, assert_equal from statsmodels.stats.libqsturng import qsturng ss = '''\ 43.9 1 1 39.0 1 2 46.7 1 3 43.8 1 4 44.2 1 5 47.7 1 6 43.6 1 7 38.9 1 8 43.6 1 9 40.0 1 10 89.8 2 1 87.1 2 2 92.7 2 3 90.6 2 4 87.7 2 5 92.4 2 6 86.1 2 7 88.1 2 8 90.8 2 9 89.1 2 10 68.4 3 1 69.3 3 2 68.5 3 3 66.4 3 4 70.0 3 5 68.1 3 6 70.6 3 7 65.2 3 8 63.8 3 9 69.2 3 10 36.2 4 1 45.2 4 2 40.7 4 3 40.5 4 4 39.3 4 5 40.3 4 6 43.2 4 7 38.7 4 8 40.9 4 9 39.7 4 10''' #idx Treatment StressReduction ss2 = '''\ 1 mental 2 2 mental 2 3 mental 3 4 mental 4 5 mental 4 6 mental 5 7 mental 3 8 mental 4 9 mental 4 10 mental 4 11 physical 4 12 physical 4 13 physical 3 14 physical 5 15 physical 4 16 physical 1 17 physical 1 18 physical 2 19 physical 3 20 physical 3 21 medical 1 22 medical 2 23 medical 2 24 medical 2 25 medical 3 26 medical 2 27 medical 3 28 medical 1 29 medical 3 30 medical 1''' ss3 = '''\ 1 24.5 1 23.5 1 26.4 1 27.1 1 29.9 2 28.4 2 34.2 2 29.5 2 32.2 2 30.1 3 26.1 3 28.3 3 24.3 3 26.2 3 27.8''' ss5 = '''\ 2 - 3\t4.340\t0.691\t7.989\t*** 2 - 1\t4.600\t0.951\t8.249\t*** 3 - 2\t-4.340\t-7.989\t-0.691\t*** 3 - 1\t0.260\t-3.389\t3.909\t- 1 - 2\t-4.600\t-8.249\t-0.951\t*** 1 - 3\t-0.260\t-3.909\t3.389\t''' cylinders = np.array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 6, 6, 6, 4, 4, 4, 4, 4, 4, 6, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 6, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 4, 4, 4, 4, 4, 8, 4, 6, 6, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4]) cyl_labels = np.array(['USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'France', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Japan', 'USA', 'USA', 'USA', 'Japan', 'Germany', 'France', 'Germany', 'Sweden', 'Germany', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'USA', 'USA', 'France', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'Japan', 'USA', 'Sweden', 'USA', 'France', 'Japan', 'Germany', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'USA', 'Germany', 'Japan', 'Japan', 'USA', 'USA', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'USA', 'USA', 'USA', 'USA', 'Japan', 'USA', 'USA', 'USA', 'Germany', 'USA', 'USA', 'USA']) #accommodate recfromtxt for python 3.2, requires bytes ss = asbytes(ss) ss2 = asbytes(ss2) ss3 = asbytes(ss3) ss5 = asbytes(ss5) dta = np.recfromtxt(BytesIO(ss), names=("Rust","Brand","Replication")) dta2 = np.recfromtxt(BytesIO(ss2), names = ("idx", "Treatment", "StressReduction")) dta3 = np.recfromtxt(BytesIO(ss3), names = ("Brand", "Relief")) dta5 = np.recfromtxt(BytesIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t') sas_ = dta5[[1,3,2]] from statsmodels.stats.multicomp import (tukeyhsd, pairwise_tukeyhsd, MultiComparison) #import statsmodels.sandbox.stats.multicomp as multi #print tukeyhsd(dta['Brand'], dta['Rust']) def get_thsd(mci, alpha=0.05): var_ = np.var(mci.groupstats.groupdemean(), ddof=len(mci.groupsunique)) means = mci.groupstats.groupmean nobs = mci.groupstats.groupnobs resi = tukeyhsd(means, nobs, var_, df=None, alpha=alpha, q_crit=qsturng(1-alpha, len(means), (nobs-1).sum())) #print resi[4] var2 = (mci.groupstats.groupvarwithin() * (nobs - 1.)).sum() \ / (nobs - 1.).sum() #print nobs, (nobs - 1).sum() #print mci.groupstats.groupvarwithin() assert_almost_equal(var_, var2, decimal=14) return resi class CheckTuckeyHSDMixin(object): @classmethod def setup_class_(self): self.mc = MultiComparison(self.endog, self.groups) self.res = self.mc.tukeyhsd(alpha=self.alpha) def test_multicomptukey(self): assert_almost_equal(self.res.meandiffs, self.meandiff2, decimal=14) assert_almost_equal(self.res.confint, self.confint2, decimal=2) assert_equal(self.res.reject, self.reject2) def test_group_tukey(self): res_t = get_thsd(self.mc, alpha=self.alpha) assert_almost_equal(res_t[4], self.confint2, decimal=2) def test_shortcut_function(self): #check wrapper function res = pairwise_tukeyhsd(self.endog, self.groups, alpha=self.alpha) assert_almost_equal(res.confint, self.res.confint, decimal=14) class TestTuckeyHSD2(CheckTuckeyHSDMixin): @classmethod def setup_class(self): #balanced case self.endog = dta2['StressReduction'] self.groups = dta2['Treatment'] self.alpha = 0.05 self.setup_class_() #in super #from R tukeyhsd2s = np.array([ 1.5,1,-0.5,0.3214915, -0.1785085,-1.678509,2.678509,2.178509, 0.6785085,0.01056279,0.1079035,0.5513904] ).reshape(3,4, order='F') self.meandiff2 = tukeyhsd2s[:, 0] self.confint2 = tukeyhsd2s[:, 1:3] pvals = tukeyhsd2s[:, 3] self.reject2 = pvals < 0.05 class TestTuckeyHSD2s(CheckTuckeyHSDMixin): @classmethod def setup_class(self): #unbalanced case self.endog = dta2['StressReduction'][3:29] self.groups = dta2['Treatment'][3:29] self.alpha = 0.01 self.setup_class_() #from R tukeyhsd2s = np.array( [1.8888888888888889, 0.888888888888889, -1, 0.2658549, -0.5908785, -2.587133, 3.511923, 2.368656, 0.5871331, 0.002837638, 0.150456, 0.1266072] ).reshape(3,4, order='F') self.meandiff2 = tukeyhsd2s[:, 0] self.confint2 = tukeyhsd2s[:, 1:3] pvals = tukeyhsd2s[:, 3] self.reject2 = pvals < 0.01 class TestTuckeyHSD3(CheckTuckeyHSDMixin): @classmethod def setup_class(self): #SAS case self.endog = dta3['Relief'] self.groups = dta3['Brand'] self.alpha = 0.05 self.setup_class_() #super(self, self).setup_class_() #CheckTuckeyHSD.setup_class_() self.meandiff2 = sas_['mean'] self.confint2 = sas_[['lower','upper']].view(float).reshape((3,2)) self.reject2 = sas_['sig'] == asbytes('***') class TestTuckeyHSD4(CheckTuckeyHSDMixin): @classmethod def setup_class(self): #unbalanced case verified in Matlab self.endog = cylinders self.groups = cyl_labels self.alpha = 0.05 self.setup_class_() self.res._simultaneous_ci() #from Matlab self.halfwidth2 = np.array([1.5228335685980883, 0.9794949704444682, 0.78673802805533644, 2.3321237694566364, 0.57355135882752939]) self.meandiff2 = np.array([0.22222222222222232, 0.13333333333333375, 0.0, 2.2898550724637685, -0.088888888888888573, -0.22222222222222232, 2.0676328502415462, -0.13333333333333375, 2.1565217391304348, 2.2898550724637685]) self.confint2 = np.array([-2.32022210717, 2.76466655161, -2.247517583, 2.51418424967, -3.66405224956, 3.66405224956, 0.113960166573, 4.46574997835, -1.87278583908, 1.6950080613, -3.529655688, 3.08521124356, 0.568180988881, 3.5670847116, -3.31822643175, 3.05155976508, 0.951206924521, 3.36183655374, -0.74487911754, 5.32458926247]).reshape(10,2) self.reject2 = np.array([False, False, False, True, False, False, True, False, True, False]) def test_hochberg_intervals(self): assert_almost_equal(self.res.halfwidths, self.halfwidth2, 14) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_panel_robustcov.py000066400000000000000000000047331224417117700276410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for panel robust covariance estimators after pooled ols this follows the example from xtscc paper/help Created on Tue May 22 20:27:57 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.regression.linear_model import OLS from statsmodels.tools.tools import add_constant import statsmodels.stats.sandwich_covariance as sw def test_panel_robust_cov(): import pandas as pa import statsmodels.datasets.grunfeld as gr from results.results_panelrobust import results as res_stata dtapa = gr.data.load_pandas() #Stata example/data seems to miss last firm dtapa_endog = dtapa.endog[:200] dtapa_exog = dtapa.exog[:200] res = OLS(dtapa_endog, add_constant(dtapa_exog[['value', 'capital']], prepend=False)).fit() #time indicator in range(max Ti) time = np.asarray(dtapa_exog[['year']]) time -= time.min() time = np.squeeze(time).astype(int) #sw.cov_nw_panel requires bounds instead of index tidx = [(i*20, 20*(i+1)) for i in range(10)] #firm index in range(n_firms) firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'), return_inverse=True) #panel newey west standard errors cov = sw.cov_nw_panel(res, 0, tidx, use_correction='hac') #dropping numpy 1.4 soon #np.testing.assert_allclose(cov, res_stata.cov_pnw0_stata, rtol=1e-6) assert_almost_equal(cov, res_stata.cov_pnw0_stata, decimal=4) cov = sw.cov_nw_panel(res, 1, tidx, use_correction='hac') #np.testing.assert_allclose(cov, res_stata.cov_pnw1_stata, rtol=1e-6) assert_almost_equal(cov, res_stata.cov_pnw1_stata, decimal=4) cov = sw.cov_nw_panel(res, 4, tidx) #check default #np.testing.assert_allclose(cov, res_stata.cov_pnw4_stata, rtol=1e-6) assert_almost_equal(cov, res_stata.cov_pnw4_stata, decimal=4) #cluster robust standard errors cov_clu = sw.cov_cluster(res, firm_id) assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4) #Driscoll and Kraay panel robust standard errors rcov = sw.cov_nw_groupsum(res, 0, time, use_correction=0) assert_almost_equal(rcov, res_stata.cov_dk0_stata, decimal=4) rcov = sw.cov_nw_groupsum(res, 1, time, use_correction=0) assert_almost_equal(rcov, res_stata.cov_dk1_stata, decimal=4) rcov = sw.cov_nw_groupsum(res, 4, time) #check default assert_almost_equal(rcov, res_stata.cov_dk4_stata, decimal=4) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_power.py000066400000000000000000000624341224417117700255720ustar00rootroot00000000000000# -*- coding: utf-8 -*- # pylint: disable=W0231, W0142 """Tests for statistical power calculations Note: tests for chisquare power are in test_gof.py Created on Sat Mar 09 08:44:49 2013 Author: Josef Perktold """ import copy import numpy as np from numpy.testing import (assert_almost_equal, assert_allclose, assert_raises, assert_equal, assert_warns) import statsmodels.stats.power as smp #from .test_weightstats import CheckPowerMixin from statsmodels.stats.tests.test_weightstats import Holder # for testing plots import nose from numpy.testing import dec try: import matplotlib.pyplot as plt #makes plt available for test functions have_matplotlib = True except ImportError: have_matplotlib = False class CheckPowerMixin(object): def test_power(self): #test against R results kwds = copy.copy(self.kwds) del kwds['power'] kwds.update(self.kwds_extra) if hasattr(self, 'decimal'): decimal = self.decimal else: decimal = 6 res1 = self.cls() assert_almost_equal(res1.power(**kwds), self.res2.power, decimal=decimal) def test_positional(self): res1 = self.cls() kwds = copy.copy(self.kwds) del kwds['power'] kwds.update(self.kwds_extra) # positional args if hasattr(self, 'args_names'): args_names = self.args_names else: nobs_ = 'nobs' if 'nobs' in kwds else 'nobs1' args_names = ['effect_size', nobs_, 'alpha'] # pop positional args args = [kwds.pop(arg) for arg in args_names] if hasattr(self, 'decimal'): decimal = self.decimal else: decimal = 6 res = res1.power(*args, **kwds) assert_almost_equal(res, self.res2.power, decimal=decimal) def test_roots(self): kwds = copy.copy(self.kwds) kwds.update(self.kwds_extra) # kwds_extra are used as argument, but not as target for root for key in self.kwds: # keep print to check whether tests are really executed #print 'testing roots', key value = kwds[key] kwds[key] = None result = self.cls().solve_power(**kwds) assert_allclose(result, value, rtol=0.001, err_msg=key+' failed') # yield can be used to investigate specific errors #yield assert_allclose, result, value, 0.001, 0, key+' failed' kwds[key] = value # reset dict @dec.skipif(not have_matplotlib) def test_power_plot(self): if self.cls == smp.FTestPower: raise nose.SkipTest('skip FTestPower plot_power') fig = plt.figure() ax = fig.add_subplot(2,1,1) fig = self.cls().plot_power(dep_var='nobs', nobs= np.arange(2, 100), effect_size=np.array([0.1, 0.2, 0.3, 0.5, 1]), #alternative='larger', ax=ax, title='Power of t-Test', **self.kwds_extra) ax = fig.add_subplot(2,1,2) fig = self.cls().plot_power(dep_var='es', nobs=np.array([10, 20, 30, 50, 70, 100]), effect_size=np.linspace(0.01, 2, 51), #alternative='larger', ax=ax, title='', **self.kwds_extra) plt.close('all') #''' test cases #one sample # two-sided one-sided #large power OneS1 OneS3 #small power OneS2 OneS4 # #two sample # two-sided one-sided #large power TwoS1 TwoS3 #small power TwoS2 TwoS4 #small p, ratio TwoS4 TwoS5 #''' class TestTTPowerOneS1(CheckPowerMixin): def __init__(self): #> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="two.sided") #> cat_items(p, prefix='tt_power2_1.') res2 = Holder() res2.n = 30 res2.d = 1 res2.sig_level = 0.05 res2.power = 0.9995636009612725 res2.alternative = 'two.sided' res2.note = 'NULL' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power':res2.power} self.kwds_extra = {} self.cls = smp.TTestPower class TestTTPowerOneS2(CheckPowerMixin): # case with small power def __init__(self): res2 = Holder() #> p = pwr.t.test(d=0.2,n=20,sig.level=0.05,type="one.sample",alternative="two.sided") #> cat_items(p, "res2.") res2.n = 20 res2.d = 0.2 res2.sig_level = 0.05 res2.power = 0.1359562887679666 res2.alternative = 'two.sided' res2.note = '''NULL''' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power':res2.power} self.kwds_extra = {} self.cls = smp.TTestPower class TestTTPowerOneS3(CheckPowerMixin): def __init__(self): res2 = Holder() #> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="one.sample",alternative="greater") #> cat_items(p, prefix='tt_power1_1g.') res2.n = 30 res2.d = 1 res2.sig_level = 0.05 res2.power = 0.999892010204909 res2.alternative = 'greater' res2.note = 'NULL' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power': res2.power} self.kwds_extra = {'alternative': 'larger'} self.cls = smp.TTestPower class TestTTPowerOneS4(CheckPowerMixin): def __init__(self): res2 = Holder() #> p = pwr.t.test(d=0.05,n=20,sig.level=0.05,type="one.sample",alternative="greater") #> cat_items(p, "res2.") res2.n = 20 res2.d = 0.05 res2.sig_level = 0.05 res2.power = 0.0764888785042198 res2.alternative = 'greater' res2.note = '''NULL''' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power': res2.power} self.kwds_extra = {'alternative': 'larger'} self.cls = smp.TTestPower class TestTTPowerOneS5(CheckPowerMixin): # case one-sided less, not implemented yet def __init__(self): res2 = Holder() #> p = pwr.t.test(d=0.2,n=20,sig.level=0.05,type="one.sample",alternative="less") #> cat_items(p, "res2.") res2.n = 20 res2.d = 0.2 res2.sig_level = 0.05 res2.power = 0.006063932667926375 res2.alternative = 'less' res2.note = '''NULL''' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power': res2.power} self.kwds_extra = {'alternative': 'smaller'} self.cls = smp.TTestPower class TestTTPowerOneS6(CheckPowerMixin): # case one-sided less, negative effect size, not implemented yet def __init__(self): res2 = Holder() #> p = pwr.t.test(d=-0.2,n=20,sig.level=0.05,type="one.sample",alternative="less") #> cat_items(p, "res2.") res2.n = 20 res2.d = -0.2 res2.sig_level = 0.05 res2.power = 0.21707518167191 res2.alternative = 'less' res2.note = '''NULL''' res2.method = 'One-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs': res2.n, 'alpha': res2.sig_level, 'power': res2.power} self.kwds_extra = {'alternative': 'smaller'} self.cls = smp.TTestPower class TestTTPowerTwoS1(CheckPowerMixin): def __init__(self): #> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="two.sided") #> cat_items(p, prefix='tt_power2_1.') res2 = Holder() res2.n = 30 res2.d = 1 res2.sig_level = 0.05 res2.power = 0.967708258242517 res2.alternative = 'two.sided' res2.note = 'n is number in *each* group' res2.method = 'Two-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power': res2.power, 'ratio': 1} self.kwds_extra = {} self.cls = smp.TTestIndPower class TestTTPowerTwoS2(CheckPowerMixin): def __init__(self): res2 = Holder() #> p = pwr.t.test(d=0.1,n=20,sig.level=0.05,type="two.sample",alternative="two.sided") #> cat_items(p, "res2.") res2.n = 20 res2.d = 0.1 res2.sig_level = 0.05 res2.power = 0.06095912465411235 res2.alternative = 'two.sided' res2.note = 'n is number in *each* group' res2.method = 'Two-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power': res2.power, 'ratio': 1} self.kwds_extra = {} self.cls = smp.TTestIndPower class TestTTPowerTwoS3(CheckPowerMixin): def __init__(self): res2 = Holder() #> p = pwr.t.test(d=1,n=30,sig.level=0.05,type="two.sample",alternative="greater") #> cat_items(p, prefix='tt_power2_1g.') res2.n = 30 res2.d = 1 res2.sig_level = 0.05 res2.power = 0.985459690251624 res2.alternative = 'greater' res2.note = 'n is number in *each* group' res2.method = 'Two-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1} self.kwds_extra = {'alternative': 'larger'} self.cls = smp.TTestIndPower class TestTTPowerTwoS4(CheckPowerMixin): # case with small power def __init__(self): res2 = Holder() #> p = pwr.t.test(d=0.01,n=30,sig.level=0.05,type="two.sample",alternative="greater") #> cat_items(p, "res2.") res2.n = 30 res2.d = 0.01 res2.sig_level = 0.05 res2.power = 0.0540740302835667 res2.alternative = 'greater' res2.note = 'n is number in *each* group' res2.method = 'Two-sample t test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power} self.kwds_extra = {'alternative': 'larger'} self.cls = smp.TTestIndPower class TestTTPowerTwoS5(CheckPowerMixin): # case with unequal n, ratio>1 def __init__(self): res2 = Holder() #> p = pwr.t2n.test(d=0.1,n1=20, n2=30,sig.level=0.05,alternative="two.sided") #> cat_items(p, "res2.") res2.n1 = 20 res2.n2 = 30 res2.d = 0.1 res2.sig_level = 0.05 res2.power = 0.0633081832564667 res2.alternative = 'two.sided' res2.method = 't test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n1, 'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5} self.kwds_extra = {'alternative': 'two-sided'} self.cls = smp.TTestIndPower class TestTTPowerTwoS6(CheckPowerMixin): # case with unequal n, ratio>1 def __init__(self): res2 = Holder() #> p = pwr.t2n.test(d=0.1,n1=20, n2=30,sig.level=0.05,alternative="greater") #> cat_items(p, "res2.") res2.n1 = 20 res2.n2 = 30 res2.d = 0.1 res2.sig_level = 0.05 res2.power = 0.09623589080917805 res2.alternative = 'greater' res2.method = 't test power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n1, 'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1.5} self.kwds_extra = {'alternative': 'larger'} self.cls = smp.TTestIndPower def test_normal_power_explicit(): # a few initial test cases for NormalIndPower sigma = 1 d = 0.3 nobs = 80 alpha = 0.05 res1 = smp.normal_power(d, nobs/2., 0.05) res2 = smp.NormalIndPower().power(d, nobs, 0.05) res3 = smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None) res_R = 0.475100870572638 assert_almost_equal(res1, res_R, decimal=13) assert_almost_equal(res2, res_R, decimal=13) assert_almost_equal(res3, res_R, decimal=13) norm_pow = smp.normal_power(-0.01, nobs/2., 0.05) norm_pow_R = 0.05045832927039234 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided") assert_almost_equal(norm_pow, norm_pow_R, decimal=11) norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05, alternative="larger") norm_pow_R = 0.056869534873146124 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater") assert_almost_equal(norm_pow, norm_pow_R, decimal=11) # Note: negative effect size is same as switching one-sided alternative # TODO: should I switch to larger/smaller instead of "one-sided" options norm_pow = smp.NormalIndPower().power(-0.01, nobs, 0.05, alternative="larger") norm_pow_R = 0.0438089705093578 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less") assert_almost_equal(norm_pow, norm_pow_R, decimal=11) class TestNormalIndPower1(CheckPowerMixin): def __init__(self): #> example from above # results copied not directly from R res2 = Holder() res2.n = 80 res2.d = 0.3 res2.sig_level = 0.05 res2.power = 0.475100870572638 res2.alternative = 'two.sided' res2.note = 'NULL' res2.method = 'two sample power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1} self.kwds_extra = {} self.cls = smp.NormalIndPower class TestNormalIndPower2(CheckPowerMixin): def __init__(self): res2 = Holder() #> np = pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less") #> cat_items(np, "res2.") res2.h = 0.01 res2.n = 80 res2.sig_level = 0.05 res2.power = 0.0438089705093578 res2.alternative = 'less' res2.method = ('Difference of proportion power calculation for' + ' binomial distribution (arcsine transformation)') res2.note = 'same sample sizes' self.res2 = res2 self.kwds = {'effect_size': res2.h, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power, 'ratio': 1} self.kwds_extra = {'alternative':'smaller'} self.cls = smp.NormalIndPower class TestNormalIndPower_onesamp1(CheckPowerMixin): def __init__(self): # forcing one-sample by using ratio=0 #> example from above # results copied not directly from R res2 = Holder() res2.n = 40 res2.d = 0.3 res2.sig_level = 0.05 res2.power = 0.475100870572638 res2.alternative = 'two.sided' res2.note = 'NULL' res2.method = 'two sample power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power} # keyword for which we don't look for root: self.kwds_extra = {'ratio': 0} self.cls = smp.NormalIndPower class TestNormalIndPower_onesamp2(CheckPowerMixin): # Note: same power as two sample case with twice as many observations def __init__(self): # forcing one-sample by using ratio=0 res2 = Holder() #> np = pwr.norm.test(d=0.01,n=40,sig.level=0.05,alternative="less") #> cat_items(np, "res2.") res2.d = 0.01 res2.n = 40 res2.sig_level = 0.05 res2.power = 0.0438089705093578 res2.alternative = 'less' res2.method = 'Mean power calculation for normal distribution with known variance' self.res2 = res2 self.kwds = {'effect_size': res2.d, 'nobs1': res2.n, 'alpha': res2.sig_level, 'power':res2.power} # keyword for which we don't look for root: self.kwds_extra = {'ratio': 0, 'alternative':'smaller'} self.cls = smp.NormalIndPower class TestChisquarePower(CheckPowerMixin): def __init__(self): # one example from test_gof, results_power res2 = Holder() res2.w = 0.1 res2.N = 5 res2.df = 4 res2.sig_level = 0.05 res2.power = 0.05246644635810126 res2.method = 'Chi squared power calculation' res2.note = 'N is the number of observations' self.res2 = res2 self.kwds = {'effect_size': res2.w, 'nobs': res2.N, 'alpha': res2.sig_level, 'power':res2.power} # keyword for which we don't look for root: # solving for n_bins doesn't work, will not be used in regular usage self.kwds_extra = {'n_bins': res2.df + 1} self.cls = smp.GofChisquarePower def _test_positional(self): res1 = self.cls() args_names = ['effect_size','nobs', 'alpha', 'n_bins'] kwds = copy.copy(self.kwds) del kwds['power'] kwds.update(self.kwds_extra) args = [kwds[arg] for arg in args_names] if hasattr(self, 'decimal'): decimal = self.decimal #pylint: disable-msg=E1101 else: decimal = 6 assert_almost_equal(res1.power(*args), self.res2.power, decimal=decimal) def test_ftest_power(): #equivalence ftest, ttest for alpha in [0.01, 0.05, 0.1, 0.20, 0.50]: res0 = smp.ttest_power(0.01, 200, alpha) res1 = smp.ftest_power(0.01, 199, 1, alpha=alpha, ncc=0) assert_almost_equal(res1, res0, decimal=6) #example from Gplus documentation F-test ANOVA #Total sample size:200 #Effect size "f":0.25 #Beta/alpha ratio:1 #Result: #Alpha:0.1592 #Power (1-beta):0.8408 #Critical F:1.4762 #Lambda: 12.50000 res1 = smp.ftest_anova_power(0.25, 200, 0.1592, k_groups=10) res0 = 0.8408 assert_almost_equal(res1, res0, decimal=4) # TODO: no class yet # examples agains R::pwr res2 = Holder() #> rf = pwr.f2.test(u=5, v=199, f2=0.1**2, sig.level=0.01) #> cat_items(rf, "res2.") res2.u = 5 res2.v = 199 res2.f2 = 0.01 res2.sig_level = 0.01 res2.power = 0.0494137732920332 res2.method = 'Multiple regression power calculation' res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u, alpha=res2.sig_level, ncc=1) assert_almost_equal(res1, res2.power, decimal=5) res2 = Holder() #> rf = pwr.f2.test(u=5, v=199, f2=0.3**2, sig.level=0.01) #> cat_items(rf, "res2.") res2.u = 5 res2.v = 199 res2.f2 = 0.09 res2.sig_level = 0.01 res2.power = 0.7967191006290872 res2.method = 'Multiple regression power calculation' res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u, alpha=res2.sig_level, ncc=1) assert_almost_equal(res1, res2.power, decimal=5) res2 = Holder() #> rf = pwr.f2.test(u=5, v=19, f2=0.3**2, sig.level=0.1) #> cat_items(rf, "res2.") res2.u = 5 res2.v = 19 res2.f2 = 0.09 res2.sig_level = 0.1 res2.power = 0.235454222377575 res2.method = 'Multiple regression power calculation' res1 = smp.ftest_power(np.sqrt(res2.f2), res2.v, res2.u, alpha=res2.sig_level, ncc=1) assert_almost_equal(res1, res2.power, decimal=5) # class based version of two above test for Ftest class TestFtestAnovaPower(CheckPowerMixin): def __init__(self): res2 = Holder() #example from Gplus documentation F-test ANOVA #Total sample size:200 #Effect size "f":0.25 #Beta/alpha ratio:1 #Result: #Alpha:0.1592 #Power (1-beta):0.8408 #Critical F:1.4762 #Lambda: 12.50000 #converted to res2 by hand res2.f = 0.25 res2.n = 200 res2.k = 10 res2.alpha = 0.1592 res2.power = 0.8408 res2.method = 'Multiple regression power calculation' self.res2 = res2 self.kwds = {'effect_size': res2.f, 'nobs': res2.n, 'alpha': res2.alpha, 'power': res2.power} # keyword for which we don't look for root: # solving for n_bins doesn't work, will not be used in regular usage self.kwds_extra = {'k_groups': res2.k} # rootfinding doesn't work #self.args_names = ['effect_size','nobs', 'alpha']#, 'k_groups'] self.cls = smp.FTestAnovaPower # precision for test_power self.decimal = 4 class TestFtestPower(CheckPowerMixin): def __init__(self): res2 = Holder() #> rf = pwr.f2.test(u=5, v=19, f2=0.3**2, sig.level=0.1) #> cat_items(rf, "res2.") res2.u = 5 res2.v = 19 res2.f2 = 0.09 res2.sig_level = 0.1 res2.power = 0.235454222377575 res2.method = 'Multiple regression power calculation' self.res2 = res2 self.kwds = {'effect_size': np.sqrt(res2.f2), 'df_num': res2.v, 'df_denom': res2.u, 'alpha': res2.sig_level, 'power': res2.power} # keyword for which we don't look for root: # solving for n_bins doesn't work, will not be used in regular usage self.kwds_extra = {} self.args_names = ['effect_size', 'df_num', 'df_denom', 'alpha'] self.cls = smp.FTestPower # precision for test_power self.decimal = 5 def test_power_solver(): # messing up the solver to trigger backup nip = smp.NormalIndPower() # check result es0 = 0.1 pow_ = nip.solve_power(es0, nobs1=1600, alpha=0.01, power=None, ratio=1, alternative='larger') # value is regression test assert_almost_equal(pow_, 0.69219411243824214, decimal=5) es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1, alternative='larger') assert_almost_equal(es, es0, decimal=4) assert_equal(nip.cache_fit_res[0], 1) assert_equal(len(nip.cache_fit_res), 2) # cause first optimizer to fail nip.start_bqexp['effect_size'] = {'upp': -10, 'low': -20} nip.start_ttp['effect_size'] = 0.14 es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1, alternative='larger') assert_almost_equal(es, es0, decimal=4) assert_equal(nip.cache_fit_res[0], 1) assert_equal(len(nip.cache_fit_res), 3, err_msg=repr(nip.cache_fit_res)) nip.start_ttp['effect_size'] = np.nan es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1, alternative='larger') assert_almost_equal(es, es0, decimal=4) assert_equal(nip.cache_fit_res[0], 1) assert_equal(len(nip.cache_fit_res), 4) # I let this case fail, could be fixed for some statistical tests # (we shouldn't get here in the first place) # effect size is negative, but last stage brentq uses [1e-8, 1-1e-8] assert_raises(ValueError, nip.solve_power, None, nobs1=1600, alpha=0.01, power=0.005, ratio=1, alternative='larger') def test_power_solver_warn(): # messing up the solver to trigger warning # I wrote this with scipy 0.9, # convergence behavior of scipy 0.11 is different, # fails at a different case, but is successful where it failed before pow_ = 0.69219411243824214 # from previous function nip = smp.NormalIndPower() # using nobs, has one backup (fsolve) nip.start_bqexp['nobs1'] = {'upp': 50, 'low': -20} val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger') import scipy if scipy.__version__ < '0.10': assert_almost_equal(val, 1600, decimal=4) assert_equal(nip.cache_fit_res[0], 1) assert_equal(len(nip.cache_fit_res), 3) # case that has convergence failure, and should warn nip.start_ttp['nobs1'] = np.nan from statsmodels.tools.sm_exceptions import ConvergenceWarning assert_warns(ConvergenceWarning, nip.solve_power, 0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger') # this converges with scipy 0.11 ??? # nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger') import warnings with warnings.catch_warnings(): # python >= 2.6 warnings.simplefilter("ignore") val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger') assert_equal(nip.cache_fit_res[0], 0) assert_equal(len(nip.cache_fit_res), 3) if __name__ == '__main__': test_normal_power_explicit() nt = TestNormalIndPower1() nt.test_power() nt.test_roots() nt = TestNormalIndPower_onesamp1() nt.test_power() nt.test_roots() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_proportion.py000066400000000000000000000463061224417117700266510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Mar 01 14:56:56 2013 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_array_less from statsmodels.stats.proportion import proportion_confint import statsmodels.stats.proportion as smprop class Holder(object): pass def test_confint_proportion(): from results.results_proportion import res_binom, res_binom_methods methods = {'agresti_coull' : 'agresti-coull', 'normal' : 'asymptotic', 'beta' : 'exact', 'wilson' : 'wilson', 'jeffrey' : 'bayes' } for case in res_binom: count, nobs = case for method in methods: idx = res_binom_methods.index(methods[method]) res_low = res_binom[case].ci_low[idx] res_upp = res_binom[case].ci_upp[idx] if np.isnan(res_low) or np.isnan(res_upp): continue ci = proportion_confint(count, nobs, alpha=0.05, method=method) assert_almost_equal(ci, [res_low, res_upp], decimal=6, err_msg=repr(case) + method) def test_samplesize_confidenceinterval_prop(): #consistency test for samplesize to achieve confidence_interval nobs = 20 ci = smprop.proportion_confint(12, nobs, alpha=0.05, method='normal') res = smprop.samplesize_confint_proportion(12./nobs, (ci[1] - ci[0]) / 2) assert_almost_equal(res, nobs, decimal=13) def test_proportion_effect_size(): # example from blog es = smprop.proportion_effectsize(0.5, 0.4) assert_almost_equal(es, 0.2013579207903309, decimal=13) class CheckProportionMixin(object): def test_proptest(self): # equality of k-samples pt = smprop.proportions_chisquare(self.n_success, self.nobs, value=None) assert_almost_equal(pt[0], self.res_prop_test.statistic, decimal=13) assert_almost_equal(pt[1], self.res_prop_test.p_value, decimal=13) # several against value pt = smprop.proportions_chisquare(self.n_success, self.nobs, value=self.res_prop_test_val.null_value[0]) assert_almost_equal(pt[0], self.res_prop_test_val.statistic, decimal=13) assert_almost_equal(pt[1], self.res_prop_test_val.p_value, decimal=13) # one proportion against value pt = smprop.proportions_chisquare(self.n_success[0], self.nobs[0], value=self.res_prop_test_1.null_value) assert_almost_equal(pt[0], self.res_prop_test_1.statistic, decimal=13) assert_almost_equal(pt[1], self.res_prop_test_1.p_value, decimal=13) def test_pairwiseproptest(self): ppt = smprop.proportions_chisquare_allpairs(self.n_success, self.nobs, multitest_method=None) assert_almost_equal(ppt.pvals_raw, self.res_ppt_pvals_raw) ppt = smprop.proportions_chisquare_allpairs(self.n_success, self.nobs, multitest_method='h') assert_almost_equal(ppt.pval_corrected(), self.res_ppt_pvals_holm) pptd = smprop.proportions_chisquare_pairscontrol(self.n_success, self.nobs, multitest_method='hommel') assert_almost_equal(pptd.pvals_raw, ppt.pvals_raw[:len(self.nobs) - 1], decimal=13) class TestProportion(CheckProportionMixin): def setup(self): self.n_success = np.array([ 73, 90, 114, 75]) self.nobs = np.array([ 86, 93, 136, 82]) self.res_ppt_pvals_raw = np.array([ 0.00533824886503131, 0.8327574849753566, 0.1880573726722516, 0.002026764254350234, 0.1309487516334318, 0.1076118730631731 ]) self.res_ppt_pvals_holm = np.array([ 0.02669124432515654, 0.8327574849753566, 0.4304474922526926, 0.0121605855261014, 0.4304474922526926, 0.4304474922526926 ]) res_prop_test = Holder() res_prop_test.statistic = 11.11938768628861 res_prop_test.parameter = 3 res_prop_test.p_value = 0.011097511366581344 res_prop_test.estimate = np.array([ 0.848837209302326, 0.967741935483871, 0.838235294117647, 0.9146341463414634 ]).reshape(4,1, order='F') res_prop_test.null_value = '''NULL''' res_prop_test.conf_int = '''NULL''' res_prop_test.alternative = 'two.sided' res_prop_test.method = '4-sample test for equality of proportions ' + \ 'without continuity correction' res_prop_test.data_name = 'smokers2 out of patients' self.res_prop_test = res_prop_test #> pt = prop.test(smokers2, patients, p=rep(c(0.9), 4), correct=FALSE) #> cat_items(pt, "res_prop_test_val.") res_prop_test_val = Holder() res_prop_test_val.statistic = np.array([ 13.20305530710751 ]).reshape(1,1, order='F') res_prop_test_val.parameter = np.array([ 4 ]).reshape(1,1, order='F') res_prop_test_val.p_value = 0.010325090041836 res_prop_test_val.estimate = np.array([ 0.848837209302326, 0.967741935483871, 0.838235294117647, 0.9146341463414634 ]).reshape(4,1, order='F') res_prop_test_val.null_value = np.array([ 0.9, 0.9, 0.9, 0.9 ]).reshape(4,1, order='F') res_prop_test_val.conf_int = '''NULL''' res_prop_test_val.alternative = 'two.sided' res_prop_test_val.method = '4-sample test for given proportions without continuity correction' res_prop_test_val.data_name = 'smokers2 out of patients, null probabilities rep(c(0.9), 4)' self.res_prop_test_val = res_prop_test_val #> pt = prop.test(smokers2[1], patients[1], p=0.9, correct=FALSE) #> cat_items(pt, "res_prop_test_1.") res_prop_test_1 = Holder() res_prop_test_1.statistic = 2.501291989664086 res_prop_test_1.parameter = 1 res_prop_test_1.p_value = 0.113752943640092 res_prop_test_1.estimate = 0.848837209302326 res_prop_test_1.null_value = 0.9 res_prop_test_1.conf_int = np.array([0.758364348004061, 0.9094787701686766]) res_prop_test_1.alternative = 'two.sided' res_prop_test_1.method = '1-sample proportions test without continuity correction' res_prop_test_1.data_name = 'smokers2[1] out of patients[1], null probability 0.9' self.res_prop_test_1 = res_prop_test_1 def test_binom_test(): #> bt = binom.test(51,235,(1/6),alternative="less") #> cat_items(bt, "binom_test_less.") binom_test_less = Holder() binom_test_less.statistic = 51 binom_test_less.parameter = 235 binom_test_less.p_value = 0.982022657605858 binom_test_less.conf_int = [0, 0.2659460862574313] binom_test_less.estimate = 0.2170212765957447 binom_test_less.null_value = 1. / 6 binom_test_less.alternative = 'less' binom_test_less.method = 'Exact binomial test' binom_test_less.data_name = '51 and 235' #> bt = binom.test(51,235,(1/6),alternative="greater") #> cat_items(bt, "binom_test_greater.") binom_test_greater = Holder() binom_test_greater.statistic = 51 binom_test_greater.parameter = 235 binom_test_greater.p_value = 0.02654424571169085 binom_test_greater.conf_int = [0.1735252778065201, 1] binom_test_greater.estimate = 0.2170212765957447 binom_test_greater.null_value = 1. / 6 binom_test_greater.alternative = 'greater' binom_test_greater.method = 'Exact binomial test' binom_test_greater.data_name = '51 and 235' #> bt = binom.test(51,235,(1/6),alternative="t") #> cat_items(bt, "binom_test_2sided.") binom_test_2sided = Holder() binom_test_2sided.statistic = 51 binom_test_2sided.parameter = 235 binom_test_2sided.p_value = 0.0437479701823997 binom_test_2sided.conf_int = [0.1660633298083073, 0.2752683640289254] binom_test_2sided.estimate = 0.2170212765957447 binom_test_2sided.null_value = 1. / 6 binom_test_2sided.alternative = 'two.sided' binom_test_2sided.method = 'Exact binomial test' binom_test_2sided.data_name = '51 and 235' alltests = [('larger', binom_test_greater), ('smaller', binom_test_less), ('two-sided', binom_test_2sided)] for alt, res0 in alltests: # only p-value is returned res = smprop.binom_test(51, 235, prop=1. / 6, alternative=alt) #assert_almost_equal(res[0], res0.statistic) assert_almost_equal(res, res0.p_value, decimal=13) # R binom_test returns Copper-Pearson confint ci_2s = smprop.proportion_confint(51, 235, alpha=0.05, method='beta') ci_low, ci_upp = smprop.proportion_confint(51, 235, alpha=0.1, method='beta') assert_almost_equal(ci_2s, binom_test_2sided.conf_int, decimal=13) assert_almost_equal(ci_upp, binom_test_less.conf_int[1], decimal=13) assert_almost_equal(ci_low, binom_test_greater.conf_int[0], decimal=13) def test_binom_rejection_interval(): # consistency check with binom_test # some code duplication but limit checks are different alpha = 0.05 nobs = 200 prop = 12./20 alternative='smaller' ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha, alternative=alternative) assert_equal(ci_upp, nobs) pval = smprop.binom_test(ci_low, nobs, prop=prop, alternative=alternative) assert_array_less(pval, alpha) pval = smprop.binom_test(ci_low + 1, nobs, prop=prop, alternative=alternative) assert_array_less(alpha, pval) alternative='larger' ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha, alternative=alternative) assert_equal(ci_low, 0) pval = smprop.binom_test(ci_upp, nobs, prop=prop, alternative=alternative) assert_array_less(pval, alpha) pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop, alternative=alternative) assert_array_less(alpha, pval) alternative='two-sided' ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha, alternative=alternative) pval = smprop.binom_test(ci_upp, nobs, prop=prop, alternative=alternative) assert_array_less(pval, alpha) pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop, alternative=alternative) assert_array_less(alpha, pval) pval = smprop.binom_test(ci_upp, nobs, prop=prop, alternative=alternative) assert_array_less(pval, alpha) pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop, alternative=alternative) assert_array_less(alpha, pval) def test_binom_tost(): # consistency check with two different implementation, # proportion_confint is tested against R # no reference case from other package available ci = smprop.proportion_confint(10, 20, method='beta', alpha=0.1) bt = smprop.binom_tost(10, 20, *ci) assert_almost_equal(bt, [0.05] * 3, decimal=12) ci = smprop.proportion_confint(5, 20, method='beta', alpha=0.1) bt = smprop.binom_tost(5, 20, *ci) assert_almost_equal(bt, [0.05] * 3, decimal=12) # vectorized, TODO: observed proportion = 0 returns nan ci = smprop.proportion_confint(np.arange(1, 20), 20, method='beta', alpha=0.05) bt = smprop.binom_tost(np.arange(1, 20), 20, *ci) bt = np.asarray(bt) assert_almost_equal(bt, 0.025 * np.ones(bt.shape), decimal=12) def test_power_binom_tost(): # comparison numbers from PASS manual p_alt = 0.6 + np.linspace(0, 0.09, 10) power = smprop.power_binom_tost(0.5, 0.7, 500, p_alt=p_alt, alpha=0.05) res_power = np.array([0.9965, 0.9940, 0.9815, 0.9482, 0.8783, 0.7583, 0.5914, 0.4041, 0.2352, 0.1139]) assert_almost_equal(power, res_power, decimal=4) rej_int = smprop.binom_tost_reject_interval(0.5, 0.7, 500) res_rej_int = (269, 332) assert_equal(rej_int, res_rej_int) # TODO: actual alpha=0.0489 for all p_alt above # another case nobs = np.arange(20, 210, 20) power = smprop.power_binom_tost(0.4, 0.6, nobs, p_alt=0.5, alpha=0.05) res_power = np.array([ 0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457, 0.6154, 0.6674, 0.7708]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) def test_power_ztost_prop(): power = smprop.power_ztost_prop(0.1, 0.9, 10, p_alt=0.6, alpha=0.05, discrete=True, dist='binom')[0] assert_almost_equal(power, 0.8204, decimal=4) # PASS example import warnings with warnings.catch_warnings(): # python >= 2.6 warnings.simplefilter("ignore") power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=False, dist='binom')[0] res_power = np.array([ 0., 0., 0., 0.0889, 0.2356, 0.4770, 0.5530, 0.6154, 0.7365, 0.7708]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) # with critval_continuity correction power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=False, dist='binom', variance_prop=None, continuity=2, critval_continuity=1)[0] res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457, 0.6154, 0.6674, 0.7708]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=False, dist='binom', variance_prop=0.5, critval_continuity=1)[0] res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457, 0.6154, 0.6674, 0.7112]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) def test_ztost(): xfair = np.repeat([1,0], [228, 762-228]) # comparing to SAS last output at # http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm # confidence interval for tost # generic ztost is moved to weightstats from statsmodels.stats.weightstats import zconfint, ztost ci01 = zconfint(xfair, alpha=0.1, ddof=0) assert_almost_equal(ci01, [0.2719, 0.3265], 4) res = ztost(xfair, 0.18, 0.38, ddof=0) assert_almost_equal(res[1][0], 7.1865, 4) assert_almost_equal(res[2][0], -4.8701, 4) assert_array_less(res[0], 0.0001) def test_power_ztost_prop_norm(): # regression test for normal distribution # from a rough comparison, the results and variations look reasonable power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=False, dist='norm', variance_prop=0.5, continuity=0, critval_continuity=0)[0] res_power = np.array([0., 0., 0., 0.11450013, 0.27752006, 0.41495922, 0.52944621, 0.62382638, 0.70092914, 0.76341806]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) # regression test for normal distribution power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=False, dist='norm', variance_prop=0.5, continuity=1, critval_continuity=0)[0] res_power = np.array([0., 0., 0.02667562, 0.20189793, 0.35099606, 0.47608598, 0.57981118, 0.66496683, 0.73427591, 0.79026127]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) # regression test for normal distribution power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=True, dist='norm', variance_prop=0.5, continuity=1, critval_continuity=0)[0] res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313, 0.55312718, 0.61549537, 0.66743625, 0.77066806]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) # regression test for normal distribution power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=True, dist='norm', variance_prop=0.5, continuity=1, critval_continuity=1)[0] res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313, 0.44588687, 0.61549537, 0.66743625, 0.71115563]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) # regression test for normal distribution power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20), p_alt=0.5, alpha=0.05, discrete=True, dist='norm', variance_prop=None, continuity=0, critval_continuity=0)[0] res_power = np.array([0., 0., 0., 0., 0.15851942, 0.41611758, 0.5010377 , 0.5708047 , 0.70328247, 0.74210096]) # TODO: I currently don't impose power>=0, i.e np.maximum(power, 0) assert_almost_equal(np.maximum(power, 0), res_power, decimal=4) def test_proportion_ztests(): # currently only consistency test with proportions chisquare # Note: alternative handling is generic res1 = smprop.proportions_ztest(15, 20., value=0.5, prop_var=0.5) res2 = smprop.proportions_chisquare(15, 20., value=0.5) assert_almost_equal(res1[1], res2[1], decimal=13) res1 = smprop.proportions_ztest(np.asarray([15, 10]), np.asarray([20., 20]), value=0, prop_var=None) res2 = smprop.proportions_chisquare(np.asarray([15, 10]), np.asarray([20., 20])) # test only p-value assert_almost_equal(res1[1], res2[1], decimal=13) if __name__ == '__main__': test_confint_proportion() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_qsturng.py000066400000000000000000000014361224417117700261340ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Mar 28 13:49:11 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.stats.libqsturng import qsturng, psturng from statsmodels.sandbox.stats.multicomp import get_tukeyQcrit def test_qstrung(): rows = [ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 30, 40, 60, 120, 9999] cols = np.arange(2,11) for alpha in [0.01, 0.05]: for k in cols: c1 = get_tukeyQcrit(k, rows, alpha=alpha) c2 = qsturng(1-alpha, k, rows) assert_almost_equal(c1, c2, decimal=2) #roundtrip assert_almost_equal(psturng(qsturng(1-alpha, k, rows), k, rows), alpha, 5) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_sandwich.py000066400000000000000000000072701224417117700262330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for sandwich robust covariance estimation see also in regression for cov_hac compared to Gretl and sandbox.panel test_random_panel for comparing cov_cluster, cov_hac_panel and cov_white Created on Sat Dec 17 08:39:16 2011 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_almost_equal from statsmodels.regression.linear_model import OLS, GLSAR from statsmodels.tools.tools import add_constant import statsmodels.stats.sandwich_covariance as sw #import statsmodels.sandbox.panel.sandwich_covariance_generic as swg def test_cov_cluster_2groups(): #comparing cluster robust standard errors to Peterson #requires Petersen's test_data #http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt import os cur_dir = os.path.abspath(os.path.dirname(__file__)) fpath = os.path.join(cur_dir,"test_data.txt") pet = np.genfromtxt(fpath) endog = pet[:,-1] group = pet[:,0].astype(int) time = pet[:,1].astype(int) exog = add_constant(pet[:,2]) res = OLS(endog, exog).fit() cov01, covg, covt = sw.cov_cluster_2groups(res, group, group2=time) #Reference number from Petersen #http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.htm bse_petw = [0.0284, 0.0284] bse_pet0 = [0.0670, 0.0506] bse_pet1 = [0.0234, 0.0334] #year bse_pet01 = [0.0651, 0.0536] #firm and year bse_0 = sw.se_cov(covg) bse_1 = sw.se_cov(covt) bse_01 = sw.se_cov(cov01) #print res.HC0_se, bse_petw - res.HC0_se #print bse_0, bse_0 - bse_pet0 #print bse_1, bse_1 - bse_pet1 #print bse_01, bse_01 - bse_pet01 assert_almost_equal(bse_petw, res.HC0_se, decimal=4) assert_almost_equal(bse_0, bse_pet0, decimal=4) assert_almost_equal(bse_1, bse_pet1, decimal=4) assert_almost_equal(bse_01, bse_pet01, decimal=4) def test_hac_simple(): from statsmodels.datasets import macrodata d2 = macrodata.load().data g_gdp = 400*np.diff(np.log(d2['realgdp'])) g_inv = 400*np.diff(np.log(d2['realinv'])) exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]]) res_olsg = OLS(g_inv, exogg).fit() #> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=TRUE) #Lag truncation parameter chosen: 4 # (Intercept) ggdp lint cov1_r = [[ 1.40643899878678802, -0.3180328707083329709, -0.060621111216488610], [ -0.31803287070833292, 0.1097308348999818661, 0.000395311760301478], [ -0.06062111121648865, 0.0003953117603014895, 0.087511528912470993]] #> NeweyWest(fm, lag = 4, prewhite = FALSE, sandwich = TRUE, verbose=TRUE, adjust=FALSE) #Lag truncation parameter chosen: 4 # (Intercept) ggdp lint cov2_r = [[ 1.3855512908840137, -0.313309610252268500, -0.059720797683570477], [ -0.3133096102522685, 0.108101169035130618, 0.000389440793564339], [ -0.0597207976835705, 0.000389440793564336, 0.086211852740503622]] cov1 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=True) se1 = sw.se_cov(cov1) cov2 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False) se2 = sw.se_cov(cov2) assert_almost_equal(cov1, cov1_r, decimal=14) assert_almost_equal(cov2, cov2_r, decimal=14) # compare default for nlags cov3 = sw.cov_hac_simple(res_olsg, use_correction=False) cov4 = sw.cov_hac_simple(res_olsg, nlags=4, use_correction=False) assert_almost_equal(cov3, cov4, decimal=14) if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x'], exit=False) #test_hac_simple() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_statstools.py000066400000000000000000000122031224417117700266420ustar00rootroot00000000000000 import numpy as np import pandas as pd from numpy.testing import assert_almost_equal from statsmodels.stats.stattools import (omni_normtest, jarque_bera, durbin_watson) from statsmodels.stats.adnorm import normal_ad #a random array, rounded to 4 decimals x = np.array([-0.1184, -1.3403, 0.0063, -0.612 , -0.3869, -0.2313, -2.8485, -0.2167, 0.4153, 1.8492, -0.3706, 0.9726, -0.1501, -0.0337, -1.4423, 1.2489, 0.9182, -0.2331, -0.6182, 0.183 ]) def test_durbin_watson(): #benchmark values from R car::durbinWatsonTest(x) #library("car") #> durbinWatsonTest(x) #[1] 1.95298958377419 #> durbinWatsonTest(x**2) #[1] 1.848802400319998 #> durbinWatsonTest(x[2:20]+0.5*x[1:19]) #[1] 1.09897993228779 #> durbinWatsonTest(x[2:20]+0.8*x[1:19]) #[1] 0.937241876707273 #> durbinWatsonTest(x[2:20]+0.9*x[1:19]) #[1] 0.921488912587806 st_R = 1.95298958377419 assert_almost_equal(durbin_watson(x), st_R, 14) st_R = 1.848802400319998 assert_almost_equal(durbin_watson(x**2), st_R, 14) st_R = 1.09897993228779 assert_almost_equal(durbin_watson(x[1:]+0.5*x[:-1]), st_R, 14) st_R = 0.937241876707273 assert_almost_equal(durbin_watson(x[1:]+0.8*x[:-1]), st_R, 14) st_R = 0.921488912587806 assert_almost_equal(durbin_watson(x[1:]+0.9*x[:-1]), st_R, 14) def test_omni_normtest(): #tests against R fBasics from scipy import stats st_pv_R = np.array( [[3.994138321207883, -1.129304302161460, 1.648881473704978], [0.1357325110375005, 0.2587694866795507, 0.0991719192710234]]) nt = omni_normtest(x) assert_almost_equal(nt, st_pv_R[:,0], 14) st = stats.skewtest(x) assert_almost_equal(st, st_pv_R[:,1], 14) kt = stats.kurtosistest(x) assert_almost_equal(kt, st_pv_R[:,2], 11) st_pv_R = np.array( [[34.523210399523926, 4.429509162503833, 3.860396220444025], [3.186985686465249e-08, 9.444780064482572e-06, 1.132033129378485e-04]]) x2 = x**2 #TODO: fix precision in these test with relative tolerance nt = omni_normtest(x2) assert_almost_equal(nt, st_pv_R[:,0], 12) st = stats.skewtest(x2) assert_almost_equal(st, st_pv_R[:,1], 12) kt = stats.kurtosistest(x2) assert_almost_equal(kt, st_pv_R[:,2], 12) def test_omni_normtest_axis(): #test axis of omni_normtest x = np.random.randn(15, 3) nt1 = omni_normtest(x) nt2 = omni_normtest(x, axis=0) nt3 = omni_normtest(x.T, axis=1) assert_almost_equal(nt2, nt1, decimal=13) assert_almost_equal(nt3, nt1, decimal=13) def test_jarque_bera(): #tests against R fBasics st_pv_R = np.array([1.9662677226861689, 0.3741367669648314]) jb = jarque_bera(x)[:2] assert_almost_equal(jb, st_pv_R, 14) st_pv_R = np.array([78.329987305556, 0.000000000000]) jb = jarque_bera(x**2)[:2] assert_almost_equal(jb, st_pv_R, 13) st_pv_R = np.array([5.7135750796706670, 0.0574530296971343]) jb = jarque_bera(np.log(x**2))[:2] assert_almost_equal(jb, st_pv_R, 14) st_pv_R = np.array([2.6489315748495761, 0.2659449923067881]) jb = jarque_bera(np.exp(-x**2))[:2] assert_almost_equal(jb, st_pv_R, 14) def test_shapiro(): #tests against R fBasics #testing scipy.stats from scipy.stats import shapiro st_pv_R = np.array([0.939984787255526, 0.239621898000460]) sh = shapiro(x) assert_almost_equal(sh, st_pv_R, 4) #st is ok -7.15e-06, pval agrees at -3.05e-10 st_pv_R = np.array([5.799574255943298e-01, 1.838456834681376e-06 * 1e4]) sh = shapiro(x**2)*np.array([1,1e4]) assert_almost_equal(sh, st_pv_R, 5) st_pv_R = np.array([0.91730442643165588, 0.08793704167882448 ]) sh = shapiro(np.log(x**2)) assert_almost_equal(sh, st_pv_R, 5) #diff is [ 9.38773155e-07, 5.48221246e-08] st_pv_R = np.array([0.818361863493919373, 0.001644620895206969 ]) sh = shapiro(np.exp(-x**2)) assert_almost_equal(sh, st_pv_R, 5) def test_adnorm(): #tests against R fBasics st_pv = [] st_pv_R = np.array([0.5867235358882148, 0.1115380760041617]) ad = normal_ad(x) assert_almost_equal(ad, st_pv_R, 12) st_pv.append(st_pv_R) st_pv_R = np.array([2.976266267594575e+00, 8.753003709960645e-08]) ad = normal_ad(x**2) assert_almost_equal(ad, st_pv_R, 11) st_pv.append(st_pv_R) st_pv_R = np.array([0.4892557856308528, 0.1968040759316307]) ad = normal_ad(np.log(x**2)) assert_almost_equal(ad, st_pv_R, 12) st_pv.append(st_pv_R) st_pv_R = np.array([1.4599014654282669312, 0.0006380009232897535]) ad = normal_ad(np.exp(-x**2)) assert_almost_equal(ad, st_pv_R, 12) st_pv.append(st_pv_R) ad = normal_ad(np.column_stack((x,x**2, np.log(x**2),np.exp(-x**2))).T, axis=1) assert_almost_equal(ad, np.column_stack(st_pv), 11) def test_durbin_watson_pandas(): x=np.random.randn(50) x_series=pd.Series(x) assert_almost_equal(durbin_watson(x), durbin_watson(x_series), decimal=13) if __name__ == '__main__': test_durbin_watson() test_omni_normtest() test_jarque_bera() test_shapiro() test_adnorm() test_durbin_watson_pandas() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_tost.py000066400000000000000000000573211224417117700254260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Wed Oct 17 09:48:34 2012 Author: Josef Perktold """ import numpy as np import statsmodels.stats.weightstats as smws from numpy.testing import assert_almost_equal, assert_equal, assert_ def assert_almost_equal_inf(x, y, decimal=6, msg=None): x = np.atleast_1d(x) y = np.atleast_1d(y) assert_equal(np.isposinf(x), np.isposinf(y)) assert_equal(np.isneginf(x), np.isneginf(y)) assert_equal(np.isnan(x), np.isnan(y)) assert_almost_equal(x[np.isfinite(x)], y[np.isfinite(y)]) class Holder(object): pass raw_clinic = '''\ 1 1 2.84 4.00 3.45 2.55 2.46 2 1 2.51 3.26 3.10 2.82 2.48 3 1 2.41 4.14 3.37 2.99 3.04 4 1 2.95 3.42 2.82 3.37 3.35 5 1 3.14 3.25 3.31 2.87 3.41 6 1 3.79 4.34 3.88 3.40 3.16 7 1 4.14 4.97 4.25 3.43 3.06 8 1 3.85 4.31 3.92 3.58 3.91 9 1 3.02 3.11 2.20 2.24 2.28 10 1 3.45 3.41 3.80 3.86 3.91 11 1 5.37 5.02 4.59 3.99 4.27 12 1 3.81 4.21 4.08 3.18 1.86 13 1 4.19 4.59 4.79 4.17 2.60 14 1 3.16 5.30 4.69 4.83 4.51 15 1 3.84 4.32 4.25 3.87 2.93 16 2 2.60 3.76 2.86 2.41 2.71 17 2 2.82 3.66 3.20 2.49 2.49 18 2 2.18 3.65 3.87 3.00 2.65 19 2 3.46 3.60 2.97 1.80 1.74 20 2 4.01 3.48 4.42 3.06 2.76 21 2 3.04 2.87 2.87 2.71 2.87 22 2 3.47 3.24 3.47 3.26 3.14 23 2 4.06 3.92 3.18 3.06 1.74 24 2 2.91 3.99 3.06 2.02 3.18 25 2 3.59 4.21 4.02 3.26 2.85 26 2 4.51 4.21 3.78 2.63 1.92 27 2 3.16 3.31 3.28 3.25 3.52 28 2 3.86 3.61 3.28 3.19 3.09 29 2 3.31 2.97 3.76 3.18 2.60 30 2 3.02 2.73 3.87 3.50 2.93'''.split() clinic = np.array(raw_clinic, float).reshape(-1,7) #t = tost(-clinic$var2[16:30] + clinic$var2[1:15], eps=0.6) tost_clinic_paired = Holder() tost_clinic_paired.sample = 'paired' tost_clinic_paired.mean_diff = 0.5626666666666665 tost_clinic_paired.se_diff = 0.2478276410785118 tost_clinic_paired.alpha = 0.05 tost_clinic_paired.ci_diff = (0.1261653305099018, 0.999168002823431) tost_clinic_paired.df = 14 tost_clinic_paired.epsilon = 0.6 tost_clinic_paired.result = 'not rejected' tost_clinic_paired.p_value = 0.4412034046017588 tost_clinic_paired.check_me = (0.525333333333333, 0.6) #> t = tost(-clinic$var1[16:30] + clinic$var1[1:15], eps=0.6) #> cat_items(t, prefix="tost_clinic_paired_1.") tost_clinic_paired_1 = Holder() tost_clinic_paired_1.mean_diff = 0.1646666666666667 tost_clinic_paired_1.se_diff = 0.1357514067862445 tost_clinic_paired_1.alpha = 0.05 tost_clinic_paired_1.ci_diff = (-0.0744336620516462, 0.4037669953849797) tost_clinic_paired_1.df = 14 tost_clinic_paired_1.epsilon = 0.6 tost_clinic_paired_1.result = 'rejected' tost_clinic_paired_1.p_value = 0.003166881489265175 tost_clinic_paired_1.check_me = (-0.2706666666666674, 0.600000000000001) #> t = tost(clinic$var2[1:15], clinic$var2[16:30], eps=0.6) #> cat_items(t, prefix="tost_clinic_indep.") tost_clinic_indep = Holder() tost_clinic_indep.sample = 'independent' tost_clinic_indep.mean_diff = 0.562666666666666 tost_clinic_indep.se_diff = 0.2149871904637392 tost_clinic_indep.alpha = 0.05 tost_clinic_indep.ci_diff = (0.194916250699966, 0.930417082633366) tost_clinic_indep.df = 24.11000151062728 tost_clinic_indep.epsilon = 0.6 tost_clinic_indep.result = 'not rejected' tost_clinic_indep.p_value = 0.4317936812594803 tost_clinic_indep.check_me = (0.525333333333332, 0.6) #> t = tost(clinic$var1[1:15], clinic$var1[16:30], eps=0.6) #> cat_items(t, prefix="tost_clinic_indep_1.") tost_clinic_indep_1 = Holder() tost_clinic_indep_1.sample = 'independent' tost_clinic_indep_1.mean_diff = 0.1646666666666667 tost_clinic_indep_1.se_diff = 0.2531625991083627 tost_clinic_indep_1.alpha = 0.05 tost_clinic_indep_1.ci_diff = (-0.2666862980722534, 0.596019631405587) tost_clinic_indep_1.df = 26.7484787582315 tost_clinic_indep_1.epsilon = 0.6 tost_clinic_indep_1.result = 'rejected' tost_clinic_indep_1.p_value = 0.04853083976236974 tost_clinic_indep_1.check_me = (-0.2706666666666666, 0.6) #pooled variance #> t = tost(clinic$var1[1:15], clinic$var1[16:30], eps=0.6, var.equal = TRUE) #> cat_items(t, prefix="tost_clinic_indep_1_pooled.") tost_clinic_indep_1_pooled = Holder() tost_clinic_indep_1_pooled.mean_diff = 0.1646666666666667 tost_clinic_indep_1_pooled.se_diff = 0.2531625991083628 tost_clinic_indep_1_pooled.alpha = 0.05 tost_clinic_indep_1_pooled.ci_diff = (-0.2659960620757337, 0.595329395409067) tost_clinic_indep_1_pooled.df = 28 tost_clinic_indep_1_pooled.epsilon = 0.6 tost_clinic_indep_1_pooled.result = 'rejected' tost_clinic_indep_1_pooled.p_value = 0.04827315100761467 tost_clinic_indep_1_pooled.check_me = (-0.2706666666666666, 0.6) #> t = tost(clinic$var2[1:15], clinic$var2[16:30], eps=0.6, var.equal = TRUE) #> cat_items(t, prefix="tost_clinic_indep_2_pooled.") tost_clinic_indep_2_pooled = Holder() tost_clinic_indep_2_pooled.mean_diff = 0.562666666666666 tost_clinic_indep_2_pooled.se_diff = 0.2149871904637392 tost_clinic_indep_2_pooled.alpha = 0.05 tost_clinic_indep_2_pooled.ci_diff = (0.1969453064978777, 0.928388026835454) tost_clinic_indep_2_pooled.df = 28 tost_clinic_indep_2_pooled.epsilon = 0.6 tost_clinic_indep_2_pooled.result = 'not rejected' tost_clinic_indep_2_pooled.p_value = 0.43169347692374 tost_clinic_indep_2_pooled.check_me = (0.525333333333332, 0.6) #tost ratio, log transformed #> t = tost(log(clinic$var1[1:15]), log(clinic$var1[16:30]), eps=log(1.25), paired=TRUE) #> cat_items(t, prefix="tost_clinic_1_paired.") tost_clinic_1_paired = Holder() tost_clinic_1_paired.mean_diff = 0.0431223318225235 tost_clinic_1_paired.se_diff = 0.03819576328421437 tost_clinic_1_paired.alpha = 0.05 tost_clinic_1_paired.ci_diff = (-0.02415225319362176, 0.1103969168386687) tost_clinic_1_paired.df = 14 tost_clinic_1_paired.epsilon = 0.2231435513142098 tost_clinic_1_paired.result = 'rejected' tost_clinic_1_paired.p_value = 0.0001664157928976468 tost_clinic_1_paired.check_me = (-0.1368988876691603, 0.2231435513142073) #> t = tost(log(clinic$var1[1:15]), log(clinic$var1[16:30]), eps=log(1.25), paired=FALSE) #> cat_items(t, prefix="tost_clinic_1_indep.") tost_clinic_1_indep = Holder() tost_clinic_1_indep.mean_diff = 0.04312233182252334 tost_clinic_1_indep.se_diff = 0.073508371131806 tost_clinic_1_indep.alpha = 0.05 tost_clinic_1_indep.ci_diff = (-0.0819851930203655, 0.1682298566654122) tost_clinic_1_indep.df = 27.61177037646526 tost_clinic_1_indep.epsilon = 0.2231435513142098 tost_clinic_1_indep.result = 'rejected' tost_clinic_1_indep.p_value = 0.01047085593138891 tost_clinic_1_indep.check_me = (-0.1368988876691633, 0.22314355131421) #> t = tost(log(y), log(x), eps=log(1.25), paired=TRUE) #> cat_items(t, prefix="tost_s_paired.") tost_s_paired = Holder() tost_s_paired.mean_diff = 0.06060076667771316 tost_s_paired.se_diff = 0.04805826005366752 tost_s_paired.alpha = 0.05 tost_s_paired.ci_diff = (-0.0257063329659993, 0.1469078663214256) tost_s_paired.df = 11 tost_s_paired.epsilon = 0.2231435513142098 tost_s_paired.result = 'rejected' tost_s_paired.p_value = 0.003059338540563293 tost_s_paired.check_me = (-0.1019420179587835, 0.2231435513142098) #multiple endpoints #> compvall <- multeq.diff(data=clinic,grp="fact",method="step.up",margin.up=rep(0.6,5), margin.lo=c(-1.0, -1.0, -1.5, -1.5, -1.5)) #> cat_items(compvall, prefix="tost_clinic_all_no_multi.") tost_clinic_all_no_multi = Holder() tost_clinic_all_no_multi.comp_name = '2-1' tost_clinic_all_no_multi.estimate = np.array([ -0.1646666666666667, -0.562666666666666, -0.3073333333333332, -0.5553333333333335, -0.469333333333333]) tost_clinic_all_no_multi.degr_fr = np.array([ 26.74847875823152, 24.1100015106273, 23.90046331918926, 25.71678948210178, 24.88436709341423]) tost_clinic_all_no_multi.test_stat = np.array([ 3.020456692101513, 2.034229724989578, 4.052967897750272, 4.37537447933403, 4.321997343344]) tost_clinic_all_no_multi.p_value = np.array([ 0.00274867705173331, 0.02653543052872217, 0.0002319468040526358, 8.916466517494902e-05, 0.00010890038649094043]) tost_clinic_all_no_multi.lower = np.array([ -0.596019631405587, -0.930417082633366, -0.690410573009442, -0.92373513818557, -0.876746448909633]) tost_clinic_all_no_multi.upper = np.array([ 0.2666862980722534, -0.194916250699966, 0.07574390634277595, -0.186931528481097, -0.06192021775703377]) tost_clinic_all_no_multi.margin_lo = np.array([ -1, -1, -1.5, -1.5, -1.5]) tost_clinic_all_no_multi.margin_up = np.array([ 0.6, 0.6, 0.6, 0.6, 0.6]) tost_clinic_all_no_multi.base = 1 tost_clinic_all_no_multi.method = 'step.up' tost_clinic_all_no_multi.var_equal = '''FALSE''' tost_clinic_all_no_multi.FWER = 0.05 #> comp <- multeq.diff(data=clinic,grp="fact", resp=c("var1"),method="step.up",margin.up=rep(0.6), margin.lo=rep(-1.5)) #> cat_items(comp, prefix="tost_clinic_1_asym.") tost_clinic_1_asym = Holder tost_clinic_1_asym.comp_name = '2-1' tost_clinic_1_asym.estimate = -0.1646666666666667 tost_clinic_1_asym.degr_fr = 26.74847875823152 tost_clinic_1_asym.test_stat = 3.020456692101513 tost_clinic_1_asym.p_value = 0.00274867705173331 tost_clinic_1_asym.lower = -0.596019631405587 tost_clinic_1_asym.upper = 0.2666862980722534 tost_clinic_1_asym.margin_lo = -1.5 tost_clinic_1_asym.margin_up = 0.6 tost_clinic_1_asym.base = 1 tost_clinic_1_asym.method = 'step.up' tost_clinic_1_asym.var_equal = '''FALSE''' tost_clinic_1_asym.FWER = 0.05 #TODO: not used yet, some p-values are multi-testing adjusted # not implemented #> compvall <- multeq.diff(data=clinic,grp="fact",method="step.up",margin.up=rep(0.6,5), margin.lo=c(-0.5, -0.5, -1.5, -1.5, -1.5)) #> cat_items(compvall, prefix="tost_clinic_all_multi.") tost_clinic_all_multi = Holder() tost_clinic_all_multi.comp_name = '2-1' tost_clinic_all_multi.estimate = np.array([ -0.1646666666666667, -0.562666666666666, -0.3073333333333332, -0.5553333333333335, -0.469333333333333]) tost_clinic_all_multi.degr_fr = np.array([ 26.74847875823152, 24.1100015106273, 23.90046331918926, 25.71678948210178, 24.88436709341423]) tost_clinic_all_multi.test_stat = np.array([ 1.324576910311299, -0.2914902349832590, 4.052967897750272, 4.37537447933403, 4.321997343344]) tost_clinic_all_multi.p_value = np.array([ 0.0982588867413542, 0.6134151998456164, 0.0006958404121579073, 0.0002674939955248471, 0.0003267011594728213]) tost_clinic_all_multi.lower = np.array([ -0.596019631405587, -0.930417082633366, -0.812901144055456, -1.040823983574101, -1.006578759345919]) tost_clinic_all_multi.upper = np.array([ 0.2666862980722534, -0.194916250699966, 0.1982344773887895, -0.0698426830925655, 0.0679120926792529]) tost_clinic_all_multi.margin_lo = np.array([ -0.5, -0.5, -1.5, -1.5, -1.5]) tost_clinic_all_multi.margin_up = np.array([ 0.6, 0.6, 0.6, 0.6, 0.6]) tost_clinic_all_multi.base = 1 tost_clinic_all_multi.method = 'step.up' tost_clinic_all_multi.var_equal = '''FALSE''' tost_clinic_all_multi.FWER = 0.05 #t-tests #> tt = t.test(clinic$var1[16:30], clinic$var1[1:15], data=clinic, mu=-0., alternative="two.sided", paired=TRUE) #> cat_items(tt, prefix="ttest_clinic_paired_1.") ttest_clinic_paired_1 = Holder() ttest_clinic_paired_1.statistic = 1.213001548676048 ttest_clinic_paired_1.parameter = 14 ttest_clinic_paired_1.p_value = 0.245199929713149 ttest_clinic_paired_1.conf_int = (-0.1264911434745851, 0.4558244768079186) ttest_clinic_paired_1.estimate = 0.1646666666666667 ttest_clinic_paired_1.null_value = 0 ttest_clinic_paired_1.alternative = 'two.sided' ttest_clinic_paired_1.method = 'Paired t-test' ttest_clinic_paired_1.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> ttless = t.test(clinic$var1[1:15], clinic$var1[16:30],, data=clinic, mu=-0., alternative="less", paired=FALSE) #> cat_items(ttless, prefix="ttest_clinic_paired_1_l.") ttest_clinic_paired_1_l = Holder() ttest_clinic_paired_1_l.statistic = 0.650438363512706 ttest_clinic_paired_1_l.parameter = 26.7484787582315 ttest_clinic_paired_1_l.p_value = 0.739521349864458 ttest_clinic_paired_1_l.conf_int = (-np.inf, 0.596019631405587) ttest_clinic_paired_1_l.estimate = (3.498, 3.333333333333333) ttest_clinic_paired_1_l.null_value = 0 ttest_clinic_paired_1_l.alternative = 'less' ttest_clinic_paired_1_l.method = 'Welch Two Sample t-test' ttest_clinic_paired_1_l.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> cat_items(tt, prefix="ttest_clinic_indep_1_g.") ttest_clinic_indep_1_g = Holder() ttest_clinic_indep_1_g.statistic = 0.650438363512706 ttest_clinic_indep_1_g.parameter = 26.7484787582315 ttest_clinic_indep_1_g.p_value = 0.2604786501355416 ttest_clinic_indep_1_g.conf_int = (-0.2666862980722534, np.inf) ttest_clinic_indep_1_g.estimate = (3.498, 3.333333333333333) ttest_clinic_indep_1_g.null_value = 0 ttest_clinic_indep_1_g.alternative = 'greater' ttest_clinic_indep_1_g.method = 'Welch Two Sample t-test' ttest_clinic_indep_1_g.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> cat_items(ttless, prefix="ttest_clinic_indep_1_l.") ttest_clinic_indep_1_l = Holder() ttest_clinic_indep_1_l.statistic = 0.650438363512706 ttest_clinic_indep_1_l.parameter = 26.7484787582315 ttest_clinic_indep_1_l.p_value = 0.739521349864458 ttest_clinic_indep_1_l.conf_int = (-np.inf, 0.596019631405587) ttest_clinic_indep_1_l.estimate = (3.498, 3.333333333333333) ttest_clinic_indep_1_l.null_value = 0 ttest_clinic_indep_1_l.alternative = 'less' ttest_clinic_indep_1_l.method = 'Welch Two Sample t-test' ttest_clinic_indep_1_l.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> ttless = t.test(clinic$var1[1:15], clinic$var1[16:30],, data=clinic, mu=1., alternative="less", paired=FALSE) #> cat_items(ttless, prefix="ttest_clinic_indep_1_l_mu.") ttest_clinic_indep_1_l_mu = Holder() ttest_clinic_indep_1_l_mu.statistic = -3.299592184135306 ttest_clinic_indep_1_l_mu.parameter = 26.7484787582315 ttest_clinic_indep_1_l_mu.p_value = 0.001372434925571605 ttest_clinic_indep_1_l_mu.conf_int = (-np.inf, 0.596019631405587) ttest_clinic_indep_1_l_mu.estimate = (3.498, 3.333333333333333) ttest_clinic_indep_1_l_mu.null_value = 1 ttest_clinic_indep_1_l_mu.alternative = 'less' ttest_clinic_indep_1_l_mu.method = 'Welch Two Sample t-test' ttest_clinic_indep_1_l_mu.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> tt2 = t.test(clinic$var1[1:15], clinic$var1[16:30],, data=clinic, mu=1, alternative="two.sided", paired=FALSE) #> cat_items(tt2, prefix="ttest_clinic_indep_1_two_mu.") ttest_clinic_indep_1_two_mu = Holder() ttest_clinic_indep_1_two_mu.statistic = -3.299592184135306 ttest_clinic_indep_1_two_mu.parameter = 26.7484787582315 ttest_clinic_indep_1_two_mu.p_value = 0.00274486985114321 ttest_clinic_indep_1_two_mu.conf_int = (-0.3550087243406, 0.6843420576739336) ttest_clinic_indep_1_two_mu.estimate = (3.498, 3.333333333333333) ttest_clinic_indep_1_two_mu.null_value = 1 ttest_clinic_indep_1_two_mu.alternative = 'two.sided' ttest_clinic_indep_1_two_mu.method = 'Welch Two Sample t-test' ttest_clinic_indep_1_two_mu.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' #> tt2 = t.test(clinic$var1[1:15], clinic$var1[16:30],, data=clinic, mu=1, alternative="two.sided", paired=FALSE, var.equal=TRUE) #> cat_items(tt2, prefix="ttest_clinic_indep_1_two_mu_pooled.") ttest_clinic_indep_1_two_mu_pooled = Holder() ttest_clinic_indep_1_two_mu_pooled.statistic = -3.299592184135305 ttest_clinic_indep_1_two_mu_pooled.parameter = 28 ttest_clinic_indep_1_two_mu_pooled.p_value = 0.002643203760742494 ttest_clinic_indep_1_two_mu_pooled.conf_int = (-0.35391340938235, 0.6832467427156834) ttest_clinic_indep_1_two_mu_pooled.estimate = (3.498, 3.333333333333333) ttest_clinic_indep_1_two_mu_pooled.null_value = 1 ttest_clinic_indep_1_two_mu_pooled.alternative = 'two.sided' ttest_clinic_indep_1_two_mu_pooled.method = ' Two Sample t-test' ttest_clinic_indep_1_two_mu_pooled.data_name = 'clinic$var1[1:15] and clinic$var1[16:30]' res1 = smws.ttost_paired(clinic[:15, 2], clinic[15:, 2], -0.6, 0.6, transform=None) res2 = smws.ttost_paired(clinic[:15, 3], clinic[15:, 3], -0.6, 0.6, transform=None) res = smws.ttost_ind(clinic[:15, 3], clinic[15:, 3], -0.6, 0.6, usevar='unequal') class CheckTostMixin(object): def test_pval(self): assert_almost_equal(self.res1.pvalue, self.res2.p_value, decimal=13) #assert_almost_equal(self.res1.df, self.res2.df, decimal=13) class TestTostp1(CheckTostMixin): #paired var1 def __init__(self): self.res2 = tost_clinic_paired_1 x1, x2 = clinic[:15, 2], clinic[15:, 2] self.res1 = Holder() res = smws.ttost_paired(x1, x2, -0.6, 0.6, transform=None) self.res1.pvalue = res[0] #self.res1.df = res[1][-1] not yet res_ds = smws.DescrStatsW(x1 - x2, weights=None, ddof=0) #tost confint 2*alpha TODO: check again self.res1.tconfint_diff = res_ds.tconfint_mean(0.1) self.res1.confint_05 = res_ds.tconfint_mean(0.05) self.res1.mean_diff = res_ds.mean self.res1.std_mean_diff = res_ds.std_mean self.res2b = ttest_clinic_paired_1 def test_special(self): #TODO: add attributes to other cases and move to superclass assert_almost_equal(self.res1.tconfint_diff, self.res2.ci_diff, decimal=13) assert_almost_equal(self.res1.mean_diff, self.res2.mean_diff, decimal=13) assert_almost_equal(self.res1.std_mean_diff, self.res2.se_diff, decimal=13) #compare with ttest assert_almost_equal(self.res1.confint_05, self.res2b.conf_int, decimal=13) class TestTostp2(CheckTostMixin): #paired var2 def __init__(self): self.res2 = tost_clinic_paired x, y = clinic[:15, 3], clinic[15:, 3] self.res1 = Holder() res = smws.ttost_paired(x, y, -0.6, 0.6, transform=None) self.res1.pvalue = res[0] class TestTosti1(CheckTostMixin): def __init__(self): self.res2 = tost_clinic_indep_1 x, y = clinic[:15, 2], clinic[15:, 2] self.res1 = Holder() res = smws.ttost_ind(x, y, -0.6, 0.6, usevar='unequal') self.res1.pvalue = res[0] class TestTosti2(CheckTostMixin): def __init__(self): self.res2 = tost_clinic_indep x, y = clinic[:15, 3], clinic[15:, 3] self.res1 = Holder() res = smws.ttost_ind(x, y, -0.6, 0.6, usevar='unequal') self.res1.pvalue = res[0] class TestTostip1(CheckTostMixin): def __init__(self): self.res2 = tost_clinic_indep_1_pooled x, y = clinic[:15, 2], clinic[15:, 2] self.res1 = Holder() res = smws.ttost_ind(x, y, -0.6, 0.6, usevar='pooled') self.res1.pvalue = res[0] class TestTostip2(CheckTostMixin): def __init__(self): self.res2 = tost_clinic_indep_2_pooled x, y = clinic[:15, 3], clinic[15:, 3] self.res1 = Holder() res = smws.ttost_ind(x, y, -0.6, 0.6, usevar='pooled') self.res1.pvalue = res[0] #transform=np.log #class TestTostp1_log(CheckTost): def test_tost_log(): x1, x2 = clinic[:15, 2], clinic[15:, 2] resp = smws.ttost_paired(x1, x2, 0.8, 1.25, transform=np.log) assert_almost_equal(resp[0], tost_clinic_1_paired.p_value, 13) resi = smws.ttost_ind(x1, x2, 0.8, 1.25, transform=np.log, usevar='unequal') assert_almost_equal(resi[0], tost_clinic_1_indep.p_value, 13) def test_tost_asym(): x1, x2 = clinic[:15, 2], clinic[15:, 2] #Note: x1, x2 reversed by definition in multeq.dif assert_almost_equal(x2.mean() - x1.mean(), tost_clinic_1_asym.estimate, 13) resa = smws.ttost_ind(x2, x1, -1.5, 0.6, usevar='unequal') assert_almost_equal(resa[0], tost_clinic_1_asym.p_value, 13) #multi-endpoints, asymmetric bounds, vectorized resall = smws.ttost_ind(clinic[15:, 2:7], clinic[:15, 2:7], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, usevar='unequal') assert_almost_equal(resall[0], tost_clinic_all_no_multi.p_value, 13) #SMOKE tests: foe multi-endpoint vectorized, k on k resall = smws.ttost_ind(clinic[15:, 2:7], clinic[:15, 2:7], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, usevar='unequal', transform=np.log) resall = smws.ttost_ind(clinic[15:, 2:7], clinic[:15, 2:7], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, usevar='unequal', transform=np.exp) resall = smws.ttost_paired(clinic[15:, 2:7], clinic[:15, 2:7], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, transform=np.log) resall = smws.ttost_paired(clinic[15:, 2:7], clinic[:15, 2:7], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, transform=np.exp) resall = smws.ttest_ind(clinic[15:, 2:7], clinic[:15, 2:7], value=[-1.0, -1.0, -1.5, -1.5, -1.5]) #k on 1: compare all with reference resall = smws.ttost_ind(clinic[15:, 2:7], clinic[:15, 2:3], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, usevar='unequal') resa3_2 = smws.ttost_ind(clinic[15:, 3:4], clinic[:15, 2:3], [-1.0, -1.0, -1.5, -1.5, -1.5], 0.6, usevar='unequal') assert_almost_equal(resall[0][1], resa3_2[0][1], decimal=13) resall = smws.ttost_ind(clinic[15:, 2], clinic[:15, 2], [-1.0, -0.5, -0.7, -1.5, -1.5], 0.6, usevar='unequal') resall = smws.ttost_ind(clinic[15:, 2], clinic[:15, 2], [-1.0, -0.5, -0.7, -1.5, -1.5], np.repeat(0.6,5), usevar='unequal') def test_ttest(): x1, x2 = clinic[:15, 2], clinic[15:, 2] all_tests = [] t1 = smws.ttest_ind(x1, x2, alternative='larger', usevar='unequal') all_tests.append((t1, ttest_clinic_indep_1_g)) t2 = smws.ttest_ind(x1, x2, alternative='smaller', usevar='unequal') all_tests.append((t2, ttest_clinic_indep_1_l)) t3 = smws.ttest_ind(x1, x2, alternative='smaller', usevar='unequal', value=1) all_tests.append((t3, ttest_clinic_indep_1_l_mu)) for res1, res2 in all_tests: assert_almost_equal(res1[0], res2.statistic, decimal=13) assert_almost_equal(res1[1], res2.p_value, decimal=13) #assert_almost_equal(res1[2], res2.df, decimal=13) cm = smws.CompareMeans(smws.DescrStatsW(x1), smws.DescrStatsW(x2)) ci = cm.tconfint_diff(alternative='two-sided', usevar='unequal') assert_almost_equal(ci, ttest_clinic_indep_1_two_mu.conf_int, decimal=13) ci = cm.tconfint_diff(alternative='two-sided', usevar='pooled') assert_almost_equal(ci, ttest_clinic_indep_1_two_mu_pooled.conf_int, decimal=13) ci = cm.tconfint_diff(alternative='smaller', usevar='unequal') assert_almost_equal_inf(ci, ttest_clinic_indep_1_l.conf_int, decimal=13) ci = cm.tconfint_diff(alternative='larger', usevar='unequal') assert_almost_equal_inf(ci, ttest_clinic_indep_1_g.conf_int, decimal=13) #test get_compare cm = smws.CompareMeans(smws.DescrStatsW(x1), smws.DescrStatsW(x2)) cm1 = cm.d1.get_compare(cm.d2) cm2 = cm.d1.get_compare(x2) cm3 = cm.d1.get_compare(np.hstack((x2,x2))) #all use the same d1, no copying assert_(cm.d1 is cm1.d1) assert_(cm.d1 is cm2.d1) assert_(cm.d1 is cm3.d1) def tost_transform_paired(): raw = np.array('''\ 103.4 90.11 59.92 77.71 68.17 77.71 94.54 97.51 69.48 58.21 72.17 101.3 74.37 79.84 84.44 96.06 96.74 89.30 94.26 97.22 48.52 61.62 95.68 85.80'''.split(), float) x, y = raw.reshape(-1,2).T res1 = smws.ttost_paired(x, y, 0.8, 1.25, transform=np.log) res_sas = (0.0031, (3.38, 0.0031), (-5.90, 0.00005)) assert_almost_equal(res1[0], res_sas[0], 3) assert_almost_equal(res1[1:], res_sas[1:], 2) #result R tost assert_almost_equal(res1[0], tost_s_paired.p_value, 13) if __name__ == '__main__': tt = TestTostp1() tt.test_special() for cls in [TestTostp1, TestTostp2, TestTosti1, TestTosti2, TestTostip1, TestTostip2]: #print cls tt = cls() tt.test_pval() test_ttest() tost_transform_paired() test_tost_log() statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/tests/test_weightstats.py000066400000000000000000000525501224417117700270020ustar00rootroot00000000000000'''tests for weightstats, compares with replication no failures but needs cleanup update 2012-09-09: added test after fixing bug in covariance TODOs: - I don't remember what all the commented out code is doing - should be refactored to use generator or inherited tests - still gaps in test coverage - value/diff in ttest_ind is tested in test_tost.py - what about pandas data structures? Author: Josef Perktold License: BSD (3-clause) ''' import numpy as np from scipy import stats from numpy.testing import assert_almost_equal, assert_equal, assert_allclose from statsmodels.stats.weightstats import \ DescrStatsW, CompareMeans, ttest_ind, ztest, zconfint #import statsmodels.stats.weightstats as smws class Holder(object): pass class TestWeightstats(object): def __init__(self): np.random.seed(9876789) n1, n2 = 20,20 m1, m2 = 1, 1.2 x1 = m1 + np.random.randn(n1) x2 = m2 + np.random.randn(n2) x1_2d = m1 + np.random.randn(n1, 3) x2_2d = m2 + np.random.randn(n2, 3) w1_ = 2. * np.ones(n1) w2_ = 2. * np.ones(n2) w1 = np.random.randint(1,4, n1) w2 = np.random.randint(1,4, n2) self.x1, self.x2 = x1, x2 self.w1, self.w2 = w1, w2 self.x1_2d, self.x2_2d = x1_2d, x2_2d def test_weightstats_1(self): x1, x2 = self.x1, self.x2 w1, w2 = self.w1, self.w2 w1_ = 2. * np.ones(len(x1)) w2_ = 2. * np.ones(len(x2)) d1 = DescrStatsW(x1) # print ttest_ind(x1, x2) # print ttest_ind(x1, x2, usevar='unequal') # #print ttest_ind(x1, x2, usevar='unequal') # print stats.ttest_ind(x1, x2) # print ttest_ind(x1, x2, usevar='unequal', alternative='larger') # print ttest_ind(x1, x2, usevar='unequal', alternative='smaller') # print ttest_ind(x1, x2, usevar='unequal', weights=(w1_, w2_)) # print stats.ttest_ind(np.r_[x1, x1], np.r_[x2,x2]) assert_almost_equal(ttest_ind(x1, x2, weights=(w1_, w2_))[:2], stats.ttest_ind(np.r_[x1, x1], np.r_[x2,x2])) def test_weightstats_2(self): x1, x2 = self.x1, self.x2 w1, w2 = self.w1, self.w2 d1 = DescrStatsW(x1) d1w = DescrStatsW(x1, weights=w1) d2w = DescrStatsW(x2, weights=w2) x1r = d1w.asrepeats() x2r = d2w.asrepeats() # print 'random weights' # print ttest_ind(x1, x2, weights=(w1, w2)) # print stats.ttest_ind(x1r, x2r) assert_almost_equal(ttest_ind(x1, x2, weights=(w1, w2))[:2], stats.ttest_ind(x1r, x2r), 14) #not the same as new version with random weights/replication # assert x1r.shape[0] == d1w.sum_weights # assert x2r.shape[0] == d2w.sum_weights assert_almost_equal(x2r.mean(0), d2w.mean, 14) assert_almost_equal(x2r.var(), d2w.var, 14) assert_almost_equal(x2r.std(), d2w.std, 14) #note: the following is for 1d assert_almost_equal(np.cov(x2r, bias=1), d2w.cov, 14) #assert_almost_equal(np.corrcoef(np.x2r), d2w.corrcoef, 19) #TODO: exception in corrcoef (scalar case) #one-sample tests # print d1.ttest_mean(3) # print stats.ttest_1samp(x1, 3) # print d1w.ttest_mean(3) # print stats.ttest_1samp(x1r, 3) assert_almost_equal(d1.ttest_mean(3)[:2], stats.ttest_1samp(x1, 3), 11) assert_almost_equal(d1w.ttest_mean(3)[:2], stats.ttest_1samp(x1r, 3), 11) def test_weightstats_3(self): x1_2d, x2_2d = self.x1_2d, self.x2_2d w1, w2 = self.w1, self.w2 d1w_2d = DescrStatsW(x1_2d, weights=w1) d2w_2d = DescrStatsW(x2_2d, weights=w2) x1r_2d = d1w_2d.asrepeats() x2r_2d = d2w_2d.asrepeats() assert_almost_equal(x2r_2d.mean(0), d2w_2d.mean, 14) assert_almost_equal(x2r_2d.var(0), d2w_2d.var, 14) assert_almost_equal(x2r_2d.std(0), d2w_2d.std, 14) assert_almost_equal(np.cov(x2r_2d.T, bias=1), d2w_2d.cov, 14) assert_almost_equal(np.corrcoef(x2r_2d.T), d2w_2d.corrcoef, 14) # print d1w_2d.ttest_mean(3) # #scipy.stats.ttest is also vectorized # print stats.ttest_1samp(x1r_2d, 3) t,p,d = d1w_2d.ttest_mean(3) assert_almost_equal([t, p], stats.ttest_1samp(x1r_2d, 3), 11) #print [stats.ttest_1samp(xi, 3) for xi in x1r_2d.T] cm = CompareMeans(d1w_2d, d2w_2d) ressm = cm.ttest_ind() resss = stats.ttest_ind(x1r_2d, x2r_2d) assert_almost_equal(ressm[:2], resss, 14) ## #doesn't work for 2d, levene doesn't use weights ## cm = CompareMeans(d1w_2d, d2w_2d) ## ressm = cm.test_equal_var() ## resss = stats.levene(x1r_2d, x2r_2d) ## assert_almost_equal(ressm[:2], resss, 14) def test_weightstats_ddof_tests(self): # explicit test that ttest and confint are independent of ddof # one sample case x1_2d = self.x1_2d w1 = self.w1 d1w_d0 = DescrStatsW(x1_2d, weights=w1, ddof=0) d1w_d1 = DescrStatsW(x1_2d, weights=w1, ddof=1) d1w_d2 = DescrStatsW(x1_2d, weights=w1, ddof=2) #check confint independent of user ddof res0 = d1w_d0.ttest_mean() res1 = d1w_d1.ttest_mean() res2 = d1w_d2.ttest_mean() # concatenate into one array with np.r_ assert_almost_equal(np.r_[res1], np.r_[res0], 14) assert_almost_equal(np.r_[res2], np.r_[res0], 14) res0 = d1w_d0.ttest_mean(0.5) res1 = d1w_d1.ttest_mean(0.5) res2 = d1w_d2.ttest_mean(0.5) assert_almost_equal(np.r_[res1], np.r_[res0], 14) assert_almost_equal(np.r_[res2], np.r_[res0], 14) #check confint independent of user ddof res0 = d1w_d0.tconfint_mean() res1 = d1w_d1.tconfint_mean() res2 = d1w_d2.tconfint_mean() assert_almost_equal(res1, res0, 14) assert_almost_equal(res2, res0, 14) class CheckWeightstats1dMixin(object): def test_basic(self): x1r = self.x1r d1w = self.d1w assert_almost_equal(x1r.mean(0), d1w.mean, 14) assert_almost_equal(x1r.var(0, ddof=d1w.ddof), d1w.var, 14) assert_almost_equal(x1r.std(0, ddof=d1w.ddof), d1w.std, 14) var1 = d1w.var_ddof(ddof=1) assert_almost_equal(x1r.var(0, ddof=1), var1, 14) std1 = d1w.std_ddof(ddof=1) assert_almost_equal(x1r.std(0, ddof=1), std1, 14) assert_almost_equal(np.cov(x1r.T, bias=1-d1w.ddof), d1w.cov, 14) # #assert_almost_equal(np.corrcoef(x1r.T), d1w.corrcoef, 14) def test_ttest(self): x1r = self.x1r d1w = self.d1w assert_almost_equal(d1w.ttest_mean(3)[:2], stats.ttest_1samp(x1r, 3), 11) # def # assert_almost_equal(ttest_ind(x1, x2, weights=(w1, w2))[:2], # stats.ttest_ind(x1r, x2r), 14) def test_ttest_2sample(self): x1, x2 = self.x1, self.x2 x1r, x2r = self.x1r, self.x2r w1, w2 = self.w1, self.w2 #Note: stats.ttest_ind handles 2d/nd arguments res_sp = stats.ttest_ind(x1r, x2r) assert_almost_equal(ttest_ind(x1, x2, weights=(w1, w2))[:2], res_sp, 14) #check correct ttest independent of user ddof cm = CompareMeans(DescrStatsW(x1, weights=w1, ddof=0), DescrStatsW(x2, weights=w2, ddof=1)) assert_almost_equal(cm.ttest_ind()[:2], res_sp, 14) cm = CompareMeans(DescrStatsW(x1, weights=w1, ddof=1), DescrStatsW(x2, weights=w2, ddof=2)) assert_almost_equal(cm.ttest_ind()[:2], res_sp, 14) cm0 = CompareMeans(DescrStatsW(x1, weights=w1, ddof=0), DescrStatsW(x2, weights=w2, ddof=0)) cm1 = CompareMeans(DescrStatsW(x1, weights=w1, ddof=0), DescrStatsW(x2, weights=w2, ddof=1)) cm2 = CompareMeans(DescrStatsW(x1, weights=w1, ddof=1), DescrStatsW(x2, weights=w2, ddof=2)) res0 = cm0.ttest_ind(usevar='unequal') res1 = cm1.ttest_ind(usevar='unequal') res2 = cm2.ttest_ind(usevar='unequal') assert_almost_equal(res1, res0, 14) assert_almost_equal(res2, res0, 14) #check confint independent of user ddof res0 = cm0.tconfint_diff(usevar='pooled') res1 = cm1.tconfint_diff(usevar='pooled') res2 = cm2.tconfint_diff(usevar='pooled') assert_almost_equal(res1, res0, 14) assert_almost_equal(res2, res0, 14) res0 = cm0.tconfint_diff(usevar='unequal') res1 = cm1.tconfint_diff(usevar='unequal') res2 = cm2.tconfint_diff(usevar='unequal') assert_almost_equal(res1, res0, 14) assert_almost_equal(res2, res0, 14) def test_confint_mean(self): #compare confint_mean with ttest d1w = self.d1w alpha = 0.05 low, upp = d1w.tconfint_mean() t, p, d = d1w.ttest_mean(low) assert_almost_equal(p, alpha * np.ones(p.shape), 8) t, p, d = d1w.ttest_mean(upp) assert_almost_equal(p, alpha * np.ones(p.shape), 8) t, p, d = d1w.ttest_mean(np.vstack((low, upp))) assert_almost_equal(p, alpha * np.ones(p.shape), 8) class CheckWeightstats2dMixin(CheckWeightstats1dMixin): def test_corr(self): x1r = self.x1r d1w = self.d1w assert_almost_equal(np.corrcoef(x1r.T), d1w.corrcoef, 14) class TestWeightstats1d_ddof(CheckWeightstats1dMixin): @classmethod def setup_class(self): np.random.seed(9876789) n1, n2 = 20,20 m1, m2 = 1, 1.2 x1 = m1 + np.random.randn(n1, 1) x2 = m2 + np.random.randn(n2, 1) w1 = np.random.randint(1,4, n1) w2 = np.random.randint(1,4, n2) self.x1, self.x2 = x1, x2 self.w1, self.w2 = w1, w2 self.d1w = DescrStatsW(x1, weights=w1, ddof=1) self.d2w = DescrStatsW(x2, weights=w2, ddof=1) self.x1r = self.d1w.asrepeats() self.x2r = self.d2w.asrepeats() class TestWeightstats2d(CheckWeightstats2dMixin): @classmethod def setup_class(self): np.random.seed(9876789) n1, n2 = 20,20 m1, m2 = 1, 1.2 x1 = m1 + np.random.randn(n1, 3) x2 = m2 + np.random.randn(n2, 3) w1_ = 2. * np.ones(n1) w2_ = 2. * np.ones(n2) w1 = np.random.randint(1,4, n1) w2 = np.random.randint(1,4, n2) self.x1, self.x2 = x1, x2 self.w1, self.w2 = w1, w2 self.d1w = DescrStatsW(x1, weights=w1) self.d2w = DescrStatsW(x2, weights=w2) self.x1r = self.d1w.asrepeats() self.x2r = self.d2w.asrepeats() class TestWeightstats2d_ddof(CheckWeightstats2dMixin): @classmethod def setup_class(self): np.random.seed(9876789) n1, n2 = 20,20 m1, m2 = 1, 1.2 x1 = m1 + np.random.randn(n1, 3) x2 = m2 + np.random.randn(n2, 3) w1 = np.random.randint(1,4, n1) w2 = np.random.randint(1,4, n2) self.x1, self.x2 = x1, x2 self.w1, self.w2 = w1, w2 self.d1w = DescrStatsW(x1, weights=w1, ddof=1) self.d2w = DescrStatsW(x2, weights=w2, ddof=1) self.x1r = self.d1w.asrepeats() self.x2r = self.d2w.asrepeats() class TestWeightstats2d_nobs(CheckWeightstats2dMixin): @classmethod def setup_class(self): np.random.seed(9876789) n1, n2 = 20,30 m1, m2 = 1, 1.2 x1 = m1 + np.random.randn(n1, 3) x2 = m2 + np.random.randn(n2, 3) w1 = np.random.randint(1,4, n1) w2 = np.random.randint(1,4, n2) self.x1, self.x2 = x1, x2 self.w1, self.w2 = w1, w2 self.d1w = DescrStatsW(x1, weights=w1, ddof=0) self.d2w = DescrStatsW(x2, weights=w2, ddof=1) self.x1r = self.d1w.asrepeats() self.x2r = self.d2w.asrepeats() def test_ttest_ind_with_uneq_var(): #from scipy # check vs. R a = (1, 2, 3) b = (1.1, 2.9, 4.2) pr = 0.53619490753126731 tr = -0.68649512735572582 t, p, df = ttest_ind(a, b, usevar='unequal') assert_almost_equal([t,p], [tr, pr], 13) a = (1, 2, 3, 4) pr = 0.84354139131608286 tr = -0.2108663315950719 t, p, df = ttest_ind(a, b, usevar='unequal') assert_almost_equal([t,p], [tr, pr], 13) def test_ztest_ztost(): # compare weightstats with separately tested proportion ztest ztost import statsmodels.stats.proportion as smprop x1 = [0, 1] w1 = [5, 15] res2 = smprop.proportions_ztest(15, 20., value=0.5) d1 = DescrStatsW(x1, w1) res1 = d1.ztest_mean(0.5) assert_allclose(res1, res2, rtol=0.03, atol=0.003) d2 = DescrStatsW(x1, np.array(w1)*21./20) res1 = d2.ztest_mean(0.5) assert_almost_equal(res1, res2, decimal=12) res1 = d2.ztost_mean(0.4, 0.6) res2 = smprop.proportions_ztost(15, 20., 0.4, 0.6) assert_almost_equal(res1[0], res2[0], decimal=12) x2 = [0, 1] w2 = [10, 10] #d2 = DescrStatsW(x1, np.array(w1)*21./20) d2 = DescrStatsW(x2, w2) res1 = ztest(d1.asrepeats(), d2.asrepeats()) res2 = smprop.proportions_chisquare(np.asarray([15, 10]), np.asarray([20., 20])) #TODO: check this is this difference expected?, see test_proportion assert_allclose(res1[1], res2[1], rtol=0.03) res1a = CompareMeans(d1, d2).ztest_ind() assert_allclose(res1a[1], res2[1], rtol=0.03) assert_almost_equal(res1a, res1, decimal=12) ###### test for ztest and z confidence interval against R BSDA z.test # Note: I needed to calculate the pooled standard deviation for R # std = np.std(np.concatenate((x-x.mean(),y-y.mean())), ddof=2) #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667) #> cat_items(zt, "ztest.") ztest_ = Holder() ztest_.statistic = 6.55109865675183 ztest_.p_value = 5.711530850508982e-11 ztest_.conf_int = np.array([1.230415246535603, 2.280948389828034]) ztest_.estimate = np.array([7.01818181818182, 5.2625]) ztest_.null_value = 0 ztest_.alternative = 'two.sided' ztest_.method = 'Two-sample z-Test' ztest_.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667, alternative="less") #> cat_items(zt, "ztest_smaller.") ztest_smaller = Holder() ztest_smaller.statistic = 6.55109865675183 ztest_smaller.p_value = 0.999999999971442 ztest_smaller.conf_int = np.array([np.nan, 2.196499421109045]) ztest_smaller.estimate = np.array([7.01818181818182, 5.2625]) ztest_smaller.null_value = 0 ztest_smaller.alternative = 'less' ztest_smaller.method = 'Two-sample z-Test' ztest_smaller.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667, alternative="greater") #> cat_items(zt, "ztest_larger.") ztest_larger = Holder() ztest_larger.statistic = 6.55109865675183 ztest_larger.p_value = 2.855760072861813e-11 ztest_larger.conf_int = np.array([1.314864215254592, np.nan]) ztest_larger.estimate = np.array([7.01818181818182, 5.2625 ]) ztest_larger.null_value = 0 ztest_larger.alternative = 'greater' ztest_larger.method = 'Two-sample z-Test' ztest_larger.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667, mu=1, alternative="two.sided") #> cat_items(zt, "ztest_mu.") ztest_mu = Holder() ztest_mu.statistic = 2.81972854805176 ztest_mu.p_value = 0.00480642898427981 ztest_mu.conf_int = np.array([1.230415246535603, 2.280948389828034]) ztest_mu.estimate = np.array([7.01818181818182, 5.2625]) ztest_mu.null_value = 1 ztest_mu.alternative = 'two.sided' ztest_mu.method = 'Two-sample z-Test' ztest_mu.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667, mu=1, alternative="greater") #> cat_items(zt, "ztest_larger_mu.") ztest_larger_mu = Holder() ztest_larger_mu.statistic = 2.81972854805176 ztest_larger_mu.p_value = 0.002403214492139871 ztest_larger_mu.conf_int = np.array([1.314864215254592, np.nan]) ztest_larger_mu.estimate = np.array([7.01818181818182, 5.2625]) ztest_larger_mu.null_value = 1 ztest_larger_mu.alternative = 'greater' ztest_larger_mu.method = 'Two-sample z-Test' ztest_larger_mu.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.57676142668828667, y, sigma.y=0.57676142668828667, mu=2, alternative="less") #> cat_items(zt, "ztest_smaller_mu.") ztest_smaller_mu = Holder() ztest_smaller_mu.statistic = -0.911641560648313 ztest_smaller_mu.p_value = 0.1809787183191324 ztest_smaller_mu.conf_int = np.array([np.nan, 2.196499421109045]) ztest_smaller_mu.estimate = np.array([7.01818181818182, 5.2625]) ztest_smaller_mu.null_value = 2 ztest_smaller_mu.alternative = 'less' ztest_smaller_mu.method = 'Two-sample z-Test' ztest_smaller_mu.data_name = 'x and y' #> zt = z.test(x, sigma.x=0.46436662631627995, mu=6.4, alternative="two.sided") #> cat_items(zt, "ztest_mu_1s.") ztest_mu_1s = Holder() ztest_mu_1s.statistic = 4.415212090914452 ztest_mu_1s.p_value = 1.009110038015147e-05 ztest_mu_1s.conf_int = np.array([6.74376372125119, 7.29259991511245]) ztest_mu_1s.estimate = 7.01818181818182 ztest_mu_1s.null_value = 6.4 ztest_mu_1s.alternative = 'two.sided' ztest_mu_1s.method = 'One-sample z-Test' ztest_mu_1s.data_name = 'x' #> zt = z.test(x, sigma.x=0.46436662631627995, mu=7.4, alternative="less") #> cat_items(zt, "ztest_smaller_mu_1s.") ztest_smaller_mu_1s = Holder() ztest_smaller_mu_1s.statistic = -2.727042762035397 ztest_smaller_mu_1s.p_value = 0.00319523783881176 ztest_smaller_mu_1s.conf_int = np.array([np.nan, 7.248480744895716]) ztest_smaller_mu_1s.estimate = 7.01818181818182 ztest_smaller_mu_1s.null_value = 7.4 ztest_smaller_mu_1s.alternative = 'less' ztest_smaller_mu_1s.method = 'One-sample z-Test' ztest_smaller_mu_1s.data_name = 'x' #> zt = z.test(x, sigma.x=0.46436662631627995, mu=6.4, alternative="greater") #> cat_items(zt, "ztest_greater_mu_1s.") ztest_larger_mu_1s = Holder() ztest_larger_mu_1s.statistic = 4.415212090914452 ztest_larger_mu_1s.p_value = 5.045550190097003e-06 ztest_larger_mu_1s.conf_int = np.array([6.78788289146792, np.nan]) ztest_larger_mu_1s.estimate = 7.01818181818182 ztest_larger_mu_1s.null_value = 6.4 ztest_larger_mu_1s.alternative = 'greater' ztest_larger_mu_1s.method = 'One-sample z-Test' ztest_larger_mu_1s.data_name = 'x' alternatives = {'less' : 'smaller', 'greater' : 'larger', 'two.sided' : 'two-sided'} class TestZTest(object): # all examples use the same data # no weights used in tests @classmethod def setup_class(cls): cls.x1 = np.array([7.8, 6.6, 6.5, 7.4, 7.3, 7., 6.4, 7.1, 6.7, 7.6, 6.8]) cls.x2 = np.array([4.5, 5.4, 6.1, 6.1, 5.4, 5., 4.1, 5.5]) cls.d1 = DescrStatsW(cls.x1) cls.d2 = DescrStatsW(cls.x2) cls.cm = CompareMeans(cls.d1, cls.d2) def test(self): x1, x2 = self.x1, self.x2 cm = self.cm # tc : test cases for tc in [ztest_, ztest_smaller, ztest_larger, ztest_mu, ztest_smaller_mu, ztest_larger_mu]: zstat, pval = ztest(x1, x2, value=tc.null_value, alternative=alternatives[tc.alternative]) assert_allclose(zstat, tc.statistic, rtol=1e-10) assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16) zstat, pval = cm.ztest_ind(value=tc.null_value, alternative=alternatives[tc.alternative]) assert_allclose(zstat, tc.statistic, rtol=1e-10) assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16) #overwrite nan in R's confint tc_conf_int = tc.conf_int.copy() if np.isnan(tc_conf_int[0]): tc_conf_int[0] = - np.inf if np.isnan(tc_conf_int[1]): tc_conf_int[1] = np.inf # Note: value is shifting our confidence interval in zconfint ci = zconfint(x1, x2, value=0, alternative=alternatives[tc.alternative]) assert_allclose(ci, tc_conf_int, rtol=1e-10) ci = cm.zconfint_diff(alternative=alternatives[tc.alternative]) assert_allclose(ci, tc_conf_int, rtol=1e-10) ci = zconfint(x1, x2, value=tc.null_value, alternative=alternatives[tc.alternative]) assert_allclose(ci, tc_conf_int - tc.null_value, rtol=1e-10) # 1 sample test copy-paste d1 = self.d1 for tc in [ztest_mu_1s, ztest_smaller_mu_1s, ztest_larger_mu_1s]: zstat, pval = ztest(x1, value=tc.null_value, alternative=alternatives[tc.alternative]) assert_allclose(zstat, tc.statistic, rtol=1e-10) assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16) zstat, pval = d1.ztest_mean(value=tc.null_value, alternative=alternatives[tc.alternative]) assert_allclose(zstat, tc.statistic, rtol=1e-10) assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16) #overwrite nan in R's confint tc_conf_int = tc.conf_int.copy() if np.isnan(tc_conf_int[0]): tc_conf_int[0] = - np.inf if np.isnan(tc_conf_int[1]): tc_conf_int[1] = np.inf # Note: value is shifting our confidence interval in zconfint ci = zconfint(x1, value=0, alternative=alternatives[tc.alternative]) assert_allclose(ci, tc_conf_int, rtol=1e-10) ci = d1.zconfint_mean(alternative=alternatives[tc.alternative]) assert_allclose(ci, tc_conf_int, rtol=1e-10) statsmodels-0.5.0+git13-g8e07d34/statsmodels/stats/weightstats.py000066400000000000000000001241701224417117700245770ustar00rootroot00000000000000'''Ttests and descriptive statistics with weights Created on 2010-09-18 Author: josef-pktd License: BSD (3-clause) References ---------- SPSS manual SAS manual This follows in large parts the SPSS manual, which is largely the same as the SAS manual with different, simpler notation. Freq, Weight in SAS seems redundant since they always show up as product, SPSS has only weights. Notes ----- This has potential problems with ddof, I started to follow numpy with ddof=0 by default and users can change it, but this might still mess up the t-tests, since the estimates for the standard deviation will be based on the ddof that the user chooses. - fixed ddof for the meandiff ttest, now matches scipy.stats.ttest_ind Note: scipy has now a separate, pooled variance option in ttest, but I haven't compared yet. ''' import numpy as np from scipy import stats from statsmodels.tools.decorators import OneTimeProperty class DescrStatsW(object): '''descriptive statistics and tests with weights for case weights Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column. If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to the sample size. This is essentially the same as replicating each observations by its weight, if the weights are integers, often called case or frequency weights. Parameters ---------- data : array_like, 1-D or 2-D dataset weights : None or 1-D ndarray weights for each observation, with same length as zero axis of data ddof : int default ddof=0, degrees of freedom correction used for second moments, var, std, cov, corrcoef. However, statistical tests are independent of `ddof`, based on the standard formulas. Examples -------- Note: I don't know the seed for the following, so the numbers will differ >>> x1_2d = 1.0 + np.random.randn(20, 3) >>> w1 = np.random.randint(1,4, 20) >>> d1 = DescrStatsW(x1_2d, weights=w1) >>> d1.mean array([ 1.42739844, 1.23174284, 1.083753 ]) >>> d1.var array([ 0.94855633, 0.52074626, 1.12309325]) >>> d1.std_mean array([ 0.14682676, 0.10878944, 0.15976497]) >>> tstat, pval, df = d1.ttest_mean(0) >>> tstat; pval; df array([ 9.72165021, 11.32226471, 6.78342055]) array([ 1.58414212e-12, 1.26536887e-14, 2.37623126e-08]) 44.0 >>> tstat, pval, df = d1.ttest_mean([0, 1, 1]) >>> tstat; pval; df array([ 9.72165021, 2.13019609, 0.52422632]) array([ 1.58414212e-12, 3.87842808e-02, 6.02752170e-01]) 44.0 #if weiqhts are integers, then asrepeats can be used >>> x1r = d1.asrepeats() >>> x1r.shape ... >>> stats.ttest_1samp(x1r, [0, 1, 1]) ... ''' def __init__(self, data, weights=None, ddof=0): self.data = np.asarray(data) if weights is None: self.weights = np.ones(self.data.shape[0]) else: #why squeeze? self.weights = np.asarray(weights).squeeze().astype(float) self.ddof = ddof @OneTimeProperty def sum_weights(self): return self.weights.sum(0) @OneTimeProperty def nobs(self): '''alias for number of observations/cases, equal to sum of weights ''' return self.sum_weights @OneTimeProperty def sum(self): '''weighted sum of data''' return np.dot(self.data.T, self.weights) @OneTimeProperty def mean(self): '''weighted mean of data''' return self.sum / self.sum_weights @OneTimeProperty def demeaned(self): '''data with weighted mean subtracted''' return self.data - self.mean @OneTimeProperty def sumsquares(self): '''weighted sum of squares of demeaned data''' return np.dot((self.demeaned**2).T, self.weights) #need memoize instead of cache decorator def var_ddof(self, ddof=0): '''variance of data given ddof Parameters ---------- ddof : int, float degrees of freedom correction, independent of attribute ddof Returns ------- var : float, ndarray variance with denominator ``sum_weights - ddof`` ''' return self.sumsquares / (self.sum_weights - ddof) def std_ddof(self, ddof=0): '''standard deviation of data with given ddof Parameters ---------- ddof : int, float degrees of freedom correction, independent of attribute ddof Returns ------- std : float, ndarray standard deviation with denominator ``sum_weights - ddof`` ''' return np.sqrt(self.var_ddof(ddof=ddof)) @OneTimeProperty def var(self): '''variance with default degrees of freedom correction ''' return self.sumsquares / (self.sum_weights - self.ddof) @OneTimeProperty def _var(self): '''variance without degrees of freedom correction used for statistical tests with controlled ddof ''' return self.sumsquares / self.sum_weights @OneTimeProperty def std(self): '''standard deviation with default degrees of freedom correction ''' return np.sqrt(self.var) @OneTimeProperty def cov(self): '''weighted covariance of data if data is 2 dimensional assumes variables in columns and observations in rows uses default ddof ''' cov_ = np.dot(self.weights * self.demeaned.T, self.demeaned) cov_ /= (self.sum_weights - self.ddof) return cov_ @OneTimeProperty def corrcoef(self): '''weighted correlation with default ddof assumes variables in columns and observations in rows ''' return self.cov / self.std / self.std[:,None] @OneTimeProperty def std_mean(self): '''standard deviation of weighted mean ''' std = self.std if self.ddof != 0: #ddof correction, (need copy of std) std = std * np.sqrt((self.sum_weights - self.ddof) / self.sum_weights) return std / np.sqrt(self.sum_weights - 1) def tconfint_mean(self, alpha=0.05, alternative='two-sided'): '''two-sided confidence interval for weighted mean of data If the data is 2d, then these are separate confidence intervals for each column. Parameters ---------- alpha : float significance level for the confidence interval, coverage is ``1-alpha`` alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: mean not equal to value (default) 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Returns ------- lower, upper : floats or ndarrays lower and upper bound of confidence interval Notes ----- In a previous version, statsmodels 0.4, alpha was the confidence level, e.g. 0.95 ''' #TODO: add asymmetric dof = self.sum_weights - 1 ci = _tconfint_generic(self.mean, self.std_mean, dof, alpha, alternative) return ci def zconfint_mean(self, alpha=0.05, alternative='two-sided'): '''two-sided confidence interval for weighted mean of data Confidence interval is based on normal distribution. If the data is 2d, then these are separate confidence intervals for each column. Parameters ---------- alpha : float significance level for the confidence interval, coverage is ``1-alpha`` alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: mean not equal to value (default) 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Returns ------- lower, upper : floats or ndarrays lower and upper bound of confidence interval Notes ----- In a previous version, statsmodels 0.4, alpha was the confidence level, e.g. 0.95 ''' return _zconfint_generic(self.mean, self.std_mean, alpha, alternative) def ttest_mean(self, value=0, alternative='two-sided'): '''ttest of Null hypothesis that mean is equal to value. The alternative hypothesis H1 is defined by the following 'two-sided': H1: mean not equal to value 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Parameters ---------- value : float or array the hypothesized value for the mean alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: mean not equal to value (default) 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test df : int or float ''' #TODO: check direction with R, smaller=less, larger=greater tstat = (self.mean - value) / self.std_mean dof = self.sum_weights - 1 #TODO: use outsourced if alternative == 'two-sided': pvalue = stats.t.sf(np.abs(tstat), dof)*2 elif alternative == 'larger': pvalue = stats.t.sf(tstat, dof) elif alternative == 'smaller': pvalue = stats.t.cdf(tstat, dof) return tstat, pvalue, dof def ttost_mean(self, low, upp): '''test of (non-)equivalence of one sample TOST: two one-sided t tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of the interval given by thresholds low and upp. Parameters ---------- low, upp : float equivalence interval low < mean < upp Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1, df1 : tuple test statistic, pvalue and degrees of freedom for lower threshold test t2, pv2, df2 : tuple test statistic, pvalue and degrees of freedom for upper threshold test ''' t1, pv1, df1 = self.ttest_mean(low, alternative='larger') t2, pv2, df2 = self.ttest_mean(upp, alternative='smaller') return np.maximum(pv1, pv2), (t1, pv1, df1), (t2, pv2, df2) def ztest_mean(self, value=0, alternative='two-sided'): '''z-test of Null hypothesis that mean is equal to value. The alternative hypothesis H1 is defined by the following 'two-sided': H1: mean not equal to value 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Parameters ---------- value : float or array the hypothesized value for the mean alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: mean not equal to value (default) 'larger' : H1: mean larger than value 'smaller' : H1: mean smaller than value Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test Notes ----- This uses the same degrees of freedom correction as the t-test in the calculation of the standard error of the mean, i.e it uses `(sum_weights - 1)` instead of `sum_weights` in the denominator. See Examples below for the difference. Examples -------- z-test on a proportion, with 20 observations, 15 of those are our event >>> x1 = [0, 1] >>> w1 = [5, 15] >>> d1 = smws.DescrStatsW(x1, w1) >>> d1.ztest_mean(0.5) (2.5166114784235836, 0.011848940928347452) This differs from the proportions_ztest because of the degrees of freedom correction: >>> smprop.proportions_ztest(15, 20., value=0.5) (2.5819888974716112, 0.009823274507519247). We can replicate the results from ``proportions_ztest`` if we increase the weights to have artificially one more observation: >>> smws.DescrStatsW(x1, np.array(w1)*21./20).ztest_mean(0.5) (2.5819888974716116, 0.0098232745075192366) ''' tstat = (self.mean - value) / self.std_mean #TODO: use outsourced if alternative == 'two-sided': pvalue = stats.norm.sf(np.abs(tstat))*2 elif alternative == 'larger': pvalue = stats.norm.sf(tstat) elif alternative == 'smaller': pvalue = stats.norm.cdf(tstat) return tstat, pvalue def ztost_mean(self, low, upp): '''test of (non-)equivalence of one sample, based on z-test TOST: two one-sided z-tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of the interval given by thresholds low and upp. Parameters ---------- low, upp : float equivalence interval low < mean < upp Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple test statistic and p-value for lower threshold test t2, pv2 : tuple test statistic and p-value for upper threshold test ''' t1, pv1 = self.ztest_mean(low, alternative='larger') t2, pv2 = self.ztest_mean(upp, alternative='smaller') return np.maximum(pv1, pv2), (t1, pv1), (t2, pv2) def get_compare(self, other, weights=None): '''return an instance of CompareMeans with self and other Parameters ---------- other : array_like or instance of DescrStatsW If array_like then this creates an instance of DescrStatsW with the given weights. weights : None or array weights are only used if other is not an instance of DescrStatsW Returns ------- cm : instance of CompareMeans the instance has self attached as d1 and other as d2. See Also -------- CompareMeans ''' if not isinstance(other, self.__class__): d2 = DescrStatsW(other, weights) else: d2 = other return CompareMeans(self, d2) def asrepeats(self): '''get array that has repeats given by floor(weights) observations with weight=0 are dropped ''' w_int = np.floor(self.weights).astype(int) return np.repeat(self.data, w_int, axis=0) def _tstat_generic(value1, value2, std_diff, dof, alternative, diff=0): '''generic ttest to save typing''' tstat = (value1 - value2 - diff) / std_diff if alternative in ['two-sided', '2-sided', '2s']: pvalue = stats.t.sf(np.abs(tstat), dof)*2 elif alternative in ['larger', 'l']: pvalue = stats.t.sf(tstat, dof) elif alternative in ['smaller', 's']: pvalue = stats.t.cdf(tstat, dof) else: raise ValueError('invalid alternative') return tstat, pvalue def _tconfint_generic(mean, std_mean, dof, alpha, alternative): '''generic t-confint to save typing''' if alternative in ['two-sided', '2-sided', '2s']: tcrit = stats.t.ppf(1 - alpha / 2., dof) lower = mean - tcrit * std_mean upper = mean + tcrit * std_mean elif alternative in ['larger', 'l']: tcrit = stats.t.ppf(alpha, dof) lower = mean + tcrit * std_mean upper = np.inf elif alternative in ['smaller', 's']: tcrit = stats.t.ppf(1 - alpha, dof) lower = -np.inf upper = mean + tcrit * std_mean else: raise ValueError('invalid alternative') return lower, upper def _zstat_generic(value1, value2, std_diff, alternative, diff=0): '''generic (normal) z-test to save typing can be used as ztest based on summary statistics ''' zstat = (value1 - value2 - diff) / std_diff if alternative in ['two-sided', '2-sided', '2s']: pvalue = stats.norm.sf(np.abs(zstat))*2 elif alternative in ['larger', 'l']: pvalue = stats.norm.sf(zstat) elif alternative in ['smaller', 's']: pvalue = stats.norm.cdf(zstat) else: raise ValueError('invalid alternative') return zstat, pvalue def _zstat_generic2(value, std_diff, alternative): '''generic (normal) z-test to save typing can be used as ztest based on summary statistics ''' zstat = value / std_diff if alternative in ['two-sided', '2-sided', '2s']: pvalue = stats.norm.sf(np.abs(zstat))*2 elif alternative in ['larger', 'l']: pvalue = stats.norm.sf(zstat) elif alternative in ['smaller', 's']: pvalue = stats.norm.cdf(zstat) else: raise ValueError('invalid alternative') return zstat, pvalue def _zconfint_generic(mean, std_mean, alpha, alternative): '''generic normal-confint to save typing''' if alternative in ['two-sided', '2-sided', '2s']: zcrit = stats.norm.ppf(1 - alpha / 2.) lower = mean - zcrit * std_mean upper = mean + zcrit * std_mean elif alternative in ['larger', 'l']: zcrit = stats.norm.ppf(alpha) lower = mean + zcrit * std_mean upper = np.inf elif alternative in ['smaller', 's']: zcrit = stats.norm.ppf(1 - alpha) lower = -np.inf upper = mean + zcrit * std_mean else: raise ValueError('invalid alternative') return lower, upper class CompareMeans(object): '''class for two sample comparison The tests and the confidence interval work for multi-endpoint comparison: If d1 and d2 have the same number of rows, then each column of the data in d1 is compared with the corresponding column in d2. Parameters ---------- d1, d2 : instances of DescrStatsW Notes ----- The result for the statistical tests and the confidence interval are independent of the user specified ddof. TODO: Extend to any number of groups or write a version that works in that case, like in SAS and SPSS. ''' def __init__(self, d1, d2): '''assume d1, d2 hold the relevant attributes ''' self.d1 = d1 self.d2 = d2 #assume nobs is available # if not hasattr(self.d1, 'nobs'): # d1.nobs1 = d1.sum_weights.astype(float) #float just to make sure # self.nobs2 = d2.sum_weights.astype(float) @OneTimeProperty def std_meandiff_separatevar(self): #this uses ``_var`` to use ddof=0 for formula d1 = self.d1 d2 = self.d2 return np.sqrt(d1._var / (d1.nobs-1) + d2._var / (d2.nobs-1)) @OneTimeProperty def std_meandiff_pooledvar(self): '''variance assuming equal variance in both data sets ''' #this uses ``_var`` to use ddof=0 for formula d1 = self.d1 d2 = self.d2 #could make var_pooled into attribute var_pooled = ((d1.sumsquares + d2.sumsquares) / #(d1.nobs - d1.ddof + d2.nobs - d2.ddof)) (d1.nobs - 1 + d2.nobs - 1)) return np.sqrt(var_pooled * (1. / d1.nobs + 1. /d2.nobs)) def dof_satt(self): '''degrees of freedom of Satterthwaite for unequal variance ''' d1 = self.d1 d2 = self.d2 #this follows blindly the SPSS manual #except I use ``_var`` which has ddof=0 sem1 = d1._var / (d1.nobs-1) sem2 = d2._var / (d2.nobs-1) semsum = sem1 + sem2 z1 = (sem1 / semsum)**2 / (d1.nobs - 1) z2 = (sem2 / semsum)**2 / (d2.nobs - 1) dof = 1. / (z1 + z2) return dof def ttest_ind(self, alternative='two-sided', usevar='pooled', value=0): '''ttest for the null hypothesis of identical means this should also be the same as onewaygls, except for ddof differences Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used value : float difference between the means under the Null hypothesis. Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test df : int or float degrees of freedom used in the t-test Notes ----- The result is independent of the user specified ddof. ''' d1 = self.d1 d2 = self.d2 if usevar == 'pooled': stdm = self.std_meandiff_pooledvar dof = (d1.nobs - 1 + d2.nobs - 1) elif usevar == 'unequal': stdm = self.std_meandiff_separatevar dof = self.dof_satt() else: raise ValueError('usevar can only be "pooled" or "unequal"') tstat, pval = _tstat_generic(d1.mean, d2.mean, stdm, dof, alternative, diff=value) return tstat, pval, dof def ztest_ind(self, alternative='two-sided', usevar='pooled', value=0): '''z-test for the null hypothesis of identical means Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used value : float difference between the means under the Null hypothesis. Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test df : int or float degrees of freedom used in the t-test ''' d1 = self.d1 d2 = self.d2 if usevar == 'pooled': stdm = self.std_meandiff_pooledvar elif usevar == 'unequal': stdm = self.std_meandiff_separatevar else: raise ValueError('usevar can only be "pooled" or "unequal"') tstat, pval = _zstat_generic(d1.mean, d2.mean, stdm, alternative, diff=value) return tstat, pval def tconfint_diff(self, alpha=0.05, alternative='two-sided', usevar='pooled'): '''confidence interval for the difference in means Parameters ---------- alpha : float significance level for the confidence interval, coverage is ``1-alpha`` alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following : 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used Returns ------- lower, upper : floats lower and upper limits of the confidence interval Notes ----- The result is independent of the user specified ddof. ''' d1 = self.d1 d2 = self.d2 diff = d1.mean - d2.mean if usevar == 'pooled': std_diff = self.std_meandiff_pooledvar dof = (d1.nobs - 1 + d2.nobs - 1) elif usevar == 'unequal': std_diff = self.std_meandiff_separatevar dof = self.dof_satt() else: raise ValueError('usevar can only be "pooled" or "unequal"') res = _tconfint_generic(diff, std_diff, dof, alpha=alpha, alternative=alternative) return res def zconfint_diff(self, alpha=0.05, alternative='two-sided', usevar='pooled'): '''confidence interval for the difference in means Parameters ---------- alpha : float significance level for the confidence interval, coverage is ``1-alpha`` alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following : 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used Returns ------- lower, upper : floats lower and upper limits of the confidence interval Notes ----- The result is independent of the user specified ddof. ''' d1 = self.d1 d2 = self.d2 diff = d1.mean - d2.mean if usevar == 'pooled': std_diff = self.std_meandiff_pooledvar elif usevar == 'unequal': std_diff = self.std_meandiff_separatevar else: raise ValueError('usevar can only be "pooled" or "unequal"') res = _zconfint_generic(diff, std_diff, alpha=alpha, alternative=alternative) return res def ttost_ind(self, low, upp, usevar='pooled'): ''' test of equivalence for two independent samples, base on t-test Parameters ---------- low, upp : float equivalence interval low < m1 - m2 < upp usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test ''' tt1 = self.ttest_ind(alternative='larger', usevar=usevar, value=low) tt2 = self.ttest_ind(alternative='smaller', usevar=usevar, value=upp) #TODO: remove tuple return, use same as for function tost_ind return np.maximum(tt1[1], tt2[1]), (tt1, tt2) def ztost_ind(self, low, upp, usevar='pooled'): ''' test of equivalence for two independent samples, based on z-test Parameters ---------- low, upp : float equivalence interval low < m1 - m2 < upp usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test ''' tt1 = self.ztest_ind(alternative='larger', usevar=usevar, value=low) tt2 = self.ztest_ind(alternative='smaller', usevar=usevar, value=upp) #TODO: remove tuple return, use same as for function tost_ind return np.maximum(tt1[1], tt2[1]), tt1, tt2 #tost.__doc__ = tost_ind.__doc__ #doesn't work for 2d, doesn't take weights into account ## def test_equal_var(self): ## '''Levene test for independence ## ## ''' ## d1 = self.d1 ## d2 = self.d2 ## #rewrite this, for now just use scipy.stats ## return stats.levene(d1.data, d2.data) def ttest_ind(x1, x2, alternative='two-sided', usevar='pooled', weights=(None, None), value=0): '''ttest independent sample convenience function that uses the classes and throws away the intermediate results, compared to scipy stats: drops axis option, adds alternative, usevar, and weights option Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used weights : tuple of None or ndarrays Case weights for the two samples. For details on weights see ``DescrStatsW`` value : float difference between the means under the Null hypothesis. Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test df : int or float degrees of freedom used in the t-test ''' cm = CompareMeans(DescrStatsW(x1, weights=weights[0], ddof=0), DescrStatsW(x2, weights=weights[1], ddof=0)) tstat, pval, dof = cm.ttest_ind(alternative=alternative, usevar=usevar, value=value) return tstat, pval, dof def ttost_ind(x1, x2, low, upp, usevar='pooled', weights=(None, None), transform=None): '''test of (non-)equivalence for two independent samples TOST: two one-sided t tests null hypothesis: m1 - m2 < low or m1 - m2 > upp alternative hypothesis: low < m1 - m2 < upp where m1, m2 are the means, expected values of the two samples. If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the difference between the two samples is larger than the the thresholds given by low and upp. Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case low, upp : float equivalence interval low < m1 - m2 < upp usevar : string, 'pooled' or 'unequal' If ``pooled``, then the standard deviation of the samples is assumed to be the same. If ``unequal``, then Welsh ttest with Satterthwait degrees of freedom is used weights : tuple of None or ndarrays Case weights for the two samples. For details on weights see ``DescrStatsW`` transform : None or function If None (default), then the data is not transformed. Given a function, sample data and thresholds are transformed. If transform is log, then the equivalence interval is in ratio: low < m1 / m2 < upp Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test Notes ----- The test rejects if the 2*alpha confidence interval for the difference is contained in the ``(low, upp)`` interval. This test works also for multi-endpoint comparisons: If d1 and d2 have the same number of columns, then each column of the data in d1 is compared with the corresponding column in d2. This is the same as comparing each of the corresponding columns separately. Currently no multi-comparison correction is used. The raw p-values reported here can be correction with the functions in ``multitest``. ''' if transform: if transform is np.log: #avoid hstack in special case x1 = transform(x1) x2 = transform(x2) else: #for transforms like rankdata that will need both datasets #concatenate works for stacking 1d and 2d arrays xx = transform(np.concatenate((x1, x2), 0)) x1 = xx[:len(x1)] x2 = xx[len(x1):] low = transform(low) upp = transform(upp) cm = CompareMeans(DescrStatsW(x1, weights=weights[0], ddof=0), DescrStatsW(x2, weights=weights[1], ddof=0)) pval, res = cm.ttost_ind(low, upp, usevar=usevar) return pval, res[0], res[1] def ttost_paired(x1, x2, low, upp, transform=None, weights=None): '''test of (non-)equivalence for two dependent, paired sample TOST: two one-sided t tests null hypothesis: md < low or md > upp alternative hypothesis: low < md < upp where md is the mean, expected value of the difference x1 - x2 If the pvalue is smaller than a threshold,say 0.05, then we reject the hypothesis that the difference between the two samples is larger than the the thresholds given by low and upp. Parameters ---------- x1, x2 : array_like two dependent samples low, upp : float equivalence interval low < mean of difference < upp weights : None or ndarray case weights for the two samples. For details on weights see ``DescrStatsW`` transform : None or function If None (default), then the data is not transformed. Given a function sample data and thresholds are transformed. If transform is log the the equivalence interval is in ratio: low < x1 / x2 < upp Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1, df1 : tuple test statistic, pvalue and degrees of freedom for lower threshold test t2, pv2, df2 : tuple test statistic, pvalue and degrees of freedom for upper threshold test ''' if transform: if transform is np.log: #avoid hstack in special case x1 = transform(x1) x2 = transform(x2) else: #for transforms like rankdata that will need both datasets #concatenate works for stacking 1d and 2d arrays xx = transform(np.concatenate((x1, x2), 0)) x1 = xx[:len(x1)] x2 = xx[len(x1):] low = transform(low) upp = transform(upp) dd = DescrStatsW(x1 - x2, weights=weights, ddof=0) t1, pv1, df1 = dd.ttest_mean(low, alternative='larger') t2, pv2, df2 = dd.ttest_mean(upp, alternative='smaller') return np.maximum(pv1, pv2), (t1, pv1, df1), (t2, pv2, df2) def ztest(x1, x2=None, value=0, alternative='two-sided', usevar='pooled', ddof=1.): '''test for mean based on normal distribution, one or two samples In the case of two samples, the samples are assumed to be independent. Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples value : float In the one sample case, value is the mean of x1 under the Null hypothesis. In the two sample case, value is the difference between mean of x1 and mean of x2 under the Null hypothesis. The test statistic is `x1_mean - x2_mean - value`. alternative : string The alternative hypothesis, H1, has to be one of the following 'two-sided': H1: difference in means not equal to value (default) 'larger' : H1: difference in means larger than value 'smaller' : H1: difference in means smaller than value usevar : string, 'pooled' Currently, only 'pooled' is implemented. If ``pooled``, then the standard deviation of the samples is assumed to be the same. see CompareMeans.ztest_ind for different options. ddof : int Degrees of freedom use in the calculation of the variance of the mean estimate. In the case of comparing means this is one, however it can be adjusted for testing other statistics (proportion, correlation) Returns ------- tstat : float test statisic pvalue : float pvalue of the t-test Notes ----- usevar not implemented, is always pooled in two sample case use CompareMeans instead. ''' # TODO: this should delegate to CompareMeans like ttest_ind # However that does not implement ddof #usevar is not used, always pooled if usevar != 'pooled': raise NotImplementedError('only usevar="pooled" is implemented') x1 = np.asarray(x1) nobs1 = x1.shape[0] x1_mean = x1.mean(0) x1_var = x1.var(0) if x2 is not None: x2 = np.asarray(x2) nobs2 = x2.shape[0] x2_mean = x2.mean(0) x2_var = x2.var(0) var_pooled = (nobs1 * x1_var + nobs2 * x2_var) var_pooled /= (nobs1 + nobs2 - 2 * ddof) var_pooled *= (1. / nobs1 + 1. / nobs2) else: var_pooled = x1_var / (nobs1 - ddof) x2_mean = 0 std_diff = np.sqrt(var_pooled) #stat = x1_mean - x2_mean - value return _zstat_generic(x1_mean, x2_mean, std_diff, alternative, diff=value) def zconfint(x1, x2=None, value=0, alpha=0.05, alternative='two-sided', usevar='pooled', ddof=1.): '''confidence interval based on normal distribution z-test Parameters ---------- x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case value : float In the one sample case, value is the mean of x1 under the Null hypothesis. In the two sample case, value is the difference between mean of x1 and mean of x2 under the Null hypothesis. The test statistic is `x1_mean - x2_mean - value`. usevar : string, 'pooled' Currently, only 'pooled' is implemented. If ``pooled``, then the standard deviation of the samples is assumed to be the same. see CompareMeans.ztest_ind for different options. ddof : int Degrees of freedom use in the calculation of the variance of the mean estimate. In the case of comparing means this is one, however it can be adjusted for testing other statistics (proportion, correlation) Notes ----- checked only for 1 sample case usevar not implemented, is always pooled in two sample case ``value`` shifts the confidence interval so it is centered at `x1_mean - x2_mean - value` See Also -------- ztest CompareMeans ''' #usevar is not used, always pooled # mostly duplicate code from ztest if usevar != 'pooled': raise NotImplementedError('only usevar="pooled" is implemented') x1 = np.asarray(x1) nobs1 = x1.shape[0] x1_mean = x1.mean(0) x1_var = x1.var(0) if x2 is not None: x2 = np.asarray(x2) nobs2 = x2.shape[0] x2_mean = x2.mean(0) x2_var = x2.var(0) var_pooled = (nobs1 * x1_var + nobs2 * x2_var) var_pooled /= (nobs1 + nobs2 - 2 * ddof) var_pooled *= (1. / nobs1 + 1. / nobs2) else: var_pooled = x1_var / (nobs1 - ddof) x2_mean = 0 std_diff = np.sqrt(var_pooled) ci = _zconfint_generic(x1_mean - x2_mean - value, std_diff, alpha, alternative) return ci def ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.): '''Equivalence test based on normal distribution Parameters ---------- x1 : array_like one sample or first sample for 2 independent samples low, upp : float equivalence interval low < m1 - m2 < upp x1 : array_like or None second sample for 2 independent samples test. If None, then a one-sample test is performed. usevar : string, 'pooled' If `pooled`, then the standard deviation of the samples is assumed to be the same. Only `pooled` is currently implemented. Returns ------- pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test Notes ----- checked only for 1 sample case ''' tt1 = ztest(x1, x2, alternative='larger', usevar=usevar, value=low, ddof=ddof) tt2 = ztest(x1, x2, alternative='smaller', usevar=usevar, value=upp, ddof=ddof) return np.maximum(tt1[1], tt2[1]), tt1, tt2, statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/000077500000000000000000000000001224417117700216565ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/R_ig.s000066400000000000000000000003461224417117700227250ustar00rootroot00000000000000### SETUP ### d <- read.table("./inv_gaussian.csv",sep=",", header=T, nrows=5000) attach(d) ### MODEL ### library(nlme) m1 <- glm(xig ~ x1 + x2, family=inverse.gaussian) results <- summary.glm(m1) results results['coefficients'] statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/R_lbw.s000066400000000000000000000006721224417117700231140ustar00rootroot00000000000000### SETUP ### d <- read.table("./stata_lbw_glm.csv",sep=",", header=T) attach(d) race.f <- factor(race) contrasts(race.f) <- contr.treatment(3, base = 3) # make white the intercept ### MODEL ### m1 <- glm(low ~ age + lwt + race.f + smoke + ptl + ht + ui, family=binomial) results <- summary.glm(m1) results results['coefficients'] library(boot) m1.diag <- glm.diag(m1) # note that this returns standardized residuals for diagnostics) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/__init__.py000066400000000000000000000000461224417117700237670ustar00rootroot00000000000000#adding test directory to python path statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/check_for_rpy.py000066400000000000000000000001411224417117700250410ustar00rootroot00000000000000def skip_rpy(): try: import rpy return False except: return True statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/coverage_sm.py000066400000000000000000000017151224417117700245260ustar00rootroot00000000000000''' create html coverage report using coverage Note that this will work on the *installed* version of statsmodels; however, the script should be run from the source tree's test directory. 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statsmodels-0.5.0+git13-g8e07d34/statsmodels/tests/rmodelwrap.py000066400000000000000000000123071224417117700244070ustar00rootroot00000000000000''' Wrapper for R models to allow comparison to scipy models ''' import numpy as np import rpy from check_for_rpy import skip_rpy skipR = skip_rpy() if not skipR: from rpy import r # MASS contains # negative binomial family for GLM # rlm #TODO: write a check_key wrapper for these class RModel(object): ''' Class gives R models scipy.models -like interface ''' def __init__(self, y, design, model_type=r.lm, **kwds): ''' Set up and estimate R model with data and design ''' r.library('MASS') # still needs to be in test, but also here for # logical tests at the end not to show an error self.y = np.array(y) self.design = np.array(design) self.model_type = model_type self._design_cols = ['x.%d' % (i+1) for i in range( self.design.shape[1])] # Note the '-1' for no intercept - this is included in the design self.formula = r('y ~ %s-1' % '+'.join(self._design_cols)) self.frame = r.data_frame(y=y, x=self.design) rpy.set_default_mode(rpy.NO_CONVERSION) results = self.model_type(self.formula, data = self.frame, **kwds) self.robj = results # keep the Robj model so it can be # used in the tests rpy.set_default_mode(rpy.BASIC_CONVERSION) rsum = r.summary(results) self.rsum = rsum # Provide compatible interface with scipy models self.results = results.as_py() # coeffs = self.results['coefficients'] # self.beta0 = np.array([coeffs[c] for c in self._design_cols]) self.nobs = len(self.results['residuals']) if isinstance(self.results['residuals'], dict): self.resid = np.zeros((len(self.results['residuals'].keys()))) for i in self.results['residuals'].keys(): self.resid[int(i)-1] = self.results['residuals'][i] else: self.resid = self.results['residuals'] self.fittedvalues = self.results['fitted.values'] self.df_resid = self.results['df.residual'] self.params = rsum['coefficients'][:,0] self.bse = rsum['coefficients'][:,1] self.bt = rsum['coefficients'][:,2] try: self.pvalues = rsum['coefficients'][:,3] except: pass self.rsquared = rsum.setdefault('r.squared', None) self.rsquared_adj = rsum.setdefault('adj.r.squared', None) self.aic_R = rsum.setdefault('aic', None) self.fvalue = rsum.setdefault('fstatistic', None) if self.fvalue and isinstance(self.fvalue, dict): self.fvalue = self.fvalue.setdefault('value', None) # for wls df = rsum.setdefault('df', None) if df: # for RLM, works for other models? self.df_model = df[0]-1 # R counts intercept self.df_resid = df[1] self.bcov_unscaled = rsum.setdefault('cov.unscaled', None) self.bcov = rsum.setdefault('cov.scaled', None) if 'sigma' in rsum: self.scale = rsum['sigma'] elif 'dispersion' in rsum: self.scale = rsum['dispersion'] else: self.scale = None self.llf = r.logLik(results) if model_type == r.glm: self.getglm() if model_type == r.rlm: self.getrlm() def getglm(self): self.deviance = self.rsum['deviance'] self.resid = [self.results['residuals'][str(k)] \ for k in range(1, 1+self.nobs)] if isinstance(self.resid, dict): tmp = np.zeros(len(self.resid)) for i in self.resid.keys(): tmp[int(i)-1] = self.resid[i] self.resid = tmp self.predict = [self.results['linear.predictors'][str(k)] \ for k in range(1, 1+self.nobs)] self.fittedvalues = [self.results['fitted.values'][str(k)] \ for k in range(1, 1+self.nobs)] self.weights = [self.results['weights'][str(k)] \ for k in range(1, 1+self.nobs)] self.resid_deviance = self.rsum['deviance.resid'] if isinstance(self.resid_deviance, dict): tmp = np.zeros(len(self.resid_deviance)) for i in self.resid_deviance.keys(): tmp[int(i)-1] = self.resid_deviance[i] self.resid_deviance = tmp self.null_deviance = self.rsum['null.deviance'] def getrlm(self): self.k2 = self.results['k2'] if isinstance(self.results['w'], dict): tmp = np.zeros((len(self.results['w'].keys()))) for i in self.results['w'].keys(): tmp[int(i)-1] = self.results['w'][i] self.weights = tmp else: self.weights = self.results['w'] self.stddev = self.rsum['stddev'] # Don't know what this is yet self.wresid = None # these equal resids always? #TODO: # function to write Rresults to results file, so this is a developers tool # and not a test dependency? def RModelConvert(model, sec_title=None, results_title=None): import os if not results_title: raise AttributeError("You need to specify a results title") outfile = open('./model_results.py', 'a') outfile.write('class '+results_title) outfile.write(' '*4) # handle indents statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/000077500000000000000000000000001224417117700216545ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/__init__.py000066400000000000000000000001601224417117700237620ustar00rootroot00000000000000from tools import add_constant, categorical from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/catadd.py000066400000000000000000000030321224417117700234440ustar00rootroot00000000000000 import numpy as np from statsmodels.tools.tools import rank as smrank def add_indep(x, varnames, dtype=None): ''' construct array with independent columns x is either iterable (list, tuple) or instance of ndarray or a subclass of it. If x is an ndarray, then each column is assumed to represent a variable with observations in rows. ''' #TODO: this needs tests for subclasses if isinstance(x, np.ndarray) and x.ndim == 2: x = x.T nvars_orig = len(x) nobs = len(x[0]) #print 'nobs, nvars_orig', nobs, nvars_orig if not dtype: dtype = np.asarray(x[0]).dtype xout = np.zeros((nobs, nvars_orig), dtype=dtype) count = 0 rank_old = 0 varnames_new = [] varnames_dropped = [] keepindx = [] for (xi, ni) in zip(x, varnames): #print xi.shape, xout.shape xout[:,count] = xi rank_new = smrank(xout) #print rank_new if rank_new > rank_old: varnames_new.append(ni) rank_old = rank_new count += 1 else: varnames_dropped.append(ni) return xout[:,:count], varnames_new if __name__ == '__main__': x1 = np.array([0,0,0,0,0,1,1,1,2,2,2]) x2 = np.array([0,0,0,0,0,1,1,1,1,1,1]) x0 = np.ones(len(x2)) x = np.column_stack([x0, x1[:,None]*np.arange(3), x2[:,None]*np.arange(2)]) varnames = ['const'] + ['var1_%d' %i for i in np.arange(3)] \ + ['var2_%d' %i for i in np.arange(2)] xo,vo = add_indep(x, varnames) print xo.shape statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/compatibility.py000066400000000000000000000014451224417117700251030ustar00rootroot00000000000000 import numpy as np try: from numpy.linalg import slogdet as np_slogdet except: def np_slogdet(x): return 1, np.log(np.linalg.det(x)) def getZipFile(): '''return ZipFile class with open method for python < 2.6 for python < 2.6, the open method returns a StringIO.StringIO file_like Examples -------- ZipFile = getZipFile() ... not fully tested yet written for pyecon ''' import sys, zipfile if sys.version >= '2.6': return zipfile.ZipFile else: class ZipFile(zipfile.ZipFile): def open(self, filename): fullfilename = [f for f in self.namelist() if filename in f][0] import StringIO return StringIO.StringIO(self.read(fullfilename)) return ZipFile statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/data.py000066400000000000000000000067341224417117700231510ustar00rootroot00000000000000""" Compatibility tools for various data structure inputs """ #TODO: question: interpret_data # looks good and could/should be merged with other check convertion functions we also have # similar also to what Nathaniel mentioned for Formula # good: if ndarray check passes then loading pandas is not triggered, import numpy as np def have_pandas(): try: import pandas return True except ImportError: return False except Exception: return False def have_patsy(): try: import patsy return True except ImportError: return False except Exception: return False def is_data_frame(obj): if not have_pandas(): return False import pandas as pn return isinstance(obj, pn.DataFrame) def is_design_matrix(obj): if not have_patsy(): return False from patsy import DesignMatrix return isinstance(obj, DesignMatrix) def _is_structured_ndarray(obj): return isinstance(obj, np.ndarray) and obj.dtype.names is not None def interpret_data(data, colnames=None, rownames=None): """ Convert passed data structure to form required by estimation classes Parameters ---------- data : ndarray-like colnames : sequence or None May be part of data structure rownames : sequence or None Returns ------- (values, colnames, rownames) : (homogeneous ndarray, list) """ if isinstance(data, np.ndarray): if _is_structured_ndarray(data): if colnames is None: colnames = data.dtype.names values = struct_to_ndarray(data) else: values = data if colnames is None: colnames = ['Y_%d' % i for i in range(values.shape[1])] elif is_data_frame(data): # XXX: hack data = data.dropna() values = data.values colnames = data.columns rownames = data.index else: # pragma: no cover raise Exception('cannot handle other input types at the moment') if not isinstance(colnames, list): colnames = list(colnames) # sanity check if len(colnames) != values.shape[1]: raise ValueError('length of colnames does not match number ' 'of columns in data') if rownames is not None and len(rownames) != len(values): raise ValueError('length of rownames does not match number ' 'of rows in data') return values, colnames, rownames def struct_to_ndarray(arr): return arr.view((float, len(arr.dtype.names))) def _is_using_ndarray_type(endog, exog): return (type(endog) is np.ndarray and (type(exog) is np.ndarray or exog is None)) def _is_using_ndarray(endog, exog): return (isinstance(endog, np.ndarray) and (isinstance(exog, np.ndarray) or exog is None)) def _is_using_pandas(endog, exog): if not have_pandas(): return False from pandas import Series, DataFrame, WidePanel klasses = (Series, DataFrame, WidePanel) return (isinstance(endog, klasses) or isinstance(exog, klasses)) def _is_array_like(endog, exog): try: # do it like this in case of mixed types, ie., ndarray and list endog = np.asarray(endog) exog = np.asarray(exog) return True except: return False def _is_using_patsy(endog, exog): # we get this when a structured array is passed through a formula return (is_design_matrix(endog) and (is_design_matrix(exog) or exog is None)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/decorators.py000066400000000000000000000205001224417117700243700ustar00rootroot00000000000000from numpy.testing import assert_equal import warnings __all__ = ['resettable_cache','cache_readonly', 'cache_writable'] class CacheWriteWarning(UserWarning): pass class ResettableCache(dict): """ Dictionary whose elements mey depend one from another. If entry `B` depends on entry `A`, changing the values of entry `A` will reset the value of entry `B` to a default (None); deleteing entry `A` will delete entry `B`. The connections between entries are stored in a `_resetdict` private attribute. Parameters ---------- reset : dictionary, optional An optional dictionary, associated a sequence of entries to any key of the object. items : var, optional An optional dictionary used to initialize the dictionary Examples -------- >>> reset = dict(a=('b',), b=('c',)) >>> cache = resettable_cache(a=0, b=1, c=2, reset=reset) >>> assert_equal(cache, dict(a=0, b=1, c=2)) >>> print "Try resetting a" >>> cache['a'] = 1 >>> assert_equal(cache, dict(a=1, b=None, c=None)) >>> cache['c'] = 2 >>> assert_equal(cache, dict(a=1, b=None, c=2)) >>> cache['b'] = 0 >>> assert_equal(cache, dict(a=1, b=0, c=None)) >>> print "Try deleting b" >>> del(cache['a']) >>> assert_equal(cache, {}) """ def __init__(self, reset=None, **items): self._resetdict = reset or {} dict.__init__(self, **items) def __setitem__(self, key, value): dict.__setitem__(self, key, value) #if hasattr needed for unpickling with protocol=2 if hasattr(self, '_resetdict'): for mustreset in self._resetdict.get(key, []): self[mustreset] = None def __delitem__(self, key): dict.__delitem__(self, key) for mustreset in self._resetdict.get(key, []): del(self[mustreset]) # def __getstate__(self): # print 'pickling wrapper', self.__dict__ # return self.__dict__ # # def __setstate__(self, dict_): # print 'unpickling wrapper', dict_ # self.__dict__.update(dict_) resettable_cache = ResettableCache class CachedAttribute(object): def __init__(self, func, cachename=None, resetlist=None): self.fget = func self.name = func.__name__ self.cachename = cachename or '_cache' self.resetlist = resetlist or () def __get__(self, obj, type=None): if obj is None: return self.fget # Get the cache or set a default one if needed _cachename = self.cachename _cache = getattr(obj, _cachename, None) if _cache is None: setattr(obj, _cachename, resettable_cache()) _cache = getattr(obj, _cachename) # Get the name of the attribute to set and cache name = self.name _cachedval = _cache.get(name, None) # print "[_cachedval=%s]" % _cachedval if _cachedval is None: # Call the "fget" function _cachedval = self.fget(obj) # Set the attribute in obj # print "Setting %s in cache to %s" % (name, _cachedval) try: _cache[name] = _cachedval except KeyError: setattr(_cache, name, _cachedval) # Update the reset list if needed (and possible) resetlist = self.resetlist if resetlist is not (): try: _cache._resetdict[name] = self.resetlist except AttributeError: pass # else: # print "Reading %s from cache (%s)" % (name, _cachedval) return _cachedval def __set__(self, obj, value): errmsg = "The attribute '%s' cannot be overwritten" % self.name warnings.warn(errmsg, CacheWriteWarning) class CachedWritableAttribute(CachedAttribute): # def __set__(self, obj, value): _cache = getattr(obj, self.cachename) name = self.name try: _cache[name] = value except KeyError: setattr(_cache, name, value) class _cache_readonly(object): """ Decorator for CachedAttribute """ def __init__(self, cachename=None, resetlist=None): self.func = None self.cachename = cachename self.resetlist = resetlist or None def __call__(self, func): return CachedAttribute(func, cachename=self.cachename, resetlist=self.resetlist) cache_readonly = _cache_readonly() class cache_writable(_cache_readonly): """ Decorator for CachedWritableAttribute """ def __call__(self, func): return CachedWritableAttribute(func, cachename=self.cachename, resetlist=self.resetlist) #this has been copied from nitime a long time ago #TODO: ceck whether class has change in nitime class OneTimeProperty(object): """A descriptor to make special properties that become normal attributes. This is meant to be used mostly by the auto_attr decorator in this module. Author: Fernando Perez, copied from nitime """ def __init__(self,func): """Create a OneTimeProperty instance. Parameters ---------- func : method The method that will be called the first time to compute a value. Afterwards, the method's name will be a standard attribute holding the value of this computation. """ self.getter = func self.name = func.func_name def __get__(self,obj,type=None): """This will be called on attribute access on the class or instance. """ if obj is None: # Being called on the class, return the original function. This way, # introspection works on the class. #return func #print 'class access' return self.getter val = self.getter(obj) #print "** auto_attr - loading '%s'" % self.name # dbg setattr(obj, self.name, val) return val try: from nose.tools import nottest except ImportError: # make a dummy decorator so people that don't have nose installed # don't get an error def nottest(fn): return fn if __name__ == "__main__": ### Tests resettable_cache ---------------------------------------------------- reset = dict(a=('b',), b=('c',)) cache = resettable_cache(a=0, b=1, c=2, reset=reset) assert_equal(cache, dict(a=0, b=1, c=2)) # print "Try resetting a" cache['a'] = 1 assert_equal(cache, dict(a=1, b=None, c=None)) cache['c'] = 2 assert_equal(cache, dict(a=1, b=None, c=2)) cache['b'] = 0 assert_equal(cache, dict(a=1, b=0, c=None)) # print "Try deleting b" del(cache['a']) assert_equal(cache, {}) ### --------------------------------------------------------------------------- class Example(object): # def __init__(self): self._cache = resettable_cache() self.a = 0 # @cache_readonly def b(self): return 1 @cache_writable(resetlist='d') def c(self): return 2 @cache_writable(resetlist=('e', 'f')) def d(self): return self.c + 1 # @cache_readonly def e(self): return 4 @cache_readonly def f(self): return self.e + 1 ex = Example() print "(attrs : %s)" % str(ex.__dict__) print "(cached : %s)" % str(ex._cache) print "Try a :", ex.a print "Try accessing/setting a readonly attribute" assert_equal(ex.__dict__, dict(a=0, _cache={})) print "Try b #1:", ex.b b = ex.b assert_equal(b, 1) assert_equal(ex.__dict__, dict(a=0, _cache=dict(b=1,))) # assert_equal(ex.__dict__, dict(a=0, b=1, _cache=dict(b=1))) ex.b = -1 print "Try dict", ex.__dict__ assert_equal(ex._cache, dict(b=1,)) # print "Try accessing/resetting a cachewritable attribute" c = ex.c assert_equal(c, 2) assert_equal(ex._cache, dict(b=1, c=2)) d = ex.d assert_equal(d, 3) assert_equal(ex._cache, dict(b=1, c=2, d=3)) ex.c = 0 assert_equal(ex._cache, dict(b=1, c=0, d=None, e=None, f=None)) d = ex.d assert_equal(ex._cache, dict(b=1, c=0, d=1, e=None, f=None)) ex.d = 5 assert_equal(ex._cache, dict(b=1, c=0, d=5, e=None, f=None)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/dump2module.py000066400000000000000000000153021224417117700244640ustar00rootroot00000000000000'''Save a set of numpy arrays to a python module file that can be imported Author : Josef Perktold ''' import numpy as np class HoldIt(object): '''Class to write numpy arrays into a python module Calling save on the instance of this class write all attributes of the instance into a module file. For details see the save method. ''' def __init__(self, name): self.name = name def save(self, what=None, filename=None, header=True, useinstance=True, comment=None, print_options=None): '''write attributes of this instance to python module given by filename Parameters ---------- what : list or None list of attributes that are added to the module. If None (default) then all attributes in __dict__ that do not start with an underline will be saved. filename : string specifies filename with path. If the file does not exist, it will be created. If the file is already exists, then the new data will be appended to the file. header : bool If true, then the imports of the module and the class definition are written before writing the data. useinstance : bool If true, then the data in the module are attached to an instance of a holder class. If false, then each array will be saved as separate variable. comment : string If comment is not empty then this string will be attached as a description comment to the data instance in the saved module. print_options : dict or None The print_options for the numpy arrays will be updated with this. see notes Notes ----- The content of an numpy array are written using repr, which can be controlled with the np.set_printoptions. The numpy default is updated with: precision=20, linewidth=100, nanstr='nan', infstr='inf' This should provide enough precision for double floating point numbers. If one array has more than 1000 elements, then threshold should be overwritten by the user, see keyword argument print_options. ''' print_opt_old = np.get_printoptions() print_opt = dict(precision=20, linewidth=100, nanstr='nan', infstr='inf') if print_options: print_opt.update(print_options) np.set_printoptions(**print_opt) #precision corrects for non-scientific notation if what is None: what = (i for i in self.__dict__ if i[0] != '_') if header: txt = ['import numpy as np\n' 'from numpy import array, rec, inf, nan\n\n'] if useinstance: txt.append('class Holder(object):\n pass\n\n') else: txt = [] if useinstance: txt.append('%s = Holder()' % self.name) prefix = '%s.' % self.name else: prefix = '' if not comment is None: txt.append("%scomment = '%s'" % (prefix, comment)) for x in what: txt.append('%s%s = %s' % (prefix, x, repr(getattr(self,x)))) txt.extend(['','']) #add empty lines at end if not filename is None: file(filename, 'a+').write('\n'.join(txt)) np.set_printoptions(**print_opt_old) self._filename = filename self._useinstance = useinstance self._what = what return txt def verify(self): '''load the saved module and verify the data This tries several ways of comparing the saved and the attached data, but might not work for all possible data structures. Returns ------- all_correct : bool true if no differences are found, for floating point numbers rtol=1e-16, atol=1e-16 is used to determine equality (allclose) correctli : list list of attribute names that compare as equal incorrectli : list list of attribute names that did not compare as equal, either because they differ or because the comparison does not handle the data structure correctly ''' module = __import__(self._filename.replace('.py','')) if not self._useinstance: raise NotImplementedError('currently only implemented when' 'useinstance is true') data = getattr(module, self.name) correctli = [] incorrectli = [] for d in self._what: self_item = getattr(data, d) saved_item = getattr(data, d) #print d, #try simple equality correct = np.all(self.item == saved_item) #try allclose if not correct and not self.item.dtype == np.dtype('object'): correct = np.allclose(self_item, saved_item, rtol=1e-16, atol=1e-16) if not correct: import warnings warnings.warm("inexact precision in "+d) #try iterating, if object array if not correct: correlem =[np.all(data[d].item()[k] == getattr(testsave.var_results, d).item()[k]) for k in data[d].item().keys()] if not correlem: #print d, "wrong" incorrectli.append(d) correctli.append(d) return len(incorrectli)==0, correctli, incorrectli if __name__ == '__main__': data = np.load(r"E:\Josef\eclipsegworkspace\statsmodels-josef-experimental-030\dist\statsmodels-0.3.0dev_with_Winhelp_a2\statsmodels-0.3.0dev\scikits\statsmodels\tsa\vector_ar\tests\results\vars_results.npz") res_var = HoldIt('var_results') for d in data: setattr(res_var, d, data[d]) np.set_printoptions(precision=120, linewidth=100) res_var.save(filename='testsave.py', header=True, comment='VAR test data converted from vars_results.npz') import testsave for d in data: print d, correct = np.all(data[d] == getattr(testsave.var_results, d)) if not correct and not data[d].dtype == np.dtype('object'): correct = np.allclose(data[d], getattr(testsave.var_results, d), rtol=1e-16, atol=1e-16) if not correct: print "inexact precision" if not correct: correlem =[np.all(data[d].item()[k] == getattr(testsave.var_results, d).item()[k]) for k in data[d].item().keys()] if not correlem: print d, "wrong" print res_var.verify() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/eval_measures.py000066400000000000000000000345271224417117700250740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """some measures for evaluation of prediction, tests and model selection Created on Tue Nov 08 15:23:20 2011 Author: Josef Perktold License: BSD-3 """ import numpy as np def mse(x1, x2, axis=0): '''mean squared error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- mse : ndarray or float mean squared error along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass, for example numpy matrices will silently produce an incorrect result. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.mean((x1-x2)**2, axis=axis) def rmse(x1, x2, axis=0): '''root mean squared error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- rmse : ndarray or float root mean squared error along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass, for example numpy matrices will silently produce an incorrect result. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.sqrt(mse(x1, x2, axis=axis)) def maxabs(x1, x2, axis=0): '''maximum absolute error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- maxabs : ndarray or float maximum absolute difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.max(np.abs(x1-x2), axis=axis) def meanabs(x1, x2, axis=0): '''mean absolute error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- meanabs : ndarray or float mean absolute difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.mean(np.abs(x1-x2), axis=axis) def medianabs(x1, x2, axis=0): '''median absolute error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- medianabs : ndarray or float median absolute difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.median(np.abs(x1-x2), axis=axis) def bias(x1, x2, axis=0): '''bias, mean error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- bias : ndarray or float bias, or mean difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.mean(x1-x2, axis=axis) def medianbias(x1, x2, axis=0): '''median bias, median error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- medianbias : ndarray or float median bias, or median difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.median(x1-x2, axis=axis) def vare(x1, x2, ddof=0, axis=0): '''variance of error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- vare : ndarray or float variance of difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.var(x1-x2, ddof=ddof, axis=axis) def stde(x1, x2, ddof=0, axis=0): '''standard deviation of error Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- stde : ndarray or float standard deviation of difference along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asanyarray`` to convert the input. Whether this is the desired result or not depends on the array subclass. ''' x1 = np.asanyarray(x1) x2 = np.asanyarray(x2) return np.std(x1-x2, ddof=ddof, axis=axis) def iqr(x1, x2, axis=0): '''interquartile range of error rounded index, no interpolations this could use newer numpy function instead Parameters ---------- x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns ------- mse : ndarray or float mean squared error along given axis. Notes ----- If ``x1`` and ``x2`` have different shapes, then they need to broadcast. This uses ``numpy.asarray`` to convert the input, in contrast to the other functions in this category. ''' x1 = np.asarray(x1) x2 = np.asarray(x2) if axis is None: x1 = np.ravel(x1) x2 = np.ravel(x2) axis = 0 xdiff = np.sort(x1 - x2) nobs = x1.shape[axis] idx = np.round((nobs-1) * np.array([0.25, 0.75])).astype(int) sl = [slice(None)] * xdiff.ndim sl[axis] = idx iqr = np.diff(xdiff[sl], axis=axis) iqr = np.squeeze(iqr) #drop reduced dimension if iqr.size == 1: return iqr #[0] else: return iqr # Information Criteria #--------------------- def aic(llf, nobs, df_modelwc): '''Akaike information criterion Parameters ---------- llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- aic : float information criterion References ---------- http://en.wikipedia.org/wiki/Akaike_information_criterion ''' return -2. * llf + 2. * df_modelwc def aicc(llf, nobs, df_modelwc): '''Akaike information criterion (AIC) with small sample correction Parameters ---------- llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- aicc : float information criterion References ---------- http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc ''' return -2. * llf + 2. * df_modelwc * nobs / (nobs - df_modelwc - 1.) #float division def bic(llf, nobs, df_modelwc): '''Bayesian information criterion (BIC) or Schwarz criterion Parameters ---------- llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- bic : float information criterion References ---------- http://en.wikipedia.org/wiki/Bayesian_information_criterion ''' return -2. * llf + np.log(nobs) * df_modelwc def hqic(llf, nobs, df_modelwc): '''Hannan-Quinn information criterion (HQC) Parameters ---------- llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- hqic : float information criterion References ---------- Wikipedia doesn't say much ''' return -2. * llf + 2 * np.log(np.log(nobs)) * df_modelwc #IC based on residual sigma def aic_sigma(sigma2, nobs, df_modelwc, islog=False): '''Akaike information criterion Parameters ---------- sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- aic : float information criterion Notes ----- A constant has been dropped in comparison to the loglikelihood base information criteria. The information criteria should be used to compare only comparable models. For example, AIC is defined in terms of the loglikelihood as :math:`-2 llf + 2 k` in terms of :math:`\hat{\sigma}^2` :math:`log(\hat{\sigma}^2) + 2 k / n` in terms of the determinant of :math:`\hat{\Sigma}` :math:`log(\|\hat{\Sigma}\|) + 2 k / n` Note: In our definition we do not divide by n in the log-likelihood version. TODO: Latex math reference for example lecture notes by Herman Bierens See Also -------- References ---------- http://en.wikipedia.org/wiki/Akaike_information_criterion ''' if not islog: sigma2 = np.log(sigma2) return sigma2 + aic(0, nobs, df_modelwc) / nobs def aicc_sigma(sigma2, nobs, df_modelwc, islog=False): '''Akaike information criterion (AIC) with small sample correction Parameters ---------- sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- aicc : float information criterion Notes ----- A constant has been dropped in comparison to the loglikelihood base information criteria. These should be used to compare for comparable models. References ---------- http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc ''' if not islog: sigma2 = np.log(sigma2) return sigma2 + aicc(0, nobs, df_modelwc) / nobs #float division def bic_sigma(sigma2, nobs, df_modelwc, islog=False): '''Bayesian information criterion (BIC) or Schwarz criterion Parameters ---------- sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- bic : float information criterion Notes ----- A constant has been dropped in comparison to the loglikelihood base information criteria. These should be used to compare for comparable models. References ---------- http://en.wikipedia.org/wiki/Bayesian_information_criterion ''' if not islog: sigma2 = np.log(sigma2) return sigma2 + bic(0, nobs, df_modelwc) / nobs def hqic_sigma(sigma2, nobs, df_modelwc, islog=False): '''Hannan-Quinn information criterion (HQC) Parameters ---------- sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns ------- hqic : float information criterion Notes ----- A constant has been dropped in comparison to the loglikelihood base information criteria. These should be used to compare for comparable models. References ---------- xxx ''' if not islog: sigma2 = np.log(sigma2) return sigma2 + hqic(0, nobs, df_modelwc) / nobs #from var_model.py, VAR only? separates neqs and k_vars per equation #def fpe_sigma(): # ((nobs + self.df_model) / self.df_resid) ** neqs * np.exp(ld) __all__ = [maxabs, meanabs, medianabs, medianbias, mse, rmse, stde, vare, aic, aic_sigma, aicc, aicc_sigma, bias, bic, bic_sigma, hqic, hqic_sigma, iqr] statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/grouputils.py000066400000000000000000000273261224417117700244550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tools for working with groups This provides several functions to work with groups and a Group class that keeps track of the different representations and has methods to work more easily with groups. Author: Josef Perktold, Author: Nathaniel Smith, recipe for sparse_dummies on scipy user mailing list Created on Tue Nov 29 15:44:53 2011 : sparse_dummies Created on Wed Nov 30 14:28:24 2011 : combine_indices changes: add Group class Notes ~~~~~ This reverses the class I used before, where the class was for the data and the group was auxiliary. Here, it is only the group, no data is kept. sparse_dummies needs checking for corner cases, e.g. what if a category level has zero elements? This can happen with subset selection even if the original groups where defined as arange. Not all methods and options have been tried out yet after refactoring need more efficient loop if groups are sorted -> see GroupSorted.group_iter """ import numpy as np from statsmodels.compatnp.np_compat import npc_unique def combine_indices(groups, prefix='', sep='.', return_labels=False): '''use np.unique to get integer group indices for product, intersection ''' if isinstance(groups, tuple): groups = np.column_stack(groups) else: groups = np.asarray(groups) dt = groups.dtype #print dt is2d = (groups.ndim == 2) #need to store if is2d: ncols = groups.shape[1] if not groups.flags.c_contiguous: groups = np.array(groups, order='C') groups_ = groups.view([('',groups.dtype)]*groups.shape[1]) else: groups_ = groups uni, uni_idx, uni_inv = npc_unique(groups_, return_index=True, return_inverse=True) if is2d: uni = uni.view(dt).reshape(-1, ncols) #avoiding a view would be # for t in uni.dtype.fields.values(): # assert (t[0] == dt) # # uni.dtype = dt # uni.shape = (uni.size//ncols, ncols) if return_labels: label = [(prefix+sep.join(['%s']*len(uni[0]))) % tuple(ii) for ii in uni] return uni_inv, uni_idx, uni, label else: return uni_inv, uni_idx, uni #written for and used in try_covariance_grouploop.py def group_sums(x, group, use_bincount=True): '''simple bincount version, again group : array, integer assumed to be consecutive integers no dtype checking because I want to raise in that case uses loop over columns of x for comparison, simple python loop ''' x = np.asarray(x) if x.ndim == 1: x = x[:,None] elif x.ndim > 2 and use_bincount: raise ValueError('not implemented yet') if use_bincount: return np.array([np.bincount(group, weights=x[:,col]) for col in range(x.shape[1])]) else: uniques = np.unique(group) result = np.zeros([len(uniques)] + list(x.shape[1:])) for ii, cat in enumerate(uniques): result[ii] = x[g==cat].sum(0) return result def group_sums_dummy(x, group_dummy): '''sum by groups given group dummy variable group_dummy can be either ndarray or sparse matrix ''' if type(group_dummy) is np.ndarray: return np.dot(x.T, group_dummy) else: #check for sparse return x.T * group_dummy def dummy_sparse(groups): '''create a sparse indicator from a group array with integer labels Parameters ---------- groups: ndarray, int, 1d (nobs,) an array of group indicators for each observation. Group levels are assumed to be defined as consecutive integers, i.e. range(n_groups) where n_groups is the number of group levels. A group level with no observations for it will still produce a column of zeros. Returns ------- indi : ndarray, int8, 2d (nobs, n_groups) an indicator array with one row per observation, that has 1 in the column of the group level for that observation Examples -------- >>> g = np.array([0, 0, 2, 1, 1, 2, 0]) >>> indi = dummy_sparse(g) >>> indi <7x3 sparse matrix of type '' with 7 stored elements in Compressed Sparse Row format> >>> indi.todense() matrix([[1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=int8) current behavior with missing groups >>> g = np.array([0, 0, 2, 0, 2, 0]) >>> indi = dummy_sparse(g) >>> indi.todense() matrix([[1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [1, 0, 0]], dtype=int8) ''' from scipy import sparse indptr = np.arange(len(groups)+1) data = np.ones(len(groups), dtype=np.int8) indi = sparse.csr_matrix((data, g, indptr)) return indi class Group(object): def __init__(self, group, name=''): #self.group = np.asarray(group) #TODO: use checks in combine_indices self.name = name uni, uni_idx, uni_inv = combine_indices(group) #TODO: rename these to something easier to remember self.group_int, self.uni_idx, self.uni = uni, uni_idx, uni_inv self.n_groups = len(self.uni) #put this here so they can be overwritten before calling labels self.separator = '.' self.prefix = self.name if self.prefix: self.prefix = self.prefix + '=' #cache decorator def counts(self): return np.bincount(self.group_int) #cache_decorator def labels(self): #is this only needed for product of groups (intersection)? prefix = self.prefix uni = self.uni sep = self.separator if uni.ndim > 1: label = [(prefix+sep.join(['%s']*len(uni[0]))) % tuple(ii) for ii in uni] else: label = [prefix + '%s' % ii for ii in uni] return label def dummy(self, drop_idx=None, sparse=False, dtype=int): ''' drop_idx is only available if sparse=False drop_idx is supposed to index into uni ''' uni = self.uni if drop_idx is not None: idx = range(len(uni)) del idx[drop_idx] uni = uni[idx] group = self.group if not sparse: return (group[:,None] == uni[None,:]).astype(dtype) else: return dummy_sparse(self.group_int) def interaction(self, other): if isinstance(other, self.__class__): other = other.group return self.__class__((self, other)) def group_sums(self, x, use_bincount=True): return group_sums(x, self.group_int, use_bincount=use_bincount) def group_demean(self, x, use_bincount=True): means_g = group_demean(x/float(nobs), self.group_int, use_bincount=use_bincount) x_demeaned = x - means_g[self.group_int] #check reverse_index? return x_demeaned, means_g class GroupSorted(Group): def __init__(self, group, name=''): super(self.__class__, self).__init__(group, name=name) idx = (np.nonzero(np.diff(group))[0]+1).tolist() self.groupidx = groupidx = zip([0]+idx, idx+[len(group)]) ngroups = len(groupidx) def group_iter(self): for low, upp in self.groupidx: yield slice(low, upp) def lag_indices(self, lag): '''return the index array for lagged values Warning: if k is larger then the number of observations for an individual, then no values for that individual are returned. TODO: for the unbalanced case, I should get the same truncation for the array with lag=0. From the return of lag_idx we wouldn't know which individual is missing. TODO: do I want the full equivalent of lagmat in tsa? maxlag or lag or lags. not tested yet ''' lag_idx = np.asarray(self.groupidx)[:,1] - lag #asarray or already? mask_ok = (low <= lag_idx) #still an observation that belongs to the same individual return lag_idx[mask_ok] if __name__ == '__main__': #---------- examples combine_indices from numpy.testing import assert_equal np.random.seed(985367) groups = np.random.randint(0,2,size=(10,2)) uv, ux, u, label = combine_indices(groups, return_labels=True) uv, ux, u, label = combine_indices(groups, prefix='g1,g2=', sep=',', return_labels=True) group0 = np.array(['sector0', 'sector1'])[groups[:,0]] group1 = np.array(['region0', 'region1'])[groups[:,1]] uv, ux, u, label = combine_indices((group0, group1), prefix='sector,region=', sep=',', return_labels=True) uv, ux, u, label = combine_indices((group0, group1), prefix='', sep='.', return_labels=True) group_joint = np.array(label)[uv] group_joint_expected = np.array( ['sector1.region0', 'sector0.region1', 'sector0.region0', 'sector0.region1', 'sector1.region1', 'sector0.region0', 'sector1.region0', 'sector1.region0', 'sector0.region1', 'sector0.region0'], dtype='|S15') assert_equal(group_joint, group_joint_expected) ''' >>> uv array([2, 1, 0, 0, 1, 0, 2, 0, 1, 0]) >>> label ['sector0.region0', 'sector1.region0', 'sector1.region1'] >>> np.array(label)[uv] array(['sector1.region1', 'sector1.region0', 'sector0.region0', 'sector0.region0', 'sector1.region0', 'sector0.region0', 'sector1.region1', 'sector0.region0', 'sector1.region0', 'sector0.region0'], dtype='|S15') >>> np.column_stack((group0, group1)) array([['sector1', 'region1'], ['sector1', 'region0'], ['sector0', 'region0'], ['sector0', 'region0'], ['sector1', 'region0'], ['sector0', 'region0'], ['sector1', 'region1'], ['sector0', 'region0'], ['sector1', 'region0'], ['sector0', 'region0']], dtype='|S7') ''' #------------- examples sparse_dummies from scipy import sparse g = np.array([0, 0, 1, 2, 1, 1, 2, 0]) u = range(3) indptr = np.arange(len(g)+1) data = np.ones(len(g), dtype=np.int8) a = sparse.csr_matrix((data, g, indptr)) print a.todense() print np.all(a.todense() == (g[:,None] == np.arange(3)).astype(int)) x = np.arange(len(g)*3).reshape(len(g), 3, order='F') print 'group means' print x.T * a print np.dot(x.T, g[:,None] == np.arange(3)) print np.array([np.bincount(g, weights=x[:,col]) for col in range(3)]) for cat in u: print x[g==cat].sum(0) for cat in u: x[g==cat].sum(0) cc = sparse.csr_matrix([[0, 1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 1, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 1, 0, 1, 0, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 1, 0, 1, 0]]) #------------- groupsums print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2), g, use_bincount=False).T print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2)[:,:,0], g) print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2)[:,:,1], g) #------------- examples class x = np.arange(len(g)*3).reshape(len(g), 3, order='F') mygroup = Group(g) print mygroup.group_int print mygroup.group_sums(x) print mygroup.labels() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/linalg.py000066400000000000000000000143061224417117700235000ustar00rootroot00000000000000'''local, adjusted version from scipy.linalg.basic.py changes: The only changes are that additional results are returned ''' import numpy as np from scipy.linalg import svd as decomp_svd #decomp_svd #check which imports we need here: from scipy.linalg.flinalg import get_flinalg_funcs from scipy.linalg.lapack import get_lapack_funcs from numpy import asarray,zeros,sum,newaxis,greater_equal,subtract,arange,\ conjugate,ravel,r_,mgrid,take,ones,dot,transpose,sqrt,add,real import numpy from numpy import asarray_chkfinite, outer, concatenate, reshape, single #from numpy import matrix as Matrix from numpy.linalg import LinAlgError from scipy.linalg import calc_lwork ### Linear Least Squares def lstsq(a, b, cond=None, overwrite_a=0, overwrite_b=0): """Compute least-squares solution to equation :m:`a x = b` Compute a vector x such that the 2-norm :m:`|b - a x|` is minimised. Parameters ---------- a : array, shape (M, N) b : array, shape (M,) or (M, K) cond : float Cutoff for 'small' singular values; used to determine effective rank of a. Singular values smaller than rcond*largest_singular_value are considered zero. overwrite_a : boolean Discard data in a (may enhance performance) overwrite_b : boolean Discard data in b (may enhance performance) Returns ------- x : array, shape (N,) or (N, K) depending on shape of b Least-squares solution residues : array, shape () or (1,) or (K,) Sums of residues, squared 2-norm for each column in :m:`b - a x` If rank of matrix a is < N or > M this is an empty array. If b was 1-d, this is an (1,) shape array, otherwise the shape is (K,) rank : integer Effective rank of matrix a s : array, shape (min(M,N),) Singular values of a. The condition number of a is abs(s[0]/s[-1]). Raises LinAlgError if computation does not converge """ a1, b1 = map(asarray_chkfinite,(a,b)) if len(a1.shape) != 2: raise ValueError('expected matrix') m,n = a1.shape if len(b1.shape)==2: nrhs = b1.shape[1] else: nrhs = 1 if m != b1.shape[0]: raise ValueError('incompatible dimensions') gelss, = get_lapack_funcs(('gelss',),(a1,b1)) if n>m: # need to extend b matrix as it will be filled with # a larger solution matrix b2 = zeros((n,nrhs), dtype=gelss.dtype) if len(b1.shape)==2: b2[:m,:] = b1 else: b2[:m,0] = b1 b1 = b2 overwrite_a = overwrite_a or (a1 is not a and not hasattr(a,'__array__')) overwrite_b = overwrite_b or (b1 is not b and not hasattr(b,'__array__')) if gelss.module_name[:7] == 'flapack': lwork = calc_lwork.gelss(gelss.prefix,m,n,nrhs)[1] v,x,s,rank,info = gelss(a1,b1,cond = cond, lwork = lwork, overwrite_a = overwrite_a, overwrite_b = overwrite_b) else: raise NotImplementedError('calling gelss from %s' % (gelss.module_name)) if info>0: raise LinAlgError("SVD did not converge in Linear Least Squares") if info<0: raise ValueError(\ 'illegal value in %-th argument of internal gelss'%(-info)) resids = asarray([], dtype=x.dtype) if n>> from numpy import * >>> a = random.randn(9, 6) >>> B = linalg.pinv(a) >>> allclose(a, dot(a, dot(B, a))) True >>> allclose(B, dot(B, dot(a, B))) True """ a = asarray_chkfinite(a) b = numpy.identity(a.shape[0], dtype=a.dtype) if rcond is not None: cond = rcond return lstsq(a, b, cond=cond)[0] eps = numpy.finfo(float).eps feps = numpy.finfo(single).eps _array_precision = {'f': 0, 'd': 1, 'F': 0, 'D': 1} def pinv2(a, cond=None, rcond=None): """Compute the (Moore-Penrose) pseudo-inverse of a matrix. Calculate a generalized inverse of a matrix using its singular-value decomposition and including all 'large' singular values. Parameters ---------- a : array, shape (M, N) Matrix to be pseudo-inverted cond, rcond : float or None Cutoff for 'small' singular values. Singular values smaller than rcond*largest_singular_value are considered zero. If None or -1, suitable machine precision is used. Returns ------- B : array, shape (N, M) Raises LinAlgError if SVD computation does not converge Examples -------- >>> from numpy import * >>> a = random.randn(9, 6) >>> B = linalg.pinv2(a) >>> allclose(a, dot(a, dot(B, a))) True >>> allclose(B, dot(B, dot(a, B))) True """ a = asarray_chkfinite(a) u, s, vh = decomp_svd(a) t = u.dtype.char if rcond is not None: cond = rcond if cond in [None,-1]: cond = {0: feps*1e3, 1: eps*1e6}[_array_precision[t]] m,n = a.shape cutoff = cond*numpy.maximum.reduce(s) psigma = zeros((m,n),t) for i in range(len(s)): if s[i] > cutoff: psigma[i,i] = 1.0/conjugate(s[i]) #XXX: use lapack/blas routines for dot return transpose(conjugate(dot(dot(u,psigma),vh))) if __name__ == '__main__': #for checking only, #Note on Windows32: # linalg doesn't always produce the same results in each call a0 = np.random.randn(100,10) b0 = a0.sum(1)[:,None] + np.random.randn(100,3) lstsq(a0,b0) pinv(a0) pinv2(a0) x = pinv(a0) x2=scipy.linalg.pinv(a0) print np.max(np.abs(x-x2)) x = pinv2(a0) x2 = scipy.linalg.pinv2(a0) print np.max(np.abs(x-x2)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/numdiff.py000066400000000000000000000345731224417117700236720ustar00rootroot00000000000000"""numerical differentiation function, gradient, Jacobian, and Hessian Author : josef-pkt License : BSD """ #These are simple forward differentiation, so that we have them available #without dependencies. # #* Jacobian should be faster than numdifftools because it doesn't use loop over observations. #* numerical precision will vary and depend on the choice of stepsizes # #Todo: #* some cleanup #* check numerical accuracy (and bugs) with numdifftools and analytical derivatives # - linear least squares case: (hess - 2*X'X) is 1e-8 or so # - gradient and Hessian agree with numdifftools when evaluated away from minimum # - forward gradient, Jacobian evaluated at minimum is inaccurate, centered (+/- epsilon) is ok #* dot product of Jacobian is different from Hessian, either wrong example or a bug (unlikely), # or a real difference # # #What are the conditions that Jacobian dotproduct and Hessian are the same? #see also: #BHHH: Greene p481 17.4.6, MLE Jacobian = d loglike / d beta , where loglike is vector for each observation # see also example 17.4 when J'J is very different from Hessian # also does it hold only at the minimum, what's relationship to covariance of Jacobian matrix #http://projects.scipy.org/scipy/ticket/1157 #http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm # objective: sum((y-f(beta,x)**2), Jacobian = d f/d beta and not d objective/d beta as in MLE Greene # similar: http://crsouza.blogspot.com/2009/11/neural-network-learning-by-levenberg_18.html#hessian # #in example: if J = d x*beta / d beta then J'J == X'X # similar to http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm import numpy as np #NOTE: we only do double precision internally so far EPS = np.MachAr().eps _hessian_docs = """ Calculate Hessian with finite difference derivative approximation Parameters ---------- x : array_like value at which function derivative is evaluated f : function function of one array f(x, `*args`, `**kwargs`) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chosen according to EPS**(1/%(scale)s)*x. args : tuple Arguments for function `f`. kwargs : dict Keyword arguments for function `f`. %(extra_params)s Returns ------- hess : ndarray array of partial second derivatives, Hessian %(extra_returns)s Notes ----- Equation (%(equation_number)s) in Ridout. Computes the Hessian as:: %(equation)s where e[j] is a vector with element j == 1 and the rest are zero and d[i] is epsilon[i]. References ----------: Ridout, M.S. (2009) Statistical applications of the complex-step method of numerical differentiation. The American Statistician, 63, 66-74 """ def _get_epsilon(x, s, epsilon, n): if epsilon is None: h = EPS**(1. / s) * np.maximum(np.abs(x), 0.1) else: if np.isscalar(epsilon): h = np.empty(n) h.fill(epsilon) else: # pragma : no cover h = np.asarray(epsilon) if h.shape != x.shape: raise ValueError("If h is not a scalar it must have the same" " shape as x.") return h def approx_fprime(x, f, epsilon=None, args=(), kwargs={}, centered=False): ''' Gradient of function, or Jacobian if function f returns 1d array Parameters ---------- x : array parameters at which the derivative is evaluated f : function `f(*((x,)+args), **kwargs)` returning either one value or 1d array epsilon : float, optional Stepsize, if None, optimal stepsize is used. This is EPS**(1/2)*x for `centered` == False and EPS**(1/3)*x for `centered` == True. args : tuple Tuple of additional arguments for function `f`. kwargs : dict Dictionary of additional keyword arguments for function `f`. centered : bool Whether central difference should be returned. If not, does forward differencing. Returns ------- grad : array gradient or Jacobian Notes ----- If f returns a 1d array, it returns a Jacobian. If a 2d array is returned by f (e.g., with a value for each observation), it returns a 3d array with the Jacobian of each observation with shape xk x nobs x xk. I.e., the Jacobian of the first observation would be [:, 0, :] ''' n = len(x) #TODO: add scaled stepsize f0 = f(*((x,)+args), **kwargs) dim = np.atleast_1d(f0).shape # it could be a scalar grad = np.zeros((n,) + dim, float) ei = np.zeros((n,), float) if not centered: epsilon = _get_epsilon(x, 2, epsilon, n) for k in range(n): ei[k] = epsilon[k] grad[k,:] = (f(*((x+ei,)+args), **kwargs) - f0)/epsilon[k] ei[k] = 0.0 else: epsilon = _get_epsilon(x, 3, epsilon, n) / 2. for k in range(len(x)): ei[k] = epsilon[k] grad[k,:] = (f(*((x+ei,)+args), **kwargs) - \ f(*((x-ei,)+args), **kwargs))/(2 * epsilon[k]) ei[k] = 0.0 return grad.squeeze().T def approx_fprime_cs(x, f, epsilon=None, args=(), kwargs={}): ''' Calculate gradient or Jacobian with complex step derivative approximation Parameters ---------- x : array parameters at which the derivative is evaluated f : function `f(*((x,)+args), **kwargs)` returning either one value or 1d array epsilon : float, optional Stepsize, if None, optimal stepsize is used. Optimal step-size is EPS*x. See note. args : tuple Tuple of additional arguments for function `f`. kwargs : dict Dictionary of additional keyword arguments for function `f`. Returns ------- partials : ndarray array of partial derivatives, Gradient or Jacobian Notes ----- The complex-step derivative has truncation error O(epsilon**2), so truncation error can be eliminated by choosing epsilon to be very small. The complex-step derivative avoids the problem of round-off error with small epsilon because there is no subtraction. ''' #From Guilherme P. de Freitas, numpy mailing list #May 04 2010 thread "Improvement of performance" #http://mail.scipy.org/pipermail/numpy-discussion/2010-May/050250.html n = len(x) epsilon = _get_epsilon(x, 1, epsilon, n) increments = np.identity(n) * 1j * epsilon #TODO: see if this can be vectorized, but usually dim is small partials = [f(x+ih, *args, **kwargs).imag / epsilon[i] for i,ih in enumerate(increments)] return np.array(partials).T def approx_hess_cs(x, f, epsilon=None, args=(), kwargs={}): '''Calculate Hessian with complex-step derivative approximation Parameters ---------- x : array_like value at which function derivative is evaluated f : function function of one array f(x) epsilon : float stepsize, if None, then stepsize is automatically chosen Returns ------- hess : ndarray array of partial second derivatives, Hessian Notes ----- based on equation 10 in M. S. RIDOUT: Statistical Applications of the Complex-step Method of Numerical Differentiation, University of Kent, Canterbury, Kent, U.K. The stepsize is the same for the complex and the finite difference part. ''' #TODO: might want to consider lowering the step for pure derivatives n = len(x) h = _get_epsilon(x, 3, epsilon, n) ee = np.diag(h) hess = np.outer(h,h) n = len(x) for i in range(n): for j in range(i,n): hess[i,j] = (f(*((x + 1j*ee[i,:] + ee[j,:],)+args), **kwargs) - f(*((x + 1j*ee[i,:] - ee[j,:],)+args), **kwargs)).imag/\ 2./hess[i,j] hess[j,i] = hess[i,j] return hess approx_hess_cs.__doc__ = "Calculate Hessian with complex-step derivative " +\ "approximation\n" +\ "\n".join(_hessian_docs.split("\n")[1:]) % dict( scale="3", extra_params="", extra_returns="", equation_number="10", equation = """1/(2*d_j*d_k) * imag(f(x + i*d[j]*e[j] + d[k]*e[k]) - f(x + i*d[j]*e[j] - d[k]*e[k])) """) def approx_hess1(x, f, epsilon=None, args=(), kwargs={}, return_grad=False): n = len(x) h = _get_epsilon(x, 3, epsilon, n) ee = np.diag(h) f0 = f(*((x,)+args), **kwargs) # Compute forward step g = np.zeros(n); for i in range(n): g[i] = f(*((x+ee[i,:],)+args), **kwargs) hess = np.outer(h,h) # this is now epsilon**2 # Compute "double" forward step for i in range(n): for j in range(i,n): hess[i,j] = (f(*((x + ee[i,:] + ee[j,:],) + args), **kwargs) - \ g[i] - g[j] + f0)/hess[i,j] hess[j,i] = hess[i,j] if return_grad: grad = (g - f0)/h return hess, grad else: return hess approx_hess1.__doc__ = _hessian_docs % dict(scale="3", extra_params = """return_grad : bool Whether or not to also return the gradient """, extra_returns = """grad : nparray Gradient if return_grad == True """, equation_number = "7", equation = """1/(d_j*d_k) * ((f(x + d[j]*e[j] + d[k]*e[k]) - f(x + d[j]*e[j]))) """) def approx_hess2(x, f, epsilon=None, args=(), kwargs={}, return_grad=False): # n = len(x) #NOTE: ridout suggesting using eps**(1/4)*theta h = _get_epsilon(x, 3, epsilon, n) ee = np.diag(h) f0 = f(*((x,)+args), **kwargs) # Compute forward step g = np.zeros(n) gg = np.zeros(n) for i in range(n): g[i] = f(*((x+ee[i,:],)+args), **kwargs) gg[i] = f(*((x-ee[i,:],)+args), **kwargs) hess = np.outer(h,h) # this is now epsilon**2 # Compute "double" forward step for i in range(n): for j in range(i,n): hess[i,j] = (f(*((x + ee[i,:] + ee[j,:],) + args), **kwargs) - \ g[i] - g[j] + f0 + \ f(*((x - ee[i,:] - ee[j,:],) + args), **kwargs) - \ gg[i] - gg[j] + f0 )/(2*hess[i,j]) hess[j,i] = hess[i,j] if return_grad: grad = (g - f0)/h return hess, grad else: return hess approx_hess2.__doc__ = _hessian_docs % dict(scale="3", extra_params = """return_grad : bool Whether or not to also return the gradient """, extra_returns = """grad : nparray Gradient if return_grad == True """, equation_number = "8", equation = """1/(2*d_j*d_k) * ((f(x + d[j]*e[j] + d[k]*e[k]) - f(x + d[j]*e[j])) - (f(x + d[k]*e[k]) - f(x)) + (f(x - d[j]*e[j] - d[k]*e[k]) - f(x + d[j]*e[j])) - (f(x - d[k]*e[k]) - f(x))) """) def approx_hess3(x, f, epsilon=None, args=(), kwargs={}): n = len(x) h = _get_epsilon(x, 4, epsilon, n) ee = np.diag(h) hess = np.outer(h,h) for i in range(n): for j in range(i,n): hess[i,j] = (f(*((x + ee[i,:] + ee[j,:],)+args), **kwargs) - f(*((x + ee[i,:] - ee[j,:],)+args), **kwargs) - (f(*((x - ee[i,:] + ee[j,:],)+args), **kwargs) - f(*((x - ee[i,:] - ee[j,:],)+args), **kwargs),) )/(4.*hess[i,j]) hess[j,i] = hess[i,j] return hess approx_hess3.__doc__ = _hessian_docs % dict(scale="4", extra_params="", extra_returns="", equation_number = "9", equation = """1/(4*d_j*d_k) * ((f(x + d[j]*e[j] + d[k]*e[k]) - f(x + d[j]*e[j] - d[k]*e[k])) - (f(x - d[j]*e[j] + d[k]*e[k]) - f(x - d[j]*e[j] - d[k]*e[k]))""") approx_hess = approx_hess3 approx_hess.__doc__ += "\n This is an alias for approx_hess3" if __name__ == '__main__': #pragma : no cover import statsmodels.api as sm from scipy.optimize.optimize import approx_fhess_p import numpy as np data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) mod = sm.Probit(data.endog, data.exog) res = mod.fit(method="newton") test_params = [1,0.25,1.4,-7] llf = mod.loglike score = mod.score hess = mod.hessian # below is Josef's scratch work def approx_hess_cs_old(x, func, args=(), h=1.0e-20, epsilon=1e-6): def grad(x): return approx_fprime_cs(x, func, args=args, h=1.0e-20) #Hessian from gradient: return (approx_fprime(x, grad, epsilon) + approx_fprime(x, grad, -epsilon))/2. def fun(beta, x): return np.dot(x, beta).sum(0) def fun1(beta, y, x): #print beta.shape, x.shape xb = np.dot(x, beta) return (y-xb)**2 #(xb-xb.mean(0))**2 def fun2(beta, y, x): #print beta.shape, x.shape return fun1(beta, y, x).sum(0) nobs = 200 x = np.arange(nobs*3).reshape(nobs,-1) x = np.random.randn(nobs,3) xk = np.array([1,2,3]) xk = np.array([1.,1.,1.]) #xk = np.zeros(3) beta = xk y = np.dot(x, beta) + 0.1*np.random.randn(nobs) xk = np.dot(np.linalg.pinv(x),y) epsilon = 1e-6 args = (y,x) from scipy import optimize xfmin = optimize.fmin(fun2, (0,0,0), args) print approx_fprime((1,2,3),fun,epsilon,x) jac = approx_fprime(xk,fun1,epsilon,args) jacmin = approx_fprime(xk,fun1,-epsilon,args) #print jac print jac.sum(0) print '\nnp.dot(jac.T, jac)' print np.dot(jac.T, jac) print '\n2*np.dot(x.T, x)' print 2*np.dot(x.T, x) jac2 = (jac+jacmin)/2. print np.dot(jac2.T, jac2) #he = approx_hess(xk,fun2,epsilon,*args) print approx_hess_old(xk,fun2,1e-3,args) he = approx_hess_old(xk,fun2,None,args) print 'hessfd' print he print 'epsilon =', None print he[0] - 2*np.dot(x.T, x) for eps in [1e-3,1e-4,1e-5,1e-6]: print 'eps =', eps print approx_hess_old(xk,fun2,eps,args)[0] - 2*np.dot(x.T, x) hcs2 = approx_hess_cs(xk,fun2,args=args) print 'hcs2' print hcs2 - 2*np.dot(x.T, x) hfd3 = approx_hess(xk,fun2,args=args) print 'hfd3' print hfd3 - 2*np.dot(x.T, x) import numdifftools as nd hnd = nd.Hessian(lambda a: fun2(a, y, x)) hessnd = hnd(xk) print 'numdiff' print hessnd - 2*np.dot(x.T, x) #assert_almost_equal(hessnd, he[0]) gnd = nd.Gradient(lambda a: fun2(a, y, x)) gradnd = gnd(xk) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/parallel.py000066400000000000000000000034561224417117700240320ustar00rootroot00000000000000'''Parallel utility function using joblib copied from https://github.com/mne-tools/mne-python Author: Alexandre Gramfort License: Simplified BSD changes for statsmodels (Josef Perktold) - try import from joblib directly, (doesn't import all of sklearn) ''' def parallel_func(func, n_jobs, verbose=5): """Return parallel instance with delayed function Util function to use joblib only if available Parameters ---------- func: callable A function n_jobs: int Number of jobs to run in parallel verbose: int Verbosity level Returns ------- parallel: instance of joblib.Parallel or list The parallel object my_func: callable func if not parallel or delayed(func) n_jobs: int Number of jobs >= 0 Examples -------- >>> from math import sqrt >>> from statsmodels.tools.parallel import parallel_func >>> parallel, p_func, n_jobs = parallel_func(sqrt, n_jobs=-1, verbose=0) >>> print n_jobs >>> parallel(p_func(i**2) for i in range(10)) """ try: try: from joblib import Parallel, delayed except ImportError: from sklearn.externals.joblib import Parallel, delayed parallel = Parallel(n_jobs, verbose=verbose) my_func = delayed(func) if n_jobs == -1: try: import multiprocessing n_jobs = multiprocessing.cpu_count() except (ImportError, NotImplementedError): print "multiprocessing not installed. Cannot run in parallel." n_jobs = 1 except ImportError: print "joblib not installed. Cannot run in parallel." n_jobs = 1 my_func = func parallel = list return parallel, my_func, n_jobs statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/print_version.py000077500000000000000000000107771224417117700251460ustar00rootroot00000000000000#!/usr/bin/env python import sys from os.path import dirname def safe_version(module, attr='__version__'): if not isinstance(attr, list): attr = [attr] try: return reduce(getattr, [module] + attr) except AttributeError: return "Cannot detect version" def show_versions(): print("\nINSTALLED VERSIONS") print("------------------") print("Python: %d.%d.%d.%s.%s" % sys.version_info[:]) try: import os (sysname, nodename, release, version, machine) = os.uname() print("OS: %s %s %s %s" % (sysname, release, version,machine)) print("byteorder: %s" % sys.byteorder) print("LC_ALL: %s" % os.environ.get('LC_ALL',"None")) print("LANG: %s" % os.environ.get('LANG',"None")) except: pass try: import statsmodels from statsmodels import version has_sm = True except ImportError: has_sm = False print('\nStatsmodels\n===========\n') if has_sm: print('Installed: %s (%s)' % (safe_version(version, 'full_version'), dirname(statsmodels.__file__))) else: print('Not installed') print("\nRequired Dependencies\n=====================\n") try: import Cython print("cython: %s (%s)" % (safe_version(Cython), dirname(Cython.__file__))) except ImportError: print("cython: Not installed") try: import numpy print("numpy: %s (%s)" % (safe_version(numpy, ['version', 'version']), dirname(numpy.__file__))) except ImportError: print("numpy: Not installed") try: import scipy print("scipy: %s (%s)" % (safe_version(scipy, ['version', 'version']), dirname(scipy.__file__))) except ImportError: print("scipy: Not installed") try: import pandas print("pandas: %s (%s)" % (safe_version(pandas, ['version', 'version']), dirname(pandas.__file__))) except ImportError: print("pandas: Not installed") try: import dateutil print(" dateutil: %s (%s)" % (safe_version(dateutil), dirname(dateutil.__file__))) except ImportError: print(" dateutil: not installed") try: import patsy print("patsy: %s (%s)" % (safe_version(patsy), dirname(patsy.__file__))) except ImportError: print("patsy: Not installed") print("\nOptional Dependencies\n=====================\n") try: import matplotlib as mpl print("matplotlib: %s (%s)" % (safe_version(mpl), dirname(mpl.__file__))) except ImportError: print("matplotlib: Not installed") try: from cvxopt import info print("cvxopt: %s (%s)" % (safe_version(info, 'version'), dirname(info.__file__))) except ImportError: print("cvxopt: Not installed") print("\nDeveloper Tools\n================\n") try: import IPython print("IPython: %s (%s)" % (safe_version(IPython), dirname(IPython.__file__))) except ImportError: print("IPython: Not installed") try: import jinja2 print(" jinja2: %s (%s)" % (safe_version(jinja2), dirname(jinja2.__file__))) except ImportError: print(" jinja2: Not installed") try: import sphinx print("sphinx: %s (%s)" % (safe_version(sphinx), dirname(sphinx.__file__))) except ImportError: print("sphinx: Not installed") try: import pygments print(" pygments: %s (%s)" % (safe_version(pygments), dirname(pygments.__file__))) except ImportError: print(" pygments: Not installed") try: import nose print("nose: %s (%s)" % (safe_version(nose), dirname(nose.__file__))) except ImportError: print("nose: Not installed") try: import virtualenv print("virtualenv: %s (%s)" % (safe_version(virtualenv), dirname(virtualenv.__file__))) except ImportError: print("virtualenv: Not installed") print("\n") if __name__ == "__main__": show_versions() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/rootfinding.py000066400000000000000000000167371224417117700245660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Mar 18 15:48:23 2013 Author: Josef Perktold TODO: - test behavior if nans or infs are encountered during the evaluation. now partially robust to nans, if increasing can be determined or is given. - rewrite core loop to use for...except instead of while. """ import numpy as np from scipy import optimize DEBUG = False # based on scipy.stats.distributions._ppf_single_call def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5, start_low=None, start_upp=None, increasing=None, max_it=100, maxiter_bq=100, factor=10, full_output=False): '''find the root of a function in one variable by expanding and brentq Assumes function ``func`` is monotonic. Parameters ---------- func : callable function for which we find the root ``x`` such that ``func(x) = 0`` low : float or None lower bound for brentq upp : float or None upper bound for brentq args : tuple optional additional arguments for ``func`` xtol : float parameter x tolerance given to brentq start_low : float (positive) or None starting bound for expansion with increasing ``x``. It needs to be positive. If None, then it is set to 1. start_upp : float (negative) or None starting bound for expansion with decreasing ``x``. It needs to be negative. If None, then it is set to -1. increasing : bool or None If None, then the function is evaluated at the initial bounds to determine wether the function is increasing or not. If increasing is True (False), then it is assumed that the function is monotonically increasing (decreasing). max_it : int maximum number of expansion steps. maxiter_bq : int maximum number of iterations of brentq. factor : float expansion factor for step of shifting the bounds interval, default is 10. full_output : bool, optional If full_output is False, the root is returned. If full_output is True, the return value is (x, r), where x is the root, and r is a RootResults object. Returns ------- x : float root of the function, value at which ``func(x) = 0``. info : RootResult (optional) returned if ``full_output`` is True. attributes: - start_bounds : starting bounds for expansion stage - brentq_bounds : bounds used with ``brentq`` - iterations_expand : number of iterations in expansion stage - converged : True if brentq converged. - flag : return status, 'converged' if brentq converged - function_calls : number of function calls by ``brentq`` - iterations : number of iterations in ``brentq`` Notes ----- If increasing is None, then whether the function is monotonically increasing or decreasing is inferred from evaluating the function at the initial bounds. This can fail if there is numerically no variation in the data in this range. In this case, using different starting bounds or directly specifying ``increasing`` can make it possible to move the expansion in the right direction. If ''' #TODO: rtol is missing, what does it do? left, right = low, upp #alias # start_upp first because of possible sl = -1 > upp if upp is not None: su = upp elif start_upp is not None: if start_upp < 0: raise ValueError('start_upp needs to be positive') su = start_upp else: su = 1. if low is not None: sl = low elif start_low is not None: if start_low > 0: raise ValueError('start_low needs to be negative') sl = start_low else: sl = min(-1., su - 1.) # need sl < su if upp is None: su = max(su, sl + 1.) # increasing or not ? if ((low is None) or (upp is None)) and increasing is None: assert sl < su # check during developement f_low = func(sl, *args) f_upp = func(su, *args) # special case for F-distribution (symmetric around zero for effect size) # chisquare also takes an indefinite time (didn't wait see if it returns) if np.max(np.abs(f_upp - f_low)) < 1e-15 and sl == -1 and su == 1: sl = 1e-8 f_low = func(sl, *args) increasing = (f_low < f_upp) if DEBUG: print 'symm', sl, su, f_low, f_upp # possibly func returns nan delta = su - sl if np.isnan(f_low): # try just 3 points to find ``increasing`` # don't change sl because brentq can handle one nan bound for fraction in [0.25, 0.5, 0.75]: sl_ = sl + fraction * delta f_low = func(sl_, *args) if not np.isnan(f_low): break else: raise ValueError('could not determine whether function is ' + 'increasing based on starting interval.' + '\nspecify increasing or change starting ' + 'bounds') if np.isnan(f_upp): for fraction in [0.25, 0.5, 0.75]: su_ = su + fraction * delta f_upp = func(su_, *args) if not np.isnan(f_upp): break else: raise ValueError('could not determine whether function is' + 'increasing based on starting interval.' + '\nspecify increasing or change starting ' + 'bounds') increasing = (f_low < f_upp) if DEBUG: print 'low, upp', low, upp, func(sl, *args), func(su, *args) print 'increasing', increasing print 'sl, su', sl, su if not increasing: sl, su = su, sl left, right = right, left n_it = 0 if left is None and sl != 0: left = sl while func(left, *args) > 0: #condition is also false if func returns nan right = left left *= factor if n_it >= max_it: break n_it += 1 # left is now such that func(left) < q if right is None and su !=0: right = su while func(right, *args) < 0: left = right right *= factor if n_it >= max_it: break n_it += 1 # right is now such that func(right) > q if n_it >= max_it: #print 'Warning: max_it reached' #TODO: use Warnings, Note: brentq might still work even with max_it f_low = func(sl, *args) f_upp = func(su, *args) if np.isnan(f_low) and np.isnan(f_upp): # can we still get here? raise ValueError('max_it reached' + '\nthe function values at boths bounds are NaN' + '\nchange the starting bounds, set bounds' + 'or increase max_it') res = optimize.brentq(func, left, right, args=args, xtol=xtol, maxiter=maxiter_bq, full_output=full_output) if full_output: val = res[0] info = res[1] info.iterations_expand = n_it info.start_bounds = (sl, su) info.brentq_bounds = (left, right) info.increasing = increasing return val, info else: return res statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/sm_exceptions.py000066400000000000000000000001431224417117700251040ustar00rootroot00000000000000class PerfectSeparationError(Exception): pass class ConvergenceWarning(UserWarning): pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/000077500000000000000000000000001224417117700230165ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/__init__.py000066400000000000000000000000001224417117700251150ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_catadd.py000066400000000000000000000013231224417117700256460ustar00rootroot00000000000000 import numpy as np from numpy.testing import assert_equal from statsmodels.tools.catadd import add_indep from scipy import linalg def test_add_indep(): x1 = np.array([0,0,0,0,0,1,1,1,2,2,2]) x2 = np.array([0,0,0,0,0,1,1,1,1,1,1]) x0 = np.ones(len(x2)) x = np.column_stack([x0, x1[:,None]*np.arange(3), x2[:,None]*np.arange(2)]) varnames = ['const'] + ['var1_%d' %i for i in np.arange(3)] \ + ['var2_%d' %i for i in np.arange(2)] xo, vo = add_indep(x, varnames) assert_equal(xo, np.column_stack((x0, x1, x2))) assert_equal((linalg.svdvals(x) > 1e-12).sum(), 3) assert_equal(vo, ['const', 'var1_1', 'var2_1']) if __name__ == '__main__': test_add_indep() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_data.py000066400000000000000000000033361224417117700253450ustar00rootroot00000000000000import pandas import numpy as np from statsmodels.tools import data def test_missing_data_pandas(): """ Fixes GH: #144 """ X = np.random.random((10,5)) X[1,2] = np.nan df = pandas.DataFrame(X) vals, cnames, rnames = data.interpret_data(df) np.testing.assert_equal(rnames.tolist(), [0,2,3,4,5,6,7,8,9]) def test_structarray(): X = np.random.random((9,)).view([('var1', 'f8'), ('var2', 'f8'), ('var3', 'f8')]) vals, cnames, rnames = data.interpret_data(X) np.testing.assert_equal(cnames, X.dtype.names) np.testing.assert_equal(vals, X.view((float,3))) np.testing.assert_equal(rnames, None) def test_recarray(): X = np.random.random((9,)).view([('var1', 'f8'), ('var2', 'f8'), ('var3', 'f8')]) vals, cnames, rnames = data.interpret_data(X.view(np.recarray)) np.testing.assert_equal(cnames, X.dtype.names) np.testing.assert_equal(vals, X.view((float,3))) np.testing.assert_equal(rnames, None) def test_dataframe(): X = np.random.random((10,5)) df = pandas.DataFrame(X) vals, cnames, rnames = data.interpret_data(df) np.testing.assert_equal(vals, df.values) np.testing.assert_equal(rnames.tolist(), df.index.tolist()) np.testing.assert_equal(cnames, df.columns.tolist()) def test_patsy_577(): X = np.random.random((10, 2)) df = pandas.DataFrame(X, columns=["var1", "var2"]) from patsy import dmatrix endog = dmatrix("var1 - 1", df) np.testing.assert_(data._is_using_patsy(endog, None)) exog = dmatrix("var2 - 1", df) np.testing.assert_(data._is_using_patsy(endog, exog)) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_eval_measures.py000066400000000000000000000066761224417117700273010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 08 22:28:48 2011 @author: josef """ import numpy as np from numpy.testing import assert_equal, assert_almost_equal, assert_ from statsmodels.tools.eval_measures import ( maxabs, meanabs, medianabs, medianbias, mse, rmse, stde, vare, aic, aic_sigma, aicc, aicc_sigma, bias, bic, bic_sigma, hqic, hqic_sigma, iqr) def test_eval_measures(): #mainly regression tests x = np.arange(20).reshape(4,5) y = np.ones((4,5)) assert_equal(iqr(x, y), 5*np.ones(5)) assert_equal(iqr(x, y, axis=1), 2*np.ones(4)) assert_equal(iqr(x, y, axis=None), 9) assert_equal(mse(x, y), np.array([ 73.5, 87.5, 103.5, 121.5, 141.5])) assert_equal(mse(x, y, axis=1), np.array([ 3., 38., 123., 258.])) assert_almost_equal(rmse(x, y), np.array([ 8.5732141 , 9.35414347, 10.17349497, 11.02270384, 11.89537725])) assert_almost_equal(rmse(x, y, axis=1), np.array([ 1.73205081, 6.164414, 11.09053651, 16.0623784 ])) assert_equal(maxabs(x, y), np.array([ 14., 15., 16., 17., 18.])) assert_equal(maxabs(x, y, axis=1), np.array([ 3., 8., 13., 18.])) assert_equal(meanabs(x, y), np.array([ 7. , 7.5, 8.5, 9.5, 10.5])) assert_equal(meanabs(x, y, axis=1), np.array([ 1.4, 6. , 11. , 16. ])) assert_equal(meanabs(x, y, axis=0), np.array([ 7. , 7.5, 8.5, 9.5, 10.5])) assert_equal(medianabs(x, y), np.array([ 6.5, 7.5, 8.5, 9.5, 10.5])) assert_equal(medianabs(x, y, axis=1), np.array([ 1., 6., 11., 16.])) assert_equal(bias(x, y), np.array([ 6.5, 7.5, 8.5, 9.5, 10.5])) assert_equal(bias(x, y, axis=1), np.array([ 1., 6., 11., 16.])) assert_equal(medianbias(x, y), np.array([ 6.5, 7.5, 8.5, 9.5, 10.5])) assert_equal(medianbias(x, y, axis=1), np.array([ 1., 6., 11., 16.])) assert_equal(vare(x, y), np.array([ 31.25, 31.25, 31.25, 31.25, 31.25])) assert_equal(vare(x, y, axis=1), np.array([ 2., 2., 2., 2.])) def test_ic(): #test information criteria #consistency check ics = [aic, aicc, bic, hqic] ics_sig = [aic_sigma, aicc_sigma, bic_sigma, hqic_sigma] for ic, ic_sig in zip(ics, ics_sig): assert_(ic(np.array(2),10,2).dtype == np.float, msg=repr(ic)) assert_(ic_sig(np.array(2),10,2).dtype == np.float, msg=repr(ic_sig) ) assert_almost_equal(ic(-10./2.*np.log(2.),10,2)/10, ic_sig(2, 10, 2), decimal=14) assert_almost_equal(ic_sig(np.log(2.),10,2, islog=True), ic_sig(2, 10, 2), decimal=14) #examples penalty directly from formula n, k = 10, 2 assert_almost_equal(aic(0, 10, 2), 2*k, decimal=14) #next see Wikipedia assert_almost_equal(aicc(0, 10, 2), aic(0, n, k) + 2*k*(k+1.)/(n-k-1.), decimal=14) assert_almost_equal(bic(0, 10, 2), np.log(n)*k, decimal=14) assert_almost_equal(hqic(0, 10, 2), 2*np.log(np.log(n))*k, decimal=14) if __name__ == '__main__': test_eval_measures() test_ic() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_numdiff.py000066400000000000000000000333561224417117700260710ustar00rootroot00000000000000'''Testing numerical differentiation Still some problems, with API (args tuple versus *args) finite difference Hessian has some problems that I didn't look at yet Should Hessian also work per observation, if fun returns 2d ''' import numpy as np from numpy.testing import assert_almost_equal, assert_allclose import statsmodels.api as sm from statsmodels.tools import numdiff from statsmodels.tools.numdiff import (approx_fprime, approx_fprime_cs, approx_hess_cs) DEC3 = 3 DEC4 = 4 DEC5 = 5 DEC6 = 6 DEC8 = 8 DEC13 = 13 DEC14 = 14 def maxabs(x,y): return np.abs(x-y).max() def fun(beta, x): return np.dot(x, beta).sum(0) def fun1(beta, y, x): #print beta.shape, x.shape xb = np.dot(x, beta) return (y-xb)**2 #(xb-xb.mean(0))**2 def fun2(beta, y, x): #print beta.shape, x.shape return fun1(beta, y, x).sum(0) #ravel() added because of MNLogit 2d params class CheckGradLoglikeMixin(object): def test_score(self): for test_params in self.params: sc = self.mod.score(test_params) scfd = numdiff.approx_fprime(test_params.ravel(), self.mod.loglike) assert_almost_equal(sc, scfd, decimal=1) sccs = numdiff.approx_fprime_cs(test_params.ravel(), self.mod.loglike) assert_almost_equal(sc, sccs, decimal=11) def test_hess(self): for test_params in self.params: he = self.mod.hessian(test_params) hefd = numdiff.approx_fprime_cs(test_params, self.mod.score) assert_almost_equal(he, hefd, decimal=DEC8) #NOTE: notice the accuracy below assert_almost_equal(he, hefd, decimal=7) hefd = numdiff.approx_fprime(test_params, self.mod.score, centered=True) assert_allclose(he, hefd, rtol=5e-10) hefd = numdiff.approx_fprime(test_params, self.mod.score, centered=False) assert_almost_equal(he, hefd, decimal=4) hescs = numdiff.approx_fprime_cs(test_params.ravel(), self.mod.score) assert_allclose(he, hescs, rtol=1e-13) hecs = numdiff.approx_hess_cs(test_params.ravel(), self.mod.loglike) assert_allclose(he, hecs, rtol=1e-9) #NOTE: Look at the lack of precision - default epsilon not always #best grad = self.mod.score(test_params) hecs, gradcs = numdiff.approx_hess1(test_params, self.mod.loglike, 1e-6, return_grad=True) assert_almost_equal(he, hecs, decimal=1) assert_almost_equal(grad, gradcs, decimal=1) hecs, gradcs = numdiff.approx_hess2(test_params, self.mod.loglike, 1e-4, return_grad=True) assert_almost_equal(he, hecs, decimal=3) assert_almost_equal(grad, gradcs, decimal=1) hecs = numdiff.approx_hess3(test_params, self.mod.loglike, 1e-5) assert_almost_equal(he, hecs, decimal=4) class TestGradMNLogit(CheckGradLoglikeMixin): def __init__(self): #from results.results_discrete import Anes data = sm.datasets.anes96.load() exog = data.exog exog = sm.add_constant(exog, prepend=False) self.mod = sm.MNLogit(data.endog, exog) #def loglikeflat(self, params): #reshapes flattened params # return self.loglike(params.reshape(6,6)) #self.mod.loglike = loglikeflat #need instance method #self.params = [np.ones((6,6)).ravel()] res = self.mod.fit(disp=0) self.params = [res.params.ravel('F')] def test_hess(self): #NOTE: I had to overwrite this to lessen the tolerance for test_params in self.params: he = self.mod.hessian(test_params) hefd = numdiff.approx_fprime_cs(test_params, self.mod.score) assert_almost_equal(he, hefd, decimal=DEC8) #NOTE: notice the accuracy below and the epsilon changes # this doesn't work well for score -> hessian with non-cs step # it's a little better around the optimum assert_almost_equal(he, hefd, decimal=7) hefd = numdiff.approx_fprime(test_params, self.mod.score, centered=True) assert_almost_equal(he, hefd, decimal=4) hefd = numdiff.approx_fprime(test_params, self.mod.score, 1e-9, centered=False) assert_almost_equal(he, hefd, decimal=2) hescs = numdiff.approx_fprime_cs(test_params, self.mod.score) assert_almost_equal(he, hescs, decimal=DEC8) hecs = numdiff.approx_hess_cs(test_params, self.mod.loglike) assert_almost_equal(he, hecs, decimal=5) #NOTE: these just don't work well #hecs = numdiff.approx_hess1(test_params, self.mod.loglike, 1e-3) #assert_almost_equal(he, hecs, decimal=1) #hecs = numdiff.approx_hess2(test_params, self.mod.loglike, 1e-4) #assert_almost_equal(he, hecs, decimal=0) hecs = numdiff.approx_hess3(test_params, self.mod.loglike, 1e-4) assert_almost_equal(he, hecs, decimal=0) class TestGradLogit(CheckGradLoglikeMixin): def __init__(self): data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) #mod = sm.Probit(data.endog, data.exog) self.mod = sm.Logit(data.endog, data.exog) #res = mod.fit(method="newton") self.params = [np.array([1,0.25,1.4,-7])] ##loglike = mod.loglike ##score = mod.score ##hess = mod.hessian class CheckDerivativeMixin(object): def __init__(self): nobs = 200 #x = np.arange(nobs*3).reshape(nobs,-1) np.random.seed(187678) x = np.random.randn(nobs,3) xk = np.array([1,2,3]) xk = np.array([1.,1.,1.]) #xk = np.zeros(3) beta = xk y = np.dot(x, beta) + 0.1*np.random.randn(nobs) xkols = np.dot(np.linalg.pinv(x),y) self.x = x self.y = y self.params = [np.array([1.,1.,1.]), xkols] self.init() def init(self): pass def test_grad_fun1_fd(self): for test_params in self.params: #gtrue = self.x.sum(0) gtrue = self.gradtrue(test_params) fun = self.fun() epsilon = 1e-6 gfd = numdiff.approx_fprime(test_params, fun, epsilon=epsilon, args=self.args) gfd += numdiff.approx_fprime(test_params, fun, epsilon=-epsilon, args=self.args) gfd /= 2. assert_almost_equal(gtrue, gfd, decimal=DEC6) def test_grad_fun1_fdc(self): for test_params in self.params: #gtrue = self.x.sum(0) gtrue = self.gradtrue(test_params) fun = self.fun() epsilon = 1e-6 #default epsilon 1e-6 is not precise enough gfd = numdiff.approx_fprime(test_params, fun, epsilon=1e-8, args=self.args, centered=True) assert_almost_equal(gtrue, gfd, decimal=DEC5) def test_grad_fun1_cs(self): for test_params in self.params: #gtrue = self.x.sum(0) gtrue = self.gradtrue(test_params) fun = self.fun() gcs = numdiff.approx_fprime_cs(test_params, fun, args=self.args) assert_almost_equal(gtrue, gcs, decimal=DEC13) def test_hess_fun1_fd(self): for test_params in self.params: #hetrue = 0 hetrue = self.hesstrue(test_params) if not hetrue is None: #Hessian doesn't work for 2d return of fun fun = self.fun() #default works, epsilon 1e-6 or 1e-8 is not precise enough hefd = numdiff.approx_hess1(test_params, fun, #epsilon=1e-8, args=self.args) #TODO:should be kwds assert_almost_equal(hetrue, hefd, decimal=DEC3) #TODO: I reduced precision to DEC3 from DEC4 because of # TestDerivativeFun hefd = numdiff.approx_hess2(test_params, fun, #epsilon=1e-8, args=self.args) #TODO:should be kwds assert_almost_equal(hetrue, hefd, decimal=DEC3) hefd = numdiff.approx_hess3(test_params, fun, #epsilon=1e-8, args=self.args) #TODO:should be kwds assert_almost_equal(hetrue, hefd, decimal=DEC3) def test_hess_fun1_cs(self): for test_params in self.params: #hetrue = 0 hetrue = self.hesstrue(test_params) if not hetrue is None: #Hessian doesn't work for 2d return of fun fun = self.fun() hecs = numdiff.approx_hess_cs(test_params, fun, args=self.args) assert_almost_equal(hetrue, hecs, decimal=DEC6) class TestDerivativeFun(CheckDerivativeMixin): def init(self): xkols = np.dot(np.linalg.pinv(self.x), self.y) self.params = [np.array([1.,1.,1.]), xkols] self.args = (self.x,) def fun(self): return fun def gradtrue(self, params): return self.x.sum(0) def hesstrue(self, params): return np.zeros((3,3)) #make it (3,3), because test fails with scalar 0 #why is precision only DEC3 class TestDerivativeFun2(CheckDerivativeMixin): def init(self): xkols = np.dot(np.linalg.pinv(self.x), self.y) self.params = [np.array([1.,1.,1.]), xkols] self.args = (self.y, self.x) def fun(self): return fun2 def gradtrue(self, params): y, x = self.y, self.x return (-x*2*(y-np.dot(x, params))[:,None]).sum(0) #2*(y-np.dot(x, params)).sum(0) def hesstrue(self, params): x = self.x return 2*np.dot(x.T, x) class TestDerivativeFun1(CheckDerivativeMixin): def init(self): xkols = np.dot(np.linalg.pinv(self.x), self.y) self.params = [np.array([1.,1.,1.]), xkols] self.args = (self.y, self.x) def fun(self): return fun1 def gradtrue(self, params): y, x = self.y, self.x return (-x*2*(y-np.dot(x, params))[:,None]) def hesstrue(self, params): return None y, x = self.y, self.x return (-x*2*(y-np.dot(x, params))[:,None]) #TODO: check shape if __name__ == '__main__': epsilon = 1e-6 nobs = 200 x = np.arange(nobs*3).reshape(nobs,-1) x = np.random.randn(nobs,3) xk = np.array([1,2,3]) xk = np.array([1.,1.,1.]) #xk = np.zeros(3) beta = xk y = np.dot(x, beta) + 0.1*np.random.randn(nobs) xkols = np.dot(np.linalg.pinv(x),y) print approx_fprime((1,2,3),fun,epsilon,x) gradtrue = x.sum(0) print x.sum(0) gradcs = approx_fprime_cs((1,2,3), fun, (x,), h=1.0e-20) print gradcs, maxabs(gradcs, gradtrue) print approx_hess_cs((1,2,3), fun, (x,), h=1.0e-20) #this is correctly zero print approx_hess_cs((1,2,3), fun2, (y,x), h=1.0e-20)-2*np.dot(x.T, x) print numdiff.approx_hess(xk,fun2,1e-3, (y,x))[0] - 2*np.dot(x.T, x) gt = (-x*2*(y-np.dot(x, [1,2,3]))[:,None]) g = approx_fprime_cs((1,2,3), fun1, (y,x), h=1.0e-20)#.T #this shouldn't be transposed gd = numdiff.approx_fprime((1,2,3),fun1,epsilon,(y,x)) print maxabs(g, gt) print maxabs(gd, gt) import statsmodels.api as sm data = sm.datasets.spector.load() data.exog = sm.add_constant(data.exog, prepend=False) #mod = sm.Probit(data.endog, data.exog) mod = sm.Logit(data.endog, data.exog) #res = mod.fit(method="newton") test_params = [1,0.25,1.4,-7] loglike = mod.loglike score = mod.score hess = mod.hessian #cs doesn't work for Probit because special.ndtr doesn't support complex #maybe calculating ndtr for real and imag parts separately, if we need it #and if it still works in this case print 'sm', score(test_params) print 'fd', numdiff.approx_fprime(test_params,loglike,epsilon) print 'cs', numdiff.approx_fprime_cs(test_params,loglike) print 'sm', hess(test_params) print 'fd', numdiff.approx_fprime(test_params,score,epsilon) print 'cs', numdiff.approx_fprime_cs(test_params, score) #print 'fd', numdiff.approx_hess(test_params, loglike, epsilon) #TODO: bug ''' Traceback (most recent call last): File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\test_numdiff.py", line 74, in print 'fd', numdiff.approx_hess(test_params, loglike, epsilon) File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\numdiff.py", line 118, in approx_hess xh = x + h TypeError: can only concatenate list (not "float") to list ''' hesscs = numdiff.approx_hess_cs(test_params, loglike) print 'cs', hesscs print maxabs(hess(test_params), hesscs) data = sm.datasets.anes96.load() exog = data.exog exog = sm.add_constant(exog, prepend=False) res1 = sm.MNLogit(data.endog, exog).fit(method="newton", disp=0) datap = sm.datasets.randhie.load() nobs = len(datap.endog) exogp = sm.add_constant(datap.exog.view(float).reshape(nobs,-1), prepend=False) modp = sm.Poisson(datap.endog, exogp) resp = modp.fit(method='newton', disp=0) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_parallel.py000066400000000000000000000004601224417117700262230ustar00rootroot00000000000000from statsmodels.tools.parallel import parallel_func from numpy import arange, testing from math import sqrt def test_parallel(): x = arange(10.) parallel, p_func, n_jobs = parallel_func(sqrt, n_jobs=-1, verbose=0) y = parallel(p_func(i**2) for i in range(10)) testing.assert_equal(x,y) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_rootfinding.py000066400000000000000000000051371224417117700267570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Sat Mar 23 13:34:19 2013 Author: Josef Perktold """ import numpy as np from statsmodels.tools.rootfinding import brentq_expanding from numpy.testing import assert_allclose, assert_equal, assert_raises def func(x, a): f = (x - a)**3 return f def func_nan(x, a, b): x = np.atleast_1d(x) f = (x - 1.*a)**3 f[x < b] = np.nan return f def funcn(x, a): f = -(x - a)**3 return f def test_brentq_expanding(): cases = [ (0, {}), (50, {}), (-50, {}), (500000, dict(low=10000)), (-50000, dict(upp=-1000)), (500000, dict(low=300000, upp=700000)), (-50000, dict(low= -70000, upp=-1000)) ] funcs = [(func, None), (func, True), (funcn, None), (funcn, False)] for f, inc in funcs: for a, kwds in cases: kw = {'increasing':inc} kw.update(kwds) res = brentq_expanding(f, args=(a,), **kwds) #print '%10d'%a, ['dec', 'inc'][f is func], res - a assert_allclose(res, a, rtol=1e-5) # wrong sign for start bounds # doesn't raise yet during development TODO: activate this # it kind of works in some cases, but not correctly or in a useful way #assert_raises(ValueError, brentq_expanding, func, args=(-500,), start_upp=-1000) #assert_raises(ValueError, brentq_expanding, func, args=(500,), start_low=1000) # low upp given, but doesn't bound root, leave brentq exception # ValueError: f(a) and f(b) must have different signs assert_raises(ValueError, brentq_expanding, funcn, args=(-50000,), low= -40000, upp=-10000) # max_it too low to find root bounds # ValueError: f(a) and f(b) must have different signs assert_raises(ValueError, brentq_expanding, func, args=(-50000,), max_it=2) # maxiter_bq too low # RuntimeError: Failed to converge after 3 iterations. assert_raises(RuntimeError, brentq_expanding, func, args=(-50000,), maxiter_bq=3) # cannot determin whether increasing, all 4 low trial points return nan assert_raises(ValueError, brentq_expanding, func_nan, args=(-20, 0.6)) # test for full_output a = 500 val, info = brentq_expanding(func, args=(a,), full_output=True) assert_allclose(val, a, rtol=1e-5) info1 = {'iterations': 63, 'start_bounds': (-1, 1), 'brentq_bounds': (100, 1000), 'flag': 'converged', 'function_calls': 64, 'iterations_expand': 3, 'converged': True} for k in info1: assert_equal(info1[k], info.__dict__[k]) assert_allclose(info.root, a, rtol=1e-5) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tests/test_tools.py000066400000000000000000000415511224417117700255750ustar00rootroot00000000000000""" Test functions for models.tools """ import numpy as np from numpy.random import standard_normal from numpy.testing import (assert_equal, assert_array_equal, assert_almost_equal, assert_string_equal, TestCase) from nose.tools import (assert_true, assert_false, assert_raises) from statsmodels.datasets import longley from statsmodels.tools import tools class TestTools(TestCase): def test_add_constant_list(self): x = range(1,5) x = tools.add_constant(x) y = np.asarray([[1,1,1,1],[1,2,3,4.]]).T assert_equal(x, y) def test_add_constant_1d(self): x = np.arange(1,5) x = tools.add_constant(x) y = np.asarray([[1,1,1,1],[1,2,3,4.]]).T assert_equal(x, y) def test_add_constant_has_constant1d(self): x = np.ones(5) x = tools.add_constant(x) assert_equal(x, np.ones(5)) def test_add_constant_has_constant2d(self): x = np.asarray([[1,1,1,1],[1,2,3,4.]]) y = tools.add_constant(x) assert_equal(x,y) def test_recipr(self): X = np.array([[2,1],[-1,0]]) Y = tools.recipr(X) assert_almost_equal(Y, np.array([[0.5,1],[0,0]])) def test_recipr0(self): X = np.array([[2,1],[-4,0]]) Y = tools.recipr0(X) assert_almost_equal(Y, np.array([[0.5,1],[-0.25,0]])) def test_rank(self): X = standard_normal((40,10)) self.assertEquals(tools.rank(X), 10) X[:,0] = X[:,1] + X[:,2] self.assertEquals(tools.rank(X), 9) def test_fullrank(self): X = standard_normal((40,10)) X[:,0] = X[:,1] + X[:,2] Y = tools.fullrank(X) self.assertEquals(Y.shape, (40,9)) self.assertEquals(tools.rank(Y), 9) X[:,5] = X[:,3] + X[:,4] Y = tools.fullrank(X) self.assertEquals(Y.shape, (40,8)) self.assertEquals(tools.rank(Y), 8) def test_estimable(): rng = np.random.RandomState(20120713) N, P = (40, 10) X = rng.normal(size=(N, P)) C = rng.normal(size=(1, P)) isestimable = tools.isestimable assert_true(isestimable(C, X)) assert_true(isestimable(np.eye(P), X)) for row in np.eye(P): assert_true(isestimable(row, X)) X = np.ones((40, 2)) assert_true(isestimable([1, 1], X)) assert_false(isestimable([1, 0], X)) assert_false(isestimable([0, 1], X)) assert_false(isestimable(np.eye(2), X)) halfX = rng.normal(size=(N, 5)) X = np.hstack([halfX, halfX]) assert_false(isestimable(np.hstack([np.eye(5), np.zeros((5, 5))]), X)) assert_false(isestimable(np.hstack([np.zeros((5, 5)), np.eye(5)]), X)) assert_true(isestimable(np.hstack([np.eye(5), np.eye(5)]), X)) # Test array-like for design XL = X.tolist() assert_true(isestimable(np.hstack([np.eye(5), np.eye(5)]), XL)) # Test ValueError for incorrect number of columns X = rng.normal(size=(N, 5)) for n in range(1, 4): assert_raises(ValueError, isestimable, np.ones((n,)), X) assert_raises(ValueError, isestimable, np.eye(4), X) class TestCategoricalNumerical(object): #TODO: use assert_raises to check that bad inputs are taken care of def __init__(self): #import string stringabc = 'abcdefghijklmnopqrstuvwxy' self.des = np.random.randn(25,2) self.instr = np.floor(np.arange(10,60, step=2)/10) x=np.zeros((25,5)) x[:5,0]=1 x[5:10,1]=1 x[10:15,2]=1 x[15:20,3]=1 x[20:25,4]=1 self.dummy = x structdes = np.zeros((25,1),dtype=[('var1', 'f4'),('var2', 'f4'), ('instrument','f4'),('str_instr','a10')]) structdes['var1'] = self.des[:,0][:,None] structdes['var2'] = self.des[:,1][:,None] structdes['instrument'] = self.instr[:,None] string_var = [stringabc[0:5], stringabc[5:10], stringabc[10:15], stringabc[15:20], stringabc[20:25]] string_var *= 5 self.string_var = np.array(sorted(string_var)) structdes['str_instr'] = self.string_var[:,None] self.structdes = structdes self.recdes = structdes.view(np.recarray) def test_array2d(self): des = np.column_stack((self.des, self.instr, self.des)) des = tools.categorical(des, col=2) assert_array_equal(des[:,-5:], self.dummy) assert_equal(des.shape[1],10) def test_array1d(self): des = tools.categorical(self.instr) assert_array_equal(des[:,-5:], self.dummy) assert_equal(des.shape[1],6) def test_array2d_drop(self): des = np.column_stack((self.des, self.instr, self.des)) des = tools.categorical(des, col=2, drop=True) assert_array_equal(des[:,-5:], self.dummy) assert_equal(des.shape[1],9) def test_array1d_drop(self): des = tools.categorical(self.instr, drop=True) assert_array_equal(des, self.dummy) assert_equal(des.shape[1],5) def test_recarray2d(self): des = tools.categorical(self.recdes, col='instrument') # better way to do this? test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_recarray2dint(self): des = tools.categorical(self.recdes, col=2) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_recarray1d(self): instr = self.structdes['instrument'].view(np.recarray) dum = tools.categorical(instr) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names[-5:]])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 6) def test_recarray1d_drop(self): instr = self.structdes['instrument'].view(np.recarray) dum = tools.categorical(instr, drop=True) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 5) def test_recarray2d_drop(self): des = tools.categorical(self.recdes, col='instrument', drop=True) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 8) def test_structarray2d(self): des = tools.categorical(self.structdes, col='instrument') test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_structarray2dint(self): des = tools.categorical(self.structdes, col=2) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_structarray1d(self): instr = self.structdes['instrument'].view(dtype=[('var1', 'f4')]) dum = tools.categorical(instr) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names[-5:]])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 6) def test_structarray2d_drop(self): des = tools.categorical(self.structdes, col='instrument', drop=True) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 8) def test_structarray1d_drop(self): instr = self.structdes['instrument'].view(dtype=[('var1', 'f4')]) dum = tools.categorical(instr, drop=True) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 5) # def test_arraylike2d(self): # des = tools.categorical(self.structdes.tolist(), col=2) # test_des = des[:,-5:] # assert_array_equal(test_des, self.dummy) # assert_equal(des.shape[1], 9) # def test_arraylike1d(self): # instr = self.structdes['instrument'].tolist() # dum = tools.categorical(instr) # test_dum = dum[:,-5:] # assert_array_equal(test_dum, self.dummy) # assert_equal(dum.shape[1], 6) # def test_arraylike2d_drop(self): # des = tools.categorical(self.structdes.tolist(), col=2, drop=True) # test_des = des[:,-5:] # assert_array_equal(test__des, self.dummy) # assert_equal(des.shape[1], 8) # def test_arraylike1d_drop(self): # instr = self.structdes['instrument'].tolist() # dum = tools.categorical(instr, drop=True) # assert_array_equal(dum, self.dummy) # assert_equal(dum.shape[1], 5) class TestCategoricalString(TestCategoricalNumerical): # comment out until we have type coercion # def test_array2d(self): # des = np.column_stack((self.des, self.instr, self.des)) # des = tools.categorical(des, col=2) # assert_array_equal(des[:,-5:], self.dummy) # assert_equal(des.shape[1],10) # def test_array1d(self): # des = tools.categorical(self.instr) # assert_array_equal(des[:,-5:], self.dummy) # assert_equal(des.shape[1],6) # def test_array2d_drop(self): # des = np.column_stack((self.des, self.instr, self.des)) # des = tools.categorical(des, col=2, drop=True) # assert_array_equal(des[:,-5:], self.dummy) # assert_equal(des.shape[1],9) def test_array1d_drop(self): des = tools.categorical(self.string_var, drop=True) assert_array_equal(des, self.dummy) assert_equal(des.shape[1],5) def test_recarray2d(self): des = tools.categorical(self.recdes, col='str_instr') # better way to do this? test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_recarray2dint(self): des = tools.categorical(self.recdes, col=3) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_recarray1d(self): instr = self.structdes['str_instr'].view(np.recarray) dum = tools.categorical(instr) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names[-5:]])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 6) def test_recarray1d_drop(self): instr = self.structdes['str_instr'].view(np.recarray) dum = tools.categorical(instr, drop=True) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 5) def test_recarray2d_drop(self): des = tools.categorical(self.recdes, col='str_instr', drop=True) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 8) def test_structarray2d(self): des = tools.categorical(self.structdes, col='str_instr') test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_structarray2dint(self): des = tools.categorical(self.structdes, col=3) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 9) def test_structarray1d(self): instr = self.structdes['str_instr'].view(dtype=[('var1', 'a10')]) dum = tools.categorical(instr) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names[-5:]])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 6) def test_structarray2d_drop(self): des = tools.categorical(self.structdes, col='str_instr', drop=True) test_des = np.column_stack(([des[_] for _ in des.dtype.names[-5:]])) assert_array_equal(test_des, self.dummy) assert_equal(len(des.dtype.names), 8) def test_structarray1d_drop(self): instr = self.structdes['str_instr'].view(dtype=[('var1', 'a10')]) dum = tools.categorical(instr, drop=True) test_dum = np.column_stack(([dum[_] for _ in dum.dtype.names])) assert_array_equal(test_dum, self.dummy) assert_equal(len(dum.dtype.names), 5) def test_arraylike2d(self): pass def test_arraylike1d(self): pass def test_arraylike2d_drop(self): pass def test_arraylike1d_drop(self): pass def test_rec_issue302(): arr = np.rec.fromrecords([[10], [11]], names='group') actual = tools.categorical(arr) expected = np.rec.array([(10, 1.0, 0.0), (11, 0.0, 1.0)], dtype=[('group', int), ('group_10', float), ('group_11', float)]) assert_array_equal(actual, expected) def test_issue302(): arr = np.rec.fromrecords([[10, 12], [11, 13]], names=['group', 'whatever']) actual = tools.categorical(arr, col=['group']) expected = np.rec.array([(10, 12, 1.0, 0.0), (11, 13, 0.0, 1.0)], dtype=[('group', int), ('whatever', int), ('group_10', float), ('group_11', float)]) assert_array_equal(actual, expected) def test_pandas_const_series(): dta = longley.load_pandas() series = dta.exog['GNP'] series = tools.add_constant(series, prepend=False) assert_string_equal('const', series.columns[1]) assert_equal(series.var(0)[1], 0) def test_pandas_const_series_prepend(): dta = longley.load_pandas() series = dta.exog['GNP'] series = tools.add_constant(series, prepend=True) assert_string_equal('const', series.columns[0]) assert_equal(series.var(0)[0], 0) def test_pandas_const_df(): dta = longley.load_pandas().exog dta = tools.add_constant(dta, prepend=False) assert_string_equal('const', dta.columns[-1]) assert_equal(dta.var(0)[-1], 0) def test_pandas_const_df_prepend(): dta = longley.load_pandas().exog # regression test for #1025 dta['UNEMP'] /= dta['UNEMP'].std() dta = tools.add_constant(dta, prepend=True) assert_string_equal('const', dta.columns[0]) assert_equal(dta.var(0)[0], 0) def test_chain_dot(): A = np.arange(1,13).reshape(3,4) B = np.arange(3,15).reshape(4,3) C = np.arange(5,8).reshape(3,1) assert_equal(tools.chain_dot(A,B,C), np.array([[1820],[4300],[6780]])) class TestNanDot(object): @classmethod def setupClass(cls): nan = np.nan cls.mx_1 = np.array([[nan, 1.], [2., 3.]]) cls.mx_2 = np.array([[nan, nan], [2., 3.]]) cls.mx_3 = np.array([[0., 0.], [0., 0.]]) cls.mx_4 = np.array([[1., 0.], [1., 0.]]) cls.mx_5 = np.array([[0., 1.], [0., 1.]]) cls.mx_6 = np.array([[1., 2.], [3., 4.]]) def test_11(self): test_res = tools.nan_dot(self.mx_1, self.mx_1) expected_res = np.array([[ np.nan, np.nan], [ np.nan, 11.]]) assert_array_equal(test_res, expected_res) def test_12(self): nan = np.nan test_res = tools.nan_dot(self.mx_1, self.mx_2) expected_res = np.array([[ nan, nan], [ nan, nan]]) assert_array_equal(test_res, expected_res) def test_13(self): nan = np.nan test_res = tools.nan_dot(self.mx_1, self.mx_3) expected_res = np.array([[ 0., 0.], [ 0., 0.]]) assert_array_equal(test_res, expected_res) def test_14(self): nan = np.nan test_res = tools.nan_dot(self.mx_1, self.mx_4) expected_res = np.array([[ nan, 0.], [ 5., 0.]]) assert_array_equal(test_res, expected_res) def test_41(self): nan = np.nan test_res = tools.nan_dot(self.mx_4, self.mx_1) expected_res = np.array([[ nan, 1.], [ nan, 1.]]) assert_array_equal(test_res, expected_res) def test_23(self): nan = np.nan test_res = tools.nan_dot(self.mx_2, self.mx_3) expected_res = np.array([[ 0., 0.], [ 0., 0.]]) assert_array_equal(test_res, expected_res) def test_32(self): nan = np.nan test_res = tools.nan_dot(self.mx_3, self.mx_2) expected_res = np.array([[ 0., 0.], [ 0., 0.]]) assert_array_equal(test_res, expected_res) def test_24(self): nan = np.nan test_res = tools.nan_dot(self.mx_2, self.mx_4) expected_res = np.array([[ nan, 0.], [ 5., 0.]]) assert_array_equal(test_res, expected_res) def test_25(self): nan = np.nan test_res = tools.nan_dot(self.mx_2, self.mx_5) expected_res = np.array([[ 0., nan], [ 0., 5.]]) assert_array_equal(test_res, expected_res) def test_66(self): nan = np.nan test_res = tools.nan_dot(self.mx_6, self.mx_6) expected_res = np.array([[ 7., 10.], [ 15., 22.]]) assert_array_equal(test_res, expected_res) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/tools.py000066400000000000000000000415121224417117700233710ustar00rootroot00000000000000''' Utility functions models code ''' import numpy as np import numpy.lib.recfunctions as nprf import numpy.linalg as L from scipy.interpolate import interp1d from scipy.linalg import svdvals from statsmodels.distributions import (ECDF, monotone_fn_inverter, StepFunction) from statsmodels.tools.data import _is_using_pandas from statsmodels.compatnp.py3k import asstr2 from pandas import DataFrame def _make_dictnames(tmp_arr, offset=0): """ Helper function to create a dictionary mapping a column number to the name in tmp_arr. """ col_map = {} for i,col_name in enumerate(tmp_arr): col_map.update({i+offset : col_name}) return col_map def drop_missing(Y,X=None, axis=1): """ Returns views on the arrays Y and X where missing observations are dropped. Y : array-like X : array-like, optional axis : int Axis along which to look for missing observations. Default is 1, ie., observations in rows. Returns ------- Y : array All Y where the X : array Notes ----- If either Y or X is 1d, it is reshaped to be 2d. """ Y = np.asarray(Y) if Y.ndim == 1: Y = Y[:,None] if X is not None: X = np.array(X) if X.ndim == 1: X = X[:,None] keepidx = np.logical_and(~np.isnan(Y).any(axis),~np.isnan(X).any(axis)) return Y[keepidx], X[keepidx] else: keepidx = ~np.isnan(Y).any(axis) return Y[keepidx] #TODO: needs to better preserve dtype and be more flexible # ie., if you still have a string variable in your array you don't # want to cast it to float #TODO: add name validator (ie., bad names for datasets.grunfeld) def categorical(data, col=None, dictnames=False, drop=False, ): ''' Returns a dummy matrix given an array of categorical variables. Parameters ---------- data : array A structured array, recarray, or array. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : 'string', int, or None If data is a structured array or a recarray, `col` can be a string that is the name of the column that contains the variable. For all arrays `col` can be an int that is the (zero-based) column index number. `col` can only be None for a 1d array. The default is None. dictnames : bool, optional If True, a dictionary mapping the column number to the categorical name is returned. Used to have information about plain arrays. drop : bool Whether or not keep the categorical variable in the returned matrix. Returns -------- dummy_matrix, [dictnames, optional] A matrix of dummy (indicator/binary) float variables for the categorical data. If dictnames is True, then the dictionary is returned as well. Notes ----- This returns a dummy variable for EVERY distinct variable. If a a structured or recarray is provided, the names for the new variable is the old variable name - underscore - category name. So if the a variable 'vote' had answers as 'yes' or 'no' then the returned array would have to new variables-- 'vote_yes' and 'vote_no'. There is currently no name checking. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm Univariate examples >>> import string >>> string_var = [string.lowercase[0:5], string.lowercase[5:10], \ string.lowercase[10:15], string.lowercase[15:20], \ string.lowercase[20:25]] >>> string_var *= 5 >>> string_var = np.asarray(sorted(string_var)) >>> design = sm.tools.categorical(string_var, drop=True) Or for a numerical categorical variable >>> instr = np.floor(np.arange(10,60, step=2)/10) >>> design = sm.tools.categorical(instr, drop=True) With a structured array >>> num = np.random.randn(25,2) >>> struct_ar = np.zeros((25,1), dtype=[('var1', 'f4'),('var2', 'f4'), \ ('instrument','f4'),('str_instr','a5')]) >>> struct_ar['var1'] = num[:,0][:,None] >>> struct_ar['var2'] = num[:,1][:,None] >>> struct_ar['instrument'] = instr[:,None] >>> struct_ar['str_instr'] = string_var[:,None] >>> design = sm.tools.categorical(struct_ar, col='instrument', drop=True) Or >>> design2 = sm.tools.categorical(struct_ar, col='str_instr', drop=True) ''' if isinstance(col, (list, tuple)): try: assert len(col) == 1 col = col[0] except: raise ValueError("Can only convert one column at a time") #TODO: add a NameValidator function # catch recarrays and structured arrays if data.dtype.names or data.__class__ is np.recarray: if not col and np.squeeze(data).ndim > 1: raise IndexError("col is None and the input array is not 1d") if isinstance(col, int): col = data.dtype.names[col] if col is None and data.dtype.names and len(data.dtype.names) == 1: col = data.dtype.names[0] tmp_arr = np.unique(data[col]) # if the cols are shape (#,) vs (#,1) need to add an axis and flip _swap = True if data[col].ndim == 1: tmp_arr = tmp_arr[:,None] _swap = False tmp_dummy = (tmp_arr==data[col]).astype(float) if _swap: tmp_dummy = np.squeeze(tmp_dummy).swapaxes(1,0) if not tmp_arr.dtype.names: # how do we get to this code path? tmp_arr = [asstr2(item) for item in np.squeeze(tmp_arr)] elif tmp_arr.dtype.names: tmp_arr = [asstr2(item) for item in np.squeeze(tmp_arr.tolist())] # prepend the varname and underscore, if col is numeric attribute lookup # is lost for recarrays... if col is None: try: col = data.dtype.names[0] except: col = 'var' #TODO: the above needs to be made robust because there could be many # var_yes, var_no varaibles for instance. tmp_arr = [col + '_'+ item for item in tmp_arr] #TODO: test this for rec and structured arrays!!! if drop is True: if len(data.dtype) <= 1: if tmp_dummy.shape[0] < tmp_dummy.shape[1]: tmp_dummy = np.squeeze(tmp_dummy).swapaxes(1,0) dt = zip(tmp_arr, [tmp_dummy.dtype.str]*len(tmp_arr)) # preserve array type return np.array(map(tuple, tmp_dummy.tolist()), dtype=dt).view(type(data)) data=nprf.drop_fields(data, col, usemask=False, asrecarray=type(data) is np.recarray) data=nprf.append_fields(data, tmp_arr, data=tmp_dummy, usemask=False, asrecarray=type(data) is np.recarray) return data # handle ndarrays and catch array-like for an error elif data.__class__ is np.ndarray or not isinstance(data,np.ndarray): if not isinstance(data, np.ndarray): raise NotImplementedError("Array-like objects are not supported") if isinstance(col, int): offset = data.shape[1] # need error catching here? tmp_arr = np.unique(data[:,col]) tmp_dummy = (tmp_arr[:,np.newaxis]==data[:,col]).astype(float) tmp_dummy = tmp_dummy.swapaxes(1,0) if drop is True: offset -= 1 data = np.delete(data, col, axis=1).astype(float) data = np.column_stack((data,tmp_dummy)) if dictnames is True: col_map = _make_dictnames(tmp_arr, offset) return data, col_map return data elif col is None and np.squeeze(data).ndim == 1: tmp_arr = np.unique(data) tmp_dummy = (tmp_arr[:,None]==data).astype(float) tmp_dummy = tmp_dummy.swapaxes(1,0) if drop is True: if dictnames is True: col_map = _make_dictnames(tmp_arr) return tmp_dummy, col_map return tmp_dummy else: data = np.column_stack((data, tmp_dummy)) if dictnames is True: col_map = _make_dictnames(tmp_arr, offset=1) return data, col_map return data else: raise IndexError("The index %s is not understood" % col) def _series_add_constant(data, prepend): const = np.ones_like(data) if not prepend: columns = [data.name, 'const'] else: columns = ['const', data.name] results = DataFrame({data.name : data, 'const' : const}, columns=columns) return results def _dataframe_add_constant(data, prepend): # check for const. if np.any(data.var(0) == 0): return data if prepend: data.insert(0, 'const', 1) else: data['const'] = 1 return data def _pandas_add_constant(data, prepend): from pandas import Series if isinstance(data, Series): return _series_add_constant(data, prepend) else: return _dataframe_add_constant(data, prepend) #TODO: add an axis argument to this for sysreg def add_constant(data, prepend=True): ''' This appends a column of ones to an array if prepend==False. For ndarrays and pandas.DataFrames, checks to make sure a constant is not already included. If there is at least one column of ones then the original object is returned. Does not check for a constant if a structured or recarray is given. Parameters ---------- data : array-like `data` is the column-ordered design matrix prepend : bool True and the constant is prepended rather than appended. Returns ------- data : array The original array with a constant (column of ones) as the first or last column. ''' if _is_using_pandas(data, None): # work on a copy return _pandas_add_constant(data.copy(), prepend) else: data = np.asarray(data) if not data.dtype.names: var0 = data.var(0) == 0 if np.any(var0): return data data = np.column_stack((data, np.ones((data.shape[0], 1)))) if prepend: return np.roll(data, 1, 1) else: return_rec = data.__class__ is np.recarray if prepend: ones = np.ones((data.shape[0], 1), dtype=[('const', float)]) data = nprf.append_fields(ones, data.dtype.names, [data[i] for i in data.dtype.names], usemask=False, asrecarray=return_rec) else: data = nprf.append_fields(data, 'const', np.ones(data.shape[0]), usemask=False, asrecarray = return_rec) return data def isestimable(C, D): """ True if (Q, P) contrast `C` is estimable for (N, P) design `D` From an Q x P contrast matrix `C` and an N x P design matrix `D`, checks if the contrast `C` is estimable by looking at the rank of ``vstack([C,D])`` and verifying it is the same as the rank of `D`. Parameters ---------- C : (Q, P) array-like contrast matrix. If `C` has is 1 dimensional assume shape (1, P) D: (N, P) array-like design matrix Returns ------- tf : bool True if the contrast `C` is estimable on design `D` Examples -------- >>> D = np.array([[1, 1, 1, 0, 0, 0], ... [0, 0, 0, 1, 1, 1], ... [1, 1, 1, 1, 1, 1]]).T >>> isestimable([1, 0, 0], D) False >>> isestimable([1, -1, 0], D) True """ C = np.asarray(C) D = np.asarray(D) if C.ndim == 1: C = C[None, :] if C.shape[1] != D.shape[1]: raise ValueError('Contrast should have %d columns' % D.shape[1]) new = np.vstack([C, D]) if rank(new) != rank(D): return False return True def recipr(X): """ Return the reciprocal of an array, setting all entries less than or equal to 0 to 0. Therefore, it presumes that X should be positive in general. """ x = np.maximum(np.asarray(X).astype(np.float64), 0) return np.greater(x, 0.) / (x + np.less_equal(x, 0.)) def recipr0(X): """ Return the reciprocal of an array, setting all entries equal to 0 as 0. It does not assume that X should be positive in general. """ test = np.equal(np.asarray(X), 0) return np.where(test, 0, 1. / X) def clean0(matrix): """ Erase columns of zeros: can save some time in pseudoinverse. """ colsum = np.add.reduce(matrix**2, 0) val = [matrix[:,i] for i in np.flatnonzero(colsum)] return np.array(np.transpose(val)) def rank(X, cond=1.0e-12): """ Return the rank of a matrix X based on its generalized inverse, not the SVD. """ X = np.asarray(X) if len(X.shape) == 2: D = svdvals(X) return int(np.add.reduce(np.greater(D / D.max(), cond).astype(np.int32))) else: return int(not np.alltrue(np.equal(X, 0.))) def fullrank(X, r=None): """ Return a matrix whose column span is the same as X. If the rank of X is known it can be specified as r -- no check is made to ensure that this really is the rank of X. """ if r is None: r = rank(X) V, D, U = L.svd(X, full_matrices=0) order = np.argsort(D) order = order[::-1] value = [] for i in range(r): value.append(V[:,order[i]]) return np.asarray(np.transpose(value)).astype(np.float64) StepFunction = np.deprecate(StepFunction, old_name = 'statsmodels.tools.tools.StepFunction', new_name = 'statsmodels.distributions.StepFunction') monotone_fn_inverter = np.deprecate(monotone_fn_inverter, old_name = 'statsmodels.tools.tools.monotone_fn_inverter', new_name = 'statsmodels.distributions.monotone_fn_inverter') ECDF = np.deprecate(ECDF, old_name = 'statsmodels.tools.tools.ECDF', new_name = 'statsmodels.distributions.ECDF') def unsqueeze(data, axis, oldshape): """ Unsqueeze a collapsed array >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) >>> """ newshape = list(oldshape) newshape[axis] = 1 return data.reshape(newshape) def chain_dot(*arrs): """ Returns the dot product of the given matrices. Parameters ---------- arrs: argument list of ndarray Returns ------- Dot product of all arguments. Example ------- >>> import numpy as np >>> from statsmodels.tools import chain_dot >>> A = np.arange(1,13).reshape(3,4) >>> B = np.arange(3,15).reshape(4,3) >>> C = np.arange(5,8).reshape(3,1) >>> chain_dot(A,B,C) array([[1820], [4300], [6780]]) """ return reduce(lambda x, y: np.dot(y, x), arrs[::-1]) def webuse(data, baseurl='http://www.stata-press.com/data/r11/', as_df=True): """ Parameters ---------- data : str Name of dataset to fetch. baseurl : str The base URL to the stata datasets. as_df : bool If True, returns a `pandas.DataFrame` Returns ------- dta : Record Array A record array containing the Stata dataset. Examples -------- >>> dta = webuse('auto') Notes ----- Make sure baseurl has trailing forward slash. Doesn't do any error checking in response URLs. """ # lazy imports from statsmodels.iolib import genfromdta from urllib2 import urlopen from urlparse import urljoin from StringIO import StringIO url = urljoin(baseurl, data+'.dta') dta = urlopen(url) #TODO: this isn't Python 3 compatibile since urlopen returns bytes? dta = StringIO(dta.read()) # make it truly file-like if as_df: # could make this faster if we don't process dta twice? from pandas import DataFrame return DataFrame.from_records(genfromdta(dta)) else: return genfromdta(dta) def nan_dot(A, B): """ Returns np.dot(left_matrix, right_matrix) with the convention that nan * 0 = 0 and nan * x = nan if x != 0. Parameters ---------- A, B : np.ndarrays """ # Find out who should be nan due to nan * nonzero should_be_nan_1 = np.dot(np.isnan(A), (B != 0)) should_be_nan_2 = np.dot((A != 0), np.isnan(B)) should_be_nan = should_be_nan_1 + should_be_nan_2 # Multiply after setting all nan to 0 # This is what happens if there were no nan * nonzero conflicts C = np.dot(np.nan_to_num(A), np.nan_to_num(B)) C[should_be_nan] = np.nan return C def maybe_unwrap_results(results): """ Gets raw results back from wrapped results. Can be used in plotting functions or other post-estimation type routines. """ return getattr(results, '_results', results) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tools/wrappers.py000066400000000000000000000024271224417117700240760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convenience Wrappers Created on Sat Oct 30 14:56:35 2010 Author: josef-pktd License: BSD """ import numpy as np import statsmodels.api as sm from statsmodels import GLS, WLS, OLS def remove_nanrows(y, x): '''remove common rows in [y,x] that contain at least one nan TODO: this should be made more flexible, arbitrary number of arrays and 1d or 2d arrays duplicate: Skipper added sm.tools.drop_missing ''' mask = ~np.isnan(y) mask *= ~(np.isnan(x).any(-1)) #* or & y = y[mask] x = x[mask] return y, x def linmod(y, x, weights=None, sigma=None, add_const=True, filter_missing=True, **kwds): '''get linear model with extra options for entry dispatches to regular model class and does not wrap the output If several options are exclusive, for example sigma and weights, then the chosen class depends on the implementation sequence. ''' if filter_missing: y, x = remove_nanrows(y, x) #do the same for masked arrays if add_const: x = sm.add_constant(x, prepend=True) if not sigma is None: return GLS(y, x, sigma=sigma, **kwds) elif not weights is None: return WLS(y, x, weights=weights, **kwds) else: return OLS(y, x, **kwds) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/000077500000000000000000000000001224417117700213035ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/__init__.py000066400000000000000000000001031224417117700234060ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/adfvalues.py000066400000000000000000000420711224417117700236330ustar00rootroot00000000000000from scipy.stats import norm from numpy import array, polyval, inf, asarray __all__ = ['mackinnonp','mackinnoncrit'] # These are the cut-off values for the left-tail vs. the rest of the # tau distribution, for getting the p-values tau_star_nc = [-1.04, -1.53, -2.68, -3.09, -3.07, -3.77] tau_min_nc = [-19.04,-19.62,-21.21,-23.25,-21.63,-25.74] tau_max_nc = [inf,1.51,0.86,0.88,1.05,1.24] tau_star_c = [-1.61, -2.62, -3.13, -3.47, -3.78, -3.93] tau_min_c = [-18.83,-18.86,-23.48,-28.07,-25.96,-23.27] tau_max_c = [2.74,0.92,0.55,0.61,0.79,1] tau_star_ct = [-2.89, -3.19, -3.50, -3.65, -3.80, -4.36] tau_min_ct = [-16.18,-21.15,-25.37,-26.63,-26.53,-26.18] tau_max_ct = [0.7,0.63,0.71,0.93,1.19,1.42] tau_star_ctt = [-3.21,-3.51,-3.81,-3.83,-4.12,-4.63] tau_min_ctt = [-17.17,-21.1,-24.33,-24.03,-24.33,-28.22] tau_max_ctt = [0.54,0.79,1.08,1.43,3.49,1.92] small_scaling = array([1,1,1e-2]) tau_nc_smallp = [ [0.6344,1.2378,3.2496], [1.9129,1.3857,3.5322], [2.7648,1.4502,3.4186], [3.4336,1.4835,3.19], [4.0999,1.5533,3.59], [4.5388,1.5344,2.9807]] tau_nc_smallp = asarray(tau_nc_smallp)*small_scaling tau_c_smallp = [ [2.1659,1.4412,3.8269], [2.92,1.5012,3.9796], [3.4699,1.4856,3.164], [3.9673,1.4777,2.6315], [4.5509,1.5338,2.9545], [5.1399,1.6036,3.4445]] tau_c_smallp = asarray(tau_c_smallp)*small_scaling tau_ct_smallp = [ [3.2512,1.6047,4.9588], [3.6646,1.5419,3.6448], [4.0983,1.5173,2.9898], [4.5844,1.5338,2.8796], [5.0722,1.5634,2.9472], [5.53,1.5914,3.0392]] tau_ct_smallp = asarray(tau_ct_smallp)*small_scaling tau_ctt_smallp = [ [4.0003,1.658,4.8288], [4.3534,1.6016,3.7947], [4.7343,1.5768,3.2396], [5.214,1.6077,3.3449], [5.6481,1.6274,3.3455], [5.9296,1.5929,2.8223]] tau_ctt_smallp = asarray(tau_ctt_smallp)*small_scaling large_scaling = array([1,1e-1,1e-1,1e-2]) tau_nc_largep = [ [0.4797,9.3557,-0.6999,3.3066], [1.5578,8.558,-2.083,-3.3549], [2.2268,6.8093,-3.2362,-5.4448], [2.7654,6.4502,-3.0811,-4.4946], [3.2684,6.8051,-2.6778,-3.4972], [3.7268,7.167,-2.3648,-2.8288]] tau_nc_largep = asarray(tau_nc_largep)*large_scaling tau_c_largep = [ [1.7339,9.3202,-1.2745,-1.0368], [2.1945,6.4695,-2.9198,-4.2377], [2.5893,4.5168,-3.6529,-5.0074], [3.0387,4.5452,-3.3666,-4.1921], [3.5049,5.2098,-2.9158,-3.3468], [3.9489,5.8933,-2.5359,-2.721]] tau_c_largep = asarray(tau_c_largep)*large_scaling tau_ct_largep = [ [2.5261,6.1654,-3.7956,-6.0285], [2.85,5.272,-3.6622,-5.1695], [3.221,5.255,-3.2685,-4.1501], [3.652,5.9758,-2.7483,-3.2081], [4.0712,6.6428,-2.3464,-2.546], [4.4735,7.1757,-2.0681,-2.1196] ] tau_ct_largep = asarray(tau_ct_largep)*large_scaling tau_ctt_largep = [ [3.0778,4.9529,-4.1477,-5.9359], [3.4713,5.967,-3.2507,-4.2286], [3.8637,6.7852,-2.6286,-3.1381], [4.2736,7.6199,-2.1534,-2.4026], [4.6679,8.2618,-1.822,-1.9147], [5.0009,8.3735,-1.6994,-1.6928]] tau_ctt_largep = asarray(tau_ctt_largep)*large_scaling #NOTE: The Z-statistic is used when lags are included to account for # serial correlation in the error term z_star_nc = [-2.9,-8.7,-14.8,-20.9,-25.7,-30.5] z_star_c = [-8.9,-14.3,-19.5,-25.1,-29.6,-34.4] z_star_ct = [-15.0,-19.6,-25.3,-29.6,-31.8,-38.4] z_star_ctt = [-20.7,-25.3,-29.9,-34.4,-38.5,-44.2] # These are Table 5 from MacKinnon (1994) # small p is defined as p in .005 to .150 ie p = .005 up to z_star # Z* is the largest value for which it is appropriate to use these # approximations # the left tail approximation is # p = norm.cdf(d_0 + d_1*log(abs(z)) + d_2*log(abs(z))**2 + d_3*log(abs(z))**3 # there is no Z-min, ie., it is well-behaved in the left tail z_nc_smallp = array([[.0342, -.6376,0,-.03872], [1.3426,-.7680,0,-.04104], [3.8607,-2.4159,.51293,-.09835], [6.1072,-3.7250,.85887,-.13102], [7.7800,-4.4579,1.00056,-.14014], [4.0253, -.8815,0,-.04887]]) z_c_smallp = array([[2.2142,-1.7863,.32828,-.07727], [1.1662,.1814,-.36707,0], [6.6584,-4.3486,1.04705,-.15011], [3.3249,-.8456,0,-.04818], [4.0356,-.9306,0,-.04776], [13.9959,-8.4314,1.97411,-.22234]]) z_ct_smallp = array([ [4.6476,-2.8932,0.5832,-0.0999], [7.2453,-4.7021,1.127,-.15665], [3.4893,-0.8914,0,-.04755], [1.6604,1.0375,-0.53377,0], [2.006,1.1197,-0.55315,0], [11.1626,-5.6858,1.21479,-.15428]]) z_ctt_smallp = array([ [3.6739,-1.1549,0,-0.03947], [3.9783,-1.0619,0,-0.04394], [2.0062,0.8907,-0.51708,0], [4.9218,-1.0663,0,-0.04691], [5.1433,-0.9877,0,-0.04993], [23.6812,-14.6485,3.42909,-.33794]]) # These are Table 6 from MacKinnon (1994). # These are well-behaved in the right tail. # the approximation function is # p = norm.cdf(d_0 + d_1 * z + d_2*z**2 + d_3*z**3 + d_4*z**4) z_large_scaling = array([1,1e-1,1e-2,1e-3,1e-5]) z_nc_largep = array([ [0.4927,6.906,13.2331,12.099,0], [1.5167,4.6859,4.2401,2.7939,7.9601], [2.2347,3.9465,2.2406,0.8746,1.4239], [2.8239,3.6265,1.6738,0.5408,0.7449], [3.3174,3.3492,1.2792,0.3416,0.3894], [3.729,3.0611,0.9579,0.2087,0.1943]]) z_nc_largep *= z_large_scaling z_c_largep = array([ [1.717,5.5243,4.3463,1.6671,0], [2.2394,4.2377,2.432,0.9241,0.4364], [2.743,3.626,1.5703,0.4612,0.567], [3.228,3.3399,1.2319,0.3162,0.3482], [3.6583,3.0934,0.9681,0.2111,0.1979], [4.0379,2.8735,0.7694,0.1433,0.1146]]) z_c_largep *= z_large_scaling z_ct_largep = array([ [2.7117,4.5731,2.2868,0.6362,0.5], [3.0972,4.0873,1.8982,0.5796,0.7384], [3.4594,3.6326,1.4284,0.3813,0.4325], [3.806,3.2634,1.0689,0.2402,0.2304], [4.1402,2.9867,0.8323,0.16,0.1315], [4.4497,2.7534,0.6582,0.1089,0.0773]]) z_ct_largep *= z_large_scaling z_ctt_largep = array([ [3.4671,4.3476,1.9231,0.5381,0.6216], [3.7827,3.9421,1.5699,0.4093,0.4485], [4.052,3.4947,1.1772,0.2642,0.2502], [4.3311,3.1625,0.9126,0.1775,0.1462], [4.594,2.8739,0.707,0.1181,0.0838], [4.8479,2.6447,0.5647,0.0827,0.0518]]) z_ctt_largep *= z_large_scaling #TODO: finish this and then integrate them into adf function def mackinnonp(teststat, regression="c", N=1, lags=None): """ Returns MacKinnon's approximate p-value for teststat. Parameters ---------- teststat : float "T-value" from an Augmented Dickey-Fuller regression. regression : str {"c", "nc", "ct", "ctt"} This is the method of regression that was used. Following MacKinnon's notation, this can be "c" for constant, "nc" for no constant, "ct" for constant and trend, and "ctt" for constant, trend, and trend-squared. N : int The number of series believed to be I(1). For (Augmented) Dickey- Fuller N = 1. Returns ------- p-value : float The p-value for the ADF statistic estimated using MacKinnon 1994. References ---------- MacKinnon, J.G. 1994 "Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests." Journal of Business & Economics Statistics, 12.2, 167-76. Notes ----- For (A)DF H_0: AR coefficient = 1 H_a: AR coefficient < 1 """ maxstat = eval("tau_max_"+regression) minstat = eval("tau_min_"+regression) starstat = eval("tau_star_"+regression) if teststat > maxstat[N-1]: return 1.0 elif teststat < minstat[N-1]: return 0.0 if teststat <= starstat[N-1]: tau_coef = eval("tau_" + regression + "_smallp["+str(N-1)+"]") # teststat = np.log(np.abs(teststat)) #above is only for z stats else: tau_coef = eval("tau_" + regression + "_largep["+str(N-1)+"]") return norm.cdf(polyval(tau_coef[::-1], teststat)) # These are the new estimates from MacKinnon 2010 # the first axis is N -1 # the second axis is 1 %, 5 %, 10 % # the last axis is the coefficients tau_nc_2010 = [[ [-2.56574,-2.2358,-3.627,0], # N = 1 [-1.94100,-0.2686,-3.365,31.223], [-1.61682, 0.2656, -2.714, 25.364]]] tau_nc_2010 = asarray(tau_nc_2010) tau_c_2010 = [[ [-3.43035,-6.5393,-16.786,-79.433], # N = 1, 1% [-2.86154,-2.8903,-4.234,-40.040], # 5 % [-2.56677,-1.5384,-2.809,0]], # 10 % [ [-3.89644,-10.9519,-33.527,0], # N = 2 [-3.33613,-6.1101,-6.823,0], [-3.04445,-4.2412,-2.720,0]], [ [-4.29374,-14.4354,-33.195,47.433], # N = 3 [-3.74066,-8.5632,-10.852,27.982], [-3.45218,-6.2143,-3.718,0]], [ [-4.64332,-18.1031,-37.972,0], # N = 4 [-4.09600,-11.2349,-11.175,0], [-3.81020,-8.3931,-4.137,0]], [ [-4.95756,-21.8883,-45.142,0], # N = 5 [-4.41519,-14.0405,-12.575,0], [-4.13157,-10.7417,-3.784,0]], [ [-5.24568,-25.6688,-57.737,88.639], # N = 6 [-4.70693,-16.9178,-17.492,60.007], [-4.42501,-13.1875,-5.104,27.877]], [ [-5.51233,-29.5760,-69.398,164.295],# N = 7 [-4.97684,-19.9021,-22.045,110.761], [-4.69648,-15.7315,-5.104,27.877]], [ [-5.76202,-33.5258,-82.189,256.289], # N = 8 [-5.22924,-23.0023,-24.646,144.479], [-4.95007,-18.3959,-7.344,94.872]], [ [-5.99742,-37.6572,-87.365,248.316],# N = 9 [-5.46697,-26.2057,-26.627,176.382], [-5.18897,-21.1377,-9.484,172.704]], [ [-6.22103,-41.7154,-102.680,389.33],# N = 10 [-5.69244,-29.4521,-30.994,251.016], [-5.41533,-24.0006,-7.514,163.049]], [ [-6.43377,-46.0084,-106.809,352.752],# N = 11 [-5.90714,-32.8336,-30.275,249.994], [-5.63086,-26.9693,-4.083,151.427]], [ [-6.63790,-50.2095,-124.156,579.622],# N = 12 [-6.11279,-36.2681,-32.505,314.802], [-5.83724,-29.9864,-2.686,184.116]]] tau_c_2010 = asarray(tau_c_2010) tau_ct_2010 = [[ [-3.95877,-9.0531,-28.428,-134.155], # N = 1 [-3.41049,-4.3904,-9.036,-45.374], [-3.12705,-2.5856,-3.925,-22.380]], [ [-4.32762,-15.4387,-35.679,0], # N = 2 [-3.78057,-9.5106,-12.074,0], [-3.49631,-7.0815,-7.538,21.892]], [ [-4.66305,-18.7688,-49.793,104.244], # N = 3 [-4.11890,-11.8922,-19.031,77.332], [-3.83511,-9.0723,-8.504,35.403]], [ [-4.96940,-22.4694,-52.599,51.314], # N = 4 [-4.42871,-14.5876,-18.228,39.647], [-4.14633,-11.2500,-9.873,54.109]], [ [-5.25276,-26.2183,-59.631,50.646], # N = 5 [-4.71537,-17.3569,-22.660,91.359], [-4.43422,-13.6078,-10.238,76.781]], [ [-5.51727,-29.9760,-75.222,202.253], # N = 6 [-4.98228,-20.3050,-25.224,132.03], [-4.70233,-16.1253,-9.836,94.272]], [ [-5.76537,-33.9165,-84.312,245.394], # N = 7 [-5.23299,-23.3328,-28.955,182.342], [-4.95405,-18.7352,-10.168,120.575]], [ [-6.00003,-37.8892,-96.428,335.92], # N = 8 [-5.46971,-26.4771,-31.034,220.165], [-5.19183,-21.4328,-10.726,157.955]], [ [-6.22288,-41.9496,-109.881,466.068], # N = 9 [-5.69447,-29.7152,-33.784,273.002], [-5.41738,-24.2882,-8.584,169.891]], [ [-6.43551,-46.1151,-120.814,566.823], # N = 10 [-5.90887,-33.0251,-37.208,346.189], [-5.63255,-27.2042,-6.792,177.666]], [ [-6.63894,-50.4287,-128.997,642.781], # N = 11 [-6.11404,-36.4610,-36.246,348.554], [-5.83850,-30.1995,-5.163,210.338]], [ [-6.83488,-54.7119,-139.800,736.376], # N = 12 [-6.31127,-39.9676,-37.021,406.051], [-6.03650,-33.2381,-6.606,317.776]]] tau_ct_2010 = asarray(tau_ct_2010) tau_ctt_2010 = [[ [-4.37113,-11.5882,-35.819,-334.047], # N = 1 [-3.83239,-5.9057,-12.490,-118.284], [-3.55326,-3.6596,-5.293,-63.559]], [ [-4.69276,-20.2284,-64.919,88.884], # N =2 [-4.15387,-13.3114,-28.402,72.741], [-3.87346,-10.4637,-17.408,66.313]], [ [-4.99071,-23.5873,-76.924,184.782], # N = 3 [-4.45311,-15.7732,-32.316,122.705], [-4.17280,-12.4909,-17.912,83.285]], [ [-5.26780,-27.2836,-78.971,137.871], # N = 4 [-4.73244,-18.4833,-31.875,111.817], [-4.45268,-14.7199,-17.969,101.92]], [ [-5.52826,-30.9051,-92.490,248.096], # N = 5 [-4.99491,-21.2360,-37.685,194.208], [-4.71587,-17.0820,-18.631,136.672]], [ [-5.77379,-34.7010,-105.937,393.991], # N = 6 [-5.24217,-24.2177,-39.153,232.528], [-4.96397,-19.6064,-18.858,174.919]], [ [-6.00609,-38.7383,-108.605,365.208], # N = 7 [-5.47664,-27.3005,-39.498,246.918], [-5.19921,-22.2617,-17.910,208.494]], [ [-6.22758,-42.7154,-119.622,421.395], # N = 8 [-5.69983,-30.4365,-44.300,345.48], [-5.42320,-24.9686,-19.688,274.462]], [ [-6.43933,-46.7581,-136.691,651.38], # N = 9 [-5.91298,-33.7584,-42.686,346.629], [-5.63704,-27.8965,-13.880,236.975]], [ [-6.64235,-50.9783,-145.462,752.228], # N = 10 [-6.11753,-37.056,-48.719,473.905], [-5.84215,-30.8119,-14.938,316.006]], [ [-6.83743,-55.2861,-152.651,792.577], # N = 11 [-6.31396,-40.5507,-46.771,487.185], [-6.03921,-33.8950,-9.122,285.164]], [ [-7.02582,-59.6037,-166.368,989.879], # N = 12 [-6.50353,-44.0797,-47.242,543.889], [-6.22941,-36.9673,-10.868,418.414]]] tau_ctt_2010 = asarray(tau_ctt_2010) def mackinnoncrit(N=1, regression ="c", nobs=inf): """ Returns the critical values for cointegrating and the ADF test. In 2010 MacKinnon updated the values of his 1994 paper with critical values for the augmented Dickey-Fuller tests. These new values are to be preferred and are used here. Parameters ---------- N : int The number of series of I(1) series for which the null of non-cointegration is being tested. For N > 12, the critical values are linearly interpolated (not yet implemented). For the ADF test, N = 1. reg : str {'c', 'tc', 'ctt', 'nc'} Following MacKinnon (1996), these stand for the type of regression run. 'c' for constant and no trend, 'tc' for constant with a linear trend, 'ctt' for constant with a linear and quadratic trend, and 'nc' for no constant. The values for the no constant case are taken from the 1996 paper, as they were not updated for 2010 due to the unrealistic assumptions that would underlie such a case. nobs : int or np.inf This is the sample size. If the sample size is numpy.inf, then the asymptotic critical values are returned. References ---------- MacKinnon, J.G. 1994 "Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests." Journal of Business & Economics Statistics, 12.2, 167-76. MacKinnon, J.G. 2010. "Critical Values for Cointegration Tests." Queen's University, Dept of Economics Working Papers 1227. http://ideas.repec.org/p/qed/wpaper/1227.html """ reg = regression if reg not in ['c','ct','nc','ctt']: raise ValueError("regression keyword %s not understood") % reg if nobs is inf: return eval("tau_"+reg+"_2010["+str(N-1)+",:,0]") else: return polyval(eval("tau_"+reg+"_2010["+str(N-1)+",:,::-1].T"),1./nobs) if __name__=="__main__": pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/api.py000066400000000000000000000010101224417117700224160ustar00rootroot00000000000000from .ar_model import AR from .arima_model import ARMA, ARIMA import vector_ar as var from .vector_ar.var_model import VAR from .vector_ar.svar_model import SVAR from .vector_ar.dynamic import DynamicVAR import filters import tsatools from .tsatools import (add_trend, detrend, lagmat, lagmat2ds, add_lag) import interp import stattools from .stattools import (adfuller, acovf, q_stat, acf, pacf_yw, pacf_ols, pacf, ccovf, ccf, periodogram, grangercausalitytests) from .base import datetools statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/ar_model.py000066400000000000000000001020471224417117700234430ustar00rootroot00000000000000from __future__ import division import numpy as np from numpy import (dot, identity, atleast_2d, atleast_1d, zeros) from numpy.linalg import inv from scipy import optimize from scipy.stats import t, norm, ss as sumofsq from statsmodels.regression.linear_model import OLS from statsmodels.tsa.tsatools import (lagmat, add_trend, _ar_transparams, _ar_invtransparams) import statsmodels.tsa.base.tsa_model as tsbase import statsmodels.base.model as base from statsmodels.tools.decorators import (resettable_cache, cache_readonly, cache_writable) from statsmodels.tools.compatibility import np_slogdet from statsmodels.tools.numdiff import (approx_fprime, approx_hess, approx_hess_cs) from statsmodels.tsa.kalmanf.kalmanfilter import KalmanFilter import statsmodels.base.wrapper as wrap from statsmodels.tsa.vector_ar import util from statsmodels.tsa.base.datetools import _index_date __all__ = ['AR'] def _check_ar_start(start, k_ar, method, dynamic): if (method == 'cmle' or dynamic) and start < k_ar: raise ValueError("Start must be >= k_ar for conditional MLE " "or dynamic forecast. Got %d" % start) def _validate(start, k_ar, dates, method): """ Checks the date and then returns an integer """ from datetime import datetime if isinstance(start, (basestring, datetime)): start_date = start start = _index_date(start, dates) if 'mle' not in method and start < k_ar: raise ValueError("Start must be >= k_ar for conditional MLE or " "dynamic forecast. Got %s" % start_date) return start def _ar_predict_out_of_sample(y, params, p, k_trend, steps, start=0): mu = params[:k_trend] or 0 # only have to worry about constant arparams = params[k_trend:][::-1] # reverse for dot # dynamic endogenous variable endog = np.zeros(p + steps) # this is one too big but doesn't matter if start: endog[:p] = y[start-p:start] else: endog[:p] = y[-p:] forecast = np.zeros(steps) for i in range(steps): fcast = mu + np.dot(arparams, endog[i:i+p]) forecast[i] = fcast endog[i + p] = fcast return forecast class AR(tsbase.TimeSeriesModel): __doc__ = tsbase._tsa_doc % {"model" : "Autoregressive AR(p) model", "params" : """endog : array-like 1-d endogenous response variable. The independent variable.""", "extra_params" : base._missing_param_doc, "extra_sections" : ""} def __init__(self, endog, dates=None, freq=None, missing='none'): super(AR, self).__init__(endog, None, dates, freq, missing=missing) endog = self.endog # original might not have been an ndarray if endog.ndim == 1: endog = endog[:,None] self.endog = endog # to get shapes right elif endog.ndim > 1 and endog.shape[1] != 1: raise ValueError("Only the univariate case is implemented") def initialize(self): pass def _transparams(self, params): """ Transforms params to induce stationarity/invertability. Reference --------- Jones(1980) """ p = self.k_ar k = self.k_trend newparams = params.copy() newparams[k:k+p] = _ar_transparams(params[k:k+p].copy()) return newparams def _invtransparams(self, start_params): """ Inverse of the Jones reparameterization """ p = self.k_ar k = self.k_trend newparams = start_params.copy() newparams[k:k+p] = _ar_invtransparams(start_params[k:k+p].copy()) return newparams def _presample_fit(self, params, start, p, end, y, predictedvalues): """ Return the pre-sample predicted values using the Kalman Filter Notes ----- See predict method for how to use start and p. """ k = self.k_trend # build system matrices T_mat = KalmanFilter.T(params, p, k, p) R_mat = KalmanFilter.R(params, p, k, 0, p) # Initial State mean and variance alpha = np.zeros((p,1)) Q_0 = dot(inv(identity(p**2)-np.kron(T_mat,T_mat)),dot(R_mat, R_mat.T).ravel('F')) Q_0 = Q_0.reshape(p,p, order='F') #TODO: order might need to be p+k P = Q_0 Z_mat = KalmanFilter.Z(p) for i in xrange(end): #iterate p-1 times to fit presample v_mat = y[i] - dot(Z_mat,alpha) F_mat = dot(dot(Z_mat, P), Z_mat.T) Finv = 1./F_mat # inv. always scalar K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # update state alpha = dot(T_mat, alpha) + dot(K,v_mat) L = T_mat - dot(K,Z_mat) P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T) # P[0,0] += 1 # for MA part, R_mat.R_mat.T above if i >= start-1: #only record if we ask for it predictedvalues[i+1-start] = dot(Z_mat,alpha) def _get_predict_start(self, start, dynamic): method = getattr(self, 'method', 'mle') k_ar = getattr(self, 'k_ar', 0) if start is None: if method == 'mle' and not dynamic: start = 0 else: # can't do presample fit for cmle or dynamic start = k_ar elif isinstance(start, int): start = super(AR, self)._get_predict_start(start) else: # should be a date start = _validate(start, k_ar, self.data.dates, method) start = super(AR, self)._get_predict_start(start) _check_ar_start(start, k_ar, method, dynamic) self._set_predict_start_date(start) return start def predict(self, params, start=None, end=None, dynamic=False): """ Returns in-sample and out-of-sample prediction. Parameters ---------- params : array The fitted model parameters. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. dynamic : bool The `dynamic` keyword affects in-sample prediction. If dynamic is False, then the in-sample lagged values are used for prediction. If `dynamic` is True, then in-sample forecasts are used in place of lagged dependent variables. The first forecasted value is `start`. Returns ------- predicted values : array Notes ----- The linear Gaussian Kalman filter is used to return pre-sample fitted values. The exact initial Kalman Filter is used. See Durbin and Koopman in the references for more information. """ # will return an index of a date start = self._get_predict_start(start, dynamic) end, out_of_sample = self._get_predict_end(end) if start - end > 1: raise ValueError("end is before start") k_ar = self.k_ar k_trend = self.k_trend method = self.method endog = self.endog.squeeze() if dynamic: out_of_sample += end - start + 1 return _ar_predict_out_of_sample(endog, params, k_ar, k_trend, out_of_sample, start) predictedvalues = np.zeros(end+1-start) # fit pre-sample if method == 'mle': # use Kalman Filter to get initial values if k_trend: mu = params[0]/(1-np.sum(params[k_trend:])) # modifies predictedvalues in place if start < k_ar: self._presample_fit(params, start, k_ar, min(k_ar-1, end), endog[:k_ar]-mu, predictedvalues) predictedvalues[:k_ar-start] += mu if end < k_ar: return predictedvalues # just do the whole thing and truncate fittedvalues = dot(self.X, params) pv_start = max(k_ar - start, 0) fv_start = max(start - k_ar, 0) fv_end = min(len(fittedvalues), end-k_ar+1) predictedvalues[pv_start:] = fittedvalues[fv_start:fv_end] if out_of_sample: forecastvalues = _ar_predict_out_of_sample(endog, params, k_ar, k_trend, out_of_sample) predictedvalues = np.r_[predictedvalues, forecastvalues] return predictedvalues def _presample_varcov(self, params): """ Returns the inverse of the presample variance-covariance. Notes ----- See Hamilton p. 125 """ k = self.k_trend p = self.k_ar p1 = p+1 # get inv(Vp) Hamilton 5.3.7 params0 = np.r_[-1, params[k:]] Vpinv = np.zeros((p,p), dtype=params.dtype) for i in range(1,p1): Vpinv[i-1,i-1:] = np.correlate(params0, params0[:i], old_behavior=False)[:-1] Vpinv[i-1,i-1:] -= np.correlate(params0[-i:], params0, old_behavior=False)[:-1] Vpinv = Vpinv + Vpinv.T - np.diag(Vpinv.diagonal()) return Vpinv def _loglike_css(self, params): """ Loglikelihood of AR(p) process using conditional sum of squares """ nobs = self.nobs Y = self.Y X = self.X ssr = sumofsq(Y.squeeze()-np.dot(X,params)) sigma2 = ssr/nobs return -nobs/2 * (np.log(2*np.pi) + np.log(sigma2)) -\ ssr/(2*sigma2) def _loglike_mle(self, params): """ Loglikelihood of AR(p) process using exact maximum likelihood """ nobs = self.nobs Y = self.Y X = self.X endog = self.endog k_ar = self.k_ar k_trend = self.k_trend # reparameterize according to Jones (1980) like in ARMA/Kalman Filter if self.transparams: params = self._transparams(params) # get mean and variance for pre-sample lags yp = endog[:k_ar].copy() if k_trend: c = [params[0]] * k_ar else: c = [0] mup = np.asarray(c/(1-np.sum(params[k_trend:]))) diffp = yp-mup[:,None] # get inv(Vp) Hamilton 5.3.7 Vpinv = self._presample_varcov(params) diffpVpinv = np.dot(np.dot(diffp.T,Vpinv),diffp).item() ssr = sumofsq(endog[k_ar:].squeeze() -np.dot(X,params)) # concentrating the likelihood means that sigma2 is given by sigma2 = 1./nobs * (diffpVpinv + ssr) self.sigma2 = sigma2 logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \ logdet + diffpVpinv/sigma2 + ssr/sigma2) return loglike def loglike(self, params): """ The loglikelihood of an AR(p) process Parameters ---------- params : array The fitted parameters of the AR model Returns ------- llf : float The loglikelihood evaluated at `params` Notes ----- Contains constant term. If the model is fit by OLS then this returns the conditonal maximum likelihood. .. math:: \\frac{\\left(n-p\\right)}{2}\\left(\\log\\left(2\\pi\\right)+\\log\\left(\\sigma^{2}\\right)\\right)-\\frac{1}{\\sigma^{2}}\\sum_{i}\\epsilon_{i}^{2} If it is fit by MLE then the (exact) unconditional maximum likelihood is returned. .. math:: -\\frac{n}{2}log\\left(2\\pi\\right)-\\frac{n}{2}\\log\\left(\\sigma^{2}\\right)+\\frac{1}{2}\\left|V_{p}^{-1}\\right|-\\frac{1}{2\\sigma^{2}}\\left(y_{p}-\\mu_{p}\\right)^{\\prime}V_{p}^{-1}\\left(y_{p}-\\mu_{p}\\right)-\\frac{1}{2\\sigma^{2}}\\sum_{t=p+1}^{n}\\epsilon_{i}^{2} where :math:`\\mu_{p}` is a (`p` x 1) vector with each element equal to the mean of the AR process and :math:`\\sigma^{2}V_{p}` is the (`p` x `p`) variance-covariance matrix of the first `p` observations. """ #TODO: Math is on Hamilton ~pp 124-5 if self.method == "cmle": return self._loglike_css(params) else: return self._loglike_mle(params) def score(self, params): """ Return the gradient of the loglikelihood at params. Parameters ---------- params : array-like The parameter values at which to evaluate the score function. Notes ----- Returns numerical gradient. """ loglike = self.loglike return approx_fprime(params, loglike, epsilon=1e-8) def information(self, params): """ Not Implemented Yet """ return def hessian(self, params): """ Returns numerical hessian for now. """ loglike = self.loglike return approx_hess(params, loglike) def _stackX(self, k_ar, trend): """ Private method to build the RHS matrix for estimation. Columns are trend terms then lags. """ endog = self.endog X = lagmat(endog, maxlag=k_ar, trim='both') k_trend = util.get_trendorder(trend) if k_trend: X = add_trend(X, prepend=True, trend=trend) self.k_trend = k_trend return X def select_order(self, maxlag, ic, trend='c', method='mle'): """ Select the lag order according to the information criterion. Parameters ---------- maxlag : int The highest lag length tried. See `AR.fit`. ic : str {'aic','bic','hqic','t-stat'} Criterion used for selecting the optimal lag length. See `AR.fit`. trend : str {'c','nc'} Whether to include a constant or not. 'c' - include constant. 'nc' - no constant. Returns ------- bestlag : int Best lag according to IC. """ endog = self.endog # make Y and X with same nobs to compare ICs Y = endog[maxlag:] self.Y = Y # attach to get correct fit stats X = self._stackX(maxlag, trend) # sets k_trend self.X = X k = self.k_trend # k_trend set in _stackX k = max(1,k) # handle if startlag is 0 results = {} if ic != 't-stat': for lag in range(k,maxlag+1): # have to reinstantiate the model to keep comparable models endog_tmp = endog[maxlag-lag:] fit = AR(endog_tmp).fit(maxlag=lag, method=method, full_output=0, trend=trend, maxiter=100, disp=0) results[lag] = eval('fit.'+ic) bestic, bestlag = min((res, k) for k,res in results.iteritems()) else: # choose by last t-stat. stop = 1.6448536269514722 # for t-stat, norm.ppf(.95) for lag in range(maxlag,k-1,-1): # have to reinstantiate the model to keep comparable models endog_tmp = endog[maxlag-lag:] fit = AR(endog_tmp).fit(maxlag=lag, method=method, full_output=0, trend=trend, maxiter=35, disp=-1) if np.abs(fit.tvalues[-1]) >= stop: bestlag = lag break return bestlag def fit(self, maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver=None, maxiter=35, full_output=1, disp=1, callback=None, **kwargs): """ Fit the unconditional maximum likelihood of an AR(p) process. Parameters ---------- maxlag : int If `ic` is None, then maxlag is the lag length used in fit. If `ic` is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(nobs/100.)**(1/4.)) method : str {'cmle', 'mle'}, optional cmle - Conditional maximum likelihood using OLS mle - Unconditional (exact) maximum likelihood. See `solver` and the Notes. ic : str {'aic','bic','hic','t-stat'} Criterion used for selecting the optimal lag length. aic - Akaike Information Criterion bic - Bayes Information Criterion t-stat - Based on last lag hqic - Hannan-Quinn Information Criterion If any of the information criteria are selected, the lag length which results in the lowest value is selected. If t-stat, the model starts with maxlag and drops a lag until the highest lag has a t-stat that is significant at the 95 % level. trend : str {'c','nc'} Whether to include a constant or not. 'c' - include constant. 'nc' - no constant. The below can be specified if method is 'mle' transparams : bool, optional Whether or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). start_params : array-like, optional A first guess on the parameters. Default is cmle estimates. solver : str or None, optional Solver to be used. The default is 'l_bfgs' (limited memory Broyden- Fletcher-Goldfarb-Shanno). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient), and 'powell'. The limited memory BFGS uses m=30 to approximate the Hessian, projected gradient tolerance of 1e-7 and factr = 1e3. These cannot currently be changed for l_bfgs. See notes for more information. maxiter : int, optional The maximum number of function evaluations. Default is 35. tol : float The convergence tolerance. Default is 1e-08. full_output : bool, optional If True, all output from solver will be available in the Results object's mle_retvals attribute. Output is dependent on the solver. See Notes for more information. disp : bool, optional If True, convergence information is output. callback : function, optional Called after each iteration as callback(xk) where xk is the current parameter vector. kwargs See Notes for keyword arguments that can be passed to fit. References ---------- Jones, R.H. 1980 "Maximum likelihood fitting of ARMA models to time series with missing observations." `Technometrics`. 22.3. 389-95. See also -------- statsmodels.base.model.LikelihoodModel.fit : for more information on using the solvers. """ method = method.lower() if method not in ['cmle','yw','mle']: raise ValueError("Method %s not recognized" % method) self.method = method self.trend = trend self.transparams = transparams nobs = len(self.endog) # overwritten if method is 'cmle' endog = self.endog if maxlag is None: maxlag = int(round(12*(nobs/100.)**(1/4.))) k_ar = maxlag # stays this if ic is None # select lag length if ic is not None: ic = ic.lower() if ic not in ['aic','bic','hqic','t-stat']: raise ValueError("ic option %s not understood" % ic) k_ar = self.select_order(k_ar, ic, trend, method) self.k_ar = k_ar # change to what was chosen by ic # redo estimation for best lag # make LHS Y = endog[k_ar:,:] # make lagged RHS X = self._stackX(k_ar, trend) # sets self.k_trend k_trend = self.k_trend k = k_trend self.exog_names = util.make_lag_names(self.endog_names, k_ar, k_trend) self.Y = Y self.X = X if solver: solver = solver.lower() if method == "cmle": # do OLS arfit = OLS(Y,X).fit() params = arfit.params self.nobs = nobs - k_ar self.sigma2 = arfit.ssr/arfit.nobs #needed for predict fcasterr if method == "mle": self.nobs = nobs if start_params is None: start_params = OLS(Y,X).fit().params else: if len(start_params) != k_trend + k_ar: raise ValueError("Length of start params is %d. There" " are %d parameters." % (len(start_params), k_trend + k_ar)) start_params = self._invtransparams(start_params) loglike = lambda params : -self.loglike(params) if solver == None: # use limited memory bfgs bounds = [(None,)*2]*(k_ar+k) mlefit = optimize.fmin_l_bfgs_b(loglike, start_params, approx_grad=True, m=12, pgtol=1e-8, factr=1e2, bounds=bounds, iprint=disp) self.mlefit = mlefit params = mlefit[0] else: mlefit = super(AR, self).fit(start_params=start_params, method=solver, maxiter=maxiter, full_output=full_output, disp=disp, callback = callback, **kwargs) self.mlefit = mlefit params = mlefit.params if self.transparams: params = self._transparams(params) self.transparams = False # turn off now for other results # don't use yw, because we can't estimate the constant #elif method == "yw": # params, omega = yule_walker(endog, order=maxlag, # method="mle", demean=False) # how to handle inference after Yule-Walker? # self.params = params #TODO: don't attach here # self.omega = omega pinv_exog = np.linalg.pinv(X) normalized_cov_params = np.dot(pinv_exog, pinv_exog.T) arfit = ARResults(self, params, normalized_cov_params) return ARResultsWrapper(arfit) class ARResults(tsbase.TimeSeriesModelResults): """ Class to hold results from fitting an AR model. Parameters ---------- model : AR Model instance Reference to the model that is fit. params : array The fitted parameters from the AR Model. normalized_cov_params : array inv(dot(X.T,X)) where X is the lagged values. scale : float, optional An estimate of the scale of the model. Returns ------- **Attributes** aic : float Akaike Information Criterion using Lutkephol's definition. :math:`log(sigma) + 2*(1 + k_ar + k_trend)/nobs` bic : float Bayes Information Criterion :math:`\\log(\\sigma) + (1 + k_ar + k_trend)*\\log(nobs)/nobs` bse : array The standard errors of the estimated parameters. If `method` is 'cmle', then the standard errors that are returned are the OLS standard errors of the coefficients. If the `method` is 'mle' then they are computed using the numerical Hessian. fittedvalues : array The in-sample predicted values of the fitted AR model. The `k_ar` initial values are computed via the Kalman Filter if the model is fit by `mle`. fpe : float Final prediction error using Lutkepohl's definition ((n_totobs+k_trend)/(n_totobs-k_ar-k_trend))*sigma hqic : float Hannan-Quinn Information Criterion. k_ar : float Lag length. Sometimes used as `p` in the docs. k_trend : float The number of trend terms included. 'nc'=0, 'c'=1. llf : float The loglikelihood of the model evaluated at `params`. See `AR.loglike` model : AR model instance A reference to the fitted AR model. nobs : float The number of available observations `nobs` - `k_ar` n_totobs : float The number of total observations in `endog`. Sometimes `n` in the docs. params : array The fitted parameters of the model. pvalues : array The p values associated with the standard errors. resid : array The residuals of the model. If the model is fit by 'mle' then the pre-sample residuals are calculated using fittedvalues from the Kalman Filter. roots : array The roots of the AR process are the solution to (1 - arparams[0]*z - arparams[1]*z**2 -...- arparams[p-1]*z**k_ar) = 0 Stability requires that the roots in modulus lie outside the unit circle. scale : float Same as sigma2 sigma2 : float The variance of the innovations (residuals). trendorder : int The polynomial order of the trend. 'nc' = None, 'c' or 't' = 0, 'ct' = 1, etc. tvalues : array The t-values associated with `params`. """ _cache = {} # for scale setter def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(ARResults, self).__init__(model, params, normalized_cov_params, scale) self._cache = resettable_cache() self.nobs = model.nobs n_totobs = len(model.endog) self.n_totobs = n_totobs self.X = model.X # copy? self.Y = model.Y k_ar = model.k_ar self.k_ar = k_ar k_trend = model.k_trend self.k_trend = k_trend trendorder = None if k_trend > 0: trendorder = k_trend - 1 self.trendorder = 1 #TODO: cmle vs mle? self.df_model = k_ar + k_trend self.df_resid = self.model.df_resid = n_totobs - self.df_model @cache_writable() def sigma2(self): model = self.model if model.method == "cmle": # do DOF correction return 1./self.nobs * sumofsq(self.resid) else: return self.model.sigma2 @cache_writable() # for compatability with RegressionResults def scale(self): return self.sigma2 @cache_readonly def bse(self): # allow user to specify? if self.model.method == "cmle": # uses different scale/sigma definition resid = self.resid ssr = np.dot(resid,resid) ols_scale = ssr/(self.nobs - self.k_ar - self.k_trend) return np.sqrt(np.diag(self.cov_params(scale=ols_scale))) else: hess = approx_hess(self.params, self.model.loglike) return np.sqrt(np.diag(-np.linalg.inv(hess))) @cache_readonly def pvalues(self): return norm.sf(np.abs(self.tvalues))*2 @cache_readonly def aic(self): #JP: this is based on loglike with dropped constant terms ? # Lutkepohl #return np.log(self.sigma2) + 1./self.model.nobs * self.k_ar # Include constant as estimated free parameter and double the loss return np.log(self.sigma2) + 2 * (1 + self.df_model)/self.nobs # Stata defintion #nobs = self.nobs #return -2 * self.llf/nobs + 2 * (self.k_ar+self.k_trend)/nobs @cache_readonly def hqic(self): nobs = self.nobs # Lutkepohl # return np.log(self.sigma2)+ 2 * np.log(np.log(nobs))/nobs * self.k_ar # R uses all estimated parameters rather than just lags return np.log(self.sigma2) + 2 * np.log(np.log(nobs))/nobs * \ (1 + self.df_model) # Stata #nobs = self.nobs #return -2 * self.llf/nobs + 2 * np.log(np.log(nobs))/nobs * \ # (self.k_ar + self.k_trend) @cache_readonly def fpe(self): nobs = self.nobs df_model = self.df_model #Lutkepohl return ((nobs+df_model)/(nobs-df_model))*self.sigma2 @cache_readonly def bic(self): nobs = self.nobs # Lutkepohl #return np.log(self.sigma2) + np.log(nobs)/nobs * self.k_ar # Include constant as est. free parameter return np.log(self.sigma2) + (1 + self.df_model) * np.log(nobs)/nobs # Stata # return -2 * self.llf/nobs + np.log(nobs)/nobs * (self.k_ar + \ # self.k_trend) @cache_readonly def resid(self): #NOTE: uses fittedvalues because it calculate presample values for mle model = self.model endog = model.endog.squeeze() if model.method == "cmle": # elimate pre-sample return endog[self.k_ar:] - self.fittedvalues else: return model.endog.squeeze() - self.fittedvalues #def ssr(self): # resid = self.resid # return np.dot(resid, resid) @cache_readonly def roots(self): k = self.k_trend return np.roots(np.r_[1, -self.params[k:]]) ** -1 @cache_readonly def fittedvalues(self): return self.model.predict(self.params) def predict(self, start=None, end=None, dynamic=False): params = self.params predictedvalues = self.model.predict(params, start, end, dynamic) return predictedvalues #start = self.model._get_predict_start(start) #end, out_of_sample = self.model._get_predict_end(end) ##TODO: return forecast errors and confidence intervals #from statsmodels.tsa.arima_process import arma2ma #ma_rep = arma2ma(np.r_[1,-params[::-1]], [1], out_of_sample) #fcasterr = np.sqrt(self.sigma2 * np.cumsum(ma_rep**2)) preddoc = AR.predict.__doc__.split('\n') extra_doc = """ confint : bool, float Whether to return confidence intervals. If `confint` == True, 95 % confidence intervals are returned. Else if `confint` is a float, then it is assumed to be the alpha value of the confidence interval. That is confint == .05 returns a 95% confidence interval, and .10 would return a 90% confidence interval.""".split('\n') #ret_doc = """ # fcasterr : array-like # confint : array-like #""" predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc + preddoc[20:]) class ARResultsWrapper(wrap.ResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(ARResultsWrapper, ARResults) if __name__ == "__main__": import statsmodels.api as sm sunspots = sm.datasets.sunspots.load() # Why does R demean the data by defaut? ar_ols = AR(sunspots.endog) res_ols = ar_ols.fit(maxlag=9) ar_mle = AR(sunspots.endog) res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs", maxiter=500, gtol=1e-10) # res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True, # tol=1e-13) # ar_yw = AR(sunspots.endog) # res_yw = ar_yw.fit(maxlag=4, method="yw") # # Timings versus talkbox # from timeit import default_timer as timer # print "Time AR fit vs. talkbox" # # generate a long series of AR(2) data # # nobs = 1000000 # y = np.empty(nobs) # y[0:2] = 0 # for i in range(2,nobs): # y[i] = .25 * y[i-1] - .75 * y[i-2] + np.random.rand() # # mod_sm = AR(y) # t = timer() # res_sm = mod_sm.fit(method="yw", trend="nc", demean=False, maxlag=2) # t_end = timer() # print str(t_end - t) + " seconds for sm.AR with yule-walker, 2 lags" # try: # import scikits.talkbox as tb # except: # raise ImportError("You need scikits.talkbox installed for timings") # t = timer() # mod_tb = tb.lpc(y, 2) # t_end = timer() # print str(t_end - t) + " seconds for talkbox.lpc" # print """For higher lag lengths ours quickly fills up memory and starts #thrashing the swap. Should we include talkbox C code or Cythonize the #Levinson recursion algorithm?""" ## Try with a pandas series import pandas import scikits.timeseries as ts d1 = ts.Date(year=1700, freq='A') #NOTE: have to have yearBegin offset for annual data until parser rewrite #should this be up to the user, or should it be done in TSM init? #NOTE: not anymore, it's end of year now ts_dr = ts.date_array(start_date=d1, length=len(sunspots.endog)) pandas_dr = pandas.DateRange(start=d1.datetime, periods=len(sunspots.endog), timeRule='A@DEC') #pandas_dr = pandas_dr.shift(-1, pandas.datetools.yearBegin) dates = np.arange(1700,1700+len(sunspots.endog)) dates = ts.date_array(dates, freq='A') #sunspots = pandas.TimeSeries(sunspots.endog, index=dates) #NOTE: pandas only does business days for dates it looks like import datetime dt_dates = np.asarray(map(datetime.datetime.fromordinal, ts_dr.toordinal().astype(int))) sunspots = pandas.TimeSeries(sunspots.endog, index=dt_dates) #NOTE: pandas can't handle pre-1900 dates mod = AR(sunspots, freq='A') res = mod.fit(method='mle', maxlag=9) # some data for an example in Box Jenkins IBM = np.asarray([460,457,452,459,462,459,463,479,493,490.]) w = np.diff(IBM) theta = .5 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/arima_model.py000066400000000000000000002040651224417117700241350ustar00rootroot00000000000000from __future__ import absolute_import # for 2to3 with extensions from datetime import datetime import numpy as np from scipy import optimize from scipy.stats import t, norm from scipy.signal import lfilter from numpy import (dot, identity, kron, log, zeros, pi, exp, eye, abs, empty, zeros_like) from numpy.linalg import inv, pinv from statsmodels.tools.decorators import (cache_readonly, cache_writable, resettable_cache) import statsmodels.base.model as base import statsmodels.tsa.base.tsa_model as tsbase import statsmodels.base.wrapper as wrap from statsmodels.regression.linear_model import yule_walker, GLS from statsmodels.tsa.tsatools import (lagmat, add_trend, _ar_transparams, _ar_invtransparams, _ma_transparams, _ma_invtransparams) from statsmodels.tsa.vector_ar import util from statsmodels.tsa.ar_model import AR from statsmodels.tsa.arima_process import arma2ma from statsmodels.tools.numdiff import (approx_fprime, approx_fprime_cs, approx_hess_cs) from statsmodels.tsa.base.datetools import _index_date from statsmodels.tsa.kalmanf import KalmanFilter from .kalmanf import kalman_loglike _armax_notes = """ Notes ----- If exogenous variables are given, then the model that is fit is .. math:: \\phi(L)(y_t - X_t\\beta) = \\theta(L)\epsilon_t where :math:`\\phi` and :math:`\\theta` are polynomials in the lag operator, :math:`L`. This is the regression model with ARMA errors, or ARMAX model. This specification is used, whether or not the model is fit using conditional sum of square or maximum-likelihood, using the `method` argument in :meth:`statsmodels.tsa.arima_model.%(Model)s.fit`. Therefore, for now, `css` and `mle` refer to estimation methods only. This may change for the case of the `css` model in future versions. """ _arma_params = """\ endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. Though optional, the order keyword in fit is deprecated and it is recommended to give order here. exog : array-like, optional An optional arry of exogenous variables. This should *not* include a constant or trend. You can specify this in the `fit` method.""" _arma_model = "Autoregressive Moving Average ARMA(p,q) Model" _arima_model = "Autoregressive Integrated Moving Average ARIMA(p,d,q) Model" _arima_params = """\ endog : array-like The endogenous variable. order : iterable The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should *not* include a constant or trend. You can specify this in the `fit` method.""" _predict_notes = """ Notes ----- Use the results predict method instead. """ _results_notes = """ Notes ----- It is recommended to use dates with the time-series models, as the below will probably make clear. However, if ARIMA is used without dates and/or `start` and `end` are given as indices, then these indices are in terms of the *original*, undifferenced series. Ie., given some undifferenced observations:: 1970Q1, 1 1970Q2, 1.5 1970Q3, 1.25 1970Q4, 2.25 1971Q1, 1.2 1971Q2, 4.1 1970Q1 is observation 0 in the original series. However, if we fit an ARIMA(p,1,q) model then we lose this first observation through differencing. Therefore, the first observation we can forecast (if using exact MLE) is index 1. In the differenced series this is index 0, but we refer to it as 1 from the original series. """ _predict = """ %(Model)s model in-sample and out-of-sample prediction Parameters ---------- %(params)s start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. exog : array-like, optional If the model is an ARMAX and out-of-sample forecasting is requested, exog must be given. Note that you'll need to pass `k_ar` additional lags for any exogenous variables. E.g., if you fit an ARMAX(2, q) model and want to predict 5 steps, you need 7 observations to do this. dynamic : bool, optional The `dynamic` keyword affects in-sample prediction. If dynamic is False, then the in-sample lagged values are used for prediction. If `dynamic` is True, then in-sample forecasts are used in place of lagged dependent variables. The first forecasted value is `start`. %(extra_params)s Returns ------- predict : array The predicted values. %(extra_section)s """ _arma_predict = _predict % {"Model" : "ARMA", "params" : """ params : array-like The fitted parameters of the model.""", "extra_params" : "", "extra_section" : _predict_notes} _arma_results_predict = _predict % {"Model" : "ARMA", "params" : "", "extra_params" : "", "extra_section" : _results_notes} _arima_predict = _predict % {"Model" : "ARIMA", "params" : """ params : array-like The fitted parameters of the model.""", "extra_params" : """ typ : str {'linear', 'levels'} - 'linear' : Linear prediction in terms of the differenced endogenous variables. - 'levels' : Predict the levels of the original endogenous variables.""", "extra_section" : _predict_notes} _arima_results_predict = _predict % {"Model" : "ARIMA", "params" : "", "extra_params" : """typ : str {'linear', 'levels'} - 'linear' : Linear prediction in terms of the differenced endogenous variables. - 'levels' : Predict the levels of the original endogenous variables. """, "extra_section" : _results_notes} def _check_arima_start(start, k_ar, k_diff, method, dynamic): if start < 0: raise ValueError("The start index %d of the original series " "has been differenced away" % start) elif (dynamic or 'mle' not in method) and start < k_ar: raise ValueError("Start must be >= k_ar for conditional MLE " "or dynamic forecast. Got %d" % start) def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors, trendparam, exparams, arparams, maparams, steps, method, exog=None): """ Returns endog, resid, mu of appropriate length for out of sample prediction. """ if q: resid = np.zeros(q) if start and 'mle' in method or (start == p and not start == 0): resid[:q] = errors[start-q:start] elif start: resid[:q] = errors[start-q-p:start-p] else: resid[:q] = errors[-q:] else: resid = None y = endog if k_trend == 1: # use expectation not constant if k_exog > 0: #TODO: technically should only hold for MLE not # conditional model. See #274. # ensure 2-d for conformability if np.ndim(exog) == 1 and k_exog == 1: # have a 1d series of observations -> 2d exog = exog[:, None] elif np.ndim(exog) == 1: # should have a 1d row of exog -> 2d if len(exog) != k_exog: raise ValueError("1d exog given and len(exog) != k_exog") exog = exog[None, :] X = lagmat(np.dot(exog, exparams), p, original='in', trim='both') mu = trendparam * (1 - arparams.sum()) # arparams were reversed in unpack for ease later mu = mu + (np.r_[1, -arparams[::-1]]*X).sum(1)[:,None] else: mu = trendparam * (1 - arparams.sum()) mu = np.array([mu]*steps) elif k_exog > 0: X = np.dot(exog, exparams) #NOTE: you shouldn't have to give in-sample exog! X = lagmat(X, p, original='in', trim='both') mu = (np.r_[1, -arparams[::-1]]*X).sum(1)[:,None] else: mu = np.zeros(steps) endog = np.zeros(p + steps - 1) if p and start: endog[:p] = y[start-p:start] elif p: endog[:p] = y[-p:] return endog, resid, mu def _arma_predict_out_of_sample(params, steps, errors, p, q, k_trend, k_exog, endog, exog=None, start=0, method='mle'): (trendparam, exparams, arparams, maparams) = _unpack_params(params, (p,q), k_trend, k_exog, reverse=True) endog, resid, mu = _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors, trendparam, exparams, arparams, maparams, steps, method, exog) forecast = np.zeros(steps) if steps == 1: if q: return mu[0] + np.dot(arparams, endog[:p]) + np.dot(maparams, resid[:q]) else: return mu[0] + np.dot(arparams, endog[:p]) if q: i = 0 # if q == 1 else: i = -1 for i in range(min(q,steps-1)): fcast = mu[i] + np.dot(arparams,endog[i:i+p]) + \ np.dot(maparams[:q-i],resid[i:i+q]) forecast[i] = fcast endog[i+p] = fcast for i in range(i+1,steps-1): fcast = mu[i] + np.dot(arparams,endog[i:i+p]) forecast[i] = fcast endog[i+p] = fcast #need to do one more without updating endog forecast[-1] = mu[-1] + np.dot(arparams,endog[steps-1:]) return forecast def _arma_predict_in_sample(start, end, endog, resid, k_ar, method): """ Pre- and in-sample fitting for ARMA. """ if 'mle' in method: fittedvalues = endog - resid #get them all then trim elif k_ar > 0: fittedvalues = endog[k_ar:] - resid fv_start = start if 'mle' not in method: fv_start -= k_ar # start is in terms of endog index predictedvalues = np.zeros(end + 1 - fv_start) fv_end = min(len(fittedvalues), end + 1) return fittedvalues[fv_start:fv_end] def _validate(start, k_ar, k_diff, dates, method): if isinstance(start, (basestring, datetime)): start_date = start start = _index_date(start, dates) start -= k_diff if 'mle' not in method and start < k_ar - k_diff: raise ValueError("Start must be >= k_ar for conditional " "MLE or dynamic forecast. Got %s" % start) return start def _unpack_params(params, order, k_trend, k_exog, reverse=False): p, q = order k = k_trend + k_exog maparams = params[k+p:] arparams = params[k:k+p] trend = params[:k_trend] exparams = params[k_trend:k] if reverse: return trend, exparams, arparams[::-1], maparams[::-1] return trend, exparams, arparams, maparams def _unpack_order(order): k_ar, k_ma, k = order k_lags = max(k_ar, k_ma+1) return k_ar, k_ma, order, k_lags def _make_arma_names(data, k_trend, order, exog_names): k_ar, k_ma = order exog_names = exog_names or [] ar_lag_names = util.make_lag_names([data.ynames], k_ar, 0) ar_lag_names = [''.join(('ar.', i)) for i in ar_lag_names] ma_lag_names = util.make_lag_names([data.ynames], k_ma, 0) ma_lag_names = [''.join(('ma.', i)) for i in ma_lag_names] trend_name = util.make_lag_names('', 0, k_trend) exog_names = trend_name + exog_names + ar_lag_names + ma_lag_names return exog_names def _make_arma_exog(endog, exog, trend): k_trend = 1 # overwritten if no constant if exog is None and trend == 'c': # constant only exog = np.ones((len(endog),1)) elif exog is not None and trend == 'c': # constant plus exogenous exog = add_trend(exog, trend='c', prepend=True) elif exog is not None and trend == 'nc': # make sure it's not holding constant from last run if exog.var() == 0: exog = None k_trend = 0 if trend == 'nc': k_trend = 0 return k_trend, exog def _check_estimable(nobs, n_params): if nobs <= n_params: raise ValueError("Insufficient degrees of freedom to estimate") class ARMA(tsbase.TimeSeriesModel): __doc__ = tsbase._tsa_doc % {"model" : _arma_model, "params" : _arma_params, "extra_params" : "", "extra_sections" : _armax_notes % {"Model" : "ARMA"}} def __init__(self, endog, order=None, exog=None, dates=None, freq=None, missing='none'): super(ARMA, self).__init__(endog, exog, dates, freq) exog = self.data.exog # get it after it's gone through processing if order is None: import warnings warnings.warn("In the next release order will not be optional " "in the model constructor.", FutureWarning) else: _check_estimable(len(self.endog), sum(order)) self.k_ar = k_ar = order[0] self.k_ma = k_ma = order[1] self.k_lags = k_lags = max(k_ar,k_ma+1) if exog is not None: if exog.ndim == 1: exog = exog[:,None] k_exog = exog.shape[1] # number of exog. variables excl. const else: k_exog = 0 self.k_exog = k_exog def _fit_start_params_hr(self, order): """ Get starting parameters for fit. Parameters ---------- order : iterable (p,q,k) - AR lags, MA lags, and number of exogenous variables including the constant. Returns ------- start_params : array A first guess at the starting parameters. Notes ----- If necessary, fits an AR process with the laglength selected according to best BIC. Obtain the residuals. Then fit an ARMA(p,q) model via OLS using these residuals for a first approximation. Uses a separate OLS regression to find the coefficients of exogenous variables. References ---------- Hannan, E.J. and Rissanen, J. 1982. "Recursive estimation of mixed autoregressive-moving average order." `Biometrika`. 69.1. """ p,q,k = order start_params = zeros((p+q+k)) endog = self.endog.copy() # copy because overwritten exog = self.exog if k != 0: ols_params = GLS(endog, exog).fit().params start_params[:k] = ols_params endog -= np.dot(exog, ols_params).squeeze() if q != 0: if p != 0: # make sure we don't run into small data problems in AR fit nobs = len(endog) maxlag = int(round(12*(nobs/100.)**(1/4.))) if maxlag >= nobs: maxlag = nobs - 1 armod = AR(endog).fit(ic='bic', trend='nc', maxlag=maxlag) arcoefs_tmp = armod.params p_tmp = armod.k_ar # it's possible in small samples that optimal lag-order # doesn't leave enough obs. No consistent way to fix. if p_tmp + q >= len(endog): raise ValueError("Proper starting parameters cannot" " be found for this order with this number " "of observations. Use the start_params " "argument.") resid = endog[p_tmp:] - np.dot(lagmat(endog, p_tmp, trim='both'), arcoefs_tmp) if p < p_tmp + q: endog_start = p_tmp + q - p resid_start = 0 else: endog_start = 0 resid_start = p - p_tmp - q lag_endog = lagmat(endog, p, 'both')[endog_start:] lag_resid = lagmat(resid, q, 'both')[resid_start:] # stack ar lags and resids X = np.column_stack((lag_endog, lag_resid)) coefs = GLS(endog[max(p_tmp+q,p):], X).fit().params start_params[k:k+p+q] = coefs else: start_params[k+p:k+p+q] = yule_walker(endog, order=q)[0] if q == 0 and p != 0: arcoefs = yule_walker(endog, order=p)[0] start_params[k:k+p] = arcoefs # check AR coefficients if p and not np.all(np.abs(np.roots(np.r_[1, -start_params[k:k+p]])) < 1): raise ValueError("The computed initial AR coefficients are not " "stationary\nYou should induce stationarity, " "choose a different model order, or you can\n" "pass your own start_params.") # check MA coefficients elif q and not np.all(np.abs(np.roots(np.r_[1, start_params[k+p:]])) < 1): raise ValueError("The computed initial MA coefficients are not " "invertible\nYou should induce invertibility, " "choose a different model order, or you can\n" "pass your own start_params.") # check MA coefficients return start_params def _fit_start_params(self, order, method): if method != 'css-mle': # use Hannan-Rissanen to get start params start_params = self._fit_start_params_hr(order) else: # use CSS to get start params func = lambda params: -self.loglike_css(params) #start_params = [.1]*(k_ar+k_ma+k_exog) # different one for k? start_params = self._fit_start_params_hr(order) if self.transparams: start_params = self._invtransparams(start_params) bounds = [(None,)*2]*sum(order) mlefit = optimize.fmin_l_bfgs_b(func, start_params, approx_grad=True, m=12, pgtol=1e-7, factr=1e3, bounds = bounds, iprint=-1) start_params = self._transparams(mlefit[0]) return start_params def score(self, params): """ Compute the score function at params. Notes ----- This is a numerical approximation. """ loglike = self.loglike #if self.transparams: # params = self._invtransparams(params) #return approx_fprime(params, loglike, epsilon=1e-5) return approx_fprime_cs(params, loglike) def hessian(self, params): """ Compute the Hessian at params, Notes ----- This is a numerical approximation. """ loglike = self.loglike #if self.transparams: # params = self._invtransparams(params) return approx_hess_cs(params, loglike) def _transparams(self, params): """ Transforms params to induce stationarity/invertability. Reference --------- Jones(1980) """ k_ar, k_ma = self.k_ar, self.k_ma k = self.k_exog + self.k_trend newparams = np.zeros_like(params) # just copy exogenous parameters if k != 0: newparams[:k] = params[:k] # AR Coeffs if k_ar != 0: newparams[k:k+k_ar] = _ar_transparams(params[k:k+k_ar].copy()) # MA Coeffs if k_ma != 0: newparams[k+k_ar:] = _ma_transparams(params[k+k_ar:].copy()) return newparams def _invtransparams(self, start_params): """ Inverse of the Jones reparameterization """ k_ar, k_ma = self.k_ar, self.k_ma k = self.k_exog + self.k_trend newparams = start_params.copy() arcoefs = newparams[k:k+k_ar] macoefs = newparams[k+k_ar:] # AR coeffs if k_ar != 0: newparams[k:k+k_ar] = _ar_invtransparams(arcoefs) # MA coeffs if k_ma != 0: newparams[k+k_ar:k+k_ar+k_ma] = _ma_invtransparams(macoefs) return newparams def _get_predict_start(self, start, dynamic): # do some defaults method = getattr(self, 'method', 'mle') k_ar = getattr(self, 'k_ar', 0) k_diff = getattr(self, 'k_diff', 0) if start is None: if 'mle' in method and not dynamic: start = 0 else: start = k_ar self._set_predict_start_date(start) # else it's done in super elif isinstance(start, int): start = super(ARMA, self)._get_predict_start(start) else: # should be on a date #elif 'mle' not in method or dynamic: # should be on a date start = _validate(start, k_ar, k_diff, self.data.dates, method) start = super(ARMA, self)._get_predict_start(start) _check_arima_start(start, k_ar, k_diff, method, dynamic) return start def _get_predict_end(self, end, dynamic=False): # pass through so predict works for ARIMA and ARMA return super(ARMA, self)._get_predict_end(end) def geterrors(self, params): """ Get the errors of the ARMA process. Parameters ---------- params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend """ #start = self._get_predict_start(start) # will be an index of a date #end, out_of_sample = self._get_predict_end(end) params = np.asarray(params) k_ar, k_ma = self.k_ar, self.k_ma k = self.k_exog + self.k_trend method = getattr(self, 'method', 'mle') if 'mle' in method: # use KalmanFilter to get errors (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat, T_mat, paramsdtype) = KalmanFilter._init_kalman_state(params, self) errors = KalmanFilter.geterrors(y,k,k_ar,k_ma, k_lags, nobs, Z_mat, m, R_mat, T_mat, paramsdtype) if isinstance(errors, tuple): errors = errors[0] # non-cython version returns a tuple else: # use scipy.signal.lfilter y = self.endog.copy() k = self.k_exog + self.k_trend if k > 0: y -= dot(self.exog, params[:k]) k_ar = self.k_ar k_ma = self.k_ma (trendparams, exparams, arparams, maparams) = _unpack_params(params, (k_ar, k_ma), self.k_trend, self.k_exog, reverse=False) b,a = np.r_[1,-arparams], np.r_[1,maparams] zi = zeros((max(k_ar, k_ma))) for i in range(k_ar): zi[i] = sum(-b[:i+1][::-1]*y[:i+1]) e = lfilter(b,a,y,zi=zi) errors = e[0][k_ar:] return errors.squeeze() def predict(self, params, start=None, end=None, exog=None, dynamic=False): method = getattr(self, 'method', 'mle') # don't assume fit #params = np.asarray(params) # will return an index of a date start = self._get_predict_start(start, dynamic) end, out_of_sample = self._get_predict_end(end, dynamic) if out_of_sample and (exog is None and self.k_exog > 0): raise ValueError("You must provide exog for ARMAX") endog = self.endog resid = self.geterrors(params) k_ar = self.k_ar if out_of_sample != 0 and self.k_exog > 0: if self.k_exog == 1 and exog.ndim == 1: exog = exog[:,None] # we need the last k_ar exog for the lag-polynomial if self.k_exog > 0: # need the last k_ar exog for the lag-polynomial exog = np.vstack((self.exog[-k_ar:, self.k_trend:], exog)) if dynamic: #TODO: now that predict does dynamic in-sample it should # also return error estimates and confidence intervals # but how? len(endog) is not tot_obs out_of_sample += end - start + 1 return _arma_predict_out_of_sample(params, out_of_sample, resid, k_ar, self.k_ma, self.k_trend, self.k_exog, endog, exog, start, method) predictedvalues = _arma_predict_in_sample(start, end, endog, resid, k_ar, method) if out_of_sample: forecastvalues = _arma_predict_out_of_sample(params, out_of_sample, resid, k_ar, self.k_ma, self.k_trend, self.k_exog, endog, exog, method=method) predictedvalues = np.r_[predictedvalues, forecastvalues] return predictedvalues predict.__doc__ = _arma_predict def loglike(self, params): """ Compute the log-likelihood for ARMA(p,q) model Notes ----- Likelihood used depends on the method set in fit """ method = self.method if method in ['mle', 'css-mle']: return self.loglike_kalman(params) elif method == 'css': return self.loglike_css(params) else: raise ValueError("Method %s not understood" % method) def loglike_kalman(self, params): """ Compute exact loglikelihood for ARMA(p,q) model using the Kalman Filter. """ return KalmanFilter.loglike(params, self) def loglike_css(self, params): """ Conditional Sum of Squares likelihood function. """ k_ar = self.k_ar k_ma = self.k_ma k = self.k_exog + self.k_trend y = self.endog.copy().astype(params.dtype) nobs = self.nobs # how to handle if empty? if self.transparams: newparams = self._transparams(params) else: newparams = params if k > 0: y -= dot(self.exog, newparams[:k]) # the order of p determines how many zeros errors to set for lfilter b,a = np.r_[1,-newparams[k:k+k_ar]], np.r_[1,newparams[k+k_ar:]] zi = np.zeros((max(k_ar,k_ma)), dtype=params.dtype) for i in range(k_ar): zi[i] = sum(-b[:i+1][::-1] * y[:i+1]) errors = lfilter(b,a, y, zi=zi)[0][k_ar:] ssr = np.dot(errors,errors) sigma2 = ssr/nobs self.sigma2 = sigma2 llf = -nobs/2.*(log(2*pi) + log(sigma2)) - ssr/(2*sigma2) return llf def fit(self, order=None, start_params=None, trend='c', method = "css-mle", transparams=True, solver=None, maxiter=35, full_output=1, disp=5, callback=None, **kwargs): """ Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters ---------- start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). If False, no checking for stationarity or invertibility is done. method : str {'css-mle','mle','css'} This is the loglikelihood to maximize. If "css-mle", the conditional sum of squares likelihood is maximized and its values are used as starting values for the computation of the exact likelihood via the Kalman filter. If "mle", the exact likelihood is maximized via the Kalman Filter. If "css" the conditional sum of squares likelihood is maximized. All three methods use `start_params` as starting parameters. See above for more information. trend : str {'c','nc'} Whehter to include a constant or not. 'c' includes constant, 'nc' no constant. solver : str or None, optional Solver to be used. The default is 'l_bfgs' (limited memory Broyden-Fletcher-Goldfarb-Shanno). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient), and 'powell'. By default, the limited memory BFGS uses m=12 to approximate the Hessian, projected gradient tolerance of 1e-8 and factr = 1e2. You can change these by using kwargs. maxiter : int, optional The maximum number of function evaluations. Default is 35. tol : float The convergence tolerance. Default is 1e-08. full_output : bool, optional If True, all output from solver will be available in the Results object's mle_retvals attribute. Output is dependent on the solver. See Notes for more information. disp : bool, optional If True, convergence information is printed. For the default l_bfgs_b solver, disp controls the frequency of the output during the iterations. disp < 0 means no output in this case. callback : function, optional Called after each iteration as callback(xk) where xk is the current parameter vector. kwargs See Notes for keyword arguments that can be passed to fit. Returns ------- statsmodels.tsa.arima_model.ARMAResults class See also -------- statsmodels.base.model.LikelihoodModel.fit : for more information on using the solvers. ARMAResults : results class returned by fit Notes ------ If fit by 'mle', it is assumed for the Kalman Filter that the initial unkown state is zero, and that the inital variance is P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')).reshape(r, r, order = 'F') """ if order is not None: import warnings warnings.warn("The order argument to fit is deprecated. " "Please use the model constructor argument order. " "This will overwrite any order given in the model " "constructor.", FutureWarning) _check_estimable(len(self.endog), sum(order)) # get model order and constants self.k_ar = k_ar = int(order[0]) self.k_ma = k_ma = int(order[1]) self.k_lags = max(k_ar,k_ma+1) else: try: assert hasattr(self, "k_ar") assert hasattr(self, "k_ma") except: raise ValueError("Please give order to the model constructor " "before calling fit.") k_ar = self.k_ar k_ma = self.k_ma # enforce invertibility self.transparams = transparams self.method = method.lower() endog, exog = self.endog, self.exog k_exog = self.k_exog self.nobs = len(endog) # this is overwritten if method is 'css' # (re)set trend and handle exogenous variables # always pass original exog k_trend, exog = _make_arma_exog(endog, self.exog, trend) # check again now that we know the trend _check_estimable(len(endog), k_ar + k_ma + k_exog + k_trend) self.k_trend = k_trend self.exog = exog # overwrites original exog from __init__ # (re)set names for this model self.exog_names = _make_arma_names(self.data, k_trend, (k_ar, k_ma), self.exog_names) k = k_trend + k_exog # choose objective function method = method.lower() # adjust nobs for css if method == 'css': self.nobs = len(self.endog) - k_ar loglike = lambda params: -self.loglike(params) if start_params is not None: start_params = np.asarray(start_params) else: # estimate starting parameters start_params = self._fit_start_params((k_ar,k_ma,k), method) if transparams: # transform initial parameters to ensure invertibility start_params = self._invtransparams(start_params) if solver is None: # use default limited memory bfgs bounds = [(None,)*2]*(k_ar+k_ma+k) pgtol = kwargs.get('pgtol', 1e-8) factr = kwargs.get('factr', 1e2) m = kwargs.get('m', 12) mlefit = optimize.fmin_l_bfgs_b(loglike, start_params, approx_grad=True, m=m, pgtol=pgtol, factr=factr, bounds=bounds, iprint=disp) self.mlefit = mlefit params = mlefit[0] else: # call the solver from LikelihoodModel mlefit = super(ARMA, self).fit(start_params, method=solver, maxiter=maxiter, full_output=full_output, disp=disp, callback = callback, **kwargs) self.mlefit = mlefit params = mlefit.params if transparams: # transform parameters back params = self._transparams(params) self.transparams = False # set to false so methods don't expect transf. normalized_cov_params = None #TODO: fix this armafit = ARMAResults(self, params, normalized_cov_params) return ARMAResultsWrapper(armafit) #NOTE: the length of endog changes when we give a difference to fit #so model methods are not the same on unfit models as fit ones #starting to think that order of model should be put in instantiation... class ARIMA(ARMA): __doc__ = tsbase._tsa_doc % {"model" : _arima_model, "params" : _arima_params, "extra_params" : "", "extra_sections" : _armax_notes % {"Model" : "ARIMA"}} def __new__(cls, endog, order, exog=None, dates=None, freq=None, missing='none'): p, d, q = order if d == 0: # then we just use an ARMA model return ARMA(endog, (p,q), exog, dates, freq, missing) else: mod = super(ARIMA, cls).__new__(cls) mod.__init__(endog, order, exog, dates, freq, missing) return mod def __init__(self, endog, order, exog=None, dates=None, freq=None, missing='none'): p,d,q = order super(ARIMA, self).__init__(endog, (p,q), exog, dates, freq, missing) self.k_diff = d self.endog = np.diff(self.endog, n=d) #NOTE: will check in ARMA but check again since differenced now _check_estimable(len(self.endog), p+q) if exog is not None: self.exog = self.exog[d:] self.data.ynames = 'D.' + self.endog_names # what about exog, should we difference it automatically before # super call? def _get_predict_start(self, start, dynamic): """ """ #TODO: remove all these getattr and move order specification to # class constructor k_diff = getattr(self, 'k_diff', 0) method = getattr(self, 'method', 'mle') k_ar = getattr(self, 'k_ar', 0) if start is None: if 'mle' in method and not dynamic: start = 0 else: start = k_ar elif isinstance(start, int): start -= k_diff try: # catch when given an integer outside of dates index start = super(ARIMA, self)._get_predict_start(start, dynamic) except IndexError, err: raise ValueError("start must be in series. " "got %d" % (start + k_diff)) else: # received a date start = _validate(start, k_ar, k_diff, self.data.dates, method) start = super(ARIMA, self)._get_predict_start(start, dynamic) # reset date for k_diff adjustment self._set_predict_start_date(start + k_diff) return start def _get_predict_end(self, end, dynamic=False): """ Returns last index to be forecast of the differenced array. Handling of inclusiveness should be done in the predict function. """ end, out_of_sample = super(ARIMA, self)._get_predict_end(end, dynamic) if 'mle' not in self.method and not dynamic: end -= self.k_ar return end - self.k_diff, out_of_sample def fit(self, start_params=None, trend='c', method = "css-mle", transparams=True, solver=None, maxiter=35, full_output=1, disp=5, callback=None, **kwargs): """ Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter. Parameters ---------- start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). If False, no checking for stationarity or invertibility is done. method : str {'css-mle','mle','css'} This is the loglikelihood to maximize. If "css-mle", the conditional sum of squares likelihood is maximized and its values are used as starting values for the computation of the exact likelihood via the Kalman filter. If "mle", the exact likelihood is maximized via the Kalman Filter. If "css" the conditional sum of squares likelihood is maximized. All three methods use `start_params` as starting parameters. See above for more information. trend : str {'c','nc'} Whether to include a constant or not. 'c' includes constant, 'nc' no constant. solver : str or None, optional Solver to be used. The default is 'l_bfgs' (limited memory Broyden-Fletcher-Goldfarb-Shanno). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient), and 'powell'. By default, the limited memory BFGS uses m=12 to approximate the Hessian, projected gradient tolerance of 1e-8 and factr = 1e2. You can change these by using kwargs. maxiter : int, optional The maximum number of function evaluations. Default is 35. tol : float The convergence tolerance. Default is 1e-08. full_output : bool, optional If True, all output from solver will be available in the Results object's mle_retvals attribute. Output is dependent on the solver. See Notes for more information. disp : bool, optional If True, convergence information is printed. For the default l_bfgs_b solver, disp controls the frequency of the output during the iterations. disp < 0 means no output in this case. callback : function, optional Called after each iteration as callback(xk) where xk is the current parameter vector. kwargs See Notes for keyword arguments that can be passed to fit. Returns ------- `statsmodels.tsa.arima.ARIMAResults` class See also -------- statsmodels.base.model.LikelihoodModel.fit : for more information on using the solvers. ARIMAResults : results class returned by fit Notes ------ If fit by 'mle', it is assumed for the Kalman Filter that the initial unkown state is zero, and that the inital variance is P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')).reshape(r, r, order = 'F') """ arima_fit = super(ARIMA, self).fit(None, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, **kwargs) normalized_cov_params = None #TODO: fix this? arima_fit = ARIMAResults(self, arima_fit._results.params, normalized_cov_params) arima_fit.k_diff = self.k_diff return ARIMAResultsWrapper(arima_fit) def predict(self, params, start=None, end=None, exog=None, typ='linear', dynamic=False): # go ahead and convert to an index for easier checking if isinstance(start, (basestring, datetime)): start = _index_date(start, self.data.dates) if typ == 'linear': if not dynamic or (start != self.k_ar + self.k_diff and start is not None): return super(ARIMA, self).predict(params, start, end, exog, dynamic) else: # need to assume pre-sample residuals are zero # do this by a hack q = self.k_ma self.k_ma = 0 predictedvalues = super(ARIMA, self).predict(params, start, end, exog, dynamic) self.k_ma = q return predictedvalues elif typ == 'levels': endog = self.data.endog if not dynamic: predict = super(ARIMA, self).predict(params, start, end, dynamic) start = self._get_predict_start(start, dynamic) end, out_of_sample = self._get_predict_end(end) if 'mle' in self.method: # add each predicted diff to lagged endog if out_of_sample: fv = predict[:-out_of_sample] + endog[start:end+1] fv = np.r_[fv, endog[-1] + np.cumsum(predict[-out_of_sample:])] else: fv = predict + endog[start:end + 1] else: k_ar = self.k_ar if out_of_sample: fv = (predict[:-out_of_sample] + endog[max(start, self.k_ar-1):end+k_ar+1]) fv = np.r_[fv, endog[-1] + np.cumsum(predict[-out_of_sample:])] else: fv = predict + endog[max(start, k_ar):end+k_ar+1] else: #IFF we need to use pre-sample values assume pre-sample # residuals are zero, do this by a hack if start == self.k_ar + self.k_diff or start is None: # do the first k_diff+1 separately p = self.k_ar q = self.k_ma k_exog = self.k_exog k_trend = self.k_trend k_diff = self.k_diff (trendparam, exparams, arparams, maparams) = _unpack_params(params, (p,q), k_trend, k_exog, reverse=True) # this is the hack self.k_ma = 0 predict = super(ARIMA, self).predict(params, start, end, exog, dynamic) if not start: start = self._get_predict_start(start, dynamic) start += k_diff self.k_ma = q return endog[start-1] + np.cumsum(predict) else: predict = super(ARIMA, self).predict(params, start, end, exog, dynamic) return endog[start-1] + np.cumsum(predict) return fv else: # pragma : no cover raise ValueError("typ %s not understood" % typ) predict.__doc__ = _arima_predict class ARMAResults(tsbase.TimeSeriesModelResults): """ Class to hold results from fitting an ARMA model. Parameters ---------- model : ARMA instance The fitted model instance params : array Fitted parameters normalized_cov_params : array, optional The normalized variance covariance matrix scale : float, optional Optional argument to scale the variance covariance matrix. Returns -------- **Attributes** aic : float Akaike Information Criterion :math:`-2*llf+2*(df_model+1)` arparams : array The parameters associated with the AR coefficients in the model. arroots : array The roots of the AR coefficients are the solution to (1 - arparams[0]*z - arparams[1]*z**2 -...- arparams[p-1]*z**k_ar) = 0 Stability requires that the roots in modulus lie outside the unit circle. bic : float Bayes Information Criterion -2*llf + log(nobs)*(df_model+1) Where if the model is fit using conditional sum of squares, the number of observations `nobs` does not include the `p` pre-sample observations. bse : array The standard errors of the parameters. These are computed using the numerical Hessian. df_model : array The model degrees of freedom = `k_exog` + `k_trend` + `k_ar` + `k_ma` df_resid : array The residual degrees of freedom = `nobs` - `df_model` fittedvalues : array The predicted values of the model. hqic : float Hannan-Quinn Information Criterion -2*llf + 2*(`df_model`)*log(log(nobs)) Like `bic` if the model is fit using conditional sum of squares then the `k_ar` pre-sample observations are not counted in `nobs`. k_ar : int The number of AR coefficients in the model. k_exog : int The number of exogenous variables included in the model. Does not include the constant. k_ma : int The number of MA coefficients. k_trend : int This is 0 for no constant or 1 if a constant is included. llf : float The value of the log-likelihood function evaluated at `params`. maparams : array The value of the moving average coefficients. maroots : array The roots of the MA coefficients are the solution to (1 + maparams[0]*z + maparams[1]*z**2 + ... + maparams[q-1]*z**q) = 0 Stability requires that the roots in modules lie outside the unit circle. model : ARMA instance A reference to the model that was fit. nobs : float The number of observations used to fit the model. If the model is fit using exact maximum likelihood this is equal to the total number of observations, `n_totobs`. If the model is fit using conditional maximum likelihood this is equal to `n_totobs` - `k_ar`. n_totobs : float The total number of observations for `endog`. This includes all observations, even pre-sample values if the model is fit using `css`. params : array The parameters of the model. The order of variables is the trend coefficients and the `k_exog` exognous coefficients, then the `k_ar` AR coefficients, and finally the `k_ma` MA coefficients. pvalues : array The p-values associated with the t-values of the coefficients. Note that the coefficients are assumed to have a Student's T distribution. resid : array The model residuals. If the model is fit using 'mle' then the residuals are created via the Kalman Filter. If the model is fit using 'css' then the residuals are obtained via `scipy.signal.lfilter` adjusted such that the first `k_ma` residuals are zero. These zero residuals are not returned. scale : float This is currently set to 1.0 and not used by the model or its results. sigma2 : float The variance of the residuals. If the model is fit by 'css', sigma2 = ssr/nobs, where ssr is the sum of squared residuals. If the model is fit by 'mle', then sigma2 = 1/nobs * sum(v**2 / F) where v is the one-step forecast error and F is the forecast error variance. See `nobs` for the difference in definitions depending on the fit. """ _cache = {} #TODO: use this for docstring when we fix nobs issue def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(ARMAResults, self).__init__(model, params, normalized_cov_params, scale) self.sigma2 = model.sigma2 nobs = model.nobs self.nobs = nobs k_exog = model.k_exog self.k_exog = k_exog k_trend = model.k_trend self.k_trend = k_trend k_ar = model.k_ar self.k_ar = k_ar self.n_totobs = len(model.endog) k_ma = model.k_ma self.k_ma = k_ma df_model = k_exog + k_trend + k_ar + k_ma self.df_model = df_model self.df_resid = self.nobs - df_model self._cache = resettable_cache() @cache_readonly def arroots(self): return np.roots(np.r_[1,-self.arparams])**-1 @cache_readonly def maroots(self): return np.roots(np.r_[1,self.maparams])**-1 @cache_readonly def arfreq(self): r""" Returns the frequency of the AR roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots. """ z = self.arroots if not z.size: return return np.arctan2(z.imag, z.real) / (2*pi) @cache_readonly def mafreq(self): r""" Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots. """ z = self.maroots if not z.size: return return np.arctan2(z.imag, z.real) / (2*pi) @cache_readonly def arparams(self): k = self.k_exog + self.k_trend return self.params[k:k+self.k_ar] @cache_readonly def maparams(self): k = self.k_exog + self.k_trend k_ar = self.k_ar return self.params[k+k_ar:] @cache_readonly def llf(self): return self.model.loglike(self.params) @cache_readonly def bse(self): params = self.params hess = self.model.hessian(params) if len(params) == 1: # can't take an inverse return np.sqrt(-1./hess) return np.sqrt(np.diag(-inv(hess))) def cov_params(self): # add scale argument? params = self.params hess = self.model.hessian(params) return -inv(hess) @cache_readonly def aic(self): return -2*self.llf + 2*(self.df_model+1) @cache_readonly def bic(self): nobs = self.nobs return -2*self.llf + np.log(nobs)*(self.df_model+1) @cache_readonly def hqic(self): nobs = self.nobs return -2*self.llf + 2*(self.df_model+1)*np.log(np.log(nobs)) @cache_readonly def fittedvalues(self): model = self.model endog = model.endog.copy() k_ar = self.k_ar exog = model.exog # this is a copy if exog is not None: if model.method == "css" and k_ar > 0: exog = exog[k_ar:] if model.method == "css" and k_ar > 0: endog = endog[k_ar:] fv = endog - self.resid # add deterministic part back in k = self.k_exog + self.k_trend #TODO: this needs to be commented out for MLE with constant # if k != 0: # fv += dot(exog, self.params[:k]) return fv @cache_readonly def resid(self): return self.model.geterrors(self.params) @cache_readonly def pvalues(self): #TODO: same for conditional and unconditional? df_resid = self.df_resid return t.sf(np.abs(self.tvalues), df_resid) * 2 def predict(self, start=None, end=None, exog=None, dynamic=False): return self.model.predict(self.params, start, end, exog, dynamic) predict.__doc__ = _arma_results_predict def forecast(self, steps=1, exog=None, alpha=.05): """ Out-of-sample forecasts Parameters ---------- steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. Note that you'll need to pass `k_ar` additional lags for any exogenous variables. E.g., if you fit an ARMAX(2, q) model and want to predict 5 steps, you need 7 observations to do this. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns ------- forecast : array Array of out of sample forecasts stderr : array Array of the standard error of the forecasts. conf_int : array 2d array of the confidence interval for the forecast """ if exog is not None: #TODO: make a convenience function for this. we're using the # pattern elsewhere in the codebase exog = np.asarray(exog) if self.k_exog == 1 and exog.ndim == 1: exog = exog[:,None] elif exog.ndim == 1: if len(exog) != self.k_exog: raise ValueError("1d exog given and len(exog) != k_exog") exog = exog[None, :] if exog.shape[0] != steps: raise ValueError("new exog needed for each step") # prepend in-sample exog observations exog = np.vstack((self.model.exog[-self.k_ar:, self.k_trend:], exog)) arparams = self.arparams maparams = self.maparams forecast = _arma_predict_out_of_sample(self.params, steps, self.resid, self.k_ar, self.k_ma, self.k_trend, self.k_exog, self.model.endog, exog, method=self.model.method) # compute the standard errors sigma2 = self.sigma2 ma_rep = arma2ma(np.r_[1,-arparams], np.r_[1, maparams], nobs=steps) fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2)) const = norm.ppf(1 - alpha/2.) conf_int = np.c_[forecast - const*fcasterr, forecast + const*fcasterr] return forecast, fcasterr, conf_int def summary(self, alpha=.05): """Summarize the Model Parameters ---------- alpha : float, optional Significance level for the confidence intervals. Returns ------- smry : Summary instance This holds the summary table and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary """ from statsmodels.iolib.summary import Summary model = self.model title = model.__class__.__name__ + ' Model Results' method = model.method # get sample TODO: make better sample machinery for estimation k_diff = getattr(self, 'k_diff', 0) if 'mle' in method: start = k_diff else: start = k_diff + self.k_ar if self.data.dates is not None: dates = self.data.dates sample = [dates[start].strftime('%m-%d-%Y')] sample += ['- ' + dates[-1].strftime('%m-%d-%Y')] else: sample = str(start) + ' - ' + str(len(self.data.orig_endog)) k_ar, k_ma = self.k_ar, self.k_ma if not k_diff: order = str((k_ar, k_ma)) else: order = str((k_ar, k_diff, k_ma)) top_left = [('Dep. Variable:', None), ('Model:', [model.__class__.__name__ + order]), ('Method:', [method]), ('Date:', None), ('Time:', None), ('Sample:', [sample[0]]), ('', [sample[1]]) ] top_right = [ ('No. Observations:', [str(len(self.model.endog))]), ('Log Likelihood', ["%#5.3f" % self.llf]), ('S.D. of innovations', ["%#5.3f" % self.sigma2**.5]), ('AIC', ["%#5.3f" % self.aic]), ('BIC', ["%#5.3f" % self.bic]), ('HQIC', ["%#5.3f" % self.hqic])] smry = Summary() smry.add_table_2cols(self, gleft=top_left, gright=top_right, title=title) smry.add_table_params(self, alpha=alpha, use_t=False) # Make the roots table from statsmodels.iolib.table import SimpleTable if k_ma and k_ar: arstubs = ["AR.%d" % i for i in range(1, k_ar + 1)] mastubs = ["MA.%d" % i for i in range(1, k_ma + 1)] stubs = arstubs + mastubs roots = np.r_[self.arroots, self.maroots] freq = np.r_[self.arfreq, self.mafreq] elif k_ma: mastubs = ["MA.%d" % i for i in range(1, k_ma + 1)] stubs = mastubs roots = self.maroots freq = self.mafreq elif k_ar: arstubs = ["AR.%d" % i for i in range(1, k_ar + 1)] stubs = arstubs roots = self.arroots freq = self.arfreq modulus = np.abs(roots) data = np.column_stack((roots.real, roots.imag, modulus, freq)) roots_table = SimpleTable(data, headers=[' Real', ' Imaginary', ' Modulus', ' Frequency'], title="Roots", stubs=stubs, data_fmts=["%17.4f", "%+17.4fj", "%17.4f", "%17.4f"]) smry.tables.append(roots_table) return smry def summary2(self, title=None, alpha=.05, float_format="%.4f"): """Experimental summary function for ARIMA Results Parameters ----------- title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string print format for floats in parameters summary Returns ------- smry : Summary instance This holds the summary table and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary2.Summary : class to hold summary results """ # get sample TODO: make better sample machinery for estimation k_diff = getattr(self, 'k_diff', 0) if 'mle' in self.model.method: start = k_diff else: start = k_diff + self.k_ar if self.data.dates is not None: dates = self.data.dates sample = [dates[start].strftime('%m-%d-%Y')] sample += [dates[-1].strftime('%m-%d-%Y')] else: sample = str(start) + ' - ' + str(len(self.data.orig_endog)) k_ar, k_ma = self.k_ar, self.k_ma if not k_diff: order = str((k_ar, k_ma)) else: order = str((k_ar, k_diff, k_ma)) # Roots table if k_ma and k_ar: arstubs = ["AR.%d" % i for i in range(1, k_ar + 1)] mastubs = ["MA.%d" % i for i in range(1, k_ma + 1)] stubs = arstubs + mastubs roots = np.r_[self.arroots, self.maroots] freq = np.r_[self.arfreq, self.mafreq] elif k_ma: mastubs = ["MA.%d" % i for i in range(1, k_ma + 1)] stubs = mastubs roots = self.maroots freq = self.mafreq elif k_ar: arstubs = ["AR.%d" % i for i in range(1, k_ar + 1)] stubs = arstubs roots = self.arroots freq = self.arfreq modulus = np.abs(roots) data = np.column_stack((roots.real, roots.imag, modulus, freq)) data = pd.DataFrame(data) data.columns = ['Real', 'Imaginary', 'Modulus', 'Frequency'] data.index = stubs # Summary from statsmodels.iolib import summary2 smry = summary2.Summary() # Model info model_info = summary2.summary_model(self) model_info['Method:'] = self.model.method model_info['Sample:'] = sample[0] model_info[' '] = sample[-1] model_info['S.D. of innovations:'] = "%#5.3f" % self.sigma2**.5 model_info['HQIC:'] = "%#5.3f" % self.hqic model_info['No. Observations:'] = str(len(self.model.endog)) # Parameters params = summary2.summary_params(self) smry.add_dict(model_info) smry.add_df(params, float_format=float_format) smry.add_df(data, float_format="%17.4f") smry.add_title(results=self, title=title) return smry class ARMAResultsWrapper(wrap.ResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts( tsbase.TimeSeriesResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(ARMAResultsWrapper, ARMAResults) class ARIMAResults(ARMAResults): def predict(self, start=None, end=None, exog=None, typ='linear', dynamic=False): return self.model.predict(self.params, start, end, exog, typ, dynamic) predict.__doc__ = _arima_results_predict def forecast(self, steps=1, exog=None, alpha=.05): """ Out-of-sample forecasts Parameters ---------- steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARIMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns ------- forecast : array Array of out of sample forecasts stderr : array Array of the standard error of the forecasts. conf_int : array 2d array of the confidence interval for the forecast Notes ----- Prediction is done in the levels of the original endogenous variable. If you would like prediction of differences in levels use `predict`. """ if exog is not None: if self.k_exog == 1 and exog.ndim == 1: exog = exog[:,None] if exog.shape[0] != steps: raise ValueError("new exog needed for each step") # prepend in-sample exog observations exog = np.vstack((self.model.exog[-self.k_ar:, self.k_trend:], exog)) forecast = _arma_predict_out_of_sample(self.params, steps, self.resid, self.k_ar, self.k_ma, self.k_trend, self.k_exog, self.model.endog, exog, method=self.model.method) forecast = self.model.data.endog[-1] + np.cumsum(forecast) # get forecast errors arparams = self.arparams maparams = self.maparams sigma2 = self.sigma2 ma_rep = arma2ma(np.r_[1, -arparams], np.r_[1, maparams], nobs=steps) fcerr = np.sqrt(np.cumsum(np.cumsum(ma_rep)**2)*sigma2) const = norm.ppf(1 - alpha/2.) conf_int = np.c_[forecast - const*fcerr, forecast + const*fcerr] return forecast, fcerr, conf_int class ARIMAResultsWrapper(ARMAResultsWrapper): pass wrap.populate_wrapper(ARIMAResultsWrapper, ARIMAResults) if __name__ == "__main__": import numpy as np import statsmodels.api as sm # simulate arma process from statsmodels.tsa.arima_process import arma_generate_sample y = arma_generate_sample([1., -.75],[1.,.25], nsample=1000) arma = ARMA(y) res = arma.fit(trend='nc', order=(1,1)) np.random.seed(12345) y_arma22 = arma_generate_sample([1.,-.85,.35],[1,.25,-.9], nsample=1000) arma22 = ARMA(y_arma22) res22 = arma22.fit(trend = 'nc', order=(2,2)) # test CSS arma22_css = ARMA(y_arma22) res22css = arma22_css.fit(trend='nc', order=(2,2), method='css') data = sm.datasets.sunspots.load() ar = ARMA(data.endog) resar = ar.fit(trend='nc', order=(9,0)) y_arma31 = arma_generate_sample([1,-.75,-.35,.25],[.1], nsample=1000) arma31css = ARMA(y_arma31) res31css = arma31css.fit(order=(3,1), method="css", trend="nc", transparams=True) y_arma13 = arma_generate_sample([1., -.75],[1,.25,-.5,.8], nsample=1000) arma13css = ARMA(y_arma13) res13css = arma13css.fit(order=(1,3), method='css', trend='nc') # check css for p < q and q < p y_arma41 = arma_generate_sample([1., -.75, .35, .25, -.3],[1,-.35], nsample=1000) arma41css = ARMA(y_arma41) res41css = arma41css.fit(order=(4,1), trend='nc', method='css') y_arma14 = arma_generate_sample([1, -.25], [1., -.75, .35, .25, -.3], nsample=1000) arma14css = ARMA(y_arma14) res14css = arma14css.fit(order=(4,1), trend='nc', method='css') # ARIMA Model from statsmodels.tools.tools import webuse dta = webuse('wpi1') wpi = dta['wpi'] mod = ARIMA(wpi, (1,1,1)).fit() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/arima_process.py000066400000000000000000000747271224417117700245250ustar00rootroot00000000000000'''ARMA process and estimation with scipy.signal.lfilter 2009-09-06: copied from try_signal.py reparameterized same as signal.lfilter (positive coefficients) Notes ----- * pretty fast * checked with Monte Carlo and cross comparison with statsmodels yule_walker for AR numbers are close but not identical to yule_walker not compared to other statistics packages, no degrees of freedom correction * ARMA(2,2) estimation (in Monte Carlo) requires longer time series to estimate parameters without large variance. There might be different ARMA parameters with similar impulse response function that cannot be well distinguished with small samples (e.g. 100 observations) * good for one time calculations for entire time series, not for recursive prediction * class structure not very clean yet * many one-liners with scipy.signal, but takes time to figure out usage * missing result statistics, e.g. t-values, but standard errors in examples * no criteria for choice of number of lags * no constant term in ARMA process * no integration, differencing for ARIMA * written without textbook, works but not sure about everything briefly checked and it looks to be standard least squares, see below * theoretical autocorrelation function of general ARMA Done, relatively easy to guess solution, time consuming to get theoretical test cases, example file contains explicit formulas for acovf of MA(1), MA(2) and ARMA(1,1) * two names for lag polynomials ar = rhoy, ma = rhoe ? Properties: Judge, ... (1985): The Theory and Practise of Econometrics BigJudge p. 237ff: If the time series process is a stationary ARMA(p,q), then minimizing the sum of squares is asymptoticaly (as T-> inf) equivalent to the exact Maximum Likelihood Estimator Because Least Squares conditional on the initial information does not use all information, in small samples exact MLE can be better. Without the normality assumption, the least squares estimator is still consistent under suitable conditions, however not efficient Author: josefpktd License: BSD ''' import numpy as np from scipy import signal, optimize, linalg from statsmodels.base.model import LikelihoodModel #this has been copied to new arma_mle.py - keep temporarily for easier lookup class ARIMAProcess(LikelihoodModel): '''currently ARMA only, no differencing used - no I parameterized as rhoy(L) y_t = rhoe(L) eta_t A instance of this class preserves state, so new class instances should be created for different examples ''' def __init__(self, endog, exog=None): super(ARIMAProcess, self).__init__(endog, exog) if endog.ndim == 1: endog = endog[:,None] elif endog.ndim > 1 and endog.shape[1] != 1: raise ValueError("Only the univariate case is implemented") self.endog = endog # overwrite endog if exog is not None: raise ValueError("Exogenous variables are not yet supported.") def fit(self, order=(0,0,0), method="ls", rhoy0=None, rhoe0=None): ''' Estimate lag coefficients of an ARIMA process. Parameters ---------- order : sequence p,d,q where p is the number of AR lags, d is the number of differences to induce stationarity, and q is the number of MA lags to estimate. method : str {"ls", "ssm"} Method of estimation. LS is conditional least squares. SSM is state-space model and the Kalman filter is used to maximize the exact likelihood. rhoy0, rhoe0 : array_like (optional) starting values for estimation Returns ------- rh, cov_x, infodict, mesg, ier : output of scipy.optimize.leastsq rh : estimate of lag parameters, concatenated [rhoy, rhoe] cov_x : unscaled (!) covariance matrix of coefficient estimates ''' if not hasattr(order, '__iter__'): raise ValueError("order must be an iterable sequence. Got type \ %s instead" % type(order)) p,d,q = order if d > 0: raise ValueError("Differencing not implemented yet") # assume no constant, ie mu = 0 # unless overwritten then use w_bar for mu Y = np.diff(endog, d, axis=0) #TODO: handle lags? x = self.endog.squeeze() # remove the squeeze might be needed later def errfn( rho): #rhoy, rhoe = rho rhoy = np.concatenate(([1], rho[:p])) rhoe = np.concatenate(([1], rho[p:])) etahatr = signal.lfilter(rhoy, rhoe, x) #print rho,np.sum(etahatr*etahatr) return etahatr if rhoy0 is None: rhoy0 = 0.5 * np.ones(p) if rhoe0 is None: rhoe0 = 0.5 * np.ones(q) method = method.lower() if method == "ls": rh, cov_x, infodict, mesg, ier = \ optimize.leastsq(errfn, np.r_[rhoy0, rhoe0],ftol=1e-10,full_output=True) #TODO: integrate this into the MLE.fit framework? elif method == "ssm": pass else: # fmin_bfgs is slow or doesn't work yet errfnsum = lambda rho : np.sum(errfn(rho)**2) #xopt, {fopt, gopt, Hopt, func_calls, grad_calls rh,fopt, gopt, cov_x, _,_, ier = \ optimize.fmin_bfgs(errfnsum, np.r_[rhoy0, rhoe0], maxiter=2, full_output=True) infodict, mesg = None, None self.rh = rh self.rhoy = np.concatenate(([1], rh[:p])) self.rhoe = np.concatenate(([1], rh[p:])) #rh[-q:])) doesnt work for q=0 self.error_estimate = errfn(rh) return rh, cov_x, infodict, mesg, ier def errfn(self, rho=None, p=None, x=None): ''' duplicate -> remove one ''' #rhoy, rhoe = rho if not rho is None: rhoy = np.concatenate(([1], rho[:p])) rhoe = np.concatenate(([1], rho[p:])) else: rhoy = self.rhoy rhoe = self.rhoe etahatr = signal.lfilter(rhoy, rhoe, x) #print rho,np.sum(etahatr*etahatr) return etahatr def predicted(self, rhoy=None, rhoe=None): '''past predicted values of time series just added, not checked yet ''' if rhoy is None: rhoy = self.rhoy if rhoe is None: rhoe = self.rhoe return self.x + self.error_estimate def forecast(self, ar=None, ma=None, nperiod=10): eta = np.r_[self.error_estimate, np.zeros(nperiod)] if ar is None: ar = self.rhoy if ma is None: ma = self.rhoe return signal.lfilter(ma, ar, eta) #TODO: is this needed as a method at all? @classmethod def generate_sample(cls, ar, ma, nsample, std=1): eta = std * np.random.randn(nsample) return signal.lfilter(ma, ar, eta) def arma_generate_sample(ar, ma, nsample, sigma=1, distrvs=np.random.randn, burnin=0): '''generate an random sample of an ARMA process Parameters ---------- ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nsample : int length of simulated time series sigma : float standard deviation of noise distrvs : function, random number generator function that generates the random numbers, and takes sample size as argument default: np.random.randn TODO: change to size argument burnin : integer (default: 0) to reduce the effect of initial conditions, burnin observations at the beginning of the sample are dropped Returns ------- acovf : array autocovariance of ARMA process given by ar, ma ''' #TODO: unify with ArmaProcess method eta = sigma * distrvs(nsample+burnin) return signal.lfilter(ma, ar, eta)[burnin:] def arma_acovf(ar, ma, nobs=10): '''theoretical autocovariance function of ARMA process Parameters ---------- ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag Returns ------- acovf : array autocovariance of ARMA process given by ar, ma See Also -------- arma_acf acovf Notes ----- Tries to do some crude numerical speed improvements for cases with high persistance. However, this algorithm is slow if the process is highly persistent and only a few autocovariances are desired. ''' #increase length of impulse response for AR closer to 1 #maybe cheap/fast enough to always keep nobs for ir large if np.abs(np.sum(ar)-1) > 0.9: nobs_ir = max(1000, 2* nobs) #no idea right now how large it is needed else: nobs_ir = max(100, 2* nobs) #no idea right now ir = arma_impulse_response(ar, ma, nobs=nobs_ir) #better save than sorry (?), I have no idea about the required precision #only checked for AR(1) while ir[-1] > 5*1e-5: nobs_ir *= 10 ir = arma_impulse_response(ar, ma, nobs=nobs_ir) #again no idea where the speed break points are: if nobs_ir > 50000 and nobs < 1001: acovf = np.array([np.dot(ir[:nobs-t], ir[t:nobs]) for t in range(nobs)]) else: acovf = np.correlate(ir,ir,'full')[len(ir)-1:] return acovf[:nobs] def arma_acf(ar, ma, nobs=10): '''theoretical autocorrelation function of ARMA process Parameters ---------- ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag Returns ------- acf : array autocorrelation of ARMA process given by ar, ma See Also -------- arma_acovf acf acovf ''' acovf = arma_acovf(ar, ma, nobs) return acovf/acovf[0] def arma_pacf(ar, ma, nobs=10): '''partial autocorrelation function of an ARMA process Notes ----- solves yule-walker equation for each lag order up to nobs lags not tested/checked yet ''' apacf = np.zeros(nobs) acov = arma_acf(ar,ma, nobs=nobs+1) apacf[0] = 1. for k in range(2,nobs+1): r = acov[:k]; apacf[k-1] = linalg.solve(linalg.toeplitz(r[:-1]), r[1:])[-1] return apacf def arma_periodogram(ar, ma, worN=None, whole=0): '''periodogram for ARMA process given by lag-polynomials ar and ma Parameters ---------- ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read "w or N") If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencies. Otherwise, compute the response at frequencies given in worN whole : {0,1}, optional options for scipy.signal.freqz Normally, frequencies are computed from 0 to pi (upper-half of unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi. Returns ------- w : array frequencies sd : array periodogram, spectral density Notes ----- Normalization ? This uses signal.freqz, which does not use fft. There is a fft version somewhere. ''' w, h = signal.freqz(ma, ar, worN=worN, whole=whole) sd = np.abs(h)**2/np.sqrt(2*np.pi) if np.sum(np.isnan(h)) > 0: # this happens with unit root or seasonal unit root' print 'Warning: nan in frequency response h, maybe a unit root' return w, sd def arma_impulse_response(ar, ma, nobs=100): '''get the impulse response function (MA representation) for ARMA process Parameters ---------- ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns ------- ir : array, 1d impulse response function with nobs elements Notes ----- This is the same as finding the MA representation of an ARMA(p,q). By reversing the role of ar and ma in the function arguments, the returned result is the AR representation of an ARMA(p,q), i.e ma_representation = arma_impulse_response(ar, ma, nobs=100) ar_representation = arma_impulse_response(ma, ar, nobs=100) fully tested against matlab Examples -------- AR(1) >>> arma_impulse_response([1.0, -0.8], [1.], nobs=10) array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 , 0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773]) this is the same as >>> 0.8**np.arange(10) array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 , 0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773]) MA(2) >>> arma_impulse_response([1.0], [1., 0.5, 0.2], nobs=10) array([ 1. , 0.5, 0.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. ]) ARMA(1,2) >>> arma_impulse_response([1.0, -0.8], [1., 0.5, 0.2], nobs=10) array([ 1. , 1.3 , 1.24 , 0.992 , 0.7936 , 0.63488 , 0.507904 , 0.4063232 , 0.32505856, 0.26004685]) ''' impulse = np.zeros(nobs) impulse[0] = 1. return signal.lfilter(ma, ar, impulse) #alias, easier to remember arma2ma = arma_impulse_response #alias, easier to remember def arma2ar(ar, ma, nobs=100): '''get the AR representation of an ARMA process Parameters ---------- ar : array_like, 1d auto regressive lag polynomial ma : array_like, 1d moving average lag polynomial nobs : int number of observations to calculate Returns ------- ar : array, 1d coefficients of AR lag polynomial with nobs elements ` Notes ----- This is just an alias for ``ar_representation = arma_impulse_response(ma, ar, nobs=100)`` fully tested against matlab Examples -------- ''' return arma_impulse_response(ma, ar, nobs=nobs) #moved from sandbox.tsa.try_fi def ar2arma(ar_des, p, q, n=20, mse='ar', start=None): '''find arma approximation to ar process This finds the ARMA(p,q) coefficients that minimize the integrated squared difference between the impulse_response functions (MA representation) of the AR and the ARMA process. This does currently not check whether the MA lagpolynomial of the ARMA process is invertible, neither does it check the roots of the AR lagpolynomial. Parameters ---------- ar_des : array_like coefficients of original AR lag polynomial, including lag zero p, q : int length of desired ARMA lag polynomials n : int number of terms of the impuls_response function to include in the objective function for the approximation mse : string, 'ar' not used yet, Returns ------- ar_app, ma_app : arrays coefficients of the AR and MA lag polynomials of the approximation res : tuple result of optimize.leastsq Notes ----- Extension is possible if we want to match autocovariance instead of impulse response function. TODO: convert MA lag polynomial, ma_app, to be invertible, by mirroring roots outside the unit intervall to ones that are inside. How do we do this? ''' #p,q = pq def msear_err(arma, ar_des): ar, ma = np.r_[1, arma[:p-1]], np.r_[1, arma[p-1:]] ar_approx = arma_impulse_response(ma, ar, n) ## print ar,ma ## print ar_des.shape, ar_approx.shape ## print ar_des ## print ar_approx return (ar_des - ar_approx) #((ar - ar_approx)**2).sum() if start is None: arma0 = np.r_[-0.9* np.ones(p-1), np.zeros(q-1)] else: arma0 = start res = optimize.leastsq(msear_err, arma0, ar_des, maxfev=5000)#, full_output=True) #print res arma_app = np.atleast_1d(res[0]) ar_app = np.r_[1, arma_app[:p-1]], ma_app = np.r_[1, arma_app[p-1:]] return ar_app, ma_app, res def lpol2index(ar): '''remove zeros from lagpolynomial, squeezed representation with index Parameters ---------- ar : array_like coefficients of lag polynomial Returns ------- coeffs : array non-zero coefficients of lag polynomial index : array index (lags) of lagpolynomial with non-zero elements ''' ar = np.asarray(ar) index = np.nonzero(ar)[0] coeffs = ar[index] return coeffs, index def index2lpol(coeffs, index): '''expand coefficients to lag poly Parameters ---------- coeffs : array non-zero coefficients of lag polynomial index : array index (lags) of lagpolynomial with non-zero elements ar : array_like coefficients of lag polynomial Returns ------- ar : array_like coefficients of lag polynomial ''' n = max(index) ar = np.zeros(n) ar[index] = coeffs return ar #moved from sandbox.tsa.try_fi def lpol_fima(d, n=20): '''MA representation of fractional integration .. math:: (1-L)^{-d} for |d|<0.5 or |d|<1 (?) Parameters ---------- d : float fractional power n : int number of terms to calculate, including lag zero Returns ------- ma : array coefficients of lag polynomial ''' #hide import inside function until we use this heavily from scipy.special import gamma, gammaln j = np.arange(n) return np.exp(gammaln(d+j) - gammaln(j+1) - gammaln(d)) #moved from sandbox.tsa.try_fi def lpol_fiar(d, n=20): '''AR representation of fractional integration .. math:: (1-L)^{d} for |d|<0.5 or |d|<1 (?) Parameters ---------- d : float fractional power n : int number of terms to calculate, including lag zero Returns ------- ar : array coefficients of lag polynomial Notes: first coefficient is 1, negative signs except for first term, ar(L)*x_t ''' #hide import inside function until we use this heavily from scipy.special import gamma, gammaln j = np.arange(n) ar = - np.exp(gammaln(-d+j) - gammaln(j+1) - gammaln(-d)) ar[0] = 1 return ar #moved from sandbox.tsa.try_fi def lpol_sdiff(s): '''return coefficients for seasonal difference (1-L^s) just a trivial convenience function Parameters ---------- s : int number of periods in season Returns ------- sdiff : list, length s+1 ''' return [1] + [0]*(s-1) + [-1] def deconvolve(num, den, n=None): """Deconvolves divisor out of signal, division of polynomials for n terms calculates den^{-1} * num Parameters ---------- num : array_like signal or lag polynomial denom : array_like coefficients of lag polynomial (linear filter) n : None or int number of terms of quotient Returns ------- quot : array quotient or filtered series rem : array remainder Notes ----- If num is a time series, then this applies the linear filter den^{-1}. If both num and den are both lagpolynomials, then this calculates the quotient polynomial for n terms and also returns the remainder. This is copied from scipy.signal.signaltools and added n as optional parameter. """ num = np.atleast_1d(num) den = np.atleast_1d(den) N = len(num) D = len(den) if D > N and n is None: quot = []; rem = num; else: if n is None: n = N-D+1 input = np.zeros(n, float) input[0] = 1 quot = signal.lfilter(num, den, input) num_approx = signal.convolve(den, quot, mode='full') if len(num) < len(num_approx): # 1d only ? num = np.concatenate((num, np.zeros(len(num_approx)-len(num)))) rem = num - num_approx return quot, rem class ArmaProcess(object): '''represents an ARMA process for given lag-polynomials This is a class to bring together properties of the process. It does not do any estimation or statistical analysis. maybe needs special handling for unit roots ''' def __init__(self, ar, ma, nobs=None): self.ar = np.asarray(ar) self.ma = np.asarray(ma) self.arcoefs = -self.ar[1:] self.macoefs = self.ma[1:] self.arpoly = np.polynomial.Polynomial(self.ar) self.mapoly = np.polynomial.Polynomial(self.ma) self.nobs = nobs @classmethod def from_coeffs(cls, arcoefs, macoefs, nobs=None): '''create ArmaProcess instance from coefficients of the lag-polynomials ''' return cls(np.r_[1, -arcoefs], np.r_[1, macoefs], nobs=nobs) @classmethod def from_estimation(cls, model_results, nobs=None): '''create ArmaProcess instance from estimation results ''' arcoefs = model_results.params[:model_results.nar] macoefs = model_results.params[model_results.nar: model_results.nar+model_results.nma] return cls(np.r_[1, -arcoefs], np.r_[1, macoefs], nobs=nobs) def __mul__(self, oth): if isinstance(oth, self.__class__): ar = (self.arpoly * oth.arpoly).coef ma = (self.mapoly * oth.mapoly).coef else: try: aroth, maoth = oth arpolyoth = np.polynomial.Polynomial(aroth) mapolyoth = np.polynomial.Polynomial(maoth) ar = (self.arpoly * arpolyoth).coef ma = (self.mapoly * mapolyoth).coef except: print('other is not a valid type') raise return self.__class__(ar, ma, nobs=self.nobs) def __repr__(self): return 'ArmaProcess(%r, %r, nobs=%d)' % (self.ar.tolist(), self.ma.tolist(), self.nobs) def __str__(self): return 'ArmaProcess\nAR: %r\nMA: %r' % (self.ar.tolist(), self.ma.tolist()) def acovf(self, nobs=None): nobs = nobs or self.nobs return arma_acovf(self.ar, self.ma, nobs=nobs) acovf.__doc__ = arma_acovf.__doc__ def acf(self, nobs=None): nobs = nobs or self.nobs return arma_acf(self.ar, self.ma, nobs=nobs) acf.__doc__ = arma_acf.__doc__ def pacf(self, nobs=None): nobs = nobs or self.nobs return arma_pacf(self.ar, self.ma, nobs=nobs) pacf.__doc__ = arma_pacf.__doc__ def periodogram(self, nobs=None): nobs = nobs or self.nobs return arma_periodogram(self.ar, self.ma, worN=nobs) periodogram.__doc__ = arma_periodogram.__doc__ def impulse_response(self, nobs=None): nobs = nobs or self.nobs return arma_impulse_response(self.ar, self.ma, worN=nobs) impulse_response.__doc__ = arma_impulse_response.__doc__ def arma2ma(self, nobs=None): nobs = nobs or self.nobs return arma2ma(self.ar, self.ma, nobs=nobs) arma2ma.__doc__ = arma2ma.__doc__ def arma2ar(self, nobs=None): nobs = nobs or self.nobs return arma2ar(self.ar, self.ma, nobs=nobs) arma2ar.__doc__ = arma2ar.__doc__ def ar_roots(self): '''roots of autoregressive lag-polynomial ''' return self.arpoly.roots() def ma_roots(self): '''roots of moving average lag-polynomial ''' return self.mapoly.roots() def isstationary(self): '''Arma process is stationary if AR roots are outside unit circle Returns ------- isstationary : boolean True if autoregressive roots are outside unit circle ''' if np.all(np.abs(self.ar_roots()) > 1): return True else: return False def isinvertible(self): '''Arma process is invertible if MA roots are outside unit circle Returns ------- isinvertible : boolean True if moving average roots are outside unit circle ''' if np.all(np.abs(self.ma_roots()) > 1): return True else: return False def invertroots(self, retnew=False): '''make MA polynomial invertible by inverting roots inside unit circle Parameters ---------- retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns ------- manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible, returned if retnew is false. armaprocess : new instance of class If retnew is true, then return a new instance with invertible MA-polynomial ''' pr = self.ma_roots() insideroots = np.abs(pr)<1 if insideroots.any(): pr[np.abs(pr)<1] = 1./pr[np.abs(pr)<1] pnew = poly.Polynomial.fromroots(pr) mainv = pn.coef/pnew.coef[0] wasinvertible = False else: mainv = self.ma wasinvertible = True if retnew: return self.__class__(self.ar, mainv, nobs=self.nobs) else: return mainv, wasinvertible def generate_sample(self, size=100, scale=1, distrvs=None, axis=0, burnin=0): '''generate ARMA samples Parameters ---------- size : int or tuple of ints If size is an integer, then this creates a 1d timeseries of length size. If size is a tuple, then the timeseries is along axis. All other axis have independent arma samples. Returns ------- rvs : ndarray random sample(s) of arma process Notes ----- Should work for n-dimensional with time series along axis, but not tested yet. Processes are sampled independently. ''' if distrvs is None: distrvs = np.random.normal if np.ndim(size) == 0: size = [size] if burnin: #handle burin time for nd arrays #maybe there is a better trick in scipy.fft code newsize = list(size) newsize[axis] += burnin newsize = tuple(newsize) fslice = [slice(None)]*len(newsize) fslice[axis] = slice(burnin, None, None) fslice = tuple(fslice) else: newsize = tuple(size) fslice = tuple([slice(None)]*np.ndim(newsize)) eta = scale * distrvs(size=newsize) return signal.lfilter(self.ma, self.ar, eta, axis=axis)[fslice] __all__ = ['arma_acf', 'arma_acovf', 'arma_generate_sample', 'arma_impulse_response', 'arma2ar', 'arma2ma', 'deconvolve', 'lpol2index', 'index2lpol'] if __name__ == '__main__': # Simulate AR(1) #-------------- # ar * y = ma * eta ar = [1, -0.8] ma = [1.0] # generate AR data eta = 0.1 * np.random.randn(1000) yar1 = signal.lfilter(ar, ma, eta) print "\nExample 0" arest = ARIMAProcess(yar1) rhohat, cov_x, infodict, mesg, ier = arest.fit((1,0,1)) print rhohat print cov_x print "\nExample 1" ar = [1.0, -0.8] ma = [1.0, 0.5] y1 = arest.generate_sample(ar,ma,1000,0.1) arest = ARIMAProcess(y1) rhohat1, cov_x1, infodict, mesg, ier = arest.fit((1,0,1)) print rhohat1 print cov_x1 err1 = arest.errfn(x=y1) print np.var(err1) import statsmodels.api as sm print sm.regression.yule_walker(y1, order=2, inv=True) print "\nExample 2" nsample = 1000 ar = [1.0, -0.6, -0.1] ma = [1.0, 0.3, 0.2] y2 = ARIMA.generate_sample(ar,ma,nsample,0.1) arest2 = ARIMAProcess(y2) rhohat2, cov_x2, infodict, mesg, ier = arest2.fit((1,0,2)) print rhohat2 print cov_x2 err2 = arest.errfn(x=y2) print np.var(err2) print arest2.rhoy print arest2.rhoe print "true" print ar print ma rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,0,2)) print rhohat2a print cov_x2a err2a = arest.errfn(x=y2) print np.var(err2a) print arest2.rhoy print arest2.rhoe print "true" print ar print ma print sm.regression.yule_walker(y2, order=2, inv=True) print "\nExample 20" nsample = 1000 ar = [1.0]#, -0.8, -0.4] ma = [1.0, 0.5, 0.2] y3 = ARIMA.generate_sample(ar,ma,nsample,0.01) arest20 = ARIMAProcess(y3) rhohat3, cov_x3, infodict, mesg, ier = arest20.fit((2,0,0)) print rhohat3 print cov_x3 err3 = arest20.errfn(x=y3) print np.var(err3) print np.sqrt(np.dot(err3,err3)/nsample) print arest20.rhoy print arest20.rhoe print "true" print ar print ma rhohat3a, cov_x3a, infodict, mesg, ier = arest20.fit((0,0,2)) print rhohat3a print cov_x3a err3a = arest20.errfn(x=y3) print np.var(err3a) print np.sqrt(np.dot(err3a,err3a)/nsample) print arest20.rhoy print arest20.rhoe print "true" print ar print ma print sm.regression.yule_walker(y3, order=2, inv=True) print "\nExample 02" nsample = 1000 ar = [1.0, -0.8, 0.4] #-0.8, -0.4] ma = [1.0]#, 0.8, 0.4] y4 = ARIMA.generate_sample(ar,ma,nsample) arest02 = ARIMAProcess(y4) rhohat4, cov_x4, infodict, mesg, ier = arest02.fit((2,0,0)) print rhohat4 print cov_x4 err4 = arest02.errfn(x=y4) print np.var(err4) sige = np.sqrt(np.dot(err4,err4)/nsample) print sige print sige * np.sqrt(np.diag(cov_x4)) print np.sqrt(np.diag(cov_x4)) print arest02.rhoy print arest02.rhoe print "true" print ar print ma rhohat4a, cov_x4a, infodict, mesg, ier = arest02.fit((0,0,2)) print rhohat4a print cov_x4a err4a = arest02.errfn(x=y4) print np.var(err4a) sige = np.sqrt(np.dot(err4a,err4a)/nsample) print sige print sige * np.sqrt(np.diag(cov_x4a)) print np.sqrt(np.diag(cov_x4a)) print arest02.rhoy print arest02.rhoe print "true" print ar print ma import statsmodels.api as sm print sm.regression.yule_walker(y4, order=2, method='mle', inv=True) import matplotlib.pyplot as plt plt.plot(arest2.forecast()[-100:]) #plt.show() ar1, ar2 = ([1, -0.4], [1, 0.5]) ar2 = [1, -1] lagpolyproduct = np.convolve(ar1, ar2) print deconvolve(lagpolyproduct, ar2, n=None) print signal.deconvolve(lagpolyproduct, ar2) print deconvolve(lagpolyproduct, ar2, n=10) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/arma_mle.py000066400000000000000000000322601224417117700234350ustar00rootroot00000000000000""" Created on Sun Oct 10 14:57:50 2010 Author: josef-pktd, Skipper Seabold License: BSD TODO: check everywhere initialization of signal.lfilter """ import numpy as np from scipy import signal, optimize from statsmodels.base.model import (LikelihoodModel, GenericLikelihoodModel) #copied from sandbox/regression/mle.py #rename until merge of classes is complete class Arma(GenericLikelihoodModel): #switch to generic mle """ univariate Autoregressive Moving Average model, conditional on initial values The ARMA model is estimated either with conditional Least Squares or with conditional Maximum Likelihood. The implementation is using scipy.filter.lfilter which makes it faster than the Kalman Filter Implementation. The Kalman Filter Implementation however uses the exact Maximum Likelihood and will be more accurate, statistically more efficent in small samples. In large samples conditional LS, conditional MLE and exact MLE should be very close to each other, they are equivalent asymptotically. Notes ----- this can subclass TSMLEModel TODO: - CondLS return raw estimation results - needs checking that there is no wrong state retained, when running fit several times with different options - still needs consistent order options. - Currently assumes that the mean is zero, no mean or effect of exogenous variables are included in the estimation. """ def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(Arma, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() def initialize(self): pass def geterrors(self, params): #copied from sandbox.tsa.arima.ARIMA p, q = self.nar, self.nma ar = np.concatenate(([1], -params[:p])) ma = np.concatenate(([1], params[p:p+q])) #lfilter_zi requires same length for ar and ma maxlag = 1+max(p,q) armax = np.zeros(maxlag) armax[:p+1] = ar mamax = np.zeros(maxlag) mamax[:q+1] = ma #remove zi again to match better with Skipper's version #zi = signal.lfilter_zi(armax, mamax) #errorsest = signal.lfilter(rhoy, rhoe, self.endog, zi=zi)[0] #zi is also returned errorsest = signal.lfilter(ar, ma, self.endog) return errorsest def loglike(self, params): """ Loglikelihood for arma model Notes ----- The ancillary parameter is assumed to be the last element of the params vector """ # #copied from sandbox.tsa.arima.ARIMA # p = self.nar # rhoy = np.concatenate(([1], params[:p])) # rhoe = np.concatenate(([1], params[p:-1])) # errorsest = signal.lfilter(rhoy, rhoe, self.endog) errorsest = self.geterrors(params) sigma2 = np.maximum(params[-1]**2, 1e-6) axis = 0 nobs = len(errorsest) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 # llike = -0.5 * (np.sum(np.log(sigma2),axis) # + np.sum((errorsest**2)/sigma2, axis) # + nobs*np.log(2*np.pi)) llike = -0.5 * (nobs*np.log(sigma2) + np.sum((errorsest**2)/sigma2, axis) + nobs*np.log(2*np.pi)) return llike #add for Jacobian calculation bsejac in GenericMLE, copied from loglike def nloglikeobs(self, params): """ Loglikelihood for arma model Notes ----- The ancillary parameter is assumed to be the last element of the params vector """ # #copied from sandbox.tsa.arima.ARIMA # p = self.nar # rhoy = np.concatenate(([1], params[:p])) # rhoe = np.concatenate(([1], params[p:-1])) # errorsest = signal.lfilter(rhoy, rhoe, self.endog) errorsest = self.geterrors(params) sigma2 = np.maximum(params[-1]**2, 1e-6) axis = 0 nobs = len(errorsest) #this doesn't help for exploding paths #errorsest[np.isnan(errorsest)] = 100 # llike = -0.5 * (np.sum(np.log(sigma2),axis) # + np.sum((errorsest**2)/sigma2, axis) # + nobs*np.log(2*np.pi)) llike = 0.5 * (np.log(sigma2) + (errorsest**2)/sigma2 + np.log(2*np.pi)) return llike #use generic instead # def score(self, params): # """ # Score vector for Arma model # """ # #return None # #print params # jac = ndt.Jacobian(self.loglike, stepMax=1e-4) # return jac(params)[-1] #use generic instead # def hessian(self, params): # """ # Hessian of arma model. Currently uses numdifftools # """ # #return None # Hfun = ndt.Jacobian(self.score, stepMax=1e-4) # return Hfun(params)[-1] #copied from arima.ARIMA, needs splitting out of method specific code def fit(self, order=(0,0), start_params=None, method="ls", **optkwds): ''' Estimate lag coefficients of an ARIMA process. Parameters ---------- order : sequence p,d,q where p is the number of AR lags, d is the number of differences to induce stationarity, and q is the number of MA lags to estimate. method : str {"ls", "ssm"} Method of estimation. LS is conditional least squares. SSM is state-space model and the Kalman filter is used to maximize the exact likelihood. rhoy0, rhoe0 : array_like (optional) starting values for estimation Returns ------- (rh, cov_x, infodict, mesg, ier) : output of scipy.optimize.leastsq rh : estimate of lag parameters, concatenated [rhoy, rhoe] cov_x : unscaled (!) covariance matrix of coefficient estimates ''' if not hasattr(order, '__iter__'): raise ValueError("order must be an iterable sequence. Got type \ %s instead" % type(order)) p,q = order self.nar = p # needed for geterrors, needs cleanup self.nma = q ## if d > 0: ## raise ValueError("Differencing not implemented yet") ## # assume no constant, ie mu = 0 ## # unless overwritten then use w_bar for mu ## Y = np.diff(endog, d, axis=0) #TODO: handle lags? x = self.endog.squeeze() # remove the squeeze might be needed later # def errfn( rho): # #rhoy, rhoe = rho # rhoy = np.concatenate(([1], rho[:p])) # rhoe = np.concatenate(([1], rho[p:])) # etahatr = signal.lfilter(rhoy, rhoe, x) # #print rho,np.sum(etahatr*etahatr) # return etahatr #replace with start_params if start_params is None: arcoefs0 = 0.5 * np.ones(p) macoefs0 = 0.5 * np.ones(q) start_params = np.r_[arcoefs0, macoefs0] method = method.lower() if method == "ls": #update optim_kwds = dict(ftol=1e-10, full_output=True) optim_kwds.update(optkwds) #changes: use self.geterrors (nobs,): # rh, cov_x, infodict, mesg, ier = \ # optimize.leastsq(errfn, np.r_[rhoy0, rhoe0],ftol=1e-10,full_output=True) rh, cov_x, infodict, mesg, ier = \ optimize.leastsq(self.geterrors, start_params, **optim_kwds) #TODO: need missing parameter estimates for LS, scale, residual-sdt #TODO: integrate this into the MLE.fit framework? elif method == "ssm": pass else: #this is also conditional least squares # fmin_bfgs is slow or doesn't work yet errfnsum = lambda rho : np.sum(self.geterrors(rho)**2) #xopt, {fopt, gopt, Hopt, func_calls, grad_calls optim_kwds = dict(maxiter=2, full_output=True) optim_kwds.update(optkwds) rh, fopt, gopt, cov_x, _,_, ier = \ optimize.fmin_bfgs(errfnsum, start_params, **optim_kwds) infodict, mesg = None, None self.params = rh self.ar_est = np.concatenate(([1], -rh[:p])) self.ma_est = np.concatenate(([1], rh[p:p+q])) #rh[-q:])) doesnt work for q=0, added p+q as endpoint for safety if var is included self.error_estimate = self.geterrors(rh) return rh, cov_x, infodict, mesg, ier #renamed and needs check with other fit def fit_mle(self, order=(0,0), start_params=None, method='nm', maxiter=5000, tol=1e-08, **kwds): '''Estimate an ARMA model with given order using Conditional Maximum Likelihood Parameters ---------- order : tuple, 2 elements specifies the number of lags(nar, nma) to include, not including lag 0 start_params : array_like, 1d, (nar+nma+1,) start parameters for the optimization, the length needs to be equal to the number of ar plus ma coefficients plus 1 for the residual variance method : str optimization method, as described in LikelihoodModel maxiter : int maximum number of iteration in the optimization tol : float tolerance (?) for the optimization Returns ------- mlefit : instance of (GenericLikelihood ?)Result class contains estimation results and additional statistics ''' nar, nma = p, q = order self.nar, self.nma = nar, nma if start_params is None: start_params = np.concatenate((0.05*np.ones(nar + nma), [1])) mlefit = super(Arma, self).fit(start_params=start_params, maxiter=maxiter, method=method, tol=tol, **kwds) #bug fix: running ls and then mle didn't overwrite this rh = mlefit.params self.params = rh self.ar_est = np.concatenate(([1], -rh[:p])) self.ma_est = np.concatenate(([1], rh[p:p+q])) self.error_estimate = self.geterrors(rh) return mlefit #copied from arima.ARIMA def predicted(self, ar=None, ma=None): '''past predicted values of time series just added, not checked yet ''' # #ar, ma not used, not useful as arguments for predicted pattern # #need it for prediction for other time series, endog # if ar is None: # ar = self.ar_est # if ma is None: # ma = self.ma_est return self.endog - self.error_estimate #copied from arima.ARIMA def forecast(self, ar=None, ma=None, nperiod=10): '''nperiod ahead forecast at the end of the data period forecast is based on the error estimates ''' eta = np.r_[self.error_estimate, np.zeros(nperiod)] if ar is None: ar = self.ar_est if ma is None: ma = self.ma_est return signal.lfilter(ma, ar, eta) def forecast2(self, step_ahead=1, start=None, end=None, endog=None): '''rolling h-period ahead forecast without reestimation, 1 period ahead only in construction: uses loop to go over data and not sure how to get (finite) forecast polynomial for h-step Notes ----- just the idea: To improve performance with expanding arrays, specify total period by endog and the conditional forecast period by step_ahead This should be used by/with results which should contain predicted error or noise. Could be either a recursive loop or lfilter with a h-step ahead forecast filter, but then I need to calculate that one. ??? further extension: allow reestimation option question: return h-step ahead or range(h)-step ahead ? ''' if step_ahead != 1: raise NotImplementedError p,q = self.nar, self.nma k = 0 errors = self.error_estimate y = self.endog #this is for 1step ahead only, still need h-step predictive polynomial arcoefs_rev = self.params[k:k+p][::-1] macoefs_rev = self.params[k+p:k+p+q][::-1] predicted = [] # create error vector iteratively for i in range(start, end): predicted.append(sum(arcoefs_rev*y[i-p:i]) + sum(macoefs_rev * errors[i-p:i])) return np.asarray(predicted) def forecast3(self, step_ahead=1, start=None): #, end=None): '''another try for h-step ahead forecasting ''' from arima_process import arma2ma, ArmaProcess p,q = self.nar, self.nma k=0 ar = self.params[k:k+p] ma = self.params[k+p:k+p+q] marep = arma2ma(ar,ma, start)[step_ahead+1:] #truncated ma representation errors = self.error_estimate forecasts = np.convolve(errors, marep) return forecasts#[-(errors.shape[0] - start-5):] #get 5 overlapping for testing #copied from arima.ARIMA #TODO: is this needed as a method at all? #JP: not needed in this form, but can be replace with using the parameters @classmethod def generate_sample(cls, ar, ma, nsample, std=1): eta = std * np.random.randn(nsample) return signal.lfilter(ma, ar, eta) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/000077500000000000000000000000001224417117700222155ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/__init__.py000066400000000000000000000000001224417117700243140ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/datetools.py000066400000000000000000000250141224417117700245670ustar00rootroot00000000000000import re import datetime from pandas import datetools as pandas_datetools import numpy as np from statsmodels.compatnp.py3k import asstr #NOTE: All of these frequencies assume end of period (except wrt time) try: from pandas.tseries.frequencies import to_offset class _freq_to_pandas_class(object): # being lazy, don't want to replace dictionary below def __getitem__(self, key): return to_offset(key) _freq_to_pandas = _freq_to_pandas_class() except ImportError: _freq_to_pandas = {'B' : pandas_datetools.BDay(1), 'D' : pandas_datetools.day, 'W' : pandas_datetools.Week(weekday=6), 'M' : pandas_datetools.monthEnd, 'A' : pandas_datetools.yearEnd, 'Q' : pandas_datetools.quarterEnd} def _index_date(date, dates): """ Gets the index number of a date in a date index. Works in-sample and will return one past the end of the dates since prediction can start one out. Currently used to validate prediction start dates. If there dates are not of a fixed-frequency and date is not on the existing dates, then a ValueError is raised. """ if isinstance(date, basestring): date = date_parser(date) try: if hasattr(dates, 'indexMap'): # 0.7.x return dates.indexMap[date] else: date = dates.get_loc(date) try: # pandas 0.8.0 returns a boolean array len(date) return np.where(date)[0].item() except TypeError: # expected behavior return date except KeyError, err: freq = _infer_freq(dates) if freq is None: #TODO: try to intelligently roll forward onto a date in the # index. Waiting to drop pandas 0.7.x support so this is # cleaner to do. raise ValueError("There is no frequency for these dates and " "date %s is not in dates index. Try giving a " "date that is in the dates index or use " "an integer" % date) # we can start prediction at the end of endog if _idx_from_dates(dates[-1], date, freq) == 1: return len(dates) raise ValueError("date %s not in date index. Try giving a " "date that is in the dates index or use an integer" % date) def _date_from_idx(d1, idx, freq): """ Returns the date from an index beyond the end of a date series. d1 is the datetime of the last date in the series. idx is the index distance of how far the next date should be from d1. Ie., 1 gives the next date from d1 at freq. Notes ----- This does not do any rounding to make sure that d1 is actually on the offset. For now, this needs to be taken care of before you get here. """ return d1 + idx * _freq_to_pandas[freq] def _idx_from_dates(d1, d2, freq): """ Returns an index offset from datetimes d1 and d2. d1 is expected to be the last date in a date series and d2 is the out of sample date. Notes ----- Rounds down the index if the end date is before the next date at freq. Does not check the start date to see whether it is on the offest but assumes that it is. """ try: # pandas 0.8.x from pandas import DatetimeIndex return len(DatetimeIndex(start=d1, end=d2, freq = _freq_to_pandas[freq])) - 1 except ImportError, err: from pandas import DateRange return len(DateRange(d1, d2, offset = _freq_to_pandas[freq])) - 1 _quarter_to_day = { "1" : (3, 31), "2" : (6, 30), "3" : (9, 30), "4" : (12, 31), "I" : (3, 31), "II" : (6, 30), "III" : (9, 30), "IV" : (12, 31) } _mdays = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] _months_with_days = zip(range(1,13), _mdays) _month_to_day = dict(zip(map(str,range(1,13)), _months_with_days)) _month_to_day.update(dict(zip(["I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX", "X", "XI", "XII"], _months_with_days))) # regex patterns _y_pattern = '^\d?\d?\d?\d$' _q_pattern = ''' ^ # beginning of string \d?\d?\d?\d # match any number 1-9999, includes leading zeros (:?q) # use q or a : as a separator ([1-4]|(I{1,3}V?)) # match 1-4 or I-IV roman numerals $ # end of string ''' _m_pattern = ''' ^ # beginning of string \d?\d?\d?\d # match any number 1-9999, includes leading zeros (:?m) # use m or a : as a separator (([1-9][0-2]?)|(I?XI{0,2}|I?VI{0,3}|I{1,3})) # match 1-12 or # I-XII roman numerals $ # end of string ''' #NOTE: see also ts.extras.isleapyear, which accepts a sequence def _is_leap(year): year = int(year) return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) def date_parser(timestr, parserinfo=None, **kwargs): """ Uses dateutil.parser.parse, but also handles monthly dates of the form 1999m4, 1999:m4, 1999:mIV, 1999mIV and the same for quarterly data with q instead of m. It is not case sensitive. The default for annual data is the end of the year, which also differs from dateutil. """ flags = re.IGNORECASE | re.VERBOSE if re.search(_q_pattern, timestr, flags): y,q = timestr.replace(":","").lower().split('q') month, day = _quarter_to_day[q.upper()] year = int(y) elif re.search(_m_pattern, timestr, flags): y,m = timestr.replace(":","").lower().split('m') month, day = _month_to_day[m.upper()] year = int(y) if _is_leap(y) and month == 2: day += 1 elif re.search(_y_pattern, timestr, flags): month, day = 12, 31 year = int(timestr) else: if (hasattr(pandas_datetools, 'parser') and not callable(pandas_datetools.parser)): # exists in 0.8.0 pandas, but it's the class not the module return pandas_datetools.parser.parse(timestr, parserinfo, **kwargs) else: # 0.8.1 pandas version didn't import this into namespace from dateutil import parser return parser.parse(timestr, parserinfo, **kwargs) return datetime.datetime(year, month, day) def date_range_str(start, end=None, length=None): """ Returns a list of abbreviated date strings. Parameters ---------- start : str The first abbreviated date, for instance, '1965q1' or '1965m1' end : str, optional The last abbreviated date if length is None. length : int, optional The length of the returned array of end is None. Returns ------- date_range : list List of strings """ flags = re.IGNORECASE | re.VERBOSE #_check_range_inputs(end, length, freq) start = start.lower() if re.search(_m_pattern, start, flags): annual_freq = 12 split = 'm' elif re.search(_q_pattern, start, flags): annual_freq = 4 split = 'q' elif re.search(_y_pattern, start, flags): annual_freq = 1 start += 'a1' # hack if end: end += 'a1' split = 'a' else: raise ValueError("Date %s not understood" % start) yr1, offset1 = map(int, start.replace(":","").split(split)) if end is not None: end = end.lower() yr2, offset2 = map(int, end.replace(":","").split(split)) length = (yr2 - yr1) * annual_freq + offset2 elif length: yr2 = yr1 + length // annual_freq offset2 = length % annual_freq + (offset1 - 1) years = np.repeat(range(yr1+1, yr2), annual_freq).tolist() years = np.r_[[str(yr1)]*(annual_freq+1-offset1), years] # tack on first year years = np.r_[years, [str(yr2)]*offset2] # tack on last year if split != 'a': offset = np.tile(np.arange(1, annual_freq+1), yr2-yr1-1) offset = np.r_[np.arange(offset1, annual_freq+1).astype('a2'), offset] offset = np.r_[offset, np.arange(1,offset2+1).astype('a2')] date_arr_range = [''.join([i, split, asstr(j)]) for i,j in zip(years, offset)] else: date_arr_range = years.tolist() return date_arr_range def dates_from_str(dates): """ Turns a sequence of date strings and returns a list of datetime. Parameters ---------- dates : array-like A sequence of abbreviated dates as string. For instance, '1996m1' or '1996Q1'. The datetime dates are at the end of the period. Returns ------- date_list : array A list of datetime types. """ return map(date_parser, dates) def dates_from_range(start, end=None, length=None): """ Turns a sequence of date strings and returns a list of datetime. Parameters ---------- start : str The first abbreviated date, for instance, '1965q1' or '1965m1' end : str, optional The last abbreviated date if length is None. length : int, optional The length of the returned array of end is None. Example ------- >>> import statsmodels.api as sm >>> dates = sm.tsa.datetools.date_range('1960m1', length=nobs) Returns ------- date_list : array A list of datetime types. """ dates = date_range_str(start, end, length) return dates_from_str(dates) def _add_datetimes(dates): return reduce(lambda x, y: y+x, dates) def _infer_freq(dates): try: from pandas.tseries.api import infer_freq freq = infer_freq(dates) return freq except ImportError: pass timedelta = datetime.timedelta nobs = min(len(dates), 6) if nobs == 1: raise ValueError("Cannot infer frequency from one date") if hasattr(dates, 'values'): dates = dates.values # can't do a diff on a DateIndex diff = np.diff(dates[:nobs]) delta = _add_datetimes(diff) nobs -= 1 # after diff if delta == timedelta(nobs): #greedily assume 'D' return 'D' elif delta == timedelta(nobs + 2): return 'B' elif delta == timedelta(7*nobs): return 'W' elif delta >= timedelta(28*nobs) and delta <= timedelta(31*nobs): return 'M' elif delta >= timedelta(90*nobs) and delta <= timedelta(92*nobs): return 'Q' elif delta >= timedelta(365 * nobs) and delta <= timedelta(366 * nobs): return 'A' else: return statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/tests/000077500000000000000000000000001224417117700233575ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/tests/__init__.py000066400000000000000000000000001224417117700254560ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/tests/test_base.py000066400000000000000000000055171224417117700257120ustar00rootroot00000000000000import numpy as np from pandas import Series from pandas.util import testing as ptesting from statsmodels.tsa.base.tsa_model import TimeSeriesModel from statsmodels.tsa.base.datetools import dates_from_range import numpy.testing as npt try: from pandas import DatetimeIndex _pandas_08x = True except ImportError: _pandas_08x = False def test_pandas_nodates_index(): from statsmodels.datasets import sunspots y = sunspots.load_pandas().data.SUNACTIVITY npt.assert_raises(ValueError, TimeSeriesModel, y) def test_predict_freq(): # test that predicted dates have same frequency x = np.arange(1,36.) if _pandas_08x: from pandas import date_range # there's a bug in pandas up to 0.10.2 for YearBegin #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR") dates = date_range("1972-4-30", "2006-4-30", freq="A-APR") series = Series(x, index=dates) model = TimeSeriesModel(series) #npt.assert_(model.data.freq == "AS-APR") npt.assert_(model.data.freq == "A-APR") start = model._get_predict_start("2006-4-30") end = model._get_predict_end("2016-4-30") model._make_predict_dates() predict_dates = model.data.predict_dates #expected_dates = date_range("2006-12-31", "2016-12-31", # freq="AS-APR") expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR") npt.assert_equal(predict_dates, expected_dates) #ptesting.assert_series_equal(predict_dates, expected_dates) else: from pandas import DateRange, datetools dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd) series = Series(x, index=dates) model = TimeSeriesModel(series) npt.assert_(model.data.freq == "A") start = model._get_predict_start("2006-12-31") end = model._get_predict_end("2016-12-31") model._make_predict_dates() predict_dates = model.data.predict_dates expected_dates = DateRange("2006-12-31", "2016-12-31", offset=datetools.yearEnd) npt.assert_array_equal(predict_dates, expected_dates) def test_keyerror_start_date(): x = np.arange(1,36.) if _pandas_08x: from pandas import date_range # there's a bug in pandas up to 0.10.2 for YearBegin #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR") dates = date_range("1972-4-30", "2006-4-30", freq="A-APR") series = Series(x, index=dates) model = TimeSeriesModel(series) else: from pandas import DateRange, datetools dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd) series = Series(x, index=dates) model = TimeSeriesModel(series) npt.assert_raises(ValueError, model._get_predict_start, "1970-4-30") statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/tests/test_datetools.py000066400000000000000000000143331224417117700267720ustar00rootroot00000000000000from datetime import datetime import numpy.testing as npt from statsmodels.tsa.base.datetools import (_date_from_idx, _idx_from_dates, date_parser, date_range_str, dates_from_str, dates_from_range, _infer_freq, _freq_to_pandas) def test_date_from_idx(): d1 = datetime(2008, 12, 31) idx = 15 npt.assert_equal(_date_from_idx(d1, idx, 'Q'), datetime(2012, 9, 30)) npt.assert_equal(_date_from_idx(d1, idx, 'A'), datetime(2023, 12, 31)) npt.assert_equal(_date_from_idx(d1, idx, 'B'), datetime(2009, 1, 21)) npt.assert_equal(_date_from_idx(d1, idx, 'D'), datetime(2009, 1, 15)) npt.assert_equal(_date_from_idx(d1, idx, 'W'), datetime(2009, 4, 12)) npt.assert_equal(_date_from_idx(d1, idx, 'M'), datetime(2010, 3, 31)) def test_idx_from_date(): d1 = datetime(2008, 12, 31) idx = 15 npt.assert_equal(_idx_from_dates(d1, datetime(2012, 9, 30), 'Q'), idx) npt.assert_equal(_idx_from_dates(d1, datetime(2023, 12, 31), 'A'), idx) npt.assert_equal(_idx_from_dates(d1, datetime(2009, 1, 21), 'B'), idx) npt.assert_equal(_idx_from_dates(d1, datetime(2009, 1, 15), 'D'), idx) # move d1 and d2 forward to end of week npt.assert_equal(_idx_from_dates(datetime(2009, 1, 4), datetime(2009, 4, 17), 'W'), idx-1) npt.assert_equal(_idx_from_dates(d1, datetime(2010, 3, 31), 'M'), idx) def test_regex_matching_month(): t1 = "1999m4" t2 = "1999:m4" t3 = "1999:mIV" t4 = "1999mIV" result = datetime(1999, 4, 30) npt.assert_equal(date_parser(t1), result) npt.assert_equal(date_parser(t2), result) npt.assert_equal(date_parser(t3), result) npt.assert_equal(date_parser(t4), result) def test_regex_matching_quarter(): t1 = "1999q4" t2 = "1999:q4" t3 = "1999:qIV" t4 = "1999qIV" result = datetime(1999, 12, 31) npt.assert_equal(date_parser(t1), result) npt.assert_equal(date_parser(t2), result) npt.assert_equal(date_parser(t3), result) npt.assert_equal(date_parser(t4), result) def test_dates_from_range(): results = [datetime(1959, 3, 31, 0, 0), datetime(1959, 6, 30, 0, 0), datetime(1959, 9, 30, 0, 0), datetime(1959, 12, 31, 0, 0), datetime(1960, 3, 31, 0, 0), datetime(1960, 6, 30, 0, 0), datetime(1960, 9, 30, 0, 0), datetime(1960, 12, 31, 0, 0), datetime(1961, 3, 31, 0, 0), datetime(1961, 6, 30, 0, 0), datetime(1961, 9, 30, 0, 0), datetime(1961, 12, 31, 0, 0), datetime(1962, 3, 31, 0, 0), datetime(1962, 6, 30, 0, 0)] dt_range = dates_from_range('1959q1', '1962q2') npt.assert_(results == dt_range) # test with starting period not the first with length results = results[2:] dt_range = dates_from_range('1959q3', length=len(results)) npt.assert_(results == dt_range) # check month results = [datetime(1959, 3, 31, 0, 0), datetime(1959, 4, 30, 0, 0), datetime(1959, 5, 31, 0, 0), datetime(1959, 6, 30, 0, 0), datetime(1959, 7, 31, 0, 0), datetime(1959, 8, 31, 0, 0), datetime(1959, 9, 30, 0, 0), datetime(1959, 10, 31, 0, 0), datetime(1959, 11, 30, 0, 0), datetime(1959, 12, 31, 0, 0), datetime(1960, 1, 31, 0, 0), datetime(1960, 2, 28, 0, 0), datetime(1960, 3, 31, 0, 0), datetime(1960, 4, 30, 0, 0), datetime(1960, 5, 31, 0, 0), datetime(1960, 6, 30, 0, 0), datetime(1960, 7, 31, 0, 0), datetime(1960, 8, 31, 0, 0), datetime(1960, 9, 30, 0, 0), datetime(1960, 10, 31, 0, 0), datetime(1960, 12, 31, 0, 0), datetime(1961, 1, 31, 0, 0), datetime(1961, 2, 28, 0, 0), datetime(1961, 3, 31, 0, 0), datetime(1961, 4, 30, 0, 0), datetime(1961, 5, 31, 0, 0), datetime(1961, 6, 30, 0, 0), datetime(1961, 7, 31, 0, 0), datetime(1961, 8, 31, 0, 0), datetime(1961, 9, 30, 0, 0), datetime(1961, 10, 31, 0, 0)] dt_range = dates_from_range("1959m3", length=len(results)) try: from pandas import DatetimeIndex _pandas_08x = True except ImportError, err: _pandas_08x = False d1 = datetime(2008, 12, 31) d2 = datetime(2012, 9, 30) def test_infer_freq(): d1 = datetime(2008, 12, 31) d2 = datetime(2012, 9, 30) if _pandas_08x: b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values assert _infer_freq(w) == 'W-SUN' assert _infer_freq(a) == 'A-DEC' assert _infer_freq(q) == 'Q-DEC' assert _infer_freq(w[:3]) == 'W-SUN' assert _infer_freq(a[:3]) == 'A-DEC' assert _infer_freq(q[:3]) == 'Q-DEC' else: from pandas import DateRange b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values assert _infer_freq(w) == 'W' assert _infer_freq(a) == 'A' assert _infer_freq(q) == 'Q' assert _infer_freq(w[:3]) == 'W' assert _infer_freq(a[:3]) == 'A' assert _infer_freq(q[:3]) == 'Q' assert _infer_freq(b[2:5]) == 'B' assert _infer_freq(b[:3]) == 'D' assert _infer_freq(b) == 'B' assert _infer_freq(d) == 'D' assert _infer_freq(m) == 'M' assert _infer_freq(d[:3]) == 'D' assert _infer_freq(m[:3]) == 'M' statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/base/tsa_model.py000066400000000000000000000237501224417117700245450ustar00rootroot00000000000000import statsmodels.base.model as base from statsmodels.base import data import statsmodels.base.wrapper as wrap from statsmodels.tsa.base import datetools from numpy import arange, asarray from pandas import Index from pandas import datetools as pandas_datetools import datetime _freq_to_pandas = datetools._freq_to_pandas _tsa_doc = """ %(model)s Parameters ---------- %(params)s dates : array-like of datetime, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or 'B', 'D', 'W', 'M', 'A', or 'Q'. This is optional if dates are given. %(extra_params)s %(extra_sections)s """ _model_doc = "Timeseries model base class" _generic_params = base._model_params_doc _missing_param_doc = base._missing_param_doc class TimeSeriesModel(base.LikelihoodModel): __doc__ = _tsa_doc % {"model" : _model_doc, "params" : _generic_params, "extra_params" : _missing_param_doc, "extra_sections" : ""} def __init__(self, endog, exog=None, dates=None, freq=None, missing='none'): super(TimeSeriesModel, self).__init__(endog, exog, missing=missing) self._init_dates(dates, freq) def _init_dates(self, dates, freq): if dates is None: dates = self.data.row_labels if dates is not None: if (not isinstance(dates[0], datetime.datetime) and isinstance(self.data, data.PandasData)): raise ValueError("Given a pandas object and the index does " "not contain dates") if not freq: try: freq = datetools._infer_freq(dates) except: raise ValueError("Frequency inference failed. Use `freq` " "keyword.") dates = Index(dates) self.data.dates = dates if freq: try: #NOTE: Can drop this once we move to pandas >= 0.8.x _freq_to_pandas[freq] except: raise ValueError("freq %s not understood" % freq) self.data.freq = freq def _get_exog_names(self): return self.data.xnames def _set_exog_names(self, vals): if not isinstance(vals, list): vals = [vals] self.data.xnames = vals #overwrite with writable property for (V)AR models exog_names = property(_get_exog_names, _set_exog_names) def _get_dates_loc(self, dates, date): if hasattr(dates, 'indexMap'): # 0.7.x date = dates.indexMap[date] else: date = dates.get_loc(date) try: # pandas 0.8.0 returns a boolean array len(date) from numpy import where date = where(date)[0].item() except TypeError: # this is expected behavior pass return date def _str_to_date(self, date): """ Takes a string and returns a datetime object """ return datetools.date_parser(date) def _set_predict_start_date(self, start): dates = self.data.dates if dates is None: return if start > len(dates): raise ValueError("Start must be <= len(endog)") if start == len(dates): self.data.predict_start = datetools._date_from_idx(dates[-1], start, self.data.freq) elif start < len(dates): self.data.predict_start = dates[start] else: raise ValueError("Start must be <= len(dates)") def _get_predict_start(self, start): """ Returns the index of the given start date. Subclasses should define default behavior for start = None. That isn't handled here. Start can be a string or an integer if self.data.dates is None. """ dates = self.data.dates if isinstance(start, str): if dates is None: raise ValueError("Got a string for start and dates is None") dtstart = self._str_to_date(start) self.data.predict_start = dtstart try: start = self._get_dates_loc(dates, dtstart) except KeyError: raise ValueError("Start must be in dates. Got %s | %s" % (str(start), str(dtstart))) self._set_predict_start_date(start) return start def _get_predict_end(self, end): """ See _get_predict_start for more information. Subclasses do not need to define anything for this. """ out_of_sample = 0 # will be overwritten if needed if end is None: # use data for ARIMA - endog changes end = len(self.data.endog) - 1 dates = self.data.dates freq = self.data.freq if isinstance(end, str): if dates is None: raise ValueError("Got a string for end and dates is None") try: dtend = self._str_to_date(end) self.data.predict_end = dtend end = self._get_dates_loc(dates, dtend) except KeyError, err: # end is greater than dates[-1]...probably if dtend > self.data.dates[-1]: end = len(self.data.endog) - 1 freq = self.data.freq out_of_sample = datetools._idx_from_dates(dates[-1], dtend, freq) else: if freq is None: raise ValueError("There is no frequency for these " "dates and date %s is not in dates " "index. Try giving a date that is in " "the dates index or use an integer." % dtend) else: #pragma: no cover raise err # should never get here self._make_predict_dates() # attaches self.data.predict_dates elif isinstance(end, int) and dates is not None: try: self.data.predict_end = dates[end] except IndexError, err: nobs = len(self.data.endog) - 1 # as an index out_of_sample = end - nobs end = nobs if freq is not None: self.data.predict_end = datetools._date_from_idx(dates[-1], out_of_sample, freq) elif out_of_sample <= 0: # have no frequency but are in sample #TODO: what error to catch here to make sure dates is #on the index? try: self.data.predict_end = self._get_dates_loc(dates, end) except KeyError: raise else: self.data.predict_end = end + out_of_sample self.data.predict_start = self._get_dates_loc(dates, self.data.predict_start) self._make_predict_dates() elif isinstance(end, int): nobs = len(self.data.endog) - 1 # is an index if end > nobs: out_of_sample = end - nobs end = nobs elif freq is None: # should have a date with freq = None raise ValueError("When freq is None, you must give an integer " "index for end.") return end, out_of_sample def _make_predict_dates(self): data = self.data dtstart = data.predict_start dtend = data.predict_end freq = data.freq if freq is not None: pandas_freq = _freq_to_pandas[freq] try: from pandas import DatetimeIndex dates = DatetimeIndex(start=dtstart, end=dtend, freq=pandas_freq) except ImportError, err: from pandas import DateRange dates = DateRange(dtstart, dtend, offset = pandas_freq).values # handle elif freq is None and (isinstance(dtstart, int) and isinstance(dtend, int)): from pandas import Index dates = Index(range(dtstart, dtend+1)) # if freq is None and dtstart and dtend aren't integers, we're # in sample else: dates = self.data.dates start = self._get_dates_loc(dates, dtstart) end = self._get_dates_loc(dates, dtend) dates = dates[start:end+1] # is this index inclusive? self.data.predict_dates = dates class TimeSeriesModelResults(base.LikelihoodModelResults): def __init__(self, model, params, normalized_cov_params, scale=1.): self.data = model.data super(TimeSeriesModelResults, self).__init__(model, params, normalized_cov_params, scale) class TimeSeriesResultsWrapper(wrap.ResultsWrapper): _attrs = {} _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_attrs, _attrs) _methods = {'predict' : 'dates'} _wrap_methods = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_methods, _methods) wrap.populate_wrapper(TimeSeriesResultsWrapper, TimeSeriesModelResults) if __name__ == "__main__": import statsmodels.api as sm import datetime import pandas data = sm.datasets.macrodata.load() #make a DataFrame #TODO: attach a DataFrame to some of the datasets, for quicker use dates = [str(int(x[0])) +':'+ str(int(x[1])) \ for x in data.data[['year','quarter']]] df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates) ex_mod = TimeSeriesModel(df) #ts_series = pandas.TimeSeries() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/descriptivestats.py000066400000000000000000000043611224417117700252610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Descriptive Statistics for Time Series Created on Sat Oct 30 14:24:08 2010 Author: josef-pktd License: BSD(3clause) """ import numpy as np import stattools as stt #todo: check subclassing for descriptive stats classes class TsaDescriptive(object): '''collection of descriptive statistical methods for time series ''' def __init__(self, data, label=None, name=''): self.data = data self.label = label self.name = name def filter(self, num, den): from scipy.signal import lfilter xfiltered = lfilter(num, den, self.data) return self.__class__(xfiltered, self.label, self.name + '_filtered') def detrend(self, order=1): import tsatools xdetrended = tsatools.detrend(self.data, order=order) return self.__class__(xdetrended, self.label, self.name + '_detrended') def fit(self, order=(1,0,1), **kwds): from arima_model import ARMA self.mod = ARMA(self.data) self.res = self.mod.fit(order=order, **kwds) #self.estimated_process = return self.res def acf(self, nlags=40): return stt.acf(self.data, nlags=nlags) def pacf(self, nlags=40): return stt.pacf(self.data, nlags=nlags) def periodogram(self): #doesn't return frequesncies return stt.periodogram(self.data) # copied from fftarma.py def plot4(self, fig=None, nobs=100, nacf=20, nfreq=100): data = self.data acf = self.acf(nacf) pacf = self.pacf(nacf) w = np.linspace(0, np.pi, nfreq, endpoint=False) spdr = self.periodogram()[:nfreq] #(w) if fig is None: import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(2,2,1) namestr = ' for %s' % self.name if self.name else '' ax.plot(data) ax.set_title('Time series' + namestr) ax = fig.add_subplot(2,2,2) ax.plot(acf) ax.set_title('Autocorrelation' + namestr) ax = fig.add_subplot(2,2,3) ax.plot(spdr) # (wr, spdr) ax.set_title('Power Spectrum' + namestr) ax = fig.add_subplot(2,2,4) ax.plot(pacf) ax.set_title('Partial Autocorrelation' + namestr) return fig statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/000077500000000000000000000000001224417117700227535ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/__init__.py000066400000000000000000000002201224417117700250560ustar00rootroot00000000000000from .bk_filter import bkfilter from .hp_filter import hpfilter from .cf_filter import cffilter from .filtertools import miso_lfilter, arfilter statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/bk_filter.py000066400000000000000000000053001224417117700252640ustar00rootroot00000000000000from __future__ import absolute_import import numpy as np from scipy.signal import fftconvolve from .utils import _maybe_get_pandas_wrapper def bkfilter(X, low=6, high=32, K=12): """ Baxter-King bandpass filter Parameters ---------- X : array-like A 1 or 2d ndarray. If 2d, variables are assumed to be in columns. low : float Minimum period for oscillations, ie., Baxter and King suggest that the Burns-Mitchell U.S. business cycle has 6 for quarterly data and 1.5 for annual data. high : float Maximum period for oscillations BK suggest that the U.S. business cycle has 32 for quarterly data and 8 for annual data. K : int Lead-lag length of the filter. Baxter and King propose a truncation length of 12 for quarterly data and 3 for annual data. Returns ------- Y : array Cyclical component of X References ---------- :: Baxter, M. and R. G. King. "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series." *Review of Economics and Statistics*, 1999, 81(4), 575-593. Notes ----- Returns a centered weighted moving average of the original series. Where the weights a[j] are computed :: a[j] = b[j] + theta, for j = 0, +/-1, +/-2, ... +/- K b[0] = (omega_2 - omega_1)/pi b[j] = 1/(pi*j)(sin(omega_2*j)-sin(omega_1*j), for j = +/-1, +/-2,... and theta is a normalizing constant :: theta = -sum(b)/(2K+1) Examples -------- >>> import statsmodels.api as sm >>> dta = sm.datasets.macrodata.load() >>> X = dta.data['realinv'] >>> Y = sm.tsa.filters.bkfilter(X, 6, 24, 12) """ #TODO: change the docstring to ..math::? #TODO: allow windowing functions to correct for Gibb's Phenomenon? # adjust bweights (symmetrically) by below before demeaning # Lancosz Sigma Factors np.sinc(2*j/(2.*K+1)) _pandas_wrapper = _maybe_get_pandas_wrapper(X, K) X = np.asarray(X) omega_1 = 2.*np.pi/high # convert from freq. to periodicity omega_2 = 2.*np.pi/low bweights = np.zeros(2*K+1) bweights[K] = (omega_2 - omega_1)/np.pi # weight at zero freq. j = np.arange(1,int(K)+1) weights = 1/(np.pi*j)*(np.sin(omega_2*j)-np.sin(omega_1*j)) bweights[K+j] = weights # j is an idx bweights[:K] = weights[::-1] # make symmetric weights bweights -= bweights.mean() # make sure weights sum to zero if X.ndim == 2: bweights = bweights[:,None] X = fftconvolve(X, bweights, mode='valid') # get a centered moving avg/ # convolution if _pandas_wrapper is not None: return _pandas_wrapper(X) return X statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/cf_filter.py000066400000000000000000000054721224417117700252720ustar00rootroot00000000000000from __future__ import absolute_import import numpy as np from .utils import _maybe_get_pandas_wrapper # the data is sampled quarterly, so cut-off frequency of 18 # Wn is normalized cut-off freq #Cutoff frequency is that frequency where the magnitude response of the filter # is sqrt(1/2.). For butter, the normalized cutoff frequency Wn must be a # number between 0 and 1, where 1 corresponds to the Nyquist frequency, p # radians per sample. #NOTE: uses a loop, could probably be sped-up for very large datasets def cffilter(X, low=6, high=32, drift=True): """ Christiano Fitzgerald asymmetric, random walk filter Parameters ---------- X : array-like 1 or 2d array to filter. If 2d, variables are assumed to be in columns. low : float Minimum period of oscillations. Features below low periodicity are filtered out. Default is 6 for quarterly data, giving a 1.5 year periodicity. high : float Maximum period of oscillations. Features above high periodicity are filtered out. Default is 32 for quarterly data, giving an 8 year periodicity. drift : bool Whether or not to remove a trend from the data. The trend is estimated as np.arange(nobs)*(X[-1] - X[0])/(len(X)-1) Returns ------- cycle : array The features of `X` between periodicities given by low and high trend : array The trend in the data with the cycles removed. """ #TODO: cythonize/vectorize loop?, add ability for symmetric filter, # and estimates of theta other than random walk. if low < 2: raise ValueError("low must be >= 2") _pandas_wrapper = _maybe_get_pandas_wrapper(X) X = np.asanyarray(X) if X.ndim == 1: X = X[:,None] nobs, nseries = X.shape a = 2*np.pi/high b = 2*np.pi/low if drift: # get drift adjusted series X = X - np.arange(nobs)[:,None]*(X[-1] - X[0])/(nobs-1) J = np.arange(1,nobs+1) Bj = (np.sin(b*J)-np.sin(a*J))/(np.pi*J) B0 = (b-a)/np.pi Bj = np.r_[B0,Bj][:,None] y = np.zeros((nobs,nseries)) for i in xrange(nobs): B = -.5*Bj[0] -np.sum(Bj[1:-i-2]) A = -Bj[0] - np.sum(Bj[1:-i-2]) - np.sum(Bj[1:i]) - B y[i] = Bj[0] * X[i] + np.dot(Bj[1:-i-2].T,X[i+1:-1]) + B*X[-1] + \ np.dot(Bj[1:i].T, X[1:i][::-1]) + A*X[0] y = y.squeeze() cycle, trend = y, X.squeeze()-y if _pandas_wrapper is not None: return _pandas_wrapper(cycle), _pandas_wrapper(trend) return cycle, trend if __name__ == "__main__": import statsmodels as sm dta = sm.datasets.macrodata.load().data[['infl','tbilrate']].view((float,2))[1:] cycle, trend = cffilter(dta, 6, 32, drift=True) dta = sm.datasets.macrodata.load().data['tbilrate'][1:] cycle2, trend2 = cffilter(dta, 6, 32, drift=True) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/filtertools.py000066400000000000000000000225111224417117700256740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Linear Filters for time series analysis and testing TODO: * check common sequence in signature of filter functions (ar,ma,x) or (x,ar,ma) Created on Sat Oct 23 17:18:03 2010 Author: Josef-pktd """ #not original copied from various experimental scripts #version control history is there import numpy as np import scipy.fftpack as fft from scipy import signal from scipy.signal.signaltools import _centered as trim_centered #original changes and examples in sandbox.tsa.try_var_convolve # don't do these imports, here just for copied fftconvolve #get rid of these imports #from scipy.fftpack import fft, ifft, ifftshift, fft2, ifft2, fftn, \ # ifftn, fftfreq #from numpy import product,array def fftconvolveinv(in1, in2, mode="full"): """Convolve two N-dimensional arrays using FFT. See convolve. copied from scipy.signal.signaltools, but here used to try out inverse filter doesn't work or I can't get it to work 2010-10-23: looks ok to me for 1d, from results below with padded data array (fftp) but it doesn't work for multidimensional inverse filter (fftn) original signal.fftconvolve also uses fftn """ s1 = np.array(in1.shape) s2 = np.array(in2.shape) complex_result = (np.issubdtype(in1.dtype, np.complex) or np.issubdtype(in2.dtype, np.complex)) size = s1+s2-1 # Always use 2**n-sized FFT fsize = 2**np.ceil(np.log2(size)) IN1 = fft.fftn(in1,fsize) #IN1 *= fftn(in2,fsize) #JP: this looks like the only change I made IN1 /= fft.fftn(in2,fsize) # use inverse filter # note the inverse is elementwise not matrix inverse # is this correct, NO doesn't seem to work for VARMA fslice = tuple([slice(0, int(sz)) for sz in size]) ret = fft.ifftn(IN1)[fslice].copy() del IN1 if not complex_result: ret = ret.real if mode == "full": return ret elif mode == "same": if np.product(s1,axis=0) > np.product(s2,axis=0): osize = s1 else: osize = s2 return trim_centered(ret,osize) elif mode == "valid": return trim_centered(ret,abs(s2-s1)+1) #code duplication with fftconvolveinv def fftconvolve3(in1, in2=None, in3=None, mode="full"): """Convolve two N-dimensional arrays using FFT. See convolve. for use with arma (old version: in1=num in2=den in3=data * better for consistency with other functions in1=data in2=num in3=den * note in2 and in3 need to have consistent dimension/shape since I'm using max of in2, in3 shapes and not the sum copied from scipy.signal.signaltools, but here used to try out inverse filter doesn't work or I can't get it to work 2010-10-23 looks ok to me for 1d, from results below with padded data array (fftp) but it doesn't work for multidimensional inverse filter (fftn) original signal.fftconvolve also uses fftn """ if (in2 is None) and (in3 is None): raise ValueError('at least one of in2 and in3 needs to be given') s1 = np.array(in1.shape) if not in2 is None: s2 = np.array(in2.shape) else: s2 = 0 if not in3 is None: s3 = np.array(in3.shape) s2 = max(s2, s3) # try this looks reasonable for ARMA #s2 = s3 complex_result = (np.issubdtype(in1.dtype, np.complex) or np.issubdtype(in2.dtype, np.complex)) size = s1+s2-1 # Always use 2**n-sized FFT fsize = 2**np.ceil(np.log2(size)) #convolve shorter ones first, not sure if it matters if not in2 is None: IN1 = fft.fftn(in2, fsize) if not in3 is None: IN1 /= fft.fftn(in3, fsize) # use inverse filter # note the inverse is elementwise not matrix inverse # is this correct, NO doesn't seem to work for VARMA IN1 *= fft.fftn(in1, fsize) fslice = tuple([slice(0, int(sz)) for sz in size]) ret = fft.ifftn(IN1)[fslice].copy() del IN1 if not complex_result: ret = ret.real if mode == "full": return ret elif mode == "same": if np.product(s1,axis=0) > np.product(s2,axis=0): osize = s1 else: osize = s2 return trim_centered(ret,osize) elif mode == "valid": return trim_centered(ret,abs(s2-s1)+1) #original changes and examples in sandbox.tsa.try_var_convolve #examples and tests are there def arfilter(x, a): '''apply an autoregressive filter to a series x x can be 2d, a can be 1d, 2d, or 3d Parameters ---------- x : array_like data array, 1d or 2d, if 2d then observations in rows a : array_like autoregressive filter coefficients, ar lag polynomial see Notes Returns ------- y : ndarray, 2d filtered array, number of columns determined by x and a Notes ----- In general form this uses the linear filter :: y = a(L)x where x : nobs, nvars a : nlags, nvars, npoly Depending on the shape and dimension of a this uses different Lag polynomial arrays case 1 : a is 1d or (nlags,1) one lag polynomial is applied to all variables (columns of x) case 2 : a is 2d, (nlags, nvars) each series is independently filtered with its own lag polynomial, uses loop over nvar case 3 : a is 3d, (nlags, nvars, npoly) the ith column of the output array is given by the linear filter defined by the 2d array a[:,:,i], i.e. :: y[:,i] = a(.,.,i)(L) * x y[t,i] = sum_p sum_j a(p,j,i)*x(t-p,j) for p = 0,...nlags-1, j = 0,...nvars-1, for all t >= nlags All filtering is done with scipy.signal.convolve, so it will be reasonably fast for medium sized arrays. For large arrays fft convolution would be faster. Note: maybe convert to axis=1, Not TODO: initial conditions, make sure tests for 3d case are done, I don't remember how much I tested the 3d case ''' x = np.asarray(x) a = np.asarray(a) if x.ndim == 1: x = x[:,None] if x.ndim > 2: raise ValueError('x array has to be 1d or 2d') nvar = x.shape[1] nlags = a.shape[0] ntrim = nlags//2 # for x is 2d with ncols >1 if a.ndim == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a[:,None], mode='valid') # alternative: #return signal.lfilter(a,[1],x.astype(float),axis=0) elif a.ndim == 2: if min(a.shape) == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a, mode='valid') # case: independent ar #(a bit like recserar in gauss, but no x yet) result = np.zeros((x.shape[0]-nlags+1, nvar)) for i in range(nvar): # could also use np.convolve, but easier for swiching to fft result[:,i] = signal.convolve(x[:,i], a[:,i], mode='valid') return result elif a.ndim == 3: # case: vector autoregressive with lag matrices # #not necessary: # if np.any(a.shape[1:] != nvar): # raise ValueError('if 3d shape of a has to be (nobs,nvar,nvar)') yf = signal.convolve(x[:,:,None], a) yvalid = yf[ntrim:-ntrim, yf.shape[1]//2,:] return yvalid #copied from sandbox.tsa.garch def miso_lfilter(ar, ma, x, useic=False): #[0.1,0.1]): ''' use nd convolution to merge inputs, then use lfilter to produce output arguments for column variables return currently 1d Parameters ---------- ar : array_like, 1d, float autoregressive lag polynomial including lag zero, ar(L)y_t ma : array_like, same ndim as x, currently 2d moving average lag polynomial ma(L)x_t x : array_like, 2d input data series, time in rows, variables in columns Returns ------- y : array, 1d filtered output series inp : array, 1d combined input series Notes ----- currently for 2d inputs only, no choice of axis Use of signal.lfilter requires that ar lag polynomial contains floating point numbers does not cut off invalid starting and final values miso_lfilter find array y such that:: ar(L)y_t = ma(L)x_t with shapes y (nobs,), x (nobs,nvars), ar (narlags,), ma (narlags,nvars) ''' ma = np.asarray(ma) ar = np.asarray(ar) #inp = signal.convolve(x, ma, mode='valid') #inp = signal.convolve(x, ma)[:, (x.shape[1]+1)//2] #Note: convolve mixes up the variable left-right flip #I only want the flip in time direction #this might also be a mistake or problem in other code where I #switched from correlate to convolve # correct convolve version, for use with fftconvolve in other cases #inp2 = signal.convolve(x, ma[:,::-1])[:, (x.shape[1]+1)//2] inp = signal.correlate(x, ma[::-1,:])[:, (x.shape[1]+1)//2] #for testing 2d equivalence between convolve and correlate #np.testing.assert_almost_equal(inp2, inp) nobs = x.shape[0] # cut of extra values at end #todo initialize also x for correlate if useic: return signal.lfilter([1], ar, inp, #zi=signal.lfilter_ic(np.array([1.,0.]),ar, ic))[0][:nobs], inp[:nobs] zi=signal.lfiltic(np.array([1.,0.]),ar, useic))[0][:nobs], inp[:nobs] else: return signal.lfilter([1], ar, inp)[:nobs], inp[:nobs] #return signal.lfilter([1], ar, inp), inp statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/filters/hp_filter.py000066400000000000000000000056121224417117700253050ustar00rootroot00000000000000from __future__ import absolute_import from scipy import sparse from scipy.sparse import dia_matrix, eye as speye from scipy.sparse.linalg import spsolve import numpy as np from .utils import _maybe_get_pandas_wrapper def hpfilter(X, lamb=1600): """ Hodrick-Prescott filter Parameters ---------- X : array-like The 1d ndarray timeseries to filter of length (nobs,) or (nobs,1) lamb : float The Hodrick-Prescott smoothing parameter. A value of 1600 is suggested for quarterly data. Ravn and Uhlig suggest using a value of 6.25 (1600/4**4) for annual data and 129600 (1600*3**4) for monthly data. Returns ------- cycle : array The estimated cycle in the data given lamb. trend : array The estimated trend in the data given lamb. Examples --------- >>> import statsmodels.api as sm >>> dta = sm.datasets.macrodata.load() >>> X = dta.data['realgdp'] >>> cycle, trend = sm.tsa.filters.hpfilter(X,1600) Notes ----- The HP filter removes a smooth trend, `T`, from the data `X`. by solving min sum((X[t] - T[t])**2 + lamb*((T[t+1] - T[t]) - (T[t] - T[t-1]))**2) T t Here we implemented the HP filter as a ridge-regression rule using scipy.sparse. In this sense, the solution can be written as T = inv(I - lamb*K'K)X where I is a nobs x nobs identity matrix, and K is a (nobs-2) x nobs matrix such that K[i,j] = 1 if i == j or i == j + 2 K[i,j] = -2 if i == j + 1 K[i,j] = 0 otherwise References ---------- Hodrick, R.J, and E. C. Prescott. 1980. "Postwar U.S. Business Cycles: An Empricial Investigation." `Carnegie Mellon University discussion paper no. 451`. Ravn, M.O and H. Uhlig. 2002. "Notes On Adjusted the Hodrick-Prescott Filter for the Frequency of Observations." `The Review of Economics and Statistics`, 84(2), 371-80. """ _pandas_wrapper = _maybe_get_pandas_wrapper(X) X = np.asarray(X, float) if X.ndim > 1: X = X.squeeze() nobs = len(X) I = speye(nobs,nobs) offsets = np.array([0,1,2]) data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1) K = dia_matrix((data, offsets), shape=(nobs-2,nobs)) import scipy if (X.dtype != np.dtype('>> indicator = [50,100,150,100] * 5 >>> benchmark = [500,400,300,400,500] >>> benchmarked = dentonm(indicator, benchmark, freq="aq") Notes ----- Denton's method minimizes the distance given by the penalty function, in a least squares sense, between the unknown benchmarked series and the indicator series subject to the condition that the sum of the benchmarked series is equal to the benchmark. The modification allows that the first value not be pre-determined as is the case with Denton's original method. If the there is no benchmark provided for the last few indicator observations, then extrapolation is performed using the last benchmark-indicator ratio of the previous period. Minimizes sum((X[t]/I[t] - X[t-1]/I[t-1])**2) s.t. sum(X) = A, for each period. Where X is the benchmarked series, I is the indicator, and A is the benchmark. References ---------- Bloem, A.M, Dippelsman, R.J. and Maehle, N.O. 2001 Quarterly National Accounts Manual--Concepts, Data Sources, and Compilation. IMF. http://www.imf.org/external/pubs/ft/qna/2000/Textbook/index.htm Cholette, P. 1988. "Benchmarking systems of socio-economic time series." Statistics Canada, Time Series Research and Analysis Division, Working Paper No TSRA-88-017E. Denton, F.T. 1971. "Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization." Journal of the American Statistical Association. 99-102. """ # penalty : str # Penalty function. Can be "D1", "D2", "D3", "D4", "D5". # X is the benchmarked series and I is the indicator. # D1 - sum((X[t] - X[t-1]) - (I[t] - I[ti-1])**2) # D2 - sum((ln(X[t]/X[t-1]) - ln(I[t]/I[t-1]))**2) # D3 - sum((X[t]/X[t-1] / I[t]/I[t-1])**2) # D4 - sum((X[t]/I[t] - X[t-1]/I[t-1])**2) # D5 - sum((X[t]/I[t] / X[t-1]/I[t-1] - 1)**2) #NOTE: only D4 is the only one implemented, see IMF chapter 6. # check arrays and make 2d indicator = asarray(indicator) if indicator.ndim == 1: indicator = indicator[:,None] benchmark = asarray(benchmark) if benchmark.ndim == 1: benchmark = benchmark[:,None] # get dimensions N = len(indicator) # total number of high-freq m = len(benchmark) # total number of low-freq # number of low-freq observations for aggregate measure # 4 for annual to quarter and 3 for quarter to monthly if freq == "aq": k = 4 elif freq == "qm": k = 3 elif freq == "other": k = kwargs.get("k") if not k: raise ValueError("k must be supplied with freq=\"other\"") else: raise ValueError("freq %s not understood" % freq) n = k*m # number of indicator series with a benchmark for back-series # if k*m != n, then we are going to extrapolate q observations if N > n: q = N - n else: q = 0 # make the aggregator matrix #B = block_diag(*(ones((k,1)),)*m) B = np.kron(np.eye(m), ones((k,1))) # following the IMF paper, we can do Zinv = diag(1./indicator.squeeze()[:n]) # this is D in Denton's notation (not using initial value correction) # D = eye(n) # make off-diagonal = -1 # D[((np.diag_indices(n)[0])[:-1]+1,(np.diag_indices(n)[1])[:-1])] = -1 # account for starting conditions # H = D[1:,:] # HTH = dot(H.T,H) # just make HTH HTH = eye(n) diag_idx0, diag_idx1 = diag_indices(n) HTH[diag_idx0[1:-1], diag_idx1[1:-1]] += 1 HTH[diag_idx0[:-1]+1, diag_idx1[:-1]] = -1 HTH[diag_idx0[:-1], diag_idx1[:-1]+1] = -1 W = dot(dot(Zinv,HTH),Zinv) # make partitioned matrices #TODO: break this out so that we can simplify the linalg? I = zeros((n+m,n+m)) I[:n,:n] = W I[:n,n:] = B I[n:,:n] = B.T A = zeros((m+n,1)) # zero first-order constraints A[-m:] = benchmark # adding up constraints X = solve(I,A) X = X[:-m] # drop the lagrange multipliers # handle extrapolation if q > 0: # get last Benchmark-Indicator ratio bi = X[n-1]/indicator[n-1] extrapolated = bi * indicator[n:] X = r_[X,extrapolated] return X.squeeze() if __name__ == "__main__": import numpy as np #these will be the tests # from IMF paper # quarterly data indicator = np.array([98.2, 100.8, 102.2, 100.8, 99.0, 101.6, 102.7, 101.5, 100.5, 103.0, 103.5, 101.5]) # two annual observations benchmark = np.array([4000.,4161.4]) x_imf = dentonm(indicator, benchmark, freq="aq") imf_stata = np.array([969.8, 998.4, 1018.3, 1013.4, 1007.2, 1042.9, 1060.3, 1051.0, 1040.6, 1066.5, 1071.7, 1051.0]) np.testing.assert_almost_equal(imf_stata, x_imf, 1) # Denton example zQ = np.array([50,100,150,100] * 5) Y = np.array([500,400,300,400,500]) x_denton = dentonm(zQ, Y, freq="aq") x_stata = np.array([64.334796,127.80616,187.82379,120.03526,56.563894, 105.97568,147.50144,89.958987,40.547201,74.445963, 108.34473,76.66211,42.763347,94.14664,153.41596, 109.67405,58.290761,122.62556,190.41409,128.66959]) """ # Examples from the Denton 1971 paper k = 4 m = 5 n = m*k zQ = [50,100,150,100] * m Y = [500,400,300,400,500] A = np.eye(n) B = block_diag(*(np.ones((k,1)),)*m) r = Y - B.T.dot(zQ) #Ainv = inv(A) Ainv = A # shortcut for identity C = Ainv.dot(B).dot(inv(B.T.dot(Ainv).dot(B))) x = zQ + C.dot(r) # minimize first difference d(x-z) R = linalg.tri(n, dtype=float) # R is tril so actually R.T in paper Ainv = R.dot(R.T) C = Ainv.dot(B).dot(inv(B.T.dot(Ainv).dot(B))) x1 = zQ + C.dot(r) # minimize the second difference d**2(x-z) Ainv = R.dot(Ainv).dot(R.T) C = Ainv.dot(B).dot(inv(B.T.dot(Ainv).dot(B))) x12 = zQ + C.dot(r) # # do it proportionately (x-z)/z Z = np.diag(zQ) Ainv = np.eye(n) C = Z.dot(Ainv).dot(Z).dot(B).dot(inv(B.T.dot(Z).dot(Ainv).dot(Z).dot(B))) x11 = zQ + C.dot(r) # do it proportionately with differencing d((x-z)/z) Ainv = R.dot(R.T) C = Z.dot(Ainv).dot(Z).dot(B).dot(inv(B.T.dot(Z).dot(Ainv).dot(Z).dot(B))) x111 = zQ + C.dot(r) x_stata = np.array([64.334796,127.80616,187.82379,120.03526,56.563894, 105.97568,147.50144,89.958987,40.547201,74.445963, 108.34473,76.66211,42.763347,94.14664,153.41596, 109.67405,58.290761,122.62556,190.41409,128.66959]) """ statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/interp/tests/000077500000000000000000000000001224417117700237465ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/interp/tests/__init__.py000066400000000000000000000000001224417117700260450ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/interp/tests/test_denton.py000066400000000000000000000023351224417117700266510ustar00rootroot00000000000000import numpy as np from statsmodels.tsa.interp import dentonm def test_denton_quarterly(): # Data and results taken from IMF paper indicator = np.array([98.2, 100.8, 102.2, 100.8, 99.0, 101.6, 102.7, 101.5, 100.5, 103.0, 103.5, 101.5]) benchmark = np.array([4000.,4161.4]) x_imf = dentonm(indicator, benchmark, freq="aq") imf_stata = np.array([969.8, 998.4, 1018.3, 1013.4, 1007.2, 1042.9, 1060.3, 1051.0, 1040.6, 1066.5, 1071.7, 1051.0]) np.testing.assert_almost_equal(imf_stata, x_imf, 1) def test_denton_quarterly2(): # Test denton vs stata. Higher precision than other test. zQ = np.array([50,100,150,100] * 5) Y = np.array([500,400,300,400,500]) x_denton = dentonm(zQ, Y, freq="aq") x_stata = np.array([64.334796,127.80616,187.82379,120.03526,56.563894, 105.97568,147.50144,89.958987,40.547201,74.445963, 108.34473,76.66211,42.763347,94.14664,153.41596, 109.67405,58.290761,122.62556,190.41409,128.66959]) np.testing.assert_almost_equal(x_denton, x_stata, 5) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__,'-vvs','-x', '--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/kalmanf/000077500000000000000000000000001224417117700227145ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/kalmanf/__init__.py000066400000000000000000000000461224417117700250250ustar00rootroot00000000000000from kalmanfilter import KalmanFilter statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/kalmanf/kalman_loglike.pyx000066400000000000000000000121711224417117700264310ustar00rootroot00000000000000# cython: profile=True from numpy cimport float64_t, ndarray, complex128_t, complex64_t from numpy import log as nplog from numpy import identity, dot, kron, zeros, pi, exp, eye, sum, empty, ones from numpy.linalg import pinv cimport cython ctypedef float64_t DOUBLE ctypedef complex128_t COMPLEX128 ctypedef complex64_t COMPLEX64 cdef extern from "math.h": double log(double x) @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def kalman_filter_double(ndarray[DOUBLE, ndim=1] y, unsigned int k, unsigned int p, unsigned int q, unsigned int r, unsigned int nobs, ndarray[DOUBLE, ndim=2] Z_mat, ndarray[DOUBLE, ndim=2] R_mat, ndarray[DOUBLE, ndim=2] T_mat): """ Cython version of the Kalman filter recursions for an ARMA process. """ m = Z_mat.shape[1] # store forecast-errors v = zeros((nobs,1)) # store variance of forecast errors F = ones((nobs,1)) loglikelihood = zeros((1,1)) cdef int i = 0 # initial state # cdef np.ndarray[DOUBLE, ndim=2] alpha = zeros((m,1)) alpha = zeros((m,1)) # initial variance P = dot(pinv(identity(m**2)-kron(T_mat, T_mat)),dot(R_mat, R_mat.T).ravel('F')).reshape(r,r, order='F') F_mat = 0 while not F_mat == 1 and i < nobs: # Predict v_mat = y[i] - dot(Z_mat,alpha) # one-step forecast error v[i] = v_mat F_mat = dot(dot(Z_mat, P), Z_mat.T) F[i] = F_mat Finv = 1./F_mat # always scalar for univariate series K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # Kalman Gain Matrix # update state alpha = dot(T_mat, alpha) + dot(K,v_mat) L = T_mat - dot(K,Z_mat) P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T) loglikelihood += log(F_mat) i+=1 for i in xrange(i,nobs): v_mat = y[i] - dot(Z_mat,alpha) v[i] = v_mat alpha = dot(T_mat, alpha) + dot(K, v_mat) return v, F, loglikelihood @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def kalman_filter_complex(ndarray[COMPLEX128, ndim=1] y, unsigned int k, unsigned int p, unsigned int q, unsigned int r, unsigned int nobs, ndarray[DOUBLE, ndim=2] Z_mat, ndarray[COMPLEX128, ndim=2] R_mat, ndarray[COMPLEX128, ndim=2] T_mat): """ Cython version of the Kalman filter recursions for an ARMA process. """ m = Z_mat.shape[1] # store forecast-errors v = zeros((nobs,1), dtype=complex) # store variance of forecast errors F = ones((nobs,1), dtype=complex) loglikelihood = zeros((1,1), dtype=complex) cdef int i = 0 # initial state # cdef np.ndarray[DOUBLE, ndim=2] alpha = zeros((m,1)) alpha = zeros((m,1)) # initial variance P = dot(pinv(identity(m**2)-kron(T_mat, T_mat)),dot(R_mat, R_mat.T).ravel('F')).reshape(r,r, order='F') F_mat = 0 while not F_mat == 1 and i < nobs: # Predict v_mat = y[i] - dot(Z_mat,alpha) # one-step forecast error v[i] = v_mat F_mat = dot(dot(Z_mat, P), Z_mat.T) F[i] = F_mat Finv = 1./F_mat # always scalar for univariate series K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # Kalman Gain Matrix # update state alpha = dot(T_mat, alpha) + dot(K,v_mat) L = T_mat - dot(K,Z_mat) P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T) loglikelihood += nplog(F_mat) i+=1 for i in xrange(i,nobs): v_mat = y[i] - dot(Z_mat,alpha) v[i] = v_mat alpha = dot(T_mat, alpha) + dot(K, v_mat) return v,F,loglikelihood @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def kalman_loglike_double(ndarray[DOUBLE, ndim=1] y, unsigned int k, unsigned int p, unsigned int q, unsigned int r, unsigned int nobs, ndarray[DOUBLE, ndim=2] Z_mat, ndarray[DOUBLE, ndim=2] R_mat, ndarray[DOUBLE, ndim=2] T_mat): """ Cython version of the Kalman filter recursions for an ARMA process. """ v, F, loglikelihood = kalman_filter_double(y,k,p,q,r,nobs,Z_mat,R_mat,T_mat) sigma2 = 1./nobs * sum(v**2 / F) loglike = -.5 *(loglikelihood + nobs*log(sigma2)) loglike -= nobs/2. * (log(2*pi) + 1) return loglike, sigma2 @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def kalman_loglike_complex(ndarray[COMPLEX128, ndim=1] y, unsigned int k, unsigned int p, unsigned int q, unsigned int r, unsigned int nobs, ndarray[DOUBLE, ndim=2] Z_mat, ndarray[COMPLEX128, ndim=2] R_mat, ndarray[COMPLEX128, ndim=2] T_mat): """ Cython version of the Kalman filter recursions for an ARMA process. """ v,F,loglikelihood = kalman_filter_complex(y,k,p,q,r,nobs,Z_mat,R_mat,T_mat) sigma2 = 1./nobs * sum(v**2 / F) loglike = -.5 *(loglikelihood + nobs*nplog(sigma2)) loglike -= nobs/2. * (log(2*pi) + 1) return loglike, sigma2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/kalmanf/kalmanfilter.py000066400000000000000000000734401224417117700257470ustar00rootroot00000000000000""" State Space Analysis using the Kalman Filter References ----------- Durbin., J and Koopman, S.J. `Time Series Analysis by State Space Methods`. Oxford, 2001. Hamilton, J.D. `Time Series Analysis`. Princeton, 1994. Harvey, A.C. `Forecasting, Structural Time Series Models and the Kalman Filter`. Cambridge, 1989. Notes ----- This file follows Hamilton's notation pretty closely. The ARMA Model class follows Durbin and Koopman notation. Harvey uses Durbin and Koopman notation. """ #Anderson and Moore `Optimal Filtering` provides a more efficient algorithm # namely the information filter # if the number of series is much greater than the number of states # e.g., with a DSGE model. See also # http://www.federalreserve.gov/pubs/oss/oss4/aimindex.html # Harvey notes that the square root filter will keep P_t pos. def. but # is not strictly needed outside of the engineering (long series) import numpy as np from numpy import dot, identity, kron, log, zeros, pi, exp, eye, issubdtype, ones from numpy.linalg import inv, pinv from statsmodels.tools.tools import chain_dot from . import kalman_loglike #Fast filtering and smoothing for multivariate state space models # and The Riksbank -- Strid and Walentin (2008) # Block Kalman filtering for large-scale DSGE models # but this is obviously macro model specific def _init_diffuse(T,R): m = T.shape[1] # number of states r = R.shape[1] # should also be the number of states? Q_0 = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')) return zeros((m,1)), Q_0.reshape(r,r,order='F') def kalmansmooth(F, A, H, Q, R, y, X, xi10): pass def kalmanfilter(F, A, H, Q, R, y, X, xi10, ntrain, history=False): """ Returns the negative log-likelihood of y conditional on the information set Assumes that the initial state and all innovations are multivariate Gaussian. Parameters ----------- F : array-like The (r x r) array holding the transition matrix for the hidden state. A : array-like The (nobs x k) array relating the predetermined variables to the observed data. H : array-like The (nobs x r) array relating the hidden state vector to the observed data. Q : array-like (r x r) variance/covariance matrix on the error term in the hidden state transition. R : array-like (nobs x nobs) variance/covariance of the noise in the observation equation. y : array-like The (nobs x 1) array holding the observed data. X : array-like The (nobs x k) array holding the predetermined variables data. xi10 : array-like Is the (r x 1) initial prior on the initial state vector. ntrain : int The number of training periods for the filter. This is the number of observations that do not affect the likelihood. Returns ------- likelihood The negative of the log likelihood history or priors, history of posterior If history is True. Notes ----- No input checking is done. """ # uses log of Hamilton 13.4.1 F = np.asarray(F) H = np.atleast_2d(np.asarray(H)) n = H.shape[1] # remember that H gets transposed y = np.asarray(y) A = np.asarray(A) X = np.asarray(X) if y.ndim == 1: # note that Y is in rows for now y = y[:,None] nobs = y.shape[0] xi10 = np.atleast_2d(np.asarray(xi10)) # if xi10.ndim == 1: # xi10[:,None] if history: state_vector = [xi10] Q = np.asarray(Q) r = xi10.shape[0] # Eq. 12.2.21, other version says P0 = Q # p10 = np.dot(np.linalg.inv(np.eye(r**2)-np.kron(F,F)),Q.ravel('F')) # p10 = np.reshape(P0, (r,r), order='F') # Assume a fixed, known intial point and set P0 = Q #TODO: this looks *slightly * different than Durbin-Koopman exact likelihood # initialization p 112 unless I've misunderstood the notational translation. p10 = Q loglikelihood = 0 for i in range(nobs): HTPHR = np.atleast_1d(np.squeeze(chain_dot(H.T,p10,H)+R)) # print HTPHR # print HTPHR.ndim # print HTPHR.shape if HTPHR.ndim == 1: HTPHRinv = 1./HTPHR else: HTPHRinv = np.linalg.inv(HTPHR) # correct # print A.T # print X # print H.T # print xi10 # print y[i] part1 = y[i] - np.dot(A.T,X) - np.dot(H.T,xi10) # correct if i >= ntrain: # zero-index, but ntrain isn't HTPHRdet = np.linalg.det(np.atleast_2d(HTPHR)) # correct part2 = -.5*chain_dot(part1.T,HTPHRinv,part1) # correct #TODO: Need to test with ill-conditioned problem. loglike_interm = (-n/2.) * np.log(2*np.pi) - .5*\ np.log(HTPHRdet) + part2 loglikelihood += loglike_interm # 13.2.15 Update current state xi_t based on y xi11 = xi10 + chain_dot(p10, H, HTPHRinv, part1) # 13.2.16 MSE of that state p11 = p10 - chain_dot(p10, H, HTPHRinv, H.T, p10) # 13.2.17 Update forecast about xi_{t+1} based on our F xi10 = np.dot(F,xi11) if history: state_vector.append(xi10) # 13.2.21 Update the MSE of the forecast p10 = chain_dot(F,p11,F.T) + Q if not history: return -loglikelihood else: return -loglikelihood, np.asarray(state_vector[:-1]) #TODO: this works if it gets refactored, but it's not quite as accurate # as KalmanFilter # def loglike_exact(self, params): # """ # Exact likelihood for ARMA process. # # Notes # ----- # Computes the exact likelihood for an ARMA process by modifying the # conditional sum of squares likelihood as suggested by Shephard (1997) # "The relationship between the conditional sum of squares and the exact # likelihood for autoregressive moving average models." # """ # p = self.p # q = self.q # k = self.k # y = self.endog.copy() # nobs = self.nobs # if self.transparams: # newparams = self._transparams(params) # else: # newparams = params # if k > 0: # y -= dot(self.exog, newparams[:k]) # if p != 0: # arcoefs = newparams[k:k+p][::-1] # T = KalmanFilter.T(arcoefs) # else: # arcoefs = 0 # if q != 0: # macoefs = newparams[k+p:k+p+q][::-1] # else: # macoefs = 0 # errors = [0] * q # psuedo-errors # rerrors = [1] * q # error correction term # # create pseudo-error and error correction series iteratively # for i in range(p,len(y)): # errors.append(y[i]-sum(arcoefs*y[i-p:i])-\ # sum(macoefs*errors[i-q:i])) # rerrors.append(-sum(macoefs*rerrors[i-q:i])) # errors = np.asarray(errors) # rerrors = np.asarray(rerrors) # # # compute bayesian expected mean and variance of initial errors # one_sumrt2 = 1 + np.sum(rerrors**2) # sum_errors2 = np.sum(errors**2) # mup = -np.sum(errors * rerrors)/one_sumrt2 # # # concentrating out the ML estimator of "true" sigma2 gives # sigma2 = 1./(2*nobs) * (sum_errors2 - mup**2*(one_sumrt2)) # # # which gives a variance of the initial errors of # sigma2p = sigma2/one_sumrt2 # # llf = -(nobs-p)/2. * np.log(2*pi*sigma2) - 1./(2*sigma2)*sum_errors2 \ # + 1./2*log(one_sumrt2) + 1./(2*sigma2) * mup**2*one_sumrt2 # Z_mat = KalmanFilter.Z(r) # R_mat = KalmanFilter.R(newparams, r, k, q, p) # T_mat = KalmanFilter.T(newparams, r, k, p) # # initial state and its variance # alpha = zeros((m,1)) # Q_0 = dot(inv(identity(m**2)-kron(T_mat,T_mat)), # dot(R_mat,R_mat.T).ravel('F')) # Q_0 = Q_0.reshape(r,r,order='F') # P = Q_0 # v = zeros((nobs,1)) # F = zeros((nobs,1)) # B = array([T_mat, 0], dtype=object) # # # for i in xrange(int(nobs)): # v_mat = (y[i],0) - dot(z_mat,B) # # B_0 = (T,0) # v_t = (y_t,0) - z*B_t # llf = -nobs/2.*np.log(2*pi*sigma2) - 1/(2.*sigma2)*se_n - \ # 1/2.*logdet(Sigma_a) + 1/(2*sigma2)*s_n_prime*sigma_a*s_n # return llf # class StateSpaceModel(object): """ Generic StateSpaceModel class. Meant to be a base class. This class lays out the methods that are to be defined by any child class. Parameters ---------- endog : array-like An `nobs` x `p` array of observations exog : array-like, optional An `nobs` x `k` array of exogenous variables. **kwargs Anything provided to the constructor will be attached as an attribute. Notes ----- The state space model is assumed to be of the form y[t] = Z[t].dot(alpha[t]) + epsilon[t] alpha[t+1] = T[t].dot(alpha[t]) + R[t].dot(eta[t]) where epsilon[t] ~ N(0, H[t]) eta[t] ~ N(0, Q[t]) alpha[0] ~ N(a[0], P[0]) Where y is the `p` x 1 observations vector, and alpha is the `m` x 1 state vector. References ----------- Durbin, J. and S.J. Koopman. 2001. `Time Series Analysis by State Space Methods.` Oxford. """ def __init__(self, endog, exog=None, **kwargs): dict.__init__(self, kwargs) self.__dict__ = self endog = np.asarray(endog) if endog.ndim == 1: endog = endog[:,None] self.endog = endog p = endog.shape[1] self.p = nobs self.nobs = endog.shape[0] if exog: self.exog = exog def T(self, params): pass def R(self, params): pass def Z(self, params): pass def H(self, params): pass def Q(self, params): pass def _univariatefilter(self, params, init_state, init_var): """ Implements the Kalman Filter recursions. Optimized for univariate case. """ y = self.endog nobs = self.nobs R = self.R T = self.T Z = self.Z H = self.H Q = self.Q if not init_state and not init_var: alpha, P = _init_diffuse(T,R) #NOTE: stopped here def _univariatefilter_update(self): pass # does the KF but calls _update after each loop to update the matrices # for time-varying coefficients def kalmanfilter(self, params, init_state=None, init_var=None): """ Runs the Kalman Filter """ # determine if if self.p == 1: return _univariatefilter(init_state, init_var) else: raise ValueError("No multivariate filter written yet") def _updateloglike(self, params, xi10, ntrain, penalty, upperbounds, lowerbounds, F,A,H,Q,R, history): """ """ paramsorig = params # are the bounds binding? if penalty: params = np.min((np.max((lowerbounds, params), axis=0),upperbounds), axis=0) #TODO: does it make sense for all of these to be allowed to be None? if F != None and callable(F): F = F(params) elif F == None: F = 0 if A != None and callable(A): A = A(params) elif A == None: A = 0 if H != None and callable(H): H = H(params) elif H == None: H = 0 print callable(Q) if Q != None and callable(Q): Q = Q(params) elif Q == None: Q = 0 if R != None and callable(R): R = R(params) elif R == None: R = 0 X = self.exog if X == None: X = 0 y = self.endog loglike = kalmanfilter(F,A,H,Q,R,y,X, xi10, ntrain, history) # use a quadratic penalty function to move away from bounds if penalty: loglike += penalty * np.sum((paramsorig-params)**2) return loglike # r = self.r # n = self.n # F = np.diagonal(np.ones(r-1), k=-1) # think this will be wrong for VAR # cf. 13.1.22 but think VAR # F[0] = params[:p] # assumes first p start_params are coeffs # of obs. vector, needs to be nxp for VAR? # self.F = F # cholQ = np.diag(start_params[p:]) # fails for bivariate # MA(1) section # 13.4.2 # Q = np.dot(cholQ,cholQ.T) # self.Q = Q # HT = np.zeros((n,r)) # xi10 = self.xi10 # y = self.endog # ntrain = self.ntrain # loglike = kalmanfilter(F,H,y,xi10,Q,ntrain) def fit_kalman(self, start_params, xi10, ntrain=1, F=None, A=None, H=None, Q=None, R=None, method="bfgs", penalty=True, upperbounds=None, lowerbounds=None): """ Parameters ---------- method : str Only "bfgs" is currently accepted. start_params : array-like The first guess on all parameters to be estimated. This can be in any order as long as the F,A,H,Q, and R functions handle the parameters appropriately. xi10 : arry-like The (r x 1) vector of initial states. See notes. F,A,H,Q,R : functions or array-like, optional If functions, they should take start_params (or the current value of params during iteration and return the F,A,H,Q,R matrices). See notes. If they are constant then can be given as array-like objects. If not included in the state-space representation then can be left as None. See example in class docstring. penalty : bool, Whether or not to include a penalty for solutions that violate the bounds given by `lowerbounds` and `upperbounds`. lowerbounds : array-like Lower bounds on the parameter solutions. Expected to be in the same order as `start_params`. upperbounds : array-like Upper bounds on the parameter solutions. Expected to be in the same order as `start_params` """ y = self.endog ntrain = ntrain _updateloglike = self._updateloglike params = start_params if method.lower() == 'bfgs': (params, llf, score, cov_params, func_calls, grad_calls, warnflag) = optimize.fmin_bfgs(_updateloglike, params, args = (xi10, ntrain, penalty, upperbounds, lowerbounds, F,A,H,Q,R, False), gtol= 1e-8, epsilon=1e-5, full_output=1) #TODO: provide more options to user for optimize # Getting history would require one more call to _updatelikelihood self.params = params self.llf = llf self.gradient = score self.cov_params = cov_params # how to interpret this? self.warnflag = warnflag def updatematrices(params, y, xi10, ntrain, penalty, upperbound, lowerbound): """ TODO: change API, update names This isn't general. Copy of Luca's matlab example. """ paramsorig = params # are the bounds binding? params = np.min((np.max((lowerbound,params),axis=0),upperbound), axis=0) rho = params[0] sigma1 = params[1] sigma2 = params[2] F = np.array([[rho, 0],[0,0]]) cholQ = np.array([[sigma1,0],[0,sigma2]]) H = np.ones((2,1)) q = np.dot(cholQ,cholQ.T) loglike = kalmanfilter(F,0,H,q,0, y, 0, xi10, ntrain) loglike = loglike + penalty*np.sum((paramsorig-params)**2) return loglike class KalmanFilter(object): """ Kalman Filter code intended for use with the ARMA model. Notes ----- The notation for the state-space form follows Durbin and Koopman (2001). The observation equations is .. math:: y_{t} = Z_{t}\\alpha_{t} + \\epsilon_{t} The state equation is .. math:: \\alpha_{t+1} = T_{t}\\alpha_{t} + R_{t}\\eta_{t} For the present purposed \epsilon_{t} is assumed to always be zero. """ @classmethod def T(cls, params, r, k, p): # F in Hamilton """ The coefficient matrix for the state vector in the state equation. Its dimension is r+k x r+k. Parameters ---------- r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. p : int The AR coefficient in an ARMA model. References ---------- Durbin and Koopman Section 3.7. """ arr = zeros((r,r), dtype=params.dtype) # allows for complex-step # derivative params_padded = zeros(r, dtype=params.dtype) # handle zero coefficients if necessary #NOTE: squeeze added for cg optimizer params_padded[:p] = params[k:p+k] arr[:,0] = params_padded # first p params are AR coeffs w/ short params arr[:-1,1:] = eye(r-1) return arr @classmethod def R(cls, params, r, k, q, p): # R is H in Hamilton """ The coefficient matrix for the state vector in the observation equation. Its dimension is r+k x 1. Parameters ---------- r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. k : int The number of exogenous variables in the ARMA model, including the constant if appropriate. q : int The MA order in an ARMA model. p : int The AR order in an ARMA model. References ---------- Durbin and Koopman Section 3.7. """ arr = zeros((r,1), dtype=params.dtype) # this allows zero coefficients # dtype allows for compl. der. arr[1:q+1,:] = params[p+k:p+k+q][:,None] arr[0] = 1.0 return arr @classmethod def Z(cls, r): """ Returns the Z selector matrix in the observation equation. Parameters ---------- r : int In the context of the ARMA model r is max(p,q+1) where p is the AR order and q is the MA order. Notes ----- Currently only returns a 1 x r vector [1,0,0,...0]. Will need to be generalized when the Kalman Filter becomes more flexible. """ arr = zeros((1,r)) arr[:,0] = 1. return arr @classmethod def geterrors(cls, y, k, k_ar, k_ma, k_lags, nobs, Z_mat, m, R_mat, T_mat, paramsdtype): """ Returns just the errors of the Kalman Filter """ if issubdtype(paramsdtype, float): return kalman_loglike.kalman_filter_double(y, k, k_ar, k_ma, k_lags, int(nobs), Z_mat, R_mat, T_mat)[0] elif issubdtype(paramsdtype, complex): return kalman_loglike.kalman_filter_complex(y, k, k_ar, k_ma, k_lags, int(nobs), Z_mat, R_mat, T_mat)[0] else: raise TypeError("dtype %s is not supported " "Please file a bug report" % paramsdtype) @classmethod def _init_kalman_state(cls, params, arma_model): """ Returns the system matrices and other info needed for the Kalman Filter recursions """ paramsdtype = params.dtype y = arma_model.endog.copy().astype(paramsdtype) k = arma_model.k_exog + arma_model.k_trend nobs = arma_model.nobs k_ar = arma_model.k_ar k_ma = arma_model.k_ma k_lags = arma_model.k_lags if arma_model.transparams: newparams = arma_model._transparams(params) else: newparams = params # don't need a copy if not modified. if k > 0: y -= dot(arma_model.exog, newparams[:k]) # system matrices Z_mat = cls.Z(k_lags) m = Z_mat.shape[1] # r R_mat = cls.R(newparams, k_lags, k, k_ma, k_ar) T_mat = cls.T(newparams, k_lags, k, k_ar) return (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat, T_mat, paramsdtype) @classmethod def loglike(cls, params, arma_model): """ The loglikelihood for an ARMA model using the Kalman Filter recursions. Parameters ---------- params : array The coefficients of the ARMA model, assumed to be in the order of trend variables and `k` exogenous coefficients, the `p` AR coefficients, then the `q` MA coefficients. arma_model : `statsmodels.tsa.arima.ARMA` instance A reference to the ARMA model instance. Notes ----- This works for both real valued and complex valued parameters. The complex values being used to compute the numerical derivative. If available will use a Cython version of the Kalman Filter. """ #TODO: see section 3.4.6 in Harvey for computing the derivatives in the # recursion itself. #TODO: this won't work for time-varying parameters (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat, T_mat, paramsdtype) = cls._init_kalman_state(params, arma_model) if issubdtype(paramsdtype, float): loglike, sigma2 = kalman_loglike.kalman_loglike_double(y, k, k_ar, k_ma, k_lags, int(nobs), Z_mat, R_mat, T_mat) elif issubdtype(paramsdtype, complex): loglike, sigma2 = kalman_loglike.kalman_loglike_complex(y, k, k_ar, k_ma, k_lags, int(nobs), Z_mat, R_mat, T_mat) else: raise TypeError("This dtype %s is not supported " " Please files a bug report." % paramsdtype) arma_model.sigma2 = sigma2 return loglike.item() # return a scalar not a 0d array if __name__ == "__main__": import numpy as np from scipy.linalg import block_diag import numpy as np # Make our observations as in 13.1.13 np.random.seed(54321) nobs = 600 y = np.zeros(nobs) rho = [.5, -.25, .35, .25] sigma = 2.0 # std dev. or noise for i in range(4,nobs): y[i] = np.dot(rho,y[i-4:i][::-1]) + np.random.normal(scale=sigma) y = y[100:] # make an MA(2) observation equation as in example 13.3 # y = mu + [1 theta][e_t e_t-1]' mu = 2. theta = .8 rho = np.array([1, theta]) np.random.randn(54321) e = np.random.randn(101) y = mu + rho[0]*e[1:]+rho[1]*e[:-1] # might need to add an axis r = len(rho) x = np.ones_like(y) # For now, assume that F,Q,A,H, and R are known F = np.array([[0,0],[1,0]]) Q = np.array([[1,0],[0,0]]) A = np.array([mu]) H = rho[:,None] R = 0 # remember that the goal is to solve recursively for the # state vector, xi, given the data, y (in this case) # we can also get a MSE matrix, P, associated with *each* observation # given that our errors are ~ NID(0,variance) # the starting E[e(1),e(0)] = [0,0] xi0 = np.array([[0],[0]]) # with variance = 1 we know that # P0 = np.eye(2) # really P_{1|0} # Using the note below P0 = np.dot(np.linalg.inv(np.eye(r**2)-np.kron(F,F)),Q.ravel('F')) P0 = np.reshape(P0, (r,r), order='F') # more generally, if the eigenvalues for F are in the unit circle # (watch out for rounding error in LAPACK!) then # the DGP of the state vector is var/cov stationary, we know that # xi0 = 0 # Furthermore, we could start with # vec(P0) = np.dot(np.linalg.inv(np.eye(r**2) - np.kron(F,F)),vec(Q)) # where vec(X) = np.ravel(X, order='F') with a possible [:,np.newaxis] # if you really want a "2-d" array # a fortran (row-) ordered raveled array # If instead, some eigenvalues are on or outside the unit circle # xi0 can be replaced with a best guess and then # P0 is a positive definite matrix repr the confidence in the guess # larger diagonal elements signify less confidence # we also know that y1 = mu # and MSE(y1) = variance*(1+theta**2) = np.dot(np.dot(H.T,P0),H) state_vector = [xi0] forecast_vector = [mu] MSE_state = [P0] # will be a list of matrices MSE_forecast = [] # must be numerical shortcuts for some of this... # this should be general enough to be reused for i in range(len(y)-1): # update the state vector sv = state_vector[i] P = MSE_state[i] HTPHR = np.dot(np.dot(H.T,P),H)+R if np.ndim(HTPHR) < 2: # we have a scalar HTPHRinv = 1./HTPHR else: HTPHRinv = np.linalg.inv(HTPHR) FPH = np.dot(np.dot(F,P),H) gain_matrix = np.dot(FPH,HTPHRinv) # correct new_sv = np.dot(F,sv) new_sv += np.dot(gain_matrix,y[i] - np.dot(A.T,x[i]) - np.dot(H.T,sv)) state_vector.append(new_sv) # update the MSE of the state vector forecast using 13.2.28 new_MSEf = np.dot(np.dot(F - np.dot(gain_matrix,H.T),P),F.T - np.dot(H, gain_matrix.T)) + np.dot(np.dot(gain_matrix,R),gain_matrix.T) + Q MSE_state.append(new_MSEf) # update the in sample forecast of y forecast_vector.append(np.dot(A.T,x[i+1]) + np.dot(H.T,new_sv)) # update the MSE of the forecast MSE_forecast.append(np.dot(np.dot(H.T,new_MSEf),H) + R) MSE_forecast = np.array(MSE_forecast).squeeze() MSE_state = np.array(MSE_state) forecast_vector = np.array(forecast_vector) state_vector = np.array(state_vector).squeeze() ########## # Luca's example # choose parameters governing the signal extraction problem rho = .9 sigma1 = 1 sigma2 = 1 nobs = 100 # get the state space representation (Hamilton's notation)\ F = np.array([[rho, 0],[0, 0]]) cholQ = np.array([[sigma1, 0],[0,sigma2]]) H = np.ones((2,1)) # generate random data np.random.seed(12345) xihistory = np.zeros((2,nobs)) for i in range(1,nobs): xihistory[:,i] = np.dot(F,xihistory[:,i-1]) + \ np.dot(cholQ,np.random.randn(2,1)).squeeze() # this makes an ARMA process? # check notes, do the math y = np.dot(H.T, xihistory) y = y.T params = np.array([rho, sigma1, sigma2]) penalty = 1e5 upperbounds = np.array([.999, 100, 100]) lowerbounds = np.array([-.999, .001, .001]) xi10 = xihistory[:,0] ntrain = 1 bounds = zip(lowerbounds,upperbounds) # if you use fmin_l_bfgs_b # results = optimize.fmin_bfgs(updatematrices, params, # args=(y,xi10,ntrain,penalty,upperbounds,lowerbounds), # gtol = 1e-8, epsilon=1e-10) # array([ 0.83111567, 1.2695249 , 0.61436685]) F = lambda x : np.array([[x[0],0],[0,0]]) def Q(x): cholQ = np.array([[x[1],0],[0,x[2]]]) return np.dot(cholQ,cholQ.T) H = np.ones((2,1)) # ssm_model = StateSpaceModel(y) # need to pass in Xi10! # ssm_model.fit_kalman(start_params=params, xi10=xi10, F=F, Q=Q, H=H, # upperbounds=upperbounds, lowerbounds=lowerbounds) # why does the above take 3 times as many iterations than direct max? # compare directly to matlab output from scipy import io # y_matlab = io.loadmat('./kalman_y.mat')['y'].reshape(-1,1) # ssm_model2 = StateSpaceModel(y_matlab) # ssm_model2.fit_kalman(start_params=params, xi10=xi10, F=F, Q=Q, H=H, # upperbounds=upperbounds, lowerbounds=lowerbounds) # matlab output # thetaunc = np.array([0.7833, 1.1688, 0.5584]) # np.testing.assert_almost_equal(ssm_model2.params, thetaunc, 4) # maybe add a line search check to make sure we didn't get stuck in a local # max for more complicated ssm? # Examples from Durbin and Koopman import zipfile try: dk = zipfile.ZipFile('/home/skipper/statsmodels/statsmodels-skipper/scikits/statsmodels/sandbox/tsa/DK-data.zip') except: raise IOError("Install DK-data.zip from http://www.ssfpack.com/DKbook.html or specify its correct local path.") nile = dk.open('Nile.dat').readlines() nile = [float(_.strip()) for _ in nile[1:]] nile = np.asarray(nile) # v = np.zeros_like(nile) # a = np.zeros_like(nile) # F = np.zeros_like(nile) # P = np.zeros_like(nile) # P[0] = 10.**7 # sigma2e = 15099. # sigma2n = 1469.1 # for i in range(len(nile)): # v[i] = nile[i] - a[i] # Kalman filter residual # F[i] = P[i] + sigma2e # the variance of the Kalman filter residual # K = P[i]/F[i] # a[i+1] = a[i] + K*v[i] # P[i+1] = P[i]*(1.-K) + sigma2n nile_ssm = StateSpaceModel(nile) R = lambda params : np.array(params[0]) Q = lambda params : np.array(params[1]) # nile_ssm.fit_kalman(start_params=[1.0,1.0], xi10=0, F=[1.], H=[1.], # Q=Q, R=R, penalty=False, ntrain=0) # p. 162 univariate structural time series example seatbelt = dk.open('Seatbelt.dat').readlines() seatbelt = [map(float,_.split()) for _ in seatbelt[2:]] sb_ssm = StateSpaceModel(seatbelt) s = 12 # monthly data # s p. H = np.zeros((s+1,1)) # Z in DK, H' in Hamilton H[::2] = 1. lambdaj = np.r_[1:6:6j] lambdaj *= 2*np.pi/s T = np.zeros((s+1,s+1)) C = lambda j : np.array([[np.cos(j), np.sin(j)],[-np.sin(j), np.cos(j)]]) Cj = [C(j) for j in lambdaj] + [-1] #NOTE: the above is for handling seasonality #TODO: it is just a rotation matrix. See if Robert's link has a better way #http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5F5145BE25D61F87478B25AD1493C8F4?doi=10.1.1.110.5134&rep=rep1&type=pdf&ei=QcetSefqF4GEsQPnx4jSBA&sig2=HjJILSBPFgJTfuifbvKrxw&usg=AFQjCNFbABIxusr-NEbgrinhtR6buvjaYA from scipy import linalg F = linalg.block_diag(*Cj) # T in DK, F in Hamilton R = np.eye(s-1) sigma2_omega = 1. Q = np.eye(s-1) * sigma2_omega statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/mlemodel.py000066400000000000000000000040651224417117700234600ustar00rootroot00000000000000"""Base Classes for Likelihood Models in time series analysis Warning: imports numdifftools Created on Sun Oct 10 15:00:47 2010 Author: josef-pktd License: BSD """ import numpy as np try: import numdifftools as ndt except ImportError: pass from statsmodels.base.model import LikelihoodModel #copied from sandbox/regression/mle.py #TODO: I take it this is only a stub and should be included in another # model class? class TSMLEModel(LikelihoodModel): """ univariate time series model for estimation with maximum likelihood Note: This is not working yet """ def __init__(self, endog, exog=None): #need to override p,q (nar,nma) correctly super(TSMLEModel, self).__init__(endog, exog) #set default arma(1,1) self.nar = 1 self.nma = 1 #self.initialize() def geterrors(self, params): raise NotImplementedError def loglike(self, params): """ Loglikelihood for timeseries model Notes ----- needs to be overwritten by subclass """ raise NotImplementedError def score(self, params): """ Score vector for Arma model """ #return None #print params jac = ndt.Jacobian(self.loglike, stepMax=1e-4) return jac(params)[-1] def hessian(self, params): """ Hessian of arma model. Currently uses numdifftools """ #return None Hfun = ndt.Jacobian(self.score, stepMax=1e-4) return Hfun(params)[-1] def fit(self, start_params=None, maxiter=5000, method='fmin', tol=1e-08): '''estimate model by minimizing negative loglikelihood does this need to be overwritten ? ''' if start_params is None and hasattr(self, '_start_params'): start_params = self._start_params #start_params = np.concatenate((0.05*np.ones(self.nar + self.nma), [1])) mlefit = super(TSMLEModel, self).fit(start_params=start_params, maxiter=maxiter, method=method, tol=tol) return mlefit statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/setup.py000066400000000000000000000010431224417117700230130ustar00rootroot00000000000000#! /usr/bin/env python import os.path base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import (Configuration, get_numpy_include_dirs) config = Configuration('tsa', parent_package, top_path) config.add_subpackage('kalmanf') config.add_data_files('vector_ar/data/*.dat') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict()) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/stattools.py000066400000000000000000000771721224417117700237270ustar00rootroot00000000000000""" Statistical tools for time series analysis """ import numpy as np from scipy import stats, signal from statsmodels.regression.linear_model import OLS, yule_walker from statsmodels.tools.tools import add_constant from tsatools import lagmat, lagmat2ds, add_trend #from statsmodels.sandbox.tsa import var from adfvalues import mackinnonp, mackinnoncrit #from statsmodels.sandbox.rls import RLS #NOTE: now in two places to avoid circular import #TODO: I like the bunch pattern for this too. class ResultsStore(object): def __str__(self): return self._str # pylint: disable=E1101 def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(), fitargs=(), regresults=False): """ Returns the results for the lag length that maximimizes the info criterion. Parameters ---------- mod : Model class Model estimator class. modargs : tuple args to pass to model. See notes. fitargs : tuple args to pass to fit. See notes. lagstart : int The first zero-indexed column to hold a lag. See Notes. maxlag : int The highest lag order for lag length selection. method : str {"aic","bic","t-stat"} aic - Akaike Information Criterion bic - Bayes Information Criterion t-stat - Based on last lag Returns ------- icbest : float Best information criteria. bestlag : int The lag length that maximizes the information criterion. Notes ----- Does estimation like mod(endog, exog[:,:i], *modargs).fit(*fitargs) where i goes from lagstart to lagstart+maxlag+1. Therefore, lags are assumed to be in contiguous columns from low to high lag length with the highest lag in the last column. """ #TODO: can tcol be replaced by maxlag + 2? #TODO: This could be changed to laggedRHS and exog keyword arguments if # this will be more general. results = {} method = method.lower() for lag in range(startlag, startlag+maxlag+1): mod_instance = mod(endog, exog[:,:lag], *modargs) results[lag] = mod_instance.fit() if method == "aic": icbest, bestlag = min((v.aic,k) for k,v in results.iteritems()) elif method == "bic": icbest, bestlag = min((v.bic,k) for k,v in results.iteritems()) elif method == "t-stat": lags = sorted(results.keys())[::-1] #stop = stats.norm.ppf(.95) stop = 1.6448536269514722 for lag in range(startlag + maxlag, startlag - 1, -1): icbest = np.abs(results[lag].tvalues[-1]) if np.abs(icbest) >= stop: bestlag = lag icbest = icbest break else: raise ValueError("Information Criterion %s not understood.") % method if not regresults: return icbest, bestlag else: return icbest, bestlag, results #this needs to be converted to a class like HetGoldfeldQuandt, 3 different returns are a mess # See: #Ng and Perron(2001), Lag length selection and the construction of unit root #tests with good size and power, Econometrica, Vol 69 (6) pp 1519-1554 #TODO: include drift keyword, only valid with regression == "c" # just changes the distribution of the test statistic to a t distribution #TODO: autolag is untested def adfuller(x, maxlag=None, regression="c", autolag='AIC', store=False, regresults=False): '''Augmented Dickey-Fuller unit root test The Augmented Dickey-Fuller test can be used to test for a unit root in a univariate process in the presence of serial correlation. Parameters ---------- x : array_like, 1d data series maxlag : int Maximum lag which is included in test, default 12*(nobs/100)^{1/4} regression : str {'c','ct','ctt','nc'} Constant and trend order to include in regression * 'c' : constant only * 'ct' : constant and trend * 'ctt' : constant, and linear and quadratic trend * 'nc' : no constant, no trend autolag : {'AIC', 'BIC', 't-stat', None} * if None, then maxlag lags are used * if 'AIC' or 'BIC', then the number of lags is chosen to minimize the corresponding information criterium * 't-stat' based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant at the 95 % level. store : bool If True, then a result instance is returned additionally to the adf statistic regresults : bool If True, the full regression results are returned. Returns ------- adf : float Test statistic pvalue : float MacKinnon's approximate p-value based on MacKinnon (1994) usedlag : int Number of lags used. nobs : int Number of observations used for the ADF regression and calculation of the critical values. critical values : dict Critical values for the test statistic at the 1 %, 5 %, and 10 % levels. Based on MacKinnon (2010) icbest : float The maximized information criterion if autolag is not None. regresults : RegressionResults instance The resstore : (optional) instance of ResultStore an instance of a dummy class with results attached as attributes Notes ----- The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the pvalue is above a critical size, then we cannot reject that there is a unit root. The p-values are obtained through regression surface approximation from MacKinnon 1994, but using the updated 2010 tables. If the p-value is close to significant, then the critical values should be used to judge whether to accept or reject the null. The autolag option and maxlag for it are described in Greene. Examples -------- see example script References ---------- Greene Hamilton P-Values (regression surface approximation) MacKinnon, J.G. 1994. "Approximate asymptotic distribution functions for unit-root and cointegration tests. `Journal of Business and Economic Statistics` 12, 167-76. Critical values MacKinnon, J.G. 2010. "Critical Values for Cointegration Tests." Queen's University, Dept of Economics, Working Papers. Available at http://ideas.repec.org/p/qed/wpaper/1227.html ''' if regresults: store = True trenddict = {None:'nc', 0:'c', 1:'ct', 2:'ctt'} if regression is None or isinstance(regression, int): regression = trenddict[regression] regression = regression.lower() if regression not in ['c','nc','ct','ctt']: raise ValueError("regression option %s not understood") % regression x = np.asarray(x) nobs = x.shape[0] if maxlag is None: #from Greene referencing Schwert 1989 maxlag = int(np.ceil(12. * np.power(nobs/100., 1/4.))) xdiff = np.diff(x) xdall = lagmat(xdiff[:,None], maxlag, trim='both', original='in') nobs = xdall.shape[0] # pylint: disable=E1103 xdall[:,0] = x[-nobs-1:-1] # replace 0 xdiff with level of x xdshort = xdiff[-nobs:] if store: resstore = ResultsStore() if autolag: if regression != 'nc': fullRHS = add_trend(xdall, regression, prepend=True) else: fullRHS = xdall startlag = fullRHS.shape[1] - xdall.shape[1] + 1 # 1 for level # pylint: disable=E1103 #search for lag length with smallest information criteria #Note: use the same number of observations to have comparable IC #aic and bic: smaller is better if not regresults: icbest, bestlag = _autolag(OLS, xdshort, fullRHS, startlag, maxlag, autolag) else: icbest, bestlag, alres = _autolag(OLS, xdshort, fullRHS, startlag, maxlag, autolag, regresults=regresults) resstore.autolag_results = alres bestlag -= startlag #convert to lag not column index #rerun ols with best autolag xdall = lagmat(xdiff[:,None], bestlag, trim='both', original='in') nobs = xdall.shape[0] # pylint: disable=E1103 xdall[:,0] = x[-nobs-1:-1] # replace 0 xdiff with level of x xdshort = xdiff[-nobs:] usedlag = bestlag else: usedlag = maxlag icbest = None if regression != 'nc': resols = OLS(xdshort, add_trend(xdall[:,:usedlag+1], regression)).fit() else: resols = OLS(xdshort, xdall[:,:usedlag+1]).fit() adfstat = resols.tvalues[0] # adfstat = (resols.params[0]-1.0)/resols.bse[0] # the "asymptotically correct" z statistic is obtained as # nobs/(1-np.sum(resols.params[1:-(trendorder+1)])) (resols.params[0] - 1) # I think this is the statistic that is used for series that are integrated # for orders higher than I(1), ie., not ADF but cointegration tests. # Get approx p-value and critical values pvalue = mackinnonp(adfstat, regression=regression, N=1) critvalues = mackinnoncrit(N=1, regression=regression, nobs=nobs) critvalues = {"1%" : critvalues[0], "5%" : critvalues[1], "10%" : critvalues[2]} if store: resstore.resols = resols resstore.maxlag = maxlag resstore.usedlag = usedlag resstore.adfstat = adfstat resstore.critvalues = critvalues resstore.nobs = nobs resstore.H0 = "The coefficient on the lagged level equals 1 - unit root" resstore.HA = "The coefficient on the lagged level < 1 - stationary" resstore.icbest = icbest return adfstat, pvalue, critvalues, resstore else: if not autolag: return adfstat, pvalue, usedlag, nobs, critvalues else: return adfstat, pvalue, usedlag, nobs, critvalues, icbest def acovf(x, unbiased=False, demean=True, fft=False): ''' Autocovariance for 1D Parameters ---------- x : array Time series data. Must be 1d. unbiased : bool If True, then denominators is n-k, otherwise n demean : bool If True, then subtract the mean x from each element of x fft : bool If True, use FFT convolution. This method should be preferred for long time series. Returns ------- acovf : array autocovariance function ''' x = np.squeeze(np.asarray(x)) if x.ndim > 1: raise ValueError("x must be 1d. Got %d dims." % x.ndim) n = len(x) if demean: xo = x - x.mean() else: xo = x if unbiased: xi = np.arange(1, n+1) d = np.hstack((xi, xi[:-1][::-1])) else: d = n if fft: nobs = len(xo) Frf = np.fft.fft(xo, n=nobs*2) acov = np.fft.ifft(Frf*np.conjugate(Frf))[:nobs]/d return acov.real else: return (np.correlate(xo, xo, 'full')/d)[n-1:] def q_stat(x,nobs, type="ljungbox"): """ Return's Ljung-Box Q Statistic x : array-like Array of autocorrelation coefficients. Can be obtained from acf. nobs : int Number of observations in the entire sample (ie., not just the length of the autocorrelation function results. Returns ------- q-stat : array Ljung-Box Q-statistic for autocorrelation parameters p-value : array P-value of the Q statistic Notes ------ Written to be used with acf. """ x = np.asarray(x) if type=="ljungbox": ret = nobs*(nobs+2)*np.cumsum((1./(nobs-np.arange(1, len(x)+1)))*x**2) chi2 = stats.chi2.sf(ret,np.arange(1,len(x)+1)) return ret,chi2 #NOTE: Changed unbiased to False #see for example # http://www.itl.nist.gov/div898/handbook/eda/section3/autocopl.htm def acf(x, unbiased=False, nlags=40, confint=None, qstat=False, fft=False, alpha=None): ''' Autocorrelation function for 1d arrays. Parameters ---------- x : array Time series data unbiased : bool If True, then denominators for autocovariance are n-k, otherwise n nlags: int, optional Number of lags to return autocorrelation for. confint : scalar, optional The use of confint is deprecated. See `alpha`. If a number is given, the confidence intervals for the given level are returned. For instance if confint=95, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett\'s formula. qstat : bool, optional If True, returns the Ljung-Box q statistic for each autocorrelation coefficient. See q_stat for more information. fft : bool, optional If True, computes the ACF via FFT. alpha : scalar, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett\'s formula. Returns ------- acf : array autocorrelation function confint : array, optional Confidence intervals for the ACF. Returned if confint is not None. qstat : array, optional The Ljung-Box Q-Statistic. Returned if q_stat is True. pvalues : array, optional The p-values associated with the Q-statistics. Returned if q_stat is True. Notes ----- The acf at lag 0 (ie., 1) is returned. This is based np.correlate which does full convolution. For very long time series it is recommended to use fft convolution instead. If unbiased is true, the denominator for the autocovariance is adjusted but the autocorrelation is not an unbiased estimtor. ''' nobs = len(x) d = nobs # changes if unbiased if not fft: avf = acovf(x, unbiased=unbiased, demean=True) #acf = np.take(avf/avf[0], range(1,nlags+1)) acf = avf[:nlags+1]/avf[0] else: #JP: move to acovf x0 = x - x.mean() Frf = np.fft.fft(x0, n=nobs*2) # zero-pad for separability if unbiased: d = nobs - np.arange(nobs) acf = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs]/d acf /= acf[0] #acf = np.take(np.real(acf), range(1,nlags+1)) acf = np.real(acf[:nlags+1]) #keep lag 0 if not (confint or qstat or alpha): return acf if not confint is None: import warnings warnings.warn("confint is deprecated. Please use the alpha keyword", FutureWarning) varacf = np.ones(nlags+1)/nobs varacf[0] = 0 varacf[1] = 1./nobs varacf[2:] *= 1 + 2*np.cumsum(acf[1:-1]**2) interval = stats.norm.ppf(1-(100-confint)/200.)*np.sqrt(varacf) confint = np.array(zip(acf-interval, acf+interval)) if not qstat: return acf, confint if alpha is not None: varacf = np.ones(nlags+1)/nobs varacf[0] = 0 varacf[1] = 1./nobs varacf[2:] *= 1 + 2*np.cumsum(acf[1:-1]**2) interval = stats.norm.ppf(1-alpha/2.)*np.sqrt(varacf) confint = np.array(zip(acf-interval, acf+interval)) if not qstat: return acf, confint if qstat: qstat, pvalue = q_stat(acf[1:], nobs=nobs) #drop lag 0 if (confint is not None or alpha is not None): return acf, confint, qstat, pvalue else: return acf, qstat, pvalue def pacf_yw(x, nlags=40, method='unbiased'): '''Partial autocorrelation estimated with non-recursive yule_walker Parameters ---------- x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : 'unbiased' (default) or 'mle' method for the autocovariance calculations in yule walker Returns ------- pacf : 1d array partial autocorrelations, maxlag+1 elements Notes ----- This solves yule_walker for each desired lag and contains currently duplicate calculations. ''' xm = x - x.mean() pacf = [1.] for k in range(1, nlags+1): pacf.append(yule_walker(x, k, method=method)[0][-1]) return np.array(pacf) #NOTE: this is incorrect. def pacf_ols(x, nlags=40): '''Calculate partial autocorrelations Parameters ---------- x : 1d array observations of time series for which pacf is calculated nlags : int Number of lags for which pacf is returned. Lag 0 is not returned. Returns ------- pacf : 1d array partial autocorrelations, maxlag+1 elements Notes ----- This solves a separate OLS estimation for each desired lag. ''' #TODO: add warnings for Yule-Walker #NOTE: demeaning and not using a constant gave incorrect answers? #JP: demeaning should have a better estimate of the constant #maybe we can compare small sample properties with a MonteCarlo xlags, x0 = lagmat(x, nlags, original='sep') #xlags = sm.add_constant(lagmat(x, nlags), prepend=True) xlags = add_constant(xlags) pacf = [1.] for k in range(1, nlags+1): res = OLS(x0[k:], xlags[k:,:k+1]).fit() #np.take(xlags[k:], range(1,k+1)+[-1], pacf.append(res.params[-1]) return np.array(pacf) def pacf(x, nlags=40, method='ywunbiased', alpha=None): '''Partial autocorrelation estimated Parameters ---------- x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : 'ywunbiased' (default) or 'ywmle' or 'ols' specifies which method for the calculations to use: - yw or ywunbiased : yule walker with bias correction in denominator for acovf - ywm or ywmle : yule walker without bias correction - ols - regression of time series on lags of it and on constant - ld or ldunbiased : Levinson-Durbin recursion with bias correction - ldb or ldbiased : Levinson-Durbin recursion without bias correction alpha : scalar, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x)) Returns ------- pacf : 1d array partial autocorrelations, nlags elements, including lag zero confint : array, optional Confidence intervals for the PACF. Returned if confint is not None. Notes ----- This solves yule_walker equations or ols for each desired lag and contains currently duplicate calculations. ''' if method == 'ols': ret = pacf_ols(x, nlags=nlags) elif method in ['yw', 'ywu', 'ywunbiased', 'yw_unbiased']: ret = pacf_yw(x, nlags=nlags, method='unbiased') elif method in ['ywm', 'ywmle', 'yw_mle']: ret = pacf_yw(x, nlags=nlags, method='mle') elif method in ['ld', 'ldu', 'ldunbiase', 'ld_unbiased']: acv = acovf(x, unbiased=True) ld_ = levinson_durbin(acv, nlags=nlags, isacov=True) #print 'ld', ld_ ret = ld_[2] elif method in ['ldb', 'ldbiased', 'ld_biased']: #inconsistent naming with ywmle acv = acovf(x, unbiased=False) ld_ = levinson_durbin(acv, nlags=nlags, isacov=True) ret = ld_[2] else: raise ValueError('method not available') if alpha is not None: varacf = 1./len(x) interval = stats.norm.ppf(1. - alpha/2.) * np.sqrt(varacf) confint = np.array(zip(ret-interval, ret+interval)) return ret, confint else: return ret def ccovf(x, y, unbiased=True, demean=True): ''' crosscovariance for 1D Parameters ---------- x, y : arrays time series data unbiased : boolean if True, then denominators is n-k, otherwise n Returns ------- ccovf : array autocovariance function Notes ----- This uses np.correlate which does full convolution. For very long time series it is recommended to use fft convolution instead. ''' n = len(x) if demean: xo = x - x.mean(); yo = y - y.mean(); else: xo = x yo = y if unbiased: xi = np.ones(n); d = np.correlate(xi, xi, 'full') else: d = n return (np.correlate(xo,yo,'full') / d)[n-1:] def ccf(x, y, unbiased=True): '''cross-correlation function for 1d Parameters ---------- x, y : arrays time series data unbiased : boolean if True, then denominators for autocovariance is n-k, otherwise n Returns ------- ccf : array cross-correlation function of x and y Notes ----- This is based np.correlate which does full convolution. For very long time series it is recommended to use fft convolution instead. If unbiased is true, the denominator for the autocovariance is adjusted but the autocorrelation is not an unbiased estimtor. ''' cvf = ccovf(x, y, unbiased=unbiased, demean=True) return cvf / (np.std(x) * np.std(y)) def periodogram(X): """ Returns the periodogram for the natural frequency of X Parameters ---------- X : array-like Array for which the periodogram is desired. Returns ------- pgram : array 1./len(X) * np.abs(np.fft.fft(X))**2 References ---------- Brockwell and Davis. """ X = np.asarray(X) # if kernel == "bartlett": # w = 1 - np.arange(M+1.)/M #JP removed integer division pergr = 1./len(X) * np.abs(np.fft.fft(X))**2 pergr[0] = 0. # what are the implications of this? return pergr #copied from nitime and scikits\statsmodels\sandbox\tsa\examples\try_ld_nitime.py #TODO: check what to return, for testing and trying out returns everything def levinson_durbin(s, nlags=10, isacov=False): '''Levinson-Durbin recursion for autoregressive processes Parameters ---------- s : array_like If isacov is False, then this is the time series. If iasacov is true then this is interpreted as autocovariance starting with lag 0 nlags : integer largest lag to include in recursion or order of the autoregressive process isacov : boolean flag to indicate whether the first argument, s, contains the autocovariances or the data series. Returns ------- sigma_v : float estimate of the error variance ? arcoefs : ndarray estimate of the autoregressive coefficients pacf : ndarray partial autocorrelation function sigma : ndarray entire sigma array from intermediate result, last value is sigma_v phi : ndarray entire phi array from intermediate result, last column contains autoregressive coefficients for AR(nlags) with a leading 1 Notes ----- This function returns currently all results, but maybe we drop sigma and phi from the returns. If this function is called with the time series (isacov=False), then the sample autocovariance function is calculated with the default options (biased, no fft). ''' s = np.asarray(s) order = nlags #rename compared to nitime #from nitime ## if sxx is not None and type(sxx) == np.ndarray: ## sxx_m = sxx[:order+1] ## else: ## sxx_m = ut.autocov(s)[:order+1] if isacov: sxx_m = s else: sxx_m = acovf(s)[:order+1] #not tested phi = np.zeros((order+1, order+1), 'd') sig = np.zeros(order+1) # initial points for the recursion phi[1,1] = sxx_m[1]/sxx_m[0] sig[1] = sxx_m[0] - phi[1,1]*sxx_m[1] for k in xrange(2,order+1): phi[k,k] = (sxx_m[k] - np.dot(phi[1:k,k-1], sxx_m[1:k][::-1]))/sig[k-1] for j in xrange(1,k): phi[j,k] = phi[j,k-1] - phi[k,k]*phi[k-j,k-1] sig[k] = sig[k-1]*(1 - phi[k,k]**2) sigma_v = sig[-1] arcoefs = phi[1:,-1] pacf_ = np.diag(phi).copy() pacf_[0] = 1. return sigma_v, arcoefs, pacf_, sig, phi #return everything def grangercausalitytests(x, maxlag, addconst=True, verbose=True): '''four tests for granger non causality of 2 timeseries all four tests give similar results `params_ftest` and `ssr_ftest` are equivalent based on F test which is identical to lmtest:grangertest in R Parameters ---------- x : array, 2d, (nobs,2) data for test whether the time series in the second column Granger causes the time series in the first column maxlag : integer the Granger causality test results are calculated for all lags up to maxlag verbose : bool print results if true Returns ------- results : dictionary all test results, dictionary keys are the number of lags. For each lag the values are a tuple, with the first element a dictionary with teststatistic, pvalues, degrees of freedom, the second element are the OLS estimation results for the restricted model, the unrestricted model and the restriction (contrast) matrix for the parameter f_test. Notes ----- TODO: convert to class and attach results properly The Null hypothesis for grangercausalitytests is that the time series in the second column, x2, does NOT Granger cause the time series in the first column, x1. Grange causality means that past values of x2 have a statistically significant effect on the current value of x1, taking past values of x1 into account as regressors. We reject the null hypothesis that x2 does not Granger cause x1 if the pvalues are below a desired size of the test. The null hypothesis for all four test is that the coefficients corresponding to past values of the second time series are zero. 'params_ftest', 'ssr_ftest' are based on F distribution 'ssr_chi2test', 'lrtest' are based on chi-square distribution References ---------- http://en.wikipedia.org/wiki/Granger_causality Greene: Econometric Analysis ''' from scipy import stats # lazy import x = np.asarray(x) resli = {} for mlg in range(1, maxlag+1): result = {} if verbose: print '\nGranger Causality' print 'number of lags (no zero)', mlg mxlg = mlg #+ 1 # Note number of lags starting at zero in lagmat # create lagmat of both time series dta = lagmat2ds(x, mxlg, trim='both', dropex=1) #add constant if addconst: dtaown = add_constant(dta[:,1:mxlg+1], prepend=False) dtajoint = add_constant(dta[:,1:], prepend=False) else: raise ValueError('Not Implemented') dtaown = dta[:,1:mxlg] dtajoint = dta[:,1:] #run ols on both models without and with lags of second variable res2down = OLS(dta[:,0], dtaown).fit() res2djoint = OLS(dta[:,0], dtajoint).fit() #print results #for ssr based tests see: http://support.sas.com/rnd/app/examples/ets/granger/index.htm #the other tests are made-up # Granger Causality test using ssr (F statistic) fgc1 = (res2down.ssr-res2djoint.ssr)/res2djoint.ssr/(mxlg)*res2djoint.df_resid if verbose: print 'ssr based F test: F=%-8.4f, p=%-8.4f, df_denom=%d, df_num=%d' % \ (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg) result['ssr_ftest'] = (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg) # Granger Causality test using ssr (ch2 statistic) fgc2 = res2down.nobs*(res2down.ssr-res2djoint.ssr)/res2djoint.ssr if verbose: print 'ssr based chi2 test: chi2=%-8.4f, p=%-8.4f, df=%d' % \ (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg) result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg) #likelihood ratio test pvalue: lr = -2*(res2down.llf-res2djoint.llf) if verbose: print 'likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' % \ (lr, stats.chi2.sf(lr, mxlg), mxlg) result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg) # F test that all lag coefficients of exog are zero rconstr = np.column_stack((np.zeros((mxlg-1,mxlg-1)), np.eye(mxlg-1, mxlg-1),\ np.zeros((mxlg-1, 1)))) rconstr = np.column_stack((np.zeros((mxlg,mxlg)), np.eye(mxlg, mxlg),\ np.zeros((mxlg, 1)))) ftres = res2djoint.f_test(rconstr) if verbose: print 'parameter F test: F=%-8.4f, p=%-8.4f, df_denom=%d, df_num=%d' % \ (ftres.fvalue, ftres.pvalue, ftres.df_denom, ftres.df_num) result['params_ftest'] = (np.squeeze(ftres.fvalue)[()], np.squeeze(ftres.pvalue)[()], ftres.df_denom, ftres.df_num) resli[mxlg] = (result, [res2down, res2djoint, rconstr]) return resli def coint(y1, y2, regression="c"): """ This is a simple cointegration test. Uses unit-root test on residuals to test for cointegrated relationship See Hamilton (1994) 19.2 Parameters ---------- y1 : array_like, 1d first element in cointegrating vector y2 : array_like remaining elements in cointegrating vector c : str {'c'} Included in regression * 'c' : Constant Returns ------- coint_t : float t-statistic of unit-root test on residuals pvalue : float MacKinnon's approximate p-value based on MacKinnon (1994) crit_value : dict Critical values for the test statistic at the 1 %, 5 %, and 10 % levels. Notes ----- The Null hypothesis is that there is no cointegration, the alternative hypothesis is that there is cointegrating relationship. If the pvalue is small, below a critical size, then we can reject the hypothesis that there is no cointegrating relationship. P-values are obtained through regression surface approximation from MacKinnon 1994. References ---------- MacKinnon, J.G. 1994. "Approximate asymptotic distribution functions for unit-root and cointegration tests. `Journal of Business and Economic Statistics` 12, 167-76. """ regression = regression.lower() if regression not in ['c','nc','ct','ctt']: raise ValueError("regression option %s not understood") % regression y1 = np.asarray(y1) y2 = np.asarray(y2) if regression == 'c': y2 = add_constant(y2, prepend=False) st1_resid = OLS(y1, y2).fit().resid #stage one residuals lgresid_cons = add_constant(st1_resid[0:-1], prepend=False) uroot_reg = OLS(st1_resid[1:], lgresid_cons).fit() coint_t = (uroot_reg.params[0]-1)/uroot_reg.bse[0] pvalue = mackinnonp(coint_t, regression="c", N=2, lags=None) crit_value = mackinnoncrit(N=1, regression="c", nobs=len(y1)) return coint_t, pvalue, crit_value __all__ = ['acovf', 'acf', 'pacf', 'pacf_yw', 'pacf_ols', 'ccovf', 'ccf', 'periodogram', 'q_stat', 'coint'] if __name__=="__main__": import statsmodels.api as sm data = sm.datasets.macrodata.load().data x = data['realgdp'] # adf is tested now. adf = adfuller(x,4, autolag=None) adfbic = adfuller(x, autolag="bic") adfaic = adfuller(x, autolag="aic") adftstat = adfuller(x, autolag="t-stat") # acf is tested now acf1,ci1,Q,pvalue = acf(x, nlags=40, confint=95, qstat=True) acf2, ci2,Q2,pvalue2 = acf(x, nlags=40, confint=95, fft=True, qstat=True) acf3,ci3,Q3,pvalue3 = acf(x, nlags=40, confint=95, qstat=True, unbiased=True) acf4, ci4,Q4,pvalue4 = acf(x, nlags=40, confint=95, fft=True, qstat=True, unbiased=True) # pacf is tested now # pacf1 = pacorr(x) # pacfols = pacf_ols(x, nlags=40) # pacfyw = pacf_yw(x, nlags=40, method="mle") y = np.random.normal(size=(100,2)) grangercausalitytests(y,2) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/000077500000000000000000000000001224417117700224455ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/__init__.py000066400000000000000000000000001224417117700245440ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/arima.do000066400000000000000000000116141224417117700240650ustar00rootroot00000000000000insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate /* here for safe keeping arima cpi, arima(4,1,1) from(0.92687596 -0.5558627 0.32086541 0.25225289 0.11362493 0.93914412 0.78437817334640536, copy) predict xb, xb dynamic(.) predict y, y dynamic(.) */ arima cpi, arima(1,1,1) mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima111_results.py") format("%16.0g") replace clear /* do it with no constant */ insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,1) noconstant mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse /*predict stdp, stdp*/ estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse /*mkmat stdp stdp*/ mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima111nc_results.py") format("%16.0g") replace clear /* Now do conditional */ insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,1) condition mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima111_css_results.py") format("%16.0g") replace clear /* do it with no constant */ insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,1) noconstant condition mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima111nc_css_results.py") format("%16.0g") replace statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/arima112.do000066400000000000000000000115031224417117700243060ustar00rootroot00000000000000insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,2) mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima112_results.py") format("%16.0g") replace /* Do it with no constant */ clear insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,2) noconstant mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse /*predict stdp, stdp*/ estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse /*mkmat stdp stdp*/ mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima112nc_results.py") format("%16.0g") replace clear /* now do conditional */ insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate /* still converges to different than x-12-arima */ arima cpi, arima(1,1,2) condition from(.905322 -.692425 1.07366 0.172024 0.682072819129, copy) gtolerance(.0001) vce(oim) mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima112_css_results.py") format("%16.0g") replace /* Do it with no constant */ clear insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(1,1,2) noconstant condition mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima112nc_css_results.py") format("%16.0g") replace statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/arima211.do000066400000000000000000000113321224417117700243060ustar00rootroot00000000000000insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(2,1,1) mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima211_results.py") format("%16.0g") replace clear /* do it with no constant */ insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(2,1,1) noconstant mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse /* can't do stdp without a constant predict stdp, stdp */ estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse /*mkmat stdp stdp*/ mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima211nc_results.py") format("%16.0g") replace /* now do conditional */ clear insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(2,1,1) condition mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima211_css_results.py") format("%16.0g") replace /* do it with no constant */ clear insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv" gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate arima cpi, arima(2,1,1) condition noconstant mat llf=e(ll) mat nobs=e(N) // number of parameters mat k=e(k) // number of dependent variables mat k_exog=e(k_dv) mat sigma=e(sigma) mat chi2=e(chi2) mat df_model=e(df_m) mat k_ar=e(ar_max) mat k_ma=e(ma_max) mat params=e(b) mat cov_params=e(V) // don't append because you'll rewrite the bunch class // mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima_results.py") format("%16.0g") replace predict xb predict y, y predict resid, resid predict yr, yr predict mse, mse predict stdp, stdp estat ic mat icstats=r(S) mkmat xb xb mkmat y y mkmat yr yr mkmat mse mse mkmat stdp stdp mkmat resid resid mat2nparray llf nobs k k_exog sigma chi2 df_model k_ar k_ma params cov_params xb y resid yr mse stdp icstats, saving("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/arima211nc_css_results.py") format("%16.0g") replace statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/000077500000000000000000000000001224417117700241465ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/ARMLEConstantPredict.csv000066400000000000000000000111271224417117700305520ustar00rootroot000000000000001700, 48.3243 1701, 12.6633 1702, 26.1958 1703, 30.7430 1704, 35.6854 1705, 48.6206 1706, 67.6159 1707, 16.0018 1708, 13.3760 1709, 11.2305 1710, 9.4877 1711, 9.0588 1712, 8.7644 1713, 11.2268 1714, 20.9667 1715, 23.5576 1716, 36.9585 1717, 50.0107 1718, 58.9465 1719, 46.3827 1720, 21.4597 1721, 18.4550 1722, 22.7606 1723, 19.9462 1724, 12.5469 1725, 33.2339 1726, 55.8533 1727, 89.9622 1728, 120.8949 1729, 74.1257 1730, 42.7777 1731, 31.2814 1732, 23.3099 1733, 5.0720 1734, 12.1647 1735, 36.4987 1736, 62.5014 1737, 93.0209 1738, 84.6001 1739, 103.9179 1740, 81.1555 1741, 41.0694 1742, 17.7797 1743, 11.1391 1744, 18.9217 1745, 18.3533 1746, 29.8649 1747, 50.0499 1748, 62.2988 1749, 73.1959 1750, 80.7216 1751, 68.4801 1752, 24.2343 1753, 36.7505 1754, 22.8928 1755, 5.0187 1756, 19.4815 1757, 24.7614 1758, 52.7583 1759, 66.4247 1760, 54.4097 1761, 63.4711 1762, 82.9175 1763, 38.9882 1764, 27.5613 1765, 29.6525 1766, 16.6044 1767, 17.0212 1768, 56.0339 1769, 81.6483 1770, 113.7808 1771, 87.3770 1772, 59.2614 1773, 51.5278 1774, 18.2411 1775, 23.3538 1776, 8.1929 1777, 34.2464 1778, 128.3357 1779, 165.3934 1780, 94.6616 1781, 56.1351 1782, 51.4397 1783, 22.6825 1784, 12.7374 1785, 10.5643 1786, 42.5939 1787, 123.6546 1788, 149.8687 1789, 112.0556 1790, 94.3949 1791, 62.0904 1792, 41.9001 1793, 45.4531 1794, 34.7052 1795, 43.7433 1796, 38.1605 1797, 38.5734 1798, 31.0695 1799, 25.0907 1800, 25.7435 1801, 32.0251 1802, 47.8522 1803, 52.0917 1804, 38.1594 1805, 43.7860 1806, 34.0097 1807, 17.4484 1808, 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statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/__init__.py000066400000000000000000000000001224417117700262450ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima.R000066400000000000000000000012611224417117700253620ustar00rootroot00000000000000dta <- read.csv("/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv") cpi <- dta$cpi /* this automatically suppresses the constant */ mod111 <- arima(cpi, order=c(1,1,1), method="CSS") /*you can use xreg=1:length(cpi)*/ dcpi <- diff(cpi) mod111 <- arima(dcpi, order=c(1,0,1), method="CSS") bse <- sqrt(diag(mod111$var.coef)) tvalues <- mod111$coef / bse pvalues <- (1 - pt(abs(tvalues), 198)) * 2 /* use starting values from X-ARIMA */ mod112 <- arima(dcpi, order=c(1,0,2), method="CSS", init=c(-0.692425, 1.07366, 0.172024, 0.905322)) bse <- sqrt(diag(mod112$var.coef)) tvalues <- mod112$coef / bse pvalues <- (1 - pt(abs(tvalues), 198)) * 2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima111_css_results.py000066400000000000000000001262071224417117700304750ustar00rootroot00000000000000import numpy as np llf = np.array([-242.06033399744]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .80201496146073]) chi2 = np.array([ 348.43324197088]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .82960638524364, .93479332833705, -.75728342544279, .64322799840686]) cov_params = np.array([ .14317811930738, -.01646077810033, .01510986837498, -.00280799533479, -.01646077810033, .00321032468661, -.00353027620719, .00097645385252, .01510986837498, -.00353027620719, .00484312817753, -.00112050648944, -.00280799533479, .00097645385252, -.00112050648944, .0007715609499]).reshape(4,4) xb = np.array([ .82960641384125, .82960641384125, .697261095047, .61113905906677, .51607495546341, .47362637519836, .41342103481293, .40238001942635, 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1.2842413187027, 1.3106474876404, 1.5614050626755, 1.4672855138779, 1.2362524271011, 1.1855486631393, 1.1294020414352, 1.1046353578568, 1.0858771800995, 1.0716745853424, 1.0786685943604, 1.0662157535553, 1.0390332937241, .96519494056702, .9802839756012, .92070508003235, .91108840703964, .95705932378769, .95637094974518, .97360169887543, 1.0221517086029, .9701629281044, .94854199886322, .98542231321335, 1.048855304718, 1.0081344842911, 1.0305507183075, 1.0475262403488, .93612504005432, .85176283121109, .89438372850418, .820152759552, .71068543195724, .76979607343674, .76130604743958, .77262878417969, .85220617055893, .84146595001221, .93983960151672, .97883212566376, 1.0793634653091, 1.1909983158112, 1.1690304279327, 1.2411522865295, 1.1360056400299, 1.0918840169907, .9164656996727, .76586949825287, .918093085289, .87360894680023, .92867678403854, 1.00588285923, .92233866453171, .84132260084152, .90422683954239, .9873673915863, .99707210063934, 1.1109310388565, 1.1971517801285, 1.138188958168, 1.2710473537445, 1.1763968467712, 1.7437561750412, 1.4101150035858, 1.3527159690857, 1.4335050582886, .99765706062317, 1.1067585945129, 1.3086627721786, 1.2968333959579, 1.3547962903976, 1.6768488883972, 1.5905654430389, 2.0774590969086, 1.3218278884888, .21813294291496, .30750840902328, .60612773895264]) y = np.array([np.nan, 29.809606552124, 29.847261428833, 29.961139678955, 29.886075973511, 30.013628005981, 29.96342086792, 30.152379989624, 30.214540481567, 30.142219543457, 30.245149612427, 30.290935516357, 30.3401927948, 30.521595001221, 30.511829376221, 30.683492660522, 30.734575271606, 30.764270782471, 30.996646881104, 31.046964645386, 31.252710342407, 31.242681503296, 31.308164596558, 31.410068511963, 31.582162857056, 31.680667877197, 31.897289276123, 31.956798553467, 32.207256317139, 32.652923583984, 32.8166847229, 33.252780914307, 33.267993927002, 33.468269348145, 33.786235809326, 34.099838256836, 34.527889251709, 34.831386566162, 35.369533538818, 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100.7755279541, 101.770362854, 103.11968994141, 104.33930969238, 105.083152771, 106.07612609863, 106.59980773926, 107.96627044678, 108.61009216309, 109.38529968262, 110.87303924561, 109.27794647217, 110.13377380371, 110.85829162598, 112.16562652588, 113.56465148926, 114.70414733887, 115.95180511475, 116.95239257813, 118.188331604, 119.53330993652, 120.98512268066, 122.30659484863, 124.3293762207, 125.73359680176, 126.54803466797, 128.79624938965, 130.18423461914, 131.81065368652, 134.96139526367, 136.16728210449, 136.33625793457, 137.38554382324, 138.32939147949, 139.40463256836, 140.48587036133, 141.57167053223, 142.77867126465, 143.86622619629, 144.83903503418, 145.46519470215, 146.58029174805, 147.220703125, 148.11108398438, 149.35705566406, 150.35636901855, 151.47360229492, 152.82215881348, 153.5701751709, 154.44854736328, 155.68542480469, 157.14886474609, 158.00813293457, 159.23054504395, 160.44752502441, 160.83612060547, 161.25175476074, 162.39437866211, 162.82015991211, 162.91067504883, 163.96978759766, 164.66130065918, 165.47262573242, 166.75219726563, 167.54145812988, 169.03984069824, 170.27883911133, 171.9793548584, 173.89099121094, 175.06903076172, 176.84115600586, 177.5359954834, 178.49188232422, 178.5164642334, 178.46586608887, 180.21809387207, 180.8736114502, 182.12867736816, 183.60589599609, 184.12232971191, 184.54132080078, 185.80421447754, 187.28736877441, 188.39706420898, 190.2109375, 191.99716186523, 192.93818664551, 195.07104492188, 195.8763885498, 200.94375610352, 200.81010437012, 202.05271911621, 204.13349914551, 202.89764404297, 204.68077087402, 207.22866821289, 208.63482666016, 210.48779296875, 214.17184448242, 215.58755493164, 220.68745422363, 218.21083068848, 212.39213562012, 212.978515625, 215.07511901855]) resid = np.array([np.nan, -.6596063375473, -.49726036190987, -.5911386013031, -.34607490897179, -.46362805366516, -.21342028677464, -.31237986683846, -.40454092621803, -.22221945226192, -.26514956355095, -.2509354352951, 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1.589346408844, -.26140204071999, -1.0672763586044, -.13626158237457, -.18554861843586, -.02939598634839, -.00464448658749, .01412893645465, .1283223181963, .02133745700121, -.06621573865414, -.33903631567955, .13481116294861, -.28028702735901, -.02071117423475, .28890857100487, .04294065013528, .14363515377045, .32640132308006, -.22214868664742, -.0701690018177, .25145494937897, .41458681225777, -.14886146783829, .19186246395111, .16944620013237, -.54752624034882, -.43612506985664, .2482432872057, -.39438369870186, -.62015581130981, .28931456804276, -.06979911774397, .03869699314237, .4273681640625, -.05220314115286, .55854320526123, .26015737652779, .62115871906281, .72063958644867, .00899865385145, .53098171949387, -.44116449356079, -.13600566983223, -.89187180995941, -.81647485494614, .83413660526276, -.21809615194798, .32638800144196, .47133237123489, -.4058920443058, -.42233863472939, .35867437720299, .49578228592873, .11262346804142, .70294010639191, .58906590938568, -.19715182483196, .86181098222733, -.37105345726013, 3.3236031532288, -1.543759226799, -.11011194437742, .64728397130966, -2.2335081100464, .67635416984558, 1.2392344474792, .10933646559715, .49816474318504, 2.0072033405304, -.17484994232655, 3.0224411487579, -3.7984521389008, -6.0368394851685, .27887633442879, 1.4904805421829, 1.3098726272583]) yr = np.array([np.nan, -.6596063375473, -.49726036190987, -.5911386013031, -.34607490897179, -.46362805366516, -.21342028677464, -.31237986683846, -.40454092621803, -.22221945226192, -.26514956355095, -.2509354352951, -.13019436597824, -.30159646272659, -.1318296790123, -.24349159002304, -.25457563996315, -.07427024841309, -.24664734303951, -.10696394741535, -.30270880460739, -.22268049418926, -.18816292285919, -.13006833195686, -.20216277241707, -.10066751390696, -.24728938937187, -.07679972797632, .07274255156517, -.20292413234711, .03331403434277, -.35277983546257, -.16799576580524, -.06826904416084, -.08623649924994, .00015908146452, -.12788754701614, .06861615926027, -.06953293830156, -.08066567778587, .11089706420898, -.03098993562162, -.04496069997549, .04446176066995, .01869462057948, -.20081178843975, -.08008606731892, -.08214038610458, -.38369914889336, -.03162068501115, -.24543529748917, -.22040157020092, -.20144037902355, -.18708138167858, -.07620526105165, .01427639275789, .48931872844696, -.11833623051643, .78889113664627, .43461054563522, .45327401161194, .27393117547035, .73159569501877, .21077930927277, -.40970605611801, -.01871551014483, -.10306061804295, -.0734596773982, -.65103828907013, .0724478662014, .05945380032063, -.05038867890835, .45991089940071, -.12104434520006, -.09359546005726, .22719417512417, .28968048095703, .64352011680603, .53756183385849, .25732442736626, .93205803632736, 1.0886732339859, .72682982683182, 1.2397809028625, 1.1673469543457, -.18098846077919, .31969723105431, .72494095563889, .05790812522173, .61364978551865, .06710703670979, -.77938556671143, -.97910648584366, 1.1435683965683, -.92507529258728, -1.5155116319656, -.11481033265591, .01764474436641, .02447287365794, .32963913679123, .18031190335751, -.23930950462818, .01684862375259, -.37613153457642, .40019443631172, -.2662724852562, -.11008904129267, .51469951868057, -2.1730391979218, .22205695509911, .06622361391783, .54170626401901, .53436845541, .2353515625, .29585054516792, .04819770529866, .24760706722736, .31166675686836, .36669155955315, .21487690508366, .79340130090714, .17062658071518, -.33359375596046, .95196217298508, .10373862832785, .31576481461525, 1.589346408844, -.26140204071999, -1.0672763586044, -.13626158237457, -.18554861843586, -.02939598634839, -.00464448658749, .01412893645465, .1283223181963, .02133745700121, -.06621573865414, -.33903631567955, .13481116294861, -.28028702735901, -.02071117423475, .28890857100487, .04294065013528, .14363515377045, .32640132308006, -.22214868664742, -.0701690018177, .25145494937897, .41458681225777, -.14886146783829, .19186246395111, .16944620013237, -.54752624034882, -.43612506985664, .2482432872057, -.39438369870186, -.62015581130981, .28931456804276, -.06979911774397, .03869699314237, .4273681640625, -.05220314115286, .55854320526123, .26015737652779, .62115871906281, .72063958644867, .00899865385145, .53098171949387, -.44116449356079, -.13600566983223, -.89187180995941, -.81647485494614, .83413660526276, -.21809615194798, .32638800144196, .47133237123489, -.4058920443058, -.42233863472939, .35867437720299, .49578228592873, .11262346804142, .70294010639191, .58906590938568, -.19715182483196, .86181098222733, -.37105345726013, 3.3236031532288, -1.543759226799, -.11011194437742, .64728397130966, -2.2335081100464, .67635416984558, 1.2392344474792, .10933646559715, .49816474318504, 2.0072033405304, -.17484994232655, 3.0224411487579, -3.7984521389008, -6.0368394851685, .27887633442879, 1.4904805421829, 1.3098726272583]) mse = np.array([ 1.0121052265167, .66349595785141, .65449619293213, .64957880973816, .64683443307877, .64528465270996, .64440369606018, .64390099048615, .64361357688904, .64344894886017, .64335465431213, .64330065250397, .64326965808868, .64325189590454, .64324170351028, .6432358622551, .64323252439499, .64323055744171, .64322948455811, .64322882890701, .64322847127914, .64322829246521, .64322817325592, .64322811365128, .64322805404663, .64322805404663, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199, .64322799444199]) stdp = np.array([ .82960641384125, .82960641384125, .697261095047, .61113905906677, .51607495546341, .47362637519836, .41342103481293, .40238001942635, .37454023957253, .33222004771233, .32514902949333, .31093680858612, .30019253492355, .31159669160843, .29182952642441, .30349296331406, .29457464814186, .28427124023438, .30664679408073, .29696446657181, .31270903348923, .29268020391464, .28816330432892, .29006817936897, .30216124653816, .30066826939583, .31728908419609, .30679926276207, .3272570669651, .37292611598969, .36668366193771, .40278288722038, .36799272894859, .36827209591866, .38623574376106, .39983862638474, .42789059877396, .43138384819031, .46953064203262, .48066720366478, .48910140991211, .53098994493484, .54496067762375, .55554050207138, .58130383491516, .60081332921982, .58008605241776, .58214038610458, .58369606733322, .53162068128586, .54543834924698, .52040082216263, .50143963098526, .48708060383797, .47620677947998, .48572361469269, .51068127155304, .61833620071411, .61110657453537, .76539021730423, .84672522544861, .92606955766678, .96840506792068, 1.0892199277878, 1.1097067594528, 1.0187155008316, 1.0030621290207, .97345739603043, .95103752613068, .82755368947983, .84054774045944, .85038793087006, .84008830785751, .92104357481003, .89359468221664, .87280809879303, .91032028198242, .95647835731506, 1.0624366998672, 1.1426770687103, 1.1679404973984, 1.311328291893, 1.473167181015, 1.5602221488953, 1.7326545715332, 1.8809853792191, 1.7803012132645, 1.7750589847565, 1.8420933485031, 1.7863517999649, 1.8328944444656, 1.7793855667114, 1.5791050195694, 1.3564316034317, 1.5250737667084, 1.3155146837234, 1.014811873436, .98235523700714, .97552710771561, .97035628557205, 1.0196926593781, 1.0393049716949, .98315137624741, .97613000869751, .89980864524841, .96626943349838, .91009211540222, .88530200719833, .97303456068039, .57794612646103, .63377332687378, .65829831361771, .76562696695328, .86465454101563, .90414637327194, .95180231332779, .95238989591599, .98833626508713, 1.0333099365234, 1.0851185321808, 1.1066001653671, 1.2293750047684, 1.233595252037, 1.1480363607407, 1.2962552309036, 1.2842413187027, 1.3106474876404, 1.5614050626755, 1.4672855138779, 1.2362524271011, 1.1855486631393, 1.1294020414352, 1.1046353578568, 1.0858771800995, 1.0716745853424, 1.0786685943604, 1.0662157535553, 1.0390332937241, .96519494056702, .9802839756012, .92070508003235, .91108840703964, .95705932378769, .95637094974518, .97360169887543, 1.0221517086029, .9701629281044, .94854199886322, .98542231321335, 1.048855304718, 1.0081344842911, 1.0305507183075, 1.0475262403488, .93612504005432, .85176283121109, .89438372850418, .820152759552, .71068543195724, .76979607343674, .76130604743958, .77262878417969, .85220617055893, .84146595001221, .93983960151672, .97883212566376, 1.0793634653091, 1.1909983158112, 1.1690304279327, 1.2411522865295, 1.1360056400299, 1.0918840169907, .9164656996727, .76586949825287, .918093085289, .87360894680023, .92867678403854, 1.00588285923, .92233866453171, .84132260084152, .90422683954239, .9873673915863, .99707210063934, 1.1109310388565, 1.1971517801285, 1.138188958168, 1.2710473537445, 1.1763968467712, 1.7437561750412, 1.4101150035858, 1.3527159690857, 1.4335050582886, .99765706062317, 1.1067585945129, 1.3086627721786, 1.2968333959579, 1.3547962903976, 1.6768488883972, 1.5905654430389, 2.0774590969086, 1.3218278884888, .21813294291496, .30750840902328, .60612773895264]) icstats = np.array([ 202, np.nan, -242.06033399744, 4, 492.12066799488, 505.35373878448]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima111_forecasts.csv000066400000000000000000000146711224417117700302610ustar00rootroot00000000000000dates, cpi, predict, stderr, conf1, conf2 1950:1, 28.980,,,, 1950:2, 29.150, 29.861 ,,, 1950:3, 29.350, 29.803 ,,, 1950:4, 29.370, 29.904 ,,, 1951:1, 29.540, 29.829 ,,, 1951:2, 29.550, 29.968 ,,, 1951:3, 29.750, 29.928 ,,, 1951:4, 29.840, 30.127 ,,, 1952:1, 29.810, 30.197 ,,, 1952:2, 29.920, 30.132 ,,, 1952:3, 29.980, 30.239 ,,, 1952:4, 30.040, 30.289 ,,, 1953:1, 30.210, 30.341 ,,, 1953:2, 30.220, 30.524 ,,, 1953:3, 30.380, 30.517 ,,, 1953:4, 30.440, 30.689 ,,, 1954:1, 30.480, 30.741 ,,, 1954:2, 30.690, 30.772 ,,, 1954:3, 30.750, 31.003 ,,, 1954:4, 30.940, 31.055 ,,, 1955:1, 30.950, 31.260 ,,, 1955:2, 31.020, 31.251 ,,, 1955:3, 31.120, 31.317 ,,, 1955:4, 31.280, 31.419 ,,, 1956:1, 31.380, 31.590 ,,, 1956:2, 31.580, 31.689 ,,, 1956:3, 31.650, 31.905 ,,, 1956:4, 31.880, 31.965 ,,, 1957:1, 32.280, 32.215 ,,, 1957:2, 32.450, 32.659 ,,, 1957:3, 32.850, 32.824 ,,, 1957:4, 32.900, 33.259 ,,, 1958:1, 33.100, 33.276 ,,, 1958:2, 33.400, 33.477 ,,, 1958:3, 33.700, 33.794 ,,, 1958:4, 34.100, 34.108 ,,, 1959:1, 34.400, 34.535 ,,, 1959:2, 34.900, 34.839 ,,, 1959:3, 35.300, 35.376 ,,, 1959:4, 35.700, 35.787 ,,, 1960:1, 36.300, 36.196 ,,, 1960:2, 36.800, 36.837 ,,, 1960:3, 37.300, 37.351 ,,, 1960:4, 37.900, 37.862 ,,, 1961:1, 38.500, 38.488 ,,, 1961:2, 38.900, 39.108 ,,, 1961:3, 39.400, 39.489 ,,, 1961:4, 39.900, 39.991 ,,, 1962:1, 40.100, 40.493 ,,, 1962:2, 40.600, 40.644 ,,, 1962:3, 40.900, 41.157 ,,, 1962:4, 41.200, 41.433 ,,, 1963:1, 41.500, 41.715 ,,, 1963:2, 41.800, 42.000 ,,, 1963:3, 42.200, 42.289 ,,, 1963:4, 42.700, 42.698 ,,, 1964:1, 43.700, 43.221 ,,, 1964:2, 44.200, 44.324 ,,, 1964:3, 45.600, 44.818 ,,, 1964:4, 46.800, 46.366 ,,, 1965:1, 48.100, 47.646 ,,, 1965:2, 49.300, 49.025 ,,, 1965:3, 51.000, 50.269 ,,, 1965:4, 52.300, 52.087 ,,, 1966:1, 53.000, 53.411 ,,, 1966:2, 54.000, 54.027 ,,, 1966:3, 54.900, 55.014 ,,, 1966:4, 55.800, 55.887 ,,, 1967:1, 56.100, 56.766 ,,, 1967:2, 57.000, 56.948 ,,, 1967:3, 57.900, 57.859 ,,, 1967:4, 58.700, 58.767 ,,, 1968:1, 60.000, 59.557 ,,, 1968:2, 60.800, 60.933 ,,, 1968:3, 61.600, 61.707 ,,, 1968:4, 62.700, 62.488 ,,, 1969:1, 63.900, 63.623 ,,, 1969:2, 65.500, 64.867 ,,, 1969:3, 67.100, 66.569 ,,, 1969:4, 68.500, 68.247 ,,, 1970:1, 70.600, 69.674 ,,, 1970:2, 73.000, 71.913 ,,, 1970:3, 75.200, 74.471 ,,, 1970:4, 78.000, 76.758 ,,, 1971:1, 80.900, 79.727 ,,, 1971:2, 82.600, 82.775 ,,, 1971:3, 84.700, 84.385 ,,, 1971:4, 87.200, 86.484 ,,, 1972:1, 89.100, 89.051 ,,, 1972:2, 91.500, 90.901 ,,, 1972:3, 93.400, 93.347 ,,, 1972:4, 94.400, 95.198 ,,, 1973:1, 95.000, 96.008 ,,, 1973:2, 97.500, 96.394 ,,, 1973:3, 98.100, 99.050 ,,, 1973:4, 97.900, 99.449 ,,, 1974:1, 98.800, 98.959 ,,, 1974:2, 99.800, 99.822 ,,, 1974:3, 100.800, 100.810 ,,, 1974:4, 102.100, 101.800 ,,, 1975:1, 103.300, 103.144 ,,, 1975:2, 104.100, 104.360 ,,, 1975:3, 105.100, 105.106 ,,, 1975:4, 105.700, 106.097 ,,, 1976:1, 107.000, 106.623 ,,, 1976:2, 107.700, 107.984 ,,, 1976:3, 108.500, 108.630 ,,, 1976:4, 109.900, 109.405 ,,, 1977:1, 108.700, 110.887 ,,, 1977:2, 109.500, 109.311 ,,, 1977:3, 110.200, 110.159 ,,, 1977:4, 111.400, 110.880 ,,, 1978:1, 112.700, 112.180 ,,, 1978:2, 113.800, 113.574 ,,, 1978:3, 115.000, 114.712 ,,, 1978:4, 116.000, 115.959 ,,, 1979:1, 117.200, 116.961 ,,, 1979:2, 118.500, 118.197 ,,, 1979:3, 119.900, 119.541 ,,, 1979:4, 121.200, 120.992 ,,, 1980:1, 123.100, 122.314 ,,, 1980:2, 124.500, 124.333 ,,, 1980:3, 125.400, 125.740 ,,, 1980:4, 127.500, 126.561 ,,, 1981:1, 128.900, 128.803 ,,, 1981:2, 130.500, 130.194 ,,, 1981:3, 133.400, 131.821 ,,, 1981:4, 134.700, 134.961 ,,, 1982:1, 135.100, 136.176 ,,, 1982:2, 136.200, 136.359 ,,, 1982:3, 137.200, 137.409 ,,, 1982:4, 138.300, 138.354 ,,, 1983:1, 139.400, 139.428 ,,, 1983:2, 140.500, 140.509 ,,, 1983:3, 141.700, 141.594 ,,, 1983:4, 142.800, 142.799 ,,, 1984:1, 143.800, 143.886 ,,, 1984:2, 144.500, 144.859 ,,, 1984:3, 145.600, 145.488 ,,, 1984:4, 146.300, 146.600 ,,, 1985:1, 147.200, 147.242 ,,, 1985:2, 148.400, 148.131 ,,, 1985:3, 149.400, 149.374 ,,, 1985:4, 150.500, 150.373 ,,, 1986:1, 151.800, 151.489 ,,, 1986:2, 152.600, 152.835 ,,, 1986:3, 153.500, 153.586 ,,, 1986:4, 154.700, 154.465 ,,, 1987:1, 156.100, 155.700 ,,, 1987:2, 157.000, 157.160 ,,, 1987:3, 158.200, 158.022 ,,, 1987:4, 159.400, 159.244 ,,, 1988:1, 159.900, 160.460 ,,, 1988:2, 160.400, 160.855 ,,, 1988:3, 161.500, 161.274 ,,, 1988:4, 162.000, 162.412 ,,, 1989:1, 162.200, 162.841 ,,, 1989:2, 163.200, 162.935 ,,, 1989:3, 163.900, 163.988 ,,, 1989:4, 164.700, 164.679 ,,, 1990:1, 165.900, 165.489 ,,, 1990:2, 166.700, 166.764 ,,, 1990:3, 168.100, 167.554 ,,, 1990:4, 169.300, 169.048 ,,, 1991:1, 170.900, 170.286 ,,, 1991:2, 172.700, 171.984 ,,, 1991:3, 173.900, 173.893 ,,, 1991:4, 175.600, 175.075 ,,, 1992:1, 176.400, 176.846 ,,, 1992:2, 177.400, 177.549 ,,, 1992:3, 177.600, 178.508 ,,, 1992:4, 177.700, 178.541 ,,, 1993:1, 179.300, 178.495 ,,, 1993:2, 180.000, 180.236 ,,, 1993:3, 181.200, 180.893 ,,, 1993:4, 182.600, 182.144 ,,, 1994:1, 183.200, 183.617 ,,, 1994:2, 183.700, 184.139 ,,, 1994:3, 184.900, 184.561 ,,, 1994:4, 186.300, 185.819 ,,, 1995:1, 187.400, 187.298 ,,, 1995:2, 189.100, 188.408 ,,, 1995:3, 190.800, 190.218 ,,, 1995:4, 191.800, 192.002 ,,, 1996:1, 193.800, 192.948 ,,, 1996:2, 194.700, 195.076 ,,, 1996:3, 199.200, 195.889 ,,, 1996:4, 199.400, 200.930 ,,, 1997:1, 200.700, 200.820 ,,, 1997:2, 202.700, 202.068 ,,, 1997:3, 201.900, 204.145 ,,, 1997:4, 203.574, 202.932 ,,, 1998:1, 205.920, 204.705 ,,, 1998:2, 207.338, 207.241 ,,, 1998:3, 209.133, 208.649 ,,, 1998:4, 212.495, 210.500 ,,, 1999:1, 213.997, 214.170 ,,, 1999:2, 218.610, 215.595 ,,, 1999:3, 216.889, 220.674 ,,, 1999:4, 212.174, 218.243 ,,, 2000:1, 212.671, 212.474 ,,, 2000:2, 214.469, 213.039 ,,, 2000:3, 216.385, 215.110 ,,, 2000:4,, 217.255, 0.7999, 215.688, 218.823 2001:1,, 218.127, 1.2305, 215.715, 220.538 2001:2,, 218.998, 1.6254, 215.813, 222.184 2001:3,, 219.870, 2.0093, 215.932, 223.809 2001:4,, 220.743, 2.3897, 216.060, 225.427 2002:1,, 221.616, 2.7691, 216.189, 227.044 2002:2,, 222.490, 3.1483, 216.320, 228.661 2002:3,, 223.364, 3.5276, 216.450, 230.278 2002:4,, 224.239, 3.9067, 216.582, 231.896 2003:1,, 225.114, 4.2853, 216.715, 233.513 2003:2,, 225.989, 4.6630, 216.850, 235.128 2003:3,, 226.865, 5.0394, 216.988, 236.742 2003:4,, 227.741, 5.4142, 217.129, 238.352 2004:1,, 228.617, 5.7870, 217.274, 239.959 2004:2,, 229.493, 6.1576, 217.425, 241.562 2004:3,, 230.370, 6.5257, 217.580, 243.160 2004:4,, 231.247, 6.8911, 217.741, 244.753 2005:1,, 232.124, 7.2535, 217.908, 246.341 2005:2,, 233.002, 7.6128, 218.081, 247.923 2005:3,, 233.879, 7.9689, 218.261, 249.498 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima111_results.py000066400000000000000000001262071224417117700276250ustar00rootroot00000000000000import numpy as np llf = np.array([-241.75576160303]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .79987660416529]) chi2 = np.array([ 342.91413339514]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .88084748605315, .93989719451385, -.7709851377434, .79987660416529]) cov_params = np.array([ .15020189867396, -.01642122563089, .01456018801049, -.00156750041014, -.01642122563089, .0032067778715, -.00350387326241, .00059634328354, .01456018801049, -.00350387326241, .00480028434835, -.00068065418463, -.00156750041014, .00059634328354, -.00068065418463, .00029322997097]).reshape(4,4) xb = np.array([ .88084751367569, .88084751367569, .65303039550781, .55365419387817, .45908725261688, .42810925841331, .37837743759155, .37686342000961, .35719576478004, .3220648765564, .31943875551224, .30907514691353, .30120712518692, .31383177638054, .29652059078217, .30856171250343, .30095273256302, .29171526432037, .31331890821457, .30463594198227, .31990340352058, .30126947164536, .29703867435455, .29884466528893, .31037190556526, .30912432074547, .32505416870117, .31537705659866, .33494210243225, .37874156236649, .37366089224815, .40859284996986, .37640652060509, .37692713737488, .39422073960304, .40755322575569, .43472331762314, .43878075480461, .47569087147713, .48725643754005, .49617394804955, .53683114051819, .55128628015518, .56243091821671, .58791494369507, .60756206512451, .58892780542374, .59145200252533, .59339815378189, .54422444105148, .55698639154434, .53304374217987, .51458370685577, .50035130977631, .48937830328941, .49780988693237, .52120143175125, .62369203567505, .6182547211647, .76608312129974, .84627467393875, .92499214410782, .96879118680954, 1.0870156288147, 1.1105998754501, 1.0274360179901, 1.013991355896, .98673474788666, .96571969985962, .84817039966583, .85888928174973, .86715340614319, .85663330554962, .93297851085663, .90738350152969, .88765007257462, .92311006784439, .96734017133713, 1.0690053701401, 1.1473876237869, 1.1740373373032, 1.3128218650818, 1.4704967737198, 1.5582785606384, 1.7273052930832, 1.8745132684708, 1.7853132486343, 1.7841064929962, 1.850741147995, 1.800768494606, 1.8466963768005, 1.7976499795914, 1.6078149080276, 1.3938897848129, 1.5498898029327, 1.3492304086685, 1.059396147728, 1.0217411518097, 1.0096007585526, 1.0002405643463, 1.0436969995499, 1.0603114366531, 1.0055546760559, .99712115526199, .92305397987366, .9841884970665, .92997401952744, .90506774187088, .9872123003006, .61137217283249, .65943044424057, .67959040403366, .77959072589874, .87357920408249, .91226226091385, .95897603034973, .96120971441269, .99671375751495, 1.0409790277481, 1.0919979810715, 1.1144404411316, 1.2330915927887, 1.2401138544083, 1.161071896553, 1.3028255701065, 1.2938764095306, 1.3207612037659, 1.5610725879669, 1.4760913848877, 1.258552312851, 1.2090681791306, 1.1540271043777, 1.12848341465, 1.1087870597839, 1.0936040878296, 1.0987877845764, 1.0858948230743, 1.0590622425079, .98770052194595, 1.0002481937408, .94235575199127, .93150353431702, .97381073236465, .9726470708847, .98864215612411, 1.0347559452057, .98585307598114, .96503925323486, .9996662735939, 1.0601476430893, 1.022319316864, 1.043828368187, 1.0604115724564, .95495897531509, .87365657091141, .91232192516327, .84078407287598, .73495537042618, .78849309682846, .77909576892853, .78874284029007, .8637443780899, .8540056347847, .94784545898438, .98641014099121, 1.0837067365646, 1.1925053596497, 1.1750392913818, 1.2460317611694, 1.1487410068512, 1.1075156927109, .94060403108597, .7950227856636, .93615245819092, .89293897151947, .94407802820206, 1.0172899961472, .93860250711441, .86104601621628, .91948908567429, .99833220243454, 1.008442401886, 1.1175880432129, 1.2017351388931, 1.1483734846115, 1.2761443853378, 1.188849568367, 1.7296310663223, 1.4202431440353, 1.3675138950348, 1.445098400116, 1.031960606575, 1.1313284635544, 1.3214453458786, 1.3112732172012, 1.367110490799, 1.674845457077, 1.5979281663895, 2.064112663269, 1.3536450862885, .30015936493874, .36831066012383, .64060544967651]) y = np.array([np.nan, 29.860847473145, 29.803030014038, 29.903654098511, 29.82908821106, 29.968111038208, 29.928377151489, 30.126863479614, 30.197195053101, 30.132064819336, 30.23943901062, 30.289073944092, 30.341207504272, 30.523830413818, 30.516519546509, 30.68856048584, 30.740953445435, 30.771715164185, 31.003318786621, 31.054636001587, 31.25990486145, 31.251270294189, 31.317039489746, 31.418846130371, 31.590372085571, 31.689123153687, 31.905054092407, 31.965375900269, 32.214942932129, 32.658740997314, 32.823661804199, 33.258590698242, 33.27640914917, 33.47692489624, 33.7942237854, 34.107555389404, 34.534721374512, 34.83878326416, 35.375694274902, 35.787254333496, 36.196174621582, 36.83683013916, 37.3512840271, 37.86243057251, 38.487915039063, 39.107563018799, 39.488929748535, 39.991455078125, 40.49340057373, 40.644222259521, 41.156986236572, 41.433044433594, 41.714584350586, 42.000350952148, 42.289379119873, 42.697811126709, 43.221202850342, 44.323692321777, 44.818256378174, 46.366081237793, 47.64627456665, 49.024990081787, 50.26879119873, 52.087017059326, 53.410598754883, 54.027435302734, 55.01399230957, 55.886737823486, 56.765720367432, 56.948169708252, 57.858890533447, 58.767154693604, 59.556632995605, 60.93297958374, 61.707382202148, 62.487648010254, 63.623111724854, 64.867340087891, 66.569007873535, 68.247383117676, 69.674034118652, 71.912818908691, 74.470497131348, 76.758277893066, 79.72730255127, 82.774513244629, 84.385314941406, 86.484100341797, 89.050735473633, 90.900764465332, 93.346694946289, 95.197654724121, 96.007820129395, 96.393890380859, 99.04988861084, 99.449226379395, 98.959396362305, 99.821746826172, 100.80960083008, 101.80024719238, 103.1436920166, 104.36031341553, 105.10555267334, 106.09712219238, 106.62305450439, 107.98419189453, 108.62997436523, 109.40506744385, 110.88721466064, 109.31137084961, 110.15943145752, 110.87958526611, 112.17959594727, 113.57357788086, 114.71226501465, 115.95897674561, 116.9612121582, 118.1967086792, 119.54097747803, 120.99199676514, 122.31443786621, 124.33309173584, 125.74011230469, 126.56107330322, 128.80282592773, 130.19386291504, 131.82075500488, 134.96105957031, 136.17608642578, 136.35855102539, 137.40905761719, 138.35401916504, 139.42848205566, 140.50877380371, 141.59359741211, 142.79878234863, 143.88589477539, 144.85906982422, 145.48770141602, 146.60025024414, 147.24235534668, 148.13150024414, 149.37380981445, 150.3726348877, 151.48864746094, 152.83476257324, 153.58586120605, 154.46504211426, 155.69966125488, 157.16015625, 158.0223236084, 159.24382019043, 160.46040344238, 160.85494995117, 161.27365112305, 162.41232299805, 162.84078979492, 162.93495178223, 163.98849487305, 164.67909240723, 165.48873901367, 166.76373291016, 167.55400085449, 169.0478515625, 170.2864074707, 171.98370361328, 173.89250183105, 175.07502746582, 176.84603881836, 177.54873657227, 178.50750732422, 178.5406036377, 178.49502563477, 180.23616027832, 180.89294433594, 182.14407348633, 183.61729431152, 184.13859558105, 184.56105041504, 185.81948852539, 187.29833984375, 188.40843200684, 190.21759033203, 192.00173950195, 192.9483795166, 195.07614135742, 195.88883972168, 200.92962646484, 200.82023620605, 202.06750488281, 204.1450958252, 202.93196105957, 204.70533752441, 207.24143981934, 208.6492767334, 210.50010681152, 214.16984558105, 215.59492492676, 220.67411804199, 218.24264526367, 212.47415161133, 213.03932189941, 215.10960388184]) resid = np.array([np.nan, -.71084743738174, -.45302960276604, -.5336537361145, -.28908717632294, -.41811093688011, -.17837668955326, -.28686326742172, -.38719645142555, -.21206425130367, -.25943928956985, -.24907378852367, -.13120894134045, -.3038315474987, -.13652075827122, -.24856032431126, -.26095372438431, -.0817142650485, -.25331944227219, -.11463540792465, -.30990317463875, -.2312697917223, -.19703827798367, -.13884480297565, -.21037344634533, -.10912357270718, -.25505447387695, -.08537751436234, .06505750864744, -.20873957872391, .02633681893349, -.35858979821205, -.1764095723629, -.07692407816648, -.09422151744366, -.00755552388728, -.13472028076649, .06121923774481, -.07569316774607, -.08725491166115, .10382451862097, -.03683112934232, -.05128625407815, .03757134452462, .0120835499838, -.20756052434444, -.08892779797316, -.09145200997591, -.3934012055397, -.04422445222735, -.25698333978653, -.23304453492165, -.21458448469639, -.2003520578146, -.08937677741051, .00219011562876, .47879853844643, -.12369203567505, .78174299001694, .43391767144203, .4537245631218, .27500861883163, .73120957612991, .21298357844353, -.41059911251068, -.02743596211076, -.11398979276419, -.08673703670502, -.66572046279907, .05183110013604, .04111221805215, -.06715416908264, .44336593151093, -.13297925889492, -.1073842421174, .21235218644142, .27689066529274, .63265830278397, .53099316358566, .25261387228966, .92596107721329, 1.0871796607971, .72950023412704, 1.2417244911194, 1.1726962327957, -.17451636493206, .31468516588211, .71589350700378, .04926039651036, .59923303127289, .05330519750714, -.79764997959137, -1.0078164339066, 1.1061102151871, -.94989138841629, -1.5492273569107, -.15939457714558, -.02174116671085, -.00960071571171, .29975482821465, .15630762279034, -.2603160738945, -.00555467186496, -.3971226811409, .37694907188416, -.28419154882431, -.12997098267078, .49493381381035, -2.1872169971466, .18863087892532, .04056651890278, .52041417360306, .52040469646454, .22642692923546, .28773468732834, .0410239957273, .2387872338295, .30328929424286, .35902243852615, .20799747109413, .78556102514267, .16690990328789, -.34011232852936, .93892657756805, .0971682742238, .30612966418266, 1.5792326927185, -.26106956601143, -1.0760822296143, -.15856145322323, -.2090682387352, -.05402099713683, -.02849259786308, -.00878097955137, .10639289021492, .00121826829854, -.08589478582144, -.35906526446342, .11230555176735, -.30025118589401, -.04236188530922, .26849341392517, .02618926763535, .12735903263092, .31136092543602, -.23475293815136, -.08585914969444, .23495768010616, .40034285187721, -.1601537913084, .17767761647701, .15616858005524, -.56041151285172, -.45495894551277, .2263495028019, -.41232195496559, -.64078712463379, .26504465937614, -.08849616348743, .02090725488961, .41125410795212, -.06374131888151, .54600352048874, .25215145945549, .61358070373535, .71629631519318, .00749156065285, .52497291564941, -.44604399800301, -.14874097704887, -.90750348567963, -.84061318635941, .80498331785202, -.23615552484989, .30705797672272, .45593112707138, -.41729912161827, -.43860253691673, .33895090222359, .48052009940147, .10165861994028, .69156980514526, .58240884542465, -.20173519849777, .85162657499313, -.37615045905113, 3.3111503124237, -1.5296341180801, -.12024004757404, .63248610496521, -2.2451014518738, .64205056428909, 1.2146645784378, .09655395895243, .48372489213943, 1.9948890209198, -.17284658551216, 3.0150785446167, -3.7851057052612, -6.0686569213867, .19684991240501, 1.4296782016754, 1.2753949165344]) yr = np.array([np.nan, -.71084743738174, -.45302960276604, -.5336537361145, -.28908717632294, -.41811093688011, -.17837668955326, -.28686326742172, -.38719645142555, -.21206425130367, -.25943928956985, -.24907378852367, -.13120894134045, -.3038315474987, -.13652075827122, -.24856032431126, -.26095372438431, -.0817142650485, -.25331944227219, -.11463540792465, -.30990317463875, -.2312697917223, -.19703827798367, -.13884480297565, -.21037344634533, -.10912357270718, -.25505447387695, -.08537751436234, .06505750864744, -.20873957872391, .02633681893349, -.35858979821205, -.1764095723629, -.07692407816648, -.09422151744366, -.00755552388728, -.13472028076649, .06121923774481, -.07569316774607, -.08725491166115, .10382451862097, -.03683112934232, -.05128625407815, .03757134452462, .0120835499838, -.20756052434444, -.08892779797316, -.09145200997591, -.3934012055397, -.04422445222735, -.25698333978653, -.23304453492165, -.21458448469639, -.2003520578146, -.08937677741051, .00219011562876, .47879853844643, -.12369203567505, .78174299001694, .43391767144203, .4537245631218, .27500861883163, .73120957612991, .21298357844353, -.41059911251068, -.02743596211076, -.11398979276419, -.08673703670502, -.66572046279907, .05183110013604, .04111221805215, -.06715416908264, .44336593151093, -.13297925889492, -.1073842421174, .21235218644142, .27689066529274, .63265830278397, .53099316358566, .25261387228966, .92596107721329, 1.0871796607971, .72950023412704, 1.2417244911194, 1.1726962327957, -.17451636493206, .31468516588211, .71589350700378, .04926039651036, .59923303127289, .05330519750714, -.79764997959137, -1.0078164339066, 1.1061102151871, -.94989138841629, -1.5492273569107, -.15939457714558, -.02174116671085, -.00960071571171, .29975482821465, .15630762279034, -.2603160738945, -.00555467186496, -.3971226811409, .37694907188416, -.28419154882431, -.12997098267078, .49493381381035, -2.1872169971466, .18863087892532, .04056651890278, .52041417360306, .52040469646454, .22642692923546, .28773468732834, .0410239957273, .2387872338295, .30328929424286, .35902243852615, .20799747109413, .78556102514267, .16690990328789, -.34011232852936, .93892657756805, .0971682742238, .30612966418266, 1.5792326927185, -.26106956601143, -1.0760822296143, -.15856145322323, -.2090682387352, -.05402099713683, -.02849259786308, -.00878097955137, .10639289021492, .00121826829854, -.08589478582144, -.35906526446342, .11230555176735, -.30025118589401, -.04236188530922, .26849341392517, .02618926763535, .12735903263092, .31136092543602, -.23475293815136, -.08585914969444, .23495768010616, .40034285187721, -.1601537913084, .17767761647701, .15616858005524, -.56041151285172, -.45495894551277, .2263495028019, -.41232195496559, -.64078712463379, .26504465937614, -.08849616348743, .02090725488961, .41125410795212, -.06374131888151, .54600352048874, .25215145945549, .61358070373535, .71629631519318, .00749156065285, .52497291564941, -.44604399800301, -.14874097704887, -.90750348567963, -.84061318635941, .80498331785202, -.23615552484989, .30705797672272, .45593112707138, -.41729912161827, -.43860253691673, .33895090222359, .48052009940147, .10165861994028, .69156980514526, .58240884542465, -.20173519849777, .85162657499313, -.37615045905113, 3.3111503124237, -1.5296341180801, -.12024004757404, .63248610496521, -2.2451014518738, .64205056428909, 1.2146645784378, .09655395895243, .48372489213943, 1.9948890209198, -.17284658551216, 3.0150785446167, -3.7851057052612, -6.0686569213867, .19684991240501, 1.4296782016754, 1.2753949165344]) mse = np.array([ .7963672876358, .7963672876358, .71457105875015, .67959600687027, .66207146644592, .65259438753128, .64725720882416, .644182741642, .64238852262497, .64133352041245, .64071041345596, .6403414607048, .64012265205383, .63999271392822, .63991558551788, .63986974954605, .63984251022339, .63982629776001, .6398167014122, .6398109793663, .63980758190155, .63980555534363, .63980436325073, .639803647995, .63980323076248, .63980293273926, .63980281352997, .63980269432068, .63980263471603, .63980263471603, .63980263471603, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139, .63980257511139]) stdp = np.array([ .88084751367569, .88084751367569, .65303039550781, .55365419387817, .45908725261688, .42810925841331, .37837743759155, .37686342000961, .35719576478004, .3220648765564, .31943875551224, .30907514691353, .30120712518692, .31383177638054, .29652059078217, .30856171250343, .30095273256302, .29171526432037, .31331890821457, .30463594198227, .31990340352058, .30126947164536, .29703867435455, .29884466528893, .31037190556526, .30912432074547, .32505416870117, .31537705659866, .33494210243225, .37874156236649, .37366089224815, .40859284996986, .37640652060509, .37692713737488, .39422073960304, .40755322575569, .43472331762314, .43878075480461, .47569087147713, .48725643754005, .49617394804955, .53683114051819, .55128628015518, .56243091821671, .58791494369507, .60756206512451, .58892780542374, .59145200252533, .59339815378189, .54422444105148, .55698639154434, .53304374217987, .51458370685577, .50035130977631, .48937830328941, .49780988693237, .52120143175125, .62369203567505, .6182547211647, .76608312129974, .84627467393875, .92499214410782, .96879118680954, 1.0870156288147, 1.1105998754501, 1.0274360179901, 1.013991355896, .98673474788666, .96571969985962, .84817039966583, .85888928174973, .86715340614319, .85663330554962, .93297851085663, .90738350152969, .88765007257462, .92311006784439, .96734017133713, 1.0690053701401, 1.1473876237869, 1.1740373373032, 1.3128218650818, 1.4704967737198, 1.5582785606384, 1.7273052930832, 1.8745132684708, 1.7853132486343, 1.7841064929962, 1.850741147995, 1.800768494606, 1.8466963768005, 1.7976499795914, 1.6078149080276, 1.3938897848129, 1.5498898029327, 1.3492304086685, 1.059396147728, 1.0217411518097, 1.0096007585526, 1.0002405643463, 1.0436969995499, 1.0603114366531, 1.0055546760559, .99712115526199, .92305397987366, .9841884970665, .92997401952744, .90506774187088, .9872123003006, .61137217283249, .65943044424057, .67959040403366, .77959072589874, .87357920408249, .91226226091385, .95897603034973, .96120971441269, .99671375751495, 1.0409790277481, 1.0919979810715, 1.1144404411316, 1.2330915927887, 1.2401138544083, 1.161071896553, 1.3028255701065, 1.2938764095306, 1.3207612037659, 1.5610725879669, 1.4760913848877, 1.258552312851, 1.2090681791306, 1.1540271043777, 1.12848341465, 1.1087870597839, 1.0936040878296, 1.0987877845764, 1.0858948230743, 1.0590622425079, .98770052194595, 1.0002481937408, .94235575199127, .93150353431702, .97381073236465, .9726470708847, .98864215612411, 1.0347559452057, .98585307598114, .96503925323486, .9996662735939, 1.0601476430893, 1.022319316864, 1.043828368187, 1.0604115724564, .95495897531509, .87365657091141, .91232192516327, .84078407287598, .73495537042618, .78849309682846, .77909576892853, .78874284029007, .8637443780899, .8540056347847, .94784545898438, .98641014099121, 1.0837067365646, 1.1925053596497, 1.1750392913818, 1.2460317611694, 1.1487410068512, 1.1075156927109, .94060403108597, .7950227856636, .93615245819092, .89293897151947, .94407802820206, 1.0172899961472, .93860250711441, .86104601621628, .91948908567429, .99833220243454, 1.008442401886, 1.1175880432129, 1.2017351388931, 1.1483734846115, 1.2761443853378, 1.188849568367, 1.7296310663223, 1.4202431440353, 1.3675138950348, 1.445098400116, 1.031960606575, 1.1313284635544, 1.3214453458786, 1.3112732172012, 1.367110490799, 1.674845457077, 1.5979281663895, 2.064112663269, 1.3536450862885, .30015936493874, .36831066012383, .64060544967651]) icstats = np.array([ 202, np.nan, -241.75576160303, 4, 491.51152320605, 504.74459399566]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima111nc_css_results.py000066400000000000000000001254731224417117700310220ustar00rootroot00000000000000import numpy as np llf = np.array([-242.89663276735]) nobs = np.array([ 202]) k = np.array([ 3]) k_exog = np.array([ 1]) sigma = np.array([ .8053519404535]) chi2 = np.array([ 15723.381396967]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .99479180506163, -.84461527652809, .64859174799221]) cov_params = np.array([ .00008904968254, -.00023560410507, .00012795903324, -.00023560410507, .00131628534915, -.00022462340695, .00012795903324, -.00022462340695, .0005651128627]).reshape(3,3) xb = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) y = np.array([np.nan, 28.979999542236, 29.178695678711, 29.40651512146, 29.420400619507, 29.608880996704, 29.609405517578, 29.830673217773, 29.921676635742, 29.874292373657, 29.990877151489, 30.048864364624, 30.10717010498, 30.292304992676, 30.290996551514, 30.464012145996, 30.519966125488, 30.553541183472, 30.783664703369, 30.838117599487, 31.042964935303, 31.038463592529, 31.105230331421, 31.207004547119, 31.377513885498, 31.477378845215, 31.692283630371, 31.755348205566, 32.003520965576, 32.444396972656, 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1.2339268922806, 1.1695692539215]) mse = np.array([ 1.1112809181213, .6632194519043, .65879660844803, .65575885772705, .65364873409271, .65217137336731, .65113133192062, .6503963470459, .64987552165985, .64950579404831, .64924287796021, .64905577898026, .64892256259918, .64882761240005, .64875996112823, .64871168136597, .64867728948593, .64865279197693, .64863526821136, .64862281084061, .64861387014389, .64860755205154, .64860302209854, .64859980344772, .64859747886658, .64859586954117, .64859467744827, .64859384298325, .6485932469368, .64859282970428, .64859253168106, .64859229326248, .64859211444855, .64859199523926, .64859193563461, .64859187602997, .64859187602997, .64859181642532, .64859181642532, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, 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.64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068]) stdp = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) icstats = np.array([ 202, np.nan, -242.89663276735, 3, 491.79326553469, 501.7180686269]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima111nc_results.py000066400000000000000000001254731224417117700301520ustar00rootroot00000000000000import numpy as np llf = np.array([-243.77512585356]) nobs = np.array([ 202]) k = np.array([ 3]) k_exog = np.array([ 1]) sigma = np.array([ .80556855709271]) chi2 = np.array([ 14938.241729056]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .99034248845249, -.83659509233745, .80556855709271]) cov_params = np.array([ .00009906057555, -.00026616895902, .00007867120825, -.00026616895902, .00137590666911, -.0001403880509, .00007867120825, -.0001403880509, .0002129852258]).reshape(3,3) xb = np.array([ 0, 0, .10457526892424, .14047028124332, .10415132343769, .11891452968121, .09492295235395, .11409470438957, .1086763292551, .08389142900705, .08740901201963, .08212262392044, .07780049741268, .09159570932388, .07793674618006, .08996207267046, .08444581180811, .07675409317017, .09658851474524, .09001308679581, .10454939305782, .08898131549358, .08520055562258, .08665482699871, .09710033237934, .09660832583904, .11157563328743, .10410477221012, .12245775014162, .16395011544228, .16329728066921, .19811384379864, .1734282374382, .17583830654621, .19323042035103, .20777989923954, .23532645404339, .24299769103527, .28016451001167, .29588291049004, .30903342366219, .35078409314156, .37033796310425, .38669663667679, .41575729846954, .44006872177124, .42965853214264, .43632391095161, .44190016388893, .40044051408768, .41188025474548, .3907016813755, .37298321723938, .3581600189209, .3457590341568, .35075950622559, .37031736969948, .46355310082436, .46467992663383, .60399496555328, .68979620933533, .77695161104202, .8344908952713, .95950168371201, 1.0025858879089, .94638174772263, .94548571109772, .92936158180237, .91587167978287, .81233781576157, .81797075271606, .8226832151413, .81125050783157, .87855970859528, .85799652338028, .84079349040985, .87252616882324, .9144481420517, 1.0110183954239, 1.0918086767197, 1.1286484003067, 1.2670909166336, 1.4290360212326, 1.533768415451, 1.7136362791061, 1.8794873952866, 1.8337399959564, 1.8569672107697, 1.9378981590271, 1.9133563041687, 1.969698548317, 1.939960360527, 1.7767087221146, 1.5786340236664, 1.7050459384918, 1.5186812877655, 1.2397723197937, 1.1755603551865, 1.1372153759003, 1.1051361560822, 1.1244224309921, 1.1251838207245, 1.06432056427, 1.0441527366638, .96578127145767, 1.0078399181366, .95077663660049, .91841346025467, .98358678817749, .63836628198624, .65705251693726, .65730959177017, .73439955711365, .81426596641541, .85033398866653, .89588165283203, .90323758125305, .94014054536819, .98638904094696, 1.040454864502, 1.0703103542328, 1.1875365972519, 1.2087339162827, 1.1495937108994, 1.2846138477325, 1.2899470329285, 1.3251601457596, 1.5544888973236, 1.5003498792648, 1.316685795784, 1.2706536054611, 1.2167699337006, 1.1870667934418, 1.1622149944305, 1.1414264440536, 1.1394081115723, 1.1223464012146, 1.0926969051361, 1.0217674970627, 1.0239287614822, .96423649787903, .94504725933075, .97511827945709, .96952658891678, .98022425174713, 1.0199228525162, .97626084089279, .95510673522949, .98353403806686, 1.0380674600601, 1.0068138837814, 1.0267919301987, 1.0435055494308, .94986528158188, .87152636051178, .89823776483536, .82833498716354, .72372996807098, .75921636819839, .74277937412262, .74440395832062, .80726110935211, .79834908246994, .88314270973206, .92332923412323, 1.0184471607208, 1.12877368927, 1.1288229227066, 1.2057402133942, 1.1317123174667, 1.100532412529, .95145136117935, .81135284900665, .92477059364319, .88128125667572, .92177194356918, .98639768362045, .91746246814728, .84441828727722, .89093261957169, .96059763431549, .97275197505951, 1.0751719474792, 1.1608537435532, 1.124911904335, 1.2485905885696, 1.1829364299774, 1.6815021038055, 1.4374854564667, 1.4024653434753, 1.4807903766632, 1.1158236265182, 1.1908674240112, 1.3569641113281, 1.3532432317734, 1.4080929756165, 1.6949023008347, 1.6488753557205, 2.0886788368225, 1.4827802181244, .51556593179703, .5077338218689, .70120370388031]) y = np.array([np.nan, 28.979999542236, 29.25457572937, 29.49047088623, 29.474151611328, 29.65891456604, 29.64492225647, 29.864093780518, 29.948677062988, 29.893890380859, 30.00740814209, 30.062122344971, 30.11780166626, 30.301595687866, 30.29793548584, 30.469961166382, 30.524446487427, 30.556753158569, 30.786588668823, 30.840013504028, 31.044549942017, 31.038982391357, 31.105201721191, 31.206655502319, 31.377101898193, 31.476608276367, 31.691576004028, 31.754104614258, 32.002456665039, 32.443950653076, 32.613296508789, 33.048110961914, 33.073429107666, 33.27583694458, 33.593231201172, 33.907779693604, 34.33532333374, 34.642997741699, 35.180164337158, 35.595882415771, 36.009033203125, 36.650783538818, 37.170337677002, 37.686695098877, 38.315757751465, 38.94006729126, 39.329658508301, 39.836326599121, 40.341899871826, 40.500438690186, 41.011878967285, 41.290702819824, 41.572982788086, 41.85816192627, 42.14575958252, 42.550758361816, 43.070316314697, 44.163555145264, 44.664680480957, 46.203994750977, 47.489795684814, 48.876949310303, 50.134490966797, 51.959503173828, 53.302585601807, 53.946380615234, 54.945484161377, 55.829364776611, 56.715869903564, 56.912334442139, 57.817970275879, 58.722682952881, 59.511249542236, 60.878559112549, 61.657997131348, 62.44079208374, 63.572528839111, 64.814453125, 66.511016845703, 68.19181060791, 69.628646850586, 71.867088317871, 74.429039001465, 76.733764648438, 79.713638305664, 82.779487609863, 84.433738708496, 86.55696105957, 89.137893676758, 91.01335144043, 93.469696044922, 95.339958190918, 96.176712036133, 96.578636169434, 99.205047607422, 99.618682861328, 99.139770507813, 99.975563049316, 100.9372177124, 101.9051361084, 103.22441864014, 104.42518615723, 105.16432189941, 106.14414978027, 106.66577911377, 108.00784301758, 108.65077209473, 109.41841125488, 110.88359069824, 109.33836364746, 110.15705108643, 110.85730743408, 112.13439941406, 113.51425933838, 114.65033721924, 115.89588165283, 116.90323638916, 118.14013671875, 119.48638916016, 120.94045257568, 122.27030944824, 124.28753662109, 125.70873260498, 126.54959869385, 128.78460693359, 130.18994140625, 131.82516479492, 134.95448303223, 136.20034790039, 136.41668701172, 137.47065734863, 138.41676330566, 139.48707580566, 140.56221008301, 141.64143371582, 142.83940124512, 143.92234802246, 144.89270019531, 145.52177429199, 146.62393188477, 147.2642364502, 148.14505004883, 149.37510681152, 150.36952209473, 151.48022460938, 152.81993103027, 153.57626342773, 154.45510864258, 155.68353271484, 157.13807678223, 158.00682067871, 159.22679138184, 160.4434967041, 160.84985351563, 161.27151489258, 162.39823913574, 162.82833862305, 162.92372131348, 163.95921325684, 164.64277648926, 165.44439697266, 166.70726013184, 167.49835205078, 168.98315429688, 170.22332763672, 171.91844177246, 173.82876586914, 175.02882385254, 176.80574035645, 177.53170776367, 178.50051879883, 178.55145263672, 178.51135253906, 180.22477722168, 180.88128662109, 182.12176513672, 183.58641052246, 184.1174621582, 184.54441833496, 185.79092407227, 187.2606048584, 188.37274169922, 190.17517089844, 191.96086120605, 192.92491149902, 195.04859924316, 195.88293457031, 200.88150024414, 200.8374786377, 202.10246276855, 204.18078613281, 203.01582336426, 204.76487731934, 207.27696228027, 208.69123840332, 210.54109191895, 214.18989562988, 215.64587402344, 220.69868469238, 218.37178039551, 212.68955993652, 213.17874145508, 215.1701965332]) resid = np.array([np.nan, .17000007629395, .09542549401522, -.12046983093023, .06584875285625, -.108916208148, .1050778105855, -.02409455552697, -.13867701590061, .02610917761922, -.02740954607725, -.02212125435472, .09219767153263, -.08159548044205, .08206310123205, -.02996070124209, -.04444679990411, .13324689865112, -.03658904880285, .09998744726181, -.09454916417599, -.01898162066936, .01479982770979, .0733450204134, .00289814220741, .10339243710041, -.04157593473792, .12589477002621, .27754187583923, .00605186540633, .23670043051243, -.14811079204082, .02656871080399, .1241647452116, .10676880925894, .1922178119421, .06467659771442, .25700229406357, .11983319371939, .10411861538887, .29096505045891, .14921590685844, .12966203689575, .21330565214157, .18424116075039, -.0400672107935, .07034146040678, .06367607414722, -.24190320074558, .0995594933629, -.11187721043825, -.09070245176554, -.07298398017883, -.05816079676151, .05424249917269, .14924050867558, .6296826004982, .03644690662622, .93531775474548, .59600579738617, .61020302772522, .42304915189743, .86550986766815, .34049755334854, -.30258512496948, .05361825972795, -.0454841889441, -.02936388924718, -.61587244272232, .0876636877656, .082030788064, -.0226839594543, .48874869942665, -.07856046408415, -.05799730122089, .25920879840851, .32747456431389, .6855503320694, .5889800786972, .30819287896156, .97135013341904, 1.1329106092453, .77096086740494, 1.2662346363068, 1.1863652467728, -.17949041724205, .26625844836235, .64303278923035, -.03789660334587, .48664516210556, -.06969699263573, -.93996042013168, -1.1767102479935, .92136597633362, -1.1050474643707, -1.7186782360077, -.33977076411247, -.17556031048298, -.13721539080143, .19485920667648, .07558213919401, -.32518845796585, -.06432051956654, -.4441542327404, .33422181010246, -.30784299969673, -.15077359974384, .48158806562424, -2.1835913658142, .16163675487041, .04294444993138, .54269498586655, .56559586524963, .2857401072979, .34966295957565, .10411833971739, .29675936698914, .35986250638962, .41361248493195, .25954058766365, .82969123125076, .21246488392353, -.30873239040375, .9504047036171, .11538010835648, .31005910038948, 1.5748337507248, -.25448590517044, -1.1003407239914, -.21669489145279, -.27065354585648, -.11676382273436, -.08707597851753, -.06220890209079, .05857050418854, -.039402063936, -.12234635651112, -.39269989728928, .07823857665062, -.32393181324005, -.06424260139465, .25494971871376, .02488171122968, .13047952950001, .31977880001068, -.21991983056068, -.07626695930958, .24489018321037, .41647511720657, -.13807360827923, .19318304955959, .17320497334003, -.54350554943085, -.4498653113842, .22847975790501, -.39823773503304, -.62833803892136, .27627000212669, -.05921940878034, .05722366645932, .45559296011925, -.00725806690753, .6016600728035, .31685426831245, .67666161060333, .78155589103699, .07122328877449, .57118928432465, -.40575236082077, -.1317123323679, -.90052020549774, -.85146051645279, .78865325450897, -.224773645401, .31871569156647, .47823718190193, -.38640683889389, -.41746246814728, .35557863116264, .50907653570175, .13939322531223, .72726023197174, .62482494115829, -.16085371375084, .87508809566498, -.34859669208527, 3.3170635700226, -1.4815051555634, -.1374823898077, .59753465652466, -2.280793428421, .5581876039505, 1.155125617981, .06103510409594, .44175490736961, 1.9539065361023, -.19290342926979, 2.9641311168671, -3.8096718788147, -6.1977920532227, -.01855664327741, 1.2902550697327, 1.2147966623306]) yr = np.array([np.nan, .17000007629395, .09542549401522, -.12046983093023, .06584875285625, -.108916208148, .1050778105855, -.02409455552697, -.13867701590061, .02610917761922, -.02740954607725, -.02212125435472, .09219767153263, -.08159548044205, .08206310123205, -.02996070124209, -.04444679990411, .13324689865112, -.03658904880285, .09998744726181, -.09454916417599, -.01898162066936, .01479982770979, .0733450204134, .00289814220741, .10339243710041, -.04157593473792, .12589477002621, .27754187583923, .00605186540633, .23670043051243, -.14811079204082, .02656871080399, .1241647452116, .10676880925894, .1922178119421, .06467659771442, .25700229406357, .11983319371939, .10411861538887, .29096505045891, .14921590685844, .12966203689575, .21330565214157, .18424116075039, -.0400672107935, .07034146040678, .06367607414722, -.24190320074558, .0995594933629, -.11187721043825, -.09070245176554, -.07298398017883, -.05816079676151, .05424249917269, .14924050867558, .6296826004982, .03644690662622, .93531775474548, .59600579738617, .61020302772522, .42304915189743, .86550986766815, .34049755334854, -.30258512496948, .05361825972795, -.0454841889441, -.02936388924718, -.61587244272232, .0876636877656, .082030788064, -.0226839594543, .48874869942665, -.07856046408415, -.05799730122089, .25920879840851, .32747456431389, .6855503320694, .5889800786972, .30819287896156, .97135013341904, 1.1329106092453, .77096086740494, 1.2662346363068, 1.1863652467728, -.17949041724205, .26625844836235, .64303278923035, -.03789660334587, .48664516210556, -.06969699263573, -.93996042013168, -1.1767102479935, .92136597633362, -1.1050474643707, -1.7186782360077, -.33977076411247, -.17556031048298, -.13721539080143, .19485920667648, .07558213919401, -.32518845796585, -.06432051956654, -.4441542327404, .33422181010246, -.30784299969673, -.15077359974384, .48158806562424, -2.1835913658142, .16163675487041, .04294444993138, .54269498586655, .56559586524963, .2857401072979, .34966295957565, .10411833971739, .29675936698914, .35986250638962, .41361248493195, .25954058766365, .82969123125076, .21246488392353, -.30873239040375, .9504047036171, .11538010835648, .31005910038948, 1.5748337507248, -.25448590517044, -1.1003407239914, -.21669489145279, -.27065354585648, -.11676382273436, -.08707597851753, -.06220890209079, .05857050418854, -.039402063936, -.12234635651112, -.39269989728928, .07823857665062, -.32393181324005, -.06424260139465, .25494971871376, .02488171122968, .13047952950001, .31977880001068, -.21991983056068, -.07626695930958, .24489018321037, .41647511720657, -.13807360827923, .19318304955959, .17320497334003, -.54350554943085, -.4498653113842, .22847975790501, -.39823773503304, -.62833803892136, .27627000212669, -.05921940878034, .05722366645932, .45559296011925, -.00725806690753, .6016600728035, .31685426831245, .67666161060333, .78155589103699, .07122328877449, .57118928432465, -.40575236082077, -.1317123323679, -.90052020549774, -.85146051645279, .78865325450897, -.224773645401, .31871569156647, .47823718190193, -.38640683889389, -.41746246814728, .35557863116264, .50907653570175, .13939322531223, .72726023197174, .62482494115829, -.16085371375084, .87508809566498, -.34859669208527, 3.3170635700226, -1.4815051555634, -.1374823898077, .59753465652466, -2.280793428421, .5581876039505, 1.155125617981, .06103510409594, .44175490736961, 1.9539065361023, -.19290342926979, 2.9641311168671, -3.8096718788147, -6.1977920532227, -.01855664327741, 1.2902550697327, 1.2147966623306]) mse = np.array([ 1.4469859600067, 1.4469859600067, .89943557977676, .77543312311172, .72303003072739, .69548159837723, .67933452129364, .6692613363266, .66273111104965, .65839165449142, .65546035766602, .6534583568573, .65208071470261, .65112781524658, .65046626329422, .65000593662262, .64968502521515, .64946103096008, .64930462837219, .64919525384903, .64911878108978, .64906531572342, .6490278840065, .64900171756744, .6489834189415, .64897060394287, .64896160364151, .64895534515381, .6489509344101, .64894789457321, .64894568920135, .64894419908524, .64894318580627, .64894241094589, .64894187450409, .64894151687622, .64894127845764, .64894109964371, .64894098043442, .64894092082977, .64894086122513, .64894080162048, .64894074201584, .64894074201584, .64894074201584, .64894074201584, .64894074201584, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119, .64894068241119]) stdp = np.array([ .88084751367569, .88084751367569, .65303039550781, .55365419387817, .45908725261688, .42810925841331, .37837743759155, .37686342000961, .35719576478004, .3220648765564, .31943875551224, .30907514691353, .30120712518692, .31383177638054, .29652059078217, .30856171250343, .30095273256302, .29171526432037, .31331890821457, .30463594198227, .31990340352058, .30126947164536, .29703867435455, .29884466528893, .31037190556526, .30912432074547, .32505416870117, .31537705659866, .33494210243225, .37874156236649, .37366089224815, .40859284996986, .37640652060509, .37692713737488, .39422073960304, .40755322575569, .43472331762314, .43878075480461, .47569087147713, .48725643754005, .49617394804955, .53683114051819, .55128628015518, .56243091821671, .58791494369507, .60756206512451, .58892780542374, .59145200252533, .59339815378189, .54422444105148, .55698639154434, .53304374217987, .51458370685577, .50035130977631, .48937830328941, .49780988693237, .52120143175125, .62369203567505, .6182547211647, .76608312129974, .84627467393875, .92499214410782, .96879118680954, 1.0870156288147, 1.1105998754501, 1.0274360179901, 1.013991355896, .98673474788666, .96571969985962, .84817039966583, .85888928174973, .86715340614319, .85663330554962, .93297851085663, .90738350152969, .88765007257462, .92311006784439, .96734017133713, 1.0690053701401, 1.1473876237869, 1.1740373373032, 1.3128218650818, 1.4704967737198, 1.5582785606384, 1.7273052930832, 1.8745132684708, 1.7853132486343, 1.7841064929962, 1.850741147995, 1.800768494606, 1.8466963768005, 1.7976499795914, 1.6078149080276, 1.3938897848129, 1.5498898029327, 1.3492304086685, 1.059396147728, 1.0217411518097, 1.0096007585526, 1.0002405643463, 1.0436969995499, 1.0603114366531, 1.0055546760559, .99712115526199, .92305397987366, .9841884970665, .92997401952744, .90506774187088, .9872123003006, .61137217283249, .65943044424057, .67959040403366, .77959072589874, .87357920408249, .91226226091385, .95897603034973, .96120971441269, .99671375751495, 1.0409790277481, 1.0919979810715, 1.1144404411316, 1.2330915927887, 1.2401138544083, 1.161071896553, 1.3028255701065, 1.2938764095306, 1.3207612037659, 1.5610725879669, 1.4760913848877, 1.258552312851, 1.2090681791306, 1.1540271043777, 1.12848341465, 1.1087870597839, 1.0936040878296, 1.0987877845764, 1.0858948230743, 1.0590622425079, .98770052194595, 1.0002481937408, .94235575199127, .93150353431702, .97381073236465, .9726470708847, .98864215612411, 1.0347559452057, .98585307598114, .96503925323486, .9996662735939, 1.0601476430893, 1.022319316864, 1.043828368187, 1.0604115724564, .95495897531509, .87365657091141, .91232192516327, .84078407287598, .73495537042618, .78849309682846, .77909576892853, .78874284029007, .8637443780899, .8540056347847, .94784545898438, .98641014099121, 1.0837067365646, 1.1925053596497, 1.1750392913818, 1.2460317611694, 1.1487410068512, 1.1075156927109, .94060403108597, .7950227856636, .93615245819092, .89293897151947, .94407802820206, 1.0172899961472, .93860250711441, .86104601621628, .91948908567429, .99833220243454, 1.008442401886, 1.1175880432129, 1.2017351388931, 1.1483734846115, 1.2761443853378, 1.188849568367, 1.7296310663223, 1.4202431440353, 1.3675138950348, 1.445098400116, 1.031960606575, 1.1313284635544, 1.3214453458786, 1.3112732172012, 1.367110490799, 1.674845457077, 1.5979281663895, 2.064112663269, 1.3536450862885, .30015936493874, .36831066012383, .64060544967651]) icstats = np.array([ 202, np.nan, -243.77512585356, 3, 493.55025170713, 503.47505479933]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima112_css_results.py000066400000000000000000001270471224417117700305010ustar00rootroot00000000000000import numpy as np llf = np.array([ -244.3852892951]) nobs = np.array([ 202]) k = np.array([ 5]) k_exog = np.array([ 1]) sigma = np.array([ .81130812929037]) chi2 = np.array([ 73901.783883385]) df_model = np.array([ 3]) k_ar = np.array([ 1]) k_ma = np.array([ 2]) params = np.array([ .91963917600489, -.89804855498306, 1.3032353997768, .30230174935463, .65822088065264]) cov_params = np.array([ .00622027554245, .00026933156699, -.00014478009121, -.00010527901395, .00006880952561, .00026933156699, .0023661521973, -.00263264462948, -.00241927046074, -.00069258998629, -.00014478009121, -.00263264462948, .003637275855, .0033625395431, .00072507981262, -.00010527901395, -.00241927046074, .0033625395431, .00312013997649, .00067166747844, .00006880952561, -.00069258998629, .00072507981262, .00067166747844, .00108477120916]).reshape(5,5) xb = np.array([ .91963917016983, .91963917016983, .69261693954468, .76115423440933, .63710719347, .77478265762329, .61527073383331, .80501782894135, .62182641029358, .72158020734787, .66619211435318, .72980058193207, .64935982227325, .77660953998566, .6069877743721, .79818457365036, .60795724391937, .7583304643631, .68147450685501, .72765469551086, .69699174165726, .69102382659912, .67753201723099, .72617518901825, .69977235794067, .713603079319, .72478419542313, .68503832817078, .75677126646042, .79034942388535, .68609654903412, .83208250999451, .60605573654175, .80849212408066, .69862711429596, .80978256464005, .7383074760437, .78789436817169, .79390448331833, .79169547557831, .76283228397369, .87939429283142, .75783687829971, .85010063648224, .80657452344894, .86508285999298, .72368890047073, .86846202611923, .75351697206497, .74047154188156, .82022970914841, .73184186220169, .7623735666275, .74929028749466, .75702118873596, .79036456346512, .81429827213287, 1.0007030963898, .70464313030243, 1.2375881671906, .82733017206192, 1.1780800819397, .83767229318619, 1.3407131433487, .7835128903389, .99667322635651, .82677388191223, 1.0330017805099, .78713357448578, .80603551864624, .91298097372055, .94862020015717, .83088356256485, 1.1405943632126, .72683191299438, 1.0197489261627, .88344657421112, 1.1016070842743, 1.0485582351685, 1.1717364788055, .94894939661026, 1.418029665947, 1.2063212394714, 1.3504880666733, 1.4053744077682, 1.5106836557388, .91192328929901, 1.4546687602997, 1.2100585699081, 1.2459771633148, 1.2914154529572, 1.1733019351959, .80550068616867, .88859277963638, 1.5257360935211, .49089628458023, .75268715620041, .92090040445328, .99410575628281, .87882828712463, 1.1253950595856, .89082646369934, .9317963719368, .90858340263367, .82737028598785, 1.0978132486343, .74325948953629, .98125350475311, 1.0478370189667, .03625157848001, 1.3422871828079, .51377469301224, 1.3643686771393, .70055514574051, 1.2559896707535, .71517109870911, 1.1997950077057, .75360465049744, 1.2862613201141, .79965251684189, 1.2606881856918, 1.018030166626, 1.1752370595932, .69517260789871, 1.597958445549, .65335071086884, 1.4763361215591, 1.2708671092987, 1.0432199239731, .561203956604, 1.2630445957184, .66821777820587, 1.2384748458862, .70777904987335, 1.2246036529541, .75373476743698, 1.199233174324, .69312900304794, 1.0659650564194, .80386221408844, .99243313074112, .78622406721115, 1.1766475439072, .74267518520355, 1.1679803133011, .85658311843872, .99335825443268, .79920876026154, 1.1595865488052, .92043119668961, .98299539089203, .94316083192825, 1.0661553144455, .6393609046936, .9456650018692, .91597771644592, .80332309007645, .65838772058487, 1.1093089580536, .68860310316086, 1.0485997200012, .89771980047226, .94581252336502, 1.0480616092682, 1.0014315843582, 1.1307729482651, 1.1770483255386, .89873492717743, 1.2652103900909, .66434383392334, 1.1431220769882, .44322970509529, .9269899725914, 1.0786435604095, .82789659500122, 1.0368362665176, 1.0712716579437, .70438456535339, .88966482877731, 1.009087562561, 1.0887442827225, .88976800441742, 1.2735350131989, 1.0157470703125, .95522791147232, 1.3003809452057, .73179203271866, 2.4736785888672, -.25176140666008, 1.9082181453705, .53501582145691, .7591078877449, 1.0281100273132, 1.6240043640137, .60095232725143, 1.6211705207825, 1.344465970993, 1.124480009079, 2.1775946617126, -.71973150968552, -.37754261493683, 1.2329530715942, 1.127131819725]) y = np.array([np.nan, 29.899639129639, 29.842617034912, 30.111154556274, 30.007108688354, 30.314783096313, 30.165269851685, 30.555017471313, 30.461826324463, 30.531579971313, 30.586193084717, 30.709800720215, 30.689361572266, 30.986608505249, 30.826986312866, 31.178184509277, 31.047958374023, 31.238330841064, 31.371475219727, 31.477655410767, 31.636991500854, 31.641023635864, 31.697532653809, 31.846176147461, 31.979772567749, 32.093601226807, 32.304782867432, 32.335037231445, 32.636772155762, 33.070346832275, 33.136096954346, 33.682079315186, 33.506057739258, 33.908489227295, 34.098628997803, 34.509784698486, 34.838306427002, 35.187896728516, 35.693904876709, 36.091693878174, 36.462833404541, 37.179393768311, 37.557834625244, 38.150100708008, 38.706577301025, 39.365081787109, 39.623691558838, 40.268463134766, 40.653518676758, 40.840469360352, 41.420227050781, 41.631843566895, 41.962375640869, 42.249290466309, 42.557022094727, 42.990364074707, 43.514297485352, 44.700702667236, 44.904644012451, 46.837585449219, 47.627330780029, 49.278079986572, 50.137672424316, 52.340713500977, 53.083511352539, 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.69261693954468, .76115423440933, .63710719347, .77478265762329, .61527073383331, .80501782894135, .62182641029358, .72158020734787, .66619211435318, .72980058193207, .64935982227325, .77660953998566, .6069877743721, .79818457365036, .60795724391937, .7583304643631, .68147450685501, .72765469551086, .69699174165726, .69102382659912, .67753201723099, .72617518901825, .69977235794067, .713603079319, .72478419542313, .68503832817078, .75677126646042, .79034942388535, .68609654903412, .83208250999451, .60605573654175, .80849212408066, .69862711429596, .80978256464005, .7383074760437, .78789436817169, .79390448331833, .79169547557831, .76283228397369, .87939429283142, .75783687829971, .85010063648224, .80657452344894, .86508285999298, .72368890047073, .86846202611923, .75351697206497, .74047154188156, .82022970914841, .73184186220169, .7623735666275, .74929028749466, .75702118873596, .79036456346512, .81429827213287, 1.0007030963898, .70464313030243, 1.2375881671906, .82733017206192, 1.1780800819397, .83767229318619, 1.3407131433487, .7835128903389, .99667322635651, .82677388191223, 1.0330017805099, .78713357448578, .80603551864624, .91298097372055, .94862020015717, .83088356256485, 1.1405943632126, .72683191299438, 1.0197489261627, .88344657421112, 1.1016070842743, 1.0485582351685, 1.1717364788055, .94894939661026, 1.418029665947, 1.2063212394714, 1.3504880666733, 1.4053744077682, 1.5106836557388, .91192328929901, 1.4546687602997, 1.2100585699081, 1.2459771633148, 1.2914154529572, 1.1733019351959, .80550068616867, .88859277963638, 1.5257360935211, .49089628458023, .75268715620041, .92090040445328, .99410575628281, .87882828712463, 1.1253950595856, .89082646369934, .9317963719368, .90858340263367, .82737028598785, 1.0978132486343, .74325948953629, .98125350475311, 1.0478370189667, .03625157848001, 1.3422871828079, .51377469301224, 1.3643686771393, .70055514574051, 1.2559896707535, .71517109870911, 1.1997950077057, .75360465049744, 1.2862613201141, .79965251684189, 1.2606881856918, 1.018030166626, 1.1752370595932, .69517260789871, 1.597958445549, .65335071086884, 1.4763361215591, 1.2708671092987, 1.0432199239731, .561203956604, 1.2630445957184, .66821777820587, 1.2384748458862, .70777904987335, 1.2246036529541, .75373476743698, 1.199233174324, .69312900304794, 1.0659650564194, .80386221408844, .99243313074112, .78622406721115, 1.1766475439072, .74267518520355, 1.1679803133011, .85658311843872, .99335825443268, .79920876026154, 1.1595865488052, .92043119668961, .98299539089203, .94316083192825, 1.0661553144455, .6393609046936, .9456650018692, .91597771644592, .80332309007645, .65838772058487, 1.1093089580536, .68860310316086, 1.0485997200012, .89771980047226, .94581252336502, 1.0480616092682, 1.0014315843582, 1.1307729482651, 1.1770483255386, .89873492717743, 1.2652103900909, .66434383392334, 1.1431220769882, .44322970509529, .9269899725914, 1.0786435604095, .82789659500122, 1.0368362665176, 1.0712716579437, .70438456535339, .88966482877731, 1.009087562561, 1.0887442827225, .88976800441742, 1.2735350131989, 1.0157470703125, .95522791147232, 1.3003809452057, .73179203271866, 2.4736785888672, -.25176140666008, 1.9082181453705, .53501582145691, .7591078877449, 1.0281100273132, 1.6240043640137, .60095232725143, 1.6211705207825, 1.344465970993, 1.124480009079, 2.1775946617126, -.71973150968552, -.37754261493683, 1.2329530715942, 1.127131819725]) icstats = np.array([ 202, np.nan, -244.3852892951, 5, 498.7705785902, 515.31191707721]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima112_results.py000066400000000000000000001270471224417117700276310ustar00rootroot00000000000000import numpy as np llf = np.array([-245.40783909604]) nobs = np.array([ 202]) k = np.array([ 5]) k_exog = np.array([ 1]) sigma = np.array([ .8100467417583]) chi2 = np.array([ 2153.20304012]) df_model = np.array([ 3]) k_ar = np.array([ 1]) k_ma = np.array([ 2]) params = np.array([ .92817025087557, -.89593490671979, 1.3025011610587, .30250063082791, .8100467417583]) cov_params = np.array([ .00638581549851, .0001858475428, 2.8222806545671, .8538806860364, -1.1429127085819, .0001858475428, .00132037832566, -.14420925344502, -.04447007102804, .0576156187095, 2.8222806545671, -.14420925344502, 40397.568324803, 12222.977216556, -16359.547340433, .8538806860364, -.04447007102804, 12222.977216556, 3698.2722243412, -4949.8609964351, -1.1429127085819, .0576156187095, -16359.547340433, -4949.8609964351, 6625.0231409853]).reshape(5,5) xb = np.array([ .92817026376724, .92817026376724, .69511789083481, .77192437648773, .66135895252228, .77525061368942, .64687132835388, .79659670591354, .65842008590698, .71215486526489, .69971066713333, .72092038393021, .68201982975006, .76510280370712, .64253836870193, .78239262104034, .64609551429749, .74087703227997, .71774411201477, .7119727730751, .73067259788513, .67785596847534, .70898467302322, .71334755420685, .72984194755554, .7017787694931, .75292426347733, .67507487535477, .78219056129456, .78040039539337, .71250075101852, .82028061151505, .63505899906158, .79452306032181, .72773635387421, .79555094242096, .76685506105423, .77427339553833, .82101213932037, .77917188405991, .78917801380157, .86641925573349, .78457218408585, .83697980642319, .83281791210175, .85224026441574, .75030690431595, .8551008105278, .78025943040848, .72790426015854, .84552866220474, .72061747312546, .78669738769531, .73868823051453, .78071022033691, .78002023696899, .83737623691559, .98988044261932, .72882527112961, 1.2245427370071, .85331875085831, 1.1637357473373, .86477434635162, 1.3248475790024, .81245219707489, .98008638620377, .85591268539429, 1.0162551403046, .8165408372879, .78947591781616, .94166398048401, .93266606330872, .85924750566483, 1.1245046854019, .75576168298721, 1.0030617713928, .91267073154449, 1.0848042964935, 1.0778224468231, 1.1551086902618, .97817331552505, 1.4012540578842, 1.2360861301422, 1.3335381746292, 1.4352362155914, 1.4941285848618, .9415163397789, 1.437669634819, 1.2404690980911, 1.2285294532776, 1.3219480514526, 1.1560415029526, .83524394035339, .87116771936417, 1.5561962127686, .47358739376068, .78093349933624, .90549737215042, 1.0217791795731, .86397403478622, 1.1526786088943, .87662625312805, .95803648233414, .89513635635376, .85281348228455, 1.0852742195129, .76808404922485, .96872144937515, 1.0732915401459, .02145584858954, 1.3687089681625, .50049883127213, 1.3895837068558, .6889950633049, 1.2795144319534, .7050421833992, 1.2218985557556, .74481928348541, 1.3074514865875, .7919961810112, 1.2807723283768, 1.0120536088943, 1.1938916444778, .68923074007034, 1.6174983978271, .64740318059921, 1.4949930906296, 1.2678960561752, 1.0586776733398, .55762887001038, 1.2790743112564, .66515874862671, 1.2538269758224, .70554333925247, 1.2391568422318, .75241559743881, 1.2129040956497, .69235223531723, 1.0785228013992, .8043577671051, 1.0037930011749, .78750842809677, 1.1880930662155, .74399447441101, 1.1791603565216, .85870295763016, 1.0032330751419, .8019300699234, 1.1696527004242, .92376220226288, .99186056852341, .94733852148056, 1.0748032331467, .64247089624405, .95419937372208, .92043441534042, .8104555606842, .66252142190933, 1.1178470849991, .69223344326019, 1.0570795536041, .90239083766937, .95320242643356, 1.0541093349457, 1.0082466602325, 1.1376332044601, 1.1841852664948, .90440809726715, 1.2733660936356, .66835701465607, 1.1515763998032, .44600257277489, .93500959873199, 1.0847823619843, .83353632688522, 1.0442448854446, 1.077241897583, .71010553836823, .89557945728302, 1.0163468122482, 1.094814658165, .89641278982162, 1.2808450460434, 1.0223702192307, .96094745397568, 1.309353351593, .73499941825867, 2.4902238845825, -.2579345703125, 1.9272556304932, .53125941753387, .7708500623703, 1.0312130451202, 1.6360099315643, .6022145152092, 1.6338716745377, 1.3494771718979, 1.1322995424271, 2.1901025772095, -.72639065980911, -.37026473879814, 1.2391144037247, 1.1353877782822]) y = np.array([np.nan, 29.908170700073, 29.84511756897, 30.121925354004, 30.031360626221, 30.315252304077, 30.196870803833, 30.5465965271, 30.498420715332, 30.52215385437, 30.619710922241, 30.70092010498, 30.722021102905, 30.975101470947, 30.862537384033, 31.162391662598, 31.086095809937, 31.220876693726, 31.407745361328, 31.461973190308, 31.670673370361, 31.627857208252, 31.728984832764, 31.833349227905, 32.009841918945, 32.08177947998, 32.33292388916, 32.325073242188, 32.662189483643, 33.060398101807, 33.162502288818, 33.670280456543, 33.535060882568, 33.894519805908, 34.127738952637, 34.495552062988, 34.866851806641, 35.17427444458, 35.721012115479, 36.079170227051, 36.489177703857, 37.16641998291, 37.584571838379, 38.136978149414, 38.732818603516, 39.352241516113, 39.65030670166, 40.255104064941, 40.68025970459, 40.827903747559, 41.445526123047, 41.620620727539, 41.986698150635, 42.238689422607, 42.580707550049, 42.98002243042, 43.537376403809, 44.689880371094, 44.928825378418, 46.824542999268, 47.653316497803, 49.263732910156, 50.164772033691, 52.324848175049, 53.112449645996, 53.980087280273, 54.855911254883, 55.916255950928, 56.616539001465, 56.889472961426, 57.941665649414, 58.832668304443, 59.55924987793, 61.124504089355, 61.555759429932, 62.603061676025, 63.612670898438, 64.984802246094, 66.577819824219, 68.255104064941, 69.478172302246, 72.001251220703, 74.236083984375, 76.533538818359, 79.435234069824, 82.39412689209, 83.541511535645, 86.137664794922, 88.440467834473, 90.32852935791, 92.82194519043, 94.556045532227, 95.235244750977, 95.871170043945, 99.056198120117, 98.573585510254, 98.680938720703, 99.705497741699, 100.82178497314, 101.66397857666, 103.25267791748, 104.17662811279, 105.0580368042, 105.99513244629, 106.55281066895, 108.08527374268, 108.46807861328, 109.46871948242, 110.97328948975, 108.72145080566, 110.86870574951, 110.70049285889, 112.78958892822, 113.38899230957, 115.0795211792, 115.70503997803, 117.22190093994, 117.94481658936, 119.80744934082, 120.69200134277, 122.48076629639, 124.11205291748, 125.69389343262, 126.08923339844, 129.11749267578, 129.54739379883, 131.99499511719, 134.66789245605, 135.75866699219, 135.6576385498, 137.47906494141, 137.86515808105, 139.55383300781, 140.10552978516, 141.73915100098, 142.45240783691, 144.01290893555, 144.49235534668, 145.57852172852, 146.40435791016, 147.30380249023, 147.98750305176, 149.58808898926, 150.14398193359, 151.67915344238, 152.65870666504, 153.6032409668, 154.30192565918, 155.86964416504, 157.02377319336, 157.99186706543, 159.14733886719, 160.47479248047, 160.54246520996, 161.35418701172, 162.42044067383, 162.81045532227, 162.86251831055, 164.31784057617, 164.59222412109, 165.75708007813, 166.80238342285, 167.65319824219, 169.15411376953, 170.30824279785, 172.03762817383, 173.88418579102, 174.80439758301, 176.87336730957, 177.06834411621, 178.55157470703, 178.04600524902, 178.63500976563, 180.38478088379, 180.83354187012, 182.24424743652, 183.67724609375, 183.91009521484, 184.59558105469, 185.91633605957, 187.39482116699, 188.29640197754, 190.38084411621, 191.82237243652, 192.76095581055, 195.10935974121, 195.43499755859, 201.69021606445, 199.14205932617, 202.62725830078, 203.23126220703, 202.67083740234, 204.60522460938, 207.55601501465, 207.94021606445, 210.76686096191, 213.84446716309, 215.12928771973, 220.80010986328, 216.16261291504, 211.80372619629, 213.91012573242, 215.60438537598]) resid = np.array([np.nan, -.7581701874733, -.49511715769768, -.75192391872406, -.49135887622833, -.76525229215622, -.44687059521675, -.70659655332565, -.68842077255249, -.60215425491333, -.63971120119095, -.66091901063919, -.51202166080475, -.75510257482529, -.48253855109215, -.72239124774933, -.60609650611877, -.53087604045868, -.65774464607239, -.52197223901749, -.7206723690033, -.60785627365112, -.6089842915535, -.55334770679474, -.62984347343445, -.50177800655365, -.68292456865311, -.44507533311844, -.38219094276428, -.61039841175079, -.31250306963921, -.77027755975723, -.4350620508194, -.494520008564, -.42773708701134, -.39555323123932, -.46685197949409, -.27427339553833, -.42101442813873, -.37917038798332, -.18917952477932, -.36641922593117, -.28457221388817, -.23697751760483, -.23281940817833, -.45223876833916, -.25030693411827, -.35510078072548, -.58026248216629, -.22790426015854, -.54552561044693, -.42061823606491, -.48669815063477, -.43868899345398, -.38070866465569, -.28002023696899, .16262374818325, -.48988044261932, .67117244005203, -.02454199641943, .44668045639992, .0362650193274, .83522641658783, -.02484837733209, -.11245145648718, .01991361007094, .0440888479352, -.1162573993206, -.51654160022736, .11052562296391, -.04166246205568, -.13266679644585, .4407517015934, -.32450538873672, .04423752427101, .0969405695796, .28733000159264, .51519411802292, .52217602729797, .24489280581474, 1.1218250989914, .99874752759933, .96391087770462, 1.4664648771286, 1.4647653102875, .20586840808392, 1.1584821939468, 1.062330365181, .65953236818314, 1.1714720726013, .57805341482162, -.15604154765606, -.23524549603462, 1.6288322210312, -.95619779825211, -.67358434200287, .1190680116415, .09450265020132, -.02177914790809, .43602138757706, .04732597246766, -.07663082331419, .0419635027647, -.29513788223267, .44718953967094, -.38527730107307, .0319189876318, .43128004670143, -2.2732961177826, .77854722738266, -.66871201992035, .69950574636459, -.08958829939365, .41101104021072, -.07951752096415, .2949578166008, -.02190163731575, .5551837682724, .0925500690937, .50799924135208, .61922925710678, .38794788718224, -.29389011859894, 1.4107677936554, -.21750450134277, .95260292291641, 1.4050008058548, .03210696578026, -.65866851806641, .54236197471619, -.27907428145409, .43484738469124, -.15383619070053, .39446276426315, -.03915995359421, .34759050607681, -.21290412545204, .00764474179596, .02148328535259, -.10436081886292, -.10379911959171, .41248852014542, -.18809306621552, .35601159930229, .12084264308214, -.05869990959764, -.10323911905289, .39806687831879, .2303563952446, -.02376830019057, .20813637971878, .25265842676163, -.57480323314667, -.14247089624405, .14580672979355, -.4204343855381, -.61045861244202, .33747857809067, -.41785016655922, .10776958614588, .14291742444038, -.1023878082633, .44680669903755, .14588765799999, .59174418449402, .66236984729767, .01581169478595, .7956041097641, -.47337827086449, .33164295554161, -.95156413316727, -.34601172804832, .66499650478363, -.38478538393974, .36646059155464, .35576421022415, -.47725108265877, -.21010553836823, .30441749095917, .38366231322289, .00517613813281, .80359941720963, .41915187239647, -.02237024717033, 1.039052605629, -.409359395504, 3.7650005817413, -2.2902269363403, 1.5579376220703, .072744384408, -1.3312624692917, .90316116809845, 1.3147799968719, -.21801064908504, 1.1927837133408, 1.7281278371811, .15252174437046, 3.4807071685791, -3.9110956192017, -3.9886209964752, .86727404594421, .55887448787689, .78061258792877]) yr = np.array([np.nan, -.7581701874733, -.49511715769768, -.75192391872406, -.49135887622833, -.76525229215622, -.44687059521675, -.70659655332565, -.68842077255249, -.60215425491333, -.63971120119095, -.66091901063919, -.51202166080475, -.75510257482529, -.48253855109215, -.72239124774933, -.60609650611877, -.53087604045868, -.65774464607239, -.52197223901749, -.7206723690033, -.60785627365112, -.6089842915535, -.55334770679474, -.62984347343445, -.50177800655365, -.68292456865311, -.44507533311844, -.38219094276428, -.61039841175079, -.31250306963921, -.77027755975723, -.4350620508194, -.494520008564, -.42773708701134, -.39555323123932, -.46685197949409, -.27427339553833, -.42101442813873, -.37917038798332, -.18917952477932, -.36641922593117, -.28457221388817, -.23697751760483, -.23281940817833, -.45223876833916, -.25030693411827, -.35510078072548, -.58026248216629, -.22790426015854, -.54552561044693, -.42061823606491, -.48669815063477, -.43868899345398, -.38070866465569, -.28002023696899, .16262374818325, -.48988044261932, .67117244005203, -.02454199641943, .44668045639992, .0362650193274, .83522641658783, -.02484837733209, -.11245145648718, .01991361007094, .0440888479352, -.1162573993206, -.51654160022736, .11052562296391, -.04166246205568, -.13266679644585, .4407517015934, -.32450538873672, .04423752427101, .0969405695796, .28733000159264, .51519411802292, .52217602729797, .24489280581474, 1.1218250989914, .99874752759933, .96391087770462, 1.4664648771286, 1.4647653102875, .20586840808392, 1.1584821939468, 1.062330365181, .65953236818314, 1.1714720726013, .57805341482162, -.15604154765606, -.23524549603462, 1.6288322210312, -.95619779825211, -.67358434200287, .1190680116415, .09450265020132, -.02177914790809, .43602138757706, .04732597246766, -.07663082331419, .0419635027647, -.29513788223267, .44718953967094, -.38527730107307, .0319189876318, .43128004670143, -2.2732961177826, .77854722738266, -.66871201992035, .69950574636459, -.08958829939365, .41101104021072, -.07951752096415, .2949578166008, -.02190163731575, .5551837682724, .0925500690937, .50799924135208, .61922925710678, .38794788718224, -.29389011859894, 1.4107677936554, -.21750450134277, .95260292291641, 1.4050008058548, .03210696578026, -.65866851806641, .54236197471619, -.27907428145409, .43484738469124, -.15383619070053, .39446276426315, -.03915995359421, .34759050607681, -.21290412545204, .00764474179596, .02148328535259, -.10436081886292, -.10379911959171, .41248852014542, -.18809306621552, .35601159930229, .12084264308214, -.05869990959764, -.10323911905289, .39806687831879, .2303563952446, -.02376830019057, .20813637971878, .25265842676163, -.57480323314667, -.14247089624405, .14580672979355, -.4204343855381, -.61045861244202, .33747857809067, -.41785016655922, .10776958614588, .14291742444038, -.1023878082633, .44680669903755, .14588765799999, .59174418449402, .66236984729767, .01581169478595, .7956041097641, -.47337827086449, .33164295554161, -.95156413316727, -.34601172804832, .66499650478363, -.38478538393974, .36646059155464, .35576421022415, -.47725108265877, -.21010553836823, .30441749095917, .38366231322289, .00517613813281, .80359941720963, .41915187239647, -.02237024717033, 1.039052605629, -.409359395504, 3.7650005817413, -2.2902269363403, 1.5579376220703, .072744384408, -1.3312624692917, .90316116809845, 1.3147799968719, -.21801064908504, 1.1927837133408, 1.7281278371811, .15252174437046, 3.4807071685791, -3.9110956192017, -3.9886209964752, .86727404594421, .55887448787689, .78061258792877]) mse = np.array([ .77732294797897, .77732294797897, .70387578010559, .69261533021927, .68906670808792, .68708789348602, .68558460474014, .68429106473923, .6831266283989, .68206071853638, .68107759952545, .68016695976257, .67932069301605, .67853212356567, .67779558897018, .67710596323013, .67645901441574, .6758508682251, .67527812719345, .67473775148392, .67422717809677, .67374390363693, .67328584194183, .6728510260582, .67243778705597, .67204451560974, .67166984081268, .67131245136261, .67097115516663, .67064493894577, .67033278942108, .67003381252289, .66974723339081, .66947221755981, .66920816898346, .66895437240601, .66871029138565, .66847538948059, .66824907064438, .66803097724915, .66782057285309, .66761755943298, .66742146015167, .66723203659058, .66704881191254, .66687160730362, .66670006513596, .66653394699097, .66637301445007, .66621696949005, .66606563329697, .66591882705688, .66577625274658, .66563785076141, .66550332307816, .66537261009216, .6652455329895, .66512185335159, .66500157117844, .66488444805145, .66477036476135, .66465926170349, .66455101966858, .66444545984268, .6643425822258, .66424214839935, .66414421796799, .66404861211777, .66395533084869, .66386413574219, .66377514600754, .66368812322617, .66360312700272, .66351997852325, .66343873739243, .6633592247963, .66328144073486, .66320532560349, .66313081979752, .66305786371231, .66298645734787, .6629164814949, .66284799575806, .66278082132339, .66271501779556, .66265046596527, .66258722543716, .6625252366066, .66246438026428, .66240465641022, .66234612464905, .66228866577148, .66223222017288, .66217684745789, .66212248802185, .66206908226013, .66201663017273, .661965072155, .66191446781158, .6618646979332, .66181582212448, .66176778078079, .66172051429749, .66167408227921, .66162836551666, .66158348321915, .66153925657272, .66149580478668, .66145300865173, .66141092777252, .6613695025444, .66132873296738, .6612885594368, .66124904155731, .66121011972427, .66117179393768, .66113406419754, .6610968708992, .66106027364731, .66102415323257, .66098862886429, .66095358133316, .66091907024384, .66088503599167, .66085147857666, .66081839799881, .66078579425812, .66075360774994, .66072189807892, .66069066524506, .66065979003906, .66062939167023, .66059935092926, .66056972742081, .66054052114487, .6605116724968, .66048324108124, .6604551076889, .66042739152908, .66040003299713, .66037303209305, .66034632921219, .66032004356384, .66029399633408, .66026836633682, .66024297475815, .66021794080734, .66019320487976, .6601687669754, .66014462709427, .66012072563171, .66009718179703, .66007387638092, .66005086898804, .66002810001373, .66000562906265, .65998339653015, .65996146202087, .65993976593018, .65991830825806, .65989708900452, .65987610816956, .65985536575317, .65983480215073, .6598145365715, .65979450941086, .65977466106415, .65975499153137, .65973562002182, .6597164273262, .65969741344452, .65967857837677, .6596599817276, .65964162349701, .65962338447571, .65960538387299, .6595875620842, .65956991910934, .65955245494843, .65953516960144, .65951806306839, .65950113534927, .65948438644409, .6594677567482, .65945136547089, .65943509340286, .65941900014877, .65940302610397, .65938723087311, .65937161445618, .65935611724854, .65934079885483, .65932559967041, .65931057929993, .65929567813873, .65928089618683, .65926629304886, .65925180912018, .65923744440079, .65922319889069, .65920913219452, .65919518470764, .65918135643005]) stdp = np.array([ .92817026376724, .92817026376724, .69511789083481, .77192437648773, .66135895252228, .77525061368942, .64687132835388, .79659670591354, .65842008590698, .71215486526489, .69971066713333, .72092038393021, .68201982975006, .76510280370712, .64253836870193, .78239262104034, .64609551429749, .74087703227997, .71774411201477, .7119727730751, .73067259788513, .67785596847534, .70898467302322, .71334755420685, .72984194755554, .7017787694931, .75292426347733, .67507487535477, .78219056129456, .78040039539337, .71250075101852, .82028061151505, .63505899906158, .79452306032181, .72773635387421, .79555094242096, .76685506105423, .77427339553833, .82101213932037, .77917188405991, .78917801380157, .86641925573349, .78457218408585, .83697980642319, .83281791210175, .85224026441574, .75030690431595, .8551008105278, .78025943040848, .72790426015854, .84552866220474, .72061747312546, .78669738769531, .73868823051453, .78071022033691, .78002023696899, .83737623691559, .98988044261932, .72882527112961, 1.2245427370071, .85331875085831, 1.1637357473373, .86477434635162, 1.3248475790024, .81245219707489, .98008638620377, .85591268539429, 1.0162551403046, .8165408372879, .78947591781616, .94166398048401, .93266606330872, .85924750566483, 1.1245046854019, .75576168298721, 1.0030617713928, .91267073154449, 1.0848042964935, 1.0778224468231, 1.1551086902618, .97817331552505, 1.4012540578842, 1.2360861301422, 1.3335381746292, 1.4352362155914, 1.4941285848618, .9415163397789, 1.437669634819, 1.2404690980911, 1.2285294532776, 1.3219480514526, 1.1560415029526, .83524394035339, .87116771936417, 1.5561962127686, .47358739376068, .78093349933624, .90549737215042, 1.0217791795731, .86397403478622, 1.1526786088943, .87662625312805, .95803648233414, .89513635635376, .85281348228455, 1.0852742195129, .76808404922485, .96872144937515, 1.0732915401459, .02145584858954, 1.3687089681625, .50049883127213, 1.3895837068558, .6889950633049, 1.2795144319534, .7050421833992, 1.2218985557556, .74481928348541, 1.3074514865875, .7919961810112, 1.2807723283768, 1.0120536088943, 1.1938916444778, .68923074007034, 1.6174983978271, .64740318059921, 1.4949930906296, 1.2678960561752, 1.0586776733398, .55762887001038, 1.2790743112564, .66515874862671, 1.2538269758224, .70554333925247, 1.2391568422318, .75241559743881, 1.2129040956497, .69235223531723, 1.0785228013992, .8043577671051, 1.0037930011749, .78750842809677, 1.1880930662155, .74399447441101, 1.1791603565216, .85870295763016, 1.0032330751419, .8019300699234, 1.1696527004242, .92376220226288, .99186056852341, .94733852148056, 1.0748032331467, .64247089624405, .95419937372208, .92043441534042, .8104555606842, .66252142190933, 1.1178470849991, .69223344326019, 1.0570795536041, .90239083766937, .95320242643356, 1.0541093349457, 1.0082466602325, 1.1376332044601, 1.1841852664948, .90440809726715, 1.2733660936356, .66835701465607, 1.1515763998032, .44600257277489, .93500959873199, 1.0847823619843, .83353632688522, 1.0442448854446, 1.077241897583, .71010553836823, .89557945728302, 1.0163468122482, 1.094814658165, .89641278982162, 1.2808450460434, 1.0223702192307, .96094745397568, 1.309353351593, .73499941825867, 2.4902238845825, -.2579345703125, 1.9272556304932, .53125941753387, .7708500623703, 1.0312130451202, 1.6360099315643, .6022145152092, 1.6338716745377, 1.3494771718979, 1.1322995424271, 2.1901025772095, -.72639065980911, -.37026473879814, 1.2391144037247, 1.1353877782822]) icstats = np.array([ 202, np.nan, -245.40783909604, 5, 500.81567819208, 517.35701667909]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima112nc_css_results.py000066400000000000000000001262071224417117700310170ustar00rootroot00000000000000import numpy as np llf = np.array([-239.75290561974]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .79291203639424]) chi2 = np.array([ 35036.682546665]) df_model = np.array([ 3]) k_ar = np.array([ 1]) k_ma = np.array([ 2]) params = np.array([ .99954097483478, -.69022779461512, -.20477682541104, .62870949745886]) cov_params = np.array([ .00007344276568, -.00016074342677, -.00018478942445, 8.040251506e-06, -.00016074342677, .00094099304774, .00017233777676, -.0000145011098, -.00018478942445, .00017233777676, .00103352686916, .00030686101903, 8.040251506e-06, -.0000145011098, .00030686101903, .00067796985496]).reshape(4,4) xb = np.array([ 0, 0, .05104803293943, .06663129478693, .02164618112147, .0773858949542, .02606418170035, .09391833096743, .05710592120886, .03083370067179, .07319989800453, .05287836492062, .05776296555996, .09105986356735, .04293738678098, .09576436132193, .06068528071046, .06157376244664, .11172580718994, .06527806818485, .11443704366684, .05653077363968, .08205550909042, .08481238037348, .10436166077852, .0875685736537, .12320486456156, .08366665989161, .13979130983353, .1902572363615, .1306214183569, .21803694963455, .11079790443182, .17274764180183, .1937662512064, .20047917962074, .24034893512726, .21783453226089, .29279819130898, .26804205775261, .28678458929062, .35651323199272, .33659368753433, .35760068893433, .39895334839821, .41131839156151, .36645981669426, .40991494059563, .41024547815323, .32657703757286, .42312324047089, .34933325648308, .35912537574768, .35077446699142, .34701564908028, .37364318966866, .40170526504517, .56070649623871, .41915491223335, .73478156328201, .67748892307281, .7744625210762, .77825599908829, .97586625814438, .88692498207092, .76232481002808, .87376874685287, .83281141519547, .84783887863159, .66423743963242, .84904235601425, .81613594293594, .80033475160599, .95782464742661, .80624777078629, .83626395463943, .91873735189438, .95130664110184, 1.0939226150513, 1.1171194314957, 1.1004731655121, 1.3512066602707, 1.4703129529953, 1.4805699586868, 1.7385860681534, 1.8268398046494, 1.5489361286163, 1.7446503639221, 1.864644408226, 1.7200467586517, 1.9223358631134, 1.775306224823, 1.5392524003983, 1.4067870378494, 1.9366238117218, 1.2984343767166, 1.1080636978149, 1.3500427007675, 1.2837564945221, 1.2670782804489, 1.3347851037979, 1.2857422828674, 1.1625040769577, 1.2111755609512, 1.0548515319824, 1.2553508281708, 1.0327949523926, 1.0740388631821, 1.222040772438, .40555971860886, 1.0233588218689, .84209614992142, 1.0186324119568, 1.0319027900696, .99487775564194, 1.0439211130142, .98785293102264, 1.0620124340057, 1.0916963815689, 1.1378232240677, 1.1243290901184, 1.3305295705795, 1.1925677061081, 1.0872994661331, 1.4599523544312, 1.2333589792252, 1.3584797382355, 1.7595859766006, 1.3009568452835, 1.1157965660095, 1.2948887348175, 1.2063180208206, 1.2332669496536, 1.2132470607758, 1.2049551010132, 1.2260574102402, 1.1875206232071, 1.1547852754593, 1.0519831180573, 1.1594845056534, 1.0069926977158, 1.0675266981125, 1.1299223899841, 1.0620901584625, 1.0999356508255, 1.1535499095917, 1.0026944875717, 1.0428657531738, 1.1120204925537, 1.1684119701385, 1.0258769989014, 1.1342295408249, 1.1183958053589, .91313683986664, .91156214475632, 1.0540328025818, .84359037876129, .75758427381516, .96401190757751, .83226495981216, .8759680390358, .98239886760712, .85917687416077, 1.0634194612503, .99442666769028, 1.153311252594, 1.2288066148758, 1.0869039297104, 1.281947016716, 1.0067318677902, 1.1028815507889, .82448446750641, .78489726781845, 1.1850204467773, .86753690242767, 1.0692945718765, 1.1030179262161, .8791960477829, .86451041698456, 1.0455346107483, 1.085998415947, 1.0172398090363, 1.2250980138779, 1.2316122055054, 1.062157869339, 1.3991860151291, 1.0520887374878, 2.2203133106232, .88833123445511, 1.4289729595184, 1.5206423997879, .68520504236221, 1.4659557342529, 1.5350053310394, 1.3178979158401, 1.4888265132904, 1.9698411226273, 1.4406447410583, 2.517040014267, .55537897348404, -.20722626149654, 1.0899519920349, 1.164245724678]) y = np.array([np.nan, 28.979999542236, 29.201047897339, 29.416631698608, 29.391647338867, 29.617385864258, 29.576063156128, 29.84391784668, 29.897106170654, 29.84083366394, 29.993200302124, 30.032876968384, 30.097763061523, 30.3010597229, 30.262937545776, 30.475763320923, 30.500686645508, 30.541572570801, 30.801725387573, 30.815279006958, 31.054437637329, 31.006530761719, 31.102056503296, 31.20481300354, 31.384363174438, 31.467567443848, 31.703205108643, 31.733665466309, 32.019790649414, 32.47025680542, 32.580623626709, 33.068035125732, 33.010799407959, 33.272747039795, 33.593769073486, 33.900478363037, 34.340347290039, 34.617835998535, 35.192798614502, 35.568042755127, 35.986785888672, 36.656513214111, 37.13659286499, 37.657600402832, 38.29895401001, 38.911319732666, 39.266460418701, 39.809917449951, 40.310245513916, 40.426574707031, 41.023120880127, 41.249336242676, 41.559127807617, 41.850772857666, 42.14701461792, 42.573642730713, 43.101707458496, 44.260707855225, 44.619155883789, 46.334781646729, 47.477489471436, 48.874462127686, 50.078254699707, 51.9758644104, 53.186923980713, 53.762325286865, 54.873767852783, 55.732814788818, 56.647838592529, 56.764236450195, 57.849040985107, 58.716136932373, 59.500335693359, 60.957824707031, 61.606246948242, 62.436264038086, 63.61873626709, 64.85131072998, 66.593925476074, 68.21711730957, 69.600471496582, 71.951202392578, 74.470314025879, 76.680564880371, 79.738586425781, 82.726844787598, 84.148933410645, 86.444648742676, 89.064643859863, 90.820045471191, 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.26804205775261, .28678458929062, .35651323199272, .33659368753433, .35760068893433, .39895334839821, .41131839156151, .36645981669426, .40991494059563, .41024547815323, .32657703757286, .42312324047089, .34933325648308, .35912537574768, .35077446699142, .34701564908028, .37364318966866, .40170526504517, .56070649623871, .41915491223335, .73478156328201, .67748892307281, .7744625210762, .77825599908829, .97586625814438, .88692498207092, .76232481002808, .87376874685287, .83281141519547, .84783887863159, .66423743963242, .84904235601425, .81613594293594, .80033475160599, .95782464742661, .80624777078629, .83626395463943, .91873735189438, .95130664110184, 1.0939226150513, 1.1171194314957, 1.1004731655121, 1.3512066602707, 1.4703129529953, 1.4805699586868, 1.7385860681534, 1.8268398046494, 1.5489361286163, 1.7446503639221, 1.864644408226, 1.7200467586517, 1.9223358631134, 1.775306224823, 1.5392524003983, 1.4067870378494, 1.9366238117218, 1.2984343767166, 1.1080636978149, 1.3500427007675, 1.2837564945221, 1.2670782804489, 1.3347851037979, 1.2857422828674, 1.1625040769577, 1.2111755609512, 1.0548515319824, 1.2553508281708, 1.0327949523926, 1.0740388631821, 1.222040772438, .40555971860886, 1.0233588218689, .84209614992142, 1.0186324119568, 1.0319027900696, .99487775564194, 1.0439211130142, .98785293102264, 1.0620124340057, 1.0916963815689, 1.1378232240677, 1.1243290901184, 1.3305295705795, 1.1925677061081, 1.0872994661331, 1.4599523544312, 1.2333589792252, 1.3584797382355, 1.7595859766006, 1.3009568452835, 1.1157965660095, 1.2948887348175, 1.2063180208206, 1.2332669496536, 1.2132470607758, 1.2049551010132, 1.2260574102402, 1.1875206232071, 1.1547852754593, 1.0519831180573, 1.1594845056534, 1.0069926977158, 1.0675266981125, 1.1299223899841, 1.0620901584625, 1.0999356508255, 1.1535499095917, 1.0026944875717, 1.0428657531738, 1.1120204925537, 1.1684119701385, 1.0258769989014, 1.1342295408249, 1.1183958053589, .91313683986664, .91156214475632, 1.0540328025818, .84359037876129, .75758427381516, .96401190757751, .83226495981216, .8759680390358, .98239886760712, .85917687416077, 1.0634194612503, .99442666769028, 1.153311252594, 1.2288066148758, 1.0869039297104, 1.281947016716, 1.0067318677902, 1.1028815507889, .82448446750641, .78489726781845, 1.1850204467773, .86753690242767, 1.0692945718765, 1.1030179262161, .8791960477829, .86451041698456, 1.0455346107483, 1.085998415947, 1.0172398090363, 1.2250980138779, 1.2316122055054, 1.062157869339, 1.3991860151291, 1.0520887374878, 2.2203133106232, .88833123445511, 1.4289729595184, 1.5206423997879, .68520504236221, 1.4659557342529, 1.5350053310394, 1.3178979158401, 1.4888265132904, 1.9698411226273, 1.4406447410583, 2.517040014267, .55537897348404, -.20722626149654, 1.0899519920349, 1.164245724678]) icstats = np.array([ 202, np.nan, -239.75290561974, 4, 487.50581123949, 500.73888202909]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima112nc_results.py000066400000000000000000001077571224417117700301600ustar00rootroot00000000000000import numpy as np llf = np.array([-240.79351748413]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .79349479007541]) chi2 = np.array([ 31643.977146904]) df_model = np.array([ 3]) k_ar = np.array([ 1]) k_ma = np.array([ 2]) params = np.array([ .99502750401315, -.68630179255403, -.19840894739396, .79349479007541]) cov_params = np.array([ .00010016992219, -.00021444523598, -.00023305572854, -6.768123591e-06, -.00021444523598, .00104186449549, .00023669747281, 3.902897504e-06, -.00023305572854, .00023669747281, .0010810935718, .00020764808165, -6.768123591e-06, 3.902897504e-06, .00020764808165, .0002668504612]).reshape(4,4) xb = np.array([ 0, 0, .11361486464739, .14230862259865, .07256115227938, .13274206221104, .06747215241194, .13822889328003, .09585004299879, .06099047139287, .10120190680027, .07761032879353, .07942545413971, .11177492141724, .06088993698359, .11208334565163, .0755797252059, .07422959059477, .12350624799728, .07623053342104, .12391770631075, .06531815230846, .08897981792688, .09103457629681, .10980048775673, .09255626052618, .12732291221619, .08764986693859, .14262486994267, .19330270588398, .13410428166389, .2202503234148, .1138079687953, .17364008724689, .19471970200539, .20120698213577, .24072274565697, .21839858591557, .29251739382744, .26838713884354, .28637075424194, .3556769490242, .33624804019928, .35650280117989, .3974232673645, .40968126058578, .36446389555931, .40641778707504, .40639826655388, .32208651304245, .41636437177658, .34307014942169, .3511538207531, .34216031432152, .33759200572968, .36354607343674, .39148297905922, .55032896995544, .4113195836544, .7244918346405, .67152947187424, .76787060499191, .77276849746704, .96944856643677, .88270664215088, .7563271522522, .86404490470886, .82250237464905, .83520317077637, .65044301748276, .83044308423996, .79827356338501, .78103590011597, .93702721595764, .78709679841995, .81435388326645, .89593154191971, .92867535352707, 1.0709822177887, 1.0957812070847, 1.0792914628983, 1.3286831378937, 1.4503024816513, 1.4619816541672, 1.7190475463867, 1.8096150159836, 1.5324629545212, 1.721804857254, 1.8408879041672, 1.6955831050873, 1.8928952217102, 1.7459137439728, 1.5055395364761, 1.3664853572845, 1.8893030881882, 1.256967663765, 1.0567245483398, 1.2921603918076, 1.2266329526901, 1.2085332870483, 1.275726556778, 1.2278587818146, 1.1046848297119, 1.1517647504807, .99646359682083, 1.194694519043, .97580307722092, 1.0148292779922, 1.1635760068893, .35167038440704, .95728904008865, .78414303064346, .95968008041382, .97746151685715, .94291216135025, .99327826499939, .93940645456314, 1.013852596283, 1.0454497337341, 1.0929356813431, 1.0810794830322, 1.2874436378479, 1.1533098220825, 1.0470397472382, 1.4171674251556, 1.1959022283554, 1.3181202411652, 1.7197531461716, 1.2677561044693, 1.0768386125565, 1.2508004903793, 1.1625586748123, 1.1872273683548, 1.1668027639389, 1.1576874256134, 1.1782459020615, 1.1398378610611, 1.1065219640732, 1.0032601356506, 1.1087976694107, .95788156986237, 1.0163568258286, 1.079482793808, 1.0131409168243, 1.0506906509399, 1.1052004098892, .95601671934128, .99452114105225, 1.0641269683838, 1.1217628717422, .98107707500458, 1.0877858400345, 1.0735836029053, .86890149116516, .86449563503265, 1.0060983896255, .79812264442444, .70991164445877, .91461282968521, .78625136613846, .8291689157486, .93680161237717, .81633454561234, 1.0196126699448, .95442569255829, 1.1131925582886, 1.1916073560715, 1.05200278759, 1.2451642751694, .97296446561813, 1.0647999048233, .78715896606445, .74267995357513, 1.1400059461594, .82839399576187, 1.0262999534607, 1.0628409385681, .84051495790482, .82304340600967, 1.0028872489929, 1.0457111597061, .97847640514374, 1.1855980157852, 1.195351600647, 1.0270363092422, 1.3610677719116, 1.0189098119736, 2.1800265312195, .86722087860107, 1.3893752098083, 1.4851142168045, .65110164880753, 1.417050242424, 1.4938380718231, 1.2786860466003, 1.446773648262, 1.9284181594849, 1.4071846008301, 2.4745123386383, .53088372945786, -.25887301564217, 1.0166070461273, 1.1028108596802]) y = np.array([np.nan, 28.979999542236, 29.263614654541, 29.492309570313, 29.442562103271, 29.672742843628, 29.617471694946, 29.888229370117, 29.935850143433, 29.870990753174, 30.021202087402, 30.057609558105, 30.119426727295, 30.321773529053, 30.280889511108, 30.492082595825, 30.515581130981, 30.554229736328, 30.813507080078, 30.826231002808, 31.063919067383, 31.015319824219, 31.108980178833, 31.21103477478, 31.389801025391, 31.472555160522, 31.707323074341, 31.737649917603, 32.022624969482, 32.473300933838, 32.584106445313, 33.070247650146, 33.013809204102, 33.273639678955, 33.594722747803, 33.901206970215, 34.340721130371, 34.61840057373, 35.192520141602, 35.568386077881, 35.98637008667, 36.65567779541, 37.136245727539, 37.65650177002, 38.297424316406, 38.909679412842, 39.264465332031, 39.806419372559, 40.306400299072, 40.42208480835, 41.016361236572, 41.243072509766, 41.551155090332, 41.84215927124, 42.137592315674, 42.563545227051, 43.091484069824, 44.250328063965, 44.611320495605, 46.324489593506, 47.471527099609, 48.867870330811, 50.072769165039, 51.9694480896, 53.182704925537, 53.756328582764, 54.864044189453, 55.722503662109, 56.635204315186, 56.750442504883, 57.830444335938, 58.698276519775, 59.481037139893, 60.937026977539, 61.587097167969, 62.414352416992, 63.595932006836, 64.828674316406, 66.570983886719, 68.195777893066, 69.579292297363, 71.928680419922, 74.450302124023, 76.661979675293, 79.719047546387, 82.709617614746, 84.132461547852, 86.421798706055, 89.040885925293, 90.79557800293, 93.39289855957, 95.14591217041, 95.905540466309, 96.366485595703, 99.389305114746, 99.356964111328, 98.956726074219, 100.09216308594, 101.02663421631, 102.00853729248, 103.37572479248, 104.52786254883, 105.20468139648, 106.25176239014, 106.69645690918, 108.19469451904, 108.67579650879, 109.51483154297, 111.06357574463, 109.05166625977, 110.45729064941, 110.98413848877, 112.35968017578, 113.6774597168, 114.74291229248, 115.99327850342, 116.93940734863, 118.21385192871, 119.54544830322, 120.99293518066, 122.28107452393, 124.38744354248, 125.65331268311, 126.44704437256, 128.91716003418, 130.09590148926, 131.81811523438, 135.11975097656, 135.96775817871, 136.17684936523, 137.45079040527, 138.36254882813, 139.48722839355, 140.56680297852, 141.65768432617, 142.87825012207, 143.93983459473, 144.9065246582, 145.50326538086, 146.70880126953, 147.25788879395, 148.21635437012, 149.47947692871, 150.41313171387, 151.55068969727, 152.90519714355, 153.55603027344, 154.49452209473, 155.76412963867, 157.22177124023, 157.98107910156, 159.28778076172, 160.47357177734, 160.76889038086, 161.26449584961, 162.50610351563, 162.7981262207, 162.90991210938, 164.11460876465, 164.6862487793, 165.5291595459, 166.83679199219, 167.5163269043, 169.11961364746, 170.25442504883, 172.01318359375, 173.8916015625, 174.95199584961, 176.84516906738, 177.37295532227, 178.46479797363, 178.38716125488, 178.44267272949, 180.44000244141, 180.8283996582, 182.22630310059, 183.66284179688, 184.04051208496, 184.52304077148, 185.90287780762, 187.34571838379, 188.37846374512, 190.28559875488, 191.99536132813, 192.82704162598, 195.16107177734, 195.71890258789, 201.3800201416, 200.26721191406, 202.08937072754, 204.18510437012, 202.55110168457, 204.99105834961, 207.41383361816, 208.61668395996, 210.57977294922, 214.4234161377, 215.40417480469, 221.08451843262, 217.41989135742, 211.91511535645, 213.68760681152, 215.57180786133]) resid = np.array([np.nan, .17000007629395, .08638589829206, -.12230817228556, .09743892401457, -.12274374067783, .13252860307693, -.04822873324156, -.12585072219372, .04901013895869, -.04120244085789, -.01760895550251, .0905727148056, -.1017746925354, .09910991042852, -.05208196863532, -.03558072075248, .13577139377594, -.0635067820549, .11377000063658, -.11391747742891, .00468154205009, .01102056354284, .0689652711153, -.00980201456696, .10744450241327, -.05732321739197, .14234967529774, .25737473368645, -.02330071851611, .26589342951775, -.17024727165699, .08618897944689, .12636296451092, .10527954250574, .19879072904587, .05928030610085, .28160139918327, .10748030990362, .13161440193653, .31362771987915, .14432306587696, .16375194489956, .24349948763847, .20257519185543, -.00967973750085, .13553610444069, .09358221292496, -.20640131831169, .17791347205639, -.11636133491993, -.04307091236115, -.05115458369255, -.04216108098626, .06240950897336, .13645392656326, .60851699113846, -.05032894387841, .98867815732956, .47550892829895, .62846976518631, .43213015794754, .92723226547241, .33055067062378, -.18270587921143, .24367287755013, .0359565988183, .07749536633492, -.53520393371582, .24955849349499, .0695584192872, .00172565307003, .51896333694458, -.13702796399593, .01290242280811, .28564843535423, .30406919121742, .67132312059402, .5290162563324, .30422034859657, 1.0207070112228, 1.0713183879852, .74969446659088, 1.3380213975906, 1.1809539794922, -.10961806029081, .56753551959991, .77819520235062, .05911364778876, .70441842079163, .0071063125506, -.74591380357742, -.90554106235504, 1.1335146427155, -1.2893046140671, -1.4569646120071, -.15672297775745, -.29216033220291, -.22663290798664, .09146212786436, -.0757219940424, -.42786338925362, -.10468477010727, -.55176627635956, .3035394847393, -.49469754099846, -.1758000254631, .38517218828201, -2.3635804653168, .44833266735077, -.25729209184647, .41586154699326, .34031534194946, .1225445792079, .25708478689194, .00672172475606, .26059049367905, .28615045547485, .35455179214478, .2070597410202, .81892204284668, .11255792528391, -.25330832600594, 1.0529587268829, -.0171735137701, .40410390496254, 1.5818736553192, -.41975006461143, -.86774694919586, .02315225638449, -.25080046057701, -.06255260109901, -.08723650127649, -.06679660826921, .04230948910117, -.07823982834816, -.13983787596226, -.40652501583099, .09674594551325, -.40880066156387, -.05788768827915, .1836401373148, -.07948285341263, .08686522394419, .24931237101555, -.30519738793373, -.05602284148335, .20547580718994, .33588215708733, -.22176894545555, .21891987323761, .11221113055944, -.57358360290527, -.36890152096748, .23551045358181, -.50609844923019, -.59812569618225, .29008835554123, -.21461589634418, .013751687482, .37082800269127, -.13679853081703, .5836746096611, .18038421869278, .64556515216827, .68681055307388, .00838960427791, .64800941944122, -.44517654180527, .02703555487096, -.8647877573967, -.68716812133789, .85732614994049, -.44000896811485, .37160298228264, .37370926141739, -.46285009384155, -.34051498770714, .37695357203484, .3971218764782, .05427964404225, .72153580188751, .51439893245697, -.19535164535046, .97296363115311, -.46107393503189, 3.4810900688171, -1.9800295829773, .43278217315674, .61062479019165, -2.2851173877716, 1.0229095220566, .92894279956818, -.07583882659674, .51631212234497, 1.9152258634567, -.42641922831535, 3.2058219909668, -4.1955056190491, -5.2458953857422, .75588232278824, .7813817858696, .81318950653076]) yr = np.array([np.nan, .17000007629395, .08638589829206, -.12230817228556, .09743892401457, -.12274374067783, .13252860307693, -.04822873324156, -.12585072219372, .04901013895869, -.04120244085789, -.01760895550251, .0905727148056, -.1017746925354, .09910991042852, -.05208196863532, -.03558072075248, .13577139377594, -.0635067820549, .11377000063658, -.11391747742891, .00468154205009, .01102056354284, .0689652711153, -.00980201456696, .10744450241327, -.05732321739197, .14234967529774, .25737473368645, -.02330071851611, .26589342951775, -.17024727165699, .08618897944689, .12636296451092, .10527954250574, .19879072904587, .05928030610085, .28160139918327, .10748030990362, .13161440193653, .31362771987915, .14432306587696, .16375194489956, .24349948763847, .20257519185543, -.00967973750085, .13553610444069, .09358221292496, -.20640131831169, .17791347205639, -.11636133491993, -.04307091236115, -.05115458369255, -.04216108098626, .06240950897336, .13645392656326, .60851699113846, -.05032894387841, .98867815732956, .47550892829895, .62846976518631, .43213015794754, .92723226547241, .33055067062378, -.18270587921143, .24367287755013, .0359565988183, .07749536633492, -.53520393371582, .24955849349499, .0695584192872, .00172565307003, .51896333694458, -.13702796399593, .01290242280811, .28564843535423, .30406919121742, .67132312059402, .5290162563324, .30422034859657, 1.0207070112228, 1.0713183879852, .74969446659088, 1.3380213975906, 1.1809539794922, -.10961806029081, .56753551959991, .77819520235062, .05911364778876, .70441842079163, .0071063125506, -.74591380357742, -.90554106235504, 1.1335146427155, -1.2893046140671, -1.4569646120071, -.15672297775745, -.29216033220291, -.22663290798664, .09146212786436, -.0757219940424, -.42786338925362, -.10468477010727, -.55176627635956, .3035394847393, -.49469754099846, -.1758000254631, .38517218828201, -2.3635804653168, .44833266735077, -.25729209184647, .41586154699326, .34031534194946, .1225445792079, .25708478689194, .00672172475606, .26059049367905, .28615045547485, .35455179214478, .2070597410202, .81892204284668, .11255792528391, -.25330832600594, 1.0529587268829, -.0171735137701, .40410390496254, 1.5818736553192, -.41975006461143, -.86774694919586, .02315225638449, -.25080046057701, -.06255260109901, -.08723650127649, -.06679660826921, .04230948910117, -.07823982834816, -.13983787596226, -.40652501583099, .09674594551325, -.40880066156387, -.05788768827915, .1836401373148, -.07948285341263, .08686522394419, .24931237101555, -.30519738793373, -.05602284148335, .20547580718994, .33588215708733, -.22176894545555, .21891987323761, .11221113055944, -.57358360290527, -.36890152096748, .23551045358181, -.50609844923019, -.59812569618225, .29008835554123, -.21461589634418, .013751687482, .37082800269127, -.13679853081703, .5836746096611, .18038421869278, .64556515216827, .68681055307388, .00838960427791, .64800941944122, -.44517654180527, .02703555487096, -.8647877573967, -.68716812133789, .85732614994049, -.44000896811485, .37160298228264, .37370926141739, -.46285009384155, -.34051498770714, .37695357203484, .3971218764782, .05427964404225, .72153580188751, .51439893245697, -.19535164535046, .97296363115311, -.46107393503189, 3.4810900688171, -1.9800295829773, .43278217315674, .61062479019165, -2.2851173877716, 1.0229095220566, .92894279956818, -.07583882659674, .51631212234497, 1.9152258634567, -.42641922831535, 3.2058219909668, -4.1955056190491, -5.2458953857422, .75588232278824, .7813817858696, .81318950653076]) mse = np.array([ 1.4407075643539, 1.4407075643539, .79720854759216, .75209444761276, .71109557151794, .68920749425888, .67417079210281, .66374856233597, .65616118907928, .65050256252289, .64619332551956, .64286160469055, .64025497436523, .63819670677185, .63655960559845, .6352499127388, .63419729471207, .63334810733795, .63266098499298, .63210368156433, .63165074586868, .63128209114075, .63098156452179, .63073641061783, .63053613901138, .63037252426147, .6302387714386, .63012927770615, .63003975152969, .62996637821198, .62990635633469, .62985718250275, .62981688976288, .62978386878967, .62975686788559, .62973469495773, .62971651554108, .62970167398453, .62968945503235, .62967944145203, .62967127561569, .62966454029083, .62965905666351, .62965452671051, .62965083122253, .62964779138565, .62964528799057, .62964326143265, .62964159250259, .62964022159576, .62963908910751, .62963819503784, .62963742017746, .62963682413101, .62963628768921, .6296358704567, .62963551282883, .62963527441025, .62963503599167, .62963485717773, .6296346783638, .62963455915451, .62963443994522, .62963438034058, .62963432073593, .62963426113129, .62963420152664, .629634141922, .629634141922, .62963408231735, .62963408231735, .62963408231735, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963402271271, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806, .62963396310806]) icstats = np.array([ 202, np.nan, -240.79351748413, 4, 489.58703496826, 502.82010575786]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima211_css_results.py000066400000000000000000001270471224417117700305010ustar00rootroot00000000000000import numpy as np llf = np.array([-240.29558272688]) nobs = np.array([ 202]) k = np.array([ 5]) k_exog = np.array([ 1]) sigma = np.array([ .79494581155191]) chi2 = np.array([ 1213.6019521322]) df_model = np.array([ 3]) k_ar = np.array([ 2]) k_ma = np.array([ 1]) params = np.array([ .72428568600554, 1.1464248419014, -.17024528879204, -.87113675466923, .63193884330392]) cov_params = np.array([ .31218565961764, -.01618380799341, .00226345462929, .01386291798401, -.0036338799176, -.01618380799341, .00705713030623, -.00395404914463, -.00685704952799, -.00018629958479, .00226345462929, -.00395404914463, .00255884492061, .00363586332269, .00039879711931, .01386291798401, -.00685704952799, .00363586332269, .00751765532203, .00008982556101, -.0036338799176, -.00018629958479, .00039879711931, .00008982556101, .00077550533053]).reshape(5,5) xb = np.array([ .72428566217422, .72428566217422, .56208884716034, .53160965442657, .45030161738396, .45229381322861, .38432359695435, .40517011284828, .36063131690025, .30754271149635, .32044330239296, .29408219456673, .27966624498367, .29743707180023, .25011941790581, .27747189998627, .24822402000427, .23426930606365, .27233305573463, .23524768650532, .26427435874939, .21787133812904, .22461311519146, .22853142023087, .24335558712482, .22953669726849, .25524401664734, .22482520341873, .26450532674789, .31863233447075, .27352628111839, .33670437335968, .25623551011086, .28701293468475, .315819054842, .3238864839077, .35844340920448, .34399557113647, .40348997712135, .39373970031738, .4022718667984, .46476069092751, .45762005448341, .46842387318611, .50536489486694, .52051961421967, .47866532206535, .50378143787384, .50863671302795, .4302790760994, .49568024277687, .44652271270752, .43774726986885, .43010330200195, .42344436049461, .44517293572426, .47460499405861, .62086409330368, .52550911903381, .77532315254211, .78466820716858, .85438597202301, .87056696414948, 1.0393311977386, .99110960960388, .85202795267105, .91560190916061, .89238166809082, .88917690515518, .72121334075928, .84221452474594, .8454754948616, .82078683376312, .95394861698151, .84718400239944, .839300096035, .91501939296722, .95743554830551, 1.0874761343002, 1.1326615810394, 1.1169674396515, 1.3300451040268, 1.4790810346603, 1.5027786493301, 1.7226468324661, 1.8395622968674, 1.5940405130386, 1.694568157196, 1.8241587877274, 1.7037791013718, 1.838702917099, 1.7334734201431, 1.4791669845581, 1.3007366657257, 1.7364456653595, 1.2694935798645, .96595168113708, 1.1405370235443, 1.1328836679459, 1.1091921329498, 1.171138882637, 1.1465038061142, 1.0319484472275, 1.055313706398, .93150246143341, 1.0844472646713, .93333613872528, .93137633800507, 1.0778160095215, .38748729228973, .77933365106583, .75266307592392, .88410103321075, .94100385904312, .91849637031555, .96046274900436, .92494148015976, .98310285806656, 1.0272513628006, 1.0762135982513, 1.0743116140366, 1.254854798317, 1.1723403930664, 1.0479376316071, 1.3550333976746, 1.2255589962006, 1.2870025634766, 1.6643482446671, 1.3312928676605, 1.0657893419266, 1.1804157495499, 1.1335761547089, 1.137326002121, 1.1235628128052, 1.1115798950195, 1.1286649703979, 1.0989991426468, 1.0626485347748, .96542054414749, 1.0419135093689, .93033194541931, .95628559589386, 1.027433514595, .98328214883804, 1.0063992738724, 1.0645687580109, .94354963302612, .95077443122864, 1.0226324796677, 1.089217543602, .97552293539047, 1.0441918373108, 1.052937746048, .86785578727722, .82579529285431, .95432937145233, .79897737503052, .68320548534393, .85365778207779, .78336101770401, .80072748661041, .9089440703392, .82500487565994, .98515397310257, .96745657920837, 1.0962044000626, 1.195325255394, 1.0824474096298, 1.2239117622375, 1.0142554044724, 1.0399018526077, .80796521902084, .7145761847496, 1.0631860494614, .86374056339264, .98086261749268, 1.0528303384781, .86123734712601, .80300676822662, .96200370788574, 1.0364016294479, .98456978797913, 1.1556725502014, 1.2025715112686, 1.0507286787033, 1.312912106514, 1.0682457685471, 2.0334177017212, 1.0775905847549, 1.2798084020615, 1.461397767067, .72960823774338, 1.2498733997345, 1.466894865036, 1.286082983017, 1.3903408050537, 1.8483582735062, 1.4685434103012, 2.3107523918152, .7711226940155, -.31598940491676, .68151205778122, 1.0212944746017]) y = np.array([np.nan, 29.704284667969, 29.712087631226, 29.881610870361, 29.820302963257, 29.992294311523, 29.934322357178, 30.155170440674, 30.200632095337, 30.117542266846, 30.24044418335, 30.274082183838, 30.319667816162, 30.507436752319, 30.470119476318, 30.657470703125, 30.68822479248, 30.714269638062, 30.962333679199, 30.985248565674, 31.204275131226, 31.16787147522, 31.244613647461, 31.348531723022, 31.523355484009, 31.609535217285, 31.835243225098, 31.874824523926, 32.144504547119, 32.5986328125, 32.723526000977, 33.186702728271, 33.156238555908, 33.387012481689, 33.7158203125, 34.023887634277, 34.458442687988, 34.743995666504, 35.303489685059, 35.693740844727, 36.102272033691, 36.764759063721, 37.257617950439, 37.768424987793, 38.405364990234, 39.020519256592, 39.378665924072, 39.903781890869, 40.408638000488, 40.530277252197, 41.095680236816, 41.346523284912, 41.637748718262, 41.930103302002, 42.223442077637, 42.645172119141, 43.174606323242, 44.320865631104, 44.725509643555, 46.37532043457, 47.584667205811, 48.954383850098, 50.170566558838, 52.039329528809, 53.291107177734, 53.852027893066, 54.915603637695, 55.792385101318, 56.6891746521, 56.821212768555, 57.842212677002, 58.745475769043, 59.5207862854, 60.953948974609, 61.6471824646, 62.439296722412, 63.615020751953, 64.857437133789, 66.587478637695, 68.23265838623, 69.616966247559, 71.930046081543, 74.479080200195, 76.702774047852, 79.722648620605, 82.739562988281, 84.194038391113, 86.394561767578, 89.024154663086, 90.803779602051, 93.33869934082, 95.133476257324, 95.879165649414, 96.300735473633, 99.236442565918, 99.369491577148, 98.865951538086, 99.940536499023, 100.93288421631, 101.90919494629, 103.27114105225, 104.44651031494, 105.13195037842, 106.15531158447, 106.63150024414, 108.08444976807, 108.63333129883, 109.43137359619, 110.9778137207, 109.08748626709, 110.27933502197, 110.95265960693, 112.28410339355, 113.64099884033, 114.71849822998, 115.96046447754, 116.9249420166, 118.18309783936, 119.52725219727, 120.97621154785, 122.27430725098, 124.35485076904, 125.67234039307, 126.44793701172, 128.85502624512, 130.12554931641, 131.78700256348, 135.06434631348, 136.03129577637, 136.16580200195, 137.38041687012, 138.3335723877, 139.43733215332, 140.52355957031, 141.61158752441, 142.82865905762, 143.8990020752, 144.86265563965, 145.46542358398, 146.64192199707, 147.2303314209, 148.15628051758, 149.42742919922, 150.38327026367, 151.50639343262, 152.86457824707, 153.54354858398, 154.45077514648, 155.72262573242, 157.18922424316, 157.97552490234, 159.24418640137, 160.45292663574, 160.7678527832, 161.22578430176, 162.45433044434, 162.79898071289, 162.88320922852, 164.05364990234, 164.68334960938, 165.50071716309, 166.80894470215, 167.52500915527, 169.08515930176, 170.26745605469, 171.99620056152, 173.89532470703, 174.98243713379, 176.82391357422, 177.41424560547, 178.43989562988, 178.40797424316, 178.41456604004, 180.36318969727, 180.86373901367, 182.18086242676, 183.65283203125, 184.06123352051, 184.50300598145, 185.86199951172, 187.33641052246, 188.38456726074, 190.25567626953, 192.00257873535, 192.85073852539, 195.11291503906, 195.76824951172, 201.23341369629, 200.47758483887, 201.97981262207, 204.16139221191, 202.6296081543, 204.82388305664, 207.38688659668, 208.62408447266, 210.52333068848, 214.34335327148, 215.46553039551, 220.92074584961, 217.66012573242, 211.85800170898, 213.35252380371, 215.49029541016]) resid = np.array([np.nan, -.55428558588028, -.36208805441856, 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.51533102989197, .34561482071877, .82943379878998, .2606680393219, -.29110881686211, .14797207713127, -.01560037955642, .00761602073908, -.58917766809464, .17878817021847, .05778701230884, -.04547626897693, .47921240329742, -.15394935011864, -.0471847653389, .26070219278336, .28498136997223, .64256292581558, .51252233982086, .2673399746418, .9830310344696, 1.0699564218521, .72091597318649, 1.2972244024277, 1.1773546934128, -.13956540822983, .50595796108246, .80543184280396, .07584273815155, .6962223649025, .06129856407642, -.73347336053848, -.87916851043701, 1.1992633342743, -1.1364471912384, -1.4694905281067, -.0659501478076, -.14053705334663, -.13288362324238, .19080325961113, .02886573970318, -.34650835394859, -.03194846212864, -.45531520247459, .36850056052208, -.38445034623146, -.13333308696747, .46862518787384, -2.2778205871582, .41251575946808, -.07933671027422, .4473415017128, .4158943593502, .1590022444725, .28150060772896, .03953726217151, .27505549788475, .31690016388893, .37275013327599, .22378182411194, .82568991184235, .14514668285847, -.27233889698982, 1.052060842514, .04496052488685, .37444713711739, 1.6129913330078, -.36434525251389, -.93128365278244, .03420155867934, -.1804157346487, -.03357006236911, -.03733511269093, -.02355666831136, .08841699361801, -.02865886501968, -.09899909794331, -.36265158653259, .13458555936813, -.34191656112671, -.03033804148436, .24371138215065, -.02743346057832, .1167239844799, .29360374808311, -.26456567645073, -.04355576634407, .24922250211239, .37737664580345, -.18922370672226, .22447402775288, .15580512583256, -.55293774604797, -.36785578727722, .27421084046364, -.45432937145233, -.59898042678833, .31679451465607, -.1536608338356, .01664204336703, .39926943182945, -.10894102603197, .57500427961349, .21484296023846, .63253426551819, .7037987112999, .00467173522338, .61756485700607, -.4239239692688, -.014255377464, -.83988964557648, -.70797437429428, .88542991876602, -.36318910121918, .33625638484955, .41914650797844, -.4528394639492, -.36123737692833, .39699018001556, .43800541758537, .06358920782804, .71544241905212, .54432433843613, -.20257151126862, .94927132129669, -.41291815042496, 3.4317541122437, -1.8334206342697, .22241242229939, .72019159793854, -2.2614006996155, .94440299272537, 1.0961196422577, -.04889564588666, .50891524553299, 1.971658706665, -.34635934233665, 3.1444630622864, -4.0317454338074, -5.4861345291138, .81299871206284, 1.1164767742157, .89470589160919]) yr = np.array([np.nan, -.55428558588028, -.36208805441856, -.5116091966629, -.28030154109001, -.4422954916954, -.18432281911373, -.31516996026039, -.39063200354576, -.19754208624363, -.26044383645058, -.23408082127571, -.10966806858778, -.2874368429184, -.09011957794428, -.21747054159641, -.20822501182556, -.02426831051707, -.21233357489109, -.0452471524477, -.25427412986755, -.14787164330482, -.12461274117231, -.06853157281876, -.14335711300373, -.02953593060374, -.18524432182312, .00517434487119, 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.50595796108246, .80543184280396, .07584273815155, .6962223649025, .06129856407642, -.73347336053848, -.87916851043701, 1.1992633342743, -1.1364471912384, -1.4694905281067, -.0659501478076, -.14053705334663, -.13288362324238, .19080325961113, .02886573970318, -.34650835394859, -.03194846212864, -.45531520247459, .36850056052208, -.38445034623146, -.13333308696747, .46862518787384, -2.2778205871582, .41251575946808, -.07933671027422, .4473415017128, .4158943593502, .1590022444725, .28150060772896, .03953726217151, .27505549788475, .31690016388893, .37275013327599, .22378182411194, .82568991184235, .14514668285847, -.27233889698982, 1.052060842514, .04496052488685, .37444713711739, 1.6129913330078, -.36434525251389, -.93128365278244, .03420155867934, -.1804157346487, -.03357006236911, -.03733511269093, -.02355666831136, .08841699361801, -.02865886501968, -.09899909794331, -.36265158653259, .13458555936813, -.34191656112671, -.03033804148436, .24371138215065, -.02743346057832, .1167239844799, .29360374808311, -.26456567645073, -.04355576634407, .24922250211239, .37737664580345, -.18922370672226, .22447402775288, .15580512583256, -.55293774604797, -.36785578727722, .27421084046364, -.45432937145233, -.59898042678833, .31679451465607, -.1536608338356, .01664204336703, .39926943182945, -.10894102603197, .57500427961349, .21484296023846, .63253426551819, .7037987112999, .00467173522338, .61756485700607, -.4239239692688, -.014255377464, -.83988964557648, -.70797437429428, .88542991876602, -.36318910121918, .33625638484955, .41914650797844, -.4528394639492, -.36123737692833, .39699018001556, .43800541758537, .06358920782804, .71544241905212, .54432433843613, -.20257151126862, .94927132129669, -.41291815042496, 3.4317541122437, -1.8334206342697, .22241242229939, .72019159793854, -2.2614006996155, .94440299272537, 1.0961196422577, -.04889564588666, .50891524553299, 1.971658706665, -.34635934233665, 3.1444630622864, -4.0317454338074, -5.4861345291138, .81299871206284, 1.1164767742157, .89470589160919]) mse = np.array([ 1.1115040779114, .69814515113831, .63478744029999, .63409090042114, .63356643915176, .63317084312439, .63287192583084, .63264590501785, .63247483968735, .63234525918961, .63224703073502, .63217264413834, .63211619853973, .63207340240479, .63204091787338, .63201630115509, .63199764490128, .63198345899582, .63197267055511, .63196450471878, .63195830583572, .63195365667343, .63195008039474, .63194733858109, .63194531202316, .6319437623024, .6319425702095, .63194167613983, .63194096088409, .63194048404694, .63194006681442, .6319397687912, .63193953037262, .63193941116333, .6319392323494, .63193917274475, .63193905353546, .63193899393082, .63193899393082, .63193893432617, .63193893432617, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193887472153, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688, .63193881511688]) stdp = np.array([ .72428566217422, .72428566217422, .56208884716034, .53160965442657, .45030161738396, .45229381322861, .38432359695435, .40517011284828, .36063131690025, .30754271149635, .32044330239296, .29408219456673, .27966624498367, .29743707180023, .25011941790581, .27747189998627, .24822402000427, .23426930606365, .27233305573463, .23524768650532, .26427435874939, .21787133812904, .22461311519146, .22853142023087, .24335558712482, .22953669726849, .25524401664734, .22482520341873, .26450532674789, .31863233447075, .27352628111839, .33670437335968, .25623551011086, .28701293468475, .315819054842, .3238864839077, .35844340920448, .34399557113647, .40348997712135, .39373970031738, .4022718667984, .46476069092751, .45762005448341, .46842387318611, .50536489486694, .52051961421967, .47866532206535, .50378143787384, .50863671302795, .4302790760994, .49568024277687, .44652271270752, .43774726986885, .43010330200195, .42344436049461, .44517293572426, .47460499405861, .62086409330368, .52550911903381, .77532315254211, .78466820716858, .85438597202301, .87056696414948, 1.0393311977386, .99110960960388, .85202795267105, .91560190916061, .89238166809082, .88917690515518, .72121334075928, .84221452474594, .8454754948616, .82078683376312, .95394861698151, .84718400239944, .839300096035, .91501939296722, .95743554830551, 1.0874761343002, 1.1326615810394, 1.1169674396515, 1.3300451040268, 1.4790810346603, 1.5027786493301, 1.7226468324661, 1.8395622968674, 1.5940405130386, 1.694568157196, 1.8241587877274, 1.7037791013718, 1.838702917099, 1.7334734201431, 1.4791669845581, 1.3007366657257, 1.7364456653595, 1.2694935798645, .96595168113708, 1.1405370235443, 1.1328836679459, 1.1091921329498, 1.171138882637, 1.1465038061142, 1.0319484472275, 1.055313706398, .93150246143341, 1.0844472646713, .93333613872528, .93137633800507, 1.0778160095215, .38748729228973, .77933365106583, .75266307592392, .88410103321075, .94100385904312, .91849637031555, .96046274900436, .92494148015976, .98310285806656, 1.0272513628006, 1.0762135982513, 1.0743116140366, 1.254854798317, 1.1723403930664, 1.0479376316071, 1.3550333976746, 1.2255589962006, 1.2870025634766, 1.6643482446671, 1.3312928676605, 1.0657893419266, 1.1804157495499, 1.1335761547089, 1.137326002121, 1.1235628128052, 1.1115798950195, 1.1286649703979, 1.0989991426468, 1.0626485347748, .96542054414749, 1.0419135093689, .93033194541931, .95628559589386, 1.027433514595, .98328214883804, 1.0063992738724, 1.0645687580109, .94354963302612, .95077443122864, 1.0226324796677, 1.089217543602, .97552293539047, 1.0441918373108, 1.052937746048, .86785578727722, .82579529285431, .95432937145233, .79897737503052, .68320548534393, .85365778207779, .78336101770401, .80072748661041, .9089440703392, .82500487565994, .98515397310257, .96745657920837, 1.0962044000626, 1.195325255394, 1.0824474096298, 1.2239117622375, 1.0142554044724, 1.0399018526077, .80796521902084, .7145761847496, 1.0631860494614, .86374056339264, .98086261749268, 1.0528303384781, .86123734712601, .80300676822662, .96200370788574, 1.0364016294479, .98456978797913, 1.1556725502014, 1.2025715112686, 1.0507286787033, 1.312912106514, 1.0682457685471, 2.0334177017212, 1.0775905847549, 1.2798084020615, 1.461397767067, .72960823774338, 1.2498733997345, 1.466894865036, 1.286082983017, 1.3903408050537, 1.8483582735062, 1.4685434103012, 2.3107523918152, .7711226940155, -.31598940491676, .68151205778122, 1.0212944746017]) icstats = np.array([ 202, np.nan, -240.29558272688, 5, 490.59116545376, 507.13250394077]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima211_results.py000066400000000000000000001270471224417117700276310ustar00rootroot00000000000000import numpy as np llf = np.array([-239.91961140777]) nobs = np.array([ 202]) k = np.array([ 5]) k_exog = np.array([ 1]) sigma = np.array([ .79249918716869]) chi2 = np.array([ 907.36856676748]) df_model = np.array([ 3]) k_ar = np.array([ 2]) k_ma = np.array([ 1]) params = np.array([ .86132870569126, 1.1429974468925, -.16912378249242, -.87400688606796, .79249918716869]) cov_params = np.array([ .22693223682292, -.00989623843683, -.00092349277863, .00706357033712, -.00195332705416, -.00989623843683, .00771565502349, -.00405286187622, -.00748374882497, -.00006513770699, -.00092349277863, -.00405286187622, .00252946620109, .00372566422545, .00024079697522, .00706357033712, -.00748374882497, .00372566422545, .00812286034649, 8.202907726e-06, -.00195332705416, -.00006513770699, .00024079697522, 8.202907726e-06, .00031052148013]).reshape(5,5) xb = np.array([ .86132872104645, .86132872104645, .58931648731232, .53695642948151, .43875110149384, .43893754482269, .36897498369217, .39383068680763, .35268598794937, .30327013134956, .3206245303154, .29862868785858, .2883597612381, .30968201160431, .26627779006958, .29635512828827, .27021989226341, .25900682806969, .29855474829674, .26389503479004, .2940701842308, .24996083974838, .25805163383484, .26306444406509, .27853038907051, .26575443148613, .29165366292, .262396723032, .30186694860458, .35503962635994, .31088310480118, .37306407094002, .29435822367668, .32511350512505, .35352823138237, .36144828796387, .39527052640915, .38102087378502, .43927636742592, .42946752905846, .43780836462975, .49889534711838, .49156260490417, .50206583738327, .53814554214478, .55276554822922, .51174682378769, .53661912679672, .54144555330276, .4649658203125, .52955776453018, .48147654533386, .47327646613121, .46611019968987, .45984682440758, .48127228021622, .50998419523239, .65266174077988, .5583056807518, .80249065160751, .80990171432495, .87710189819336, .89202457666397, 1.0564744472504, 1.0080462694168, .87197554111481, .93521976470947, .9128600358963, .91022855043411, .7465353012085, .866335272789, .86956661939621, .84549117088318, .97585707902908, .87074065208435, .86343002319336, .93773847818375, .97884559631348, 1.1054569482803, 1.1484670639038, 1.1322609186172, 1.3402134180069, 1.4842771291733, 1.5056529045105, 1.719556927681, 1.8319338560104, 1.5904501676559, 1.6899375915527, 1.8168371915817, 1.6987046003342, 1.8314251899719, 1.7283667325974, 1.4807628393173, 1.308970451355, 1.7375549077988, 1.2797228097916, .98571860790253, 1.1599444150925, 1.1530816555023, 1.13017141819, 1.1908437013626, 1.1662386655807, 1.0540459156036, 1.0774390697479, .95646268129349, 1.1066728830338, .95817422866821, .95676136016846, 1.1000070571899, .42435362935066, .81153392791748, .78478264808655, .91281259059906, .9670450091362, .94373846054077, .98408794403076, .9486455321312, 1.0052901506424, 1.0478744506836, 1.095078587532, 1.0925225019455, 1.2685979604721, 1.1865184307098, 1.0648469924927, 1.3658550977707, 1.2376955747604, 1.2978720664978, 1.6663243770599, 1.338112950325, 1.0797605514526, 1.1944576501846, 1.1494234800339, 1.153874874115, 1.1408479213715, 1.1294689178467, 1.1464176177979, 1.1174235343933, 1.0820926427841, .98742854595184, 1.0630278587341, .95385235548019, .97988063097, 1.049503326416, 1.0058189630508, 1.0283635854721, 1.0849515199661, .96609032154083, .97366327047348, 1.0440692901611, 1.1086683273315, .99680209159851, 1.0642927885056, 1.0725424289703, .8914600610733, .85157895088196, .97811859846115, .8258438706398, .71353197097778, .88130152225494, .81193578243256, .82894796133041, .9344978928566, .85150623321533, 1.0080153942108, .9895287156105, 1.1147927045822, 1.2104271650314, 1.0987895727158, 1.23719227314, 1.0314946174622, 1.0577303171158, .8316445350647, .74243623018265, 1.0848734378815, .88838368654251, 1.0033586025238, 1.0730868577957, .88500571250916, .82902699708939, .98530465364456, 1.057307600975, 1.0057097673416, 1.1727533340454, 1.2172684669495, 1.0678850412369, 1.3246995210648, 1.0841422080994, 2.0282983779907, 1.0879902839661, 1.289278626442, 1.4674614667892, .75163400173187, 1.2650294303894, 1.4760826826096, 1.2972749471664, 1.3993507623672, 1.8463147878647, 1.4716247320175, 2.2955448627472, .7857254743576, -.26799991726875, .71938097476959, 1.0508332252502]) y = np.array([np.nan, 29.841327667236, 29.739316940308, 29.886957168579, 29.808752059937, 29.978939056396, 29.918973922729, 30.143831253052, 30.192686080933, 30.113269805908, 30.24062538147, 30.27862739563, 30.32836151123, 30.519681930542, 30.486276626587, 30.67635345459, 30.710220336914, 30.73900604248, 30.988555908203, 31.01389503479, 31.234069824219, 31.199960708618, 31.278051376343, 31.383066177368, 31.558530807495, 31.645753860474, 31.871654510498, 31.912395477295, 32.181865692139, 32.635040283203, 32.760883331299, 33.223064422607, 33.194358825684, 33.425113677979, 33.753528594971, 34.061447143555, 34.495269775391, 34.781021118164, 35.339279174805, 35.729465484619, 36.137809753418, 36.798892974854, 37.291561126709, 37.802066802979, 38.438148498535, 39.052764892578, 39.41174697876, 39.936618804932, 40.44144821167, 40.564964294434, 41.129554748535, 41.381477355957, 41.673278808594, 41.966110229492, 42.259845733643, 42.681274414063, 43.209983825684, 44.352661132813, 44.758304595947, 46.402488708496, 47.609901428223, 48.977100372314, 50.192024230957, 52.05647277832, 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126.46485137939, 128.86585998535, 130.1376953125, 131.79786682129, 135.06631469727, 136.03811645508, 136.17976379395, 137.39445495605, 138.34942626953, 139.45387268066, 140.54084777832, 141.6294708252, 142.84642028809, 143.91741943359, 144.88209533691, 145.48742675781, 146.66304016113, 147.25386047363, 148.17987060547, 149.4494934082, 150.40580749512, 151.52836608887, 152.88494873047, 153.56610107422, 154.47366333008, 155.74406433105, 157.20867919922, 157.9967956543, 159.26428222656, 160.47253417969, 160.79145812988, 161.25157165527, 162.47811889648, 162.82585144043, 162.91352844238, 164.08129882813, 164.71192932129, 165.52894592285, 166.83448791504, 167.55149841309, 169.10801696777, 170.28953552246, 172.0147857666, 173.9104309082, 174.99877929688, 176.83720397949, 177.43148803711, 178.45771789551, 178.43165588379, 178.44242858887, 180.38487243652, 180.88838195801, 182.20335388184, 183.67309570313, 184.08500671387, 184.5290222168, 185.88529968262, 187.35731506348, 188.40570068359, 190.27276611328, 192.01727294922, 192.8678894043, 195.12471008301, 195.78413391113, 201.22830200195, 200.48799133301, 201.98927307129, 204.16746520996, 202.65162658691, 204.83903503418, 207.39608764648, 208.63526916504, 210.53234863281, 214.34130859375, 215.4686126709, 220.9055480957, 217.67472839355, 211.90599060059, 213.39038085938, 215.51982116699]) resid = np.array([np.nan, -.6913286447525, -.38931575417519, -.51695597171783, -.26875102519989, -.42893922328949, -.16897420585155, -.30383053421974, -.38268667459488, -.19326950609684, -.26062506437302, -.2386272996664, -.11836158484221, -.29968178272247, -.10627794265747, -.23635374009609, -.2302208840847, -.04900583252311, -.23855529725552, -.07389451563358, -.28406995534897, -.17996114492416, -.15805125236511, -.10306460410357, -.17853192985058, -.06575367599726, -.22165396809578, -.03239719197154, .09813265502453, -.18503764271736, .08911459892988, -.32306101918221, -.09436126798391, -.02511045895517, -.05352900549769, 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.62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332, .62805497646332]) stdp = np.array([ .86132872104645, .86132872104645, .58931648731232, .53695642948151, .43875110149384, .43893754482269, .36897498369217, .39383068680763, .35268598794937, .30327013134956, .3206245303154, .29862868785858, .2883597612381, .30968201160431, .26627779006958, .29635512828827, .27021989226341, .25900682806969, .29855474829674, .26389503479004, .2940701842308, .24996083974838, .25805163383484, .26306444406509, .27853038907051, .26575443148613, .29165366292, .262396723032, .30186694860458, .35503962635994, .31088310480118, .37306407094002, .29435822367668, .32511350512505, .35352823138237, .36144828796387, .39527052640915, .38102087378502, .43927636742592, .42946752905846, .43780836462975, .49889534711838, .49156260490417, .50206583738327, .53814554214478, .55276554822922, .51174682378769, .53661912679672, .54144555330276, .4649658203125, .52955776453018, .48147654533386, .47327646613121, .46611019968987, .45984682440758, .48127228021622, .50998419523239, .65266174077988, .5583056807518, .80249065160751, .80990171432495, .87710189819336, .89202457666397, 1.0564744472504, 1.0080462694168, .87197554111481, .93521976470947, .9128600358963, .91022855043411, .7465353012085, .866335272789, .86956661939621, .84549117088318, .97585707902908, .87074065208435, .86343002319336, .93773847818375, .97884559631348, 1.1054569482803, 1.1484670639038, 1.1322609186172, 1.3402134180069, 1.4842771291733, 1.5056529045105, 1.719556927681, 1.8319338560104, 1.5904501676559, 1.6899375915527, 1.8168371915817, 1.6987046003342, 1.8314251899719, 1.7283667325974, 1.4807628393173, 1.308970451355, 1.7375549077988, 1.2797228097916, .98571860790253, 1.1599444150925, 1.1530816555023, 1.13017141819, 1.1908437013626, 1.1662386655807, 1.0540459156036, 1.0774390697479, .95646268129349, 1.1066728830338, .95817422866821, .95676136016846, 1.1000070571899, .42435362935066, .81153392791748, .78478264808655, .91281259059906, .9670450091362, .94373846054077, .98408794403076, .9486455321312, 1.0052901506424, 1.0478744506836, 1.095078587532, 1.0925225019455, 1.2685979604721, 1.1865184307098, 1.0648469924927, 1.3658550977707, 1.2376955747604, 1.2978720664978, 1.6663243770599, 1.338112950325, 1.0797605514526, 1.1944576501846, 1.1494234800339, 1.153874874115, 1.1408479213715, 1.1294689178467, 1.1464176177979, 1.1174235343933, 1.0820926427841, .98742854595184, 1.0630278587341, .95385235548019, .97988063097, 1.049503326416, 1.0058189630508, 1.0283635854721, 1.0849515199661, .96609032154083, .97366327047348, 1.0440692901611, 1.1086683273315, .99680209159851, 1.0642927885056, 1.0725424289703, .8914600610733, .85157895088196, .97811859846115, .8258438706398, .71353197097778, .88130152225494, .81193578243256, .82894796133041, .9344978928566, .85150623321533, 1.0080153942108, .9895287156105, 1.1147927045822, 1.2104271650314, 1.0987895727158, 1.23719227314, 1.0314946174622, 1.0577303171158, .8316445350647, .74243623018265, 1.0848734378815, .88838368654251, 1.0033586025238, 1.0730868577957, .88500571250916, .82902699708939, .98530465364456, 1.057307600975, 1.0057097673416, 1.1727533340454, 1.2172684669495, 1.0678850412369, 1.3246995210648, 1.0841422080994, 2.0282983779907, 1.0879902839661, 1.289278626442, 1.4674614667892, .75163400173187, 1.2650294303894, 1.4760826826096, 1.2972749471664, 1.3993507623672, 1.8463147878647, 1.4716247320175, 2.2955448627472, .7857254743576, -.26799991726875, .71938097476959, 1.0508332252502]) icstats = np.array([ 202, np.nan, -239.91961140777, 5, 489.83922281554, 506.38056130255]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima211nc_css_results.py000066400000000000000000001262071224417117700310170ustar00rootroot00000000000000import numpy as np llf = np.array([-240.21658671417]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .79473430527544]) chi2 = np.array([ 54633.096432541]) df_model = np.array([ 3]) k_ar = np.array([ 2]) k_ma = np.array([ 1]) params = np.array([ 1.1970355174119, -.19724105359909, -.91770441432171, .63160261598163]) cov_params = np.array([ .00182362158934, -.00163271308366, -.00140915922719, -.00044400541718, -.00163271308366, .00148731108625, .00117094734518, .00044939207189, -.00140915922719, .00117094734518, .00181742086472, .00032737864417, -.00044400541718, .00044939207189, .00032737864417, .00071193796554]).reshape(4,4) xb = np.array([ 0, 0, .04999904707074, .06866559386253, .02903163060546, .07047952711582, .03383458778262, .08519082516432, .06387747824192, .0323903337121, .0664371997118, .056028008461, .05634552612901, .08741936087608, .04945361241698, .0881454795599, .06608437746763, .05997726693749, .1058451384306, .07246486097574, .10775856673717, .06419499218464, .07649331539869, .08432687073946, .10236189514399, .09031195193529, .11902847886086, .08933536708355, .13242828845978, .18790599703789, .1410321444273, .2076324224472, .12561346590519, .16128000617027, .19236192107201, .20115886628628, .23716603219509, .22255305945873, .28473255038261, .27441227436066, .28466689586639, .34994292259216, .3424659371376, .35532799363136, .39506548643112, .41180691123009, .37130555510521, .40151777863503, .40951976180077, .33306270837784, .40587121248245, .35764938592911, .35284259915352, .34843212366104, .34438464045525, .36860400438309, .3990384042263, .54691004753113, .44432625174522, .70020270347595, .70163971185684, .77033877372742, .78572767972946, .95923948287964, .90811860561371, .77250319719315, .85019183158875, .83438217639923, .83959633111954, .67678385972977, .81331378221512, .8202628493309, .79870623350143, .93831378221512, .8281461596489, .8256653547287, .9071888923645, .95076316595078, 1.0827594995499, 1.1249971389771, 1.1078934669495, 1.3271758556366, 1.4741442203522, 1.4939774274826, 1.7192279100418, 1.8355281352997, 1.5873348712921, 1.7079899311066, 1.8515517711639, 1.7368041276932, 1.8895095586777, 1.7913619279861, 1.5485136508942, 1.3914349079132, 1.8569093942642, 1.378589630127, 1.0909280776978, 1.2919955253601, 1.2874838113785, 1.2636196613312, 1.3255174160004, 1.2952193021774, 1.1754019260406, 1.2002106904984, 1.0717958211899, 1.2283787727356, 1.0664055347443, 1.0640426874161, 1.2097471952438, .49885395169258, .91795635223389, .88015007972717, 1.0048481225967, 1.0485925674438, 1.0131514072418, 1.0480036735535, 1.0044000148773, 1.0596977472305, 1.0989319086075, 1.1431447267532, 1.1360602378845, 1.316884636879, 1.2248164415359, 1.0992801189423, 1.4178918600082, 1.2780615091324, 1.3436778783798, 1.727570772171, 1.376532793045, 1.1185711622238, 1.2548811435699, 1.2139776945114, 1.22409760952, 1.2136551141739, 1.2040791511536, 1.2232189178467, 1.1931306123734, 1.1573059558868, 1.0603547096252, 1.1422899961472, 1.0268490314484, 1.0556720495224, 1.1264756917953, 1.0764141082764, 1.0978548526764, 1.1536711454391, 1.025780916214, 1.034966468811, 1.1074740886688, 1.1707112789154, 1.0496238470078, 1.1209251880646, 1.1271858215332, .93740028142929, .90130144357681, 1.0357736349106, .87323325872421, .75861483812332, .93606770038605, .85732334852219, .87216699123383, .97779452800751, .88410341739655, 1.0446182489395, 1.0177079439163, 1.144193649292, 1.2372444868088, 1.1155867576599, 1.2619564533234, 1.0462523698807, 1.0816910266876, .85130125284195, .76972281932831, 1.1335872411728, .92024201154709, 1.0416384935379, 1.1102936267853, .91037821769714, .85678082704544, 1.022847533226, 1.0930491685867, 1.0342184305191, 1.2070096731186, 1.2472279071808, 1.0886085033417, 1.3604420423508, 1.1053978204727, 2.0939025878906, 1.0898643732071, 1.3238569498062, 1.5171576738358, .77435439825058, 1.3360253572464, 1.5512014627457, 1.3569095134735, 1.4669530391693, 1.9312930107117, 1.52878677845, 2.3952746391296, .80755305290222, -.2365039139986, .85178333520889, 1.1858888864517]) y = np.array([np.nan, 28.979999542236, 29.199998855591, 29.4186668396, 29.399032592773, 29.610481262207, 29.583833694458, 29.835191726685, 29.903877258301, 29.842390060425, 29.986436843872, 30.036027908325, 30.096345901489, 30.29741859436, 30.269453048706, 30.468145370483, 30.506084442139, 30.539976119995, 30.795845031738, 30.822463989258, 31.047760009766, 31.014196395874, 31.096494674683, 31.204328536987, 31.382362365723, 31.470310211182, 31.699028015137, 31.739334106445, 32.012428283691, 32.467903137207, 32.591033935547, 33.057632446289, 33.025615692139, 33.261280059814, 33.592365264893, 33.901161193848, 34.33716583252, 34.622554779053, 35.184734344482, 35.574413299561, 35.984668731689, 36.649940490723, 37.142463684082, 37.655326843262, 38.295066833496, 38.911808013916, 39.271308898926, 39.801517486572, 40.309520721436, 40.433059692383, 41.005870819092, 41.257652282715, 41.552845001221, 41.848430633545, 42.144382476807, 42.568603515625, 43.099040985107, 44.246910095215, 44.644325256348, 46.300201416016, 47.501640319824, 48.870338439941, 50.08572769165, 51.959239959717, 53.208118438721, 53.77250289917, 54.850193023682, 55.734382629395, 56.639595031738, 56.776782989502, 57.813312530518, 58.720264434814, 59.498706817627, 60.938312530518, 61.628147125244, 62.425662994385, 63.607189178467, 64.850761413574, 66.58275604248, 68.224998474121, 69.607894897461, 71.927177429199, 74.474143981934, 76.693977355957, 79.719230651855, 82.735527038574, 84.18733215332, 86.407989501953, 89.051551818848, 90.836799621582, 93.389511108398, 95.191360473633, 95.948516845703, 96.39143371582, 99.356910705566, 99.478584289551, 98.990928649902, 100.09199523926, 101.08748626709, 102.063621521, 103.42551422119, 104.59522247314, 105.27539825439, 106.30020904541, 106.77178955078, 108.2283782959, 108.76640319824, 109.5640411377, 111.10974884033, 109.19885253906, 110.41795349121, 111.08014678955, 112.40484619141, 113.74858856201, 114.81315612793, 116.04800415039, 117.00440216064, 118.25969696045, 119.59893035889, 121.04314422607, 122.33605957031, 124.41688537598, 125.72481536865, 126.49928283691, 128.91789245605, 130.17805480957, 131.84367370605, 135.12756347656, 136.07652282715, 136.21858215332, 137.45487976074, 138.41397094727, 139.52409362793, 140.61364746094, 141.70408630371, 142.92321777344, 143.99313354492, 144.9573059082, 145.56034851074, 146.74229431152, 147.32685852051, 148.25567626953, 149.52647399902, 150.47640991211, 151.59785461426, 152.95367431641, 153.62579345703, 154.53497314453, 155.80746459961, 157.27072143555, 158.04962158203, 159.32092285156, 160.52717590332, 160.83738708496, 161.30130004883, 162.53576660156, 162.87322998047, 162.95861816406, 164.13606262207, 164.75732421875, 165.57215881348, 166.8777923584, 167.58410644531, 169.14462280273, 170.31771850586, 172.04418945313, 173.93724060059, 175.01557922363, 176.86196899414, 177.44624328613, 178.48168945313, 178.4513092041, 178.4697265625, 180.43359375, 180.92024230957, 182.24163818359, 183.71029663086, 184.11038208008, 184.5567779541, 185.92283630371, 187.39305114746, 188.43421936035, 190.30702209473, 192.04722595215, 192.88861083984, 195.16044616699, 195.8053894043, 201.29389953613, 200.48985290527, 202.0238494873, 204.21714782715, 202.67434692383, 204.91003417969, 207.47120666504, 208.6949005127, 210.59994506836, 214.42628479004, 215.52578735352, 221.00527954102, 217.6965637207, 211.93748474121, 213.52278137207, 215.65487670898]) resid = np.array([np.nan, .17000007629395, .150001719594, -.04866513609886, .1409684419632, -.06048120185733, .16616617143154, .00480932369828, -.09387816488743, .07761027663946, -.00643773004413, .00397336483002, .1136526465416, -.07741913199425, .11054623872042, -.02814410813153, -.02608536928892, .15002372860909, -.04584567248821, .11753567308187, -.09775833785534, .00580469891429, .02350706420839, .07567297667265, -.00236342288554, .10968881100416, -.04902878031135, .14066417515278, .2675713300705, -.01790400780737, .25896555185318, -.15762937068939, .074383482337, .13872304558754, .10763731598854, .19883884489536, .06283701956272, .27744692564011, .11526516824961, .12558923661709, .3153315782547, .15005706250668, .1575340628624, .24467428028584, .20493300259113, -.01180539745837, .12869445979595, .09848223626614, -.20952282845974, .166937276721, -.1058681756258, -.05765015259385, -.05284334719181, -.04843288660049, .05561690032482, .13139598071575, .60096162557602, -.04691004380584, .95567148923874, .49979802966118, .5983595252037, .42966198921204, .91427308320999, .34075975418091, -.20811785757542, .22749677300453, .049809679389, .06561553478241, -.53959709405899, .2232176810503, .08668774366379, -.02026358619332, .50129300355911, -.13831453025341, -.02814690209925, .27433693408966, .29281187057495, .64923530817032, .51723897457123, .27500438690186, .99210506677628, 1.0728256702423, .72585266828537, 1.3060256242752, 1.1807736158371, -.13553117215633, .51266354322433, .79201000928879, .0484497398138, .66319739818573, .0104919327423, -.79136198759079, -.94851511716843, 1.1085650920868, -1.2569109201431, -1.5785865783691, -.19092650711536, -.29199549555779, -.2874838411808, .03637577593327, -.12551285326481, -.49522390961647, -.17540194094181, -.60021221637726, .22820720076561, -.52838176488876, -.26640248298645, .33595886826515, -2.4097516536713, .30114910006523, -.21795941889286, .31985449790955, .29514732956886, .05141358822584, .18684551119804, -.04800364747643, .19559693336487, .24030530452728, .30106961727142, .15685074031353, .76394122838974, .08311692625284, -.32481494545937, 1.0007183551788, -.01789797656238, .32194453477859, 1.5563160181046, -.42756772041321, -.97652357816696, -.01858037337661, -.25488117337227, -.11397163569927, -.12410674989223, -.1136489585042, -.00408222479746, -.12321277707815, -.19313062727451, -.45730903744698, .03965143114328, -.44229298830032, -.12685517966747, .1443248540163, -.12647566199303, .02359197475016, .20214818418026, -.35366812348366, -.12578700482845, .16503044962883, .29253509640694, -.27071738243103, .15037304162979, .07907173037529, -.6271858215332, -.43740031123161, .19870468974113, -.53577369451523, -.67323631048203, .24138513207436, -.23607075214386, -.05732027813792, .32782995700836, -.17779149115086, .51590573787689, .15537866950035, .5822828412056, .65580940246582, -.03724759072065, .58442544937134, -.46196871995926, -.04625232890248, -.88167881965637, -.75131040811539, .83028328418732, -.43359026312828, .2797549366951, .35837066173553, -.51030284166336, -.41037824749947, .34321609139442, .37716165184975, .00694172456861, .66579383611679, .49298724532127, -.24722795188427, .91139149665833, -.46044808626175, 3.3946022987366, -1.8939057588577, .21013870835304, .67614299058914, -2.3171606063843, .89965683221817, 1.0099676847458, -.13320215046406, .43808862566948, 1.8950464725494, -.42929407954216, 3.0842199325562, -4.1162676811218, -5.5225644111633, .73351317644119, .94620555639267, .73011147975922]) yr = np.array([np.nan, .17000007629395, .150001719594, -.04866513609886, .1409684419632, -.06048120185733, .16616617143154, .00480932369828, -.09387816488743, .07761027663946, -.00643773004413, .00397336483002, .1136526465416, -.07741913199425, .11054623872042, -.02814410813153, -.02608536928892, .15002372860909, -.04584567248821, .11753567308187, -.09775833785534, .00580469891429, .02350706420839, .07567297667265, -.00236342288554, .10968881100416, -.04902878031135, .14066417515278, .2675713300705, -.01790400780737, .25896555185318, -.15762937068939, .074383482337, .13872304558754, .10763731598854, .19883884489536, .06283701956272, .27744692564011, .11526516824961, .12558923661709, .3153315782547, .15005706250668, .1575340628624, .24467428028584, .20493300259113, -.01180539745837, .12869445979595, .09848223626614, -.20952282845974, .166937276721, -.1058681756258, -.05765015259385, -.05284334719181, -.04843288660049, .05561690032482, .13139598071575, .60096162557602, -.04691004380584, .95567148923874, .49979802966118, .5983595252037, .42966198921204, .91427308320999, .34075975418091, -.20811785757542, .22749677300453, .049809679389, .06561553478241, -.53959709405899, .2232176810503, .08668774366379, -.02026358619332, .50129300355911, -.13831453025341, -.02814690209925, .27433693408966, .29281187057495, .64923530817032, .51723897457123, .27500438690186, .99210506677628, 1.0728256702423, .72585266828537, 1.3060256242752, 1.1807736158371, -.13553117215633, .51266354322433, .79201000928879, .0484497398138, .66319739818573, .0104919327423, -.79136198759079, -.94851511716843, 1.1085650920868, -1.2569109201431, -1.5785865783691, -.19092650711536, -.29199549555779, -.2874838411808, .03637577593327, -.12551285326481, -.49522390961647, -.17540194094181, -.60021221637726, .22820720076561, -.52838176488876, -.26640248298645, .33595886826515, -2.4097516536713, .30114910006523, -.21795941889286, .31985449790955, .29514732956886, .05141358822584, .18684551119804, -.04800364747643, .19559693336487, .24030530452728, .30106961727142, .15685074031353, .76394122838974, .08311692625284, -.32481494545937, 1.0007183551788, -.01789797656238, .32194453477859, 1.5563160181046, -.42756772041321, -.97652357816696, -.01858037337661, -.25488117337227, -.11397163569927, -.12410674989223, -.1136489585042, -.00408222479746, -.12321277707815, -.19313062727451, -.45730903744698, .03965143114328, -.44229298830032, -.12685517966747, .1443248540163, -.12647566199303, .02359197475016, .20214818418026, -.35366812348366, -.12578700482845, .16503044962883, .29253509640694, -.27071738243103, .15037304162979, .07907173037529, -.6271858215332, -.43740031123161, .19870468974113, -.53577369451523, -.67323631048203, .24138513207436, -.23607075214386, -.05732027813792, .32782995700836, -.17779149115086, .51590573787689, .15537866950035, .5822828412056, .65580940246582, -.03724759072065, .58442544937134, -.46196871995926, -.04625232890248, -.88167881965637, -.75131040811539, .83028328418732, -.43359026312828, .2797549366951, .35837066173553, -.51030284166336, -.41037824749947, .34321609139442, .37716165184975, .00694172456861, .66579383611679, .49298724532127, -.24722795188427, .91139149665833, -.46044808626175, 3.3946022987366, -1.8939057588577, .21013870835304, .67614299058914, -2.3171606063843, .89965683221817, 1.0099676847458, -.13320215046406, .43808862566948, 1.8950464725494, -.42929407954216, 3.0842199325562, -4.1162676811218, -5.5225644111633, .73351317644119, .94620555639267, .73011147975922]) mse = np.array([ 1.1635265350342, .70545583963394, .63365471363068, .633325278759, .63304948806763, .63281834125519, .63262450695038, .63246184587479, .63232523202896, .63221049308777, .63211411237717, .63203299045563, .63196486234665, .63190752267838, .63185924291611, .63181865215302, .63178449869156, .63175576925278, .631731569767, .63171118497849, .63169401884079, .63167959451675, .63166743516922, .63165718317032, .63164860010147, .63164132833481, .6316351890564, .63163006305695, .63162571191788, .63162207603455, .63161903619766, .63161641359329, .63161426782608, .63161242008209, .63161087036133, .63160955905914, .63160848617554, .63160753250122, .63160675764084, .63160610198975, .63160556554794, .63160508871078, .63160473108292, .63160437345505, .63160407543182, .63160383701324, .63160365819931, .63160347938538, .63160336017609, .6316032409668, .63160312175751, .63160306215286, .63160300254822, .63160294294357, .63160288333893, .63160282373428, .63160282373428, .63160276412964, .63160276412964, .63160270452499, .63160270452499, .63160270452499, .63160270452499, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035, .63160264492035]) stdp = np.array([ 0, 0, .04999904707074, .06866559386253, .02903163060546, .07047952711582, .03383458778262, .08519082516432, .06387747824192, .0323903337121, .0664371997118, .056028008461, .05634552612901, .08741936087608, .04945361241698, .0881454795599, .06608437746763, .05997726693749, .1058451384306, .07246486097574, .10775856673717, .06419499218464, .07649331539869, .08432687073946, .10236189514399, .09031195193529, .11902847886086, .08933536708355, .13242828845978, .18790599703789, .1410321444273, .2076324224472, .12561346590519, .16128000617027, .19236192107201, .20115886628628, .23716603219509, .22255305945873, .28473255038261, .27441227436066, .28466689586639, .34994292259216, .3424659371376, .35532799363136, .39506548643112, .41180691123009, .37130555510521, .40151777863503, .40951976180077, .33306270837784, .40587121248245, .35764938592911, .35284259915352, .34843212366104, .34438464045525, .36860400438309, .3990384042263, .54691004753113, .44432625174522, .70020270347595, .70163971185684, .77033877372742, .78572767972946, .95923948287964, .90811860561371, .77250319719315, .85019183158875, .83438217639923, .83959633111954, .67678385972977, .81331378221512, .8202628493309, .79870623350143, .93831378221512, .8281461596489, .8256653547287, .9071888923645, .95076316595078, 1.0827594995499, 1.1249971389771, 1.1078934669495, 1.3271758556366, 1.4741442203522, 1.4939774274826, 1.7192279100418, 1.8355281352997, 1.5873348712921, 1.7079899311066, 1.8515517711639, 1.7368041276932, 1.8895095586777, 1.7913619279861, 1.5485136508942, 1.3914349079132, 1.8569093942642, 1.378589630127, 1.0909280776978, 1.2919955253601, 1.2874838113785, 1.2636196613312, 1.3255174160004, 1.2952193021774, 1.1754019260406, 1.2002106904984, 1.0717958211899, 1.2283787727356, 1.0664055347443, 1.0640426874161, 1.2097471952438, .49885395169258, .91795635223389, .88015007972717, 1.0048481225967, 1.0485925674438, 1.0131514072418, 1.0480036735535, 1.0044000148773, 1.0596977472305, 1.0989319086075, 1.1431447267532, 1.1360602378845, 1.316884636879, 1.2248164415359, 1.0992801189423, 1.4178918600082, 1.2780615091324, 1.3436778783798, 1.727570772171, 1.376532793045, 1.1185711622238, 1.2548811435699, 1.2139776945114, 1.22409760952, 1.2136551141739, 1.2040791511536, 1.2232189178467, 1.1931306123734, 1.1573059558868, 1.0603547096252, 1.1422899961472, 1.0268490314484, 1.0556720495224, 1.1264756917953, 1.0764141082764, 1.0978548526764, 1.1536711454391, 1.025780916214, 1.034966468811, 1.1074740886688, 1.1707112789154, 1.0496238470078, 1.1209251880646, 1.1271858215332, .93740028142929, .90130144357681, 1.0357736349106, .87323325872421, .75861483812332, .93606770038605, .85732334852219, .87216699123383, .97779452800751, .88410341739655, 1.0446182489395, 1.0177079439163, 1.144193649292, 1.2372444868088, 1.1155867576599, 1.2619564533234, 1.0462523698807, 1.0816910266876, .85130125284195, .76972281932831, 1.1335872411728, .92024201154709, 1.0416384935379, 1.1102936267853, .91037821769714, .85678082704544, 1.022847533226, 1.0930491685867, 1.0342184305191, 1.2070096731186, 1.2472279071808, 1.0886085033417, 1.3604420423508, 1.1053978204727, 2.0939025878906, 1.0898643732071, 1.3238569498062, 1.5171576738358, .77435439825058, 1.3360253572464, 1.5512014627457, 1.3569095134735, 1.4669530391693, 1.9312930107117, 1.52878677845, 2.3952746391296, .80755305290222, -.2365039139986, .85178333520889, 1.1858888864517]) icstats = np.array([ 202, np.nan, -240.21658671417, 4, 488.43317342834, 501.66624421795]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima211nc_results.py000066400000000000000000001077571224417117700301600ustar00rootroot00000000000000import numpy as np llf = np.array([-241.25977940638]) nobs = np.array([ 202]) k = np.array([ 4]) k_exog = np.array([ 1]) sigma = np.array([ .79533686587485]) chi2 = np.array([ 48655.961417345]) df_model = np.array([ 3]) k_ar = np.array([ 2]) k_ma = np.array([ 1]) params = np.array([ 1.1870704073154, -.19095698898571, -.90853757573555, .79533686587485]) cov_params = np.array([ .00204336743511, -.00177522179187, -.00165894353702, -.00031352141782, -.00177522179187, .00157376214003, .00132907629148, .00030367391511, -.00165894353702, .00132907629148, .00210988984438, .00024199988464, -.00031352141782, .00030367391511, .00024199988464, .00027937875185]).reshape(4,4) xb = np.array([ 0, 0, .11248598247766, .14283391833305, .0800810828805, .12544548511505, .07541109621525, .1297073662281, .10287435352802, .06303016841412, .09501431882381, .08120259642601, .07862555980682, .10874316096306, .06787430495024, .10527064651251, .08142036944628, .07337106764317, .11828763782978, .08380854874849, .11801292747259, .07338324189186, .0842502862215, .09106454998255, .10832596570253, .09570593386889, .1236881390214, .09362822026014, .13587079942226, .19111332297325, .14459040760994, .21043147146702, .12866979837418, .16308072209358, .19356986880302, .20215991139412, .23782986402512, .22326464951038, .28485587239265, .27474755048752, .28465977311134, .34938132762909, .3421268761158, .35463020205498, .39384591579437, .41037485003471, .36968034505844, .39875456690788, .40607318282127, .32915702462196, .40012913942337, .35161358118057, .34572568535805, .34037715196609, .3355179131031, .35895752906799, .38901025056839, .53648668527603, .43572762608528, .69034379720688, .69410443305969, .76356476545334, .77972346544266, .95276647806168, .9030898809433, .76722019910812, .84191131591797, .82463103532791, .82802563905716, .66399103403091, .79665386676788, .80260843038559, .78016436100006, .91813576221466, .80874294042587, .80483394861221, .8848432302475, .92809981107712, 1.0597171783447, 1.1029140949249, 1.0864543914795, 1.3046631813049, 1.4528053998947, 1.4744025468826, 1.6993381977081, 1.816978096962, 1.5705223083496, 1.6871707439423, 1.8281806707382, 1.7127912044525, 1.8617957830429, 1.7624272108078, 1.5169456005096, 1.3543643951416, 1.8122490644455, 1.3362231254578, 1.0437293052673, 1.2371381521225, 1.2306576967239, 1.2056746482849, 1.2665351629257, 1.2366921901703, 1.1172571182251, 1.1408381462097, 1.0126565694809, 1.1675561666489, 1.0074961185455, 1.0045058727264, 1.1498116254807, .44306626915932, .85451871156693, .81856834888458, .94427144527435, .99084824323654, .95836746692657, .994897544384, .95328682661057, 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.63256251811981, .63256222009659, .63256192207336, .63256174325943, .6325615644455, .63256138563156, .63256126642227, .63256120681763, .63256108760834, .63256102800369, .63256096839905, .63256096839905, .6325609087944, .63256084918976, .63256084918976, .63256084918976, .63256078958511, .63256078958511, .63256078958511, .63256078958511, .63256078958511, .63256078958511, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047, .63256072998047]) icstats = np.array([ 202, np.nan, -241.25977940638, 4, 490.51955881276, 503.75262960236]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, icstats=icstats, ) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima212_forecast.csv000066400000000000000000000142151224417117700300720ustar00rootroot00000000000000dates, cpi, predict, stderr, conf1, conf2 1950:1, 28.980,,,, 1950:2, 29.150, 29.848,, 1950:3, 29.350, 29.751,, 1950:4, 29.370, 29.925,, 1951:1, 29.540, 29.788,, 1951:2, 29.550, 30.005,, 1951:3, 29.750, 29.872,, 1951:4, 29.840, 30.181,, 1952:1, 29.810, 30.125,, 1952:2, 29.920, 30.154,, 1952:3, 29.980, 30.203,, 1952:4, 30.040, 30.290,, 1953:1, 30.210, 30.306,, 1953:2, 30.220, 30.535,, 1953:3, 30.380, 30.450,, 1953:4, 30.440, 30.721,, 1954:1, 30.480, 30.657,, 1954:2, 30.690, 30.793,, 1954:3, 30.750, 30.955,, 1954:4, 30.940, 31.034,, 1955:1, 30.950, 31.237,, 1955:2, 31.020, 31.194,, 1955:3, 31.120, 31.310,, 1955:4, 31.280, 31.371,, 1956:1, 31.380, 31.588,, 1956:2, 31.580, 31.629,, 1956:3, 31.650, 31.915,, 1956:4, 31.880, 31.882,, 1957:1, 32.280, 32.248,, 1957:2, 32.450, 32.601,, 1957:3, 32.850, 32.794,, 1957:4, 32.900, 33.245,, 1958:1, 33.100, 33.179,, 1958:2, 33.400, 33.502,, 1958:3, 33.700, 33.723,, 1958:4, 34.100, 34.119,, 1959:1, 34.400, 34.481,, 1959:2, 34.900, 34.819,, 1959:3, 35.300, 35.358,, 1959:4, 35.700, 35.737,, 1960:1, 36.300, 36.176,, 1960:2, 36.800, 36.821,, 1960:3, 37.300, 37.301,, 1960:4, 37.900, 37.844,, 1961:1, 38.500, 38.456,, 1961:2, 38.900, 39.082,, 1961:3, 39.400, 39.423,, 1961:4, 39.900, 39.995,, 1962:1, 40.100, 40.436,, 1962:2, 40.600, 40.599,, 1962:3, 40.900, 41.175,, 1962:4, 41.200, 41.354,, 1963:1, 41.500, 41.738,, 1963:2, 41.800, 41.936,, 1963:3, 42.200, 42.311,, 1963:4, 42.700, 42.658,, 1964:1, 43.700, 43.247,, 1964:2, 44.200, 44.354,, 1964:3, 45.600, 44.715,, 1964:4, 46.800, 46.538,, 1965:1, 48.100, 47.464,, 1965:2, 49.300, 49.162,, 1965:3, 51.000, 50.071,, 1965:4, 52.300, 52.276,, 1966:1, 53.000, 53.138,, 1966:2, 54.000, 54.101,, 1966:3, 54.900, 54.875,, 1966:4, 55.800, 55.948,, 1967:1, 56.100, 56.663,, 1967:2, 57.000, 56.919,, 1967:3, 57.900, 57.917,, 1967:4, 58.700, 58.733,, 1968:1, 60.000, 59.585,, 1968:2, 60.800, 60.985,, 1968:3, 61.600, 61.618,, 1968:4, 62.700, 62.545,, 1969:1, 63.900, 63.602,, 1969:2, 65.500, 64.917,, 1969:3, 67.100, 66.596,, 1969:4, 68.500, 68.249,, 1970:1, 70.600, 69.639,, 1970:2, 73.000, 72.023,, 1970:3, 75.200, 74.438,, 1970:4, 78.000, 76.768,, 1971:1, 80.900, 79.775,, 1971:2, 82.600, 82.748,, 1971:3, 84.700, 84.201,, 1971:4, 87.200, 86.591,, 1972:1, 89.100, 88.976,, 1972:2, 91.500, 90.888,, 1972:3, 93.400, 93.401,, 1972:4, 94.400, 95.103,, 1973:1, 95.000, 95.950,, 1973:2, 97.500, 96.342,, 1973:3, 98.100, 99.392,, 1973:4, 97.900, 99.031,, 1974:1, 98.800, 99.216,, 1974:2, 99.800, 99.751,, 1974:3, 100.800, 101.044,, 1974:4, 102.100, 101.727 ,, 1975:1, 103.300, 103.382 ,, 1975:2, 104.100, 104.251 ,, 1975:3, 105.100, 105.233 ,, 1975:4, 105.700, 106.052 ,, 1976:1, 107.000, 106.667 ,, 1976:2, 107.700, 108.081 ,, 1976:3, 108.500, 108.538 ,, 1976:4, 109.900, 109.523 ,, 1977:1, 108.700, 110.907 ,, 1977:2, 109.500, 108.963 ,, 1977:3, 110.200, 110.651 ,, 1977:4, 111.400, 110.524 ,, 1978:1, 112.700, 112.660 ,, 1978:2, 113.800, 113.230 ,, 1978:3, 115.000, 115.051 ,, 1978:4, 116.000, 115.662 ,, 1979:1, 117.200, 117.204 ,, 1979:2, 118.500, 117.975 ,, 1979:3, 119.900, 119.754 ,, 1979:4, 121.200, 120.802 ,, 1980:1, 123.100, 122.467 ,, 1980:2, 124.500, 124.261 ,, 1980:3, 125.400, 125.749 ,, 1980:4, 127.500, 126.429 ,, 1981:1, 128.900, 129.043 ,, 1981:2, 130.500, 129.915 ,, 1981:3, 133.400, 132.075 ,, 1981:4, 134.700, 134.922 ,, 1982:1, 135.100, 136.050 ,, 1982:2, 136.200, 136.239 ,, 1982:3, 137.200, 137.516 ,, 1982:4, 138.300, 138.258 ,, 1983:1, 139.400, 139.559 ,, 1983:2, 140.500, 140.434 ,, 1983:3, 141.700, 141.711 ,, 1983:4, 142.800, 142.753 ,, 1984:1, 143.800, 143.966 ,, 1984:2, 144.500, 144.807 ,, 1984:3, 145.600, 145.519 ,, 1984:4, 146.300, 146.646 ,, 1985:1, 147.200, 147.195 ,, 1985:2, 148.400, 148.223 ,, 1985:3, 149.400, 149.379 ,, 1985:4, 150.500, 150.396 ,, 1986:1, 151.800, 151.507 ,, 1986:2, 152.600, 152.878 ,, 1986:3, 153.500, 153.502 ,, 1986:4, 154.700, 154.540 ,, 1987:1, 156.100, 155.683 ,, 1987:2, 157.000, 157.240 ,, 1987:3, 158.200, 157.903 ,, 1987:4, 159.400, 159.379 ,, 1988:1, 159.900, 160.350 ,, 1988:2, 160.400, 160.856 ,, 1988:3, 161.500, 161.226 ,, 1988:4, 162.000, 162.540 ,, 1989:1, 162.200, 162.682 ,, 1989:2, 163.200, 163.025 ,, 1989:3, 163.900, 164.017 ,, 1989:4, 164.700, 164.677 ,, 1990:1, 165.900, 165.527 ,, 1990:2, 166.700, 166.815 ,, 1990:3, 168.100, 167.488 ,, 1990:4, 169.300, 169.190 ,, 1991:1, 170.900, 170.157 ,, 1991:2, 172.700, 172.157 ,, 1991:3, 173.900, 173.775 ,, 1991:4, 175.600, 175.092 ,, 1992:1, 176.400, 176.843 ,, 1992:2, 177.400, 177.401 ,, 1992:3, 177.600, 178.590 ,, 1992:4, 177.700, 178.310 ,, 1993:1, 179.300, 178.624 ,, 1993:2, 180.000, 180.330 ,, 1993:3, 181.200, 180.791 ,, 1993:4, 182.600, 182.320 ,, 1994:1, 183.200, 183.535 ,, 1994:2, 183.700, 184.132 ,, 1994:3, 184.900, 184.510 ,, 1994:4, 186.300, 185.955 ,, 1995:1, 187.400, 187.256 ,, 1995:2, 189.100, 188.443 ,, 1995:3, 190.800, 190.274 ,, 1995:4, 191.800, 191.988 ,, 1996:1, 193.800, 192.858 ,, 1996:2, 194.700, 195.254 ,, 1996:3, 199.200, 195.589 ,, 1996:4, 199.400, 201.698 ,, 1997:1, 200.700, 199.660 ,, 1997:2, 202.700, 203.065 ,, 1997:3, 201.900, 203.288 ,, 1997:4, 203.574, 203.327 ,, 1998:1, 205.920, 204.543 ,, 1998:2, 207.338, 207.635 ,, 1998:3, 209.133, 208.278 ,, 1998:4, 212.495, 210.909 ,, 1999:1, 213.997, 214.077 ,, 1999:2, 218.610, 215.531 ,, 1999:3, 216.889, 221.137 ,, 1999:4, 212.174, 217.006 ,, 2000:1, 212.671, 212.741 ,, 2000:2, 214.469, 213.240 ,, 2000:3, 216.385, 215.464,,, 2000:4,, 217.291, 0.7691, 215.784, 218.799 2001:1,, 218.197, 1.2775, 215.693, 220.701 2001:2,, 219.100, 1.6382, 215.889, 222.311 2001:3,, 220.002, 2.0322, 216.019, 223.985 2001:4,, 220.902, 2.3838, 216.230, 225.574 2002:1,, 221.801, 2.7544, 216.403, 227.200 2002:2,, 222.699, 3.1082, 216.607, 228.791 2002:3,, 223.595, 3.4721, 216.790, 230.400 2002:4,, 224.490, 3.8288, 216.986, 231.994 2003:1,, 225.384, 4.1908, 217.170, 233.598 2003:2,, 226.276, 4.5494, 217.360, 235.193 2003:3,, 227.168, 4.9105, 217.544, 236.793 2003:4,, 228.059, 5.2698, 217.730, 238.387 2004:1,, 228.948, 5.6300, 217.914, 239.983 2004:2,, 229.837, 5.9888, 218.099, 241.574 2004:3,, 230.724, 6.3475, 218.283, 243.165 2004:4,, 231.611, 6.7049, 218.470, 244.752 2005:1,, 232.497, 7.0614, 218.657, 246.337 2005:2,, 233.382, 7.4165, 218.846, 247.918 2005:3,, 234.266, 7.7704, 219.037, 249.496 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arima_forecast.inp000066400000000000000000000013441224417117700276370ustar00rootroot00000000000000open /home/skipper/statsmodels/statsmodels/statsmodels/datasets/macrodata/macrodata.csv setobs 4 1959:1 --time-series dataset addobs 25 # (1,1,1) arima 1 1 1 ; cpi --nc fcast 2000:1 2015:04 fc111nc fcast 2000:1 2015:04 fc111ncdyn --dynamic arima 1 1 1 ; cpi fcast 2000:1 2015:04 fc111c fcast 2000:1 2015:04 fc111cdyn --dynamic # (2,1,1) arima 2 1 1 ; cpi --nc fcast 2000:1 2015:04 fc211nc fcast 2000:1 2015:04 fc211ncdyn --dynamic arima 2 1 1 ; cpi fcast 2000:1 2015:04 fc211c fcast 2000:1 2015:04 fc211cdyn --dynamic smpl 2000:1 2015:04 store '/home/skipper/statsmodels/statsmodels-skipper/statsmodels/tsa/tests/results/results_arima_forecasts.csv' fc111nc fc111ncdyn fc111c fc111cdyn fc211nc fc211ncdyn fc211c fc211cdyn --omit-obs statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/arma_forecast.inp000066400000000000000000000013261224417117700274660ustar00rootroot00000000000000open \ /home/skipper/statsmodels/statsmodels-git/scikits/statsmodels/tsa/tests/results/y_arma_data.csv setobs 12 1980:01 --time-series dataset addobs 10 # (0,0) arima 1 0 1 ; 1 --nc fcast 2000:11 2001:08 # (4,1) arima 4 0 1 ; 3 --nc fcast 2000:11 2001:08 # (5,0) arima 5 0 0 ; 5 --nc fcast 2000:11 2001:08 # (1,1) c arima 1 0 1 ; 7 fcast 2000:11 2001:08 # (4,1) c arima 4 0 1 ; 9 fcast 2000:11 2001:08 # (5,0) c arima 5 0 0 ; 11 fcast 2000:11 2001:08 smpl 2000:11 2001:08 #store '/home/skipper/statsmodels/statsmodels-git/scikits/statsmodels/tsa/tests/results/results_arma_forecasts.csv' fc11 fe11 fc41 fe41 fc50 fe50 fc11c fe11c fc41c fe41c fc50c fe50c --omit-obs # no way to capture errors as far as I can tell statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/corrgram.do000066400000000000000000000011231224417117700263030ustar00rootroot00000000000000* Stata do file for getting test results insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv", double clear gen qtrdate=yq(year,quarter) format qtrdate %tq tsset qtrdate ac realgdp, gen(acvar) ac realgdp, gen(acvarfft) fft corrgram realgdp matrix Q = r(Q)' svmat Q, names(Q) matrix PAC = r(PAC)' svmat PAC, names(PAC) rename PAC1 PACOLS pac realgdp, yw gen(PACYW) outsheet acvar acvarfft Q1 PACOLS PACYW using "/home/skipper/statsmodels/statsmodels-skipper/scikits/statsmodels/sandbox/tsa/tests/results/results_corrgram.csv", comma replace statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/datamlw_tls.py000066400000000000000000000172131224417117700270370ustar00rootroot00000000000000import numpy as np from numpy import array class Holder(object): pass mlpacf = Holder() mlpacf.comment = 'mlab.parcorr(x, [], 2, nout=3)' mlpacf.name = 'mlpacf' mlpacf.lags1000 = array([[ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlpacf.bounds1000 = array([[ 0.06334064], [-0.06334064]]) mlpacf.lags100 = array([[ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlpacf.pacf100 = array([[ 1. ], [ 0.47253777], [-0.49466966], [-0.02689319], [-0.00122204], [ 0.08419183], [ 0.03220774], [ 0.10404012], [ 0.05304617], [-0.04129564], [-0.04049451], [ 0.11727754], [ 0.11804158], [-0.05864957], [-0.15681802], [ 0.11828684], [ 0.05156002], [ 0.00694629], [ 0.01668964], [ 0.02236851], [-0.0909443 ]]) mlpacf.pacf1000 = array([[ 1.00000000e+00], [ 5.29288262e-01], [ -5.31849027e-01], [ 1.17440051e-02], [ -5.37941905e-02], [ -4.11119348e-02], [ -2.40367432e-02], [ 2.24289891e-02], [ 3.33007235e-02], [ 4.59658302e-02], [ 6.65850553e-03], [ -3.76714278e-02], [ 5.27229738e-02], [ 2.50796558e-02], [ -4.42597301e-02], [ -1.95819186e-02], [ 4.70451394e-02], [ -1.70963705e-03], [ 3.04262524e-04], [ -6.22001614e-03], [ -1.16694989e-02]]) mlpacf.bounds100 = array([[ 0.20306923], [-0.20306923]]) mlacf = Holder() mlacf.comment = 'mlab.autocorr(x, [], 2, nout=3)' mlacf.name = 'mlacf' mlacf.acf1000 = array([[ 1. ], [ 0.5291635 ], [-0.10186759], [-0.35798372], [-0.25894203], [-0.06398397], [ 0.0513664 ], [ 0.08222289], [ 0.08115406], [ 0.07674254], [ 0.04540619], [-0.03024699], [-0.05886634], [-0.01422948], [ 0.01277825], [-0.01013384], [-0.00765693], [ 0.02183677], [ 0.03618889], [ 0.01622553], [-0.02073507]]) mlacf.lags1000 = array([[ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlacf.bounds1000 = array([[ 0.0795181], [-0.0795181]]) mlacf.lags100 = array([[ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlacf.bounds100 = array([[ 0.24319646], [-0.24319646]]) mlacf.acf100 = array([[ 1. ], [ 0.47024791], [-0.1348087 ], [-0.32905777], [-0.18632437], [ 0.06223404], [ 0.16645194], [ 0.12589966], [ 0.04805397], [-0.03785273], [-0.0956997 ], [ 0.00644021], [ 0.17157144], [ 0.12370327], [-0.07597526], [-0.13865131], [ 0.02730275], [ 0.13624193], [ 0.10417949], [ 0.01114516], [-0.09727938]]) mlccf = Holder() mlccf.comment = 'mlab.crosscorr(x[4:], x[:-4], [], 2, nout=3)' mlccf.name = 'mlccf' mlccf.ccf100 = array([[ 0.20745123], [ 0.12351939], [-0.03436893], [-0.14550879], [-0.10570855], [ 0.0108839 ], [ 0.1108941 ], [ 0.14562415], [ 0.02872607], [-0.14976649], [-0.08274954], [ 0.13158485], [ 0.18350343], [ 0.00633845], [-0.10359988], [-0.0416147 ], [ 0.05056298], [ 0.13438945], [ 0.17832125], [ 0.06665153], [-0.19999538], [-0.31700548], [-0.09727956], [ 0.46547234], [ 0.92934645], [ 0.44480271], [-0.09228691], [-0.21627289], [-0.05447732], [ 0.13786254], [ 0.15409039], [ 0.07466298], [-0.01000896], [-0.06744264], [-0.0607185 ], [ 0.04338471], [ 0.12336618], [ 0.07712367], [-0.08739259], [-0.09319212], [ 0.04426167]]) mlccf.lags1000 = array([[-20.], [-19.], [-18.], [-17.], [-16.], [-15.], [-14.], [-13.], [-12.], [-11.], [-10.], [ -9.], [ -8.], [ -7.], [ -6.], [ -5.], [ -4.], [ -3.], [ -2.], [ -1.], [ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlccf.bounds1000 = array([[ 0.06337243], [-0.06337243]]) mlccf.ccf1000 = array([[ 0.02733339], [ 0.04372407], [ 0.01082335], [-0.02755073], [-0.02076039], [ 0.01624263], [ 0.03622844], [ 0.02186092], [-0.00766506], [-0.0101448 ], [ 0.01279167], [-0.01424596], [-0.05893064], [-0.03028013], [ 0.04545462], [ 0.076825 ], [ 0.08124118], [ 0.08231121], [ 0.05142144], [-0.06405412], [-0.25922346], [-0.35806674], [-0.1017256 ], [ 0.5293535 ], [ 0.99891094], [ 0.52941977], [-0.10127572], [-0.35691466], [-0.25943369], [-0.06458511], [ 0.05026194], [ 0.08196501], [ 0.08242852], [ 0.07775845], [ 0.04590431], [-0.03195209], [-0.06162966], [-0.01395345], [ 0.01448736], [-0.00952503], [-0.00927344]]) mlccf.lags100 = array([[-20.], [-19.], [-18.], [-17.], [-16.], [-15.], [-14.], [-13.], [-12.], [-11.], [-10.], [ -9.], [ -8.], [ -7.], [ -6.], [ -5.], [ -4.], [ -3.], [ -2.], [ -1.], [ 0.], [ 1.], [ 2.], [ 3.], [ 4.], [ 5.], [ 6.], [ 7.], [ 8.], [ 9.], [ 10.], [ 11.], [ 12.], [ 13.], [ 14.], [ 15.], [ 16.], [ 17.], [ 18.], [ 19.], [ 20.]]) mlccf.bounds100 = array([[ 0.20412415], [-0.20412415]]) mlywar = Holder() mlywar.comment = "mlab.ar(x100-x100.mean(), 10, 'yw').a.ravel()" mlywar.arcoef100 = array([ 1. , -0.66685531, 0.43519425, -0.00399862, 0.05521524, -0.09366752, 0.01093454, -0.00688404, -0.04739089, 0.00127931, 0.03946846]) mlywar.arcoef1000 = array([ 1. , -0.81230253, 0.55766432, -0.02370962, 0.02688963, 0.01110911, 0.02239171, -0.01891209, -0.00240527, -0.01752532, -0.06348611, 0.0609686 , -0.00717163, -0.0467326 , -0.00122755, 0.06004768, -0.04893984, 0.00575949, 0.00249315, -0.00560358, 0.01248498]) mlywar.name = 'mlywar' statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/make_arma.py000066400000000000000000000032321224417117700264350ustar00rootroot00000000000000import numpy as np from statsmodels.tsa.arima_process import arma_generate_sample from statsmodels.iolib import savetxt np.random.seed(12345) # no constant y_arma11 = arma_generate_sample([1., -.75],[1., .35], nsample=250) y_arma14 = arma_generate_sample([1., -.75],[1., .35, -.75, .1, .35], nsample=250) y_arma41 = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35], nsample=250) y_arma22 = arma_generate_sample([1., -.75, .45],[1., .35, -.9], nsample=250) y_arma50 = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.], nsample=250) y_arma02 = arma_generate_sample([1.], [1., .35, -.75], nsample=250) # constant constant = 4.5 y_arma11c = arma_generate_sample([1., -.75],[1., .35], nsample=250) + constant y_arma14c = arma_generate_sample([1., -.75],[1., .35, -.75, .1, .35], nsample=250) + constant y_arma41c = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35], nsample=250) + constant y_arma22c = arma_generate_sample([1., -.75, .45],[1., .35, -.9], nsample=250) + \ constant y_arma50c = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.], nsample=250) + constant y_arma02c = arma_generate_sample([1.], [1., .35, -.75], nsample=250) + constant savetxt('y_arma_data.csv', np.column_stack((y_arma11, y_arma14, y_arma41, y_arma22,y_arma50, y_arma02,y_arma11c,y_arma14c,y_arma41c,y_arma22c, y_arma50c,y_arma02c)), names=['y_arma11','y_arma14','y_arma41', 'y_arma22','y_arma50', 'y_arma02','y_arma11c','y_arma14c', 'y_arma41c','y_arma22c', 'y_arma50c','y_arma02c'], delimiter=",") statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/resids_css_c.csv000066400000000000000000000472701224417117700273400ustar00rootroot00000000000000uhat1,uhat2,uhat3,uhat4,uhat5,uhat6 NA,NA,NA,NA,NA,0.5003943106 -0.0654604069,1.2630851631,NA,NA,NA,-0.3492146815 -0.6354860870,-0.3745540861,NA,-0.0568351332,NA,-0.4057551504 0.2946689399,1.9320851419,NA,0.4708046227,NA,1.1354217062 0.3069409327,-2.9442407231,1.6018525285,-0.1746956878,NA,0.1874478049 1.1046618847,-0.4539618378,1.6539689425,-0.2014228655,0.6393283864,1.6055802746 -0.3828416754,0.1344632210,-1.3129222016,-0.8717798107,-0.7314782509,1.0374903213 1.4702204261,0.7281252447,-0.5567108484,-2.4315672313,0.4423561929,1.2018542806 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0.6768033834,1.5538048490,-0.9729001401,1.8032642048,1.4887937090,0.8395074269 -0.3741392058,1.1962552335,1.0153800771,0.4098058451,0.5662269008,1.1278183643 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_ar.py000066400000000000000000000226321224417117700267100ustar00rootroot00000000000000import numpy as np import os class ARLagResults(object): """ Results are from R vars::VARselect for sunspot data. Comands run were var_select <- VARselect(SUNACTIVITY, lag.max=16, type=c("const")) """ def __init__(self, type="const"): # order of results is AIC, HQ, SC, FPE if type == "const": ic = [6.311751824815273, 6.321813007357017, 6.336872456958734, 551.009492543133547, 5.647615009344886, 5.662706783157502, 5.685295957560077, 283.614444209634655, 5.634199640773091, 5.654322005856580, 5.684440905060013, 279.835333966272003, 5.639415797766900, 5.664568754121261, 5.702217378125553, 281.299267441683185, 5.646102475432464, 5.676286023057697, 5.721464371862848, 283.187210932784524, 5.628416873122441, 5.663631012018546, 5.716339085624555, 278.223839284844701, 5.584204185137150, 5.624448915304128, 5.684686713710994, 266.191975554941564, 5.541163244029505, 5.586438565467356, 5.654206088675081, 254.979353737235556, 5.483155367013447, 5.533461279722170, 5.608758527730753, 240.611088468544949, 5.489939895595428, 5.545276399575022, 5.628103372384465, 242.251199397394288, 5.496713895370946, 5.557080990621412, 5.647437688231713, 243.900349905069504, 5.503539311586831, 5.568936998108170, 5.666823420519329, 245.573823561989144, 5.510365149977393, 5.580793427769605, 5.686209574981622, 247.259396991133599, 5.513740912139918, 5.589199781203001, 5.702145653215877, 248.099655693709479, 5.515627471325321, 5.596116931659277, 5.716592528473011, 248.572915484827206, 5.515935627515806, 5.601455679120634, 5.729461000735226, 248.654927915301300] self.ic = np.asarray(ic).reshape(4,-1, order='F') class ARResultsOLS(object): """ Results of fitting an AR(9) model to the sunspot data. Results were taken from Stata using the var command. """ def __init__(self, constant=True): self.avobs = 300. if constant: self.params = [ 6.7430535917332, 1.1649421971129, -.40535742259304, -.16653934246587, .14980629416032, -.09462417064796, .00491001240749, .0504665930841, -.08635349190816, .25349103194757] # These are returned by stata VAR, using the (V)AR scale/sigma # we return the true OLS bse by default # the stata residuals can be achived by np.sqrt(np.diag(res1.cov_params())) self.bse_stata = [2.413485601, .0560359041, .0874490762, .0900894414, .0899348339, .0900100797, .0898385666, .0896997939, .0869773089, .0559505756] # The below are grom gretl's ARIMA command with conditional maxium likelihood self.bse_gretl = [2.45474, 0.0569939, 0.0889440, 0.0916295, 0.0914723, 0.0915488, 0.0913744, 0.0912332, 0.0884642, 0.0569071] self.rmse = 15.1279294937327 self.fpe = 236.4827257929261 self.llf = -1235.559128419549 #NOTE: we use a different definition of these ic than Stata # but our order selection results agree with R VARselect # close to Stata for Lutkepohl but we penalize the ic for the trend terms # self.bic = 8.427186938618863 # self.aic = 8.30372752279699 # self.hqic = 8.353136159250697 #NOTE: predictions were taken from gretl, but agree with Stata # test predict #TODO: remove one of the files filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "AROLSConstantPredict.csv") predictresults = np.loadtxt(filename) fv = predictresults[:300,0] pv = predictresults[300:,1] pv_lb = predictresults[300:,2] pv_ub = predictresults[300:,3] pv_se = predictresults[300:,4] del predictresults # cases - in sample predict # n = -1, start = 0 (fitted values) self.FVOLSnneg1start0 = fv # n=-1, start=9 self.FVOLSnneg1start9 = fv # n=-1, start=100 self.FVOLSnneg1start100 = fv[100-9:] # n = 200, start = 0 self.FVOLSn200start0 = fv[:192] # n = 200, start = 200 self.FVOLSn200start200 = np.hstack((fv[200-9:],pv[:101-9])) # n = 200, start = -109 use above self.FVOLSn200startneg109 = self.FVOLSn200start200 # n = 100, start = 325, post-sample forecasting self.FVOLSn100start325 = np.hstack((fv[-1],pv)) # n = 301, start = 9 self.FVOLSn301start9 = np.hstack((fv,pv[:2])) # n = 301, start = 0 self.FVOLSdefault = fv # n = 4, start = 312 self.FVOLSn4start312 = np.hstack((fv[-1],pv[:8])) # n = 15, start = 312 self.FVOLSn15start312 = np.hstack((fv[-1],pv[:19])) elif not constant: self.params = [1.19582389902985, -0.40591818219637, -0.15813796884843, 0.16620079925202, -0.08570200254617, 0.01876298948686, 0.06130211910707, -0.08461507700047, 0.27995084653313] self.bse_stata = [.055645055, .088579237, .0912031179, .0909032462, .0911161784, .0908611473, .0907743174, .0880993504, .0558560278] self.bse_gretl = [0.0564990, 0.0899386, 0.0926027, 0.0922983, 0.0925145, 0.0922555, 0.0921674, 0.0894513, 0.0567132] self.rmse = 15.29712618677774 self.sigma = 226.9820074869752 self.llf = -1239.41217278661 # See note above # self.bic = 8.433861292817106 # self.hqic = 8.367215591385756 # self.aic = 8.322747818577421 self.fpe = 241.0221316614273 filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "AROLSNoConstantPredict.csv") predictresults = np.loadtxt(filename) fv = predictresults[:300,0] pv = predictresults[300:,1] pv_lb = predictresults[300:,2] pv_ub = predictresults[300:,3] pv_se = predictresults[300:,4] del predictresults # cases - in sample predict # n = -1, start = 0 (fitted values) self.FVOLSnneg1start0 = fv # n=-1, start=9 self.FVOLSnneg1start9 = fv # n=-1, start=100 self.FVOLSnneg1start100 = fv[100-9:] # n = 200, start = 0 self.FVOLSn200start0 = fv[:192] # n = 200, start = 200 self.FVOLSn200start200 = np.hstack((fv[200-9:],pv[:101-9])) # n = 200, start = -109 use above self.FVOLSn200startneg109 = self.FVOLSn200start200 # n = 100, start = 325, post-sample forecasting self.FVOLSn100start325 = np.hstack((fv[-1],pv)) # n = 301, start = 9 self.FVOLSn301start9 = np.hstack((fv,pv[:2])) # n = 301, start = 0 self.FVOLSdefault = fv # n = 4, start = 312 self.FVOLSn4start312 = np.hstack((fv[-1],pv[:8])) # n = 15, start = 312 self.FVOLSn15start312 = np.hstack((fv[-1],pv[:19])) class ARResultsMLE(object): """ Results of fitting an AR(9) model to the sunspot data using exact MLE. Results were taken from gretl. """ def __init__(self, constant=True): self.avobs = 300 if constant: # NOTE: Stata's estimated parameters differ from gretl filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "ARMLEConstantPredict.csv") filename2 = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'results_ar_forecast_mle_dynamic.csv') predictresults = np.loadtxt(filename, delimiter=",") year = predictresults[:,0] pv = predictresults[:,1] dynamicpv = np.genfromtxt(filename2, delimiter=",", skip_header=1) # cases - in sample predict # start = 0 (fitted values) self.FVMLEdefault = pv[:309] # start=9 self.FVMLEstart9end308 = pv[9:309] # start=100, end=309 self.FVMLEstart100end308 = pv[100:309] # start = 0, end self.FVMLEstart0end200 = pv[:201] # n = 200, start = 200 self.FVMLEstart200end334 = pv[200:] # start = 309, end=334 post-sample forecasting self.FVMLEstart308end334 = pv[308:] # end = 310, start = 9 self.FVMLEstart9end309 = pv[9:310] # end = 301, start = 0 self.FVMLEstart0end301 = pv[:302] # end = 312, start = 4 self.FVMLEstart4end312 = pv[4:313] # end = 7, start = 2 self.FVMLEstart2end7 = pv[2:8] self.fcdyn = dynamicpv[:,0] self.fcdyn2 = dynamicpv[:,1] self.fcdyn3 = dynamicpv[:,2] self.fcdyn4 = dynamicpv[:,3] else: pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv000066400000000000000000000244321224417117700333020ustar00rootroot00000000000000fcdyn,fcdyn2,fcdyn3,fcdyn4,year,index NA,NA,NA,NA,1700,0 NA,NA,NA,NA,1701,1 NA,NA,NA,NA,1702,2 NA,NA,NA,NA,1703,3 NA,NA,NA,NA,1704,4 NA,NA,NA,NA,1705,5 NA,NA,NA,NA,1706,6 NA,NA,NA,NA,1707,7 NA,NA,NA,NA,1708,8 11.23047,NA,NA,NA,1709,9 13.23731,NA,NA,NA,1710,10 19.66407,NA,NA,NA,1711,11 27.00373,NA,NA,NA,1712,12 33.57858,NA,NA,NA,1713,13 44.90784,NA,NA,NA,1714,14 47.95866,NA,NA,NA,1715,15 45.09347,NA,NA,NA,1716,16 36.50656,NA,NA,NA,1717,17 28.78818,NA,NA,NA,1718,18 23.48711,NA,NA,NA,1719,19 22.53206,NA,NA,NA,1720,20 25.68737,NA,NA,NA,1721,21 31.07822,NA,NA,NA,1722,22 38.54836,NA,NA,NA,1723,23 45.41845,NA,NA,NA,1724,24 49.67139,NA,NA,NA,1725,25 49.37283,NA,NA,NA,1726,26 45.30287,NA,NA,NA,1727,27 39.27502,NA,NA,NA,1728,28 33.76503,NA,NA,NA,1729,29 30.76819,NA,NA,NA,1730,30 31.00231,NA,NA,NA,1731,31 34.36712,NA,NA,NA,1732,32 39.73509,NA,NA,NA,1733,33 45.49821,NA,NA,NA,1734,34 49.73386,NA,NA,NA,1735,35 51.13904,NA,NA,NA,1736,36 49.42444,NA,NA,NA,1737,37 45.44374,NA,NA,NA,1738,38 40.80805,NA,NA,NA,1739,39 37.19994,NA,NA,NA,1740,40 35.84322,NA,NA,NA,1741,41 37.12242,NA,NA,NA,1742,42 40.5707,NA,NA,NA,1743,43 45.0109,NA,NA,NA,1744,44 48.97761,NA,NA,NA,1745,45 51.20812,NA,NA,NA,1746,46 51.08132,NA,NA,NA,1747,47 48.81563,NA,NA,NA,1748,48 45.34622,NA,NA,NA,1749,49 41.96479,NA,NA,NA,1750,50 39.85285,NA,NA,NA,1751,51 39.70252,NA,NA,NA,1752,52 41.51253,NA,NA,NA,1753,53 44.62548,NA,NA,NA,1754,54 47.97401,NA,NA,NA,1755,55 50.45678,NA,NA,NA,1756,56 51.31412,NA,NA,NA,1757,57 50.37143,NA,NA,NA,1758,58 48.07163,NA,NA,NA,1759,59 45.29209,NA,NA,NA,1760,60 43.02564,NA,NA,NA,1761,61 42.04014,NA,NA,NA,1762,62 42.63295,NA,NA,NA,1763,63 44.55469,NA,NA,NA,1764,64 47.11933,NA,NA,NA,1765,65 49.45578,NA,NA,NA,1766,66 50.81053,NA,NA,NA,1767,67 50.79702,NA,NA,NA,1768,68 49.50859,NA,NA,NA,1769,69 47.46335,NA,NA,NA,1770,70 45.40802,NA,NA,NA,1771,71 44.05476,NA,NA,NA,1772,72 43.84306,NA,NA,NA,1773,73 44.80533,NA,NA,NA,1774,74 46.57591,NA,NA,NA,1775,75 48.53369,NA,NA,NA,1776,76 50.02399,NA,NA,NA,1777,77 50.58108,NA,NA,NA,1778,78 50.07622,NA,NA,NA,1779,79 48.74461,NA,NA,NA,1780,80 47.0881,NA,NA,NA,1781,81 45.69285,NA,NA,NA,1782,82 45.02758,NA,NA,NA,1783,83 45.29136,NA,NA,NA,1784,84 46.35975,NA,NA,NA,1785,85 47.84262,NA,NA,NA,1786,86 49.22875,NA,NA,NA,1787,87 50.06412,NA,NA,NA,1788,88 50.10232,NA,NA,NA,1789,89 49.37749,NA,NA,NA,1790,90 48.17905,NA,NA,NA,1791,91 46.94158,NA,NA,NA,1792,92 46.09115,NA,NA,NA,1793,93 45.9021,NA,NA,NA,1794,94 46.41197,NA,NA,NA,1795,95 47.4205,NA,NA,NA,1796,96 48.5688,NA,NA,NA,1797,97 49.46817,NA,NA,NA,1798,98 49.83268,NA,NA,NA,1799,99 49.57053,25.74347,NA,NA,1800,100 48.80555,45.07553,NA,NA,1801,101 47.82483,56.26223,NA,NA,1802,102 46.97392,58.91456,NA,NA,1803,103 46.53746,51.91183,NA,NA,1804,104 46.64617,41.38568,NA,NA,1805,105 47.24034,29.44033,NA,NA,1806,106 48.09929,20.594,NA,NA,1807,107 48.92374,15.65894,NA,NA,1808,108 49.44053,18.22081,NA,NA,1809,109 49.49352,27.80714,NA,NA,1810,110 49.09048,40.67458,NA,NA,1811,111 48.39246,52.10051,NA,NA,1812,112 47.65195,57.96601,NA,NA,1813,113 47.12326,57.18009,NA,NA,1814,114 46.97669,50.58997,NA,NA,1815,115 47.24521,41.19133,NA,NA,1816,116 47.82008,31.97262,NA,NA,1817,117 48.49449,26.00973,NA,NA,1818,118 49.0381,25.3424,NA,NA,1819,119 49.2759,30.23195,NA,NA,1820,120 49.14449,38.94292,NA,NA,1821,121 48.708,48.32435,NA,NA,1822,122 48.13022,55.21302,NA,NA,1823,123 47.61448,57.45876,NA,NA,1824,124 47.33314,54.73303,NA,NA,1825,125 47.371,48.31932,NA,NA,1826,126 47.70098,40.61562,NA,NA,1827,127 48.19876,34.30102,NA,NA,1828,128 48.68947,31.49938,NA,NA,1829,129 49.00915,33.09014,NA,NA,1830,130 49.05949,38.4097,NA,NA,1831,131 48.83779,45.54427,NA,NA,1832,132 48.43316,52.02924,NA,NA,1833,133 47.99199,55.75885,NA,NA,1834,134 47.66576,55.67565,NA,NA,1835,135 47.55974,52.06329,NA,NA,1836,136 47.6997,46.36284,NA,NA,1837,137 48.02718,40.63523,NA,NA,1838,138 48.42331,36.8523,NA,NA,1839,139 48.75186,36.24014,NA,NA,1840,140 48.90604,38.89879,NA,NA,1841,141 48.84306,43.81192,NA,NA,1842,142 48.59544,49.24557,NA,NA,1843,143 48.25633,53.37311,NA,NA,1844,144 47.94516,54.90133,NA,NA,1845,145 47.76602,53.47803,NA,NA,1846,146 47.77337,49.76026,NA,NA,1847,147 47.95608,45.14231,NA,NA,1848,148 48.2444,41.25146,NA,NA,1849,149 48.53638,39.3907,NA,NA,1850,150 48.73379,40.11474,NA,NA,1851,151 48.77512,43.07797,NA,NA,1852,152 48.65446,47.194,NA,NA,1853,153 48.42082,51.03932,NA,NA,1854,154 48.15889,53.35115,NA,NA,1855,155 47.95871,53.44565,NA,NA,1856,156 47.88509,51.41935,NA,NA,1857,157 47.95695,48.07774,NA,NA,1858,158 48.14322,44.62913,NA,NA,1859,159 48.37574,42.25802,NA,NA,1860,160 48.5741,41.72712,NA,NA,1861,161 48.67334,43.14053,NA,NA,1862,162 48.64509,45.94062,NA,NA,1863,163 48.50536,49.1286,NA,NA,1864,164 48.30696,51.62327,NA,NA,1865,165 48.11988,52.62979,NA,NA,1866,166 48.00684,51.8937,NA,NA,1867,167 48.00254,49.76002,NA,NA,1868,168 48.10326,47.0275,NA,NA,1869,169 48.27007,44.65717,NA,NA,1870,170 48.44363,43.44038,NA,NA,1871,171 48.56524,43.74025,NA,NA,1872,172 48.59658,45.3902,NA,NA,1873,173 48.53167,47.77605,NA,NA,1874,174 48.39723,50.06516,NA,NA,1875,175 48.24216,51.49802,NA,NA,1876,176 48.11987,51.64078,NA,NA,1877,177 48.07012,50.51556,NA,NA,1878,178 48.10623,48.57068,NA,NA,1879,179 48.21193,46.50852,NA,NA,1880,180 48.34826,45.03631,NA,NA,1881,181 48.46784,44.62735,NA,NA,1882,182 48.53126,45.37293,NA,NA,1883,183 48.51997,46.97024,NA,NA,1884,184 48.44153,48.84385,NA,NA,1885,185 48.32577,50.35313,NA,NA,1886,186 48.21362,51.01153,NA,NA,1887,187 48.14281,50.64309,NA,NA,1888,188 48.13525,49.42654,NA,NA,1889,189 48.19046,47.81766,NA,NA,1890,190 48.28681,46.382,NA,NA,1891,191 48.38984,45.59878,NA,NA,1892,192 48.46454,45.70354,NA,NA,1893,193 48.48717,46.62066,NA,NA,1894,194 48.45269,48.00411,NA,NA,1895,195 48.37559,49.36743,NA,NA,1896,196 48.284,50.25469,NA,NA,1897,197 48.20957,50.39271,NA,NA,1898,198 48.1766,49.77424,NA,NA,1899,199 48.19421,48.64739,NA,NA,1900,200 48.25402,47.41943,NA,NA,1901,201 48.33384,46.51162,NA,NA,1902,202 48.40579,46.21651,NA,NA,1903,203 48.44604,46.6056,NA,NA,1904,204 48.4426,47.51603,NA,NA,1905,205 48.3988,48.61704,NA,NA,1906,206 48.33142,49.52975,NA,NA,1907,207 48.26437,49.95725,NA,NA,1908,208 48.22027,49.78077,NA,NA,1909,209 48.21289,49.09095,NA,NA,1910,210 48.24294,48.14704,NA,NA,1911,211 48.29848,47.28117,NA,NA,1912,212 48.35955,46.78283,NA,NA,1913,213 48.4053,46.8033,NA,NA,1914,214 48.42108,47.31141,NA,NA,1915,215 48.40305,48.11303,NA,NA,1916,216 48.35898,48.92453,NA,NA,1917,217 48.30501,49.4728,NA,NA,1918,218 48.25985,49.58682,NA,NA,1919,219 48.23831,49.25039,NA,NA,1920,220 48.24652,48.59994,NA,NA,1921,221 48.28025,47.87108,NA,NA,1922,222 48.3269,47.31424,NA,NA,1923,223 48.37012,47.10955,NA,NA,1924,224 48.3955,47.30951,NA,NA,1925,225 48.39538,47.82753,NA,NA,1926,226 48.37106,48.474,NA,NA,1927,227 48.33194,49.02525,NA,NA,1928,228 48.29195,49.30067,NA,NA,1929,229 48.26462,49.22147,NA,NA,1930,230 48.25856,48.83232,NA,NA,1931,231 48.27478,48.28018,NA,NA,1932,232 48.30673,47.75973,NA,NA,1933,233 48.34286,47.44541,NA,NA,1934,234 48.3708,47.43357,NA,NA,1935,235 48.38153,47.7138,NA,NA,1936,236 48.37229,48.17769,NA,NA,1937,237 48.34718,48.66019,NA,NA,1938,238 48.31544,48.99806,NA,NA,1939,239 48.28812,49.0848,NA,NA,1940,240 48.27422,48.90384,NA,NA,1941,241 48.27777,48.52964,NA,NA,1942,242 48.29672,48.09819,NA,NA,1943,243 48.32394,47.75808,NA,NA,1944,244 48.34984,47.61982,NA,NA,1945,245 48.36576,47.7204,NA,NA,1946,246 48.36682,48.01445,NA,NA,1947,247 48.3534,48.39355,NA,NA,1948,248 48.33074,48.72594,NA,NA,1949,249 48.30694,48.90203,NA,NA,1950,250 48.29008,48.87032,NA,NA,1951,251 48.28551,48.6519,NA,NA,1952,252 48.29417,48.32977,NA,NA,1953,253 48.3125,48.01783,NA,NA,1954,254 48.33385,47.82096,NA,NA,1955,255 48.35086,47.80007,NA,NA,1956,256 48.35802,47.95373,NA,NA,1957,257 48.3534,48.22171,NA,NA,1958,258 48.33914,48.50819,NA,NA,1959,259 48.32052,48.71578,NA,NA,1960,260 48.30403,48.77848,NA,NA,1961,261 48.29514,48.68238,NA,NA,1962,262 48.29647,48.46779,NA,NA,1963,263 48.30708,48.21301,NA,NA,1964,264 48.32293,48.00602,NA,NA,1965,265 48.33842,47.9144,NA,NA,1966,266 48.34835,47.96348,NA,NA,1967,267 48.34964,48.12991,NA,NA,1968,268 48.34229,48.35188,NA,NA,1969,269 48.3292,48.55192,NA,NA,1970,270 48.31506,48.66372,NA,NA,1971,271 48.3047,48.65398,NA,NA,1972,272 48.30142,48.53202,NA,NA,1973,273 48.30599,48.34455,NA,NA,1974,274 48.31648,48.15806,NA,NA,1975,275 48.32907,48.03546,NA,NA,1976,276 48.3394,48.01503,NA,NA,1977,277 48.34411,48.09869,NA,NA,1978,278 48.34188,48.25319,NA,NA,1979,279 48.33381,48.42301,NA,NA,1980,280 48.3229,48.55017,NA,NA,1981,281 48.31298,48.59394,NA,NA,1982,282 48.30734,48.54369,NA,NA,1983,283 48.30768,48.42102,NA,NA,1984,284 48.31358,48.27089,NA,NA,1985,285 48.32279,48.14532,NA,NA,1986,286 48.33204,48.08548,NA,NA,1987,287 48.3382,48.10837,NA,NA,1988,288 48.33936,48.20225,NA,NA,1989,289 48.33537,48.332,NA,NA,1990,290 48.32782,48.45215,NA,NA,1991,291 48.31944,48.52268,NA,NA,1992,292 48.31309,48.52226,NA,NA,1993,293 48.31082,48.45455,NA,NA,1994,294 48.31319,48.3457,NA,NA,1995,295 48.31918,48.23446,NA,NA,1996,296 48.32658,48.15849,NA,NA,1997,297 48.33284,48.1417,NA,NA,1998,298 48.3359,48.18687,NA,NA,1999,299 48.33488,48.27574,NA,NA,2000,300 48.33033,48.37623,NA,NA,2001,301 48.32396,48.4539,NA,NA,2002,302 48.318,48.48368,NA,NA,2003,303 48.31445,48.45791,NA,NA,2004,304 48.31438,48.38801,NA,NA,2005,305 48.31765,48.29974,NA,NA,2006,306 48.32299,48.22377,NA,NA,2007,307 48.3285,48.18514,23.47364,NA,2008,308 48.33231,48.19506,54.73861,30.85857,2009,309 48.33322,48.24781,80.91882,61.33544,2010,310 48.33107,48.32352,96.89133,87.02467,2011,311 48.32673,48.39553,94.15365,91.32123,2012,312 48.32178,48.43977,77.70128,79.9216,2013,313 48.3179,48.44268,56.33983,60.7995,2014,314 48.31637,48.40531,36.52842,40.37485,2015,315 48.31757,48.34226,21.34622,24.52903,2016,316 48.32098,48.27606,15.74889,13.53051,2017,317 48.32533,48.22919,23.33969,14.44262,2018,318 48.32911,48.21651,40.86964,26.82511,2019,319 48.33107,48.24065,61.94895,47.5587,2020,320 48.33065,48.29165,78.24312,67.30043,2021,321 48.3281,48.35101,84.08843,78.69045,2022,322 48.32438,48.3983,78.31338,78.43595,2023,323 48.32081,48.41819,64.1858,67.94477,2024,324 48.31858,48.4053,46.7631,52.09101,2025,325 48.31839,48.36561,31.46734,35.70807,2026,326 48.32018,48.31381,23.18735,24.0176,2027,327 48.32327,48.26797,24.45816,20.48491,2028,328 48.32655,48.24327,34.63706,26.46583,2029,329 48.3289,48.247,49.85213,39.67762,2030,330 48.32957,48.27651,64.55238,55.23003,2031,331 48.32843,48.3206,73.5886,67.49796,2032,332 48.32594,48.36368,74.20407,72.37827,2033,333 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arima.py000066400000000000000000000570621224417117700274040ustar00rootroot00000000000000import os import numpy as np from numpy import genfromtxt cur_dir = os.path.dirname(os.path.abspath(__file__)) forecast_results = genfromtxt(open(cur_dir+"/results_arima_forecasts.csv", "rb"), names=True, delimiter=",", dtype=float) #NOTE: # stata gives no indication of no convergence for 112 CSS but gives a # different answer than x12arima, gretl simply fails to converge # redid stata with starting parameters from x12arima # it looks like stata uses a different formula for the CSS likelihood # they appear to be using a larger sample than R, gretl, or us. # CSS results are therefore taken from R and gretl class ARIMA111(object): def __init__(self, method="mle"): self.k_ar = 1 self.k_diff = 1 self.k_ma = 1 if method == "mle": # from stata from arima111_results import results # unpack stata results self.__dict__.update(results) self.resid = self.resid[1:] self.params = self.params[:-1] self.sigma2 = self.sigma**2 self.aic = self.icstats[4] self.bic = self.icstats[5] self.fittedvalues = self.xb[1:] # no idea why this initial value self.linear = self.y[1:] #their bse are OPG #self.bse = np.diag(self.cov_params) ** .5 # from gretl self.arroots = [1.0640 + 0j] self.maroots = [1.2971 + 0j] self.hqic = 496.8653 self.aic_gretl = 491.5112 self.bic_gretl = 504.7442 #self.bse = [.205811, .0457010, .0897565] self.tvalues = [4.280, 20.57, -8.590] self.pvalues = [1.87e-5, 5.53e-94, 8.73e-18] self.cov_params = [[0.0423583, -0.00167449, 0.00262911], [-0.00167449, 0.00208858, -0.0035068], [0.00262911, -0.0035068, 0.00805622]] self.bse = np.diag(np.sqrt(self.cov_params)) # from stata #forecast = genfromtxt(open(cur_dir+"/arima111_forecasts.csv"), # delimiter=",", skip_header=1, usecols=[1,2,3,4,5]) #self.forecast = forecast[203:,1] #self.fcerr = forecast[203:,2] #self.fc_conf_int = forecast[203:,3:] # from gretl self.forecast = forecast_results['fc111c'][-25:] self.forecasterr = forecast_results['fc111cse'][-25:] self.forecast_dyn = forecast_results['fc111cdyn'] self.forecasterr_dyn = forecast_results['fc111cdynse'] else: #from arima111_css_results import results # coefs, bse, tvalues, and pvalues taken from R because gretl # uses mean not constant self.bse = [0.21583833, 0.03844939, 0.08566390] self.params = [1.0087257, 0.9455393, -0.8021834] self.sigma2 = 0.6355913 self.tvalues = [4.673524, 24.591788, -9.364311] self.pvalues = [5.464467e-06, 0, 0] self.cov_params = np.array([ [ 0.046586183, 0.002331183, -0.004647432 ], [ 0.002331183, 0.001478356, -0.002726201 ], [-0.004647432, -0.002726201, 0.007338304 ]]) # from gretl self.llf = -239.6601 self.aic = 487.3202 self.bic = 500.5334 self.hqic = 492.6669 self.arroots = [1.0578 + 0j] self.maroots = [1.2473 + 0j] #cov_params = np.array([[0.00369569, -0.00271777, 0.00269806], # [0, 0.00209573, -0.00224559], # [0, 0, 0.00342769]]) #self.cov_params = cov_params + cov_params.T - \ # np.diag(np.diag(cov_params)) #self.bse = np.diag(np.sqrt(self.cov_params)) self.resid = [-0.015830, -0.236884, -0.093946, -0.281152, -0.089983, -0.226336, -0.351666, -0.198703, -0.258418, -0.259026, -0.149513, -0.325703, -0.165703, -0.279229, -0.295711, -0.120018, -0.289870, -0.154243, -0.348403, -0.273902, -0.240894, -0.182791, -0.252930, -0.152441, -0.296412, -0.128941, 0.024068, -0.243972, -0.011436, -0.392437, -0.217022, -0.118190, -0.133489, -0.045755, -0.169953, 0.025010, -0.107754, -0.119661, 0.070794, -0.065586, -0.080390, 0.007741, -0.016138, -0.235283, -0.121907, -0.125546, -0.428463, -0.087713, -0.298131, -0.277757, -0.261422, -0.248326, -0.137826, -0.043771, 0.437100, -0.150051, 0.751890, 0.424180, 0.450514, 0.277089, 0.732583, 0.225086, -0.403648, -0.040509, -0.132975, -0.112572, -0.696214, 0.003079, -0.003491, -0.108758, 0.401383, -0.162302, -0.141547, 0.175094, 0.245346, 0.607134, 0.519045, 0.248419, 0.920521, 1.097613, 0.755983, 1.271156, 1.216969, -0.121014, 0.340712, 0.732750, 0.068915, 0.603912, 0.060157, -0.803110, -1.044392, 1.040311, -0.984497, -1.611668, -0.258198, -0.112970, -0.091071, 0.226487, 0.097475, -0.311423, -0.061105, -0.449488, 0.317277, -0.329734, -0.181248, 0.443263, -2.223262, 0.096836, -0.033782, 0.456032, 0.476052, 0.197564, 0.263362, 0.021578, 0.216803, 0.284249, 0.343786, 0.196981, 0.773819, 0.169070, -0.343097, 0.918962, 0.096363, 0.298610, 1.571685, -0.236620, -1.073822, -0.194208, -0.250742, -0.101530, -0.076437, -0.056319, 0.059811, -0.041620, -0.128404, -0.403446, 0.059654, -0.347208, -0.095257, 0.217668, -0.015057, 0.087431, 0.275062, -0.263580, -0.122746, 0.195629, 0.367272, -0.184188, 0.146368, 0.127777, -0.587128, -0.498538, 0.172490, -0.456741, -0.694000, 0.199392, -0.140634, -0.029636, 0.364818, -0.097080, 0.510745, 0.230842, 0.595504, 0.709721, 0.012218, 0.520223, -0.445174, -0.168341, -0.935465, -0.894203, 0.733417, -0.279707, 0.258861, 0.417969, -0.443542, -0.477955, 0.288992, 0.442126, 0.075826, 0.665759, 0.571509, -0.204055, 0.835901, -0.375693, 3.292828, -1.469299, -0.122206, 0.617909, -2.250468, 0.570871, 1.166013, 0.079873, 0.463372, 1.981434, -0.142869, 3.023376, -3.713161, -6.120150, -0.007487, 1.267027, 1.176930] self.linear = [29.3658, 29.6069, 29.6339, 29.8312, 29.8400, 30.0663, 30.1617, 30.1187, 30.2384, 30.2990, 30.3595, 30.5457, 30.5457, 30.7192, 30.7757, 30.8100, 31.0399, 31.0942, 31.2984, 31.2939, 31.3609, 31.4628, 31.6329, 31.7324, 31.9464, 32.0089, 32.2559, 32.6940, 32.8614, 33.2924, 33.3170, 33.5182, 33.8335, 34.1458, 34.5700, 34.8750, 35.4078, 35.8197, 36.2292, 36.8656, 37.3804, 37.8923, 38.5161, 39.1353, 39.5219, 40.0255, 40.5285, 40.6877, 41.1981, 41.4778, 41.7614, 42.0483, 42.3378, 42.7438, 43.2629, 44.3501, 44.8481, 46.3758, 47.6495, 49.0229, 50.2674, 52.0749, 53.4036, 54.0405, 55.0330, 55.9126, 56.7962, 56.9969, 57.9035, 58.8088, 59.5986, 60.9623, 61.7415, 62.5249, 63.6547, 64.8929, 66.5810, 68.2516, 69.6795, 71.9024, 74.4440, 76.7288, 79.6830, 82.7210, 84.3593, 86.4672, 89.0311, 90.8961, 93.3398, 95.2031, 96.0444, 96.4597, 99.0845, 99.5117, 99.0582, 99.9130, 100.8911, 101.8735, 103.2025, 104.4114, 105.1611, 106.1495, 106.6827, 108.0297, 108.6812, 109.4567, 110.9233, 109.4032, 110.2338, 110.9440, 112.2239, 113.6024, 114.7366, 115.9784, 116.9832, 118.2158, 119.5562, 121.0030, 122.3262, 124.3309, 125.7431, 126.5810, 128.8036, 130.2014, 131.8283, 134.9366, 136.1738, 136.3942, 137.4507, 138.4015, 139.4764, 140.5563, 141.6402, 142.8416, 143.9284, 144.9034, 145.5403, 146.6472, 147.2953, 148.1823, 149.4151, 150.4126, 151.5249, 152.8636, 153.6227, 154.5044, 155.7327, 157.1842, 158.0536, 159.2722, 160.4871, 160.8985, 161.3275, 162.4567, 162.8940, 163.0006, 164.0406, 164.7296, 165.5352, 166.7971, 167.5893, 169.0692, 170.3045, 171.9903, 173.8878, 175.0798, 176.8452, 177.5683, 178.5355, 178.5942, 178.5666, 180.2797, 180.9411, 182.1820, 183.6435, 184.1780, 184.6110, 185.8579, 187.3242, 188.4342, 190.2285, 192.0041, 192.9641, 195.0757, 195.9072, 200.8693, 200.8222, 202.0821, 204.1505, 203.0031, 204.7540, 207.2581, 208.6696, 210.5136, 214.1399, 215.5866, 220.6022, 218.2942, 212.6785, 213.2020, 215.2081] # forecasting isn't any different for css # except you lose the first p+1 observations for in-sample # these results are from x-12 arima self.forecast = forecast_results['fc111c_css'][-25:] self.forecasterr = forecast_results['fc111cse_css'][-25:] self.forecast_dyn = forecast_results['fc111cdyn_css'] self.forecasterr_dyn = forecast_results['fc111cdynse_css'] class ARIMA211(object): def __init__(self, method="mle"): if method == 'mle': # from stata from arima111_results import results self.__dict__.update(results) self.resid = self.resid[1:] self.params = self.params[:-1] self.sigma2 = self.sigma**2 self.aic = self.icstats[4] self.bic = self.icstats[5] self.fittedvalues = self.xb[1:] # no idea why this initial value self.linear = self.y[1:] self.k_diff = 1 #their bse are OPG #self.bse = np.diag(self.cov_params) ** .5 # from gretl self.arroots = [1.027 + 0j, 5.7255+ 0j] self.maroots = [1.1442+0j] self.hqic = 496.5314 self.aic_gretl = 489.8388 self.bic_gretl = 506.3801 #self.bse = [0.248376, 0.102617, 0.0871312, 0.0696346] self.tvalues = [3.468, 11.14, -1.941, 12.55] self.pvalues = [.0005, 8.14e-29, .0522, 3.91e-36] cov_params = np.array([ [0.0616906, -0.00250187, 0.0010129, 0.00260485], [0, 0.0105302, -0.00867819, -0.00525614], [ 0 ,0, 0.00759185, 0.00361962], [ 0 ,0,0, 0.00484898]]) self.cov_params = cov_params + cov_params.T - \ np.diag(np.diag(cov_params)) self.bse = np.diag(np.sqrt(self.cov_params)) self.forecast = forecast_results['fc211c'][-25:] self.forecasterr = forecast_results['fc211cse'][-25:] self.forecast_dyn = forecast_results['fc211cdyn'][-25:] self.forecasterr_dyn = forecast_results['fc211cdynse'][-25:] else: from arima211_css_results import results self.__dict__.update(results) self.resid = self.resid[1:] self.params = self.params[:-1] self.sigma2 = self.sigma**2 self.aic = self.icstats[4] self.bic = self.icstats[5] self.fittedvalues = self.xb[1:] # no idea why this initial value self.linear = self.y[1:] self.k_diff = 1 # from gretl self.arroots = [1.0229 + 0j, 4.4501 + 0j] self.maroots = [1.0604 + 0j] self.hqic = 489.3225 self.aic_gretl = 482.6486 self.bic_gretl = 499.1402 self.tvalues = [.7206, 22.54, -19.04] self.pvalues = [.4712, 1.52e-112, 2.19e-10, 8.00e-81] cov_parmas = np.array([ [8.20496e-04, -0.0011992, 4.57078e-04, 0.00109907], [0, 0.00284432, -0.0016752, -0.00220223], [0, 0, 0.00119783, 0.00108868], [0, 0, 0, 0.00245324]]) self.cov_params = cov_params + cov_params.T - \ np.diag(np.diag(cov_params)) self.bse = np.diag(np.sqrt(self.cov_params)) # forecasting isn't any different for css # except you lose the first p+1 observations for in-sample self.forecast = forecast_results['fc111c_css'][-25:] self.forecasterr = forecast_results['fc111cse_css'][-25:] self.forecast_dyn = forecast_results['fc111cdyn_css'] self.forecasterr_dyn = forecast_results['fc111cdynse_css'] class ARIMA112(object): def __init__(self, method="mle"): self.df_model = 3 self.k = 5 self.k_ar = 1 self.k_ma = 2 self.k_exog = 1 self.k_diff = 1 if method == "mle": from arima112_results import results # from gretl self.arroots = [1.0324 + 0j] self.maroots = [1.1447 + 0j, -4.8613+0j] self.hqic = 495.5852 self.aic_gretl = 488.8925 self.bic_gretl = 505.4338 self.tvalues = [3.454, 31.10, -7.994, -2.127] self.pvalues = [0.0006, 2.1e-212, 1.31e-15, .0334] cov_params = np.array([ [0.0620096, -0.00172172, 0.00181301, 0.00103271], [0, 9.69682e-04, -9.70767e-04, -8.99814e-04], [0, 0, 0.00698068, -0.00443871], [0, 0, 0, 0.00713662]]) self.cov_params = cov_params + cov_params.T - \ np.diag(np.diag(cov_params)) self.bse = np.diag(np.sqrt(self.cov_params)) # from gretl self.forecast = forecast_results['fc112c'][-25:] self.forecasterr = forecast_results['fc112cse'][-25:] self.forecast_dyn = forecast_results['fc112cdyn'] self.forecasterr_dyn = forecast_results['fc112cdynse'] # unpack stata results self.__dict__ = results self.resid = self.resid[1:] self.params = self.params[:-1] self.sigma2 = self.sigma**2 self.aic = self.icstats[4] self.bic = self.icstats[5] self.fittedvalues = self.xb[1:] # no idea why this initial value self.linear = self.y[1:] #their bse are OPG #self.bse = np.diag(self.cov_params) ** .5 else: #NOTE: this looks like a "hard" problem #unable to replicate stata's results even with their starting #values # unable to replicate x12 results in stata using their starting # values. x-12 has better likelihood and we can replicate so # use their results #from arima112_css_results import results # taken from R using X12-arima values as init params self.bse = [0.07727588, 0.09356658, 0.10503567, 0.07727970] self.params = [ 0.9053219, -0.692412, 1.0736728, 0.1720008] self.sigma2 = 0.6820727 self.tvalues = [11.715452, -7.400215, 10.221983, 2.225692] self.pvalues = [0, 3.791634e-12, 0, 2.716275e-02] self.cov_params = np.array([ [ 0.0059715623, 0.001327824, -0.001592129, -0.0008061933], [ 0.0013278238, 0.008754705, -0.008024634, -0.0045933413], [-0.0015921293,-0.008024634, 0.011032492, 0.0072509641], [-0.0008061933,-0.004593341, 0.007250964, 0.0059721516]]) # from x12arima via gretl # gretl did not converge for this model... self.llf = -246.7534 self.nobs = 202 #self.params = [.905322, -.692425, 1.07366, 0.172024] #self.sigma2 = 0.682072819129 #self.bse = [0.0756430, 0.118440, 0.140691, 0.105266] self.resid = resid = [-1.214477, -0.069772, -1.064510, -0.249555, -0.874206, -0.322177, -1.003579, -0.310040, -0.890506, -0.421211, -0.715219, -0.564119, -0.636560, -0.580912, -0.717440, -0.424277, -0.747835, -0.424739, -0.805958, -0.516877, -0.690127, -0.473072, -0.694766, -0.435627, -0.736474, -0.388060, -0.429596, -0.557224, -0.342308, -0.741842, -0.442199, -0.491319, -0.420884, -0.388057, -0.466176, -0.257193, -0.429646, -0.349683, -0.205870, -0.335547, -0.290300, -0.216572, -0.234272, -0.427951, -0.255446, -0.338097, -0.579033, -0.213860, -0.556756, -0.389907, -0.510060, -0.409759, -0.396778, -0.258727, 0.160063, -0.467109, 0.688004, -0.021120, 0.503044, 0.031500, 0.878365, -0.003548, -0.079327, 0.038289, 0.032773, -0.050780, -0.560124, 0.185655, -0.111981, -0.020714, 0.363254, -0.218484, -0.006161, 0.165950, 0.252365, 0.599220, 0.488921, 0.347677, 1.079814, 1.102745, 0.959907, 1.570836, 1.454934, 0.343521, 1.125826, 1.154059, 0.666141, 1.269685, 0.551831, -0.027476, -0.305192, 1.715665, -0.990662, -0.548239, -0.011636, 0.197796, -0.050128, 0.480031, 0.061198, -0.049562, 0.064436, -0.300420, 0.494730, -0.411527, 0.109242, 0.375255, -2.184482, 0.717733, -0.673064, 0.751681, -0.092543, 0.438016, -0.024881, 0.250085, 0.096010, 0.452618, 0.265491, 0.374299, 0.820424, 0.238176, -0.059646, 1.214061, 0.028679, 0.797567, 1.614444, -0.094717, -0.408067, 0.299198, -0.021561, 0.231915, 0.084190, 0.199192, 0.201132, 0.148509, 0.035431, -0.203352, 0.264744, -0.319785, 0.150305, 0.184628, 0.074637, 0.148340, 0.357372, -0.241250, 0.119294, 0.204413, 0.458730, -0.190477, 0.416587, 0.084216, -0.363361, -0.310339, 0.309728, -0.549677, -0.449092, 0.183025, -0.259015, -0.000883, 0.267255, -0.188068, 0.577697, 0.049310, 0.746401, 0.565829, 0.178270, 0.709983, -0.348012, 0.273262, -0.873288, -0.403100, 0.720072, -0.428076, 0.488246, 0.248152, -0.313214, -0.323137, 0.414843, 0.308909, 0.134180, 0.732275, 0.535639, -0.056128, 1.128355, -0.449151, 3.879123, -2.303860, 1.712549, -0.074407, -1.162052, 0.848316, 1.262031, 0.009320, 1.017563, 1.978597, -0.001637, 3.782223, -4.119563, -3.666488, 0.345244, 0.869998, 0.635321] self.linear = [30.5645, 29.4398, 30.6045, 29.7996, 30.6242, 30.1622, 30.8136, 30.2300, 30.8705, 30.4612, 30.9252, 30.7841, 31.0166, 31.0209, 31.1974, 31.1143, 31.4978, 31.3647, 31.7560, 31.5369, 31.8101, 31.7531, 32.0748, 32.0156, 32.3865, 32.2681, 32.7096, 33.0072, 33.1923, 33.6418, 33.5422, 33.8913, 34.1209, 34.4881, 34.8662, 35.1572, 35.7296, 36.0497, 36.5059, 37.1355, 37.5903, 38.1166, 38.7343, 39.3280, 39.6554, 40.2381, 40.6790, 40.8139, 41.4568, 41.5899, 42.0101, 42.2098, 42.5968, 42.9587, 43.5399, 44.6671, 44.9120, 46.8211, 47.5970, 49.2685, 50.1216, 52.3035, 53.0793, 53.9617, 54.8672, 55.8508, 56.6601, 56.8143, 58.0120, 58.7207, 59.6367, 61.0185, 61.6062, 62.5340, 63.6476, 64.9008, 66.6111, 68.1523, 69.5202, 71.8973, 74.2401, 76.4292, 79.4451, 82.2565, 83.5742, 86.0459, 88.4339, 90.2303, 92.8482, 94.4275, 95.3052, 95.7843, 99.0907, 98.4482, 98.8116, 99.6022, 100.8501, 101.6200, 103.2388, 104.1496, 105.0356, 106.0004, 106.5053, 108.1115, 108.3908, 109.5247, 110.8845, 108.7823, 110.8731, 110.6483, 112.7925, 113.3620, 115.0249, 115.7499, 117.1040, 118.0474, 119.6345, 120.8257, 122.2796, 124.2618, 125.4596, 126.2859, 128.8713, 129.7024, 131.7856, 134.7947, 135.5081, 135.9008, 137.2216, 138.0681, 139.3158, 140.3008, 141.4989, 142.6515, 143.7646, 144.7034, 145.3353, 146.6198, 147.0497, 148.2154, 149.3254, 150.3517, 151.4426, 152.8413, 153.3807, 154.4956, 155.6413, 157.1905, 157.7834, 159.3158, 160.2634, 160.7103, 161.1903, 162.5497, 162.6491, 163.0170, 164.1590, 164.7009, 165.6327, 166.8881, 167.5223, 169.2507, 170.1536, 172.1342, 173.7217, 174.8900, 176.7480, 177.1267, 178.4733, 178.1031, 178.5799, 180.4281, 180.7118, 182.3518, 183.5132, 184.0231, 184.4852, 185.9911, 187.2658, 188.3677, 190.2644, 191.8561, 192.6716, 195.1492, 195.3209, 201.7039, 198.9875, 202.7744, 203.0621, 202.7257, 204.6580, 207.3287, 208.1154, 210.5164, 213.9986, 214.8278, 221.0086, 215.8405, 212.3258, 213.5990, 215.7497] self.yr = [] self.arroots = [-1.4442 + 0j] self.maroots = [-1.1394 + 0j, -5.1019+0j] self.hqic = 510.1902 self.aic = 503.5069 self.bic = 520.0234 #self.tvalues = [11.97, -5.846, 7.631, 1.634] #self.pvalues = [5.21e-33, 5.03e-9, 2.32e-14, .1022] #cov_params = np.array([ # [0.0620096, -0.00172172, 0.00181301, 0.00103271], # [0, 9.69682e-04, -9.70767e-04, -8.99814e-04], # [0, 0, 0.00698068, -0.00443871], # [0, 0, 0, 0.00713662]]) #self.cov_params = cov_params + cov_params.T - \ # np.diag(np.diag(cov_params)) #self.bse = np.diag(np.sqrt(self.cov_params)) self.forecast = forecast_results['fc112c_css'][-25:] self.forecasterr = forecast_results['fc112cse_css'][-25:] self.forecast_dyn = forecast_results['fc112cdyn_css'] self.forecasterr_dyn = forecast_results['fc112cdynse_css'] statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv000066400000000000000000000104221224417117700334770ustar00rootroot00000000000000orig_index,year,quarter,y,predict,std_err,lower,upper 0,1950,1,3.198371,,,, 1,1950,2,0.951462,,,, 2,1950,3,2.044147,1.637865,,, 3,1950,4,5.387144,3.026386,,, 4,1951,1,4.278298,4.758931,,, 5,1951,2,2.608896,2.744609,,, 6,1951,3,2.2891,2.190297,,, 7,1951,4,0.542828,2.572409,,, 8,1952,1,1.818848,1.464508,,, 9,1952,2,1.141287,2.995619,,, 10,1952,3,1.390376,1.964605,,, 11,1952,4,3.519065,2.452368,,, 12,1953,1,2.687199,3.739905,,, 13,1953,2,2.72044,2.352415,,, 14,1953,3,3.640325,2.782552,,, 15,1953,4,3.388688,3.337867,,, 16,1954,1,1.285691,2.807582,,, 17,1954,2,1.193271,1.532043,,, 18,1954,3,4.125089,2.281349,,, 19,1954,4,1.826838,4.190755,,, 20,1955,1,1.211262,1.496953,,, 21,1955,2,1.494882,2.104899,,, 22,1955,3,2.407813,2.42813,,, 23,1955,4,3.476123,2.908037,,, 24,1956,1,4.683225,3.270582,,, 25,1956,2,3.209492,3.687261,,, 26,1956,3,3.045272,2.260033,,, 27,1956,4,3.189591,2.771537,,, 28,1957,1,3.223306,2.86421,,, 29,1957,2,2.160318,2.825037,,, 30,1957,3,1.084407,2.10187,,, 31,1957,4,0.698262,1.797678,,, 32,1958,1,1.121673,1.916263,,, 33,1958,2,2.538408,2.308136,,, 34,1958,3,3.671068,3.072406,,, 35,1958,4,3.45499,3.284601,,, 36,1959,1,2.136544,2.743738,,, 37,1959,2,3.821324,1.982434,,, 38,1959,3,4.975221,3.611985,,, 39,1959,4,3.157348,3.662564,,, 40,1960,1,1.30047,2.059906,,, 41,1960,2,1.802575,1.56944,,, 42,1960,3,3.772872,2.555923,,, 43,1960,4,3.770009,3.604042,,, 44,1961,1,3.359749,2.847162,,, 45,1961,2,2.440787,2.64582,,, 46,1961,3,2.682876,2.180379,,, 47,1961,4,4.210082,2.684437,,, 48,1962,1,3.215831,3.570286,,, 49,1962,2,2.501961,2.311421,,, 50,1962,3,2.597237,2.281679,,, 51,1962,4,2.865064,2.576134,,, 52,1963,1,1.27064,2.688936,,, 53,1963,2,-0.23313,1.51842,,, 54,1963,3,0.276246,1.147103,,, 55,1963,4,2.197587,2.011094,,, 56,1964,1,3.4989,3.036608,,, 57,1964,2,2.577512,3.170317,,, 58,1964,3,3.582812,2.110702,,, 59,1964,4,3.0313,3.186722,,, 60,1965,1,2.467222,2.374814,,, 61,1965,2,2.161508,2.255183,,, 62,1965,3,2.460806,2.24175,,, 63,1965,4,3.291564,2.537855,,, 64,1966,1,3.637327,2.960457,,, 65,1966,2,2.015331,2.872379,,, 66,1966,3,3.386468,1.678517,,, 67,1966,4,2.153601,3.241487,,, 68,1967,1,2.037476,1.80477,,, 69,1967,2,2.115952,2.270841,,, 70,1967,3,1.352658,2.308425,,, 71,1967,4,1.847239,1.76327,,, 72,1968,1,1.741011,2.393571,,, 73,1968,2,2.28343,2.091623,,, 74,1968,3,3.006313,2.514739,,, 75,1968,4,4.314375,2.772906,,, 76,1969,1,3.718064,3.380456,,, 77,1969,2,2.029417,2.48767,,, 78,1969,3,1.500418,1.638736,,, 79,1969,4,2.249069,1.917292,,, 80,1970,1,2.751434,2.556965,,, 81,1970,2,4.149486,2.578243,,, 82,1970,3,4.793702,3.342115,,, 83,1970,4,2.970161,3.233744,,, 84,1971,1,1.535665,1.809793,,, 85,1971,2,-0.690842,1.585436,,, 86,1971,3,0.372726,0.581896,,, 87,1971,4,1.280042,2.116903,,, 88,1972,1,1.981304,2.214855,,, 89,1972,2,2.909937,2.372742,,, 90,1972,3,3.050076,2.743802,,, 91,1972,4,2.641661,2.491526,,, 92,1973,1,2.621315,2.193228,,, 93,1973,2,2.422996,2.338137,,, 94,1973,3,2.613121,2.192145,,, 95,1973,4,-0.631347,2.393736,,, 96,1974,1,0.308662,0.135852,,, 97,1974,2,2.14549,2.048404,,, 98,1974,3,2.530416,2.769812,,, 99,1974,4,2.333179,2.349218,,, 100,1975,1,,2.129509,0.943872,0.279554,3.979465 101,1975,2,,2.074884,1.135543,-0.150738,4.300507 102,1975,3,,2.105186,1.136638,-0.122583,4.332955 103,1975,4,,2.134998,1.15044,-0.119824,4.389819 104,1976,1,,2.136828,1.156371,-0.129617,4.403272 105,1976,2,,2.12296,1.156401,-0.143544,4.389463 106,1976,3,,2.109324,1.156884,-0.158127,4.376774 107,1976,4,,2.100939,1.157087,-0.166909,4.368787 108,1977,1,,2.095456,1.157087,-0.172394,4.363306 109,1977,2,,2.089903,1.157105,-0.17798,4.357787 110,1977,3,,2.083365,1.157111,-0.184532,4.351262 111,1977,4,,2.076291,1.157111,-0.191606,4.344188 112,1978,1,,2.069235,1.157112,-0.198663,4.337133 113,1978,2,,2.062363,1.157112,-0.205535,4.330262 114,1978,3,,2.055591,1.157112,-0.212308,4.323489 115,1978,4,,2.048814,1.157112,-0.219084,4.316713 116,1979,1,,2.042003,1.157112,-0.225896,4.309901 117,1979,2,,2.035173,1.157112,-0.232725,4.303072 118,1979,3,,2.028345,1.157112,-0.239554,4.296243 119,1979,4,,2.021522,1.157112,-0.246376,4.289421 120,1980,1,,2.014704,1.157112,-0.253195,4.282602 121,1980,2,,2.007885,1.157112,-0.260014,4.275783 122,1980,3,,2.001064,1.157112,-0.266834,4.268963 123,1980,4,,1.994244,1.157112,-0.273655,4.262142 124,1981,1,,1.987423,1.157112,-0.280476,4.255321 ,,,,1.980602,1.157112,-0.287296,4.248501 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv000066400000000000000000000110341224417117700334640ustar00rootroot00000000000000orig_index,year,quarter,y,predict,std_err,lower,upper 0,1950,1,3.198371,2.786912,0,0,0 1,1950,2,0.951462,2.974749,0,0,0 2,1950,3,2.044147,1.436963,0,0,0 3,1950,4,5.387144,3.04366,0,0,0 4,1951,1,4.278298,4.703375,0,0,0 5,1951,2,2.608896,2.671842,0,0,0 6,1951,3,2.2891,2.179081,0,0,0 7,1951,4,0.542828,2.559835,0,0,0 8,1952,1,1.818848,1.44334,0,0,0 9,1952,2,1.141287,2.994511,0,0,0 10,1952,3,1.390376,1.924747,0,0,0 11,1952,4,3.519065,2.444375,0,0,0 12,1953,1,2.687199,3.708894,0,0,0 13,1953,2,2.72044,2.302143,0,0,0 14,1953,3,3.640325,2.772049,0,0,0 15,1953,4,3.388688,3.306912,0,0,0 16,1954,1,1.285691,2.76964,0,0,0 17,1954,2,1.193271,1.515752,0,0,0 18,1954,3,4.125089,2.28757,0,0,0 19,1954,4,1.826838,4.162606,0,0,0 20,1955,1,1.211262,1.441483,0,0,0 21,1955,2,1.494882,2.122148,0,0,0 22,1955,3,2.407813,2.41238,0,0,0 23,1955,4,3.476123,2.886529,0,0,0 24,1956,1,4.683225,3.238772,0,0,0 25,1956,2,3.209492,3.650045,0,0,0 26,1956,3,3.045272,2.223427,0,0,0 27,1956,4,3.189591,2.769419,0,0,0 28,1957,1,3.223306,2.84062,0,0,0 29,1957,2,2.160318,2.802709,0,0,0 30,1957,3,1.084407,2.084095,0,0,0 31,1957,4,0.698262,1.797552,0,0,0 32,1958,1,1.121673,1.917181,0,0,0 33,1958,2,2.538408,2.301813,0,0,0 34,1958,3,3.671068,3.054365,0,0,0 35,1958,4,3.45499,3.251589,0,0,0 36,1959,1,2.136544,2.71492,0,0,0 37,1959,2,3.821324,1.972191,0,0,0 38,1959,3,4.975221,3.609349,0,0,0 39,1959,4,3.157348,3.617571,0,0,0 40,1960,1,1.30047,2.032269,0,0,0 41,1960,2,1.802575,1.581756,0,0,0 42,1960,3,3.772872,2.56424,0,0,0 43,1960,4,3.770009,3.580948,0,0,0 44,1961,1,3.359749,2.809913,0,0,0 45,1961,2,2.440787,2.635187,0,0,0 46,1961,3,2.682876,2.172471,0,0,0 47,1961,4,4.210082,2.68386,0,0,0 48,1962,1,3.215831,3.551058,0,0,0 49,1962,2,2.501961,2.281018,0,0,0 50,1962,3,2.597237,2.286959,0,0,0 51,1962,4,2.865064,2.572783,0,0,0 52,1963,1,1.27064,2.677956,0,0,0 53,1963,2,-0.23313,1.511573,0,0,0 54,1963,3,0.276246,1.169244,0,0,0 55,1963,4,2.197587,2.029432,0,0,0 56,1964,1,3.4989,3.027459,0,0,0 57,1964,2,2.577512,3.143262,0,0,0 58,1964,3,3.582812,2.093742,0,0,0 59,1964,4,3.0313,3.192262,0,0,0 60,1965,1,2.467222,2.353064,0,0,0 61,1965,2,2.161508,2.260144,0,0,0 62,1965,3,2.460806,2.244168,0,0,0 63,1965,4,3.291564,2.538462,0,0,0 64,1966,1,3.637327,2.952329,0,0,0 65,1966,2,2.015331,2.85753,0,0,0 66,1966,3,3.386468,1.674466,0,0,0 67,1966,4,2.153601,3.258153,0,0,0 68,1967,1,2.037476,1.783801,0,0,0 69,1967,2,2.115952,2.291254,0,0,0 70,1967,3,1.352658,2.308358,0,0,0 71,1967,4,1.847239,1.767768,0,0,0 72,1968,1,1.741011,2.40798,0,0,0 73,1968,2,2.28343,2.089385,0,0,0 74,1968,3,3.006313,2.522133,0,0,0 75,1968,4,4.314375,2.768106,0,0,0 76,1969,1,3.718064,3.37106,0,0,0 77,1969,2,2.029417,2.471187,0,0,0 78,1969,3,1.500418,1.651553,0,0,0 79,1969,4,2.249069,1.942538,0,0,0 80,1970,1,2.751434,2.566664,0,0,0 81,1970,2,4.149486,2.574459,0,0,0 82,1970,3,4.793702,3.339428,0,0,0 83,1970,4,2.970161,3.215612,0,0,0 84,1971,1,1.535665,1.806844,0,0,0 85,1971,2,-0.690842,1.616034,0,0,0 86,1971,3,0.372726,0.611575,0,0,0 87,1971,4,1.280042,2.159702,0,0,0 88,1972,1,1.981304,2.213886,0,0,0 89,1972,2,2.909937,2.379235,0,0,0 90,1972,3,3.050076,2.747067,0,0,0 91,1972,4,2.641661,2.490161,0,0,0 92,1973,1,2.621315,2.202678,0,0,0 93,1973,2,2.422996,2.352931,0,0,0 94,1973,3,2.613121,2.20254,0,0,0 95,1973,4,-0.631347,2.407679,0,0,0 96,1974,1,0.308662,0.15372,0,0,0 97,1974,2,2.14549,2.110661,0,0,0 98,1974,3,2.530416,2.769133,0,0,0 99,1974,4,2.333179,2.346634,0,0,0 100,1975,1,,2.143652,0.954284,0.273289,4.014014 101,1975,2,,2.102544,1.146611,-0.144772,4.34986 102,1975,3,,2.136329,1.147121,-0.111987,4.384644 103,1975,4,,2.16265,1.161904,-0.11464,4.43994 104,1976,1,,2.161075,1.167047,-0.126294,4.448444 105,1976,2,,2.147009,1.167047,-0.140361,4.434379 106,1976,3,,2.135191,1.16758,-0.153225,4.423606 107,1976,4,,2.12856,1.167729,-0.160146,4.417266 108,1977,1,,2.123977,1.167729,-0.16473,4.412685 109,1977,2,,2.11883,1.167748,-0.169914,4.407575 110,1977,3,,2.112729,1.167753,-0.176024,4.401481 111,1977,4,,2.106298,1.167753,-0.182456,4.395051 112,1978,1,,2.099996,1.167753,-0.188758,4.388751 113,1978,2,,2.093869,1.167753,-0.194886,4.382623 114,1978,3,,2.087793,1.167753,-0.200962,4.376548 115,1978,4,,2.081689,1.167753,-0.207066,4.370444 116,1979,1,,2.075554,1.167753,-0.213201,4.364308 117,1979,2,,2.069411,1.167753,-0.219344,4.358165 118,1979,3,,2.063273,1.167753,-0.225481,4.352028 119,1979,4,,2.057142,1.167753,-0.231613,4.345896 120,1980,1,,2.051011,1.167753,-0.237744,4.339766 121,1980,2,,2.044879,1.167753,-0.243875,4.333634 122,1980,3,,2.038747,1.167753,-0.250008,4.327501 123,1980,4,,2.032614,1.167753,-0.256141,4.321369 124,1981,1,,2.026481,1.167753,-0.262273,4.315236 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arima_forecasts.csv000066400000000000000000000563051224417117700316170ustar00rootroot00000000000000year,quarter,fc111nc,fc111ncse,fc111ncconf1,fc111ncconf2,fc111ncdyn,fc111ncdynse,fc111ncdynconf1,fc111ncdynconf2,fc111c,fc111cse,fc111cconf1,fc111cconf2,fc111cdyn,fc111cdynse,fc111cdynconf1,fc111cdynconf2,fc211nc,fc211ncse,fc211ncconf1,fc211ncconf2,fc211ncdyn,fc211ncdynse,fc211ncdynconf1,fc211ncdynconf2,fc211c,fc211cse,fc211cconf1,fc211cconf2,fc211cdyn,fc211cdynse,fc211cdynconf1,fc211ncdynconf2,fc111c_css,fc111cse_css,fc111cconf1_css,fc111cconf2_css,fc111cdyn_css,fc111cdynse_css,fc111cdynconf1_css,fc111cdynconf2_css,fc112c_css,fc112cse_css,fc112cconf1_css,fc112cconf2_css,fc112cdyn_css,fc112cdynse_css,fc112cdynconf1_css,fc112cdynconf2_css 2000,1,170.2233243826,,,,170.2233243826,0.8056,168.644,171.802,170.2864191564,,,,170.2864191564,0.7999,168.719,171.854,170.2746065512,,,,170.2746065512,0.7953,168.716,171.833,170.2895305662,,,,170.2895305662,0.7925,168.736,171.843,170.304,,,,170.304,0.7972,168.742,171.867,170.154,,,,170.154,0.8259,168.535,171.772 2000,2,171.9184524902,,,,171.1377283933,1.23,168.727,173.549,171.9837202702,,,,171.2664932271,1.2305,168.855,173.678,172.0019713519,,,,171.2023842717,1.291,168.672,173.733,172.0147981675,,,,171.2401153995,1.2804,168.731,173.75,171.99,,,,171.309,1.211,168.935,173.682,172.134,,,,171.103,1.4083,168.343,173.863 2000,3,173.828787093,,,,172.0432982132,1.6186,168.871,175.216,173.892520498,,,,172.2406036174,1.6254,169.055,175.426,173.897011282,,,,172.1176130768,1.7143,168.758,175.478,173.9104306451,,,,172.1817815353,1.698,168.854,175.51,173.888,,,,172.313,1.5831,169.21,175.416,173.722,,,,171.978,1.8547,168.343,175.613 2000,4,175.0288317109,,,,172.9401191906,1.9993,169.021,176.859,175.0750500309,,,,173.2091088061,2.0093,169.271,177.147,174.9780464837,,,,173.0268876834,2.1027,168.906,177.148,174.9987913625,,,,173.1198404087,2.0819,169.039,177.2,175.08,,,,173.318,1.9403,169.515,177.121,174.89,,,,172.904,2.1881,168.616,177.193 2001,1,176.8057511331,,,,173.8282758494,2.3815,169.161,178.496,176.8460409347,,,,174.1723457236,2.3897,169.489,178.856,176.823803435,,,,173.9314905347,2.4735,169.084,178.779,176.837192481,,,,174.0552845768,2.4481,169.257,178.854,176.845,,,,174.323,2.2912,169.833,178.814,176.748,,,,173.795,2.4921,168.911,178.679 2001,2,177.5317122,,,,174.7078518968,2.7692,169.28,180.135,177.5487445729,,,,175.1306310477,2.7691,169.703,180.558,177.4100697987,,,,174.8316846814,2.8356,169.274,180.389,177.4314962031,,,,174.9883502198,2.8046,169.491,180.485,177.568,,,,175.328,2.6392,170.156,180.501,177.127,,,,174.711,2.7534,169.314,180.107 2001,3,178.5005259429,,,,175.5789302315,3.1643,169.377,181.781,178.5075160891,,,,176.0842624201,3.1483,169.914,182.255,178.4434523593,,,,175.7275374931,3.194,169.467,181.988,178.4577307847,,,,175.9191394228,3.1554,169.735,182.104,178.535,,,,176.334,2.9858,170.482,182.186,178.473,,,,175.609,2.998,169.733,181.485 2001,4,178.551424957,,,,176.4415929509,3.5677,169.449,183.434,178.5405940261,,,,177.0335195914,3.5276,170.12,183.947,178.4127628861,,,,176.6190787111,3.5515,169.658,183.58,178.431638159,,,,176.8477289245,3.503,169.982,183.713,178.594,,,,177.34,3.3315,170.81,183.869,178.103,,,,176.519,3.2202,170.207,182.831 2002,1,178.5113144309,,,,177.2959213597,3.9799,169.495,185.096,178.4950101925,,,,177.9786654965,3.9067,170.322,185.636,178.4280846262,,,,177.5063307758,3.91,169.843,185.17,178.4424347618,,,,177.7741891719,3.8486,170.231,185.317,178.567,,,,178.345,3.6766,171.139,185.551,178.58,,,,177.421,3.4306,170.697,184.145 2002,2,180.2247478027,,,,178.1419959768,4.4009,169.516,186.768,180.2361498037,,,,178.9199472648,4.2853,170.521,187.319,180.3880488337,,,,178.3893146464,4.2706,170.019,186.76,180.384875915,,,,178.6985877074,4.1932,170.48,186.917,180.28,,,,179.351,4.021,171.47,187.232,180.428,,,,178.329,3.6271,171.22,185.438 2002,3,180.8812555147,,,,178.9798965432,4.8306,169.512,188.448,180.8929359533,,,,179.8575971709,4.663,170.718,188.997,180.8779742943,,,,179.2680509174,4.6342,170.185,188.351,180.8883836647,,,,179.6209898179,4.5371,170.728,188.514,180.941,,,,180.357,4.3645,171.803,188.912,180.712,,,,179.232,3.8146,171.756,186.709 2002,4,182.1217530645,,,,179.8097020298,5.2691,169.483,190.137,182.1440794946,,,,180.7918335272,5.0394,170.915,190.669,182.1982416071,,,,180.1425600339,5.0013,170.34,189.945,182.2033611924,,,,180.5414587027,4.8808,170.975,190.108,182.182,,,,181.364,4.707,172.138,190.589,182.352,,,,180.139,3.9926,172.313,187.964 2003,1,183.5863851269,,,,180.6314906442,5.7159,169.428,191.835,183.6172936436,,,,181.7228615235,5.4142,171.111,192.334,183.6677376966,,,,181.0128623329,5.3723,170.483,191.542,183.6730872079,,,,181.4600555571,5.2243,171.221,191.699,183.644,,,,182.37,5.0481,172.476,192.264,183.513,,,,181.043,4.1635,172.883,189.204 2003,2,184.1174433192,,,,181.4453398387,6.171,169.35,193.54,184.138603356,,,,182.6508740151,5.787,171.308,193.993,184.0698585717,,,,181.8789780516,5.7474,170.614,193.144,184.0850064378,,,,182.376839639,5.5676,171.464,193.289,184.178,,,,183.376,5.3878,172.817,193.936,184.023,,,,181.949,4.3273,173.468,190.43 2003,3,184.5443919305,,,,182.251326317,6.6341,169.249,195.254,184.561043527,,,,183.5760522653,6.1576,171.507,195.645,184.5149896099,,,,182.7409273298,6.1268,170.733,194.749,184.5290257015,,,,183.2918683299,5.9109,171.707,194.877,184.611,,,,184.383,5.7256,173.161,195.605,184.485,,,,182.854,4.4853,174.063,191.645 2003,4,185.790913924,,,,183.0495260422,7.1048,169.124,196.975,185.8194914079,,,,184.4985666411,6.5257,171.708,197.289,185.879208987,,,,183.5987302099,6.5106,170.838,196.359,185.885307579,,,,184.2051971955,6.2541,171.947,196.463,185.858,,,,185.39,6.0616,173.509,197.27,185.991,,,,183.76,4.6378,174.67,192.85 2004,1,187.2605858834,,,,183.840014243,7.583,168.978,198.702,187.2983369795,,,,185.4185772689,6.8911,171.912,198.925,187.3504459251,,,,184.4524066383,6.8989,170.931,197.974,187.3573082078,,,,185.1168800421,6.5971,172.187,198.047,187.324,,,,186.397,6.3954,173.862,198.931,187.266,,,,184.665,4.7856,175.285,194.044 2004,2,188.3727424216,,,,184.6228654218,8.0684,168.809,200.437,188.4084484474,,,,186.3362346499,7.2535,172.12,200.553,188.3934156532,,,,185.3019764651,7.2916,171.011,199.593,188.4057130567,,,,186.026968973,6.9399,172.425,199.629,188.434,,,,187.403,6.727,174.219,200.588,188.368,,,,185.57,4.9288,175.91,195.231 2004,3,190.1751707887,,,,185.3981533608,8.5607,168.619,202.177,190.217596201,,,,187.2516802385,7.6128,172.331,202.173,190.2660090321,,,,186.1474594451,7.6889,171.078,201.217,190.2727541662,,,,186.9355144424,7.2823,172.662,201.209,190.228,,,,188.41,7.0562,174.581,202.24,190.264,,,,186.476,5.0681,176.542,196.409 2004,4,191.9608597652,,,,186.1659511297,9.0598,168.409,203.923,192.0017457862,,,,188.1650469862,7.9689,172.546,203.784,192.0082426728,,,,186.9888752382,8.0906,171.132,202.846,192.0172722694,,,,187.8425653072,7.6243,172.899,202.786,192.004,,,,189.418,7.3829,174.947,203.888,191.856,,,,187.381,5.2036,177.182,197.58 2005,1,192.9249100574,,,,186.9263310921,9.5653,168.179,205.674,192.9483784061,,,,189.076459853,8.3216,172.766,205.387,192.8516392196,,,,187.8262434093,8.4968,171.173,204.48,192.867884817,,,,188.7481688778,7.9659,173.135,204.361,192.964,,,,190.425,7.7069,175.32,205.53,192.672,,,,188.286,5.3357,177.828,198.744 2005,2,195.0485996347,,,,187.6793649125,10.077,167.929,207.43,195.0761539867,,,,189.9860362873,8.6709,172.991,206.981,195.1215629644,,,,188.6595834296,8.9073,171.202,206.118,195.1247041843,,,,189.6523709671,8.3068,173.371,205.933,195.076,,,,191.432,8.0282,175.697,207.167,195.149,,,,189.192,5.4645,178.481,199.902 2005,3,195.8829345899,,,,188.425123563,10.5947,167.66,209.19,195.8888528366,,,,190.8938866775,9.0167,173.222,208.566,195.769454439,,,,189.4889146764,9.3223,171.218,207.76,195.7841430678,,,,190.5552159377,8.6471,173.607,207.503,195.907,,,,192.439,8.3467,176.08,208.798,195.321,,,,190.097,5.5904,179.14,201.054 2005,4,200.881551968,,,,189.1636773301,11.1182,167.372,210.955,200.9296590393,,,,191.8001147769,9.3588,173.457,210.143,201.2531803919,,,,190.3142564339,9.7415,171.221,209.407,201.2283148262,,,,191.4567467481,8.9866,173.843,209.07,200.869,,,,193.447,8.6623,176.469,210.424,201.704,,,,191.002,5.7136,179.804,202.201 2006,1,200.8374966075,,,,189.8950958213,11.6472,167.067,212.723,200.8202472437,,,,192.7048181018,9.6974,173.698,211.711,200.4617908111,,,,191.1356278936,10.165,171.212,211.059,200.4879862602,,,,192.3570049969,9.3252,174.08,210.634,200.822,,,,194.454,8.975,176.863,212.045,198.987,,,,191.907,5.8341,180.473,203.342 2006,2,202.1024666878,,,,190.6194479714,12.1817,166.744,214.495,202.0675124183,,,,193.6080883069,10.0323,173.945,213.271,201.9885761817,,,,191.9530481544,10.5927,171.192,212.714,201.9892792336,,,,193.256030966,9.663,174.317,212.195,202.082,,,,195.462,9.2848,177.264,213.66,202.774,,,,192.813,5.9522,181.147,204.479 2006,3,204.1807964789,,,,191.3368020496,12.7213,166.404,216.27,204.1450996188,,,,194.5100115376,10.3635,174.198,214.822,204.179541508,,,,192.7665362236,11.0246,171.159,214.374,204.1674660761,,,,194.1538636621,9.9997,174.555,213.753,204.15,,,,196.469,9.5916,177.67,215.268,203.062,,,,193.718,6.068,181.825,205.611 2006,4,203.0157854602,,,,192.0472256651,13.2659,166.046,218.048,202.9319404185,,,,195.410668761,10.6911,174.457,216.365,202.6394746314,,,,193.5761110169,11.4604,171.114,216.038,202.6516262814,,,,195.0505408572,10.3355,174.793,215.308,203.003,,,,197.477,9.8954,178.082,216.872,202.726,,,,194.623,6.1816,182.508,206.739 2007,1,204.7648391013,,,,192.7507857741,13.8154,165.673,219.828,204.7053158681,,,,196.3101360771,11.015,174.721,217.899,204.864868405,,,,194.3817913592,11.9003,171.058,217.706,204.8390307301,,,,195.9460991278,10.67,175.033,216.859,204.754,,,,198.485,10.1962,178.5,218.469,204.658,,,,195.529,6.2932,183.194,207.863 2007,2,207.2769566127,,,,193.447548686,14.3694,165.284,221.611,207.2414456805,,,,197.2084850116,11.3352,174.992,219.425,207.4265796922,,,,195.1835959846,12.3441,170.99,219.378,207.3960909405,,,,196.8405738924,11.0034,175.274,218.407,207.258,,,,199.492,10.494,178.924,220.06,207.329,,,,196.434,6.4029,183.885,208.983 2007,3,208.6912331435,,,,194.1375800696,14.928,164.879,223.396,208.6492720137,,,,198.105782791,11.6518,175.269,220.943,208.6537586221,,,,195.9815435372,12.7917,170.91,221.053,208.6352773018,,,,197.7339994486,11.3356,175.517,219.951,208.67,,,,200.5,10.7888,179.355,221.646,208.115,,,,197.339,6.5107,184.579,210.1 2007,4,210.5410872839,,,,194.8209449591,15.4908,164.459,225.182,210.500111343,,,,199.0020926004,11.9647,175.552,222.453,210.5576053658,,,,196.7756525715,13.2432,170.819,222.732,210.532355268,,,,198.626409008,11.6665,175.761,221.492,210.514,,,,201.508,11.0805,179.791,223.225,210.516,,,,198.245,6.6167,185.276,211.213 2008,1,214.1899253565,,,,195.4977077604,16.0578,164.025,226.971,214.1698597159,,,,199.8974738271,12.2741,175.841,223.954,214.3829696128,,,,197.5659415525,13.6983,170.718,224.414,214.3413261881,,,,199.5178347313,11.996,176.006,223.03,214.14,,,,202.516,11.3693,180.232,224.799,213.999,,,,199.15,6.721,185.977,212.323 2008,2,215.6458854232,,,,196.1679322573,16.6289,163.576,228.76,215.5949327403,,,,200.7919822885,12.58,176.136,225.448,215.4886503677,,,,198.3524288567,14.1572,170.605,226.1,215.4686267913,,,,200.4083077608,12.3242,176.253,224.563,215.587,,,,203.524,11.6551,180.68,226.367,214.828,,,,200.055,6.8238,186.681,213.43 2008,3,220.6987299825,,,,196.8316816174,17.2038,163.113,230.55,220.6741351366,,,,201.6856704468,12.8823,176.437,226.935,220.9632787766,,,,199.1351327719,14.6196,170.481,227.789,220.9055597068,,,,201.2978582533,12.6509,176.503,226.093,220.602,,,,204.532,11.9379,181.134,227.93,221.009,,,,200.961,6.925,187.388,214.534 2008,4,218.3717504048,,,,197.4890183978,17.7824,162.636,232.342,218.2426223953,,,,202.5785876106,13.1812,176.744,228.413,217.6667975573,,,,199.9140714981,15.0855,170.347,229.481,217.6747091231,,,,202.186515411,12.9761,176.754,227.619,218.294,,,,205.54,12.2178,181.593,229.486,215.84,,,,201.866,7.0248,188.098,215.634 2009,1,212.6894332539,,,,198.1400045515,18.3646,162.146,234.134,212.4740916853,,,,203.4707801248,13.4766,177.057,229.884,211.8959992321,,,,200.6892631477,15.5549,170.202,231.176,211.9059766187,,,,203.0743075115,13.2999,177.007,229.142,212.678,,,,206.548,12.4947,182.059,231.037,212.326,,,,202.771,7.1232,188.81,216.733 2009,2,213.1786192035,,,,198.7847014328,18.9502,161.643,235.926,213.0392637652,,,,204.3622915483,13.7687,177.376,231.348,213.4572131423,,,,201.460725746,16.0277,170.047,232.874,213.3903799208,,,,203.9612619376,13.6221,177.262,230.66,213.202,,,,207.556,12.7688,182.529,232.582,213.599,,,,203.677,7.2203,189.525,217.828 2009,3,215.1701293805,,,,199.4231698032,19.5392,161.127,237.719,215.1095875387,,,,205.2531628216,14.0574,177.701,232.805,215.5892010821,,,,202.2284772315,16.5038,169.882,234.575,215.5198428062,,,,204.847405205,13.9428,177.52,232.175,215.208,,,,208.564,13.04,183.006,234.122,215.75,,,,204.582,7.316,190.243,218.921 2009,4,217.2661592661,0.8056,215.687,218.845,200.0554698373,20.1314,160.599,239.512,217.2554734364,0.7999,215.688,218.823,206.1434324244,14.3429,178.032,234.255,217.5930744392,0.7953,216.034,219.152,202.9925354565,16.9832,169.706,236.279,217.5372491403,0.7925,215.984,219.091,205.7327629904,14.2619,177.78,233.686,217.308,0.7972,215.745,218.871,209.572,13.3085,183.488,235.656,217.422,0.8259,215.804,219.041,205.487,7.4105,190.963,220.012 2010,1,218.1388055236,1.23,215.728,220.55,200.6816611283,20.7267,160.058,241.305,218.1265713231,1.2305,215.715,220.538,207.0331365232,14.6251,178.368,235.698,218.6612703593,1.291,216.131,221.192,203.7529181874,17.4658,169.521,237.985,218.5527257447,1.2804,216.043,221.062,206.6173601574,14.5794,178.042,235.192,218.236,1.211,215.862,220.609,210.58,13.5741,183.975,237.185,218.346,1.4083,215.585,221.106,206.393,7.5039,191.685,221.1 2010,2,219.003021018,1.6186,215.831,222.175,201.3018026937,21.3249,159.506,243.098,218.9982561241,1.6254,215.813,222.184,207.9223091109,14.9042,178.711,237.134,219.6986031575,1.7143,216.339,223.059,204.5096431051,17.9516,169.325,239.694,219.5410430132,1.698,216.213,222.869,207.5012207828,14.8952,178.307,236.695,219.168,1.5831,216.065,222.271,211.588,13.837,184.468,238.708,219.238,1.8547,215.603,222.874,207.298,7.596,192.41,222.186 2010,3,219.8588872002,1.9993,215.94,223.778,201.9159529807,21.9261,158.942,244.89,219.8704925599,2.0093,215.932,223.809,208.8109821365,15.1802,179.058,238.564,220.7260098869,2.1027,216.605,224.847,205.2627278056,18.4404,169.12,241.405,220.5214492212,2.0819,216.441,224.602,208.3843681813,15.2095,178.574,238.194,220.104,1.9403,216.301,223.907,212.597,14.0972,184.967,240.226,220.152,2.1881,215.864,224.441,208.203,7.6871,193.137,223.27 2010,4,220.706484734,2.3815,216.039,225.374,202.524169872,22.5299,158.366,246.682,220.7432474715,2.3897,216.06,225.427,209.6991856291,15.4531,179.412,239.987,221.7475271771,2.4735,216.9,226.595,206.0121897999,18.9323,168.905,243.119,221.4974064825,2.4481,216.699,226.296,209.2668249293,15.522,178.844,239.689,221.044,2.2912,216.553,225.535,213.605,14.3547,185.47,241.739,221.052,2.4921,216.167,225.936,209.109,7.7771,193.866,224.351 2011,1,221.545893504,2.7692,216.118,226.973,203.126510691,23.1365,157.78,248.473,221.6164896931,2.7691,216.189,227.044,210.5869478123,15.723,179.771,241.403,222.7639487392,2.8356,217.206,228.322,206.7580465151,19.4272,168.681,244.835,222.4696165704,2.8046,216.973,227.967,210.1486128889,15.8329,179.117,241.18,221.988,2.6392,216.815,227.161,214.613,14.6096,185.979,243.247,221.961,2.7534,216.565,227.358,210.014,7.8661,194.597,225.431 2011,2,222.3771926233,3.1643,216.175,228.579,203.7230322072,23.7456,157.183,250.264,222.4901899324,3.1483,216.32,228.661,211.4742952135,15.9899,180.135,242.814,223.775445946,3.194,217.515,230.036,207.5003152945,19.9251,168.448,246.553,223.4382960718,3.1554,217.254,229.623,211.0297532299,16.1421,179.392,242.668,222.935,2.9858,217.083,228.787,215.621,14.8619,186.493,244.75,222.864,2.998,216.988,228.74,210.919,7.9541,195.33,226.509 2011,3,223.2004604401,3.5677,216.208,230.193,204.3137906419,24.3571,156.575,252.053,223.3643206578,3.5276,216.45,230.278,212.3612527653,16.2539,180.504,244.218,224.7820706652,3.5515,217.821,231.743,208.2390133976,20.4258,168.205,248.273,224.4035738561,3.503,217.538,231.269,211.9102664522,16.4495,179.67,244.151,223.886,3.3315,217.357,230.416,216.63,15.1116,187.011,246.248,223.771,3.2202,217.459,230.082,211.825,8.0411,196.064,227.585 2011,4,224.0157745462,3.9799,216.215,231.816,204.8988416729,24.9711,155.956,253.841,224.2388559925,3.9067,216.582,231.896,213.2478439017,16.5152,180.879,245.617,225.7838517429,3.91,218.12,233.447,208.9741580012,20.9293,167.954,249.995,225.3655605905,3.8486,217.822,232.909,212.7901724066,16.7553,179.95,245.63,224.84,3.6766,217.634,232.046,217.638,15.3588,187.535,247.741,224.675,3.4306,217.951,231.399,212.73,8.1272,196.801,228.659 2012,1,224.8232117835,4.4009,216.198,233.449,205.4782404403,25.5873,155.328,255.628,225.1137716155,4.2853,216.715,233.513,214.134090648,16.7736,181.258,247.01,226.7808135168,4.2706,218.411,235.151,209.7057661993,21.4356,167.693,251.719,226.3243609722,4.1932,218.106,234.543,213.6694903158,17.0594,180.234,247.105,225.797,4.021,217.916,233.678,218.646,15.6036,188.064,249.229,225.581,3.6271,218.472,232.69,213.635,8.2124,197.539,229.731 2012,2,225.6228482515,4.8306,216.155,235.091,206.0520415515,26.2056,154.69,257.414,225.9890446675,4.663,216.85,235.128,215.0200137057,17.0293,181.643,248.397,227.7729793693,4.6342,218.69,236.856,210.4338550039,21.9446,167.423,253.444,227.2800759537,4.5371,218.387,236.173,214.5482387938,17.3618,180.52,248.577,226.757,4.3645,218.202,235.311,219.654,15.8459,188.597,250.712,226.486,3.8146,219.009,233.962,214.541,8.2967,198.279,230.802 2012,3,226.4147593147,5.2691,216.088,236.742,206.6202990863,26.8261,154.042,259.198,226.8646536632,5.0394,216.988,236.742,215.9056325317,17.2823,182.033,249.778,228.7603724093,5.0013,218.958,238.563,211.1584413449,22.4563,167.145,255.172,228.2328032167,4.8808,218.667,237.799,215.4264358655,17.6624,180.809,250.044,227.719,4.707,218.494,236.945,220.663,16.0859,189.135,252.191,227.392,3.9926,219.566,235.217,215.446,8.3802,199.021,231.871 2012,4,227.1990196094,5.7159,215.996,238.402,207.183066602,27.4486,153.385,260.981,227.740578409,5.4142,217.129,238.352,216.7909654136,17.5327,182.428,251.154,229.7430156036,5.3723,219.213,240.273,211.8795420709,22.9706,166.858,256.901,229.1826373376,5.2243,218.943,239.422,216.304098985,17.9613,181.101,251.508,228.684,5.0481,218.79,238.578,221.671,16.3234,189.678,253.665,228.297,4.1635,220.136,236.457,216.351,8.4628,199.764,232.938 2013,1,227.9757030506,6.171,215.881,240.071,207.7403971383,28.073,152.718,262.762,228.6167999251,5.787,217.274,239.959,217.6760295396,17.7805,182.827,252.525,230.7209318028,5.7474,219.456,241.986,212.5971739494,23.4874,166.563,258.632,230.1296698969,5.5676,219.217,241.042,217.1812450544,18.2585,181.395,252.967,229.651,5.3878,219.091,240.211,222.68,16.5587,190.225,255.134,229.202,4.3273,220.721,237.683,217.257,8.5447,200.509,234.004 2013,2,228.7448828395,6.6341,215.742,241.747,208.2923432229,28.6993,152.043,264.542,229.4933003724,6.1576,217.425,241.562,218.5608410646,18.0257,183.231,253.891,231.6941437462,6.1268,219.686,243.702,213.3113536672,24.0068,166.259,260.364,231.0739895756,5.9109,219.489,242.659,218.0578904402,18.5539,181.693,254.423,230.621,5.7256,219.399,241.843,223.688,16.7917,190.777,256.599,230.107,4.4853,221.316,238.898,218.162,8.6257,201.256,235.068 2013,3,229.50663147,7.1048,215.581,243.432,208.8389568756,29.3273,151.358,266.319,230.3700629844,6.5257,217.58,243.16,219.4454151726,18.2686,183.64,255.251,232.6626740638,6.5106,219.902,245.423,214.0220978309,24.5287,165.947,262.097,232.0156822477,6.2541,219.758,244.274,218.9340509913,18.8477,181.993,255.875,231.592,6.0616,219.712,243.473,224.696,17.0225,191.333,258.06,231.013,4.6378,221.923,240.103,219.067,8.706,202.004,236.131 2013,4,230.2610207356,7.583,215.399,245.123,209.3802896139,29.957,150.666,268.095,231.2470720022,6.8911,217.741,244.753,220.3297661348,18.5089,184.053,256.607,233.6265452764,6.8989,220.105,247.148,214.7294229673,25.053,165.626,263.832,232.9548310688,6.5971,220.025,245.885,219.8097420547,19.1397,182.297,257.323,232.566,6.3954,220.031,245.101,225.705,17.2511,191.893,259.516,231.918,4.7856,222.538,241.298,219.972,8.7856,202.753,237.192 2014,1,231.0081217362,8.0684,215.194,246.822,209.9163924575,30.5884,149.964,269.869,232.1243126143,7.2535,217.908,246.341,221.2139073645,18.747,184.471,257.957,234.5857797965,7.2916,220.294,248.877,215.4333455235,25.5797,165.298,265.569,233.8915165624,6.9399,220.29,247.493,220.6849784915,19.43,182.603,258.767,233.542,6.727,220.357,246.726,226.713,17.4776,192.458,260.969,232.823,4.9288,223.163,242.484,220.878,8.8645,203.504,238.252 2014,2,231.7480048848,8.5607,214.969,248.527,210.4473159331,31.2213,149.255,271.64,233.0017708995,7.6128,218.081,247.923,222.0978514689,18.9827,184.893,259.303,235.5403999291,7.6889,220.471,250.61,216.1338818676,26.1088,164.962,267.306,234.8258167029,7.2823,220.553,249.099,221.5597746927,19.7186,182.912,260.208,234.519,7.0562,220.689,248.349,227.722,17.7019,193.027,262.417,233.729,5.0681,223.795,243.662,221.783,8.9426,204.256,239.31 2014,3,232.480739914,9.0598,214.724,250.238,210.9731100793,31.8558,148.537,273.409,233.8794337734,7.9689,218.261,249.498,222.9816102973,19.2161,185.319,260.644,236.4904278715,8.0906,220.633,252.348,216.8310482889,26.6402,164.617,269.045,235.7578069968,7.6243,220.814,250.701,222.4341445934,20.0055,183.224,261.644,235.498,7.3829,221.028,249.968,228.73,17.9242,193.599,263.861,234.634,5.2036,224.435,244.833,222.688,9.0201,205.009,240.368 2014,4,233.206395883,9.5653,214.459,251.954,211.4938244514,32.4916,147.811,275.176,234.7572889381,8.3216,218.447,251.067,223.8651949865,19.4472,185.749,261.981,237.4358857144,8.4968,220.783,254.089,217.5248609985,27.1738,164.265,270.785,236.6875605602,7.9659,221.075,252.3,223.3081016879,20.2907,183.539,263.077,236.479,7.7069,221.374,251.584,229.739,18.1444,194.176,265.301,235.539,5.3357,225.082,245.997,223.594,9.097,205.764,241.423 2015,1,233.9250411836,10.077,214.175,253.676,212.0095081258,33.1288,147.078,276.941,235.6353248348,8.6709,218.641,252.63,224.7486160043,19.6762,186.184,263.313,238.3767954422,8.9073,220.919,255.835,218.2153361293,27.7096,163.905,272.525,237.6151481949,8.3068,221.334,253.896,224.1816590432,20.5741,183.857,264.506,237.461,8.0282,221.726,253.196,230.747,18.3627,194.757,266.737,236.445,5.4645,225.734,247.155,224.499,9.1731,206.52,242.478 2015,2,234.6367435468,10.5947,213.872,255.402,212.5202097049,33.7673,146.338,278.703,236.5135305999,9.0167,218.841,254.186,225.6318831888,19.9031,186.623,264.641,239.3131789336,9.3223,221.042,257.585,218.9024897366,28.2477,163.538,274.267,238.5406384615,8.6471,221.593,255.489,225.0548293128,20.8559,184.178,265.932,238.445,8.3467,222.086,254.804,231.755,18.5789,195.341,268.17,237.35,5.5904,226.393,248.307,225.404,9.2487,207.277,243.532 2015,3,235.3415700494,11.1182,213.55,257.133,213.0259773212,34.407,145.589,280.463,237.3918960223,9.3588,219.049,255.735,226.5150057872,20.1278,187.065,265.965,240.245057962,9.7415,221.152,259.338,219.5863377988,28.7878,163.163,276.009,239.4640977504,8.9866,221.851,257.077,225.9276247493,21.136,184.502,267.354,239.43,8.6623,222.452,256.408,232.764,18.7933,195.93,269.598,238.255,5.7136,227.057,249.454,226.31,9.3237,208.036,244.584 2015,4,236.0395871201,11.6472,213.211,258.868,213.5268586427,35.0479,144.834,282.22,238.2704115052,9.6974,219.264,257.277,227.3979924905,20.3505,187.512,267.284,241.1724541961,10.165,221.249,261.096,220.2668962169,29.3301,162.781,277.753,240.3855903505,9.3252,222.108,258.663,226.800057218,21.4145,184.828,268.772,240.416,8.975,222.825,258.007,233.772,19.0058,196.522,271.023,239.161,5.8341,227.726,250.595,227.215,9.398,208.795,245.635 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv000066400000000000000000000353441224417117700333170ustar00rootroot00000000000000fc,fcdyn,fcdyn2,fcdyn3,fcdyn4,orig_index,year,quarter,,,,,, NA,NA,NA,NA,NA,0,1959,1,,,,,, NA,NA,NA,NA,NA,1,1959,2,,,,,, NA,NA,NA,NA,NA,2,1959,3,,,,,, NA,NA,NA,NA,NA,3,1959,4,,,,,, NA,NA,NA,NA,NA,4,1960,1,,,,,, 29.7575819218,29.7575819218,NA,NA,NA,5,1960,2,,,,,, 29.6889197051,29.9572171332,NA,NA,NA,6,1960,3,,,,,, 29.9626330839,30.1680969792,NA,NA,NA,7,1960,4,,,,,, 29.9910430709,30.3882531469,NA,NA,NA,8,1961,1,,,,,, 29.9504240714,30.6079779559,NA,NA,NA,9,1961,2,,,,,, 30.0841410663,30.8285844839,NA,NA,NA,10,1961,3,,,,,, 30.1048255382,31.0506468204,NA,NA,NA,11,1961,4,,,,,, 30.1800344216,31.2730827044,NA,NA,NA,12,1962,1,,,,,, 30.3714349362,31.4957681649,NA,NA,NA,13,1962,2,,,,,, 30.3295604942,31.7187987306,NA,NA,NA,14,1962,3,,,,,, 30.5486434278,31.9420751464,NA,NA,NA,15,1962,4,,,,,, 30.5522722863,32.1655550591,NA,NA,NA,16,1963,1,,,,,, 30.6061743416,32.3892452608,NA,NA,NA,17,1963,2,,,,,, 30.8498630373,32.6131379091,NA,NA,NA,18,1963,3,,,,,, 30.8580075917,32.837225959,NA,NA,NA,19,1963,4,,,,,, 31.1110006502,33.0615091134,NA,NA,NA,20,1964,1,,,,,, 31.045159744,33.28598686,NA,NA,NA,21,1964,2,,,,,, 31.1522012578,33.5106583813,NA,NA,NA,22,1964,3,,,,,, 31.2328229409,33.7355235973,NA,NA,NA,23,1964,4,,,,,, 31.4151846198,33.9605825887,31.4151846198,NA,NA,24,1965,1,,,,,, 31.4996864174,34.1858353991,31.5451621646,NA,NA,25,1965,2,,,,,, 31.7379939232,34.411282142,31.6823961791,NA,NA,26,1965,3,,,,,, 31.7603857461,34.6369229685,31.8187238201,NA,NA,27,1965,4,,,,,, 32.0533148842,34.8627580301,31.9539331921,NA,NA,28,1966,1,,,,,, 32.4829850845,35.088787485,32.0898712134,NA,NA,29,1966,2,,,,,, 32.6081136012,35.3150114973,32.2260104566,NA,NA,30,1966,3,,,,,, 33.1082366398,35.5414302325,32.3621085756,NA,NA,31,1966,4,,,,,, 33.0256737089,35.7680438567,32.4983553035,NA,NA,32,1967,1,,,,,, 33.3102788967,35.9948525371,32.634744717,NA,NA,33,1967,2,,,,,, 33.5898710208,36.2218564413,32.7712370524,NA,NA,34,1967,3,,,,,, 33.9093400596,36.4490557371,32.9078459341,NA,NA,35,1967,4,,,,,, 34.3561409186,36.6764505927,33.044575937,NA,NA,36,1968,1,,,,,, 34.6307558557,36.9040411762,33.1814227831,NA,NA,37,1968,2,,,,,, 35.2106795469,37.1318276561,33.3183869295,NA,NA,38,1968,3,,,,,, 35.5711327549,37.3598102011,33.4554693153,NA,NA,39,1968,4,,,,,, 36.0080894273,37.5879889798,33.5926696993,NA,NA,40,1969,1,,,,,, 36.6606355815,37.8163641612,33.7299881027,NA,NA,41,1969,2,,,,,, 37.1360332408,38.0449359144,33.8674247253,NA,NA,42,1969,3,,,,,, 37.6751991393,38.2737044085,34.0049796551,NA,NA,43,1969,4,,,,,, 38.3005703403,38.5026698128,34.1426529764,NA,NA,44,1970,1,,,,,, 38.9079571589,38.7318322969,34.280444799,NA,NA,45,1970,2,,,,,, 39.2733991806,38.9611920304,34.4183552264,NA,NA,46,1970,3,,,,,, 39.8148885174,39.190749183,34.5563843583,NA,NA,47,1970,4,,,,,, 40.2915758461,39.4205039248,34.6945322972,NA,NA,48,1971,1,,,,,, 40.4225895126,39.6504564257,34.8327991459,NA,NA,49,1971,2,,,,,, 41.0185075749,39.880606856,34.9711850064,NA,NA,50,1971,3,,,,,, 41.2142853032,40.110955386,35.1096899812,NA,NA,51,1971,4,,,,,, 41.5594233571,40.3415021864,35.2483141727,NA,NA,52,1972,1,,,,,, 41.8323265312,40.5722474276,35.3870576838,NA,NA,53,1972,2,,,,,, 42.1305292337,40.8031912806,35.5259206169,NA,NA,54,1972,3,,,,,, 42.5580908985,41.0343339163,35.664903075,NA,NA,55,1972,4,,,,,, 43.0849626521,41.2656755057,35.8040051609,NA,NA,56,1973,1,,,,,, 44.2451877539,41.4972162202,35.9432269775,NA,NA,57,1973,2,,,,,, 44.6132865443,41.7289562311,36.082568628,NA,NA,58,1973,3,,,,,, 46.3671644035,41.96089571,36.2220302155,NA,NA,59,1973,4,,,,,, 47.4424883646,42.1930348285,36.3616118431,NA,NA,60,1974,1,,,,,, 48.9233461033,42.4253737585,36.5013136143,NA,NA,61,1974,2,,,,,, 50.0952378443,42.6579126721,36.6411356325,NA,NA,62,1974,3,,,,,, 51.9839926027,42.8906517412,36.7810780011,NA,NA,63,1974,4,,,,,, 53.1741129397,43.1235911382,36.9211408237,NA,NA,64,1975,1,,,,,, 53.8053193308,43.3567310356,37.0613242041,NA,NA,65,1975,2,,,,,, 54.8708705552,43.5900716059,37.2016282459,NA,NA,66,1975,3,,,,,, 55.6636550103,43.8236130218,37.3420530531,NA,NA,67,1975,4,,,,,, 56.6131925387,44.0573554562,37.4825987296,NA,NA,68,1976,1,,,,,, 56.7317375164,44.2912990822,37.6232653794,NA,NA,69,1976,2,,,,,, 57.8182142576,44.5254440728,37.7640531066,NA,NA,70,1976,3,,,,,, 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1.263254580758,0.926875951549,0.926875951549,1.263254580758,NA,183,2004,4 1.101757976061,0.926875951549,0.926875951549,1.101757976061,NA,184,2005,1 1.480277083021,0.926875951549,0.926875951549,1.480277083021,NA,185,2005,2 0.8474719581,0.926875951549,0.926875951549,0.8474719581,NA,186,2005,3 2.641343677264,0.926875951549,0.926875951549,2.641343677264,NA,187,2005,4 0.299792134874,0.926875951549,0.926875951549,0.299792134874,NA,188,2006,1 2.323857621587,0.926875951549,0.926875951549,2.323857621587,NA,189,2006,2 0.368579502305,0.926875951549,0.926875951549,0.368579502305,NA,190,2006,3 1.145176180268,0.926875951549,0.926875951549,1.145176180268,NA,191,2006,4 0.767219433255,0.926875951549,0.926875951549,0.767219433255,NA,192,2007,1 1.546790605375,0.926875951549,0.926875951549,1.546790605375,NA,193,2007,2 0.98052154915,0.926875951549,0.926875951549,0.98052154915,NA,194,2007,3 1.809685838489,0.926875951549,0.926875951549,1.809685838489,NA,195,2007,4 1.594814571946,0.926875951549,0.926875951549,1.594814571946,NA,196,2008,1 1.576157878722,0.926875951549,0.926875951549,1.576157878722,NA,197,2008,2 2.627374926492,0.926875951549,0.926875951549,2.627374926492,NA,198,2008,3 -0.080501465061,0.926875951549,0.926875951549,-0.080501465061,NA,199,2008,4 -0.143905364796,0.926875951549,0.926875951549,-0.143905364796,NA,200,2009,1 -0.291651949853,0.926875951549,0.926875951549,-0.291651949853,NA,201,2009,2 0.543157406421,0.926875951549,0.926875951549,0.543157406423,NA,202,2009,3 1.196374008885,0.926875951549,0.926875951549,0.670188970211,1.196374008885,203,2009,4 1.265346864378,0.926875951549,0.926875951549,1.117335644106,1.265346864378,204,2010,1 1.173696210516,0.926875951549,0.926875951549,0.740831804756,1.173696210516,205,2010,2 1.078652308679,0.926875951549,0.926875951549,0.983052812221,1.078652308679,206,2010,3 1.037707266894,0.926875951549,0.926875951549,0.85483217265,1.037707266894,207,2010,4 1.014688660435,0.926875951549,0.926875951549,0.959658388297,1.014688660435,208,2011,1 0.979957079474,0.926875951549,0.926875951549,0.878568582037,0.979957079474,209,2011,2 0.970749320849,0.926875951549,0.926875951549,0.952456798029,0.970749320849,210,2011,3 0.954264512229,0.926875951549,0.926875951549,0.89723984453,0.954264512229,211,2011,4 0.949096714807,0.926875951549,0.926875951549,0.943096789796,0.949096714807,212,2012,1 0.940410834647,0.926875951549,0.926875951549,0.909314112718,0.940410834647,213,2012,2 0.938376248966,0.926875951549,0.926875951549,0.937273453716,0.938376248966,214,2012,3 0.933543521689,0.926875951549,0.926875951549,0.91618573467,0.933543521689,215,2012,4 0.932798795546,0.926875951549,0.926875951549,0.933567508384,0.932798795546,216,2013,1 0.930161942404,0.926875951549,0.926875951549,0.920353581631,0.930161942404,217,2013,2 0.929938463825,0.926875951549,0.926875951549,0.931133359137,0.929938463825,218,2013,3 0.928479634179,0.926875951549,0.926875951549,0.922889904339,0.928479634179,219,2013,4 0.928469062843,0.926875951549,0.926875951549,0.929572741746,0.928469062843,220,2014,1 0.927650865487,0.926875951549,0.926875951549,0.924430761588,0.927650865487,221,2014,2 0.927708891911,0.926875951549,0.926875951549,0.928578703482,0.927708891911,222,2014,3 0.927245679784,0.926875951549,0.926875951549,0.925372237336,0.927245679784,223,2014,4 0.927314186847,0.926875951549,0.926875951549,0.927947780535,0.927314186847,224,2015,1 0.927049147244,0.926875951549,0.926875951549,0.925949361587,0.927049147244,225,2015,2 0.927108200998,0.926875951549,0.926875951549,0.927549079877,0.927108200998,226,2015,3 0.926954981816,0.926875951549,0.926875951549,0.92630398656,0.926954981816,227,2015,4 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arma.py000066400000000000000000001136401224417117700272260ustar00rootroot00000000000000""" Results for ARMA models. Produced by gretl. """ import os from numpy import genfromtxt current_path = os.path.dirname(os.path.abspath(__file__)) yhat_mle = genfromtxt(open(current_path+"/yhat_exact_nc.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) yhat_css = genfromtxt(open(current_path+"/yhat_css_nc.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) yhatc_mle = genfromtxt(open(current_path+"/yhat_exact_c.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) yhatc_css = genfromtxt(open(current_path+"/yhat_css_c.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) resids_mle = genfromtxt(open(current_path+"/resids_exact_nc.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) resids_css = genfromtxt(open(current_path+"/resids_css_nc.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) residsc_mle = genfromtxt(open(current_path+"/resids_exact_c.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) residsc_css = genfromtxt(open(current_path+"/resids_css_c.csv", "rb"), delimiter=",", skip_header = 1, dtype=float) forecast_results = genfromtxt(open(current_path+"/results_arma_forecasts.csv", "rb"), names=True, delimiter=",", dtype=float) class Y_arma11(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.788452102751, 0.381793815167] self.aic = 714.489820273473 self.bic = 725.054203027060 self.arroots = 1.2683 + 0j self.maroots = -2.6192 + 0j self.bse = [0.042075906061, 0.060925105865] self.hqic = 718.741675179309 self.llf = -354.244910136737 self.resid = resids_mle[:,0] self.fittedvalues = yhat_mle[:,0] self.pvalues = [2.39e-78, 3.69e-10] self.tvalues = [18.74, 6.267] self.sigma2 = 0.994743350844 ** 2 self.cov_params = [[ 0.0017704, -0.0010612], [-0.0010612, 0.0037119 ]] self.forecast = forecast_results['fc11'] self.forecasterr = forecast_results['fe11'] elif method =="css": self.params = [0.791515576984, 0.383078056824] self.aic = 710.994047176570 self.bic = 721.546405865964 self.arroots = [ 1.2634 + 0.0000j] self.maroots = [-2.6104 +0.0000j] # self.bse = [0.042369318062, 0.065703859674] #NOTE: bse, cov_params, tvalues taken from R self.bse = [0.0424015620491, 0.0608752234378] # self.cov_params = [ #[ 0.0017952, -0.0010996], #[ -0.0010996, 0.0043170]] self.cov_params = [ [0.00179789246421, -0.00106195321540], [-0.00106195321540, 0.00370579282860]] self.hqic = 715.241545108550 self.llf = -352.497023588285 self.resid = resids_css[1:,0] self.fittedvalues = yhat_css[1:,0] self.pvalues = [ 7.02e-78, 5.53e-09] # self.tvalues = [18.68, 5.830] self.tvalues = [18.6671317239, 6.2928857557] self.sigma2 = 0.996717562780**2 class Y_arma14(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.763798613302, 0.306453049063, -0.835653786888, 0.151382611965, 0.421169903784] self.aic = 736.001094752429 self.bic = 757.129860259603 self.arroots = 1.3092 + 0j self.maroots = [1.0392 -0.7070j, 1.0392 + 0.7070j, -1.2189 -0.1310j, -1.2189 + 0.1310j] self.bse = [0.064888368113, 0.078031359430, 0.076246826219, 0.069267771804, 0.071567389557] self.cov_params = [[ 0.0042105, -0.0031074, -0.0027947, -0.00027766, -0.00037373 ], [ -0.0031074, 0.0060889, 0.0033958, -0.0026825, -0.00062289 ], [ -0.0027947, 0.0033958, 0.0058136, -0.00063747, -0.0028984 ], [ -0.00027766, -0.0026825, -0.00063747, 0.0047980, 0.0026998 ], [ -0.00037373, -0.00062289, -0.0028984, 0.0026998, 0.0051219 ]] self.hqic = 744.504804564101 self.llf = -362.000547376215 self.resid = resids_mle[:,1] self.fittedvalues = yhat_mle[:,1] self.pvalues = [5.51e-32, 8.59e-05, 5.96e-28, 0.0289, 3.98e-09] self.tvalues = [11.77, 3.927, -10.96, 2.185, 5.885] self.sigma2 = 1.022607088673 ** 2 self.bse = [0.064888368113, 0.078031359430, 0.076246826219, 0.069267771804, 0.071567389557] elif method =="css": self.params = [0.772072791055, 0.283961556581, -0.834797380642, 0.157773469382, 0.431616426021] self.aic = 734.294057687460 self.bic = 755.398775066249 self.arroots = [1.2952 +0.0000j] self.maroots = [1.0280 -0.6987j, 1.0280 +0.6987j, -1.2108 -0.1835j, -1.2108 +0.1835j] #NOTE: bse, cov_params, and tvalues taken from R # self.bse = [0.083423762397, 0.086852297123, 0.093883465705, # 0.068170451942, 0.065938183073] self.bse = [0.06106330, 0.07381130, 0.07257705, 0.06857992, 0.07046048] # self.cov_params = [ #[ 0.0069595, -0.0053083, -0.0054522, -0.0016324, -0.00099984], #[ -0.0053083, 0.0075433, 0.0052442, -0.00071680, 0.0010335], #[ -0.0054522, 0.0052442, 0.0088141, 0.0019754, -0.0018231], #[ -0.0016324, -0.00071680, 0.0019754, 0.0046472, 0.0011853], #[ -0.00099984, 0.0010335, -0.0018231, 0.0011853, 0.0043478]] self.cov_params = [ [ 0.0037287270, -0.0025337305, -0.0023475489, -0.0001894180, -0.0002716368], [-0.0025337305, 0.0054481087, 0.0029356374, -0.0027307668, -0.0008073432], [-0.0023475489, 0.0029356374, 0.0052674275, -0.0007578638, -0.0028534882], [-0.0001894180, -0.0027307668, -0.0007578638, 0.0047032056, 0.0026710177], [-0.0002716368, -0.0008073432, -0.0028534882, 0.0026710177, 0.0049646795] ] self.hqic = 742.789053551421 self.llf = -361.147028843730 self.resid = resids_css[1:,1] self.fittedvalues = yhat_css[1:,1] self.pvalues = [2.15e-20, 0.0011, 6.01e-19, 0.0206, 5.92e-11] # self.tvalues = [9.255, 3.269, -8.892, 2.314, 6.546] self.tvalues = [ 12.643194, 3.847252, -11.501785, 2.301399, 6.126120 ] self.sigma2 = 1.031950951582**2 class Y_arma41(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.859167822255, -0.445990454620, -0.094364739597, 0.633504596270, 0.039251240870] self.aic = 680.801215465509 self.bic = 701.929980972682 self.arroots = [1.0209-0j, 0.2966-0.9835j , 0.2966+0.9835j , -1.4652 + 0.0000j ] self.maroots = [-25.4769 + 0.0000] self.bse = [0.097363938243, 0.136020728785, 0.128467873077, 0.081059611396, 0.138536155409] self.cov_params = [ [ 0.0094797, -0.012908, 0.011870, -0.0073247, -0.011669], [ -0.012908, 0.018502, -0.017103, 0.010456, 0.015892], [ 0.011870, -0.017103, 0.016504, -0.010091, -0.014626], [ -0.0073247, 0.010456, -0.010091, 0.0065707, 0.0089767], [ -0.011669, 0.015892, -0.014626, 0.0089767, 0.019192]] self.hqic = 689.304925277181 self.llf = -334.400607732754 self.resid = resids_mle[:,2] self.fittedvalues = yhat_mle[:,2] self.pvalues = [1.10e-18, 0.0010, 0.4626, 5.48e-15, 0.7769] self.tvalues = [8.824, -3.279, -.7345, 7.815, .2833] self.sigma2 = 0.911409665692 ** 2 self.forecast = forecast_results['fc41'] self.forecasterr = forecast_results['fe41'] elif method =="css": self.params = [0.868370308475, -0.459433478113, -0.086098063077, 0.635050245511, 0.033645204508] self.aic = 666.171731561927 self.bic = 687.203720777521 self.arroots = [1.0184 +0.0000j, 0.2960 -0.9803j, 0.2960 +0.9803j, -1.4747 +0.0000j] self.maroots = [-29.7219 +0.0000j] #NOTE: bse, cov_params, and t are from R # self.bse = [0.077822066628, 0.112199961491, 0.104986211369, # 0.068394652456, 0.113996438269] self.bse = [0.09554032, 0.13387533, 0.12691479, 0.08045129, 0.13456419] # self.cov_params = [ #[ 0.0060563, -0.0083712, 0.0076270, -0.0047067, -0.0070610], #[ -0.0083712, 0.012589, -0.011391, 0.0069576, 0.0098601], #[ 0.0076270, -0.011391, 0.011022, -0.0067771, -0.0089971], #[ -0.0047067, 0.0069576, -0.0067771, 0.0046778, 0.0054205], #[ -0.0070610, 0.0098601, -0.0089971, 0.0054205, 0.012995] # ] self.cov_params = [ [ 0.009127952, -0.01243259, 0.011488329, -0.007070855, -0.011031907], [-0.012432590, 0.01792260, -0.016597806, 0.010136298, 0.015053122], [ 0.011488329, -0.01659781, 0.016107364, -0.009851695, -0.013923062], [-0.007070855, 0.01013630, -0.009851695, 0.006472410, 0.008562476], [-0.011031907, 0.01505312, -0.013923062, 0.008562476, 0.018107521] ] self.hqic = 674.640335476392 self.llf = -327.085865780964 self.resid = resids_css[4:,2] self.fittedvalues = yhat_css[4:,2] self.pvalues = [6.51e-29, 4.23e-05, 0.4122, 1.62e-20, 0.7679] # self.tvalues = [11.16, -4.095, -0.8201, 9.285, 0.2951] self.tvalues = [9.0887381, -3.4315100, -0.6786792, 7.8938778, 0.2503143 ] self.sigma2 = 0.914551777765**2 class Y_arma22(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.810898877154, -0.535753742985, 0.101765385197, -0.691891368356] self.aic = 756.286535543453 self.bic = 773.893840132765 self.arroots = [ 0.7568 -1.1375j, 0.7568 +1.1375j] self.maroots = [-1.1309, 1.2780] self.bse = [0.065073834100, 0.060522519771, 0.065569474599, 0.071275323591] self.cov_params = [ [ 0.0042346, -0.0012416, -0.0024319, -0.0012756], [ -0.0012416, 0.0036630, -0.00022460, -0.0019999], [ -0.0024319, -0.00022460, 0.0042994, 0.0017842], [ -0.0012756, -0.0019999, 0.0017842, 0.0050802]] self.hqic = 763.372960386513 self.llf = -373.143267771727 self.resid = resids_mle[:,3] self.fittedvalues = yhat_mle[:,3] self.pvalues = [1.22e-35 , 8.59e-19, 0.1207, 2.81e-22] self.tvalues = [12.46, -8.852, 1.552, -9.707] self.sigma2 = 1.069529754715**2 elif method =="css": self.params = [0.811172493623, -0.538952207139, 0.108020549805, -0.697398037845] self.aic = 749.652327535412 self.bic = 767.219471266237 self.arroots = [ 0.7525 -1.1354j, 0.7525 +1.1354j] self.maroots = [-1.1225 +0.0000j, 1.2774 +0.0000j] #NOTE: bse, cov_params, and tvalues taken from R # self.bse = [0.063356402845, 0.064719801680, 0.058293106832, # 0.061453528114] self.bse = [0.06549657, 0.06127495, 0.06514116, 0.07148213] # self.cov_params = [ #[ 0.0040140, -0.0016670, -0.0019069, -0.0011369], #[ -0.0016670, 0.0041887, -0.00019356, -0.0014322], #[ -0.0019069, -0.00019356, 0.0033981, 0.0020063], #[ -0.0011369, -0.0014322, 0.0020063, 0.0037765]] self.cov_params = [ [ 0.004289801, -0.0012980774, -0.0024461381, -0.001244467], [-0.001298077, 0.0037546193, -0.0001725373, -0.002039177], [-0.002446138, -0.0001725373, 0.0042433713, 0.001720042], [-0.001244467, -0.0020391767, 0.0017200417, 0.005109695] ] self.hqic = 756.724194601530 self.llf = -369.826163767706 self.resid = resids_css[2:,3] self.fittedvalues = yhat_css[2:,3] self.pvalues = [1.57e-37, 8.26e-17, 0.0639, 7.55e-30] # self.tvalues = [ 12.80, -8.327, 1.853, -11.35] self.tvalues = [12.385077, -8.795883, 1.657944, -9.755738] self.sigma2 = 1.074973483083**2 class Y_arma50(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.726892679311, -0.312619864536, 0.323740181610, 0.226499145083, -0.089562902305] self.aic = 691.422630427314 self.bic = 712.551395934487 self.arroots = [1.0772 +0.0000j, 0.0087 -1.2400j, 0.0087 + 1.2400j, -1.9764 +0.0000j, 3.4107 + 0.0000j] self.maroots = None #TODO: empty array? self.bse = [0.062942787895, 0.076539691571, 0.076608230545, 0.077330717503, 0.063499540628] self.cov_params = [ [ 0.0039618, -0.0028252, 0.0013351, -0.0013901, -0.00066624], [ -0.0028252, 0.0058583, -0.0040200, 0.0026059, -0.0014275], [ 0.0013351, -0.0040200, 0.0058688, -0.0041018, 0.0013917], [ -0.0013901, 0.0026059, -0.0041018, 0.0059800, -0.0028959], [ -0.00066624, -0.0014275, 0.0013917, -0.0028959, 0.0040322]] self.hqic = 699.926340238986 self.llf = -339.711315213657 self.resid = resids_mle[:,4] self.fittedvalues = yhat_mle[:,4] self.pvalues = [7.51e-31, 4.42e-05, 2.38e-05, 0.0034, 0.1584] self.tvalues = [11.55, -4.084, 4.226, 2.929, -1.410] self.sigma2 = 0.938374940397 ** 2 self.forecast = forecast_results['fc50'] self.forecasterr = forecast_results['fe50'] elif method =="css": #NOTE: some results use x-12 arima because gretl uses LS estimates for AR CSS self.params = [0.725706505843, -0.305501865989, 0.320719417706, 0.226552951649, -0.089852608091 ] # self.aic = 674.817286564674 self.aic = 676.8173 # self.bic = 692.323577617397 self.bic = 697.8248 self.arroots = [1.0755 +0.0000j,0.0075-1.2434j, 0.0075 +1.2434j, -1.9686 +0.0000j, 3.3994 +0.0000j] self.maroots = None self.bse = [0.064344956583, 0.078060866211, 0.077980166982, 0.078390791831, 0.064384559496] self.cov_params = [ [ 0.0041403, -0.0029335, 0.0013775, -0.0014298, -0.00068813], [ -0.0029335, 0.0060935, -0.0041786, 0.0026980, -0.0014765], [ 0.0013775, -0.0041786, 0.0060809, -0.0042177, 0.0014572], [ -0.0014298, 0.0026980, -0.0042177, 0.0061451, -0.0029853], [ -0.00068813, -0.0014765, 0.0014572, -0.0029853, 0.0041454]] # self.hqic = 681.867054880965 self.hqic = 685.2770 self.llf = -332.408643282337 self.resid = resids_css[5:,4] self.fittedvalues = yhat_css[5:,4] self.pvalues = [1.68e-29, 9.09e-05, 3.91e-05, 0.0039, 0.1628] self.tvalues = [11.28, -3.914, 4.113, 2.890, -1.396] # self.sigma2 = 0.949462810435**2 self.sigma2 = .939724 ** 2 class Y_arma02(object): def __init__(self, method="mle"): if method == "mle": self.params = [0.169096401142, -0.683713393265] self.aic = 775.017701544762 self.bic = 785.582084298349 self.arroots = None self.maroots = [-1.0920 + 0j, 1.3393 + 0j] self.bse = [0.049254112414, 0.050541821979] self.cov_params = [[0.0024260, 0.00078704], [0.00078704, 0.0025545]] self.hqic = 779.269556450598 self.llf = -384.508850772381 self.resid = resids_mle[:,5] self.fittedvalues = yhat_mle[:,5] self.pvalues = [.0006, 1.07e-41] self.tvalues = [3.433, -13.53] self.sigma2 = 1.122887152869 ** 2 elif method =="css": # bse, cov_params, tvalues taken from R self.params = [0.175605240783, -0.688421349504] self.aic = 773.725350463014 self.bic = 784.289733216601 self.arroots = None self.maroots = [-1.0844 + 0.j, 1.3395 +0.j] # self.bse = [0.044465497496, 0.045000813836] self.bse = [0.04850046, 0.05023068] # self.cov_params = [ #[ 0.0019772, 0.00090016], #[ 0.00090016, 0.0020251]] self.cov_params = [ [0.0023522942, 0.0007545702], [0.0007545702, 0.0025231209] ] self.hqic = 777.977205368850 self.llf = -383.862675231507 self.resid = resids_css[:,5] self.fittedvalues = yhat_css[:,5] self.pvalues = [7.84e-05, 7.89e-53] # self.tvalues = [3.949, -15.30] self.tvalues = [3.620967, -13.705514 ] self.sigma2 = 1.123571177436**2 class Y_arma11c(object): def __init__(self, method="mle"): if method == "mle": self.params = [4.856475759430, 0.664363281011, 0.407547531124] self.aic = 737.922644877973 self.bic = 752.008488549422 self.arroots = [1.5052 + 0j] self.maroots = [-2.4537 + 0j] self.bse = [0.273164176960, 0.055495689209, 0.068249092654] self.cov_params = [ [ 0.074619, -0.00012834, 1.5413e-05], [ -0.00012834, 0.0030798, -0.0020242], [ 1.5413e-05, -0.0020242, 0.0046579]] self.hqic = 743.591784752421 self.llf = -364.961322438987 self.resid = residsc_mle[:,0] self.fittedvalues = yhatc_mle[:,0] self.pvalues = [1.04e-70, 5.02e-33, 2.35e-9] self.tvalues = [17.78, 11.97, 5.971] self.sigma2 = 1.039168068701 ** 2 self.forecast = forecast_results['fc11c'] self.forecasterr = forecast_results['fe11c'] elif method =="css": # self.params = [1.625462134333, 0.666386002049, 0.409512270580] #NOTE: gretl gives the intercept not the mean, x-12-arima and R agree with us #NOTE: params, bse, cov_params, tvals from R self.params = [4.872477127267, 0.666395534262, 0.409517026658] self.aic = 734.613526514951 self.bic = 748.683338100810 self.arroots = [1.5006 +0.0000j] self.maroots = [-2.4419 +0.0000] # self.bse = [0.294788633992, 0.057503298669, 0.063059352497] self.bse = [ 0.2777238133284, 0.0557583459688, 0.0681432545482] # self.cov_params = [ #[ 0.086900, -0.016074, 0.010536], #[ -0.016074, 0.0033066, -0.0021977], #[ 0.010536, -0.0021977, 0.0039765] # ] self.cov_params = [ [7.71305164897e-02, 5.65375305967e-06, 1.29481824075e-06 ], [5.65375305967e-06, 3.10899314518e-03, -2.02754322743e-03], [1.29481824075e-06, -2.02754322743e-03, 4.64350314042e-03 ] ] self.hqic = 740.276857090925 self.llf = -363.306763257476 self.resid = residsc_css[1:,0] self.fittedvalues = yhatc_css[1:,0] self.pvalues = [ 3.51e-08, 4.70e-31, 8.35e-11] # self.tvalues = [5.514, 11.59, 6.494] self.tvalues = [17.544326, 11.951494, 6.009649] self.sigma2 = 1.040940645447**2 class Y_arma14c(object): def __init__(self, method="mle"): if method == "mle": self.params = [4.773779823083, 0.591149657917, 0.322267595204, -0.702933089342, 0.116129490967, 0.323009574097] self.aic = 720.814886758937 self.bic = 745.465113183973 self.arroots = [ 1.6916 +0.0000j] self.maroots = [1.1071 -0.7821j, 1.1071 +0.7821j, -1.2868 -0.1705j,-1.2868 +0.1705j] # had to change order? self.bse = [0.160891073193, 0.151756542096, 0.152996852330, 0.140231020145, 0.064663675882, 0.065045468010] self.cov_params = [ [0.025886, 0.00026606, -0.00020969, -0.00021435, 4.2558e-05, 5.2904e-05], [0.00026606, 0.023030, -0.021269, -0.018787, 0.0015423, 0.0011363], [-0.00020969, -0.021269, 0.023408, 0.018469, -0.0035048, -0.0010750], [-0.00021435, -0.018787, 0.018469, 0.019665, -0.00085717, -0.0033840], [4.2558e-05, 0.0015423, -0.0035048, -0.00085717, 0.0041814, 0.0014543], [5.2904e-05, 0.0011363, -0.0010750, -0.0033840, 0.0014543, 0.0042309]] self.hqic = 730.735881539221 self.llf = -353.407443379469 self.resid = residsc_mle[:,1] self.fittedvalues = yhatc_mle[:,1] self.pvalues = [1.82e-193, 9.80e-05, 0.0352, 5.37e-07, 0.0725, 6.84e-07] self.tvalues = [29.67, 3.895, 2.106, -5.013, 1.796, 4.966] self.sigma2 = 0.990262659233 ** 2 elif method =="css": #NOTE: params, bse, cov_params, and tvalues from R # self.params = [1.502401748545, 0.683090744792, 0.197636417391, # -0.763847295045, 0.137000823589, 0.304781097398] self.params = [4.740785760452, 0.683056278882, 0.197681128402, -0.763804443884, 0.136991271488, 0.304776424257] self.aic = 719.977407193363 self.bic = 744.599577468616 self.arroots = [1.4639 +0.0000j] self.maroots = [1.1306-0.7071j, 1.1306+0.7071j, -1.3554 -0.0896j, -1.3554 +0.0896j] # self.bse = [0.534723749868, 0.111273280223, 0.119840296133, # 0.111263606843, 0.070759105676, 0.061783181500] self.bse = [0.1750455599911, 0.0942341854820, 0.0999988749541, 0.0929630759694, 0.0628352649371, 0.0645444272345] # self.cov_params = [ #[ 0.28593, -0.059175, 0.053968, 0.046974, 0.00085168, 0.0028000 ], #[ -0.059175, 0.012382, -0.011333, -0.0098375, -0.00012631,-0.00058518 ], #[ 0.053968, -0.011333, 0.014362, 0.010298, -0.0028117, -0.00011132 ], #[ 0.046974, -0.0098375, 0.010298, 0.012380, 0.00031018, -0.0021617 ], #[ 0.00085168, -0.00012631, -0.0028117, 0.00031018, 0.0050069, 0.00079958 ], #[ .0028000, -0.00058518, -0.00011132, -0.0021617, 0.00079958, 0.0038172 ]] self.cov_params = [ [0.030640948072601, -1.61599091345e-03, 0.001707084515950, 0.001163372764659, -1.78587340563e-04, 0.000116062673743], [-0.001615990913449, 8.88008171345e-03, -0.007454252059003, -0.006468410832237, 5.66645379098e-05, -0.000381880917361], [0.001707084515950, -7.45425205900e-03, 0.009999774992092, 0.005860013051220, -2.27726197200e-03, 0.000757683049669], [0.001163372764659, -6.46841083224e-03, 0.005860013051220, 0.008642133493695, 4.40550745987e-04, -0.002170706208320], [-0.000178587340563, 5.66645379098e-05, -0.002277261972002, 0.000440550745987, 3.94827051971e-03, 0.000884171120090 ], [0.000116062673743, -3.81880917361e-04, 0.000757683049669, -0.002170706208320, 8.84171120090e-04, 0.004165983087027] ] self.hqic = 729.888235701317 self.llf = -352.988703596681 self.resid = residsc_css[1:,1] self.fittedvalues = yhatc_css[1:,1] self.pvalues = [0.0050, 8.31e-10, 0.0991, 6.64e-12, 0.0528, 8.09e-07] # self.tvalues = [2.810, 6.139, 1.649, -6.865, 1.936, 4.933] self.tvalues = [27.08315344127, 7.24849772286, 1.97683352430, -8.21621311385, 2.18016541548, 4.72196341831] self.sigma2 = 0.998687642867**2 class Y_arma41c(object): def __init__(self, method="mle"): if method == "mle": self.params = [1.062980233899, 0.768972932892, -0.264824839032, -0.279936544064, 0.756963578430, 0.231557444097] self.aic = 686.468309958027 self.bic = 711.118536383063 self.arroots = [1.0077 +0j, .3044-.9793j, .3044+.9793j, -1.2466 +0j] self.maroots = [-4.3186 + 0.j] self.bse = [2.781653916478, 0.063404432598, 0.091047664068, 0.084679571389, 0.054747989396, 0.098952817806] self.cov_params =[ [ 7.7376, 0.0080220, -0.0039840, 0.0064925, 0.0022936, -0.0098015], [ 0.0080220, 0.0040201, -0.0054843, 0.0046548, -0.0029922, -0.0047964], [ -0.0039840, -0.0054843, 0.0082897, -0.0072913, 0.0043566, 0.0067289], [ 0.0064925, 0.0046548, -0.0072913, 0.0071706, -0.0043610, -0.0057962], [ 0.0022936, -0.0029922, 0.0043566, -0.0043610, 0.0029973, 0.0036193], [ -0.0098015, -0.0047964, 0.0067289, -0.0057962, 0.0036193, 0.0097917]] self.hqic = 696.389304738311 self.llf = -336.234154979014 self.resid = residsc_mle[:,2] self.fittedvalues = yhatc_mle[:,2] self.pvalues = [0.7024, 7.50e-34, 0.0036, 0.0009, 1.77e-43, 0.0193] self.tvalues = [0.3821, 12.13, -2.909, -3.306, 13.83, 2.340] self.sigma2 = 0.915487643192 ** 2 self.forecast = forecast_results['fc41c'] self.forecasterr = forecast_results['fe41c'] elif method =="css": # self.params = [-0.077068926631, 0.763816531155, -0.270949972390, # -0.284496499726, 0.757135838677, 0.225247299659] #NOTE: params, cov_params, bse, and tvalues from R self.params = [-2.234160612756, 0.763815335585, -0.270946894536, -0.284497190744, 0.757136686518, 0.225260672575] self.aic = 668.907200379791 self.bic = 693.444521131318 self.arroots = [1.0141 +0.0000j, 0.3036 -0.9765j, 0.3036 +0.9765j, -1.2455 +0.0000j] self.maroots = [-4.4396 +0.0000j] # self.bse = [0.076048453921, 0.067854052128, 0.098041415680, # 0.090698349822, 0.057331126067, 0.099985455449] self.bse = [2.1842857865614, 0.0644148863289, 0.0923502391706, 0.0860004491012, 0.0558014467639, 0.1003832271008] # self.cov_params = [ #[ 0.0057834, 0.00052477, -0.00079965, 0.00061291, -0.00013618, -0.0018963 ], #[ 0.00052477, 0.0046042, -0.0062505, 0.0053416, -0.0032941, -0.0047957 ], #[-0.00079965, -0.0062505, 0.0096121, -0.0084500, 0.0047967, 0.0064755 ], #[ 0.00061291, 0.0053416, -0.0084500, 0.0082262, -0.0048029, -0.0057908 ], #[-0.00013618, -0.0032941, 0.0047967, -0.0048029, 0.0032869, 0.0035716 ], #[ -0.0018963, -0.0047957, 0.0064755, -0.0057908, 0.0035716, 0.0099971] # ] self.cov_params = [ [4.77110439737413, -0.00908682223670, 0.00330914414276, -0.00684678121434, -0.00232348925409, 0.00950558295301], [-0.00908682223670, -0.00562941039954, 0.00852856667488, -0.00749429397372, -0.00304322809665, -0.00494984519949], [0.00330914414276, -0.00562941039954, 0.00852856667488, -0.00749429397372, 0.00443590637587, 0.00693146988144], [-0.00684678121434, 0.00482359594764, -0.00749429397372, 0.00739607724561, -0.00448059420947, -0.00600908311031], [-0.00232348925409, -0.00304322809665, 0.00443590637587, -0.00448059420947, 0.00311380146095, 0.00373734623817 ], [0.00950558295301, -0.00494984519949, 0.00693146988144, -0.00600908311031, 0.00373734623817, 0.01007679228317]] self.hqic = 678.787238280001 self.llf = -327.453600189896 self.resid = residsc_css[4:,2] self.fittedvalues = yhatc_css[4:,2] self.pvalues = [0.3109, 2.15e-29, 0.0057, 0.0017, 8.06e-40, 0.0243] # self.tvalues = [-1.013, 11.26, -2.764, -3.137, 13.21, 2.253] self.tvalues = [-1.02283347101, 11.85774561000, -2.93390571556, -3.30808959392, 13.56840602577, 2.24400708246] self.sigma2 = 0.915919923456**2 class Y_arma22c(object): def __init__(self, method="mle"): if method == "mle": self.params = [4.507728587708, 0.788365037622, -0.358656861792, 0.035886565643, -0.699600200796] self.aic = 813.417242529788 self.bic = 834.546008036962 self.arroots = [1.0991 -1.2571j, 1.0991 +1.2571j] self.maroots = [-1.1702 +0.0000j, 1.2215 +0.0000j] self.bse = [0.045346684035, 0.078382496509, 0.07004802526, 0.069227816205, 0.070668181454] self.cov_params = [ [ 0.0020563, -2.3845e-05, -6.3775e-06, 4.6698e-05, 5.8515e-05], [ -2.3845e-05, 0.0061438, -0.0014403, -0.0035405, -0.0019265], [ -6.3775e-06, -0.0014403, 0.0049067, -0.00059888, -0.0025716], [ 4.6698e-05, -0.0035405, -0.00059888, 0.0047925, 0.0022931], [ 5.8515e-05, -0.0019265, -0.0025716, 0.0022931, 0.0049940]] self.hqic = 821.920952341460 self.llf = -400.708621264894 self.resid = residsc_mle[:,3] self.fittedvalues = yhatc_mle[:,3] self.pvalues = [0.0000, 8.48e-24, 3.05e-07, 0.6042, 4.17e-23] self.tvalues = [99.41, 10.06, -5.120, 0.5184, -9.900] self.sigma2 = 1.196309833136 ** 2 elif method =="css": #NOTE: params, bse, cov_params, and tvalues from R # self.params = [2.571274348147, 0.793030965872, -0.363511071688, # 0.033543918525, -0.702593972949] self.params = [4.507207454494, 0.793055048760, -0.363521072479, 0.033519062805, -0.702595834943] self.aic = 806.807171655455 self.bic = 827.887744132445 # self.bse = [0.369201481343, 0.076041378729, 0.070029488852, # 0.062547355221, 0.068166970089] self.bse = [0.0446913896589, 0.0783060902603, 0.0697866176073, 0.0681463870772, 0.068958002297] # self.cov_params = [ #[ 0.13631, -0.017255, -0.012852, 0.014091, 0.017241], #[ -0.017255, 0.0057823, -0.0020013, -0.0026493, -0.0014131], #[ -0.012852, -0.0020013, 0.0049041, -0.00042960, -0.0023845], #[ 0.014091, -0.0026493, -0.00042960, 0.0039122, 0.0022028], #[ 0.017241, -0.0014131, -0.0023845, 0.0022028, 0.0046467] # ] self.cov_params =[ [1.99732030964e-03, -2.22972353619e-05, -0.000009957435095, 4.64825632252e-05, 5.98134427402e-05], [-2.22972353619e-05, 6.13184377186e-03, -0.001435210779968, -3.47284237940e-03, -1.95077811843e-03 ], [-9.95743509501e-06,-1.43521077997e-03, 0.004870171997068, -6.54767224831e-04, -2.44459075151e-03], [ 4.64825632252e-05,-3.47284237940e-03, -0.000654767224831, 4.64393007167e-03, 2.34032945541e-03], [ 5.98134427402e-05,-1.95077811843e-03, -0.002444590751509, 2.34032945541e-03, 4.75520608091e-03]] self.arroots = [1.0908 -1.2494j, 1.0908 +1.2494j] self.maroots = [-1.1694 + 0.0000j, 1.2171 +0.0000j] self.hqic = 815.293412134796 self.llf = -397.403585827727 self.resid = residsc_css[2:,3] self.fittedvalues = yhatc_css[2:,3] self.pvalues = [3.30e-12, 1.83e-25, 2.09e-07, 0.5918, 6.55e-25] # self.tvalues = [ 6.964, 10.43, -5.191, 0.5363, -10.31] self.tvalues = [100.851808120009, 10.127629231947, -5.209036989363, 0.491868523669, -10.188749840927] self.sigma2 = 1.201409294941**2 class Y_arma50c(object): def __init__(self, method="mle"): if method == "mle": self.params = [4.562207236168, 0.754284447885, -0.305849188005, 0.253824706641,0.281161230244,-0.172263847479] self.aic = 711.817562780112 self.bic = 736.467789205148 self.arroots = [-1.6535 + 0.j, .0129 -1.2018j, .0129 + 1.2018j, 1.1546 + 0.j, 2.1052 + 0j] self.maroots = None self.bse = [0.318447388812, 0.062272737541, 0.076600312879, 0.077310728819, 0.076837326995, 0.062642955733] self.cov_params = [ [ 0.10141, -6.6930e-05, -7.3157e-05, -4.4815e-05, 7.7676e-05, -0.00013170], [-6.6930e-05, 0.0038779, -0.0028465, 0.0013770, -0.0012194, -0.00058978], [-7.3157e-05, -0.0028465, 0.0058676, -0.0040145, 0.0024694, -0.0012307], [-4.4815e-05, 0.0013770, -0.0040145, 0.0059769, -0.0040413, 0.0013481], [ 7.7676e-05, -0.0012194, 0.0024694, -0.0040413, 0.0059040, -0.0028575], [-0.00013170, -0.00058978, -0.0012307, 0.0013481, -0.0028575, 0.0039241]] self.hqic = 721.738557560396 self.llf = -348.908781390056 self.resid = residsc_mle[:,4] self.fittedvalues = yhatc_mle[:,4] self.pvalues = [1.50e-46, 9.06e-34, 6.53e-05, 0.0010, 0.0003, 0.0060] self.tvalues = [14.33, 12.11, -3.993, 3.283, 3.659, -2.750] self.sigma2 = 0.973930886014 ** 2 self.forecast = forecast_results['fc50c'] self.forecasterr = forecast_results['fe50c'] elif method =="css": #NOTE: params, bse, cov_params, tvalues from R #likelihood based results from x-12 arima # self.params = [0.843173779572, 0.755433266689, -0.296886816205, # 0.253572751789, 0.276975022313, -0.172637420881] self.params = [4.593494860193, 0.755427402630, -0.296867127441, 0.253556723526, 0.276987447724, -0.172647993470] # self.aic = 694.843378847617 self.aic = 696.8434 # self.bic = 715.850928110886 self.bic = 721.3522 self.arroots = [-1.6539 +0.0000j, 0.0091-1.2069j, 0.0091 +1.2069j, 1.1508 +0.0000j, 2.0892 +0.0000j] self.maroots = None # self.bse = [0.236922950898, 0.063573574389, 0.078206936773, # 0.078927252266, 0.078183651496, 0.063596048046] self.bse = [0.3359627893565, 0.0621593755265, 0.0764672280408, 0.0771715117870, 0.0764444608104, 0.0621813373935] # self.cov_params = [ #[ 0.056132, -0.0028895, -0.0012291, -0.0031424, -0.0012502, -0.0028739], #[ -0.0028895, 0.0040416, -0.0029508, 0.0014229, -0.0012546,-0.00062818], #[ -0.0012291, -0.0029508, 0.0061163, -0.0041939, 0.0025537, -0.0012585], #[ -0.0031424, 0.0014229, -0.0041939, 0.0062295, -0.0041928, 0.0014204], #[ -0.0012502, -0.0012546, 0.0025537, -0.0041928, 0.0061127, -0.0029479], #[ -0.0028739,-0.00062818, -0.0012585, 0.0014204, -0.0029479, 0.0040445] # ] self.cov_params = [ [ 1.12870995832e-01, 4.32810158586e-05, -1.89697385245e-05, 0.0000465331836881, -0.000024151327384, 0.000109807500875], [ 4.32810158586e-05, 3.86378796585e-03, -2.82098637123e-03, 0.001360256141301, -0.001199382243647, -0.000600542191229], [-1.89697385245e-05, -2.82098637123e-03, 5.84723696424e-03, -0.004009391809667, 0.002441359768335, -0.001203154760767], [ 4.65331836880e-05, 1.36025614130e-03, -4.00939180967e-03, 0.005955442231484, -0.004008307295820, 0.001357917028471], [-2.41513273840e-05, -1.19938224365e-03, 2.44135976834e-03, -0.004008307295820, 0.005843755588588, -0.002818181279545], [ 1.09807500875e-04, -6.00542191229e-04, -1.20315476077e-03, 0.001357917028471, -0.002818181279545, 0.003866518720043]] # self.hqic = 703.303100827167 self.hqic = 706.7131 self.llf = -341.421689423809 self.resid = residsc_css[5:,4] self.fittedvalues = yhatc_css[5:,4] self.pvalues = [0.0004, 1.45e-32, 0.0001, 0.0013, 0.0004, 0.0066] # self.tvalues = [ 3.559, 11.88, -3.796, 3.213, 3.543, -2.715] self.tvalues = [13.67262984389, 12.15307258528, -3.88227918086, 3.28562597329, 3.62338153462, -2.77652428699 ] # self.sigma2 = 0.987100631424**2 self.sigma2 = 0.974939 ** 2 class Y_arma02c(object): def __init__(self, method="mle"): if method == "mle": self.params = [4.519277801954, 0.200385403960, -0.643766305844] self.aic = 758.051194540770 self.bic = 772.137038212219 self.arroots = None self.maroots = [-1.1004 + 0.j, 1.4117 + 0.j] self.bse = [0.038397713362, 0.049314652466, 0.048961366071] self.cov_params = [ [ 0.0014744, 6.2363e-05, 6.4093e-05 ], [ 6.2363e-05, 0.0024319, 0.0014083 ], [ 6.4093e-05, 0.0014083, 0.0023972 ]] self.hqic = 763.720334415218 self.llf = -375.025597270385 self.resid = residsc_mle[:,5] self.fittedvalues = yhatc_mle[:,5] self.pvalues = [0.0000, 4.84e-5, 1.74e-39] self.tvalues = [117.7, 4.063, -13.15] self.sigma2 = 1.081406299967 ** 2 elif method =="css": #NOTE: cov_params and tvalues taken from R self.params = [4.519869870853, 0.202414429306, -0.647482560461] self.aic = 756.679105324347 self.bic = 770.764948995796 self.arroots = None self.maroots = [ -1.0962 + 0.0000j, 1.4089 + 0.0000j] self.bse = [0.038411589816, 0.047983057239, 0.043400749866] # self.cov_params = [ #[ 0.0014755, 9.0191e-05, 7.3561e-06], #[ 9.0191e-05, 0.0023024, 0.0012479], #[ 7.3561e-06, 0.0012479, 0.0018836]] self.cov_params = [ [1.46121526606e-03, 5.30770136338e-05, 5.34796521051e-05], [5.30770136338e-05, 2.37105883909e-03, 1.41090983316e-03], [5.34796521051e-05, 1.41090983316e-03, 2.35584355080e-03]] self.hqic = 762.348245198795 self.llf = -374.339552662174 self.resid = residsc_css[:,5] self.fittedvalues = yhatc_css[:,5] self.pvalues = [ 0.0000, 2.46e-05, 2.49e-50] # self.tvalues = [117.7, 4.218, -14.92] self.tvalues = [118.24120637494, 4.15691796413, -13.33981086206] self.sigma2 = 1.081576475937**2 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_arma_forecasts.csv000066400000000000000000000024421224417117700314370ustar00rootroot00000000000000"fc11","fe11","fc41","fe41","fc50","fe50","fc11c","fe11c","fc41c","fe41c","fc50c","fe50c" -0.0931139795,0.994743,-0.4298095393,0.91141,1.24361,0.938375,4.2276592922,1.03917,-1.3985445949,0.915488,4.779739852,0.973931 -0.073415913,1.53122,-1.4961938371,1.22521,0.484195,1.16009,4.4387131881,1.52336,3.0228581596,1.29504,4.692859693,1.21992 -0.057884931,1.78523,-3.770193621,1.2607,0.722602,1.17762,4.5789296469,1.6936,4.071345984,1.37495,3.8280155603,1.24654 -0.0456394955,1.92633,-3.6148341892,1.27585,1.09664,1.20138,4.6720843135,1.76352,0.8457602276,1.38243,3.9467079457,1.26508 -0.0359845562,2.00905,-1.5553772244,1.29131,0.854109,1.31833,4.7339728534,1.79351,-2.3126724383,1.39737,4.5998286564,1.37737 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statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/results_process.py000066400000000000000000000031001224417117700277510ustar00rootroot00000000000000import numpy as np from numpy import array class Holder(object): pass armarep = Holder() armarep.comment = 'mlab.garchma(-res_armarep.ar[1:], res_armarep.ma[1:], 20)' +\ 'mlab.garchar(-res_armarep.ar[1:], res_armarep.ma[1:], 20)' armarep.marep = array([[-0.1 ], [-0.77 ], [-0.305 ], [ 0.4635 ], [ 0.47575 ], [-0.132925 ], [-0.4470625 ], [-0.11719125 ], [ 0.299054375 ], [ 0.2432801875 ], [-0.11760340625 ], [-0.253425853125 ], [-0.0326302015625 ], [ 0.18642558171875], [ 0.11931695210938], [-0.08948198932031], [-0.14019455634766], [ 0.00148831328242], [ 0.11289980171934], [ 0.05525925023373]]) armarep.ar = array([ 1. , -0.5, 0.8]) armarep.ma = array([ 1. , -0.6 , 0.08]) armarep.name = 'armarep' armarep.arrep = array([[ -1.00000000000000e-01], [ -7.80000000000000e-01], [ -4.60000000000000e-01], [ -2.13600000000000e-01], [ -9.13600000000000e-02], [ -3.77280000000000e-02], [ -1.53280000000000e-02], [ -6.17856000000000e-03], [ -2.48089600000000e-03], [ -9.94252799999999e-04], [ -3.98080000000000e-04], [ -1.59307776000000e-04], [ -6.37382655999999e-05], [ -2.54983372800000e-05], [ -1.01999411200000e-05], [ -4.08009768959999e-06], [ -1.63206332416000e-06], [ -6.52830179327999e-07], [ -2.61133041663999e-07], [ -1.04453410652160e-07]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/results/savedrvs.py000066400000000000000000000546261224417117700263720ustar00rootroot00000000000000'''Generated Random Processes for tests autogenerated by savervs.py ''' import numpy as np from numpy import array class Holder(object): pass rvsdata = Holder() rvsdata.comment = 'generated data, divide by 1000, see savervs' rvsdata.xarma32 = array([-1271, -1222, -840, -169, -1016, -980, -1272, -926, 445, 833, -91, -1974, -2231, -549, 424, 238, -1665, -1815, 685, 3361, 1912, -1931, -3555, -1817, 387, 730, -1154, -702, 973, 1340, -161, 276, 200, 1785, 834, -1469, -1593, -134, 555, -422, -2314, -1326, -2268, -3579, -3049, -930, 1155, 962, -644, -217, -561, 224, 810, 2445, 2710, 2152, 502, 21, 164, -499, -1093, -492, 531, -605, -1535, -2081, -3816, -2257, 487, 2134, 1785, 1495, 1259, 1895, 1339, 617, 1143, 385, -1220, -738, 1171, 1047, -234, -107, -1458, -1244, -2737, 33, 2373, 2749, 2725, 3331, 1054, 418, 1231, -1171, -1446, -1187, 863, 1386, 757, 734, 283, -735, 550, 417, -236, 324, 318, -102, 2126, 3246, 2358, 2156, 726, -983, -803, -242, -500, -13, 49, 308, -227, 243, -612, -2329, -2476, -3441, -5435, -4693, -2538, -2159, -2656, -906, -211, -288, 1777, 1363, 564, -2035, -1134, -609, -1112, 560, 658, 1533, 796, 523, 456, 76, -1164, -749, -1084, -3218, -2107, -310, -686, -1625, 2008, 4155, 1650, -1086, -673, 1634, 1999, 449, -1077, -648, -155, -327, 228, 1295, 2036, 542, -197, -451, -1554, -2416, -2066, -2146, -1524, -1976, -2962, -2621, -2313, -2052, -3314, -2363, -1522, -3305, -3445, -3206, -1501, 2029, 1963, 1168, 2050, 2927, 2019, 84, 213, 1783, 617, -767, -425, 739, 281, 506, -749, -938, -284, -147, 51, 1296, 3033, 2263, 1409, -1702, -819, -1295, -1831, -539, 1327, 1954, 1473, -1535, -1187, -1310, 380, 1621, 2035, 2234, 559, 51, -1071, 590, 2128, 1483, 848, 1198, 2707, 1447, -629, 237, 909, 453, -734, -802, 1026, 521, -9, 919, 441, -118, -1073, -2428, 98, 823, 102, -438, -233, -613, 440, 1143, -743, -1345, 186, -1999, -2351, -887, -584, -883, -623, -1522, 974, 2318, 1329, -523, -2599, -1555, 826, -859, -2790, -2753, 807, 1889, -95, -1454, 443, 845, -291, 1516, 2804, 1018, 402, -446, -1721, -1824, 1678, 2889, -663, -560, 628, 1213, 520, -1344, -3029, -3100, -1603, -1480, -1667, -3356, -4405, -2556, 532, 1602, -15, 646, 2279, 1893, -945, -258, 344, -316, 1130, 1119, 695, 276, 56, -682, -610, 412, 1058, 259, 746, 1197, 1959, 1896, 127, -1301, 1036, 3094, 5213, 3846, 1728, 40, -520, -173, 330, -480, 649, 1621, 1622, -1011, -1851, -2687, -756, 401, 1888, 2372, 4153, 2531, -150, 485, 2600, 2193, -1238, -2702, -184, 1336, 370, -1196, -1737, 637, 634, 77, -1314, -688, -1375, -1973, -1229, -1414, -2230, -1922, -584, 93, 180, 2158, 2976, 1433, -173, -1073, -1362, -446, 242, 7, 354, 332, 2003, 1866, -729, -1446, -294, 2438, 3955, 1829, 485, 1028, 981, 1335, 513, -1386, -2583, -1063, 465, 1104, 85, -892, -78, 766, 1995, 891, -170, 2, -428, -562, -1078, -2591, -2077, -135, -238, -1150, -1207, -185, -46, -1319, -1829, -1409, -926, 576, 1119, 454, -747, -538, -739, -2994, -3052, -1626, -2472, -1340, -254, -972, -1182, -258, 831, 876, -244, -724, -208, -428, -110, 188, -2187, -2695, -1161, 597, 1492, 1594, -403, 695, 1834, 1737, 586, -740, 259, -714, -1607, -1082, -365, 2040, 604, -1253, -1269, -419, -713, -482, 1379, 2335, 1730, 325, -1377, -1721, -1762, -602, -1224, -839, 70, -1058, -118, -691, -1397, -245, -291, -648, -1489, -1088, -1083, -160, 1310, 169, -1539, -1558, -2095, -3421, -1609, -465, -867, 311, 272, -157, -936, -1003, -492, -1526, -2179, -1237, -662, -144, 638, 596, -629, -1893, -671, 324, 408, 367, 1438, 4568, 2576, 677, 701, 2667, 1288, 449, -357, 776, 2250, 2324, 968, 245, 1432, 1597, 843, 88, -274, -256, 830, 348, 534, 140, -560, -1582, -2012, -287, 1470, -729, -2398, -1433, -1409, -1547, 70, 1438, 2246, 408, -293, -566, 374, 1793, 2355, 1104, 358, 2301, 2994, 572, 278, 508, -2406, -2767, -1216, -231, -1717, -1038, 2015, 1469, 1471, 1395, 860, 1148, 1211, 1189, 494, -536, 383, -136, -2171, -2334, -1181, -294, -841, -2051, -3304, -2254, -926, -811, 160, 1960, 2945, 2466, 1922, 2833, 2421, 1197, 3025, 4033, 3210, 1497, 1912, 1138, 174, -630, -2423, -999, 296, 1519, 2061, 1400, -424, -609, -978, -1747, -1637, -2454, -1547, 885, 2065, 1530, -1956, 846, 2811, 3105, 2220, 2732, 4631, 3504, 1996, 246, -419, -1541, -1955, -3171, -2742, -811, -318, -1303, -2002, -997, -487, -2089, -3453, -3373, -1940, -620, 384, 365, -133, -1300, -833, -1544, -1711, -1981, -315, -155, -1995, -2384, -4010, -5394, -6186, -3794, -1829, -2637, -4255, -2014, 282, -174, -2623, -2023, -749, -168, -2387, -3959, -4101, -2004, -2070, -2468, -1831, -1518, 606, 305, 684, 2183, 1218, -1008, -2261, -1276, -99, 889, 740, -525, -1786, -1716, -452, -872, -1384, -1867, -547, -900, -1464, -1898, -1493, -990, 965, 810, 636, -335, -57, 1761, 2837, 773, 215, 920, 483, -234, 1301, 2610, 3083, 2329, 920, -827, 22, 4317, 5366, 3711, 2220, 1356, 198, -1385, 656, 1163, -370, -1721, -1005, -832, -1455, -1485, 221, -1445, -1502, -79, -4, -599, -850, -507, 902, 1909, 1642, -326, -3379, -5642, -7068, -4275, -1044, 528, 548, 249, -1384, -2485, -1533, -1776, -2930, -2058, -1721, -475, -166, -1761, -2550, -1586, -240, -1584, -1954, 623, 3826, 2094, -1004, -1782, -267, 2490, 3336, 2293, 189, -108, -315, -965, -125, 1201, 360, -544, -1602, -2150, -901, 1430, 968, -1100, 505, 2880, 2554, 928, 918, 689, -2829, -2478, -2904, -1615, -242, 243, -1668, -877, 2385, 543, -2462, -1762, 470, 1344, 1493, 1624, 257, -1833, -1947, -805, -413, 905, 2909, 3272, 1148, -1473, -2368, -1054, -2143, -4330, -3257, -1939, -1831, -414, -1157, -1212, -1644, -1360, -2409, -4136, -5747, -3415, -1752, 373, -1680, -1267, 2267, 2701, 1101, -1714, -3138, -3153, -3256, -3328, -2661, -879, 2115, 1795, -324, -1930, -1432, -1613, -2301, -1401, 88, 1369, 1063, -854, -2125, 243, 1683, 2011, 2646, 1289, -938, -1205, -1214, 562, 2641, 3335, 2858, 2650, 1965, 478, 1391, 486, 255, -1764, -813, 84, -453, -809, -1203, -1590, 730, 2059, 234, -319, 0, 624, 1273, 1470, 1882, 2215, 1611, 485, -16, 397, 593, -95, 125, 1435, 2673, 3073, 2262, 1803, 983, 666, 1516, 2821, 2395, 299, 86, 1150, 1214, 751, -1096, -962, -39, -366, -2125, -2086, -1032, -966, -863, -1522, -1793, 1228, 207, -2243, -1916, -1320, -1530, -2318, -1050, -663, -1137, -2035, -1198, -246, 753, -185, -709, -231, -1111, -1121, -11, 976, 555, -1947, -1304, 807, 529, 231, -285, -553, -695, -2006, -1090, -424, 318, -1113]) rvsdata.name = 'rvsdata' rvsdata.xnormal = array([-1271, 176, -296, 327, -973, 228, -819, 107, 975, 82, -477, -1492, -403, 695, 212, 91, -1549, -45, 1557, 1947, -785, -2139, -1264, 295, 806, 278, -1244, 787, 752, 173, -738, 969, -646, 1811, -990, -1369, 72, 408, 169, -587, -1517, 720, -2150, -1233, -121, 682, 1268, -29, -802, 679, -1041, 934, 344, 1788, 486, 460, -834, 40, -93, -751, -374, 345, 500, -1167, -387, -966, -2369, 1119, 1148, 1193, 411, 626, 45, 960, -293, 11, 806, -866, -1043, 574, 1072, -328, -381, 433, -1857, 409, -2190, 2614, 1005, 864, 1243, 1268, -1701, 680, 560, -2567, 639, -663, 1513, 215, 69, 498, -504, -771, 1392, -739, -131, 744, -382, -158, 2394, 712, 162, 1064, -1146, -1062, 248, -171, -411, 665, -236, 350, -503, 645, -1105, -1447, -337, -2050, -2539, -151, 85, -840, -555, 1235, -427, 106, 2227, -828, 252, -2248, 992, -737, -559, 1801, -593, 1438, -583, 342, -10, -331, -1053, 466, -1020, -2163, 1003, 224, -794, -406, 3338, 1021, -1157, -788, 242, 1219, 267, -455, -843, 183, -196, -181, 699, 822, 825, -920, 110, -482, -1332, -843, -256, -989, 232, -1082, -1221, -200, -800, -344, -1779, 559, -485, -2241, -84, -1173, 701, 2685, -395, 644, 1374, 741, -144, -788, 542, 1044, -1150, -296, 281, 485, -495, 829, -1385, 80, 192, -237, 340, 1213, 1634, -264, 479, -2698, 1282, -1759, -422, 1003, 1015, 668, 332, -2367, 835, -1347, 1532, 828, 766, 907, -1122, 265, -1357, 1658, 879, -199, 433, 532, 1482, -925, -812, 1081, -191, -126, -637, -45, 1306, -863, 326, 954, -806, 42, -885, -1504, 2167, -496, -63, -19, -61, -654, 1153, 340, -1586, 97, 836, -2868, 439, 380, -652, -34, 197, -1342, 2507, 481, -228, -748, -1941, 596, 1137, -1978, -857, -546, 2208, 151, -864, -446, 1297, -507, -417, 2265, 596, -1011, 719, -1112, -1279, -184, 2721, 371, -2244, 1511, -127, 365, -53, -1399, -1628, -736, 312, -785, -182, -2070, -1452, 640, 1479, 648, -754, 1396, 1005, -183, -1661, 1296, -547, -532, 1901, -511, 232, -27, -191, -734, 140, 647, 406, -449, 997, 204, 1035, 352, -1083, -788, 2025, 1127, 2903, -184, -197, -888, -536, 82, 279, -775, 1426, 490, 362, -1900, -219, -1753, 1342, 166, 1677, 753, 2518, -1078, -990, 1138, 1235, -197, -2026, -747, 1329, 255, -323, -722, -716, 1677, -746, 298, -1190, 509, -1420, -498, 302, -996, -883, -12, 443, 139, 260, 2131, 620, -535, -443, -870, -671, 535, 213, -172, 612, -169, 1841, -195, -1629, -3, 265, 2091, 1611, -929, 225, 486, -338, 922, -582, -1433, -1072, 765, 424, 696, -541, -524, 612, 278, 1405, -777, -163, 188, -805, 36, -692, -1680, 268, 810, -688, -359, -120, 386, -248, -1015, -387, -273, -158, 1263, 271, -209, -716, 208, -738, -2268, -37, 5, -1793, 1277, 46, -967, 151, 427, 579, 178, -621, -189, 167, -563, 481, 93, -2388, -120, 359, 751, 946, 613, -1484, 1690, 387, 285, -258, -870, 936, -1574, -400, 204, -70, 2254, -1548, -593, -89, -66, -599, 452, 1518, 658, 195, -496, -1363, -468, -759, 681, -1159, 579, 368, -1393, 1360, -1277, -474, 959, -779, -77, -864, 141, -632, 770, 1119, -1125, -857, -153, -1451, -1597, 1318, -337, -436, 1453, -619, -64, -625, -214, 78, -1334, -519, 313, -293, 446, 719, -93, -860, -1006, 823, 57, 199, 332, 1112, 3079, -1616, 338, 298, 1515, -1213, 603, -811, 1023, 1171, 464, -372, -24, 1105, -43, -1, -208, -340, -102, 970, -632, 743, -459, -520, -977, -671, 1096, 974, -1956, -601, 251, -1197, -108, 1305, 661, 1135, -1164, 195, -628, 708, 1211, 773, -470, 102, 1923, 405, -1286, 932, -349, -2927, 277, 144, -19, -1333, 988, 2027, -952, 1495, 91, -288, 784, 127, 341, -324, -670, 967, -1015, -1599, -98, -9, 157, -541, -1040, -1576, 297, 67, -285, 1094, 1433, 1051, 440, 491, 1410, -145, -138, 2498, 764, 408, -237, 1099, -888, -184, -541, -1975, 1272, 87, 1229, 908, -97, -1121, 168, -949, -891, -88, -1521, 596, 1512, 747, 259, -2640, 3297, 236, 1135, 500, 1288, 2137, -399, 380, -1022, -439, -1345, -514, -1828, -69, 770, -307, -693, -606, 370, -290, -1584, -1193, -834, 148, 404, 771, 18, -207, -1068, 393, -1392, -163, -807, 1152, -564, -1495, -210, -2692, -1930, -2043, 660, -58, -1329, -1511, 1339, 458, -536, -1669, 511, -210, 167, -2028, -1333, -1271, 661, -1274, -334, 64, -619, 1818, -811, 1078, 1446, -927, -1106, -984, 181, 235, 902, 44, -836, -1021, -298, 479, -916, -219, -811, 804, -1060, -300, -710, -163, -115, 1647, -480, 582, -807, 412, 1486, 972, -1188, 649, 326, -605, -109, 1607, 847, 1140, 266, -492, -1229, 912, 3582, 914, 580, 283, -375, -834, -1206, 1985, -468, -814, -702, 211, -700, -608, -113, 1086, -2231, 662, 581, -565, -131, -197, -39, 1113, 863, 236, -1199, -2557, -2587, -2859, 948, 822, 911, 448, 71, -1579, -959, 292, -1273, -1206, 474, -908, 929, -126, -1497, -608, 106, 371, -1552, 61, 1758, 2286, -1107, -1079, -534, 404, 2099, 1035, 219, -997, 143, -666, -652, 850, 788, -780, -118, -1156, -878, 675, 1530, -576, -1022, 1862, 1282, 75, -105, 604, -507, -3169, 907, -2181, 792, 540, 180, -1660, 1113, 2179, -2179, -1223, 567, 560, 544, 906, 580, -1054, -1474, -186, 110, -189, 1452, 1823, 684, -856, -1508, -974, 353, -1953, -1918, 418, -598, -513, 1332, -1457, 226, -905, -78, -1575, -1908, -2347, 923, -460, 1745, -2262, 1119, 2445, -200, 25, -1892, -1465, -1012, -1193, -797, -204, 784, 2291, -382, -782, -1097, -103, -1085, -802, 521, 547, 1020, 17, -1255, -948, 1733, 352, 971, 1444, -955, -1251, -38, -844, 1473, 1673, 1071, 687, 818, -109, -758, 1446, -1260, 507, -2043, 1091, -143, -573, 40, -705, -693, 1992, 582, -1145, 532, -176, 341, 802, 455, 858, 713, -47, -389, -137, 288, 63, -443, 499, 1048, 1202, 1044, 157, 472, -423, 135, 978, 1325, 104, -958, 465, 564, -14, 220, -1498, 330, 158, -601, -1414, -14, -29, -535, 186, -954, -468, 2492, -1869, -1194, 345, -835, -578, -838, 802, -596, -418, -898, 414, 91, 851, -824, -34, 193, -1276, 208, 593, 538, -175, -1954, 860, 843, -610, 617, -511, -339, -261, -1495, 833, -298, 637, -1384]) rvsdata.xar2 = array([-1271, -841, -333, 481, -422, -350, -889, -428, 1077, 1158, -89, -2142, -2073, 108, 1335, 1105, -1332, -1663, 892, 3493, 1563, -2635, -4154, -1710, 1515, 2345, -125, -486, 426, 757, -346, 314, -222, 1476, 302, -1866, -1571, 84, 1022, 189, -1877, -876, -1912, -2325, 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-0.4500016338,1.1752951286,-3.5066103886,-1.9759240640,-0.6709051154,0.8818860021 0.4372161970,2.4192075123,-2.7256311999,2.8077499707,1.1706347223,-0.6429555574 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_adfuller_lag.py000066400000000000000000000034541224417117700265050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for autolag of adfuller, unitroot_adf Created on Wed May 30 21:39:46 2012 Author: Josef Perktold """ import numpy as np from numpy.testing import assert_equal, assert_almost_equal import statsmodels.tsa.stattools as tsast from statsmodels.datasets import macrodata def test_adf_autolag(): #see issue #246 #this is mostly a unit test d2 = macrodata.load().data for k_trend, tr in enumerate(['nc', 'c', 'ct', 'ctt']): #[None:'nc', 0:'c', 1:'ct', 2:'ctt'] x = np.log(d2['realgdp']) xd = np.diff(x) #check exog adf3 = tsast.adfuller(x, maxlag=None, autolag='aic', regression=tr, store=True, regresults=True) st2 = adf3[-1] assert_equal(len(st2.autolag_results), 15 + 1) #+1 for lagged level for l, res in sorted(st2.autolag_results.iteritems())[:5]: lag = l-k_trend #assert correct design matrices in _autolag assert_equal(res.model.exog[-10:,k_trend], x[-11:-1]) assert_equal(res.model.exog[-1,k_trend+1:], xd[-lag:-1][::-1]) #min-ic lag of dfgls in Stata is also 2, or 9 for maic with notrend assert_equal(st2.usedlag, 2) #same result with lag fixed at usedlag of autolag adf2 = tsast.adfuller(x, maxlag=2, autolag=None, regression=tr) assert_almost_equal(adf3[:2], adf2[:2], decimal=12) tr = 'c' #check maxlag with autolag adf3 = tsast.adfuller(x, maxlag=5, autolag='aic', regression=tr, store=True, regresults=True) assert_equal(len(adf3[-1].autolag_results), 5 + 1) adf3 = tsast.adfuller(x, maxlag=0, autolag='aic', regression=tr, store=True, regresults=True) assert_equal(len(adf3[-1].autolag_results), 0 + 1) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_ar.py000066400000000000000000000263631224417117700244720ustar00rootroot00000000000000""" Test AR Model """ import statsmodels.api as sm from statsmodels.tsa.ar_model import AR from numpy.testing import (assert_almost_equal, assert_equal, assert_allclose, assert_) from results import results_ar import numpy as np import numpy.testing as npt from pandas import Series, Index DECIMAL_6 = 6 DECIMAL_5 = 5 DECIMAL_4 = 4 class CheckARMixin(object): def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_6) def test_bse(self): bse = np.sqrt(np.diag(self.res1.cov_params())) # no dof correction # for compatability with Stata assert_almost_equal(bse, self.res2.bse_stata, DECIMAL_6) assert_almost_equal(self.res1.bse, self.res2.bse_gretl, DECIMAL_5) def test_llf(self): assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_6) def test_fpe(self): assert_almost_equal(self.res1.fpe, self.res2.fpe, DECIMAL_6) def test_pickle(self): from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() #test wrapped results load save pickle self.res1.save(fh) fh.seek(0,0) res_unpickled = self.res1.__class__.load(fh) assert_(type(res_unpickled) is type(self.res1)) class TestAROLSConstant(CheckARMixin): """ Test AR fit by OLS with a constant. """ @classmethod def setupClass(cls): data = sm.datasets.sunspots.load() cls.res1 = AR(data.endog).fit(maxlag=9, method='cmle') cls.res2 = results_ar.ARResultsOLS(constant=True) def test_predict(self): model = self.res1.model params = self.res1.params assert_almost_equal(model.predict(params),self.res2.FVOLSnneg1start0, DECIMAL_4) assert_almost_equal(model.predict(params),self.res2.FVOLSnneg1start9, DECIMAL_4) assert_almost_equal(model.predict(params, start=100), self.res2.FVOLSnneg1start100, DECIMAL_4) assert_almost_equal(model.predict(params, start=9, end=200), self.res2.FVOLSn200start0, DECIMAL_4) assert_almost_equal(model.predict(params, start=200, end=400), self.res2.FVOLSn200start200, DECIMAL_4) #assert_almost_equal(model.predict(params, n=200,start=-109), # self.res2.FVOLSn200startneg109, DECIMAL_4) assert_almost_equal(model.predict(params, start=308, end=424), self.res2.FVOLSn100start325, DECIMAL_4) assert_almost_equal(model.predict(params, start=9, end=310), self.res2.FVOLSn301start9, DECIMAL_4) assert_almost_equal(model.predict(params), self.res2.FVOLSdefault, DECIMAL_4) assert_almost_equal(model.predict(params, start=308, end=316), self.res2.FVOLSn4start312, DECIMAL_4) assert_almost_equal(model.predict(params, start=308, end=327), self.res2.FVOLSn15start312, DECIMAL_4) class TestAROLSNoConstant(CheckARMixin): """f Test AR fit by OLS without a constant. """ @classmethod def setupClass(cls): data = sm.datasets.sunspots.load() cls.res1 = AR(data.endog).fit(maxlag=9,method='cmle',trend='nc') cls.res2 = results_ar.ARResultsOLS(constant=False) def test_predict(self): model = self.res1.model params = self.res1.params assert_almost_equal(model.predict(params),self.res2.FVOLSnneg1start0, DECIMAL_4) assert_almost_equal(model.predict(params),self.res2.FVOLSnneg1start9, DECIMAL_4) assert_almost_equal(model.predict(params, start=100), self.res2.FVOLSnneg1start100, DECIMAL_4) assert_almost_equal(model.predict(params, start=9, end=200), self.res2.FVOLSn200start0, DECIMAL_4) assert_almost_equal(model.predict(params, start=200, end=400), self.res2.FVOLSn200start200, DECIMAL_4) #assert_almost_equal(model.predict(params, n=200,start=-109), # self.res2.FVOLSn200startneg109, DECIMAL_4) assert_almost_equal(model.predict(params, start=308,end=424), self.res2.FVOLSn100start325, DECIMAL_4) assert_almost_equal(model.predict(params, start=9, end=310), self.res2.FVOLSn301start9, DECIMAL_4) assert_almost_equal(model.predict(params), self.res2.FVOLSdefault, DECIMAL_4) assert_almost_equal(model.predict(params, start=308, end=316), self.res2.FVOLSn4start312, DECIMAL_4) assert_almost_equal(model.predict(params, start=308, end=327), self.res2.FVOLSn15start312, DECIMAL_4) #class TestARMLEConstant(CheckAR): class TestARMLEConstant(object): @classmethod def setupClass(cls): data = sm.datasets.sunspots.load() cls.res1 = AR(data.endog).fit(maxlag=9,method="mle", disp=-1) cls.res2 = results_ar.ARResultsMLE(constant=True) def test_predict(self): model = self.res1.model params = self.res1.params assert_almost_equal(model.predict(params), self.res2.FVMLEdefault, DECIMAL_4) assert_almost_equal(model.predict(params, start=9, end=308), self.res2.FVMLEstart9end308, DECIMAL_4) assert_almost_equal(model.predict(params, start=100, end=308), self.res2.FVMLEstart100end308, DECIMAL_4) assert_almost_equal(model.predict(params, start=0, end=200), self.res2.FVMLEstart0end200, DECIMAL_4) # Note: factor 0.5 in below two tests needed to meet precision on OS X. assert_almost_equal(0.5 * model.predict(params, start=200, end=333), 0.5 * self.res2.FVMLEstart200end334, DECIMAL_4) assert_almost_equal(0.5 * model.predict(params, start=308, end=333), 0.5 * self.res2.FVMLEstart308end334, DECIMAL_4) assert_almost_equal(model.predict(params, start=9,end=309), self.res2.FVMLEstart9end309, DECIMAL_4) assert_almost_equal(model.predict(params, end=301), self.res2.FVMLEstart0end301, DECIMAL_4) assert_almost_equal(model.predict(params, start=4, end=312), self.res2.FVMLEstart4end312, DECIMAL_4) assert_almost_equal(model.predict(params, start=2, end=7), self.res2.FVMLEstart2end7, DECIMAL_4) def test_dynamic_predict(self): res1 = self.res1 res2 = self.res2 rtol = 8e-6 # assert_raises pre-sample # 9, 51 start, end = 9, 51 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol) # 9, 308 start, end = 9, 308 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol) # 9, 333 start, end = 9, 333 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol) # 100, 151 start, end = 100, 151 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol) # 100, 308 start, end = 100, 308 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol) # 100, 333 start, end = 100, 333 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol) # 308, 308 start, end = 308, 308 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn3[start:end+1], rtol=rtol) # 308, 333 start, end = 308, 333 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn3[start:end+1], rtol=rtol) # 309, 333 start, end = 309, 333 fv = res1.predict(start, end, dynamic=True) assert_allclose(fv, res2.fcdyn4[start:end+1], rtol=rtol) # None, None start, end = None, None fv = res1.predict(dynamic=True) assert_allclose(fv, res2.fcdyn[9:309], rtol=rtol) class TestAutolagAR(object): @classmethod def setupClass(cls): data = sm.datasets.sunspots.load() endog = data.endog results = [] for lag in range(1,16+1): endog_tmp = endog[16-lag:] r = AR(endog_tmp).fit(maxlag=lag) # See issue #324 for why we're doing these corrections vs. R # results k_ar = r.k_ar k_trend = r.k_trend log_sigma2 = np.log(r.sigma2) #import ipdb; ipdb.set_trace() aic = r.aic aic = (aic - log_sigma2) * (1 + k_ar)/(1 + k_ar + k_trend) aic += log_sigma2 hqic = r.hqic hqic = (hqic - log_sigma2) * (1 + k_ar)/(1 + k_ar + k_trend) hqic += log_sigma2 bic = r.bic bic = (bic - log_sigma2) * (1 + k_ar)/(1 + k_ar + k_trend) bic += log_sigma2 results.append([aic, hqic, bic, r.fpe]) res1 = np.asarray(results).T.reshape(4,-1, order='C') # aic correction to match R cls.res1 = res1 cls.res2 = results_ar.ARLagResults("const").ic def test_ic(self): npt.assert_almost_equal(self.res1, self.res2, DECIMAL_6) def test_ar_dates(): # just make sure they work data = sm.datasets.sunspots.load() dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog)) endog = Series(data.endog, index=dates) ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1) pred = ar_model.predict(start='2005', end='2015') predict_dates = sm.tsa.datetools.dates_from_range('2005', '2015') try: from pandas import DatetimeIndex # pylint: disable-msg=E0611 predict_dates = DatetimeIndex(predict_dates, freq='infer') except ImportError: pass assert_equal(ar_model.data.predict_dates, predict_dates) assert_equal(pred.index, predict_dates) def test_ar_named_series(): dates = sm.tsa.datetools.dates_from_range("2011m1", length=72) y = Series(np.random.randn(72), name="foobar", index=dates) results = sm.tsa.AR(y).fit(2) assert_(results.params.index.equals(Index(["const", "L1.foobar", "L2.foobar"]))) def test_ar_start_params(): # fix 236 # smoke test data = sm.datasets.sunspots.load() res = AR(data.endog).fit(maxlag=9, start_params=0.1*np.ones(10.), method="mle", disp=-1) def test_ar_series(): # smoke test for 773 dta = sm.datasets.macrodata.load_pandas().data["cpi"].diff().dropna() dates = sm.tsa.datetools.dates_from_range("1959Q1", length=len(dta)) dta.index = dates ar = AR(dta).fit(maxlags=15) ar.bse #TODO: likelihood for ARX model? #class TestAutolagARX(object): # def setup(self): # data = sm.datasets.macrodata.load() # endog = data.data.realgdp # exog = data.data.realint # results = [] # for lag in range(1, 26): # endog_tmp = endog[26-lag:] # exog_tmp = exog[26-lag:] # r = AR(endog_tmp, exog_tmp).fit(maxlag=lag, trend='ct') # results.append([r.aic, r.hqic, r.bic, r.fpe]) # self.res1 = np.asarray(results).T.reshape(4,-1, order='C') statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_arima.py000066400000000000000000002223671224417117700251630ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_almost_equal, assert_equal, assert_, assert_raises, dec) import statsmodels.sandbox.tsa.fftarma as fa from statsmodels.tsa.descriptivestats import TsaDescriptive from statsmodels.tsa.arma_mle import Arma from statsmodels.tsa.arima_model import ARMA, ARIMA from statsmodels.tsa.base.datetools import dates_from_range from results import results_arma, results_arima import os from statsmodels.tsa.base import datetools from statsmodels.tsa.arima_process import arma_generate_sample import pandas try: from statsmodels.tsa.kalmanf import kalman_loglike fast_kalman = 1 except: fast_kalman = 0 #NOTE: the KF with complex input returns a different precision for # the hessian imaginary part, so we use approx_hess and the the # resulting stats are slightly different. DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 current_path = os.path.dirname(os.path.abspath(__file__)) y_arma = np.genfromtxt(open(current_path + '/results/y_arma_data.csv', "rb"), delimiter=",", skip_header=1, dtype=float) cpi_dates = dates_from_range('1959Q1', '2009Q3') cpi_predict_dates = dates_from_range('2009Q3', '2015Q4') sun_dates = dates_from_range('1700', '2008') sun_predict_dates = dates_from_range('2008', '2033') try: from pandas import DatetimeIndex # pylint: disable-msg=E0611 cpi_dates = DatetimeIndex(cpi_dates, freq='infer') sun_dates = DatetimeIndex(sun_dates, freq='infer') cpi_predict_dates = DatetimeIndex(cpi_predict_dates, freq='infer') sun_predict_dates = DatetimeIndex(sun_predict_dates, freq='infer') except ImportError: pass def test_compare_arma(): #this is a preliminary test to compare arma_kf, arma_cond_ls and arma_cond_mle #the results returned by the fit methods are incomplete #for now without random.seed np.random.seed(9876565) x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) # this used kalman filter through descriptive # d = ARMA(x) # d.fit((1,1), trend='nc') # dres = d.res modkf = ARMA(x, (1,1)) ##rkf = mkf.fit((1,1)) ##rkf.params reskf = modkf.fit(trend='nc', disp=-1) dres = reskf modc = Arma(x) resls = modc.fit(order=(1,1)) rescm = modc.fit_mle(order=(1,1), start_params=[0.4,0.4, 1.], disp=0) #decimal 1 corresponds to threshold of 5% difference #still different sign corrcted #assert_almost_equal(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(resls[0] / dres.params, np.ones(dres.params.shape), decimal=1) #rescm also contains variance estimate as last element of params #assert_almost_equal(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(rescm.params[:-1] / dres.params, np.ones(dres.params.shape), decimal=1) #return resls[0], d.params, rescm.params class CheckArmaResultsMixin(object): """ res2 are the results from gretl. They are in results/results_arma. res1 are from statsmodels """ decimal_params = DECIMAL_4 def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, self.decimal_params) decimal_aic = DECIMAL_4 def test_aic(self): assert_almost_equal(self.res1.aic, self.res2.aic, self.decimal_aic) decimal_bic = DECIMAL_4 def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic, self.decimal_bic) decimal_arroots = DECIMAL_4 def test_arroots(self): assert_almost_equal(self.res1.arroots, self.res2.arroots, self.decimal_arroots) decimal_maroots = DECIMAL_4 def test_maroots(self): assert_almost_equal(self.res1.maroots, self.res2.maroots, self.decimal_maroots) decimal_bse = DECIMAL_2 def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, self.decimal_bse) decimal_cov_params = DECIMAL_4 def test_covparams(self): assert_almost_equal(self.res1.cov_params(), self.res2.cov_params, self.decimal_cov_params) decimal_hqic = DECIMAL_4 def test_hqic(self): assert_almost_equal(self.res1.hqic, self.res2.hqic, self.decimal_hqic) decimal_llf = DECIMAL_4 def test_llf(self): assert_almost_equal(self.res1.llf, self.res2.llf, self.decimal_llf) decimal_resid = DECIMAL_4 def test_resid(self): assert_almost_equal(self.res1.resid, self.res2.resid, self.decimal_resid) decimal_fittedvalues = DECIMAL_4 def test_fittedvalues(self): assert_almost_equal(self.res1.fittedvalues, self.res2.fittedvalues, self.decimal_fittedvalues) decimal_pvalues = DECIMAL_2 def test_pvalues(self): assert_almost_equal(self.res1.pvalues, self.res2.pvalues, self.decimal_pvalues) decimal_t = DECIMAL_2 # only 2 decimal places in gretl output def test_tvalues(self): assert_almost_equal(self.res1.tvalues, self.res2.tvalues, self.decimal_t) decimal_sigma2 = DECIMAL_4 def test_sigma2(self): assert_almost_equal(self.res1.sigma2, self.res2.sigma2, self.decimal_sigma2) def test_summary(self): # smoke tests table = self.res1.summary() class CheckForecastMixin(object): decimal_forecast = DECIMAL_4 def test_forecast(self): assert_almost_equal(self.res1.forecast_res, self.res2.forecast, self.decimal_forecast) decimal_forecasterr = DECIMAL_4 def test_forecasterr(self): assert_almost_equal(self.res1.forecast_err, self.res2.forecasterr, self.decimal_forecasterr) class CheckDynamicForecastMixin(object): decimal_forecast_dyn = 4 def test_dynamic_forecast(self): assert_almost_equal(self.res1.forecast_res_dyn, self.res2.forecast_dyn, self.decimal_forecast_dyn) #def test_forecasterr(self): # assert_almost_equal(self.res1.forecast_err_dyn, # self.res2.forecasterr_dyn, # DECIMAL_4) class CheckArimaResultsMixin(CheckArmaResultsMixin): def test_order(self): assert self.res1.k_diff == self.res2.k_diff assert self.res1.k_ar == self.res2.k_ar assert self.res1.k_ma == self.res2.k_ma decimal_predict_levels = DECIMAL_4 def test_predict_levels(self): assert_almost_equal(self.res1.predict(typ='levels'), self.res2.linear, self.decimal_predict_levels) #NOTE: Ok class Test_Y_ARMA11_NoConst(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,0] cls.res1 = ARMA(endog, order=(1,1)).fit(trend='nc', disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma11() def test_pickle(self): from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() #test wrapped results load save pickle self.res1.save(fh) fh.seek(0,0) res_unpickled = self.res1.__class__.load(fh) assert_(type(res_unpickled) is type(self.res1)) #NOTE: Ok class Test_Y_ARMA14_NoConst(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,1] cls.res1 = ARMA(endog, order=(1,4)).fit(trend='nc', disp=-1) cls.res2 = results_arma.Y_arma14() #NOTE: Ok #can't use class decorators in 2.5.... #@dec.slow class Test_Y_ARMA41_NoConst(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,2] cls.res1 = ARMA(endog, order=(4,1)).fit(trend='nc', disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma41() cls.decimal_maroots = DECIMAL_3 #NOTE: Ok class Test_Y_ARMA22_NoConst(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,3] cls.res1 = ARMA(endog, order=(2,2)).fit(trend='nc', disp=-1) cls.res2 = results_arma.Y_arma22() #NOTE: Ok class Test_Y_ARMA50_NoConst(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,4] cls.res1 = ARMA(endog, order=(5,0)).fit(trend='nc', disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma50() #NOTE: Ok class Test_Y_ARMA02_NoConst(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,5] cls.res1 = ARMA(endog, order=(0,2)).fit(trend='nc', disp=-1) cls.res2 = results_arma.Y_arma02() #NOTE: Ok class Test_Y_ARMA11_Const(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,6] cls.res1 = ARMA(endog, order=(1,1)).fit(trend="c", disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma11c() #NOTE: OK class Test_Y_ARMA14_Const(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,7] cls.res1 = ARMA(endog, order=(1,4)).fit(trend="c", disp=-1) cls.res2 = results_arma.Y_arma14c() #NOTE: Ok #@dec.slow class Test_Y_ARMA41_Const(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,8] cls.res1 = ARMA(endog, order=(4,1)).fit(trend="c", disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma41c() cls.decimal_cov_params = DECIMAL_3 cls.decimal_fittedvalues = DECIMAL_3 cls.decimal_resid = DECIMAL_3 cls.decimal_params = DECIMAL_3 if fast_kalman: cls.decimal_cov_params -= 2 cls.decimal_bse -= 1 #NOTE: Ok class Test_Y_ARMA22_Const(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,9] cls.res1 = ARMA(endog, order=(2,2)).fit(trend="c", disp=-1) cls.res2 = results_arma.Y_arma22c() #NOTE: Ok class Test_Y_ARMA50_Const(CheckArmaResultsMixin, CheckForecastMixin): @classmethod def setupClass(cls): endog = y_arma[:,10] cls.res1 = ARMA(endog, order=(5,0)).fit(trend="c", disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma50c() #NOTE: Ok class Test_Y_ARMA02_Const(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,11] cls.res1 = ARMA(endog, order=(0,2)).fit(trend="c", disp=-1) cls.res2 = results_arma.Y_arma02c() #NOTE: # cov_params and tvalues are off still but not as much vs. R class Test_Y_ARMA11_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,0] cls.res1 = ARMA(endog, order=(1,1)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma11("css") cls.decimal_t = DECIMAL_1 # better vs. R class Test_Y_ARMA14_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,1] cls.res1 = ARMA(endog, order=(1,4)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma14("css") cls.decimal_fittedvalues = DECIMAL_3 cls.decimal_resid = DECIMAL_3 cls.decimal_t = DECIMAL_1 #NOTE: Ok #NOTE: # bse, etc. better vs. R # maroot is off because maparams is off a bit (adjust tolerance?) class Test_Y_ARMA41_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,2] cls.res1 = ARMA(endog, order=(4,1)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma41("css") cls.decimal_t = DECIMAL_1 cls.decimal_pvalues = 0 cls.decimal_cov_params = DECIMAL_3 cls.decimal_maroots = DECIMAL_1 #NOTE: Ok #same notes as above class Test_Y_ARMA22_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,3] cls.res1 = ARMA(endog, order=(2,2)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma22("css") cls.decimal_t = DECIMAL_1 cls.decimal_resid = DECIMAL_3 cls.decimal_pvalues = DECIMAL_1 cls.decimal_fittedvalues = DECIMAL_3 #NOTE: Ok #NOTE: gretl just uses least squares for AR CSS # so BIC, etc. is # -2*res1.llf + np.log(nobs)*(res1.q+res1.p+res1.k) # with no adjustment for p and no extra sigma estimate #NOTE: so our tests use x-12 arima results which agree with us and are # consistent with the rest of the models class Test_Y_ARMA50_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,4] cls.res1 = ARMA(endog, order=(5,0)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma50("css") cls.decimal_t = 0 cls.decimal_llf = DECIMAL_1 # looks like rounding error? #NOTE: ok class Test_Y_ARMA02_NoConst_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,5] cls.res1 = ARMA(endog, order=(0,2)).fit(method="css", trend='nc', disp=-1) cls.res2 = results_arma.Y_arma02("css") #NOTE: Ok #NOTE: our results are close to --x-12-arima option and R class Test_Y_ARMA11_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,6] cls.res1 = ARMA(endog, order=(1,1)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma11c("css") cls.decimal_params = DECIMAL_3 cls.decimal_cov_params = DECIMAL_3 cls.decimal_t = DECIMAL_1 #NOTE: Ok class Test_Y_ARMA14_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,7] cls.res1 = ARMA(endog, order=(1,4)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma14c("css") cls.decimal_t = DECIMAL_1 cls.decimal_pvalues = DECIMAL_1 #NOTE: Ok class Test_Y_ARMA41_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,8] cls.res1 = ARMA(endog, order=(4,1)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma41c("css") cls.decimal_t = DECIMAL_1 cls.decimal_cov_params = DECIMAL_1 cls.decimal_maroots = DECIMAL_3 cls.decimal_bse = DECIMAL_1 #NOTE: Ok class Test_Y_ARMA22_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,9] cls.res1 = ARMA(endog, order=(2,2)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma22c("css") cls.decimal_t = 0 cls.decimal_pvalues = DECIMAL_1 #NOTE: Ok class Test_Y_ARMA50_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,10] cls.res1 = ARMA(endog, order=(5,0)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma50c("css") cls.decimal_t = DECIMAL_1 cls.decimal_params = DECIMAL_3 cls.decimal_cov_params = DECIMAL_2 #NOTE: Ok class Test_Y_ARMA02_Const_CSS(CheckArmaResultsMixin): @classmethod def setupClass(cls): endog = y_arma[:,11] cls.res1 = ARMA(endog, order=(0,2)).fit(trend="c", method="css", disp=-1) cls.res2 = results_arma.Y_arma02c("css") def test_reset_trend(): endog = y_arma[:,0] mod = ARMA(endog, order=(1,1)) res1 = mod.fit(trend="c", disp=-1) res2 = mod.fit(trend="nc", disp=-1) assert_equal(len(res1.params), len(res2.params)+1) #@dec.slow def test_start_params_bug(): data = np.array([1368., 1187, 1090, 1439, 2362, 2783, 2869, 2512, 1804, 1544, 1028, 869, 1737, 2055, 1947, 1618, 1196, 867, 997, 1862, 2525, 3250, 4023, 4018, 3585, 3004, 2500, 2441, 2749, 2466, 2157, 1847, 1463, 1146, 851, 993, 1448, 1719, 1709, 1455, 1950, 1763, 2075, 2343, 3570, 4690, 3700, 2339, 1679, 1466, 998, 853, 835, 922, 851, 1125, 1299, 1105, 860, 701, 689, 774, 582, 419, 846, 1132, 902, 1058, 1341, 1551, 1167, 975, 786, 759, 751, 649, 876, 720, 498, 553, 459, 543, 447, 415, 377, 373, 324, 320, 306, 259, 220, 342, 558, 825, 994, 1267, 1473, 1601, 1896, 1890, 2012, 2198, 2393, 2825, 3411, 3406, 2464, 2891, 3685, 3638, 3746, 3373, 3190, 2681, 2846, 4129, 5054, 5002, 4801, 4934, 4903, 4713, 4745, 4736, 4622, 4642, 4478, 4510, 4758, 4457, 4356, 4170, 4658, 4546, 4402, 4183, 3574, 2586, 3326, 3948, 3983, 3997, 4422, 4496, 4276, 3467, 2753, 2582, 2921, 2768, 2789, 2824, 2482, 2773, 3005, 3641, 3699, 3774, 3698, 3628, 3180, 3306, 2841, 2014, 1910, 2560, 2980, 3012, 3210, 3457, 3158, 3344, 3609, 3327, 2913, 2264, 2326, 2596, 2225, 1767, 1190, 792, 669, 589, 496, 354, 246, 250, 323, 495, 924, 1536, 2081, 2660, 2814, 2992, 3115, 2962, 2272, 2151, 1889, 1481, 955, 631, 288, 103, 60, 82, 107, 185, 618, 1526, 2046, 2348, 2584, 2600, 2515, 2345, 2351, 2355, 2409, 2449, 2645, 2918, 3187, 2888, 2610, 2740, 2526, 2383, 2936, 2968, 2635, 2617, 2790, 3906, 4018, 4797, 4919, 4942, 4656, 4444, 3898, 3908, 3678, 3605, 3186, 2139, 2002, 1559, 1235, 1183, 1096, 673, 389, 223, 352, 308, 365, 525, 779, 894, 901, 1025, 1047, 981, 902, 759, 569, 519, 408, 263, 156, 72, 49, 31, 41, 192, 423, 492, 552, 564, 723, 921, 1525, 2768, 3531, 3824, 3835, 4294, 4533, 4173, 4221, 4064, 4641, 4685, 4026, 4323, 4585, 4836, 4822, 4631, 4614, 4326, 4790, 4736, 4104, 5099, 5154, 5121, 5384, 5274, 5225, 4899, 5382, 5295, 5349, 4977, 4597, 4069, 3733, 3439, 3052, 2626, 1939, 1064, 713, 916, 832, 658, 817, 921, 772, 764, 824, 967, 1127, 1153, 824, 912, 957, 990, 1218, 1684, 2030, 2119, 2233, 2657, 2652, 2682, 2498, 2429, 2346, 2298, 2129, 1829, 1816, 1225, 1010, 748, 627, 469, 576, 532, 475, 582, 641, 605, 699, 680, 714, 670, 666, 636, 672, 679, 446, 248, 134, 160, 178, 286, 413, 676, 1025, 1159, 952, 1398, 1833, 2045, 2072, 1798, 1799, 1358, 727, 353, 347, 844, 1377, 1829, 2118, 2272, 2745, 4263, 4314, 4530, 4354, 4645, 4547, 5391, 4855, 4739, 4520, 4573, 4305, 4196, 3773, 3368, 2596, 2596, 2305, 2756, 3747, 4078, 3415, 2369, 2210, 2316, 2263, 2672, 3571, 4131, 4167, 4077, 3924, 3738, 3712, 3510, 3182, 3179, 2951, 2453, 2078, 1999, 2486, 2581, 1891, 1997, 1366, 1294, 1536, 2794, 3211, 3242, 3406, 3121, 2425, 2016, 1787, 1508, 1304, 1060, 1342, 1589, 2361, 3452, 2659, 2857, 3255, 3322, 2852, 2964, 3132, 3033, 2931, 2636, 2818, 3310, 3396, 3179, 3232, 3543, 3759, 3503, 3758, 3658, 3425, 3053, 2620, 1837, 923, 712, 1054, 1376, 1556, 1498, 1523, 1088, 728, 890, 1413, 2524, 3295, 4097, 3993, 4116, 3874, 4074, 4142, 3975, 3908, 3907, 3918, 3755, 3648, 3778, 4293, 4385, 4360, 4352, 4528, 4365, 3846, 4098, 3860, 3230, 2820, 2916, 3201, 3721, 3397, 3055, 2141, 1623, 1825, 1716, 2232, 2939, 3735, 4838, 4560, 4307, 4975, 5173, 4859, 5268, 4992, 5100, 5070, 5270, 4760, 5135, 5059, 4682, 4492, 4933, 4737, 4611, 4634, 4789, 4811, 4379, 4689, 4284, 4191, 3313, 2770, 2543, 3105, 2967, 2420, 1996, 2247, 2564, 2726, 3021, 3427, 3509, 3759, 3324, 2988, 2849, 2340, 2443, 2364, 1252, 623, 742, 867, 684, 488, 348, 241, 187, 279, 355, 423, 678, 1375, 1497, 1434, 2116, 2411, 1929, 1628, 1635, 1609, 1757, 2090, 2085, 1790, 1846, 2038, 2360, 2342, 2401, 2920, 3030, 3132, 4385, 5483, 5865, 5595, 5485, 5727, 5553, 5560, 5233, 5478, 5159, 5155, 5312, 5079, 4510, 4628, 4535, 3656, 3698, 3443, 3146, 2562, 2304, 2181, 2293, 1950, 1930, 2197, 2796, 3441, 3649, 3815, 2850, 4005, 5305, 5550, 5641, 4717, 5131, 2831, 3518, 3354, 3115, 3515, 3552, 3244, 3658, 4407, 4935, 4299, 3166, 3335, 2728, 2488, 2573, 2002, 1717, 1645, 1977, 2049, 2125, 2376, 2551, 2578, 2629, 2750, 3150, 3699, 4062, 3959, 3264, 2671, 2205, 2128, 2133, 2095, 1964, 2006, 2074, 2201, 2506, 2449, 2465, 2064, 1446, 1382, 983, 898, 489, 319, 383, 332, 276, 224, 144, 101, 232, 429, 597, 750, 908, 960, 1076, 951, 1062, 1183, 1404, 1391, 1419, 1497, 1267, 963, 682, 777, 906, 1149, 1439, 1600, 1876, 1885, 1962, 2280, 2711, 2591, 2411]) res = ARMA(data, order=(4,1)).fit(disp=-1) class Test_ARIMA101(CheckArmaResultsMixin): # just make sure this works @classmethod def setupClass(cls): endog = y_arma[:,6] cls.res1 = ARIMA(endog, (1,0,1)).fit(trend="c", disp=-1) (cls.res1.forecast_res, cls.res1.forecast_err, confint) = cls.res1.forecast(10) cls.res2 = results_arma.Y_arma11c() cls.res2.k_diff = 0 cls.res2.k_ar = 1 cls.res2.k_ma = 1 class Test_ARIMA111(CheckArimaResultsMixin, CheckForecastMixin, CheckDynamicForecastMixin): @classmethod def setupClass(cls): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] cls.res1 = ARIMA(cpi, (1,1,1)).fit(disp=-1) cls.res2 = results_arima.ARIMA111() # make sure endog names changes to D.cpi cls.decimal_llf = 3 cls.decimal_aic = 3 cls.decimal_bic = 3 cls.decimal_cov_params = 2 # this used to be better? cls.decimal_t = 0 (cls.res1.forecast_res, cls.res1.forecast_err, conf_int) = cls.res1.forecast(25) #cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=226, typ='levels', dynamic=True) #TODO: fix the indexing for the end here, I don't think this is right # if we're going to treat it like indexing # the forecast from 2005Q1 through 2009Q4 is indices # 184 through 227 not 226 # note that the first one counts in the count so 164 + 64 is 65 # predictions cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=164+63, typ='levels', dynamic=True) def test_freq(self): assert_almost_equal(self.res1.arfreq, [0.0000], 4) assert_almost_equal(self.res1.mafreq, [0.0000], 4) class Test_ARIMA111CSS(CheckArimaResultsMixin, CheckForecastMixin, CheckDynamicForecastMixin): @classmethod def setupClass(cls): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] cls.res1 = ARIMA(cpi, (1,1,1)).fit(disp=-1, method='css') cls.res2 = results_arima.ARIMA111(method='css') cls.res2.fittedvalues = - cpi[1:-1] + cls.res2.linear # make sure endog names changes to D.cpi (cls.res1.forecast_res, cls.res1.forecast_err, conf_int) = cls.res1.forecast(25) cls.decimal_forecast = 2 cls.decimal_forecast_dyn = 2 cls.decimal_forecasterr = 3 cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=164+63, typ='levels', dynamic=True) # precisions cls.decimal_arroots = 3 cls.decimal_cov_params = 3 cls.decimal_hqic = 3 cls.decimal_maroots = 3 cls.decimal_t = 1 cls.decimal_fittedvalues = 2 # because of rounding when copying cls.decimal_resid = 2 #cls.decimal_llf = 3 #cls.decimal_aic = 3 #cls.decimal_bic = 3 cls.decimal_predict_levels = DECIMAL_2 class Test_ARIMA112CSS(CheckArimaResultsMixin): @classmethod def setupClass(cls): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] cls.res1 = ARIMA(cpi, (1,1,2)).fit(disp=-1, method='css', start_params = [.905322, -.692425, 1.07366, 0.172024]) cls.res2 = results_arima.ARIMA112(method='css') cls.res2.fittedvalues = - cpi[1:-1] + cls.res2.linear # make sure endog names changes to D.cpi cls.decimal_llf = 3 cls.decimal_aic = 3 cls.decimal_bic = 3 #(cls.res1.forecast_res, # cls.res1.forecast_err, # conf_int) = cls.res1.forecast(25) #cls.res1.forecast_res_dyn = cls.res1.predict(start=164, end=226, typ='levels', dynamic=True) #TODO: fix the indexing for the end here, I don't think this is right # if we're going to treat it like indexing # the forecast from 2005Q1 through 2009Q4 is indices # 184 through 227 not 226 # note that the first one counts in the count so 164 + 64 is 65 # predictions #cls.res1.forecast_res_dyn = self.predict(start=164, end=164+63, # typ='levels', dynamic=True) # since we got from gretl don't have linear prediction in differences cls.decimal_arroots = 3 cls.decimal_maroots = 2 cls.decimal_t = 1 cls.decimal_resid = 2 cls.decimal_fittedvalues = 3 cls.decimal_predict_levels = DECIMAL_3 def test_freq(self): assert_almost_equal(self.res1.arfreq, [0.5000], 4) assert_almost_equal(self.res1.mafreq, [0.5000, 0.5000], 4) #class Test_ARIMADates(CheckArmaResults, CheckForecast, CheckDynamicForecast): # @classmethod # def setupClass(cls): # from statsmodels.datasets.macrodata import load # from statsmodels.tsa.datetools import dates_from_range # # cpi = load().data['cpi'] # dates = dates_from_range('1959q1', length=203) # cls.res1 = ARIMA(cpi, dates=dates, freq='Q').fit(order=(1,1,1), disp=-1) # cls.res2 = results_arima.ARIMA111() # # make sure endog names changes to D.cpi # cls.decimal_llf = 3 # cls.decimal_aic = 3 # cls.decimal_bic = 3 # (cls.res1.forecast_res, # cls.res1.forecast_err, # conf_int) = cls.res1.forecast(25) def test_arima_predict_mle_dates(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] res1 = ARIMA(cpi, (4,1,1), dates=cpi_dates, freq='Q').fit(disp=-1) arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_mle.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] start, end = 2, 51 fv = res1.predict('1959Q3', '1971Q4', typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) assert_equal(res1.data.predict_dates, cpi_dates[start:end+1]) start, end = 202, 227 fv = res1.predict('2009Q3', '2015Q4', typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) assert_equal(res1.data.predict_dates, cpi_predict_dates) # make sure dynamic works start, end = '1960q2', '1971q4' fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[5:51+1], DECIMAL_4) start, end = '1965q1', '2015q4' fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[24:227+1], DECIMAL_4) def test_arma_predict_mle_dates(): from statsmodels.datasets.sunspots import load sunspots = load().data['SUNACTIVITY'] mod = ARMA(sunspots, (9,0), dates=sun_dates, freq='A') mod.method = 'mle' assert_raises(ValueError, mod._get_predict_start, *('1701', True)) start, end = 2, 51 _ = mod._get_predict_start('1702', False) _ = mod._get_predict_end('1751') assert_equal(mod.data.predict_dates, sun_dates[start:end+1]) start, end = 308, 333 _ = mod._get_predict_start('2008', False) _ = mod._get_predict_end('2033') assert_equal(mod.data.predict_dates, sun_predict_dates) def test_arima_predict_css_dates(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] res1 = ARIMA(cpi, (4,1,1), dates=cpi_dates, freq='Q').fit(disp=-1, method='css', trend='nc') params = np.array([ 1.231272508473910, -0.282516097759915, 0.170052755782440, -0.118203728504945, -0.938783134717947]) arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_css.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] start, end = 5, 51 fv = res1.model.predict(params, '1960Q2', '1971Q4', typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) assert_equal(res1.data.predict_dates, cpi_dates[start:end+1]) start, end = 202, 227 fv = res1.model.predict(params, '2009Q3', '2015Q4', typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) assert_equal(res1.data.predict_dates, cpi_predict_dates) # make sure dynamic works start, end = 5, 51 fv = res1.model.predict(params, '1960Q2', '1971Q4', typ='levels', dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) start, end = '1965q1', '2015q4' fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[24:227+1], DECIMAL_4) def test_arma_predict_css_dates(): from statsmodels.datasets.sunspots import load sunspots = load().data['SUNACTIVITY'] mod = ARMA(sunspots, (9,0), dates=sun_dates, freq='A') mod.method = 'css' assert_raises(ValueError, mod._get_predict_start, *('1701', False)) def test_arima_predict_mle(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] res1 = ARIMA(cpi, (4,1,1)).fit(disp=-1) # fit the model so that we get correct endog length but use arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_mle.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] fcdyn3 = arima_forecasts[:,3] fcdyn4 = arima_forecasts[:,4] # 0 indicates the first sample-observation below # ie., the index after the pre-sample, these are also differenced once # so the indices are moved back once from the cpi in levels # start < p, end

    0 1959q3 - 1971q4 start, end = 2, 51 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start < p, end nobs 1959q3 - 2009q3 start, end = 2, 202 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start < p, end >nobs 1959q3 - 2015q4 start, end = 2, 227 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end >0 1960q1 - 1971q4 start, end = 4, 51 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 4, 202 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 start, end = 4, 227 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 #NOTE: raises #start, end = 202, 202 #fv = res1.predict(start, end, typ='levels') #assert_almost_equal(fv, []) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_3) # start >nobs, end >nobs 2009q4 - 2015q4 #NOTE: this raises but shouldn't, dynamic forecasts could start #one period out start, end = 203, 227 fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.predict(start, end, typ='levels') assert_almost_equal(fv, fc[1:203], DECIMAL_4) #### Dynamic ##### # start < p, end

    0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.predict(start, end, dynamic=True, typ='levels') #assert_almost_equal(fv, fcdyn[5:end+1], DECIMAL_4) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.predict(start, end, dynamic=True, typ='levels') #assert_almost_equal(fv, fcdyn[5:end+1], DECIMAL_4) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.predict(start, end, dynamic=True, typ='levels') #assert_almost_equal(fv, fcdyn[5:end+1], DECIMAL_4) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 start, end = 5, 227 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn3[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn3[start:end+1], DECIMAL_4) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn4[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.predict(start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[5:203], DECIMAL_4) def _check_start(model, given, expected, dynamic): start = model._get_predict_start(given, dynamic) assert_equal(start, expected) def _check_end(model, given, end_expect, out_of_sample_expect): end, out_of_sample = model._get_predict_end(given) assert_equal((end, out_of_sample), (end_expect, out_of_sample_expect)) def test_arma_predict_indices(): from statsmodels.datasets.sunspots import load sunspots = load().data['SUNACTIVITY'] model = ARMA(sunspots, (9,0), dates=sun_dates, freq='A') model.method = 'mle' # raises - pre-sample + dynamic assert_raises(ValueError, model._get_predict_start, *(0, True)) assert_raises(ValueError, model._get_predict_start, *(8, True)) assert_raises(ValueError, model._get_predict_start, *('1700', True)) assert_raises(ValueError, model._get_predict_start, *('1708', True)) # raises - start out of sample assert_raises(ValueError, model._get_predict_start, *(311, True)) assert_raises(ValueError, model._get_predict_start, *(311, False)) assert_raises(ValueError, model._get_predict_start, *('2010', True)) assert_raises(ValueError, model._get_predict_start, *('2010', False)) # works - in-sample # None # given, expected, dynamic start_test_cases = [ (None, 9, True), # all start get moved back by k_diff (9, 9, True), (10, 10, True), # what about end of sample start - last value is first # forecast (309, 309, True), (308, 308, True), (0, 0, False), (1, 1, False), (4, 4, False), # all start get moved back by k_diff ('1709', 9, True), ('1710', 10, True), # what about end of sample start - last value is first # forecast ('2008', 308, True), ('2009', 309, True), ('1700', 0, False), ('1708', 8, False), ('1709', 9, False), ] for case in start_test_cases: _check_start(*((model,) + case)) # the length of sunspot is 309, so last index is 208 end_test_cases = [(None, 308, 0), (307, 307, 0), (308, 308, 0), (309, 308, 1), (312, 308, 4), (51, 51, 0), (333, 308, 25), ('2007', 307, 0), ('2008', 308, 0), ('2009', 308, 1), ('2012', 308, 4), ('1815', 115, 0), ('2033', 308, 25), ] for case in end_test_cases: _check_end(*((model,)+case)) def test_arima_predict_indices(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] model = ARIMA(cpi, (4,1,1), dates=cpi_dates, freq='Q') model.method = 'mle' # starting indices # raises - pre-sample + dynamic assert_raises(ValueError, model._get_predict_start, *(0, True)) assert_raises(ValueError, model._get_predict_start, *(4, True)) assert_raises(ValueError, model._get_predict_start, *('1959Q1', True)) assert_raises(ValueError, model._get_predict_start, *('1960Q1', True)) # raises - index differenced away assert_raises(ValueError, model._get_predict_start, *(0, False)) assert_raises(ValueError, model._get_predict_start, *('1959Q1', False)) # raises - start out of sample assert_raises(ValueError, model._get_predict_start, *(204, True)) assert_raises(ValueError, model._get_predict_start, *(204, False)) assert_raises(ValueError, model._get_predict_start, *('2010Q1', True)) assert_raises(ValueError, model._get_predict_start, *('2010Q1', False)) # works - in-sample # None # given, expected, dynamic start_test_cases = [ (None, 4, True), # all start get moved back by k_diff (5, 4, True), (6, 5, True), # what about end of sample start - last value is first # forecast (203, 202, True), (1, 0, False), (4, 3, False), (5, 4, False), # all start get moved back by k_diff ('1960Q2', 4, True), ('1960Q3', 5, True), # what about end of sample start - last value is first # forecast ('2009Q4', 202, True), ('1959Q2', 0, False), ('1960Q1', 3, False), ('1960Q2', 4, False), ] for case in start_test_cases: _check_start(*((model,) + case)) # check raises #TODO: make sure dates are passing through unmolested #assert_raises(ValueError, model._get_predict_end, ("2001-1-1",)) # the length of diff(cpi) is 202, so last index is 201 end_test_cases = [(None, 201, 0), (201, 200, 0), (202, 201, 0), (203, 201, 1), (204, 201, 2), (51, 50, 0), (164+63, 201, 25), ('2009Q2', 200, 0), ('2009Q3', 201, 0), ('2009Q4', 201, 1), ('2010Q1', 201, 2), ('1971Q4', 50, 0), ('2015Q4', 201, 25), ] for case in end_test_cases: _check_end(*((model,)+case)) # check higher k_diff model.k_diff = 2 # raises - pre-sample + dynamic assert_raises(ValueError, model._get_predict_start, *(0, True)) assert_raises(ValueError, model._get_predict_start, *(5, True)) assert_raises(ValueError, model._get_predict_start, *('1959Q1', True)) assert_raises(ValueError, model._get_predict_start, *('1960Q1', True)) # raises - index differenced away assert_raises(ValueError, model._get_predict_start, *(1, False)) assert_raises(ValueError, model._get_predict_start, *('1959Q2', False)) start_test_cases = [(None, 4, True), # all start get moved back by k_diff (6, 4, True), # what about end of sample start - last value is first # forecast (203, 201, True), (2, 0, False), (4, 2, False), (5, 3, False), ('1960Q3', 4, True), # what about end of sample start - last value is first # forecast ('2009Q4', 201, True), ('2009Q4', 201, True), ('1959Q3', 0, False), ('1960Q1', 2, False), ('1960Q2', 3, False), ] for case in start_test_cases: _check_start(*((model,)+case)) end_test_cases = [(None, 200, 0), (201, 199, 0), (202, 200, 0), (203, 200, 1), (204, 200, 2), (51, 49, 0), (164+63, 200, 25), ('2009Q2', 199, 0), ('2009Q3', 200, 0), ('2009Q4', 200, 1), ('2010Q1', 200, 2), ('1971Q4', 49, 0), ('2015Q4', 200, 25), ] for case in end_test_cases: _check_end(*((model,)+case)) def test_arima_predict_indices_css(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] #NOTE: Doing no-constant for now to kick the conditional exogenous #issue 274 down the road # go ahead and git the model to set up necessary variables model = ARIMA(cpi, (4,1,1)) model.method = 'css' assert_raises(ValueError, model._get_predict_start, *(0, False)) assert_raises(ValueError, model._get_predict_start, *(0, True)) assert_raises(ValueError, model._get_predict_start, *(2, False)) assert_raises(ValueError, model._get_predict_start, *(2, True)) def test_arima_predict_css(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] #NOTE: Doing no-constant for now to kick the conditional exogenous #issue 274 down the road # go ahead and git the model to set up necessary variables res1 = ARIMA(cpi, (4,1,1)).fit(disp=-1, method="css", trend="nc") # but use gretl parameters to predict to avoid precision problems params = np.array([ 1.231272508473910, -0.282516097759915, 0.170052755782440, -0.118203728504945, -0.938783134717947]) arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_css.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] fcdyn3 = arima_forecasts[:,3] fcdyn4 = arima_forecasts[:,4] #NOTE: should raise #start, end = 1,3 #fv = res1.model.predict(params, start, end) ## start < p, end 0 1959q3 - 1960q1 #start, end = 2, 4 #fv = res1.model.predict(params, start, end) ## start < p, end >0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.model.predict(params, start, end) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.model.predict(params, start, end) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.model.predict(params, start, end) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 #TODO: why detoriating precision? fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end, typ='levels') assert_almost_equal(fv, fc[5:203], DECIMAL_4) #### Dynamic ##### #NOTE: should raise # start < p, end

    0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.predict(start, end, dynamic=True) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.predict(start, end, dynamic=True) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.predict(start, end, dynamic=True) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 start, end = 5, 227 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn3[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn4[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end, dynamic=True, typ='levels') assert_almost_equal(fv, fcdyn[5:203], DECIMAL_4) def test_arima_predict_css_diffs(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] #NOTE: Doing no-constant for now to kick the conditional exogenous #issue 274 down the road # go ahead and git the model to set up necessary variables res1 = ARIMA(cpi, (4,1,1)).fit(disp=-1, method="css", trend="c") # but use gretl parameters to predict to avoid precision problems params = np.array([0.78349893861244, -0.533444105973324, 0.321103691668809, 0.264012463189186, 0.107888256920655, 0.920132542916995]) # we report mean, should we report constant? params[0] = params[0] / (1 - params[1:5].sum()) arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_css_diff.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] fcdyn3 = arima_forecasts[:,3] fcdyn4 = arima_forecasts[:,4] #NOTE: should raise #start, end = 1,3 #fv = res1.model.predict(params, start, end) ## start < p, end 0 1959q3 - 1960q1 #start, end = 2, 4 #fv = res1.model.predict(params, start, end) ## start < p, end >0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.model.predict(params, start, end) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.model.predict(params, start, end) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.model.predict(params, start, end) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 #TODO: why detoriating precision? fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[5:203], DECIMAL_4) #### Dynamic ##### #NOTE: should raise # start < p, end

    0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.predict(start, end, dynamic=True) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.predict(start, end, dynamic=True) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.predict(start, end, dynamic=True) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 start, end = 5, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn3[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end, dynamic=True) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn4[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[5:203], DECIMAL_4) def test_arima_predict_mle_diffs(): from statsmodels.datasets.macrodata import load cpi = load().data['cpi'] #NOTE: Doing no-constant for now to kick the conditional exogenous #issue 274 down the road # go ahead and git the model to set up necessary variables res1 = ARIMA(cpi, (4,1,1)).fit(disp=-1, trend="c") # but use gretl parameters to predict to avoid precision problems params = np.array([0.926875951549299, -0.555862621524846, 0.320865492764400, 0.252253019082800, 0.113624958031799, 0.939144026934634]) arima_forecasts = np.genfromtxt(open( current_path + '/results/results_arima_forecasts_all_mle_diff.csv', "rb"), delimiter=",", skip_header=1, dtype=float) fc = arima_forecasts[:,0] fcdyn = arima_forecasts[:,1] fcdyn2 = arima_forecasts[:,2] fcdyn3 = arima_forecasts[:,3] fcdyn4 = arima_forecasts[:,4] #NOTE: should raise start, end = 1,3 fv = res1.model.predict(params, start, end) ## start < p, end 0 1959q3 - 1960q1 start, end = 2, 4 fv = res1.model.predict(params, start, end) ## start < p, end >0 1959q3 - 1971q4 start, end = 2, 51 fv = res1.model.predict(params, start, end) ## start < p, end nobs 1959q3 - 2009q3 start, end = 2, 202 fv = res1.model.predict(params, start, end) ## start < p, end >nobs 1959q3 - 2015q4 start, end = 2, 227 fv = res1.model.predict(params, start, end) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 #TODO: why detoriating precision? fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end) assert_almost_equal(fv, fc[1:203], DECIMAL_4) #### Dynamic ##### #NOTE: should raise # start < p, end

    0 1959q3 - 1971q4 #start, end = 2, 51 #fv = res1.predict(start, end, dynamic=True) ## start < p, end nobs 1959q3 - 2009q3 #start, end = 2, 202 #fv = res1.predict(start, end, dynamic=True) ## start < p, end >nobs 1959q3 - 2015q4 #start, end = 2, 227 #fv = res1.predict(start, end, dynamic=True) # start 0, end >0 1960q1 - 1971q4 start, end = 5, 51 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end nobs 1960q1 - 2009q3 start, end = 5, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start 0, end >nobs 1960q1 - 2015q4 start, end = 5, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[start:end+1], DECIMAL_4) # start >p, end >0 1965q1 - 1971q4 start, end = 24, 51 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end nobs 1965q1 - 2009q3 start, end = 24, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start >p, end >nobs 1965q1 - 2015q4 start, end = 24, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn2[start:end+1], DECIMAL_4) # start nobs, end nobs 2009q3 - 2009q3 start, end = 202, 202 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn3[start:end+1], DECIMAL_4) # start nobs, end >nobs 2009q3 - 2015q4 start, end = 202, 227 fv = res1.model.predict(params, start, end, dynamic=True) # start >nobs, end >nobs 2009q4 - 2015q4 start, end = 203, 227 fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn4[start:end+1], DECIMAL_4) # defaults start, end = None, None fv = res1.model.predict(params, start, end, dynamic=True) assert_almost_equal(fv, fcdyn[5:203], DECIMAL_4) def test_arima_wrapper(): from statsmodels.datasets.macrodata import load_pandas cpi = load_pandas().data['cpi'] cpi.index = pandas.Index(cpi_dates) res = ARIMA(cpi, (4,1,1), freq='Q').fit(disp=-1) assert_equal(res.params.index, ['const', 'ar.L1.D.cpi', 'ar.L2.D.cpi', 'ar.L3.D.cpi', 'ar.L4.D.cpi', 'ma.L1.D.cpi']) assert_equal(res.model.endog_names, 'D.cpi') def test_1dexog(): # smoke test, this will raise an error if broken from statsmodels.datasets.macrodata import load_pandas dta = load_pandas().data endog = dta['realcons'].values exog = dta['m1'].values.squeeze() mod = ARMA(endog, (1,1), exog).fit(disp=-1) def test_arima_predict_bug(): #predict_start_date wasn't getting set on start = None from statsmodels.datasets import sunspots dta = sunspots.load_pandas().data.SUNACTIVITY dta.index = pandas.Index(dates_from_range('1700', '2008')) arma_mod20 = ARMA(dta, (2,0)).fit(disp=-1) arma_mod20.predict(None, None) def test_arima_predict_q2(): # bug with q > 1 for arima predict from statsmodels.datasets import macrodata inv = macrodata.load().data['realinv'] arima_mod = ARIMA(np.log(inv), (1,1,2)).fit(start_params=[0,0,0,0], disp=-1) fc, stderr, conf_int = arima_mod.forecast(5) # values copy-pasted from gretl assert_almost_equal(fc, [7.306320, 7.313825, 7.321749, 7.329827, 7.337962], 5) def test_arima_predict_pandas_nofreq(): # this is issue 712 try: from pandas.tseries.api import infer_freq # pylint: disable-msg=E0611, F0401 except ImportError: import nose raise nose.SkipTest from pandas import DataFrame dates = ["2010-01-04", "2010-01-05", "2010-01-06", "2010-01-07", "2010-01-08", "2010-01-11", "2010-01-12", "2010-01-11", "2010-01-12", "2010-01-13", "2010-01-17"] close = [626.75, 623.99, 608.26, 594.1, 602.02, 601.11, 590.48, 587.09, 589.85, 580.0,587.62] data = DataFrame(close, index=DatetimeIndex(dates), columns=["close"]) #TODO: fix this names bug for non-string names names arma = ARMA(data, order=(1,0)).fit(disp=-1) # first check that in-sample prediction works predict = arma.predict() assert_(predict.index.equals(data.index)) # check that this raises an exception when date not on index assert_raises(ValueError, arma.predict, start="2010-1-9", end=10) assert_raises(ValueError, arma.predict, start="2010-1-9", end="2010-1-17") # raise because end not on index assert_raises(ValueError, arma.predict, start="2010-1-4", end="2010-1-10") # raise because end not on index assert_raises(ValueError, arma.predict, start=3, end="2010-1-10") predict = arma.predict(start="2010-1-7", end=10) # should be of length 10 assert_(len(predict) == 8) assert_(predict.index.equals(data.index[3:10+1])) predict = arma.predict(start="2010-1-7", end=14) assert_(predict.index.equals(pandas.Index(range(3, 15)))) predict = arma.predict(start=3, end=14) assert_(predict.index.equals(pandas.Index(range(3, 15)))) # end can be a date if it's in the sample and on the index # predict dates is just a slice of the dates index then predict = arma.predict(start="2010-1-6", end="2010-1-13") assert_(predict.index.equals(data.index[2:10])) predict = arma.predict(start=2, end="2010-1-13") assert_(predict.index.equals(data.index[2:10])) def test_arima_predict_exog(): # check 625 and 626 #from statsmodels.tsa.arima_process import arma_generate_sample #arparams = np.array([1, -.45, .25]) #maparams = np.array([1, .15]) #nobs = 100 #np.random.seed(123) #y = arma_generate_sample(arparams, maparams, nobs, burnin=100) ## make an exogenous trend #X = np.array(range(nobs)) / 20.0 ## add a constant #y += 2.5 from pandas import read_csv arima_forecasts = read_csv(current_path + "/results/" "results_arima_exog_forecasts_mle.csv") y = arima_forecasts["y"].dropna() X = np.arange(len(y) + 25)/20. predict_expected = arima_forecasts["predict"] arma_res = ARMA(y.values, order=(2,1), exog=X[:100]).fit(trend="c", disp=-1) # params from gretl params = np.array([2.786912485145725, -0.122650190196475, 0.533223846028938, -0.319344321763337, 0.132883233000064]) assert_almost_equal(arma_res.params, params, 5) # no exog for in-sample predict = arma_res.predict() assert_almost_equal(predict, predict_expected.values[:100], 5) # check 626 assert_(len(arma_res.model.exog_names) == 5) # exog for out-of-sample and in-sample dynamic predict = arma_res.model.predict(params, end=124, exog=X[100:]) assert_almost_equal(predict, predict_expected.values, 6) # conditional sum of squares #arima_forecasts = read_csv(current_path + "/results/" # "results_arima_exog_forecasts_css.csv") #predict_expected = arima_forecasts["predict"].dropna() #arma_res = ARMA(y.values, order=(2,1), exog=X[:100]).fit(trend="c", # method="css", # disp=-1) #params = np.array([2.152350033809826, -0.103602399018814, # 0.566716580421188, -0.326208009247944, # 0.102142932143421]) #predict = arma_res.model.predict(params) ## in-sample #assert_almost_equal(predict, predict_expected.values[:98], 6) #predict = arma_res.model.predict(params, end=124, exog=X[100:]) ## exog for out-of-sample and in-sample dynamic #assert_almost_equal(predict, predict_expected.values, 3) def test_arima_no_diff(): # issue 736 # smoke test, predict will break if we have ARIMAResults but # ARMA model, need ARIMA(p, 0, q) to return an ARMA in init. ar = [1, -.75, .15, .35] ma = [1, .25, .9] y = arma_generate_sample(ar, ma, 100) mod = ARIMA(y, (3, 0, 2)) assert_(isinstance(mod, ARMA)) res = mod.fit(disp=-1) # smoke test just to be sure res.predict() def test_arima_predict_noma(): # issue 657 # smoke test ar = [1, .75] ma = [1] data = arma_generate_sample(ar, ma, 100) arma = ARMA(data, order=(0,1)) arma_res = arma.fit(disp=-1) arma_res.forecast(1) def test_arimax(): from statsmodels.datasets.macrodata import load_pandas dta = load_pandas().data dates = dates_from_range("1959Q1", length=len(dta)) dta.index = cpi_dates dta = dta[["realdpi", "m1", "realgdp"]] y = dta.pop("realdpi") # 1 exog #X = dta.ix[1:]["m1"] #res = ARIMA(y, (2, 1, 1), X).fit(disp=-1) #params = [23.902305009084373, 0.024650911502790, -0.162140641341602, # 0.165262136028113, -0.066667022903974] #assert_almost_equal(res.params.values, params, 6) # 2 exog X = dta res = ARIMA(y, (2, 1, 1), X).fit(disp=-1, solver="nm", maxiter=1000, ftol=1e-12, xtol=1e-12) # from gretl #params = [13.113976653926638, -0.003792125069387, 0.004123504809217, # -0.199213760940898, 0.151563643588008, -0.033088661096699] # from stata using double stata_llf = -1076.108614859121 params = [13.1259220104, -0.00376814509403812, 0.00411970083135622, -0.19921477896158524, 0.15154396192855729, -0.03308400760360837] # we can get close assert_almost_equal(res.params.values, params, 4) # This shows that it's an optimizer problem and not a problem in the code assert_almost_equal(res.model.loglike(np.array(params)), stata_llf, 6) X = dta.diff() res = ARIMA(y, (2, 1, 1), X).fit(disp=-1) # gretl won't estimate this - looks like maybe a bug on their part, # but we can just fine, we're close to Stata's answer # from Stata params = [19.5656863783347, 0.32653841355833396198, 0.36286527042965188716, -1.01133792126884, -0.15722368379307766206, 0.69359822544092153418] assert_almost_equal(res.params.values, params, 3) def test_bad_start_params(): endog = np.array([820.69093, 781.0103028, 785.8786988, 767.64282267, 778.9837648 , 824.6595702 , 813.01877867, 751.65598567, 753.431091 , 746.920813 , 795.6201904 , 772.65732833, 793.4486454 , 868.8457766 , 823.07226547, 783.09067747, 791.50723847, 770.93086347, 835.34157333, 810.64147947, 738.36071367, 776.49038513, 822.93272333, 815.26461227, 773.70552987, 777.3726522 , 811.83444853, 840.95489133, 777.51031933, 745.90077307, 806.95113093, 805.77521973, 756.70927733, 749.89091773, 1694.2266924 , 2398.4802244 , 1434.6728516 , 909.73940427, 929.01291907, 769.07561453, 801.1112548 , 796.16163313, 817.2496376 , 857.73046447, 838.849345 , 761.92338873, 731.7842242 , 770.4641844 ]) mod = ARMA(endog, (15, 0)) assert_raises(ValueError, mod.fit) from statsmodels.datasets.macrodata import load inv = load().data['realinv'] arima_mod = ARIMA(np.log(inv), (1,1,2)) assert_raises(ValueError, mod.fit) def test_arima_small_data_bug(): # Issue 1038, too few observations with given order from datetime import datetime import statsmodels.api as sm vals = [96.2, 98.3, 99.1, 95.5, 94.0, 87.1, 87.9, 86.7402777504474] dr = dates_from_range("1990q1", length=len(vals)) ts = pandas.TimeSeries(vals, index=dr) df = pandas.DataFrame(ts) mod = sm.tsa.ARIMA(df, (2, 0, 2)) assert_raises(ValueError, mod.fit) def test_arima_dataframe_integer_name(): # Smoke Test for Issue 1038 from datetime import datetime import statsmodels.api as sm vals = [96.2, 98.3, 99.1, 95.5, 94.0, 87.1, 87.9, 86.7402777504474, 94.0, 96.5, 93.3, 97.5, 96.3, 92.] dr = dates_from_range("1990q1", length=len(vals)) ts = pandas.TimeSeries(vals, index=dr) df = pandas.DataFrame(ts) mod = sm.tsa.ARIMA(df, (2, 0, 2)) def test_arima_exog_predict_1d(): # test 1067 np.random.seed(12345) y = np.random.random(100) x = np.random.random(100) mod = ARMA(y, (2, 1), x).fit(disp=-1) newx = np.random.random(10) results = mod.forecast(steps=10, alpha=0.05, exog=newx) def test_arima_1123(): # test ARMAX predict when trend is none np.random.seed(12345) arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) arparam = np.r_[1, -arparams] maparam = np.r_[1, maparams] nobs = 20 dates = dates_from_range('1980',length=nobs) y = arma_generate_sample(arparams, maparams, nobs) X = np.random.randn(nobs) y += 5*X mod = ARMA(y[:-1], order=(1,0), exog=X[:-1]) res = mod.fit(trend='nc', disp=False) fc = res.forecast(exog=X[-1:]) # results from gretl assert_almost_equal(fc[0], 2.200393, 6) assert_almost_equal(fc[1], 1.030743, 6) assert_almost_equal(fc[2][0,0], 0.180175, 6) assert_almost_equal(fc[2][0,1], 4.220611, 6) mod = ARMA(y[:-1], order=(1,1), exog=X[:-1]) res = mod.fit(trend='nc', disp=False) fc = res.forecast(exog=X[-1:]) assert_almost_equal(fc[0], 2.765688, 6) assert_almost_equal(fc[1], 0.835048, 6) assert_almost_equal(fc[2][0,0], 1.129023, 6) assert_almost_equal(fc[2][0,1], 4.402353, 6) # make sure this works to. code looked fishy. mod = ARMA(y[:-1], order=(1,0), exog=X[:-1]) res = mod.fit(trend='c', disp=False) fc = res.forecast(exog=X[-1:]) assert_almost_equal(fc[0], 2.481219, 6) assert_almost_equal(fc[1], 0.968759, 6) assert_almost_equal(fc[2][0], [0.582485, 4.379952], 6) def test_small_data(): # 1146 y = [-1214.360173, -1848.209905, -2100.918158, -3647.483678, -4711.186773] # refuse to estimate these assert_raises(ValueError, ARIMA, y, (2, 0, 3)) assert_raises(ValueError, ARIMA, y, (1, 1, 3)) mod = ARIMA(y, (1, 0, 3)) assert_raises(ValueError, mod.fit, trend="c") # try to estimate these...leave it up to the user to check for garbage # and be clear, these are garbage parameters. # X-12 arima will estimate, gretl refuses to estimate likely a problem # in start params regression. res = mod.fit(trend="nc", disp=0, start_params=[.1,.1,.1,.1]) mod = ARIMA(y, (1, 0, 2)) res = mod.fit(disp=0, start_params=[.1, .1, .1, .1]) if __name__ == "__main__": import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_arima_process.py000066400000000000000000000106131224417117700267060ustar00rootroot00000000000000 import numpy as np from numpy.testing import (assert_array_almost_equal, assert_almost_equal, assert_equal) from statsmodels.tsa.arima_process import (arma_generate_sample, arma_acovf, arma_acf, arma_impulse_response, lpol_fiar, lpol_fima) from statsmodels.sandbox.tsa.fftarma import ArmaFft from results.results_process import armarep #benchmarkdata arlist = [[1.], [1, -0.9], #ma representation will need many terms to get high precision [1, 0.9], [1, -0.9, 0.3]] malist = [[1.], [1, 0.9], [1, -0.9], [1, 0.9, -0.3]] def test_arma_acovf(): # Check for specific AR(1) N = 20; phi = 0.9; sigma = 1; # rep 1: from module function rep1 = arma_acovf([1, -phi], [1], N); # rep 2: manually rep2 = [1.*sigma*phi**i/(1-phi**2) for i in range(N)]; assert_almost_equal(rep1, rep2, 7); # 7 is max precision here def test_arma_acf(): # Check for specific AR(1) N = 20; phi = 0.9; sigma = 1; # rep 1: from module function rep1 = arma_acf([1, -phi], [1], N); # rep 2: manually acovf = np.array([1.*sigma*phi**i/(1-phi**2) for i in range(N)]) rep2 = acovf / (1./(1-phi**2)); assert_almost_equal(rep1, rep2, 8); # 8 is max precision here def _manual_arma_generate_sample(ar, ma, eta): T = len(eta); ar = ar[::-1]; ma = ma[::-1]; p,q = len(ar), len(ma); rep2 = [0]*max(p,q); # initialize with zeroes for t in range(T): yt = eta[t]; if p: yt += np.dot(rep2[-p:], ar); if q: # left pad shocks with zeros yt += np.dot([0]*(q-t) + list(eta[max(0,t-q):t]), ma); rep2.append(yt); return np.array(rep2[max(p,q):]); def test_arma_generate_sample(): # Test that this generates a true ARMA process # (amounts to just a test that scipy.signal.lfilter does what we want) T = 100; dists = [np.random.randn] for dist in dists: np.random.seed(1234); eta = dist(T); for ar in arlist: for ma in malist: # rep1: from module function np.random.seed(1234); rep1 = arma_generate_sample(ar, ma, T, distrvs=dist); # rep2: "manually" create the ARMA process ar_params = -1*np.array(ar[1:]); ma_params = np.array(ma[1:]); rep2 = _manual_arma_generate_sample(ar_params, ma_params, eta) assert_array_almost_equal(rep1, rep2, 13); def test_fi(): #test identity of ma and ar representation of fi lag polynomial n = 100 mafromar = arma_impulse_response(lpol_fiar(0.4, n=n), [1], n) assert_array_almost_equal(mafromar, lpol_fima(0.4, n=n), 13) def test_arma_impulse_response(): arrep = arma_impulse_response(armarep.ma, armarep.ar, nobs=21)[1:] marep = arma_impulse_response(armarep.ar, armarep.ma, nobs=21)[1:] assert_array_almost_equal(armarep.marep.ravel(), marep, 14) #difference in sign convention to matlab for AR term assert_array_almost_equal(-armarep.arrep.ravel(), arrep, 14) def test_spectrum(): nfreq = 20 w = np.linspace(0, np.pi, nfreq, endpoint=False) for ar in arlist: for ma in malist: arma = ArmaFft(ar, ma, 20) spdr, wr = arma.spdroots(w) spdp, wp = arma.spdpoly(w, 200) spdd, wd = arma.spddirect(nfreq*2) assert_equal(w, wr) assert_equal(w, wp) assert_almost_equal(w, wd[:nfreq], decimal=14) assert_almost_equal(spdr, spdp, decimal=7, err_msg='spdr spdp not equal for %s, %s' % (ar, ma)) assert_almost_equal(spdr, spdd[:nfreq], decimal=7, err_msg='spdr spdd not equal for %s, %s' % (ar, ma)) def test_armafft(): #test other methods nfreq = 20 w = np.linspace(0, np.pi, nfreq, endpoint=False) for ar in arlist: for ma in malist: arma = ArmaFft(ar, ma, 20) ac1 = arma.invpowerspd(1024)[:10] ac2 = arma.acovf(10)[:10] assert_almost_equal(ac1, ac2, decimal=7, err_msg='acovf not equal for %s, %s' % (ar, ma)) if __name__ == '__main__': test_arma_acovf() test_arma_acf() test_arma_generate_sample() test_fi() test_arma_impulse_response() test_spectrum() test_armafft() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_stattools.py000066400000000000000000000172701224417117700261210ustar00rootroot00000000000000from statsmodels.tsa.stattools import (adfuller, acf, pacf_ols, pacf_yw, pacf, grangercausalitytests, coint, acovf) from statsmodels.tsa.base.datetools import dates_from_range import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_raises from numpy import genfromtxt#, concatenate from statsmodels.datasets import macrodata, sunspots from pandas import Series, Index import os DECIMAL_8 = 8 DECIMAL_6 = 6 DECIMAL_5 = 5 DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 class CheckADF(object): """ Test Augmented Dickey-Fuller Test values taken from Stata. """ levels = ['1%', '5%', '10%'] data = macrodata.load() x = data.data['realgdp'] y = data.data['infl'] def test_teststat(self): assert_almost_equal(self.res1[0], self.teststat, DECIMAL_5) def test_pvalue(self): assert_almost_equal(self.res1[1], self.pvalue, DECIMAL_5) def test_critvalues(self): critvalues = [self.res1[4][lev] for lev in self.levels] assert_almost_equal(critvalues, self.critvalues, DECIMAL_2) class TestADFConstant(CheckADF): """ Dickey-Fuller test for unit root """ def __init__(self): self.res1 = adfuller(self.x, regression="c", autolag=None, maxlag=4) self.teststat = .97505319 self.pvalue = .99399563 self.critvalues = [-3.476, -2.883, -2.573] class TestADFConstantTrend(CheckADF): """ """ def __init__(self): self.res1 = adfuller(self.x, regression="ct", autolag=None, maxlag=4) self.teststat = -1.8566374 self.pvalue = .67682968 self.critvalues = [-4.007, -3.437, -3.137] #class TestADFConstantTrendSquared(CheckADF): # """ # """ # pass #TODO: get test values from R? class TestADFNoConstant(CheckADF): """ """ def __init__(self): self.res1 = adfuller(self.x, regression="nc", autolag=None, maxlag=4) self.teststat = 3.5227498 self.pvalue = .99999 # Stata does not return a p-value for noconstant. # Tau^max in MacKinnon (1994) is missing, so it is # assumed that its right-tail is well-behaved self.critvalues = [-2.587, -1.950, -1.617] # No Unit Root class TestADFConstant2(CheckADF): def __init__(self): self.res1 = adfuller(self.y, regression="c", autolag=None, maxlag=1) self.teststat = -4.3346988 self.pvalue = .00038661 self.critvalues = [-3.476, -2.883, -2.573] class TestADFConstantTrend2(CheckADF): def __init__(self): self.res1 = adfuller(self.y, regression="ct", autolag=None, maxlag=1) self.teststat = -4.425093 self.pvalue = .00199633 self.critvalues = [-4.006, -3.437, -3.137] class TestADFNoConstant2(CheckADF): def __init__(self): self.res1 = adfuller(self.y, regression="nc", autolag=None, maxlag=1) self.teststat = -2.4511596 self.pvalue = 0.013747 # Stata does not return a p-value for noconstant # this value is just taken from our results self.critvalues = [-2.587,-1.950,-1.617] class CheckCorrGram(object): """ Set up for ACF, PACF tests. """ data = macrodata.load() x = data.data['realgdp'] filename = os.path.dirname(os.path.abspath(__file__))+\ "/results/results_corrgram.csv" results = genfromtxt(open(filename, "rb"), delimiter=",", names=True,dtype=float) #not needed: add 1. for lag zero #self.results['acvar'] = np.concatenate(([1.], self.results['acvar'])) class TestACF(CheckCorrGram): """ Test Autocorrelation Function """ def __init__(self): self.acf = self.results['acvar'] #self.acf = np.concatenate(([1.], self.acf)) self.qstat = self.results['Q1'] self.res1 = acf(self.x, nlags=40, qstat=True, alpha=.05) self.confint_res = self.results[['acvar_lb','acvar_ub']].view((float, 2)) def test_acf(self): assert_almost_equal(self.res1[0][1:41], self.acf, DECIMAL_8) def test_confint(self): centered = self.res1[1] - self.res1[1].mean(1)[:,None] assert_almost_equal(centered[1:41], self.confint_res, DECIMAL_8) def test_qstat(self): assert_almost_equal(self.res1[2][:40], self.qstat, DECIMAL_3) # 3 decimal places because of stata rounding # def pvalue(self): # pass #NOTE: shouldn't need testing if Q stat is correct class TestACF_FFT(CheckCorrGram): """ Test Autocorrelation Function using FFT """ def __init__(self): self.acf = self.results['acvarfft'] self.qstat = self.results['Q1'] self.res1 = acf(self.x, nlags=40, qstat=True, fft=True) def test_acf(self): assert_almost_equal(self.res1[0][1:], self.acf, DECIMAL_8) def test_qstat(self): #todo why is res1/qstat 1 short assert_almost_equal(self.res1[1], self.qstat, DECIMAL_3) class TestPACF(CheckCorrGram): def __init__(self): self.pacfols = self.results['PACOLS'] self.pacfyw = self.results['PACYW'] def test_ols(self): pacfols, confint = pacf(self.x, nlags=40, alpha=.05, method="ols") assert_almost_equal(pacfols[1:], self.pacfols, DECIMAL_6) centered = confint - confint.mean(1)[:,None] # from edited Stata ado file res = [[-.1375625, .1375625]] * 40 assert_almost_equal(centered[1:41], res, DECIMAL_6) def test_yw(self): pacfyw = pacf_yw(self.x, nlags=40, method="mle") assert_almost_equal(pacfyw[1:], self.pacfyw, DECIMAL_8) def test_ld(self): pacfyw = pacf_yw(self.x, nlags=40, method="mle") pacfld = pacf(self.x, nlags=40, method="ldb") assert_almost_equal(pacfyw, pacfld, DECIMAL_8) pacfyw = pacf(self.x, nlags=40, method="yw") pacfld = pacf(self.x, nlags=40, method="ldu") assert_almost_equal(pacfyw, pacfld, DECIMAL_8) class CheckCoint(object): """ Test Cointegration Test Results for 2-variable system Test values taken from Stata """ levels = ['1%', '5%', '10%'] data = macrodata.load() y1 = data.data['realcons'] y2 = data.data['realgdp'] def test_tstat(self): assert_almost_equal(self.coint_t,self.teststat, DECIMAL_4) class TestCoint_t(CheckCoint): """ Get AR(1) parameter on residuals """ def __init__(self): self.coint_t = coint(self.y1, self.y2, regression ="c")[0] self.teststat = -1.8208817 def test_grangercausality(): # some example data mdata = macrodata.load().data mdata = mdata[['realgdp','realcons']] data = mdata.view((float,2)) data = np.diff(np.log(data), axis=0) #R: lmtest:grangertest r_result = [0.243097, 0.7844328, 195, 2] #f_test gr = grangercausalitytests(data[:,1::-1], 2, verbose=False) assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7) assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'], decimal=7) def test_pandasacovf(): s = Series(range(1, 11)) assert_almost_equal(acovf(s), acovf(s.values)) def test_acovf2d(): dta = sunspots.load_pandas().data dta.index = Index(dates_from_range('1700', '2008')) del dta["YEAR"] res = acovf(dta) assert_equal(res, acovf(dta.values)) X = np.random.random((10,2)) assert_raises(ValueError, acovf, X) if __name__=="__main__": import nose # nose.runmodule(argv=[__file__, '-vvs','-x','-pdb'], exit=False) import numpy as np np.testing.run_module_suite() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tests/test_tsa_tools.py000066400000000000000000000174721224417117700261000ustar00rootroot00000000000000'''tests for some time series analysis functions ''' import numpy as np from numpy.testing import assert_array_almost_equal, assert_equal import statsmodels.api as sm import statsmodels.tsa.stattools as tsa import statsmodels.tsa.tsatools as tools from statsmodels.tsa.tsatools import vec, vech from results import savedrvs from results.datamlw_tls import mlacf, mlccf, mlpacf, mlywar xo = savedrvs.rvsdata.xar2 x100 = xo[-100:]/1000. x1000 = xo/1000. def test_acf(): acf_x = tsa.acf(x100, unbiased=False)[:21] assert_array_almost_equal(mlacf.acf100.ravel(), acf_x, 8) #why only dec=8 acf_x = tsa.acf(x1000, unbiased=False)[:21] assert_array_almost_equal(mlacf.acf1000.ravel(), acf_x, 8) #why only dec=9 def test_ccf(): ccf_x = tsa.ccf(x100[4:], x100[:-4], unbiased=False)[:21] assert_array_almost_equal(mlccf.ccf100.ravel()[:21][::-1], ccf_x, 8) ccf_x = tsa.ccf(x1000[4:], x1000[:-4], unbiased=False)[:21] assert_array_almost_equal(mlccf.ccf1000.ravel()[:21][::-1], ccf_x, 8) def test_pacf_yw(): pacfyw = tsa.pacf_yw(x100, 20, method='mle') assert_array_almost_equal(mlpacf.pacf100.ravel(), pacfyw, 1) pacfyw = tsa.pacf_yw(x1000, 20, method='mle') assert_array_almost_equal(mlpacf.pacf1000.ravel(), pacfyw, 2) #assert False def test_pacf_ols(): pacfols = tsa.pacf_ols(x100, 20) assert_array_almost_equal(mlpacf.pacf100.ravel(), pacfols, 8) pacfols = tsa.pacf_ols(x1000, 20) assert_array_almost_equal(mlpacf.pacf1000.ravel(), pacfols, 8) #assert False def test_ywcoef(): assert_array_almost_equal(mlywar.arcoef100[1:], -sm.regression.yule_walker(x100, 10, method='mle')[0], 8) assert_array_almost_equal(mlywar.arcoef1000[1:], -sm.regression.yule_walker(x1000, 20, method='mle')[0], 8) def test_duplication_matrix(): for k in range(2, 10): m = tools.unvech(np.random.randn(k * (k + 1) / 2)) Dk = tools.duplication_matrix(k) assert(np.array_equal(vec(m), np.dot(Dk, vech(m)))) def test_elimination_matrix(): for k in range(2, 10): m = np.random.randn(k, k) Lk = tools.elimination_matrix(k) assert(np.array_equal(vech(m), np.dot(Lk, vec(m)))) def test_commutation_matrix(): m = np.random.randn(4, 3) K = tools.commutation_matrix(4, 3) assert(np.array_equal(vec(m.T), np.dot(K, vec(m)))) def test_vec(): arr = np.array([[1, 2], [3, 4]]) assert(np.array_equal(vec(arr), [1, 3, 2, 4])) def test_vech(): arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) assert(np.array_equal(vech(arr), [1, 4, 7, 5, 8, 9])) def test_add_lag_insert(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,:3],lagmat,nddata[3:,-1])) lag_data = sm.tsa.add_lag(data, 'realgdp', 3) assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results) def test_add_lag_noinsert(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,:],lagmat)) lag_data = sm.tsa.add_lag(data, 'realgdp', 3, insert=False) assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results) def test_add_lag_noinsert_atend(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,-1],3,trim='Both') results = np.column_stack((nddata[3:,:],lagmat)) lag_data = sm.tsa.add_lag(data, 'cpi', 3, insert=False) assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results) # should be the same as insert lag_data2 = sm.tsa.add_lag(data, 'cpi', 3, insert=True) assert_equal(lag_data2.view((float,len(lag_data2.dtype.names))), results) def test_add_lag_ndarray(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,:3],lagmat,nddata[3:,-1])) lag_data = sm.tsa.add_lag(nddata, 2, 3) assert_equal(lag_data, results) def test_add_lag_noinsert_ndarray(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,:],lagmat)) lag_data = sm.tsa.add_lag(nddata, 2, 3, insert=False) assert_equal(lag_data, results) def test_add_lag_noinsertatend_ndarray(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,-1],3,trim='Both') results = np.column_stack((nddata[3:,:],lagmat)) lag_data = sm.tsa.add_lag(nddata, 3, 3, insert=False) assert_equal(lag_data, results) # should be the same as insert also check negative col number lag_data2 = sm.tsa.add_lag(nddata, -1, 3, insert=True) assert_equal(lag_data2, results) def test_add_lag1d(): data = np.random.randn(100) lagmat = sm.tsa.lagmat(data,3,trim='Both') results = np.column_stack((data[3:],lagmat)) lag_data = sm.tsa.add_lag(data, lags=3, insert=True) assert_equal(results, lag_data) # add index data = data[:,None] lagmat = sm.tsa.lagmat(data,3,trim='Both') # test for lagmat too results = np.column_stack((data[3:],lagmat)) lag_data = sm.tsa.add_lag(data,lags=3, insert=True) assert_equal(results, lag_data) def test_add_lag1d_drop(): data = np.random.randn(100) lagmat = sm.tsa.lagmat(data,3,trim='Both') lag_data = sm.tsa.add_lag(data, lags=3, drop=True, insert=True) assert_equal(lagmat, lag_data) # no insert, should be the same lag_data = sm.tsa.add_lag(data, lags=3, drop=True, insert=False) assert_equal(lagmat, lag_data) def test_add_lag1d_struct(): data = np.zeros(100, dtype=[('variable',float)]) nddata = np.random.randn(100) data['variable'] = nddata lagmat = sm.tsa.lagmat(nddata,3,trim='Both', original='in') lag_data = sm.tsa.add_lag(data, 'variable', lags=3, insert=True) assert_equal(lagmat, lag_data.view((float,4))) lag_data = sm.tsa.add_lag(data, 'variable', lags=3, insert=False) assert_equal(lagmat, lag_data.view((float,4))) lag_data = sm.tsa.add_lag(data, lags=3, insert=True) assert_equal(lagmat, lag_data.view((float,4))) def test_add_lag_1d_drop_struct(): data = np.zeros(100, dtype=[('variable',float)]) nddata = np.random.randn(100) data['variable'] = nddata lagmat = sm.tsa.lagmat(nddata,3,trim='Both') lag_data = sm.tsa.add_lag(data, lags=3, drop=True) assert_equal(lagmat, lag_data.view((float,3))) def test_add_lag_drop_insert(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,:2],lagmat,nddata[3:,-1])) lag_data = sm.tsa.add_lag(data, 'realgdp', 3, drop=True) assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results) def test_add_lag_drop_noinsert(): data = sm.datasets.macrodata.load().data[['year','quarter','realgdp','cpi']] nddata = data.view((float,4)) lagmat = sm.tsa.lagmat(nddata[:,2],3,trim='Both') results = np.column_stack((nddata[3:,np.array([0,1,3])],lagmat)) lag_data = sm.tsa.add_lag(data, 'realgdp', 3, insert=False, drop=True) assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results) if __name__ == '__main__': #running them directly # test_acf() # test_ccf() # test_pacf_yw() # test_pacf_ols() # test_ywcoef() import nose nose.runmodule() statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/tsatools.py000066400000000000000000000424711224417117700235350ustar00rootroot00000000000000import numpy as np import numpy.lib.recfunctions as nprf from statsmodels.tools.tools import add_constant def add_trend(X, trend="c", prepend=False): """ Adds a trend and/or constant to an array. Parameters ---------- X : array-like Original array of data. trend : str {"c","t","ct","ctt"} "c" add constant only "t" add trend only "ct" add constant and linear trend "ctt" add constant and linear and quadratic trend. prepend : bool If True, prepends the new data to the columns of X. Notes ----- Returns columns as ["ctt","ct","c"] whenever applicable. There is currently no checking for an existing constant or trend. See also -------- statsmodels.add_constant """ #TODO: could be generalized for trend of aribitrary order trend = trend.lower() if trend == "c": # handles structured arrays return add_constant(X, prepend=prepend) elif trend == "ct" or trend == "t": trendorder = 1 elif trend == "ctt": trendorder = 2 else: raise ValueError("trend %s not understood" % trend) X = np.asanyarray(X) nobs = len(X) trendarr = np.vander(np.arange(1,nobs+1, dtype=float), trendorder+1) # put in order ctt trendarr = np.fliplr(trendarr) if trend == "t": trendarr = trendarr[:,1] if not X.dtype.names: if not prepend: X = np.column_stack((X, trendarr)) else: X = np.column_stack((trendarr, X)) else: return_rec = data.__clas__ is np.recarray if trendorder == 1: if trend == "ct": dt = [('const',float),('trend',float)] else: dt = [('trend', float)] elif trendorder == 2: dt = [('const',float),('trend',float),('trend_squared', float)] trendarr = trendarr.view(dt) if prepend: X = nprf.append_fields(trendarr, X.dtype.names, [X[i] for i in data.dtype.names], usemask=False, asrecarray=return_rec) else: X = nprf.append_fields(X, trendarr.dtype.names, [trendarr[i] for i in trendarr.dtype.names], usemask=false, asrecarray=return_rec) return X def add_lag(x, col=None, lags=1, drop=False, insert=True): """ Returns an array with lags included given an array. Parameters ---------- x : array An array or NumPy ndarray subclass. Can be either a 1d or 2d array with observations in columns. col : 'string', int, or None If data is a structured array or a recarray, `col` can be a string that is the name of the column containing the variable. Or `col` can be an int of the zero-based column index. If it's a 1d array `col` can be None. lags : int The number of lags desired. drop : bool Whether to keep the contemporaneous variable for the data. insert : bool or int If True, inserts the lagged values after `col`. If False, appends the data. If int inserts the lags at int. Returns ------- array : ndarray Array with lags Examples -------- >>> import statsmodels.api as sm >>> data = sm.datasets.macrodata.load() >>> data = data.data[['year','quarter','realgdp','cpi']] >>> data = sm.tsa.add_lag(data, 'realgdp', lags=2) Notes ----- Trims the array both forward and backward, so that the array returned so that the length of the returned array is len(`X`) - lags. The lags are returned in increasing order, ie., t-1,t-2,...,t-lags """ if x.dtype.names: names = x.dtype.names if not col and np.squeeze(x).ndim > 1: raise IndexError, "col is None and the input array is not 1d" elif len(names) == 1: col = names[0] if isinstance(col, int): col = x.dtype.names[col] contemp = x[col] # make names for lags tmp_names = [col + '_'+'L(%i)' % i for i in range(1,lags+1)] ndlags = lagmat(contemp, maxlag=lags, trim='Both') # get index for return if insert is True: ins_idx = list(names).index(col) + 1 elif insert is False: ins_idx = len(names) + 1 else: # insert is an int if insert > len(names): raise Warning("insert > number of variables, inserting at the"+ " last position") ins_idx = insert first_names = list(names[:ins_idx]) last_names = list(names[ins_idx:]) if drop: if col in first_names: first_names.pop(first_names.index(col)) else: last_names.pop(last_names.index(col)) if first_names: # only do this if x isn't "empty" first_arr = nprf.append_fields(x[first_names][lags:],tmp_names, ndlags.T, usemask=False) else: first_arr = np.zeros(len(x)-lags, dtype=zip(tmp_names, (x[col].dtype,)*lags)) for i,name in enumerate(tmp_names): first_arr[name] = ndlags[:,i] if last_names: return nprf.append_fields(first_arr, last_names, [x[name][lags:] for name in last_names], usemask=False) else: # lags for last variable return first_arr else: # we have an ndarray if x.ndim == 1: # make 2d if 1d x = x[:,None] if col is None: col = 0 # handle negative index if col < 0: col = x.shape[1] + col contemp = x[:,col] if insert is True: ins_idx = col + 1 elif insert is False: ins_idx = x.shape[1] else: if insert < 0: # handle negative index insert = x.shape[1] + insert + 1 if insert > x.shape[1]: insert = x.shape[1] raise Warning("insert > number of variables, inserting at the"+ " last position") ins_idx = insert ndlags = lagmat(contemp, lags, trim='Both') first_cols = range(ins_idx) last_cols = range(ins_idx,x.shape[1]) if drop: if col in first_cols: first_cols.pop(first_cols.index(col)) else: last_cols.pop(last_cols.index(col)) return np.column_stack((x[lags:,first_cols],ndlags, x[lags:,last_cols])) def detrend(x, order=1, axis=0): '''detrend an array with a trend of given order along axis 0 or 1 Parameters ---------- x : array_like, 1d or 2d data, if 2d, then each row or column is independently detrended with the same trendorder, but independent trend estimates order : int specifies the polynomial order of the trend, zero is constant, one is linear trend, two is quadratic trend axis : int for detrending with order > 0, axis can be either 0 observations by rows, or 1, observations by columns Returns ------- detrended data series : ndarray The detrended series is the residual of the linear regression of the data on the trend of given order. ''' x = np.asarray(x) nobs = x.shape[0] if order == 0: return x - np.expand_dims(x.mean(ax), x) else: if x.ndim == 2 and range(2)[axis]==1: x = x.T elif x.ndim > 2: raise NotImplementedError('x.ndim>2 is not implemented until it is needed') #could use a polynomial, but this should work also with 2d x, but maybe not yet trends = np.vander(np.arange(nobs).astype(float), N=order+1) beta = np.linalg.lstsq(trends, x)[0] resid = x - np.dot(trends, beta) if x.ndim == 2 and range(2)[axis]==1: resid = resid.T return resid def lagmat(x, maxlag, trim='forward', original='ex'): '''create 2d array of lags Parameters ---------- x : array_like, 1d or 2d data; if 2d, observation in rows and variables in columns maxlag : int or sequence of ints all lags from zero to maxlag are included trim : str {'forward', 'backward', 'both', 'none'} or None * 'forward' : trim invalid observations in front * 'backward' : trim invalid initial observations * 'both' : trim invalid observations on both sides * 'none', None : no trimming of observations original : str {'ex','sep','in'} * 'ex' : drops the original array returning only the lagged values. * 'in' : returns the original array and the lagged values as a single array. * 'sep' : returns a tuple (original array, lagged values). The original array is truncated to have the same number of rows as the returned lagmat. Returns ------- lagmat : 2d array array with lagged observations y : 2d array, optional Only returned if original == 'sep' Examples -------- >>> from statsmodels.tsa.tsatools import lagmat >>> import numpy as np >>> X = np.arange(1,7).reshape(-1,2) >>> lagmat(X, maxlag=2, trim="forward", original='in') array([[ 1., 2., 0., 0., 0., 0.], [ 3., 4., 1., 2., 0., 0.], [ 5., 6., 3., 4., 1., 2.]]) >>> lagmat(X, maxlag=2, trim="backward", original='in') array([[ 5., 6., 3., 4., 1., 2.], [ 0., 0., 5., 6., 3., 4.], [ 0., 0., 0., 0., 5., 6.]]) >>> lagmat(X, maxlag=2, trim="both", original='in') array([[ 5., 6., 3., 4., 1., 2.]]) >>> lagmat(X, maxlag=2, trim="none", original='in') array([[ 1., 2., 0., 0., 0., 0.], [ 3., 4., 1., 2., 0., 0.], [ 5., 6., 3., 4., 1., 2.], [ 0., 0., 5., 6., 3., 4.], [ 0., 0., 0., 0., 5., 6.]]) Notes ----- TODO: * allow list of lags additional to maxlag * create varnames for columns ''' x = np.asarray(x) dropidx = 0 if x.ndim == 1: x = x[:,None] nobs, nvar = x.shape if original in ['ex','sep']: dropidx = nvar if maxlag >= nobs: raise ValueError("maxlag should be < nobs") lm = np.zeros((nobs+maxlag, nvar*(maxlag+1))) for k in range(0, int(maxlag+1)): lm[maxlag-k:nobs+maxlag-k, nvar*(maxlag-k):nvar*(maxlag-k+1)] = x if trim: trimlower = trim.lower() else: trimlower = trim if trimlower == 'none' or not trimlower: startobs = 0 stopobs = len(lm) elif trimlower == 'forward': startobs = 0 stopobs = nobs+maxlag-k elif trimlower == 'both': startobs = maxlag stopobs = nobs+maxlag-k elif trimlower == 'backward': startobs = maxlag stopobs = len(lm) else: raise ValueError('trim option not valid') if original == 'sep': return lm[startobs:stopobs,dropidx:], x[startobs:stopobs] else: return lm[startobs:stopobs,dropidx:] def lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward'): '''generate lagmatrix for 2d array, columns arranged by variables Parameters ---------- x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude first dropex lags from other variables for all variables, except the first, lags from dropex to maxlagex are included trim : string * 'forward' : trim invalid observations in front * 'backward' : trim invalid initial observations * 'both' : trim invalid observations on both sides * 'none' : no trimming of observations Returns ------- lagmat : 2d array array with lagged observations, columns ordered by variable Notes ----- very inefficient for unequal lags, just done for convenience ''' if maxlagex is None: maxlagex = maxlag0 maxlag = max(maxlag0, maxlagex) nobs, nvar = x.shape lagsli = [lagmat(x[:,0], maxlag, trim=trim, original='in')[:,:maxlag0+1]] for k in range(1,nvar): lagsli.append(lagmat(x[:,k], maxlag, trim=trim, original='in')[:,dropex:maxlagex+1]) return np.column_stack(lagsli) def vec(mat): return mat.ravel('F') def vech(mat): # Gets Fortran-order return mat.T.take(_triu_indices(len(mat))) # tril/triu/diag, suitable for ndarray.take def _tril_indices(n): rows, cols = np.tril_indices(n) return rows * n + cols def _triu_indices(n): rows, cols = np.triu_indices(n) return rows * n + cols def _diag_indices(n): rows, cols = np.diag_indices(n) return rows * n + cols def unvec(v): k = int(np.sqrt(len(v))) assert(k * k == len(v)) return v.reshape((k, k), order='F') def unvech(v): # quadratic formula, correct fp error rows = .5 * (-1 + np.sqrt(1 + 8 * len(v))) rows = int(np.round(rows)) result = np.zeros((rows, rows)) result[np.triu_indices(rows)] = v result = result + result.T # divide diagonal elements by 2 result[np.diag_indices(rows)] /= 2 return result def duplication_matrix(n): """ Create duplication matrix D_n which satisfies vec(S) = D_n vech(S) for symmetric matrix S Returns ------- D_n : ndarray """ tmp = np.eye(n * (n + 1) / 2) return np.array([unvech(x).ravel() for x in tmp]).T def elimination_matrix(n): """ Create the elimination matrix L_n which satisfies vech(M) = L_n vec(M) for any matrix M Parameters ---------- Returns ------- """ vech_indices = vec(np.tril(np.ones((n, n)))) return np.eye(n * n)[vech_indices != 0] def commutation_matrix(p, q): """ Create the commutation matrix K_{p,q} satisfying vec(A') = K_{p,q} vec(A) Parameters ---------- p : int q : int Returns ------- K : ndarray (pq x pq) """ K = np.eye(p * q) indices = np.arange(p * q).reshape((p, q), order='F') return K.take(indices.ravel(), axis=0) def _ar_transparams(params): """ Transforms params to induce stationarity/invertability. Parameters ---------- params : array The AR coefficients Reference --------- Jones(1980) """ newparams = ((1-np.exp(-params))/ (1+np.exp(-params))).copy() tmp = ((1-np.exp(-params))/ (1+np.exp(-params))).copy() for j in range(1,len(params)): a = newparams[j] for kiter in range(j): tmp[kiter] -= a * newparams[j-kiter-1] newparams[:j] = tmp[:j] return newparams def _ar_invtransparams(params): """ Inverse of the Jones reparameterization Parameters ---------- params : array The transformed AR coefficients """ # AR coeffs tmp = params.copy() for j in range(len(params)-1,0,-1): a = params[j] for kiter in range(j): tmp[kiter] = (params[kiter] + a * params[j-kiter-1])/\ (1-a**2) params[:j] = tmp[:j] invarcoefs = -np.log((1-params)/(1+params)) return invarcoefs def _ma_transparams(params): """ Transforms params to induce stationarity/invertability. Parameters ---------- params : array The ma coeffecients of an (AR)MA model. Reference --------- Jones(1980) """ newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy() tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy() # levinson-durbin to get macf for j in range(1,len(params)): b = newparams[j] for kiter in range(j): tmp[kiter] += b * newparams[j-kiter-1] newparams[:j] = tmp[:j] return newparams def _ma_invtransparams(macoefs): """ Inverse of the Jones reparameterization Parameters ---------- params : array The transformed MA coefficients """ tmp = macoefs.copy() for j in range(len(macoefs)-1,0,-1): b = macoefs[j] for kiter in range(j): tmp[kiter] = (macoefs[kiter]-b *macoefs[j-kiter-1])/(1-b**2) macoefs[:j] = tmp[:j] invmacoefs = -np.log((1-macoefs)/(1+macoefs)) return invmacoefs def unintegrate(x, levels): """ After taking n-differences of a series, return the original series Parameters ---------- x : array-like The n-th differenced series levels : list A list of the first-value in each differenced series, for [first-difference, second-difference, ..., n-th difference] Returns ------- y : array-like The original series de-differenced Examples -------- >>> x = np.array([1, 3, 9., 19, 8.]) >>> levels = [x[0], np.diff(x, 1)[0]] >>> _unintegrate(np.diff(x, 2), levels) array([ 1., 3., 9., 19., 8.]) """ levels = levels[:] # copy if len(levels) > 1: x0 = levels.pop(-1) return _unintegrate(np.cumsum(np.r_[x0, x]), levels) x0 = levels[0] return np.cumsum(np.r_[x0, x]) __all__ = ['lagmat', 'lagmat2ds','add_trend', 'duplication_matrix', 'elimination_matrix', 'commutation_matrix', 'vec', 'vech', 'unvec', 'unvech'] if __name__ == '__main__': # sanity check, mainly for imports x = np.random.normal(size=(100,2)) tmp = lagmat(x,2) tmp = lagmat2ds(x,2) # grangercausalitytests(x, 2) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/varma_process.py000066400000000000000000000467411224417117700245350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Helper and filter functions for VAR and VARMA, and basic VAR class Created on Mon Jan 11 11:04:23 2010 Author: josef-pktd License: BSD This is a new version, I didn't look at the old version again, but similar ideas. not copied/cleaned yet: * fftn based filtering, creating samples with fft * Tests: I ran examples but did not convert them to tests examples look good for parameter estimate and forecast, and filter functions main TODOs: * result statistics * see whether Bayesian dummy observation can be included without changing the single call to linalg.lstsq * impulse response function does not treat correlation, see Hamilton and jplv Extensions * constraints, Bayesian priors/penalization * Error Correction Form and Cointegration * Factor Models Stock-Watson, ??? see also VAR section in Notes.txt """ import numpy as np from numpy.testing import assert_equal from scipy import signal #might not (yet) need the following from scipy.signal.signaltools import _centered as trim_centered from statsmodels.tsa.tsatools import lagmat def varfilter(x, a): '''apply an autoregressive filter to a series x Warning: I just found out that convolve doesn't work as I thought, this likely doesn't work correctly for nvars>3 x can be 2d, a can be 1d, 2d, or 3d Parameters ---------- x : array_like data array, 1d or 2d, if 2d then observations in rows a : array_like autoregressive filter coefficients, ar lag polynomial see Notes Returns ------- y : ndarray, 2d filtered array, number of columns determined by x and a Notes ----- In general form this uses the linear filter :: y = a(L)x where x : nobs, nvars a : nlags, nvars, npoly Depending on the shape and dimension of a this uses different Lag polynomial arrays case 1 : a is 1d or (nlags,1) one lag polynomial is applied to all variables (columns of x) case 2 : a is 2d, (nlags, nvars) each series is independently filtered with its own lag polynomial, uses loop over nvar case 3 : a is 3d, (nlags, nvars, npoly) the ith column of the output array is given by the linear filter defined by the 2d array a[:,:,i], i.e. :: y[:,i] = a(.,.,i)(L) * x y[t,i] = sum_p sum_j a(p,j,i)*x(t-p,j) for p = 0,...nlags-1, j = 0,...nvars-1, for all t >= nlags Note: maybe convert to axis=1, Not TODO: initial conditions ''' x = np.asarray(x) a = np.asarray(a) if x.ndim == 1: x = x[:,None] if x.ndim > 2: raise ValueError('x array has to be 1d or 2d') nvar = x.shape[1] nlags = a.shape[0] ntrim = nlags//2 # for x is 2d with ncols >1 if a.ndim == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a[:,None], mode='valid') # alternative: #return signal.lfilter(a,[1],x.astype(float),axis=0) elif a.ndim == 2: if min(a.shape) == 1: # case: identical ar filter (lag polynomial) return signal.convolve(x, a, mode='valid') # case: independent ar #(a bit like recserar in gauss, but no x yet) #(no, reserar is inverse filter) result = np.zeros((x.shape[0]-nlags+1, nvar)) for i in range(nvar): # could also use np.convolve, but easier for swiching to fft result[:,i] = signal.convolve(x[:,i], a[:,i], mode='valid') return result elif a.ndim == 3: # case: vector autoregressive with lag matrices # #not necessary: # if np.any(a.shape[1:] != nvar): # raise ValueError('if 3d shape of a has to be (nobs,nvar,nvar)') yf = signal.convolve(x[:,:,None], a) yvalid = yf[ntrim:-ntrim, yf.shape[1]//2,:] return yvalid def varinversefilter(ar, nobs, version=1): '''creates inverse ar filter (MA representation) recursively The VAR lag polynomial is defined by :: ar(L) y_t = u_t or y_t = -ar_{-1}(L) y_{t-1} + u_t the returned lagpolynomial is arinv(L)=ar^{-1}(L) in :: y_t = arinv(L) u_t Parameters ---------- ar : array, (nlags,nvars,nvars) matrix lagpolynomial, currently no exog first row should be identity Returns ------- arinv : array, (nobs,nvars,nvars) Notes ----- ''' nlags, nvars, nvarsex = ar.shape if nvars != nvarsex: print 'exogenous variables not implemented not tested' arinv = np.zeros((nobs+1, nvarsex, nvars)) arinv[0,:,:] = ar[0] arinv[1:nlags,:,:] = -ar[1:] if version == 1: for i in range(2,nobs+1): tmp = np.zeros((nvars,nvars)) for p in range(1,nlags): tmp += np.dot(-ar[p],arinv[i-p,:,:]) arinv[i,:,:] = tmp if version == 0: for i in range(nlags+1,nobs+1): print ar[1:].shape, arinv[i-1:i-nlags:-1,:,:].shape #arinv[i,:,:] = np.dot(-ar[1:],arinv[i-1:i-nlags:-1,:,:]) #print np.tensordot(-ar[1:],arinv[i-1:i-nlags:-1,:,:],axes=([2],[1])).shape #arinv[i,:,:] = np.tensordot(-ar[1:],arinv[i-1:i-nlags:-1,:,:],axes=([2],[1])) raise NotImplementedError('waiting for generalized ufuncs or something') return arinv def vargenerate(ar, u, initvalues=None): '''generate an VAR process with errors u similar to gauss uses loop Parameters ---------- ar : array (nlags,nvars,nvars) matrix lagpolynomial u : array (nobs,nvars) exogenous variable, error term for VAR Returns ------- sar : array (1+nobs,nvars) sample of var process, inverse filtered u does not trim initial condition y_0 = 0 Examples -------- # generate random sample of VAR nobs, nvars = 10, 2 u = numpy.random.randn(nobs,nvars) a21 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0. ], [ 0., -0.6]]]) vargenerate(a21,u) # Impulse Response to an initial shock to the first variable imp = np.zeros((nobs, nvars)) imp[0,0] = 1 vargenerate(a21,imp) ''' nlags, nvars, nvarsex = ar.shape nlagsm1 = nlags - 1 nobs = u.shape[0] if nvars != nvarsex: print 'exogenous variables not implemented not tested' if u.shape[1] != nvars: raise ValueError('u needs to have nvars columns') if initvalues is None: sar = np.zeros((nobs+nlagsm1, nvars)) start = nlagsm1 else: start = max(nlagsm1, initvalues.shape[0]) sar = np.zeros((nobs+start, nvars)) sar[start-initvalues.shape[0]:start] = initvalues #sar[nlagsm1:] = u sar[start:] = u #if version == 1: for i in range(start,start+nobs): for p in range(1,nlags): sar[i] += np.dot(sar[i-p,:],-ar[p]) return sar def padone(x, front=0, back=0, axis=0, fillvalue=0): '''pad with zeros along one axis, currently only axis=0 can be used sequentially to pad several axis Examples -------- >>> padone(np.ones((2,3)),1,3,axis=1) array([[ 0., 1., 1., 1., 0., 0., 0.], [ 0., 1., 1., 1., 0., 0., 0.]]) >>> padone(np.ones((2,3)),1,1, fillvalue=np.nan) array([[ NaN, NaN, NaN], [ 1., 1., 1.], [ 1., 1., 1.], [ NaN, NaN, NaN]]) ''' #primitive version shape = np.array(x.shape) shape[axis] += (front + back) shapearr = np.array(x.shape) out = np.empty(shape) out.fill(fillvalue) startind = np.zeros(x.ndim) startind[axis] = front endind = startind + shapearr myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] #print myslice #print out.shape #print out[tuple(myslice)].shape out[tuple(myslice)] = x return out def trimone(x, front=0, back=0, axis=0): '''trim number of array elements along one axis Examples -------- >>> xp = padone(np.ones((2,3)),1,3,axis=1) >>> xp array([[ 0., 1., 1., 1., 0., 0., 0.], [ 0., 1., 1., 1., 0., 0., 0.]]) >>> trimone(xp,1,3,1) array([[ 1., 1., 1.], [ 1., 1., 1.]]) ''' shape = np.array(x.shape) shape[axis] -= (front + back) #print shape, front, back shapearr = np.array(x.shape) startind = np.zeros(x.ndim) startind[axis] = front endind = startind + shape myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] #print myslice #print shape, endind #print x[tuple(myslice)].shape return x[tuple(myslice)] def ar2full(ar): '''make reduced lagpolynomial into a right side lagpoly array ''' nlags, nvar,nvarex = ar.shape return np.r_[np.eye(nvar,nvarex)[None,:,:],-ar] def ar2lhs(ar): '''convert full (rhs) lagpolynomial into a reduced, left side lagpoly array this is mainly a reminder about the definition ''' return -ar[1:] class _Var(object): '''obsolete VAR class, use tsa.VAR instead, for internal use only Example ------- >>> v = Var(ar2s) >>> v.fit(1) >>> v.arhat array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.77784898, 0.01726193], [ 0.10733009, -0.78665335]]]) ''' def __init__(self, y): self.y = y self.nobs, self.nvars = y.shape def fit(self, nlags): '''estimate parameters using ols Parameters ---------- nlags : integer number of lags to include in regression, same for all variables Returns ------- None, but attaches arhat : array (nlags, nvar, nvar) full lag polynomial array arlhs : array (nlags-1, nvar, nvar) reduced lag polynomial for left hand side other statistics as returned by linalg.lstsq : need to be completed This currently assumes all parameters are estimated without restrictions. In this case SUR is identical to OLS estimation results are attached to the class instance ''' self.nlags = nlags # without current period nvars = self.nvars #TODO: ar2s looks like a module variable, bug? #lmat = lagmat(ar2s, nlags, trim='both', original='in') lmat = lagmat(self.y, nlags, trim='both', original='in') self.yred = lmat[:,:nvars] self.xred = lmat[:,nvars:] res = np.linalg.lstsq(self.xred, self.yred) self.estresults = res self.arlhs = res[0].reshape(nlags, nvars, nvars) self.arhat = ar2full(self.arlhs) self.rss = res[1] self.xredrank = res[2] def predict(self): '''calculate estimated timeseries (yhat) for sample ''' if not hasattr(self, 'yhat'): self.yhat = varfilter(self.y, self.arhat) return self.yhat def covmat(self): ''' covariance matrix of estimate # not sure it's correct, need to check orientation everywhere # looks ok, display needs getting used to >>> v.rss[None,None,:]*np.linalg.inv(np.dot(v.xred.T,v.xred))[:,:,None] array([[[ 0.37247445, 0.32210609], [ 0.1002642 , 0.08670584]], [[ 0.1002642 , 0.08670584], [ 0.45903637, 0.39696255]]]) >>> >>> v.rss[0]*np.linalg.inv(np.dot(v.xred.T,v.xred)) array([[ 0.37247445, 0.1002642 ], [ 0.1002642 , 0.45903637]]) >>> v.rss[1]*np.linalg.inv(np.dot(v.xred.T,v.xred)) array([[ 0.32210609, 0.08670584], [ 0.08670584, 0.39696255]]) ''' #check if orientation is same as self.arhat self.paramcov = (self.rss[None,None,:] * np.linalg.inv(np.dot(self.xred.T, self.xred))[:,:,None]) def forecast(self, horiz=1, u=None): '''calculates forcast for horiz number of periods at end of sample Parameters ---------- horiz : int (optional, default=1) forecast horizon u : array (horiz, nvars) error term for forecast periods. If None, then u is zero. Returns ------- yforecast : array (nobs+horiz, nvars) this includes the sample and the forecasts ''' if u is None: u = np.zeros((horiz, self.nvars)) return vargenerate(self.arhat, u, initvalues=self.y) class VarmaPoly(object): '''class to keep track of Varma polynomial format Examples -------- ar23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) ma22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[ 0.4, 0. ], [ 0.2, 0.3]]]) ''' def __init__(self, ar, ma=None): self.ar = ar self.ma = ma nlags, nvarall, nvars = ar.shape self.nlags, self.nvarall, self.nvars = nlags, nvarall, nvars self.isstructured = not (ar[0,:nvars] == np.eye(nvars)).all() if self.ma is None: self.ma = np.eye(nvars)[None,...] self.isindependent = True else: self.isindependent = not (ma[0] == np.eye(nvars)).all() self.malags = ar.shape[0] self.hasexog = nvarall > nvars self.arm1 = -ar[1:] #@property def vstack(self, a=None, name='ar'): '''stack lagpolynomial vertically in 2d array ''' if not a is None: a = a elif name == 'ar': a = self.ar elif name == 'ma': a = self.ma else: raise ValueError('no array or name given') return a.reshape(-1, self.nvarall) #@property def hstack(self, a=None, name='ar'): '''stack lagpolynomial horizontally in 2d array ''' if not a is None: a = a elif name == 'ar': a = self.ar elif name == 'ma': a = self.ma else: raise ValueError('no array or name given') return a.swapaxes(1,2).reshape(-1, self.nvarall).T #@property def stacksquare(self, a=None, name='ar', orientation='vertical'): '''stack lagpolynomial vertically in 2d square array with eye ''' if not a is None: a = a elif name == 'ar': a = self.ar elif name == 'ma': a = self.ma else: raise ValueError('no array or name given') astacked = a.reshape(-1, self.nvarall) lenpk, nvars = astacked.shape #[0] amat = np.eye(lenpk, k=nvars) amat[:,:nvars] = astacked return amat #@property def vstackarma_minus1(self): '''stack ar and lagpolynomial vertically in 2d array ''' a = np.concatenate((self.ar[1:], self.ma[1:]),0) return a.reshape(-1, self.nvarall) #@property def hstackarma_minus1(self): '''stack ar and lagpolynomial vertically in 2d array this is the Kalman Filter representation, I think ''' a = np.concatenate((self.ar[1:], self.ma[1:]),0) return a.swapaxes(1,2).reshape(-1, self.nvarall) def getisstationary(self, a=None): '''check whether the auto-regressive lag-polynomial is stationary Returns ------- isstationary : boolean *attaches* areigenvalues : complex array eigenvalues sorted by absolute value References ---------- formula taken from NAG manual ''' if not a is None: a = a else: if self.isstructured: a = -self.reduceform(self.ar)[1:] else: a = -self.ar[1:] amat = self.stacksquare(a) ev = np.sort(np.linalg.eigvals(amat))[::-1] self.areigenvalues = ev return (np.abs(ev) < 1).all() def getisinvertible(self, a=None): '''check whether the auto-regressive lag-polynomial is stationary Returns ------- isinvertible : boolean *attaches* maeigenvalues : complex array eigenvalues sorted by absolute value References ---------- formula taken from NAG manual ''' if not a is None: a = a else: if self.isindependent: a = self.reduceform(self.ma)[1:] else: a = self.ma[1:] if a.shape[0] == 0: # no ma lags self.maeigenvalues = np.array([], np.complex) return True amat = self.stacksquare(a) ev = np.sort(np.linalg.eigvals(amat))[::-1] self.maeigenvalues = ev return (np.abs(ev) < 1).all() def reduceform(self, apoly): ''' this assumes no exog, todo ''' if apoly.ndim != 3: raise ValueError('apoly needs to be 3d') nlags, nvarsex, nvars = apoly.shape a = np.empty_like(apoly) try: a0inv = np.linalg.inv(a[0,:nvars, :]) except np.linalg.LinAlgError: raise ValueError('matrix not invertible', 'ask for implementation of pinv') for lag in range(nlags): a[lag] = np.dot(a0inv, apoly[lag]) return a if __name__ == "__main__": # some example lag polynomials a21 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0. ], [ 0., -0.6]]]) a22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0. ], [ 0.1, -0.8]]]) a23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.8, 0.2], [ 0.1, -0.6]]]) a24 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) a31 = np.r_[np.eye(3)[None,:,:], 0.8*np.eye(3)[None,:,:]] a32 = np.array([[[ 1. , 0. , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[ 0.8, 0. , 0. ], [ 0.1, 0.6, 0. ], [ 0. , 0. , 0.9]]]) ######## ut = np.random.randn(1000,2) ar2s = vargenerate(a22,ut) #res = np.linalg.lstsq(lagmat(ar2s,1)[:,1:], ar2s) res = np.linalg.lstsq(lagmat(ar2s,1), ar2s) bhat = res[0].reshape(1,2,2) arhat = ar2full(bhat) #print maxabs(arhat - a22) v = _Var(ar2s) v.fit(1) v.forecast() v.forecast(25)[-30:] ar23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) ma22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[ 0.4, 0. ], [ 0.2, 0.3]]]) ar23ns = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-1.9, 0. ], [ 0.4, -0.6]], [[ 0.3, 0. ], [ 0.1, -0.1]]]) vp = VarmaPoly(ar23, ma22) print vars(vp) print vp.vstack() print vp.vstack(a24) print vp.hstackarma_minus1() print vp.getisstationary() print vp.getisinvertible() vp2 = VarmaPoly(ar23ns) print vp2.getisstationary() print vp2.getisinvertible() # no ma lags statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/000077500000000000000000000000001224417117700232675ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/__init__.py000066400000000000000000000001031224417117700253720ustar00rootroot00000000000000from statsmodels import NoseWrapper as Tester test = Tester().test statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/api.py000066400000000000000000000001611224417117700244100ustar00rootroot00000000000000# pylint: disable=W0611 from .var_model import VAR from .svar_model import SVAR from .dynamic import DynamicVAR statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/000077500000000000000000000000001224417117700242005ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/e1.dat000066400000000000000000000050531224417117700252020ustar00rootroot00000000000000/*quarterly, seasonally adjusted, West German fixed investment, disposable income, consumption expenditures in billions of DM, 1960Q1-1982Q4; source: Deutsche Bundesbank */ <1960 Q1> invest income cons 180 451 415 179 465 421 185 485 434 192 493 448 211 509 459 202 520 458 207 521 479 214 540 487 231 548 497 229 558 510 234 574 516 237 583 525 206 591 529 250 599 538 259 610 546 263 627 555 264 642 574 280 653 574 282 660 586 292 694 602 286 709 617 302 734 639 304 751 653 307 763 668 317 766 679 314 779 686 306 808 697 304 785 688 292 794 704 275 799 699 273 799 709 301 812 715 280 837 724 289 853 746 303 876 758 322 897 779 315 922 798 339 949 816 364 979 837 371 988 858 375 1025 881 432 1063 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Commerce, Bureau of Economic Analysis, The National Income and Product Accounts of the United States, 1929-1974 */ <1947 Q1> y1 y2 69.6 0.1 67.6 -0.9 69.5 -2.9 74.7 2.7 77.1 4.1 77.4 5.6 76.6 6.9 76.1 5.3 71.8 -0.3 68.9 -7.1 68.5 -2.5 70.6 -7.7 75.4 4.4 82.3 7.7 88.2 8.0 86.9 22.1 83.4 13.4 80.3 19.9 79.4 14.6 78.6 7.0 79.3 7.3 80.3 -2.7 75.3 5.4 80.6 7.2 83.9 3.9 84.2 5.1 84.4 1.9 83.8 -5.0 82.8 -3.4 84.1 -4.1 87.0 -2.7 88.5 1.5 92.1 5.9 96.1 8.0 98.3 7.8 98.8 9.2 96.6 7.5 97.4 5.5 97.6 4.9 96.6 5.4 96.2 2.5 95.3 2.9 96.4 3.7 94.9 -3.0 90.0 -6.8 87.2 -6.2 88.0 0.3 93.0 5.3 98.3 5.0 101.6 13.0 102.6 -0.4 101.4 8.2 104.9 13.5 101.8 4.9 98.8 3.0 98.6 -3.9 97.7 -3.8 99.2 1.9 101.3 6.6 104.6 6.7 106.1 10.6 109.9 9.2 111.1 8.0 110.1 4.7 110.7 7.6 116.0 7.0 118.5 9.3 122.0 7.1 124.0 6.1 124.0 8.0 124.9 7.3 126.4 7.9 133.4 13.4 137.9 10.6 140.1 12.4 143.8 8.8 147.5 13.5 146.2 17.8 145.0 15.1 139.7 20.5 136.4 14.6 139.6 7.5 141.1 12.2 145.5 13.8 148.9 6.3 148.9 11.8 150.7 9.2 155.0 7.6 159.1 9.8 158.4 12.2 158.1 13.4 154.3 6.8 151.8 2.9 150.0 4.8 150.4 6.3 149.5 3.3 154.3 7.9 158.4 10.0 162.1 5.0 166.0 3.7 174.3 4.8 176.1 10.1 178.2 12.1 186.7 10.8 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/e3.dat000066400000000000000000000167031224417117700252100ustar00rootroot00000000000000/*quarterly, seasonally adjusted real U.S. money (M1), GNP in 1982 Dollars, discount rate on 91-day treasury bills (rd), yield on long term treasury bonds (rb), 1954Q1-1987Q4; source: Business Conditions Digest */ <1954 Q1> M1 gnp rd rb 450.9 1406.8 0.010800000 0.026133333 453.0 1401.2 0.0081333333 0.025233333 459.1 1418.0 0.0087000000 0.024900000 464.6 1438.8 0.010366667 0.025666667 469.6 1469.6 0.012600000 0.027466667 473.1 1485.7 0.015133333 0.028166667 474.6 1505.5 0.018633333 0.029266667 474.3 1518.7 0.023466667 0.028900000 475.4 1515.7 0.023800000 0.028866667 472.9 1522.6 0.025966667 0.029900000 468.7 1523.7 0.025966667 0.031266667 467.5 1540.6 0.030633333 0.033000000 464.7 1553.3 0.031700000 0.032733333 461.2 1552.4 0.031566667 0.034333333 457.1 1561.5 0.033800000 0.036300000 453.0 1537.3 0.033433333 0.035333333 447.5 1506.1 0.018366667 0.032566667 449.6 1514.2 0.010200000 0.031533333 454.2 1550.0 0.017100000 0.035700000 458.5 1586.7 0.027866667 0.037533333 464.1 1606.4 0.028000000 0.039166667 466.3 1637.0 0.030200000 0.040600000 468.1 1629.5 0.035333333 0.041566667 460.0 1643.4 0.043000000 0.041666667 459.2 1671.6 0.039433333 0.042233333 455.7 1666.8 0.030900000 0.041066667 459.5 1668.4 0.023933333 0.038300000 455.9 1654.1 0.023600000 0.039066667 458.0 1671.3 0.023766667 0.038266667 461.5 1692.1 0.023266667 0.038033333 462.2 1716.3 0.023233333 0.039733333 465.4 1754.9 0.024766667 0.040066667 467.4 1777.9 0.027400000 0.040600000 468.5 1796.4 0.027166667 0.038900000 466.5 1813.1 0.028566667 0.039800000 467.7 1810.1 0.028033333 0.038766667 471.9 1834.6 0.029100000 0.039133333 474.8 1860.0 0.029433333 0.039800000 477.7 1892.5 0.032800000 0.040133333 479.9 1906.1 0.034966667 0.041066667 481.9 1948.7 0.035366667 0.041566667 484.8 1965.4 0.034800000 0.041633333 491.3 1985.2 0.035066667 0.041433333 495.6 1993.7 0.036866667 0.041400000 498.3 2036.9 0.039000000 0.041500000 497.6 2066.4 0.038800000 0.041433333 501.7 2099.3 0.038600000 0.041966667 507.8 2147.6 0.041566667 0.043500000 511.8 2190.1 0.046333333 0.045566667 511.8 2195.8 0.045966667 0.045833333 506.2 2218.3 0.050500000 0.047800000 503.1 2229.2 0.045800000 0.046966667 507.1 2241.8 0.045266667 0.044400000 510.8 2255.2 0.036566667 0.047100000 518.0 2287.7 0.043466667 0.049333333 521.3 2300.6 0.047866667 0.053300000 521.9 2327.3 0.050633333 0.052433333 525.4 2366.9 0.055066667 0.053033333 528.3 2385.3 0.052266667 0.050733333 533.8 2383.0 0.055800000 0.054200000 536.5 2416.5 0.061400000 0.058833333 532.8 2419.8 0.062400000 0.059133333 527.6 2433.2 0.070466667 0.061366667 523.2 2423.5 0.073166667 0.065333333 521.4 2408.6 0.072600000 0.065633333 518.1 2406.5 0.067533333 0.068200000 519.4 2435.8 0.063833333 0.066500000 521.2 2413.8 0.053600000 0.062666667 524.7 2478.6 0.038600000 0.058233333 530.8 2478.4 0.042066667 0.058833333 534.1 2491.1 0.050500000 0.057500000 536.5 2491.0 0.042333333 0.055200000 542.6 2545.6 0.034333333 0.056500000 547.8 2595.1 0.037466667 0.056566667 554.4 2622.1 0.042400000 0.056266667 562.5 2671.3 0.048500000 0.056100000 565.2 2734.0 0.056400000 0.061000000 560.1 2741.0 0.066100000 0.062266667 556.1 2738.3 0.083900000 0.065966667 548.5 2762.8 0.074633333 0.063000000 542.1 2747.4 0.076033333 0.066366667 532.8 2755.2 0.082666667 0.070500000 522.8 2719.3 0.082833333 0.072700000 511.9 2695.4 0.073333333 0.069733333 505.3 2642.7 0.058700000 0.067033333 506.8 2669.6 0.054000000 0.069733333 505.5 2714.9 0.063333333 0.070933333 501.0 2752.7 0.056833333 0.072233333 502.2 2804.4 0.049533333 0.069100000 505.5 2816.9 0.051666667 0.068866667 502.6 2828.6 0.051700000 0.067900000 505.3 2856.8 0.046966667 0.065500000 508.1 2896.0 0.046233333 0.070133333 507.7 2942.7 0.048266667 0.070966667 509.4 3001.8 0.054733333 0.069766667 513.0 2994.1 0.061366667 0.071600000 513.7 3020.5 0.064100000 0.075800000 514.1 3115.9 0.064833333 0.078500000 512.5 3142.6 0.073166667 0.079333333 509.7 3181.6 0.086800000 0.081966667 503.3 3181.7 0.093600000 0.084366667 496.1 3178.9 0.093733333 0.084366667 497.2 3207.4 0.096300000 0.084833333 487.6 3201.3 0.11803333 0.096066667 474.0 3233.4 0.13460000 0.11150000 451.2 3157.0 0.10050000 0.10016667 464.9 3159.1 0.092366667 0.10433333 465.3 3199.2 0.13710000 0.11640000 455.3 3261.1 0.14366667 0.12010000 453.7 3250.2 0.14830000 0.12656667 448.9 3264.6 0.15086667 0.13600000 447.1 3219.3 0.12023333 0.13230000 451.2 3170.4 0.12893333 0.13446667 447.1 3179.9 0.12360000 0.12943333 449.1 3154.5 0.097066667 0.12200000 464.9 3159.3 0.079333333 0.10340000 475.8 3186.6 0.080800000 0.10436667 484.3 3258.3 0.084200000 0.10346667 493.6 3306.4 0.091866667 0.11260000 496.4 3365.1 0.087933333 0.11323333 497.5 3451.7 0.091333333 0.11543333 500.4 3498.0 0.098433333 0.12686667 501.5 3520.6 0.10343333 0.12340000 502.2 3535.2 0.089733333 0.11373333 511.0 3577.5 0.081833333 0.11426667 518.2 3599.2 0.075233333 0.10913333 533.9 3635.8 0.071033333 0.10590000 543.2 3662.4 0.071466667 0.10080000 553.4 3719.3 0.068866667 0.089033333 576.8 3711.6 0.061300000 0.079466667 598.0 3721.3 0.055333333 0.078866667 620.0 3734.7 0.053400000 0.078400000 631.9 3776.7 0.055333333 0.076366667 634.8 3823.0 0.057333333 0.085766667 630.1 3865.3 0.060333333 0.090833333 630.5 3923.0 0.060033333 0.092400000 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/e4.dat000066400000000000000000000062551224417117700252120ustar00rootroot00000000000000/*quarterly, unadjusted, West German real per capita disposable income, personal consumption expenditures, 1960Q1-1987Q4; source: Deutsches Institut fuer Wirtschaftsforschung, Berlin */ <1960 Q1> inc cons 1684.6080 1505.3198 1757.2068 1632.8338 1842.7211 1655.8021 2027.3177 1889.8641 1859.0561 1600.8018 1832.6429 1686.0382 1893.5360 1739.6939 2098.9234 1958.8067 1873.2676 1645.0461 1908.6426 1798.0200 1989.5797 1805.1480 2203.5812 2038.5067 1931.2619 1673.9302 1980.3270 1829.8065 2065.5097 1848.9135 2256.8667 2065.9135 2040.4667 1758.5866 2098.8673 1894.7205 2145.8642 1910.8631 2427.6995 2165.6707 2178.3079 1830.4957 2268.4690 2024.4865 2313.5771 2040.4966 2548.2016 2274.2020 2239.8707 1919.7148 2286.0157 2070.1321 2379.5459 2085.6752 2535.5248 2269.4856 2247.8021 1942.0016 2311.8334 2063.5617 2332.9532 2088.5606 2579.1389 2320.2063 2348.2887 1965.2127 2404.7787 2164.5546 2478.4108 2182.9295 2808.6086 2467.3345 2535.4286 2121.2628 2605.4529 2307.8719 2688.1167 2333.5107 2998.7831 2626.8251 2695.4440 2252.5326 2764.9544 2445.3018 2879.4832 2484.5415 3255.6348 2810.1885 2825.4761 2383.4010 2877.2233 2575.9185 2955.7203 2578.9525 3378.4279 2872.9650 3014.1083 2517.0234 3030.1896 2645.5602 3092.0762 2682.6305 3491.3989 2962.6573 3098.3107 2607.4582 3091.6898 2755.3575 3144.9089 2732.3144 3535.0880 2988.6982 3096.3842 2606.4549 3110.8586 2748.4852 3222.0775 2775.8346 3613.9943 3012.7843 3233.5315 2647.7614 3319.4594 2854.9956 3323.1825 2880.3328 3723.1654 3164.0112 3312.5138 2808.3615 3324.6223 2965.6652 3418.3893 2978.0314 3824.4625 3280.6247 3414.6557 2914.1164 3438.2362 3095.7771 3487.2942 3122.1189 3973.8991 3438.6965 3546.9684 3053.1464 3553.1513 3216.7262 3655.2346 3254.8354 4102.7937 3543.8848 3680.9675 3130.1370 3783.8147 3406.7708 3755.9600 3327.0867 4261.4059 3669.2334 3797.3271 3279.6486 3786.5219 3324.2316 3790.9261 3369.4241 4282.9525 3682.5376 3848.1635 3250.4014 3764.6489 3308.7353 3759.0830 3339.1146 4294.2124 3661.0499 3811.5833 3224.8444 3709.8919 3291.6504 3641.5356 3260.4145 4180.7126 3611.8419 3734.1328 3259.6856 3698.3154 3361.7589 3664.9417 3347.9925 4237.3911 3700.2983 3847.0869 3323.8678 3766.3393 3424.4785 3783.3845 3430.6044 4327.6089 3753.7867 3887.8617 3335.6273 3839.1482 3469.0466 3859.0148 3533.5180 4431.0216 3859.7411 4049.0191 3452.2034 4053.1962 3669.5399 4077.4384 3673.5696 4653.9432 3999.3784 4191.4281 3540.2152 4163.4328 3750.5110 4177.7770 3775.5819 4811.1361 4150.3215 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/e5.dat000066400000000000000000000266301224417117700252120ustar00rootroot00000000000000/*monthly West German interest rate on 3-months loans in the money market (i_short), yields on bonds outstanding for total fixed interest securities (i_long), 1960M1-1987M12; source: Deutsche Bundesbank */ <1960 M1> i_short i_long 4.3600000 6.2000000 4.4700000 6.2000000 4.7100000 6.2000000 4.5900000 6.2000000 4.6400000 6.2000000 5.2500000 6.4000000 5.5800000 6.6000000 5.4400000 6.5000000 5.6100000 6.4000000 6.1000000 6.4000000 5.3800000 6.2000000 5.0600000 6.2000000 4.6600000 6.1000000 4.1300000 6.1000000 3.7300000 6.0000000 3.3400000 5.8000000 3.2300000 5.7000000 3.1300000 5.7000000 3.1600000 5.8000000 3.0900000 5.9000000 3.0600000 6.0000000 4.0500000 6.0000000 3.7100000 6.0000000 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9.4600000 10.900000 9.4800000 10.900000 9.6500000 10.900000 9.6900000 10.800000 9.7800000 10.900000 9.0400000 10.600000 8.6000000 9.9000000 7.7400000 9.4000000 6.4300000 9.0000000 5.7100000 8.9000000 4.8900000 8.8000000 4.9900000 8.5000000 4.8800000 8.4000000 4.6600000 8.4000000 3.8800000 8.6000000 3.9300000 8.7000000 4.0700000 8.7000000 4.1200000 8.7000000 4.2100000 8.6000000 3.9300000 8.4000000 3.7200000 8.2000000 3.7400000 7.8000000 3.6200000 7.8000000 3.7700000 8.0000000 4.1400000 8.3000000 4.4700000 8.4000000 4.5600000 8.3000000 4.5600000 8.1000000 4.8500000 8.0000000 4.6900000 7.6000000 4.9300000 7.4000000 4.7800000 7.2000000 4.7100000 7.1000000 4.7300000 7.0000000 4.6200000 6.6000000 4.4400000 6.4000000 4.2800000 6.4000000 4.2900000 6.3000000 4.1200000 6.1000000 4.1500000 6.0000000 4.1300000 6.0000000 4.1500000 6.0000000 3.9800000 6.0000000 3.5800000 5.8000000 3.4600000 5.7000000 3.5100000 5.6000000 3.5600000 5.6000000 3.6000000 5.8000000 3.6800000 6.0000000 3.7500000 6.3000000 3.7000000 6.6000000 3.7000000 6.4000000 3.9500000 6.3000000 3.8500000 6.6000000 4.0600000 6.6000000 3.8900000 6.7000000 4.1500000 7.0000000 4.4700000 7.1000000 5.5400000 7.2000000 5.9200000 7.6000000 6.4600000 8.0000000 6.8400000 7.9000000 7.0900000 7.7000000 7.8900000 7.8000000 8.7600000 7.9000000 9.6500000 8.3000000 9.5800000 8.0000000 8.8600000 8.1000000 8.9700000 8.5000000 9.6400000 9.5000000 10.220000 9.6000000 10.260000 8.8000000 10.110000 8.3000000 9.7000000 8.0000000 8.9800000 7.9000000 8.9700000 8.3000000 9.0800000 8.5000000 9.4500000 9.0000000 10.200000 9.1000000 9.4700000 9.2000000 10.670000 9.9000000 13.600000 10.400000 13.190000 10.400000 13.200000 11.000000 13.090000 11.100000 12.960000 11.200000 12.900000 11.500000 12.500000 11.300000 11.780000 10.600000 11.080000 10.200000 10.820000 9.9000000 10.460000 10.000000 10.270000 9.9000000 9.8700000 9.6000000 9.3300000 9.1000000 9.1800000 8.9000000 9.2800000 9.2000000 9.4600000 9.5000000 9.0000000 9.2000000 8.1800000 8.8000000 7.5800000 8.4000000 7.3100000 8.2000000 6.6200000 8.0000000 5.8200000 7.7000000 5.8300000 7.7000000 5.4500000 7.4000000 5.2000000 7.4000000 5.3300000 7.7000000 5.5700000 8.1000000 5.5700000 8.2000000 5.7100000 8.3000000 5.8800000 8.4000000 6.1800000 8.2000000 6.3000000 8.2000000 6.4800000 8.3000000 6.1200000 8.2000000 5.9500000 8.1000000 5.8600000 7.9000000 5.8400000 7.9000000 6.1000000 8.0000000 6.1300000 8.1000000 6.1300000 8.1000000 6.0200000 7.9000000 5.8200000 7.7000000 6.0700000 7.4000000 5.9600000 7.2000000 5.8300000 7.0000000 5.8700000 7.1000000 6.1600000 7.5000000 6.3900000 7.7000000 6.0200000 7.3000000 5.8400000 7.1000000 5.6800000 7.0000000 5.3400000 6.8000000 4.7900000 6.5000000 4.6900000 6.4000000 4.8100000 6.6000000 4.8400000 6.7000000 4.8300000 6.6000000 4.6700000 6.4000000 4.4900000 6.3000000 4.5400000 6.0000000 4.4900000 5.6000000 4.6000000 5.9000000 4.6000000 6.0000000 4.6300000 6.0000000 4.5700000 5.8000000 4.5000000 5.8000000 4.5900000 6.0000000 4.6900000 6.1000000 4.8100000 6.0000000 4.4900000 5.9000000 3.9700000 5.7000000 3.9900000 5.6000000 3.8900000 5.5000000 3.7600000 5.4000000 3.7000000 5.5000000 3.8300000 5.8000000 3.9500000 6.0000000 3.9900000 6.2000000 4.7000000 6.5000000 3.9400000 6.0000000 3.6500000 5.8000000 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/data/e6.dat000066400000000000000000000117261224417117700252130ustar00rootroot00000000000000/* sample: 1972Q2 -- 1998Q4 West German data until 1990Q2, all of Germany aferwards Dp - \Delta log gdp deflator (source: Deutsches Institut für Wirtschaftsforschung, Volkswirtschaftliche Gesamtrechnung) R - nominal long term interest rate (Umlaufsrendite) (source: Monatsberichte der Deutschen Bundesbank, quarterly values are values of last month of quarter) */ <1972 Q2> Dp R -0.00313258 0.083 0.0188713 0.083 0.0248036 0.087 0.0162776 0.087 2.89679E-4 0.102 0.016829 0.098 0.0385835 0.097 -0.00144386 0.107 0.0127239 0.109 0.0238972 0.108 0.0427728 0.099 -0.0103779 0.089 0.00558424 0.084 0.00870609 0.087 0.0389199 0.086 -0.0196176 0.078 0.00731993 0.083 0.0180764 0.081 0.0237775 0.074 -0.0144377 0.07 0.0110059 0.064 0.0104017 0.06 0.0354648 0.06 -0.0157599 0.056 0.0100193 0.06 0.0180621 0.064 0.0256915 0.066 -0.0170689 0.071 0.00350475 0.08 0.0256438 0.078 0.0315075 0.08 -0.0132775 0.095 0.0130968 0.083 0.0151458 0.083 0.0312119 0.091 -0.0207896 0.104 0.0104489 0.111 0.018106 0.113 0.0405002 0.099 -0.0201483 0.096 0.00380802 0.092 0.0215569 0.088 0.0327106 0.08 -0.0201726 0.074 -0.00333166 0.081 0.0192871 0.084 0.0345473 0.083 -0.0262156 0.079 -0.00519753 0.081 0.0132318 0.077 0.0374279 0.07 -0.0291948 0.077 -0.00261354 0.07 0.0172353 0.064 0.0376387 0.066 -0.0204763 0.06 9.00269E-4 0.06 0.0125732 0.058 0.0344677 0.06 -0.0220394 0.056 -0.00202703 0.055 0.00173426 0.062 0.0365901 0.058 -0.0252557 0.056 9.2268E-4 0.06 0.00451374 0.063 0.0383272 0.062 -0.0211368 0.07 -5.33104E-4 0.071 0.00981092 0.071 0.0373549 0.078 -0.016232 0.09 0.00197124 0.09 0.00687361 0.091 0.0274582 0.09 -0.0140786 0.086 0.0166497 0.086 0.0144682 0.088 0.043004 0.087 -0.0127769 0.082 0.00927353 0.084 0.0179157 0.082 0.030508 0.074 -0.0105906 0.065 0.00747108 0.067 0.00467587 0.061 0.0310678 0.056 -0.0153685 0.062 8.10146E-4 0.069 0.00806475 0.074 0.0287657 0.074 -0.0183783 0.071 0.00449467 0.064 0.00988674 0.061 0.0245948 0.055 -0.0185189 0.058 -0.00590181 0.059 0.00581503 0.055 0.0243654 0.051 -0.0157485 0.051 -0.00749254 0.05 0.00388288 0.051 0.0242448 0.051 -0.014647 0.047 -0.00204897 0.047 0.00247526 0.041 0.0239234 0.038 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/dynamic.py000066400000000000000000000235241224417117700252730ustar00rootroot00000000000000# pylint: disable=W0201 import numpy as np from statsmodels.tools.decorators import cache_readonly import var_model as _model import util import plotting FULL_SAMPLE = 0 ROLLING = 1 EXPANDING = 2 try: import pandas as pn except ImportError: pass def _get_window_type(window_type): if window_type in (FULL_SAMPLE, ROLLING, EXPANDING): return window_type elif isinstance(window_type, basestring): window_type_up = window_type.upper() if window_type_up in ('FULL SAMPLE', 'FULL_SAMPLE'): return FULL_SAMPLE elif window_type_up == 'ROLLING': return ROLLING elif window_type_up == 'EXPANDING': return EXPANDING raise Exception('Unrecognized window type: %s' % window_type) def require_pandas(): try: import pandas as pn except ImportError: raise ImportError('pandas is required to use this code (for now)') class DynamicVAR(object): """ Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares Parameters ---------- data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {'expanding', 'rolling'} min_periods : int or None Minimum number of observations to require in window, defaults to window size if None specified trend : {'c', 'nc', 'ct', 'ctt'} TODO Returns ------- **Attributes**: coefs : WidePanel items : coefficient names major_axis : dates minor_axis : VAR equation names """ def __init__(self, data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None): require_pandas() self.lag_order = lag_order self.names = list(data.columns) self.neqs = len(self.names) self._y_orig = data # TODO: deal with trend self._x_orig = _make_lag_matrix(data, lag_order) self._x_orig['intercept'] = 1 (self.y, self.x, self.x_filtered, self._index, self._time_has_obs) = _filter_data(self._y_orig, self._x_orig) self.lag_order = lag_order self.trendorder = util.get_trendorder(trend) self._set_window(window_type, window, min_periods) def _set_window(self, window_type, window, min_periods): self._window_type = _get_window_type(window_type) if self._is_rolling: if window is None: raise Exception('Must pass window when doing rolling ' 'regression') if min_periods is None: min_periods = window else: window = len(self.x) if min_periods is None: min_periods = 1 self._window = int(window) self._min_periods = min_periods @cache_readonly def T(self): """ Number of time periods in results """ return len(self.result_index) @property def nobs(self): # Stub, do I need this? data = dict((eq, r.nobs) for eq, r in self.equations.iteritems()) return pn.DataFrame(data) @cache_readonly def equations(self): eqs = {} for col, ts in self.y.iteritems(): model = pn.ols(y=ts, x=self.x, window=self._window, window_type=self._window_type, min_periods=self._min_periods) eqs[col] = model return eqs @cache_readonly def coefs(self): """ Return dynamic regression coefficients as WidePanel """ data = {} for eq, result in self.equations.iteritems(): data[eq] = result.beta panel = pn.WidePanel.fromDict(data) # Coefficient names become items return panel.swapaxes('items', 'minor') @property def result_index(self): return self.coefs.major_axis @cache_readonly def _coefs_raw(self): """ Reshape coefficients to be more amenable to dynamic calculations Returns ------- coefs : (time_periods x lag_order x neqs x neqs) """ coef_panel = self.coefs.copy() del coef_panel['intercept'] coef_values = coef_panel.swapaxes('items', 'major').values coef_values = coef_values.reshape((len(coef_values), self.lag_order, self.neqs, self.neqs)) return coef_values @cache_readonly def _intercepts_raw(self): """ Similar to _coefs_raw, return intercept values in easy-to-use matrix form Returns ------- intercepts : (T x K) """ return self.coefs['intercept'].values @cache_readonly def resid(self): data = {} for eq, result in self.equations.iteritems(): data[eq] = result.resid return pn.DataFrame(data) def forecast(self, steps=1): """ Produce dynamic forecast Parameters ---------- steps Returns ------- forecasts : pandas.DataFrame """ output = np.empty((self.T - steps, self.neqs)) y_values = self.y.values y_index_map = dict((d, idx) for idx, d in enumerate(self.y.index)) result_index_map = dict((d, idx) for idx, d in enumerate(self.result_index)) coefs = self._coefs_raw intercepts = self._intercepts_raw # can only produce this many forecasts forc_index = self.result_index[steps:] for i, date in enumerate(forc_index): # TODO: check that this does the right thing in weird cases... idx = y_index_map[date] - steps result_idx = result_index_map[date] - steps y_slice = y_values[:idx] forcs = _model.forecast(y_slice, coefs[result_idx], intercepts[result_idx], steps) output[i] = forcs[-1] return pn.DataFrame(output, index=forc_index, columns=self.names) def plot_forecast(self, steps=1, figsize=(10, 10)): """ Plot h-step ahead forecasts against actual realizations of time series. Note that forecasts are lined up with their respective realizations. Parameters ---------- steps : """ import matplotlib.pyplot as plt fig, axes = plt.subplots(figsize=figsize, nrows=self.neqs, sharex=True) forc = self.forecast(steps=steps) dates = forc.index y_overlay = self.y.reindex(dates) for i, col in enumerate(forc.columns): ax = axes[i] y_ts = y_overlay[col] forc_ts = forc[col] y_handle = ax.plot(dates, y_ts.values, 'k.', ms=2) forc_handle = ax.plot(dates, forc_ts.values, 'k-') fig.legend((y_handle, forc_handle), ('Y', 'Forecast')) fig.autofmt_xdate() fig.suptitle('Dynamic %d-step forecast' % steps) # pretty things up a bit plotting.adjust_subplots(bottom=0.15, left=0.10) plt.draw_if_interactive() @property def _is_rolling(self): return self._window_type == ROLLING @cache_readonly def r2(self): """Returns the r-squared values.""" data = dict((eq, r.r2) for eq, r in self.equations.iteritems()) return pn.DataFrame(data) class DynamicPanelVAR(DynamicVAR): """ Dynamic (time-varying) panel vector autoregression using panel ordinary least squares Parameters ---------- """ def __init__(self, data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None): self.lag_order = lag_order self.neqs = len(data.columns) self._y_orig = data # TODO: deal with trend self._x_orig = _make_lag_matrix(data, lag_order) self._x_orig['intercept'] = 1 (self.y, self.x, self.x_filtered, self._index, self._time_has_obs) = _filter_data(self._y_orig, self._x_orig) self.lag_order = lag_order self.trendorder = util.get_trendorder(trend) self._set_window(window_type, window, min_periods) def _filter_data(lhs, rhs): """ Data filtering routine for dynamic VAR lhs : DataFrame original data rhs : DataFrame lagged variables Returns ------- """ def _has_all_columns(df): return np.isfinite(df.values).sum(1) == len(df.columns) rhs_valid = _has_all_columns(rhs) if not rhs_valid.all(): pre_filtered_rhs = rhs[rhs_valid] else: pre_filtered_rhs = rhs index = lhs.index.union(rhs.index) if not index.equals(rhs.index) or not index.equals(lhs.index): rhs = rhs.reindex(index) lhs = lhs.reindex(index) rhs_valid = _has_all_columns(rhs) lhs_valid = _has_all_columns(lhs) valid = rhs_valid & lhs_valid if not valid.all(): filt_index = rhs.index[valid] filtered_rhs = rhs.reindex(filt_index) filtered_lhs = lhs.reindex(filt_index) else: filtered_rhs, filtered_lhs = rhs, lhs return filtered_lhs, filtered_rhs, pre_filtered_rhs, index, valid def _make_lag_matrix(x, lags): data = {} columns = [] for i in range(1, 1 + lags): lagstr = 'L%d.'% i lag = x.shift(i).rename(columns=lambda c: lagstr + c) data.update(lag._series) columns.extend(lag.columns) return pn.DataFrame(data, columns=columns) class Equation(object): """ Stub, estimate one equation """ def __init__(self, y, x): pass if __name__ == '__main__': import pandas.util.testing as ptest ptest.N = 500 data = ptest.makeTimeDataFrame().cumsum(0) var = DynamicVAR(data, lag_order=2, window_type='expanding') var2 = DynamicVAR(data, lag_order=2, window=10, window_type='rolling') statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/irf.py000066400000000000000000000564421224417117700244340ustar00rootroot00000000000000""" Impulse reponse-related code """ from __future__ import division import numpy as np import numpy.linalg as la import scipy.linalg as L from scipy import stats from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.tools import chain_dot #from statsmodels.tsa.api import VAR import statsmodels.tsa.tsatools as tsa import statsmodels.tsa.vector_ar.plotting as plotting import statsmodels.tsa.vector_ar.util as util mat = np.array class BaseIRAnalysis(object): """ Base class for plotting and computing IRF-related statistics, want to be able to handle known and estimated processes """ def __init__(self, model, P=None, periods=10, order=None, svar=False): self.model = model self.periods = periods self.neqs, self.lags, self.T = model.neqs, model.k_ar, model.nobs self.order = order if P is None: sigma = model.sigma_u # TODO, may be difficult at the moment # if order is not None: # indexer = [model.get_eq_index(name) for name in order] # sigma = sigma[:, indexer][indexer, :] # if sigma.shape != model.sigma_u.shape: # raise ValueError('variable order is wrong length') P = la.cholesky(sigma) self.P = P self.svar = svar self.irfs = model.ma_rep(periods) if svar: self.svar_irfs = model.svar_ma_rep(periods, P=P) else: self.orth_irfs = model.orth_ma_rep(periods) self.cum_effects = self.irfs.cumsum(axis=0) if svar: self.svar_cum_effects = self.svar_irfs.cumsum(axis=0) else: self.orth_cum_effects = self.orth_irfs.cumsum(axis=0) self.lr_effects = model.long_run_effects() if svar: self.svar_lr_effects = np.dot(model.long_run_effects(), P) else: self.orth_lr_effects = np.dot(model.long_run_effects(), P) # auxiliary stuff self._A = util.comp_matrix(model.coefs) def cov(self, *args, **kwargs): raise NotImplementedError def cum_effect_cov(self, *args, **kwargs): raise NotImplementedError def plot(self, orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None, component=None): """ Plot impulse responses Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI subplot_params : dict To pass to subplot plotting funcions. Example: if fonts are too big, pass {'fontsize' : 8} or some number to your taste. plot_params : dict plot_stderr: bool, default True Plot standard impulse response error bands stderr_type: string 'asym': default, computes asymptotic standard errors 'mc': monte carlo standard errors (use rpl) repl: int, default 1000 Number of replications for Monte Carlo and Sims-Zha standard errors seed: int np.random.seed for Monte Carlo replications component: array or vector of principal component indices """ periods = self.periods model = self.model svar = self.svar if orth and svar: raise ValueError("For SVAR system, set orth=False") if orth: title = 'Impulse responses (orthogonalized)' irfs = self.orth_irfs elif svar: title = 'Impulse responses (structural)' irfs = self.svar_irfs else: title = 'Impulse responses' irfs = self.irfs if plot_stderr == False: stderr = None elif stderr_type not in ['asym', 'mc', 'sz1', 'sz2','sz3']: raise ValueError("Error type must be either 'asym', 'mc','sz1','sz2', or 'sz3'") else: if stderr_type == 'asym': stderr = self.cov(orth=orth) if stderr_type == 'mc': stderr = self.errband_mc(orth=orth, svar=svar, repl=repl, signif=signif, seed=seed) if stderr_type == 'sz1': stderr = self.err_band_sz1(orth=orth, svar=svar, repl=repl, signif=signif, seed=seed, component=component) if stderr_type == 'sz2': stderr = self.err_band_sz2(orth=orth, svar=svar, repl=repl, signif=signif, seed=seed, component=component) if stderr_type == 'sz3': stderr = self.err_band_sz3(orth=orth, svar=svar, repl=repl, signif=signif, seed=seed, component=component) plotting.irf_grid_plot(irfs, stderr, impulse, response, self.model.names, title, signif=signif, subplot_params=subplot_params, plot_params=plot_params, stderr_type=stderr_type) def plot_cum_effects(self, orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None): """ Plot cumulative impulse response functions Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI subplot_params : dict To pass to subplot plotting funcions. Example: if fonts are too big, pass {'fontsize' : 8} or some number to your taste. plot_params : dict plot_stderr: bool, default True Plot standard impulse response error bands stderr_type: string 'asym': default, computes asymptotic standard errors 'mc': monte carlo standard errors (use rpl) repl: int, default 1000 Number of replications for monte carlo standard errors seed: int np.random.seed for Monte Carlo replications """ if orth: title = 'Cumulative responses responses (orthogonalized)' cum_effects = self.orth_cum_effects lr_effects = self.orth_lr_effects else: title = 'Cumulative responses' cum_effects = self.cum_effects lr_effects = self.lr_effects if stderr_type not in ['asym', 'mc']: raise TypeError else: if stderr_type == 'asym': stderr = self.cum_effect_cov(orth=orth) if stderr_type == 'mc': stderr = self.cum_errband_mc(orth=orth, repl=repl, signif=signif, seed=seed) if not plot_stderr: stderr = None plotting.irf_grid_plot(cum_effects, stderr, impulse, response, self.model.names, title, signif=signif, hlines=lr_effects, subplot_params=subplot_params, plot_params=plot_params, stderr_type=stderr_type) class IRAnalysis(BaseIRAnalysis): """ Impulse response analysis class. Computes impulse responses, asymptotic standard errors, and produces relevant plots Parameters ---------- model : VAR instance Notes ----- Using Lutkepohl (2005) notation """ def __init__(self, model, P=None, periods=10, order=None, svar=False): BaseIRAnalysis.__init__(self, model, P=P, periods=periods, order=order, svar=svar) self.cov_a = model._cov_alpha self.cov_sig = model._cov_sigma # memoize dict for G matrix function self._g_memo = {} def cov(self, orth=False): """ Compute asymptotic standard errors for impulse response coefficients Notes ----- Lutkepohl eq 3.7.5 Returns ------- """ if orth: return self._orth_cov() covs = self._empty_covm(self.periods + 1) covs[0] = np.zeros((self.neqs ** 2, self.neqs ** 2)) for i in range(1, self.periods + 1): Gi = self.G[i - 1] covs[i] = chain_dot(Gi, self.cov_a, Gi.T) return covs def errband_mc(self, orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100): """ IRF Monte Carlo integrated error bands """ model = self.model periods = self.periods if svar == True: return model.sirf_errband_mc(orth=orth, repl=repl, T=periods, signif=signif, seed=seed, burn=burn, cum=False) else: return model.irf_errband_mc(orth=orth, repl=repl, T=periods, signif=signif, seed=seed, burn=burn, cum=False) def err_band_sz1(self, orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100, component=None): """ IRF Sims-Zha error band method 1. Assumes symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : neqs x neqs array, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. """ model = self.model periods = self.periods if orth: irfs = self.orth_irfs elif svar: irfs = self.svar_irfs else: irfs = self.irfs neqs = self.neqs irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed, burn=100) q = util.norm_signif_level(signif) W, eigva, k =self._eigval_decomp_SZ(irf_resim) if component != None: if np.shape(component) != (neqs,neqs): raise ValueError("Component array must be " + str(neqs) + " x " + str(neqs)) if np.argmax(component) >= neqs*periods: raise ValueError("Atleast one of the components does not exist") else: k = component # here take the kth column of W, which we determine by finding the largest eigenvalue of the covaraince matrix lower = np.copy(irfs) upper = np.copy(irfs) for i in xrange(neqs): for j in xrange(neqs): lower[1:,i,j] = irfs[1:,i,j] + W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]]) upper[1:,i,j] = irfs[1:,i,j] - W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]]) return lower, upper def err_band_sz2(self, orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None): """ IRF Sims-Zha error band method 2. This method Does not assume symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : neqs x neqs array, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. """ model = self.model periods = self.periods if orth: irfs = self.orth_irfs elif svar: irfs = self.svar_irfs else: irfs = self.irfs neqs = self.neqs irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed, burn=100) W, eigva, k = self._eigval_decomp_SZ(irf_resim) if component != None: if np.shape(component) != (neqs,neqs): raise ValueError("Component array must be " + str(neqs) + " x " + str(neqs)) if np.argmax(component) >= neqs*periods: raise ValueError("Atleast one of the components does not exist") else: k = component gamma = np.zeros((repl, periods+1, neqs, neqs)) for p in xrange(repl): for i in xrange(neqs): for j in xrange(neqs): gamma[p,1:,i,j] = W[i,j,k[i,j],:] * irf_resim[p,1:,i,j] gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles indx = round(signif/2*repl)-1,round((1-signif/2)*repl)-1 lower = np.copy(irfs) upper = np.copy(irfs) for i in xrange(neqs): for j in xrange(neqs): lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j] upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j] return lower, upper def err_band_sz3(self, orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None): """ IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : vector length neqs, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. """ model = self.model periods = self.periods if orth: irfs = self.orth_irfs elif svar: irfs = self.svar_irfs else: irfs = self.irfs neqs = self.neqs irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed, burn=100) stack = np.zeros((neqs, repl, periods*neqs)) #stack left to right, up and down for p in xrange(repl): for i in xrange(neqs): stack[i, p,:] = np.ravel(irf_resim[p,1:,:,i].T) stack_cov=np.zeros((neqs, periods*neqs, periods*neqs)) W = np.zeros((neqs, periods*neqs, periods*neqs)) eigva = np.zeros((neqs, periods*neqs)) k = np.zeros((neqs)) if component != None: if np.size(component) != (neqs): raise ValueError("Component array must be of length " + str(neqs)) if np.argmax(component) >= neqs*periods: raise ValueError("Atleast one of the components does not exist") else: k = component #compute for eigen decomp for each stack for i in xrange(neqs): stack_cov[i] = np.cov(stack[i],rowvar=0) W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i]) gamma = np.zeros((repl, periods+1, neqs, neqs)) for p in xrange(repl): c=0 for j in xrange(neqs): for i in xrange(neqs): gamma[p,1:,i,j] = W[j,k[j],i*periods:(i+1)*periods] * irf_resim[p,1:,i,j] if i == neqs-1: gamma[p,1:,i,j] = W[j,k[j],i*periods:] * irf_resim[p,1:,i,j] gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles indx = round(signif/2*repl)-1,round((1-signif/2)*repl)-1 lower = np.copy(irfs) upper = np.copy(irfs) for i in xrange(neqs): for j in xrange(neqs): lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j] upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j] return lower, upper def _eigval_decomp_SZ(self, irf_resim): """ Returns ------- W: array of eigenvectors eigva: list of eigenvalues k: matrix indicating column # of largest eigenvalue for each c_i,j """ neqs = self.neqs periods = self.periods cov_hold = np.zeros((neqs, neqs, periods, periods)) for i in xrange(neqs): for j in xrange(neqs): cov_hold[i,j,:,:] = np.cov(irf_resim[:,1:,i,j],rowvar=0) W = np.zeros((neqs, neqs, periods, periods)) eigva = np.zeros((neqs, neqs, periods, 1)) k = np.zeros((neqs, neqs)) for i in xrange(neqs): for j in xrange(neqs): W[i,j,:,:], eigva[i,j,:,0], k[i,j] = util.eigval_decomp(cov_hold[i,j,:,:]) return W, eigva, k @cache_readonly def G(self): # Gi matrices as defined on p. 111 K = self.neqs # nlags = self.model.p # J = np.hstack((np.eye(K),) + (np.zeros((K, K)),) * (nlags - 1)) def _make_g(i): # p. 111 Lutkepohl G = 0. for m in range(i): # be a bit cute to go faster idx = i - 1 - m if idx in self._g_memo: apow = self._g_memo[idx] else: apow = la.matrix_power(self._A.T, idx) # apow = np.dot(J, apow) apow = apow[:K] self._g_memo[idx] = apow # take first K rows piece = np.kron(apow, self.irfs[m]) G = G + piece return G return [_make_g(i) for i in range(1, self.periods + 1)] def _orth_cov(self): # Lutkepohl 3.7.8 Ik = np.eye(self.neqs) PIk = np.kron(self.P.T, Ik) H = self.H covs = self._empty_covm(self.periods + 1) for i in range(self.periods + 1): if i == 0: apiece = 0 else: Ci = np.dot(PIk, self.G[i-1]) apiece = chain_dot(Ci, self.cov_a, Ci.T) Cibar = np.dot(np.kron(Ik, self.irfs[i]), H) bpiece = chain_dot(Cibar, self.cov_sig, Cibar.T) / self.T # Lutkepohl typo, cov_sig correct covs[i] = apiece + bpiece return covs def cum_effect_cov(self, orth=False): """ Compute asymptotic standard errors for cumulative impulse response coefficients Parameters ---------- orth : boolean Notes ----- eq. 3.7.7 (non-orth), 3.7.10 (orth) Returns ------- """ Ik = np.eye(self.neqs) PIk = np.kron(self.P.T, Ik) F = 0. covs = self._empty_covm(self.periods + 1) for i in range(self.periods + 1): if i > 0: F = F + self.G[i - 1] if orth: if i == 0: apiece = 0 else: Bn = np.dot(PIk, F) apiece = chain_dot(Bn, self.cov_a, Bn.T) Bnbar = np.dot(np.kron(Ik, self.cum_effects[i]), self.H) bpiece = chain_dot(Bnbar, self.cov_sig, Bnbar.T) / self.T covs[i] = apiece + bpiece else: if i == 0: covs[i] = np.zeros((self.neqs**2, self.neqs**2)) continue covs[i] = chain_dot(F, self.cov_a, F.T) return covs def cum_errband_mc(self, orth=False, repl=1000, signif=0.05, seed=None, burn=100): """ IRF Monte Carlo integrated error bands of cumulative effect """ model = self.model periods = self.periods return model.irf_errband_mc(orth=orth, repl=repl, T=periods, signif=signif, seed=seed, burn=burn, cum=True) def lr_effect_cov(self, orth=False): """ Returns ------- """ lre = self.lr_effects Finfty = np.kron(np.tile(lre.T, self.lags), lre) Ik = np.eye(self.neqs) if orth: Binf = np.dot(np.kron(self.P.T, np.eye(self.neqs)), Finfty) Binfbar = np.dot(np.kron(Ik, lre), self.H) return (chain_dot(Binf, self.cov_a, Binf.T) + chain_dot(Binfbar, self.cov_sig, Binfbar.T)) else: return chain_dot(Finfty, self.cov_a, Finfty.T) def stderr(self, orth=False): return np.array([tsa.unvec(np.sqrt(np.diag(c))) for c in self.cov(orth=orth)]) def cum_effect_stderr(self, orth=False): return np.array([tsa.unvec(np.sqrt(np.diag(c))) for c in self.cum_effect_cov(orth=orth)]) def lr_effect_stderr(self, orth=False): cov = self.lr_effect_cov(orth=orth) return tsa.unvec(np.sqrt(np.diag(cov))) def _empty_covm(self, periods): return np.zeros((periods, self.neqs ** 2, self.neqs ** 2), dtype=float) @cache_readonly def H(self): k = self.neqs Lk = tsa.elimination_matrix(k) Kkk = tsa.commutation_matrix(k, k) Ik = np.eye(k) # B = chain_dot(Lk, np.eye(k**2) + commutation_matrix(k, k), # np.kron(self.P, np.eye(k)), Lk.T) # return np.dot(Lk.T, L.inv(B)) B = chain_dot(Lk, np.dot(np.kron(Ik, self.P), Kkk) + np.kron(self.P, Ik), Lk.T) return np.dot(Lk.T, L.inv(B)) def fevd_table(self): pass statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/output.py000066400000000000000000000172441224417117700252110ustar00rootroot00000000000000from cStringIO import StringIO import numpy as np from statsmodels.iolib import SimpleTable import statsmodels.tsa.vector_ar.util as util mat = np.array _default_table_fmt = dict( empty_cell = '', colsep=' ', row_pre = '', row_post = '', table_dec_above='=', table_dec_below='=', header_dec_below='-', header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = 'r', stubs_align = 'l', fmt = 'txt' ) class VARSummary(object): default_fmt = dict( #data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"], #data_fmts = ["%#10.4g","%#10.4g","%#10.4g","%#6.4g"], data_fmts = ["%#15.6F","%#15.6F","%#15.3F","%#14.3F"], empty_cell = '', #colwidths = 10, colsep=' ', row_pre = '', row_post = '', table_dec_above='=', table_dec_below='=', header_dec_below='-', header_fmt = '%s', stub_fmt = '%s', title_align='c', header_align = 'r', data_aligns = 'r', stubs_align = 'l', fmt = 'txt' ) part1_fmt = dict(default_fmt, data_fmts = ["%s"], colwidths = 15, colsep=' ', table_dec_below='', header_dec_below=None, ) part2_fmt = dict(default_fmt, data_fmts = ["%#12.6g","%#12.6g","%#10.4g","%#5.4g"], colwidths = None, colsep=' ', table_dec_above='-', table_dec_below='-', header_dec_below=None, ) def __init__(self, estimator): self.model = estimator self.summary = self.make() def __repr__(self): return self.summary def make(self, endog_names=None, exog_names=None): """ Summary of VAR model """ buf = StringIO() print >> buf, self._header_table() print >> buf, self._stats_table() print >> buf, self._coef_table() print >> buf, self._resid_info() return buf.getvalue() def _header_table(self): import time model = self.model t = time.localtime() # TODO: change when we allow coef restrictions # ncoefs = len(model.beta) # Header information part1title = "Summary of Regression Results" part1data = [[model._model_type], ["OLS"], #TODO: change when fit methods change [time.strftime("%a, %d, %b, %Y", t)], [time.strftime("%H:%M:%S", t)]] part1header = None part1stubs = ('Model:', 'Method:', 'Date:', 'Time:') part1 = SimpleTable(part1data, part1header, part1stubs, title=part1title, txt_fmt=self.part1_fmt) return str(part1) def _stats_table(self): # TODO: do we want individual statistics or should users just # use results if wanted? # Handle overall fit statistics model = self.model part2Lstubs = ('No. of Equations:', 'Nobs:', 'Log likelihood:', 'AIC:') part2Rstubs = ('BIC:', 'HQIC:', 'FPE:', 'Det(Omega_mle):') part2Ldata = [[model.neqs], [model.nobs], [model.llf], [model.aic]] part2Rdata = [[model.bic], [model.hqic], [model.fpe], [model.detomega]] part2Lheader = None part2L = SimpleTable(part2Ldata, part2Lheader, part2Lstubs, txt_fmt = self.part2_fmt) part2R = SimpleTable(part2Rdata, part2Lheader, part2Rstubs, txt_fmt = self.part2_fmt) part2L.extend_right(part2R) return str(part2L) def _coef_table(self): model = self.model k = model.neqs Xnames = self.model.exog_names data = zip(model.params.T.ravel(), model.stderr.T.ravel(), model.tvalues.T.ravel(), model.pvalues.T.ravel()) header = ('coefficient','std. error','t-stat','prob') buf = StringIO() dim = k * model.k_ar + model.k_trend for i in range(k): section = "Results for equation %s" % model.names[i] print >> buf, section table = SimpleTable(data[dim * i : dim * (i + 1)], header, Xnames, title=None, txt_fmt = self.default_fmt) print >> buf, str(table) if i < k - 1: buf.write('\n') return buf.getvalue() def _resid_info(self): buf = StringIO() names = self.model.names print >> buf, "Correlation matrix of residuals" print >> buf, pprint_matrix(self.model.resid_corr, names, names) return buf.getvalue() def causality_summary(results, variables, equation, kind): title = "Granger causality %s-test" % kind null_hyp = 'H_0: %s do not Granger-cause %s' % (variables, equation) return hypothesis_test_table(results, title, null_hyp) def normality_summary(results): title = "Normality skew/kurtosis Chi^2-test" null_hyp = 'H_0: data generated by normally-distributed process' return hypothesis_test_table(results, title, null_hyp) def hypothesis_test_table(results, title, null_hyp): fmt = dict(_default_table_fmt, data_fmts=["%#15.6F","%#15.6F","%#15.3F", "%s"]) buf = StringIO() table = SimpleTable([[results['statistic'], results['crit_value'], results['pvalue'], str(results['df'])]], ['Test statistic', 'Critical Value', 'p-value', 'df'], [''], title=None, txt_fmt=fmt) print >> buf, title print >> buf, table print >> buf, null_hyp buf.write("Conclusion: %s H_0" % results['conclusion']) buf.write(" at %.2f%% significance level" % (results['signif'] * 100)) return buf.getvalue() def print_ic_table(ics, selected_orders): """ For VAR order selection """ # Can factor this out into a utility method if so desired cols = sorted(ics) data = mat([["%#10.4g" % v for v in ics[c]] for c in cols], dtype=object).T # start minimums for i, col in enumerate(cols): idx = int(selected_orders[col]), i data[idx] = data[idx] + '*' # data[idx] = data[idx][:-1] + '*' # super hack, ugh fmt = dict(_default_table_fmt, data_fmts=("%s",) * len(cols)) buf = StringIO() table = SimpleTable(data, cols, range(len(data)), title='VAR Order Selection', txt_fmt=fmt) print >> buf, table print >> buf, '* Minimum' print buf.getvalue() def pprint_matrix(values, rlabels, clabels, col_space=None): buf = StringIO() T, K = len(rlabels), len(clabels) if col_space is None: min_space = 10 col_space = [max(len(str(c)) + 2, min_space) for c in clabels] else: col_space = (col_space,) * K row_space = max([len(str(x)) for x in rlabels]) + 2 head = _pfixed('', row_space) for j, h in enumerate(clabels): head += _pfixed(h, col_space[j]) print >> buf, head for i, rlab in enumerate(rlabels): line = ('%s' % rlab).ljust(row_space) for j in range(K): line += _pfixed(values[i,j], col_space[j]) print >> buf, line return buf.getvalue() def _pfixed(s, space, nanRep=None, float_format=None): if isinstance(s, float): if float_format: formatted = float_format(s) else: formatted = "%#8.6F" % s return formatted.rjust(space) else: return ('%s' % s)[:space].rjust(space) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/plotting.py000066400000000000000000000161051224417117700255040ustar00rootroot00000000000000import numpy as np import statsmodels.tsa.vector_ar.util as util class MPLConfigurator(object): def __init__(self): self._inverse_actions = [] def revert(self): for action in self._inverse_actions: action() def set_fontsize(self, size): import matplotlib as mpl old_size = mpl.rcParams['font.size'] mpl.rcParams['font.size'] = size def revert(): mpl.rcParams['font.size'] = old_size self._inverse_actions.append(revert) #------------------------------------------------------------------------------- # Plotting functions def plot_mts(Y, names=None, index=None): """ Plot multiple time series """ import matplotlib.pyplot as plt k = Y.shape[1] rows, cols = k, 1 plt.figure(figsize=(10, 10)) for j in range(k): ts = Y[:, j] ax = plt.subplot(rows, cols, j+1) if index is not None: ax.plot(index, ts) else: ax.plot(ts) if names is not None: ax.set_title(names[j]) def plot_var_forc(prior, forc, err_upper, err_lower, index=None, names=None, plot_stderr=True): import matplotlib.pyplot as plt n, k = prior.shape rows, cols = k, 1 fig = plt.figure(figsize=(10, 10)) prange = np.arange(n) rng_f = np.arange(n - 1, n + len(forc)) rng_err = np.arange(n, n + len(forc)) for j in range(k): ax = plt.subplot(rows, cols, j+1) p1 = ax.plot(prange, prior[:, j], 'k', label='Observed') p2 = ax.plot(rng_f, np.r_[prior[-1:, j], forc[:, j]], 'k--', label='Forecast') if plot_stderr: p3 = ax.plot(rng_err, err_upper[:, j], 'k-.', label='Forc 2 STD err') ax.plot(rng_err, err_lower[:, j], 'k-.') if names is not None: ax.set_title(names[j]) ax.legend(loc='upper right') def plot_with_error(y, error, x=None, axes=None, value_fmt='k', error_fmt='k--', alpha=0.05, stderr_type = 'asym'): """ Make plot with optional error bars Parameters ---------- y : error : array or None """ import matplotlib.pyplot as plt if axes is None: axes = plt.gca() x = x if x is not None else range(len(y)) plot_action = lambda y, fmt: axes.plot(x, y, fmt) plot_action(y, value_fmt) #changed this if error is not None: if stderr_type == 'asym': q = util.norm_signif_level(alpha) plot_action(y - q * error, error_fmt) plot_action(y + q * error, error_fmt) if stderr_type in ('mc','sz1','sz2','sz3'): plot_action(error[0], error_fmt) plot_action(error[1], error_fmt) def plot_full_acorr(acorr, fontsize=8, linewidth=8, xlabel=None, err_bound=None): """ Parameters ---------- """ import matplotlib.pyplot as plt config = MPLConfigurator() config.set_fontsize(fontsize) k = acorr.shape[1] fig, axes = plt.subplots(k, k, figsize=(10, 10), squeeze=False) for i in range(k): for j in range(k): ax = axes[i][j] acorr_plot(acorr[:, i, j], linewidth=linewidth, xlabel=xlabel, ax=ax) if err_bound is not None: ax.axhline(err_bound, color='k', linestyle='--') ax.axhline(-err_bound, color='k', linestyle='--') adjust_subplots() config.revert() return fig def acorr_plot(acorr, linewidth=8, xlabel=None, ax=None): import matplotlib.pyplot as plt if ax is None: ax = plt.gca() if xlabel is None: xlabel = np.arange(len(acorr)) ax.vlines(xlabel, [0], acorr, lw=linewidth) ax.axhline(0, color='k') ax.set_ylim([-1, 1]) # hack? ax.set_xlim([-1, xlabel[-1] + 1]) def plot_acorr_with_error(): pass def adjust_subplots(**kwds): import matplotlib.pyplot as plt passed_kwds = dict(bottom=0.05, top=0.925, left=0.05, right=0.95, hspace=0.2) passed_kwds.update(kwds) plt.subplots_adjust(**passed_kwds) #------------------------------------------------------------------------------- # Multiple impulse response (cum_effects, etc.) cplots def irf_grid_plot(values, stderr, impcol, rescol, names, title, signif=0.05, hlines=None, subplot_params=None, plot_params=None, figsize=(10,10), stderr_type='asym'): """ Reusable function to make flexible grid plots of impulse responses and comulative effects values : (T + 1) x k x k stderr : T x k x k hlines : k x k """ import matplotlib.pyplot as plt if subplot_params is None: subplot_params = {} if plot_params is None: plot_params = {} nrows, ncols, to_plot = _get_irf_plot_config(names, impcol, rescol) fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, squeeze=False, figsize=figsize) # fill out space adjust_subplots() fig.suptitle(title, fontsize=14) subtitle_temp = r'%s$\rightarrow$%s' k = len(names) rng = range(len(values)) for (j, i, ai, aj) in to_plot: ax = axes[ai][aj] # HACK? if stderr is not None: if stderr_type == 'asym': sig = np.sqrt(stderr[:, j * k + i, j * k + i]) plot_with_error(values[:, i, j], sig, x=rng, axes=ax, alpha=signif, value_fmt='b', stderr_type=stderr_type) if stderr_type in ('mc','sz1','sz2','sz3'): errs = stderr[0][:, i, j], stderr[1][:, i, j] plot_with_error(values[:, i, j], errs, x=rng, axes=ax, alpha=signif, value_fmt='b', stderr_type=stderr_type) else: plot_with_error(values[:, i, j], None, x=rng, axes=ax, value_fmt='b') ax.axhline(0, color='k') if hlines is not None: ax.axhline(hlines[i,j], color='k') sz = subplot_params.get('fontsize', 12) ax.set_title(subtitle_temp % (names[j], names[i]), fontsize=sz) def _get_irf_plot_config(names, impcol, rescol): nrows = ncols = k = len(names) if impcol is not None and rescol is not None: # plot one impulse-response pair nrows = ncols = 1 j = util.get_index(names, impcol) i = util.get_index(names, rescol) to_plot = [(j, i, 0, 0)] elif impcol is not None: # plot impacts of impulse in one variable ncols = 1 j = util.get_index(names, impcol) to_plot = [(j, i, i, 0) for i in range(k)] elif rescol is not None: # plot only things having impact on particular variable ncols = 1 i = util.get_index(names, rescol) to_plot = [(j, i, j, 0) for j in range(k)] else: # plot everything to_plot = [(j, i, i, j) for i in range(k) for j in range(k)] return nrows, ncols, to_plot #------------------------------------------------------------------------------- # Forecast error variance decomposition statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/svar_model.py000066400000000000000000000577051224417117700260120ustar00rootroot00000000000000""" Vector Autoregression (VAR) processes References ---------- Lutkepohl (2005) New Introduction to Multiple Time Series Analysis """ from __future__ import division import numpy as np import numpy.linalg as npl try: from numpy.linalg import slogdet as np_slogdet except: def np_slogdet(x): return 1, np.log(np.linalg.det(x)) from statsmodels.tools.numdiff import (approx_hess, approx_fprime) from statsmodels.tools.decorators import cache_readonly from statsmodels.tsa.vector_ar.irf import IRAnalysis from statsmodels.tsa.vector_ar.var_model import VARProcess, \ VARResults import statsmodels.tsa.vector_ar.util as util import statsmodels.tsa.base.tsa_model as tsbase from statsmodels.tools.tools import rank as smrank mat = np.array def svar_ckerr(svar_type, A, B): if A is None and (svar_type == 'A' or svar_type == 'AB'): raise ValueError('SVAR of type A or AB but A array not given.') if B is None and (svar_type == 'B' or svar_type == 'AB'): raise ValueError('SVAR of type B or AB but B array not given.') class SVAR(tsbase.TimeSeriesModel): """ Fit VAR and then estimate structural components of A and B, defined: .. math:: Ay_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + B\var(\epsilon_t) Parameters ---------- endog : array-like 1-d endogenous response variable. The independent variable. names : array-like must match number of columns or endog dates : array-like must match number of rows of endog svar_type : str "A" - estimate structural parameters of A matrix, B assumed = I "B" - estimate structural parameters of B matrix, A assumed = I "AB" - estimate structural parameters indicated in both A and B matrix A : array-like neqs x neqs with unknown parameters marked with 'E' for estimate B : array-like neqs x neqs with unknown parameters marked with 'E' for estimate References ---------- Hamilton (1994) Time Series Analysis """ def __init__(self, endog, svar_type, names=None, dates=None, freq=None, A=None, B=None, missing='none'): super(SVAR, self).__init__(endog, None, dates, freq, missing=missing) if names is not None: import warnings warnings.warn("The names argument is deprecated and will be " "removed in the next release.", FutureWarning) self.names = names else: self.names = self.endog_names #(self.endog, self.names, # self.dates) = data_util.interpret_data(endog, names, dates) self.y = self.endog #keep alias for now self.neqs = self.endog.shape[1] types = ['A', 'B', 'AB'] if svar_type not in types: raise ValueError('SVAR type not recognized, must be in ' + str(types)) self.svar_type = svar_type svar_ckerr(svar_type, A, B) #initialize A, B as I if not given #Initialize SVAR masks if A is None: A = np.identity(self.neqs) self.A_mask = A_mask = np.zeros(A.shape, dtype=bool) else: A_mask = np.logical_or(A == 'E', A == 'e') self.A_mask = A_mask if B is None: B = np.identity(self.neqs) self.B_mask = B_mask = np.zeros(B.shape, dtype=bool) else: B_mask = np.logical_or(B == 'E', B == 'e') self.B_mask = B_mask # convert A and B to numeric #TODO: change this when masked support is better or with formula #integration Anum = np.zeros(A.shape, dtype=float) Anum[~A_mask] = A[~A_mask] Anum[A_mask] = np.nan self.A = Anum Bnum = np.zeros(B.shape, dtype=float) Bnum[~B_mask] = B[~B_mask] Bnum[B_mask] = np.nan self.B = Bnum #LikelihoodModel.__init__(self, endog) #super(SVAR, self).__init__(endog) def fit(self, A_guess=None, B_guess=None, maxlags=None, method='ols', ic=None, trend='c', verbose=False, s_method='mle', solver="bfgs", override=False, maxiter=500, maxfun=500): """ Fit the SVAR model and solve for structural parameters Parameters ---------- A_guess : array-like, optional A vector of starting values for all parameters to be estimated in A. B_guess : array-like, optional A vector of starting values for all parameters to be estimated in B. maxlags : int Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), see select_order function method : {'ols'} Estimation method to use ic : {'aic', 'fpe', 'hqic', 'bic', None} Information criterion to use for VAR order selection. aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic : Bayesian a.k.a. Schwarz verbose : bool, default False Print order selection output to the screen trend, str {"c", "ct", "ctt", "nc"} "c" - add constant "ct" - constant and trend "ctt" - constant, linear and quadratic trend "nc" - co constant, no trend Note that these are prepended to the columns of the dataset. s_method : {'mle'} Estimation method for structural parameters solver : {'nm', 'newton', 'bfgs', 'cg', 'ncg', 'powell'} Solution method See statsmodels.base for details override : bool, default False If True, returns estimates of A and B without checking order or rank condition maxiter : int, default 500 Number of iterations to perform in solution method maxfun : int Number of function evaluations to perform Notes ----- Lutkepohl pp. 146-153 Hamilton pp. 324-336 Returns ------- est : SVARResults """ lags = maxlags if ic is not None: selections = self.select_order(maxlags=maxlags, verbose=verbose) if ic not in selections: raise Exception("%s not recognized, must be among %s" % (ic, sorted(selections))) lags = selections[ic] if verbose: print 'Using %d based on %s criterion' % (lags, ic) else: if lags is None: lags = 1 self.nobs = len(self.endog) - lags # initialize starting parameters start_params = self._get_init_params(A_guess, B_guess) return self._estimate_svar(start_params, lags, trend=trend, solver=solver, override=override, maxiter=maxiter, maxfun=maxfun) def _get_init_params(self, A_guess, B_guess): """ Returns either the given starting or .1 if none are given. """ var_type = self.svar_type.lower() n_masked_a = self.A_mask.sum() if var_type in ['ab', 'a']: if A_guess is None: A_guess = np.array([.1]*n_masked_a) else: if len(A_guess) != n_masked_a: msg = 'len(A_guess) = %s, there are %s parameters in A' raise ValueError(msg % (len(A_guess), n_masked_a)) else: A_guess = [] n_masked_b = self.B_mask.sum() if var_type in ['ab', 'b']: if B_guess is None: B_guess = np.array([.1]*n_masked_b) else: if len(B_guess) != n_masked_b: msg = 'len(B_guess) = %s, there are %s parameters in B' raise ValueError(msg % (len(B_guess), n_masked_b)) else: B_guess = [] return np.r_[A_guess, B_guess] def _estimate_svar(self, start_params, lags, maxiter, maxfun, trend='c', solver="nm", override=False): """ lags : int trend : string or None As per above """ k_trend = util.get_trendorder(trend) y = self.endog z = util.get_var_endog(y, lags, trend=trend) y_sample = y[lags:] # Lutkepohl p75, about 5x faster than stated formula var_params = np.linalg.lstsq(z, y_sample)[0] resid = y_sample - np.dot(z, var_params) # Unbiased estimate of covariance matrix $\Sigma_u$ of the white noise # process $u$ # equivalent definition # .. math:: \frac{1}{T - Kp - 1} Y^\prime (I_T - Z (Z^\prime Z)^{-1} # Z^\prime) Y # Ref: Lutkepohl p.75 # df_resid right now is T - Kp - 1, which is a suggested correction avobs = len(y_sample) df_resid = avobs - (self.neqs * lags + k_trend) sse = np.dot(resid.T, resid) #TODO: should give users the option to use a dof correction or not omega = sse / df_resid self.sigma_u = omega A, B = self._solve_AB(start_params, override=override, solver=solver, maxiter=maxiter, maxfun=maxfun) A_mask = self.A_mask B_mask = self.B_mask return SVARResults(y, z, var_params, omega, lags, names=self.endog_names, trend=trend, dates=self.data.dates, model=self, A=A, B=B, A_mask=A_mask, B_mask=B_mask) def loglike(self, params): """ Loglikelihood for SVAR model Notes ----- This method assumes that the autoregressive parameters are first estimated, then likelihood with structural parameters is estimated """ #TODO: this doesn't look robust if A or B is None A = self.A B = self.B A_mask = self.A_mask B_mask = self.B_mask A_len = len(A[A_mask]) B_len = len(B[B_mask]) if A is not None: A[A_mask] = params[:A_len] if B is not None: B[B_mask] = params[A_len:A_len+B_len] nobs = self.nobs neqs = self.neqs sigma_u = self.sigma_u W = np.dot(npl.inv(B),A) trc_in = np.dot(np.dot(W.T,W),sigma_u) sign, b_logdet = np_slogdet(B**2) #numpy 1.4 compat b_slogdet = sign * b_logdet likl = -nobs/2. * (neqs * np.log(2 * np.pi) - \ np.log(npl.det(A)**2) + b_slogdet + \ np.trace(trc_in)) return likl def score(self, AB_mask): """ Return the gradient of the loglike at AB_mask. Parameters ---------- AB_mask : unknown values of A and B matrix concatenated Notes ----- Return numerical gradient """ loglike = self.loglike return approx_fprime(AB_mask, loglike, epsilon=1e-8) def hessian(self, AB_mask): """ Returns numerical hessian. """ loglike = self.loglike return approx_hess(AB_mask, loglike) def _solve_AB(self, start_params, maxiter, maxfun, override=False, solver='bfgs'): """ Solves for MLE estimate of structural parameters Parameters ---------- override : bool, default False If True, returns estimates of A and B without checking order or rank condition solver : str or None, optional Solver to be used. The default is 'nm' (Nelder-Mead). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'cg' conjugate, 'ncg' (non-conjugate gradient), and 'powell'. maxiter : int, optional The maximum number of iterations. Default is 500. maxfun : int, optional The maximum number of function evalutions. Returns ------- A_solve, B_solve: ML solutions for A, B matrices """ #TODO: this could stand a refactor A_mask = self.A_mask B_mask = self.B_mask A = self.A B = self.B A_len = len(A[A_mask]) A[A_mask] = start_params[:A_len] B[B_mask] = start_params[A_len:] if override == False: J = self._compute_J(A, B) self.check_order(J) self.check_rank(J) else: #TODO: change to a warning? print "Order/rank conditions have not been checked" retvals = super(SVAR, self).fit(start_params=start_params, method=solver, maxiter=maxiter, maxfun=maxfun, ftol=1e-20, disp=0).params A[A_mask] = retvals[:A_len] B[B_mask] = retvals[A_len:] return A, B def _compute_J(self, A_solve, B_solve): #first compute appropriate duplication matrix # taken from Magnus and Neudecker (1980), #"The Elimination Matrix: Some Lemmas and Applications # the creation of the D_n matrix follows MN (1980) directly, #while the rest follows Hamilton (1994) neqs = self.neqs sigma_u = self.sigma_u A_mask = self.A_mask B_mask = self.B_mask #first generate duplication matrix, see MN (1980) for notation D_nT=np.zeros([(1.0/2)*(neqs)*(neqs+1),neqs**2]) for j in xrange(neqs): i=j while j <= i < neqs: u=np.zeros([(1.0/2)*neqs*(neqs+1),1]) u[(j)*neqs+(i+1)-(1.0/2)*(j+1)*j-1]=1 Tij=np.zeros([neqs,neqs]) Tij[i,j]=1 Tij[j,i]=1 D_nT=D_nT+np.dot(u,(Tij.ravel('F')[:,None]).T) i=i+1 D_n=D_nT.T D_pl=npl.pinv(D_n) #generate S_B S_B = np.zeros((neqs**2, len(A_solve[A_mask]))) S_D = np.zeros((neqs**2, len(B_solve[B_mask]))) j = 0 j_d = 0 if len(A_solve[A_mask]) is not 0: A_vec = np.ravel(A_mask, order='F') for k in xrange(neqs**2): if A_vec[k] == True: S_B[k,j] = -1 j += 1 if len(B_solve[B_mask]) is not 0: B_vec = np.ravel(B_mask, order='F') for k in xrange(neqs**2): if B_vec[k] == True: S_D[k,j_d] = 1 j_d +=1 #now compute J invA = npl.inv(A_solve) J_p1i = np.dot(np.dot(D_pl, np.kron(sigma_u, invA)), S_B) J_p1 = -2.0 * J_p1i J_p2 = np.dot(np.dot(D_pl, np.kron(invA, invA)), S_D) J = np.append(J_p1, J_p2, axis=1) return J def check_order(self, J): if np.size(J, axis=0) < np.size(J, axis=1): raise ValueError("Order condition not met: " "solution may not be unique") def check_rank(self, J): rank = smrank(J) if rank < np.size(J, axis=1): raise ValueError("Rank condition not met: " "solution may not be unique.") class SVARProcess(VARProcess): """ Class represents a known SVAR(p) process Parameters ---------- coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) A : neqs x neqs np.ndarray with unknown parameters marked with 'E' A_mask : neqs x neqs mask array with known parameters masked B : neqs x neqs np.ndarry with unknown parameters marked with 'E' B_mask : neqs x neqs mask array with known parameters masked Returns ------- **Attributes**: """ def __init__(self, coefs, intercept, sigma_u, A_solve, B_solve, names=None): self.k_ar = len(coefs) self.neqs = coefs.shape[1] self.coefs = coefs self.intercept = intercept self.sigma_u = sigma_u self.A_solve = A_solve self.B_solve = B_solve self.names = names def orth_ma_rep(self, maxn=10, P=None): """ Unavailable for SVAR """ raise NotImplementedError def svar_ma_rep(self, maxn=10, P=None): """ Compute Structural MA coefficient matrices using MLE of A, B """ if P is None: A_solve = self.A_solve B_solve = self.B_solve P = np.dot(npl.inv(A_solve), B_solve) ma_mats = self.ma_rep(maxn=maxn) return mat([np.dot(coefs, P) for coefs in ma_mats]) class SVARResults(SVARProcess, VARResults): """ Estimate VAR(p) process with fixed number of lags Parameters ---------- endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {'nc', 'c', 'ct'} names : array-like List of names of the endogenous variables in order of appearance in `endog`. dates Returns ------- **Attributes** aic bic bse coefs : ndarray (p x K x K) Estimated A_i matrices, A_i = coefs[i-1] cov_params dates detomega df_model : int df_resid : int endog endog_lagged fittedvalues fpe intercept info_criteria k_ar : int k_trend : int llf model names neqs : int Number of variables (equations) nobs : int n_totobs : int params k_ar : int Order of VAR process params : ndarray (Kp + 1) x K A_i matrices and intercept in stacked form [int A_1 ... A_p] pvalue names : list variables names resid sigma_u : ndarray (K x K) Estimate of white noise process variance Var[u_t] sigma_u_mle stderr trenorder tvalues y : ys_lagged """ _model_type = 'SVAR' def __init__(self, endog, endog_lagged, params, sigma_u, lag_order, A=None, B=None, A_mask=None, B_mask=None, model=None, trend='c', names=None, dates=None): self.model = model self.y = self.endog = endog #keep alias for now self.ys_lagged = self.endog_lagged = endog_lagged #keep alias for now self.dates = dates self.n_totobs, self.neqs = self.y.shape self.nobs = self.n_totobs - lag_order k_trend = util.get_trendorder(trend) if k_trend > 0: # make this the polynomial trend order trendorder = k_trend - 1 else: trendorder = None self.k_trend = k_trend self.trendorder = trendorder self.exog_names = util.make_lag_names(names, lag_order, k_trend) self.params = params self.sigma_u = sigma_u # Each matrix needs to be transposed reshaped = self.params[self.k_trend:] reshaped = reshaped.reshape((lag_order, self.neqs, self.neqs)) # Need to transpose each coefficient matrix intercept = self.params[0] coefs = reshaped.swapaxes(1, 2).copy() #SVAR components #TODO: if you define these here, you don't also have to define #them in SVAR process, but I left them for now -ss self.A = A self.B = B self.A_mask = A_mask self.B_mask = B_mask super(SVARResults, self).__init__(coefs, intercept, sigma_u, A, B, names=names) @cache_readonly def coef_names(self): """Coefficient names (deprecated) """ from warnings import warn warn("coef_names is deprecated and will be removed in 0.6.0." "Use exog_names", FutureWarning) return self.exog_names def irf(self, periods=10, var_order=None): """ Analyze structural impulse responses to shocks in system Parameters ---------- periods : int Returns ------- irf : IRAnalysis """ A = self.A B= self.B P = np.dot(npl.inv(A), B) return IRAnalysis(self, P=P, periods=periods, svar=True) def sirf_errband_mc(self, orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False): """ Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters ---------- orth: bool, default False Compute orthoganalized impulse response error bands repl: int number of Monte Carlo replications to perform T: int, default 10 number of impulse response periods signif: float (0 < signif <1) Significance level for error bars, defaults to 95% CI seed: int np.random.seed for replications burn: int number of initial observations to discard for simulation cum: bool, default False produce cumulative irf error bands Notes ----- Lutkepohl (2005) Appendix D Returns ------- Tuple of lower and upper arrays of ma_rep monte carlo standard errors """ neqs = self.neqs mean = self.mean() k_ar = self.k_ar coefs = self.coefs sigma_u = self.sigma_u intercept = self.intercept df_model = self.df_model nobs = self.nobs ma_coll = np.zeros((repl, T+1, neqs, neqs)) A = self.A B = self.B A_mask = self.A_mask B_mask = self.B_mask A_pass = np.zeros(A.shape, dtype='|S1') B_pass = np.zeros(B.shape, dtype='|S1') A_pass[~A_mask] = A[~A_mask] B_pass[~B_mask] = B[~B_mask] A_pass[A_mask] = 'E' B_pass[B_mask] = 'E' if A_mask.sum() == 0: s_type = 'B' elif B_mask.sum() == 0: s_type = 'A' else: s_type = 'AB' g_list = [] for i in range(repl): #discard first hundred to correct for starting bias sim = util.varsim(coefs, intercept, sigma_u, steps=nobs+burn) sim = sim[burn:] if cum == True: if i < 10: sol = SVAR(sim, svar_type=s_type, A=A_pass, B=B_pass).fit(maxlags=k_ar) g_list.append(np.append(sol.A[sol.A_mask].\ tolist(), sol.B[sol.B_mask].\ tolist())) ma_coll[i] = sol.svar_ma_rep(maxn=T).cumsum(axis=0) elif i >= 10: if i == 10: mean_AB = np.mean(g_list, axis = 0) split = len(A_pass[A_mask]) opt_A = mean_AB[:split] opt_A = mean_AB[split:] ma_coll[i] = SVAR(sim, svar_type=s_type, A=A_pass, B=B_pass).fit(maxlags=k_ar,\ A_guess=opt_A, B_guess=opt_B).\ svar_ma_rep(maxn=T).cumsum(axis=0) elif cum == False: if i < 10: sol = SVAR(sim, svar_type=s_type, A=A_pass, B=B_pass).fit(maxlags=k_ar) g_list.append(np.append(sol.A[A_mask].tolist(), sol.B[B_mask].tolist())) ma_coll[i] = sol.svar_ma_rep(maxn=T) elif i >= 10: if i == 10: mean_AB = np.mean(g_list, axis = 0) split = len(A[A_mask]) opt_A = mean_AB[:split] opt_B = mean_AB[split:] ma_coll[i] = SVAR(sim, svar_type=s_type, A=A_pass, B=B_pass).fit(maxlags=k_ar,\ A_guess = opt_A, B_guess = opt_B).\ svar_ma_rep(maxn=T) ma_sort = np.sort(ma_coll, axis=0) #sort to get quantiles index = round(signif/2*repl)-1,round((1-signif/2)*repl)-1 lower = ma_sort[index[0],:, :, :] upper = ma_sort[index[1],:, :, :] return lower, upper statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/000077500000000000000000000000001224417117700244315ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/__init__.py000066400000000000000000000000001224417117700265300ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/example_svar.py000066400000000000000000000016721224417117700274770ustar00rootroot00000000000000import numpy as np import statsmodels.api as sm from statsmodels.tsa.api import VAR, SVAR import matplotlib.pyplot as plt import statsmodels.tsa.vector_ar.util as util import pandas as px mdatagen = sm.datasets.macrodata.load().data mdata = mdatagen[['realgdp','realcons','realinv']] names = mdata.dtype.names start = px.datetime(1959, 3, 31) end = px.datetime(2009, 9, 30) qtr = px.DateRange(start, end, offset=px.datetools.BQuarterEnd()) data = px.DataFrame(mdata, index=qtr) data = (np.log(data)).diff().dropna() #define structural inputs A = np.asarray([[1, 0, 0],['E', 1, 0],['E', 'E', 1]]) B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']]) A_guess = np.asarray([0.5, 0.25, -0.38]) B_guess = np.asarray([0.5, 0.1, 0.05]) mymodel = SVAR(data, svar_type='AB', A=A, B=B, freq='Q') res = mymodel.fit(maxlags=3, maxiter=10000, maxfun=10000, solver='bfgs') res.irf(periods=30).plot(impulse='realgdp', plot_stderr=True, stderr_type='mc', repl=100) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/results/000077500000000000000000000000001224417117700261325ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/results/__init__.py000066400000000000000000000000011224417117700302320ustar00rootroot00000000000000 statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/results/results_svar.py000066400000000000000000000006141224417117700312410ustar00rootroot00000000000000""" Test Results for the SVAR model. Obtained from R using svartest.R """ import numpy as np class SVARdataResults(object): def __init__(self): self.A = ([[1.0, 0.0, 0], [-0.506802245, 1.0, 0], [-5.536056520, 3.04117686, 1.0]]) self.B = ([[0.0075756676, 0.0, 0.0], [0.0, 0.00512051886, 0.0], [0.0, 0.0, 0.020708948]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/results/results_var.py000066400000000000000000000055561224417117700310700ustar00rootroot00000000000000""" Test Results for the VAR model. Obtained from Stata using datasets/macrodata/var.do """ import numpy as np class MacrodataResults(object): def __init__(self): params = [-0.2794863875, 0.0082427826, 0.6750534746, 0.2904420695, 0.0332267098, -0.0073250059, 0.0015269951, -0.1004938623, -0.1231841792, 0.2686635768, 0.2325045441, 0.0257430635, 0.0235035714, 0.0054596064, -1.97116e+00, 0.3809752365, 4.4143364022, 0.8001168377, 0.2255078864, -0.1241109271, -0.0239026118] params = np.asarray(params).reshape(3,-1) params = np.hstack((params[:,-1][:,None],params[:,:-1:2],params[:,1::2])) self.params = params self.neqs = 3 self.nobs = 200 self.df_eq = 7 self.nobs_1 = 200 self.df_model_1 = 6 self.rmse_1 = .0075573716985351 self.rsquared_1 = .2739094844780006 self.llf_1 = 696.8213727557811 self.nobs_2 = 200 self.rmse_2 = .0065444260782597 self.rsquared_2 = .1423626064753714 self.llf_2 = 725.6033255319256 self.nobs_3 = 200 self.rmse_3 = .0395942039671031 self.rsquared_3 = .2955406949737428 self.llf_3 = 365.5895183036045 # These are from Stata. They use the LL based definition # We return Lutkepohl statistics. See Stata TS manual page 436 # self.bic = -19.06939794312953 # self.aic = -19.41572126661708 # self.hqic = -19.27556951526737 # These are from R. See var.R in macrodata folder self.bic = -2.758301611618373e+01 self.aic = -2.792933943967127e+01 self.hqic = -2.778918768832157e+01 self.fpe = 7.421287668357018e-13 self.detsig = 6.01498432283e-13 self.llf = 1962.572126661708 self.chi2_1 = 75.44775165699033 # don't know how they calculate this # it's not -2 * (ll1 - ll0) self.chi2_2 = 33.19878716815366 self.chi2_3 = 83.90568280242312 bse = [.1666662376, .1704584393, .1289691456, .1433308696, .0257313781, .0253307796, .0010992645,.1443272761,.1476111934,.1116828804, .1241196435, .0222824956, .021935591, .0009519255, .8731894193, .8930573331, .6756886998, .7509319263, .1348105496, .1327117543, .0057592114] bse = np.asarray(bse).reshape(3,-1) bse = np.hstack((bse[:,-1][:,None],bse[:,:-1:2],bse[:,1::2])) self.bse = bse #array([[ -2.79434736e-01, 6.75015752e-01, 3.32194508e-02, # 8.22108491e-03, 2.90457628e-01, -7.32090753e-03, # 1.52697235e-03], # [ -1.00467978e-01, 2.68639553e-01, 2.57387265e-02, # -1.23173928e-01, 2.32499436e-01, 2.35037610e-02, # 5.45960305e-03], # [ -1.97097367e+00, 4.41416233e+00, 2.25478953e-01, # 3.80785849e-01, 8.00280918e-01, -1.24079062e-01, # -2.39025209e-02]]) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/results/results_var_data.py000066400000000000000000000151621224417117700320530ustar00rootroot00000000000000import numpy as np from numpy import array, rec, inf, nan class Holder(object): pass var_results = Holder() var_results.comment = 'VAR test data converted from vars_results.npz' var_results.causality = array([(9.317172089406967e-08,), (0.5183914225971917,), (4.8960835385969403e-14,)], dtype=[('causedby', 'float')]) var_results.name = 'var_results' var_results.orthirf = array({'realgdp': rec.array([(0.007557357219752236, 0.003948403413668315, 0.02972434157321242), (0.0015408726821578582, 0.0010664916255201816, 0.00923575489996933), (0.0015874964105555918, 0.0010551760558416706, 0.006102514196485799), (0.0007262051539604352, 0.0005562787500837443, 0.003199064883156089), (0.0005537000868358786, 0.0003520396722562061, 0.0024372344590635623), (0.0003079984190444812, 0.00021674897409108682, 0.0013369479853037147)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')]), 'realinv': rec.array([(0.0, 0.0, 0.020741992721114832), (0.0006890376065674764, 0.0005338724781743238, 0.004676882806534488), (0.00017134455810606506, 0.000682084896451223, -0.0005205835547221123), (0.0005217378718553543, 0.00030179909990059973, 0.0026650577026759623), (0.00034979575853114173, 0.00022249591743758265, 0.0015804716569096742), (0.00017738402507880077, 0.00013384975583249413, 0.0007585745605878197)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')]), 'realcons': rec.array([(0.0, 0.005219256972675799, -0.01593559385372415), (0.0029937089930834665, 0.000991936965470755, 0.019445506482528015), (0.002111631850388755, 0.0013051320985438103, 0.00901674690789421), (0.000760821255683657, 0.0006894587307924545, 0.0031531908594739904), (0.0006879775935656037, 0.0004452087814044681, 0.0029845646103117077), (0.0003908528054047277, 0.00026799713044743445, 0.0017324202804285334)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')])}, dtype=object) var_results.detomega = array([ 6.69357627913858242322e-13]) var_results.nirfs = array([ 5.]) var_results.loglike = array([ 1962.57082404432503608405]) var_results.stderr = array([(0.0011190205021849168, 0.0009690469778955306, 0.0058627441569700095), (0.169662667084976, 0.14692411307869205, 0.8888923913067142), (0.13128502534984174, 0.11368992508162204, 0.6878252130006585), (0.02619387125804535, 0.022683312533087908, 0.13723427351570822), (0.17352233516355128, 0.15026650017519333, 0.9091138675275061), (0.14590394087772843, 0.12634958224138662, 0.7644162686828472), (0.02578605367156698, 0.022330151532965133, 0.13509764584216039)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')]) var_results.crit = array({'sic': [-27.583016116183746], 'hqic': [-27.789187688321583], 'fpe': [7.421287668356912e-13], 'aic': [-27.92933943967129]}, dtype=object) var_results.phis = array([[[ 1. , 0. , 0. ], [ 0. , 1. , 0. ], [ 0. , 0. , 1. ]], [[-0.27943473587305184269, 0.67501575174854244743, 0.03321945079394677397], [-0.10046797808205484848, 0.26863955252271337626, 0.02573872652220415141], [-1.97097367379580989954, 4.41416232699026434005, 0.22547895322388852857]], [[-0.04698727419951594098, 0.42980675754266339794, 0.00826075683324488733], [-0.17281970983411520937, 0.35046409430422942322, 0.03288425107568751504], [ 0.04364931246903419604, 1.65096193457053663778, -0.02509804924346402399]], [[-0.11912577490062775665, 0.22257198988626961111, 0.02515370046023775175], [-0.07584698388980917749, 0.17652397885529469423, 0.01455014973529403233], [-0.60265240582682233494, 0.99644320159959076655, 0.12848609767194643649]], [[-0.08883245330826733399, 0.18330532474980901214, 0.01686413466798001443], [-0.05728563445875266974, 0.11805267650029765969, 0.01072683422606196188], [-0.39750493711843060129, 0.80448318531634888107, 0.07619671254154830597]], [[-0.04564844379524263945, 0.10099768165139233478, 0.00855192784337583181], [-0.03382143062494445684, 0.07105049775782870669, 0.00645308084098585207], [-0.19869441802790854812, 0.44359103295144985957, 0.03657192299636715521]]]) var_results.nahead = array([ 5.]) var_results.totobs = array([ 202.]) var_results.type = array(['const'], dtype='|S5') var_results.obs = array([ 200.]) var_results.irf = array({'realgdp': rec.array([(1.0, 0.0, 0.0), (-0.27943473587305184, -0.10046797808205485, -1.97097367379581), (-0.04698727419951594, -0.1728197098341152, 0.043649312469034196), (-0.11912577490062776, -0.07584698388980918, -0.6026524058268223), (-0.08883245330826733, -0.05728563445875267, -0.3975049371184306), (-0.04564844379524264, -0.03382143062494446, -0.19869441802790855)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')]), 'realinv': rec.array([(0.0, 0.0, 1.0), (0.033219450793946774, 0.02573872652220415, 0.22547895322388853), (0.008260756833244887, 0.032884251075687515, -0.025098049243464024), (0.025153700460237752, 0.014550149735294032, 0.12848609767194644), (0.016864134667980014, 0.010726834226061962, 0.0761967125415483), (0.008551927843375832, 0.006453080840985852, 0.036571922996367155)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')]), 'realcons': rec.array([(0.0, 1.0, 0.0), (0.6750157517485424, 0.2686395525227134, 4.414162326990264), (0.4298067575426634, 0.3504640943042294, 1.6509619345705366), (0.2225719898862696, 0.1765239788552947, 0.9964432015995908), (0.183305324749809, 0.11805267650029766, 0.8044831853163489), (0.10099768165139233, 0.0710504977578287, 0.44359103295144986)], dtype=[('realgdp', 'float'), ('realcons', 'float'), ('realinv', 'float')])}, dtype=object) var_results.coefs = array([(0.0015269723529158544, 0.005459603048402539, 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pylint: disable=W0612,W0231 from cStringIO import StringIO from nose.tools import assert_raises import nose import os import sys import numpy as np import statsmodels.api as sm import statsmodels.tsa.vector_ar.var_model as model import statsmodels.tsa.vector_ar.util as util import statsmodels.tools.data as data_util from statsmodels.tsa.vector_ar.var_model import VAR from statsmodels.compatnp.py3k import BytesIO from numpy.testing import assert_almost_equal, assert_equal, assert_ DECIMAL_12 = 12 DECIMAL_6 = 6 DECIMAL_5 = 5 DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 class CheckVAR(object): # just so pylint won't complain res1 = None res2 = None def test_params(self): assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_3) def test_neqs(self): assert_equal(self.res1.neqs, self.res2.neqs) def test_nobs(self): assert_equal(self.res1.avobs, self.res2.nobs) def test_df_eq(self): assert_equal(self.res1.df_eq, self.res2.df_eq) def test_rmse(self): results = self.res1.results for i in range(len(results)): assert_almost_equal(results[i].mse_resid**.5, eval('self.res2.rmse_'+str(i+1)), DECIMAL_6) def test_rsquared(self): results = self.res1.results for i in range(len(results)): assert_almost_equal(results[i].rsquared, eval('self.res2.rsquared_'+str(i+1)), DECIMAL_3) def test_llf(self): results = self.res1.results assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_2) for i in range(len(results)): assert_almost_equal(results[i].llf, eval('self.res2.llf_'+str(i+1)), DECIMAL_2) def test_aic(self): assert_almost_equal(self.res1.aic, self.res2.aic) def test_bic(self): assert_almost_equal(self.res1.bic, self.res2.bic) def test_hqic(self): assert_almost_equal(self.res1.hqic, self.res2.hqic) def test_fpe(self): assert_almost_equal(self.res1.fpe, self.res2.fpe) def test_detsig(self): assert_almost_equal(self.res1.detomega, self.res2.detsig) def test_bse(self): assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4) def get_macrodata(): data = sm.datasets.macrodata.load().data[['realgdp','realcons','realinv']] names = data.dtype.names nd = data.view((float,3)) nd = np.diff(np.log(nd), axis=0) return nd.ravel().view(data.dtype) def generate_var(): from rpy2.robjects import r import pandas.rpy.common as prp r.source('tests/var.R') return prp.convert_robj(r['result'], use_pandas=False) def write_generate_var(): result = generate_var() np.savez('tests/results/vars_results.npz', **result) class RResults(object): """ Simple interface with results generated by "vars" package in R. """ def __init__(self): #data = np.load(resultspath + 'vars_results.npz') from results.results_var_data import var_results data = var_results.__dict__ self.names = data['coefs'].dtype.names self.params = data['coefs'].view((float, len(self.names))) self.stderr = data['stderr'].view((float, len(self.names))) self.irf = data['irf'].item() self.orth_irf = data['orthirf'].item() self.nirfs = int(data['nirfs'][0]) self.nobs = int(data['obs'][0]) self.totobs = int(data['totobs'][0]) crit = data['crit'].item() self.aic = crit['aic'][0] self.sic = self.bic = crit['sic'][0] self.hqic = crit['hqic'][0] self.fpe = crit['fpe'][0] self.detomega = data['detomega'][0] self.loglike = data['loglike'][0] self.nahead = int(data['nahead'][0]) self.ma_rep = data['phis'] self.causality = data['causality'] def close_plots(): try: import matplotlib.pyplot as plt plt.close('all') except ImportError: pass _orig_stdout = None def setup_module(): global _orig_stdout _orig_stdout = sys.stdout sys.stdout = StringIO() def teardown_module(): sys.stdout = _orig_stdout close_plots() def have_matplotlib(): try: import matplotlib if matplotlib.__version__ < '1': raise return True except: return False class CheckIRF(object): ref = None; res = None; irf = None k = None #--------------------------------------------------------------------------- # IRF tests def test_irf_coefs(self): self._check_irfs(self.irf.irfs, self.ref.irf) self._check_irfs(self.irf.orth_irfs, self.ref.orth_irf) def _check_irfs(self, py_irfs, r_irfs): for i, name in enumerate(self.res.names): ref_irfs = r_irfs[name].view((float, self.k)) res_irfs = py_irfs[:, :, i] assert_almost_equal(ref_irfs, res_irfs) def test_plot_irf(self): if not have_matplotlib(): raise nose.SkipTest self.irf.plot() self.irf.plot(plot_stderr=False) self.irf.plot(impulse=0, response=1) self.irf.plot(impulse=0) self.irf.plot(response=0) self.irf.plot(orth=True) self.irf.plot(impulse=0, response=1, orth=True) close_plots() def test_plot_cum_effects(self): if not have_matplotlib(): raise nose.SkipTest self.irf.plot_cum_effects() self.irf.plot_cum_effects(plot_stderr=False) self.irf.plot_cum_effects(impulse=0, response=1) self.irf.plot_cum_effects(orth=True) self.irf.plot_cum_effects(impulse=0, response=1, orth=True) close_plots() class CheckFEVD(object): fevd = None #--------------------------------------------------------------------------- # FEVD tests def test_fevd_plot(self): if not have_matplotlib(): raise nose.SkipTest self.fevd.plot() close_plots() def test_fevd_repr(self): print self.fevd def test_fevd_summary(self): self.fevd.summary() def test_fevd_cov(self): # test does not crash # not implemented # covs = self.fevd.cov() pass class TestVARResults(CheckIRF, CheckFEVD): @classmethod def setupClass(cls): cls.p = 2 cls.data = get_macrodata() cls.model = VAR(cls.data) cls.names = cls.model.names cls.ref = RResults() cls.k = len(cls.ref.names) cls.res = cls.model.fit(maxlags=cls.p) cls.irf = cls.res.irf(cls.ref.nirfs) cls.nahead = cls.ref.nahead cls.fevd = cls.res.fevd() def test_constructor(self): # make sure this works with no names ndarr = self.data.view((float, 3)) model = VAR(ndarr) res = model.fit(self.p) def test_names(self): assert_equal(self.model.names, self.ref.names) model2 = VAR(self.data, names=self.names) assert_equal(model2.names, self.ref.names) def test_get_eq_index(self): assert(isinstance(self.res.names, list)) for i, name in enumerate(self.names): idx = self.res.get_eq_index(i) idx2 = self.res.get_eq_index(name) assert_equal(idx, i) assert_equal(idx, idx2) assert_raises(Exception, self.res.get_eq_index, 'foo') def test_repr(self): # just want this to work foo = str(self.res) bar = repr(self.res) def test_params(self): assert_almost_equal(self.res.params, self.ref.params, DECIMAL_3) def test_cov_params(self): # do nothing for now self.res.cov_params def test_cov_ybar(self): self.res.cov_ybar() def test_tstat(self): self.res.tvalues def test_pvalues(self): self.res.pvalues def test_summary(self): summ = self.res.summary() print summ def test_detsig(self): assert_almost_equal(self.res.detomega, self.ref.detomega) def test_aic(self): assert_almost_equal(self.res.aic, self.ref.aic) def test_bic(self): assert_almost_equal(self.res.bic, self.ref.bic) def test_hqic(self): assert_almost_equal(self.res.hqic, self.ref.hqic) def test_fpe(self): assert_almost_equal(self.res.fpe, self.ref.fpe) def test_lagorder_select(self): ics = ['aic', 'fpe', 'hqic', 'bic'] for ic in ics: res = self.model.fit(maxlags=10, ic=ic, verbose=True) assert_raises(Exception, self.model.fit, ic='foo') def test_nobs(self): assert_equal(self.res.nobs, self.ref.nobs) def test_stderr(self): assert_almost_equal(self.res.stderr, self.ref.stderr, DECIMAL_4) def test_loglike(self): assert_almost_equal(self.res.llf, self.ref.loglike) def test_ma_rep(self): ma_rep = self.res.ma_rep(self.nahead) assert_almost_equal(ma_rep, self.ref.ma_rep) #-------------------------------------------------- # Lots of tests to make sure stuff works...need to check correctness def test_causality(self): causedby = self.ref.causality['causedby'] for i, name in enumerate(self.names): variables = self.names[:i] + self.names[i + 1:] result = self.res.test_causality(name, variables, kind='f') assert_almost_equal(result['pvalue'], causedby[i], DECIMAL_4) rng = range(self.k) rng.remove(i) result2 = self.res.test_causality(i, rng, kind='f') assert_almost_equal(result['pvalue'], result2['pvalue'], DECIMAL_12) # make sure works result = self.res.test_causality(name, variables, kind='wald') # corner cases _ = self.res.test_causality(self.names[0], self.names[1]) _ = self.res.test_causality(0, 1) assert_raises(Exception,self.res.test_causality, 0, 1, kind='foo') def test_select_order(self): result = self.model.fit(10, ic='aic', verbose=True) result = self.model.fit(10, ic='fpe', verbose=True) # bug model = VAR(self.model.endog) model.select_order() def test_is_stable(self): # may not necessarily be true for other datasets assert(self.res.is_stable(verbose=True)) def test_acf(self): # test that it works...for now acfs = self.res.acf(10) # defaults to nlags=lag_order acfs = self.res.acf() assert(len(acfs) == self.p + 1) def test_acorr(self): acorrs = self.res.acorr(10) def test_forecast(self): point = self.res.forecast(self.res.y[-5:], 5) def test_forecast_interval(self): y = self.res.y[:-self.p:] point, lower, upper = self.res.forecast_interval(y, 5) def test_plot_sim(self): if not have_matplotlib(): raise nose.SkipTest self.res.plotsim(steps=100) close_plots() def test_plot(self): if not have_matplotlib(): raise nose.SkipTest self.res.plot() close_plots() def test_plot_acorr(self): if not have_matplotlib(): raise nose.SkipTest self.res.plot_acorr() close_plots() def test_plot_forecast(self): if not have_matplotlib(): raise nose.SkipTest self.res.plot_forecast(5) close_plots() def test_reorder(self): #manually reorder data = self.data.view((float,3)) names = self.names data2 = np.append(np.append(data[:,2,None], data[:,0,None], axis=1), data[:,1,None], axis=1) names2 = [] names2.append(names[2]) names2.append(names[0]) names2.append(names[1]) res2 = VAR(data2,names=names2).fit(maxlags=self.p) #use reorder function res3 = self.res.reorder(['realinv','realgdp', 'realcons']) #check if the main results match assert_almost_equal(res2.params, res3.params) assert_almost_equal(res2.sigma_u, res3.sigma_u) assert_almost_equal(res2.bic, res3.bic) assert_almost_equal(res2.stderr, res3.stderr) def test_pickle(self): from statsmodels.compatnp.py3k import BytesIO fh = BytesIO() #test wrapped results load save pickle self.res.save(fh) fh.seek(0,0) res_unpickled = self.res.__class__.load(fh) assert_(type(res_unpickled) is type(self.res)) class E1_Results(object): """ Results from Lutkepohl (2005) using E2 dataset """ def __init__(self): # Lutkepohl p. 120 results # I asked the author about these results and there is probably rounding # error in the book, so I adjusted these test results to match what is # coming out of the Python (double-checked) calculations self.irf_stderr = np.array([[[.125, 0.546, 0.664 ], [0.032, 0.139, 0.169], [0.026, 0.112, 0.136]], [[0.129, 0.547, 0.663], [0.032, 0.134, 0.163], [0.026, 0.108, 0.131]], [[0.084, .385, .479], [.016, .079, .095], [.016, .078, .103]]]) self.cum_irf_stderr = np.array([[[.125, 0.546, 0.664 ], [0.032, 0.139, 0.169], [0.026, 0.112, 0.136]], [[0.149, 0.631, 0.764], [0.044, 0.185, 0.224], [0.033, 0.140, 0.169]], [[0.099, .468, .555], [.038, .170, .205], [.033, .150, .185]]]) self.lr_stderr = np.array([[.134, .645, .808], [.048, .230, .288], [.043, .208, .260]]) basepath = os.path.split(sm.__file__)[0] resultspath = basepath + '/tsa/vector_ar/tests/results/' def get_lutkepohl_data(name='e2'): lut_data = basepath + '/tsa/vector_ar/data/' path = lut_data + '%s.dat' % name return util.parse_lutkepohl_data(path) def test_lutkepohl_parse(): files = ['e%d' % i for i in range(1, 7)] for f in files: get_lutkepohl_data(f) class TestVARResultsLutkepohl(object): """ Verify calculations using results from Lutkepohl's book """ def __init__(self): self.p = 2 sdata, dates = get_lutkepohl_data('e1') names = sdata.dtype.names data = data_util.struct_to_ndarray(sdata) adj_data = np.diff(np.log(data), axis=0) # est = VAR(adj_data, p=2, dates=dates[1:], names=names) self.model = VAR(adj_data[:-16], dates=dates[1:-16], names=names, freq='Q') self.res = self.model.fit(maxlags=self.p) self.irf = self.res.irf(10) self.lut = E1_Results() def test_approx_mse(self): # 3.5.18, p. 99 mse2 = np.array([[25.12, .580, 1.300], [.580, 1.581, .586], [1.300, .586, 1.009]]) * 1e-4 assert_almost_equal(mse2, self.res.forecast_cov(3)[1], DECIMAL_3) def test_irf_stderr(self): irf_stderr = self.irf.stderr(orth=False) for i in range(1, 1 + len(self.lut.irf_stderr)): assert_almost_equal(np.round(irf_stderr[i], 3), self.lut.irf_stderr[i-1]) def test_cum_irf_stderr(self): stderr = self.irf.cum_effect_stderr(orth=False) for i in range(1, 1 + len(self.lut.cum_irf_stderr)): assert_almost_equal(np.round(stderr[i], 3), self.lut.cum_irf_stderr[i-1]) def test_lr_effect_stderr(self): stderr = self.irf.lr_effect_stderr(orth=False) orth_stderr = self.irf.lr_effect_stderr(orth=True) assert_almost_equal(np.round(stderr, 3), self.lut.lr_stderr) def test_get_trendorder(): results = { 'c' : 1, 'nc' : 0, 'ct' : 2, 'ctt' : 3 } for t, trendorder in results.iteritems(): assert(util.get_trendorder(t) == trendorder) if __name__ == '__main__': import nose nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'], exit=False) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/tests/var.R000066400000000000000000000037661224417117700253600ustar00rootroot00000000000000library(vars) data <- read.csv('/home/wesm/code/statsmodels/scikits/statsmodels/datasets/macrodata/macrodata.csv') names <- colnames(data) data <- log(data[c('realgdp', 'realcons', 'realinv')]) data <- sapply(data, diff) reorder.coefs <- function(coefs) { n <- dim(coefs)[1] # put constant first... coefs[c(n, seq(1:(n-1))),] } extract.mat <- function(lst, i) { sapply(lst, function(x) x[,i]) } get.coefs <- function(est) { reorder.coefs(extract.mat(coef(est), 1)) } get.stderr <- function(est) { reorder.coefs(extract.mat(coef(est), 2)) } reorder.phi <- function(phis) { # Puts things in more proper C order for comparison purposes in Python k <- dim(phis)[1] n <- dim(phis)[3] arr <- array(dim=c(n, k, k)) for (i in 1:n) arr[i,,] <- phis[,,i] arr } causality.matrix <- function(est) { names <- colnames(est$y) K <- est$K # p-values result <- matrix(0, nrow=K, ncol=) for (i in 1:K) { ## # causes ## result[i,1] <- causality(est, cause=names[i])$Granger$p.value # caused by others result[i,1] <- causality(est, cause=names[-i])$Granger$p.value } colnames(result) <- c("causedby") result } get.results <- function(data, p=1) { sel <- VARselect(data, p) # do at most p est <- VAR(data, p=p) K <- ncol(data) nirfs <- 5 orth.irf <- irf(est, n.ahead=nirfs, boot=F)$irf irf <- irf(est, n.ahead=nirfs, boot=F, orth=F)$irf crit <- t(sel$criteria) colnames(crit) <- c('aic', 'hqic', 'sic', 'fpe') resid <- resid(est) detomega <- det(crossprod(resid) / (est$obs - K * p - 1)) n.ahead <- 5 list(coefs=get.coefs(est), stderr=get.stderr(est), obs=est$obs, totobs=est$totobs, type=est$type, crit=as.list(crit[p,]), nirfs=nirfs, orthirf=orth.irf, irf=irf, causality=causality.matrix(est), detomega=detomega, loglike=as.numeric(logLik(est)), nahead=n.ahead, phis=Phi(est, n.ahead)) } k <- dim(data)[2] result <- get.results(data, p=2) est = VAR(data, p=2) statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/util.py000066400000000000000000000143131224417117700246200ustar00rootroot00000000000000""" Miscellaneous utility code for VAR estimation """ import numpy as np import scipy.stats as stats import scipy.linalg as L import scipy.linalg.decomp as decomp import statsmodels.tsa.tsatools as tsa from scipy.linalg import cholesky #------------------------------------------------------------------------------- # Auxiliary functions for estimation def get_var_endog(y, lags, trend='c'): """ Make predictor matrix for VAR(p) process Z := (Z_0, ..., Z_T).T (T x Kp) Z_t = [1 y_t y_{t-1} ... y_{t - p + 1}] (Kp x 1) Ref: Lutkepohl p.70 (transposed) """ nobs = len(y) # Ravel C order, need to put in descending order Z = np.array([y[t-lags : t][::-1].ravel() for t in xrange(lags, nobs)]) # Add constant, trend, etc. if trend != 'nc': Z = tsa.add_trend(Z, prepend=True, trend=trend) return Z def get_trendorder(trend='c'): # Handle constant, etc. if trend == 'c': trendorder = 1 elif trend == 'nc': trendorder = 0 elif trend == 'ct': trendorder = 2 elif trend == 'ctt': trendorder = 3 return trendorder def make_lag_names(names, lag_order, trendorder=1): """ Produce list of lag-variable names. Constant / trends go at the beginning Example ------- >>> make_lag_names(['foo', 'bar'], 2, 1) ['const', 'L1.foo', 'L1.bar', 'L2.foo', 'L2.bar'] """ lag_names = [] if isinstance(names, basestring): # python 3? names = [names] # take care of lagged endogenous names for i in range(1, lag_order + 1): for name in names: if not isinstance(name, basestring): name = str(name) # will need consistent unicode handling lag_names.append('L'+str(i)+'.'+name) # handle the constant name if trendorder != 0: lag_names.insert(0, 'const') if trendorder > 1: lag_names.insert(0, 'trend') if trendorder > 2: lag_names.insert(0, 'trend**2') return lag_names def comp_matrix(coefs): """ Return compansion matrix for the VAR(1) representation for a VAR(p) process (companion form) A = [A_1 A_2 ... A_p-1 A_p I_K 0 0 0 0 I_K ... 0 0 0 ... I_K 0] """ p, k, k2 = coefs.shape assert(k == k2) kp = k * p result = np.zeros((kp, kp)) result[:k] = np.concatenate(coefs, axis=1) # Set I_K matrices if p > 1: result[np.arange(k, kp), np.arange(kp-k)] = 1 return result #------------------------------------------------------------------------------- # Miscellaneous stuff def parse_lutkepohl_data(path): # pragma: no cover """ Parse data files from Lutkepohl (2005) book Source for data files: www.jmulti.de """ from collections import deque from datetime import datetime import pandas import pandas.core.datetools as dt import re from statsmodels.compatnp.py3k import asbytes regex = re.compile(asbytes('<(.*) (\w)([\d]+)>.*')) lines = deque(open(path, 'rb')) to_skip = 0 while asbytes('*/') not in lines.popleft(): #while '*/' not in lines.popleft(): to_skip += 1 while True: to_skip += 1 line = lines.popleft() m = regex.match(line) if m: year, freq, start_point = m.groups() break data = np.genfromtxt(path, names=True, skip_header=to_skip+1) n = len(data) # generate the corresponding date range (using pandas for now) start_point = int(start_point) year = int(year) offsets = { asbytes('Q') : dt.BQuarterEnd(), asbytes('M') : dt.BMonthEnd(), asbytes('A') : dt.BYearEnd() } # create an instance offset = offsets[freq] inc = offset * (start_point - 1) start_date = offset.rollforward(datetime(year, 1, 1)) + inc offset = offsets[freq] try: from pandas import DatetimeIndex # pylint: disable=E0611 date_range = DatetimeIndex(start=start_date, freq=offset, periods=n) except ImportError: from pandas import DateRange date_range = DateRange(start_date, offset=offset, periods=n) return data, date_range def get_logdet(m): from statsmodels.tools.compatibility import np_slogdet logdet = np_slogdet(m) if logdet[0] == -1: # pragma: no cover raise ValueError("Matrix is not positive definite") elif logdet[0] == 0: # pragma: no cover raise ValueError("Matrix is singular") else: logdet = logdet[1] return logdet def norm_signif_level(alpha=0.05): return stats.norm.ppf(1 - alpha / 2) def acf_to_acorr(acf): diag = np.diag(acf[0]) # numpy broadcasting sufficient return acf / np.sqrt(np.outer(diag, diag)) def varsim(coefs, intercept, sig_u, steps=100, initvalues=None, seed=None): """ Simulate simple VAR(p) process with known coefficients, intercept, white noise covariance, etc. """ if seed is not None: np.random.seed(seed=seed) from numpy.random import multivariate_normal as rmvnorm p, k, k = coefs.shape ugen = rmvnorm(np.zeros(len(sig_u)), sig_u, steps) result = np.zeros((steps, k)) result[p:] = intercept + ugen[p:] # add in AR terms for t in xrange(p, steps): ygen = result[t] for j in xrange(p): ygen += np.dot(coefs[j], result[t-j-1]) return result def get_index(lst, name): try: result = lst.index(name) except Exception: if not isinstance(name, int): raise result = name return result #method used repeatedly in Sims-Zha error bands def eigval_decomp(sym_array): """ Returns ------- W: array of eigenvectors eigva: list of eigenvalues k: largest eigenvector """ #check if symmetric, do not include shock period eigva, W = decomp.eig(sym_array, left=True, right=False) k = np.argmax(eigva) return W, eigva, k def vech(A): """ Simple vech operator Returns ------- vechvec: vector of all elements on and below diagonal """ length=A.shape[1] vechvec=[] for i in xrange(length): b=i while b < length: vechvec.append(A[b,i]) b=b+1 vechvec=np.asarray(vechvec) return vechvec statsmodels-0.5.0+git13-g8e07d34/statsmodels/tsa/vector_ar/var_model.py000066400000000000000000001431361224417117700256210ustar00rootroot00000000000000""" Vector Autoregression (VAR) processes References ---------- Lutkepohl (2005) New Introduction to Multiple Time Series Analysis """ from __future__ import division from collections import defaultdict from cStringIO import StringIO import numpy as np import numpy.linalg as npl from numpy.linalg import cholesky as chol, solve import scipy.stats as stats import scipy.linalg as L from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.tools import chain_dot from statsmodels.tsa.tsatools import vec, unvec from statsmodels.tsa.vector_ar.irf import IRAnalysis from statsmodels.tsa.vector_ar.output import VARSummary import statsmodels.tsa.tsatools as tsa import statsmodels.tsa.vector_ar.output as output import statsmodels.tsa.vector_ar.plotting as plotting import statsmodels.tsa.vector_ar.util as util import statsmodels.tsa.base.tsa_model as tsbase import statsmodels.base.wrapper as wrap mat = np.array #------------------------------------------------------------------------------- # VAR process routines def ma_rep(coefs, maxn=10): r""" MA(\infty) representation of VAR(p) process Parameters ---------- coefs : ndarray (p x k x k) maxn : int Number of MA matrices to compute Notes ----- VAR(p) process as .. math:: y_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + u_t can be equivalently represented as .. math:: y_t = \mu + \sum_{i=0}^\infty \Phi_i u_{t-i} e.g. can recursively compute the \Phi_i matrices with \Phi_0 = I_k Returns ------- phis : ndarray (maxn + 1 x k x k) """ p, k, k = coefs.shape phis = np.zeros((maxn+1, k, k)) phis[0] = np.eye(k) # recursively compute Phi matrices for i in xrange(1, maxn + 1): for j in xrange(1, i+1): if j > p: break phis[i] += np.dot(phis[i-j], coefs[j-1]) return phis def is_stable(coefs, verbose=False): """ Determine stability of VAR(p) system by examining the eigenvalues of the VAR(1) representation Parameters ---------- coefs : ndarray (p x k x k) Returns ------- is_stable : bool """ A_var1 = util.comp_matrix(coefs) eigs = np.linalg.eigvals(A_var1) if verbose: print 'Eigenvalues of VAR(1) rep' for val in np.abs(eigs): print val return (np.abs(eigs) <= 1).all() def var_acf(coefs, sig_u, nlags=None): """ Compute autocovariance function ACF_y(h) up to nlags of stable VAR(p) process Parameters ---------- coefs : ndarray (p x k x k) Coefficient matrices A_i sig_u : ndarray (k x k) Covariance of white noise process u_t nlags : int, optional Defaults to order p of system Notes ----- Ref: Lutkepohl p.28-29 Returns ------- acf : ndarray, (p, k, k) """ p, k, _ = coefs.shape if nlags is None: nlags = p # p x k x k, ACF for lags 0, ..., p-1 result = np.zeros((nlags + 1, k, k)) result[:p] = _var_acf(coefs, sig_u) # yule-walker equations for h in xrange(p, nlags + 1): # compute ACF for lag=h # G(h) = A_1 G(h-1) + ... + A_p G(h-p) for j in xrange(p): result[h] += np.dot(coefs[j], result[h-j-1]) return result def _var_acf(coefs, sig_u): """ Compute autocovariance function ACF_y(h) for h=1,...,p Notes ----- Lutkepohl (2005) p.29 """ p, k, k2 = coefs.shape assert(k == k2) A = util.comp_matrix(coefs) # construct VAR(1) noise covariance SigU = np.zeros((k*p, k*p)) SigU[:k,:k] = sig_u # vec(ACF) = (I_(kp)^2 - kron(A, A))^-1 vec(Sigma_U) vecACF = L.solve(np.eye((k*p)**2) - np.kron(A, A), vec(SigU)) acf = unvec(vecACF) acf = acf[:k].T.reshape((p, k, k)) return acf def forecast(y, coefs, intercept, steps): """ Produce linear MSE forecast Parameters ---------- y : coefs : intercept : steps : Returns ------- forecasts : ndarray (steps x neqs) Notes ----- Lutkepohl p. 37 Also used by DynamicVAR class """ p = len(coefs) k = len(coefs[0]) # initial value forcs = np.zeros((steps, k)) + intercept # h=0 forecast should be latest observation # forcs[0] = y[-1] # make indices easier to think about for h in xrange(1, steps + 1): # y_t(h) = intercept + sum_1^p A_i y_t_(h-i) f = forcs[h - 1] for i in xrange(1, p + 1): # slightly hackish if h - i <= 0: # e.g. when h=1, h-1 = 0, which is y[-1] prior_y = y[h - i - 1] else: # e.g. when h=2, h-1=1, which is forcs[0] prior_y = forcs[h - i - 1] # i=1 is coefs[0] f = f + np.dot(coefs[i - 1], prior_y) forcs[h - 1] = f return forcs def forecast_cov(ma_coefs, sig_u, steps): """ Compute theoretical forecast error variance matrices Parameters ---------- Returns ------- forc_covs : ndarray (steps x neqs x neqs) """ k = len(sig_u) forc_covs = np.zeros((steps, k, k)) prior = np.zeros((k, k)) for h in xrange(steps): # Sigma(h) = Sigma(h-1) + Phi Sig_u Phi' phi = ma_coefs[h] var = chain_dot(phi, sig_u, phi.T) forc_covs[h] = prior = prior + var return forc_covs def var_loglike(resid, omega, nobs): r""" Returns the value of the VAR(p) log-likelihood. Parameters ---------- resid : ndarray (T x K) omega : ndarray Sigma hat matrix. Each element i,j is the average product of the OLS residual for variable i and the OLS residual for variable j or np.dot(resid.T,resid)/nobs. There should be no correction for the degrees of freedom. nobs : int Returns ------- llf : float The value of the loglikelihood function for a VAR(p) model Notes ----- The loglikelihood function for the VAR(p) is .. math:: -\left(\frac{T}{2}\right) \left(\ln\left|\Omega\right|-K\ln\left(2\pi\right)-K\right) """ logdet = util.get_logdet(np.asarray(omega)) neqs = len(omega) part1 = - (nobs * neqs / 2) * np.log(2 * np.pi) part2 = - (nobs / 2) * (logdet + neqs) return part1 + part2 def _reordered(self, order): #Create new arrays to hold rearranged results from .fit() endog = self.endog endog_lagged = self.endog_lagged params = self.params sigma_u = self.sigma_u names = self.names k_ar = self.k_ar endog_new = np.zeros([np.size(endog,0),np.size(endog,1)]) endog_lagged_new = np.zeros([np.size(endog_lagged,0), np.size(endog_lagged,1)]) params_new_inc, params_new = [np.zeros([np.size(params,0), np.size(params,1)]) for i in range(2)] sigma_u_new_inc, sigma_u_new = [np.zeros([np.size(sigma_u,0), np.size(sigma_u,1)]) for i in range(2)] num_end = len(self.params[0]) names_new = [] #Rearrange elements and fill in new arrays k = self.k_trend for i, c in enumerate(order): endog_new[:,i] = self.endog[:,c] if k > 0: params_new_inc[0,i] = params[0,i] endog_lagged_new[:,0] = endog_lagged[:,0] for j in range(k_ar): params_new_inc[i+j*num_end+k,:] = self.params[c+j*num_end+k,:] endog_lagged_new[:,i+j*num_end+k] = endog_lagged[:,c+j*num_end+k] sigma_u_new_inc[i,:] = sigma_u[c,:] names_new.append(names[c]) for i, c in enumerate(order): params_new[:,i] = params_new_inc[:,c] sigma_u_new[:,i] = sigma_u_new_inc[:,c] return VARResults(endog=endog_new, endog_lagged=endog_lagged_new, params=params_new, sigma_u=sigma_u_new, lag_order=self.k_ar, model=self.model, trend='c', names=names_new, dates=self.dates) #------------------------------------------------------------------------------- # VARProcess class: for known or unknown VAR process class VAR(tsbase.TimeSeriesModel): r""" Fit VAR(p) process and do lag order selection .. math:: y_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + u_t Parameters ---------- endog : array-like 2-d endogenous response variable. The independent variable. names : array-like must match number of columns of endog dates : array-like must match number of rows of endog References ---------- Lutkepohl (2005) New Introduction to Multiple Time Series Analysis """ def __init__(self, endog, dates=None, names=None, freq=None, missing='none'): super(VAR, self).__init__(endog, None, dates, freq, missing=missing) if self.endog.ndim == 1: raise ValueError("Only gave one variable to VAR") if names is not None: import warnings warnings.warn("The names argument is deprecated and will be " "removed in the next release.", FutureWarning) self.names = names else: self.names = self.endog_names self.y = self.endog #keep alias for now self.neqs = self.endog.shape[1] def _get_predict_start(self, start, k_ar): if start is None: start = k_ar return super(VAR, self)._get_predict_start(start) def predict(self, params, start=None, end=None, lags=1, trend='c'): """ Returns in-sample predictions or forecasts """ start = self._get_predict_start(start, lags) end, out_of_sample = self._get_predict_end(end) if end < start: raise ValueError("end is before start") if end == start + out_of_sample: return np.array([]) k_trend = util.get_trendorder(trend) k = self.neqs k_ar = lags predictedvalues = np.zeros((end + 1 - start + out_of_sample, k)) if k_trend != 0: intercept = params[:k_trend] predictedvalues += intercept y = self.y X = util.get_var_endog(y, lags, trend=trend) fittedvalues = np.dot(X, params) fv_start = start - k_ar pv_end = min(len(predictedvalues), len(fittedvalues) - fv_start) fv_end = min(len(fittedvalues), end-k_ar+1) predictedvalues[:pv_end] = fittedvalues[fv_start:fv_end] if not out_of_sample: return predictedvalues # fit out of sample y = y[-k_ar:] coefs = params[k_trend:].reshape((k_ar, k, k)).swapaxes(1,2) predictedvalues[pv_end:] = forecast(y, coefs, intercept, out_of_sample) return predictedvalues def fit(self, maxlags=None, method='ols', ic=None, trend='c', verbose=False): """ Fit the VAR model Parameters ---------- maxlags : int Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), see select_order function method : {'ols'} Estimation method to use ic : {'aic', 'fpe', 'hqic', 'bic', None} Information criterion to use for VAR order selection. aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic : Bayesian a.k.a. Schwarz verbose : bool, default False Print order selection output to the screen trend, str {"c", "ct", "ctt", "nc"} "c" - add constant "ct" - constant and trend "ctt" - constant, linear and quadratic trend "nc" - co constant, no trend Note that these are prepended to the columns of the dataset. Notes ----- Lutkepohl pp. 146-153 Returns ------- est : VARResults """ lags = maxlags if ic is not None: selections = self.select_order(maxlags=maxlags, verbose=verbose) if ic not in selections: raise Exception("%s not recognized, must be among %s" % (ic, sorted(selections))) lags = selections[ic] if verbose: print 'Using %d based on %s criterion' % (lags, ic) else: if lags is None: lags = 1 k_trend = util.get_trendorder(trend) self.exog_names = util.make_lag_names(self.endog_names, lags, k_trend) self.nobs = len(self.endog) - lags return self._estimate_var(lags, trend=trend) def _estimate_var(self, lags, offset=0, trend='c'): """ lags : int offset : int Periods to drop from beginning-- for order selection so it's an apples-to-apples comparison trend : string or None As per above """ # have to do this again because select_order doesn't call fit self.k_trend = k_trend = util.get_trendorder(trend) if offset < 0: # pragma: no cover raise ValueError('offset must be >= 0') y = self.y[offset:] z = util.get_var_endog(y, lags, trend=trend) y_sample = y[lags:] # Lutkepohl p75, about 5x faster than stated formula params = np.linalg.lstsq(z, y_sample)[0] resid = y_sample - np.dot(z, params) # Unbiased estimate of covariance matrix $\Sigma_u$ of the white noise # process $u$ # equivalent definition # .. math:: \frac{1}{T - Kp - 1} Y^\prime (I_T - Z (Z^\prime Z)^{-1} # Z^\prime) Y # Ref: Lutkepohl p.75 # df_resid right now is T - Kp - 1, which is a suggested correction avobs = len(y_sample) df_resid = avobs - (self.neqs * lags + k_trend) sse = np.dot(resid.T, resid) omega = sse / df_resid varfit = VARResults(y, z, params, omega, lags, names=self.endog_names, trend=trend, dates=self.data.dates, model=self) return VARResultsWrapper(varfit) def select_order(self, maxlags=None, verbose=True): """ Compute lag order selections based on each of the available information criteria Parameters ---------- maxlags : int if None, defaults to 12 * (nobs/100.)**(1./4) verbose : bool, default True If True, print table of info criteria and selected orders Returns ------- selections : dict {info_crit -> selected_order} """ if maxlags is None: maxlags = int(round(12*(len(self.endog)/100.)**(1/4.))) ics = defaultdict(list) for p in range(maxlags + 1): # exclude some periods to same amount of data used for each lag # order result = self._estimate_var(p, offset=maxlags-p) for k, v in result.info_criteria.iteritems(): ics[k].append(v) selected_orders = dict((k, mat(v).argmin()) for k, v in ics.iteritems()) if verbose: output.print_ic_table(ics, selected_orders) return selected_orders class VARProcess(object): """ Class represents a known VAR(p) process Parameters ---------- coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) Returns ------- **Attributes**: """ def __init__(self, coefs, intercept, sigma_u, names=None): self.k_ar = len(coefs) self.neqs = coefs.shape[1] self.coefs = coefs self.intercept = intercept self.sigma_u = sigma_u self.names = names def get_eq_index(self, name): "Return integer position of requested equation name" return util.get_index(self.names, name) def __str__(self): output = ('VAR(%d) process for %d-dimensional response y_t' % (self.k_ar, self.neqs)) output += '\nstable: %s' % self.is_stable() output += '\nmean: %s' % self.mean() return output def is_stable(self, verbose=False): """Determine stability based on model coefficients Parameters ---------- verbose : bool Print eigenvalues of the VAR(1) companion Notes ----- Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle """ return is_stable(self.coefs, verbose=verbose) def plotsim(self, steps=1000): """ Plot a simulation from the VAR(p) process for the desired number of steps """ Y = util.varsim(self.coefs, self.intercept, self.sigma_u, steps=steps) plotting.plot_mts(Y) def mean(self): r"""Mean of stable process Lutkepohl eq. 2.1.23 .. math:: \mu = (I - A_1 - \dots - A_p)^{-1} \alpha """ return solve(self._char_mat, self.intercept) def ma_rep(self, maxn=10): r"""Compute MA(:math:`\infty`) coefficient matrices Parameters ---------- maxn : int Number of coefficient matrices to compute Returns ------- coefs : ndarray (maxn x k x k) """ return ma_rep(self.coefs, maxn=maxn) def orth_ma_rep(self, maxn=10, P=None): r"""Compute Orthogonalized MA coefficient matrices using P matrix such that :math:`\Sigma_u = PP^\prime`. P defaults to the Cholesky decomposition of :math:`\Sigma_u` Parameters ---------- maxn : int Number of coefficient matrices to compute P : ndarray (k x k), optional Matrix such that Sigma_u = PP', defaults to Cholesky descomp Returns ------- coefs : ndarray (maxn x k x k) """ if P is None: P = self._chol_sigma_u ma_mats = self.ma_rep(maxn=maxn) return mat([np.dot(coefs, P) for coefs in ma_mats]) def long_run_effects(self): """Compute long-run effect of unit impulse .. math:: \Psi_\infty = \sum_{i=0}^\infty \Phi_i """ return L.inv(self._char_mat) @cache_readonly def _chol_sigma_u(self): return chol(self.sigma_u) @cache_readonly def _char_mat(self): return np.eye(self.neqs) - self.coefs.sum(0) def acf(self, nlags=None): """Compute theoretical autocovariance function Returns ------- acf : ndarray (p x k x k) """ return var_acf(self.coefs, self.sigma_u, nlags=nlags) def acorr(self, nlags=None): """Compute theoretical autocorrelation function Returns ------- acorr : ndarray (p x k x k) """ return util.acf_to_acorr(self.acf(nlags=nlags)) def plot_acorr(self, nlags=10, linewidth=8): "Plot theoretical autocorrelation function" plotting.plot_full_acorr(self.acorr(nlags=nlags), linewidth=linewidth) def forecast(self, y, steps): """Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters ---------- y : ndarray (p x k) steps : int Returns ------- forecasts : ndarray (steps x neqs) Notes ----- Lutkepohl pp 37-38 """ return forecast(y, self.coefs, self.intercept, steps) def mse(self, steps): """ Compute theoretical forecast error variance matrices Parameters ---------- steps : int Number of steps ahead Notes ----- .. math:: \mathrm{MSE}(h) = \sum_{i=0}^{h-1} \Phi \Sigma_u \Phi^T Returns ------- forc_covs : ndarray (steps x neqs x neqs) """ ma_coefs = self.ma_rep(steps) k = len(self.sigma_u) forc_covs = np.zeros((steps, k, k)) prior = np.zeros((k, k)) for h in xrange(steps): # Sigma(h) = Sigma(h-1) + Phi Sig_u Phi' phi = ma_coefs[h] var = chain_dot(phi, self.sigma_u, phi.T) forc_covs[h] = prior = prior + var return forc_covs forecast_cov = mse def _forecast_vars(self, steps): covs = self.forecast_cov(steps) # Take diagonal for each cov inds = np.arange(self.neqs) return covs[:, inds, inds] def forecast_interval(self, y, steps, alpha=0.05): """Construct forecast interval estimates assuming the y are Gaussian Parameters ---------- Notes ----- Lutkepohl pp. 39-40 Returns ------- (lower, mid, upper) : (ndarray, ndarray, ndarray) """ assert(0 < alpha < 1) q = util.norm_signif_level(alpha) point_forecast = self.forecast(y, steps) sigma = np.sqrt(self._forecast_vars(steps)) forc_lower = point_forecast - q * sigma forc_upper = point_forecast + q * sigma return point_forecast, forc_lower, forc_upper #------------------------------------------------------------------------------- # VARResults class class VARResults(VARProcess): """Estimate VAR(p) process with fixed number of lags Parameters ---------- endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {'nc', 'c', 'ct'} names : array-like List of names of the endogenous variables in order of appearance in `endog`. dates Returns ------- **Attributes** aic bic bse coefs : ndarray (p x K x K) Estimated A_i matrices, A_i = coefs[i-1] cov_params dates detomega df_model : int df_resid : int endog endog_lagged fittedvalues fpe intercept info_criteria k_ar : int k_trend : int llf model names neqs : int Number of variables (equations) nobs : int n_totobs : int params k_ar : int Order of VAR process params : ndarray (Kp + 1) x K A_i matrices and intercept in stacked form [int A_1 ... A_p] pvalues names : list variables names resid roots : array The roots of the VAR process are the solution to (I - coefs[0]*z - coefs[1]*z**2 ... - coefs[p-1]*z**k_ar) = 0. Note that the inverse roots are returned, and stability requires that the roots lie outside the unit circle. sigma_u : ndarray (K x K) Estimate of white noise process variance Var[u_t] sigma_u_mle stderr trenorder tvalues y : ys_lagged """ _model_type = 'VAR' def __init__(self, endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None): self.model = model self.y = self.endog = endog #keep alias for now self.ys_lagged = self.endog_lagged = endog_lagged #keep alias for now self.dates = dates self.n_totobs, neqs = self.y.shape self.nobs = self.n_totobs - lag_order k_trend = util.get_trendorder(trend) if k_trend > 0: # make this the polynomial trend order trendorder = k_trend - 1 else: trendorder = None self.k_trend = k_trend self.trendorder = trendorder self.exog_names = util.make_lag_names(names, lag_order, k_trend) self.params = params # Initialize VARProcess parent class # construct coefficient matrices # Each matrix needs to be transposed reshaped = self.params[self.k_trend:] reshaped = reshaped.reshape((lag_order, neqs, neqs)) # Need to transpose each coefficient matrix intercept = self.params[0] coefs = reshaped.swapaxes(1, 2).copy() super(VARResults, self).__init__(coefs, intercept, sigma_u, names=names) @cache_readonly def coef_names(self): """Coefficient names (deprecated) """ from warnings import warn warn("coef_names is deprecated and will be removed in 0.6.0." "Use exog_names", FutureWarning) return self.exog_names def plot(self): """Plot input time series """ plotting.plot_mts(self.y, names=self.names, index=self.dates) @property def df_model(self): """Number of estimated parameters, including the intercept / trends """ return self.neqs * self.k_ar + self.k_trend @property def df_resid(self): "Number of observations minus number of estimated parameters" return self.nobs - self.df_model @cache_readonly def fittedvalues(self): """The predicted insample values of the response variables of the model. """ return np.dot(self.ys_lagged, self.params) @cache_readonly def resid(self): """Residuals of response variable resulting from estimated coefficients """ return self.y[self.k_ar:] - self.fittedvalues def sample_acov(self, nlags=1): return _compute_acov(self.y[self.k_ar:], nlags=nlags) def sample_acorr(self, nlags=1): acovs = self.sample_acov(nlags=nlags) return _acovs_to_acorrs(acovs) def plot_sample_acorr(self, nlags=10, linewidth=8): "Plot theoretical autocorrelation function" plotting.plot_full_acorr(self.sample_acorr(nlags=nlags), linewidth=linewidth) def resid_acov(self, nlags=1): """ Compute centered sample autocovariance (including lag 0) Parameters ---------- nlags : int Returns ------- """ return _compute_acov(self.resid, nlags=nlags) def resid_acorr(self, nlags=1): """ Compute sample autocorrelation (including lag 0) Parameters ---------- nlags : int Returns ------- """ acovs = self.resid_acov(nlags=nlags) return _acovs_to_acorrs(acovs) @cache_readonly def resid_corr(self): "Centered residual correlation matrix" return self.resid_acorr(0)[0] @cache_readonly def sigma_u_mle(self): """(Biased) maximum likelihood estimate of noise process covariance """ return self.sigma_u * self.df_resid / self.nobs @cache_readonly def cov_params(self): """Estimated variance-covariance of model coefficients Notes ----- Covariance of vec(B), where B is the matrix [intercept, A_1, ..., A_p] (K x (Kp + 1)) Adjusted to be an unbiased estimator Ref: Lutkepohl p.74-75 """ z = self.ys_lagged return np.kron(L.inv(np.dot(z.T, z)), self.sigma_u) def cov_ybar(self): r"""Asymptotically consistent estimate of covariance of the sample mean .. math:: \sqrt(T) (\bar{y} - \mu) \rightarrow {\cal N}(0, \Sigma_{\bar{y}})\\ \Sigma_{\bar{y}} = B \Sigma_u B^\prime, \text{where } B = (I_K - A_1 - \cdots - A_p)^{-1} Notes ----- Lutkepohl Proposition 3.3 """ Ainv = L.inv(np.eye(self.neqs) - self.coefs.sum(0)) return chain_dot(Ainv, self.sigma_u, Ainv.T) #------------------------------------------------------------ # Estimation-related things @cache_readonly def _zz(self): # Z'Z return np.dot(self.ys_lagged.T, self.ys_lagged) @property def _cov_alpha(self): """ Estimated covariance matrix of model coefficients ex intercept """ # drop intercept return self.cov_params[self.neqs:, self.neqs:] @cache_readonly def _cov_sigma(self): """ Estimated covariance matrix of vech(sigma_u) """ D_K = tsa.duplication_matrix(self.neqs) D_Kinv = npl.pinv(D_K) sigxsig = np.kron(self.sigma_u, self.sigma_u) return 2 * chain_dot(D_Kinv, sigxsig, D_Kinv.T) @cache_readonly def llf(self): "Compute VAR(p) loglikelihood" return var_loglike(self.resid, self.sigma_u_mle, self.nobs) @cache_readonly def stderr(self): """Standard errors of coefficients, reshaped to match in size """ stderr = np.sqrt(np.diag(self.cov_params)) return stderr.reshape((self.df_model, self.neqs), order='C') bse = stderr # statsmodels interface? @cache_readonly def tvalues(self): """Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance. """ return self.params / self.stderr @cache_readonly def pvalues(self): """Two-sided p-values for model coefficients from Student t-distribution """ return stats.t.sf(np.abs(self.tvalues), self.df_resid)*2 def plot_forecast(self, steps, alpha=0.05, plot_stderr=True): """ Plot forecast """ mid, lower, upper = self.forecast_interval(self.y[-self.k_ar:], steps, alpha=alpha) plotting.plot_var_forc(self.y, mid, lower, upper, names=self.names, plot_stderr=plot_stderr) # Forecast error covariance functions def forecast_cov(self, steps=1): r"""Compute forecast covariance matrices for desired number of steps Parameters ---------- steps : int Notes ----- .. math:: \Sigma_{\hat y}(h) = \Sigma_y(h) + \Omega(h) / T Ref: Lutkepohl pp. 96-97 Returns ------- covs : ndarray (steps x k x k) """ mse = self.mse(steps) omegas = self._omega_forc_cov(steps) return mse + omegas / self.nobs #Monte Carlo irf standard errors def irf_errband_mc(self, orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False): """ Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters ---------- orth: bool, default False Compute orthoganalized impulse response error bands repl: int number of Monte Carlo replications to perform T: int, default 10 number of impulse response periods signif: float (0 < signif <1) Significance level for error bars, defaults to 95% CI seed: int np.random.seed for replications burn: int number of initial observations to discard for simulation cum: bool, default False produce cumulative irf error bands Notes ----- Lutkepohl (2005) Appendix D Returns ------- Tuple of lower and upper arrays of ma_rep monte carlo standard errors """ neqs = self.neqs mean = self.mean() k_ar = self.k_ar coefs = self.coefs sigma_u = self.sigma_u intercept = self.intercept df_model = self.df_model nobs = self.nobs ma_coll = np.zeros((repl, T+1, neqs, neqs)) if (orth == True and cum == True): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ orth_ma_rep(maxn=T).cumsum(axis=0) elif (orth == True and cum == False): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ orth_ma_rep(maxn=T) elif (orth == False and cum == True): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ ma_rep(maxn=T).cumsum(axis=0) elif (orth == False and cum == False): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ ma_rep(maxn=T) for i in range(repl): #discard first hundred to eliminate correct for starting bias sim = util.varsim(coefs, intercept, sigma_u, steps=nobs+burn) sim = sim[burn:] ma_coll[i,:,:,:] = fill_coll(sim) ma_sort = np.sort(ma_coll, axis=0) #sort to get quantiles index = round(signif/2*repl)-1,round((1-signif/2)*repl)-1 lower = ma_sort[index[0],:, :, :] upper = ma_sort[index[1],:, :, :] return lower, upper def irf_resim(self, orth=False, repl=1000, T=10, seed=None, burn=100, cum=False): """ Simulates impulse response function, returning an array of simulations. Used for Sims-Zha error band calculation. Parameters ---------- orth: bool, default False Compute orthoganalized impulse response error bands repl: int number of Monte Carlo replications to perform T: int, default 10 number of impulse response periods signif: float (0 < signif <1) Significance level for error bars, defaults to 95% CI seed: int np.random.seed for replications burn: int number of initial observations to discard for simulation cum: bool, default False produce cumulative irf error bands Notes ----- Sims, Christoper A., and Tao Zha. 1999. "Error Bands for Impulse Response." Econometrica 67: 1113-1155. Returns ------- Array of simulated impulse response functions """ neqs = self.neqs mean = self.mean() k_ar = self.k_ar coefs = self.coefs sigma_u = self.sigma_u intercept = self.intercept df_model = self.df_model nobs = self.nobs if seed is not None: np.random.seed(seed=seed) ma_coll = np.zeros((repl, T+1, neqs, neqs)) if (orth == True and cum == True): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ orth_ma_rep(maxn=T).cumsum(axis=0) elif (orth == True and cum == False): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ orth_ma_rep(maxn=T) elif (orth == False and cum == True): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ ma_rep(maxn=T).cumsum(axis=0) elif (orth == False and cum == False): fill_coll = lambda sim : VAR(sim).fit(maxlags=k_ar).\ ma_rep(maxn=T) for i in range(repl): #discard first hundred to eliminate correct for starting bias sim = util.varsim(coefs, intercept, sigma_u, steps=nobs+burn) sim = sim[burn:] ma_coll[i,:,:,:] = fill_coll(sim) return ma_coll def _omega_forc_cov(self, steps): # Approximate MSE matrix \Omega(h) as defined in Lut p97 G = self._zz Ginv = L.inv(G) # memoize powers of B for speedup # TODO: see if can memoize better B = self._bmat_forc_cov() _B = {} def bpow(i): if i not in _B: _B[i] = np.linalg.matrix_power(B, i) return _B[i] phis = self.ma_rep(steps) sig_u = self.sigma_u omegas = np.zeros((steps, self.neqs, self.neqs)) for h in range(1, steps + 1): if h == 1: omegas[h-1] = self.df_model * self.sigma_u continue om = omegas[h-1] for i in range(h): for j in range(h): Bi = bpow(h - 1 - i) Bj = bpow(h - 1 - j) mult = np.trace(chain_dot(Bi.T, Ginv, Bj, G)) om += mult * chain_dot(phis[i], sig_u, phis[j].T) omegas[h-1] = om return omegas def _bmat_forc_cov(self): # B as defined on p. 96 of Lut upper = np.zeros((1, self.df_model)) upper[0,0] = 1 lower_dim = self.neqs * (self.k_ar - 1) I = np.eye(lower_dim) lower = np.column_stack((np.zeros((lower_dim, 1)), I, np.zeros((lower_dim, self.neqs)))) return np.vstack((upper, self.params.T, lower)) def summary(self): """Compute console output summary of estimates Returns ------- summary : VARSummary """ return VARSummary(self) def irf(self, periods=10, var_decomp=None, var_order=None): """Analyze impulse responses to shocks in system Parameters ---------- periods : int var_decomp : ndarray (k x k), lower triangular Must satisfy Omega = P P', where P is the passed matrix. Defaults to Cholesky decomposition of Omega var_order : sequence Alternate variable order for Cholesky decomposition Returns ------- irf : IRAnalysis """ if var_order is not None: raise NotImplementedError('alternate variable order not implemented' ' (yet)') return IRAnalysis(self, P=var_decomp, periods=periods) def fevd(self, periods=10, var_decomp=None): """ Compute forecast error variance decomposition ("fevd") Returns ------- fevd : FEVD instance """ return FEVD(self, P=var_decomp, periods=periods) def reorder(self, order): """Reorder variables for structural specification """ if len(order) != len(self.params[0,:]): raise ValueError("Reorder specification length should match number of endogenous variables") #This convert order to list of integers if given as strings if type(order[0]) is str: order_new = [] for i, nam in enumerate(order): order_new.append(self.names.index(order[i])) order = order_new return _reordered(self, order) #------------------------------------------------------------------------------- # VAR Diagnostics: Granger-causality, whiteness of residuals, normality, etc. def test_causality(self, equation, variables, kind='f', signif=0.05, verbose=True): """Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables Parameters ---------- equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {'f', 'wald'} Perform F-test or Wald (chi-sq) test signif : float, default 5% Significance level for computing critical values for test, defaulting to standard 0.95 level Notes ----- Null hypothesis is that there is no Granger-causality for the indicated variables. The degrees of freedom in the F-test are based on the number of variables in the VAR system, that is, degrees of freedom are equal to the number of equations in the VAR times degree of freedom of a single equation. Returns ------- results : dict """ if isinstance(variables, (basestring, int, np.integer)): variables = [variables] k, p = self.neqs, self.k_ar # number of restrictions N = len(variables) * self.k_ar # Make restriction matrix C = np.zeros((N, k ** 2 * p + k), dtype=float) eq_index = self.get_eq_index(equation) vinds = mat([self.get_eq_index(v) for v in variables]) # remember, vec is column order! offsets = np.concatenate([k + k ** 2 * j + k * vinds + eq_index for j in range(p)]) C[np.arange(N), offsets] = 1 # Lutkepohl 3.6.5 Cb = np.dot(C, vec(self.params.T)) middle = L.inv(chain_dot(C, self.cov_params, C.T)) # wald statistic lam_wald = statistic = chain_dot(Cb, middle, Cb) if kind.lower() == 'wald': df = N dist = stats.chi2(df) elif kind.lower() == 'f': statistic = lam_wald / N df = (N, k * self.df_resid) dist = stats.f(*df) else: raise Exception('kind %s not recognized' % kind) pvalue = dist.sf(statistic) crit_value = dist.ppf(1 - signif) conclusion = 'fail to reject' if statistic < crit_value else 'reject' results = { 'statistic' : statistic, 'crit_value' : crit_value, 'pvalue' : pvalue, 'df' : df, 'conclusion' : conclusion, 'signif' : signif } if verbose: summ = output.causality_summary(results, variables, equation, kind) print summ return results def test_whiteness(self, nlags=10, plot=True, linewidth=8): """ Test white noise assumption. Sample (Y) autocorrelations are compared with the standard :math:`2 / \sqrt(T)` bounds. Parameters ---------- plot : boolean, default True Plot autocorrelations with 2 / sqrt(T) bounds """ acorrs = self.sample_acorr(nlags) bound = 2 / np.sqrt(self.nobs) # TODO: this probably needs some UI work if (np.abs(acorrs) > bound).any(): print ('FAIL: Some autocorrelations exceed %.4f bound. ' 'See plot' % bound) else: print 'PASS: No autocorrelations exceed %.4f bound' % bound if plot: fig = plotting.plot_full_acorr(acorrs[1:], xlabel=np.arange(1, nlags+1), err_bound=bound, linewidth=linewidth) fig.suptitle(r"ACF plots with $2 / \sqrt{T}$ bounds " "for testing whiteness assumption") def test_normality(self, signif=0.05, verbose=True): """ Test assumption of normal-distributed errors using Jarque-Bera-style omnibus Chi^2 test Parameters ---------- signif : float Test significance threshold Notes ----- H0 (null) : data are generated by a Gaussian-distributed process """ Pinv = npl.inv(self._chol_sigma_u) w = np.array([np.dot(Pinv, u) for u in self.resid]) b1 = (w ** 3).sum(0) / self.nobs lam_skew = self.nobs * np.dot(b1, b1) / 6 b2 = (w ** 4).sum(0) / self.nobs - 3 lam_kurt = self.nobs * np.dot(b2, b2) / 24 lam_omni = lam_skew + lam_kurt omni_dist = stats.chi2(self.neqs * 2) omni_pvalue = omni_dist.sf(lam_omni) crit_omni = omni_dist.ppf(1 - signif) conclusion = 'fail to reject' if lam_omni < crit_omni else 'reject' results = { 'statistic' : lam_omni, 'crit_value' : crit_omni, 'pvalue' : omni_pvalue, 'df' : self.neqs * 2, 'conclusion' : conclusion, 'signif' : signif } if verbose: summ = output.normality_summary(results) print summ return results @cache_readonly def detomega(self): r""" Return determinant of white noise covariance with degrees of freedom correction: .. math:: \hat \Omega = \frac{T}{T - Kp - 1} \hat \Omega_{\mathrm{MLE}} """ return L.det(self.sigma_u) @cache_readonly def info_criteria(self): "information criteria for lagorder selection" nobs = self.nobs neqs = self.neqs lag_order = self.k_ar free_params = lag_order * neqs ** 2 + neqs * self.k_trend ld = util.get_logdet(self.sigma_u_mle) # See Lutkepohl pp. 146-150 aic = ld + (2. / nobs) * free_params bic = ld + (np.log(nobs) / nobs) * free_params hqic = ld + (2. * np.log(np.log(nobs)) / nobs) * free_params fpe = ((nobs + self.df_model) / self.df_resid) ** neqs * np.exp(ld) return { 'aic' : aic, 'bic' : bic, 'hqic' : hqic, 'fpe' : fpe } @property def aic(self): "Akaike information criterion" return self.info_criteria['aic'] @property def fpe(self): """Final Prediction Error (FPE) Lutkepohl p. 147, see info_criteria """ return self.info_criteria['fpe'] @property def hqic(self): "Hannan-Quinn criterion" return self.info_criteria['hqic'] @property def bic(self): "Bayesian a.k.a. Schwarz info criterion" return self.info_criteria['bic'] @cache_readonly def roots(self): neqs = self.neqs k_ar = self.k_ar p = neqs * k_ar arr = np.zeros((p,p)) arr[:neqs,:] = np.column_stack(self.coefs) arr[neqs:,:-neqs] = np.eye(p-neqs) roots = np.linalg.eig(arr)[0]**-1 idx = np.argsort(np.abs(roots))[::-1] # sort by reverse modulus return roots[idx] class VARResultsWrapper(wrap.ResultsWrapper): _attrs = {'bse' : 'columns_eq', 'cov_params' : 'cov', 'params' : 'columns_eq', 'pvalues' : 'columns_eq', 'tvalues' : 'columns_eq', 'sigma_u' : 'cov_eq', 'sigma_u_mle' : 'cov_eq', 'stderr' : 'columns_eq'} _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs, _attrs) _methods = {} _wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods, _methods) _wrap_methods.pop('cov_params') # not yet a method in VARResults wrap.populate_wrapper(VARResultsWrapper, VARResults) class FEVD(object): """ Compute and plot Forecast error variance decomposition and asymptotic standard errors """ def __init__(self, model, P=None, periods=None): self.periods = periods self.model = model self.neqs = model.neqs self.names = model.names self.irfobj = model.irf(var_decomp=P, periods=periods) self.orth_irfs = self.irfobj.orth_irfs # cumulative impulse responses irfs = (self.orth_irfs[:periods] ** 2).cumsum(axis=0) rng = range(self.neqs) mse = self.model.mse(periods)[:, rng, rng] # lag x equation x component fevd = np.empty_like(irfs) for i in range(periods): fevd[i] = (irfs[i].T / mse[i]).T # switch to equation x lag x component self.decomp = fevd.swapaxes(0, 1) def summary(self): buf = StringIO() rng = range(self.periods) for i in range(self.neqs): ppm = output.pprint_matrix(self.decomp[i], rng, self.names) print >> buf, 'FEVD for %s' % self.names[i] print >> buf, ppm print buf.getvalue() def cov(self): """Compute asymptotic standard errors Returns ------- """ raise NotImplementedError def plot(self, periods=None, figsize=(10,10), **plot_kwds): """Plot graphical display of FEVD Parameters ---------- periods : int, default None Defaults to number originally specified. Can be at most that number """ import matplotlib.pyplot as plt k = self.neqs periods = periods or self.periods fig, axes = plt.subplots(nrows=k, figsize=figsize) fig.suptitle('Forecast error variance decomposition (FEVD)') colors = [str(c) for c in np.arange(k, dtype=float) / k] ticks = np.arange(periods) limits = self.decomp.cumsum(2) for i in range(k): ax = axes[i] this_limits = limits[i].T handles = [] for j in range(k): lower = this_limits[j - 1] if j > 0 else 0 upper = this_limits[j] handle = ax.bar(ticks, upper - lower, bottom=lower, color=colors[j], label=self.names[j], **plot_kwds) handles.append(handle) ax.set_title(self.names[i]) # just use the last axis to get handles for plotting handles, labels = ax.get_legend_handles_labels() fig.legend(handles, labels, loc='upper right') plotting.adjust_subplots(right=0.85) #------------------------------------------------------------------------------- def _compute_acov(x, nlags=1): x = x - x.mean(0) result = [] for lag in xrange(nlags + 1): if lag > 0: r = np.dot(x[lag:].T, x[:-lag]) else: r = np.dot(x.T, x) result.append(r) return np.array(result) / len(x) def _acovs_to_acorrs(acovs): sd = np.sqrt(np.diag(acovs[0])) return acovs / np.outer(sd, sd) if __name__ == '__main__': import statsmodels.api as sm from statsmodels.tsa.vector_ar.util import parse_lutkepohl_data import statsmodels.tools.data as data_util np.set_printoptions(linewidth=140, precision=5) sdata, dates = parse_lutkepohl_data('data/%s.dat' % 'e1') names = sdata.dtype.names data = data_util.struct_to_ndarray(sdata) adj_data = np.diff(np.log(data), axis=0) # est = VAR(adj_data, p=2, dates=dates[1:], names=names) model = VAR(adj_data[:-16], dates=dates[1:-16], names=names) # model = VAR(adj_data[:-16], dates=dates[1:-16], names=names) est = model.fit(maxlags=2) irf = est.irf() y = est.y[-2:] """ # irf.plot_irf() # i = 2; j = 1 # cv = irf.cum_effect_cov(orth=True) # print np.sqrt(cv[:, j * 3 + i, j * 3 + i]) / 1e-2 # data = np.genfromtxt('Canada.csv', delimiter=',', names=True) # data = data.view((float, 4)) """ ''' mdata = sm.datasets.macrodata.load().data mdata2 = mdata[['realgdp','realcons','realinv']] names = mdata2.dtype.names data = mdata2.view((float,3)) data = np.diff(np.log(data), axis=0) import pandas as pn df = pn.DataFrame.fromRecords(mdata) df = np.log(df.reindex(columns=names)) df = (df - df.shift(1)).dropna() model = VAR(df) est = model.fit(maxlags=2) irf = est.irf() ''' statsmodels-0.5.0+git13-g8e07d34/tools/000077500000000000000000000000001224417117700173125ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/000077500000000000000000000000001224417117700211725ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/DESCRIPTION000066400000000000000000000005141224417117700227000ustar00rootroot00000000000000Package: R2nparray Version: 0.1 Date: 2011-08-23 Title: R to Numpy Arrays Author: Skipper Seabold Maintainer: Skipper Seabold Description: Writes R matrices, vectors, and scalars to a file as numpy arrays License: BSD URL: http://www.github.com/statsmodels/statsmodels Repository: github statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/R/000077500000000000000000000000001224417117700213735ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/R/R2nparray.R000066400000000000000000000017471224417117700234070ustar00rootroot00000000000000# This function respects the digits option mkarray <- function(X, name) { cat(name); cat(" = np.array(["); cat(X, sep=","); cat("])") if (is.matrix(X)) { i <- as.character(nrow(X)) j <- as.character(ncol(X)) cat(".reshape("); cat(i); cat(","); cat(j); cat(", order='F')") } cat("\n\n") } R2nparray <- function(..., fname, append=FALSE) { if (!is.list(...)) { to_write <- list(...) } else { to_write <- (...) } sink(file=fname, append=append) # assumes appended file already imports numpy if (file.info(fname)$size == 0) { cat("import numpy as np\n\n") } for (i in c(1:length(to_write))) { name <- names(to_write)[i] X <- to_write[[i]] name <- gsub("\\.", "_", name) # make name pythonic mkarray(X=X, name=name) } sink() } #fname <- "RResults.py" #R2array(A=A,B=B,params=params,fname=fname) #also takes a lsit #R2array(list(A=A,B=B,params=params),fname=fname) statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/R/R2nparray.Rd000066400000000000000000000035461224417117700235520ustar00rootroot00000000000000\name{R2nparray} \alias{R2nparray} %- Also NEED an '\alias' for EACH other topic documented here. \title{ %% ~~function to do ... ~~ } \description{ %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ R2nparray(..., fname, append = FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{\dots}{ %% ~~Describe \code{\dots} here~~ } \item{fname}{ %% ~~Describe \code{fname} here~~ } \item{append}{ %% ~~Describe \code{append} here~~ } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ %% ~put references to the literature/web site here ~ } \author{ %% ~~who you are~~ } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ ##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function(..., fname, append=FALSE) { if (!is.list(...)) { to_write <- list(...) } else { to_write <- (...) } sink(file=fname, append=append) # assumes appended file already imports numpy if (file.info(fname)$size == 0) { cat("import numpy as np\n\n") } for (i in c(1:length(to_write))) { name <- names(to_write)[i] X <- to_write[[i]] name <- gsub("\\.", "_", name) # make name pythonic mkarray(X=X, name=name) } sink() } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ ~kwd1 } \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/README000066400000000000000000000001141224417117700220460ustar00rootroot00000000000000To install run in the parent of this directory R CMD INSTALL R2nparray statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/man/000077500000000000000000000000001224417117700217455ustar00rootroot00000000000000statsmodels-0.5.0+git13-g8e07d34/tools/R2nparray/man/R2nparray.Rd000066400000000000000000000033171224417117700241200ustar00rootroot00000000000000\name{R2nparray} \alias{R2nparray} %- Also NEED an '\alias' for EACH other topic documented here. \title{Write R data to file as Numpy Arrays %% ~~function to do ... ~~ } \description{Takes a matrix, scalar, or vector in R and dumps it to a file as a NumPy array. %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ R2nparray(..., fname, append = FALSE) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{\dots}{Scalars, vectors, or matrices. Can be part of a list or not. %% ~~Describe \code{\dots} here~~ } \item{fname}{Filename to write the arrays to. %% ~~Describe \code{fname} here~~ } \item{append}{Whether or not to append to an existing file or overwrite it. %% ~~Describe \code{append} here~~ } } \details{The names of the arugments are the names of the arrays in the file. %% ~~ If necessary, more details than the description above ~~ } \value{ %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ %% ~put references to the literature/web site here ~ } \author{Skipper Seabold %% ~~who you are~~ } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ mdat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol=3) scalar <- 127.5 vec <- c(1,2,3) R2nparray(mdat=mdat, myscalar=scalar, vec=vec, fname="./numpyarrays") # or use a list lst = list(mdat=mdat, myscalar=scalar, vec=vec) R2nparray(lst, fname="./numpyarrays") } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ IO } statsmodels-0.5.0+git13-g8e07d34/tools/README.txt000066400000000000000000000027611224417117700210160ustar00rootroot00000000000000This directory is only of interest to developers. It contains files needed to build the docs automatically and to do code maintenance. The below is just a reminder of the commands to update things. It may not necessarily reflect the current workflow. How to update the main entry page --------------------------------- If you want to update the main docs page from the statsmodels-website then run the following (with your credentials) make clean make html rsync -avPr -e ssh build/html/* jseabold,statsmodels@web.sourceforge.net:htdocs/ How to update the nightly builds -------------------------------- Note that this is done automatically with the update_web.py script except for new releases. They should be done by hand if there are any backported changes. Important: Make sure you have the version installed for which you are building the documentation if done by hand. To update devel branch (from the master branch) Make sure you have master installed cd to docs directory make clean make html rsync -avPr -e ssh build/html/* jseabold,statsmodels@web.sourceforge.net:htdocs/devel How to add a new directory --------------------------- If you want to create a new directory on the sourceforge site. This can be done on linux as follows sftp jseabold,statsmodels@web.sourceforge.net mkdir 0.2 bye Then make sure you have the release installed, cd to the docs directory and run make clean make html rsync -avPr -e ssh build/html/* jseabold,statsmodels@web.sourceforge.net:htdocs/0.2 statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist32-py26.bat000066400000000000000000000006141224417117700241070ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x86 /release set DISTUTILS_USE_SDK=1 C:\Python26_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst rem bdist_msi uses StrictVersion so can't be used for release candidates rem C:\Python26_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist32-py27.bat000066400000000000000000000006141224417117700241100ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x86 /release set DISTUTILS_USE_SDK=1 C:\Python27_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst rem bdist_msi uses StrictVersion so can't be used for release candidates rem C:\Python27_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist32-py32.bat000066400000000000000000000005031224417117700241010ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x86 /release set DISTUTILS_USE_SDK=1 rem C:\Python27_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi C:\Python32_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist64-py26.bat000066400000000000000000000004671224417117700241220ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x64 /release set DISTUTILS_USE_SDK=1 rem C:\Python26\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi C:\Python26\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist64-py27.bat000066400000000000000000000004671224417117700241230ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x64 /release set DISTUTILS_USE_SDK=1 rem C:\Python27\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi C:\Python27\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst statsmodels-0.5.0+git13-g8e07d34/tools/build_win_bdist64-py32.bat000066400000000000000000000004751224417117700241160ustar00rootroot00000000000000setlocal EnableDelayedExpansion CALL "C:\Program Files\Microsoft SDKs\Windows\v7.0\Bin\SetEnv.cmd" /x64 /release set DISTUTILS_USE_SDK=1 rem C:\Python27\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi C:\Python32_64bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst statsmodels-0.5.0+git13-g8e07d34/tools/check_dirs.py000066400000000000000000000012201224417117700217550ustar00rootroot00000000000000'''helper script to check which directories are not on python path all test folders should have and ``__init__.py`` ''' import os root = '../statsmodels' print('base, dnames, len(fnames), n_py') for base, dnames, fnames in os.walk(root): if not '__init__.py' in fnames: #I have some empty directories when I switch git branches if (len(dnames) + len(fnames)) != 0: n_py = len([f for f in fnames if f[-3:] == '.py']) if n_py > 0: print(base, dnames, len(fnames), n_py) if '__pycache__' in dnames: dnames.remove('__pycache__') if 'src' in dnames: dnames.remove('src') statsmodels-0.5.0+git13-g8e07d34/tools/code_maintenance.py000066400000000000000000000044031224417117700231410ustar00rootroot00000000000000""" Code maintenance script modified from PyMC """ #!/usr/bin/env python import sys import os # This is a function, not a test case, because it has to be run from inside # the source tree to work well. mod_strs = ['IPython', 'pylab', 'matplotlib', 'scipy','Pdb'] dep_files = {} for mod_str in mod_strs: dep_files[mod_str] = [] def remove_whitespace(fname): # Remove trailing whitespace fd = open(fname,mode='U') # open in universal newline mode lines = [] for line in fd.readlines(): lines.append( line.rstrip() ) fd.close() fd = open(fname,mode='w') fd.seek(0) for line in lines: fd.write(line+'\n') fd.close() # print 'Removed whitespace from %s'%fname def find_whitespace(fname): fd = open(fname, mode='U') for line in fd.readlines(): #print repr(line) if ' \n' in line: print fname break # print print_only = True # ==================== # = Strip whitespace = # ==================== for dirname, dirs, files in os.walk('.'): if dirname[1:].find('.')==-1: # print dirname for fname in files: if fname[-2:] in ['c', 'f'] or fname[-3:]=='.py' or fname[-4:] in ['.pyx', '.txt', '.tex', '.sty', '.cls'] or fname.find('.')==-1: # print fname if print_only: find_whitespace(dirname + '/' + fname) else: remove_whitespace(dirname + '/' + fname) """ # ========================== # = Check for dependencies = # ========================== for dirname, dirs, files in os.walk('pymc'): for fname in files: if fname[-3:]=='.py' or fname[-4:]=='.pyx': if dirname.find('sandbox')==-1 and fname != 'test_dependencies.py'\ and dirname.find('examples')==-1: for mod_str in mod_strs: if file(dirname+'/'+fname).read().find(mod_str)>=0: dep_files[mod_str].append(dirname+'/'+fname) print 'Instances of optional dependencies found are:' for mod_str in mod_strs: print '\t'+mod_str+':' for fname in dep_files[mod_str]: print '\t\t'+fname if len(dep_files['Pdb'])>0: raise ValueError, 'Looks like Pdb was not commented out in '+', '.join(dep_files[mod_str]) """ statsmodels-0.5.0+git13-g8e07d34/tools/dataset_rst.py000077500000000000000000000026161224417117700222110ustar00rootroot00000000000000#! /usr/bin/env python """ Run this script to convert dataset documentation to ReST files. Relies on the meta-information from the datasets of the currently installed version. Ie., it imports the datasets package to scrape the meta-information. """ import statsmodels.api as sm import os from os.path import join import inspect from string import Template datasets = dict(inspect.getmembers(sm.datasets, inspect.ismodule)) datasets.pop('utils') datasets.pop('nile') #TODO: fix docstring in nile doc_template = Template(u"""$TITLE $title_ Description ----------- $DESCRIPTION Notes ----- $NOTES Source ------ $SOURCE Copyright --------- $COPYRIGHT """) for dataset in datasets: write_pth = join('../docs/source/datasets/generated', dataset+'.rst') data_mod = datasets[dataset] with open(os.path.realpath(write_pth), 'w') as rst_file: title = getattr(data_mod,'TITLE') descr = getattr(data_mod, 'DESCRLONG') copyr = getattr(data_mod, 'COPYRIGHT') notes = getattr(data_mod, 'NOTE') source = getattr(data_mod, 'SOURCE') write_file = doc_template.substitute(TITLE=title, title_='='*len(title), DESCRIPTION=descr, NOTES=notes, SOURCE=source, COPYRIGHT=copyr) rst_file.write(write_file) statsmodels-0.5.0+git13-g8e07d34/tools/estmat2nparray.ado000066400000000000000000000100251224417117700227510ustar00rootroot00000000000000* * Save estimation results and matrices to a Python module * * Based on mat2nparray by Skipper * Changes by Josef * * changes * ------- * write also column and row names of matrices to py module * replace missing values by np.nan in matrices * make namelist optional * add estimation results from e(), e(scalars) and e(macros), not the matrices in e * make estimation result optional * add aliases for params_table * don't split col or row names if only 1 - changed my mind: always list * Issues * ------ * row and colum names if only a single row or column - list or string capture program drop estmat2nparray program define estmat2nparray version 11.0 syntax [namelist(min=1)], SAVing(str) [ Format(str) APPend REPlace NOEst] if "`format'"=="" local format "%16.0g" local saving: subinstr local saving "." ".", count(local ext) if !`ext' local saving "`saving'.py" tempname myfile file open `myfile' using "`saving'", write text `append' `replace' file write `myfile' "import numpy as np" _n _n /* get results from e()*/ if "`noest'" == "" { file write `myfile' "est = dict(" _n local escalars : e(scalars) foreach ii in `escalars'{ file write `myfile' " " "`ii'" " = " "`e(`ii')'" "," _n } local emacros : e(macros) foreach ii in `emacros' { file write `myfile' " " "`ii'" " = " `"""' "`e(`ii')'" `"""' "," _n } file write `myfile' " )" _n _n } /* end write e()*/ foreach mat of local namelist { mkarray `mat' `myfile' `format' } file write `myfile' "class Bunch(dict):" _n file write `myfile' " def __init__(self, **kw):" _n file write `myfile' " dict.__init__(self, kw)" _n file write `myfile' " self.__dict__ = self" _n _n if "`noest'" == "" { file write `myfile' " for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):" _n file write `myfile' " self[att] = self.params_table[:,i]" _n _n } file write `myfile' "" _n file write `myfile' "results = Bunch(" _n foreach mat of local namelist { file write `myfile' " `mat'=`mat', " _n file write `myfile' " `mat'_colnames=`mat'_colnames, " _n file write `myfile' " `mat'_rownames=`mat'_rownames, " _n } if "`noest'" == "" { file write `myfile' " **est" _n } file write `myfile' " )" _n _n file close `myfile' end capture program drop mkarray program define mkarray args mat myfile fmt local nrows = rowsof(`mat') local ncols = colsof(`mat') local i 1 local j 1 file write `myfile' "`mat' = np.array([" local justifyn = length("`mat' = np.array([") forvalues i=1/`nrows' { forvalues j = 1/`ncols' { if `i' > 1 | `j' > 1 { // then we need to indent forvalues k=1/`justifyn' { file write `myfile' " " } } if `i' < `nrows' | `j' < `ncols' { if mi(`mat'[`i',`j']) { file write `myfile' "np.nan" ", " _n } else { file write `myfile' `fmt' (`mat'[`i',`j']) ", " _n } } else { if mi(`mat'[`i',`j']) { file write `myfile' "np.nan" } else { file write `myfile' `fmt' (`mat'[`i',`j']) } } } } if `nrows' == 1 | `ncols' == 1 { file write `myfile' "])" _n _n } else { file write `myfile' "]).reshape(`nrows',`ncols')" _n _n } capture drop colnms local colnms: coln `mat' capture file write `myfile' "`mat'_colnames = '" "`colnms'" "'" * always split -> return list for single column * set > 1 to avoid list for single column if `ncols' > 0 { file write `myfile' ".split()" _n _n } else { file write `myfile' _n _n } capture drop rownms local rownms: rown `mat' capture file write `myfile' "`mat'_rownames = '" "`rownms'" "'" * always split -> return list for single row if `nrows' > 0 { file write `myfile' ".split()" _n _n } else { file write `myfile' _n _n } end statsmodels-0.5.0+git13-g8e07d34/tools/ex_sur.R000066400000000000000000000030341224417117700207420ustar00rootroot00000000000000data <- read.csv('E:\\path_to_repo\\statsmodels\\datasets\\grunfeld\\grunfeld.csv') data <- data[data$firm %in% c('General Motors','Chrysler','General Electric','Westinghouse','US Steel'),] attach(data) library('plm') library('systemfit') panel <- plm.data(data,c('firm','year')) formula <- invest ~ value + capital SUR <- systemfit(formula,method='SUR',data=panel) f <- fitted(SUR) ff <- c(f[,'Chrysler'],f[,'General.Electric'],f[,'General.Motors'],f[,'US.Steel'],f[,'Westinghouse']) # save results to python module #load functions, (windows path separators) source("E:\\path_to_repo\\tools\\R2nparray\\R\\R2nparray.R") source("E:\\path_to_repo\\tools\\topy.R") #translation table for names (could be dict in python) translate = list(coefficients="params", coefCov="cov_params", residCovEst="resid_cov_est", residCov="resid_cov", df_residual="df_resid", df_residual_sys="df_resid_sys", #nCoef="k_vars", #not sure about this fitted_values="fittedvalues" ) fname = "tmp_sur_0.py" append = FALSE #TRUE #redirect output to file sink(file=fname, append=append) write_header() cat("\nsur = Bunch()\n") cat_items(SUR, prefix="sur.", blacklist=c("eq", "control"), trans=translate) equations = SUR[["eq"]] for (ii in c(1:length(equations))) { equ_name = paste("sur.equ", ii, sep="") cat("\n\n", equ_name, sep=""); cat(" = Bunch()\n") cat_items(equations[[ii]], prefix=paste(equ_name, ".", sep=""), trans=translate) } sink() statsmodels-0.5.0+git13-g8e07d34/tools/examples_rst.py000077500000000000000000000134061224417117700224010ustar00rootroot00000000000000#! /usr/bin/env python import os import sys import re import subprocess import pickle from StringIO import StringIO # 3rd party from matplotlib import pyplot as plt # Ours import hash_funcs #---------------------------------------------------- # Globals #---------------------------------------------------- # these files do not get made into .rst files because of # some problems, they may need a simple cleaning up exclude_list = ['run_all.py', # these need to be cleaned up 'example_ols_tftest.py', 'example_glsar.py', 'example_ols_table.py', #not finished yet 'example_arima.py', 'try_wls.py'] file_path = os.path.dirname(__file__) docs_rst_dir = os.path.realpath(os.path.join(file_path, '../docs/source/examples/generated/')) example_dir = os.path.realpath(os.path.join(file_path, '../examples/')) def check_script(filename): """ Run all the files in filelist from run_all. Add any with problems to exclude_list and return it. """ file_to_run = "python -c\"import warnings; " file_to_run += "warnings.simplefilter('ignore'); " file_to_run += "from matplotlib import use; use('Agg'); " file_to_run += "execfile(r'%s')\"" % os.path.join(example_dir, filename) proc = subprocess.Popen(file_to_run, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) #NOTE: use communicate to wait for process termination stdout, stderr = proc.communicate() result = proc.returncode if result != 0: # raised an error msg = "Not generating reST from %s. An error occurred.\n" % filename msg += stderr print msg return False return True def parse_docstring(block): """ Strips the docstring from a string representation of the file. Returns the docstring and block without it """ ds = "\"{3}|'{3}" try: start = re.search(ds, block).end() end = re.search(ds, block[start:]).start() except: #TODO: make more informative raise IOError("File %s does not have a docstring?") docstring = block[start:start+end] block = block[start+end+3:] return docstring.strip(), block def parse_file(block): """ Block is a raw string file. """ docstring, block = parse_docstring(block) # just get the first line from the docstring docstring = docstring.split('\n')[0] or docstring.split('\n')[1] outfile = [docstring,'='*len(docstring),''] block = block.split('\n') # iterate through the rest of block, anything in comments is stripped of # # anything else is fair game to go in an ipython directive code_snippet = False for line in block: #if not len(line): # continue # preserve blank lines if line.startswith('#') and not (line.startswith('#%') or line.startswith('#@')): # on some ReST text if code_snippet: # were on a code snippet outfile.append('') code_snippet = False line = line.strip() # try to remove lines like # hello -> #hello line = re.sub("(?<=#) (?!\s)", "", line) # make sure commented out things have a space line = re.sub("#\.\.(?!\s)", "#.. ", line) line = re.sub("^#+", "", line) # strip multiple hashes outfile.append(line) else: if not code_snippet: # new code block outfile.append('\n.. ipython:: python\n') code_snippet = True # handle decorators and magic functions if line.startswith('#%') or line.startswith('#@'): line = line[1:] outfile.append(' '+line.strip('\n')) return '\n'.join(outfile) def write_file(outfile, rst_file_pth): """ Write outfile to rst_file_pth """ print "Writing ", os.path.basename(rst_file_pth) write_file = open(rst_file_pth, 'w') write_file.writelines(outfile) write_file.close() def restify(example_file, filehash, fname): """ Takes a whole file ie., the result of file.read(), its md5 hash, and the filename Parse the file Write the new .rst Update the hash_dict """ write_filename = os.path.join(docs_rst_dir, fname[:-2] + 'rst') try: rst_file = parse_file(example_file) except IOError as err: raise IOError(err.message % fname) write_file(rst_file, write_filename) if filehash is not None: hash_funcs.update_hash_dict(filehash, fname) if __name__ == "__main__": sys.path.insert(0, example_dir) from run_all import filelist sys.path.remove(example_dir) if not os.path.exists(docs_rst_dir): os.makedirs(docs_rst_dir) if len(sys.argv) > 1: # given a file,files to process, no help flag yet for example_file in sys.argv[1:]: whole_file = open(example_file, 'r').read() restify(whole_file, None, example_file) else: # process the whole directory for root, dirnames, filenames in os.walk(example_dir): if 'notebooks' in root: continue for example in filenames: example_file = os.path.join(root, example) whole_file = open(example_file, 'r').read() to_write, filehash = hash_funcs.check_hash(whole_file, example) if not to_write: print "Hash has not changed for file %s" % example continue elif (not example.endswith('.py') or example in exclude_list or not check_script(example_file)): continue restify(whole_file, filehash, example) statsmodels-0.5.0+git13-g8e07d34/tools/fold_toc.py000077500000000000000000000026651224417117700214710ustar00rootroot00000000000000#!/usr/bin/env python import sys import re # Read doc to string filename = sys.argv[1] try: static_path = sys.argv[2] except: static_path = '_static' doc = open(filename).read() # Add mktree to head pre = '' post = ''' ''' post = re.sub('static_path', static_path, post) doc = re.sub(pre, post, doc) # TOC class pre = '''

      ''' post = '''
      Expand all. Collapse all.
        ''' toc_n = doc.count('toctree-wrapper') for i in range(toc_n): post_n = re.sub('#', str(i), post) doc = re.sub(pre, post_n, doc, count=1) ## TOC entries pre = '
      • ' post = '
      • ' doc = re.sub(pre, post, doc) # TOC entries 2nd level pre = '
      • ' post = '
      • ' doc = re.sub(pre, post, doc) # TOC entries 3rd level pre = '
      • ' post = '
      • ' doc = re.sub(pre, post, doc) # TOC entries 4th level pre = '
      • ' post = '
      • ' doc = re.sub(pre, post, doc) # Write to file f = open(filename, 'w') f.write(doc) f.close() statsmodels-0.5.0+git13-g8e07d34/tools/generate_formula_api.py000077500000000000000000000052241224417117700240420ustar00rootroot00000000000000#! /usr/bin/python """ This will generate an API file for formula. in dir/statsmodels/formula/api.py It first builds statsmodels in place, then generates the file. It's to be run by developers to add files to the formula API without having to maintain this by hand. usage generate_formula_api /home/skipper/statsmodels/statsmodels/ """ import sys import os def iter_subclasses(cls, _seen=None, template_classes=[]): """ Generator to iterate over all the subclasses of Model. Based on http://code.activestate.com/recipes/576949-find-all-subclasses-of-a-given-class/ Yields class """ if not isinstance(cls, type): raise TypeError('itersubclasses must be called with ' 'new-style classes, not %.100r' % cls) if _seen is None: _seen = set() try: subs = cls.__subclasses__() except TypeError: # fails only when cls is type subs = cls.__subclasses__(cls) for sub in subs: if sub not in _seen and sub.__name__ not in template_classes: _seen.add(sub) # we don't want to yield the templates, but we do want to # recurse on them yield sub for sub in iter_subclasses(sub, _seen, template_classes): yield sub def write_formula_api(directory): template_classes = ['DiscreteModel', 'BinaryModel', 'MultinomialModel', 'OrderedModel', 'CountModel', 'LikelihoodModel', 'GenericLikelihoodModel', 'TimeSeriesModel', # this class should really be deleted 'ARIMAProcess', # these need some more work, so don't expose them 'ARIMA', 'VAR', 'SVAR', 'AR', 'NBin', 'NbReg', 'ARMA', ] fout = open(os.path.join(directory, 'statsmodels', 'formula', 'api.py'), 'w') for model in iter_subclasses(Model, template_classes=template_classes): print "Generating API for %s" % model.__name__ fout.write( 'from '+model.__module__+' import ' + model.__name__ + '\n' ) fout.write( model.__name__.lower() +' = '+ model.__name__ +'.from_formula\n' ) fout.close() if __name__ == "__main__": import statsmodels.api as sm print "Generating formula API for statsmodels version %s" % sm.version.full_version directory = sys.argv[1] cur_dir = os.path.dirname(__file__) os.chdir(directory) # it needs to be installed to walk the whole subclass chain? from statsmodels.base.model import Model write_formula_api(directory) statsmodels-0.5.0+git13-g8e07d34/tools/gh_api.py000066400000000000000000000173701224417117700211230ustar00rootroot00000000000000"""Functions for Github API requests. Copied from IPython 732be29 https://github.com/ipython/ipython/blob/master/tools/gh_api.py""" from __future__ import print_function try: input = raw_input except NameError: pass import os import re import sys import requests import getpass import json try: import requests_cache except ImportError: print("no cache") else: requests_cache.install_cache("gh_api") # Keyring stores passwords by a 'username', but we're not storing a username and # password fake_username = 'statsmodels_tools' class Obj(dict): """Dictionary with attribute access to names.""" def __getattr__(self, name): try: return self[name] except KeyError: raise AttributeError(name) def __setattr__(self, name, val): self[name] = val token = None def get_auth_token(): global token if token is not None: return token import keyring token = keyring.get_password('github', fake_username) if token is not None: return token print("Please enter your github username and password. These are not " "stored, only used to get an oAuth token. You can revoke this at " "any time on Github.") user = input("Username: ") pw = getpass.getpass("Password: ") auth_request = { "scopes": [ "public_repo", "gist" ], "note": "Statsmodels tools", "note_url": "https://github.com/statsmodels/statsmodels/tree/master/tools", } response = requests.post('https://api.github.com/authorizations', auth=(user, pw), data=json.dumps(auth_request)) response.raise_for_status() token = json.loads(response.text)['token'] keyring.set_password('github', fake_username, token) return token def make_auth_header(): return {'Authorization': 'token ' + get_auth_token()} def post_issue_comment(project, num, body): url = 'https://api.github.com/repos/{project}/issues/{num}/comments'.format(project=project, num=num) payload = json.dumps({'body': body}) requests.post(url, data=payload, headers=make_auth_header()) def post_gist(content, description='', filename='file', auth=False): """Post some text to a Gist, and return the URL.""" post_data = json.dumps({ "description": description, "public": True, "files": { filename: { "content": content } } }).encode('utf-8') headers = make_auth_header() if auth else {} response = requests.post("https://api.github.com/gists", data=post_data, headers=headers) response.raise_for_status() response_data = json.loads(response.text) return response_data['html_url'] def get_pull_request(project, num, auth=False): """get pull request info by number """ url = "https://api.github.com/repos/{project}/pulls/{num}".format(project=project, num=num) if auth: header = make_auth_header() else: header = None response = requests.get(url, headers=header) response.raise_for_status() return json.loads(response.text, object_hook=Obj) element_pat = re.compile(r'<(.+?)>') rel_pat = re.compile(r'rel=[\'"](\w+)[\'"]') def get_paged_request(url, headers=None): """get a full list, handling APIv3's paging""" results = [] while True: print("fetching %s" % url, file=sys.stderr) response = requests.get(url, headers=headers) response.raise_for_status() results.extend(response.json()) if 'next' in response.links: url = response.links['next']['url'] else: break return results def get_pulls_list(project, state="closed", auth=False): """get pull request list """ url = "https://api.github.com/repos/{project}/pulls?state={state}&per_page=100".format(project=project, state=state) if auth: headers = make_auth_header() else: headers = None pages = get_paged_request(url, headers=headers) return pages def get_issues_list(project, state="closed", auth=False): """get pull request list """ url = "https://api.github.com/repos/{project}/pulls?state={state}&per_page=100".format(project=project, state=state) if auth: headers = make_auth_header() else: headers = None pages = get_paged_request(url, headers=headers) return pages # encode_multipart_formdata is from urllib3.filepost # The only change is to iter_fields, to enforce S3's required key ordering def iter_fields(fields): fields = fields.copy() for key in ('key', 'acl', 'Filename', 'success_action_status', 'AWSAccessKeyId', 'Policy', 'Signature', 'Content-Type', 'file'): yield (key, fields.pop(key)) for (k,v) in fields.items(): yield k,v def encode_multipart_formdata(fields, boundary=None): """ Encode a dictionary of ``fields`` using the multipart/form-data mime format. :param fields: Dictionary of fields or list of (key, value) field tuples. The key is treated as the field name, and the value as the body of the form-data bytes. If the value is a tuple of two elements, then the first element is treated as the filename of the form-data section. Field names and filenames must be unicode. :param boundary: If not specified, then a random boundary will be generated using :func:`mimetools.choose_boundary`. """ # copy requests imports in here: from io import BytesIO from requests.packages.urllib3.filepost import ( choose_boundary, six, writer, b, get_content_type ) body = BytesIO() if boundary is None: boundary = choose_boundary() for fieldname, value in iter_fields(fields): body.write(b('--%s\r\n' % (boundary))) if isinstance(value, tuple): filename, data = value writer(body).write('Content-Disposition: form-data; name="%s"; ' 'filename="%s"\r\n' % (fieldname, filename)) body.write(b('Content-Type: %s\r\n\r\n' % (get_content_type(filename)))) else: data = value writer(body).write('Content-Disposition: form-data; name="%s"\r\n' % (fieldname)) body.write(b'Content-Type: text/plain\r\n\r\n') if isinstance(data, int): data = str(data) # Backwards compatibility if isinstance(data, six.text_type): writer(body).write(data) else: body.write(data) body.write(b'\r\n') body.write(b('--%s--\r\n' % (boundary))) content_type = b('multipart/form-data; boundary=%s' % boundary) return body.getvalue(), content_type def post_download(project, filename, name=None, description=""): """Upload a file to the GitHub downloads area""" if name is None: name = os.path.basename(filename) with open(filename, 'rb') as f: filedata = f.read() url = "https://api.github.com/repos/{project}/downloads".format(project=project) payload = json.dumps(dict(name=name, size=len(filedata), description=description)) response = requests.post(url, data=payload, headers=make_auth_header()) response.raise_for_status() reply = json.loads(response.content) s3_url = reply['s3_url'] fields = dict( key=reply['path'], acl=reply['acl'], success_action_status=201, Filename=reply['name'], AWSAccessKeyId=reply['accesskeyid'], Policy=reply['policy'], Signature=reply['signature'], file=(reply['name'], filedata), ) fields['Content-Type'] = reply['mime_type'] data, content_type = encode_multipart_formdata(fields) s3r = requests.post(s3_url, data=data, headers={'Content-Type': content_type}) return s3r statsmodels-0.5.0+git13-g8e07d34/tools/github_stats.py000066400000000000000000000160431224417117700223700ustar00rootroot00000000000000#!/usr/bin/env python """Simple tools to query github.com and gather stats about issues. Copied from IPython 732be29 https://github.com/ipython/ipython/blob/master/tools/github_stats.py """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from __future__ import print_function import json import re import sys from datetime import datetime, timedelta from subprocess import check_output from gh_api import get_paged_request, make_auth_header, get_pull_request #----------------------------------------------------------------------------- # Globals #----------------------------------------------------------------------------- ISO8601 = "%Y-%m-%dT%H:%M:%SZ" PER_PAGE = 100 #----------------------------------------------------------------------------- # Functions #----------------------------------------------------------------------------- def get_issues(project="statsmodels/statsmodels", state="closed", pulls=False): """Get a list of the issues from the Github API.""" which = 'pulls' if pulls else 'issues' url = "https://api.github.com/repos/%s/%s?state=%s&per_page=%i" % (project, which, state, PER_PAGE) return get_paged_request(url, headers=make_auth_header()) def round_hour(dt): return dt.replace(minute=0,second=0,microsecond=0) def _parse_datetime(s): """Parse dates in the format returned by the Github API.""" if s: return datetime.strptime(s, ISO8601) else: return datetime.fromtimestamp(0) def issues2dict(issues): """Convert a list of issues to a dict, keyed by issue number.""" idict = {} for i in issues: idict[i['number']] = i return idict def is_pull_request(issue): """Return True if the given issue is a pull request.""" return bool(issue.get('pull_request', {}).get('html_url', None)) def split_pulls(all_issues, project="statsmodels/statsmodels"): """split a list of closed issues into non-PR Issues and Pull Requests""" pulls = [] issues = [] for i in all_issues: if is_pull_request(i): pull = get_pull_request(project, i['number'], auth=True) pulls.append(pull) else: issues.append(i) return issues, pulls def issues_closed_since(period=timedelta(days=365), project="statsmodels/statsmodels", pulls=False): """Get all issues closed since a particular point in time. period can either be a datetime object, or a timedelta object. In the latter case, it is used as a time before the present. """ which = 'pulls' if pulls else 'issues' if isinstance(period, timedelta): since = round_hour(datetime.utcnow() - period) else: since = period url = "https://api.github.com/repos/%s/%s?state=closed&sort=updated&since=%s&per_page=%i" % (project, which, since.strftime(ISO8601), PER_PAGE) allclosed = get_paged_request(url, headers=make_auth_header()) filtered = [ i for i in allclosed if _parse_datetime(i['closed_at']) > since ] if pulls: filtered = [ i for i in filtered if _parse_datetime(i['merged_at']) > since ] # filter out PRs not against master (backports) filtered = [ i for i in filtered if i['base']['ref'] == 'master' ] else: filtered = [ i for i in filtered if not is_pull_request(i) ] return filtered def sorted_by_field(issues, field='closed_at', reverse=False): """Return a list of issues sorted by closing date date.""" return sorted(issues, key = lambda i:i[field], reverse=reverse) def report(issues, show_urls=False): """Summary report about a list of issues, printing number and title. """ # titles may have unicode in them, so we must encode everything below if show_urls: for i in issues: role = 'ghpull' if 'merged_at' in i else 'ghissue' print('* :%s:`%d`: %s' % (role, i['number'], i['title'].encode('utf-8'))) else: for i in issues: print('* %d: %s' % (i['number'], i['title'].encode('utf-8'))) #----------------------------------------------------------------------------- # Main script #----------------------------------------------------------------------------- if __name__ == "__main__": # deal with unicode import codecs sys.stdout = codecs.getwriter('utf8')(sys.stdout) # Whether to add reST urls for all issues in printout. show_urls = True # By default, search one month back tag = None if len(sys.argv) > 1: try: days = int(sys.argv[1]) except: tag = sys.argv[1] else: tag = check_output(['git', 'describe', '--abbrev=0']).strip() if tag: cmd = ['git', 'log', '-1', '--format=%ai', tag] tagday, tz = check_output(cmd).strip().rsplit(' ', 1) since = datetime.strptime(tagday, "%Y-%m-%d %H:%M:%S") h = int(tz[1:3]) m = int(tz[3:]) td = timedelta(hours=h, minutes=m) if tz[0] == '-': since += td else: since -= td else: since = datetime.utcnow() - timedelta(days=days) since = round_hour(since) print("fetching GitHub stats since %s (tag: %s)" % (since, tag), file=sys.stderr) # turn off to play interactively without redownloading, use %run -i if 1: issues = issues_closed_since(since, pulls=False) pulls = issues_closed_since(since, pulls=True) # For regular reports, it's nice to show them in reverse chronological order issues = sorted_by_field(issues, reverse=True) pulls = sorted_by_field(pulls, reverse=True) n_issues, n_pulls = map(len, (issues, pulls)) n_total = n_issues + n_pulls # Print summary report we can directly include into release notes. print() since_day = since.strftime("%Y/%m/%d") today = datetime.today().strftime("%Y/%m/%d") print("GitHub stats for %s - %s (tag: %s)" % (since_day, today, tag)) print() print("These lists are automatically generated, and may be incomplete or contain duplicates.") print() if tag: # print git info, in addition to GitHub info: since_tag = tag+'..' cmd = ['git', 'log', '--oneline', since_tag] ncommits = len(check_output(cmd).splitlines()) author_cmd = ['git', 'log', "--format='* %aN'", since_tag] all_authors = check_output(author_cmd).decode('utf-8', 'replace').splitlines() unique_authors = sorted(set(all_authors), key=lambda s: s.lower()) print("The following %i authors contributed %i commits." % (len(unique_authors), ncommits)) print() print('\n'.join(unique_authors)) print() print() print("We closed a total of %d issues, %d pull requests and %d regular issues;\n" "this is the full list (generated with the script \n" ":file:`tools/github_stats.py`):" % (n_total, n_pulls, n_issues)) print() print('Pull Requests (%d):\n' % n_pulls) report(pulls, show_urls) print() print('Issues (%d):\n' % n_issues) report(issues, show_urls) statsmodels-0.5.0+git13-g8e07d34/tools/hash_funcs.py000066400000000000000000000023751224417117700220140ustar00rootroot00000000000000""" A collection of utilities to see if new ReST files need to be automatically generated from certain files in the project (examples, datasets). """ import os import pickle file_path = os.path.dirname(__file__) def get_hash(f): """ Gets hexadmecimal md5 hash of a string """ import hashlib m = hashlib.md5() m.update(f) return m.hexdigest() def update_hash_dict(filehash, filename): """ Opens the pickled hash dictionary, adds an entry, and dumps it back. """ try: with open(file_path+'/hash_dict.pickle','r') as f: hash_dict = pickle.load(f) except IOError as err: hash_dict = {} hash_dict.update({filename : filehash}) with open(os.path.join(file_path,'hash_dict.pickle'),'w') as f: pickle.dump(hash_dict, f) def check_hash(rawfile, filename): """ Returns True if hash does not match the previous one. """ try: with open(file_path+'/hash_dict.pickle','r') as f: hash_dict = pickle.load(f) except IOError as err: hash_dict = {} try: checkhash = hash_dict[filename] except: checkhash = None filehash = get_hash(rawfile) if filehash == checkhash: return False, None return True, filehash statsmodels-0.5.0+git13-g8e07d34/tools/km_cox1.do000066400000000000000000000041521224417117700212010ustar00rootroot00000000000000/* Run Survival models and save results Author: Josef Perktold based on example from Stata help */ clear *basic Kaplan-Meier capture use "E:\Josef\statawork\stan3.dta", clear if _rc != 0 webuse stan3 capture save "E:\Josef\statawork\stan3.dta" capture erase surf.dta sts list, saving("surf") use "E:\Josef\statawork\surf.dta", clear outsheet using "surv_km.csv", comma replace * Kaplan-Meier with by use "E:\Josef\statawork\stan3.dta", clear capture erase surf2.dta sts list, by(posttran) saving("surf2") use "E:\Josef\statawork\surf2.dta", clear outsheet using "surv_km2.csv", comma replace * Cox Proportional Hazard use "E:\Josef\statawork\stan3.dta", clear stcox age posttran , estimate norobust ereturn list, matlist e(V) matlist e(p) * the next doesn't work * predict predictall, hr xb stdp basesurv basechazard basehc mgale csnell deviance ldisplace lmax effects /* generate in python: >>> for i in 'hr xb stdp basesurv basechazard basehc mgale csnell deviance ldisplace lmax effects'.split(): print 'predict %s, %s' % (i,i) */ predict hr, hr predict xb, xb predict stdp, stdp predict basesurv, basesurv predict basechazard, basechazard predict basehc, basehc predict mgale, mgale predict csnell, csnell predict deviance, deviance predict ldisplace, ldisplace predict lmax, lmax *capture predict effects, effects outsheet hr xb stdp basesurv basechazard basehc mgale csnell deviance ldisplace lmax using "surv_coxph.csv", comma replace * replay stcox matrix cov = e(V) svmat cov, names(cov) * get the colnames and rownames capture drop nacol narow matrix params_table = r(table)' gen nacol = "`: colnames params_table'" gen narow = "`: rownames params_table'" di nacol di narow *2nd version capture drop rown2 local colnms: coln params_table gen str rown2 = "`colnms'" di rown2 svmat params_table, names(params_table) estmat2nparray params_table cov, saving("results_coxphrobust.py") format("%16.0g") append * other options, no matrices or no est results *estmat2nparray params_table cov, saving("results_coxphrobust_2.py") format("%16.0g") append noest *estmat2nparray , saving("results_coxphrobust_2.py") format("%16.0g") append statsmodels-0.5.0+git13-g8e07d34/tools/matplotlibrc000066400000000000000000000462051224417117700217400ustar00rootroot00000000000000### MATPLOTLIBRC FORMAT # This is a sample matplotlib configuration file - you can find a copy # of it on your system in # site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it # there, please note that it will be overridden in your next install. # If you want to keep a permanent local copy that will not be # over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux # like systems) and C:\Documents and Settings\yourname\.matplotlib # (win32 systems). # # This file is best viewed in a editor which supports python mode # syntax highlighting. Blank lines, or lines starting with a comment # symbol, are ignored, as are trailing comments. Other lines must # have the format # key : val # optional comment # # Colors: for the color values below, you can either use - a # matplotlib color string, such as r, k, or b - an rgb tuple, such as # (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar # grayscale intensity such as 0.75 - a legal html color name, eg red, # blue, darkslategray #### CONFIGURATION BEGINS HERE # the default backend; one of GTK GTKAgg GTKCairo CocoaAgg FltkAgg # MacOSX QtAgg Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG Template # You can also deploy your own backend outside of matplotlib by # referring to the module name (which must be in the PYTHONPATH) as # 'module://my_backend' backend : Qt4Agg # if you are runing pyplot inside a GUI and your backend choice # conflicts, we will automatically try and find a compatible one for # you if backend_fallback is True #backend_fallback: True interactive : True toolbar : toolbar2 # None | classic | toolbar2 #timezone : UTC # a pytz timezone string, eg US/Central or Europe/Paris # Where your matplotlib data lives if you installed to a non-default # location. This is where the matplotlib fonts, bitmaps, etc reside #datapath : /home/jdhunter/mpldata ### LINES # See http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.lines for more # information on line properties. lines.linewidth : 1.0 # line width in points #lines.linestyle : - # solid line #lines.color : blue #lines.marker : None # the default marker #lines.markeredgewidth : 0.5 # the line width around the marker symbol #lines.markersize : 6 # markersize, in points #lines.dash_joinstyle : miter # miter|round|bevel #lines.dash_capstyle : butt # butt|round|projecting #lines.solid_joinstyle : miter # miter|round|bevel #lines.solid_capstyle : projecting # butt|round|projecting lines.antialiased : True # render lines in antialised (no jaggies) ### PATCHES # Patches are graphical objects that fill 2D space, like polygons or # circles. See # http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.patches # information on patch properties patch.linewidth : 0.5 # edge width in points patch.facecolor : 348ABD patch.edgecolor : eeeeee patch.antialiased : True # render patches in antialised (no jaggies) ### FONT # # font properties used by text.Text. See # http://matplotlib.sourceforge.net/api/font_manager_api.html for more # information on font properties. The 6 font properties used for font # matching are given below with their default values. # # The font.family property has five values: 'serif' (e.g. Times), # 'sans-serif' (e.g. Helvetica), 'cursive' (e.g. Zapf-Chancery), # 'fantasy' (e.g. Western), and 'monospace' (e.g. Courier). Each of # these font families has a default list of font names in decreasing # order of priority associated with them. # # The font.style property has three values: normal (or roman), italic # or oblique. The oblique style will be used for italic, if it is not # present. # # The font.variant property has two values: normal or small-caps. For # TrueType fonts, which are scalable fonts, small-caps is equivalent # to using a font size of 'smaller', or about 83% of the current font # size. # # The font.weight property has effectively 13 values: normal, bold, # bolder, lighter, 100, 200, 300, ..., 900. Normal is the same as # 400, and bold is 700. bolder and lighter are relative values with # respect to the current weight. # # The font.stretch property has 11 values: ultra-condensed, # extra-condensed, condensed, semi-condensed, normal, semi-expanded, # expanded, extra-expanded, ultra-expanded, wider, and narrower. This # property is not currently implemented. # # The font.size property is the default font size for text, given in pts. # 12pt is the standard value. # font.family : monospace #font.style : normal #font.variant : normal #font.weight : medium #font.stretch : normal # note that font.size controls default text sizes. To configure # special text sizes tick labels, axes, labels, title, etc, see the rc # settings for axes and ticks. Special text sizes can be defined # relative to font.size, using the following values: xx-small, x-small, # small, medium, large, x-large, xx-large, larger, or smaller #font.size : 12.0 #font.serif : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif #font.sans-serif : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif #font.cursive : Apple Chancery, Textile, Zapf Chancery, Sand, cursive #font.fantasy : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy font.monospace : Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace ### TEXT # text properties used by text.Text. See # http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.text for more # information on text properties #text.color : black ### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex #text.usetex : False # use latex for all text handling. The following fonts # are supported through the usual rc parameter settings: # new century schoolbook, bookman, times, palatino, # zapf chancery, charter, serif, sans-serif, helvetica, # avant garde, courier, monospace, computer modern roman, # computer modern sans serif, computer modern typewriter # If another font is desired which can loaded using the # LaTeX \usepackage command, please inquire at the # matplotlib mailing list #text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling # unicode strings. #text.latex.preamble : # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO. # preamble is a comma separated list of LaTeX statements # that are included in the LaTeX document preamble. # An example: # text.latex.preamble : \usepackage{bm},\usepackage{euler} # The following packages are always loaded with usetex, so # beware of package collisions: color, geometry, graphicx, # type1cm, textcomp. Adobe Postscript (PSSNFS) font packages # may also be loaded, depending on your font settings #text.dvipnghack : None # some versions of dvipng don't handle alpha # channel properly. Use True to correct # and flush ~/.matplotlib/tex.cache # before testing and False to force # correction off. None will try and # guess based on your dvipng version #text.hinting : True # If True, text will be hinted, otherwise not. This only # affects the Agg backend. # The following settings allow you to select the fonts in math mode. # They map from a TeX font name to a fontconfig font pattern. # These settings are only used if mathtext.fontset is 'custom'. # Note that this "custom" mode is unsupported and may go away in the # future. #mathtext.cal : cursive #mathtext.rm : serif #mathtext.tt : monospace #mathtext.it : serif:italic #mathtext.bf : serif:bold #mathtext.sf : sans #mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix', # 'stixsans' or 'custom' #mathtext.fallback_to_cm : True # When True, use symbols from the Computer Modern # fonts when a symbol can not be found in one of # the custom math fonts. #mathtext.default : it # The default font to use for math. # Can be any of the LaTeX font names, including # the special name "regular" for the same font # used in regular text. ### AXES # default face and edge color, default tick sizes, # default fontsizes for ticklabels, and so on. See # http://matplotlib.sourceforge.net/api/axes_api.html#module-matplotlib.axes #axes.hold : True # whether to clear the axes by default on axes.facecolor : eeeeee # axes background color axes.edgecolor : bcbcbc # axes edge color axes.linewidth : 1.0 # edge linewidth axes.grid : True # display grid or not axes.titlesize : large # fontsize of the axes title axes.labelsize : large # fontsize of the x any y labels axes.labelcolor : 555555 axes.axisbelow : True # whether axis gridlines and ticks are below # the axes elements (lines, text, etc) #axes.formatter.limits : -7, 7 # use scientific notation if log10 # of the axis range is smaller than the # first or larger than the second #axes.unicode_minus : True # use unicode for the minus symbol # rather than hypen. See http://en.wikipedia.org/wiki/Plus_sign#Plus_sign axes.color_cycle : 348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33 # E24A33 : orange # 7A68A6 : purple # 348ABD : blue # 188487 : turquoise # A60628 : red # CF4457 : pink # 467821 : green #polaraxes.grid : True # display grid on polar axes #axes3d.grid : True # display grid on 3d axes ### TICKS # see http://matplotlib.sourceforge.net/api/axis_api.html#matplotlib.axis.Tick xtick.major.size : 0 # major tick size in points xtick.minor.size : 0 # minor tick size in points xtick.major.pad : 6 # distance to major tick label in points xtick.minor.pad : 6 # distance to the minor tick label in points xtick.color : 555555 # color of the tick labels #xtick.labelsize : medium # fontsize of the tick labels xtick.direction : in # direction: in or out ytick.major.size : 0 # major tick size in points ytick.minor.size : 0 # minor tick size in points ytick.major.pad : 6 # distance to major tick label in points ytick.minor.pad : 6 # distance to the minor tick label in points ytick.color : 555555 # color of the tick labels #ytick.labelsize : medium # fontsize of the tick labels ytick.direction : in # direction: in or out ### GRIDS #grid.color : black # grid color #grid.linestyle : : # dotted #grid.linewidth : 0.5 # in points ### Legend legend.fancybox : True # if True, use a rounded box for the # legend, else a rectangle #legend.isaxes : True #legend.numpoints : 2 # the number of points in the legend line #legend.fontsize : large #legend.pad : 0.0 # deprecated; the fractional whitespace inside the legend border #legend.borderpad : 0.5 # border whitspace in fontsize units #legend.markerscale : 1.0 # the relative size of legend markers vs. original # the following dimensions are in axes coords #legend.labelsep : 0.010 # the vertical space between the legend entries #legend.handlelen : 0.05 # the length of the legend lines #legend.handletextsep : 0.02 # the space between the legend line and legend text #legend.axespad : 0.02 # the border between the axes and legend edge #legend.shadow : False ### FIGURE # See http://matplotlib.sourceforge.net/api/figure_api.html#matplotlib.figure.Figure figure.figsize : 8, 6 # figure size in inches #figure.dpi : 80 # figure dots per inch figure.facecolor : 0.85 # figure facecolor; 0.75 is scalar gray figure.edgecolor : 0.5 # figure edgecolor # The figure subplot parameters. All dimensions are fraction of the # figure width or height #figure.subplot.left : 0.125 # the left side of the subplots of the figure #figure.subplot.right : 0.9 # the right side of the subplots of the figure #figure.subplot.bottom : 0.1 # the bottom of the subplots of the figure #figure.subplot.top : 0.9 # the top of the subplots of the figure #figure.subplot.wspace : 0.2 # the amount of width reserved for blank space between subplots figure.subplot.hspace : 0.5 # the amount of height reserved for white space between subplots ### IMAGES #image.aspect : equal # equal | auto | a number #image.interpolation : bilinear # see help(imshow) for options #image.cmap : jet # gray | jet etc... #image.lut : 256 # the size of the colormap lookup table #image.origin : upper # lower | upper #image.resample : False ### CONTOUR PLOTS #contour.negative_linestyle : dashed # dashed | solid ### Agg rendering ### Warning: experimental, 2008/10/10 #agg.path.chunksize : 0 # 0 to disable; values in the range # 10000 to 100000 can improve speed slightly # and prevent an Agg rendering failure # when plotting very large data sets, # especially if they are very gappy. # It may cause minor artifacts, though. # A value of 20000 is probably a good # starting point. ### SAVING FIGURES #path.simplify : True # When True, simplify paths by removing "invisible" # points to reduce file size and increase rendering # speed #path.simplify_threshold : 0.1 # The threshold of similarity below which # vertices will be removed in the simplification # process #path.snap : True # When True, rectilinear axis-aligned paths will be snapped to # the nearest pixel when certain criteria are met. When False, # paths will never be snapped. # the default savefig params can be different from the display params # Eg, you may want a higher resolution, or to make the figure # background white #savefig.dpi : 100 # figure dots per inch #savefig.facecolor : white # figure facecolor when saving #savefig.edgecolor : white # figure edgecolor when saving #savefig.extension : auto # what extension to use for savefig('foo'), or 'auto' #cairo.format : png # png, ps, pdf, svg # tk backend params #tk.window_focus : False # Maintain shell focus for TkAgg # ps backend params #ps.papersize : letter # auto, letter, legal, ledger, A0-A10, B0-B10 #ps.useafm : False # use of afm fonts, results in small files #ps.usedistiller : False # can be: None, ghostscript or xpdf # Experimental: may produce smaller files. # xpdf intended for production of publication quality files, # but requires ghostscript, xpdf and ps2eps #ps.distiller.res : 6000 # dpi #ps.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType) # pdf backend params #pdf.compression : 6 # integer from 0 to 9 # 0 disables compression (good for debugging) #pdf.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType) # svg backend params #svg.image_inline : True # write raster image data directly into the svg file #svg.image_noscale : False # suppress scaling of raster data embedded in SVG #svg.embed_char_paths : True # embed character outlines in the SVG file # docstring params #docstring.hardcopy = False # set this when you want to generate hardcopy docstring # Set the verbose flags. This controls how much information # matplotlib gives you at runtime and where it goes. The verbosity # levels are: silent, helpful, debug, debug-annoying. Any level is # inclusive of all the levels below it. If your setting is "debug", # you'll get all the debug and helpful messages. When submitting # problems to the mailing-list, please set verbose to "helpful" or "debug" # and paste the output into your report. # # The "fileo" gives the destination for any calls to verbose.report. # These objects can a filename, or a filehandle like sys.stdout. # # You can override the rc default verbosity from the command line by # giving the flags --verbose-LEVEL where LEVEL is one of the legal # levels, eg --verbose-helpful. # # You can access the verbose instance in your code # from matplotlib import verbose. #verbose.level : silent # one of silent, helpful, debug, debug-annoying #verbose.fileo : sys.stdout # a log filename, sys.stdout or sys.stderr # Event keys to interact with figures/plots via keyboard. # Customize these settings according to your needs. # Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '') keymap.fullscreen : f # toggling keymap.home : h, r, home # home or reset mnemonic keymap.back : left, c, backspace # forward / backward keys to enable keymap.forward : right, v # left handed quick navigation keymap.pan : p # pan mnemonic keymap.zoom : o # zoom mnemonic keymap.save : s # saving current figure keymap.grid : g # switching on/off a grid in current axes keymap.yscale : l # toggle scaling of y-axes ('log'/'linear') keymap.xscale : L, k # toggle scaling of x-axes ('log'/'linear') keymap.all_axes : a # enable all axes # Control downloading of example data. Various examples download some # data from the Matplotlib svn repository to avoid distributing extra # files, but sometimes you want to avoid that. In that case set # examples.download to False and examples.directory to the directory # where you have a checkout of # https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/sample_data #examples.download : True # False to bypass downloading mechanism #examples.directory : '' # directory to look in if download is false statsmodels-0.5.0+git13-g8e07d34/tools/migrate_issues_gh.py000066400000000000000000000303521224417117700233700ustar00rootroot00000000000000#!/usr/bin/env python """Launchpad to github bug migration script. There's a ton of code from Hydrazine copied here: https://launchpad.net/hydrazine Usage ----- This code is meant to port a bug database for a project from Launchpad to GitHub. It was used to port the IPython bug history. The code is meant to be used interactively. I ran it multiple times in one long IPython session, until the data structures I was getting from Launchpad looked right. Then I turned off (see 'if 0' markers below) the Launchpad part, and ran it again with the github part executing and using the 'bugs' variable from my interactive namespace (via"%run -i" in IPython). This code is NOT fire and forget, it's meant to be used with some intelligent supervision at the wheel. Start by making a test repository (I made one called ipython/BugsTest) and upload only a few issues into that. Once you are sure that everything is OK, run it against your real repo with all your issues. You should read all the code below and roughly understand what's going on before using this. Since I didn't intend to use this more than once, it's not particularly robust or documented. It got the job done and I've never used it again. Configuration ------------- To pull things off LP, you need to log in first (see the Hydrazine docs). Your Hydrazine credentials will be cached locally and this script can reuse them. To push to GH, you need to set below the GH repository owner, API token and repository name you wan to push issues into. See the GH section for the necessary variables. """ import collections import os.path import subprocess import sys import time from pprint import pformat import launchpadlib from launchpadlib.credentials import Credentials from launchpadlib.launchpad import ( Launchpad, STAGING_SERVICE_ROOT, EDGE_SERVICE_ROOT ) #----------------------------------------------------------------------------- # Launchpad configuration #----------------------------------------------------------------------------- # The official LP project name PROJECT_NAME = 'statsmodels' # How LP marks your bugs, I don't know where this is stored, but they use it to # generate bug descriptions and we need to split on this string to create # shorter Github bug titles PROJECT_ID = 'statsmodels' # Default Launchpad server, see their docs for details service_root = EDGE_SERVICE_ROOT #----------------------------------------------------------------------------- # Code copied/modified from Hydrazine (https://launchpad.net/hydrazine) #----------------------------------------------------------------------------- # Constants for the names in LP of certain lp_importances = ['Critical', 'High', 'Medium', 'Low', 'Wishlist', 'Undecided'] lp_status = ['Confirmed', 'Triaged', 'Fix Committed', 'Fix Released', 'In Progress',"Won't Fix", "Incomplete", "Invalid", "New"] def squish(a): return a.lower().replace(' ', '_').replace("'",'') lp_importances_c = set(map(squish, lp_importances)) lp_status_c = set(map(squish, lp_status)) def trace(s): sys.stderr.write(s + '\n') def create_session(): lplib_cachedir = os.path.expanduser("~/.cache/launchpadlib/") hydrazine_cachedir = os.path.expanduser("~/.cache/hydrazine/") rrd_dir = os.path.expanduser("~/.cache/hydrazine/rrd") for d in [lplib_cachedir, hydrazine_cachedir, rrd_dir]: if not os.path.isdir(d): os.makedirs(d, mode=0700) hydrazine_credentials_filename = os.path.join(hydrazine_cachedir, 'credentials') if os.path.exists(hydrazine_credentials_filename): credentials = Credentials() credentials.load(file( os.path.expanduser("~/.cache/hydrazine/credentials"), "r")) trace('loaded existing credentials') return Launchpad(credentials, service_root, lplib_cachedir) # TODO: handle the case of having credentials that have expired etc else: launchpad = Launchpad.get_token_and_login( 'Hydrazine', service_root, lplib_cachedir) trace('saving credentials...') launchpad.credentials.save(file( hydrazine_credentials_filename, "w")) return launchpad def canonical_enum(entered, options): entered = squish(entered) return entered if entered in options else None def canonical_importance(from_importance): return canonical_enum(from_importance, lp_importances_c) def canonical_status(entered): return canonical_enum(entered, lp_status_c) #----------------------------------------------------------------------------- # Functions and classes #----------------------------------------------------------------------------- class Base(object): def __str__(self): a = dict([(k,v) for (k,v) in self.__dict__.iteritems() if not k.startswith('_')]) return pformat(a) __repr__ = __str__ class Message(Base): def __init__(self, m): self.content = m.content o = m.owner self.owner = o.name self.owner_name = o.display_name self.date = m.date_created class Bug(Base): def __init__(self, bt): # Cache a few things for which launchpad will make a web request each # time. bug = bt.bug o = bt.owner a = bt.assignee dupe = bug.duplicate_of # Store from the launchpadlib bug objects only what we want, and as # local data self.id = bug.id self.lp_url = 'https://bugs.launchpad.net/%s/+bug/%i' % \ (PROJECT_NAME, self.id) self.title = bt.title self.description = bug.description # Every bug has an owner (who created it) self.owner = o.name self.owner_name = o.display_name # Not all bugs have been assigned to someone yet try: self.assignee = a.name self.assignee_name = a.display_name except AttributeError: self.assignee = self.assignee_name = None # Store status/importance in canonical format self.status = canonical_status(bt.status) self.importance = canonical_importance(bt.importance) self.tags = bug.tags # Store the bug discussion messages, but skip m[0], which is the same # as the bug description we already stored self.messages = map(Message, list(bug.messages)[1:]) self.milestone = getattr(bt.milestone, 'name', None) # Duplicate handling disabled, since the default query already filters # out the duplicates. Keep the code here in case we ever want to look # into this... if 0: # Track duplicates conveniently try: self.duplicate_of = dupe.id self.is_duplicate = True except AttributeError: self.duplicate_of = None self.is_duplicate = False # dbg dupe info if bug.number_of_duplicates > 0: self.duplicates = [b.id for b in bug.duplicates] else: self.duplicates = [] # tmp - debug self._bt = bt self._bug = bug #----------------------------------------------------------------------------- # Main script #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Launchpad part #----------------------------------------------------------------------------- # launchpad = create_session() launchpad = Launchpad.login_with('statsmodels', 'production') project = launchpad.projects[PROJECT_NAME] # Note: by default, this will give us all bugs except duplicates and those # with status "won't fix" or 'invalid' bug_tasks = project.searchTasks(status=lp_status) bugs = {} for bt in list(bug_tasks): b = Bug(bt) bugs[b.id] = b print b.title sys.stdout.flush() #----------------------------------------------------------------------------- # Github part #----------------------------------------------------------------------------- #http://pypi.python.org/pypi/github2 #http://github.com/ask/python-github2 # Github libraries from github2 import core, issues, client for mod in (core, issues, client): reload(mod) def format_title(bug): return bug.title.split('{0}: '.format(PROJECT_ID), 1)[1].strip('"') def format_body(bug): body = \ """Original Launchpad bug {bug.id}: {bug.lp_url} Reported by: {bug.owner} ({owner_name}). {description}""".format(bug=bug, owner_name=bug.owner_name.encode('utf-8'), description=bug.description.encode('utf-8')) return body def format_message(num, m): body = \ """[ LP comment {num} by: {owner_name}, on {m.date!s} ] {content}""".format(num=num, m=m, owner_name=m.owner_name.encode('utf-8'), content=m.content.encode('utf-8')) return body # Config user = 'wesm' token= '12efaff85b8e17f63ee835c5632b8cf0' repo = 'statsmodels/statsmodels' #repo = 'ipython/ipython' # Skip bugs with this status: # to_skip = set([u'fix_committed', u'incomplete']) to_skip = set() # Only label these importance levels: gh_importances = set([u'critical', u'high', u'low', u'medium', u'wishlist']) # Start script gh = client.Github(username=user, api_token=token) # Filter out the full LP bug dict to process only the ones we want bugs_todo = dict( (id, b) for (id, b) in bugs.iteritems() if not b.status in to_skip ) # Select which bug ids to run #bids = bugs_todo.keys()[50:100] # bids = bugs_todo.keys()[12:] bids = bugs_todo.keys() #bids = bids[:5]+[502787] # Start loop over bug ids and file them on Github nbugs = len(bids) gh_issues = [] # for reporting at the end for n, bug_id in enumerate(bids): bug = bugs[bug_id] title = format_title(bug) body = format_body(bug) print if len(title)<65: print bug.id, '[{0}/{1}]'.format(n+1, nbugs), title else: print bug.id, title[:65]+'...' # still check bug.status, in case we manually added other bugs to the list # above (mostly during testing) if bug.status in to_skip: print '--- Skipping - status:',bug.status continue print '+++ Filing...', sys.stdout.flush() # Create github issue for this bug issue = gh.issues.open(repo, title=title, body=body) print 'created GitHub #', issue.number gh_issues.append(issue.number) sys.stdout.flush() # Mark status as a label #status = 'status-{0}'.format(b.status) #gh.issues.add_label(repo, issue.number, status) # Mark any extra tags we might have as labels for tag in b.tags: label = 'tag-{0}'.format(tag) gh.issues.add_label(repo, issue.number, label) # If bug has assignee, add it as label if bug.assignee: gh.issues.add_label(repo, issue.number, #bug.assignee # Github bug, gets confused with dots in labels. bug.assignee.replace('.','_') ) if bug.importance in gh_importances: if bug.importance == 'wishlist': label = bug.importance else: label = 'prio-{0}'.format(bug.importance) gh.issues.add_label(repo, issue.number, label) if bug.milestone: label = 'milestone-{0}'.format(bug.milestone).replace('.','_') gh.issues.add_label(repo, issue.number, label) # Add original message thread for num, message in enumerate(bug.messages): # Messages on LP are numbered from 1 comment = format_message(num+1, message) gh.issues.comment(repo, issue.number, comment) time.sleep(0.5) # soft sleep after each message to prevent gh block if bug.status in ['fix_committed', 'fix_released', 'invalid']: gh.issues.close(repo, issue.number) # too many fast requests and gh will block us, so sleep for a while # I just eyeballed these values by trial and error. time.sleep(1) # soft sleep after each request # And longer one after every batch batch_size = 10 tsleep = 60 if (len(gh_issues) % batch_size)==0: print print '*** SLEEPING for {0} seconds to avoid github blocking... ***'.format(tsleep) sys.stdout.flush() time.sleep(tsleep) # Summary report print print '*'*80 print 'Summary of GitHub issues filed:' print gh_issues print 'Total:', len(gh_issues) statsmodels-0.5.0+git13-g8e07d34/tools/nbgenerate.py000077500000000000000000000255711224417117700220130ustar00rootroot00000000000000#! /usr/bin/env python """ Script to generate notebooks with output from notebooks that don't have output. """ # prefer HTML over rST for now until nbconvert changes drop OUTPUT = "html" SOURCE_DIR = ("/home/skipper/statsmodels/statsmodels-skipper/examples/" "notebooks") import os import io import sys import time import shutil from Queue import Empty try: # IPython has been refactored from IPython.kernel import KernelManager except ImportError: from IPython.zmq.blockingkernelmanager import (BlockingKernelManager as KernelManager) from IPython.nbformat.current import reads, write, NotebookNode cur_dir = os.path.abspath(os.path.dirname(__file__)) # for conversion of .ipynb -> html/rst from IPython.config import Config try: from nbconvert.converters.template import ConverterTemplate from nbconvert.converters.rst import ConverterRST except ImportError: if os.path.exists("/home/skipper/src/nbconvert"): if not os.path.exists(os.path.join(cur_dir, "nbconvert")): os.symlink("/home/skipper/src/nbconvert", os.path.join(cur_dir, "nbconvert")) from nbconvert.converters.template import ConverterTemplate from nbconvert.converters.rst import ConverterRST else: from warnings import warn warn("Notebook examples not built. Add nbconvert to path or update " "paths in tools/nbgenerate.py") sys.exit(0) import hash_funcs class NotebookRunner: """ Paramters --------- notebook_dir : str Path to the notebooks to convert extra_args : list These are command line arguments passed to start the notebook kernel profile : str The profile name to use timeout : int How many seconds to wait for each sell to complete running """ def __init__(self, notebook_dir, extra_args=None, profile=None, timeout=90): self.notebook_dir = os.path.abspath(notebook_dir) self.profile = profile self.timeout = timeout km = KernelManager() if extra_args is None: extra_args = [] if profile is not None: extra_args += ["--profile=%s" % profile] km.start_kernel(stderr=open(os.devnull, 'w'), extra_arguments=extra_args) try: kc = km.client() kc.start_channels() iopub = kc.iopub_channel except AttributeError: # still on 0.13 kc = km kc.start_channels() iopub = kc.sub_channel shell = kc.shell_channel # make sure it's working shell.execute("pass") shell.get_msg() # all of these should be run pylab inline shell.execute("%pylab inline") shell.get_msg() self.kc = kc self.km = km self.iopub = iopub def __iter__(self): notebooks = [os.path.join(self.notebook_dir, i) for i in os.listdir(self.notebook_dir) if i.endswith('.ipynb') and 'generated' not in i] for ipynb in notebooks: with open(ipynb, 'r') as f: nb = reads(f.read(), 'json') yield ipynb, nb def __call__(self, nb): return self.run_notebook(nb) def run_cell(self, shell, iopub, cell, exec_count): outs = [] shell.execute(cell.input) # hard-coded timeout, problem? shell.get_msg(timeout=20) cell.prompt_number = exec_count # msg["content"]["execution_count"] while True: try: # whats the assumption on timeout here? # is it asynchronous? msg = iopub.get_msg(timeout=.2) except Empty: break msg_type = msg["msg_type"] if msg_type in ["status" , "pyin"]: continue elif msg_type == "clear_output": outs = [] continue content = msg["content"] out = NotebookNode(output_type=msg_type) if msg_type == "stream": out.stream = content["name"] out.text = content["data"] elif msg_type in ["display_data", "pyout"]: for mime, data in content["data"].iteritems(): attr = mime.split("/")[-1].lower() # this gets most right, but fix svg+html, plain attr = attr.replace('+xml', '').replace('plain', 'text') setattr(out, attr, data) if msg_type == "pyout": out.prompt_number = exec_count #content["execution_count"] elif msg_type == "pyerr": out.ename = content["ename"] out.evalue = content["evalue"] out.traceback = content["traceback"] else: print "unhandled iopub msg:", msg_type outs.append(out) return outs def run_notebook(self, nb): """ """ shell = self.kc.shell_channel iopub = self.iopub cells = 0 errors = 0 exec_count = 1 #TODO: What are the worksheets? -ss for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type != 'code': # there won't be any output continue cells += 1 try: # attaches the output to cell inplace outs = self.run_cell(shell, iopub, cell, exec_count) exec_count += 1 except Exception as e: print "failed to run cell:", repr(e) print cell.input errors += 1 continue cell.outputs = outs print "ran notebook %s" % nb.metadata.name print " ran %3i cells" % cells if errors: print " %3i cells raised exceptions" % errors else: print " there were no errors" def __del__(self): self.kc.stop_channels() self.km.shutdown_kernel() del self.km def _get_parser(): try: import argparse except ImportError: raise ImportError("This script only runs on Python >= 2.7") parser = argparse.ArgumentParser(description="Convert .ipynb notebook " "inputs to HTML page with output") parser.add_argument("path", type=str, default=SOURCE_DIR, nargs="?", help="path to folder containing notebooks") parser.add_argument("--profile", type=str, help="profile name to use") parser.add_argument("--timeout", default=90, type=int, metavar="N", help="how long to wait for cells to run in seconds") return parser def nb2html(nb): """ Cribbed from nbviewer """ config = Config() config.ConverterTemplate.template_file = 'basichtml' config.NbconvertApp.fileext = "html" config.CSSHtmlHeaderTransformer.enabled = False C = ConverterTemplate(config=config) return C.convert(nb)[0] def nb2rst(nb, files_dir): """ nb should be a NotebookNode """ config = Config() C = ConverterRST(config=config) # bastardize how this is supposed to be called # either the API is broken, or I'm not using it right # why can't I set this using the config? C.files_dir = files_dir + "_files" if not os.path.exists(C.files_dir): os.makedirs(C.files_dir) # already parsed into a NotebookNode C.nb = nb return C.convert() if __name__ == '__main__': rst_target_dir = os.path.join(cur_dir, '..', 'docs/source/examples/notebooks/generated/') if not os.path.exists(rst_target_dir): os.makedirs(rst_target_dir) parser = _get_parser() arg_ns, other_args = parser.parse_known_args() os.chdir(arg_ns.path) # so we execute in notebook dir notebook_runner = NotebookRunner(arg_ns.path, other_args, arg_ns.profile, arg_ns.timeout) try: for fname, nb in notebook_runner: base, ext = os.path.splitext(fname) fname_only = os.path.basename(base) # check if we need to write towrite, filehash = hash_funcs.check_hash(open(fname, "r").read(), fname_only) if not towrite: print "Hash has not changed for file %s" % fname_only continue print "Writing ", fname_only # This edits the notebook cells inplace notebook_runner(nb) # for debugging writes ipynb file with output #new_ipynb = "%s_generated%s" % (base, ".ipynb") #with io.open(new_ipynb, "w", encoding="utf-8") as f: # write(nb, f, "json") # use nbconvert to convert to rst support_file_dir = os.path.join(rst_target_dir, fname_only+"_files") if OUTPUT == "rst": new_rst = os.path.join(rst_target_dir, fname_only+".rst") rst_out = nb2rst(nb, fname_only) # write them to source directory if not os.path.exists(rst_target_dir): os.makedirs(rst_target_dir) with io.open(new_rst, "w", encoding="utf-8") as f: f.write(rst_out) # move support files if os.path.exists(fname_only+"_files"): shutil.move(fname_only+"_files", os.path.join(rst_target_dir, fname_only+"_files")) elif OUTPUT == "html": from notebook_output_template import notebook_template new_html = os.path.join(rst_target_dir, fname_only+".rst") # get the title out of the notebook because sphinx needs it title_cell = nb['worksheets'][0]['cells'].pop(0) try: assert title_cell['cell_type'] == 'heading' except: print "Title not in first cell for ", fname_only print "Not generating rST" html_out = nb2html(nb) # indent for insertion into raw html block in rST html_out = "\n".join([" "+i for i in html_out.split("\n")]) with io.open(new_html, "w", encoding="utf-8") as f: f.write(title_cell["source"]+u"\n") f.write(u"="*len(title_cell["source"])+u"\n\n") f.write(notebook_template.substitute(name=fname_only, body=html_out)) hash_funcs.update_hash_dict(filehash, fname_only) except Exception, err: raise err finally: os.chdir(cur_dir) # probably not necessary del notebook_runner statsmodels-0.5.0+git13-g8e07d34/tools/notebook_output_template.py000066400000000000000000000034051224417117700250210ustar00rootroot00000000000000from string import Template notebook_template = Template(""" .. _${name}_notebook: `Link to Notebook GitHub `_ .. raw:: html $body """) statsmodels-0.5.0+git13-g8e07d34/tools/topy.R000066400000000000000000000104041224417117700204270ustar00rootroot00000000000000sanitize_name <- function(name) { #"[%s]" % "]|[".join(map(re.escape, list(string.punctuation.replace("_","") punctuation <- '[\\!]|[\\"]|[\\#]|[\\$]|[\\%]|[\\&]|[\\\']|[\\(]|[\\)]|[\\*]|[\\+]|[\\,]|[\\-]|[\\.]|[\\/]|[\\:]|[\\;]|[\\<]|[\\=]|[\\>]|[\\?]|[\\@]|[\\[]|[\\\\]|[\\]]|[\\^]|[\\`]|[\\{]|[\\|]|[\\}]|[\\~]' # handle spaces,tabs,etc. and periods specially name <- gsub("[[:blank:]\\.]", "_", name) name <- gsub(punctuation, "", name) return(name) } cat_items <- function(object, prefix="", blacklist=NULL, trans=list()) { #print content (names) of object into python expressions for defining variables # #Parameters #---------- #object : object with names attribute # names(object) will be written as python assignments #prefix : string # string that is prepended to the variable names #blacklist : list of strings # names that are in the blacklist are ignored #trans : named list (dict_like) # names that are in trans will be replaced by the corresponding value # #example cat_items(fitresult, blacklist=c("eq")) # #currently limited type inference, mainly numerical items = names(object) for (name in items) { if (is.element(name, blacklist)) next #cat(name); cat("\n") item = object[[name]] #fix name #Skipper's sanitize name name_ <- gsub("\\.", "_", name) # make name pythonic #translate name newname = trans[[name_]] #translation table on sanitized names if (!is.null(newname)) { name_ = newname } name_ = paste(prefix, name_, sep="") if (is.numeric(item)) { if (!is.null(names(item))) { #named list, class numeric ? mkarray2(as.matrix(item), name_); if (!is.null(dimnames(item))) write_dimnames(item, prefix=name_) } else if (class(item) == 'matrix') { mkarray2(item, name_); if (!is.null(dimnames(item))) write_dimnames(item, prefix=name_) } else if (class(item) == 'numeric') { #scalar cat(name_); cat(" = "); cat(item); cat("\n") } } else if (is.character(item)) { #assume string doesn't contain single quote cat(name_); cat(" = '"); cat(item); cat("'\n") } else { cat(name_); cat(" = '''"); cat(deparse(item)); cat("'''"); cat("\n") } } } #end function write_dimnames <- function(mat, prefix="") { if (prefix != "") { prefix = paste(prefix, "_", sep="") } dimn = list("rownames", "colnames", "thirdnames") #up to 3 dimension ? for (ii in c(1:length(dimnames(mat)))) { cat(paste(prefix, dimn[[ii]], sep="")) cat (" = ["); for (dname in dimnames(mat)[[ii]]) { cat("'"); cat(dname); cat("', ") } cat("]\n") } } write_header <-function() { cat("import numpy as np\n\n") cat("class Bunch(dict):\n") cat(" def __init__(self, **kw):\n") cat(" dict.__init__(self, kw)\n") cat(" self.__dict__ = self\n\n") } mkhtest <- function(ht, name, distr="f") { #function to write results of a statistical test of class htest to a python dict # #Parameters #---------- #ht : instance of ht # return of many statistical tests #name : string # name of variable that holds results dict #distr : string # distribution of the test statistic # cat(name); cat(" = dict("); cat("statistic="); cat(ht$statistic); cat(", "); cat("pvalue="); cat(ht$p.value); cat(", "); cat("parameters=("); cat(ht$parameter, sep=","); cat(",), "); cat("distr='"); cat(distr); cat("'"); cat(")"); cat("\n\n") } mkarray2 <- function(X, name, sanitize=FALSE) { indent = " " if (sanitize) { cat(sanitize_name(name)); cat(" = np.array([\n"); cat(X, sep=", ", fill=76, labels=indent); cat(indent); cat("])") } else{ cat(name); cat(" = np.array([\n"); cat(X, sep=", ", fill=76, labels=indent); cat(indent); cat("])") } if (is.matrix(X)) { i <- as.character(nrow(X)) j <- as.character(ncol(X)) cat(".reshape("); cat(i); cat(","); cat(j); cat(", order='F')") } cat("\n") } statsmodels-0.5.0+git13-g8e07d34/tools/update_web.py000077500000000000000000000216611224417117700220140ustar00rootroot00000000000000#!/usr/bin/python """ This script installs the trunk version, builds the docs, then uploads them to ... Then it installs the devel version, builds the docs, and uploads them to ... Depends ------- virtualenv """ import base64 import subprocess import os import shutil import re import smtplib import sys from email.MIMEText import MIMEText ######### INITIAL SETUP ########## #hard-coded "current working directory" ie., you will need file permissions #for this folder script = os.path.abspath(sys.argv[0]) dname = os.path.abspath(os.path.dirname(script)) gname = 'statsmodels' gitdname = os.path.join(dname, gname) os.chdir(dname) # hard-coded git branch names repo = 'git://github.com/statsmodels/statsmodels.git' stable_trunk = 'master' last_release = 'v0.4.3' branches = [stable_trunk] # change last_release above and uncomment the below to update for a release #branches = [stable_trunk, last_release] # virtual environment directory virtual_dir = 'BUILDENV' virtual_dir = os.path.join(dname, virtual_dir) # this points to the newly installed python in the virtualenv virtual_python = os.path.join(virtual_dir,'bin','python') # my security holes with open('/home/skipper/statsmodels/gmail.txt') as f: pwd = f.readline().strip() gmail_pwd = base64.b64decode(pwd) ########### EMAIL ############# email_name ='statsmodels.dev' + 'AT' + 'gmail' +'.com' email_name = email_name.replace('AT','@') gmail_pwd= gmail_pwd to_email = [email_name, ('josef.pktd' + 'AT' + 'gmail' + '.com').replace('AT', '@')] #to_email = [email_name] ########### FUNCTIONS ############### def create_virtualenv(): # make a virtualenv for installation if it doesn't exist # and easy_install sphinx if not os.path.exists(virtual_dir): retcode = subprocess.call(['/usr/local/bin/virtualenv', "--system-site-packages", virtual_dir]) if retcode != 0: msg = """There was a problem creating the virtualenv""" raise Exception(msg) retcode = subprocess.call([virtual_dir+'/bin/easy_install', 'sphinx']) if retcode != 0: msg = """There was a problem installing sphinx""" raise Exception(msg) def create_update_gitdir(): """ Creates a directory for local repo if it doesn't exist, updates repo otherwise. """ if not os.path.exists(gitdname): retcode = subprocess.call('git clone '+repo, shell=True) if retcode != 0: msg = """There was a problem cloning the repo""" raise Exception(msg) else: # directory exists, can't pull if you're not on a branch # just delete it and clone again. Lazy but clean solution. shutil.rmtree(gitdname) create_update_gitdir() def getdirs(): """ Get current directories of cwd in order to restore to this """ dirs = [i for i in os.listdir(dname) if not \ os.path.isfile(os.path.join(dname, i))] return dirs def newdir(dirs): """ Returns difference in directories between dirs and current directories If the difference is greater than one directory it raises an error. """ dirs = set(dirs) newdirs = set([i for i in os.listdir(dname) if not \ os.path.isfile(os.path.join(dname,i))]) newdir = newdirs.difference(dirs) if len(newdir) != 1: msg = """There was more than one directory created. Don't know what to delete.""" raise Exception(msg) newdir = newdir.pop() return newdir def install_branch(branch): """ Installs the branch in a virtualenv. """ # if it's already in the virtualenv, remove it ver = '.'.join(map(str,(sys.version_info.major,sys.version_info.minor))) sitepack = os.path.join(virtual_dir, 'lib','python'+ver, 'site-packages') if os.path.exists(sitepack): dir_list = os.listdir(sitepack) else: dir_list = [] for f in dir_list: if 'statsmodels' in f: shutil.rmtree(os.path.join(sitepack, f)) # checkout the branch os.chdir(gitdname) retcode = subprocess.call('git checkout ' + branch, shell=True) if retcode != 0: msg = """Could not checkout out branch %s""" % branch raise Exception(msg) # build and install retcode = subprocess.call(" ".join([virtual_python, 'setup.py', 'build']), shell=True) if retcode != 0: msg = """ Could not build branch %s""" % branch raise Exception(msg) retcode = subprocess.call(" ".join([virtual_python, os.path.join(gitdname, 'setup.py'), 'install']), shell=True) if retcode != 0: os.chdir(dname) msg = """Could not install branch %s""" % branch raise Exception(msg) os.chdir(dname) def build_docs(branch): """ Changes into gitdname and builds the docs using BUILDENV virtualenv """ os.chdir(os.path.join(gitdname, 'docs')) sphinx_dir = os.path.join(virtual_dir,'bin') retcode = subprocess.call("make clean", shell=True) if retcode != 0: os.chdir(dname) msg = """Could not clean the html docs for branch %s""" % branch raise Exception(msg) #NOTE: The python call in the below makes sure that it uses the Python # that is referenced after entering the virtualenv sphinx_call = " ".join(['make','html', "SPHINXBUILD=' python /usr/local/bin/sphinx-build'"]) activate = os.path.join(virtual_dir, "bin", "activate") activate_virtualenv = ". " + activate #NOTE: You have to enter virtualenv in the same call. As soon as the # child process is done, the env variables from activate are lost. # getting the correct env from bin/activate and passing to env is # annoying retcode = subprocess.call(" && ".join([activate_virtualenv, sphinx_call]), shell=True, env = {'MATPLOTLIBRC' : # put this in the environment to use local rc '/home/skipper/statsmodels/statsmodels/tools/'}) if retcode != 0: os.chdir(dname) msg = """Could not build the html docs for branch %s""" % branch raise Exception(msg) os.chdir(dname) def build_pdf(branch): """ Changes into new_branch_dir and builds the docs using sphinx in the BUILDENV virtualenv """ os.chdir(os.path.join(gitdname,'statsmodels','docs')) sphinx_dir = os.path.join(virtual_dir,'bin') retcode = subprocess.call(" ".join(['make','latexpdf', 'SPHINXBUILD='+sphinx_dir+'/sphinx-build']), shell=True) if retcode != 0: os.chdir(old_cwd) msg = """Could not build the pdf docs for branch %s""" % branch raise Exception(msg) os.chdir(dname) def upload_docs(branch): if branch == 'master': remote_dir = 'devel' else: remote_dir = 'stable' # old_cwd = os.getcwd() os.chdir(os.path.join(gitdname, 'docs')) retcode = subprocess.call(['rsync', '-avPr' ,'-e ssh', 'build/html/', 'jseabold,statsmodels@web.sourceforge.net:htdocs/'+remote_dir]) if retcode != 0: os.chdir(old_cwd) msg = """Could not upload html to %s for branch %s""" % (remote_dir, branch) raise Exception(msg) os.chdir(dname) #TODO: upload pdf is not tested def upload_pdf(branch): if branch == 'master': remote_dir = 'devel' else: remote_dir = 'stable' os.chdir(os.path.join(dname, new_branch_dir, 'statsmodels','docs')) retcode = subprocess.call(['rsync', '-avPr', '-e ssh', 'build/latex/statsmodels.pdf', 'jseabold,statsmodels@web.sourceforge.net:htdocs/'+remote_dir+'pdf/']) if retcode != 0: os.chdir(old_cwd) msg = """Could not upload pdf to %s for branch %s""" % (remote_dir+'/pdf', branch) raise Exception(msg) os.chdir(dname) def email_me(status='ok'): if status == 'ok': message = """ HTML Documentation uploaded successfully. """ subject = "Statsmodels HTML Build OK" else: message = status subject = "Statsmodels HTML Build Failed" msg = MIMEText(message) msg['Subject'] = subject msg['From'] = email_name msg['To'] = email_name server = smtplib.SMTP('smtp.gmail.com',587) server.ehlo() server.starttls() server.ehlo() server.login(email_name, gmail_pwd) server.sendmail(email_name, to_email, msg.as_string()) server.close() ############### MAIN ################### def main(): # get branch, install in virtualenv, build the docs, upload, and cleanup msg = '' for branch in branches: try: create_virtualenv() create_update_gitdir() install_branch(branch) build_docs(branch) upload_docs(branch) # build_pdf(new_branch_dir) # upload_pdf(branch, new_branch_dir) except Exception as status: msg += str(status) + '\n' if msg == '': # if it doesn't something went wrong and was caught above email_me() else: email_me(msg) if __name__ == "__main__": main()